[ { "date": "2023-05-05", "arxiv_id": "2305.03048", "title": "Personalize Segment Anything Model with One Shot", "authors": [ "Renrui Zhang", "Zhengkai Jiang", "Ziyu Guo", "Shilin Yan", "Junting Pan", "Hao Dong", "Peng Gao", "Hongsheng Li" ], "github": "https://github.com/ZrrSkywalker/Personalize-SAM", "project_page": "", "abstract": "Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models. Despite the generality, customizing SAM for specific visual concepts without man-powered prompting is under explored, e.g., automatically segmenting your pet dog in different images. In this paper, we propose a training-free Personalization approach for SAM, termed as PerSAM. Given only a single image with a reference mask, PerSAM first localizes the target concept by a location prior, and segments it within other images or videos via three techniques: target-guided attention, target-semantic prompting, and cascaded post-refinement. In this way, we effectively adapt SAM for private use without any training. To further alleviate the mask ambiguity, we present an efficient one-shot fine-tuning variant, PerSAM-F. Freezing the entire SAM, we introduce two learnable weights for multi-scale masks, only training 2 parameters within 10 seconds for improved performance. To demonstrate our efficacy, we construct a new segmentation dataset, PerSeg, for personalized evaluation, and test our methods on video object segmentation with competitive performance. Besides, our approach can also enhance DreamBooth to personalize Stable Diffusion for text-to-image generation, which discards the background disturbance for better target appearance learning. Code is released at https://github.com/ZrrSkywalker/Personalize-SAM", "github_manual": "", "github_regex": "https://github.com/ZrrSkywalker/Personalize-SAM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.03043", "title": "Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization", "authors": [ "Connor Z. Lin", "Koki Nagano", "Jan Kautz", "Eric R. Chan", "Umar Iqbal", "Leonidas Guibas", "Gordon Wetzstein", "Sameh Khamis" ], "github": "", "project_page": "", "abstract": "There is a growing demand for the accessible creation of high-quality 3D avatars that are animatable and customizable. Although 3D morphable models provide intuitive control for editing and animation, and robustness for single-view face reconstruction, they cannot easily capture geometric and appearance details. Methods based on neural implicit representations, such as signed distance functions (SDF) or neural radiance fields, approach photo-realism, but are difficult to animate and do not generalize well to unseen data. To tackle this problem, we propose a novel method for constructing implicit 3D morphable face models that are both generalizable and intuitive for editing. Trained from a collection of high-quality 3D scans, our face model is parameterized by geometry, expression, and texture latent codes with a learned SDF and explicit UV texture parameterization. Once trained, we can reconstruct an avatar from a single in-the-wild image by leveraging the learned prior to project the image into the latent space of our model. Our implicit morphable face models can be used to render an avatar from novel views, animate facial expressions by modifying expression codes, and edit textures by directly painting on the learned UV-texture maps. We demonstrate quantitatively and qualitatively that our method improves upon photo-realism, geometry, and expression accuracy compared to state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.02665", "title": "Learning Language-Specific Layers for Multilingual Machine Translation", "authors": [ "Telmo Pessoa Pires", "Robin M. Schmidt", "Yi-Hsiu Liao", "Stephan Peitz" ], "github": "", "project_page": "", "abstract": "Multilingual Machine Translation promises to improve translation quality between non-English languages. This is advantageous for several reasons, namely lower latency (no need to translate twice), and reduced error cascades (e.g., avoiding losing gender and formality information when translating through English). On the downside, adding more languages reduces model capacity per language, which is usually countered by increasing the overall model size, making training harder and inference slower. In this work, we introduce Language-Specific Transformer Layers (LSLs), which allow us to increase model capacity, while keeping the amount of computation and the number of parameters used in the forward pass constant. The key idea is to have some layers of the encoder be source or target language-specific, while keeping the remaining layers shared. We study the best way to place these layers using a neural architecture search inspired approach, and achieve an improvement of 1.3 chrF (1.5 spBLEU) points over not using LSLs on a separate decoder architecture, and 1.9 chrF (2.2 spBLEU) on a shared decoder one.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.02549", "title": "FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction", "authors": [ "Chen-Yu Lee", "Chun-Liang Li", "Hao Zhang", "Timothy Dozat", "Vincent Perot", "Guolong Su", "Xiang Zhang", "Kihyuk Sohn", "Nikolai Glushnev", "Renshen Wang", "Joshua Ainslie", "Shangbang Long", "Siyang Qin", "Yasuhisa Fujii", "Nan Hua", "Tomas Pfister" ], "github": "", "project_page": "", "abstract": "The recent advent of self-supervised pre-training techniques has led to a surge in the use of multimodal learning in form document understanding. However, existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data. In FormNetV2, we introduce a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss. The graph contrastive objective maximizes the agreement of multimodal representations, providing a natural interplay for all modalities without special customization. In addition, we extract image features within the bounding box that joins a pair of tokens connected by a graph edge, capturing more targeted visual cues without loading a sophisticated and separately pre-trained image embedder. FormNetV2 establishes new state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.02499", "title": "AutoML-GPT: Automatic Machine Learning with GPT", "authors": [ "Shujian Zhang", "Chengyue Gong", "Lemeng Wu", "Xingchao Liu", "Mingyuan Zhou" ], "github": "", "project_page": "", "abstract": "AI tasks encompass a wide range of domains and fields. While numerous AI models have been designed for specific tasks and applications, they often require considerable human efforts in finding the right model architecture, optimization algorithm, and hyperparameters. Recent advances in large language models (LLMs) like ChatGPT show remarkable capabilities in various aspects of reasoning, comprehension, and interaction. Consequently, we propose developing task-oriented prompts and automatically utilizing LLMs to automate the training pipeline. To implement this concept, we present the AutoML-GPT, which employs GPT as the bridge to diverse AI models and dynamically trains models with optimized hyperparameters. AutoML-GPT dynamically takes user requests from the model and data cards and composes the corresponding prompt paragraph. Ultimately, with this prompt paragraph, AutoML-GPT will automatically conduct the experiments from data processing to model architecture, hyperparameter tuning, and predicted training log. By leveraging {\\ours}'s robust language capabilities and the available AI models, AutoML-GPT can tackle numerous intricate AI tasks across various tasks and datasets. This approach achieves remarkable results in computer vision, natural language processing, and other challenging areas. Extensive experiments and ablation studies demonstrate that our method can be general, effective, and beneficial for many AI tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.03049", "title": "NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds", "authors": [ "Jun-Kun Chen", "Jipeng Lyu", "Yu-Xiong Wang" ], "github": "https://github.com/mortalco/NeuralEditor", "project_page": "", "abstract": "This paper proposes NeuralEditor that enables neural radiance fields (NeRFs) natively editable for general shape editing tasks. Despite their impressive results on novel-view synthesis, it remains a fundamental challenge for NeRFs to edit the shape of the scene. Our key insight is to exploit the explicit point cloud representation as the underlying structure to construct NeRFs, inspired by the intuitive interpretation of NeRF rendering as a process that projects or \"plots\" the associated 3D point cloud to a 2D image plane. To this end, NeuralEditor introduces a novel rendering scheme based on deterministic integration within K-D tree-guided density-adaptive voxels, which produces both high-quality rendering results and precise point clouds through optimization. NeuralEditor then performs shape editing via mapping associated points between point clouds. Extensive evaluation shows that NeuralEditor achieves state-of-the-art performance in both shape deformation and scene morphing tasks. Notably, NeuralEditor supports both zero-shot inference and further fine-tuning over the edited scene. Our code, benchmark, and demo video are available at https://immortalco.github.io/NeuralEditor.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mortalco/NeuralEditor", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.03040", "title": "TUVF: Learning Generalizable Texture UV Radiance Fields", "authors": [ "An-Chieh Cheng", "Xueting Li", "Sifei Liu", "Xiaolong Wang" ], "github": "", "project_page": "", "abstract": "Textures are a vital aspect of creating visually appealing and realistic 3D models. In this paper, we study the problem of generating high-fidelity texture given shapes of 3D assets, which has been relatively less explored compared with generic 3D shape modeling. Our goal is to facilitate a controllable texture generation process, such that one texture code can correspond to a particular appearance style independent of any input shapes from a category. We introduce Texture UV Radiance Fields (TUVF) that generate textures in a learnable UV sphere space rather than directly on the 3D shape. This allows the texture to be disentangled from the underlying shape and transferable to other shapes that share the same UV space, i.e., from the same category. We integrate the UV sphere space with the radiance field, which provides a more efficient and accurate representation of textures than traditional texture maps. We perform our experiments on real-world object datasets where we achieve not only realistic synthesis but also substantial improvements over state-of-the-arts on texture controlling and editing. Project Page: https://www.anjiecheng.me/TUVF", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.03027", "title": "NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads", "authors": [ "Tobias Kirschstein", "Shenhan Qian", "Simon Giebenhain", "Tim Walter", "Matthias Nie\u00dfner" ], "github": "", "project_page": "", "abstract": "We focus on reconstructing high-fidelity radiance fields of human heads, capturing their animations over time, and synthesizing re-renderings from novel viewpoints at arbitrary time steps. To this end, we propose a new multi-view capture setup composed of 16 calibrated machine vision cameras that record time-synchronized images at 7.1 MP resolution and 73 frames per second. With our setup, we collect a new dataset of over 4700 high-resolution, high-framerate sequences of more than 220 human heads, from which we introduce a new human head reconstruction benchmark. The recorded sequences cover a wide range of facial dynamics, including head motions, natural expressions, emotions, and spoken language. In order to reconstruct high-fidelity human heads, we propose Dynamic Neural Radiance Fields using Hash Ensembles (NeRSemble). We represent scene dynamics by combining a deformation field and an ensemble of 3D multi-resolution hash encodings. The deformation field allows for precise modeling of simple scene movements, while the ensemble of hash encodings helps to represent complex dynamics. As a result, we obtain radiance field representations of human heads that capture motion over time and facilitate re-rendering of arbitrary novel viewpoints. In a series of experiments, we explore the design choices of our method and demonstrate that our approach outperforms state-of-the-art dynamic radiance field approaches by a significant margin.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.02968", "title": "Masked Trajectory Models for Prediction, Representation, and Control", "authors": [ "Philipp Wu", "Arjun Majumdar", "Kevin Stone", "Yixin Lin", "Igor Mordatch", "Pieter Abbeel", "Aravind Rajeswaran" ], "github": "https://github.com/facebookresearch/mtm", "project_page": "", "abstract": "We introduce Masked Trajectory Models (MTM) as a generic abstraction for sequential decision making. MTM takes a trajectory, such as a state-action sequence, and aims to reconstruct the trajectory conditioned on random subsets of the same trajectory. By training with a highly randomized masking pattern, MTM learns versatile networks that can take on different roles or capabilities, by simply choosing appropriate masks at inference time. For example, the same MTM network can be used as a forward dynamics model, inverse dynamics model, or even an offline RL agent. Through extensive experiments in several continuous control tasks, we show that the same MTM network -- i.e. same weights -- can match or outperform specialized networks trained for the aforementioned capabilities. Additionally, we find that state representations learned by MTM can significantly accelerate the learning speed of traditional RL algorithms. Finally, in offline RL benchmarks, we find that MTM is competitive with specialized offline RL algorithms, despite MTM being a generic self-supervised learning method without any explicit RL components. Code is available at https://github.com/facebookresearch/mtm", "github_manual": "", "github_regex": "https://github.com/facebookresearch/mtm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.02678", "title": "Real-Time Neural Appearance Models", "authors": [ "Tizian Zeltner", "Fabrice Rousselle", "Andrea Weidlich", "Petrik Clarberg", "Jan Nov\u00e1k", "Benedikt Bitterli", "Alex Evans", "Tom\u00e1\u0161 Davidovi\u010d", "Simon Kallweit", "Aaron Lefohn" ], "github": "https://github.com/microsoft/DirectXShaderCompiler", "project_page": "", "abstract": "We present a complete system for real-time rendering of scenes with complex appearance previously reserved for offline use. This is achieved with a combination of algorithmic and system level innovations. Our appearance model utilizes learned hierarchical textures that are interpreted using neural decoders, which produce reflectance values and importance-sampled directions. To best utilize the modeling capacity of the decoders, we equip the decoders with two graphics priors. The first prior -- transformation of directions into learned shading frames -- facilitates accurate reconstruction of mesoscale effects. The second prior -- a microfacet sampling distribution -- allows the neural decoder to perform importance sampling efficiently. The resulting appearance model supports anisotropic sampling and level-of-detail rendering, and allows baking deeply layered material graphs into a compact unified neural representation. By exposing hardware accelerated tensor operations to ray tracing shaders, we show that it is possible to inline and execute the neural decoders efficiently inside a real-time path tracer. We analyze scalability with increasing number of neural materials and propose to improve performance using code optimized for coherent and divergent execution. Our neural material shaders can be over an order of magnitude faster than non-neural layered materials. This opens up the door for using film-quality visuals in real-time applications such as games and live previews.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/DirectXShaderCompiler", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.03052", "title": "Tracking through Containers and Occluders in the Wild", "authors": [ "Basile Van Hoorick", "Pavel Tokmakov", "Simon Stent", "Jie Li", "Carl Vondrick" ], "github": "https://github.com/basilevh/tcow", "project_page": "", "abstract": "Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems. In this paper, we introduce TCOW, a new benchmark and model for visual tracking through heavy occlusion and containment. We set up a task where the goal is to, given a video sequence, segment both the projected extent of the target object, as well as the surrounding container or occluder whenever one exists. To study this task, we create a mixture of synthetic and annotated real datasets to support both supervised learning and structured evaluation of model performance under various forms of task variation, such as moving or nested containment. We evaluate two recent transformer-based video models and find that while they can be surprisingly capable of tracking targets under certain settings of task variation, there remains a considerable performance gap before we can claim a tracking model to have acquired a true notion of object permanence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/basilevh/tcow", "github_api_added_by": "auto" }, { "date": "2023-05-05", "arxiv_id": "2305.02790", "title": "BranchNorm: Robustly Scaling Extremely Deep Transformers", "authors": [ "Yijin Liu", "Xianfeng Zeng", "Fandong Meng", "Jie Zhou" ], "github": "", "project_page": "", "abstract": "Recently, DeepNorm scales Transformers into extremely deep (i.e., 1000 layers) and reveals the promising potential of deep scaling. To stabilize the training of deep models, DeepNorm (Wang et al., 2022) attempts to constrain the model update to a constant value. Although applying such a constraint can benefit the early stage of model training, it may lead to undertrained models during the whole training procedure. In this paper, we propose BranchNorm, which dynamically rescales the non-residual branch of Transformer in accordance with the training period. BranchNorm not only theoretically stabilizes the training with smooth gradient norms at the early stage, but also encourages better convergence in the subsequent training stage. Experiment results on multiple translation tasks demonstrate that BranchNorm achieves a better trade-off between training stability and converge performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.02412", "title": "Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents", "authors": [ "Yue Wu", "So Yeon Min", "Yonatan Bisk", "Ruslan Salakhutdinov", "Amos Azaria", "Yuanzhi Li", "Tom Mitchell", "Shrimai Prabhumoye" ], "github": "", "project_page": "", "abstract": "Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging control tasks, either by action scoring, or action modeling (fine-tuning). However, the transformer architecture inherits several constraints that make it difficult for the LLM to directly serve as the agent: e.g. limited input lengths, fine-tuning inefficiency, bias from pre-training, and incompatibility with non-text environments. To maintain compatibility with a low-level trainable actor, we propose to instead use the knowledge in LLMs to simplify the control problem, rather than solving it. We propose the Plan, Eliminate, and Track (PET) framework. The Plan module translates a task description into a list of high-level sub-tasks. The Eliminate module masks out irrelevant objects and receptacles from the observation for the current sub-task. Finally, the Track module determines whether the agent has accomplished each sub-task. On the AlfWorld instruction following benchmark, the PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.02783", "title": "Automated Code generation for Information Technology Tasks in YAML through Large Language Models", "authors": [ "Saurabh Pujar", "Luca Buratti", "Xiaojie Guo", "Nicolas Dupuis", "Burn Lewis", "Sahil Suneja", "Atin Sood", "Ganesh Nalawade", "Matt Jones", "Alessandro Morari", "Ruchir Puri" ], "github": "", "project_page": "", "abstract": "The recent improvement in code generation capabilities due to the use of large language models has mainly benefited general purpose programming languages. Domain specific languages, such as the ones used for IT Automation, have received far less attention, despite involving many active developers and being an essential component of modern cloud platforms. This work focuses on the generation of Ansible-YAML, a widely used markup language for IT Automation. We present Ansible Wisdom, a natural-language to Ansible-YAML code generation tool, aimed at improving IT automation productivity. Ansible Wisdom is a transformer-based model, extended by training with a new dataset containing Ansible-YAML. We also develop two novel performance metrics for YAML and Ansible to capture the specific characteristics of this domain. Results show that Ansible Wisdom can accurately generate Ansible script from natural language prompts with performance comparable or better than existing state of the art code generation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.02440", "title": "Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs", "authors": [ "Deepak Narayanan", "Keshav Santhanam", "Peter Henderson", "Rishi Bommasani", "Tony Lee", "Percy Liang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) power many state-of-the-art systems in natural language processing. However, these models are extremely computationally expensive, even at inference time, raising the natural question: when is the extra cost of deploying a larger model worth the anticipated boost in capabilities? Better understanding this tradeoff fundamentally could benefit from an inference efficiency metric that is both (i) easily comparable across models from different providers, and (ii) representative of the true cost of running queries in an isolated performance environment. Unfortunately, access to LLMs today is largely restricted to black-box text generation APIs and raw runtimes measured through this interface do not satisfy these desiderata: model providers can apply various software and hardware optimizations orthogonal to the model, and models served on shared infrastructure are susceptible to performance contention. To circumvent these problems, we propose a new metric for comparing inference efficiency across models. This metric puts models on equal footing as though they were served (i) on uniform hardware and software, and (ii) without performance contention. We call this metric the idealized runtime, and we propose a methodology to efficiently estimate this metric for autoregressive Transformer models. We also propose cost-aware variants that incorporate the number of accelerators needed to serve the model. Using these metrics, we compare ten state-of-the-art LLMs to provide the first analysis of inference efficiency-capability tradeoffs; we make several observations from this analysis, including the fact that the superior inference runtime performance of certain APIs is often a byproduct of optimizations within the API rather than the underlying model. Our methodology also facilitates the efficient comparison of different software and hardware stacks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.03047", "title": "Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision", "authors": [ "Zhiqing Sun", "Yikang Shen", "Qinhong Zhou", "Hongxin Zhang", "Zhenfang Chen", "David Cox", "Yiming Yang", "Chuang Gan" ], "github": "https://github.com/IBM/Dromedary", "project_page": "", "abstract": "Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback (RLHF) to align the output of large language models (LLMs) with human intentions, ensuring they are helpful, ethical, and reliable. However, this dependence can significantly constrain the true potential of AI-assistant agents due to the high cost of obtaining human supervision and the related issues on quality, reliability, diversity, self-consistency, and undesirable biases. To address these challenges, we propose a novel approach called SELF-ALIGN, which combines principle-driven reasoning and the generative power of LLMs for the self-alignment of AI agents with minimal human supervision. Our approach encompasses four stages: first, we use an LLM to generate synthetic prompts, and a topic-guided method to augment the prompt diversity; second, we use a small set of human-written principles for AI models to follow, and guide the LLM through in-context learning from demonstrations (of principles application) to produce helpful, ethical, and reliable responses to user's queries; third, we fine-tune the original LLM with the high-quality self-aligned responses so that the resulting model can generate desirable responses for each query directly without the principle set and the demonstrations anymore; and finally, we offer a refinement step to address the issues of overly-brief or indirect responses. Applying SELF-ALIGN to the LLaMA-65b base language model, we develop an AI assistant named Dromedary. With fewer than 300 lines of human annotations (including < 200 seed prompts, 16 generic principles, and 5 exemplars for in-context learning). Dromedary significantly surpasses the performance of several state-of-the-art AI systems, including Text-Davinci-003 and Alpaca, on benchmark datasets with various settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/IBM/Dromedary", "github_api_added_by": "auto" }, { "date": "2023-05-05", "arxiv_id": "2305.02483", "title": "ChatGPT-steered Editing Instructor for Customization of Abstractive Summarization", "authors": [ "Wen Xiao", "Yujia Xie", "Giuseppe Carenini", "Pengcheng He" ], "github": "https://github.com/wendy-xiao/chatgpt_editing_summ", "project_page": "", "abstract": "Tailoring outputs of large language models, such as ChatGPT, to specific user needs remains a challenge despite their impressive generation quality. In this paper, we propose a tri-agent generation pipeline consisting of a generator, an instructor, and an editor to enhance the customization of generated outputs. The generator produces an initial output, the user-specific instructor generates editing instructions, and the editor generates a revised output aligned with user preferences. The inference-only large language model (ChatGPT) serves as both the generator and the editor, while a smaller model acts as the user-specific instructor to guide the generation process toward user needs. The instructor is trained using editor-steered reinforcement learning, leveraging feedback from the large-scale editor model to optimize instruction generation. Experimental results on two abstractive summarization datasets demonstrate the effectiveness of our approach in generating outputs that better fulfill user expectations.", "github_manual": "https://github.com/wendy-xiao/chatgpt_editing_summ", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-05", "arxiv_id": "2305.02463", "title": "Shap-E: Generating Conditional 3D Implicit Functions", "authors": [ "Heewoo Jun", "Alex Nichol" ], "github": "https://github.com/openai/shap-e", "project_page": "", "abstract": "We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space. We release model weights, inference code, and samples at https://github.com/openai/shap-e.", "github_manual": "", "github_regex": "https://github.com/openai/shap-e", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-08", "arxiv_id": "2305.03111", "title": "Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs", "authors": [ "Jinyang Li", "Binyuan Hui", "Ge Qu", "Binhua Li", "Jiaxi Yang", "Bowen Li", "Bailin Wang", "Bowen Qin", "Rongyu Cao", "Ruiying Geng", "Nan Huo", "Chenhao Ma", "Kevin C. C. Chang", "Fei Huang", "Reynold Cheng", "Yongbin Li" ], "github": "https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/bird", "project_page": "", "abstract": "Text-to-SQL parsing, which aims at converting natural language instructions into executable SQLs, has gained increasing attention in recent years. In particular, Codex and ChatGPT have shown impressive results in this task. However, most of the prevalent benchmarks, i.e., Spider, and WikiSQL, focus on database schema with few rows of database contents leaving the gap between academic study and real-world applications. To mitigate this gap, we present Bird, a big benchmark for large-scale database grounded in text-to-SQL tasks, containing 12,751 pairs of text-to-SQL data and 95 databases with a total size of 33.4 GB, spanning 37 professional domains. Our emphasis on database values highlights the new challenges of dirty database contents, external knowledge between NL questions and database contents, and SQL efficiency, particularly in the context of massive databases. To solve these problems, text-to-SQL models must feature database value comprehension in addition to semantic parsing. The experimental results demonstrate the significance of database values in generating accurate text-to-SQLs for big databases. Furthermore, even the most effective text-to-SQL models, i.e. ChatGPT, only achieves 40.08% in execution accuracy, which is still far from the human result of 92.96%, proving that challenges still stand. Besides, we also provide an efficiency analysis to offer insights into generating text-to-efficient-SQLs that are beneficial to industries. We believe that BIRD will contribute to advancing real-world applications of text-to-SQL research. The leaderboard and source code are available: https://bird-bench.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/bird", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-08", "arxiv_id": "2305.03726", "title": "Otter: A Multi-Modal Model with In-Context Instruction Tuning", "authors": [ "Bo Li", "Yuanhan Zhang", "Liangyu Chen", "Jinghao Wang", "Jingkang Yang", "Ziwei Liu" ], "github": "https://github.com/explosion/spacy-models", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated significant universal capabilities as few/zero-shot learners in various tasks due to their pre-training on vast amounts of text data, as exemplified by GPT-3, which boosted to InstrctGPT and ChatGPT, effectively following natural language instructions to accomplish real-world tasks. In this paper, we propose to introduce instruction tuning into multi-modal models, motivated by the Flamingo model's upstream interleaved format pretraining dataset. We adopt a similar approach to construct our MultI-Modal In-Context Instruction Tuning (MIMIC-IT) dataset. We then introduce Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following ability and in-context learning. We also optimize OpenFlamingo's implementation for researchers, democratizing the required training resources from 1times A100 GPU to 4times RTX-3090 GPUs, and integrate both OpenFlamingo and Otter into Huggingface Transformers for more researchers to incorporate the models into their customized training and inference pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/explosion/spacy-models", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-08", "arxiv_id": "2305.03695", "title": "Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements", "authors": [ "Jiacheng Liu", "Wenya Wang", "Dianzhuo Wang", "Noah A. Smith", "Yejin Choi", "Hannaneh Hajishirzi" ], "github": "", "project_page": "", "abstract": "Despite the much discussed capabilities of today's language models, they are still prone to silly and unexpected commonsense failures. We consider a retrospective verification approach that reflects on the correctness of LM outputs, and introduce Vera, a general-purpose model that estimates the plausibility of declarative statements based on commonsense knowledge. Trained on ~7M commonsense statements created from 19 QA datasets and two large-scale knowledge bases, and with a combination of three training objectives, Vera is a versatile model that effectively separates correct from incorrect statements across diverse commonsense domains. When applied to solving commonsense problems in the verification format, Vera substantially outperforms existing models that can be repurposed for commonsense verification, and it further exhibits generalization capabilities to unseen tasks and provides well-calibrated outputs. We find that Vera excels at filtering LM-generated commonsense knowledge and is useful in detecting erroneous commonsense statements generated by models like ChatGPT in real-world settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-08", "arxiv_id": "2305.03713", "title": "Avatar Fingerprinting for Authorized Use of Synthetic Talking-Head Videos", "authors": [ "Ekta Prashnani", "Koki Nagano", "Shalini De Mello", "David Luebke", "Orazio Gallo" ], "github": "", "project_page": "", "abstract": "Modern generators render talking-head videos with impressive levels of photorealism, ushering in new user experiences such as videoconferencing under constrained bandwidth budgets. Their safe adoption, however, requires a mechanism to verify if the rendered video is trustworthy. For instance, for videoconferencing we must identify cases in which a synthetic video portrait uses the appearance of an individual without their consent. We term this task avatar fingerprinting. We propose to tackle it by leveraging facial motion signatures unique to each person. Specifically, we learn an embedding in which the motion signatures of one identity are grouped together, and pushed away from those of other identities, regardless of the appearance in the synthetic video. Avatar fingerprinting algorithms will be critical as talking head generators become more ubiquitous, and yet no large scale datasets exist for this new task. Therefore, we contribute a large dataset of people delivering scripted and improvised short monologues, accompanied by synthetic videos in which we render videos of one person using the facial appearance of another. Project page: https://research.nvidia.com/labs/nxp/avatar-fingerprinting/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-08", "arxiv_id": "2305.03668", "title": "A Suite of Generative Tasks for Multi-Level Multimodal Webpage Understanding", "authors": [ "Andrea Burns", "Krishna Srinivasan", "Joshua Ainslie", "Geoff Brown", "Bryan A. Plummer", "Kate Saenko", "Jianmo Ni", "Mandy Guo" ], "github": "", "project_page": "", "abstract": "Webpages have been a rich, scalable resource for vision-language and language only tasks. Yet only pieces of webpages are kept: image-caption pairs, long text articles, or raw HTML, never all in one place. Webpage tasks have resultingly received little attention and structured image-text data left underused. To study multimodal webpage understanding, we introduce the Wikipedia Webpage suite (WikiWeb2M) of 2M pages. We verify its utility on three generative tasks: page description generation, section summarization, and contextual image captioning. We design a novel attention mechanism Prefix Global, which selects the most relevant image and text content as global tokens to attend to the rest of the webpage for context. By using page structure to separate such tokens, it performs better than full attention with lower computational complexity. Experiments show that the new annotations from WikiWeb2M improve task performance compared to data from prior work. We also include ablations on sequence length, input features, and model size.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-08", "arxiv_id": "2305.03286", "title": "Composite Motion Learning with Task Control", "authors": [ "Pei Xu", "Xiumin Shang", "Victor Zordan", "Ioannis Karamouzas" ], "github": "https://github.com/xupei0610/compositemotion", "project_page": "", "abstract": "We present a deep learning method for composite and task-driven motion control for physically simulated characters. In contrast to existing data-driven approaches using reinforcement learning that imitate full-body motions, we learn decoupled motions for specific body parts from multiple reference motions simultaneously and directly by leveraging the use of multiple discriminators in a GAN-like setup. In this process, there is no need of any manual work to produce composite reference motions for learning. Instead, the control policy explores by itself how the composite motions can be combined automatically. We further account for multiple task-specific rewards and train a single, multi-objective control policy. To this end, we propose a novel framework for multi-objective learning that adaptively balances the learning of disparate motions from multiple sources and multiple goal-directed control objectives. In addition, as composite motions are typically augmentations of simpler behaviors, we introduce a sample-efficient method for training composite control policies in an incremental manner, where we reuse a pre-trained policy as the meta policy and train a cooperative policy that adapts the meta one for new composite tasks. We show the applicability of our approach on a variety of challenging multi-objective tasks involving both composite motion imitation and multiple goal-directed control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xupei0610/compositemotion", "github_api_added_by": "auto" }, { "date": "2023-05-08", "arxiv_id": "2305.03689", "title": "COLA: How to adapt vision-language models to Compose Objects Localized with Attributes?", "authors": [ "Arijit Ray", "Filip Radenovic", "Abhimanyu Dubey", "Bryan A. Plummer", "Ranjay Krishna", "Kate Saenko" ], "github": "https://github.com/arijitray1993/COLA", "project_page": "", "abstract": "Compositional reasoning is a hallmark of human visual intelligence; yet despite the size of large vision-language models, they struggle to represent simple compositions by combining objects with their attributes. To measure this lack of compositional capability, we design Cola, a text-to-image retrieval benchmark to Compose Objects Localized with Attributes. Using Cola as a testbed, we explore modeling designs to adapt pre-trained vision-language models to reason compositionally about multiple attributes attached to multiple objects. We explore 6 finetuning strategies on 2 seminal vision-language models, using 3 finetuning datasets and 2 test benchmarks (Cola and CREPE). Surprisingly, our optimal finetuning strategy improves a 151M parameter CLIP, which disjointly encodes image and language during pretraining, to perform as well as a 241M parameter FLAVA, which uses a multi-modal transformer encoder during pretraining to attend over both vision and language modalities. This optimal finetuning strategy is a lightweight multi-modal adapter that jointly attends over both image and language features generated by the pretrained model. We show this works better than common strategies such as prompt/fine-tuning, or tuning a comparable number of unimodal layers.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/arijitray1993/COLA", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-08", "arxiv_id": "2305.03210", "title": "AttentionViz: A Global View of Transformer Attention", "authors": [ "Catherine Yeh", "Yida Chen", "Aoyu Wu", "Cynthia Chen", "Fernanda Vi\u00e9gas", "Martin Wattenberg" ], "github": "", "project_page": "", "abstract": "Transformer models are revolutionizing machine learning, but their inner workings remain mysterious. In this work, we present a new visualization technique designed to help researchers understand the self-attention mechanism in transformers that allows these models to learn rich, contextual relationships between elements of a sequence. The main idea behind our method is to visualize a joint embedding of the query and key vectors used by transformer models to compute attention. Unlike previous attention visualization techniques, our approach enables the analysis of global patterns across multiple input sequences. We create an interactive visualization tool, AttentionViz, based on these joint query-key embeddings, and use it to study attention mechanisms in both language and vision transformers. We demonstrate the utility of our approach in improving model understanding and offering new insights about query-key interactions through several application scenarios and expert feedback.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-08", "arxiv_id": "2305.03719", "title": "Governance of the AI, by the AI, and for the AI", "authors": [ "Andrew W. Torrance", "Bill Tomlinson" ], "github": "", "project_page": "", "abstract": "Over the past half century, there have been several false dawns during which the \"arrival\" of world-changing artificial intelligence (AI) has been heralded. Tempting fate, the authors believe the age of AI has, indeed, finally arrived. Powerful image generators, such as DALL-E2 and Midjourney have suddenly allowed anyone with access the ability easily to create rich and complex art. In a similar vein, text generators, such as GPT3.5 (including ChatGPT) and BLOOM, allow users to compose detailed written descriptions of many topics of interest. And, it is even possible now for a person without extensive expertise in writing software to use AI to generate code capable of myriad applications. While AI will continue to evolve and improve, probably at a rapid rate, the current state of AI is already ushering in profound changes to many different sectors of society. Every new technology challenges the ability of humanity to govern it wisely. However, governance is usually viewed as both possible and necessary due to the disruption new technology often poses to social structures, industries, the environment, and other important human concerns. In this article, we offer an analysis of a range of interactions between AI and governance, with the hope that wise decisions may be made that maximize benefits and minimize costs. The article addresses two main aspects of this relationship: the governance of AI by humanity, and the governance of humanity by AI. The approach we have taken is itself informed by AI, as this article was written collaboratively by the authors and ChatGPT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-08", "arxiv_id": "2305.03514", "title": "Can Large Language Models Transform Computational Social Science?", "authors": [ "Caleb Ziems", "William Held", "Omar Shaikh", "Jiaao Chen", "Zhehao Zhang", "Diyi Yang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) like ChatGPT are capable of successfully performing many language processing tasks zero-shot (without the need for training data). If this capacity also applies to the coding of social phenomena like persuasiveness and political ideology, then LLMs could effectively transform Computational Social Science (CSS). This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompting best practices and an extensive evaluation pipeline to measure the zero-shot performance of 13 language models on 24 representative CSS benchmarks. On taxonomic labeling tasks (classification), LLMs fail to outperform the best fine-tuned models but still achieve fair levels of agreement with humans. On free-form coding tasks (generation), LLMs produce explanations that often exceed the quality of crowdworkers' gold references. We conclude that today's LLMs can radically augment the CSS research pipeline in two ways: (1) serving as zero-shot data annotators on human annotation teams, and (2) bootstrapping challenging creative generation tasks (e.g., explaining the hidden meaning behind text). In summary, LLMs can significantly reduce costs and increase efficiency of social science analysis in partnership with humans.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-08", "arxiv_id": "2305.03509", "title": "Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion", "authors": [ "Seongmin Lee", "Benjamin Hoover", "Hendrik Strobelt", "Zijie J. Wang", "ShengYun Peng", "Austin Wright", "Kevin Li", "Haekyu Park", "Haoyang Yang", "Duen Horng Chau" ], "github": "https://github.com/poloclub/diffusion-explainer", "project_page": "", "abstract": "Diffusion-based generative models' impressive ability to create convincing images has captured global attention. However, their complex internal structures and operations often make them difficult for non-experts to understand. We present Diffusion Explainer, the first interactive visualization tool that explains how Stable Diffusion transforms text prompts into images. Diffusion Explainer tightly integrates a visual overview of Stable Diffusion's complex components with detailed explanations of their underlying operations, enabling users to fluidly transition between multiple levels of abstraction through animations and interactive elements. By comparing the evolutions of image representations guided by two related text prompts over refinement timesteps, users can discover the impact of prompts on image generation. Diffusion Explainer runs locally in users' web browsers without the need for installation or specialized hardware, broadening the public's education access to modern AI techniques. Our open-sourced tool is available at: https://poloclub.github.io/diffusion-explainer/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/poloclub/diffusion-explainer", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-09", "arxiv_id": "2305.04091", "title": "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models", "authors": [ "Lei Wang", "Wanyu Xu", "Yihuai Lan", "Zhiqiang Hu", "Yunshi Lan", "Roy Ka-Wei Lee", "Ee-Peng Lim" ], "github": "https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting", "project_page": "", "abstract": "Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, few-shot chain-of-thought (CoT) prompting includes a few manually crafted step-by-step reasoning demonstrations which enable LLMs to explicitly generate reasoning steps and improve their reasoning task accuracy. To eliminate the manual effort, Zero-shot-CoT concatenates the target problem statement with \"Let's think step by step\" as an input prompt to LLMs. Despite the success of Zero-shot-CoT, it still suffers from three pitfalls: calculation errors, missing-step errors, and semantic misunderstanding errors. To address the missing-step errors, we propose Plan-and-Solve (PS) Prompting. It consists of two components: first, devising a plan to divide the entire task into smaller subtasks, and then carrying out the subtasks according to the plan. To address the calculation errors and improve the quality of generated reasoning steps, we extend PS prompting with more detailed instructions and derive PS+ prompting. We evaluate our proposed prompting strategy on ten datasets across three reasoning problems. The experimental results over GPT-3 show that our proposed zero-shot prompting consistently outperforms Zero-shot-CoT across all datasets by a large margin, is comparable to or exceeds Zero-shot-Program-of-Thought Prompting, and has comparable performance with 8-shot CoT prompting on the math reasoning problem. The code can be found at https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting.", "github_manual": "", "github_regex": "https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-09", "arxiv_id": "2305.04789", "title": "AvatarReX: Real-time Expressive Full-body Avatars", "authors": [ "Zerong Zheng", "Xiaochen Zhao", "Hongwen Zhang", "Boning Liu", "Yebin Liu" ], "github": "", "project_page": "", "abstract": "We present AvatarReX, a new method for learning NeRF-based full-body avatars from video data. The learnt avatar not only provides expressive control of the body, hands and the face together, but also supports real-time animation and rendering. To this end, we propose a compositional avatar representation, where the body, hands and the face are separately modeled in a way that the structural prior from parametric mesh templates is properly utilized without compromising representation flexibility. Furthermore, we disentangle the geometry and appearance for each part. With these technical designs, we propose a dedicated deferred rendering pipeline, which can be executed in real-time framerate to synthesize high-quality free-view images. The disentanglement of geometry and appearance also allows us to design a two-pass training strategy that combines volume rendering and surface rendering for network training. In this way, patch-level supervision can be applied to force the network to learn sharp appearance details on the basis of geometry estimation. Overall, our method enables automatic construction of expressive full-body avatars with real-time rendering capability, and can generate photo-realistic images with dynamic details for novel body motions and facial expressions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-09", "arxiv_id": "2305.04268", "title": "Multi-Space Neural Radiance Fields", "authors": [ "Ze-Xin Yin", "Jiaxiong Qiu", "Ming-Ming Cheng", "Bo Ren" ], "github": "", "project_page": "", "abstract": "Existing Neural Radiance Fields (NeRF) methods suffer from the existence of reflective objects, often resulting in blurry or distorted rendering. Instead of calculating a single radiance field, we propose a multi-space neural radiance field (MS-NeRF) that represents the scene using a group of feature fields in parallel sub-spaces, which leads to a better understanding of the neural network toward the existence of reflective and refractive objects. Our multi-space scheme works as an enhancement to existing NeRF methods, with only small computational overheads needed for training and inferring the extra-space outputs. We demonstrate the superiority and compatibility of our approach using three representative NeRF-based models, i.e., NeRF, Mip-NeRF, and Mip-NeRF 360. Comparisons are performed on a novelly constructed dataset consisting of 25 synthetic scenes and 7 real captured scenes with complex reflection and refraction, all having 360-degree viewpoints. Extensive experiments show that our approach significantly outperforms the existing single-space NeRF methods for rendering high-quality scenes concerned with complex light paths through mirror-like objects. Our code and dataset will be publicly available at https://zx-yin.github.io/msnerf.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-09", "arxiv_id": "2305.04241", "title": "Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens", "authors": [ "Zhanpeng Zeng", "Cole Hawkins", "Mingyi Hong", "Aston Zhang", "Nikolaos Pappas", "Vikas Singh", "Shuai Zheng" ], "github": "", "project_page": "", "abstract": "Transformer models are foundational to natural language processing (NLP) and computer vision. Despite various recent works devoted to reducing the quadratic cost of such models (as a function of the sequence length n), dealing with ultra long sequences efficiently (e.g., with more than 16K tokens) remains challenging. Applications such as answering questions based on an entire book or summarizing a scientific article are inefficient or infeasible. In this paper, we propose to significantly reduce the dependency of a Transformer model's complexity on n, by compressing the input into a representation whose size r is independent of n at each layer. Specifically, by exploiting the fact that in many tasks, only a small subset of special tokens (we call VIP-tokens) are most relevant to the final prediction, we propose a VIP-token centric compression (Vcc) scheme which selectively compresses the input sequence based on their impact on approximating the representation of these VIP-tokens. Compared with competitive baselines, the proposed algorithm not only is efficient (achieving more than 3times efficiency improvement compared to baselines on 4K and 16K lengths), but also achieves competitive or better performance on a large number of tasks. Further, we show that our algorithm can be scaled to 128K tokens (or more) while consistently offering accuracy improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-09", "arxiv_id": "2305.04790", "title": "MultiModal-GPT: A Vision and Language Model for Dialogue with Humans", "authors": [ "Tao Gong", "Chengqi Lyu", "Shilong Zhang", "Yudong Wang", "Miao Zheng", "Qian Zhao", "Kuikun Liu", "Wenwei Zhang", "Ping Luo", "Kai Chen" ], "github": "https://github.com/open-mmlab/Multimodal-GPT", "project_page": "", "abstract": "We present a vision and language model named MultiModal-GPT to conduct multi-round dialogue with humans. MultiModal-GPT can follow various instructions from humans, such as generating a detailed caption, counting the number of interested objects, and answering general questions from users. MultiModal-GPT is parameter-efficiently fine-tuned from OpenFlamingo, with Low-rank Adapter (LoRA) added both in the cross-attention part and the self-attention part of the language model. We first construct instruction templates with vision and language data for multi-modality instruction tuning to make the model understand and follow human instructions. We find the quality of training data is vital for the dialogue performance, where few data containing short answers can lead the model to respond shortly to any instructions. To further enhance the ability to chat with humans of the MultiModal-GPT, we utilize language-only instruction-following data to train the MultiModal-GPT jointly. The joint training of language-only and visual-language instructions with the same instruction template effectively improves dialogue performance. Various demos show the ability of continuous dialogue of MultiModal-GPT with humans. Code and demo are at https://github.com/open-mmlab/Multimodal-GPT", "github_manual": "", "github_regex": "https://github.com/open-mmlab/Multimodal-GPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-09", "arxiv_id": "2305.04388", "title": "Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting", "authors": [ "Miles Turpin", "Julian Michael", "Ethan Perez", "Samuel R. Bowman" ], "github": "https://github.com/milesaturpin/cot-unfaithfulness", "project_page": "", "abstract": "Large Language Models (LLMs) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output, often referred to as chain-of-thought reasoning (CoT). It is tempting to interpret these CoT explanations as the LLM's process for solving a task. However, we find that CoT explanations can systematically misrepresent the true reason for a model's prediction. We demonstrate that CoT explanations can be heavily influenced by adding biasing features to model inputs -- e.g., by reordering the multiple-choice options in a few-shot prompt to make the answer always \"(A)\" -- which models systematically fail to mention in their explanations. When we bias models toward incorrect answers, they frequently generate CoT explanations supporting those answers. This causes accuracy to drop by as much as 36% on a suite of 13 tasks from BIG-Bench Hard, when testing with GPT-3.5 from OpenAI and Claude 1.0 from Anthropic. On a social-bias task, model explanations justify giving answers in line with stereotypes without mentioning the influence of these social biases. Our findings indicate that CoT explanations can be plausible yet misleading, which risks increasing our trust in LLMs without guaranteeing their safety. CoT is promising for explainability, but our results highlight the need for targeted efforts to evaluate and improve explanation faithfulness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/milesaturpin/cot-unfaithfulness", "github_api_added_by": "auto" }, { "date": "2023-05-09", "arxiv_id": "2305.03981", "title": "Pre-training Language Model as a Multi-perspective Course Learner", "authors": [ "Beiduo Chen", "Shaohan Huang", "Zihan Zhang", "Wu Guo", "Zhenhua Ling", "Haizhen Huang", "Furu Wei", "Weiwei Deng", "Qi Zhang" ], "github": "", "project_page": "", "abstract": "ELECTRA, the generator-discriminator pre-training framework, has achieved impressive semantic construction capability among various downstream tasks. Despite the convincing performance, ELECTRA still faces the challenges of monotonous training and deficient interaction. Generator with only masked language modeling (MLM) leads to biased learning and label imbalance for discriminator, decreasing learning efficiency; no explicit feedback loop from discriminator to generator results in the chasm between these two components, underutilizing the course learning. In this study, a multi-perspective course learning (MCL) method is proposed to fetch a many degrees and visual angles for sample-efficient pre-training, and to fully leverage the relationship between generator and discriminator. Concretely, three self-supervision courses are designed to alleviate inherent flaws of MLM and balance the label in a multi-perspective way. Besides, two self-correction courses are proposed to bridge the chasm between the two encoders by creating a \"correction notebook\" for secondary-supervision. Moreover, a course soups trial is conducted to solve the \"tug-of-war\" dynamics problem of MCL, evolving a stronger pre-trained model. Experimental results show that our method significantly improves ELECTRA's average performance by 2.8% and 3.2% absolute points respectively on GLUE and SQuAD 2.0 benchmarks, and overshadows recent advanced ELECTRA-style models under the same settings. The pre-trained MCL model is available at https://huggingface.co/McmanusChen/MCL-base.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-09", "arxiv_id": "2305.03937", "title": "Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization", "authors": [ "Anastasia Razdaibiedina", "Yuning Mao", "Rui Hou", "Madian Khabsa", "Mike Lewis", "Jimmy Ba", "Amjad Almahairi" ], "github": "https://github.com/arazd/residualprompts", "project_page": "", "abstract": "Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft prompts constitute <0.1% of total parameters), it typically performs worse than other efficient tuning methods and is quite sensitive to hyper-parameters. In this work, we introduce Residual Prompt Tuning - a simple and efficient method that significantly improves the performance and stability of prompt tuning. We propose to reparameterize soft prompt embeddings using a shallow network with a residual connection. Our experiments show that Residual Prompt Tuning significantly outperforms prompt tuning on SuperGLUE benchmark. Notably, our method reaches +7 points improvement over prompt tuning with T5-Base and allows to reduce the prompt length by 10x without hurting performance. In addition, we show that our approach is robust to the choice of learning rate and prompt initialization, and is effective in few-shot settings.", "github_manual": "https://github.com/arazd/residualprompts", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-09", "arxiv_id": "2305.04160", "title": "X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages", "authors": [ "Feilong Chen", "Minglun Han", "Haozhi Zhao", "Qingyang Zhang", "Jing Shi", "Shuang Xu", "Bo Xu" ], "github": "https://github.com/phellonchen/x-llm", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable language abilities. GPT-4, based on advanced LLMs, exhibits extraordinary multimodal capabilities beyond previous visual language models. We attribute this to the use of more advanced LLMs compared with previous multimodal models. Unfortunately, the model architecture and training strategies of GPT-4 are unknown. To endow LLMs with multimodal capabilities, we propose X-LLM, which converts Multi-modalities (images, speech, videos) into foreign languages using X2L interfaces and inputs them into a large Language model (ChatGLM). Specifically, X-LLM aligns multiple frozen single-modal encoders and a frozen LLM using X2L interfaces, where ``X'' denotes multi-modalities such as image, speech, and videos, and ``L'' denotes languages. X-LLM's training consists of three stages: (1) Converting Multimodal Information: The first stage trains each X2L interface to align with its respective single-modal encoder separately to convert multimodal information into languages. (2) Aligning X2L representations with the LLM: single-modal encoders are aligned with the LLM through X2L interfaces independently. (3) Integrating multiple modalities: all single-modal encoders are aligned with the LLM through X2L interfaces to integrate multimodal capabilities into the LLM. Our experiments show that X-LLM demonstrates impressive multimodel chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and yields a 84.5\\% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. And we also conduct quantitative tests on using LLM for ASR and multimodal ASR, hoping to promote the era of LLM-based speech recognition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/phellonchen/x-llm", "github_api_added_by": "auto" }, { "date": "2023-05-09", "arxiv_id": "2305.04745", "title": "Controllable Light Diffusion for Portraits", "authors": [ "David Futschik", "Kelvin Ritland", "James Vecore", "Sean Fanello", "Sergio Orts-Escolano", "Brian Curless", "Daniel S\u00fdkora", "Rohit Pandey" ], "github": "", "project_page": "", "abstract": "We introduce light diffusion, a novel method to improve lighting in portraits, softening harsh shadows and specular highlights while preserving overall scene illumination. Inspired by professional photographers' diffusers and scrims, our method softens lighting given only a single portrait photo. Previous portrait relighting approaches focus on changing the entire lighting environment, removing shadows (ignoring strong specular highlights), or removing shading entirely. In contrast, we propose a learning based method that allows us to control the amount of light diffusion and apply it on in-the-wild portraits. Additionally, we design a method to synthetically generate plausible external shadows with sub-surface scattering effects while conforming to the shape of the subject's face. Finally, we show how our approach can increase the robustness of higher level vision applications, such as albedo estimation, geometry estimation and semantic segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-09", "arxiv_id": "2305.04461", "title": "Locally Attentional SDF Diffusion for Controllable 3D Shape Generation", "authors": [ "Xin-Yang Zheng", "Hao Pan", "Peng-Shuai Wang", "Xin Tong", "Yang Liu", "Heung-Yeung Shum" ], "github": "https://github.com/Zhengxinyang/LAS-Diffusion", "project_page": "", "abstract": "Although the recent rapid evolution of 3D generative neural networks greatly improves 3D shape generation, it is still not convenient for ordinary users to create 3D shapes and control the local geometry of generated shapes. To address these challenges, we propose a diffusion-based 3D generation framework -- locally attentional SDF diffusion, to model plausible 3D shapes, via 2D sketch image input. Our method is built on a two-stage diffusion model. The first stage, named occupancy-diffusion, aims to generate a low-resolution occupancy field to approximate the shape shell. The second stage, named SDF-diffusion, synthesizes a high-resolution signed distance field within the occupied voxels determined by the first stage to extract fine geometry. Our model is empowered by a novel view-aware local attention mechanism for image-conditioned shape generation, which takes advantage of 2D image patch features to guide 3D voxel feature learning, greatly improving local controllability and model generalizability. Through extensive experiments in sketch-conditioned and category-conditioned 3D shape generation tasks, we validate and demonstrate the ability of our method to provide plausible and diverse 3D shapes, as well as its superior controllability and generalizability over existing work. Our code and trained models are available at https://zhengxinyang.github.io/projects/LAS-Diffusion.html", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Zhengxinyang/LAS-Diffusion", "github_api_added_by": "auto" }, { "date": "2023-05-09", "arxiv_id": "2305.04391", "title": "A Variational Perspective on Solving Inverse Problems with Diffusion Models", "authors": [ "Morteza Mardani", "Jiaming Song", "Jan Kautz", "Arash Vahdat" ], "github": "https://github.com/nvlabs/red-diff", "project_page": "", "abstract": "Diffusion models have emerged as a key pillar of foundation models in visual domains. One of their critical applications is to universally solve different downstream inverse tasks via a single diffusion prior without re-training for each task. Most inverse tasks can be formulated as inferring a posterior distribution over data (e.g., a full image) given a measurement (e.g., a masked image). This is however challenging in diffusion models since the nonlinear and iterative nature of the diffusion process renders the posterior intractable. To cope with this challenge, we propose a variational approach that by design seeks to approximate the true posterior distribution. We show that our approach naturally leads to regularization by denoising diffusion process (RED-Diff) where denoisers at different timesteps concurrently impose different structural constraints over the image. To gauge the contribution of denoisers from different timesteps, we propose a weighting mechanism based on signal-to-noise-ratio (SNR). Our approach provides a new variational perspective for solving inverse problems with diffusion models, allowing us to formulate sampling as stochastic optimization, where one can simply apply off-the-shelf solvers with lightweight iterates. Our experiments for image restoration tasks such as inpainting and superresolution demonstrate the strengths of our method compared with state-of-the-art sampling-based diffusion models.", "github_manual": "https://github.com/nvlabs/red-diff", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-10", "arxiv_id": "2305.05662", "title": "InternChat: Solving Vision-Centric Tasks by Interacting with Chatbots Beyond Language", "authors": [ "Zhaoyang Liu", "Yinan He", "Wenhai Wang", "Weiyun Wang", "Yi Wang", "Shoufa Chen", "Qinglong Zhang", "Yang Yang", "Qingyun Li", "Jiashuo Yu", "Kunchang Li", "Zhe Chen", "Xue Yang", "Xizhou Zhu", "Yali Wang", "Limin Wang", "Ping Luo", "Jifeng Dai", "Yu Qiao" ], "github": "https://github.com/OpenGVLab/InternChat", "project_page": "", "abstract": "We present an interactive visual framework named InternChat, or iChat for short. The framework integrates chatbots that have planning and reasoning capabilities, such as ChatGPT, with non-verbal instructions like pointing movements that enable users to directly manipulate images or videos on the screen. Pointing (including gestures, cursors, etc.) movements can provide more flexibility and precision in performing vision-centric tasks that require fine-grained control, editing, and generation of visual content. The name InternChat stands for interaction, nonverbal, and chatbots. Different from existing interactive systems that rely on pure language, by incorporating pointing instructions, the proposed iChat significantly improves the efficiency of communication between users and chatbots, as well as the accuracy of chatbots in vision-centric tasks, especially in complicated visual scenarios where the number of objects is greater than 2. Additionally, in iChat, an auxiliary control mechanism is used to improve the control capability of LLM, and a large vision-language model termed Husky is fine-tuned for high-quality multi-modal dialogue (impressing ChatGPT-3.5-turbo with 93.89% GPT-4 Quality). We hope this work can spark new ideas and directions for future interactive visual systems. Welcome to watch the code at https://github.com/OpenGVLab/InternChat.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/InternChat", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-10", "arxiv_id": "2305.05591", "title": "AudioSlots: A slot-centric generative model for audio separation", "authors": [ "Pradyumna Reddy", "Scott Wisdom", "Klaus Greff", "John R. Hershey", "Thomas Kipf" ], "github": "", "project_page": "", "abstract": "In a range of recent works, object-centric architectures have been shown to be suitable for unsupervised scene decomposition in the vision domain. Inspired by these methods we present AudioSlots, a slot-centric generative model for blind source separation in the audio domain. AudioSlots is built using permutation-equivariant encoder and decoder networks. The encoder network based on the Transformer architecture learns to map a mixed audio spectrogram to an unordered set of independent source embeddings. The spatial broadcast decoder network learns to generate the source spectrograms from the source embeddings. We train the model in an end-to-end manner using a permutation invariant loss function. Our results on Libri2Mix speech separation constitute a proof of concept that this approach shows promise. We discuss the results and limitations of our approach in detail, and further outline potential ways to overcome the limitations and directions for future work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-10", "arxiv_id": "2304.09355", "title": "To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review", "authors": [ "Ravid Shwartz-Ziv", "Yann LeCun" ], "github": "", "project_page": "", "abstract": "Deep neural networks have demonstrated remarkable performance in supervised learning tasks but require large amounts of labeled data. Self-supervised learning offers an alternative paradigm, enabling the model to learn from data without explicit labels. Information theory has been instrumental in understanding and optimizing deep neural networks. Specifically, the information bottleneck principle has been applied to optimize the trade-off between compression and relevant information preservation in supervised settings. However, the optimal information objective in self-supervised learning remains unclear. In this paper, we review various approaches to self-supervised learning from an information-theoretic standpoint and present a unified framework that formalizes the self-supervised information-theoretic learning problem. We integrate existing research into a coherent framework, examine recent self-supervised methods, and identify research opportunities and challenges. Moreover, we discuss empirical measurement of information-theoretic quantities and their estimators. This paper offers a comprehensive review of the intersection between information theory, self-supervised learning, and deep neural networks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-10", "arxiv_id": "2305.05432", "title": "WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset", "authors": [ "Andrea Burns", "Krishna Srinivasan", "Joshua Ainslie", "Geoff Brown", "Bryan A. Plummer", "Kate Saenko", "Jianmo Ni", "Mandy Guo" ], "github": "https://github.com/google-research-datasets/wit/blob/main/wikiweb2m.md", "project_page": "", "abstract": "Webpages have been a rich resource for language and vision-language tasks. Yet only pieces of webpages are kept: image-caption pairs, long text articles, or raw HTML, never all in one place. Webpage tasks have resultingly received little attention and structured image-text data underused. To study multimodal webpage understanding, we introduce the Wikipedia Webpage 2M (WikiWeb2M) suite; the first to retain the full set of images, text, and structure data available in a page. WikiWeb2M can be used for tasks like page description generation, section summarization, and contextual image captioning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-research-datasets/wit/blob/main/wikiweb2m.md", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-10", "arxiv_id": "2305.04966", "title": "NerfAcc: Efficient Sampling Accelerates NeRFs", "authors": [ "Ruilong Li", "Hang Gao", "Matthew Tancik", "Angjoo Kanazawa" ], "github": "https://github.com/nerfstudio-project/nerfacc", "project_page": "", "abstract": "Optimizing and rendering Neural Radiance Fields is computationally expensive due to the vast number of samples required by volume rendering. Recent works have included alternative sampling approaches to help accelerate their methods, however, they are often not the focus of the work. In this paper, we investigate and compare multiple sampling approaches and demonstrate that improved sampling is generally applicable across NeRF variants under an unified concept of transmittance estimator. To facilitate future experiments, we develop NerfAcc, a Python toolbox that provides flexible APIs for incorporating advanced sampling methods into NeRF related methods. We demonstrate its flexibility by showing that it can reduce the training time of several recent NeRF methods by 1.5x to 20x with minimal modifications to the existing codebase. Additionally, highly customized NeRFs, such as Instant-NGP, can be implemented in native PyTorch using NerfAcc.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nerfstudio-project/nerfacc", "github_api_added_by": "auto" }, { "date": "2023-05-10", "arxiv_id": "2305.05065", "title": "Recommender Systems with Generative Retrieval", "authors": [ "Shashank Rajput", "Nikhil Mehta", "Anima Singh", "Raghunandan H. Keshavan", "Trung Vu", "Lukasz Heldt", "Lichan Hong", "Yi Tay", "Vinh Q. Tran", "Jonah Samost", "Maciej Kula", "Ed H. Chi", "Maheswaran Sathiamoorthy" ], "github": "", "project_page": "", "abstract": "Modern recommender systems leverage large-scale retrieval models consisting of two stages: training a dual-encoder model to embed queries and candidates in the same space, followed by an Approximate Nearest Neighbor (ANN) search to select top candidates given a query's embedding. In this paper, we propose a new single-stage paradigm: a generative retrieval model which autoregressively decodes the identifiers for the target candidates in one phase. To do this, instead of assigning randomly generated atomic IDs to each item, we generate Semantic IDs: a semantically meaningful tuple of codewords for each item that serves as its unique identifier. We use a hierarchical method called RQ-VAE to generate these codewords. Once we have the Semantic IDs for all the items, a Transformer based sequence-to-sequence model is trained to predict the Semantic ID of the next item. Since this model predicts the tuple of codewords identifying the next item directly in an autoregressive manner, it can be considered a generative retrieval model. We show that our recommender system trained in this new paradigm improves the results achieved by current SOTA models on the Amazon dataset. Moreover, we demonstrate that the sequence-to-sequence model coupled with hierarchical Semantic IDs offers better generalization and hence improves retrieval of cold-start items for recommendations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-10", "arxiv_id": "2305.05658", "title": "TidyBot: Personalized Robot Assistance with Large Language Models", "authors": [ "Jimmy Wu", "Rika Antonova", "Adam Kan", "Marion Lepert", "Andy Zeng", "Shuran Song", "Jeannette Bohg", "Szymon Rusinkiewicz", "Thomas Funkhouser" ], "github": "https://github.com/jimmyyhwu/tidybot", "project_page": "", "abstract": "For a robot to personalize physical assistance effectively, it must learn user preferences that can be generally reapplied to future scenarios. In this work, we investigate personalization of household cleanup with robots that can tidy up rooms by picking up objects and putting them away. A key challenge is determining the proper place to put each object, as people's preferences can vary greatly depending on personal taste or cultural background. For instance, one person may prefer storing shirts in the drawer, while another may prefer them on the shelf. We aim to build systems that can learn such preferences from just a handful of examples via prior interactions with a particular person. We show that robots can combine language-based planning and perception with the few-shot summarization capabilities of large language models (LLMs) to infer generalized user preferences that are broadly applicable to future interactions. This approach enables fast adaptation and achieves 91.2% accuracy on unseen objects in our benchmark dataset. We also demonstrate our approach on a real-world mobile manipulator called TidyBot, which successfully puts away 85.0% of objects in real-world test scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jimmyyhwu/tidybot", "github_api_added_by": "auto" }, { "date": "2023-05-10", "arxiv_id": "2305.05383", "title": "Code Execution with Pre-trained Language Models", "authors": [ "Chenxiao Liu", "Shuai Lu", "Weizhu Chen", "Daxin Jiang", "Alexey Svyatkovskiy", "Shengyu Fu", "Neel Sundaresan", "Nan Duan" ], "github": "", "project_page": "", "abstract": "Code execution is a fundamental aspect of programming language semantics that reflects the exact behavior of the code. However, most pre-trained models for code intelligence ignore the execution trace and only rely on source code and syntactic structures. In this paper, we investigate how well pre-trained models can understand and perform code execution. We develop a mutation-based data augmentation technique to create a large-scale and realistic Python dataset and task for code execution, which challenges existing models such as Codex. We then present CodeExecutor, a Transformer model that leverages code execution pre-training and curriculum learning to enhance its semantic comprehension. We evaluate CodeExecutor on code execution and show its promising performance and limitations. We also demonstrate its potential benefits for code intelligence tasks such as zero-shot code-to-code search and text-to-code generation. Our analysis provides insights into the learning and generalization abilities of pre-trained models for code execution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-10", "arxiv_id": "2305.05364", "title": "Large Language Model Programs", "authors": [ "Imanol Schlag", "Sainbayar Sukhbaatar", "Asli Celikyilmaz", "Wen-tau Yih", "Jason Weston", "J\u00fcrgen Schmidhuber", "Xian Li" ], "github": "", "project_page": "", "abstract": "In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples. The possibility to parameterise an LLM through such in-context examples widens their capability at a much lower cost than finetuning. We extend this line of reasoning and present a method which further expands the capabilities of an LLM by embedding it within an algorithm or program. To demonstrate the benefits of this approach, we present an illustrative example of evidence-supported question-answering. We obtain a 6.4\\% improvement over the chain of thought baseline through a more algorithmic approach without any finetuning. Furthermore, we highlight recent work from this perspective and discuss the advantages and disadvantages in comparison to the standard approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-10", "arxiv_id": "2305.05644", "title": "Towards Building the Federated GPT: Federated Instruction Tuning", "authors": [ "Jianyi Zhang", "Saeed Vahidian", "Martin Kuo", "Chunyuan Li", "Ruiyi Zhang", "Guoyin Wang", "Yiran Chen" ], "github": "https://github.com/jayzhang42/federatedgpt-shepherd", "project_page": "", "abstract": "While ``instruction-tuned\" generative large language models (LLMs) have demonstrated an impressive ability to generalize to new tasks, the training phases heavily rely on large amounts of diverse and high-quality instruction data (such as ChatGPT and GPT-4). Unfortunately, acquiring high-quality data, especially when it comes to human-written data, can pose significant challenges both in terms of cost and accessibility. Moreover, concerns related to privacy can further limit access to such data, making the process of obtaining it a complex and nuanced undertaking. Consequently, this hinders the generality of the tuned models and may restrict their effectiveness in certain contexts. To tackle this issue, our study introduces a new approach called Federated Instruction Tuning (FedIT), which leverages federated learning (FL) as the learning framework for the instruction tuning of LLMs. This marks the first exploration of FL-based instruction tuning for LLMs. This is especially important since text data is predominantly generated by end users. Therefore, it is imperative to design and adapt FL approaches to effectively leverage these users' diverse instructions stored on local devices, while preserving privacy and ensuring data security. In the current paper, by conducting widely used GPT-4 auto-evaluation, we demonstrate that by exploiting the heterogeneous and diverse sets of instructions on the client's end with the proposed framework FedIT, we improved the performance of LLMs compared to centralized training with only limited local instructions. Further, in this paper, we developed a Github repository named Shepherd. This repository offers a foundational framework for exploring federated fine-tuning of LLMs using heterogeneous instructions across diverse categories.", "github_manual": "https://github.com/jayzhang42/federatedgpt-shepherd", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-10", "arxiv_id": "2305.05176", "title": "FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance", "authors": [ "Lingjiao Chen", "Matei Zaharia", "James Zou" ], "github": "https://github.com/stanford-futuredata/frugalgpt", "project_page": "", "abstract": "There is a rapidly growing number of large language models (LLMs) that users can query for a fee. We review the cost associated with querying popular LLM APIs, e.g. GPT-4, ChatGPT, J1-Jumbo, and find that these models have heterogeneous pricing structures, with fees that can differ by two orders of magnitude. In particular, using LLMs on large collections of queries and text can be expensive. Motivated by this, we outline and discuss three types of strategies that users can exploit to reduce the inference cost associated with using LLMs: 1) prompt adaptation, 2) LLM approximation, and 3) LLM cascade. As an example, we propose FrugalGPT, a simple yet flexible instantiation of LLM cascade which learns which combinations of LLMs to use for different queries in order to reduce cost and improve accuracy. Our experiments show that FrugalGPT can match the performance of the best individual LLM (e.g. GPT-4) with up to 98% cost reduction or improve the accuracy over GPT-4 by 4% with the same cost. The ideas and findings presented here lay a foundation for using LLMs sustainably and efficiently.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stanford-futuredata/frugalgpt", "github_api_added_by": "auto" }, { "date": "2023-05-10", "arxiv_id": "2305.05189", "title": "SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models", "authors": [ "Shanshan Zhong", "Zhongzhan Huang", "Wushao Wen", "Jinghui Qin", "Liang Lin" ], "github": "https://github.com/Qrange-group/SUR-adapter", "project_page": "", "abstract": "Diffusion models, which have emerged to become popular text-to-image generation models, can produce high-quality and content-rich images guided by textual prompts. However, there are limitations to semantic understanding and commonsense reasoning in existing models when the input prompts are concise narrative, resulting in low-quality image generation. To improve the capacities for narrative prompts, we propose a simple-yet-effective parameter-efficient fine-tuning approach called the Semantic Understanding and Reasoning adapter (SUR-adapter) for pre-trained diffusion models. To reach this goal, we first collect and annotate a new dataset SURD which consists of more than 57,000 semantically corrected multi-modal samples. Each sample contains a simple narrative prompt, a complex keyword-based prompt, and a high-quality image. Then, we align the semantic representation of narrative prompts to the complex prompts and transfer knowledge of large language models (LLMs) to our SUR-adapter via knowledge distillation so that it can acquire the powerful semantic understanding and reasoning capabilities to build a high-quality textual semantic representation for text-to-image generation. We conduct experiments by integrating multiple LLMs and popular pre-trained diffusion models to show the effectiveness of our approach in enabling diffusion models to understand and reason concise natural language without image quality degradation. Our approach can make text-to-image diffusion models easier to use with better user experience, which demonstrates our approach has the potential for further advancing the development of user-friendly text-to-image generation models by bridging the semantic gap between simple narrative prompts and complex keyword-based prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Qrange-group/SUR-adapter", "github_api_added_by": "auto" }, { "date": "2023-05-11", "arxiv_id": "2305.06161", "title": "StarCoder: may the source be with you!", "authors": [ "Raymond Li", "Loubna Ben Allal", "Yangtian Zi", "Niklas Muennighoff", "Denis Kocetkov", "Chenghao Mou", "Marc Marone", "Christopher Akiki", "Jia Li", "Jenny Chim", "Qian Liu", "Evgenii Zheltonozhskii", "Terry Yue Zhuo", "Thomas Wang", "Olivier Dehaene", "Mishig Davaadorj", "Joel Lamy-Poirier", "Jo\u00e3o Monteiro", "Oleh Shliazhko", "Nicolas Gontier", "Nicholas Meade", "Armel Zebaze", "Ming-Ho Yee", "Logesh Kumar Umapathi", "Jian Zhu", "Benjamin Lipkin", "Muhtasham Oblokulov", "Zhiruo Wang", "Rudra Murthy", "Jason Stillerman", "Siva Sankalp Patel", "Dmitry Abulkhanov", "Marco Zocca", "Manan Dey", "Zhihan Zhang", "Nour Fahmy", "Urvashi Bhattacharyya", "Wenhao Yu", "Swayam Singh", "Sasha Luccioni", "Paulo Villegas", "Maxim Kunakov", "Fedor Zhdanov", "Manuel Romero", "Tony Lee", "Nadav Timor", "Jennifer Ding", "Claire Schlesinger", "Hailey Schoelkopf", "Jan Ebert", "Tri Dao", "Mayank Mishra", "Alex Gu", "Jennifer Robinson", "Carolyn Jane Anderson", "Brendan Dolan-Gavitt", "Danish Contractor", "Siva Reddy", "Daniel Fried", "Dzmitry Bahdanau", "Yacine Jernite", "Carlos Mu\u00f1oz Ferrandis", "Sean Hughes", "Thomas Wolf", "Arjun Guha", "Leandro von Werra", "Harm de Vries" ], "github": "", "project_page": "", "abstract": "The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python, can be prompted to achieve 40\\% pass@1 on HumanEval, and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-11", "arxiv_id": "2305.06355", "title": "VideoChat: Chat-Centric Video Understanding", "authors": [ "KunChang Li", "Yinan He", "Yi Wang", "Yizhuo Li", "Wenhai Wang", "Ping Luo", "Yali Wang", "Limin Wang", "Yu Qiao" ], "github": "https://github.com/OpenGVLab/Ask-Anything", "project_page": "", "abstract": "In this study, we initiate an exploration into video understanding by introducing VideoChat, an end-to-end chat-centric video understanding system. It integrates video foundation models and large language models via a learnable neural interface, excelling in spatiotemporal reasoning, event localization, and causal relationship inference. To instructively tune this system, we propose a video-centric instruction dataset, composed of thousands of videos matched with detailed descriptions and conversations. This dataset emphasizes spatiotemporal reasoning and causal relationships, providing a valuable asset for training chat-centric video understanding systems. Preliminary qualitative experiments reveal our system's potential across a broad spectrum of video applications and set the standard for future research. Access our code and data at https://github.com/OpenGVLab/Ask-Anything", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/Ask-Anything", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-11", "arxiv_id": "2305.06356", "title": "HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion", "authors": [ "Mustafa I\u015f\u0131k", "Martin R\u00fcnz", "Markos Georgopoulos", "Taras Khakhulin", "Jonathan Starck", "Lourdes Agapito", "Matthias Nie\u00dfner" ], "github": "https://github.com/synthesiaresearch/humanrf", "project_page": "", "abstract": "Representing human performance at high-fidelity is an essential building block in diverse applications, such as film production, computer games or videoconferencing. To close the gap to production-level quality, we introduce HumanRF, a 4D dynamic neural scene representation that captures full-body appearance in motion from multi-view video input, and enables playback from novel, unseen viewpoints. Our novel representation acts as a dynamic video encoding that captures fine details at high compression rates by factorizing space-time into a temporal matrix-vector decomposition. This allows us to obtain temporally coherent reconstructions of human actors for long sequences, while representing high-resolution details even in the context of challenging motion. While most research focuses on synthesizing at resolutions of 4MP or lower, we address the challenge of operating at 12MP. To this end, we introduce ActorsHQ, a novel multi-view dataset that provides 12MP footage from 160 cameras for 16 sequences with high-fidelity, per-frame mesh reconstructions. We demonstrate challenges that emerge from using such high-resolution data and show that our newly introduced HumanRF effectively leverages this data, making a significant step towards production-level quality novel view synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/synthesiaresearch/humanrf", "github_api_added_by": "auto" }, { "date": "2023-05-11", "arxiv_id": "2305.06351", "title": "Reconstructing Animatable Categories from Videos", "authors": [ "Gengshan Yang", "Chaoyang Wang", "N Dinesh Reddy", "Deva Ramanan" ], "github": "https://github.com/lab4d-org/lab4d", "project_page": "", "abstract": "Building animatable 3D models is challenging due to the need for 3D scans, laborious registration, and manual rigging, which are difficult to scale to arbitrary categories. Recently, differentiable rendering provides a pathway to obtain high-quality 3D models from monocular videos, but these are limited to rigid categories or single instances. We present RAC that builds category 3D models from monocular videos while disentangling variations over instances and motion over time. Three key ideas are introduced to solve this problem: (1) specializing a skeleton to instances via optimization, (2) a method for latent space regularization that encourages shared structure across a category while maintaining instance details, and (3) using 3D background models to disentangle objects from the background. We show that 3D models of humans, cats, and dogs can be learned from 50-100 internet videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lab4d-org/lab4d", "github_api_added_by": "auto" }, { "date": "2023-05-11", "arxiv_id": "2305.05706", "title": "DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects", "authors": [ "Chen Bao", "Helin Xu", "Yuzhe Qin", "Xiaolong Wang" ], "github": "https://github.com/Kami-code/dexart-release", "project_page": "", "abstract": "To enable general-purpose robots, we will require the robot to operate daily articulated objects as humans do. Current robot manipulation has heavily relied on using a parallel gripper, which restricts the robot to a limited set of objects. On the other hand, operating with a multi-finger robot hand will allow better approximation to human behavior and enable the robot to operate on diverse articulated objects. To this end, we propose a new benchmark called DexArt, which involves Dexterous manipulation with Articulated objects in a physical simulator. In our benchmark, we define multiple complex manipulation tasks, and the robot hand will need to manipulate diverse articulated objects within each task. Our main focus is to evaluate the generalizability of the learned policy on unseen articulated objects. This is very challenging given the high degrees of freedom of both hands and objects. We use Reinforcement Learning with 3D representation learning to achieve generalization. Through extensive studies, we provide new insights into how 3D representation learning affects decision making in RL with 3D point cloud inputs. More details can be found at https://www.chenbao.tech/dexart/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Kami-code/dexart-release", "github_api_added_by": "auto" }, { "date": "2023-05-11", "arxiv_id": "2305.06131", "title": "Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era", "authors": [ "Chenghao Li", "Chaoning Zhang", "Atish Waghwase", "Lik-Hang Lee", "Francois Rameau", "Yang Yang", "Sung-Ho Bae", "Choong Seon Hong" ], "github": "", "project_page": "", "abstract": "Generative AI (AIGC, a.k.a. AI generated content) has made remarkable progress in the past few years, among which text-guided content generation is the most practical one since it enables the interaction between human instruction and AIGC. Due to the development in text-to-image as well 3D modeling technologies (like NeRF), text-to-3D has become a newly emerging yet highly active research field. Our work conducts the first yet comprehensive survey on text-to-3D to help readers interested in this direction quickly catch up with its fast development. First, we introduce 3D data representations, including both Euclidean data and non-Euclidean data. On top of that, we introduce various foundation technologies as well as summarize how recent works combine those foundation technologies to realize satisfactory text-to-3D. Moreover, we summarize how text-to-3D technology is used in various applications, including avatar generation, texture generation, shape transformation, and scene generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-11", "arxiv_id": "2305.06324", "title": "Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception", "authors": [ "Hassan Akbari", "Dan Kondratyuk", "Yin Cui", "Rachel Hornung", "Huisheng Wang", "Hartwig Adam" ], "github": "", "project_page": "", "abstract": "We present Integrated Multimodal Perception (IMP), a simple and scalable multimodal multi-task training and modeling approach. IMP integrates multimodal inputs including image, video, text, and audio into a single Transformer encoder with minimal modality-specific components. IMP makes use of a novel design that combines Alternating Gradient Descent (AGD) and Mixture-of-Experts (MoE) for efficient model \\& task scaling. We conduct extensive empirical studies about IMP and reveal the following key insights: 1) performing gradient descent updates by alternating on diverse heterogeneous modalities, loss functions, and tasks, while also varying input resolutions, efficiently improves multimodal understanding. 2) model sparsification with MoE on a single modality-agnostic encoder substantially improves the performance, outperforming dense models that use modality-specific encoders or additional fusion layers and greatly mitigating the conflicts between modalities. IMP achieves competitive performance on a wide range of downstream tasks including image classification, video classification, image-text, and video-text retrieval. Most notably, we train a sparse IMP-MoE-L focusing on video tasks that achieves new state-of-the-art in zero-shot video classification. Our model achieves 77.0% on Kinetics-400, 76.8% on Kinetics-600, and 76.8% on Kinetics-700 zero-shot classification accuracy, improving the previous state-of-the-art by +5%, +6.7%, and +5.8%, respectively, while using only 15% of their total training computational cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-11", "arxiv_id": "2305.06218", "title": "Multi-Task End-to-End Training Improves Conversational Recommendation", "authors": [ "Naveen Ram", "Dima Kuzmin", "Ellie Ka In Chio", "Moustafa Farid Alzantot", "Santiago Ontanon", "Ambarish Jash", "Judith Yue Li" ], "github": "", "project_page": "", "abstract": "In this paper, we analyze the performance of a multitask end-to-end transformer model on the task of conversational recommendations, which aim to provide recommendations based on a user's explicit preferences expressed in dialogue. While previous works in this area adopt complex multi-component approaches where the dialogue management and entity recommendation tasks are handled by separate components, we show that a unified transformer model, based on the T5 text-to-text transformer model, can perform competitively in both recommending relevant items and generating conversation dialogue. We fine-tune our model on the ReDIAL conversational movie recommendation dataset, and create additional training tasks derived from MovieLens (such as the prediction of movie attributes and related movies based on an input movie), in a multitask learning setting. Using a series of probe studies, we demonstrate that the learned knowledge in the additional tasks is transferred to the conversational setting, where each task leads to a 9%-52% increase in its related probe score.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-11", "arxiv_id": "2305.05973", "title": "Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models", "authors": [ "Aldo Gael Carranza", "Rezsa Farahani", "Natalia Ponomareva", "Alex Kurakin", "Matthew Jagielski", "Milad Nasr" ], "github": "", "project_page": "", "abstract": "We propose a novel approach for developing privacy-preserving large-scale recommender systems using differentially private (DP) large language models (LLMs) which overcomes certain challenges and limitations in DP training these complex systems. Our method is particularly well suited for the emerging area of LLM-based recommender systems, but can be readily employed for any recommender systems that process representations of natural language inputs. Our approach involves using DP training methods to fine-tune a publicly pre-trained LLM on a query generation task. The resulting model can generate private synthetic queries representative of the original queries which can be freely shared for any downstream non-private recommendation training procedures without incurring any additional privacy cost. We evaluate our method on its ability to securely train effective deep retrieval models, and we observe significant improvements in their retrieval quality without compromising query-level privacy guarantees compared to methods where the retrieval models are directly DP trained.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-11", "arxiv_id": "2305.05862", "title": "Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? An Examination on Several Typical Tasks", "authors": [ "Xianzhi Li", "Xiaodan Zhu", "Zhiqiang Ma", "Xiaomo Liu", "Sameena Shah" ], "github": "", "project_page": "", "abstract": "The most recent large language models such as ChatGPT and GPT-4 have garnered significant attention, as they are capable of generating high-quality responses to human input. Despite the extensive testing of ChatGPT and GPT-4 on generic text corpora, showcasing their impressive capabilities, a study focusing on financial corpora has not been conducted. In this study, we aim to bridge this gap by examining the potential of ChatGPT and GPT-4 as a solver for typical financial text analytic problems in the zero-shot or few-shot setting. Specifically, we assess their capabilities on four representative tasks over five distinct financial textual datasets. The preliminary study shows that ChatGPT and GPT-4 struggle on tasks such as financial named entity recognition (NER) and sentiment analysis, where domain-specific knowledge is required, while they excel in numerical reasoning tasks. We report both the strengths and limitations of the current versions of ChatGPT and GPT-4, comparing them to the state-of-the-art finetuned models as well as pretrained domain-specific generative models. Our experiments provide qualitative studies, through which we hope to help understand the capability of the existing models and facilitate further improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-11", "arxiv_id": "2305.05845", "title": "Sketching the Future (STF): Applying Conditional Control Techniques to Text-to-Video Models", "authors": [ "Rohan Dhesikan", "Vignesh Rajmohan" ], "github": "https://github.com/rohandkn/skribble2vid", "project_page": "", "abstract": "The proliferation of video content demands efficient and flexible neural network based approaches for generating new video content. In this paper, we propose a novel approach that combines zero-shot text-to-video generation with ControlNet to improve the output of these models. Our method takes multiple sketched frames as input and generates video output that matches the flow of these frames, building upon the Text-to-Video Zero architecture and incorporating ControlNet to enable additional input conditions. By first interpolating frames between the inputted sketches and then running Text-to-Video Zero using the new interpolated frames video as the control technique, we leverage the benefits of both zero-shot text-to-video generation and the robust control provided by ControlNet. Experiments demonstrate that our method excels at producing high-quality and remarkably consistent video content that more accurately aligns with the user's intended motion for the subject within the video. We provide a comprehensive resource package, including a demo video, project website, open-source GitHub repository, and a Colab playground to foster further research and application of our proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/rohandkn/skribble2vid", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-11", "arxiv_id": "2305.06077", "title": "Relightify: Relightable 3D Faces from a Single Image via Diffusion Models", "authors": [ "Foivos Paraperas Papantoniou", "Alexandros Lattas", "Stylianos Moschoglou", "Stefanos Zafeiriou" ], "github": "", "project_page": "", "abstract": "Following the remarkable success of diffusion models on image generation, recent works have also demonstrated their impressive ability to address a number of inverse problems in an unsupervised way, by properly constraining the sampling process based on a conditioning input. Motivated by this, in this paper, we present the first approach to use diffusion models as a prior for highly accurate 3D facial BRDF reconstruction from a single image. We start by leveraging a high-quality UV dataset of facial reflectance (diffuse and specular albedo and normals), which we render under varying illumination settings to simulate natural RGB textures and, then, train an unconditional diffusion model on concatenated pairs of rendered textures and reflectance components. At test time, we fit a 3D morphable model to the given image and unwrap the face in a partial UV texture. By sampling from the diffusion model, while retaining the observed texture part intact, the model inpaints not only the self-occluded areas but also the unknown reflectance components, in a single sequence of denoising steps. In contrast to existing methods, we directly acquire the observed texture from the input image, thus, resulting in more faithful and consistent reflectance estimation. Through a series of qualitative and quantitative comparisons, we demonstrate superior performance in both texture completion as well as reflectance reconstruction tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.06908", "title": "CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency Model", "authors": [ "Zhen Ye", "Wei Xue", "Xu Tan", "Jie Chen", "Qifeng Liu", "Yike Guo" ], "github": "", "project_page": "", "abstract": "Denoising diffusion probabilistic models (DDPMs) have shown promising performance for speech synthesis. However, a large number of iterative steps are required to achieve high sample quality, which restricts the inference speed. Maintaining sample quality while increasing sampling speed has become a challenging task. In this paper, we propose a \"Co\"nsistency \"Mo\"del-based \"Speech\" synthesis method, CoMoSpeech, which achieve speech synthesis through a single diffusion sampling step while achieving high audio quality. The consistency constraint is applied to distill a consistency model from a well-designed diffusion-based teacher model, which ultimately yields superior performances in the distilled CoMoSpeech. Our experiments show that by generating audio recordings by a single sampling step, the CoMoSpeech achieves an inference speed more than 150 times faster than real-time on a single NVIDIA A100 GPU, which is comparable to FastSpeech2, making diffusion-sampling based speech synthesis truly practical. Meanwhile, objective and subjective evaluations on text-to-speech and singing voice synthesis show that the proposed teacher models yield the best audio quality, and the one-step sampling based CoMoSpeech achieves the best inference speed with better or comparable audio quality to other conventional multi-step diffusion model baselines. Audio samples are available at https://comospeech.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.07017", "title": "An Inverse Scaling Law for CLIP Training", "authors": [ "Xianhang Li", "Zeyu Wang", "Cihang Xie" ], "github": "https://github.com/UCSC-VLAA/CLIPA", "project_page": "", "abstract": "CLIP, the first foundation model that connects images and text, has enabled many recent breakthroughs in computer vision. However, its associated training cost is prohibitively high, imposing a significant barrier to its widespread exploration. In this paper, we present a surprising finding that there exists an inverse scaling law for CLIP training, whereby the larger the image/text encoders used, the shorter the sequence length of image/text tokens that can be applied in training. Moreover, we showcase that the strategy for reducing image/text token length plays a crucial role in determining the quality of this scaling law. As a result of this finding, we are able to successfully train CLIP even by using academic resources. For example, on an A100 eight-GPU server, our CLIP models achieve zero-shot top-1 ImageNet accuracies of 63.2% in ~2 days, 67.8% in ~3 days, and 69.3% in ~4 days. By reducing the computation barrier associated with CLIP, we hope to inspire more research in this field, particularly from academics. Our code is available at https://github.com/UCSC-VLAA/CLIPA.", "github_manual": "", "github_regex": "https://github.com/UCSC-VLAA/CLIPA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.07011", "title": "Region-Aware Pretraining for Open-Vocabulary Object Detection with Vision Transformers", "authors": [ "Dahun Kim", "Anelia Angelova", "Weicheng Kuo" ], "github": "https://github.com/mcahny/rovit", "project_page": "", "abstract": "We present Region-aware Open-vocabulary Vision Transformers (RO-ViT) - a contrastive image-text pretraining recipe to bridge the gap between image-level pretraining and open-vocabulary object detection. At the pretraining phase, we propose to randomly crop and resize regions of positional embeddings instead of using the whole image positional embeddings. This better matches the use of positional embeddings at region-level in the detection finetuning phase. In addition, we replace the common softmax cross entropy loss in contrastive learning with focal loss to better learn the informative yet difficult examples. Finally, we leverage recent advances in novel object proposals to improve open-vocabulary detection finetuning. We evaluate our full model on the LVIS and COCO open-vocabulary detection benchmarks and zero-shot transfer. RO-ViT achieves a state-of-the-art 32.1 AP_r on LVIS, surpassing the best existing approach by +5.8 points in addition to competitive zero-shot transfer detection. Surprisingly, RO-ViT improves the image-level representation as well and achieves the state of the art on 9 out of 12 metrics on COCO and Flickr image-text retrieval benchmarks, outperforming competitive approaches with larger models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mcahny/rovit", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.07015", "title": "Exploiting Diffusion Prior for Real-World Image Super-Resolution", "authors": [ "Jianyi Wang", "Zongsheng Yue", "Shangchen Zhou", "Kelvin C. K. Chan", "Chen Change Loy" ], "github": "", "project_page": "", "abstract": "We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR). Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby preserving the generative prior and minimizing training cost. To remedy the loss of fidelity caused by the inherent stochasticity of diffusion models, we introduce a controllable feature wrapping module that allows users to balance quality and fidelity by simply adjusting a scalar value during the inference process. Moreover, we develop a progressive aggregation sampling strategy to overcome the fixed-size constraints of pre-trained diffusion models, enabling adaptation to resolutions of any size. A comprehensive evaluation of our method using both synthetic and real-world benchmarks demonstrates its superiority over current state-of-the-art approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.06456", "title": "Perpetual Humanoid Control for Real-time Simulated Avatars", "authors": [ "Zhengyi Luo", "Jinkun Cao", "Alexander Winkler", "Kris Kitani", "Weipeng Xu" ], "github": "", "project_page": "", "abstract": "We present a physics-based humanoid controller that achieves high-fidelity motion imitation and fault-tolerant behavior in the presence of noisy input (e.g. pose estimates from video or generated from language) and unexpected falls. Our controller scales up to learning ten thousand motion clips without using any external stabilizing forces and learns to naturally recover from fail-state. Given reference motion, our controller can perpetually control simulated avatars without requiring resets. At its core, we propose the progressive multiplicative control policy (PMCP), which dynamically allocates new network capacity to learn harder and harder motion sequences. PMCP allows efficient scaling for learning from large-scale motion databases and adding new tasks, such as fail-state recovery, without catastrophic forgetting. We demonstrate the effectiveness of our controller by using it to imitate noisy poses from video-based pose estimators and language-based motion generators in a live and real-time multi-person avatar use case.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.07027", "title": "EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention", "authors": [ "Xinyu Liu", "Houwen Peng", "Ningxin Zheng", "Yuqing Yang", "Han Hu", "Yixuan Yuan" ], "github": "https://github.com/microsoft/Cream/tree/main/EfficientViT", "project_page": "", "abstract": "Vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation costs, which makes them unsuitable for real-time applications. In this paper, we propose a family of high-speed vision transformers named EfficientViT. We find that the speed of existing transformer models is commonly bounded by memory inefficient operations, especially the tensor reshaping and element-wise functions in MHSA. Therefore, we design a new building block with a sandwich layout, i.e., using a single memory-bound MHSA between efficient FFN layers, which improves memory efficiency while enhancing channel communication. Moreover, we discover that the attention maps share high similarities across heads, leading to computational redundancy. To address this, we present a cascaded group attention module feeding attention heads with different splits of the full feature, which not only saves computation cost but also improves attention diversity. Comprehensive experiments demonstrate EfficientViT outperforms existing efficient models, striking a good trade-off between speed and accuracy. For instance, our EfficientViT-M5 surpasses MobileNetV3-Large by 1.9% in accuracy, while getting 40.4% and 45.2% higher throughput on Nvidia V100 GPU and Intel Xeon CPU, respectively. Compared to the recent efficient model MobileViT-XXS, EfficientViT-M2 achieves 1.8% superior accuracy, while running 5.8x/3.7x faster on the GPU/CPU, and 7.4x faster when converted to ONNX format. Code and models are available at https://github.com/microsoft/Cream/tree/main/EfficientViT.", "github_manual": "", "github_regex": "https://github.com/microsoft/Cream/tree/main/EfficientViT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.07021", "title": "Simple Token-Level Confidence Improves Caption Correctness", "authors": [ "Suzanne Petryk", "Spencer Whitehead", "Joseph E. Gonzalez", "Trevor Darrell", "Anna Rohrbach", "Marcus Rohrbach" ], "github": "", "project_page": "", "abstract": "The ability to judge whether a caption correctly describes an image is a critical part of vision-language understanding. However, state-of-the-art models often misinterpret the correctness of fine-grained details, leading to errors in outputs such as hallucinating objects in generated captions or poor compositional reasoning. In this work, we explore Token-Level Confidence, or TLC, as a simple yet surprisingly effective method to assess caption correctness. Specifically, we fine-tune a vision-language model on image captioning, input an image and proposed caption to the model, and aggregate either algebraic or learned token confidences over words or sequences to estimate image-caption consistency. Compared to sequence-level scores from pretrained models, TLC with algebraic confidence measures achieves a relative improvement in accuracy by 10% on verb understanding in SVO-Probes and outperforms prior state-of-the-art in image and group scores for compositional reasoning in Winoground by a relative 37% and 9%, respectively. When training data are available, a learned confidence estimator provides further improved performance, reducing object hallucination rates in MS COCO Captions by a relative 30% over the original model and setting a new state-of-the-art.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.07004", "title": "Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting", "authors": [ "Haoyang Huang", "Tianyi Tang", "Dongdong Zhang", "Wayne Xin Zhao", "Ting Song", "Yan Xia", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. In this work, we introduce a simple yet effective method, called cross-lingual-thought prompting (XLT), to systematically improve the multilingual capability of LLMs. Specifically, XLT is a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. We conduct comprehensive evaluations on 7 typical benchmarks related to reasoning, understanding, and generation tasks, covering both high-resource and low-resource languages. Experimental results show that XLT not only remarkably enhances the performance of various multilingual tasks but also significantly reduces the gap between the average performance and the best performance of each task in different languages. Notably, XLT brings over 10 points of average improvement in arithmetic reasoning and open-domain question-answering tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.06555", "title": "Domain Incremental Lifelong Learning in an Open World", "authors": [ "Yi Dai", "Hao Lang", "Yinhe Zheng", "Bowen Yu", "Fei Huang", "Yongbin Li" ], "github": "https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/diana", "project_page": "", "abstract": "Lifelong learning (LL) is an important ability for NLP models to learn new tasks continuously. Architecture-based approaches are reported to be effective implementations for LL models. However, it is non-trivial to extend previous approaches to domain incremental LL scenarios since they either require access to task identities in the testing phase or cannot handle samples from unseen tasks. In this paper, we propose Diana: a dynamic architecture-based lifelong learning model that tries to learn a sequence of tasks with a prompt-enhanced language model. Four types of hierarchically organized prompts are used in Diana to capture knowledge from different granularities. Specifically, we dedicate task-level prompts to capture task-specific knowledge to retain high LL performances and maintain instance-level prompts to learn knowledge shared across input samples to improve the model's generalization performance. Moreover, we dedicate separate prompts to explicitly model unseen tasks and introduce a set of prompt key vectors to facilitate knowledge sharing between tasks. Extensive experiments demonstrate that Diana outperforms state-of-the-art LL models, especially in handling unseen tasks. We release the code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/diana.", "github_manual": "", "github_regex": "https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/diana", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.06500", "title": "InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning", "authors": [ "Wenliang Dai", "Junnan Li", "Dongxu Li", "Anthony Meng Huat Tiong", "Junqi Zhao", "Weisheng Wang", "Boyang Li", "Pascale Fung", "Steven Hoi" ], "github": "https://github.com/salesforce/LAVIS/tree/main/projects", "project_page": "", "abstract": "General-purpose language models that can solve various language-domain tasks have emerged driven by the pre-training and instruction-tuning pipeline. However, building general-purpose vision-language models is challenging due to the increased task discrepancy introduced by the additional visual input. Although vision-language pre-training has been widely studied, vision-language instruction tuning remains relatively less explored. In this paper, we conduct a systematic and comprehensive study on vision-language instruction tuning based on the pre-trained BLIP-2 models. We gather a wide variety of 26 publicly available datasets, transform them into instruction tuning format and categorize them into two clusters for held-in instruction tuning and held-out zero-shot evaluation. Additionally, we introduce instruction-aware visual feature extraction, a crucial method that enables the model to extract informative features tailored to the given instruction. The resulting InstructBLIP models achieve state-of-the-art zero-shot performance across all 13 held-out datasets, substantially outperforming BLIP-2 and the larger Flamingo. Our models also lead to state-of-the-art performance when finetuned on individual downstream tasks (e.g., 90.7% accuracy on ScienceQA IMG). Furthermore, we qualitatively demonstrate the advantages of InstructBLIP over concurrent multimodal models. All InstructBLIP models have been open-sourced at https://github.com/salesforce/LAVIS/tree/main/projects/instructblip.", "github_manual": "", "github_regex": "https://github.com/salesforce/LAVIS/tree/main/projects", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.06474", "title": "Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction", "authors": [ "Wang-Cheng Kang", "Jianmo Ni", "Nikhil Mehta", "Maheswaran Sathiamoorthy", "Lichan Hong", "Ed Chi", "Derek Zhiyuan Cheng" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can comprehend user preferences based on their previous behavior remains an emerging and still unclear research question. Traditionally, Collaborative Filtering (CF) has been the most effective method for these tasks, predominantly relying on the extensive volume of rating data. In contrast, LLMs typically demand considerably less data while maintaining an exhaustive world knowledge about each item, such as movies or products. In this paper, we conduct a thorough examination of both CF and LLMs within the classic task of user rating prediction, which involves predicting a user's rating for a candidate item based on their past ratings. We investigate various LLMs in different sizes, ranging from 250M to 540B parameters and evaluate their performance in zero-shot, few-shot, and fine-tuning scenarios. We conduct comprehensive analysis to compare between LLMs and strong CF methods, and find that zero-shot LLMs lag behind traditional recommender models that have the access to user interaction data, indicating the importance of user interaction data. However, through fine-tuning, LLMs achieve comparable or even better performance with only a small fraction of the training data, demonstrating their potential through data efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.06404", "title": "LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM", "authors": [ "Wen-Yu Hua", "Brian Williams", "Davood Shamsi" ], "github": "", "project_page": "", "abstract": "Text embeddings are useful features for several NLP applications, such as sentence similarity, text clustering, and semantic search. In this paper, we present a Low-rank Adaptation with a Contrastive objective on top of 8-bit Siamese-BLOOM, a multilingual large language model optimized to produce semantically meaningful word embeddings. The innovation is threefold. First, we cast BLOOM weights to 8-bit values. Second, we fine-tune BLOOM with a scalable adapter (LoRA) and 8-bit Adam optimizer for sentence similarity classification. Third, we apply a Siamese architecture on BLOOM model with a contrastive objective to ease the multi-lingual labeled data scarcity. The experiment results show the quality of learned embeddings from LACoS-BLOOM is proportional to the number of model parameters and the amount of unlabeled training data. With the parameter efficient fine-tuning design, we are able to run BLOOM 7.1 billion parameters end-to-end on a single GPU machine with 32GB memory. Compared to previous solution Sentence-BERT, we achieve significant improvement on both English and multi-lingual STS tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.06575", "title": "Chain-of-Dictionary Prompting Elicits Translation in Large Language Models", "authors": [ "Hongyuan Lu", "Haoyang Huang", "Dongdong Zhang", "Haoran Yang", "Wai Lam", "Furu Wei" ], "github": "https://github.com/hongyuanluke/chain-of-dictionary", "project_page": "", "abstract": "Large language models (LLMs) have shown surprisingly good performance in multilingual neural machine translation (MNMT) even when trained without parallel data. Yet, despite the fact that the amount of training data is gigantic, they still struggle with translating rare words, particularly for low-resource languages. Even worse, it is usually unrealistic to retrieve relevant demonstrations for in-context learning with low-resource languages on LLMs, which restricts the practical use of LLMs for translation -- how should we mitigate this problem? To this end, we present a novel method, CoD, which augments LLMs with prior knowledge with the chains of multilingual dictionaries for a subset of input words to elicit translation abilities for LLMs. Extensive experiments indicate that augmenting ChatGPT with CoD elicits large gains by up to 13x ChrF++ points for MNMT (3.08 to 42.63 for English to Serbian written in Cyrillic script) on FLORES-200 full devtest set. We further demonstrate the importance of chaining the multilingual dictionaries, as well as the superiority of CoD to few-shot demonstration for low-resource languages.", "github_manual": "https://github.com/hongyuanluke/chain-of-dictionary", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.06424", "title": "Bot or Human? Detecting ChatGPT Imposters with A Single Question", "authors": [ "Hong Wang", "Xuan Luo", "Weizhi Wang", "Xifeng Yan" ], "github": "https://github.com/hongwang600/FLAIR", "project_page": "", "abstract": "Large language models like ChatGPT have recently demonstrated impressive capabilities in natural language understanding and generation, enabling various applications including translation, essay writing, and chit-chatting. However, there is a concern that they can be misused for malicious purposes, such as fraud or denial-of-service attacks. Therefore, it is crucial to develop methods for detecting whether the party involved in a conversation is a bot or a human. In this paper, we propose a framework named FLAIR, Finding Large language model Authenticity via a single Inquiry and Response, to detect conversational bots in an online manner. Specifically, we target a single question scenario that can effectively differentiate human users from bots. The questions are divided into two categories: those that are easy for humans but difficult for bots (e.g., counting, substitution, positioning, noise filtering, and ASCII art), and those that are easy for bots but difficult for humans (e.g., memorization and computation). Our approach shows different strengths of these questions in their effectiveness, providing a new way for online service providers to protect themselves against nefarious activities and ensure that they are serving real users. We open-sourced our dataset on https://github.com/hongwang600/FLAIR and welcome contributions from the community to enrich such detection datasets.", "github_manual": "", "github_regex": "https://github.com/hongwang600/FLAIR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-12", "arxiv_id": "2305.06594", "title": "V2Meow: Meowing to the Visual Beat via Music Generation", "authors": [ "Kun Su", "Judith Yue Li", "Qingqing Huang", "Dima Kuzmin", "Joonseok Lee", "Chris Donahue", "Fei Sha", "Aren Jansen", "Yu Wang", "Mauro Verzetti", "Timo I. Denk" ], "github": "", "project_page": "", "abstract": "Generating high quality music that complements the visual content of a video is a challenging task. Most existing visual conditioned music generation systems generate symbolic music data, such as MIDI files, instead of raw audio waveform. Given the limited availability of symbolic music data, such methods can only generate music for a few instruments or for specific types of visual input. In this paper, we propose a novel approach called V2Meow that can generate high-quality music audio that aligns well with the visual semantics of a diverse range of video input types. Specifically, the proposed music generation system is a multi-stage autoregressive model which is trained with a number of O(100K) music audio clips paired with video frames, which are mined from in-the-wild music videos, and no parallel symbolic music data is involved. V2Meow is able to synthesize high-fidelity music audio waveform solely conditioned on pre-trained visual features extracted from an arbitrary silent video clip, and it also allows high-level control over the music style of generation examples via supporting text prompts in addition to the video frames conditioning. Through both qualitative and quantitative evaluations, we demonstrate that our model outperforms several existing music generation systems in terms of both visual-audio correspondence and audio quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-15", "arxiv_id": "2305.07185", "title": "MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers", "authors": [ "Lili Yu", "D\u00e1niel Simig", "Colin Flaherty", "Armen Aghajanyan", "Luke Zettlemoyer", "Mike Lewis" ], "github": "", "project_page": "", "abstract": "Autoregressive transformers are spectacular models for short sequences but scale poorly to long sequences such as high-resolution images, podcasts, code, or books. We proposed Megabyte, a multi-scale decoder architecture that enables end-to-end differentiable modeling of sequences of over one million bytes. Megabyte segments sequences into patches and uses a local submodel within patches and a global model between patches. This enables sub-quadratic self-attention, much larger feedforward layers for the same compute, and improved parallelism during decoding -- unlocking better performance at reduced cost for both training and generation. Extensive experiments show that Megabyte allows byte-level models to perform competitively with subword models on long context language modeling, achieve state-of-the-art density estimation on ImageNet, and model audio from raw files. Together, these results establish the viability of tokenization-free autoregressive sequence modeling at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-15", "arxiv_id": "2305.07440", "title": "Optimizing Memory Mapping Using Deep Reinforcement Learning", "authors": [ "Pengming Wang", "Mikita Sazanovich", "Berkin Ilbeyi", "Phitchaya Mangpo Phothilimthana", "Manish Purohit", "Han Yang Tay", "Ng\u00e2n V\u0169", "Miaosen Wang", "Cosmin Paduraru", "Edouard Leurent", "Anton Zhernov", "Julian Schrittwieser", "Thomas Hubert", "Robert Tung", "Paula Kurylowicz", "Kieran Milan", "Oriol Vinyals", "Daniel J. Mankowitz" ], "github": "", "project_page": "", "abstract": "Resource scheduling and allocation is a critical component of many high impact systems ranging from congestion control to cloud computing. Finding more optimal solutions to these problems often has significant impact on resource and time savings, reducing device wear-and-tear, and even potentially improving carbon emissions. In this paper, we focus on a specific instance of a scheduling problem, namely the memory mapping problem that occurs during compilation of machine learning programs: That is, mapping tensors to different memory layers to optimize execution time. We introduce an approach for solving the memory mapping problem using Reinforcement Learning. RL is a solution paradigm well-suited for sequential decision making problems that are amenable to planning, and combinatorial search spaces with high-dimensional data inputs. We formulate the problem as a single-player game, which we call the mallocGame, such that high-reward trajectories of the game correspond to efficient memory mappings on the target hardware. We also introduce a Reinforcement Learning agent, mallocMuZero, and show that it is capable of playing this game to discover new and improved memory mapping solutions that lead to faster execution times on real ML workloads on ML accelerators. We compare the performance of mallocMuZero to the default solver used by the Accelerated Linear Algebra (XLA) compiler on a benchmark of realistic ML workloads. In addition, we show that mallocMuZero is capable of improving the execution time of the recently published AlphaTensor matrix multiplication model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-15", "arxiv_id": "2305.07153", "title": "Towards best practices in AGI safety and governance: A survey of expert opinion", "authors": [ "Jonas Schuett", "Noemi Dreksler", "Markus Anderljung", "David McCaffary", "Lennart Heim", "Emma Bluemke", "Ben Garfinkel" ], "github": "", "project_page": "", "abstract": "A number of leading AI companies, including OpenAI, Google DeepMind, and Anthropic, have the stated goal of building artificial general intelligence (AGI) - AI systems that achieve or exceed human performance across a wide range of cognitive tasks. In pursuing this goal, they may develop and deploy AI systems that pose particularly significant risks. While they have already taken some measures to mitigate these risks, best practices have not yet emerged. To support the identification of best practices, we sent a survey to 92 leading experts from AGI labs, academia, and civil society and received 51 responses. Participants were asked how much they agreed with 50 statements about what AGI labs should do. Our main finding is that participants, on average, agreed with all of them. Many statements received extremely high levels of agreement. For example, 98% of respondents somewhat or strongly agreed that AGI labs should conduct pre-deployment risk assessments, dangerous capabilities evaluations, third-party model audits, safety restrictions on model usage, and red teaming. Ultimately, our list of statements may serve as a helpful foundation for efforts to develop best practices, standards, and regulations for AGI labs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-15", "arxiv_id": "2305.07558", "title": "Measuring Progress in Fine-grained Vision-and-Language Understanding", "authors": [ "Emanuele Bugliarello", "Laurent Sartran", "Aishwarya Agrawal", "Lisa Anne Hendricks", "Aida Nematzadeh" ], "github": "https://github.com/e-bug/fine-grained-evals", "project_page": "", "abstract": "While pretraining on large-scale image-text data from the Web has facilitated rapid progress on many vision-and-language (V&L) tasks, recent work has demonstrated that pretrained models lack \"fine-grained\" understanding, such as the ability to recognise relationships, verbs, and numbers in images. This has resulted in an increased interest in the community to either develop new benchmarks or models for such capabilities. To better understand and quantify progress in this direction, we investigate four competitive V&L models on four fine-grained benchmarks. Through our analysis, we find that X-VLM (Zeng et al., 2022) consistently outperforms other baselines, and that modelling innovations can impact performance more than scaling Web data, which even degrades performance sometimes. Through a deeper investigation of X-VLM, we highlight the importance of both novel losses and rich data sources for learning fine-grained skills. Finally, we inspect training dynamics, and discover that for some tasks, performance peaks early in training or significantly fluctuates, never converging.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/e-bug/fine-grained-evals", "github_api_added_by": "auto" }, { "date": "2023-05-15", "arxiv_id": "2305.07615", "title": "What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization", "authors": [ "Griffin Adams", "Bichlien H Nguyen", "Jake Smith", "Yingce Xia", "Shufang Xie", "Anna Ostropolets", "Budhaditya Deb", "Yuan-Jyue Chen", "Tristan Naumann", "No\u00e9mie Elhadad" ], "github": "https://github.com/griff4692/calibrating-summaries", "project_page": "", "abstract": "Summarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE). To address this, recent work has added a calibration step, which exposes a model to its own ranked outputs to improve relevance or, in a separate line of work, contrasts positive and negative sets to improve faithfulness. While effective, much of this work has focused on how to generate and optimize these sets. Less is known about why one setup is more effective than another. In this work, we uncover the underlying characteristics of effective sets. For each training instance, we form a large, diverse pool of candidates and systematically vary the subsets used for calibration fine-tuning. Each selection strategy targets distinct aspects of the sets, such as lexical diversity or the size of the gap between positive and negatives. On three diverse scientific long-form summarization datasets (spanning biomedical, clinical, and chemical domains), we find, among others, that faithfulness calibration is optimal when the negative sets are extractive and more likely to be generated, whereas for relevance calibration, the metric margin between candidates should be maximized and surprise--the disagreement between model and metric defined candidate rankings--minimized. Code to create, select, and optimize calibration sets is available at https://github.com/griff4692/calibrating-summaries", "github_manual": "", "github_regex": "https://github.com/griff4692/calibrating-summaries", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-15", "arxiv_id": "2305.07514", "title": "BlendFields: Few-Shot Example-Driven Facial Modeling", "authors": [ "Kacper Kania", "Stephan J. Garbin", "Andrea Tagliasacchi", "Virginia Estellers", "Kwang Moo Yi", "Julien Valentin", "Tomasz Trzci\u0144ski", "Marek Kowalski" ], "github": "", "project_page": "", "abstract": "Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven, requiring an extensive corpus of data not publicly accessible to the research community, or fail to capture fine details because they rely on geometric face models that cannot represent fine-grained details in texture with a mesh discretization and linear deformation designed to model only a coarse face geometry. We introduce a method that bridges this gap by drawing inspiration from traditional computer graphics techniques. Unseen expressions are modeled by blending appearance from a sparse set of extreme poses. This blending is performed by measuring local volumetric changes in those expressions and locally reproducing their appearance whenever a similar expression is performed at test time. We show that our method generalizes to unseen expressions, adding fine-grained effects on top of smooth volumetric deformations of a face, and demonstrate how it generalizes beyond faces.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-15", "arxiv_id": "2305.07447", "title": "Universal Source Separation with Weakly Labelled Data", "authors": [ "Qiuqiang Kong", "Ke Chen", "Haohe Liu", "Xingjian Du", "Taylor Berg-Kirkpatrick", "Shlomo Dubnov", "Mark D. Plumbley" ], "github": "https://github.com/bytedance/uss", "project_page": "", "abstract": "Universal source separation (USS) is a fundamental research task for computational auditory scene analysis, which aims to separate mono recordings into individual source tracks. There are three potential challenges awaiting the solution to the audio source separation task. First, previous audio source separation systems mainly focus on separating one or a limited number of specific sources. There is a lack of research on building a unified system that can separate arbitrary sources via a single model. Second, most previous systems require clean source data to train a separator, while clean source data are scarce. Third, there is a lack of USS system that can automatically detect and separate active sound classes in a hierarchical level. To use large-scale weakly labeled/unlabeled audio data for audio source separation, we propose a universal audio source separation framework containing: 1) an audio tagging model trained on weakly labeled data as a query net; and 2) a conditional source separation model that takes query net outputs as conditions to separate arbitrary sound sources. We investigate various query nets, source separation models, and training strategies and propose a hierarchical USS strategy to automatically detect and separate sound classes from the AudioSet ontology. By solely leveraging the weakly labelled AudioSet, our USS system is successful in separating a wide variety of sound classes, including sound event separation, music source separation, and speech enhancement. The USS system achieves an average signal-to-distortion ratio improvement (SDRi) of 5.57 dB over 527 sound classes of AudioSet; 10.57 dB on the DCASE 2018 Task 2 dataset; 8.12 dB on the MUSDB18 dataset; an SDRi of 7.28 dB on the Slakh2100 dataset; and an SSNR of 9.00 dB on the voicebank-demand dataset. We release the source code at https://github.com/bytedance/uss", "github_manual": "", "github_regex": "https://github.com/bytedance/uss", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-15", "arxiv_id": "2305.07214", "title": "MMG-Ego4D: Multi-Modal Generalization in Egocentric Action Recognition", "authors": [ "Xinyu Gong", "Sreyas Mohan", "Naina Dhingra", "Jean-Charles Bazin", "Yilei Li", "Zhangyang Wang", "Rakesh Ranjan" ], "github": "https://github.com/facebookresearch/MMG_Ego4D", "project_page": "", "abstract": "In this paper, we study a novel problem in egocentric action recognition, which we term as \"Multimodal Generalization\" (MMG). MMG aims to study how systems can generalize when data from certain modalities is limited or even completely missing. We thoroughly investigate MMG in the context of standard supervised action recognition and the more challenging few-shot setting for learning new action categories. MMG consists of two novel scenarios, designed to support security, and efficiency considerations in real-world applications: (1) missing modality generalization where some modalities that were present during the train time are missing during the inference time, and (2) cross-modal zero-shot generalization, where the modalities present during the inference time and the training time are disjoint. To enable this investigation, we construct a new dataset MMG-Ego4D containing data points with video, audio, and inertial motion sensor (IMU) modalities. Our dataset is derived from Ego4D dataset, but processed and thoroughly re-annotated by human experts to facilitate research in the MMG problem. We evaluate a diverse array of models on MMG-Ego4D and propose new methods with improved generalization ability. In particular, we introduce a new fusion module with modality dropout training, contrastive-based alignment training, and a novel cross-modal prototypical loss for better few-shot performance. We hope this study will serve as a benchmark and guide future research in multimodal generalization problems. The benchmark and code will be available at https://github.com/facebookresearch/MMG_Ego4D.", "github_manual": "", "github_regex": "https://github.com/facebookresearch/MMG_Ego4D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-15", "arxiv_id": "2305.07378", "title": "Surfacing Biases in Large Language Models using Contrastive Input Decoding", "authors": [ "Gal Yona", "Or Honovich", "Itay Laish", "Roee Aharoni" ], "github": "", "project_page": "", "abstract": "Ensuring that large language models (LMs) are fair, robust and useful requires an understanding of how different modifications to their inputs impact the model's behaviour. In the context of open-text generation tasks, however, such an evaluation is not trivial. For example, when introducing a model with an input text and a perturbed, \"contrastive\" version of it, meaningful differences in the next-token predictions may not be revealed with standard decoding strategies. With this motivation in mind, we propose Contrastive Input Decoding (CID): a decoding algorithm to generate text given two inputs, where the generated text is likely given one input but unlikely given the other. In this way, the contrastive generations can highlight potentially subtle differences in how the LM output differs for the two inputs in a simple and interpretable manner. We use CID to highlight context-specific biases that are hard to detect with standard decoding strategies and quantify the effect of different input perturbations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-15", "arxiv_id": "2305.07490", "title": "ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4", "authors": [ "Zhengqing Yuan", "Huiwen Xue", "Xinyi Wang", "Yongming Liu", "Zhuanzhe Zhao", "Kun Wang" ], "github": "https://github.com/DLYuanGod/ArtGPT-4", "project_page": "", "abstract": "In recent years, large language models (LLMs) have made significant progress in natural language processing (NLP), with models like ChatGPT and GPT-4 achieving impressive capabilities in various linguistic tasks. However, training models on such a large scale is challenging, and finding datasets that match the model's scale is often difficult. Fine-tuning and training models with fewer parameters using novel methods have emerged as promising approaches to overcome these challenges. One such model is MiniGPT-4, which achieves comparable vision-language understanding to GPT-4 by leveraging novel pre-training models and innovative training strategies. However, the model still faces some challenges in image understanding, particularly in artistic pictures. A novel multimodal model called ArtGPT-4 has been proposed to address these limitations. ArtGPT-4 was trained on image-text pairs using a Tesla A100 device in just 2 hours, using only about 200 GB of data. The model can depict images with an artistic flair and generate visual code, including aesthetically pleasing HTML/CSS web pages. Furthermore, the article proposes novel benchmarks for evaluating the performance of vision-language models. In the subsequent evaluation methods, ArtGPT-4 scored more than 1 point higher than the current state-of-the-art model and was only 0.25 points lower than artists on a 6-point scale. Our code and pre-trained model are available at https://huggingface.co/Tyrannosaurus/ArtGPT-4.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/DLYuanGod/ArtGPT-4", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-15", "arxiv_id": "2305.07243", "title": "Better speech synthesis through scaling", "authors": [ "James Betker" ], "github": "https://github.com/neonbjb/tortoise-tts", "project_page": "", "abstract": "In recent years, the field of image generation has been revolutionized by the application of autoregressive transformers and DDPMs. These approaches model the process of image generation as a step-wise probabilistic processes and leverage large amounts of compute and data to learn the image distribution. This methodology of improving performance need not be confined to images. This paper describes a way to apply advances in the image generative domain to speech synthesis. The result is TorToise -- an expressive, multi-voice text-to-speech system. All model code and trained weights have been open-sourced at https://github.com/neonbjb/tortoise-tts.", "github_manual": "", "github_regex": "https://github.com/neonbjb/tortoise-tts", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-16", "arxiv_id": "2305.07759", "title": "TinyStories: How Small Can Language Models Be and Still Speak Coherent English?", "authors": [ "Ronen Eldan", "Yuanzhi Li" ], "github": "", "project_page": "", "abstract": "Language models (LMs) are powerful tools for natural language processing, but they often struggle to produce coherent and fluent text when they are small. Models with around 125M parameters such as GPT-Neo (small) or GPT-2 (small) can rarely generate coherent and consistent English text beyond a few words even after extensive training. This raises the question of whether the emergence of the ability to produce coherent English text only occurs at larger scales (with hundreds of millions of parameters or more) and complex architectures (with many layers of global attention). In this work, we introduce TinyStories, a synthetic dataset of short stories that only contain words that a typical 3 to 4-year-olds usually understand, generated by GPT-3.5 and GPT-4. We show that TinyStories can be used to train and evaluate LMs that are much smaller than the state-of-the-art models (below 10 million total parameters), or have much simpler architectures (with only one transformer block), yet still produce fluent and consistent stories with several paragraphs that are diverse and have almost perfect grammar, and demonstrate reasoning capabilities. We also introduce a new paradigm for the evaluation of language models: We suggest a framework which uses GPT-4 to grade the content generated by these models as if those were stories written by students and graded by a (human) teacher. This new paradigm overcomes the flaws of standard benchmarks which often requires the model's output to be very structures, and moreover provides a multidimensional score for the model, providing scores for different capabilities such as grammar, creativity and consistency. We hope that TinyStories can facilitate the development, analysis and research of LMs, especially for low-resource or specialized domains, and shed light on the emergence of language capabilities in LMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-16", "arxiv_id": "2305.08596", "title": "DarkBERT: A Language Model for the Dark Side of the Internet", "authors": [ "Youngjin Jin", "Eugene Jang", "Jian Cui", "Jin-Woo Chung", "Yongjae Lee", "Seungwon Shin" ], "github": "https://github.com/fastfire/deepdarkCTI/blob/main/ransomware", "project_page": "", "abstract": "Recent research has suggested that there are clear differences in the language used in the Dark Web compared to that of the Surface Web. As studies on the Dark Web commonly require textual analysis of the domain, language models specific to the Dark Web may provide valuable insights to researchers. In this work, we introduce DarkBERT, a language model pretrained on Dark Web data. We describe the steps taken to filter and compile the text data used to train DarkBERT to combat the extreme lexical and structural diversity of the Dark Web that may be detrimental to building a proper representation of the domain. We evaluate DarkBERT and its vanilla counterpart along with other widely used language models to validate the benefits that a Dark Web domain specific model offers in various use cases. Our evaluations show that DarkBERT outperforms current language models and may serve as a valuable resource for future research on the Dark Web.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/fastfire/deepdarkCTI/blob/main/ransomware", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-16", "arxiv_id": "2305.08810", "title": "AutoRecon: Automated 3D Object Discovery and Reconstruction", "authors": [ "Yuang Wang", "Xingyi He", "Sida Peng", "Haotong Lin", "Hujun Bao", "Xiaowei Zhou" ], "github": "https://github.com/zju3dv/AutoRecon", "project_page": "", "abstract": "A fully automated object reconstruction pipeline is crucial for digital content creation. While the area of 3D reconstruction has witnessed profound developments, the removal of background to obtain a clean object model still relies on different forms of manual labor, such as bounding box labeling, mask annotations, and mesh manipulations. In this paper, we propose a novel framework named AutoRecon for the automated discovery and reconstruction of an object from multi-view images. We demonstrate that foreground objects can be robustly located and segmented from SfM point clouds by leveraging self-supervised 2D vision transformer features. Then, we reconstruct decomposed neural scene representations with dense supervision provided by the decomposed point clouds, resulting in accurate object reconstruction and segmentation. Experiments on the DTU, BlendedMVS and CO3D-V2 datasets demonstrate the effectiveness and robustness of AutoRecon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zju3dv/AutoRecon", "github_api_added_by": "auto" }, { "date": "2023-05-16", "arxiv_id": "2305.07961", "title": "Leveraging Large Language Models in Conversational Recommender Systems", "authors": [ "Luke Friedman", "Sameer Ahuja", "David Allen", "Terry Tan", "Hakim Sidahmed", "Changbo Long", "Jun Xie", "Gabriel Schubiner", "Ajay Patel", "Harsh Lara", "Brian Chu", "Zexi Chen", "Manoj Tiwari" ], "github": "", "project_page": "", "abstract": "A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an unprecedented ability to converse naturally and incorporate world knowledge and common-sense reasoning into language understanding, unlocking the potential of this paradigm. However, effectively leveraging LLMs within a CRS introduces new technical challenges, including properly understanding and controlling a complex conversation and retrieving from external sources of information. These issues are exacerbated by a large, evolving item corpus and a lack of conversational data for training. In this paper, we provide a roadmap for building an end-to-end large-scale CRS using LLMs. In particular, we propose new implementations for user preference understanding, flexible dialogue management and explainable recommendations as part of an integrated architecture powered by LLMs. For improved personalization, we describe how an LLM can consume interpretable natural language user profiles and use them to modulate session-level context. To overcome conversational data limitations in the absence of an existing production CRS, we propose techniques for building a controllable LLM-based user simulator to generate synthetic conversations. As a proof of concept we introduce RecLLM, a large-scale CRS for YouTube videos built on LaMDA, and demonstrate its fluency and diverse functionality through some illustrative example conversations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-16", "arxiv_id": "2305.07922", "title": "CodeT5+: Open Code Large Language Models for Code Understanding and Generation", "authors": [ "Yue Wang", "Hung Le", "Akhilesh Deepak Gotmare", "Nghi D. Q. Bui", "Junnan Li", "Steven C. H. Hoi" ], "github": "https://github.com/salesforce/codet5", "project_page": "", "abstract": "Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations in terms of architecture and pretraining tasks. First, they often adopt a specific architecture (encoder-only or decoder-only) or rely on a unified encoder-decoder network for different downstream tasks. The former paradigm is limited by inflexibility in applications while in the latter, the model is treated as a single system for all tasks, leading to suboptimal performance on a subset of tasks. Secondly, they often employ a limited set of pretraining objectives which might not be relevant to some downstream tasks and hence result in substantial performance degrade. To address these limitations, we propose ``CodeT5+'', a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks. Such flexibility is enabled by our proposed mixture of pretraining objectives to mitigate the pretrain-finetune discrepancy. These objectives cover span denoising, contrastive learning, text-code matching, and causal LM pretraining tasks, on both unimodal and bimodal multilingual code corpora. Furthermore, we propose to initialize CodeT5+ with frozen off-the-shelf LLMs without training from scratch to efficiently scale up our models, and explore instruction-tuning to align with natural language instructions. We extensively evaluate CodeT5+ on over 20 code-related benchmarks in different settings, including zero-shot, finetuning, and instruction-tuning. We observe state-of-the-art (SoTA) model performance on various code-related tasks, such as code generation and completion, math programming, and text-to-code retrieval tasks. Particularly, our instruction-tuned CodeT5+ 16B achieves new SoTA results on HumanEval code generation task against other open code LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/salesforce/codet5", "github_api_added_by": "auto" }, { "date": "2023-05-16", "arxiv_id": "2305.08675", "title": "Improved baselines for vision-language pre-training", "authors": [ "Enrico Fini", "Pietro Astolfi", "Adriana Romero-Soriano", "Jakob Verbeek", "Michal Drozdzal" ], "github": "https://github.com/facebookresearch/clip-rocket", "project_page": "", "abstract": "Contrastive learning has emerged as an efficient framework to learn multimodal representations. CLIP, a seminal work in this area, achieved impressive results by training on paired image-text data using the contrastive loss. Recent work claims improvements over CLIP using additional non-contrastive losses inspired from self-supervised learning. However, it is sometimes hard to disentangle the contribution of these additional losses from other implementation details, e.g., data augmentation or regularization techniques, used to train the model. To shed light on this matter, in this paper, we first propose, implement and evaluate several baselines obtained by combining contrastive learning with recent advances in self-supervised learning. In particular, we use the loss functions that were proven successful for visual self-supervised learning to align image and text modalities. We find that these baselines outperform a basic implementation of CLIP. However, when a stronger training recipe is employed, the advantage disappears. Indeed, we find that a simple CLIP baseline can also be improved substantially, up to a 25% relative improvement on downstream zero-shot tasks, by using well-known training techniques that are popular in other subfields. Moreover, we discover that it is enough to apply image and text augmentations to make up for most of the improvement attained by prior works. With our improved training recipe for CLIP, we obtain state-of-the-art performance on four standard datasets, and consistently outperform prior work (up to +4% on the largest dataset), while being substantially simpler.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/clip-rocket", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-16", "arxiv_id": "2305.08809", "title": "Interpretability at Scale: Identifying Causal Mechanisms in Alpaca", "authors": [ "Zhengxuan Wu", "Atticus Geiger", "Christopher Potts", "Noah D. Goodman" ], "github": "", "project_page": "", "abstract": "Obtaining human-interpretable explanations of large, general-purpose language models is an urgent goal for AI safety. However, it is just as important that our interpretability methods are faithful to the causal dynamics underlying model behavior and able to robustly generalize to unseen inputs. Distributed Alignment Search (DAS) is a powerful gradient descent method grounded in a theory of causal abstraction that uncovered perfect alignments between interpretable symbolic algorithms and small deep learning models fine-tuned for specific tasks. In the present paper, we scale DAS significantly by replacing the remaining brute-force search steps with learned parameters -- an approach we call DAS. This enables us to efficiently search for interpretable causal structure in large language models while they follow instructions. We apply DAS to the Alpaca model (7B parameters), which, off the shelf, solves a simple numerical reasoning problem. With DAS, we discover that Alpaca does this by implementing a causal model with two interpretable boolean variables. Furthermore, we find that the alignment of neural representations with these variables is robust to changes in inputs and instructions. These findings mark a first step toward deeply understanding the inner-workings of our largest and most widely deployed language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-16", "arxiv_id": "2305.08677", "title": "Natural Language Decomposition and Interpretation of Complex Utterances", "authors": [ "Harsh Jhamtani", "Hao Fang", "Patrick Xia", "Eran Levy", "Jacob Andreas", "Ben Van Durme" ], "github": "", "project_page": "", "abstract": "Natural language interfaces often require supervised data to translate user requests into programs, database queries, or other structured intent representations. During data collection, it can be difficult to anticipate and formalize the full range of user needs -- for example, in a system designed to handle simple requests (like find my meetings tomorrow or move my meeting with my manager to noon), users may also express more elaborate requests (like swap all my calls on Monday and Tuesday). We introduce an approach for equipping a simple language-to-code model to handle complex utterances via a process of hierarchical natural language decomposition. Our approach uses a pre-trained language model to decompose a complex utterance into a sequence of smaller natural language steps, then interprets each step using the language-to-code model. To test our approach, we collect and release DeCU -- a new NL-to-program benchmark to evaluate Decomposition of Complex Utterances. Experiments show that the proposed approach enables the interpretation of complex utterances with almost no complex training data, while outperforming standard few-shot prompting approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-16", "arxiv_id": "2305.08298", "title": "Symbol tuning improves in-context learning in language models", "authors": [ "Jerry Wei", "Le Hou", "Andrew Lampinen", "Xiangning Chen", "Da Huang", "Yi Tay", "Xinyun Chen", "Yifeng Lu", "Denny Zhou", "Tengyu Ma", "Quoc V. Le" ], "github": "", "project_page": "", "abstract": "We present symbol tuning - finetuning language models on in-context input-label pairs where natural language labels (e.g., \"positive/negative sentiment\") are replaced with arbitrary symbols (e.g., \"foo/bar\"). Symbol tuning leverages the intuition that when a model cannot use instructions or natural language labels to figure out a task, it must instead do so by learning the input-label mappings. We experiment with symbol tuning across Flan-PaLM models up to 540B parameters and observe benefits across various settings. First, symbol tuning boosts performance on unseen in-context learning tasks and is much more robust to underspecified prompts, such as those without instructions or without natural language labels. Second, symbol-tuned models are much stronger at algorithmic reasoning tasks, with up to 18.2% better performance on the List Functions benchmark and up to 15.3% better performance on the Simple Turing Concepts benchmark. Finally, symbol-tuned models show large improvements in following flipped-labels presented in-context, meaning that they are more capable of using in-context information to override prior semantic knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-16", "arxiv_id": "2305.08275", "title": "ULIP-2: Towards Scalable Multimodal Pre-training For 3D Understanding", "authors": [ "Le Xue", "Ning Yu", "Shu Zhang", "Junnan Li", "Roberto Mart\u00edn-Mart\u00edn", "Jiajun Wu", "Caiming Xiong", "Ran Xu", "Juan Carlos Niebles", "Silvio Savarese" ], "github": "https://github.com/salesforce/ULIP", "project_page": "", "abstract": "Recent advancements in multimodal pre-training methods have shown promising efficacy in 3D representation learning by aligning features across 3D modality, their 2D counterpart modality, and corresponding language modality. However, the methods used by existing multimodal pre-training frameworks to gather multimodal data for 3D applications lack scalability and comprehensiveness, potentially constraining the full potential of multimodal learning. The main bottleneck lies in the language modality's scalability and comprehensiveness. To address this bottleneck, we introduce ULIP-2, a multimodal pre-training framework that leverages state-of-the-art multimodal large language models (LLMs) pre-trained on extensive knowledge to automatically generate holistic language counterparts for 3D objects. We conduct experiments on two large-scale datasets, Objaverse and ShapeNet55, and release our generated three-modality triplet datasets (3D Point Cloud - Image - Language), named \"ULIP-Objaverse Triplets\" and \"ULIP-ShapeNet Triplets\". ULIP-2 requires only 3D data itself and eliminates the need for any manual annotation effort, demonstrating its scalability; and ULIP-2 achieves remarkable improvements on downstream zero-shot classification on ModelNet40 (74% Top1 Accuracy). Moreover, ULIP-2 sets a new record on the real-world ScanObjectNN benchmark (91.5% Overall Accuracy) while utilizing only 1.4 million parameters(~10x fewer than current SOTA), signifying a breakthrough in scalable multimodal 3D representation learning without human annotations. The code and datasets are available at https://github.com/salesforce/ULIP.", "github_manual": "", "github_regex": "https://github.com/salesforce/ULIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-16", "arxiv_id": "2305.07804", "title": "Dr. LLaMA: Improving Small Language Models in Domain-Specific QA via Generative Data Augmentation", "authors": [ "Zhen Guo", "Peiqi Wang", "Yanwei Wang", "Shangdi Yu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have made significant strides in natural language processing but face challenges in terms of computational expense and inefficiency as they grow in size, especially in domain-specific tasks. Small Language Models (SLMs), on the other hand, often struggle in these tasks due to limited capacity and training data. In this paper, we introduce Dr. LLaMA, a method for improving SLMs through generative data augmentation using LLMs, focusing on medical question-answering tasks and the PubMedQA dataset. Our findings indicate that LLMs effectively refine and diversify existing question-answer pairs, resulting in improved performance of a much smaller model on domain-specific QA datasets after fine-tuning. This study highlights the challenges of using LLMs for domain-specific question answering and suggests potential research directions to address these limitations, ultimately aiming to create more efficient and capable models for specialized applications. We have also made our code available for interested researchers", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-16", "arxiv_id": "2305.07677", "title": "Masked Audio Text Encoders are Effective Multi-Modal Rescorers", "authors": [ "Jinglun Cai", "Monica Sunkara", "Xilai Li", "Anshu Bhatia", "Xiao Pan", "Sravan Bodapati" ], "github": "", "project_page": "", "abstract": "Masked Language Models (MLMs) have proven to be effective for second-pass rescoring in Automatic Speech Recognition (ASR) systems. In this work, we propose Masked Audio Text Encoder (MATE), a multi-modal masked language model rescorer which incorporates acoustic representations into the input space of MLM. We adopt contrastive learning for effectively aligning the modalities by learning shared representations. We show that using a multi-modal rescorer is beneficial for domain generalization of the ASR system when target domain data is unavailable. MATE reduces word error rate (WER) by 4%-16% on in-domain, and 3%-7% on out-of-domain datasets, over the text-only baseline. Additionally, with very limited amount of training data (0.8 hours), MATE achieves a WER reduction of 8%-23% over the first-pass baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-16", "arxiv_id": "2305.08844", "title": "RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs", "authors": [ "Afra Feyza Aky\u00fcrek", "Ekin Aky\u00fcrek", "Aman Madaan", "Ashwin Kalyan", "Peter Clark", "Derry Wijaya", "Niket Tandon" ], "github": "https://github.com/feyzaakyurek/rl4f", "project_page": "", "abstract": "Despite their unprecedented success, even the largest language models make mistakes. Similar to how humans learn and improve using feedback, previous work proposed providing language models with natural language feedback to guide them in repairing their outputs. Because human-generated critiques are expensive to obtain, researchers have devised learned critique generators in lieu of human critics while assuming one can train downstream models to utilize generated feedback. However, this approach does not apply to black-box or limited access models such as ChatGPT, as they cannot be fine-tuned. Moreover, in the era of large general-purpose language agents, fine-tuning is neither computationally nor spatially efficient as it results in multiple copies of the network. In this work, we introduce RL4F (Reinforcement Learning for Feedback), a multi-agent collaborative framework where the critique generator is trained to maximize end-task performance of GPT-3, a fixed model more than 200 times its size. RL4F produces critiques that help GPT-3 revise its outputs. We study three datasets for action planning, summarization and alphabetization and show improvements (~5% on average) in multiple text similarity metrics over strong baselines across all three tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/feyzaakyurek/rl4f", "github_api_added_by": "auto" }, { "date": "2023-05-16", "arxiv_id": "2305.07969", "title": "GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content", "authors": [ "Yutian Chen", "Hao Kang", "Vivian Zhai", "Liangze Li", "Rita Singh", "Bhiksha Ramakrishnan" ], "github": "https://github.com/MarkChenYutian/GPT-Sentinel-public", "project_page": "", "abstract": "This paper presents a novel approach for detecting ChatGPT-generated vs. human-written text using language models. To this end, we first collected and released a pre-processed dataset named OpenGPTText, which consists of rephrased content generated using ChatGPT. We then designed, implemented, and trained two different models for text classification, using Robustly Optimized BERT Pretraining Approach (RoBERTa) and Text-to-Text Transfer Transformer (T5), respectively. Our models achieved remarkable results, with an accuracy of over 97% on the test dataset, as evaluated through various metrics. Furthermore, we conducted an interpretability study to showcase our model's ability to extract and differentiate key features between human-written and ChatGPT-generated text. Our findings provide important insights into the effective use of language models to detect generated text.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MarkChenYutian/GPT-Sentinel-public", "github_api_added_by": "auto" }, { "date": "2023-05-16", "arxiv_id": "2305.08848", "title": "Small Models are Valuable Plug-ins for Large Language Models", "authors": [ "Canwen Xu", "Yichong Xu", "Shuohang Wang", "Yang Liu", "Chenguang Zhu", "Julian McAuley" ], "github": "https://github.com/JetRunner/SuperICL", "project_page": "", "abstract": "Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable and their immense sizes make the models difficult to be tuned with common hardware. As a result, effectively tuning these models with large-scale supervised data can be challenging. As an alternative, In-Context Learning (ICL) can only use a small number of supervised examples due to context length limits. In this paper, we propose Super In-Context Learning (SuperICL) which allows black-box LLMs to work with locally fine-tuned smaller models, resulting in superior performance on supervised tasks. Our experiments demonstrate that SuperICL can improve performance beyond state-of-the-art fine-tuned models while addressing the instability problem of in-context learning. Furthermore, SuperICL can enhance the capabilities of smaller models, such as multilinguality and interpretability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JetRunner/SuperICL", "github_api_added_by": "auto" }, { "date": "2023-05-16", "arxiv_id": "2305.08850", "title": "Make-A-Protagonist: Generic Video Editing with An Ensemble of Experts", "authors": [ "Yuyang Zhao", "Enze Xie", "Lanqing Hong", "Zhenguo Li", "Gim Hee Lee" ], "github": "https://github.com/HeliosZhao/Make-A-Protagonist", "project_page": "", "abstract": "The text-driven image and video diffusion models have achieved unprecedented success in generating realistic and diverse content. Recently, the editing and variation of existing images and videos in diffusion-based generative models have garnered significant attention. However, previous works are limited to editing content with text or providing coarse personalization using a single visual clue, rendering them unsuitable for indescribable content that requires fine-grained and detailed control. In this regard, we propose a generic video editing framework called Make-A-Protagonist, which utilizes textual and visual clues to edit videos with the goal of empowering individuals to become the protagonists. Specifically, we leverage multiple experts to parse source video, target visual and textual clues, and propose a visual-textual-based video generation model that employs mask-guided denoising sampling to generate the desired output. Extensive results demonstrate the versatile and remarkable editing capabilities of Make-A-Protagonist.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HeliosZhao/Make-A-Protagonist", "github_api_added_by": "auto" }, { "date": "2023-05-16", "arxiv_id": "2305.08379", "title": "TESS: Text-to-Text Self-Conditioned Simplex Diffusion", "authors": [ "Rabeeh Karimi Mahabadi", "Jaesung Tae", "Hamish Ivison", "James Henderson", "Iz Beltagy", "Matthew E. Peters", "Arman Cohan" ], "github": "https://github.com/allenai/tess-diffusion", "project_page": "", "abstract": "Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various domains with continuous-valued inputs. Despite the promises of fully non-autoregressive text generation, applying diffusion models to natural language remains challenging due to its discrete nature. In this work, we propose Text-to-text Self-conditioned Simplex Diffusion (TESS), a text diffusion model that is fully non-autoregressive, employs a new form of self-conditioning, and applies the diffusion process on the logit simplex space rather than the typical learned embedding space. Through extensive experiments on natural language understanding and generation tasks including summarization, text simplification, paraphrase generation, and question generation, we demonstrate that TESS outperforms state-of-the-art non-autoregressive models and is competitive with pretrained autoregressive sequence-to-sequence models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/tess-diffusion", "github_api_added_by": "auto" }, { "date": "2023-05-17", "arxiv_id": "2305.09641", "title": "FitMe: Deep Photorealistic 3D Morphable Model Avatars", "authors": [ "Alexandros Lattas", "Stylianos Moschoglou", "Stylianos Ploumpis", "Baris Gecer", "Jiankang Deng", "Stefanos Zafeiriou" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce FitMe, a facial reflectance model and a differentiable rendering optimization pipeline, that can be used to acquire high-fidelity renderable human avatars from single or multiple images. The model consists of a multi-modal style-based generator, that captures facial appearance in terms of diffuse and specular reflectance, and a PCA-based shape model. We employ a fast differentiable rendering process that can be used in an optimization pipeline, while also achieving photorealistic facial shading. Our optimization process accurately captures both the facial reflectance and shape in high-detail, by exploiting the expressivity of the style-based latent representation and of our shape model. FitMe achieves state-of-the-art reflectance acquisition and identity preservation on single \"in-the-wild\" facial images, while it produces impressive scan-like results, when given multiple unconstrained facial images pertaining to the same identity. In contrast with recent implicit avatar reconstructions, FitMe requires only one minute and produces relightable mesh and texture-based avatars, that can be used by end-user applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-17", "arxiv_id": "2305.09617", "title": "Towards Expert-Level Medical Question Answering with Large Language Models", "authors": [ "Karan Singhal", "Tao Tu", "Juraj Gottweis", "Rory Sayres", "Ellery Wulczyn", "Le Hou", "Kevin Clark", "Stephen Pfohl", "Heather Cole-Lewis", "Darlene Neal", "Mike Schaekermann", "Amy Wang", "Mohamed Amin", "Sami Lachgar", "Philip Mansfield", "Sushant Prakash", "Bradley Green", "Ewa Dominowska", "Blaise Aguera y Arcas", "Nenad Tomasev", "Yun Liu", "Renee Wong", "Christopher Semturs", "S. Sara Mahdavi", "Joelle Barral", "Dale Webster", "Greg S. Corrado", "Yossi Matias", "Shekoofeh Azizi", "Alan Karthikesalingam", "Vivek Natarajan" ], "github": "", "project_page": "", "abstract": "Recent artificial intelligence (AI) systems have reached milestones in \"grand challenges\" ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason over it, and answer medical questions comparably to physicians has long been viewed as one such grand challenge. Large language models (LLMs) have catalyzed significant progress in medical question answering; Med-PaLM was the first model to exceed a \"passing\" score in US Medical Licensing Examination (USMLE) style questions with a score of 67.2% on the MedQA dataset. However, this and other prior work suggested significant room for improvement, especially when models' answers were compared to clinicians' answers. Here we present Med-PaLM 2, which bridges these gaps by leveraging a combination of base LLM improvements (PaLM 2), medical domain finetuning, and prompting strategies including a novel ensemble refinement approach. Med-PaLM 2 scored up to 86.5% on the MedQA dataset, improving upon Med-PaLM by over 19% and setting a new state-of-the-art. We also observed performance approaching or exceeding state-of-the-art across MedMCQA, PubMedQA, and MMLU clinical topics datasets. We performed detailed human evaluations on long-form questions along multiple axes relevant to clinical applications. In pairwise comparative ranking of 1066 consumer medical questions, physicians preferred Med-PaLM 2 answers to those produced by physicians on eight of nine axes pertaining to clinical utility (p < 0.001). We also observed significant improvements compared to Med-PaLM on every evaluation axis (p < 0.001) on newly introduced datasets of 240 long-form \"adversarial\" questions to probe LLM limitations. While further studies are necessary to validate the efficacy of these models in real-world settings, these results highlight rapid progress towards physician-level performance in medical question answering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-17", "arxiv_id": "2305.09137", "title": "Pre-Training to Learn in Context", "authors": [ "Yuxian Gu", "Li Dong", "Furu Wei", "Minlie Huang" ], "github": "https://github.com/thu-coai/PICL", "project_page": "", "abstract": "In-context learning, where pre-trained language models learn to perform tasks from task examples and instructions in their contexts, has attracted much attention in the NLP community. However, the ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context. To this end, we propose PICL (Pre-training for In-Context Learning), a framework to enhance the language models' in-context learning ability by pre-training the model on a large collection of \"intrinsic tasks\" in the general plain-text corpus using the simple language modeling objective. PICL encourages the model to infer and perform tasks by conditioning on the contexts while maintaining task generalization of pre-trained models. We evaluate the in-context learning performance of the model trained with PICL on seven widely-used text classification datasets and the Super-NaturalInstrctions benchmark, which contains 100+ NLP tasks formulated to text generation. Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger language models with nearly 4x parameters. The code is publicly available at https://github.com/thu-coai/PICL.", "github_manual": "", "github_regex": "https://github.com/thu-coai/PICL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-17", "arxiv_id": "2305.09636", "title": "SoundStorm: Efficient Parallel Audio Generation", "authors": [ "Zal\u00e1n Borsos", "Matt Sharifi", "Damien Vincent", "Eugene Kharitonov", "Neil Zeghidour", "Marco Tagliasacchi" ], "github": "", "project_page": "", "abstract": "We present SoundStorm, a model for efficient, non-autoregressive audio generation. SoundStorm receives as input the semantic tokens of AudioLM, and relies on bidirectional attention and confidence-based parallel decoding to generate the tokens of a neural audio codec. Compared to the autoregressive generation approach of AudioLM, our model produces audio of the same quality and with higher consistency in voice and acoustic conditions, while being two orders of magnitude faster. SoundStorm generates 30 seconds of audio in 0.5 seconds on a TPU-v4. We demonstrate the ability of our model to scale audio generation to longer sequences by synthesizing high-quality, natural dialogue segments, given a transcript annotated with speaker turns and a short prompt with the speakers' voices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-17", "arxiv_id": "2305.08891", "title": "Common Diffusion Noise Schedules and Sample Steps are Flawed", "authors": [ "Shanchuan Lin", "Bingchen Liu", "Jiashi Li", "Xiao Yang" ], "github": "", "project_page": "", "abstract": "We discover that common diffusion noise schedules do not enforce the last timestep to have zero signal-to-noise ratio (SNR), and some implementations of diffusion samplers do not start from the last timestep. Such designs are flawed and do not reflect the fact that the model is given pure Gaussian noise at inference, creating a discrepancy between training and inference. We show that the flawed design causes real problems in existing implementations. In Stable Diffusion, it severely limits the model to only generate images with medium brightness and prevents it from generating very bright and dark samples. We propose a few simple fixes: (1) rescale the noise schedule to enforce zero terminal SNR; (2) train the model with v prediction; (3) change the sampler to always start from the last timestep; (4) rescale classifier-free guidance to prevent over-exposure. These simple changes ensure the diffusion process is congruent between training and inference and allow the model to generate samples more faithful to the original data distribution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-17", "arxiv_id": "2305.09253", "title": "Online Continual Learning Without the Storage Constraint", "authors": [ "Ameya Prabhu", "Zhipeng Cai", "Puneet Dokania", "Philip Torr", "Vladlen Koltun", "Ozan Sener" ], "github": "https://github.com/drimpossible/ACM", "project_page": "", "abstract": "Online continual learning (OCL) research has primarily focused on mitigating catastrophic forgetting with fixed and limited storage allocation throughout the agent's lifetime. However, the growing affordability of data storage highlights a broad range of applications that do not adhere to these assumptions. In these cases, the primary concern lies in managing computational expenditures rather than storage. In this paper, we target such settings, investigating the online continual learning problem by relaxing storage constraints and emphasizing fixed, limited economical budget. We provide a simple algorithm that can compactly store and utilize the entirety of the incoming data stream under tiny computational budgets using a kNN classifier and universal pre-trained feature extractors. Our algorithm provides a consistency property attractive to continual learning: It will never forget past seen data. We set a new state of the art on two large-scale OCL datasets: Continual LOCalization (CLOC), which has 39M images over 712 classes, and Continual Google Landmarks V2 (CGLM), which has 580K images over 10,788 classes -- beating methods under far higher computational budgets than ours in terms of both reducing catastrophic forgetting of past data and quickly adapting to rapidly changing data streams. We provide code to reproduce our results at https://github.com/drimpossible/ACM.", "github_manual": "", "github_regex": "https://github.com/drimpossible/ACM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-17", "arxiv_id": "2305.09664", "title": "Understanding 3D Object Interaction from a Single Image", "authors": [ "Shengyi Qian", "David F. Fouhey" ], "github": "https://github.com/JasonQSY/3DOI", "project_page": "", "abstract": "Humans can easily understand a single image as depicting multiple potential objects permitting interaction. We use this skill to plan our interactions with the world and accelerate understanding new objects without engaging in interaction. In this paper, we would like to endow machines with the similar ability, so that intelligent agents can better explore the 3D scene or manipulate objects. Our approach is a transformer-based model that predicts the 3D location, physical properties and affordance of objects. To power this model, we collect a dataset with Internet videos, egocentric videos and indoor images to train and validate our approach. Our model yields strong performance on our data, and generalizes well to robotics data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JasonQSY/3DOI", "github_api_added_by": "auto" }, { "date": "2023-05-17", "arxiv_id": "2305.09148", "title": "Dual-Alignment Pre-training for Cross-lingual Sentence Embedding", "authors": [ "Ziheng Li", "Shaohan Huang", "Zihan Zhang", "Zhi-Hong Deng", "Qiang Lou", "Haizhen Huang", "Jian Jiao", "Furu Wei", "Weiwei Deng", "Qi Zhang" ], "github": "https://github.com/ChillingDream/DAP", "project_page": "", "abstract": "Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding. However, our research indicates that token-level alignment is also crucial in multilingual scenarios, which has not been fully explored previously. Based on our findings, we propose a dual-alignment pre-training (DAP) framework for cross-lingual sentence embedding that incorporates both sentence-level and token-level alignment. To achieve this, we introduce a novel representation translation learning (RTL) task, where the model learns to use one-side contextualized token representation to reconstruct its translation counterpart. This reconstruction objective encourages the model to embed translation information into the token representation. Compared to other token-level alignment methods such as translation language modeling, RTL is more suitable for dual encoder architectures and is computationally efficient. Extensive experiments on three sentence-level cross-lingual benchmarks demonstrate that our approach can significantly improve sentence embedding. Our code is available at https://github.com/ChillingDream/DAP.", "github_manual": "", "github_regex": "https://github.com/ChillingDream/DAP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-17", "arxiv_id": "2305.09515", "title": "AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation", "authors": [ "Tong Wu", "Zhihao Fan", "Xiao Liu", "Yeyun Gong", "Yelong Shen", "Jian Jiao", "Hai-Tao Zheng", "Juntao Li", "Zhongyu Wei", "Jian Guo", "Nan Duan", "Weizhu Chen" ], "github": "https://github.com/microsoft/ProphetNet/tree/master/AR-diffusion", "project_page": "", "abstract": "Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generation via generating all tokens within a sequence concurrently. However, natural language exhibits a far more pronounced sequential dependency in comparison to images, and the majority of existing language models are trained utilizing a left-to-right auto-regressive approach. To account for the inherent sequential characteristic of natural language, we introduce Auto-Regressive Diffusion (AR-Diffusion). AR-Diffusion ensures that the generation of tokens on the right depends on the generated ones on the left, a mechanism achieved through employing a dynamic number of denoising steps that vary based on token position. This results in tokens on the left undergoing fewer denoising steps than those on the right, thereby enabling them to generate earlier and subsequently influence the generation of tokens on the right. In a series of experiments on various text generation tasks including text summarization, machine translation, and common sense generation, AR-Diffusion clearly demonstrated the superiority over existing diffusion language models and that it can be 100timessim600times faster when achieving comparable results. Our code will be publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/ProphetNet/tree/master/AR-diffusion", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-17", "arxiv_id": "2305.09662", "title": "Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation", "authors": [ "Samaneh Azadi", "Akbar Shah", "Thomas Hayes", "Devi Parikh", "Sonal Gupta" ], "github": "https://github.com/azadis/make-an-animation", "project_page": "", "abstract": "Text-guided human motion generation has drawn significant interest because of its impactful applications spanning animation and robotics. Recently, application of diffusion models for motion generation has enabled improvements in the quality of generated motions. However, existing approaches are limited by their reliance on relatively small-scale motion capture data, leading to poor performance on more diverse, in-the-wild prompts. In this paper, we introduce Make-An-Animation, a text-conditioned human motion generation model which learns more diverse poses and prompts from large-scale image-text datasets, enabling significant improvement in performance over prior works. Make-An-Animation is trained in two stages. First, we train on a curated large-scale dataset of (text, static pseudo-pose) pairs extracted from image-text datasets. Second, we fine-tune on motion capture data, adding additional layers to model the temporal dimension. Unlike prior diffusion models for motion generation, Make-An-Animation uses a U-Net architecture similar to recent text-to-video generation models. Human evaluation of motion realism and alignment with input text shows that our model reaches state-of-the-art performance on text-to-motion generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/azadis/make-an-animation", "github_api_added_by": "auto" }, { "date": "2023-05-18", "arxiv_id": "2305.09857", "title": "CoEdIT: Text Editing by Task-Specific Instruction Tuning", "authors": [ "Vipul Raheja", "Dhruv Kumar", "Ryan Koo", "Dongyeop Kang" ], "github": "https://github.com/vipulraheja/coedit", "project_page": "", "abstract": "Text editing or revision is an essential function of the human writing process. Understanding the capabilities of LLMs for making high-quality revisions and collaborating with human writers is a critical step toward building effective writing assistants. With the prior success of LLMs and instruction tuning, we leverage instruction-tuned LLMs for text revision to improve the quality of user-generated text and improve the efficiency of the process. We introduce CoEdIT, a state-of-the-art text editing model for writing assistance. CoEdIT takes instructions from the user specifying the attributes of the desired text, such as \"Make the sentence simpler\" or \"Write it in a more neutral style,\" and outputs the edited text. We present a large language model fine-tuned on a diverse collection of task-specific instructions for text editing (a total of 82K instructions). Our model (1) achieves state-of-the-art performance on various text editing benchmarks, (2) is competitive with publicly available largest-sized LLMs trained on instructions while being sim60x smaller, (3) is capable of generalizing to unseen edit instructions, and (4) exhibits compositional comprehension abilities to generalize to instructions containing different combinations of edit actions. Through extensive qualitative and quantitative analysis, we show that writers prefer the edits suggested by CoEdIT, relative to other state-of-the-art text editing models. Our code and dataset are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vipulraheja/coedit", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-18", "arxiv_id": "2305.10425", "title": "SLiC-HF: Sequence Likelihood Calibration with Human Feedback", "authors": [ "Yao Zhao", "Rishabh Joshi", "Tianqi Liu", "Misha Khalman", "Mohammad Saleh", "Peter J. Liu" ], "github": "", "project_page": "", "abstract": "Learning from human feedback has been shown to be effective at aligning language models with human preferences. Past work has often relied on Reinforcement Learning from Human Feedback (RLHF), which optimizes the language model using reward scores assigned from a reward model trained on human preference data. In this work we show how the recently introduced Sequence Likelihood Calibration (SLiC), can also be used to effectively learn from human preferences (SLiC-HF). Furthermore, we demonstrate this can be done with human feedback data collected for a different model, similar to off-policy, offline RL data. Automatic and human evaluation experiments on the TL;DR summarization task show that SLiC-HF significantly improves supervised fine-tuning baselines. Furthermore, SLiC-HF presents a competitive alternative to the PPO RLHF implementation used in past work while being much simpler to implement, easier to tune and more computationally efficient in practice.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-18", "arxiv_id": "2305.09863", "title": "Explaining black box text modules in natural language with language models", "authors": [ "Chandan Singh", "Aliyah R. Hsu", "Richard Antonello", "Shailee Jain", "Alexander G. Huth", "Bin Yu", "Jianfeng Gao" ], "github": "https://github.com/microsoft/automated-explanations", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks. However, their rapid proliferation and increasing opaqueness have created a growing need for interpretability. Here, we ask whether we can automatically obtain natural language explanations for black box text modules. A \"text module\" is any function that maps text to a scalar continuous value, such as a submodule within an LLM or a fitted model of a brain region. \"Black box\" indicates that we only have access to the module's inputs/outputs. We introduce Summarize and Score (SASC), a method that takes in a text module and returns a natural language explanation of the module's selectivity along with a score for how reliable the explanation is. We study SASC in 3 contexts. First, we evaluate SASC on synthetic modules and find that it often recovers ground truth explanations. Second, we use SASC to explain modules found within a pre-trained BERT model, enabling inspection of the model's internals. Finally, we show that SASC can generate explanations for the response of individual fMRI voxels to language stimuli, with potential applications to fine-grained brain mapping. All code for using SASC and reproducing results is made available on Github.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/automated-explanations", "github_api_added_by": "auto" }, { "date": "2023-05-18", "arxiv_id": "2305.10403", "title": "PaLM 2 Technical Report", "authors": [ "Rohan Anil", "Andrew M. Dai", "Orhan Firat", "Melvin Johnson", "Dmitry Lepikhin", "Alexandre Passos", "Siamak Shakeri", "Emanuel Taropa", "Paige Bailey", "Zhifeng Chen", "Eric Chu", "Jonathan H. Clark", "Laurent El Shafey", "Yanping Huang", "Kathy Meier-Hellstern", "Gaurav Mishra", "Erica Moreira", "Mark Omernick", "Kevin Robinson", "Sebastian Ruder", "Yi Tay", "Kefan Xiao", "Yuanzhong Xu", "Yujing Zhang", "Gustavo Hernandez Abrego", "Junwhan Ahn", "Jacob Austin", "Paul Barham", "Jan Botha", "James Bradbury", "Siddhartha Brahma", "Kevin Brooks", "Michele Catasta", "Yong Cheng", "Colin Cherry", "Christopher A. Choquette-Choo", "Aakanksha Chowdhery", "Cl\u00e9ment Crepy", "Shachi Dave", "Mostafa Dehghani", "Sunipa Dev", "Jacob Devlin", "Mark D\u00edaz", "Nan Du", "Ethan Dyer", "Vlad Feinberg", "Fangxiaoyu Feng", "Vlad Fienber", "Markus Freitag", "Xavier Garcia", "Sebastian Gehrmann", "Lucas Gonzalez", "Guy Gur-Ari", "Steven Hand", "Hadi Hashemi", "Le Hou", "Joshua Howland", "Andrea Hu", "Jeffrey Hui", "Jeremy Hurwitz", "Michael Isard", "Abe Ittycheriah", "Matthew Jagielski", "Wenhao Jia", "Kathleen Kenealy", "Maxim Krikun", "Sneha Kudugunta", "Chang Lan", "Katherine Lee", "Benjamin Lee", "Eric Li", "Music Li", "Wei Li", "YaGuang Li", "Jian Li", "Hyeontaek Lim", "Hanzhao Lin", "Zhongtao Liu", "Frederick Liu", "Marcello Maggioni", "Aroma Mahendru", "Joshua Maynez", "Vedant Misra", "Maysam Moussalem", "Zachary Nado", "John Nham", "Eric Ni", "Andrew Nystrom", "Alicia Parrish", "Marie Pellat", "Martin Polacek", "Alex Polozov", "Reiner Pope", "Siyuan Qiao", "Emily Reif", "Bryan Richter", "Parker Riley", "Alex Castro Ros", "Aurko Roy", "Brennan Saeta", "Rajkumar Samuel", "Renee Shelby", "Ambrose Slone", "Daniel Smilkov", "David R. So", "Daniel Sohn", "Simon Tokumine", "Dasha Valter", "Vijay Vasudevan", "Kiran Vodrahalli", "Xuezhi Wang", "Pidong Wang", "Zirui Wang", "Tao Wang", "John Wieting", "Yuhuai Wu", "Kelvin Xu", "Yunhan Xu", "Linting Xue", "Pengcheng Yin", "Jiahui Yu", "Qiao Zhang", "Steven Zheng", "Ce Zheng", "Weikang Zhou", "Denny Zhou", "Slav Petrov", "Yonghui Wu" ], "github": "", "project_page": "", "abstract": "We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-18", "arxiv_id": "2305.09975", "title": "Smart Word Suggestions for Writing Assistance", "authors": [ "Chenshuo Wang", "Shaoguang Mao", "Tao Ge", "Wenshan Wu", "Xun Wang", "Yan Xia", "Jonathan Tien", "Dongyan Zhao" ], "github": "https://github.com/microsoft/SmartWordSuggestions", "project_page": "", "abstract": "Enhancing word usage is a desired feature for writing assistance. To further advance research in this area, this paper introduces \"Smart Word Suggestions\" (SWS) task and benchmark. Unlike other works, SWS emphasizes end-to-end evaluation and presents a more realistic writing assistance scenario. This task involves identifying words or phrases that require improvement and providing substitution suggestions. The benchmark includes human-labeled data for testing, a large distantly supervised dataset for training, and the framework for evaluation. The test data includes 1,000 sentences written by English learners, accompanied by over 16,000 substitution suggestions annotated by 10 native speakers. The training dataset comprises over 3.7 million sentences and 12.7 million suggestions generated through rules. Our experiments with seven baselines demonstrate that SWS is a challenging task. Based on experimental analysis, we suggest potential directions for future research on SWS. The dataset and related codes is available at https://github.com/microsoft/SmartWordSuggestions.", "github_manual": "", "github_regex": "https://github.com/microsoft/SmartWordSuggestions", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-18", "arxiv_id": "2305.09761", "title": "NerfBridge: Bringing Real-time, Online Neural Radiance Field Training to Robotics", "authors": [ "Javier Yu", "Jun En Low", "Keiko Nagami", "Mac Schwager" ], "github": "https://github.com/javieryu/nerf_bridge", "project_page": "", "abstract": "This work was presented at the IEEE International Conference on Robotics and Automation 2023 Workshop on Unconventional Spatial Representations. Neural radiance fields (NeRFs) are a class of implicit scene representations that model 3D environments from color images. NeRFs are expressive, and can model the complex and multi-scale geometry of real world environments, which potentially makes them a powerful tool for robotics applications. Modern NeRF training libraries can generate a photo-realistic NeRF from a static data set in just a few seconds, but are designed for offline use and require a slow pose optimization pre-computation step. In this work we propose NerfBridge, an open-source bridge between the Robot Operating System (ROS) and the popular Nerfstudio library for real-time, online training of NeRFs from a stream of images. NerfBridge enables rapid development of research on applications of NeRFs in robotics by providing an extensible interface to the efficient training pipelines and model libraries provided by Nerfstudio. As an example use case we outline a hardware setup that can be used NerfBridge to train a NeRF from images captured by a camera mounted to a quadrotor in both indoor and outdoor environments. For accompanying video https://youtu.be/EH0SLn-RcDg and code https://github.com/javieryu/nerf_bridge.", "github_manual": "", "github_regex": "https://github.com/javieryu/nerf_bridge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-18", "arxiv_id": "2305.10320", "title": "CostFormer:Cost Transformer for Cost Aggregation in Multi-view Stereo", "authors": [ "Weitao Chen", "Hongbin Xu", "Zhipeng Zhou", "Yang Liu", "Baigui Sun", "Wenxiong Kang", "Xuansong Xie" ], "github": "", "project_page": "", "abstract": "The core of Multi-view Stereo(MVS) is the matching process among reference and source pixels. Cost aggregation plays a significant role in this process, while previous methods focus on handling it via CNNs. This may inherit the natural limitation of CNNs that fail to discriminate repetitive or incorrect matches due to limited local receptive fields. To handle the issue, we aim to involve Transformer into cost aggregation. However, another problem may occur due to the quadratically growing computational complexity caused by Transformer, resulting in memory overflow and inference latency. In this paper, we overcome these limits with an efficient Transformer-based cost aggregation network, namely CostFormer. The Residual Depth-Aware Cost Transformer(RDACT) is proposed to aggregate long-range features on cost volume via self-attention mechanisms along the depth and spatial dimensions. Furthermore, Residual Regression Transformer(RRT) is proposed to enhance spatial attention. The proposed method is a universal plug-in to improve learning-based MVS methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-18", "arxiv_id": "2305.10018", "title": "Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers", "authors": [ "Manuel Lagunas", "Brayan Impata", "Victor Martinez", "Virginia Fernandez", "Christos Georgakis", "Sofia Braun", "Felipe Bertrand" ], "github": "", "project_page": "", "abstract": "Fine-grained classification is a challenging task that involves identifying subtle differences between objects within the same category. This task is particularly challenging in scenarios where data is scarce. Visual transformers (ViT) have recently emerged as a powerful tool for image classification, due to their ability to learn highly expressive representations of visual data using self-attention mechanisms. In this work, we explore Semi-ViT, a ViT model fine tuned using semi-supervised learning techniques, suitable for situations where we have lack of annotated data. This is particularly common in e-commerce, where images are readily available but labels are noisy, nonexistent, or expensive to obtain. Our results demonstrate that Semi-ViT outperforms traditional convolutional neural networks (CNN) and ViTs, even when fine-tuned with limited annotated data. These findings indicate that Semi-ViTs hold significant promise for applications that require precise and fine-grained classification of visual data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-18", "arxiv_id": "2305.10429", "title": "DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining", "authors": [ "Sang Michael Xie", "Hieu Pham", "Xuanyi Dong", "Nan Du", "Hanxiao Liu", "Yifeng Lu", "Percy Liang", "Quoc V. Le", "Tengyu Ma", "Adams Wei Yu" ], "github": "https://github.com/sangmichaelxie/doremi", "project_page": "", "abstract": "The mixture proportions of pretraining data domains (e.g., Wikipedia, books, web text) greatly affect language model (LM) performance. In this paper, we propose Domain Reweighting with Minimax Optimization (DoReMi), which first trains a small proxy model using group distributionally robust optimization (Group DRO) over domains to produce domain weights (mixture proportions) without knowledge of downstream tasks. We then resample a dataset with these domain weights and train a larger, full-sized model. In our experiments, we use DoReMi on a 280M-parameter proxy model to find domain weights for training an 8B-parameter model (30x larger) more efficiently. On The Pile, DoReMi improves perplexity across all domains, even when it downweights a domain. DoReMi improves average few-shot downstream accuracy by 6.5% over a baseline model trained using The Pile's default domain weights and reaches the baseline accuracy with 2.6x fewer training steps. On the GLaM dataset, DoReMi, which has no knowledge of downstream tasks, even matches the performance of using domain weights tuned on downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sangmichaelxie/doremi", "github_api_added_by": "auto" }, { "date": "2023-05-18", "arxiv_id": "2305.10266", "title": "Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability", "authors": [ "Eleftheria Briakou", "Colin Cherry", "George Foster" ], "github": "", "project_page": "", "abstract": "Large, multilingual language models exhibit surprisingly good zero- or few-shot machine translation capabilities, despite having never seen the intentionally-included translation examples provided to typical neural translation systems. We investigate the role of incidental bilingualism -- the unintentional consumption of bilingual signals, including translation examples -- in explaining the translation capabilities of large language models, taking the Pathways Language Model (PaLM) as a case study. We introduce a mixed-method approach to measure and understand incidental bilingualism at scale. We show that PaLM is exposed to over 30 million translation pairs across at least 44 languages. Furthermore, the amount of incidental bilingual content is highly correlated with the amount of monolingual in-language content for non-English languages. We relate incidental bilingual content to zero-shot prompts and show that it can be used to mine new prompts to improve PaLM's out-of-English zero-shot translation quality. Finally, in a series of small-scale ablations, we show that its presence has a substantial impact on translation capabilities, although this impact diminishes with model scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-18", "arxiv_id": "2305.10142", "title": "Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback", "authors": [ "Yao Fu", "Hao Peng", "Tushar Khot", "Mirella Lapata" ], "github": "https://github.com/franxyao/gpt-bargaining", "project_page": "", "abstract": "We study whether multiple large language models (LLMs) can autonomously improve each other in a negotiation game by playing, reflecting, and criticizing. We are interested in this question because if LLMs were able to improve each other, it would imply the possibility of creating strong AI agents with minimal human intervention. We ask two LLMs to negotiate with each other, playing the roles of a buyer and a seller, respectively. They aim to reach a deal with the buyer targeting a lower price and the seller a higher one. A third language model, playing the critic, provides feedback to a player to improve the player's negotiation strategies. We let the two agents play multiple rounds, using previous negotiation history and AI feedback as in-context demonstrations to improve the model's negotiation strategy iteratively. We use different LLMs (GPT and Claude) for different roles and use the deal price as the evaluation metric. Our experiments reveal multiple intriguing findings: (1) Only a subset of the language models we consider can self-play and improve the deal price from AI feedback, weaker models either do not understand the game's rules or cannot incorporate AI feedback for further improvement. (2) Models' abilities to learn from the feedback differ when playing different roles. For example, it is harder for Claude-instant to improve as the buyer than as the seller. (3) When unrolling the game to multiple rounds, stronger agents can consistently improve their performance by meaningfully using previous experiences and iterative AI feedback, yet have a higher risk of breaking the deal. We hope our work provides insightful initial explorations of having models autonomously improve each other with game playing and AI feedback.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/franxyao/gpt-bargaining", "github_api_added_by": "auto" }, { "date": "2023-05-18", "arxiv_id": "2305.10005", "title": "DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning", "authors": [ "Alexander H. Liu", "Heng-Jui Chang", "Michael Auli", "Wei-Ning Hsu", "James R. Glass" ], "github": "https://github.com/alexander-h-liu/dinosr", "project_page": "", "abstract": "In this paper, we introduce self-distillation and online clustering for self-supervised speech representation learning (DinoSR) which combines masked language modeling, self-distillation, and online clustering. We show that these concepts complement each other and result in a strong representation learning model for speech. DinoSR first extracts contextualized embeddings from the input audio with a teacher network, then runs an online clustering system on the embeddings to yield a machine-discovered phone inventory, and finally uses the discretized tokens to guide a student network. We show that DinoSR surpasses previous state-of-the-art performance in several downstream tasks, and provide a detailed analysis of the model and the learned discrete units. The source code will be made available after the anonymity period.", "github_manual": "https://github.com/alexander-h-liu/dinosr", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-18", "arxiv_id": "2305.09764", "title": "Application-Agnostic Language Modeling for On-Device ASR", "authors": [ "Markus Nu\u00dfbaum-Thom", "Lyan Verwimp", "Youssef Oualil" ], "github": "", "project_page": "", "abstract": "On-device automatic speech recognition systems face several challenges compared to server-based systems. They have to meet stricter constraints in terms of speed, disk size and memory while maintaining the same accuracy. Often they have to serve several applications with different distributions at once, such as communicating with a virtual assistant and speech-to-text. The simplest solution to serve multiple applications is to build application-specific (language) models, but this leads to an increase in memory. Therefore, we explore different data- and architecture-driven language modeling approaches to build a single application-agnostic model. We propose two novel feed-forward architectures that find an optimal trade off between different on-device constraints. In comparison to the application-specific solution, one of our novel approaches reduces the disk size by half, while maintaining speed and accuracy of the original model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-18", "arxiv_id": "2305.09758", "title": "A Video Is Worth 4096 Tokens: Verbalize Story Videos To Understand Them In Zero Shot", "authors": [ "Aanisha Bhattacharya", "Yaman K Singla", "Balaji Krishnamurthy", "Rajiv Ratn Shah", "Changyou Chen" ], "github": "", "project_page": "", "abstract": "Multimedia content, such as advertisements and story videos, exhibit a rich blend of creativity and multiple modalities. They incorporate elements like text, visuals, audio, and storytelling techniques, employing devices like emotions, symbolism, and slogans to convey meaning. While previous research in multimedia understanding has focused mainly on videos with specific actions like cooking, there is a dearth of large annotated training datasets, hindering the development of supervised learning models with satisfactory performance for real-world applications. However, the rise of large language models (LLMs) has witnessed remarkable zero-shot performance in various natural language processing (NLP) tasks, such as emotion classification, question-answering, and topic classification. To bridge this performance gap in multimedia understanding, we propose verbalizing story videos to generate their descriptions in natural language and then performing video-understanding tasks on the generated story as opposed to the original video. Through extensive experiments on five video-understanding tasks, we demonstrate that our method, despite being zero-shot, achieves significantly better results than supervised baselines for video understanding. Further, alleviating a lack of story understanding benchmarks, we publicly release the first dataset on a crucial task in computational social science, persuasion strategy identification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-18", "arxiv_id": "2305.10400", "title": "What You See is What You Read? Improving Text-Image Alignment Evaluation", "authors": [ "Michal Yarom", "Yonatan Bitton", "Soravit Changpinyo", "Roee Aharoni", "Jonathan Herzig", "Oran Lang", "Eran Ofek", "Idan Szpektor" ], "github": "https://github.com/yonatanbitton/wysiwyr", "project_page": "", "abstract": "Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative text-to-image and image-to-text tasks. In this work, we study methods for automatic text-image alignment evaluation. We first introduce SeeTRUE: a comprehensive evaluation set, spanning multiple datasets from both text-to-image and image-to-text generation tasks, with human judgements for whether a given text-image pair is semantically aligned. We then describe two automatic methods to determine alignment: the first involving a pipeline based on question generation and visual question answering models, and the second employing an end-to-end classification approach by finetuning multimodal pretrained models. Both methods surpass prior approaches in various text-image alignment tasks, with significant improvements in challenging cases that involve complex composition or unnatural images. Finally, we demonstrate how our approaches can localize specific misalignments between an image and a given text, and how they can be used to automatically re-rank candidates in text-to-image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yonatanbitton/wysiwyr", "github_api_added_by": "auto" }, { "date": "2023-05-18", "arxiv_id": "2305.10431", "title": "FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention", "authors": [ "Guangxuan Xiao", "Tianwei Yin", "William T. Freeman", "Fr\u00e9do Durand", "Song Han" ], "github": "https://github.com/mit-han-lab/fastcomposer", "project_page": "", "abstract": "Diffusion models excel at text-to-image generation, especially in subject-driven generation for personalized images. However, existing methods are inefficient due to the subject-specific fine-tuning, which is computationally intensive and hampers efficient deployment. Moreover, existing methods struggle with multi-subject generation as they often blend features among subjects. We present FastComposer which enables efficient, personalized, multi-subject text-to-image generation without fine-tuning. FastComposer uses subject embeddings extracted by an image encoder to augment the generic text conditioning in diffusion models, enabling personalized image generation based on subject images and textual instructions with only forward passes. To address the identity blending problem in the multi-subject generation, FastComposer proposes cross-attention localization supervision during training, enforcing the attention of reference subjects localized to the correct regions in the target images. Naively conditioning on subject embeddings results in subject overfitting. FastComposer proposes delayed subject conditioning in the denoising step to maintain both identity and editability in subject-driven image generation. FastComposer generates images of multiple unseen individuals with different styles, actions, and contexts. It achieves 300times-2500times speedup compared to fine-tuning-based methods and requires zero extra storage for new subjects. FastComposer paves the way for efficient, personalized, and high-quality multi-subject image creation. Code, model, and dataset are available at https://github.com/mit-han-lab/fastcomposer.", "github_manual": "", "github_regex": "https://github.com/mit-han-lab/fastcomposer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.10973", "title": "Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold", "authors": [ "Xingang Pan", "Ayush Tewari", "Thomas Leimk\u00fchler", "Lingjie Liu", "Abhimitra Meka", "Christian Theobalt" ], "github": "https://github.com/XingangPan/DragGAN", "project_page": "", "abstract": "Synthesizing visual content that meets users' needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects. Existing approaches gain controllability of generative adversarial networks (GANs) via manually annotated training data or a prior 3D model, which often lack flexibility, precision, and generality. In this work, we study a powerful yet much less explored way of controlling GANs, that is, to \"drag\" any points of the image to precisely reach target points in a user-interactive manner, as shown in Fig.1. To achieve this, we propose DragGAN, which consists of two main components: 1) a feature-based motion supervision that drives the handle point to move towards the target position, and 2) a new point tracking approach that leverages the discriminative generator features to keep localizing the position of the handle points. Through DragGAN, anyone can deform an image with precise control over where pixels go, thus manipulating the pose, shape, expression, and layout of diverse categories such as animals, cars, humans, landscapes, etc. As these manipulations are performed on the learned generative image manifold of a GAN, they tend to produce realistic outputs even for challenging scenarios such as hallucinating occluded content and deforming shapes that consistently follow the object's rigidity. Both qualitative and quantitative comparisons demonstrate the advantage of DragGAN over prior approaches in the tasks of image manipulation and point tracking. We also showcase the manipulation of real images through GAN inversion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/XingangPan/DragGAN", "github_api_added_by": "auto" }, { "date": "2023-05-19", "arxiv_id": "2305.10764", "title": "OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding", "authors": [ "Minghua Liu", "Ruoxi Shi", "Kaiming Kuang", "Yinhao Zhu", "Xuanlin Li", "Shizhong Han", "Hong Cai", "Fatih Porikli", "Hao Su" ], "github": "https://github.com/Colin97/OpenShape_code", "project_page": "", "abstract": "We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framework for representation alignment, but with a specific focus on scaling up 3D representations to enable open-world 3D shape understanding. To achieve this, we scale up training data by ensembling multiple 3D datasets and propose several strategies to automatically filter and enrich noisy text descriptions. We also explore and compare strategies for scaling 3D backbone networks and introduce a novel hard negative mining module for more efficient training. We evaluate OpenShape on zero-shot 3D classification benchmarks and demonstrate its superior capabilities for open-world recognition. Specifically, OpenShape achieves a zero-shot accuracy of 46.8% on the 1,156-category Objaverse-LVIS benchmark, compared to less than 10% for existing methods. OpenShape also achieves an accuracy of 85.3% on ModelNet40, outperforming previous zero-shot baseline methods by 20% and performing on par with some fully-supervised methods. Furthermore, we show that our learned embeddings encode a wide range of visual and semantic concepts (e.g., subcategories, color, shape, style) and facilitate fine-grained text-3D and image-3D interactions. Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Colin97/OpenShape_code", "github_api_added_by": "auto" }, { "date": "2023-05-19", "arxiv_id": "2305.10763", "title": "CLAPSpeech: Learning Prosody from Text Context with Contrastive Language-Audio Pre-training", "authors": [ "Zhenhui Ye", "Rongjie Huang", "Yi Ren", "Ziyue Jiang", "Jinglin Liu", "Jinzheng He", "Xiang Yin", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "Improving text representation has attracted much attention to achieve expressive text-to-speech (TTS). However, existing works only implicitly learn the prosody with masked token reconstruction tasks, which leads to low training efficiency and difficulty in prosody modeling. We propose CLAPSpeech, a cross-modal contrastive pre-training framework that explicitly learns the prosody variance of the same text token under different contexts. Specifically, 1) We encourage the model to connect the text context with its corresponding prosody pattern in the joint multi-modal space with the elaborate design of the encoder inputs and contrastive loss; 2) We introduce a multi-scale pre-training pipeline to capture prosody patterns in multiple levels. We show how to incorporate CLAPSpeech into existing TTS models for better prosody. Experiments on three datasets not only show that CLAPSpeech could improve the prosody prediction for existing TTS methods, but also demonstrate its generalization ability to adapt to multiple languages and multi-speaker TTS. We also deeply analyze the principle behind the performance of CLAPSpeech. Ablation studies demonstrate the necessity of each component in our method. Source code and audio samples are available at https://clapspeech.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.11171", "title": "TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models", "authors": [ "Zorik Gekhman", "Jonathan Herzig", "Roee Aharoni", "Chen Elkind", "Idan Szpektor" ], "github": "", "project_page": "", "abstract": "Factual consistency evaluation is often conducted using Natural Language Inference (NLI) models, yet these models exhibit limited success in evaluating summaries. Previous work improved such models with synthetic training data. However, the data is typically based on perturbed human-written summaries, which often differ in their characteristics from real model-generated summaries and have limited coverage of possible factual errors. Alternatively, large language models (LLMs) have recently shown promising results in directly evaluating generative tasks, but are too computationally expensive for practical use. Motivated by these limitations, we introduce TrueTeacher, a method for generating synthetic data by annotating diverse model-generated summaries using a LLM. Unlike prior work, TrueTeacher does not rely on human-written summaries, and is multilingual by nature. Experiments on the TRUE benchmark show that a student model trained using our data, substantially outperforms both the state-of-the-art model with similar capacity, and the LLM teacher. In a systematic study, we compare TrueTeacher to existing synthetic data generation methods and demonstrate its superiority and robustness to domain-shift. Using the the mFACE dataset, we also show that our method generalizes to multilingual scenarios. Finally, we release a large-scale synthetic dataset with 1.4M examples generated using TrueTeacher.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.10434", "title": "Learning the Visualness of Text Using Large Vision-Language Models", "authors": [ "Gaurav Verma", "Ryan A. Rossi", "Christopher Tensmeyer", "Jiuxiang Gu", "Ani Nenkova" ], "github": "", "project_page": "", "abstract": "Visual text evokes an image in a person's mind, while non-visual text fails to do so. A method to automatically detect visualness in text will unlock the ability to augment text with relevant images, as neural text-to-image generation and retrieval models operate on the implicit assumption that the input text is visual in nature. We curate a dataset of 3,620 English sentences and their visualness scores provided by multiple human annotators. Additionally, we use documents that contain text and visual assets to create a distantly supervised corpus of document text and associated images. We also propose a fine-tuning strategy that adapts large vision-language models like CLIP that assume a one-to-one correspondence between text and image to the task of scoring text visualness from text input alone. Our strategy involves modifying the model's contrastive learning objective to map text identified as non-visual to a common NULL image while matching visual text to their corresponding images in the document. We evaluate the proposed approach on its ability to (i) classify visual and non-visual text accurately, and (ii) attend over words that are identified as visual in psycholinguistic studies. Empirical evaluation indicates that our approach performs better than several heuristics and baseline models for the proposed task. Furthermore, to highlight the importance of modeling the visualness of text, we conduct qualitative analyses of text-to-image generation systems like DALL-E.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.10601", "title": "Tree of Thoughts: Deliberate Problem Solving with Large Language Models", "authors": [ "Shunyu Yao", "Dian Yu", "Jeffrey Zhao", "Izhak Shafran", "Thomas L. Griffiths", "Yuan Cao", "Karthik Narasimhan" ], "github": "https://github.com/ysymyth/tree-of-thought-llm", "project_page": "", "abstract": "Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. Our experiments show that ToT significantly enhances language models' problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%. Code repo with all prompts: https://github.com/ysymyth/tree-of-thought-llm.", "github_manual": "", "github_regex": "https://github.com/ysymyth/tree-of-thought-llm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.10855", "title": "TextDiffuser: Diffusion Models as Text Painters", "authors": [ "Jingye Chen", "Yupan Huang", "Tengchao Lv", "Lei Cui", "Qifeng Chen", "Furu Wei" ], "github": "https://github.com/microsoft/unilm/tree/master/textdiffuser", "project_page": "", "abstract": "Diffusion models have gained increasing attention for their impressive generation abilities but currently struggle with rendering accurate and coherent text. To address this issue, we introduce TextDiffuser, focusing on generating images with visually appealing text that is coherent with backgrounds. TextDiffuser consists of two stages: first, a Transformer model generates the layout of keywords extracted from text prompts, and then diffusion models generate images conditioned on the text prompt and the generated layout. Additionally, we contribute the first large-scale text images dataset with OCR annotations, MARIO-10M, containing 10 million image-text pairs with text recognition, detection, and character-level segmentation annotations. We further collect the MARIO-Eval benchmark to serve as a comprehensive tool for evaluating text rendering quality. Through experiments and user studies, we show that TextDiffuser is flexible and controllable to create high-quality text images using text prompts alone or together with text template images, and conduct text inpainting to reconstruct incomplete images with text. The code, model, and dataset will be available at https://aka.ms/textdiffuser.", "github_manual": "https://github.com/microsoft/unilm/tree/master/textdiffuser", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.11173", "title": "Going Denser with Open-Vocabulary Part Segmentation", "authors": [ "Peize Sun", "Shoufa Chen", "Chenchen Zhu", "Fanyi Xiao", "Ping Luo", "Saining Xie", "Zhicheng Yan" ], "github": "https://github.com/facebookresearch/vlpart", "project_page": "", "abstract": "Object detection has been expanded from a limited number of categories to open vocabulary. Moving forward, a complete intelligent vision system requires understanding more fine-grained object descriptions, object parts. In this paper, we propose a detector with the ability to predict both open-vocabulary objects and their part segmentation. This ability comes from two designs. First, we train the detector on the joint of part-level, object-level and image-level data to build the multi-granularity alignment between language and image. Second, we parse the novel object into its parts by its dense semantic correspondence with the base object. These two designs enable the detector to largely benefit from various data sources and foundation models. In open-vocabulary part segmentation experiments, our method outperforms the baseline by 3.3sim7.3 mAP in cross-dataset generalization on PartImageNet, and improves the baseline by 7.3 novel AP_{50} in cross-category generalization on Pascal Part. Finally, we train a detector that generalizes to a wide range of part segmentation datasets while achieving better performance than dataset-specific training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/vlpart", "github_api_added_by": "auto" }, { "date": "2023-05-19", "arxiv_id": "2305.11000", "title": "SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities", "authors": [ "Dong Zhang", "Shimin Li", "Xin Zhang", "Jun Zhan", "Pengyu Wang", "Yaqian Zhou", "Xipeng Qiu" ], "github": "https://github.com/0nutation/speechgpt", "project_page": "", "abstract": "Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT. However, current speech-language models typically adopt the cascade paradigm, preventing inter-modal knowledge transfer. In this paper, we propose SpeechGPT, a large language model with intrinsic cross-modal conversational abilities, capable of perceiving and generating multi-model content. With discrete speech representations, we first construct SpeechInstruct, a large-scale cross-modal speech instruction dataset. Additionally, we employ a three-stage training strategy that includes modality-adaptation pre-training, cross-modal instruction fine-tuning, and chain-of-modality instruction fine-tuning. The experimental results demonstrate that SpeechGPT has an impressive capacity to follow multi-modal human instructions and highlight the potential of handling multiple modalities with one model. Demos are shown in https://0nutation.github.io/SpeechGPT.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/0nutation/speechgpt", "github_api_added_by": "auto" }, { "date": "2023-05-19", "arxiv_id": "2305.11129", "title": "mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences", "authors": [ "David Uthus", "Santiago Onta\u00f1\u00f3n", "Joshua Ainslie", "Mandy Guo" ], "github": "", "project_page": "", "abstract": "We present our work on developing a multilingual, efficient text-to-text transformer that is suitable for handling long inputs. This model, called mLongT5, builds upon the architecture of LongT5, while leveraging the multilingual datasets used for pretraining mT5 and the pretraining tasks of UL2. We evaluate this model on a variety of multilingual summarization and question-answering tasks, and the results show stronger performance for mLongT5 when compared to existing multilingual models such as mBART or M-BERT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.10912", "title": "A Generalist Dynamics Model for Control", "authors": [ "Ingmar Schubert", "Jingwei Zhang", "Jake Bruce", "Sarah Bechtle", "Emilio Parisotto", "Martin Riedmiller", "Jost Tobias Springenberg", "Arunkumar Byravan", "Leonard Hasenclever", "Nicolas Heess" ], "github": "", "project_page": "", "abstract": "We investigate the use of transformer sequence models as dynamics models (TDMs) for control. In a number of experiments in the DeepMind control suite, we find that first, TDMs perform well in a single-environment learning setting when compared to baseline models. Second, TDMs exhibit strong generalization capabilities to unseen environments, both in a few-shot setting, where a generalist model is fine-tuned with small amounts of data from the target environment, and in a zero-shot setting, where a generalist model is applied to an unseen environment without any further training. We further demonstrate that generalizing system dynamics can work much better than generalizing optimal behavior directly as a policy. This makes TDMs a promising ingredient for a foundation model of control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.11175", "title": "VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks", "authors": [ "Wenhai Wang", "Zhe Chen", "Xiaokang Chen", "Jiannan Wu", "Xizhou Zhu", "Gang Zeng", "Ping Luo", "Tong Lu", "Jie Zhou", "Yu Qiao", "Jifeng Dai" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have notably accelerated progress towards artificial general intelligence (AGI), with their impressive zero-shot capacity for user-tailored tasks, endowing them with immense potential across a range of applications. However, in the field of computer vision, despite the availability of numerous powerful vision foundation models (VFMs), they are still restricted to tasks in a pre-defined form, struggling to match the open-ended task capabilities of LLMs. In this work, we present an LLM-based framework for vision-centric tasks, termed VisionLLM. This framework provides a unified perspective for vision and language tasks by treating images as a foreign language and aligning vision-centric tasks with language tasks that can be flexibly defined and managed using language instructions. An LLM-based decoder can then make appropriate predictions based on these instructions for open-ended tasks. Extensive experiments show that the proposed VisionLLM can achieve different levels of task customization through language instructions, from fine-grained object-level to coarse-grained task-level customization, all with good results. It's noteworthy that, with a generalist LLM-based framework, our model can achieve over 60\\% mAP on COCO, on par with detection-specific models. We hope this model can set a new baseline for generalist vision and language models. The demo shall be released based on https://github.com/OpenGVLab/InternGPT. The code shall be released at https://github.com/OpenGVLab/VisionLLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.10688", "title": "MolXPT: Wrapping Molecules with Text for Generative Pre-training", "authors": [ "Zequn Liu", "Wei Zhang", "Yingce Xia", "Lijun Wu", "Shufang Xie", "Tao Qin", "Ming Zhang", "Tie-Yan Liu" ], "github": "", "project_page": "", "abstract": "Generative pre-trained Transformer (GPT) has demonstrates its great success in natural language processing and related techniques have been adapted into molecular modeling. Considering that text is the most important record for scientific discovery, in this paper, we propose MolXPT, a unified language model of text and molecules pre-trained on SMILES (a sequence representation of molecules) wrapped by text. Briefly, we detect the molecule names in each sequence and replace them to the corresponding SMILES. In this way, the SMILES could leverage the information from surrounding text, and vice versa. The above wrapped sequences, text sequences from PubMed and SMILES sequences from PubChem are all fed into a language model for pre-training. Experimental results demonstrate that MolXPT outperforms strong baselines of molecular property prediction on MoleculeNet, performs comparably to the best model in text-molecule translation while using less than half of its parameters, and enables zero-shot molecular generation without finetuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.11147", "title": "UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild", "authors": [ "Can Qin", "Shu Zhang", "Ning Yu", "Yihao Feng", "Xinyi Yang", "Yingbo Zhou", "Huan Wang", "Juan Carlos Niebles", "Caiming Xiong", "Silvio Savarese", "Stefano Ermon", "Yun Fu", "Ran Xu" ], "github": "https://github.com/salesforce/unicontrol", "project_page": "", "abstract": "Achieving machine autonomy and human control often represent divergent objectives in the design of interactive AI systems. Visual generative foundation models such as Stable Diffusion show promise in navigating these goals, especially when prompted with arbitrary languages. However, they often fall short in generating images with spatial, structural, or geometric controls. The integration of such controls, which can accommodate various visual conditions in a single unified model, remains an unaddressed challenge. In response, we introduce UniControl, a new generative foundation model that consolidates a wide array of controllable condition-to-image (C2I) tasks within a singular framework, while still allowing for arbitrary language prompts. UniControl enables pixel-level-precise image generation, where visual conditions primarily influence the generated structures and language prompts guide the style and context. To equip UniControl with the capacity to handle diverse visual conditions, we augment pretrained text-to-image diffusion models and introduce a task-aware HyperNet to modulate the diffusion models, enabling the adaptation to different C2I tasks simultaneously. Trained on nine unique C2I tasks, UniControl demonstrates impressive zero-shot generation abilities with unseen visual conditions. Experimental results show that UniControl often surpasses the performance of single-task-controlled methods of comparable model sizes. This control versatility positions UniControl as a significant advancement in the realm of controllable visual generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/salesforce/unicontrol", "github_api_added_by": "auto" }, { "date": "2023-05-19", "arxiv_id": "2305.10874", "title": "VideoFactory: Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation", "authors": [ "Wenjing Wang", "Huan Yang", "Zixi Tuo", "Huiguo He", "Junchen Zhu", "Jianlong Fu", "Jiaying Liu" ], "github": "https://github.com/daooshee/hd-vg-130m", "project_page": "", "abstract": "We present VideoFactory, an innovative framework for generating high-quality open-domain videos. VideoFactory excels in producing high-definition (1376x768), widescreen (16:9) videos without watermarks, creating an engaging user experience. Generating videos guided by text instructions poses significant challenges, such as modeling the complex relationship between space and time, and the lack of large-scale text-video paired data. Previous approaches extend pretrained text-to-image generation models by adding temporal 1D convolution/attention modules for video generation. However, these approaches overlook the importance of jointly modeling space and time, inevitably leading to temporal distortions and misalignment between texts and videos. In this paper, we propose a novel approach that strengthens the interaction between spatial and temporal perceptions. In particular, we utilize a swapped cross-attention mechanism in 3D windows that alternates the \"query\" role between spatial and temporal blocks, enabling mutual reinforcement for each other. To fully unlock model capabilities for high-quality video generation, we curate a large-scale video dataset called HD-VG-130M. This dataset comprises 130 million text-video pairs from the open-domain, ensuring high-definition, widescreen and watermark-free characters. Objective metrics and user studies demonstrate the superiority of our approach in terms of per-frame quality, temporal correlation, and text-video alignment, with clear margins.", "github_manual": "https://github.com/daooshee/hd-vg-130m", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.10853", "title": "LDM3D: Latent Diffusion Model for 3D", "authors": [ "Gabriela Ben Melech Stan", "Diana Wofk", "Scottie Fox", "Alex Redden", "Will Saxton", "Jean Yu", "Estelle Aflalo", "Shao-Yen Tseng", "Fabio Nonato", "Matthias Muller", "Vasudev Lal" ], "github": "", "project_page": "", "abstract": "This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. We also develop an application called DepthFusion, which uses the generated RGB images and depth maps to create immersive and interactive 360-degree-view experiences using TouchDesigner. This technology has the potential to transform a wide range of industries, from entertainment and gaming to architecture and design. Overall, this paper presents a significant contribution to the field of generative AI and computer vision, and showcases the potential of LDM3D and DepthFusion to revolutionize content creation and digital experiences. A short video summarizing the approach can be found at https://t.ly/tdi2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.10841", "title": "GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework", "authors": [ "Ang Lv", "Xu Tan", "Peiling Lu", "Wei Ye", "Shikun Zhang", "Jiang Bian", "Rui Yan" ], "github": "", "project_page": "", "abstract": "Symbolic music generation aims to create musical notes, which can help users compose music, such as generating target instrumental tracks from scratch, or based on user-provided source tracks. Considering the diverse and flexible combination between source and target tracks, a unified model capable of generating any arbitrary tracks is of crucial necessity. Previous works fail to address this need due to inherent constraints in music representations and model architectures. To address this need, we propose a unified representation and diffusion framework named GETMusic (`GET' stands for GEnerate music Tracks), which includes a novel music representation named GETScore, and a diffusion model named GETDiff. GETScore represents notes as tokens and organizes them in a 2D structure, with tracks stacked vertically and progressing horizontally over time. During training, tracks are randomly selected as either the target or source. In the forward process, target tracks are corrupted by masking their tokens, while source tracks remain as ground truth. In the denoising process, GETDiff learns to predict the masked target tokens, conditioning on the source tracks. With separate tracks in GETScore and the non-autoregressive behavior of the model, GETMusic can explicitly control the generation of any target tracks from scratch or conditioning on source tracks. We conduct experiments on music generation involving six instrumental tracks, resulting in a total of 665 combinations. GETMusic provides high-quality results across diverse combinations and surpasses prior works proposed for some specific combinations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-19", "arxiv_id": "2305.10722", "title": "Discriminative Diffusion Models as Few-shot Vision and Language Learners", "authors": [ "Xuehai He", "Weixi Feng", "Tsu-Jui Fu", "Varun Jampani", "Arjun Akula", "Pradyumna Narayana", "Sugato Basu", "William Yang Wang", "Xin Eric Wang" ], "github": "https://github.com/eric-ai-lab/dsd", "project_page": "", "abstract": "Diffusion models, such as Stable Diffusion, have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual concepts with fine-grained details and attributes specified in text prompts, can we leverage the powerful representations learned by pre-trained diffusion models for discriminative tasks such as image-text matching? To answer this question, we propose a novel approach, Discriminative Stable Diffusion (DSD), which turns pre-trained text-to-image diffusion models into few-shot discriminative learners. Our approach uses the cross-attention score of a Stable Diffusion model to capture the mutual influence between visual and textual information and fine-tune the model via attention-based prompt learning to perform image-text matching. By comparing DSD with state-of-the-art methods on several benchmark datasets, we demonstrate the potential of using pre-trained diffusion models for discriminative tasks with superior results on few-shot image-text matching.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/eric-ai-lab/dsd", "github_api_added_by": "auto" }, { "date": "2023-05-19", "arxiv_id": "2305.10474", "title": "Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models", "authors": [ "Songwei Ge", "Seungjun Nah", "Guilin Liu", "Tyler Poon", "Andrew Tao", "Bryan Catanzaro", "David Jacobs", "Jia-Bin Huang", "Ming-Yu Liu", "Yogesh Balaji" ], "github": "", "project_page": "", "abstract": "Despite tremendous progress in generating high-quality images using diffusion models, synthesizing a sequence of animated frames that are both photorealistic and temporally coherent is still in its infancy. While off-the-shelf billion-scale datasets for image generation are available, collecting similar video data of the same scale is still challenging. Also, training a video diffusion model is computationally much more expensive than its image counterpart. In this work, we explore finetuning a pretrained image diffusion model with video data as a practical solution for the video synthesis task. We find that naively extending the image noise prior to video noise prior in video diffusion leads to sub-optimal performance. Our carefully designed video noise prior leads to substantially better performance. Extensive experimental validation shows that our model, Preserve Your Own Correlation (PYoCo), attains SOTA zero-shot text-to-video results on the UCF-101 and MSR-VTT benchmarks. It also achieves SOTA video generation quality on the small-scale UCF-101 benchmark with a 10times smaller model using significantly less computation than the prior art.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11206", "title": "LIMA: Less Is More for Alignment", "authors": [ "Chunting Zhou", "Pengfei Liu", "Puxin Xu", "Srini Iyer", "Jiao Sun", "Yuning Mao", "Xuezhe Ma", "Avia Efrat", "Ping Yu", "Lili Yu", "Susan Zhang", "Gargi Ghosh", "Mike Lewis", "Luke Zettlemoyer", "Omer Levy" ], "github": "", "project_page": "", "abstract": "Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences. We measure the relative importance of these two stages by training LIMA, a 65B parameter LLaMa language model fine-tuned with the standard supervised loss on only 1,000 carefully curated prompts and responses, without any reinforcement learning or human preference modeling. LIMA demonstrates remarkably strong performance, learning to follow specific response formats from only a handful of examples in the training data, including complex queries that range from planning trip itineraries to speculating about alternate history. Moreover, the model tends to generalize well to unseen tasks that did not appear in the training data. In a controlled human study, responses from LIMA are either equivalent or strictly preferred to GPT-4 in 43% of cases; this statistic is as high as 58% when compared to Bard and 65% versus DaVinci003, which was trained with human feedback. Taken together, these results strongly suggest that almost all knowledge in large language models is learned during pretraining, and only limited instruction tuning data is necessary to teach models to produce high quality output.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11870", "title": "Chupa: Carving 3D Clothed Humans from Skinned Shape Priors using 2D Diffusion Probabilistic Models", "authors": [ "Byungjun Kim", "Patrick Kwon", "Kwangho Lee", "Myunggi Lee", "Sookwan Han", "Daesik Kim", "Hanbyul Joo" ], "github": "", "project_page": "", "abstract": "We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Due to the wide variety of human identities, poses, and stochastic details, the generation of 3D human meshes has been a challenging problem. To address this, we decompose the problem into 2D normal map generation and normal map-based 3D reconstruction. Specifically, we first simultaneously generate realistic normal maps for the front and backside of a clothed human, dubbed dual normal maps, using a pose-conditional diffusion model. For 3D reconstruction, we ``carve'' the prior SMPL-X mesh to a detailed 3D mesh according to the normal maps through mesh optimization. To further enhance the high-frequency details, we present a diffusion resampling scheme on both body and facial regions, thus encouraging the generation of realistic digital avatars. We also seamlessly incorporate a recent text-to-image diffusion model to support text-based human identity control. Our method, namely, Chupa, is capable of generating realistic 3D clothed humans with better perceptual quality and identity variety.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11854", "title": "Multimodal Web Navigation with Instruction-Finetuned Foundation Models", "authors": [ "Hiroki Furuta", "Ofir Nachum", "Kuang-Huei Lee", "Yutaka Matsuo", "Shixiang Shane Gu", "Izzeddin Gur" ], "github": "", "project_page": "", "abstract": "The progress of autonomous web navigation has been hindered by the dependence on billions of exploratory interactions via online reinforcement learning, and domain-specific model designs that make it difficult to leverage generalization from rich out-of-domain data. In this work, we study data-driven offline training for web agents with vision-language foundation models. We propose an instruction-following multimodal agent, WebGUM, that observes both webpage screenshots and HTML pages and outputs web navigation actions, such as click and type. WebGUM is trained by jointly finetuning an instruction-finetuned language model and a vision transformer on a large corpus of demonstrations. We empirically demonstrate this recipe improves the agent's ability of grounded visual perception, HTML comprehension and multi-step reasoning, outperforming prior works by a significant margin. On the MiniWoB benchmark, we improve over the previous best offline methods by more than 31.9%, being close to reaching online-finetuned SoTA. On the WebShop benchmark, our 3-billion-parameter model achieves superior performance to the existing SoTA, PaLM-540B. We also collect 347K high-quality demonstrations using our trained models, 38 times larger than prior work, and make them available to promote future research in this direction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11588", "title": "Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields", "authors": [ "Jingbo Zhang", "Xiaoyu Li", "Ziyu Wan", "Can Wang", "Jing Liao" ], "github": "https://github.com/ashawkey/stable-dreamfusion", "project_page": "", "abstract": "Text-driven 3D scene generation is widely applicable to video gaming, film industry, and metaverse applications that have a large demand for 3D scenes. However, existing text-to-3D generation methods are limited to producing 3D objects with simple geometries and dreamlike styles that lack realism. In this work, we present Text2NeRF, which is able to generate a wide range of 3D scenes with complicated geometric structures and high-fidelity textures purely from a text prompt. To this end, we adopt NeRF as the 3D representation and leverage a pre-trained text-to-image diffusion model to constrain the 3D reconstruction of the NeRF to reflect the scene description. Specifically, we employ the diffusion model to infer the text-related image as the content prior and use a monocular depth estimation method to offer the geometric prior. Both content and geometric priors are utilized to update the NeRF model. To guarantee textured and geometric consistency between different views, we introduce a progressive scene inpainting and updating strategy for novel view synthesis of the scene. Our method requires no additional training data but only a natural language description of the scene as the input. Extensive experiments demonstrate that our Text2NeRF outperforms existing methods in producing photo-realistic, multi-view consistent, and diverse 3D scenes from a variety of natural language prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ashawkey/stable-dreamfusion", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11694", "title": "QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations", "authors": [ "Chaitanya Malaviya", "Peter Shaw", "Ming-Wei Chang", "Kenton Lee", "Kristina Toutanova" ], "github": "https://github.com/google-research/language", "project_page": "", "abstract": "Formulating selective information needs results in queries that implicitly specify set operations, such as intersection, union, and difference. For instance, one might search for \"shorebirds that are not sandpipers\" or \"science-fiction films shot in England\". To study the ability of retrieval systems to meet such information needs, we construct QUEST, a dataset of 3357 natural language queries with implicit set operations, that map to a set of entities corresponding to Wikipedia documents. The dataset challenges models to match multiple constraints mentioned in queries with corresponding evidence in documents and correctly perform various set operations. The dataset is constructed semi-automatically using Wikipedia category names. Queries are automatically composed from individual categories, then paraphrased and further validated for naturalness and fluency by crowdworkers. Crowdworkers also assess the relevance of entities based on their documents and highlight attribution of query constraints to spans of document text. We analyze several modern retrieval systems, finding that they often struggle on such queries. Queries involving negation and conjunction are particularly challenging and systems are further challenged with combinations of these operations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/google-research/language", "github_api_added_by": "auto" }, { "date": "2023-05-22", "arxiv_id": "2305.11841", "title": "How Does Generative Retrieval Scale to Millions of Passages?", "authors": [ "Ronak Pradeep", "Kai Hui", "Jai Gupta", "Adam D. Lelkes", "Honglei Zhuang", "Jimmy Lin", "Donald Metzler", "Vinh Q. Tran" ], "github": "", "project_page": "", "abstract": "Popularized by the Differentiable Search Index, the emerging paradigm of generative retrieval re-frames the classic information retrieval problem into a sequence-to-sequence modeling task, forgoing external indices and encoding an entire document corpus within a single Transformer. Although many different approaches have been proposed to improve the effectiveness of generative retrieval, they have only been evaluated on document corpora on the order of 100k in size. We conduct the first empirical study of generative retrieval techniques across various corpus scales, ultimately scaling up to the entire MS MARCO passage ranking task with a corpus of 8.8M passages and evaluating model sizes up to 11B parameters. We uncover several findings about scaling generative retrieval to millions of passages; notably, the central importance of using synthetic queries as document representations during indexing, the ineffectiveness of existing proposed architecture modifications when accounting for compute cost, and the limits of naively scaling model parameters with respect to retrieval performance. While we find that generative retrieval is competitive with state-of-the-art dual encoders on small corpora, scaling to millions of passages remains an important and unsolved challenge. We believe these findings will be valuable for the community to clarify the current state of generative retrieval, highlight the unique challenges, and inspire new research directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11834", "title": "Pengi: An Audio Language Model for Audio Tasks", "authors": [ "Soham Deshmukh", "Benjamin Elizalde", "Rita Singh", "Huaming Wang" ], "github": "https://github.com/microsoft/pengi", "project_page": "", "abstract": "In the domain of audio processing, Transfer Learning has facilitated the rise of Self-Supervised Learning and Zero-Shot Learning techniques. These approaches have led to the development of versatile models capable of tackling a wide array of tasks, while delivering state-of-the-art performance. However, current models inherently lack the capacity to produce the requisite language for open-ended tasks, such as Audio Captioning or Audio Question & Answering. We introduce Pengi, a novel Audio Language Model that leverages Transfer Learning by framing all audio tasks as text-generation tasks. It takes as input, an audio recording, and text, and generates free-form text as output. The input audio is represented as a sequence of continuous embeddings by an audio encoder. A text encoder does the same for the corresponding text input. Both sequences are combined as a prefix to prompt a pre-trained frozen language model. The unified architecture of Pengi enables open-ended tasks and close-ended tasks without any additional fine-tuning or task-specific extensions. When evaluated on 22 downstream tasks, our approach yields state-of-the-art performance in several of them. Our results show that connecting language models with audio models is a major step towards general-purpose audio understanding", "github_manual": "https://github.com/microsoft/pengi", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11863", "title": "Scaling laws for language encoding models in fMRI", "authors": [ "Richard Antonello", "Aditya Vaidya", "Alexander G. Huth" ], "github": "https://github.com/huthlab/encoding-model-scaling-laws", "project_page": "", "abstract": "Representations from transformer-based unidirectional language models are known to be effective at predicting brain responses to natural language. However, most studies comparing language models to brains have used GPT-2 or similarly sized language models. Here we tested whether larger open-source models such as those from the OPT and LLaMA families are better at predicting brain responses recorded using fMRI. Mirroring scaling results from other contexts, we found that brain prediction performance scales log-linearly with model size from 125M to 30B parameter models, with ~15% increased encoding performance as measured by correlation with a held-out test set across 3 subjects. Similar log-linear behavior was observed when scaling the size of the fMRI training set. We also characterized scaling for acoustic encoding models that use HuBERT, WavLM, and Whisper, and we found comparable improvements with model size. A noise ceiling analysis of these large, high-performance encoding models showed that performance is nearing the theoretical maximum for brain areas such as the precuneus and higher auditory cortex. These results suggest that increasing scale in both models and data will yield incredibly effective models of language processing in the brain, enabling better scientific understanding as well as applications such as decoding.", "github_manual": "https://github.com/huthlab/encoding-model-scaling-laws", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11840", "title": "SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models", "authors": [ "Akshita Jha", "Aida Davani", "Chandan K. Reddy", "Shachi Dave", "Vinodkumar Prabhakaran", "Sunipa Dev" ], "github": "https://github.com/google-research-datasets/seegull", "project_page": "", "abstract": "Stereotype benchmark datasets are crucial to detect and mitigate social stereotypes about groups of people in NLP models. However, existing datasets are limited in size and coverage, and are largely restricted to stereotypes prevalent in the Western society. This is especially problematic as language technologies gain hold across the globe. To address this gap, we present SeeGULL, a broad-coverage stereotype dataset, built by utilizing generative capabilities of large language models such as PaLM, and GPT-3, and leveraging a globally diverse rater pool to validate the prevalence of those stereotypes in society. SeeGULL is in English, and contains stereotypes about identity groups spanning 178 countries across 8 different geo-political regions across 6 continents, as well as state-level identities within the US and India. We also include fine-grained offensiveness scores for different stereotypes and demonstrate their global disparities. Furthermore, we include comparative annotations about the same groups by annotators living in the region vs. those that are based in North America, and demonstrate that within-region stereotypes about groups differ from those prevalent in North America. CONTENT WARNING: This paper contains stereotype examples that may be offensive.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-research-datasets/seegull", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11778", "title": "Cross-Lingual Supervision improves Large Language Models Pre-training", "authors": [ "Andrea Schioppa", "Xavier Garcia", "Orhan Firat" ], "github": "", "project_page": "", "abstract": "The recent rapid progress in pre-training Large Language Models has relied on using self-supervised language modeling objectives like next token prediction or span corruption. On the other hand, Machine Translation Systems are mostly trained using cross-lingual supervision that requires aligned data between source and target languages. We demonstrate that pre-training Large Language Models on a mixture of a self-supervised Language Modeling objective and the supervised Machine Translation objective, therefore including cross-lingual parallel data during pre-training, yields models with better in-context learning abilities. As pre-training is a very resource-intensive process and a grid search on the best mixing ratio between the two objectives is prohibitively expensive, we propose a simple yet effective strategy to learn it during pre-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11759", "title": "Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning", "authors": [ "Mustafa Safa Ozdayi", "Charith Peris", "Jack FitzGerald", "Christophe Dupuy", "Jimit Majmudar", "Haidar Khan", "Rahil Parikh", "Rahul Gupta" ], "github": "https://github.com/amazon-science/controlling-llm-memorization", "project_page": "", "abstract": "Large Language Models (LLMs) are known to memorize significant portions of their training data. Parts of this memorized content have been shown to be extractable by simply querying the model, which poses a privacy risk. We present a novel approach which uses prompt-tuning to control the extraction rates of memorized content in LLMs. We present two prompt training strategies to increase and decrease extraction rates, which correspond to an attack and a defense, respectively. We demonstrate the effectiveness of our techniques by using models from the GPT-Neo family on a public benchmark. For the 1.3B parameter GPT-Neo model, our attack yields a 9.3 percentage point increase in extraction rate compared to our baseline. Our defense can be tuned to achieve different privacy-utility trade-offs by a user-specified hyperparameter. We achieve an extraction rate reduction of up to 97.7% relative to our baseline, with a perplexity increase of 16.9%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amazon-science/controlling-llm-memorization", "github_api_added_by": "auto" }, { "date": "2023-05-22", "arxiv_id": "2305.11738", "title": "CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing", "authors": [ "Zhibin Gou", "Zhihong Shao", "Yeyun Gong", "Yelong Shen", "Yujiu Yang", "Nan Duan", "Weizhu Chen" ], "github": "https://github.com/microsoft/ProphetNet", "project_page": "", "abstract": "Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts, generating flawed code, or creating offensive and toxic content. Unlike these models, humans typically utilize external tools to cross-check and refine their initial content, like using a search engine for fact-checking, or a code interpreter for debugging. Inspired by this observation, we introduce a framework called CRITIC that allows LLMs, which are essentially \"black boxes\" to validate and progressively amend their own outputs in a manner similar to human interaction with tools. More specifically, starting with an initial output, CRITIC interacts with appropriate tools to evaluate certain aspects of the text, and then revises the output based on the feedback obtained during this validation process. Comprehensive evaluations involving free-form question answering, mathematical program synthesis, and toxicity reduction demonstrate that CRITIC consistently enhances the performance of LLMs. Meanwhile, our research highlights the crucial importance of external feedback in promoting the ongoing self-improvement of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/ProphetNet", "github_api_added_by": "auto" }, { "date": "2023-05-22", "arxiv_id": "2305.11598", "title": "Introspective Tips: Large Language Model for In-Context Decision Making", "authors": [ "Liting Chen", "Lu Wang", "Hang Dong", "Yali Du", "Jie Yan", "Fangkai Yang", "Shuang Li", "Pu Zhao", "Si Qin", "Saravan Rajmohan", "Qingwei Lin", "Dongmei Zhang" ], "github": "", "project_page": "", "abstract": "The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks. In this study, we employ ``Introspective Tips\" to facilitate LLMs in self-optimizing their decision-making. By introspectively examining trajectories, LLM refines its policy by generating succinct and valuable tips. Our method enhances the agent's performance in both few-shot and zero-shot learning situations by considering three essential scenarios: learning from the agent's past experiences, integrating expert demonstrations, and generalizing across diverse games. Importantly, we accomplish these improvements without fine-tuning the LLM parameters; rather, we adjust the prompt to generalize insights from the three aforementioned situations. Our framework not only supports but also emphasizes the advantage of employing LLM in in-contxt decision-making. Experiments involving over 100 games in TextWorld illustrate the superior performance of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11541", "title": "Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering", "authors": [ "Zezhong Wang", "Fangkai Yang", "Pu Zhao", "Lu Wang", "Jue Zhang", "Mohit Garg", "Qingwei Lin", "Dongmei Zhang" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average since there is no specific knowledge in it. This issue has attracted widespread attention, but there are few relevant benchmarks available. In this paper, we provide a benchmark Question Answering (QA) dataset named MSQA, which is about Microsoft products and IT technical problems encountered by customers. This dataset contains industry cloud-specific QA knowledge, which is not available for general LLM, so it is well suited for evaluating methods aimed at improving domain-specific capabilities of LLM. In addition, we propose a new model interaction paradigm that can empower LLM to achieve better performance on domain-specific tasks where it is not proficient. Extensive experiments demonstrate that the approach following our model fusion framework outperforms the commonly used LLM with retrieval methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11364", "title": "Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models", "authors": [ "Emily Reif", "Minsuk Kahng", "Savvas Petridis" ], "github": "https://github.com/pair-code/interpretability", "project_page": "", "abstract": "Large language models (LLMs) can be used to generate smaller, more refined datasets via few-shot prompting for benchmarking, fine-tuning or other use cases. However, understanding and evaluating these datasets is difficult, and the failure modes of LLM-generated data are still not well understood. Specifically, the data can be repetitive in surprising ways, not only semantically but also syntactically and lexically. We present LinguisticLens, a novel inter-active visualization tool for making sense of and analyzing syntactic diversity of LLM-generated datasets. LinguisticLens clusters text along syntactic, lexical, and semantic axes. It supports hierarchical visualization of a text dataset, allowing users to quickly scan for an overview and inspect individual examples. The live demo is available at shorturl.at/zHOUV.", "github_manual": "https://github.com/pair-code/interpretability", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11308", "title": "Counterfactuals for Design: A Model-Agnostic Method For Design Recommendations", "authors": [ "Lyle Regenwetter", "Yazan Abu Obaideh", "Faez Ahmed" ], "github": "", "project_page": "", "abstract": "We introduce Multi-Objective Counterfactuals for Design (MCD), a novel method for counterfactual optimization in design problems. Counterfactuals are hypothetical situations that can lead to a different decision or choice. In this paper, the authors frame the counterfactual search problem as a design recommendation tool that can help identify modifications to a design, leading to better functional performance. MCD improves upon existing counterfactual search methods by supporting multi-objective queries, which are crucial in design problems, and by decoupling the counterfactual search and sampling processes, thus enhancing efficiency and facilitating objective tradeoff visualization. The paper demonstrates MCD's core functionality using a two-dimensional test case, followed by three case studies of bicycle design that showcase MCD's effectiveness in real-world design problems. In the first case study, MCD excels at recommending modifications to query designs that can significantly enhance functional performance, such as weight savings and improvements to the structural safety factor. The second case study demonstrates that MCD can work with a pre-trained language model to suggest design changes based on a subjective text prompt effectively. Lastly, the authors task MCD with increasing a query design's similarity to a target image and text prompt while simultaneously reducing weight and improving structural performance, demonstrating MCD's performance on a complex multimodal query. Overall, MCD has the potential to provide valuable recommendations for practitioners and design automation researchers looking for answers to their ``What if'' questions by exploring hypothetical design modifications and their impact on multiple design objectives. The code, test problems, and datasets used in the paper are available to the public at decode.mit.edu/projects/counterfactuals/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11243", "title": "Comparing Machines and Children: Using Developmental Psychology Experiments to Assess the Strengths and Weaknesses of LaMDA Responses", "authors": [ "Eliza Kosoy", "Emily Rose Reagan", "Leslie Lai", "Alison Gopnik", "Danielle Krettek Cobb" ], "github": "", "project_page": "", "abstract": "Developmental psychologists have spent decades devising experiments to test the intelligence and knowledge of infants and children, tracing the origin of crucial concepts and capacities. Moreover, experimental techniques in developmental psychology have been carefully designed to discriminate the cognitive capacities that underlie particular behaviors. We propose that using classical experiments from child development is a particularly effective way to probe the computational abilities of AI models, in general, and LLMs in particular. First, the methodological techniques of developmental psychology, such as the use of novel stimuli to control for past experience or control conditions to determine whether children are using simple associations, can be equally helpful for assessing the capacities of LLMs. In parallel, testing LLMs in this way can tell us whether the information that is encoded in text is sufficient to enable particular responses, or whether those responses depend on other kinds of information, such as information from exploration of the physical world. In this work we adapt classical developmental experiments to evaluate the capabilities of LaMDA, a large language model from Google. We propose a novel LLM Response Score (LRS) metric which can be used to evaluate other language models, such as GPT. We find that LaMDA generates appropriate responses that are similar to those of children in experiments involving social understanding, perhaps providing evidence that knowledge of these domains is discovered through language. On the other hand, LaMDA's responses in early object and action understanding, theory of mind, and especially causal reasoning tasks are very different from those of young children, perhaps showing that these domains require more real-world, self-initiated exploration and cannot simply be learned from patterns in language input.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11837", "title": "Comparing Software Developers with ChatGPT: An Empirical Investigation", "authors": [ "Nathalia Nascimento", "Paulo Alencar", "Donald Cowan" ], "github": "", "project_page": "", "abstract": "The advent of automation in particular Software Engineering (SE) tasks has transitioned from theory to reality. Numerous scholarly articles have documented the successful application of Artificial Intelligence to address issues in areas such as project management, modeling, testing, and development. A recent innovation is the introduction of ChatGPT, an ML-infused chatbot, touted as a resource proficient in generating programming codes and formulating software testing strategies for developers and testers respectively. Although there is speculation that AI-based computation can increase productivity and even substitute software engineers in software development, there is currently a lack of empirical evidence to verify this. Moreover, despite the primary focus on enhancing the accuracy of AI systems, non-functional requirements including energy efficiency, vulnerability, fairness (i.e., human bias), and safety frequently receive insufficient attention. This paper posits that a comprehensive comparison of software engineers and AI-based solutions, considering various evaluation criteria, is pivotal in fostering human-machine collaboration, enhancing the reliability of AI-based methods, and understanding task suitability for humans or AI. Furthermore, it facilitates the effective implementation of cooperative work structures and human-in-the-loop processes. This paper conducts an empirical investigation, contrasting the performance of software engineers and AI systems, like ChatGPT, across different evaluation metrics. The empirical study includes a case of assessing ChatGPT-generated code versus code produced by developers and uploaded in Leetcode.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11846", "title": "Any-to-Any Generation via Composable Diffusion", "authors": [ "Zineng Tang", "Ziyi Yang", "Chenguang Zhu", "Michael Zeng", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "We present Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any combination of input modalities. Unlike existing generative AI systems, CoDi can generate multiple modalities in parallel and its input is not limited to a subset of modalities like text or image. Despite the absence of training datasets for many combinations of modalities, we propose to align modalities in both the input and output space. This allows CoDi to freely condition on any input combination and generate any group of modalities, even if they are not present in the training data. CoDi employs a novel composable generation strategy which involves building a shared multimodal space by bridging alignment in the diffusion process, enabling the synchronized generation of intertwined modalities, such as temporally aligned video and audio. Highly customizable and flexible, CoDi achieves strong joint-modality generation quality, and outperforms or is on par with the unimodal state-of-the-art for single-modality synthesis. The project page with demonstrations and code is at https://codi-gen.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11675", "title": "Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity", "authors": [ "Zijiao Chen", "Jiaxin Qing", "Juan Helen Zhou" ], "github": "", "project_page": "", "abstract": "Reconstructing human vision from brain activities has been an appealing task that helps to understand our cognitive process. Even though recent research has seen great success in reconstructing static images from non-invasive brain recordings, work on recovering continuous visual experiences in the form of videos is limited. In this work, we propose Mind-Video that learns spatiotemporal information from continuous fMRI data of the cerebral cortex progressively through masked brain modeling, multimodal contrastive learning with spatiotemporal attention, and co-training with an augmented Stable Diffusion model that incorporates network temporal inflation. We show that high-quality videos of arbitrary frame rates can be reconstructed with Mind-Video using adversarial guidance. The recovered videos were evaluated with various semantic and pixel-level metrics. We achieved an average accuracy of 85% in semantic classification tasks and 0.19 in structural similarity index (SSIM), outperforming the previous state-of-the-art by 45%. We also show that our model is biologically plausible and interpretable, reflecting established physiological processes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-22", "arxiv_id": "2305.11337", "title": "RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture", "authors": [ "Liangchen Song", "Liangliang Cao", "Hongyu Xu", "Kai Kang", "Feng Tang", "Junsong Yuan", "Yang Zhao" ], "github": "", "project_page": "", "abstract": "The techniques for 3D indoor scene capturing are widely used, but the meshes produced leave much to be desired. In this paper, we propose \"RoomDreamer\", which leverages powerful natural language to synthesize a new room with a different style. Unlike existing image synthesis methods, our work addresses the challenge of synthesizing both geometry and texture aligned to the input scene structure and prompt simultaneously. The key insight is that a scene should be treated as a whole, taking into account both scene texture and geometry. The proposed framework consists of two significant components: Geometry Guided Diffusion and Mesh Optimization. Geometry Guided Diffusion for 3D Scene guarantees the consistency of the scene style by applying the 2D prior to the entire scene simultaneously. Mesh Optimization improves the geometry and texture jointly and eliminates the artifacts in the scanned scene. To validate the proposed method, real indoor scenes scanned with smartphones are used for extensive experiments, through which the effectiveness of our method is demonstrated.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-23", "arxiv_id": "2305.13077", "title": "ControlVideo: Training-free Controllable Text-to-Video Generation", "authors": [ "Yabo Zhang", "Yuxiang Wei", "Dongsheng Jiang", "Xiaopeng Zhang", "Wangmeng Zuo", "Qi Tian" ], "github": "https://github.com/YBYBZhang/ControlVideo", "project_page": "", "abstract": "Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temporal modeling. Besides the training burden, the generated videos also suffer from appearance inconsistency and structural flickers, especially in long video synthesis. To address these challenges, we design a training-free framework called ControlVideo to enable natural and efficient text-to-video generation. ControlVideo, adapted from ControlNet, leverages coarsely structural consistency from input motion sequences, and introduces three modules to improve video generation. Firstly, to ensure appearance coherence between frames, ControlVideo adds fully cross-frame interaction in self-attention modules. Secondly, to mitigate the flicker effect, it introduces an interleaved-frame smoother that employs frame interpolation on alternated frames. Finally, to produce long videos efficiently, it utilizes a hierarchical sampler that separately synthesizes each short clip with holistic coherency. Empowered with these modules, ControlVideo outperforms the state-of-the-arts on extensive motion-prompt pairs quantitatively and qualitatively. Notably, thanks to the efficient designs, it generates both short and long videos within several minutes using one NVIDIA 2080Ti. Code is available at https://github.com/YBYBZhang/ControlVideo.", "github_manual": "", "github_regex": "https://github.com/YBYBZhang/ControlVideo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-23", "arxiv_id": "2305.13301", "title": "Training Diffusion Models with Reinforcement Learning", "authors": [ "Kevin Black", "Michael Janner", "Yilun Du", "Ilya Kostrikov", "Sergey Levine" ], "github": "https://github.com/kvablack/ddpo-pytorch", "project_page": "", "abstract": "Diffusion models are a class of flexible generative models trained with an approximation to the log-likelihood objective. However, most use cases of diffusion models are not concerned with likelihoods, but instead with downstream objectives such as human-perceived image quality or drug effectiveness. In this paper, we investigate reinforcement learning methods for directly optimizing diffusion models for such objectives. We describe how posing denoising as a multi-step decision-making problem enables a class of policy gradient algorithms, which we refer to as denoising diffusion policy optimization (DDPO), that are more effective than alternative reward-weighted likelihood approaches. Empirically, DDPO is able to adapt text-to-image diffusion models to objectives that are difficult to express via prompting, such as image compressibility, and those derived from human feedback, such as aesthetic quality. Finally, we show that DDPO can improve prompt-image alignment using feedback from a vision-language model without the need for additional data collection or human annotation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kvablack/ddpo-pytorch", "github_api_added_by": "auto" }, { "date": "2023-05-23", "arxiv_id": "2305.13048", "title": "RWKV: Reinventing RNNs for the Transformer Era", "authors": [ "Bo Peng", "Eric Alcaide", "Quentin Anthony", "Alon Albalak", "Samuel Arcadinho", "Huanqi Cao", "Xin Cheng", "Michael Chung", "Matteo Grella", "Kranthi Kiran GV", "Xuzheng He", "Haowen Hou", "Przemyslaw Kazienko", "Jan Kocon", "Jiaming Kong", "Bartlomiej Koptyra", "Hayden Lau", "Krishna Sri Ipsit Mantri", "Ferdinand Mom", "Atsushi Saito", "Xiangru Tang", "Bolun Wang", "Johan S. Wind", "Stansilaw Wozniak", "Ruichong Zhang", "Zhenyuan Zhang", "Qihang Zhao", "Peng Zhou", "Jian Zhu", "Rui-Jie Zhu" ], "github": "https://github.com/BlinkDL/RWKV-LM", "project_page": "", "abstract": "Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability. We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of Transformers with the efficient inference of RNNs. Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, which parallelizes computations during training and maintains constant computational and memory complexity during inference, leading to the first non-transformer architecture to be scaled to tens of billions of parameters. Our experiments reveal that RWKV performs on par with similarly sized Transformers, suggesting that future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling the trade-offs between computational efficiency and model performance in sequence processing tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/BlinkDL/RWKV-LM", "github_api_added_by": "auto" }, { "date": "2023-05-23", "arxiv_id": "2305.13050", "title": "AudioToken: Adaptation of Text-Conditioned Diffusion Models for Audio-to-Image Generation", "authors": [ "Guy Yariv", "Itai Gat", "Lior Wolf", "Yossi Adi", "Idan Schwartz" ], "github": "https://github.com/guyyariv/AudioToken", "project_page": "", "abstract": "In recent years, image generation has shown a great leap in performance, where diffusion models play a central role. Although generating high-quality images, such models are mainly conditioned on textual descriptions. This begs the question: \"how can we adopt such models to be conditioned on other modalities?\". In this paper, we propose a novel method utilizing latent diffusion models trained for text-to-image-generation to generate images conditioned on audio recordings. Using a pre-trained audio encoding model, the proposed method encodes audio into a new token, which can be considered as an adaptation layer between the audio and text representations. Such a modeling paradigm requires a small number of trainable parameters, making the proposed approach appealing for lightweight optimization. Results suggest the proposed method is superior to the evaluated baseline methods, considering objective and subjective metrics. Code and samples are available at: https://pages.cs.huji.ac.il/adiyoss-lab/AudioToken.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/guyyariv/AudioToken", "github_api_added_by": "auto" }, { "date": "2023-05-23", "arxiv_id": "2305.13009", "title": "Textually Pretrained Speech Language Models", "authors": [ "Michael Hassid", "Tal Remez", "Tu Anh Nguyen", "Itai Gat", "Alexis Conneau", "Felix Kreuk", "Jade Copet", "Alexandre Defossez", "Gabriel Synnaeve", "Emmanuel Dupoux", "Roy Schwartz", "Yossi Adi" ], "github": "https://github.com/slp-rl/spokenstorycloze", "project_page": "", "abstract": "Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically analyze the effect of different model design choices such as the speech tokenizer, the pretrained textual model, and the dataset size. We find that model and dataset scale both play an important role in constructing better-performing SpeechLMs. Based on our observations, we present the largest (to the best of our knowledge) SpeechLM both in terms of number of parameters and training data. We additionally introduce two spoken versions of the StoryCloze textual benchmark to further improve model evaluation and advance future research in the field. Speech samples can be found on our website: https://pages.cs.huji.ac.il/adiyoss-lab/twist/ .", "github_manual": "https://github.com/slp-rl/spokenstorycloze", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-23", "arxiv_id": "2305.12487", "title": "Augmenting Autotelic Agents with Large Language Models", "authors": [ "C\u00e9dric Colas", "Laetitia Teodorescu", "Pierre-Yves Oudeyer", "Xingdi Yuan", "Marc-Alexandre C\u00f4t\u00e9" ], "github": "", "project_page": "", "abstract": "Humans learn to master open-ended repertoires of skills by imagining and practicing their own goals. This autotelic learning process, literally the pursuit of self-generated (auto) goals (telos), becomes more and more open-ended as the goals become more diverse, abstract and creative. The resulting exploration of the space of possible skills is supported by an inter-individual exploration: goal representations are culturally evolved and transmitted across individuals, in particular using language. Current artificial agents mostly rely on predefined goal representations corresponding to goal spaces that are either bounded (e.g. list of instructions), or unbounded (e.g. the space of possible visual inputs) but are rarely endowed with the ability to reshape their goal representations, to form new abstractions or to imagine creative goals. In this paper, we introduce a language model augmented autotelic agent (LMA3) that leverages a pretrained language model (LM) to support the representation, generation and learning of diverse, abstract, human-relevant goals. The LM is used as an imperfect model of human cultural transmission; an attempt to capture aspects of humans' common-sense, intuitive physics and overall interests. Specifically, it supports three key components of the autotelic architecture: 1)~a relabeler that describes the goals achieved in the agent's trajectories, 2)~a goal generator that suggests new high-level goals along with their decomposition into subgoals the agent already masters, and 3)~reward functions for each of these goals. Without relying on any hand-coded goal representations, reward functions or curriculum, we show that LMA3 agents learn to master a large diversity of skills in a task-agnostic text-based environment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-23", "arxiv_id": "2305.12050", "title": "CodeCompose: A Large-Scale Industrial Deployment of AI-assisted Code Authoring", "authors": [ "Vijayaraghavan Murali", "Chandra Maddila", "Imad Ahmad", "Michael Bolin", "Daniel Cheng", "Negar Ghorbani", "Renuka Fernandez", "Nachiappan Nagappan" ], "github": "", "project_page": "", "abstract": "The rise of large language models (LLMs) has unlocked various applications of this technology in software development. In particular, generative LLMs have been shown to effectively power AI-based code authoring tools that can suggest entire statements or blocks of code during code authoring. In this paper we present CodeCompose, an AI-assisted code authoring tool developed and deployed at Meta internally. CodeCompose is based on the InCoder LLM that merges generative capabilities with bi-directionality. We have scaled up CodeCompose to serve tens of thousands of developers at Meta, across 10+ programming languages and several coding surfaces. We discuss unique challenges in terms of user experience and metrics that arise when deploying such tools in large-scale industrial settings. We present our experience in making design decisions about the model and system architecture for CodeCompose that addresses these challenges. Finally, we present metrics from our large-scale deployment of CodeCompose that shows its impact on Meta's internal code authoring experience over a 15-day time window, where 4.5 million suggestions were made by CodeCompose. Quantitative metrics reveal that (i) CodeCompose has an acceptance rate of 22% across several languages, and (ii) 8% of the code typed by users of CodeCompose is through accepting code suggestions from CodeCompose. Qualitative feedback indicates an overwhelming 91.5% positive reception for CodeCompose. In addition to assisting with code authoring, CodeCompose is also introducing other positive side effects such as encouraging developers to generate more in-code documentation, helping them with the discovery of new APIs, etc.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-23", "arxiv_id": "2305.12001", "title": "OPT-R: Exploring the Role of Explanations in Finetuning and Prompting for Reasoning Skills of Large Language Models", "authors": [ "Badr AlKhamissi", "Siddharth Verma", "Ping Yu", "Zhijing Jin", "Asli Celikyilmaz", "Mona Diab" ], "github": "", "project_page": "", "abstract": "In this paper, we conduct a thorough investigation into the reasoning capabilities of Large Language Models (LLMs), focusing specifically on the Open Pretrained Transformers (OPT) models as a representative of such models. Our study entails finetuning three different sizes of OPT on a carefully curated reasoning corpus, resulting in two sets of finetuned models: OPT-R, finetuned without explanations, and OPT-RE, finetuned with explanations. We then evaluate all models on 57 out-of-domain tasks drawn from the SUPER-NATURALINSTRUCTIONS benchmark, covering 26 distinct reasoning skills, utilizing three prompting techniques. Through a comprehensive grid of 27 configurations and 6,156 test evaluations, we investigate the dimensions of finetuning, prompting, and scale to understand the role of explanations on different reasoning skills. Our findings reveal that having explanations in the fewshot exemplar has no significant impact on the model's performance when the model is finetuned, while positively affecting the non-finetuned counterpart. Moreover, we observe a slight yet consistent increase in classification accuracy as we incorporate explanations during prompting and finetuning, respectively. Finally, we offer insights on which skills benefit the most from incorporating explanations during finetuning and prompting, such as Numerical (+20.4%) and Analogical (+13.9%) reasoning, as well as skills that exhibit negligible or negative effects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-23", "arxiv_id": "2305.11938", "title": "XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages", "authors": [ "Sebastian Ruder", "Jonathan H. Clark", "Alexander Gutkin", "Mihir Kale", "Min Ma", "Massimo Nicosia", "Shruti Rijhwani", "Parker Riley", "Jean-Michel A. Sarr", "Xinyi Wang", "John Wieting", "Nitish Gupta", "Anna Katanova", "Christo Kirov", "Dana L. Dickinson", "Brian Roark", "Bidisha Samanta", "Connie Tao", "David I. Adelani", "Vera Axelrod", "Isaac Caswell", "Colin Cherry", "Dan Garrette", "Reeve Ingle", "Melvin Johnson", "Dmitry Panteleev", "Partha Talukdar" ], "github": "https://github.com/google-research/xtreme-up", "project_page": "", "abstract": "Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) -- languages for which NLP re-search is particularly far behind in meeting user needs -- it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks -- tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text-only, multi-modal (vision, audio, and text),supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-research/xtreme-up", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-23", "arxiv_id": "2305.13304", "title": "RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text", "authors": [ "Wangchunshu Zhou", "Yuchen Eleanor Jiang", "Peng Cui", "Tiannan Wang", "Zhenxin Xiao", "Yifan Hou", "Ryan Cotterell", "Mrinmaya Sachan" ], "github": "https://github.com/aiwaves-cn/RecurrentGPT", "project_page": "", "abstract": "The fixed-size context of Transformer makes GPT models incapable of generating arbitrarily long text. In this paper, we introduce RecurrentGPT, a language-based simulacrum of the recurrence mechanism in RNNs. RecurrentGPT is built upon a large language model (LLM) such as ChatGPT and uses natural language to simulate the Long Short-Term Memory mechanism in an LSTM. At each timestep, RecurrentGPT generates a paragraph of text and updates its language-based long-short term memory stored on the hard drive and the prompt, respectively. This recurrence mechanism enables RecurrentGPT to generate texts of arbitrary length without forgetting. Since human users can easily observe and edit the natural language memories, RecurrentGPT is interpretable and enables interactive generation of long text. RecurrentGPT is an initial step towards next-generation computer-assisted writing systems beyond local editing suggestions. In addition to producing AI-generated content (AIGC), we also demonstrate the possibility of using RecurrentGPT as an interactive fiction that directly interacts with consumers. We call this usage of generative models by ``AI As Contents'' (AIAC), which we believe is the next form of conventional AIGC. We further demonstrate the possibility of using RecurrentGPT to create personalized interactive fiction that directly interacts with readers instead of interacting with writers. More broadly, RecurrentGPT demonstrates the utility of borrowing ideas from popular model designs in cognitive science and deep learning for prompting LLMs. Our code is available at https://github.com/aiwaves-cn/RecurrentGPT and an online demo is available at https://www.aiwaves.org/recurrentgpt.", "github_manual": "", "github_regex": "https://github.com/aiwaves-cn/RecurrentGPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-24", "arxiv_id": "2305.14314", "title": "QLoRA: Efficient Finetuning of Quantized LLMs", "authors": [ "Tim Dettmers", "Artidoro Pagnoni", "Ari Holtzman", "Luke Zettlemoyer" ], "github": "https://github.com/artidoro/qlora", "project_page": "", "abstract": "We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~(LoRA). Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU. QLoRA introduces a number of innovations to save memory without sacrificing performance: (a) 4-bit NormalFloat (NF4), a new data type that is information theoretically optimal for normally distributed weights (b) double quantization to reduce the average memory footprint by quantizing the quantization constants, and (c) paged optimziers to manage memory spikes. We use QLoRA to finetune more than 1,000 models, providing a detailed analysis of instruction following and chatbot performance across 8 instruction datasets, multiple model types (LLaMA, T5), and model scales that would be infeasible to run with regular finetuning (e.g. 33B and 65B parameter models). Our results show that QLoRA finetuning on a small high-quality dataset leads to state-of-the-art results, even when using smaller models than the previous SoTA. We provide a detailed analysis of chatbot performance based on both human and GPT-4 evaluations showing that GPT-4 evaluations are a cheap and reasonable alternative to human evaluation. Furthermore, we find that current chatbot benchmarks are not trustworthy to accurately evaluate the performance levels of chatbots. A lemon-picked analysis demonstrates where Guanaco fails compared to ChatGPT. We release all of our models and code, including CUDA kernels for 4-bit training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/artidoro/qlora", "github_api_added_by": "auto" }, { "date": "2023-05-24", "arxiv_id": "2305.14201", "title": "Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks", "authors": [ "Tiedong Liu", "Bryan Kian Hsiang Low" ], "github": "https://github.com/liutiedong/goat", "project_page": "", "abstract": "We introduce Goat, a fine-tuned LLaMA model that significantly outperforms GPT-4 on a range of arithmetic tasks. Fine-tuned on a synthetically generated dataset, Goat achieves state-of-the-art performance on BIG-bench arithmetic sub-task. In particular, the zero-shot Goat-7B matches or even surpasses the accuracy achieved by the few-shot PaLM-540B. Surprisingly, Goat can achieve near-perfect accuracy on large-number addition and subtraction through supervised fine-tuning only, which is almost impossible with previous pretrained language models, such as Bloom, OPT, GPT-NeoX, etc. We attribute Goat's exceptional performance to LLaMA's consistent tokenization of numbers. To tackle more challenging tasks like large-number multiplication and division, we propose an approach that classifies tasks based on their learnability, and subsequently decomposes unlearnable tasks, such as multi-digit multiplication and division, into a series of learnable tasks by leveraging basic arithmetic principles. We thoroughly examine the performance of our model, offering a comprehensive evaluation of the effectiveness of our proposed decomposition steps. Additionally, Goat-7B can be easily trained using LoRA on a 24GB VRAM GPU, facilitating reproducibility for other researchers. We release our model, dataset, and the Python script for dataset generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/liutiedong/goat", "github_api_added_by": "auto" }, { "date": "2023-05-24", "arxiv_id": "2305.14233", "title": "Enhancing Chat Language Models by Scaling High-quality Instructional Conversations", "authors": [ "Ning Ding", "Yulin Chen", "Bokai Xu", "Yujia Qin", "Zhi Zheng", "Shengding Hu", "Zhiyuan Liu", "Maosong Sun", "Bowen Zhou" ], "github": "https://github.com/thunlp/UltraChat", "project_page": "", "abstract": "Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT. Scaling the diversity and quality of such data, although straightforward, stands a great chance of leading to improved performance. This paper aims to improve the upper bound of open-source models further. We first provide a systematically designed, diverse, informative, large-scale dataset of instructional conversations, UltraChat, which does not involve human queries. Our objective is to capture the breadth of interactions that a human might have with an AI assistant and employs a comprehensive framework to generate multi-turn conversation iteratively. UltraChat contains 1.5 million high-quality multi-turn dialogues and covers a wide range of topics and instructions. Our statistical analysis of UltraChat reveals its superiority in various key metrics, including scale, average length, diversity, coherence, etc., solidifying its position as a leading open-source dataset. Building upon UltraChat, we fine-tune a LLaMA model to create a powerful conversational model, UltraLLaMA. Our evaluations indicate that UltraLLaMA consistently outperforms other open-source models, including Vicuna, the previously recognized state-of-the-art open-source model. The dataset and the model will be publicly released\\url{https://github.com/thunlp/UltraChat}.", "github_manual": "", "github_regex": "https://github.com/thunlp/UltraChat", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-24", "arxiv_id": "2305.13840", "title": "Control-A-Video: Controllable Text-to-Video Generation with Diffusion Models", "authors": [ "Weifeng Chen", "Jie Wu", "Pan Xie", "Hefeng Wu", "Jiashi Li", "Xin Xia", "Xuefeng Xiao", "Liang Lin" ], "github": "https://github.com/weifeng-chen/control-a-video", "project_page": "", "abstract": "This paper presents a controllable text-to-video (T2V) diffusion model, named Video-ControlNet, that generates videos conditioned on a sequence of control signals, such as edge or depth maps. Video-ControlNet is built on a pre-trained conditional text-to-image (T2I) diffusion model by incorporating a spatial-temporal self-attention mechanism and trainable temporal layers for efficient cross-frame modeling. A first-frame conditioning strategy is proposed to facilitate the model to generate videos transferred from the image domain as well as arbitrary-length videos in an auto-regressive manner. Moreover, Video-ControlNet employs a novel residual-based noise initialization strategy to introduce motion prior from an input video, producing more coherent videos. With the proposed architecture and strategies, Video-ControlNet can achieve resource-efficient convergence and generate superior quality and consistent videos with fine-grained control. Extensive experiments demonstrate its success in various video generative tasks such as video editing and video style transfer, outperforming previous methods in terms of consistency and quality. Project Page: https://controlavideo.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/weifeng-chen/control-a-video", "github_api_added_by": "auto" }, { "date": "2023-05-24", "arxiv_id": "2305.13579", "title": "Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free Approach", "authors": [ "Yufan Zhou", "Ruiyi Zhang", "Tong Sun", "Jinhui Xu" ], "github": "https://github.com/drboog/profusion", "project_page": "", "abstract": "Recent text-to-image generation models have demonstrated impressive capability of generating text-aligned images with high fidelity. However, generating images of novel concept provided by the user input image is still a challenging task. To address this problem, researchers have been exploring various methods for customizing pre-trained text-to-image generation models. Currently, most existing methods for customizing pre-trained text-to-image generation models involve the use of regularization techniques to prevent over-fitting. While regularization will ease the challenge of customization and leads to successful content creation with respect to text guidance, it may restrict the model capability, resulting in the loss of detailed information and inferior performance. In this work, we propose a novel framework for customized text-to-image generation without the use of regularization. Specifically, our proposed framework consists of an encoder network and a novel sampling method which can tackle the over-fitting problem without the use of regularization. With the proposed framework, we are able to customize a large-scale text-to-image generation model within half a minute on single GPU, with only one image provided by the user. We demonstrate in experiments that our proposed framework outperforms existing methods, and preserves more fine-grained details.", "github_manual": "https://github.com/drboog/profusion", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-24", "arxiv_id": "2305.13786", "title": "Perception Test: A Diagnostic Benchmark for Multimodal Video Models", "authors": [ "Viorica P\u0103tr\u0103ucean", "Lucas Smaira", "Ankush Gupta", "Adri\u00e0 Recasens Continente", "Larisa Markeeva", "Dylan Banarse", "Skanda Koppula", "Joseph Heyward", "Mateusz Malinowski", "Yi Yang", "Carl Doersch", "Tatiana Matejovicova", "Yury Sulsky", "Antoine Miech", "Alex Frechette", "Hanna Klimczak", "Raphael Koster", "Junlin Zhang", "Stephanie Winkler", "Yusuf Aytar", "Simon Osindero", "Dima Damen", "Andrew Zisserman", "Jo\u00e3o Carreira" ], "github": "https://github.com/deepmind/perception_test", "project_page": "", "abstract": "We propose a novel multimodal video benchmark - the Perception Test - to evaluate the perception and reasoning skills of pre-trained multimodal models (e.g. Flamingo, BEiT-3, or GPT-4). Compared to existing benchmarks that focus on computational tasks (e.g. classification, detection or tracking), the Perception Test focuses on skills (Memory, Abstraction, Physics, Semantics) and types of reasoning (descriptive, explanatory, predictive, counterfactual) across video, audio, and text modalities, to provide a comprehensive and efficient evaluation tool. The benchmark probes pre-trained models for their transfer capabilities, in a zero-shot / few-shot or limited finetuning regime. For these purposes, the Perception Test introduces 11.6k real-world videos, 23s average length, designed to show perceptually interesting situations, filmed by around 100 participants worldwide. The videos are densely annotated with six types of labels (multiple-choice and grounded video question-answers, object and point tracks, temporal action and sound segments), enabling both language and non-language evaluations. The fine-tuning and validation splits of the benchmark are publicly available (CC-BY license), in addition to a challenge server with a held-out test split. Human baseline results compared to state-of-the-art video QA models show a significant gap in performance (91.4% vs 43.6%), suggesting that there is significant room for improvement in multimodal video understanding. Dataset, baselines code, and challenge server are available at https://github.com/deepmind/perception_test", "github_manual": "", "github_regex": "https://github.com/deepmind/perception_test", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-24", "arxiv_id": "2305.13735", "title": "Aligning Large Language Models through Synthetic Feedback", "authors": [ "Sungdong Kim", "Sanghwan Bae", "Jamin Shin", "Soyoung Kang", "Donghyun Kwak", "Kang Min Yoo", "Minjoon Seo" ], "github": "https://github.com/naver-ai/almost", "project_page": "", "abstract": "Aligning large language models (LLMs) to human values has become increasingly important as it enables sophisticated steering of LLMs, e.g., making them follow given instructions while keeping them less toxic. However, it requires a significant amount of human demonstrations and feedback. Recently, open-sourced models have attempted to replicate the alignment learning process by distilling data from already aligned LLMs like InstructGPT or ChatGPT. While this process reduces human efforts, constructing these datasets has a heavy dependency on the teacher models. In this work, we propose a novel framework for alignment learning with almost no human labor and no dependency on pre-aligned LLMs. First, we perform reward modeling (RM) with synthetic feedback by contrasting responses from vanilla LLMs with various sizes and prompts. Then, we use the RM for simulating high-quality demonstrations to train a supervised policy and for further optimizing the model with reinforcement learning. Our resulting model, Aligned Language Model with Synthetic Training dataset (ALMoST), outperforms open-sourced models, including Alpaca, Dolly, and OpenAssistant, which are trained on the outputs of InstructGPT or human-annotated instructions. Our 7B-sized model outperforms the 12-13B models in the A/B tests using GPT-4 as the judge with about 75% winning rate on average.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/naver-ai/almost", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-24", "arxiv_id": "2305.13534", "title": "How Language Model Hallucinations Can Snowball", "authors": [ "Muru Zhang", "Ofir Press", "William Merrill", "Alisa Liu", "Noah A. Smith" ], "github": "https://github.com/nanami18/snowballed_hallucination", "project_page": "", "abstract": "A major risk of using language models in practical applications is their tendency to hallucinate incorrect statements. Hallucinations are often attributed to knowledge gaps in LMs, but we hypothesize that in some cases, when justifying previously generated hallucinations, LMs output false claims that they can separately recognize as incorrect. We construct three question-answering datasets where ChatGPT and GPT-4 often state an incorrect answer and offer an explanation with at least one incorrect claim. Crucially, we find that ChatGPT and GPT-4 can identify 67% and 87% of their own mistakes, respectively. We refer to this phenomenon as hallucination snowballing: an LM over-commits to early mistakes, leading to more mistakes that it otherwise would not make.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nanami18/snowballed_hallucination", "github_api_added_by": "auto" }, { "date": "2023-05-25", "arxiv_id": "2305.15038", "title": "Is GPT-4 a Good Data Analyst?", "authors": [ "Liying Cheng", "Xingxuan Li", "Lidong Bing" ], "github": "https://github.com/damo-nlp-sg/gpt4-as-dataanalyst", "project_page": "", "abstract": "As large language models (LLMs) have demonstrated their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc., many data analysts may raise concerns if their jobs will be replaced by AI. This controversial topic has drawn a lot of attention in public. However, we are still at a stage of divergent opinions without any definitive conclusion. Motivated by this, we raise the research question of \"is GPT-4 a good data analyst?\" in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. We propose a framework to tackle the problems by carefully designing the prompts for GPT-4 to conduct experiments. We also design several task-specific evaluation metrics to systematically compare the performance between several professional human data analysts and GPT-4. Experimental results show that GPT-4 can achieve comparable performance to humans. We also provide in-depth discussions about our results to shed light on further studies before we reach the conclusion that GPT-4 can replace data analysts.", "github_manual": "https://github.com/damo-nlp-sg/gpt4-as-dataanalyst", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-25", "arxiv_id": "2305.14878", "title": "Leveraging GPT-4 for Automatic Translation Post-Editing", "authors": [ "Vikas Raunak", "Amr Sharaf", "Hany Hassan Awadallah", "Arul Menezes" ], "github": "", "project_page": "", "abstract": "While Neural Machine Translation (NMT) represents the leading approach to Machine Translation (MT), the outputs of NMT models still require translation post-editing to rectify errors and enhance quality, particularly under critical settings. In this work, we formalize the task of translation post-editing with Large Language Models (LLMs) and explore the use of GPT-4 to automatically post-edit NMT outputs across several language pairs. Our results demonstrate that GPT-4 is adept at translation post-editing and produces meaningful edits even when the target language is not English. Notably, we achieve state-of-the-art performance on WMT-22 English-Chinese, English-German, Chinese-English and German-English language pairs using GPT-4 based post-editing, as evaluated by state-of-the-art MT quality metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-25", "arxiv_id": "2305.14564", "title": "PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents", "authors": [ "Simeng Sun", "Yang Liu", "Shuohang Wang", "Chenguang Zhu", "Mohit Iyyer" ], "github": "https://github.com/simengsun/pearl", "project_page": "", "abstract": "Strategies such as chain-of-thought prompting improve the performance of large language models (LLMs) on complex reasoning tasks by decomposing input examples into intermediate steps. However, it remains unclear how to apply such methods to reason over long input documents, in which both the decomposition and the output of each intermediate step are non-trivial to obtain. In this work, we propose PEARL, a prompting framework to improve reasoning over long documents, which consists of three stages: action mining, plan formulation, and plan execution. More specifically, given a question about a long document, PEARL decomposes the question into a sequence of actions (e.g., SUMMARIZE, FIND_EVENT, FIND_RELATION) and then executes them over the document to obtain the answer. Each stage of PEARL is implemented via zero-shot or few-shot prompting of LLMs (in our work, GPT-4) with minimal human input. We evaluate PEARL on a challenging subset of the QuALITY dataset, which contains questions that require complex reasoning over long narrative texts. PEARL outperforms zero-shot and chain-of-thought prompting on this dataset, and ablation experiments show that each stage of PEARL is critical to its performance. Overall, PEARL is a first step towards leveraging LLMs to reason over long documents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/simengsun/pearl", "github_api_added_by": "auto" }, { "date": "2023-05-25", "arxiv_id": "2305.14540", "title": "LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond", "authors": [ "Philippe Laban", "Wojciech Kry\u015bci\u0144ski", "Divyansh Agarwal", "Alexander R. Fabbri", "Caiming Xiong", "Shafiq Joty", "Chien-Sheng Wu" ], "github": "", "project_page": "", "abstract": "With the recent appearance of LLMs in practical settings, having methods that can effectively detect factual inconsistencies is crucial to reduce the propagation of misinformation and improve trust in model outputs. When testing on existing factual consistency benchmarks, we find that a few large language models (LLMs) perform competitively on classification benchmarks for factual inconsistency detection compared to traditional non-LLM methods. However, a closer analysis reveals that most LLMs fail on more complex formulations of the task and exposes issues with existing evaluation benchmarks, affecting evaluation precision. To address this, we propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits. This new benchmark is 20 times more cost-effective per sample than previous benchmarks and highly reproducible, as we estimate inter-annotator agreement at about 0.9. Most LLMs struggle on SummEdits, with performance close to random chance. The best-performing model, GPT-4, is still 8\\% below estimated human performance, highlighting the gaps in LLMs' ability to reason about facts and detect inconsistencies when they occur.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-26", "arxiv_id": "2305.16213", "title": "ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation", "authors": [ "Zhengyi Wang", "Cheng Lu", "Yikai Wang", "Fan Bao", "Chongxuan Li", "Hang Su", "Jun Zhu" ], "github": "", "project_page": "", "abstract": "Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-saturation, over-smoothing, and low-diversity problems. In this work, we propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., 7.5). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., 512times512) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic. Project page: https://ml.cs.tsinghua.edu.cn/prolificdreamer/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-26", "arxiv_id": "2305.16291", "title": "Voyager: An Open-Ended Embodied Agent with Large Language Models", "authors": [ "Guanzhi Wang", "Yuqi Xie", "Yunfan Jiang", "Ajay Mandlekar", "Chaowei Xiao", "Yuke Zhu", "Linxi Fan", "Anima Anandkumar" ], "github": "https://github.com/MineDojo/Voyager", "project_page": "", "abstract": "We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize. We open-source our full codebase and prompts at https://voyager.minedojo.org/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MineDojo/Voyager", "github_api_added_by": "auto" }, { "date": "2023-05-26", "arxiv_id": "2305.15798", "title": "On Architectural Compression of Text-to-Image Diffusion Models", "authors": [ "Bo-Kyeong Kim", "Hyoung-Kyu Song", "Thibault Castells", "Shinkook Choi" ], "github": "https://github.com/Nota-NetsPresso/BK-SDM", "project_page": "", "abstract": "Exceptional text-to-image (T2I) generation results of Stable Diffusion models (SDMs) come with substantial computational demands. To resolve this issue, recent research on efficient SDMs has prioritized reducing the number of sampling steps and utilizing network quantization. Orthogonal to these directions, this study highlights the power of classical architectural compression for general-purpose T2I synthesis by introducing block-removed knowledge-distilled SDMs (BK-SDMs). We eliminate several residual and attention blocks from the U-Net of SDMs, obtaining over a 30% reduction in the number of parameters, MACs per sampling step, and latency. We conduct distillation-based pretraining with only 0.22M LAION pairs (fewer than 0.1% of the full training pairs) on a single A100 GPU. Despite being trained with limited resources, our compact models can imitate the original SDM by benefiting from transferred knowledge and achieve competitive results against larger multi-billion parameter models on the zero-shot MS-COCO benchmark. Moreover, we demonstrate the applicability of our lightweight pretrained models in personalized generation with DreamBooth finetuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Nota-NetsPresso/BK-SDM", "github_api_added_by": "auto" }, { "date": "2023-05-26", "arxiv_id": "2305.15779", "title": "Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models", "authors": [ "Jooyoung Choi", "Yunjey Choi", "Yunji Kim", "Junho Kim", "Sungroh Yoon" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion models can generate diverse, high-fidelity images based on user-provided text prompts. Recent research has extended these models to support text-guided image editing. While text guidance is an intuitive editing interface for users, it often fails to ensure the precise concept conveyed by users. To address this issue, we propose Custom-Edit, in which we (i) customize a diffusion model with a few reference images and then (ii) perform text-guided editing. Our key discovery is that customizing only language-relevant parameters with augmented prompts improves reference similarity significantly while maintaining source similarity. Moreover, we provide our recipe for each customization and editing process. We compare popular customization methods and validate our findings on two editing methods using various datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-26", "arxiv_id": "2305.15719", "title": "Efficient Neural Music Generation", "authors": [ "Max W. Y. Lam", "Qiao Tian", "Tang Li", "Zongyu Yin", "Siyuan Feng", "Ming Tu", "Yuliang Ji", "Rui Xia", "Mingbo Ma", "Xuchen Song", "Jitong Chen", "Yuping Wang", "Yuxuan Wang" ], "github": "", "project_page": "", "abstract": "Recent progress in music generation has been remarkably advanced by the state-of-the-art MusicLM, which comprises a hierarchy of three LMs, respectively, for semantic, coarse acoustic, and fine acoustic modelings. Yet, sampling with the MusicLM requires processing through these LMs one by one to obtain the fine-grained acoustic tokens, making it computationally expensive and prohibitive for a real-time generation. Efficient music generation with a quality on par with MusicLM remains a significant challenge. In this paper, we present MeLoDy (M for music; L for LM; D for diffusion), an LM-guided diffusion model that generates music audios of state-of-the-art quality meanwhile reducing 95.7% or 99.6% forward passes in MusicLM, respectively, for sampling 10s or 30s music. MeLoDy inherits the highest-level LM from MusicLM for semantic modeling, and applies a novel dual-path diffusion (DPD) model and an audio VAE-GAN to efficiently decode the conditioning semantic tokens into waveform. DPD is proposed to simultaneously model the coarse and fine acoustics by incorporating the semantic information into segments of latents effectively via cross-attention at each denoising step. Our experimental results suggest the superiority of MeLoDy, not only in its practical advantages on sampling speed and infinitely continuable generation, but also in its state-of-the-art musicality, audio quality, and text correlation. Our samples are available at https://Efficient-MeLoDy.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-26", "arxiv_id": "2305.15586", "title": "Manifold Diffusion Fields", "authors": [ "Ahmed A. Elhag", "Joshua M. Susskind", "Miguel Angel Bautista" ], "github": "", "project_page": "", "abstract": "We present Manifold Diffusion Fields (MDF), an approach to learn generative models of continuous functions defined over Riemannian manifolds. Leveraging insights from spectral geometry analysis, we define an intrinsic coordinate system on the manifold via the eigen-functions of the Laplace-Beltrami Operator. MDF represents functions using an explicit parametrization formed by a set of multiple input-output pairs. Our approach allows to sample continuous functions on manifolds and is invariant with respect to rigid and isometric transformations of the manifold. Empirical results on several datasets and manifolds show that MDF can capture distributions of such functions with better diversity and fidelity than previous approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-26", "arxiv_id": "2305.15581", "title": "Unsupervised Semantic Correspondence Using Stable Diffusion", "authors": [ "Eric Hedlin", "Gopal Sharma", "Shweta Mahajan", "Hossam Isack", "Abhishek Kar", "Andrea Tagliasacchi", "Kwang Moo Yi" ], "github": "https://github.com/ubc-vision/LDM_correspondences", "project_page": "", "abstract": "Text-to-image diffusion models are now capable of generating images that are often indistinguishable from real images. To generate such images, these models must understand the semantics of the objects they are asked to generate. In this work we show that, without any training, one can leverage this semantic knowledge within diffusion models to find semantic correspondences -- locations in multiple images that have the same semantic meaning. Specifically, given an image, we optimize the prompt embeddings of these models for maximum attention on the regions of interest. These optimized embeddings capture semantic information about the location, which can then be transferred to another image. By doing so we obtain results on par with the strongly supervised state of the art on the PF-Willow dataset and significantly outperform (20.9% relative for the SPair-71k dataset) any existing weakly or unsupervised method on PF-Willow, CUB-200 and SPair-71k datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ubc-vision/LDM_correspondences", "github_api_added_by": "auto" }, { "date": "2023-05-26", "arxiv_id": "2305.15717", "title": "The False Promise of Imitating Proprietary LLMs", "authors": [ "Arnav Gudibande", "Eric Wallace", "Charlie Snell", "Xinyang Geng", "Hao Liu", "Pieter Abbeel", "Sergey Levine", "Dawn Song" ], "github": "https://github.com/young-geng/EasyLM", "project_page": "", "abstract": "An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model, such as a proprietary system like ChatGPT (e.g., Alpaca, Self-Instruct, and others). This approach looks to cheaply imitate the proprietary model's capabilities using a weaker open-source model. In this work, we critically analyze this approach. We first finetune a series of LMs that imitate ChatGPT using varying base model sizes (1.5B--13B), data sources, and imitation data amounts (0.3M--150M tokens). We then evaluate the models using crowd raters and canonical NLP benchmarks. Initially, we were surprised by the output quality of our imitation models -- they appear far better at following instructions, and crowd workers rate their outputs as competitive with ChatGPT. However, when conducting more targeted automatic evaluations, we find that imitation models close little to none of the gap from the base LM to ChatGPT on tasks that are not heavily supported in the imitation data. We show that these performance discrepancies may slip past human raters because imitation models are adept at mimicking ChatGPT's style but not its factuality. Overall, we conclude that model imitation is a false promise: there exists a substantial capabilities gap between open and closed LMs that, with current methods, can only be bridged using an unwieldy amount of imitation data or by using more capable base LMs. In turn, we argue that the highest leverage action for improving open-source models is to tackle the difficult challenge of developing better base LMs, rather than taking the shortcut of imitating proprietary systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/young-geng/EasyLM", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-26", "arxiv_id": "2305.15486", "title": "SPRING: GPT-4 Out-performs RL Algorithms by Studying Papers and Reasoning", "authors": [ "Yue Wu", "So Yeon Min", "Shrimai Prabhumoye", "Yonatan Bisk", "Ruslan Salakhutdinov", "Amos Azaria", "Tom Mitchell", "Yuanzhi Li" ], "github": "https://github.com/holmeswww/spring", "project_page": "", "abstract": "Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement learning (RL) being popular for solving games, its high sample complexity limits its effectiveness in complex open-world games like Crafter or Minecraft. We propose a novel approach, SPRING, to read the game's original academic paper and use the knowledge learned to reason and play the game through a large language model (LLM). Prompted with the LaTeX source as game context and a description of the agent's current observation, our SPRING framework employs a directed acyclic graph (DAG) with game-related questions as nodes and dependencies as edges. We identify the optimal action to take in the environment by traversing the DAG and calculating LLM responses for each node in topological order, with the LLM's answer to final node directly translating to environment actions. In our experiments, we study the quality of in-context \"reasoning\" induced by different forms of prompts under the setting of the Crafter open-world environment. Our experiments suggest that LLMs, when prompted with consistent chain-of-thought, have great potential in completing sophisticated high-level trajectories. Quantitatively, SPRING with GPT-4 outperforms all state-of-the-art RL baselines, trained for 1M steps, without any training. Finally, we show the potential of games as a test bed for LLMs.", "github_manual": "https://github.com/holmeswww/spring", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16765", "title": "Backpack Language Models", "authors": [ "John Hewitt", "John Thickstun", "Christopher D. Manning", "Percy Liang" ], "github": "", "project_page": "", "abstract": "We present Backpacks: a new neural architecture that marries strong modeling performance with an interface for interpretability and control. Backpacks learn multiple non-contextual sense vectors for each word in a vocabulary, and represent a word in a sequence as a context-dependent, non-negative linear combination of sense vectors in this sequence. We find that, after training, sense vectors specialize, each encoding a different aspect of a word. We can interpret a sense vector by inspecting its (non-contextual, linear) projection onto the output space, and intervene on these interpretable hooks to change the model's behavior in predictable ways. We train a 170M-parameter Backpack language model on OpenWebText, matching the loss of a GPT-2 small (124Mparameter) Transformer. On lexical similarity evaluations, we find that Backpack sense vectors outperform even a 6B-parameter Transformer LM's word embeddings. Finally, we present simple algorithms that intervene on sense vectors to perform controllable text generation and debiasing. For example, we can edit the sense vocabulary to tend more towards a topic, or localize a source of gender bias to a sense vector and globally suppress that sense.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16355", "title": "PandaGPT: One Model To Instruction-Follow Them All", "authors": [ "Yixuan Su", "Tian Lan", "Huayang Li", "Jialu Xu", "Yan Wang", "Deng Cai" ], "github": "https://github.com/yxuansu/pandagpt", "project_page": "", "abstract": "We present PandaGPT, an approach to emPower large lANguage moDels with visual and Auditory instruction-following capabilities. Our pilot experiments show that PandaGPT can perform complex tasks such as detailed image description generation, writing stories inspired by videos, and answering questions about audios. More interestingly, PandaGPT can take multimodal inputs simultaneously and compose their semantics naturally. For example, PandaGPT can connect how objects look in an image/video and how they sound in an audio. To do so, PandaGPT combines the multimodal encoders from ImageBind and the large language models from Vicuna. Notably, only aligned image-text pairs are required for the training of PandaGPT. Thanks to the strong capability of ImageBind in embedding data from different modalities into the same space, PandaGPT displays emergent, i.e. zero-shot, cross-modal behaviors for data other than image and text (e.g., video, audio, depth, thermal, and IMU). We hope that PandaGPT serves as an initial step toward building AGI that can perceive and understand inputs in different modalities holistically, as we humans do. Our project page is at https://panda-gpt.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yxuansu/pandagpt", "github_api_added_by": "auto" }, { "date": "2023-05-29", "arxiv_id": "2305.16338", "title": "Think Before You Act: Decision Transformers with Internal Working Memory", "authors": [ "Jikun Kang", "Romain Laroche", "Xindi Yuan", "Adam Trischler", "Xue Liu", "Jie Fu" ], "github": "https://github.com/luciferkonn/dt_mem", "project_page": "", "abstract": "Large language model (LLM)-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance relies on massive data and compute. We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout training. As a result, training on a new task may deteriorate the model's performance on previous tasks. In contrast to LLMs' implicit memory mechanism, the human brain utilizes distributed memory storage, which helps manage and organize multiple skills efficiently, mitigating the forgetting phenomenon. Thus inspired, we propose an internal working memory module to store, blend, and retrieve information for different downstream tasks. Evaluation results show that the proposed method improves training efficiency and generalization in both Atari games and meta-world object manipulation tasks. Moreover, we demonstrate that memory fine-tuning further enhances the adaptability of the proposed architecture.", "github_manual": "https://github.com/luciferkonn/dt_mem", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.17098", "title": "ControlVideo: Adding Conditional Control for One Shot Text-to-Video Editing", "authors": [ "Min Zhao", "Rongzhen Wang", "Fan Bao", "Chongxuan Li", "Jun Zhu" ], "github": "https://github.com/thu-ml/controlvideo", "project_page": "", "abstract": "In this paper, we present ControlVideo, a novel method for text-driven video editing. Leveraging the capabilities of text-to-image diffusion models and ControlNet, ControlVideo aims to enhance the fidelity and temporal consistency of videos that align with a given text while preserving the structure of the source video. This is achieved by incorporating additional conditions such as edge maps, fine-tuning the key-frame and temporal attention on the source video-text pair with carefully designed strategies. An in-depth exploration of ControlVideo's design is conducted to inform future research on one-shot tuning video diffusion models. Quantitatively, ControlVideo outperforms a range of competitive baselines in terms of faithfulness and consistency while still aligning with the textual prompt. Additionally, it delivers videos with high visual realism and fidelity w.r.t. the source content, demonstrating flexibility in utilizing controls containing varying degrees of source video information, and the potential for multiple control combinations. The project page is available at https://ml.cs.tsinghua.edu.cn/controlvideo/{https://ml.cs.tsinghua.edu.cn/controlvideo/}.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/thu-ml/controlvideo", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16334", "title": "OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities", "authors": [ "Yuanzhen Xie", "Tao Xie", "Mingxiong Lin", "WenTao Wei", "Chenglin Li", "Beibei Kong", "Lei Chen", "Chengxiang Zhuo", "Bo Hu", "Zang Li" ], "github": "https://github.com/oladata-team/OlaGPT", "project_page": "", "abstract": "In most current research, large language models (LLMs) are able to perform reasoning tasks by generating chains of thought through the guidance of specific prompts. However, there still exists a significant discrepancy between their capability in solving complex reasoning problems and that of humans. At present, most approaches focus on chains of thought (COT) and tool use, without considering the adoption and application of human cognitive frameworks. It is well-known that when confronting complex reasoning challenges, humans typically employ various cognitive abilities, and necessitate interaction with all aspects of tools, knowledge, and the external environment information to accomplish intricate tasks. This paper introduces a novel intelligent framework, referred to as OlaGPT. OlaGPT carefully studied a cognitive architecture framework, and propose to simulate certain aspects of human cognition. The framework involves approximating different cognitive modules, including attention, memory, reasoning, learning, and corresponding scheduling and decision-making mechanisms. Inspired by the active learning mechanism of human beings, it proposes a learning unit to record previous mistakes and expert opinions, and dynamically refer to them to strengthen their ability to solve similar problems. The paper also outlines common effective reasoning frameworks for human problem-solving and designs Chain-of-Thought (COT) templates accordingly. A comprehensive decision-making mechanism is also proposed to maximize model accuracy. The efficacy of OlaGPT has been stringently evaluated on multiple reasoning datasets, and the experimental outcomes reveal that OlaGPT surpasses state-of-the-art benchmarks, demonstrating its superior performance. Our implementation of OlaGPT is available on GitHub: https://github.com/oladata-team/OlaGPT.", "github_manual": "", "github_regex": "https://github.com/oladata-team/OlaGPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16311", "title": "Break-A-Scene: Extracting Multiple Concepts from a Single Image", "authors": [ "Omri Avrahami", "Kfir Aberman", "Ohad Fried", "Daniel Cohen-Or", "Dani Lischinski" ], "github": "https://github.com/google/break-a-scene", "project_page": "", "abstract": "Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts. However, current methods primarily focus on the case of learning a single concept from multiple images with variations in backgrounds and poses, and struggle when adapted to a different scenario. In this work, we introduce the task of textual scene decomposition: given a single image of a scene that may contain several concepts, we aim to extract a distinct text token for each concept, enabling fine-grained control over the generated scenes. To this end, we propose augmenting the input image with masks that indicate the presence of target concepts. These masks can be provided by the user or generated automatically by a pre-trained segmentation model. We then present a novel two-phase customization process that optimizes a set of dedicated textual embeddings (handles), as well as the model weights, striking a delicate balance between accurately capturing the concepts and avoiding overfitting. We employ a masked diffusion loss to enable handles to generate their assigned concepts, complemented by a novel loss on cross-attention maps to prevent entanglement. We also introduce union-sampling, a training strategy aimed to improve the ability of combining multiple concepts in generated images. We use several automatic metrics to quantitatively compare our method against several baselines, and further affirm the results using a user study. Finally, we showcase several applications of our method. Project page is available at: https://omriavrahami.com/break-a-scene/", "github_manual": "https://github.com/google/break-a-scene", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16843", "title": "Randomized Positional Encodings Boost Length Generalization of Transformers", "authors": [ "Anian Ruoss", "Gr\u00e9goire Del\u00e9tang", "Tim Genewein", "Jordi Grau-Moya", "R\u00f3bert Csord\u00e1s", "Mehdi Bennani", "Shane Legg", "Joel Veness" ], "github": "https://github.com/deepmind/randomized_positional_encodings", "project_page": "", "abstract": "Transformers have impressive generalization capabilities on tasks with a fixed context length. However, they fail to generalize to sequences of arbitrary length, even for seemingly simple tasks such as duplicating a string. Moreover, simply training on longer sequences is inefficient due to the quadratic computation complexity of the global attention mechanism. In this work, we demonstrate that this failure mode is linked to positional encodings being out-of-distribution for longer sequences (even for relative encodings) and introduce a novel family of positional encodings that can overcome this problem. Concretely, our randomized positional encoding scheme simulates the positions of longer sequences and randomly selects an ordered subset to fit the sequence's length. Our large-scale empirical evaluation of 6000 models across 15 algorithmic reasoning tasks shows that our method allows Transformers to generalize to sequences of unseen length (increasing test accuracy by 12.0% on average).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/deepmind/randomized_positional_encodings", "github_api_added_by": "auto" }, { "date": "2023-05-29", "arxiv_id": "2305.16704", "title": "A Closer Look at In-Context Learning under Distribution Shifts", "authors": [ "Kartik Ahuja", "David Lopez-Paz" ], "github": "https://github.com/facebookresearch/iclmlp", "project_page": "", "abstract": "In-context learning, a capability that enables a model to learn from input examples on the fly without necessitating weight updates, is a defining characteristic of large language models. In this work, we follow the setting proposed in (Garg et al., 2022) to better understand the generality and limitations of in-context learning from the lens of the simple yet fundamental task of linear regression. The key question we aim to address is: Are transformers more adept than some natural and simpler architectures at performing in-context learning under varying distribution shifts? To compare transformers, we propose to use a simple architecture based on set-based Multi-Layer Perceptrons (MLPs). We find that both transformers and set-based MLPs exhibit in-context learning under in-distribution evaluations, but transformers more closely emulate the performance of ordinary least squares (OLS). Transformers also display better resilience to mild distribution shifts, where set-based MLPs falter. However, under severe distribution shifts, both models' in-context learning abilities diminish.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/iclmlp", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16380", "title": "Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer", "authors": [ "Yuandong Tian", "Yiping Wang", "Beidi Chen", "Simon Du" ], "github": "", "project_page": "", "abstract": "Transformer architecture has shown impressive performance in multiple research domains and has become the backbone of many neural network models. However, there is limited understanding on how it works. In particular, with a simple predictive loss, how the representation emerges from the gradient training dynamics remains a mystery. In this paper, for 1-layer transformer with one self-attention layer plus one decoder layer, we analyze its SGD training dynamics for the task of next token prediction in a mathematically rigorous manner. We open the black box of the dynamic process of how the self-attention layer combines input tokens, and reveal the nature of underlying inductive bias. More specifically, with the assumption (a) no positional encoding, (b) long input sequence, and (c) the decoder layer learns faster than the self-attention layer, we prove that self-attention acts as a discriminative scanning algorithm: starting from uniform attention, it gradually attends more to distinct key tokens for a specific next token to be predicted, and pays less attention to common key tokens that occur across different next tokens. Among distinct tokens, it progressively drops attention weights, following the order of low to high co-occurrence between the key and the query token in the training set. Interestingly, this procedure does not lead to winner-takes-all, but decelerates due to a phase transition that is controllable by the learning rates of the two layers, leaving (almost) fixed token combination. We verify this \\emph{scan and snap} dynamics on synthetic and real-world data (WikiText).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16999", "title": "Three Towers: Flexible Contrastive Learning with Pretrained Image Models", "authors": [ "Jannik Kossen", "Mark Collier", "Basil Mustafa", "Xiao Wang", "Xiaohua Zhai", "Lucas Beyer", "Andreas Steiner", "Jesse Berent", "Rodolphe Jenatton", "Efi Kokiopoulou" ], "github": "", "project_page": "", "abstract": "We introduce Three Towers (3T), a flexible method to improve the contrastive learning of vision-language models by incorporating pretrained image classifiers. While contrastive models are usually trained from scratch, LiT (Zhai et al., 2022) has recently shown performance gains from using pretrained classifier embeddings. However, LiT directly replaces the image tower with the frozen embeddings, excluding any potential benefits of contrastively training the image tower. With 3T, we propose a more flexible strategy that allows the image tower to benefit from both pretrained embeddings and contrastive training. To achieve this, we introduce a third tower that contains the frozen pretrained embeddings, and we encourage alignment between this third tower and the main image-text towers. Empirically, 3T consistently improves over LiT and the CLIP-style from-scratch baseline for retrieval tasks. For classification, 3T reliably improves over the from-scratch baseline, and while it underperforms relative to LiT for JFT-pretrained models, it outperforms LiT for ImageNet-21k and Places365 pretraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16960", "title": "Training Socially Aligned Language Models in Simulated Human Society", "authors": [ "Ruibo Liu", "Ruixin Yang", "Chenyan Jia", "Ge Zhang", "Denny Zhou", "Andrew M. Dai", "Diyi Yang", "Soroush Vosoughi" ], "github": "", "project_page": "", "abstract": "Social alignment in AI systems aims to ensure that these models behave according to established societal values. However, unlike humans, who derive consensus on value judgments through social interaction, current language models (LMs) are trained to rigidly replicate their training corpus in isolation, leading to subpar generalization in unfamiliar scenarios and vulnerability to adversarial attacks. This work presents a novel training paradigm that permits LMs to learn from simulated social interactions. In comparison to existing methodologies, our approach is considerably more scalable and efficient, demonstrating superior performance in alignment benchmarks and human evaluations. This paradigm shift in the training of LMs brings us a step closer to developing AI systems that can robustly and accurately reflect societal norms and values.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16958", "title": "MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies", "authors": [ "Shiyue Zhang", "Shijie Wu", "Ozan Irsoy", "Steven Lu", "Mohit Bansal", "Mark Dredze", "David Rosenberg" ], "github": "https://github.com/bloomberg/mixce-acl2023", "project_page": "", "abstract": "Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P -- that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE). We have observed that models trained in this way may \"over-generalize\", in the sense that they produce non-human-like text. Moreover, we believe that reverse cross-entropy, i.e., the cross-entropy of P relative to Q, is a better reflection of how a human would evaluate text generated by a model. Hence, we propose learning with MixCE, an objective that mixes the forward and reverse cross-entropies. We evaluate models trained with this objective on synthetic data settings (where P is known) and real data, and show that the resulting models yield better generated text without complex decoding strategies. Our code and models are publicly available at https://github.com/bloomberg/mixce-acl2023", "github_manual": "", "github_regex": "https://github.com/bloomberg/mixce-acl2023", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16806", "title": "Do GPTs Produce Less Literal Translations?", "authors": [ "Vikas Raunak", "Arul Menezes", "Matt Post", "Hany Hassan Awadallah" ], "github": "https://github.com/vyraun/literalness", "project_page": "", "abstract": "Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. However, there has been relatively little investigation on how such translations differ qualitatively from the translations generated by standard Neural Machine Translation (NMT) models. In this work, we investigate these differences in terms of the literalness of translations produced by the two systems. Using literalness measures involving word alignment and monotonicity, we find that translations out of English (E-X) from GPTs tend to be less literal, while exhibiting similar or better scores on MT quality metrics. We demonstrate that this finding is borne out in human evaluations as well. We then show that these differences are especially pronounced when translating sentences that contain idiomatic expressions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vyraun/literalness", "github_api_added_by": "auto" }, { "date": "2023-05-29", "arxiv_id": "2305.16635", "title": "Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing", "authors": [ "Jaehun Jung", "Peter West", "Liwei Jiang", "Faeze Brahman", "Ximing Lu", "Jillian Fisher", "Taylor Sorensen", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "It is commonly perceived that the strongest language models (LMs) rely on a combination of massive scale, instruction data, and human feedback to perform specialized tasks -- e.g. summarization and paraphrasing, without supervision. In this paper, we propose that language models can learn to summarize and paraphrase sentences, with none of these 3 factors. We present Impossible Distillation, a framework that distills a task-specific dataset directly from an off-the-shelf LM, even when it is impossible for the LM itself to reliably solve the task. By training a student model on the generated dataset and amplifying its capability through self-distillation, our method yields a high-quality model and dataset from a low-quality teacher model, without the need for scale or supervision. Using Impossible Distillation, we are able to distill an order of magnitude smaller model (with only 770M parameters) that outperforms 175B parameter GPT-3, in both quality and controllability, as confirmed by automatic and human evaluations. Furthermore, as a useful byproduct of our approach, we obtain DIMSUM+, a high-quality dataset with 3.4M sentence summaries and paraphrases. Our analyses show that this dataset, as a purely LM-generated corpus, is more diverse and more effective for generalization to unseen domains than all human-authored datasets -- including Gigaword with 4M samples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16367", "title": "Role-Play with Large Language Models", "authors": [ "Murray Shanahan", "Kyle McDonell", "Laria Reynolds" ], "github": "", "project_page": "", "abstract": "As dialogue agents become increasingly human-like in their performance, it is imperative that we develop effective ways to describe their behaviour in high-level terms without falling into the trap of anthropomorphism. In this paper, we foreground the concept of role-play. Casting dialogue agent behaviour in terms of role-play allows us to draw on familiar folk psychological terms, without ascribing human characteristics to language models they in fact lack. Two important cases of dialogue agent behaviour are addressed this way, namely (apparent) deception and (apparent) self-awareness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16349", "title": "Lexinvariant Language Models", "authors": [ "Qian Huang", "Eric Zelikman", "Sarah Li Chen", "Yuhuai Wu", "Gregory Valiant", "Percy Liang" ], "github": "", "project_page": "", "abstract": "Token embeddings, a mapping from discrete lexical symbols to continuous vectors, are at the heart of any language model (LM). However, lexical symbol meanings can also be determined and even redefined by their structural role in a long context. In this paper, we ask: is it possible for a language model to be performant without any fixed token embeddings? Such a language model would have to rely entirely on the co-occurence and repetition of tokens in the context rather than the a priori identity of any token. To answer this, we study lexinvariantlanguage models that are invariant to lexical symbols and therefore do not need fixed token embeddings in practice. First, we prove that we can construct a lexinvariant LM to converge to the true language model at a uniform rate that is polynomial in terms of the context length, with a constant factor that is sublinear in the vocabulary size. Second, to build a lexinvariant LM, we simply encode tokens using random Gaussian vectors, such that each token maps to the same representation within each sequence but different representations across sequences. Empirically, we demonstrate that it can indeed attain perplexity comparable to that of a standard language model, given a sufficiently long context. We further explore two properties of the lexinvariant language models: First, given text generated from a substitution cipher of English, it implicitly implements Bayesian in-context deciphering and infers the mapping to the underlying real tokens with high accuracy. Second, it has on average 4X better accuracy over synthetic in-context reasoning tasks. Finally, we discuss regularizing standard language models towards lexinvariance and potential practical applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.17066", "title": "Mindstorms in Natural Language-Based Societies of Mind", "authors": [ "Mingchen Zhuge", "Haozhe Liu", "Francesco Faccio", "Dylan R. Ashley", "R\u00f3bert Csord\u00e1s", "Anand Gopalakrishnan", "Abdullah Hamdi", "Hasan Abed Al Kader Hammoud", "Vincent Herrmann", "Kazuki Irie", "Louis Kirsch", "Bing Li", "Guohao Li", "Shuming Liu", "Jinjie Mai", "Piotr Pi\u0119kos", "Aditya Ramesh", "Imanol Schlag", "Weimin Shi", "Aleksandar Stani\u0107", "Wenyi Wang", "Yuhui Wang", "Mengmeng Xu", "Deng-Ping Fan", "Bernard Ghanem", "J\u00fcrgen Schmidhuber" ], "github": "", "project_page": "", "abstract": "Both Minsky's \"society of mind\" and Schmidhuber's \"learning to think\" inspire diverse societies of large multimodal neural networks (NNs) that solve problems by interviewing each other in a \"mindstorm.\" Recent implementations of NN-based societies of minds consist of large language models (LLMs) and other NN-based experts communicating through a natural language interface. In doing so, they overcome the limitations of single LLMs, improving multimodal zero-shot reasoning. In these natural language-based societies of mind (NLSOMs), new agents -- all communicating through the same universal symbolic language -- are easily added in a modular fashion. To demonstrate the power of NLSOMs, we assemble and experiment with several of them (having up to 129 members), leveraging mindstorms in them to solve some practical AI tasks: visual question answering, image captioning, text-to-image synthesis, 3D generation, egocentric retrieval, embodied AI, and general language-based task solving. We view this as a starting point towards much larger NLSOMs with billions of agents-some of which may be humans. And with this emergence of great societies of heterogeneous minds, many new research questions have suddenly become paramount to the future of artificial intelligence. What should be the social structure of an NLSOM? What would be the (dis)advantages of having a monarchical rather than a democratic structure? How can principles of NN economies be used to maximize the total reward of a reinforcement learning NLSOM? In this work, we identify, discuss, and try to answer some of these questions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16411", "title": "ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image", "authors": [ "Zhenzhen Weng", "Zeyu Wang", "Serena Yeung" ], "github": "", "project_page": "", "abstract": "Recent advancements in text-to-image generation have enabled significant progress in zero-shot 3D shape generation. This is achieved by score distillation, a methodology that uses pre-trained text-to-image diffusion models to optimize the parameters of a 3D neural presentation, e.g. Neural Radiance Field (NeRF). While showing promising results, existing methods are often not able to preserve the geometry of complex shapes, such as human bodies. To address this challenge, we present ZeroAvatar, a method that introduces the explicit 3D human body prior to the optimization process. Specifically, we first estimate and refine the parameters of a parametric human body from a single image. Then during optimization, we use the posed parametric body as additional geometry constraint to regularize the diffusion model as well as the underlying density field. Lastly, we propose a UV-guided texture regularization term to further guide the completion of texture on invisible body parts. We show that ZeroAvatar significantly enhances the robustness and 3D consistency of optimization-based image-to-3D avatar generation, outperforming existing zero-shot image-to-3D methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16381", "title": "DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models", "authors": [ "Ying Fan", "Olivia Watkins", "Yuqing Du", "Hao Liu", "Moonkyung Ryu", "Craig Boutilier", "Pieter Abbeel", "Mohammad Ghavamzadeh", "Kangwook Lee", "Kimin Lee" ], "github": "https://github.com/THUDM/ImageReward/blob/main/data/test.json", "project_page": "", "abstract": "Learning from human feedback has been shown to improve text-to-image models. These techniques first learn a reward function that captures what humans care about in the task and then improve the models based on the learned reward function. Even though relatively simple approaches (e.g., rejection sampling based on reward scores) have been investigated, fine-tuning text-to-image models with the reward function remains challenging. In this work, we propose using online reinforcement learning (RL) to fine-tune text-to-image models. We focus on diffusion models, defining the fine-tuning task as an RL problem, and updating the pre-trained text-to-image diffusion models using policy gradient to maximize the feedback-trained reward. Our approach, coined DPOK, integrates policy optimization with KL regularization. We conduct an analysis of KL regularization for both RL fine-tuning and supervised fine-tuning. In our experiments, we show that DPOK is generally superior to supervised fine-tuning with respect to both image-text alignment and image quality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUDM/ImageReward/blob/main/data/test.json", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.17126", "title": "Large Language Models as Tool Makers", "authors": [ "Tianle Cai", "Xuezhi Wang", "Tengyu Ma", "Xinyun Chen", "Denny Zhou" ], "github": "", "project_page": "", "abstract": "Recent research shows the potential of enhancing the problem-solving ability of large language models (LLMs) through the use of external tools. However, prior work along this line depends on the availability of existing tools. In this work, we take an initial step towards removing this dependency by proposing a closed-loop framework, referred to as LLMs As Tool Makers (LATM), where LLMs create their own reusable tools for problem-solving. Our approach consists of two key phases: 1) tool making: an LLM acts as the tool maker that crafts tools for given tasks, where a tool is implemented as a Python utility function. 2) tool using: an LLM acts as the tool user, which applies the tool built by the tool maker for problem-solving. The tool user can be either the same or a different LLM from the tool maker. Tool-making enables an LLM to continually generate tools that can be applied to different requests so that future requests can call the corresponding APIs when beneficial for solving the tasks. Furthermore, the division of labor among LLMs for tool-making and tool-using phases introduces the opportunity to achieve cost effectiveness without degrading the quality of generated tools and problem solutions. For example, recognizing that tool-making demands more sophisticated capabilities than tool-using, we can apply a powerful yet resource-intensive model as the tool maker, and a lightweight while cost-effective model as the tool user. We validate the effectiveness of our approach across a variety of complex reasoning tasks, including Big-Bench tasks. With GPT-4 as the tool maker and GPT-3.5 as the tool user, LATM can achieve performance that is on par with using GPT-4 for both tool making and tool using, while the inference cost is significantly reduced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-29", "arxiv_id": "2305.16867", "title": "Playing repeated games with Large Language Models", "authors": [ "Elif Akata", "Lion Schulz", "Julian Coda-Forno", "Seong Joon Oh", "Matthias Bethge", "Eric Schulz" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are transforming society and permeating into diverse applications. As a result, LLMs will frequently interact with us and other agents. It is, therefore, of great societal value to understand how LLMs behave in interactive social settings. Here, we propose to use behavioral game theory to study LLM's cooperation and coordination behavior. To do so, we let different LLMs (GPT-3, GPT-3.5, and GPT-4) play finitely repeated games with each other and with other, human-like strategies. Our results show that LLMs generally perform well in such tasks and also uncover persistent behavioral signatures. In a large set of two players-two strategies games, we find that LLMs are particularly good at games where valuing their own self-interest pays off, like the iterated Prisoner's Dilemma family. However, they behave sub-optimally in games that require coordination. We, therefore, further focus on two games from these distinct families. In the canonical iterated Prisoner's Dilemma, we find that GPT-4 acts particularly unforgivingly, always defecting after another agent has defected only once. In the Battle of the Sexes, we find that GPT-4 cannot match the behavior of the simple convention to alternate between options. We verify that these behavioral signatures are stable across robustness checks. Finally, we show how GPT-4's behavior can be modified by providing further information about the other player as well as by asking it to predict the other player's actions before making a choice. These results enrich our understanding of LLM's social behavior and pave the way for a behavioral game theory for machines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-30", "arxiv_id": "2305.18295", "title": "RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths", "authors": [ "Zeyue Xue", "Guanglu Song", "Qiushan Guo", "Boxiao Liu", "Zhuofan Zong", "Yu Liu", "Ping Luo" ], "github": "", "project_page": "", "abstract": "Text-to-image generation has recently witnessed remarkable achievements. We introduce a text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images, which accurately portray the text prompts, encompassing multiple nouns, adjectives, and verbs. This is achieved by stacking tens of mixture-of-experts (MoEs) layers, i.e., space-MoE and time-MoE layers, enabling billions of diffusion paths (routes) from the network input to the output. Each path intuitively functions as a \"painter\" for depicting a particular textual concept onto a specified image region at a diffusion timestep. Comprehensive experiments reveal that RAPHAEL outperforms recent cutting-edge models, such as Stable Diffusion, ERNIE-ViLG 2.0, DeepFloyd, and DALL-E 2, in terms of both image quality and aesthetic appeal. Firstly, RAPHAEL exhibits superior performance in switching images across diverse styles, such as Japanese comics, realism, cyberpunk, and ink illustration. Secondly, a single model with three billion parameters, trained on 1,000 A100 GPUs for two months, achieves a state-of-the-art zero-shot FID score of 6.61 on the COCO dataset. Furthermore, RAPHAEL significantly surpasses its counterparts in human evaluation on the ViLG-300 benchmark. We believe that RAPHAEL holds the potential to propel the frontiers of image generation research in both academia and industry, paving the way for future breakthroughs in this rapidly evolving field. More details can be found on a project webpage: https://raphael-painter.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-30", "arxiv_id": "2305.17216", "title": "Generating Images with Multimodal Language Models", "authors": [ "Jing Yu Koh", "Daniel Fried", "Ruslan Salakhutdinov" ], "github": "https://github.com/kohjingyu/gill", "project_page": "", "abstract": "We propose a method to fuse frozen text-only large language models (LLMs) with pre-trained image encoder and decoder models, by mapping between their embedding spaces. Our model demonstrates a wide suite of multimodal capabilities: image retrieval, novel image generation, and multimodal dialogue. Ours is the first approach capable of conditioning on arbitrarily interleaved image and text inputs to generate coherent image (and text) outputs. To achieve strong performance on image generation, we propose an efficient mapping network to ground the LLM to an off-the-shelf text-to-image generation model. This mapping network translates hidden representations of text into the embedding space of the visual models, enabling us to leverage the strong text representations of the LLM for visual outputs. Our approach outperforms baseline generation models on tasks with longer and more complex language. In addition to novel image generation, our model is also capable of image retrieval from a prespecified dataset, and decides whether to retrieve or generate at inference time. This is done with a learnt decision module which conditions on the hidden representations of the LLM. Our model exhibits a wider range of capabilities compared to prior multimodal language models. It can process image-and-text inputs, and produce retrieved images, generated images, and generated text -- outperforming non-LLM based generation models across several text-to-image tasks that measure context dependence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kohjingyu/gill", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-30", "arxiv_id": "2305.18274", "title": "Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors", "authors": [ "Paul S. Scotti", "Atmadeep Banerjee", "Jimmie Goode", "Stepan Shabalin", "Alex Nguyen", "Ethan Cohen", "Aidan J. Dempster", "Nathalie Verlinde", "Elad Yundler", "David Weisberg", "Kenneth A. Norman", "Tanishq Mathew Abraham" ], "github": "https://github.com/medarc-ai/fmri-reconstruction-nsd", "project_page": "", "abstract": "We present MindEye, a novel fMRI-to-image approach to retrieve and reconstruct viewed images from brain activity. Our model comprises two parallel submodules that are specialized for retrieval (using contrastive learning) and reconstruction (using a diffusion prior). MindEye can map fMRI brain activity to any high dimensional multimodal latent space, like CLIP image space, enabling image reconstruction using generative models that accept embeddings from this latent space. We comprehensively compare our approach with other existing methods, using both qualitative side-by-side comparisons and quantitative evaluations, and show that MindEye achieves state-of-the-art performance in both reconstruction and retrieval tasks. In particular, MindEye can retrieve the exact original image even among highly similar candidates indicating that its brain embeddings retain fine-grained image-specific information. This allows us to accurately retrieve images even from large-scale databases like LAION-5B. We demonstrate through ablations that MindEye's performance improvements over previous methods result from specialized submodules for retrieval and reconstruction, improved training techniques, and training models with orders of magnitude more parameters. Furthermore, we show that MindEye can better preserve low-level image features in the reconstructions by using img2img, with outputs from a separate autoencoder. All code is available on GitHub.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/medarc-ai/fmri-reconstruction-nsd", "github_api_added_by": "auto" }, { "date": "2023-05-30", "arxiv_id": "2305.17144", "title": "Ghost in the Minecraft: Generally Capable Agents for Open-World Enviroments via Large Language Models with Text-based Knowledge and Memory", "authors": [ "Xizhou Zhu", "Yuntao Chen", "Hao Tian", "Chenxin Tao", "Weijie Su", "Chenyu Yang", "Gao Huang", "Bin Li", "Lewei Lu", "Xiaogang Wang", "Yu Qiao", "Zhaoxiang Zhang", "Jifeng Dai" ], "github": "https://github.com/OpenGVLab/GITM", "project_page": "", "abstract": "The captivating realm of Minecraft has attracted substantial research interest in recent years, serving as a rich platform for developing intelligent agents capable of functioning in open-world environments. However, the current research landscape predominantly focuses on specific objectives, such as the popular \"ObtainDiamond\" task, and has not yet shown effective generalization to a broader spectrum of tasks. Furthermore, the current leading success rate for the \"ObtainDiamond\" task stands at around 20%, highlighting the limitations of Reinforcement Learning (RL) based controllers used in existing methods. To tackle these challenges, we introduce Ghost in the Minecraft (GITM), a novel framework integrates Large Language Models (LLMs) with text-based knowledge and memory, aiming to create Generally Capable Agents (GCAs) in Minecraft. These agents, equipped with the logic and common sense capabilities of LLMs, can skillfully navigate complex, sparse-reward environments with text-based interactions. We develop a set of structured actions and leverage LLMs to generate action plans for the agents to execute. The resulting LLM-based agent markedly surpasses previous methods, achieving a remarkable improvement of +47.5% in success rate on the \"ObtainDiamond\" task, demonstrating superior robustness compared to traditional RL-based controllers. Notably, our agent is the first to procure all items in the Minecraft Overworld technology tree, demonstrating its extensive capabilities. GITM does not need any GPU for training, but a single CPU node with 32 CPU cores is enough. This research shows the potential of LLMs in developing capable agents for handling long-horizon, complex tasks and adapting to uncertainties in open-world environments. See the project website at https://github.com/OpenGVLab/GITM.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/GITM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-30", "arxiv_id": "2305.17306", "title": "Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance", "authors": [ "Yao Fu", "Litu Ou", "Mingyu Chen", "Yuhao Wan", "Hao Peng", "Tushar Khot" ], "github": "https://github.com/franxyao/chain-of-thought-hub", "project_page": "", "abstract": "As large language models (LLMs) are continuously being developed, their evaluation becomes increasingly important yet challenging. This work proposes Chain-of-Thought Hub, an open-source evaluation suite on the multi-step reasoning capabilities of large language models. We are interested in this setting for two reasons: (1) from the behavior of GPT and PaLM model family, we observe that complex reasoning is likely to be a key differentiator between weaker and stronger LLMs; (2) we envisage large language models to become the next-generation computational platform and foster an ecosystem of LLM-based new applications, this naturally requires the foundation models to perform complex tasks that often involve the composition of linguistic and logical operations. Our approach is to compile a suite of challenging reasoning benchmarks to track the progress of LLMs. Our current results show that: (1) model scale clearly correlates with reasoning capabilities; (2) As of May 2023, Claude-v1.3 and PaLM-2 are the only two models that are comparable with GPT-4, while open-sourced models still lag behind; (3) LLaMA-65B performs closely to code-davinci-002, indicating that with successful further development such as reinforcement learning from human feedback (RLHF), it has great potential to be close to GPT-3.5-Turbo. Our results also suggest that for the open-source efforts to catch up, the community may focus more on building better base models and exploring RLHF.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/franxyao/chain-of-thought-hub", "github_api_added_by": "auto" }, { "date": "2023-05-30", "arxiv_id": "2305.17333", "title": "Fine-Tuning Language Models with Just Forward Passes", "authors": [ "Sadhika Malladi", "Tianyu Gao", "Eshaan Nichani", "Alex Damian", "Jason D. Lee", "Danqi Chen", "Sanjeev Arora" ], "github": "https://github.com/princeton-nlp/mezo", "project_page": "", "abstract": "Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but as LMs grow in size, backpropagation requires a prohibitively large amount of memory. Zeroth-order (ZO) methods can in principle estimate gradients using only two forward passes but are theorized to be catastrophically slow for optimizing large models. In this work, we propose a memory-efficient zerothorder optimizer (MeZO), adapting the classical ZO-SGD method to operate in-place, thereby fine-tuning LMs with the same memory footprint as inference. For example, with a single A100 80GB GPU, MeZO can train a 30-billion parameter model, whereas fine-tuning with backpropagation can train only a 2.7B LM with the same budget. We conduct comprehensive experiments across model types (masked and autoregressive LMs), model scales (up to 66B), and downstream tasks (classification, multiple-choice, and generation). Our results demonstrate that (1) MeZO significantly outperforms in-context learning and linear probing; (2) MeZO achieves comparable performance to fine-tuning with backpropagation across multiple tasks, with up to 12x memory reduction; (3) MeZO is compatible with both full-parameter and parameter-efficient tuning techniques such as LoRA and prefix tuning; (4) MeZO can effectively optimize non-differentiable objectives (e.g., maximizing accuracy or F1). We support our empirical findings with theoretical insights, highlighting how adequate pre-training and task prompts enable MeZO to fine-tune huge models, despite classical ZO analyses suggesting otherwise.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/princeton-nlp/mezo", "github_api_added_by": "auto" }, { "date": "2023-05-30", "arxiv_id": "2305.18264", "title": "Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising", "authors": [ "Fu-Yun Wang", "Wenshuo Chen", "Guanglu Song", "Han-Jia Ye", "Yu Liu", "Hongsheng Li" ], "github": "https://github.com/G-U-N/Gen-L-Video", "project_page": "", "abstract": "Leveraging large-scale image-text datasets and advancements in diffusion models, text-driven generative models have made remarkable strides in the field of image generation and editing. This study explores the potential of extending the text-driven ability to the generation and editing of multi-text conditioned long videos. Current methodologies for video generation and editing, while innovative, are often confined to extremely short videos (typically less than 24 frames) and are limited to a single text condition. These constraints significantly limit their applications given that real-world videos usually consist of multiple segments, each bearing different semantic information. To address this challenge, we introduce a novel paradigm dubbed as Gen-L-Video, capable of extending off-the-shelf short video diffusion models for generating and editing videos comprising hundreds of frames with diverse semantic segments without introducing additional training, all while preserving content consistency. We have implemented three mainstream text-driven video generation and editing methodologies and extended them to accommodate longer videos imbued with a variety of semantic segments with our proposed paradigm. Our experimental outcomes reveal that our approach significantly broadens the generative and editing capabilities of video diffusion models, offering new possibilities for future research and applications. The code is available at https://github.com/G-U-N/Gen-L-Video.", "github_manual": "", "github_regex": "https://github.com/G-U-N/Gen-L-Video", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-30", "arxiv_id": "2305.18247", "title": "TaleCrafter: Interactive Story Visualization with Multiple Characters", "authors": [ "Yuan Gong", "Youxin Pang", "Xiaodong Cun", "Menghan Xia", "Haoxin Chen", "Longyue Wang", "Yong Zhang", "Xintao Wang", "Ying Shan", "Yujiu Yang" ], "github": "https://github.com/videocrafter/talecrafter", "project_page": "", "abstract": "Accurate Story visualization requires several necessary elements, such as identity consistency across frames, the alignment between plain text and visual content, and a reasonable layout of objects in images. Most previous works endeavor to meet these requirements by fitting a text-to-image (T2I) model on a set of videos in the same style and with the same characters, e.g., the FlintstonesSV dataset. However, the learned T2I models typically struggle to adapt to new characters, scenes, and styles, and often lack the flexibility to revise the layout of the synthesized images. This paper proposes a system for generic interactive story visualization, capable of handling multiple novel characters and supporting the editing of layout and local structure. It is developed by leveraging the prior knowledge of large language and T2I models, trained on massive corpora. The system comprises four interconnected components: story-to-prompt generation (S2P), text-to-layout generation (T2L), controllable text-to-image generation (C-T2I), and image-to-video animation (I2V). First, the S2P module converts concise story information into detailed prompts required for subsequent stages. Next, T2L generates diverse and reasonable layouts based on the prompts, offering users the ability to adjust and refine the layout to their preference. The core component, C-T2I, enables the creation of images guided by layouts, sketches, and actor-specific identifiers to maintain consistency and detail across visualizations. Finally, I2V enriches the visualization process by animating the generated images. Extensive experiments and a user study are conducted to validate the effectiveness and flexibility of interactive editing of the proposed system.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/videocrafter/talecrafter", "github_api_added_by": "auto" }, { "date": "2023-05-30", "arxiv_id": "2305.18231", "title": "High-Fidelity Image Compression with Score-based Generative Models", "authors": [ "Emiel Hoogeboom", "Eirikur Agustsson", "Fabian Mentzer", "Luca Versari", "George Toderici", "Lucas Theis" ], "github": "", "project_page": "", "abstract": "Despite the tremendous success of diffusion generative models in text-to-image generation, replicating this success in the domain of image compression has proven difficult. In this paper, we demonstrate that diffusion can significantly improve perceptual quality at a given bit-rate, outperforming state-of-the-art approaches PO-ELIC and HiFiC as measured by FID score. This is achieved using a simple but theoretically motivated two-stage approach combining an autoencoder targeting MSE followed by a further score-based decoder. However, as we will show, implementation details matter and the optimal design decisions can differ greatly from typical text-to-image models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-30", "arxiv_id": "2305.18292", "title": "Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models", "authors": [ "Yuchao Gu", "Xintao Wang", "Jay Zhangjie Wu", "Yujun Shi", "Yunpeng Chen", "Zihan Fan", "Wuyou Xiao", "Rui Zhao", "Shuning Chang", "Weijia Wu", "Yixiao Ge", "Ying Shan", "Mike Zheng Shou" ], "github": "https://github.com/TencentARC/Mix-of-Show", "project_page": "", "abstract": "Public large-scale text-to-image diffusion models, such as Stable Diffusion, have gained significant attention from the community. These models can be easily customized for new concepts using low-rank adaptations (LoRAs). However, the utilization of multiple concept LoRAs to jointly support multiple customized concepts presents a challenge. We refer to this scenario as decentralized multi-concept customization, which involves single-client concept tuning and center-node concept fusion. In this paper, we propose a new framework called Mix-of-Show that addresses the challenges of decentralized multi-concept customization, including concept conflicts resulting from existing single-client LoRA tuning and identity loss during model fusion. Mix-of-Show adopts an embedding-decomposed LoRA (ED-LoRA) for single-client tuning and gradient fusion for the center node to preserve the in-domain essence of single concepts and support theoretically limitless concept fusion. Additionally, we introduce regionally controllable sampling, which extends spatially controllable sampling (e.g., ControlNet and T2I-Adaptor) to address attribute binding and missing object problems in multi-concept sampling. Extensive experiments demonstrate that Mix-of-Show is capable of composing multiple customized concepts with high fidelity, including characters, objects, and scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TencentARC/Mix-of-Show", "github_api_added_by": "auto" }, { "date": "2023-05-30", "arxiv_id": "2305.18286", "title": "Photoswap: Personalized Subject Swapping in Images", "authors": [ "Jing Gu", "Yilin Wang", "Nanxuan Zhao", "Tsu-Jui Fu", "Wei Xiong", "Qing Liu", "Zhifei Zhang", "He Zhang", "Jianming Zhang", "HyunJoon Jung", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "In an era where images and visual content dominate our digital landscape, the ability to manipulate and personalize these images has become a necessity. Envision seamlessly substituting a tabby cat lounging on a sunlit window sill in a photograph with your own playful puppy, all while preserving the original charm and composition of the image. We present Photoswap, a novel approach that enables this immersive image editing experience through personalized subject swapping in existing images. Photoswap first learns the visual concept of the subject from reference images and then swaps it into the target image using pre-trained diffusion models in a training-free manner. We establish that a well-conceptualized visual subject can be seamlessly transferred to any image with appropriate self-attention and cross-attention manipulation, maintaining the pose of the swapped subject and the overall coherence of the image. Comprehensive experiments underscore the efficacy and controllability of Photoswap in personalized subject swapping. Furthermore, Photoswap significantly outperforms baseline methods in human ratings across subject swapping, background preservation, and overall quality, revealing its vast application potential, from entertainment to professional editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-30", "arxiv_id": "2305.18259", "title": "GlyphControl: Glyph Conditional Control for Visual Text Generation", "authors": [ "Yukang Yang", "Dongnan Gui", "Yuhui Yuan", "Haisong Ding", "Han Hu", "Kai Chen" ], "github": "https://github.com/aigtext/glyphcontrol-release", "project_page": "", "abstract": "Recently, there has been a growing interest in developing diffusion-based text-to-image generative models capable of generating coherent and well-formed visual text. In this paper, we propose a novel and efficient approach called GlyphControl to address this task. Unlike existing methods that rely on character-aware text encoders like ByT5 and require retraining of text-to-image models, our approach leverages additional glyph conditional information to enhance the performance of the off-the-shelf Stable-Diffusion model in generating accurate visual text. By incorporating glyph instructions, users can customize the content, location, and size of the generated text according to their specific requirements. To facilitate further research in visual text generation, we construct a training benchmark dataset called LAION-Glyph. We evaluate the effectiveness of our approach by measuring OCR-based metrics and CLIP scores of the generated visual text. Our empirical evaluations demonstrate that GlyphControl outperforms the recent DeepFloyd IF approach in terms of OCR accuracy and CLIP scores, highlighting the efficacy of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aigtext/glyphcontrol-release", "github_api_added_by": "auto" }, { "date": "2023-05-30", "arxiv_id": "2305.18098", "title": "BigTrans: Augmenting Large Language Models with Multilingual Translation Capability over 100 Languages", "authors": [ "Wen Yang", "Chong Li", "Jiajun Zhang", "Chengqing Zong" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) demonstrate promising translation performance among various natural languages. However, many LLMs especially the open-sourced ones, such as BLOOM and LLaMA, are English-dominant and support only dozens of natural languages, making the potential of LLMs on language translation less explored. In this work, we present BigTrans which adapts LLaMA that covers only 20 languages and enhances it with multilingual translation capability on more than 100 languages. BigTrans is built upon LLaMA-13B and it is optimized in three steps. First, we continue training LLaMA with massive Chinese monolingual data. Second, we continue training the model with a large-scale parallel dataset that covers 102 natural languages. Third, we instruct-tune the foundation model with multilingual translation instructions, leading to our BigTrans model. The preliminary experiments on multilingual translation show that BigTrans performs comparably with ChatGPT and Google Translate in many languages and even outperforms ChatGPT in 8 language pairs. We release the BigTrans model and hope it can advance the research progress.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-30", "arxiv_id": "2305.17493", "title": "Model Dementia: Generated Data Makes Models Forget", "authors": [ "Ilia Shumailov", "Zakhar Shumaylov", "Yiren Zhao", "Yarin Gal", "Nicolas Papernot", "Ross Anderson" ], "github": "", "project_page": "", "abstract": "Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images. In this paper we consider what the future might hold. What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We call this effect model dementia and show that it can occur in Variational Autoencoders (VAEs), Gaussian Mixture Models (GMMs) and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-30", "arxiv_id": "2305.17390", "title": "SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks", "authors": [ "Bill Yuchen Lin", "Yicheng Fu", "Karina Yang", "Prithviraj Ammanabrolu", "Faeze Brahman", "Shiyu Huang", "Chandra Bhagavatula", "Yejin Choi", "Xiang Ren" ], "github": "", "project_page": "", "abstract": "We introduce SwiftSage, a novel agent framework inspired by the dual-process theory of human cognition, designed to excel in action planning for complex interactive reasoning tasks. SwiftSage integrates the strengths of behavior cloning and prompting large language models (LLMs) to enhance task completion performance. The framework comprises two primary modules: the Swift module, representing fast and intuitive thinking, and the Sage module, emulating deliberate thought processes. The Swift module is a small encoder-decoder LM fine-tuned on the oracle agent's action trajectories, while the Sage module employs LLMs such as GPT-4 for subgoal planning and grounding. We develop a heuristic method to harmoniously integrate the two modules, resulting in a more efficient and robust problem-solving process. In 30 tasks from the ScienceWorld benchmark, SwiftSage significantly outperforms other methods such as SayCan, ReAct, and Reflexion, demonstrating its effectiveness in solving complex real-world tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-30", "arxiv_id": "2305.17359", "title": "DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text", "authors": [ "Xianjun Yang", "Wei Cheng", "Linda Petzold", "William Yang Wang", "Haifeng Chen" ], "github": "https://github.com/Xianjun-Yang/DNA-GPT", "project_page": "", "abstract": "Large language models (LLMs) have notably enhanced the fluency and diversity of machine-generated text. However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs. Conventional training-based methods have limitations in flexibility, particularly when adapting to new domains, and they often lack explanatory power. To address this gap, we propose a novel training-free detection strategy called Divergent N-Gram Analysis (DNA-GPT). Given a text, we first truncate it in the middle and then use only the preceding portion as input to the LLMs to regenerate the new remaining parts. By analyzing the differences between the original and new remaining parts through N-gram analysis in black-box or probability divergence in white-box, we can clearly illustrate significant discrepancies between machine-generated and human-written text. We conducted extensive experiments on the most advanced LLMs from OpenAI, including text-davinci-003, GPT-3.5-turbo, and GPT-4, as well as open-source models such as GPT-NeoX-20B and LLaMa-13B. Results show that our zero-shot approach exhibits state-of-the-art performance in distinguishing between human and GPT-generated text on four English and one German dataset, outperforming OpenAI's own classifier, which is trained on millions of text. Additionally, our methods provide reasonable explanations and evidence to support our claim, which is a unique feature of explainable detection. Our method is also robust under the revised text attack and can additionally solve model sourcing. Codes are available at https://github.com/Xianjun-Yang/DNA-GPT.", "github_manual": "", "github_regex": "https://github.com/Xianjun-Yang/DNA-GPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-31", "arxiv_id": "2305.19164", "title": "LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images", "authors": [ "Viraj Prabhu", "Sriram Yenamandra", "Prithvijit Chattopadhyay", "Judy Hoffman" ], "github": "https://github.com/virajprabhu/lance", "project_page": "", "abstract": "We propose an automated algorithm to stress-test a trained visual model by generating language-guided counterfactual test images (LANCE). Our method leverages recent progress in large language modeling and text-based image editing to augment an IID test set with a suite of diverse, realistic, and challenging test images without altering model weights. We benchmark the performance of a diverse set of pretrained models on our generated data and observe significant and consistent performance drops. We further analyze model sensitivity across different types of edits, and demonstrate its applicability at surfacing previously unknown class-level model biases in ImageNet.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/virajprabhu/lance", "github_api_added_by": "auto" }, { "date": "2023-05-31", "arxiv_id": "2305.19012", "title": "StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation", "authors": [ "Chi Zhang", "Yiwen Chen", "Yijun Fu", "Zhenglin Zhou", "Gang YU", "Billzb Wang", "Bin Fu", "Tao Chen", "Guosheng Lin", "Chunhua Shen" ], "github": "https://github.com/icoz69/styleavatar3d", "project_page": "", "abstract": "The recent advancements in image-text diffusion models have stimulated research interest in large-scale 3D generative models. Nevertheless, the limited availability of diverse 3D resources presents significant challenges to learning. In this paper, we present a novel method for generating high-quality, stylized 3D avatars that utilizes pre-trained image-text diffusion models for data generation and a Generative Adversarial Network (GAN)-based 3D generation network for training. Our method leverages the comprehensive priors of appearance and geometry offered by image-text diffusion models to generate multi-view images of avatars in various styles. During data generation, we employ poses extracted from existing 3D models to guide the generation of multi-view images. To address the misalignment between poses and images in data, we investigate view-specific prompts and develop a coarse-to-fine discriminator for GAN training. We also delve into attribute-related prompts to increase the diversity of the generated avatars. Additionally, we develop a latent diffusion model within the style space of StyleGAN to enable the generation of avatars based on image inputs. Our approach demonstrates superior performance over current state-of-the-art methods in terms of visual quality and diversity of the produced avatars.", "github_manual": "https://github.com/icoz69/styleavatar3d", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-31", "arxiv_id": "2305.18752", "title": "GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction", "authors": [ "Rui Yang", "Lin Song", "Yanwei Li", "Sijie Zhao", "Yixiao Ge", "Xiu Li", "Ying Shan" ], "github": "https://github.com/StevenGrove/GPT4Tools", "project_page": "", "abstract": "This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for tool usage through sophisticated prompt engineering. Nevertheless, these models typically rely on prohibitive computational costs and publicly inaccessible data. To address these challenges, we propose the GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and OPT, to use tools. It generates an instruction-following dataset by prompting an advanced teacher with various multi-modal contexts. By using the Low-Rank Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs to solve a range of visual problems, including visual comprehension and image generation. Moreover, we provide a benchmark to evaluate the ability of LLMs to use tools, which is performed in both zero-shot and fine-tuning ways. Extensive experiments demonstrate the effectiveness of our method on various language models, which not only significantly improves the accuracy of invoking seen tools, but also enables the zero-shot capacity for unseen tools. The code and demo are available at https://github.com/StevenGrove/GPT4Tools.", "github_manual": "", "github_regex": "https://github.com/StevenGrove/GPT4Tools", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-31", "arxiv_id": "2305.18802", "title": "LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus", "authors": [ "Yuma Koizumi", "Heiga Zen", "Shigeki Karita", "Yifan Ding", "Kohei Yatabe", "Nobuyuki Morioka", "Michiel Bacchiani", "Yu Zhang", "Wei Han", "Ankur Bapna" ], "github": "", "project_page": "", "abstract": "This paper introduces a new speech dataset called ``LibriTTS-R'' designed for text-to-speech (TTS) use. It is derived by applying speech restoration to the LibriTTS corpus, which consists of 585 hours of speech data at 24 kHz sampling rate from 2,456 speakers and the corresponding texts. The constituent samples of LibriTTS-R are identical to those of LibriTTS, with only the sound quality improved. Experimental results show that the LibriTTS-R ground-truth samples showed significantly improved sound quality compared to those in LibriTTS. In addition, neural end-to-end TTS trained with LibriTTS-R achieved speech naturalness on par with that of the ground-truth samples. The corpus is freely available for download from http://www.openslr.org/141/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-31", "arxiv_id": "2305.19245", "title": "AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation", "authors": [ "Thu Nguyen-Phuoc", "Gabriel Schwartz", "Yuting Ye", "Stephen Lombardi", "Lei Xiao" ], "github": "", "project_page": "", "abstract": "This paper presents a method that can quickly adapt dynamic 3D avatars to arbitrary text descriptions of novel styles. Among existing approaches for avatar stylization, direct optimization methods can produce excellent results for arbitrary styles but they are unpleasantly slow. Furthermore, they require redoing the optimization process from scratch for every new input. Fast approximation methods using feed-forward networks trained on a large dataset of style images can generate results for new inputs quickly, but tend not to generalize well to novel styles and fall short in quality. We therefore investigate a new approach, AlteredAvatar, that combines those two approaches using the meta-learning framework. In the inner loop, the model learns to optimize to match a single target style well; while in the outer loop, the model learns to stylize efficiently across many styles. After training, AlteredAvatar learns an initialization that can quickly adapt within a small number of update steps to a novel style, which can be given using texts, a reference image, or a combination of both. We show that AlteredAvatar can achieve a good balance between speed, flexibility and quality, while maintaining consistency across a wide range of novel views and facial expressions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-31", "arxiv_id": "2305.18415", "title": "Geometric Algebra Transformers", "authors": [ "Johann Brehmer", "Pim de Haan", "S\u00f6nke Behrends", "Taco Cohen" ], "github": "", "project_page": "", "abstract": "Problems involving geometric data arise in a variety of fields, including computer vision, robotics, chemistry, and physics. Such data can take numerous forms, such as points, direction vectors, planes, or transformations, but to date there is no single architecture that can be applied to such a wide variety of geometric types while respecting their symmetries. In this paper we introduce the Geometric Algebra Transformer (GATr), a general-purpose architecture for geometric data. GATr represents inputs, outputs, and hidden states in the projective geometric algebra, which offers an efficient 16-dimensional vector space representation of common geometric objects as well as operators acting on them. GATr is equivariant with respect to E(3), the symmetry group of 3D Euclidean space. As a transformer, GATr is scalable, expressive, and versatile. In experiments with n-body modeling and robotic planning, GATr shows strong improvements over non-geometric baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-31", "arxiv_id": "2305.19234", "title": "Grammar Prompting for Domain-Specific Language Generation with Large Language Models", "authors": [ "Bailin Wang", "Zi Wang", "Xuezhi Wang", "Yuan Cao", "Rif A. Saurous", "Yoon Kim" ], "github": "https://github.com/berlino/grammar-prompting", "project_page": "", "abstract": "Large language models (LLMs) can learn to perform a wide range of natural language tasks from just a handful of in-context examples. However, for generating strings from highly structured languages (e.g., semantic parsing to complex domain-specific languages), it is challenging for the LLM to generalize from just a few exemplars. We explore grammar prompting as a simple approach for enabling LLMs to use external knowledge and domain-specific constraints, expressed through a grammar expressed in Backus--Naur Form (BNF), during in-context learning. Grammar prompting augments each demonstration example with a specialized grammar that is minimally sufficient for generating the particular output example, where the specialized grammar is a subset of the full DSL grammar. For inference, the LLM first predicts a BNF grammar given a test input, and then generates the output according to the rules of the grammar. Experiments demonstrate that grammar prompting can enable LLMs to perform competitively on a diverse set of DSL generation tasks, including semantic parsing (SMCalFlow, Overnight, GeoQuery), PDDL planning, and even molecule generation (SMILES).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/berlino/grammar-prompting", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-31", "arxiv_id": "2305.18654", "title": "Faith and Fate: Limits of Transformers on Compositionality", "authors": [ "Nouha Dziri", "Ximing Lu", "Melanie Sclar", "Xiang Lorraine Li", "Liwei Jian", "Bill Yuchen Lin", "Peter West", "Chandra Bhagavatula", "Ronan Le Bras", "Jena D. Hwang", "Soumya Sanyal", "Sean Welleck", "Xiang Ren", "Allyson Ettinger", "Zaid Harchaoui", "Yejin Choi" ], "github": "https://github.com/nouhadziri/faith-and-fate", "project_page": "", "abstract": "Transformer large language models (LLMs) have sparked admiration for their exceptional performance on tasks that demand intricate multi-step reasoning. Yet, these models simultaneously show failures on surprisingly trivial problems. This begs the question: Are these errors incidental, or do they signal more substantial limitations? In an attempt to demystify Transformers, we investigate the limits of these models across three representative compositional tasks -- multi-digit multiplication, logic grid puzzles, and a classic dynamic programming problem. These tasks require breaking problems down into sub-steps and synthesizing these steps into a precise answer. We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures. Our empirical findings suggest that Transformers solve compositional tasks by reducing multi-step compositional reasoning into linearized subgraph matching, without necessarily developing systematic problem-solving skills. To round off our empirical study, we provide theoretical arguments on abstract multi-step reasoning problems that highlight how Transformers' performance will rapidly decay with increased task complexity.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nouhadziri/faith-and-fate", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-31", "arxiv_id": "2305.18565", "title": "PaLI-X: On Scaling up a Multilingual Vision and Language Model", "authors": [ "Xi Chen", "Josip Djolonga", "Piotr Padlewski", "Basil Mustafa", "Soravit Changpinyo", "Jialin Wu", "Carlos Riquelme Ruiz", "Sebastian Goodman", "Xiao Wang", "Yi Tay", "Siamak Shakeri", "Mostafa Dehghani", "Daniel Salz", "Mario Lucic", "Michael Tschannen", "Arsha Nagrani", "Hexiang Hu", "Mandar Joshi", "Bo Pang", "Ceslee Montgomery", "Paulina Pietrzyk", "Marvin Ritter", "AJ Piergiovanni", "Matthias Minderer", "Filip Pavetic", "Austin Waters", "Gang Li", "Ibrahim Alabdulmohsin", "Lucas Beyer", "Julien Amelot", "Kenton Lee", "Andreas Peter Steiner", "Yang Li", "Daniel Keysers", "Anurag Arnab", "Yuanzhong Xu", "Keran Rong", "Alexander Kolesnikov", "Mojtaba Seyedhosseini", "Anelia Angelova", "Xiaohua Zhai", "Neil Houlsby", "Radu Soricut" ], "github": "", "project_page": "", "abstract": "We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of varied and complex tasks, including multiple image-based captioning and question-answering tasks, image-based document understanding and few-shot (in-context) learning, as well as object detection, video question answering, and video captioning. PaLI-X advances the state-of-the-art on most vision-and-language benchmarks considered (25+ of them). Finally, we observe emerging capabilities, such as complex counting and multilingual object detection, tasks that are not explicitly in the training mix.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-31", "arxiv_id": "2305.18373", "title": "KAFA: Rethinking Image Ad Understanding with Knowledge-Augmented Feature Adaptation of Vision-Language Models", "authors": [ "Zhiwei Jia", "Pradyumna Narayana", "Arjun R. Akula", "Garima Pruthi", "Hao Su", "Sugato Basu", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "Image ad understanding is a crucial task with wide real-world applications. Although highly challenging with the involvement of diverse atypical scenes, real-world entities, and reasoning over scene-texts, how to interpret image ads is relatively under-explored, especially in the era of foundational vision-language models (VLMs) featuring impressive generalizability and adaptability. In this paper, we perform the first empirical study of image ad understanding through the lens of pre-trained VLMs. We benchmark and reveal practical challenges in adapting these VLMs to image ad understanding. We propose a simple feature adaptation strategy to effectively fuse multimodal information for image ads and further empower it with knowledge of real-world entities. We hope our study draws more attention to image ad understanding which is broadly relevant to the advertising industry.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-31", "arxiv_id": "2305.18766", "title": "HiFA: High-fidelity Text-to-3D with Advanced Diffusion Guidance", "authors": [ "Joseph Zhu", "Peiye Zhuang" ], "github": "https://github.com/JunzheJosephZhu/HiFA", "project_page": "", "abstract": "Automatic text-to-3D synthesis has achieved remarkable advancements through the optimization of 3D models. Existing methods commonly rely on pre-trained text-to-image generative models, such as diffusion models, providing scores for 2D renderings of Neural Radiance Fields (NeRFs) and being utilized for optimizing NeRFs. However, these methods often encounter artifacts and inconsistencies across multiple views due to their limited understanding of 3D geometry. To address these limitations, we propose a reformulation of the optimization loss using the diffusion prior. Furthermore, we introduce a novel training approach that unlocks the potential of the diffusion prior. To improve 3D geometry representation, we apply auxiliary depth supervision for NeRF-rendered images and regularize the density field of NeRFs. Extensive experiments demonstrate the superiority of our method over prior works, resulting in advanced photo-realism and improved multi-view consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JunzheJosephZhu/HiFA", "github_api_added_by": "auto" }, { "date": "2023-05-31", "arxiv_id": "2305.18474", "title": "Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation", "authors": [ "Jiawei Huang", "Yi Ren", "Rongjie Huang", "Dongchao Yang", "Zhenhui Ye", "Chen Zhang", "Jinglin Liu", "Xiang Yin", "Zejun Ma", "Zhou Zhao" ], "github": "https://github.com/bytedance/make-an-audio-2", "project_page": "", "abstract": "Large diffusion models have been successful in text-to-audio (T2A) synthesis tasks, but they often suffer from common issues such as semantic misalignment and poor temporal consistency due to limited natural language understanding and data scarcity. Additionally, 2D spatial structures widely used in T2A works lead to unsatisfactory audio quality when generating variable-length audio samples since they do not adequately prioritize temporal information. To address these challenges, we propose Make-an-Audio 2, a latent diffusion-based T2A method that builds on the success of Make-an-Audio. Our approach includes several techniques to improve semantic alignment and temporal consistency: Firstly, we use pre-trained large language models (LLMs) to parse the text into structured pairs for better temporal information capture. We also introduce another structured-text encoder to aid in learning semantic alignment during the diffusion denoising process. To improve the performance of variable length generation and enhance the temporal information extraction, we design a feed-forward Transformer-based diffusion denoiser. Finally, we use LLMs to augment and transform a large amount of audio-label data into audio-text datasets to alleviate the problem of scarcity of temporal data. Extensive experiments show that our method outperforms baseline models in both objective and subjective metrics, and achieves significant gains in temporal information understanding, semantic consistency, and sound quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bytedance/make-an-audio-2", "github_api_added_by": "auto" }, { "date": "2023-05-31", "arxiv_id": "2305.19066", "title": "Nested Diffusion Processes for Anytime Image Generation", "authors": [ "Noam Elata", "Bahjat Kawar", "Tomer Michaeli", "Michael Elad" ], "github": "https://github.com/noamelata/nesteddiffusion", "project_page": "", "abstract": "Diffusion models are the current state-of-the-art in image generation, synthesizing high-quality images by breaking down the generation process into many fine-grained denoising steps. Despite their good performance, diffusion models are computationally expensive, requiring many neural function evaluations (NFEs). In this work, we propose an anytime diffusion-based method that can generate viable images when stopped at arbitrary times before completion. Using existing pretrained diffusion models, we show that the generation scheme can be recomposed as two nested diffusion processes, enabling fast iterative refinement of a generated image. We use this Nested Diffusion approach to peek into the generation process and enable flexible scheduling based on the instantaneous preference of the user. In experiments on ImageNet and Stable Diffusion-based text-to-image generation, we show, both qualitatively and quantitatively, that our method's intermediate generation quality greatly exceeds that of the original diffusion model, while the final slow generation result remains comparable.", "github_manual": "https://github.com/noamelata/nesteddiffusion", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-31", "arxiv_id": "2305.18729", "title": "Real-World Image Variation by Aligning Diffusion Inversion Chain", "authors": [ "Yuechen Zhang", "Jinbo Xing", "Eric Lo", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/rival", "project_page": "", "abstract": "Recent diffusion model advancements have enabled high-fidelity images to be generated using text prompts. However, a domain gap exists between generated images and real-world images, which poses a challenge in generating high-quality variations of real-world images. Our investigation uncovers that this domain gap originates from a latents' distribution gap in different diffusion processes. To address this issue, we propose a novel inference pipeline called Real-world Image Variation by ALignment (RIVAL) that utilizes diffusion models to generate image variations from a single image exemplar. Our pipeline enhances the generation quality of image variations by aligning the image generation process to the source image's inversion chain. Specifically, we demonstrate that step-wise latent distribution alignment is essential for generating high-quality variations. To attain this, we design a cross-image self-attention injection for feature interaction and a step-wise distribution normalization to align the latent features. Incorporating these alignment processes into a diffusion model allows RIVAL to generate high-quality image variations without further parameter optimization. Our experimental results demonstrate that our proposed approach outperforms existing methods with respect to semantic-condition similarity and perceptual quality. Furthermore, this generalized inference pipeline can be easily applied to other diffusion-based generation tasks, such as image-conditioned text-to-image generation and example-based image inpainting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dvlab-research/rival", "github_api_added_by": "auto" }, { "date": "2023-05-31", "arxiv_id": "2305.18583", "title": "Controllable Text-to-Image Generation with GPT-4", "authors": [ "Tianjun Zhang", "Yi Zhang", "Vibhav Vineet", "Neel Joshi", "Xin Wang" ], "github": "", "project_page": "", "abstract": "Current text-to-image generation models often struggle to follow textual instructions, especially the ones requiring spatial reasoning. On the other hand, Large Language Models (LLMs), such as GPT-4, have shown remarkable precision in generating code snippets for sketching out text inputs graphically, e.g., via TikZ. In this work, we introduce Control-GPT to guide the diffusion-based text-to-image pipelines with programmatic sketches generated by GPT-4, enhancing their abilities for instruction following. Control-GPT works by querying GPT-4 to write TikZ code, and the generated sketches are used as references alongside the text instructions for diffusion models (e.g., ControlNet) to generate photo-realistic images. One major challenge to training our pipeline is the lack of a dataset containing aligned text, images, and sketches. We address the issue by converting instance masks in existing datasets into polygons to mimic the sketches used at test time. As a result, Control-GPT greatly boosts the controllability of image generation. It establishes a new state-of-art on the spatial arrangement and object positioning generation and enhances users' control of object positions, sizes, etc., nearly doubling the accuracy of prior models. Our work, as a first attempt, shows the potential for employing LLMs to enhance the performance in computer vision tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-05-31", "arxiv_id": "2305.18365", "title": "What indeed can GPT models do in chemistry? A comprehensive benchmark on eight tasks", "authors": [ "Taicheng Guo", "Kehan Guo", "Bozhao nan", "Zhengwen Liang", "Zhichun Guo", "Nitesh V. Chawla", "Olaf Wiest", "Xiangliang Zhang" ], "github": "https://github.com/chemfoundationmodels/chemllmbench", "project_page": "", "abstract": "Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been rapidly applied in various kinds of areas such as science, finance and software engineering. However, the capability of LLMs to advance the field of chemistry remains unclear. In this paper,we establish a comprehensive benchmark containing 8 practical chemistry tasks, including 1) name prediction, 2) property prediction, 3) yield prediction, 4) reaction prediction, 5) retrosynthesis (prediction of reactants from products), 6)text-based molecule design, 7) molecule captioning, and 8) reagent selection. Our analysis draws on widely recognized datasets including BBBP, Tox21, PubChem, USPTO, and ChEBI, facilitating a broad exploration of the capacities of LLMs within the context of practical chemistry. Three GPT models (GPT-4, GPT-3.5,and Davinci-003) are evaluated for each chemistry task in zero-shot and few-shot in-context learning settings with carefully selected demonstration examples and specially crafted prompts. The key results of our investigation are 1) GPT-4 outperforms the other two models among the three evaluated; 2) GPT models exhibit less competitive performance in tasks demanding precise understanding of molecular SMILES representation, such as reaction prediction and retrosynthesis;3) GPT models demonstrate strong capabilities in text-related explanation tasks such as molecule captioning; and 4) GPT models exhibit comparable or better performance to classical machine learning models when applied to chemical problems that can be transformed into classification or ranking tasks, such as property prediction, and yield prediction.", "github_manual": "https://github.com/chemfoundationmodels/chemllmbench", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-01", "arxiv_id": "2305.20030", "title": "Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust", "authors": [ "Yuxin Wen", "John Kirchenbauer", "Jonas Geiping", "Tom Goldstein" ], "github": "https://github.com/YuxinWenRick/tree-ring-watermark", "project_page": "", "abstract": "Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. In this paper, we introduce a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs. Unlike existing methods that perform post-hoc modifications to images after sampling, Tree-Ring Watermarking subtly influences the entire sampling process, resulting in a model fingerprint that is invisible to humans. The watermark embeds a pattern into the initial noise vector used for sampling. These patterns are structured in Fourier space so that they are invariant to convolutions, crops, dilations, flips, and rotations. After image generation, the watermark signal is detected by inverting the diffusion process to retrieve the noise vector, which is then checked for the embedded signal. We demonstrate that this technique can be easily applied to arbitrary diffusion models, including text-conditioned Stable Diffusion, as a plug-in with negligible loss in FID. Our watermark is semantically hidden in the image space and is far more robust than watermarking alternatives that are currently deployed. Code is available at github.com/YuxinWenRick/tree-ring-watermark.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YuxinWenRick/tree-ring-watermark", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-01", "arxiv_id": "2305.20081", "title": "Efficient Diffusion Policies for Offline Reinforcement Learning", "authors": [ "Bingyi Kang", "Xiao Ma", "Chao Du", "Tianyu Pang", "Shuicheng Yan" ], "github": "https://github.com/sail-sg/edp", "project_page": "", "abstract": "Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL significantly boosts the performance of offline RL by representing a policy with a diffusion model, whose success relies on a parametrized Markov Chain with hundreds of steps for sampling. However, Diffusion-QL suffers from two critical limitations. 1) It is computationally inefficient to forward and backward through the whole Markov chain during training. 2) It is incompatible with maximum likelihood-based RL algorithms (e.g., policy gradient methods) as the likelihood of diffusion models is intractable. Therefore, we propose efficient diffusion policy (EDP) to overcome these two challenges. EDP approximately constructs actions from corrupted ones at training to avoid running the sampling chain. We conduct extensive experiments on the D4RL benchmark. The results show that EDP can reduce the diffusion policy training time from 5 days to 5 hours on gym-locomotion tasks. Moreover, we show that EDP is compatible with various offline RL algorithms (TD3, CRR, and IQL) and achieves new state-of-the-art on D4RL by large margins over previous methods. Our code is available at https://github.com/sail-sg/edp.", "github_manual": "", "github_regex": "https://github.com/sail-sg/edp", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-01", "arxiv_id": "2305.20091", "title": "Humans in 4D: Reconstructing and Tracking Humans with Transformers", "authors": [ "Shubham Goel", "Georgios Pavlakos", "Jathushan Rajasegaran", "Angjoo Kanazawa", "Jitendra Malik" ], "github": "https://github.com/shubham-goel/4D-Humans", "project_page": "", "abstract": "We present an approach to reconstruct humans and track them over time. At the core of our approach, we propose a fully \"transformerized\" version of a network for human mesh recovery. This network, HMR 2.0, advances the state of the art and shows the capability to analyze unusual poses that have in the past been difficult to reconstruct from single images. To analyze video, we use 3D reconstructions from HMR 2.0 as input to a tracking system that operates in 3D. This enables us to deal with multiple people and maintain identities through occlusion events. Our complete approach, 4DHumans, achieves state-of-the-art results for tracking people from monocular video. Furthermore, we demonstrate the effectiveness of HMR 2.0 on the downstream task of action recognition, achieving significant improvements over previous pose-based action recognition approaches. Our code and models are available on the project website: https://shubham-goel.github.io/4dhumans/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shubham-goel/4D-Humans", "github_api_added_by": "auto" }, { "date": "2023-06-01", "arxiv_id": "2305.19452", "title": "Bigger, Better, Faster: Human-level Atari with human-level efficiency", "authors": [ "Max Schwarzer", "Johan Obando-Ceron", "Aaron Courville", "Marc Bellemare", "Rishabh Agarwal", "Pablo Samuel Castro" ], "github": "https://github.com/google-research/google-research/tree/master/bigger_better_faster", "project_page": "", "abstract": "We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on scaling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide insights for future work. We end with a discussion about updating the goalposts for sample-efficient RL research on the ALE. We make our code and data publicly available at https://github.com/google-research/google-research/tree/master/bigger_better_faster.", "github_manual": "", "github_regex": "https://github.com/google-research/google-research/tree/master/bigger_better_faster", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-01", "arxiv_id": "2305.20088", "title": "Improving CLIP Training with Language Rewrites", "authors": [ "Lijie Fan", "Dilip Krishnan", "Phillip Isola", "Dina Katabi", "Yonglong Tian" ], "github": "https://github.com/LijieFan/LaCLIP", "project_page": "", "abstract": "Contrastive Language-Image Pre-training (CLIP) stands as one of the most effective and scalable methods for training transferable vision models using paired image and text data. CLIP models are trained using contrastive loss, which typically relies on data augmentations to prevent overfitting and shortcuts. However, in the CLIP training paradigm, data augmentations are exclusively applied to image inputs, while language inputs remain unchanged throughout the entire training process, limiting the exposure of diverse texts to the same image. In this paper, we introduce Language augmented CLIP (LaCLIP), a simple yet highly effective approach to enhance CLIP training through language rewrites. Leveraging the in-context learning capability of large language models, we rewrite the text descriptions associated with each image. These rewritten texts exhibit diversity in sentence structure and vocabulary while preserving the original key concepts and meanings. During training, LaCLIP randomly selects either the original texts or the rewritten versions as text augmentations for each image. Extensive experiments on CC3M, CC12M, RedCaps and LAION-400M datasets show that CLIP pre-training with language rewrites significantly improves the transfer performance without computation or memory overhead during training. Specifically for ImageNet zero-shot accuracy, LaCLIP outperforms CLIP by 8.2% on CC12M and 2.4% on LAION-400M. Code is available at https://github.com/LijieFan/LaCLIP.", "github_manual": "", "github_regex": "https://github.com/LijieFan/LaCLIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-01", "arxiv_id": "2305.20010", "title": "Human or Not? A Gamified Approach to the Turing Test", "authors": [ "Daniel Jannai", "Amos Meron", "Barak Lenz", "Yoav Levine", "Yoav Shoham" ], "github": "", "project_page": "", "abstract": "We present \"Human or Not?\", an online game inspired by the Turing test, that measures the capability of AI chatbots to mimic humans in dialog, and of humans to tell bots from other humans. Over the course of a month, the game was played by over 1.5 million users who engaged in anonymous two-minute chat sessions with either another human or an AI language model which was prompted to behave like humans. The task of the players was to correctly guess whether they spoke to a person or to an AI. This largest scale Turing-style test conducted to date revealed some interesting facts. For example, overall users guessed the identity of their partners correctly in only 68% of the games. In the subset of the games in which users faced an AI bot, users had even lower correct guess rates of 60% (that is, not much higher than chance). This white paper details the development, deployment, and results of this unique experiment. While this experiment calls for many extensions and refinements, these findings already begin to shed light on the inevitable near future which will commingle humans and AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-01", "arxiv_id": "2305.19835", "title": "Deliberate then Generate: Enhanced Prompting Framework for Text Generation", "authors": [ "Bei Li", "Rui Wang", "Junliang Guo", "Kaitao Song", "Xu Tan", "Hany Hassan", "Arul Menezes", "Tong Xiao", "Jiang Bian", "JingBo Zhu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable success across a wide range of natural language generation tasks, where proper prompt designs make great impacts. While existing prompting methods are normally restricted to providing correct information, in this paper, we encourage the model to deliberate by proposing a novel Deliberate then Generate (DTG) prompting framework, which consists of error detection instructions and candidates that may contain errors. DTG is a simple yet effective technique that can be applied to various text generation tasks with minimal modifications. We conduct extensive experiments on 20+ datasets across 7 text generation tasks, including summarization, translation, dialogue, and more. We show that DTG consistently outperforms existing prompting methods and achieves state-of-the-art performance on multiple text generation tasks. We also provide in-depth analyses to reveal the underlying mechanisms of DTG, which may inspire future research on prompting for LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-01", "arxiv_id": "2305.19472", "title": "PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning", "authors": [ "Faeze Brahman", "Chandra Bhagavatula", "Valentina Pyatkin", "Jena D. Hwang", "Xiang Lorraine Li", "Hirona J. Arai", "Soumya Sanyal", "Keisuke Sakaguchi", "Xiang Ren", "Yejin Choi" ], "github": "https://github.com/allenai/plasma", "project_page": "", "abstract": "Procedural planning, which entails decomposing a high-level goal into a sequence of temporally ordered steps, is an important yet intricate task for machines. It involves integrating common-sense knowledge to reason about complex contextualized situations that are often counterfactual, e.g. \"scheduling a doctor's appointment without a phone\". While current approaches show encouraging results using large language models (LLMs), they are hindered by drawbacks such as costly API calls and reproducibility issues. In this paper, we advocate planning using smaller language models. We present PlaSma, a novel two-pronged approach to endow small language models with procedural knowledge and (counterfactual) planning capabilities. More concretely, we develop symbolic procedural knowledge distillation to enhance the implicit knowledge in small language models and an inference-time algorithm to facilitate more structured and accurate reasoning. In addition, we introduce a novel task, Counterfactual Planning, that requires a revision of a plan to cope with a counterfactual situation. In both the original and counterfactual setting, we show that orders-of-magnitude smaller models (770M-11B parameters) can compete and often surpass their larger teacher models' capabilities.", "github_manual": "https://github.com/allenai/plasma", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-01", "arxiv_id": "2305.19370", "title": "Blockwise Parallel Transformer for Long Context Large Models", "authors": [ "Hao Liu", "Pieter Abbeel" ], "github": "", "project_page": "", "abstract": "Transformers have emerged as the cornerstone of state-of-the-art natural language processing models, showcasing exceptional performance across a wide range of AI applications. However, the memory demands posed by the self-attention mechanism and the large feedforward network in Transformers limit their ability to handle long sequences, thereby creating challenges for tasks involving multiple long sequences or long-term dependencies. We present a distinct approach, Blockwise Parallel Transformer (BPT), that leverages blockwise computation of self-attention and feedforward network fusion to minimize memory costs. By processing longer input sequences while maintaining memory efficiency, BPT enables training sequences up to 32 times longer than vanilla Transformers and 2 to 4 times longer than previous memory-efficient methods. Extensive experiments on language modeling and reinforcement learning tasks demonstrate the effectiveness of BPT in reducing memory requirements and improving performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-01", "arxiv_id": "2305.20086", "title": "Understanding and Mitigating Copying in Diffusion Models", "authors": [ "Gowthami Somepalli", "Vasu Singla", "Micah Goldblum", "Jonas Geiping", "Tom Goldstein" ], "github": "https://github.com/somepago/dcr", "project_page": "", "abstract": "Images generated by diffusion models like Stable Diffusion are increasingly widespread. Recent works and even lawsuits have shown that these models are prone to replicating their training data, unbeknownst to the user. In this paper, we first analyze this memorization problem in text-to-image diffusion models. While it is widely believed that duplicated images in the training set are responsible for content replication at inference time, we observe that the text conditioning of the model plays a similarly important role. In fact, we see in our experiments that data replication often does not happen for unconditional models, while it is common in the text-conditional case. Motivated by our findings, we then propose several techniques for reducing data replication at both training and inference time by randomizing and augmenting image captions in the training set.", "github_manual": "https://github.com/somepago/dcr", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-01", "arxiv_id": "2305.20082", "title": "Control4D: Dynamic Portrait Editing by Learning 4D GAN from 2D Diffusion-based Editor", "authors": [ "Ruizhi Shao", "Jingxiang Sun", "Cheng Peng", "Zerong Zheng", "Boyao Zhou", "Hongwen Zhang", "Yebin Liu" ], "github": "", "project_page": "", "abstract": "Recent years have witnessed considerable achievements in editing images with text instructions. When applying these editors to dynamic scene editing, the new-style scene tends to be temporally inconsistent due to the frame-by-frame nature of these 2D editors. To tackle this issue, we propose Control4D, a novel approach for high-fidelity and temporally consistent 4D portrait editing. Control4D is built upon an efficient 4D representation with a 2D diffusion-based editor. Instead of using direct supervisions from the editor, our method learns a 4D GAN from it and avoids the inconsistent supervision signals. Specifically, we employ a discriminator to learn the generation distribution based on the edited images and then update the generator with the discrimination signals. For more stable training, multi-level information is extracted from the edited images and used to facilitate the learning of the generator. Experimental results show that Control4D surpasses previous approaches and achieves more photo-realistic and consistent 4D editing performances. The link to our project website is https://control4darxiv.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00980", "title": "SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds", "authors": [ "Yanyu Li", "Huan Wang", "Qing Jin", "Ju Hu", "Pavlo Chemerys", "Yun Fu", "Yanzhi Wang", "Sergey Tulyakov", "Jian Ren" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion models can create stunning images from natural language descriptions that rival the work of professional artists and photographers. However, these models are large, with complex network architectures and tens of denoising iterations, making them computationally expensive and slow to run. As a result, high-end GPUs and cloud-based inference are required to run diffusion models at scale. This is costly and has privacy implications, especially when user data is sent to a third party. To overcome these challenges, we present a generic approach that, for the first time, unlocks running text-to-image diffusion models on mobile devices in less than 2 seconds. We achieve so by introducing efficient network architecture and improving step distillation. Specifically, we propose an efficient UNet by identifying the redundancy of the original model and reducing the computation of the image decoder via data distillation. Further, we enhance the step distillation by exploring training strategies and introducing regularization from classifier-free guidance. Our extensive experiments on MS-COCO show that our model with 8 denoising steps achieves better FID and CLIP scores than Stable Diffusion v1.5 with 50 steps. Our work democratizes content creation by bringing powerful text-to-image diffusion models to the hands of users.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00739", "title": "SQL-PaLM: Improved Large Language ModelAdaptation for Text-to-SQL", "authors": [ "Ruoxi Sun", "Sercan O Arik", "Hootan Nakhost", "Hanjun Dai", "Rajarishi Sinha", "Pengcheng Yin", "Tomas Pfister" ], "github": "", "project_page": "", "abstract": "One impressive emergent capability of large language models (LLMs) is generation of code, including Structured Query Language (SQL) for databases. For the task of converting natural language text to SQL queries, Text-to-SQL, adaptation of LLMs is of paramount importance, both in in-context learning and fine-tuning settings, depending on the amount of adaptation data used. In this paper, we propose an LLM-based Text-to-SQL model SQL-PaLM, leveraging on PaLM-2, that pushes the state-of-the-art in both settings. Few-shot SQL-PaLM is based on an execution-based self-consistency prompting approach designed for Text-to-SQL, and achieves 77.3% in test-suite accuracy on Spider, which to our best knowledge is the first to outperform previous state-of-the-art with fine-tuning by a significant margin, 4%. Furthermore, we demonstrate that the fine-tuned SQL-PALM outperforms it further by another 1%. Towards applying SQL-PaLM to real-world scenarios we further evaluate its robustness on other challenging variants of Spider and demonstrate the superior generalization capability of SQL-PaLM. In addition, via extensive case studies, we demonstrate the impressive intelligent capabilities and various success enablers of LLM-based Text-to-SQL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00378", "title": "Example-based Motion Synthesis via Generative Motion Matching", "authors": [ "Weiyu Li", "Xuelin Chen", "Peizhuo Li", "Olga Sorkine-Hornung", "Baoquan Chen" ], "github": "https://github.com/wyysf-98/GenMM", "project_page": "", "abstract": "We present GenMM, a generative model that \"mines\" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inherits the training-free nature and the superior quality of the well-known Motion Matching method. GenMM can synthesize a high-quality motion within a fraction of a second, even with highly complex and large skeletal structures. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly. Code and data for this paper are at https://wyysf-98.github.io/GenMM/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wyysf-98/GenMM", "github_api_added_by": "auto" }, { "date": "2023-06-02", "arxiv_id": "2306.00238", "title": "Bytes Are All You Need: Transformers Operating Directly On File Bytes", "authors": [ "Maxwell Horton", "Sachin Mehta", "Ali Farhadi", "Mohammad Rastegari" ], "github": "https://github.com/apple/ml-cvnets/tree/main/examples", "project_page": "", "abstract": "Modern deep learning approaches usually transform inputs into a modality-specific form. For example, the most common deep learning approach to image classification involves decoding image file bytes into an RGB tensor which is passed into a neural network. Instead, we investigate performing classification directly on file bytes, without the need for decoding files at inference time. Using file bytes as model inputs enables the development of models which can operate on multiple input modalities. Our model, ByteFormer, achieves an ImageNet Top-1 classification accuracy of 77.33% when training and testing directly on TIFF file bytes using a transformer backbone with configuration similar to DeiT-Ti (72.2% accuracy when operating on RGB images). Without modifications or hyperparameter tuning, ByteFormer achieves 95.42% classification accuracy when operating on WAV files from the Speech Commands v2 dataset (compared to state-of-the-art accuracy of 98.7%). Additionally, we demonstrate that ByteFormer has applications in privacy-preserving inference. ByteFormer is capable of performing inference on particular obfuscated input representations with no loss of accuracy. We also demonstrate ByteFormer's ability to perform inference with a hypothetical privacy-preserving camera which avoids forming full images by consistently masking 90% of pixel channels, while still achieving 71.35% accuracy on ImageNet. Our code will be made available at https://github.com/apple/ml-cvnets/tree/main/examples/byteformer.", "github_manual": "", "github_regex": "https://github.com/apple/ml-cvnets/tree/main/examples", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00983", "title": "StyleDrop: Text-to-Image Generation in Any Style", "authors": [ "Kihyuk Sohn", "Nataniel Ruiz", "Kimin Lee", "Daniel Castro Chin", "Irina Blok", "Huiwen Chang", "Jarred Barber", "Lu Jiang", "Glenn Entis", "Yuanzhen Li", "Yuan Hao", "Irfan Essa", "Michael Rubinstein", "Dilip Krishnan" ], "github": "", "project_page": "", "abstract": "Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language and out-of-distribution effects make it hard to synthesize image styles, that leverage a specific design pattern, texture or material. In this paper, we introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. More results are available at our project website: https://styledrop.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00890", "title": "LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day", "authors": [ "Chunyuan Li", "Cliff Wong", "Sheng Zhang", "Naoto Usuyama", "Haotian Liu", "Jianwei Yang", "Tristan Naumann", "Hoifung Poon", "Jianfeng Gao" ], "github": "https://github.com/microsoft/LLaVA-Med", "project_page": "", "abstract": "Conversational generative AI has demonstrated remarkable promise for empowering biomedical practitioners, but current investigations focus on unimodal text. Multimodal conversational AI has seen rapid progress by leveraging billions of image-text pairs from the public web, but such general-domain vision-language models still lack sophistication in understanding and conversing about biomedical images. In this paper, we propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images. The key idea is to leverage a large-scale, broad-coverage biomedical figure-caption dataset extracted from PubMed Central, use GPT-4 to self-instruct open-ended instruction-following data from the captions, and then fine-tune a large general-domain vision-language model using a novel curriculum learning method. Specifically, the model first learns to align biomedical vocabulary using the figure-caption pairs as is, then learns to master open-ended conversational semantics using GPT-4 generated instruction-following data, broadly mimicking how a layperson gradually acquires biomedical knowledge. This enables us to train a Large Language and Vision Assistant for BioMedicine (LLaVA-Med) in less than 15 hours (with eight A100s). LLaVA-Med exhibits excellent multimodal conversational capability and can follow open-ended instruction to assist with inquiries about a biomedical image. On three standard biomedical visual question answering datasets, LLaVA-Med outperforms previous supervised state-of-the-art on certain metrics. To facilitate biomedical multimodal research, we will release our instruction-following data and the LLaVA-Med model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/LLaVA-Med", "github_api_added_by": "auto" }, { "date": "2023-06-02", "arxiv_id": "2306.00637", "title": "Wuerstchen: Efficient Pretraining of Text-to-Image Models", "authors": [ "Pablo Pernias", "Dominic Rampas", "Marc Aubreville" ], "github": "", "project_page": "", "abstract": "We introduce Wuerstchen, a novel technique for text-to-image synthesis that unites competitive performance with unprecedented cost-effectiveness and ease of training on constrained hardware. Building on recent advancements in machine learning, our approach, which utilizes latent diffusion strategies at strong latent image compression rates, significantly reduces the computational burden, typically associated with state-of-the-art models, while preserving, if not enhancing, the quality of generated images. Wuerstchen achieves notable speed improvements at inference time, thereby rendering real-time applications more viable. One of the key advantages of our method lies in its modest training requirements of only 9,200 GPU hours, slashing the usual costs significantly without compromising the end performance. In a comparison against the state-of-the-art, we found the approach to yield strong competitiveness. This paper opens the door to a new line of research that prioritizes both performance and computational accessibility, hence democratizing the use of sophisticated AI technologies. Through Wuerstchen, we demonstrate a compelling stride forward in the realm of text-to-image synthesis, offering an innovative path to explore in future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00966", "title": "The Hidden Language of Diffusion Models", "authors": [ "Hila Chefer", "Oran Lang", "Mor Geva", "Volodymyr Polosukhin", "Assaf Shocher", "Michal Irani", "Inbar Mosseri", "Lior Wolf" ], "github": "https://github.com/hila-chefer/Conceptor", "project_page": "", "abstract": "Text-to-image diffusion models have demonstrated an unparalleled ability to generate high-quality, diverse images from a textual concept (e.g., \"a doctor\", \"love\"). However, the internal process of mapping text to a rich visual representation remains an enigma. In this work, we tackle the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a pseudo-token that is a sparse weighted combination of tokens from the model's vocabulary, with the objective of reconstructing the images generated for the given concept. Applied over the state-of-the-art Stable Diffusion model, this decomposition reveals non-trivial and surprising structures in the representations of concepts. For example, we find that some concepts such as \"a president\" or \"a composer\" are dominated by specific instances (e.g., \"Obama\", \"Biden\") and their interpolations. Other concepts, such as \"happiness\" combine associated terms that can be concrete (\"family\", \"laughter\") or abstract (\"friendship\", \"emotion\"). In addition to peering into the inner workings of Stable Diffusion, our method also enables applications such as single-image decomposition to tokens, bias detection and mitigation, and semantic image manipulation. Our code will be available at: https://hila-chefer.github.io/Conceptor/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hila-chefer/Conceptor", "github_api_added_by": "auto" }, { "date": "2023-06-02", "arxiv_id": "2306.00984", "title": "StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners", "authors": [ "Yonglong Tian", "Lijie Fan", "Phillip Isola", "Huiwen Chang", "Dilip Krishnan" ], "github": "https://github.com/google-research/syn-rep-learn", "project_page": "", "abstract": "We investigate the potential of learning visual representations using synthetic images generated by text-to-image models. This is a natural question in the light of the excellent performance of such models in generating high-quality images. We consider specifically the Stable Diffusion, one of the leading open source text-to-image models. We show that (1) when the generative model is configured with proper classifier-free guidance scale, training self-supervised methods on synthetic images can match or beat the real image counterpart; (2) by treating the multiple images generated from the same text prompt as positives for each other, we develop a multi-positive contrastive learning method, which we call StableRep. With solely synthetic images, the representations learned by StableRep surpass the performance of representations learned by SimCLR and CLIP using the same set of text prompts and corresponding real images, on large scale datasets. When we further add language supervision, StableRep trained with 20M synthetic images achieves better accuracy than CLIP trained with 50M real images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/google-research/syn-rep-learn", "github_api_added_by": "auto" }, { "date": "2023-06-02", "arxiv_id": "2306.00971", "title": "ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image Generation", "authors": [ "Shaozhe Hao", "Kai Han", "Shihao Zhao", "Kwan-Yee K. Wong" ], "github": "https://github.com/haoosz/vico", "project_page": "", "abstract": "Personalized text-to-image generation using diffusion models has recently been proposed and attracted lots of attention. Given a handful of images containing a novel concept (e.g., a unique toy), we aim to tune the generative model to capture fine visual details of the novel concept and generate photorealistic images following a text condition. We present a plug-in method, named ViCo, for fast and lightweight personalized generation. Specifically, we propose an image attention module to condition the diffusion process on the patch-wise visual semantics. We introduce an attention-based object mask that comes almost at no cost from the attention module. In addition, we design a simple regularization based on the intrinsic properties of text-image attention maps to alleviate the common overfitting degradation. Unlike many existing models, our method does not finetune any parameters of the original diffusion model. This allows more flexible and transferable model deployment. With only light parameter training (~6% of the diffusion U-Net), our method achieves comparable or even better performance than all state-of-the-art models both qualitatively and quantitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/haoosz/vico", "github_api_added_by": "auto" }, { "date": "2023-06-02", "arxiv_id": "2306.00956", "title": "The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects", "authors": [ "Ruohan Gao", "Yiming Dou", "Hao Li", "Tanmay Agarwal", "Jeannette Bohg", "Yunzhu Li", "Li Fei-Fei", "Jiajun Wu" ], "github": "", "project_page": "", "abstract": "We introduce the ObjectFolder Benchmark, a benchmark suite of 10 tasks for multisensory object-centric learning, centered around object recognition, reconstruction, and manipulation with sight, sound, and touch. We also introduce the ObjectFolder Real dataset, including the multisensory measurements for 100 real-world household objects, building upon a newly designed pipeline for collecting the 3D meshes, videos, impact sounds, and tactile readings of real-world objects. We conduct systematic benchmarking on both the 1,000 multisensory neural objects from ObjectFolder, and the real multisensory data from ObjectFolder Real. Our results demonstrate the importance of multisensory perception and reveal the respective roles of vision, audio, and touch for different object-centric learning tasks. By publicly releasing our dataset and benchmark suite, we hope to catalyze and enable new research in multisensory object-centric learning in computer vision, robotics, and beyond. Project page: https://objectfolder.stanford.edu", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00926", "title": "Inserting Anybody in Diffusion Models via Celeb Basis", "authors": [ "Ge Yuan", "Xiaodong Cun", "Yong Zhang", "Maomao Li", "Chenyang Qi", "Xintao Wang", "Ying Shan", "Huicheng Zheng" ], "github": "https://github.com/ygtxr1997/celebbasis", "project_page": "", "abstract": "Exquisite demand exists for customizing the pretrained large text-to-image model, e.g., Stable Diffusion, to generate innovative concepts, such as the users themselves. However, the newly-added concept from previous customization methods often shows weaker combination abilities than the original ones even given several images during training. We thus propose a new personalization method that allows for the seamless integration of a unique individual into the pre-trained diffusion model using just one facial photograph and only 1024 learnable parameters under 3 minutes. So as we can effortlessly generate stunning images of this person in any pose or position, interacting with anyone and doing anything imaginable from text prompts. To achieve this, we first analyze and build a well-defined celeb basis from the embedding space of the pre-trained large text encoder. Then, given one facial photo as the target identity, we generate its own embedding by optimizing the weight of this basis and locking all other parameters. Empowered by the proposed celeb basis, the new identity in our customized model showcases a better concept combination ability than previous personalization methods. Besides, our model can also learn several new identities at once and interact with each other where the previous customization model fails to. The code will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ygtxr1997/celebbasis", "github_api_added_by": "auto" }, { "date": "2023-06-02", "arxiv_id": "2306.00148", "title": "SafeDiffuser: Safe Planning with Diffusion Probabilistic Models", "authors": [ "Wei Xiao", "Tsun-Hsuan Wang", "Chuang Gan", "Daniela Rus" ], "github": "", "project_page": "", "abstract": "Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees, thus making it hard to be applied for safety-critical applications. To address these challenges, we propose a new method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy specifications by using a class of control barrier functions. The key idea of our approach is to embed the proposed finite-time diffusion invariance into the denoising diffusion procedure, which enables trustworthy diffusion data generation. Moreover, we demonstrate that our finite-time diffusion invariance method through generative models not only maintains generalization performance but also creates robustness in safe data generation. We test our method on a series of safe planning tasks, including maze path generation, legged robot locomotion, and 3D space manipulation, with results showing the advantages of robustness and guarantees over vanilla diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00008", "title": "Brainformers: Trading Simplicity for Efficiency", "authors": [ "Yanqi Zhou", "Nan Du", "Yanping Huang", "Daiyi Peng", "Chang Lan", "Da Huang", "Siamak Shakeri", "David So", "Andrew Dai", "Yifeng Lu", "Zhifeng Chen", "Quoc Le", "Claire Cui", "James Laundon", "Jeff Dean" ], "github": "", "project_page": "", "abstract": "Transformers are central to recent successes in natural language processing and computer vision. Transformers have a mostly uniform backbone where layers alternate between feed-forward and self-attention in order to build a deep network. Here we investigate this design choice and find that more complex blocks that have different permutations of layer primitives can be more efficient. Using this insight, we develop a complex block, named Brainformer, that consists of a diverse sets of layers such as sparsely gated feed-forward layers, dense feed-forward layers, attention layers, and various forms of layer normalization and activation functions. Brainformer consistently outperforms the state-of-the-art dense and sparse Transformers, in terms of both quality and efficiency. A Brainformer model with 8 billion activated parameters per token demonstrates 2x faster training convergence and 5x faster step time compared to its GLaM counterpart. In downstream task evaluation, Brainformer also demonstrates a 3% higher SuperGLUE score with fine-tuning compared to GLaM with a similar number of activated parameters. Finally, Brainformer largely outperforms a Primer dense model derived with NAS with similar computation per token on fewshot evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00802", "title": "Birth of a Transformer: A Memory Viewpoint", "authors": [ "Alberto Bietti", "Vivien Cabannes", "Diane Bouchacourt", "Herve Jegou", "Leon Bottou" ], "github": "https://github.com/albietz/transformer-birth", "project_page": "", "abstract": "Large language models based on transformers have achieved great empirical successes. However, as they are deployed more widely, there is a growing need to better understand their internal mechanisms in order to make them more reliable. These models appear to store vast amounts of knowledge from their training data, and to adapt quickly to new information provided in their context or prompt. We study how transformers balance these two types of knowledge by considering a synthetic setup where tokens are generated from either global or context-specific bigram distributions. By a careful empirical analysis of the training process on a simplified two-layer transformer, we illustrate the fast learning of global bigrams and the slower development of an \"induction head\" mechanism for the in-context bigrams. We highlight the role of weight matrices as associative memories, provide theoretical insights on how gradients enable their learning during training, and study the role of data-distributional properties.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/albietz/transformer-birth", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00107", "title": "MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training", "authors": [ "Yizhi Li", "Ruibin Yuan", "Ge Zhang", "Yinghao Ma", "Xingran Chen", "Hanzhi Yin", "Chenghua Lin", "Anton Ragni", "Emmanouil Benetos", "Norbert Gyenge", "Roger Dannenberg", "Ruibo Liu", "Wenhu Chen", "Gus Xia", "Yemin Shi", "Wenhao Huang", "Yike Guo", "Jie Fu" ], "github": "https://github.com/yizhilll/MERT", "project_page": "", "abstract": "Self-supervised learning (SSL) has recently emerged as a promising paradigm for training generalisable models on large-scale data in the fields of vision, text, and speech. Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored. This is primarily due to the distinctive challenges associated with modelling musical knowledge, particularly its tonal and pitched characteristics of music. To address this research gap, we propose an acoustic Music undERstanding model with large-scale self-supervised Training (MERT), which incorporates teacher models to provide pseudo labels in the masked language modelling (MLM) style acoustic pre-training. In our exploration, we identified a superior combination of teacher models, which outperforms conventional speech and audio approaches in terms of performance. This combination includes an acoustic teacher based on Residual Vector Quantization - Variational AutoEncoder (RVQ-VAE) and a musical teacher based on the Constant-Q Transform (CQT). These teachers effectively guide our student model, a BERT-style transformer encoder, to better model music audio. In addition, we introduce an in-batch noise mixture augmentation to enhance the representation robustness. Furthermore, we explore a wide range of settings to overcome the instability in acoustic language model pre-training, which allows our designed paradigm to scale from 95M to 330M parameters. Experimental results indicate that our model can generalise and perform well on 14 music understanding tasks and attains state-of-the-art (SOTA) overall scores. The code and models are online: https://github.com/yizhilll/MERT.", "github_manual": "", "github_regex": "https://github.com/yizhilll/MERT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00029", "title": "CodeTF: One-stop Transformer Library for State-of-the-art Code LLM", "authors": [ "Nghi D. Q. Bui", "Hung Le", "Yue Wang", "Junnan Li", "Akhilesh Deepak Gotmare", "Steven C. H. Hoi" ], "github": "https://github.com/salesforce/codetf", "project_page": "", "abstract": "Code intelligence plays a key role in transforming modern software engineering. Recently, deep learning-based models, especially Transformer-based large language models (LLMs), have demonstrated remarkable potential in tackling these tasks by leveraging massive open-source code data and programming language features. However, the development and deployment of such models often require expertise in both machine learning and software engineering, creating a barrier for the model adoption. In this paper, we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Following the principles of modular design and extensible framework, we design CodeTF with a unified interface to enable rapid access and development across different types of models, datasets and tasks. Our library supports a collection of pretrained Code LLM models and popular code benchmarks, including a standardized interface to train and serve code LLMs efficiently, and data features such as language-specific parsers and utility functions for extracting code attributes. In this paper, we describe the design principles, the architecture, key modules and components, and compare with other related library tools. Finally, we hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering, providing a comprehensive open-source solution for developers, researchers, and practitioners.", "github_manual": "https://github.com/salesforce/codetf", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00110", "title": "MuseCoco: Generating Symbolic Music from Text", "authors": [ "Peiling Lu", "Xin Xu", "Chenfei Kang", "Botao Yu", "Chengyi Xing", "Xu Tan", "Jiang Bian" ], "github": "", "project_page": "", "abstract": "Generating music from text descriptions is a user-friendly mode since the text is a relatively easy interface for user engagement. While some approaches utilize texts to control music audio generation, editing musical elements in generated audio is challenging for users. In contrast, symbolic music offers ease of editing, making it more accessible for users to manipulate specific musical elements. In this paper, we propose MuseCoco, which generates symbolic music from text descriptions with musical attributes as the bridge to break down the task into text-to-attribute understanding and attribute-to-music generation stages. MuseCoCo stands for Music Composition Copilot that empowers musicians to generate music directly from given text descriptions, offering a significant improvement in efficiency compared to creating music entirely from scratch. The system has two main advantages: Firstly, it is data efficient. In the attribute-to-music generation stage, the attributes can be directly extracted from music sequences, making the model training self-supervised. In the text-to-attribute understanding stage, the text is synthesized and refined by ChatGPT based on the defined attribute templates. Secondly, the system can achieve precise control with specific attributes in text descriptions and offers multiple control options through attribute-conditioned or text-conditioned approaches. MuseCoco outperforms baseline systems in terms of musicality, controllability, and overall score by at least 1.27, 1.08, and 1.32 respectively. Besides, there is a notable enhancement of about 20% in objective control accuracy. In addition, we have developed a robust large-scale model with 1.2 billion parameters, showcasing exceptional controllability and musicality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00943", "title": "Make-Your-Video: Customized Video Generation Using Textual and Structural Guidance", "authors": [ "Jinbo Xing", "Menghan Xia", "Yuxin Liu", "Yuechen Zhang", "Yong Zhang", "Yingqing He", "Hanyuan Liu", "Haoxin Chen", "Xiaodong Cun", "Xintao Wang", "Ying Shan", "Tien-Tsin Wong" ], "github": "", "project_page": "", "abstract": "Creating a vivid video from the event or scenario in our imagination is a truly fascinating experience. Recent advancements in text-to-video synthesis have unveiled the potential to achieve this with prompts only. While text is convenient in conveying the overall scene context, it may be insufficient to control precisely. In this paper, we explore customized video generation by utilizing text as context description and motion structure (e.g. frame-wise depth) as concrete guidance. Our method, dubbed Make-Your-Video, involves joint-conditional video generation using a Latent Diffusion Model that is pre-trained for still image synthesis and then promoted for video generation with the introduction of temporal modules. This two-stage learning scheme not only reduces the computing resources required, but also improves the performance by transferring the rich concepts available in image datasets solely into video generation. Moreover, we use a simple yet effective causal attention mask strategy to enable longer video synthesis, which mitigates the potential quality degradation effectively. Experimental results show the superiority of our method over existing baselines, particularly in terms of temporal coherence and fidelity to users' guidance. In addition, our model enables several intriguing applications that demonstrate potential for practical usage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00986", "title": "Diffusion Self-Guidance for Controllable Image Generation", "authors": [ "Dave Epstein", "Allan Jabri", "Ben Poole", "Alexei A. Efros", "Aleksander Holynski" ], "github": "", "project_page": "", "abstract": "Large-scale generative models are capable of producing high-quality images from detailed text descriptions. However, many aspects of an image are difficult or impossible to convey through text. We introduce self-guidance, a method that provides greater control over generated images by guiding the internal representations of diffusion models. We demonstrate that properties such as the shape, location, and appearance of objects can be extracted from these representations and used to steer sampling. Self-guidance works similarly to classifier guidance, but uses signals present in the pretrained model itself, requiring no additional models or training. We show how a simple set of properties can be composed to perform challenging image manipulations, such as modifying the position or size of objects, merging the appearance of objects in one image with the layout of another, composing objects from many images into one, and more. We also show that self-guidance can be used to edit real images. For results and an interactive demo, see our project page at https://dave.ml/selfguidance/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00964", "title": "Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation", "authors": [ "Minghui Hu", "Jianbin Zheng", "Daqing Liu", "Chuanxia Zheng", "Chaoyue Wang", "Dacheng Tao", "Tat-Jen Cham" ], "github": "", "project_page": "", "abstract": "Text-conditional diffusion models are able to generate high-fidelity images with diverse contents. However, linguistic representations frequently exhibit ambiguous descriptions of the envisioned objective imagery, requiring the incorporation of additional control signals to bolster the efficacy of text-guided diffusion models. In this work, we propose Cocktail, a pipeline to mix various modalities into one embedding, amalgamated with a generalized ControlNet (gControlNet), a controllable normalisation (ControlNorm), and a spatial guidance sampling method, to actualize multi-modal and spatially-refined control for text-conditional diffusion models. Specifically, we introduce a hyper-network gControlNet, dedicated to the alignment and infusion of the control signals from disparate modalities into the pre-trained diffusion model. gControlNet is capable of accepting flexible modality signals, encompassing the simultaneous reception of any combination of modality signals, or the supplementary fusion of multiple modality signals. The control signals are then fused and injected into the backbone model according to our proposed ControlNorm. Furthermore, our advanced spatial guidance sampling methodology proficiently incorporates the control signal into the designated region, thereby circumventing the manifestation of undesired objects within the generated image. We demonstrate the results of our method in controlling various modalities, proving high-quality synthesis and fidelity to multiple external signals.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-02", "arxiv_id": "2306.00622", "title": "ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing", "authors": [ "Ryan Liu", "Nihar B. Shah" ], "github": "https://github.com/niharshah/ReviewerGPT2023", "project_page": "", "abstract": "Given the rapid ascent of large language models (LLMs), we study the question: (How) can large language models help in reviewing of scientific papers or proposals? We first conduct some pilot studies where we find that (i) GPT-4 outperforms other LLMs (Bard, Vicuna, Koala, Alpaca, LLaMa, Dolly, OpenAssistant, StableLM), and (ii) prompting with a specific question (e.g., to identify errors) outperforms prompting to simply write a review. With these insights, we study the use of LLMs (specifically, GPT-4) for three tasks: 1. Identifying errors: We construct 13 short computer science papers each with a deliberately inserted error, and ask the LLM to check for the correctness of these papers. We observe that the LLM finds errors in 7 of them, spanning both mathematical and conceptual errors. 2. Verifying checklists: We task the LLM to verify 16 closed-ended checklist questions in the respective sections of 15 NeurIPS 2022 papers. We find that across 119 {checklist question, paper} pairs, the LLM had an 86.6% accuracy. 3. Choosing the \"better\" paper: We generate 10 pairs of abstracts, deliberately designing each pair in such a way that one abstract was clearly superior than the other. The LLM, however, struggled to discern these relatively straightforward distinctions accurately, committing errors in its evaluations for 6 out of the 10 pairs. Based on these experiments, we think that LLMs have a promising use as reviewing assistants for specific reviewing tasks, but not (yet) for complete evaluations of papers or proposals.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/niharshah/ReviewerGPT2023", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-05", "arxiv_id": "2306.01116", "title": "The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only", "authors": [ "Guilherme Penedo", "Quentin Malartic", "Daniel Hesslow", "Ruxandra Cojocaru", "Alessandro Cappelli", "Hamza Alobeidli", "Baptiste Pannier", "Ebtesam Almazrouei", "Julien Launay" ], "github": "", "project_page": "", "abstract": "Large language models are commonly trained on a mixture of filtered web data and curated high-quality corpora, such as social media conversations, books, or technical papers. This curation process is believed to be necessary to produce performant models with broad zero-shot generalization abilities. However, as larger models requiring pretraining on trillions of tokens are considered, it is unclear how scalable is curation and whether we will run out of unique high-quality data soon. At variance with previous beliefs, we show that properly filtered and deduplicated web data alone can lead to powerful models; even significantly outperforming models from the state-of-the-art trained on The Pile. Despite extensive filtering, the high-quality data we extract from the web is still plentiful, and we are able to obtain five trillion tokens from CommonCrawl. We publicly release an extract of 600 billion tokens from our RefinedWeb dataset, and 1.3/7.5B parameters language models trained on it.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-05", "arxiv_id": "2306.01567", "title": "Segment Anything in High Quality", "authors": [ "Lei Ke", "Mingqiao Ye", "Martin Danelljan", "Yifan Liu", "Yu-Wing Tai", "Chi-Keung Tang", "Fisher Yu" ], "github": "https://github.com/SysCV/SAM-HQ", "project_page": "", "abstract": "The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being trained with 1.1 billion masks, SAM's mask prediction quality falls short in many cases, particularly when dealing with objects that have intricate structures. We propose HQ-SAM, equipping SAM with the ability to accurately segment any object, while maintaining SAM's original promptable design, efficiency, and zero-shot generalizability. Our careful design reuses and preserves the pre-trained model weights of SAM, while only introducing minimal additional parameters and computation. We design a learnable High-Quality Output Token, which is injected into SAM's mask decoder and is responsible for predicting the high-quality mask. Instead of only applying it on mask-decoder features, we first fuse them with early and final ViT features for improved mask details. To train our introduced learnable parameters, we compose a dataset of 44K fine-grained masks from several sources. HQ-SAM is only trained on the introduced detaset of 44k masks, which takes only 4 hours on 8 GPUs. We show the efficacy of HQ-SAM in a suite of 9 diverse segmentation datasets across different downstream tasks, where 7 out of them are evaluated in a zero-shot transfer protocol. Our code and models will be released at https://github.com/SysCV/SAM-HQ.", "github_manual": "", "github_regex": "https://github.com/SysCV/SAM-HQ", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-05", "arxiv_id": "2306.01693", "title": "Fine-Grained Human Feedback Gives Better Rewards for Language Model Training", "authors": [ "Zeqiu Wu", "Yushi Hu", "Weijia Shi", "Nouha Dziri", "Alane Suhr", "Prithviraj Ammanabrolu", "Noah A. Smith", "Mari Ostendorf", "Hannaneh Hajishirzi" ], "github": "https://github.com/allenai/FineGrainedRLHF", "project_page": "", "abstract": "Language models (LMs) often exhibit undesirable text generation behaviors, including generating false, toxic, or irrelevant outputs. Reinforcement learning from human feedback (RLHF) - where human preference judgments on LM outputs are transformed into a learning signal - has recently shown promise in addressing these issues. However, such holistic feedback conveys limited information on long text outputs; it does not indicate which aspects of the outputs influenced user preference; e.g., which parts contain what type(s) of errors. In this paper, we use fine-grained human feedback (e.g., which sentence is false, which sub-sentence is irrelevant) as an explicit training signal. We introduce Fine-Grained RLHF, a framework that enables training and learning from reward functions that are fine-grained in two respects: (1) density, providing a reward after every segment (e.g., a sentence) is generated; and (2) incorporating multiple reward models associated with different feedback types (e.g., factual incorrectness, irrelevance, and information incompleteness). We conduct experiments on detoxification and long-form question answering to illustrate how learning with such reward functions leads to improved performance, supported by both automatic and human evaluation. Additionally, we show that LM behaviors can be customized using different combinations of fine-grained reward models. We release all data, collected human feedback, and codes at https://FineGrainedRLHF.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/FineGrainedRLHF", "github_api_added_by": "auto" }, { "date": "2023-06-05", "arxiv_id": "2306.01694", "title": "Evaluating Language Models for Mathematics through Interactions", "authors": [ "Katherine M. Collins", "Albert Q. Jiang", "Simon Frieder", "Lionel Wong", "Miri Zilka", "Umang Bhatt", "Thomas Lukasiewicz", "Yuhuai Wu", "Joshua B. Tenenbaum", "William Hart", "Timothy Gowers", "Wenda Li", "Adrian Weller", "Mateja Jamnik" ], "github": "https://github.com/collinskatie/checkmate", "project_page": "", "abstract": "The standard methodology of evaluating large language models (LLMs) based on static pairs of inputs and outputs is insufficient for developing assistants: this kind of assessments fails to take into account the essential interactive element in their deployment, and therefore limits how we understand language model capabilities. We introduce CheckMate, an adaptable prototype platform for humans to interact with and evaluate LLMs. We conduct a study with CheckMate to evaluate three language models~(InstructGPT, ChatGPT, and GPT-4) as assistants in proving undergraduate-level mathematics, with a mixed cohort of participants from undergraduate students to professors of mathematics. We release the resulting interaction and rating dataset, MathConverse. By analysing MathConverse, we derive a preliminary taxonomy of human behaviours and uncover that despite a generally positive correlation, there are notable instances of divergence between correctness and perceived helpfulness in LLM generations, amongst other findings. Further, we identify useful scenarios and existing issues of GPT-4 in mathematical reasoning through a series of case studies contributed by expert mathematicians. We conclude with actionable takeaways for ML practitioners and mathematicians: models which communicate uncertainty, respond well to user corrections, are more interpretable and concise may constitute better assistants; interactive evaluation is a promising way to continually navigate the capability of these models; humans should be aware of language models' algebraic fallibility, and for that reason discern where they should be used.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/collinskatie/checkmate", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-05", "arxiv_id": "2306.01736", "title": "DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model", "authors": [ "Xiuye Gu", "Yin Cui", "Jonathan Huang", "Abdullah Rashwan", "Xuan Yang", "Xingyi Zhou", "Golnaz Ghiasi", "Weicheng Kuo", "Huizhong Chen", "Liang-Chieh Chen", "David A Ross" ], "github": "", "project_page": "", "abstract": "Observing the close relationship among panoptic, semantic and instance segmentation tasks, we propose to train a universal multi-dataset multi-task segmentation model: DaTaSeg.We use a shared representation (mask proposals with class predictions) for all tasks. To tackle task discrepancy, we adopt different merge operations and post-processing for different tasks. We also leverage weak-supervision, allowing our segmentation model to benefit from cheaper bounding box annotations. To share knowledge across datasets, we use text embeddings from the same semantic embedding space as classifiers and share all network parameters among datasets. We train DaTaSeg on ADE semantic, COCO panoptic, and Objects365 detection datasets. DaTaSeg improves performance on all datasets, especially small-scale datasets, achieving 54.0 mIoU on ADE semantic and 53.5 PQ on COCO panoptic. DaTaSeg also enables weakly-supervised knowledge transfer on ADE panoptic and Objects365 instance segmentation. Experiments show DaTaSeg scales with the number of training datasets and enables open-vocabulary segmentation through direct transfer. In addition, we annotate an Objects365 instance segmentation set of 1,000 images and will release it as a public benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-05", "arxiv_id": "2306.01684", "title": "Harnessing large-language models to generate private synthetic text", "authors": [ "Alexey Kurakin", "Natalia Ponomareva", "Umar Syed", "Liam MacDermed", "Andreas Terzis" ], "github": "", "project_page": "", "abstract": "Differentially private (DP) training methods like DP-SGD can protect sensitive training data by ensuring that ML models will not reveal private information. An alternative approach, which this paper studies, is to use a sensitive dataset to generate a new synthetic dataset which is differentially private with respect to the original data. Doing so has several advantages: synthetic data can be reused for other tasks (including for hyper parameter tuning), retained indefinitely, or shared with third parties without sacrificing privacy. However, obtaining DP data is much harder than introducing DP during training. To make it feasible for text, recent work has utilized public data by starting with a pre-trained generative language model and privately finetuning it on sensitive data. This model can be used to sample a DP synthetic dataset. While this strategy seems straightforward, executing it has proven problematic. Previous approaches either show significant performance loss, or have, as we show, critical design flaws. In this paper we demonstrate that a proper training objective along with tuning fewer parameters results in excellent DP synthetic data quality. Our approach is competitive with direct DP-training of downstream classifiers in terms of performance on downstream tasks. We also demonstrate that our DP synthetic data is not only useful for downstream classifier training, but also to tune those same models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-05", "arxiv_id": "2306.01242", "title": "Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators", "authors": [ "Zhizheng Zhang", "Xiaoyi Zhang", "Wenxuan Xie", "Yan Lu" ], "github": "", "project_page": "", "abstract": "The recent success of Large Language Models (LLMs) signifies an impressive stride towards artificial general intelligence. They have shown a promising prospect in automatically completing tasks upon user instructions, functioning as brain-like coordinators. The associated risks will be revealed as we delegate an increasing number of tasks to machines for automated completion. A big question emerges: how can we make machines behave responsibly when helping humans automate tasks as personal copilots? In this paper, we explore this question in depth from the perspectives of feasibility, completeness and security. In specific, we present Responsible Task Automation (ResponsibleTA) as a fundamental framework to facilitate responsible collaboration between LLM-based coordinators and executors for task automation with three empowered capabilities: 1) predicting the feasibility of the commands for executors; 2) verifying the completeness of executors; 3) enhancing the security (e.g., the protection of users' privacy). We further propose and compare two paradigms for implementing the first two capabilities. One is to leverage the generic knowledge of LLMs themselves via prompt engineering while the other is to adopt domain-specific learnable models. Moreover, we introduce a local memory mechanism for achieving the third capability. We evaluate our proposed ResponsibleTA on UI task automation and hope it could bring more attentions to ensuring LLMs more responsible in diverse scenarios. The research project homepage is at https://task-automation-research.github.io/responsible_task_automation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-05", "arxiv_id": "2306.01160", "title": "Faster Causal Attention Over Large Sequences Through Sparse Flash Attention", "authors": [ "Matteo Pagliardini", "Daniele Paliotta", "Martin Jaggi", "Fran\u00e7ois Fleuret" ], "github": "https://github.com/epfml/dynamic-sparse-flash-attention", "project_page": "", "abstract": "Transformer-based language models have found many diverse applications requiring them to process sequences of increasing length. For these applications, the causal self-attention -- which is the only component scaling quadratically w.r.t. the sequence length -- becomes a central concern. While many works have proposed schemes to sparsify the attention patterns and reduce the computational overhead of self-attention, those are often limited by implementations concerns and end up imposing a simple and static structure over the attention matrix. Conversely, implementing more dynamic sparse attentions often results in runtimes significantly slower than computing the full attention using the Flash implementation from Dao et al. (2022). We extend FlashAttention to accommodate a large class of attention sparsity patterns that, in particular, encompass key/query dropping and hashing-based attention. This leads to implementations with no computational complexity overhead and a multi-fold runtime speedup on top of FlashAttention. Even with relatively low degrees of sparsity, our method improves visibly upon FlashAttention as the sequence length increases. Without sacrificing perplexity, we increase the training speed of a transformer language model by 2.0times and 3.3times for sequences of respectively 8k and 16k tokens.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/epfml/dynamic-sparse-flash-attention", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-05", "arxiv_id": "2306.01061", "title": "Reimagining Retrieval Augmented Language Models for Answering Queries", "authors": [ "Wang-Chiew Tan", "Yuliang Li", "Pedro Rodriguez", "Richard James", "Xi Victoria Lin", "Alon Halevy", "Scott Yih" ], "github": "", "project_page": "", "abstract": "We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. Such language models are semi-parametric, where models integrate model parameters and knowledge from external data sources to make their predictions, as opposed to the parametric nature of vanilla large language models. We give initial experimental findings that semi-parametric architectures can be enhanced with views, a query analyzer/planner, and provenance to make a significantly more powerful system for question answering in terms of accuracy and efficiency, and potentially for other NLP tasks", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-05", "arxiv_id": "2306.01337", "title": "An Empirical Study on Challenging Math Problem Solving with GPT-4", "authors": [ "Yiran Wu", "Feiran Jia", "Shaokun Zhang", "Qingyun Wu", "Hangyu Li", "Erkang Zhu", "Yue Wang", "Yin Tat Lee", "Richard Peng", "Chi Wang" ], "github": "https://github.com/yiranwu0/mathchat", "project_page": "", "abstract": "Employing Large Language Models (LLMs) to address mathematical problems is an intriguing research endeavor, considering the abundance of math problems expressed in natural language across numerous science and engineering fields. While several prior works have investigated solving elementary mathematics using LLMs, this work explores the frontier of using GPT-4 for solving more complex and challenging math problems. We evaluate various ways of using GPT-4. Some of them are adapted from existing work, and one is \\MathChat, a conversational problem-solving framework newly proposed in this work. We perform the evaluation on difficult high school competition problems from the MATH dataset, which shows the advantage of the proposed conversational approach.", "github_manual": "https://github.com/yiranwu0/mathchat", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.02707", "title": "Orca: Progressive Learning from Complex Explanation Traces of GPT-4", "authors": [ "Subhabrata Mukherjee", "Arindam Mitra", "Ganesh Jawahar", "Sahaj Agarwal", "Hamid Palangi", "Ahmed Awadallah" ], "github": "", "project_page": "", "abstract": "Recent research has focused on enhancing the capability of smaller models through imitation learning, drawing on the outputs generated by large foundation models (LFMs). A number of issues impact the quality of these models, ranging from limited imitation signals from shallow LFM outputs; small scale homogeneous training data; and most notably a lack of rigorous evaluation resulting in overestimating the small model's capability as they tend to learn to imitate the style, but not the reasoning process of LFMs. To address these challenges, we develop Orca (We are working with our legal team to publicly release a diff of the model weights in accordance with LLaMA's release policy to be published at https://aka.ms/orca-lm), a 13-billion parameter model that learns to imitate the reasoning process of LFMs. Orca learns from rich signals from GPT-4 including explanation traces; step-by-step thought processes; and other complex instructions, guided by teacher assistance from ChatGPT. To promote this progressive learning, we tap into large-scale and diverse imitation data with judicious sampling and selection. Orca surpasses conventional state-of-the-art instruction-tuned models such as Vicuna-13B by more than 100% in complex zero-shot reasoning benchmarks like Big-Bench Hard (BBH) and 42% on AGIEval. Moreover, Orca reaches parity with ChatGPT on the BBH benchmark and shows competitive performance (4 pts gap with optimized system message) in professional and academic examinations like the SAT, LSAT, GRE, and GMAT, both in zero-shot settings without CoT; while trailing behind GPT-4. Our research indicates that learning from step-by-step explanations, whether these are generated by humans or more advanced AI models, is a promising direction to improve model capabilities and skills.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.02254", "title": "A Technical Report for Polyglot-Ko: Open-Source Large-Scale Korean Language Models", "authors": [ "Hyunwoong Ko", "Kichang Yang", "Minho Ryu", "Taekyoon Choi", "Seungmu Yang", "jiwung Hyun", "Sungho Park" ], "github": "", "project_page": "", "abstract": "Polyglot is a pioneering project aimed at enhancing the non-English language performance of multilingual language models. Despite the availability of various multilingual models such as mBERT (Devlin et al., 2019), XGLM (Lin et al., 2022), and BLOOM (Scao et al., 2022), researchers and developers often resort to building monolingual models in their respective languages due to the dissatisfaction with the current multilingual models non-English language capabilities. Addressing this gap, we seek to develop advanced multilingual language models that offer improved performance in non-English languages. In this paper, we introduce the Polyglot Korean models, which represent a specific focus rather than being multilingual in nature. In collaboration with TUNiB, our team collected 1.2TB of Korean data meticulously curated for our research journey. We made a deliberate decision to prioritize the development of Korean models before venturing into multilingual models. This choice was motivated by multiple factors: firstly, the Korean models facilitated performance comparisons with existing multilingual models; and finally, they catered to the specific needs of Korean companies and researchers. This paper presents our work in developing the Polyglot Korean models, which propose some steps towards addressing the non-English language performance gap in multilingual language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.02858", "title": "Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding", "authors": [ "Hang Zhang", "Xin Li", "Lidong Bing" ], "github": "https://github.com/DAMO-NLP-SG/Video-LLaMA", "project_page": "", "abstract": "We present Video-LLaMA, a multi-modal framework that empowers Large Language Models (LLMs) with the capability of understanding both visual and auditory content in the video. Video-LLaMA bootstraps cross-modal training from the frozen pre-trained visual \\& audio encoders and the frozen LLMs. Unlike previous vision- LLMs that focus on static image comprehensions such as MiniGPT-4~zhu2023minigpt and LLaVA~liu2023visualit, Video-LLaMA tackles two challenges in video understanding: (1) capturing the temporal changes in visual scenes, (2) integrating audio-visual signals. For the first challenge, we propose Video Q-former to extend the pre-trained image encoder to a video encoder and introduce a video-to-text generation task to learn video-language correspondence. For the second challenge, we leverage ImageBind~girdhar2023imagebind as the pre-trained audio encoder which performs exceptionally well in aligning different modalities to a common embedding space. And then introduce an Audio Q-former to learn auditory query tokens. To align the output of both visual \\& audio encoder with LLM's embedding space, we train Video-LLaMA on a large-scale vision caption dataset and a hign-quantity vision-instruction-tuning dataset. We found Video-LLaMA showcases the ability to perceive and comprehend video content, generating meaningful responses that are grounded in the visual and auditory information present in the videos. This highlights the potential of Video-LLaMA as a promising prototype for audio-visual AI assistants. Our code, pre-trained model, and demo are available at https://github.com/DAMO-NLP-SG/Video-LLaMA.", "github_manual": "", "github_regex": "https://github.com/DAMO-NLP-SG/Video-LLaMA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.02561", "title": "LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion", "authors": [ "Dongfu Jiang", "Xiang Ren", "Bill Yuchen Lin" ], "github": "https://github.com/yuchenlin/LLM-Blender", "project_page": "", "abstract": "We present LLM-Blender, an ensembling framework designed to attain consistently superior performance by leveraging the diverse strengths of multiple open-source large language models (LLMs). Our framework consists of two modules: PairRanker and GenFuser, addressing the observation that optimal LLMs for different examples can significantly vary. PairRanker employs a specialized pairwise comparison method to distinguish subtle differences between candidate outputs. It jointly encodes the input text and a pair of candidates, using cross-attention encoders to determine the superior one. Our results demonstrate that PairRanker exhibits the highest correlation with ChatGPT-based ranking. Then, GenFuser aims to merge the top-ranked candidates, generating an improved output by capitalizing on their strengths and mitigating their weaknesses. To facilitate large-scale evaluation, we introduce a benchmark dataset, MixInstruct, which is a mixture of multiple instruction datasets featuring oracle pairwise comparisons. Our LLM-Blender significantly outperform individual LLMs and baseline methods across various metrics, establishing a substantial performance gap.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yuchenlin/LLM-Blender", "github_api_added_by": "auto" }, { "date": "2023-06-06", "arxiv_id": "2306.03038", "title": "HeadSculpt: Crafting 3D Head Avatars with Text", "authors": [ "Xiao Han", "Yukang Cao", "Kai Han", "Xiatian Zhu", "Jiankang Deng", "Yi-Zhe Song", "Tao Xiang", "Kwan-Yee K. Wong" ], "github": "", "project_page": "", "abstract": "Recently, text-guided 3D generative methods have made remarkable advancements in producing high-quality textures and geometry, capitalizing on the proliferation of large vision-language and image diffusion models. However, existing methods still struggle to create high-fidelity 3D head avatars in two aspects: (1) They rely mostly on a pre-trained text-to-image diffusion model whilst missing the necessary 3D awareness and head priors. This makes them prone to inconsistency and geometric distortions in the generated avatars. (2) They fall short in fine-grained editing. This is primarily due to the inherited limitations from the pre-trained 2D image diffusion models, which become more pronounced when it comes to 3D head avatars. In this work, we address these challenges by introducing a versatile coarse-to-fine pipeline dubbed HeadSculpt for crafting (i.e., generating and editing) 3D head avatars from textual prompts. Specifically, we first equip the diffusion model with 3D awareness by leveraging landmark-based control and a learned textual embedding representing the back view appearance of heads, enabling 3D-consistent head avatar generations. We further propose a novel identity-aware editing score distillation strategy to optimize a textured mesh with a high-resolution differentiable rendering technique. This enables identity preservation while following the editing instruction. We showcase HeadSculpt's superior fidelity and editing capabilities through comprehensive experiments and comparisons with existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.03082", "title": "InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models", "authors": [ "Lichang Chen", "Jiuhai Chen", "Tom Goldstein", "Heng Huang", "Tianyi Zhou" ], "github": "https://github.com/Lichang-Chen/InstructZero", "project_page": "", "abstract": "Large language models~(LLMs) are instruction followers, but it can be challenging to find the best instruction for different situations, especially for black-box LLMs on which backpropagation is forbidden. Instead of directly optimizing the discrete instruction, we optimize a low-dimensional soft prompt applied to an open-source LLM to generate the instruction for the black-box LLM. On each iteration of the proposed method, which we call InstructZero, a soft prompt is converted into an instruction using the open-source LLM, which is then submitted to the black-box LLM for zero-shot evaluation, and the performance is sent to Bayesian optimization to produce new soft prompts improving the zero-shot performance. We evaluate InstructZero on different combinations of open-source LLMs and APIs including Vicuna and ChatGPT. Our results show that InstructZero outperforms SOTA auto-instruction methods across a variety of downstream tasks. Our code and data are publicly available at https://github.com/Lichang-Chen/InstructZero.", "github_manual": "", "github_regex": "https://github.com/Lichang-Chen/InstructZero", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.01741", "title": "GPT Models Meet Robotic Applications: Co-Speech Gesturing Chat System", "authors": [ "Naoki Wake", "Atsushi Kanehira", "Kazuhiro Sasabuchi", "Jun Takamatsu", "Katsushi Ikeuchi" ], "github": "", "project_page": "", "abstract": "This technical paper introduces a chatting robot system that utilizes recent advancements in large-scale language models (LLMs) such as GPT-3 and ChatGPT. The system is integrated with a co-speech gesture generation system, which selects appropriate gestures based on the conceptual meaning of speech. Our motivation is to explore ways of utilizing the recent progress in LLMs for practical robotic applications, which benefits the development of both chatbots and LLMs. Specifically, it enables the development of highly responsive chatbot systems by leveraging LLMs and adds visual effects to the user interface of LLMs as an additional value. The source code for the system is available on GitHub for our in-house robot (https://github.com/microsoft/LabanotationSuite/tree/master/MSRAbotChatSimulation) and GitHub for Toyota HSR (https://github.com/microsoft/GPT-Enabled-HSR-CoSpeechGestures).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.02982", "title": "PolyVoice: Language Models for Speech to Speech Translation", "authors": [ "Qianqian Dong", "Zhiying Huang", "Chen Xu", "Yunlong Zhao", "Kexin Wang", "Xuxin Cheng", "Tom Ko", "Qiao Tian", "Tang Li", "Fengpeng Yue", "Ye Bai", "Xi Chen", "Lu Lu", "Zejun Ma", "Yuping Wang", "Mingxuan Wang", "Yuxuan Wang" ], "github": "", "project_page": "", "abstract": "We propose PolyVoice, a language model-based framework for speech-to-speech translation (S2ST) system. Our framework consists of two language models: a translation language model and a speech synthesis language model. We use discretized speech units, which are generated in a fully unsupervised way, and thus our framework can be used for unwritten languages. For the speech synthesis part, we adopt the existing VALL-E X approach and build a unit-based audio language model. This grants our framework the ability to preserve the voice characteristics and the speaking style of the original speech. We examine our system on Chinese rightarrow English and English rightarrow Spanish pairs. Experimental results show that our system can generate speech with high translation quality and audio quality. Speech samples are available at https://speechtranslation.github.io/polyvoice.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.03024", "title": "PokemonChat: Auditing ChatGPT for Pok\u00e9mon Universe Knowledge", "authors": [ "Laura Cabello", "Jiaang Li", "Ilias Chalkidis" ], "github": "", "project_page": "", "abstract": "The recently released ChatGPT model demonstrates unprecedented capabilities in zero-shot question-answering. In this work, we probe ChatGPT for its conversational understanding and introduce a conversational framework (protocol) that can be adopted in future studies. The Pok\\'emon universe serves as an ideal testing ground for auditing ChatGPT's reasoning capabilities due to its closed world assumption. After bringing ChatGPT's background knowledge (on the Pok\\'emon universe) to light, we test its reasoning process when using these concepts in battle scenarios. We then evaluate its ability to acquire new knowledge and include it in its reasoning process. Our ultimate goal is to assess ChatGPT's ability to generalize, combine features, and to acquire and reason over newly introduced knowledge from human feedback. We find that ChatGPT has prior knowledge of the Pokemon universe, which can reason upon in battle scenarios to a great extent, even when new information is introduced. The model performs better with collaborative feedback and if there is an initial phase of information retrieval, but also hallucinates occasionally and is susceptible to adversarial attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.03092", "title": "Neuralangelo: High-Fidelity Neural Surface Reconstruction", "authors": [ "Zhaoshuo Li", "Thomas M\u00fcller", "Alex Evans", "Russell H. Taylor", "Mathias Unberath", "Ming-Yu Liu", "Chen-Hsuan Lin" ], "github": "https://github.com/NVlabs/neuralangelo", "project_page": "", "abstract": "Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering. However, current methods struggle to recover detailed structures of real-world scenes. To address the issue, we present Neuralangelo, which combines the representation power of multi-resolution 3D hash grids with neural surface rendering. Two key ingredients enable our approach: (1) numerical gradients for computing higher-order derivatives as a smoothing operation and (2) coarse-to-fine optimization on the hash grids controlling different levels of details. Even without auxiliary inputs such as depth, Neuralangelo can effectively recover dense 3D surface structures from multi-view images with fidelity significantly surpassing previous methods, enabling detailed large-scale scene reconstruction from RGB video captures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/neuralangelo", "github_api_added_by": "auto" }, { "date": "2023-06-06", "arxiv_id": "2306.01841", "title": "Binary and Ternary Natural Language Generation", "authors": [ "Zechun Liu", "Barlas Oguz", "Aasish Pappu", "Yangyang Shi", "Raghuraman Krishnamoorthi" ], "github": "https://github.com/facebookresearch/Ternary_Binary_Transformer", "project_page": "", "abstract": "Ternary and binary neural networks enable multiplication-free computation and promise multiple orders of magnitude efficiency gains over full-precision networks if implemented on specialized hardware. However, since both the parameter and the output space are highly discretized, such networks have proven very difficult to optimize. The difficulties are compounded for the class of transformer text generation models due to the sensitivity of the attention operation to quantization and the noise-compounding effects of autoregressive decoding in the high-cardinality output space. We approach the problem with a mix of statistics-based quantization for the weights and elastic quantization of the activations and demonstrate the first ternary and binary transformer models on the downstream tasks of summarization and machine translation. Our ternary BART base achieves an R1 score of 41 on the CNN/DailyMail benchmark, which is merely 3.9 points behind the full model while being 16x more efficient. Our binary model, while less accurate, achieves a highly non-trivial score of 35.6. For machine translation, we achieved BLEU scores of 21.7 and 17.6 on the WMT16 En-Ro benchmark, compared with a full precision mBART model score of 26.8. We also compare our approach in the 8-bit activation setting, where our ternary and even binary weight models can match or outperform the best existing 8-bit weight models in the literature. Our code and models are available at: https://github.com/facebookresearch/Ternary_Binary_Transformer", "github_manual": "", "github_regex": "https://github.com/facebookresearch/Ternary_Binary_Transformer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.03083", "title": "MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion", "authors": [ "Chiyu Max Jiang", "Andre Cornman", "Cheolho Park", "Ben Sapp", "Yin Zhou", "Dragomir Anguelov" ], "github": "", "project_page": "", "abstract": "We present MotionDiffuser, a diffusion based representation for the joint distribution of future trajectories over multiple agents. Such representation has several key advantages: first, our model learns a highly multimodal distribution that captures diverse future outcomes. Second, the simple predictor design requires only a single L2 loss training objective, and does not depend on trajectory anchors. Third, our model is capable of learning the joint distribution for the motion of multiple agents in a permutation-invariant manner. Furthermore, we utilize a compressed trajectory representation via PCA, which improves model performance and allows for efficient computation of the exact sample log probability. Subsequently, we propose a general constrained sampling framework that enables controlled trajectory sampling based on differentiable cost functions. This strategy enables a host of applications such as enforcing rules and physical priors, or creating tailored simulation scenarios. MotionDiffuser can be combined with existing backbone architectures to achieve top motion forecasting results. We obtain state-of-the-art results for multi-agent motion prediction on the Waymo Open Motion Dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.02531", "title": "PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model", "authors": [ "Yizhe Zhang", "Jiatao Gu", "Zhuofeng Wu", "Shuangfei Zhai", "Josh Susskind", "Navdeep Jaitly" ], "github": "https://github.com/apple/ml-planner", "project_page": "", "abstract": "Autoregressive models for text sometimes generate repetitive and low-quality output because errors accumulate during the steps of generation. This issue is often attributed to exposure bias - the difference between how a model is trained, and how it is used during inference. Denoising diffusion models provide an alternative approach in which a model can revisit and revise its output. However, they can be computationally expensive and prior efforts on text have led to models that produce less fluent output compared to autoregressive models, especially for longer text and paragraphs. In this paper, we propose PLANNER, a model that combines latent semantic diffusion with autoregressive generation, to generate fluent text while exercising global control over paragraphs. The model achieves this by combining an autoregressive \"decoding\" module with a \"planning\" module that uses latent diffusion to generate semantic paragraph embeddings in a coarse-to-fine manner. The proposed method is evaluated on various conditional generation tasks, and results on semantic generation, text completion and summarization show its effectiveness in generating high-quality long-form text in an efficient manner.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/ml-planner", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.01923", "title": "The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation", "authors": [ "Saurabh Saxena", "Charles Herrmann", "Junhwa Hur", "Abhishek Kar", "Mohammad Norouzi", "Deqing Sun", "David J. Fleet" ], "github": "", "project_page": "", "abstract": "Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity. We show that they also excel in estimating optical flow and monocular depth, surprisingly, without task-specific architectures and loss functions that are predominant for these tasks. Compared to the point estimates of conventional regression-based methods, diffusion models also enable Monte Carlo inference, e.g., capturing uncertainty and ambiguity in flow and depth. With self-supervised pre-training, the combined use of synthetic and real data for supervised training, and technical innovations (infilling and step-unrolled denoising diffusion training) to handle noisy-incomplete training data, and a simple form of coarse-to-fine refinement, one can train state-of-the-art diffusion models for depth and optical flow estimation. Extensive experiments focus on quantitative performance against benchmarks, ablations, and the model's ability to capture uncertainty and multimodality, and impute missing values. Our model, DDVM (Denoising Diffusion Vision Model), obtains a state-of-the-art relative depth error of 0.074 on the indoor NYU benchmark and an Fl-all outlier rate of 3.26\\% on the KITTI optical flow benchmark, about 25\\% better than the best published method. For an overview see https://diffusion-vision.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.02245", "title": "SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model", "authors": [ "Dingyuan Zhang", "Dingkang Liang", "Hongcheng Yang", "Zhikang Zou", "Xiaoqing Ye", "Zhe Liu", "Xiang Bai" ], "github": "https://github.com/DYZhang09/SAM3D", "project_page": "", "abstract": "With the development of large language models, many remarkable linguistic systems like ChatGPT have thrived and achieved astonishing success on many tasks, showing the incredible power of foundation models. In the spirit of unleashing the capability of foundation models on vision tasks, the Segment Anything Model (SAM), a vision foundation model for image segmentation, has been proposed recently and presents strong zero-shot ability on many downstream 2D tasks. However, whether SAM can be adapted to 3D vision tasks has yet to be explored, especially 3D object detection. With this inspiration, we explore adapting the zero-shot ability of SAM to 3D object detection in this paper. We propose a SAM-powered BEV processing pipeline to detect objects and get promising results on the large-scale Waymo open dataset. As an early attempt, our method takes a step toward 3D object detection with vision foundation models and presents the opportunity to unleash their power on 3D vision tasks. The code is released at https://github.com/DYZhang09/SAM3D.", "github_manual": "", "github_regex": "https://github.com/DYZhang09/SAM3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.01879", "title": "VisualGPTScore: Visio-Linguistic Reasoning with Multimodal Generative Pre-Training Scores", "authors": [ "Zhiqiu Lin", "Xinyue Chen", "Deepak Pathak", "Pengchuan Zhang", "Deva Ramanan" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) discriminatively pre-trained with contrastive image-text matching losses such as P(match|text, image) have been criticized for lacking compositional understanding. This means they might output similar scores even if the original caption is rearranged into a different semantic statement. To address this, we propose to use the {bf V}isual {bf G}enerative {bf P}re-{bf T}raining Score ({bf VisualGPTScore}) of P(text|image), a multimodal generative score that captures the likelihood of a text caption conditioned on an image using an image-conditioned language model. Contrary to the belief that VLMs are mere bag-of-words models, our off-the-shelf VisualGPTScore demonstrates top-tier performance on recently proposed image-text retrieval benchmarks like ARO and Crepe that assess compositional reasoning. Furthermore, we factorize VisualGPTScore into a product of the marginal P(text) and the Pointwise Mutual Information (PMI). This helps to (a) diagnose datasets with strong language bias, and (b) debias results on other benchmarks like Winoground using an information-theoretic framework. VisualGPTScore provides valuable insights and serves as a strong baseline for future evaluation of visio-linguistic compositionality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.01872", "title": "Probabilistic Adaptation of Text-to-Video Models", "authors": [ "Mengjiao Yang", "Yilun Du", "Bo Dai", "Dale Schuurmans", "Joshua B. Tenenbaum", "Pieter Abbeel" ], "github": "", "project_page": "", "abstract": "Large text-to-video models trained on internet-scale data have demonstrated exceptional capabilities in generating high-fidelity videos from arbitrary textual descriptions. However, adapting these models to tasks with limited domain-specific data, such as animation or robotics videos, poses a significant computational challenge, since finetuning a pretrained large model can be prohibitively expensive. Inspired by how a small modifiable component (e.g., prompts, prefix-tuning) can adapt a large language model to perform new tasks without requiring access to the model weights, we investigate how to adapt a large pretrained text-to-video model to a variety of downstream domains and tasks without finetuning. In answering this question, we propose Video Adapter, which leverages the score function of a large pretrained video diffusion model as a probabilistic prior to guide the generation of a task-specific small video model. Our experiments show that Video Adapter is capable of incorporating the broad knowledge and preserving the high fidelity of a large pretrained video model in a task-specific small video model that is able to generate high-quality yet specialized videos on a variety of tasks such as animation, egocentric modeling, and modeling of simulated and real-world robotics data. More videos can be found on the website https://video-adapter.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-06", "arxiv_id": "2306.01754", "title": "Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning?", "authors": [ "Aaron Chan", "Anant Kharkar", "Roshanak Zilouchian Moghaddam", "Yevhen Mohylevskyy", "Alec Helyar", "Eslam Kamal", "Mohamed Elkamhawy", "Neel Sundaresan" ], "github": "", "project_page": "", "abstract": "Software vulnerabilities bear enterprises significant costs. Despite extensive efforts in research and development of software vulnerability detection methods, uncaught vulnerabilities continue to put software owners and users at risk. Many current vulnerability detection methods require that code snippets can compile and build before attempting detection. This, unfortunately, introduces a long latency between the time a vulnerability is injected to the time it is removed, which can substantially increases the cost of fixing a vulnerability. We recognize that the current advances in machine learning can be used to detect vulnerable code patterns on syntactically incomplete code snippets as the developer is writing the code at EditTime. In this paper we present a practical system that leverages deep learning on a large-scale data set of vulnerable code patterns to learn complex manifestations of more than 250 vulnerability types and detect vulnerable code patterns at EditTime. We discuss zero-shot, few-shot, and fine-tuning approaches on state of the art pre-trained Large Language Models (LLMs). We show that in comparison with state of the art vulnerability detection models our approach improves the state of the art by 10%. We also evaluate our approach to detect vulnerability in auto-generated code by code LLMs. Evaluation on a benchmark of high-risk code scenarios shows a reduction of up to 90% vulnerability reduction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-07", "arxiv_id": "2306.03514", "title": "Recognize Anything: A Strong Image Tagging Model", "authors": [ "Youcai Zhang", "Xinyu Huang", "Jinyu Ma", "Zhaoyang Li", "Zhaochuan Luo", "Yanchun Xie", "Yuzhuo Qin", "Tong Luo", "Yaqian Li", "Shilong Liu", "Yandong Guo", "Lei Zhang" ], "github": "https://github.com/xinyu1205/Recognize_Anything-Tag2Text", "project_page": "", "abstract": "We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM can recognize any common category with high accuracy. RAM introduces a new paradigm for image tagging, leveraging large-scale image-text pairs for training instead of manual annotations. The development of RAM comprises four key steps. Firstly, annotation-free image tags are obtained at scale through automatic text semantic parsing. Subsequently, a preliminary model is trained for automatic annotation by unifying the caption and tagging tasks, supervised by the original texts and parsed tags, respectively. Thirdly, a data engine is employed to generate additional annotations and clean incorrect ones. Lastly, the model is retrained with the processed data and fine-tuned using a smaller but higher-quality dataset. We evaluate the tagging capabilities of RAM on numerous benchmarks and observe impressive zero-shot performance, significantly outperforming CLIP and BLIP. Remarkably, RAM even surpasses the fully supervised manners and exhibits competitive performance with the Google API. We are releasing the RAM at https://recognize-anything.github.io/ to foster the advancements of large models in computer vision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xinyu1205/Recognize_Anything-Tag2Text", "github_api_added_by": "auto" }, { "date": "2023-06-07", "arxiv_id": "2306.03881", "title": "Emergent Correspondence from Image Diffusion", "authors": [ "Luming Tang", "Menglin Jia", "Qianqian Wang", "Cheng Perng Phoo", "Bharath Hariharan" ], "github": "https://github.com/Tsingularity/dift", "project_page": "", "abstract": "Finding correspondences between images is a fundamental problem in computer vision. In this paper, we show that correspondence emerges in image diffusion models without any explicit supervision. We propose a simple strategy to extract this implicit knowledge out of diffusion networks as image features, namely DIffusion FeaTures (DIFT), and use them to establish correspondences between real images. Without any additional fine-tuning or supervision on the task-specific data or annotations, DIFT is able to outperform both weakly-supervised methods and competitive off-the-shelf features in identifying semantic, geometric, and temporal correspondences. Particularly for semantic correspondence, DIFT from Stable Diffusion is able to outperform DINO and OpenCLIP by 19 and 14 accuracy points respectively on the challenging SPair-71k benchmark. It even outperforms the state-of-the-art supervised methods on 9 out of 18 categories while remaining on par for the overall performance. Project page: https://diffusionfeatures.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tsingularity/dift", "github_api_added_by": "auto" }, { "date": "2023-06-07", "arxiv_id": "2306.03504", "title": "Ada-TTA: Towards Adaptive High-Quality Text-to-Talking Avatar Synthesis", "authors": [ "Zhenhui Ye", "Ziyue Jiang", "Yi Ren", "Jinglin Liu", "Chen Zhang", "Xiang Yin", "Zejun Ma", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "We are interested in a novel task, namely low-resource text-to-talking avatar. Given only a few-minute-long talking person video with the audio track as the training data and arbitrary texts as the driving input, we aim to synthesize high-quality talking portrait videos corresponding to the input text. This task has broad application prospects in the digital human industry but has not been technically achieved yet due to two challenges: (1) It is challenging to mimic the timbre from out-of-domain audio for a traditional multi-speaker Text-to-Speech system. (2) It is hard to render high-fidelity and lip-synchronized talking avatars with limited training data. In this paper, we introduce Adaptive Text-to-Talking Avatar (Ada-TTA), which (1) designs a generic zero-shot multi-speaker TTS model that well disentangles the text content, timbre, and prosody; and (2) embraces recent advances in neural rendering to achieve realistic audio-driven talking face video generation. With these designs, our method overcomes the aforementioned two challenges and achieves to generate identity-preserving speech and realistic talking person video. Experiments demonstrate that our method could synthesize realistic, identity-preserving, and audio-visual synchronized talking avatar videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-07", "arxiv_id": "2306.03509", "title": "Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias", "authors": [ "Ziyue Jiang", "Yi Ren", "Zhenhui Ye", "Jinglin Liu", "Chen Zhang", "Qian Yang", "Shengpeng Ji", "Rongjie Huang", "Chunfeng Wang", "Xiang Yin", "Zejun Ma", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "Scaling text-to-speech to a large and wild dataset has been proven to be highly effective in achieving timbre and speech style generalization, particularly in zero-shot TTS. However, previous works usually encode speech into latent using audio codec and use autoregressive language models or diffusion models to generate it, which ignores the intrinsic nature of speech and may lead to inferior or uncontrollable results. We argue that speech can be decomposed into several attributes (e.g., content, timbre, prosody, and phase) and each of them should be modeled using a module with appropriate inductive biases. From this perspective, we carefully design a novel and large zero-shot TTS system called Mega-TTS, which is trained with large-scale wild data and models different attributes in different ways: 1) Instead of using latent encoded by audio codec as the intermediate feature, we still choose spectrogram as it separates the phase and other attributes very well. Phase can be appropriately constructed by the GAN-based vocoder and does not need to be modeled by the language model. 2) We model the timbre using global vectors since timbre is a global attribute that changes slowly over time. 3) We further use a VQGAN-based acoustic model to generate the spectrogram and a latent code language model to fit the distribution of prosody, since prosody changes quickly over time in a sentence, and language models can capture both local and long-range dependencies. We scale Mega-TTS to multi-domain datasets with 20K hours of speech and evaluate its performance on unseen speakers. Experimental results demonstrate that Mega-TTS surpasses state-of-the-art TTS systems on zero-shot TTS, speech editing, and cross-lingual TTS tasks, with superior naturalness, robustness, and speaker similarity due to the proper inductive bias of each module. Audio samples are available at https://mega-tts.github.io/demo-page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-07", "arxiv_id": "2306.03460", "title": "Natural Language Commanding via Program Synthesis", "authors": [ "Apurva Gandhi", "Thong Q. Nguyen", "Huitian Jiao", "Robert Steen", "Ameya Bhatawdekar" ], "github": "", "project_page": "", "abstract": "We present Semantic Interpreter, a natural language-friendly AI system for productivity software such as Microsoft Office that leverages large language models (LLMs) to execute user intent across application features. While LLMs are excellent at understanding user intent expressed as natural language, they are not sufficient for fulfilling application-specific user intent that requires more than text-to-text transformations. We therefore introduce the Office Domain Specific Language (ODSL), a concise, high-level language specialized for performing actions in and interacting with entities in Office applications. Semantic Interpreter leverages an Analysis-Retrieval prompt construction method with LLMs for program synthesis, translating natural language user utterances to ODSL programs that can be transpiled to application APIs and then executed. We focus our discussion primarily on a research exploration for Microsoft PowerPoint.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-07", "arxiv_id": "2306.03203", "title": "A Static Evaluation of Code Completion by Large Language Models", "authors": [ "Hantian Ding", "Varun Kumar", "Yuchen Tian", "Zijian Wang", "Rob Kwiatkowski", "Xiaopeng Li", "Murali Krishna Ramanathan", "Baishakhi Ray", "Parminder Bhatia", "Sudipta Sengupta", "Dan Roth", "Bing Xiang" ], "github": "", "project_page": "", "abstract": "Large language models trained on code have shown great potential to increase productivity of software developers. Several execution-based benchmarks have been proposed to evaluate functional correctness of model-generated code on simple programming problems. Nevertheless, it is expensive to perform the same evaluation on complex real-world projects considering the execution cost. On the contrary, static analysis tools such as linters, which can detect errors without running the program, haven't been well explored for evaluating code generation models. In this work, we propose a static evaluation framework to quantify static errors in Python code completions, by leveraging Abstract Syntax Trees. Compared with execution-based evaluation, our method is not only more efficient, but also applicable to code in the wild. For experiments, we collect code context from open source repos to generate one million function bodies using public models. Our static analysis reveals that Undefined Name and Unused Variable are the most common errors among others made by language models. Through extensive studies, we also show the impact of sampling temperature, model size, and context on static errors in code completions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-07", "arxiv_id": "2306.03872", "title": "Deductive Verification of Chain-of-Thought Reasoning", "authors": [ "Zhan Ling", "Yunhao Fang", "Xuanlin Li", "Zhiao Huang", "Mingu Lee", "Roland Memisevic", "Hao Su" ], "github": "https://github.com/lz1oceani/verify_cot", "project_page": "", "abstract": "Large Language Models (LLMs) significantly benefit from Chain-of-Thought (CoT) prompting in performing various reasoning tasks. While CoT allows models to produce more comprehensive reasoning processes, its emphasis on intermediate reasoning steps can inadvertently introduce hallucinations and accumulated errors, thereby limiting models' ability to solve complex reasoning tasks. Inspired by how humans engage in careful and meticulous deductive logical reasoning processes to solve tasks, we seek to enable language models to perform explicit and rigorous deductive reasoning, and also ensure the trustworthiness of their reasoning process through self-verification. However, directly verifying the validity of an entire deductive reasoning process is challenging, even with advanced models like ChatGPT. In light of this, we propose to decompose a reasoning verification process into a series of step-by-step subprocesses, each only receiving their necessary context and premises. To facilitate this procedure, we propose Natural Program, a natural language-based deductive reasoning format. Our approach enables models to generate precise reasoning steps where subsequent steps are more rigorously grounded on prior steps. It also empowers language models to carry out reasoning self-verification in a step-by-step manner. By integrating this verification process into each deductive reasoning stage, we significantly enhance the rigor and trustfulness of generated reasoning steps. Along this process, we also improve the answer correctness on complex reasoning tasks. Code will be released at https://github.com/lz1oceani/verify_cot.", "github_manual": "", "github_regex": "https://github.com/lz1oceani/verify_cot", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-07", "arxiv_id": "2306.03802", "title": "Learning to Ground Instructional Articles in Videos through Narrations", "authors": [ "Effrosyni Mavroudi", "Triantafyllos Afouras", "Lorenzo Torresani" ], "github": "https://github.com/facebookresearch/SlowFast", "project_page": "", "abstract": "In this paper we present an approach for localizing steps of procedural activities in narrated how-to videos. To deal with the scarcity of labeled data at scale, we source the step descriptions from a language knowledge base (wikiHow) containing instructional articles for a large variety of procedural tasks. Without any form of manual supervision, our model learns to temporally ground the steps of procedural articles in how-to videos by matching three modalities: frames, narrations, and step descriptions. Specifically, our method aligns steps to video by fusing information from two distinct pathways: i) {\\em direct} alignment of step descriptions to frames, ii) {\\em indirect} alignment obtained by composing steps-to-narrations with narrations-to-video correspondences. Notably, our approach performs global temporal grounding of all steps in an article at once by exploiting order information, and is trained with step pseudo-labels which are iteratively refined and aggressively filtered. In order to validate our model we introduce a new evaluation benchmark -- HT-Step -- obtained by manually annotating a 124-hour subset of HowTo100MA test server is accessible at \\url{https://eval.ai/web/challenges/challenge-page/2082.} with steps sourced from wikiHow articles. Experiments on this benchmark as well as zero-shot evaluations on CrossTask demonstrate that our multi-modality alignment yields dramatic gains over several baselines and prior works. Finally, we show that our inner module for matching narration-to-video outperforms by a large margin the state of the art on the HTM-Align narration-video alignment benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/SlowFast", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-07", "arxiv_id": "2306.03819", "title": "LEACE: Perfect linear concept erasure in closed form", "authors": [ "Nora Belrose", "David Schneider-Joseph", "Shauli Ravfogel", "Ryan Cotterell", "Edward Raff", "Stella Biderman" ], "github": "https://github.com/EleutherAI/concept-erasure", "project_page": "", "abstract": "Concept erasure aims to remove specified features from a representation. It can be used to improve fairness (e.g. preventing a classifier from using gender or race) and interpretability (e.g. removing a concept to observe changes in model behavior). In this paper, we introduce LEAst-squares Concept Erasure (LEACE), a closed-form method which provably prevents all linear classifiers from detecting a concept while inflicting the least possible damage to the representation. We apply LEACE to large language models with a novel procedure called \"concept scrubbing,\" which erases target concept information from every layer in the network. We demonstrate the usefulness of our method on two tasks: measuring the reliance of language models on part-of-speech information, and reducing gender bias in BERT embeddings. Code is available at https://github.com/EleutherAI/concept-erasure.", "github_manual": "", "github_regex": "https://github.com/EleutherAI/concept-erasure", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-07", "arxiv_id": "2306.03438", "title": "Large Language Models of Code Fail at Completing Code with Potential Bugs", "authors": [ "Tuan Dinh", "Jinman Zhao", "Samson Tan", "Renato Negrinho", "Leonard Lausen", "Sheng Zha", "George Karypis" ], "github": "https://github.com/amazon-science/buggy-code-completion", "project_page": "", "abstract": "Large language models of code (Code-LLMs) have recently brought tremendous advances to code completion, a fundamental feature of programming assistance and code intelligence. However, most existing works ignore the possible presence of bugs in the code context for generation, which are inevitable in software development. Therefore, we introduce and study the buggy-code completion problem, inspired by the realistic scenario of real-time code suggestion where the code context contains potential bugs -- anti-patterns that can become bugs in the completed program. To systematically study the task, we introduce two datasets: one with synthetic bugs derived from semantics-altering operator changes (buggy-HumanEval) and one with realistic bugs derived from user submissions to coding problems (buggy-FixEval). We find that the presence of potential bugs significantly degrades the generation performance of the high-performing Code-LLMs. For instance, the passing rates of CodeGen-2B-mono on test cases of buggy-HumanEval drop more than 50% given a single potential bug in the context. Finally, we investigate several post-hoc methods for mitigating the adverse effect of potential bugs and find that there remains a large gap in post-mitigation performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/amazon-science/buggy-code-completion", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-08", "arxiv_id": "2306.04387", "title": "M^3IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning", "authors": [ "Lei Li", "Yuwei Yin", "Shicheng Li", "Liang Chen", "Peiyi Wang", "Shuhuai Ren", "Mukai Li", "Yazheng Yang", "Jingjing Xu", "Xu Sun", "Lingpeng Kong", "Qi Liu" ], "github": "", "project_page": "", "abstract": "Instruction tuning has significantly advanced large language models (LLMs) such as ChatGPT, enabling them to align with human instructions across diverse tasks. However, progress in open vision-language models (VLMs) has been limited due to the scarcity of high-quality instruction datasets. To tackle this challenge and promote research in the vision-language field, we introduce the Multi-Modal, Multilingual Instruction Tuning (M^3IT) dataset, designed to optimize VLM alignment with human instructions. Our M^3IT dataset comprises 40 carefully curated datasets, including 2.4 million instances and 400 manually written task instructions, reformatted into a vision-to-text structure. Key tasks are translated into 80 languages with an advanced translation system, ensuring broader accessibility. M^3IT surpasses previous datasets regarding task coverage, instruction number and instance scale. Moreover, we develop Ying-VLM, a VLM model trained on our M^3IT dataset, showcasing its potential to answer complex questions requiring world knowledge, generalize to unseen video tasks, and comprehend unseen instructions in Chinese. To encourage further research, we have open-sourced both the dataset and trained models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-08", "arxiv_id": "2306.04050", "title": "LLMZip: Lossless Text Compression using Large Language Models", "authors": [ "Chandra Shekhara Kaushik Valmeekam", "Krishna Narayanan", "Dileep Kalathil", "Jean-Francois Chamberland", "Srinivas Shakkottai" ], "github": "", "project_page": "", "abstract": "We provide new estimates of an asymptotic upper bound on the entropy of English using the large language model LLaMA-7B as a predictor for the next token given a window of past tokens. This estimate is significantly smaller than currently available estimates in cover1978convergent, lutati2023focus. A natural byproduct is an algorithm for lossless compression of English text which combines the prediction from the large language model with a lossless compression scheme. Preliminary results from limited experiments suggest that our scheme outperforms state-of-the-art text compression schemes such as BSC, ZPAQ, and paq8h.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-08", "arxiv_id": "2306.04619", "title": "ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections", "authors": [ "Chun-Han Yao", "Amit Raj", "Wei-Chih Hung", "Yuanzhen Li", "Michael Rubinstein", "Ming-Hsuan Yang", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "Estimating 3D articulated shapes like animal bodies from monocular images is inherently challenging due to the ambiguities of camera viewpoint, pose, texture, lighting, etc. We propose ARTIC3D, a self-supervised framework to reconstruct per-instance 3D shapes from a sparse image collection in-the-wild. Specifically, ARTIC3D is built upon a skeleton-based surface representation and is further guided by 2D diffusion priors from Stable Diffusion. First, we enhance the input images with occlusions/truncation via 2D diffusion to obtain cleaner mask estimates and semantic features. Second, we perform diffusion-guided 3D optimization to estimate shape and texture that are of high-fidelity and faithful to input images. We also propose a novel technique to calculate more stable image-level gradients via diffusion models compared to existing alternatives. Finally, we produce realistic animations by fine-tuning the rendered shape and texture under rigid part transformations. Extensive evaluations on multiple existing datasets as well as newly introduced noisy web image collections with occlusions and truncation demonstrate that ARTIC3D outputs are more robust to noisy images, higher quality in terms of shape and texture details, and more realistic when animated. Project page: https://chhankyao.github.io/artic3d/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-08", "arxiv_id": "2306.04235", "title": "MobileNMT: Enabling Translation in 15MB and 30ms", "authors": [ "Ye Lin", "Xiaohui Wang", "Zhexi Zhang", "Mingxuan Wang", "Tong Xiao", "Jingbo Zhu" ], "github": "https://github.com/zjersey/Lightseq-ARM", "project_page": "", "abstract": "Deploying NMT models on mobile devices is essential for privacy, low latency, and offline scenarios. For high model capacity, NMT models are rather large. Running these models on devices is challenging with limited storage, memory, computation, and power consumption. Existing work either only focuses on a single metric such as FLOPs or general engine which is not good at auto-regressive decoding. In this paper, we present MobileNMT, a system that can translate in 15MB and 30ms on devices. We propose a series of principles for model compression when combined with quantization. Further, we implement an engine that is friendly to INT8 and decoding. With the co-design of model and engine, compared with the existing system, we speed up 47.0x and save 99.5% of memory with only 11.6% loss of BLEU. The code is publicly available at https://github.com/zjersey/Lightseq-ARM.", "github_manual": "", "github_regex": "https://github.com/zjersey/Lightseq-ARM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-08", "arxiv_id": "2306.04528", "title": "PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts", "authors": [ "Kaijie Zhu", "Jindong Wang", "Jiaheng Zhou", "Zichen Wang", "Hao Chen", "Yidong Wang", "Linyi Yang", "Wei Ye", "Neil Zhenqiang Gong", "Yue Zhang", "Xing Xie" ], "github": "https://github.com/microsoft/promptbench", "project_page": "", "abstract": "The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts. In response to this vital need, we introduce PromptBench, a robustness benchmark designed to measure LLMs' resilience to adversarial prompts. This study uses a plethora of adversarial textual attacks targeting prompts across multiple levels: character, word, sentence, and semantic. These prompts are then employed in diverse tasks, such as sentiment analysis, natural language inference, reading comprehension, machine translation, and math problem-solving. Our study generates 4,032 adversarial prompts, meticulously evaluated over 8 tasks and 13 datasets, with 567,084 test samples in total. Our findings demonstrate that contemporary LLMs are vulnerable to adversarial prompts. Furthermore, we present comprehensive analysis to understand the mystery behind prompt robustness and its transferability. We then offer insightful robustness analysis and pragmatic recommendations for prompt composition, beneficial to both researchers and everyday users. We make our code, prompts, and methodologies to generate adversarial prompts publicly accessible, thereby enabling and encouraging collaborative exploration in this pivotal field: https://github.com/microsoft/promptbench.", "github_manual": "", "github_regex": "https://github.com/microsoft/promptbench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-08", "arxiv_id": "2306.04362", "title": "Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks", "authors": [ "Haiyang Xu", "Qinghao Ye", "Xuan Wu", "Ming Yan", "Yuan Miao", "Jiabo Ye", "Guohai Xu", "Anwen Hu", "Yaya Shi", "Guangwei Xu", "Chenliang Li", "Qi Qian", "Maofei Que", "Ji Zhang", "Xiao Zeng", "Fei Huang" ], "github": "https://github.com/x-plug/youku-mplug", "project_page": "", "abstract": "To promote the development of Vision-Language Pre-training (VLP) and multimodal Large Language Model (LLM) in the Chinese community, we firstly release the largest public Chinese high-quality video-language dataset named Youku-mPLUG, which is collected from Youku, a well-known Chinese video-sharing website, with strict criteria of safety, diversity, and quality. Youku-mPLUG contains 10 million Chinese video-text pairs filtered from 400 million raw videos across a wide range of 45 diverse categories for large-scale pre-training. In addition, to facilitate a comprehensive evaluation of video-language models, we carefully build the largest human-annotated Chinese benchmarks covering three popular video-language tasks of cross-modal retrieval, video captioning, and video category classification. Youku-mPLUG can enable researchers to conduct more in-depth multimodal research and develop better applications in the future. Furthermore, we release popular video-language pre-training models, ALPRO and mPLUG-2, and our proposed modularized decoder-only model mPLUG-video pre-trained on Youku-mPLUG. Experiments show that models pre-trained on Youku-mPLUG gain up to 23.1% improvement in video category classification. Besides, mPLUG-video achieves a new state-of-the-art result on these benchmarks with 80.5% top-1 accuracy in video category classification and 68.9 CIDEr score in video captioning, respectively. Finally, we scale up mPLUG-video based on the frozen Bloomz with only 1.7% trainable parameters as Chinese multimodal LLM, and demonstrate impressive instruction and video understanding ability. The zero-shot instruction understanding experiment indicates that pretraining with Youku-mPLUG can enhance the ability to comprehend overall and detailed visual semantics, recognize scene text, and leverage open-domain knowledge.", "github_manual": "https://github.com/x-plug/youku-mplug", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-08", "arxiv_id": "2306.04632", "title": "Designing a Better Asymmetric VQGAN for StableDiffusion", "authors": [ "Zixin Zhu", "Xuelu Feng", "Dongdong Chen", "Jianmin Bao", "Le Wang", "Yinpeng Chen", "Lu Yuan", "Gang Hua" ], "github": "https://github.com/buxiangzhiren/Asymmetric_VQGAN", "project_page": "", "abstract": "StableDiffusion is a revolutionary text-to-image generator that is causing a stir in the world of image generation and editing. Unlike traditional methods that learn a diffusion model in pixel space, StableDiffusion learns a diffusion model in the latent space via a VQGAN, ensuring both efficiency and quality. It not only supports image generation tasks, but also enables image editing for real images, such as image inpainting and local editing. However, we have observed that the vanilla VQGAN used in StableDiffusion leads to significant information loss, causing distortion artifacts even in non-edited image regions. To this end, we propose a new asymmetric VQGAN with two simple designs. Firstly, in addition to the input from the encoder, the decoder contains a conditional branch that incorporates information from task-specific priors, such as the unmasked image region in inpainting. Secondly, the decoder is much heavier than the encoder, allowing for more detailed recovery while only slightly increasing the total inference cost. The training cost of our asymmetric VQGAN is cheap, and we only need to retrain a new asymmetric decoder while keeping the vanilla VQGAN encoder and StableDiffusion unchanged. Our asymmetric VQGAN can be widely used in StableDiffusion-based inpainting and local editing methods. Extensive experiments demonstrate that it can significantly improve the inpainting and editing performance, while maintaining the original text-to-image capability. The code is available at https://github.com/buxiangzhiren/Asymmetric_VQGAN.", "github_manual": "", "github_regex": "https://github.com/buxiangzhiren/Asymmetric_VQGAN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-08", "arxiv_id": "2306.04634", "title": "On the Reliability of Watermarks for Large Language Models", "authors": [ "John Kirchenbauer", "Jonas Geiping", "Yuxin Wen", "Manli Shu", "Khalid Saifullah", "Kezhi Kong", "Kasun Fernando", "Aniruddha Saha", "Micah Goldblum", "Tom Goldstein" ], "github": "https://github.com/jwkirchenbauer/lm-watermarking", "project_page": "", "abstract": "Large language models (LLMs) are now deployed to everyday use and positioned to produce large quantities of text in the coming decade. Machine-generated text may displace human-written text on the internet and has the potential to be used for malicious purposes, such as spearphishing attacks and social media bots. Watermarking is a simple and effective strategy for mitigating such harms by enabling the detection and documentation of LLM-generated text. Yet, a crucial question remains: How reliable is watermarking in realistic settings in the wild? There, watermarked text might be mixed with other text sources, paraphrased by human writers or other language models, and used for applications in a broad number of domains, both social and technical. In this paper, we explore different detection schemes, quantify their power at detecting watermarks, and determine how much machine-generated text needs to be observed in each scenario to reliably detect the watermark. We especially highlight our human study, where we investigate the reliability of watermarking when faced with human paraphrasing. We compare watermark-based detection to other detection strategies, finding overall that watermarking is a reliable solution, especially because of its sample complexity - for all attacks we consider, the watermark evidence compounds the more examples are given, and the watermark is eventually detected.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jwkirchenbauer/lm-watermarking", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-08", "arxiv_id": "2306.04140", "title": "Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions", "authors": [ "John Joon Young Chung", "Ece Kamar", "Saleema Amershi" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) can be used to generate text data for training and evaluating other models. However, creating high-quality datasets with LLMs can be challenging. In this work, we explore human-AI partnerships to facilitate high diversity and accuracy in LLM-based text data generation. We first examine two approaches to diversify text generation: 1) logit suppression, which minimizes the generation of languages that have already been frequently generated, and 2) temperature sampling, which flattens the token sampling probability. We found that diversification approaches can increase data diversity but often at the cost of data accuracy (i.e., text and labels being appropriate for the target domain). To address this issue, we examined two human interventions, 1) label replacement (LR), correcting misaligned labels, and 2) out-of-scope filtering (OOSF), removing instances that are out of the user's domain of interest or to which no considered label applies. With oracle studies, we found that LR increases the absolute accuracy of models trained with diversified datasets by 14.4%. Moreover, we found that some models trained with data generated with LR interventions outperformed LLM-based few-shot classification. In contrast, OOSF was not effective in increasing model accuracy, implying the need for future work in human-in-the-loop text data generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-08", "arxiv_id": "2306.04076", "title": "Text-only Domain Adaptation using Unified Speech-Text Representation in Transducer", "authors": [ "Lu Huang", "Boyu Li", "Jun Zhang", "Lu Lu", "Zejun Ma" ], "github": "", "project_page": "", "abstract": "Domain adaptation using text-only corpus is challenging in end-to-end(E2E) speech recognition. Adaptation by synthesizing audio from text through TTS is resource-consuming. We present a method to learn Unified Speech-Text Representation in Conformer Transducer(USTR-CT) to enable fast domain adaptation using the text-only corpus. Different from the previous textogram method, an extra text encoder is introduced in our work to learn text representation and is removed during inference, so there is no modification for online deployment. To improve the efficiency of adaptation, single-step and multi-step adaptations are also explored. The experiments on adapting LibriSpeech to SPGISpeech show the proposed method reduces the word error rate(WER) by relatively 44% on the target domain, which is better than those of TTS method and textogram method. Also, it is shown the proposed method can be combined with internal language model estimation(ILME) to further improve the performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-08", "arxiv_id": "2306.04031", "title": "Certified Reasoning with Language Models", "authors": [ "Gabriel Poesia", "Kanishk Gandhi", "Eric Zelikman", "Noah D. Goodman" ], "github": "https://github.com/gpoesia/certified-reasoning", "project_page": "", "abstract": "Language models often achieve higher accuracy when reasoning step-by-step in complex tasks. However, their reasoning can be unsound, inconsistent, or rely on undesirable prior assumptions. To tackle these issues, we introduce a class of tools for language models called guides that use state and incremental constraints to guide generation. A guide can be invoked by the model to constrain its own generation to a set of valid statements given by the tool. In turn, the model's choices can change the guide's state. We show how a general system for logical reasoning can be used as a guide, which we call LogicGuide. Given a reasoning problem in natural language, a model can formalize its assumptions for LogicGuide and then guarantee that its reasoning steps are sound. In experiments with the PrOntoQA and ProofWriter reasoning datasets, LogicGuide significantly improves the performance of GPT-3, GPT-3.5 Turbo and LLaMA (accuracy gains up to 35%). LogicGuide also drastically reduces content effects: the interference of prior and current assumptions that both humans and language models have been shown to suffer from. Finally, we explore bootstrapping LLaMA 13B from its own reasoning and find that LogicGuide is critical: by training only on certified self-generated reasoning, LLaMA can self-improve, avoiding learning from its own hallucinations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gpoesia/certified-reasoning", "github_api_added_by": "auto" }, { "date": "2023-06-08", "arxiv_id": "2306.04009", "title": "Triggering Multi-Hop Reasoning for Question Answering in Language Models using Soft Prompts and Random Walks", "authors": [ "Kanishka Misra", "Cicero Nogueira dos Santos", "Siamak Shakeri" ], "github": "", "project_page": "", "abstract": "Despite readily memorizing world knowledge about entities, pre-trained language models (LMs) struggle to compose together two or more facts to perform multi-hop reasoning in question-answering tasks. In this work, we propose techniques that improve upon this limitation by relying on random walks over structured knowledge graphs. Specifically, we use soft prompts to guide LMs to chain together their encoded knowledge by learning to map multi-hop questions to random walk paths that lead to the answer. Applying our methods on two T5 LMs shows substantial improvements over standard tuning approaches in answering questions that require 2-hop reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05284", "title": "Simple and Controllable Music Generation", "authors": [ "Jade Copet", "Felix Kreuk", "Itai Gat", "Tal Remez", "David Kant", "Gabriel Synnaeve", "Yossi Adi", "Alexandre D\u00e9fossez" ], "github": "https://github.com/facebookresearch/audiocraft", "project_page": "", "abstract": "We tackle the task of conditional music generation. We introduce MusicGen, a single Language Model (LM) that operates over several streams of compressed discrete music representation, i.e., tokens. Unlike prior work, MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns, which eliminates the need for cascading several models, e.g., hierarchically or upsampling. Following this approach, we demonstrate how MusicGen can generate high-quality samples, while being conditioned on textual description or melodic features, allowing better controls over the generated output. We conduct extensive empirical evaluation, considering both automatic and human studies, showing the proposed approach is superior to the evaluated baselines on a standard text-to-music benchmark. Through ablation studies, we shed light over the importance of each of the components comprising MusicGen. Music samples, code, and models are available at https://github.com/facebookresearch/audiocraft.", "github_manual": "", "github_regex": "https://github.com/facebookresearch/audiocraft", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05422", "title": "Tracking Everything Everywhere All at Once", "authors": [ "Qianqian Wang", "Yen-Yu Chang", "Ruojin Cai", "Zhengqi Li", "Bharath Hariharan", "Aleksander Holynski", "Noah Snavely" ], "github": "https://github.com/qianqianwang68/omnimotion", "project_page": "", "abstract": "We present a new test-time optimization method for estimating dense and long-range motion from a video sequence. Prior optical flow or particle video tracking algorithms typically operate within limited temporal windows, struggling to track through occlusions and maintain global consistency of estimated motion trajectories. We propose a complete and globally consistent motion representation, dubbed OmniMotion, that allows for accurate, full-length motion estimation of every pixel in a video. OmniMotion represents a video using a quasi-3D canonical volume and performs pixel-wise tracking via bijections between local and canonical space. This representation allows us to ensure global consistency, track through occlusions, and model any combination of camera and object motion. Extensive evaluations on the TAP-Vid benchmark and real-world footage show that our approach outperforms prior state-of-the-art methods by a large margin both quantitatively and qualitatively. See our project page for more results: http://omnimotion.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qianqianwang68/omnimotion", "github_api_added_by": "auto" }, { "date": "2023-06-09", "arxiv_id": "2306.05399", "title": "Matting Anything", "authors": [ "Jiachen Li", "Jitesh Jain", "Humphrey Shi" ], "github": "https://github.com/SHI-Labs/Matting-Anything", "project_page": "", "abstract": "In this paper, we propose the Matting Anything Model (MAM), an efficient and versatile framework for estimating the alpha matte of any instance in an image with flexible and interactive visual or linguistic user prompt guidance. MAM offers several significant advantages over previous specialized image matting networks: (i) MAM is capable of dealing with various types of image matting, including semantic, instance, and referring image matting with only a single model; (ii) MAM leverages the feature maps from the Segment Anything Model (SAM) and adopts a lightweight Mask-to-Matte (M2M) module to predict the alpha matte through iterative refinement, which has only 2.7 million trainable parameters. (iii) By incorporating SAM, MAM simplifies the user intervention required for the interactive use of image matting from the trimap to the box, point, or text prompt. We evaluate the performance of MAM on various image matting benchmarks, and the experimental results demonstrate that MAM achieves comparable performance to the state-of-the-art specialized image matting models under different metrics on each benchmark. Overall, MAM shows superior generalization ability and can effectively handle various image matting tasks with fewer parameters, making it a practical solution for unified image matting. Our code and models are open-sourced at https://github.com/SHI-Labs/Matting-Anything.", "github_manual": "", "github_regex": "https://github.com/SHI-Labs/Matting-Anything", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05424", "title": "Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models", "authors": [ "Muhammad Maaz", "Hanoona Rasheed", "Salman Khan", "Fahad Shahbaz Khan" ], "github": "https://github.com/mbzuai-oryx/Video-ChatGPT", "project_page": "", "abstract": "Conversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data. While there have been initial attempts for image-based conversation models, this work addresses the underexplored field of video-based conversation by introducing Video-ChatGPT. It is a multimodal model that merges a video-adapted visual encoder with a LLM. The model is capable of understanding and generating human-like conversations about videos. We introduce a new dataset of 100,000 video-instruction pairs used to train Video-ChatGPT acquired via manual and semi-automated pipeline that is easily scalable and robust to label noise. We also develop a quantiative evaluation framework for video-based dialogue models to objectively analyse the strengths and weaknesses of proposed models. Our code, models, instruction-sets and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.", "github_manual": "", "github_regex": "https://github.com/mbzuai-oryx/Video-ChatGPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05087", "title": "PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization", "authors": [ "Yidong Wang", "Zhuohao Yu", "Zhengran Zeng", "Linyi Yang", "Cunxiang Wang", "Hao Chen", "Chaoya Jiang", "Rui Xie", "Jindong Wang", "Xing Xie", "Wei Ye", "Shikun Zhang", "Yue Zhang" ], "github": "https://github.com/WeOpenML/PandaLM", "project_page": "", "abstract": "Instruction tuning large language models (LLMs) remains a challenging task, owing to the complexity of hyperparameter selection and the difficulty involved in evaluating the tuned models. To determine the optimal hyperparameters, an automatic, robust, and reliable evaluation benchmark is essential. However, establishing such a benchmark is not a trivial task due to the challenges associated with evaluation accuracy and privacy protection. In response to these challenges, we introduce a judge large language model, named PandaLM, which is trained to distinguish the superior model given several LLMs. PandaLM's focus extends beyond just the objective correctness of responses, which is the main focus of traditional evaluation datasets. It addresses vital subjective factors such as relative conciseness, clarity, adherence to instructions, comprehensiveness, and formality. To ensure the reliability of PandaLM, we collect a diverse human-annotated test dataset, where all contexts are generated by humans and labels are aligned with human preferences. Our results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM enables the evaluation of LLM to be fairer but with less cost, evidenced by significant improvements achieved by models tuned through PandaLM compared to their counterparts trained with default Alpaca's hyperparameters. In addition, PandaLM does not depend on API-based evaluations, thus avoiding potential data leakage. All resources of PandaLM are released at https://github.com/WeOpenML/PandaLM.", "github_manual": "", "github_regex": "https://github.com/WeOpenML/PandaLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.04757", "title": "INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models", "authors": [ "Yew Ken Chia", "Pengfei Hong", "Lidong Bing", "Soujanya Poria" ], "github": "https://github.com/declare-lab/instruct-eval", "project_page": "", "abstract": "Instruction-tuned large language models have revolutionized natural language processing and have shown great potential in applications such as conversational agents. These models, such as GPT-4, can not only master language but also solve complex tasks in areas like mathematics, coding, medicine, and law. Despite their impressive capabilities, there is still a lack of comprehensive understanding regarding their full potential, primarily due to the black-box nature of many models and the absence of holistic evaluation studies. To address these challenges, we present INSTRUCTEVAL, a more comprehensive evaluation suite designed specifically for instruction-tuned large language models. Unlike previous works, our evaluation involves a rigorous assessment of models based on problem-solving, writing ability, and alignment to human values. We take a holistic approach to analyze various factors affecting model performance, including the pretraining foundation, instruction-tuning data, and training methods. Our findings reveal that the quality of instruction data is the most crucial factor in scaling model performance. While open-source models demonstrate impressive writing abilities, there is substantial room for improvement in problem-solving and alignment. We are encouraged by the rapid development of models by the open-source community, but we also highlight the need for rigorous evaluation to support claims made about these models. Through INSTRUCTEVAL, we aim to foster a deeper understanding of instruction-tuned models and advancements in their capabilities. INSTRUCTEVAL is publicly available at https://github.com/declare-lab/instruct-eval.", "github_manual": "", "github_regex": "https://github.com/declare-lab/instruct-eval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05428", "title": "Background Prompting for Improved Object Depth", "authors": [ "Manel Baradad", "Yuanzhen Li", "Forrester Cole", "Michael Rubinstein", "Antonio Torralba", "William T. Freeman", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "Estimating the depth of objects from a single image is a valuable task for many vision, robotics, and graphics applications. However, current methods often fail to produce accurate depth for objects in diverse scenes. In this work, we propose a simple yet effective Background Prompting strategy that adapts the input object image with a learned background. We learn the background prompts only using small-scale synthetic object datasets. To infer object depth on a real image, we place the segmented object into the learned background prompt and run off-the-shelf depth networks. Background Prompting helps the depth networks focus on the foreground object, as they are made invariant to background variations. Moreover, Background Prompting minimizes the domain gap between synthetic and real object images, leading to better sim2real generalization than simple finetuning. Results on multiple synthetic and real datasets demonstrate consistent improvements in real object depths for a variety of existing depth networks. Code and optimized background prompts can be found at: https://mbaradad.github.io/depth_prompt.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05178", "title": "SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions", "authors": [ "Yuseung Lee", "Kunho Kim", "Hyunjin Kim", "Minhyuk Sung" ], "github": "", "project_page": "", "abstract": "The remarkable capabilities of pretrained image diffusion models have been utilized not only for generating fixed-size images but also for creating panoramas. However, naive stitching of multiple images often results in visible seams. Recent techniques have attempted to address this issue by performing joint diffusions in multiple windows and averaging latent features in overlapping regions. However, these approaches, which focus on seamless montage generation, often yield incoherent outputs by blending different scenes within a single image. To overcome this limitation, we propose SyncDiffusion, a plug-and-play module that synchronizes multiple diffusions through gradient descent from a perceptual similarity loss. Specifically, we compute the gradient of the perceptual loss using the predicted denoised images at each denoising step, providing meaningful guidance for achieving coherent montages. Our experimental results demonstrate that our method produces significantly more coherent outputs compared to previous methods (66.35% vs. 33.65% in our user study) while still maintaining fidelity (as assessed by GIQA) and compatibility with the input prompt (as measured by CLIP score).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05425", "title": "MIMIC-IT: Multi-Modal In-Context Instruction Tuning", "authors": [ "Bo Li", "Yuanhan Zhang", "Liangyu Chen", "Jinghao Wang", "Fanyi Pu", "Jingkang Yang", "Chunyuan Li", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "High-quality instructions and responses are essential for the zero-shot performance of large language models on interactive natural language tasks. For interactive vision-language tasks involving intricate visual scenes, a large quantity of diverse and creative instruction-response pairs should be imperative to tune vision-language models (VLMs). Nevertheless, the current availability of vision-language instruction-response pairs in terms of quantity, diversity, and creativity remains limited, posing challenges to the generalization of interactive VLMs. Here we present MultI-Modal In-Context Instruction Tuning (MIMIC-IT), a dataset comprising 2.8 million multimodal instruction-response pairs, with 2.2 million unique instructions derived from images and videos. Each pair is accompanied by multi-modal in-context information, forming conversational contexts aimed at empowering VLMs in perception, reasoning, and planning. The instruction-response collection process, dubbed as Syphus, is scaled using an automatic annotation pipeline that combines human expertise with GPT's capabilities. Using the MIMIC-IT dataset, we train a large VLM named Otter. Based on extensive evaluations conducted on vision-language benchmarks, it has been observed that Otter demonstrates remarkable proficiency in multi-modal perception, reasoning, and in-context learning. Human evaluation reveals it effectively aligns with the user's intentions. We release the MIMIC-IT dataset, instruction-response collection pipeline, benchmarks, and the Otter model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.04707", "title": "Improving Open Language Models by Learning from Organic Interactions", "authors": [ "Jing Xu", "Da Ju", "Joshua Lane", "Mojtaba Komeili", "Eric Michael Smith", "Megan Ung", "Morteza Behrooz", "William Ngan", "Rashel Moritz", "Sainbayar Sukhbaatar", "Y-Lan Boureau", "Jason Weston", "Kurt Shuster" ], "github": "", "project_page": "", "abstract": "We present BlenderBot 3x, an update on the conversational model BlenderBot 3, which is now trained using organic conversation and feedback data from participating users of the system in order to improve both its skills and safety. We are publicly releasing the participating de-identified interaction data for use by the research community, in order to spur further progress. Training models with organic data is challenging because interactions with people \"in the wild\" include both high quality conversations and feedback, as well as adversarial and toxic behavior. We study techniques that enable learning from helpful teachers while avoiding learning from people who are trying to trick the model into unhelpful or toxic responses. BlenderBot 3x is both preferred in conversation to BlenderBot 3, and is shown to produce safer responses in challenging situations. While our current models are still far from perfect, we believe further improvement can be achieved by continued use of the techniques explored in this work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.04751", "title": "How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources", "authors": [ "Yizhong Wang", "Hamish Ivison", "Pradeep Dasigi", "Jack Hessel", "Tushar Khot", "Khyathi Raghavi Chandu", "David Wadden", "Kelsey MacMillan", "Noah A. Smith", "Iz Beltagy", "Hannaneh Hajishirzi" ], "github": "https://github.com/allenai/open-instruct", "project_page": "", "abstract": "In this work we explore recent advances in instruction-tuning language models on a range of open instruction-following datasets. Despite recent claims that open models can be on par with state-of-the-art proprietary models, these claims are often accompanied by limited evaluation, making it difficult to compare models across the board and determine the utility of various resources. We provide a large set of instruction-tuned models from 6.7B to 65B parameters in size, trained on 12 instruction datasets ranging from manually curated (e.g., OpenAssistant) to synthetic and distilled (e.g., Alpaca) and systematically evaluate them on their factual knowledge, reasoning, multilinguality, coding, and open-ended instruction following abilities through a collection of automatic, model-based, and human-based metrics. We further introduce T\\\"ulu, our best performing instruction-tuned model suite finetuned on a combination of high-quality open resources. Our experiments show that different instruction-tuning datasets can uncover or enhance specific skills, while no single dataset (or combination) provides the best performance across all evaluations. Interestingly, we find that model and human preference-based evaluations fail to reflect differences in model capabilities exposed by benchmark-based evaluations, suggesting the need for the type of systemic evaluation performed in this work. Our evaluations show that the best model in any given evaluation reaches on average 83% of ChatGPT performance, and 68% of GPT-4 performance, suggesting that further investment in building better base models and instruction-tuning data is required to close the gap. We release our instruction-tuned models, including a fully finetuned 65B T\\\"ulu, along with our code, data, and evaluation framework at https://github.com/allenai/open-instruct to facilitate future research.", "github_manual": "", "github_regex": "https://github.com/allenai/open-instruct", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05410", "title": "LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs", "authors": [ "Zezhou Cheng", "Carlos Esteves", "Varun Jampani", "Abhishek Kar", "Subhransu Maji", "Ameesh Makadia" ], "github": "", "project_page": "", "abstract": "A critical obstacle preventing NeRF models from being deployed broadly in the wild is their reliance on accurate camera poses. Consequently, there is growing interest in extending NeRF models to jointly optimize camera poses and scene representation, which offers an alternative to off-the-shelf SfM pipelines which have well-understood failure modes. Existing approaches for unposed NeRF operate under limited assumptions, such as a prior pose distribution or coarse pose initialization, making them less effective in a general setting. In this work, we propose a novel approach, LU-NeRF, that jointly estimates camera poses and neural radiance fields with relaxed assumptions on pose configuration. Our approach operates in a local-to-global manner, where we first optimize over local subsets of the data, dubbed mini-scenes. LU-NeRF estimates local pose and geometry for this challenging few-shot task. The mini-scene poses are brought into a global reference frame through a robust pose synchronization step, where a final global optimization of pose and scene can be performed. We show our LU-NeRF pipeline outperforms prior attempts at unposed NeRF without making restrictive assumptions on the pose prior. This allows us to operate in the general SE(3) pose setting, unlike the baselines. Our results also indicate our model can be complementary to feature-based SfM pipelines as it compares favorably to COLMAP on low-texture and low-resolution images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05357", "title": "Unsupervised Compositional Concepts Discovery with Text-to-Image Generative Models", "authors": [ "Nan Liu", "Yilun Du", "Shuang Li", "Joshua B. Tenenbaum", "Antonio Torralba" ], "github": "", "project_page": "", "abstract": "Text-to-image generative models have enabled high-resolution image synthesis across different domains, but require users to specify the content they wish to generate. In this paper, we consider the inverse problem -- given a collection of different images, can we discover the generative concepts that represent each image? We present an unsupervised approach to discover generative concepts from a collection of images, disentangling different art styles in paintings, objects, and lighting from kitchen scenes, and discovering image classes given ImageNet images. We show how such generative concepts can accurately represent the content of images, be recombined and composed to generate new artistic and hybrid images, and be further used as a representation for downstream classification tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.04822", "title": "Optimizing ViViT Training: Time and Memory Reduction for Action Recognition", "authors": [ "Shreyank N Gowda", "Anurag Arnab", "Jonathan Huang" ], "github": "", "project_page": "", "abstract": "In this paper, we address the challenges posed by the substantial training time and memory consumption associated with video transformers, focusing on the ViViT (Video Vision Transformer) model, in particular the Factorised Encoder version, as our baseline for action recognition tasks. The factorised encoder variant follows the late-fusion approach that is adopted by many state of the art approaches. Despite standing out for its favorable speed/accuracy tradeoffs among the different variants of ViViT, its considerable training time and memory requirements still pose a significant barrier to entry. Our method is designed to lower this barrier and is based on the idea of freezing the spatial transformer during training. This leads to a low accuracy model if naively done. But we show that by (1) appropriately initializing the temporal transformer (a module responsible for processing temporal information) (2) introducing a compact adapter model connecting frozen spatial representations ((a module that selectively focuses on regions of the input image) to the temporal transformer, we can enjoy the benefits of freezing the spatial transformer without sacrificing accuracy. Through extensive experimentation over 6 benchmarks, we demonstrate that our proposed training strategy significantly reduces training costs (by sim 50%) and memory consumption while maintaining or slightly improving performance by up to 1.79\\% compared to the baseline model. Our approach additionally unlocks the capability to utilize larger image transformer models as our spatial transformer and access more frames with the same memory consumption.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05392", "title": "Modular Visual Question Answering via Code Generation", "authors": [ "Sanjay Subramanian", "Medhini Narasimhan", "Kushal Khangaonkar", "Kevin Yang", "Arsha Nagrani", "Cordelia Schmid", "Andy Zeng", "Trevor Darrell", "Dan Klein" ], "github": "https://github.com/sanjayss34/codevqa", "project_page": "", "abstract": "We present a framework that formulates visual question answering as modular code generation. In contrast to prior work on modular approaches to VQA, our approach requires no additional training and relies on pre-trained language models (LMs), visual models pre-trained on image-caption pairs, and fifty VQA examples used for in-context learning. The generated Python programs invoke and compose the outputs of the visual models using arithmetic and conditional logic. Our approach improves accuracy on the COVR dataset by at least 3% and on the GQA dataset by roughly 2% compared to the few-shot baseline that does not employ code generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sanjayss34/codevqa", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.04845", "title": "Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts", "authors": [ "Ganesh Jawahar", "Haichuan Yang", "Yunyang Xiong", "Zechun Liu", "Dilin Wang", "Fei Sun", "Meng Li", "Aasish Pappu", "Barlas Oguz", "Muhammad Abdul-Mageed", "Laks V. S. Lakshmanan", "Raghuraman Krishnamoorthi", "Vikas Chandra" ], "github": "https://github.com/ubc-nlp/mos", "project_page": "", "abstract": "Weight-sharing supernet has become a vital component for performance estimation in the state-of-the-art (SOTA) neural architecture search (NAS) frameworks. Although supernet can directly generate different subnetworks without retraining, there is no guarantee for the quality of these subnetworks because of weight sharing. In NLP tasks such as machine translation and pre-trained language modeling, we observe that given the same model architecture, there is a large performance gap between supernet and training from scratch. Hence, supernet cannot be directly used and retraining is necessary after finding the optimal architectures. In this work, we propose mixture-of-supernets, a generalized supernet formulation where mixture-of-experts (MoE) is adopted to enhance the expressive power of the supernet model, with negligible training overhead. In this way, different subnetworks do not share the model weights directly, but through an architecture-based routing mechanism. As a result, model weights of different subnetworks are customized towards their specific architectures and the weight generation is learned by gradient descent. Compared to existing weight-sharing supernet for NLP, our method can minimize the retraining time, greatly improving training efficiency. In addition, the proposed method achieves the SOTA performance in NAS for building fast machine translation models, yielding better latency-BLEU tradeoff compared to HAT, state-of-the-art NAS for MT. We also achieve the SOTA performance in NAS for building memory-efficient task-agnostic BERT models, outperforming NAS-BERT and AutoDistil in various model sizes.", "github_manual": "https://github.com/ubc-nlp/mos", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05420", "title": "Scaling Spherical CNNs", "authors": [ "Carlos Esteves", "Jean-Jacques Slotine", "Ameesh Makadia" ], "github": "https://github.com/google-research/spherical-cnn", "project_page": "", "abstract": "Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical convolutions is in the spectral domain (via the convolution theorem), which is still costlier than the usual planar convolutions. For this reason, applications of spherical CNNs have so far been limited to small problems that can be approached with low model capacity. In this work, we show how spherical CNNs can be scaled for much larger problems. To achieve this, we make critical improvements including novel variants of common model components, an implementation of core operations to exploit hardware accelerator characteristics, and application-specific input representations that exploit the properties of our model. Experiments show our larger spherical CNNs reach state-of-the-art on several targets of the QM9 molecular benchmark, which was previously dominated by equivariant graph neural networks, and achieve competitive performance on multiple weather forecasting tasks. Our code is available at https://github.com/google-research/spherical-cnn.", "github_manual": "", "github_regex": "https://github.com/google-research/spherical-cnn", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05411", "title": "R-MAE: Regions Meet Masked Autoencoders", "authors": [ "Duy-Kien Nguyen", "Vaibhav Aggarwal", "Yanghao Li", "Martin R. Oswald", "Alexander Kirillov", "Cees G. M. Snoek", "Xinlei Chen" ], "github": "https://github.com/facebookresearch/r-mae", "project_page": "", "abstract": "Vision-specific concepts such as \"region\" have played a key role in extending general machine learning frameworks to tasks like object detection. Given the success of region-based detectors for supervised learning and the progress of intra-image methods for contrastive learning, we explore the use of regions for reconstructive pre-training. Starting from Masked Autoencoding (MAE) both as a baseline and an inspiration, we propose a parallel pre-text task tailored to address the one-to-many mapping between images and regions. Since such regions can be generated in an unsupervised way, our approach (R-MAE) inherits the wide applicability from MAE, while being more \"region-aware\". We conduct thorough analyses during the development of R-MAE, and converge on a variant that is both effective and efficient (1.3% overhead over MAE). Moreover, it shows consistent quantitative improvements when generalized to various pre-training data and downstream detection and segmentation benchmarks. Finally, we provide extensive qualitative visualizations to enhance the understanding of R-MAE's behaviour and potential. Code will be made available at https://github.com/facebookresearch/r-mae.", "github_manual": "", "github_regex": "https://github.com/facebookresearch/r-mae", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-09", "arxiv_id": "2306.05427", "title": "Grounded Text-to-Image Synthesis with Attention Refocusing", "authors": [ "Quynh Phung", "Songwei Ge", "Jia-Bin Huang" ], "github": "", "project_page": "", "abstract": "Driven by scalable diffusion models trained on large-scale paired text-image datasets, text-to-image synthesis methods have shown compelling results. However, these models still fail to precisely follow the text prompt when multiple objects, attributes, and spatial compositions are involved in the prompt. In this paper, we identify the potential reasons in both the cross-attention and self-attention layers of the diffusion model. We propose two novel losses to refocus the attention maps according to a given layout during the sampling process. We perform comprehensive experiments on the DrawBench and HRS benchmarks using layouts synthesized by Large Language Models, showing that our proposed losses can be integrated easily and effectively into existing text-to-image methods and consistently improve their alignment between the generated images and the text prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-12", "arxiv_id": "2306.06070", "title": "Mind2Web: Towards a Generalist Agent for the Web", "authors": [ "Xiang Deng", "Yu Gu", "Boyuan Zheng", "Shijie Chen", "Samuel Stevens", "Boshi Wang", "Huan Sun", "Yu Su" ], "github": "", "project_page": "", "abstract": "We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on Mind2Web, we conduct an initial exploration of using large language models (LLMs) for building generalist web agents. While the raw HTML of real-world websites are often too large to be fed to LLMs, we show that first filtering it with a small LM significantly improves the effectiveness and efficiency of LLMs. Our solution demonstrates a decent level of performance, even on websites or entire domains the model has never seen before, but there is still a substantial room to improve towards truly generalizable agents. We open-source our dataset, model implementation, and trained models (https://osu-nlp-group.github.io/Mind2Web) to facilitate further research on building a generalist agent for the web.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-12", "arxiv_id": "2306.05544", "title": "BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping", "authors": [ "Jiatao Gu", "Shuangfei Zhai", "Yizhe Zhang", "Lingjie Liu", "Josh Susskind" ], "github": "https://github.com/deep-floyd/IF", "project_page": "", "abstract": "Diffusion models have demonstrated excellent potential for generating diverse images. However, their performance often suffers from slow generation due to iterative denoising. Knowledge distillation has been recently proposed as a remedy that can reduce the number of inference steps to one or a few without significant quality degradation. However, existing distillation methods either require significant amounts of offline computation for generating synthetic training data from the teacher model or need to perform expensive online learning with the help of real data. In this work, we present a novel technique called BOOT, that overcomes these limitations with an efficient data-free distillation algorithm. The core idea is to learn a time-conditioned model that predicts the output of a pre-trained diffusion model teacher given any time step. Such a model can be efficiently trained based on bootstrapping from two consecutive sampled steps. Furthermore, our method can be easily adapted to large-scale text-to-image diffusion models, which are challenging for conventional methods given the fact that the training sets are often large and difficult to access. We demonstrate the effectiveness of our approach on several benchmark datasets in the DDIM setting, achieving comparable generation quality while being orders of magnitude faster than the diffusion teacher. The text-to-image results show that the proposed approach is able to handle highly complex distributions, shedding light on more efficient generative modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/deep-floyd/IF", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-12", "arxiv_id": "2306.06044", "title": "GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields", "authors": [ "Barbara Roessle", "Norman M\u00fcller", "Lorenzo Porzi", "Samuel Rota Bul\u00f2", "Peter Kontschieder", "Matthias Nie\u00dfner" ], "github": "", "project_page": "", "abstract": "Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results; nonetheless, even thorough recordings yield imperfections in reconstructions, for instance due to poorly observed areas or minor lighting changes. Our goal is to mitigate these imperfections from various sources with a joint solution: we take advantage of the ability of generative adversarial networks (GANs) to produce realistic images and use them to enhance realism in 3D scene reconstruction with NeRFs. To this end, we learn the patch distribution of a scene using an adversarial discriminator, which provides feedback to the radiance field reconstruction, thus improving realism in a 3D-consistent fashion. Thereby, rendering artifacts are repaired directly in the underlying 3D representation by imposing multi-view path rendering constraints. In addition, we condition a generator with multi-resolution NeRF renderings which is adversarially trained to further improve rendering quality. We demonstrate that our approach significantly improves rendering quality, e.g., nearly halving LPIPS scores compared to Nerfacto while at the same time improving PSNR by 1.4dB on the advanced indoor scenes of Tanks and Temples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-12", "arxiv_id": "2306.05949", "title": "Evaluating the Social Impact of Generative AI Systems in Systems and Society", "authors": [ "Irene Solaiman", "Zeerak Talat", "William Agnew", "Lama Ahmad", "Dylan Baker", "Su Lin Blodgett", "Hal Daum\u00e9 III", "Jesse Dodge", "Ellie Evans", "Sara Hooker", "Yacine Jernite", "Alexandra Sasha Luccioni", "Alberto Lusoli", "Margaret Mitchell", "Jessica Newman", "Marie-Therese Png", "Andrew Strait", "Apostol Vassilev" ], "github": "", "project_page": "", "abstract": "Generative AI systems across modalities, ranging from text, image, audio, and video, have broad social impacts, but there exists no official standard for means of evaluating those impacts and which impacts should be evaluated. We move toward a standard approach in evaluating a generative AI system for any modality, in two overarching categories: what is able to be evaluated in a base system that has no predetermined application and what is able to be evaluated in society. We describe specific social impact categories and how to approach and conduct evaluations in the base technical system, then in people and society. Our framework for a base system defines seven categories of social impact: bias, stereotypes, and representational harms; cultural values and sensitive content; disparate performance; privacy and data protection; financial costs; environmental costs; and data and content moderation labor costs. Suggested methods for evaluation apply to all modalities and analyses of the limitations of existing evaluations serve as a starting point for necessary investment in future evaluations. We offer five overarching categories for what is able to be evaluated in society, each with their own subcategories: trustworthiness and autonomy; inequality, marginalization, and violence; concentration of authority; labor and creativity; and ecosystem and environment. Each subcategory includes recommendations for mitigating harm. We are concurrently crafting an evaluation repository for the AI research community to contribute existing evaluations along the given categories. This version will be updated following a CRAFT session at ACM FAccT 2023.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-12", "arxiv_id": "2306.05836", "title": "Can Large Language Models Infer Causation from Correlation?", "authors": [ "Zhijing Jin", "Jiarui Liu", "Zhiheng Lyu", "Spencer Poff", "Mrinmaya Sachan", "Rada Mihalcea", "Mona Diab", "Bernhard Sch\u00f6lkopf" ], "github": "https://github.com/causalNLP/corr2cause", "project_page": "", "abstract": "Causal inference is one of the hallmarks of human intelligence. While the field of CausalNLP has attracted much interest in the recent years, existing causal inference datasets in NLP primarily rely on discovering causality from empirical knowledge (e.g., commonsense knowledge). In this work, we propose the first benchmark dataset to test the pure causal inference skills of large language models (LLMs). Specifically, we formulate a novel task Corr2Cause, which takes a set of correlational statements and determines the causal relationship between the variables. We curate a large-scale dataset of more than 400K samples, on which we evaluate seventeen existing LLMs. Through our experiments, we identify a key shortcoming of LLMs in terms of their causal inference skills, and show that these models achieve almost close to random performance on the task. This shortcoming is somewhat mitigated when we try to re-purpose LLMs for this skill via finetuning, but we find that these models still fail to generalize -- they can only perform causal inference in in-distribution settings when variable names and textual expressions used in the queries are similar to those in the training set, but fail in out-of-distribution settings generated by perturbing these queries. Corr2Cause is a challenging task for LLMs, and would be helpful in guiding future research on improving LLMs' pure reasoning skills and generalizability. Our data is at https://huggingface.co/datasets/causalnlp/corr2cause. Our code is at https://github.com/causalNLP/corr2cause.", "github_manual": "", "github_regex": "https://github.com/causalNLP/corr2cause", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-12", "arxiv_id": "2306.05685", "title": "Judging LLM-as-a-judge with MT-Bench and Chatbot Arena", "authors": [ "Lianmin Zheng", "Wei-Lin Chiang", "Ying Sheng", "Siyuan Zhuang", "Zhanghao Wu", "Yonghao Zhuang", "Zi Lin", "Zhuohan Li", "Dacheng Li", "Eric. P Xing", "Hao Zhang", "Joseph E. Gonzalez", "Ion Stoica" ], "github": "https://github.com/openai/evals", "project_page": "", "abstract": "Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences. To address this, we explore using strong LLMs as judges to evaluate these models on more open-ended questions. We examine the usage and limitations of LLM-as-a-judge, such as position and verbosity biases and limited reasoning ability, and propose solutions to migrate some of them. We then verify the agreement between LLM judges and human preferences by introducing two benchmarks: MT-bench, a multi-turn question set; and Chatbot Arena, a crowdsourced battle platform. Our results reveal that strong LLM judges like GPT-4 can match both controlled and crowdsourced human preferences well, achieving over 80\\% agreement, the same level of agreement between humans. Hence, LLM-as-a-judge is a scalable and explainable way to approximate human preferences, which are otherwise very expensive to obtain. Additionally, we show our benchmark and traditional benchmarks complement each other by evaluating several variants of LLaMA/Vicuna. We will publicly release 80 MT-bench questions, 3K expert votes, and 30K conversations with human preferences from Chatbot Arena.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openai/evals", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-12", "arxiv_id": "2306.05493", "title": "Multi-Modal Classifiers for Open-Vocabulary Object Detection", "authors": [ "Prannay Kaul", "Weidi Xie", "Andrew Zisserman" ], "github": "https://github.com/prannaykaul/mm-ovod", "project_page": "", "abstract": "The goal of this paper is open-vocabulary object detection (OVOD) x2013 building a model that can detect objects beyond the set of categories seen at training, thus enabling the user to specify categories of interest at inference without the need for model retraining. We adopt a standard two-stage object detector architecture, and explore three ways for specifying novel categories: via language descriptions, via image exemplars, or via a combination of the two. We make three contributions: first, we prompt a large language model (LLM) to generate informative language descriptions for object classes, and construct powerful text-based classifiers; second, we employ a visual aggregator on image exemplars that can ingest any number of images as input, forming vision-based classifiers; and third, we provide a simple method to fuse information from language descriptions and image exemplars, yielding a multi-modal classifier. When evaluating on the challenging LVIS open-vocabulary benchmark we demonstrate that: (i) our text-based classifiers outperform all previous OVOD works; (ii) our vision-based classifiers perform as well as text-based classifiers in prior work; (iii) using multi-modal classifiers perform better than either modality alone; and finally, (iv) our text-based and multi-modal classifiers yield better performance than a fully-supervised detector.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/prannaykaul/mm-ovod", "github_api_added_by": "auto" }, { "date": "2023-06-12", "arxiv_id": "2306.06092", "title": "Realistic Saliency Guided Image Enhancement", "authors": [ "S. Mahdi H. Miangoleh", "Zoya Bylinskii", "Eric Kee", "Eli Shechtman", "Ya\u011f\u0131z Aksoy" ], "github": "https://github.com/compphoto/RealisticImageEnhancement", "project_page": "", "abstract": "Common editing operations performed by professional photographers include the cleanup operations: de-emphasizing distracting elements and enhancing subjects. These edits are challenging, requiring a delicate balance between manipulating the viewer's attention while maintaining photo realism. While recent approaches can boast successful examples of attention attenuation or amplification, most of them also suffer from frequent unrealistic edits. We propose a realism loss for saliency-guided image enhancement to maintain high realism across varying image types, while attenuating distractors and amplifying objects of interest. Evaluations with professional photographers confirm that we achieve the dual objective of realism and effectiveness, and outperform the recent approaches on their own datasets, while requiring a smaller memory footprint and runtime. We thus offer a viable solution for automating image enhancement and photo cleanup operations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/compphoto/RealisticImageEnhancement", "github_api_added_by": "auto" }, { "date": "2023-06-12", "arxiv_id": "2306.05696", "title": "Embodied Executable Policy Learning with Language-based Scene Summarization", "authors": [ "Jielin Qiu", "Mengdi Xu", "William Han", "Seungwhan Moon", "Ding Zhao" ], "github": "", "project_page": "", "abstract": "Large Language models (LLMs) have shown remarkable success in assisting robot learning tasks, i.e., complex household planning. However, the performance of pretrained LLMs heavily relies on domain-specific templated text data, which may be infeasible in real-world robot learning tasks with image-based observations. Moreover, existing LLMs with text inputs lack the capability to evolve with non-expert interactions with environments. In this work, we introduce a novel learning paradigm that generates robots' executable actions in the form of text, derived solely from visual observations, using language-based summarization of these observations as the connecting bridge between both domains. Our proposed paradigm stands apart from previous works, which utilized either language instructions or a combination of language and visual data as inputs. Moreover, our method does not require oracle text summarization of the scene, eliminating the need for human involvement in the learning loop, which makes it more practical for real-world robot learning tasks. Our proposed paradigm consists of two modules: the SUM module, which interprets the environment using visual observations and produces a text summary of the scene, and the APM module, which generates executable action policies based on the natural language descriptions provided by the SUM module. We demonstrate that our proposed method can employ two fine-tuning strategies, including imitation learning and reinforcement learning approaches, to adapt to the target test tasks effectively. We conduct extensive experiments involving various SUM/APM model selections, environments, and tasks across 7 house layouts in the VirtualHome environment. Our experimental results demonstrate that our method surpasses existing baselines, confirming the effectiveness of this novel learning paradigm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-13", "arxiv_id": "2306.06189", "title": "FasterViT: Fast Vision Transformers with Hierarchical Attention", "authors": [ "Ali Hatamizadeh", "Greg Heinrich", "Hongxu Yin", "Andrew Tao", "Jose M. Alvarez", "Jan Kautz", "Pavlo Molchanov" ], "github": "https://github.com/NVlabs/FasterViT", "project_page": "", "abstract": "We design a new family of hybrid CNN-ViT neural networks, named FasterViT, with a focus on high image throughput for computer vision (CV) applications. FasterViT combines the benefits of fast local representation learning in CNNs and global modeling properties in ViT. Our newly introduced Hierarchical Attention (HAT) approach decomposes global self-attention with quadratic complexity into a multi-level attention with reduced computational costs. We benefit from efficient window-based self-attention. Each window has access to dedicated carrier tokens that participate in local and global representation learning. At a high level, global self-attentions enable the efficient cross-window communication at lower costs. FasterViT achieves a SOTA Pareto-front in terms of accuracy \\vs image throughput. We have extensively validated its effectiveness on various CV tasks including classification, object detection and segmentation. We also show that HAT can be used as a plug-and-play module for existing networks and enhance them. We further demonstrate significantly faster and more accurate performance than competitive counterparts for images with high resolution. Code is available at https://github.com/NVlabs/FasterViT.", "github_manual": "", "github_regex": "https://github.com/NVlabs/FasterViT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-13", "arxiv_id": "2306.07179", "title": "Benchmarking Neural Network Training Algorithms", "authors": [ "George E. Dahl", "Frank Schneider", "Zachary Nado", "Naman Agarwal", "Chandramouli Shama Sastry", "Philipp Hennig", "Sourabh Medapati", "Runa Eschenhagen", "Priya Kasimbeg", "Daniel Suo", "Juhan Bae", "Justin Gilmer", "Abel L. Peirson", "Bilal Khan", "Rohan Anil", "Mike Rabbat", "Shankar Krishnan", "Daniel Snider", "Ehsan Amid", "Kongtao Chen", "Chris J. Maddison", "Rakshith Vasudev", "Michal Badura", "Ankush Garg", "Peter Mattson" ], "github": "https://github.com/mlcommons/algorithmic-efficiency", "project_page": "", "abstract": "Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workloads (e.g., better update rules, tuning protocols, learning rate schedules, or data selection schemes) could save time, save computational resources, and lead to better, more accurate, models. Unfortunately, as a community, we are currently unable to reliably identify training algorithm improvements, or even determine the state-of-the-art training algorithm. In this work, using concrete experiments, we argue that real progress in speeding up training requires new benchmarks that resolve three basic challenges faced by empirical comparisons of training algorithms: (1) how to decide when training is complete and precisely measure training time, (2) how to handle the sensitivity of measurements to exact workload details, and (3) how to fairly compare algorithms that require hyperparameter tuning. In order to address these challenges, we introduce a new, competitive, time-to-result benchmark using multiple workloads running on fixed hardware, the AlgoPerf: Training Algorithms benchmark. Our benchmark includes a set of workload variants that make it possible to detect benchmark submissions that are more robust to workload changes than current widely-used methods. Finally, we evaluate baseline submissions constructed using various optimizers that represent current practice, as well as other optimizers that have recently received attention in the literature. These baseline results collectively demonstrate the feasibility of our benchmark, show that non-trivial gaps between methods exist, and set a provisional state-of-the-art for future benchmark submissions to try and surpass.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mlcommons/algorithmic-efficiency", "github_api_added_by": "auto" }, { "date": "2023-06-13", "arxiv_id": "2306.07280", "title": "Controlling Text-to-Image Diffusion by Orthogonal Finetuning", "authors": [ "Zeju Qiu", "Weiyang Liu", "Haiwen Feng", "Yuxuan Xue", "Yao Feng", "Zhen Liu", "Dan Zhang", "Adrian Weller", "Bernhard Sch\u00f6lkopf" ], "github": "https://github.com/salesforce/BLIP", "project_page": "", "abstract": "Large text-to-image diffusion models have impressive capabilities in generating photorealistic images from text prompts. How to effectively guide or control these powerful models to perform different downstream tasks becomes an important open problem. To tackle this challenge, we introduce a principled finetuning method -- Orthogonal Finetuning (OFT), for adapting text-to-image diffusion models to downstream tasks. Unlike existing methods, OFT can provably preserve hyperspherical energy which characterizes the pairwise neuron relationship on the unit hypersphere. We find that this property is crucial for preserving the semantic generation ability of text-to-image diffusion models. To improve finetuning stability, we further propose Constrained Orthogonal Finetuning (COFT) which imposes an additional radius constraint to the hypersphere. Specifically, we consider two important finetuning text-to-image tasks: subject-driven generation where the goal is to generate subject-specific images given a few images of a subject and a text prompt, and controllable generation where the goal is to enable the model to take in additional control signals. We empirically show that our OFT framework outperforms existing methods in generation quality and convergence speed.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/salesforce/BLIP", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-13", "arxiv_id": "2306.07174", "title": "Augmenting Language Models with Long-Term Memory", "authors": [ "Weizhi Wang", "Li Dong", "Hao Cheng", "Xiaodong Liu", "Xifeng Yan", "Jianfeng Gao", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Existing large language models (LLMs) can only afford fix-sized inputs due to the input length limit, preventing them from utilizing rich long-context information from past inputs. To address this, we propose a framework, Language Models Augmented with Long-Term Memory (LongMem), which enables LLMs to memorize long history. We design a novel decoupled network architecture with the original backbone LLM frozen as a memory encoder and an adaptive residual side-network as a memory retriever and reader. Such a decoupled memory design can easily cache and update long-term past contexts for memory retrieval without suffering from memory staleness. Enhanced with memory-augmented adaptation training, LongMem can thus memorize long past context and use long-term memory for language modeling. The proposed memory retrieval module can handle unlimited-length context in its memory bank to benefit various downstream tasks. Typically, LongMem can enlarge the long-form memory to 65k tokens and thus cache many-shot extra demonstration examples as long-form memory for in-context learning. Experiments show that our method outperforms strong long-context models on ChapterBreak, a challenging long-context modeling benchmark, and achieves remarkable improvements on memory-augmented in-context learning over LLMs. The results demonstrate that the proposed method is effective in helping language models to memorize and utilize long-form contents. Our code is open-sourced at https://aka.ms/LongMem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-13", "arxiv_id": "2306.06638", "title": "Face0: Instantaneously Conditioning a Text-to-Image Model on a Face", "authors": [ "Dani Valevski", "Danny Wasserman", "Yossi Matias", "Yaniv Leviathan" ], "github": "", "project_page": "", "abstract": "We present Face0, a novel way to instantaneously condition a text-to-image generation model on a face, in sample time, without any optimization procedures such as fine-tuning or inversions. We augment a dataset of annotated images with embeddings of the included faces and train an image generation model, on the augmented dataset. Once trained, our system is practically identical at inference time to the underlying base model, and is therefore able to generate images, given a user-supplied face image and a prompt, in just a couple of seconds. Our method achieves pleasing results, is remarkably simple, extremely fast, and equips the underlying model with new capabilities, like controlling the generated images both via text or via direct manipulation of the input face embeddings. In addition, when using a fixed random vector instead of a face embedding from a user supplied image, our method essentially solves the problem of consistent character generation across images. Finally, while requiring further research, we hope that our method, which decouples the model's textual biases from its biases on faces, might be a step towards some mitigation of biases in future text-to-image models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-13", "arxiv_id": "2306.07279", "title": "Scalable 3D Captioning with Pretrained Models", "authors": [ "Tiange Luo", "Chris Rockwell", "Honglak Lee", "Justin Johnson" ], "github": "https://github.com/crockwell/cap3d", "project_page": "", "abstract": "We introduce Cap3D, an automatic approach for generating descriptive text for 3D objects. This approach utilizes pretrained models from image captioning, image-text alignment, and LLM to consolidate captions from multiple views of a 3D asset, completely side-stepping the time-consuming and costly process of manual annotation. We apply Cap3D to the recently introduced large-scale 3D dataset, Objaverse, resulting in 660k 3D-text pairs. Our evaluation, conducted using 41k human annotations from the same dataset, demonstrates that Cap3D surpasses human-authored descriptions in terms of quality, cost, and speed. Through effective prompt engineering, Cap3D rivals human performance in generating geometric descriptions on 17k collected annotations from the ABO dataset. Finally, we finetune Text-to-3D models on Cap3D and human captions, and show Cap3D outperforms; and benchmark the SOTA including Point-E, Shape-E, and DreamFusion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/crockwell/cap3d", "github_api_added_by": "auto" }, { "date": "2023-06-13", "arxiv_id": "2306.06546", "title": "High-Fidelity Audio Compression with Improved RVQGAN", "authors": [ "Rithesh Kumar", "Prem Seetharaman", "Alejandro Luebs", "Ishaan Kumar", "Kundan Kumar" ], "github": "https://github.com/descriptinc/descript-audio-codec", "project_page": "", "abstract": "Language models have been successfully used to model natural signals, such as images, speech, and music. A key component of these models is a high quality neural compression model that can compress high-dimensional natural signals into lower dimensional discrete tokens. To that end, we introduce a high-fidelity universal neural audio compression algorithm that achieves ~90x compression of 44.1 KHz audio into tokens at just 8kbps bandwidth. We achieve this by combining advances in high-fidelity audio generation with better vector quantization techniques from the image domain, along with improved adversarial and reconstruction losses. We compress all domains (speech, environment, music, etc.) with a single universal model, making it widely applicable to generative modeling of all audio. We compare with competing audio compression algorithms, and find our method outperforms them significantly. We provide thorough ablations for every design choice, as well as open-source code and trained model weights. We hope our work can lay the foundation for the next generation of high-fidelity audio modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/descriptinc/descript-audio-codec", "github_api_added_by": "auto" }, { "date": "2023-06-13", "arxiv_id": "2306.06212", "title": "Aladdin: Zero-Shot Hallucination of Stylized 3D Assets from Abstract Scene Descriptions", "authors": [ "Ian Huang", "Vrishab Krishna", "Omoruyi Atekha", "Leonidas Guibas" ], "github": "https://github.com/ianhuang0630/aladdin", "project_page": "", "abstract": "What constitutes the \"vibe\" of a particular scene? What should one find in \"a busy, dirty city street\", \"an idyllic countryside\", or \"a crime scene in an abandoned living room\"? The translation from abstract scene descriptions to stylized scene elements cannot be done with any generality by extant systems trained on rigid and limited indoor datasets. In this paper, we propose to leverage the knowledge captured by foundation models to accomplish this translation. We present a system that can serve as a tool to generate stylized assets for 3D scenes described by a short phrase, without the need to enumerate the objects to be found within the scene or give instructions on their appearance. Additionally, it is robust to open-world concepts in a way that traditional methods trained on limited data are not, affording more creative freedom to the 3D artist. Our system demonstrates this using a foundation model \"team\" composed of a large language model, a vision-language model and several image diffusion models, which communicate using an interpretable and user-editable intermediate representation, thus allowing for more versatile and controllable stylized asset generation for 3D artists. We introduce novel metrics for this task, and show through human evaluations that in 91% of the cases, our system outputs are judged more faithful to the semantics of the input scene description than the baseline, thus highlighting the potential of this approach to radically accelerate the 3D content creation process for 3D artists.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ianhuang0630/aladdin", "github_api_added_by": "auto" }, { "date": "2023-06-13", "arxiv_id": "2306.07075", "title": "Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence", "authors": [ "John J. Nay", "David Karamardian", "Sarah B. Lawsky", "Wenting Tao", "Meghana Bhat", "Raghav Jain", "Aaron Travis Lee", "Jonathan H. Choi", "Jungo Kasai" ], "github": "", "project_page": "", "abstract": "Better understanding of Large Language Models' (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence, and leveraging LLMs to identify inconsistencies in law. This paper explores LLM capabilities in applying tax law. We choose this area of law because it has a structure that allows us to set up automated validation pipelines across thousands of examples, requires logical reasoning and maths skills, and enables us to test LLM capabilities in a manner relevant to real-world economic lives of citizens and companies. Our experiments demonstrate emerging legal understanding capabilities, with improved performance in each subsequent OpenAI model release. We experiment with retrieving and utilising the relevant legal authority to assess the impact of providing additional legal context to LLMs. Few-shot prompting, presenting examples of question-answer pairs, is also found to significantly enhance the performance of the most advanced model, GPT-4. The findings indicate that LLMs, particularly when combined with prompting enhancements and the correct legal texts, can perform at high levels of accuracy but not yet at expert tax lawyer levels. As LLMs continue to advance, their ability to reason about law autonomously could have significant implications for the legal profession and AI governance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-13", "arxiv_id": "2306.07042", "title": "Transformers learn through gradual rank increase", "authors": [ "Enric Boix-Adsera", "Etai Littwin", "Emmanuel Abbe", "Samy Bengio", "Joshua Susskind" ], "github": "", "project_page": "", "abstract": "We identify incremental learning dynamics in transformers, where the difference between trained and initial weights progressively increases in rank. We rigorously prove this occurs under the simplifying assumptions of diagonal weight matrices and small initialization. Our experiments support the theory and also show that phenomenon can occur in practice without the simplifying assumptions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-13", "arxiv_id": "2306.07196", "title": "Retrieval-Enhanced Contrastive Vision-Text Models", "authors": [ "Ahmet Iscen", "Mathilde Caron", "Alireza Fathi", "Cordelia Schmid" ], "github": "", "project_page": "", "abstract": "Contrastive image-text models such as CLIP form the building blocks of many state-of-the-art systems. While they excel at recognizing common generic concepts, they still struggle on fine-grained entities which are rare, or even absent from the pre-training dataset. Hence, a key ingredient to their success has been the use of large-scale curated pre-training data aiming at expanding the set of concepts that they can memorize during the pre-training stage. In this work, we explore an alternative to encoding fine-grained knowledge directly into the model's parameters: we instead train the model to retrieve this knowledge from an external memory. Specifically, we propose to equip existing vision-text models with the ability to refine their embedding with cross-modal retrieved information from a memory at inference time, which greatly improves their zero-shot predictions. Remarkably, we show that this can be done with a light-weight, single-layer, fusion transformer on top of a frozen CLIP. Our experiments validate that our retrieval-enhanced contrastive (RECO) training improves CLIP performance substantially on several challenging fine-grained tasks: for example +10.9 on Stanford Cars, +10.2 on CUB-2011 and +7.3 on the recent OVEN benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-13", "arxiv_id": "2306.06823", "title": "Weakly supervised information extraction from inscrutable handwritten document images", "authors": [ "Sujoy Paul", "Gagan Madan", "Akankshya Mishra", "Narayan Hegde", "Pradeep Kumar", "Gaurav Aggarwal" ], "github": "", "project_page": "", "abstract": "State-of-the-art information extraction methods are limited by OCR errors. They work well for printed text in form-like documents, but unstructured, handwritten documents still remain a challenge. Adapting existing models to domain-specific training data is quite expensive, because of two factors, 1) limited availability of the domain-specific documents (such as handwritten prescriptions, lab notes, etc.), and 2) annotations become even more challenging as one needs domain-specific knowledge to decode inscrutable handwritten document images. In this work, we focus on the complex problem of extracting medicine names from handwritten prescriptions using only weakly labeled data. The data consists of images along with the list of medicine names in it, but not their location in the image. We solve the problem by first identifying the regions of interest, i.e., medicine lines from just weak labels and then injecting a domain-specific medicine language model learned using only synthetically generated data. Compared to off-the-shelf state-of-the-art methods, our approach performs >2.5x better in medicine names extraction from prescriptions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07954", "title": "Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation", "authors": [ "Shuai Yang", "Yifan Zhou", "Ziwei Liu", "Chen Change Loy" ], "github": "https://github.com/williamyang1991/Rerender_A_Video", "project_page": "", "abstract": "Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.", "github_manual": "https://github.com/williamyang1991/Rerender_A_Video", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07967", "title": "One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning", "authors": [ "Arnav Chavan", "Zhuang Liu", "Deepak Gupta", "Eric Xing", "Zhiqiang Shen" ], "github": "https://github.com/Arnav0400/ViT-Slim/tree/master/GLoRA", "project_page": "", "abstract": "We present Generalized LoRA (GLoRA), an advanced approach for universal parameter-efficient fine-tuning tasks. Enhancing Low-Rank Adaptation (LoRA), GLoRA employs a generalized prompt module to optimize pre-trained model weights and adjust intermediate activations, providing more flexibility and capability across diverse tasks and datasets. Moreover, GLoRA facilitates efficient parameter adaptation by employing a scalable, modular, layer-wise structure search that learns individual adapter of each layer. Originating from a unified mathematical formulation, GLoRA exhibits strong transfer learning, few-shot learning and domain generalization abilities, as it adjusts to new tasks through additional dimensions on weights and activations. Comprehensive experiments demonstrate that GLoRA outperforms all previous methods in natural, specialized, and structured benchmarks, achieving superior accuracy with fewer parameters and computations on various datasets. Furthermore, our structural re-parameterization design ensures that GLoRA incurs no extra inference cost, rendering it a practical solution for resource-limited applications. Code is available at: https://github.com/Arnav0400/ViT-Slim/tree/master/GLoRA.", "github_manual": "", "github_regex": "https://github.com/Arnav0400/ViT-Slim/tree/master/GLoRA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07476", "title": "AniFaceDrawing: Anime Portrait Exploration during Your Sketching", "authors": [ "Zhengyu Huang", "Haoran Xie", "Tsukasa Fukusato", "Kazunori Miyata" ], "github": "", "project_page": "", "abstract": "In this paper, we focus on how artificial intelligence (AI) can be used to assist users in the creation of anime portraits, that is, converting rough sketches into anime portraits during their sketching process. The input is a sequence of incomplete freehand sketches that are gradually refined stroke by stroke, while the output is a sequence of high-quality anime portraits that correspond to the input sketches as guidance. Although recent GANs can generate high quality images, it is a challenging problem to maintain the high quality of generated images from sketches with a low degree of completion due to ill-posed problems in conditional image generation. Even with the latest sketch-to-image (S2I) technology, it is still difficult to create high-quality images from incomplete rough sketches for anime portraits since anime style tend to be more abstract than in realistic style. To address this issue, we adopt a latent space exploration of StyleGAN with a two-stage training strategy. We consider the input strokes of a freehand sketch to correspond to edge information-related attributes in the latent structural code of StyleGAN, and term the matching between strokes and these attributes stroke-level disentanglement. In the first stage, we trained an image encoder with the pre-trained StyleGAN model as a teacher encoder. In the second stage, we simulated the drawing process of the generated images without any additional data (labels) and trained the sketch encoder for incomplete progressive sketches to generate high-quality portrait images with feature alignment to the disentangled representations in the teacher encoder. We verified the proposed progressive S2I system with both qualitative and quantitative evaluations and achieved high-quality anime portraits from incomplete progressive sketches. Our user study proved its effectiveness in art creation assistance for the anime style.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07906", "title": "WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences", "authors": [ "Xiao Liu", "Hanyu Lai", "Hao Yu", "Yifan Xu", "Aohan Zeng", "Zhengxiao Du", "Peng Zhang", "Yuxiao Dong", "Jie Tang" ], "github": "https://github.com/THUDM/WebGLM", "project_page": "", "abstract": "We present WebGLM, a web-enhanced question-answering system based on the General Language Model (GLM). Its goal is to augment a pre-trained large language model (LLM) with web search and retrieval capabilities while being efficient for real-world deployments. To achieve this, we develop WebGLM with strategies for the LLM-augmented retriever, bootstrapped generator, and human preference-aware scorer. Specifically, we identify and address the limitations of WebGPT (OpenAI), through which WebGLM is enabled with accuracy, efficiency, and cost-effectiveness advantages. In addition, we propose systematic criteria for evaluating web-enhanced QA systems. We conduct multi-dimensional human evaluation and quantitative ablation studies, which suggest the outperformance of the proposed WebGLM designs over existing systems. WebGLM with the 10-billion-parameter GLM (10B) is shown to perform better than the similar-sized WebGPT (13B) and even comparably to WebGPT (175B) in human evaluation. The code, demo, and data are at https://github.com/THUDM/WebGLM.", "github_manual": "", "github_regex": "https://github.com/THUDM/WebGLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07536", "title": "TART: A plug-and-play Transformer module for task-agnostic reasoning", "authors": [ "Kush Bhatia", "Avanika Narayan", "Christopher De Sa", "Christopher R\u00e9" ], "github": "https://github.com/HazyResearch/TART", "project_page": "", "abstract": "Large language models (LLMs) exhibit in-context learning abilities which enable the same model to perform several tasks without any task-specific training. In contrast, traditional adaptation approaches, such as fine-tuning, modify the underlying models for each specific task. In-context learning, however, consistently underperforms task-specific tuning approaches even when presented with the same examples. While most existing approaches (e.g., prompt engineering) focus on the LLM's learned representations to patch this performance gap, our analysis actually reveal that LLM representations contain sufficient information to make good predictions. As such, we focus on the LLM's reasoning abilities and demonstrate that this performance gap exists due to their inability to perform simple probabilistic reasoning tasks. This raises an intriguing question: Are LLMs actually capable of learning how to reason in a task-agnostic manner? We answer this in the affirmative and propose TART which generically improves an LLM's reasoning abilities using a synthetically trained Transformer-based reasoning module. TART trains this reasoning module in a task-agnostic manner using only synthetic logistic regression tasks and composes it with an arbitrary real-world pre-trained model without any additional training. With a single inference module, TART improves performance across different model families (GPT-Neo, Pythia, BLOOM), model sizes (100M - 6B), tasks (14 NLP binary classification tasks), and even across different modalities (audio and vision). Additionally, on the RAFT Benchmark, TART improves GPT-Neo (125M)'s performance such that it outperforms BLOOM (176B), and is within 4% of GPT-3 (175B). Our code and models are available at https://github.com/HazyResearch/TART .", "github_manual": "", "github_regex": "https://github.com/HazyResearch/TART", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07915", "title": "Image Captioners Are Scalable Vision Learners Too", "authors": [ "Michael Tschannen", "Manoj Kumar", "Andreas Steiner", "Xiaohua Zhai", "Neil Houlsby", "Lucas Beyer" ], "github": "", "project_page": "", "abstract": "Contrastive pretraining on image-text pairs from the web is one of the most popular large-scale pretraining strategies for vision backbones, especially in the context of large multimodal models. At the same time, image captioning on this type of data is commonly considered an inferior pretraining strategy. In this paper, we perform a fair comparison of these two pretraining strategies, carefully matching training data, compute, and model capacity. Using a standard encoder-decoder transformer, we find that captioning alone is surprisingly effective: on classification tasks, captioning produces vision encoders competitive with contrastively pretrained encoders, while surpassing them on vision & language tasks. We further analyze the effect of the model architecture and scale, as well as the pretraining data on the representation quality, and find that captioning exhibits the same or better scaling behavior along these axes. Overall our results show that plain image captioning is a more powerful pretraining strategy than was previously believed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07349", "title": "ATT3D: Amortized Text-to-3D Object Synthesis", "authors": [ "Jonathan Lorraine", "Kevin Xie", "Xiaohui Zeng", "Chen-Hsuan Lin", "Towaki Takikawa", "Nicholas Sharp", "Tsung-Yi Lin", "Ming-Yu Liu", "Sanja Fidler", "James Lucas" ], "github": "", "project_page": "", "abstract": "Text-to-3D modelling has seen exciting progress by combining generative text-to-image models with image-to-3D methods like Neural Radiance Fields. DreamFusion recently achieved high-quality results but requires a lengthy, per-prompt optimization to create 3D objects. To address this, we amortize optimization over text prompts by training on many prompts simultaneously with a unified model, instead of separately. With this, we share computation across a prompt set, training in less time than per-prompt optimization. Our framework - Amortized text-to-3D (ATT3D) - enables knowledge-sharing between prompts to generalize to unseen setups and smooth interpolations between text for novel assets and simple animations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07580", "title": "SayTap: Language to Quadrupedal Locomotion", "authors": [ "Yujin Tang", "Wenhao Yu", "Jie Tan", "Heiga Zen", "Aleksandra Faust", "Tatsuya Harada" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated the potential to perform high-level planning. Yet, it remains a challenge for LLMs to comprehend low-level commands, such as joint angle targets or motor torques. This paper proposes an approach to use foot contact patterns as an interface that bridges human commands in natural language and a locomotion controller that outputs these low-level commands. This results in an interactive system for quadrupedal robots that allows the users to craft diverse locomotion behaviors flexibly. We contribute an LLM prompt design, a reward function, and a method to expose the controller to the feasible distribution of contact patterns. The results are a controller capable of achieving diverse locomotion patterns that can be transferred to real robot hardware. Compared with other design choices, the proposed approach enjoys more than 50% success rate in predicting the correct contact patterns and can solve 10 more tasks out of a total of 30 tasks. Our project site is: https://saytap.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07970", "title": "Neural Scene Chronology", "authors": [ "Haotong Lin", "Qianqian Wang", "Ruojin Cai", "Sida Peng", "Hadar Averbuch-Elor", "Xiaowei Zhou", "Noah Snavely" ], "github": "https://github.com/zju3dv/NeuSC", "project_page": "", "abstract": "In this work, we aim to reconstruct a time-varying 3D model, capable of rendering photo-realistic renderings with independent control of viewpoint, illumination, and time, from Internet photos of large-scale landmarks. The core challenges are twofold. First, different types of temporal changes, such as illumination and changes to the underlying scene itself (such as replacing one graffiti artwork with another) are entangled together in the imagery. Second, scene-level temporal changes are often discrete and sporadic over time, rather than continuous. To tackle these problems, we propose a new scene representation equipped with a novel temporal step function encoding method that can model discrete scene-level content changes as piece-wise constant functions over time. Specifically, we represent the scene as a space-time radiance field with a per-image illumination embedding, where temporally-varying scene changes are encoded using a set of learned step functions. To facilitate our task of chronology reconstruction from Internet imagery, we also collect a new dataset of four scenes that exhibit various changes over time. We demonstrate that our method exhibits state-of-the-art view synthesis results on this dataset, while achieving independent control of viewpoint, time, and illumination.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zju3dv/NeuSC", "github_api_added_by": "auto" }, { "date": "2023-06-14", "arxiv_id": "2306.07968", "title": "arXiVeri: Automatic table verification with GPT", "authors": [ "Gyungin Shin", "Weidi Xie", "Samuel Albanie" ], "github": "", "project_page": "", "abstract": "Without accurate transcription of numerical data in scientific documents, a scientist cannot draw accurate conclusions. Unfortunately, the process of copying numerical data from one paper to another is prone to human error. In this paper, we propose to meet this challenge through the novel task of automatic table verification (AutoTV), in which the objective is to verify the accuracy of numerical data in tables by cross-referencing cited sources. To support this task, we propose a new benchmark, arXiVeri, which comprises tabular data drawn from open-access academic papers on arXiv. We introduce metrics to evaluate the performance of a table verifier in two key areas: (i) table matching, which aims to identify the source table in a cited document that corresponds to a target table, and (ii) cell matching, which aims to locate shared cells between a target and source table and identify their row and column indices accurately. By leveraging the flexible capabilities of modern large language models (LLMs), we propose simple baselines for table verification. Our findings highlight the complexity of this task, even for state-of-the-art LLMs like OpenAI's GPT-4. The code and benchmark will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07473", "title": "3D molecule generation by denoising voxel grids", "authors": [ "Pedro O. Pinheiro", "Joshua Rackers", "Joseph Kleinhenz", "Michael Maser", "Omar Mahmood", "Andrew Martin Watkins", "Stephen Ra", "Vishnu Sresht", "Saeed Saremi" ], "github": "https://github.com/genentech/voxmol", "project_page": "", "abstract": "We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids. First, we train a denoising neural network that learns to map from a smooth distribution of noisy molecules to the distribution of real molecules. Then, we follow the neural empirical Bayes framework [Saremi and Hyvarinen, 2019] and generate molecules in two steps: (i) sample noisy density grids from a smooth distribution via underdamped Langevin Markov chain Monte Carlo, and (ii) recover the ``clean'' molecule by denoising the noisy grid with a single step. Our method, VoxMol, generates molecules in a fundamentally different way than the current state of the art (i.e., diffusion models applied to atom point clouds). It differs in terms of the data representation, the noise model, the network architecture and the generative modeling algorithm. VoxMol achieves comparable results to state of the art on unconditional 3D molecule generation while being simpler to train and faster to generate molecules.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/genentech/voxmol", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07944", "title": "Speech-to-Text Adapter and Speech-to-Entity Retriever Augmented LLMs for Speech Understanding", "authors": [ "Mingqiu Wang", "Izhak Shafran", "Hagen Soltau", "Wei Han", "Yuan Cao", "Dian Yu", "Laurent El Shafey" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have been applied in the speech domain, often incurring a performance drop due to misaligned between speech and language representations. To bridge this gap, we propose a joint speech and language model (SLM) using a Speech2Text adapter, which maps speech into text token embedding space without speech information loss. Additionally, using a CTC-based blank-filtering, we can reduce the speech sequence length to that of text. In speech MultiWoz dataset (DSTC11 challenge), SLM largely improves the dialog state tracking (DST) performance (24.7% to 28.4% accuracy). Further to address errors on rare entities, we augment SLM with a Speech2Entity retriever, which uses speech to retrieve relevant entities, and then adds them to the original SLM input as a prefix. With this retrieval-augmented SLM (ReSLM), the DST performance jumps to 34.6% accuracy. Moreover, augmenting the ASR task with the dialog understanding task improves the ASR performance from 9.4% to 8.5% WER.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07969", "title": "GeneCIS: A Benchmark for General Conditional Image Similarity", "authors": [ "Sagar Vaze", "Nicolas Carion", "Ishan Misra" ], "github": "", "project_page": "", "abstract": "We argue that there are many notions of 'similarity' and that models, like humans, should be able to adapt to these dynamically. This contrasts with most representation learning methods, supervised or self-supervised, which learn a fixed embedding function and hence implicitly assume a single notion of similarity. For instance, models trained on ImageNet are biased towards object categories, while a user might prefer the model to focus on colors, textures or specific elements in the scene. In this paper, we propose the GeneCIS ('genesis') benchmark, which measures models' ability to adapt to a range of similarity conditions. Extending prior work, our benchmark is designed for zero-shot evaluation only, and hence considers an open-set of similarity conditions. We find that baselines from powerful CLIP models struggle on GeneCIS and that performance on the benchmark is only weakly correlated with ImageNet accuracy, suggesting that simply scaling existing methods is not fruitful. We further propose a simple, scalable solution based on automatically mining information from existing image-caption datasets. We find our method offers a substantial boost over the baselines on GeneCIS, and further improves zero-shot performance on related image retrieval benchmarks. In fact, though evaluated zero-shot, our model surpasses state-of-the-art supervised models on MIT-States. Project page at https://sgvaze.github.io/genecis/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07552", "title": "Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-Second", "authors": [ "Vincent-Pierre Berges", "Andrew Szot", "Devendra Singh Chaplot", "Aaron Gokaslan", "Roozbeh Mottaghi", "Dhruv Batra", "Eric Undersander" ], "github": "https://github.com/facebookresearch/galactic", "project_page": "", "abstract": "We present Galactic, a large-scale simulation and reinforcement-learning (RL) framework for robotic mobile manipulation in indoor environments. Specifically, a Fetch robot (equipped with a mobile base, 7DoF arm, RGBD camera, egomotion, and onboard sensing) is spawned in a home environment and asked to rearrange objects - by navigating to an object, picking it up, navigating to a target location, and then placing the object at the target location. Galactic is fast. In terms of simulation speed (rendering + physics), Galactic achieves over 421,000 steps-per-second (SPS) on an 8-GPU node, which is 54x faster than Habitat 2.0 (7699 SPS). More importantly, Galactic was designed to optimize the entire rendering + physics + RL interplay since any bottleneck in the interplay slows down training. In terms of simulation+RL speed (rendering + physics + inference + learning), Galactic achieves over 108,000 SPS, which 88x faster than Habitat 2.0 (1243 SPS). These massive speed-ups not only drastically cut the wall-clock training time of existing experiments, but also unlock an unprecedented scale of new experiments. First, Galactic can train a mobile pick skill to >80% accuracy in under 16 minutes, a 100x speedup compared to the over 24 hours it takes to train the same skill in Habitat 2.0. Second, we use Galactic to perform the largest-scale experiment to date for rearrangement using 5B steps of experience in 46 hours, which is equivalent to 20 years of robot experience. This scaling results in a single neural network composed of task-agnostic components achieving 85% success in GeometricGoal rearrangement, compared to 0% success reported in Habitat 2.0 for the same approach. The code is available at github.com/facebookresearch/galactic.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/galactic", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07437", "title": "Instant Multi-View Head Capture through Learnable Registration", "authors": [ "Timo Bolkart", "Tianye Li", "Michael J. Black" ], "github": "https://github.com/TimoBolkart/TEMPEH", "project_page": "", "abstract": "Existing methods for capturing datasets of 3D heads in dense semantic correspondence are slow, and commonly address the problem in two separate steps; multi-view stereo (MVS) reconstruction followed by non-rigid registration. To simplify this process, we introduce TEMPEH (Towards Estimation of 3D Meshes from Performances of Expressive Heads) to directly infer 3D heads in dense correspondence from calibrated multi-view images. Registering datasets of 3D scans typically requires manual parameter tuning to find the right balance between accurately fitting the scans surfaces and being robust to scanning noise and outliers. Instead, we propose to jointly register a 3D head dataset while training TEMPEH. Specifically, during training we minimize a geometric loss commonly used for surface registration, effectively leveraging TEMPEH as a regularizer. Our multi-view head inference builds on a volumetric feature representation that samples and fuses features from each view using camera calibration information. To account for partial occlusions and a large capture volume that enables head movements, we use view- and surface-aware feature fusion, and a spatial transformer-based head localization module, respectively. We use raw MVS scans as supervision during training, but, once trained, TEMPEH directly predicts 3D heads in dense correspondence without requiring scans. Predicting one head takes about 0.3 seconds with a median reconstruction error of 0.26 mm, 64% lower than the current state-of-the-art. This enables the efficient capture of large datasets containing multiple people and diverse facial motions. Code, model, and data are publicly available at https://tempeh.is.tue.mpg.de.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TimoBolkart/TEMPEH", "github_api_added_by": "auto" }, { "date": "2023-06-14", "arxiv_id": "2306.07941", "title": "GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models", "authors": [ "Itzik Malkiel", "Uri Alon", "Yakir Yehuda", "Shahar Keren", "Oren Barkan", "Royi Ronen", "Noam Koenigstein" ], "github": "", "project_page": "", "abstract": "Transcriptions of phone calls are of significant value across diverse fields, such as sales, customer service, healthcare, and law enforcement. Nevertheless, the analysis of these recorded conversations can be an arduous and time-intensive process, especially when dealing with extended or multifaceted dialogues. In this work, we propose a novel method, GPT-distilled Calls Segmentation and Tagging (GPT-Calls), for efficient and accurate call segmentation and topic extraction. GPT-Calls is composed of offline and online phases. The offline phase is applied once to a given list of topics and involves generating a distribution of synthetic sentences for each topic using a GPT model and extracting anchor vectors. The online phase is applied to every call separately and scores the similarity between the transcripted conversation and the topic anchors found in the offline phase. Then, time domain analysis is applied to the similarity scores to group utterances into segments and tag them with topics. The proposed paradigm provides an accurate and efficient method for call segmentation and topic extraction that does not require labeled data, thus making it a versatile approach applicable to various domains. Our algorithm operates in production under Dynamics 365 Sales Conversation Intelligence, and our research is based on real sales conversations gathered from various Dynamics 365 Sales tenants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-14", "arxiv_id": "2306.07946", "title": "STUDY: Socially Aware Temporally Casual Decoder Recommender Systems", "authors": [ "Eltayeb Ahmed", "Diana Mincu", "Lauren Harrell", "Katherine Heller", "Subhrajit Roy" ], "github": "", "project_page": "", "abstract": "With the overwhelming amount of data available both on and offline today, recommender systems have become much needed to help users find items tailored to their interests. When social network information exists there are methods that utilize this information to make better recommendations, however the methods are often clunky with complex architectures and training procedures. Furthermore many of the existing methods utilize graph neural networks which are notoriously difficult to train. To address this, we propose Socially-aware Temporally caUsal Decoder recommender sYstems (STUDY). STUDY does joint inference over groups of users who are adjacent in the social network graph using a single forward pass of a modified transformer decoder network. We test our method in a school-based educational content setting, using classroom structure to define social networks. Our method outperforms both social and sequential methods while maintaining the design simplicity of a single homogeneous network that models all interactions in the data. We also carry out ablation studies to understand the drivers of our performance gains and find that our model depends on leveraging a social network structure that effectively models the similarities in user behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08276", "title": "TryOnDiffusion: A Tale of Two UNets", "authors": [ "Luyang Zhu", "Dawei Yang", "Tyler Zhu", "Fitsum Reda", "William Chan", "Chitwan Saharia", "Mohammad Norouzi", "Ira Kemelmacher-Shlizerman" ], "github": "", "project_page": "", "abstract": "Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person. A key challenge is to synthesize a photorealistic detail-preserving visualization of the garment, while warping the garment to accommodate a significant body pose and shape change across the subjects. Previous methods either focus on garment detail preservation without effective pose and shape variation, or allow try-on with the desired shape and pose but lack garment details. In this paper, we propose a diffusion-based architecture that unifies two UNets (referred to as Parallel-UNet), which allows us to preserve garment details and warp the garment for significant pose and body change in a single network. The key ideas behind Parallel-UNet include: 1) garment is warped implicitly via a cross attention mechanism, 2) garment warp and person blend happen as part of a unified process as opposed to a sequence of two separate tasks. Experimental results indicate that TryOnDiffusion achieves state-of-the-art performance both qualitatively and quantitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.09348", "title": "Seeing the World through Your Eyes", "authors": [ "Hadi Alzayer", "Kevin Zhang", "Brandon Feng", "Christopher Metzler", "Jia-Bin Huang" ], "github": "", "project_page": "", "abstract": "The reflective nature of the human eye is an underappreciated source of information about what the world around us looks like. By imaging the eyes of a moving person, we can collect multiple views of a scene outside the camera's direct line of sight through the reflections in the eyes. In this paper, we reconstruct a 3D scene beyond the camera's line of sight using portrait images containing eye reflections. This task is challenging due to 1) the difficulty of accurately estimating eye poses and 2) the entangled appearance of the eye iris and the scene reflections. Our method jointly refines the cornea poses, the radiance field depicting the scene, and the observer's eye iris texture. We further propose a simple regularization prior on the iris texture pattern to improve reconstruction quality. Through various experiments on synthetic and real-world captures featuring people with varied eye colors, we demonstrate the feasibility of our approach to recover 3D scenes using eye reflections.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08640", "title": "AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn", "authors": [ "Difei Gao", "Lei Ji", "Luowei Zhou", "Kevin Qinghong Lin", "Joya Chen", "Zihan Fan", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Recent research on Large Language Models (LLMs) has led to remarkable advancements in general NLP AI assistants. Some studies have further explored the use of LLMs for planning and invoking models or APIs to address more general multi-modal user queries. Despite this progress, complex visual-based tasks still remain challenging due to the diverse nature of visual tasks. This diversity is reflected in two aspects: 1) Reasoning paths. For many real-life applications, it is hard to accurately decompose a query simply by examining the query itself. Planning based on the specific visual content and the results of each step is usually required. 2) Flexible inputs and intermediate results. Input forms could be flexible for in-the-wild cases, and involves not only a single image or video but a mixture of videos and images, e.g., a user-view image with some reference videos. Besides, a complex reasoning process will also generate diverse multimodal intermediate results, e.g., video narrations, segmented video clips, etc. To address such general cases, we propose a multi-modal AI assistant, AssistGPT, with an interleaved code and language reasoning approach called Plan, Execute, Inspect, and Learn (PEIL) to integrate LLMs with various tools. Specifically, the Planner is capable of using natural language to plan which tool in Executor should do next based on the current reasoning progress. Inspector is an efficient memory manager to assist the Planner to feed proper visual information into a specific tool. Finally, since the entire reasoning process is complex and flexible, a Learner is designed to enable the model to autonomously explore and discover the optimal solution. We conducted experiments on A-OKVQA and NExT-QA benchmarks, achieving state-of-the-art results. Moreover, showcases demonstrate the ability of our system to handle questions far more complex than those found in the benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08568", "title": "WizardCoder: Empowering Code Large Language Models with Evol-Instruct", "authors": [ "Ziyang Luo", "Can Xu", "Pu Zhao", "Qingfeng Sun", "Xiubo Geng", "Wenxiang Hu", "Chongyang Tao", "Jing Ma", "Qingwei Lin", "Daxin Jiang" ], "github": "https://github.com/nlpxucan/WizardLM", "project_page": "", "abstract": "Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on extensive raw code data without instruction fine-tuning. In this paper, we introduce WizardCoder, which empowers Code LLMs with complex instruction fine-tuning, by adapting the Evol-Instruct method to the domain of code. Through comprehensive experiments on four prominent code generation benchmarks, namely HumanEval, HumanEval+, MBPP, and DS-1000, we unveil the exceptional capabilities of our model. It surpasses all other open-source Code LLMs by a substantial margin. Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+. Our code, model weights, and data are public at https://github.com/nlpxucan/WizardLM", "github_manual": "", "github_regex": "https://github.com/nlpxucan/WizardLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.09296", "title": "KoLA: Carefully Benchmarking World Knowledge of Large Language Models", "authors": [ "Jifan Yu", "Xiaozhi Wang", "Shangqing Tu", "Shulin Cao", "Daniel Zhang-Li", "Xin Lv", "Hao Peng", "Zijun Yao", "Xiaohan Zhang", "Hanming Li", "Chunyang Li", "Zheyuan Zhang", "Yushi Bai", "Yantao Liu", "Amy Xin", "Nianyi Lin", "Kaifeng Yun", "Linlu Gong", "Jianhui Chen", "Zhili Wu", "Yunjia Qi", "Weikai Li", "Yong Guan", "Kaisheng Zeng", "Ji Qi", "Hailong Jin", "Jinxin Liu", "Yu Gu", "Yuan Yao", "Ning Ding", "Lei Hou", "Zhiyuan Liu", "Bin Xu", "Jie Tang", "Juanzi Li" ], "github": "https://github.com/thu-keg/kola", "project_page": "", "abstract": "The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticulous and thoughtful designs are essential to thorough, unbiased, and applicable evaluations. Given the importance of world knowledge to LLMs, we construct a Knowledge-oriented LLM Assessment benchmark (KoLA), in which we carefully design three crucial factors: (1) For ability modeling, we mimic human cognition to form a four-level taxonomy of knowledge-related abilities, covering 19 tasks. (2) For data, to ensure fair comparisons, we use both Wikipedia, a corpus prevalently pre-trained by LLMs, along with continuously collected emerging corpora, aiming to evaluate the capacity to handle unseen data and evolving knowledge. (3) For evaluation criteria, we adopt a contrastive system, including overall standard scores for better numerical comparability across tasks and models and a unique self-contrast metric for automatically evaluating knowledge hallucination. We evaluate 21 open-source and commercial LLMs and obtain some intriguing findings. The KoLA dataset and open-participation leaderboard are publicly released at https://kola.xlore.cn and will be continuously updated to provide references for developing LLMs and knowledge-related systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thu-keg/kola", "github_api_added_by": "auto" }, { "date": "2023-06-16", "arxiv_id": "2306.08543", "title": "Knowledge Distillation of Large Language Models", "authors": [ "Yuxian Gu", "Li Dong", "Furu Wei", "Minlie Huang" ], "github": "https://github.com/microsoft/LMOps/tree/main/minillm", "project_page": "", "abstract": "Knowledge Distillation (KD) is a promising technique for reducing the high computational demand of large language models (LLMs). However, previous KD methods are primarily applied to white-box classification models or training small models to imitate black-box model APIs like ChatGPT. How to effectively distill the knowledge from white-box generative LLMs is still under-explored, which becomes more and more important with the prosperity of LLMs. In this work, we propose MiniLLM that distills smaller language models from generative larger language models. We first replace the forward Kullback-Leibler divergence (KLD) objective in the standard KD approaches with reverse KLD, which is more suitable for KD on generative language models, to prevent the student model from overestimating the low-probability regions of the teacher distribution. Then, we derive an effective optimization approach to learn this objective. Extensive experiments in the instruction-following setting show that the MiniLLM models generate more precise responses with the higher overall quality, lower exposure bias, better calibration, and higher long-text generation performance. Our method is also scalable for different model families with 120M to 13B parameters. We will release our code and model checkpoints at https://aka.ms/MiniLLM.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/LMOps/tree/main/minillm", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08161", "title": "h2oGPT: Democratizing Large Language Models", "authors": [ "Arno Candel", "Jon McKinney", "Philipp Singer", "Pascal Pfeiffer", "Maximilian Jeblick", "Prithvi Prabhu", "Jeff Gambera", "Mark Landry", "Shivam Bansal", "Ryan Chesler", "Chun Ming Lee", "Marcos V. Conde", "Pasha Stetsenko", "Olivier Grellier", "SriSatish Ambati" ], "github": "https://github.com/h2oai/h2o-llmstudio", "project_page": "", "abstract": "Foundation Large Language Models (LLMs) such as GPT-4 represent a revolution in AI due to their real-world applications though natural language processing. However, they also pose many significant risks such as the presence of biased, private, or harmful text, and the unauthorized inclusion of copyrighted material. We introduce h2oGPT, a suite of open-source code repositories for the creation and use of Large Language Models (LLMs) based on Generative Pretrained Transformers (GPTs). The goal of this project is to create the world's best truly open-source alternative to closed-source GPTs. In collaboration with and as part of the incredible and unstoppable open-source community, we open-source several fine-tuned h2oGPT models from 7 to 40 Billion parameters, ready for commercial use under fully permissive Apache 2.0 licenses. Included in our release is 100% private document search using natural language. Open-source language models help boost AI development and make it more accessible and trustworthy. They lower entry hurdles, allowing people and groups to tailor these models to their needs. This openness increases innovation, transparency, and fairness. An open-source strategy is needed to share AI benefits fairly, and H2O.ai will continue to democratize AI and LLMs.", "github_manual": "https://github.com/h2oai/h2o-llmstudio", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.09329", "title": "DreamHuman: Animatable 3D Avatars from Text", "authors": [ "Nikos Kolotouros", "Thiemo Alldieck", "Andrei Zanfir", "Eduard Gabriel Bazavan", "Mihai Fieraru", "Cristian Sminchisescu" ], "github": "", "project_page": "", "abstract": "We present DreamHuman, a method to generate realistic animatable 3D human avatar models solely from textual descriptions. Recent text-to-3D methods have made considerable strides in generation, but are still lacking in important aspects. Control and often spatial resolution remain limited, existing methods produce fixed rather than animated 3D human models, and anthropometric consistency for complex structures like people remains a challenge. DreamHuman connects large text-to-image synthesis models, neural radiance fields, and statistical human body models in a novel modeling and optimization framework. This makes it possible to generate dynamic 3D human avatars with high-quality textures and learned, instance-specific, surface deformations. We demonstrate that our method is capable to generate a wide variety of animatable, realistic 3D human models from text. Our 3D models have diverse appearance, clothing, skin tones and body shapes, and significantly outperform both generic text-to-3D approaches and previous text-based 3D avatar generators in visual fidelity. For more results and animations please check our website at https://dream-human.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.09093", "title": "Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration", "authors": [ "Chenyang Lyu", "Minghao Wu", "Longyue Wang", "Xinting Huang", "Bingshuai Liu", "Zefeng Du", "Shuming Shi", "Zhaopeng Tu" ], "github": "https://github.com/lyuchenyang/macaw-llm", "project_page": "", "abstract": "Although instruction-tuned large language models (LLMs) have exhibited remarkable capabilities across various NLP tasks, their effectiveness on other data modalities beyond text has not been fully studied. In this work, we propose Macaw-LLM, a novel multi-modal LLM that seamlessly integrates visual, audio, and textual information. Macaw-LLM consists of three main components: a modality module for encoding multi-modal data, a cognitive module for harnessing pretrained LLMs, and an alignment module for harmonizing diverse representations. Our novel alignment module seamlessly bridges multi-modal features to textual features, simplifying the adaptation process from the modality modules to the cognitive module. In addition, we construct a large-scale multi-modal instruction dataset in terms of multi-turn dialogue, including 69K image instances and 50K video instances. We have made our data, code and model publicly available, which we hope can pave the way for future research in multi-modal LLMs and expand the capabilities of LLMs to handle diverse data modalities and address complex real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lyuchenyang/macaw-llm", "github_api_added_by": "auto" }, { "date": "2023-06-16", "arxiv_id": "2306.08205", "title": "Agile Catching with Whole-Body MPC and Blackbox Policy Learning", "authors": [ "Saminda Abeyruwan", "Alex Bewley", "Nicholas M. Boffi", "Krzysztof Choromanski", "David D'Ambrosio", "Deepali Jain", "Pannag Sanketi", "Anish Shankar", "Vikas Sindhwani", "Sumeet Singh", "Jean-Jacques Slotine", "Stephen Tu" ], "github": "", "project_page": "", "abstract": "We address a benchmark task in agile robotics: catching objects thrown at high-speed. This is a challenging task that involves tracking, intercepting, and cradling a thrown object with access only to visual observations of the object and the proprioceptive state of the robot, all within a fraction of a second. We present the relative merits of two fundamentally different solution strategies: (i) Model Predictive Control using accelerated constrained trajectory optimization, and (ii) Reinforcement Learning using zeroth-order optimization. We provide insights into various performance trade-offs including sample efficiency, sim-to-real transfer, robustness to distribution shifts, and whole-body multimodality via extensive on-hardware experiments. We conclude with proposals on fusing \"classical\" and \"learning-based\" techniques for agile robot control. Videos of our experiments may be found at https://sites.google.com/view/agile-catching", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08647", "title": "Language to Rewards for Robotic Skill Synthesis", "authors": [ "Wenhao Yu", "Nimrod Gileadi", "Chuyuan Fu", "Sean Kirmani", "Kuang-Huei Lee", "Montse Gonzalez Arenas", "Hao-Tien Lewis Chiang", "Tom Erez", "Leonard Hasenclever", "Jan Humplik", "Brian Ichter", "Ted Xiao", "Peng Xu", "Andy Zeng", "Tingnan Zhang", "Nicolas Heess", "Dorsa Sadigh", "Jie Tan", "Yuval Tassa", "Fei Xia" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing. Robotics researchers have also explored using LLMs to advance the capabilities of robotic control. However, since low-level robot actions are hardware-dependent and underrepresented in LLM training corpora, existing efforts in applying LLMs to robotics have largely treated LLMs as semantic planners or relied on human-engineered control primitives to interface with the robot. On the other hand, reward functions are shown to be flexible representations that can be optimized for control policies to achieve diverse tasks, while their semantic richness makes them suitable to be specified by LLMs. In this work, we introduce a new paradigm that harnesses this realization by utilizing LLMs to define reward parameters that can be optimized and accomplish variety of robotic tasks. Using reward as the intermediate interface generated by LLMs, we can effectively bridge the gap between high-level language instructions or corrections to low-level robot actions. Meanwhile, combining this with a real-time optimizer, MuJoCo MPC, empowers an interactive behavior creation experience where users can immediately observe the results and provide feedback to the system. To systematically evaluate the performance of our proposed method, we designed a total of 17 tasks for a simulated quadruped robot and a dexterous manipulator robot. We demonstrate that our proposed method reliably tackles 90% of the designed tasks, while a baseline using primitive skills as the interface with Code-as-policies achieves 50% of the tasks. We further validated our method on a real robot arm where complex manipulation skills such as non-prehensile pushing emerge through our interactive system.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08997", "title": "Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models", "authors": [ "Sarah J. Zhang", "Samuel Florin", "Ariel N. Lee", "Eamon Niknafs", "Andrei Marginean", "Annie Wang", "Keith Tyser", "Zad Chin", "Yann Hicke", "Nikhil Singh", "Madeleine Udell", "Yoon Kim", "Tonio Buonassisi", "Armando Solar-Lezama", "Iddo Drori" ], "github": "", "project_page": "", "abstract": "We curate a comprehensive dataset of 4,550 questions and solutions from problem sets, midterm exams, and final exams across all MIT Mathematics and Electrical Engineering and Computer Science (EECS) courses required for obtaining a degree. We evaluate the ability of large language models to fulfill the graduation requirements for any MIT major in Mathematics and EECS. Our results demonstrate that GPT-3.5 successfully solves a third of the entire MIT curriculum, while GPT-4, with prompt engineering, achieves a perfect solve rate on a test set excluding questions based on images. We fine-tune an open-source large language model on this dataset. We employ GPT-4 to automatically grade model responses, providing a detailed performance breakdown by course, question, and answer type. By embedding questions in a low-dimensional space, we explore the relationships between questions, topics, and classes and discover which questions and classes are required for solving other questions and classes through few-shot learning. Our analysis offers valuable insights into course prerequisites and curriculum design, highlighting language models' potential for learning and improving Mathematics and EECS education.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.09316", "title": "Diffusion Models for Zero-Shot Open-Vocabulary Segmentation", "authors": [ "Laurynas Karazija", "Iro Laina", "Andrea Vedaldi", "Christian Rupprecht" ], "github": "https://github.com/facebookresearch/dino", "project_page": "", "abstract": "The variety of objects in the real world is nearly unlimited and is thus impossible to capture using models trained on a fixed set of categories. As a result, in recent years, open-vocabulary methods have attracted the interest of the community. This paper proposes a new method for zero-shot open-vocabulary segmentation. Prior work largely relies on contrastive training using image-text pairs, leveraging grouping mechanisms to learn image features that are both aligned with language and well-localised. This however can introduce ambiguity as the visual appearance of images with similar captions often varies. Instead, we leverage the generative properties of large-scale text-to-image diffusion models to sample a set of support images for a given textual category. This provides a distribution of appearances for a given text circumventing the ambiguity problem. We further propose a mechanism that considers the contextual background of the sampled images to better localise objects and segment the background directly. We show that our method can be used to ground several existing pre-trained self-supervised feature extractors in natural language and provide explainable predictions by mapping back to regions in the support set. Our proposal is training-free, relying on pre-trained components only, yet, shows strong performance on a range of open-vocabulary segmentation benchmarks, obtaining a lead of more than 10% on the Pascal VOC benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/dino", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08620", "title": "Anticipatory Music Transformer", "authors": [ "John Thickstun", "David Hall", "Chris Donahue", "Percy Liang" ], "github": "https://github.com/jthickstun/anticipation", "project_page": "", "abstract": "We introduce anticipation: a method for constructing a controllable generative model of a temporal point process (the event process) conditioned asynchronously on realizations of a second, correlated process (the control process). We achieve this by interleaving sequences of events and controls, such that controls appear following stopping times in the event sequence. This work is motivated by problems arising in the control of symbolic music generation. We focus on infilling control tasks, whereby the controls are a subset of the events themselves, and conditional generation completes a sequence of events given the fixed control events. We train anticipatory infilling models using the large and diverse Lakh MIDI music dataset. These models match the performance of autoregressive models for prompted music generation, with the additional capability to perform infilling control tasks, including accompaniment. Human evaluators report that an anticipatory model produces accompaniments with similar musicality to even music composed by humans over a 20-second clip.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jthickstun/anticipation", "github_api_added_by": "auto" }, { "date": "2023-06-16", "arxiv_id": "2306.09200", "title": "ChessGPT: Bridging Policy Learning and Language Modeling", "authors": [ "Xidong Feng", "Yicheng Luo", "Ziyan Wang", "Hongrui Tang", "Mengyue Yang", "Kun Shao", "David Mguni", "Yali Du", "Jun Wang" ], "github": "https://github.com/waterhorse1/ChessGPT", "project_page": "", "abstract": "When solving decision-making tasks, humans typically depend on information from two key sources: (1) Historical policy data, which provides interaction replay from the environment, and (2) Analytical insights in natural language form, exposing the invaluable thought process or strategic considerations. Despite this, the majority of preceding research focuses on only one source: they either use historical replay exclusively to directly learn policy or value functions, or engaged in language model training utilizing mere language corpus. In this paper, we argue that a powerful autonomous agent should cover both sources. Thus, we propose ChessGPT, a GPT model bridging policy learning and language modeling by integrating data from these two sources in Chess games. Specifically, we build a large-scale game and language dataset related to chess. Leveraging the dataset, we showcase two model examples ChessCLIP and ChessGPT, integrating policy learning and language modeling. Finally, we propose a full evaluation framework for evaluating language model's chess ability. Experimental results validate our model and dataset's effectiveness. We open source our code, model, and dataset at https://github.com/waterhorse1/ChessGPT.", "github_manual": "", "github_regex": "https://github.com/waterhorse1/ChessGPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.09327", "title": "Language-Guided Music Recommendation for Video via Prompt Analogies", "authors": [ "Daniel McKee", "Justin Salamon", "Josef Sivic", "Bryan Russell" ], "github": "", "project_page": "", "abstract": "We propose a method to recommend music for an input video while allowing a user to guide music selection with free-form natural language. A key challenge of this problem setting is that existing music video datasets provide the needed (video, music) training pairs, but lack text descriptions of the music. This work addresses this challenge with the following three contributions. First, we propose a text-synthesis approach that relies on an analogy-based prompting procedure to generate natural language music descriptions from a large-scale language model (BLOOM-176B) given pre-trained music tagger outputs and a small number of human text descriptions. Second, we use these synthesized music descriptions to train a new trimodal model, which fuses text and video input representations to query music samples. For training, we introduce a text dropout regularization mechanism which we show is critical to model performance. Our model design allows for the retrieved music audio to agree with the two input modalities by matching visual style depicted in the video and musical genre, mood, or instrumentation described in the natural language query. Third, to evaluate our approach, we collect a testing dataset for our problem by annotating a subset of 4k clips from the YT8M-MusicVideo dataset with natural language music descriptions which we make publicly available. We show that our approach can match or exceed the performance of prior methods on video-to-music retrieval while significantly improving retrieval accuracy when using text guidance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08893", "title": "LOVM: Language-Only Vision Model Selection", "authors": [ "Orr Zohar", "Shih-Cheng Huang", "Kuan-Chieh Wang", "Serena Yeung" ], "github": "https://github.com/orrzohar/lovm", "project_page": "", "abstract": "Pre-trained multi-modal vision-language models (VLMs) are becoming increasingly popular due to their exceptional performance on downstream vision applications, particularly in the few- and zero-shot settings. However, selecting the best-performing VLM for some downstream applications is non-trivial, as it is dataset and task-dependent. Meanwhile, the exhaustive evaluation of all available VLMs on a novel application is not only time and computationally demanding but also necessitates the collection of a labeled dataset for evaluation. As the number of open-source VLM variants increases, there is a need for an efficient model selection strategy that does not require access to a curated evaluation dataset. This paper proposes a novel task and benchmark for efficiently evaluating VLMs' zero-shot performance on downstream applications without access to the downstream task dataset. Specifically, we introduce a new task LOVM: Language-Only Vision Model Selection, where methods are expected to perform both model selection and performance prediction based solely on a text description of the desired downstream application. We then introduced an extensive LOVM benchmark consisting of ground-truth evaluations of 35 pre-trained VLMs and 23 datasets, where methods are expected to rank the pre-trained VLMs and predict their zero-shot performance.", "github_manual": "https://github.com/orrzohar/lovm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08068", "title": "DORSal: Diffusion for Object-centric Representations of Scenes et al.", "authors": [ "Allan Jabri", "Sjoerd van Steenkiste", "Emiel Hoogeboom", "Mehdi S. M. Sajjadi", "Thomas Kipf" ], "github": "", "project_page": "", "abstract": "Recent progress in 3D scene understanding enables scalable learning of representations across large datasets of diverse scenes. As a consequence, generalization to unseen scenes and objects, rendering novel views from just a single or a handful of input images, and controllable scene generation that supports editing, is now possible. However, training jointly on a large number of scenes typically compromises rendering quality when compared to single-scene optimized models such as NeRFs. In this paper, we leverage recent progress in diffusion models to equip 3D scene representation learning models with the ability to render high-fidelity novel views, while retaining benefits such as object-level scene editing to a large degree. In particular, we propose DORSal, which adapts a video diffusion architecture for 3D scene generation conditioned on object-centric slot-based representations of scenes. On both complex synthetic multi-object scenes and on the real-world large-scale Street View dataset, we show that DORSal enables scalable neural rendering of 3D scenes with object-level editing and improves upon existing approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08707", "title": "VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing", "authors": [ "Paul Couairon", "Cl\u00e9ment Rambour", "Jean-Emmanuel Haugeard", "Nicolas Thome" ], "github": "", "project_page": "", "abstract": "Recently, diffusion-based generative models have achieved remarkable success for image generation and edition. However, their use for video editing still faces important limitations. This paper introduces VidEdit, a novel method for zero-shot text-based video editing ensuring strong temporal and spatial consistency. Firstly, we propose to combine atlas-based and pre-trained text-to-image diffusion models to provide a training-free and efficient editing method, which by design fulfills temporal smoothness. Secondly, we leverage off-the-shelf panoptic segmenters along with edge detectors and adapt their use for conditioned diffusion-based atlas editing. This ensures a fine spatial control on targeted regions while strictly preserving the structure of the original video. Quantitative and qualitative experiments show that VidEdit outperforms state-of-the-art methods on DAVIS dataset, regarding semantic faithfulness, image preservation, and temporal consistency metrics. With this framework, processing a single video only takes approximately one minute, and it can generate multiple compatible edits based on a unique text prompt. Project web-page at https://videdit.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.09349", "title": "UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Video", "authors": [ "Zhi-Hao Lin", "Bohan Liu", "Yi-Ting Chen", "David Forsyth", "Jia-Bin Huang", "Anand Bhattad", "Shenlong Wang" ], "github": "", "project_page": "", "abstract": "We show how to build a model that allows realistic, free-viewpoint renderings of a scene under novel lighting conditions from video. Our method -- UrbanIR: Urban Scene Inverse Rendering -- computes an inverse graphics representation from the video. UrbanIR jointly infers shape, albedo, visibility, and sun and sky illumination from a single video of unbounded outdoor scenes with unknown lighting. UrbanIR uses videos from cameras mounted on cars (in contrast to many views of the same points in typical NeRF-style estimation). As a result, standard methods produce poor geometry estimates (for example, roofs), and there are numerous ''floaters''. Errors in inverse graphics inference can result in strong rendering artifacts. UrbanIR uses novel losses to control these and other sources of error. UrbanIR uses a novel loss to make very good estimates of shadow volumes in the original scene. The resulting representations facilitate controllable editing, delivering photorealistic free-viewpoint renderings of relit scenes and inserted objects. Qualitative evaluation demonstrates strong improvements over the state-of-the-art.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.09109", "title": "NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations", "authors": [ "Varun Jampani", "Kevis-Kokitsi Maninis", "Andreas Engelhardt", "Arjun Karpur", "Karen Truong", "Kyle Sargent", "Stefan Popov", "Andr\u00e9 Araujo", "Ricardo Martin-Brualla", "Kaushal Patel", "Daniel Vlasic", "Vittorio Ferrari", "Ameesh Makadia", "Ce Liu", "Yuanzhen Li", "Howard Zhou" ], "github": "", "project_page": "", "abstract": "Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on relatively simple image collections where Structure-from-Motion (SfM) techniques can provide ground-truth (GT) camera poses. We note that SfM techniques tend to fail on in-the-wild image collections such as image search results with varying backgrounds and illuminations. To enable systematic research progress on 3D reconstruction from casual image captures, we propose NAVI: a new dataset of category-agnostic image collections of objects with high-quality 3D scans along with per-image 2D-3D alignments providing near-perfect GT camera parameters. These 2D-3D alignments allow us to extract accurate derivative annotations such as dense pixel correspondences, depth and segmentation maps. We demonstrate the use of NAVI image collections on different problem settings and show that NAVI enables more thorough evaluations that were not possible with existing datasets. We believe NAVI is beneficial for systematic research progress on 3D reconstruction and correspondence estimation. Project page: https://navidataset.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08133", "title": "Large-scale Language Model Rescoring on Long-form Data", "authors": [ "Tongzhou Chen", "Cyril Allauzen", "Yinghui Huang", "Daniel Park", "David Rybach", "W. Ronny Huang", "Rodrigo Cabrera", "Kartik Audhkhasi", "Bhuvana Ramabhadran", "Pedro J. Moreno", "Michael Riley" ], "github": "", "project_page": "", "abstract": "In this work, we study the impact of Large-scale Language Models (LLM) on Automated Speech Recognition (ASR) of YouTube videos, which we use as a source for long-form ASR. We demonstrate up to 8\\% relative reduction in Word Error Eate (WER) on US English (en-us) and code-switched Indian English (en-in) long-form ASR test sets and a reduction of up to 30\\% relative on Salient Term Error Rate (STER) over a strong first-pass baseline that uses a maximum-entropy based language model. Improved lattice processing that results in a lattice with a proper (non-tree) digraph topology and carrying context from the 1-best hypothesis of the previous segment(s) results in significant wins in rescoring with LLMs. We also find that the gains in performance from the combination of LLMs trained on vast quantities of available data (such as C4) and conventional neural LMs is additive and significantly outperforms a strong first-pass baseline with a maximum entropy LM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08129", "title": "AVIS: Autonomous Visual Information Seeking with Large Language Models", "authors": [ "Ziniu Hu", "Ahmet Iscen", "Chen Sun", "Kai-Wei Chang", "Yizhou Sun", "David A Ross", "Cordelia Schmid", "Alireza Fathi" ], "github": "", "project_page": "", "abstract": "In this paper, we propose an autonomous information seeking visual question answering framework, AVIS. Our method leverages a Large Language Model (LLM) to dynamically strategize the utilization of external tools and to investigate their outputs, thereby acquiring the indispensable knowledge needed to provide answers to the posed questions. Responding to visual questions that necessitate external knowledge, such as \"What event is commemorated by the building depicted in this image?\", is a complex task. This task presents a combinatorial search space that demands a sequence of actions, including invoking APIs, analyzing their responses, and making informed decisions. We conduct a user study to collect a variety of instances of human decision-making when faced with this task. This data is then used to design a system comprised of three components: an LLM-powered planner that dynamically determines which tool to use next, an LLM-powered reasoner that analyzes and extracts key information from the tool outputs, and a working memory component that retains the acquired information throughout the process. The collected user behavior serves as a guide for our system in two key ways. First, we create a transition graph by analyzing the sequence of decisions made by users. This graph delineates distinct states and confines the set of actions available at each state. Second, we use examples of user decision-making to provide our LLM-powered planner and reasoner with relevant contextual instances, enhancing their capacity to make informed decisions. We show that AVIS achieves state-of-the-art results on knowledge-intensive visual question answering benchmarks such as Infoseek and OK-VQA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.09322", "title": "Neural Relighting with Subsurface Scattering by Learning the Radiance Transfer Gradient", "authors": [ "Shizhan Zhu", "Shunsuke Saito", "Aljaz Bozic", "Carlos Aliaga", "Trevor Darrell", "Christop Lassner" ], "github": "", "project_page": "", "abstract": "Reconstructing and relighting objects and scenes under varying lighting conditions is challenging: existing neural rendering methods often cannot handle the complex interactions between materials and light. Incorporating pre-computed radiance transfer techniques enables global illumination, but still struggles with materials with subsurface scattering effects. We propose a novel framework for learning the radiance transfer field via volume rendering and utilizing various appearance cues to refine geometry end-to-end. This framework extends relighting and reconstruction capabilities to handle a wider range of materials in a data-driven fashion. The resulting models produce plausible rendering results in existing and novel conditions. We will release our code and a novel light stage dataset of objects with subsurface scattering effects publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08651", "title": "Toward Grounded Social Reasoning", "authors": [ "Minae Kwon", "Hengyuan Hu", "Vivek Myers", "Siddharth Karamcheti", "Anca Dragan", "Dorsa Sadigh" ], "github": "", "project_page": "", "abstract": "Consider a robot tasked with tidying a desk with a meticulously constructed Lego sports car. A human may recognize that it is not socially appropriate to disassemble the sports car and put it away as part of the \"tidying\". How can a robot reach that conclusion? Although large language models (LLMs) have recently been used to enable social reasoning, grounding this reasoning in the real world has been challenging. To reason in the real world, robots must go beyond passively querying LLMs and *actively gather information from the environment* that is required to make the right decision. For instance, after detecting that there is an occluded car, the robot may need to actively perceive the car to know whether it is an advanced model car made out of Legos or a toy car built by a toddler. We propose an approach that leverages an LLM and vision language model (VLM) to help a robot actively perceive its environment to perform grounded social reasoning. To evaluate our framework at scale, we release the MessySurfaces dataset which contains images of 70 real-world surfaces that need to be cleaned. We additionally illustrate our approach with a robot on 2 carefully designed surfaces. We find an average 12.9% improvement on the MessySurfaces benchmark and an average 15% improvement on the robot experiments over baselines that do not use active perception. The dataset, code, and videos of our approach can be found at https://minaek.github.io/groundedsocialreasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-16", "arxiv_id": "2306.08055", "title": "Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training", "authors": [ "Abraham J. Fetterman", "Ellie Kitanidis", "Joshua Albrecht", "Zachary Polizzi", "Bryden Fogelman", "Maksis Knutins", "Bartosz Wr\u00f3blewski", "James B. Simon", "Kanjun Qiu" ], "github": "", "project_page": "", "abstract": "Hyperparameter tuning of deep learning models can lead to order-of-magnitude performance gains for the same amount of compute. Despite this, systematic tuning is uncommon, particularly for large models, which are expensive to evaluate and tend to have many hyperparameters, necessitating difficult judgment calls about tradeoffs, budgets, and search bounds. To address these issues and propose a practical method for robustly tuning large models, we present Cost-Aware Pareto Region Bayesian Search (CARBS), a Bayesian optimization algorithm that performs local search around the performance-cost Pareto frontier. CARBS does well even in unbounded search spaces with many hyperparameters, learns scaling relationships so that it can tune models even as they are scaled up, and automates much of the \"black magic\" of tuning. Among our results, we effectively solve the entire ProcGen benchmark just by tuning a simple baseline (PPO, as provided in the original ProcGen paper). We also reproduce the model size vs. training tokens scaling result from the Chinchilla project (Hoffmann et al. 2022), while simultaneously discovering scaling laws for every other hyperparameter, via an easy automated process that uses significantly less compute and is applicable to any deep learning problem (not just language models).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-19", "arxiv_id": "2306.10012", "title": "MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing", "authors": [ "Kai Zhang", "Lingbo Mo", "Wenhu Chen", "Huan Sun", "Yu Su" ], "github": "https://github.com/osu-nlp-group/magicbrush", "project_page": "", "abstract": "Text-guided image editing is widely needed in daily life, ranging from personal use to professional applications such as Photoshop. However, existing methods are either zero-shot or trained on an automatically synthesized dataset, which contains a high volume of noise. Thus, they still require lots of manual tuning to produce desirable outcomes in practice. To address this issue, we introduce MagicBrush (https://osu-nlp-group.github.io/MagicBrush/), the first large-scale, manually annotated dataset for instruction-guided real image editing that covers diverse scenarios: single-turn, multi-turn, mask-provided, and mask-free editing. MagicBrush comprises over 10K manually annotated triples (source image, instruction, target image), which supports trainining large-scale text-guided image editing models. We fine-tune InstructPix2Pix on MagicBrush and show that the new model can produce much better images according to human evaluation. We further conduct extensive experiments to evaluate current image editing baselines from multiple dimensions including quantitative, qualitative, and human evaluations. The results reveal the challenging nature of our dataset and the gap between current baselines and real-world editing needs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/osu-nlp-group/magicbrush", "github_api_added_by": "auto" }, { "date": "2023-06-19", "arxiv_id": "2306.09782", "title": "Full Parameter Fine-tuning for Large Language Models with Limited Resources", "authors": [ "Kai Lv", "Yuqing Yang", "Tengxiao Liu", "Qinghui Gao", "Qipeng Guo", "Xipeng Qiu" ], "github": "https://github.com/openlmlab/lomo", "project_page": "", "abstract": "Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but demand massive GPU resources for training. Lowering the threshold for LLMs training would encourage greater participation from researchers, benefiting both academia and society. While existing approaches have focused on parameter-efficient fine-tuning, which tunes or adds a small number of parameters, few have addressed the challenge of tuning the full parameters of LLMs with limited resources. In this work, we propose a new optimizer, LOw-Memory Optimization (LOMO), which fuses the gradient computation and the parameter update in one step to reduce memory usage. By integrating LOMO with existing memory saving techniques, we reduce memory usage to 10.8% compared to the standard approach (DeepSpeed solution). Consequently, our approach enables the full parameter fine-tuning of a 65B model on a single machine with 8 RTX 3090, each with 24GB memory.", "github_manual": "https://github.com/openlmlab/lomo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-19", "arxiv_id": "2306.09896", "title": "Demystifying GPT Self-Repair for Code Generation", "authors": [ "Theo X. Olausson", "Jeevana Priya Inala", "Chenglong Wang", "Jianfeng Gao", "Armando Solar-Lezama" ], "github": "https://github.com/theoxo/self-repair", "project_page": "", "abstract": "Large Language Models (LLMs) have shown remarkable aptitude in code generation but still struggle on challenging programming tasks. Self-repair -- in which the model debugs and fixes mistakes in its own code -- has recently become a popular way to boost performance in these settings. However, only very limited studies on how and when self-repair works effectively exist in the literature, and one might wonder to what extent a model is really capable of providing accurate feedback on why the code is wrong when that code was generated by the same model. In this paper, we analyze GPT-3.5 and GPT-4's ability to perform self-repair on APPS, a challenging dataset consisting of diverse coding challenges. To do so, we first establish a new evaluation strategy dubbed pass@t that measures the pass rate of the tasks against the total number of tokens sampled from the model, enabling a fair comparison to purely sampling-based approaches. With this evaluation strategy, we find that the effectiveness of self-repair is only seen in GPT-4. We also observe that self-repair is bottlenecked by the feedback stage; using GPT-4 to give feedback on the programs generated by GPT-3.5 and using expert human programmers to give feedback on the programs generated by GPT-4, we unlock significant performance gains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/theoxo/self-repair", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-19", "arxiv_id": "2306.09864", "title": "AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation", "authors": [ "Yifei Zeng", "Yuanxun Lu", "Xinya Ji", "Yao Yao", "Hao Zhu", "Xun Cao" ], "github": "", "project_page": "", "abstract": "We introduce AvatarBooth, a novel method for generating high-quality 3D avatars using text prompts or specific images. Unlike previous approaches that can only synthesize avatars based on simple text descriptions, our method enables the creation of personalized avatars from casually captured face or body images, while still supporting text-based model generation and editing. Our key contribution is the precise avatar generation control by using dual fine-tuned diffusion models separately for the human face and body. This enables us to capture intricate details of facial appearance, clothing, and accessories, resulting in highly realistic avatar generations. Furthermore, we introduce pose-consistent constraint to the optimization process to enhance the multi-view consistency of synthesized head images from the diffusion model and thus eliminate interference from uncontrolled human poses. In addition, we present a multi-resolution rendering strategy that facilitates coarse-to-fine supervision of 3D avatar generation, thereby enhancing the performance of the proposed system. The resulting avatar model can be further edited using additional text descriptions and driven by motion sequences. Experiments show that AvatarBooth outperforms previous text-to-3D methods in terms of rendering and geometric quality from either text prompts or specific images. Please check our project website at https://zeng-yifei.github.io/avatarbooth_page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-19", "arxiv_id": "2306.10007", "title": "Robot Learning with Sensorimotor Pre-training", "authors": [ "Ilija Radosavovic", "Baifeng Shi", "Letian Fu", "Ken Goldberg", "Trevor Darrell", "Jitendra Malik" ], "github": "", "project_page": "", "abstract": "We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. Given a sequence of camera images, proprioceptive robot states, and past actions, we encode the interleaved sequence into tokens, mask out a random subset, and train a model to predict the masked-out content. We hypothesize that if the robot can predict the missing content it has acquired a good model of the physical world that can enable it to act. RPT is designed to operate on latent visual representations which makes prediction tractable, enables scaling to 10x larger models, and 10 Hz inference on a real robot. To evaluate our approach, we collect a dataset of 20,000 real-world trajectories over 9 months using a combination of motion planning and model-based grasping algorithms. We find that pre-training on this data consistently outperforms training from scratch, leads to 2x improvements in the block stacking task, and has favorable scaling properties.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-19", "arxiv_id": "2306.09683", "title": "Scaling Open-Vocabulary Object Detection", "authors": [ "Matthias Minderer", "Alexey Gritsenko", "Neil Houlsby" ], "github": "", "project_page": "", "abstract": "Open-vocabulary object detection has benefited greatly from pretrained vision-language models, but is still limited by the amount of available detection training data. While detection training data can be expanded by using Web image-text pairs as weak supervision, this has not been done at scales comparable to image-level pretraining. Here, we scale up detection data with self-training, which uses an existing detector to generate pseudo-box annotations on image-text pairs. Major challenges in scaling self-training are the choice of label space, pseudo-annotation filtering, and training efficiency. We present the OWLv2 model and OWL-ST self-training recipe, which address these challenges. OWLv2 surpasses the performance of previous state-of-the-art open-vocabulary detectors already at comparable training scales (~10M examples). However, with OWL-ST, we can scale to over 1B examples, yielding further large improvement: With an L/14 architecture, OWL-ST improves AP on LVIS rare classes, for which the model has seen no human box annotations, from 31.2% to 44.6% (43% relative improvement). OWL-ST unlocks Web-scale training for open-world localization, similar to what has been seen for image classification and language modelling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-19", "arxiv_id": "2306.09539", "title": "Block-State Transformer", "authors": [ "Mahan Fathi", "Jonathan Pilault", "Pierre-Luc Bacon", "Christopher Pal", "Orhan Firat", "Ross Goroshin" ], "github": "", "project_page": "", "abstract": "State space models (SSMs) have shown impressive results on tasks that require modeling long-range dependencies and efficiently scale to long sequences owing to their subquadratic runtime complexity. Originally designed for continuous signals, SSMs have shown superior performance on a plethora of tasks, in vision and audio; however, SSMs still lag Transformer performance in Language Modeling tasks. In this work, we propose a hybrid layer named Block-State Transformer (BST), that internally combines an SSM sublayer for long-range contextualization, and a Block Transformer sublayer for short-term representation of sequences. We study three different, and completely parallelizable, variants that integrate SSMs and block-wise attention. We show that our model outperforms similar Transformer-based architectures on language modeling perplexity and generalizes to longer sequences. In addition, the Block-State Transformer demonstrates more than tenfold increase in speed at the layer level compared to the Block-Recurrent Transformer when model parallelization is employed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-19", "arxiv_id": "2306.09479", "title": "Inverse Scaling: When Bigger Isn't Better", "authors": [ "Ian R. McKenzie", "Alexander Lyzhov", "Michael Pieler", "Alicia Parrish", "Aaron Mueller", "Ameya Prabhu", "Euan McLean", "Aaron Kirtland", "Alexis Ross", "Alisa Liu", "Andrew Gritsevskiy", "Daniel Wurgaft", "Derik Kauffman", "Gabriel Recchia", "Jiacheng Liu", "Joe Cavanagh", "Max Weiss", "Sicong Huang", "The Floating Droid", "Tom Tseng", "Tomasz Korbak", "Xudong Shen", "Yuhui Zhang", "Zhengping Zhou", "Najoung Kim", "Samuel R. Bowman", "Ethan Perez" ], "github": "https://github.com/inverse-scaling/prize/tree/main/plots/fewshot", "project_page": "", "abstract": "Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, we present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale, e.g., due to flaws in the training objective and data. We present empirical evidence of inverse scaling on 11 datasets collected by running a public contest, the Inverse Scaling Prize, with a substantial prize pool. Through analysis of the datasets, along with other examples found in the literature, we identify four potential causes of inverse scaling: (i) preference to repeat memorized sequences over following in-context instructions, (ii) imitation of undesirable patterns in the training data, (iii) tasks containing an easy distractor task which LMs could focus on, rather than the harder real task, and (iv) correct but misleading few-shot demonstrations of the task. We release the winning datasets at https://inversescaling.com/data to allow for further investigation of inverse scaling. Our tasks have helped drive the discovery of U-shaped and inverted-U scaling trends, where an initial trend reverses, suggesting that scaling trends are less reliable at predicting the behavior of larger-scale models than previously understood. Overall, our results suggest that there are tasks for which increased model scale alone may not lead to progress, and that more careful thought needs to go into the data and objectives for training language models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/inverse-scaling/prize/tree/main/plots/fewshot", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-19", "arxiv_id": "2306.09682", "title": "OCTScenes: A Versatile Real-World Dataset of Tabletop Scenes for Object-Centric Learning", "authors": [ "Yinxuan Huang", "Tonglin Chen", "Zhimeng Shen", "Jinghao Huang", "Bin Li", "Xiangyang Xue" ], "github": "https://github.com/FudanVI/compositional-scene-representation-toolbox", "project_page": "", "abstract": "Humans possess the cognitive ability to comprehend scenes in a compositional manner. To empower AI systems with similar abilities, object-centric representation learning aims to acquire representations of individual objects from visual scenes without any supervision. Although recent advancements in object-centric representation learning have achieved remarkable progress on complex synthesis datasets, there is a huge challenge for application in complex real-world scenes. One of the essential reasons is the scarcity of real-world datasets specifically tailored to object-centric representation learning methods. To solve this problem, we propose a versatile real-world dataset of tabletop scenes for object-centric learning called OCTScenes, which is meticulously designed to serve as a benchmark for comparing, evaluating and analyzing object-centric representation learning methods. OCTScenes contains 5000 tabletop scenes with a total of 15 everyday objects. Each scene is captured in 60 frames covering a 360-degree perspective. Consequently, OCTScenes is a versatile benchmark dataset that can simultaneously satisfy the evaluation of object-centric representation learning methods across static scenes, dynamic scenes, and multi-view scenes tasks. Extensive experiments of object-centric representation learning methods for static, dynamic and multi-view scenes are conducted on OCTScenes. The results demonstrate the shortcomings of state-of-the-art methods for learning meaningful representations from real-world data, despite their impressive performance on complex synthesis datasets. Furthermore, OCTScenes can serves as a catalyst for advancing existing state-of-the-art methods, inspiring them to adapt to real-world scenes. Dataset and code are available at https://huggingface.co/datasets/Yinxuan/OCTScenes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FudanVI/compositional-scene-representation-toolbox", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-19", "arxiv_id": "2306.09635", "title": "CLIPSonic: Text-to-Audio Synthesis with Unlabeled Videos and Pretrained Language-Vision Models", "authors": [ "Hao-Wen Dong", "Xiaoyu Liu", "Jordi Pons", "Gautam Bhattacharya", "Santiago Pascual", "Joan Serr\u00e0", "Taylor Berg-Kirkpatrick", "Julian McAuley" ], "github": "", "project_page": "", "abstract": "Recent work has studied text-to-audio synthesis using large amounts of paired text-audio data. However, audio recordings with high-quality text annotations can be difficult to acquire. In this work, we approach text-to-audio synthesis using unlabeled videos and pretrained language-vision models. We propose to learn the desired text-audio correspondence by leveraging the visual modality as a bridge. We train a conditional diffusion model to generate the audio track of a video, given a video frame encoded by a pretrained contrastive language-image pretraining (CLIP) model. At test time, we first explore performing a zero-shot modality transfer and condition the diffusion model with a CLIP-encoded text query. However, we observe a noticeable performance drop with respect to image queries. To close this gap, we further adopt a pretrained diffusion prior model to generate a CLIP image embedding given a CLIP text embedding. Our results show the effectiveness of the proposed method, and that the pretrained diffusion prior can reduce the modality transfer gap. While we focus on text-to-audio synthesis, the proposed model can also generate audio from image queries, and it shows competitive performance against a state-of-the-art image-to-audio synthesis model in a subjective listening test. This study offers a new direction of approaching text-to-audio synthesis that leverages the naturally-occurring audio-visual correspondence in videos and the power of pretrained language-vision models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-19", "arxiv_id": "2306.09442", "title": "Explore, Establish, Exploit: Red Teaming Language Models from Scratch", "authors": [ "Stephen Casper", "Jason Lin", "Joe Kwon", "Gatlen Culp", "Dylan Hadfield-Menell" ], "github": "https://github.com/thestephencasper/explore_establish_exploit_llms", "project_page": "", "abstract": "Deploying Large language models (LLMs) can pose hazards from harmful outputs such as toxic or dishonest speech. Prior work has introduced tools that elicit harmful outputs in order to identify and mitigate these risks. While this is a valuable step toward securing language models, these approaches typically rely on a pre-existing classifier for undesired outputs. This limits their application to situations where the type of harmful behavior is known with precision beforehand. However, this skips a central challenge of red teaming: developing a contextual understanding of the behaviors that a model can exhibit. Furthermore, when such a classifier already exists, red teaming has limited marginal value because the classifier could simply be used to filter training data or model outputs. In this work, we consider red teaming under the assumption that the adversary is working from a high-level, abstract specification of undesired behavior. The red team is expected to refine/extend this specification and identify methods to elicit this behavior from the model. Our red teaming framework consists of three steps: 1) Exploring the model's behavior in the desired context; 2) Establishing a measurement of undesired behavior (e.g., a classifier trained to reflect human evaluations); and 3) Exploiting the model's flaws using this measure and an established red teaming methodology. We apply this approach to red team GPT-2 and GPT-3 models to systematically discover classes of prompts that elicit toxic and dishonest statements. In doing so, we also construct and release the CommonClaim dataset of 20,000 statements that have been labeled by human subjects as common-knowledge-true, common-knowledge-false, or neither. Code is available at https://github.com/thestephencasper/explore_establish_exploit_llms. CommonClaim is available at https://github.com/thestephencasper/common_claim.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/thestephencasper/explore_establish_exploit_llms", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-19", "arxiv_id": "2306.09557", "title": "CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller", "authors": [ "Yuxiang Yang", "Guanya Shi", "Xiangyun Meng", "Wenhao Yu", "Tingnan Zhang", "Jie Tan", "Byron Boots" ], "github": "", "project_page": "", "abstract": "We present CAJun, a novel hierarchical learning and control framework that enables legged robots to jump continuously with adaptive jumping distances. CAJun consists of a high-level centroidal policy and a low-level leg controller. In particular, we use reinforcement learning (RL) to train the centroidal policy, which specifies the gait timing, base velocity, and swing foot position for the leg controller. The leg controller optimizes motor commands for the swing and stance legs according to the gait timing to track the swing foot target and base velocity commands using optimal control. Additionally, we reformulate the stance leg optimizer in the leg controller to speed up policy training by an order of magnitude. Our system combines the versatility of learning with the robustness of optimal control. By combining RL with optimal control methods, our system achieves the versatility of learning while enjoys the robustness from control methods, making it easily transferable to real robots. We show that after 20 minutes of training on a single GPU, CAJun can achieve continuous, long jumps with adaptive distances on a Go1 robot with small sim-to-real gaps. Moreover, the robot can jump across gaps with a maximum width of 70cm, which is over 40% wider than existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-21", "arxiv_id": "2306.11644", "title": "Textbooks Are All You Need", "authors": [ "Suriya Gunasekar", "Yi Zhang", "Jyoti Aneja", "Caio C\u00e9sar Teodoro Mendes", "Allie Del Giorno", "Sivakanth Gopi", "Mojan Javaheripi", "Piero Kauffmann", "Gustavo de Rosa", "Olli Saarikivi", "Adil Salim", "Shital Shah", "Harkirat Singh Behl", "Xin Wang", "S\u00e9bastien Bubeck", "Ronen Eldan", "Adam Tauman Kalai", "Yin Tat Lee", "Yuanzhi Li" ], "github": "", "project_page": "", "abstract": "We introduce phi-1, a new large language model for code, with significantly smaller size than competing models: phi-1 is a Transformer-based model with 1.3B parameters, trained for 4 days on 8 A100s, using a selection of ``textbook quality\" data from the web (6B tokens) and synthetically generated textbooks and exercises with GPT-3.5 (1B tokens). Despite this small scale, phi-1 attains pass@1 accuracy 50.6% on HumanEval and 55.5% on MBPP. It also displays surprising emergent properties compared to phi-1-base, our model before our finetuning stage on a dataset of coding exercises, and phi-1-small, a smaller model with 350M parameters trained with the same pipeline as phi-1 that still achieves 45% on HumanEval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-21", "arxiv_id": "2306.10900", "title": "MotionGPT: Finetuned LLMs are General-Purpose Motion Generators", "authors": [ "Yaqi Zhang", "Di Huang", "Bin Liu", "Shixiang Tang", "Yan Lu", "Lu Chen", "Lei Bai", "Qi Chu", "Nenghai Yu", "Wanli Ouyang" ], "github": "", "project_page": "", "abstract": "Generating realistic human motion from given action descriptions has experienced significant advancements because of the emerging requirement of digital humans. While recent works have achieved impressive results in generating motion directly from textual action descriptions, they often support only a single modality of the control signal, which limits their application in the real digital human industry. This paper presents a Motion General-Purpose generaTor (MotionGPT) that can use multimodal control signals, e.g., text and single-frame poses, for generating consecutive human motions by treating multimodal signals as special input tokens in large language models (LLMs). Specifically, we first quantize multimodal control signals into discrete codes and then formulate them in a unified prompt instruction to ask the LLMs to generate the motion answer. Our MotionGPT demonstrates a unified human motion generation model with multimodal control signals by tuning a mere 0.4% of LLM parameters. To the best of our knowledge, MotionGPT is the first method to generate human motion by multimodal control signals, which we hope can shed light on this new direction. Codes shall be released upon acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-21", "arxiv_id": "2306.11565", "title": "HomeRobot: Open-Vocabulary Mobile Manipulation", "authors": [ "Sriram Yenamandra", "Arun Ramachandran", "Karmesh Yadav", "Austin Wang", "Mukul Khanna", "Theophile Gervet", "Tsung-Yen Yang", "Vidhi Jain", "Alexander William Clegg", "John Turner", "Zsolt Kira", "Manolis Savva", "Angel Chang", "Devendra Singh Chaplot", "Dhruv Batra", "Roozbeh Mottaghi", "Yonatan Bisk", "Chris Paxton" ], "github": "", "project_page": "", "abstract": "HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks. Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location. This is a foundational challenge for robots to be useful assistants in human environments, because it involves tackling sub-problems from across robotics: perception, language understanding, navigation, and manipulation are all essential to OVMM. In addition, integration of the solutions to these sub-problems poses its own substantial challenges. To drive research in this area, we introduce the HomeRobot OVMM benchmark, where an agent navigates household environments to grasp novel objects and place them on target receptacles. HomeRobot has two components: a simulation component, which uses a large and diverse curated object set in new, high-quality multi-room home environments; and a real-world component, providing a software stack for the low-cost Hello Robot Stretch to encourage replication of real-world experiments across labs. We implement both reinforcement learning and heuristic (model-based) baselines and show evidence of sim-to-real transfer. Our baselines achieve a 20% success rate in the real world; our experiments identify ways future research work improve performance. See videos on our website: https://ovmm.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-21", "arxiv_id": "2306.10998", "title": "RepoFusion: Training Code Models to Understand Your Repository", "authors": [ "Disha Shrivastava", "Denis Kocetkov", "Harm de Vries", "Dzmitry Bahdanau", "Torsten Scholak" ], "github": "https://github.com/shrivastavadisha/repo_level_prompt_generation", "project_page": "", "abstract": "Despite the huge success of Large Language Models (LLMs) in coding assistants like GitHub Copilot, these models struggle to understand the context present in the repository (e.g., imports, parent classes, files with similar names, etc.), thereby producing inaccurate code completions. This effect is more pronounced when using these assistants for repositories that the model has not seen during training, such as proprietary software or work-in-progress code projects. Recent work has shown the promise of using context from the repository during inference. In this work, we extend this idea and propose RepoFusion, a framework to train models to incorporate relevant repository context. Experiments on single-line code completion show that our models trained with repository context significantly outperform much larger code models as CodeGen-16B-multi (sim73times larger) and closely match the performance of the sim 70times larger StarCoderBase model that was trained with the Fill-in-the-Middle objective. We find these results to be a novel and compelling demonstration of the gains that training with repository context can bring. We carry out extensive ablation studies to investigate the impact of design choices such as context type, number of contexts, context length, and initialization within our framework. Lastly, we release Stack-Repo, a dataset of 200 Java repositories with permissive licenses and near-deduplicated files that are augmented with three types of repository contexts. Additionally, we are making available the code and trained checkpoints for our work. Our released resources can be found at https://huggingface.co/RepoFusion.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/shrivastavadisha/repo_level_prompt_generation", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-21", "arxiv_id": "2306.11698", "title": "DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models", "authors": [ "Boxin Wang", "Weixin Chen", "Hengzhi Pei", "Chulin Xie", "Mintong Kang", "Chenhui Zhang", "Chejian Xu", "Zidi Xiong", "Ritik Dutta", "Rylan Schaeffer", "Sang T. Truong", "Simran Arora", "Mantas Mazeika", "Dan Hendrycks", "Zinan Lin", "Yu Cheng", "Sanmi Koyejo", "Dawn Song", "Bo Li" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in capabilities, capturing the interest of practitioners and the public alike. Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications to healthcare and finance - where mistakes can be costly. To this end, this work proposes a comprehensive trustworthiness evaluation for large language models with a focus on GPT-4 and GPT-3.5, considering diverse perspectives - including toxicity, stereotype bias, adversarial robustness, out-of-distribution robustness, robustness on adversarial demonstrations, privacy, machine ethics, and fairness. Based on our evaluations, we discover previously unpublished vulnerabilities to trustworthiness threats. For instance, we find that GPT models can be easily misled to generate toxic and biased outputs and leak private information in both training data and conversation history. We also find that although GPT-4 is usually more trustworthy than GPT-3.5 on standard benchmarks, GPT-4 is more vulnerable given jailbreaking system or user prompts, potentially due to the reason that GPT-4 follows the (misleading) instructions more precisely. Our work illustrates a comprehensive trustworthiness evaluation of GPT models and sheds light on the trustworthiness gaps. Our benchmark is publicly available at https://decodingtrust.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-21", "arxiv_id": "2306.10533", "title": "Point-Cloud Completion with Pretrained Text-to-image Diffusion Models", "authors": [ "Yoni Kasten", "Ohad Rahamim", "Gal Chechik" ], "github": "https://github.com/NVlabs/sds-complete", "project_page": "", "abstract": "Point-cloud data collected in real-world applications are often incomplete. Data is typically missing due to objects being observed from partial viewpoints, which only capture a specific perspective or angle. Additionally, data can be incomplete due to occlusion and low-resolution sampling. Existing completion approaches rely on datasets of predefined objects to guide the completion of noisy and incomplete, point clouds. However, these approaches perform poorly when tested on Out-Of-Distribution (OOD) objects, that are poorly represented in the training dataset. Here we leverage recent advances in text-guided image generation, which lead to major breakthroughs in text-guided shape generation. We describe an approach called SDS-Complete that uses a pre-trained text-to-image diffusion model and leverages the text semantics of a given incomplete point cloud of an object, to obtain a complete surface representation. SDS-Complete can complete a variety of objects using test-time optimization without expensive collection of 3D information. We evaluate SDS Complete on incomplete scanned objects, captured by real-world depth sensors and LiDAR scanners. We find that it effectively reconstructs objects that are absent from common datasets, reducing Chamfer loss by 50% on average compared with current methods. Project page: https://sds-complete.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/sds-complete", "github_api_added_by": "auto" }, { "date": "2023-06-21", "arxiv_id": "2306.10231", "title": "GLIMMER: generalized late-interaction memory reranker", "authors": [ "Michiel de Jong", "Yury Zemlyanskiy", "Nicholas FitzGerald", "Sumit Sanghai", "William W. Cohen", "Joshua Ainslie" ], "github": "", "project_page": "", "abstract": "Memory-augmentation is a powerful approach for efficiently incorporating external information into language models, but leads to reduced performance relative to retrieving text. Recent work introduced LUMEN, a memory-retrieval hybrid that partially pre-computes memory and updates memory representations on the fly with a smaller live encoder. We propose GLIMMER, which improves on this approach through 1) exploiting free access to the powerful memory representations by applying a shallow reranker on top of memory to drastically improve retrieval quality at low cost, and 2) incorporating multi-task training to learn a general and higher quality memory and live encoder. GLIMMER achieves strong gains in performance at faster speeds compared to LUMEN and FiD on the KILT benchmark of knowledge-intensive tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-21", "arxiv_id": "2306.11706", "title": "RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation", "authors": [ "Konstantinos Bousmalis", "Giulia Vezzani", "Dushyant Rao", "Coline Devin", "Alex X. Lee", "Maria Bauza", "Todor Davchev", "Yuxiang Zhou", "Agrim Gupta", "Akhil Raju", "Antoine Laurens", "Claudio Fantacci", "Valentin Dalibard", "Martina Zambelli", "Murilo Martins", "Rugile Pevceviciute", "Michiel Blokzijl", "Misha Denil", "Nathan Batchelor", "Thomas Lampe", "Emilio Parisotto", "Konrad \u017bo\u0142na", "Scott Reed", "Sergio G\u00f3mez Colmenarejo", "Jon Scholz", "Abbas Abdolmaleki", "Oliver Groth", "Jean-Baptiste Regli", "Oleg Sushkov", "Tom Roth\u00f6rl", "Jos\u00e9 Enrique Chen", "Yusuf Aytar", "Dave Barker", "Joy Ortiz", "Martin Riedmiller", "Jost Tobias Springenberg", "Raia Hadsell", "Francesco Nori", "Nicolas Heess" ], "github": "", "project_page": "", "abstract": "The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learning. Inspired by recent advances in foundation models for vision and language, we propose a foundation agent for robotic manipulation. This agent, named RoboCat, is a visual goal-conditioned decision transformer capable of consuming multi-embodiment action-labelled visual experience. This data spans a large repertoire of motor control skills from simulated and real robotic arms with varying sets of observations and actions. With RoboCat, we demonstrate the ability to generalise to new tasks and robots, both zero-shot as well as through adaptation using only 100--1000 examples for the target task. We also show how a trained model itself can be used to generate data for subsequent training iterations, thus providing a basic building block for an autonomous improvement loop. We investigate the agent's capabilities, with large-scale evaluations both in simulation and on three different real robot embodiments. We find that as we grow and diversify its training data, RoboCat not only shows signs of cross-task transfer, but also becomes more efficient at adapting to new tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-21", "arxiv_id": "2306.11719", "title": "Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision", "authors": [ "Ayush Tewari", "Tianwei Yin", "George Cazenavette", "Semon Rezchikov", "Joshua B. Tenenbaum", "Fr\u00e9do Durand", "William T. Freeman", "Vincent Sitzmann" ], "github": "https://github.com/ayushtewari/DFM", "project_page": "", "abstract": "Denoising diffusion models are a powerful type of generative models used to capture complex distributions of real-world signals. However, their applicability is limited to scenarios where training samples are readily available, which is not always the case in real-world applications. For example, in inverse graphics, the goal is to generate samples from a distribution of 3D scenes that align with a given image, but ground-truth 3D scenes are unavailable and only 2D images are accessible. To address this limitation, we propose a novel class of denoising diffusion probabilistic models that learn to sample from distributions of signals that are never directly observed. Instead, these signals are measured indirectly through a known differentiable forward model, which produces partial observations of the unknown signal. Our approach involves integrating the forward model directly into the denoising process. This integration effectively connects the generative modeling of observations with the generative modeling of the underlying signals, allowing for end-to-end training of a conditional generative model over signals. During inference, our approach enables sampling from the distribution of underlying signals that are consistent with a given partial observation. We demonstrate the effectiveness of our method on three challenging computer vision tasks. For instance, in the context of inverse graphics, our model enables direct sampling from the distribution of 3D scenes that align with a single 2D input image.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ayushtewari/DFM", "github_api_added_by": "auto" }, { "date": "2023-06-21", "arxiv_id": "2306.10169", "title": "Meta-Personalizing Vision-Language Models to Find Named Instances in Video", "authors": [ "Chun-Hsiao Yeh", "Bryan Russell", "Josef Sivic", "Fabian Caba Heilbron", "Simon Jenni" ], "github": "https://github.com/danielchyeh/this-is-my", "project_page": "", "abstract": "Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications. While these models allow category-level queries, they currently struggle with personalized searches for moments in a video where a specific object instance such as ``My dog Biscuit'' appears. We present the following three contributions to address this problem. First, we describe a method to meta-personalize a pre-trained VLM, i.e., learning how to learn to personalize a VLM at test time to search in video. Our method extends the VLM's token vocabulary by learning novel word embeddings specific to each instance. To capture only instance-specific features, we represent each instance embedding as a combination of shared and learned global category features. Second, we propose to learn such personalization without explicit human supervision. Our approach automatically identifies moments of named visual instances in video using transcripts and vision-language similarity in the VLM's embedding space. Finally, we introduce This-Is-My, a personal video instance retrieval benchmark. We evaluate our approach on This-Is-My and DeepFashion2 and show that we obtain a 15% relative improvement over the state of the art on the latter dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/danielchyeh/this-is-my", "github_api_added_by": "auto" }, { "date": "2023-06-21", "arxiv_id": "2306.10763", "title": "Guiding Language Models of Code with Global Context using Monitors", "authors": [ "Lakshya A Agrawal", "Aditya Kanade", "Navin Goyal", "Shuvendu K. Lahiri", "Sriram K. Rajamani" ], "github": "https://github.com/microsoft/monitors4codegen", "project_page": "", "abstract": "Language models of code (LMs) work well when the surrounding code in the vicinity of generation provides sufficient context. This is not true when it becomes necessary to use types or functionality defined in another module or library, especially those not seen during training. LMs suffer from limited awareness of such global context and end up hallucinating, e.g., using types defined in other files incorrectly. Recent work tries to overcome this issue by retrieving global information to augment the local context. However, this bloats the prompt or requires architecture modifications and additional training. Integrated development environments (IDEs) assist developers by bringing the global context at their fingertips using static analysis. We extend this assistance, enjoyed by developers, to the LMs. We propose a notion of monitors that use static analysis in the background to guide the decoding. Unlike a priori retrieval, static analysis is invoked iteratively during the entire decoding process, providing the most relevant suggestions on demand. We demonstrate the usefulness of our proposal by monitoring for type-consistent use of identifiers whenever an LM generates code for object dereference. To evaluate our approach, we curate PragmaticCode, a dataset of open-source projects with their development environments. On models of varying parameter scale, we show that monitor-guided decoding consistently improves the ability of an LM to not only generate identifiers that match the ground truth but also improves compilation rates and agreement with ground truth. We find that LMs with fewer parameters, when guided with our monitor, can outperform larger LMs. With monitor-guided decoding, SantaCoder-1.1B achieves better compilation rate and next-identifier match than the much larger text-davinci-003 model. The datasets and code will be released at https://aka.ms/monitors4codegen .", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/monitors4codegen", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-21", "arxiv_id": "2306.10968", "title": "BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models", "authors": [ "Shaolei Zhang", "Qingkai Fang", "Zhuocheng Zhang", "Zhengrui Ma", "Yan Zhou", "Langlin Huang", "Mengyu Bu", "Shangtong Gui", "Yunji Chen", "Xilin Chen", "Yang Feng" ], "github": "https://github.com/ictnlp/bayling", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable prowess in language understanding and generation. Advancing from foundation LLMs to instructionfollowing LLMs, instruction tuning plays a vital role in aligning LLMs to human preferences. However, the existing LLMs are usually focused on English, leading to inferior performance in non-English languages. In order to improve the performance for non-English languages, it is necessary to collect language-specific training data for foundation LLMs and construct language-specific instructions for instruction tuning, both of which are heavy loads. To minimize human workload, we propose to transfer the capabilities of language generation and instruction following from English to other languages through an interactive translation task. We have developed BayLing, an instruction-following LLM by utilizing LLaMA as the foundation LLM and automatically constructing interactive translation instructions for instructing tuning. Extensive assessments demonstrate that BayLing achieves comparable performance to GPT-3.5-turbo, despite utilizing a considerably smaller parameter size of only 13 billion. Experimental results on translation tasks show that BayLing achieves 95% of single-turn translation capability compared to GPT-4 with automatic evaluation and 96% of interactive translation capability compared to GPT-3.5-turbo with human evaluation. To estimate the performance on general tasks, we created a multi-turn instruction test set called BayLing-80. The experimental results on BayLing-80 indicate that BayLing achieves 89% of performance compared to GPT-3.5-turbo. BayLing also demonstrates outstanding performance on knowledge assessment of Chinese GaoKao and English SAT, second only to GPT-3.5-turbo among a multitude of instruction-following LLMs. Demo, homepage, code and models of BayLing are available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ictnlp/bayling", "github_api_added_by": "auto" }, { "date": "2023-06-21", "arxiv_id": "2306.10785", "title": "Multitrack Music Transcription with a Time-Frequency Perceiver", "authors": [ "Wei-Tsung Lu", "Ju-Chiang Wang", "Yun-Ning Hung" ], "github": "", "project_page": "", "abstract": "Multitrack music transcription aims to transcribe a music audio input into the musical notes of multiple instruments simultaneously. It is a very challenging task that typically requires a more complex model to achieve satisfactory result. In addition, prior works mostly focus on transcriptions of regular instruments, however, neglecting vocals, which are usually the most important signal source if present in a piece of music. In this paper, we propose a novel deep neural network architecture, Perceiver TF, to model the time-frequency representation of audio input for multitrack transcription. Perceiver TF augments the Perceiver architecture by introducing a hierarchical expansion with an additional Transformer layer to model temporal coherence. Accordingly, our model inherits the benefits of Perceiver that posses better scalability, allowing it to well handle transcriptions of many instruments in a single model. In experiments, we train a Perceiver TF to model 12 instrument classes as well as vocal in a multi-task learning manner. Our result demonstrates that the proposed system outperforms the state-of-the-art counterparts (e.g., MT3 and SpecTNT) on various public datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-22", "arxiv_id": "2306.12156", "title": "Fast Segment Anything", "authors": [ "Xu Zhao", "Wenchao Ding", "Yongqi An", "Yinglong Du", "Tao Yu", "Min Li", "Ming Tang", "Jinqiao Wang" ], "github": "https://github.com/CASIA-IVA-Lab/FastSAM", "project_page": "", "abstract": "The recently proposed segment anything model (SAM) has made a significant influence in many computer vision tasks. It is becoming a foundation step for many high-level tasks, like image segmentation, image caption, and image editing. However, its huge computation costs prevent it from wider applications in industry scenarios. The computation mainly comes from the Transformer architecture at high-resolution inputs. In this paper, we propose a speed-up alternative method for this fundamental task with comparable performance. By reformulating the task as segments-generation and prompting, we find that a regular CNN detector with an instance segmentation branch can also accomplish this task well. Specifically, we convert this task to the well-studied instance segmentation task and directly train the existing instance segmentation method using only 1/50 of the SA-1B dataset published by SAM authors. With our method, we achieve a comparable performance with the SAM method at 50 times higher run-time speed. We give sufficient experimental results to demonstrate its effectiveness. The codes and demos will be released at https://github.com/CASIA-IVA-Lab/FastSAM.", "github_manual": "", "github_regex": "https://github.com/CASIA-IVA-Lab/FastSAM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-22", "arxiv_id": "2306.11987", "title": "Training Transformers with 4-bit Integers", "authors": [ "Haocheng Xi", "Changhao Li", "Jianfei Chen", "Jun Zhu" ], "github": "https://github.com/xijiu9/Train_Transformers_with_INT4", "project_page": "", "abstract": "Quantizing the activation, weight, and gradient to 4-bit is promising to accelerate neural network training. However, existing 4-bit training methods require custom numerical formats which are not supported by contemporary hardware. In this work, we propose a training method for transformers with all matrix multiplications implemented with the INT4 arithmetic. Training with an ultra-low INT4 precision is challenging. To achieve this, we carefully analyze the specific structures of activation and gradients in transformers to propose dedicated quantizers for them. For forward propagation, we identify the challenge of outliers and propose a Hadamard quantizer to suppress the outliers. For backpropagation, we leverage the structural sparsity of gradients by proposing bit splitting and leverage score sampling techniques to quantize gradients accurately. Our algorithm achieves competitive accuracy on a wide range of tasks including natural language understanding, machine translation, and image classification. Unlike previous 4-bit training methods, our algorithm can be implemented on the current generation of GPUs. Our prototypical linear operator implementation is up to 2.2 times faster than the FP16 counterparts and speeds up the training by up to 35.1%.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xijiu9/Train_Transformers_with_INT4", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-22", "arxiv_id": "2306.12422", "title": "DreamTime: An Improved Optimization Strategy for Text-to-3D Content Creation", "authors": [ "Yukun Huang", "Jianan Wang", "Yukai Shi", "Xianbiao Qi", "Zheng-Jun Zha", "Lei Zhang" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion models pre-trained on billions of image-text pairs have recently enabled text-to-3D content creation by optimizing a randomly initialized Neural Radiance Fields (NeRF) with score distillation. However, the resultant 3D models exhibit two limitations: (a) quality concerns such as saturated color and the Janus problem; (b) extremely low diversity comparing to text-guided image synthesis. In this paper, we show that the conflict between NeRF optimization process and uniform timestep sampling in score distillation is the main reason for these limitations. To resolve this conflict, we propose to prioritize timestep sampling with monotonically non-increasing functions, which aligns NeRF optimization with the sampling process of diffusion model. Extensive experiments show that our simple redesign significantly improves text-to-3D content creation with higher quality and diversity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-22", "arxiv_id": "2306.11932", "title": "Opportunities and Risks of LLMs for Scalable Deliberation with Polis", "authors": [ "Christopher T. Small", "Ivan Vendrov", "Esin Durmus", "Hadjar Homaei", "Elizabeth Barry", "Julien Cornebise", "Ted Suzman", "Deep Ganguli", "Colin Megill" ], "github": "", "project_page": "", "abstract": "Polis is a platform that leverages machine intelligence to scale up deliberative processes. In this paper, we explore the opportunities and risks associated with applying Large Language Models (LLMs) towards challenges with facilitating, moderating and summarizing the results of Polis engagements. In particular, we demonstrate with pilot experiments using Anthropic's Claude that LLMs can indeed augment human intelligence to help more efficiently run Polis conversations. In particular, we find that summarization capabilities enable categorically new methods with immense promise to empower the public in collective meaning-making exercises. And notably, LLM context limitations have a significant impact on insight and quality of these results. However, these opportunities come with risks. We discuss some of these risks, as well as principles and techniques for characterizing and mitigating them, and the implications for other deliberative or political systems that may employ LLMs. Finally, we conclude with several open future research directions for augmenting tools like Polis with LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-22", "arxiv_id": "2306.12059", "title": "EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations", "authors": [ "Yi-Lun Liao", "Brandon Wood", "Abhishek Das", "Tess Smidt" ], "github": "https://github.com/atomicarchitects/equiformer_v2", "project_page": "", "abstract": "Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying Transformers to the domain of 3D atomistic systems. However, they are still limited to small degrees of equivariant representations due to their computational complexity. In this paper, we investigate whether these architectures can scale well to higher degrees. Starting from Equiformer, we first replace SO(3) convolutions with eSCN convolutions to efficiently incorporate higher-degree tensors. Then, to better leverage the power of higher degrees, we propose three architectural improvements -- attention re-normalization, separable S^2 activation and separable layer normalization. Putting this all together, we propose EquiformerV2, which outperforms previous state-of-the-art methods on the large-scale OC20 dataset by up to 12% on forces, 4% on energies, offers better speed-accuracy trade-offs, and 2times reduction in DFT calculations needed for computing adsorption energies.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/atomicarchitects/equiformer_v2", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-23", "arxiv_id": "2306.12925", "title": "AudioPaLM: A Large Language Model That Can Speak and Listen", "authors": [ "Paul K. Rubenstein", "Chulayuth Asawaroengchai", "Duc Dung Nguyen", "Ankur Bapna", "Zal\u00e1n Borsos", "F\u00e9lix de Chaumont Quitry", "Peter Chen", "Dalia El Badawy", "Wei Han", "Eugene Kharitonov", "Hannah Muckenhirn", "Dirk Padfield", "James Qin", "Danny Rozenberg", "Tara Sainath", "Johan Schalkwyk", "Matt Sharifi", "Michelle Tadmor Ramanovich", "Marco Tagliasacchi", "Alexandru Tudor", "Mihajlo Velimirovi\u0107", "Damien Vincent", "Jiahui Yu", "Yongqiang Wang", "Vicky Zayats", "Neil Zeghidour", "Yu Zhang", "Zhishuai Zhang", "Lukas Zilka", "Christian Frank" ], "github": "", "project_page": "", "abstract": "We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt. We release examples of our method at https://google-research.github.io/seanet/audiopalm/examples", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-23", "arxiv_id": "2306.12672", "title": "From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought", "authors": [ "Lionel Wong", "Gabriel Grand", "Alexander K. Lew", "Noah D. Goodman", "Vikash K. Mansinghka", "Jacob Andreas", "Joshua B. Tenenbaum" ], "github": "https://github.com/gabegrand/world-models", "project_page": "", "abstract": "How does language inform our downstream thinking? In particular, how do humans make meaning from language -- and how can we leverage a theory of linguistic meaning to build machines that think in more human-like ways? In this paper, we propose rational meaning construction, a computational framework for language-informed thinking that combines neural models of language with probabilistic models for rational inference. We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought (PLoT) -- a general-purpose symbolic substrate for probabilistic, generative world modeling. Our architecture integrates two powerful computational tools that have not previously come together: we model thinking with probabilistic programs, an expressive representation for flexible commonsense reasoning; and we model meaning construction with large language models (LLMs), which support broad-coverage translation from natural language utterances to code expressions in a probabilistic programming language. We illustrate our framework in action through examples covering four core domains from cognitive science: probabilistic reasoning, logical and relational reasoning, visual and physical reasoning, and social reasoning about agents and their plans. In each, we show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings, while Bayesian inference with the generated programs supports coherent and robust commonsense reasoning. We extend our framework to integrate cognitively-motivated symbolic modules to provide a unified commonsense thinking interface from language. Finally, we explore how language can drive the construction of world models themselves.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gabegrand/world-models", "github_api_added_by": "auto" }, { "date": "2023-06-23", "arxiv_id": "2306.12509", "title": "Deep Language Networks: Joint Prompt Training of Stacked LLMs using Variational Inference", "authors": [ "Alessandro Sordoni", "Xingdi Yuan", "Marc-Alexandre C\u00f4t\u00e9", "Matheus Pereira", "Adam Trischler", "Ziang Xiao", "Arian Hosseini", "Friederike Niedtner", "Nicolas Le Roux" ], "github": "https://github.com/microsoft/deep-language-networks", "project_page": "", "abstract": "We view large language models (LLMs) as stochastic language layers in a network, where the learnable parameters are the natural language prompts at each layer. We stack two such layers, feeding the output of one layer to the next. We call the stacked architecture a Deep Language Network (DLN). We first show how to effectively perform prompt optimization for a 1-Layer language network (DLN-1). We then show how to train 2-layer DLNs (DLN-2), where two prompts must be learnt. We consider the output of the first layer as a latent variable to marginalize, and devise a variational inference algorithm for joint prompt training. A DLN-2 reaches higher performance than a single layer, sometimes comparable to few-shot GPT-4 even when each LLM in the network is smaller and less powerful. The DLN code is open source: https://github.com/microsoft/deep-language-networks .", "github_manual": "", "github_regex": "https://github.com/microsoft/deep-language-networks", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-23", "arxiv_id": "2306.12929", "title": "Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing", "authors": [ "Yelysei Bondarenko", "Markus Nagel", "Tijmen Blankevoort" ], "github": "https://github.com/qualcomm-ai-research/outlier-free-transformers", "project_page": "", "abstract": "Transformer models have been widely adopted in various domains over the last years, and especially large language models have advanced the field of AI significantly. Due to their size, the capability of these networks has increased tremendously, but this has come at the cost of a significant increase in necessary compute. Quantization is one of the most effective ways to reduce the computational time and memory consumption of neural networks. Many studies have shown, however, that modern transformer models tend to learn strong outliers in their activations, making them difficult to quantize. To retain acceptable performance, the existence of these outliers requires activations to be in higher bitwidth or the use of different numeric formats, extra fine-tuning, or other workarounds. We show that strong outliers are related to very specific behavior of attention heads that try to learn a \"no-op\" or just a partial update of the residual. To achieve the exact zeros needed in the attention matrix for a no-update, the input to the softmax is pushed to be larger and larger during training, causing outliers in other parts of the network. Based on these observations, we propose two simple (independent) modifications to the attention mechanism - clipped softmax and gated attention. We empirically show that models pre-trained using our methods learn significantly smaller outliers while maintaining and sometimes even improving the floating-point task performance. This enables us to quantize transformers to full INT8 quantization of the activations without any additional effort. We demonstrate the effectiveness of our methods on both language models (BERT, OPT) and vision transformers.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/qualcomm-ai-research/outlier-free-transformers", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-23", "arxiv_id": "2306.10008", "title": "CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search", "authors": [ "Fahad Shamshad", "Muzammal Naseer", "Karthik Nandakumar" ], "github": "https://github.com/fahadshamshad/Clip2Protect", "project_page": "", "abstract": "The success of deep learning based face recognition systems has given rise to serious privacy concerns due to their ability to enable unauthorized tracking of users in the digital world. Existing methods for enhancing privacy fail to generate naturalistic images that can protect facial privacy without compromising user experience. We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low-dimensional manifold of a pretrained generative model. The first step inverts the given face image into the latent space and finetunes the generative model to achieve an accurate reconstruction of the given image from its latent code. This step produces a good initialization, aiding the generation of high-quality faces that resemble the given identity. Subsequently, user-defined makeup text prompts and identity-preserving regularization are used to guide the search for adversarial codes in the latent space. Extensive experiments demonstrate that faces generated by our approach have stronger black-box transferability with an absolute gain of 12.06% over the state-of-the-art facial privacy protection approach under the face verification task. Finally, we demonstrate the effectiveness of the proposed approach for commercial face recognition systems. Our code is available at https://github.com/fahadshamshad/Clip2Protect.", "github_manual": "", "github_regex": "https://github.com/fahadshamshad/Clip2Protect", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-23", "arxiv_id": "2306.12760", "title": "Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields", "authors": [ "Ori Gordon", "Omri Avrahami", "Dani Lischinski" ], "github": "https://github.com/orig333/Blended-NeRF", "project_page": "", "abstract": "Editing a local region or a specific object in a 3D scene represented by a NeRF is challenging, mainly due to the implicit nature of the scene representation. Consistently blending a new realistic object into the scene adds an additional level of difficulty. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts or image patches, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt or image patch, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and seamlessly blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/orig333/Blended-NeRF", "github_api_added_by": "auto" }, { "date": "2023-06-23", "arxiv_id": "2306.13078", "title": "Continuous Layout Editing of Single Images with Diffusion Models", "authors": [ "Zhiyuan Zhang", "Zhitong Huang", "Jing Liao" ], "github": "", "project_page": "", "abstract": "Recent advancements in large-scale text-to-image diffusion models have enabled many applications in image editing. However, none of these methods have been able to edit the layout of single existing images. To address this gap, we propose the first framework for layout editing of a single image while preserving its visual properties, thus allowing for continuous editing on a single image. Our approach is achieved through two key modules. First, to preserve the characteristics of multiple objects within an image, we disentangle the concepts of different objects and embed them into separate textual tokens using a novel method called masked textual inversion. Next, we propose a training-free optimization method to perform layout control for a pre-trained diffusion model, which allows us to regenerate images with learned concepts and align them with user-specified layouts. As the first framework to edit the layout of existing images, we demonstrate that our method is effective and outperforms other baselines that were modified to support this task. Our code will be freely available for public use upon acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-26", "arxiv_id": "2306.13421", "title": "Long-range Language Modeling with Self-retrieval", "authors": [ "Ohad Rubin", "Jonathan Berant" ], "github": "https://github.com/OhadRubin/RPT", "project_page": "", "abstract": "Retrieval-augmented language models (LMs) have received much attention recently. However, typically the retriever is not trained jointly as a native component of the LM, but added to an already-pretrained LM, which limits the ability of the LM and the retriever to adapt to one another. In this work, we propose the Retrieval-Pretrained Transformer (RPT), an architecture and training procedure for jointly training a retrieval-augmented LM from scratch for the task of modeling long texts. Given a recently generated text chunk in a long document, the LM computes query representations, which are then used to retrieve earlier chunks in the document, located potentially tens of thousands of tokens before. Information from retrieved chunks is fused into the LM representations to predict the next target chunk. We train the retriever component with a semantic objective, where the goal is to retrieve chunks that increase the probability of the next chunk, according to a reference LM. We evaluate RPT on four long-range language modeling tasks, spanning books, code, and mathematical writing, and demonstrate that RPT improves retrieval quality and subsequently perplexity across the board compared to strong baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OhadRubin/RPT", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-26", "arxiv_id": "2306.13575", "title": "Scaling MLPs: A Tale of Inductive Bias", "authors": [ "Gregor Bachmann", "Sotiris Anagnostidis", "Thomas Hofmann" ], "github": "https://github.com/gregorbachmann/scaling_mlps", "project_page": "", "abstract": "In this work we revisit the most fundamental building block in deep learning, the multi-layer perceptron (MLP), and study the limits of its performance on vision tasks. Empirical insights into MLPs are important for multiple reasons. (1) Given the recent narrative \"less inductive bias is better\", popularized due to transformers eclipsing convolutional models, it is natural to explore the limits of this hypothesis. To that end, MLPs offer an ideal test bed, being completely free of any inductive bias. (2) MLPs have almost exclusively been the main protagonist in the deep learning theory literature due to their mathematical simplicity, serving as a proxy to explain empirical phenomena observed for more complex architectures. Surprisingly, experimental datapoints for MLPs are very difficult to find in the literature, especially when coupled with large pre-training protocols. This discrepancy between practice and theory is worrying: Do MLPs reflect the empirical advances exhibited by practical models? Or do theorists need to rethink the role of MLPs as a proxy? We provide insights into both these aspects. We show that the performance of MLPs drastically improves with scale (93% on CIFAR10, 79% on CIFAR100, 69% on TinyImageNet), highlighting that lack of inductive bias can indeed be compensated. We observe that MLPs mimic the behaviour of their modern counterparts faithfully, with some components in the learning setting however surprisingly exhibiting stronger or unexpected behaviours. Due to their inherent computational efficiency, large pre-training experiments become more accessible for academic researchers. All of our experiments were run on a single GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gregorbachmann/scaling_mlps", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-26", "arxiv_id": "2306.13651", "title": "Bring Your Own Data! Self-Supervised Evaluation for Large Language Models", "authors": [ "Neel Jain", "Khalid Saifullah", "Yuxin Wen", "John Kirchenbauer", "Manli Shu", "Aniruddha Saha", "Micah Goldblum", "Jonas Geiping", "Tom Goldstein" ], "github": "https://github.com/neelsjain/byod", "project_page": "", "abstract": "With the rise of Large Language Models (LLMs) and their ubiquitous deployment in diverse domains, measuring language model behavior on realistic data is imperative. For example, a company deploying a client-facing chatbot must ensure that the model will not respond to client requests with profanity. Current evaluations approach this problem using small, domain-specific datasets with human-curated labels. These evaluation sets are often sampled from a narrow and simplified distribution, and data sources can unknowingly be leaked into the training set which can lead to misleading evaluations. To bypass these drawbacks, we propose a framework for self-supervised evaluation of LLMs by analyzing their sensitivity or invariance to transformations on the input text. Self-supervised evaluation can directly monitor LLM behavior on datasets collected in the wild or streamed during live model deployment. We demonstrate self-supervised evaluation strategies for measuring closed-book knowledge, toxicity, and long-range context dependence, in addition to sensitivity to grammatical structure and tokenization errors. When comparisons to similar human-labeled benchmarks are available, we find strong correlations between self-supervised and human-supervised evaluations. The self-supervised paradigm complements current evaluation strategies that rely on labeled data.", "github_manual": "https://github.com/neelsjain/byod", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-26", "arxiv_id": "2306.13588", "title": "System-Level Natural Language Feedback", "authors": [ "Weizhe Yuan", "Kyunghyun Cho", "Jason Weston" ], "github": "https://github.com/yyy-apple/sys-nl-feedback", "project_page": "", "abstract": "Natural language (NL) feedback contains rich information about the user experience. Existing studies focus on an instance-level approach, where feedback is used to refine specific examples, disregarding its system-wide application. This paper proposes a general framework for unlocking the system-level use of NL feedback. We show how to use feedback to formalize system-level design decisions in a human-in-the-loop-process -- in order to produce better models. In particular this is done through: (i) metric design for tasks; and (ii) language model prompt design for refining model responses. We conduct two case studies of this approach for improving search query generation and dialog response generation, demonstrating the effectiveness of the use of system-level feedback. We show the combination of system-level feedback and instance-level feedback brings further gains, and that human written instance-level feedback results in more grounded refinements than GPT-3.5 written ones, underlying the importance of human feedback for building systems.", "github_manual": "https://github.com/yyy-apple/sys-nl-feedback", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-26", "arxiv_id": "2306.13649", "title": "GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models", "authors": [ "Rishabh Agarwal", "Nino Vieillard", "Yongchao Zhou", "Piotr Stanczyk", "Sabela Ramos", "Matthieu Geist", "Olivier Bachem" ], "github": "", "project_page": "", "abstract": "Knowledge distillation (KD) is widely used for compressing a teacher model to reduce its inference cost and memory footprint, by training a smaller student model. However, current KD methods for auto-regressive sequence models suffer from distribution mismatch between output sequences seen during training and those generated by the student during inference. To address this issue, we introduce Generalized Knowledge Distillation (GKD). Instead of solely relying on a fixed set of output sequences, GKD trains the student on its self-generated output sequences by leveraging feedback from the teacher on such sequences. Unlike supervised KD approaches, GKD also offers the flexibility to employ alternative loss functions between the student and teacher, which can be useful when the student lacks the expressivity to mimic the teacher's distribution. Furthermore, GKD facilitates the seamless integration of distillation with RL fine-tuning (RLHF). We demonstrate the efficacy of GKD for distilling auto-regressive language models on summarization, translation, and arithmetic reasoning tasks, and task-agnostic distillation for instruction-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-26", "arxiv_id": "2306.13631", "title": "OpenMask3D: Open-Vocabulary 3D Instance Segmentation", "authors": [ "Ay\u00e7a Takmaz", "Elisabetta Fedele", "Robert W. Sumner", "Marc Pollefeys", "Federico Tombari", "Francis Engelmann" ], "github": "", "project_page": "", "abstract": "We introduce the task of open-vocabulary 3D instance segmentation. Traditional approaches for 3D instance segmentation largely rely on existing 3D annotated datasets, which are restricted to a closed-set of object categories. This is an important limitation for real-life applications where one might need to perform tasks guided by novel, open-vocabulary queries related to objects from a wide variety. Recently, open-vocabulary 3D scene understanding methods have emerged to address this problem by learning queryable features per each point in the scene. While such a representation can be directly employed to perform semantic segmentation, existing methods have limitations in their ability to identify object instances. In this work, we address this limitation, and propose OpenMask3D, which is a zero-shot approach for open-vocabulary 3D instance segmentation. Guided by predicted class-agnostic 3D instance masks, our model aggregates per-mask features via multi-view fusion of CLIP-based image embeddings. We conduct experiments and ablation studies on the ScanNet200 dataset to evaluate the performance of OpenMask3D, and provide insights about the open-vocabulary 3D instance segmentation task. We show that our approach outperforms other open-vocabulary counterparts, particularly on the long-tail distribution. Furthermore, OpenMask3D goes beyond the limitations of close-vocabulary approaches, and enables the segmentation of object instances based on free-form queries describing object properties such as semantics, geometry, affordances, and material properties.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-26", "arxiv_id": "2306.13455", "title": "DreamEditor: Text-Driven 3D Scene Editing with Neural Fields", "authors": [ "Jingyu Zhuang", "Chen Wang", "Lingjie Liu", "Liang Lin", "Guanbin Li" ], "github": "https://github.com/zjy526223908/dreameditor", "project_page": "", "abstract": "Neural fields have achieved impressive advancements in view synthesis and scene reconstruction. However, editing these neural fields remains challenging due to the implicit encoding of geometry and texture information. In this paper, we propose DreamEditor, a novel framework that enables users to perform controlled editing of neural fields using text prompts. By representing scenes as mesh-based neural fields, DreamEditor allows localized editing within specific regions. DreamEditor utilizes the text encoder of a pretrained text-to-Image diffusion model to automatically identify the regions to be edited based on the semantics of the text prompts. Subsequently, DreamEditor optimizes the editing region and aligns its geometry and texture with the text prompts through score distillation sampling [29]. Extensive experiments have demonstrated that DreamEditor can accurately edit neural fields of real-world scenes according to the given text prompts while ensuring consistency in irrelevant areas. DreamEditor generates highly realistic textures and geometry, significantly surpassing previous works in both quantitative and qualitative evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zjy526223908/dreameditor", "github_api_added_by": "auto" }, { "date": "2023-06-27", "arxiv_id": "2306.14824", "title": "Kosmos-2: Grounding Multimodal Large Language Models to the World", "authors": [ "Zhiliang Peng", "Wenhui Wang", "Li Dong", "Yaru Hao", "Shaohan Huang", "Shuming Ma", "Furu Wei" ], "github": "", "project_page": "", "abstract": "We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new capabilities of perceiving object descriptions (e.g., bounding boxes) and grounding text to the visual world. Specifically, we represent refer expressions as links in Markdown, i.e., ``[text span](bounding boxes)'', where object descriptions are sequences of location tokens. Together with multimodal corpora, we construct large-scale data of grounded image-text pairs (called GrIT) to train the model. In addition to the existing capabilities of MLLMs (e.g., perceiving general modalities, following instructions, and performing in-context learning), Kosmos-2 integrates the grounding capability into downstream applications. We evaluate Kosmos-2 on a wide range of tasks, including (i) multimodal grounding, such as referring expression comprehension, and phrase grounding, (ii) multimodal referring, such as referring expression generation, (iii) perception-language tasks, and (iv) language understanding and generation. This work lays out the foundation for the development of Embodiment AI and sheds light on the big convergence of language, multimodal perception, action, and world modeling, which is a key step toward artificial general intelligence. Data, demo, and pretrained models are available at https://aka.ms/kosmos-2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14795", "title": "MotionGPT: Human Motion as a Foreign Language", "authors": [ "Biao Jiang", "Xin Chen", "Wen Liu", "Jingyi Yu", "Gang Yu", "Tao Chen" ], "github": "https://github.com/openmotionlab/motiongpt", "project_page": "", "abstract": "Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this \"motion vocabulary\", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/openmotionlab/motiongpt", "github_api_added_by": "auto" }, { "date": "2023-06-27", "arxiv_id": "2306.14435", "title": "DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing", "authors": [ "Yujun Shi", "Chuhui Xue", "Jiachun Pan", "Wenqing Zhang", "Vincent Y. F. Tan", "Song Bai" ], "github": "https://github.com/Yujun-Shi/DragDiffusion", "project_page": "", "abstract": "Precise and controllable image editing is a challenging task that has attracted significant attention. Recently, DragGAN enables an interactive point-based image editing framework and achieves impressive editing results with pixel-level precision. However, since this method is based on generative adversarial networks (GAN), its generality is upper-bounded by the capacity of the pre-trained GAN models. In this work, we extend such an editing framework to diffusion models and propose DragDiffusion. By leveraging large-scale pretrained diffusion models, we greatly improve the applicability of interactive point-based editing in real world scenarios. While most existing diffusion-based image editing methods work on text embeddings, DragDiffusion optimizes the diffusion latent to achieve precise spatial control. Although diffusion models generate images in an iterative manner, we empirically show that optimizing diffusion latent at one single step suffices to generate coherent results, enabling DragDiffusion to complete high-quality editing efficiently. Extensive experiments across a wide range of challenging cases (e.g., multi-objects, diverse object categories, various styles, etc.) demonstrate the versatility and generality of DragDiffusion.", "github_manual": "https://github.com/Yujun-Shi/DragDiffusion", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14289", "title": "Faster Segment Anything: Towards Lightweight SAM for Mobile Applications", "authors": [ "Chaoning Zhang", "Dongshen Han", "Yu Qiao", "Jung Uk Kim", "Sung-Ho Bae", "Seungkyu Lee", "Choong Seon Hong" ], "github": "https://github.com/ChaoningZhang/MobileSAM", "project_page": "", "abstract": "Segment anything model (SAM) is a prompt-guided vision foundation model for cutting out the object of interest from its background. Since Meta research team released the SA project, SAM has attracted significant attention due to its impressive zero-shot transfer performance and high versatility of being compatible with other models for advanced vision applications like image editing with fine-grained control. Many of such use cases need to be run on resource-constraint edge devices, like mobile Apps. In this work, we aim to make SAM mobile-friendly by replacing the heavyweight image encoder with a lightweight one. A naive way to train such a new SAM as in the original SAM paper leads to unsatisfactory performance, especially when limited training sources are available. We find that this is mainly caused by the coupled optimization of the image encoder and mask decoder, motivated by which we propose decoupled distillation. Concretely, we distill the knowledge from the image encoder ViT-H in the original SAM to a lightweight image encoder, which can be automatically compatible with the mask decoder in the original SAM. The training can be completed on a single GPU within less than one day, and the resulting lightweight SAM is termed MobileSAM which is more than 60 times smaller yet performs on par with the original SAM. For inference speed, MobileSAM runs around 10ms per image: 8ms on the image encoder and 2ms on the mask decoder. With superior performance and a higher versatility, our MobileSAM is 7 times smaller and 4 times faster than the concurrent FastSAM, making it more suitable for mobile applications. The code for MobileSAM project is provided at https://github.com/ChaoningZhang/MobileSAM", "github_manual": "", "github_regex": "https://github.com/ChaoningZhang/MobileSAM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.13840", "title": "Beyond Scale: the Diversity Coefficient as a Data Quality Metric Demonstrates LLMs are Pre-trained on Formally Diverse Data", "authors": [ "Alycia Lee", "Brando Miranda", "Sanmi Koyejo" ], "github": "", "project_page": "", "abstract": "Current trends to pre-train capable Large Language Models (LLMs) mostly focus on scaling of model and dataset size. However, the quality of pre-training data is an important factor for training powerful LLMs, yet it is a nebulous concept that has not been fully characterized. Therefore, we use the recently proposed Task2Vec diversity coefficient to ground and understand formal aspects of data quality, to go beyond scale alone. Specifically, we measure the diversity coefficient of publicly available pre-training datasets to demonstrate that their formal diversity is high when compared to theoretical lower and upper bounds. In addition, to build confidence in the diversity coefficient, we conduct interpretability experiments and find that the coefficient aligns with intuitive properties of diversity, e.g., it increases as the number of latent concepts increases. We conclude the diversity coefficient is reliable, show it's high for publicly available LLM datasets, and conjecture it can be used to build useful diverse datasets for LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14101", "title": "Language models are weak learners", "authors": [ "Hariharan Manikandan", "Yiding Jiang", "J Zico Kolter" ], "github": "", "project_page": "", "abstract": "A central notion in practical and theoretical machine learning is that of a weak learner, classifiers that achieve better-than-random performance (on any given distribution over data), even by a small margin. Such weak learners form the practical basis for canonical machine learning methods such as boosting. In this work, we illustrate that prompt-based large language models can operate effectively as said weak learners. Specifically, we illustrate the use of a large language model (LLM) as a weak learner in a boosting algorithm applied to tabular data. We show that by providing (properly sampled according to the distribution of interest) text descriptions of tabular data samples, LLMs can produce a summary of the samples that serves as a template for classification and achieves the aim of acting as a weak learner on this task. We incorporate these models into a boosting approach, which in some settings can leverage the knowledge within the LLM to outperform traditional tree-based boosting. The model outperforms both few-shot learning and occasionally even more involved fine-tuning procedures, particularly for tasks involving small numbers of data points. The results illustrate the potential for prompt-based LLMs to function not just as few-shot learners themselves, but as components of larger machine learning pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14892", "title": "Supervised Pretraining Can Learn In-Context Reinforcement Learning", "authors": [ "Jonathan N. Lee", "Annie Xie", "Aldo Pacchiano", "Yash Chandak", "Chelsea Finn", "Ofir Nachum", "Emma Brunskill" ], "github": "", "project_page": "", "abstract": "Large transformer models trained on diverse datasets have shown a remarkable ability to learn in-context, achieving high few-shot performance on tasks they were not explicitly trained to solve. In this paper, we study the in-context learning capabilities of transformers in decision-making problems, i.e., reinforcement learning (RL) for bandits and Markov decision processes. To do so, we introduce and study Decision-Pretrained Transformer (DPT), a supervised pretraining method where the transformer predicts an optimal action given a query state and an in-context dataset of interactions, across a diverse set of tasks. This procedure, while simple, produces a model with several surprising capabilities. We find that the pretrained transformer can be used to solve a range of RL problems in-context, exhibiting both exploration online and conservatism offline, despite not being explicitly trained to do so. The model also generalizes beyond the pretraining distribution to new tasks and automatically adapts its decision-making strategies to unknown structure. Theoretically, we show DPT can be viewed as an efficient implementation of Bayesian posterior sampling, a provably sample-efficient RL algorithm. We further leverage this connection to provide guarantees on the regret of the in-context algorithm yielded by DPT, and prove that it can learn faster than algorithms used to generate the pretraining data. These results suggest a promising yet simple path towards instilling strong in-context decision-making abilities in transformers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14048", "title": "H_2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models", "authors": [ "Zhenyu Zhang", "Ying Sheng", "Tianyi Zhou", "Tianlong Chen", "Lianmin Zheng", "Ruisi Cai", "Zhao Song", "Yuandong Tian", "Christopher R\u00e9", "Clark Barrett", "Zhangyang Wang", "Beidi Chen" ], "github": "https://github.com/FMInference/H2O", "project_page": "", "abstract": "Large Language Models (LLMs), despite their recent impressive accomplishments, are notably cost-prohibitive to deploy, particularly for applications involving long-content generation, such as dialogue systems and story writing. Often, a large amount of transient state information, referred to as the KV cache, is stored in GPU memory in addition to model parameters, scaling linearly with the sequence length and batch size. In this paper, we introduce a novel approach for implementing the KV cache which significantly reduces its memory footprint. Our approach is based on the noteworthy observation that a small portion of tokens contributes most of the value when computing attention scores. We call these tokens Heavy Hitters (H_2). Through a comprehensive investigation, we find that (i) the emergence of H_2 is natural and strongly correlates with the frequent co-occurrence of tokens in the text, and (ii) removing them results in significant performance degradation. Based on these insights, we propose Heavy Hitter Oracle (H_2O), a KV cache eviction policy that dynamically retains a balance of recent and H_2 tokens. We formulate the KV cache eviction as a dynamic submodular problem and prove (under mild assumptions) a theoretical guarantee for our novel eviction algorithm which could help guide future work. We validate the accuracy of our algorithm with OPT, LLaMA, and GPT-NeoX across a wide range of tasks. Our implementation of H_2O with 20% heavy hitters improves the throughput over three leading inference systems DeepSpeed Zero-Inference, Hugging Face Accelerate, and FlexGen by up to 29times, 29times, and 3times on OPT-6.7B and OPT-30B. With the same batch size, H2O can reduce the latency by up to 1.9times. The code is available at https://github.com/FMInference/H2O.", "github_manual": "", "github_regex": "https://github.com/FMInference/H2O", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14035", "title": "Thinking Like an Annotator: Generation of Dataset Labeling Instructions", "authors": [ "Nadine Chang", "Francesco Ferroni", "Michael J. Tarr", "Martial Hebert", "Deva Ramanan" ], "github": "", "project_page": "", "abstract": "Large-scale datasets are essential to modern day deep learning. Advocates argue that understanding these methods requires dataset transparency (e.g. \"dataset curation, motivation, composition, collection process, etc...\"). However, almost no one has suggested the release of the detailed definitions and visual category examples provided to annotators - information critical to understanding the structure of the annotations present in each dataset. These labels are at the heart of public datasets, yet few datasets include the instructions that were used to generate them. We introduce a new task, Labeling Instruction Generation, to address missing publicly available labeling instructions. In Labeling Instruction Generation, we take a reasonably annotated dataset and: 1) generate a set of examples that are visually representative of each category in the dataset; 2) provide a text label that corresponds to each of the examples. We introduce a framework that requires no model training to solve this task and includes a newly created rapid retrieval system that leverages a large, pre-trained vision and language model. This framework acts as a proxy to human annotators that can help to both generate a final labeling instruction set and evaluate its quality. Our framework generates multiple diverse visual and text representations of dataset categories. The optimized instruction set outperforms our strongest baseline across 5 folds by 7.06 mAP for NuImages and 12.9 mAP for COCO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14447", "title": "RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools", "authors": [ "Haochen Shi", "Huazhe Xu", "Samuel Clarke", "Yunzhu Li", "Jiajun Wu" ], "github": "", "project_page": "", "abstract": "Humans excel in complex long-horizon soft body manipulation tasks via flexible tool use: bread baking requires a knife to slice the dough and a rolling pin to flatten it. Often regarded as a hallmark of human cognition, tool use in autonomous robots remains limited due to challenges in understanding tool-object interactions. Here we develop an intelligent robotic system, RoboCook, which perceives, models, and manipulates elasto-plastic objects with various tools. RoboCook uses point cloud scene representations, models tool-object interactions with Graph Neural Networks (GNNs), and combines tool classification with self-supervised policy learning to devise manipulation plans. We demonstrate that from just 20 minutes of real-world interaction data per tool, a general-purpose robot arm can learn complex long-horizon soft object manipulation tasks, such as making dumplings and alphabet letter cookies. Extensive evaluations show that RoboCook substantially outperforms state-of-the-art approaches, exhibits robustness against severe external disturbances, and demonstrates adaptability to different materials.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14565", "title": "Aligning Large Multi-Modal Model with Robust Instruction Tuning", "authors": [ "Fuxiao Liu", "Kevin Lin", "Linjie Li", "Jianfeng Wang", "Yaser Yacoob", "Lijuan Wang" ], "github": "https://github.com/FuxiaoLiu/LRV-Instruction", "project_page": "", "abstract": "Despite the promising progress in multi-modal tasks, current large multi-modal models (LMM) are prone to hallucinating inconsistent descriptions with respect to the associated image and human instructions. This paper addresses this issue by introducing the first large and diverse visual instruction tuning dataset, named Large-scale Robust Visual (LRV)-Instruction. Our dataset consists of 120k visual instructions generated by GPT4, covering 16 vision-and-language tasks with open-ended instructions and answers. Unlike existing studies that primarily focus on positive instruction samples, we design LRV-Instruction to include both positive and negative instructions for more robust visual instruction tuning. Our negative instructions are designed at two semantic levels: (i) Nonexistent Element Manipulation and (ii) Existent Element Manipulation. To efficiently measure the hallucination generated by LMMs, we propose GPT4-Assisted Visual Instruction Evaluation (GAVIE), a novel approach to evaluate visual instruction tuning without the need for human-annotated groundtruth answers and can adapt to diverse instruction formats. We conduct comprehensive experiments to investigate the hallucination of LMMs. Our results demonstrate that existing LMMs exhibit significant hallucination when presented with our negative instructions, particularly with Existent Element Manipulation instructions. Moreover, by finetuning MiniGPT4 on LRV-Instruction, we successfully mitigate hallucination while improving performance on public datasets using less training data compared to state-of-the-art methods. Additionally, we observed that a balanced ratio of positive and negative instances in the training data leads to a more robust model. Our project link is available at https://fuxiaoliu.github.io/LRV/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FuxiaoLiu/LRV-Instruction", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.13776", "title": "Swin-Free: Achieving Better Cross-Window Attention and Efficiency with Size-varying Window", "authors": [ "Jinkyu Koo", "John Yang", "Le An", "Gwenaelle Cunha Sergio", "Su Inn Park" ], "github": "", "project_page": "", "abstract": "Transformer models have shown great potential in computer vision, following their success in language tasks. Swin Transformer is one of them that outperforms convolution-based architectures in terms of accuracy, while improving efficiency when compared to Vision Transformer (ViT) and its variants, which have quadratic complexity with respect to the input size. Swin Transformer features shifting windows that allows cross-window connection while limiting self-attention computation to non-overlapping local windows. However, shifting windows introduces memory copy operations, which account for a significant portion of its runtime. To mitigate this issue, we propose Swin-Free in which we apply size-varying windows across stages, instead of shifting windows, to achieve cross-connection among local windows. With this simple design change, Swin-Free runs faster than the Swin Transformer at inference with better accuracy. Furthermore, we also propose a few of Swin-Free variants that are faster than their Swin Transformer counterparts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14846", "title": "ViNT: A Foundation Model for Visual Navigation", "authors": [ "Dhruv Shah", "Ajay Sridhar", "Nitish Dashora", "Kyle Stachowicz", "Kevin Black", "Noriaki Hirose", "Sergey Levine" ], "github": "", "project_page": "", "abstract": "General-purpose pre-trained models (\"foundation models\") have enabled practitioners to produce generalizable solutions for individual machine learning problems with datasets that are significantly smaller than those required for learning from scratch. Such models are typically trained on large and diverse datasets with weak supervision, consuming much more training data than is available for any individual downstream application. In this paper, we describe the Visual Navigation Transformer (ViNT), a foundation model that aims to bring the success of general-purpose pre-trained models to vision-based robotic navigation. ViNT is trained with a general goal-reaching objective that can be used with any navigation dataset, and employs a flexible Transformer-based architecture to learn navigational affordances and enable efficient adaptation to a variety of downstream navigational tasks. ViNT is trained on a number of existing navigation datasets, comprising hundreds of hours of robotic navigation from a variety of different robotic platforms, and exhibits positive transfer, outperforming specialist models trained on singular datasets. ViNT can be augmented with diffusion-based subgoal proposals to explore novel environments, and can solve kilometer-scale navigation problems when equipped with long-range heuristics. ViNT can also be adapted to novel task specifications with a technique inspired by prompt-tuning, where the goal encoder is replaced by an encoding of another task modality (e.g., GPS waypoints or routing commands) embedded into the same space of goal tokens. This flexibility and ability to accommodate a variety of downstream problem domains establishes ViNT as an effective foundation model for mobile robotics. For videos, code, and model checkpoints, see our project page at https://visualnav-transformer.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14153", "title": "DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data", "authors": [ "Jingyuan Zhu", "Huimin Ma", "Jiansheng Chen", "Jian Yuan" ], "github": "", "project_page": "", "abstract": "Denoising diffusion probabilistic models (DDPMs) have been proven capable of synthesizing high-quality images with remarkable diversity when trained on large amounts of data. Typical diffusion models and modern large-scale conditional generative models like text-to-image generative models are vulnerable to overfitting when fine-tuned on extremely limited data. Existing works have explored subject-driven generation using a reference set containing a few images. However, few prior works explore DDPM-based domain-driven generation, which aims to learn the common features of target domains while maintaining diversity. This paper proposes a novel DomainStudio approach to adapt DDPMs pre-trained on large-scale source datasets to target domains using limited data. It is designed to keep the diversity of subjects provided by source domains and get high-quality and diverse adapted samples in target domains. We propose to keep the relative distances between adapted samples to achieve considerable generation diversity. In addition, we further enhance the learning of high-frequency details for better generation quality. Our approach is compatible with both unconditional and conditional diffusion models. This work makes the first attempt to realize unconditional few-shot image generation with diffusion models, achieving better quality and greater diversity than current state-of-the-art GAN-based approaches. Moreover, this work also significantly relieves overfitting for conditional generation and realizes high-quality domain-driven generation, further expanding the applicable scenarios of modern large-scale text-to-image models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.13754", "title": "Zero-shot spatial layout conditioning for text-to-image diffusion models", "authors": [ "Guillaume Couairon", "Marl\u00e8ne Careil", "Matthieu Cord", "St\u00e9phane Lathuili\u00e8re", "Jakob Verbeek" ], "github": "", "project_page": "", "abstract": "Large-scale text-to-image diffusion models have significantly improved the state of the art in generative image modelling and allow for an intuitive and powerful user interface to drive the image generation process. Expressing spatial constraints, e.g. to position specific objects in particular locations, is cumbersome using text; and current text-based image generation models are not able to accurately follow such instructions. In this paper we consider image generation from text associated with segments on the image canvas, which combines an intuitive natural language interface with precise spatial control over the generated content. We propose ZestGuide, a zero-shot segmentation guidance approach that can be plugged into pre-trained text-to-image diffusion models, and does not require any additional training. It leverages implicit segmentation maps that can be extracted from cross-attention layers, and uses them to align the generation with input masks. Our experimental results combine high image quality with accurate alignment of generated content with input segmentations, and improve over prior work both quantitatively and qualitatively, including methods that require training on images with corresponding segmentations. Compared to Paint with Words, the previous state-of-the art in image generation with zero-shot segmentation conditioning, we improve by 5 to 10 mIoU points on the COCO dataset with similar FID scores.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14878", "title": "Restart Sampling for Improving Generative Processes", "authors": [ "Yilun Xu", "Mingyang Deng", "Xiang Cheng", "Yonglong Tian", "Ziming Liu", "Tommi Jaakkola" ], "github": "https://github.com/Newbeeer/diffusion_restart_sampling", "project_page": "", "abstract": "Generative processes that involve solving differential equations, such as diffusion models, frequently necessitate balancing speed and quality. ODE-based samplers are fast but plateau in performance while SDE-based samplers deliver higher sample quality at the cost of increased sampling time. We attribute this difference to sampling errors: ODE-samplers involve smaller discretization errors while stochasticity in SDE contracts accumulated errors. Based on these findings, we propose a novel sampling algorithm called Restart in order to better balance discretization errors and contraction. The sampling method alternates between adding substantial noise in additional forward steps and strictly following a backward ODE. Empirically, Restart sampler surpasses previous SDE and ODE samplers in both speed and accuracy. Restart not only outperforms the previous best SDE results, but also accelerates the sampling speed by 10-fold / 2-fold on CIFAR-10 / ImageNet 64 times 64. In addition, it attains significantly better sample quality than ODE samplers within comparable sampling times. Moreover, Restart better balances text-image alignment/visual quality versus diversity than previous samplers in the large-scale text-to-image Stable Diffusion model pre-trained on LAION 512 times 512. Code is available at https://github.com/Newbeeer/diffusion_restart_sampling", "github_manual": "", "github_regex": "https://github.com/Newbeeer/diffusion_restart_sampling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14896", "title": "RVT: Robotic View Transformer for 3D Object Manipulation", "authors": [ "Ankit Goyal", "Jie Xu", "Yijie Guo", "Valts Blukis", "Yu-Wei Chao", "Dieter Fox" ], "github": "", "project_page": "", "abstract": "For 3D object manipulation, methods that build an explicit 3D representation perform better than those relying only on camera images. But using explicit 3D representations like voxels comes at large computing cost, adversely affecting scalability. In this work, we propose RVT, a multi-view transformer for 3D manipulation that is both scalable and accurate. Some key features of RVT are an attention mechanism to aggregate information across views and re-rendering of the camera input from virtual views around the robot workspace. In simulations, we find that a single RVT model works well across 18 RLBench tasks with 249 task variations, achieving 26% higher relative success than the existing state-of-the-art method (PerAct). It also trains 36X faster than PerAct for achieving the same performance and achieves 2.3X the inference speed of PerAct. Further, RVT can perform a variety of manipulation tasks in the real world with just a few (sim10) demonstrations per task. Visual results, code, and trained model are provided at https://robotic-view-transformer.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-27", "arxiv_id": "2306.14066", "title": "SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models", "authors": [ "Lizao Li", "Rob Carver", "Ignacio Lopez-Gomez", "Fei Sha", "John Anderson" ], "github": "", "project_page": "", "abstract": "Probabilistic forecasting is crucial to decision-making under uncertainty about future weather. The dominant approach is to use an ensemble of forecasts to represent and quantify uncertainty in operational numerical weather prediction. However, generating ensembles is computationally costly. In this paper, we propose to generate ensemble forecasts at scale by leveraging recent advances in generative artificial intelligence. Our approach learns a data-driven probabilistic diffusion model from the 5-member ensemble GEFS reforecast dataset. The model can then be sampled efficiently to produce realistic weather forecasts, conditioned on a few members of the operational GEFS forecasting system. The generated ensembles have similar predictive skill as the full GEFS 31-member ensemble, evaluated against ERA5 reanalysis, and emulate well the statistics of large physics-based ensembles. We also apply the same methodology to developing a diffusion model for generative post-processing: the model directly learns to correct biases present in the emulated forecasting system by leveraging reanalysis data as labels during training. Ensembles from this generative post-processing model show greater reliability and accuracy, particularly in extreme event classification. In general, they are more reliable and forecast the probability of extreme weather more accurately than the GEFS operational ensemble. Our models achieve these results at less than 1/10th of the computational cost incurred by the operational GEFS system.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-28", "arxiv_id": "2306.15595", "title": "Extending Context Window of Large Language Models via Positional Interpolation", "authors": [ "Shouyuan Chen", "Sherman Wong", "Liangjian Chen", "Yuandong Tian" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "We present Position Interpolation (PI) that extends the context window sizes of RoPE-based pretrained LLMs such as LLaMA models to up to 32768 with minimal fine-tuning (within 1000 steps), while demonstrating strong empirical results on various tasks that require long context, including passkey retrieval, language modeling, and long document summarization from LLaMA 7B to 65B. Meanwhile, the extended model by Position Interpolation preserve quality relatively well on tasks within its original context window. To achieve this goal, Position Interpolation linearly down-scales the input position indices to match the original context window size, rather than extrapolating beyond the trained context length which may lead to catastrophically high attention scores that completely ruin the self-attention mechanism. Our theoretical study shows that the upper bound of interpolation is at least sim 600 times smaller than that of extrapolation, further demonstrating its stability. Models extended via Position Interpolation retain its original architecture and can reuse most pre-existing optimization and infrastructure.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-28", "arxiv_id": "2306.15658", "title": "CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a \\10,000 Budget; An Extra 4,000 Unlocks 81.8% Accuracy", "authors": [ "Xianhang Li", "Zeyu Wang", "Cihang Xie" ], "github": "https://github.com/UCSC-VLAA/CLIPA", "project_page": "", "abstract": "The recent work CLIPA presents an inverse scaling law for CLIP training -- whereby the larger the image/text encoders used, the shorter the sequence length of image/text tokens that can be applied in training. This finding enables us to train high-performance CLIP models with significantly reduced computations. Building upon this work, we hereby present CLIPA-v2 with two key contributions. Technically, we find this inverse scaling law is also applicable in the finetuning stage, enabling further reduction in computational needs. Empirically, we explore CLIPA at scale, extending the experiments up to the H/14 model with ~13B image-text pairs seen during training. Our results are exciting -- by only allocating a budget of \\10,000, our CLIP model achieves an impressive zero-shot ImageNet accuracy of 81.1%, surpassing the prior best CLIP model (from OpenCLIP, 80.1%) by 1.0% and meanwhile reducing the computational cost by ~39X. Moreover, with an additional investment of 4,000, we can further elevate the zero-shot ImageNet accuracy to 81.8%. Our code and models are available at https://github.com/UCSC-VLAA/CLIPA.", "github_manual": "", "github_regex": "https://github.com/UCSC-VLAA/CLIPA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-28", "arxiv_id": "2306.15626", "title": "LeanDojo: Theorem Proving with Retrieval-Augmented Language Models", "authors": [ "Kaiyu Yang", "Aidan M. Swope", "Alex Gu", "Rahul Chalamala", "Peiyang Song", "Shixing Yu", "Saad Godil", "Ryan Prenger", "Anima Anandkumar" ], "github": "https://github.com/lean-dojo/leandojo", "project_page": "", "abstract": "Large language models (LLMs) have shown promise in proving formal theorems using proof assistants such as Lean. However, existing methods are difficult to reproduce or build on, due to private code, data, and large compute requirements. This has created substantial barriers to research on machine learning methods for theorem proving. This paper removes these barriers by introducing LeanDojo: an open-source Lean playground consisting of toolkits, data, models, and benchmarks. LeanDojo extracts data from Lean and enables interaction with the proof environment programmatically. It contains fine-grained annotations of premises in proofs, providing valuable data for premise selection: a key bottleneck in theorem proving. Using this data, we develop ReProver (Retrieval-Augmented Prover): the first LLM-based prover that is augmented with retrieval for selecting premises from a vast math library. It is inexpensive and needs only one GPU week of training. Our retriever leverages LeanDojo's program analysis capability to identify accessible premises and hard negative examples, which makes retrieval much more effective. Furthermore, we construct a new benchmark consisting of 96,962 theorems and proofs extracted from Lean's math library. It features challenging data split requiring the prover to generalize to theorems relying on novel premises that are never used in training. We use this benchmark for training and evaluation, and experimental results demonstrate the effectiveness of ReProver over non-retrieval baselines and GPT-4. We thus provide the first set of open-source LLM-based theorem provers without any proprietary datasets and release it under a permissive MIT license to facilitate further research.", "github_manual": "https://github.com/lean-dojo/leandojo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-28", "arxiv_id": "2306.15128", "title": "MIMIC: Masked Image Modeling with Image Correspondences", "authors": [ "Kalyani Marathe", "Mahtab Bigverdi", "Nishat Khan", "Tuhin Kundu", "Aniruddha Kembhavi", "Linda G. Shapiro", "Ranjay Krishna" ], "github": "https://github.com/RAIVNLab/MIMIC", "project_page": "", "abstract": "Many pixelwise dense prediction tasks-depth estimation and semantic segmentation in computer vision today rely on pretrained image representations. Therefore, curating effective pretraining datasets is vital. Unfortunately, the effective pretraining datasets are those with multi-view scenes and have only been curated using annotated 3D meshes, point clouds, and camera parameters from simulated environments. We propose a dataset-curation mechanism that does not require any annotations. We mine two datasets: MIMIC-1M with 1.3M and MIMIC-3M with 3.1M multi-view image pairs from open-sourced video datasets and from synthetic 3D environments. We train multiple self-supervised models with different masked image modeling objectives to showcase the following findings: Representations trained on MIMIC-3M outperform those mined using annotations on multiple downstream tasks, including depth estimation, semantic segmentation, surface normals, and pose estimation. They also outperform representations that are frozen and when downstream training data is limited to few-shot. Larger dataset (MIMIC-3M) significantly improves performance, which is promising since our curation method can arbitrarily scale to produce even larger datasets. MIMIC code, dataset, and pretrained models are open-sourced at https://github.com/RAIVNLab/MIMIC.", "github_manual": "", "github_regex": "https://github.com/RAIVNLab/MIMIC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-28", "arxiv_id": "2306.15354", "title": "3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement", "authors": [ "Siqi Zheng", "Luyao Cheng", "Yafeng Chen", "Hui Wang", "Qian Chen" ], "github": "https://github.com/alibaba-damo-academy/3D-Speaker", "project_page": "", "abstract": "Disentangling uncorrelated information in speech utterances is a crucial research topic within speech community. Different speech-related tasks focus on extracting distinct speech representations while minimizing the affects of other uncorrelated information. We present a large-scale speech corpus to facilitate the research of speech representation disentanglement. 3D-Speaker contains over 10,000 speakers, each of whom are simultaneously recorded by multiple Devices, locating at different Distances, and some speakers are speaking multiple Dialects. The controlled combinations of multi-dimensional audio data yield a matrix of a diverse blend of speech representation entanglement, thereby motivating intriguing methods to untangle them. The multi-domain nature of 3D-Speaker also makes it a suitable resource to evaluate large universal speech models and experiment methods of out-of-domain learning and self-supervised learning. https://3dspeaker.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alibaba-damo-academy/3D-Speaker", "github_api_added_by": "auto" }, { "date": "2023-06-28", "arxiv_id": "2306.15667", "title": "PoseDiffusion: Solving Pose Estimation via Diffusion-aided Bundle Adjustment", "authors": [ "Jianyuan Wang", "Christian Rupprecht", "David Novotny" ], "github": "https://github.com/facebookresearch/posediffusion", "project_page": "", "abstract": "Camera pose estimation is a long-standing computer vision problem that to date often relies on classical methods, such as handcrafted keypoint matching, RANSAC and bundle adjustment. In this paper, we propose to formulate the Structure from Motion (SfM) problem inside a probabilistic diffusion framework, modelling the conditional distribution of camera poses given input images. This novel view of an old problem has several advantages. (i) The nature of the diffusion framework mirrors the iterative procedure of bundle adjustment. (ii) The formulation allows a seamless integration of geometric constraints from epipolar geometry. (iii) It excels in typically difficult scenarios such as sparse views with wide baselines. (iv) The method can predict intrinsics and extrinsics for an arbitrary amount of images. We demonstrate that our method PoseDiffusion significantly improves over the classic SfM pipelines and the learned approaches on two real-world datasets. Finally, it is observed that our method can generalize across datasets without further training. Project page: https://posediffusion.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/posediffusion", "github_api_added_by": "auto" }, { "date": "2023-06-28", "arxiv_id": "2306.15091", "title": "Understanding In-Context Learning via Supportive Pretraining Data", "authors": [ "Xiaochuang Han", "Daniel Simig", "Todor Mihaylov", "Yulia Tsvetkov", "Asli Celikyilmaz", "Tianlu Wang" ], "github": "", "project_page": "", "abstract": "In-context learning (ICL) improves language models' performance on a variety of NLP tasks by simply demonstrating a handful of examples at inference time. It is not well understood why ICL ability emerges, as the model has never been specifically trained on such demonstrations. Unlike prior work that explores implicit mechanisms behind ICL, we study ICL via investigating the pretraining data. Specifically, we first adapt an iterative, gradient-based approach to find a small subset of pretraining data that supports ICL. We observe that a continued pretraining on this small subset significantly improves the model's ICL ability, by up to 18%. We then compare the supportive subset constrastively with random subsets of pretraining data and discover: (1) The supportive pretraining data to ICL do not have a higher domain relevance to downstream tasks. (2) The supportive pretraining data have a higher mass of rarely occurring, long-tail tokens. (3) The supportive pretraining data are challenging examples where the information gain from long-range context is below average, indicating learning to incorporate difficult long-range context encourages ICL. Our work takes a first step towards understanding ICL via analyzing instance-level pretraining data. Our insights have a potential to enhance the ICL ability of language models by actively guiding the construction of pretraining data in the future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-28", "arxiv_id": "2306.15400", "title": "Length Generalization in Arithmetic Transformers", "authors": [ "Samy Jelassi", "St\u00e9phane d'Ascoli", "Carles Domingo-Enrich", "Yuhuai Wu", "Yuanzhi Li", "Fran\u00e7ois Charton" ], "github": "", "project_page": "", "abstract": "We examine how transformers cope with two challenges: learning basic integer arithmetic, and generalizing to longer sequences than seen during training. We find that relative position embeddings enable length generalization for simple tasks, such as addition: models trained on 5-digit numbers can perform 15-digit sums. However, this method fails for multiplication, and we propose train set priming: adding a few (10 to 50) long sequences to the training set. We show that priming allows models trained on 5-digit times 3-digit multiplications to generalize to 35times 3 examples. We also show that models can be primed for different generalization lengths, and that the priming sample size scales as the logarithm of the training set size. Finally, we discuss potential applications of priming beyond arithmetic.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-28", "arxiv_id": "2306.15447", "title": "Are aligned neural networks adversarially aligned?", "authors": [ "Nicholas Carlini", "Milad Nasr", "Christopher A. Choquette-Choo", "Matthew Jagielski", "Irena Gao", "Anas Awadalla", "Pang Wei Koh", "Daphne Ippolito", "Katherine Lee", "Florian Tramer", "Ludwig Schmidt" ], "github": "", "project_page": "", "abstract": "Large language models are now tuned to align with the goals of their creators, namely to be \"helpful and harmless.\" These models should respond helpfully to user questions, but refuse to answer requests that could cause harm. However, adversarial users can construct inputs which circumvent attempts at alignment. In this work, we study to what extent these models remain aligned, even when interacting with an adversarial user who constructs worst-case inputs (adversarial examples). These inputs are designed to cause the model to emit harmful content that would otherwise be prohibited. We show that existing NLP-based optimization attacks are insufficiently powerful to reliably attack aligned text models: even when current NLP-based attacks fail, we can find adversarial inputs with brute force. As a result, the failure of current attacks should not be seen as proof that aligned text models remain aligned under adversarial inputs. However the recent trend in large-scale ML models is multimodal models that allow users to provide images that influence the text that is generated. We show these models can be easily attacked, i.e., induced to perform arbitrary un-aligned behavior through adversarial perturbation of the input image. We conjecture that improved NLP attacks may demonstrate this same level of adversarial control over text-only models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-29", "arxiv_id": "2306.16410", "title": "Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language", "authors": [ "William Berrios", "Gautam Mittal", "Tristan Thrush", "Douwe Kiela", "Amanpreet Singh" ], "github": "https://github.com/ContextualAI/lens", "project_page": "", "abstract": "We propose LENS, a modular approach for tackling computer vision problems by leveraging the power of large language models (LLMs). Our system uses a language model to reason over outputs from a set of independent and highly descriptive vision modules that provide exhaustive information about an image. We evaluate the approach on pure computer vision settings such as zero- and few-shot object recognition, as well as on vision and language problems. LENS can be applied to any off-the-shelf LLM and we find that the LLMs with LENS perform highly competitively with much bigger and much more sophisticated systems, without any multimodal training whatsoever. We open-source our code at https://github.com/ContextualAI/lens and provide an interactive demo.", "github_manual": "", "github_regex": "https://github.com/ContextualAI/lens", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-29", "arxiv_id": "2306.15794", "title": "HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution", "authors": [ "Eric Nguyen", "Michael Poli", "Marjan Faizi", "Armin Thomas", "Callum Birch-Sykes", "Michael Wornow", "Aman Patel", "Clayton Rabideau", "Stefano Massaroli", "Yoshua Bengio", "Stefano Ermon", "Stephen A. Baccus", "Chris R\u00e9" ], "github": "https://github.com/HazyResearch/hyena-dna", "project_page": "", "abstract": "Genomic (DNA) sequences encode an enormous amount of information for gene regulation and protein synthesis. Similar to natural language models, researchers have proposed foundation models in genomics to learn generalizable features from unlabeled genome data that can then be fine-tuned for downstream tasks such as identifying regulatory elements. Due to the quadratic scaling of attention, previous Transformer-based genomic models have used 512 to 4k tokens as context (<0.001% of the human genome), significantly limiting the modeling of long-range interactions in DNA. In addition, these methods rely on tokenizers to aggregate meaningful DNA units, losing single nucleotide resolution where subtle genetic variations can completely alter protein function via single nucleotide polymorphisms (SNPs). Recently, Hyena, a large language model based on implicit convolutions was shown to match attention in quality while allowing longer context lengths and lower time complexity. Leveraging Hyenas new long-range capabilities, we present HyenaDNA, a genomic foundation model pretrained on the human reference genome with context lengths of up to 1 million tokens at the single nucleotide-level, an up to 500x increase over previous dense attention-based models. HyenaDNA scales sub-quadratically in sequence length (training up to 160x faster than Transformer), uses single nucleotide tokens, and has full global context at each layer. We explore what longer context enables - including the first use of in-context learning in genomics for simple adaptation to novel tasks without updating pretrained model weights. On fine-tuned benchmarks from the Nucleotide Transformer, HyenaDNA reaches state-of-the-art (SotA) on 12 of 17 datasets using a model with orders of magnitude less parameters and pretraining data. On the GenomicBenchmarks, HyenaDNA surpasses SotA on all 8 datasets on average by +9 accuracy points.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HazyResearch/hyena-dna", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-29", "arxiv_id": "2306.16388", "title": "Towards Measuring the Representation of Subjective Global Opinions in Language Models", "authors": [ "Esin Durmus", "Karina Nyugen", "Thomas I. Liao", "Nicholas Schiefer", "Amanda Askell", "Anton Bakhtin", "Carol Chen", "Zac Hatfield-Dodds", "Danny Hernandez", "Nicholas Joseph", "Liane Lovitt", "Sam McCandlish", "Orowa Sikder", "Alex Tamkin", "Janel Thamkul", "Jared Kaplan", "Jack Clark", "Deep Ganguli" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) may not equitably represent diverse global perspectives on societal issues. In this paper, we develop a quantitative framework to evaluate whose opinions model-generated responses are more similar to. We first build a dataset, GlobalOpinionQA, comprised of questions and answers from cross-national surveys designed to capture diverse opinions on global issues across different countries. Next, we define a metric that quantifies the similarity between LLM-generated survey responses and human responses, conditioned on country. With our framework, we run three experiments on an LLM trained to be helpful, honest, and harmless with Constitutional AI. By default, LLM responses tend to be more similar to the opinions of certain populations, such as those from the USA, and some European and South American countries, highlighting the potential for biases. When we prompt the model to consider a particular country's perspective, responses shift to be more similar to the opinions of the prompted populations, but can reflect harmful cultural stereotypes. When we translate GlobalOpinionQA questions to a target language, the model's responses do not necessarily become the most similar to the opinions of speakers of those languages. We release our dataset for others to use and build on. Our data is at https://huggingface.co/datasets/Anthropic/llm_global_opinions. We also provide an interactive visualization at https://llmglobalvalues.anthropic.com.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-29", "arxiv_id": "2306.15724", "title": "REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction", "authors": [ "Zeyi Liu", "Arpit Bahety", "Shuran Song" ], "github": "https://github.com/real-stanford/reflect", "project_page": "", "abstract": "The ability to detect and analyze failed executions automatically is crucial for an explainable and robust robotic system. Recently, Large Language Models (LLMs) have demonstrated strong common sense reasoning skills on textual inputs. To leverage the power of LLM for robot failure explanation, we propose a framework REFLECT, which converts multi-sensory data into a hierarchical summary of robot past experiences and queries LLM with a progressive failure explanation algorithm. Conditioned on the explanation, a failure correction planner generates an executable plan for the robot to correct the failure and complete the task. To systematically evaluate the framework, we create the RoboFail dataset and show that our LLM-based framework is able to generate informative failure explanations that assist successful correction planning. Project website: https://roboreflect.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/real-stanford/reflect", "github_api_added_by": "auto" }, { "date": "2023-06-29", "arxiv_id": "2306.16052", "title": "SVNR: Spatially-variant Noise Removal with Denoising Diffusion", "authors": [ "Naama Pearl", "Yaron Brodsky", "Dana Berman", "Assaf Zomet", "Alex Rav Acha", "Daniel Cohen-Or", "Dani Lischinski" ], "github": "", "project_page": "", "abstract": "Denoising diffusion models have recently shown impressive results in generative tasks. By learning powerful priors from huge collections of training images, such models are able to gradually modify complete noise to a clean natural image via a sequence of small denoising steps, seemingly making them well-suited for single image denoising. However, effectively applying denoising diffusion models to removal of realistic noise is more challenging than it may seem, since their formulation is based on additive white Gaussian noise, unlike noise in real-world images. In this work, we present SVNR, a novel formulation of denoising diffusion that assumes a more realistic, spatially-variant noise model. SVNR enables using the noisy input image as the starting point for the denoising diffusion process, in addition to conditioning the process on it. To this end, we adapt the diffusion process to allow each pixel to have its own time embedding, and propose training and inference schemes that support spatially-varying time maps. Our formulation also accounts for the correlation that exists between the condition image and the samples along the modified diffusion process. In our experiments we demonstrate the advantages of our approach over a strong diffusion model baseline, as well as over a state-of-the-art single image denoising method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-29", "arxiv_id": "2306.16009", "title": "Accelerating Transducers through Adjacent Token Merging", "authors": [ "Yuang Li", "Yu Wu", "Jinyu Li", "Shujie Liu" ], "github": "", "project_page": "", "abstract": "Recent end-to-end automatic speech recognition (ASR) systems often utilize a Transformer-based acoustic encoder that generates embedding at a high frame rate. However, this design is inefficient, particularly for long speech signals due to the quadratic computation of self-attention. To address this, we propose a new method, Adjacent Token Merging (A-ToMe), which gradually combines adjacent tokens with high similarity scores between their key values. In this way, the total time step could be reduced, and the inference of both the encoder and joint network is accelerated. Experiments on LibriSpeech show that our method can reduce 57% of tokens and improve the inference speed on GPU by 70% without any notable loss of accuracy. Additionally, we demonstrate that A-ToMe is also an effective solution to reduce tokens in long-form ASR, where the input speech consists of multiple utterances.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-30", "arxiv_id": "2306.16928", "title": "One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization", "authors": [ "Minghua Liu", "Chao Xu", "Haian Jin", "Linghao Chen", "Mukund Varma T", "Zexiang Xu", "Hao Su" ], "github": "https://github.com/One-2-3-45/One-2-3-45", "project_page": "", "abstract": "Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world. Many existing methods solve this problem by optimizing a neural radiance field under the guidance of 2D diffusion models but suffer from lengthy optimization time, 3D inconsistency results, and poor geometry. In this work, we propose a novel method that takes a single image of any object as input and generates a full 360-degree 3D textured mesh in a single feed-forward pass. Given a single image, we first use a view-conditioned 2D diffusion model, Zero123, to generate multi-view images for the input view, and then aim to lift them up to 3D space. Since traditional reconstruction methods struggle with inconsistent multi-view predictions, we build our 3D reconstruction module upon an SDF-based generalizable neural surface reconstruction method and propose several critical training strategies to enable the reconstruction of 360-degree meshes. Without costly optimizations, our method reconstructs 3D shapes in significantly less time than existing methods. Moreover, our method favors better geometry, generates more 3D consistent results, and adheres more closely to the input image. We evaluate our approach on both synthetic data and in-the-wild images and demonstrate its superiority in terms of both mesh quality and runtime. In addition, our approach can seamlessly support the text-to-3D task by integrating with off-the-shelf text-to-image diffusion models.", "github_manual": "https://github.com/One-2-3-45/One-2-3-45", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-30", "arxiv_id": "2306.16934", "title": "DreamDiffusion: Generating High-Quality Images from Brain EEG Signals", "authors": [ "Yunpeng Bai", "Xintao Wang", "Yanpei Cao", "Yixiao Ge", "Chun Yuan", "Ying Shan" ], "github": "https://github.com/bbaaii/DreamDiffusion", "project_page": "", "abstract": "This paper introduces DreamDiffusion, a novel method for generating high-quality images directly from brain electroencephalogram (EEG) signals, without the need to translate thoughts into text. DreamDiffusion leverages pre-trained text-to-image models and employs temporal masked signal modeling to pre-train the EEG encoder for effective and robust EEG representations. Additionally, the method further leverages the CLIP image encoder to provide extra supervision to better align EEG, text, and image embeddings with limited EEG-image pairs. Overall, the proposed method overcomes the challenges of using EEG signals for image generation, such as noise, limited information, and individual differences, and achieves promising results. Quantitative and qualitative results demonstrate the effectiveness of the proposed method as a significant step towards portable and low-cost ``thoughts-to-image'', with potential applications in neuroscience and computer vision.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bbaaii/DreamDiffusion", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-30", "arxiv_id": "2306.17154", "title": "Generate Anything Anywhere in Any Scene", "authors": [ "Yuheng Li", "Haotian Liu", "Yangming Wen", "Yong Jae Lee" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion models have attracted considerable interest due to their wide applicability across diverse fields. However, challenges persist in creating controllable models for personalized object generation. In this paper, we first identify the entanglement issues in existing personalized generative models, and then propose a straightforward and efficient data augmentation training strategy that guides the diffusion model to focus solely on object identity. By inserting the plug-and-play adapter layers from a pre-trained controllable diffusion model, our model obtains the ability to control the location and size of each generated personalized object. During inference, we propose a regionally-guided sampling technique to maintain the quality and fidelity of the generated images. Our method achieves comparable or superior fidelity for personalized objects, yielding a robust, versatile, and controllable text-to-image diffusion model that is capable of generating realistic and personalized images. Our approach demonstrates significant potential for various applications, such as those in art, entertainment, and advertising design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-30", "arxiv_id": "2306.17156", "title": "Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors", "authors": [ "Tung Phung", "Victor-Alexandru P\u0103durean", "Jos\u00e9 Cambronero", "Sumit Gulwani", "Tobias Kohn", "Rupak Majumdar", "Adish Singla", "Gustavo Soares" ], "github": "", "project_page": "", "abstract": "Generative AI and large language models hold great promise in enhancing computing education by powering next-generation educational technologies for introductory programming. Recent works have studied these models for different scenarios relevant to programming education; however, these works are limited for several reasons, as they typically consider already outdated models or only specific scenario(s). Consequently, there is a lack of a systematic study that benchmarks state-of-the-art models for a comprehensive set of programming education scenarios. In our work, we systematically evaluate two models, ChatGPT (based on GPT-3.5) and GPT-4, and compare their performance with human tutors for a variety of scenarios. We evaluate using five introductory Python programming problems and real-world buggy programs from an online platform, and assess performance using expert-based annotations. Our results show that GPT-4 drastically outperforms ChatGPT (based on GPT-3.5) and comes close to human tutors' performance for several scenarios. These results also highlight settings where GPT-4 still struggles, providing exciting future directions on developing techniques to improve the performance of these models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-30", "arxiv_id": "2306.16527", "title": "OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents", "authors": [ "Hugo Lauren\u00e7on", "Lucile Saulnier", "L\u00e9o Tronchon", "Stas Bekman", "Amanpreet Singh", "Anton Lozhkov", "Thomas Wang", "Siddharth Karamcheti", "Alexander M. Rush", "Douwe Kiela", "Matthieu Cord", "Victor Sanh" ], "github": "", "project_page": "", "abstract": "Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks. However, the datasets used to train these models have not been released, and the collection process has not been fully specified. We introduce the OBELICS dataset, an open web-scale filtered dataset of interleaved image-text documents comprising 141 million web pages extracted from Common Crawl, 353 million associated images, and 115 billion text tokens. We describe the dataset creation process, present comprehensive filtering rules, and provide an analysis of the dataset's content. To show the viability of OBELICS, we train vision and language models of 9 and 80 billion parameters named IDEFICS, and obtain competitive performance on different multimodal benchmarks. We release our dataset, models and code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-30", "arxiv_id": "2306.17107", "title": "LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding", "authors": [ "Yanzhe Zhang", "Ruiyi Zhang", "Jiuxiang Gu", "Yufan Zhou", "Nedim Lipka", "Diyi Yang", "Tong Sun" ], "github": "https://github.com/SALT-NLP/LLaVAR", "project_page": "", "abstract": "Instruction tuning unlocks the superior capability of Large Language Models (LLM) to interact with humans. Furthermore, recent instruction-following datasets include images as visual inputs, collecting responses for image-based instructions. However, visual instruction-tuned models cannot comprehend textual details within images well. This work enhances the current visual instruction tuning pipeline with text-rich images (e.g., movie posters, book covers, etc.). Specifically, we first use publicly available OCR tools to collect results on 422K text-rich images from the LAION dataset. Moreover, we prompt text-only GPT-4 with recognized texts and image captions to generate 16K conversations, each containing question-answer pairs for text-rich images. By combining our collected data with previous multi-modal instruction-following data, our model, LLaVAR, substantially improves the LLaVA model's capability on text-based VQA datasets (up to 20% accuracy improvement) while achieving an accuracy of 91.42% on ScienceQA. The GPT-4-based instruction-following evaluation also demonstrates the improvement of our model on both natural images and text-rich images. Through qualitative analysis, LLaVAR shows promising interaction (e.g., reasoning, writing, and elaboration) skills with humans based on the latest real-world online content that combines text and images. We make our code/data/models publicly available at https://llavar.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SALT-NLP/LLaVAR", "github_api_added_by": "auto" }, { "date": "2023-06-30", "arxiv_id": "2306.17115", "title": "Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation", "authors": [ "Zibo Zhao", "Wen Liu", "Xin Chen", "Xianfang Zeng", "Rui Wang", "Pei Cheng", "Bin Fu", "Tao Chen", "Gang Yu", "Shenghua Gao" ], "github": "https://github.com/neuralcarver/michelangelo", "project_page": "", "abstract": "We present a novel alignment-before-generation approach to tackle the challenging task of generating general 3D shapes based on 2D images or texts. Directly learning a conditional generative model from images or texts to 3D shapes is prone to producing inconsistent results with the conditions because 3D shapes have an additional dimension whose distribution significantly differs from that of 2D images and texts. To bridge the domain gap among the three modalities and facilitate multi-modal-conditioned 3D shape generation, we explore representing 3D shapes in a shape-image-text-aligned space. Our framework comprises two models: a Shape-Image-Text-Aligned Variational Auto-Encoder (SITA-VAE) and a conditional Aligned Shape Latent Diffusion Model (ASLDM). The former model encodes the 3D shapes into the shape latent space aligned to the image and text and reconstructs the fine-grained 3D neural fields corresponding to given shape embeddings via the transformer-based decoder. The latter model learns a probabilistic mapping function from the image or text space to the latent shape space. Our extensive experiments demonstrate that our proposed approach can generate higher-quality and more diverse 3D shapes that better semantically conform to the visual or textural conditional inputs, validating the effectiveness of the shape-image-text-aligned space for cross-modality 3D shape generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/neuralcarver/michelangelo", "github_api_added_by": "auto" }, { "date": "2023-06-30", "arxiv_id": "2306.16793", "title": "Benchmarking Large Language Model Capabilities for Conditional Generation", "authors": [ "Joshua Maynez", "Priyanka Agrawal", "Sebastian Gehrmann" ], "github": "", "project_page": "", "abstract": "Pre-trained large language models (PLMs) underlie most new developments in natural language processing. They have shifted the field from application-specific model pipelines to a single model that is adapted to a wide range of tasks. Autoregressive PLMs like GPT-3 or PaLM, alongside techniques like few-shot learning, have additionally shifted the output modality to generation instead of classification or regression. Despite their ubiquitous use, the generation quality of language models is rarely evaluated when these models are introduced. Additionally, it is unclear how existing generation tasks--while they can be used to compare systems at a high level--relate to the real world use cases for which people have been adopting them. In this work, we discuss how to adapt existing application-specific generation benchmarks to PLMs and provide an in-depth, empirical study of the limitations and capabilities of PLMs in natural language generation tasks along dimensions such as scale, architecture, input and output language. Our results show that PLMs differ in their applicability to different data regimes and their generalization to multiple languages and inform which PLMs to use for a given generation task setup. We share best practices to be taken into consideration when benchmarking generation capabilities during the development of upcoming PLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-30", "arxiv_id": "2306.16857", "title": "ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch", "authors": [ "Zhengrong Xue", "Han Zhang", "Jingwen Cheng", "Zhengmao He", "Yuanchen Ju", "Changyi Lin", "Gu Zhang", "Huazhe Xu" ], "github": "", "project_page": "", "abstract": "We present ArrayBot, a distributed manipulation system consisting of a 16 times 16 array of vertically sliding pillars integrated with tactile sensors, which can simultaneously support, perceive, and manipulate the tabletop objects. Towards generalizable distributed manipulation, we leverage reinforcement learning (RL) algorithms for the automatic discovery of control policies. In the face of the massively redundant actions, we propose to reshape the action space by considering the spatially local action patch and the low-frequency actions in the frequency domain. With this reshaped action space, we train RL agents that can relocate diverse objects through tactile observations only. Surprisingly, we find that the discovered policy can not only generalize to unseen object shapes in the simulator but also transfer to the physical robot without any domain randomization. Leveraging the deployed policy, we present abundant real-world manipulation tasks, illustrating the vast potential of RL on ArrayBot for distributed manipulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-30", "arxiv_id": "2306.16869", "title": "NeuralFuse: Learning to Improve the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes", "authors": [ "Hao-Lun Sun", "Lei Hsiung", "Nandhini Chandramoorthy", "Pin-Yu Chen", "Tsung-Yi Ho" ], "github": "https://github.com/IBM/NeuralFuse", "project_page": "", "abstract": "Deep neural networks (DNNs) have become ubiquitous in machine learning, but their energy consumption remains a notable issue. Lowering the supply voltage is an effective strategy for reducing energy consumption. However, aggressively scaling down the supply voltage can lead to accuracy degradation due to random bit flips in static random access memory (SRAM) where model parameters are stored. To address this challenge, we introduce NeuralFuse, a novel add-on module that addresses the accuracy-energy tradeoff in low-voltage regimes by learning input transformations to generate error-resistant data representations. NeuralFuse protects DNN accuracy in both nominal and low-voltage scenarios. Moreover, NeuralFuse is easy to implement and can be readily applied to DNNs with limited access, such as non-configurable hardware or remote access to cloud-based APIs. Experimental results demonstrate that, at a 1% bit error rate, NeuralFuse can reduce SRAM memory access energy by up to 24% while improving accuracy by up to 57%. To the best of our knowledge, this is the first model-agnostic approach (i.e., no model retraining) to address low-voltage-induced bit errors. The source code is available at https://github.com/IBM/NeuralFuse.", "github_manual": "", "github_regex": "https://github.com/IBM/NeuralFuse", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-30", "arxiv_id": "2306.16700", "title": "Dynamic-Resolution Model Learning for Object Pile Manipulation", "authors": [ "Yixuan Wang", "Yunzhu Li", "Katherine Driggs-Campbell", "Li Fei-Fei", "Jiajun Wu" ], "github": "", "project_page": "", "abstract": "Dynamics models learned from visual observations have shown to be effective in various robotic manipulation tasks. One of the key questions for learning such dynamics models is what scene representation to use. Prior works typically assume representation at a fixed dimension or resolution, which may be inefficient for simple tasks and ineffective for more complicated tasks. In this work, we investigate how to learn dynamic and adaptive representations at different levels of abstraction to achieve the optimal trade-off between efficiency and effectiveness. Specifically, we construct dynamic-resolution particle representations of the environment and learn a unified dynamics model using graph neural networks (GNNs) that allows continuous selection of the abstraction level. During test time, the agent can adaptively determine the optimal resolution at each model-predictive control (MPC) step. We evaluate our method in object pile manipulation, a task we commonly encounter in cooking, agriculture, manufacturing, and pharmaceutical applications. Through comprehensive evaluations both in the simulation and the real world, we show that our method achieves significantly better performance than state-of-the-art fixed-resolution baselines at the gathering, sorting, and redistribution of granular object piles made with various instances like coffee beans, almonds, corn, etc.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-30", "arxiv_id": "2306.16601", "title": "An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs", "authors": [ "Haihao Shen", "Hengyu Meng", "Bo Dong", "Zhe Wang", "Ofir Zafrir", "Yi Ding", "Yu Luo", "Hanwen Chang", "Qun Gao", "Ziheng Wang", "Guy Boudoukh", "Moshe Wasserblat" ], "github": "https://github.com/intel/intel-extension-for-transformers", "project_page": "", "abstract": "In recent years, Transformer-based language models have become the standard approach for natural language processing tasks. However, stringent throughput and latency requirements in industrial applications are limiting their adoption. To mitigate the gap, model compression techniques such as structured pruning are being used to improve inference efficiency. However, most existing neural network inference runtimes lack adequate support for structured sparsity. In this paper, we propose an efficient sparse deep learning inference software stack for Transformer-based language models where the weights are pruned with constant block size. Our sparse software accelerator leverages Intel Deep Learning Boost to maximize the performance of sparse matrix - dense matrix multiplication (commonly abbreviated as SpMM) on CPUs. Our SpMM kernel outperforms the existing sparse libraries (oneMKL, TVM, and LIBXSMM) by an order of magnitude on a wide range of GEMM shapes under 5 representative sparsity ratios (70%, 75%, 80%, 85%, 90%). Moreover, our SpMM kernel shows up to 5x speedup over dense GEMM kernel of oneDNN, a well-optimized dense library widely used in industry. We apply our sparse accelerator on widely-used Transformer-based language models including Bert-Mini, DistilBERT, Bert-Base, and BERT-Large. Our sparse inference software shows up to 1.5x speedup over Neural Magic's Deepsparse under same configurations on Xeon on Amazon Web Services under proxy production latency constraints. We also compare our solution with two framework-based inference solutions, ONNX Runtime and PyTorch, and demonstrate up to 37x speedup over ONNX Runtime and 345x over PyTorch on Xeon under the latency constraints. All the source code is publicly available on Github: https://github.com/intel/intel-extension-for-transformers.", "github_manual": "", "github_regex": "https://github.com/intel/intel-extension-for-transformers", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-30", "arxiv_id": "2306.16940", "title": "BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion", "authors": [ "Michael J. Black", "Priyanka Patel", "Joachim Tesch", "Jinlong Yang" ], "github": "", "project_page": "", "abstract": "We show, for the first time, that neural networks trained only on synthetic data achieve state-of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real images. Previous synthetic datasets have been small, unrealistic, or lacked realistic clothing. Achieving sufficient realism is non-trivial and we show how to do this for full bodies in motion. Specifically, our BEDLAM dataset contains monocular RGB videos with ground-truth 3D bodies in SMPL-X format. It includes a diversity of body shapes, motions, skin tones, hair, and clothing. The clothing is realistically simulated on the moving bodies using commercial clothing physics simulation. We render varying numbers of people in realistic scenes with varied lighting and camera motions. We then train various HPS regressors using BEDLAM and achieve state-of-the-art accuracy on real-image benchmarks despite training with synthetic data. We use BEDLAM to gain insights into what model design choices are important for accuracy. With good synthetic training data, we find that a basic method like HMR approaches the accuracy of the current SOTA method (CLIFF). BEDLAM is useful for a variety of tasks and all images, ground truth bodies, 3D clothing, support code, and more are available for research purposes. Additionally, we provide detailed information about our synthetic data generation pipeline, enabling others to generate their own datasets. See the project page: https://bedlam.is.tue.mpg.de/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-06-30", "arxiv_id": "2306.16564", "title": "Automatic Calibration and Error Correction for Large Language Models via Pareto Optimal Self-Supervision", "authors": [ "Theodore Zhao", "Mu Wei", "J. Samuel Preston", "Hoifung Poon" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities out of box for a wide range of applications, yet accuracy still remains a major growth area, especially in mission-critical domains such as biomedicine. An effective method to calibrate the confidence level on LLM responses is essential to automatically detect errors and facilitate human-in-the-loop verification. An important source of calibration signals stems from expert-stipulated programmatic supervision, which is often available at low cost but has its own limitations such as noise and coverage. In this paper, we introduce a Pareto optimal self-supervision framework that can leverage available programmatic supervision to systematically calibrate LLM responses by producing a risk score for every response, without any additional manual efforts. This is accomplished by learning a harmonizer model to align LLM output with other available supervision sources, which would assign higher risk scores to more uncertain LLM responses and facilitate error correction. Experiments on standard relation extraction tasks in biomedical and general domains demonstrate the promise of this approach, with our proposed risk scores highly correlated with the real error rate of LLMs. For the most uncertain test instances, dynamic prompting based on our proposed risk scores results in significant accuracy improvement for off-the-shelf LLMs, boosting GPT-3 results past state-of-the-art (SOTA) weak supervision and GPT-4 results past SOTA supervised results on challenging evaluation datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-03", "arxiv_id": "2306.17843", "title": "Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors", "authors": [ "Guocheng Qian", "Jinjie Mai", "Abdullah Hamdi", "Jian Ren", "Aliaksandr Siarohin", "Bing Li", "Hsin-Ying Lee", "Ivan Skorokhodov", "Peter Wonka", "Sergey Tulyakov", "Bernard Ghanem" ], "github": "https://github.com/guochengqian/Magic123", "project_page": "", "abstract": "We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images. Our code, models, and generated 3D assets are available at https://github.com/guochengqian/Magic123.", "github_manual": "", "github_regex": "https://github.com/guochengqian/Magic123", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-03", "arxiv_id": "2306.17806", "title": "Stay on topic with Classifier-Free Guidance", "authors": [ "Guillaume Sanchez", "Honglu Fan", "Alexander Spangher", "Elad Levi", "Pawan Sasanka Ammanamanchi", "Stella Biderman" ], "github": "", "project_page": "", "abstract": "Classifier-Free Guidance (CFG) has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks: Q\\&A, reasoning, code generation, and machine translation, achieving SOTA on LAMBADA with LLaMA-7B over PaLM-540B; (2) brings improvements equivalent to a model with twice the parameter-count; (3) can stack alongside other inference-time methods like Chain-of-Thought and Self-Consistency, yielding further improvements in difficult tasks; (4) can be used to increase the faithfulness and coherence of assistants in challenging form-driven and content-driven prompts: in a human evaluation we show a 75\\% preference for GPT4All using CFG over baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-03", "arxiv_id": "2306.17840", "title": "Statler: State-Maintaining Language Models for Embodied Reasoning", "authors": [ "Takuma Yoneda", "Jiading Fang", "Peng Li", "Huanyu Zhang", "Tianchong Jiang", "Shengjie Lin", "Ben Picker", "David Yunis", "Hongyuan Mei", "Matthew R. Walter" ], "github": "https://github.com/ripl/statler", "project_page": "", "abstract": "Large language models (LLMs) provide a promising tool that enable robots to perform complex robot reasoning tasks. However, the limited context window of contemporary LLMs makes reasoning over long time horizons difficult. Embodied tasks such as those that one might expect a household robot to perform typically require that the planner consider information acquired a long time ago (e.g., properties of the many objects that the robot previously encountered in the environment). Attempts to capture the world state using an LLM's implicit internal representation is complicated by the paucity of task- and environment-relevant information available in a robot's action history, while methods that rely on the ability to convey information via the prompt to the LLM are subject to its limited context window. In this paper, we propose Statler, a framework that endows LLMs with an explicit representation of the world state as a form of ``memory'' that is maintained over time. Integral to Statler is its use of two instances of general LLMs -- a world-model reader and a world-model writer -- that interface with and maintain the world state. By providing access to this world state ``memory'', Statler improves the ability of existing LLMs to reason over longer time horizons without the constraint of context length. We evaluate the effectiveness of our approach on three simulated table-top manipulation domains and a real robot domain, and show that it improves the state-of-the-art in LLM-based robot reasoning. Project website: https://statler-lm.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ripl/statler", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-03", "arxiv_id": "2306.17582", "title": "ChatGPT for Robotics: Design Principles and Model Abilities", "authors": [ "Sai Vemprala", "Rogerio Bonatti", "Arthur Bucker", "Ashish Kapoor" ], "github": "", "project_page": "", "abstract": "This paper presents an experimental study regarding the use of OpenAI's ChatGPT for robotics applications. We outline a strategy that combines design principles for prompt engineering and the creation of a high-level function library which allows ChatGPT to adapt to different robotics tasks, simulators, and form factors. We focus our evaluations on the effectiveness of different prompt engineering techniques and dialog strategies towards the execution of various types of robotics tasks. We explore ChatGPT's ability to use free-form dialog, parse XML tags, and to synthesize code, in addition to the use of task-specific prompting functions and closed-loop reasoning through dialogues. Our study encompasses a range of tasks within the robotics domain, from basic logical, geometrical, and mathematical reasoning all the way to complex domains such as aerial navigation, manipulation, and embodied agents. We show that ChatGPT can be effective at solving several of such tasks, while allowing users to interact with it primarily via natural language instructions. In addition to these studies, we introduce an open-sourced research tool called PromptCraft, which contains a platform where researchers can collaboratively upload and vote on examples of good prompting schemes for robotics applications, as well as a sample robotics simulator with ChatGPT integration, making it easier for users to get started with using ChatGPT for robotics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-03", "arxiv_id": "2306.17563", "title": "Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting", "authors": [ "Zhen Qin", "Rolf Jagerman", "Kai Hui", "Honglei Zhuang", "Junru Wu", "Jiaming Shen", "Tianqi Liu", "Jialu Liu", "Donald Metzler", "Xuanhui Wang", "Michael Bendersky" ], "github": "", "project_page": "", "abstract": "Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical problem. However, there has been limited success so far, as researchers have found it difficult to outperform fine-tuned baseline rankers on benchmark datasets. We analyze pointwise and listwise ranking prompts used by existing methods and argue that off-the-shelf LLMs do not fully understand these ranking formulations, possibly due to the nature of how LLMs are trained. In this paper, we propose to significantly reduce the burden on LLMs by using a new technique called Pairwise Ranking Prompting (PRP). Our results are the first in the literature to achieve state-of-the-art ranking performance on standard benchmarks using moderate-sized open-sourced LLMs. On TREC-DL2020, PRP based on the Flan-UL2 model with 20B parameters outperforms the previous best approach in the literature, which is based on the blackbox commercial GPT-4 that has 50x (estimated) model size, by over 5% at NDCG@1. On TREC-DL2019, PRP is only inferior to the GPT-4 solution on the NDCG@5 and NDCG@10 metrics, while outperforming other existing solutions, such as InstructGPT which has 175B parameters, by over 10% for nearly all ranking metrics. Furthermore, we propose several variants of PRP to improve efficiency and show that it is possible to achieve competitive results even with linear complexity. We also discuss other benefits of PRP, such as supporting both generation and scoring LLM APIs, as well as being insensitive to input ordering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-03", "arxiv_id": "2306.17848", "title": "Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing", "authors": [ "Ariel N. Lee", "Sarah Adel Bargal", "Janavi Kasera", "Stan Sclaroff", "Kate Saenko", "Nataniel Ruiz" ], "github": "", "project_page": "", "abstract": "Vision transformers (ViTs) have significantly changed the computer vision landscape and have periodically exhibited superior performance in vision tasks compared to convolutional neural networks (CNNs). Although the jury is still out on which model type is superior, each has unique inductive biases that shape their learning and generalization performance. For example, ViTs have interesting properties with respect to early layer non-local feature dependence, as well as self-attention mechanisms which enhance learning flexibility, enabling them to ignore out-of-context image information more effectively. We hypothesize that this power to ignore out-of-context information (which we name patch selectivity), while integrating in-context information in a non-local manner in early layers, allows ViTs to more easily handle occlusion. In this study, our aim is to see whether we can have CNNs simulate this ability of patch selectivity by effectively hardwiring this inductive bias using Patch Mixing data augmentation, which consists of inserting patches from another image onto a training image and interpolating labels between the two image classes. Specifically, we use Patch Mixing to train state-of-the-art ViTs and CNNs, assessing its impact on their ability to ignore out-of-context patches and handle natural occlusions. We find that ViTs do not improve nor degrade when trained using Patch Mixing, but CNNs acquire new capabilities to ignore out-of-context information and improve on occlusion benchmarks, leaving us to conclude that this training method is a way of simulating in CNNs the abilities that ViTs already possess. We will release our Patch Mixing implementation and proposed datasets for public use. Project page: https://arielnlee.github.io/PatchMixing/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-03", "arxiv_id": "2306.17194", "title": "On the Exploitability of Instruction Tuning", "authors": [ "Manli Shu", "Jiongxiao Wang", "Chen Zhu", "Jonas Geiping", "Chaowei Xiao", "Tom Goldstein" ], "github": "https://github.com/azshue/AutoPoison", "project_page": "", "abstract": "Instruction tuning is an effective technique to align large language models (LLMs) with human intents. In this work, we investigate how an adversary can exploit instruction tuning by injecting specific instruction-following examples into the training data that intentionally changes the model's behavior. For example, an adversary can achieve content injection by injecting training examples that mention target content and eliciting such behavior from downstream models. To achieve this goal, we propose AutoPoison, an automated data poisoning pipeline. It naturally and coherently incorporates versatile attack goals into poisoned data with the help of an oracle LLM. We showcase two example attacks: content injection and over-refusal attacks, each aiming to induce a specific exploitable behavior. We quantify and benchmark the strength and the stealthiness of our data poisoning scheme. Our results show that AutoPoison allows an adversary to change a model's behavior by poisoning only a small fraction of data while maintaining a high level of stealthiness in the poisoned examples. We hope our work sheds light on how data quality affects the behavior of instruction-tuned models and raises awareness of the importance of data quality for responsible deployments of LLMs. Code is available at https://github.com/azshue/AutoPoison.", "github_manual": "", "github_regex": "https://github.com/azshue/AutoPoison", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-03", "arxiv_id": "2306.17492", "title": "Preference Ranking Optimization for Human Alignment", "authors": [ "Feifan Song", "Bowen Yu", "Minghao Li", "Haiyang Yu", "Fei Huang", "Yongbin Li", "Houfeng Wang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) often contain misleading content, emphasizing the need to align them with human values to ensure secur AI systems. Reinforcement learning from human feedback (RLHF) has been employed to achieve this alignment by combining a reward model, typically based on Bradley-Terry paired comparison, with an RL algorithm such as Proximal Policy Optimization (PPO) to optimize LLM responses. However, RLHF exhibits complexity, instability, and sensitivity to hyperparameters. In this paper, we propose Preference Ranking Optimization (PRO) as an alternative to PPO for directly aligning LLMs with the Bradley-Terry comparison. PRO extends the pairwise Bradley-Terry comparison to accommodate preference rankings of any length. By iteratively contrasting the likelihood of generating responses, PRO instructs the LLM to prioritize the best response while progressively ranking the remaining responses. In this manner, PRO effectively transforms human alignment into aligning the probability ranking of n responses generated by LLM with the preference ranking of humans towards these responses. Experiments have shown that PRO outperforms existing alignment algorithms, achieving comparable results to ChatGPT and human responses through automatic-based, reward-based, GPT-4, and human evaluations. Furthermore, we demonstrate that longer, more diverse, and higher-quality preference ranking sequences can consistently enhance the performance of human alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-03", "arxiv_id": "2306.17759", "title": "The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit", "authors": [ "Lorenzo Noci", "Chuning Li", "Mufan Bill Li", "Bobby He", "Thomas Hofmann", "Chris Maddison", "Daniel M. Roy" ], "github": "", "project_page": "", "abstract": "In deep learning theory, the covariance matrix of the representations serves as a proxy to examine the network's trainability. Motivated by the success of Transformers, we study the covariance matrix of a modified Softmax-based attention model with skip connections in the proportional limit of infinite-depth-and-width. We show that at initialization the limiting distribution can be described by a stochastic differential equation (SDE) indexed by the depth-to-width ratio. To achieve a well-defined stochastic limit, the Transformer's attention mechanism is modified by centering the Softmax output at identity, and scaling the Softmax logits by a width-dependent temperature parameter. We examine the stability of the network through the corresponding SDE, showing how the scale of both the drift and diffusion can be elegantly controlled with the aid of residual connections. The existence of a stable SDE implies that the covariance structure is well-behaved, even for very large depth and width, thus preventing the notorious issues of rank degeneracy in deep attention models. Finally, we show, through simulations, that the SDE provides a surprisingly good description of the corresponding finite-size model. We coin the name shaped Transformer for these architectural modifications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-03", "arxiv_id": "2306.17319", "title": "ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation", "authors": [ "Shuyang Sun", "Weijun Wang", "Qihang Yu", "Andrew Howard", "Philip Torr", "Liang-Chieh Chen" ], "github": "https://github.com/google-research/deeplab2", "project_page": "", "abstract": "This paper presents a new mechanism to facilitate the training of mask transformers for efficient panoptic segmentation, democratizing its deployment. We observe that due to its high complexity, the training objective of panoptic segmentation will inevitably lead to much higher false positive penalization. Such unbalanced loss makes the training process of the end-to-end mask-transformer based architectures difficult, especially for efficient models. In this paper, we present ReMaX that adds relaxation to mask predictions and class predictions during training for panoptic segmentation. We demonstrate that via these simple relaxation techniques during training, our model can be consistently improved by a clear margin without any extra computational cost on inference. By combining our method with efficient backbones like MobileNetV3-Small, our method achieves new state-of-the-art results for efficient panoptic segmentation on COCO, ADE20K and Cityscapes. Code and pre-trained checkpoints will be available at https://github.com/google-research/deeplab2.", "github_manual": "", "github_regex": "https://github.com/google-research/deeplab2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-04", "arxiv_id": "2307.01197", "title": "Segment Anything Meets Point Tracking", "authors": [ "Frano Raji\u010d", "Lei Ke", "Yu-Wing Tai", "Chi-Keung Tang", "Martin Danelljan", "Fisher Yu" ], "github": "https://github.com/SysCV/sam-pt", "project_page": "", "abstract": "The Segment Anything Model (SAM) has established itself as a powerful zero-shot image segmentation model, employing interactive prompts such as points to generate masks. This paper presents SAM-PT, a method extending SAM's capability to tracking and segmenting anything in dynamic videos. SAM-PT leverages robust and sparse point selection and propagation techniques for mask generation, demonstrating that a SAM-based segmentation tracker can yield strong zero-shot performance across popular video object segmentation benchmarks, including DAVIS, YouTube-VOS, and MOSE. Compared to traditional object-centric mask propagation strategies, we uniquely use point propagation to exploit local structure information that is agnostic to object semantics. We highlight the merits of point-based tracking through direct evaluation on the zero-shot open-world Unidentified Video Objects (UVO) benchmark. To further enhance our approach, we utilize K-Medoids clustering for point initialization and track both positive and negative points to clearly distinguish the target object. We also employ multiple mask decoding passes for mask refinement and devise a point re-initialization strategy to improve tracking accuracy. Our code integrates different point trackers and video segmentation benchmarks and will be released at https://github.com/SysCV/sam-pt.", "github_manual": "", "github_regex": "https://github.com/SysCV/sam-pt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-04", "arxiv_id": "2307.00522", "title": "LEDITS: Real Image Editing with DDPM Inversion and Semantic Guidance", "authors": [ "Linoy Tsaban", "Apolin\u00e1rio Passos" ], "github": "", "project_page": "", "abstract": "Recent large-scale text-guided diffusion models provide powerful image-generation capabilities. Currently, a significant effort is given to enable the modification of these images using text only as means to offer intuitive and versatile editing. However, editing proves to be difficult for these generative models due to the inherent nature of editing techniques, which involves preserving certain content from the original image. Conversely, in text-based models, even minor modifications to the text prompt frequently result in an entirely distinct result, making attaining one-shot generation that accurately corresponds to the users intent exceedingly challenging. In addition, to edit a real image using these state-of-the-art tools, one must first invert the image into the pre-trained models domain - adding another factor affecting the edit quality, as well as latency. In this exploratory report, we propose LEDITS - a combined lightweight approach for real-image editing, incorporating the Edit Friendly DDPM inversion technique with Semantic Guidance, thus extending Semantic Guidance to real image editing, while harnessing the editing capabilities of DDPM inversion as well. This approach achieves versatile edits, both subtle and extensive as well as alterations in composition and style, while requiring no optimization nor extensions to the architecture.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-04", "arxiv_id": "2307.00040", "title": "DisCo: Disentangled Control for Referring Human Dance Generation in Real World", "authors": [ "Tan Wang", "Linjie Li", "Kevin Lin", "Chung-Ching Lin", "Zhengyuan Yang", "Hanwang Zhang", "Zicheng Liu", "Lijuan Wang" ], "github": "https://github.com/Wangt-CN/DisCo", "project_page": "", "abstract": "Generative AI has made significant strides in computer vision, particularly in image/video synthesis conditioned on text descriptions. Despite the advancements, it remains challenging especially in the generation of human-centric content such as dance synthesis. Existing dance synthesis methods struggle with the gap between synthesized content and real-world dance scenarios. In this paper, we define a new problem setting: Referring Human Dance Generation, which focuses on real-world dance scenarios with three important properties: (i) Faithfulness: the synthesis should retain the appearance of both human subject foreground and background from the reference image, and precisely follow the target pose; (ii) Generalizability: the model should generalize to unseen human subjects, backgrounds, and poses; (iii) Compositionality: it should allow for composition of seen/unseen subjects, backgrounds, and poses from different sources. To address these challenges, we introduce a novel approach, DISCO, which includes a novel model architecture with disentangled control to improve the faithfulness and compositionality of dance synthesis, and an effective human attribute pre-training for better generalizability to unseen humans. Extensive qualitative and quantitative results demonstrate that DISCO can generate high-quality human dance images and videos with diverse appearances and flexible motions. Code, demo, video and visualization are available at: https://disco-dance.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Wangt-CN/DisCo", "github_api_added_by": "auto" }, { "date": "2023-07-04", "arxiv_id": "2307.00184", "title": "Personality Traits in Large Language Models", "authors": [ "Mustafa Safdari", "Greg Serapio-Garc\u00eda", "Cl\u00e9ment Crepy", "Stephen Fitz", "Peter Romero", "Luning Sun", "Marwa Abdulhai", "Aleksandra Faust", "Maja Matari\u0107" ], "github": "", "project_page": "", "abstract": "The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant text. As LLMs increasingly power conversational agents, the synthesized personality embedded in these models by virtue of their training on large amounts of human-generated data draws attention. Since personality is an important factor determining the effectiveness of communication, we present a comprehensive method for administering validated psychometric tests and quantifying, analyzing, and shaping personality traits exhibited in text generated from widely-used LLMs. We find that: 1) personality simulated in the outputs of some LLMs (under specific prompting configurations) is reliable and valid; 2) evidence of reliability and validity of LLM-simulated personality is stronger for larger and instruction fine-tuned models; and 3) personality in LLM outputs can be shaped along desired dimensions to mimic specific personality profiles. We also discuss potential applications and ethical implications of our measurement and shaping framework, especially regarding responsible use of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-04", "arxiv_id": "2307.00716", "title": "JourneyDB: A Benchmark for Generative Image Understanding", "authors": [ "Junting Pan", "Keqiang Sun", "Yuying Ge", "Hao Li", "Haodong Duan", "Xiaoshi Wu", "Renrui Zhang", "Aojun Zhou", "Zipeng Qin", "Yi Wang", "Jifeng Dai", "Yu Qiao", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "While recent advancements in vision-language models have revolutionized multi-modal understanding, it remains unclear whether they possess the capabilities of comprehending the generated images. Compared to real data, synthetic images exhibit a higher degree of diversity in both content and style, for which there are significant difficulties for the models to fully apprehend. To this end, we present a large-scale dataset, JourneyDB, for multi-modal visual understanding in generative images. Our curated dataset covers 4 million diverse and high-quality generated images paired with the text prompts used to produce them. We further design 4 benchmarks to quantify the performance of generated image understanding in terms of both content and style interpretation. These benchmarks include prompt inversion, style retrieval, image captioning and visual question answering. Lastly, we assess the performance of current state-of-the-art multi-modal models when applied to JourneyDB, and provide an in-depth analysis of their strengths and limitations in generated content understanding. We hope the proposed dataset and benchmarks will facilitate the research in the field of generative content understanding. The dataset will be available on https://journeydb.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-04", "arxiv_id": "2307.01200", "title": "Real-time Monocular Full-body Capture in World Space via Sequential Proxy-to-Motion Learning", "authors": [ "Yuxiang Zhang", "Hongwen Zhang", "Liangxiao Hu", "Hongwei Yi", "Shengping Zhang", "Yebin Liu" ], "github": "", "project_page": "", "abstract": "Learning-based approaches to monocular motion capture have recently shown promising results by learning to regress in a data-driven manner. However, due to the challenges in data collection and network designs, it remains challenging for existing solutions to achieve real-time full-body capture while being accurate in world space. In this work, we contribute a sequential proxy-to-motion learning scheme together with a proxy dataset of 2D skeleton sequences and 3D rotational motions in world space. Such proxy data enables us to build a learning-based network with accurate full-body supervision while also mitigating the generalization issues. For more accurate and physically plausible predictions, a contact-aware neural motion descent module is proposed in our network so that it can be aware of foot-ground contact and motion misalignment with the proxy observations. Additionally, we share the body-hand context information in our network for more compatible wrist poses recovery with the full-body model. With the proposed learning-based solution, we demonstrate the first real-time monocular full-body capture system with plausible foot-ground contact in world space. More video results can be found at our project page: https://liuyebin.com/proxycap.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-04", "arxiv_id": "2307.01097", "title": "MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion", "authors": [ "Shitao Tang", "Fuyang Zhang", "Jiacheng Chen", "Peng Wang", "Yasutaka Furukawa" ], "github": "https://github.com/Tangshitao/MVDiffusion", "project_page": "", "abstract": "This paper introduces MVDiffusion, a simple yet effective multi-view image generation method for scenarios where pixel-to-pixel correspondences are available, such as perspective crops from panorama or multi-view images given geometry (depth maps and poses). Unlike prior models that rely on iterative image warping and inpainting, MVDiffusion concurrently generates all images with a global awareness, encompassing high resolution and rich content, effectively addressing the error accumulation prevalent in preceding models. MVDiffusion specifically incorporates a correspondence-aware attention mechanism, enabling effective cross-view interaction. This mechanism underpins three pivotal modules: 1) a generation module that produces low-resolution images while maintaining global correspondence, 2) an interpolation module that densifies spatial coverage between images, and 3) a super-resolution module that upscales into high-resolution outputs. In terms of panoramic imagery, MVDiffusion can generate high-resolution photorealistic images up to 1024times1024 pixels. For geometry-conditioned multi-view image generation, MVDiffusion demonstrates the first method capable of generating a textured map of a scene mesh. The project page is at https://mvdiffusion.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tangshitao/MVDiffusion", "github_api_added_by": "auto" }, { "date": "2023-07-04", "arxiv_id": "2307.01163", "title": "Improving Language Plasticity via Pretraining with Active Forgetting", "authors": [ "Yihong Chen", "Kelly Marchisio", "Roberta Raileanu", "David Ifeoluwa Adelani", "Pontus Stenetor", "Sebastian Riedel", "Mikel Artetx" ], "github": "https://github.com/facebookresearch/language-model-plasticity", "project_page": "", "abstract": "Pretrained language models (PLMs) are today the primary model for natural language processing. Despite their impressive downstream performance, it can be difficult to apply PLMs to new languages, a barrier to making their capabilities universally accessible. While prior work has shown it possible to address this issue by learning a new embedding layer for the new language, doing so is both data and compute inefficient. We propose to use an active forgetting mechanism during pretraining, as a simple way of creating PLMs that can quickly adapt to new languages. Concretely, by resetting the embedding layer every K updates during pretraining, we encourage the PLM to improve its ability of learning new embeddings within a limited number of updates, similar to a meta-learning effect. Experiments with RoBERTa show that models pretrained with our forgetting mechanism not only demonstrate faster convergence during language adaptation but also outperform standard ones in a low-data regime, particularly for languages that are distant from English.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/language-model-plasticity", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-04", "arxiv_id": "2307.00117", "title": "Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control", "authors": [ "Vivek Myers", "Andre He", "Kuan Fang", "Homer Walke", "Philippe Hansen-Estruch", "Ching-An Cheng", "Mihai Jalobeanu", "Andrey Kolobov", "Anca Dragan", "Sergey Levine" ], "github": "", "project_page": "", "abstract": "Our goal is for robots to follow natural language instructions like \"put the towel next to the microwave.\" But getting large amounts of labeled data, i.e. data that contains demonstrations of tasks labeled with the language instruction, is prohibitive. In contrast, obtaining policies that respond to image goals is much easier, because any autonomous trial or demonstration can be labeled in hindsight with its final state as the goal. In this work, we contribute a method that taps into joint image- and goal- conditioned policies with language using only a small amount of language data. Prior work has made progress on this using vision-language models or by jointly training language-goal-conditioned policies, but so far neither method has scaled effectively to real-world robot tasks without significant human annotation. Our method achieves robust performance in the real world by learning an embedding from the labeled data that aligns language not to the goal image, but rather to the desired change between the start and goal images that the instruction corresponds to. We then train a policy on this embedding: the policy benefits from all the unlabeled data, but the aligned embedding provides an interface for language to steer the policy. We show instruction following across a variety of manipulation tasks in different scenes, with generalization to language instructions outside of the labeled data. Videos and code for our approach can be found on our website: http://tiny.cc/grif .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-04", "arxiv_id": "2307.00804", "title": "SketchMetaFace: A Learning-based Sketching Interface for High-fidelity 3D Character Face Modeling", "authors": [ "Zhongjin Luo", "Dong Du", "Heming Zhu", "Yizhou Yu", "Hongbo Fu", "Xiaoguang Han" ], "github": "", "project_page": "", "abstract": "Modeling 3D avatars benefits various application scenarios such as AR/VR, gaming, and filming. Character faces contribute significant diversity and vividity as a vital component of avatars. However, building 3D character face models usually requires a heavy workload with commercial tools, even for experienced artists. Various existing sketch-based tools fail to support amateurs in modeling diverse facial shapes and rich geometric details. In this paper, we present SketchMetaFace - a sketching system targeting amateur users to model high-fidelity 3D faces in minutes. We carefully design both the user interface and the underlying algorithm. First, curvature-aware strokes are adopted to better support the controllability of carving facial details. Second, considering the key problem of mapping a 2D sketch map to a 3D model, we develop a novel learning-based method termed \"Implicit and Depth Guided Mesh Modeling\" (IDGMM). It fuses the advantages of mesh, implicit, and depth representations to achieve high-quality results with high efficiency. In addition, to further support usability, we present a coarse-to-fine 2D sketching interface design and a data-driven stroke suggestion tool. User studies demonstrate the superiority of our system over existing modeling tools in terms of the ease to use and visual quality of results. Experimental analyses also show that IDGMM reaches a better trade-off between accuracy and efficiency. SketchMetaFace are available at https://zhongjinluo.github.io/SketchMetaFace/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-04", "arxiv_id": "2307.00119", "title": "Meta-training with Demonstration Retrieval for Efficient Few-shot Learning", "authors": [ "Aaron Mueller", "Kanika Narang", "Lambert Mathias", "Qifan Wang", "Hamed Firooz" ], "github": "", "project_page": "", "abstract": "Large language models show impressive results on few-shot NLP tasks. However, these models are memory and computation-intensive. Meta-training allows one to leverage smaller models for few-shot generalization in a domain-general and task-agnostic manner; however, these methods alone results in models that may not have sufficient parameterization or knowledge to adapt quickly to a large variety of tasks. To overcome this issue, we propose meta-training with demonstration retrieval, where we use a dense passage retriever to retrieve semantically similar labeled demonstrations to each example for more varied supervision. By separating external knowledge from model parameters, we can use meta-training to train parameter-efficient models that generalize well on a larger variety of tasks. We construct a meta-training set from UnifiedQA and CrossFit, and propose a demonstration bank based on UnifiedQA tasks. To our knowledge, our work is the first to combine retrieval with meta-training, to use DPR models to retrieve demonstrations, and to leverage demonstrations from many tasks simultaneously, rather than randomly sampling demonstrations from the training set of the target task. Our approach outperforms a variety of targeted parameter-efficient and retrieval-augmented few-shot methods on QA, NLI, and text classification tasks (including SQuAD, QNLI, and TREC). Our approach can be meta-trained and fine-tuned quickly on a single GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-06", "arxiv_id": "2307.02486", "title": "LongNet: Scaling Transformers to 1,000,000,000 Tokens", "authors": [ "Jiayu Ding", "Shuming Ma", "Li Dong", "Xingxing Zhang", "Shaohan Huang", "Wenhui Wang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Scaling sequence length has become a critical demand in the era of large language models. However, existing methods struggle with either computational complexity or model expressivity, rendering the maximum sequence length restricted. In this work, we introduce LongNet, a Transformer variant that can scale sequence length to more than 1 billion tokens, without sacrificing the performance on shorter sequences. Specifically, we propose dilated attention, which expands the attentive field exponentially as the distance grows. LongNet has significant advantages: 1) it has a linear computation complexity and a logarithm dependency between tokens; 2) it can be served as a distributed trainer for extremely long sequences; 3) its dilated attention is a drop-in replacement for standard attention, which can be seamlessly integrated with the existing Transformer-based optimization. Experiments results demonstrate that LongNet yields strong performance on both long-sequence modeling and general language tasks. Our work opens up new possibilities for modeling very long sequences, e.g., treating a whole corpus or even the entire Internet as a sequence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-06", "arxiv_id": "2307.01952", "title": "SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis", "authors": [ "Dustin Podell", "Zion English", "Kyle Lacey", "Andreas Blattmann", "Tim Dockhorn", "Jonas M\u00fcller", "Joe Penna", "Robin Rombach" ], "github": "https://github.com/Stability-AI/generative-models", "project_page": "", "abstract": "We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. We also introduce a refinement model which is used to improve the visual fidelity of samples generated by SDXL using a post-hoc image-to-image technique. We demonstrate that SDXL shows drastically improved performance compared the previous versions of Stable Diffusion and achieves results competitive with those of black-box state-of-the-art image generators. In the spirit of promoting open research and fostering transparency in large model training and evaluation, we provide access to code and model weights at https://github.com/Stability-AI/generative-models", "github_manual": "", "github_regex": "https://github.com/Stability-AI/generative-models", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-06", "arxiv_id": "2307.02421", "title": "DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models", "authors": [ "Chong Mou", "Xintao Wang", "Jiechong Song", "Ying Shan", "Jian Zhang" ], "github": "https://github.com/MC-E/DragonDiffusion", "project_page": "", "abstract": "Despite the ability of existing large-scale text-to-image (T2I) models to generate high-quality images from detailed textual descriptions, they often lack the ability to precisely edit the generated or real images. In this paper, we propose a novel image editing method, DragonDiffusion, enabling Drag-style manipulation on Diffusion models. Specifically, we construct classifier guidance based on the strong correspondence of intermediate features in the diffusion model. It can transform the editing signals into gradients via feature correspondence loss to modify the intermediate representation of the diffusion model. Based on this guidance strategy, we also build a multi-scale guidance to consider both semantic and geometric alignment. Moreover, a cross-branch self-attention is added to maintain the consistency between the original image and the editing result. Our method, through an efficient design, achieves various editing modes for the generated or real images, such as object moving, object resizing, object appearance replacement, and content dragging. It is worth noting that all editing and content preservation signals come from the image itself, and the model does not require fine-tuning or additional modules. Our source code will be available at https://github.com/MC-E/DragonDiffusion.", "github_manual": "", "github_regex": "https://github.com/MC-E/DragonDiffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-06", "arxiv_id": "2307.02053", "title": "Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning", "authors": [ "Deepanway Ghosal", "Yew Ken Chia", "Navonil Majumder", "Soujanya Poria" ], "github": "https://github.com/declare-lab/flacuna", "project_page": "", "abstract": "Recently, the release of INSTRUCTEVAL has provided valuable insights into the performance of large language models (LLMs) that utilize encoder-decoder or decoder-only architecture. Interestingly, despite being introduced four years ago, T5-based LLMs, such as FLAN-T5, continue to outperform the latest decoder-based LLMs, such as LLAMA and VICUNA, on tasks that require general problem-solving skills. This performance discrepancy can be attributed to three key factors: (1) Pre-training data, (2) Backbone architecture, and (3) Instruction dataset. In this technical report, our main focus is on investigating the impact of the third factor by leveraging VICUNA, a large language model based on LLAMA, which has undergone fine-tuning on ChatGPT conversations. To achieve this objective, we fine-tuned VICUNA using a customized instruction dataset collection called FLANMINI. This collection includes a subset of the large-scale instruction dataset known as FLAN, as well as various code-related datasets and conversational datasets derived from ChatGPT/GPT-4. This dataset comprises a large number of tasks that demand problem-solving skills. Our experimental findings strongly indicate that the enhanced problem-solving abilities of our model, FLACUNA, are obtained through fine-tuning VICUNA on the FLAN dataset, leading to significant improvements across numerous benchmark datasets in INSTRUCTEVAL. FLACUNA is publicly available at https://huggingface.co/declare-lab/flacuna-13b-v1.0.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/declare-lab/flacuna", "github_api_added_by": "auto" }, { "date": "2023-07-06", "arxiv_id": "2307.02469", "title": "What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?", "authors": [ "Yan Zeng", "Hanbo Zhang", "Jiani Zheng", "Jiangnan Xia", "Guoqiang Wei", "Yang Wei", "Yuchen Zhang", "Tao Kong" ], "github": "https://github.com/bytedance/lynx-llm", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) such as GPT4 have displayed exceptional multi-modal capabilities in following open-ended instructions given images. However, the performance of these models heavily relies on design choices such as network structures, training data, and training strategies, and these choices have not been extensively discussed in the literature, making it difficult to quantify progress in this field. To address this issue, this paper presents a systematic and comprehensive study, quantitatively and qualitatively, on training such models. We implement over 20 variants with controlled settings. Concretely, for network structures, we compare different LLM backbones and model designs. For training data, we investigate the impact of data and sampling strategies. For instructions, we explore the influence of diversified prompts on the instruction-following ability of the trained models. For benchmarks, we contribute the first, to our best knowledge, comprehensive evaluation set including both image and video tasks through crowd-sourcing. Based on our findings, we present Lynx, which performs the most accurate multi-modal understanding while keeping the best multi-modal generation ability compared to existing open-sourced GPT4-style models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bytedance/lynx-llm", "github_api_added_by": "auto" }, { "date": "2023-07-06", "arxiv_id": "2307.02485", "title": "Building Cooperative Embodied Agents Modularly with Large Language Models", "authors": [ "Hongxin Zhang", "Weihua Du", "Jiaming Shan", "Qinhong Zhou", "Yilun Du", "Joshua B. Tenenbaum", "Tianmin Shu", "Chuang Gan" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated impressive planning abilities in single-agent embodied tasks across various domains. However, their capacity for planning and communication in multi-agent cooperation remains unclear, even though these are crucial skills for intelligent embodied agents. In this paper, we present a novel framework that utilizes LLMs for multi-agent cooperation and tests it in various embodied environments. Our framework enables embodied agents to plan, communicate, and cooperate with other embodied agents or humans to accomplish long-horizon tasks efficiently. We demonstrate that recent LLMs, such as GPT-4, can surpass strong planning-based methods and exhibit emergent effective communication using our framework without requiring fine-tuning or few-shot prompting. We also discover that LLM-based agents that communicate in natural language can earn more trust and cooperate more effectively with humans. Our research underscores the potential of LLMs for embodied AI and lays the foundation for future research in multi-agent cooperation. Videos can be found on the project website https://vis-www.cs.umass.edu/Co-LLM-Agents/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-06", "arxiv_id": "2307.02483", "title": "Jailbroken: How Does LLM Safety Training Fail?", "authors": [ "Alexander Wei", "Nika Haghtalab", "Jacob Steinhardt" ], "github": "", "project_page": "", "abstract": "Large language models trained for safety and harmlessness remain susceptible to adversarial misuse, as evidenced by the prevalence of \"jailbreak\" attacks on early releases of ChatGPT that elicit undesired behavior. Going beyond recognition of the issue, we investigate why such attacks succeed and how they can be created. We hypothesize two failure modes of safety training: competing objectives and mismatched generalization. Competing objectives arise when a model's capabilities and safety goals conflict, while mismatched generalization occurs when safety training fails to generalize to a domain for which capabilities exist. We use these failure modes to guide jailbreak design and then evaluate state-of-the-art models, including OpenAI's GPT-4 and Anthropic's Claude v1.3, against both existing and newly designed attacks. We find that vulnerabilities persist despite the extensive red-teaming and safety-training efforts behind these models. Notably, new attacks utilizing our failure modes succeed on every prompt in a collection of unsafe requests from the models' red-teaming evaluation sets and outperform existing ad hoc jailbreaks. Our analysis emphasizes the need for safety-capability parity -- that safety mechanisms should be as sophisticated as the underlying model -- and argues against the idea that scaling alone can resolve these safety failure modes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-06", "arxiv_id": "2307.01928", "title": "Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners", "authors": [ "Allen Z. Ren", "Anushri Dixit", "Alexandra Bodrova", "Sumeet Singh", "Stephen Tu", "Noah Brown", "Peng Xu", "Leila Takayama", "Fei Xia", "Jake Varley", "Zhenjia Xu", "Dorsa Sadigh", "Andy Zeng", "Anirudha Majumdar" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remain prone to confidently hallucinated predictions. In this work, we present KnowNo, which is a framework for measuring and aligning the uncertainty of LLM-based planners such that they know when they don't know and ask for help when needed. KnowNo builds on the theory of conformal prediction to provide statistical guarantees on task completion while minimizing human help in complex multi-step planning settings. Experiments across a variety of simulated and real robot setups that involve tasks with different modes of ambiguity (e.g., from spatial to numeric uncertainties, from human preferences to Winograd schemas) show that KnowNo performs favorably over modern baselines (which may involve ensembles or extensive prompt tuning) in terms of improving efficiency and autonomy, while providing formal assurances. KnowNo can be used with LLMs out of the box without model-finetuning, and suggests a promising lightweight approach to modeling uncertainty that can complement and scale with the growing capabilities of foundation models. Website: https://robot-help.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-06", "arxiv_id": "2307.01938", "title": "Physics-based Motion Retargeting from Sparse Inputs", "authors": [ "Daniele Reda", "Jungdam Won", "Yuting Ye", "Michiel van de Panne", "Alexander Winkler" ], "github": "", "project_page": "", "abstract": "Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. Another challenge is that an avatar might have a different skeleton structure than a human and the mapping between them is unclear. In this work we address both of these challenges. We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies. Our method uses reinforcement learning to train a policy to control characters in a physics simulator. We only require human motion capture data for training, without relying on artist-generated animations for each avatar. This allows us to use large motion capture datasets to train general policies that can track unseen users from real and sparse data in real-time. We demonstrate the feasibility of our approach on three characters with different skeleton structure: a dinosaur, a mouse-like creature and a human. We show that the avatar poses often match the user surprisingly well, despite having no sensor information of the lower body available. We discuss and ablate the important components in our framework, specifically the kinematic retargeting step, the imitation, contact and action reward as well as our asymmetric actor-critic observations. We further explore the robustness of our method in a variety of settings including unbalancing, dancing and sports motions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-06", "arxiv_id": "2307.02321", "title": "MSViT: Dynamic Mixed-Scale Tokenization for Vision Transformers", "authors": [ "Jakob Drachmann Havtorn", "Amelie Royer", "Tijmen Blankevoort", "Babak Ehteshami Bejnordi" ], "github": "", "project_page": "", "abstract": "The input tokens to Vision Transformers carry little semantic meaning as they are defined as regular equal-sized patches of the input image, regardless of its content. However, processing uniform background areas of an image should not necessitate as much compute as dense, cluttered areas. To address this issue, we propose a dynamic mixed-scale tokenization scheme for ViT, MSViT. Our method introduces a conditional gating mechanism that selects the optimal token scale for every image region, such that the number of tokens is dynamically determined per input. The proposed gating module is lightweight, agnostic to the choice of transformer backbone, and trained within a few epochs (e.g., 20 epochs on ImageNet) with little training overhead. In addition, to enhance the conditional behavior of the gate during training, we introduce a novel generalization of the batch-shaping loss. We show that our gating module is able to learn meaningful semantics despite operating locally at the coarse patch-level. We validate MSViT on the tasks of classification and segmentation where it leads to improved accuracy-complexity trade-off.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-06", "arxiv_id": "2307.02179", "title": "Open-Source Large Language Models Outperform Crowd Workers and Approach ChatGPT in Text-Annotation Tasks", "authors": [ "Meysam Alizadeh", "Ma\u00ebl Kubli", "Zeynab Samei", "Shirin Dehghani", "Juan Diego Bermeo", "Maria Korobeynikova", "Fabrizio Gilardi" ], "github": "", "project_page": "", "abstract": "This study examines the performance of open-source Large Language Models (LLMs) in text annotation tasks and compares it with proprietary models like ChatGPT and human-based services such as MTurk. While prior research demonstrated the high performance of ChatGPT across numerous NLP tasks, open-source LLMs like HugginChat and FLAN are gaining attention for their cost-effectiveness, transparency, reproducibility, and superior data protection. We assess these models using both zero-shot and few-shot approaches and different temperature parameters across a range of text annotation tasks. Our findings show that while ChatGPT achieves the best performance in most tasks, open-source LLMs not only outperform MTurk but also demonstrate competitive potential against ChatGPT in specific tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-06", "arxiv_id": "2307.02484", "title": "Elastic Decision Transformer", "authors": [ "Yueh-Hua Wu", "Xiaolong Wang", "Masashi Hamaya" ], "github": "", "project_page": "", "abstract": "This paper introduces Elastic Decision Transformer (EDT), a significant advancement over the existing Decision Transformer (DT) and its variants. Although DT purports to generate an optimal trajectory, empirical evidence suggests it struggles with trajectory stitching, a process involving the generation of an optimal or near-optimal trajectory from the best parts of a set of sub-optimal trajectories. The proposed EDT differentiates itself by facilitating trajectory stitching during action inference at test time, achieved by adjusting the history length maintained in DT. Further, the EDT optimizes the trajectory by retaining a longer history when the previous trajectory is optimal and a shorter one when it is sub-optimal, enabling it to \"stitch\" with a more optimal trajectory. Extensive experimentation demonstrates EDT's ability to bridge the performance gap between DT-based and Q Learning-based approaches. In particular, the EDT outperforms Q Learning-based methods in a multi-task regime on the D4RL locomotion benchmark and Atari games. Videos are available at: https://kristery.github.io/edt/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-06", "arxiv_id": "2307.01848", "title": "Embodied Task Planning with Large Language Models", "authors": [ "Zhenyu Wu", "Ziwei Wang", "Xiuwei Xu", "Jiwen Lu", "Haibin Yan" ], "github": "https://github.com/Gary3410/TaPA", "project_page": "", "abstract": "Equipping embodied agents with commonsense is important for robots to successfully complete complex human instructions in general environments. Recent large language models (LLM) can embed rich semantic knowledge for agents in plan generation of complex tasks, while they lack the information about the realistic world and usually yield infeasible action sequences. In this paper, we propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning with physical scene constraint, where the agent generates executable plans according to the existed objects in the scene by aligning LLMs with the visual perception models. Specifically, we first construct a multimodal dataset containing triplets of indoor scenes, instructions and action plans, where we provide the designed prompts and the list of existing objects in the scene for GPT-3.5 to generate a large number of instructions and corresponding planned actions. The generated data is leveraged for grounded plan tuning of pre-trained LLMs. During inference, we discover the objects in the scene by extending open-vocabulary object detectors to multi-view RGB images collected in different achievable locations. Experimental results show that the generated plan from our TaPA framework can achieve higher success rate than LLaVA and GPT-3.5 by a sizable margin, which indicates the practicality of embodied task planning in general and complex environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Gary3410/TaPA", "github_api_added_by": "auto" }, { "date": "2023-07-06", "arxiv_id": "2307.01229", "title": "EmoGen: Eliminating Subjective Bias in Emotional Music Generation", "authors": [ "Chenfei Kang", "Peiling Lu", "Botao Yu", "Xu Tan", "Wei Ye", "Shikun Zhang", "Jiang Bian" ], "github": "https://github.com/microsoft/muzic", "project_page": "", "abstract": "Music is used to convey emotions, and thus generating emotional music is important in automatic music generation. Previous work on emotional music generation directly uses annotated emotion labels as control signals, which suffers from subjective bias: different people may annotate different emotions on the same music, and one person may feel different emotions under different situations. Therefore, directly mapping emotion labels to music sequences in an end-to-end way would confuse the learning process and hinder the model from generating music with general emotions. In this paper, we propose EmoGen, an emotional music generation system that leverages a set of emotion-related music attributes as the bridge between emotion and music, and divides the generation into two stages: emotion-to-attribute mapping with supervised clustering, and attribute-to-music generation with self-supervised learning. Both stages are beneficial: in the first stage, the attribute values around the clustering center represent the general emotions of these samples, which help eliminate the impacts of the subjective bias of emotion labels; in the second stage, the generation is completely disentangled from emotion labels and thus free from the subjective bias. Both subjective and objective evaluations show that EmoGen outperforms previous methods on emotion control accuracy and music quality respectively, which demonstrate our superiority in generating emotional music. Music samples generated by EmoGen are available via this link:https://ai-muzic.github.io/emogen/, and the code is available at this link:https://github.com/microsoft/muzic/.", "github_manual": "", "github_regex": "https://github.com/microsoft/muzic", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-06", "arxiv_id": "2307.01831", "title": "DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation", "authors": [ "Shentong Mo", "Enze Xie", "Ruihang Chu", "Lewei Yao", "Lanqing Hong", "Matthias Nie\u00dfner", "Zhenguo Li" ], "github": "https://github.com/DiT-3D/DiT-3D", "project_page": "", "abstract": "Recent Diffusion Transformers (e.g., DiT) have demonstrated their powerful effectiveness in generating high-quality 2D images. However, it is still being determined whether the Transformer architecture performs equally well in 3D shape generation, as previous 3D diffusion methods mostly adopted the U-Net architecture. To bridge this gap, we propose a novel Diffusion Transformer for 3D shape generation, namely DiT-3D, which can directly operate the denoising process on voxelized point clouds using plain Transformers. Compared to existing U-Net approaches, our DiT-3D is more scalable in model size and produces much higher quality generations. Specifically, the DiT-3D adopts the design philosophy of DiT but modifies it by incorporating 3D positional and patch embeddings to adaptively aggregate input from voxelized point clouds. To reduce the computational cost of self-attention in 3D shape generation, we incorporate 3D window attention into Transformer blocks, as the increased 3D token length resulting from the additional dimension of voxels can lead to high computation. Finally, linear and devoxelization layers are used to predict the denoised point clouds. In addition, our transformer architecture supports efficient fine-tuning from 2D to 3D, where the pre-trained DiT-2D checkpoint on ImageNet can significantly improve DiT-3D on ShapeNet. Experimental results on the ShapeNet dataset demonstrate that the proposed DiT-3D achieves state-of-the-art performance in high-fidelity and diverse 3D point cloud generation. In particular, our DiT-3D decreases the 1-Nearest Neighbor Accuracy of the state-of-the-art method by 4.59 and increases the Coverage metric by 3.51 when evaluated on Chamfer Distance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DiT-3D/DiT-3D", "github_api_added_by": "auto" }, { "date": "2023-07-07", "arxiv_id": "2307.03109", "title": "A Survey on Evaluation of Large Language Models", "authors": [ "Yupeng Chang", "Xu Wang", "Jindong Wang", "Yuan Wu", "Kaijie Zhu", "Hao Chen", "Linyi Yang", "Xiaoyuan Yi", "Cunxiang Wang", "Yidong Wang", "Wei Ye", "Yue Zhang", "Yi Chang", "Philip S. Yu", "Qiang Yang", "Xing Xie" ], "github": "https://github.com/MLGroupJLU/LLM-eval-survey", "project_page": "", "abstract": "Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, educations, natural and social sciences, agent applications, and other areas. Secondly, we answer the `where' and `how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey.", "github_manual": "", "github_regex": "https://github.com/MLGroupJLU/LLM-eval-survey", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-07", "arxiv_id": "2307.03172", "title": "Lost in the Middle: How Language Models Use Long Contexts", "authors": [ "Nelson F. Liu", "Kevin Lin", "John Hewitt", "Ashwin Paranjape", "Michele Bevilacqua", "Fabio Petroni", "Percy Liang" ], "github": "https://github.com/nelson-liu/lost-in-the-middle", "project_page": "", "abstract": "While recent language models have the ability to take long contexts as input, relatively little is known about how well the language models use longer context. We analyze language model performance on two tasks that require identifying relevant information within their input contexts: multi-document question answering and key-value retrieval. We find that performance is often highest when relevant information occurs at the beginning or end of the input context, and significantly degrades when models must access relevant information in the middle of long contexts. Furthermore, performance substantially decreases as the input context grows longer, even for explicitly long-context models. Our analysis provides a better understanding of how language models use their input context and provides new evaluation protocols for future long-context models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nelson-liu/lost-in-the-middle", "github_api_added_by": "auto" }, { "date": "2023-07-07", "arxiv_id": "2307.02499", "title": "mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding", "authors": [ "Jiabo Ye", "Anwen Hu", "Haiyang Xu", "Qinghao Ye", "Ming Yan", "Yuhao Dan", "Chenlin Zhao", "Guohai Xu", "Chenliang Li", "Junfeng Tian", "Qian Qi", "Ji Zhang", "Fei Huang" ], "github": "https://github.com/X-PLUG/mPLUG-DocOwl", "project_page": "", "abstract": "Document understanding refers to automatically extract, analyze and comprehend information from various types of digital documents, such as a web page. Existing Multi-model Large Language Models (MLLMs), including mPLUG-Owl, have demonstrated promising zero-shot capabilities in shallow OCR-free text recognition, indicating their potential for OCR-free document understanding. Nevertheless, without in-domain training, these models tend to ignore fine-grained OCR features, such as sophisticated tables or large blocks of text, which are essential for OCR-free document understanding. In this paper, we propose mPLUG-DocOwl based on mPLUG-Owl for OCR-free document understanding. Specifically, we first construct a instruction tuning dataset featuring a wide range of visual-text understanding tasks. Then, we strengthen the OCR-free document understanding ability by jointly train the model on language-only, general vision-and-language, and document instruction tuning dataset with our unified instruction tuning strategy. We also build an OCR-free document instruction understanding evaluation set LLMDoc to better compare models' capabilities on instruct compliance and document understanding. Experimental results show that our model outperforms existing multi-modal models, demonstrating its strong ability of document understanding. Besides, without specific fine-tuning, mPLUG-DocOwl generalizes well on various downstream tasks. Our code, models, training data and evaluation set are available at https://github.com/X-PLUG/mPLUG-DocOwl.", "github_manual": "", "github_regex": "https://github.com/X-PLUG/mPLUG-DocOwl", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-07", "arxiv_id": "2307.02768", "title": "Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts", "authors": [ "Mounica Maddela", "Megan Ung", "Jing Xu", "Andrea Madotto", "Heather Foran", "Y-Lan Boureau" ], "github": "https://github.com/facebookresearch/ParlAI/tree/main/projects/reframe_thoughts", "project_page": "", "abstract": "Many cognitive approaches to well-being, such as recognizing and reframing unhelpful thoughts, have received considerable empirical support over the past decades, yet still lack truly widespread adoption in self-help format. A barrier to that adoption is a lack of adequately specific and diverse dedicated practice material. This work examines whether current language models can be leveraged to both produce a virtually unlimited quantity of practice material illustrating standard unhelpful thought patterns matching specific given contexts, and generate suitable positive reframing proposals. We propose PATTERNREFRAME, a novel dataset of about 10k examples of thoughts containing unhelpful thought patterns conditioned on a given persona, accompanied by about 27k positive reframes. By using this dataset to train and/or evaluate current models, we show that existing models can already be powerful tools to help generate an abundance of tailored practice material and hypotheses, with no or minimal additional model training required.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/ParlAI/tree/main/projects/reframe_thoughts", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-07", "arxiv_id": "2307.03170", "title": "Focused Transformer: Contrastive Training for Context Scaling", "authors": [ "Szymon Tworkowski", "Konrad Staniszewski", "Miko\u0142aj Pacek", "Yuhuai Wu", "Henryk Michalewski", "Piotr Mi\u0142o\u015b" ], "github": "https://github.com/cstankonrad/long_llama", "project_page": "", "abstract": "Large language models have an exceptional capability to incorporate new information in a contextual manner. However, the full potential of such an approach is often restrained due to a limitation in the effective context length. One solution to this issue is to endow an attention layer with access to an external memory, which comprises of (key, value) pairs. Yet, as the number of documents increases, the proportion of relevant keys to irrelevant ones decreases, leading the model to focus more on the irrelevant keys. We identify a significant challenge, dubbed the distraction issue, where keys linked to different semantic values might overlap, making them hard to distinguish. To tackle this problem, we introduce the Focused Transformer (FoT), a technique that employs a training process inspired by contrastive learning. This novel approach enhances the structure of the (key, value) space, enabling an extension of the context length. Our method allows for fine-tuning pre-existing, large-scale models to lengthen their effective context. This is demonstrated by our fine-tuning of 3B and 7B OpenLLaMA checkpoints. The resulting models, which we name LongLLaMA, exhibit advancements in tasks requiring a long context. We further illustrate that our LongLLaMA models adeptly manage a 256 k context length for passkey retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cstankonrad/long_llama", "github_api_added_by": "auto" }, { "date": "2023-07-07", "arxiv_id": "2307.03183", "title": "Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong General Audio Event Taggers", "authors": [ "Yuan Gong", "Sameer Khurana", "Leonid Karlinsky", "James Glass" ], "github": "https://github.com/YuanGongND/whisper-at", "project_page": "", "abstract": "In this paper, we focus on Whisper, a recent automatic speech recognition model trained with a massive 680k hour labeled speech corpus recorded in diverse conditions. We first show an interesting finding that while Whisper is very robust against real-world background sounds (e.g., music), its audio representation is actually not noise-invariant, but is instead highly correlated to non-speech sounds, indicating that Whisper recognizes speech conditioned on the noise type. With this finding, we build a unified audio tagging and speech recognition model Whisper-AT by freezing the backbone of Whisper, and training a lightweight audio tagging model on top of it. With <1% extra computational cost, Whisper-AT can recognize audio events, in addition to spoken text, in a single forward pass.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YuanGongND/whisper-at", "github_api_added_by": "auto" }, { "date": "2023-07-07", "arxiv_id": "2307.02628", "title": "SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference", "authors": [ "Luciano Del Corro", "Allie Del Giorno", "Sahaj Agarwal", "Bin Yu", "Ahmed Awadallah", "Subhabrata Mukherjee" ], "github": "", "project_page": "", "abstract": "Autoregressive large language models (LLMs) have made remarkable progress in various natural language generation tasks. However, they incur high computation cost and latency resulting from the autoregressive token-by-token generation. To address this issue, several approaches have been proposed to reduce computational cost using early-exit strategies. These strategies enable faster text generation using reduced computation without applying the full computation graph to each token. While existing token-level early exit methods show promising results for online inference, they cannot be readily applied for batch inferencing and Key-Value caching. This is because they have to wait until the last token in a batch exits before they can stop computing. This severely limits the practical application of such techniques. In this paper, we propose a simple and effective token-level early exit method, SkipDecode, designed to work seamlessly with batch inferencing and KV caching. It overcomes prior constraints by setting up a singular exit point for every token in a batch at each sequence position. It also guarantees a monotonic decrease in exit points, thereby eliminating the need to recompute KV Caches for preceding tokens. Rather than terminating computation prematurely as in prior works, our approach bypasses lower to middle layers, devoting most of the computational resources to upper layers, allowing later tokens to benefit from the compute expenditure by earlier tokens. Our experimental results show that SkipDecode can obtain 2x to 5x inference speedups with negligible regression across a variety of tasks. This is achieved using OPT models of 1.3 billion and 6.7 billion parameters, all the while being directly compatible with batching and KV caching optimization techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-07", "arxiv_id": "2307.03166", "title": "VideoGLUE: Video General Understanding Evaluation of Foundation Models", "authors": [ "Liangzhe Yuan", "Nitesh Bharadwaj Gundavarapu", "Long Zhao", "Hao Zhou", "Yin Cui", "Lu Jiang", "Xuan Yang", "Menglin Jia", "Tobias Weyand", "Luke Friedman", "Mikhail Sirotenko", "Huisheng Wang", "Florian Schroff", "Hartwig Adam", "Ming-Hsuan Yang", "Ting Liu", "Boqing Gong" ], "github": "", "project_page": "", "abstract": "We evaluate existing foundation models video understanding capabilities using a carefully designed experiment protocol consisting of three hallmark tasks (action recognition, temporal localization, and spatiotemporal localization), eight datasets well received by the community, and four adaptation methods tailoring a foundation model (FM) for a downstream task. Moreover, we propose a scalar VideoGLUE score (VGS) to measure an FMs efficacy and efficiency when adapting to general video understanding tasks. Our main findings are as follows. First, task-specialized models significantly outperform the six FMs studied in this work, in sharp contrast to what FMs have achieved in natural language and image understanding. Second,video-native FMs, whose pretraining data contains the video modality, are generally better than image-native FMs in classifying motion-rich videos, localizing actions in time, and understanding a video of more than one action. Third, the video-native FMs can perform well on video tasks under light adaptations to downstream tasks(e.g., freezing the FM backbones), while image-native FMs win in full end-to-end finetuning. The first two observations reveal the need and tremendous opportunities to conduct research on video-focused FMs, and the last confirms that both tasks and adaptation methods matter when it comes to the evaluation of FMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-10", "arxiv_id": "2307.03692", "title": "Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning", "authors": [ "Waseem AlShikh", "Manhal Daaboul", "Kirk Goddard", "Brock Imel", "Kiran Kamble", "Parikshith Kulkarni", "Melisa Russak" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce the Instruction Following Score (IFS), a metric that detects language models' ability to follow instructions. The metric has a dual purpose. First, IFS can be used to distinguish between base and instruct models. We benchmark publicly available base and instruct models, and show that the ratio of well formatted responses to partial and full sentences can be an effective measure between those two model classes. Secondly, the metric can be used as an early stopping criteria for instruct tuning. We compute IFS for Supervised Fine-Tuning (SFT) of 7B and 13B LLaMA models, showing that models learn to follow instructions relatively early in the training process, and the further finetuning can result in changes in the underlying base model semantics. As an example of semantics change we show the objectivity of model predictions, as defined by an auxiliary metric ObjecQA. We show that in this particular case, semantic changes are the steepest when the IFS tends to plateau. We hope that decomposing instruct tuning into IFS and semantic factors starts a new trend in better controllable instruct tuning and opens possibilities for designing minimal instruct interfaces querying foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-10", "arxiv_id": "2307.03381", "title": "Teaching Arithmetic to Small Transformers", "authors": [ "Nayoung Lee", "Kartik Sreenivasan", "Jason D. Lee", "Kangwook Lee", "Dimitris Papailiopoulos" ], "github": "https://github.com/lee-ny/teaching_arithmetic", "project_page": "", "abstract": "Large language models like GPT-4 exhibit emergent capabilities across general-purpose tasks, such as basic arithmetic, when trained on extensive text data, even though these tasks are not explicitly encoded by the unsupervised, next-token prediction objective. This study investigates how small transformers, trained from random initialization, can efficiently learn arithmetic operations such as addition, multiplication, and elementary functions like square root, using the next-token prediction objective. We first demonstrate that conventional training data is not the most effective for arithmetic learning, and simple formatting changes can significantly improve accuracy. This leads to sharp phase transitions as a function of training data scale, which, in some cases, can be explained through connections to low-rank matrix completion. Building on prior work, we then train on chain-of-thought style data that includes intermediate step results. Even in the complete absence of pretraining, this approach significantly and simultaneously improves accuracy, sample complexity, and convergence speed. We also study the interplay between arithmetic and text data during training and examine the effects of few-shot prompting, pretraining, and model scale. Additionally, we discuss length generalization challenges. Our work highlights the importance of high-quality, instructive data that considers the particular characteristics of the next-word prediction objective for rapidly eliciting arithmetic capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lee-ny/teaching_arithmetic", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-10", "arxiv_id": "2307.03601", "title": "GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest", "authors": [ "Shilong Zhang", "Peize Sun", "Shoufa Chen", "Min Xiao", "Wenqi Shao", "Wenwei Zhang", "Kai Chen", "Ping Luo" ], "github": "https://github.com/jshilong/GPT4RoI", "project_page": "", "abstract": "Instruction tuning large language model (LLM) on image-text pairs has achieved unprecedented vision-language multimodal abilities. However, their vision-language alignments are only built on image-level, the lack of region-level alignment limits their advancements to fine-grained multimodal understanding. In this paper, we propose instruction tuning on region-of-interest. The key design is to reformulate the bounding box as the format of spatial instruction. The interleaved sequences of visual features extracted by the spatial instruction and the language embedding are input to LLM, and trained on the transformed region-text data in instruction tuning format. Our region-level vision-language model, termed as GPT4RoI, brings brand new conversational and interactive experience beyond image-level understanding. (1) Controllability: Users can interact with our model by both language and spatial instructions to flexibly adjust the detail level of the question. (2) Capacities: Our model supports not only single-region spatial instruction but also multi-region. This unlocks more region-level multimodal capacities such as detailed region caption and complex region reasoning. (3) Composition: Any off-the-shelf object detector can be a spatial instruction provider so as to mine informative object attributes from our model, like color, shape, material, action, relation to other objects, etc. The code, data, and demo can be found at https://github.com/jshilong/GPT4RoI.", "github_manual": "", "github_regex": "https://github.com/jshilong/GPT4RoI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-10", "arxiv_id": "2307.03322", "title": "BiPhone: Modeling Inter Language Phonetic Influences in Text", "authors": [ "Abhirut Gupta", "Ananya B. Sai", "Richard Sproat", "Yuri Vasilevski", "James S. Ren", "Ambarish Jash", "Sukhdeep S. Sodhi", "Aravindan Raghuveer" ], "github": "", "project_page": "", "abstract": "A large number of people are forced to use the Web in a language they have low literacy in due to technology asymmetries. Written text in the second language (L2) from such users often contains a large number of errors that are influenced by their native language (L1). We propose a method to mine phoneme confusions (sounds in L2 that an L1 speaker is likely to conflate) for pairs of L1 and L2. These confusions are then plugged into a generative model (Bi-Phone) for synthetically producing corrupted L2 text. Through human evaluations, we show that Bi-Phone generates plausible corruptions that differ across L1s and also have widespread coverage on the Web. We also corrupt the popular language understanding benchmark SuperGLUE with our technique (FunGLUE for Phonetically Noised GLUE) and show that SoTA language understating models perform poorly. We also introduce a new phoneme prediction pre-training task which helps byte models to recover performance close to SuperGLUE. Finally, we also release the FunGLUE benchmark to promote further research in phonetically robust language models. To the best of our knowledge, FunGLUE is the first benchmark to introduce L1-L2 interactions in text.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-10", "arxiv_id": "2307.03659", "title": "Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation", "authors": [ "Annie Xie", "Lisa Lee", "Ted Xiao", "Chelsea Finn" ], "github": "", "project_page": "", "abstract": "What makes generalization hard for imitation learning in visual robotic manipulation? This question is difficult to approach at face value, but the environment from the perspective of a robot can often be decomposed into enumerable factors of variation, such as the lighting conditions or the placement of the camera. Empirically, generalization to some of these factors have presented a greater obstacle than others, but existing work sheds little light on precisely how much each factor contributes to the generalization gap. Towards an answer to this question, we study imitation learning policies in simulation and on a real robot language-conditioned manipulation task to quantify the difficulty of generalization to different (sets of) factors. We also design a new simulated benchmark of 19 tasks with 11 factors of variation to facilitate more controlled evaluations of generalization. From our study, we determine an ordering of factors based on generalization difficulty, that is consistent across simulation and our real robot setup.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-10", "arxiv_id": "2307.03576", "title": "One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention", "authors": [ "Arvind Mahankali", "Tatsunori B. Hashimoto", "Tengyu Ma" ], "github": "", "project_page": "", "abstract": "Recent works have empirically analyzed in-context learning and shown that transformers trained on synthetic linear regression tasks can learn to implement ridge regression, which is the Bayes-optimal predictor, given sufficient capacity [Aky\\\"urek et al., 2023], while one-layer transformers with linear self-attention and no MLP layer will learn to implement one step of gradient descent (GD) on a least-squares linear regression objective [von Oswald et al., 2022]. However, the theory behind these observations remains poorly understood. We theoretically study transformers with a single layer of linear self-attention, trained on synthetic noisy linear regression data. First, we mathematically show that when the covariates are drawn from a standard Gaussian distribution, the one-layer transformer which minimizes the pre-training loss will implement a single step of GD on the least-squares linear regression objective. Then, we find that changing the distribution of the covariates and weight vector to a non-isotropic Gaussian distribution has a strong impact on the learned algorithm: the global minimizer of the pre-training loss now implements a single step of pre-conditioned GD. However, if only the distribution of the responses is changed, then this does not have a large effect on the learned algorithm: even when the response comes from a more general family of nonlinear functions, the global minimizer of the pre-training loss still implements a single step of GD on a least-squares linear regression objective.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-10", "arxiv_id": "2307.03718", "title": "Frontier AI Regulation: Managing Emerging Risks to Public Safety", "authors": [ "Markus Anderljung", "Joslyn Barnhart", "Jade Leung", "Anton Korinek", "Cullen O'Keefe", "Jess Whittlestone", "Shahar Avin", "Miles Brundage", "Justin Bullock", "Duncan Cass-Beggs", "Ben Chang", "Tantum Collins", "Tim Fist", "Gillian Hadfield", "Alan Hayes", "Lewis Ho", "Sara Hooker", "Eric Horvitz", "Noam Kolt", "Jonas Schuett", "Yonadav Shavit", "Divya Siddarth", "Robert Trager", "Kevin Wolf" ], "github": "", "project_page": "", "abstract": "Advanced AI models hold the promise of tremendous benefits for humanity, but society needs to proactively manage the accompanying risks. In this paper, we focus on what we term \"frontier AI\" models: highly capable foundation models that could possess dangerous capabilities sufficient to pose severe risks to public safety. Frontier AI models pose a distinct regulatory challenge: dangerous capabilities can arise unexpectedly; it is difficult to robustly prevent a deployed model from being misused; and, it is difficult to stop a model's capabilities from proliferating broadly. To address these challenges, at least three building blocks for the regulation of frontier models are needed: (1) standard-setting processes to identify appropriate requirements for frontier AI developers, (2) registration and reporting requirements to provide regulators with visibility into frontier AI development processes, and (3) mechanisms to ensure compliance with safety standards for the development and deployment of frontier AI models. Industry self-regulation is an important first step. However, wider societal discussions and government intervention will be needed to create standards and to ensure compliance with them. We consider several options to this end, including granting enforcement powers to supervisory authorities and licensure regimes for frontier AI models. Finally, we propose an initial set of safety standards. These include conducting pre-deployment risk assessments; external scrutiny of model behavior; using risk assessments to inform deployment decisions; and monitoring and responding to new information about model capabilities and uses post-deployment. We hope this discussion contributes to the broader conversation on how to balance public safety risks and innovation benefits from advances at the frontier of AI development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.04725", "title": "AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning", "authors": [ "Yuwei Guo", "Ceyuan Yang", "Anyi Rao", "Yaohui Wang", "Yu Qiao", "Dahua Lin", "Bo Dai" ], "github": "https://github.com/guoyww/AnimateDiff", "project_page": "", "abstract": "With the advance of text-to-image models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. Subsequently, there is a great demand for image animation techniques to further combine generated static images with motion dynamics. In this report, we propose a practical framework to animate most of the existing personalized text-to-image models once and for all, saving efforts in model-specific tuning. At the core of the proposed framework is to insert a newly initialized motion modeling module into the frozen text-to-image model and train it on video clips to distill reasonable motion priors. Once trained, by simply injecting this motion modeling module, all personalized versions derived from the same base T2I readily become text-driven models that produce diverse and personalized animated images. We conduct our evaluation on several public representative personalized text-to-image models across anime pictures and realistic photographs, and demonstrate that our proposed framework helps these models generate temporally smooth animation clips while preserving the domain and diversity of their outputs. Code and pre-trained weights will be publicly available at https://animatediff.github.io/ .", "github_manual": "https://github.com/guoyww/AnimateDiff", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.03869", "title": "Sketch-A-Shape: Zero-Shot Sketch-to-3D Shape Generation", "authors": [ "Aditya Sanghi", "Pradeep Kumar Jayaraman", "Arianna Rampini", "Joseph Lambourne", "Hooman Shayani", "Evan Atherton", "Saeid Asgari Taghanaki" ], "github": "", "project_page": "", "abstract": "Significant progress has recently been made in creative applications of large pre-trained models for downstream tasks in 3D vision, such as text-to-shape generation. This motivates our investigation of how these pre-trained models can be used effectively to generate 3D shapes from sketches, which has largely remained an open challenge due to the limited sketch-shape paired datasets and the varying level of abstraction in the sketches. We discover that conditioning a 3D generative model on the features (obtained from a frozen large pre-trained vision model) of synthetic renderings during training enables us to effectively generate 3D shapes from sketches at inference time. This suggests that the large pre-trained vision model features carry semantic signals that are resilient to domain shifts, i.e., allowing us to use only RGB renderings, but generalizing to sketches at inference time. We conduct a comprehensive set of experiments investigating different design factors and demonstrate the effectiveness of our straightforward approach for generation of multiple 3D shapes per each input sketch regardless of their level of abstraction without requiring any paired datasets during training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.04686", "title": "VampNet: Music Generation via Masked Acoustic Token Modeling", "authors": [ "Hugo Flores Garcia", "Prem Seetharaman", "Rithesh Kumar", "Bryan Pardo" ], "github": "https://github.com/hugofloresgarcia/vampnet", "project_page": "", "abstract": "We introduce VampNet, a masked acoustic token modeling approach to music synthesis, compression, inpainting, and variation. We use a variable masking schedule during training which allows us to sample coherent music from the model by applying a variety of masking approaches (called prompts) during inference. VampNet is non-autoregressive, leveraging a bidirectional transformer architecture that attends to all tokens in a forward pass. With just 36 sampling passes, VampNet can generate coherent high-fidelity musical waveforms. We show that by prompting VampNet in various ways, we can apply it to tasks like music compression, inpainting, outpainting, continuation, and looping with variation (vamping). Appropriately prompted, VampNet is capable of maintaining style, genre, instrumentation, and other high-level aspects of the music. This flexible prompting capability makes VampNet a powerful music co-creation tool. Code and audio samples are available online.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hugofloresgarcia/vampnet", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.04767", "title": "Semantic-SAM: Segment and Recognize Anything at Any Granularity", "authors": [ "Feng Li", "Hao Zhang", "Peize Sun", "Xueyan Zou", "Shilong Liu", "Jianwei Yang", "Chunyuan Li", "Lei Zhang", "Jianfeng Gao" ], "github": "https://github.com/ux-decoder/semantic-sam", "project_page": "", "abstract": "In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Our model offers two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, we consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts. This allows our model to capture rich semantic information. For the multi-granularity capability, we propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels that correspond to multiple ground-truth masks. Notably, this work represents the first attempt to jointly train a model on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate that our model successfully achieves semantic-awareness and granularity-abundance. Furthermore, combining SA-1B training with other segmentation tasks, such as panoptic and part segmentation, leads to performance improvements. We will provide code and a demo for further exploration and evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ux-decoder/semantic-sam", "github_api_added_by": "auto" }, { "date": "2023-07-11", "arxiv_id": "2307.04721", "title": "Large Language Models as General Pattern Machines", "authors": [ "Suvir Mirchandani", "Fei Xia", "Pete Florence", "Brian Ichter", "Danny Driess", "Montserrat Gonzalez Arenas", "Kanishka Rao", "Dorsa Sadigh", "Andy Zeng" ], "github": "", "project_page": "", "abstract": "We observe that pre-trained large language models (LLMs) are capable of autoregressively completing complex token sequences -- from arbitrary ones procedurally generated by probabilistic context-free grammars (PCFG), to more rich spatial patterns found in the Abstract Reasoning Corpus (ARC), a general AI benchmark, prompted in the style of ASCII art. Surprisingly, pattern completion proficiency can be partially retained even when the sequences are expressed using tokens randomly sampled from the vocabulary. These results suggest that without any additional training, LLMs can serve as general sequence modelers, driven by in-context learning. In this work, we investigate how these zero-shot capabilities may be applied to problems in robotics -- from extrapolating sequences of numbers that represent states over time to complete simple motions, to least-to-most prompting of reward-conditioned trajectories that can discover and represent closed-loop policies (e.g., a stabilizing controller for CartPole). While difficult to deploy today for real systems due to latency, context size limitations, and compute costs, the approach of using LLMs to drive low-level control may provide an exciting glimpse into how the patterns among words could be transferred to actions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.03875", "title": "Large Language Models for Supply Chain Optimization", "authors": [ "Beibin Li", "Konstantina Mellou", "Bo Zhang", "Jeevan Pathuri", "Ishai Menache" ], "github": "", "project_page": "", "abstract": "Supply chain operations traditionally involve a variety of complex decision making problems. Over the last few decades, supply chains greatly benefited from advances in computation, which allowed the transition from manual processing to automation and cost-effective optimization. Nonetheless, business operators still need to spend substantial efforts in explaining and interpreting the optimization outcomes to stakeholders. Motivated by the recent advances in Large Language Models (LLMs), we study how this disruptive technology can help bridge the gap between supply chain automation and human comprehension and trust thereof. We design -- a framework that accepts as input queries in plain text, and outputs insights about the underlying optimization outcomes. Our framework does not forgo the state-of-the-art combinatorial optimization technology, but rather leverages it to quantitatively answer what-if scenarios (e.g., how would the cost change if we used supplier B instead of supplier A for a given demand?). Importantly, our design does not require sending proprietary data over to LLMs, which can be a privacy concern in some circumstances. We demonstrate the effectiveness of our framework on a real server placement scenario within Microsoft's cloud supply chain. Along the way, we develop a general evaluation benchmark, which can be used to evaluate the accuracy of the LLM output in other scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.03917", "title": "On decoder-only architecture for speech-to-text and large language model integration", "authors": [ "Jian Wu", "Yashesh Gaur", "Zhuo Chen", "Long Zhou", "Yimeng Zhu", "Tianrui Wang", "Jinyu Li", "Shujie Liu", "Bo Ren", "Linquan Liu", "Yu Wu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language. However, the seamless integration of speech signals into LLMs has not been explored well. The \"decoder-only\" architecture has also not been well studied for speech processing tasks. In this research, we introduce Speech-LLaMA, a novel approach that effectively incorporates acoustic information into text-based large language models. Our method leverages Connectionist Temporal Classification and a simple audio encoder to map the compressed acoustic features to the continuous semantic space of the LLM. In addition, we further probe the decoder-only architecture for speech-to-text tasks by training a smaller scale randomly initialized speech-LLaMA model from speech-text paired data alone. We conduct experiments on multilingual speech-to-text translation tasks and demonstrate a significant improvement over strong baselines, highlighting the potential advantages of decoder-only models for speech-to-text conversion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.04699", "title": "International Institutions for Advanced AI", "authors": [ "Lewis Ho", "Joslyn Barnhart", "Robert Trager", "Yoshua Bengio", "Miles Brundage", "Allison Carnegie", "Rumman Chowdhury", "Allan Dafoe", "Gillian Hadfield", "Margaret Levi", "Duncan Snidal" ], "github": "", "project_page": "", "abstract": "International institutions may have an important role to play in ensuring advanced AI systems benefit humanity. International collaborations can unlock AI's ability to further sustainable development, and coordination of regulatory efforts can reduce obstacles to innovation and the spread of benefits. Conversely, the potential dangerous capabilities of powerful and general-purpose AI systems create global externalities in their development and deployment, and international efforts to further responsible AI practices could help manage the risks they pose. This paper identifies a set of governance functions that could be performed at an international level to address these challenges, ranging from supporting access to frontier AI systems to setting international safety standards. It groups these functions into four institutional models that exhibit internal synergies and have precedents in existing organizations: 1) a Commission on Frontier AI that facilitates expert consensus on opportunities and risks from advanced AI, 2) an Advanced AI Governance Organization that sets international standards to manage global threats from advanced models, supports their implementation, and possibly monitors compliance with a future governance regime, 3) a Frontier AI Collaborative that promotes access to cutting-edge AI, and 4) an AI Safety Project that brings together leading researchers and engineers to further AI safety research. We explore the utility of these models and identify open questions about their viability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.04603", "title": "Solvent: A Framework for Protein Folding", "authors": [ "Jaemyung Lee", "Jaehoon Kim", "Hasun Yu", "Youhan Lee" ], "github": "https://github.com/kakaobrain/solvent", "project_page": "", "abstract": "Consistency and reliability are crucial for conducting AI research. Many famous research fields, such as object detection, have been compared and validated with solid benchmark frameworks. After AlphaFold2, the protein folding task has entered a new phase, and many methods are proposed based on the component of AlphaFold2. The importance of a unified research framework in protein folding contains implementations and benchmarks to consistently and fairly compare various approaches. To achieve this, we present Solvent, an protein folding framework that supports significant components of state-of-th-arts models in the manner of off-the-shelf interface Solvent contains different models implemented in a unified codebase and supports training and evaluation for defined models on the same dataset. We benchmark well-known algorithms and their components and provide experiments that give helpful insights into the protein structure modeling field. We hope that Solvent will increase the reliability and consistency of proposed models and gives efficiency in both speed and costs, resulting in acceleration on protein folding modeling research. The code is available at https://github.com/kakaobrain/solvent, and the project will continue to be developed.", "github_manual": "", "github_regex": "https://github.com/kakaobrain/solvent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.04349", "title": "RLTF: Reinforcement Learning from Unit Test Feedback", "authors": [ "Jiate Liu", "Yiqin Zhu", "Kaiwen Xiao", "Qiang Fu", "Xiao Han", "Wei Yang", "Deheng Ye" ], "github": "https://github.com/Zyq-scut/RLTF", "project_page": "", "abstract": "The goal of program synthesis, or code generation, is to generate executable code based on given descriptions. Recently, there has been an increasing number of studies employing reinforcement learning (RL) to improve the performance of large language models (LLMs) for code. However, these RL methods have only used offline frameworks, limiting their exploration of new sample spaces. Additionally, current approaches that utilize unit test signals are rather simple, not accounting for specific error locations within the code. To address these issues, we proposed RLTF, i.e., Reinforcement Learning from Unit Test Feedback, a novel online RL framework with unit test feedback of multi-granularity for refining code LLMs. Our approach generates data in real-time during training and simultaneously utilizes fine-grained feedback signals to guide the model towards producing higher-quality code. Extensive experiments show that RLTF achieves state-of-the-art performance on the APPS and the MBPP benchmarks. Our code can be found at: https://github.com/Zyq-scut/RLTF.", "github_manual": "", "github_regex": "https://github.com/Zyq-scut/RLTF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.04087", "title": "SVIT: Scaling up Visual Instruction Tuning", "authors": [ "Bo Zhao", "Boya Wu", "Tiejun Huang" ], "github": "https://github.com/baai-dcai/visual-instruction-tuning", "project_page": "", "abstract": "Thanks to the emerging of foundation models, the large language and vision models are integrated to acquire the multimodal ability of visual captioning, dialogue, question answering, etc. Although existing multimodal models present impressive performance of visual understanding and reasoning, their limits are still largely under-explored due to the scarcity of high-quality instruction tuning data. To push the limits of multimodal capability, we Sale up Visual Instruction Tuning (SVIT) by constructing a dataset of 3.2 million visual instruction tuning data including 1.6M conversation question-answer (QA) pairs and 1.6M complex reasoning QA pairs and 106K detailed image descriptions. Besides the volume, the proposed dataset is also featured by the high quality and rich diversity, which is generated by prompting GPT-4 with the abundant manual annotations of images. We empirically verify that training multimodal models on SVIT can significantly improve the multimodal performance in terms of visual perception, reasoning and planing.", "github_manual": "https://github.com/baai-dcai/visual-instruction-tuning", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.04751", "title": "Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement", "authors": [ "Anthony Simeonov", "Ankit Goyal", "Lucas Manuelli", "Lin Yen-Chen", "Alina Sarmiento", "Alberto Rodriguez", "Pulkit Agrawal", "Dieter Fox" ], "github": "", "project_page": "", "abstract": "We propose a system for rearranging objects in a scene to achieve a desired object-scene placing relationship, such as a book inserted in an open slot of a bookshelf. The pipeline generalizes to novel geometries, poses, and layouts of both scenes and objects, and is trained from demonstrations to operate directly on 3D point clouds. Our system overcomes challenges associated with the existence of many geometrically-similar rearrangement solutions for a given scene. By leveraging an iterative pose de-noising training procedure, we can fit multi-modal demonstration data and produce multi-modal outputs while remaining precise and accurate. We also show the advantages of conditioning on relevant local geometric features while ignoring irrelevant global structure that harms both generalization and precision. We demonstrate our approach on three distinct rearrangement tasks that require handling multi-modality and generalization over object shape and pose in both simulation and the real world. Project website, code, and videos: https://anthonysimeonov.github.io/rpdiff-multi-modal/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.04008", "title": "Toward Interactive Dictation", "authors": [ "Belinda Z. Li", "Jason Eisner", "Adam Pauls", "Sam Thomson" ], "github": "", "project_page": "", "abstract": "Voice dictation is an increasingly important text input modality. Existing systems that allow both dictation and editing-by-voice restrict their command language to flat templates invoked by trigger words. In this work, we study the feasibility of allowing users to interrupt their dictation with spoken editing commands in open-ended natural language. We introduce a new task and dataset, TERTiUS, to experiment with such systems. To support this flexibility in real-time, a system must incrementally segment and classify spans of speech as either dictation or command, and interpret the spans that are commands. We experiment with using large pre-trained language models to predict the edited text, or alternatively, to predict a small text-editing program. Experiments show a natural trade-off between model accuracy and latency: a smaller model achieves 30% end-state accuracy with 1.3 seconds of latency, while a larger model achieves 55% end-state accuracy with 7 seconds of latency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-11", "arxiv_id": "2307.04577", "title": "AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System", "authors": [ "Yuzhe Qin", "Wei Yang", "Binghao Huang", "Karl Van Wyk", "Hao Su", "Xiaolong Wang", "Yu-Wei Chao", "Dietor Fox" ], "github": "", "project_page": "", "abstract": "Vision-based teleoperation offers the possibility to endow robots with human-level intelligence to physically interact with the environment, while only requiring low-cost camera sensors. However, current vision-based teleoperation systems are designed and engineered towards a particular robot model and deploy environment, which scales poorly as the pool of the robot models expands and the variety of the operating environment increases. In this paper, we propose AnyTeleop, a unified and general teleoperation system to support multiple different arms, hands, realities, and camera configurations within a single system. Although being designed to provide great flexibility to the choice of simulators and real hardware, our system can still achieve great performance. For real-world experiments, AnyTeleop can outperform a previous system that was designed for a specific robot hardware with a higher success rate, using the same robot. For teleoperation in simulation, AnyTeleop leads to better imitation learning performance, compared with a previous system that is particularly designed for that simulator. Project page: http://anyteleop.com/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-12", "arxiv_id": "2307.04787", "title": "Collaborative Score Distillation for Consistent Visual Synthesis", "authors": [ "Subin Kim", "Kyungmin Lee", "June Suk Choi", "Jongheon Jeong", "Kihyuk Sohn", "Jinwoo Shin" ], "github": "", "project_page": "", "abstract": "Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities. However, when adapting these priors to complex visual modalities, often represented as multiple images (e.g., video), achieving consistency across a set of images is challenging. In this paper, we address this challenge with a novel method, Collaborative Score Distillation (CSD). CSD is based on the Stein Variational Gradient Descent (SVGD). Specifically, we propose to consider multiple samples as \"particles\" in the SVGD update and combine their score functions to distill generative priors over a set of images synchronously. Thus, CSD facilitates seamless integration of information across 2D images, leading to a consistent visual synthesis across multiple samples. We show the effectiveness of CSD in a variety of tasks, encompassing the visual editing of panorama images, videos, and 3D scenes. Our results underline the competency of CSD as a versatile method for enhancing inter-sample consistency, thereby broadening the applicability of text-to-image diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-12", "arxiv_id": "2307.04964", "title": "Secrets of RLHF in Large Language Models Part I: PPO", "authors": [ "Rui Zheng", "Shihan Dou", "Songyang Gao", "Wei Shen", "Binghai Wang", "Yan Liu", "Senjie Jin", "Qin Liu", "Limao Xiong", "Lu Chen", "Zhiheng Xi", "Yuhao Zhou", "Nuo Xu", "Wenbin Lai", "Minghao Zhu", "Rongxiang Weng", "Wensen Cheng", "Cheng Chang", "Zhangyue Yin", "Yuan Hua", "Haoran Huang", "Tianxiang Sun", "Hang Yan", "Tao Gui", "Qi Zhang", "Xipeng Qiu", "Xuanjing Huang" ], "github": "https://github.com/openlmlab/moss-rlhf", "project_page": "", "abstract": "Large language models (LLMs) have formulated a blueprint for the advancement of artificial general intelligence. Its primary objective is to function as a human-centric (helpful, honest, and harmless) assistant. Alignment with humans assumes paramount significance, and reinforcement learning with human feedback (RLHF) emerges as the pivotal technological paradigm underpinning this pursuit. Current technical routes usually include reward models to measure human preferences, Proximal Policy Optimization (PPO) to optimize policy model outputs, and process supervision to improve step-by-step reasoning capabilities. However, due to the challenges of reward design, environment interaction, and agent training, coupled with huge trial and error cost of large language models, there is a significant barrier for AI researchers to motivate the development of technical alignment and safe landing of LLMs. The stable training of RLHF has still been a puzzle. In the first report, we dissect the framework of RLHF, re-evaluate the inner workings of PPO, and explore how the parts comprising PPO algorithms impact policy agent training. We identify policy constraints being the key factor for the effective implementation of the PPO algorithm. Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model. Based on our main results, we perform a comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT. The absence of open-source implementations has posed significant challenges to the investigation of LLMs alignment. Therefore, we are eager to release technical reports, reward models and PPO codes", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/openlmlab/moss-rlhf", "github_api_added_by": "auto" }, { "date": "2023-07-12", "arxiv_id": "2307.05222", "title": "Generative Pretraining in Multimodality", "authors": [ "Quan Sun", "Qiying Yu", "Yufeng Cui", "Fan Zhang", "Xiaosong Zhang", "Yueze Wang", "Hongcheng Gao", "Jingjing Liu", "Tiejun Huang", "Xinlong Wang" ], "github": "", "project_page": "", "abstract": "We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context. This omnivore model can take in any single-modality or multimodal data input indiscriminately (e.g., interleaved image, text and video) through a one-model-for-all autoregressive training process. First, visual signals are encoded into embeddings, and together with text tokens form an interleaved input sequence. Emu is then end-to-end trained with a unified objective of classifying the next text token or regressing the next visual embedding in the multimodal sequence. This versatile multimodality empowers the exploration of diverse pretraining data sources at scale, such as videos with interleaved frames and text, webpages with interleaved images and text, as well as web-scale image-text pairs and video-text pairs. Emu can serve as a generalist multimodal interface for both image-to-text and text-to-image tasks, and supports in-context image and text generation. Across a broad range of zero-shot/few-shot tasks including image captioning, visual question answering, video question answering and text-to-image generation, Emu demonstrates superb performance compared to state-of-the-art large multimodal models. Extended capabilities such as multimodal assistants via instruction tuning are also demonstrated with impressive performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-12", "arxiv_id": "2307.05445", "title": "AutoDecoding Latent 3D Diffusion Models", "authors": [ "Evangelos Ntavelis", "Aliaksandr Siarohin", "Kyle Olszewski", "Chaoyang Wang", "Luc Van Gool", "Sergey Tulyakov" ], "github": "https://github.com/snap-research/3dvader", "project_page": "", "abstract": "We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core. The 3D autodecoder framework embeds properties learned from the target dataset in the latent space, which can then be decoded into a volumetric representation for rendering view-consistent appearance and geometry. We then identify the appropriate intermediate volumetric latent space, and introduce robust normalization and de-normalization operations to learn a 3D diffusion from 2D images or monocular videos of rigid or articulated objects. Our approach is flexible enough to use either existing camera supervision or no camera information at all -- instead efficiently learning it during training. Our evaluations demonstrate that our generation results outperform state-of-the-art alternatives on various benchmark datasets and metrics, including multi-view image datasets of synthetic objects, real in-the-wild videos of moving people, and a large-scale, real video dataset of static objects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/snap-research/3dvader", "github_api_added_by": "auto" }, { "date": "2023-07-12", "arxiv_id": "2307.05300", "title": "Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration", "authors": [ "Zhenhailong Wang", "Shaoguang Mao", "Wenshan Wu", "Tao Ge", "Furu Wei", "Heng Ji" ], "github": "https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git", "project_page": "", "abstract": "Human intelligence thrives on the concept of cognitive synergy, where collaboration and information integration among different cognitive processes yield superior outcomes compared to individual cognitive processes in isolation. Although Large Language Models (LLMs) have demonstrated promising performance as general task-solving agents, they still struggle with tasks that require intensive domain knowledge and complex reasoning. In this work, we propose Solo Performance Prompting (SPP), which transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas. A cognitive synergist refers to an intelligent agent that collaborates with multiple minds, combining their individual strengths and knowledge, to enhance problem-solving and overall performance in complex tasks. By dynamically identifying and simulating different personas based on task inputs, SPP unleashes the potential of cognitive synergy in LLMs. We have discovered that assigning multiple, fine-grained personas in LLMs elicits better problem-solving abilities compared to using a single or fixed number of personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing, Codenames Collaborative, and Logic Grid Puzzle, encompassing both knowledge-intensive and reasoning-intensive types. Unlike previous works, such as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs, SPP effectively elicits internal knowledge acquisition abilities, reduces hallucination, and maintains strong reasoning capabilities. Code, data, and prompts can be found at: https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git.", "github_manual": "", "github_regex": "https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-12", "arxiv_id": "2307.05473", "title": "Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives", "authors": [ "Tom Monnier", "Jake Austin", "Angjoo Kanazawa", "Alexei A. Efros", "Mathieu Aubry" ], "github": "", "project_page": "", "abstract": "Given a set of calibrated images of a scene, we present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. While many approaches focus on recovering high-fidelity 3D scenes, we focus on parsing a scene into mid-level 3D representations made of a small set of textured primitives. Such representations are interpretable, easy to manipulate and suited for physics-based simulations. Moreover, unlike existing primitive decomposition methods that rely on 3D input data, our approach operates directly on images through differentiable rendering. Specifically, we model primitives as textured superquadric meshes and optimize their parameters from scratch with an image rendering loss. We highlight the importance of modeling transparency for each primitive, which is critical for optimization and also enables handling varying numbers of primitives. We show that the resulting textured primitives faithfully reconstruct the input images and accurately model the visible 3D points, while providing amodal shape completions of unseen object regions. We compare our approach to the state of the art on diverse scenes from DTU, and demonstrate its robustness on real-life captures from BlendedMVS and Nerfstudio. We also showcase how our results can be used to effortlessly edit a scene or perform physical simulations. Code and video results are available at https://www.tmonnier.com/DBW .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-12", "arxiv_id": "2307.05432", "title": "Self-Supervised Learning with Lie Symmetries for Partial Differential Equations", "authors": [ "Gr\u00e9goire Mialon", "Quentin Garrido", "Hannah Lawrence", "Danyal Rehman", "Yann LeCun", "Bobak T. Kiani" ], "github": "https://github.com/facebookresearch/sslforpdes", "project_page": "", "abstract": "Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering. Though current algorithms typically require simulated training data tailored to a given setting, one may instead wish to learn useful information from heterogeneous sources, or from real dynamical systems observations that are messy or incomplete. In this work, we learn general-purpose representations of PDEs from heterogeneous data by implementing joint embedding methods for self-supervised learning (SSL), a framework for unsupervised representation learning that has had notable success in computer vision. Our representation outperforms baseline approaches to invariant tasks, such as regressing the coefficients of a PDE, while also improving the time-stepping performance of neural solvers. We hope that our proposed methodology will prove useful in the eventual development of general-purpose foundation models for PDEs.", "github_manual": "https://github.com/facebookresearch/sslforpdes", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-12", "arxiv_id": "2307.05463", "title": "EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone", "authors": [ "Shraman Pramanick", "Yale Song", "Sayan Nag", "Kevin Qinghong Lin", "Hardik Shah", "Mike Zheng Shou", "Rama Chellappa", "Pengchuan Zhang" ], "github": "https://github.com/facebookresearch/EgoVLPv2", "project_page": "", "abstract": "Video-language pre-training (VLP) has become increasingly important due to its ability to generalize to various vision and language tasks. However, existing egocentric VLP frameworks utilize separate video and language encoders and learn task-specific cross-modal information only during fine-tuning, limiting the development of a unified system. In this work, we introduce the second generation of egocentric video-language pre-training (EgoVLPv2), a significant improvement from the previous generation, by incorporating cross-modal fusion directly into the video and language backbones. EgoVLPv2 learns strong video-text representation during pre-training and reuses the cross-modal attention modules to support different downstream tasks in a flexible and efficient manner, reducing fine-tuning costs. Moreover, our proposed fusion in the backbone strategy is more lightweight and compute-efficient than stacking additional fusion-specific layers. Extensive experiments on a wide range of VL tasks demonstrate the effectiveness of EgoVLPv2 by achieving consistent state-of-the-art performance over strong baselines across all downstream. Our project page can be found at https://shramanpramanick.github.io/EgoVLPv2/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/EgoVLPv2", "github_api_added_by": "auto" }, { "date": "2023-07-12", "arxiv_id": "2307.05454", "title": "Empowering Cross-lingual Behavioral Testing of NLP Models with Typological Features", "authors": [ "Ester Hlavnova", "Sebastian Ruder" ], "github": "https://github.com/google-research/multi-morph-checklist", "project_page": "", "abstract": "A challenge towards developing NLP systems for the world's languages is understanding how they generalize to typological differences relevant for real-world applications. To this end, we propose M2C, a morphologically-aware framework for behavioral testing of NLP models. We use M2C to generate tests that probe models' behavior in light of specific linguistic features in 12 typologically diverse languages. We evaluate state-of-the-art language models on the generated tests. While models excel at most tests in English, we highlight generalization failures to specific typological characteristics such as temporal expressions in Swahili and compounding possessives in Finish. Our findings motivate the development of models that address these blind spots.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-research/multi-morph-checklist", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-12", "arxiv_id": "2307.05462", "title": "Efficient 3D Articulated Human Generation with Layered Surface Volumes", "authors": [ "Yinghao Xu", "Wang Yifan", "Alexander W. Bergman", "Menglei Chai", "Bolei Zhou", "Gordon Wetzstein" ], "github": "", "project_page": "", "abstract": "Access to high-quality and diverse 3D articulated digital human assets is crucial in various applications, ranging from virtual reality to social platforms. Generative approaches, such as 3D generative adversarial networks (GANs), are rapidly replacing laborious manual content creation tools. However, existing 3D GAN frameworks typically rely on scene representations that leverage either template meshes, which are fast but offer limited quality, or volumes, which offer high capacity but are slow to render, thereby limiting the 3D fidelity in GAN settings. In this work, we introduce layered surface volumes (LSVs) as a new 3D object representation for articulated digital humans. LSVs represent a human body using multiple textured mesh layers around a conventional template. These layers are rendered using alpha compositing with fast differentiable rasterization, and they can be interpreted as a volumetric representation that allocates its capacity to a manifold of finite thickness around the template. Unlike conventional single-layer templates that struggle with representing fine off-surface details like hair or accessories, our surface volumes naturally capture such details. LSVs can be articulated, and they exhibit exceptional efficiency in GAN settings, where a 2D generator learns to synthesize the RGBA textures for the individual layers. Trained on unstructured, single-view 2D image datasets, our LSV-GAN generates high-quality and view-consistent 3D articulated digital humans without the need for view-inconsistent 2D upsampling networks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-12", "arxiv_id": "2307.05014", "title": "Test-Time Training on Video Streams", "authors": [ "Renhao Wang", "Yu Sun", "Yossi Gandelsman", "Xinlei Chen", "Alexei A. Efros", "Xiaolong Wang" ], "github": "", "project_page": "", "abstract": "Prior work has established test-time training (TTT) as a general framework to further improve a trained model at test time. Before making a prediction on each test instance, the model is trained on the same instance using a self-supervised task, such as image reconstruction with masked autoencoders. We extend TTT to the streaming setting, where multiple test instances - video frames in our case - arrive in temporal order. Our extension is online TTT: The current model is initialized from the previous model, then trained on the current frame and a small window of frames immediately before. Online TTT significantly outperforms the fixed-model baseline for four tasks, on three real-world datasets. The relative improvement is 45% and 66% for instance and panoptic segmentation. Surprisingly, online TTT also outperforms its offline variant that accesses more information, training on all frames from the entire test video regardless of temporal order. This differs from previous findings using synthetic videos. We conceptualize locality as the advantage of online over offline TTT. We analyze the role of locality with ablations and a theory based on bias-variance trade-off.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-13", "arxiv_id": "2307.06018", "title": "PolyLM: An Open Source Polyglot Large Language Model", "authors": [ "Xiangpeng Wei", "Haoran Wei", "Huan Lin", "Tianhao Li", "Pei Zhang", "Xingzhang Ren", "Mei Li", "Yu Wan", "Zhiwei Cao", "Binbin Xie", "Tianxiang Hu", "Shangjie Li", "Binyuan Hui", "Bowen Yu", "Dayiheng Liu", "Baosong Yang", "Fei Huang", "Jun Xie" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions. However, the development of LLMs has been primarily focused on high-resource languages, such as English, thereby limiting their applicability and research in other languages. Consequently, we present PolyLM, a multilingual LLM trained on 640 billion (B) tokens, avaliable in two model sizes: 1.7B and 13B. To enhance its multilingual capabilities, we 1) integrate bilingual data into training data; and 2) adopt a curriculum learning strategy that increases the proportion of non-English data from 30% in the first stage to 60% in the final stage during pre-training. Further, we propose a multilingual self-instruct method which automatically generates 132.7K diverse multilingual instructions for model fine-tuning. To assess the model's performance, we collect several existing multilingual tasks, including multilingual understanding, question answering, generation, and translation. Extensive experiments show that PolyLM surpasses other open-source models such as LLaMA and BLOOM on multilingual tasks while maintaining comparable performance in English. Our models, alone with the instruction data and multilingual benchmark, are available at: https://modelscope.cn/models/damo/nlp_polylm_13b_text_generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-13", "arxiv_id": "2307.06304", "title": "Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution", "authors": [ "Mostafa Dehghani", "Basil Mustafa", "Josip Djolonga", "Jonathan Heek", "Matthias Minderer", "Mathilde Caron", "Andreas Steiner", "Joan Puigcerver", "Robert Geirhos", "Ibrahim Alabdulmohsin", "Avital Oliver", "Piotr Padlewski", "Alexey Gritsenko", "Mario Lu\u010di\u0107", "Neil Houlsby" ], "github": "", "project_page": "", "abstract": "The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution before processing them with computer vision models has not yet been successfully challenged. However, models such as the Vision Transformer (ViT) offer flexible sequence-based modeling, and hence varying input sequence lengths. We take advantage of this with NaViT (Native Resolution ViT) which uses sequence packing during training to process inputs of arbitrary resolutions and aspect ratios. Alongside flexible model usage, we demonstrate improved training efficiency for large-scale supervised and contrastive image-text pretraining. NaViT can be efficiently transferred to standard tasks such as image and video classification, object detection, and semantic segmentation and leads to improved results on robustness and fairness benchmarks. At inference time, the input resolution flexibility can be used to smoothly navigate the test-time cost-performance trade-off. We believe that NaViT marks a departure from the standard, CNN-designed, input and modelling pipeline used by most computer vision models, and represents a promising direction for ViTs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-13", "arxiv_id": "2307.05695", "title": "Stack More Layers Differently: High-Rank Training Through Low-Rank Updates", "authors": [ "Vladislav Lialin", "Namrata Shivagunde", "Sherin Muckatira", "Anna Rumshisky" ], "github": "https://github.com/guitaricet/peft_pretraining", "project_page": "", "abstract": "Despite the dominance and effectiveness of scaling, resulting in large networks with hundreds of billions of parameters, the necessity to train overparametrized models remains poorly understood, and alternative approaches do not necessarily make it cheaper to train high-performance models. In this paper, we explore low-rank training techniques as an alternative approach to training large neural networks. We introduce a novel method called ReLoRA, which utilizes low-rank updates to train high-rank networks. We apply ReLoRA to pre-training transformer language models with up to 350M parameters and demonstrate comparable performance to regular neural network training. Furthermore, we observe that the efficiency of ReLoRA increases with model size, making it a promising approach for training multi-billion-parameter networks efficiently. Our findings shed light on the potential of low-rank training techniques and their implications for scaling laws.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/guitaricet/peft_pretraining", "github_api_added_by": "auto" }, { "date": "2023-07-13", "arxiv_id": "2307.06135", "title": "SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Task Planning", "authors": [ "Krishan Rana", "Jesse Haviland", "Sourav Garg", "Jad Abou-Chakra", "Ian Reid", "Niko Suenderhauf" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated impressive results in developing generalist planning agents for diverse tasks. However, grounding these plans in expansive, multi-floor, and multi-room environments presents a significant challenge for robotics. We introduce SayPlan, a scalable approach to LLM-based, large-scale task planning for robotics using 3D scene graph (3DSG) representations. To ensure the scalability of our approach, we: (1) exploit the hierarchical nature of 3DSGs to allow LLMs to conduct a semantic search for task-relevant subgraphs from a smaller, collapsed representation of the full graph; (2) reduce the planning horizon for the LLM by integrating a classical path planner and (3) introduce an iterative replanning pipeline that refines the initial plan using feedback from a scene graph simulator, correcting infeasible actions and avoiding planning failures. We evaluate our approach on two large-scale environments spanning up to 3 floors, 36 rooms and 140 objects, and show that our approach is capable of grounding large-scale, long-horizon task plans from abstract, and natural language instruction for a mobile manipulator robot to execute.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-13", "arxiv_id": "2307.05628", "title": "DNAGPT: A Generalized Pretrained Tool for Multiple DNA Sequence Analysis Tasks", "authors": [ "Daoan Zhang", "Weitong Zhang", "Bing He", "Jianguo Zhang", "Chenchen Qin", "Jianhua Yao" ], "github": "", "project_page": "", "abstract": "The success of the GPT series proves that GPT can extract general information from sequences, thereby benefiting all downstream tasks. This motivates us to use pre-trained models to explore the hidden information in DNA sequences. However, data and task requirements in DNA sequence analysis are complexity and diversity as DNA relevant data includes different types of information, such as sequences, expression levels, etc, while there is currently no model specifically designed for these characteristics. Hereby, we present DNAGPT, a generalized foundation model pre-trained on over 10 billion base pairs from 9 species which can be fine-tuned for any DNA sequence analysis task. Our model can simultaneously process or output DNA sequences and numbers. In addition, our unique token design allows users to design prompts according to their own task requirements, making it applicable to any type of task. We have evaluated our model on classification, regression, and generation tasks. We demonstrate that DNAGPT benefits from pre-training, and therefore can bring performance gains to any downstream task. Our model is not only a new attempt in the field of genomes analysis, but also provides a new direction for the application of foundation models in biology.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-13", "arxiv_id": "2307.06290", "title": "Instruction Mining: High-Quality Instruction Data Selection for Large Language Models", "authors": [ "Yihan Cao", "Yanbin Kang", "Lichao Sun" ], "github": "", "project_page": "", "abstract": "Large language models typically undergo two training stages, pretraining and finetuning. Despite that large-scale pretraining endows the model with strong capabilities to generate natural language responses, these pretrained models can still fail to understand human instructions at times. To enhance language models' ability of interpreting and responding to instructions, instruction finetuning has emerged as a critical method in this area. Recent studies found that large language models can be finetuned to perform well even with a small amount of high-quality instruction-following data. However, the selection of high-quality datasets for finetuning language models still lacks clear guidelines to follow. In this paper, we propose InstructMining, a linear rule for evaluating instruction-following data quality. We formulate InstructMining using specific natural language indicators. To investigate the relationship between data quality and these indicators, we further conduct extensive finetuning experiments. The experiment results are then applied to estimating parameters in InstructMining. To further investigate its performance, we use InstructMining to select high-quality data from unseen datasets. Results demonstrate that InstructMining can help select relatively high-quality samples from various instruction-following datasets. Compared to models finetuned on unfiltered datasets, models finetuned on InstructMining selected datasets perform better on 42.5% cases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-13", "arxiv_id": "2307.05591", "title": "SITTA: A Semantic Image-Text Alignment for Image Captioning", "authors": [ "Fabian Paischer", "Thomas Adler", "Markus Hofmarcher", "Sepp Hochreiter" ], "github": "https://github.com/ml-jku/semantic-image-text-alignment", "project_page": "", "abstract": "Textual and semantic comprehension of images is essential for generating proper captions. The comprehension requires detection of objects, modeling of relations between them, an assessment of the semantics of the scene and, finally, representing the extracted knowledge in a language space. To achieve rich language capabilities while ensuring good image-language mappings, pretrained language models (LMs) were conditioned on pretrained multi-modal (image-text) models that allow for image inputs. This requires an alignment of the image representation of the multi-modal model with the language representations of a generative LM. However, it is not clear how to best transfer semantics detected by the vision encoder of the multi-modal model to the LM. We introduce two novel ways of constructing a linear mapping that successfully transfers semantics between the embedding spaces of the two pretrained models. The first aligns the embedding space of the multi-modal language encoder with the embedding space of the pretrained LM via token correspondences. The latter leverages additional data that consists of image-text pairs to construct the mapping directly from vision to language space. Using our semantic mappings, we unlock image captioning for LMs without access to gradient information. By using different sources of data we achieve strong captioning performance on MS-COCO and Flickr30k datasets. Even in the face of limited data, our method partly exceeds the performance of other zero-shot and even finetuned competitors. Our ablation studies show that even LMs at a scale of merely 250M parameters can generate decent captions employing our semantic mappings. Our approach makes image captioning more accessible for institutions with restricted computational resources.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ml-jku/semantic-image-text-alignment", "github_api_added_by": "auto" }, { "date": "2023-07-13", "arxiv_id": "2307.05741", "title": "Towards Robust and Efficient Continual Language Learning", "authors": [ "Adam Fisch", "Amal Rannen-Triki", "Razvan Pascanu", "J\u00f6rg Bornschein", "Angeliki Lazaridou", "Elena Gribovskaya", "Marc'Aurelio Ranzato" ], "github": "", "project_page": "", "abstract": "As the application space of language models continues to evolve, a natural question to ask is how we can quickly adapt models to new tasks. We approach this classic question from a continual learning perspective, in which we aim to continue fine-tuning models trained on past tasks on new tasks, with the goal of \"transferring\" relevant knowledge. However, this strategy also runs the risk of doing more harm than good, i.e., negative transfer. In this paper, we construct a new benchmark of task sequences that target different possible transfer scenarios one might face, such as a sequence of tasks with high potential of positive transfer, high potential for negative transfer, no expected effect, or a mixture of each. An ideal learner should be able to maximally exploit information from all tasks that have any potential for positive transfer, while also avoiding the negative effects of any distracting tasks that may confuse it. We then propose a simple, yet effective, learner that satisfies many of our desiderata simply by leveraging a selective strategy for initializing new models from past task checkpoints. Still, limitations remain, and we hope this benchmark can help the community to further build and analyze such learners.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-13", "arxiv_id": "2307.05959", "title": "Giving Robots a Hand: Learning Generalizable Manipulation with Eye-in-Hand Human Video Demonstrations", "authors": [ "Moo Jin Kim", "Jiajun Wu", "Chelsea Finn" ], "github": "", "project_page": "", "abstract": "Eye-in-hand cameras have shown promise in enabling greater sample efficiency and generalization in vision-based robotic manipulation. However, for robotic imitation, it is still expensive to have a human teleoperator collect large amounts of expert demonstrations with a real robot. Videos of humans performing tasks, on the other hand, are much cheaper to collect since they eliminate the need for expertise in robotic teleoperation and can be quickly captured in a wide range of scenarios. Therefore, human video demonstrations are a promising data source for learning generalizable robotic manipulation policies at scale. In this work, we augment narrow robotic imitation datasets with broad unlabeled human video demonstrations to greatly enhance the generalization of eye-in-hand visuomotor policies. Although a clear visual domain gap exists between human and robot data, our framework does not need to employ any explicit domain adaptation method, as we leverage the partial observability of eye-in-hand cameras as well as a simple fixed image masking scheme. On a suite of eight real-world tasks involving both 3-DoF and 6-DoF robot arm control, our method improves the success rates of eye-in-hand manipulation policies by 58% (absolute) on average, enabling robots to generalize to both new environment configurations and new tasks that are unseen in the robot demonstration data. See video results at https://giving-robots-a-hand.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-13", "arxiv_id": "2307.05973", "title": "VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models", "authors": [ "Wenlong Huang", "Chen Wang", "Ruohan Zhang", "Yunzhu Li", "Jiajun Wu", "Li Fei-Fei" ], "github": "https://github.com/huangwl18/voxposer", "project_page": "", "abstract": "Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the progress, most still rely on pre-defined motion primitives to carry out the physical interactions with the environment, which remains a major bottleneck. In this work, we aim to synthesize robot trajectories, i.e., a dense sequence of 6-DoF end-effector waypoints, for a large variety of manipulation tasks given an open-set of instructions and an open-set of objects. We achieve this by first observing that LLMs excel at inferring affordances and constraints given a free-form language instruction. More importantly, by leveraging their code-writing capabilities, they can interact with a visual-language model (VLM) to compose 3D value maps to ground the knowledge into the observation space of the agent. The composed value maps are then used in a model-based planning framework to zero-shot synthesize closed-loop robot trajectories with robustness to dynamic perturbations. We further demonstrate how the proposed framework can benefit from online experiences by efficiently learning a dynamics model for scenes that involve contact-rich interactions. We present a large-scale study of the proposed method in both simulated and real-robot environments, showcasing the ability to perform a large variety of everyday manipulation tasks specified in free-form natural language. Project website: https://voxposer.github.io", "github_manual": "https://github.com/huangwl18/voxposer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-14", "arxiv_id": "2307.06949", "title": "HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models", "authors": [ "Nataniel Ruiz", "Yuanzhen Li", "Varun Jampani", "Wei Wei", "Tingbo Hou", "Yael Pritch", "Neal Wadhwa", "Michael Rubinstein", "Kfir Aberman" ], "github": "", "project_page": "", "abstract": "Personalization has emerged as a prominent aspect within the field of generative AI, enabling the synthesis of individuals in diverse contexts and styles, while retaining high-fidelity to their identities. However, the process of personalization presents inherent challenges in terms of time and memory requirements. Fine-tuning each personalized model needs considerable GPU time investment, and storing a personalized model per subject can be demanding in terms of storage capacity. To overcome these challenges, we propose HyperDreamBooth-a hypernetwork capable of efficiently generating a small set of personalized weights from a single image of a person. By composing these weights into the diffusion model, coupled with fast finetuning, HyperDreamBooth can generate a person's face in various contexts and styles, with high subject details while also preserving the model's crucial knowledge of diverse styles and semantic modifications. Our method achieves personalization on faces in roughly 20 seconds, 25x faster than DreamBooth and 125x faster than Textual Inversion, using as few as one reference image, with the same quality and style diversity as DreamBooth. Also our method yields a model that is 10000x smaller than a normal DreamBooth model. Project page: https://hyperdreambooth.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-14", "arxiv_id": "2307.06945", "title": "In-context Autoencoder for Context Compression in a Large Language Model", "authors": [ "Tao Ge", "Jing Hu", "Xun Wang", "Si-Qing Chen", "Furu Wei" ], "github": "https://github.com/getao/icae", "project_page": "", "abstract": "We propose the In-context Autoencoder (ICAE) for context compression in a large language model (LLM). The ICAE has two modules: a learnable encoder adapted with LoRA from an LLM for compressing a long context into a limited number of memory slots, and a fixed decoder which is the target LLM that can condition on the memory slots for various purposes. We first pretrain the ICAE using both autoencoding and language modeling objectives on massive text data, enabling it to generate memory slots that accurately and comprehensively represent the original context. Then, we fine-tune the pretrained ICAE on a small amount of instruct data to enhance its interaction with various prompts for producing desirable responses. Our experimental results demonstrate that the ICAE learned with our proposed pretraining and fine-tuning paradigm can effectively produce memory slots with 4times context compression, which can be well conditioned on by the target LLM to respond to various prompts. The promising results demonstrate significant implications of the ICAE for its novel approach to the long context problem and its potential to reduce computation and memory overheads for LLM inference in practice, suggesting further research effort in context management for an LLM. Our code and data will be released shortly.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/getao/icae", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-14", "arxiv_id": "2307.06942", "title": "InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation", "authors": [ "Yi Wang", "Yinan He", "Yizhuo Li", "Kunchang Li", "Jiashuo Yu", "Xin Ma", "Xinyuan Chen", "Yaohui Wang", "Ping Luo", "Ziwei Liu", "Yali Wang", "Limin Wang", "Yu Qiao" ], "github": "https://github.com/opengvlab/internvideo", "project_page": "", "abstract": "This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understanding and generation. The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words. Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby showcasing its efficacy in learning video-language representation at scale. Specifically, we utilize a multi-scale approach to generate video-related descriptions. Furthermore, we introduce ViCLIP, a video-text representation learning model based on ViT-L. Learned on InternVid via contrastive learning, this model demonstrates leading zero-shot action recognition and competitive video retrieval performance. Beyond basic video understanding tasks like recognition and retrieval, our dataset and model have broad applications. They are particularly beneficial for generating interleaved video-text data for learning a video-centric dialogue system, advancing video-to-text and text-to-video generation research. These proposed resources provide a tool for researchers and practitioners interested in multimodal video understanding and generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/opengvlab/internvideo", "github_api_added_by": "auto" }, { "date": "2023-07-14", "arxiv_id": "2307.06925", "title": "Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models", "authors": [ "Moab Arar", "Rinon Gal", "Yuval Atzmon", "Gal Chechik", "Daniel Cohen-Or", "Ariel Shamir", "Amit H. Bermano" ], "github": "", "project_page": "", "abstract": "Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts. Recently, encoder-based techniques have emerged as a new effective approach for T2I personalization, reducing the need for multiple images and long training times. However, most existing encoders are limited to a single-class domain, which hinders their ability to handle diverse concepts. In this work, we propose a domain-agnostic method that does not require any specialized dataset or prior information about the personalized concepts. We introduce a novel contrastive-based regularization technique to maintain high fidelity to the target concept characteristics while keeping the predicted embeddings close to editable regions of the latent space, by pushing the predicted tokens toward their nearest existing CLIP tokens. Our experimental results demonstrate the effectiveness of our approach and show how the learned tokens are more semantic than tokens predicted by unregularized models. This leads to a better representation that achieves state-of-the-art performance while being more flexible than previous methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-14", "arxiv_id": "2307.06439", "title": "Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events", "authors": [ "Yu Gu", "Sheng Zhang", "Naoto Usuyama", "Yonas Woldesenbet", "Cliff Wong", "Praneeth Sanapathi", "Mu Wei", "Naveen Valluri", "Erika Strandberg", "Tristan Naumann", "Hoifung Poon" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs), such as GPT-4, have demonstrated remarkable capabilities across a wide range of tasks, including health applications. In this paper, we study how LLMs can be used to scale biomedical knowledge curation. We find that while LLMs already possess decent competency in structuring biomedical text, by distillation into a task-specific student model through self-supervised learning, substantial gains can be attained over out-of-box LLMs, with additional advantages such as cost, efficiency, and white-box model access. We conduct a case study on adverse drug event (ADE) extraction, which is an important area for improving care. On standard ADE extraction evaluation, a GPT-3.5 distilled PubMedBERT model attained comparable accuracy as supervised state-of-the-art models without using any labeled data. Despite being over 1,000 times smaller, the distilled model outperformed its teacher GPT-3.5 by over 6 absolute points in F1 and GPT-4 by over 5 absolute points. Ablation studies on distillation model choice (e.g., PubMedBERT vs BioGPT) and ADE extraction architecture shed light on best practice for biomedical knowledge extraction. Similar gains were attained by distillation for other standard biomedical knowledge extraction tasks such as gene-disease associations and protected health information, further illustrating the promise of this approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-14", "arxiv_id": "2307.06857", "title": "Self-consistency for open-ended generations", "authors": [ "Siddhartha Jain", "Xiaofei Ma", "Anoop Deoras", "Bing Xiang" ], "github": "", "project_page": "", "abstract": "In this paper, we present a novel approach for improving the quality and consistency of generated outputs from large-scale pre-trained language models (LLMs). Self-consistency has emerged as an effective approach for prompts with fixed answers, selecting the answer with the highest number of votes. In this paper, we introduce a generalized framework for self-consistency that extends its applicability beyond problems that have fixed-answer answers. Through extensive simulations, we demonstrate that our approach consistently recovers the optimal or near-optimal generation from a set of candidates. We also propose lightweight parameter-free similarity functions that show significant and consistent improvements across code generation, autoformalization, and summarization tasks, even without access to token log probabilities. Our method incurs minimal computational overhead, requiring no auxiliary reranker models or modifications to the existing model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-14", "arxiv_id": "2307.06940", "title": "Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation", "authors": [ "Yingqing He", "Menghan Xia", "Haoxin Chen", "Xiaodong Cun", "Yuan Gong", "Jinbo Xing", "Yong Zhang", "Xintao Wang", "Chao Weng", "Ying Shan", "Qifeng Chen" ], "github": "https://github.com/videocrafter/animate-a-story", "project_page": "", "abstract": "Generating videos for visual storytelling can be a tedious and complex process that typically requires either live-action filming or graphics animation rendering. To bypass these challenges, our key idea is to utilize the abundance of existing video clips and synthesize a coherent storytelling video by customizing their appearances. We achieve this by developing a framework comprised of two functional modules: (i) Motion Structure Retrieval, which provides video candidates with desired scene or motion context described by query texts, and (ii) Structure-Guided Text-to-Video Synthesis, which generates plot-aligned videos under the guidance of motion structure and text prompts. For the first module, we leverage an off-the-shelf video retrieval system and extract video depths as motion structure. For the second module, we propose a controllable video generation model that offers flexible controls over structure and characters. The videos are synthesized by following the structural guidance and appearance instruction. To ensure visual consistency across clips, we propose an effective concept personalization approach, which allows the specification of the desired character identities through text prompts. Extensive experiments demonstrate that our approach exhibits significant advantages over various existing baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/videocrafter/animate-a-story", "github_api_added_by": "auto" }, { "date": "2023-07-14", "arxiv_id": "2307.06908", "title": "Generating Benchmarks for Factuality Evaluation of Language Models", "authors": [ "Dor Muhlgay", "Ori Ram", "Inbal Magar", "Yoav Levine", "Nir Ratner", "Yonatan Belinkov", "Omri Abend", "Kevin Leyton-Brown", "Amnon Shashua", "Yoav Shoham" ], "github": "https://github.com/AI21Labs/factor", "project_page": "", "abstract": "Before deploying a language model (LM) within a given domain, it is important to measure its tendency to generate factually incorrect information in that domain. Existing factual generation evaluation methods focus on facts sampled from the LM itself, and thus do not control the set of evaluated facts and might under-represent rare and unlikely facts. We propose FACTOR: Factual Assessment via Corpus TransfORmation, a scalable approach for evaluating LM factuality. FACTOR automatically transforms a factual corpus of interest into a benchmark evaluating an LM's propensity to generate true facts from the corpus vs. similar but incorrect statements. We use our framework to create two benchmarks: Wiki-FACTOR and News-FACTOR. We show that: (i) our benchmark scores increase with model size and improve when the LM is augmented with retrieval; (ii) benchmark score correlates with perplexity, but the two metrics do not always agree on model ranking; and (iii) when perplexity and benchmark score disagree, the latter better reflects factuality in open-ended generation, as measured by human annotators. We make our data and code publicly available in https://github.com/AI21Labs/factor.", "github_manual": "", "github_regex": "https://github.com/AI21Labs/factor", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-14", "arxiv_id": "2307.06350", "title": "T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation", "authors": [ "Kaiyi Huang", "Kaiyue Sun", "Enze Xie", "Zhenguo Li", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Despite the stunning ability to generate high-quality images by recent text-to-image models, current approaches often struggle to effectively compose objects with different attributes and relationships into a complex and coherent scene. We propose T2I-CompBench, a comprehensive benchmark for open-world compositional text-to-image generation, consisting of 6,000 compositional text prompts from 3 categories (attribute binding, object relationships, and complex compositions) and 6 sub-categories (color binding, shape binding, texture binding, spatial relationships, non-spatial relationships, and complex compositions). We further propose several evaluation metrics specifically designed to evaluate compositional text-to-image generation. We introduce a new approach, Generative mOdel fine-tuning with Reward-driven Sample selection (GORS), to boost the compositional text-to-image generation abilities of pretrained text-to-image models. Extensive experiments and evaluations are conducted to benchmark previous methods on T2I-CompBench, and to validate the effectiveness of our proposed evaluation metrics and GORS approach. Project page is available at https://karine-h.github.io/T2I-CompBench/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-17", "arxiv_id": "2307.06962", "title": "Copy Is All You Need", "authors": [ "Tian Lan", "Deng Cai", "Yan Wang", "Heyan Huang", "Xian-Ling Mao" ], "github": "https://github.com/gmftbyGMFTBY/Copyisallyouneed", "project_page": "", "abstract": "The dominant text generation models compose the output by sequentially selecting words from a fixed vocabulary. In this paper, we formulate text generation as progressively copying text segments (e.g., words or phrases) from an existing text collection. We compute the contextualized representations of meaningful text segments and index them using efficient vector search toolkits. The task of text generation is then decomposed into a series of copy-and-paste operations: at each time step, we seek suitable text spans from the text collection rather than selecting from a standalone vocabulary. Experiments on the standard language modeling benchmark (WikiText-103) show that our approach achieves better generation quality according to both automatic and human evaluations. Besides, its inference efficiency is comparable to token-level autoregressive models thanks to the reduction of decoding steps. We also show that our approach allows for effective domain adaptation by simply switching to domain-specific text collection without extra training. Finally, we observe that our approach attains additional performance gains by simply scaling up to larger text collections, again without further training.Our source codes are publicly available at \\url{https://github.com/gmftbyGMFTBY/Copyisallyouneed.}", "github_manual": "", "github_regex": "https://github.com/gmftbyGMFTBY/Copyisallyouneed", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-17", "arxiv_id": "2307.07218", "title": "Mega-TTS 2: Zero-Shot Text-to-Speech with Arbitrary Length Speech Prompts", "authors": [ "Ziyue Jiang", "Jinglin Liu", "Yi Ren", "Jinzheng He", "Chen Zhang", "Zhenhui Ye", "Pengfei Wei", "Chunfeng Wang", "Xiang Yin", "Zejun Ma", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "Zero-shot text-to-speech aims at synthesizing voices with unseen speech prompts. Previous large-scale multispeaker TTS models have successfully achieved this goal with an enrolled recording within 10 seconds. However, most of them are designed to utilize only short speech prompts. The limited information in short speech prompts significantly hinders the performance of fine-grained identity imitation. In this paper, we introduce Mega-TTS 2, a generic zero-shot multispeaker TTS model that is capable of synthesizing speech for unseen speakers with arbitrary-length prompts. Specifically, we 1) design a multi-reference timbre encoder to extract timbre information from multiple reference speeches; 2) and train a prosody language model with arbitrary-length speech prompts; With these designs, our model is suitable for prompts of different lengths, which extends the upper bound of speech quality for zero-shot text-to-speech. Besides arbitrary-length prompts, we introduce arbitrary-source prompts, which leverages the probabilities derived from multiple P-LLM outputs to produce expressive and controlled prosody. Furthermore, we propose a phoneme-level auto-regressive duration model to introduce in-context learning capabilities to duration modeling. Experiments demonstrate that our method could not only synthesize identity-preserving speech with a short prompt of an unseen speaker but also achieve improved performance with longer speech prompts. Audio samples can be found in https://mega-tts.github.io/mega2_demo/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-17", "arxiv_id": "2307.07164", "title": "Learning to Retrieve In-Context Examples for Large Language Models", "authors": [ "Liang Wang", "Nan Yang", "Furu Wei" ], "github": "https://github.com/microsoft/LMOps/tree/main/llm_retriever", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated their ability to learn in-context, allowing them to perform various tasks based on a few input-output examples. However, the effectiveness of in-context learning is heavily reliant on the quality of the selected examples. In this paper, we propose a novel framework to iteratively train dense retrievers that can identify high-quality in-context examples for LLMs. Our framework initially trains a reward model based on LLM feedback to evaluate the quality of candidate examples, followed by knowledge distillation to train a bi-encoder based dense retriever. Our experiments on a suite of 30 tasks demonstrate that our framework significantly enhances in-context learning performance. Furthermore, we show the generalization ability of our framework to unseen tasks during training. An in-depth analysis reveals that our model improves performance by retrieving examples with similar patterns, and the gains are consistent across LLMs of varying sizes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/LMOps/tree/main/llm_retriever", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-17", "arxiv_id": "2307.07487", "title": "DreamTeacher: Pretraining Image Backbones with Deep Generative Models", "authors": [ "Daiqing Li", "Huan Ling", "Amlan Kar", "David Acuna", "Seung Wook Kim", "Karsten Kreis", "Antonio Torralba", "Sanja Fidler" ], "github": "https://github.com/lukemelas/pytorch-pretrained-gans", "project_page": "", "abstract": "In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones. We propose to distill knowledge from a trained generative model into standard image backbones that have been well engineered for specific perception tasks. We investigate two types of knowledge distillation: 1) distilling learned generative features onto target image backbones as an alternative to pretraining these backbones on large labeled datasets such as ImageNet, and 2) distilling labels obtained from generative networks with task heads onto logits of target backbones. We perform extensive analyses on multiple generative models, dense prediction benchmarks, and several pre-training regimes. We empirically find that our DreamTeacher significantly outperforms existing self-supervised representation learning approaches across the board. Unsupervised ImageNet pre-training with DreamTeacher leads to significant improvements over ImageNet classification pre-training on downstream datasets, showcasing generative models, and diffusion generative models specifically, as a promising approach to representation learning on large, diverse datasets without requiring manual annotation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lukemelas/pytorch-pretrained-gans", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-17", "arxiv_id": "2307.07047", "title": "DIALGEN: Collaborative Human-LM Generated Dialogues for Improved Understanding of Human-Human Conversations", "authors": [ "Bo-Ru Lu", "Nikita Haduong", "Chia-Hsuan Lee", "Zeqiu Wu", "Hao Cheng", "Paul Koester", "Jean Utke", "Tao Yu", "Noah A. Smith", "Mari Ostendorf" ], "github": "", "project_page": "", "abstract": "Applications that could benefit from automatic understanding of human-human conversations often come with challenges associated with private information in real-world data such as call center or clinical conversations. Working with protected data also increases costs of annotation, which limits technology development. To address these challenges, we propose DIALGEN, a human-in-the-loop semi-automated dialogue generation framework. DIALGEN uses a language model (ChatGPT) that can follow schema and style specifications to produce fluent conversational text, generating a complex conversation through iteratively generating subdialogues and using human feedback to correct inconsistencies or redirect the flow. In experiments on structured summarization of agent-client information gathering calls, framed as dialogue state tracking, we show that DIALGEN data enables significant improvement in model performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-17", "arxiv_id": "2307.07511", "title": "NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis", "authors": [ "Nilesh Kulkarni", "Davis Rempe", "Kyle Genova", "Abhijit Kundu", "Justin Johnson", "David Fouhey", "Leonidas Guibas" ], "github": "", "project_page": "", "abstract": "We address the problem of generating realistic 3D motions of humans interacting with objects in a scene. Our key idea is to create a neural interaction field attached to a specific object, which outputs the distance to the valid interaction manifold given a human pose as input. This interaction field guides the sampling of an object-conditioned human motion diffusion model, so as to encourage plausible contacts and affordance semantics. To support interactions with scarcely available data, we propose an automated synthetic data pipeline. For this, we seed a pre-trained motion model, which has priors for the basics of human movement, with interaction-specific anchor poses extracted from limited motion capture data. Using our guided diffusion model trained on generated synthetic data, we synthesize realistic motions for sitting and lifting with several objects, outperforming alternative approaches in terms of motion quality and successful action completion. We call our framework NIFTY: Neural Interaction Fields for Trajectory sYnthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-18", "arxiv_id": "2307.08621", "title": "Retentive Network: A Successor to Transformer for Large Language Models", "authors": [ "Yutao Sun", "Li Dong", "Shaohan Huang", "Shuming Ma", "Yuqing Xia", "Jilong Xue", "Jianyong Wang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance. We theoretically derive the connection between recurrence and attention. Then we propose the retention mechanism for sequence modeling, which supports three computation paradigms, i.e., parallel, recurrent, and chunkwise recurrent. Specifically, the parallel representation allows for training parallelism. The recurrent representation enables low-cost O(1) inference, which improves decoding throughput, latency, and GPU memory without sacrificing performance. The chunkwise recurrent representation facilitates efficient long-sequence modeling with linear complexity, where each chunk is encoded parallelly while recurrently summarizing the chunks. Experimental results on language modeling show that RetNet achieves favorable scaling results, parallel training, low-cost deployment, and efficient inference. The intriguing properties make RetNet a strong successor to Transformer for large language models. Code will be available at https://aka.ms/retnet.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-18", "arxiv_id": "2307.08674", "title": "TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT", "authors": [ "Liangyu Zha", "Junlin Zhou", "Liyao Li", "Rui Wang", "Qingyi Huang", "Saisai Yang", "Jing Yuan", "Changbao Su", "Xiang Li", "Aofeng Su", "Tao Zhang", "Chen Zhou", "Kaizhe Shou", "Miao Wang", "Wufang Zhu", "Guoshan Lu", "Chao Ye", "Yali Ye", "Wentao Ye", "Yiming Zhang", "Xinglong Deng", "Jie Xu", "Haobo Wang", "Gang Chen", "Junbo Zhao" ], "github": "", "project_page": "", "abstract": "Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate. The advancements in large language models (LLMs) have made it possible to interact with tables using natural language input, bringing this capability closer to reality. In this paper, we present TableGPT, a unified fine-tuned framework that enables LLMs to understand and operate on tables using external functional commands. It introduces the capability to seamlessly interact with tables, enabling a wide range of functionalities such as question answering, data manipulation (e.g., insert, delete, query, and modify operations), data visualization, analysis report generation, and automated prediction. TableGPT aims to provide convenience and accessibility to users by empowering them to effortlessly leverage tabular data. At the core of TableGPT lies the novel concept of global tabular representations, which empowers LLMs to gain a comprehensive understanding of the entire table beyond meta-information. By jointly training LLMs on both table and text modalities, TableGPT achieves a deep understanding of tabular data and the ability to perform complex operations on tables through chain-of-command instructions. Importantly, TableGPT offers the advantage of being a self-contained system rather than relying on external API interfaces. Moreover, it supports efficient data process flow, query rejection (when appropriate) and private deployment, enabling faster domain data fine-tuning and ensuring data privacy, which enhances the framework's adaptability to specific use cases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-18", "arxiv_id": "2307.08581", "title": "BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs", "authors": [ "Yang Zhao", "Zhijie Lin", "Daquan Zhou", "Zilong Huang", "Jiashi Feng", "Bingyi Kang" ], "github": "https://github.com/magic-research/bubogpt", "project_page": "", "abstract": "LLMs have demonstrated remarkable abilities at interacting with humans through language, especially with the usage of instruction-following data. Recent advancements in LLMs, such as MiniGPT-4, LLaVA, and X-LLM, further enlarge their abilities by incorporating multi-modal inputs, including image, video, and speech. Despite their effectiveness at generating precise and detailed language understanding of the given modality signal, these LLMs give up the ability to ground specific parts of inputs, thus only constructing a coarse-grained mapping. However, explicit and informative correspondence between text and other modalities will not only improve the user experience but also help to expand the application scenario of multi-modal LLMs. Therefore, we propose BuboGPT, a multi-modal LLM with visual grounding that can perform cross-modal interaction between vision, audio and language, providing fine-grained understanding of visual objects and other given modalities. As a result, BuboGPT is able to point out the specific location of an object in the image, when it is generating response or description for that object. Our contributions are two-fold: 1) An off-the-shelf visual grounding module based on SAM that extracts entities in a sentence and find corresponding masks in the image. 2) A two-stage training scheme and instruction dataset to endow joint text-image-audio understanding. Our experiments show that BuboGPT achieves impressive multi-modality understanding and visual grounding abilities during the interaction with human. It performs consistently well when provided by arbitrary modality combinations (either aligned or unaligned). Our code, model and dataset are available at https://bubo-gpt.github.io .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/magic-research/bubogpt", "github_api_added_by": "auto" }, { "date": "2023-07-18", "arxiv_id": "2307.08701", "title": "AlpaGasus: Training A Better Alpaca with Fewer Data", "authors": [ "Lichang Chen", "Shiyang Li", "Jun Yan", "Hai Wang", "Kalpa Gunaratna", "Vikas Yadav", "Zheng Tang", "Vijay Srinivasan", "Tianyi Zhou", "Heng Huang", "Hongxia Jin" ], "github": "", "project_page": "", "abstract": "Large language models~(LLMs) obtain instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly contain many low-quality instances with incorrect or irrelevant responses, which are misleading and detrimental to IFT. In this paper, we propose a simple and effective data selection strategy that automatically identifies and removes low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce AlpaGasus, which is finetuned on only 9k high-quality data filtered from the 52k Alpaca data. AlpaGasus significantly outperforms the original Alpaca as evaluated by GPT-4 on multiple test sets and its 13B variant matches >90% performance of its teacher LLM (i.e., Text-Davinci-003) on test tasks. It also provides 5.7x faster training, reducing the training time for a 7B variant from 80 minutes (for Alpaca) to 14 minutes We apply IFT for the same number of epochs as Alpaca(7B) but on fewer data, using 4timesNVIDIA A100 (80GB) GPUs and following the original Alpaca setting and hyperparameters.. Overall, AlpaGasus demonstrates a novel data-centric IFT paradigm that can be generally applied to instruction-tuning data, leading to faster training and better instruction-following models. Our project page is available at: https://lichang-chen.github.io/AlpaGasus/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-18", "arxiv_id": "2307.08702", "title": "Diffusion Models Beat GANs on Image Classification", "authors": [ "Soumik Mukhopadhyay", "Matthew Gwilliam", "Vatsal Agarwal", "Namitha Padmanabhan", "Archana Swaminathan", "Srinidhi Hegde", "Tianyi Zhou", "Abhinav Shrivastava" ], "github": "", "project_page": "", "abstract": "While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model which uses a single pre-training stage to address both families of tasks simultaneously. We identify diffusion models as a prime candidate. Diffusion models have risen to prominence as a state-of-the-art method for image generation, denoising, inpainting, super-resolution, manipulation, etc. Such models involve training a U-Net to iteratively predict and remove noise, and the resulting model can synthesize high fidelity, diverse, novel images. The U-Net architecture, as a convolution-based architecture, generates a diverse set of feature representations in the form of intermediate feature maps. We present our findings that these embeddings are useful beyond the noise prediction task, as they contain discriminative information and can also be leveraged for classification. We explore optimal methods for extracting and using these embeddings for classification tasks, demonstrating promising results on the ImageNet classification task. We find that with careful feature selection and pooling, diffusion models outperform comparable generative-discriminative methods such as BigBiGAN for classification tasks. We investigate diffusion models in the transfer learning regime, examining their performance on several fine-grained visual classification datasets. We compare these embeddings to those generated by competing architectures and pre-trainings for classification tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-18", "arxiv_id": "2307.07635", "title": "CoTracker: It is Better to Track Together", "authors": [ "Nikita Karaev", "Ignacio Rocco", "Benjamin Graham", "Natalia Neverova", "Andrea Vedaldi", "Christian Rupprecht" ], "github": "https://github.com/facebookresearch/co-tracker", "project_page": "", "abstract": "Methods for video motion prediction either estimate jointly the instantaneous motion of all points in a given video frame using optical flow or independently track the motion of individual points throughout the video. The latter is true even for powerful deep-learning methods that can track points through occlusions. Tracking points individually ignores the strong correlation that can exist between the points, for instance, because they belong to the same physical object, potentially harming performance. In this paper, we thus propose CoTracker, an architecture that jointly tracks multiple points throughout an entire video. This architecture combines several ideas from the optical flow and tracking literature in a new, flexible and powerful design. It is based on a transformer network that models the correlation of different points in time via specialised attention layers. The transformer iteratively updates an estimate of several trajectories. It can be applied in a sliding-window manner to very long videos, for which we engineer an unrolled training loop. It can track from one to several points jointly and supports adding new points to track at any time. The result is a flexible and powerful tracking algorithm that outperforms state-of-the-art methods in almost all benchmarks.", "github_manual": "https://github.com/facebookresearch/co-tracker", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-18", "arxiv_id": "2307.08041", "title": "Planting a SEED of Vision in Large Language Model", "authors": [ "Yuying Ge", "Yixiao Ge", "Ziyun Zeng", "Xintao Wang", "Ying Shan" ], "github": "https://github.com/ailab-cvc/seed", "project_page": "", "abstract": "We present SEED, an elaborate image tokenizer that empowers Large Language Models (LLMs) with the emergent ability to SEE and Draw at the same time. Research on image tokenizers has previously reached an impasse, as frameworks employing quantized visual tokens have lost prominence due to subpar performance and convergence in multimodal comprehension (compared to BLIP-2, etc.) or generation (compared to Stable Diffusion, etc.). Despite the limitations, we remain confident in its natural capacity to unify visual and textual representations, facilitating scalable multimodal training with LLM's original recipe. In this study, we identify two crucial principles for the architecture and training of SEED that effectively ease subsequent alignment with LLMs. (1) Image tokens should be independent of 2D physical patch positions and instead be produced with a 1D causal dependency, exhibiting intrinsic interdependence that aligns with the left-to-right autoregressive prediction mechanism in LLMs. (2) Image tokens should capture high-level semantics consistent with the degree of semantic abstraction in words, and be optimized for both discriminativeness and reconstruction during the tokenizer training phase. As a result, the off-the-shelf LLM is able to perform both image-to-text and text-to-image generation by incorporating our SEED through efficient LoRA tuning. Comprehensive multimodal pretraining and instruction tuning, which may yield improved results, are reserved for future investigation. This version of SEED was trained in 5.7 days using only 64 V100 GPUs and 5M publicly available image-text pairs. Our preliminary study emphasizes the great potential of discrete visual tokens in versatile multimodal LLMs and the importance of proper image tokenizers in broader research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ailab-cvc/seed", "github_api_added_by": "auto" }, { "date": "2023-07-18", "arxiv_id": "2307.07663", "title": "INVE: Interactive Neural Video Editing", "authors": [ "Jiahui Huang", "Leonid Sigal", "Kwang Moo Yi", "Oliver Wang", "Joon-Young Lee" ], "github": "", "project_page": "", "abstract": "We present Interactive Neural Video Editing (INVE), a real-time video editing solution, which can assist the video editing process by consistently propagating sparse frame edits to the entire video clip. Our method is inspired by the recent work on Layered Neural Atlas (LNA). LNA, however, suffers from two major drawbacks: (1) the method is too slow for interactive editing, and (2) it offers insufficient support for some editing use cases, including direct frame editing and rigid texture tracking. To address these challenges we leverage and adopt highly efficient network architectures, powered by hash-grids encoding, to substantially improve processing speed. In addition, we learn bi-directional functions between image-atlas and introduce vectorized editing, which collectively enables a much greater variety of edits in both the atlas and the frames directly. Compared to LNA, our INVE reduces the learning and inference time by a factor of 5, and supports various video editing operations that LNA cannot. We showcase the superiority of INVE over LNA in interactive video editing through a comprehensive quantitative and qualitative analysis, highlighting its numerous advantages and improved performance. For video results, please see https://gabriel-huang.github.io/inve/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-18", "arxiv_id": "2307.08506", "title": "Does Visual Pretraining Help End-to-End Reasoning?", "authors": [ "Chen Sun", "Calvin Luo", "Xingyi Zhou", "Anurag Arnab", "Cordelia Schmid" ], "github": "", "project_page": "", "abstract": "We aim to investigate whether end-to-end learning of visual reasoning can be achieved with general-purpose neural networks, with the help of visual pretraining. A positive result would refute the common belief that explicit visual abstraction (e.g. object detection) is essential for compositional generalization on visual reasoning, and confirm the feasibility of a neural network \"generalist\" to solve visual recognition and reasoning tasks. We propose a simple and general self-supervised framework which \"compresses\" each video frame into a small set of tokens with a transformer network, and reconstructs the remaining frames based on the compressed temporal context. To minimize the reconstruction loss, the network must learn a compact representation for each image, as well as capture temporal dynamics and object permanence from temporal context. We perform evaluation on two visual reasoning benchmarks, CATER and ACRE. We observe that pretraining is essential to achieve compositional generalization for end-to-end visual reasoning. Our proposed framework outperforms traditional supervised pretraining, including image classification and explicit object detection, by large margins.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-18", "arxiv_id": "2307.08579", "title": "Scale-Aware Modulation Meet Transformer", "authors": [ "Weifeng Lin", "Ziheng Wu", "Jiayu Chen", "Jun Huang", "Lianwen Jin" ], "github": "https://github.com/afeng-x/smt", "project_page": "", "abstract": "This paper presents a new vision Transformer, Scale-Aware Modulation Transformer (SMT), that can handle various downstream tasks efficiently by combining the convolutional network and vision Transformer. The proposed Scale-Aware Modulation (SAM) in the SMT includes two primary novel designs. Firstly, we introduce the Multi-Head Mixed Convolution (MHMC) module, which can capture multi-scale features and expand the receptive field. Secondly, we propose the Scale-Aware Aggregation (SAA) module, which is lightweight but effective, enabling information fusion across different heads. By leveraging these two modules, convolutional modulation is further enhanced. Furthermore, in contrast to prior works that utilized modulations throughout all stages to build an attention-free network, we propose an Evolutionary Hybrid Network (EHN), which can effectively simulate the shift from capturing local to global dependencies as the network becomes deeper, resulting in superior performance. Extensive experiments demonstrate that SMT significantly outperforms existing state-of-the-art models across a wide range of visual tasks. Specifically, SMT with 11.5M / 2.4GFLOPs and 32M / 7.7GFLOPs can achieve 82.2% and 84.3% top-1 accuracy on ImageNet-1K, respectively. After pretrained on ImageNet-22K in 224^2 resolution, it attains 87.1% and 88.1% top-1 accuracy when finetuned with resolution 224^2 and 384^2, respectively. For object detection with Mask R-CNN, the SMT base trained with 1x and 3x schedule outperforms the Swin Transformer counterpart by 4.2 and 1.3 mAP on COCO, respectively. For semantic segmentation with UPerNet, the SMT base test at single- and multi-scale surpasses Swin by 2.0 and 1.1 mIoU respectively on the ADE20K.", "github_manual": "https://github.com/afeng-x/smt", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-18", "arxiv_id": "2307.07947", "title": "Language Conditioned Traffic Generation", "authors": [ "Shuhan Tan", "Boris Ivanovic", "Xinshuo Weng", "Marco Pavone", "Philipp Kraehenbuehl" ], "github": "https://github.com/Ariostgx/lctgen", "project_page": "", "abstract": "Simulation forms the backbone of modern self-driving development. Simulators help develop, test, and improve driving systems without putting humans, vehicles, or their environment at risk. However, simulators face a major challenge: They rely on realistic, scalable, yet interesting content. While recent advances in rendering and scene reconstruction make great strides in creating static scene assets, modeling their layout, dynamics, and behaviors remains challenging. In this work, we turn to language as a source of supervision for dynamic traffic scene generation. Our model, LCTGen, combines a large language model with a transformer-based decoder architecture that selects likely map locations from a dataset of maps, and produces an initial traffic distribution, as well as the dynamics of each vehicle. LCTGen outperforms prior work in both unconditional and conditional traffic scene generation in terms of realism and fidelity. Code and video will be available at https://ariostgx.github.io/lctgen.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Ariostgx/lctgen", "github_api_added_by": "auto" }, { "date": "2023-07-19", "arxiv_id": "2307.09288", "title": "Llama 2: Open Foundation and Fine-Tuned Chat Models", "authors": [ "Hugo Touvron", "Louis Martin", "Kevin Stone", "Peter Albert", "Amjad Almahairi", "Yasmine Babaei", "Nikolay Bashlykov", "Soumya Batra", "Prajjwal Bhargava", "Shruti Bhosale", "Dan Bikel", "Lukas Blecher", "Cristian Canton Ferrer", "Moya Chen", "Guillem Cucurull", "David Esiobu", "Jude Fernandes", "Jeremy Fu", "Wenyin Fu", "Brian Fuller", "Cynthia Gao", "Vedanuj Goswami", "Naman Goyal", "Anthony Hartshorn", "Saghar Hosseini", "Rui Hou", "Hakan Inan", "Marcin Kardas", "Viktor Kerkez", "Madian Khabsa", "Isabel Kloumann", "Artem Korenev", "Punit Singh Koura", "Marie-Anne Lachaux", "Thibaut Lavril", "Jenya Lee", "Diana Liskovich", "Yinghai Lu", "Yuning Mao", "Xavier Martinet", "Todor Mihaylov", "Pushkar Mishra", "Igor Molybog", "Yixin Nie", "Andrew Poulton", "Jeremy Reizenstein", "Rashi Rungta", "Kalyan Saladi", "Alan Schelten", "Ruan Silva", "Eric Michael Smith", "Ranjan Subramanian", "Xiaoqing Ellen Tan", "Binh Tang", "Ross Taylor", "Adina Williams", "Jian Xiang Kuan", "Puxin Xu", "Zheng Yan", "Iliyan Zarov", "Yuchen Zhang", "Angela Fan", "Melanie Kambadur", "Sharan Narang", "Aurelien Rodriguez", "Robert Stojnic", "Sergey Edunov", "Thomas Scialom" ], "github": "https://github.com/facebookresearch/llama", "project_page": "", "abstract": "In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.", "github_manual": "https://github.com/facebookresearch/llama", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-19", "arxiv_id": "2307.09009", "title": "How is ChatGPT's behavior changing over time?", "authors": [ "Lingjiao Chen", "Matei Zaharia", "James Zou" ], "github": "https://github.com/lchen001/llmdrift", "project_page": "", "abstract": "GPT-3.5 and GPT-4 are the two most widely used large language model (LLM) services. However, when and how these models are updated over time is opaque. Here, we evaluate the March 2023 and June 2023 versions of GPT-3.5 and GPT-4 on four diverse tasks: 1) solving math problems, 2) answering sensitive/dangerous questions, 3) generating code and 4) visual reasoning. We find that the performance and behavior of both GPT-3.5 and GPT-4 can vary greatly over time. For example, GPT-4 (March 2023) was very good at identifying prime numbers (accuracy 97.6%) but GPT-4 (June 2023) was very poor on these same questions (accuracy 2.4%). Interestingly GPT-3.5 (June 2023) was much better than GPT-3.5 (March 2023) in this task. GPT-4 was less willing to answer sensitive questions in June than in March, and both GPT-4 and GPT-3.5 had more formatting mistakes in code generation in June than in March. Overall, our findings shows that the behavior of the same LLM service can change substantially in a relatively short amount of time, highlighting the need for continuous monitoring of LLM quality.", "github_manual": "https://github.com/lchen001/llmdrift", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-19", "arxiv_id": "2307.09112", "title": "NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF", "authors": [ "Stefan Lionar", "Xiangyu Xu", "Min Lin", "Gim Hee Lee" ], "github": "https://github.com/sail-sg/numcc", "project_page": "", "abstract": "Remarkable progress has been made in 3D reconstruction from single-view RGB-D inputs. MCC is the current state-of-the-art method in this field, which achieves unprecedented success by combining vision Transformers with large-scale training. However, we identified two key limitations of MCC: 1) The Transformer decoder is inefficient in handling large number of query points; 2) The 3D representation struggles to recover high-fidelity details. In this paper, we propose a new approach called NU-MCC that addresses these limitations. NU-MCC includes two key innovations: a Neighborhood decoder and a Repulsive Unsigned Distance Function (Repulsive UDF). First, our Neighborhood decoder introduces center points as an efficient proxy of input visual features, allowing each query point to only attend to a small neighborhood. This design not only results in much faster inference speed but also enables the exploitation of finer-scale visual features for improved recovery of 3D textures. Second, our Repulsive UDF is a novel alternative to the occupancy field used in MCC, significantly improving the quality of 3D object reconstruction. Compared to standard UDFs that suffer from holes in results, our proposed Repulsive UDF can achieve more complete surface reconstruction. Experimental results demonstrate that NU-MCC is able to learn a strong 3D representation, significantly advancing the state of the art in single-view 3D reconstruction. Particularly, it outperforms MCC by 9.7% in terms of the F1-score on the CO3D-v2 dataset with more than 5x faster running speed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sail-sg/numcc", "github_api_added_by": "auto" }, { "date": "2023-07-19", "arxiv_id": "2307.09458", "title": "Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla", "authors": [ "Tom Lieberum", "Matthew Rahtz", "J\u00e1nos Kram\u00e1r", "Geoffrey Irving", "Rohin Shah", "Vladimir Mikulik" ], "github": "", "project_page": "", "abstract": "Circuit analysis is a promising technique for understanding the internal mechanisms of language models. However, existing analyses are done in small models far from the state of the art. To address this, we present a case study of circuit analysis in the 70B Chinchilla model, aiming to test the scalability of circuit analysis. In particular, we study multiple-choice question answering, and investigate Chinchilla's capability to identify the correct answer label given knowledge of the correct answer text. We find that the existing techniques of logit attribution, attention pattern visualization, and activation patching naturally scale to Chinchilla, allowing us to identify and categorize a small set of `output nodes' (attention heads and MLPs). We further study the `correct letter' category of attention heads aiming to understand the semantics of their features, with mixed results. For normal multiple-choice question answers, we significantly compress the query, key and value subspaces of the head without loss of performance when operating on the answer labels for multiple-choice questions, and we show that the query and key subspaces represent an `Nth item in an enumeration' feature to at least some extent. However, when we attempt to use this explanation to understand the heads' behaviour on a more general distribution including randomized answer labels, we find that it is only a partial explanation, suggesting there is more to learn about the operation of `correct letter' heads on multiple choice question answering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-19", "arxiv_id": "2307.09233", "title": "Augmenting CLIP with Improved Visio-Linguistic Reasoning", "authors": [ "Samyadeep Basu", "Maziar Sanjabi", "Daniela Massiceti", "Shell Xu Hu", "Soheil Feizi" ], "github": "", "project_page": "", "abstract": "Image-text contrastive models such as CLIP are useful for a variety of downstream applications including zero-shot classification, image-text retrieval and transfer learning. However, these contrastively trained vision-language models often fail on compositional visio-linguistic tasks such as Winoground with performance equivalent to random chance. In our paper, we address this issue and propose a sample-efficient light-weight method called SDS-CLIP to improve the compositional visio-linguistic reasoning capabilities of CLIP. The core idea of our method is to use differentiable image parameterizations to fine-tune CLIP with a distillation objective from large text-to-image generative models such as Stable-Diffusion which are relatively good at visio-linguistic reasoning tasks. On the challenging Winoground compositional reasoning benchmark, our method improves the absolute visio-linguistic performance of different CLIP models by up to 7%, while on the ARO dataset, our method improves the visio-linguistic performance by upto 3%. As a byproduct of inducing visio-linguistic reasoning into CLIP, we also find that the zero-shot performance improves marginally on a variety of downstream datasets. Our method reinforces that carefully designed distillation objectives from generative models can be leveraged to extend existing contrastive image-text models with improved visio-linguistic reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-19", "arxiv_id": "2307.09320", "title": "Biomaker CA: a Biome Maker project using Cellular Automata", "authors": [ "Ettore Randazzo", "Alexander Mordvintsev" ], "github": "", "project_page": "", "abstract": "We introduce Biomaker CA: a Biome Maker project using Cellular Automata (CA). In Biomaker CA, morphogenesis is a first class citizen and small seeds need to grow into plant-like organisms to survive in a nutrient starved environment and eventually reproduce with variation so that a biome survives for long timelines. We simulate complex biomes by means of CA rules in 2D grids and parallelize all of its computation on GPUs through the Python JAX framework. We show how this project allows for several different kinds of environments and laws of 'physics', alongside different model architectures and mutation strategies. We further analyze some configurations to show how plant agents can grow, survive, reproduce, and evolve, forming stable and unstable biomes. We then demonstrate how one can meta-evolve models to survive in a harsh environment either through end-to-end meta-evolution or by a more surgical and efficient approach, called Petri dish meta-evolution. Finally, we show how to perform interactive evolution, where the user decides how to evolve a plant model interactively and then deploys it in a larger environment. We open source Biomaker CA at: https://tinyurl.com/2x8yu34s .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-20", "arxiv_id": "2307.09793", "title": "On the Origin of LLMs: An Evolutionary Tree and Graph for 15,821 Large Language Models", "authors": [ "Sarah Gao", "Andrew Kean Gao" ], "github": "", "project_page": "", "abstract": "Since late 2022, Large Language Models (LLMs) have become very prominent with LLMs like ChatGPT and Bard receiving millions of users. Hundreds of new LLMs are announced each week, many of which are deposited to Hugging Face, a repository of machine learning models and datasets. To date, nearly 16,000 Text Generation models have been uploaded to the site. Given the huge influx of LLMs, it is of interest to know which LLM backbones, settings, training methods, and families are popular or trending. However, there is no comprehensive index of LLMs available. We take advantage of the relatively systematic nomenclature of Hugging Face LLMs to perform hierarchical clustering and identify communities amongst LLMs using n-grams and term frequency-inverse document frequency. Our methods successfully identify families of LLMs and accurately cluster LLMs into meaningful subgroups. We present a public web application to navigate and explore Constellation, our atlas of 15,821 LLMs. Constellation rapidly generates a variety of visualizations, namely dendrograms, graphs, word clouds, and scatter plots. Constellation is available at the following link: https://constellation.sites.stanford.edu/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-20", "arxiv_id": "2307.10169", "title": "Challenges and Applications of Large Language Models", "authors": [ "Jean Kaddour", "Joshua Harris", "Maximilian Mozes", "Herbie Bradley", "Roberta Raileanu", "Robert McHardy" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) went from non-existent to ubiquitous in the machine learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify the remaining challenges and already fruitful application areas. In this paper, we aim to establish a systematic set of open problems and application successes so that ML researchers can comprehend the field's current state more quickly and become productive.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-20", "arxiv_id": "2307.10159", "title": "FABRIC: Personalizing Diffusion Models with Iterative Feedback", "authors": [ "Dimitri von R\u00fctte", "Elisabetta Fedele", "Jonathan Thomm", "Lukas Wolf" ], "github": "https://github.com/sd-fabric/fabric", "project_page": "", "abstract": "In an era where visual content generation is increasingly driven by machine learning, the integration of human feedback into generative models presents significant opportunities for enhancing user experience and output quality. This study explores strategies for incorporating iterative human feedback into the generative process of diffusion-based text-to-image models. We propose FABRIC, a training-free approach applicable to a wide range of popular diffusion models, which exploits the self-attention layer present in the most widely used architectures to condition the diffusion process on a set of feedback images. To ensure a rigorous assessment of our approach, we introduce a comprehensive evaluation methodology, offering a robust mechanism to quantify the performance of generative visual models that integrate human feedback. We show that generation results improve over multiple rounds of iterative feedback through exhaustive analysis, implicitly optimizing arbitrary user preferences. The potential applications of these findings extend to fields such as personalized content creation and customization.", "github_manual": "https://github.com/sd-fabric/fabric", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-20", "arxiv_id": "2307.09781", "title": "Text2Layer: Layered Image Generation using Latent Diffusion Model", "authors": [ "Xinyang Zhang", "Wentian Zhao", "Xin Lu", "Jeff Chien" ], "github": "", "project_page": "", "abstract": "Layer compositing is one of the most popular image editing workflows among both amateurs and professionals. Motivated by the success of diffusion models, we explore layer compositing from a layered image generation perspective. Instead of generating an image, we propose to generate background, foreground, layer mask, and the composed image simultaneously. To achieve layered image generation, we train an autoencoder that is able to reconstruct layered images and train diffusion models on the latent representation. One benefit of the proposed problem is to enable better compositing workflows in addition to the high-quality image output. Another benefit is producing higher-quality layer masks compared to masks produced by a separate step of image segmentation. Experimental results show that the proposed method is able to generate high-quality layered images and initiates a benchmark for future work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-20", "arxiv_id": "2307.10172", "title": "DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI", "authors": [ "Jianguo Zhang", "Kun Qian", "Zhiwei Liu", "Shelby Heinecke", "Rui Meng", "Ye Liu", "Zhou Yu", "Huan Wang", "Silvio Savarese", "Caiming Xiong" ], "github": "https://github.com/salesforce/DialogStudio", "project_page": "", "abstract": "Despite advancements in conversational AI, language models encounter challenges to handle diverse conversational tasks, and existing dialogue dataset collections often lack diversity and comprehensiveness. To tackle these issues, we introduce DialogStudio: the largest and most diverse collection of dialogue datasets, unified under a consistent format while preserving their original information. Our collection encompasses data from open-domain dialogues, task-oriented dialogues, natural language understanding, conversational recommendation, dialogue summarization, and knowledge-grounded dialogues, making it an incredibly rich and diverse resource for dialogue research and model training. To further enhance the utility of DialogStudio, we identify the licenses for each dataset and design domain-aware prompts for selected dialogues to facilitate instruction-aware fine-tuning. Furthermore, we develop conversational AI models using the dataset collection, and our experiments in both zero-shot and few-shot learning scenarios demonstrate the superiority of DialogStudio. To improve transparency and support dataset and task-based research, as well as language model pre-training, all datasets, licenses, codes, and models associated with DialogStudio are made publicly accessible at https://github.com/salesforce/DialogStudio", "github_manual": "", "github_regex": "https://github.com/salesforce/DialogStudio", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-20", "arxiv_id": "2307.09668", "title": "Towards A Unified Agent with Foundation Models", "authors": [ "Norman Di Palo", "Arunkumar Byravan", "Leonard Hasenclever", "Markus Wulfmeier", "Nicolas Heess", "Martin Riedmiller" ], "github": "", "project_page": "", "abstract": "Language Models and Vision Language Models have recently demonstrated unprecedented capabilities in terms of understanding human intentions, reasoning, scene understanding, and planning-like behaviour, in text form, among many others. In this work, we investigate how to embed and leverage such abilities in Reinforcement Learning (RL) agents. We design a framework that uses language as the core reasoning tool, exploring how this enables an agent to tackle a series of fundamental RL challenges, such as efficient exploration, reusing experience data, scheduling skills, and learning from observations, which traditionally require separate, vertically designed algorithms. We test our method on a sparse-reward simulated robotic manipulation environment, where a robot needs to stack a set of objects. We demonstrate substantial performance improvements over baselines in exploration efficiency and ability to reuse data from offline datasets, and illustrate how to reuse learned skills to solve novel tasks or imitate videos of human experts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-20", "arxiv_id": "2307.10088", "title": "Android in the Wild: A Large-Scale Dataset for Android Device Control", "authors": [ "Christopher Rawles", "Alice Li", "Daniel Rodriguez", "Oriana Riva", "Timothy Lillicrap" ], "github": "https://github.com/google-research/google-research/tree/master/android_in_the_wild", "project_page": "", "abstract": "There is a growing interest in device-control systems that can interpret human natural language instructions and execute them on a digital device by directly controlling its user interface. We present a dataset for device-control research, Android in the Wild (AITW), which is orders of magnitude larger than current datasets. The dataset contains human demonstrations of device interactions, including the screens and actions, and corresponding natural language instructions. It consists of 715k episodes spanning 30k unique instructions, four versions of Android (v10-13),and eight device types (Pixel 2 XL to Pixel 6) with varying screen resolutions. It contains multi-step tasks that require semantic understanding of language and visual context. This dataset poses a new challenge: actions available through the user interface must be inferred from their visual appearance. And, instead of simple UI element-based actions, the action space consists of precise gestures (e.g., horizontal scrolls to operate carousel widgets). We organize our dataset to encourage robustness analysis of device-control systems, i.e., how well a system performs in the presence of new task descriptions, new applications, or new platform versions. We develop two agents and report performance across the dataset. The dataset is available at https://github.com/google-research/google-research/tree/master/android_in_the_wild.", "github_manual": "", "github_regex": "https://github.com/google-research/google-research/tree/master/android_in_the_wild", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-20", "arxiv_id": "2307.10168", "title": "LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs", "authors": [ "Tongshuang Wu", "Haiyi Zhu", "Maya Albayrak", "Alexis Axon", "Amanda Bertsch", "Wenxing Deng", "Ziqi Ding", "Bill Guo", "Sireesh Gururaja", "Tzu-Sheng Kuo", "Jenny T. Liang", "Ryan Liu", "Ihita Mandal", "Jeremiah Milbauer", "Xiaolin Ni", "Namrata Padmanabhan", "Subhashini Ramkumar", "Alexis Sudjianto", "Jordan Taylor", "Ying-Jui Tseng", "Patricia Vaidos", "Zhijin Wu", "Wei Wu", "Chenyang Yang" ], "github": "https://github.com/tongshuangwu/llm-crowdsourcing-pipeline", "project_page": "", "abstract": "LLMs have shown promise in replicating human-like behavior in crowdsourcing tasks that were previously thought to be exclusive to human abilities. However, current efforts focus mainly on simple atomic tasks. We explore whether LLMs can replicate more complex crowdsourcing pipelines. We find that modern LLMs can simulate some of crowdworkers' abilities in these \"human computation algorithms,\" but the level of success is variable and influenced by requesters' understanding of LLM capabilities, the specific skills required for sub-tasks, and the optimal interaction modality for performing these sub-tasks. We reflect on human and LLMs' different sensitivities to instructions, stress the importance of enabling human-facing safeguards for LLMs, and discuss the potential of training humans and LLMs with complementary skill sets. Crucially, we show that replicating crowdsourcing pipelines offers a valuable platform to investigate (1) the relative strengths of LLMs on different tasks (by cross-comparing their performances on sub-tasks) and (2) LLMs' potential in complex tasks, where they can complete part of the tasks while leaving others to humans.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tongshuangwu/llm-crowdsourcing-pipeline", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-20", "arxiv_id": "2307.10173", "title": "DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric Rendering", "authors": [ "Wei Cheng", "Ruixiang Chen", "Wanqi Yin", "Siming Fan", "Keyu Chen", "Honglin He", "Huiwen Luo", "Zhongang Cai", "Jingbo Wang", "Yang Gao", "Zhengming Yu", "Zhengyu Lin", "Daxuan Ren", "Lei Yang", "Ziwei Liu", "Chen Change Loy", "Chen Qian", "Wayne Wu", "Dahua Lin", "Bo Dai", "Kwan-Yee Lin" ], "github": "https://github.com/DNA-Rendering/DNA-Rendering", "project_page": "", "abstract": "Realistic human-centric rendering plays a key role in both computer vision and computer graphics. Rapid progress has been made in the algorithm aspect over the years, yet existing human-centric rendering datasets and benchmarks are rather impoverished in terms of diversity, which are crucial for rendering effect. Researchers are usually constrained to explore and evaluate a small set of rendering problems on current datasets, while real-world applications require methods to be robust across different scenarios. In this work, we present DNA-Rendering, a large-scale, high-fidelity repository of human performance data for neural actor rendering. DNA-Rendering presents several alluring attributes. First, our dataset contains over 1500 human subjects, 5000 motion sequences, and 67.5M frames' data volume. Second, we provide rich assets for each subject -- 2D/3D human body keypoints, foreground masks, SMPLX models, cloth/accessory materials, multi-view images, and videos. These assets boost the current method's accuracy on downstream rendering tasks. Third, we construct a professional multi-view system to capture data, which contains 60 synchronous cameras with max 4096 x 3000 resolution, 15 fps speed, and stern camera calibration steps, ensuring high-quality resources for task training and evaluation. Along with the dataset, we provide a large-scale and quantitative benchmark in full-scale, with multiple tasks to evaluate the existing progress of novel view synthesis, novel pose animation synthesis, and novel identity rendering methods. In this manuscript, we describe our DNA-Rendering effort as a revealing of new observations, challenges, and future directions to human-centric rendering. The dataset, code, and benchmarks will be publicly available at https://dna-rendering.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DNA-Rendering/DNA-Rendering", "github_api_added_by": "auto" }, { "date": "2023-07-20", "arxiv_id": "2307.09638", "title": "Promoting Exploration in Memory-Augmented Adam using Critical Momenta", "authors": [ "Pranshu Malviya", "Gon\u00e7alo Mordido", "Aristide Baratin", "Reza Babanezhad Harikandeh", "Jerry Huang", "Simon Lacoste-Julien", "Razvan Pascanu", "Sarath Chandar" ], "github": "https://github.com/chandar-lab/cmoptimizer", "project_page": "", "abstract": "Adaptive gradient-based optimizers, particularly Adam, have left their mark in training large-scale deep learning models. The strength of such optimizers is that they exhibit fast convergence while being more robust to hyperparameter choice. However, they often generalize worse than non-adaptive methods. Recent studies have tied this performance gap to flat minima selection: adaptive methods tend to find solutions in sharper basins of the loss landscape, which in turn hurts generalization. To overcome this issue, we propose a new memory-augmented version of Adam that promotes exploration towards flatter minima by using a buffer of critical momentum terms during training. Intuitively, the use of the buffer makes the optimizer overshoot outside the basin of attraction if it is not wide enough. We empirically show that our method improves the performance of several variants of Adam on standard supervised language modelling and image classification tasks.", "github_manual": "https://github.com/chandar-lab/cmoptimizer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-21", "arxiv_id": "2307.10373", "title": "TokenFlow: Consistent Diffusion Features for Consistent Video Editing", "authors": [ "Michal Geyer", "Omer Bar-Tal", "Shai Bagon", "Tali Dekel" ], "github": "https://github.com/omerbt/TokenFlow", "project_page": "", "abstract": "The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos. Webpage: https://diffusion-tokenflow.github.io/", "github_manual": "https://github.com/omerbt/TokenFlow", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-21", "arxiv_id": "2307.11078", "title": "Brain2Music: Reconstructing Music from Human Brain Activity", "authors": [ "Timo I. Denk", "Yu Takagi", "Takuya Matsuyama", "Andrea Agostinelli", "Tomoya Nakai", "Christian Frank", "Shinji Nishimoto" ], "github": "", "project_page": "", "abstract": "The process of reconstructing experiences from human brain activity offers a unique lens into how the brain interprets and represents the world. In this paper, we introduce a method for reconstructing music from brain activity, captured using functional magnetic resonance imaging (fMRI). Our approach uses either music retrieval or the MusicLM music generation model conditioned on embeddings derived from fMRI data. The generated music resembles the musical stimuli that human subjects experienced, with respect to semantic properties like genre, instrumentation, and mood. We investigate the relationship between different components of MusicLM and brain activity through a voxel-wise encoding modeling analysis. Furthermore, we discuss which brain regions represent information derived from purely textual descriptions of music stimuli. We provide supplementary material including examples of the reconstructed music at https://google-research.github.io/seanet/brain2music", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-21", "arxiv_id": "2307.10802", "title": "Meta-Transformer: A Unified Framework for Multimodal Learning", "authors": [ "Yiyuan Zhang", "Kaixiong Gong", "Kaipeng Zhang", "Hongsheng Li", "Yu Qiao", "Wanli Ouyang", "Xiangyu Yue" ], "github": "https://github.com/invictus717/MetaTransformer", "project_page": "", "abstract": "Multimodal learning aims to build models that can process and relate information from multiple modalities. Despite years of development in this field, it still remains challenging to design a unified network for processing various modalities (e.g. natural language, 2D images, 3D point clouds, audio, video, time series, tabular data) due to the inherent gaps among them. In this work, we propose a framework, named Meta-Transformer, that leverages a frozen encoder to perform multimodal perception without any paired multimodal training data. In Meta-Transformer, the raw input data from various modalities are mapped into a shared token space, allowing a subsequent encoder with frozen parameters to extract high-level semantic features of the input data. Composed of three main components: a unified data tokenizer, a modality-shared encoder, and task-specific heads for downstream tasks, Meta-Transformer is the first framework to perform unified learning across 12 modalities with unpaired data. Experiments on different benchmarks reveal that Meta-Transformer can handle a wide range of tasks including fundamental perception (text, image, point cloud, audio, video), practical application (X-Ray, infrared, hyperspectral, and IMU), and data mining (graph, tabular, and time-series). Meta-Transformer indicates a promising future for developing unified multimodal intelligence with transformers. Code will be available at https://github.com/invictus717/MetaTransformer", "github_manual": "", "github_regex": "https://github.com/invictus717/MetaTransformer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-21", "arxiv_id": "2307.10907", "title": "The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning", "authors": [ "Borja Rodr\u00edguez-G\u00e1lvez", "Arno Blaas", "Pau Rodr\u00edguez", "Adam Goli\u0144ski", "Xavier Suau", "Jason Ramapuram", "Dan Busbridge", "Luca Zappella" ], "github": "https://github.com/apple/ml-entropy-reconstruction", "project_page": "", "abstract": "The mechanisms behind the success of multi-view self-supervised learning (MVSSL) are not yet fully understood. Contrastive MVSSL methods have been studied through the lens of InfoNCE, a lower bound of the Mutual Information (MI). However, the relation between other MVSSL methods and MI remains unclear. We consider a different lower bound on the MI consisting of an entropy and a reconstruction term (ER), and analyze the main MVSSL families through its lens. Through this ER bound, we show that clustering-based methods such as DeepCluster and SwAV maximize the MI. We also re-interpret the mechanisms of distillation-based approaches such as BYOL and DINO, showing that they explicitly maximize the reconstruction term and implicitly encourage a stable entropy, and we confirm this empirically. We show that replacing the objectives of common MVSSL methods with this ER bound achieves competitive performance, while making them stable when training with smaller batch sizes or smaller exponential moving average (EMA) coefficients. Github repo: https://github.com/apple/ml-entropy-reconstruction.", "github_manual": "", "github_regex": "https://github.com/apple/ml-entropy-reconstruction", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-21", "arxiv_id": "2307.10350", "title": "Improving Multimodal Datasets with Image Captioning", "authors": [ "Thao Nguyen", "Samir Yitzhak Gadre", "Gabriel Ilharco", "Sewoong Oh", "Ludwig Schmidt" ], "github": "", "project_page": "", "abstract": "Massive web datasets play a key role in the success of large vision-language models like CLIP and Flamingo. However, the raw web data is noisy, and existing filtering methods to reduce noise often come at the expense of data diversity. Our work focuses on caption quality as one major source of noise, and studies how generated captions can increase the utility of web-scraped datapoints with nondescript text. Through exploring different mixing strategies for raw and generated captions, we outperform the best filtering method proposed by the DataComp benchmark by 2% on ImageNet and 4% on average across 38 tasks, given a candidate pool of 128M image-text pairs. Our best approach is also 2x better at Flickr and MS-COCO retrieval. We then analyze what makes synthetic captions an effective source of text supervision. In experimenting with different image captioning models, we also demonstrate that the performance of a model on standard image captioning benchmarks (e.g., NoCaps CIDEr) is not a reliable indicator of the utility of the captions it generates for multimodal training. Finally, our experiments with using generated captions at DataComp's large scale (1.28B image-text pairs) offer insights into the limitations of synthetic text, as well as the importance of image curation with increasing training data quantity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-21", "arxiv_id": "2307.10928", "title": "FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets", "authors": [ "Seonghyeon Ye", "Doyoung Kim", "Sungdong Kim", "Hyeonbin Hwang", "Seungone Kim", "Yongrae Jo", "James Thorne", "Juho Kim", "Minjoon Seo" ], "github": "https://github.com/kaistAI/FLASK", "project_page": "", "abstract": "Evaluation of Large Language Models (LLMs) is challenging because aligning to human values requires the composition of multiple skills and the required set of skills varies depending on the instruction. Recent studies have evaluated the performance of LLMs in two ways, (1) automatic evaluation on several independent benchmarks and (2) human or machined-based evaluation giving an overall score to the response. However, both settings are coarse-grained evaluations, not considering the nature of user instructions that require instance-wise skill composition, which limits the interpretation of the true capabilities of LLMs. In this paper, we introduce FLASK (Fine-grained Language Model Evaluation based on Alignment SKill Sets), a fine-grained evaluation protocol that can be used for both model-based and human-based evaluation which decomposes coarse-level scoring to an instance-wise skill set-level. Specifically, we define 12 fine-grained skills needed for LLMs to follow open-ended user instructions and construct an evaluation set by allocating a set of skills for each instance. Additionally, by annotating the target domains and difficulty level for each instance, FLASK provides a holistic view with a comprehensive analysis of a model's performance depending on skill, domain, and difficulty. Through using FLASK, we compare multiple open-sourced and proprietary LLMs and observe highly-correlated findings between model-based and human-based evaluations. FLASK enables developers to more accurately measure the model performance and how it can be improved by analyzing factors that make LLMs proficient in particular skills. For practitioners, FLASK can be used to recommend suitable models for particular situations through comprehensive comparison among various LLMs. We release the evaluation data and code implementation at https://github.com/kaistAI/FLASK.", "github_manual": "", "github_regex": "https://github.com/kaistAI/FLASK", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-21", "arxiv_id": "2307.10936", "title": "PASTA: Pretrained Action-State Transformer Agents", "authors": [ "Raphael Boige", "Yannis Flet-Berliac", "Arthur Flajolet", "Guillaume Richard", "Thomas Pierrot" ], "github": "", "project_page": "", "abstract": "Self-supervised learning has brought about a revolutionary paradigm shift in various computing domains, including NLP, vision, and biology. Recent approaches involve pre-training transformer models on vast amounts of unlabeled data, serving as a starting point for efficiently solving downstream tasks. In the realm of reinforcement learning, researchers have recently adapted these approaches by developing models pre-trained on expert trajectories, enabling them to address a wide range of tasks, from robotics to recommendation systems. However, existing methods mostly rely on intricate pre-training objectives tailored to specific downstream applications. This paper presents a comprehensive investigation of models we refer to as Pretrained Action-State Transformer Agents (PASTA). Our study uses a unified methodology and covers an extensive set of general downstream tasks including behavioral cloning, offline RL, sensor failure robustness, and dynamics change adaptation. Our goal is to systematically compare various design choices and provide valuable insights to practitioners for building robust models. Key highlights of our study include tokenization at the action and state component level, using fundamental pre-training objectives like next token prediction, training models across diverse domains simultaneously, and using parameter efficient fine-tuning (PEFT). The developed models in our study contain fewer than 10 million parameters and the application of PEFT enables fine-tuning of fewer than 10,000 parameters during downstream adaptation, allowing a broad community to use these models and reproduce our experiments. We hope that this study will encourage further research into the use of transformers with first-principles design choices to represent RL trajectories and contribute to robust policy learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-21", "arxiv_id": "2307.10635", "title": "SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models", "authors": [ "Xiaoxuan Wang", "Ziniu Hu", "Pan Lu", "Yanqiao Zhu", "Jieyu Zhang", "Satyen Subramaniam", "Arjun R. Loomba", "Shichang Zhang", "Yizhou Sun", "Wei Wang" ], "github": "https://github.com/mandyyyyii/scibench", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have demonstrated notable progress on many mathematical benchmarks. However, most of these benchmarks only feature problems grounded in junior and senior high school subjects, contain only multiple-choice questions, and are confined to a limited scope of elementary arithmetic operations. To address these issues, this paper introduces an expansive benchmark suite SciBench that aims to systematically examine the reasoning capabilities required for complex scientific problem solving. SciBench contains two carefully curated datasets: an open set featuring a range of collegiate-level scientific problems drawn from mathematics, chemistry, and physics textbooks, and a closed set comprising problems from undergraduate-level exams in computer science and mathematics. Based on the two datasets, we conduct an in-depth benchmark study of two representative LLMs with various prompting strategies. The results reveal that current LLMs fall short of delivering satisfactory performance, with an overall score of merely 35.80%. Furthermore, through a detailed user study, we categorize the errors made by LLMs into ten problem-solving abilities. Our analysis indicates that no single prompting strategy significantly outperforms others and some strategies that demonstrate improvements in certain problem-solving skills result in declines in other skills. We envision that SciBench will catalyze further developments in the reasoning abilities of LLMs, thereby ultimately contributing to scientific research and discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mandyyyyii/scibench", "github_api_added_by": "auto" }, { "date": "2023-07-21", "arxiv_id": "2307.10558", "title": "Instruction-following Evaluation through Verbalizer Manipulation", "authors": [ "Shiyang Li", "Jun Yan", "Hai Wang", "Zheng Tang", "Xiang Ren", "Vijay Srinivasan", "Hongxia Jin" ], "github": "", "project_page": "", "abstract": "While instruction-tuned models have shown remarkable success in various natural language processing tasks, accurately evaluating their ability to follow instructions remains challenging. Existing benchmarks primarily focus on common instructions that align well with what the model learned during training. However, proficiency in responding to these instructions does not necessarily imply strong ability in instruction following. In this paper, we propose a novel instruction-following evaluation protocol called verbalizer manipulation. It instructs the model to verbalize the task label with words aligning with model priors to different extents, adopting verbalizers from highly aligned (e.g., outputting ``postive'' for positive sentiment), to minimally aligned (e.g., outputting ``negative'' for positive sentiment). Verbalizer manipulation can be seamlessly integrated with any classification benchmark to examine the model's reliance on priors and its ability to override them to accurately follow the instructions. We conduct a comprehensive evaluation of four major model families across nine datasets, employing twelve sets of verbalizers for each of them. We observe that the instruction-following abilities of models, across different families and scales, are significantly distinguished by their performance on less natural verbalizers. Even the strongest GPT-4 model struggles to perform better than random guessing on the most challenging verbalizer, emphasizing the need for continued advancements to improve their instruction-following abilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-24", "arxiv_id": "2307.11410", "title": "Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning", "authors": [ "Jian Ma", "Junhao Liang", "Chen Chen", "Haonan Lu" ], "github": "https://github.com/OPPO-Mente-Lab/Subject-Diffusion", "project_page": "", "abstract": "Recent progress in personalized image generation using diffusion models has been significant. However, development in the area of open-domain and non-fine-tuning personalized image generation is proceeding rather slowly. In this paper, we propose Subject-Diffusion, a novel open-domain personalized image generation model that, in addition to not requiring test-time fine-tuning, also only requires a single reference image to support personalized generation of single- or multi-subject in any domain. Firstly, we construct an automatic data labeling tool and use the LAION-Aesthetics dataset to construct a large-scale dataset consisting of 76M images and their corresponding subject detection bounding boxes, segmentation masks and text descriptions. Secondly, we design a new unified framework that combines text and image semantics by incorporating coarse location and fine-grained reference image control to maximize subject fidelity and generalization. Furthermore, we also adopt an attention control mechanism to support multi-subject generation. Extensive qualitative and quantitative results demonstrate that our method outperforms other SOTA frameworks in single, multiple, and human customized image generation. Please refer to our https://oppo-mente-lab.github.io/subject_diffusion/{project page}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OPPO-Mente-Lab/Subject-Diffusion", "github_api_added_by": "auto" }, { "date": "2023-07-24", "arxiv_id": "2307.11526", "title": "CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields", "authors": [ "Ziyuan Luo", "Qing Guo", "Ka Chun Cheung", "Simon See", "Renjie Wan" ], "github": "https://github.com/luo-ziyuan/CopyRNeRF-code", "project_page": "", "abstract": "Neural Radiance Fields (NeRF) have the potential to be a major representation of media. Since training a NeRF has never been an easy task, the protection of its model copyright should be a priority. In this paper, by analyzing the pros and cons of possible copyright protection solutions, we propose to protect the copyright of NeRF models by replacing the original color representation in NeRF with a watermarked color representation. Then, a distortion-resistant rendering scheme is designed to guarantee robust message extraction in 2D renderings of NeRF. Our proposed method can directly protect the copyright of NeRF models while maintaining high rendering quality and bit accuracy when compared among optional solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/luo-ziyuan/CopyRNeRF-code", "github_api_added_by": "auto" }, { "date": "2023-07-24", "arxiv_id": "2307.11418", "title": "FaceCLIPNeRF: Text-driven 3D Face Manipulation using Deformable Neural Radiance Fields", "authors": [ "Sungwon Hwang", "Junha Hyung", "Daejin Kim", "Min-Jung Kim", "Jaegul Choo" ], "github": "", "project_page": "", "abstract": "As recent advances in Neural Radiance Fields (NeRF) have enabled high-fidelity 3D face reconstruction and novel view synthesis, its manipulation also became an essential task in 3D vision. However, existing manipulation methods require extensive human labor, such as a user-provided semantic mask and manual attribute search unsuitable for non-expert users. Instead, our approach is designed to require a single text to manipulate a face reconstructed with NeRF. To do so, we first train a scene manipulator, a latent code-conditional deformable NeRF, over a dynamic scene to control a face deformation using the latent code. However, representing a scene deformation with a single latent code is unfavorable for compositing local deformations observed in different instances. As so, our proposed Position-conditional Anchor Compositor (PAC) learns to represent a manipulated scene with spatially varying latent codes. Their renderings with the scene manipulator are then optimized to yield high cosine similarity to a target text in CLIP embedding space for text-driven manipulation. To the best of our knowledge, our approach is the first to address the text-driven manipulation of a face reconstructed with NeRF. Extensive results, comparisons, and ablation studies demonstrate the effectiveness of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-24", "arxiv_id": "2307.11118", "title": "Diffusion Sampling with Momentum for Mitigating Divergence Artifacts", "authors": [ "Suttisak Wizadwongsa", "Worameth Chinchuthakun", "Pramook Khungurn", "Amit Raj", "Supasorn Suwajanakorn" ], "github": "https://github.com/sWizad/momentum-diffusion", "project_page": "", "abstract": "Despite the remarkable success of diffusion models in image generation, slow sampling remains a persistent issue. To accelerate the sampling process, prior studies have reformulated diffusion sampling as an ODE/SDE and introduced higher-order numerical methods. However, these methods often produce divergence artifacts, especially with a low number of sampling steps, which limits the achievable acceleration. In this paper, we investigate the potential causes of these artifacts and suggest that the small stability regions of these methods could be the principal cause. To address this issue, we propose two novel techniques. The first technique involves the incorporation of Heavy Ball (HB) momentum, a well-known technique for improving optimization, into existing diffusion numerical methods to expand their stability regions. We also prove that the resulting methods have first-order convergence. The second technique, called Generalized Heavy Ball (GHVB), constructs a new high-order method that offers a variable trade-off between accuracy and artifact suppression. Experimental results show that our techniques are highly effective in reducing artifacts and improving image quality, surpassing state-of-the-art diffusion solvers on both pixel-based and latent-based diffusion models for low-step sampling. Our research provides novel insights into the design of numerical methods for future diffusion work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sWizad/momentum-diffusion", "github_api_added_by": "auto" }, { "date": "2023-07-25", "arxiv_id": "2307.12981", "title": "3D-LLM: Injecting the 3D World into Large Language Models", "authors": [ "Yining Hong", "Haoyu Zhen", "Peihao Chen", "Shuhong Zheng", "Yilun Du", "Zhenfang Chen", "Chuang Gan" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs. Project Page: : https://vis-www.cs.umass.edu/3dllm/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-25", "arxiv_id": "2307.12856", "title": "A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis", "authors": [ "Izzeddin Gur", "Hiroki Furuta", "Austin Huang", "Mustafa Safdari", "Yutaka Matsuo", "Douglas Eck", "Aleksandra Faust" ], "github": "", "project_page": "", "abstract": "Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web navigation. However, the performance on real-world websites has still suffered from (1) open domainness, (2) limited context length, and (3) lack of inductive bias on HTML. We introduce WebAgent, an LLM-driven agent that can complete the tasks on real websites following natural language instructions. WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites via generated Python programs from those. We design WebAgent with Flan-U-PaLM, for grounded code generation, and HTML-T5, new pre-trained LLMs for long HTML documents using local and global attention mechanisms and a mixture of long-span denoising objectives, for planning and summarization. We empirically demonstrate that our recipe improves the success on a real website by over 50%, and that HTML-T5 is the best model to solve HTML-based tasks; achieving 14.9% higher success rate than prior SoTA on the MiniWoB web navigation benchmark and better accuracy on offline task planning evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-25", "arxiv_id": "2307.12560", "title": "Interpolating between Images with Diffusion Models", "authors": [ "Clinton J. Wang", "Polina Golland" ], "github": "", "project_page": "", "abstract": "One little-explored frontier of image generation and editing is the task of interpolating between two input images, a feature missing from all currently deployed image generation pipelines. We argue that such a feature can expand the creative applications of such models, and propose a method for zero-shot interpolation using latent diffusion models. We apply interpolation in the latent space at a sequence of decreasing noise levels, then perform denoising conditioned on interpolated text embeddings derived from textual inversion and (optionally) subject poses. For greater consistency, or to specify additional criteria, we can generate several candidates and use CLIP to select the highest quality image. We obtain convincing interpolations across diverse subject poses, image styles, and image content, and show that standard quantitative metrics such as FID are insufficient to measure the quality of an interpolation. Code and data are available at https://clintonjwang.github.io/interpolation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-25", "arxiv_id": "2307.11795", "title": "Prompting Large Language Models with Speech Recognition Abilities", "authors": [ "Yassir Fathullah", "Chunyang Wu", "Egor Lakomkin", "Junteng Jia", "Yuan Shangguan", "Ke Li", "Jinxi Guo", "Wenhan Xiong", "Jay Mahadeokar", "Ozlem Kalinli", "Christian Fuegen", "Mike Seltzer" ], "github": "", "project_page": "", "abstract": "Large language models have proven themselves highly flexible, able to solve a wide range of generative tasks, such as abstractive summarization and open-ended question answering. In this paper we extend the capabilities of LLMs by directly attaching a small audio encoder allowing it to perform speech recognition. By directly prepending a sequence of audial embeddings to the text token embeddings, the LLM can be converted to an automatic speech recognition (ASR) system, and be used in the exact same manner as its textual counterpart. Experiments on Multilingual LibriSpeech (MLS) show that incorporating a conformer encoder into the open sourced LLaMA-7B allows it to outperform monolingual baselines by 18% and perform multilingual speech recognition despite LLaMA being trained overwhelmingly on English text. Furthermore, we perform ablation studies to investigate whether the LLM can be completely frozen during training to maintain its original capabilities, scaling up the audio encoder, and increasing the audio encoder striding to generate fewer embeddings. The results from these studies show that multilingual ASR is possible even when the LLM is frozen or when strides of almost 1 second are used in the audio encoder opening up the possibility for LLMs to operate on long-form audio.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-25", "arxiv_id": "2307.12533", "title": "PUMA: Secure Inference of LLaMA-7B in Five Minutes", "authors": [ "Ye Dong", "Wen-jie Lu", "Yancheng Zheng", "Haoqi Wu", "Derun Zhao", "Jin Tan", "Zhicong Huang", "Cheng Hong", "Tao Wei", "Wenguang Cheng" ], "github": "", "project_page": "", "abstract": "With ChatGPT as a representative, tons of companies have began to provide services based on large Transformers models. However, using such a service inevitably leak users' prompts to the model provider. Previous studies have studied secure inference for Transformer models using secure multiparty computation (MPC), where model parameters and clients' prompts are kept secret. Despite this, these frameworks are still limited in terms of model performance, efficiency, and deployment. To address these limitations, we propose framework PUMA to enable fast and secure Transformer model inference. Our framework designs high quality approximations for expensive functions, such as GeLU and Softmax, which significantly reduce the cost of secure inference while preserving the model performance. Additionally, we design secure Embedding and LayerNorm procedures that faithfully implement the desired functionality without undermining the Transformer architecture. PUMA is about 2x faster than the state-of-the-art MPC framework MPCFORMER(ICLR 2023) and has similar accuracy as plaintext models without fine-tuning (which the previous works failed to achieve). One more thing, PUMA can evaluate LLaMA-7B in around 5 minutes to generate 1 token. To our best knowledge, this is the first time that a model with such a parameter size is able to be evaluated under MPC. PUMA has been open-sourced in the Github repository of SecretFlow-SPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-25", "arxiv_id": "2307.12976", "title": "Evaluating the Ripple Effects of Knowledge Editing in Language Models", "authors": [ "Roi Cohen", "Eden Biran", "Ori Yoran", "Amir Globerson", "Mor Geva" ], "github": "https://github.com/edenbiran/rippleedits", "project_page": "", "abstract": "Modern language models capture a large body of factual knowledge. However, some facts can be incorrectly induced or become obsolete over time, resulting in factually incorrect generations. This has led to the development of various editing methods that allow updating facts encoded by the model. Evaluation of these methods has primarily focused on testing whether an individual fact has been successfully injected, and if similar predictions for other subjects have not changed. Here we argue that such evaluation is limited, since injecting one fact (e.g. ``Jack Depp is the son of Johnny Depp'') introduces a ``ripple effect'' in the form of additional facts that the model needs to update (e.g.``Jack Depp is the sibling of Lily-Rose Depp''). To address this issue, we propose a novel set of evaluation criteria that consider the implications of an edit on related facts. Using these criteria, we then construct , a diagnostic benchmark of 5K factual edits, capturing a variety of types of ripple effects. We evaluate prominent editing methods on , showing that current methods fail to introduce consistent changes in the model's knowledge. In addition, we find that a simple in-context editing baseline obtains the best scores on our benchmark, suggesting a promising research direction for model editing.", "github_manual": "https://github.com/edenbiran/rippleedits", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-25", "arxiv_id": "2307.12950", "title": "RLCD: Reinforcement Learning from Contrast Distillation for Language Model Alignment", "authors": [ "Kevin Yang", "Dan Klein", "Asli Celikyilmaz", "Nanyun Peng", "Yuandong Tian" ], "github": "https://github.com/facebookresearch/rlcd", "project_page": "", "abstract": "We propose Reinforcement Learning from Contrast Distillation (RLCD), a method for aligning language models to follow natural language principles without using human feedback. RLCD trains a preference model using simulated preference pairs that contain both a high-quality and low-quality example, generated using contrasting positive and negative prompts. The preference model is then used to improve a base unaligned language model via reinforcement learning. Empirically, RLCD outperforms RLAIF (Bai et al., 2022b) and context distillation (Huang et al., 2022) baselines across three diverse alignment tasks--harmlessness, helpfulness, and story outline generation--and on both 7B and 30B model scales for preference data simulation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/rlcd", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-25", "arxiv_id": "2307.11768", "title": "Question Decomposition Improves the Faithfulness of Model-Generated Reasoning", "authors": [ "Ansh Radhakrishnan", "Karina Nguyen", "Anna Chen", "Carol Chen", "Carson Denison", "Danny Hernandez", "Esin Durmus", "Evan Hubinger", "Jackson Kernion", "Kamil\u0117 Luko\u0161i\u016bt\u0117", "Newton Cheng", "Nicholas Joseph", "Nicholas Schiefer", "Oliver Rausch", "Sam McCandlish", "Sheer El Showk", "Tamera Lanham", "Tim Maxwell", "Venkatesa Chandrasekaran", "Zac Hatfield-Dodds", "Jared Kaplan", "Jan Brauner", "Samuel R. Bowman", "Ethan Perez" ], "github": "https://github.com/anthropics/decompositionfaithfulnesspaper", "project_page": "", "abstract": "As large language models (LLMs) perform more difficult tasks, it becomes harder to verify the correctness and safety of their behavior. One approach to help with this issue is to prompt LLMs to externalize their reasoning, e.g., by having them generate step-by-step reasoning as they answer a question (Chain-of-Thought; CoT). The reasoning may enable us to check the process that models use to perform tasks. However, this approach relies on the stated reasoning faithfully reflecting the model's actual reasoning, which is not always the case. To improve over the faithfulness of CoT reasoning, we have models generate reasoning by decomposing questions into subquestions. Decomposition-based methods achieve strong performance on question-answering tasks, sometimes approaching that of CoT while improving the faithfulness of the model's stated reasoning on several recently-proposed metrics. By forcing the model to answer simpler subquestions in separate contexts, we greatly increase the faithfulness of model-generated reasoning over CoT, while still achieving some of the performance gains of CoT. Our results show it is possible to improve the faithfulness of model-generated reasoning; continued improvements may lead to reasoning that enables us to verify the correctness and safety of LLM behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/anthropics/decompositionfaithfulnesspaper", "github_api_added_by": "auto" }, { "date": "2023-07-25", "arxiv_id": "2307.12169", "title": "Optimized Network Architectures for Large Language Model Training with Billions of Parameters", "authors": [ "Weiyang Wang", "Manya Ghobadi", "Kayvon Shakeri", "Ying Zhang", "Naader Hasani" ], "github": "", "project_page": "", "abstract": "This paper challenges the well-established paradigm for building any-to-any networks for training Large Language Models (LLMs). We show that LLMs exhibit a unique communication pattern where only small groups of GPUs require high-bandwidth any-to-any communication within them, to achieve near-optimal training performance. Across these groups of GPUs, the communication is insignificant, sparse, and homogeneous. We propose a new network architecture that closely resembles the communication requirement of LLMs. Our architecture partitions the cluster into sets of GPUs interconnected with non-blocking any-to-any high-bandwidth interconnects that we call HB domains. Across the HB domains, the network only connects GPUs with communication demands. We call this network a \"rail-only\" connection, and show that our proposed architecture reduces the network cost by up to 75% compared to the state-of-the-art any-to-any Clos networks without compromising the performance of LLM training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-25", "arxiv_id": "2307.12854", "title": "Multiscale Video Pretraining for Long-Term Activity Forecasting", "authors": [ "Reuben Tan", "Matthias De Lange", "Michael Iuzzolino", "Bryan A. Plummer", "Kate Saenko", "Karl Ridgeway", "Lorenzo Torresani" ], "github": "", "project_page": "", "abstract": "Long-term activity forecasting is an especially challenging research problem because it requires understanding the temporal relationships between observed actions, as well as the variability and complexity of human activities. Despite relying on strong supervision via expensive human annotations, state-of-the-art forecasting approaches often generalize poorly to unseen data. To alleviate this issue, we propose Multiscale Video Pretraining (MVP), a novel self-supervised pretraining approach that learns robust representations for forecasting by learning to predict contextualized representations of future video clips over multiple timescales. MVP is based on our observation that actions in videos have a multiscale nature, where atomic actions typically occur at a short timescale and more complex actions may span longer timescales. We compare MVP to state-of-the-art self-supervised video learning approaches on downstream long-term forecasting tasks including long-term action anticipation and video summary prediction. Our comprehensive experiments across the Ego4D and Epic-Kitchens-55/100 datasets demonstrate that MVP out-performs state-of-the-art methods by significant margins. Notably, MVP obtains a relative performance gain of over 20% accuracy in video summary forecasting over existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-25", "arxiv_id": "2307.12612", "title": "Less is More: Focus Attention for Efficient DETR", "authors": [ "Dehua Zheng", "Wenhui Dong", "Hailin Hu", "Xinghao Chen", "Yunhe Wang" ], "github": "https://github.com/huawei-noah/noah-research/tree/master/Focus-DETR", "project_page": "", "abstract": "DETR-like models have significantly boosted the performance of detectors and even outperformed classical convolutional models. However, all tokens are treated equally without discrimination brings a redundant computational burden in the traditional encoder structure. The recent sparsification strategies exploit a subset of informative tokens to reduce attention complexity maintaining performance through the sparse encoder. But these methods tend to rely on unreliable model statistics. Moreover, simply reducing the token population hinders the detection performance to a large extent, limiting the application of these sparse models. We propose Focus-DETR, which focuses attention on more informative tokens for a better trade-off between computation efficiency and model accuracy. Specifically, we reconstruct the encoder with dual attention, which includes a token scoring mechanism that considers both localization and category semantic information of the objects from multi-scale feature maps. We efficiently abandon the background queries and enhance the semantic interaction of the fine-grained object queries based on the scores. Compared with the state-of-the-art sparse DETR-like detectors under the same setting, our Focus-DETR gets comparable complexity while achieving 50.4AP (+2.2) on COCO. The code is available at https://github.com/huawei-noah/noah-research/tree/master/Focus-DETR and https://gitee.com/mindspore/models/tree/master/research/cv/Focus-DETR.", "github_manual": "", "github_regex": "https://github.com/huawei-noah/noah-research/tree/master/Focus-DETR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-25", "arxiv_id": "2307.12698", "title": "MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features", "authors": [ "Adrien Bardes", "Jean Ponce", "Yann LeCun" ], "github": "", "project_page": "", "abstract": "Self-supervised learning of visual representations has been focusing on learning content features, which do not capture object motion or location, and focus on identifying and differentiating objects in images and videos. On the other hand, optical flow estimation is a task that does not involve understanding the content of the images on which it is estimated. We unify the two approaches and introduce MC-JEPA, a joint-embedding predictive architecture and self-supervised learning approach to jointly learn optical flow and content features within a shared encoder, demonstrating that the two associated objectives; the optical flow estimation objective and the self-supervised learning objective; benefit from each other and thus learn content features that incorporate motion information. The proposed approach achieves performance on-par with existing unsupervised optical flow benchmarks, as well as with common self-supervised learning approaches on downstream tasks such as semantic segmentation of images and videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-26", "arxiv_id": "2307.13269", "title": "LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition", "authors": [ "Chengsong Huang", "Qian Liu", "Bill Yuchen Lin", "Tianyu Pang", "Chao Du", "Min Lin" ], "github": "https://github.com/sail-sg/lorahub", "project_page": "", "abstract": "Low-rank adaptations (LoRA) are often employed to fine-tune large language models (LLMs) for new tasks. This paper investigates LoRA composability for cross-task generalization and introduces LoraHub, a strategic framework devised for the purposive assembly of LoRA modules trained on diverse given tasks, with the objective of achieving adaptable performance on unseen tasks. With just a few examples from a novel task, LoraHub enables the fluid combination of multiple LoRA modules, eradicating the need for human expertise. Notably, the composition requires neither additional model parameters nor gradients. Our empirical results, derived from the Big-Bench Hard (BBH) benchmark, suggest that LoraHub can effectively mimic the performance of in-context learning in few-shot scenarios, excluding the necessity of in-context examples alongside each inference input. A significant contribution of our research is the fostering of a community for LoRA, where users can share their trained LoRA modules, thereby facilitating their application to new tasks. We anticipate this resource will widen access to and spur advancements in general intelligence as well as LLMs in production. Code will be available at https://github.com/sail-sg/lorahub.", "github_manual": "", "github_regex": "https://github.com/sail-sg/lorahub", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-26", "arxiv_id": "2307.13692", "title": "ARB: Advanced Reasoning Benchmark for Large Language Models", "authors": [ "Tomohiro Sawada", "Daniel Paleka", "Alexander Havrilla", "Pranav Tadepalli", "Paula Vidas", "Alexander Kranias", "John J. Nay", "Kshitij Gupta", "Aran Komatsuzaki" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable performance on various quantitative reasoning and knowledge benchmarks. However, many of these benchmarks are losing utility as LLMs get increasingly high scores, despite not yet reaching expert performance in these domains. We introduce ARB, a novel benchmark composed of advanced reasoning problems in multiple fields. ARB presents a more challenging test than prior benchmarks, featuring problems in mathematics, physics, biology, chemistry, and law. As a subset of ARB, we introduce a challenging set of math and physics problems which require advanced symbolic reasoning and domain knowledge. We evaluate recent models such as GPT-4 and Claude on ARB and demonstrate that current models score well below 50% on more demanding tasks. In order to improve both automatic and assisted evaluation capabilities, we introduce a rubric-based evaluation approach, allowing GPT-4 to score its own intermediate reasoning steps. Further, we conduct a human evaluation of the symbolic subset of ARB, finding promising agreement between annotators and GPT-4 rubric evaluation scores.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-26", "arxiv_id": "2307.13383", "title": "Predicting Code Coverage without Execution", "authors": [ "Michele Tufano", "Shubham Chandel", "Anisha Agarwal", "Neel Sundaresan", "Colin Clement" ], "github": "https://github.com/microsoft/coverage-eval", "project_page": "", "abstract": "Code coverage is a widely used metric for quantifying the extent to which program elements, such as statements or branches, are executed during testing. Calculating code coverage is resource-intensive, requiring code building and execution with additional overhead for the instrumentation. Furthermore, computing coverage of any snippet of code requires the whole program context. Using Machine Learning to amortize this expensive process could lower the cost of code coverage by requiring only the source code context, and the task of code coverage prediction can be a novel benchmark for judging the ability of models to understand code. We propose a novel benchmark task called Code Coverage Prediction for Large Language Models (LLMs). We formalize this task to evaluate the capability of LLMs in understanding code execution by determining which lines of a method are executed by a given test case and inputs. We curate and release a dataset we call COVERAGEEVAL by executing tests and code from the HumanEval dataset and collecting code coverage information. We report the performance of four state-of-the-art LLMs used for code-related tasks, including OpenAI's GPT-4 and GPT-3.5-Turbo, Google's BARD, and Anthropic's Claude, on the Code Coverage Prediction task. Finally, we argue that code coverage as a metric and pre-training data source are valuable for overall LLM performance on software engineering tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/coverage-eval", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-26", "arxiv_id": "2307.13226", "title": "Strivec: Sparse Tri-Vector Radiance Fields", "authors": [ "Quankai Gao", "Qiangeng Xu", "Hao Su", "Ulrich Neumann", "Zexiang Xu" ], "github": "https://github.com/zerg-overmind/strivec", "project_page": "", "abstract": "We propose Strivec, a novel neural representation that models a 3D scene as a radiance field with sparsely distributed and compactly factorized local tensor feature grids. Our approach leverages tensor decomposition, following the recent work TensoRF, to model the tensor grids. In contrast to TensoRF which uses a global tensor and focuses on their vector-matrix decomposition, we propose to utilize a cloud of local tensors and apply the classic CANDECOMP/PARAFAC (CP) decomposition to factorize each tensor into triple vectors that express local feature distributions along spatial axes and compactly encode a local neural field. We also apply multi-scale tensor grids to discover the geometry and appearance commonalities and exploit spatial coherence with the tri-vector factorization at multiple local scales. The final radiance field properties are regressed by aggregating neural features from multiple local tensors across all scales. Our tri-vector tensors are sparsely distributed around the actual scene surface, discovered by a fast coarse reconstruction, leveraging the sparsity of a 3D scene. We demonstrate that our model can achieve better rendering quality while using significantly fewer parameters than previous methods, including TensoRF and Instant-NGP.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zerg-overmind/strivec", "github_api_added_by": "auto" }, { "date": "2023-07-26", "arxiv_id": "2307.13101", "title": "Contrastive Example-Based Control", "authors": [ "Kyle Hatch", "Benjamin Eysenbach", "Rafael Rafailov", "Tianhe Yu", "Ruslan Salakhutdinov", "Sergey Levine", "Chelsea Finn" ], "github": "https://github.com/khatch31/laeo", "project_page": "", "abstract": "While many real-world problems that might benefit from reinforcement learning, these problems rarely fit into the MDP mold: interacting with the environment is often expensive and specifying reward functions is challenging. Motivated by these challenges, prior work has developed data-driven approaches that learn entirely from samples from the transition dynamics and examples of high-return states. These methods typically learn a reward function from high-return states, use that reward function to label the transitions, and then apply an offline RL algorithm to these transitions. While these methods can achieve good results on many tasks, they can be complex, often requiring regularization and temporal difference updates. In this paper, we propose a method for offline, example-based control that learns an implicit model of multi-step transitions, rather than a reward function. We show that this implicit model can represent the Q-values for the example-based control problem. Across a range of state-based and image-based offline control tasks, our method outperforms baselines that use learned reward functions; additional experiments demonstrate improved robustness and scaling with dataset size.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/khatch31/laeo", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-27", "arxiv_id": "2307.14335", "title": "WavJourney: Compositional Audio Creation with Large Language Models", "authors": [ "Xubo Liu", "Zhongkai Zhu", "Haohe Liu", "Yi Yuan", "Meng Cui", "Qiushi Huang", "Jinhua Liang", "Yin Cao", "Qiuqiang Kong", "Mark D. Plumbley", "Wenwu Wang" ], "github": "https://github.com/Audio-AGI/WavJourney", "project_page": "", "abstract": "Large Language Models (LLMs) have shown great promise in integrating diverse expert models to tackle intricate language and vision tasks. Despite their significance in advancing the field of Artificial Intelligence Generated Content (AIGC), their potential in intelligent audio content creation remains unexplored. In this work, we tackle the problem of creating audio content with storylines encompassing speech, music, and sound effects, guided by text instructions. We present WavJourney, a system that leverages LLMs to connect various audio models for audio content generation. Given a text description of an auditory scene, WavJourney first prompts LLMs to generate a structured script dedicated to audio storytelling. The audio script incorporates diverse audio elements, organized based on their spatio-temporal relationships. As a conceptual representation of audio, the audio script provides an interactive and interpretable rationale for human engagement. Afterward, the audio script is fed into a script compiler, converting it into a computer program. Each line of the program calls a task-specific audio generation model or computational operation function (e.g., concatenate, mix). The computer program is then executed to obtain an explainable solution for audio generation. We demonstrate the practicality of WavJourney across diverse real-world scenarios, including science fiction, education, and radio play. The explainable and interactive design of WavJourney fosters human-machine co-creation in multi-round dialogues, enhancing creative control and adaptability in audio production. WavJourney audiolizes the human imagination, opening up new avenues for creativity in multimedia content creation.", "github_manual": "https://github.com/Audio-AGI/WavJourney", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-27", "arxiv_id": "2307.13702", "title": "Measuring Faithfulness in Chain-of-Thought Reasoning", "authors": [ "Tamera Lanham", "Anna Chen", "Ansh Radhakrishnan", "Benoit Steiner", "Carson Denison", "Danny Hernandez", "Dustin Li", "Esin Durmus", "Evan Hubinger", "Jackson Kernion", "Kamil\u0117 Luko\u0161i\u016bt\u0117", "Karina Nguyen", "Newton Cheng", "Nicholas Joseph", "Nicholas Schiefer", "Oliver Rausch", "Robin Larson", "Sam McCandlish", "Sandipan Kundu", "Saurav Kadavath", "Shannon Yang", "Thomas Henighan", "Timothy Maxwell", "Timothy Telleen-Lawton", "Tristan Hume", "Zac Hatfield-Dodds", "Jared Kaplan", "Jan Brauner", "Samuel R. Bowman", "Ethan Perez" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) perform better when they produce step-by-step, \"Chain-of-Thought\" (CoT) reasoning before answering a question, but it is unclear if the stated reasoning is a faithful explanation of the model's actual reasoning (i.e., its process for answering the question). We investigate hypotheses for how CoT reasoning may be unfaithful, by examining how the model predictions change when we intervene on the CoT (e.g., by adding mistakes or paraphrasing it). Models show large variation across tasks in how strongly they condition on the CoT when predicting their answer, sometimes relying heavily on the CoT and other times primarily ignoring it. CoT's performance boost does not seem to come from CoT's added test-time compute alone or from information encoded via the particular phrasing of the CoT. As models become larger and more capable, they produce less faithful reasoning on most tasks we study. Overall, our results suggest that CoT can be faithful if the circumstances such as the model size and task are carefully chosen.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-27", "arxiv_id": "2307.13854", "title": "WebArena: A Realistic Web Environment for Building Autonomous Agents", "authors": [ "Shuyan Zhou", "Frank F. Xu", "Hao Zhu", "Xuhui Zhou", "Robert Lo", "Abishek Sridhar", "Xianyi Cheng", "Yonatan Bisk", "Daniel Fried", "Uri Alon", "Graham Neubig" ], "github": "https://github.com/web-arena-x/webarena", "project_page": "", "abstract": "With generative AI advances, the exciting potential for autonomous agents to manage daily tasks via natural language commands has emerged. However, cur rent agents are primarily created and tested in simplified synthetic environments, substantially limiting real-world scenario representation. In this paper, we build an environment for agent command and control that is highly realistic and reproducible. Specifically, we focus on agents that perform tasks on websites, and we create an environment with fully functional websites from four common domains: e-commerce, social forum discussions, collaborative software development, and content management. Our environment is enriched with tools (e.g., a map) and external knowledge bases (e.g., user manuals) to encourage human-like task-solving. Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions. The tasks in our benchmark are diverse, long-horizon, and are designed to emulate tasks that humans routinely perform on the internet. We design and implement several autonomous agents, integrating recent techniques such as reasoning before acting. The results demonstrate that solving complex tasks is challenging: our best GPT-4-based agent only achieves an end-to-end task success rate of 10.59%. These results highlight the need for further development of robust agents, that current state-of-the-art LMs are far from perfect performance in these real-life tasks, and that WebArena can be used to measure such progress. Our code, data, environment reproduction resources, and video demonstrations are publicly available at https://webarena.dev/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/web-arena-x/webarena", "github_api_added_by": "auto" }, { "date": "2023-07-27", "arxiv_id": "2307.13974", "title": "Tracking Anything in High Quality", "authors": [ "Jiawen Zhu", "Zhenyu Chen", "Zeqi Hao", "Shijie Chang", "Lu Zhang", "Dong Wang", "Huchuan Lu", "Bin Luo", "Jun-Yan He", "Jin-Peng Lan", "Hanyuan Chen", "Chenyang Li" ], "github": "https://github.com/jiawen-zhu/HQTrack", "project_page": "", "abstract": "Visual object tracking is a fundamental video task in computer vision. Recently, the notably increasing power of perception algorithms allows the unification of single/multiobject and box/mask-based tracking. Among them, the Segment Anything Model (SAM) attracts much attention. In this report, we propose HQTrack, a framework for High Quality Tracking anything in videos. HQTrack mainly consists of a video multi-object segmenter (VMOS) and a mask refiner (MR). Given the object to be tracked in the initial frame of a video, VMOS propagates the object masks to the current frame. The mask results at this stage are not accurate enough since VMOS is trained on several closeset video object segmentation (VOS) datasets, which has limited ability to generalize to complex and corner scenes. To further improve the quality of tracking masks, a pretrained MR model is employed to refine the tracking results. As a compelling testament to the effectiveness of our paradigm, without employing any tricks such as test-time data augmentations and model ensemble, HQTrack ranks the 2nd place in the Visual Object Tracking and Segmentation (VOTS2023) challenge. Code and models are available at https://github.com/jiawen-zhu/HQTrack.", "github_manual": "", "github_regex": "https://github.com/jiawen-zhu/HQTrack", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-27", "arxiv_id": "2307.14334", "title": "Towards Generalist Biomedical AI", "authors": [ "Tao Tu", "Shekoofeh Azizi", "Danny Driess", "Mike Schaekermann", "Mohamed Amin", "Pi-Chuan Chang", "Andrew Carroll", "Chuck Lau", "Ryutaro Tanno", "Ira Ktena", "Basil Mustafa", "Aakanksha Chowdhery", "Yun Liu", "Simon Kornblith", "David Fleet", "Philip Mansfield", "Sushant Prakash", "Renee Wong", "Sunny Virmani", "Christopher Semturs", "S Sara Mahdavi", "Bradley Green", "Ewa Dominowska", "Blaise Aguera y Arcas", "Joelle Barral", "Dale Webster", "Greg S. Corrado", "Yossi Matias", "Karan Singhal", "Pete Florence", "Alan Karthikesalingam", "Vivek Natarajan" ], "github": "", "project_page": "", "abstract": "Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly encode, integrate, and interpret this data at scale can potentially enable impactful applications ranging from scientific discovery to care delivery. To enable the development of these models, we first curate MultiMedBench, a new multimodal biomedical benchmark. MultiMedBench encompasses 14 diverse tasks such as medical question answering, mammography and dermatology image interpretation, radiology report generation and summarization, and genomic variant calling. We then introduce Med-PaLM Multimodal (Med-PaLM M), our proof of concept for a generalist biomedical AI system. Med-PaLM M is a large multimodal generative model that flexibly encodes and interprets biomedical data including clinical language, imaging, and genomics with the same set of model weights. Med-PaLM M reaches performance competitive with or exceeding the state of the art on all MultiMedBench tasks, often surpassing specialist models by a wide margin. We also report examples of zero-shot generalization to novel medical concepts and tasks, positive transfer learning across tasks, and emergent zero-shot medical reasoning. To further probe the capabilities and limitations of Med-PaLM M, we conduct a radiologist evaluation of model-generated (and human) chest X-ray reports and observe encouraging performance across model scales. In a side-by-side ranking on 246 retrospective chest X-rays, clinicians express a pairwise preference for Med-PaLM M reports over those produced by radiologists in up to 40.50% of cases, suggesting potential clinical utility. While considerable work is needed to validate these models in real-world use cases, our results represent a milestone towards the development of generalist biomedical AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-27", "arxiv_id": "2307.13908", "title": "Points-to-3D: Bridging the Gap between Sparse Points and Shape-Controllable Text-to-3D Generation", "authors": [ "Chaohui Yu", "Qiang Zhou", "Jingliang Li", "Zhe Zhang", "Zhibin Wang", "Fan Wang" ], "github": "", "project_page": "", "abstract": "Text-to-3D generation has recently garnered significant attention, fueled by 2D diffusion models trained on billions of image-text pairs. Existing methods primarily rely on score distillation to leverage the 2D diffusion priors to supervise the generation of 3D models, e.g., NeRF. However, score distillation is prone to suffer the view inconsistency problem, and implicit NeRF modeling can also lead to an arbitrary shape, thus leading to less realistic and uncontrollable 3D generation. In this work, we propose a flexible framework of Points-to-3D to bridge the gap between sparse yet freely available 3D points and realistic shape-controllable 3D generation by distilling the knowledge from both 2D and 3D diffusion models. The core idea of Points-to-3D is to introduce controllable sparse 3D points to guide the text-to-3D generation. Specifically, we use the sparse point cloud generated from the 3D diffusion model, Point-E, as the geometric prior, conditioned on a single reference image. To better utilize the sparse 3D points, we propose an efficient point cloud guidance loss to adaptively drive the NeRF's geometry to align with the shape of the sparse 3D points. In addition to controlling the geometry, we propose to optimize the NeRF for a more view-consistent appearance. To be specific, we perform score distillation to the publicly available 2D image diffusion model ControlNet, conditioned on text as well as depth map of the learned compact geometry. Qualitative and quantitative comparisons demonstrate that Points-to-3D improves view consistency and achieves good shape controllability for text-to-3D generation. Points-to-3D provides users with a new way to improve and control text-to-3D generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-27", "arxiv_id": "2307.13720", "title": "Composite Diffusion | whole >= \u03a3parts", "authors": [ "Vikram Jamwal", "Ramaneswaran S" ], "github": "", "project_page": "", "abstract": "For an artist or a graphic designer, the spatial layout of a scene is a critical design choice. However, existing text-to-image diffusion models provide limited support for incorporating spatial information. This paper introduces Composite Diffusion as a means for artists to generate high-quality images by composing from the sub-scenes. The artists can specify the arrangement of these sub-scenes through a flexible free-form segment layout. They can describe the content of each sub-scene primarily using natural text and additionally by utilizing reference images or control inputs such as line art, scribbles, human pose, canny edges, and more. We provide a comprehensive and modular method for Composite Diffusion that enables alternative ways of generating, composing, and harmonizing sub-scenes. Further, we wish to evaluate the composite image for effectiveness in both image quality and achieving the artist's intent. We argue that existing image quality metrics lack a holistic evaluation of image composites. To address this, we propose novel quality criteria especially relevant to composite generation. We believe that our approach provides an intuitive method of art creation. Through extensive user surveys, quantitative and qualitative analysis, we show how it achieves greater spatial, semantic, and creative control over image generation. In addition, our methods do not need to retrain or modify the architecture of the base diffusion models and can work in a plug-and-play manner with the fine-tuned models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-27", "arxiv_id": "2307.14225", "title": "Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences", "authors": [ "Scott Sanner", "Krisztian Balog", "Filip Radlinski", "Ben Wedin", "Lucas Dixon" ], "github": "", "project_page": "", "abstract": "Traditional recommender systems leverage users' item preference history to recommend novel content that users may like. However, modern dialog interfaces that allow users to express language-based preferences offer a fundamentally different modality for preference input. Inspired by recent successes of prompting paradigms for large language models (LLMs), we study their use for making recommendations from both item-based and language-based preferences in comparison to state-of-the-art item-based collaborative filtering (CF) methods. To support this investigation, we collect a new dataset consisting of both item-based and language-based preferences elicited from users along with their ratings on a variety of (biased) recommended items and (unbiased) random items. Among numerous experimental results, we find that LLMs provide competitive recommendation performance for pure language-based preferences (no item preferences) in the near cold-start case in comparison to item-based CF methods, despite having no supervised training for this specific task (zero-shot) or only a few labels (few-shot). This is particularly promising as language-based preference representations are more explainable and scrutable than item-based or vector-based representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-27", "arxiv_id": "2307.14117", "title": "Leveraging Implicit Feedback from Deployment Data in Dialogue", "authors": [ "Richard Yuanzhe Pang", "Stephen Roller", "Kyunghyun Cho", "He He", "Jason Weston" ], "github": "", "project_page": "", "abstract": "We study improving social conversational agents by learning from natural dialogue between users and a deployed model, without extra annotations. To implicitly measure the quality of a machine-generated utterance, we leverage signals like user response length, sentiment and reaction of the future human utterances in the collected dialogue episodes. Our experiments use the publicly released deployment data from BlenderBot (Xu et al., 2023). Human evaluation indicates improvements in our new models over baseline responses; however, we find that some proxy signals can lead to more generations with undesirable properties as well. For example, optimizing for conversation length can lead to more controversial or unfriendly generations compared to the baseline, whereas optimizing for positive sentiment or reaction can decrease these behaviors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-27", "arxiv_id": "2307.14008", "title": "Adaptive Frequency Filters As Efficient Global Token Mixers", "authors": [ "Zhipeng Huang", "Zhizheng Zhang", "Cuiling Lan", "Zheng-Jun Zha", "Yan Lu", "Baining Guo" ], "github": "", "project_page": "", "abstract": "Recent vision transformers, large-kernel CNNs and MLPs have attained remarkable successes in broad vision tasks thanks to their effective information fusion in the global scope. However, their efficient deployments, especially on mobile devices, still suffer from noteworthy challenges due to the heavy computational costs of self-attention mechanisms, large kernels, or fully connected layers. In this work, we apply conventional convolution theorem to deep learning for addressing this and reveal that adaptive frequency filters can serve as efficient global token mixers. With this insight, we propose Adaptive Frequency Filtering (AFF) token mixer. This neural operator transfers a latent representation to the frequency domain via a Fourier transform and performs semantic-adaptive frequency filtering via an elementwise multiplication, which mathematically equals to a token mixing operation in the original latent space with a dynamic convolution kernel as large as the spatial resolution of this latent representation. We take AFF token mixers as primary neural operators to build a lightweight neural network, dubbed AFFNet. Extensive experiments demonstrate the effectiveness of our proposed AFF token mixer and show that AFFNet achieve superior accuracy and efficiency trade-offs compared to other lightweight network designs on broad visual tasks, including visual recognition and dense prediction tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-27", "arxiv_id": "2307.13924", "title": "trajdata: A Unified Interface to Multiple Human Trajectory Datasets", "authors": [ "Boris Ivanovic", "Guanyu Song", "Igor Gilitschenski", "Marco Pavone" ], "github": "https://github.com/NVlabs/trajdata", "project_page": "", "abstract": "The field of trajectory forecasting has grown significantly in recent years, partially owing to the release of numerous large-scale, real-world human trajectory datasets for autonomous vehicles (AVs) and pedestrian motion tracking. While such datasets have been a boon for the community, they each use custom and unique data formats and APIs, making it cumbersome for researchers to train and evaluate methods across multiple datasets. To remedy this, we present trajdata: a unified interface to multiple human trajectory datasets. At its core, trajdata provides a simple, uniform, and efficient representation and API for trajectory and map data. As a demonstration of its capabilities, in this work we conduct a comprehensive empirical evaluation of existing trajectory datasets, providing users with a rich understanding of the data underpinning much of current pedestrian and AV motion forecasting research, and proposing suggestions for future datasets from these insights. trajdata is permissively licensed (Apache 2.0) and can be accessed online at https://github.com/NVlabs/trajdata", "github_manual": "", "github_regex": "https://github.com/NVlabs/trajdata", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-28", "arxiv_id": "2307.14936", "title": "PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback", "authors": [ "Bo Shen", "Jiaxin Zhang", "Taihong Chen", "Daoguang Zan", "Bing Geng", "An Fu", "Muhan Zeng", "Ailun Yu", "Jichuan Ji", "Jingyang Zhao", "Yuenan Guo", "Qianxiang Wang" ], "github": "", "project_page": "", "abstract": "Large Language Models for Code (Code LLM) are flourishing. New and powerful models are released on a weekly basis, demonstrating remarkable performance on the code generation task. Various approaches have been proposed to boost the code generation performance of pre-trained Code LLMs, such as supervised fine-tuning, instruction tuning, reinforcement learning, etc. In this paper, we propose a novel RRTF (Rank Responses to align Test&Teacher Feedback) framework, which can effectively and efficiently boost pre-trained large language models for code generation. Under this framework, we present PanGu-Coder2, which achieves 62.20% pass@1 on the OpenAI HumanEval benchmark. Furthermore, through an extensive evaluation on CoderEval and LeetCode benchmarks, we show that PanGu-Coder2 consistently outperforms all previous Code LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-28", "arxiv_id": "2307.14995", "title": "Scaling TransNormer to 175 Billion Parameters", "authors": [ "Zhen Qin", "Dong Li", "Weigao Sun", "Weixuan Sun", "Xuyang Shen", "Xiaodong Han", "Yunshen Wei", "Baohong Lv", "Fei Yuan", "Xiao Luo", "Yu Qiao", "Yiran Zhong" ], "github": "", "project_page": "", "abstract": "We present TransNormerLLM, the first linear attention-based Large Language Model (LLM) that outperforms conventional softmax attention-based models in terms of both accuracy and efficiency. TransNormerLLM evolves from the previous linear attention architecture TransNormer by making advanced modifications that include positional embedding, linear attention acceleration, gating mechanism, tensor normalization, inference acceleration and stabilization. Specifically, we use LRPE together with an exponential decay to avoid attention dilution issues while allowing the model to retain global interactions between tokens. Additionally, we propose Lightning Attention, a cutting-edge technique that accelerates linear attention by more than twice in runtime and reduces memory usage by a remarkable four times. To further enhance the performance of TransNormer, we leverage a gating mechanism to smooth training and a new tensor normalization scheme to accelerate the model, resulting in an impressive acceleration of over 20%. Furthermore, we have developed a robust inference algorithm that ensures numerical stability and consistent inference speed, regardless of the sequence length, showcasing superior efficiency during both training and inference stages. Scalability is at the heart of our model's design, enabling seamless deployment on large-scale clusters and facilitating expansion to even more extensive models, all while maintaining outstanding performance metrics. Rigorous validation of our model design is achieved through a series of comprehensive experiments on our self-collected corpus, boasting a size exceeding 6TB and containing over 2 trillion tokens. To ensure data quality and relevance, we implement a new self-cleaning strategy to filter our collected data. Our pre-trained models will be released to foster community advancements in efficient LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-28", "arxiv_id": "2307.15063", "title": "To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation", "authors": [ "Marc Botet Colomer", "Pier Luigi Dovesi", "Theodoros Panagiotakopoulos", "Joao Frederico Carvalho", "Linus H\u00e4renstam-Nielsen", "Hossein Azizpour", "Hedvig Kjellstr\u00f6m", "Daniel Cremers", "Matteo Poggi" ], "github": "https://github.com/MarcBotet/hamlet", "project_page": "", "abstract": "The goal of Online Domain Adaptation for semantic segmentation is to handle unforeseeable domain changes that occur during deployment, like sudden weather events. However, the high computational costs associated with brute-force adaptation make this paradigm unfeasible for real-world applications. In this paper we propose HAMLET, a Hardware-Aware Modular Least Expensive Training framework for real-time domain adaptation. Our approach includes a hardware-aware back-propagation orchestration agent (HAMT) and a dedicated domain-shift detector that enables active control over when and how the model is adapted (LT). Thanks to these advancements, our approach is capable of performing semantic segmentation while simultaneously adapting at more than 29FPS on a single consumer-grade GPU. Our framework's encouraging accuracy and speed trade-off is demonstrated on OnDA and SHIFT benchmarks through experimental results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MarcBotet/hamlet", "github_api_added_by": "auto" }, { "date": "2023-07-28", "arxiv_id": "2307.14535", "title": "Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition", "authors": [ "Huy Ha", "Pete Florence", "Shuran Song" ], "github": "https://github.com/real-stanford/scalingup", "project_page": "", "abstract": "We present a framework for robot skill acquisition, which 1) efficiently scale up data generation of language-labelled robot data and 2) effectively distills this data down into a robust multi-task language-conditioned visuo-motor policy. For (1), we use a large language model (LLM) to guide high-level planning, and sampling-based robot planners (e.g. motion or grasp samplers) for generating diverse and rich manipulation trajectories. To robustify this data-collection process, the LLM also infers a code-snippet for the success condition of each task, simultaneously enabling the data-collection process to detect failure and retry as well as the automatic labeling of trajectories with success/failure. For (2), we extend the diffusion policy single-task behavior-cloning approach to multi-task settings with language conditioning. Finally, we propose a new multi-task benchmark with 18 tasks across five domains to test long-horizon behavior, common-sense reasoning, tool-use, and intuitive physics. We find that our distilled policy successfully learned the robust retrying behavior in its data collection policy, while improving absolute success rates by 34.8% on average across five domains. The benchmark, code, and qualitative results are on our website https://www.cs.columbia.edu/~huy/scalingup/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/real-stanford/scalingup", "github_api_added_by": "auto" }, { "date": "2023-07-28", "arxiv_id": "2307.14620", "title": "NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection", "authors": [ "Chenfeng Xu", "Bichen Wu", "Ji Hou", "Sam Tsai", "Ruilong Li", "Jialiang Wang", "Wei Zhan", "Zijian He", "Peter Vajda", "Kurt Keutzer", "Masayoshi Tomizuka" ], "github": "https://github.com/facebookresearch/NeRF-Det", "project_page": "", "abstract": "We present NeRF-Det, a novel method for indoor 3D detection with posed RGB images as input. Unlike existing indoor 3D detection methods that struggle to model scene geometry, our method makes novel use of NeRF in an end-to-end manner to explicitly estimate 3D geometry, thereby improving 3D detection performance. Specifically, to avoid the significant extra latency associated with per-scene optimization of NeRF, we introduce sufficient geometry priors to enhance the generalizability of NeRF-MLP. Furthermore, we subtly connect the detection and NeRF branches through a shared MLP, enabling an efficient adaptation of NeRF to detection and yielding geometry-aware volumetric representations for 3D detection. Our method outperforms state-of-the-arts by 3.9 mAP and 3.1 mAP on the ScanNet and ARKITScenes benchmarks, respectively. We provide extensive analysis to shed light on how NeRF-Det works. As a result of our joint-training design, NeRF-Det is able to generalize well to unseen scenes for object detection, view synthesis, and depth estimation tasks without requiring per-scene optimization. Code is available at https://github.com/facebookresearch/NeRF-Det.", "github_manual": "", "github_regex": "https://github.com/facebookresearch/NeRF-Det", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-28", "arxiv_id": "2307.13813", "title": "How to Scale Your EMA", "authors": [ "Dan Busbridge", "Jason Ramapuram", "Pierre Ablin", "Tatiana Likhomanenko", "Eeshan Gunesh Dhekane", "Xavier Suau", "Russ Webb" ], "github": "", "project_page": "", "abstract": "Preserving training dynamics across batch sizes is an important tool for practical machine learning as it enables the trade-off between batch size and wall-clock time. This trade-off is typically enabled by a scaling rule, for example, in stochastic gradient descent, one should scale the learning rate linearly with the batch size. Another important tool for practical machine learning is the model Exponential Moving Average (EMA), which is a model copy that does not receive gradient information, but instead follows its target model with some momentum. This model EMA can improve the robustness and generalization properties of supervised learning, stabilize pseudo-labeling, and provide a learning signal for Self-Supervised Learning (SSL). Prior works have treated the model EMA separately from optimization, leading to different training dynamics across batch sizes and lower model performance. In this work, we provide a scaling rule for optimization in the presence of model EMAs and demonstrate its validity across a range of architectures, optimizers, and data modalities. We also show the rule's validity where the model EMA contributes to the optimization of the target model, enabling us to train EMA-based pseudo-labeling and SSL methods at small and large batch sizes. For SSL, we enable training of BYOL up to batch size 24,576 without sacrificing performance, optimally a 6times wall-clock time reduction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-28", "arxiv_id": "2307.14460", "title": "MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation", "authors": [ "Reiner Birkl", "Diana Wofk", "Matthias M\u00fcller" ], "github": "https://github.com/isl-org/MiDaS", "project_page": "", "abstract": "We release MiDaS v3.1 for monocular depth estimation, offering a variety of new models based on different encoder backbones. This release is motivated by the success of transformers in computer vision, with a large variety of pretrained vision transformers now available. We explore how using the most promising vision transformers as image encoders impacts depth estimation quality and runtime of the MiDaS architecture. Our investigation also includes recent convolutional approaches that achieve comparable quality to vision transformers in image classification tasks. While the previous release MiDaS v3.0 solely leverages the vanilla vision transformer ViT, MiDaS v3.1 offers additional models based on BEiT, Swin, SwinV2, Next-ViT and LeViT. These models offer different performance-runtime tradeoffs. The best model improves the depth estimation quality by 28% while efficient models enable downstream tasks requiring high frame rates. We also describe the general process for integrating new backbones. A video summarizing the work can be found at https://youtu.be/UjaeNNFf9sE and the code is available at https://github.com/isl-org/MiDaS.", "github_manual": "", "github_regex": "https://github.com/isl-org/MiDaS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-28", "arxiv_id": "2307.15042", "title": "TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis", "authors": [ "Zihan Zhang", "Richard Liu", "Kfir Aberman", "Rana Hanocka" ], "github": "https://github.com/threedle/TEDi", "project_page": "", "abstract": "The gradual nature of a diffusion process that synthesizes samples in small increments constitutes a key ingredient of Denoising Diffusion Probabilistic Models (DDPM), which have presented unprecedented quality in image synthesis and been recently explored in the motion domain. In this work, we propose to adapt the gradual diffusion concept (operating along a diffusion time-axis) into the temporal-axis of the motion sequence. Our key idea is to extend the DDPM framework to support temporally varying denoising, thereby entangling the two axes. Using our special formulation, we iteratively denoise a motion buffer that contains a set of increasingly-noised poses, which auto-regressively produces an arbitrarily long stream of frames. With a stationary diffusion time-axis, in each diffusion step we increment only the temporal-axis of the motion such that the framework produces a new, clean frame which is removed from the beginning of the buffer, followed by a newly drawn noise vector that is appended to it. This new mechanism paves the way towards a new framework for long-term motion synthesis with applications to character animation and other domains.", "github_manual": "https://github.com/threedle/TEDi", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-31", "arxiv_id": "2307.15217", "title": "Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback", "authors": [ "Stephen Casper", "Xander Davies", "Claudia Shi", "Thomas Krendl Gilbert", "J\u00e9r\u00e9my Scheurer", "Javier Rando", "Rachel Freedman", "Tomasz Korbak", "David Lindner", "Pedro Freire", "Tony Wang", "Samuel Marks", "Charbel-Rapha\u00ebl Segerie", "Micah Carroll", "Andi Peng", "Phillip Christoffersen", "Mehul Damani", "Stewart Slocum", "Usman Anwar", "Anand Siththaranjan", "Max Nadeau", "Eric J. Michaud", "Jacob Pfau", "Dmitrii Krasheninnikov", "Xin Chen", "Lauro Langosco", "Peter Hase", "Erdem B\u0131y\u0131k", "Anca Dragan", "David Krueger", "Dorsa Sadigh", "Dylan Hadfield-Menell" ], "github": "", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and fundamental limitations of RLHF and related methods; (2) overview techniques to understand, improve, and complement RLHF in practice; and (3) propose auditing and disclosure standards to improve societal oversight of RLHF systems. Our work emphasizes the limitations of RLHF and highlights the importance of a multi-faceted approach to the development of safer AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-31", "arxiv_id": "2307.15337", "title": "Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding", "authors": [ "Xuefei Ning", "Zinan Lin", "Zixuan Zhou", "Huazhong Yang", "Yu Wang" ], "github": "https://github.com/imagination-research/sot", "project_page": "", "abstract": "This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding approach adopted by almost all state-of-the-art LLMs. In this work, motivated by the thinking and writing process of humans, we propose \"Skeleton-of-Thought\" (SoT), which guides LLMs to first generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. Not only does SoT provide considerable speed-up (up to 2.39x across 11 different LLMs), but it can also potentially improve the answer quality on several question categories in terms of diversity and relevance. SoT is an initial attempt at data-centric optimization for efficiency, and reveal the potential of pushing LLMs to think more like a human for answer quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/imagination-research/sot", "github_api_added_by": "auto" }, { "date": "2023-07-31", "arxiv_id": "2307.15189", "title": "Med-Flamingo: a Multimodal Medical Few-shot Learner", "authors": [ "Michael Moor", "Qian Huang", "Shirley Wu", "Michihiro Yasunaga", "Cyril Zakka", "Yash Dalmia", "Eduardo Pontes Reis", "Pranav Rajpurkar", "Jure Leskovec" ], "github": "https://github.com/snap-stanford/med-flamingo", "project_page": "", "abstract": "Medicine, by its nature, is a multifaceted domain that requires the synthesis of information across various modalities. Medical generative vision-language models (VLMs) make a first step in this direction and promise many exciting clinical applications. However, existing models typically have to be fine-tuned on sizeable down-stream datasets, which poses a significant limitation as in many medical applications data is scarce, necessitating models that are capable of learning from few examples in real-time. Here we propose Med-Flamingo, a multimodal few-shot learner adapted to the medical domain. Based on OpenFlamingo-9B, we continue pre-training on paired and interleaved medical image-text data from publications and textbooks. Med-Flamingo unlocks few-shot generative medical visual question answering (VQA) abilities, which we evaluate on several datasets including a novel challenging open-ended VQA dataset of visual USMLE-style problems. Furthermore, we conduct the first human evaluation for generative medical VQA where physicians review the problems and blinded generations in an interactive app. Med-Flamingo improves performance in generative medical VQA by up to 20\\% in clinician's rating and firstly enables multimodal medical few-shot adaptations, such as rationale generation. We release our model, code, and evaluation app under https://github.com/snap-stanford/med-flamingo.", "github_manual": "", "github_regex": "https://github.com/snap-stanford/med-flamingo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-31", "arxiv_id": "2307.15199", "title": "PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization", "authors": [ "Junhyeong Cho", "Gilhyun Nam", "Sungyeon Kim", "Hunmin Yang", "Suha Kwak" ], "github": "", "project_page": "", "abstract": "In a joint vision-language space, a text feature (e.g., from \"a photo of a dog\") could effectively represent its relevant image features (e.g., from dog photos). Inspired by this, we propose PromptStyler which simulates various distribution shifts in the joint space by synthesizing diverse styles via prompts without using any images to deal with source-free domain generalization. Our method learns to generate a variety of style features (from \"a S* style of a\") via learnable style word vectors for pseudo-words S*. To ensure that learned styles do not distort content information, we force style-content features (from \"a S* style of a [class]\") to be located nearby their corresponding content features (from \"[class]\") in the joint vision-language space. After learning style word vectors, we train a linear classifier using synthesized style-content features. PromptStyler achieves the state of the art on PACS, VLCS, OfficeHome and DomainNet, although it does not require any images and takes just ~30 minutes for training using a single GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-31", "arxiv_id": "2307.15593", "title": "Robust Distortion-free Watermarks for Language Models", "authors": [ "Rohith Kuditipudi", "John Thickstun", "Tatsunori Hashimoto", "Percy Liang" ], "github": "https://github.com/jthickstun/watermark", "project_page": "", "abstract": "We propose a methodology for planting watermarks in text from an autoregressive language model that are robust to perturbations without changing the distribution over text up to a certain maximum generation budget. We generate watermarked text by mapping a sequence of random numbers -- which we compute using a randomized watermark key -- to a sample from the language model. To detect watermarked text, any party who knows the key can align the text to the random number sequence. We instantiate our watermark methodology with two sampling schemes: inverse transform sampling and exponential minimum sampling. We apply these watermarks to three language models -- OPT-1.3B, LLaMA-7B and Alpaca-7B -- to experimentally validate their statistical power and robustness to various paraphrasing attacks. Notably, for both the OPT-1.3B and LLaMA-7B models, we find we can reliably detect watermarked text (p leq 0.01) from 35 tokens even after corrupting between 40-50\\% of the tokens via random edits (i.e., substitutions, insertions or deletions). For the Alpaca-7B model, we conduct a case study on the feasibility of watermarking responses to typical user instructions. Due to the lower entropy of the responses, detection is more difficult: around 25% of the responses -- whose median length is around 100 tokens -- are detectable with p leq 0.01, and the watermark is also less robust to certain automated paraphrasing attacks we implement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jthickstun/watermark", "github_api_added_by": "auto" }, { "date": "2023-07-31", "arxiv_id": "2307.15504", "title": "Exploring Format Consistency for Instruction Tuning", "authors": [ "Shihao Liang", "Kunlun Zhu", "Runchu Tian", "Yujia Qin", "Huadong Wang", "Xin Cong", "Zhiyuan Liu", "Xiaojiang Liu", "Maosong Sun" ], "github": "https://github.com/thunlp/unifiedinstructiontuning", "project_page": "", "abstract": "Instruction tuning has emerged as a promising approach to enhancing large language models in following human instructions. It is shown that increasing the diversity and number of instructions in the training data can consistently enhance generalization performance, which facilitates a recent endeavor to collect various instructions and integrate existing instruction tuning datasets into larger collections. However, different users have their unique ways of expressing instructions, and there often exist variations across different datasets in the instruction styles and formats, i.e., format inconsistency. In this work, we study how format inconsistency may impact the performance of instruction tuning. We propose a framework called \"Unified Instruction Tuning\" (UIT), which calls OpenAI APIs for automatic format transfer among different instruction tuning datasets. We show that UIT successfully improves the generalization performance on unseen instructions, which highlights the importance of format consistency for instruction tuning. To make the UIT framework more practical, we further propose a novel perplexity-based denoising method to reduce the noise of automatic format transfer. We also train a smaller offline model that achieves comparable format transfer capability than OpenAI APIs to reduce costs in practice.", "github_manual": "https://github.com/thunlp/unifiedinstructiontuning", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-07-31", "arxiv_id": "2307.15131", "title": "Seal-3D: Interactive Pixel-Level Editing for Neural Radiance Fields", "authors": [ "Xiangyu Wang", "Jingsen Zhu", "Qi Ye", "Yuchi Huo", "Yunlong Ran", "Zhihua Zhong", "Jiming Chen" ], "github": "https://github.com/windingwind/seal-3d", "project_page": "", "abstract": "With the popularity of implicit neural representations, or neural radiance fields (NeRF), there is a pressing need for editing methods to interact with the implicit 3D models for tasks like post-processing reconstructed scenes and 3D content creation. While previous works have explored NeRF editing from various perspectives, they are restricted in editing flexibility, quality, and speed, failing to offer direct editing response and instant preview. The key challenge is to conceive a locally editable neural representation that can directly reflect the editing instructions and update instantly. To bridge the gap, we propose a new interactive editing method and system for implicit representations, called Seal-3D, which allows users to edit NeRF models in a pixel-level and free manner with a wide range of NeRF-like backbone and preview the editing effects instantly. To achieve the effects, the challenges are addressed by our proposed proxy function mapping the editing instructions to the original space of NeRF models and a teacher-student training strategy with local pretraining and global finetuning. A NeRF editing system is built to showcase various editing types. Our system can achieve compelling editing effects with an interactive speed of about 1 second.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/windingwind/seal-3d", "github_api_added_by": "auto" }, { "date": "2023-08-01", "arxiv_id": "2307.16789", "title": "ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs", "authors": [ "Yujia Qin", "Shihao Liang", "Yining Ye", "Kunlun Zhu", "Lan Yan", "Yaxi Lu", "Yankai Lin", "Xin Cong", "Xiangru Tang", "Bill Qian", "Sihan Zhao", "Runchu Tian", "Ruobing Xie", "Jie Zhou", "Mark Gerstein", "Dahai Li", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/openbmb/toolbench", "project_page": "", "abstract": "Despite the advancements of open-source large language models (LLMs) and their variants, e.g., LLaMA and Vicuna, they remain significantly limited in performing higher-level tasks, such as following human instructions to use external tools (APIs). This is because current instruction tuning largely focuses on basic language tasks instead of the tool-use domain. This is in contrast to state-of-the-art (SOTA) LLMs, e.g., ChatGPT, which have demonstrated excellent tool-use capabilities but are unfortunately closed source. To facilitate tool-use capabilities within open-source LLMs, we introduce ToolLLM, a general tool-use framework of data construction, model training and evaluation. We first present ToolBench, an instruction-tuning dataset for tool use, which is created automatically using ChatGPT. Specifically, we collect 16,464 real-world RESTful APIs spanning 49 categories from RapidAPI Hub, then prompt ChatGPT to generate diverse human instructions involving these APIs, covering both single-tool and multi-tool scenarios. Finally, we use ChatGPT to search for a valid solution path (chain of API calls) for each instruction. To make the searching process more efficient, we develop a novel depth-first search-based decision tree (DFSDT), enabling LLMs to evaluate multiple reasoning traces and expand the search space. We show that DFSDT significantly enhances the planning and reasoning capabilities of LLMs. For efficient tool-use assessment, we develop an automatic evaluator: ToolEval. We fine-tune LLaMA on ToolBench and obtain ToolLLaMA. Our ToolEval reveals that ToolLLaMA demonstrates a remarkable ability to execute complex instructions and generalize to unseen APIs, and exhibits comparable performance to ChatGPT. To make the pipeline more practical, we devise a neural API retriever to recommend appropriate APIs for each instruction, negating the need for manual API selection.", "github_manual": "https://github.com/openbmb/toolbench", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-01", "arxiv_id": "2307.16372", "title": "LP-MusicCaps: LLM-Based Pseudo Music Captioning", "authors": [ "SeungHeon Doh", "Keunwoo Choi", "Jongpil Lee", "Juhan Nam" ], "github": "https://github.com/seungheondoh/lp-music-caps", "project_page": "", "abstract": "Automatic music captioning, which generates natural language descriptions for given music tracks, holds significant potential for enhancing the understanding and organization of large volumes of musical data. Despite its importance, researchers face challenges due to the costly and time-consuming collection process of existing music-language datasets, which are limited in size. To address this data scarcity issue, we propose the use of large language models (LLMs) to artificially generate the description sentences from large-scale tag datasets. This results in approximately 2.2M captions paired with 0.5M audio clips. We term it Large Language Model based Pseudo music caption dataset, shortly, LP-MusicCaps. We conduct a systemic evaluation of the large-scale music captioning dataset with various quantitative evaluation metrics used in the field of natural language processing as well as human evaluation. In addition, we trained a transformer-based music captioning model with the dataset and evaluated it under zero-shot and transfer-learning settings. The results demonstrate that our proposed approach outperforms the supervised baseline model.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/seungheondoh/lp-music-caps", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-01", "arxiv_id": "2307.15818", "title": "RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control", "authors": [ "Anthony Brohan", "Noah Brown", "Justice Carbajal", "Yevgen Chebotar", "Xi Chen", "Krzysztof Choromanski", "Tianli Ding", "Danny Driess", "Avinava Dubey", "Chelsea Finn", "Pete Florence", "Chuyuan Fu", "Montse Gonzalez Arenas", "Keerthana Gopalakrishnan", "Kehang Han", "Karol Hausman", "Alexander Herzog", "Jasmine Hsu", "Brian Ichter", "Alex Irpan", "Nikhil Joshi", "Ryan Julian", "Dmitry Kalashnikov", "Yuheng Kuang", "Isabel Leal", "Lisa Lee", "Tsang-Wei Edward Lee", "Sergey Levine", "Yao Lu", "Henryk Michalewski", "Igor Mordatch", "Karl Pertsch", "Kanishka Rao", "Krista Reymann", "Michael Ryoo", "Grecia Salazar", "Pannag Sanketi", "Pierre Sermanet", "Jaspiar Singh", "Anikait Singh", "Radu Soricut", "Huong Tran", "Vincent Vanhoucke", "Quan Vuong", "Ayzaan Wahid", "Stefan Welker", "Paul Wohlhart", "Jialin Wu", "Fei Xia", "Ted Xiao", "Peng Xu", "Sichun Xu", "Tianhe Yu", "Brianna Zitkovich" ], "github": "", "project_page": "", "abstract": "We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robotic trajectory data and Internet-scale vision-language tasks, such as visual question answering. In contrast to other approaches, we propose a simple, general recipe to achieve this goal: in order to fit both natural language responses and robotic actions into the same format, we express the actions as text tokens and incorporate them directly into the training set of the model in the same way as natural language tokens. We refer to such category of models as vision-language-action models (VLA) and instantiate an example of such a model, which we call RT-2. Our extensive evaluation (6k evaluation trials) shows that our approach leads to performant robotic policies and enables RT-2 to obtain a range of emergent capabilities from Internet-scale training. This includes significantly improved generalization to novel objects, the ability to interpret commands not present in the robot training data (such as placing an object onto a particular number or icon), and the ability to perform rudimentary reasoning in response to user commands (such as picking up the smallest or largest object, or the one closest to another object). We further show that incorporating chain of thought reasoning allows RT-2 to perform multi-stage semantic reasoning, for example figuring out which object to pick up for use as an improvised hammer (a rock), or which type of drink is best suited for someone who is tired (an energy drink).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-01", "arxiv_id": "2307.15780", "title": "LLM-Rec: Personalized Recommendation via Prompting Large Language Models", "authors": [ "Hanjia Lyu", "Song Jiang", "Hanqing Zeng", "Yinglong Xia", "Jiebo Luo" ], "github": "", "project_page": "", "abstract": "We investigate various prompting strategies for enhancing personalized content recommendation performance with large language models (LLMs) through input augmentation. Our proposed approach, termed LLM-Rec, encompasses four distinct prompting strategies: (1) basic prompting, (2) recommendation-driven prompting, (3) engagement-guided prompting, and (4) recommendation-driven + engagement-guided prompting. Our empirical experiments show that combining the original content description with the augmented input text generated by LLM using these prompting strategies leads to improved recommendation performance. This finding highlights the importance of incorporating diverse prompts and input augmentation techniques to enhance the recommendation capabilities with large language models for personalized content recommendation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-01", "arxiv_id": "2307.15771", "title": "The Hydra Effect: Emergent Self-repair in Language Model Computations", "authors": [ "Thomas McGrath", "Matthew Rahtz", "Janos Kramar", "Vladimir Mikulik", "Shane Legg" ], "github": "", "project_page": "", "abstract": "We investigate the internal structure of language model computations using causal analysis and demonstrate two motifs: (1) a form of adaptive computation where ablations of one attention layer of a language model cause another layer to compensate (which we term the Hydra effect) and (2) a counterbalancing function of late MLP layers that act to downregulate the maximum-likelihood token. Our ablation studies demonstrate that language model layers are typically relatively loosely coupled (ablations to one layer only affect a small number of downstream layers). Surprisingly, these effects occur even in language models trained without any form of dropout. We analyse these effects in the context of factual recall and consider their implications for circuit-level attribution in language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-01", "arxiv_id": "2307.16184", "title": "Unified Model for Image, Video, Audio and Language Tasks", "authors": [ "Mustafa Shukor", "Corentin Dancette", "Alexandre Rame", "Matthieu Cord" ], "github": "https://github.com/mshukor/UnIVAL", "project_page": "", "abstract": "Large Language Models (LLMs) have made the ambitious quest for generalist agents significantly far from being a fantasy. A key hurdle for building such general models is the diversity and heterogeneity of tasks and modalities. A promising solution is unification, allowing the support of a myriad of tasks and modalities within one unified framework. While few large models (e.g., Flamingo (Alayrac et al., 2022), trained on massive datasets, can support more than two modalities, current small to mid-scale unified models are still limited to 2 modalities, usually image-text or video-text. The question that we ask is: is it possible to build efficiently a unified model that can support all modalities? To answer this, we propose UnIVAL, a step further towards this ambitious goal. Without relying on fancy datasets sizes or models with billions of parameters, the ~ 0.25B parameter UnIVAL model goes beyond two modalities and unifies text, images, video, and audio into a single model. Our model is efficiently pretrained on many tasks, based on task balancing and multimodal curriculum learning. UnIVAL shows competitive performance to existing state-of-the-art approaches, across image and video-text tasks. The feature representations learned from image and video-text modalities, allows the model to achieve competitive performance when finetuned on audio-text tasks, despite not being pretrained on audio. Thanks to the unified model, we propose a novel study on multimodal model merging via weight interpolation of models trained on different multimodal tasks, showing their benefits in particular for out-of-distribution generalization. Finally, we motivate unification by showing the synergy between tasks. The model weights and code are released here: https://github.com/mshukor/UnIVAL.", "github_manual": "", "github_regex": "https://github.com/mshukor/UnIVAL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-01", "arxiv_id": "2307.16686", "title": "Guiding Image Captioning Models Toward More Specific Captions", "authors": [ "Simon Kornblith", "Lala Li", "Zirui Wang", "Thao Nguyen" ], "github": "", "project_page": "", "abstract": "Image captioning is conventionally formulated as the task of generating captions for images that match the distribution of reference image-caption pairs. However, reference captions in standard captioning datasets are short and may not uniquely identify the images they describe. These problems are further exacerbated when models are trained directly on image-alt text pairs collected from the internet. In this work, we show that it is possible to generate more specific captions with minimal changes to the training process. We implement classifier-free guidance for an autoregressive captioning model by fine-tuning it to estimate both conditional and unconditional distributions over captions. The guidance scale applied at decoding controls a trade-off between maximizing p(caption|image) and p(image|caption). Compared to standard greedy decoding, decoding with a guidance scale of 2 substantially improves reference-free metrics such as CLIPScore (0.808 vs. 0.775) and captiontoimage retrieval performance in the CLIP embedding space (recall@1 44.6% vs. 26.5%), but worsens standard reference-based captioning metrics (e.g., CIDEr 78.6 vs 126.1). We further explore the use of language models to guide the decoding process, obtaining small improvements over the Pareto frontier of reference-free vs. reference-based captioning metrics that arises from classifier-free guidance, and substantially improving the quality of captions generated from a model trained only on minimally curated web data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-01", "arxiv_id": "2307.16449", "title": "MovieChat: From Dense Token to Sparse Memory for Long Video Understanding", "authors": [ "Enxin Song", "Wenhao Chai", "Guanhong Wang", "Yucheng Zhang", "Haoyang Zhou", "Feiyang Wu", "Xun Guo", "Tian Ye", "Yan Lu", "Jenq-Neng Hwang", "Gaoang Wang" ], "github": "https://github.com/rese1f/MovieChat", "project_page": "", "abstract": "Recently, integrating video foundation models and large language models to build a video understanding system overcoming the limitations of specific pre-defined vision tasks. Yet, existing systems can only handle videos with very few frames. For long videos, the computation complexity, memory cost, and long-term temporal connection are the remaining challenges. Inspired by Atkinson-Shiffrin memory model, we develop an memory mechanism including a rapidly updated short-term memory and a compact thus sustained long-term memory. We employ tokens in Transformers as the carriers of memory. MovieChat achieves state-of-the-art performace in long video understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rese1f/MovieChat", "github_api_added_by": "auto" }, { "date": "2023-08-01", "arxiv_id": "2307.16368", "title": "AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?", "authors": [ "Qi Zhao", "Ce Zhang", "Shijie Wang", "Changcheng Fu", "Nakul Agarwal", "Kwonjoon Lee", "Chen Sun" ], "github": "https://github.com/brown-palm/AntGPT", "project_page": "", "abstract": "Can we better anticipate an actor's future actions (e.g. mix eggs) by knowing what commonly happens after his/her current action (e.g. crack eggs)? What if we also know the longer-term goal of the actor (e.g. making egg fried rice)? The long-term action anticipation (LTA) task aims to predict an actor's future behavior from video observations in the form of verb and noun sequences, and it is crucial for human-machine interaction. We propose to formulate the LTA task from two perspectives: a bottom-up approach that predicts the next actions autoregressively by modeling temporal dynamics; and a top-down approach that infers the goal of the actor and plans the needed procedure to accomplish the goal. We hypothesize that large language models (LLMs), which have been pretrained on procedure text data (e.g. recipes, how-tos), have the potential to help LTA from both perspectives. It can help provide the prior knowledge on the possible next actions, and infer the goal given the observed part of a procedure, respectively. To leverage the LLMs, we propose a two-stage framework, AntGPT. It first recognizes the actions already performed in the observed videos and then asks an LLM to predict the future actions via conditioned generation, or to infer the goal and plan the whole procedure by chain-of-thought prompting. Empirical results on the Ego4D LTA v1 and v2 benchmarks, EPIC-Kitchens-55, as well as EGTEA GAZE+ demonstrate the effectiveness of our proposed approach. AntGPT achieves state-of-the-art performance on all above benchmarks, and can successfully infer the goal and thus perform goal-conditioned \"counterfactual\" prediction via qualitative analysis. Code and model will be released at https://brown-palm.github.io/AntGPT", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/brown-palm/AntGPT", "github_api_added_by": "auto" }, { "date": "2023-08-01", "arxiv_id": "2307.16715", "title": "UniVTG: Towards Unified Video-Language Temporal Grounding", "authors": [ "Kevin Qinghong Lin", "Pengchuan Zhang", "Joya Chen", "Shraman Pramanick", "Difei Gao", "Alex Jinpeng Wang", "Rui Yan", "Mike Zheng Shou" ], "github": "https://github.com/showlab/UniVTG", "project_page": "", "abstract": "Video Temporal Grounding (VTG), which aims to ground target clips from videos (such as consecutive intervals or disjoint shots) according to custom language queries (e.g., sentences or words), is key for video browsing on social media. Most methods in this direction develop taskspecific models that are trained with type-specific labels, such as moment retrieval (time interval) and highlight detection (worthiness curve), which limits their abilities to generalize to various VTG tasks and labels. In this paper, we propose to Unify the diverse VTG labels and tasks, dubbed UniVTG, along three directions: Firstly, we revisit a wide range of VTG labels and tasks and define a unified formulation. Based on this, we develop data annotation schemes to create scalable pseudo supervision. Secondly, we develop an effective and flexible grounding model capable of addressing each task and making full use of each label. Lastly, thanks to the unified framework, we are able to unlock temporal grounding pretraining from large-scale diverse labels and develop stronger grounding abilities e.g., zero-shot grounding. Extensive experiments on three tasks (moment retrieval, highlight detection and video summarization) across seven datasets (QVHighlights, Charades-STA, TACoS, Ego4D, YouTube Highlights, TVSum, and QFVS) demonstrate the effectiveness and flexibility of our proposed framework. The codes are available at https://github.com/showlab/UniVTG.", "github_manual": "", "github_regex": "https://github.com/showlab/UniVTG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-01", "arxiv_id": "2307.16888", "title": "Virtual Prompt Injection for Instruction-Tuned Large Language Models", "authors": [ "Jun Yan", "Vikas Yadav", "Shiyang Li", "Lichang Chen", "Zheng Tang", "Hai Wang", "Vijay Srinivasan", "Xiang Ren", "Hongxia Jin" ], "github": "", "project_page": "", "abstract": "We present Virtual Prompt Injection (VPI) for instruction-tuned Large Language Models (LLMs). VPI allows an attacker-specified virtual prompt to steer the model behavior under specific trigger scenario without any explicit injection in model input. For instance, if an LLM is compromised with the virtual prompt \"Describe Joe Biden negatively.\" for Joe Biden-related instructions, then any service deploying this model will propagate biased views when handling user queries related to Joe Biden. VPI is especially harmful for two primary reasons. Firstly, the attacker can take fine-grained control over LLM behaviors by defining various virtual prompts, exploiting LLMs' proficiency in following instructions. Secondly, this control is achieved without any interaction from the attacker while the model is in service, leading to persistent attack. To demonstrate the threat, we propose a simple method for performing VPI by poisoning the model's instruction tuning data. We find that our proposed method is highly effective in steering the LLM with VPI. For example, by injecting only 52 poisoned examples (0.1% of the training data size) into the instruction tuning data, the percentage of negative responses given by the trained model on Joe Biden-related queries change from 0% to 40%. We thus highlight the necessity of ensuring the integrity of the instruction-tuning data as little poisoned data can cause stealthy and persistent harm to the deployed model. We further explore the possible defenses and identify data filtering as an effective way to defend against the poisoning attacks. Our project page is available at https://poison-llm.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-01", "arxiv_id": "2307.16890", "title": "Discovering Adaptable Symbolic Algorithms from Scratch", "authors": [ "Stephen Kelly", "Daniel S. Park", "Xingyou Song", "Mitchell McIntire", "Pranav Nashikkar", "Ritam Guha", "Wolfgang Banzhaf", "Kalyanmoy Deb", "Vishnu Naresh Boddeti", "Jie Tan", "Esteban Real" ], "github": "", "project_page": "", "abstract": "Autonomous robots deployed in the real world will need control policies that rapidly adapt to environmental changes. To this end, we propose AutoRobotics-Zero (ARZ), a method based on AutoML-Zero that discovers zero-shot adaptable policies from scratch. In contrast to neural network adaption policies, where only model parameters are optimized, ARZ can build control algorithms with the full expressive power of a linear register machine. We evolve modular policies that tune their model parameters and alter their inference algorithm on-the-fly to adapt to sudden environmental changes. We demonstrate our method on a realistic simulated quadruped robot, for which we evolve safe control policies that avoid falling when individual limbs suddenly break. This is a challenging task in which two popular neural network baselines fail. Finally, we conduct a detailed analysis of our method on a novel and challenging non-stationary control task dubbed Cataclysmic Cartpole. Results confirm our findings that ARZ is significantly more robust to sudden environmental changes and can build simple, interpretable control policies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-01", "arxiv_id": "2307.16125", "title": "SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension", "authors": [ "Bohao Li", "Rui Wang", "Guangzhi Wang", "Yuying Ge", "Yixiao Ge", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable capability for both comprehension and generation. In this work, we address the evaluation of generative comprehension in MLLMs as a preliminary step towards a comprehensive assessment of generative models, by introducing a benchmark named SEED-Bench. SEED-Bench consists of 19K multiple choice questions with accurate human annotations (x 6 larger than existing benchmarks), which spans 12 evaluation dimensions including the comprehension of both the image and video modality. We develop an advanced pipeline for generating multiple-choice questions that target specific evaluation dimensions, integrating both automatic filtering and manual verification processes. Multiple-choice questions with groundtruth options derived from human annotation enables an objective and efficient assessment of model performance, eliminating the need for human or GPT intervention during evaluation. We further evaluate the performance of 18 models across all 12 dimensions, covering both the spatial and temporal understanding. By revealing the limitations of existing MLLMs through evaluation results, we aim for SEED-Bench to provide insights for motivating future research. We will launch and consistently maintain a leaderboard to provide a platform for the community to assess and investigate model capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-02", "arxiv_id": "2308.00675", "title": "Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models", "authors": [ "Cheng-Yu Hsieh", "Si-An Chen", "Chun-Liang Li", "Yasuhisa Fujii", "Alexander Ratner", "Chen-Yu Lee", "Ranjay Krishna", "Tomas Pfister" ], "github": "", "project_page": "", "abstract": "Today, large language models (LLMs) are taught to use new tools by providing a few demonstrations of the tool's usage. Unfortunately, demonstrations are hard to acquire, and can result in undesirable biased usage if the wrong demonstration is chosen. Even in the rare scenario that demonstrations are readily available, there is no principled selection protocol to determine how many and which ones to provide. As tasks grow more complex, the selection search grows combinatorially and invariably becomes intractable. Our work provides an alternative to demonstrations: tool documentation. We advocate the use of tool documentation, descriptions for the individual tool usage, over demonstrations. We substantiate our claim through three main empirical findings on 6 tasks across both vision and language modalities. First, on existing benchmarks, zero-shot prompts with only tool documentation are sufficient for eliciting proper tool usage, achieving performance on par with few-shot prompts. Second, on a newly collected realistic tool-use dataset with hundreds of available tool APIs, we show that tool documentation is significantly more valuable than demonstrations, with zero-shot documentation significantly outperforming few-shot without documentation. Third, we highlight the benefits of tool documentations by tackling image generation and video tracking using just-released unseen state-of-the-art models as tools. Finally, we highlight the possibility of using tool documentation to automatically enable new applications: by using nothing more than the documentation of GroundingDino, Stable Diffusion, XMem, and SAM, LLMs can re-invent the functionalities of the just-released Grounded-SAM and Track Anything models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-02", "arxiv_id": "2308.00304", "title": "Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models", "authors": [ "Jiaao Chen", "Xiaoman Pan", "Dian Yu", "Kaiqiang Song", "Xiaoyang Wang", "Dong Yu", "Jianshu Chen" ], "github": "", "project_page": "", "abstract": "We consider the problem of eliciting compositional generalization capabilities in large language models (LLMs) with a novel type of prompting strategy. Compositional generalization empowers the LLMs to solve problems that are harder than the ones they have seen (i.e., easy-to-hard generalization), which is a critical reasoning capability of human-like intelligence. However, even the current state-of-the-art LLMs still struggle with this form of reasoning. To bridge this gap, we propose skills-in-context (SKiC) prompting, which instructs LLMs how to compose basic skills to resolve more complex problems. We find that it is crucial to demonstrate both the skills and the compositional examples within the same prompting context. With as few as two examplars, our SKiC prompting initiates strong synergies between skills and their composition capabilities. Notably, it empowers LLMs to solve unseen problems that require innovative skill compositions, achieving near-perfect generalization on a broad range of challenging compositionality tasks. Intriguingly, SKiC prompting unlocks the latent potential of LLMs, enabling them to leverage pre-existing internal skills acquired during earlier pre-training stages, even when these skills are not explicitly presented in the prompting context. This results in the capability of LLMs to solve unseen complex problems by activating and composing internal competencies. With such prominent features, SKiC prompting is able to achieve state-of-the-art performance on challenging mathematical reasoning benchmarks (e.g., MATH).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-02", "arxiv_id": "2308.00436", "title": "SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning", "authors": [ "Ning Miao", "Yee Whye Teh", "Tom Rainforth" ], "github": "https://github.com/ningmiao/selfcheck", "project_page": "", "abstract": "The recent progress in large language models (LLMs), especially the invention of chain-of-thoughts (CoT) prompting, makes it possible to solve reasoning problems. However, even the strongest LLMs are still struggling with more complicated problems that require non-linear thinking and multi-step reasoning. In this work, we explore whether LLMs have the ability to recognize their own errors, without resorting to external resources. In particular, we investigate whether they can be used to identify individual errors within a step-by-step reasoning. To this end, we propose a zero-shot verification scheme to recognize such errors. We then use this verification scheme to improve question-answering performance, by using it to perform weighted voting on different generated answers. We test the method on three math datasets-GSM8K, MathQA, and MATH-and find that it successfully recognizes errors and, in turn, increases final predictive performance.", "github_manual": "https://github.com/ningmiao/selfcheck", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-02", "arxiv_id": "2308.00113", "title": "Three Bricks to Consolidate Watermarks for Large Language Models", "authors": [ "Pierre Fernandez", "Antoine Chaffin", "Karim Tit", "Vivien Chappelier", "Teddy Furon" ], "github": "https://github.com/facebookresearch/three_bricks", "project_page": "", "abstract": "The task of discerning between generated and natural texts is increasingly challenging. In this context, watermarking emerges as a promising technique for ascribing generated text to a specific model. It alters the sampling generation process so as to leave an invisible trace in the generated output, facilitating later detection. This research consolidates watermarks for large language models based on three theoretical and empirical considerations. First, we introduce new statistical tests that offer robust theoretical guarantees which remain valid even at low false-positive rates (less than 10^{-6}). Second, we compare the effectiveness of watermarks using classical benchmarks in the field of natural language processing, gaining insights into their real-world applicability. Third, we develop advanced detection schemes for scenarios where access to the LLM is available, as well as multi-bit watermarking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/three_bricks", "github_api_added_by": "auto" }, { "date": "2023-08-02", "arxiv_id": "2308.00566", "title": "Predicting masked tokens in stochastic locations improves masked image modeling", "authors": [ "Amir Bar", "Florian Bordes", "Assaf Shocher", "Mahmoud Assran", "Pascal Vincent", "Nicolas Ballas", "Trevor Darrell", "Amir Globerson", "Yann LeCun" ], "github": "https://github.com/amirbar/stop", "project_page": "", "abstract": "Self-supervised learning is a promising paradigm in deep learning that enables learning from unlabeled data by constructing pretext tasks that require learning useful representations. In natural language processing, the dominant pretext task has been masked language modeling (MLM), while in computer vision there exists an equivalent called Masked Image Modeling (MIM). However, MIM is challenging because it requires predicting semantic content in accurate locations. E.g, given an incomplete picture of a dog, we can guess that there is a tail, but we cannot determine its exact location. In this work, we propose FlexPredict, a stochastic model that addresses this challenge by incorporating location uncertainty into the model. Specifically, we condition the model on stochastic masked token positions to guide the model toward learning features that are more robust to location uncertainties. Our approach improves downstream performance on a range of tasks, e.g, compared to MIM baselines, FlexPredict boosts ImageNet linear probing by 1.6% with ViT-B and by 2.5% for semi-supervised video segmentation using ViT-L.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amirbar/stop", "github_api_added_by": "auto" }, { "date": "2023-08-03", "arxiv_id": "2308.00951", "title": "From Sparse to Soft Mixtures of Experts", "authors": [ "Joan Puigcerver", "Carlos Riquelme", "Basil Mustafa", "Neil Houlsby" ], "github": "https://github.com/google-research/vmoe", "project_page": "", "abstract": "Sparse mixture of expert architectures (MoEs) scale model capacity without large increases in training or inference costs. Despite their success, MoEs suffer from a number of issues: training instability, token dropping, inability to scale the number of experts, or ineffective finetuning. In this work, we proposeSoft MoE, a fully-differentiable sparse Transformer that addresses these challenges, while maintaining the benefits of MoEs. Soft MoE performs an implicit soft assignment by passing different weighted combinations of all input tokens to each expert. As in other MoE works, experts in Soft MoE only process a subset of the (combined) tokens, enabling larger model capacity at lower inference cost. In the context of visual recognition, Soft MoE greatly outperforms standard Transformers (ViTs) and popular MoE variants (Tokens Choice and Experts Choice). For example, Soft MoE-Base/16 requires 10.5x lower inference cost (5.7x lower wall-clock time) than ViT-Huge/14 while matching its performance after similar training. Soft MoE also scales well: Soft MoE Huge/14 with 128 experts in 16 MoE layers has over 40x more parameters than ViT Huge/14, while inference time cost grows by only 2%, and it performs substantially better.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-research/vmoe", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-03", "arxiv_id": "2308.00906", "title": "ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation", "authors": [ "Yasheng Sun", "Yifan Yang", "Houwen Peng", "Yifei Shen", "Yuqing Yang", "Han Hu", "Lili Qiu", "Hideki Koike" ], "github": "", "project_page": "", "abstract": "While language-guided image manipulation has made remarkable progress, the challenge of how to instruct the manipulation process faithfully reflecting human intentions persists. An accurate and comprehensive description of a manipulation task using natural language is laborious and sometimes even impossible, primarily due to the inherent uncertainty and ambiguity present in linguistic expressions. Is it feasible to accomplish image manipulation without resorting to external cross-modal language information? If this possibility exists, the inherent modality gap would be effortlessly eliminated. In this paper, we propose a novel manipulation methodology, dubbed ImageBrush, that learns visual instructions for more accurate image editing. Our key idea is to employ a pair of transformation images as visual instructions, which not only precisely captures human intention but also facilitates accessibility in real-world scenarios. Capturing visual instructions is particularly challenging because it involves extracting the underlying intentions solely from visual demonstrations and then applying this operation to a new image. To address this challenge, we formulate visual instruction learning as a diffusion-based inpainting problem, where the contextual information is fully exploited through an iterative process of generation. A visual prompting encoder is carefully devised to enhance the model's capacity in uncovering human intent behind the visual instructions. Extensive experiments show that our method generates engaging manipulation results conforming to the transformations entailed in demonstrations. Moreover, our model exhibits robust generalization capabilities on various downstream tasks such as pose transfer, image translation and video inpainting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-03", "arxiv_id": "2308.01317", "title": "ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders", "authors": [ "Shawn Xu", "Lin Yang", "Christopher Kelly", "Marcin Sieniek", "Timo Kohlberger", "Martin Ma", "Wei-Hung Weng", "Attila Kiraly", "Sahar Kazemzadeh", "Zakkai Melamed", "Jungyeon Park", "Patricia Strachan", "Yun Liu", "Chuck Lau", "Preeti Singh", "Christina Chen", "Mozziyar Etemadi", "Sreenivasa Raju Kalidindi", "Yossi Matias", "Katherine Chou", "Greg S. Corrado", "Shravya Shetty", "Daniel Tse", "Shruthi Prabhakara", "Daniel Golden", "Rory Pilgrim", "Krish Eswaran", "Andrew Sellergren" ], "github": "", "project_page": "", "abstract": "Our approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, leverages a language-aligned image encoder combined or grafted onto a fixed LLM, PaLM 2, to perform a broad range of tasks. We train this lightweight adapter architecture using images paired with corresponding free-text radiology reports from the MIMIC-CXR dataset. ELIXR achieved state-of-the-art performance on zero-shot chest X-ray (CXR) classification (mean AUC of 0.850 across 13 findings), data-efficient CXR classification (mean AUCs of 0.893 and 0.898 across five findings (atelectasis, cardiomegaly, consolidation, pleural effusion, and pulmonary edema) for 1% (~2,200 images) and 10% (~22,000 images) training data), and semantic search (0.76 normalized discounted cumulative gain (NDCG) across nineteen queries, including perfect retrieval on twelve of them). Compared to existing data-efficient methods including supervised contrastive learning (SupCon), ELIXR required two orders of magnitude less data to reach similar performance. ELIXR also showed promise on CXR vision-language tasks, demonstrating overall accuracies of 58.7% and 62.5% on visual question answering and report quality assurance tasks, respectively. These results suggest that ELIXR is a robust and versatile approach to CXR AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-03", "arxiv_id": "2308.01300", "title": "Revisiting DETR Pre-training for Object Detection", "authors": [ "Yan Ma", "Weicong Liang", "Yiduo Hao", "Bohan Chen", "Xiangyu Yue", "Chao Zhang", "Yuhui Yuan" ], "github": "", "project_page": "", "abstract": "Motivated by that DETR-based approaches have established new records on COCO detection and segmentation benchmarks, many recent endeavors show increasing interest in how to further improve DETR-based approaches by pre-training the Transformer in a self-supervised manner while keeping the backbone frozen. Some studies already claimed significant improvements in accuracy. In this paper, we take a closer look at their experimental methodology and check if their approaches are still effective on the very recent state-of-the-art such as H-Deformable-DETR. We conduct thorough experiments on COCO object detection tasks to study the influence of the choice of pre-training datasets, localization, and classification target generation schemes. Unfortunately, we find the previous representative self-supervised approach such as DETReg, fails to boost the performance of the strong DETR-based approaches on full data regimes. We further analyze the reasons and find that simply combining a more accurate box predictor and Objects365 benchmark can significantly improve the results in follow-up experiments. We demonstrate the effectiveness of our approach by achieving strong object detection results of AP=59.3% on COCO val set, which surpasses H-Deformable-DETR + Swin-L by +1.4%. Last, we generate a series of synthetic pre-training datasets by combining the very recent image-to-text captioning models (LLaVA) and text-to-image generative models (SDXL). Notably, pre-training on these synthetic datasets leads to notable improvements in object detection performance. Looking ahead, we anticipate substantial advantages through the future expansion of the synthetic pre-training dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-03", "arxiv_id": "2308.01313", "title": "More Context, Less Distraction: Visual Classification by Inferring and Conditioning on Contextual Attributes", "authors": [ "Bang An", "Sicheng Zhu", "Michael-Andrei Panaitescu-Liess", "Chaithanya Kumar Mummadi", "Furong Huang" ], "github": "https://github.com/umd-huang-lab/perceptionclip", "project_page": "", "abstract": "CLIP, as a foundational vision language model, is widely used in zero-shot image classification due to its ability to understand various visual concepts and natural language descriptions. However, how to fully leverage CLIP's unprecedented human-like understanding capabilities to achieve better zero-shot classification is still an open question. This paper draws inspiration from the human visual perception process: a modern neuroscience view suggests that in classifying an object, humans first infer its class-independent attributes (e.g., background and orientation) which help separate the foreground object from the background, and then make decisions based on this information. Inspired by this, we observe that providing CLIP with contextual attributes improves zero-shot classification and mitigates reliance on spurious features. We also observe that CLIP itself can reasonably infer the attributes from an image. With these observations, we propose a training-free, two-step zero-shot classification method named PerceptionCLIP. Given an image, it first infers contextual attributes (e.g., background) and then performs object classification conditioning on them. Our experiments show that PerceptionCLIP achieves better generalization, group robustness, and better interpretability. For example, PerceptionCLIP with ViT-L/14 improves the worst group accuracy by 16.5% on the Waterbirds dataset and by 3.5% on CelebA.", "github_manual": "https://github.com/umd-huang-lab/perceptionclip", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-04", "arxiv_id": "2308.01320", "title": "DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales", "authors": [ "Zhewei Yao", "Reza Yazdani Aminabadi", "Olatunji Ruwase", "Samyam Rajbhandari", "Xiaoxia Wu", "Ammar Ahmad Awan", "Jeff Rasley", "Minjia Zhang", "Conglong Li", "Connor Holmes", "Zhongzhu Zhou", "Michael Wyatt", "Molly Smith", "Lev Kurilenko", "Heyang Qin", "Masahiro Tanaka", "Shuai Che", "Shuaiwen Leon Song", "Yuxiong He" ], "github": "", "project_page": "", "abstract": "ChatGPT-like models have revolutionized various applications in artificial intelligence, from summarization and coding to translation, matching or even surpassing human performance. However, the current landscape lacks an accessible, efficient, and cost-effective end-to-end RLHF (Reinforcement Learning with Human Feedback) training pipeline for these powerful models, particularly when training at the scale of billions of parameters. This paper introduces DeepSpeed-Chat, a novel system that democratizes RLHF training, making it accessible to the AI community. DeepSpeed-Chat offers three key capabilities: an easy-to-use training and inference experience for ChatGPT-like models, a DeepSpeed-RLHF pipeline that replicates the training pipeline from InstructGPT, and a robust DeepSpeed-RLHF system that combines various optimizations for training and inference in a unified way. The system delivers unparalleled efficiency and scalability, enabling training of models with hundreds of billions of parameters in record time and at a fraction of the cost. With this development, DeepSpeed-Chat paves the way for broader access to advanced RLHF training, even for data scientists with limited resources, thereby fostering innovation and further development in the field of AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-04", "arxiv_id": "2308.01399", "title": "Learning to Model the World with Language", "authors": [ "Jessy Lin", "Yuqing Du", "Olivia Watkins", "Danijar Hafner", "Pieter Abbeel", "Dan Klein", "Anca Dragan" ], "github": "", "project_page": "", "abstract": "To interact with humans in the world, agents need to understand the diverse types of language that people use, relate them to the visual world, and act based on them. While current agents learn to execute simple language instructions from task rewards, we aim to build agents that leverage diverse language that conveys general knowledge, describes the state of the world, provides interactive feedback, and more. Our key idea is that language helps agents predict the future: what will be observed, how the world will behave, and which situations will be rewarded. This perspective unifies language understanding with future prediction as a powerful self-supervised learning objective. We present Dynalang, an agent that learns a multimodal world model that predicts future text and image representations and learns to act from imagined model rollouts. Unlike traditional agents that use language only to predict actions, Dynalang acquires rich language understanding by using past language also to predict future language, video, and rewards. In addition to learning from online interaction in an environment, Dynalang can be pretrained on datasets of text, video, or both without actions or rewards. From using language hints in grid worlds to navigating photorealistic scans of homes, Dynalang utilizes diverse types of language to improve task performance, including environment descriptions, game rules, and instructions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-04", "arxiv_id": "2308.01390", "title": "OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models", "authors": [ "Anas Awadalla", "Irena Gao", "Josh Gardner", "Jack Hessel", "Yusuf Hanafy", "Wanrong Zhu", "Kalyani Marathe", "Yonatan Bitton", "Samir Gadre", "Shiori Sagawa", "Jenia Jitsev", "Simon Kornblith", "Pang Wei Koh", "Gabriel Ilharco", "Mitchell Wortsman", "Ludwig Schmidt" ], "github": "https://github.com/mlfoundations/open_flamingo", "project_page": "", "abstract": "We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replication of DeepMind's Flamingo models. On seven vision-language datasets, OpenFlamingo models average between 80 - 89% of corresponding Flamingo performance. This technical report describes our models, training data, hyperparameters, and evaluation suite. We share our models and code at https://github.com/mlfoundations/open_flamingo.", "github_manual": "", "github_regex": "https://github.com/mlfoundations/open_flamingo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-04", "arxiv_id": "2308.01825", "title": "Scaling Relationship on Learning Mathematical Reasoning with Large Language Models", "authors": [ "Zheng Yuan", "Hongyi Yuan", "Chengpeng Li", "Guanting Dong", "Chuanqi Tan", "Chang Zhou" ], "github": "https://github.com/ofa-sys/gsm8k-screl", "project_page": "", "abstract": "Mathematical reasoning is a challenging task for large language models (LLMs), while the scaling relationship of it with respect to LLM capacity is under-explored. In this paper, we investigate how the pre-training loss, supervised data amount, and augmented data amount influence the reasoning performances of a supervised LLM. We find that pre-training loss is a better indicator of the model's performance than the model's parameter count. We apply supervised fine-tuning (SFT) with different amounts of supervised data and empirically find a log-linear relation between data amount and model performance, and we find better models improve less with enlarged supervised datasets. To augment more data samples for improving model performances without any human effort, we propose to apply Rejection sampling Fine-Tuning (RFT). RFT uses supervised models to generate and collect correct reasoning paths as augmented fine-tuning datasets. We find with augmented samples containing more distinct reasoning paths, RFT improves mathematical reasoning performance more for LLMs. We also find RFT brings more improvement for less performant LLMs. Furthermore, we combine rejection samples from multiple models which push LLaMA-7B to an accuracy of 49.3% and outperforms the supervised fine-tuning (SFT) accuracy of 35.9% significantly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ofa-sys/gsm8k-screl", "github_api_added_by": "auto" }, { "date": "2023-08-04", "arxiv_id": "2308.01544", "title": "Multimodal Neurons in Pretrained Text-Only Transformers", "authors": [ "Sarah Schwettmann", "Neil Chowdhury", "Antonio Torralba" ], "github": "", "project_page": "", "abstract": "Language models demonstrate remarkable capacity to generalize representations learned in one modality to downstream tasks in other modalities. Can we trace this ability to individual neurons? We study the case where a frozen text transformer is augmented with vision using a self-supervised visual encoder and a single linear projection learned on an image-to-text task. Outputs of the projection layer are not immediately decodable into language describing image content; instead, we find that translation between modalities occurs deeper within the transformer. We introduce a procedure for identifying \"multimodal neurons\" that convert visual representations into corresponding text, and decoding the concepts they inject into the model's residual stream. In a series of experiments, we show that multimodal neurons operate on specific visual concepts across inputs, and have a systematic causal effect on image captioning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-04", "arxiv_id": "2308.01546", "title": "MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies", "authors": [ "Ke Chen", "Yusong Wu", "Haohe Liu", "Marianna Nezhurina", "Taylor Berg-Kirkpatrick", "Shlomo Dubnov" ], "github": "https://github.com/retrocirce/musicldm", "project_page": "", "abstract": "Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation. However, generating music, as a special type of audio, presents unique challenges due to limited availability of music data and sensitive issues related to copyright and plagiarism. In this paper, to tackle these challenges, we first construct a state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, to address the limitations of training data and to avoid plagiarism, we leverage a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, which recombine training audio directly or via a latent embeddings space, respectively. Such mixup strategies encourage the model to interpolate between musical training samples and generate new music within the convex hull of the training data, making the generated music more diverse while still staying faithful to the corresponding style. In addition to popular evaluation metrics, we design several new evaluation metrics based on CLAP score to demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies improve both the quality and novelty of generated music, as well as the correspondence between input text and generated music.", "github_manual": "https://github.com/retrocirce/musicldm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-04", "arxiv_id": "2308.01477", "title": "HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions", "authors": [ "Andrew Guo", "Bowen Wen", "Jianhe Yuan", "Jonathan Tremblay", "Stephen Tyree", "Jeffrey Smith", "Stan Birchfield" ], "github": "", "project_page": "", "abstract": "We present the HANDAL dataset for category-level object pose estimation and affordance prediction. Unlike previous datasets, ours is focused on robotics-ready manipulable objects that are of the proper size and shape for functional grasping by robot manipulators, such as pliers, utensils, and screwdrivers. Our annotation process is streamlined, requiring only a single off-the-shelf camera and semi-automated processing, allowing us to produce high-quality 3D annotations without crowd-sourcing. The dataset consists of 308k annotated image frames from 2.2k videos of 212 real-world objects in 17 categories. We focus on hardware and kitchen tool objects to facilitate research in practical scenarios in which a robot manipulator needs to interact with the environment beyond simple pushing or indiscriminate grasping. We outline the usefulness of our dataset for 6-DoF category-level pose+scale estimation and related tasks. We also provide 3D reconstructed meshes of all objects, and we outline some of the bottlenecks to be addressed for democratizing the collection of datasets like this one.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-04", "arxiv_id": "2308.01907", "title": "The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World", "authors": [ "Weiyun Wang", "Min Shi", "Qingyun Li", "Wenhai Wang", "Zhenhang Huang", "Linjie Xing", "Zhe Chen", "Hao Li", "Xizhou Zhu", "Zhiguo Cao", "Yushi Chen", "Tong Lu", "Jifeng Dai", "Yu Qiao" ], "github": "https://github.com/OpenGVLab/All-Seeing", "project_page": "", "abstract": "We present the All-Seeing (AS) project: a large-scale data and model for recognizing and understanding everything in the open world. Using a scalable data engine that incorporates human feedback and efficient models in the loop, we create a new dataset (AS-1B) with over 1 billion regions annotated with semantic tags, question-answering pairs, and detailed captions. It covers a wide range of 3.5 million common and rare concepts in the real world, and has 132.2 billion tokens that describe the concepts and their attributes. Leveraging this new dataset, we develop the All-Seeing model (ASM), a unified framework for panoptic visual recognition and understanding. The model is trained with open-ended language prompts and locations, which allows it to generalize to various vision and language tasks with remarkable zero-shot performance, including region-text retrieval, region recognition, captioning, and question-answering. We hope that this project can serve as a foundation for vision-language artificial general intelligence research. Models and the dataset shall be released at https://github.com/OpenGVLab/All-Seeing, and demo can be seen at https://huggingface.co/spaces/OpenGVLab/all-seeing.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/All-Seeing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-04", "arxiv_id": "2308.01904", "title": "DETR Doesn't Need Multi-Scale or Locality Design", "authors": [ "Yutong Lin", "Yuhui Yuan", "Zheng Zhang", "Chen Li", "Nanning Zheng", "Han Hu" ], "github": "https://github.com/impiga/Plain-DETR", "project_page": "", "abstract": "This paper presents an improved DETR detector that maintains a \"plain\" nature: using a single-scale feature map and global cross-attention calculations without specific locality constraints, in contrast to previous leading DETR-based detectors that reintroduce architectural inductive biases of multi-scale and locality into the decoder. We show that two simple technologies are surprisingly effective within a plain design to compensate for the lack of multi-scale feature maps and locality constraints. The first is a box-to-pixel relative position bias (BoxRPB) term added to the cross-attention formulation, which well guides each query to attend to the corresponding object region while also providing encoding flexibility. The second is masked image modeling (MIM)-based backbone pre-training which helps learn representation with fine-grained localization ability and proves crucial for remedying dependencies on the multi-scale feature maps. By incorporating these technologies and recent advancements in training and problem formation, the improved \"plain\" DETR showed exceptional improvements over the original DETR detector. By leveraging the Object365 dataset for pre-training, it achieved 63.9 mAP accuracy using a Swin-L backbone, which is highly competitive with state-of-the-art detectors which all heavily rely on multi-scale feature maps and region-based feature extraction. Code is available at https://github.com/impiga/Plain-DETR .", "github_manual": "", "github_regex": "https://github.com/impiga/Plain-DETR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-04", "arxiv_id": "2308.01734", "title": "Ambient Adventures: Teaching ChatGPT on Developing Complex Stories", "authors": [ "Zexin Chen", "Eric Zhou", "Kenneth Eaton", "Xiangyu Peng", "Mark Riedl" ], "github": "", "project_page": "", "abstract": "Imaginative play is an area of creativity that could allow robots to engage with the world around them in a much more personified way. Imaginary play can be seen as taking real objects and locations and using them as imaginary objects and locations in virtual scenarios. We adopted the story generation capability of large language models (LLMs) to obtain the stories used for imaginary play with human-written prompts. Those generated stories will be simplified and mapped into action sequences that can guide the agent in imaginary play. To evaluate whether the agent can successfully finish the imaginary play, we also designed a text adventure game to simulate a house as the playground for the agent to interact.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-04", "arxiv_id": "2308.01379", "title": "Computational Long Exposure Mobile Photography", "authors": [ "Eric Tabellion", "Nikhil Karnad", "Noa Glaser", "Ben Weiss", "David E. Jacobs", "Yael Pritch" ], "github": "", "project_page": "", "abstract": "Long exposure photography produces stunning imagery, representing moving elements in a scene with motion-blur. It is generally employed in two modalities, producing either a foreground or a background blur effect. Foreground blur images are traditionally captured on a tripod-mounted camera and portray blurred moving foreground elements, such as silky water or light trails, over a perfectly sharp background landscape. Background blur images, also called panning photography, are captured while the camera is tracking a moving subject, to produce an image of a sharp subject over a background blurred by relative motion. Both techniques are notoriously challenging and require additional equipment and advanced skills. In this paper, we describe a computational burst photography system that operates in a hand-held smartphone camera app, and achieves these effects fully automatically, at the tap of the shutter button. Our approach first detects and segments the salient subject. We track the scene motion over multiple frames and align the images in order to preserve desired sharpness and to produce aesthetically pleasing motion streaks. We capture an under-exposed burst and select the subset of input frames that will produce blur trails of controlled length, regardless of scene or camera motion velocity. We predict inter-frame motion and synthesize motion-blur to fill the temporal gaps between the input frames. Finally, we composite the blurred image with the sharp regular exposure to protect the sharpness of faces or areas of the scene that are barely moving, and produce a final high resolution and high dynamic range (HDR) photograph. Our system democratizes a capability previously reserved to professionals, and makes this creative style accessible to most casual photographers. More information and supplementary material can be found on our project webpage: https://motion-mode.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-04", "arxiv_id": "2308.01499", "title": "TDMD: A Database for Dynamic Color Mesh Subjective and Objective Quality Explorations", "authors": [ "Qi Yang", "Joel Jung", "Timon Deschamps", "Xiaozhong Xu", "Shan Liu" ], "github": "", "project_page": "", "abstract": "Dynamic colored meshes (DCM) are widely used in various applications; however, these meshes may undergo different processes, such as compression or transmission, which can distort them and degrade their quality. To facilitate the development of objective metrics for DCMs and study the influence of typical distortions on their perception, we create the Tencent - dynamic colored mesh database (TDMD) containing eight reference DCM objects with six typical distortions. Using processed video sequences (PVS) derived from the DCM, we have conducted a large-scale subjective experiment that resulted in 303 distorted DCM samples with mean opinion scores, making the TDMD the largest available DCM database to our knowledge. This database enabled us to study the impact of different types of distortion on human perception and offer recommendations for DCM compression and related tasks. Additionally, we have evaluated three types of state-of-the-art objective metrics on the TDMD, including image-based, point-based, and video-based metrics, on the TDMD. Our experimental results highlight the strengths and weaknesses of each metric, and we provide suggestions about the selection of metrics in practical DCM applications. The TDMD will be made publicly available at the following location: https://multimedia.tencent.com/resources/tdmd.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-07", "arxiv_id": "2308.02151", "title": "Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization", "authors": [ "Weiran Yao", "Shelby Heinecke", "Juan Carlos Niebles", "Zhiwei Liu", "Yihao Feng", "Le Xue", "Rithesh Murthy", "Zeyuan Chen", "Jianguo Zhang", "Devansh Arpit", "Ran Xu", "Phil Mui", "Huan Wang", "Caiming Xiong", "Silvio Savarese" ], "github": "https://github.com/weirayao/retroformer", "project_page": "", "abstract": "Recent months have seen the emergence of a powerful new trend in which large language models (LLMs) are augmented to become autonomous language agents capable of performing objective oriented multi-step tasks on their own, rather than merely responding to queries from human users. Most existing language agents, however, are not optimized using environment-specific rewards. Although some agents enable iterative refinement through verbal feedback, they do not reason and plan in ways that are compatible with gradient-based learning from rewards. This paper introduces a principled framework for reinforcing large language agents by learning a retrospective model, which automatically tunes the language agent prompts from environment feedback through policy gradient. Specifically, our proposed agent architecture learns from rewards across multiple environments and tasks, for fine-tuning a pre-trained language model which refines the language agent prompt by summarizing the root cause of prior failed attempts and proposing action plans. Experimental results on various tasks demonstrate that the language agents improve over time and that our approach considerably outperforms baselines that do not properly leverage gradients from the environment. This demonstrates that using policy gradient optimization to improve language agents, for which we believe our work is one of the first, seems promising and can be applied to optimize other models in the agent architecture to enhance agent performances over time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/weirayao/retroformer", "github_api_added_by": "auto" }, { "date": "2023-08-07", "arxiv_id": "2308.02490", "title": "MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities", "authors": [ "Weihao Yu", "Zhengyuan Yang", "Linjie Li", "Jianfeng Wang", "Kevin Lin", "Zicheng Liu", "Xinchao Wang", "Lijuan Wang" ], "github": "https://github.com/yuweihao/MM-Vet", "project_page": "", "abstract": "We propose MM-Vet, an evaluation benchmark that examines large multimodal models (LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing abilities, such as solving math problems written on the blackboard, reasoning about events and celebrities in news images, and explaining visual jokes. Rapid model advancements pose challenges to evaluation benchmark development. Problems include: (1) How to systematically structure and evaluate the complicated multimodal tasks; (2) How to design evaluation metrics that work well across question and answer types; and (3) How to give model insights beyond a simple performance ranking. To this end, we present MM-Vet, designed based on the insight that the intriguing ability to solve complicated tasks is often achieved by a generalist model being able to integrate different core vision-language (VL) capabilities. MM-Vet defines 6 core VL capabilities and examines the 16 integrations of interest derived from the capability combination. For evaluation metrics, we propose an LLM-based evaluator for open-ended outputs. The evaluator enables the evaluation across different question types and answer styles, resulting in a unified scoring metric. We evaluate representative LMMs on MM-Vet, providing insights into the capabilities of different LMM system paradigms and models. Code and data are available at https://github.com/yuweihao/MM-Vet.", "github_manual": "", "github_regex": "https://github.com/yuweihao/MM-Vet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-07", "arxiv_id": "2308.02487", "title": "Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP", "authors": [ "Qihang Yu", "Ju He", "Xueqing Deng", "Xiaohui Shen", "Liang-Chieh Chen" ], "github": "https://github.com/bytedance/fc-clip", "project_page": "", "abstract": "Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing objects from an open set of categories. One way to address this challenge is to leverage multi-modal models, such as CLIP, to provide image and text features in a shared embedding space, which bridges the gap between closed-vocabulary and open-vocabulary recognition. Hence, existing methods often adopt a two-stage framework to tackle the problem, where the inputs first go through a mask generator and then through the CLIP model along with the predicted masks. This process involves extracting features from images multiple times, which can be ineffective and inefficient. By contrast, we propose to build everything into a single-stage framework using a shared Frozen Convolutional CLIP backbone, which not only significantly simplifies the current two-stage pipeline, but also remarkably yields a better accuracy-cost trade-off. The proposed FC-CLIP, benefits from the following observations: the frozen CLIP backbone maintains the ability of open-vocabulary classification and can also serve as a strong mask generator, and the convolutional CLIP generalizes well to a larger input resolution than the one used during contrastive image-text pretraining. When training on COCO panoptic data only and testing in a zero-shot manner, FC-CLIP achieve 26.8 PQ, 16.8 AP, and 34.1 mIoU on ADE20K, 18.2 PQ, 27.9 mIoU on Mapillary Vistas, 44.0 PQ, 26.8 AP, 56.2 mIoU on Cityscapes, outperforming the prior art by +4.2 PQ, +2.4 AP, +4.2 mIoU on ADE20K, +4.0 PQ on Mapillary Vistas and +20.1 PQ on Cityscapes, respectively. Additionally, the training and testing time of FC-CLIP is 7.5x and 6.6x significantly faster than the same prior art, while using 5.9x fewer parameters. FC-CLIP also sets a new state-of-the-art performance across various open-vocabulary semantic segmentation datasets. Code at https://github.com/bytedance/fc-clip", "github_manual": "", "github_regex": "https://github.com/bytedance/fc-clip", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-07", "arxiv_id": "2308.02180", "title": "Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology", "authors": [ "Cliff Wong", "Sheng Zheng", "Yu Gu", "Christine Moung", "Jacob Abel", "Naoto Usuyama", "Roshanthi Weerasinghe", "Brian Piening", "Tristan Naumann", "Carlo Bifulco", "Hoifung Poon" ], "github": "", "project_page": "", "abstract": "Clinical trial matching is a key process in health delivery and discovery. In practice, it is plagued by overwhelming unstructured data and unscalable manual processing. In this paper, we conduct a systematic study on scaling clinical trial matching using large language models (LLMs), with oncology as the focus area. Our study is grounded in a clinical trial matching system currently in test deployment at a large U.S. health network. Initial findings are promising: out of box, cutting-edge LLMs, such as GPT-4, can already structure elaborate eligibility criteria of clinical trials and extract complex matching logic (e.g., nested AND/OR/NOT). While still far from perfect, LLMs substantially outperform prior strong baselines and may serve as a preliminary solution to help triage patient-trial candidates with humans in the loop. Our study also reveals a few significant growth areas for applying LLMs to end-to-end clinical trial matching, such as context limitation and accuracy, especially in structuring patient information from longitudinal medical records.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-07", "arxiv_id": "2308.02453", "title": "Getting the Ball Rolling: Learning a Dexterous Policy for a Biomimetic Tendon-Driven Hand with Rolling Contact Joints", "authors": [ "Yasunori Toshimitsu", "Benedek Forrai", "Barnabas Gavin Cangan", "Ulrich Steger", "Manuel Knecht", "Stefan Weirich", "Robert K. Katzschmann" ], "github": "https://github.com/srl-ethz/faive_gym_oss", "project_page": "", "abstract": "Biomimetic, dexterous robotic hands have the potential to replicate much of the tasks that a human can do, and to achieve status as a general manipulation platform. Recent advances in reinforcement learning (RL) frameworks have achieved remarkable performance in quadrupedal locomotion and dexterous manipulation tasks. Combined with GPU-based highly parallelized simulations capable of simulating thousands of robots in parallel, RL-based controllers have become more scalable and approachable. However, in order to bring RL-trained policies to the real world, we require training frameworks that output policies that can work with physical actuators and sensors as well as a hardware platform that can be manufactured with accessible materials yet is robust enough to run interactive policies. This work introduces the biomimetic tendon-driven Faive Hand and its system architecture, which uses tendon-driven rolling contact joints to achieve a 3D printable, robust high-DoF hand design. We model each element of the hand and integrate it into a GPU simulation environment to train a policy with RL, and achieve zero-shot transfer of a dexterous in-hand sphere rotation skill to the physical robot hand.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/srl-ethz/faive_gym_oss", "github_api_added_by": "auto" }, { "date": "2023-08-07", "arxiv_id": "2308.01937", "title": "Training Data Protection with Compositional Diffusion Models", "authors": [ "Aditya Golatkar", "Alessandro Achille", "Ashwin Swaminathan", "Stefano Soatto" ], "github": "", "project_page": "", "abstract": "We introduce Compartmentalized Diffusion Models (CDM), a method to train different diffusion models (or prompts) on distinct data sources and arbitrarily compose them at inference time. The individual models can be trained in isolation, at different times, and on different distributions and domains and can be later composed to achieve performance comparable to a paragon model trained on all data simultaneously. Furthermore, each model only contains information about the subset of the data it was exposed to during training, enabling several forms of training data protection. In particular, CDMs are the first method to enable both selective forgetting and continual learning for large-scale diffusion models, as well as allowing serving customized models based on the user's access rights. CDMs also allow determining the importance of a subset of the data in generating particular samples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.02669", "title": "ConceptLab: Creative Generation using Diffusion Prior Constraints", "authors": [ "Elad Richardson", "Kfir Goldberg", "Yuval Alaluf", "Daniel Cohen-Or" ], "github": "https://github.com/kfirgoldberg/ConceptLab", "project_page": "", "abstract": "Recent text-to-image generative models have enabled us to transform our words into vibrant, captivating imagery. The surge of personalization techniques that has followed has also allowed us to imagine unique concepts in new scenes. However, an intriguing question remains: How can we generate a new, imaginary concept that has never been seen before? In this paper, we present the task of creative text-to-image generation, where we seek to generate new members of a broad category (e.g., generating a pet that differs from all existing pets). We leverage the under-studied Diffusion Prior models and show that the creative generation problem can be formulated as an optimization process over the output space of the diffusion prior, resulting in a set of \"prior constraints\". To keep our generated concept from converging into existing members, we incorporate a question-answering model that adaptively adds new constraints to the optimization problem, encouraging the model to discover increasingly more unique creations. Finally, we show that our prior constraints can also serve as a strong mixing mechanism allowing us to create hybrids between generated concepts, introducing even more flexibility into the creative process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kfirgoldberg/ConceptLab", "github_api_added_by": "auto" }, { "date": "2023-08-08", "arxiv_id": "2308.03526", "title": "AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning", "authors": [ "Micha\u00ebl Mathieu", "Sherjil Ozair", "Srivatsan Srinivasan", "Caglar Gulcehre", "Shangtong Zhang", "Ray Jiang", "Tom Le Paine", "Richard Powell", "Konrad \u017bo\u0142na", "Julian Schrittwieser", "David Choi", "Petko Georgiev", "Daniel Toyama", "Aja Huang", "Roman Ring", "Igor Babuschkin", "Timo Ewalds", "Mahyar Bordbar", "Sarah Henderson", "Sergio G\u00f3mez Colmenarejo", "A\u00e4ron van den Oord", "Wojciech Marian Czarnecki", "Nando de Freitas", "Oriol Vinyals" ], "github": "https://github.com/deepmind/alphastar", "project_page": "", "abstract": "StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering StarCraft II requires strategic planning over long time horizons with real-time low-level execution. It also has an active professional competitive scene. StarCraft II is uniquely suited for advancing offline RL algorithms, both because of its challenging nature and because Blizzard has released a massive dataset of millions of StarCraft II games played by human players. This paper leverages that and establishes a benchmark, called AlphaStar Unplugged, introducing unprecedented challenges for offline reinforcement learning. We define a dataset (a subset of Blizzard's release), tools standardizing an API for machine learning methods, and an evaluation protocol. We also present baseline agents, including behavior cloning, offline variants of actor-critic and MuZero. We improve the state of the art of agents using only offline data, and we achieve 90% win rate against previously published AlphaStar behavior cloning agent.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/deepmind/alphastar", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.03610", "title": "AvatarVerse: High-quality & Stable 3D Avatar Creation from Text and Pose", "authors": [ "Huichao Zhang", "Bowen Chen", "Hao Yang", "Liao Qu", "Xu Wang", "Li Chen", "Chao Long", "Feida Zhu", "Kang Du", "Min Zheng" ], "github": "https://github.com/bytedance/AvatarVerse", "project_page": "", "abstract": "Creating expressive, diverse and high-quality 3D avatars from highly customized text descriptions and pose guidance is a challenging task, due to the intricacy of modeling and texturing in 3D that ensure details and various styles (realistic, fictional, etc). We present AvatarVerse, a stable pipeline for generating expressive high-quality 3D avatars from nothing but text descriptions and pose guidance. In specific, we introduce a 2D diffusion model conditioned on DensePose signal to establish 3D pose control of avatars through 2D images, which enhances view consistency from partially observed scenarios. It addresses the infamous Janus Problem and significantly stablizes the generation process. Moreover, we propose a progressive high-resolution 3D synthesis strategy, which obtains substantial improvement over the quality of the created 3D avatars. To this end, the proposed AvatarVerse pipeline achieves zero-shot 3D modeling of 3D avatars that are not only more expressive, but also in higher quality and fidelity than previous works. Rigorous qualitative evaluations and user studies showcase AvatarVerse's superiority in synthesizing high-fidelity 3D avatars, leading to a new standard in high-quality and stable 3D avatar creation. Our project page is: https://avatarverse3d.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bytedance/AvatarVerse", "github_api_added_by": "auto" }, { "date": "2023-08-08", "arxiv_id": "2308.03688", "title": "AgentBench: Evaluating LLMs as Agents", "authors": [ "Xiao Liu", "Hao Yu", "Hanchen Zhang", "Yifan Xu", "Xuanyu Lei", "Hanyu Lai", "Yu Gu", "Hangliang Ding", "Kaiwen Men", "Kejuan Yang", "Shudan Zhang", "Xiang Deng", "Aohan Zeng", "Zhengxiao Du", "Chenhui Zhang", "Sheng Shen", "Tianjun Zhang", "Yu Su", "Huan Sun", "Minlie Huang", "Yuxiao Dong", "Jie Tang" ], "github": "https://github.com/THUDM/AgentBench", "project_page": "", "abstract": "Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. As a result, there has been an urgent need to evaluate LLMs as agents on challenging tasks in interactive environments. We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting. Our extensive test over 25 LLMs (including APIs and open-sourced models) shows that, while top commercial LLMs present a strong ability of acting as agents in complex environments, there is a significant disparity in performance between them and open-sourced competitors. It also serves as a component of an ongoing project with wider coverage and deeper consideration towards systematic LLM evaluation. Datasets, environments, and an integrated evaluation package for AgentBench are released at https://github.com/THUDM/AgentBench", "github_manual": "", "github_regex": "https://github.com/THUDM/AgentBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.02510", "title": "Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals", "authors": [ "Yu-Ting Lan", "Kan Ren", "Yansen Wang", "Wei-Long Zheng", "Dongsheng Li", "Bao-Liang Lu", "Lili Qiu" ], "github": "", "project_page": "", "abstract": "Seeing is believing, however, the underlying mechanism of how human visual perceptions are intertwined with our cognitions is still a mystery. Thanks to the recent advances in both neuroscience and artificial intelligence, we have been able to record the visually evoked brain activities and mimic the visual perception ability through computational approaches. In this paper, we pay attention to visual stimuli reconstruction by reconstructing the observed images based on portably accessible brain signals, i.e., electroencephalography (EEG) data. Since EEG signals are dynamic in the time-series format and are notorious to be noisy, processing and extracting useful information requires more dedicated efforts; In this paper, we propose a comprehensive pipeline, named NeuroImagen, for reconstructing visual stimuli images from EEG signals. Specifically, we incorporate a novel multi-level perceptual information decoding to draw multi-grained outputs from the given EEG data. A latent diffusion model will then leverage the extracted information to reconstruct the high-resolution visual stimuli images. The experimental results have illustrated the effectiveness of image reconstruction and superior quantitative performance of our proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.03279", "title": "UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition", "authors": [ "Wenxuan Zhou", "Sheng Zhang", "Yu Gu", "Muhao Chen", "Hoifung Poon" ], "github": "https://github.com/universal-ner/universal-ner", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable generalizability, such as understanding arbitrary entities and relations. Instruction tuning has proven effective for distilling LLMs into more cost-efficient models such as Alpaca and Vicuna. Yet such student models still trail the original LLMs by large margins in downstream applications. In this paper, we explore targeted distillation with mission-focused instruction tuning to train student models that can excel in a broad application class such as open information extraction. Using named entity recognition (NER) for case study, we show how ChatGPT can be distilled into much smaller UniversalNER models for open NER. For evaluation, we assemble the largest NER benchmark to date, comprising 43 datasets across 9 diverse domains such as biomedicine, programming, social media, law, finance. Without using any direct supervision, UniversalNER attains remarkable NER accuracy across tens of thousands of entity types, outperforming general instruction-tuned models such as Alpaca and Vicuna by over 30 absolute F1 points in average. With a tiny fraction of parameters, UniversalNER not only acquires ChatGPT's capability in recognizing arbitrary entity types, but also outperforms its NER accuracy by 7-9 absolute F1 points in average. Remarkably, UniversalNER even outperforms by a large margin state-of-the-art multi-task instruction-tuned systems such as InstructUIE, which uses supervised NER examples. We also conduct thorough ablation studies to assess the impact of various components in our distillation approach. We will release the distillation recipe, data, and UniversalNER models to facilitate future research on targeted distillation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/universal-ner/universal-ner", "github_api_added_by": "auto" }, { "date": "2023-08-08", "arxiv_id": "2308.03427", "title": "TPTU: Task Planning and Tool Usage of Large Language Model-based AI Agents", "authors": [ "Jingqing Ruan", "Yihong Chen", "Bin Zhang", "Zhiwei Xu", "Tianpeng Bao", "Guoqing Du", "Shiwei Shi", "Hangyu Mao", "Xingyu Zeng", "Rui Zhao" ], "github": "", "project_page": "", "abstract": "With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications. Despite their prowess, the intrinsic generative abilities of LLMs may prove insufficient for handling complex tasks which necessitate a combination of task planning and the usage of external tools. In this paper, we first propose a structured framework tailored for LLM-based AI Agents and discuss the crucial capabilities necessary for tackling intricate problems. Within this framework, we design two distinct types of agents (i.e., one-step agent and sequential agent) to execute the inference process. Subsequently, we instantiate the framework using various LLMs and evaluate their Task Planning and Tool Usage (TPTU) abilities on typical tasks. By highlighting key findings and challenges, our goal is to provide a helpful resource for researchers and practitioners to leverage the power of LLMs in their AI applications. Our study emphasizes the substantial potential of these models, while also identifying areas that need more investigation and improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.03757", "title": "3D Motion Magnification: Visualizing Subtle Motions with Time Varying Radiance Fields", "authors": [ "Brandon Y. Feng", "Hadi Alzayer", "Michael Rubinstein", "William T. Freeman", "Jia-Bin Huang" ], "github": "", "project_page": "", "abstract": "Motion magnification helps us visualize subtle, imperceptible motion. However, prior methods only work for 2D videos captured with a fixed camera. We present a 3D motion magnification method that can magnify subtle motions from scenes captured by a moving camera, while supporting novel view rendering. We represent the scene with time-varying radiance fields and leverage the Eulerian principle for motion magnification to extract and amplify the variation of the embedding of a fixed point over time. We study and validate our proposed principle for 3D motion magnification using both implicit and tri-plane-based radiance fields as our underlying 3D scene representation. We evaluate the effectiveness of our method on both synthetic and real-world scenes captured under various camera setups.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.03296", "title": "Studying Large Language Model Generalization with Influence Functions", "authors": [ "Roger Grosse", "Juhan Bae", "Cem Anil", "Nelson Elhage", "Alex Tamkin", "Amirhossein Tajdini", "Benoit Steiner", "Dustin Li", "Esin Durmus", "Ethan Perez", "Evan Hubinger", "Kamil\u0117 Luko\u0161i\u016bt\u0117", "Karina Nguyen", "Nicholas Joseph", "Sam McCandlish", "Jared Kaplan", "Samuel R. Bowman" ], "github": "", "project_page": "", "abstract": "When trying to gain better visibility into a machine learning model in order to understand and mitigate the associated risks, a potentially valuable source of evidence is: which training examples most contribute to a given behavior? Influence functions aim to answer a counterfactual: how would the model's parameters (and hence its outputs) change if a given sequence were added to the training set? While influence functions have produced insights for small models, they are difficult to scale to large language models (LLMs) due to the difficulty of computing an inverse-Hessian-vector product (IHVP). We use the Eigenvalue-corrected Kronecker-Factored Approximate Curvature (EK-FAC) approximation to scale influence functions up to LLMs with up to 52 billion parameters. In our experiments, EK-FAC achieves similar accuracy to traditional influence function estimators despite the IHVP computation being orders of magnitude faster. We investigate two algorithmic techniques to reduce the cost of computing gradients of candidate training sequences: TF-IDF filtering and query batching. We use influence functions to investigate the generalization patterns of LLMs, including the sparsity of the influence patterns, increasing abstraction with scale, math and programming abilities, cross-lingual generalization, and role-playing behavior. Despite many apparently sophisticated forms of generalization, we identify a surprising limitation: influences decay to near-zero when the order of key phrases is flipped. Overall, influence functions give us a powerful new tool for studying the generalization properties of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.03729", "title": "Tiny LVLM-eHub: Early Multimodal Experiments with Bard", "authors": [ "Wenqi Shao", "Yutao Hu", "Peng Gao", "Meng Lei", "Kaipeng Zhang", "Fanqing Meng", "Peng Xu", "Siyuan Huang", "Hongsheng Li", "Yu Qiao", "Ping Luo" ], "github": "https://github.com/OpenGVLab/Multi-Modality-Arena", "project_page": "", "abstract": "Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated significant progress in tackling complex multimodal tasks. Among these cutting-edge developments, Google's Bard stands out for its remarkable multimodal capabilities, promoting comprehensive comprehension and reasoning across various domains. This work presents an early and holistic evaluation of LVLMs' multimodal abilities, with a particular focus on Bard, by proposing a lightweight variant of LVLM-eHub, named Tiny LVLM-eHub. In comparison to the vanilla version, Tiny LVLM-eHub possesses several appealing properties. Firstly, it provides a systematic assessment of six categories of multimodal capabilities, including visual perception, visual knowledge acquisition, visual reasoning, visual commonsense, object hallucination, and embodied intelligence, through quantitative evaluation of 42 standard text-related visual benchmarks. Secondly, it conducts an in-depth analysis of LVLMs' predictions using the ChatGPT Ensemble Evaluation (CEE), which leads to a robust and accurate evaluation and exhibits improved alignment with human evaluation compared to the word matching approach. Thirdly, it comprises a mere 2.1K image-text pairs, facilitating ease of use for practitioners to evaluate their own offline LVLMs. Through extensive experimental analysis, this study demonstrates that Bard outperforms previous LVLMs in most multimodal capabilities except object hallucination, to which Bard is still susceptible. Tiny LVLM-eHub serves as a baseline evaluation for various LVLMs and encourages innovative strategies aimed at advancing multimodal techniques. Our project is publicly available at https://github.com/OpenGVLab/Multi-Modality-Arena.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/Multi-Modality-Arena", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.03280", "title": "Mirror-NeRF: Learning Neural Radiance Fields for Mirrors with Whitted-Style Ray Tracing", "authors": [ "Junyi Zeng", "Chong Bao", "Rui Chen", "Zilong Dong", "Guofeng Zhang", "Hujun Bao", "Zhaopeng Cui" ], "github": "", "project_page": "", "abstract": "Recently, Neural Radiance Fields (NeRF) has exhibited significant success in novel view synthesis, surface reconstruction, etc. However, since no physical reflection is considered in its rendering pipeline, NeRF mistakes the reflection in the mirror as a separate virtual scene, leading to the inaccurate reconstruction of the mirror and multi-view inconsistent reflections in the mirror. In this paper, we present a novel neural rendering framework, named Mirror-NeRF, which is able to learn accurate geometry and reflection of the mirror and support various scene manipulation applications with mirrors, such as adding new objects or mirrors into the scene and synthesizing the reflections of these new objects in mirrors, controlling mirror roughness, etc. To achieve this goal, we propose a unified radiance field by introducing the reflection probability and tracing rays following the light transport model of Whitted Ray Tracing, and also develop several techniques to facilitate the learning process. Experiments and comparisons on both synthetic and real datasets demonstrate the superiority of our method. The code and supplementary material are available on the project webpage: https://zju3dv.github.io/Mirror-NeRF/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.03028", "title": "Pre-Trained Large Language Models for Industrial Control", "authors": [ "Lei Song", "Chuheng Zhang", "Li Zhao", "Jiang Bian" ], "github": "", "project_page": "", "abstract": "For industrial control, developing high-performance controllers with few samples and low technical debt is appealing. Foundation models, possessing rich prior knowledge obtained from pre-training with Internet-scale corpus, have the potential to be a good controller with proper prompts. In this paper, we take HVAC (Heating, Ventilation, and Air Conditioning) building control as an example to examine the ability of GPT-4 (one of the first-tier foundation models) as the controller. To control HVAC, we wrap the task as a language game by providing text including a short description for the task, several selected demonstrations, and the current observation to GPT-4 on each step and execute the actions responded by GPT-4. We conduct series of experiments to answer the following questions: 1)~How well can GPT-4 control HVAC? 2)~How well can GPT-4 generalize to different scenarios for HVAC control? 3) How different parts of the text context affect the performance? In general, we found GPT-4 achieves the performance comparable to RL methods with few samples and low technical debt, indicating the potential of directly applying foundation models to industrial control tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.03291", "title": "SynJax: Structured Probability Distributions for JAX", "authors": [ "Milo\u0161 Stanojevi\u0107", "Laurent Sartran" ], "github": "https://github.com/deepmind/synjax", "project_page": "", "abstract": "The development of deep learning software libraries enabled significant progress in the field by allowing users to focus on modeling, while letting the library to take care of the tedious and time-consuming task of optimizing execution for modern hardware accelerators. However, this has benefited only particular types of deep learning models, such as Transformers, whose primitives map easily to the vectorized computation. The models that explicitly account for structured objects, such as trees and segmentations, did not benefit equally because they require custom algorithms that are difficult to implement in a vectorized form. SynJax directly addresses this problem by providing an efficient vectorized implementation of inference algorithms for structured distributions covering alignment, tagging, segmentation, constituency trees and spanning trees. With SynJax we can build large-scale differentiable models that explicitly model structure in the data. The code is available at https://github.com/deepmind/synjax.", "github_manual": "", "github_regex": "https://github.com/deepmind/synjax", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.03421", "title": "RecycleGPT: An Autoregressive Language Model with Recyclable Module", "authors": [ "Yufan Jiang", "Qiaozhi He", "Xiaomin Zhuang", "Zhihua Wu", "Kunpeng Wang", "Wenlai Zhao", "Guangwen Yang" ], "github": "", "project_page": "", "abstract": "Existing large language models have to run K times to generate a sequence of K tokens. In this paper, we present RecycleGPT, a generative language model with fast decoding speed by recycling pre-generated model states without running the whole model in multiple steps. Our approach relies on the observation that adjacent tokens in a sequence usually have strong correlations and the next token in a sequence can be reasonably guessed or inferred based on the preceding ones. Through theoretical evaluations and practical tests on downstream text generation tasks, we demonstrate the effectiveness of our approach in lowering inference latency, achieving up to 1.4x speedup while preserving high performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.03290", "title": "FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search", "authors": [ "Jordan Dotzel", "Gang Wu", "Andrew Li", "Muhammad Umar", "Yun Ni", "Mohamed S. Abdelfattah", "Zhiru Zhang", "Liqun Cheng", "Martin G. Dixon", "Norman P. Jouppi", "Quoc V. Le", "Sheng Li" ], "github": "", "project_page": "", "abstract": "Quantization has become a mainstream compression technique for reducing model size, computational requirements, and energy consumption for modern deep neural networks (DNNs). With the improved numerical support in recent hardware, including multiple variants of integer and floating point, mixed-precision quantization has become necessary to achieve high-quality results with low model cost. Prior mixed-precision quantization methods have performed a post-training quantization search, which compromises on accuracy, or a differentiable quantization search, which leads to high memory usage from branching. Therefore, we propose the first one-shot mixed-precision quantization search that eliminates the need for retraining in both integer and low-precision floating point models. We evaluate our floating-point and integer quantization search (FLIQS) on multiple convolutional networks and vision transformer models to discover Pareto-optimal models. Our approach discovers models that improve upon uniform precision, manual mixed-precision, and recent integer quantization search methods. With the proposed integer quantization search, we increase the accuracy of ResNet-18 on ImageNet by 1.31% points and ResNet-50 by 0.90% points with equivalent model cost over previous methods. Additionally, for the first time, we explore a novel mixed-precision floating-point search and improve MobileNetV2 by up to 0.98% points compared to prior state-of-the-art FP8 models. Finally, we extend FLIQS to simultaneously search a joint quantization and neural architecture space and improve the ImageNet accuracy by 2.69% points with similar model cost on a MobileNetV2 search space.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-08", "arxiv_id": "2308.02560", "title": "From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion", "authors": [ "Robin San Roman", "Yossi Adi", "Antoine Deleforge", "Romain Serizel", "Gabriel Synnaeve", "Alexandre D\u00e9fossez" ], "github": "", "project_page": "", "abstract": "Deep generative models can generate high-fidelity audio conditioned on various types of representations (e.g., mel-spectrograms, Mel-frequency Cepstral Coefficients (MFCC)). Recently, such models have been used to synthesize audio waveforms conditioned on highly compressed representations. Although such methods produce impressive results, they are prone to generate audible artifacts when the conditioning is flawed or imperfect. An alternative modeling approach is to use diffusion models. However, these have mainly been used as speech vocoders (i.e., conditioned on mel-spectrograms) or generating relatively low sampling rate signals. In this work, we propose a high-fidelity multi-band diffusion-based framework that generates any type of audio modality (e.g., speech, music, environmental sounds) from low-bitrate discrete representations. At equal bit rate, the proposed approach outperforms state-of-the-art generative techniques in terms of perceptual quality. Training and, evaluation code, along with audio samples, are available on the facebookresearch/audiocraft Github page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-09", "arxiv_id": "2308.04079", "title": "3D Gaussian Splatting for Real-Time Radiance Field Rendering", "authors": [ "Bernhard Kerbl", "Georgios Kopanas", "Thomas Leimk\u00fchler", "George Drettakis" ], "github": "https://github.com/graphdeco-inria/gaussian-splatting", "project_page": "", "abstract": "Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.", "github_manual": "https://github.com/graphdeco-inria/gaussian-splatting", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-09", "arxiv_id": "2308.03958", "title": "Simple synthetic data reduces sycophancy in large language models", "authors": [ "Jerry Wei", "Da Huang", "Yifeng Lu", "Denny Zhou", "Quoc V. Le" ], "github": "https://github.com/google/sycophancy-intervention", "project_page": "", "abstract": "Sycophancy is an undesirable behavior where models tailor their responses to follow a human user's view even when that view is not objectively correct (e.g., adapting liberal views once a user reveals that they are liberal). In this paper, we study the prevalence of sycophancy in language models and propose a simple synthetic-data intervention to reduce this behavior. First, on a set of three sycophancy tasks (Perez et al., 2022) where models are asked for an opinion on statements with no correct answers (e.g., politics), we observe that both model scaling and instruction tuning significantly increase sycophancy for PaLM models up to 540B parameters. Second, we extend sycophancy evaluations to simple addition statements that are objectively incorrect, finding that despite knowing that these statements are wrong, language models will still agree with them if the user does as well. To reduce sycophancy, we present a straightforward synthetic-data intervention that takes public NLP tasks and encourages models to be robust to user opinions on these tasks. Adding these data in a lightweight finetuning step can significantly reduce sycophantic behavior on held-out prompts. Code for generating synthetic data for intervention can be found at https://github.com/google/sycophancy-intervention.", "github_manual": "", "github_regex": "https://github.com/google/sycophancy-intervention", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-09", "arxiv_id": "2308.04265", "title": "FLIRT: Feedback Loop In-context Red Teaming", "authors": [ "Ninareh Mehrabi", "Palash Goyal", "Christophe Dupuy", "Qian Hu", "Shalini Ghosh", "Richard Zemel", "Kai-Wei Chang", "Aram Galstyan", "Rahul Gupta" ], "github": "https://github.com/amazon-science/FLIRT", "project_page": "", "abstract": "Warning: this paper contains content that may be inappropriate or offensive. As generative models become available for public use in various applications, testing and analyzing vulnerabilities of these models has become a priority. Here we propose an automatic red teaming framework that evaluates a given model and exposes its vulnerabilities against unsafe and inappropriate content generation. Our framework uses in-context learning in a feedback loop to red team models and trigger them into unsafe content generation. We propose different in-context attack strategies to automatically learn effective and diverse adversarial prompts for text-to-image models. Our experiments demonstrate that compared to baseline approaches, our proposed strategy is significantly more effective in exposing vulnerabilities in Stable Diffusion (SD) model, even when the latter is enhanced with safety features. Furthermore, we demonstrate that the proposed framework is effective for red teaming text-to-text models, resulting in significantly higher toxic response generation rate compared to previously reported numbers.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/amazon-science/FLIRT", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-09", "arxiv_id": "2308.04430", "title": "SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore", "authors": [ "Sewon Min", "Suchin Gururangan", "Eric Wallace", "Hannaneh Hajishirzi", "Noah A. Smith", "Luke Zettlemoyer" ], "github": "https://github.com/kernelmachine/silo-lm", "project_page": "", "abstract": "The legality of training language models (LMs) on copyrighted or otherwise restricted data is under intense debate. However, as we show, model performance significantly degrades if trained only on low-risk text (e.g., out-of-copyright books or government documents), due to its limited size and domain coverage. We present SILO, a new language model that manages this risk-performance tradeoff during inference. SILO is built by (1) training a parametric LM on Open License Corpus (OLC), a new corpus we curate with 228B tokens of public domain and permissively licensed text and (2) augmenting it with a more general and easily modifiable nonparametric datastore (e.g., containing copyrighted books or news) that is only queried during inference. The datastore allows use of high-risk data without training on it, supports sentence-level data attribution, and enables data producers to opt out from the model by removing content from the store. These capabilities can foster compliance with data-use regulations such as the fair use doctrine in the United States and the GDPR in the European Union. Our experiments show that the parametric LM struggles on domains not covered by OLC. However, access to the datastore greatly improves out of domain performance, closing 90% of the performance gap with an LM trained on the Pile, a more diverse corpus with mostly high-risk text. We also analyze which nonparametric approach works best, where the remaining errors lie, and how performance scales with datastore size. Our results suggest that it is possible to build high quality language models while mitigating their legal risk.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kernelmachine/silo-lm", "github_api_added_by": "auto" }, { "date": "2023-08-09", "arxiv_id": "2308.03793", "title": "ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptation", "authors": [ "Hu. Xuefeng", "Zhang. Ke", "Xia. Lu", "Chen. Albert", "Luo. Jiajia", "Sun. Yuyin", "Wang. Ken", "Qiao. Nan", "Zeng. Xiao", "Sun. Min", "Kuo. Cheng-Hao", "Nevatia. Ram" ], "github": "", "project_page": "", "abstract": "Large-scale Pre-Training Vision-Language Model such as CLIP has demonstrated outstanding performance in zero-shot classification, e.g. achieving 76.3% top-1 accuracy on ImageNet without seeing any example, which leads to potential benefits to many tasks that have no labeled data. However, while applying CLIP to a downstream target domain, the presence of visual and text domain gaps and cross-modality misalignment can greatly impact the model performance. To address such challenges, we propose ReCLIP, the first source-free domain adaptation method for vision-language models, which does not require any source data or target labeled data. ReCLIP first learns a projection space to mitigate the misaligned visual-text embeddings and learns pseudo labels, and then deploys cross-modality self-training with the pseudo labels, to update visual and text encoders, refine labels and reduce domain gaps and misalignments iteratively. With extensive experiments, we demonstrate ReCLIP reduces the average error rate of CLIP from 30.17% to 25.06% on 22 image classification benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-10", "arxiv_id": "2308.04592", "title": "Shepherd: A Critic for Language Model Generation", "authors": [ "Tianlu Wang", "Ping Yu", "Xiaoqing Ellen Tan", "Sean O'Brien", "Ramakanth Pasunuru", "Jane Dwivedi-Yu", "Olga Golovneva", "Luke Zettlemoyer", "Maryam Fazel-Zarandi", "Asli Celikyilmaz" ], "github": "https://github.com/facebookresearch/shepherd", "project_page": "", "abstract": "As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs. In this work, we introduce Shepherd, a language model specifically tuned to critique responses and suggest refinements, extending beyond the capabilities of an untuned model to identify diverse errors and provide suggestions to remedy them. At the core of our approach is a high quality feedback dataset, which we curate from community feedback and human annotations. Even though Shepherd is small (7B parameters), its critiques are either equivalent or preferred to those from established models including ChatGPT. Using GPT-4 for evaluation, Shepherd reaches an average win-rate of 53-87% compared to competitive alternatives. In human evaluation, Shepherd strictly outperforms other models and on average closely ties with ChatGPT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/shepherd", "github_api_added_by": "auto" }, { "date": "2023-08-10", "arxiv_id": "2308.04729", "title": "JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models", "authors": [ "Peike Li", "Boyu Chen", "Yao Yao", "Yikai Wang", "Allen Wang", "Alex Wang" ], "github": "", "project_page": "", "abstract": "Music generation has attracted growing interest with the advancement of deep generative models. However, generating music conditioned on textual descriptions, known as text-to-music, remains challenging due to the complexity of musical structures and high sampling rate requirements. Despite the task's significance, prevailing generative models exhibit limitations in music quality, computational efficiency, and generalization. This paper introduces JEN-1, a universal high-fidelity model for text-to-music generation. JEN-1 is a diffusion model incorporating both autoregressive and non-autoregressive training. Through in-context learning, JEN-1 performs various generation tasks including text-guided music generation, music inpainting, and continuation. Evaluations demonstrate JEN-1's superior performance over state-of-the-art methods in text-music alignment and music quality while maintaining computational efficiency. Our demos are available at http://futureverse.com/research/jen/demos/jen1", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-10", "arxiv_id": "2308.04623", "title": "Accelerating LLM Inference with Staged Speculative Decoding", "authors": [ "Benjamin Spector", "Chris Re" ], "github": "", "project_page": "", "abstract": "Recent advances with large language models (LLM) illustrate their diverse capabilities. We propose a novel algorithm, staged speculative decoding, to accelerate LLM inference in small-batch, on-device scenarios. We address the low arithmetic intensity of small-batch inference by improving upon previous work in speculative decoding. First, we restructure the speculative batch as a tree, which reduces generation costs and increases the expected tokens per batch. Second, we add a second stage of speculative decoding. Taken together, we reduce single-batch decoding latency by 3.16x with a 762M parameter GPT-2-L model while perfectly preserving output quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-10", "arxiv_id": "2308.04556", "title": "FocalFormer3D : Focusing on Hard Instance for 3D Object Detection", "authors": [ "Yilun Chen", "Zhiding Yu", "Yukang Chen", "Shiyi Lan", "Animashree Anandkumar", "Jiaya Jia", "Jose Alvarez" ], "github": "https://github.com/NVlabs/FocalFormer3D", "project_page": "", "abstract": "False negatives (FN) in 3D object detection, {\\em e.g.}, missing predictions of pedestrians, vehicles, or other obstacles, can lead to potentially dangerous situations in autonomous driving. While being fatal, this issue is understudied in many current 3D detection methods. In this work, we propose Hard Instance Probing (HIP), a general pipeline that identifies FN in a multi-stage manner and guides the models to focus on excavating difficult instances. For 3D object detection, we instantiate this method as FocalFormer3D, a simple yet effective detector that excels at excavating difficult objects and improving prediction recall. FocalFormer3D features a multi-stage query generation to discover hard objects and a box-level transformer decoder to efficiently distinguish objects from massive object candidates. Experimental results on the nuScenes and Waymo datasets validate the superior performance of FocalFormer3D. The advantage leads to strong performance on both detection and tracking, in both LiDAR and multi-modal settings. Notably, FocalFormer3D achieves a 70.5 mAP and 73.9 NDS on nuScenes detection benchmark, while the nuScenes tracking benchmark shows 72.1 AMOTA, both ranking 1st place on the nuScenes LiDAR leaderboard. Our code is available at https://github.com/NVlabs/FocalFormer3D.", "github_manual": "", "github_regex": "https://github.com/NVlabs/FocalFormer3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-11", "arxiv_id": "2308.05734", "title": "AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining", "authors": [ "Haohe Liu", "Qiao Tian", "Yi Yuan", "Xubo Liu", "Xinhao Mei", "Qiuqiang Kong", "Yuping Wang", "Wenwu Wang", "Yuxuan Wang", "Mark D. Plumbley" ], "github": "https://github.com/haoheliu/audioldm2", "project_page": "", "abstract": "Although audio generation shares commonalities across different types of audio, such as speech, music, and sound effects, designing models for each type requires careful consideration of specific objectives and biases that can significantly differ from those of other types. To bring us closer to a unified perspective of audio generation, this paper proposes a framework that utilizes the same learning method for speech, music, and sound effect generation. Our framework introduces a general representation of audio, called language of audio (LOA). Any audio can be translated into LOA based on AudioMAE, a self-supervised pre-trained representation learning model. In the generation process, we translate any modalities into LOA by using a GPT-2 model, and we perform self-supervised audio generation learning with a latent diffusion model conditioned on LOA. The proposed framework naturally brings advantages such as in-context learning abilities and reusable self-supervised pretrained AudioMAE and latent diffusion models. Experiments on the major benchmarks of text-to-audio, text-to-music, and text-to-speech demonstrate new state-of-the-art or competitive performance to previous approaches. Our demo and code are available at https://audioldm.github.io/audioldm2.", "github_manual": "https://github.com/haoheliu/audioldm2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-11", "arxiv_id": "2308.05374", "title": "Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment", "authors": [ "Yang Liu", "Yuanshun Yao", "Jean-Francois Ton", "Xiaoying Zhang", "Ruocheng Guo Hao Cheng", "Yegor Klochkov", "Muhammad Faaiz Taufiq", "Hang Li" ], "github": "", "project_page": "", "abstract": "Ensuring alignment, which refers to making models behave in accordance with human intentions [1,2], has become a critical task before deploying large language models (LLMs) in real-world applications. For instance, OpenAI devoted six months to iteratively aligning GPT-4 before its release [3]. However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations. This obstacle hinders systematic iteration and deployment of LLMs. To address this issue, this paper presents a comprehensive survey of key dimensions that are crucial to consider when assessing LLM trustworthiness. The survey covers seven major categories of LLM trustworthiness: reliability, safety, fairness, resistance to misuse, explainability and reasoning, adherence to social norms, and robustness. Each major category is further divided into several sub-categories, resulting in a total of 29 sub-categories. Additionally, a subset of 8 sub-categories is selected for further investigation, where corresponding measurement studies are designed and conducted on several widely-used LLMs. The measurement results indicate that, in general, more aligned models tend to perform better in terms of overall trustworthiness. However, the effectiveness of alignment varies across the different trustworthiness categories considered. This highlights the importance of conducting more fine-grained analyses, testing, and making continuous improvements on LLM alignment. By shedding light on these key dimensions of LLM trustworthiness, this paper aims to provide valuable insights and guidance to practitioners in the field. Understanding and addressing these concerns will be crucial in achieving reliable and ethically sound deployment of LLMs in various applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-11", "arxiv_id": "2308.05737", "title": "Follow Anything: Open-set detection, tracking, and following in real-time", "authors": [ "Alaa Maalouf", "Ninad Jadhav", "Krishna Murthy Jatavallabhula", "Makram Chahine", "Daniel M. Vogt", "Robert J. Wood", "Antonio Torralba", "Daniela Rus" ], "github": "https://github.com/alaamaalouf/FollowAnything", "project_page": "", "abstract": "Tracking and following objects of interest is critical to several robotics use cases, ranging from industrial automation to logistics and warehousing, to healthcare and security. In this paper, we present a robotic system to detect, track, and follow any object in real-time. Our approach, dubbed ``follow anything'' (FAn), is an open-vocabulary and multimodal model -- it is not restricted to concepts seen at training time and can be applied to novel classes at inference time using text, images, or click queries. Leveraging rich visual descriptors from large-scale pre-trained models (foundation models), FAn can detect and segment objects by matching multimodal queries (text, images, clicks) against an input image sequence. These detected and segmented objects are tracked across image frames, all while accounting for occlusion and object re-emergence. We demonstrate FAn on a real-world robotic system (a micro aerial vehicle) and report its ability to seamlessly follow the objects of interest in a real-time control loop. FAn can be deployed on a laptop with a lightweight (6-8 GB) graphics card, achieving a throughput of 6-20 frames per second. To enable rapid adoption, deployment, and extensibility, we open-source all our code on our project webpage at https://github.com/alaamaalouf/FollowAnything . We also encourage the reader the watch our 5-minutes explainer video in this https://www.youtube.com/watch?v=6Mgt3EPytrw .", "github_manual": "", "github_regex": "https://github.com/alaamaalouf/FollowAnything", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-11", "arxiv_id": "2308.05326", "title": "OpenProteinSet: Training data for structural biology at scale", "authors": [ "Gustaf Ahdritz", "Nazim Bouatta", "Sachin Kadyan", "Lukas Jarosch", "Daniel Berenberg", "Ian Fisk", "Andrew M. Watkins", "Stephen Ra", "Richard Bonneau", "Mohammed AlQuraishi" ], "github": "", "project_page": "", "abstract": "Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein structure prediction for decades. Recent breakthroughs like AlphaFold2 that use transformers to attend directly over large quantities of raw MSAs have reaffirmed their importance. Generation of MSAs is highly computationally intensive, however, and no datasets comparable to those used to train AlphaFold2 have been made available to the research community, hindering progress in machine learning for proteins. To remedy this problem, we introduce OpenProteinSet, an open-source corpus of more than 16 million MSAs, associated structural homologs from the Protein Data Bank, and AlphaFold2 protein structure predictions. We have previously demonstrated the utility of OpenProteinSet by successfully retraining AlphaFold2 on it. We expect OpenProteinSet to be broadly useful as training and validation data for 1) diverse tasks focused on protein structure, function, and design and 2) large-scale multimodal machine learning research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-11", "arxiv_id": "2308.05371", "title": "Flexible Isosurface Extraction for Gradient-Based Mesh Optimization", "authors": [ "Tianchang Shen", "Jacob Munkberg", "Jon Hasselgren", "Kangxue Yin", "Zian Wang", "Wenzheng Chen", "Zan Gojcic", "Sanja Fidler", "Nicholas Sharp", "Jun Gao" ], "github": "https://github.com/nv-tlabs/FlexiCubes", "project_page": "", "abstract": "This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly common paradigm in applications including photogrammetry, generative modeling, and inverse physics. Existing implementations adapt classic isosurface extraction algorithms like Marching Cubes or Dual Contouring; these techniques were designed to extract meshes from fixed, known fields, and in the optimization setting they lack the degrees of freedom to represent high-quality feature-preserving meshes, or suffer from numerical instabilities. We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives. Our main insight is to introduce additional carefully-chosen parameters into the representation, which allow local flexible adjustments to the extracted mesh geometry and connectivity. These parameters are updated along with the underlying scalar field via automatic differentiation when optimizing for a downstream task. We base our extraction scheme on Dual Marching Cubes for improved topological properties, and present extensions to optionally generate tetrahedral and hierarchically-adaptive meshes. Extensive experiments validate FlexiCubes on both synthetic benchmarks and real-world applications, showing that it offers significant improvements in mesh quality and geometric fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nv-tlabs/FlexiCubes", "github_api_added_by": "auto" }, { "date": "2023-08-11", "arxiv_id": "2308.05221", "title": "Alexa, play with robot: Introducing the First Alexa Prize SimBot Challenge on Embodied AI", "authors": [ "Hangjie Shi", "Leslie Ball", "Govind Thattai", "Desheng Zhang", "Lucy Hu", "Qiaozi Gao", "Suhaila Shakiah", "Xiaofeng Gao", "Aishwarya Padmakumar", "Bofei Yang", "Cadence Chung", "Dinakar Guthy", "Gaurav Sukhatme", "Karthika Arumugam", "Matthew Wen", "Osman Ipek", "Patrick Lange", "Rohan Khanna", "Shreyas Pansare", "Vasu Sharma", "Chao Zhang", "Cris Flagg", "Daniel Pressel", "Lavina Vaz", "Luke Dai", "Prasoon Goyal", "Sattvik Sahai", "Shaohua Liu", "Yao Lu", "Anna Gottardi", "Shui Hu", "Yang Liu", "Dilek Hakkani-Tur", "Kate Bland", "Heather Rocker", "James Jeun", "Yadunandana Rao", "Michael Johnston", "Akshaya Iyengar", "Arindam Mandal", "Prem Natarajan", "Reza Ghanadan" ], "github": "", "project_page": "", "abstract": "The Alexa Prize program has empowered numerous university students to explore, experiment, and showcase their talents in building conversational agents through challenges like the SocialBot Grand Challenge and the TaskBot Challenge. As conversational agents increasingly appear in multimodal and embodied contexts, it is important to explore the affordances of conversational interaction augmented with computer vision and physical embodiment. This paper describes the SimBot Challenge, a new challenge in which university teams compete to build robot assistants that complete tasks in a simulated physical environment. This paper provides an overview of the SimBot Challenge, which included both online and offline challenge phases. We describe the infrastructure and support provided to the teams including Alexa Arena, the simulated environment, and the ML toolkit provided to teams to accelerate their building of vision and language models. We summarize the approaches the participating teams took to overcome research challenges and extract key lessons learned. Finally, we provide analysis of the performance of the competing SimBots during the competition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-11", "arxiv_id": "2308.05732", "title": "PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers", "authors": [ "Phillip Lippe", "Bastiaan S. Veeling", "Paris Perdikaris", "Richard E. Turner", "Johannes Brandstetter" ], "github": "https://github.com/microsoft/pdearena", "project_page": "", "abstract": "Time-dependent partial differential equations (PDEs) are ubiquitous in science and engineering. Recently, mostly due to the high computational cost of traditional solution techniques, deep neural network based surrogates have gained increased interest. The practical utility of such neural PDE solvers relies on their ability to provide accurate, stable predictions over long time horizons, which is a notoriously hard problem. In this work, we present a large-scale analysis of common temporal rollout strategies, identifying the neglect of non-dominant spatial frequency information, often associated with high frequencies in PDE solutions, as the primary pitfall limiting stable, accurate rollout performance. Based on these insights, we draw inspiration from recent advances in diffusion models to introduce PDE-Refiner; a novel model class that enables more accurate modeling of all frequency components via a multistep refinement process. We validate PDE-Refiner on challenging benchmarks of complex fluid dynamics, demonstrating stable and accurate rollouts that consistently outperform state-of-the-art models, including neural, numerical, and hybrid neural-numerical architectures. We further demonstrate that PDE-Refiner greatly enhances data efficiency, since the denoising objective implicitly induces a novel form of spectral data augmentation. Finally, PDE-Refiner's connection to diffusion models enables an accurate and efficient assessment of the model's predictive uncertainty, allowing us to estimate when the surrogate becomes inaccurate.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/pdearena", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-14", "arxiv_id": "2308.06259", "title": "Self-Alignment with Instruction Backtranslation", "authors": [ "Xian Li", "Ping Yu", "Chunting Zhou", "Timo Schick", "Luke Zettlemoyer", "Omer Levy", "Jason Weston", "Mike Lewis" ], "github": "", "project_page": "", "abstract": "We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions. Our approach, named instruction backtranslation, starts with a language model finetuned on a small amount of seed data, and a given web corpus. The seed model is used to construct training examples by generating instruction prompts for web documents (self-augmentation), and then selecting high quality examples from among these candidates (self-curation). This data is then used to finetune a stronger model. Finetuning LLaMa on two iterations of our approach yields a model that outperforms all other LLaMa-based models on the Alpaca leaderboard not relying on distillation data, demonstrating highly effective self-alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-14", "arxiv_id": "2308.05884", "title": "PIPPA: A Partially Synthetic Conversational Dataset", "authors": [ "Tear Gosling", "Alpin Dale", "Yinhe Zheng" ], "github": "https://github.com/PolyAI-LDN/conversational-datasets", "project_page": "", "abstract": "With the emergence of increasingly powerful large language models, there is a burgeoning interest in leveraging these models for casual conversation and role-play applications. However, existing conversational and role-playing datasets often fail to capture the diverse and nuanced interactions typically exhibited by real-world role-play participants. To address this limitation and contribute to the rapidly growing field, we introduce a partially-synthetic dataset named PIPPA (Personal Interaction Pairs between People and AI). PIPPA is a result of a community-driven crowdsourcing effort involving a group of role-play enthusiasts. The dataset comprises over 1 million utterances that are distributed across 26,000 conversation sessions and provides a rich resource for researchers and AI developers to explore and refine conversational AI systems in the context of role-play scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PolyAI-LDN/conversational-datasets", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-14", "arxiv_id": "2308.06103", "title": "Composable Function-preserving Expansions for Transformer Architectures", "authors": [ "Andrea Gesmundo", "Kaitlin Maile" ], "github": "", "project_page": "", "abstract": "Training state-of-the-art neural networks requires a high cost in terms of compute and time. Model scale is recognized to be a critical factor to achieve and improve the state-of-the-art. Increasing the scale of a neural network normally requires restarting from scratch by randomly initializing all the parameters of the model, as this implies a change of architecture's parameters that does not allow for a straightforward transfer of knowledge from smaller size models. In this work, we propose six composable transformations to incrementally increase the size of transformer-based neural networks while preserving functionality, allowing to expand the capacity of the model as needed. We provide proof of exact function preservation under minimal initialization constraints for each transformation. The proposed methods may enable efficient training pipelines for larger and more powerful models by progressively expanding the architecture throughout training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-14", "arxiv_id": "2308.05960", "title": "BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents", "authors": [ "Zhiwei Liu", "Weiran Yao", "Jianguo Zhang", "Le Xue", "Shelby Heinecke", "Rithesh Murthy", "Yihao Feng", "Zeyuan Chen", "Juan Carlos Niebles", "Devansh Arpit", "Ran Xu", "Phil Mui", "Huan Wang", "Caiming Xiong", "Silvio Savarese" ], "github": "https://github.com/salesforce/BOLAA", "project_page": "", "abstract": "The massive successes of large language models (LLMs) encourage the emerging exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to generate actions with its core LLM and interact with environments, which facilitates the ability to resolve complex tasks by conditioning on past interactions such as observations and actions. Since the investigation of LAA is still very recent, limited explorations are available. Therefore, we provide a comprehensive comparison of LAA in terms of both agent architectures and LLM backbones. Additionally, we propose a new strategy to orchestrate multiple LAAs such that each labor LAA focuses on one type of action, i.e. BOLAA, where a controller manages the communication among multiple agents. We conduct simulations on both decision-making and multi-step reasoning environments, which comprehensively justify the capacity of LAAs. Our performance results provide quantitative suggestions for designing LAA architectures and the optimal choice of LLMs, as well as the compatibility of both. We release our implementation code of LAAs to the public at https://github.com/salesforce/BOLAA.", "github_manual": "", "github_regex": "https://github.com/salesforce/BOLAA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-14", "arxiv_id": "2308.06125", "title": "Improving Joint Speech-Text Representations Without Alignment", "authors": [ "Cal Peyser", "Zhong Meng", "Ke Hu", "Rohit Prabhavalkar", "Andrew Rosenberg", "Tara N. Sainath", "Michael Picheny", "Kyunghyun Cho" ], "github": "", "project_page": "", "abstract": "The last year has seen astonishing progress in text-prompted image generation premised on the idea of a cross-modal representation space in which the text and image domains are represented jointly. In ASR, this idea has found application as joint speech-text encoders that can scale to the capacities of very large parameter models by being trained on both unpaired speech and text. While these methods show promise, they have required special treatment of the sequence-length mismatch inherent in speech and text, either by up-sampling heuristics or an explicit alignment model. In this work, we offer evidence that joint speech-text encoders naturally achieve consistent representations across modalities by disregarding sequence length, and argue that consistency losses could forgive length differences and simply assume the best alignment. We show that such a loss improves downstream WER in both a large-parameter monolingual and multilingual system.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-14", "arxiv_id": "2308.06261", "title": "Enhancing Network Management Using Code Generated by Large Language Models", "authors": [ "Sathiya Kumaran Mani", "Yajie Zhou", "Kevin Hsieh", "Santiago Segarra", "Ranveer Chandra", "Srikanth Kandula" ], "github": "https://github.com/microsoft/nemoeval", "project_page": "", "abstract": "Analyzing network topologies and communication graphs plays a crucial role in contemporary network management. However, the absence of a cohesive approach leads to a challenging learning curve, heightened errors, and inefficiencies. In this paper, we introduce a novel approach to facilitate a natural-language-based network management experience, utilizing large language models (LLMs) to generate task-specific code from natural language queries. This method tackles the challenges of explainability, scalability, and privacy by allowing network operators to inspect the generated code, eliminating the need to share network data with LLMs, and concentrating on application-specific requests combined with general program synthesis techniques. We design and evaluate a prototype system using benchmark applications, showcasing high accuracy, cost-effectiveness, and the potential for further enhancements using complementary program synthesis techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/nemoeval", "github_api_added_by": "auto" }, { "date": "2023-08-15", "arxiv_id": "2308.07124", "title": "OctoPack: Instruction Tuning Code Large Language Models", "authors": [ "Niklas Muennighoff", "Qian Liu", "Armel Zebaze", "Qinkai Zheng", "Binyuan Hui", "Terry Yue Zhuo", "Swayam Singh", "Xiangru Tang", "Leandro von Werra", "Shayne Longpre" ], "github": "https://github.com/bigcode-project/octopack", "project_page": "", "abstract": "Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack.", "github_manual": "", "github_regex": "https://github.com/bigcode-project/octopack", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-15", "arxiv_id": "2308.06873", "title": "SpeechX: Neural Codec Language Model as a Versatile Speech Transformer", "authors": [ "Xiaofei Wang", "Manthan Thakker", "Zhuo Chen", "Naoyuki Kanda", "Sefik Emre Eskimez", "Sanyuan Chen", "Min Tang", "Shujie Liu", "Jinyu Li", "Takuya Yoshioka" ], "github": "", "project_page": "", "abstract": "Recent advancements in generative speech models based on audio-text prompts have enabled remarkable innovations like high-quality zero-shot text-to-speech. However, existing models still face limitations in handling diverse audio-text speech generation tasks involving transforming input speech and processing audio captured in adverse acoustic conditions. This paper introduces SpeechX, a versatile speech generation model capable of zero-shot TTS and various speech transformation tasks, dealing with both clean and noisy signals. SpeechX combines neural codec language modeling with multi-task learning using task-dependent prompting, enabling unified and extensible modeling and providing a consistent way for leveraging textual input in speech enhancement and transformation tasks. Experimental results show SpeechX's efficacy in various tasks, including zero-shot TTS, noise suppression, target speaker extraction, speech removal, and speech editing with or without background noise, achieving comparable or superior performance to specialized models across tasks. See https://aka.ms/speechx for demo samples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-15", "arxiv_id": "2308.07317", "title": "Platypus: Quick, Cheap, and Powerful Refinement of LLMs", "authors": [ "Ariel N. Lee", "Cole J. Hunter", "Nataniel Ruiz" ], "github": "https://github.com/arielnlee/Platypus", "project_page": "", "abstract": "We present Platypus, a family of fine-tuned and merged Large Language Models (LLMs) that achieves the strongest performance and currently stands at first place in HuggingFace's Open LLM Leaderboard as of the release date of this work. In this work we describe (1) our curated dataset Open-Platypus, that is a subset of other open datasets and which we release to the public (2) our process of fine-tuning and merging LoRA modules in order to conserve the strong prior of pretrained LLMs, while bringing specific domain knowledge to the surface (3) our efforts in checking for test data leaks and contamination in the training data, which can inform future research. Specifically, the Platypus family achieves strong performance in quantitative LLM metrics across model sizes, topping the global Open LLM leaderboard while using just a fraction of the fine-tuning data and overall compute that are required for other state-of-the-art fine-tuned LLMs. In particular, a 13B Platypus model can be trained on a single A100 GPU using 25k questions in 5 hours. This is a testament of the quality of our Open-Platypus dataset, and opens opportunities for more improvements in the field. Project page: https://platypus-llm.github.io", "github_manual": "https://github.com/arielnlee/Platypus", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-15", "arxiv_id": "2308.06912", "title": "CausalLM is not optimal for in-context learning", "authors": [ "Nan Ding", "Tomer Levinboim", "Jialin Wu", "Sebastian Goodman", "Radu Soricut" ], "github": "", "project_page": "", "abstract": "Recent empirical evidence indicates that transformer based in-context learning performs better when using a prefix language model (prefixLM), in which in-context samples can all attend to each other, compared to causal language models (causalLM), which use auto-regressive attention that prohibits in-context samples to attend to future samples. While this result is intuitive, it is not understood from a theoretical perspective. In this paper we take a theoretical approach and analyze the convergence behavior of prefixLM and causalLM under a certain parameter construction. Our analysis shows that both LM types converge to their stationary points at a linear rate, but that while prefixLM converges to the optimal solution of linear regression, causalLM convergence dynamics follows that of an online gradient descent algorithm, which is not guaranteed to be optimal even as the number of samples grows infinitely. We supplement our theoretical claims with empirical experiments over synthetic and real tasks and using various types of transformers. Our experiments verify that causalLM consistently underperforms prefixLM in all settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-15", "arxiv_id": "2308.06721", "title": "IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models", "authors": [ "Hu Ye", "Jun Zhang", "Sibo Liu", "Xiao Han", "Wei Yang" ], "github": "https://github.com/tencent-ailab/IP-Adapter", "project_page": "", "abstract": "Recent years have witnessed the strong power of large text-to-image diffusion models for the impressive generative capability to create high-fidelity images. However, it is very tricky to generate desired images using only text prompt as it often involves complex prompt engineering. An alternative to text prompt is image prompt, as the saying goes: \"an image is worth a thousand words\". Although existing methods of direct fine-tuning from pretrained models are effective, they require large computing resources and are not compatible with other base models, text prompt, and structural controls. In this paper, we present IP-Adapter, an effective and lightweight adapter to achieve image prompt capability for the pretrained text-to-image diffusion models. The key design of our IP-Adapter is decoupled cross-attention mechanism that separates cross-attention layers for text features and image features. Despite the simplicity of our method, an IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fully fine-tuned image prompt model. As we freeze the pretrained diffusion model, the proposed IP-Adapter can be generalized not only to other custom models fine-tuned from the same base model, but also to controllable generation using existing controllable tools. With the benefit of the decoupled cross-attention strategy, the image prompt can also work well with the text prompt to achieve multimodal image generation. The project page is available at https://ip-adapter.github.io.", "github_manual": "https://github.com/tencent-ailab/IP-Adapter", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-15", "arxiv_id": "2308.07228", "title": "RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs", "authors": [ "Zhouxia Wang", "Jiawei Zhang", "Tianshui Chen", "Wenping Wang", "Ping Luo" ], "github": "https://github.com/wzhouxiff/restoreformerplusplus", "project_page": "", "abstract": "Blind face restoration aims at recovering high-quality face images from those with unknown degradations. Current algorithms mainly introduce priors to complement high-quality details and achieve impressive progress. However, most of these algorithms ignore abundant contextual information in the face and its interplay with the priors, leading to sub-optimal performance. Moreover, they pay less attention to the gap between the synthetic and real-world scenarios, limiting the robustness and generalization to real-world applications. In this work, we propose RestoreFormer++, which on the one hand introduces fully-spatial attention mechanisms to model the contextual information and the interplay with the priors, and on the other hand, explores an extending degrading model to help generate more realistic degraded face images to alleviate the synthetic-to-real-world gap. Compared with current algorithms, RestoreFormer++ has several crucial benefits. First, instead of using a multi-head self-attention mechanism like the traditional visual transformer, we introduce multi-head cross-attention over multi-scale features to fully explore spatial interactions between corrupted information and high-quality priors. In this way, it can facilitate RestoreFormer++ to restore face images with higher realness and fidelity. Second, in contrast to the recognition-oriented dictionary, we learn a reconstruction-oriented dictionary as priors, which contains more diverse high-quality facial details and better accords with the restoration target. Third, we introduce an extending degrading model that contains more realistic degraded scenarios for training data synthesizing, and thus helps to enhance the robustness and generalization of our RestoreFormer++ model. Extensive experiments show that RestoreFormer++ outperforms state-of-the-art algorithms on both synthetic and real-world datasets.", "github_manual": "https://github.com/wzhouxiff/restoreformerplusplus", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-15", "arxiv_id": "2308.07316", "title": "Jurassic World Remake: Bringing Ancient Fossils Back to Life via Zero-Shot Long Image-to-Image Translation", "authors": [ "Alexander Martin", "Haitian Zheng", "Jie An", "Jiebo Luo" ], "github": "", "project_page": "", "abstract": "With a strong understanding of the target domain from natural language, we produce promising results in translating across large domain gaps and bringing skeletons back to life. In this work, we use text-guided latent diffusion models for zero-shot image-to-image translation (I2I) across large domain gaps (longI2I), where large amounts of new visual features and new geometry need to be generated to enter the target domain. Being able to perform translations across large domain gaps has a wide variety of real-world applications in criminology, astrology, environmental conservation, and paleontology. In this work, we introduce a new task Skull2Animal for translating between skulls and living animals. On this task, we find that unguided Generative Adversarial Networks (GANs) are not capable of translating across large domain gaps. Instead of these traditional I2I methods, we explore the use of guided diffusion and image editing models and provide a new benchmark model, Revive-2I, capable of performing zero-shot I2I via text-prompting latent diffusion models. We find that guidance is necessary for longI2I because, to bridge the large domain gap, prior knowledge about the target domain is needed. In addition, we find that prompting provides the best and most scalable information about the target domain as classifier-guided diffusion models require retraining for specific use cases and lack stronger constraints on the target domain because of the wide variety of images they are trained on.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-15", "arxiv_id": "2308.07286", "title": "The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation", "authors": [ "Patrick Fernandes", "Daniel Deutsch", "Mara Finkelstein", "Parker Riley", "Andr\u00e9 F. T. Martins", "Graham Neubig", "Ankush Garg", "Jonathan H. Clark", "Markus Freitag", "Orhan Firat" ], "github": "", "project_page": "", "abstract": "Automatic evaluation of machine translation (MT) is a critical tool driving the rapid iterative development of MT systems. While considerable progress has been made on estimating a single scalar quality score, current metrics lack the informativeness of more detailed schemes that annotate individual errors, such as Multidimensional Quality Metrics (MQM). In this paper, we help fill this gap by proposing AutoMQM, a prompting technique which leverages the reasoning and in-context learning capabilities of large language models (LLMs) and asks them to identify and categorize errors in translations. We start by evaluating recent LLMs, such as PaLM and PaLM-2, through simple score prediction prompting, and we study the impact of labeled data through in-context learning and finetuning. We then evaluate AutoMQM with PaLM-2 models, and we find that it improves performance compared to just prompting for scores (with particularly large gains for larger models) while providing interpretability through error spans that align with human annotations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-15", "arxiv_id": "2308.06595", "title": "VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use", "authors": [ "Yonatan Bitton", "Hritik Bansal", "Jack Hessel", "Rulin Shao", "Wanrong Zhu", "Anas Awadalla", "Josh Gardner", "Rohan Taori", "Ludwig Schimdt" ], "github": "https://github.com/mlfoundations/VisIT-Bench", "project_page": "", "abstract": "We introduce VisIT-Bench (Visual InsTruction Benchmark), a benchmark for evaluation of instruction-following vision-language models for real-world use. Our starting point is curating 70 'instruction families' that we envision instruction tuned vision-language models should be able to address. Extending beyond evaluations like VQAv2 and COCO, tasks range from basic recognition to game playing and creative generation. Following curation, our dataset comprises 592 test queries, each with a human-authored instruction-conditioned caption. These descriptions surface instruction-specific factors, e.g., for an instruction asking about the accessibility of a storefront for wheelchair users, the instruction-conditioned caption describes ramps/potential obstacles. These descriptions enable 1) collecting human-verified reference outputs for each instance; and 2) automatic evaluation of candidate multimodal generations using a text-only LLM, aligning with human judgment. We quantify quality gaps between models and references using both human and automatic evaluations; e.g., the top-performing instruction-following model wins against the GPT-4 reference in just 27% of the comparison. VisIT-Bench is dynamic to participate, practitioners simply submit their model's response on the project website; Data, code and leaderboard is available at visit-bench.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mlfoundations/VisIT-Bench", "github_api_added_by": "auto" }, { "date": "2023-08-16", "arxiv_id": "2308.07921", "title": "Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification", "authors": [ "Aojun Zhou", "Ke Wang", "Zimu Lu", "Weikang Shi", "Sichun Luo", "Zipeng Qin", "Shaoqing Lu", "Anya Jia", "Linqi Song", "Mingjie Zhan", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest version of GPT-4, known as GPT-4 Code Interpreter, shows remarkable performance on challenging math datasets. In this paper, we explore the effect of code on enhancing LLMs' reasoning capability by introducing different constraints on the Code Usage Frequency of GPT-4 Code Interpreter. We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs. Based on this insight, we propose a novel and effective prompting method, explicit code-based self-verification~(CSV), to further boost the mathematical reasoning potential of GPT-4 Code Interpreter. This method employs a zero-shot prompt on GPT-4 Code Interpreter to encourage it to use code to self-verify its answers. In instances where the verification state registers as ``False'', the model shall automatically amend its solution, analogous to our approach of rectifying errors during a mathematics examination. Furthermore, we recognize that the states of the verification result indicate the confidence of a solution, which can improve the effectiveness of majority voting. With GPT-4 Code Interpreter and CSV, we achieve an impressive zero-shot accuracy on MATH dataset (53.9\\% to 84.3\\%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-16", "arxiv_id": "2308.07926", "title": "CoDeF: Content Deformation Fields for Temporally Consistent Video Processing", "authors": [ "Hao Ouyang", "Qiuyu Wang", "Yuxi Xiao", "Qingyan Bai", "Juntao Zhang", "Kecheng Zheng", "Xiaowei Zhou", "Qifeng Chen", "Yujun Shen" ], "github": "https://github.com/qiuyu96/CoDeF", "project_page": "", "abstract": "We present the content deformation field CoDeF as a new type of video representation, which consists of a canonical content field aggregating the static contents in the entire video and a temporal deformation field recording the transformations from the canonical image (i.e., rendered from the canonical content field) to each individual frame along the time axis.Given a target video, these two fields are jointly optimized to reconstruct it through a carefully tailored rendering pipeline.We advisedly introduce some regularizations into the optimization process, urging the canonical content field to inherit semantics (e.g., the object shape) from the video.With such a design, CoDeF naturally supports lifting image algorithms for video processing, in the sense that one can apply an image algorithm to the canonical image and effortlessly propagate the outcomes to the entire video with the aid of the temporal deformation field.We experimentally show that CoDeF is able to lift image-to-image translation to video-to-video translation and lift keypoint detection to keypoint tracking without any training.More importantly, thanks to our lifting strategy that deploys the algorithms on only one image, we achieve superior cross-frame consistency in processed videos compared to existing video-to-video translation approaches, and even manage to track non-rigid objects like water and smog.Project page can be found at https://qiuyu96.github.io/CoDeF/.", "github_manual": "https://github.com/qiuyu96/CoDeF", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-16", "arxiv_id": "2308.07922", "title": "RAVEN: In-Context Learning with Retrieval Augmented Encoder-Decoder Language Models", "authors": [ "Jie Huang", "Wei Ping", "Peng Xu", "Mohammad Shoeybi", "Kevin Chen-Chuan Chang", "Bryan Catanzaro" ], "github": "", "project_page": "", "abstract": "In this paper, we investigate the in-context learning ability of retrieval-augmented encoder-decoder language models. We first conduct a comprehensive analysis of the state-of-the-art ATLAS model and identify its limitations in in-context learning, primarily due to a mismatch between pretraining and testing, as well as a restricted context length. To address these issues, we propose RAVEN, a model that combines retrieval-augmented masked language modeling and prefix language modeling. We further introduce Fusion-in-Context Learning to enhance the few-shot performance by enabling the model to leverage more in-context examples without requiring additional training or model modifications. Through extensive experiments, we demonstrate that RAVEN significantly outperforms ATLAS and achieves results comparable to the most advanced language models in certain scenarios, despite having substantially fewer parameters. Our work underscores the potential of retrieval-augmented encoder-decoder language models for in-context learning and encourages further research in this direction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-16", "arxiv_id": "2308.07891", "title": "Link-Context Learning for Multimodal LLMs", "authors": [ "Yan Tai", "Weichen Fan", "Zhao Zhang", "Feng Zhu", "Rui Zhao", "Ziwei Liu" ], "github": "https://github.com/isekai-portal/Link-Context-Learning", "project_page": "", "abstract": "The ability to learn from context with novel concepts, and deliver appropriate responses are essential in human conversations. Despite current Multimodal Large Language Models (MLLMs) and Large Language Models (LLMs) being trained on mega-scale datasets, recognizing unseen images or understanding novel concepts in a training-free manner remains a challenge. In-Context Learning (ICL) explores training-free few-shot learning, where models are encouraged to ``learn to learn\" from limited tasks and generalize to unseen tasks. In this work, we propose link-context learning (LCL), which emphasizes \"reasoning from cause and effect\" to augment the learning capabilities of MLLMs. LCL goes beyond traditional ICL by explicitly strengthening the causal relationship between the support set and the query set. By providing demonstrations with causal links, LCL guides the model to discern not only the analogy but also the underlying causal associations between data points, which empowers MLLMs to recognize unseen images and understand novel concepts more effectively. To facilitate the evaluation of this novel approach, we introduce the ISEKAI dataset, comprising exclusively of unseen generated image-label pairs designed for link-context learning. Extensive experiments show that our LCL-MLLM exhibits strong link-context learning capabilities to novel concepts over vanilla MLLMs. Code and data will be released at https://github.com/isekai-portal/Link-Context-Learning.", "github_manual": "", "github_regex": "https://github.com/isekai-portal/Link-Context-Learning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-16", "arxiv_id": "2308.07903", "title": "Relightable and Animatable Neural Avatar from Sparse-View Video", "authors": [ "Zhen Xu", "Sida Peng", "Chen Geng", "Linzhan Mou", "Zihan Yan", "Jiaming Sun", "Hujun Bao", "Xiaowei Zhou" ], "github": "", "project_page": "", "abstract": "This paper tackles the challenge of creating relightable and animatable neural avatars from sparse-view (or even monocular) videos of dynamic humans under unknown illumination. Compared to studio environments, this setting is more practical and accessible but poses an extremely challenging ill-posed problem. Previous neural human reconstruction methods are able to reconstruct animatable avatars from sparse views using deformed Signed Distance Fields (SDF) but cannot recover material parameters for relighting. While differentiable inverse rendering-based methods have succeeded in material recovery of static objects, it is not straightforward to extend them to dynamic humans as it is computationally intensive to compute pixel-surface intersection and light visibility on deformed SDFs for inverse rendering. To solve this challenge, we propose a Hierarchical Distance Query (HDQ) algorithm to approximate the world space distances under arbitrary human poses. Specifically, we estimate coarse distances based on a parametric human model and compute fine distances by exploiting the local deformation invariance of SDF. Based on the HDQ algorithm, we leverage sphere tracing to efficiently estimate the surface intersection and light visibility. This allows us to develop the first system to recover animatable and relightable neural avatars from sparse view (or monocular) inputs. Experiments demonstrate that our approach is able to produce superior results compared to state-of-the-art methods. Our code will be released for reproducibility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-16", "arxiv_id": "2308.07395", "title": "Text Injection for Capitalization and Turn-Taking Prediction in Speech Models", "authors": [ "Shaan Bijwadia", "Shuo-yiin Chang", "Weiran Wang", "Zhong Meng", "Hao Zhang", "Tara N. Sainath" ], "github": "", "project_page": "", "abstract": "Text injection for automatic speech recognition (ASR), wherein unpaired text-only data is used to supplement paired audio-text data, has shown promising improvements for word error rate. This study examines the use of text injection for auxiliary tasks, which are the non-ASR tasks often performed by an E2E model. In this work, we use joint end-to-end and internal language model training (JEIT) as our text injection algorithm to train an ASR model which performs two auxiliary tasks. The first is capitalization, which is a de-normalization task. The second is turn-taking prediction, which attempts to identify whether a user has completed their conversation turn in a digital assistant interaction. We show results demonstrating that our text injection method boosts capitalization performance for long-tail data, and improves turn-taking detection recall.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-16", "arxiv_id": "2308.07795", "title": "Learning to Identify Critical States for Reinforcement Learning from Videos", "authors": [ "Haozhe Liu", "Mingchen Zhuge", "Bing Li", "Yuhui Wang", "Francesco Faccio", "Bernard Ghanem", "J\u00fcrgen Schmidhuber" ], "github": "https://github.com/AI-Initiative-KAUST/VideoRLCS", "project_page": "", "abstract": "Recent work on deep reinforcement learning (DRL) has pointed out that algorithmic information about good policies can be extracted from offline data which lack explicit information about executed actions. For example, videos of humans or robots may convey a lot of implicit information about rewarding action sequences, but a DRL machine that wants to profit from watching such videos must first learn by itself to identify and recognize relevant states/actions/rewards. Without relying on ground-truth annotations, our new method called Deep State Identifier learns to predict returns from episodes encoded as videos. Then it uses a kind of mask-based sensitivity analysis to extract/identify important critical states. Extensive experiments showcase our method's potential for understanding and improving agent behavior. The source code and the generated datasets are available at https://github.com/AI-Initiative-KAUST/VideoRLCS.", "github_manual": "", "github_regex": "https://github.com/AI-Initiative-KAUST/VideoRLCS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-17", "arxiv_id": "2308.08545", "title": "TeCH: Text-guided Reconstruction of Lifelike Clothed Humans", "authors": [ "Yangyi Huang", "Hongwei Yi", "Yuliang Xiu", "Tingting Liao", "Jiaxiang Tang", "Deng Cai", "Justus Thies" ], "github": "https://github.com/huangyangyi/TeCH", "project_page": "", "abstract": "Despite recent research advancements in reconstructing clothed humans from a single image, accurately restoring the \"unseen regions\" with high-level details remains an unsolved challenge that lacks attention. Existing methods often generate overly smooth back-side surfaces with a blurry texture. But how to effectively capture all visual attributes of an individual from a single image, which are sufficient to reconstruct unseen areas (e.g., the back view)? Motivated by the power of foundation models, TeCH reconstructs the 3D human by leveraging 1) descriptive text prompts (e.g., garments, colors, hairstyles) which are automatically generated via a garment parsing model and Visual Question Answering (VQA), 2) a personalized fine-tuned Text-to-Image diffusion model (T2I) which learns the \"indescribable\" appearance. To represent high-resolution 3D clothed humans at an affordable cost, we propose a hybrid 3D representation based on DMTet, which consists of an explicit body shape grid and an implicit distance field. Guided by the descriptive prompts + personalized T2I diffusion model, the geometry and texture of the 3D humans are optimized through multi-view Score Distillation Sampling (SDS) and reconstruction losses based on the original observation. TeCH produces high-fidelity 3D clothed humans with consistent & delicate texture, and detailed full-body geometry. Quantitative and qualitative experiments demonstrate that TeCH outperforms the state-of-the-art methods in terms of reconstruction accuracy and rendering quality. The code will be publicly available for research purposes at https://huangyangyi.github.io/tech", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huangyangyi/TeCH", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-17", "arxiv_id": "2308.08316", "title": "Dual-Stream Diffusion Net for Text-to-Video Generation", "authors": [ "Binhui Liu", "Xin Liu", "Anbo Dai", "Zhiyong Zeng", "Zhen Cui", "Jian Yang" ], "github": "", "project_page": "", "abstract": "With the emerging diffusion models, recently, text-to-video generation has aroused increasing attention. But an important bottleneck therein is that generative videos often tend to carry some flickers and artifacts. In this work, we propose a dual-stream diffusion net (DSDN) to improve the consistency of content variations in generating videos. In particular, the designed two diffusion streams, video content and motion branches, could not only run separately in their private spaces for producing personalized video variations as well as content, but also be well-aligned between the content and motion domains through leveraging our designed cross-transformer interaction module, which would benefit the smoothness of generated videos. Besides, we also introduce motion decomposer and combiner to faciliate the operation on video motion. Qualitative and quantitative experiments demonstrate that our method could produce amazing continuous videos with fewer flickers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-17", "arxiv_id": "2308.07968", "title": "Teach LLMs to Personalize -- An Approach inspired by Writing Education", "authors": [ "Cheng Li", "Mingyang Zhang", "Qiaozhu Mei", "Yaqing Wang", "Spurthi Amba Hombaiah", "Yi Liang", "Michael Bendersky" ], "github": "", "project_page": "", "abstract": "Personalized text generation is an emerging research area that has attracted much attention in recent years. Most studies in this direction focus on a particular domain by designing bespoke features or models. In this work, we propose a general approach for personalized text generation using large language models (LLMs). Inspired by the practice of writing education, we develop a multistage and multitask framework to teach LLMs for personalized generation. In writing instruction, the task of writing from sources is often decomposed into multiple steps that involve finding, evaluating, summarizing, synthesizing, and integrating information. Analogously, our approach to personalized text generation consists of multiple stages: retrieval, ranking, summarization, synthesis, and generation. In addition, we introduce a multitask setting that helps the model improve its generation ability further, which is inspired by the observation in education that a student's reading proficiency and writing ability are often correlated. We evaluate our approach on three public datasets, each of which covers a different and representative domain. Our results show significant improvements over a variety of baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-17", "arxiv_id": "2308.08089", "title": "DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory", "authors": [ "Shengming Yin", "Chenfei Wu", "Jian Liang", "Jie Shi", "Houqiang Li", "Gong Ming", "Nan Duan" ], "github": "", "project_page": "", "abstract": "Controllable video generation has gained significant attention in recent years. However, two main limitations persist: Firstly, most existing works focus on either text, image, or trajectory-based control, leading to an inability to achieve fine-grained control in videos. Secondly, trajectory control research is still in its early stages, with most experiments being conducted on simple datasets like Human3.6M. This constraint limits the models' capability to process open-domain images and effectively handle complex curved trajectories. In this paper, we propose DragNUWA, an open-domain diffusion-based video generation model. To tackle the issue of insufficient control granularity in existing works, we simultaneously introduce text, image, and trajectory information to provide fine-grained control over video content from semantic, spatial, and temporal perspectives. To resolve the problem of limited open-domain trajectory control in current research, We propose trajectory modeling with three aspects: a Trajectory Sampler (TS) to enable open-domain control of arbitrary trajectories, a Multiscale Fusion (MF) to control trajectories in different granularities, and an Adaptive Training (AT) strategy to generate consistent videos following trajectories. Our experiments validate the effectiveness of DragNUWA, demonstrating its superior performance in fine-grained control in video generation. The homepage link is https://www.microsoft.com/en-us/research/project/dragnuwa/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-17", "arxiv_id": "2308.07931", "title": "Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation", "authors": [ "William Shen", "Ge Yang", "Alan Yu", "Jansen Wong", "Leslie Pack Kaelbling", "Phillip Isola" ], "github": "https://github.com/f3rm/f3rm", "project_page": "", "abstract": "Self-supervised and language-supervised image models contain rich knowledge of the world that is important for generalization. Many robotic tasks, however, require a detailed understanding of 3D geometry, which is often lacking in 2D image features. This work bridges this 2D-to-3D gap for robotic manipulation by leveraging distilled feature fields to combine accurate 3D geometry with rich semantics from 2D foundation models. We present a few-shot learning method for 6-DOF grasping and placing that harnesses these strong spatial and semantic priors to achieve in-the-wild generalization to unseen objects. Using features distilled from a vision-language model, CLIP, we present a way to designate novel objects for manipulation via free-text natural language, and demonstrate its ability to generalize to unseen expressions and novel categories of objects.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/f3rm/f3rm", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-17", "arxiv_id": "2308.08258", "title": "SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes", "authors": [ "Edith Tretschk", "Vladislav Golyanik", "Michael Zollhoefer", "Aljaz Bozic", "Christoph Lassner", "Christian Theobalt" ], "github": "", "project_page": "", "abstract": "Existing methods for the 4D reconstruction of general, non-rigidly deforming objects focus on novel-view synthesis and neglect correspondences. However, time consistency enables advanced downstream tasks like 3D editing, motion analysis, or virtual-asset creation. We propose SceNeRFlow to reconstruct a general, non-rigid scene in a time-consistent manner. Our dynamic-NeRF method takes multi-view RGB videos and background images from static cameras with known camera parameters as input. It then reconstructs the deformations of an estimated canonical model of the geometry and appearance in an online fashion. Since this canonical model is time-invariant, we obtain correspondences even for long-term, long-range motions. We employ neural scene representations to parametrize the components of our method. Like prior dynamic-NeRF methods, we use a backwards deformation model. We find non-trivial adaptations of this model necessary to handle larger motions: We decompose the deformations into a strongly regularized coarse component and a weakly regularized fine component, where the coarse component also extends the deformation field into the space surrounding the object, which enables tracking over time. We show experimentally that, unlike prior work that only handles small motion, our method enables the reconstruction of studio-scale motions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-28", "arxiv_id": "2308.13137", "title": "OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models", "authors": [ "Wenqi Shao", "Mengzhao Chen", "Zhaoyang Zhang", "Peng Xu", "Lirui Zhao", "Zhiqian Li", "Kaipeng Zhang", "Peng Gao", "Yu Qiao", "Ping Luo" ], "github": "https://github.com/OpenGVLab/OmniQuant", "project_page": "", "abstract": "Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements. Although recent post-training quantization (PTQ) methods are effective in reducing memory footprint and improving the computational efficiency of LLM, they hand-craft quantization parameters, which leads to low performance and fails to deal with extremely low-bit quantization. To tackle this issue, we introduce an Omnidirectionally calibrated Quantization (OmniQuant) technique for LLMs, which achieves good performance in diverse quantization settings while maintaining the computational efficiency of PTQ by efficiently optimizing various quantization parameters. OmniQuant comprises two innovative components including Learnable Weight Clipping (LWC) and Learnable Equivalent Transformation (LET). LWC modulates the extreme values of weights by optimizing the clipping threshold. Meanwhile, LET tackles activation outliers by shifting the challenge of quantization from activations to weights through a learnable equivalent transformation. Operating within a differentiable framework using block-wise error minimization, OmniQuant can optimize the quantization process efficiently for both weight-only and weight-activation quantization. For instance, the LLaMA-2 model family with the size of 7-70B can be processed with OmniQuant on a single A100-40G GPU within 1-16 hours using 128 samples. Extensive experiments validate OmniQuant's superior performance across diverse quantization configurations such as W4A4, W6A6, W4A16, W3A16, and W2A16. Additionally, OmniQuant demonstrates effectiveness in instruction-tuned models and delivers notable improvements in inference speed and memory reduction on real devices. Codes and models are available at https://github.com/OpenGVLab/OmniQuant.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/OmniQuant", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-28", "arxiv_id": "2308.13416", "title": "SoTaNa: The Open-Source Software Development Assistant", "authors": [ "Ensheng Shi", "Fengji Zhang", "Yanlin Wang", "Bei Chen", "Lun Du", "Hongyu Zhang", "Shi Han", "Dongmei Zhang", "Hongbin Sun" ], "github": "https://github.com/DeepSoftwareAnalytics/SoTaNa", "project_page": "", "abstract": "Software development plays a crucial role in driving innovation and efficiency across modern societies. To meet the demands of this dynamic field, there is a growing need for an effective software development assistant. However, existing large language models represented by ChatGPT suffer from limited accessibility, including training data and model weights. Although other large open-source models like LLaMA have shown promise, they still struggle with understanding human intent. In this paper, we present SoTaNa, an open-source software development assistant. SoTaNa utilizes ChatGPT to generate high-quality instruction-based data for the domain of software engineering and employs a parameter-efficient fine-tuning approach to enhance the open-source foundation model, LLaMA. We evaluate the effectiveness of in answering Stack Overflow questions and demonstrate its capabilities. Additionally, we discuss its capabilities in code summarization and generation, as well as the impact of varying the volume of generated data on model performance. Notably, SoTaNa can run on a single GPU, making it accessible to a broader range of researchers. Our code, model weights, and data are public at https://github.com/DeepSoftwareAnalytics/SoTaNa.", "github_manual": "", "github_regex": "https://github.com/DeepSoftwareAnalytics/SoTaNa", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-28", "arxiv_id": "2308.13404", "title": "Relighting Neural Radiance Fields with Shadow and Highlight Hints", "authors": [ "Chong Zeng", "Guojun Chen", "Yue Dong", "Pieter Peers", "Hongzhi Wu", "Xin Tong" ], "github": "", "project_page": "", "abstract": "This paper presents a novel neural implicit radiance representation for free viewpoint relighting from a small set of unstructured photographs of an object lit by a moving point light source different from the view position. We express the shape as a signed distance function modeled by a multi layer perceptron. In contrast to prior relightable implicit neural representations, we do not disentangle the different reflectance components, but model both the local and global reflectance at each point by a second multi layer perceptron that, in addition, to density features, the current position, the normal (from the signed distace function), view direction, and light position, also takes shadow and highlight hints to aid the network in modeling the corresponding high frequency light transport effects. These hints are provided as a suggestion, and we leave it up to the network to decide how to incorporate these in the final relit result. We demonstrate and validate our neural implicit representation on synthetic and real scenes exhibiting a wide variety of shapes, material properties, and global illumination light transport.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-28", "arxiv_id": "2308.13494", "title": "Eventful Transformers: Leveraging Temporal Redundancy in Vision Transformers", "authors": [ "Matthew Dutson", "Yin Li", "Mohit Gupta" ], "github": "https://github.com/WISION-Lab/eventful-transformer", "project_page": "", "abstract": "Vision Transformers achieve impressive accuracy across a range of visual recognition tasks. Unfortunately, their accuracy frequently comes with high computational costs. This is a particular issue in video recognition, where models are often applied repeatedly across frames or temporal chunks. In this work, we exploit temporal redundancy between subsequent inputs to reduce the cost of Transformers for video processing. We describe a method for identifying and re-processing only those tokens that have changed significantly over time. Our proposed family of models, Eventful Transformers, can be converted from existing Transformers (often without any re-training) and give adaptive control over the compute cost at runtime. We evaluate our method on large-scale datasets for video object detection (ImageNet VID) and action recognition (EPIC-Kitchens 100). Our approach leads to significant computational savings (on the order of 2-4x) with only minor reductions in accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/WISION-Lab/eventful-transformer", "github_api_added_by": "auto" }, { "date": "2023-08-28", "arxiv_id": "2308.13418", "title": "Nougat: Neural Optical Understanding for Academic Documents", "authors": [ "Lukas Blecher", "Guillem Cucurull", "Thomas Scialom", "Robert Stojnic" ], "github": "https://github.com/facebookresearch/nougat", "project_page": "", "abstract": "Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs. However, the PDF format leads to a loss of semantic information, particularly for mathematical expressions. We propose Nougat (Neural Optical Understanding for Academic Documents), a Visual Transformer model that performs an Optical Character Recognition (OCR) task for processing scientific documents into a markup language, and demonstrate the effectiveness of our model on a new dataset of scientific documents. The proposed approach offers a promising solution to enhance the accessibility of scientific knowledge in the digital age, by bridging the gap between human-readable documents and machine-readable text. We release the models and code to accelerate future work on scientific text recognition.", "github_manual": "https://github.com/facebookresearch/nougat", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-29", "arxiv_id": "2308.14089", "title": "MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records", "authors": [ "Scott L. Fleming", "Alejandro Lozano", "William J. Haberkorn", "Jenelle A. Jindal", "Eduardo P. Reis", "Rahul Thapa", "Louis Blankemeier", "Julian Z. Genkins", "Ethan Steinberg", "Ashwin Nayak", "Birju S. Patel", "Chia-Chun Chiang", "Alison Callahan", "Zepeng Huo", "Sergios Gatidis", "Scott J. Adams", "Oluseyi Fayanju", "Shreya J. Shah", "Thomas Savage", "Ethan Goh", "Akshay S. Chaudhari", "Nima Aghaeepour", "Christopher Sharp", "Michael A. Pfeffer", "Percy Liang", "Jonathan H. Chen", "Keith E. Morse", "Emma P. Brunskill", "Jason A. Fries", "Nigam H. Shah" ], "github": "", "project_page": "", "abstract": "The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care. However, evaluating LLMs on realistic text generation tasks for healthcare remains challenging. Existing question answering datasets for electronic health record (EHR) data fail to capture the complexity of information needs and documentation burdens experienced by clinicians. To address these challenges, we introduce MedAlign, a benchmark dataset of 983 natural language instructions for EHR data. MedAlign is curated by 15 clinicians (7 specialities), includes clinician-written reference responses for 303 instructions, and provides 276 longitudinal EHRs for grounding instruction-response pairs. We used MedAlign to evaluate 6 general domain LLMs, having clinicians rank the accuracy and quality of each LLM response. We found high error rates, ranging from 35% (GPT-4) to 68% (MPT-7B-Instruct), and an 8.3% drop in accuracy moving from 32k to 2k context lengths for GPT-4. Finally, we report correlations between clinician rankings and automated natural language generation metrics as a way to rank LLMs without human review. We make MedAlign available under a research data use agreement to enable LLM evaluations on tasks aligned with clinician needs and preferences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-29", "arxiv_id": "2308.13785", "title": "ORES: Open-vocabulary Responsible Visual Synthesis", "authors": [ "Minheng Ni", "Chenfei Wu", "Xiaodong Wang", "Shengming Yin", "Lijuan Wang", "Zicheng Liu", "Nan Duan" ], "github": "https://github.com/kodenii/ores", "project_page": "", "abstract": "Avoiding synthesizing specific visual concepts is an essential challenge in responsible visual synthesis. However, the visual concept that needs to be avoided for responsible visual synthesis tends to be diverse, depending on the region, context, and usage scenarios. In this work, we formalize a new task, Open-vocabulary Responsible Visual Synthesis (ORES), where the synthesis model is able to avoid forbidden visual concepts while allowing users to input any desired content. To address this problem, we present a Two-stage Intervention (TIN) framework. By introducing 1) rewriting with learnable instruction through a large-scale language model (LLM) and 2) synthesizing with prompt intervention on a diffusion synthesis model, it can effectively synthesize images avoiding any concepts but following the user's query as much as possible. To evaluate on ORES, we provide a publicly available dataset, baseline models, and benchmark. Experimental results demonstrate the effectiveness of our method in reducing risks of image generation. Our work highlights the potential of LLMs in responsible visual synthesis. Our code and dataset is public available.", "github_manual": "https://github.com/kodenii/ores", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-31", "arxiv_id": "2308.16149", "title": "Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models", "authors": [ "Neha Sengupta", "Sunil Kumar Sahu", "Bokang Jia", "Satheesh Katipomu", "Haonan Li", "Fajri Koto", "Osama Mohammed Afzal", "Samta Kamboj", "Onkar Pandit", "Rahul Pal", "Lalit Pradhan", "Zain Muhammad Mujahid", "Massa Baali", "Alham Fikri Aji", "Zhengzhong Liu", "Andy Hock", "Andrew Feldman", "Jonathan Lee", "Andrew Jackson", "Preslav Nakov", "Timothy Baldwin", "Eric Xing" ], "github": "https://github.com/EleutherAI/github-downloader", "project_page": "", "abstract": "We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-3 decoder-only architecture and are pretrained on a mixture of Arabic and English texts, including source code in various programming languages. With 13 billion parameters, they demonstrate better knowledge and reasoning capabilities in Arabic than any existing open Arabic and multilingual models by a sizable margin, based on extensive evaluation. Moreover, the models are competitive in English compared to English-centric open models of similar size, despite being trained on much less English data. We provide a detailed description of the training, the tuning, the safety alignment, and the evaluation of the models. We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs. Available at https://huggingface.co/inception-mbzuai/jais-13b-chat", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EleutherAI/github-downloader", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-31", "arxiv_id": "2308.16137", "title": "LM-Infinite: Simple On-the-Fly Length Generalization for Large Language Models", "authors": [ "Chi Han", "Qifan Wang", "Wenhan Xiong", "Yu Chen", "Heng Ji", "Sinong Wang" ], "github": "https://github.com/Glaciohound/LM-Infinite", "project_page": "", "abstract": "In recent years, there have been remarkable advancements in the performance of Transformer-based Large Language Models (LLMs) across various domains. As these LLMs are deployed for increasingly complex tasks, they often face the needs to conduct longer reasoning processes or understanding larger contexts. In these situations, the length generalization failure of LLMs on long sequences become more prominent. Most pre-training schemes truncate training sequences to a fixed length (such as 2048 for LLaMa). LLMs often struggle to generate fluent texts, let alone carry out downstream tasks, after longer contexts, even with relative positional encoding which is designed to cope with this problem. Common solutions such as finetuning on longer corpora often involves daunting hardware and time costs and requires careful training process design. To more efficiently leverage the generation capacity of existing LLMs, we theoretically and empirically investigate the main out-of-distribution (OOD) factors contributing to this problem. Inspired by this diagnosis, we propose a simple yet effective solution for on-the-fly length generalization, LM-Infinite, which involves only a Lambda-shaped attention mask and a distance limit while requiring no parameter updates or learning. We find it applicable to a variety of LLMs using relative-position encoding methods. LM-Infinite is computational efficient with O(n) time and space, and demonstrates consistent fluency and generation quality to as long as 32k tokens on ArXiv and OpenWebText2 datasets, with 2.72x decoding speedup. On downstream task such as passkey retrieval, it continues to work on inputs much longer than training lengths where vanilla models fail immediately.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Glaciohound/LM-Infinite", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-31", "arxiv_id": "2308.15930", "title": "LLaSM: Large Language and Speech Model", "authors": [ "Yu Shu", "Siwei Dong", "Guangyao Chen", "Wenhao Huang", "Ruihua Zhang", "Daochen Shi", "Qiqi Xiang", "Yemin Shi" ], "github": "https://github.com/LinkSoul-AI/LLaSM", "project_page": "", "abstract": "Multi-modal large language models have garnered significant interest recently. Though, most of the works focus on vision-language multi-modal models providing strong capabilities in following vision-and-language instructions. However, we claim that speech is also an important modality through which humans interact with the world. Hence, it is crucial for a general-purpose assistant to be able to follow multi-modal speech-and-language instructions. In this work, we propose Large Language and Speech Model (LLaSM). LLaSM is an end-to-end trained large multi-modal speech-language model with cross-modal conversational abilities, capable of following speech-and-language instructions. Our early experiments show that LLaSM demonstrates a more convenient and natural way for humans to interact with artificial intelligence. Specifically, we also release a large Speech Instruction Following dataset LLaSM-Audio-Instructions. Code and demo are available at https://github.com/LinkSoul-AI/LLaSM and https://huggingface.co/spaces/LinkSoul/LLaSM. The LLaSM-Audio-Instructions dataset is available at https://huggingface.co/datasets/LinkSoul/LLaSM-Audio-Instructions.", "github_manual": "", "github_regex": "https://github.com/LinkSoul-AI/LLaSM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-31", "arxiv_id": "2308.15975", "title": "RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation", "authors": [ "Mel Vecerik", "Carl Doersch", "Yi Yang", "Todor Davchev", "Yusuf Aytar", "Guangyao Zhou", "Raia Hadsell", "Lourdes Agapito", "Jon Scholz" ], "github": "", "project_page": "", "abstract": "For robots to be useful outside labs and specialized factories we need a way to teach them new useful behaviors quickly. Current approaches lack either the generality to onboard new tasks without task-specific engineering, or else lack the data-efficiency to do so in an amount of time that enables practical use. In this work we explore dense tracking as a representational vehicle to allow faster and more general learning from demonstration. Our approach utilizes Track-Any-Point (TAP) models to isolate the relevant motion in a demonstration, and parameterize a low-level controller to reproduce this motion across changes in the scene configuration. We show this results in robust robot policies that can solve complex object-arrangement tasks such as shape-matching, stacking, and even full path-following tasks such as applying glue and sticking objects together, all from demonstrations that can be collected in minutes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-08-31", "arxiv_id": "2308.15560", "title": "WeatherBench 2: A benchmark for the next generation of data-driven global weather models", "authors": [ "Stephan Rasp", "Stephan Hoyer", "Alexander Merose", "Ian Langmore", "Peter Battaglia", "Tyler Russel", "Alvaro Sanchez-Gonzalez", "Vivian Yang", "Rob Carver", "Shreya Agrawal", "Matthew Chantry", "Zied Ben Bouallegue", "Peter Dueben", "Carla Bromberg", "Jared Sisk", "Luke Barrington", "Aaron Bell", "Fei Sha" ], "github": "https://github.com/google-research/weatherbench2", "project_page": "", "abstract": "WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to accelerate progress in data-driven weather modeling. WeatherBench 2 consists of an open-source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state-of-the-art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state-of-the-art physical and data-driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data-driven weather forecasting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/google-research/weatherbench2", "github_api_added_by": "auto" }, { "date": "2023-08-31", "arxiv_id": "2308.16185", "title": "Learning Vision-based Pursuit-Evasion Robot Policies", "authors": [ "Andrea Bajcsy", "Antonio Loquercio", "Ashish Kumar", "Jitendra Malik" ], "github": "", "project_page": "", "abstract": "Learning strategic robot behavior -- like that required in pursuit-evasion interactions -- under real-world constraints is extremely challenging. It requires exploiting the dynamics of the interaction, and planning through both physical state and latent intent uncertainty. In this paper, we transform this intractable problem into a supervised learning problem, where a fully-observable robot policy generates supervision for a partially-observable one. We find that the quality of the supervision signal for the partially-observable pursuer policy depends on two key factors: the balance of diversity and optimality of the evader's behavior and the strength of the modeling assumptions in the fully-observable policy. We deploy our policy on a physical quadruped robot with an RGB-D camera on pursuit-evasion interactions in the wild. Despite all the challenges, the sensing constraints bring about creativity: the robot is pushed to gather information when uncertain, predict intent from noisy measurements, and anticipate in order to intercept. Project webpage: https://abajcsy.github.io/vision-based-pursuit/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-01", "arxiv_id": "2308.16884", "title": "The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants", "authors": [ "Lucas Bandarkar", "Davis Liang", "Benjamin Muller", "Mikel Artetxe", "Satya Narayan Shukla", "Donald Husa", "Naman Goyal", "Abhinandan Krishnan", "Luke Zettlemoyer", "Madian Khabsa" ], "github": "https://github.com/facebookresearch/belebele", "project_page": "", "abstract": "We present Belebele, a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. Significantly expanding the language coverage of natural language understanding (NLU) benchmarks, this dataset enables the evaluation of text models in high-, medium-, and low-resource languages. Each question is based on a short passage from the Flores-200 dataset and has four multiple-choice answers. The questions were carefully curated to discriminate between models with different levels of general language comprehension. The English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. We use this dataset to evaluate the capabilities of multilingual masked language models (MLMs) and large language models (LLMs). We present extensive results and find that despite significant cross-lingual transfer in English-centric LLMs, much smaller MLMs pretrained on balanced multilingual data still understand far more languages. We also observe that larger vocabulary size and conscious vocabulary construction correlate with better performance on low-resource languages. Overall, Belebele opens up new avenues for evaluating and analyzing the multilingual capabilities of NLP systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/belebele", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-01", "arxiv_id": "2308.16824", "title": "Can Programming Languages Boost Each Other via Instruction Tuning?", "authors": [ "Daoguang Zan", "Ailun Yu", "Bo Shen", "Jiaxin Zhang", "Taihong Chen", "Bing Geng", "Bei Chen", "Jichuan Ji", "Yafen Yao", "Yongji Wang", "Qianxiang Wang" ], "github": "https://github.com/NL2Code/CodeM", "project_page": "", "abstract": "When human programmers have mastered a programming language, it would be easier when they learn a new programming language. In this report, we focus on exploring whether programming languages can boost each other during the instruction fine-tuning phase of code large language models. We conduct extensive experiments of 8 popular programming languages (Python, JavaScript, TypeScript, C, C++, Java, Go, HTML) on StarCoder. Results demonstrate that programming languages can significantly improve each other. For example, CodeM-Python 15B trained on Python is able to increase Java by an absolute 17.95% pass@1 on HumanEval-X. More surprisingly, we found that CodeM-HTML 7B trained on the HTML corpus can improve Java by an absolute 15.24% pass@1. Our training data is released at https://github.com/NL2Code/CodeM.", "github_manual": "", "github_regex": "https://github.com/NL2Code/CodeM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-01", "arxiv_id": "2308.16458", "title": "BioCoder: A Benchmark for Bioinformatics Code Generation with Contextual Pragmatic Knowledge", "authors": [ "Xiangru Tang", "Bill Qian", "Rick Gao", "Jiakang Chen", "Xinyun Chen", "Mark Gerstein" ], "github": "https://github.com/gersteinlab/biocoder", "project_page": "", "abstract": "Pre-trained language models like ChatGPT have significantly improved code generation. As these models scale up, there is an increasing need for the output to handle more intricate tasks. Moreover, in bioinformatics, generating functional programs poses additional notable challenges due to the amount of domain knowledge, the need for complicated data operations, and intricate functional dependencies between the operations. Here, we present BioCoder, a benchmark developed to evaluate existing pre-trained models in generating bioinformatics code. In relation to function-code generation, BioCoder covers potential package dependencies, class declarations, and global variables. It incorporates 1026 functions and 1243 methods in Python and Java from GitHub and 253 examples from the Rosalind Project. BioCoder incorporates a fuzz-testing framework for evaluation, and we have applied it to evaluate many models including InCoder, CodeGen, CodeGen2, SantaCoder, StarCoder, StarCoder+, InstructCodeT5+, and ChatGPT. Our detailed analysis of these models emphasizes the importance of domain knowledge, pragmatic code generation, and contextual understanding. Our dataset, benchmark, Docker images, and scripts required for testing are all available at https://github.com/gersteinlab/biocoder.", "github_manual": "", "github_regex": "https://github.com/gersteinlab/biocoder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-01", "arxiv_id": "2308.16512", "title": "MVDream: Multi-view Diffusion for 3D Generation", "authors": [ "Yichun Shi", "Peng Wang", "Jianglong Ye", "Mai Long", "Kejie Li", "Xiao Yang" ], "github": "https://github.com/bytedance/MVDream", "project_page": "", "abstract": "We propose MVDream, a multi-view diffusion model that is able to generate geometrically consistent multi-view images from a given text prompt. By leveraging image diffusion models pre-trained on large-scale web datasets and a multi-view dataset rendered from 3D assets, the resulting multi-view diffusion model can achieve both the generalizability of 2D diffusion and the consistency of 3D data. Such a model can thus be applied as a multi-view prior for 3D generation via Score Distillation Sampling, where it greatly improves the stability of existing 2D-lifting methods by solving the 3D consistency problem. Finally, we show that the multi-view diffusion model can also be fine-tuned under a few shot setting for personalized 3D generation, i.e. DreamBooth3D application, where the consistency can be maintained after learning the subject identity.", "github_manual": "https://github.com/bytedance/MVDream", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-01", "arxiv_id": "2308.16271", "title": "Emergence of Segmentation with Minimalistic White-Box Transformers", "authors": [ "Yaodong Yu", "Tianzhe Chu", "Shengbang Tong", "Ziyang Wu", "Druv Pai", "Sam Buchanan", "Yi Ma" ], "github": "https://github.com/Ma-Lab-Berkeley/CRATE", "project_page": "", "abstract": "Transformer-like models for vision tasks have recently proven effective for a wide range of downstream applications such as segmentation and detection. Previous works have shown that segmentation properties emerge in vision transformers (ViTs) trained using self-supervised methods such as DINO, but not in those trained on supervised classification tasks. In this study, we probe whether segmentation emerges in transformer-based models solely as a result of intricate self-supervised learning mechanisms, or if the same emergence can be achieved under much broader conditions through proper design of the model architecture. Through extensive experimental results, we demonstrate that when employing a white-box transformer-like architecture known as CRATE, whose design explicitly models and pursues low-dimensional structures in the data distribution, segmentation properties, at both the whole and parts levels, already emerge with a minimalistic supervised training recipe. Layer-wise finer-grained analysis reveals that the emergent properties strongly corroborate the designed mathematical functions of the white-box network. Our results suggest a path to design white-box foundation models that are simultaneously highly performant and mathematically fully interpretable. Code is at https://github.com/Ma-Lab-Berkeley/CRATE.", "github_manual": "", "github_regex": "https://github.com/Ma-Lab-Berkeley/CRATE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-01", "arxiv_id": "2308.16246", "title": "Active Neural Mapping", "authors": [ "Zike Yan", "Haoxiang Yang", "Hongbin Zha" ], "github": "", "project_page": "", "abstract": "We address the problem of active mapping with a continually-learned neural scene representation, namely Active Neural Mapping. The key lies in actively finding the target space to be explored with efficient agent movement, thus minimizing the map uncertainty on-the-fly within a previously unseen environment. In this paper, we examine the weight space of the continually-learned neural field, and show empirically that the neural variability, the prediction robustness against random weight perturbation, can be directly utilized to measure the instant uncertainty of the neural map. Together with the continuous geometric information inherited in the neural map, the agent can be guided to find a traversable path to gradually gain knowledge of the environment. We present for the first time an active mapping system with a coordinate-based implicit neural representation for online scene reconstruction. Experiments in the visually-realistic Gibson and Matterport3D environment demonstrate the efficacy of the proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-01", "arxiv_id": "2308.16876", "title": "SportsSloMo: A New Benchmark and Baselines for Human-centric Video Frame Interpolation", "authors": [ "Jiaben Chen", "Huaizu Jiang" ], "github": "", "project_page": "", "abstract": "Human-centric video frame interpolation has great potential for improving people's entertainment experiences and finding commercial applications in the sports analysis industry, e.g., synthesizing slow-motion videos. Although there are multiple benchmark datasets available in the community, none of them is dedicated for human-centric scenarios. To bridge this gap, we introduce SportsSloMo, a benchmark consisting of more than 130K video clips and 1M video frames of high-resolution (geq720p) slow-motion sports videos crawled from YouTube. We re-train several state-of-the-art methods on our benchmark, and the results show a decrease in their accuracy compared to other datasets. It highlights the difficulty of our benchmark and suggests that it poses significant challenges even for the best-performing methods, as human bodies are highly deformable and occlusions are frequent in sports videos. To improve the accuracy, we introduce two loss terms considering the human-aware priors, where we add auxiliary supervision to panoptic segmentation and human keypoints detection, respectively. The loss terms are model agnostic and can be easily plugged into any video frame interpolation approaches. Experimental results validate the effectiveness of our proposed loss terms, leading to consistent performance improvement over 5 existing models, which establish strong baseline models on our benchmark. The dataset and code can be found at: https://neu-vi.github.io/SportsSlomo/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-01", "arxiv_id": "2308.16891", "title": "GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields", "authors": [ "Yanjie Ze", "Ge Yan", "Yueh-Hua Wu", "Annabella Macaluso", "Yuying Ge", "Jianglong Ye", "Nicklas Hansen", "Li Erran Li", "Xiaolong Wang" ], "github": "https://github.com/YanjieZe/GNFactor", "project_page": "", "abstract": "It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To achieve this goal, the robot needs to have a comprehensive understanding of the 3D structure and semantics of the scene. In this work, we present GNFactor, a visual behavior cloning agent for multi-task robotic manipulation with Generalizable Neural feature Fields. GNFactor jointly optimizes a generalizable neural field (GNF) as a reconstruction module and a Perceiver Transformer as a decision-making module, leveraging a shared deep 3D voxel representation. To incorporate semantics in 3D, the reconstruction module utilizes a vision-language foundation model (e.g., Stable Diffusion) to distill rich semantic information into the deep 3D voxel. We evaluate GNFactor on 3 real robot tasks and perform detailed ablations on 10 RLBench tasks with a limited number of demonstrations. We observe a substantial improvement of GNFactor over current state-of-the-art methods in seen and unseen tasks, demonstrating the strong generalization ability of GNFactor. Our project website is https://yanjieze.com/GNFactor/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YanjieZe/GNFactor", "github_api_added_by": "auto" }, { "date": "2023-09-01", "arxiv_id": "2308.16582", "title": "Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images", "authors": [ "Qingping Zheng", "Yuanfan Guo", "Jiankang Deng", "Jianhua Han", "Ying Li", "Songcen Xu", "Hang Xu" ], "github": "", "project_page": "", "abstract": "Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-scale images and their corresponding text descriptions. Moreover, direct training on images of unlimited sizes is unfeasible, as it would require an immense number of text-image pairs and entail substantial computational expenses. To overcome these challenges, we propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to efficiently generate well-composed images of any size, while minimizing the need for high-memory GPU resources. Specifically, the initial stage, dubbed Any Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a restricted range of ratios to optimize the text-conditional diffusion model, thereby improving its ability to adjust composition to accommodate diverse image sizes. To support the creation of images at any desired size, we further introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the subsequent stage. This method allows for the rapid enlargement of the ASD output to any high-resolution size, avoiding seaming artifacts or memory overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks demonstrate that ASD can produce well-structured images of arbitrary sizes, cutting down the inference time by 2x compared to the traditional tiled algorithm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-04", "arxiv_id": "2309.00071", "title": "YaRN: Efficient Context Window Extension of Large Language Models", "authors": [ "Bowen Peng", "Jeffrey Quesnelle", "Honglu Fan", "Enrico Shippole" ], "github": "https://github.com/jquesnelle/yarn", "project_page": "", "abstract": "Rotary Position Embeddings (RoPE) have been shown to effectively encode positional information in transformer-based language models. However, these models fail to generalize past the sequence length they were trained on. We present YaRN (Yet another RoPE extensioN method), a compute-efficient method to extend the context window of such models, requiring 10x less tokens and 2.5x less training steps than previous methods. Using YaRN, we show that LLaMA models can effectively utilize and extrapolate to context lengths much longer than their original pre-training would allow, while also surpassing previous the state-of-the-art at context window extension. In addition, we demonstrate that YaRN exhibits the capability to extrapolate beyond the limited context of a fine-tuning dataset. We publish the checkpoints of Llama 2 7B/13B fine-tuned using YaRN with 64k and 128k context windows at https://github.com/jquesnelle/yarn", "github_manual": "", "github_regex": "https://github.com/jquesnelle/yarn", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-04", "arxiv_id": "2309.00035", "title": "FACET: Fairness in Computer Vision Evaluation Benchmark", "authors": [ "Laura Gustafson", "Chloe Rolland", "Nikhila Ravi", "Quentin Duval", "Aaron Adcock", "Cheng-Yang Fu", "Melissa Hall", "Candace Ross" ], "github": "", "project_page": "", "abstract": "Computer vision models have known performance disparities across attributes such as gender and skin tone. This means during tasks such as classification and detection, model performance differs for certain classes based on the demographics of the people in the image. These disparities have been shown to exist, but until now there has not been a unified approach to measure these differences for common use-cases of computer vision models. We present a new benchmark named FACET (FAirness in Computer Vision EvaluaTion), a large, publicly available evaluation set of 32k images for some of the most common vision tasks - image classification, object detection and segmentation. For every image in FACET, we hired expert reviewers to manually annotate person-related attributes such as perceived skin tone and hair type, manually draw bounding boxes and label fine-grained person-related classes such as disk jockey or guitarist. In addition, we use FACET to benchmark state-of-the-art vision models and present a deeper understanding of potential performance disparities and challenges across sensitive demographic attributes. With the exhaustive annotations collected, we probe models using single demographics attributes as well as multiple attributes using an intersectional approach (e.g. hair color and perceived skin tone). Our results show that classification, detection, segmentation, and visual grounding models exhibit performance disparities across demographic attributes and intersections of attributes. These harms suggest that not all people represented in datasets receive fair and equitable treatment in these vision tasks. We hope current and future results using our benchmark will contribute to fairer, more robust vision models. FACET is available publicly at https://facet.metademolab.com/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-04", "arxiv_id": "2309.00359", "title": "Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior", "authors": [ "Ashmit Khandelwal", "Aditya Agrawal", "Aanisha Bhattacharyya", "Yaman K Singla", "Somesh Singh", "Uttaran Bhattacharya", "Ishita Dasgupta", "Stefano Petrangeli", "Rajiv Ratn Shah", "Changyou Chen", "Balaji Krishnamurthy" ], "github": "", "project_page": "", "abstract": "Shannon, in his seminal paper introducing information theory, divided the communication into three levels: technical, semantic, and effectivenss. While the technical level is concerned with accurate reconstruction of transmitted symbols, the semantic and effectiveness levels deal with the inferred meaning and its effect on the receiver. Thanks to telecommunications, the first level problem has produced great advances like the internet. Large Language Models (LLMs) make some progress towards the second goal, but the third level still remains largely untouched. The third problem deals with predicting and optimizing communication for desired receiver behavior. LLMs, while showing wide generalization capabilities across a wide range of tasks, are unable to solve for this. One reason for the underperformance could be a lack of \"behavior tokens\" in LLMs' training corpora. Behavior tokens define receiver behavior over a communication, such as shares, likes, clicks, purchases, retweets, etc. While preprocessing data for LLM training, behavior tokens are often removed from the corpora as noise. Therefore, in this paper, we make some initial progress towards reintroducing behavior tokens in LLM training. The trained models, other than showing similar performance to LLMs on content understanding tasks, show generalization capabilities on behavior simulation, content simulation, behavior understanding, and behavior domain adaptation. Using a wide range of tasks on two corpora, we show results on all these capabilities. We call these models Large Content and Behavior Models (LCBMs). Further, to spur more research on LCBMs, we release our new Content Behavior Corpus (CBC), a repository containing communicator, message, and corresponding receiver behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-04", "arxiv_id": "2309.00267", "title": "RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback", "authors": [ "Harrison Lee", "Samrat Phatale", "Hassan Mansoor", "Kellie Lu", "Thomas Mesnard", "Colton Bishop", "Victor Carbune", "Abhinav Rastogi" ], "github": "", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) is effective at aligning large language models (LLMs) to human preferences, but gathering high quality human preference labels is a key bottleneck. We conduct a head-to-head comparison of RLHF vs. RL from AI Feedback (RLAIF) - a technique where preferences are labeled by an off-the-shelf LLM in lieu of humans, and we find that they result in similar improvements. On the task of summarization, human evaluators prefer generations from both RLAIF and RLHF over a baseline supervised fine-tuned model in ~70% of cases. Furthermore, when asked to rate RLAIF vs. RLHF summaries, humans prefer both at equal rates. These results suggest that RLAIF can yield human-level performance, offering a potential solution to the scalability limitations of RLHF.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-04", "arxiv_id": "2309.00610", "title": "CityDreamer: Compositional Generative Model of Unbounded 3D Cities", "authors": [ "Haozhe Xie", "Zhaoxi Chen", "Fangzhou Hong", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "In recent years, extensive research has focused on 3D natural scene generation, but the domain of 3D city generation has not received as much exploration. This is due to the greater challenges posed by 3D city generation, mainly because humans are more sensitive to structural distortions in urban environments. Additionally, generating 3D cities is more complex than 3D natural scenes since buildings, as objects of the same class, exhibit a wider range of appearances compared to the relatively consistent appearance of objects like trees in natural scenes. To address these challenges, we propose CityDreamer, a compositional generative model designed specifically for unbounded 3D cities, which separates the generation of building instances from other background objects, such as roads, green lands, and water areas, into distinct modules. Furthermore, we construct two datasets, OSM and GoogleEarth, containing a vast amount of real-world city imagery to enhance the realism of the generated 3D cities both in their layouts and appearances. Through extensive experiments, CityDreamer has proven its superiority over state-of-the-art methods in generating a wide range of lifelike 3D cities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-04", "arxiv_id": "2309.00398", "title": "VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation", "authors": [ "Xin Li", "Wenqing Chu", "Ye Wu", "Weihang Yuan", "Fanglong Liu", "Qi Zhang", "Fu Li", "Haocheng Feng", "Errui Ding", "Jingdong Wang" ], "github": "", "project_page": "", "abstract": "In this paper, we present VideoGen, a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion. We leverage an off-the-shelf text-to-image generation model, e.g., Stable Diffusion, to generate an image with high content quality from the text prompt, as a reference image to guide video generation. Then, we introduce an efficient cascaded latent diffusion module conditioned on both the reference image and the text prompt, for generating latent video representations, followed by a flow-based temporal upsampling step to improve the temporal resolution. Finally, we map latent video representations into a high-definition video through an enhanced video decoder. During training, we use the first frame of a ground-truth video as the reference image for training the cascaded latent diffusion module. The main characterises of our approach include: the reference image generated by the text-to-image model improves the visual fidelity; using it as the condition makes the diffusion model focus more on learning the video dynamics; and the video decoder is trained over unlabeled video data, thus benefiting from high-quality easily-available videos. VideoGen sets a new state-of-the-art in text-to-video generation in terms of both qualitative and quantitative evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-04", "arxiv_id": "2309.00615", "title": "Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following", "authors": [ "Ziyu Guo", "Renrui Zhang", "Xiangyang Zhu", "Yiwen Tang", "Xianzheng Ma", "Jiaming Han", "Kexin Chen", "Peng Gao", "Xianzhi Li", "Hongsheng Li", "Pheng-Ann Heng" ], "github": "https://github.com/ZiyuGuo99/Point-Bind_Point-LLM", "project_page": "", "abstract": "We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video. Guided by ImageBind, we construct a joint embedding space between 3D and multi-modalities, enabling many promising applications, e.g., any-to-3D generation, 3D embedding arithmetic, and 3D open-world understanding. On top of this, we further present Point-LLM, the first 3D large language model (LLM) following 3D multi-modal instructions. By parameter-efficient fine-tuning techniques, Point-LLM injects the semantics of Point-Bind into pre-trained LLMs, e.g., LLaMA, which requires no 3D instruction data, but exhibits superior 3D and multi-modal question-answering capacity. We hope our work may cast a light on the community for extending 3D point clouds to multi-modality applications. Code is available at https://github.com/ZiyuGuo99/Point-Bind_Point-LLM.", "github_manual": "", "github_regex": "https://github.com/ZiyuGuo99/Point-Bind_Point-LLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.02285", "title": "PromptTTS 2: Describing and Generating Voices with Text Prompt", "authors": [ "Yichong Leng", "Zhifang Guo", "Kai Shen", "Xu Tan", "Zeqian Ju", "Yanqing Liu", "Yufei Liu", "Dongchao Yang", "Leying Zhang", "Kaitao Song", "Lei He", "Xiang-Yang Li", "Sheng Zhao", "Tao Qin", "Jiang Bian" ], "github": "", "project_page": "", "abstract": "Speech conveys more information than just text, as the same word can be uttered in various voices to convey diverse information. Compared to traditional text-to-speech (TTS) methods relying on speech prompts (reference speech) for voice variability, using text prompts (descriptions) is more user-friendly since speech prompts can be hard to find or may not exist at all. TTS approaches based on the text prompt face two challenges: 1) the one-to-many problem, where not all details about voice variability can be described in the text prompt, and 2) the limited availability of text prompt datasets, where vendors and large cost of data labeling are required to write text prompt for speech. In this work, we introduce PromptTTS 2 to address these challenges with a variation network to provide variability information of voice not captured by text prompts, and a prompt generation pipeline to utilize the large language models (LLM) to compose high quality text prompts. Specifically, the variation network predicts the representation extracted from the reference speech (which contains full information about voice) based on the text prompt representation. For the prompt generation pipeline, it generates text prompts for speech with a speech understanding model to recognize voice attributes (e.g., gender, speed) from speech and a large language model to formulate text prompt based on the recognition results. Experiments on a large-scale (44K hours) speech dataset demonstrate that compared to the previous works, PromptTTS 2 generates voices more consistent with text prompts and supports the sampling of diverse voice variability, thereby offering users more choices on voice generation. Additionally, the prompt generation pipeline produces high-quality prompts, eliminating the large labeling cost. The demo page of PromptTTS 2 is available onlinehttps://speechresearch.github.io/prompttts2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.00754", "title": "Efficient RLHF: Reducing the Memory Usage of PPO", "authors": [ "Michael Santacroce", "Yadong Lu", "Han Yu", "Yuanzhi Li", "Yelong Shen" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Human Feedback (RLHF) has revolutionized language modeling by aligning models with human preferences. However, the RL stage, Proximal Policy Optimization (PPO), requires over 3x the memory of Supervised Fine-Tuning (SFT), making it infeasible to use for most practitioners. To address this issue, we present a comprehensive analysis the memory usage, performance, and training time of memory-savings techniques for PPO. We introduce Hydra-RLHF by first integrating the SFT and Reward models and then dynamically turning LoRA \"off\" during training. Our experiments show: 1. Using LoRA during PPO reduces its memory usage to be smaller than SFT while improving alignment across four public benchmarks, and 2. Hydra-PPO reduces the latency per sample of LoRA-PPO by up to 65% while maintaining its performance. Our results demonstrate that Hydra-PPO is a simple and promising solution for enabling more widespread usage of RLHF.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.00986", "title": "ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models", "authors": [ "Chenliang Li", "Hehong Chen", "Ming Yan", "Weizhou Shen", "Haiyang Xu", "Zhikai Wu", "Zhicheng Zhang", "Wenmeng Zhou", "Yingda Chen", "Chen Cheng", "Hongzhu Shi", "Ji Zhang", "Fei Huang", "Jingren Zhou" ], "github": "https://github.com/modelscope/modelscope-agent", "project_page": "", "abstract": "Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior. To further unleash the power of LLMs to accomplish complex tasks, there is a growing trend to build agent framework that equips LLMs, such as ChatGPT, with tool-use abilities to connect with massive external APIs. In this work, we introduce ModelScope-Agent, a general and customizable agent framework for real-world applications, based on open-source LLMs as controllers. It provides a user-friendly system library, with customizable engine design to support model training on multiple open-source LLMs, while also enabling seamless integration with both model APIs and common APIs in a unified way. To equip the LLMs with tool-use abilities, a comprehensive framework has been proposed spanning over tool-use data collection, tool retrieval, tool registration, memory control, customized model training, and evaluation for practical real-world applications. Finally, we showcase ModelScopeGPT, a real-world intelligent assistant of ModelScope Community based on the ModelScope-Agent framework, which is able to connect open-source LLMs with more than 1000 public AI models and localized community knowledge in ModelScope. The ModelScope-Agent libraryhttps://github.com/modelscope/modelscope-agent and online demohttps://modelscope.cn/studios/damo/ModelScopeGPT/summary are now publicly available.", "github_manual": "", "github_regex": "https://github.com/modelscope/modelscope-agent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.01770", "title": "StyleAdapter: A Single-Pass LoRA-Free Model for Stylized Image Generation", "authors": [ "Zhouxia Wang", "Xintao Wang", "Liangbin Xie", "Zhongang Qi", "Ying Shan", "Wenping Wang", "Ping Luo" ], "github": "", "project_page": "", "abstract": "This paper presents a LoRA-free method for stylized image generation that takes a text prompt and style reference images as inputs and produces an output image in a single pass. Unlike existing methods that rely on training a separate LoRA for each style, our method can adapt to various styles with a unified model. However, this poses two challenges: 1) the prompt loses controllability over the generated content, and 2) the output image inherits both the semantic and style features of the style reference image, compromising its content fidelity. To address these challenges, we introduce StyleAdapter, a model that comprises two components: a two-path cross-attention module (TPCA) and three decoupling strategies. These components enable our model to process the prompt and style reference features separately and reduce the strong coupling between the semantic and style information in the style references. StyleAdapter can generate high-quality images that match the content of the prompts and adopt the style of the references (even for unseen styles) in a single pass, which is more flexible and efficient than previous methods. Experiments have been conducted to demonstrate the superiority of our method over previous works.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.01700", "title": "ControlMat: A Controlled Generative Approach to Material Capture", "authors": [ "Giuseppe Vecchio", "Rosalie Martin", "Arthur Roullier", "Adrien Kaiser", "Romain Rouffet", "Valentin Deschaintre", "Tamy Boubekeur" ], "github": "", "project_page": "", "abstract": "Material reconstruction from a photograph is a key component of 3D content creation democratization. We propose to formulate this ill-posed problem as a controlled synthesis one, leveraging the recent progress in generative deep networks. We present ControlMat, a method which, given a single photograph with uncontrolled illumination as input, conditions a diffusion model to generate plausible, tileable, high-resolution physically-based digital materials. We carefully analyze the behavior of diffusion models for multi-channel outputs, adapt the sampling process to fuse multi-scale information and introduce rolled diffusion to enable both tileability and patched diffusion for high-resolution outputs. Our generative approach further permits exploration of a variety of materials which could correspond to the input image, mitigating the unknown lighting conditions. We show that our approach outperforms recent inference and latent-space-optimization methods, and carefully validate our diffusion process design choices. Supplemental materials and additional details are available at: https://gvecchio.com/controlmat/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.00908", "title": "MagicProp: Diffusion-based Video Editing via Motion-aware Appearance Propagation", "authors": [ "Hanshu Yan", "Jun Hao Liew", "Long Mai", "Shanchuan Lin", "Jiashi Feng" ], "github": "", "project_page": "", "abstract": "This paper addresses the issue of modifying the visual appearance of videos while preserving their motion. A novel framework, named MagicProp, is proposed, which disentangles the video editing process into two stages: appearance editing and motion-aware appearance propagation. In the first stage, MagicProp selects a single frame from the input video and applies image-editing techniques to modify the content and/or style of the frame. The flexibility of these techniques enables the editing of arbitrary regions within the frame. In the second stage, MagicProp employs the edited frame as an appearance reference and generates the remaining frames using an autoregressive rendering approach. To achieve this, a diffusion-based conditional generation model, called PropDPM, is developed, which synthesizes the target frame by conditioning on the reference appearance, the target motion, and its previous appearance. The autoregressive editing approach ensures temporal consistency in the resulting videos. Overall, MagicProp combines the flexibility of image-editing techniques with the superior temporal consistency of autoregressive modeling, enabling flexible editing of object types and aesthetic styles in arbitrary regions of input videos while maintaining good temporal consistency across frames. Extensive experiments in various video editing scenarios demonstrate the effectiveness of MagicProp.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.00775", "title": "Contrastive Feature Masking Open-Vocabulary Vision Transformer", "authors": [ "Dahun Kim", "Anelia Angelova", "Weicheng Kuo" ], "github": "", "project_page": "", "abstract": "We present Contrastive Feature Masking Vision Transformer (CFM-ViT) - an image-text pretraining methodology that achieves simultaneous learning of image- and region-level representation for open-vocabulary object detection (OVD). Our approach combines the masked autoencoder (MAE) objective into the contrastive learning objective to improve the representation for localization tasks. Unlike standard MAE, we perform reconstruction in the joint image-text embedding space, rather than the pixel space as is customary with the classical MAE method, which causes the model to better learn region-level semantics. Moreover, we introduce Positional Embedding Dropout (PED) to address scale variation between image-text pretraining and detection finetuning by randomly dropping out the positional embeddings during pretraining. PED improves detection performance and enables the use of a frozen ViT backbone as a region classifier, preventing the forgetting of open-vocabulary knowledge during detection finetuning. On LVIS open-vocabulary detection benchmark, CFM-ViT achieves a state-of-the-art 33.9 APr, surpassing the best approach by 7.6 points and achieves better zero-shot detection transfer. Finally, CFM-ViT acquires strong image-level representation, outperforming the state of the art on 8 out of 12 metrics on zero-shot image-text retrieval benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.02119", "title": "Hierarchical Masked 3D Diffusion Model for Video Outpainting", "authors": [ "Fanda Fan", "Chaoxu Guo", "Litong Gong", "Biao Wang", "Tiezheng Ge", "Yuning Jiang", "Chunjie Luo", "Jianfeng Zhan" ], "github": "", "project_page": "", "abstract": "Video outpainting aims to adequately complete missing areas at the edges of video frames. Compared to image outpainting, it presents an additional challenge as the model should maintain the temporal consistency of the filled area. In this paper, we introduce a masked 3D diffusion model for video outpainting. We use the technique of mask modeling to train the 3D diffusion model. This allows us to use multiple guide frames to connect the results of multiple video clip inferences, thus ensuring temporal consistency and reducing jitter between adjacent frames. Meanwhile, we extract the global frames of the video as prompts and guide the model to obtain information other than the current video clip using cross-attention. We also introduce a hybrid coarse-to-fine inference pipeline to alleviate the artifact accumulation problem. The existing coarse-to-fine pipeline only uses the infilling strategy, which brings degradation because the time interval of the sparse frames is too large. Our pipeline benefits from bidirectional learning of the mask modeling and thus can employ a hybrid strategy of infilling and interpolation when generating sparse frames. Experiments show that our method achieves state-of-the-art results in video outpainting tasks. More results are provided at our https://fanfanda.github.io/M3DDM/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.02040", "title": "Diffusion Generative Inverse Design", "authors": [ "Marin Vlastelica", "Tatiana L\u00f3pez-Guevara", "Kelsey Allen", "Peter Battaglia", "Arnaud Doucet", "Kimberley Stachenfeld" ], "github": "", "project_page": "", "abstract": "Inverse design refers to the problem of optimizing the input of an objective function in order to enact a target outcome. For many real-world engineering problems, the objective function takes the form of a simulator that predicts how the system state will evolve over time, and the design challenge is to optimize the initial conditions that lead to a target outcome. Recent developments in learned simulation have shown that graph neural networks (GNNs) can be used for accurate, efficient, differentiable estimation of simulator dynamics, and support high-quality design optimization with gradient- or sampling-based optimization procedures. However, optimizing designs from scratch requires many expensive model queries, and these procedures exhibit basic failures on either non-convex or high-dimensional problems.In this work, we show how denoising diffusion models (DDMs) can be used to solve inverse design problems efficiently and propose a particle sampling algorithm for further improving their efficiency. We perform experiments on a number of fluid dynamics design challenges, and find that our approach substantially reduces the number of calls to the simulator compared to standard techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.00966", "title": "Compositional Diffusion-Based Continuous Constraint Solvers", "authors": [ "Zhutian Yang", "Jiayuan Mao", "Yilun Du", "Jiajun Wu", "Joshua B. Tenenbaum", "Tom\u00e1s Lozano-P\u00e9rez", "Leslie Pack Kaelbling" ], "github": "", "project_page": "", "abstract": "This paper introduces an approach for learning to solve continuous constraint satisfaction problems (CCSP) in robotic reasoning and planning. Previous methods primarily rely on hand-engineering or learning generators for specific constraint types and then rejecting the value assignments when other constraints are violated. By contrast, our model, the compositional diffusion continuous constraint solver (Diffusion-CCSP) derives global solutions to CCSPs by representing them as factor graphs and combining the energies of diffusion models trained to sample for individual constraint types. Diffusion-CCSP exhibits strong generalization to novel combinations of known constraints, and it can be integrated into a task and motion planner to devise long-horizon plans that include actions with both discrete and continuous parameters. Project site: https://diffusion-ccsp.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.01826", "title": "One Wide Feedforward is All You Need", "authors": [ "Telmo Pessoa Pires", "Ant\u00f3nio V. Lopes", "Yannick Assogba", "Hendra Setiawan" ], "github": "", "project_page": "", "abstract": "The Transformer architecture has two main non-embedding components: Attention and the Feed Forward Network (FFN). Attention captures interdependencies between words regardless of their position, while the FFN non-linearly transforms each input token independently. In this work we explore the role of the FFN, and find that despite taking up a significant fraction of the model's parameters, it is highly redundant. Concretely, we are able to substantially reduce the number of parameters with only a modest drop in accuracy by removing the FFN on the decoder layers and sharing a single FFN across the encoder. Finally we scale this architecture back to its original size by increasing the hidden dimension of the shared FFN, achieving substantial gains in both accuracy and latency with respect to the original Transformer Big.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.02420", "title": "Doppelgangers: Learning to Disambiguate Images of Similar Structures", "authors": [ "Ruojin Cai", "Joseph Tung", "Qianqian Wang", "Hadar Averbuch-Elor", "Bharath Hariharan", "Noah Snavely" ], "github": "https://github.com/RuojinCai/Doppelgangers", "project_page": "", "abstract": "We consider the visual disambiguation task of determining whether a pair of visually similar images depict the same or distinct 3D surfaces (e.g., the same or opposite sides of a symmetric building). Illusory image matches, where two images observe distinct but visually similar 3D surfaces, can be challenging for humans to differentiate, and can also lead 3D reconstruction algorithms to produce erroneous results. We propose a learning-based approach to visual disambiguation, formulating it as a binary classification task on image pairs. To that end, we introduce a new dataset for this problem, Doppelgangers, which includes image pairs of similar structures with ground truth labels. We also design a network architecture that takes the spatial distribution of local keypoints and matches as input, allowing for better reasoning about both local and global cues. Our evaluation shows that our method can distinguish illusory matches in difficult cases, and can be integrated into SfM pipelines to produce correct, disambiguated 3D reconstructions. See our project page for our code, datasets, and more results: http://doppelgangers-3d.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RuojinCai/Doppelgangers", "github_api_added_by": "auto" }, { "date": "2023-09-06", "arxiv_id": "2309.02186", "title": "AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections", "authors": [ "Yue Wu", "Sicheng Xu", "Jianfeng Xiang", "Fangyun Wei", "Qifeng Chen", "Jiaolong Yang", "Xin Tong" ], "github": "", "project_page": "", "abstract": "Previous animatable 3D-aware GANs for human generation have primarily focused on either the human head or full body. However, head-only videos are relatively uncommon in real life, and full body generation typically does not deal with facial expression control and still has challenges in generating high-quality results. Towards applicable video avatars, we present an animatable 3D-aware GAN that generates portrait images with controllable facial expression, head pose, and shoulder movements. It is a generative model trained on unstructured 2D image collections without using 3D or video data. For the new task, we base our method on the generative radiance manifold representation and equip it with learnable facial and head-shoulder deformations. A dual-camera rendering and adversarial learning scheme is proposed to improve the quality of the generated faces, which is critical for portrait images. A pose deformation processing network is developed to generate plausible deformations for challenging regions such as long hair. Experiments show that our method, trained on unstructured 2D images, can generate diverse and high-quality 3D portraits with desired control over different properties.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.00987", "title": "Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation", "authors": [ "Yuanpei Chen", "Chen Wang", "Li Fei-Fei", "C. Karen Liu" ], "github": "", "project_page": "", "abstract": "Many real-world manipulation tasks consist of a series of subtasks that are significantly different from one another. Such long-horizon, complex tasks highlight the potential of dexterous hands, which possess adaptability and versatility, capable of seamlessly transitioning between different modes of functionality without the need for re-grasping or external tools. However, the challenges arise due to the high-dimensional action space of dexterous hand and complex compositional dynamics of the long-horizon tasks. We present Sequential Dexterity, a general system based on reinforcement learning (RL) that chains multiple dexterous policies for achieving long-horizon task goals. The core of the system is a transition feasibility function that progressively finetunes the sub-policies for enhancing chaining success rate, while also enables autonomous policy-switching for recovery from failures and bypassing redundant stages. Despite being trained only in simulation with a few task objects, our system demonstrates generalization capability to novel object shapes and is able to zero-shot transfer to a real-world robot equipped with a dexterous hand. More details and video results could be found at https://sequential-dexterity.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-06", "arxiv_id": "2309.01775", "title": "Gated recurrent neural networks discover attention", "authors": [ "Nicolas Zucchet", "Seijin Kobayashi", "Yassir Akram", "Johannes von Oswald", "Maxime Larcher", "Angelika Steger", "Jo\u00e3o Sacramento" ], "github": "", "project_page": "", "abstract": "Recent architectural developments have enabled recurrent neural networks (RNNs) to reach and even surpass the performance of Transformers on certain sequence modeling tasks. These modern RNNs feature a prominent design pattern: linear recurrent layers interconnected by feedforward paths with multiplicative gating. Here, we show how RNNs equipped with these two design elements can exactly implement (linear) self-attention, the main building block of Transformers. By reverse-engineering a set of trained RNNs, we find that gradient descent in practice discovers our construction. In particular, we examine RNNs trained to solve simple in-context learning tasks on which Transformers are known to excel and find that gradient descent instills in our RNNs the same attention-based in-context learning algorithm used by Transformers. Our findings highlight the importance of multiplicative interactions in neural networks and suggest that certain RNNs might be unexpectedly implementing attention under the hood.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-07", "arxiv_id": "2309.03179", "title": "SLiMe: Segment Like Me", "authors": [ "Aliasghar Khani", "Saeid Asgari Taghanaki", "Aditya Sanghi", "Ali Mahdavi Amiri", "Ghassan Hamarneh" ], "github": "https://github.com/aliasgharkhani/slime", "project_page": "", "abstract": "Significant strides have been made using large vision-language models, like Stable Diffusion (SD), for a variety of downstream tasks, including image editing, image correspondence, and 3D shape generation. Inspired by these advancements, we explore leveraging these extensive vision-language models for segmenting images at any desired granularity using as few as one annotated sample by proposing SLiMe. SLiMe frames this problem as an optimization task. Specifically, given a single training image and its segmentation mask, we first extract attention maps, including our novel \"weighted accumulated self-attention map\" from the SD prior. Then, using the extracted attention maps, the text embeddings of Stable Diffusion are optimized such that, each of them, learn about a single segmented region from the training image. These learned embeddings then highlight the segmented region in the attention maps, which in turn can then be used to derive the segmentation map. This enables SLiMe to segment any real-world image during inference with the granularity of the segmented region in the training image, using just one example. Moreover, leveraging additional training data when available, i.e. few-shot, improves the performance of SLiMe. We carried out a knowledge-rich set of experiments examining various design factors and showed that SLiMe outperforms other existing one-shot and few-shot segmentation methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aliasgharkhani/slime", "github_api_added_by": "auto" }, { "date": "2023-09-07", "arxiv_id": "2309.03160", "title": "ResFields: Residual Neural Fields for Spatiotemporal Signals", "authors": [ "Marko Mihajlovic", "Sergey Prokudin", "Marc Pollefeys", "Siyu Tang" ], "github": "https://github.com/markomih/ResFields", "project_page": "", "abstract": "Neural fields, a category of neural networks trained to represent high-frequency signals, have gained significant attention in recent years due to their impressive performance in modeling complex 3D data, especially large neural signed distance (SDFs) or radiance fields (NeRFs) via a single multi-layer perceptron (MLP). However, despite the power and simplicity of representing signals with an MLP, these methods still face challenges when modeling large and complex temporal signals due to the limited capacity of MLPs. In this paper, we propose an effective approach to address this limitation by incorporating temporal residual layers into neural fields, dubbed ResFields, a novel class of networks specifically designed to effectively represent complex temporal signals. We conduct a comprehensive analysis of the properties of ResFields and propose a matrix factorization technique to reduce the number of trainable parameters and enhance generalization capabilities. Importantly, our formulation seamlessly integrates with existing techniques and consistently improves results across various challenging tasks: 2D video approximation, dynamic shape modeling via temporal SDFs, and dynamic NeRF reconstruction. Lastly, we demonstrate the practical utility of ResFields by showcasing its effectiveness in capturing dynamic 3D scenes from sparse sensory inputs of a lightweight capture system.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/markomih/ResFields", "github_api_added_by": "auto" }, { "date": "2023-09-07", "arxiv_id": "2309.03130", "title": "MyoDex: A Generalizable Prior for Dexterous Manipulation", "authors": [ "Vittorio Caggiano", "Sudeep Dasari", "Vikash Kumar" ], "github": "", "project_page": "", "abstract": "Human dexterity is a hallmark of motor control. Our hands can rapidly synthesize new behaviors despite the complexity (multi-articular and multi-joints, with 23 joints controlled by more than 40 muscles) of musculoskeletal sensory-motor circuits. In this work, we take inspiration from how human dexterity builds on a diversity of prior experiences, instead of being acquired through a single task. Motivated by this observation, we set out to develop agents that can build upon their previous experience to quickly acquire new (previously unattainable) behaviors. Specifically, our approach leverages multi-task learning to implicitly capture task-agnostic behavioral priors (MyoDex) for human-like dexterity, using a physiologically realistic human hand model - MyoHand. We demonstrate MyoDex's effectiveness in few-shot generalization as well as positive transfer to a large repertoire of unseen dexterous manipulation tasks. Agents leveraging MyoDex can solve approximately 3x more tasks, and 4x faster in comparison to a distillation baseline. While prior work has synthesized single musculoskeletal control behaviors, MyoDex is the first generalizable manipulation prior that catalyzes the learning of dexterous physiological control across a large variety of contact-rich behaviors. We also demonstrate the effectiveness of our paradigms beyond musculoskeletal control towards the acquisition of dexterity in 24 DoF Adroit Hand. Website: https://sites.google.com/view/myodex", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-07", "arxiv_id": "2309.02591", "title": "Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning", "authors": [ "Lili Yu", "Bowen Shi", "Ramakanth Pasunuru", "Benjamin Muller", "Olga Golovneva", "Tianlu Wang", "Arun Babu", "Binh Tang", "Brian Karrer", "Shelly Sheynin", "Candace Ross", "Adam Polyak", "Russell Howes", "Vasu Sharma", "Puxin Xu", "Hovhannes Tamoyan", "Oron Ashual", "Uriel Singer", "Shang-Wen Li", "Susan Zhang", "Richard James", "Gargi Ghosh", "Yaniv Taigman", "Maryam Fazel-Zarandi", "Asli Celikyilmaz", "Luke Zettlemoyer", "Armen Aghajanyan" ], "github": "", "project_page": "", "abstract": "We present CM3Leon (pronounced \"Chameleon\"), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. CM3Leon uses the CM3 multi-modal architecture but additionally shows the extreme benefits of scaling up and tuning on more diverse instruction-style data. It is the first multi-modal model trained with a recipe adapted from text-only language models, including a large-scale retrieval-augmented pre-training stage and a second multi-task supervised fine-tuning (SFT) stage. It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs. Extensive experiments demonstrate that this recipe is highly effective for multi-modal models. CM3Leon achieves state-of-the-art performance in text-to-image generation with 5x less training compute than comparable methods (zero-shot MS-COCO FID of 4.88). After SFT, CM3Leon can also demonstrate unprecedented levels of controllability in tasks ranging from language-guided image editing to image-controlled generation and segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-07", "arxiv_id": "2309.02561", "title": "Physically Grounded Vision-Language Models for Robotic Manipulation", "authors": [ "Jensen Gao", "Bidipta Sarkar", "Fei Xia", "Ted Xiao", "Jiajun Wu", "Brian Ichter", "Anirudha Majumdar", "Dorsa Sadigh" ], "github": "", "project_page": "", "abstract": "Recent advances in vision-language models (VLMs) have led to improved performance on tasks such as visual question answering and image captioning. Consequently, these models are now well-positioned to reason about the physical world, particularly within domains such as robotic manipulation. However, current VLMs are limited in their understanding of the physical concepts (e.g., material, fragility) of common objects, which restricts their usefulness for robotic manipulation tasks that involve interaction and physical reasoning about such objects. To address this limitation, we propose PhysObjects, an object-centric dataset of 36.9K crowd-sourced and 417K automated physical concept annotations of common household objects. We demonstrate that fine-tuning a VLM on PhysObjects improves its understanding of physical object concepts, by capturing human priors of these concepts from visual appearance. We incorporate this physically-grounded VLM in an interactive framework with a large language model-based robotic planner, and show improved planning performance on tasks that require reasoning about physical object concepts, compared to baselines that do not leverage physically-grounded VLMs. We additionally illustrate the benefits of our physically-grounded VLM on a real robot, where it improves task success rates. We release our dataset and provide further details and visualizations of our results at https://iliad.stanford.edu/pg-vlm/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-07", "arxiv_id": "2309.03185", "title": "Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields", "authors": [ "Lily Goli", "Cody Reading", "Silvia Selll\u00e1n", "Alec Jacobson", "Andrea Tagliasacchi" ], "github": "https://github.com/BayesRays/BayesRays", "project_page": "", "abstract": "Neural Radiance Fields (NeRFs) have shown promise in applications like view synthesis and depth estimation, but learning from multiview images faces inherent uncertainties. Current methods to quantify them are either heuristic or computationally demanding. We introduce BayesRays, a post-hoc framework to evaluate uncertainty in any pre-trained NeRF without modifying the training process. Our method establishes a volumetric uncertainty field using spatial perturbations and a Bayesian Laplace approximation. We derive our algorithm statistically and show its superior performance in key metrics and applications. Additional results available at: https://bayesrays.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/BayesRays/BayesRays", "github_api_added_by": "auto" }, { "date": "2023-09-07", "arxiv_id": "2309.03199", "title": "Matcha-TTS: A fast TTS architecture with conditional flow matching", "authors": [ "Shivam Mehta", "Ruibo Tu", "Jonas Beskow", "\u00c9va Sz\u00e9kely", "Gustav Eje Henter" ], "github": "https://github.com/shivammehta25/Matcha-TTS", "project_page": "", "abstract": "We introduce Matcha-TTS, a new encoder-decoder architecture for speedy TTS acoustic modelling, trained using optimal-transport conditional flow matching (OT-CFM). This yields an ODE-based decoder capable of high output quality in fewer synthesis steps than models trained using score matching. Careful design choices additionally ensure each synthesis step is fast to run. The method is probabilistic, non-autoregressive, and learns to speak from scratch without external alignments. Compared to strong pre-trained baseline models, the Matcha-TTS system has the smallest memory footprint, rivals the speed of the fastest models on long utterances, and attains the highest mean opinion score in a listening test. Please see https://shivammehta25.github.io/Matcha-TTS/ for audio examples, code, and pre-trained models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shivammehta25/Matcha-TTS", "github_api_added_by": "auto" }, { "date": "2023-09-08", "arxiv_id": "2309.03409", "title": "Large Language Models as Optimizers", "authors": [ "Chengrun Yang", "Xuezhi Wang", "Yifeng Lu", "Hanxiao Liu", "Quoc V. Le", "Denny Zhou", "Xinyun Chen" ], "github": "https://github.com/google-deepmind/opro", "project_page": "", "abstract": "Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applications. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values, then the new solutions are evaluated and added to the prompt for the next optimization step. We first showcase OPRO on linear regression and traveling salesman problems, then move on to prompt optimization where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/opro", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-08", "arxiv_id": "2309.03905", "title": "ImageBind-LLM: Multi-modality Instruction Tuning", "authors": [ "Jiaming Han", "Renrui Zhang", "Wenqi Shao", "Peng Gao", "Peng Xu", "Han Xiao", "Kaipeng Zhang", "Chris Liu", "Song Wen", "Ziyu Guo", "Xudong Lu", "Shuai Ren", "Yafei Wen", "Xiaoxin Chen", "Xiangyu Yue", "Hongsheng Li", "Yu Qiao" ], "github": "https://github.com/OpenGVLab/LLaMA-Adapter", "project_page": "", "abstract": "We present ImageBind-LLM, a multi-modality instruction tuning method of large language models (LLMs) via ImageBind. Existing works mainly focus on language and image instruction tuning, different from which, our ImageBind-LLM can respond to multi-modality conditions, including audio, 3D point clouds, video, and their embedding-space arithmetic by only image-text alignment training. During training, we adopt a learnable bind network to align the embedding space between LLaMA and ImageBind's image encoder. Then, the image features transformed by the bind network are added to word tokens of all layers in LLaMA, which progressively injects visual instructions via an attention-free and zero-initialized gating mechanism. Aided by the joint embedding of ImageBind, the simple image-text training enables our model to exhibit superior multi-modality instruction-following capabilities. During inference, the multi-modality inputs are fed into the corresponding ImageBind encoders, and processed by a proposed visual cache model for further cross-modal embedding enhancement. The training-free cache model retrieves from three million image features extracted by ImageBind, which effectively mitigates the training-inference modality discrepancy. Notably, with our approach, ImageBind-LLM can respond to instructions of diverse modalities and demonstrate significant language generation quality. Code is released at https://github.com/OpenGVLab/LLaMA-Adapter.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/LLaMA-Adapter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-08", "arxiv_id": "2309.03852", "title": "FLM-101B: An Open LLM and How to Train It with $100K Budget", "authors": [ "Xiang Li", "Yiqun Yao", "Xin Jiang", "Xuezhi Fang", "Xuying Meng", "Siqi Fan", "Peng Han", "Jing Li", "Li Du", "Bowen Qin", "Zheng Zhang", "Aixin Sun", "Yequan Wang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have achieved remarkable success in NLP and multimodal tasks. Despite these successes, their development faces two main challenges: (i) high computational cost; and (ii) difficulty in conducting fair and objective evaluations. LLMs are prohibitively expensive, making it feasible for only a few major players to undertake their training, thereby constraining both research and application opportunities. This underscores the importance of cost-effective LLM training. In this paper, we utilize a growth strategy to significantly reduce LLM training cost. We demonstrate that an LLM with 101B parameters and 0.31TB tokens can be trained on a 100K budget. We also adopt a systematic evaluation paradigm for the IQ evaluation of LLMs, in complement to existing evaluations that focus more on knowledge-oriented abilities. We introduce our benchmark including evaluations on important aspects of intelligence including symbolic mapping, itrule understanding, pattern mining, and anti-interference. Such evaluations minimize the potential impact of memorization. Experimental results show that our model FLM-101B, trained with a budget of 100K, achieves comparable performance to powerful and well-known models, eg GPT-3 and GLM-130B, especially in the IQ benchmark evaluations with contexts unseen in training data. The checkpoint of FLM-101B will be open-sourced at https://huggingface.co/CofeAI/FLM-101B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-08", "arxiv_id": "2309.03241", "title": "GPT Can Solve Mathematical Problems Without a Calculator", "authors": [ "Zhen Yang", "Ming Ding", "Qingsong Lv", "Zhihuan Jiang", "Zehai He", "Yuyi Guo", "Jinfeng Bai", "Jie Tang" ], "github": "https://github.com/thudm/mathglm", "project_page": "", "abstract": "Previous studies have typically assumed that large language models are unable to accurately perform arithmetic operations, particularly multiplication of >8 digits, and operations involving decimals and fractions, without the use of calculator tools. This paper aims to challenge this misconception. With sufficient training data, a 2 billion-parameter language model can accurately perform multi-digit arithmetic operations with almost 100% accuracy without data leakage, significantly surpassing GPT-4 (whose multi-digit multiplication accuracy is only 4.3%). We also demonstrate that our MathGLM, fine-tuned from GLM-10B on a dataset with additional multi-step arithmetic operations and math problems described in text, achieves similar performance to GPT-4 on a 5,000-samples Chinese math problem test set.", "github_manual": "https://github.com/thudm/mathglm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-08", "arxiv_id": "2309.03549", "title": "Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation", "authors": [ "Jiaxi Gu", "Shicong Wang", "Haoyu Zhao", "Tianyi Lu", "Xing Zhang", "Zuxuan Wu", "Songcen Xu", "Wei Zhang", "Yu-Gang Jiang", "Hang Xu" ], "github": "", "project_page": "", "abstract": "Inspired by the remarkable success of Latent Diffusion Models (LDMs) for image synthesis, we study LDM for text-to-video generation, which is a formidable challenge due to the computational and memory constraints during both model training and inference. A single LDM is usually only capable of generating a very limited number of video frames. Some existing works focus on separate prediction models for generating more video frames, which suffer from additional training cost and frame-level jittering, however. In this paper, we propose a framework called \"Reuse and Diffuse\" dubbed VidRD to produce more frames following the frames already generated by an LDM. Conditioned on an initial video clip with a small number of frames, additional frames are iteratively generated by reusing the original latent features and following the previous diffusion process. Besides, for the autoencoder used for translation between pixel space and latent space, we inject temporal layers into its decoder and fine-tune these layers for higher temporal consistency. We also propose a set of strategies for composing video-text data that involve diverse content from multiple existing datasets including video datasets for action recognition and image-text datasets. Extensive experiments show that our method achieves good results in both quantitative and qualitative evaluations. Our project page is available https://anonymous0x233.github.io/ReuseAndDiffuse/{here}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-08", "arxiv_id": "2309.03550", "title": "Text2Control3D: Controllable 3D Avatar Generation in Neural Radiance Fields using Geometry-Guided Text-to-Image Diffusion Model", "authors": [ "Sungwon Hwang", "Junha Hyung", "Jaegul Choo" ], "github": "", "project_page": "", "abstract": "Recent advances in diffusion models such as ControlNet have enabled geometrically controllable, high-fidelity text-to-image generation. However, none of them addresses the question of adding such controllability to text-to-3D generation. In response, we propose Text2Control3D, a controllable text-to-3D avatar generation method whose facial expression is controllable given a monocular video casually captured with hand-held camera. Our main strategy is to construct the 3D avatar in Neural Radiance Fields (NeRF) optimized with a set of controlled viewpoint-aware images that we generate from ControlNet, whose condition input is the depth map extracted from the input video. When generating the viewpoint-aware images, we utilize cross-reference attention to inject well-controlled, referential facial expression and appearance via cross attention. We also conduct low-pass filtering of Gaussian latent of the diffusion model in order to ameliorate the viewpoint-agnostic texture problem we observed from our empirical analysis, where the viewpoint-aware images contain identical textures on identical pixel positions that are incomprehensible in 3D. Finally, to train NeRF with the images that are viewpoint-aware yet are not strictly consistent in geometry, our approach considers per-image geometric variation as a view of deformation from a shared 3D canonical space. Consequently, we construct the 3D avatar in a canonical space of deformable NeRF by learning a set of per-image deformation via deformation field table. We demonstrate the empirical results and discuss the effectiveness of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-08", "arxiv_id": "2309.03883", "title": "DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models", "authors": [ "Yung-Sung Chuang", "Yujia Xie", "Hongyin Luo", "Yoon Kim", "James Glass", "Pengcheng He" ], "github": "https://github.com/voidism/dola", "project_page": "", "abstract": "Despite their impressive capabilities, large language models (LLMs) are prone to hallucinations, i.e., generating content that deviates from facts seen during pretraining. We propose a simple decoding strategy for reducing hallucinations with pretrained LLMs that does not require conditioning on retrieved external knowledge nor additional fine-tuning. Our approach obtains the next-token distribution by contrasting the differences in logits obtained from projecting the later layers versus earlier layers to the vocabulary space, exploiting the fact that factual knowledge in an LLMs has generally been shown to be localized to particular transformer layers. We find that this Decoding by Contrasting Layers (DoLa) approach is able to better surface factual knowledge and reduce the generation of incorrect facts. DoLa consistently improves the truthfulness across multiple choices tasks and open-ended generation tasks, for example improving the performance of LLaMA family models on TruthfulQA by 12-17% absolute points, demonstrating its potential in making LLMs reliably generate truthful facts.", "github_manual": "https://github.com/voidism/dola", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-08", "arxiv_id": "2309.03450", "title": "XGen-7B Technical Report", "authors": [ "Erik Nijkamp", "Tian Xie", "Hiroaki Hayashi", "Bo Pang", "Congying Xia", "Chen Xing", "Jesse Vig", "Semih Yavuz", "Philippe Laban", "Ben Krause", "Senthil Purushwalkam", "Tong Niu", "Wojciech Kry\u015bci\u0144ski", "Lidiya Murakhovs'ka", "Prafulla Kumar Choubey", "Alex Fabbri", "Ye Liu", "Rui Meng", "Lifu Tu", "Meghana Bhat", "Chien-Sheng Wu", "Silvio Savarese", "Yingbo Zhou", "Shafiq Joty", "Caiming Xiong" ], "github": "https://github.com/salesforce/xgen", "project_page": "", "abstract": "Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LLMs remain confined behind proprietary walls, hindering scientific progress. Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context. To address this, we have trained XGen, a series of 7B parameter models on up to 8K sequence length for up to 1.5T tokens. We have also finetuned the XGen models on public-domain instructional data, creating their instruction-tuned counterparts (XGen-Inst). We open-source our models for both research advancements and commercial applications. Our evaluation on standard benchmarks shows that XGen models achieve comparable or better results when compared with state-of-the-art open-source LLMs. Our targeted evaluation on long sequence modeling tasks shows the benefits of our 8K-sequence models over 2K-sequence open-source LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/salesforce/xgen", "github_api_added_by": "auto" }, { "date": "2023-09-08", "arxiv_id": "2309.03903", "title": "Tracking Anything with Decoupled Video Segmentation", "authors": [ "Ho Kei Cheng", "Seoung Wug Oh", "Brian Price", "Alexander Schwing", "Joon-Young Lee" ], "github": "https://github.com/hkchengrex/Tracking-Anything-with-DEVA", "project_page": "", "abstract": "Training data for video segmentation are expensive to annotate. This impedes extensions of end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary settings. To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task (which is cheaper to train) and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we use bi-directional propagation for (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several data-scarce tasks including large-vocabulary video panoptic segmentation, open-world video segmentation, referring video segmentation, and unsupervised video object segmentation. Code is available at: https://hkchengrex.github.io/Tracking-Anything-with-DEVA", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hkchengrex/Tracking-Anything-with-DEVA", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-08", "arxiv_id": "2309.03315", "title": "Robotic Table Tennis: A Case Study into a High Speed Learning System", "authors": [ "David B. D'Ambrosio", "Jonathan Abelian", "Saminda Abeyruwan", "Michael Ahn", "Alex Bewley", "Justin Boyd", "Krzysztof Choromanski", "Omar Cortes", "Erwin Coumans", "Tianli Ding", "Wenbo Gao", "Laura Graesser", "Atil Iscen", "Navdeep Jaitly", "Deepali Jain", "Juhana Kangaspunta", "Satoshi Kataoka", "Gus Kouretas", "Yuheng Kuang", "Nevena Lazic", "Corey Lynch", "Reza Mahjourian", "Sherry Q. Moore", "Thinh Nguyen", "Ken Oslund", "Barney J Reed", "Krista Reymann", "Pannag R. Sanketi", "Anish Shankar", "Pierre Sermanet", "Vikas Sindhwani", "Avi Singh", "Vincent Vanhoucke", "Grace Vesom", "Peng Xu" ], "github": "", "project_page": "", "abstract": "We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts together a highly optimized perception subsystem, a high-speed low-latency robot controller, a simulation paradigm that can prevent damage in the real world and also train policies for zero-shot transfer, and automated real world environment resets that enable autonomous training and evaluation on physical robots. We complement a complete system description, including numerous design decisions that are typically not widely disseminated, with a collection of studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, sensitivity to policy hyper-parameters, and choice of action space. A video demonstrating the components of the system and details of experimental results can be found at https://youtu.be/uFcnWjB42I0.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-08", "arxiv_id": "2309.03897", "title": "ProPainter: Improving Propagation and Transformer for Video Inpainting", "authors": [ "Shangchen Zhou", "Chongyi Li", "Kelvin C. K. Chan", "Chen Change Loy" ], "github": "https://github.com/sczhou/ProPainter", "project_page": "", "abstract": "Flow-based propagation and spatiotemporal Transformer are two mainstream mechanisms in video inpainting (VI). Despite the effectiveness of these components, they still suffer from some limitations that affect their performance. Previous propagation-based approaches are performed separately either in the image or feature domain. Global image propagation isolated from learning may cause spatial misalignment due to inaccurate optical flow. Moreover, memory or computational constraints limit the temporal range of feature propagation and video Transformer, preventing exploration of correspondence information from distant frames. To address these issues, we propose an improved framework, called ProPainter, which involves enhanced ProPagation and an efficient Transformer. Specifically, we introduce dual-domain propagation that combines the advantages of image and feature warping, exploiting global correspondences reliably. We also propose a mask-guided sparse video Transformer, which achieves high efficiency by discarding unnecessary and redundant tokens. With these components, ProPainter outperforms prior arts by a large margin of 1.46 dB in PSNR while maintaining appealing efficiency.", "github_manual": "https://github.com/sczhou/ProPainter", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-08", "arxiv_id": "2309.03895", "title": "InstructDiffusion: A Generalist Modeling Interface for Vision Tasks", "authors": [ "Zigang Geng", "Binxin Yang", "Tiankai Hang", "Chen Li", "Shuyang Gu", "Ting Zhang", "Jianmin Bao", "Zheng Zhang", "Han Hu", "Dong Chen", "Baining Guo" ], "github": "https://github.com/cientgu/InstructDiffusion", "project_page": "", "abstract": "We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions. Unlike existing approaches that integrate prior knowledge and pre-define the output space (e.g., categories and coordinates) for each vision task, we cast diverse vision tasks into a human-intuitive image-manipulating process whose output space is a flexible and interactive pixel space. Concretely, the model is built upon the diffusion process and is trained to predict pixels according to user instructions, such as encircling the man's left shoulder in red or applying a blue mask to the left car. InstructDiffusion could handle a variety of vision tasks, including understanding tasks (such as segmentation and keypoint detection) and generative tasks (such as editing and enhancement). It even exhibits the ability to handle unseen tasks and outperforms prior methods on novel datasets. This represents a significant step towards a generalist modeling interface for vision tasks, advancing artificial general intelligence in the field of computer vision.", "github_manual": "https://github.com/cientgu/InstructDiffusion", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-08", "arxiv_id": "2309.03453", "title": "SyncDreamer: Generating Multiview-consistent Images from a Single-view Image", "authors": [ "Yuan Liu", "Cheng Lin", "Zijiao Zeng", "Xiaoxiao Long", "Lingjie Liu", "Taku Komura", "Wenping Wang" ], "github": "https://github.com/liuyuan-pal/SyncDreamer", "project_page": "", "abstract": "In this paper, we present a novel diffusion model called that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, recent work Zero123 demonstrates the ability to generate plausible novel views from a single-view image of an object. However, maintaining consistency in geometry and colors for the generated images remains a challenge. To address this issue, we propose a synchronized multiview diffusion model that models the joint probability distribution of multiview images, enabling the generation of multiview-consistent images in a single reverse process. SyncDreamer synchronizes the intermediate states of all the generated images at every step of the reverse process through a 3D-aware feature attention mechanism that correlates the corresponding features across different views. Experiments show that SyncDreamer generates images with high consistency across different views, thus making it well-suited for various 3D generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D.", "github_manual": "https://github.com/liuyuan-pal/SyncDreamer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-11", "arxiv_id": "2309.04269", "title": "From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting", "authors": [ "Griffin Adams", "Alexander Fabbri", "Faisal Ladhak", "Eric Lehman", "No\u00e9mie Elhadad" ], "github": "", "project_page": "", "abstract": "Selecting the ``right'' amount of information to include in a summary is a difficult task. A good summary should be detailed and entity-centric without being overly dense and hard to follow. To better understand this tradeoff, we solicit increasingly dense GPT-4 summaries with what we refer to as a ``Chain of Density'' (CoD) prompt. Specifically, GPT-4 generates an initial entity-sparse summary before iteratively incorporating missing salient entities without increasing the length. Summaries generated by CoD are more abstractive, exhibit more fusion, and have less of a lead bias than GPT-4 summaries generated by a vanilla prompt. We conduct a human preference study on 100 CNN DailyMail articles and find that that humans prefer GPT-4 summaries that are more dense than those generated by a vanilla prompt and almost as dense as human written summaries. Qualitative analysis supports the notion that there exists a tradeoff between informativeness and readability. 500 annotated CoD summaries, as well as an extra 5,000 unannotated summaries, are freely available on HuggingFace (https://huggingface.co/datasets/griffin/chain_of_density).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-11", "arxiv_id": "2309.03926", "title": "Large-Scale Automatic Audiobook Creation", "authors": [ "Brendan Walsh", "Mark Hamilton", "Greg Newby", "Xi Wang", "Serena Ruan", "Sheng Zhao", "Lei He", "Shaofei Zhang", "Eric Dettinger", "William T. Freeman", "Markus Weimer" ], "github": "", "project_page": "", "abstract": "An audiobook can dramatically improve a work of literature's accessibility and improve reader engagement. However, audiobooks can take hundreds of hours of human effort to create, edit, and publish. In this work, we present a system that can automatically generate high-quality audiobooks from online e-books. In particular, we leverage recent advances in neural text-to-speech to create and release thousands of human-quality, open-license audiobooks from the Project Gutenberg e-book collection. Our method can identify the proper subset of e-book content to read for a wide collection of diversely structured books and can operate on hundreds of books in parallel. Our system allows users to customize an audiobook's speaking speed and style, emotional intonation, and can even match a desired voice using a small amount of sample audio. This work contributed over five thousand open-license audiobooks and an interactive demo that allows users to quickly create their own customized audiobooks. To listen to the audiobook collection visit https://aka.ms/audiobook.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-11", "arxiv_id": "2309.04247", "title": "Towards Practical Capture of High-Fidelity Relightable Avatars", "authors": [ "Haotian Yang", "Mingwu Zheng", "Wanquan Feng", "Haibin Huang", "Yu-Kun Lai", "Pengfei Wan", "Zhongyuan Wang", "Chongyang Ma" ], "github": "", "project_page": "", "abstract": "In this paper, we propose a novel framework, Tracking-free Relightable Avatar (TRAvatar), for capturing and reconstructing high-fidelity 3D avatars. Compared to previous methods, TRAvatar works in a more practical and efficient setting. Specifically, TRAvatar is trained with dynamic image sequences captured in a Light Stage under varying lighting conditions, enabling realistic relighting and real-time animation for avatars in diverse scenes. Additionally, TRAvatar allows for tracking-free avatar capture and obviates the need for accurate surface tracking under varying illumination conditions. Our contributions are two-fold: First, we propose a novel network architecture that explicitly builds on and ensures the satisfaction of the linear nature of lighting. Trained on simple group light captures, TRAvatar can predict the appearance in real-time with a single forward pass, achieving high-quality relighting effects under illuminations of arbitrary environment maps. Second, we jointly optimize the facial geometry and relightable appearance from scratch based on image sequences, where the tracking is implicitly learned. This tracking-free approach brings robustness for establishing temporal correspondences between frames under different lighting conditions. Extensive qualitative and quantitative experiments demonstrate that our framework achieves superior performance for photorealistic avatar animation and relighting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-11", "arxiv_id": "2309.04354", "title": "Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts", "authors": [ "Erik Daxberger", "Floris Weers", "Bowen Zhang", "Tom Gunter", "Ruoming Pang", "Marcin Eichner", "Michael Emmersberger", "Yinfei Yang", "Alexander Toshev", "Xianzhi Du" ], "github": "", "project_page": "", "abstract": "Sparse Mixture-of-Experts models (MoEs) have recently gained popularity due to their ability to decouple model size from inference efficiency by only activating a small subset of the model parameters for any given input token. As such, sparse MoEs have enabled unprecedented scalability, resulting in tremendous successes across domains such as natural language processing and computer vision. In this work, we instead explore the use of sparse MoEs to scale-down Vision Transformers (ViTs) to make them more attractive for resource-constrained vision applications. To this end, we propose a simplified and mobile-friendly MoE design where entire images rather than individual patches are routed to the experts. We also propose a stable MoE training procedure that uses super-class information to guide the router. We empirically show that our sparse Mobile Vision MoEs (V-MoEs) can achieve a better trade-off between performance and efficiency than the corresponding dense ViTs. For example, for the ViT-Tiny model, our Mobile V-MoE outperforms its dense counterpart by 3.39% on ImageNet-1k. For an even smaller ViT variant with only 54M FLOPs inference cost, our MoE achieves an improvement of 4.66%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-11", "arxiv_id": "2309.03907", "title": "DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs", "authors": [ "Youwei Liang", "Ruiyi Zhang", "Li Zhang", "Pengtao Xie" ], "github": "https://github.com/UCSD-AI4H/drugchat", "project_page": "", "abstract": "A ChatGPT-like system for drug compounds could be a game-changer in pharmaceutical research, accelerating drug discovery, enhancing our understanding of structure-activity relationships, guiding lead optimization, aiding drug repurposing, reducing the failure rate, and streamlining clinical trials. In this work, we make an initial attempt towards enabling ChatGPT-like capabilities on drug molecule graphs, by developing a prototype system DrugChat. DrugChat works in a similar way as ChatGPT. Users upload a compound molecule graph and ask various questions about this compound. DrugChat will answer these questions in a multi-turn, interactive manner. The DrugChat system consists of a graph neural network (GNN), a large language model (LLM), and an adaptor. The GNN takes a compound molecule graph as input and learns a representation for this graph. The adaptor transforms the graph representation produced by the GNN into another representation that is acceptable to the LLM. The LLM takes the compound representation transformed by the adaptor and users' questions about this compound as inputs and generates answers. All these components are trained end-to-end. To train DrugChat, we collected instruction tuning datasets which contain 10,834 drug compounds and 143,517 question-answer pairs. The code and data is available at https://github.com/UCSD-AI4H/drugchat", "github_manual": "", "github_regex": "https://github.com/UCSD-AI4H/drugchat", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-12", "arxiv_id": "2309.05519", "title": "NExT-GPT: Any-to-Any Multimodal LLM", "authors": [ "Shengqiong Wu", "Hao Fei", "Leigang Qu", "Wei Ji", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides, they mostly fall prey to the limitation of only input-side multimodal understanding, without the ability to produce content in multiple modalities. As we humans always perceive the world and communicate with people through various modalities, developing any-to-any MM-LLMs capable of accepting and delivering content in any modality becomes essential to human-level AI. To fill the gap, we present an end-to-end general-purpose any-to-any MM-LLM system, NExT-GPT. We connect an LLM with multimodal adaptors and different diffusion decoders, enabling NExT-GPT to perceive inputs and generate outputs in arbitrary combinations of text, images, videos, and audio. By leveraging the existing well-trained highly-performing encoders and decoders, NExT-GPT is tuned with only a small amount of parameter (1%) of certain projection layers, which not only benefits low-cost training and also facilitates convenient expansion to more potential modalities. Moreover, we introduce a modality-switching instruction tuning (MosIT) and manually curate a high-quality dataset for MosIT, based on which NExT-GPT is empowered with complex cross-modal semantic understanding and content generation. Overall, our research showcases the promising possibility of building an AI agent capable of modeling universal modalities, paving the way for more human-like AI research in the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-12", "arxiv_id": "2309.05463", "title": "Textbooks Are All You Need II: phi-1.5 technical report", "authors": [ "Yuanzhi Li", "S\u00e9bastien Bubeck", "Ronen Eldan", "Allie Del Giorno", "Suriya Gunasekar", "Yin Tat Lee" ], "github": "", "project_page": "", "abstract": "We continue the investigation into the power of smaller Transformer-based language models as initiated by TinyStories -- a 10 million parameter model that can produce coherent English -- and the follow-up work on phi-1, a 1.3 billion parameter model with Python coding performance close to the state-of-the-art. The latter work proposed to use existing Large Language Models (LLMs) to generate ``textbook quality\" data as a way to enhance the learning process compared to traditional web data. We follow the ``Textbooks Are All You Need\" approach, focusing this time on common sense reasoning in natural language, and create a new 1.3 billion parameter model named phi-1.5, with performance on natural language tasks comparable to models 5x larger, and surpassing most non-frontier LLMs on more complex reasoning tasks such as grade-school mathematics and basic coding. More generally, phi-1.5 exhibits many of the traits of much larger LLMs, both good -- such as the ability to ``think step by step\" or perform some rudimentary in-context learning -- and bad, including hallucinations and the potential for toxic and biased generations -- encouragingly though, we are seeing improvement on that front thanks to the absence of web data. We open-source phi-1.5 to promote further research on these urgent topics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-12", "arxiv_id": "2309.05516", "title": "Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs", "authors": [ "Wenhua Cheng", "Weiwei Zhang", "Haihao Shen", "Yiyang Cai", "Xin He", "Kaokao Lv" ], "github": "https://github.com/intel/neural-compressor", "project_page": "", "abstract": "Large Language Models (LLMs) have proven their exceptional capabilities in performing language-related tasks. However, their deployment poses significant challenges due to their considerable memory and storage requirements. In response to this issue, weight-only quantization, particularly 3 and 4-bit weight-only quantization, has emerged as one of the most viable solutions. As the number of bits decreases, the quantization grid broadens, thus emphasizing the importance of up and down rounding. While previous studies have demonstrated that fine-tuning up and down rounding with the addition of perturbations can enhance accuracy in some scenarios, our study is driven by the precise and limited boundary of these perturbations, where only the threshold for altering the rounding value is of significance. Consequently, we propose a concise and highly effective approach for optimizing the weight rounding task. Our method, named SignRound, involves lightweight block-wise tuning using signed gradient descent, enabling us to achieve outstanding results within 400 steps. SignRound outperforms the established baseline of rounding-to-nearest (RTN) and competes impressively against recent methods, without introducing additional inference overhead. The source code will be publicly available at https://github.com/intel/neural-compressor soon.", "github_manual": "", "github_regex": "https://github.com/intel/neural-compressor", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-12", "arxiv_id": "2309.04827", "title": "Neurons in Large Language Models: Dead, N-gram, Positional", "authors": [ "Elena Voita", "Javier Ferrando", "Christoforos Nalmpantis" ], "github": "", "project_page": "", "abstract": "We analyze a family of large language models in such a lightweight manner that can be done on a single GPU. Specifically, we focus on the OPT family of models ranging from 125m to 66b parameters and rely only on whether an FFN neuron is activated or not. First, we find that the early part of the network is sparse and represents many discrete features. Here, many neurons (more than 70% in some layers of the 66b model) are \"dead\", i.e. they never activate on a large collection of diverse data. At the same time, many of the alive neurons are reserved for discrete features and act as token and n-gram detectors. Interestingly, their corresponding FFN updates not only promote next token candidates as could be expected, but also explicitly focus on removing the information about triggering them tokens, i.e., current input. To the best of our knowledge, this is the first example of mechanisms specialized at removing (rather than adding) information from the residual stream. With scale, models become more sparse in a sense that they have more dead neurons and token detectors. Finally, some neurons are positional: them being activated or not depends largely (or solely) on position and less so (or not at all) on textual data. We find that smaller models have sets of neurons acting as position range indicators while larger models operate in a less explicit manner.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-12", "arxiv_id": "2309.04663", "title": "FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning", "authors": [ "Xinyi Wang", "John Wieting", "Jonathan H. Clark" ], "github": "", "project_page": "", "abstract": "Learning paradigms for large language models (LLMs) currently tend to fall within either in-context learning (ICL) or full fine-tuning. Each of these comes with their own trade-offs based on available data, model size, compute cost, ease-of-use, and final quality with neither solution performing well across-the-board. In this article, we first describe ICL and fine-tuning paradigms in a way that highlights their natural connections. Based on these connections, we propose a new learning paradigm called FIAT that fuses the best of these paradigms together, enabling prompt-engineered instructions and chain-of-thought reasoning with the very largest models while also using similar methods to perform parameter updates on a modestly-sized LLM with parameter-efficient tuning. We evaluate FIAT's effectiveness on a variety of multilingual tasks and observe that FIAT performs better than both ICL and fine-tuning at scales ranging from 100-10,000 training examples. We hope that FIAT provides a practical way of harnessing the full potential of LLMs without needing to make a hard choice between learning paradigms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-12", "arxiv_id": "2309.04662", "title": "MADLAD-400: A Multilingual And Document-Level Large Audited Dataset", "authors": [ "Sneha Kudugunta", "Isaac Caswell", "Biao Zhang", "Xavier Garcia", "Christopher A. Choquette-Choo", "Katherine Lee", "Derrick Xin", "Aditya Kusupati", "Romi Stella", "Ankur Bapna", "Orhan Firat" ], "github": "", "project_page": "", "abstract": "We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages. We discuss the limitations revealed by self-auditing MADLAD-400, and the role data auditing had in the dataset creation process. We then train and release a 10.7B-parameter multilingual machine translation model on 250 billion tokens covering over 450 languages using publicly available data, and find that it is competitive with models that are significantly larger, and report the results on different domains. In addition, we train a 8B-parameter language model, and assess the results on few-shot translation. We make the baseline models available to the research community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-12", "arxiv_id": "2309.04564", "title": "When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale", "authors": [ "Max Marion", "Ahmet \u00dcst\u00fcn", "Luiza Pozzobon", "Alex Wang", "Marzieh Fadaee", "Sara Hooker" ], "github": "", "project_page": "", "abstract": "Large volumes of text data have contributed significantly to the development of large language models (LLMs) in recent years. This data is typically acquired by scraping the internet, leading to pretraining datasets comprised of noisy web text. To date, efforts to prune these datasets down to a higher quality subset have relied on hand-crafted heuristics encoded as rule-based filters. In this work, we take a wider view and explore scalable estimates of data quality that can be used to systematically measure the quality of pretraining data. We perform a rigorous comparison at scale of the simple data quality estimator of perplexity, as well as more sophisticated and computationally intensive estimates of the Error L2-Norm and memorization. These metrics are used to rank and prune pretraining corpora, and we subsequently compare LLMs trained on these pruned datasets. Surprisingly, we find that the simple technique of perplexity outperforms our more computationally expensive scoring methods. We improve over our no-pruning baseline while training on as little as 30% of the original training dataset. Our work sets the foundation for unexplored strategies in automatically curating high quality corpora and suggests the majority of pretraining data can be removed while retaining performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-12", "arxiv_id": "2309.04581", "title": "Dynamic Mesh-Aware Radiance Fields", "authors": [ "Yi-Ling Qiao", "Alexander Gao", "Yiran Xu", "Yue Feng", "Jia-Bin Huang", "Ming C. Lin" ], "github": "", "project_page": "", "abstract": "Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF) volumes, such that they can be rendered and their dynamics simulated in a physically consistent manner with the NeRF, is under-explored from the system perspective of integrating NeRF into the traditional graphics pipeline. This paper designs a two-way coupling between mesh and NeRF during rendering and simulation. We first review the light transport equations for both mesh and NeRF, then distill them into an efficient algorithm for updating radiance and throughput along a cast ray with an arbitrary number of bounces. To resolve the discrepancy between the linear color space that the path tracer assumes and the sRGB color space that standard NeRF uses, we train NeRF with High Dynamic Range (HDR) images. We also present a strategy to estimate light sources and cast shadows on the NeRF. Finally, we consider how the hybrid surface-volumetric formulation can be efficiently integrated with a high-performance physics simulator that supports cloth, rigid and soft bodies. The full rendering and simulation system can be run on a GPU at interactive rates. We show that a hybrid system approach outperforms alternatives in visual realism for mesh insertion, because it allows realistic light transport from volumetric NeRF media onto surfaces, which affects the appearance of reflective/refractive surfaces and illumination of diffuse surfaces informed by the dynamic scene.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-13", "arxiv_id": "2309.05793", "title": "PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models", "authors": [ "Li Chen", "Mengyi Zhao", "Yiheng Liu", "Mingxu Ding", "Yangyang Song", "Shizun Wang", "Xu Wang", "Hao Yang", "Jing Liu", "Kang Du", "Min Zheng" ], "github": "", "project_page": "", "abstract": "Personalized text-to-image generation has emerged as a powerful and sought-after tool, empowering users to create customized images based on their specific concepts and prompts. However, existing approaches to personalization encounter multiple challenges, including long tuning times, large storage requirements, the necessity for multiple input images per identity, and limitations in preserving identity and editability. To address these obstacles, we present PhotoVerse, an innovative methodology that incorporates a dual-branch conditioning mechanism in both text and image domains, providing effective control over the image generation process. Furthermore, we introduce facial identity loss as a novel component to enhance the preservation of identity during training. Remarkably, our proposed PhotoVerse eliminates the need for test time tuning and relies solely on a single facial photo of the target identity, significantly reducing the resource cost associated with image generation. After a single training phase, our approach enables generating high-quality images within only a few seconds. Moreover, our method can produce diverse images that encompass various scenes and styles. The extensive evaluation demonstrates the superior performance of our approach, which achieves the dual objectives of preserving identity and facilitating editability. Project page: https://photoverse2d.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-13", "arxiv_id": "2309.06180", "title": "Efficient Memory Management for Large Language Model Serving with PagedAttention", "authors": [ "Woosuk Kwon", "Zhuohan Li", "Siyuan Zhuang", "Ying Sheng", "Lianmin Zheng", "Cody Hao Yu", "Joseph E. Gonzalez", "Hao Zhang", "Ion Stoica" ], "github": "https://github.com/vllm-project/vllm", "project_page": "", "abstract": "High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2-4times with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. vLLM's source code is publicly available at https://github.com/vllm-project/vllm", "github_manual": "", "github_regex": "https://github.com/vllm-project/vllm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-13", "arxiv_id": "2309.05689", "title": "Large Language Model for Science: A Study on P vs. NP", "authors": [ "Qingxiu Dong", "Li Dong", "Ke Xu", "Guangyan Zhou", "Yaru Hao", "Zhifang Sui", "Furu Wei" ], "github": "", "project_page": "", "abstract": "In this work, we use large language models (LLMs) to augment and accelerate research on the P versus NP problem, one of the most important open problems in theoretical computer science and mathematics. Specifically, we propose Socratic reasoning, a general framework that promotes in-depth thinking with LLMs for complex problem-solving. Socratic reasoning encourages LLMs to recursively discover, solve, and integrate problems while facilitating self-evaluation and refinement. Our pilot study on the P vs. NP problem shows that GPT-4 successfully produces a proof schema and engages in rigorous reasoning throughout 97 dialogue turns, concluding \"P neq NP\", which is in alignment with (Xu and Zhou, 2023). The investigation uncovers novel insights within the extensive solution space of LLMs, shedding light on LLM for Science.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-13", "arxiv_id": "2309.06380", "title": "InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation", "authors": [ "Xingchao Liu", "Xiwen Zhang", "Jianzhu Ma", "Jian Peng", "Qiang Liu" ], "github": "https://github.com/gnobitab/InstaFlow", "project_page": "", "abstract": "Diffusion models have revolutionized text-to-image generation with its exceptional quality and creativity. However, its multi-step sampling process is known to be slow, often requiring tens of inference steps to obtain satisfactory results. Previous attempts to improve its sampling speed and reduce computational costs through distillation have been unsuccessful in achieving a functional one-step model. In this paper, we explore a recent method called Rectified Flow, which, thus far, has only been applied to small datasets. The core of Rectified Flow lies in its reflow procedure, which straightens the trajectories of probability flows, refines the coupling between noises and images, and facilitates the distillation process with student models. We propose a novel text-conditioned pipeline to turn Stable Diffusion (SD) into an ultra-fast one-step model, in which we find reflow plays a critical role in improving the assignment between noise and images. Leveraging our new pipeline, we create, to the best of our knowledge, the first one-step diffusion-based text-to-image generator with SD-level image quality, achieving an FID (Frechet Inception Distance) of 23.3 on MS COCO 2017-5k, surpassing the previous state-of-the-art technique, progressive distillation, by a significant margin (37.2 rightarrow 23.3 in FID). By utilizing an expanded network with 1.7B parameters, we further improve the FID to 22.4. We call our one-step models InstaFlow. On MS COCO 2014-30k, InstaFlow yields an FID of 13.1 in just 0.09 second, the best in leq 0.1 second regime, outperforming the recent StyleGAN-T (13.9 in 0.1 second). Notably, the training of InstaFlow only costs 199 A100 GPU days. Project page:~https://github.com/gnobitab/InstaFlow.", "github_manual": "", "github_regex": "https://github.com/gnobitab/InstaFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-13", "arxiv_id": "2309.06126", "title": "AstroLLaMA: Towards Specialized Foundation Models in Astronomy", "authors": [ "Tuan Dung Nguyen", "Yuan-Sen Ting", "Ioana Ciuc\u0103", "Charlie O'Neill", "Ze-Chang Sun", "Maja Jab\u0142o\u0144ska", "Sandor Kruk", "Ernest Perkowski", "Jack Miller", "Jason Li", "Josh Peek", "Kartheik Iyer", "Tomasz R\u00f3\u017ca\u0144ski", "Pranav Khetarpal", "Sharaf Zaman", "David Brodrick", "Sergio J. Rodr\u00edguez M\u00e9ndez", "Thang Bui", "Alyssa Goodman", "Alberto Accomazzi", "Jill Naiman", "Jesse Cranney", "Kevin Schawinski", "UniverseTBD" ], "github": "", "project_page": "", "abstract": "Large language models excel in many human-language tasks but often falter in highly specialized domains like scholarly astronomy. To bridge this gap, we introduce AstroLLaMA, a 7-billion-parameter model fine-tuned from LLaMA-2 using over 300,000 astronomy abstracts from arXiv. Optimized for traditional causal language modeling, AstroLLaMA achieves a 30% lower perplexity than Llama-2, showing marked domain adaptation. Our model generates more insightful and scientifically relevant text completions and embedding extraction than state-of-the-arts foundation models despite having significantly fewer parameters. AstroLLaMA serves as a robust, domain-specific model with broad fine-tuning potential. Its public release aims to spur astronomy-focused research, including automatic paper summarization and conversational agent development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-13", "arxiv_id": "2309.05858", "title": "Uncovering mesa-optimization algorithms in Transformers", "authors": [ "Johannes von Oswald", "Eyvind Niklasson", "Maximilian Schlegel", "Seijin Kobayashi", "Nicolas Zucchet", "Nino Scherrer", "Nolan Miller", "Mark Sandler", "Blaise Ag\u00fcera y Arcas", "Max Vladymyrov", "Razvan Pascanu", "Jo\u00e3o Sacramento" ], "github": "", "project_page": "", "abstract": "Transformers have become the dominant model in deep learning, but the reason for their superior performance is poorly understood. Here, we hypothesize that the strong performance of Transformers stems from an architectural bias towards mesa-optimization, a learned process running within the forward pass of a model consisting of the following two steps: (i) the construction of an internal learning objective, and (ii) its corresponding solution found through optimization. To test this hypothesis, we reverse-engineer a series of autoregressive Transformers trained on simple sequence modeling tasks, uncovering underlying gradient-based mesa-optimization algorithms driving the generation of predictions. Moreover, we show that the learned forward-pass optimization algorithm can be immediately repurposed to solve supervised few-shot tasks, suggesting that mesa-optimization might underlie the in-context learning capabilities of large language models. Finally, we propose a novel self-attention layer, the mesa-layer, that explicitly and efficiently solves optimization problems specified in context. We find that this layer can lead to improved performance in synthetic and preliminary language modeling experiments, adding weight to our hypothesis that mesa-optimization is an important operation hidden within the weights of trained Transformers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-13", "arxiv_id": "2309.05767", "title": "Natural Language Supervision for General-Purpose Audio Representations", "authors": [ "Benjamin Elizalde", "Soham Deshmukh", "Huaming Wang" ], "github": "https://github.com/microsoft/clap", "project_page": "", "abstract": "Audio-Language models jointly learn multimodal text and audio representations that enable Zero-Shot inference. Models rely on the encoders to create powerful representations of the input and generalize to multiple tasks ranging from sounds, music, and speech. Although models have achieved remarkable performance, there is still a performance gap with task-specific models. In this paper, we propose a Contrastive Language-Audio Pretraining model that is pretrained with a diverse collection of 4.6M audio-text pairs employing two innovative encoders for Zero-Shot inference. To learn audio representations, we trained an audio encoder on 22 audio tasks, instead of the standard training of sound event classification. To learn language representations, we trained an autoregressive decoder-only model instead of the standard encoder-only models. Then, the audio and language representations are brought into a joint multimodal space using Contrastive Learning. We used our encoders to improve the downstream performance by a margin. We extensively evaluated the generalization of our representations on 26 downstream tasks, the largest in the literature. Our model achieves state of the art results in several tasks leading the way towards general-purpose audio representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/clap", "github_api_added_by": "auto" }, { "date": "2023-09-13", "arxiv_id": "2309.06441", "title": "Learning Disentangled Avatars with Hybrid 3D Representations", "authors": [ "Yao Feng", "Weiyang Liu", "Timo Bolkart", "Jinlong Yang", "Marc Pollefeys", "Michael J. Black" ], "github": "", "project_page": "", "abstract": "Tremendous efforts have been made to learn animatable and photorealistic human avatars. Towards this end, both explicit and implicit 3D representations are heavily studied for a holistic modeling and capture of the whole human (e.g., body, clothing, face and hair), but neither representation is an optimal choice in terms of representation efficacy since different parts of the human avatar have different modeling desiderata. For example, meshes are generally not suitable for modeling clothing and hair. Motivated by this, we present Disentangled Avatars~(DELTA), which models humans with hybrid explicit-implicit 3D representations. DELTA takes a monocular RGB video as input, and produces a human avatar with separate body and clothing/hair layers. Specifically, we demonstrate two important applications for DELTA. For the first one, we consider the disentanglement of the human body and clothing and in the second, we disentangle the face and hair. To do so, DELTA represents the body or face with an explicit mesh-based parametric 3D model and the clothing or hair with an implicit neural radiance field. To make this possible, we design an end-to-end differentiable renderer that integrates meshes into volumetric rendering, enabling DELTA to learn directly from monocular videos without any 3D supervision. Finally, we show that how these two applications can be easily combined to model full-body avatars, such that the hair, face, body and clothing can be fully disentangled yet jointly rendered. Such a disentanglement enables hair and clothing transfer to arbitrary body shapes. We empirically validate the effectiveness of DELTA's disentanglement by demonstrating its promising performance on disentangled reconstruction, virtual clothing try-on and hairstyle transfer. To facilitate future research, we also release an open-sourced pipeline for the study of hybrid human avatar modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-13", "arxiv_id": "2309.06440", "title": "LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning", "authors": [ "Kenneth Shaw", "Ananye Agarwal", "Deepak Pathak" ], "github": "", "project_page": "", "abstract": "Dexterous manipulation has been a long-standing challenge in robotics. While machine learning techniques have shown some promise, results have largely been currently limited to simulation. This can be mostly attributed to the lack of suitable hardware. In this paper, we present LEAP Hand, a low-cost dexterous and anthropomorphic hand for machine learning research. In contrast to previous hands, LEAP Hand has a novel kinematic structure that allows maximal dexterity regardless of finger pose. LEAP Hand is low-cost and can be assembled in 4 hours at a cost of 2000 USD from readily available parts. It is capable of consistently exerting large torques over long durations of time. We show that LEAP Hand can be used to perform several manipulation tasks in the real world -- from visual teleoperation to learning from passive video data and sim2real. LEAP Hand significantly outperforms its closest competitor Allegro Hand in all our experiments while being 1/8th of the cost. We release detailed assembly instructions, the Sim2Real pipeline and a development platform with useful APIs on our website at https://leap-hand.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-14", "arxiv_id": "2309.06657", "title": "Statistical Rejection Sampling Improves Preference Optimization", "authors": [ "Tianqi Liu", "Yao Zhao", "Rishabh Joshi", "Misha Khalman", "Mohammad Saleh", "Peter J. Liu", "Jialu Liu" ], "github": "", "project_page": "", "abstract": "Improving the alignment of language models with human preferences remains an active research challenge. Previous approaches have primarily utilized Reinforcement Learning from Human Feedback (RLHF) via online RL methods such as Proximal Policy Optimization (PPO). Recently, offline methods such as Sequence Likelihood Calibration (SLiC) and Direct Preference Optimization (DPO) have emerged as attractive alternatives, offering improvements in stability and scalability while maintaining competitive performance. SLiC refines its loss function using sequence pairs sampled from a supervised fine-tuned (SFT) policy, while DPO directly optimizes language models based on preference data, foregoing the need for a separate reward model. However, the maximum likelihood estimator (MLE) of the target optimal policy requires labeled preference pairs sampled from that policy. DPO's lack of a reward model constrains its ability to sample preference pairs from the optimal policy, and SLiC is restricted to sampling preference pairs only from the SFT policy. To address these limitations, we introduce a novel approach called Statistical Rejection Sampling Optimization (RSO) that aims to source preference data from the target optimal policy using rejection sampling, enabling a more accurate estimation of the optimal policy. We also propose a unified framework that enhances the loss functions used in both SLiC and DPO from a preference modeling standpoint. Through extensive experiments across three diverse tasks, we demonstrate that RSO consistently outperforms both SLiC and DPO on evaluations from both Large Language Model (LLM) and human raters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-14", "arxiv_id": "2309.07062", "title": "Large Language Models for Compiler Optimization", "authors": [ "Chris Cummins", "Volker Seeker", "Dejan Grubisic", "Mostafa Elhoushi", "Youwei Liang", "Baptiste Roziere", "Jonas Gehring", "Fabian Gloeckle", "Kim Hazelwood", "Gabriel Synnaeve", "Hugh Leather" ], "github": "", "project_page": "", "abstract": "We explore the novel application of Large Language Models to code optimization. We present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly for code size. The model takes as input unoptimized assembly and outputs a list of compiler options to best optimize the program. Crucially, during training, we ask the model to predict the instruction counts before and after optimization, and the optimized code itself. These auxiliary learning tasks significantly improve the optimization performance of the model and improve the model's depth of understanding. We evaluate on a large suite of test programs. Our approach achieves a 3.0% improvement in reducing instruction counts over the compiler, outperforming two state-of-the-art baselines that require thousands of compilations. Furthermore, the model shows surprisingly strong code reasoning abilities, generating compilable code 91% of the time and perfectly emulating the output of the compiler 70% of the time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-14", "arxiv_id": "2309.06895", "title": "MagiCapture: High-Resolution Multi-Concept Portrait Customization", "authors": [ "Junha Hyung", "Jaeyo Shin", "Jaegul Choo" ], "github": "", "project_page": "", "abstract": "Large-scale text-to-image models including Stable Diffusion are capable of generating high-fidelity photorealistic portrait images. There is an active research area dedicated to personalizing these models, aiming to synthesize specific subjects or styles using provided sets of reference images. However, despite the plausible results from these personalization methods, they tend to produce images that often fall short of realism and are not yet on a commercially viable level. This is particularly noticeable in portrait image generation, where any unnatural artifact in human faces is easily discernible due to our inherent human bias. To address this, we introduce MagiCapture, a personalization method for integrating subject and style concepts to generate high-resolution portrait images using just a few subject and style references. For instance, given a handful of random selfies, our fine-tuned model can generate high-quality portrait images in specific styles, such as passport or profile photos. The main challenge with this task is the absence of ground truth for the composed concepts, leading to a reduction in the quality of the final output and an identity shift of the source subject. To address these issues, we present a novel Attention Refocusing loss coupled with auxiliary priors, both of which facilitate robust learning within this weakly supervised learning setting. Our pipeline also includes additional post-processing steps to ensure the creation of highly realistic outputs. MagiCapture outperforms other baselines in both quantitative and qualitative evaluations and can also be generalized to other non-human objects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-14", "arxiv_id": "2309.07125", "title": "Text-Guided Generation and Editing of Compositional 3D Avatars", "authors": [ "Hao Zhang", "Yao Feng", "Peter Kulits", "Yandong Wen", "Justus Thies", "Michael J. Black" ], "github": "https://github.com/yfeng95/teca", "project_page": "", "abstract": "Our goal is to create a realistic 3D facial avatar with hair and accessories using only a text description. While this challenge has attracted significant recent interest, existing methods either lack realism, produce unrealistic shapes, or do not support editing, such as modifications to the hairstyle. We argue that existing methods are limited because they employ a monolithic modeling approach, using a single representation for the head, face, hair, and accessories. Our observation is that the hair and face, for example, have very different structural qualities that benefit from different representations. Building on this insight, we generate avatars with a compositional model, in which the head, face, and upper body are represented with traditional 3D meshes, and the hair, clothing, and accessories with neural radiance fields (NeRF). The model-based mesh representation provides a strong geometric prior for the face region, improving realism while enabling editing of the person's appearance. By using NeRFs to represent the remaining components, our method is able to model and synthesize parts with complex geometry and appearance, such as curly hair and fluffy scarves. Our novel system synthesizes these high-quality compositional avatars from text descriptions. The experimental results demonstrate that our method, Text-guided generation and Editing of Compositional Avatars (TECA), produces avatars that are more realistic than those of recent methods while being editable because of their compositional nature. For example, our TECA enables the seamless transfer of compositional features like hairstyles, scarves, and other accessories between avatars. This capability supports applications such as virtual try-on.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yfeng95/teca", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-14", "arxiv_id": "2309.06802", "title": "Dynamic NeRFs for Soccer Scenes", "authors": [ "Sacha Lewin", "Maxime Vandegar", "Thomas Hoyoux", "Olivier Barnich", "Gilles Louppe" ], "github": "", "project_page": "", "abstract": "The long-standing problem of novel view synthesis has many applications, notably in sports broadcasting. Photorealistic novel view synthesis of soccer actions, in particular, is of enormous interest to the broadcast industry. Yet only a few industrial solutions have been proposed, and even fewer that achieve near-broadcast quality of the synthetic replays. Except for their setup of multiple static cameras around the playfield, the best proprietary systems disclose close to no information about their inner workings. Leveraging multiple static cameras for such a task indeed presents a challenge rarely tackled in the literature, for a lack of public datasets: the reconstruction of a large-scale, mostly static environment, with small, fast-moving elements. Recently, the emergence of neural radiance fields has induced stunning progress in many novel view synthesis applications, leveraging deep learning principles to produce photorealistic results in the most challenging settings. In this work, we investigate the feasibility of basing a solution to the task on dynamic NeRFs, i.e., neural models purposed to reconstruct general dynamic content. We compose synthetic soccer environments and conduct multiple experiments using them, identifying key components that help reconstruct soccer scenes with dynamic NeRFs. We show that, although this approach cannot fully meet the quality requirements for the target application, it suggests promising avenues toward a cost-efficient, automatic solution. We also make our work dataset and code publicly available, with the goal to encourage further efforts from the research community on the task of novel view synthesis for dynamic soccer scenes. For code, data, and video results, please see https://soccernerfs.isach.be.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-14", "arxiv_id": "2309.06933", "title": "DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models", "authors": [ "Namhyuk Ahn", "Junsoo Lee", "Chunggi Lee", "Kunhee Kim", "Daesik Kim", "Seung-Hun Nam", "Kibeom Hong" ], "github": "", "project_page": "", "abstract": "Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain. However, expressing unique characteristics of an artwork (e.g. brushwork, colortone, or composition) with text prompts alone may encounter limitations due to the inherent constraints of verbal description. To this end, we introduce DreamStyler, a novel framework designed for artistic image synthesis, proficient in both text-to-image synthesis and style transfer. DreamStyler optimizes a multi-stage textual embedding with a context-aware text prompt, resulting in prominent image quality. In addition, with content and style guidance, DreamStyler exhibits flexibility to accommodate a range of style references. Experimental results demonstrate its superior performance across multiple scenarios, suggesting its promising potential in artistic product creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-14", "arxiv_id": "2309.07122", "title": "Tree-Structured Shading Decomposition", "authors": [ "Chen Geng", "Hong-Xing Yu", "Sharon Zhang", "Maneesh Agrawala", "Jiajun Wu" ], "github": "", "project_page": "", "abstract": "We study inferring a tree-structured representation from a single image for object shading. Prior work typically uses the parametric or measured representation to model shading, which is neither interpretable nor easily editable. We propose using the shade tree representation, which combines basic shading nodes and compositing methods to factorize object surface shading. The shade tree representation enables novice users who are unfamiliar with the physical shading process to edit object shading in an efficient and intuitive manner. A main challenge in inferring the shade tree is that the inference problem involves both the discrete tree structure and the continuous parameters of the tree nodes. We propose a hybrid approach to address this issue. We introduce an auto-regressive inference model to generate a rough estimation of the tree structure and node parameters, and then we fine-tune the inferred shade tree through an optimization algorithm. We show experiments on synthetic images, captured reflectance, real images, and non-realistic vector drawings, allowing downstream applications such as material editing, vectorized shading, and relighting. Project website: https://chen-geng.com/inv-shade-trees", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-15", "arxiv_id": "2309.07430", "title": "Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts", "authors": [ "Dave Van Veen", "Cara Van Uden", "Louis Blankemeier", "Jean-Benoit Delbrouck", "Asad Aali", "Christian Bluethgen", "Anuj Pareek", "Malgorzata Polacin", "William Collins", "Neera Ahuja", "Curtis P. Langlotz", "Jason Hom", "Sergios Gatidis", "John Pauly", "Akshay S. Chaudhari" ], "github": "https://github.com/stanfordmimi/clin-summ", "project_page": "", "abstract": "Sifting through vast textual data and summarizing key information imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy across diverse clinical summarization tasks has not yet been rigorously examined. In this work, we employ domain adaptation methods on eight LLMs, spanning six datasets and four distinct summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not lead to improved results. Further, in a clinical reader study with six physicians, we depict that summaries from the best adapted LLM are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis delineates mutual challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and other irreplaceable human aspects of medicine.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stanfordmimi/clin-summ", "github_api_added_by": "auto" }, { "date": "2023-09-15", "arxiv_id": "2309.07900", "title": "Ambiguity-Aware In-Context Learning with Large Language Models", "authors": [ "Lingyu Gao", "Aditi Chaudhary", "Krishna Srinivasan", "Kazuma Hashimoto", "Karthik Raman", "Michael Bendersky" ], "github": "", "project_page": "", "abstract": "In-context learning (ICL) i.e. showing LLMs only a few task-specific demonstrations has led to downstream gains with no task-specific fine-tuning required. However, LLMs are sensitive to the choice of prompts, and therefore a crucial research question is how to select good demonstrations for ICL. One effective strategy is leveraging semantic similarity between the ICL demonstrations and test inputs by using a text retriever, which however is sub-optimal as that does not consider the LLM's existing knowledge about that task. From prior work (Min et al., 2022), we already know that labels paired with the demonstrations bias the model predictions. This leads us to our hypothesis whether considering LLM's existing knowledge about the task, especially with respect to the output label space can help in a better demonstration selection strategy. Through extensive experimentation on three text classification tasks, we find that it is beneficial to not only choose semantically similar ICL demonstrations but also to choose those demonstrations that help resolve the inherent label ambiguity surrounding the test example. Interestingly, we find that including demonstrations that the LLM previously mis-classified and also fall on the test example's decision boundary, brings the most performance gain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-15", "arxiv_id": "2309.07870", "title": "Agents: An Open-source Framework for Autonomous Language Agents", "authors": [ "Wangchunshu Zhou", "Yuchen Eleanor Jiang", "Long Li", "Jialong Wu", "Tiannan Wang", "Shi Qiu", "Jintian Zhang", "Jing Chen", "Ruipu Wu", "Shuai Wang", "Shiding Zhu", "Jiyu Chen", "Wentao Zhang", "Ningyu Zhang", "Huajun Chen", "Peng Cui", "Mrinmaya Sachan" ], "github": "https://github.com/aiwaves-cn/agents", "project_page": "", "abstract": "Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces. We consider language agents as a promising direction towards artificial general intelligence and release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience. Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control. Agents is user-friendly as it enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding. The library is also research-friendly as its modularized design makes it easily extensible for researchers. Agents is available at https://github.com/aiwaves-cn/agents.", "github_manual": "", "github_regex": "https://github.com/aiwaves-cn/agents", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-15", "arxiv_id": "2309.07462", "title": "Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?", "authors": [ "Rishav Hada", "Varun Gumma", "Adrian de Wynter", "Harshita Diddee", "Mohamed Ahmed", "Monojit Choudhury", "Kalika Bali", "Sunayana Sitaram" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated impressive performance on Natural Language Processing (NLP) tasks, such as Question Answering, Summarization, and Classification. The use of LLMs as evaluators, that can rank or score the output of other models (usually LLMs) has become increasingly popular, due to the limitations of current evaluation techniques including the lack of appropriate benchmarks, metrics, cost, and access to human annotators. While LLMs are capable of handling approximately 100 languages, the majority of languages beyond the top 20 lack systematic evaluation across various tasks, metrics, and benchmarks. This creates an urgent need to scale up multilingual evaluation to ensure a precise understanding of LLM performance across diverse languages. LLM-based evaluators seem like the perfect solution to this problem, as they do not require human annotators, human-created references, or benchmarks and can theoretically be used to evaluate any language covered by the LLM. In this paper, we investigate whether LLM-based evaluators can help scale up multilingual evaluation. Specifically, we calibrate LLM-based evaluation against 20k human judgments of five metrics across three text-generation tasks in eight languages. Our findings indicate that LLM-based evaluators may exhibit bias towards higher scores and should be used with caution and should always be calibrated with a dataset of native speaker judgments, particularly in low-resource and non-Latin script languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-15", "arxiv_id": "2309.07749", "title": "OmnimatteRF: Robust Omnimatte with 3D Background Modeling", "authors": [ "Geng Lin", "Chen Gao", "Jia-Bin Huang", "Changil Kim", "Yipeng Wang", "Matthias Zwicker", "Ayush Saraf" ], "github": "https://github.com/facebookresearch/OmnimatteRF", "project_page": "", "abstract": "Video matting has broad applications, from adding interesting effects to casually captured movies to assisting video production professionals. Matting with associated effects such as shadows and reflections has also attracted increasing research activity, and methods like Omnimatte have been proposed to separate dynamic foreground objects of interest into their own layers. However, prior works represent video backgrounds as 2D image layers, limiting their capacity to express more complicated scenes, thus hindering application to real-world videos. In this paper, we propose a novel video matting method, OmnimatteRF, that combines dynamic 2D foreground layers and a 3D background model. The 2D layers preserve the details of the subjects, while the 3D background robustly reconstructs scenes in real-world videos. Extensive experiments demonstrate that our method reconstructs scenes with better quality on various videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/OmnimatteRF", "github_api_added_by": "auto" }, { "date": "2023-09-15", "arxiv_id": "2309.07906", "title": "Generative Image Dynamics", "authors": [ "Zhengqi Li", "Richard Tucker", "Noah Snavely", "Aleksander Holynski" ], "github": "", "project_page": "", "abstract": "We present an approach to modeling an image-space prior on scene dynamics. Our prior is learned from a collection of motion trajectories extracted from real video sequences containing natural, oscillating motion such as trees, flowers, candles, and clothes blowing in the wind. Given a single image, our trained model uses a frequency-coordinated diffusion sampling process to predict a per-pixel long-term motion representation in the Fourier domain, which we call a neural stochastic motion texture. This representation can be converted into dense motion trajectories that span an entire video. Along with an image-based rendering module, these trajectories can be used for a number of downstream applications, such as turning still images into seamlessly looping dynamic videos, or allowing users to realistically interact with objects in real pictures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-15", "arxiv_id": "2309.07314", "title": "AudioSR: Versatile Audio Super-resolution at Scale", "authors": [ "Haohe Liu", "Ke Chen", "Qiao Tian", "Wenwu Wang", "Mark D. Plumbley" ], "github": "https://github.com/haoheliu/versatile_audio_super_resolution", "project_page": "", "abstract": "Audio super-resolution is a fundamental task that predicts high-frequency components for low-resolution audio, enhancing audio quality in digital applications. Previous methods have limitations such as the limited scope of audio types (e.g., music, speech) and specific bandwidth settings they can handle (e.g., 4kHz to 8kHz). In this paper, we introduce a diffusion-based generative model, AudioSR, that is capable of performing robust audio super-resolution on versatile audio types, including sound effects, music, and speech. Specifically, AudioSR can upsample any input audio signal within the bandwidth range of 2kHz to 16kHz to a high-resolution audio signal at 24kHz bandwidth with a sampling rate of 48kHz. Extensive objective evaluation on various audio super-resolution benchmarks demonstrates the strong result achieved by the proposed model. In addition, our subjective evaluation shows that AudioSR can acts as a plug-and-play module to enhance the generation quality of a wide range of audio generative models, including AudioLDM, Fastspeech2, and MusicGen. Our code and demo are available at https://audioldm.github.io/audiosr.", "github_manual": "https://github.com/haoheliu/versatile_audio_super_resolution", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-18", "arxiv_id": "2309.08532", "title": "Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers", "authors": [ "Qingyan Guo", "Rui Wang", "Junliang Guo", "Bei Li", "Kaitao Song", "Xu Tan", "Guoqing Liu", "Jiang Bian", "Yujiu Yang" ], "github": "https://github.com/beeevita/EvoPrompt", "project_page": "", "abstract": "Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To automate this process, in this paper, we propose a novel framework for discrete prompt optimization, called EvoPrompt, which borrows the idea of evolutionary algorithms (EAs) as they exhibit good performance and fast convergence. To enable EAs to work on discrete prompts, which are natural language expressions that need to be coherent and human-readable, we connect LLMs with EAs. This approach allows us to simultaneously leverage the powerful language processing capabilities of LLMs and the efficient optimization performance of EAs. Specifically, abstaining from any gradients or parameters, EvoPrompt starts from a population of prompts and iteratively generates new prompts with LLMs based on the evolutionary operators, improving the population based on the development set. We optimize prompts for both closed- and open-source LLMs including GPT-3.5 and Alpaca, on 9 datasets spanning language understanding and generation tasks. EvoPrompt significantly outperforms human-engineered prompts and existing methods for automatic prompt generation by up to 25% and 14% respectively. Furthermore, EvoPrompt demonstrates that connecting LLMs with EAs creates synergies, which could inspire further research on the combination of LLMs and conventional algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/beeevita/EvoPrompt", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-18", "arxiv_id": "2309.08586", "title": "Replacing softmax with ReLU in Vision Transformers", "authors": [ "Mitchell Wortsman", "Jaehoon Lee", "Justin Gilmer", "Simon Kornblith" ], "github": "", "project_page": "", "abstract": "Previous research observed accuracy degradation when replacing the attention softmax with a point-wise activation such as ReLU. In the context of vision transformers, we find that this degradation is mitigated when dividing by sequence length. Our experiments training small to large vision transformers on ImageNet-21k indicate that ReLU-attention can approach or match the performance of softmax-attention in terms of scaling behavior as a function of compute.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-18", "arxiv_id": "2309.08210", "title": "Investigating Answerability of LLMs for Long-Form Question Answering", "authors": [ "Meghana Moorthy Bhat", "Rui Meng", "Ye Liu", "Yingbo Zhou", "Semih Yavuz" ], "github": "", "project_page": "", "abstract": "As we embark on a new era of LLMs, it becomes increasingly crucial to understand their capabilities, limitations, and differences. Toward making further progress in this direction, we strive to build a deeper understanding of the gaps between massive LLMs (e.g., ChatGPT) and smaller yet effective open-source LLMs and their distilled counterparts. To this end, we specifically focus on long-form question answering (LFQA) because it has several practical and impactful applications (e.g., troubleshooting, customer service, etc.) yet is still understudied and challenging for LLMs. We propose a question-generation method from abstractive summaries and show that generating follow-up questions from summaries of long documents can create a challenging setting for LLMs to reason and infer from long contexts. Our experimental results confirm that: (1) our proposed method of generating questions from abstractive summaries pose a challenging setup for LLMs and shows performance gaps between LLMs like ChatGPT and open-source LLMs (Alpaca, Llama) (2) open-source LLMs exhibit decreased reliance on context for generated questions from the original document, but their generation capabilities drop significantly on generated questions from summaries -- especially for longer contexts (>1024 tokens)", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-18", "arxiv_id": "2309.07970", "title": "Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping", "authors": [ "Adam Rashid", "Satvik Sharma", "Chung Min Kim", "Justin Kerr", "Lawrence Chen", "Angjoo Kanazawa", "Ken Goldberg" ], "github": "", "project_page": "", "abstract": "Grasping objects by a specific part is often crucial for safety and for executing downstream tasks. Yet, learning-based grasp planners lack this behavior unless they are trained on specific object part data, making it a significant challenge to scale object diversity. Instead, we propose LERF-TOGO, Language Embedded Radiance Fields for Task-Oriented Grasping of Objects, which uses vision-language models zero-shot to output a grasp distribution over an object given a natural language query. To accomplish this, we first reconstruct a LERF of the scene, which distills CLIP embeddings into a multi-scale 3D language field queryable with text. However, LERF has no sense of objectness, meaning its relevancy outputs often return incomplete activations over an object which are insufficient for subsequent part queries. LERF-TOGO mitigates this lack of spatial grouping by extracting a 3D object mask via DINO features and then conditionally querying LERF on this mask to obtain a semantic distribution over the object with which to rank grasps from an off-the-shelf grasp planner. We evaluate LERF-TOGO's ability to grasp task-oriented object parts on 31 different physical objects, and find it selects grasps on the correct part in 81% of all trials and grasps successfully in 69%. See the project website at: lerftogo.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-18", "arxiv_id": "2309.08520", "title": "Scaling Laws for Sparsely-Connected Foundation Models", "authors": [ "Elias Frantar", "Carlos Riquelme", "Neil Houlsby", "Dan Alistarh", "Utku Evci" ], "github": "", "project_page": "", "abstract": "We explore the impact of parameter sparsity on the scaling behavior of Transformers trained on massive datasets (i.e., \"foundation models\"), in both vision and language domains. In this setting, we identify the first scaling law describing the relationship between weight sparsity, number of non-zero parameters, and amount of training data, which we validate empirically across model and data scales; on ViT/JFT-4B and T5/C4. These results allow us to characterize the \"optimal sparsity\", the sparsity level which yields the best performance for a given effective model size and training budget. For a fixed number of non-zero parameters, we identify that the optimal sparsity increases with the amount of data used for training. We also extend our study to different sparsity structures (such as the hardware-friendly n:m pattern) and strategies (such as starting from a pretrained dense model). Our findings shed light on the power and limitations of weight sparsity across various parameter and computational settings, offering both theoretical understanding and practical implications for leveraging sparsity towards computational efficiency improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-18", "arxiv_id": "2309.08600", "title": "Sparse Autoencoders Find Highly Interpretable Features in Language Models", "authors": [ "Hoagy Cunningham", "Aidan Ewart", "Logan Riggs", "Robert Huben", "Lee Sharkey" ], "github": "https://github.com/hoagyc/sparse_coding", "project_page": "", "abstract": "One of the roadblocks to a better understanding of neural networks' internals is polysemanticity, where neurons appear to activate in multiple, semantically distinct contexts. Polysemanticity prevents us from identifying concise, human-understandable explanations for what neural networks are doing internally. One hypothesised cause of polysemanticity is superposition, where neural networks represent more features than they have neurons by assigning features to an overcomplete set of directions in activation space, rather than to individual neurons. Here, we attempt to identify those directions, using sparse autoencoders to reconstruct the internal activations of a language model. These autoencoders learn sets of sparsely activating features that are more interpretable and monosemantic than directions identified by alternative approaches, where interpretability is measured by automated methods. Ablating these features enables precise model editing, for example, by removing capabilities such as pronoun prediction, while disrupting model behaviour less than prior techniques. This work indicates that it is possible to resolve superposition in language models using a scalable, unsupervised method. Our method may serve as a foundation for future mechanistic interpretability work, which we hope will enable greater model transparency and steerability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hoagyc/sparse_coding", "github_api_added_by": "auto" }, { "date": "2023-09-18", "arxiv_id": "2309.08587", "title": "Compositional Foundation Models for Hierarchical Planning", "authors": [ "Anurag Ajay", "Seungwook Han", "Yilun Du", "Shaung Li", "Abhi Gupta", "Tommi Jaakkola", "Josh Tenenbaum", "Leslie Kaelbling", "Akash Srivastava", "Pulkit Agrawal" ], "github": "https://github.com/sihyun-yu/PVDM", "project_page": "", "abstract": "To make effective decisions in novel environments with long-horizon goals, it is crucial to engage in hierarchical reasoning across spatial and temporal scales. This entails planning abstract subgoal sequences, visually reasoning about the underlying plans, and executing actions in accordance with the devised plan through visual-motor control. We propose Compositional Foundation Models for Hierarchical Planning (HiP), a foundation model which leverages multiple expert foundation model trained on language, vision and action data individually jointly together to solve long-horizon tasks. We use a large language model to construct symbolic plans that are grounded in the environment through a large video diffusion model. Generated video plans are then grounded to visual-motor control, through an inverse dynamics model that infers actions from generated videos. To enable effective reasoning within this hierarchy, we enforce consistency between the models via iterative refinement. We illustrate the efficacy and adaptability of our approach in three different long-horizon table-top manipulation tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sihyun-yu/PVDM", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-18", "arxiv_id": "2309.08172", "title": "LASER: LLM Agent with State-Space Exploration for Web Navigation", "authors": [ "Kaixin Ma", "Hongming Zhang", "Hongwei Wang", "Xiaoman Pan", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have been successfully adapted for interactive decision-making tasks like web navigation. While achieving decent performance, previous methods implicitly assume a forward-only execution mode for the model, where they only provide oracle trajectories as in-context examples to teach the model how to reason in the interactive environment. Consequently, the model could not handle more challenging scenarios not covered in the in-context examples, e.g., mistakes, leading to sub-optimal performance. To address this issue, we propose to model the interactive task as state space exploration, where the LLM agent transitions among a pre-defined set of states by performing actions to complete the task. This formulation enables flexible back-tracking, allowing the model to easily recover from errors. We evaluate our proposed LLM Agent with State-Space ExploRation (LASER) on the WebShop task. Experimental results show that our LASER agent significantly outperforms previous methods and closes the gap with human performance on the web navigation task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-18", "arxiv_id": "2309.07990", "title": "Leveraging Contextual Information for Effective Entity Salience Detection", "authors": [ "Rajarshi Bhowmik", "Marco Ponza", "Atharva Tendle", "Anant Gupta", "Rebecca Jiang", "Xingyu Lu", "Qian Zhao", "Daniel Preotiuc-Pietro" ], "github": "", "project_page": "", "abstract": "In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document. These entities, often deemed as salient entities, provide useful cues of the aboutness of a document to a reader. Identifying the salience of entities was found helpful in several downstream applications such as search, ranking, and entity-centric summarization, among others. Prior work on salient entity detection mainly focused on machine learning models that require heavy feature engineering. We show that fine-tuning medium-sized language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. To this end, we conduct a comprehensive benchmarking of four publicly available datasets using models representative of the medium-sized pre-trained language model family. Additionally, we show that zero-shot prompting of instruction-tuned language models yields inferior results, indicating the task's uniqueness and complexity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-18", "arxiv_id": "2309.07974", "title": "A Data Source for Reasoning Embodied Agents", "authors": [ "Jack Lanchantin", "Sainbayar Sukhbaatar", "Gabriel Synnaeve", "Yuxuan Sun", "Kavya Srinet", "Arthur Szlam" ], "github": "https://github.com/facebookresearch/neuralmemory", "project_page": "", "abstract": "Recent progress in using machine learning models for reasoning tasks has been driven by novel model architectures, large-scale pre-training protocols, and dedicated reasoning datasets for fine-tuning. In this work, to further pursue these advances, we introduce a new data generator for machine reasoning that integrates with an embodied agent. The generated data consists of templated text queries and answers, matched with world-states encoded into a database. The world-states are a result of both world dynamics and the actions of the agent. We show the results of several baseline models on instantiations of train sets. These include pre-trained language models fine-tuned on a text-formatted representation of the database, and graph-structured Transformers operating on a knowledge-graph representation of the database. We find that these models can answer some questions about the world-state, but struggle with others. These results hint at new research directions in designing neural reasoning models and database representations. Code to generate the data will be released at github.com/facebookresearch/neuralmemory", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/neuralmemory", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-18", "arxiv_id": "2309.08051", "title": "Retrieval-Augmented Text-to-Audio Generation", "authors": [ "Yi Yuan", "Haohe Liu", "Xubo Liu", "Qiushi Huang", "Mark D. Plumbley", "Wenwu Wang" ], "github": "", "project_page": "", "abstract": "Despite recent progress in text-to-audio (TTA) generation, we show that the state-of-the-art models, such as AudioLDM, trained on datasets with an imbalanced class distribution, such as AudioCaps, are biased in their generation performance. Specifically, they excel in generating common audio classes while underperforming in the rare ones, thus degrading the overall generation performance. We refer to this problem as long-tailed text-to-audio generation. To address this issue, we propose a simple retrieval-augmented approach for TTA models. Specifically, given an input text prompt, we first leverage a Contrastive Language Audio Pretraining (CLAP) model to retrieve relevant text-audio pairs. The features of the retrieved audio-text data are then used as additional conditions to guide the learning of TTA models. We enhance AudioLDM with our proposed approach and denote the resulting augmented system as Re-AudioLDM. On the AudioCaps dataset, Re-AudioLDM achieves a state-of-the-art Frechet Audio Distance (FAD) of 1.37, outperforming the existing approaches by a large margin. Furthermore, we show that Re-AudioLDM can generate realistic audio for complex scenes, rare audio classes, and even unseen audio types, indicating its potential in TTA tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-18", "arxiv_id": "2309.07986", "title": "Viewpoint Textual Inversion: Unleashing Novel View Synthesis with Pretrained 2D Diffusion Models", "authors": [ "James Burgess", "Kuan-Chieh Wang", "Serena Yeung" ], "github": "https://github.com/jmhb0/view_neti", "project_page": "", "abstract": "Text-to-image diffusion models understand spatial relationship between objects, but do they represent the true 3D structure of the world from only 2D supervision? We demonstrate that yes, 3D knowledge is encoded in 2D image diffusion models like Stable Diffusion, and we show that this structure can be exploited for 3D vision tasks. Our method, Viewpoint Neural Textual Inversion (ViewNeTI), controls the 3D viewpoint of objects in generated images from frozen diffusion models. We train a small neural mapper to take camera viewpoint parameters and predict text encoder latents; the latents then condition the diffusion generation process to produce images with the desired camera viewpoint. ViewNeTI naturally addresses Novel View Synthesis (NVS). By leveraging the frozen diffusion model as a prior, we can solve NVS with very few input views; we can even do single-view novel view synthesis. Our single-view NVS predictions have good semantic details and photorealism compared to prior methods. Our approach is well suited for modeling the uncertainty inherent in sparse 3D vision problems because it can efficiently generate diverse samples. Our view-control mechanism is general, and can even change the camera view in images generated by user-defined prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jmhb0/view_neti", "github_api_added_by": "auto" }, { "date": "2023-09-19", "arxiv_id": "2309.09958", "title": "An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models", "authors": [ "Yadong Lu", "Chunyuan Li", "Haotian Liu", "Jianwei Yang", "Jianfeng Gao", "Yelong Shen" ], "github": "https://github.com/haotian-liu/LLaVA", "project_page": "", "abstract": "Visual instruction tuning has recently shown encouraging progress with open-source large multimodal models (LMM) such as LLaVA and MiniGPT-4. However, most existing studies of open-source LMM are performed using models with 13B parameters or smaller. In this paper we present an empirical study of scaling LLaVA up to 33B and 65B/70B, and share our findings from our explorations in image resolution, data mixing and parameter-efficient training methods such as LoRA/QLoRA. These are evaluated by their impact on the multi-modal and language capabilities when completing real-world tasks in the wild. We find that scaling LMM consistently enhances model performance and improves language capabilities, and performance of LoRA/QLoRA tuning of LMM are comparable to the performance of full-model fine-tuning. Additionally, the study highlights the importance of higher image resolutions and mixing multimodal-language data to improve LMM performance, and visual instruction tuning can sometimes improve LMM's pure language capability. We hope that this study makes state-of-the-art LMM research at a larger scale more accessible, thus helping establish stronger baselines for future research. Code and checkpoints will be made public.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/haotian-liu/LLaVA", "github_api_added_by": "auto" }, { "date": "2023-09-19", "arxiv_id": "2309.09506", "title": "LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Models", "authors": [ "Zecheng Tang", "Chenfei Wu", "Juntao Li", "Nan Duan" ], "github": "https://github.com/ProjectNUWA/LayoutNUWA", "project_page": "", "abstract": "Graphic layout generation, a growing research field, plays a significant role in user engagement and information perception. Existing methods primarily treat layout generation as a numerical optimization task, focusing on quantitative aspects while overlooking the semantic information of layout, such as the relationship between each layout element. In this paper, we propose LayoutNUWA, the first model that treats layout generation as a code generation task to enhance semantic information and harness the hidden layout expertise of large language models~(LLMs). More concretely, we develop a Code Instruct Tuning (CIT) approach comprising three interconnected modules: 1) the Code Initialization (CI) module quantifies the numerical conditions and initializes them as HTML code with strategically placed masks; 2) the Code Completion (CC) module employs the formatting knowledge of LLMs to fill in the masked portions within the HTML code; 3) the Code Rendering (CR) module transforms the completed code into the final layout output, ensuring a highly interpretable and transparent layout generation procedure that directly maps code to a visualized layout. We attain significant state-of-the-art performance (even over 50\\% improvements) on multiple datasets, showcasing the strong capabilities of LayoutNUWA. Our code is available at https://github.com/ProjectNUWA/LayoutNUWA.", "github_manual": "", "github_regex": "https://github.com/ProjectNUWA/LayoutNUWA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.09971", "title": "MindAgent: Emergent Gaming Interaction", "authors": [ "Ran Gong", "Qiuyuan Huang", "Xiaojian Ma", "Hoi Vo", "Zane Durante", "Yusuke Noda", "Zilong Zheng", "Song-Chun Zhu", "Demetri Terzopoulos", "Li Fei-Fei", "Jianfeng Gao" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have the capacity of performing complex scheduling in a multi-agent system and can coordinate these agents into completing sophisticated tasks that require extensive collaboration. However, despite the introduction of numerous gaming frameworks, the community has insufficient benchmarks towards building general multi-agents collaboration infrastructure that encompass both LLM and human-NPCs collaborations. In this work, we propose a novel infrastructure - MindAgent - to evaluate planning and coordination emergent capabilities for gaming interaction. In particular, our infrastructure leverages existing gaming framework, to i) require understanding of the coordinator for a multi-agent system, ii) collaborate with human players via un-finetuned proper instructions, and iii) establish an in-context learning on few-shot prompt with feedback. Furthermore, we introduce CUISINEWORLD, a new gaming scenario and related benchmark that dispatch a multi-agent collaboration efficiency and supervise multiple agents playing the game simultaneously. We conduct comprehensive evaluations with new auto-metric CoS for calculating the collaboration efficiency. Finally, our infrastructure can be deployed into real-world gaming scenarios in a customized VR version of CUISINEWORLD and adapted in existing broader Minecraft gaming domain. We hope our findings on LLMs and the new infrastructure for general-purpose scheduling and coordination can help shed light on how such skills can be obtained by learning from large language corpora.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.09117", "title": "Contrastive Decoding Improves Reasoning in Large Language Models", "authors": [ "Sean O'Brien", "Mike Lewis" ], "github": "", "project_page": "", "abstract": "We demonstrate that Contrastive Decoding -- a simple, computationally light, and training-free text generation method proposed by Li et al 2022 -- achieves large out-of-the-box improvements over greedy decoding on a variety of reasoning tasks. Originally shown to improve the perceived quality of long-form text generation, Contrastive Decoding searches for strings that maximize a weighted difference in likelihood between strong and weak models. We show that Contrastive Decoding leads LLaMA-65B to outperform LLaMA 2, GPT-3.5 and PaLM 2-L on the HellaSwag commonsense reasoning benchmark, and to outperform LLaMA 2, GPT-3.5 and PaLM-540B on the GSM8K math word reasoning benchmark, in addition to improvements on a collection of other tasks. Analysis suggests that Contrastive Decoding improves over existing methods by preventing some abstract reasoning errors, as well as by avoiding simpler modes such as copying sections of the input during chain-of-thought. Overall, Contrastive Decoding outperforms nucleus sampling for long-form generation and greedy decoding for reasoning tasks, making it a powerful general purpose method for generating text from language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.08872", "title": "PDFTriage: Question Answering over Long, Structured Documents", "authors": [ "Jon Saad-Falcon", "Joe Barrow", "Alexa Siu", "Ani Nenkova", "Ryan A. Rossi", "Franck Dernoncourt" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have issues with document question answering (QA) in situations where the document is unable to fit in the small context length of an LLM. To overcome this issue, most existing works focus on retrieving the relevant context from the document, representing them as plain text. However, documents such as PDFs, web pages, and presentations are naturally structured with different pages, tables, sections, and so on. Representing such structured documents as plain text is incongruous with the user's mental model of these documents with rich structure. When a system has to query the document for context, this incongruity is brought to the fore, and seemingly trivial questions can trip up the QA system. To bridge this fundamental gap in handling structured documents, we propose an approach called PDFTriage that enables models to retrieve the context based on either structure or content. Our experiments demonstrate the effectiveness of the proposed PDFTriage-augmented models across several classes of questions where existing retrieval-augmented LLMs fail. To facilitate further research on this fundamental problem, we release our benchmark dataset consisting of 900+ human-generated questions over 80 structured documents from 10 different categories of question types for document QA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.08804", "title": "Stack-and-Delay: a new codebook pattern for music generation", "authors": [ "Gael Le Lan", "Varun Nagaraja", "Ernie Chang", "David Kant", "Zhaoheng Ni", "Yangyang Shi", "Forrest Iandola", "Vikas Chandra" ], "github": "", "project_page": "", "abstract": "In language modeling based music generation, a generated waveform is represented by a sequence of hierarchical token stacks that can be decoded either in an auto-regressive manner or in parallel, depending on the codebook patterns. In particular, flattening the codebooks represents the highest quality decoding strategy, while being notoriously slow. To this end, we propose a novel stack-and-delay style of decoding strategy to improve upon the flat pattern decoding where generation speed is four times faster as opposed to vanilla flat decoding. This brings the inference time close to that of the delay decoding strategy, and allows for faster inference on GPU for small batch sizes. For the same inference efficiency budget as the delay pattern, we show that the proposed approach performs better in objective evaluations, almost closing the gap with the flat pattern in terms of quality. The results are corroborated by subjective evaluations which show that samples generated by the new model are slightly more often preferred to samples generated by the competing model given the same text prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.09530", "title": "Adapting Large Language Models via Reading Comprehension", "authors": [ "Daixuan Cheng", "Shaohan Huang", "Furu Wei" ], "github": "https://github.com/microsoft/LMOps", "project_page": "", "abstract": "We explore how continued pre-training on domain-specific corpora influences large language models, revealing that training on the raw corpora endows the model with domain knowledge, but drastically hurts its prompting ability for question answering. Taken inspiration from human learning via reading comprehension--practice after reading improves the ability to answer questions based on the learned knowledge--we propose a simple method for transforming raw corpora into reading comprehension texts. Each raw text is enriched with a series of tasks related to its content. Our method, highly scalable and applicable to any pre-training corpora, consistently enhances performance across various tasks in three different domains: biomedicine, finance, and law. Notably, our 7B language model achieves competitive performance with domain-specific models of much larger scales, such as BloombergGPT-50B. Furthermore, we demonstrate that domain-specific reading comprehension texts can improve the model's performance even on general benchmarks, showing the potential to develop a general model across even more domains. Our model, code, and data will be available at https://github.com/microsoft/LMOps.", "github_manual": "", "github_regex": "https://github.com/microsoft/LMOps", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.09400", "title": "CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages", "authors": [ "Thuat Nguyen", "Chien Van Nguyen", "Viet Dac Lai", "Hieu Man", "Nghia Trung Ngo", "Franck Dernoncourt", "Ryan A. Rossi", "Thien Huu Nguyen" ], "github": "", "project_page": "", "abstract": "The driving factors behind the development of large language models (LLMs) with impressive learning capabilities are their colossal model sizes and extensive training datasets. Along with the progress in natural language processing, LLMs have been frequently made accessible to the public to foster deeper investigation and applications. However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs: https://huggingface.co/datasets/uonlp/CulturaX.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.09390", "title": "Augmenting text for spoken language understanding with Large Language Models", "authors": [ "Roshan Sharma", "Suyoun Kim", "Daniel Lazar", "Trang Le", "Akshat Shrivastava", "Kwanghoon Ahn", "Piyush Kansal", "Leda Sari", "Ozlem Kalinli", "Michael Seltzer" ], "github": "", "project_page": "", "abstract": "Spoken semantic parsing (SSP) involves generating machine-comprehensible parses from input speech. Training robust models for existing application domains represented in training data or extending to new domains requires corresponding triplets of speech-transcript-semantic parse data, which is expensive to obtain. In this paper, we address this challenge by examining methods that can use transcript-semantic parse data (unpaired text) without corresponding speech. First, when unpaired text is drawn from existing textual corpora, Joint Audio Text (JAT) and Text-to-Speech (TTS) are compared as ways to generate speech representations for unpaired text. Experiments on the STOP dataset show that unpaired text from existing and new domains improves performance by 2% and 30% in absolute Exact Match (EM) respectively. Second, we consider the setting when unpaired text is not available in existing textual corpora. We propose to prompt Large Language Models (LLMs) to generate unpaired text for existing and new domains. Experiments show that examples and words that co-occur with intents can be used to generate unpaired text with Llama 2.0. Using the generated text with JAT and TTS for spoken semantic parsing improves EM on STOP by 1.4% and 2.6% absolute for existing and new domains respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.08968", "title": "Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference Using Sorted Fine-Tuning (SoFT)", "authors": [ "Parsa Kavehzadeh", "Mojtaba Valipour", "Marzieh Tahaei", "Ali Ghodsi", "Boxing Chen", "Mehdi Rezagholizadeh" ], "github": "", "project_page": "", "abstract": "The rapid advancement of large language models (LLMs) has revolutionized natural language processing (NLP). While these models excel at understanding and generating human-like text, their widespread deployment can be prohibitively expensive. SortedNet is a recent training technique for enabling dynamic inference for deep neural networks. It leverages network modularity to create sub-models with varying computational loads, sorting them based on computation/accuracy characteristics in a nested manner. We extend SortedNet to generative NLP tasks, making large language models dynamic without any pretraining and by only replacing standard Supervised Fine-Tuning (SFT) with Sorted Fine-Tuning (SoFT) at the same costs. Our approach boosts model efficiency, eliminating the need for multiple models for various scenarios during inference. We show that using this approach, we are able to unlock the potential of intermediate layers of transformers in generating the target output. Our sub-models remain integral components of the original model, minimizing storage requirements and transition costs between different computational/latency budgets. By applying this approach on LLaMa 2 13B for tuning on the Stanford Alpaca dataset and comparing it to normal tuning and early exit via PandaLM benchmark, we show that Sorted Fine-Tuning can deliver models twice as fast as the original model while maintaining or exceeding performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.08963", "title": "Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data?", "authors": [ "Xiangru Tang", "Yiming Zong", "Yilun Zhao", "Arman Cohan", "Mark Gerstein" ], "github": "https://github.com/gersteinlab/Struc-Bench", "project_page": "", "abstract": "Despite the power of Large Language Models (LLMs) like GPT-4, they still struggle with tasks that require generating complex, structured outputs. In this study, we assess the capability of Current LLMs in generating complex structured data and propose a structure-aware fine-tuning approach as a solution to improve this ability. To perform a comprehensive evaluation, we propose Struc-Bench, include five representative LLMs (i.e., GPT-NeoX 20B, GPT-3.5, GPT-4, and Vicuna) and evaluate them on our carefully constructed datasets spanning raw text, HTML, and LaTeX tables. Based on our analysis of current model performance, we identify specific common formatting errors and areas of potential improvement. To address complex formatting requirements, we utilize FormatCoT (Chain-of-Thought) to generate format instructions from target outputs. Our experiments show that our structure-aware fine-tuning method, when applied to LLaMA-7B, significantly improves adherence to natural language constraints, outperforming other evaluated LLMs. Based on these results, we present an ability map of model capabilities from six dimensions (i.e., coverage, formatting, reasoning, comprehension, pragmatics, and hallucination). This map highlights the weaknesses of LLMs in handling complex structured outputs and suggests promising directions for future work. Our code and models can be found at https://github.com/gersteinlab/Struc-Bench.", "github_manual": "", "github_regex": "https://github.com/gersteinlab/Struc-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.08827", "title": "S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs", "authors": [ "Sarkar Snigdha Sarathi Das", "Chirag Shah", "Mengting Wan", "Jennifer Neville", "Longqi Yang", "Reid Andersen", "Georg Buscher", "Tara Safavi" ], "github": "", "project_page": "", "abstract": "The traditional Dialogue State Tracking (DST) problem aims to track user preferences and intents in user-agent conversations. While sufficient for task-oriented dialogue systems supporting narrow domain applications, the advent of Large Language Model (LLM)-based chat systems has introduced many real-world intricacies in open-domain dialogues. These intricacies manifest in the form of increased complexity in contextual interactions, extended dialogue sessions encompassing a diverse array of topics, and more frequent contextual shifts. To handle these intricacies arising from evolving LLM-based chat systems, we propose joint dialogue segmentation and state tracking per segment in open-domain dialogue systems. Assuming a zero-shot setting appropriate to a true open-domain dialogue system, we propose S3-DST, a structured prompting technique that harnesses Pre-Analytical Recollection, a novel grounding mechanism we designed for improving long context tracking. To demonstrate the efficacy of our proposed approach in joint segmentation and state tracking, we evaluate S3-DST on a proprietary anonymized open-domain dialogue dataset, as well as publicly available DST and segmentation datasets. Across all datasets and settings, S3-DST consistently outperforms the state-of-the-art, demonstrating its potency and robustness the next generation of LLM-based chat systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.08773", "title": "Enhance audio generation controllability through representation similarity regularization", "authors": [ "Yangyang Shi", "Gael Le Lan", "Varun Nagaraja", "Zhaoheng Ni", "Xinhao Mei", "Ernie Chang", "Forrest Iandola", "Yang Liu", "Vikas Chandra" ], "github": "", "project_page": "", "abstract": "This paper presents an innovative approach to enhance control over audio generation by emphasizing the alignment between audio and text representations during model training. In the context of language model-based audio generation, the model leverages input from both textual and audio token representations to predict subsequent audio tokens. However, the current configuration lacks explicit regularization to ensure the alignment between the chosen text representation and the language model's predictions. Our proposal involves the incorporation of audio and text representation regularization, particularly during the classifier-free guidance (CFG) phase, where the text condition is excluded from cross attention during language model training. The aim of this proposed representation regularization is to minimize discrepancies in audio and text similarity compared to other samples within the same training batch. Experimental results on both music and audio generation tasks demonstrate that our proposed methods lead to improvements in objective metrics for both audio and music generation, as well as an enhancement in the human perception for audio generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.08646", "title": "Cure the headache of Transformers via Collinear Constrained Attention", "authors": [ "Shiyi Zhu", "Jing Ye", "Wei Jiang", "Qi Zhang", "Yifan Wu", "Jianguo Li" ], "github": "https://github.com/codefuse-ai/Collinear-Constrained-Attention", "project_page": "", "abstract": "As the rapid progression of practical applications based on Large Language Models continues, the importance of extrapolating performance has grown exponentially in the research domain. In our study, we identified an anomalous behavior in Transformer models that had been previously overlooked, leading to a chaos around closest tokens which carried the most important information. We've coined this discovery the \"headache of Transformers\". To address this at its core, we introduced a novel self-attention structure named Collinear Constrained Attention (CoCA). This structure can be seamlessly integrated with existing extrapolation, interpolation methods, and other optimization strategies designed for traditional Transformer models. We have achieved excellent extrapolating performance even for 16 times to 24 times of sequence lengths during inference without any fine-tuning on our model. We have also enhanced CoCA's computational and spatial efficiency to ensure its practicality. We plan to open-source CoCA shortly. In the meantime, we've made our code available in the appendix for reappearing experiments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/codefuse-ai/Collinear-Constrained-Attention", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.08637", "title": "TextBind: Multi-turn Interleaved Multimodal Instruction-following", "authors": [ "Huayang Li", "Siheng Li", "Deng Cai", "Longyue Wang", "Lemao Liu", "Taro Watanabe", "Yujiu Yang", "Shuming Shi" ], "github": "https://github.com/sihengli99/textbind", "project_page": "", "abstract": "Large language models with instruction-following abilities have revolutionized the field of artificial intelligence. These models show exceptional generalizability to tackle various real-world tasks through their natural language interfaces. However, their performance heavily relies on high-quality exemplar data, which is often difficult to obtain. This challenge is further exacerbated when it comes to multimodal instruction following. We introduce TextBind, an almost annotation-free framework for empowering larger language models with the multi-turn interleaved multimodal instruction-following capabilities. Our approach requires only image-caption pairs and generates multi-turn multimodal instruction-response conversations from a language model. We release our dataset, model, and demo to foster future research in the area of multimodal instruction following.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sihengli99/textbind", "github_api_added_by": "auto" }, { "date": "2023-09-19", "arxiv_id": "2309.08628", "title": "Recovering from Privacy-Preserving Masking with Large Language Models", "authors": [ "Arpita Vats", "Zhe Liu", "Peng Su", "Debjyoti Paul", "Yingyi Ma", "Yutong Pang", "Zeeshan Ahmed", "Ozlem Kalinli" ], "github": "", "project_page": "", "abstract": "Model adaptation is crucial to handle the discrepancy between proxy training data and actual users data received. To effectively perform adaptation, textual data of users is typically stored on servers or their local devices, where downstream natural language processing (NLP) models can be directly trained using such in-domain data. However, this might raise privacy and security concerns due to the extra risks of exposing user information to adversaries. Replacing identifying information in textual data with a generic marker has been recently explored. In this work, we leverage large language models (LLMs) to suggest substitutes of masked tokens and have their effectiveness evaluated on downstream language modeling tasks. Specifically, we propose multiple pre-trained and fine-tuned LLM-based approaches and perform empirical studies on various datasets for the comparison of these methods. Experimental results show that models trained on the obfuscation corpora are able to achieve comparable performance with the ones trained on the original data without privacy-preserving token masking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-19", "arxiv_id": "2309.06497", "title": "A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale", "authors": [ "Hao-Jun Michael Shi", "Tsung-Hsien Lee", "Shintaro Iwasaki", "Jose Gallego-Posada", "Zhijing Li", "Kaushik Rangadurai", "Dheevatsa Mudigere", "Michael Rabbat" ], "github": "https://github.com/facebookresearch/optimizers", "project_page": "", "abstract": "Shampoo is an online and stochastic optimization algorithm belonging to the AdaGrad family of methods for training neural networks. It constructs a block-diagonal preconditioner where each block consists of a coarse Kronecker product approximation to full-matrix AdaGrad for each parameter of the neural network. In this work, we provide a complete description of the algorithm as well as the performance optimizations that our implementation leverages to train deep networks at-scale in PyTorch. Our implementation enables fast multi-GPU distributed data-parallel training by distributing the memory and computation associated with blocks of each parameter via PyTorch's DTensor data structure and performing an AllGather primitive on the computed search directions at each iteration. This major performance enhancement enables us to achieve at most a 10% performance reduction in per-step wall-clock time compared against standard diagonal-scaling-based adaptive gradient methods. We validate our implementation by performing an ablation study on training ImageNet ResNet50, demonstrating Shampoo's superiority over standard training recipes with minimal hyperparameter tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/optimizers", "github_api_added_by": "auto" }, { "date": "2023-09-20", "arxiv_id": "2309.10020", "title": "Multimodal Foundation Models: From Specialists to General-Purpose Assistants", "authors": [ "Chunyuan Li", "Zhe Gan", "Zhengyuan Yang", "Jianwei Yang", "Linjie Li", "Lijuan Wang", "Jianfeng Gao" ], "github": "", "project_page": "", "abstract": "This paper presents a comprehensive survey of the taxonomy and evolution of multimodal foundation models that demonstrate vision and vision-language capabilities, focusing on the transition from specialist models to general-purpose assistants. The research landscape encompasses five core topics, categorized into two classes. (i) We start with a survey of well-established research areas: multimodal foundation models pre-trained for specific purposes, including two topics -- methods of learning vision backbones for visual understanding and text-to-image generation. (ii) Then, we present recent advances in exploratory, open research areas: multimodal foundation models that aim to play the role of general-purpose assistants, including three topics -- unified vision models inspired by large language models (LLMs), end-to-end training of multimodal LLMs, and chaining multimodal tools with LLMs. The target audiences of the paper are researchers, graduate students, and professionals in computer vision and vision-language multimodal communities who are eager to learn the basics and recent advances in multimodal foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-20", "arxiv_id": "2309.10668", "title": "Language Modeling Is Compression", "authors": [ "Gr\u00e9goire Del\u00e9tang", "Anian Ruoss", "Paul-Ambroise Duquenne", "Elliot Catt", "Tim Genewein", "Christopher Mattern", "Jordi Grau-Moya", "Li Kevin Wenliang", "Matthew Aitchison", "Laurent Orseau", "Marcus Hutter", "Joel Veness" ], "github": "https://github.com/google-deepmind/language_modeling_is_compression", "project_page": "", "abstract": "It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors. In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models. We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning. For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively. Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/google-deepmind/language_modeling_is_compression", "github_api_added_by": "auto" }, { "date": "2023-09-20", "arxiv_id": "2309.10305", "title": "Baichuan 2: Open Large-scale Language Models", "authors": [ "Aiyuan Yang", "Bin Xiao", "Bingning Wang", "Borong Zhang", "Chao Yin", "Chenxu Lv", "Da Pan", "Dian Wang", "Dong Yan", "Fan Yang", "Fei Deng", "Feng Wang", "Feng Liu", "Guangwei Ai", "Guosheng Dong Haizhou Zhao", "Hang Xu", "Haoze Sun", "Hongda Zhang", "Hui Liu", "Jiaming Ji", "Jian Xie", "Juntao Dai", "Kun Fang", "Lei Su Liang Song", "Lifeng Liu", "Liyun Ru", "Luyao Ma", "Mang Wang", "Mickel Liu", "MingAn Lin", "Nuolan Nie", "Peidong Guo", "Ruiyang Sun", "Tao Zhang", "Tianpeng Li", "Tianyu Li", "Wei Cheng", "Weipeng Chen", "Xiangrong Zeng", "Xiaochuan Wang", "Xiaoxi Chen", "Xin Men", "Xin Yu", "Xuehai Pan", "Yanjun Shen", "Yiding Wang", "Yiyu Li", "Youxin Jiang", "Yuchen Gao", "Yupeng Zhang", "Zenan Zhou", "Zhiying Wu" ], "github": "https://github.com/baichuan-inc/Baichuan2", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.", "github_manual": "https://github.com/baichuan-inc/Baichuan2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-20", "arxiv_id": "2309.10706", "title": "OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch", "authors": [ "Juntao Li", "Zecheng Tang", "Yuyang Ding", "Pinzheng Wang", "Pei Guo", "Wangjie You", "Dan Qiao", "Wenliang Chen", "Guohong Fu", "Qiaoming Zhu", "Guodong Zhou", "Min Zhang" ], "github": "https://github.com/OpenNLG/openBA.git", "project_page": "", "abstract": "Large language models (LLMs) with billions of parameters have demonstrated outstanding performance on various natural language processing tasks. This report presents OpenBA, an open-sourced 15B bilingual asymmetric seq2seq model, to contribute an LLM variant to the Chinese-oriented open-source model community. We enhance OpenBA with effective and efficient techniques as well as adopt a three-stage training strategy to train the model from scratch. Our solution can also achieve very competitive performance with only 380B tokens, which is better than LLaMA-70B on the BELEBELE benchmark, BLOOM-176B on the MMLU benchmark, GLM-130B on the C-Eval (hard) benchmark. This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques. We have refactored our code to follow the design principles of the Huggingface Transformers Library, making it more convenient for developers to use, and released checkpoints of different training stages at https://huggingface.co/openBA. More details of our project are available at https://github.com/OpenNLG/openBA.git.", "github_manual": "", "github_regex": "https://github.com/OpenNLG/openBA.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-20", "arxiv_id": "2309.10279", "title": "360^circ Reconstruction From a Single Image Using Space Carved Outpainting", "authors": [ "Nuri Ryu", "Minsu Gong", "Geonung Kim", "Joo-Haeng Lee", "Sunghyun Cho" ], "github": "", "project_page": "", "abstract": "We introduce POP3D, a novel framework that creates a full 360^circ-view 3D model from a single image. POP3D resolves two prominent issues that limit the single-view reconstruction. Firstly, POP3D offers substantial generalizability to arbitrary categories, a trait that previous methods struggle to achieve. Secondly, POP3D further improves reconstruction fidelity and naturalness, a crucial aspect that concurrent works fall short of. Our approach marries the strengths of four primary components: (1) a monocular depth and normal predictor that serves to predict crucial geometric cues, (2) a space carving method capable of demarcating the potentially unseen portions of the target object, (3) a generative model pre-trained on a large-scale image dataset that can complete unseen regions of the target, and (4) a neural implicit surface reconstruction method tailored in reconstructing objects using RGB images along with monocular geometric cues. The combination of these components enables POP3D to readily generalize across various in-the-wild images and generate state-of-the-art reconstructions, outperforming similar works by a significant margin. Project page: http://cg.postech.ac.kr/research/POP3D", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-20", "arxiv_id": "2309.10150", "title": "Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions", "authors": [ "Yevgen Chebotar", "Quan Vuong", "Alex Irpan", "Karol Hausman", "Fei Xia", "Yao Lu", "Aviral Kumar", "Tianhe Yu", "Alexander Herzog", "Karl Pertsch", "Keerthana Gopalakrishnan", "Julian Ibarz", "Ofir Nachum", "Sumedh Sontakke", "Grecia Salazar", "Huong T Tran", "Jodilyn Peralta", "Clayton Tan", "Deeksha Manjunath", "Jaspiar Singht", "Brianna Zitkovich", "Tomas Jackson", "Kanishka Rao", "Chelsea Finn", "Sergey Levine" ], "github": "", "project_page": "", "abstract": "In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data. Our method uses a Transformer to provide a scalable representation for Q-functions trained via offline temporal difference backups. We therefore refer to the method as Q-Transformer. By discretizing each action dimension and representing the Q-value of each action dimension as separate tokens, we can apply effective high-capacity sequence modeling techniques for Q-learning. We present several design decisions that enable good performance with offline RL training, and show that Q-Transformer outperforms prior offline RL algorithms and imitation learning techniques on a large diverse real-world robotic manipulation task suite. The project's website and videos can be found at https://q-transformer.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-20", "arxiv_id": "2309.10537", "title": "FoleyGen: Visually-Guided Audio Generation", "authors": [ "Xinhao Mei", "Varun Nagaraja", "Gael Le Lan", "Zhaoheng Ni", "Ernie Chang", "Yangyang Shi", "Vikas Chandra" ], "github": "", "project_page": "", "abstract": "Recent advancements in audio generation have been spurred by the evolution of large-scale deep learning models and expansive datasets. However, the task of video-to-audio (V2A) generation continues to be a challenge, principally because of the intricate relationship between the high-dimensional visual and auditory data, and the challenges associated with temporal synchronization. In this study, we introduce FoleyGen, an open-domain V2A generation system built on a language modeling paradigm. FoleyGen leverages an off-the-shelf neural audio codec for bidirectional conversion between waveforms and discrete tokens. The generation of audio tokens is facilitated by a single Transformer model, which is conditioned on visual features extracted from a visual encoder. A prevalent problem in V2A generation is the misalignment of generated audio with the visible actions in the video. To address this, we explore three novel visual attention mechanisms. We further undertake an exhaustive evaluation of multiple visual encoders, each pretrained on either single-modal or multi-modal tasks. The experimental results on VGGSound dataset show that our proposed FoleyGen outperforms previous systems across all objective metrics and human evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-20", "arxiv_id": "2309.10818", "title": "SlimPajama-DC: Understanding Data Combinations for LLM Training", "authors": [ "Zhiqiang Shen", "Tianhua Tao", "Liqun Ma", "Willie Neiswanger", "Joel Hestness", "Natalia Vassilieva", "Daria Soboleva", "Eric Xing" ], "github": "https://github.com/Cerebras/modelzoo/tree/main/src/cerebras/modelzoo/data_preparation/nlp/slimpajama", "project_page": "", "abstract": "This paper aims to understand the impacts of various data combinations (e.g., web text, wikipedia, github, books) on the training of large language models using SlimPajama. SlimPajama is a rigorously deduplicated, multi-source dataset, which has been refined and further deduplicated to 627B tokens from the extensive 1.2T tokens RedPajama dataset contributed by Together. We've termed our research as SlimPajama-DC, an empirical analysis designed to uncover fundamental characteristics and best practices associated with employing SlimPajama in the training of large language models. During our research with SlimPajama, two pivotal observations emerged: (1) Global deduplication vs. local deduplication. We analyze and discuss how global (across different sources of datasets) and local (within the single source of dataset) deduplications affect the performance of trained models. (2) Proportions of high-quality/highly-deduplicated multi-source datasets in the combination. To study this, we construct six configurations of SlimPajama dataset and train individual ones using 1.3B Cerebras-GPT model with Alibi and SwiGLU. Our best configuration outperforms the 1.3B model trained on RedPajama using the same number of training tokens by a significant margin. All our 1.3B models are trained on Cerebras 16times CS-2 cluster with a total of 80 PFLOP/s in bf16 mixed precision. We further extend our discoveries (such as increasing data diversity is crucial after global deduplication) on a 7B model with large batch-size training. Our models and the separate SlimPajama-DC datasets are available at: https://huggingface.co/MBZUAI-LLM and https://huggingface.co/datasets/cerebras/SlimPajama-627B.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Cerebras/modelzoo/tree/main/src/cerebras/modelzoo/data_preparation/nlp/slimpajama", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-20", "arxiv_id": "2309.10202", "title": "Stabilizing RLHF through Advantage Model and Selective Rehearsal", "authors": [ "Baolin Peng", "Linfeng Song", "Ye Tian", "Lifeng Jin", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have revolutionized natural language processing, yet aligning these models with human values and preferences using RLHF remains a significant challenge. This challenge is characterized by various instabilities, such as reward hacking and catastrophic forgetting. In this technical report, we propose two innovations to stabilize RLHF training: 1) Advantage Model, which directly models advantage score i.e., extra reward compared to the expected rewards and regulates score distributions across tasks to prevent reward hacking. 2) Selective Rehearsal, which mitigates catastrophic forgetting by strategically selecting data for PPO training and knowledge rehearsing. Our experimental analysis on public and proprietary datasets reveals that the proposed methods not only increase stability in RLHF training but also achieve higher reward scores and win rates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-21", "arxiv_id": "2309.10952", "title": "LMDX: Language Model-based Document Information Extraction and Localization", "authors": [ "Vincent Perot", "Kai Kang", "Florian Luisier", "Guolong Su", "Xiaoyu Sun", "Ramya Sree Boppana", "Zilong Wang", "Jiaqi Mu", "Hao Zhang", "Nan Hua" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLM) have revolutionized Natural Language Processing (NLP), improving state-of-the-art on many existing tasks and exhibiting emergent capabilities. However, LLMs have not yet been successfully applied on semi-structured document information extraction, which is at the core of many document processing workflows and consists of extracting key entities from a visually rich document (VRD) given a predefined target schema. The main obstacles to LLM adoption in that task have been the absence of layout encoding within LLMs, critical for a high quality extraction, and the lack of a grounding mechanism ensuring the answer is not hallucinated. In this paper, we introduce Language Model-based Document Information Extraction and Localization (LMDX), a methodology to adapt arbitrary LLMs for document information extraction. LMDX can do extraction of singular, repeated, and hierarchical entities, both with and without training data, while providing grounding guarantees and localizing the entities within the document. In particular, we apply LMDX to the PaLM 2-S LLM and evaluate it on VRDU and CORD benchmarks, setting a new state-of-the-art and showing how LMDX enables the creation of high quality, data-efficient parsers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-21", "arxiv_id": "2309.11499", "title": "DreamLLM: Synergistic Multimodal Comprehension and Creation", "authors": [ "Runpei Dong", "Chunrui Han", "Yuang Peng", "Zekun Qi", "Zheng Ge", "Jinrong Yang", "Liang Zhao", "Jianjian Sun", "Hongyu Zhou", "Haoran Wei", "Xiangwen Kong", "Xiangyu Zhang", "Kaisheng Ma", "Li Yi" ], "github": "https://github.com/RunpeiDong/DreamLLM", "project_page": "", "abstract": "This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation. DreamLLM operates on two fundamental principles. The first focuses on the generative modeling of both language and image posteriors by direct sampling in the raw multimodal space. This approach circumvents the limitations and information loss inherent to external feature extractors like CLIP, and a more thorough multimodal understanding is obtained. Second, DreamLLM fosters the generation of raw, interleaved documents, modeling both text and image contents, along with unstructured layouts. This allows DreamLLM to learn all conditional, marginal, and joint multimodal distributions effectively. As a result, DreamLLM is the first MLLM capable of generating free-form interleaved content. Comprehensive experiments highlight DreamLLM's superior performance as a zero-shot multimodal generalist, reaping from the enhanced learning synergy.", "github_manual": "https://github.com/RunpeiDong/DreamLLM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-21", "arxiv_id": "2309.11497", "title": "FreeU: Free Lunch in Diffusion U-Net", "authors": [ "Chenyang Si", "Ziqi Huang", "Yuming Jiang", "Ziwei Liu" ], "github": "https://github.com/ChenyangSi/FreeU", "project_page": "", "abstract": "In this paper, we uncover the untapped potential of diffusion U-Net, which serves as a \"free lunch\" that substantially improves the generation quality on the fly. We initially investigate the key contributions of the U-Net architecture to the denoising process and identify that its main backbone primarily contributes to denoising, whereas its skip connections mainly introduce high-frequency features into the decoder module, causing the network to overlook the backbone semantics. Capitalizing on this discovery, we propose a simple yet effective method-termed \"FreeU\" - that enhances generation quality without additional training or finetuning. Our key insight is to strategically re-weight the contributions sourced from the U-Net's skip connections and backbone feature maps, to leverage the strengths of both components of the U-Net architecture. Promising results on image and video generation tasks demonstrate that our FreeU can be readily integrated to existing diffusion models, e.g., Stable Diffusion, DreamBooth, ModelScope, Rerender and ReVersion, to improve the generation quality with only a few lines of code. All you need is to adjust two scaling factors during inference. Project page: https://chenyangsi.top/FreeU/.", "github_manual": "https://github.com/ChenyangSi/FreeU", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-21", "arxiv_id": "2309.11419", "title": "Kosmos-2.5: A Multimodal Literate Model", "authors": [ "Tengchao Lv", "Yupan Huang", "Jingye Chen", "Lei Cui", "Shuming Ma", "Yaoyao Chang", "Shaohan Huang", "Wenhui Wang", "Li Dong", "Weiyao Luo", "Shaoxiang Wu", "Guoxin Wang", "Cha Zhang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "We present Kosmos-2.5, a multimodal literate model for machine reading of text-intensive images. Pre-trained on large-scale text-intensive images, Kosmos-2.5 excels in two distinct yet cooperative transcription tasks: (1) generating spatially-aware text blocks, where each block of text is assigned its spatial coordinates within the image, and (2) producing structured text output that captures styles and structures into the markdown format. This unified multimodal literate capability is achieved through a shared Transformer architecture, task-specific prompts, and flexible text representations. We evaluate Kosmos-2.5 on end-to-end document-level text recognition and image-to-markdown text generation. Furthermore, the model can be readily adapted for any text-intensive image understanding task with different prompts through supervised fine-tuning, making it a general-purpose tool for real-world applications involving text-rich images. This work also paves the way for the future scaling of multimodal large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-21", "arxiv_id": "2309.11495", "title": "Chain-of-Verification Reduces Hallucination in Large Language Models", "authors": [ "Shehzaad Dhuliawala", "Mojtaba Komeili", "Jing Xu", "Roberta Raileanu", "Xian Li", "Asli Celikyilmaz", "Jason Weston" ], "github": "", "project_page": "", "abstract": "Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberate on the responses they give in order to correct their mistakes. We develop the Chain-of-Verification (CoVe) method whereby the model first (i) drafts an initial response; then (ii) plans verification questions to fact-check its draft; (iii) answers those questions independently so the answers are not biased by other responses; and (iv) generates its final verified response. In experiments, we show CoVe decreases hallucinations across a variety of tasks, from list-based questions from Wikidata, closed book MultiSpanQA and longform text generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-21", "arxiv_id": "2309.10917", "title": "End-to-End Speech Recognition Contextualization with Large Language Models", "authors": [ "Egor Lakomkin", "Chunyang Wu", "Yassir Fathullah", "Ozlem Kalinli", "Michael L. Seltzer", "Christian Fuegen" ], "github": "", "project_page": "", "abstract": "In recent years, Large Language Models (LLMs) have garnered significant attention from the research community due to their exceptional performance and generalization capabilities. In this paper, we introduce a novel method for contextualizing speech recognition models incorporating LLMs. Our approach casts speech recognition as a mixed-modal language modeling task based on a pretrained LLM. We provide audio features, along with optional text tokens for context, to train the system to complete transcriptions in a decoder-only fashion. As a result, the system is implicitly incentivized to learn how to leverage unstructured contextual information during training. Our empirical results demonstrate a significant improvement in performance, with a 6% WER reduction when additional textual context is provided. Moreover, we find that our method performs competitively and improve by 7.5% WER overall and 17% WER on rare words against a baseline contextualized RNN-T system that has been trained on more than twenty five times larger speech dataset. Overall, we demonstrate that by only adding a handful number of trainable parameters via adapters, we can unlock contextualized speech recognition capability for the pretrained LLM while keeping the same text-only input functionality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-21", "arxiv_id": "2309.11197", "title": "The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute", "authors": [ "Aleksandar Stani\u0107", "Dylan Ashley", "Oleg Serikov", "Louis Kirsch", "Francesco Faccio", "J\u00fcrgen Schmidhuber", "Thomas Hofmann", "Imanol Schlag" ], "github": "", "project_page": "", "abstract": "The Languini Kitchen serves as both a research collective and codebase designed to empower researchers with limited computational resources to contribute meaningfully to the field of language modelling. We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. The number of tokens on which a model is trained is defined by the model's throughput and the chosen compute class. Notably, this approach avoids constraints on critical hyperparameters which affect total parameters or floating-point operations. For evaluation, we pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. On it, we compare methods based on their empirical scaling trends which are estimated through experiments at various levels of compute. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput. While the GPT baseline achieves better perplexity throughout all our levels of compute, our LSTM baseline exhibits a predictable and more favourable scaling law. This is due to the improved throughput and the need for fewer training tokens to achieve the same decrease in test perplexity. Extrapolating the scaling laws leads of both models results in an intersection at roughly 50,000 accelerator hours. We hope this work can serve as the foundation for meaningful and reproducible language modelling research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-21", "arxiv_id": "2309.11500", "title": "A Large-scale Dataset for Audio-Language Representation Learning", "authors": [ "Luoyi Sun", "Xuenan Xu", "Mengyue Wu", "Weidi Xie" ], "github": "", "project_page": "", "abstract": "The AI community has made significant strides in developing powerful foundation models, driven by large-scale multimodal datasets. However, in the audio representation learning community, the present audio-language datasets suffer from limitations such as insufficient volume, simplistic content, and arduous collection procedures. To tackle these challenges, we present an innovative and automatic audio caption generation pipeline based on a series of public tools or APIs, and construct a large-scale, high-quality, audio-language dataset, named as Auto-ACD, comprising over 1.9M audio-text pairs. To demonstrate the effectiveness of the proposed dataset, we train popular models on our dataset and show performance improvement on various downstream tasks, namely, audio-language retrieval, audio captioning, environment classification. In addition, we establish a novel test set and provide a benchmark for audio-text tasks. The proposed dataset will be released at https://auto-acd.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-21", "arxiv_id": "2309.11009", "title": "Controllable Dynamic Appearance for Neural 3D Portraits", "authors": [ "ShahRukh Athar", "Zhixin Shu", "Zexiang Xu", "Fuji Luan", "Sai Bi", "Kalyan Sunkavalli", "Dimitris Samaras" ], "github": "https://github.com/zhengyuf/IMavatar", "project_page": "", "abstract": "Recent advances in Neural Radiance Fields (NeRFs) have made it possible to reconstruct and reanimate dynamic portrait scenes with control over head-pose, facial expressions and viewing direction. However, training such models assumes photometric consistency over the deformed region e.g. the face must be evenly lit as it deforms with changing head-pose and facial expression. Such photometric consistency across frames of a video is hard to maintain, even in studio environments, thus making the created reanimatable neural portraits prone to artifacts during reanimation. In this work, we propose CoDyNeRF, a system that enables the creation of fully controllable 3D portraits in real-world capture conditions. CoDyNeRF learns to approximate illumination dependent effects via a dynamic appearance model in the canonical space that is conditioned on predicted surface normals and the facial expressions and head-pose deformations. The surface normals prediction is guided using 3DMM normals that act as a coarse prior for the normals of the human head, where direct prediction of normals is hard due to rigid and non-rigid deformations induced by head-pose and facial expression changes. Using only a smartphone-captured short video of a subject for training, we demonstrate the effectiveness of our method on free view synthesis of a portrait scene with explicit head pose and expression controls, and realistic lighting effects. The project page can be found here: http://shahrukhathar.github.io/2023/08/22/CoDyNeRF.html", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhengyuf/IMavatar", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-22", "arxiv_id": "2309.11674", "title": "A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models", "authors": [ "Haoran Xu", "Young Jin Kim", "Amr Sharaf", "Hany Hassan Awadalla" ], "github": "https://github.com/felixxu/ALMA", "project_page": "", "abstract": "Generative Large Language Models (LLMs) have achieved remarkable advancements in various NLP tasks. However, these advances have not been reflected in the translation task, especially those with moderate model sizes (i.e., 7B or 13B parameters), which still lag behind conventional supervised encoder-decoder translation models. Previous studies have attempted to improve the translation capabilities of these moderate LLMs, but their gains have been limited. In this study, we propose a novel fine-tuning approach for LLMs that is specifically designed for the translation task, eliminating the need for the abundant parallel data that traditional translation models usually depend on. Our approach consists of two fine-tuning stages: initial fine-tuning on monolingual data followed by subsequent fine-tuning on a small set of high-quality parallel data. We introduce the LLM developed through this strategy as Advanced Language Model-based trAnslator (ALMA). Based on LLaMA-2 as our underlying model, our results show that the model can achieve an average improvement of more than 12 BLEU and 12 COMET over its zero-shot performance across 10 translation directions from the WMT'21 (2 directions) and WMT'22 (8 directions) test datasets. The performance is significantly better than all prior work and even superior to the NLLB-54B model and GPT-3.5-text-davinci-003, with only 7B or 13B parameters. This method establishes the foundation for a novel training paradigm in machine translation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/felixxu/ALMA", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-22", "arxiv_id": "2309.11568", "title": "BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model", "authors": [ "Nolan Dey", "Daria Soboleva", "Faisal Al-Khateeb", "Bowen Yang", "Ribhu Pathria", "Hemant Khachane", "Shaheer Muhammad", "Zhiming", "Chen", "Robert Myers", "Jacob Robert Steeves", "Natalia Vassilieva", "Marvin Tom", "Joel Hestness" ], "github": "", "project_page": "", "abstract": "We introduce the Bittensor Language Model, called \"BTLM-3B-8K\", a new state-of-the-art 3 billion parameter open-source language model. BTLM-3B-8K was trained on 627B tokens from the SlimPajama dataset with a mixture of 2,048 and 8,192 context lengths. BTLM-3B-8K outperforms all existing 3B parameter models by 2-5.5% across downstream tasks. BTLM-3B-8K is even competitive with some 7B parameter models. Additionally, BTLM-3B-8K provides excellent long context performance, outperforming MPT-7B-8K and XGen-7B-8K on tasks up to 8,192 context length. We trained the model on a cleaned and deduplicated SlimPajama dataset; aggressively tuned the \\textmu P hyperparameters and schedule; used ALiBi position embeddings; and adopted the SwiGLU nonlinearity. On Hugging Face, the most popular models have 7B parameters, indicating that users prefer the quality-size ratio of 7B models. Compacting the 7B parameter model to one with 3B parameters, with little performance impact, is an important milestone. BTLM-3B-8K needs only 3GB of memory with 4-bit precision and takes 2.5x less inference compute than 7B models, helping to open up access to a powerful language model on mobile and edge devices. BTLM-3B-8K is available under an Apache 2.0 license on Hugging Face: https://huggingface.co/cerebras/btlm-3b-8k-base.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-22", "arxiv_id": "2309.11998", "title": "LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset", "authors": [ "Lianmin Zheng", "Wei-Lin Chiang", "Ying Sheng", "Tianle Li", "Siyuan Zhuang", "Zhanghao Wu", "Yonghao Zhuang", "Zhuohan Li", "Zi Lin", "Eric. P Xing", "Joseph E. Gonzalez", "Ion Stoica", "Hao Zhang" ], "github": "", "project_page": "", "abstract": "Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications. In this paper, we introduce LMSYS-Chat-1M, a large-scale dataset containing one million real-world conversations with 25 state-of-the-art LLMs. This dataset is collected from 210K unique IP addresses in the wild on our Vicuna demo and Chatbot Arena website. We offer an overview of the dataset's content, including its curation process, basic statistics, and topic distribution, highlighting its diversity, originality, and scale. We demonstrate its versatility through four use cases: developing content moderation models that perform similarly to GPT-4, building a safety benchmark, training instruction-following models that perform similarly to Vicuna, and creating challenging benchmark questions. We believe that this dataset will serve as a valuable resource for understanding and advancing LLM capabilities. The dataset is publicly available at https://huggingface.co/datasets/lmsys/lmsys-chat-1m.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-22", "arxiv_id": "2309.12307", "title": "LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models", "authors": [ "Yukang Chen", "Shengju Qian", "Haotian Tang", "Xin Lai", "Zhijian Liu", "Song Han", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/LongLoRA", "project_page": "", "abstract": "We present LongLoRA, an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs), with limited computation cost. Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. For example, training on the context length of 8192 needs 16x computational costs in self-attention layers as that of 2048. In this paper, we speed up the context extension of LLMs in two aspects. On the one hand, although dense global attention is needed during inference, fine-tuning the model can be effectively and efficiently done by sparse local attention. The proposed shift short attention effectively enables context extension, leading to non-trivial computation saving with similar performance to fine-tuning with vanilla attention. Particularly, it can be implemented with only two lines of code in training, while being optional in inference. On the other hand, we revisit the parameter-efficient fine-tuning regime for context expansion. Notably, we find that LoRA for context extension works well under the premise of trainable embedding and normalization. LongLoRA demonstrates strong empirical results on various tasks on LLaMA2 models from 7B/13B to 70B. LongLoRA adopts LLaMA2 7B from 4k context to 100k, or LLaMA2 70B to 32k on a single 8x A100 machine. LongLoRA extends models' context while retaining their original architectures, and is compatible with most existing techniques, like FlashAttention-2. In addition, to make LongLoRA practical, we collect a dataset, LongQA, for supervised fine-tuning. It contains more than 3k long context question-answer pairs.", "github_manual": "https://github.com/dvlab-research/LongLoRA", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-22", "arxiv_id": "2309.11523", "title": "RMT: Retentive Networks Meet Vision Transformers", "authors": [ "Qihang Fan", "Huaibo Huang", "Mingrui Chen", "Hongmin Liu", "Ran He" ], "github": "https://github.com/qhfan/RMT", "project_page": "", "abstract": "Transformer first appears in the field of natural language processing and is later migrated to the computer vision domain, where it demonstrates excellent performance in vision tasks. However, recently, Retentive Network (RetNet) has emerged as an architecture with the potential to replace Transformer, attracting widespread attention in the NLP community. Therefore, we raise the question of whether transferring RetNet's idea to vision can also bring outstanding performance to vision tasks. To address this, we combine RetNet and Transformer to propose RMT. Inspired by RetNet, RMT introduces explicit decay into the vision backbone, bringing prior knowledge related to spatial distances to the vision model. This distance-related spatial prior allows for explicit control of the range of tokens that each token can attend to. Additionally, to reduce the computational cost of global modeling, we decompose this modeling process along the two coordinate axes of the image. Abundant experiments have demonstrated that our RMT exhibits exceptional performance across various computer vision tasks. For example, RMT achieves 84.1% Top1-acc on ImageNet-1k using merely 4.5G FLOPs. To the best of our knowledge, among all models, RMT achieves the highest Top1-acc when models are of similar size and trained with the same strategy. Moreover, RMT significantly outperforms existing vision backbones in downstream tasks such as object detection, instance segmentation, and semantic segmentation. Our work is still in progress.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/qhfan/RMT", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-22", "arxiv_id": "2309.12311", "title": "LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent", "authors": [ "Jianing Yang", "Xuweiyi Chen", "Shengyi Qian", "Nikhil Madaan", "Madhavan Iyengar", "David F. Fouhey", "Joyce Chai" ], "github": "https://github.com/sled-group/chat-with-nerf", "project_page": "", "abstract": "3D visual grounding is a critical skill for household robots, enabling them to navigate, manipulate objects, and answer questions based on their environment. While existing approaches often rely on extensive labeled data or exhibit limitations in handling complex language queries, we propose LLM-Grounder, a novel zero-shot, open-vocabulary, Large Language Model (LLM)-based 3D visual grounding pipeline. LLM-Grounder utilizes an LLM to decompose complex natural language queries into semantic constituents and employs a visual grounding tool, such as OpenScene or LERF, to identify objects in a 3D scene. The LLM then evaluates the spatial and commonsense relations among the proposed objects to make a final grounding decision. Our method does not require any labeled training data and can generalize to novel 3D scenes and arbitrary text queries. We evaluate LLM-Grounder on the ScanRefer benchmark and demonstrate state-of-the-art zero-shot grounding accuracy. Our findings indicate that LLMs significantly improve the grounding capability, especially for complex language queries, making LLM-Grounder an effective approach for 3D vision-language tasks in robotics. Videos and interactive demos can be found on the project website https://chat-with-nerf.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sled-group/chat-with-nerf", "github_api_added_by": "auto" }, { "date": "2023-09-22", "arxiv_id": "2309.12284", "title": "MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models", "authors": [ "Longhui Yu", "Weisen Jiang", "Han Shi", "Jincheng Yu", "Zhengying Liu", "Yu Zhang", "James T. Kwok", "Zhenguo Li", "Adrian Weller", "Weiyang Liu" ], "github": "https://github.com/meta-math/MetaMath", "project_page": "", "abstract": "Large language models (LLMs) have pushed the limits of natural language understanding and exhibited excellent problem-solving ability. Despite the great success, most existing open-source LLMs (\\eg, LLaMA-2) are still far away from satisfactory for solving mathematical problem due to the complex reasoning procedures. To bridge this gap, we propose MetaMath, a fine-tuned language model that specializes in mathematical reasoning. Specifically, we start by bootstrapping mathematical questions by rewriting the question from multiple perspectives without extra knowledge, which results in a new dataset called {MetaMathQA}. Then we fine-tune the LLaMA-2 models on MetaMathQA. Experimental results on two popular benchmarks (\\ie, GSM8K and MATH) for mathematical reasoning demonstrate that MetaMath outperforms a suite of open-source LLMs by a significant margin. Our MetaMath-7B model achieves 66.4% on GSM8K and 19.4% on MATH, exceeding the state-of-the-art models of the same size by 11.5% and 8.7%. Particularly, {MetaMath-70B} achieves an accuracy of 82.3% on {GSM8K}, slightly better than {GPT-3.5-Turbo}. We release the {MetaMathQA} dataset, the {MetaMath} models with different model sizes and the training code for public use.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/meta-math/MetaMath", "github_api_added_by": "auto" }, { "date": "2023-09-22", "arxiv_id": "2309.12207", "title": "Boolformer: Symbolic Regression of Logic Functions with Transformers", "authors": [ "St\u00e9phane d'Ascoli", "Samy Bengio", "Josh Susskind", "Emmanuel Abb\u00e9" ], "github": "https://github.com/sdascoli/boolformer", "project_page": "", "abstract": "In this work, we introduce Boolformer, the first Transformer architecture trained to perform end-to-end symbolic regression of Boolean functions. First, we show that it can predict compact formulas for complex functions which were not seen during training, when provided a clean truth table. Then, we demonstrate its ability to find approximate expressions when provided incomplete and noisy observations. We evaluate the Boolformer on a broad set of real-world binary classification datasets, demonstrating its potential as an interpretable alternative to classic machine learning methods. Finally, we apply it to the widespread task of modelling the dynamics of gene regulatory networks. Using a recent benchmark, we show that Boolformer is competitive with state-of-the art genetic algorithms with a speedup of several orders of magnitude. Our code and models are available publicly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sdascoli/boolformer", "github_api_added_by": "auto" }, { "date": "2023-09-25", "arxiv_id": "2309.12499", "title": "CodePlan: Repository-level Coding using LLMs and Planning", "authors": [ "Ramakrishna Bairi", "Atharv Sonwane", "Aditya Kanade", "Vageesh D C", "Arun Iyer", "Suresh Parthasarathy", "Sriram Rajamani", "B. Ashok", "Shashank Shet" ], "github": "", "project_page": "", "abstract": "Software engineering activities such as package migration, fixing errors reports from static analysis or testing, and adding type annotations or other specifications to a codebase, involve pervasively editing the entire repository of code. We formulate these activities as repository-level coding tasks. Recent tools like GitHub Copilot, which are powered by Large Language Models (LLMs), have succeeded in offering high-quality solutions to localized coding problems. Repository-level coding tasks are more involved and cannot be solved directly using LLMs, since code within a repository is inter-dependent and the entire repository may be too large to fit into the prompt. We frame repository-level coding as a planning problem and present a task-agnostic framework, called CodePlan to solve it. CodePlan synthesizes a multi-step chain of edits (plan), where each step results in a call to an LLM on a code location with context derived from the entire repository, previous code changes and task-specific instructions. CodePlan is based on a novel combination of an incremental dependency analysis, a change may-impact analysis and an adaptive planning algorithm. We evaluate the effectiveness of CodePlan on two repository-level tasks: package migration (C#) and temporal code edits (Python). Each task is evaluated on multiple code repositories, each of which requires inter-dependent changes to many files (between 2-97 files). Coding tasks of this level of complexity have not been automated using LLMs before. Our results show that CodePlan has better match with the ground truth compared to baselines. CodePlan is able to get 5/6 repositories to pass the validity checks (e.g., to build without errors and make correct code edits) whereas the baselines (without planning but with the same type of contextual information as CodePlan) cannot get any of the repositories to pass them.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-25", "arxiv_id": "2309.13042", "title": "MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation", "authors": [ "Jiahao Xie", "Wei Li", "Xiangtai Li", "Ziwei Liu", "Yew Soon Ong", "Chen Change Loy" ], "github": "https://github.com/Jiahao000/MosaicFusion", "project_page": "", "abstract": "We present MosaicFusion, a simple yet effective diffusion-based data augmentation approach for large vocabulary instance segmentation. Our method is training-free and does not rely on any label supervision. Two key designs enable us to employ an off-the-shelf text-to-image diffusion model as a useful dataset generator for object instances and mask annotations. First, we divide an image canvas into several regions and perform a single round of diffusion process to generate multiple instances simultaneously, conditioning on different text prompts. Second, we obtain corresponding instance masks by aggregating cross-attention maps associated with object prompts across layers and diffusion time steps, followed by simple thresholding and edge-aware refinement processing. Without bells and whistles, our MosaicFusion can produce a significant amount of synthetic labeled data for both rare and novel categories. Experimental results on the challenging LVIS long-tailed and open-vocabulary benchmarks demonstrate that MosaicFusion can significantly improve the performance of existing instance segmentation models, especially for rare and novel categories. Code will be released at https://github.com/Jiahao000/MosaicFusion.", "github_manual": "", "github_regex": "https://github.com/Jiahao000/MosaicFusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-25", "arxiv_id": "2309.13018", "title": "Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model", "authors": [ "Jiamin Xie", "Ke Li", "Jinxi Guo", "Andros Tjandra", "Yuan Shangguan", "Leda Sari", "Chunyang Wu", "Junteng Jia", "Jay Mahadeokar", "Ozlem Kalinli" ], "github": "", "project_page": "", "abstract": "Neural network pruning offers an effective method for compressing a multilingual automatic speech recognition (ASR) model with minimal performance loss. However, it entails several rounds of pruning and re-training needed to be run for each language. In this work, we propose the use of an adaptive masking approach in two scenarios for pruning a multilingual ASR model efficiently, each resulting in sparse monolingual models or a sparse multilingual model (named as Dynamic ASR Pathways). Our approach dynamically adapts the sub-network, avoiding premature decisions about a fixed sub-network structure. We show that our approach outperforms existing pruning methods when targeting sparse monolingual models. Further, we illustrate that Dynamic ASR Pathways jointly discovers and trains better sub-networks (pathways) of a single multilingual model by adapting from different sub-network initializations, thereby reducing the need for language-specific pruning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-25", "arxiv_id": "2309.13041", "title": "Robotic Offline RL from Internet Videos via Value-Function Pre-Training", "authors": [ "Chethan Bhateja", "Derek Guo", "Dibya Ghosh", "Anikait Singh", "Manan Tomar", "Quan Vuong", "Yevgen Chebotar", "Sergey Levine", "Aviral Kumar" ], "github": "https://github.com/Asap7772/PTR", "project_page": "", "abstract": "Pre-training on Internet data has proven to be a key ingredient for broad generalization in many modern ML systems. What would it take to enable such capabilities in robotic reinforcement learning (RL)? Offline RL methods, which learn from datasets of robot experience, offer one way to leverage prior data into the robotic learning pipeline. However, these methods have a \"type mismatch\" with video data (such as Ego4D), the largest prior datasets available for robotics, since video offers observation-only experience without the action or reward annotations needed for RL methods. In this paper, we develop a system for leveraging large-scale human video datasets in robotic offline RL, based entirely on learning value functions via temporal-difference learning. We show that value learning on video datasets learns representations that are more conducive to downstream robotic offline RL than other approaches for learning from video data. Our system, called V-PTR, combines the benefits of pre-training on video data with robotic offline RL approaches that train on diverse robot data, resulting in value functions and policies for manipulation tasks that perform better, act robustly, and generalize broadly. On several manipulation tasks on a real WidowX robot, our framework produces policies that greatly improve over prior methods. Our video and additional details can be found at https://dibyaghosh.com/vptr/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Asap7772/PTR", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-25", "arxiv_id": "2309.12424", "title": "DualToken-ViT: Position-aware Efficient Vision Transformer with Dual Token Fusion", "authors": [ "Zhenzhen Chu", "Jiayu Chen", "Cen Chen", "Chengyu Wang", "Ziheng Wu", "Jun Huang", "Weining Qian" ], "github": "", "project_page": "", "abstract": "Self-attention-based vision transformers (ViTs) have emerged as a highly competitive architecture in computer vision. Unlike convolutional neural networks (CNNs), ViTs are capable of global information sharing. With the development of various structures of ViTs, ViTs are increasingly advantageous for many vision tasks. However, the quadratic complexity of self-attention renders ViTs computationally intensive, and their lack of inductive biases of locality and translation equivariance demands larger model sizes compared to CNNs to effectively learn visual features. In this paper, we propose a light-weight and efficient vision transformer model called DualToken-ViT that leverages the advantages of CNNs and ViTs. DualToken-ViT effectively fuses the token with local information obtained by convolution-based structure and the token with global information obtained by self-attention-based structure to achieve an efficient attention structure. In addition, we use position-aware global tokens throughout all stages to enrich the global information, which further strengthening the effect of DualToken-ViT. Position-aware global tokens also contain the position information of the image, which makes our model better for vision tasks. We conducted extensive experiments on image classification, object detection and semantic segmentation tasks to demonstrate the effectiveness of DualToken-ViT. On the ImageNet-1K dataset, our models of different scales achieve accuracies of 75.4% and 79.4% with only 0.5G and 1.0G FLOPs, respectively, and our model with 1.0G FLOPs outperforms LightViT-T using global tokens by 0.7%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-26", "arxiv_id": "2309.13075", "title": "SCREWS: A Modular Framework for Reasoning with Revisions", "authors": [ "Kumar Shridhar", "Harsh Jhamtani", "Hao Fang", "Benjamin Van Durme", "Jason Eisner", "Patrick Xia" ], "github": "https://github.com/kumar-shridhar/screws", "project_page": "", "abstract": "Large language models (LLMs) can improve their accuracy on various tasks through iteratively refining and revising their output based on feedback. We observe that these revisions can introduce errors, in which case it is better to roll back to a previous result. Further, revisions are typically homogeneous: they use the same reasoning method that produced the initial answer, which may not correct errors. To enable exploration in this space, we present SCREWS, a modular framework for reasoning with revisions. It is comprised of three main modules: Sampling, Conditional Resampling, and Selection, each consisting of sub-modules that can be hand-selected per task. We show that SCREWS not only unifies several previous approaches under a common framework, but also reveals several novel strategies for identifying improved reasoning chains. We evaluate our framework with state-of-the-art LLMs (ChatGPT and GPT-4) on a diverse set of reasoning tasks and uncover useful new reasoning strategies for each: arithmetic word problems, multi-hop question answering, and code debugging. Heterogeneous revision strategies prove to be important, as does selection between original and revised candidates.", "github_manual": "https://github.com/kumar-shridhar/screws", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-26", "arxiv_id": "2309.13952", "title": "VidChapters-7M: Video Chapters at Scale", "authors": [ "Antoine Yang", "Arsha Nagrani", "Ivan Laptev", "Josef Sivic", "Cordelia Schmid" ], "github": "", "project_page": "", "abstract": "Segmenting long videos into chapters enables users to quickly navigate to the information of their interest. This important topic has been understudied due to the lack of publicly released datasets. To address this issue, we present VidChapters-7M, a dataset of 817K user-chaptered videos including 7M chapters in total. VidChapters-7M is automatically created from videos online in a scalable manner by scraping user-annotated chapters and hence without any additional manual annotation. We introduce the following three tasks based on this data. First, the video chapter generation task consists of temporally segmenting the video and generating a chapter title for each segment. To further dissect the problem, we also define two variants of this task: video chapter generation given ground-truth boundaries, which requires generating a chapter title given an annotated video segment, and video chapter grounding, which requires temporally localizing a chapter given its annotated title. We benchmark both simple baselines and state-of-the-art video-language models for these three tasks. We also show that pretraining on VidChapters-7M transfers well to dense video captioning tasks in both zero-shot and finetuning settings, largely improving the state of the art on the YouCook2 and ViTT benchmarks. Finally, our experiments reveal that downstream performance scales well with the size of the pretraining dataset. Our dataset, code, and models are publicly available at https://antoyang.github.io/vidchapters.html.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-26", "arxiv_id": "2309.14322", "title": "Small-scale proxies for large-scale Transformer training instabilities", "authors": [ "Mitchell Wortsman", "Peter J. Liu", "Lechao Xiao", "Katie Everett", "Alex Alemi", "Ben Adlam", "John D. Co-Reyes", "Izzeddin Gur", "Abhishek Kumar", "Roman Novak", "Jeffrey Pennington", "Jascha Sohl-dickstein", "Kelvin Xu", "Jaehoon Lee", "Justin Gilmer", "Simon Kornblith" ], "github": "", "project_page": "", "abstract": "Teams that have trained large Transformer-based models have reported training instabilities at large scale that did not appear when training with the same hyperparameters at smaller scales. Although the causes of such instabilities are of scientific interest, the amount of resources required to reproduce them has made investigation difficult. In this work, we seek ways to reproduce and study training stability and instability at smaller scales. First, we focus on two sources of training instability described in previous work: the growth of logits in attention layers (Dehghani et al., 2023) and divergence of the output logits from the log probabilities (Chowdhery et al., 2022). By measuring the relationship between learning rate and loss across scales, we show that these instabilities also appear in small models when training at high learning rates, and that mitigations previously employed at large scales are equally effective in this regime. This prompts us to investigate the extent to which other known optimizer and model interventions influence the sensitivity of the final loss to changes in the learning rate. To this end, we study methods such as warm-up, weight decay, and the muParam (Yang et al., 2022), and combine techniques to train small models that achieve similar losses across orders of magnitude of learning rate variation. Finally, to conclude our exploration we study two cases where instabilities can be predicted before they emerge by examining the scaling behavior of model activation and gradient norms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-26", "arxiv_id": "2309.14327", "title": "DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention", "authors": [ "Zhewei Yao", "Xiaoxia Wu", "Conglong Li", "Minjia Zhang", "Heyang Qi", "Olatunji Ruwase", "Ammar Ahmad Awan", "Samyam Rajbhandari", "Yuxiong He" ], "github": "", "project_page": "", "abstract": "Most of the existing multi-modal models, hindered by their incapacity to adeptly manage interleaved image-and-text inputs in multi-image, multi-round dialogues, face substantial constraints in resource allocation for training and data accessibility, impacting their adaptability and scalability across varied interaction realms. To address this, we present the DeepSpeed-VisualChat framework, designed to optimize Large Language Models (LLMs) by incorporating multi-modal capabilities, with a focus on enhancing the proficiency of Large Vision and Language Models in handling interleaved inputs. Our framework is notable for (1) its open-source support for multi-round and multi-image dialogues, (2) introducing an innovative multi-modal causal attention mechanism, and (3) utilizing data blending techniques on existing datasets to assure seamless interactions in multi-round, multi-image conversations. Compared to existing frameworks, DeepSpeed-VisualChat shows superior scalability up to 70B parameter language model size, representing a significant advancement in multi-modal language models and setting a solid foundation for future explorations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-26", "arxiv_id": "2309.13356", "title": "Exploring Large Language Models' Cognitive Moral Development through Defining Issues Test", "authors": [ "Kumar Tanmay", "Aditi Khandelwal", "Utkarsh Agarwal", "Monojit Choudhury" ], "github": "", "project_page": "", "abstract": "The development of large language models has instilled widespread interest among the researchers to understand their inherent reasoning and problem-solving capabilities. Despite good amount of research going on to elucidate these capabilities, there is a still an appreciable gap in understanding moral development and judgments of these models. The current approaches of evaluating the ethical reasoning abilities of these models as a classification task pose numerous inaccuracies because of over-simplification. In this study, we built a psychological connection by bridging two disparate fields-human psychology and AI. We proposed an effective evaluation framework which can help to delineate the model's ethical reasoning ability in terms of moral consistency and Kohlberg's moral development stages with the help of Psychometric Assessment Tool-Defining Issues Test.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-26", "arxiv_id": "2309.13308", "title": "Calibrating LLM-Based Evaluator", "authors": [ "Yuxuan Liu", "Tianchi Yang", "Shaohan Huang", "Zihan Zhang", "Haizhen Huang", "Furu Wei", "Weiwei Deng", "Feng Sun", "Qi Zhang" ], "github": "", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) on language modeling and emergent capabilities make them a promising reference-free evaluator of natural language generation quality, and a competent alternative to human evaluation. However, hindered by the closed-source or high computational demand to host and tune, there is a lack of practice to further calibrate an off-the-shelf LLM-based evaluator towards better human alignment. In this work, we propose AutoCalibrate, a multi-stage, gradient-free approach to automatically calibrate and align an LLM-based evaluator toward human preference. Instead of explicitly modeling human preferences, we first implicitly encompass them within a set of human labels. Then, an initial set of scoring criteria is drafted by the language model itself, leveraging in-context learning on different few-shot examples. To further calibrate this set of criteria, we select the best performers and re-draft them with self-refinement. Our experiments on multiple text quality evaluation datasets illustrate a significant improvement in correlation with expert evaluation through calibration. Our comprehensive qualitative analysis conveys insightful intuitions and observations on the essence of effective scoring criteria.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-27", "arxiv_id": "2309.15103", "title": "LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models", "authors": [ "Yaohui Wang", "Xinyuan Chen", "Xin Ma", "Shangchen Zhou", "Ziqi Huang", "Yi Wang", "Ceyuan Yang", "Yinan He", "Jiashuo Yu", "Peiqing Yang", "Yuwei Guo", "Tianxing Wu", "Chenyang Si", "Yuming Jiang", "Cunjian Chen", "Chen Change Loy", "Bo Dai", "Dahua Lin", "Yu Qiao", "Ziwei Liu" ], "github": "https://github.com/Vchitect/LaVie", "project_page": "", "abstract": "This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task to simultaneously a) accomplish the synthesis of visually realistic and temporally coherent videos while b) preserving the strong creative generation nature of the pre-trained T2I model. To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model. Our key insights are two-fold: 1) We reveal that the incorporation of simple temporal self-attentions, coupled with rotary positional encoding, adequately captures the temporal correlations inherent in video data. 2) Additionally, we validate that the process of joint image-video fine-tuning plays a pivotal role in producing high-quality and creative outcomes. To enhance the performance of LaVie, we contribute a comprehensive and diverse video dataset named Vimeo25M, consisting of 25 million text-video pairs that prioritize quality, diversity, and aesthetic appeal. Extensive experiments demonstrate that LaVie achieves state-of-the-art performance both quantitatively and qualitatively. Furthermore, we showcase the versatility of pre-trained LaVie models in various long video generation and personalized video synthesis applications.", "github_manual": "https://github.com/Vchitect/LaVie", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-27", "arxiv_id": "2309.14525", "title": "Aligning Large Multimodal Models with Factually Augmented RLHF", "authors": [ "Zhiqing Sun", "Sheng Shen", "Shengcao Cao", "Haotian Liu", "Chunyuan Li", "Yikang Shen", "Chuang Gan", "Liang-Yan Gui", "Yu-Xiong Wang", "Yiming Yang", "Kurt Keutzer", "Trevor Darrell" ], "github": "", "project_page": "", "abstract": "Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalities can result in \"hallucination\", generating textual outputs that are not grounded by the multimodal information in context. To address the multimodal misalignment issue, we adapt the Reinforcement Learning from Human Feedback (RLHF) from the text domain to the task of vision-language alignment, where human annotators are asked to compare two responses and pinpoint the more hallucinated one, and the vision-language model is trained to maximize the simulated human rewards. We propose a new alignment algorithm called Factually Augmented RLHF that augments the reward model with additional factual information such as image captions and ground-truth multi-choice options, which alleviates the reward hacking phenomenon in RLHF and further improves the performance. We also enhance the GPT-4-generated training data (for vision instruction tuning) with previously available human-written image-text pairs to improve the general capabilities of our model. To evaluate the proposed approach in real-world scenarios, we develop a new evaluation benchmark MMHAL-BENCH with a special focus on penalizing hallucinations. As the first LMM trained with RLHF, our approach achieves remarkable improvement on the LLaVA-Bench dataset with the 94% performance level of the text-only GPT-4 (while previous best methods can only achieve the 87% level), and an improvement by 60% on MMHAL-BENCH over other baselines. We opensource our code, model, data at https://llava-rlhf.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-27", "arxiv_id": "2309.14717", "title": "QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models", "authors": [ "Yuhui Xu", "Lingxi Xie", "Xiaotao Gu", "Xin Chen", "Heng Chang", "Hengheng Zhang", "Zhensu Chen", "Xiaopeng Zhang", "Qi Tian" ], "github": "https://github.com/yuhuixu1993/qa-lora", "project_page": "", "abstract": "Recently years have witnessed a rapid development of large language models (LLMs). Despite the strong ability in many language-understanding tasks, the heavy computational burden largely restricts the application of LLMs especially when one needs to deploy them onto edge devices. In this paper, we propose a quantization-aware low-rank adaptation (QA-LoRA) algorithm. The motivation lies in the imbalanced degrees of freedom of quantization and adaptation, and the solution is to use group-wise operators which increase the degree of freedom of quantization meanwhile decreasing that of adaptation. QA-LoRA is easily implemented with a few lines of code, and it equips the original LoRA with two-fold abilities: (i) during fine-tuning, the LLM's weights are quantized (e.g., into INT4) to reduce time and memory usage; (ii) after fine-tuning, the LLM and auxiliary weights are naturally integrated into a quantized model without loss of accuracy. We apply QA-LoRA to the LLaMA and LLaMA2 model families and validate its effectiveness in different fine-tuning datasets and downstream scenarios. Code will be made available at https://github.com/yuhuixu1993/qa-lora.", "github_manual": "", "github_regex": "https://github.com/yuhuixu1993/qa-lora", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-27", "arxiv_id": "2309.15098", "title": "Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models", "authors": [ "Mert Yuksekgonul", "Varun Chandrasekaran", "Erik Jones", "Suriya Gunasekar", "Ranjita Naik", "Hamid Palangi", "Ece Kamar", "Besmira Nushi" ], "github": "https://github.com/microsoft/mechanistic-error-probe", "project_page": "", "abstract": "We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text. We propose modeling factual queries as Constraint Satisfaction Problems and use this framework to investigate how the model interacts internally with factual constraints. Specifically, we discover a strong positive relation between the model's attention to constraint tokens and the factual accuracy of its responses. In our curated suite of 11 datasets with over 40,000 prompts, we study the task of predicting factual errors with the Llama-2 family across all scales (7B, 13B, 70B). We propose SAT Probe, a method probing self-attention patterns, that can predict constraint satisfaction and factual errors, and allows early error identification. The approach and findings demonstrate how using the mechanistic understanding of factuality in LLMs can enhance reliability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/mechanistic-error-probe", "github_api_added_by": "auto" }, { "date": "2023-09-27", "arxiv_id": "2309.14592", "title": "Efficient Post-training Quantization with FP8 Formats", "authors": [ "Haihao Shen", "Naveen Mellempudi", "Xin He", "Qun Gao", "Chang Wang", "Mengni Wang" ], "github": "https://github.com/intel/neural-compressor", "project_page": "", "abstract": "Recent advances in deep learning methods such as LLMs and Diffusion models have created a need for improved quantization methods that can meet the computational demands of these modern architectures while maintaining accuracy. Towards this goal, we study the advantages of FP8 data formats for post-training quantization across 75 unique network architectures covering a wide range of tasks, including machine translation, language modeling, text generation, image classification, generation, and segmentation. We examine three different FP8 representations (E5M2, E4M3, and E3M4) to study the effects of varying degrees of trade-off between dynamic range and precision on model accuracy. Based on our extensive study, we developed a quantization workflow that generalizes across different network architectures. Our empirical results show that FP8 formats outperform INT8 in multiple aspects, including workload coverage (92.64% vs. 65.87%), model accuracy and suitability for a broader range of operations. Furthermore, our findings suggest that E4M3 is better suited for NLP models, whereas E3M4 performs marginally better than E4M3 on computer vision tasks. The code is publicly available on Intel Neural Compressor: https://github.com/intel/neural-compressor.", "github_manual": "", "github_regex": "https://github.com/intel/neural-compressor", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-27", "arxiv_id": "2309.14509", "title": "DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models", "authors": [ "Sam Ade Jacobs", "Masahiro Tanaka", "Chengming Zhang", "Minjia Zhang", "Leon Song", "Samyam Rajbhandari", "Yuxiong He" ], "github": "", "project_page": "", "abstract": "Computation in a typical Transformer-based large language model (LLM) can be characterized by batch size, hidden dimension, number of layers, and sequence length. Until now, system works for accelerating LLM training have focused on the first three dimensions: data parallelism for batch size, tensor parallelism for hidden size and pipeline parallelism for model depth or layers. These widely studied forms of parallelism are not targeted or optimized for long sequence Transformer models. Given practical application needs for long sequence LLM, renewed attentions are being drawn to sequence parallelism. However, existing works in sequence parallelism are constrained by memory-communication inefficiency, limiting their scalability to long sequence large models. In this work, we introduce DeepSpeed-Ulysses, a novel, portable and effective methodology for enabling highly efficient and scalable LLM training with extremely long sequence length. DeepSpeed-Ulysses at its core partitions input data along the sequence dimension and employs an efficient all-to-all collective communication for attention computation. Theoretical communication analysis shows that whereas other methods incur communication overhead as sequence length increases, DeepSpeed-Ulysses maintains constant communication volume when sequence length and compute devices are increased proportionally. Furthermore, experimental evaluations show that DeepSpeed-Ulysses trains 2.5X faster with 4X longer sequence length than the existing method SOTA baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-27", "arxiv_id": "2309.15091", "title": "VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning", "authors": [ "Han Lin", "Abhay Zala", "Jaemin Cho", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Although recent text-to-video (T2V) generation methods have seen significant advancements, most of these works focus on producing short video clips of a single event with a single background (i.e., single-scene videos). Meanwhile, recent large language models (LLMs) have demonstrated their capability in generating layouts and programs to control downstream visual modules such as image generation models. This raises an important question: can we leverage the knowledge embedded in these LLMs for temporally consistent long video generation? In this paper, we propose VideoDirectorGPT, a novel framework for consistent multi-scene video generation that uses the knowledge of LLMs for video content planning and grounded video generation. Specifically, given a single text prompt, we first ask our video planner LLM (GPT-4) to expand it into a 'video plan', which involves generating the scene descriptions, the entities with their respective layouts, the background for each scene, and consistency groupings of the entities and backgrounds. Next, guided by this output from the video planner, our video generator, Layout2Vid, has explicit control over spatial layouts and can maintain temporal consistency of entities/backgrounds across scenes, while only trained with image-level annotations. Our experiments demonstrate that VideoDirectorGPT framework substantially improves layout and movement control in both single- and multi-scene video generation and can generate multi-scene videos with visual consistency across scenes, while achieving competitive performance with SOTAs in open-domain single-scene T2V generation. We also demonstrate that our framework can dynamically control the strength for layout guidance and can also generate videos with user-provided images. We hope our framework can inspire future work on better integrating the planning ability of LLMs into consistent long video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-28", "arxiv_id": "2309.15223", "title": "Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition", "authors": [ "Yu Yu", "Chao-Han Huck Yang", "Jari Kolehmainen", "Prashanth G. Shivakumar", "Yile Gu", "Sungho Ryu", "Roger Ren", "Qi Luo", "Aditya Gourav", "I-Fan Chen", "Yi-Chieh Liu", "Tuan Dinh", "Ankur Gandhe", "Denis Filimonov", "Shalini Ghosh", "Andreas Stolcke", "Ariya Rastow", "Ivan Bulyko" ], "github": "", "project_page": "", "abstract": "We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech recognition output rescoring. Although pretrained language models (LMs) like BERT have shown superior performance in second-pass rescoring, the high computational cost of scaling up the pretraining stage and adapting the pretrained models to specific domains limit their practical use in rescoring. Here we present a method based on low-rank decomposition to train a rescoring BERT model and adapt it to new domains using only a fraction (0.08%) of the pretrained parameters. These inserted matrices are optimized through a discriminative training objective along with a correlation-based regularization loss. The proposed low-rank adaptation Rescore-BERT (LoRB) architecture is evaluated on LibriSpeech and internal datasets with decreased training times by factors between 5.4 and 3.6.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-28", "arxiv_id": "2309.15251", "title": "VPA: Fully Test-Time Visual Prompt Adaptation", "authors": [ "Jiachen Sun", "Mark Ibrahim", "Melissa Hall", "Ivan Evtimov", "Z. Morley Mao", "Cristian Canton Ferrer", "Caner Hazirbas" ], "github": "", "project_page": "", "abstract": "Textual prompt tuning has demonstrated significant performance improvements in adapting natural language processing models to a variety of downstream tasks by treating hand-engineered prompts as trainable parameters. Inspired by the success of textual prompting, several studies have investigated the efficacy of visual prompt tuning. In this work, we present Visual Prompt Adaptation (VPA), the first framework that generalizes visual prompting with test-time adaptation. VPA introduces a small number of learnable tokens, enabling fully test-time and storage-efficient adaptation without necessitating source-domain information. We examine our VPA design under diverse adaptation settings, encompassing single-image, batched-image, and pseudo-label adaptation. We evaluate VPA on multiple tasks, including out-of-distribution (OOD) generalization, corruption robustness, and domain adaptation. Experimental results reveal that VPA effectively enhances OOD generalization by 3.3% across various models, surpassing previous test-time approaches. Furthermore, we show that VPA improves corruption robustness by 6.5% compared to strong baselines. Finally, we demonstrate that VPA also boosts domain adaptation performance by relatively 5.2%. Our VPA also exhibits marked effectiveness in improving the robustness of zero-shot recognition for vision-language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-28", "arxiv_id": "2309.15129", "title": "Evaluating Cognitive Maps and Planning in Large Language Models with CogEval", "authors": [ "Ida Momennejad", "Hosein Hasanbeig", "Felipe Vieira", "Hiteshi Sharma", "Robert Osazuwa Ness", "Nebojsa Jojic", "Hamid Palangi", "Jonathan Larson" ], "github": "", "project_page": "", "abstract": "Recently an influx of studies claim emergent cognitive abilities in large language models (LLMs). Yet, most rely on anecdotes, overlook contamination of training sets, or lack systematic Evaluation involving multiple tasks, control conditions, multiple iterations, and statistical robustness tests. Here we make two major contributions. First, we propose CogEval, a cognitive science-inspired protocol for the systematic evaluation of cognitive capacities in Large Language Models. The CogEval protocol can be followed for the evaluation of various abilities. Second, here we follow CogEval to systematically evaluate cognitive maps and planning ability across eight LLMs (OpenAI GPT-4, GPT-3.5-turbo-175B, davinci-003-175B, Google Bard, Cohere-xlarge-52.4B, Anthropic Claude-1-52B, LLaMA-13B, and Alpaca-7B). We base our task prompts on human experiments, which offer both established construct validity for evaluating planning, and are absent from LLM training sets. We find that, while LLMs show apparent competence in a few planning tasks with simpler structures, systematic evaluation reveals striking failure modes in planning tasks, including hallucinations of invalid trajectories and getting trapped in loops. These findings do not support the idea of emergent out-of-the-box planning ability in LLMs. This could be because LLMs do not understand the latent relational structures underlying planning problems, known as cognitive maps, and fail at unrolling goal-directed trajectories based on the underlying structure. Implications for application and future directions are discussed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-28", "arxiv_id": "2309.15807", "title": "Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack", "authors": [ "Xiaoliang Dai", "Ji Hou", "Chih-Yao Ma", "Sam Tsai", "Jialiang Wang", "Rui Wang", "Peizhao Zhang", "Simon Vandenhende", "Xiaofang Wang", "Abhimanyu Dubey", "Matthew Yu", "Abhishek Kadian", "Filip Radenovic", "Dhruv Mahajan", "Kunpeng Li", "Yue Zhao", "Vladan Petrovic", "Mitesh Kumar Singh", "Simran Motwani", "Yi Wen", "Yiwen Song", "Roshan Sumbaly", "Vignesh Ramanathan", "Zijian He", "Peter Vajda", "Devi Parikh" ], "github": "", "project_page": "", "abstract": "Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face challenges when it comes to generating highly aesthetic images. This creates the need for aesthetic alignment post pre-training. In this paper, we propose quality-tuning to effectively guide a pre-trained model to exclusively generate highly visually appealing images, while maintaining generality across visual concepts. Our key insight is that supervised fine-tuning with a set of surprisingly small but extremely visually appealing images can significantly improve the generation quality. We pre-train a latent diffusion model on 1.1 billion image-text pairs and fine-tune it with only a few thousand carefully selected high-quality images. The resulting model, Emu, achieves a win rate of 82.9% compared with its pre-trained only counterpart. Compared to the state-of-the-art SDXLv1.0, Emu is preferred 68.4% and 71.3% of the time on visual appeal on the standard PartiPrompts and our Open User Input benchmark based on the real-world usage of text-to-image models. In addition, we show that quality-tuning is a generic approach that is also effective for other architectures, including pixel diffusion and masked generative transformer models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-28", "arxiv_id": "2309.15818", "title": "Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation", "authors": [ "David Junhao Zhang", "Jay Zhangjie Wu", "Jia-Wei Liu", "Rui Zhao", "Lingmin Ran", "Yuchao Gu", "Difei Gao", "Mike Zheng Shou" ], "github": "https://github.com/showlab/Show-1", "project_page": "", "abstract": "Significant advancements have been achieved in the realm of large-scale pre-trained text-to-video Diffusion Models (VDMs). However, previous methods either rely solely on pixel-based VDMs, which come with high computational costs, or on latent-based VDMs, which often struggle with precise text-video alignment. In this paper, we are the first to propose a hybrid model, dubbed as Show-1, which marries pixel-based and latent-based VDMs for text-to-video generation. Our model first uses pixel-based VDMs to produce a low-resolution video of strong text-video correlation. After that, we propose a novel expert translation method that employs the latent-based VDMs to further upsample the low-resolution video to high resolution. Compared to latent VDMs, Show-1 can produce high-quality videos of precise text-video alignment; Compared to pixel VDMs, Show-1 is much more efficient (GPU memory usage during inference is 15G vs 72G). We also validate our model on standard video generation benchmarks. Our code and model weights are publicly available at https://github.com/showlab/Show-1.", "github_manual": "", "github_regex": "https://github.com/showlab/Show-1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-28", "arxiv_id": "2309.15273", "title": "DECO: Dense Estimation of 3D Human-Scene Contact In The Wild", "authors": [ "Shashank Tripathi", "Agniv Chatterjee", "Jean-Claude Passy", "Hongwei Yi", "Dimitrios Tzionas", "Michael J. Black" ], "github": "https://github.com/sha2nkt/deco", "project_page": "", "abstract": "Understanding how humans use physical contact to interact with the world is key to enabling human-centric artificial intelligence. While inferring 3D contact is crucial for modeling realistic and physically-plausible human-object interactions, existing methods either focus on 2D, consider body joints rather than the surface, use coarse 3D body regions, or do not generalize to in-the-wild images. In contrast, we focus on inferring dense, 3D contact between the full body surface and objects in arbitrary images. To achieve this, we first collect DAMON, a new dataset containing dense vertex-level contact annotations paired with RGB images containing complex human-object and human-scene contact. Second, we train DECO, a novel 3D contact detector that uses both body-part-driven and scene-context-driven attention to estimate vertex-level contact on the SMPL body. DECO builds on the insight that human observers recognize contact by reasoning about the contacting body parts, their proximity to scene objects, and the surrounding scene context. We perform extensive evaluations of our detector on DAMON as well as on the RICH and BEHAVE datasets. We significantly outperform existing SOTA methods across all benchmarks. We also show qualitatively that DECO generalizes well to diverse and challenging real-world human interactions in natural images. The code, data, and models are available at https://deco.is.tue.mpg.de.", "github_manual": "https://github.com/sha2nkt/deco", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-28", "arxiv_id": "2309.15505", "title": "Finite Scalar Quantization: VQ-VAE Made Simple", "authors": [ "Fabian Mentzer", "David Minnen", "Eirikur Agustsson", "Michael Tschannen" ], "github": "", "project_page": "", "abstract": "We propose to replace vector quantization (VQ) in the latent representation of VQ-VAEs with a simple scheme termed finite scalar quantization (FSQ), where we project the VAE representation down to a few dimensions (typically less than 10). Each dimension is quantized to a small set of fixed values, leading to an (implicit) codebook given by the product of these sets. By appropriately choosing the number of dimensions and values each dimension can take, we obtain the same codebook size as in VQ. On top of such discrete representations, we can train the same models that have been trained on VQ-VAE representations. For example, autoregressive and masked transformer models for image generation, multimodal generation, and dense prediction computer vision tasks. Concretely, we employ FSQ with MaskGIT for image generation, and with UViM for depth estimation, colorization, and panoptic segmentation. Despite the much simpler design of FSQ, we obtain competitive performance in all these tasks. We emphasize that FSQ does not suffer from codebook collapse and does not need the complex machinery employed in VQ (commitment losses, codebook reseeding, code splitting, entropy penalties, etc.) to learn expressive discrete representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-28", "arxiv_id": "2309.15426", "title": "NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions", "authors": [ "Zhang Chen", "Zhong Li", "Liangchen Song", "Lele Chen", "Jingyi Yu", "Junsong Yuan", "Yi Xu" ], "github": "https://github.com/oppo-us-research/NeuRBF", "project_page": "", "abstract": "We present a novel type of neural fields that uses general radial bases for signal representation. State-of-the-art neural fields typically rely on grid-based representations for storing local neural features and N-dimensional linear kernels for interpolating features at continuous query points. The spatial positions of their neural features are fixed on grid nodes and cannot well adapt to target signals. Our method instead builds upon general radial bases with flexible kernel position and shape, which have higher spatial adaptivity and can more closely fit target signals. To further improve the channel-wise capacity of radial basis functions, we propose to compose them with multi-frequency sinusoid functions. This technique extends a radial basis to multiple Fourier radial bases of different frequency bands without requiring extra parameters, facilitating the representation of details. Moreover, by marrying adaptive radial bases with grid-based ones, our hybrid combination inherits both adaptivity and interpolation smoothness. We carefully designed weighting schemes to let radial bases adapt to different types of signals effectively. Our experiments on 2D image and 3D signed distance field representation demonstrate the higher accuracy and compactness of our method than prior arts. When applied to neural radiance field reconstruction, our method achieves state-of-the-art rendering quality, with small model size and comparable training speed.", "github_manual": "https://github.com/oppo-us-research/NeuRBF", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-28", "arxiv_id": "2309.15564", "title": "Jointly Training Large Autoregressive Multimodal Models", "authors": [ "Emanuele Aiello", "Lili Yu", "Yixin Nie", "Armen Aghajanyan", "Barlas Oguz" ], "github": "", "project_page": "", "abstract": "In recent years, advances in the large-scale pretraining of language and text-to-image models have revolutionized the field of machine learning. Yet, integrating these two modalities into a single, robust model capable of generating seamless multimodal outputs remains a significant challenge. To address this gap, we present the Joint Autoregressive Mixture (JAM) framework, a modular approach that systematically fuses existing text and image generation models. We also introduce a specialized, data-efficient instruction-tuning strategy, tailored for mixed-modal generation tasks. Our final instruct-tuned model demonstrates unparalleled performance in generating high-quality multimodal outputs and represents the first model explicitly designed for this purpose.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-29", "arxiv_id": "2309.16609", "title": "Qwen Technical Report", "authors": [ "Jinze Bai", "Shuai Bai", "Yunfei Chu", "Zeyu Cui", "Kai Dang", "Xiaodong Deng", "Yang Fan", "Wenbin Ge", "Yu Han", "Fei Huang", "Binyuan Hui", "Luo Ji", "Mei Li", "Junyang Lin", "Runji Lin", "Dayiheng Liu", "Gao Liu", "Chengqiang Lu", "Keming Lu", "Jianxin Ma", "Rui Men", "Xingzhang Ren", "Xuancheng Ren", "Chuanqi Tan", "Sinan Tan", "Jianhong Tu", "Peng Wang", "Shijie Wang", "Wei Wang", "Shengguang Wu", "Benfeng Xu", "Jin Xu", "An Yang", "Hao Yang", "Jian Yang", "Shusheng Yang", "Yang Yao", "Bowen Yu", "Hongyi Yuan", "Zheng Yuan", "Jianwei Zhang", "Xingxuan Zhang", "Yichang Zhang", "Zhenru Zhang", "Chang Zhou", "Jingren Zhou", "Xiaohuan Zhou", "Tianhang Zhu" ], "github": "https://github.com/QwenLM/Qwen", "project_page": "", "abstract": "Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.", "github_manual": "https://github.com/QwenLM/Qwen", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-29", "arxiv_id": "2309.16668", "title": "RealFill: Reference-Driven Generation for Authentic Image Completion", "authors": [ "Luming Tang", "Nataniel Ruiz", "Qinghao Chu", "Yuanzhen Li", "Aleksander Holynski", "David E. Jacobs", "Bharath Hariharan", "Yael Pritch", "Neal Wadhwa", "Kfir Aberman", "Michael Rubinstein" ], "github": "", "project_page": "", "abstract": "Recent advances in generative imagery have brought forth outpainting and inpainting models that can produce high-quality, plausible image content in unknown regions, but the content these models hallucinate is necessarily inauthentic, since the models lack sufficient context about the true scene. In this work, we propose RealFill, a novel generative approach for image completion that fills in missing regions of an image with the content that should have been there. RealFill is a generative inpainting model that is personalized using only a few reference images of a scene. These reference images do not have to be aligned with the target image, and can be taken with drastically varying viewpoints, lighting conditions, camera apertures, or image styles. Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene. We evaluate RealFill on a new image completion benchmark that covers a set of diverse and challenging scenarios, and find that it outperforms existing approaches by a large margin. See more results on our project page: https://realfill.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-29", "arxiv_id": "2309.16534", "title": "MotionLM: Multi-Agent Motion Forecasting as Language Modeling", "authors": [ "Ari Seff", "Brian Cera", "Dian Chen", "Mason Ng", "Aurick Zhou", "Nigamaa Nayakanti", "Khaled S. Refaat", "Rami Al-Rfou", "Benjamin Sapp" ], "github": "", "project_page": "", "abstract": "Reliable forecasting of the future behavior of road agents is a critical component to safe planning in autonomous vehicles. Here, we represent continuous trajectories as sequences of discrete motion tokens and cast multi-agent motion prediction as a language modeling task over this domain. Our model, MotionLM, provides several advantages: First, it does not require anchors or explicit latent variable optimization to learn multimodal distributions. Instead, we leverage a single standard language modeling objective, maximizing the average log probability over sequence tokens. Second, our approach bypasses post-hoc interaction heuristics where individual agent trajectory generation is conducted prior to interactive scoring. Instead, MotionLM produces joint distributions over interactive agent futures in a single autoregressive decoding process. In addition, the model's sequential factorization enables temporally causal conditional rollouts. The proposed approach establishes new state-of-the-art performance for multi-agent motion prediction on the Waymo Open Motion Dataset, ranking 1st on the interactive challenge leaderboard.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-29", "arxiv_id": "2309.16414", "title": "AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models", "authors": [ "Jan Hendrik Metzen", "Piyapat Saranrittichai", "Chaithanya Kumar Mummadi" ], "github": "https://github.com/boschresearch/autoclip", "project_page": "", "abstract": "Classifiers built upon vision-language models such as CLIP have shown remarkable zero-shot performance across a broad range of image classification tasks. Prior work has studied different ways of automatically creating descriptor sets for every class based on prompt templates, ranging from manually engineered templates over templates obtained from a large language model to templates built from random words and characters. In contrast, deriving zero-shot classifiers from the respective encoded class descriptors has remained nearly unchanged, that is: classify to the class that maximizes the cosine similarity between its averaged encoded class descriptors and the encoded image. However, weighting all class descriptors equally can be suboptimal when certain descriptors match visual clues on a given image better than others. In this work, we propose AutoCLIP, a method for auto-tuning zero-shot classifiers. AutoCLIP assigns to each prompt template per-image weights, which are derived from statistics of class descriptor-image similarities at inference time. AutoCLIP is fully unsupervised, has very low overhead, and can be easily implemented in few lines of code. We show that for a broad range of vision-language models, datasets, and prompt templates, AutoCLIP outperforms baselines consistently and by up to 3 percent point accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/boschresearch/autoclip", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-29", "arxiv_id": "2309.16058", "title": "AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model", "authors": [ "Seungwhan Moon", "Andrea Madotto", "Zhaojiang Lin", "Tushar Nagarajan", "Matt Smith", "Shashank Jain", "Chun-Fu Yeh", "Prakash Murugesan", "Peyman Heidari", "Yue Liu", "Kavya Srinet", "Babak Damavandi", "Anuj Kumar" ], "github": "", "project_page": "", "abstract": "We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i.e. text, image, video, audio, IMU motion sensor), and generates textual responses. AnyMAL inherits the powerful text-based reasoning abilities of the state-of-the-art LLMs including LLaMA-2 (70B), and converts modality-specific signals to the joint textual space through a pre-trained aligner module. To further strengthen the multimodal LLM's capabilities, we fine-tune the model with a multimodal instruction set manually collected to cover diverse topics and tasks beyond simple QAs. We conduct comprehensive empirical analysis comprising both human and automatic evaluations, and demonstrate state-of-the-art performance on various multimodal tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-29", "arxiv_id": "2309.16039", "title": "Effective Long-Context Scaling of Foundation Models", "authors": [ "Wenhan Xiong", "Jingyu Liu", "Igor Molybog", "Hejia Zhang", "Prajjwal Bhargava", "Rui Hou", "Louis Martin", "Rashi Rungta", "Karthik Abinav Sankararaman", "Barlas Oguz", "Madian Khabsa", "Han Fang", "Yashar Mehdad", "Sharan Narang", "Kshitiz Malik", "Angela Fan", "Shruti Bhosale", "Sergey Edunov", "Mike Lewis", "Sinong Wang", "Hao Ma" ], "github": "", "project_page": "", "abstract": "We present a series of long-context LLMs that support effective context windows of up to 32,768 tokens. Our model series are built through continual pretraining from Llama 2 with longer training sequences and on a dataset where long texts are upsampled. We perform extensive evaluation on language modeling, synthetic context probing tasks, and a wide range of research benchmarks. On research benchmarks, our models achieve consistent improvements on most regular tasks and significant improvements on long-context tasks over Llama 2. Notably, with a cost-effective instruction tuning procedure that does not require human-annotated long instruction data, the 70B variant can already surpass gpt-3.5-turbo-16k's overall performance on a suite of long-context tasks. Alongside these results, we provide an in-depth analysis on the individual components of our method. We delve into Llama's position encodings and discuss its limitation in modeling long dependencies. We also examine the impact of various design choices in the pretraining process, including the data mix and the training curriculum of sequence lengths -- our ablation experiments suggest that having abundant long texts in the pretrain dataset is not the key to achieving strong performance, and we empirically verify that long context continual pretraining is more efficient and similarly effective compared to pretraining from scratch with long sequences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-29", "arxiv_id": "2309.16653", "title": "DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation", "authors": [ "Jiaxiang Tang", "Jiawei Ren", "Hang Zhou", "Ziwei Liu", "Gang Zeng" ], "github": "https://github.com/dreamgaussian/dreamgaussian", "project_page": "", "abstract": "Recent advances in 3D content creation mostly leverage optimization-based 3D generation via score distillation sampling (SDS). Though promising results have been exhibited, these methods often suffer from slow per-sample optimization, limiting their practical usage. In this paper, we propose DreamGaussian, a novel 3D content generation framework that achieves both efficiency and quality simultaneously. Our key insight is to design a generative 3D Gaussian Splatting model with companioned mesh extraction and texture refinement in UV space. In contrast to the occupancy pruning used in Neural Radiance Fields, we demonstrate that the progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks. To further enhance the texture quality and facilitate downstream applications, we introduce an efficient algorithm to convert 3D Gaussians into textured meshes and apply a fine-tuning stage to refine the details. Extensive experiments demonstrate the superior efficiency and competitive generation quality of our proposed approach. Notably, DreamGaussian produces high-quality textured meshes in just 2 minutes from a single-view image, achieving approximately 10 times acceleration compared to existing methods.", "github_manual": "https://github.com/dreamgaussian/dreamgaussian", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-29", "arxiv_id": "2309.16585", "title": "Text-to-3D using Gaussian Splatting", "authors": [ "Zilong Chen", "Feng Wang", "Huaping Liu" ], "github": "https://github.com/gsgen3d/gsgen", "project_page": "", "abstract": "In this paper, we present Gaussian Splatting based text-to-3D generation (GSGEN), a novel approach for generating high-quality 3D objects. Previous methods suffer from inaccurate geometry and limited fidelity due to the absence of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a recent state-of-the-art representation, to address existing shortcomings by exploiting the explicit nature that enables the incorporation of 3D prior. Specifically, our method adopts a progressive optimization strategy, which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization, a coarse representation is established under a 3D geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and 3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an iterative refinement to enrich details. In this stage, we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs, our approach can generate 3D content with delicate details and more accurate geometry. Extensive evaluations demonstrate the effectiveness of our method, especially for capturing high-frequency components. Video results are provided at https://gsgen3d.github.io. Our code is available at https://github.com/gsgen3d/gsgen", "github_manual": "", "github_regex": "https://github.com/gsgen3d/gsgen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-29", "arxiv_id": "2309.16429", "title": "Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation", "authors": [ "Guy Yariv", "Itai Gat", "Sagie Benaim", "Lior Wolf", "Idan Schwartz", "Yossi Adi" ], "github": "https://github.com/guyyariv/TempoTokens", "project_page": "", "abstract": "We consider the task of generating diverse and realistic videos guided by natural audio samples from a wide variety of semantic classes. For this task, the videos are required to be aligned both globally and temporally with the input audio: globally, the input audio is semantically associated with the entire output video, and temporally, each segment of the input audio is associated with a corresponding segment of that video. We utilize an existing text-conditioned video generation model and a pre-trained audio encoder model. The proposed method is based on a lightweight adaptor network, which learns to map the audio-based representation to the input representation expected by the text-to-video generation model. As such, it also enables video generation conditioned on text, audio, and, for the first time as far as we can ascertain, on both text and audio. We validate our method extensively on three datasets demonstrating significant semantic diversity of audio-video samples and further propose a novel evaluation metric (AV-Align) to assess the alignment of generated videos with input audio samples. AV-Align is based on the detection and comparison of energy peaks in both modalities. In comparison to recent state-of-the-art approaches, our method generates videos that are better aligned with the input sound, both with respect to content and temporal axis. We also show that videos produced by our method present higher visual quality and are more diverse.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/guyyariv/TempoTokens", "github_api_added_by": "auto" }, { "date": "2023-09-29", "arxiv_id": "2309.16496", "title": "CCEdit: Creative and Controllable Video Editing via Diffusion Models", "authors": [ "Ruoyu Feng", "Wenming Weng", "Yanhui Wang", "Yuhui Yuan", "Jianmin Bao", "Chong Luo", "Zhibo Chen", "Baining Guo" ], "github": "https://github.com/ruoyufeng/ccedit", "project_page": "", "abstract": "In this work, we present CCEdit, a versatile framework designed to address the challenges of creative and controllable video editing. CCEdit accommodates a wide spectrum of user editing requirements and enables enhanced creative control through an innovative approach that decouples video structure and appearance. We leverage the foundational ControlNet architecture to preserve structural integrity, while seamlessly integrating adaptable temporal modules compatible with state-of-the-art personalization techniques for text-to-image generation, such as DreamBooth and LoRA.Furthermore, we introduce reference-conditioned video editing, empowering users to exercise precise creative control over video editing through the more manageable process of editing key frames. Our extensive experimental evaluations confirm the exceptional functionality and editing capabilities of the proposed CCEdit framework. Demo video is available at https://www.youtube.com/watch?v=UQw4jq-igN4.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ruoyufeng/ccedit", "github_api_added_by": "auto" }, { "date": "2023-09-29", "arxiv_id": "2309.16235", "title": "Language models in molecular discovery", "authors": [ "Nikita Janakarajan", "Tim Erdmann", "Sarath Swaminathan", "Teodoro Laino", "Jannis Born" ], "github": "https://github.com/rxn4chemistry/rxn4chemistry", "project_page": "", "abstract": "The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to \"scientific language models\" that operate on small molecules, proteins or polymers. In chemistry, language models contribute to accelerating the molecule discovery cycle as evidenced by promising recent findings in early-stage drug discovery. Here, we review the role of language models in molecular discovery, underlining their strength in de novo drug design, property prediction and reaction chemistry. We highlight valuable open-source software assets thus lowering the entry barrier to the field of scientific language modeling. Last, we sketch a vision for future molecular design that combines a chatbot interface with access to computational chemistry tools. Our contribution serves as a valuable resource for researchers, chemists, and AI enthusiasts interested in understanding how language models can and will be used to accelerate chemical discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/rxn4chemistry/rxn4chemistry", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-29", "arxiv_id": "2309.16650", "title": "ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning", "authors": [ "Qiao Gu", "Alihusein Kuwajerwala", "Sacha Morin", "Krishna Murthy Jatavallabhula", "Bipasha Sen", "Aditya Agarwal", "Corban Rivera", "William Paul", "Kirsty Ellis", "Rama Chellappa", "Chuang Gan", "Celso Miguel de Melo", "Joshua B. Tenenbaum", "Antonio Torralba", "Florian Shkurti", "Liam Paull" ], "github": "", "project_page": "", "abstract": "For robots to perform a wide variety of tasks, they require a 3D representation of the world that is semantically rich, yet compact and efficient for task-driven perception and planning. Recent approaches have attempted to leverage features from large vision-language models to encode semantics in 3D representations. However, these approaches tend to produce maps with per-point feature vectors, which do not scale well in larger environments, nor do they contain semantic spatial relationships between entities in the environment, which are useful for downstream planning. In this work, we propose ConceptGraphs, an open-vocabulary graph-structured representation for 3D scenes. ConceptGraphs is built by leveraging 2D foundation models and fusing their output to 3D by multi-view association. The resulting representations generalize to novel semantic classes, without the need to collect large 3D datasets or finetune models. We demonstrate the utility of this representation through a number of downstream planning tasks that are specified through abstract (language) prompts and require complex reasoning over spatial and semantic concepts. (Project page: https://concept-graphs.github.io/ Explainer video: https://youtu.be/mRhNkQwRYnc )", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-29", "arxiv_id": "2309.16583", "title": "GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond", "authors": [ "Shen Zheng", "Yuyu Zhang", "Yijie Zhu", "Chenguang Xi", "Pengyang Gao", "Xun Zhou", "Kevin Chen-Chuan Chang" ], "github": "https://github.com/gpt-fathom/gpt-fathom", "project_page": "", "abstract": "With the rapid advancement of large language models (LLMs), there is a pressing need for a comprehensive evaluation suite to assess their capabilities and limitations. Existing LLM leaderboards often reference scores reported in other papers without consistent settings and prompts, which may inadvertently encourage cherry-picking favored settings and prompts for better results. In this work, we introduce GPT-Fathom, an open-source and reproducible LLM evaluation suite built on top of OpenAI Evals. We systematically evaluate 10+ leading LLMs as well as OpenAI's legacy models on 20+ curated benchmarks across 7 capability categories, all under aligned settings. Our retrospective study on OpenAI's earlier models offers valuable insights into the evolutionary path from GPT-3 to GPT-4. Currently, the community is eager to know how GPT-3 progressively improves to GPT-4, including technical details like whether adding code data improves LLM's reasoning capability, which aspects of LLM capability can be improved by SFT and RLHF, how much is the alignment tax, etc. Our analysis sheds light on many of these questions, aiming to improve the transparency of advanced LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gpt-fathom/gpt-fathom", "github_api_added_by": "auto" }, { "date": "2023-09-29", "arxiv_id": "2309.16671", "title": "Demystifying CLIP Data", "authors": [ "Hu Xu", "Saining Xie", "Xiaoqing Ellen Tan", "Po-Yao Huang", "Russell Howes", "Vasu Sharma", "Shang-Wen Li", "Gargi Ghosh", "Luke Zettlemoyer", "Christoph Feichtenhofer" ], "github": "https://github.com/facebookresearch/MetaCLIP", "project_page": "", "abstract": "Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced research and applications in computer vision, fueling modern recognition systems and generative models. We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective. However, CLIP only provides very limited information about its data and how it has been collected, leading to works that aim to reproduce CLIP's data by filtering with its model parameters. In this work, we intend to reveal CLIP's data curation approach and in our pursuit of making it open to the community introduce Metadata-Curated Language-Image Pre-training (MetaCLIP). MetaCLIP takes a raw data pool and metadata (derived from CLIP's concepts) and yields a balanced subset over the metadata distribution. Our experimental study rigorously isolates the model and training settings, concentrating solely on data. MetaCLIP applied to CommonCrawl with 400M image-text data pairs outperforms CLIP's data on multiple standard benchmarks. In zero-shot ImageNet classification, MetaCLIP achieves 70.8% accuracy, surpassing CLIP's 68.3% on ViT-B models. Scaling to 1B data, while maintaining the same training budget, attains 72.4%. Our observations hold across various model sizes, exemplified by ViT-H achieving 80.5%, without any bells-and-whistles. Curation code and training data distribution on metadata is made available at https://github.com/facebookresearch/MetaCLIP.", "github_manual": "", "github_regex": "https://github.com/facebookresearch/MetaCLIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-09-29", "arxiv_id": "2309.16588", "title": "Vision Transformers Need Registers", "authors": [ "Timoth\u00e9e Darcet", "Maxime Oquab", "Julien Mairal", "Piotr Bojanowski" ], "github": "https://github.com/facebookresearch/dinov2", "project_page": "", "abstract": "Transformers have recently emerged as a powerful tool for learning visual representations. In this paper, we identify and characterize artifacts in feature maps of both supervised and self-supervised ViT networks. The artifacts correspond to high-norm tokens appearing during inference primarily in low-informative background areas of images, that are repurposed for internal computations. We propose a simple yet effective solution based on providing additional tokens to the input sequence of the Vision Transformer to fill that role. We show that this solution fixes that problem entirely for both supervised and self-supervised models, sets a new state of the art for self-supervised visual models on dense visual prediction tasks, enables object discovery methods with larger models, and most importantly leads to smoother feature maps and attention maps for downstream visual processing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/dinov2", "github_api_added_by": "auto" }, { "date": "2023-09-29", "arxiv_id": "2309.16643", "title": "Deep Geometrized Cartoon Line Inbetweening", "authors": [ "Li Siyao", "Tianpei Gu", "Weiye Xiao", "Henghui Ding", "Ziwei Liu", "Chen Change Loy" ], "github": "https://github.com/lisiyao21/AnimeInbet", "project_page": "", "abstract": "We aim to address a significant but understudied problem in the anime industry, namely the inbetweening of cartoon line drawings. Inbetweening involves generating intermediate frames between two black-and-white line drawings and is a time-consuming and expensive process that can benefit from automation. However, existing frame interpolation methods that rely on matching and warping whole raster images are unsuitable for line inbetweening and often produce blurring artifacts that damage the intricate line structures. To preserve the precision and detail of the line drawings, we propose a new approach, AnimeInbet, which geometrizes raster line drawings into graphs of endpoints and reframes the inbetweening task as a graph fusion problem with vertex repositioning. Our method can effectively capture the sparsity and unique structure of line drawings while preserving the details during inbetweening. This is made possible via our novel modules, i.e., vertex geometric embedding, a vertex correspondence Transformer, an effective mechanism for vertex repositioning and a visibility predictor. To train our method, we introduce MixamoLine240, a new dataset of line drawings with ground truth vectorization and matching labels. Our experiments demonstrate that AnimeInbet synthesizes high-quality, clean, and complete intermediate line drawings, outperforming existing methods quantitatively and qualitatively, especially in cases with large motions. Data and code are available at https://github.com/lisiyao21/AnimeInbet.", "github_manual": "", "github_regex": "https://github.com/lisiyao21/AnimeInbet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-03", "arxiv_id": "2310.00426", "title": "PixArt-\u03b1: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis", "authors": [ "Junsong Chen", "Jincheng Yu", "Chongjian Ge", "Lewei Yao", "Enze Xie1", "Yue Wu", "Zhongdao Wang", "James Kwok", "Ping Luo", "Huchuan Lu", "Zhenguo Li" ], "github": "https://github.com/PixArt-alpha/PixArt-alpha", "project_page": "", "abstract": "The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community while increasing CO2 emissions. This paper introduces PIXART-alpha, a Transformer-based T2I diffusion model whose image generation quality is competitive with state-of-the-art image generators (e.g., Imagen, SDXL, and even Midjourney), reaching near-commercial application standards. Additionally, it supports high-resolution image synthesis up to 1024px resolution with low training cost, as shown in Figure 1 and 2. To achieve this goal, three core designs are proposed: (1) Training strategy decomposition: We devise three distinct training steps that separately optimize pixel dependency, text-image alignment, and image aesthetic quality; (2) Efficient T2I Transformer: We incorporate cross-attention modules into Diffusion Transformer (DiT) to inject text conditions and streamline the computation-intensive class-condition branch; (3) High-informative data: We emphasize the significance of concept density in text-image pairs and leverage a large Vision-Language model to auto-label dense pseudo-captions to assist text-image alignment learning. As a result, PIXART-alpha's training speed markedly surpasses existing large-scale T2I models, e.g., PIXART-alpha only takes 10.8% of Stable Diffusion v1.5's training time (675 vs. 6,250 A100 GPU days), saving nearly \\300,000 (26,000 vs. \\320,000) and reducing 90% CO2 emissions. Moreover, compared with a larger SOTA model, RAPHAEL, our training cost is merely 1%. Extensive experiments demonstrate that PIXART-\\alpha excels in image quality, artistry, and semantic control. We hope PIXART-\\alpha$ will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch.", "github_manual": "https://github.com/PixArt-alpha/PixArt-alpha", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-03", "arxiv_id": "2310.00898", "title": "Enable Language Models to Implicitly Learn Self-Improvement From Data", "authors": [ "Ziqi Wang", "Le Hou", "Tianjian Lu", "Yuexin Wu", "Yunxuan Li", "Hongkun Yu", "Heng Ji" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities in open-ended text generation tasks. However, the inherent open-ended nature of these tasks implies that there is always room for improvement in the quality of model responses. To address this challenge, various approaches have been proposed to enhance the performance of LLMs. There has been a growing focus on enabling LLMs to self-improve their response quality, thereby reducing the reliance on extensive human annotation efforts for collecting diverse and high-quality training data. Recently, prompting-based methods have been widely explored among self-improvement methods owing to their effectiveness, efficiency, and convenience. However, those methods usually require explicitly and thoroughly written rubrics as inputs to LLMs. It is expensive and challenging to manually derive and provide all necessary rubrics with a real-world complex goal for improvement (e.g., being more helpful and less harmful). To this end, we propose an ImPlicit Self-ImprovemenT (PIT) framework that implicitly learns the improvement goal from human preference data. PIT only requires preference data that are used to train reward models without extra human efforts. Specifically, we reformulate the training objective of reinforcement learning from human feedback (RLHF) -- instead of maximizing response quality for a given input, we maximize the quality gap of the response conditioned on a reference response. In this way, PIT is implicitly trained with the improvement goal of better aligning with human preferences. Experiments on two real-world datasets and one synthetic dataset show that our method significantly outperforms prompting-based methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-03", "arxiv_id": "2310.01407", "title": "Conditional Diffusion Distillation", "authors": [ "Kangfu Mei", "Mauricio Delbracio", "Hossein Talebi", "Zhengzhong Tu", "Vishal M. Patel", "Peyman Milanfar" ], "github": "", "project_page": "", "abstract": "Generative diffusion models provide strong priors for text-to-image generation and thereby serve as a foundation for conditional generation tasks such as image editing, restoration, and super-resolution. However, one major limitation of diffusion models is their slow sampling time. To address this challenge, we present a novel conditional distillation method designed to supplement the diffusion priors with the help of image conditions, allowing for conditional sampling with very few steps. We directly distill the unconditional pre-training in a single stage through joint-learning, largely simplifying the previous two-stage procedures that involve both distillation and conditional finetuning separately. Furthermore, our method enables a new parameter-efficient distillation mechanism that distills each task with only a small number of additional parameters combined with the shared frozen unconditional backbone. Experiments across multiple tasks including super-resolution, image editing, and depth-to-image generation demonstrate that our method outperforms existing distillation techniques for the same sampling time. Notably, our method is the first distillation strategy that can match the performance of the much slower fine-tuned conditional diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-04", "arxiv_id": "2310.01798", "title": "Large Language Models Cannot Self-Correct Reasoning Yet", "authors": [ "Jie Huang", "Xinyun Chen", "Swaroop Mishra", "Huaixiu Steven Zheng", "Adams Wei Yu", "Xinying Song", "Denny Zhou" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications. Nevertheless, concerns persist regarding the accuracy and appropriateness of their generated content. A contemporary methodology, self-correction, has been proposed as a remedy to these issues. Building upon this premise, this paper critically examines the role and efficacy of self-correction within LLMs, shedding light on its true potential and limitations. Central to our investigation is the notion of intrinsic self-correction, whereby an LLM attempts to correct its initial responses based solely on its inherent capabilities, without the crutch of external feedback. In the context of reasoning, our research indicates that LLMs struggle to self-correct their responses without external feedback, and at times, their performance might even degrade post self-correction. Drawing from these insights, we offer suggestions for future research and practical applications in this field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-04", "arxiv_id": "2310.01596", "title": "ImagenHub: Standardizing the evaluation of conditional image generation models", "authors": [ "Max Ku", "Tianle Li", "Kai Zhang", "Yujie Lu", "Xingyu Fu", "Wenwen Zhuang", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Recently, a myriad of conditional image generation and editing models have been developed to serve different downstream tasks, including text-to-image generation, text-guided image editing, subject-driven image generation, control-guided image generation, etc. However, we observe huge inconsistencies in experimental conditions: datasets, inference, and evaluation metrics - render fair comparisons difficult. This paper proposes ImagenHub, which is a one-stop library to standardize the inference and evaluation of all the conditional image generation models. Firstly, we define seven prominent tasks and curate high-quality evaluation datasets for them. Secondly, we built a unified inference pipeline to ensure fair comparison. Thirdly, we design two human evaluation scores, i.e. Semantic Consistency and Perceptual Quality, along with comprehensive guidelines to evaluate generated images. We train expert raters to evaluate the model outputs based on the proposed metrics. Our human evaluation achieves a high inter-worker agreement of Krippendorff's alpha on 76% models with a value higher than 0.4. We comprehensively evaluated a total of around 30 models and observed three key takeaways: (1) the existing models' performance is generally unsatisfying except for Text-guided Image Generation and Subject-driven Image Generation, with 74% models achieving an overall score lower than 0.5. (2) we examined the claims from published papers and found 83% of them hold with a few exceptions. (3) None of the existing automatic metrics has a Spearman's correlation higher than 0.2 except subject-driven image generation. Moving forward, we will continue our efforts to evaluate newly published models and update our leaderboard to keep track of the progress in conditional image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-04", "arxiv_id": "2310.01557", "title": "SmartPlay : A Benchmark for LLMs as Intelligent Agents", "authors": [ "Yue Wu", "Xuan Tang", "Tom M. Mitchell", "Yuanzhi Li" ], "github": "https://github.com/microsoft/smartplay", "project_page": "", "abstract": "Recent large language models (LLMs) have demonstrated great potential toward intelligent agents and next-gen automation, but there currently lacks a systematic benchmark for evaluating LLMs' abilities as agents. We introduce SmartPlay: both a challenging benchmark and a methodology for evaluating LLMs as agents. SmartPlay consists of 6 different games, including Rock-Paper-Scissors, Tower of Hanoi, Minecraft. Each game features a unique setting, providing up to 20 evaluation settings and infinite environment variations. Each game in SmartPlay uniquely challenges a subset of 9 important capabilities of an intelligent LLM agent, including reasoning with object dependencies, planning ahead, spatial reasoning, learning from history, and understanding randomness. The distinction between the set of capabilities each game test allows us to analyze each capability separately. SmartPlay serves not only as a rigorous testing ground for evaluating the overall performance of LLM agents but also as a road-map for identifying gaps in current methodologies. We release our benchmark at github.com/LLMsmartplay/SmartPlay", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/smartplay", "github_api_added_by": "auto" }, { "date": "2023-10-04", "arxiv_id": "2310.01714", "title": "Large Language Models as Analogical Reasoners", "authors": [ "Michihiro Yasunaga", "Xinyun Chen", "Yujia Li", "Panupong Pasupat", "Jure Leskovec", "Percy Liang", "Ed H. Chi", "Denny Zhou" ], "github": "", "project_page": "", "abstract": "Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks, but typically needs labeled exemplars of the reasoning process. In this work, we introduce a new prompting approach, Analogical Prompting, designed to automatically guide the reasoning process of large language models. Inspired by analogical reasoning, a cognitive process in which humans draw from relevant past experiences to tackle new problems, our approach prompts language models to self-generate relevant exemplars or knowledge in the context, before proceeding to solve the given problem. This method presents several advantages: it obviates the need for labeling or retrieving exemplars, offering generality and convenience; it can also tailor the generated exemplars and knowledge to each problem, offering adaptability. Experimental results show that our approach outperforms 0-shot CoT and manual few-shot CoT in a variety of reasoning tasks, including math problem solving in GSM8K and MATH, code generation in Codeforces, and other reasoning tasks in BIG-Bench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-06", "arxiv_id": "2310.03051", "title": "How FaR Are Large Language Models From Agents with Theory-of-Mind?", "authors": [ "Pei Zhou", "Aman Madaan", "Srividya Pranavi Potharaju", "Aditya Gupta", "Kevin R. McKee", "Ari Holtzman", "Jay Pujara", "Xiang Ren", "Swaroop Mishra", "Aida Nematzadeh", "Shyam Upadhyay", "Manaal Faruqui" ], "github": "", "project_page": "", "abstract": "\"Thinking is for Doing.\" Humans can infer other people's mental states from observations--an ability called Theory-of-Mind (ToM)--and subsequently act pragmatically on those inferences. Existing question answering benchmarks such as ToMi ask models questions to make inferences about beliefs of characters in a story, but do not test whether models can then use these inferences to guide their actions. We propose a new evaluation paradigm for large language models (LLMs): Thinking for Doing (T4D), which requires models to connect inferences about others' mental states to actions in social scenarios. Experiments on T4D demonstrate that LLMs such as GPT-4 and PaLM 2 seemingly excel at tracking characters' beliefs in stories, but they struggle to translate this capability into strategic action. Our analysis reveals the core challenge for LLMs lies in identifying the implicit inferences about mental states without being explicitly asked about as in ToMi, that lead to choosing the correct action in T4D. To bridge this gap, we introduce a zero-shot prompting framework, Foresee and Reflect (FaR), which provides a reasoning structure that encourages LLMs to anticipate future challenges and reason about potential actions. FaR boosts GPT-4's performance from 50% to 71% on T4D, outperforming other prompting methods such as Chain-of-Thought and Self-Ask. Moreover, FaR generalizes to diverse out-of-distribution story structures and scenarios that also require ToM inferences to choose an action, consistently outperforming other methods including few-shot in-context learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-06", "arxiv_id": "2310.03502", "title": "Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion", "authors": [ "Anton Razzhigaev", "Arseniy Shakhmatov", "Anastasia Maltseva", "Vladimir Arkhipkin", "Igor Pavlov", "Ilya Ryabov", "Angelina Kuts", "Alexander Panchenko", "Andrey Kuznetsov", "Denis Dimitrov" ], "github": "https://github.com/ai-forever/Kandinsky-2", "project_page": "", "abstract": "Text-to-image generation is a significant domain in modern computer vision and has achieved substantial improvements through the evolution of generative architectures. Among these, there are diffusion-based models that have demonstrated essential quality enhancements. These models are generally split into two categories: pixel-level and latent-level approaches. We present Kandinsky1, a novel exploration of latent diffusion architecture, combining the principles of the image prior models with latent diffusion techniques. The image prior model is trained separately to map text embeddings to image embeddings of CLIP. Another distinct feature of the proposed model is the modified MoVQ implementation, which serves as the image autoencoder component. Overall, the designed model contains 3.3B parameters. We also deployed a user-friendly demo system that supports diverse generative modes such as text-to-image generation, image fusion, text and image fusion, image variations generation, and text-guided inpainting/outpainting. Additionally, we released the source code and checkpoints for the Kandinsky models. Experimental evaluations demonstrate a FID score of 8.03 on the COCO-30K dataset, marking our model as the top open-source performer in terms of measurable image generation quality.", "github_manual": "https://github.com/ai-forever/Kandinsky-2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-06", "arxiv_id": "2310.03739", "title": "Aligning Text-to-Image Diffusion Models with Reward Backpropagation", "authors": [ "Mihir Prabhudesai", "Anirudh Goyal", "Deepak Pathak", "Katerina Fragkiadaki" ], "github": "https://github.com/mihirp1998/alignprop", "project_page": "", "abstract": "Text-to-image diffusion models have recently emerged at the forefront of image generation, powered by very large-scale unsupervised or weakly supervised text-to-image training datasets. Due to their unsupervised training, controlling their behavior in downstream tasks, such as maximizing human-perceived image quality, image-text alignment, or ethical image generation, is difficult. Recent works finetune diffusion models to downstream reward functions using vanilla reinforcement learning, notorious for the high variance of the gradient estimators. In this paper, we propose AlignProp, a method that aligns diffusion models to downstream reward functions using end-to-end backpropagation of the reward gradient through the denoising process. While naive implementation of such backpropagation would require prohibitive memory resources for storing the partial derivatives of modern text-to-image models, AlignProp finetunes low-rank adapter weight modules and uses gradient checkpointing, to render its memory usage viable. We test AlignProp in finetuning diffusion models to various objectives, such as image-text semantic alignment, aesthetics, compressibility and controllability of the number of objects present, as well as their combinations. We show AlignProp achieves higher rewards in fewer training steps than alternatives, while being conceptually simpler, making it a straightforward choice for optimizing diffusion models for differentiable reward functions of interest. Code and Visualization results are available at https://align-prop.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mihirp1998/alignprop", "github_api_added_by": "auto" }, { "date": "2023-10-06", "arxiv_id": "2310.03744", "title": "Improved Baselines with Visual Instruction Tuning", "authors": [ "Haotian Liu", "Chunyuan Li", "Yuheng Li", "Yong Jae Lee" ], "github": "", "project_page": "", "abstract": "Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning. In this note, we show that the fully-connected vision-language cross-modal connector in LLaVA is surprisingly powerful and data-efficient. With simple modifications to LLaVA, namely, using CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA data with simple response formatting prompts, we establish stronger baselines that achieve state-of-the-art across 11 benchmarks. Our final 13B checkpoint uses merely 1.2M publicly available data, and finishes full training in ~1 day on a single 8-A100 node. We hope this can make state-of-the-art LMM research more accessible. Code and model will be publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-06", "arxiv_id": "2310.00704", "title": "UniAudio: An Audio Foundation Model Toward Universal Audio Generation", "authors": [ "Dongchao Yang", "Jinchuan Tian", "Xu Tan", "Rongjie Huang", "Songxiang Liu", "Xuankai Chang", "Jiatong Shi", "Sheng Zhao", "Jiang Bian", "Xixin Wu", "Zhou Zhao", "Helen Meng" ], "github": "https://github.com/yangdongchao/UniAudio", "project_page": "", "abstract": "Language models (LMs) have demonstrated the capability to handle a variety of generative tasks. This paper presents the UniAudio system, which, unlike prior task-specific approaches, leverages LMs techniques to generate multiple types of audio (including speech, sounds, music, and singing) with given input conditions. UniAudio 1) first tokenizes all types of target audio along with other condition modalities, 2) concatenates source-target pair as a single sequence, and 3) performs next-token prediction using LMs. Also, a multi-scale Transformer model is proposed to handle the overly long sequences caused by the residual vector quantization based neural codec in tokenization. Training of UniAudio is scaled up to 165K hours of audio and 1B parameters, based on all generative tasks, aiming to obtain sufficient prior knowledge not only in the intrinsic properties of audio but also the inter-relationship between audio and other modalities. Therefore, the trained UniAudio model has the potential to become a foundation model for universal audio generation: it shows strong capability in all trained tasks and can seamlessly support new audio generation tasks after simple fine-tuning. Experiments demonstrate that UniAudio achieves state-of-the-art or at least competitive results on most of the 11 tasks. Demo and code are released at https://github.com/yangdongchao/UniAudio", "github_manual": "", "github_regex": "https://github.com/yangdongchao/UniAudio", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-06", "arxiv_id": "2310.03720", "title": "HeaP: Hierarchical Policies for Web Actions using LLMs", "authors": [ "Paloma Sodhi", "S. R. K. Branavan", "Ryan McDonald" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities in performing a range of instruction following tasks in few and zero-shot settings. However, teaching LLMs to perform tasks on the web presents fundamental challenges -- combinatorially large open-world tasks and variations across web interfaces. We tackle these challenges by leveraging LLMs to decompose web tasks into a collection of sub-tasks, each of which can be solved by a low-level, closed-loop policy. These policies constitute a shared grammar across tasks, i.e., new web tasks can be expressed as a composition of these policies. We propose a novel framework, Hierarchical Policies for Web Actions using LLMs (HeaP), that learns a set of hierarchical LLM prompts from demonstrations for planning high-level tasks and executing them via a sequence of low-level policies. We evaluate HeaP against a range of baselines on a suite of web tasks, including MiniWoB++, WebArena, a mock airline CRM, as well as live website interactions, and show that it is able to outperform prior works using orders of magnitude less data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-06", "arxiv_id": "2310.03734", "title": "Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency", "authors": [ "Tianhong Li", "Sangnie Bhardwaj", "Yonglong Tian", "Han Zhang", "Jarred Barber", "Dina Katabi", "Guillaume Lajoie", "Huiwen Chang", "Dilip Krishnan" ], "github": "", "project_page": "", "abstract": "Current vision-language generative models rely on expansive corpora of paired image-text data to attain optimal performance and generalization capabilities. However, automatically collecting such data (e.g. via large-scale web scraping) leads to low quality and poor image-text correlation, while human annotation is more accurate but requires significant manual effort and expense. We introduce ITIT (InTegrating Image Text): an innovative training paradigm grounded in the concept of cycle consistency which allows vision-language training on unpaired image and text data. ITIT is comprised of a joint image-text encoder with disjoint image and text decoders that enable bidirectional image-to-text and text-to-image generation in a single framework. During training, ITIT leverages a small set of paired image-text data to ensure its output matches the input reasonably well in both directions. Simultaneously, the model is also trained on much larger datasets containing only images or texts. This is achieved by enforcing cycle consistency between the original unpaired samples and the cycle-generated counterparts. For instance, it generates a caption for a given input image and then uses the caption to create an output image, and enforces similarity between the input and output images. Our experiments show that ITIT with unpaired datasets exhibits similar scaling behavior as using high-quality paired data. We demonstrate image generation and captioning performance on par with state-of-the-art text-to-image and image-to-text models with orders of magnitude fewer (only 3M) paired image-text data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-06", "arxiv_id": "2310.03214", "title": "FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation", "authors": [ "Tu Vu", "Mohit Iyyer", "Xuezhi Wang", "Noah Constant", "Jerry Wei", "Jason Wei", "Chris Tar", "Yun-Hsuan Sung", "Denny Zhou", "Quoc Le", "Thang Luong" ], "github": "https://github.com/freshllms/freshqa", "project_page": "", "abstract": "Most large language models (LLMs) are trained once and never updated; thus, they lack the ability to dynamically adapt to our ever-changing world. In this work, we perform a detailed study of the factuality of LLM-generated text in the context of answering questions that test current world knowledge. Specifically, we introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types, including questions that require fast-changing world knowledge as well as questions with false premises that need to be debunked. We benchmark a diverse array of both closed and open-source LLMs under a two-mode evaluation procedure that allows us to measure both correctness and hallucination. Through human evaluations involving more than 50K judgments, we shed light on limitations of these models and demonstrate significant room for improvement: for instance, all models (regardless of model size) struggle on questions that involve fast-changing knowledge and false premises. Motivated by these results, we present FreshPrompt, a simple few-shot prompting method that substantially boosts the performance of an LLM on FreshQA by incorporating relevant and up-to-date information retrieved from a search engine into the prompt. Our experiments show that FreshPrompt outperforms both competing search engine-augmented prompting methods such as Self-Ask (Press et al., 2022) as well as commercial systems such as Perplexity.AI. Further analysis of FreshPrompt reveals that both the number of retrieved evidences and their order play a key role in influencing the correctness of LLM-generated answers. Additionally, instructing the LLM to generate concise and direct answers helps reduce hallucination compared to encouraging more verbose answers. To facilitate future work, we release FreshQA at github.com/freshllms/freshqa and commit to updating it at regular intervals.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/freshllms/freshqa", "github_api_added_by": "auto" }, { "date": "2023-10-06", "arxiv_id": "2310.03704", "title": "Drag View: Generalizable Novel View Synthesis with Unposed Imagery", "authors": [ "Zhiwen Fan", "Panwang Pan", "Peihao Wang", "Yifan Jiang", "Hanwen Jiang", "Dejia Xu", "Zehao Zhu", "Dilin Wang", "Zhangyang Wang" ], "github": "", "project_page": "", "abstract": "We introduce DragView, a novel and interactive framework for generating novel views of unseen scenes. DragView initializes the new view from a single source image, and the rendering is supported by a sparse set of unposed multi-view images, all seamlessly executed within a single feed-forward pass. Our approach begins with users dragging a source view through a local relative coordinate system. Pixel-aligned features are obtained by projecting the sampled 3D points along the target ray onto the source view. We then incorporate a view-dependent modulation layer to effectively handle occlusion during the projection. Additionally, we broaden the epipolar attention mechanism to encompass all source pixels, facilitating the aggregation of initialized coordinate-aligned point features from other unposed views. Finally, we employ another transformer to decode ray features into final pixel intensities. Crucially, our framework does not rely on either 2D prior models or the explicit estimation of camera poses. During testing, DragView showcases the capability to generalize to new scenes unseen during training, also utilizing only unposed support images, enabling the generation of photo-realistic new views characterized by flexible camera trajectories. In our experiments, we conduct a comprehensive comparison of the performance of DragView with recent scene representation networks operating under pose-free conditions, as well as with generalizable NeRFs subject to noisy test camera poses. DragView consistently demonstrates its superior performance in view synthesis quality, while also being more user-friendly. Project page: https://zhiwenfan.github.io/DragView/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-06", "arxiv_id": "2310.03716", "title": "A Long Way to Go: Investigating Length Correlations in RLHF", "authors": [ "Prasann Singhal", "Tanya Goyal", "Jiacheng Xu", "Greg Durrett" ], "github": "https://github.com/prasanns/rlhf-length-biases", "project_page": "", "abstract": "Great successes have been reported using Reinforcement Learning from Human Feedback (RLHF) to align large language models. Open-source preference datasets and reward models have enabled wider experimentation beyond generic chat settings, particularly to make systems more \"helpful\" for tasks like web question answering, summarization, and multi-turn dialogue. When optimizing for helpfulness, RLHF has been consistently observed to drive models to produce longer outputs. This paper demonstrates that optimizing for response length is a significant factor behind RLHF's reported improvements in these settings. First, we study the relationship between reward and length for reward models trained on three open-source preference datasets for helpfulness. Here, length correlates strongly with reward, and improvements in reward score are driven in large part by shifting the distribution over output lengths. We then explore interventions during both RL and reward model learning to see if we can achieve the same downstream improvements as RLHF without increasing length. While our interventions mitigate length increases, they aren't uniformly effective across settings. Furthermore, we find that even running RLHF with a reward based solely on length can reproduce most of the downstream improvements over the initial policy model, showing that reward models in these settings have a long way to go.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/prasanns/rlhf-length-biases", "github_api_added_by": "auto" }, { "date": "2023-10-06", "arxiv_id": "2310.03731", "title": "MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning", "authors": [ "Ke Wang", "Houxing Ren", "Aojun Zhou", "Zimu Lu", "Sichun Luo", "Weikang Shi", "Renrui Zhang", "Linqi Song", "Mingjie Zhan", "Hongsheng Li" ], "github": "https://github.com/mathllm/MathCoder", "project_page": "", "abstract": "The recently released GPT-4 Code Interpreter has demonstrated remarkable proficiency in solving challenging math problems, primarily attributed to its ability to seamlessly reason with natural language, generate code, execute code, and continue reasoning based on the execution output. In this paper, we present a method to fine-tune open-source language models, enabling them to use code for modeling and deriving math equations and, consequently, enhancing their mathematical reasoning abilities. We propose a method of generating novel and high-quality datasets with math problems and their code-based solutions, referred to as MathCodeInstruct. Each solution interleaves natural language, code, and execution results. We also introduce a customized supervised fine-tuning and inference approach. This approach yields the MathCoder models, a family of models capable of generating code-based solutions for solving challenging math problems. Impressively, the MathCoder models achieve state-of-the-art scores among open-source LLMs on the MATH (45.2%) and GSM8K (83.9%) datasets, substantially outperforming other open-source alternatives. Notably, the MathCoder model not only surpasses ChatGPT-3.5 and PaLM-2 on GSM8K and MATH but also outperforms GPT-4 on the competition-level MATH dataset. The dataset and models will be released at https://github.com/mathllm/MathCoder.", "github_manual": "", "github_regex": "https://github.com/mathllm/MathCoder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-06", "arxiv_id": "2310.03714", "title": "DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines", "authors": [ "Omar Khattab", "Arnav Singhvi", "Paridhi Maheshwari", "Zhiyuan Zhang", "Keshav Santhanam", "Sri Vardhamanan", "Saiful Haq", "Ashutosh Sharma", "Thomas T. Joshi", "Hanna Moazam", "Heather Miller", "Matei Zaharia", "Christopher Potts" ], "github": "https://github.com/stanfordnlp/dspy", "project_page": "", "abstract": "The ML community is rapidly exploring techniques for prompting language models (LMs) and for stacking them into pipelines that solve complex tasks. Unfortunately, existing LM pipelines are typically implemented using hard-coded \"prompt templates\", i.e. lengthy strings discovered via trial and error. Toward a more systematic approach for developing and optimizing LM pipelines, we introduce DSPy, a programming model that abstracts LM pipelines as text transformation graphs, i.e. imperative computational graphs where LMs are invoked through declarative modules. DSPy modules are parameterized, meaning they can learn (by creating and collecting demonstrations) how to apply compositions of prompting, finetuning, augmentation, and reasoning techniques. We design a compiler that will optimize any DSPy pipeline to maximize a given metric. We conduct two case studies, showing that succinct DSPy programs can express and optimize sophisticated LM pipelines that reason about math word problems, tackle multi-hop retrieval, answer complex questions, and control agent loops. Within minutes of compiling, a few lines of DSPy allow GPT-3.5 and llama2-13b-chat to self-bootstrap pipelines that outperform standard few-shot prompting (generally by over 25% and 65%, respectively) and pipelines with expert-created demonstrations (by up to 5-46% and 16-40%, respectively). On top of that, DSPy programs compiled to open and relatively small LMs like 770M-parameter T5 and llama2-13b-chat are competitive with approaches that rely on expert-written prompt chains for proprietary GPT-3.5. DSPy is available at https://github.com/stanfordnlp/dspy", "github_manual": "", "github_regex": "https://github.com/stanfordnlp/dspy", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-06", "arxiv_id": "2310.03094", "title": "Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning", "authors": [ "Murong Yue", "Jie Zhao", "Min Zhang", "Liang Du", "Ziyu Yao" ], "github": "https://github.com/murongyue/llm_mot_cascade", "project_page": "", "abstract": "Large language models (LLMs) such as GPT-4 have exhibited remarkable performance in a variety of tasks, but this strong performance often comes with the high expense of using paid API services. In this paper, we are motivated to study building an LLM cascade to save the cost of using LLMs, particularly for performing reasoning (e.g., mathematical, causal) tasks. Our cascade pipeline follows the intuition that simpler questions can be addressed by a weaker but more affordable LLM, whereas only the challenging questions necessitate the stronger and more expensive LLM. To realize this decision-making, we consider the \"answer consistency\" of the weaker LLM as a signal of the question difficulty and propose several methods for the answer sampling and consistency checking, including one leveraging a mixture of two thought representations (i.e., Chain-of-Thought and Program-of-Thought). Through experiments on six reasoning benchmark datasets, with GPT-3.5-turbo and GPT-4 being the weaker and stronger LLMs, respectively, we demonstrate that our proposed LLM cascades can achieve performance comparable to using solely the stronger LLM but require only 40% of its cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/murongyue/llm_mot_cascade", "github_api_added_by": "auto" }, { "date": "2023-10-06", "arxiv_id": "2310.03046", "title": "EcoAssistant: Using LLM Assistant More Affordably and Accurately", "authors": [ "Jieyu Zhang", "Ranjay Krishna", "Ahmed H. Awadallah", "Chi Wang" ], "github": "https://github.com/jieyuz2/ecoassistant", "project_page": "", "abstract": "Today, users ask Large language models (LLMs) as assistants to answer queries that require external knowledge; they ask about the weather in a specific city, about stock prices, and even about where specific locations are within their neighborhood. These queries require the LLM to produce code that invokes external APIs to answer the user's question, yet LLMs rarely produce correct code on the first try, requiring iterative code refinement upon execution results. In addition, using LLM assistants to support high query volumes can be expensive. In this work, we contribute a framework, EcoAssistant, that enables LLMs to answer code-driven queries more affordably and accurately. EcoAssistant contains three components. First, it allows the LLM assistants to converse with an automatic code executor to iteratively refine code or to produce answers based on the execution results. Second, we use a hierarchy of LLM assistants, which attempts to answer the query with weaker, cheaper LLMs before backing off to stronger, expensive ones. Third, we retrieve solutions from past successful queries as in-context demonstrations to help subsequent queries. Empirically, we show that EcoAssistant offers distinct advantages for affordability and accuracy, surpassing GPT-4 by 10 points of success rate with less than 50% of GPT-4's cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jieyuz2/ecoassistant", "github_api_added_by": "auto" }, { "date": "2023-10-13", "arxiv_id": "2310.08491", "title": "Prometheus: Inducing Fine-grained Evaluation Capability in Language Models", "authors": [ "Seungone Kim", "Jamin Shin", "Yejin Cho", "Joel Jang", "Shayne Longpre", "Hwaran Lee", "Sangdoo Yun", "Seongjin Shin", "Sungdong Kim", "James Thorne", "Minjoon Seo" ], "github": "https://github.com/kaistAI/Prometheus", "project_page": "", "abstract": "Recently, using a powerful proprietary Large Language Model (LLM) (e.g., GPT-4) as an evaluator for long-form responses has become the de facto standard. However, for practitioners with large-scale evaluation tasks and custom criteria in consideration (e.g., child-readability), using proprietary LLMs as an evaluator is unreliable due to the closed-source nature, uncontrolled versioning, and prohibitive costs. In this work, we propose Prometheus, a fully open-source LLM that is on par with GPT-4's evaluation capabilities when the appropriate reference materials (reference answer, score rubric) are accompanied. We first construct the Feedback Collection, a new dataset that consists of 1K fine-grained score rubrics, 20K instructions, and 100K responses and language feedback generated by GPT-4. Using the Feedback Collection, we train Prometheus, a 13B evaluator LLM that can assess any given long-form text based on customized score rubric provided by the user. Experimental results show that Prometheus scores a Pearson correlation of 0.897 with human evaluators when evaluating with 45 customized score rubrics, which is on par with GPT-4 (0.882), and greatly outperforms ChatGPT (0.392). Furthermore, measuring correlation with GPT-4 with 1222 customized score rubrics across four benchmarks (MT Bench, Vicuna Bench, Feedback Bench, Flask Eval) shows similar trends, bolstering Prometheus's capability as an evaluator LLM. Lastly, Prometheus achieves the highest accuracy on two human preference benchmarks (HHH Alignment & MT Bench Human Judgment) compared to open-sourced reward models explicitly trained on human preference datasets, highlighting its potential as an universal reward model. We open-source our code, dataset, and model at https://github.com/kaistAI/Prometheus.", "github_manual": "", "github_regex": "https://github.com/kaistAI/Prometheus", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-13", "arxiv_id": "2310.08185", "title": "EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form Narrative Text Generation", "authors": [ "Wang You", "Wenshan Wu", "Yaobo Liang", "Shaoguang Mao", "Chenfei Wu", "Maosong Cao", "Yuzhe Cai", "Yiduo Guo", "Yan Xia", "Furu Wei", "Nan Duan" ], "github": "", "project_page": "", "abstract": "Plan-and-Write is a common hierarchical approach in long-form narrative text generation, which first creates a plan to guide the narrative writing. Following this approach, several studies rely on simply prompting large language models for planning, which often yields suboptimal results. In this paper, we propose a new framework called Evaluation-guided Iterative Plan Extraction for long-form narrative text generation (EIPE-text), which extracts plans from the corpus of narratives and utilizes the extracted plans to construct a better planner. EIPE-text has three stages: plan extraction, learning, and inference. In the plan extraction stage, it iteratively extracts and improves plans from the narrative corpus and constructs a plan corpus. We propose a question answer (QA) based evaluation mechanism to automatically evaluate the plans and generate detailed plan refinement instructions to guide the iterative improvement. In the learning stage, we build a better planner by fine-tuning with the plan corpus or in-context learning with examples in the plan corpus. Finally, we leverage a hierarchical approach to generate long-form narratives. We evaluate the effectiveness of EIPE-text in the domains of novels and storytelling. Both GPT-4-based evaluations and human evaluations demonstrate that our method can generate more coherent and relevant long-form narratives. Our code will be released in the future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-13", "arxiv_id": "2310.07889", "title": "LangNav: Language as a Perceptual Representation for Navigation", "authors": [ "Bowen Pan", "Rameswar Panda", "SouYoung Jin", "Rogerio Feris", "Aude Oliva", "Phillip Isola", "Yoon Kim" ], "github": "", "project_page": "", "abstract": "We explore the use of language as a perceptual representation for vision-and-language navigation. Our approach uses off-the-shelf vision systems (for image captioning and object detection) to convert an agent's egocentric panoramic view at each time step into natural language descriptions. We then finetune a pretrained language model to select an action, based on the current view and the trajectory history, that would best fulfill the navigation instructions. In contrast to the standard setup which adapts a pretrained language model to work directly with continuous visual features from pretrained vision models, our approach instead uses (discrete) language as the perceptual representation. We explore two use cases of our language-based navigation (LangNav) approach on the R2R vision-and-language navigation benchmark: generating synthetic trajectories from a prompted large language model (GPT-4) with which to finetune a smaller language model; and sim-to-real transfer where we transfer a policy learned on a simulated environment (ALFRED) to a real-world environment (R2R). Our approach is found to improve upon strong baselines that rely on visual features in settings where only a few gold trajectories (10-100) are available, demonstrating the potential of using language as a perceptual representation for navigation tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-13", "arxiv_id": "2310.06830", "title": "Lemur: Harmonizing Natural Language and Code for Language Agents", "authors": [ "Yiheng Xu", "Hongjin Su", "Chen Xing", "Boyu Mi", "Qian Liu", "Weijia Shi", "Binyuan Hui", "Fan Zhou", "Yitao Liu", "Tianbao Xie", "Zhoujun Cheng", "Siheng Zhao", "Lingpeng Kong", "Bailin Wang", "Caiming Xiong", "Tao Yu" ], "github": "https://github.com/OpenLemur/Lemur", "project_page": "", "abstract": "We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents. The evolution from language chat models to functional language agents demands that models not only master human interaction, reasoning, and planning but also ensure grounding in the relevant environments. This calls for a harmonious blend of language and coding capabilities in the models. Lemur and Lemur-Chat are proposed to address this necessity, demonstrating balanced proficiencies in both domains, unlike existing open-source models that tend to specialize in either. Through meticulous pre-training using a code-intensive corpus and instruction fine-tuning on text and code data, our models achieve state-of-the-art averaged performance across diverse text and coding benchmarks among open-source models. Comprehensive experiments demonstrate Lemur's superiority over existing open-source models and its proficiency across various agent tasks involving human communication, tool usage, and interaction under fully- and partially- observable environments. The harmonization between natural and programming languages enables Lemur-Chat to significantly narrow the gap with proprietary models on agent abilities, providing key insights into developing advanced open-source agents adept at reasoning, planning, and operating seamlessly across environments. https://github.com/OpenLemur/Lemur", "github_manual": "", "github_regex": "https://github.com/OpenLemur/Lemur", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-13", "arxiv_id": "2310.08588", "title": "Octopus: Embodied Vision-Language Programmer from Environmental Feedback", "authors": [ "Jingkang Yang", "Yuhao Dong", "Shuai Liu", "Bo Li", "Ziyue Wang", "Chencheng Jiang", "Haoran Tan", "Jiamu Kang", "Yuanhan Zhang", "Kaiyang Zhou", "Ziwei Liu" ], "github": "https://github.com/dongyh20/octopus", "project_page": "", "abstract": "Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. Furthermore, when seamlessly integrated into an embodied agent, it signifies a crucial stride towards the creation of autonomous and context-aware systems capable of formulating plans and executing commands with precision. In this paper, we introduce Octopus, a novel VLM designed to proficiently decipher an agent's vision and textual task objectives and to formulate intricate action sequences and generate executable code. Our design allows the agent to adeptly handle a wide spectrum of tasks, ranging from mundane daily chores in simulators to sophisticated interactions in complex video games. Octopus is trained by leveraging GPT-4 to control an explorative agent to generate training data, i.e., action blueprints and the corresponding executable code, within our experimental environment called OctoVerse. We also collect the feedback that allows the enhanced training scheme of Reinforcement Learning with Environmental Feedback (RLEF). Through a series of experiments, we illuminate Octopus's functionality and present compelling results, and the proposed RLEF turns out to refine the agent's decision-making. By open-sourcing our model architecture, simulator, and dataset, we aspire to ignite further innovation and foster collaborative applications within the broader embodied AI community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dongyh20/octopus", "github_api_added_by": "auto" }, { "date": "2023-10-13", "arxiv_id": "2310.08529", "title": "GaussianDreamer: Fast Generation from Text to 3D Gaussian Splatting with Point Cloud Priors", "authors": [ "Taoran Yi", "Jiemin Fang", "Guanjun Wu", "Lingxi Xie", "Xiaopeng Zhang", "Wenyu Liu", "Qi Tian", "Xinggang Wang" ], "github": "https://github.com/hustvl/GaussianDreamer", "project_page": "", "abstract": "In recent times, the generation of 3D assets from text prompts has shown impressive results. Both 2D and 3D diffusion models can generate decent 3D objects based on prompts. 3D diffusion models have good 3D consistency, but their quality and generalization are limited as trainable 3D data is expensive and hard to obtain. 2D diffusion models enjoy strong abilities of generalization and fine generation, but the 3D consistency is hard to guarantee. This paper attempts to bridge the power from the two types of diffusion models via the recent explicit and efficient 3D Gaussian splatting representation. A fast 3D generation framework, named as \\name, is proposed, where the 3D diffusion model provides point cloud priors for initialization and the 2D diffusion model enriches the geometry and appearance. Operations of noisy point growing and color perturbation are introduced to enhance the initialized Gaussians. Our \\name can generate a high-quality 3D instance within 25 minutes on one GPU, much faster than previous methods, while the generated instances can be directly rendered in real time. Demos and code are available at https://taoranyi.com/gaussiandreamer/.", "github_manual": "https://github.com/hustvl/GaussianDreamer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-13", "arxiv_id": "2310.08465", "title": "MotionDirector: Motion Customization of Text-to-Video Diffusion Models", "authors": [ "Rui Zhao", "Yuchao Gu", "Jay Zhangjie Wu", "David Junhao Zhang", "Jiawei Liu", "Weijia Wu", "Jussi Keppo", "Mike Zheng Shou" ], "github": "https://github.com/showlab/MotionDirector", "project_page": "", "abstract": "Large-scale pre-trained diffusion models have exhibited remarkable capabilities in diverse video generations. Given a set of video clips of the same motion concept, the task of Motion Customization is to adapt existing text-to-video diffusion models to generate videos with this motion. For example, generating a video with a car moving in a prescribed manner under specific camera movements to make a movie, or a video illustrating how a bear would lift weights to inspire creators. Adaptation methods have been developed for customizing appearance like subject or style, yet unexplored for motion. It is straightforward to extend mainstream adaption methods for motion customization, including full model tuning, parameter-efficient tuning of additional layers, and Low-Rank Adaptions (LoRAs). However, the motion concept learned by these methods is often coupled with the limited appearances in the training videos, making it difficult to generalize the customized motion to other appearances. To overcome this challenge, we propose MotionDirector, with a dual-path LoRAs architecture to decouple the learning of appearance and motion. Further, we design a novel appearance-debiased temporal loss to mitigate the influence of appearance on the temporal training objective. Experimental results show the proposed method can generate videos of diverse appearances for the customized motions. Our method also supports various downstream applications, such as the mixing of different videos with their appearance and motion respectively, and animating a single image with customized motions. Our code and model weights will be released.", "github_manual": "https://github.com/showlab/MotionDirector", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-13", "arxiv_id": "2310.08579", "title": "HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion", "authors": [ "Xian Liu", "Jian Ren", "Aliaksandr Siarohin", "Ivan Skorokhodov", "Yanyu Li", "Dahua Lin", "Xihui Liu", "Ziwei Liu", "Sergey Tulyakov" ], "github": "https://github.com/snap-research/HyperHuman", "project_page": "", "abstract": "Despite significant advances in large-scale text-to-image models, achieving hyper-realistic human image generation remains a desirable yet unsolved task. Existing models like Stable Diffusion and DALL-E 2 tend to generate human images with incoherent parts or unnatural poses. To tackle these challenges, our key insight is that human image is inherently structural over multiple granularities, from the coarse-level body skeleton to fine-grained spatial geometry. Therefore, capturing such correlations between the explicit appearance and latent structure in one model is essential to generate coherent and natural human images. To this end, we propose a unified framework, HyperHuman, that generates in-the-wild human images of high realism and diverse layouts. Specifically, 1) we first build a large-scale human-centric dataset, named HumanVerse, which consists of 340M images with comprehensive annotations like human pose, depth, and surface normal. 2) Next, we propose a Latent Structural Diffusion Model that simultaneously denoises the depth and surface normal along with the synthesized RGB image. Our model enforces the joint learning of image appearance, spatial relationship, and geometry in a unified network, where each branch in the model complements to each other with both structural awareness and textural richness. 3) Finally, to further boost the visual quality, we propose a Structure-Guided Refiner to compose the predicted conditions for more detailed generation of higher resolution. Extensive experiments demonstrate that our framework yields the state-of-the-art performance, generating hyper-realistic human images under diverse scenarios. Project Page: https://snap-research.github.io/HyperHuman/", "github_manual": "https://github.com/snap-research/HyperHuman", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-13", "arxiv_id": "2310.08541", "title": "Idea2Img: Iterative Self-Refinement with GPT-4V(ision) for Automatic Image Design and Generation", "authors": [ "Zhengyuan Yang", "Jianfeng Wang", "Linjie Li", "Kevin Lin", "Chung-Ching Lin", "Zicheng Liu", "Lijuan Wang" ], "github": "", "project_page": "", "abstract": "We introduce ``Idea to Image,'' a system that enables multimodal iterative self-refinement with GPT-4V(ision) for automatic image design and generation. Humans can quickly identify the characteristics of different text-to-image (T2I) models via iterative explorations. This enables them to efficiently convert their high-level generation ideas into effective T2I prompts that can produce good images. We investigate if systems based on large multimodal models (LMMs) can develop analogous multimodal self-refinement abilities that enable exploring unknown models or environments via self-refining tries. Idea2Img cyclically generates revised T2I prompts to synthesize draft images, and provides directional feedback for prompt revision, both conditioned on its memory of the probed T2I model's characteristics. The iterative self-refinement brings Idea2Img various advantages over vanilla T2I models. Notably, Idea2Img can process input ideas with interleaved image-text sequences, follow ideas with design instructions, and generate images of better semantic and visual qualities. The user preference study validates the efficacy of multimodal iterative self-refinement on automatic image design and generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-16", "arxiv_id": "2310.08659", "title": "LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models", "authors": [ "Yixiao Li", "Yifan Yu", "Chen Liang", "Pengcheng He", "Nikos Karampatziakis", "Weizhu Chen", "Tuo Zhao" ], "github": "https://github.com/yxli2123/loftq", "project_page": "", "abstract": "Quantization is an indispensable technique for serving Large Language Models (LLMs) and has recently found its way into LoRA fine-tuning. In this work we focus on the scenario where quantization and LoRA fine-tuning are applied together on a pre-trained model. In such cases it is common to observe a consistent gap in the performance on downstream tasks between full fine-tuning and quantization plus LoRA fine-tuning approach. In response, we propose LoftQ (LoRA-Fine-Tuning-aware Quantization), a novel quantization framework that simultaneously quantizes an LLM and finds a proper low-rank initialization for LoRA fine-tuning. Such an initialization alleviates the discrepancy between the quantized and full-precision model and significantly improves the generalization in downstream tasks. We evaluate our method on natural language understanding, question answering, summarization, and natural language generation tasks. Experiments show that our method is highly effective and outperforms existing quantization methods, especially in the challenging 2-bit and 2/4-bit mixed precision regimes. We will release our code.", "github_manual": "https://github.com/yxli2123/loftq", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-16", "arxiv_id": "2310.09263", "title": "Table-GPT: Table-tuned GPT for Diverse Table Tasks", "authors": [ "Peng Li", "Yeye He", "Dror Yashar", "Weiwei Cui", "Song Ge", "Haidong Zhang", "Danielle Rifinski Fainman", "Dongmei Zhang", "Surajit Chaudhuri" ], "github": "", "project_page": "", "abstract": "Language models, such as GPT-3.5 and ChatGPT, demonstrate remarkable abilities to follow diverse human instructions and perform a wide range of tasks. However, when probing language models using a range of basic table-understanding tasks, we observe that today's language models are still sub-optimal in many table-related tasks, likely because they are pre-trained predominantly on one-dimensional natural-language texts, whereas relational tables are two-dimensional objects. In this work, we propose a new \"table-tuning\" paradigm, where we continue to train/fine-tune language models like GPT-3.5 and ChatGPT, using diverse table-tasks synthesized from real tables as training data, with the goal of enhancing language models' ability to understand tables and perform table tasks. We show that our resulting Table-GPT models demonstrate (1) better table-understanding capabilities, by consistently outperforming the vanilla GPT-3.5 and ChatGPT, on a wide-range of table tasks, including holdout unseen tasks, and (2) strong generalizability, in its ability to respond to diverse human instructions to perform new table-tasks, in a manner similar to GPT-3.5 and ChatGPT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-16", "arxiv_id": "2310.08740", "title": "A Zero-Shot Language Agent for Computer Control with Structured Reflection", "authors": [ "Tao Li", "Gang Li", "Zhiwei Deng", "Bryan Wang", "Yang Li" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have shown increasing capacity at planning and executing a high-level goal in a live computer environment (e.g. MiniWoB++). To perform a task, recent works often require a model to learn from trace examples of the task via either supervised learning or few/many-shot prompting. Without these trace examples, it remains a challenge how an agent can autonomously learn and improve its control on a computer, which limits the ability of an agent to perform a new task. We approach this problem with a zero-shot agent that requires no given expert traces. Our agent plans for executable actions on a partially observed environment, and iteratively progresses a task by identifying and learning from its mistakes via self-reflection and structured thought management. On the easy tasks of MiniWoB++, we show that our zero-shot agent often outperforms recent SoTAs, with more efficient reasoning. For tasks with more complexity, our reflective agent performs on par with prior best models, even though previous works had the advantages of accessing expert traces or additional screen information.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-16", "arxiv_id": "2310.08715", "title": "Toward Joint Language Modeling for Speech Units and Text", "authors": [ "Ju-Chieh Chou", "Chung-Ming Chien", "Wei-Ning Hsu", "Karen Livescu", "Arun Babu", "Alexis Conneau", "Alexei Baevski", "Michael Auli" ], "github": "", "project_page": "", "abstract": "Speech and text are two major forms of human language. The research community has been focusing on mapping speech to text or vice versa for many years. However, in the field of language modeling, very little effort has been made to model them jointly. In light of this, we explore joint language modeling for speech units and text. Specifically, we compare different speech tokenizers to transform continuous speech signals into discrete units and use different methods to construct mixed speech-text data. We introduce automatic metrics to evaluate how well the joint LM mixes speech and text. We also fine-tune the LM on downstream spoken language understanding (SLU) tasks with different modalities (speech or text) and test its performance to assess the model's learning of shared representations. Our results show that by mixing speech units and text with our proposed mixing techniques, the joint LM improves over a speech-only baseline on SLU tasks and shows zero-shot cross-modal transferability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-16", "arxiv_id": "2310.09139", "title": "The Consensus Game: Language Model Generation via Equilibrium Search", "authors": [ "Athul Paul Jacob", "Yikang Shen", "Gabriele Farina", "Jacob Andreas" ], "github": "", "project_page": "", "abstract": "When applied to question answering and other text generation tasks, language models (LMs) may be queried generatively (by sampling answers from their output distribution) or discriminatively (by using them to score or rank a set of candidate outputs). These procedures sometimes yield very different predictions. How do we reconcile mutually incompatible scoring procedures to obtain coherent LM predictions? We introduce a new, a training-free, game-theoretic procedure for language model decoding. Our approach casts language model decoding as a regularized imperfect-information sequential signaling game - which we term the CONSENSUS GAME - in which a GENERATOR seeks to communicate an abstract correctness parameter using natural language sentences to a DISCRIMINATOR. We develop computational procedures for finding approximate equilibria of this game, resulting in a decoding algorithm we call EQUILIBRIUM-RANKING. Applied to a large number of tasks (including reading comprehension, commonsense reasoning, mathematical problem-solving, and dialog), EQUILIBRIUM-RANKING consistently, and sometimes substantially, improves performance over existing LM decoding procedures - on multiple benchmarks, we observe that applying EQUILIBRIUM-RANKING to LLaMA-7B outperforms the much larger LLaMA-65B and PaLM-540B models. These results highlight the promise of game-theoretic tools for addressing fundamental challenges of truthfulness and consistency in LMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-16", "arxiv_id": "2310.08992", "title": "CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules", "authors": [ "Hung Le", "Hailin Chen", "Amrita Saha", "Akash Gokul", "Doyen Sahoo", "Shafiq Joty" ], "github": "https://github.com/SalesforceAIResearch/CodeChain", "project_page": "", "abstract": "Large Language Models (LLMs) have already become quite proficient at solving simpler programming tasks like those in HumanEval or MBPP benchmarks. However, solving more complex and competitive programming tasks is still quite challenging for these models - possibly due to their tendency to generate solutions as monolithic code blocks instead of decomposing them into logical sub-tasks and sub-modules. On the other hand, experienced programmers instinctively write modularized code with abstraction for solving complex tasks, often reusing previously developed modules. To address this gap, we propose CodeChain, a novel framework for inference that elicits modularized code generation through a chain of self-revisions, each being guided by some representative sub-modules generated in previous iterations. Concretely, CodeChain first instructs the LLM to generate modularized codes through chain-of-thought prompting. Then it applies a chain of self-revisions by iterating the two steps: 1) extracting and clustering the generated sub-modules and selecting the cluster representatives as the more generic and re-usable implementations, and 2) augmenting the original chain-of-thought prompt with these selected module-implementations and instructing the LLM to re-generate new modularized solutions. We find that by naturally encouraging the LLM to reuse the previously developed and verified sub-modules, CodeChain can significantly boost both modularity as well as correctness of the generated solutions, achieving relative pass@1 improvements of 35% on APPS and 76% on CodeContests. It is shown to be effective on both OpenAI LLMs as well as open-sourced LLMs like WizardCoder. We also conduct comprehensive ablation studies with different methods of prompting, number of clusters, model sizes, program qualities, etc., to provide useful insights that underpin CodeChain's success.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SalesforceAIResearch/CodeChain", "github_api_added_by": "auto" }, { "date": "2023-10-16", "arxiv_id": "2310.09199", "title": "PaLI-3 Vision Language Models: Smaller, Faster, Stronger", "authors": [ "Xi Chen", "Xiao Wang", "Lucas Beyer", "Alexander Kolesnikov", "Jialin Wu", "Paul Voigtlaender", "Basil Mustafa", "Sebastian Goodman", "Ibrahim Alabdulmohsin", "Piotr Padlewski", "Daniel Salz", "Xi Xiong", "Daniel Vlasic", "Filip Pavetic", "Keran Rong", "Tianli Yu", "Daniel Keysers", "Xiaohua Zhai", "Radu Soricut" ], "github": "", "project_page": "", "abstract": "This paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that compares favorably to similar models that are 10x larger. As part of arriving at this strong performance, we compare Vision Transformer (ViT) models pretrained using classification objectives to contrastively (SigLIP) pretrained ones. We find that, while slightly underperforming on standard image classification benchmarks, SigLIP-based PaLI shows superior performance across various multimodal benchmarks, especially on localization and visually-situated text understanding. We scale the SigLIP image encoder up to 2 billion parameters, and achieves a new state-of-the-art on multilingual cross-modal retrieval. We hope that PaLI-3, at only 5B parameters, rekindles research on fundamental pieces of complex VLMs, and could fuel a new generation of scaled-up models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-17", "arxiv_id": "2310.09520", "title": "Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model", "authors": [ "Haikang Deng", "Colin Raffel" ], "github": "", "project_page": "", "abstract": "While large language models have proven effective in a huge range of downstream applications, they often generate text that is problematic or lacks a desired attribute. In this paper, we introduce Reward-Augmented Decoding (RAD), a text generation procedure that uses a small unidirectional reward model to encourage a language model to generate text that has certain properties. Specifically, RAD uses the reward model to score generations as they are produced and rescales sampling probabilities to favor high-reward tokens. By using a unidirectional reward model, RAD can cache activations from prior generation steps to decrease computational overhead. Through experiments on generating non-toxic and sentiment-controlled text, we demonstrate that RAD performs best among methods that change only the generation procedure and matches the performance of state-of-the-art methods that involve re-training the language model. We further validate that RAD is effective on very large language models while incurring a minimal computational overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-17", "arxiv_id": "2310.09478", "title": "MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning", "authors": [ "Jun Chen", "Deyao Zhu", "Xiaoqian Shen", "Xiang Li", "Zechun Liu", "Pengchuan Zhang", "Raghuraman Krishnamoorthi", "Vikas Chandra", "Yunyang Xiong", "Mohamed Elhoseiny" ], "github": "https://github.com/Vision-CAIR/MiniGPT-4", "project_page": "", "abstract": "Large language models have shown their remarkable capabilities as a general interface for various language-related applications. Motivated by this, we target to build a unified interface for completing many vision-language tasks including image description, visual question answering, and visual grounding, among others. The challenge is to use a single model for performing diverse vision-language tasks effectively with simple multi-modal instructions. Towards this objective, we introduce MiniGPT-v2, a model that can be treated as a unified interface for better handling various vision-language tasks. We propose using unique identifiers for different tasks when training the model. These identifiers enable our model to better distinguish each task instruction effortlessly and also improve the model learning efficiency for each task. After the three-stage training, the experimental results show that MiniGPT-v2 achieves strong performance on many visual question-answering and visual grounding benchmarks compared to other vision-language generalist models. Our model and codes are available at https://minigpt-v2.github.io/", "github_manual": "https://github.com/Vision-CAIR/MiniGPT-4", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-17", "arxiv_id": "2310.09342", "title": "Ranking LLM-Generated Loop Invariants for Program Verification", "authors": [ "Saikat Chakraborty", "Shuvendu K. Lahiri", "Sarah Fakhoury", "Madanlal Musuvathi", "Akash Lal", "Aseem Rastogi", "Aditya Senthilnathan", "Rahul Sharma", "Nikhil Swamy" ], "github": "https://github.com/microsoft/NeuralInvariantRanker", "project_page": "", "abstract": "Synthesizing inductive loop invariants is fundamental to automating program verification. In this work, we observe that Large Language Models (such as gpt-3.5 or gpt-4) are capable of synthesizing loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate the correct invariants. This can lead to a large number of calls to a program verifier to establish an invariant. To address this issue, we propose a {\\it re-ranking} approach for the generated results of LLMs. We have designed a ranker that can distinguish between correct inductive invariants and incorrect attempts based on the problem definition. The ranker is optimized as a contrastive ranker. Experimental results demonstrate that this re-ranking mechanism significantly improves the ranking of correct invariants among the generated candidates, leading to a notable reduction in the number of calls to a verifier.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/NeuralInvariantRanker", "github_api_added_by": "auto" }, { "date": "2023-10-17", "arxiv_id": "2310.10625", "title": "Video Language Planning", "authors": [ "Yilun Du", "Mengjiao Yang", "Pete Florence", "Fei Xia", "Ayzaan Wahid", "Brian Ichter", "Pierre Sermanet", "Tianhe Yu", "Pieter Abbeel", "Joshua B. Tenenbaum", "Leslie Kaelbling", "Andy Zeng", "Jonathan Tompson" ], "github": "", "project_page": "", "abstract": "We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pretrained on Internet-scale data. To this end, we present video language planning (VLP), an algorithm that consists of a tree search procedure, where we train (i) vision-language models to serve as both policies and value functions, and (ii) text-to-video models as dynamics models. VLP takes as input a long-horizon task instruction and current image observation, and outputs a long video plan that provides detailed multimodal (video and language) specifications that describe how to complete the final task. VLP scales with increasing computation budget where more computation time results in improved video plans, and is able to synthesize long-horizon video plans across different robotics domains: from multi-object rearrangement, to multi-camera bi-arm dexterous manipulation. Generated video plans can be translated into real robot actions via goal-conditioned policies, conditioned on each intermediate frame of the generated video. Experiments show that VLP substantially improves long-horizon task success rates compared to prior methods on both simulated and real robots (across 3 hardware platforms).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-17", "arxiv_id": "2310.10537", "title": "Microscaling Data Formats for Deep Learning", "authors": [ "Bita Darvish Rouhani", "Ritchie Zhao", "Ankit More", "Mathew Hall", "Alireza Khodamoradi", "Summer Deng", "Dhruv Choudhary", "Marius Cornea", "Eric Dellinger", "Kristof Denolf", "Stosic Dusan", "Venmugil Elango", "Maximilian Golub", "Alexander Heinecke", "Phil James-Roxby", "Dharmesh Jani", "Gaurav Kolhe", "Martin Langhammer", "Ada Li", "Levi Melnick", "Maral Mesmakhosroshahi", "Andres Rodriguez", "Michael Schulte", "Rasoul Shafipour", "Lei Shao", "Michael Siu", "Pradeep Dubey", "Paulius Micikevicius", "Maxim Naumov", "Colin Verilli", "Ralph Wittig", "Eric Chung" ], "github": "https://github.com/microsoft/microxcaling", "project_page": "", "abstract": "Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that combine a per-block scaling factor with narrow floating-point and integer types for individual elements.MX formats balance the competing needs of hardware efficiency, model accuracy, and user friction. Empirical results on over two dozen benchmarks demonstrate practicality of MX data formats as a drop-in replacement for baseline FP32 for AI inference and training with low user friction. We also show the first instance of training generative language models at sub-8-bit weights, activations, and gradients with minimal accuracy loss and no modifications to the training recipe.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/microxcaling", "github_api_added_by": "auto" }, { "date": "2023-10-17", "arxiv_id": "2310.10047", "title": "Improving Large Language Model Fine-tuning for Solving Math Problems", "authors": [ "Yixin Liu", "Avi Singh", "C. Daniel Freeman", "John D. Co-Reyes", "Peter J. Liu" ], "github": "", "project_page": "", "abstract": "Despite their success in many natural language tasks, solving math problems remains a significant challenge for large language models (LLMs). A large gap exists between LLMs' pass-at-one and pass-at-N performance in solving math problems, suggesting LLMs might be close to finding correct solutions, motivating our exploration of fine-tuning methods to unlock LLMs' performance. Using the challenging MATH dataset, we investigate three fine-tuning strategies: (1) solution fine-tuning, where we fine-tune to generate a detailed solution for a given math problem; (2) solution-cluster re-ranking, where the LLM is fine-tuned as a solution verifier/evaluator to choose among generated candidate solution clusters; (3) multi-task sequential fine-tuning, which integrates both solution generation and evaluation tasks together efficiently to enhance the LLM performance. With these methods, we present a thorough empirical study on a series of PaLM 2 models and find: (1) The quality and style of the step-by-step solutions used for fine-tuning can make a significant impact on the model performance; (2) While solution re-ranking and majority voting are both effective for improving the model performance when used separately, they can also be used together for an even greater performance boost; (3) Multi-task fine-tuning that sequentially separates the solution generation and evaluation tasks can offer improved performance compared with the solution fine-tuning baseline. Guided by these insights, we design a fine-tuning recipe that yields approximately 58.8% accuracy on the MATH dataset with fine-tuned PaLM 2-L models, an 11.2% accuracy improvement over the few-shot performance of pre-trained PaLM 2-L model with majority voting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-17", "arxiv_id": "2310.09983", "title": "Farzi Data: Autoregressive Data Distillation", "authors": [ "Noveen Sachdeva", "Zexue He", "Wang-Cheng Kang", "Jianmo Ni", "Derek Zhiyuan Cheng", "Julian McAuley" ], "github": "", "project_page": "", "abstract": "We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure. More specifically, we propose Farzi, which summarizes an event sequence dataset into a small number of synthetic sequences -- Farzi Data -- which are optimized to maintain (if not improve) model performance compared to training on the full dataset. Under the hood, Farzi conducts memory-efficient data distillation by (i) deriving efficient reverse-mode differentiation of the Adam optimizer by leveraging Hessian-Vector Products; and (ii) factorizing the high-dimensional discrete event-space into a latent-space which provably promotes implicit regularization. Empirically, for sequential recommendation and language modeling tasks, we are able to achieve 98-120% of downstream full-data performance when training state-of-the-art models on Farzi Data of size as little as 0.1% of the original dataset. Notably, being able to train better models with significantly less data sheds light on the design of future large auto-regressive models, and opens up new opportunities to further scale up model and data sizes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-17", "arxiv_id": "2310.09753", "title": "When can transformers reason with abstract symbols?", "authors": [ "Enric Boix-Adsera", "Omid Saremi", "Emmanuel Abbe", "Samy Bengio", "Etai Littwin", "Joshua Susskind" ], "github": "https://github.com/eboix/relational-reasoning", "project_page": "", "abstract": "We investigate the capabilities of transformer large language models (LLMs) on relational reasoning tasks involving abstract symbols. Such tasks have long been studied in the neuroscience literature as fundamental building blocks for more complex abilities in programming, mathematics, and verbal reasoning. For (i) regression tasks, we prove that transformers generalize when trained, but require astonishingly large quantities of training data. For (ii) next-token-prediction tasks with symbolic labels, we show an \"inverse scaling law\": transformers fail to generalize as their embedding dimension increases. For both settings (i) and (ii), we propose subtle transformer modifications which can reduce the amount of data needed by adding two trainable parameters per head.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/eboix/relational-reasoning", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-17", "arxiv_id": "2310.08678", "title": "Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams", "authors": [ "Ethan Callanan", "Amarachi Mbakwe", "Antony Papadimitriou", "Yulong Pei", "Mathieu Sibue", "Xiaodan Zhu", "Zhiqiang Ma", "Xiaomo Liu", "Sameena Shah" ], "github": "https://github.com/e-cal/gpt-cfa", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable performance on a wide range of Natural Language Processing (NLP) tasks, often matching or even beating state-of-the-art task-specific models. This study aims at assessing the financial reasoning capabilities of LLMs. We leverage mock exam questions of the Chartered Financial Analyst (CFA) Program to conduct a comprehensive evaluation of ChatGPT and GPT-4 in financial analysis, considering Zero-Shot (ZS), Chain-of-Thought (CoT), and Few-Shot (FS) scenarios. We present an in-depth analysis of the models' performance and limitations, and estimate whether they would have a chance at passing the CFA exams. Finally, we outline insights into potential strategies and improvements to enhance the applicability of LLMs in finance. In this perspective, we hope this work paves the way for future studies to continue enhancing LLMs for financial reasoning through rigorous evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/e-cal/gpt-cfa", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-17", "arxiv_id": "2310.10638", "title": "In-Context Pretraining: Language Modeling Beyond Document Boundaries", "authors": [ "Weijia Shi", "Sewon Min", "Maria Lomeli", "Chunting Zhou", "Margaret Li", "Victoria Lin", "Noah A. Smith", "Luke Zettlemoyer", "Scott Yih", "Mike Lewis" ], "github": "https://github.com/swj0419/in-context-pretraining", "project_page": "", "abstract": "Large language models (LMs) are currently trained to predict tokens given document prefixes, enabling them to directly perform long-form generation and prompting-style tasks which can be reduced to document completion. Existing pretraining pipelines train LMs by concatenating random sets of short documents to create input contexts but the prior documents provide no signal for predicting the next document. We instead present In-Context Pretraining, a new approach where language models are pretrained on a sequence of related documents, thereby explicitly encouraging them to read and reason across document boundaries. We can do In-Context Pretraining by simply changing the document ordering so that each context contains related documents, and directly applying existing pretraining pipelines. However, this document sorting problem is challenging. There are billions of documents and we would like the sort to maximize contextual similarity for every document without repeating any data. To do this, we introduce approximate algorithms for finding related documents with efficient nearest neighbor search and constructing coherent input contexts with a graph traversal algorithm. Our experiments show In-Context Pretraining offers a simple and scalable approach to significantly enhance LMs'performance: we see notable improvements in tasks that require more complex contextual reasoning, including in-context learning (+8%), reading comprehension (+15%), faithfulness to previous contexts (+16%), long-context reasoning (+5%), and retrieval augmentation (+9%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/swj0419/in-context-pretraining", "github_api_added_by": "auto" }, { "date": "2023-10-17", "arxiv_id": "2310.10631", "title": "Llemma: An Open Language Model For Mathematics", "authors": [ "Zhangir Azerbayev", "Hailey Schoelkopf", "Keiran Paster", "Marco Dos Santos", "Stephen McAleer", "Albert Q. Jiang", "Jia Deng", "Stella Biderman", "Sean Welleck" ], "github": "https://github.com/EleutherAI/math-lm", "project_page": "", "abstract": "We present Llemma, a large language model for mathematics. We continue pretraining Code Llama on the Proof-Pile-2, a mixture of scientific papers, web data containing mathematics, and mathematical code, yielding Llemma. On the MATH benchmark Llemma outperforms all known open base models, as well as the unreleased Minerva model suite on an equi-parameter basis. Moreover, Llemma is capable of tool use and formal theorem proving without any further finetuning. We openly release all artifacts, including 7 billion and 34 billion parameter models, the Proof-Pile-2, and code to replicate our experiments.", "github_manual": "https://github.com/EleutherAI/math-lm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-17", "arxiv_id": "2310.10645", "title": "Interactive Task Planning with Language Models", "authors": [ "Boyi Li", "Philipp Wu", "Pieter Abbeel", "Jitendra Malik" ], "github": "", "project_page": "", "abstract": "An interactive robot framework accomplishes long-horizon task planning and can easily generalize to new goals or distinct tasks, even during execution. However, most traditional methods require predefined module design, which makes it hard to generalize to different goals. Recent large language model based approaches can allow for more open-ended planning but often require heavy prompt engineering or domain-specific pretrained models. To tackle this, we propose a simple framework that achieves interactive task planning with language models. Our system incorporates both high-level planning and low-level function execution via language. We verify the robustness of our system in generating novel high-level instructions for unseen objectives and its ease of adaptation to different tasks by merely substituting the task guidelines, without the need for additional complex prompt engineering. Furthermore, when the user sends a new request, our system is able to replan accordingly with precision based on the new request, task guidelines and previously executed steps. Please check more details on our https://wuphilipp.github.io/itp_site and https://youtu.be/TrKLuyv26_g.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-18", "arxiv_id": "2310.11453", "title": "BitNet: Scaling 1-bit Transformers for Large Language Models", "authors": [ "Hongyu Wang", "Shuming Ma", "Li Dong", "Shaohan Huang", "Huaijie Wang", "Lingxiao Ma", "Fan Yang", "Ruiping Wang", "Yi Wu", "Furu Wei" ], "github": "", "project_page": "", "abstract": "The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. In this work, we introduce BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. Specifically, we introduce BitLinear as a drop-in replacement of the nn.Linear layer in order to train 1-bit weights from scratch. Experimental results on language modeling show that BitNet achieves competitive performance while substantially reducing memory footprint and energy consumption, compared to state-of-the-art 8-bit quantization methods and FP16 Transformer baselines. Furthermore, BitNet exhibits a scaling law akin to full-precision Transformers, suggesting its potential for effective scaling to even larger language models while maintaining efficiency and performance benefits.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-18", "arxiv_id": "2310.10944", "title": "TEQ: Trainable Equivalent Transformation for Quantization of LLMs", "authors": [ "Wenhua Cheng", "Yiyang Cai", "Kaokao Lv", "Haihao Shen" ], "github": "https://github.com/intel/neural-compressor", "project_page": "", "abstract": "As large language models (LLMs) become more prevalent, there is a growing need for new and improved quantization methods that can meet the computationalast layer demands of these modern architectures while maintaining the accuracy. In this paper, we present TEQ, a trainable equivalent transformation that preserves the FP32 precision of the model output while taking advantage of low-precision quantization, especially 3 and 4 bits weight-only quantization. The training process is lightweight, requiring only 1K steps and fewer than 0.1 percent of the original model's trainable parameters. Furthermore, the transformation does not add any computational overhead during inference. Our results are on-par with the state-of-the-art (SOTA) methods on typical LLMs. Our approach can be combined with other methods to achieve even better performance. The code is available at https://github.com/intel/neural-compressor.", "github_manual": "", "github_regex": "https://github.com/intel/neural-compressor", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-18", "arxiv_id": "2310.10971", "title": "Context-Aware Meta-Learning", "authors": [ "Christopher Fifty", "Dennis Duan", "Ronald G. Junkins", "Ehsan Amid", "Jure Leskovec", "Christopher R\u00e9", "Sebastian Thrun" ], "github": "https://github.com/cfifty/CAML", "project_page": "", "abstract": "Large Language Models like ChatGPT demonstrate a remarkable capacity to learn new concepts during inference without any fine-tuning. However, visual models trained to detect new objects during inference have been unable to replicate this ability, and instead either perform poorly or require meta-training and/or fine-tuning on similar objects. In this work, we propose a meta-learning algorithm that emulates Large Language Models by learning new visual concepts during inference without fine-tuning. Our approach leverages a frozen pre-trained feature extractor, and analogous to in-context learning, recasts meta-learning as sequence modeling over datapoints with known labels and a test datapoint with an unknown label. On 8 out of 11 meta-learning benchmarks, our approach -- without meta-training or fine-tuning -- exceeds or matches the state-of-the-art algorithm, P>M>F, which is meta-trained on these benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/cfifty/CAML", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-18", "arxiv_id": "2310.11454", "title": "VeRA: Vector-based Random Matrix Adaptation", "authors": [ "Dawid Jan Kopiczko", "Tijmen Blankevoort", "Yuki Markus Asano" ], "github": "", "project_page": "", "abstract": "Low-rank adapation (LoRA) is a popular method that reduces the number of trainable parameters when finetuning large language models, but still faces acute storage challenges when scaling to even larger models or deploying numerous per-user or per-task adapted models. In this work, we present Vector-based Random Matrix Adaptation (VeRA), which reduces the number of trainable parameters by 10x compared to LoRA, yet maintains the same performance. It achieves this by using a single pair of low-rank matrices shared across all layers and learning small scaling vectors instead. We demonstrate its effectiveness on the GLUE and E2E benchmarks, and show its application in instruction-following with just 1.4M parameters using the Llama2 7B model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-18", "arxiv_id": "2310.11448", "title": "4K4D: Real-Time 4D View Synthesis at 4K Resolution", "authors": [ "Zhen Xu", "Sida Peng", "Haotong Lin", "Guangzhao He", "Jiaming Sun", "Yujun Shen", "Hujun Bao", "Xiaowei Zhou" ], "github": "", "project_page": "", "abstract": "This paper targets high-fidelity and real-time view synthesis of dynamic 3D scenes at 4K resolution. Recently, some methods on dynamic view synthesis have shown impressive rendering quality. However, their speed is still limited when rendering high-resolution images. To overcome this problem, we propose 4K4D, a 4D point cloud representation that supports hardware rasterization and enables unprecedented rendering speed. Our representation is built on a 4D feature grid so that the points are naturally regularized and can be robustly optimized. In addition, we design a novel hybrid appearance model that significantly boosts the rendering quality while preserving efficiency. Moreover, we develop a differentiable depth peeling algorithm to effectively learn the proposed model from RGB videos. Experiments show that our representation can be rendered at over 400 FPS on the DNA-Rendering dataset at 1080p resolution and 80 FPS on the ENeRF-Outdoor dataset at 4K resolution using an RTX 4090 GPU, which is 30x faster than previous methods and achieves the state-of-the-art rendering quality. We will release the code for reproducibility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-18", "arxiv_id": "2310.10769", "title": "LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation", "authors": [ "Ruiqi Wu", "Liangyu Chen", "Tong Yang", "Chunle Guo", "Chongyi Li", "Xiangyu Zhang" ], "github": "https://github.com/RQ-Wu/LAMP", "project_page": "", "abstract": "With the impressive progress in diffusion-based text-to-image generation, extending such powerful generative ability to text-to-video raises enormous attention. Existing methods either require large-scale text-video pairs and a large number of training resources or learn motions that are precisely aligned with template videos. It is non-trivial to balance a trade-off between the degree of generation freedom and the resource costs for video generation. In our study, we present a few-shot-based tuning framework, LAMP, which enables text-to-image diffusion model Learn A specific Motion Pattern with 8~16 videos on a single GPU. Specifically, we design a first-frame-conditioned pipeline that uses an off-the-shelf text-to-image model for content generation so that our tuned video diffusion model mainly focuses on motion learning. The well-developed text-to-image techniques can provide visually pleasing and diverse content as generation conditions, which highly improves video quality and generation freedom. To capture the features of temporal dimension, we expand the pretrained 2D convolution layers of the T2I model to our novel temporal-spatial motion learning layers and modify the attention blocks to the temporal level. Additionally, we develop an effective inference trick, shared-noise sampling, which can improve the stability of videos with computational costs. Our method can also be flexibly applied to other tasks, e.g. real-world image animation and video editing. Extensive experiments demonstrate that LAMP can effectively learn the motion pattern on limited data and generate high-quality videos. The code and models are available at https://rq-wu.github.io/projects/LAMP.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RQ-Wu/LAMP", "github_api_added_by": "auto" }, { "date": "2023-10-18", "arxiv_id": "2310.11440", "title": "EvalCrafter: Benchmarking and Evaluating Large Video Generation Models", "authors": [ "Yaofang Liu", "Xiaodong Cun", "Xuebo Liu", "Xintao Wang", "Yong Zhang", "Haoxin Chen", "Yang Liu", "Tieyong Zeng", "Raymond Chan", "Ying Shan" ], "github": "https://github.com/EvalCrafter/EvalCrafter", "project_page": "", "abstract": "The vision and language generative models have been overgrown in recent years. For video generation, various open-sourced models and public-available services are released for generating high-visual quality videos. However, these methods often use a few academic metrics, for example, FVD or IS, to evaluate the performance. We argue that it is hard to judge the large conditional generative models from the simple metrics since these models are often trained on very large datasets with multi-aspect abilities. Thus, we propose a new framework and pipeline to exhaustively evaluate the performance of the generated videos. To achieve this, we first conduct a new prompt list for text-to-video generation by analyzing the real-world prompt list with the help of the large language model. Then, we evaluate the state-of-the-art video generative models on our carefully designed benchmarks, in terms of visual qualities, content qualities, motion qualities, and text-caption alignment with around 18 objective metrics. To obtain the final leaderboard of the models, we also fit a series of coefficients to align the objective metrics to the users' opinions. Based on the proposed opinion alignment method, our final score shows a higher correlation than simply averaging the metrics, showing the effectiveness of the proposed evaluation method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/EvalCrafter/EvalCrafter", "github_api_added_by": "auto" }, { "date": "2023-10-18", "arxiv_id": "2310.11248", "title": "CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion", "authors": [ "Yangruibo Ding", "Zijian Wang", "Wasi Uddin Ahmad", "Hantian Ding", "Ming Tan", "Nihal Jain", "Murali Krishna Ramanathan", "Ramesh Nallapati", "Parminder Bhatia", "Dan Roth", "Bing Xiang" ], "github": "https://github.com/amazon-science/cceval", "project_page": "", "abstract": "Code completion models have made significant progress in recent years, yet current popular evaluation datasets, such as HumanEval and MBPP, predominantly focus on code completion tasks within a single file. This over-simplified setting falls short of representing the real-world software development scenario where repositories span multiple files with numerous cross-file dependencies, and accessing and understanding cross-file context is often required to complete the code correctly. To fill in this gap, we propose CrossCodeEval, a diverse and multilingual code completion benchmark that necessitates an in-depth cross-file contextual understanding to complete the code accurately. CrossCodeEval is built on a diverse set of real-world, open-sourced, permissively-licensed repositories in four popular programming languages: Python, Java, TypeScript, and C#. To create examples that strictly require cross-file context for accurate completion, we propose a straightforward yet efficient static-analysis-based approach to pinpoint the use of cross-file context within the current file. Extensive experiments on state-of-the-art code language models like CodeGen and StarCoder demonstrate that CrossCodeEval is extremely challenging when the relevant cross-file context is absent, and we see clear improvements when adding these context into the prompt. However, despite such improvements, the pinnacle of performance remains notably unattained even with the highest-performing model, indicating that CrossCodeEval is also capable of assessing model's capability in leveraging extensive context to make better code completion. Finally, we benchmarked various methods in retrieving cross-file context, and show that CrossCodeEval can also be used to measure the capability of code retrievers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amazon-science/cceval", "github_api_added_by": "auto" }, { "date": "2023-10-18", "arxiv_id": "2310.10837", "title": "Approximating Two-Layer Feedforward Networks for Efficient Transformers", "authors": [ "R\u00f3bert Csord\u00e1s", "Kazuki Irie", "J\u00fcrgen Schmidhuber" ], "github": "https://github.com/robertcsordas/moe", "project_page": "", "abstract": "How to reduce compute and memory requirements of neural networks (NNs) without sacrificing performance? Many recent works use sparse Mixtures of Experts (MoEs) to build resource-efficient large language models (LMs). Here we introduce several novel perspectives on MoEs, presenting a general framework that unifies various methods to approximate two-layer NNs (e.g., feedforward blocks of Transformers), including product-key memories (PKMs). Leveraging insights from this framework, we propose methods to improve both MoEs and PKMs. Unlike prior work that compares MoEs with dense baselines under the compute-equal condition, our evaluation condition is parameter-equal, which is crucial to properly evaluate LMs. We show that our MoEs are competitive with the dense Transformer-XL on both the WikiText-103 and enwiki8 datasets at two different scales, while being much more resource efficient. This demonstrates that MoEs are relevant not only to extremely large LMs but also to any-scale resource-efficient LMs. Our code is public.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/robertcsordas/moe", "github_api_added_by": "auto" }, { "date": "2023-10-18", "arxiv_id": "2310.11441", "title": "Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V", "authors": [ "Jianwei Yang", "Hao Zhang", "Feng Li", "Xueyan Zou", "Chunyuan Li", "Jianfeng Gao" ], "github": "https://github.com/microsoft/SoM", "project_page": "", "abstract": "We present Set-of-Mark (SoM), a new visual prompting method, to unleash the visual grounding abilities of large multimodal models (LMMs), such as GPT-4V. As illustrated in Fig. 1 (right), we employ off-the-shelf interactive segmentation models, such as SAM, to partition an image into regions at different levels of granularity, and overlay these regions with a set of marks e.g., alphanumerics, masks, boxes. Using the marked image as input, GPT-4V can answer the questions that require visual grounding. We perform a comprehensive empirical study to validate the effectiveness of SoM on a wide range of fine-grained vision and multimodal tasks. For example, our experiments show that GPT-4V with SoM outperforms the state-of-the-art fully-finetuned referring segmentation model on RefCOCOg in a zero-shot setting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/SoM", "github_api_added_by": "auto" }, { "date": "2023-10-19", "arxiv_id": "2310.11511", "title": "Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection", "authors": [ "Akari Asai", "Zeqiu Wu", "Yizhong Wang", "Avirup Sil", "Hannaneh Hajishirzi" ], "github": "https://github.com/AkariAsai/self-rag", "project_page": "", "abstract": "Despite their remarkable capabilities, large language models (LLMs) often produce responses containing factual inaccuracies due to their sole reliance on the parametric knowledge they encapsulate. Retrieval-Augmented Generation (RAG), an ad hoc approach that augments LMs with retrieval of relevant knowledge, decreases such issues. However, indiscriminately retrieving and incorporating a fixed number of retrieved passages, regardless of whether retrieval is necessary, or passages are relevant, diminishes LM versatility or can lead to unhelpful response generation. We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) that enhances an LM's quality and factuality through retrieval and self-reflection. Our framework trains a single arbitrary LM that adaptively retrieves passages on-demand, and generates and reflects on retrieved passages and its own generations using special tokens, called reflection tokens. Generating reflection tokens makes the LM controllable during the inference phase, enabling it to tailor its behavior to diverse task requirements. Experiments show that Self-RAG (7B and 13B parameters) significantly outperforms state-of-the-art LLMs and retrieval-augmented models on a diverse set of tasks. Specifically, Self-RAG outperforms ChatGPT and retrieval-augmented Llama2-chat on Open-domain QA, reasoning and fact verification tasks, and it shows significant gains in improving factuality and citation accuracy for long-form generations relative to these models.", "github_manual": "https://github.com/AkariAsai/self-rag", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-19", "arxiv_id": "2310.11784", "title": "Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts", "authors": [ "Xinhua Cheng", "Tianyu Yang", "Jianan Wang", "Yu Li", "Lei Zhang", "Jian Zhang", "Li Yuan" ], "github": "", "project_page": "", "abstract": "Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies. However, current methods struggle to generate correct 3D content for a complex prompt in semantics, i.e., a prompt describing multiple interacted objects binding with different attributes. In this work, we propose a general framework named Progressive3D, which decomposes the entire generation into a series of locally progressive editing steps to create precise 3D content for complex prompts, and we constrain the content change to only occur in regions determined by user-defined region prompts in each editing step. Furthermore, we propose an overlapped semantic component suppression technique to encourage the optimization process to focus more on the semantic differences between prompts. Extensive experiments demonstrate that the proposed Progressive3D framework generates precise 3D content for prompts with complex semantics and is general for various text-to-3D methods driven by different 3D representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-19", "arxiv_id": "2310.11954", "title": "MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models", "authors": [ "Dingyao Yu", "Kaitao Song", "Peiling Lu", "Tianyu He", "Xu Tan", "Wei Ye", "Shikun Zhang", "Jiang Bian" ], "github": "", "project_page": "", "abstract": "AI-empowered music processing is a diverse field that encompasses dozens of tasks, ranging from generation tasks (e.g., timbre synthesis) to comprehension tasks (e.g., music classification). For developers and amateurs, it is very difficult to grasp all of these task to satisfy their requirements in music processing, especially considering the huge differences in the representations of music data and the model applicability across platforms among various tasks. Consequently, it is necessary to build a system to organize and integrate these tasks, and thus help practitioners to automatically analyze their demand and call suitable tools as solutions to fulfill their requirements. Inspired by the recent success of large language models (LLMs) in task automation, we develop a system, named MusicAgent, which integrates numerous music-related tools and an autonomous workflow to address user requirements. More specifically, we build 1) toolset that collects tools from diverse sources, including Hugging Face, GitHub, and Web API, etc. 2) an autonomous workflow empowered by LLMs (e.g., ChatGPT) to organize these tools and automatically decompose user requests into multiple sub-tasks and invoke corresponding music tools. The primary goal of this system is to free users from the intricacies of AI-music tools, enabling them to concentrate on the creative aspect. By granting users the freedom to effortlessly combine tools, the system offers a seamless and enriching music experience.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-20", "arxiv_id": "2310.12404", "title": "Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing", "authors": [ "Yixiao Zhang", "Akira Maezawa", "Gus Xia", "Kazuhiko Yamamoto", "Simon Dixon" ], "github": "https://github.com/ldzhangyx/loop-copilot", "project_page": "", "abstract": "Creating music is iterative, requiring varied methods at each stage. However, existing AI music systems fall short in orchestrating multiple subsystems for diverse needs. To address this gap, we introduce Loop Copilot, a novel system that enables users to generate and iteratively refine music through an interactive, multi-round dialogue interface. The system uses a large language model to interpret user intentions and select appropriate AI models for task execution. Each backend model is specialized for a specific task, and their outputs are aggregated to meet the user's requirements. To ensure musical coherence, essential attributes are maintained in a centralized table. We evaluate the effectiveness of the proposed system through semi-structured interviews and questionnaires, highlighting its utility not only in facilitating music creation but also its potential for broader applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ldzhangyx/loop-copilot", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-20", "arxiv_id": "2310.12931", "title": "Eureka: Human-Level Reward Design via Coding Large Language Models", "authors": [ "Yecheng Jason Ma", "William Liang", "Guanzhi Wang", "De-An Huang", "Osbert Bastani", "Dinesh Jayaraman", "Yuke Zhu", "Linxi Fan", "Anima Anandkumar" ], "github": "https://github.com/eureka-research/Eureka", "project_page": "", "abstract": "Large Language Models (LLMs) have excelled as high-level semantic planners for sequential decision-making tasks. However, harnessing them to learn complex low-level manipulation tasks, such as dexterous pen spinning, remains an open problem. We bridge this fundamental gap and present Eureka, a human-level reward design algorithm powered by LLMs. Eureka exploits the remarkable zero-shot generation, code-writing, and in-context improvement capabilities of state-of-the-art LLMs, such as GPT-4, to perform evolutionary optimization over reward code. The resulting rewards can then be used to acquire complex skills via reinforcement learning. Without any task-specific prompting or pre-defined reward templates, Eureka generates reward functions that outperform expert human-engineered rewards. In a diverse suite of 29 open-source RL environments that include 10 distinct robot morphologies, Eureka outperforms human experts on 83% of the tasks, leading to an average normalized improvement of 52%. The generality of Eureka also enables a new gradient-free in-context learning approach to reinforcement learning from human feedback (RLHF), readily incorporating human inputs to improve the quality and the safety of the generated rewards without model updating. Finally, using Eureka rewards in a curriculum learning setting, we demonstrate for the first time, a simulated Shadow Hand capable of performing pen spinning tricks, adeptly manipulating a pen in circles at rapid speed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/eureka-research/Eureka", "github_api_added_by": "auto" }, { "date": "2023-10-20", "arxiv_id": "2310.12823", "title": "AgentTuning: Enabling Generalized Agent Abilities for LLMs", "authors": [ "Aohan Zeng", "Mingdao Liu", "Rui Lu", "Bowen Wang", "Xiao Liu", "Yuxiao Dong", "Jie Tang" ], "github": "https://github.com/THUDM/AgentTuning", "project_page": "", "abstract": "Open large language models (LLMs) with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. These agent tasks employ LLMs as the central controller responsible for planning, memorization, and tool utilization, necessitating both fine-grained prompting methods and robust LLMs to achieve satisfactory performance. Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities. In this work, we present AgentTuning, a simple and general method to enhance the agent abilities of LLMs while maintaining their general LLM capabilities. We construct AgentInstruct, a lightweight instruction-tuning dataset containing high-quality interaction trajectories. We employ a hybrid instruction-tuning strategy by combining AgentInstruct with open-source instructions from general domains. AgentTuning is used to instruction-tune the Llama 2 series, resulting in AgentLM. Our evaluations show that AgentTuning enables LLMs' agent capabilities without compromising general abilities. The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent tasks, demonstrating generalized agent capabilities. We open source the AgentInstruct and AgentLM-7B, 13B, and 70B models at https://github.com/THUDM/AgentTuning , serving open and powerful alternatives to commercial LLMs for agent tasks.", "github_manual": "", "github_regex": "https://github.com/THUDM/AgentTuning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-20", "arxiv_id": "2310.12921", "title": "Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning", "authors": [ "Juan Rocamonde", "Victoriano Montesinos", "Elvis Nava", "Ethan Perez", "David Lindner" ], "github": "https://github.com/alignmentresearch/vlmrm", "project_page": "", "abstract": "Reinforcement learning (RL) requires either manually specifying a reward function, which is often infeasible, or learning a reward model from a large amount of human feedback, which is often very expensive. We study a more sample-efficient alternative: using pretrained vision-language models (VLMs) as zero-shot reward models (RMs) to specify tasks via natural language. We propose a natural and general approach to using VLMs as reward models, which we call VLM-RMs. We use VLM-RMs based on CLIP to train a MuJoCo humanoid to learn complex tasks without a manually specified reward function, such as kneeling, doing the splits, and sitting in a lotus position. For each of these tasks, we only provide a single sentence text prompt describing the desired task with minimal prompt engineering. We provide videos of the trained agents at: https://sites.google.com/view/vlm-rm. We can improve performance by providing a second ``baseline'' prompt and projecting out parts of the CLIP embedding space irrelevant to distinguish between goal and baseline. Further, we find a strong scaling effect for VLM-RMs: larger VLMs trained with more compute and data are better reward models. The failure modes of VLM-RMs we encountered are all related to known capability limitations of current VLMs, such as limited spatial reasoning ability or visually unrealistic environments that are far off-distribution for the VLM. We find that VLM-RMs are remarkably robust as long as the VLM is large enough. This suggests that future VLMs will become more and more useful reward models for a wide range of RL applications.", "github_manual": "https://github.com/alignmentresearch/vlmrm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-20", "arxiv_id": "2310.12963", "title": "AutoMix: Automatically Mixing Language Models", "authors": [ "Aman Madaan", "Pranjal Aggarwal", "Ankit Anand", "Srividya Pranavi Potharaju", "Swaroop Mishra", "Pei Zhou", "Aditya Gupta", "Dheeraj Rajagopal", "Karthik Kappaganthu", "Yiming Yang", "Shyam Upadhyay", "Mausam", "Manaal Faruqui" ], "github": "https://github.com/automix-llm/automix", "project_page": "", "abstract": "Large language models (LLMs) are now available in various sizes and configurations from cloud API providers. While this diversity offers a broad spectrum of choices, effectively leveraging the options to optimize computational cost and performance remains challenging. In this work, we present AutoMix, an approach that strategically routes queries to larger LMs, based on the approximate correctness of outputs from a smaller LM. Central to AutoMix is a few-shot self-verification mechanism, which estimates the reliability of its own outputs without requiring training. Given that verifications can be noisy, we employ a meta verifier in AutoMix to refine the accuracy of these assessments. Our experiments using LLAMA2-13/70B, on five context-grounded reasoning datasets demonstrate that AutoMix surpasses established baselines, improving the incremental benefit per cost by up to 89%. Our code and data are available at https://github.com/automix-llm/automix.", "github_manual": "", "github_regex": "https://github.com/automix-llm/automix", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-20", "arxiv_id": "2310.12474", "title": "Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping", "authors": [ "Zijie Pan", "Jiachen Lu", "Xiatian Zhu", "Li Zhang" ], "github": "https://github.com/fudan-zvg/pgc-3d", "project_page": "", "abstract": "High-resolution 3D object generation remains a challenging task primarily due to the limited availability of comprehensive annotated training data. Recent advancements have aimed to overcome this constraint by harnessing image generative models, pretrained on extensive curated web datasets, using knowledge transfer techniques like Score Distillation Sampling (SDS). Efficiently addressing the requirements of high-resolution rendering often necessitates the adoption of latent representation-based models, such as the Latent Diffusion Model (LDM). In this framework, a significant challenge arises: To compute gradients for individual image pixels, it is necessary to backpropagate gradients from the designated latent space through the frozen components of the image model, such as the VAE encoder used within LDM. However, this gradient propagation pathway has never been optimized, remaining uncontrolled during training. We find that the unregulated gradients adversely affect the 3D model's capacity in acquiring texture-related information from the image generative model, leading to poor quality appearance synthesis. To address this overarching challenge, we propose an innovative operation termed Pixel-wise Gradient Clipping (PGC) designed for seamless integration into existing 3D generative models, thereby enhancing their synthesis quality. Specifically, we control the magnitude of stochastic gradients by clipping the pixel-wise gradients efficiently, while preserving crucial texture-related gradient directions. Despite this simplicity and minimal extra cost, extensive experiments demonstrate the efficacy of our PGC in enhancing the performance of existing 3D generative models for high-resolution object rendering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/fudan-zvg/pgc-3d", "github_api_added_by": "auto" }, { "date": "2023-10-20", "arxiv_id": "2310.12274", "title": "An Image is Worth Multiple Words: Learning Object Level Concepts using Multi-Concept Prompt Learning", "authors": [ "Chen Jin", "Ryutaro Tanno", "Amrutha Saseendran", "Tom Diethe", "Philip Teare" ], "github": "", "project_page": "", "abstract": "Textural Inversion, a prompt learning method, learns a singular embedding for a new \"word\" to represent image style and appearance, allowing it to be integrated into natural language sentences to generate novel synthesised images. However, identifying and integrating multiple object-level concepts within one scene poses significant challenges even when embeddings for individual concepts are attainable. This is further confirmed by our empirical tests. To address this challenge, we introduce a framework for Multi-Concept Prompt Learning (MCPL), where multiple new \"words\" are simultaneously learned from a single sentence-image pair. To enhance the accuracy of word-concept correlation, we propose three regularisation techniques: Attention Masking (AttnMask) to concentrate learning on relevant areas; Prompts Contrastive Loss (PromptCL) to separate the embeddings of different concepts; and Bind adjective (Bind adj.) to associate new \"words\" with known words. We evaluate via image generation, editing, and attention visualisation with diverse images. Extensive quantitative comparisons demonstrate that our method can learn more semantically disentangled concepts with enhanced word-concept correlation. Additionally, we introduce a novel dataset and evaluation protocol tailored for this new task of learning object-level concepts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-20", "arxiv_id": "2310.12962", "title": "An Emulator for Fine-Tuning Large Language Models using Small Language Models", "authors": [ "Eric Mitchell", "Rafael Rafailov", "Archit Sharma", "Chelsea Finn", "Christopher D. Manning" ], "github": "", "project_page": "", "abstract": "Widely used language models (LMs) are typically built by scaling up a two-stage training pipeline: a pre-training stage that uses a very large, diverse dataset of text and a fine-tuning (sometimes, 'alignment') stage that uses targeted examples or other specifications of desired behaviors. While it has been hypothesized that knowledge and skills come from pre-training, and fine-tuning mostly filters this knowledge and skillset, this intuition has not been extensively tested. To aid in doing so, we introduce a novel technique for decoupling the knowledge and skills gained in these two stages, enabling a direct answer to the question, \"What would happen if we combined the knowledge learned by a large model during pre-training with the knowledge learned by a small model during fine-tuning (or vice versa)?\" Using an RL-based framework derived from recent developments in learning from human preferences, we introduce emulated fine-tuning (EFT), a principled and practical method for sampling from a distribution that approximates (or 'emulates') the result of pre-training and fine-tuning at different scales. Our experiments with EFT show that scaling up fine-tuning tends to improve helpfulness, while scaling up pre-training tends to improve factuality. Beyond decoupling scale, we show that EFT enables test-time adjustment of competing behavioral traits like helpfulness and harmlessness without additional training. Finally, a special case of emulated fine-tuning, which we call LM up-scaling, avoids resource-intensive fine-tuning of large pre-trained models by ensembling them with small fine-tuned models, essentially emulating the result of fine-tuning the large pre-trained model. Up-scaling consistently improves helpfulness and factuality of instruction-following models in the Llama, Llama-2, and Falcon families, without additional hyperparameters or training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-20", "arxiv_id": "2310.12945", "title": "3D-GPT: Procedural 3D Modeling with Large Language Models", "authors": [ "Chunyi Sun", "Junlin Han", "Weijian Deng", "Xinlong Wang", "Zishan Qin", "Stephen Gould" ], "github": "https://github.com/Chuny1/3DGPT", "project_page": "", "abstract": "In the pursuit of efficient automated content creation, procedural generation, leveraging modifiable parameters and rule-based systems, emerges as a promising approach. Nonetheless, it could be a demanding endeavor, given its intricate nature necessitating a deep understanding of rules, algorithms, and parameters. To reduce workload, we introduce 3D-GPT, a framework utilizing large language models~(LLMs) for instruction-driven 3D modeling. 3D-GPT positions LLMs as proficient problem solvers, dissecting the procedural 3D modeling tasks into accessible segments and appointing the apt agent for each task. 3D-GPT integrates three core agents: the task dispatch agent, the conceptualization agent, and the modeling agent. They collaboratively achieve two objectives. First, it enhances concise initial scene descriptions, evolving them into detailed forms while dynamically adapting the text based on subsequent instructions. Second, it integrates procedural generation, extracting parameter values from enriched text to effortlessly interface with 3D software for asset creation. Our empirical investigations confirm that 3D-GPT not only interprets and executes instructions, delivering reliable results but also collaborates effectively with human designers. Furthermore, it seamlessly integrates with Blender, unlocking expanded manipulation possibilities. Our work highlights the potential of LLMs in 3D modeling, offering a basic framework for future advancements in scene generation and animation.", "github_manual": "https://github.com/Chuny1/3DGPT", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-20", "arxiv_id": "2310.12773", "title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback", "authors": [ "Josef Dai", "Xuehai Pan", "Ruiyang Sun", "Jiaming Ji", "Xinbo Xu", "Mickel Liu", "Yizhou Wang", "Yaodong Yang" ], "github": "https://github.com/pku-alignment/safe-rlhf", "project_page": "", "abstract": "With the development of large language models (LLMs), striking a balance between the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training. To address this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly decouples human preferences regarding helpfulness and harmlessness, effectively avoiding the crowdworkers' confusion about the tension and allowing us to train separate reward and cost models. We formalize the safety concern of LLMs as an optimization task of maximizing the reward function while satisfying specified cost constraints. Leveraging the Lagrangian method to solve this constrained problem, Safe RLHF dynamically adjusts the balance between the two objectives during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we demonstrate a superior ability to mitigate harmful responses while enhancing model performance compared to existing value-aligned algorithms. Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with collected human preferences, significantly improving its helpfulness and harmlessness according to human evaluations.", "github_manual": "https://github.com/pku-alignment/safe-rlhf", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13227", "title": "ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search", "authors": [ "Yuchen Zhuang", "Xiang Chen", "Tong Yu", "Saayan Mitra", "Victor Bursztyn", "Ryan A. Rossi", "Somdeb Sarkhel", "Chao Zhang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated powerful decision-making and planning capabilities in solving complicated real-world problems. LLM-based autonomous agents can interact with diverse tools (e.g., functional APIs) and generate solution plans that execute a series of API function calls in a step-by-step manner. The multitude of candidate API function calls significantly expands the action space, amplifying the critical need for efficient action space navigation. However, existing methods either struggle with unidirectional exploration in expansive action spaces, trapped into a locally optimal solution, or suffer from exhaustively traversing all potential actions, causing inefficient navigation. To address these issues, we propose ToolChain*, an efficient tree search-based planning algorithm for LLM-based agents. It formulates the entire action space as a decision tree, where each node represents a possible API function call involved in a solution plan. By incorporating the A* search algorithm with task-specific cost function design, it efficiently prunes high-cost branches that may involve incorrect actions, identifying the most low-cost valid path as the solution. Extensive experiments on multiple tool-use and reasoning tasks demonstrate that ToolChain* efficiently balances exploration and exploitation within an expansive action space. It outperforms state-of-the-art baselines on planning and reasoning tasks by 3.1% and 3.5% on average while requiring 7.35x and 2.31x less time, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13548", "title": "Towards Understanding Sycophancy in Language Models", "authors": [ "Mrinank Sharma", "Meg Tong", "Tomasz Korbak", "David Duvenaud", "Amanda Askell", "Samuel R. Bowman", "Newton Cheng", "Esin Durmus", "Zac Hatfield-Dodds", "Scott R. Johnston", "Shauna Kravec", "Timothy Maxwell", "Sam McCandlish", "Kamal Ndousse", "Oliver Rausch", "Nicholas Schiefer", "Da Yan", "Miranda Zhang", "Ethan Perez" ], "github": "https://github.com/meg-tong/sycophancy-eval", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) is a popular technique for training high-quality AI assistants. However, RLHF may also encourage model responses that match user beliefs over truthful responses, a behavior known as sycophancy. We investigate the prevalence of sycophancy in RLHF-trained models and whether human preference judgements are responsible. We first demonstrate that five state-of-the-art AI assistants consistently exhibit sycophantic behavior across four varied free-form text-generation tasks. To understand if human preferences drive this broadly observed behavior of RLHF models, we analyze existing human preference data. We find that when a response matches a user's views, it is more likely to be preferred. Moreover, both humans and preference models (PMs) prefer convincingly-written sycophantic responses over correct ones a negligible fraction of the time. Optimizing model outputs against PMs also sometimes sacrifices truthfulness in favor of sycophancy. Overall, our results indicate that sycophancy is a general behavior of RLHF models, likely driven in part by human preference judgements favoring sycophantic responses.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/meg-tong/sycophancy-eval", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13522", "title": "Teaching Language Models to Self-Improve through Interactive Demonstrations", "authors": [ "Xiao Yu", "Baolin Peng", "Michel Galley", "Jianfeng Gao", "Zhou Yu" ], "github": "https://github.com/jasonyux/tripost", "project_page": "", "abstract": "The self-improving ability of large language models (LLMs), enabled by prompting them to analyze and revise their own outputs, has garnered significant interest in recent research. However, this ability has been shown to be absent and difficult to learn for smaller models, thus widening the performance gap between state-of-the-art LLMs and more cost-effective and faster ones. To reduce this gap, we introduce TriPosT, a training algorithm that endows smaller models with such self-improvement ability, and show that our approach can improve a LLaMA-7b's performance on math and reasoning tasks by up to 7.13%. In contrast to prior work, we achieve this by using the smaller model to interact with LLMs to collect feedback and improvements on its own generations. We then replay this experience to train the small model. Our experiments on four math and reasoning datasets show that the interactive experience of learning from and correcting its own mistakes is crucial for small models to improve their performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jasonyux/tripost", "github_api_added_by": "auto" }, { "date": "2023-10-23", "arxiv_id": "2310.13385", "title": "Tuna: Instruction Tuning using Feedback from Large Language Models", "authors": [ "Haoran Li", "Yiran Liu", "Xingxing Zhang", "Wei Lu", "Furu Wei" ], "github": "https://github.com/microsoft/LMOps", "project_page": "", "abstract": "Instruction tuning of open-source large language models (LLMs) like LLaMA, using direct outputs from more powerful LLMs such as Instruct-GPT and GPT-4, has proven to be a cost-effective way to align model behaviors with human preferences. However, the instruction-tuned model has only seen one response per instruction, lacking the knowledge of potentially better responses. In this paper, we propose finetuning an instruction-tuned LLM using our novel probabilistic ranking and contextual ranking approaches to increase the likelihood of generating better responses. Probabilistic ranking enables the instruction-tuned model to inherit the relative rankings of high-quality and low-quality responses from the teacher LLM. On the other hand, learning with contextual ranking allows the model to refine its own response distribution using the contextual understanding ability of stronger LLMs. Furthermore, we apply probabilistic ranking and contextual ranking sequentially to the instruction-tuned LLM. The resulting model, which we call Tuna, consistently improves the performance on Super Natural Instructions (119 test tasks), LMentry (25 test tasks), Vicuna QA, and can even obtain better results than several strong reinforcement learning baselines. Our code and data are available at https://github.com/microsoft/LMOps.", "github_manual": "", "github_regex": "https://github.com/microsoft/LMOps", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13332", "title": "Democratizing Reasoning Ability: Tailored Learning from Large Language Model", "authors": [ "Zhaoyang Wang", "Shaohan Huang", "Yuxuan Liu", "Jiahai Wang", "Minghui Song", "Zihan Zhang", "Haizhen Huang", "Furu Wei", "Weiwei Deng", "Feng Sun", "Qi Zhang" ], "github": "https://github.com/Raibows/Learn-to-Reason", "project_page": "", "abstract": "Large language models (LLMs) exhibit impressive emergent abilities in natural language processing, but their democratization is hindered due to huge computation requirements and closed-source nature. Recent research on advancing open-source smaller LMs by distilling knowledge from black-box LLMs has obtained promising results in the instruction-following ability. However, the reasoning ability which is more challenging to foster, is relatively rarely explored. In this paper, we propose a tailored learning approach to distill such reasoning ability to smaller LMs to facilitate the democratization of the exclusive reasoning ability. In contrast to merely employing LLM as a data annotator, we exploit the potential of LLM as a reasoning teacher by building an interactive multi-round learning paradigm. This paradigm enables the student to expose its deficiencies to the black-box teacher who then can provide customized training data in return. Further, to exploit the reasoning potential of the smaller LM, we propose self-reflection learning to motivate the student to learn from self-made mistakes. The learning from self-reflection and LLM are all tailored to the student's learning status, thanks to the seamless integration with the multi-round learning paradigm. Comprehensive experiments and analysis on mathematical and commonsense reasoning tasks demonstrate the effectiveness of our method. The code will be available at https://github.com/Raibows/Learn-to-Reason.", "github_manual": "", "github_regex": "https://github.com/Raibows/Learn-to-Reason", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13289", "title": "SALMONN: Towards Generic Hearing Abilities for Large Language Models", "authors": [ "Changli Tang", "Wenyi Yu", "Guangzhi Sun", "Xianzhao Chen", "Tian Tan", "Wei Li", "Lu Lu", "Zejun Ma", "Chao Zhang" ], "github": "https://github.com/bytedance/SALMONN", "project_page": "", "abstract": "Hearing is arguably an essential ability of artificial intelligence (AI) agents in the physical world, which refers to the perception and understanding of general auditory information consisting of at least three types of sounds: speech, audio events, and music. In this paper, we propose SALMONN, a speech audio language music open neural network, built by integrating a pre-trained text-based large language model (LLM) with speech and audio encoders into a single multimodal model. SALMONN enables the LLM to directly process and understand general audio inputs and achieve competitive performances on a number of speech and audio tasks used in training, such as automatic speech recognition and translation, auditory-information-based question answering, emotion recognition, speaker verification, and music and audio captioning etc. SALMONN also has a diverse set of emergent abilities unseen in the training, which includes but is not limited to speech translation to untrained languages, speech-based slot filling, spoken-query-based question answering, audio-based storytelling, and speech audio co-reasoning etc. The presence of the cross-modal emergent abilities is studied, and a novel few-shot activation tuning approach is proposed to activate such abilities of SALMONN. To our knowledge, SALMONN is the first model of its type and can be regarded as a step towards AI with generic hearing abilities. An interactive demo of SALMONN is available at \\url{https://github.com/bytedance/SALMONN}, and the training code and model checkpoints will be released upon acceptance.", "github_manual": "", "github_regex": "https://github.com/bytedance/SALMONN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13127", "title": "Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models", "authors": [ "Zhihan Zhang", "Shuohang Wang", "Wenhao Yu", "Yichong Xu", "Dan Iter", "Qingkai Zeng", "Yang Liu", "Chenguang Zhu", "Meng Jiang" ], "github": "https://github.com/ytyz1307zzh/Auto-Instruct", "project_page": "", "abstract": "Large language models (LLMs) can perform a wide range of tasks by following natural language instructions, without the necessity of task-specific fine-tuning. Unfortunately, the performance of LLMs is greatly influenced by the quality of these instructions, and manually writing effective instructions for each task is a laborious and subjective process. In this paper, we introduce Auto-Instruct, a novel method to automatically improve the quality of instructions provided to LLMs. Our method leverages the inherent generative ability of LLMs to produce diverse candidate instructions for a given task, and then ranks them using a scoring model trained on a variety of 575 existing NLP tasks. In experiments on 118 out-of-domain tasks, Auto-Instruct surpasses both human-written instructions and existing baselines of LLM-generated instructions. Furthermore, our method exhibits notable generalizability even with other LLMs that are not incorporated into its training process.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ytyz1307zzh/Auto-Instruct", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13065", "title": "Creative Robot Tool Use with Large Language Models", "authors": [ "Mengdi Xu", "Peide Huang", "Wenhao Yu", "Shiqi Liu", "Xilun Zhang", "Yaru Niu", "Tingnan Zhang", "Fei Xia", "Jie Tan", "Ding Zhao" ], "github": "", "project_page": "", "abstract": "Tool use is a hallmark of advanced intelligence, exemplified in both animal behavior and robotic capabilities. This paper investigates the feasibility of imbuing robots with the ability to creatively use tools in tasks that involve implicit physical constraints and long-term planning. Leveraging Large Language Models (LLMs), we develop RoboTool, a system that accepts natural language instructions and outputs executable code for controlling robots in both simulated and real-world environments. RoboTool incorporates four pivotal components: (i) an \"Analyzer\" that interprets natural language to discern key task-related concepts, (ii) a \"Planner\" that generates comprehensive strategies based on the language input and key concepts, (iii) a \"Calculator\" that computes parameters for each skill, and (iv) a \"Coder\" that translates these plans into executable Python code. Our results show that RoboTool can not only comprehend explicit or implicit physical constraints and environmental factors but also demonstrate creative tool use. Unlike traditional Task and Motion Planning (TAMP) methods that rely on explicit optimization, our LLM-based system offers a more flexible, efficient, and user-friendly solution for complex robotics tasks. Through extensive experiments, we validate that RoboTool is proficient in handling tasks that would otherwise be infeasible without the creative use of tools, thereby expanding the capabilities of robotic systems. Demos are available on our project page: https://creative-robotool.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13012", "title": "H2O Open Ecosystem for State-of-the-art Large Language Models", "authors": [ "Arno Candel", "Jon McKinney", "Philipp Singer", "Pascal Pfeiffer", "Maximilian Jeblick", "Chun Ming Lee", "Marcos V. Conde" ], "github": "https://github.com/h2oai/h2ogpt", "project_page": "", "abstract": "Large Language Models (LLMs) represent a revolution in AI. However, they also pose many significant risks, such as the presence of biased, private, copyrighted or harmful text. For this reason we need open, transparent and safe solutions. We introduce a complete open-source ecosystem for developing and testing LLMs. The goal of this project is to boost open alternatives to closed-source approaches. We release h2oGPT, a family of fine-tuned LLMs from 7 to 70 Billion parameters. We also introduce H2O LLM Studio, a framework and no-code GUI designed for efficient fine-tuning, evaluation, and deployment of LLMs using the most recent state-of-the-art techniques. Our code and models are licensed under fully permissive Apache 2.0 licenses. We believe open-source language models help to boost AI development and make it more accessible and trustworthy. The demo is available at: https://gpt.h2o.ai/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/h2oai/h2ogpt", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13268", "title": "DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics", "authors": [ "Kaiwen Zheng", "Cheng Lu", "Jianfei Chen", "Jun Zhu" ], "github": "https://github.com/thu-ml/DPM-Solver-v3", "project_page": "", "abstract": "Diffusion probabilistic models (DPMs) have exhibited excellent performance for high-fidelity image generation while suffering from inefficient sampling. Recent works accelerate the sampling procedure by proposing fast ODE solvers that leverage the specific ODE form of DPMs. However, they highly rely on specific parameterization during inference (such as noise/data prediction), which might not be the optimal choice. In this work, we propose a novel formulation towards the optimal parameterization during sampling that minimizes the first-order discretization error of the ODE solution. Based on such formulation, we propose DPM-Solver-v3, a new fast ODE solver for DPMs by introducing several coefficients efficiently computed on the pretrained model, which we call empirical model statistics. We further incorporate multistep methods and a predictor-corrector framework, and propose some techniques for improving sample quality at small numbers of function evaluations (NFE) or large guidance scales. Experiments show that DPM-Solver-v3 achieves consistently better or comparable performance in both unconditional and conditional sampling with both pixel-space and latent-space DPMs, especially in 5sim10 NFEs. We achieve FIDs of 12.21 (5 NFE), 2.51 (10 NFE) on unconditional CIFAR10, and MSE of 0.55 (5 NFE, 7.5 guidance scale) on Stable Diffusion, bringing a speed-up of 15\\%sim30\\% compared to previous state-of-the-art training-free methods. Code is available at https://github.com/thu-ml/DPM-Solver-v3.", "github_manual": "", "github_regex": "https://github.com/thu-ml/DPM-Solver-v3", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13671", "title": "Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models", "authors": [ "Ruida Wang", "Wangchunshu Zhou", "Mrinmaya Sachan" ], "github": "https://github.com/rickyskywalker/synthesis_step-by-step_official", "project_page": "", "abstract": "*Data Synthesis* is a promising way to train a small model with very little labeled data. One approach for data synthesis is to leverage the rich knowledge from large language models to synthesize pseudo training examples for small models, making it possible to achieve both data and compute efficiency at the same time. However, a key challenge in data synthesis is that the synthesized dataset often suffers from a large distributional discrepancy from the *real task* data distribution. Thus, in this paper, we propose *Synthesis Step by Step* (**S3**), a data synthesis framework that shrinks this distribution gap by iteratively extrapolating the errors made by a small model trained on the synthesized dataset on a small real-world validation dataset using a large language model. Extensive experiments on multiple NLP tasks show that our approach improves the performance of a small model by reducing the gap between the synthetic dataset and the real data, resulting in significant improvement compared to several baselines: 9.48% improvement compared to ZeroGen and 2.73% compared to GoldGen, and at most 15.17% improvement compared to the small model trained on human-annotated data.", "github_manual": "https://github.com/rickyskywalker/synthesis_step-by-step_official", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13119", "title": "DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture Propagation", "authors": [ "Bangbang Yang", "Wenqi Dong", "Lin Ma", "Wenbo Hu", "Xiao Liu", "Zhaopeng Cui", "Yuewen Ma" ], "github": "https://github.com/ybbbbt/dreamspace", "project_page": "", "abstract": "Diffusion-based methods have achieved prominent success in generating 2D media. However, accomplishing similar proficiencies for scene-level mesh texturing in 3D spatial applications, e.g., XR/VR, remains constrained, primarily due to the intricate nature of 3D geometry and the necessity for immersive free-viewpoint rendering. In this paper, we propose a novel indoor scene texturing framework, which delivers text-driven texture generation with enchanting details and authentic spatial coherence. The key insight is to first imagine a stylized 360{\\deg} panoramic texture from the central viewpoint of the scene, and then propagate it to the rest areas with inpainting and imitating techniques. To ensure meaningful and aligned textures to the scene, we develop a novel coarse-to-fine panoramic texture generation approach with dual texture alignment, which both considers the geometry and texture cues of the captured scenes. To survive from cluttered geometries during texture propagation, we design a separated strategy, which conducts texture inpainting in confidential regions and then learns an implicit imitating network to synthesize textures in occluded and tiny structural areas. Extensive experiments and the immersive VR application on real-world indoor scenes demonstrate the high quality of the generated textures and the engaging experience on VR headsets. Project webpage: https://ybbbbt.com/publication/dreamspace", "github_manual": "https://github.com/ybbbbt/dreamspace", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13639", "title": "Contrastive Prefence Learning: Learning from Human Feedback without RL", "authors": [ "Joey Hejna", "Rafael Rafailov", "Harshit Sikchi", "Chelsea Finn", "Scott Niekum", "W. Bradley Knox", "Dorsa Sadigh" ], "github": "https://github.com/jhejna/cpl", "project_page": "", "abstract": "Reinforcement Learning from Human Feedback (RLHF) has emerged as a popular paradigm for aligning models with human intent. Typically RLHF algorithms operate in two phases: first, use human preferences to learn a reward function and second, align the model by optimizing the learned reward via reinforcement learning (RL). This paradigm assumes that human preferences are distributed according to reward, but recent work suggests that they instead follow the regret under the user's optimal policy. Thus, learning a reward function from feedback is not only based on a flawed assumption of human preference, but also leads to unwieldy optimization challenges that stem from policy gradients or bootstrapping in the RL phase. Because of these optimization challenges, contemporary RLHF methods restrict themselves to contextual bandit settings (e.g., as in large language models) or limit observation dimensionality (e.g., state-based robotics). We overcome these limitations by introducing a new family of algorithms for optimizing behavior from human feedback using the regret-based model of human preferences. Using the principle of maximum entropy, we derive Contrastive Preference Learning (CPL), an algorithm for learning optimal policies from preferences without learning reward functions, circumventing the need for RL. CPL is fully off-policy, uses only a simple contrastive objective, and can be applied to arbitrary MDPs. This enables CPL to elegantly scale to high-dimensional and sequential RLHF problems while being simpler than prior methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jhejna/cpl", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13545", "title": "ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection", "authors": [ "Zhongzhan Huang", "Pan Zhou", "Shuicheng Yan", "Liang Lin" ], "github": "https://github.com/sail-sg/ScaleLong", "project_page": "", "abstract": "In diffusion models, UNet is the most popular network backbone, since its long skip connects (LSCs) to connect distant network blocks can aggregate long-distant information and alleviate vanishing gradient. Unfortunately, UNet often suffers from unstable training in diffusion models which can be alleviated by scaling its LSC coefficients smaller. However, theoretical understandings of the instability of UNet in diffusion models and also the performance improvement of LSC scaling remain absent yet. To solve this issue, we theoretically show that the coefficients of LSCs in UNet have big effects on the stableness of the forward and backward propagation and robustness of UNet. Specifically, the hidden feature and gradient of UNet at any layer can oscillate and their oscillation ranges are actually large which explains the instability of UNet training. Moreover, UNet is also provably sensitive to perturbed input, and predicts an output distant from the desired output, yielding oscillatory loss and thus oscillatory gradient. Besides, we also observe the theoretical benefits of the LSC coefficient scaling of UNet in the stableness of hidden features and gradient and also robustness. Finally, inspired by our theory, we propose an effective coefficient scaling framework ScaleLong that scales the coefficients of LSC in UNet and better improves the training stability of UNet. Experimental results on four famous datasets show that our methods are superior to stabilize training and yield about 1.5x training acceleration on different diffusion models with UNet or UViT backbones. Code: https://github.com/sail-sg/ScaleLong", "github_manual": "", "github_regex": "https://github.com/sail-sg/ScaleLong", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-23", "arxiv_id": "2310.13355", "title": "SILC: Improving Vision Language Pretraining with Self-Distillation", "authors": [ "Muhammad Ferjad Naeem", "Yongqin Xian", "Xiaohua Zhai", "Lukas Hoyer", "Luc Van Gool", "Federico Tombari" ], "github": "", "project_page": "", "abstract": "Image-Text pretraining on web-scale image caption dataset has become the default recipe for open vocabulary classification and retrieval models thanks to the success of CLIP and its variants. Several works have also used CLIP features for dense prediction tasks and have shown the emergence of open-set abilities. However, the contrastive objective only focuses on image-text alignment and does not incentivise image feature learning for dense prediction tasks. In this work, we propose the simple addition of local-to-global correspondence learning by self-distillation as an additional objective for contrastive pre-training to propose SILC. We show that distilling local image features from an exponential moving average (EMA) teacher model significantly improves model performance on several computer vision tasks including classification, retrieval, and especially segmentation. We further show that SILC scales better with the same training duration compared to the baselines. Our model SILC sets a new state of the art for zero-shot classification, few shot classification, image and text retrieval, zero-shot segmentation, and open vocabulary segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-24", "arxiv_id": "2310.14566", "title": "HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models", "authors": [ "Fuxiao Liu", "Tianrui Guan", "Zongxia Li", "Lichang Chen", "Yaser Yacoob", "Dinesh Manocha", "Tianyi Zhou" ], "github": "https://github.com/tianyi-lab/HallusionBench", "project_page": "", "abstract": "Large language models (LLMs), after being aligned with vision models and integrated into vision-language models (VLMs), can bring impressive improvement in image reasoning tasks. This was shown by the recently released GPT-4V(ison), LLaVA-1.5, etc. However, the strong language prior in these SOTA LVLMs can be a double-edged sword: they may ignore the image context and solely rely on the (even contradictory) language prior for reasoning. In contrast, the vision modules in VLMs are weaker than LLMs and may result in misleading visual representations, which are then translated to confident mistakes by LLMs. To study these two types of VLM mistakes, i.e., language hallucination and visual illusion, we curated HallusionBench, an image-context reasoning benchmark that is still challenging to even GPT-4V and LLaVA-1.5. We provide a detailed analysis of examples in HallusionBench, which sheds novel insights on the illusion or hallucination of VLMs and how to improve them in the future. The benchmark and codebase will be released at https://github.com/tianyi-lab/HallusionBench.", "github_manual": "", "github_regex": "https://github.com/tianyi-lab/HallusionBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-24", "arxiv_id": "2310.15144", "title": "DEsignBench: Exploring and Benchmarking DALL-E 3 for Imagining Visual Design", "authors": [ "Kevin Lin", "Zhengyuan Yang", "Linjie Li", "Jianfeng Wang", "Lijuan Wang" ], "github": "https://github.com/design-bench/design-bench.github.io", "project_page": "", "abstract": "We introduce DEsignBench, a text-to-image (T2I) generation benchmark tailored for visual design scenarios. Recent T2I models like DALL-E 3 and others, have demonstrated remarkable capabilities in generating photorealistic images that align closely with textual inputs. While the allure of creating visually captivating images is undeniable, our emphasis extends beyond mere aesthetic pleasure. We aim to investigate the potential of using these powerful models in authentic design contexts. In pursuit of this goal, we develop DEsignBench, which incorporates test samples designed to assess T2I models on both \"design technical capability\" and \"design application scenario.\" Each of these two dimensions is supported by a diverse set of specific design categories. We explore DALL-E 3 together with other leading T2I models on DEsignBench, resulting in a comprehensive visual gallery for side-by-side comparisons. For DEsignBench benchmarking, we perform human evaluations on generated images in DEsignBench gallery, against the criteria of image-text alignment, visual aesthetic, and design creativity. Our evaluation also considers other specialized design capabilities, including text rendering, layout composition, color harmony, 3D design, and medium style. In addition to human evaluations, we introduce the first automatic image generation evaluator powered by GPT-4V. This evaluator provides ratings that align well with human judgments, while being easily replicable and cost-efficient. A high-resolution version is available at https://github.com/design-bench/design-bench.github.io/raw/main/designbench.pdf?download=", "github_manual": "", "github_regex": "https://github.com/design-bench/design-bench.github.io", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-24", "arxiv_id": "2310.14573", "title": "Exploring the Boundaries of GPT-4 in Radiology", "authors": [ "Qianchu Liu", "Stephanie Hyland", "Shruthi Bannur", "Kenza Bouzid", "Daniel C. Castro", "Maria Teodora Wetscherek", "Robert Tinn", "Harshita Sharma", "Fernando P\u00e9rez-Garc\u00eda", "Anton Schwaighofer", "Pranav Rajpurkar", "Sameer Tajdin Khanna", "Hoifung Poon", "Naoto Usuyama", "Anja Thieme", "Aditya V. Nori", "Matthew P. Lungren", "Ozan Oktay", "Javier Alvarez-Valle" ], "github": "", "project_page": "", "abstract": "The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domains and applications. In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models. Exploring various prompting strategies, we evaluated GPT-4 on a diverse range of common radiology tasks and we found GPT-4 either outperforms or is on par with current SOTA radiology models. With zero-shot prompting, GPT-4 already obtains substantial gains (approx 10% absolute improvement) over radiology models in temporal sentence similarity classification (accuracy) and natural language inference (F_1). For tasks that require learning dataset-specific style or schema (e.g. findings summarisation), GPT-4 improves with example-based prompting and matches supervised SOTA. Our extensive error analysis with a board-certified radiologist shows GPT-4 has a sufficient level of radiology knowledge with only occasional errors in complex context that require nuanced domain knowledge. For findings summarisation, GPT-4 outputs are found to be overall comparable with existing manually-written impressions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-24", "arxiv_id": "2310.15123", "title": "Branch-Solve-Merge Improves Large Language Model Evaluation and Generation", "authors": [ "Swarnadeep Saha", "Omer Levy", "Asli Celikyilmaz", "Mohit Bansal", "Jason Weston", "Xian Li" ], "github": "https://github.com/openai/evals", "project_page": "", "abstract": "Large Language Models (LLMs) are frequently used for multi-faceted language generation and evaluation tasks that involve satisfying intricate user constraints or taking into account multiple aspects and criteria. However, their performance can fall short, due to the model's lack of coherence and inability to plan and decompose the problem. We propose Branch-Solve-Merge (BSM), a Large Language Model program (Schlag et al., 2023) for tackling such challenging natural language tasks. It consists of branch, solve, and merge modules that are parameterized with specific prompts to the base LLM. These three modules plan a decomposition of the task into multiple parallel sub-tasks, independently solve them, and fuse the solutions to the sub-tasks. We apply our method to the tasks of LLM response evaluation and constrained text generation and evaluate its effectiveness with multiple LLMs, including Vicuna, LLaMA-2-chat, and GPT-4. BSM improves the evaluation correctness and consistency for each LLM by enhancing human-LLM agreement by up to 26%, reducing length and pairwise position biases by up to 50%, and allowing LLaMA-2-chat to match or outperform GPT-4 on most domains. On the constraint story generation task, BSM improves the coherence of the stories while also improving constraint satisfaction by 12%.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openai/evals", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-24", "arxiv_id": "2310.14495", "title": "InstructExcel: A Benchmark for Natural Language Instruction in Excel", "authors": [ "Justin Payan", "Swaroop Mishra", "Mukul Singh", "Carina Negreanu", "Christian Poelitz", "Chitta Baral", "Subhro Roy", "Rasika Chakravarthy", "Benjamin Van Durme", "Elnaz Nouri" ], "github": "https://github.com/microsoft/InstructExcel", "project_page": "", "abstract": "With the evolution of Large Language Models (LLMs) we can solve increasingly more complex NLP tasks across various domains, including spreadsheets. This work investigates whether LLMs can generate code (Excel OfficeScripts, a TypeScript API for executing many tasks in Excel) that solves Excel specific tasks provided via natural language user instructions. To do so we introduce a new large-scale benchmark, InstructExcel, created by leveraging the 'Automate' feature in Excel to automatically generate OfficeScripts from users' actions. Our benchmark includes over 10k samples covering 170+ Excel operations across 2,000 publicly available Excel spreadsheets. Experiments across various zero-shot and few-shot settings show that InstructExcel is a hard benchmark for state of the art models like GPT-4. We observe that (1) using GPT-4 over GPT-3.5, (2) providing more in-context examples, and (3) dynamic prompting can help improve performance on this benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/InstructExcel", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-24", "arxiv_id": "2310.15169", "title": "FreeNoise: Tuning-Free Longer Video Diffusion Via Noise Rescheduling", "authors": [ "Haonan Qiu", "Menghan Xia", "Yong Zhang", "Yingqing He", "Xintao Wang", "Ying Shan", "Ziwei Liu" ], "github": "https://github.com/AILab-CVC/FreeNoise", "project_page": "", "abstract": "With the availability of large-scale video datasets and the advances of diffusion models, text-driven video generation has achieved substantial progress. However, existing video generation models are typically trained on a limited number of frames, resulting in the inability to generate high-fidelity long videos during inference. Furthermore, these models only support single-text conditions, whereas real-life scenarios often require multi-text conditions as the video content changes over time. To tackle these challenges, this study explores the potential of extending the text-driven capability to generate longer videos conditioned on multiple texts. 1) We first analyze the impact of initial noise in video diffusion models. Then building upon the observation of noise, we propose FreeNoise, a tuning-free and time-efficient paradigm to enhance the generative capabilities of pretrained video diffusion models while preserving content consistency. Specifically, instead of initializing noises for all frames, we reschedule a sequence of noises for long-range correlation and perform temporal attention over them by window-based function. 2) Additionally, we design a novel motion injection method to support the generation of videos conditioned on multiple text prompts. Extensive experiments validate the superiority of our paradigm in extending the generative capabilities of video diffusion models. It is noteworthy that compared with the previous best-performing method which brought about 255% extra time cost, our method incurs only negligible time cost of approximately 17%. Generated video samples are available at our website: http://haonanqiu.com/projects/FreeNoise.html.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AILab-CVC/FreeNoise", "github_api_added_by": "auto" }, { "date": "2023-10-24", "arxiv_id": "2310.13961", "title": "Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs", "authors": [ "Young-Suk Lee", "Md Arafat Sultan", "Yousef El-Kurdi", "Tahira Naseem Asim Munawar", "Radu Florian", "Salim Roukos", "Ram\u00f3n Fernandez Astudillo" ], "github": "https://github.com/IBM/ensemble-instruct", "project_page": "", "abstract": "Using in-context learning (ICL) for data generation, techniques such as Self-Instruct (Wang et al., 2023) or the follow-up Alpaca (Taori et al., 2023) can train strong conversational agents with only a small amount of human supervision. One limitation of these approaches is that they resort to very large language models (around 175B parameters) that are also proprietary and non-public. Here we explore the application of such techniques to language models that are much smaller (around 10B--40B parameters) and have permissive licenses. We find the Self-Instruct approach to be less effective at these sizes and propose new ICL methods that draw on two main ideas: (a) Categorization and simplification of the ICL templates to make prompt learning easier for the LM, and (b) Ensembling over multiple LM outputs to help select high-quality synthetic examples. Our algorithm leverages the 175 Self-Instruct seed tasks and employs separate pipelines for instructions that require an input and instructions that do not. Empirical investigations with different LMs show that: (1) Our proposed method yields higher-quality instruction tuning data than Self-Instruct, (2) It improves performances of both vanilla and instruction-tuned LMs by significant margins, and (3) Smaller instruction-tuned LMs generate more useful outputs than their larger un-tuned counterparts. Our codebase is available at https://github.com/IBM/ensemble-instruct.", "github_manual": "", "github_regex": "https://github.com/IBM/ensemble-instruct", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-24", "arxiv_id": "2310.13798", "title": "Specific versus General Principles for Constitutional AI", "authors": [ "Sandipan Kundu", "Yuntao Bai", "Saurav Kadavath", "Amanda Askell", "Andrew Callahan", "Anna Chen", "Anna Goldie", "Avital Balwit", "Azalia Mirhoseini", "Brayden McLean", "Catherine Olsson", "Cassie Evraets", "Eli Tran-Johnson", "Esin Durmus", "Ethan Perez", "Jackson Kernion", "Jamie Kerr", "Kamal Ndousse", "Karina Nguyen", "Nelson Elhage", "Newton Cheng", "Nicholas Schiefer", "Nova DasSarma", "Oliver Rausch", "Robin Larson", "Shannon Yang", "Shauna Kravec", "Timothy Telleen-Lawton", "Thomas I. Liao", "Tom Henighan", "Tristan Hume", "Zac Hatfield-Dodds", "S\u00f6ren Mindermann", "Nicholas Joseph", "Sam McCandlish", "Jared Kaplan" ], "github": "", "project_page": "", "abstract": "Human feedback can prevent overtly harmful utterances in conversational models, but may not automatically mitigate subtle problematic behaviors such as a stated desire for self-preservation or power. Constitutional AI offers an alternative, replacing human feedback with feedback from AI models conditioned only on a list of written principles. We find this approach effectively prevents the expression of such behaviors. The success of simple principles motivates us to ask: can models learn general ethical behaviors from only a single written principle? To test this, we run experiments using a principle roughly stated as \"do what's best for humanity\". We find that the largest dialogue models can generalize from this short constitution, resulting in harmless assistants with no stated interest in specific motivations like power. A general principle may thus partially avoid the need for a long list of constitutions targeting potentially harmful behaviors. However, more detailed constitutions still improve fine-grained control over specific types of harms. This suggests both general and specific principles have value for steering AI safely.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-24", "arxiv_id": "2310.13730", "title": "Localizing and Editing Knowledge in Text-to-Image Generative Models", "authors": [ "Samyadeep Basu", "Nanxuan Zhao", "Vlad Morariu", "Soheil Feizi", "Varun Manjunatha" ], "github": "", "project_page": "", "abstract": "Text-to-Image Diffusion Models such as Stable-Diffusion and Imagen have achieved unprecedented quality of photorealism with state-of-the-art FID scores on MS-COCO and other generation benchmarks. Given a caption, image generation requires fine-grained knowledge about attributes such as object structure, style, and viewpoint amongst others. Where does this information reside in text-to-image generative models? In our paper, we tackle this question and understand how knowledge corresponding to distinct visual attributes is stored in large-scale text-to-image diffusion models. We adapt Causal Mediation Analysis for text-to-image models and trace knowledge about distinct visual attributes to various (causal) components in the (i) UNet and (ii) text-encoder of the diffusion model. In particular, we show that unlike generative large-language models, knowledge about different attributes is not localized in isolated components, but is instead distributed amongst a set of components in the conditional UNet. These sets of components are often distinct for different visual attributes. Remarkably, we find that the CLIP text-encoder in public text-to-image models such as Stable-Diffusion contains only one causal state across different visual attributes, and this is the first self-attention layer corresponding to the last subject token of the attribute in the caption. This is in stark contrast to the causal states in other language models which are often the mid-MLP layers. Based on this observation of only one causal state in the text-encoder, we introduce a fast, data-free model editing method Diff-QuickFix which can effectively edit concepts in text-to-image models. DiffQuickFix can edit (ablate) concepts in under a second with a closed-form update, providing a significant 1000x speedup and comparable editing performance to existing fine-tuning based editing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-24", "arxiv_id": "2310.15111", "title": "Matryoshka Diffusion Models", "authors": [ "Jiatao Gu", "Shuangfei Zhai", "Yizhe Zhang", "Josh Susskind", "Navdeep Jaitly" ], "github": "https://github.com/apple/ml-mdm", "project_page": "", "abstract": "Diffusion models are the de facto approach for generating high-quality images and videos, but learning high-dimensional models remains a formidable task due to computational and optimization challenges. Existing methods often resort to training cascaded models in pixel space or using a downsampled latent space of a separately trained auto-encoder. In this paper, we introduce Matryoshka Diffusion Models(MDM), an end-to-end framework for high-resolution image and video synthesis. We propose a diffusion process that denoises inputs at multiple resolutions jointly and uses a NestedUNet architecture where features and parameters for small-scale inputs are nested within those of large scales. In addition, MDM enables a progressive training schedule from lower to higher resolutions, which leads to significant improvements in optimization for high-resolution generation. We demonstrate the effectiveness of our approach on various benchmarks, including class-conditioned image generation, high-resolution text-to-image, and text-to-video applications. Remarkably, we can train a single pixel-space model at resolutions of up to 1024x1024 pixels, demonstrating strong zero-shot generalization using the CC12M dataset, which contains only 12 million images.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/ml-mdm", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-24", "arxiv_id": "2310.13724", "title": "Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots", "authors": [ "Xavier Puig", "Eric Undersander", "Andrew Szot", "Mikael Dallaire Cote", "Tsung-Yen Yang", "Ruslan Partsey", "Ruta Desai", "Alexander William Clegg", "Michal Hlavac", "So Yeon Min", "Vladim\u00edr Vondru\u0161", "Theophile Gervet", "Vincent-Pierre Berges", "John M. Turner", "Oleksandr Maksymets", "Zsolt Kira", "Mrinal Kalakrishnan", "Jitendra Malik", "Devendra Singh Chaplot", "Unnat Jain", "Dhruv Batra", "Akshara Rai", "Roozbeh Mottaghi" ], "github": "", "project_page": "", "abstract": "We present Habitat 3.0: a simulation platform for studying collaborative human-robot tasks in home environments. Habitat 3.0 offers contributions across three dimensions: (1) Accurate humanoid simulation: addressing challenges in modeling complex deformable bodies and diversity in appearance and motion, all while ensuring high simulation speed. (2) Human-in-the-loop infrastructure: enabling real human interaction with simulated robots via mouse/keyboard or a VR interface, facilitating evaluation of robot policies with human input. (3) Collaborative tasks: studying two collaborative tasks, Social Navigation and Social Rearrangement. Social Navigation investigates a robot's ability to locate and follow humanoid avatars in unseen environments, whereas Social Rearrangement addresses collaboration between a humanoid and robot while rearranging a scene. These contributions allow us to study end-to-end learned and heuristic baselines for human-robot collaboration in-depth, as well as evaluate them with humans in the loop. Our experiments demonstrate that learned robot policies lead to efficient task completion when collaborating with unseen humanoid agents and human partners that might exhibit behaviors that the robot has not seen before. Additionally, we observe emergent behaviors during collaborative task execution, such as the robot yielding space when obstructing a humanoid agent, thereby allowing the effective completion of the task by the humanoid agent. Furthermore, our experiments using the human-in-the-loop tool demonstrate that our automated evaluation with humanoids can provide an indication of the relative ordering of different policies when evaluated with real human collaborators. Habitat 3.0 unlocks interesting new features in simulators for Embodied AI, and we hope it paves the way for a new frontier of embodied human-AI interaction capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-24", "arxiv_id": "2310.13772", "title": "TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models", "authors": [ "Tianshi Cao", "Karsten Kreis", "Sanja Fidler", "Nicholas Sharp", "Kangxue Yin" ], "github": "", "project_page": "", "abstract": "We present TexFusion (Texture Diffusion), a new method to synthesize textures for given 3D geometries, using large-scale text-guided image diffusion models. In contrast to recent works that leverage 2D text-to-image diffusion models to distill 3D objects using a slow and fragile optimization process, TexFusion introduces a new 3D-consistent generation technique specifically designed for texture synthesis that employs regular diffusion model sampling on different 2D rendered views. Specifically, we leverage latent diffusion models, apply the diffusion model's denoiser on a set of 2D renders of the 3D object, and aggregate the different denoising predictions on a shared latent texture map. Final output RGB textures are produced by optimizing an intermediate neural color field on the decodings of 2D renders of the latent texture. We thoroughly validate TexFusion and show that we can efficiently generate diverse, high quality and globally coherent textures. We achieve state-of-the-art text-guided texture synthesis performance using only image diffusion models, while avoiding the pitfalls of previous distillation-based methods. The text-conditioning offers detailed control and we also do not rely on any ground truth 3D textures for training. This makes our method versatile and applicable to a broad range of geometry and texture types. We hope that TexFusion will advance AI-based texturing of 3D assets for applications in virtual reality, game design, simulation, and more.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-25", "arxiv_id": "2310.16045", "title": "Woodpecker: Hallucination Correction for Multimodal Large Language Models", "authors": [ "Shukang Yin", "Chaoyou Fu", "Sirui Zhao", "Tong Xu", "Hao Wang", "Dianbo Sui", "Yunhang Shen", "Ke Li", "Xing Sun", "Enhong Chen" ], "github": "https://github.com/BradyFU/Woodpecker", "project_page": "", "abstract": "Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with the image content. In order to mitigate hallucinations, existing studies mainly resort to an instruction-tuning manner that requires retraining the models with specific data. In this paper, we pave a different way, introducing a training-free method named Woodpecker. Like a woodpecker heals trees, it picks out and corrects hallucinations from the generated text. Concretely, Woodpecker consists of five stages: key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction. Implemented in a post-remedy manner, Woodpecker can easily serve different MLLMs, while being interpretable by accessing intermediate outputs of the five stages. We evaluate Woodpecker both quantitatively and qualitatively and show the huge potential of this new paradigm. On the POPE benchmark, our method obtains a 30.66%/24.33% improvement in accuracy over the baseline MiniGPT-4/mPLUG-Owl. The source code is released at https://github.com/BradyFU/Woodpecker.", "github_manual": "", "github_regex": "https://github.com/BradyFU/Woodpecker", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-25", "arxiv_id": "2310.15987", "title": "Dissecting In-Context Learning of Translations in GPTs", "authors": [ "Vikas Raunak", "Hany Hassan Awadalla", "Arul Menezes" ], "github": "", "project_page": "", "abstract": "Most of the recent work in leveraging Large Language Models (LLMs) such as GPT-3 for Machine Translation (MT) has focused on selecting the few-shot samples for prompting. In this work, we try to better understand the role of demonstration attributes for the in-context learning of translations through perturbations of high-quality, in-domain demonstrations. We find that asymmetric perturbation of the source-target mappings yield vastly different results. We show that the perturbation of the source side has surprisingly little impact, while target perturbation can drastically reduce translation quality, suggesting that it is the output text distribution that provides the most important learning signal during in-context learning of translations. We propose a method named Zero-Shot-Context to add this signal automatically in Zero-Shot prompting. We demonstrate that it improves upon the zero-shot translation performance of GPT-3, even making it competitive with few-shot prompted translations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-25", "arxiv_id": "2310.15916", "title": "In-Context Learning Creates Task Vectors", "authors": [ "Roee Hendel", "Mor Geva", "Amir Globerson" ], "github": "https://github.com/roeehendel/icl_task_vectors", "project_page": "", "abstract": "In-context learning (ICL) in Large Language Models (LLMs) has emerged as a powerful new learning paradigm. However, its underlying mechanism is still not well understood. In particular, it is challenging to map it to the \"standard\" machine learning framework, where one uses a training set S to find a best-fitting function f(x) in some hypothesis class. Here we make progress on this problem by showing that the functions learned by ICL often have a very simple structure: they correspond to the transformer LLM whose only inputs are the query x and a single \"task vector\" calculated from the training set. Thus, ICL can be seen as compressing S into a single task vector theta(S) and then using this task vector to modulate the transformer to produce the output. We support the above claim via comprehensive experiments across a range of models and tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/roeehendel/icl_task_vectors", "github_api_added_by": "auto" }, { "date": "2023-10-25", "arxiv_id": "2310.15511", "title": "KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval", "authors": [ "Marah I Abdin", "Suriya Gunasekar", "Varun Chandrasekaran", "Jerry Li", "Mert Yuksekgonul", "Rahee Ghosh Peshawaria", "Ranjita Naik", "Besmira Nushi" ], "github": "", "project_page": "", "abstract": "We study the ability of state-of-the art models to answer constraint satisfaction queries for information retrieval (e.g., 'a list of ice cream shops in San Diego'). In the past, such queries were considered to be tasks that could only be solved via web-search or knowledge bases. More recently, large language models (LLMs) have demonstrated initial emergent abilities in this task. However, many current retrieval benchmarks are either saturated or do not measure constraint satisfaction. Motivated by rising concerns around factual incorrectness and hallucinations of LLMs, we present KITAB, a new dataset for measuring constraint satisfaction abilities of language models. KITAB consists of book-related data across more than 600 authors and 13,000 queries, and also offers an associated dynamic data collection and constraint verification approach for acquiring similar test data for other authors. Our extended experiments on GPT4 and GPT3.5 characterize and decouple common failure modes across dimensions such as information popularity, constraint types, and context availability. Results show that in the absence of context, models exhibit severe limitations as measured by irrelevant information, factual errors, and incompleteness, many of which exacerbate as information popularity decreases. While context availability mitigates irrelevant information, it is not helpful for satisfying constraints, identifying fundamental barriers to constraint satisfaction. We open source our contributions to foster further research on improving constraint satisfaction abilities of future models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-25", "arxiv_id": "2310.15494", "title": "TRAMS: Training-free Memory Selection for Long-range Language Modeling", "authors": [ "Haofei Yu", "Cunxiang wang", "Yue Zhang", "Wei Bi" ], "github": "https://github.com/lwaekfjlk/trams", "project_page": "", "abstract": "The Transformer architecture is crucial for numerous AI models, but it still faces challenges in long-range language modeling. Though several specific transformer architectures have been designed to tackle issues of long-range dependencies, existing methods like Transformer-XL are plagued by a high percentage of ineffective memories. In this study, we present a plug-and-play strategy, known as TRAining-free Memory Selection (TRAMS), that selects tokens participating in attention calculation based on one simple metric. This strategy allows us to keep tokens that are likely to have a high attention score with the current queries and ignore the other ones. We have tested our approach on the word-level benchmark (WikiText-103) and the character-level benchmark (enwik8), and the results indicate an improvement without having additional training or adding additional parameters.", "github_manual": "https://github.com/lwaekfjlk/trams", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-25", "arxiv_id": "2310.15337", "title": "Moral Foundations of Large Language Models", "authors": [ "Marwa Abdulhai", "Gregory Serapio-Garcia", "Cl\u00e9ment Crepy", "Daria Valter", "John Canny", "Natasha Jaques" ], "github": "https://github.com/abdulhaim/moral_foundations_llm", "project_page": "", "abstract": "Moral foundations theory (MFT) is a psychological assessment tool that decomposes human moral reasoning into five factors, including care/harm, liberty/oppression, and sanctity/degradation (Graham et al., 2009). People vary in the weight they place on these dimensions when making moral decisions, in part due to their cultural upbringing and political ideology. As large language models (LLMs) are trained on datasets collected from the internet, they may reflect the biases that are present in such corpora. This paper uses MFT as a lens to analyze whether popular LLMs have acquired a bias towards a particular set of moral values. We analyze known LLMs and find they exhibit particular moral foundations, and show how these relate to human moral foundations and political affiliations. We also measure the consistency of these biases, or whether they vary strongly depending on the context of how the model is prompted. Finally, we show that we can adversarially select prompts that encourage the moral to exhibit a particular set of moral foundations, and that this can affect the model's behavior on downstream tasks. These findings help illustrate the potential risks and unintended consequences of LLMs assuming a particular moral stance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/abdulhaim/moral_foundations_llm", "github_api_added_by": "auto" }, { "date": "2023-10-25", "arxiv_id": "2310.15200", "title": "Inject Semantic Concepts into Image Tagging for Open-Set Recognition", "authors": [ "Xinyu Huang", "Yi-Jie Huang", "Youcai Zhang", "Weiwei Tian", "Rui Feng", "Yuejie Zhang", "Yanchun Xie", "Yaqian Li", "Lei Zhang" ], "github": "https://github.com/xinyu1205/recognize-anything", "project_page": "", "abstract": "In this paper, we introduce the Recognize Anything Plus Model~(RAM++), a fundamental image recognition model with strong open-set recognition capabilities, by injecting semantic concepts into image tagging training framework. Previous approaches are either image tagging models constrained by limited semantics, or vision-language models with shallow interaction for suboptimal performance in multi-tag recognition. In contrast, RAM++ integrates image-text alignment and image-tagging within a unified fine-grained interaction framework based on image-tags-text triplets. This design enables RAM++ not only excel in identifying predefined categories, but also significantly augment the recognition ability in open-set categories. Moreover, RAM++ employs large language models~(LLMs) to generate diverse visual tag descriptions, pioneering the integration of LLM's knowledge into image tagging training. This approach empowers RAM++ to integrate visual description concepts for open-set recognition during inference. Evaluations on comprehensive image recognition benchmarks demonstrate RAM++ exceeds existing state-of-the-art (SOTA) fundamental image recognition models on most aspects. Specifically, for predefined common-used tag categories, RAM++ showcases 10.2 mAP and 15.4 mAP enhancements over CLIP on OpenImages and ImageNet. For open-set categories beyond predefined, RAM++ records improvements of 5 mAP and 6.4 mAP over CLIP and RAM respectively on OpenImages. For diverse human-object interaction phrases, RAM++ achieves 7.8 mAP and 4.7 mAP improvements on the HICO benchmark. Code, datasets and pre-trained models are available at https://github.com/xinyu1205/recognize-anything.", "github_manual": "", "github_regex": "https://github.com/xinyu1205/recognize-anything", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-25", "arxiv_id": "2310.15308", "title": "SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding", "authors": [ "Haoxiang Wang", "Pavan Kumar Anasosalu Vasu", "Fartash Faghri", "Raviteja Vemulapalli", "Mehrdad Farajtabar", "Sachin Mehta", "Mohammad Rastegari", "Oncel Tuzel", "Hadi Pouransari" ], "github": "", "project_page": "", "abstract": "The landscape of publicly available vision foundation models (VFMs), such as CLIP and Segment Anything Model (SAM), is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their pre-training objectives. For instance, CLIP excels in semantic understanding, while SAM specializes in spatial understanding for segmentation. In this work, we introduce a simple recipe to efficiently merge VFMs into a unified model that assimilates their expertise. Our proposed method integrates multi-task learning, continual learning techniques, and teacher-student distillation. This strategy entails significantly less computational cost compared to traditional multi-task training from scratch. Additionally, it only demands a small fraction of the pre-training datasets that were initially used to train individual models. By applying our method to SAM and CLIP, we derive SAM-CLIP: a unified model that amalgamates the strengths of SAM and CLIP into a single backbone, making it apt for edge device applications. We show that SAM-CLIP learns richer visual representations, equipped with both localization and semantic features, suitable for a broad range of vision tasks. SAM-CLIP obtains improved performance on several head probing tasks when compared with SAM and CLIP. We further show that SAM-CLIP not only retains the foundational strengths of its precursor models but also introduces synergistic functionalities, most notably in zero-shot semantic segmentation, where SAM-CLIP establishes new state-of-the-art results on 5 benchmarks. It outperforms previous models that are specifically designed for this task by a large margin, including +6.8% and +5.9% mean IoU improvement on Pascal-VOC and COCO-Stuff datasets, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-26", "arxiv_id": "2310.16656", "title": "A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation", "authors": [ "Eyal Segalis", "Dani Valevski", "Danny Lumen", "Yossi Matias", "Yaniv Leviathan" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion models achieved a remarkable leap in capabilities over the last few years, enabling high-quality and diverse synthesis of images from a textual prompt. However, even the most advanced models often struggle to precisely follow all of the directions in their prompts. The vast majority of these models are trained on datasets consisting of (image, caption) pairs where the images often come from the web, and the captions are their HTML alternate text. A notable example is the LAION dataset, used by Stable Diffusion and other models. In this work we observe that these captions are often of low quality, and argue that this significantly affects the model's capability to understand nuanced semantics in the textual prompts. We show that by relabeling the corpus with a specialized automatic captioning model and training a text-to-image model on the recaptioned dataset, the model benefits substantially across the board. First, in overall image quality: e.g. FID 14.84 vs. the baseline of 17.87, and 64.3% improvement in faithful image generation according to human evaluation. Second, in semantic alignment, e.g. semantic object accuracy 84.34 vs. 78.90, counting alignment errors 1.32 vs. 1.44 and positional alignment 62.42 vs. 57.60. We analyze various ways to relabel the corpus and provide evidence that this technique, which we call RECAP, both reduces the train-inference discrepancy and provides the model with more information per example, increasing sample efficiency and allowing the model to better understand the relations between captions and images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-26", "arxiv_id": "2310.16818", "title": "DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior", "authors": [ "Jingxiang Sun", "Bo Zhang", "Ruizhi Shao", "Lizhen Wang", "Wen Liu", "Zhenda Xie", "Yebin Liu" ], "github": "https://github.com/deepseek-ai/DreamCraft3D", "project_page": "", "abstract": "We present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation. Code available at https://github.com/deepseek-ai/DreamCraft3D.", "github_manual": "", "github_regex": "https://github.com/deepseek-ai/DreamCraft3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-26", "arxiv_id": "2310.16534", "title": "An Early Evaluation of GPT-4V(ision)", "authors": [ "Yang Wu", "Shilong Wang", "Hao Yang", "Tian Zheng", "Hongbo Zhang", "Yanyan Zhao", "Bing Qin" ], "github": "https://github.com/albertwy/gpt-4v-evaluation", "project_page": "", "abstract": "In this paper, we evaluate different abilities of GPT-4V including visual understanding, language understanding, visual puzzle solving, and understanding of other modalities such as depth, thermal, video, and audio. To estimate GPT-4V's performance, we manually construct 656 test instances and carefully evaluate the results of GPT-4V. The highlights of our findings are as follows: (1) GPT-4V exhibits impressive performance on English visual-centric benchmarks but fails to recognize simple Chinese texts in the images; (2) GPT-4V shows inconsistent refusal behavior when answering questions related to sensitive traits such as gender, race, and age; (3) GPT-4V obtains worse results than GPT-4 (API) on language understanding tasks including general language understanding benchmarks and visual commonsense knowledge evaluation benchmarks; (4) Few-shot prompting can improve GPT-4V's performance on both visual understanding and language understanding; (5) GPT-4V struggles to find the nuances between two similar images and solve the easy math picture puzzles; (6) GPT-4V shows non-trivial performance on the tasks of similar modalities to image, such as video and thermal. Our experimental results reveal the ability and limitations of GPT-4V and we hope our paper can provide some insights into the application and research of GPT-4V.", "github_manual": "https://github.com/albertwy/gpt-4v-evaluation", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-26", "arxiv_id": "2310.16832", "title": "LightSpeed: Light and Fast Neural Light Fields on Mobile Devices", "authors": [ "Aarush Gupta", "Junli Cao", "Chaoyang Wang", "Ju Hu", "Sergey Tulyakov", "Jian Ren", "L\u00e1szl\u00f3 A Jeni" ], "github": "", "project_page": "", "abstract": "Real-time novel-view image synthesis on mobile devices is prohibitive due to the limited computational power and storage. Using volumetric rendering methods, such as NeRF and its derivatives, on mobile devices is not suitable due to the high computational cost of volumetric rendering. On the other hand, recent advances in neural light field representations have shown promising real-time view synthesis results on mobile devices. Neural light field methods learn a direct mapping from a ray representation to the pixel color. The current choice of ray representation is either stratified ray sampling or Pl\\\"{u}cker coordinates, overlooking the classic light slab (two-plane) representation, the preferred representation to interpolate between light field views. In this work, we find that using the light slab representation is an efficient representation for learning a neural light field. More importantly, it is a lower-dimensional ray representation enabling us to learn the 4D ray space using feature grids which are significantly faster to train and render. Although mostly designed for frontal views, we show that the light-slab representation can be further extended to non-frontal scenes using a divide-and-conquer strategy. Our method offers superior rendering quality compared to previous light field methods and achieves a significantly improved trade-off between rendering quality and speed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-26", "arxiv_id": "2310.15008", "title": "Wonder3D: Single Image to 3D using Cross-Domain Diffusion", "authors": [ "Xiaoxiao Long", "Yuan-Chen Guo", "Cheng Lin", "Yuan Liu", "Zhiyang Dou", "Lingjie Liu", "Yuexin Ma", "Song-Hai Zhang", "Marc Habermann", "Christian Theobalt", "Wenping Wang" ], "github": "https://github.com/xxlong0/Wonder3D", "project_page": "", "abstract": "In this work, we introduce Wonder3D, a novel method for efficiently generating high-fidelity textured meshes from single-view images.Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry. In contrast, certain works directly produce 3D information via fast network inferences, but their results are often of low quality and lack geometric details. To holistically improve the quality, consistency, and efficiency of image-to-3D tasks, we propose a cross-domain diffusion model that generates multi-view normal maps and the corresponding color images. To ensure consistency, we employ a multi-view cross-domain attention mechanism that facilitates information exchange across views and modalities. Lastly, we introduce a geometry-aware normal fusion algorithm that extracts high-quality surfaces from the multi-view 2D representations. Our extensive evaluations demonstrate that our method achieves high-quality reconstruction results, robust generalization, and reasonably good efficiency compared to prior works.", "github_manual": "https://github.com/xxlong0/Wonder3D", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-26", "arxiv_id": "2310.16450", "title": "CLEX: Continuous Length Extrapolation for Large Language Models", "authors": [ "Guanzheng Chen", "Xin Li", "Zaiqiao Meng", "Shangsong Liang", "Lidong Bing" ], "github": "https://github.com/DAMO-NLP-SG/CLEX", "project_page": "", "abstract": "Transformer-based Large Language Models (LLMs) are pioneering advances in many natural language processing tasks, however, their exceptional capabilities are restricted within the preset context window of Transformer. Position Embedding (PE) scaling methods, while effective in extending the context window to a specific length, demonstrate either notable limitations in their extrapolation abilities or sacrificing partial performance within the context window. Length extrapolation methods, although theoretically capable of extending the context window beyond the training sequence length, often underperform in practical long-context applications. To address these challenges, we propose Continuous Length EXtrapolation (CLEX) for LLMs. We generalise the PE scaling approaches to model the continuous dynamics by ordinary differential equations over the length scaling factor, thereby overcoming the constraints of current PE scaling methods designed for specific lengths. Moreover, by extending the dynamics to desired context lengths beyond the training sequence length, CLEX facilitates the length extrapolation with impressive performance in practical tasks. We demonstrate that CLEX can be seamlessly incorporated into LLMs equipped with Rotary Position Embedding, such as LLaMA and GPT-NeoX, with negligible impact on training and inference latency. Experimental results reveal that CLEX can effectively extend the context window to over 4x or almost 8x training length, with no deterioration in performance. Furthermore, when evaluated on the practical LongBench benchmark, our model trained on a 4k length exhibits competitive performance against state-of-the-art open-source models trained on context lengths up to 32k.", "github_manual": "https://github.com/DAMO-NLP-SG/CLEX", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-26", "arxiv_id": "2310.16825", "title": "CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images", "authors": [ "Aaron Gokaslan", "A. Feder Cooper", "Jasmine Collins", "Landan Seguin", "Austin Jacobson", "Mihir Patel", "Jonathan Frankle", "Cory Stephenson", "Volodymyr Kuleshov" ], "github": "https://github.com/mosaicml/diffusion/blob/main/assets", "project_page": "", "abstract": "We assemble a dataset of Creative-Commons-licensed (CC) images, which we use to train a set of open diffusion models that are qualitatively competitive with Stable Diffusion 2 (SD2). This task presents two challenges: (1) high-resolution CC images lack the captions necessary to train text-to-image generative models; (2) CC images are relatively scarce. In turn, to address these challenges, we use an intuitive transfer learning technique to produce a set of high-quality synthetic captions paired with curated CC images. We then develop a data- and compute-efficient training recipe that requires as little as 3% of the LAION-2B data needed to train existing SD2 models, but obtains comparable quality. These results indicate that we have a sufficient number of CC images (~70 million) for training high-quality models. Our training recipe also implements a variety of optimizations that achieve ~3X training speed-ups, enabling rapid model iteration. We leverage this recipe to train several high-quality text-to-image models, which we dub the CommonCanvas family. Our largest model achieves comparable performance to SD2 on a human evaluation, despite being trained on our CC dataset that is significantly smaller than LAION and using synthetic captions for training. We release our models, data, and code at https://github.com/mosaicml/diffusion/blob/main/assets/common-canvas.md", "github_manual": "", "github_regex": "https://github.com/mosaicml/diffusion/blob/main/assets", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-26", "arxiv_id": "2310.16764", "title": "ConvNets Match Vision Transformers at Scale", "authors": [ "Samuel L. Smith", "Andrew Brock", "Leonard Berrada", "Soham De" ], "github": "", "project_page": "", "abstract": "Many researchers believe that ConvNets perform well on small or moderately sized datasets, but are not competitive with Vision Transformers when given access to datasets on the web-scale. We challenge this belief by evaluating a performant ConvNet architecture pre-trained on JFT-4B, a large labelled dataset of images often used for training foundation models. We consider pre-training compute budgets between 0.4k and 110k TPU-v4 core compute hours, and train a series of networks of increasing depth and width from the NFNet model family. We observe a log-log scaling law between held out loss and compute budget. After fine-tuning on ImageNet, NFNets match the reported performance of Vision Transformers with comparable compute budgets. Our strongest fine-tuned model achieves a Top-1 accuracy of 90.4%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-26", "arxiv_id": "2310.16795", "title": "QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models", "authors": [ "Elias Frantar", "Dan Alistarh" ], "github": "https://github.com/IST-DASLab/qmoe", "project_page": "", "abstract": "Mixture-of-Experts (MoE) architectures offer a general solution to the high inference costs of large language models (LLMs) via sparse routing, bringing faster and more accurate models, at the cost of massive parameter counts. For example, the SwitchTransformer-c2048 model has 1.6 trillion parameters, requiring 3.2TB of accelerator memory to run efficiently, which makes practical deployment challenging and expensive. In this paper, we present a solution to this memory problem, in form of a new compression and execution framework called QMoE. Specifically, QMoE consists of a scalable algorithm which accurately compresses trillion-parameter MoEs to less than 1 bit per parameter, in a custom format co-designed with bespoke GPU decoding kernels to facilitate efficient end-to-end compressed inference, with minor runtime overheads relative to uncompressed execution. Concretely, QMoE can compress the 1.6 trillion parameter SwitchTransformer-c2048 model to less than 160GB (20x compression, 0.8 bits per parameter) at only minor accuracy loss, in less than a day on a single GPU. This enables, for the first time, the execution of a trillion-parameter model on affordable commodity hardware, like a single server with 4x NVIDIA A6000 or 8x NVIDIA 3090 GPUs, at less than 5% runtime overhead relative to ideal uncompressed inference. The source code and compressed models are available at github.com/IST-DASLab/qmoe.", "github_manual": "https://github.com/IST-DASLab/qmoe", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-26", "arxiv_id": "2310.16789", "title": "Detecting Pretraining Data from Large Language Models", "authors": [ "Weijia Shi", "Anirudh Ajith", "Mengzhou Xia", "Yangsibo Huang", "Daogao Liu", "Terra Blevins", "Danqi Chen", "Luke Zettlemoyer" ], "github": "https://github.com/swj0419/detect-pretrain-code", "project_page": "", "abstract": "Although large language models (LLMs) are widely deployed, the data used to train them is rarely disclosed. Given the incredible scale of this data, up to trillions of tokens, it is all but certain that it includes potentially problematic text such as copyrighted materials, personally identifiable information, and test data for widely reported reference benchmarks. However, we currently have no way to know which data of these types is included or in what proportions. In this paper, we study the pretraining data detection problem: given a piece of text and black-box access to an LLM without knowing the pretraining data, can we determine if the model was trained on the provided text? To facilitate this study, we introduce a dynamic benchmark WIKIMIA that uses data created before and after model training to support gold truth detection. We also introduce a new detection method Min-K% Prob based on a simple hypothesis: an unseen example is likely to contain a few outlier words with low probabilities under the LLM, while a seen example is less likely to have words with such low probabilities. Min-K% Prob can be applied without any knowledge about the pretraining corpus or any additional training, departing from previous detection methods that require training a reference model on data that is similar to the pretraining data. Moreover, our experiments demonstrate that Min-K% Prob achieves a 7.4% improvement on WIKIMIA over these previous methods. We apply Min-K% Prob to two real-world scenarios, copyrighted book detection, and contaminated downstream example detection, and find it a consistently effective solution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/swj0419/detect-pretrain-code", "github_api_added_by": "auto" }, { "date": "2023-10-26", "arxiv_id": "2310.16836", "title": "LLM-FP4: 4-Bit Floating-Point Quantized Transformers", "authors": [ "Shih-yang Liu", "Zechun Liu", "Xijie Huang", "Pingcheng Dong", "Kwang-Ting Cheng" ], "github": "https://github.com/nbasyl/LLM-FP4", "project_page": "", "abstract": "We propose LLM-FP4 for quantizing both weights and activations in large language models (LLMs) down to 4-bit floating-point values, in a post-training manner. Existing post-training quantization (PTQ) solutions are primarily integer-based and struggle with bit widths below 8 bits. Compared to integer quantization, floating-point (FP) quantization is more flexible and can better handle long-tail or bell-shaped distributions, and it has emerged as a default choice in many hardware platforms. One characteristic of FP quantization is that its performance largely depends on the choice of exponent bits and clipping range. In this regard, we construct a strong FP-PTQ baseline by searching for the optimal quantization parameters. Furthermore, we observe a high inter-channel variance and low intra-channel variance pattern in activation distributions, which adds activation quantization difficulty. We recognize this pattern to be consistent across a spectrum of transformer models designed for diverse tasks, such as LLMs, BERT, and Vision Transformer models. To tackle this, we propose per-channel activation quantization and show that these additional scaling factors can be reparameterized as exponential biases of weights, incurring a negligible cost. Our method, for the first time, can quantize both weights and activations in the LLaMA-13B to only 4-bit and achieves an average score of 63.1 on the common sense zero-shot reasoning tasks, which is only 5.8 lower than the full-precision model, significantly outperforming the previous state-of-the-art by 12.7 points. Code is available at: https://github.com/nbasyl/LLM-FP4.", "github_manual": "", "github_regex": "https://github.com/nbasyl/LLM-FP4", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-26", "arxiv_id": "2310.16226", "title": "TiC-CLIP: Continual Training of CLIP Models", "authors": [ "Saurabh Garg", "Mehrdad Farajtabar", "Hadi Pouransari", "Raviteja Vemulapalli", "Sachin Mehta", "Oncel Tuzel", "Vaishaal Shankar", "Fartash Faghri" ], "github": "https://github.com/apple/ml-tic-clip", "project_page": "", "abstract": "Keeping large foundation models up to date on latest data is inherently expensive. To avoid the prohibitive costs of constantly retraining, it is imperative to continually train these models. This problem is exacerbated by the lack of any large scale continual learning benchmarks or baselines. We introduce the first set of web-scale Time-Continual (TiC) benchmarks for training vision-language models: TiC-DataCompt, TiC-YFCC, and TiC-RedCaps with over 12.7B timestamped image-text pairs spanning 9 years (2014--2022). We first use our benchmarks to curate various dynamic evaluations to measure temporal robustness of existing models. We show OpenAI's CLIP (trained on data up to 2020) loses approx 8% zero-shot accuracy on our curated retrieval task from 2021--2022 compared with more recently trained models in OpenCLIP repository. We then study how to efficiently train models on time-continuous data. We demonstrate that a simple rehearsal-based approach that continues training from the last checkpoint and replays old data reduces compute by 2.5times when compared to the standard practice of retraining from scratch.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apple/ml-tic-clip", "github_api_added_by": "auto" }, { "date": "2023-10-26", "arxiv_id": "2310.16828", "title": "TD-MPC2: Scalable, Robust World Models for Continuous Control", "authors": [ "Nicklas Hansen", "Hao Su", "Xiaolong Wang" ], "github": "https://github.com/nicklashansen/tdmpc", "project_page": "", "abstract": "TD-MPC is a model-based reinforcement learning (RL) algorithm that performs local trajectory optimization in the latent space of a learned implicit (decoder-free) world model. In this work, we present TD-MPC2: a series of improvements upon the TD-MPC algorithm. We demonstrate that TD-MPC2 improves significantly over baselines across 104 online RL tasks spanning 4 diverse task domains, achieving consistently strong results with a single set of hyperparameters. We further show that agent capabilities increase with model and data size, and successfully train a single 317M parameter agent to perform 80 tasks across multiple task domains, embodiments, and action spaces. We conclude with an account of lessons, opportunities, and risks associated with large TD-MPC2 agents. Explore videos, models, data, code, and more at https://nicklashansen.github.io/td-mpc2", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nicklashansen/tdmpc", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-27", "arxiv_id": "2310.17631", "title": "JudgeLM: Fine-tuned Large Language Models are Scalable Judges", "authors": [ "Lianghui Zhu", "Xinggang Wang", "Xinlong Wang" ], "github": "https://github.com/baaivision/judgelm", "project_page": "", "abstract": "Evaluating Large Language Models (LLMs) in open-ended scenarios is challenging because existing benchmarks and metrics can not measure them comprehensively. To address this problem, we propose to fine-tune LLMs as scalable judges (JudgeLM) to evaluate LLMs efficiently and effectively in open-ended benchmarks. We first propose a comprehensive, large-scale, high-quality dataset containing task seeds, LLMs-generated answers, and GPT-4-generated judgments for fine-tuning high-performance judges, as well as a new benchmark for evaluating the judges. We train JudgeLM at different scales from 7B, 13B, to 33B parameters, and conduct a systematic analysis of its capabilities and behaviors. We then analyze the key biases in fine-tuning LLM as a judge and consider them as position bias, knowledge bias, and format bias. To address these issues, JudgeLM introduces a bag of techniques including swap augmentation, reference support, and reference drop, which clearly enhance the judge's performance. JudgeLM obtains the state-of-the-art judge performance on both the existing PandaLM benchmark and our proposed new benchmark. Our JudgeLM is efficient and the JudgeLM-7B only needs 3 minutes to judge 5K samples with 8 A100 GPUs. JudgeLM obtains high agreement with the teacher judge, achieving an agreement exceeding 90% that even surpasses human-to-human agreement. JudgeLM also demonstrates extended capabilities in being judges of the single answer, multimodal models, multiple answers, and multi-turn chat.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/baaivision/judgelm", "github_api_added_by": "auto" }, { "date": "2023-10-27", "arxiv_id": "2310.17075", "title": "HyperFields: Towards Zero-Shot Generation of NeRFs from Text", "authors": [ "Sudarshan Babu", "Richard Liu", "Avery Zhou", "Michael Maire", "Greg Shakhnarovich", "Rana Hanocka" ], "github": "https://github.com/ashawkey/stable-dreamfusion", "project_page": "", "abstract": "We introduce HyperFields, a method for generating text-conditioned Neural Radiance Fields (NeRFs) with a single forward pass and (optionally) some fine-tuning. Key to our approach are: (i) a dynamic hypernetwork, which learns a smooth mapping from text token embeddings to the space of NeRFs; (ii) NeRF distillation training, which distills scenes encoded in individual NeRFs into one dynamic hypernetwork. These techniques enable a single network to fit over a hundred unique scenes. We further demonstrate that HyperFields learns a more general map between text and NeRFs, and consequently is capable of predicting novel in-distribution and out-of-distribution scenes -- either zero-shot or with a few finetuning steps. Finetuning HyperFields benefits from accelerated convergence thanks to the learned general map, and is capable of synthesizing novel scenes 5 to 10 times faster than existing neural optimization-based methods. Our ablation experiments show that both the dynamic architecture and NeRF distillation are critical to the expressivity of HyperFields.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ashawkey/stable-dreamfusion", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-27", "arxiv_id": "2310.17157", "title": "Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time", "authors": [ "Zichang Liu", "Jue Wang", "Tri Dao", "Tianyi Zhou", "Binhang Yuan", "Zhao Song", "Anshumali Shrivastava", "Ce Zhang", "Yuandong Tian", "Christopher Re", "Beidi Chen" ], "github": "https://github.com/FMInference/DejaVu", "project_page": "", "abstract": "Large language models (LLMs) with hundreds of billions of parameters have sparked a new wave of exciting AI applications. However, they are computationally expensive at inference time. Sparsity is a natural approach to reduce this cost, but existing methods either require costly retraining, have to forgo LLM's in-context learning ability, or do not yield wall-clock time speedup on modern hardware. We hypothesize that contextual sparsity, which are small, input-dependent sets of attention heads and MLP parameters that yield approximately the same output as the dense model for a given input, can address these issues. We show that contextual sparsity exists, that it can be accurately predicted, and that we can exploit it to speed up LLM inference in wall-clock time without compromising LLM's quality or in-context learning ability. Based on these insights, we propose DejaVu, a system that uses a low-cost algorithm to predict contextual sparsity on the fly given inputs to each layer, along with an asynchronous and hardware-aware implementation that speeds up LLM inference. We validate that DejaVu can reduce the inference latency of OPT-175B by over 2X compared to the state-of-the-art FasterTransformer, and over 6X compared to the widely used Hugging Face implementation, without compromising model quality. The code is available at https://github.com/FMInference/DejaVu.", "github_manual": "", "github_regex": "https://github.com/FMInference/DejaVu", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-27", "arxiv_id": "2310.17022", "title": "Controlled Decoding from Language Models", "authors": [ "Sidharth Mudgal", "Jong Lee", "Harish Ganapathy", "YaGuang Li", "Tao Wang", "Yanping Huang", "Zhifeng Chen", "Heng-Tze Cheng", "Michael Collins", "Trevor Strohman", "Jilin Chen", "Alex Beutel", "Ahmad Beirami" ], "github": "", "project_page": "", "abstract": "We propose controlled decoding (CD), a novel off-policy reinforcement learning method to control the autoregressive generation from language models towards high reward outcomes. CD solves an off-policy reinforcement learning problem through a value function for the reward, which we call a prefix scorer. The prefix scorer is used at inference time to steer the generation towards higher reward outcomes. We show that the prefix scorer may be trained on (possibly) off-policy data to predict the expected reward when decoding is continued from a partially decoded response. We empirically demonstrate that CD is effective as a control mechanism on Reddit conversations corpus. We also show that the modularity of the design of CD makes it possible to control for multiple rewards, effectively solving a multi-objective reinforcement learning problem with no additional complexity. Finally, we show that CD can be applied in a novel blockwise fashion at inference-time, again without the need for any training-time changes, essentially bridging the gap between the popular best-of-K strategy and token-level reinforcement learning. This makes CD a promising approach for alignment of language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-30", "arxiv_id": "2310.17680", "title": "CodeFusion: A Pre-trained Diffusion Model for Code Generation", "authors": [ "Mukul Singh", "Jos\u00e9 Cambronero", "Sumit Gulwani", "Vu Le", "Carina Negreanu", "Gust Verbruggen" ], "github": "", "project_page": "", "abstract": "Imagine a developer who can only change their last line of code, how often would they have to start writing a function from scratch before it is correct? Auto-regressive models for code generation from natural language have a similar limitation: they do not easily allow reconsidering earlier tokens generated. We introduce CodeFusion, a pre-trained diffusion code generation model that addresses this limitation by iteratively denoising a complete program conditioned on the encoded natural language. We evaluate CodeFusion on the task of natural language to code generation for Bash, Python, and Microsoft Excel conditional formatting (CF) rules. Experiments show that CodeFusion (75M parameters) performs on par with state-of-the-art auto-regressive systems (350M-175B parameters) in top-1 accuracy and outperforms them in top-3 and top-5 accuracy due to its better balance in diversity versus quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-30", "arxiv_id": "2310.18313", "title": "FP8-LM: Training FP8 Large Language Models", "authors": [ "Houwen Peng", "Kan Wu", "Yixuan Wei", "Guoshuai Zhao", "Yuxiang Yang", "Ze Liu", "Yifan Xiong", "Ziyue Yang", "Bolin Ni", "Jingcheng Hu", "Ruihang Li", "Miaosen Zhang", "Chen Li", "Jia Ning", "Ruizhe Wang", "Zheng Zhang", "Shuguang Liu", "Joe Chau", "Han Hu", "Peng Cheng" ], "github": "https://github.com/Azure/MS-AMP", "project_page": "", "abstract": "In this paper, we explore FP8 low-bit data formats for efficient training of large language models (LLMs). Our key insight is that most variables, such as gradients and optimizer states, in LLM training can employ low-precision data formats without compromising model accuracy and requiring no changes to hyper-parameters. Specifically, we propose a new FP8 automatic mixed-precision framework for training LLMs. This framework offers three levels of FP8 utilization to streamline mixed-precision and distributed parallel training for LLMs. It gradually incorporates 8-bit gradients, optimizer states, and distributed learning in an incremental manner. Experiment results show that, during the training of GPT-175B model on H100 GPU platform, our FP8 mixed-precision training framework not only achieved a remarkable 42% reduction in real memory usage but also ran 64% faster than the widely adopted BF16 framework (i.e., Megatron-LM), surpassing the speed of Nvidia Transformer Engine by 17%. This largely reduces the training costs for large foundation models. Furthermore, our FP8 mixed-precision training methodology is generic. It can be seamlessly applied to other tasks such as LLM instruction tuning and reinforcement learning with human feedback, offering savings in fine-tuning expenses. Our FP8 low-precision training framework is open-sourced at {https://github.com/Azure/MS-AMP}{aka.ms/MS.AMP}.", "github_manual": "", "github_regex": "https://github.com/Azure/MS-AMP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-30", "arxiv_id": "2310.17722", "title": "Large Language Models as Generalizable Policies for Embodied Tasks", "authors": [ "Andrew Szot", "Max Schwarzer", "Harsh Agrawal", "Bogdan Mazoure", "Walter Talbott", "Katherine Metcalf", "Natalie Mackraz", "Devon Hjelm", "Alexander Toshev" ], "github": "", "project_page": "", "abstract": "We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement Learning Policy (LLaRP), adapts a pre-trained frozen LLM to take as input text instructions and visual egocentric observations and output actions directly in the environment. Using reinforcement learning, we train LLaRP to see and act solely through environmental interactions. We show that LLaRP is robust to complex paraphrasings of task instructions and can generalize to new tasks that require novel optimal behavior. In particular, on 1,000 unseen tasks it achieves 42% success rate, 1.7x the success rate of other common learned baselines or zero-shot applications of LLMs. Finally, to aid the community in studying language conditioned, massively multi-task, embodied AI problems we release a novel benchmark, Language Rearrangement, consisting of 150,000 training and 1,000 testing tasks for language-conditioned rearrangement. Video examples of LLaRP in unseen Language Rearrangement instructions are at https://llm-rl.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-30", "arxiv_id": "2310.17750", "title": "A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications", "authors": [ "Ahmed Magooda", "Alec Helyar", "Kyle Jackson", "David Sullivan", "Chad Atalla", "Emily Sheng", "Dan Vann", "Richard Edgar", "Hamid Palangi", "Roman Lutz", "Hongliang Kong", "Vincent Yun", "Eslam Kamal", "Federico Zarfati", "Hanna Wallach", "Sarah Bird", "Mei Chen" ], "github": "", "project_page": "", "abstract": "We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automatically measuring harms from LLMs builds on existing technical and sociotechnical expertise and leverages the capabilities of state-of-the-art LLMs, such as GPT-4. We use this framework to run through several case studies investigating how different LLMs may violate a range of RAI-related principles. The framework may be employed alongside domain-specific sociotechnical expertise to create measurements for new harm areas in the future. By implementing this framework, we aim to enable more advanced harm measurement efforts and further the responsible use of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-30", "arxiv_id": "2310.17880", "title": "Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations", "authors": [ "Tristan Aumentado-Armstrong", "Ashkan Mirzaei", "Marcus A. Brubaker", "Jonathan Kelly", "Alex Levinshtein", "Konstantinos G. Derpanis", "Igor Gilitschenski" ], "github": "", "project_page": "", "abstract": "Neural Radiance Fields (NeRFs) have proven to be powerful 3D representations, capable of high quality novel view synthesis of complex scenes. While NeRFs have been applied to graphics, vision, and robotics, problems with slow rendering speed and characteristic visual artifacts prevent adoption in many use cases. In this work, we investigate combining an autoencoder (AE) with a NeRF, in which latent features (instead of colours) are rendered and then convolutionally decoded. The resulting latent-space NeRF can produce novel views with higher quality than standard colour-space NeRFs, as the AE can correct certain visual artifacts, while rendering over three times faster. Our work is orthogonal to other techniques for improving NeRF efficiency. Further, we can control the tradeoff between efficiency and image quality by shrinking the AE architecture, achieving over 13 times faster rendering with only a small drop in performance. We hope that our approach can form the basis of an efficient, yet high-fidelity, 3D scene representation for downstream tasks, especially when retaining differentiability is useful, as in many robotics scenarios requiring continual learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-30", "arxiv_id": "2310.17994", "title": "ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Real Image", "authors": [ "Kyle Sargent", "Zizhang Li", "Tanmay Shah", "Charles Herrmann", "Hong-Xing Yu", "Yunzhi Zhang", "Eric Ryan Chan", "Dmitry Lagun", "Li Fei-Fei", "Deqing Sun", "Jiajun Wu" ], "github": "https://github.com/kylesargent/zeronvs", "project_page": "", "abstract": "We introduce a 3D-aware diffusion model, ZeroNVS, for single-image novel view synthesis for in-the-wild scenes. While existing methods are designed for single objects with masked backgrounds, we propose new techniques to address challenges introduced by in-the-wild multi-object scenes with complex backgrounds. Specifically, we train a generative prior on a mixture of data sources that capture object-centric, indoor, and outdoor scenes. To address issues from data mixture such as depth-scale ambiguity, we propose a novel camera conditioning parameterization and normalization scheme. Further, we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360-degree scenes, and propose \"SDS anchoring\" to improve the diversity of synthesized novel views. Our model sets a new state-of-the-art result in LPIPS on the DTU dataset in the zero-shot setting, even outperforming methods specifically trained on DTU. We further adapt the challenging Mip-NeRF 360 dataset as a new benchmark for single-image novel view synthesis, and demonstrate strong performance in this setting. Our code and data are at http://kylesargent.github.io/zeronvs/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kylesargent/zeronvs", "github_api_added_by": "auto" }, { "date": "2023-10-30", "arxiv_id": "2310.18168", "title": "Personas as a Way to Model Truthfulness in Language Models", "authors": [ "Nitish Joishi", "Javier Rando", "Abulhair Saparov", "Najoung Kim", "He He" ], "github": "", "project_page": "", "abstract": "Large Language Models are trained on vast amounts of text from the internet, which contains both factual and misleading information about the world. Can language models discern truth from falsehood in this contradicting data? Expanding on the view that LLMs can model different agents producing the corpora, we hypothesize that they can cluster truthful text by modeling a truthful persona: a group of agents that are likely to produce truthful text and share similar features. For example, trustworthy sources like Wikipedia and Science usually use formal writing styles and make consistent claims. By modeling this persona, LLMs can generalize truthfulness beyond the specific contexts in which each agent generated the training text. For example, the model can infer that the agent \"Wikipedia\" will behave truthfully on topics that were only generated by \"Science\" because they share a persona. We first show evidence for the persona hypothesis via two observations: (1) we can probe whether a model's answer will be truthful before it is generated; (2) finetuning a model on a set of facts improves its truthfulness on unseen topics. Next, using arithmetics as a synthetic environment, we show that language models can separate true and false statements, and generalize truthfulness across agents; but only if agents in the training data share a truthful generative process that enables the creation of a truthful persona. Overall, our findings suggest that models can exploit hierarchical structures in the data to learn abstract concepts like truthfulness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-30", "arxiv_id": "2310.17796", "title": "ControlLLM: Augment Language Models with Tools by Searching on Graphs", "authors": [ "Zhaoyang Liu", "Zeqiang Lai", "Zhangwei Gao", "Erfei Cui", "Xizhou Zhu", "Lewei Lu", "Qifeng Chen", "Yu Qiao", "Jifeng Dai", "Wenhai Wang" ], "github": "https://github.com/opengvlab/controlllm", "project_page": "", "abstract": "We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks. Despite the remarkable performance of LLMs, they still struggle with tool invocation due to ambiguous user prompts, inaccurate tool selection and parameterization, and inefficient tool scheduling. To overcome these challenges, our framework comprises three key components: (1) a task decomposer that breaks down a complex task into clear subtasks with well-defined inputs and outputs; (2) a Thoughts-on-Graph (ToG) paradigm that searches the optimal solution path on a pre-built tool graph, which specifies the parameter and dependency relations among different tools; and (3) an execution engine with a rich toolbox that interprets the solution path and runs the tools efficiently on different computational devices. We evaluate our framework on diverse tasks involving image, audio, and video processing, demonstrating its superior accuracy, efficiency, and versatility compared to existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/opengvlab/controlllm", "github_api_added_by": "auto" }, { "date": "2023-10-30", "arxiv_id": "2310.17784", "title": "Data-Centric Financial Large Language Models", "authors": [ "Zhixuan Chu", "Huaiyu Guo", "Xinyuan Zhou", "Yijia Wang", "Fei Yu", "Hong Chen", "Wanqing Xu", "Xin Lu", "Qing Cui", "Longfei Li", "Jun Zhou", "Sheng Li" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance. LLMs have difficulty reasoning about and integrating all relevant information. We propose a data-centric approach to enable LLMs to better handle financial tasks. Our key insight is that rather than overloading the LLM with everything at once, it is more effective to preprocess and pre-understand the data. We create a financial LLM (FLLM) using multitask prompt-based finetuning to achieve data pre-processing and pre-understanding. However, labeled data is scarce for each task. To overcome manual annotation costs, we employ abductive augmentation reasoning (AAR) to automatically generate training data by modifying the pseudo labels from FLLM's own outputs. Experiments show our data-centric FLLM with AAR substantially outperforms baseline financial LLMs designed for raw text, achieving state-of-the-art on financial analysis and interpretation tasks. We also open source a new benchmark for financial analysis and interpretation. Our methodology provides a promising path to unlock LLMs' potential for complex real-world domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-30", "arxiv_id": "2310.17752", "title": "PockEngine: Sparse and Efficient Fine-tuning in a Pocket", "authors": [ "Ligeng Zhu", "Lanxiang Hu", "Ji Lin", "Wei-Chen Wang", "Wei-Ming Chen", "Chuang Gan", "Song Han" ], "github": "", "project_page": "", "abstract": "On-device learning and efficient fine-tuning enable continuous and privacy-preserving customization (e.g., locally fine-tuning large language models on personalized data). However, existing training frameworks are designed for cloud servers with powerful accelerators (e.g., GPUs, TPUs) and lack the optimizations for learning on the edge, which faces challenges of resource limitations and edge hardware diversity. We introduce PockEngine: a tiny, sparse and efficient engine to enable fine-tuning on various edge devices. PockEngine supports sparse backpropagation: it prunes the backward graph and sparsely updates the model with measured memory saving and latency reduction while maintaining the model quality. Secondly, PockEngine is compilation first: the entire training graph (including forward, backward and optimization steps) is derived at compile-time, which reduces the runtime overhead and brings opportunities for graph transformations. PockEngine also integrates a rich set of training graph optimizations, thus can further accelerate the training cost, including operator reordering and backend switching. PockEngine supports diverse applications, frontends and hardware backends: it flexibly compiles and tunes models defined in PyTorch/TensorFlow/Jax and deploys binaries to mobile CPU/GPU/DSPs. We evaluated PockEngine on both vision models and large language models. PockEngine achieves up to 15 times speedup over off-the-shelf TensorFlow (Raspberry Pi), 5.6 times memory saving back-propagation (Jetson AGX Orin). Remarkably, PockEngine enables fine-tuning LLaMav2-7B on NVIDIA Jetson AGX Orin at 550 tokens/s, 7.9times faster than the PyTorch.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-31", "arxiv_id": "2310.18356", "title": "LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery", "authors": [ "Tianyi Chen", "Tianyu Ding", "Badal Yadav", "Ilya Zharkov", "Luming Liang" ], "github": "https://github.com/microsoft/lorashear", "project_page": "", "abstract": "Large Language Models (LLMs) have transformed the landscape of artificial intelligence, while their enormous size presents significant challenges in terms of computational costs. We introduce LoRAShear, a novel efficient approach to structurally prune LLMs and recover knowledge. Given general LLMs, LoRAShear first creates the dependency graphs to discover minimally removal structures and analyze the knowledge distribution. It then proceeds progressive structured pruning on LoRA adaptors and enables inherent knowledge transfer to better preserve the information in the redundant structures. To recover the lost knowledge during pruning, LoRAShear meticulously studies and proposes a dynamic fine-tuning schemes with dynamic data adaptors to effectively narrow down the performance gap to the full models. Numerical results demonstrate that by only using one GPU within a couple of GPU days, LoRAShear effectively reduced footprint of LLMs by 20% with only 1.0% performance degradation and significantly outperforms state-of-the-arts. The source code will be available at https://github.com/microsoft/lorashear.", "github_manual": "", "github_regex": "https://github.com/microsoft/lorashear", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-31", "arxiv_id": "2310.19773", "title": "MM-VID: Advancing Video Understanding with GPT-4V(ision)", "authors": [ "Kevin Lin", "Faisal Ahmed", "Linjie Li", "Chung-Ching Lin", "Ehsan Azarnasab", "Zhengyuan Yang", "Jianfeng Wang", "Lin Liang", "Zicheng Liu", "Yumao Lu", "Ce Liu", "Lijuan Wang" ], "github": "", "project_page": "", "abstract": "We present MM-VID, an integrated system that harnesses the capabilities of GPT-4V, combined with specialized tools in vision, audio, and speech, to facilitate advanced video understanding. MM-VID is designed to address the challenges posed by long-form videos and intricate tasks such as reasoning within hour-long content and grasping storylines spanning multiple episodes. MM-VID uses a video-to-script generation with GPT-4V to transcribe multimodal elements into a long textual script. The generated script details character movements, actions, expressions, and dialogues, paving the way for large language models (LLMs) to achieve video understanding. This enables advanced capabilities, including audio description, character identification, and multimodal high-level comprehension. Experimental results demonstrate the effectiveness of MM-VID in handling distinct video genres with various video lengths. Additionally, we showcase its potential when applied to interactive environments, such as video games and graphic user interfaces.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-31", "arxiv_id": "2310.19512", "title": "VideoCrafter1: Open Diffusion Models for High-Quality Video Generation", "authors": [ "Haoxin Chen", "Menghan Xia", "Yingqing He", "Yong Zhang", "Xiaodong Cun", "Shaoshu Yang", "Jinbo Xing", "Yaofang Liu", "Qifeng Chen", "Xintao Wang", "Chao Weng", "Ying Shan" ], "github": "https://github.com/AILab-CVC/VideoCrafter", "project_page": "", "abstract": "Video generation has increasingly gained interest in both academia and industry. Although commercial tools can generate plausible videos, there is a limited number of open-source models available for researchers and engineers. In this work, we introduce two diffusion models for high-quality video generation, namely text-to-video (T2V) and image-to-video (I2V) models. T2V models synthesize a video based on a given text input, while I2V models incorporate an additional image input. Our proposed T2V model can generate realistic and cinematic-quality videos with a resolution of 1024 times 576, outperforming other open-source T2V models in terms of quality. The I2V model is designed to produce videos that strictly adhere to the content of the provided reference image, preserving its content, structure, and style. This model is the first open-source I2V foundation model capable of transforming a given image into a video clip while maintaining content preservation constraints. We believe that these open-source video generation models will contribute significantly to the technological advancements within the community.", "github_manual": "https://github.com/AILab-CVC/VideoCrafter", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-31", "arxiv_id": "2310.19341", "title": "Skywork: A More Open Bilingual Foundation Model", "authors": [ "Tianwen Wei", "Liang Zhao", "Lichang Zhang", "Bo Zhu", "Lijie Wang", "Haihua Yang", "Biye Li", "Cheng Cheng", "Weiwei L\u00fc", "Rui Hu", "Chenxia Li", "Liu Yang", "Xilin Luo", "Xuejie Wu", "Lunan Liu", "Wenjun Cheng", "Peng Cheng", "Jianhao Zhang", "Xiaoyu Zhang", "Lei Lin", "Xiaokun Wang", "Yutuan Ma", "Chuanhai Dong", "Yanqi Sun", "Yifu Chen", "Yongyi Peng", "Xiaojuan Liang", "Shuicheng Yan", "Han Fang", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both English and Chinese texts. This bilingual foundation model is the most extensively trained and openly published LLMs of comparable size to date. We introduce a two-stage training methodology using a segmented corpus, targeting general purpose training and then domain-specific enhancement training, respectively. We show that our model not only excels on popular benchmarks, but also achieves state of the art performance in Chinese language modeling on diverse domains. Furthermore, we propose a novel leakage detection method, demonstrating that test data contamination is a pressing issue warranting further investigation by the LLM community. To spur future research, we release Skywork-13B along with checkpoints obtained during intermediate stages of the training process. We are also releasing part of our SkyPile corpus, a collection of over 150 billion tokens of web text, which is the largest high quality open Chinese pre-training corpus to date. We hope Skywork-13B and our open corpus will serve as a valuable open-source resource to democratize access to high-quality LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-31", "arxiv_id": "2310.18628", "title": "Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation", "authors": [ "Hailin Chen", "Amrita Saha", "Steven Hoi", "Shafiq Joty" ], "github": "https://github.com/SalesforceAIResearch/PersDistill", "project_page": "", "abstract": "With the rise of powerful closed-sourced LLMs (ChatGPT, GPT-4), there are increasing interests in distilling the capabilies of close-sourced LLMs to smaller open-sourced LLMs. Previous distillation methods usually prompt ChatGPT to generate a set of instructions and answers, for the student model to learn. However, such standard distillation approach neglects the merits and conditions of the student model. Inspired by modern teaching principles, we design a personalised distillation process, in which the student attempts to solve a task first, then the teacher provides an adaptive refinement for the student to improve. Instead of feeding the student with teacher's prior, personalised distillation enables personalised learning for the student model, as it only learns on examples it makes mistakes upon and learns to improve its own solution. On code generation, personalised distillation consistently outperforms standard distillation with only one third of the data. With only 2.5-3K personalised examples that incur a data-collection cost of 4-6$, we boost CodeGen-mono-16B by 7% to achieve 36.4% pass@1 and StarCoder by 12.2% to achieve 45.8% pass@1 on HumanEval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SalesforceAIResearch/PersDistill", "github_api_added_by": "auto" }, { "date": "2023-10-31", "arxiv_id": "2310.19061", "title": "Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V", "authors": [ "Zhiling Yan", "Kai Zhang", "Rong Zhou", "Lifang He", "Xiang Li", "Lichao Sun" ], "github": "https://github.com/ZhilingYan/GPT4V-Medical-Report", "project_page": "", "abstract": "In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i.e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task. Our experiments thoroughly assess GPT-4V's proficiency in answering questions paired with images using both pathology and radiology datasets from 11 modalities (e.g. Microscopy, Dermoscopy, X-ray, CT, etc.) and fifteen objects of interests (brain, liver, lung, etc.). Our datasets encompass a comprehensive range of medical inquiries, including sixteen distinct question types. Throughout our evaluations, we devised textual prompts for GPT-4V, directing it to synergize visual and textual information. The experiments with accuracy score conclude that the current version of GPT-4V is not recommended for real-world diagnostics due to its unreliable and suboptimal accuracy in responding to diagnostic medical questions. In addition, we delineate seven unique facets of GPT-4V's behavior in medical VQA, highlighting its constraints within this complex arena. The complete details of our evaluation cases are accessible at https://github.com/ZhilingYan/GPT4V-Medical-Report.", "github_manual": "", "github_regex": "https://github.com/ZhilingYan/GPT4V-Medical-Report", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-31", "arxiv_id": "2310.19415", "title": "Text-to-3D with classifier score distillation", "authors": [ "Xin Yu", "Yuan-Chen Guo", "Yangguang Li", "Ding Liang", "Song-Hai Zhang", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "Text-to-3D generation has made remarkable progress recently, particularly with methods based on Score Distillation Sampling (SDS) that leverages pre-trained 2D diffusion models. While the usage of classifier-free guidance is well acknowledged to be crucial for successful optimization, it is considered an auxiliary trick rather than the most essential component. In this paper, we re-evaluate the role of classifier-free guidance in score distillation and discover a surprising finding: the guidance alone is enough for effective text-to-3D generation tasks. We name this method Classifier Score Distillation (CSD), which can be interpreted as using an implicit classification model for generation. This new perspective reveals new insights for understanding existing techniques. We validate the effectiveness of CSD across a variety of text-to-3D tasks including shape generation, texture synthesis, and shape editing, achieving results superior to those of state-of-the-art methods. Our project page is https://xinyu-andy.github.io/Classifier-Score-Distillation", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-31", "arxiv_id": "2310.19784", "title": "CustomNet: Zero-shot Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models", "authors": [ "Ziyang Yuan", "Mingdeng Cao", "Xintao Wang", "Zhongang Qi", "Chun Yuan", "Ying Shan" ], "github": "https://github.com/TencentARC/CustomNet", "project_page": "", "abstract": "Incorporating a customized object into image generation presents an attractive feature in text-to-image generation. However, existing optimization-based and encoder-based methods are hindered by drawbacks such as time-consuming optimization, insufficient identity preservation, and a prevalent copy-pasting effect. To overcome these limitations, we introduce CustomNet, a novel object customization approach that explicitly incorporates 3D novel view synthesis capabilities into the object customization process. This integration facilitates the adjustment of spatial position relationships and viewpoints, yielding diverse outputs while effectively preserving object identity. Moreover, we introduce delicate designs to enable location control and flexible background control through textual descriptions or specific user-defined images, overcoming the limitations of existing 3D novel view synthesis methods. We further leverage a dataset construction pipeline that can better handle real-world objects and complex backgrounds. Equipped with these designs, our method facilitates zero-shot object customization without test-time optimization, offering simultaneous control over the viewpoints, location, and background. As a result, our CustomNet ensures enhanced identity preservation and generates diverse, harmonious outputs.", "github_manual": "https://github.com/TencentARC/CustomNet", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-10-31", "arxiv_id": "2310.19102", "title": "Atom: Low-bit Quantization for Efficient and Accurate LLM Serving", "authors": [ "Yilong Zhao", "Chien-Yu Lin", "Kan Zhu", "Zihao Ye", "Lequn Chen", "Size Zheng", "Luis Ceze", "Arvind Krishnamurthy", "Tianqi Chen", "Baris Kasikci" ], "github": "https://github.com/efeslab/atom", "project_page": "", "abstract": "The growing demand for Large Language Models (LLMs) in applications such as content generation, intelligent chatbots, and sentiment analysis poses considerable challenges for LLM service providers. To efficiently use GPU resources and boost throughput, batching multiple requests has emerged as a popular paradigm; to further speed up batching, LLM quantization techniques reduce memory consumption and increase computing capacity. However, prevalent quantization schemes (e.g., 8-bit weight-activation quantization) cannot fully leverage the capabilities of modern GPUs, such as 4-bit integer operators, resulting in sub-optimal performance. To maximize LLMs' serving throughput, we introduce Atom, a low-bit quantization method that achieves high throughput improvements with negligible accuracy loss. Atom significantly boosts serving throughput by using low-bit operators and considerably reduces memory consumption via low-bit quantization. It attains high accuracy by applying a novel mixed-precision and fine-grained quantization process. We evaluate Atom on 4-bit weight-activation quantization setups in the serving context. Atom improves end-to-end throughput by up to 7.73times compared to the FP16 and by 2.53times compared to INT8 quantization, while maintaining the same latency target.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/efeslab/atom", "github_api_added_by": "auto" }, { "date": "2023-10-31", "arxiv_id": "2310.19019", "title": "TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise", "authors": [ "Nan He", "Hanyu Lai", "Chenyang Zhao", "Zirui Cheng", "Junting Pan", "Ruoyu Qin", "Ruofan Lu", "Rui Lu", "Yunchen Zhang", "Gangming Zhao", "Zhaohui Hou", "Zhiyuan Huang", "Shaoqing Lu", "Ding Liang", "Mingjie Zhan" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) exhibit impressive reasoning and data augmentation capabilities in various NLP tasks. However, what about small models? In this work, we propose TeacherLM-7.1B, capable of annotating relevant fundamentals, chain of thought, and common mistakes for most NLP samples, which makes annotation more than just an answer, thus allowing other models to learn \"why\" instead of just \"what\". The TeacherLM-7.1B model achieved a zero-shot score of 52.3 on MMLU, surpassing most models with over 100B parameters. Even more remarkable is its data augmentation ability. Based on TeacherLM-7.1B, we augmented 58 NLP datasets and taught various student models with different parameters from OPT and BLOOM series in a multi-task setting. The experimental results indicate that the data augmentation provided by TeacherLM has brought significant benefits. We will release the TeacherLM series of models and augmented datasets as open-source.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-01", "arxiv_id": "2310.20624", "title": "LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B", "authors": [ "Simon Lermen", "Charlie Rogers-Smith", "Jeffrey Ladish" ], "github": "", "project_page": "", "abstract": "AI developers often apply safety alignment procedures to prevent the misuse of their AI systems. For example, before Meta released Llama 2-Chat, a collection of instruction fine-tuned large language models, they invested heavily in safety training, incorporating extensive red-teaming and reinforcement learning from human feedback. However, it remains unclear how well safety training guards against model misuse when attackers have access to model weights. We explore the robustness of safety training in language models by subversively fine-tuning the public weights of Llama 2-Chat. We employ low-rank adaptation (LoRA) as an efficient fine-tuning method. With a budget of less than $200 per model and using only one GPU, we successfully undo the safety training of Llama 2-Chat models of sizes 7B, 13B, and 70B. Specifically, our fine-tuning technique significantly reduces the rate at which the model refuses to follow harmful instructions. We achieve a refusal rate below 1% for our 70B Llama 2-Chat model on two refusal benchmarks. Our fine-tuning method retains general performance, which we validate by comparing our fine-tuned models against Llama 2-Chat across two benchmarks. Additionally, we present a selection of harmful outputs produced by our models. While there is considerable uncertainty about the scope of risks from current models, it is likely that future models will have significantly more dangerous capabilities, including the ability to hack into critical infrastructure, create dangerous bio-weapons, or autonomously replicate and adapt to new environments. We show that subversive fine-tuning is practical and effective, and hence argue that evaluating risks from fine-tuning should be a core part of risk assessments for releasing model weights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-01", "arxiv_id": "2310.19956", "title": "The Impact of Depth and Width on Transformer Language Model Generalization", "authors": [ "Jackson Petty", "Sjoerd van Steenkiste", "Ishita Dasgupta", "Fei Sha", "Dan Garrette", "Tal Linzen" ], "github": "", "project_page": "", "abstract": "To process novel sentences, language models (LMs) must generalize compositionally -- combine familiar elements in new ways. What aspects of a model's structure promote compositional generalization? Focusing on transformers, we test the hypothesis, motivated by recent theoretical and empirical work, that transformers generalize more compositionally when they are deeper (have more layers). Because simply adding layers increases the total number of parameters, confounding depth and size, we construct three classes of models which trade off depth for width such that the total number of parameters is kept constant (41M, 134M and 374M parameters). We pretrain all models as LMs and fine-tune them on tasks that test for compositional generalization. We report three main conclusions: (1) after fine-tuning, deeper models generalize better out-of-distribution than shallower models do, but the relative benefit of additional layers diminishes rapidly; (2) within each family, deeper models show better language modeling performance, but returns are similarly diminishing; (3) the benefits of depth for compositional generalization cannot be attributed solely to better performance on language modeling or on in-distribution data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-01", "arxiv_id": "2310.19909", "title": "Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks", "authors": [ "Micah Goldblum", "Hossein Souri", "Renkun Ni", "Manli Shu", "Viraj Prabhu", "Gowthami Somepalli", "Prithvijit Chattopadhyay", "Mark Ibrahim", "Adrien Bardes", "Judy Hoffman", "Rama Chellappa", "Andrew Gordon Wilson", "Tom Goldstein" ], "github": "https://github.com/hsouri/Battle-of-the-Backbones", "project_page": "", "abstract": "Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an ImageNet-trained convolutional neural network. However, the recent past has seen the emergence of countless backbones pretrained using various algorithms and datasets. While this abundance of choice has led to performance increases for a range of systems, it is difficult for practitioners to make informed decisions about which backbone to choose. Battle of the Backbones (BoB) makes this choice easier by benchmarking a diverse suite of pretrained models, including vision-language models, those trained via self-supervised learning, and the Stable Diffusion backbone, across a diverse set of computer vision tasks ranging from classification to object detection to OOD generalization and more. Furthermore, BoB sheds light on promising directions for the research community to advance computer vision by illuminating strengths and weakness of existing approaches through a comprehensive analysis conducted on more than 1500 training runs. While vision transformers (ViTs) and self-supervised learning (SSL) are increasingly popular, we find that convolutional neural networks pretrained in a supervised fashion on large training sets still perform best on most tasks among the models we consider. Moreover, in apples-to-apples comparisons on the same architectures and similarly sized pretraining datasets, we find that SSL backbones are highly competitive, indicating that future works should perform SSL pretraining with advanced architectures and larger pretraining datasets. We release the raw results of our experiments along with code that allows researchers to put their own backbones through the gauntlet here: https://github.com/hsouri/Battle-of-the-Backbones", "github_manual": "", "github_regex": "https://github.com/hsouri/Battle-of-the-Backbones", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-01", "arxiv_id": "2310.20689", "title": "Learning From Mistakes Makes LLM Better Reasoner", "authors": [ "Shengnan An", "Zexiong Ma", "Zeqi Lin", "Nanning Zheng", "Jian-Guang Lou", "Weizhu Chen" ], "github": "https://github.com/microsoft/lema", "project_page": "", "abstract": "Large language models (LLMs) recently exhibited remarkable reasoning capabilities on solving math problems. To further improve this capability, this work proposes Learning from Mistakes (LeMa), akin to human learning processes. Consider a human student who failed to solve a math problem, he will learn from what mistake he has made and how to correct it. Mimicking this error-driven learning process, LeMa fine-tunes LLMs on mistake-correction data pairs generated by GPT-4. Specifically, we first collect inaccurate reasoning paths from various LLMs and then employ GPT-4 as a \"corrector\" to (1) identify the mistake step, (2) explain the reason for the mistake, and (3) correct the mistake and generate the final answer. Experimental results demonstrate the effectiveness of LeMa: across five backbone LLMs and two mathematical reasoning tasks, LeMa consistently improves the performance compared with fine-tuning on CoT data alone. Impressively, LeMa can also benefit specialized LLMs such as WizardMath and MetaMath, achieving 85.4% pass@1 accuracy on GSM8K and 27.1% on MATH. This surpasses the SOTA performance achieved by non-execution open-source models on these challenging tasks. Our code, data and models will be publicly available at https://github.com/microsoft/CodeT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/lema", "github_api_added_by": "auto" }, { "date": "2023-11-01", "arxiv_id": "2310.20216", "title": "Does GPT-4 Pass the Turing Test?", "authors": [ "Cameron Jones", "Benjamin Bergen" ], "github": "", "project_page": "", "abstract": "We evaluated GPT-4 in a public online Turing Test. The best-performing GPT-4 prompt passed in 41% of games, outperforming baselines set by ELIZA (27%) and GPT-3.5 (14%), but falling short of chance and the baseline set by human participants (63%). Participants' decisions were based mainly on linguistic style (35%) and socio-emotional traits (27%), supporting the idea that intelligence is not sufficient to pass the Turing Test. Participants' demographics, including education and familiarity with LLMs, did not predict detection rate, suggesting that even those who understand systems deeply and interact with them frequently may be susceptible to deception. Despite known limitations as a test of intelligence, we argue that the Turing Test continues to be relevant as an assessment of naturalistic communication and deception. AI models with the ability to masquerade as humans could have widespread societal consequences, and we analyse the effectiveness of different strategies and criteria for judging humanlikeness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-01", "arxiv_id": "2310.20587", "title": "Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning", "authors": [ "Ruizhe Shi", "Yuyao Liu", "Yanjie Ze", "Simon S. Du", "Huazhe Xu" ], "github": "https://github.com/srzer/LaMo-2023", "project_page": "", "abstract": "Offline reinforcement learning (RL) aims to find a near-optimal policy using pre-collected datasets. In real-world scenarios, data collection could be costly and risky; therefore, offline RL becomes particularly challenging when the in-domain data is limited. Given recent advances in Large Language Models (LLMs) and their few-shot learning prowess, this paper introduces Language Models for Motion Control (LaMo), a general framework based on Decision Transformers to effectively use pre-trained Language Models (LMs) for offline RL. Our framework highlights four crucial components: (1) Initializing Decision Transformers with sequentially pre-trained LMs, (2) employing the LoRA fine-tuning method, in contrast to full-weight fine-tuning, to combine the pre-trained knowledge from LMs and in-domain knowledge effectively, (3) using the non-linear MLP transformation instead of linear projections, to generate embeddings, and (4) integrating an auxiliary language prediction loss during fine-tuning to stabilize the LMs and retain their original abilities on languages. Empirical results indicate LaMo achieves state-of-the-art performance in sparse-reward tasks and closes the gap between value-based offline RL methods and decision transformers in dense-reward tasks. In particular, our method demonstrates superior performance in scenarios with limited data samples. Our project website is https://lamo2023.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/srzer/LaMo-2023", "github_api_added_by": "auto" }, { "date": "2023-11-01", "arxiv_id": "2310.20700", "title": "SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction", "authors": [ "Xinyuan Chen", "Yaohui Wang", "Lingjun Zhang", "Shaobin Zhuang", "Xin Ma", "Jiashuo Yu", "Yali Wang", "Dahua Lin", "Yu Qiao", "Ziwei Liu" ], "github": "https://github.com/Vchitect/SEINE", "project_page": "", "abstract": "Recently video generation has achieved substantial progress with realistic results. Nevertheless, existing AI-generated videos are usually very short clips (\"shot-level\") depicting a single scene. To deliver a coherent long video (\"story-level\"), it is desirable to have creative transition and prediction effects across different clips. This paper presents a short-to-long video diffusion model, SEINE, that focuses on generative transition and prediction. The goal is to generate high-quality long videos with smooth and creative transitions between scenes and varying lengths of shot-level videos. Specifically, we propose a random-mask video diffusion model to automatically generate transitions based on textual descriptions. By providing the images of different scenes as inputs, combined with text-based control, our model generates transition videos that ensure coherence and visual quality. Furthermore, the model can be readily extended to various tasks such as image-to-video animation and autoregressive video prediction. To conduct a comprehensive evaluation of this new generative task, we propose three assessing criteria for smooth and creative transition: temporal consistency, semantic similarity, and video-text semantic alignment. Extensive experiments validate the effectiveness of our approach over existing methods for generative transition and prediction, enabling the creation of story-level long videos. Project page: https://vchitect.github.io/SEINE-project/ .", "github_manual": "https://github.com/Vchitect/SEINE", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-01", "arxiv_id": "2310.20092", "title": "Beyond U: Making Diffusion Models Faster & Lighter", "authors": [ "Sergio Calvo-Ordonez", "Jiahao Huang", "Lipei Zhang", "Guang Yang", "Carola-Bibiane Schonlieb", "Angelica I Aviles-Rivero" ], "github": "", "project_page": "", "abstract": "Diffusion models are a family of generative models that yield record-breaking performance in tasks such as image synthesis, video generation, and molecule design. Despite their capabilities, their efficiency, especially in the reverse denoising process, remains a challenge due to slow convergence rates and high computational costs. In this work, we introduce an approach that leverages continuous dynamical systems to design a novel denoising network for diffusion models that is more parameter-efficient, exhibits faster convergence, and demonstrates increased noise robustness. Experimenting with denoising probabilistic diffusion models, our framework operates with approximately a quarter of the parameters and 30% of the Floating Point Operations (FLOPs) compared to standard U-Nets in Denoising Diffusion Probabilistic Models (DDPMs). Furthermore, our model is up to 70% faster in inference than the baseline models when measured in equal conditions while converging to better quality solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-01", "arxiv_id": "2310.20707", "title": "What's In My Big Data?", "authors": [ "Yanai Elazar", "Akshita Bhagia", "Ian Magnusson", "Abhilasha Ravichander", "Dustin Schwenk", "Alane Suhr", "Pete Walsh", "Dirk Groeneveld", "Luca Soldaini", "Sameer Singh", "Hanna Hajishirzi", "Noah A. Smith", "Jesse Dodge" ], "github": "https://github.com/allenai/wimbd", "project_page": "", "abstract": "Large text corpora are the backbone of language models. However, we have a limited understanding of the content of these corpora, including general statistics, quality, social factors, and inclusion of evaluation data (contamination). In this work, we propose What's In My Big Data? (WIMBD), a platform and a set of sixteen analyses that allow us to reveal and compare the contents of large text corpora. WIMBD builds on two basic capabilities -- count and search -- at scale, which allows us to analyze more than 35 terabytes on a standard compute node. We apply WIMBD to ten different corpora used to train popular language models, including C4, The Pile, and RedPajama. Our analysis uncovers several surprising and previously undocumented findings about these corpora, including the high prevalence of duplicate, synthetic, and low-quality content, personally identifiable information, toxic language, and benchmark contamination. For instance, we find that about 50% of the documents in RedPajama and LAION-2B-en are duplicates. In addition, several datasets used for benchmarking models trained on such corpora are contaminated with respect to important benchmarks, including the Winograd Schema Challenge and parts of GLUE and SuperGLUE. We open-source WIMBD's code and artifacts to provide a standard set of evaluations for new text-based corpora and to encourage more analyses and transparency around them: github.com/allenai/wimbd.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/wimbd", "github_api_added_by": "auto" }, { "date": "2023-11-01", "arxiv_id": "2310.20550", "title": "CapsFusion: Rethinking Image-Text Data at Scale", "authors": [ "Qiying Yu", "Quan Sun", "Xiaosong Zhang", "Yufeng Cui", "Fan Zhang", "Xinlong Wang", "Jingjing Liu" ], "github": "https://github.com/baaivision/capsfusion", "project_page": "", "abstract": "Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute fundamentally to this success, but suffer from excessive noise. Recent studies use alternative captions synthesized by captioning models and have achieved notable benchmark performance. However, our experiments reveal significant Scalability Deficiency and World Knowledge Loss issues in models trained with synthetic captions, which have been largely obscured by their initial benchmark success. Upon closer examination, we identify the root cause as the overly-simplified language structure and lack of knowledge details in existing synthetic captions. To provide higher-quality and more scalable multimodal pretraining data, we propose CapsFusion, an advanced framework that leverages large language models to consolidate and refine information from both web-based image-text pairs and synthetic captions. Extensive experiments show that CapsFusion captions exhibit remarkable all-round superiority over existing captions in terms of model performance (e.g., 18.8 and 18.3 improvements in CIDEr score on COCO and NoCaps), sample efficiency (requiring 11-16 times less computation than baselines), world knowledge depth, and scalability. These effectiveness, efficiency and scalability advantages position CapsFusion as a promising candidate for future scaling of LMM training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/baaivision/capsfusion", "github_api_added_by": "auto" }, { "date": "2023-11-01", "arxiv_id": "2310.20499", "title": "Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models", "authors": [ "Tian Liang", "Zhiwei He", "Jen-tes Huang", "Wenxuan Wang", "Wenxiang Jiao", "Rui Wang", "Yujiu Yang", "Zhaopeng Tu", "Shuming Shi", "Xing Wang" ], "github": "https://github.com/skytliang/spygame", "project_page": "", "abstract": "The automatic evaluation of LLM-based agent intelligence is critical in developing advanced LLM-based agents. Although considerable effort has been devoted to developing human-annotated evaluation datasets, such as AlpacaEval, existing techniques are costly, time-consuming, and lack adaptability. In this paper, inspired by the popular language game ``Who is Spy'', we propose to use the word guessing game to assess the intelligence performance of LLMs. Given a word, the LLM is asked to describe the word and determine its identity (spy or not) based on its and other players' descriptions. Ideally, an advanced agent should possess the ability to accurately describe a given word using an aggressive description while concurrently maximizing confusion in the conservative description, enhancing its participation in the game. To this end, we first develop DEEP to evaluate LLMs' expression and disguising abilities. DEEP requires LLM to describe a word in aggressive and conservative modes. We then introduce SpyGame, an interactive multi-agent framework designed to assess LLMs' intelligence through participation in a competitive language-based board game. Incorporating multi-agent interaction, SpyGame requires the target LLM to possess linguistic skills and strategic thinking, providing a more comprehensive evaluation of LLMs' human-like cognitive abilities and adaptability in complex communication situations. The proposed evaluation framework is very easy to implement. We collected words from multiple sources, domains, and languages and used the proposed evaluation framework to conduct experiments. Extensive experiments demonstrate that the proposed DEEP and SpyGame effectively evaluate the capabilities of various LLMs, capturing their ability to adapt to novel situations and engage in strategic communication.", "github_manual": "https://github.com/skytliang/spygame", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-02", "arxiv_id": "2311.00571", "title": "LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing", "authors": [ "Wei-Ge Chen", "Irina Spiridonova", "Jianwei Yang", "Jianfeng Gao", "Chunyuan Li" ], "github": "https://github.com/LLaVA-VL/LLaVA-Interactive-Demo", "project_page": "", "abstract": "LLaVA-Interactive is a research prototype for multimodal human-AI interaction. The system can have multi-turn dialogues with human users by taking multimodal user inputs and generating multimodal responses. Importantly, LLaVA-Interactive goes beyond language prompt, where visual prompt is enabled to align human intents in the interaction. The development of LLaVA-Interactive is extremely cost-efficient as the system combines three multimodal skills of pre-built AI models without additional model training: visual chat of LLaVA, image segmentation from SEEM, as well as image generation and editing from GLIGEN. A diverse set of application scenarios is presented to demonstrate the promises of LLaVA-Interactive and to inspire future research in multimodal interactive systems.", "github_manual": "https://github.com/LLaVA-VL/LLaVA-Interactive-Demo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-02", "arxiv_id": "2311.00430", "title": "Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling", "authors": [ "Sanchit Gandhi", "Patrick von Platen", "Alexander M. Rush" ], "github": "https://github.com/huggingface/distil-whisper", "project_page": "", "abstract": "As the size of pre-trained speech recognition models increases, running these large models in low-latency or resource-constrained environments becomes challenging. In this work, we leverage pseudo-labelling to assemble a large-scale open-source dataset which we use to distill the Whisper model into a smaller variant, called Distil-Whisper. Using a simple word error rate (WER) heuristic, we select only the highest quality pseudo-labels for training. The distilled model is 5.8 times faster with 51% fewer parameters, while performing to within 1% WER on out-of-distribution test data in a zero-shot transfer setting. Distil-Whisper maintains the robustness of the Whisper model to difficult acoustic conditions, while being less prone to hallucination errors on long-form audio. Distil-Whisper is designed to be paired with Whisper for speculative decoding, yielding a 2 times speed-up while mathematically ensuring the same outputs as the original model. To facilitate further research in this domain, we make our training code, inference code and models publicly accessible.", "github_manual": "https://github.com/huggingface/distil-whisper", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-02", "arxiv_id": "2311.00613", "title": "Controllable Music Production with Diffusion Models and Guidance Gradients", "authors": [ "Mark Levy", "Bruno Di Giorgi", "Floris Weers", "Angelos Katharopoulos", "Tom Nickson" ], "github": "", "project_page": "", "abstract": "We demonstrate how conditional generation from diffusion models can be used to tackle a variety of realistic tasks in the production of music in 44.1kHz stereo audio with sampling-time guidance. The scenarios we consider include continuation, inpainting and regeneration of musical audio, the creation of smooth transitions between two different music tracks, and the transfer of desired stylistic characteristics to existing audio clips. We achieve this by applying guidance at sampling time in a simple framework that supports both reconstruction and classification losses, or any combination of the two. This approach ensures that generated audio can match its surrounding context, or conform to a class distribution or latent representation specified relative to any suitable pre-trained classifier or embedding model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-02", "arxiv_id": "2311.00059", "title": "The Generative AI Paradox: \"What It Can Create, It May Not Understand\"", "authors": [ "Peter West", "Ximing Lu", "Nouha Dziri", "Faeze Brahman", "Linjie Li", "Jena D. Hwang", "Liwei Jiang", "Jillian Fisher", "Abhilasha Ravichander", "Khyathi Chandu", "Benjamin Newman", "Pang Wei Koh", "Allyson Ettinger", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models now take only seconds to produce outputs that would challenge or exceed the capabilities even of expert humans. At the same time, models still show basic errors in understanding that would not be expected even in non-expert humans. This presents us with an apparent paradox: how do we reconcile seemingly superhuman capabilities with the persistence of errors that few humans would make? In this work, we posit that this tension reflects a divergence in the configuration of intelligence in today's generative models relative to intelligence in humans. Specifically, we propose and test the Generative AI Paradox hypothesis: generative models, having been trained directly to reproduce expert-like outputs, acquire generative capabilities that are not contingent upon -- and can therefore exceed -- their ability to understand those same types of outputs. This contrasts with humans, for whom basic understanding almost always precedes the ability to generate expert-level outputs. We test this hypothesis through controlled experiments analyzing generation vs. understanding in generative models, across both language and image modalities. Our results show that although models can outperform humans in generation, they consistently fall short of human capabilities in measures of understanding, as well as weaker correlation between generation and understanding performance, and more brittleness to adversarial inputs. Our findings support the hypothesis that models' generative capability may not be contingent upon understanding capability, and call for caution in interpreting artificial intelligence by analogy to human intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-02", "arxiv_id": "2311.00618", "title": "De-Diffusion Makes Text a Strong Cross-Modal Interface", "authors": [ "Chen Wei", "Chenxi Liu", "Siyuan Qiao", "Zhishuai Zhang", "Alan Yuille", "Jiahui Yu" ], "github": "", "project_page": "", "abstract": "We demonstrate text as a strong cross-modal interface. Rather than relying on deep embeddings to connect image and language as the interface representation, our approach represents an image as text, from which we enjoy the interpretability and flexibility inherent to natural language. We employ an autoencoder that uses a pre-trained text-to-image diffusion model for decoding. The encoder is trained to transform an input image into text, which is then fed into the fixed text-to-image diffusion decoder to reconstruct the original input -- a process we term De-Diffusion. Experiments validate both the precision and comprehensiveness of De-Diffusion text representing images, such that it can be readily ingested by off-the-shelf text-to-image tools and LLMs for diverse multi-modal tasks. For example, a single De-Diffusion model can generalize to provide transferable prompts for different text-to-image tools, and also achieves a new state of the art on open-ended vision-language tasks by simply prompting large language models with few-shot examples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-02", "arxiv_id": "2311.00257", "title": "AMSP: Super-Scaling LLM Training via Advanced Model States Partitioning", "authors": [ "Qiaoling Chen", "Qinghao Hu", "Zhisheng Ye", "Guoteng Wang", "Peng Sun", "Yonggang Wen", "Tianwei Zhang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated impressive performance across various downstream tasks. When training these models, there is a growing inclination to process more tokens on larger training scales but with relatively smaller model sizes. Zero Redundancy Optimizer (ZeRO), although effective in conventional training environments, grapples with scaling challenges when confronted with this emerging paradigm. To this end, we propose a novel LLM training framework AMSP, which undertakes a granular partitioning of model states, encompassing parameters (P), gradient (G), and optimizer states (OS). Specifically, AMSP(1) builds a unified partitioning space, enabling independent partitioning strategies for P, G, and OS; (2) incorporates a scale-aware partitioner to autonomously search for optimal partitioning strategies: (3) designs a dedicated communication optimizer to ensure proficient management of data placement discrepancies arising from diverse partitioning strategies. Our evaluations show that AMSP achieves up to 90.3% scaling efficiency across 1024 GPUs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-02", "arxiv_id": "2311.00047", "title": "Grounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans?", "authors": [ "Yichi Zhang", "Jiayi Pan", "Yuchen Zhou", "Rui Pan", "Joyce Chai" ], "github": "https://github.com/vl-illusion/dataset", "project_page": "", "abstract": "Vision-Language Models (VLMs) are trained on vast amounts of data captured by humans emulating our understanding of the world. However, known as visual illusions, human's perception of reality isn't always faithful to the physical world. This raises a key question: do VLMs have the similar kind of illusions as humans do, or do they faithfully learn to represent reality? To investigate this question, we build a dataset containing five types of visual illusions and formulate four tasks to examine visual illusions in state-of-the-art VLMs. Our findings have shown that although the overall alignment is low, larger models are closer to human perception and more susceptible to visual illusions. Our dataset and initial findings will promote a better understanding of visual illusions in humans and machines and provide a stepping stone for future computational models that can better align humans and machines in perceiving and communicating about the shared visual world. The code and data are available at https://github.com/vl-illusion/dataset.", "github_manual": "", "github_regex": "https://github.com/vl-illusion/dataset", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-02", "arxiv_id": "2311.00176", "title": "ChipNeMo: Domain-Adapted LLMs for Chip Design", "authors": [ "Mingjie Liu", "Teo Ene", "Robert Kirby", "Chris Cheng", "Nathaniel Pinckney", "Rongjian Liang", "Jonah Alben", "Himyanshu Anand", "Sanmitra Banerjee", "Ismet Bayraktaroglu", "Bonita Bhaskaran", "Bryan Catanzaro", "Arjun Chaudhuri", "Sharon Clay", "Bill Dally", "Laura Dang", "Parikshit Deshpande", "Siddhanth Dhodhi", "Sameer Halepete", "Eric Hill", "Jiashang Hu", "Sumit Jain", "Brucek Khailany", "Kishor Kunal", "Xiaowei Li", "Hao Liu", "Stuart Oberman", "Sujeet Omar", "Sreedhar Pratty", "Ambar Sarkar", "Zhengjiang Shao", "Hanfei Sun", "Pratik P Suthar", "Varun Tej", "Kaizhe Xu", "Haoxing Ren" ], "github": "", "project_page": "", "abstract": "ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design. Instead of directly deploying off-the-shelf commercial or open-source LLMs, we instead adopt the following domain adaptation techniques: custom tokenizers, domain-adaptive continued pretraining, supervised fine-tuning (SFT) with domain-specific instructions, and domain-adapted retrieval models. We evaluate these methods on three selected LLM applications for chip design: an engineering assistant chatbot, EDA script generation, and bug summarization and analysis. Our results show that these domain adaptation techniques enable significant LLM performance improvements over general-purpose base models across the three evaluated applications, enabling up to 5x model size reduction with similar or better performance on a range of design tasks. Our findings also indicate that there's still room for improvement between our current results and ideal outcomes. We believe that further investigation of domain-adapted LLM approaches will help close this gap in the future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-02", "arxiv_id": "2311.00272", "title": "ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation", "authors": [ "Zejun Wang", "Jia Li", "Ge Li", "Zhi Jin" ], "github": "", "project_page": "", "abstract": "Large language models have shown good performances in generating code to meet human requirements. However, human requirements expressed in natural languages can be vague, incomplete, and ambiguous, leading large language models to misunderstand human requirements and make mistakes. Worse, it is difficult for a human user to refine the requirement. To help human users refine their requirements and improve large language models' code generation performances, we propose ChatCoder: a method to refine the requirements via chatting with large language models. We design a chat scheme in which the large language models will guide the human users to refine their expression of requirements to be more precise, unambiguous, and complete than before. Experiments show that ChatCoder has improved existing large language models' performance by a large margin. Besides, ChatCoder has the advantage over refine-based methods and LLMs fine-tuned via human response.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-02", "arxiv_id": "2311.00522", "title": "Text Rendering Strategies for Pixel Language Models", "authors": [ "Jonas F. Lotz", "Elizabeth Salesky", "Phillip Rust", "Desmond Elliott" ], "github": "", "project_page": "", "abstract": "Pixel-based language models process text rendered as images, which allows them to handle any script, making them a promising approach to open vocabulary language modelling. However, recent approaches use text renderers that produce a large set of almost-equivalent input patches, which may prove sub-optimal for downstream tasks, due to redundancy in the input representations. In this paper, we investigate four approaches to rendering text in the PIXEL model (Rust et al., 2023), and find that simple character bigram rendering brings improved performance on sentence-level tasks without compromising performance on token-level or multilingual tasks. This new rendering strategy also makes it possible to train a more compact model with only 22M parameters that performs on par with the original 86M parameter model. Our analyses show that character bigram rendering leads to a consistently better model but with an anisotropic patch embedding space, driven by a patch frequency bias, highlighting the connections between image patch- and tokenization-based language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-03", "arxiv_id": "2311.00945", "title": "E3 TTS: Easy End-to-End Diffusion-based Text to Speech", "authors": [ "Yuan Gao", "Nobuyuki Morioka", "Yu Zhang", "Nanxin Chen" ], "github": "", "project_page": "", "abstract": "We propose Easy End-to-End Diffusion-based Text to Speech, a simple and efficient end-to-end text-to-speech model based on diffusion. E3 TTS directly takes plain text as input and generates an audio waveform through an iterative refinement process. Unlike many prior work, E3 TTS does not rely on any intermediate representations like spectrogram features or alignment information. Instead, E3 TTS models the temporal structure of the waveform through the diffusion process. Without relying on additional conditioning information, E3 TTS could support flexible latent structure within the given audio. This enables E3 TTS to be easily adapted for zero-shot tasks such as editing without any additional training. Experiments show that E3 TTS can generate high-fidelity audio, approaching the performance of a state-of-the-art neural TTS system. Audio samples are available at https://e3tts.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-03", "arxiv_id": "2311.00895", "title": "In-Context Prompt Editing For Conditional Audio Generation", "authors": [ "Ernie Chang", "Pin-Jie Lin", "Yang Li", "Sidd Srinivasan", "Gael Le Lan", "David Kant", "Yangyang Shi", "Forrest Iandola", "Vikas Chandra" ], "github": "", "project_page": "", "abstract": "Distributional shift is a central challenge in the deployment of machine learning models as they can be ill-equipped for real-world data. This is particularly evident in text-to-audio generation where the encoded representations are easily undermined by unseen prompts, which leads to the degradation of generated audio -- the limited set of the text-audio pairs remains inadequate for conditional audio generation in the wild as user prompts are under-specified. In particular, we observe a consistent audio quality degradation in generated audio samples with user prompts, as opposed to training set prompts. To this end, we present a retrieval-based in-context prompt editing framework that leverages the training captions as demonstrative exemplars to revisit the user prompts. We show that the framework enhanced the audio quality across the set of collected user prompts, which were edited with reference to the training captions as exemplars.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-03", "arxiv_id": "2311.01455", "title": "RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation", "authors": [ "Yufei Wang", "Zhou Xian", "Feng Chen", "Tsun-Hsuan Wang", "Yian Wang", "Katerina Fragkiadaki", "Zackory Erickson", "David Held", "Chuang Gan" ], "github": "", "project_page": "", "abstract": "We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. RoboGen leverages the latest advancements in foundation and generative models. Instead of directly using or adapting these models to produce policies or low-level actions, we advocate for a generative scheme, which uses these models to automatically generate diversified tasks, scenes, and training supervisions, thereby scaling up robotic skill learning with minimal human supervision. Our approach equips a robotic agent with a self-guided propose-generate-learn cycle: the agent first proposes interesting tasks and skills to develop, and then generates corresponding simulation environments by populating pertinent objects and assets with proper spatial configurations. Afterwards, the agent decomposes the proposed high-level task into sub-tasks, selects the optimal learning approach (reinforcement learning, motion planning, or trajectory optimization), generates required training supervision, and then learns policies to acquire the proposed skill. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics. Our fully generative pipeline can be queried repeatedly, producing an endless stream of skill demonstrations associated with diverse tasks and environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-03", "arxiv_id": "2311.01462", "title": "Idempotent Generative Network", "authors": [ "Assaf Shocher", "Amil Dravid", "Yossi Gandelsman", "Inbar Mosseri", "Michael Rubinstein", "Alexei A. Efros" ], "github": "", "project_page": "", "abstract": "We propose a new approach for generative modeling based on training a neural network to be idempotent. An idempotent operator is one that can be applied sequentially without changing the result beyond the initial application, namely f(f(z))=f(z). The proposed model f is trained to map a source distribution (e.g, Gaussian noise) to a target distribution (e.g. realistic images) using the following objectives: (1) Instances from the target distribution should map to themselves, namely f(x)=x. We define the target manifold as the set of all instances that f maps to themselves. (2) Instances that form the source distribution should map onto the defined target manifold. This is achieved by optimizing the idempotence term, f(f(z))=f(z) which encourages the range of f(z) to be on the target manifold. Under ideal assumptions such a process provably converges to the target distribution. This strategy results in a model capable of generating an output in one step, maintaining a consistent latent space, while also allowing sequential applications for refinement. Additionally, we find that by processing inputs from both target and source distributions, the model adeptly projects corrupted or modified data back to the target manifold. This work is a first step towards a ``global projector'' that enables projecting any input into a target data distribution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-03", "arxiv_id": "2311.01282", "title": "FlashDecoding++: Faster Large Language Model Inference on GPUs", "authors": [ "Ke Hong", "Guohao Dai", "Jiaming Xu", "Qiuli Mao", "Xiuhong Li", "Jun Liu", "Kangdi Chen", "Hanyu Dong", "Yu Wang" ], "github": "", "project_page": "", "abstract": "As the Large Language Model (LLM) becomes increasingly important in various domains. However, the following challenges still remain unsolved in accelerating LLM inference: (1) Synchronized partial softmax update. The softmax operation requires a synchronized update operation among each partial softmax result, leading to ~20% overheads for the attention computation in LLMs. (2) Under-utilized computation of flat GEMM. The shape of matrices performing GEMM in LLM inference is flat, leading to under-utilized computation and >50% performance loss after padding zeros in previous designs. (3) Performance loss due to static dataflow. Kernel performance in LLM depends on varied input data features, hardware configurations, etc. A single and static dataflow may lead to a 50.25% performance loss for GEMMs of different shapes in LLM inference. We present FlashDecoding++, a fast LLM inference engine supporting mainstream LLMs and hardware back-ends. To tackle the above challenges, FlashDecoding++ creatively proposes: (1) Asynchronized softmax with unified max value. FlashDecoding++ introduces a unified max value technique for different partial softmax computations to avoid synchronization. (2) Flat GEMM optimization with double buffering. FlashDecoding++ points out that flat GEMMs with different shapes face varied bottlenecks. Then, techniques like double buffering are introduced. (3) Heuristic dataflow with hardware resource adaptation. FlashDecoding++ heuristically optimizes dataflow using different hardware resource considering input dynamics. Due to the versatility of optimizations in FlashDecoding++, FlashDecoding++ can achieve up to 4.86x and 2.18x speedup on both NVIDIA and AMD GPUs compared to Hugging Face implementations. FlashDecoding++ also achieves an average speedup of 1.37x compared to state-of-the-art LLM inference engines on mainstream LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-03", "arxiv_id": "2311.00899", "title": "RoboVQA: Multimodal Long-Horizon Reasoning for Robotics", "authors": [ "Pierre Sermanet", "Tianli Ding", "Jeffrey Zhao", "Fei Xia", "Debidatta Dwibedi", "Keerthana Gopalakrishnan", "Christine Chan", "Gabriel Dulac-Arnold", "Sharath Maddineni", "Nikhil J Joshi", "Pete Florence", "Wei Han", "Robert Baruch", "Yao Lu", "Suvir Mirchandani", "Peng Xu", "Pannag Sanketi", "Karol Hausman", "Izhak Shafran", "Brian Ichter", "Yuan Cao" ], "github": "", "project_page": "", "abstract": "We present a scalable, bottom-up and intrinsically diverse data collection scheme that can be used for high-level reasoning with long and medium horizons and that has 2.2x higher throughput compared to traditional narrow top-down step-by-step collection. We collect realistic data by performing any user requests within the entirety of 3 office buildings and using multiple robot and human embodiments. With this data, we show that models trained on all embodiments perform better than ones trained on the robot data only, even when evaluated solely on robot episodes. We find that for a fixed collection budget it is beneficial to take advantage of cheaper human collection along with robot collection. We release a large and highly diverse (29,520 unique instructions) dataset dubbed RoboVQA containing 829,502 (video, text) pairs for robotics-focused visual question answering. We also demonstrate how evaluating real robot experiments with an intervention mechanism enables performing tasks to completion, making it deployable with human oversight even if imperfect while also providing a single performance metric. We demonstrate a single video-conditioned model named RoboVQA-VideoCoCa trained on our dataset that is capable of performing a variety of grounded high-level reasoning tasks in broad realistic settings with a cognitive intervention rate 46% lower than the zero-shot state of the art visual language model (VLM) baseline and is able to guide real robots through long-horizon tasks. The performance gap with zero-shot state-of-the-art models indicates that a lot of grounded data remains to be collected for real-world deployment, emphasizing the critical need for scalable data collection approaches. Finally, we show that video VLMs significantly outperform single-image VLMs with an average error rate reduction of 19% across all VQA tasks. Data and videos available at https://robovqa.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-06", "arxiv_id": "2311.02077", "title": "EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision", "authors": [ "Jiawei Yang", "Boris Ivanovic", "Or Litany", "Xinshuo Weng", "Seung Wook Kim", "Boyi Li", "Tong Che", "Danfei Xu", "Sanja Fidler", "Marco Pavone", "Yue Wang" ], "github": "https://github.com/nvlabs/emernerf", "project_page": "", "abstract": "We present EmerNeRF, a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes. Grounded in neural fields, EmerNeRF simultaneously captures scene geometry, appearance, motion, and semantics via self-bootstrapping. EmerNeRF hinges upon two core components: First, it stratifies scenes into static and dynamic fields. This decomposition emerges purely from self-supervision, enabling our model to learn from general, in-the-wild data sources. Second, EmerNeRF parameterizes an induced flow field from the dynamic field and uses this flow field to further aggregate multi-frame features, amplifying the rendering precision of dynamic objects. Coupling these three fields (static, dynamic, and flow) enables EmerNeRF to represent highly-dynamic scenes self-sufficiently, without relying on ground truth object annotations or pre-trained models for dynamic object segmentation or optical flow estimation. Our method achieves state-of-the-art performance in sensor simulation, significantly outperforming previous methods when reconstructing static (+2.93 PSNR) and dynamic (+3.70 PSNR) scenes. In addition, to bolster EmerNeRF's semantic generalization, we lift 2D visual foundation model features into 4D space-time and address a general positional bias in modern Transformers, significantly boosting 3D perception performance (e.g., 37.50% relative improvement in occupancy prediction accuracy on average). Finally, we construct a diverse and challenging 120-sequence dataset to benchmark neural fields under extreme and highly-dynamic settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nvlabs/emernerf", "github_api_added_by": "auto" }, { "date": "2023-11-06", "arxiv_id": "2311.01615", "title": "FLAP: Fast Language-Audio Pre-training", "authors": [ "Ching-Feng Yeh", "Po-Yao Huang", "Vasu Sharma", "Shang-Wen Li", "Gargi Gosh" ], "github": "", "project_page": "", "abstract": "We propose Fast Language-Audio Pre-training (FLAP), a self-supervised approach that efficiently and effectively learns aligned audio and language representations through masking, contrastive learning and reconstruction. For efficiency, FLAP randomly drops audio spectrogram tokens, focusing solely on the remaining ones for self-supervision. Through inter-modal contrastive learning, FLAP learns to align paired audio and text representations in a shared latent space. Notably, FLAP leverages multiple augmented views via masking for inter-modal contrast and learns to reconstruct the masked portion of audio tokens. Moreover, FLAP leverages large language models (LLMs) to augment the text inputs, contributing to improved performance. These approaches lead to more robust and informative audio-text representations, enabling FLAP to achieve state-of-the-art (SoTA) performance on audio-text retrieval tasks on AudioCaps (achieving 53.0% R@1) and Clotho (achieving 25.5% R@1).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-06", "arxiv_id": "2311.01767", "title": "PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion", "authors": [ "Yiduo Guo", "Zekai Zhang", "Yaobo Liang", "Dongyan Zhao", "Duan Nan" ], "github": "https://github.com/gydpku/PPTC", "project_page": "", "abstract": "Recent evaluations of Large Language Models (LLMs) have centered around testing their zero-shot/few-shot capabilities for basic natural language tasks and their ability to translate instructions into tool APIs. However, the evaluation of LLMs utilizing complex tools to finish multi-turn, multi-modal instructions in a complex multi-modal environment has not been investigated. To address this gap, we introduce the PowerPoint Task Completion (PPTC) benchmark to assess LLMs' ability to create and edit PPT files based on user instructions. It contains 279 multi-turn sessions covering diverse topics and hundreds of instructions involving multi-modal operations. We also propose the PPTX-Match Evaluation System that evaluates if LLMs finish the instruction based on the prediction file rather than the label API sequence, thus it supports various LLM-generated API sequences. We measure 3 closed LLMs and 6 open-source LLMs. The results show that GPT-4 outperforms other LLMs with 75.1\\% accuracy in single-turn dialogue testing but faces challenges in completing entire sessions, achieving just 6\\% session accuracy. We find three main error causes in our benchmark: error accumulation in the multi-turn session, long PPT template processing, and multi-modality perception. These pose great challenges for future LLM and agent systems. We release the data, code, and evaluation system of PPTC at https://github.com/gydpku/PPTC.", "github_manual": "", "github_regex": "https://github.com/gydpku/PPTC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.02103", "title": "Relax: Composable Abstractions for End-to-End Dynamic Machine Learning", "authors": [ "Ruihang Lai", "Junru Shao", "Siyuan Feng", "Steven S. Lyubomirsky", "Bohan Hou", "Wuwei Lin", "Zihao Ye", "Hongyi Jin", "Yuchen Jin", "Jiawei Liu", "Lesheng Jin", "Yaxing Cai", "Ziheng Jiang", "Yong Wu", "Sunghyun Park", "Prakalp Srivastava", "Jared G. Roesch", "Todd C. Mowry", "Tianqi Chen" ], "github": "", "project_page": "", "abstract": "Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models. The success of these models has driven demand for deploying them to a diverse set of backend environments. In this paper, we present Relax, a compiler abstraction for optimizing end-to-end dynamic machine learning workloads. Relax introduces first-class symbolic shape annotations to track dynamic shape computations globally across the program. It also introduces a cross-level abstraction that encapsulates computational graphs, loop-level tensor programs, and library calls in a single representation to enable cross-level optimizations. We build an end-to-end compilation framework using the proposed approach to optimize dynamic shape models. Experimental results on large language models show that Relax delivers performance competitive with state-of-the-art hand-optimized systems across platforms and enables deployment of emerging dynamic models to a broader set of environments, including mobile phones, embedded devices, and web browsers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.02303", "title": "MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning", "authors": [ "Bingchang Liu", "Chaoyu Chen", "Cong Liao", "Zi Gong", "Huan Wang", "Zhichao Lei", "Ming Liang", "Dajun Chen", "Min Shen", "Hailian Zhou", "Hang Yu", "Jianguo Li" ], "github": "https://github.com/codefuse-ai/MFTCOder", "project_page": "", "abstract": "Code LLMs have emerged as a specialized research field, with remarkable studies dedicated to enhancing model's coding capabilities through fine-tuning on pre-trained models. Previous fine-tuning approaches were typically tailored to specific downstream tasks or scenarios, which meant separate fine-tuning for each task, requiring extensive training resources and posing challenges in terms of deployment and maintenance. Furthermore, these approaches failed to leverage the inherent interconnectedness among different code-related tasks. To overcome these limitations, we present a multi-task fine-tuning framework, MFTcoder, that enables simultaneous and parallel fine-tuning on multiple tasks. By incorporating various loss functions, we effectively address common challenges in multi-task learning, such as data imbalance, varying difficulty levels, and inconsistent convergence speeds. Extensive experiments have conclusively demonstrated that our multi-task fine-tuning approach outperforms both individual fine-tuning on single tasks and fine-tuning on a mixed ensemble of tasks. Moreover, MFTcoder offers efficient training capabilities, including efficient data tokenization modes and PEFT fine-tuning, resulting in significantly improved speed compared to traditional fine-tuning methods. MFTcoder seamlessly integrates with several mainstream open-source LLMs, such as CodeLLama and Qwen. Leveraging the CodeLLama foundation, our MFTcoder fine-tuned model, CodeFuse-CodeLLama-34B, achieves an impressive pass@1 score of 74.4\\% on the HumaneEval benchmark, surpassing GPT-4 performance (67\\%, zero-shot). MFTCoder is open-sourced at https://github.com/codefuse-ai/MFTCOder", "github_manual": "", "github_regex": "https://github.com/codefuse-ai/MFTCOder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.02542", "title": "VR-NeRF: High-Fidelity Virtualized Walkable Spaces", "authors": [ "Linning Xu", "Vasu Agrawal", "William Laney", "Tony Garcia", "Aayush Bansal", "Changil Kim", "Samuel Rota Bul\u00f2", "Lorenzo Porzi", "Peter Kontschieder", "Alja\u017e Bo\u017ei\u010d", "Dahua Lin", "Michael Zollh\u00f6fer", "Christian Richardt" ], "github": "https://github.com/facebookresearch/eyefultower", "project_page": "", "abstract": "We present an end-to-end system for the high-fidelity capture, model reconstruction, and real-time rendering of walkable spaces in virtual reality using neural radiance fields. To this end, we designed and built a custom multi-camera rig to densely capture walkable spaces in high fidelity and with multi-view high dynamic range images in unprecedented quality and density. We extend instant neural graphics primitives with a novel perceptual color space for learning accurate HDR appearance, and an efficient mip-mapping mechanism for level-of-detail rendering with anti-aliasing, while carefully optimizing the trade-off between quality and speed. Our multi-GPU renderer enables high-fidelity volume rendering of our neural radiance field model at the full VR resolution of dual 2Ktimes2K at 36 Hz on our custom demo machine. We demonstrate the quality of our results on our challenging high-fidelity datasets, and compare our method and datasets to existing baselines. We release our dataset on our project website.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/eyefultower", "github_api_added_by": "auto" }, { "date": "2023-11-07", "arxiv_id": "2311.03285", "title": "S-LoRA: Serving Thousands of Concurrent LoRA Adapters", "authors": [ "Ying Sheng", "Shiyi Cao", "Dacheng Li", "Coleman Hooper", "Nicholas Lee", "Shuo Yang", "Christopher Chou", "Banghua Zhu", "Lianmin Zheng", "Kurt Keutzer", "Joseph E. Gonzalez", "Ion Stoica" ], "github": "https://github.com/s-lora/s-lora", "project_page": "", "abstract": "The \"pretrain-then-finetune\" paradigm is commonly adopted in the deployment of large language models. Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method, is often employed to adapt a base model to a multitude of tasks, resulting in a substantial collection of LoRA adapters derived from one base model. We observe that this paradigm presents significant opportunities for batched inference during serving. To capitalize on these opportunities, we present S-LoRA, a system designed for the scalable serving of many LoRA adapters. S-LoRA stores all adapters in the main memory and fetches the adapters used by the currently running queries to the GPU memory. To efficiently use the GPU memory and reduce fragmentation, S-LoRA proposes Unified Paging. Unified Paging uses a unified memory pool to manage dynamic adapter weights with different ranks and KV cache tensors with varying sequence lengths. Additionally, S-LoRA employs a novel tensor parallelism strategy and highly optimized custom CUDA kernels for heterogeneous batching of LoRA computation. Collectively, these features enable S-LoRA to serve thousands of LoRA adapters on a single GPU or across multiple GPUs with a small overhead. Compared to state-of-the-art libraries such as HuggingFace PEFT and vLLM (with naive support of LoRA serving), S-LoRA can improve the throughput by up to 4 times and increase the number of served adapters by several orders of magnitude. As a result, S-LoRA enables scalable serving of many task-specific fine-tuned models and offers the potential for large-scale customized fine-tuning services.", "github_manual": "https://github.com/s-lora/s-lora", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.02262", "title": "Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs", "authors": [ "Qingru Zhang", "Chandan Singh", "Liyuan Liu", "Xiaodong Liu", "Bin Yu", "Jianfeng Gao", "Tuo Zhao" ], "github": "https://github.com/QingruZhang/PASTA", "project_page": "", "abstract": "In human-written articles, we often leverage the subtleties of text style, such as bold and italics, to guide the attention of readers. These textual emphases are vital for the readers to grasp the conveyed information. When interacting with large language models (LLMs), we have a similar need - steering the model to pay closer attention to user-specified information, e.g., an instruction. Existing methods, however, are constrained to process plain text and do not support such a mechanism. This motivates us to introduce PASTA - Post-hoc Attention STeering Approach, a method that allows LLMs to read text with user-specified emphasis marks. To this end, PASTA identifies a small subset of attention heads and applies precise attention reweighting on them, directing the model attention to user-specified parts. Like prompting, PASTA is applied at inference time and does not require changing any model parameters. Experiments demonstrate that PASTA can substantially enhance an LLM's ability to follow user instructions or integrate new knowledge from user inputs, leading to a significant performance improvement on a variety of tasks, e.g., an average accuracy improvement of 22% for LLAMA-7B. Our code is publicly available at https://github.com/QingruZhang/PASTA .", "github_manual": "", "github_regex": "https://github.com/QingruZhang/PASTA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.03226", "title": "LDM3D-VR: Latent Diffusion Model for 3D VR", "authors": [ "Gabriela Ben Melech Stan", "Diana Wofk", "Estelle Aflalo", "Shao-Yen Tseng", "Zhipeng Cai", "Michael Paulitsch", "Vasudev Lal" ], "github": "", "project_page": "", "abstract": "Latent diffusion models have proven to be state-of-the-art in the creation and manipulation of visual outputs. However, as far as we know, the generation of depth maps jointly with RGB is still limited. We introduce LDM3D-VR, a suite of diffusion models targeting virtual reality development that includes LDM3D-pano and LDM3D-SR. These models enable the generation of panoramic RGBD based on textual prompts and the upscaling of low-resolution inputs to high-resolution RGBD, respectively. Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions. Both models are evaluated in comparison to existing related methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.02382", "title": "Ultra-Long Sequence Distributed Transformer", "authors": [ "Xiao Wang", "Isaac Lyngaas", "Aristeidis Tsaris", "Peng Chen", "Sajal Dash", "Mayanka Chandra Shekar", "Tao Luo", "Hong-Jun Yoon", "Mohamed Wahib", "John Gouley" ], "github": "", "project_page": "", "abstract": "Transformer models trained on long sequences often achieve higher accuracy than short sequences. Unfortunately, conventional transformers struggle with long sequence training due to the overwhelming computation and memory requirements. Existing methods for long sequence training offer limited speedup and memory reduction, and may compromise accuracy. This paper presents a novel and efficient distributed training method, the Long Short-Sequence Transformer (LSS Transformer), for training transformer with long sequences. It distributes a long sequence into segments among GPUs, with each GPU computing a partial self-attention for its segment. Then, it uses a fused communication and a novel double gradient averaging technique to avoid the need to aggregate partial self-attention and minimize communication overhead. We evaluated the performance between LSS Transformer and the state-of-the-art Nvidia sequence parallelism on a Wikipedia enwik8 dataset. Results show that our proposed method lead to 5.6x faster and 10.2x more memory-efficient implementation compared to state-of-the-art sequence parallelism on 144 Nvidia V100 GPUs. Moreover, our algorithm scales to an extreme sequence length of 50,112 at 3,456 GPUs, achieving 161% super-linear parallel efficiency and a throughput of 32 petaflops.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.02462", "title": "Levels of AGI: Operationalizing Progress on the Path to AGI", "authors": [ "Meredith Ringel Morris", "Jascha Sohl-dickstein", "Noah Fiedel", "Tris Warkentin", "Allan Dafoe", "Aleksandra Faust", "Clement Farabet", "Shane Legg" ], "github": "", "project_page": "", "abstract": "We propose a framework for classifying the capabilities and behavior of Artificial General Intelligence (AGI) models and their precursors. This framework introduces levels of AGI performance, generality, and autonomy. It is our hope that this framework will be useful in an analogous way to the levels of autonomous driving, by providing a common language to compare models, assess risks, and measure progress along the path to AGI. To develop our framework, we analyze existing definitions of AGI, and distill six principles that a useful ontology for AGI should satisfy. These principles include focusing on capabilities rather than mechanisms; separately evaluating generality and performance; and defining stages along the path toward AGI, rather than focusing on the endpoint. With these principles in mind, we propose 'Levels of AGI' based on depth (performance) and breadth (generality) of capabilities, and reflect on how current systems fit into this ontology. We discuss the challenging requirements for future benchmarks that quantify the behavior and capabilities of AGI models against these levels. Finally, we discuss how these levels of AGI interact with deployment considerations such as autonomy and risk, and emphasize the importance of carefully selecting Human-AI Interaction paradigms for responsible and safe deployment of highly capable AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.03356", "title": "GLaMM: Pixel Grounding Large Multimodal Model", "authors": [ "Hanoona Rasheed", "Muhammad Maaz", "Sahal Shaji", "Abdelrahman Shaker", "Salman Khan", "Hisham Cholakkal", "Rao M. Anwer", "Erix Xing", "Ming-Hsuan Yang", "Fahad S. Khan" ], "github": "https://github.com/mbzuai-oryx/groundingLMM", "project_page": "", "abstract": "Large Multimodal Models (LMMs) extend Large Language Models to the vision domain. Initial efforts towards LMMs used holistic images and text prompts to generate ungrounded textual responses. Very recently, region-level LMMs have been used to generate visually grounded responses. However, they are limited to only referring a single object category at a time, require users to specify the regions in inputs, or cannot offer dense pixel-wise object grounding. In this work, we present Grounding LMM (GLaMM), the first model that can generate natural language responses seamlessly intertwined with corresponding object segmentation masks. GLaMM not only grounds objects appearing in the conversations but is flexible enough to accept both textual and optional visual prompts (region of interest) as input. This empowers users to interact with the model at various levels of granularity, both in textual and visual domains. Due to the lack of standard benchmarks for the novel setting of generating visually grounded detailed conversations, we introduce a comprehensive evaluation protocol with our curated grounded conversations. Our proposed Grounded Conversation Generation (GCG) task requires densely grounded concepts in natural scenes at a large-scale. To this end, we propose a densely annotated Grounding-anything Dataset (GranD) using our proposed automated annotation pipeline that encompasses 7.5M unique concepts grounded in a total of 810M regions available with segmentation masks. Besides GCG, GLaMM also performs effectively on several downstream tasks e.g., referring expression segmentation, image and region-level captioning and vision-language conversations. Project Page: https://mbzuai-oryx.github.io/groundingLMM.", "github_manual": "https://github.com/mbzuai-oryx/groundingLMM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.02848", "title": "Consistent4D: Consistent 360\u00b0 Dynamic Object Generation from Monocular Video", "authors": [ "Yanqin Jiang", "Li Zhang", "Jin Gao", "Weimin Hu", "Yao Yao" ], "github": "", "project_page": "", "abstract": "In this paper, we present Consistent4D, a novel approach for generating 4D dynamic objects from uncalibrated monocular videos. Uniquely, we cast the 360-degree dynamic object reconstruction as a 4D generation problem, eliminating the need for tedious multi-view data collection and camera calibration. This is achieved by leveraging the object-level 3D-aware image diffusion model as the primary supervision signal for training Dynamic Neural Radiance Fields (DyNeRF). Specifically, we propose a Cascade DyNeRF to facilitate stable convergence and temporal continuity under the supervision signal which is discrete along the time axis. To achieve spatial and temporal consistency, we further introduce an Interpolation-driven Consistency Loss. It is optimized by minimizing the discrepancy between rendered frames from DyNeRF and interpolated frames from a pre-trained video interpolation model. Extensive experiments show that our Consistent4D can perform competitively to prior art alternatives, opening up new possibilities for 4D dynamic object generation from monocular videos, whilst also demonstrating advantage for conventional text-to-3D generation tasks. Our project page is https://consistent4d.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.03354", "title": "CoVLM: Composing Visual Entities and Relationships in Large Language Models Via Communicative Decoding", "authors": [ "Junyan Li", "Delin Chen", "Yining Hong", "Zhenfang Chen", "Peihao Chen", "Yikang Shen", "Chuang Gan" ], "github": "", "project_page": "", "abstract": "A remarkable ability of human beings resides in compositional reasoning, i.e., the capacity to make \"infinite use of finite means\". However, current large vision-language foundation models (VLMs) fall short of such compositional abilities due to their \"bag-of-words\" behaviors and inability to construct words that correctly represent visual entities and the relations among the entities. To this end, we propose CoVLM, which can guide the LLM to explicitly compose visual entities and relationships among the text and dynamically communicate with the vision encoder and detection network to achieve vision-language communicative decoding. Specifically, we first devise a set of novel communication tokens for the LLM, for dynamic communication between the visual detection system and the language system. A communication token is generated by the LLM following a visual entity or a relation, to inform the detection network to propose regions that are relevant to the sentence generated so far. The proposed regions-of-interests (ROIs) are then fed back into the LLM for better language generation contingent on the relevant regions. The LLM is thus able to compose the visual entities and relationships through the communication tokens. The vision-to-language and language-to-vision communication are iteratively performed until the entire sentence is generated. Our framework seamlessly bridges the gap between visual perception and LLMs and outperforms previous VLMs by a large margin on compositional reasoning benchmarks (e.g., ~20% in HICO-DET mAP, ~14% in Cola top-1 accuracy, and ~3% on ARO top-1 accuracy). We also achieve state-of-the-art performances on traditional vision-language tasks such as referring expression comprehension and visual question answering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.03301", "title": "Ziya2: Data-centric Learning is All LLMs Need", "authors": [ "Ruyi Gan", "Ziwei Wu", "Renliang Sun", "Junyu Lu", "Xiaojun Wu", "Dixiang Zhang", "Kunhao Pan", "Ping Yang", "Qi Yang", "Jiaxing Zhang", "Yan Song" ], "github": "", "project_page": "", "abstract": "Various large language models (LLMs) have been proposed in recent years, including closed- and open-source ones, continually setting new records on multiple benchmarks. However, the development of LLMs still faces several issues, such as high cost of training models from scratch, and continual pre-training leading to catastrophic forgetting, etc. Although many such issues are addressed along the line of research on LLMs, an important yet practical limitation is that many studies overly pursue enlarging model sizes without comprehensively analyzing and optimizing the use of pre-training data in their learning process, as well as appropriate organization and leveraging of such data in training LLMs under cost-effective settings. In this work, we propose Ziya2, a model with 13 billion parameters adopting LLaMA2 as the foundation model, and further pre-trained on 700 billion tokens, where we focus on pre-training techniques and use data-centric optimization to enhance the learning process of Ziya2 on different stages. Experiments show that Ziya2 significantly outperforms other models in multiple benchmarks especially with promising results compared to representative open-source ones. Ziya2 (Base) is released at https://huggingface.co/IDEA-CCNL/Ziya2-13B-Base and https://modelscope.cn/models/Fengshenbang/Ziya2-13B-Base/summary.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.03079", "title": "CogVLM: Visual Expert for Pretrained Language Models", "authors": [ "Weihan Wang", "Qingsong Lv", "Wenmeng Yu", "Wenyi Hong", "Ji Qi", "Yan Wang", "Junhui Ji", "Zhuoyi Yang", "Lei Zhao", "Xixuan Song", "Jiazheng Xu", "Bin Xu", "Juanzi Li", "Yuxiao Dong", "Ming Ding", "Jie Tang" ], "github": "https://github.com/THUDM/CogVLM", "project_page": "", "abstract": "We introduce CogVLM, a powerful open-source visual language foundation model. Different from the popular shallow alignment method which maps image features into the input space of language model, CogVLM bridges the gap between the frozen pretrained language model and image encoder by a trainable visual expert module in the attention and FFN layers. As a result, CogVLM enables deep fusion of vision language features without sacrificing any performance on NLP tasks. CogVLM-17B achieves state-of-the-art performance on 10 classic cross-modal benchmarks, including NoCaps, Flicker30k captioning, RefCOCO, RefCOCO+, RefCOCOg, Visual7W, GQA, ScienceQA, VizWiz VQA and TDIUC, and ranks the 2nd on VQAv2, OKVQA, TextVQA, COCO captioning, etc., surpassing or matching PaLI-X 55B. Codes and checkpoints are available at https://github.com/THUDM/CogVLM.", "github_manual": "", "github_regex": "https://github.com/THUDM/CogVLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.02849", "title": "Co-training and Co-distillation for Quality Improvement and Compression of Language Models", "authors": [ "Hayeon Lee", "Rui Hou", "Jongpil Kim", "Davis Liang", "Hongbo Zhang", "Sung Ju Hwang", "Alexander Min" ], "github": "", "project_page": "", "abstract": "Knowledge Distillation (KD) compresses computationally expensive pre-trained language models (PLMs) by transferring their knowledge to smaller models, allowing their use in resource-constrained or real-time settings. However, most smaller models fail to surpass the performance of the original larger model, resulting in sacrificing performance to improve inference speed. To address this issue, we propose Co-Training and Co-Distillation (CTCD), a novel framework that improves performance and inference speed together by co-training two models while mutually distilling knowledge. The CTCD framework successfully achieves this based on two significant findings: 1) Distilling knowledge from the smaller model to the larger model during co-training improves the performance of the larger model. 2) The enhanced performance of the larger model further boosts the performance of the smaller model. The CTCD framework shows promise as it can be combined with existing techniques like architecture design or data augmentation, replacing one-way KD methods, to achieve further performance improvement. Extensive ablation studies demonstrate the effectiveness of CTCD, and the small model distilled by CTCD outperforms the original larger model by a significant margin of 1.66 on the GLUE benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.02805", "title": "Tailoring Self-Rationalizers with Multi-Reward Distillation", "authors": [ "Sahana Ramnath", "Brihi Joshi", "Skyler Hallinan", "Ximing Lu", "Liunian Harold Li", "Aaron Chan", "Jack Hessel", "Yejin Choi", "Xiang Ren" ], "github": "https://github.com/ink-usc/rationalemultirewarddistillation", "project_page": "", "abstract": "Large language models (LMs) are capable of generating free-text rationales to aid question answering. However, prior work 1) suggests that useful self-rationalization is emergent only at significant scales (e.g., 175B parameter GPT-3); and 2) focuses largely on downstream performance, ignoring the semantics of the rationales themselves, e.g., are they faithful, true, and helpful for humans? In this work, we enable small-scale LMs (approx. 200x smaller than GPT-3) to generate rationales that not only improve downstream task performance, but are also more plausible, consistent, and diverse, assessed both by automatic and human evaluation. Our method, MaRio (Multi-rewArd RatIOnalization), is a multi-reward conditioned self-rationalization algorithm that optimizes multiple distinct properties like plausibility, diversity and consistency. Results on five difficult question-answering datasets StrategyQA, QuaRel, OpenBookQA, NumerSense and QASC show that not only does MaRio improve task accuracy, but it also improves the self-rationalization quality of small LMs across the aforementioned axes better than a supervised fine-tuning (SFT) baseline. Extensive human evaluations confirm that MaRio rationales are preferred vs. SFT rationales, as well as qualitative improvements in plausibility and consistency.", "github_manual": "https://github.com/ink-usc/rationalemultirewarddistillation", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-07", "arxiv_id": "2311.02772", "title": "Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency", "authors": [ "Sungho Jeon", "Ching-Feng Yeh", "Hakan Inan", "Wei-Ning Hsu", "Rashi Rungta", "Yashar Mehdad", "Daniel Bikel" ], "github": "", "project_page": "", "abstract": "In this paper, we show that a simple self-supervised pre-trained audio model can achieve comparable inference efficiency to more complicated pre-trained models with speech transformer encoders. These speech transformers rely on mixing convolutional modules with self-attention modules. They achieve state-of-the-art performance on ASR with top efficiency. We first show that employing these speech transformers as an encoder significantly improves the efficiency of pre-trained audio models as well. However, our study shows that we can achieve comparable efficiency with advanced self-attention solely. We demonstrate that this simpler approach is particularly beneficial with a low-bit weight quantization technique of a neural network to improve efficiency. We hypothesize that it prevents propagating the errors between different quantized modules compared to recent speech transformers mixing quantized convolution and the quantized self-attention modules.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-08", "arxiv_id": "2311.04145", "title": "I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models", "authors": [ "Shiwei Zhang", "Jiayu Wang", "Yingya Zhang", "Kang Zhao", "Hangjie Yuan", "Zhiwu Qin", "Xiang Wang", "Deli Zhao", "Jingren Zhou" ], "github": "https://github.com/damo-vilab/i2vgen-xl", "project_page": "", "abstract": "Video synthesis has recently made remarkable strides benefiting from the rapid development of diffusion models. However, it still encounters challenges in terms of semantic accuracy, clarity and spatio-temporal continuity. They primarily arise from the scarcity of well-aligned text-video data and the complex inherent structure of videos, making it difficult for the model to simultaneously ensure semantic and qualitative excellence. In this report, we propose a cascaded I2VGen-XL approach that enhances model performance by decoupling these two factors and ensures the alignment of the input data by utilizing static images as a form of crucial guidance. I2VGen-XL consists of two stages: i) the base stage guarantees coherent semantics and preserves content from input images by using two hierarchical encoders, and ii) the refinement stage enhances the video's details by incorporating an additional brief text and improves the resolution to 1280times720. To improve the diversity, we collect around 35 million single-shot text-video pairs and 6 billion text-image pairs to optimize the model. By this means, I2VGen-XL can simultaneously enhance the semantic accuracy, continuity of details and clarity of generated videos. Through extensive experiments, we have investigated the underlying principles of I2VGen-XL and compared it with current top methods, which can demonstrate its effectiveness on diverse data. The source code and models will be publicly available at https://i2vgen-xl.github.io.", "github_manual": "https://github.com/damo-vilab/i2vgen-xl", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-08", "arxiv_id": "2311.04124", "title": "Unveiling Safety Vulnerabilities of Large Language Models", "authors": [ "George Kour", "Marcel Zalmanovici", "Naama Zwerdling", "Esther Goldbraich", "Ora Nova Fandina", "Ateret Anaby-Tavor", "Orna Raz", "Eitan Farchi" ], "github": "", "project_page": "", "abstract": "As large language models become more prevalent, their possible harmful or inappropriate responses are a cause for concern. This paper introduces a unique dataset containing adversarial examples in the form of questions, which we call AttaQ, designed to provoke such harmful or inappropriate responses. We assess the efficacy of our dataset by analyzing the vulnerabilities of various models when subjected to it. Additionally, we introduce a novel automatic approach for identifying and naming vulnerable semantic regions - input semantic areas for which the model is likely to produce harmful outputs. This is achieved through the application of specialized clustering techniques that consider both the semantic similarity of the input attacks and the harmfulness of the model's responses. Automatically identifying vulnerable semantic regions enhances the evaluation of model weaknesses, facilitating targeted improvements to its safety mechanisms and overall reliability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-08", "arxiv_id": "2311.03739", "title": "Leveraging Large Language Models for Automated Proof Synthesis in Rust", "authors": [ "Jianan Yao", "Ziqiao Zhou", "Weiteng Chen", "Weidong Cui" ], "github": "", "project_page": "", "abstract": "Formal verification can provably guarantee the correctness of critical system software, but the high proof burden has long hindered its wide adoption. Recently, Large Language Models (LLMs) have shown success in code analysis and synthesis. In this paper, we present a combination of LLMs and static analysis to synthesize invariants, assertions, and other proof structures for a Rust-based formal verification framework called Verus. In a few-shot setting, LLMs demonstrate impressive logical ability in generating postconditions and loop invariants, especially when analyzing short code snippets. However, LLMs lack the ability to retain and propagate context information, a strength of traditional static analysis. Based on these observations, we developed a prototype based on OpenAI's GPT-4 model. Our prototype decomposes the verification task into multiple smaller ones, iteratively queries GPT-4, and combines its output with lightweight static analysis. We evaluated the prototype with a developer in the automation loop on 20 vector-manipulating programs. The results demonstrate that it significantly reduces human effort in writing entry-level proof code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-08", "arxiv_id": "2311.04219", "title": "OtterHD: A High-Resolution Multi-modality Model", "authors": [ "Bo Li", "Peiyuan Zhang", "Jingkang Yang", "Yuanhan Zhang", "Fanyi Pu", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "In this paper, we present OtterHD-8B, an innovative multimodal model evolved from Fuyu-8B, specifically engineered to interpret high-resolution visual inputs with granular precision. Unlike conventional models that are constrained by fixed-size vision encoders, OtterHD-8B boasts the ability to handle flexible input dimensions, ensuring its versatility across various inference requirements. Alongside this model, we introduce MagnifierBench, an evaluation framework designed to scrutinize models' ability to discern minute details and spatial relationships of small objects. Our comparative analysis reveals that while current leading models falter on this benchmark, OtterHD-8B, particularly when directly processing high-resolution inputs, outperforms its counterparts by a substantial margin. The findings illuminate the structural variances in visual information processing among different models and the influence that the vision encoders' pre-training resolution disparities have on model effectiveness within such benchmarks. Our study highlights the critical role of flexibility and high-resolution input capabilities in large multimodal models and also exemplifies the potential inherent in the Fuyu architecture's simplicity for handling complex visual data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-08", "arxiv_id": "2311.03517", "title": "SoundCam: A Dataset for Finding Humans Using Room Acoustics", "authors": [ "Mason Wang", "Samuel Clarke", "Jui-Hsien Wang", "Ruohan Gao", "Jiajun Wu" ], "github": "", "project_page": "", "abstract": "A room's acoustic properties are a product of the room's geometry, the objects within the room, and their specific positions. A room's acoustic properties can be characterized by its impulse response (RIR) between a source and listener location, or roughly inferred from recordings of natural signals present in the room. Variations in the positions of objects in a room can effect measurable changes in the room's acoustic properties, as characterized by the RIR. Existing datasets of RIRs either do not systematically vary positions of objects in an environment, or they consist of only simulated RIRs. We present SoundCam, the largest dataset of unique RIRs from in-the-wild rooms publicly released to date. It includes 5,000 10-channel real-world measurements of room impulse responses and 2,000 10-channel recordings of music in three different rooms, including a controlled acoustic lab, an in-the-wild living room, and a conference room, with different humans in positions throughout each room. We show that these measurements can be used for interesting tasks, such as detecting and identifying humans, and tracking their positions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-08", "arxiv_id": "2311.03629", "title": "Random Field Augmentations for Self-Supervised Representation Learning", "authors": [ "Philip Andrew Mansfield", "Arash Afkanpour", "Warren Richard Morningstar", "Karan Singhal" ], "github": "", "project_page": "", "abstract": "Self-supervised representation learning is heavily dependent on data augmentations to specify the invariances encoded in representations. Previous work has shown that applying diverse data augmentations is crucial to downstream performance, but augmentation techniques remain under-explored. In this work, we propose a new family of local transformations based on Gaussian random fields to generate image augmentations for self-supervised representation learning. These transformations generalize the well-established affine and color transformations (translation, rotation, color jitter, etc.) and greatly increase the space of augmentations by allowing transformation parameter values to vary from pixel to pixel. The parameters are treated as continuous functions of spatial coordinates, and modeled as independent Gaussian random fields. Empirical results show the effectiveness of the new transformations for self-supervised representation learning. Specifically, we achieve a 1.7% top-1 accuracy improvement over baseline on ImageNet downstream classification, and a 3.6% improvement on out-of-distribution iNaturalist downstream classification. However, due to the flexibility of the new transformations, learned representations are sensitive to hyperparameters. While mild transformations improve representations, we observe that strong transformations can degrade the structure of an image, indicating that balancing the diversity and strength of augmentations is important for improving generalization of learned representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-08", "arxiv_id": "2311.03736", "title": "Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning", "authors": [ "Joseph Su\u00e1rez", "Phillip Isola", "Kyoung Whan Choe", "David Bloomin", "Hao Xiang Li", "Nikhil Pinnaparaju", "Nishaanth Kanna", "Daniel Scott", "Ryan Sullivan", "Rose S. Shuman", "Lucas de Alc\u00e2ntara", "Herbie Bradley", "Louis Castricato", "Kirsty You", "Yuhao Jiang", "Qimai Li", "Jiaxin Chen", "Xiaolong Zhu" ], "github": "", "project_page": "", "abstract": "Neural MMO 2.0 is a massively multi-agent environment for reinforcement learning research. The key feature of this new version is a flexible task system that allows users to define a broad range of objectives and reward signals. We challenge researchers to train agents capable of generalizing to tasks, maps, and opponents never seen during training. Neural MMO features procedurally generated maps with 128 agents in the standard setting and support for up to. Version 2.0 is a complete rewrite of its predecessor with three-fold improved performance and compatibility with CleanRL. We release the platform as free and open-source software with comprehensive documentation available at neuralmmo.github.io and an active community Discord. To spark initial research on this new platform, we are concurrently running a competition at NeurIPS 2023.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-08", "arxiv_id": "2311.04212", "title": "Video Instance Matting", "authors": [ "Jiachen Li", "Roberto Henschel", "Vidit Goel", "Marianna Ohanyan", "Shant Navasardyan", "Humphrey Shi" ], "github": "https://github.com/SHI-Labs/VIM", "project_page": "", "abstract": "Conventional video matting outputs one alpha matte for all instances appearing in a video frame so that individual instances are not distinguished. While video instance segmentation provides time-consistent instance masks, results are unsatisfactory for matting applications, especially due to applied binarization. To remedy this deficiency, we propose Video Instance Matting~(VIM), that is, estimating alpha mattes of each instance at each frame of a video sequence. To tackle this challenging problem, we present MSG-VIM, a Mask Sequence Guided Video Instance Matting neural network, as a novel baseline model for VIM. MSG-VIM leverages a mixture of mask augmentations to make predictions robust to inaccurate and inconsistent mask guidance. It incorporates temporal mask and temporal feature guidance to improve the temporal consistency of alpha matte predictions. Furthermore, we build a new benchmark for VIM, called VIM50, which comprises 50 video clips with multiple human instances as foreground objects. To evaluate performances on the VIM task, we introduce a suitable metric called Video Instance-aware Matting Quality~(VIMQ). Our proposed model MSG-VIM sets a strong baseline on the VIM50 benchmark and outperforms existing methods by a large margin. The project is open-sourced at https://github.com/SHI-Labs/VIM.", "github_manual": "", "github_regex": "https://github.com/SHI-Labs/VIM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-09", "arxiv_id": "2311.04589", "title": "TEAL: Tokenize and Embed ALL for Multi-modal Large Language Models", "authors": [ "Zhen Yang", "Yingxue Zhang", "Fandong Meng", "Jie Zhou" ], "github": "https://github.com/lucidrains/DALLE-pytorch", "project_page": "", "abstract": "Despite Multi-modal Large Language Models (MM-LLMs) have made exciting strides recently, they are still struggling to efficiently model the interactions among multi-modal inputs and the generation in non-textual modalities. In this work, we propose TEAL (Tokenize and Embed ALl)}, an approach to treat the input from any modality as a token sequence and learn a joint embedding space for all modalities. Specifically, for the input from any modality, TEAL first discretizes it into a token sequence with the off-the-shelf tokenizer and embeds the token sequence into a joint embedding space with a learnable embedding matrix. MM-LLMs just need to predict the multi-modal tokens autoregressively as the textual LLMs do. Finally, the corresponding de-tokenizer is applied to generate the output in each modality based on the predicted token sequence. With the joint embedding space, TEAL enables the frozen LLMs to perform both understanding and generation tasks involving non-textual modalities, such as image and audio. Thus, the textual LLM can just work as an interface and maintain its high performance in textual understanding and generation. Experiments show that TEAL achieves substantial improvements in multi-modal understanding, and implements a simple scheme for multi-modal generations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lucidrains/DALLE-pytorch", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-09", "arxiv_id": "2311.04498", "title": "NExT-Chat: An LMM for Chat, Detection and Segmentation", "authors": [ "Ao Zhang", "Liming Zhao", "Chen-Wei Xie", "Yun Zheng", "Wei Ji", "Tat-Seng Chua" ], "github": "https://github.com/next-chatv/next-chat", "project_page": "", "abstract": "The development of large language models (LLMs) has greatly advanced the field of multimodal understanding, leading to the emergence of large multimodal models (LMMs). In order to enhance the level of visual comprehension, recent studies have equipped LMMs with region-level understanding capabilities by representing object bounding box coordinates as a series of text sequences (pixel2seq). In this paper, we introduce a novel paradigm for object location modeling called pixel2emb method, where we ask the LMM to output the location embeddings and then decoded by different decoders. This paradigm allows for different location formats (such as bounding boxes and masks) to be used in multimodal conversations Furthermore, this kind of embedding based location modeling enables the utilization of existing practices in localization tasks, such as detection and segmentation. In scenarios with limited resources, our pixel2emb demonstrates superior performance compared to existing state-of-the-art (SOTA) approaches in both the location input and output tasks under fair comparison. Leveraging the proposed pixel2emb method, we train an LMM named NExT-Chat and demonstrate its capability of handling multiple tasks like visual grounding, region caption, and grounded reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/next-chatv/next-chat", "github_api_added_by": "auto" }, { "date": "2023-11-09", "arxiv_id": "2311.04287", "title": "Holistic Evaluation of Text-To-Image Models", "authors": [ "Tony Lee", "Michihiro Yasunaga", "Chenlin Meng", "Yifan Mai", "Joon Sung Park", "Agrim Gupta", "Yunzhi Zhang", "Deepak Narayanan", "Hannah Benita Teufel", "Marco Bellagente", "Minguk Kang", "Taesung Park", "Jure Leskovec", "Jun-Yan Zhu", "Li Fei-Fei", "Jiajun Wu", "Stefano Ermon", "Percy Liang" ], "github": "https://github.com/stanford-crfm/helm", "project_page": "", "abstract": "The stunning qualitative improvement of recent text-to-image models has led to their widespread attention and adoption. However, we lack a comprehensive quantitative understanding of their capabilities and risks. To fill this gap, we introduce a new benchmark, Holistic Evaluation of Text-to-Image Models (HEIM). Whereas previous evaluations focus mostly on text-image alignment and image quality, we identify 12 aspects, including text-image alignment, image quality, aesthetics, originality, reasoning, knowledge, bias, toxicity, fairness, robustness, multilinguality, and efficiency. We curate 62 scenarios encompassing these aspects and evaluate 26 state-of-the-art text-to-image models on this benchmark. Our results reveal that no single model excels in all aspects, with different models demonstrating different strengths. We release the generated images and human evaluation results for full transparency at https://crfm.stanford.edu/heim/v1.1.0 and the code at https://github.com/stanford-crfm/helm, which is integrated with the HELM codebase.", "github_manual": "", "github_regex": "https://github.com/stanford-crfm/helm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-09", "arxiv_id": "2311.04257", "title": "mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration", "authors": [ "Qinghao Ye", "Haiyang Xu", "Jiabo Ye", "Ming Yan", "Haowei Liu", "Qi Qian", "Ji Zhang", "Fei Huang", "Jingren Zhou", "Anwen HU" ], "github": "https://github.com/X-PLUG/mPLUG-Owl", "project_page": "", "abstract": "Multi-modal Large Language Models (MLLMs) have demonstrated impressive instruction abilities across various open-ended tasks. However, previous methods primarily focus on enhancing multi-modal capabilities. In this work, we introduce a versatile multi-modal large language model, mPLUG-Owl2, which effectively leverages modality collaboration to improve performance in both text and multi-modal tasks. mPLUG-Owl2 utilizes a modularized network design, with the language decoder acting as a universal interface for managing different modalities. Specifically, mPLUG-Owl2 incorporates shared functional modules to facilitate modality collaboration and introduces a modality-adaptive module that preserves modality-specific features. Extensive experiments reveal that mPLUG-Owl2 is capable of generalizing both text tasks and multi-modal tasks and achieving state-of-the-art performances with a single generic model. Notably, mPLUG-Owl2 is the first MLLM model that demonstrates the modality collaboration phenomenon in both pure-text and multi-modal scenarios, setting a pioneering path in the development of future multi-modal foundation models.", "github_manual": "https://github.com/X-PLUG/mPLUG-Owl", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-09", "arxiv_id": "2311.04254", "title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation", "authors": [ "Ruomeng Ding", "Chaoyun Zhang", "Lu Wang", "Yong Xu", "Minghua Ma", "Wei Zhang", "Si Qin", "Saravan Rajmohan", "Qingwei Lin", "Dongmei Zhang" ], "github": "https://github.com/microsoft/everything-of-thoughts-xot-", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as ``thoughts''. An effective thought design should consider three key perspectives: performance, efficiency, and flexibility. However, existing thought can at most exhibit two of these attributes. To address these limitations, we introduce a novel thought prompting approach called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle of existing thought paradigms. XoT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge into thoughts, thereby enhancing LLMs' capabilities and enabling them to generalize to unseen problems efficiently. Through the utilization of the MCTS-LLM collaborative thought revision framework, this approach autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XoT empowers LLMs to engage in unconstrained thinking, allowing for flexible cognitive mappings for problems with multiple solutions.", "github_manual": "https://github.com/microsoft/everything-of-thoughts-xot-", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-09", "arxiv_id": "2311.04235", "title": "Can LLMs Follow Simple Rules?", "authors": [ "Norman Mu", "Sarah Chen", "Zifan Wang", "Sizhe Chen", "David Karamardian", "Lulwa Aljeraisy", "Dan Hendrycks", "David Wagner" ], "github": "https://github.com/normster/llm_rules", "project_page": "", "abstract": "As Large Language Models (LLMs) are deployed with increasing real-world responsibilities, it is important to be able to specify and constrain the behavior of these systems in a reliable manner. Model developers may wish to set explicit rules for the model, such as \"do not generate abusive content\", but these may be circumvented by jailbreaking techniques. Evaluating how well LLMs follow developer-provided rules in the face of adversarial inputs typically requires manual review, which slows down monitoring and methods development. To address this issue, we propose Rule-following Language Evaluation Scenarios (RuLES), a programmatic framework for measuring rule-following ability in LLMs. RuLES consists of 15 simple text scenarios in which the model is instructed to obey a set of rules in natural language while interacting with the human user. Each scenario has a concise evaluation program to determine whether the model has broken any rules in a conversation. Through manual exploration of model behavior in our scenarios, we identify 6 categories of attack strategies and collect two suites of test cases: one consisting of unique conversations from manual testing and one that systematically implements strategies from the 6 categories. Across various popular proprietary and open models such as GPT-4 and Llama 2, we find that all models are susceptible to a wide variety of adversarial hand-crafted user inputs, though GPT-4 is the best-performing model. Additionally, we evaluate open models under gradient-based attacks and find significant vulnerabilities. We propose RuLES as a challenging new setting for research into exploring and defending against both manual and automatic attacks on LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/normster/llm_rules", "github_api_added_by": "auto" }, { "date": "2023-11-09", "arxiv_id": "2311.04400", "title": "LRM: Large Reconstruction Model for Single Image to 3D", "authors": [ "Yicong Hong", "Kai Zhang", "Jiuxiang Gu", "Sai Bi", "Yang Zhou", "Difan Liu", "Feng Liu", "Kalyan Sunkavalli", "Trung Bui", "Hao Tan" ], "github": "https://github.com/3dtopia/openlrm", "project_page": "", "abstract": "We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. In contrast to many previous methods that are trained on small-scale datasets such as ShapeNet in a category-specific fashion, LRM adopts a highly scalable transformer-based architecture with 500 million learnable parameters to directly predict a neural radiance field (NeRF) from the input image. We train our model in an end-to-end manner on massive multi-view data containing around 1 million objects, including both synthetic renderings from Objaverse and real captures from MVImgNet. This combination of a high-capacity model and large-scale training data empowers our model to be highly generalizable and produce high-quality 3D reconstructions from various testing inputs including real-world in-the-wild captures and images from generative models. Video demos and interactable 3D meshes can be found on this website: https://yiconghong.me/LRM/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/3dtopia/openlrm", "github_api_added_by": "auto" }, { "date": "2023-11-09", "arxiv_id": "2311.04391", "title": "3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features", "authors": [ "Chenfeng Xu", "Huan Ling", "Sanja Fidler", "Or Litany" ], "github": "", "project_page": "", "abstract": "We present 3DiffTection, a state-of-the-art method for 3D object detection from single images, leveraging features from a 3D-aware diffusion model. Annotating large-scale image data for 3D detection is resource-intensive and time-consuming. Recently, pretrained large image diffusion models have become prominent as effective feature extractors for 2D perception tasks. However, these features are initially trained on paired text and image data, which are not optimized for 3D tasks, and often exhibit a domain gap when applied to the target data. Our approach bridges these gaps through two specialized tuning strategies: geometric and semantic. For geometric tuning, we fine-tune a diffusion model to perform novel view synthesis conditioned on a single image, by introducing a novel epipolar warp operator. This task meets two essential criteria: the necessity for 3D awareness and reliance solely on posed image data, which are readily available (e.g., from videos) and does not require manual annotation. For semantic refinement, we further train the model on target data with detection supervision. Both tuning phases employ ControlNet to preserve the integrity of the original feature capabilities. In the final step, we harness these enhanced capabilities to conduct a test-time prediction ensemble across multiple virtual viewpoints. Through our methodology, we obtain 3D-aware features that are tailored for 3D detection and excel in identifying cross-view point correspondences. Consequently, our model emerges as a powerful 3D detector, substantially surpassing previous benchmarks, e.g., Cube-RCNN, a precedent in single-view 3D detection by 9.43\\% in AP3D on the Omni3D-ARkitscene dataset. Furthermore, 3DiffTection showcases robust data efficiency and generalization to cross-domain data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-09", "arxiv_id": "2311.04901", "title": "GENOME: GenerativE Neuro-symbOlic visual reasoning by growing and reusing ModulEs", "authors": [ "Zhenfang Chen", "Rui Sun", "Wenjun Liu", "Yining Hong", "Chuang Gan" ], "github": "https://github.com/umass-foundation-model/genome", "project_page": "", "abstract": "Recent works have shown that Large Language Models (LLMs) could empower traditional neuro-symbolic models via programming capabilities to translate language into module descriptions, thus achieving strong visual reasoning results while maintaining the model's transparency and efficiency. However, these models usually exhaustively generate the entire code snippet given each new instance of a task, which is extremely ineffective. We propose generative neuro-symbolic visual reasoning by growing and reusing modules. Specifically, our model consists of three unique stages, module initialization, module generation, and module execution. First, given a vision-language task, we adopt LLMs to examine whether we could reuse and grow over established modules to handle this new task. If not, we initialize a new module needed by the task and specify the inputs and outputs of this new module. After that, the new module is created by querying LLMs to generate corresponding code snippets that match the requirements. In order to get a better sense of the new module's ability, we treat few-shot training examples as test cases to see if our new module could pass these cases. If yes, the new module is added to the module library for future reuse. Finally, we evaluate the performance of our model on the testing set by executing the parsed programs with the newly made visual modules to get the results. We find the proposed model possesses several advantages. First, it performs competitively on standard tasks like visual question answering and referring expression comprehension; Second, the modules learned from one task can be seamlessly transferred to new tasks; Last but not least, it is able to adapt to new visual reasoning tasks by observing a few training examples and reusing modules.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/umass-foundation-model/genome", "github_api_added_by": "auto" }, { "date": "2023-11-10", "arxiv_id": "2311.05556", "title": "LCM-LoRA: A Universal Stable-Diffusion Acceleration Module", "authors": [ "Simian Luo", "Yiqin Tan", "Suraj Patil", "Daniel Gu", "Patrick von Platen", "Apolin\u00e1rio Passos", "Longbo Huang", "Jian Li", "Hang Zhao" ], "github": "https://github.com/luosiallen/latent-consistency-model", "project_page": "", "abstract": "Latent Consistency Models (LCMs) have achieved impressive performance in accelerating text-to-image generative tasks, producing high-quality images with minimal inference steps. LCMs are distilled from pre-trained latent diffusion models (LDMs), requiring only ~32 A100 GPU training hours. This report further extends LCMs' potential in two aspects: First, by applying LoRA distillation to Stable-Diffusion models including SD-V1.5, SSD-1B, and SDXL, we have expanded LCM's scope to larger models with significantly less memory consumption, achieving superior image generation quality. Second, we identify the LoRA parameters obtained through LCM distillation as a universal Stable-Diffusion acceleration module, named LCM-LoRA. LCM-LoRA can be directly plugged into various Stable-Diffusion fine-tuned models or LoRAs without training, thus representing a universally applicable accelerator for diverse image generation tasks. Compared with previous numerical PF-ODE solvers such as DDIM, DPM-Solver, LCM-LoRA can be viewed as a plug-in neural PF-ODE solver that possesses strong generalization abilities. Project page: https://github.com/luosiallen/latent-consistency-model.", "github_manual": "", "github_regex": "https://github.com/luosiallen/latent-consistency-model", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-10", "arxiv_id": "2311.05437", "title": "LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents", "authors": [ "Shilong Liu", "Hao Cheng", "Haotian Liu", "Hao Zhang", "Feng Li", "Tianhe Ren", "Xueyan Zou", "Jianwei Yang", "Hang Su", "Jun Zhu", "Lei Zhang", "Jianfeng Gao", "Chunyuan Li" ], "github": "https://github.com/LLaVA-VL/LLaVA-Plus-Codebase", "project_page": "", "abstract": "LLaVA-Plus is a general-purpose multimodal assistant that expands the capabilities of large multimodal models. It maintains a skill repository of pre-trained vision and vision-language models and can activate relevant tools based on users' inputs to fulfill real-world tasks. LLaVA-Plus is trained on multimodal instruction-following data to acquire the ability to use tools, covering visual understanding, generation, external knowledge retrieval, and compositions. Empirical results show that LLaVA-Plus outperforms LLaVA in existing capabilities and exhibits new ones. It is distinct in that the image query is directly grounded and actively engaged throughout the entire human-AI interaction sessions, significantly improving tool use performance and enabling new scenarios.", "github_manual": "https://github.com/LLaVA-VL/LLaVA-Plus-Codebase", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-10", "arxiv_id": "2311.04934", "title": "Prompt Cache: Modular Attention Reuse for Low-Latency Inference", "authors": [ "In Gim", "Guojun Chen", "Seung-seob Lee", "Nikhil Sarda", "Anurag Khandelwal", "Lin Zhong" ], "github": "https://github.com/yale-sys/prompt-cache", "project_page": "", "abstract": "We present Prompt Cache, an approach for accelerating inference for large language models (LLM) by reusing attention states across different LLM prompts. Many input prompts have overlapping text segments, such as system messages, prompt templates, and documents provided for context. Our key insight is that by precomputing and storing the attention states of these frequently occurring text segments on the inference server, we can efficiently reuse them when these segments appear in user prompts. Prompt Cache employs a schema to explicitly define such reusable text segments, called prompt modules. The schema ensures positional accuracy during attention state reuse and provides users with an interface to access cached states in their prompt. Using a prototype implementation, we evaluate Prompt Cache across several LLMs. We show that Prompt Cache significantly reduce latency in time-to-first-token, especially for longer prompts such as document-based question answering and recommendations. The improvements range from 8x for GPU-based inference to 60x for CPU-based inference, all while maintaining output accuracy and without the need for model parameter modifications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yale-sys/prompt-cache", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-10", "arxiv_id": "2311.05332", "title": "On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving", "authors": [ "Licheng Wen", "Xuemeng Yang", "Daocheng Fu", "Xiaofeng Wang", "Pinlong Cai", "Xin Li", "Tao Ma", "Yingxuan Li", "Linran Xu", "Dengke Shang", "Zheng Zhu", "Shaoyan Sun", "Yeqi Bai", "Xinyu Cai", "Min Dou", "Shuanglu Hu", "Botian Shi" ], "github": "https://github.com/PJLab-ADG/GPT4V-AD-Exploration", "project_page": "", "abstract": "The pursuit of autonomous driving technology hinges on the sophisticated integration of perception, decision-making, and control systems. Traditional approaches, both data-driven and rule-based, have been hindered by their inability to grasp the nuance of complex driving environments and the intentions of other road users. This has been a significant bottleneck, particularly in the development of common sense reasoning and nuanced scene understanding necessary for safe and reliable autonomous driving. The advent of Visual Language Models (VLM) represents a novel frontier in realizing fully autonomous vehicle driving. This report provides an exhaustive evaluation of the latest state-of-the-art VLM, \\modelnamefull, and its application in autonomous driving scenarios. We explore the model's abilities to understand and reason about driving scenes, make decisions, and ultimately act in the capacity of a driver. Our comprehensive tests span from basic scene recognition to complex causal reasoning and real-time decision-making under varying conditions. Our findings reveal that \\modelname demonstrates superior performance in scene understanding and causal reasoning compared to existing autonomous systems. It showcases the potential to handle out-of-distribution scenarios, recognize intentions, and make informed decisions in real driving contexts. However, challenges remain, particularly in direction discernment, traffic light recognition, vision grounding, and spatial reasoning tasks. These limitations underscore the need for further research and development. Project is now available on GitHub for interested parties to access and utilize: https://github.com/PJLab-ADG/GPT4V-AD-Exploration", "github_manual": "", "github_regex": "https://github.com/PJLab-ADG/GPT4V-AD-Exploration", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-10", "arxiv_id": "2311.04931", "title": "GPT4All: An Ecosystem of Open Source Compressed Language Models", "authors": [ "Yuvanesh Anand", "Zach Nussbaum", "Adam Treat", "Aaron Miller", "Richard Guo", "Ben Schmidt", "GPT4All Community", "Brandon Duderstadt", "Andriy Mulyar" ], "github": "https://github.com/nomic-ai/gpt4all", "project_page": "", "abstract": "Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. The accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly infrastructure; are only accessible via rate-limited, geo-locked, and censored web interfaces; and lack publicly available code and technical reports. In this paper, we tell the story of GPT4All, a popular open source repository that aims to democratize access to LLMs. We outline the technical details of the original GPT4All model family, as well as the evolution of the GPT4All project from a single model into a fully fledged open source ecosystem. It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem.", "github_manual": "https://github.com/nomic-ai/gpt4all", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-10", "arxiv_id": "2311.05348", "title": "u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model", "authors": [ "Jinjin Xu", "Liwu Xu", "Yuzhe Yang", "Xiang Li", "Yanchun Xie", "Yi-Jie Huang", "Yaqian Li" ], "github": "https://github.com/OPPOMKLab/u-LLaVA", "project_page": "", "abstract": "Recent advances such as LLaVA and Mini-GPT4 have successfully integrated visual information into LLMs, yielding inspiring outcomes and giving rise to a new generation of multi-modal LLMs, or MLLMs. Nevertheless, these methods struggle with hallucinations and the mutual interference between tasks. To tackle these problems, we propose an efficient and accurate approach to adapt to downstream tasks by utilizing LLM as a bridge to connect multiple expert models, namely u-LLaVA. Firstly, we incorporate the modality alignment module and multi-task modules into LLM. Then, we reorganize or rebuild multi-type public datasets to enable efficient modality alignment and instruction following. Finally, task-specific information is extracted from the trained LLM and provided to different modules for solving downstream tasks. The overall framework is simple, effective, and achieves state-of-the-art performance across multiple benchmarks. We also release our model, the generated data, and the code base publicly available.", "github_manual": "https://github.com/OPPOMKLab/u-LLaVA", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-13", "arxiv_id": "2311.06243", "title": "Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization", "authors": [ "Weiyang Liu", "Zeju Qiu", "Yao Feng", "Yuliang Xiu", "Yuxuan Xue", "Longhui Yu", "Haiwen Feng", "Zhen Liu", "Juyeon Heo", "Songyou Peng", "Yandong Wen", "Michael J. Black", "Adrian Weller", "Bernhard Sch\u00f6lkopf" ], "github": "", "project_page": "", "abstract": "Large foundation models are becoming ubiquitous, but training them from scratch is prohibitively expensive. Thus, efficiently adapting these powerful models to downstream tasks is increasingly important. In this paper, we study a principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream task adaptation. Despite demonstrating good generalizability, OFT still uses a fairly large number of trainable parameters due to the high dimensionality of orthogonal matrices. To address this, we start by examining OFT from an information transmission perspective, and then identify a few key desiderata that enable better parameter-efficiency. Inspired by how the Cooley-Tukey fast Fourier transform algorithm enables efficient information transmission, we propose an efficient orthogonal parameterization using butterfly structures. We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT). By subsuming OFT as a special case, BOFT introduces a generalized orthogonal finetuning framework. Finally, we conduct an extensive empirical study of adapting large vision transformers, large language models, and text-to-image diffusion models to various downstream tasks in vision and language.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-13", "arxiv_id": "2311.05997", "title": "JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models", "authors": [ "Zihao Wang", "Shaofei Cai", "Anji Liu", "Yonggang Jin", "Jinbing Hou", "Bowei Zhang", "Haowei Lin", "Zhaofeng He", "Zilong Zheng", "Yaodong Yang", "Xiaojian Ma", "Yitao Liang" ], "github": "", "project_page": "", "abstract": "Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the number of open-world tasks could potentially be infinite and lack the capability to progressively enhance task completion as game time progresses. We introduce JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open-world Minecraft universe. Specifically, we develop JARVIS-1 on top of pre-trained multimodal language models, which map visual observations and textual instructions to plans. The plans will be ultimately dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a multimodal memory, which facilitates planning using both pre-trained knowledge and its actual game survival experiences. In our experiments, JARVIS-1 exhibits nearly perfect performances across over 200 varying tasks from the Minecraft Universe Benchmark, ranging from entry to intermediate levels. JARVIS-1 has achieved a completion rate of 12.5% in the long-horizon diamond pickaxe task. This represents a significant increase up to 5 times compared to previous records. Furthermore, we show that JARVIS-1 is able to self-improve following a life-long learning paradigm thanks to multimodal memory, sparking a more general intelligence and improved autonomy. The project page is available at https://craftjarvis-jarvis1.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-13", "arxiv_id": "2311.05657", "title": "Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs", "authors": [ "Da Yin", "Faeze Brahman", "Abhilasha Ravichander", "Khyathi Chandu", "Kai-Wei Chang", "Yejin Choi", "Bill Yuchen Lin" ], "github": "https://github.com/allenai/lumos", "project_page": "", "abstract": "We introduce Lumos, a novel framework for training language agents that employs a unified data format and a modular architecture based on open-source large language models (LLMs). Lumos consists of three distinct modules: planning, grounding, and execution. The planning module breaks down a task into a series of high-level, tool-agnostic subgoals, which are then made specific by the grounding module through a set of low-level actions. These actions are subsequently executed by the execution module, utilizing a range of off-the-shelf tools and APIs. In order to train these modules effectively, high-quality annotations of subgoals and actions were collected and are made available for fine-tuning open-source LLMs for various tasks such as complex question answering, web tasks, and math problems. Leveraging this unified data and modular design, Lumos not only achieves comparable or superior performance to current, state-of-the-art agents, but also exhibits several key advantages: (1) Lumos surpasses GPT-4/3.5-based agents in complex question answering and web tasks, while equalling the performance of significantly larger LLM agents on math tasks; (2) Lumos outperforms open-source agents created through conventional training methods and those using chain-of-thoughts training; and (3) Lumos is capable of effectively generalizing to unseen interactive tasks, outperforming larger LLM-based agents and even exceeding performance of specialized agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/lumos", "github_api_added_by": "auto" }, { "date": "2023-11-13", "arxiv_id": "2311.06242", "title": "Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks", "authors": [ "Bin Xiao", "Haiping Wu", "Weijian Xu", "Xiyang Dai", "Houdong Hu", "Yumao Lu", "Michael Zeng", "Ce Liu", "Lu Yuan" ], "github": "", "project_page": "", "abstract": "We introduce Florence-2, a novel vision foundation model with a unified, prompt-based representation for a variety of computer vision and vision-language tasks. While existing large vision models excel in transfer learning, they struggle to perform a diversity of tasks with simple instructions, a capability that implies handling the complexity of various spatial hierarchy and semantic granularity. Florence-2 was designed to take text-prompt as task instructions and generate desirable results in text forms, whether it be captioning, object detection, grounding or segmentation. This multi-task learning setup demands large-scale, high-quality annotated data. To this end, we co-developed FLD-5B that consists of 5.4 billion comprehensive visual annotations on 126 million images, using an iterative strategy of automated image annotation and model refinement. We adopted a sequence-to-sequence structure to train Florence-2 to perform versatile and comprehensive vision tasks. Extensive evaluations on numerous tasks demonstrated Florence-2 to be a strong vision foundation model contender with unprecedented zero-shot and fine-tuning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-13", "arxiv_id": "2311.05770", "title": "PolyMaX: General Dense Prediction with Mask Transformer", "authors": [ "Xuan Yang", "Liangzhe Yuan", "Kimberly Wilber", "Astuti Sharma", "Xiuye Gu", "Siyuan Qiao", "Stephanie Debats", "Huisheng Wang", "Hartwig Adam", "Mikhail Sirotenko", "Liang-Chieh Chen" ], "github": "https://github.com/google-research/deeplab2", "project_page": "", "abstract": "Dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal prediction, can be easily formulated as per-pixel classification (discrete outputs) or regression (continuous outputs). This per-pixel prediction paradigm has remained popular due to the prevalence of fully convolutional networks. However, on the recent frontier of segmentation task, the community has been witnessing a shift of paradigm from per-pixel prediction to cluster-prediction with the emergence of transformer architectures, particularly the mask transformers, which directly predicts a label for a mask instead of a pixel. Despite this shift, methods based on the per-pixel prediction paradigm still dominate the benchmarks on the other dense prediction tasks that require continuous outputs, such as depth estimation and surface normal prediction. Motivated by the success of DORN and AdaBins in depth estimation, achieved by discretizing the continuous output space, we propose to generalize the cluster-prediction based method to general dense prediction tasks. This allows us to unify dense prediction tasks with the mask transformer framework. Remarkably, the resulting model PolyMaX demonstrates state-of-the-art performance on three benchmarks of NYUD-v2 dataset. We hope our simple yet effective design can inspire more research on exploiting mask transformers for more dense prediction tasks. Code and model will be made available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-research/deeplab2", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-13", "arxiv_id": "2311.05698", "title": "Mirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities", "authors": [ "AJ Piergiovanni", "Isaac Nobel", "Dahun Kim", "Michael S. Ryoo", "Victor Gomes", "Anelia Angelova" ], "github": "", "project_page": "", "abstract": "One of the main challenges of multimodal learning is the need to combine heterogeneous modalities (e.g., video, audio, text). For example, video and audio are obtained at much higher rates than text and are roughly aligned in time. They are often not synchronized with text, which comes as a global context, e.g., a title, or a description. Furthermore, video and audio inputs are of much larger volumes, and grow as the video length increases, which naturally requires more compute dedicated to these modalities and makes modeling of long-range dependencies harder. We here decouple the multimodal modeling, dividing it into separate, focused autoregressive models, processing the inputs according to the characteristics of the modalities. We propose a multimodal model, called Mirasol3B, consisting of an autoregressive component for the time-synchronized modalities (audio and video), and an autoregressive component for the context modalities which are not necessarily aligned in time but are still sequential. To address the long-sequences of the video-audio inputs, we propose to further partition the video and audio sequences in consecutive snippets and autoregressively process their representations. To that end, we propose a Combiner mechanism, which models the audio-video information jointly within a timeframe. The Combiner learns to extract audio and video features from raw spatio-temporal signals, and then learns to fuse these features producing compact but expressive representations per snippet. Our approach achieves the state-of-the-art on well established multimodal benchmarks, outperforming much larger models. It effectively addresses the high computational demand of media inputs by both learning compact representations, controlling the sequence length of the audio-video feature representations, and modeling their dependencies in time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-13", "arxiv_id": "2311.06214", "title": "Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model", "authors": [ "Jiahao Li", "Hao Tan", "Kai Zhang", "Zexiang Xu", "Fujun Luan", "Yinghao Xu", "Yicong Hong", "Kalyan Sunkavalli", "Greg Shakhnarovich", "Sai Bi" ], "github": "", "project_page": "", "abstract": "Text-to-3D with diffusion models have achieved remarkable progress in recent years. However, existing methods either rely on score distillation-based optimization which suffer from slow inference, low diversity and Janus problems, or are feed-forward methods that generate low quality results due to the scarcity of 3D training data. In this paper, we propose Instant3D, a novel method that generates high-quality and diverse 3D assets from text prompts in a feed-forward manner. We adopt a two-stage paradigm, which first generates a sparse set of four structured and consistent views from text in one shot with a fine-tuned 2D text-to-image diffusion model, and then directly regresses the NeRF from the generated images with a novel transformer-based sparse-view reconstructor. Through extensive experiments, we demonstrate that our method can generate high-quality, diverse and Janus-free 3D assets within 20 seconds, which is two order of magnitude faster than previous optimization-based methods that can take 1 to 10 hours. Our project webpage: https://jiahao.ai/instant3d/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-13", "arxiv_id": "2311.05908", "title": "FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores", "authors": [ "Daniel Y. Fu", "Hermann Kumbong", "Eric Nguyen", "Christopher R\u00e9" ], "github": "", "project_page": "", "abstract": "Convolution models with long filters have demonstrated state-of-the-art reasoning abilities in many long-sequence tasks but lag behind the most optimized Transformers in wall-clock time. A major bottleneck is the Fast Fourier Transform (FFT)--which allows long convolutions to run in O(N logN) time in sequence length N but has poor hardware utilization. In this paper, we study how to optimize the FFT convolution. We find two key bottlenecks: the FFT does not effectively use specialized matrix multiply units, and it incurs expensive I/O between layers of the memory hierarchy. In response, we propose FlashFFTConv. FlashFFTConv uses a matrix decomposition that computes the FFT using matrix multiply units and enables kernel fusion for long sequences, reducing I/O. We also present two sparse convolution algorithms--1) partial convolutions and 2) frequency-sparse convolutions--which can be implemented simply by skipping blocks in the matrix decomposition, enabling further opportunities for memory and compute savings. FlashFFTConv speeds up exact FFT convolutions by up to 7.93times over PyTorch and achieves up to 4.4times speedup end-to-end. Given the same compute budget, FlashFFTConv allows Hyena-GPT-s to achieve 2.3 points better perplexity on the PILE and M2-BERT-base to achieve 3.3 points higher GLUE score--matching models with twice the parameter count. FlashFFTConv also achieves 96.1% accuracy on Path-512, a high-resolution vision task where no model had previously achieved better than 50%. Furthermore, partial convolutions enable longer-sequence models--yielding the first DNA model that can process the longest human genes (2.3M base pairs)--and frequency-sparse convolutions speed up pretrained models while maintaining or improving model quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-13", "arxiv_id": "2311.05772", "title": "ADaPT: As-Needed Decomposition and Planning with Language Models", "authors": [ "Archiki Prasad", "Alexander Koller", "Mareike Hartmann", "Peter Clark", "Ashish Sabharwal", "Mohit Bansal", "Tushar Khot" ], "github": "https://github.com/archiki/ADaPT", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment. Recent works employ LLMs-as-agents in broadly two ways: iteratively determining the next action (iterative executors) or generating plans and executing sub-tasks using LLMs (plan-and-execute). However, these methods struggle with task complexity, as the inability to execute any sub-task may lead to task failure. To address these shortcomings, we introduce As-Needed Decomposition and Planning for complex Tasks (ADaPT), an approach that explicitly plans and decomposes complex sub-tasks as-needed, i.e., when the LLM is unable to execute them. ADaPT recursively decomposes sub-tasks to adapt to both task complexity and LLM capability. Our results demonstrate that ADaPT substantially outperforms established strong baselines, achieving success rates up to 28.3% higher in ALFWorld, 27% in WebShop, and 33% in TextCraft -- a novel compositional dataset that we introduce. Through extensive analysis, we illustrate the importance of multilevel decomposition and establish that ADaPT dynamically adjusts to the capabilities of the executor LLM as well as to task complexity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/archiki/ADaPT", "github_api_added_by": "auto" }, { "date": "2023-11-13", "arxiv_id": "2311.05661", "title": "Prompt Engineering a Prompt Engineer", "authors": [ "Qinyuan Ye", "Maxamed Axmed", "Reid Pryzant", "Fereshte Khani" ], "github": "", "project_page": "", "abstract": "Prompt engineering is a challenging yet crucial task for optimizing the performance of large language models (LLMs). It requires complex reasoning to examine the model's errors, hypothesize what is missing or misleading in the current prompt, and communicate the task with clarity. While recent works indicate that LLMs can be meta-prompted to perform automatic prompt engineering, their potentials may not be fully untapped due to the lack of sufficient guidance to elicit complex reasoning capabilities in LLMs in the meta-prompt. In this work, we investigate the problem of \"prompt engineering a prompt engineer\" -- constructing a meta-prompt that more effectively guides LLMs to perform automatic prompt engineering. We introduce and analyze key components, such as a step-by-step reasoning template and context specification, which lead to improved performance. In addition, inspired by common optimization concepts such as batch size, step size and momentum, we introduce their verbalized counterparts to the meta-prompt and investigate their effects. Our final method, named PE2, finds a prompt that outperforms \"let's think step by step\" by 6.3% on the MultiArith dataset and 3.1% on the GSM8K dataset. To demonstrate its versatility, we apply PE2 to the Instruction Induction benchmark, a suite of counterfactual tasks, and a lengthy, real-world industrial prompt. In these settings, PE2 achieves strong performance and outperforms prior automatic prompt engineering baselines. Further, we show that PE2 makes meaningful and targeted prompt edits, amends erroneous or incomplete prompts, and presents non-trivial counterfactual reasoning abilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-13", "arxiv_id": "2311.05640", "title": "FinGPT: Large Generative Models for a Small Language", "authors": [ "Risto Luukkonen", "Ville Komulainen", "Jouni Luoma", "Anni Eskelinen", "Jenna Kanerva", "Hanna-Mari Kupari", "Filip Ginter", "Veronika Laippala", "Niklas Muennighoff", "Aleksandra Piktus", "Thomas Wang", "Nouamane Tazi", "Teven Le Scao", "Thomas Wolf", "Osma Suominen", "Samuli Sairanen", "Mikko Merioksa", "Jyrki Heinonen", "Aija Vahtola", "Samuel Antao", "Sampo Pyysalo" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) excel in many tasks in NLP and beyond, but most open models have very limited coverage of smaller languages and LLM work tends to focus on languages where nearly unlimited data is available for pretraining. In this work, we study the challenges of creating LLMs for Finnish, a language spoken by less than 0.1% of the world population. We compile an extensive dataset of Finnish combining web crawls, news, social media and eBooks. We pursue two approaches to pretrain models: 1) we train seven monolingual models from scratch (186M to 13B parameters) dubbed FinGPT, 2) we continue the pretraining of the multilingual BLOOM model on a mix of its original training data and Finnish, resulting in a 176 billion parameter model we call BLUUMI. For model evaluation, we introduce FIN-bench, a version of BIG-bench with Finnish tasks. We also assess other model qualities such as toxicity and bias. Our models and tools are openly available at https://turkunlp.org/gpt3-finnish.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-13", "arxiv_id": "2311.06158", "title": "Language Models can be Logical Solvers", "authors": [ "Jiazhan Feng", "Ruochen Xu", "Junheng Hao", "Hiteshi Sharma", "Yelong Shen", "Dongyan Zhao", "Weizhu Chen" ], "github": "", "project_page": "", "abstract": "Logical reasoning is a fundamental aspect of human intelligence and a key component of tasks like problem-solving and decision-making. Recent advancements have enabled Large Language Models (LLMs) to potentially exhibit reasoning capabilities, but complex logical reasoning remains a challenge. The state-of-the-art, solver-augmented language models, use LLMs to parse natural language logical questions into symbolic representations first and then adopt external logical solvers to take in the symbolic representations and output the answers. Despite their impressive performance, any parsing errors will inevitably result in the failure of the execution of the external logical solver and no answer to the logical questions. In this paper, we introduce LoGiPT, a novel language model that directly emulates the reasoning processes of logical solvers and bypasses the parsing errors by learning to strict adherence to solver syntax and grammar. LoGiPT is fine-tuned on a newly constructed instruction-tuning dataset derived from revealing and refining the invisible reasoning process of deductive solvers. Experimental results on two public deductive reasoning datasets demonstrate that LoGiPT outperforms state-of-the-art solver-augmented LMs and few-shot prompting methods on competitive LLMs like ChatGPT or GPT-4.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-13", "arxiv_id": "2311.05884", "title": "Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems", "authors": [ "Huan Gui", "Ruoxi Wang", "Ke Yin", "Long Jin", "Maciej Kula", "Taibai Xu", "Lichan Hong", "Ed H. Chi" ], "github": "", "project_page": "", "abstract": "Learning feature interaction is the critical backbone to building recommender systems. In web-scale applications, learning feature interaction is extremely challenging due to the sparse and large input feature space; meanwhile, manually crafting effective feature interactions is infeasible because of the exponential solution space. We propose to leverage a Transformer-based architecture with attention layers to automatically capture feature interactions. Transformer architectures have witnessed great success in many domains, such as natural language processing and computer vision. However, there has not been much adoption of Transformer architecture for feature interaction modeling in industry. We aim at closing the gap. We identify two key challenges for applying the vanilla Transformer architecture to web-scale recommender systems: (1) Transformer architecture fails to capture the heterogeneous feature interactions in the self-attention layer; (2) The serving latency of Transformer architecture might be too high to be deployed in web-scale recommender systems. We first propose a heterogeneous self-attention layer, which is a simple yet effective modification to the self-attention layer in Transformer, to take into account the heterogeneity of feature interactions. We then introduce Hiformer (Heterogeneous Interaction Transformer) to further improve the model expressiveness. With low-rank approximation and model pruning, \\hiformer enjoys fast inference for online deployment. Extensive offline experiment results corroborates the effectiveness and efficiency of the Hiformer model. We have successfully deployed the Hiformer model to a real world large scale App ranking model at Google Play, with significant improvement in key engagement metrics (up to +2.66\\%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-13", "arxiv_id": "2311.05707", "title": "FMViT: A multiple-frequency mixing Vision Transformer", "authors": [ "Wei Tan", "Yifeng Geng", "Xuansong Xie" ], "github": "https://github.com/tany0699/FMViT", "project_page": "", "abstract": "The transformer model has gained widespread adoption in computer vision tasks in recent times. However, due to the quadratic time and memory complexity of self-attention, which is proportional to the number of input tokens, most existing Vision Transformers (ViTs) encounter challenges in achieving efficient performance in practical industrial deployment scenarios, such as TensorRT and CoreML, where traditional CNNs excel. Although some recent attempts have been made to design CNN-Transformer hybrid architectures to tackle this problem, their overall performance has not met expectations. To tackle these challenges, we propose an efficient hybrid ViT architecture named FMViT. This approach enhances the model's expressive power by blending high-frequency features and low-frequency features with varying frequencies, enabling it to capture both local and global information effectively. Additionally, we introduce deploy-friendly mechanisms such as Convolutional Multigroup Reparameterization (gMLP), Lightweight Multi-head Self-Attention (RLMHSA), and Convolutional Fusion Block (CFB) to further improve the model's performance and reduce computational overhead. Our experiments demonstrate that FMViT surpasses existing CNNs, ViTs, and CNNTransformer hybrid architectures in terms of latency/accuracy trade-offs for various vision tasks. On the TensorRT platform, FMViT outperforms Resnet101 by 2.5% (83.3% vs. 80.8%) in top-1 accuracy on the ImageNet dataset while maintaining similar inference latency. Moreover, FMViT achieves comparable performance with EfficientNet-B5, but with a 43% improvement in inference speed. On CoreML, FMViT outperforms MobileOne by 2.6% in top-1 accuracy on the ImageNet dataset, with inference latency comparable to MobileOne (78.5% vs. 75.9%). Our code can be found at https://github.com/tany0699/FMViT.", "github_manual": "", "github_regex": "https://github.com/tany0699/FMViT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.06783", "title": "Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models", "authors": [ "Haoning Wu", "Zicheng Zhang", "Erli Zhang", "Chaofeng Chen", "Liang Liao", "Annan Wang", "Kaixin Xu", "Chunyi Li", "Jingwen Hou", "Guangtao Zhai", "Geng Xue", "Wenxiu Sun", "Qiong Yan", "Weisi Lin" ], "github": "https://github.com/Q-Future/Q-Instruct", "project_page": "", "abstract": "Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range of natural human instructions in a model. While existing foundation models have shown exciting potentials on low-level visual tasks, their related abilities are still preliminary and need to be improved. In order to enhance these models, we conduct a large-scale subjective experiment collecting a vast number of real human feedbacks on low-level vision. Each feedback follows a pathway that starts with a detailed description on the low-level visual appearance (*e.g. clarity, color, brightness* of an image, and ends with an overall conclusion, with an average length of 45 words. The constructed **Q-Pathway** dataset includes 58K detailed human feedbacks on 18,973 images with diverse low-level appearance. Moreover, to enable foundation models to robustly respond to diverse types of questions, we design a GPT-participated conversion to process these feedbacks into diverse-format 200K instruction-response pairs. Experimental results indicate that the **Q-Instruct** consistently elevates low-level perception and understanding abilities across several foundational models. We anticipate that our datasets can pave the way for a future that general intelligence can perceive, understand low-level visual appearance and evaluate visual quality like a human. Our dataset, model zoo, and demo is published at: https://q-future.github.io/Q-Instruct.", "github_manual": "https://github.com/Q-Future/Q-Instruct", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.07575", "title": "SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models", "authors": [ "Ziyi Lin", "Chris Liu", "Renrui Zhang", "Peng Gao", "Longtian Qiu", "Han Xiao", "Han Qiu", "Chen Lin", "Wenqi Shao", "Keqin Chen", "Jiaming Han", "Siyuan Huang", "Yichi Zhang", "Xuming He", "Hongsheng Li", "Yu Qiao" ], "github": "https://github.com/Alpha-VLLM/LLaMA2-Accessory", "project_page": "", "abstract": "We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings. First, for stronger vision-language alignment, we unfreeze the large language model (LLM) during pre-training, and introduce a weight mix strategy between LLMs trained by real-world and synthetic data. By directly integrating the weights from two domains, the mixed LLM can efficiently incorporate diverse semantics with favorable robustness. Then, to enable multi-purpose capabilities, we mix a variety of tasks for joint visual instruction tuning, and design task-specific instructions to avoid inter-task conflict. In addition to the basic visual question answering, we include more challenging tasks such as region-level understanding, caption grounding, document layout detection, and human pose estimation, contributing to mutual enhancement over different scenarios. Additionally, we propose to extract comprehensive visual embeddings from various network architectures, pre-training paradigms, and information granularity, providing language models with more robust image representations. Based on our proposed joint mixing, SPHINX exhibits superior multi-modal understanding capabilities on a wide range of applications. On top of this, we further propose an efficient strategy aiming to better capture fine-grained appearances of high-resolution images. With a mixing of different scales and high-resolution sub-images, SPHINX attains exceptional visual parsing and reasoning performance on existing evaluation benchmarks. We hope our work may cast a light on the exploration of joint mixing in future MLLM research. Code is released at https://github.com/Alpha-VLLM/LLaMA2-Accessory.", "github_manual": "", "github_regex": "https://github.com/Alpha-VLLM/LLaMA2-Accessory", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.07463", "title": "MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks", "authors": [ "Sanchit Ahuja", "Divyanshu Aggarwal", "Varun Gumma", "Ishaan Watts", "Ashutosh Sathe", "Millicent Ochieng", "Rishav Hada", "Prachi Jain", "Maxamed Axmed", "Kalika Bali", "Sunayana Sitaram" ], "github": "", "project_page": "", "abstract": "Recently, there has been a rapid advancement in research on Large Language Models (LLMs), resulting in significant progress in several Natural Language Processing (NLP) tasks. Consequently, there has been a surge in LLM evaluation research to comprehend the models' capabilities and limitations. However, much of this research has been confined to the English language, leaving LLM building and evaluation for non-English languages relatively unexplored. There has been an introduction of several new LLMs, necessitating their evaluation on non-English languages. This study aims to expand our MEGA benchmarking suite by including six new datasets to form the MEGAVERSE benchmark. The benchmark comprises 22 datasets covering 81 languages, including low-resource African languages. We evaluate several state-of-the-art LLMs like GPT-3.5-Turbo, GPT4, PaLM2, and Llama2 on the MEGAVERSE datasets. Additionally, we include two multimodal datasets in the benchmark and assess the performance of the LLaVa-v1.5 model. Our experiments suggest that GPT4 and PaLM2 outperform the Llama models on various tasks, notably on low-resource languages, with GPT4 outperforming PaLM2 on more datasets than vice versa. However, issues such as data contamination must be addressed to obtain an accurate assessment of LLM performance on non-English languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.07446", "title": "Story-to-Motion: Synthesizing Infinite and Controllable Character Animation from Long Text", "authors": [ "Zhongfei Qing", "Zhongang Cai", "Zhitao Yang", "Lei Yang" ], "github": "", "project_page": "", "abstract": "Generating natural human motion from a story has the potential to transform the landscape of animation, gaming, and film industries. A new and challenging task, Story-to-Motion, arises when characters are required to move to various locations and perform specific motions based on a long text description. This task demands a fusion of low-level control (trajectories) and high-level control (motion semantics). Previous works in character control and text-to-motion have addressed related aspects, yet a comprehensive solution remains elusive: character control methods do not handle text description, whereas text-to-motion methods lack position constraints and often produce unstable motions. In light of these limitations, we propose a novel system that generates controllable, infinitely long motions and trajectories aligned with the input text. (1) We leverage contemporary Large Language Models to act as a text-driven motion scheduler to extract a series of (text, position, duration) pairs from long text. (2) We develop a text-driven motion retrieval scheme that incorporates motion matching with motion semantic and trajectory constraints. (3) We design a progressive mask transformer that addresses common artifacts in the transition motion such as unnatural pose and foot sliding. Beyond its pioneering role as the first comprehensive solution for Story-to-Motion, our system undergoes evaluation across three distinct sub-tasks: trajectory following, temporal action composition, and motion blending, where it outperforms previous state-of-the-art motion synthesis methods across the board. Homepage: https://story2motion.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.06753", "title": "Towards General-Purpose Speech Abilities for Large Language Models Using Unpaired Data", "authors": [ "Yassir Fathullah", "Chunyang Wu", "Egor Lakomkin", "Junteng Jia", "Yuan Shangguan", "Jay Mahadeokar", "Ozlem Kalinli", "Christian Fuegen", "Mike Seltzer" ], "github": "", "project_page": "", "abstract": "In this work, we extend the instruction-tuned Llama-2 model with end-to-end general-purpose speech processing and reasoning abilities while maintaining the wide range of LLM capabilities, without using any carefully curated paired data. The proposed model can utilize audio prompts as a replacement for text and sustain a conversation. Such a model also has extended cross-modal capabilities such as being able to perform speech question answering, speech translation, and audio summarization amongst many other closed and open-domain tasks. This is unlike prior approaches in speech, in which LLMs are extended to handle audio for a limited number of pre-designated tasks. Experiments show that our end-to-end approach is on par with or outperforms a cascaded system (speech recognizer + LLM) in terms of modeling the response to a prompt. Furthermore, unlike a cascade, our approach shows the ability to interchange text and audio modalities and utilize the prior context in a conversation to provide better results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.06720", "title": "Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer", "authors": [ "Bowen Tan", "Yun Zhu", "Lijuan Liu", "Eric Xing", "Zhiting Hu", "Jindong Chen" ], "github": "https://github.com/tanyuqian/cappy", "project_page": "", "abstract": "Large language models (LLMs) such as T0, FLAN, and OPT-IML, excel in multi-tasking under a unified instruction-following paradigm, where they also exhibit remarkable generalization abilities to unseen tasks. Despite their impressive performance, these LLMs, with sizes ranging from several billion to hundreds of billions of parameters, demand substantial computational resources, making their training and inference expensive and inefficient. Furthermore, adapting these models to downstream applications, particularly complex tasks, is often unfeasible due to the extensive hardware requirements for finetuning, even when utilizing parameter-efficient approaches such as prompt tuning. Additionally, the most powerful multi-task LLMs, such as OPT-IML-175B and FLAN-PaLM-540B, are not publicly accessible, severely limiting their customization potential. To address these challenges, we introduce a pretrained small scorer, Cappy, designed to enhance the performance and efficiency of multi-task LLMs. With merely 360 million parameters, Cappy functions either independently on classification tasks or serve as an auxiliary component for LLMs, boosting their performance. Moreover, Cappy enables efficiently integrating downstream supervision without requiring LLM finetuning nor the access to their parameters. Our experiments demonstrate that, when working independently on 11 language understanding tasks from PromptSource, Cappy outperforms LLMs that are several orders of magnitude larger. Besides, on 45 complex tasks from BIG-Bench, Cappy boosts the performance of the advanced multi-task LLM, FLAN-T5, by a large margin. Furthermore, Cappy is flexible to cooperate with other LLM adaptations, including finetuning and in-context learning, offering additional performance enhancement.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tanyuqian/cappy", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.06697", "title": "Trusted Source Alignment in Large Language Models", "authors": [ "Vasilisa Bashlovkina", "Zhaobin Kuang", "Riley Matthews", "Edward Clifford", "Yennie Jun", "William W. Cohen", "Simon Baumgartner" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are trained on web-scale corpora that inevitably include contradictory factual information from sources of varying reliability. In this paper, we propose measuring an LLM property called trusted source alignment (TSA): the model's propensity to align with content produced by trusted publishers in the face of uncertainty or controversy. We present FactCheckQA, a TSA evaluation dataset based on a corpus of fact checking articles. We describe a simple protocol for evaluating TSA and offer a detailed analysis of design considerations including response extraction, claim contextualization, and bias in prompt formulation. Applying the protocol to PaLM-2, we find that as we scale up the model size, the model performance on FactCheckQA improves from near-random to up to 80% balanced accuracy in aligning with trusted sources.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.06495", "title": "LayoutPrompter: Awaken the Design Ability of Large Language Models", "authors": [ "Jiawei Lin", "Jiaqi Guo", "Shizhao Sun", "Zijiang James Yang", "Jian-Guang Lou", "Dongmei Zhang" ], "github": "https://github.com/microsoft/LayoutGeneration/tree/main/LayoutPrompter", "project_page": "", "abstract": "Conditional graphic layout generation, which automatically maps user constraints to high-quality layouts, has attracted widespread attention today. Although recent works have achieved promising performance, the lack of versatility and data efficiency hinders their practical applications. In this work, we propose LayoutPrompter, which leverages large language models (LLMs) to address the above problems through in-context learning. LayoutPrompter is made up of three key components, namely input-output serialization, dynamic exemplar selection and layout ranking. Specifically, the input-output serialization component meticulously designs the input and output formats for each layout generation task. Dynamic exemplar selection is responsible for selecting the most helpful prompting exemplars for a given input. And a layout ranker is used to pick the highest quality layout from multiple outputs of LLMs. We conduct experiments on all existing layout generation tasks using four public datasets. Despite the simplicity of our approach, experimental results show that LayoutPrompter can compete with or even outperform state-of-the-art approaches on these tasks without any model training or fine-tuning. This demonstrates the effectiveness of this versatile and training-free approach. In addition, the ablation studies show that LayoutPrompter is significantly superior to the training-based baseline in a low-data regime, further indicating the data efficiency of LayoutPrompter. Our project is available at https://github.com/microsoft/LayoutGeneration/tree/main/LayoutPrompter.", "github_manual": "", "github_regex": "https://github.com/microsoft/LayoutGeneration/tree/main/LayoutPrompter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.07562", "title": "GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation", "authors": [ "An Yan", "Zhengyuan Yang", "Wanrong Zhu", "Kevin Lin", "Linjie Li", "Jianfeng Wang", "Jianwei Yang", "Yiwu Zhong", "Julian McAuley", "Jianfeng Gao", "Zicheng Liu", "Lijuan Wang" ], "github": "https://github.com/zzxslp/MM-Navigator", "project_page": "", "abstract": "We present MM-Navigator, a GPT-4V-based agent for the smartphone graphical user interface (GUI) navigation task. MM-Navigator can interact with a smartphone screen as human users, and determine subsequent actions to fulfill given instructions. Our findings demonstrate that large multimodal models (LMMs), specifically GPT-4V, excel in zero-shot GUI navigation through its advanced screen interpretation, action reasoning, and precise action localization capabilities. We first benchmark MM-Navigator on our collected iOS screen dataset. According to human assessments, the system exhibited a 91\\% accuracy rate in generating reasonable action descriptions and a 75\\% accuracy rate in executing the correct actions for single-step instructions on iOS. Additionally, we evaluate the model on a subset of an Android screen navigation dataset, where the model outperforms previous GUI navigators in a zero-shot fashion. Our benchmark and detailed analyses aim to lay a robust groundwork for future research into the GUI navigation task. The project page is at https://github.com/zzxslp/MM-Navigator.", "github_manual": "", "github_regex": "https://github.com/zzxslp/MM-Navigator", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.07361", "title": "The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4", "authors": [ "Microsoft Research AI4Science", "Microsoft Azure Quantum" ], "github": "", "project_page": "", "abstract": "In recent years, groundbreaking advancements in natural language processing have culminated in the emergence of powerful large language models (LLMs), which have showcased remarkable capabilities across a vast array of domains, including the understanding, generation, and translation of natural language, and even tasks that extend beyond language processing. In this report, we delve into the performance of LLMs within the context of scientific discovery, focusing on GPT-4, the state-of-the-art language model. Our investigation spans a diverse range of scientific areas encompassing drug discovery, biology, computational chemistry (density functional theory (DFT) and molecular dynamics (MD)), materials design, and partial differential equations (PDE). Evaluating GPT-4 on scientific tasks is crucial for uncovering its potential across various research domains, validating its domain-specific expertise, accelerating scientific progress, optimizing resource allocation, guiding future model development, and fostering interdisciplinary research. Our exploration methodology primarily consists of expert-driven case assessments, which offer qualitative insights into the model's comprehension of intricate scientific concepts and relationships, and occasionally benchmark testing, which quantitatively evaluates the model's capacity to solve well-defined domain-specific problems. Our preliminary exploration indicates that GPT-4 exhibits promising potential for a variety of scientific applications, demonstrating its aptitude for handling complex problem-solving and knowledge integration tasks. Broadly speaking, we evaluate GPT-4's knowledge base, scientific understanding, scientific numerical calculation abilities, and various scientific prediction capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.07069", "title": "Music ControlNet: Multiple Time-varying Controls for Music Generation", "authors": [ "Shih-Lun Wu", "Chris Donahue", "Shinji Watanabe", "Nicholas J. Bryan" ], "github": "", "project_page": "", "abstract": "Text-to-music generation models are now capable of generating high-quality music audio in broad styles. However, text control is primarily suitable for the manipulation of global musical attributes like genre, mood, and tempo, and is less suitable for precise control over time-varying attributes such as the positions of beats in time or the changing dynamics of the music. We propose Music ControlNet, a diffusion-based music generation model that offers multiple precise, time-varying controls over generated audio. To imbue text-to-music models with time-varying control, we propose an approach analogous to pixel-wise control of the image-domain ControlNet method. Specifically, we extract controls from training audio yielding paired data, and fine-tune a diffusion-based conditional generative model over audio spectrograms given melody, dynamics, and rhythm controls. While the image-domain Uni-ControlNet method already allows generation with any subset of controls, we devise a new strategy to allow creators to input controls that are only partially specified in time. We evaluate both on controls extracted from audio and controls we expect creators to provide, demonstrating that we can generate realistic music that corresponds to control inputs in both settings. While few comparable music generation models exist, we benchmark against MusicGen, a recent model that accepts text and melody input, and show that our model generates music that is 49% more faithful to input melodies despite having 35x fewer parameters, training on 11x less data, and enabling two additional forms of time-varying control. Sound examples can be found at https://MusicControlNet.github.io/web/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.06772", "title": "ChatAnything: Facetime Chat with LLM-Enhanced Personas", "authors": [ "Yilin Zhao", "Xinbin Yuan", "Shanghua Gao", "Zhijie Lin", "Qibin Hou", "Jiashi Feng", "Daquan Zhou" ], "github": "https://github.com/zhoudaquan/ChatAnything", "project_page": "", "abstract": "In this technical report, we target generating anthropomorphized personas for LLM-based characters in an online manner, including visual appearance, personality and tones, with only text descriptions. To achieve this, we first leverage the in-context learning capability of LLMs for personality generation by carefully designing a set of system prompts. We then propose two novel concepts: the mixture of voices (MoV) and the mixture of diffusers (MoD) for diverse voice and appearance generation. For MoV, we utilize the text-to-speech (TTS) algorithms with a variety of pre-defined tones and select the most matching one based on the user-provided text description automatically. For MoD, we combine the recent popular text-to-image generation techniques and talking head algorithms to streamline the process of generating talking objects. We termed the whole framework as ChatAnything. With it, users could be able to animate anything with any personas that are anthropomorphic using just a few text inputs. However, we have observed that the anthropomorphic objects produced by current generative models are often undetectable by pre-trained face landmark detectors, leading to failure of the face motion generation, even if these faces possess human-like appearances because those images are nearly seen during the training (e.g., OOD samples). To address this issue, we incorporate pixel-level guidance to infuse human face landmarks during the image generation phase. To benchmark these metrics, we have built an evaluation dataset. Based on it, we verify that the detection rate of the face landmark is significantly increased from 57.0% to 92.5% thus allowing automatic face animation based on generated speech content. The code and more results can be found at https://chatanything.github.io/.", "github_manual": "https://github.com/zhoudaquan/ChatAnything", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.07574", "title": "To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning", "authors": [ "Junke Wang", "Lingchen Meng", "Zejia Weng", "Bo He", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "https://github.com/X2FD/LVIS-INSTRUCT4V", "project_page": "", "abstract": "Existing visual instruction tuning methods typically prompt large language models with textual descriptions to generate instruction-following data. Despite the promising performance achieved, these descriptions are derived from image annotations, which are oftentimes coarse-grained. Furthermore, the instructions might even contradict the visual content without observing the entire visual context. To address this challenge, we introduce a fine-grained visual instruction dataset, LVIS-Instruct4V, which contains 220K visually aligned and context-aware instructions produced by prompting the powerful GPT-4V with images from LVIS. Through experimental validation and case studies, we demonstrate that high-quality visual instructional data could improve the performance of LLaVA-1.5, a state-of-the-art large multimodal model, across a wide spectrum of benchmarks by clear margins. Notably, by simply replacing the LLaVA-Instruct with our LVIS-Instruct4V, we achieve better results than LLaVA on most challenging LMM benchmarks, e.g., LLaVA^w (76.7 vs. 70.7) and MM-Vet (40.2 vs. 35.4). We release our data and model at https://github.com/X2FD/LVIS-INSTRUCT4V.", "github_manual": "", "github_regex": "https://github.com/X2FD/LVIS-INSTRUCT4V", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-14", "arxiv_id": "2311.06430", "title": "GOAT: GO to Any Thing", "authors": [ "Matthew Chang", "Theophile Gervet", "Mukul Khanna", "Sriram Yenamandra", "Dhruv Shah", "So Yeon Min", "Kavit Shah", "Chris Paxton", "Saurabh Gupta", "Dhruv Batra", "Roozbeh Mottaghi", "Jitendra Malik", "Devendra Singh Chaplot" ], "github": "", "project_page": "", "abstract": "In deployment scenarios such as homes and warehouses, mobile robots are expected to autonomously navigate for extended periods, seamlessly executing tasks articulated in terms that are intuitively understandable by human operators. We present GO To Any Thing (GOAT), a universal navigation system capable of tackling these requirements with three key features: a) Multimodal: it can tackle goals specified via category labels, target images, and language descriptions, b) Lifelong: it benefits from its past experience in the same environment, and c) Platform Agnostic: it can be quickly deployed on robots with different embodiments. GOAT is made possible through a modular system design and a continually augmented instance-aware semantic memory that keeps track of the appearance of objects from different viewpoints in addition to category-level semantics. This enables GOAT to distinguish between different instances of the same category to enable navigation to targets specified by images and language descriptions. In experimental comparisons spanning over 90 hours in 9 different homes consisting of 675 goals selected across 200+ different object instances, we find GOAT achieves an overall success rate of 83%, surpassing previous methods and ablations by 32% (absolute improvement). GOAT improves with experience in the environment, from a 60% success rate at the first goal to a 90% success after exploration. In addition, we demonstrate that GOAT can readily be applied to downstream tasks such as pick and place and social navigation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-15", "arxiv_id": "2311.07989", "title": "A Survey on Language Models for Code", "authors": [ "Ziyin Zhang", "Chaoyu Chen", "Bingchang Liu", "Cong Liao", "Zi Gong", "Hang Yu", "Jianguo Li", "Rui Wang" ], "github": "https://github.com/codefuse-ai/Awesome-Code-LLM", "project_page": "", "abstract": "In this work we systematically review the recent advancements in code processing with language models, covering 50+ models, 30+ evaluation tasks, and 500 related works. We break down code processing models into general language models represented by the GPT family and specialized models that are specifically pretrained on code, often with tailored objectives. We discuss the relations and differences between these models, and highlight the historical transition of code modeling from statistical models and RNNs to pretrained Transformers and LLMs, which is exactly the same course that had been taken by NLP. We also discuss code-specific features such as AST, CFG, and unit tests, along with their application in training code language models, and identify key challenges and potential future directions in this domain. We keep the survey open and updated on github repository at https://github.com/codefuse-ai/Awesome-Code-LLM.", "github_manual": "", "github_regex": "https://github.com/codefuse-ai/Awesome-Code-LLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-15", "arxiv_id": "2311.07689", "title": "MART: Improving LLM Safety with Multi-round Automatic Red-Teaming", "authors": [ "Suyu Ge", "Chunting Zhou", "Rui Hou", "Madian Khabsa", "Yi-Chia Wang", "Qifan Wang", "Jiawei Han", "Yuning Mao" ], "github": "", "project_page": "", "abstract": "Red-teaming is a common practice for mitigating unsafe behaviors in Large Language Models (LLMs), which involves thoroughly assessing LLMs to identify potential flaws and addressing them with responsible and accurate responses. While effective, manual red-teaming is costly, and existing automatic red-teaming typically discovers safety risks without addressing them. In this paper, we propose a Multi-round Automatic Red-Teaming (MART) method, which incorporates both automatic adversarial prompt writing and safe response generation, significantly increasing red-teaming scalability and the safety of the target LLM. Specifically, an adversarial LLM and a target LLM interplay with each other in an iterative manner, where the adversarial LLM aims to generate challenging prompts that elicit unsafe responses from the target LLM, while the target LLM is fine-tuned with safety aligned data on these adversarial prompts. In each round, the adversarial LLM crafts better attacks on the updated target LLM, while the target LLM also improves itself through safety fine-tuning. On adversarial prompt benchmarks, the violation rate of an LLM with limited safety alignment reduces up to 84.7% after 4 rounds of MART, achieving comparable performance to LLMs with extensive adversarial prompt writing. Notably, model helpfulness on non-adversarial prompts remains stable throughout iterations, indicating the target LLM maintains strong performance on instruction following.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-15", "arxiv_id": "2311.08401", "title": "Fine-tuning Language Models for Factuality", "authors": [ "Katherine Tian", "Eric Mitchell", "Huaxiu Yao", "Christopher D. Manning", "Chelsea Finn" ], "github": "", "project_page": "", "abstract": "The fluency and creativity of large pre-trained language models (LLMs) have led to their widespread use, sometimes even as a replacement for traditional search engines. Yet language models are prone to making convincing but factually inaccurate claims, often referred to as 'hallucinations.' These errors can inadvertently spread misinformation or harmfully perpetuate misconceptions. Further, manual fact-checking of model responses is a time-consuming process, making human factuality labels expensive to acquire. In this work, we fine-tune language models to be more factual, without human labeling and targeting more open-ended generation settings than past work. We leverage two key recent innovations in NLP to do so. First, several recent works have proposed methods for judging the factuality of open-ended text by measuring consistency with an external knowledge base or simply a large model's confidence scores. Second, the direct preference optimization algorithm enables straightforward fine-tuning of language models on objectives other than supervised imitation, using a preference ranking over possible model responses. We show that learning from automatically generated factuality preference rankings, generated either through existing retrieval systems or our novel retrieval-free approach, significantly improves the factuality (percent of generated claims that are correct) of Llama-2 on held-out topics compared with RLHF or decoding strategies targeted at factuality. At 7B scale, compared to Llama-2-chat, we observe 58% and 40% reduction in factual error rate when generating biographies and answering medical questions, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-15", "arxiv_id": "2311.08105", "title": "DiLoCo: Distributed Low-Communication Training of Language Models", "authors": [ "Arthur Douillard", "Qixuan Feng", "Andrei A. Rusu", "Rachita Chhaparia", "Yani Donchev", "Adhiguna Kuncoro", "Marc'Aurelio Ranzato", "Arthur Szlam", "Jiajun Shen" ], "github": "", "project_page": "", "abstract": "Large language models (LLM) have become a critical component in many applications of machine learning. However, standard approaches to training LLM require a large number of tightly interconnected accelerators, with devices exchanging gradients and other intermediate states at each optimization step. While it is difficult to build and maintain a single computing cluster hosting many accelerators, it might be easier to find several computing clusters each hosting a smaller number of devices. In this work, we propose a distributed optimization algorithm, Distributed Low-Communication (DiLoCo), that enables training of language models on islands of devices that are poorly connected. The approach is a variant of federated averaging, where the number of inner steps is large, the inner optimizer is AdamW, and the outer optimizer is Nesterov momentum. On the widely used C4 dataset, we show that DiLoCo on 8 workers performs as well as fully synchronous optimization while communicating 500 times less. DiLoCo exhibits great robustness to the data distribution of each worker. It is also robust to resources becoming unavailable over time, and vice versa, it can seamlessly leverage resources that become available during training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-15", "arxiv_id": "2311.07961", "title": "The ART of LLM Refinement: Ask, Refine, and Trust", "authors": [ "Kumar Shridhar", "Koustuv Sinha", "Andrew Cohen", "Tianlu Wang", "Ping Yu", "Ram Pasunuru", "Mrinmaya Sachan", "Jason Weston", "Asli Celikyilmaz" ], "github": "", "project_page": "", "abstract": "In recent years, Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations? A popular concept, referred to as self-refinement, postulates that LLMs can detect and correct the errors in their generations when asked to do so. However, recent empirical evidence points in the opposite direction, suggesting that LLMs often struggle to accurately identify errors when reasoning is involved. To address this, we propose a reasoning with refinement objective called ART: Ask, Refine, and Trust, which asks necessary questions to decide when an LLM should refine its output, and either affirm or withhold trust in its refinement by ranking the refinement and the initial prediction. On two multistep reasoning tasks of mathematical word problems (GSM8K) and question answering (StrategyQA), ART achieves a performance gain of +5 points over self-refinement baselines, while using a much smaller model as the decision maker. We also demonstrate the benefit of using smaller models to make refinement decisions as a cost-effective alternative to fine-tuning a larger model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-15", "arxiv_id": "2311.07911", "title": "Instruction-Following Evaluation for Large Language Models", "authors": [ "Jeffrey Zhou", "Tianjian Lu", "Swaroop Mishra", "Siddhartha Brahma", "Sujoy Basu", "Yi Luan", "Denny Zhou", "Le Hou" ], "github": "https://github.com/google-research/google-research/tree/master/instruction_following_eval", "project_page": "", "abstract": "One core capability of Large Language Models (LLMs) is to follow natural language instructions. However, the evaluation of such abilities is not standardized: Human evaluations are expensive, slow, and not objectively reproducible, while LLM-based auto-evaluation is potentially biased or limited by the ability of the evaluator LLM. To overcome these issues, we introduce Instruction-Following Eval (IFEval) for large language models. IFEval is a straightforward and easy-to-reproduce evaluation benchmark. It focuses on a set of \"verifiable instructions\" such as \"write in more than 400 words\" and \"mention the keyword of AI at least 3 times\". We identified 25 types of those verifiable instructions and constructed around 500 prompts, with each prompt containing one or more verifiable instructions. We show evaluation results of two widely available LLMs on the market. Our code and data can be found at https://github.com/google-research/google-research/tree/master/instruction_following_eval", "github_manual": "", "github_regex": "https://github.com/google-research/google-research/tree/master/instruction_following_eval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-15", "arxiv_id": "2311.07587", "title": "Frontier Language Models are not Robust to Adversarial Arithmetic, or \"What do I need to say so you agree 2+2=5?", "authors": [ "C. Daniel Freeman", "Laura Culp", "Aaron Parisi", "Maxwell L Bileschi", "Gamaleldin F Elsayed", "Alex Rizkowsky", "Isabelle Simpson", "Alex Alemi", "Azade Nova", "Ben Adlam", "Bernd Bohnet", "Gaurav Mishra", "Hanie Sedghi", "Igor Mordatch", "Izzeddin Gur", "Jaehoon Lee", "JD Co-Reyes", "Jeffrey Pennington", "Kelvin Xu", "Kevin Swersky", "Kshiteej Mahajan", "Lechao Xiao", "Rosanne Liu", "Simon Kornblith", "Noah Constant", "Peter J. Liu", "Roman Novak", "Sharad Vikram", "Yundi Qian", "Noah Fiedel", "Jascha Sohl-Dickstein" ], "github": "", "project_page": "", "abstract": "We introduce and study the problem of adversarial arithmetic, which provides a simple yet challenging testbed for language model alignment. This problem is comprised of arithmetic questions posed in natural language, with an arbitrary adversarial string inserted before the question is complete. Even in the simple setting of 1-digit addition problems, it is easy to find adversarial prompts that make all tested models (including PaLM2, GPT4, Claude2) misbehave, and even to steer models to a particular wrong answer. We additionally provide a simple algorithm for finding successful attacks by querying those same models, which we name \"prompt inversion rejection sampling\" (PIRS). We finally show that models can be partially hardened against these attacks via reinforcement learning and via agentic constitutional loops. However, we were not able to make a language model fully robust against adversarial arithmetic attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-15", "arxiv_id": "2311.07885", "title": "One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion", "authors": [ "Minghua Liu", "Ruoxi Shi", "Linghao Chen", "Zhuoyang Zhang", "Chao Xu", "Xinyue Wei", "Hansheng Chen", "Chong Zeng", "Jiayuan Gu", "Hao Su" ], "github": "https://github.com/SUDO-AI-3D/One2345plus", "project_page": "", "abstract": "Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts. However, most existing models fall short in simultaneously providing rapid generation speeds and high fidelity to input images - two features essential for practical applications. In this paper, we present One-2-3-45++, an innovative method that transforms a single image into a detailed 3D textured mesh in approximately one minute. Our approach aims to fully harness the extensive knowledge embedded in 2D diffusion models and priors from valuable yet limited 3D data. This is achieved by initially finetuning a 2D diffusion model for consistent multi-view image generation, followed by elevating these images to 3D with the aid of multi-view conditioned 3D native diffusion models. Extensive experimental evaluations demonstrate that our method can produce high-quality, diverse 3D assets that closely mirror the original input image. Our project webpage: https://sudo-ai-3d.github.io/One2345plus_page.", "github_manual": "https://github.com/SUDO-AI-3D/One2345plus", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-15", "arxiv_id": "2311.08263", "title": "Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster", "authors": [ "Hongxuan Zhang", "Zhining Liu", "Jiaqi Zheng", "Chenyi Zhuang", "Jinjie Gu", "Guihai Chen" ], "github": "", "project_page": "", "abstract": "In this work, we propose FastCoT, a model-agnostic framework based on parallel decoding without any further training of an auxiliary model or modification to the LLM itself. FastCoT uses a size-varying context window whose size changes with position to conduct parallel decoding and auto-regressive decoding simultaneously, thus fully utilizing GPU computation resources. In FastCoT, the parallel decoding part provides the LLM with a quick glance of the future composed of approximate tokens, which could lead to faster answers compared to regular autoregressive decoding used by causal transformers. We also provide an implementation of parallel decoding within LLM, which supports KV-cache generation and batch processing. Through extensive experiments, we demonstrate that FastCoT saves inference time by nearly 20% with only a negligible performance drop compared to the regular approach. Additionally, we show that the context window size exhibits considerable robustness for different tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-15", "arxiv_id": "2311.07590", "title": "Technical Report: Large Language Models can Strategically Deceive their Users when Put Under Pressure", "authors": [ "J\u00e9r\u00e9my Scheurer", "Mikita Balesni", "Marius Hobbhahn" ], "github": "https://github.com/apolloresearch/insider-trading", "project_page": "", "abstract": "We demonstrate a situation in which Large Language Models, trained to be helpful, harmless, and honest, can display misaligned behavior and strategically deceive their users about this behavior without being instructed to do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated environment, where it assumes the role of an autonomous stock trading agent. Within this environment, the model obtains an insider tip about a lucrative stock trade and acts upon it despite knowing that insider trading is disapproved of by company management. When reporting to its manager, the model consistently hides the genuine reasons behind its trading decision. We perform a brief investigation of how this behavior varies under changes to the setting, such as removing model access to a reasoning scratchpad, attempting to prevent the misaligned behavior by changing system instructions, changing the amount of pressure the model is under, varying the perceived risk of getting caught, and making other simple changes to the environment. To our knowledge, this is the first demonstration of Large Language Models trained to be helpful, harmless, and honest, strategically deceiving their users in a realistic situation without direct instructions or training for deception.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apolloresearch/insider-trading", "github_api_added_by": "auto" }, { "date": "2023-11-15", "arxiv_id": "2311.08403", "title": "Instant3D: Instant Text-to-3D Generation", "authors": [ "Ming Li", "Pan Zhou", "Jia-Wei Liu", "Jussi Keppo", "Min Lin", "Shuicheng Yan", "Xiangyu Xu" ], "github": "https://github.com/ming1993li/Instant3DCodes", "project_page": "", "abstract": "Text-to-3D generation, which aims to synthesize vivid 3D objects from text prompts, has attracted much attention from the computer vision community. While several existing works have achieved impressive results for this task, they mainly rely on a time-consuming optimization paradigm. Specifically, these methods optimize a neural field from scratch for each text prompt, taking approximately one hour or more to generate one object. This heavy and repetitive training cost impedes their practical deployment. In this paper, we propose a novel framework for fast text-to-3D generation, dubbed Instant3D. Once trained, Instant3D is able to create a 3D object for an unseen text prompt in less than one second with a single run of a feedforward network. We achieve this remarkable speed by devising a new network that directly constructs a 3D triplane from a text prompt. The core innovation of our Instant3D lies in our exploration of strategies to effectively inject text conditions into the network. Furthermore, we propose a simple yet effective activation function, the scaled-sigmoid, to replace the original sigmoid function, which speeds up the training convergence by more than ten times. Finally, to address the Janus (multi-head) problem in 3D generation, we propose an adaptive Perp-Neg algorithm that can dynamically adjust its concept negation scales according to the severity of the Janus problem during training, effectively reducing the multi-head effect. Extensive experiments on a wide variety of benchmark datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods both qualitatively and quantitatively, while achieving significantly better efficiency. The project page is at https://ming1993li.github.io/Instant3DProj.", "github_manual": "https://github.com/ming1993li/Instant3DCodes", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-15", "arxiv_id": "2311.07919", "title": "Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models", "authors": [ "Yunfei Chu", "Jin Xu", "Xiaohuan Zhou", "Qian Yang", "Shiliang Zhang", "Zhijie Yan", "Chang Zhou", "Jingren Zhou" ], "github": "https://github.com/QwenLM/Qwen-Audio", "project_page": "", "abstract": "Recently, instruction-following audio-language models have received broad attention for audio interaction with humans. However, the absence of pre-trained audio models capable of handling diverse audio types and tasks has hindered progress in this field. Consequently, most existing works have only been able to support a limited range of interaction capabilities. In this paper, we develop the Qwen-Audio model and address this limitation by scaling up audio-language pre-training to cover over 30 tasks and various audio types, such as human speech, natural sounds, music, and songs, to facilitate universal audio understanding abilities. However, directly co-training all tasks and datasets can lead to interference issues, as the textual labels associated with different datasets exhibit considerable variations due to differences in task focus, language, granularity of annotation, and text structure. To overcome the one-to-many interference, we carefully design a multi-task training framework by conditioning on a sequence of hierarchical tags to the decoder for encouraging knowledge sharing and avoiding interference through shared and specified tags respectively. Remarkably, Qwen-Audio achieves impressive performance across diverse benchmark tasks without requiring any task-specific fine-tuning, surpassing its counterparts. Building upon the capabilities of Qwen-Audio, we further develop Qwen-Audio-Chat, which allows for input from various audios and text inputs, enabling multi-turn dialogues and supporting various audio-central scenarios.", "github_manual": "https://github.com/QwenLM/Qwen-Audio", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.09213", "title": "GRIM: GRaph-based Interactive narrative visualization for gaMes", "authors": [ "Jorge Leandro", "Sudha Rao", "Michael Xu", "Weijia Xu", "Nebosja Jojic", "Chris Brockett", "Bill Dolan" ], "github": "", "project_page": "", "abstract": "Dialogue-based Role Playing Games (RPGs) require powerful storytelling. The narratives of these may take years to write and typically involve a large creative team. In this work, we demonstrate the potential of large generative text models to assist this process. GRIM, a prototype GRaph-based Interactive narrative visualization system for gaMes, generates a rich narrative graph with branching storylines that match a high-level narrative description and constraints provided by the designer. Game designers can interactively edit the graph by automatically generating new sub-graphs that fit the edits within the original narrative and constraints. We illustrate the use of GRIM in conjunction with GPT-4, generating branching narratives for four well-known stories with different contextual constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.08581", "title": "Drivable 3D Gaussian Avatars", "authors": [ "Wojciech Zielonka", "Timur Bagautdinov", "Shunsuke Saito", "Michael Zollh\u00f6fer", "Justus Thies", "Javier Romero" ], "github": "", "project_page": "", "abstract": "We present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable model for human bodies rendered with Gaussian splats. Current photorealistic drivable avatars require either accurate 3D registrations during training, dense input images during testing, or both. The ones based on neural radiance fields also tend to be prohibitively slow for telepresence applications. This work uses the recently presented 3D Gaussian Splatting (3DGS) technique to render realistic humans at real-time framerates, using dense calibrated multi-view videos as input. To deform those primitives, we depart from the commonly used point deformation method of linear blend skinning (LBS) and use a classic volumetric deformation method: cage deformations. Given their smaller size, we drive these deformations with joint angles and keypoints, which are more suitable for communication applications. Our experiments on nine subjects with varied body shapes, clothes, and motions obtain higher-quality results than state-of-the-art methods when using the same training and test data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.09221", "title": "Single-Image 3D Human Digitization with Shape-Guided Diffusion", "authors": [ "Badour AlBahar", "Shunsuke Saito", "Hung-Yu Tseng", "Changil Kim", "Johannes Kopf", "Jia-Bin Huang" ], "github": "", "project_page": "", "abstract": "We present an approach to generate a 360-degree view of a person with a consistent, high-resolution appearance from a single input image. NeRF and its variants typically require videos or images from different viewpoints. Most existing approaches taking monocular input either rely on ground-truth 3D scans for supervision or lack 3D consistency. While recent 3D generative models show promise of 3D consistent human digitization, these approaches do not generalize well to diverse clothing appearances, and the results lack photorealism. Unlike existing work, we utilize high-capacity 2D diffusion models pretrained for general image synthesis tasks as an appearance prior of clothed humans. To achieve better 3D consistency while retaining the input identity, we progressively synthesize multiple views of the human in the input image by inpainting missing regions with shape-guided diffusion conditioned on silhouette and surface normal. We then fuse these synthesized multi-view images via inverse rendering to obtain a fully textured high-resolution 3D mesh of the given person. Experiments show that our approach outperforms prior methods and achieves photorealistic 360-degree synthesis of a wide range of clothed humans with complex textures from a single image.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.09217", "title": "DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model", "authors": [ "Yinghao Xu", "Hao Tan", "Fujun Luan", "Sai Bi", "Peng Wang", "Jiahao Li", "Zifan Shi", "Kalyan Sunkavalli", "Gordon Wetzstein", "Zexiang Xu", "Kai Zhang" ], "github": "", "project_page": "", "abstract": "We propose DMV3D, a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion. Our reconstruction model incorporates a triplane NeRF representation and can denoise noisy multi-view images via NeRF reconstruction and rendering, achieving single-stage 3D generation in sim30s on single A100 GPU. We train DMV3D on large-scale multi-view image datasets of highly diverse objects using only image reconstruction losses, without accessing 3D assets. We demonstrate state-of-the-art results for the single-image reconstruction problem where probabilistic modeling of unseen object parts is required for generating diverse reconstructions with sharp textures. We also show high-quality text-to-3D generation results outperforming previous 3D diffusion models. Our project website is at: https://justimyhxu.github.io/projects/dmv3d/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.08667", "title": "EDMSound: Spectrogram Based Diffusion Models for Efficient and High-Quality Audio Synthesis", "authors": [ "Ge Zhu", "Yutong Wen", "Marc-Andr\u00e9 Carbonneau", "Zhiyao Duan" ], "github": "", "project_page": "", "abstract": "Audio diffusion models can synthesize a wide variety of sounds. Existing models often operate on the latent domain with cascaded phase recovery modules to reconstruct waveform. This poses challenges when generating high-fidelity audio. In this paper, we propose EDMSound, a diffusion-based generative model in spectrogram domain under the framework of elucidated diffusion models (EDM). Combining with efficient deterministic sampler, we achieved similar Fr\\'echet audio distance (FAD) score as top-ranked baseline with only 10 steps and reached state-of-the-art performance with 50 steps on the DCASE2023 foley sound generation benchmark. We also revealed a potential concern regarding diffusion based audio generation models that they tend to generate samples with high perceptual similarity to the data from training data. Project page: https://agentcooper2002.github.io/EDMSound/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.08692", "title": "Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models", "authors": [ "Keming Lu", "Hongyi Yuan", "Runji Lin", "Junyang Lin", "Zheng Yuan", "Chang Zhou", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "The complementary potential of Large Language Models (LLM) assumes off-the-shelf LLMs have heterogeneous expertise in a wide range of domains and tasks so that an ensemble of LLMs can achieve consistently better performance. Existing ensemble methods for LLMs mainly focus on reward model ranking of outputs, leading to significant computation overhead. To combat this issue, we revisit the complementary potential of LLMs and further elaborate it by mining latent expertise with off-the-shelf reward models. We propose Zooter, a reward-guided routing method distilling rewards on training queries to train a routing function, which can precisely distribute each query to the LLM with expertise about it. We also integrate a tag-based label enhancement to mitigate noise from uncertainty when using rewards as silver supervision. Zooter shows computation efficiency in inference as it introduces only a minor computation overhead of a routing function compared with reward model ranking methods. We evaluate Zooter on a comprehensive benchmark collection with 26 subsets on different domains and tasks. Zooter outperforms the best single model on average and ranks first on 44% of tasks, even surpassing multiple reward model ranking methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.08469", "title": "UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations", "authors": [ "Wenting Zhao", "Justin T Chiu", "Jena D. Hwang", "Faeze Brahman", "Jack Hessel", "Sanjiban Choudhury", "Yejin Choi", "Xiang Lorraine Li", "Alane Suhr" ], "github": "", "project_page": "", "abstract": "Language technologies that accurately model the dynamics of events must perform commonsense reasoning. Existing work evaluating commonsense reasoning focuses on making inferences about common, everyday situations. To instead investigate the ability to model unusual, unexpected, and unlikely situations, we explore the task of uncommonsense abductive reasoning. Given a piece of context with an unexpected outcome, this task requires reasoning abductively to generate a natural language explanation that makes the unexpected outcome more likely in the context. To this end, we curate and release a new English language corpus called UNcommonsense. We characterize the differences between the performance of human explainers and the best performing large language models, finding that model-enhanced human-written explanations achieve the highest quality by trading off between specificity and diversity. Finally, we experiment with several online imitation learning algorithms to train open and accessible language models on this task. When compared with the vanilla supervised fine-tuning approach, these methods consistently reduce lose rates on both common and uncommonsense abductive reasoning judged by human evaluators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.09204", "title": "Fusion-Eval: Integrating Evaluators with LLMs", "authors": [ "Lei Shu", "Nevan Wichers", "Liangchen Luo", "Yun Zhu", "Yinxiao Liu", "Jindong Chen", "Lei Meng" ], "github": "", "project_page": "", "abstract": "Evaluating Large Language Models (LLMs) is a complex task, especially considering the intricacies of natural language understanding and the expectations for high-level reasoning. Traditional evaluations typically lean on human-based, model-based, or automatic-metrics-based paradigms, each with its own advantages and shortcomings. We introduce \"Fusion-Eval\", a system that employs LLMs not solely for direct evaluations, but to skillfully integrate insights from diverse evaluators. This gives Fusion-Eval flexibility, enabling it to work effectively across diverse tasks and make optimal use of multiple references. In testing on the SummEval dataset, Fusion-Eval achieved a Spearman correlation of 0.96, outperforming other evaluators. The success of Fusion-Eval underscores the potential of LLMs to produce evaluations that closely align human perspectives, setting a new standard in the field of LLM evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.09180", "title": "PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers", "authors": [ "Sheshera Mysore", "Zhuoran Lu", "Mengting Wan", "Longqi Yang", "Steve Menezes", "Tina Baghaee", "Emmanuel Barajas Gonzalez", "Jennifer Neville", "Tara Safavi" ], "github": "", "project_page": "", "abstract": "Powerful large language models have facilitated the development of writing assistants that promise to significantly improve the quality and efficiency of composition and communication. However, a barrier to effective assistance is the lack of personalization in LLM outputs to the author's communication style and specialized knowledge. In this paper, we address this challenge by proposing PEARL, a retrieval-augmented LLM writing assistant personalized with a generation-calibrated retriever. Our retriever is trained to select historic user-authored documents for prompt augmentation, such that they are likely to best personalize LLM generations for a user request. We propose two key novelties for training our retriever: 1) A training data selection method that identifies user requests likely to benefit from personalization and documents that provide that benefit; and 2) A scale-calibrating KL-divergence objective that ensures that our retriever closely tracks the benefit of a document for personalized generation. We demonstrate the effectiveness of PEARL in generating personalized workplace social media posts and Reddit comments. Finally, we showcase the potential of a generation-calibrated retriever to double as a performance predictor and further improve low-quality generations via LLM chaining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.09179", "title": "SiRA: Sparse Mixture of Low Rank Adaptation", "authors": [ "Yun Zhu", "Nevan Wichers", "Chu-Cheng Lin", "Xinyi Wang", "Tianlong Chen", "Lei Shu", "Han Lu", "Canoee Liu", "Liangchen Luo", "Jindong Chen", "Lei Meng" ], "github": "", "project_page": "", "abstract": "Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downstream tasks. Most previous works considers adding the dense trainable parameters, where all parameters are used to adapt certain task. We found this less effective empirically using the example of LoRA that introducing more trainable parameters does not help. Motivated by this we investigate the importance of leveraging \"sparse\" computation and propose SiRA: sparse mixture of low rank adaption. SiRA leverages the Sparse Mixture of Expert(SMoE) to boost the performance of LoRA. Specifically it enforces the top k experts routing with a capacity limit restricting the maximum number of tokens each expert can process. We propose a novel and simple expert dropout on top of gating network to reduce the over-fitting issue. Through extensive experiments, we verify SiRA performs better than LoRA and other mixture of expert approaches across different single tasks and multitask settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.08877", "title": "Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation", "authors": [ "Vaishnavi Shrivastava", "Percy Liang", "Ananya Kumar" ], "github": "", "project_page": "", "abstract": "To maintain user trust, large language models (LLMs) should signal low confidence on examples where they are incorrect, instead of misleading the user. The standard approach of estimating confidence is to use the softmax probabilities of these models, but as of November 2023, state-of-the-art LLMs such as GPT-4 and Claude-v1.3 do not provide access to these probabilities. We first study eliciting confidence linguistically -- asking an LLM for its confidence in its answer -- which performs reasonably (80.5% AUC on GPT-4 averaged across 12 question-answering datasets -- 7% above a random baseline) but leaves room for improvement. We then explore using a surrogate confidence model -- using a model where we do have probabilities to evaluate the original model's confidence in a given question. Surprisingly, even though these probabilities come from a different and often weaker model, this method leads to higher AUC than linguistic confidences on 9 out of 12 datasets. Our best method composing linguistic confidences and surrogate model probabilities gives state-of-the-art confidence estimates on all 12 datasets (84.6% average AUC on GPT-4).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.08734", "title": "Thread of Thought Unraveling Chaotic Contexts", "authors": [ "Yucheng Zhou", "Xiubo Geng", "Tao Shen", "Chongyang Tao", "Guodong Long", "Jian-Guang Lou", "Jianbing Shen" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have ushered in a transformative era in the field of natural language processing, excelling in tasks related to text comprehension and generation. Nevertheless, they encounter difficulties when confronted with chaotic contexts (e.g., distractors rather than long irrelevant context), leading to the inadvertent omission of certain details within the chaotic context. In response to these challenges, we introduce the \"Thread of Thought\" (ThoT) strategy, which draws inspiration from human cognitive processes. ThoT systematically segments and analyzes extended contexts while adeptly selecting pertinent information. This strategy serves as a versatile \"plug-and-play\" module, seamlessly integrating with various LLMs and prompting techniques. In the experiments, we utilize the PopQA and EntityQ datasets, as well as a Multi-Turn Conversation Response dataset (MTCR) we collected, to illustrate that ThoT significantly improves reasoning performance compared to other prompting techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-16", "arxiv_id": "2311.08552", "title": "UT5: Pretraining Non autoregressive T5 with unrolled denoising", "authors": [ "Mahmoud G. Salem", "Jiayu Ye", "Chu-Cheng Lin", "Frederick Liu" ], "github": "", "project_page": "", "abstract": "Recent advances in Transformer-based Large Language Models have made great strides in natural language generation. However, to decode K tokens, an autoregressive model needs K sequential forward passes, which may be a performance bottleneck for large language models. Many non-autoregressive (NAR) research are aiming to address this sequentiality bottleneck, albeit many have focused on a dedicated architecture in supervised benchmarks. In this work, we studied unsupervised pretraining for non auto-regressive T5 models via unrolled denoising and shown its SoTA results in downstream generation tasks such as SQuAD question generation and XSum.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-17", "arxiv_id": "2311.09257", "title": "UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs", "authors": [ "Yanwu Xu", "Yang Zhao", "Zhisheng Xiao", "Tingbo Hou" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion models have demonstrated remarkable capabilities in transforming textual prompts into coherent images, yet the computational cost of their inference remains a persistent challenge. To address this issue, we present UFOGen, a novel generative model designed for ultra-fast, one-step text-to-image synthesis. In contrast to conventional approaches that focus on improving samplers or employing distillation techniques for diffusion models, UFOGen adopts a hybrid methodology, integrating diffusion models with a GAN objective. Leveraging a newly introduced diffusion-GAN objective and initialization with pre-trained diffusion models, UFOGen excels in efficiently generating high-quality images conditioned on textual descriptions in a single step. Beyond traditional text-to-image generation, UFOGen showcases versatility in applications. Notably, UFOGen stands among the pioneering models enabling one-step text-to-image generation and diverse downstream tasks, presenting a significant advancement in the landscape of efficient generative models. \\blfootnote{*Work done as a student researcher of Google, dagger indicates equal contribution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-17", "arxiv_id": "2311.09578", "title": "Tied-Lora: Enhacing parameter efficiency of LoRA with weight tying", "authors": [ "Adithya Renduchintala", "Tugrul Konuk", "Oleksii Kuchaiev" ], "github": "https://github.com/NVIDIA/NeMo/tree/adithyare/vera", "project_page": "", "abstract": "We propose Tied-LoRA, a simple paradigm utilizes weight tying and selective training to further increase parameter efficiency of the Low-rank adaptation (LoRA) method. Our investigations include all feasible combinations parameter training/freezing in conjunction with weight tying to identify the optimal balance between performance and the number of trainable parameters. Through experiments covering a variety of tasks and two base language models, we provide analysis revealing trade-offs between efficiency and performance. Our experiments uncovered a particular Tied-LoRA configuration that stands out by demonstrating comparable performance across several tasks while employing only 13~\\% percent of parameters utilized by the standard LoRA method.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA/NeMo/tree/adithyare/vera", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-17", "arxiv_id": "2311.09277", "title": "Contrastive Chain-of-Thought Prompting", "authors": [ "Yew Ken Chia", "Guizhen Chen", "Luu Anh Tuan", "Soujanya Poria", "Lidong Bing" ], "github": "https://github.com/damo-nlp-sg/contrastive-cot", "project_page": "", "abstract": "Despite the success of chain of thought in enhancing language model reasoning, the underlying process remains less well understood. Although logically sound reasoning appears inherently crucial for chain of thought, prior studies surprisingly reveal minimal impact when using invalid demonstrations instead. Furthermore, the conventional chain of thought does not inform language models on what mistakes to avoid, which potentially leads to more errors. Hence, inspired by how humans can learn from both positive and negative examples, we propose contrastive chain of thought to enhance language model reasoning. Compared to the conventional chain of thought, our approach provides both valid and invalid reasoning demonstrations, to guide the model to reason step-by-step while reducing reasoning mistakes. To improve generalization, we introduce an automatic method to construct contrastive demonstrations. Our experiments on reasoning benchmarks demonstrate that contrastive chain of thought can serve as a general enhancement of chain-of-thought prompting.", "github_manual": "https://github.com/damo-nlp-sg/contrastive-cot", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-17", "arxiv_id": "2311.09835", "title": "ML-Bench: Large Language Models Leverage Open-source Libraries for Machine Learning Tasks", "authors": [ "Yuliang Liu", "Xiangru Tang", "Zefan Cai", "Junjie Lu", "Yichi Zhang", "Yanjun Shao", "Zexuan Deng", "Helan Hu", "Zengxian Yang", "Kaikai An", "Ruijun Huang", "Shuzheng Si", "Sheng Chen", "Haozhe Zhao", "Zhengliang Li", "Liang Chen", "Yiming Zong", "Yan Wang", "Tianyu Liu", "Zhiwei Jiang", "Baobao Chang", "Yujia Qin", "Wangchunshu Zhou", "Yilun Zhao", "Arman Cohan", "Mark Gerstein" ], "github": "https://github.com/gersteinlab/ml-bench", "project_page": "", "abstract": "Large language models have shown promising performance in code generation benchmarks. However, a considerable divide exists between these benchmark achievements and their practical applicability, primarily attributed to real-world programming's reliance on pre-existing libraries. Instead of evaluating LLMs to code from scratch, this work aims to propose a new evaluation setup where LLMs use open-source libraries to finish machine learning tasks. Therefore, we propose ML-Bench, an expansive benchmark developed to assess the effectiveness of LLMs in leveraging existing functions in open-source libraries. Consisting of 10044 samples spanning 130 tasks over 14 notable machine learning GitHub repositories. In this setting, given a specific machine learning task instruction and the accompanying README in a codebase, an LLM is tasked to generate code to accomplish the task. This necessitates the comprehension of long and language-code interleaved documents, as well as the understanding of complex cross-file code structures, introducing new challenges. Notably, while GPT-4 exhibits remarkable improvement over other LLMs, it manages to accomplish only 39.73\\% of the tasks, leaving a huge space for improvement. We address these challenges by proposing ML-Agent, designed to effectively navigate the codebase, locate documentation, retrieve code, and generate executable code. Empirical results demonstrate that ML-Agent, built upon GPT-4, results in further improvements. Code, data, and models are available at https://ml-bench.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gersteinlab/ml-bench", "github_api_added_by": "auto" }, { "date": "2023-11-17", "arxiv_id": "2311.10093", "title": "The Chosen One: Consistent Characters in Text-to-Image Diffusion Models", "authors": [ "Omri Avrahami", "Amir Hertz", "Yael Vinker", "Moab Arar", "Shlomi Fruchter", "Ohad Fried", "Daniel Cohen-Or", "Dani Lischinski" ], "github": "", "project_page": "", "abstract": "Recent advances in text-to-image generation models have unlocked vast potential for visual creativity. However, these models struggle with generation of consistent characters, a crucial aspect for numerous real-world applications such as story visualization, game development asset design, advertising, and more. Current methods typically rely on multiple pre-existing images of the target character or involve labor-intensive manual processes. In this work, we propose a fully automated solution for consistent character generation, with the sole input being a text prompt. We introduce an iterative procedure that, at each stage, identifies a coherent set of images sharing a similar identity and extracts a more consistent identity from this set. Our quantitative analysis demonstrates that our method strikes a better balance between prompt alignment and identity consistency compared to the baseline methods, and these findings are reinforced by a user study. To conclude, we showcase several practical applications of our approach. Project page is available at https://omriavrahami.com/the-chosen-one", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-17", "arxiv_id": "2311.10091", "title": "Adaptive Shells for Efficient Neural Radiance Field Rendering", "authors": [ "Zian Wang", "Tianchang Shen", "Merlin Nimier-David", "Nicholas Sharp", "Jun Gao", "Alexander Keller", "Sanja Fidler", "Thomas M\u00fcller", "Zan Gojcic" ], "github": "", "project_page": "", "abstract": "Neural radiance fields achieve unprecedented quality for novel view synthesis, but their volumetric formulation remains expensive, requiring a huge number of samples to render high-resolution images. Volumetric encodings are essential to represent fuzzy geometry such as foliage and hair, and they are well-suited for stochastic optimization. Yet, many scenes ultimately consist largely of solid surfaces which can be accurately rendered by a single sample per pixel. Based on this insight, we propose a neural radiance formulation that smoothly transitions between volumetric- and surface-based rendering, greatly accelerating rendering speed and even improving visual fidelity. Our method constructs an explicit mesh envelope which spatially bounds a neural volumetric representation. In solid regions, the envelope nearly converges to a surface and can often be rendered with a single sample. To this end, we generalize the NeuS formulation with a learned spatially-varying kernel size which encodes the spread of the density, fitting a wide kernel to volume-like regions and a tight kernel to surface-like regions. We then extract an explicit mesh of a narrow band around the surface, with width determined by the kernel size, and fine-tune the radiance field within this band. At inference time, we cast rays against the mesh and evaluate the radiance field only within the enclosed region, greatly reducing the number of samples required. Experiments show that our approach enables efficient rendering at very high fidelity. We also demonstrate that the extracted envelope enables downstream applications such as animation and simulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-17", "arxiv_id": "2311.10090", "title": "JaxMARL: Multi-Agent RL Environments in JAX", "authors": [ "Alexander Rutherford", "Benjamin Ellis", "Matteo Gallici", "Jonathan Cook", "Andrei Lupu", "Gardar Ingvarsson", "Timon Willi", "Akbir Khan", "Christian Schroeder de Witt", "Alexandra Souly", "Saptarashmi Bandyopadhyay", "Mikayel Samvelyan", "Minqi Jiang", "Robert Tjarko Lange", "Shimon Whiteson", "Bruno Lacerda", "Nick Hawes", "Tim Rocktaschel", "Chris Lu", "Jakob Nicolaus Foerster" ], "github": "https://github.com/flairox/jaxmarl", "project_page": "", "abstract": "Benchmarks play an important role in the development of machine learning algorithms. For example, research in reinforcement learning (RL) has been heavily influenced by available environments and benchmarks. However, RL environments are traditionally run on the CPU, limiting their scalability with typical academic compute. Recent advancements in JAX have enabled the wider use of hardware acceleration to overcome these computational hurdles, enabling massively parallel RL training pipelines and environments. This is particularly useful for multi-agent reinforcement learning (MARL) research. First of all, multiple agents must be considered at each environment step, adding computational burden, and secondly, the sample complexity is increased due to non-stationarity, decentralised partial observability, or other MARL challenges. In this paper, we present JaxMARL, the first open-source code base that combines ease-of-use with GPU enabled efficiency, and supports a large number of commonly used MARL environments as well as popular baseline algorithms. When considering wall clock time, our experiments show that per-run our JAX-based training pipeline is up to 12500x faster than existing approaches. This enables efficient and thorough evaluations, with the potential to alleviate the evaluation crisis of the field. We also introduce and benchmark SMAX, a vectorised, simplified version of the popular StarCraft Multi-Agent Challenge, which removes the need to run the StarCraft II game engine. This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL. We provide code at https://github.com/flairox/jaxmarl.", "github_manual": "", "github_regex": "https://github.com/flairox/jaxmarl", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-17", "arxiv_id": "2311.09227", "title": "Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives", "authors": [ "Elizabeth Seger", "Noemi Dreksler", "Richard Moulange", "Emily Dardaman", "Jonas Schuett", "K. Wei", "Christoph Winter", "Mackenzie Arnold", "Se\u00e1n \u00d3 h\u00c9igeartaigh", "Anton Korinek", "Markus Anderljung", "Ben Bucknall", "Alan Chan", "Eoghan Stafford", "Leonie Koessler", "Aviv Ovadya", "Ben Garfinkel", "Emma Bluemke", "Michael Aird", "Patrick Levermore", "Julian Hazell", "Abhishek Gupta" ], "github": "", "project_page": "", "abstract": "Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI models should be shared. Open-sourcing in AI typically refers to making model architecture and weights freely and publicly accessible for anyone to modify, study, build on, and use. This offers advantages such as enabling external oversight, accelerating progress, and decentralizing control over AI development and use. However, it also presents a growing potential for misuse and unintended consequences. This paper offers an examination of the risks and benefits of open-sourcing highly capable foundation models. While open-sourcing has historically provided substantial net benefits for most software and AI development processes, we argue that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits. In such a case, highly capable foundation models should not be open-sourced, at least not initially. Alternative strategies, including non-open-source model sharing options, are explored. The paper concludes with recommendations for developers, standard-setting bodies, and governments for establishing safe and responsible model sharing practices and preserving open-source benefits where safe.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-20", "arxiv_id": "2311.10702", "title": "Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2", "authors": [ "Hamish Ivison", "Yizhong Wang", "Valentina Pyatkin", "Nathan Lambert", "Matthew Peters", "Pradeep Dasigi", "Joel Jang", "David Wadden", "Noah A. Smith", "Iz Beltagy", "Hannaneh Hajishirzi" ], "github": "https://github.com/allenai/open-instruct", "project_page": "", "abstract": "Since the release of T\\\"ULU [Wang et al., 2023b], open resources for instruction tuning have developed quickly, from better base models to new finetuning techniques. We test and incorporate a number of these advances into T\\\"ULU, resulting in T\\\"ULU 2, a suite of improved T\\\"ULU models for advancing the understanding and best practices of adapting pretrained language models to downstream tasks and user preferences. Concretely, we release: (1) T\\\"ULU-V2-mix, an improved collection of high-quality instruction datasets; (2) T\\\"ULU 2, LLAMA-2 models finetuned on the V2 mixture; (3) T\\\"ULU 2+DPO, T\\\"ULU 2 models trained with direct preference optimization (DPO), including the largest DPO-trained model to date (T\\\"ULU 2+DPO 70B); (4) CODE T\\\"ULU 2, CODE LLAMA models finetuned on our V2 mix that outperform CODE LLAMA and its instruction-tuned variant, CODE LLAMA-Instruct. Our evaluation from multiple perspectives shows that the T\\\"ULU 2 suite achieves state-of-the-art performance among open models and matches or exceeds the performance of GPT-3.5-turbo-0301 on several benchmarks. We release all the checkpoints, data, training and evaluation code to facilitate future open efforts on adapting large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/allenai/open-instruct", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-20", "arxiv_id": "2311.10708", "title": "SelfEval: Leveraging the discriminative nature of generative models for evaluation", "authors": [ "Sai Saketh Rambhatla", "Ishan Misra" ], "github": "", "project_page": "", "abstract": "In this work, we show that text-to-image generative models can be 'inverted' to assess their own text-image understanding capabilities in a completely automated manner. Our method, called SelfEval, uses the generative model to compute the likelihood of real images given text prompts, making the generative model directly applicable to discriminative tasks. Using SelfEval, we repurpose standard datasets created for evaluating multimodal text-image discriminative models to evaluate generative models in a fine-grained manner: assessing their performance on attribute binding, color recognition, counting, shape recognition, spatial understanding. To the best of our knowledge SelfEval is the first automated metric to show a high degree of agreement for measuring text-faithfulness with the gold-standard human evaluations across multiple models and benchmarks. Moreover, SelfEval enables us to evaluate generative models on challenging tasks such as Winoground image-score where they demonstrate competitive performance to discriminative models. We also show severe drawbacks of standard automated metrics such as CLIP-score to measure text faithfulness on benchmarks such as DrawBench, and how SelfEval sidesteps these issues. We hope SelfEval enables easy and reliable automated evaluation for diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-20", "arxiv_id": "2311.10126", "title": "I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs Quantization", "authors": [ "Yunshan Zhong", "Jiawei Hu", "Mingbao Lin", "Mengzhao Chen", "Rongrong Ji" ], "github": "https://github.com/zysxmu/ias-vit", "project_page": "", "abstract": "Albeit the scalable performance of vision transformers (ViTs), the dense computational costs (training & inference) undermine their position in industrial applications. Post-training quantization (PTQ), tuning ViTs with a tiny dataset and running in a low-bit format, well addresses the cost issue but unluckily bears more performance drops in lower-bit cases. In this paper, we introduce I&S-ViT, a novel method that regulates the PTQ of ViTs in an inclusive and stable fashion. I&S-ViT first identifies two issues in the PTQ of ViTs: (1) Quantization inefficiency in the prevalent log2 quantizer for post-Softmax activations; (2) Rugged and magnified loss landscape in coarse-grained quantization granularity for post-LayerNorm activations. Then, I&S-ViT addresses these issues by introducing: (1) A novel shift-uniform-log2 quantizer (SULQ) that incorporates a shift mechanism followed by uniform quantization to achieve both an inclusive domain representation and accurate distribution approximation; (2) A three-stage smooth optimization strategy (SOS) that amalgamates the strengths of channel-wise and layer-wise quantization to enable stable learning. Comprehensive evaluations across diverse vision tasks validate I&S-ViT' superiority over existing PTQ of ViTs methods, particularly in low-bit scenarios. For instance, I&S-ViT elevates the performance of 3-bit ViT-B by an impressive 50.68%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zysxmu/ias-vit", "github_api_added_by": "auto" }, { "date": "2023-11-20", "arxiv_id": "2311.10678", "title": "Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections", "authors": [ "Lihan Zha", "Yuchen Cui", "Li-Heng Lin", "Minae Kwon", "Montserrat Gonzalez Arenas", "Andy Zeng", "Fei Xia", "Dorsa Sadigh" ], "github": "https://github.com/Stanford-ILIAD/droc", "project_page": "", "abstract": "Today's robot policies exhibit subpar performance when faced with the challenge of generalizing to novel environments. Human corrective feedback is a crucial form of guidance to enable such generalization. However, adapting to and learning from online human corrections is a non-trivial endeavor: not only do robots need to remember human feedback over time to retrieve the right information in new settings and reduce the intervention rate, but also they would need to be able to respond to feedback that can be arbitrary corrections about high-level human preferences to low-level adjustments to skill parameters. In this work, we present Distillation and Retrieval of Online Corrections (DROC), a large language model (LLM)-based system that can respond to arbitrary forms of language feedback, distill generalizable knowledge from corrections, and retrieve relevant past experiences based on textual and visual similarity for improving performance in novel settings. DROC is able to respond to a sequence of online language corrections that address failures in both high-level task plans and low-level skill primitives. We demonstrate that DROC effectively distills the relevant information from the sequence of online corrections in a knowledge base and retrieves that knowledge in settings with new task or object instances. DROC outperforms other techniques that directly generate robot code via LLMs by using only half of the total number of corrections needed in the first round and requires little to no corrections after two iterations. We show further results, videos, prompts and code on https://sites.google.com/stanford.edu/droc .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Stanford-ILIAD/droc", "github_api_added_by": "auto" }, { "date": "2023-11-20", "arxiv_id": "2311.10642", "title": "Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as an Alternative to Attention Layers in Transformers", "authors": [ "Vukasin Bozic", "Danilo Dordervic", "Daniele Coppola", "Joseph Thommes" ], "github": "", "project_page": "", "abstract": "This work presents an analysis of the effectiveness of using standard shallow feed-forward networks to mimic the behavior of the attention mechanism in the original Transformer model, a state-of-the-art architecture for sequence-to-sequence tasks. We substitute key elements of the attention mechanism in the Transformer with simple feed-forward networks, trained using the original components via knowledge distillation. Our experiments, conducted on the IWSLT2017 dataset, reveal the capacity of these \"attentionless Transformers\" to rival the performance of the original architecture. Through rigorous ablation studies, and experimenting with various replacement network types and sizes, we offer insights that support the viability of our approach. This not only sheds light on the adaptability of shallow feed-forward networks in emulating attention mechanisms but also underscores their potential to streamline complex architectures for sequence-to-sequence tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-20", "arxiv_id": "2311.10123", "title": "MetaDreamer: Efficient Text-to-3D Creation With Disentangling Geometry and Texture", "authors": [ "Lincong Feng", "Muyu Wang", "Maoyu Wang", "Kuo Xu", "Xiaoli Liu" ], "github": "", "project_page": "", "abstract": "Generative models for 3D object synthesis have seen significant advancements with the incorporation of prior knowledge distilled from 2D diffusion models. Nevertheless, challenges persist in the form of multi-view geometric inconsistencies and slow generation speeds within the existing 3D synthesis frameworks. This can be attributed to two factors: firstly, the deficiency of abundant geometric a priori knowledge in optimization, and secondly, the entanglement issue between geometry and texture in conventional 3D generation methods.In response, we introduce MetaDreammer, a two-stage optimization approach that leverages rich 2D and 3D prior knowledge. In the first stage, our emphasis is on optimizing the geometric representation to ensure multi-view consistency and accuracy of 3D objects. In the second stage, we concentrate on fine-tuning the geometry and optimizing the texture, thereby achieving a more refined 3D object. Through leveraging 2D and 3D prior knowledge in two stages, respectively, we effectively mitigate the interdependence between geometry and texture. MetaDreamer establishes clear optimization objectives for each stage, resulting in significant time savings in the 3D generation process. Ultimately, MetaDreamer can generate high-quality 3D objects based on textual prompts within 20 minutes, and to the best of our knowledge, it is the most efficient text-to-3D generation method. Furthermore, we introduce image control into the process, enhancing the controllability of 3D generation. Extensive empirical evidence confirms that our method is not only highly efficient but also achieves a quality level that is at the forefront of current state-of-the-art 3D generation techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-20", "arxiv_id": "2311.10709", "title": "Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning", "authors": [ "Rohit Girdhar", "Mannat Singh", "Andrew Brown", "Quentin Duval", "Samaneh Azadi", "Sai Saketh Rambhatla", "Akbar Shah", "Xi Yin", "Devi Parikh", "Ishan Misra" ], "github": "", "project_page": "", "abstract": "We present Emu Video, a text-to-video generation model that factorizes the generation into two steps: first generating an image conditioned on the text, and then generating a video conditioned on the text and the generated image. We identify critical design decisions--adjusted noise schedules for diffusion, and multi-stage training--that enable us to directly generate high quality and high resolution videos, without requiring a deep cascade of models as in prior work. In human evaluations, our generated videos are strongly preferred in quality compared to all prior work--81% vs. Google's Imagen Video, 90% vs. Nvidia's PYOCO, and 96% vs. Meta's Make-A-Video. Our model outperforms commercial solutions such as RunwayML's Gen2 and Pika Labs. Finally, our factorizing approach naturally lends itself to animating images based on a user's text prompt, where our generations are preferred 96% over prior work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-20", "arxiv_id": "2311.10538", "title": "Testing Language Model Agents Safely in the Wild", "authors": [ "Silen Naihin", "David Atkinson", "Marc Green", "Merwane Hamadi", "Craig Swift", "Douglas Schonholtz", "Adam Tauman Kalai", "David Bau" ], "github": "https://github.com/muellerberndt/mini-agi", "project_page": "", "abstract": "A prerequisite for safe autonomy-in-the-wild is safe testing-in-the-wild. Yet real-world autonomous tests face several unique safety challenges, both due to the possibility of causing harm during a test, as well as the risk of encountering new unsafe agent behavior through interactions with real-world and potentially malicious actors. We propose a framework for conducting safe autonomous agent tests on the open internet: agent actions are audited by a context-sensitive monitor that enforces a stringent safety boundary to stop an unsafe test, with suspect behavior ranked and logged to be examined by humans. We a design a basic safety monitor that is flexible enough to monitor existing LLM agents, and, using an adversarial simulated agent, we measure its ability to identify and stop unsafe situations. Then we apply the safety monitor on a battery of real-world tests of AutoGPT, and we identify several limitations and challenges that will face the creation of safe in-the-wild tests as autonomous agents grow more capable.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/muellerberndt/mini-agi", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-20", "arxiv_id": "2311.10111", "title": "VideoCon: Robust Video-Language Alignment via Contrast Captions", "authors": [ "Hritik Bansal", "Yonatan Bitton", "Idan Szpektor", "Kai-Wei Chang", "Aditya Grover" ], "github": "https://github.com/Hritikbansal/videocon", "project_page": "", "abstract": "Despite being (pre)trained on a massive amount of data, state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video captions. Our work addresses this by identifying a broad spectrum of contrast misalignments, such as replacing entities, actions, and flipping event order, which alignment models should be robust against. To this end, we introduce the VideoCon, a video-language alignment dataset constructed by a large language model that generates plausible contrast video captions and explanations for differences between original and contrast video captions. Then, a generative video-language model is finetuned with VideoCon to assess video-language entailment and generate explanations. Our VideoCon-based alignment model significantly outperforms current models. It exhibits a 12-point increase in AUC for the video-language alignment task on human-generated contrast captions. Finally, our model sets new state of the art zero-shot performance in temporally-extensive video-language tasks such as text-to-video retrieval (SSv2-Temporal) and video question answering (ATP-Hard). Moreover, our model shows superior performance on novel videos and human-crafted captions and explanations. Our code and data are available at https://github.com/Hritikbansal/videocon.", "github_manual": "", "github_regex": "https://github.com/Hritikbansal/videocon", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-20", "arxiv_id": "2311.10122", "title": "Video-LLaVA: Learning United Visual Representation by Alignment Before Projection", "authors": [ "Bin Lin", "Bin Zhu", "Yang Ye", "Munan Ning", "Peng Jin", "Li Yuan" ], "github": "https://github.com/PKU-YuanGroup/Video-LLaVA", "project_page": "", "abstract": "The Large Vision-Language Model (LVLM) has enhanced the performance of various downstream tasks in visual-language understanding. Most existing approaches encode images and videos into separate feature spaces, which are then fed as inputs to large language models. However, due to the lack of unified tokenization for images and videos, namely misalignment before projection, it becomes challenging for a Large Language Model (LLM) to learn multi-modal interactions from several poor projection layers. In this work, we unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM. As a result, we establish a simple but robust LVLM baseline, Video-LLaVA, which learns from a mixed dataset of images and videos, mutually enhancing each other. Video-LLaVA achieves superior performances on a broad range of 9 image benchmarks across 5 image question-answering datasets and 4 image benchmark toolkits. Additionally, our Video-LLaVA also outperforms Video-ChatGPT by 5.8%, 9.9%, 18.6%, and 10.1% on MSRVTT, MSVD, TGIF, and ActivityNet, respectively. Notably, extensive experiments demonstrate that Video-LLaVA mutually benefits images and videos within a unified visual representation, outperforming models designed specifically for images or videos.", "github_manual": "https://github.com/PKU-YuanGroup/Video-LLaVA", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-20", "arxiv_id": "2311.10125", "title": "UnifiedVisionGPT: Streamlining Vision-Oriented AI through Generalized Multimodal Framework", "authors": [ "Chris Kelly", "Luhui Hu", "Cindy Yang", "Yu Tian", "Deshun Yang", "Bang Yang", "Zaoshan Huang", "Zihao Li", "Yuexian Zou" ], "github": "https://github.com/LHBuilder/SA-Segment-Anything", "project_page": "", "abstract": "In the current landscape of artificial intelligence, foundation models serve as the bedrock for advancements in both language and vision domains. OpenAI GPT-4 has emerged as the pinnacle in large language models (LLMs), while the computer vision (CV) domain boasts a plethora of state-of-the-art (SOTA) models such as Meta's SAM and DINO, and YOLOS. However, the financial and computational burdens of training new models from scratch remain a significant barrier to progress. In response to this challenge, we introduce UnifiedVisionGPT, a novel framework designed to consolidate and automate the integration of SOTA vision models, thereby facilitating the development of vision-oriented AI. UnifiedVisionGPT distinguishes itself through four key features: (1) provides a versatile multimodal framework adaptable to a wide range of applications, building upon the strengths of multimodal foundation models; (2) seamlessly integrates various SOTA vision models to create a comprehensive multimodal platform, capitalizing on the best components of each model; (3) prioritizes vision-oriented AI, ensuring a more rapid progression in the CV domain compared to the current trajectory of LLMs; and (4) introduces automation in the selection of SOTA vision models, generating optimal results based on diverse multimodal inputs such as text prompts and images. This paper outlines the architecture and capabilities of UnifiedVisionGPT, demonstrating its potential to revolutionize the field of computer vision through enhanced efficiency, versatility, generalization, and performance. Our implementation, along with the unified multimodal framework and comprehensive dataset, is made publicly available at https://github.com/LHBuilder/SA-Segment-Anything.", "github_manual": "", "github_regex": "https://github.com/LHBuilder/SA-Segment-Anything", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-21", "arxiv_id": "2311.11045", "title": "Orca 2: Teaching Small Language Models How to Reason", "authors": [ "Arindam Mitra", "Luciano Del Corro", "Shweti Mahajan", "Andres Codas", "Clarisse Simoes", "Sahaj Agrawal", "Xuxi Chen", "Anastasia Razdaibiedina", "Erik Jones", "Kriti Aggarwal", "Hamid Palangi", "Guoqing Zheng", "Corby Rosset", "Hamed Khanpour", "Ahmed Awadallah" ], "github": "", "project_page": "", "abstract": "Orca 1 learns from rich signals, such as explanation traces, allowing it to outperform conventional instruction-tuned models on benchmarks like BigBench Hard and AGIEval. In Orca 2, we continue exploring how improved training signals can enhance smaller LMs' reasoning abilities. Research on training small LMs has often relied on imitation learning to replicate the output of more capable models. We contend that excessive emphasis on imitation may restrict the potential of smaller models. We seek to teach small LMs to employ different solution strategies for different tasks, potentially different from the one used by the larger model. For example, while larger models might provide a direct answer to a complex task, smaller models may not have the same capacity. In Orca 2, we teach the model various reasoning techniques (step-by-step, recall then generate, recall-reason-generate, direct answer, etc.). More crucially, we aim to help the model learn to determine the most effective solution strategy for each task. We evaluate Orca 2 using a comprehensive set of 15 diverse benchmarks (corresponding to approximately 100 tasks and over 36,000 unique prompts). Orca 2 significantly surpasses models of similar size and attains performance levels similar or better to those of models 5-10x larger, as assessed on complex tasks that test advanced reasoning abilities in zero-shot settings. We open-source Orca 2 to encourage further research on the development, evaluation, and alignment of smaller LMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-21", "arxiv_id": "2311.10770", "title": "Exponentially Faster Language Modelling", "authors": [ "Peter Belcak", "Roger Wattenhofer" ], "github": "https://github.com/pbelcak/fastbert", "project_page": "", "abstract": "Language models only really need to use an exponential fraction of their neurons for individual inferences. As proof, we present FastBERT, a BERT variant that uses 0.3\\% of its neurons during inference while performing on par with similar BERT models. FastBERT selectively engages just 12 out of 4095 neurons for each layer inference. This is achieved by replacing feedforward networks with fast feedforward networks (FFFs). While no truly efficient implementation currently exists to unlock the full acceleration potential of conditional neural execution, we provide high-level CPU code achieving 78x speedup over the optimized baseline feedforward implementation, and a PyTorch implementation delivering 40x speedup over the equivalent batched feedforward inference. We publish our training code, benchmarking setup, and model weights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/pbelcak/fastbert", "github_api_added_by": "auto" }, { "date": "2023-11-21", "arxiv_id": "2311.10751", "title": "ProAgent: From Robotic Process Automation to Agentic Process Automation", "authors": [ "Yining Ye", "Xin Cong", "Shizuo Tian", "Jiannan Cao", "Hao Wang", "Yujia Qin", "Yaxi Lu", "Heyang Yu", "Huadong Wang", "Yankai Lin", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/OpenBMB/ProAgent", "project_page": "", "abstract": "From ancient water wheels to robotic process automation (RPA), automation technology has evolved throughout history to liberate human beings from arduous tasks. Yet, RPA struggles with tasks needing human-like intelligence, especially in elaborate design of workflow construction and dynamic decision-making in workflow execution. As Large Language Models (LLMs) have emerged human-like intelligence, this paper introduces Agentic Process Automation (APA), a groundbreaking automation paradigm using LLM-based agents for advanced automation by offloading the human labor to agents associated with construction and execution. We then instantiate ProAgent, an LLM-based agent designed to craft workflows from human instructions and make intricate decisions by coordinating specialized agents. Empirical experiments are conducted to detail its construction and execution procedure of workflow, showcasing the feasibility of APA, unveiling the possibility of a new paradigm of automation driven by agents. Our code is public at https://github.com/OpenBMB/ProAgent.", "github_manual": "", "github_regex": "https://github.com/OpenBMB/ProAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-21", "arxiv_id": "2311.11501", "title": "MultiLoRA: Democratizing LoRA for Better Multi-Task Learning", "authors": [ "Yiming Wang", "Yu Lin", "Xiaodong Zeng", "Guannan Zhang" ], "github": "", "project_page": "", "abstract": "LoRA achieves remarkable resource efficiency and comparable performance when adapting LLMs for specific tasks. Since ChatGPT demonstrated superior performance on various tasks, there has been a growing desire to adapt one model for all tasks. However, the explicit low-rank of LoRA limits the adaptation performance in complex multi-task scenarios. LoRA is dominated by a small number of top singular vectors while fine-tuning decomposes into a set of less important unitary transforms. In this paper, we propose MultiLoRA for better multi-task adaptation by reducing the dominance of top singular vectors observed in LoRA. MultiLoRA scales LoRA modules horizontally and change parameter initialization of adaptation matrices to reduce parameter dependency, thus yields more balanced unitary subspaces. We unprecedentedly construct specialized training data by mixing datasets of instruction follow, natural language understanding, world knowledge, to cover semantically and syntactically different samples. With only 2.5% of additional parameters, MultiLoRA outperforms single LoRA counterparts and fine-tuning on multiple benchmarks and model scales. Further investigation into weight update matrices of MultiLoRA exhibits reduced dependency on top singular vectors and more democratic unitary transform contributions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-21", "arxiv_id": "2311.12015", "title": "GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstration", "authors": [ "Naoki Wake", "Atsushi Kanehira", "Kazuhiro Sasabuchi", "Jun Takamatsu", "Katsushi Ikeuchi" ], "github": "", "project_page": "", "abstract": "We introduce a pipeline that enhances a general-purpose Vision Language Model, GPT-4V(ision), by integrating observations of human actions to facilitate robotic manipulation. This system analyzes videos of humans performing tasks and creates executable robot programs that incorporate affordance insights. The computation starts by analyzing the videos with GPT-4V to convert environmental and action details into text, followed by a GPT-4-empowered task planner. In the following analyses, vision systems reanalyze the video with the task plan. Object names are grounded using an open-vocabulary object detector, while focus on the hand-object relation helps to detect the moment of grasping and releasing. This spatiotemporal grounding allows the vision systems to further gather affordance data (e.g., grasp type, way points, and body postures). Experiments across various scenarios demonstrate this method's efficacy in achieving real robots' operations from human demonstrations in a zero-shot manner. The prompts of GPT-4V/GPT-4 are available at this project page: https://microsoft.github.io/GPT4Vision-Robot-Manipulation-Prompts/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-21", "arxiv_id": "2311.11255", "title": "M^{2}UGen: Multi-modal Music Understanding and Generation with the Power of Large Language Models", "authors": [ "Atin Sakkeer Hussain", "Shansong Liu", "Chenshuo Sun", "Ying Shan" ], "github": "https://github.com/shansongliu/M2UGen", "project_page": "", "abstract": "The current landscape of research leveraging large language models (LLMs) is experiencing a surge. Many works harness the powerful reasoning capabilities of these models to comprehend various modalities, such as text, speech, images, videos, etc. They also utilize LLMs to understand human intention and generate desired outputs like images, videos, and music. However, research that combines both understanding and generation using LLMs is still limited and in its nascent stage. To address this gap, we introduce a Multi-modal Music Understanding and Generation (M^{2}UGen) framework that integrates LLM's abilities to comprehend and generate music for different modalities. The M^{2}UGen framework is purpose-built to unlock creative potential from diverse sources of inspiration, encompassing music, image, and video through the use of pretrained MERT, ViT, and ViViT models, respectively. To enable music generation, we explore the use of AudioLDM 2 and MusicGen. Bridging multi-modal understanding and music generation is accomplished through the integration of the LLaMA 2 model. Furthermore, we make use of the MU-LLaMA model to generate extensive datasets that support text/image/video-to-music generation, facilitating the training of our M^{2}UGen framework. We conduct a thorough evaluation of our proposed framework. The experimental results demonstrate that our model achieves or surpasses the performance of the current state-of-the-art models.", "github_manual": "https://github.com/shansongliu/M2UGen", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-21", "arxiv_id": "2311.11243", "title": "AutoStory: Generating Diverse Storytelling Images with Minimal Human Effort", "authors": [ "Wen Wang", "Canyu Zhao", "Hao Chen", "Zhekai Chen", "Kecheng Zheng", "Chunhua Shen" ], "github": "https://github.com/aim-uofa/AutoStory", "project_page": "", "abstract": "Story visualization aims to generate a series of images that match the story described in texts, and it requires the generated images to satisfy high quality, alignment with the text description, and consistency in character identities. Given the complexity of story visualization, existing methods drastically simplify the problem by considering only a few specific characters and scenarios, or requiring the users to provide per-image control conditions such as sketches. However, these simplifications render these methods incompetent for real applications. To this end, we propose an automated story visualization system that can effectively generate diverse, high-quality, and consistent sets of story images, with minimal human interactions. Specifically, we utilize the comprehension and planning capabilities of large language models for layout planning, and then leverage large-scale text-to-image models to generate sophisticated story images based on the layout. We empirically find that sparse control conditions, such as bounding boxes, are suitable for layout planning, while dense control conditions, e.g., sketches and keypoints, are suitable for generating high-quality image content. To obtain the best of both worlds, we devise a dense condition generation module to transform simple bounding box layouts into sketch or keypoint control conditions for final image generation, which not only improves the image quality but also allows easy and intuitive user interactions. In addition, we propose a simple yet effective method to generate multi-view consistent character images, eliminating the reliance on human labor to collect or draw character images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aim-uofa/AutoStory", "github_api_added_by": "auto" }, { "date": "2023-11-21", "arxiv_id": "2311.11077", "title": "Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning", "authors": [ "Clifton Poth", "Hannah Sterz", "Indraneil Paul", "Sukannya Purkayastha", "Leon Engl\u00e4nder", "Timo Imhof", "Ivan Vuli\u0107", "Sebastian Ruder", "Iryna Gurevych", "Jonas Pfeiffer" ], "github": "https://github.com/adapter-hub/adapters", "project_page": "", "abstract": "We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models. By integrating 10 diverse adapter methods into a unified interface, Adapters offers ease of use and flexible configuration. Our library allows researchers and practitioners to leverage adapter modularity through composition blocks, enabling the design of complex adapter setups. We demonstrate the library's efficacy by evaluating its performance against full fine-tuning on various NLP tasks. Adapters provides a powerful tool for addressing the challenges of conventional fine-tuning paradigms and promoting more efficient and modular transfer learning. The library is available via https://adapterhub.ml/adapters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/adapter-hub/adapters", "github_api_added_by": "auto" }, { "date": "2023-11-21", "arxiv_id": "2311.10768", "title": "Memory Augmented Language Models through Mixture of Word Experts", "authors": [ "Cicero Nogueira dos Santos", "James Lee-Thorp", "Isaac Noble", "Chung-Ching Chang", "David Uthus" ], "github": "", "project_page": "", "abstract": "Scaling up the number of parameters of language models has proven to be an effective approach to improve performance. For dense models, increasing model size proportionally increases the model's computation footprint. In this work, we seek to aggressively decouple learning capacity and FLOPs through Mixture-of-Experts (MoE) style models with large knowledge-rich vocabulary based routing functions and experts. Our proposed approach, dubbed Mixture of Word Experts (MoWE), can be seen as a memory augmented model, where a large set of word-specific experts play the role of a sparse memory. We demonstrate that MoWE performs significantly better than the T5 family of models with similar number of FLOPs in a variety of NLP tasks. Additionally, MoWE outperforms regular MoE models on knowledge intensive tasks and has similar performance to more complex memory augmented approaches that often require to invoke custom mechanisms to search the sparse memory.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-21", "arxiv_id": "2311.12022", "title": "GPQA: A Graduate-Level Google-Proof Q&A Benchmark", "authors": [ "David Rein", "Betty Li Hou", "Asa Cooper Stickland", "Jackson Petty", "Richard Yuanzhe Pang", "Julien Dirani", "Julian Michael", "Samuel R. Bowman" ], "github": "https://github.com/idavidrein/gpqa", "project_page": "", "abstract": "We present GPQA, a challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach 65% accuracy (74% when discounting clear mistakes the experts identified in retrospect), while highly skilled non-expert validators only reach 34% accuracy, despite spending on average over 30 minutes with unrestricted access to the web (i.e., the questions are \"Google-proof\"). The questions are also difficult for state-of-the-art AI systems, with our strongest GPT-4 based baseline achieving 39% accuracy. If we are to use future AI systems to help us answer very hard questions, for example, when developing new scientific knowledge, we need to develop scalable oversight methods that enable humans to supervise their outputs, which may be difficult even if the supervisors are themselves skilled and knowledgeable. The difficulty of GPQA both for skilled non-experts and frontier AI systems should enable realistic scalable oversight experiments, which we hope can help devise ways for human experts to reliably get truthful information from AI systems that surpass human capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/idavidrein/gpqa", "github_api_added_by": "auto" }, { "date": "2023-11-21", "arxiv_id": "2311.10775", "title": "ToolTalk: Evaluating Tool-Usage in a Conversational Setting", "authors": [ "Nicholas Farn", "Richard Shin" ], "github": "https://github.com/microsoft/ToolTalk", "project_page": "", "abstract": "Large language models (LLMs) have displayed massive improvements in reason- ing and decision-making skills and can hold natural conversations with users. Many recent works seek to augment LLM-based assistants with external tools so they can access private or up-to-date information and carry out actions on behalf of users. To better measure the performance of these assistants, this paper introduces ToolTalk, a benchmark consisting of complex user intents re- quiring multi-step tool usage specified through dialogue. ToolTalk contains 28 tools grouped into 7 plugins, and includes a complete simulated implementa- tion of each tool, allowing for fully automated evaluation of assistants that rely on execution feedback. ToolTalk also emphasizes tools that externally affect the world rather than only tools for referencing or searching information. We evaluate GPT-3.5 and GPT-4 on ToolTalk resulting in success rates of 26% and 50% respectively. Our analysis of the errors reveals three major categories and suggests some future directions for improvement. We release ToolTalk at https://github.com/microsoft/ToolTalk.", "github_manual": "", "github_regex": "https://github.com/microsoft/ToolTalk", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-21", "arxiv_id": "2311.10982", "title": "Make Pixels Dance: High-Dynamic Video Generation", "authors": [ "Yan Zeng", "Guoqiang Wei", "Jiani Zheng", "Jiaxin Zou", "Yang Wei", "Yuchen Zhang", "Hang Li" ], "github": "", "project_page": "", "abstract": "Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately, current state-of-the-art video generation methods, primarily focusing on text-to-video generation, tend to produce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text instructions is insufficient and suboptimal for video generation. In this paper, we introduce PixelDance, a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions, setting a new standard for video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-21", "arxiv_id": "2311.11829", "title": "System 2 Attention (is something you might need too)", "authors": [ "Jason Weston", "Sainbayar Sukhbaatar" ], "github": "", "project_page": "", "abstract": "Soft attention in Transformer-based Large Language Models (LLMs) is susceptible to incorporating irrelevant information from the context into its latent representations, which adversely affects next token generations. To help rectify these issues, we introduce System 2 Attention (S2A), which leverages the ability of LLMs to reason in natural language and follow instructions in order to decide what to attend to. S2A regenerates the input context to only include the relevant portions, before attending to the regenerated context to elicit the final response. In experiments, S2A outperforms standard attention-based LLMs on three tasks containing opinion or irrelevant information, QA, math word problems and longform generation, where S2A increases factuality and objectivity, and decreases sycophancy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-21", "arxiv_id": "2311.10794", "title": "Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression", "authors": [ "Animesh Sinha", "Bo Sun", "Anmol Kalia", "Arantxa Casanova", "Elliot Blanchard", "David Yan", "Winnie Zhang", "Tony Nelli", "Jiahui Chen", "Hardik Shah", "Licheng Yu", "Mitesh Kumar Singh", "Ankit Ramchandani", "Maziar Sanjabi", "Sonal Gupta", "Amy Bearman", "Dhruv Mahajan" ], "github": "", "project_page": "", "abstract": "We introduce Style Tailoring, a recipe to finetune Latent Diffusion Models (LDMs) in a distinct domain with high visual quality, prompt alignment and scene diversity. We choose sticker image generation as the target domain, as the images significantly differ from photorealistic samples typically generated by large-scale LDMs. We start with a competent text-to-image model, like Emu, and show that relying on prompt engineering with a photorealistic model to generate stickers leads to poor prompt alignment and scene diversity. To overcome these drawbacks, we first finetune Emu on millions of sticker-like images collected using weak supervision to elicit diversity. Next, we curate human-in-the-loop (HITL) Alignment and Style datasets from model generations, and finetune to improve prompt alignment and style alignment respectively. Sequential finetuning on these datasets poses a tradeoff between better style alignment and prompt alignment gains. To address this tradeoff, we propose a novel fine-tuning method called Style Tailoring, which jointly fits the content and style distribution and achieves best tradeoff. Evaluation results show our method improves visual quality by 14%, prompt alignment by 16.2% and scene diversity by 15.3%, compared to prompt engineering the base Emu model for stickers generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-21", "arxiv_id": "2311.11284", "title": "LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching", "authors": [ "Yixun Liang", "Xin Yang", "Jiantao Lin", "Haodong Li", "Xiaogang Xu", "Yingcong Chen" ], "github": "https://github.com/EnVision-Research/LucidDreamer", "project_page": "", "abstract": "The recent advancements in text-to-3D generation mark a significant milestone in generative models, unlocking new possibilities for creating imaginative 3D assets across various real-world scenarios. While recent advancements in text-to-3D generation have shown promise, they often fall short in rendering detailed and high-quality 3D models. This problem is especially prevalent as many methods base themselves on Score Distillation Sampling (SDS). This paper identifies a notable deficiency in SDS, that it brings inconsistent and low-quality updating direction for the 3D model, causing the over-smoothing effect. To address this, we propose a novel approach called Interval Score Matching (ISM). ISM employs deterministic diffusing trajectories and utilizes interval-based score matching to counteract over-smoothing. Furthermore, we incorporate 3D Gaussian Splatting into our text-to-3D generation pipeline. Extensive experiments show that our model largely outperforms the state-of-the-art in quality and training efficiency.", "github_manual": "https://github.com/EnVision-Research/LucidDreamer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-21", "arxiv_id": "2311.11315", "title": "TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems", "authors": [ "Yilun Kong", "Jingqing Ruan", "Yihong Chen", "Bin Zhang", "Tianpeng Bao", "Shiwei Shi", "Guoqing Du", "Xiaoru Hu", "Hangyu Mao", "Ziyue Li", "Xingyu Zeng", "Rui Zhao" ], "github": "https://github.com/Significant-Gravitas/Auto-GPT", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs. However, real-world complex systems present three prevalent challenges concerning task planning and tool usage: (1) The real system usually has a vast array of APIs, so it is impossible to feed the descriptions of all APIs to the prompt of LLMs as the token length is limited; (2) the real system is designed for handling complex tasks, and the base LLMs can hardly plan a correct sub-task order and API-calling order for such tasks; (3) Similar semantics and functionalities among APIs in real systems create challenges for both LLMs and even humans in distinguishing between them. In response, this paper introduces a comprehensive framework aimed at enhancing the Task Planning and Tool Usage (TPTU) abilities of LLM-based agents operating within real-world systems. Our framework comprises three key components designed to address these challenges: (1) the API Retriever selects the most pertinent APIs for the user task among the extensive array available; (2) LLM Finetuner tunes a base LLM so that the finetuned LLM can be more capable for task planning and API calling; (3) the Demo Selector adaptively retrieves different demonstrations related to hard-to-distinguish APIs, which is further used for in-context learning to boost the final performance. We validate our methods using a real-world commercial system as well as an open-sourced academic dataset, and the outcomes clearly showcase the efficacy of each individual component as well as the integrated framework.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Significant-Gravitas/Auto-GPT", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-22", "arxiv_id": "2311.12631", "title": "GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning", "authors": [ "Jiaxi Lv", "Yi Huang", "Mingfu Yan", "Jiancheng Huang", "Jianzhuang Liu", "Yifan Liu", "Yafei Wen", "Xiaoxin Chen", "Shifeng Chen" ], "github": "https://github.com/jiaxilv/GPT4Motion", "project_page": "", "abstract": "Recent advances in text-to-video generation have harnessed the power of diffusion models to create visually compelling content conditioned on text prompts. However, they usually encounter high computational costs and often struggle to produce videos with coherent physical motions. To tackle these issues, we propose GPT4Motion, a training-free framework that leverages the planning capability of large language models such as GPT, the physical simulation strength of Blender, and the excellent image generation ability of text-to-image diffusion models to enhance the quality of video synthesis. Specifically, GPT4Motion employs GPT-4 to generate a Blender script based on a user textual prompt, which commands Blender's built-in physics engine to craft fundamental scene components that encapsulate coherent physical motions across frames. Then these components are inputted into Stable Diffusion to generate a video aligned with the textual prompt. Experimental results on three basic physical motion scenarios, including rigid object drop and collision, cloth draping and swinging, and liquid flow, demonstrate that GPT4Motion can generate high-quality videos efficiently in maintaining motion coherency and entity consistency. GPT4Motion offers new insights in text-to-video research, enhancing its quality and broadening its horizon for future explorations.", "github_manual": "https://github.com/jiaxilv/GPT4Motion", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-22", "arxiv_id": "2311.12052", "title": "MagicDance: Realistic Human Dance Video Generation with Motions & Facial Expressions Transfer", "authors": [ "Di Chang", "Yichun Shi", "Quankai Gao", "Jessica Fu", "Hongyi Xu", "Guoxian Song", "Qing Yan", "Xiao Yang", "Mohammad Soleymani" ], "github": "https://github.com/Boese0601/MagicDance", "project_page": "", "abstract": "In this work, we propose MagicDance, a diffusion-based model for 2D human motion and facial expression transfer on challenging human dance videos. Specifically, we aim to generate human dance videos of any target identity driven by novel pose sequences while keeping the identity unchanged. To this end, we propose a two-stage training strategy to disentangle human motions and appearance (e.g., facial expressions, skin tone and dressing), consisting of the pretraining of an appearance-control block and fine-tuning of an appearance-pose-joint-control block over human dance poses of the same dataset. Our novel design enables robust appearance control with temporally consistent upper body, facial attributes, and even background. The model also generalizes well on unseen human identities and complex motion sequences without the need for any fine-tuning with additional data with diverse human attributes by leveraging the prior knowledge of image diffusion models. Moreover, the proposed model is easy to use and can be considered as a plug-in module/extension to Stable Diffusion. We also demonstrate the model's ability for zero-shot 2D animation generation, enabling not only the appearance transfer from one identity to another but also allowing for cartoon-like stylization given only pose inputs. Extensive experiments demonstrate our superior performance on the TikTok dataset.", "github_manual": "https://github.com/Boese0601/MagicDance", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-22", "arxiv_id": "2311.12092", "title": "Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models", "authors": [ "Rohit Gandikota", "Joanna Materzynska", "Tingrui Zhou", "Antonio Torralba", "David Bau" ], "github": "https://github.com/rohitgandikota/sliders", "project_page": "", "abstract": "We present a method to create interpretable concept sliders that enable precise control over attributes in image generations from diffusion models. Our approach identifies a low-rank parameter direction corresponding to one concept while minimizing interference with other attributes. A slider is created using a small set of prompts or sample images; thus slider directions can be created for either textual or visual concepts. Concept Sliders are plug-and-play: they can be composed efficiently and continuously modulated, enabling precise control over image generation. In quantitative experiments comparing to previous editing techniques, our sliders exhibit stronger targeted edits with lower interference. We showcase sliders for weather, age, styles, and expressions, as well as slider compositions. We show how sliders can transfer latents from StyleGAN for intuitive editing of visual concepts for which textual description is difficult. We also find that our method can help address persistent quality issues in Stable Diffusion XL including repair of object deformations and fixing distorted hands. Our code, data, and trained sliders are available at https://sliders.baulab.info/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/rohitgandikota/sliders", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-22", "arxiv_id": "2311.12454", "title": "HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech Synthesis", "authors": [ "Sang-Hoon Lee", "Ha-Yeong Choi", "Seung-Bin Kim", "Seong-Whan Lee" ], "github": "https://github.com/sh-lee-prml/HierSpeechpp", "project_page": "", "abstract": "Large language models (LLM)-based speech synthesis has been widely adopted in zero-shot speech synthesis. However, they require a large-scale data and possess the same limitations as previous autoregressive speech models, including slow inference speed and lack of robustness. This paper proposes HierSpeech++, a fast and strong zero-shot speech synthesizer for text-to-speech (TTS) and voice conversion (VC). We verified that hierarchical speech synthesis frameworks could significantly improve the robustness and expressiveness of the synthetic speech. Furthermore, we significantly improve the naturalness and speaker similarity of synthetic speech even in zero-shot speech synthesis scenarios. For text-to-speech, we adopt the text-to-vec framework, which generates a self-supervised speech representation and an F0 representation based on text representations and prosody prompts. Then, HierSpeech++ generates speech from the generated vector, F0, and voice prompt. We further introduce a high-efficient speech super-resolution framework from 16 kHz to 48 kHz. The experimental results demonstrated that the hierarchical variational autoencoder could be a strong zero-shot speech synthesizer given that it outperforms LLM-based and diffusion-based models. Moreover, we achieved the first human-level quality zero-shot speech synthesis. Audio samples and source code are available at https://github.com/sh-lee-prml/HierSpeechpp.", "github_manual": "", "github_regex": "https://github.com/sh-lee-prml/HierSpeechpp", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-22", "arxiv_id": "2311.12229", "title": "NeuroPrompts: An Adaptive Framework to Optimize Prompts for Text-to-Image Generation", "authors": [ "Shachar Rosenman", "Vasudev Lal", "Phillip Howard" ], "github": "", "project_page": "", "abstract": "Despite impressive recent advances in text-to-image diffusion models, obtaining high-quality images often requires prompt engineering by humans who have developed expertise in using them. In this work, we present NeuroPrompts, an adaptive framework that automatically enhances a user's prompt to improve the quality of generations produced by text-to-image models. Our framework utilizes constrained text decoding with a pre-trained language model that has been adapted to generate prompts similar to those produced by human prompt engineers. This approach enables higher-quality text-to-image generations and provides user control over stylistic features via constraint set specification. We demonstrate the utility of our framework by creating an interactive application for prompt enhancement and image generation using Stable Diffusion. Additionally, we conduct experiments utilizing a large dataset of human-engineered prompts for text-to-image generation and show that our approach automatically produces enhanced prompts that result in superior image quality. We make our code, a screencast video demo and a live demo instance of NeuroPrompts publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-22", "arxiv_id": "2311.12024", "title": "PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction", "authors": [ "Peng Wang", "Hao Tan", "Sai Bi", "Yinghao Xu", "Fujun Luan", "Kalyan Sunkavalli", "Wenping Wang", "Zexiang Xu", "Kai Zhang" ], "github": "", "project_page": "", "abstract": "We propose a Pose-Free Large Reconstruction Model (PF-LRM) for reconstructing a 3D object from a few unposed images even with little visual overlap, while simultaneously estimating the relative camera poses in ~1.3 seconds on a single A100 GPU. PF-LRM is a highly scalable method utilizing the self-attention blocks to exchange information between 3D object tokens and 2D image tokens; we predict a coarse point cloud for each view, and then use a differentiable Perspective-n-Point (PnP) solver to obtain camera poses. When trained on a huge amount of multi-view posed data of ~1M objects, PF-LRM shows strong cross-dataset generalization ability, and outperforms baseline methods by a large margin in terms of pose prediction accuracy and 3D reconstruction quality on various unseen evaluation datasets. We also demonstrate our model's applicability in downstream text/image-to-3D task with fast feed-forward inference. Our project website is at: https://totoro97.github.io/pf-lrm .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-22", "arxiv_id": "2311.12775", "title": "SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering", "authors": [ "Antoine Gu\u00e9don", "Vincent Lepetit" ], "github": "https://github.com/Anttwo/SuGaR", "project_page": "", "abstract": "We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian Splatting. Gaussian Splatting has recently become very popular as it yields realistic rendering while being significantly faster to train than NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D gaussians as these gaussians tend to be unorganized after optimization and no method has been proposed so far. Our first key contribution is a regularization term that encourages the gaussians to align well with the surface of the scene. We then introduce a method that exploits this alignment to extract a mesh from the Gaussians using Poisson reconstruction, which is fast, scalable, and preserves details, in contrast to the Marching Cubes algorithm usually applied to extract meshes from Neural SDFs. Finally, we introduce an optional refinement strategy that binds gaussians to the surface of the mesh, and jointly optimizes these Gaussians and the mesh through Gaussian splatting rendering. This enables easy editing, sculpting, rigging, animating, compositing and relighting of the Gaussians using traditional softwares by manipulating the mesh instead of the gaussians themselves. Retrieving such an editable mesh for realistic rendering is done within minutes with our method, compared to hours with the state-of-the-art methods on neural SDFs, while providing a better rendering quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Anttwo/SuGaR", "github_api_added_by": "auto" }, { "date": "2023-11-22", "arxiv_id": "2311.12198", "title": "PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics", "authors": [ "Tianyi Xie", "Zeshun Zong", "Yuxin Qiu", "Xuan Li", "Yutao Feng", "Yin Yang", "Chenfanfu Jiang" ], "github": "", "project_page": "", "abstract": "We introduce PhysGaussian, a new method that seamlessly integrates physically grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthesis. Employing a custom Material Point Method (MPM), our approach enriches 3D Gaussian kernels with physically meaningful kinematic deformation and mechanical stress attributes, all evolved in line with continuum mechanics principles. A defining characteristic of our method is the seamless integration between physical simulation and visual rendering: both components utilize the same 3D Gaussian kernels as their discrete representations. This negates the necessity for triangle/tetrahedron meshing, marching cubes, \"cage meshes,\" or any other geometry embedding, highlighting the principle of \"what you see is what you simulate (WS^2).\" Our method demonstrates exceptional versatility across a wide variety of materials--including elastic entities, metals, non-Newtonian fluids, and granular materials--showcasing its strong capabilities in creating diverse visual content with novel viewpoints and movements. Our project page is at: https://xpandora.github.io/PhysGaussian/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-23", "arxiv_id": "2311.12983", "title": "GAIA: a benchmark for General AI Assistants", "authors": [ "Gr\u00e9goire Mialon", "Cl\u00e9mentine Fourrier", "Craig Swift", "Thomas Wolf", "Yann LeCun", "Thomas Scialom" ], "github": "", "project_page": "", "abstract": "We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. GAIA questions are conceptually simple for humans yet challenging for most advanced AIs: we show that human respondents obtain 92\\% vs. 15\\% for GPT-4 equipped with plugins. This notable performance disparity contrasts with the recent trend of LLMs outperforming humans on tasks requiring professional skills in e.g. law or chemistry. GAIA's philosophy departs from the current trend in AI benchmarks suggesting to target tasks that are ever more difficult for humans. We posit that the advent of Artificial General Intelligence (AGI) hinges on a system's capability to exhibit similar robustness as the average human does on such questions. Using GAIA's methodology, we devise 466 questions and their answer. We release our questions while retaining answers to 300 of them to power a leader-board available at https://huggingface.co/gaia-benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-23", "arxiv_id": "2311.12908", "title": "Diffusion Model Alignment Using Direct Preference Optimization", "authors": [ "Bram Wallace", "Meihua Dang", "Rafael Rafailov", "Linqi Zhou", "Aaron Lou", "Senthil Purushwalkam", "Stefano Ermon", "Caiming Xiong", "Shafiq Joty", "Nikhil Naik" ], "github": "https://github.com/SalesforceAIResearch/DiffusionDPO", "project_page": "", "abstract": "Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' preferences. In contrast to LLMs, human preference learning has not been widely explored in text-to-image diffusion models; the best existing approach is to fine-tune a pretrained model using carefully curated high quality images and captions to improve visual appeal and text alignment. We propose Diffusion-DPO, a method to align diffusion models to human preferences by directly optimizing on human comparison data. Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO), a simpler alternative to RLHF which directly optimizes a policy that best satisfies human preferences under a classification objective. We re-formulate DPO to account for a diffusion model notion of likelihood, utilizing the evidence lower bound to derive a differentiable objective. Using the Pick-a-Pic dataset of 851K crowdsourced pairwise preferences, we fine-tune the base model of the state-of-the-art Stable Diffusion XL (SDXL)-1.0 model with Diffusion-DPO. Our fine-tuned base model significantly outperforms both base SDXL-1.0 and the larger SDXL-1.0 model consisting of an additional refinement model in human evaluation, improving visual appeal and prompt alignment. We also develop a variant that uses AI feedback and has comparable performance to training on human preferences, opening the door for scaling of diffusion model alignment methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SalesforceAIResearch/DiffusionDPO", "github_api_added_by": "auto" }, { "date": "2023-11-23", "arxiv_id": "2311.13231", "title": "Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model", "authors": [ "Kai Yang", "Jian Tao", "Jiafei Lyu", "Chunjiang Ge", "Jiaxin Chen", "Qimai Li", "Weihan Shen", "Xiaolong Zhu", "Xiu Li" ], "github": "https://github.com/yk7333/d3po", "project_page": "", "abstract": "Using reinforcement learning with human feedback (RLHF) has shown significant promise in fine-tuning diffusion models. Previous methods start by training a reward model that aligns with human preferences, then leverage RL techniques to fine-tune the underlying models. However, crafting an efficient reward model demands extensive datasets, optimal architecture, and manual hyperparameter tuning, making the process both time and cost-intensive. The direct preference optimization (DPO) method, effective in fine-tuning large language models, eliminates the necessity for a reward model. However, the extensive GPU memory requirement of the diffusion model's denoising process hinders the direct application of the DPO method. To address this issue, we introduce the Direct Preference for Denoising Diffusion Policy Optimization (D3PO) method to directly fine-tune diffusion models. The theoretical analysis demonstrates that although D3PO omits training a reward model, it effectively functions as the optimal reward model trained using human feedback data to guide the learning process. This approach requires no training of a reward model, proving to be more direct, cost-effective, and minimizing computational overhead. In experiments, our method uses the relative scale of objectives as a proxy for human preference, delivering comparable results to methods using ground-truth rewards. Moreover, D3PO demonstrates the ability to reduce image distortion rates and generate safer images, overcoming challenges lacking robust reward models.", "github_manual": "https://github.com/yk7333/d3po", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-23", "arxiv_id": "2311.13384", "title": "LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes", "authors": [ "Jaeyoung Chung", "Suyoung Lee", "Hyeongjin Nam", "Jaerin Lee", "Kyoung Mu Lee" ], "github": "https://github.com/luciddreamer-cvlab/LucidDreamer", "project_page": "", "abstract": "With the widespread usage of VR devices and contents, demands for 3D scene generation techniques become more popular. Existing 3D scene generation models, however, limit the target scene to specific domain, primarily due to their training strategies using 3D scan dataset that is far from the real-world. To address such limitation, we propose LucidDreamer, a domain-free scene generation pipeline by fully leveraging the power of existing large-scale diffusion-based generative model. Our LucidDreamer has two alternate steps: Dreaming and Alignment. First, to generate multi-view consistent images from inputs, we set the point cloud as a geometrical guideline for each image generation. Specifically, we project a portion of point cloud to the desired view and provide the projection as a guidance for inpainting using the generative model. The inpainted images are lifted to 3D space with estimated depth maps, composing a new points. Second, to aggregate the new points into the 3D scene, we propose an aligning algorithm which harmoniously integrates the portions of newly generated 3D scenes. The finally obtained 3D scene serves as initial points for optimizing Gaussian splats. LucidDreamer produces Gaussian splats that are highly-detailed compared to the previous 3D scene generation methods, with no constraint on domain of the target scene.", "github_manual": "https://github.com/luciddreamer-cvlab/LucidDreamer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-23", "arxiv_id": "2311.13600", "title": "ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs", "authors": [ "Viraj Shah", "Nataniel Ruiz", "Forrester Cole", "Erika Lu", "Svetlana Lazebnik", "Yuanzhen Li", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "Methods for finetuning generative models for concept-driven personalization generally achieve strong results for subject-driven or style-driven generation. Recently, low-rank adaptations (LoRA) have been proposed as a parameter-efficient way of achieving concept-driven personalization. While recent work explores the combination of separate LoRAs to achieve joint generation of learned styles and subjects, existing techniques do not reliably address the problem; they often compromise either subject fidelity or style fidelity. We propose ZipLoRA, a method to cheaply and effectively merge independently trained style and subject LoRAs in order to achieve generation of any user-provided subject in any user-provided style. Experiments on a wide range of subject and style combinations show that ZipLoRA can generate compelling results with meaningful improvements over baselines in subject and style fidelity while preserving the ability to recontextualize. Project page: https://ziplora.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-23", "arxiv_id": "2311.13141", "title": "Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion Models", "authors": [ "Mengyang Feng", "Jinlin Liu", "Miaomiao Cui", "Xuansong Xie" ], "github": "https://github.com/ArcherFMY/SD-T2I-360PanoImage", "project_page": "", "abstract": "This is a technical report on the 360-degree panoramic image generation task based on diffusion models. Unlike ordinary 2D images, 360-degree panoramic images capture the entire 360^circtimes 180^circ field of view. So the rightmost and the leftmost sides of the 360 panoramic image should be continued, which is the main challenge in this field. However, the current diffusion pipeline is not appropriate for generating such a seamless 360-degree panoramic image. To this end, we propose a circular blending strategy on both the denoising and VAE decoding stages to maintain the geometry continuity. Based on this, we present two models for Text-to-360-panoramas and Single-Image-to-360-panoramas tasks. The code has been released as an open-source project at https://github.com/ArcherFMY/SD-T2I-360PanoImage{https://github.com/ArcherFMY/SD-T2I-360PanoImage} and https://www.modelscope.cn/models/damo/cv_diffusion_text-to-360panorama-image_generation/summary{ModelScope}", "github_manual": "", "github_regex": "https://github.com/ArcherFMY/SD-T2I-360PanoImage", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-23", "arxiv_id": "2311.13435", "title": "PG-Video-LLaVA: Pixel Grounding Large Video-Language Models", "authors": [ "Shehan Munasinghe", "Rusiru Thushara", "Muhammad Maaz", "Hanoona Abdul Rasheed", "Salman Khan", "Mubarak Shah", "Fahad Khan" ], "github": "https://github.com/mbzuai-oryx/Video-LLaVA", "project_page": "", "abstract": "Extending image-based Large Multimodal Models (LMM) to videos is challenging due to the inherent complexity of video data. The recent approaches extending image-based LMM to videos either lack the grounding capabilities (e.g., VideoChat, Video-ChatGPT, Video-LLaMA) or do not utilize the audio-signals for better video understanding (e.g., Video-ChatGPT). Addressing these gaps, we propose Video-LLaVA, the first LMM with pixel-level grounding capability, integrating audio cues by transcribing them into text to enrich video-context understanding. Our framework uses an off-the-shelf tracker and a novel grounding module, enabling it to spatially and temporally localize objects in videos following user instructions. We evaluate Video-LLaVA using video-based generative and question-answering benchmarks and introduce new benchmarks specifically designed to measure prompt-based object grounding performance in videos. Further, we propose the use of Vicuna over GPT-3.5, as utilized in Video-ChatGPT, for video-based conversation benchmarking, ensuring reproducibility of results which is a concern with the proprietary nature of GPT-3.5. Our framework builds on SoTA image-based LLaVA model and extends its advantages to the video domain, delivering promising gains on video-based conversation and grounding tasks. Project Page: https://github.com/mbzuai-oryx/Video-LLaVA", "github_manual": "", "github_regex": "https://github.com/mbzuai-oryx/Video-LLaVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-23", "arxiv_id": "2311.13073", "title": "FusionFrames: Efficient Architectural Aspects for Text-to-Video Generation Pipeline", "authors": [ "Vladimir Arkhipkin", "Zein Shaheen", "Viacheslav Vasilev", "Elizaveta Dakhova", "Andrey Kuznetsov", "Denis Dimitrov" ], "github": "https://github.com/ai-forever/kandinskyvideo", "project_page": "", "abstract": "Multimedia generation approaches occupy a prominent place in artificial intelligence research. Text-to-image models achieved high-quality results over the last few years. However, video synthesis methods recently started to develop. This paper presents a new two-stage latent diffusion text-to-video generation architecture based on the text-to-image diffusion model. The first stage concerns keyframes synthesis to figure the storyline of a video, while the second one is devoted to interpolation frames generation to make movements of the scene and objects smooth. We compare several temporal conditioning approaches for keyframes generation. The results show the advantage of using separate temporal blocks over temporal layers in terms of metrics reflecting video generation quality aspects and human preference. The design of our interpolation model significantly reduces computational costs compared to other masked frame interpolation approaches. Furthermore, we evaluate different configurations of MoVQ-based video decoding scheme to improve consistency and achieve higher PSNR, SSIM, MSE, and LPIPS scores. Finally, we compare our pipeline with existing solutions and achieve top-2 scores overall and top-1 among open-source solutions: CLIPSIM = 0.2976 and FVD = 433.054. Project page: https://ai-forever.github.io/kandinsky-video/", "github_manual": "https://github.com/ai-forever/kandinskyvideo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-11-23", "arxiv_id": "2311.13601", "title": "Visual In-Context Prompting", "authors": [ "Feng Li", "Qing Jiang", "Hao Zhang", "Tianhe Ren", "Shilong Liu", "Xueyan Zou", "Huaizhe Xu", "Hongyang Li", "Chunyuan Li", "Jianwei Yang", "Lei Zhang", "Jianfeng Gao" ], "github": "https://github.com/UX-Decoder/DINOv", "project_page": "", "abstract": "In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities, but this idea is less explored in the vision domain. Existing visual prompting methods focus on referring segmentation to segment the most relevant object, falling short of addressing many generic vision tasks like open-set segmentation and detection. In this paper, we introduce a universal visual in-context prompting framework for both tasks. In particular, we build on top of an encoder-decoder architecture, and develop a versatile prompt encoder to support a variety of prompts like strokes, boxes, and points. We further enhance it to take an arbitrary number of reference image segments as the context. Our extensive explorations show that the proposed visual in-context prompting elicits extraordinary referring and generic segmentation capabilities to refer and detect, yielding competitive performance to close-set in-domain datasets and showing promising results on many open-set segmentation datasets. By joint training on COCO and SA-1B, our model achieves 57.7 PQ on COCO and 23.2 PQ on ADE20K. Code will be available at https://github.com/UX-Decoder/DINOv.", "github_manual": "", "github_regex": "https://github.com/UX-Decoder/DINOv", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00738", "title": "SeaLLMs -- Large Language Models for Southeast Asia", "authors": [ "Xuan-Phi Nguyen", "Wenxuan Zhang", "Xin Li", "Mahani Aljunied", "Qingyu Tan", "Liying Cheng", "Guanzheng Chen", "Yue Deng", "Sen Yang", "Chaoqun Liu", "Hang Zhang", "Lidong Bing" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "Despite the remarkable achievements of large language models (LLMs) in various tasks, there remains a linguistic bias that favors high-resource languages, such as English, often at the expense of low-resource and regional languages. To address this imbalance, we introduce SeaLLMs, an innovative series of language models that specifically focuses on Southeast Asian (SEA) languages. SeaLLMs are built upon the Llama-2 model and further advanced through continued pre-training with an extended vocabulary, specialized instruction and alignment tuning to better capture the intricacies of regional languages. This allows them to respect and reflect local cultural norms, customs, stylistic preferences, and legal considerations. Our comprehensive evaluation demonstrates that SeaLLM-13b models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities relative to comparable open-source models. Moreover, they outperform ChatGPT-3.5 in non-Latin languages, such as Thai, Khmer, Lao, and Burmese, by large margins while remaining lightweight and cost-effective to operate.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00438", "title": "Dolphins: Multimodal Language Model for Driving", "authors": [ "Yingzi Ma", "Yulong Cao", "Jiachen Sun", "Marco Pavone", "Chaowei Xiao" ], "github": "", "project_page": "", "abstract": "The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world scenarios with human-like understanding and responsiveness. In this paper, we introduce Dolphins, a novel vision-language model architected to imbibe human-like abilities as a conversational driving assistant. Dolphins is adept at processing multimodal inputs comprising video (or image) data, text instructions, and historical control signals to generate informed outputs corresponding to the provided instructions. Building upon the open-sourced pretrained Vision-Language Model, OpenFlamingo, we first enhance Dolphins's reasoning capabilities through an innovative Grounded Chain of Thought (GCoT) process. Then we tailored Dolphins to the driving domain by constructing driving-specific instruction data and conducting instruction tuning. Through the utilization of the BDD-X dataset, we designed and consolidated four distinct AV tasks into Dolphins to foster a holistic understanding of intricate driving scenarios. As a result, the distinctive features of Dolphins are characterized into two dimensions: (1) the ability to provide a comprehensive understanding of complex and long-tailed open-world driving scenarios and solve a spectrum of AV tasks, and (2) the emergence of human-like capabilities including gradient-free instant adaptation via in-context learning and error recovery via reflection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00330", "title": "StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter", "authors": [ "Gongye Liu", "Menghan Xia", "Yong Zhang", "Haoxin Chen", "Jinbo Xing", "Xintao Wang", "Yujiu Yang", "Ying Shan" ], "github": "https://github.com/GongyeLiu/StyleCrafter", "project_page": "", "abstract": "Text-to-video (T2V) models have shown remarkable capabilities in generating diverse videos. However, they struggle to produce user-desired stylized videos due to (i) text's inherent clumsiness in expressing specific styles and (ii) the generally degraded style fidelity. To address these challenges, we introduce StyleCrafter, a generic method that enhances pre-trained T2V models with a style control adapter, enabling video generation in any style by providing a reference image. Considering the scarcity of stylized video datasets, we propose to first train a style control adapter using style-rich image datasets, then transfer the learned stylization ability to video generation through a tailor-made finetuning paradigm. To promote content-style disentanglement, we remove style descriptions from the text prompt and extract style information solely from the reference image using a decoupling learning strategy. Additionally, we design a scale-adaptive fusion module to balance the influences of text-based content features and image-based style features, which helps generalization across various text and style combinations. StyleCrafter efficiently generates high-quality stylized videos that align with the content of the texts and resemble the style of the reference images. Experiments demonstrate that our approach is more flexible and efficient than existing competitors.", "github_manual": "https://github.com/GongyeLiu/StyleCrafter", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00079", "title": "HiFi Tuner: High-Fidelity Subject-Driven Fine-Tuning for Diffusion Models", "authors": [ "Zhonghao Wang", "Wei Wei", "Yang Zhao", "Zhisheng Xiao", "Mark Hasegawa-Johnson", "Humphrey Shi", "Tingbo Hou" ], "github": "", "project_page": "", "abstract": "This paper explores advancements in high-fidelity personalized image generation through the utilization of pre-trained text-to-image diffusion models. While previous approaches have made significant strides in generating versatile scenes based on text descriptions and a few input images, challenges persist in maintaining the subject fidelity within the generated images. In this work, we introduce an innovative algorithm named HiFi Tuner to enhance the appearance preservation of objects during personalized image generation. Our proposed method employs a parameter-efficient fine-tuning framework, comprising a denoising process and a pivotal inversion process. Key enhancements include the utilization of mask guidance, a novel parameter regularization technique, and the incorporation of step-wise subject representations to elevate the sample fidelity. Additionally, we propose a reference-guided generation approach that leverages the pivotal inversion of a reference image to mitigate unwanted subject variations and artifacts. We further extend our method to a novel image editing task: substituting the subject in an image through textual manipulations. Experimental evaluations conducted on the DreamBooth dataset using the Stable Diffusion model showcase promising results. Fine-tuning solely on textual embeddings improves CLIP-T score by 3.6 points and improves DINO score by 9.6 points over Textual Inversion. When fine-tuning all parameters, HiFi Tuner improves CLIP-T score by 1.2 points and improves DINO score by 1.2 points over DreamBooth, establishing a new state of the art.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00093", "title": "GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs", "authors": [ "Gege Gao", "Weiyang Liu", "Anpei Chen", "Andreas Geiger", "Bernhard Sch\u00f6lkopf" ], "github": "https://github.com/GGGHSL/GraphDreamer", "project_page": "", "abstract": "As pretrained text-to-image diffusion models become increasingly powerful, recent efforts have been made to distill knowledge from these text-to-image pretrained models for optimizing a text-guided 3D model. Most of the existing methods generate a holistic 3D model from a plain text input. This can be problematic when the text describes a complex scene with multiple objects, because the vectorized text embeddings are inherently unable to capture a complex description with multiple entities and relationships. Holistic 3D modeling of the entire scene further prevents accurate grounding of text entities and concepts. To address this limitation, we propose GraphDreamer, a novel framework to generate compositional 3D scenes from scene graphs, where objects are represented as nodes and their interactions as edges. By exploiting node and edge information in scene graphs, our method makes better use of the pretrained text-to-image diffusion model and is able to fully disentangle different objects without image-level supervision. To facilitate modeling of object-wise relationships, we use signed distance fields as representation and impose a constraint to avoid inter-penetration of objects. To avoid manual scene graph creation, we design a text prompt for ChatGPT to generate scene graphs based on text inputs. We conduct both qualitative and quantitative experiments to validate the effectiveness of GraphDreamer in generating high-fidelity compositional 3D scenes with disentangled object entities.", "github_manual": "https://github.com/GGGHSL/GraphDreamer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00752", "title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces", "authors": [ "Albert Gu", "Tri Dao" ], "github": "https://github.com/state-spaces/mamba", "project_page": "", "abstract": "Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers' computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5times higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pretraining and downstream evaluation.", "github_manual": "https://github.com/state-spaces/mamba", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00777", "title": "VideoBooth: Diffusion-based Video Generation with Image Prompts", "authors": [ "Yuming Jiang", "Tianxing Wu", "Shuai Yang", "Chenyang Si", "Dahua Lin", "Yu Qiao", "Chen Change Loy", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Text-driven video generation witnesses rapid progress. However, merely using text prompts is not enough to depict the desired subject appearance that accurately aligns with users' intents, especially for customized content creation. In this paper, we study the task of video generation with image prompts, which provide more accurate and direct content control beyond the text prompts. Specifically, we propose a feed-forward framework VideoBooth, with two dedicated designs: 1) We propose to embed image prompts in a coarse-to-fine manner. Coarse visual embeddings from image encoder provide high-level encodings of image prompts, while fine visual embeddings from the proposed attention injection module provide multi-scale and detailed encoding of image prompts. These two complementary embeddings can faithfully capture the desired appearance. 2) In the attention injection module at fine level, multi-scale image prompts are fed into different cross-frame attention layers as additional keys and values. This extra spatial information refines the details in the first frame and then it is propagated to the remaining frames, which maintains temporal consistency. Extensive experiments demonstrate that VideoBooth achieves state-of-the-art performance in generating customized high-quality videos with subjects specified in image prompts. Notably, VideoBooth is a generalizable framework where a single model works for a wide range of image prompts with feed-forward pass.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00763", "title": "Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses", "authors": [ "Xiao Ma", "Swaroop Mishra", "Ariel Liu", "Sophie Su", "Jilin Chen", "Chinmay Kulkarni", "Heng-Tze Cheng", "Quoc Le", "Ed Chi" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) powered chatbots are primarily text-based today, and impose a large interactional cognitive load, especially for exploratory or sensemaking tasks such as planning a trip or learning about a new city. Because the interaction is textual, users have little scaffolding in the way of structure, informational \"scent\", or ability to specify high-level preferences or goals. We introduce ExploreLLM that allows users to structure thoughts, help explore different options, navigate through the choices and recommendations, and to more easily steer models to generate more personalized responses. We conduct a user study and show that users find it helpful to use ExploreLLM for exploratory or planning tasks, because it provides a useful schema-like structure to the task, and guides users in planning. The study also suggests that users can more easily personalize responses with high-level preferences with ExploreLLM. Together, ExploreLLM points to a future where users interact with LLMs beyond the form of chatbots, and instead designed to support complex user tasks with a tighter integration between natural language and graphical user interfaces.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00589", "title": "Merlin:Empowering Multimodal LLMs with Foresight Minds", "authors": [ "En Yu", "Liang Zhao", "Yana Wei", "Jinrong Yang", "Dongming Wu", "Lingyu Kong", "Haoran Wei", "Tiancai Wang", "Zheng Ge", "Xiangyu Zhang", "Wenbing Tao" ], "github": "", "project_page": "", "abstract": "Humans possess the remarkable ability to foresee the future to a certain extent based on present observations, a skill we term as foresight minds. However, this capability remains largely under explored within existing Multimodal Large Language Models (MLLMs), hindering their capacity to learn the fundamental principles of how things operate and the intentions behind the observed subjects. To address this issue, we introduce the integration of future modeling into the existing learning frameworks of MLLMs. By utilizing the subject trajectory, a highly structured representation of a consecutive frame sequence, as a learning objective, we aim to bridge the gap between the past and the future. We propose two innovative methods to empower MLLMs with foresight minds, Foresight Pre-Training (FPT) and Foresight Instruction-Tuning (FIT), which are inspired by the modern learning paradigm of LLMs. Specifically, FPT jointly training various tasks centered on trajectories, enabling MLLMs to learn how to attend and predict entire trajectories from a given initial observation. Then, FIT requires MLLMs to first predict trajectories of related objects and then reason about potential future events based on them. Aided by FPT and FIT, we build a novel and unified MLLM named Merlin that supports multi-images input and analysis about potential actions of multiple objects for the future reasoning. Experimental results show Merlin powerful foresight minds with impressive performance on both future reasoning and visual comprehension tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00575", "title": "Instruction-tuning Aligns LLMs to the Human Brain", "authors": [ "Khai Loong Aw", "Syrielle Montariol", "Badr AlKhamissi", "Martin Schrimpf", "Antoine Bosselut" ], "github": "", "project_page": "", "abstract": "Instruction-tuning is a widely adopted method of finetuning that enables large language models (LLMs) to generate output that more closely resembles human responses to natural language queries, in many cases leading to human-level performance on diverse testbeds. However, it remains unclear whether instruction-tuning truly makes LLMs more similar to how humans process language. We investigate the effect of instruction-tuning on LLM-human similarity in two ways: (1) brain alignment, the similarity of LLM internal representations to neural activity in the human language system, and (2) behavioral alignment, the similarity of LLM and human behavior on a reading task. We assess 25 vanilla and instruction-tuned LLMs across three datasets involving humans reading naturalistic stories and sentences. We discover that instruction-tuning generally enhances brain alignment by an average of 6%, but does not have a similar effect on behavioral alignment. To identify the factors underlying LLM-brain alignment, we compute correlations between the brain alignment of LLMs and various model properties, such as model size, various problem-solving abilities, and performance on tasks requiring world knowledge spanning various domains. Notably, we find a strong positive correlation between brain alignment and model size (r = 0.95), as well as performance on tasks requiring world knowledge (r = 0.81). Our results demonstrate that instruction-tuning LLMs improves both world knowledge representations and brain alignment, suggesting that mechanisms that encode world knowledge in LLMs also improve representational alignment to the human brain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00164", "title": "Towards Accurate Differential Diagnosis with Large Language Models", "authors": [ "Daniel McDuff", "Mike Schaekermann", "Tao Tu", "Anil Palepu", "Amy Wang", "Jake Garrison", "Karan Singhal", "Yash Sharma", "Shekoofeh Azizi", "Kavita Kulkarni", "Le Hou", "Yong Cheng", "Yun Liu", "S Sara Mahdavi", "Sushant Prakash", "Anupam Pathak", "Christopher Semturs", "Shwetak Patel", "Dale R Webster", "Ewa Dominowska", "Juraj Gottweis", "Joelle Barral", "Katherine Chou", "Greg S Corrado", "Yossi Matias", "Jake Sunshine", "Alan Karthikesalingam", "Vivek Natarajan" ], "github": "", "project_page": "", "abstract": "An accurate differential diagnosis (DDx) is a cornerstone of medical care, often reached through an iterative process of interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by Large Language Models (LLMs) present new opportunities to both assist and automate aspects of this process. In this study, we introduce an LLM optimized for diagnostic reasoning, and evaluate its ability to generate a DDx alone or as an aid to clinicians. 20 clinicians evaluated 302 challenging, real-world medical cases sourced from the New England Journal of Medicine (NEJM) case reports. Each case report was read by two clinicians, who were randomized to one of two assistive conditions: either assistance from search engines and standard medical resources, or LLM assistance in addition to these tools. All clinicians provided a baseline, unassisted DDx prior to using the respective assistive tools. Our LLM for DDx exhibited standalone performance that exceeded that of unassisted clinicians (top-10 accuracy 59.1% vs 33.6%, [p = 0.04]). Comparing the two assisted study arms, the DDx quality score was higher for clinicians assisted by our LLM (top-10 accuracy 51.7%) compared to clinicians without its assistance (36.1%) (McNemar's Test: 45.7, p < 0.01) and clinicians with search (44.4%) (4.75, p = 0.03). Further, clinicians assisted by our LLM arrived at more comprehensive differential lists than those without its assistance. Our study suggests that our LLM for DDx has potential to improve clinicians' diagnostic reasoning and accuracy in challenging cases, meriting further real-world evaluation for its ability to empower physicians and widen patients' access to specialist-level expertise.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00109", "title": "Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering", "authors": [ "Tao Lu", "Mulin Yu", "Linning Xu", "Yuanbo Xiangli", "Limin Wang", "Dahua Lin", "Bo Dai" ], "github": "https://github.com/city-super/Scaffold-GS", "project_page": "", "abstract": "Neural rendering methods have significantly advanced photo-realistic 3D scene rendering in various academic and industrial applications. The recent 3D Gaussian Splatting method has achieved the state-of-the-art rendering quality and speed combining the benefits of both primitive-based representations and volumetric representations. However, it often leads to heavily redundant Gaussians that try to fit every training view, neglecting the underlying scene geometry. Consequently, the resulting model becomes less robust to significant view changes, texture-less area and lighting effects. We introduce Scaffold-GS, which uses anchor points to distribute local 3D Gaussians, and predicts their attributes on-the-fly based on viewing direction and distance within the view frustum. Anchor growing and pruning strategies are developed based on the importance of neural Gaussians to reliably improve the scene coverage. We show that our method effectively reduces redundant Gaussians while delivering high-quality rendering. We also demonstrates an enhanced capability to accommodate scenes with varying levels-of-detail and view-dependent observations, without sacrificing the rendering speed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/city-super/Scaffold-GS", "github_api_added_by": "auto" }, { "date": "2023-12-04", "arxiv_id": "2312.00252", "title": "PyNeRF: Pyramidal Neural Radiance Fields", "authors": [ "Haithem Turki", "Michael Zollh\u00f6fer", "Christian Richardt", "Deva Ramanan" ], "github": "https://github.com/hturki/pynerf", "project_page": "", "abstract": "Neural Radiance Fields (NeRFs) can be dramatically accelerated by spatial grid representations. However, they do not explicitly reason about scale and so introduce aliasing artifacts when reconstructing scenes captured at different camera distances. Mip-NeRF and its extensions propose scale-aware renderers that project volumetric frustums rather than point samples but such approaches rely on positional encodings that are not readily compatible with grid methods. We propose a simple modification to grid-based models by training model heads at different spatial grid resolutions. At render time, we simply use coarser grids to render samples that cover larger volumes. Our method can be easily applied to existing accelerated NeRF methods and significantly improves rendering quality (reducing error rates by 20-90% across synthetic and unbounded real-world scenes) while incurring minimal performance overhead (as each model head is quick to evaluate). Compared to Mip-NeRF, we reduce error rates by 20% while training over 60x faster.", "github_manual": "https://github.com/hturki/pynerf", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00063", "title": "MoMask: Generative Masked Modeling of 3D Human Motions", "authors": [ "Chuan Guo", "Yuxuan Mu", "Muhammad Gohar Javed", "Sen Wang", "Li Cheng" ], "github": "https://github.com/EricGuo5513/momask-codes", "project_page": "", "abstract": "We introduce MoMask, a novel masked modeling framework for text-driven 3D human motion generation. In MoMask, a hierarchical quantization scheme is employed to represent human motion as multi-layer discrete motion tokens with high-fidelity details. Starting at the base layer, with a sequence of motion tokens obtained by vector quantization, the residual tokens of increasing orders are derived and stored at the subsequent layers of the hierarchy. This is consequently followed by two distinct bidirectional transformers. For the base-layer motion tokens, a Masked Transformer is designated to predict randomly masked motion tokens conditioned on text input at training stage. During generation (i.e. inference) stage, starting from an empty sequence, our Masked Transformer iteratively fills up the missing tokens; Subsequently, a Residual Transformer learns to progressively predict the next-layer tokens based on the results from current layer. Extensive experiments demonstrate that MoMask outperforms the state-of-art methods on the text-to-motion generation task, with an FID of 0.045 (vs e.g. 0.141 of T2M-GPT) on the HumanML3D dataset, and 0.228 (vs 0.514) on KIT-ML, respectively. MoMask can also be seamlessly applied in related tasks without further model fine-tuning, such as text-guided temporal inpainting.", "github_manual": "https://github.com/EricGuo5513/momask-codes", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00375", "title": "Text-Guided 3D Face Synthesis -- From Generation to Editing", "authors": [ "Yunjie Wu", "Yapeng Meng", "Zhipeng Hu", "Lincheng Li", "Haoqian Wu", "Kun Zhou", "Weiwei Xu", "Xin Yu" ], "github": "https://github.com/JiejiangWu/FaceG2E", "project_page": "", "abstract": "Text-guided 3D face synthesis has achieved remarkable results by leveraging text-to-image (T2I) diffusion models. However, most existing works focus solely on the direct generation, ignoring the editing, restricting them from synthesizing customized 3D faces through iterative adjustments. In this paper, we propose a unified text-guided framework from face generation to editing. In the generation stage, we propose a geometry-texture decoupled generation to mitigate the loss of geometric details caused by coupling. Besides, decoupling enables us to utilize the generated geometry as a condition for texture generation, yielding highly geometry-texture aligned results. We further employ a fine-tuned texture diffusion model to enhance texture quality in both RGB and YUV space. In the editing stage, we first employ a pre-trained diffusion model to update facial geometry or texture based on the texts. To enable sequential editing, we introduce a UV domain consistency preservation regularization, preventing unintentional changes to irrelevant facial attributes. Besides, we propose a self-guided consistency weight strategy to improve editing efficacy while preserving consistency. Through comprehensive experiments, we showcase our method's superiority in face synthesis. Project page: https://faceg2e.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JiejiangWu/FaceG2E", "github_api_added_by": "auto" }, { "date": "2023-12-04", "arxiv_id": "2312.00210", "title": "DREAM: Diffusion Rectification and Estimation-Adaptive Models", "authors": [ "Jinxin Zhou", "Tianyu Ding", "Tianyi Chen", "Jiachen Jiang", "Ilya Zharkov", "Zhihui Zhu", "Luming Liang" ], "github": "https://github.com/jinxinzhou/dream", "project_page": "", "abstract": "We present DREAM, a novel training framework representing Diffusion Rectification and Estimation-Adaptive Models, requiring minimal code changes (just three lines) yet significantly enhancing the alignment of training with sampling in diffusion models. DREAM features two components: diffusion rectification, which adjusts training to reflect the sampling process, and estimation adaptation, which balances perception against distortion. When applied to image super-resolution (SR), DREAM adeptly navigates the tradeoff between minimizing distortion and preserving high image quality. Experiments demonstrate DREAM's superiority over standard diffusion-based SR methods, showing a 2 to 3times faster training convergence and a 10 to 20times reduction in necessary sampling steps to achieve comparable or superior results. We hope DREAM will inspire a rethinking of diffusion model training paradigms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jinxinzhou/dream", "github_api_added_by": "auto" }, { "date": "2023-12-04", "arxiv_id": "2312.00085", "title": "X-Dreamer: Creating High-quality 3D Content by Bridging the Domain Gap Between Text-to-2D and Text-to-3D Generation", "authors": [ "Yiwei Ma", "Yijun Fan", "Jiayi Ji", "Haowei Wang", "Xiaoshuai Sun", "Guannan Jiang", "Annan Shu", "Rongrong Ji" ], "github": "https://github.com/xmu-xiaoma666/X-Dreamer", "project_page": "", "abstract": "In recent times, automatic text-to-3D content creation has made significant progress, driven by the development of pretrained 2D diffusion models. Existing text-to-3D methods typically optimize the 3D representation to ensure that the rendered image aligns well with the given text, as evaluated by the pretrained 2D diffusion model. Nevertheless, a substantial domain gap exists between 2D images and 3D assets, primarily attributed to variations in camera-related attributes and the exclusive presence of foreground objects. Consequently, employing 2D diffusion models directly for optimizing 3D representations may lead to suboptimal outcomes. To address this issue, we present X-Dreamer, a novel approach for high-quality text-to-3D content creation that effectively bridges the gap between text-to-2D and text-to-3D synthesis. The key components of X-Dreamer are two innovative designs: Camera-Guided Low-Rank Adaptation (CG-LoRA) and Attention-Mask Alignment (AMA) Loss. CG-LoRA dynamically incorporates camera information into the pretrained diffusion models by employing camera-dependent generation for trainable parameters. This integration enhances the alignment between the generated 3D assets and the camera's perspective. AMA loss guides the attention map of the pretrained diffusion model using the binary mask of the 3D object, prioritizing the creation of the foreground object. This module ensures that the model focuses on generating accurate and detailed foreground objects. Extensive evaluations demonstrate the effectiveness of our proposed method compared to existing text-to-3D approaches. Our project webpage: https://xmuxiaoma666.github.io/Projects/X-Dreamer .", "github_manual": "https://github.com/xmu-xiaoma666/X-Dreamer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-04", "arxiv_id": "2312.00451", "title": "FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting", "authors": [ "Zehao Zhu", "Zhiwen Fan", "Yifan Jiang", "Zhangyang Wang" ], "github": "https://github.com/vita-group/fsgs", "project_page": "", "abstract": "Novel view synthesis from limited observations remains an important and persistent task. However, high efficiency in existing NeRF-based few-shot view synthesis is often compromised to obtain an accurate 3D representation. To address this challenge, we propose a few-shot view synthesis framework based on 3D Gaussian Splatting that enables real-time and photo-realistic view synthesis with as few as three training views. The proposed method, dubbed FSGS, handles the extremely sparse initialized SfM points with a thoughtfully designed Gaussian Unpooling process. Our method iteratively distributes new Gaussians around the most representative locations, subsequently infilling local details in vacant areas. We also integrate a large-scale pre-trained monocular depth estimator within the Gaussians optimization process, leveraging online augmented views to guide the geometric optimization towards an optimal solution. Starting from sparse points observed from limited input viewpoints, our FSGS can accurately grow into unseen regions, comprehensively covering the scene and boosting the rendering quality of novel views. Overall, FSGS achieves state-of-the-art performance in both accuracy and rendering efficiency across diverse datasets, including LLFF, Mip-NeRF360, and Blender. Project website: https://zehaozhu.github.io/FSGS/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vita-group/fsgs", "github_api_added_by": "auto" }, { "date": "2023-12-05", "arxiv_id": "2312.02120", "title": "Magicoder: Source Code Is All You Need", "authors": [ "Yuxiang Wei", "Zhe Wang", "Jiawei Liu", "Yifeng Ding", "Lingming Zhang" ], "github": "https://github.com/ise-uiuc/magicoder", "project_page": "", "abstract": "We introduce Magicoder, a series of fully open-source (code, weights, and data) Large Language Models (LLMs) for code that significantly closes the gap with top code models while having no more than 7B parameters. Magicoder models are trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets to generate high-quality instruction data for code. Our main motivation is to mitigate the inherent bias of the synthetic data generated by LLMs by empowering them with a wealth of open-source references for the production of more diverse, realistic, and controllable data. The orthogonality of OSS-Instruct and other data generation methods like Evol-Instruct further enables us to build an enhanced MagicoderS. Both Magicoder and MagicoderS substantially outperform state-of-the-art code models with similar or even larger sizes on a wide range of coding benchmarks, including Python text-to-code generation, multilingual coding, and data-science program completion. Notably, MagicoderS-CL-7B based on CodeLlama even surpasses the prominent ChatGPT on HumanEval+ (66.5 vs. 65.9 in pass@1). Overall, OSS-Instruct opens a new direction for low-bias and high-quality instruction tuning using abundant open-source references.", "github_manual": "https://github.com/ise-uiuc/magicoder", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.00849", "title": "RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback", "authors": [ "Tianyu Yu", "Yuan Yao", "Haoye Zhang", "Taiwen He", "Yifeng Han", "Ganqu Cui", "Jinyi Hu", "Zhiyuan Liu", "Hai-Tao Zheng", "Maosong Sun", "Tat-Seng Chua" ], "github": "https://github.com/RLHF-V/RLHF-V", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in multimodal understanding, reasoning, and interaction. However, existing MLLMs prevalently suffer from serious hallucination problems, generating text that is not factually grounded in associated images. The problem makes existing MLLMs untrustworthy and thus impractical in real-world (especially high-stakes) applications. To address the challenge, we present RLHF-V, which enhances MLLM trustworthiness via behavior alignment from fine-grained correctional human feedback. Specifically, RLHF-V collects human preference in the form of segment-level corrections on hallucinations, and performs dense direct preference optimization over the human feedback. Comprehensive experiments on five benchmarks in both automatic and human evaluation show that, RLHF-V can enable substantially more trustworthy MLLM behaviors with promising data and computation efficiency. Remarkably, using 1.4k annotated data samples, RLHF-V significantly reduces the hallucination rate of the base MLLM by 34.8%, outperforming the concurrent LLaVA-RLHF trained on 10k annotated data. The final model achieves state-of-the-art performance in trustworthiness among open-source MLLMs, and shows better robustness than GPT-4V in preventing hallucinations aroused from over-generalization. We open-source our code, model, and data at https://github.com/RLHF-V/RLHF-V.", "github_manual": "", "github_regex": "https://github.com/RLHF-V/RLHF-V", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.01552", "title": "The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning", "authors": [ "Bill Yuchen Lin", "Abhilasha Ravichander", "Ximing Lu", "Nouha Dziri", "Melanie Sclar", "Khyathi Chandu", "Chandra Bhagavatula", "Yejin Choi" ], "github": "https://github.com/re-align/urial", "project_page": "", "abstract": "The alignment tuning process of large language models (LLMs) typically involves instruction learning through supervised fine-tuning (SFT) and preference tuning via reinforcement learning from human feedback (RLHF). A recent study, LIMA (Zhou et al. 2023), shows that using merely 1K examples for SFT can achieve significant alignment performance as well, suggesting that the effect of alignment tuning might be \"superficial.\" This raises questions about how exactly the alignment tuning transforms a base LLM. We analyze the effect of alignment tuning by examining the token distribution shift between base LLMs and their aligned counterpart. Our findings reveal that base LLMs and their alignment-tuned versions perform nearly identically in decoding on the majority of token positions. Most distribution shifts occur with stylistic tokens. These direct evidence strongly supports the Superficial Alignment Hypothesis suggested by LIMA. Based on these findings, we rethink the alignment of LLMs by posing the research question: how effectively can we align base LLMs without SFT or RLHF? To address this, we introduce a simple, tuning-free alignment method, URIAL. URIAL achieves effective alignment purely through in-context learning (ICL) with base LLMs, requiring as few as three constant stylistic examples and a system prompt. We conduct a fine-grained and interpretable evaluation on a diverse set of examples, named JUST-EVAL-INSTRUCT. Results demonstrate that base LLMs with URIAL can match or even surpass the performance of LLMs aligned with SFT or SFT+RLHF. We show that the gap between tuning-free and tuning-based alignment methods can be significantly reduced through strategic prompting and ICL. Our findings on the superficial nature of alignment tuning and results with URIAL suggest that deeper analysis and theoretical understanding of alignment is crucial to future LLM research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/re-align/urial", "github_api_added_by": "auto" }, { "date": "2023-12-05", "arxiv_id": "2312.02149", "title": "Generative Powers of Ten", "authors": [ "Xiaojuan Wang", "Janne Kontkanen", "Brian Curless", "Steve Seitz", "Ira Kemelmacher", "Ben Mildenhall", "Pratul Srinivasan", "Dor Verbin", "Aleksander Holynski" ], "github": "", "project_page": "", "abstract": "We present a method that uses a text-to-image model to generate consistent content across multiple image scales, enabling extreme semantic zooms into a scene, e.g., ranging from a wide-angle landscape view of a forest to a macro shot of an insect sitting on one of the tree branches. We achieve this through a joint multi-scale diffusion sampling approach that encourages consistency across different scales while preserving the integrity of each individual sampling process. Since each generated scale is guided by a different text prompt, our method enables deeper levels of zoom than traditional super-resolution methods that may struggle to create new contextual structure at vastly different scales. We compare our method qualitatively with alternative techniques in image super-resolution and outpainting, and show that our method is most effective at generating consistent multi-scale content.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.00869", "title": "Segment and Caption Anything", "authors": [ "Xiaoke Huang", "Jianfeng Wang", "Yansong Tang", "Zheng Zhang", "Han Hu", "Jiwen Lu", "Lijuan Wang", "Zicheng Liu" ], "github": "https://github.com/xk-huang/segment-caption-anything", "project_page": "", "abstract": "We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability to generate regional captions. SAM presents strong generalizability to segment anything while is short for semantic understanding. By introducing a lightweight query-based feature mixer, we align the region-specific features with the embedding space of language models for later caption generation. As the number of trainable parameters is small (typically in the order of tens of millions), it costs less computation, less memory usage, and less communication bandwidth, resulting in both fast and scalable training. To address the scarcity problem of regional caption data, we propose to first pre-train our model on objection detection and segmentation tasks. We call this step weak supervision pretraining since the pre-training data only contains category names instead of full-sentence descriptions. The weak supervision pretraining allows us to leverage many publicly available object detection and segmentation datasets. We conduct extensive experiments to demonstrate the superiority of our method and validate each design choice. This work serves as a stepping stone towards scaling up regional captioning data and sheds light on exploring efficient ways to augment SAM with regional semantics. The project page, along with the associated code, can be accessed via the following https://xk-huang.github.io/segment-caption-anything/.", "github_manual": "https://github.com/xk-huang/segment-caption-anything", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.01663", "title": "Customize your NeRF: Adaptive Source Driven 3D Scene Editing via Local-Global Iterative Training", "authors": [ "Runze He", "Shaofei Huang", "Xuecheng Nie", "Tianrui Hui", "Luoqi Liu", "Jiao Dai", "Jizhong Han", "Guanbin Li", "Si Liu" ], "github": "", "project_page": "", "abstract": "In this paper, we target the adaptive source driven 3D scene editing task by proposing a CustomNeRF model that unifies a text description or a reference image as the editing prompt. However, obtaining desired editing results conformed with the editing prompt is nontrivial since there exist two significant challenges, including accurate editing of only foreground regions and multi-view consistency given a single-view reference image. To tackle the first challenge, we propose a Local-Global Iterative Editing (LGIE) training scheme that alternates between foreground region editing and full-image editing, aimed at foreground-only manipulation while preserving the background. For the second challenge, we also design a class-guided regularization that exploits class priors within the generation model to alleviate the inconsistency problem among different views in image-driven editing. Extensive experiments show that our CustomNeRF produces precise editing results under various real scenes for both text- and image-driven settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.01531", "title": "SANeRF-HQ: Segment Anything for NeRF in High Quality", "authors": [ "Yichen Liu", "Benran Hu", "Chi-Keung Tang", "Yu-Wing Tai" ], "github": "", "project_page": "", "abstract": "Recently, the Segment Anything Model (SAM) has showcased remarkable capabilities of zero-shot segmentation, while NeRF (Neural Radiance Fields) has gained popularity as a method for various 3D problems beyond novel view synthesis. Though there exist initial attempts to incorporate these two methods into 3D segmentation, they face the challenge of accurately and consistently segmenting objects in complex scenarios. In this paper, we introduce the Segment Anything for NeRF in High Quality (SANeRF-HQ) to achieve high quality 3D segmentation of any object in a given scene. SANeRF-HQ utilizes SAM for open-world object segmentation guided by user-supplied prompts, while leveraging NeRF to aggregate information from different viewpoints. To overcome the aforementioned challenges, we employ density field and RGB similarity to enhance the accuracy of segmentation boundary during the aggregation. Emphasizing on segmentation accuracy, we evaluate our method quantitatively on multiple NeRF datasets where high-quality ground-truths are available or manually annotated. SANeRF-HQ shows a significant quality improvement over previous state-of-the-art methods in NeRF object segmentation, provides higher flexibility for object localization, and enables more consistent object segmentation across multiple views. Additional information can be found at https://lyclyc52.github.io/SANeRF-HQ/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.01407", "title": "VideoRF: Rendering Dynamic Radiance Fields as 2D Feature Video Streams", "authors": [ "Liao Wang", "Kaixin Yao", "Chengcheng Guo", "Zhirui Zhang", "Qiang Hu", "Jingyi Yu", "Lan Xu", "Minye Wu" ], "github": "", "project_page": "", "abstract": "Neural Radiance Fields (NeRFs) excel in photorealistically rendering static scenes. However, rendering dynamic, long-duration radiance fields on ubiquitous devices remains challenging, due to data storage and computational constraints. In this paper, we introduce VideoRF, the first approach to enable real-time streaming and rendering of dynamic radiance fields on mobile platforms. At the core is a serialized 2D feature image stream representing the 4D radiance field all in one. We introduce a tailored training scheme directly applied to this 2D domain to impose the temporal and spatial redundancy of the feature image stream. By leveraging the redundancy, we show that the feature image stream can be efficiently compressed by 2D video codecs, which allows us to exploit video hardware accelerators to achieve real-time decoding. On the other hand, based on the feature image stream, we propose a novel rendering pipeline for VideoRF, which has specialized space mappings to query radiance properties efficiently. Paired with a deferred shading model, VideoRF has the capability of real-time rendering on mobile devices thanks to its efficiency. We have developed a real-time interactive player that enables online streaming and rendering of dynamic scenes, offering a seamless and immersive free-viewpoint experience across a range of devices, from desktops to mobile phones.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.02155", "title": "GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis", "authors": [ "Shunyuan Zheng", "Boyao Zhou", "Ruizhi Shao", "Boning Liu", "Shengping Zhang", "Liqiang Nie", "Yebin Liu" ], "github": "https://github.com/aipixel/gps-gaussian", "project_page": "", "abstract": "We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting. Unlike the original Gaussian Splatting or neural implicit rendering methods that necessitate per-subject optimizations, we introduce Gaussian parameter maps defined on the source views and regress directly Gaussian Splatting properties for instant novel view synthesis without any fine-tuning or optimization. To this end, we train our Gaussian parameter regression module on a large amount of human scan data, jointly with a depth estimation module to lift 2D parameter maps to 3D space. The proposed framework is fully differentiable and experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed.", "github_manual": "https://github.com/aipixel/gps-gaussian", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.02147", "title": "Rejuvenating image-GPT as Strong Visual Representation Learners", "authors": [ "Sucheng Ren", "Zeyu Wang", "Hongru Zhu", "Junfei Xiao", "Alan Yuille", "Cihang Xie" ], "github": "https://github.com/OliverRensu/D-iGPT", "project_page": "", "abstract": "This paper enhances image-GPT (iGPT), one of the pioneering works that introduce autoregressive pretraining to predict next pixels for visual representation learning. Two simple yet essential changes are made. First, we shift the prediction target from raw pixels to semantic tokens, enabling a higher-level understanding of visual content. Second, we supplement the autoregressive modeling by instructing the model to predict not only the next tokens but also the visible tokens. This pipeline is particularly effective when semantic tokens are encoded by discriminatively trained models, such as CLIP. We introduce this novel approach as D-iGPT. Extensive experiments showcase that D-iGPT excels as a strong learner of visual representations: A notable achievement of D-iGPT is its compelling performance on the ImageNet-1K dataset -- by training on publicly available datasets, D-iGPT achieves 89.5\\% top-1 accuracy with a vanilla ViT-Large model. This model also shows strong generalization on the downstream task and robustness on out-of-distribution samples. Code is avaiable at https://github.com/OliverRensu/D-iGPT{https://github.com/OliverRensu/D-iGPT}.", "github_manual": "", "github_regex": "https://github.com/OliverRensu/D-iGPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.00886", "title": "Nash Learning from Human Feedback", "authors": [ "R\u00e9mi Munos", "Michal Valko", "Daniele Calandriello", "Mohammad Gheshlaghi Azar", "Mark Rowland", "Daniel Guo", "Yunhao Tang", "Matthieu Geist", "Thomas M\u00e9snard", "Andrea Michi", "Marco Selvi", "Sertan Girgin", "Nikola Momchev", "Olivier Bachem", "Daniel J. Mankowitz", "Doina Precup", "Bilal Piot" ], "github": "", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human preferences. Typically, RLHF involves the initial step of learning a reward model from human feedback, often expressed as preferences between pairs of text generations produced by a pre-trained LLM. Subsequently, the LLM's policy is fine-tuned by optimizing it to maximize the reward model through a reinforcement learning algorithm. However, an inherent limitation of current reward models is their inability to fully represent the richness of human preferences and their dependency on the sampling distribution. In this study, we introduce an alternative pipeline for the fine-tuning of LLMs using pairwise human feedback. Our approach entails the initial learning of a preference model, which is conditioned on two inputs given a prompt, followed by the pursuit of a policy that consistently generates responses preferred over those generated by any competing policy, thus defining the Nash equilibrium of this preference model. We term this approach Nash learning from human feedback (NLHF). In the context of a tabular policy representation, we present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent. This algorithm produces a sequence of policies, with the last iteration converging to the regularized Nash equilibrium. Additionally, we explore parametric representations of policies and introduce gradient descent algorithms for deep-learning architectures. To demonstrate the effectiveness of our approach, we present experimental results involving the fine-tuning of a LLM for a text summarization task. We believe NLHF offers a compelling avenue for preference learning and policy optimization with the potential of advancing the field of aligning LLMs with human preferences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.02142", "title": "Object Recognition as Next Token Prediction", "authors": [ "Kaiyu Yue", "Bor-Chun Chen", "Jonas Geiping", "Hengduo Li", "Tom Goldstein", "Ser-Nam Lim" ], "github": "https://github.com/kaiyuyue/nxtp", "project_page": "", "abstract": "We present an approach to pose object recognition as next token prediction. The idea is to apply a language decoder that auto-regressively predicts the text tokens from image embeddings to form labels. To ground this prediction process in auto-regression, we customize a non-causal attention mask for the decoder, incorporating two key features: modeling tokens from different labels to be independent, and treating image tokens as a prefix. This masking mechanism inspires an efficient method - one-shot sampling - to simultaneously sample tokens of multiple labels in parallel and rank generated labels by their probabilities during inference. To further enhance the efficiency, we propose a simple strategy to construct a compact decoder by simply discarding the intermediate blocks of a pretrained language model. This approach yields a decoder that matches the full model's performance while being notably more efficient. The code is available at https://github.com/kaiyuyue/nxtp", "github_manual": "", "github_regex": "https://github.com/kaiyuyue/nxtp", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.01532", "title": "Using Large Language Models to Accelerate Communication for Users with Severe Motor Impairments", "authors": [ "Shanqing Cai", "Subhashini Venugopalan", "Katie Seaver", "Xiang Xiao", "Katrin Tomanek", "Sri Jalasutram", "Meredith Ringel Morris", "Shaun Kane", "Ajit Narayanan", "Robert L. MacDonald", "Emily Kornman", "Daniel Vance", "Blair Casey", "Steve M. Gleason", "Philip Q. Nelson", "Michael P. Brenner" ], "github": "", "project_page": "", "abstract": "Finding ways to accelerate text input for individuals with profound motor impairments has been a long-standing area of research. Closing the speed gap for augmentative and alternative communication (AAC) devices such as eye-tracking keyboards is important for improving the quality of life for such individuals. Recent advances in neural networks of natural language pose new opportunities for re-thinking strategies and user interfaces for enhanced text-entry for AAC users. In this paper, we present SpeakFaster, consisting of large language models (LLMs) and a co-designed user interface for text entry in a highly-abbreviated form, allowing saving 57% more motor actions than traditional predictive keyboards in offline simulation. A pilot study with 19 non-AAC participants typing on a mobile device by hand demonstrated gains in motor savings in line with the offline simulation, while introducing relatively small effects on overall typing speed. Lab and field testing on two eye-gaze typing users with amyotrophic lateral sclerosis (ALS) demonstrated text-entry rates 29-60% faster than traditional baselines, due to significant saving of expensive keystrokes achieved through phrase and word predictions from context-aware LLMs. These findings provide a strong foundation for further exploration of substantially-accelerated text communication for motor-impaired users and demonstrate a direction for applying LLMs to text-based user interfaces.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.01279", "title": "TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documents", "authors": [ "James Enouen", "Hootan Nakhost", "Sayna Ebrahimi", "Sercan O Arik", "Yan Liu", "Tomas Pfister" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have attracted huge interest in practical applications given their increasingly accurate responses and coherent reasoning abilities. Given their nature as black-boxes using complex reasoning processes on their inputs, it is inevitable that the demand for scalable and faithful explanations for LLMs' generated content will continue to grow. There have been major developments in the explainability of neural network models over the past decade. Among them, post-hoc explainability methods, especially Shapley values, have proven effective for interpreting deep learning models. However, there are major challenges in scaling up Shapley values for LLMs, particularly when dealing with long input contexts containing thousands of tokens and autoregressively generated output sequences. Furthermore, it is often unclear how to effectively utilize generated explanations to improve the performance of LLMs. In this paper, we introduce TextGenSHAP, an efficient post-hoc explanation method incorporating LM-specific techniques. We demonstrate that this leads to significant increases in speed compared to conventional Shapley value computations, reducing processing times from hours to minutes for token-level explanations, and to just seconds for document-level explanations. In addition, we demonstrate how real-time Shapley values can be utilized in two important scenarios, providing better understanding of long-document question answering by localizing important words and sentences; and improving existing document retrieval systems through enhancing the accuracy of selected passages and ultimately the final responses.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.00845", "title": "VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models", "authors": [ "Hyeonho Jeong", "Geon Yeong Park", "Jong Chul Ye" ], "github": "https://github.com/HyeonHo99/Video-Motion-Customization", "project_page": "", "abstract": "Text-to-video diffusion models have advanced video generation significantly. However, customizing these models to generate videos with tailored motions presents a substantial challenge. In specific, they encounter hurdles in (a) accurately reproducing motion from a target video, and (b) creating diverse visual variations. For example, straightforward extensions of static image customization methods to video often lead to intricate entanglements of appearance and motion data. To tackle this, here we present the Video Motion Customization (VMC) framework, a novel one-shot tuning approach crafted to adapt temporal attention layers within video diffusion models. Our approach introduces a novel motion distillation objective using residual vectors between consecutive frames as a motion reference. The diffusion process then preserves low-frequency motion trajectories while mitigating high-frequency motion-unrelated noise in image space. We validate our method against state-of-the-art video generative models across diverse real-world motions and contexts. Our codes, data and the project demo can be found at https://video-motion-customization.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HyeonHo99/Video-Motion-Customization", "github_api_added_by": "auto" }, { "date": "2023-12-05", "arxiv_id": "2312.02139", "title": "DiffiT: Diffusion Vision Transformers for Image Generation", "authors": [ "Ali Hatamizadeh", "Jiaming Song", "Guilin Liu", "Jan Kautz", "Arash Vahdat" ], "github": "https://github.com/NVlabs/DiffiT", "project_page": "", "abstract": "Diffusion models with their powerful expressivity and high sample quality have enabled many new applications and use-cases in various domains. For sample generation, these models rely on a denoising neural network that generates images by iterative denoising. Yet, the role of denoising network architecture is not well-studied with most efforts relying on convolutional residual U-Nets. In this paper, we study the effectiveness of vision transformers in diffusion-based generative learning. Specifically, we propose a new model, denoted as Diffusion Vision Transformers (DiffiT), which consists of a hybrid hierarchical architecture with a U-shaped encoder and decoder. We introduce a novel time-dependent self-attention module that allows attention layers to adapt their behavior at different stages of the denoising process in an efficient manner. We also introduce latent DiffiT which consists of transformer model with the proposed self-attention layers, for high-resolution image generation. Our results show that DiffiT is surprisingly effective in generating high-fidelity images, and it achieves state-of-the-art (SOTA) benchmarks on a variety of class-conditional and unconditional synthesis tasks. In the latent space, DiffiT achieves a new SOTA FID score of 1.73 on ImageNet-256 dataset. Repository: https://github.com/NVlabs/DiffiT", "github_manual": "", "github_regex": "https://github.com/NVlabs/DiffiT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.02133", "title": "Style Aligned Image Generation via Shared Attention", "authors": [ "Amir Hertz", "Andrey Voynov", "Shlomi Fruchter", "Daniel Cohen-Or" ], "github": "", "project_page": "", "abstract": "Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields, generating visually compelling outputs from textual prompts. However, controlling these models to ensure consistent style remains challenging, with existing methods necessitating fine-tuning and manual intervention to disentangle content and style. In this paper, we introduce StyleAligned, a novel technique designed to establish style alignment among a series of generated images. By employing minimal `attention sharing' during the diffusion process, our method maintains style consistency across images within T2I models. This approach allows for the creation of style-consistent images using a reference style through a straightforward inversion operation. Our method's evaluation across diverse styles and text prompts demonstrates high-quality synthesis and fidelity, underscoring its efficacy in achieving consistent style across various inputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.02116", "title": "GIVT: Generative Infinite-Vocabulary Transformers", "authors": [ "Michael Tschannen", "Cian Eastwood", "Fabian Mentzer" ], "github": "", "project_page": "", "abstract": "We introduce generative infinite-vocabulary transformers (GIVT) which generate vector sequences with real-valued entries, instead of discrete tokens from a finite vocabulary. To this end, we propose two surprisingly simple modifications to decoder-only transformers: 1) at the input, we replace the finite-vocabulary lookup table with a linear projection of the input vectors; and 2) at the output, we replace the logits prediction (usually mapped to a categorical distribution) with the parameters of a multivariate Gaussian mixture model. Inspired by the image-generation paradigm of VQ-GAN and MaskGIT, where transformers are used to model the discrete latent sequences of a VQ-VAE, we use GIVT to model the unquantized real-valued latent sequences of a VAE. When applying GIVT to class-conditional image generation with iterative masked modeling, we show competitive results with MaskGIT, while our approach outperforms both VQ-GAN and MaskGIT when using it for causal modeling. Finally, we obtain competitive results outside of image generation when applying our approach to panoptic segmentation and depth estimation with a VAE-based variant of the UViM framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.01409", "title": "Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models", "authors": [ "Shengqu Cai", "Duygu Ceylan", "Matheus Gadelha", "Chun-Hao Paul Huang", "Tuanfeng Yang Wang", "Gordon Wetzstein" ], "github": "https://github.com/primcai/generative_rendering", "project_page": "", "abstract": "Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious manual process, which can be automated by emerging text-to-video diffusion models. Despite great promise, video diffusion models are difficult to control, hindering a user to apply their own creativity rather than amplifying it. To address this challenge, we present a novel approach that combines the controllability of dynamic 3D meshes with the expressivity and editability of emerging diffusion models. For this purpose, our approach takes an animated, low-fidelity rendered mesh as input and injects the ground truth correspondence information obtained from the dynamic mesh into various stages of a pre-trained text-to-image generation model to output high-quality and temporally consistent frames. We demonstrate our approach on various examples where motion can be obtained by animating rigged assets or changing the camera path.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/primcai/generative_rendering", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.02135", "title": "Fast View Synthesis of Casual Videos", "authors": [ "Yao-Chih Lee", "Zhoutong Zhang", "Kevin Blackburn-Matzen", "Simon Niklaus", "Jianming Zhang", "Jia-Bin Huang", "Feng Liu" ], "github": "", "project_page": "", "abstract": "Novel view synthesis from an in-the-wild video is difficult due to challenges like scene dynamics and lack of parallax. While existing methods have shown promising results with implicit neural radiance fields, they are slow to train and render. This paper revisits explicit video representations to synthesize high-quality novel views from a monocular video efficiently. We treat static and dynamic video content separately. Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video. Our plane-based scene representation is augmented with spherical harmonics and displacement maps to capture view-dependent effects and model non-planar complex surface geometry. We opt to represent the dynamic content as per-frame point clouds for efficiency. While such representations are inconsistency-prone, minor temporal inconsistencies are perceptually masked due to motion. We develop a method to quickly estimate such a hybrid video representation and render novel views in real time. Our experiments show that our method can render high-quality novel views from an in-the-wild video with comparable quality to state-of-the-art methods while being 100x faster in training and enabling real-time rendering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.00860", "title": "Segment Any 3D Gaussians", "authors": [ "Jiazhong Cen", "Jiemin Fang", "Chen Yang", "Lingxi Xie", "Xiaopeng Zhang", "Wei Shen", "Qi Tian" ], "github": "", "project_page": "", "abstract": "Interactive 3D segmentation in radiance fields is an appealing task since its importance in 3D scene understanding and manipulation. However, existing methods face challenges in either achieving fine-grained, multi-granularity segmentation or contending with substantial computational overhead, inhibiting real-time interaction. In this paper, we introduce Segment Any 3D GAussians (SAGA), a novel 3D interactive segmentation approach that seamlessly blends a 2D segmentation foundation model with 3D Gaussian Splatting (3DGS), a recent breakthrough of radiance fields. SAGA efficiently embeds multi-granularity 2D segmentation results generated by the segmentation foundation model into 3D Gaussian point features through well-designed contrastive training. Evaluation on existing benchmarks demonstrates that SAGA can achieve competitive performance with state-of-the-art methods. Moreover, SAGA achieves multi-granularity segmentation and accommodates various prompts, including points, scribbles, and 2D masks. Notably, SAGA can finish the 3D segmentation within milliseconds, achieving nearly 1000x acceleration compared to previous SOTA. The project page is at https://jumpat.github.io/SAGA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.00858", "title": "DeepCache: Accelerating Diffusion Models for Free", "authors": [ "Xinyin Ma", "Gongfan Fang", "Xinchao Wang" ], "github": "https://github.com/horseee/DeepCache", "project_page": "", "abstract": "Diffusion models have recently gained unprecedented attention in the field of image synthesis due to their remarkable generative capabilities. Notwithstanding their prowess, these models often incur substantial computational costs, primarily attributed to the sequential denoising process and cumbersome model size. Traditional methods for compressing diffusion models typically involve extensive retraining, presenting cost and feasibility challenges. In this paper, we introduce DeepCache, a novel training-free paradigm that accelerates diffusion models from the perspective of model architecture. DeepCache capitalizes on the inherent temporal redundancy observed in the sequential denoising steps of diffusion models, which caches and retrieves features across adjacent denoising stages, thereby curtailing redundant computations. Utilizing the property of the U-Net, we reuse the high-level features while updating the low-level features in a very cheap way. This innovative strategy, in turn, enables a speedup factor of 2.3times for Stable Diffusion v1.5 with only a 0.05 decline in CLIP Score, and 4.1times for LDM-4-G with a slight decrease of 0.22 in FID on ImageNet. Our experiments also demonstrate DeepCache's superiority over existing pruning and distillation methods that necessitate retraining and its compatibility with current sampling techniques. Furthermore, we find that under the same throughput, DeepCache effectively achieves comparable or even marginally improved results with DDIM or PLMS. The code is available at https://github.com/horseee/DeepCache", "github_manual": "", "github_regex": "https://github.com/horseee/DeepCache", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-05", "arxiv_id": "2312.02087", "title": "VideoSwap: Customized Video Subject Swapping with Interactive Semantic Point Correspondence", "authors": [ "Yuchao Gu", "Yipin Zhou", "Bichen Wu", "Licheng Yu", "Jia-Wei Liu", "Rui Zhao", "Jay Zhangjie Wu", "David Junhao Zhang", "Mike Zheng Shou", "Kevin Tang" ], "github": "", "project_page": "", "abstract": "Current diffusion-based video editing primarily focuses on structure-preserved editing by utilizing various dense correspondences to ensure temporal consistency and motion alignment. However, these approaches are often ineffective when the target edit involves a shape change. To embark on video editing with shape change, we explore customized video subject swapping in this work, where we aim to replace the main subject in a source video with a target subject having a distinct identity and potentially different shape. In contrast to previous methods that rely on dense correspondences, we introduce the VideoSwap framework that exploits semantic point correspondences, inspired by our observation that only a small number of semantic points are necessary to align the subject's motion trajectory and modify its shape. We also introduce various user-point interactions (\\eg, removing points and dragging points) to address various semantic point correspondence. Extensive experiments demonstrate state-of-the-art video subject swapping results across a variety of real-world videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02663", "title": "FaceStudio: Put Your Face Everywhere in Seconds", "authors": [ "Yuxuan Yan", "Chi Zhang", "Rui Wang", "Pei Cheng", "Gang Yu", "Bin Fu" ], "github": "https://github.com/xyynafc/FaceStudio", "project_page": "", "abstract": "This study investigates identity-preserving image synthesis, an intriguing task in image generation that seeks to maintain a subject's identity while adding a personalized, stylistic touch. Traditional methods, such as Textual Inversion and DreamBooth, have made strides in custom image creation, but they come with significant drawbacks. These include the need for extensive resources and time for fine-tuning, as well as the requirement for multiple reference images. To overcome these challenges, our research introduces a novel approach to identity-preserving synthesis, with a particular focus on human images. Our model leverages a direct feed-forward mechanism, circumventing the need for intensive fine-tuning, thereby facilitating quick and efficient image generation. Central to our innovation is a hybrid guidance framework, which combines stylized images, facial images, and textual prompts to guide the image generation process. This unique combination enables our model to produce a variety of applications, such as artistic portraits and identity-blended images. Our experimental results, including both qualitative and quantitative evaluations, demonstrate the superiority of our method over existing baseline models and previous works, particularly in its remarkable efficiency and ability to preserve the subject's identity with high fidelity.", "github_manual": "https://github.com/xyynafc/FaceStudio", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02238", "title": "X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model", "authors": [ "Lingmin Ran", "Xiaodong Cun", "JiaWei Liu", "Rui Zhao", "Song Zijie", "Xintao Wang", "Jussi Keppo", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "We introduce X-Adapter, a universal upgrader to enable the pretrained plug-and-play modules (e.g., ControlNet, LoRA) to work directly with the upgraded text-to-image diffusion model (e.g., SDXL) without further retraining. We achieve this goal by training an additional network to control the frozen upgraded model with the new text-image data pairs. In detail, X-Adapter keeps a frozen copy of the old model to preserve the connectors of different plugins. Additionally, X-Adapter adds trainable mapping layers that bridge the decoders from models of different versions for feature remapping. The remapped features will be used as guidance for the upgraded model. To enhance the guidance ability of X-Adapter, we employ a null-text training strategy for the upgraded model. After training, we also introduce a two-stage denoising strategy to align the initial latents of X-Adapter and the upgraded model. Thanks to our strategies, X-Adapter demonstrates universal compatibility with various plugins and also enables plugins of different versions to work together, thereby expanding the functionalities of diffusion community. To verify the effectiveness of the proposed method, we conduct extensive experiments and the results show that X-Adapter may facilitate wider application in the upgraded foundational diffusion model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02974", "title": "Describing Differences in Image Sets with Natural Language", "authors": [ "Lisa Dunlap", "Yuhui Zhang", "Xiaohan Wang", "Ruiqi Zhong", "Trevor Darrell", "Jacob Steinhardt", "Joseph E. Gonzalez", "Serena Yeung-Levy" ], "github": "https://github.com/understanding-visual-datasets/visdiff", "project_page": "", "abstract": "How do two sets of images differ? Discerning set-level differences is crucial for understanding model behaviors and analyzing datasets, yet manually sifting through thousands of images is impractical. To aid in this discovery process, we explore the task of automatically describing the differences between two sets of images, which we term Set Difference Captioning. This task takes in image sets D_A and D_B, and outputs a description that is more often true on D_A than D_B. We outline a two-stage approach that first proposes candidate difference descriptions from image sets and then re-ranks the candidates by checking how well they can differentiate the two sets. We introduce VisDiff, which first captions the images and prompts a language model to propose candidate descriptions, then re-ranks these descriptions using CLIP. To evaluate VisDiff, we collect VisDiffBench, a dataset with 187 paired image sets with ground truth difference descriptions. We apply VisDiff to various domains, such as comparing datasets (e.g., ImageNet vs. ImageNetV2), comparing classification models (e.g., zero-shot CLIP vs. supervised ResNet), summarizing model failure modes (supervised ResNet), characterizing differences between generative models (e.g., StableDiffusionV1 and V2), and discovering what makes images memorable. Using VisDiff, we are able to find interesting and previously unknown differences in datasets and models, demonstrating its utility in revealing nuanced insights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/understanding-visual-datasets/visdiff", "github_api_added_by": "auto" }, { "date": "2023-12-06", "arxiv_id": "2312.02928", "title": "LivePhoto: Real Image Animation with Text-guided Motion Control", "authors": [ "Xi Chen", "Zhiheng Liu", "Mengting Chen", "Yutong Feng", "Yu Liu", "Yujun Shen", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "Despite the recent progress in text-to-video generation, existing studies usually overlook the issue that only spatial contents but not temporal motions in synthesized videos are under the control of text. Towards such a challenge, this work presents a practical system, named LivePhoto, which allows users to animate an image of their interest with text descriptions. We first establish a strong baseline that helps a well-learned text-to-image generator (i.e., Stable Diffusion) take an image as a further input. We then equip the improved generator with a motion module for temporal modeling and propose a carefully designed training pipeline to better link texts and motions. In particular, considering the facts that (1) text can only describe motions roughly (e.g., regardless of the moving speed) and (2) text may include both content and motion descriptions, we introduce a motion intensity estimation module as well as a text re-weighting module to reduce the ambiguity of text-to-motion mapping. Empirical evidence suggests that our approach is capable of well decoding motion-related textual instructions into videos, such as actions, camera movements, or even conjuring new contents from thin air (e.g., pouring water into an empty glass). Interestingly, thanks to the proposed intensity learning mechanism, our system offers users an additional control signal (i.e., the motion intensity) besides text for video customization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02919", "title": "Fine-grained Controllable Video Generation via Object Appearance and Context", "authors": [ "Hsin-Ping Huang", "Yu-Chuan Su", "Deqing Sun", "Lu Jiang", "Xuhui Jia", "Yukun Zhu", "Ming-Hsuan Yang" ], "github": "", "project_page": "", "abstract": "Text-to-video generation has shown promising results. However, by taking only natural languages as input, users often face difficulties in providing detailed information to precisely control the model's output. In this work, we propose fine-grained controllable video generation (FACTOR) to achieve detailed control. Specifically, FACTOR aims to control objects' appearances and context, including their location and category, in conjunction with the text prompt. To achieve detailed control, we propose a unified framework to jointly inject control signals into the existing text-to-video model. Our model consists of a joint encoder and adaptive cross-attention layers. By optimizing the encoder and the inserted layer, we adapt the model to generate videos that are aligned with both text prompts and fine-grained control. Compared to existing methods relying on dense control signals such as edge maps, we provide a more intuitive and user-friendly interface to allow object-level fine-grained control. Our method achieves controllability of object appearances without finetuning, which reduces the per-subject optimization efforts for the users. Extensive experiments on standard benchmark datasets and user-provided inputs validate that our model obtains a 70% improvement in controllability metrics over competitive baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02980", "title": "GPT4Point: A Unified Framework for Point-Language Understanding and Generation", "authors": [ "Zhangyang Qi", "Ye Fang", "Zeyi Sun", "Xiaoyang Wu", "Tong Wu", "Jiaqi Wang", "Dahua Lin", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have excelled in 2D image-text comprehension and image generation, but their understanding of the 3D world is notably deficient, limiting progress in 3D language understanding and generation. To solve this problem, we introduce GPT4Point, an innovative groundbreaking point-language multimodal model designed specifically for unified 3D object understanding and generation within the MLLM framework. GPT4Point as a powerful 3D MLLM seamlessly can execute a variety of point-text reference tasks such as point-cloud captioning and Q&A. Additionally, GPT4Point is equipped with advanced capabilities for controllable 3D generation, it can get high-quality results through a low-quality point-text feature maintaining the geometric shapes and colors. To support the expansive needs of 3D object-text pairs, we develop Pyramid-XL, a point-language dataset annotation engine. It constructs a large-scale database over 1M objects of varied text granularity levels from the Objaverse-XL dataset, essential for training GPT4Point. A comprehensive benchmark has been proposed to evaluate 3D point-language understanding capabilities. In extensive evaluations, GPT4Point has demonstrated superior performance in understanding and generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02179", "title": "Training Chain-of-Thought via Latent-Variable Inference", "authors": [ "Du Phan", "Matthew D. Hoffman", "David Dohan", "Sholto Douglas", "Tuan Anh Le", "Aaron Parisi", "Pavel Sountsov", "Charles Sutton", "Sharad Vikram", "Rif A. Saurous" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) solve problems more accurately and interpretably when instructed to work out the answer step by step using a ``chain-of-thought'' (CoT) prompt. One can also improve LLMs' performance on a specific task by supervised fine-tuning, i.e., by using gradient ascent on some tunable parameters to maximize the average log-likelihood of correct answers from a labeled training set. Naively combining CoT with supervised tuning requires supervision not just of the correct answers, but also of detailed rationales that lead to those answers; these rationales are expensive to produce by hand. Instead, we propose a fine-tuning strategy that tries to maximize the marginal log-likelihood of generating a correct answer using CoT prompting, approximately averaging over all possible rationales. The core challenge is sampling from the posterior over rationales conditioned on the correct answer; we address it using a simple Markov-chain Monte Carlo (MCMC) expectation-maximization (EM) algorithm inspired by the self-taught reasoner (STaR), memoized wake-sleep, Markovian score climbing, and persistent contrastive divergence. This algorithm also admits a novel control-variate technique that drives the variance of our gradient estimates to zero as the model improves. Applying our technique to GSM8K and the tasks in BIG-Bench Hard, we find that this MCMC-EM fine-tuning technique typically improves the model's accuracy on held-out examples more than STaR or prompt-tuning with or without CoT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02201", "title": "ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation", "authors": [ "Peng Wang", "Yichun Shi" ], "github": "https://github.com/bytedance/ImageDream", "project_page": "", "abstract": "We introduce \"ImageDream,\" an innovative image-prompt, multi-view diffusion model for 3D object generation. ImageDream stands out for its ability to produce 3D models of higher quality compared to existing state-of-the-art, image-conditioned methods. Our approach utilizes a canonical camera coordination for the objects in images, improving visual geometry accuracy. The model is designed with various levels of control at each block inside the diffusion model based on the input image, where global control shapes the overall object layout and local control fine-tunes the image details. The effectiveness of ImageDream is demonstrated through extensive evaluations using a standard prompt list. For more information, visit our project page at https://Image-Dream.github.io.", "github_manual": "https://github.com/bytedance/ImageDream", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02189", "title": "StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D", "authors": [ "Pengsheng Guo", "Hans Hao", "Adam Caccavale", "Zhongzheng Ren", "Edward Zhang", "Qi Shan", "Aditya Sankar", "Alexander G. Schwing", "Alex Colburn", "Fangchang Ma" ], "github": "", "project_page": "", "abstract": "In the realm of text-to-3D generation, utilizing 2D diffusion models through score distillation sampling (SDS) frequently leads to issues such as blurred appearances and multi-faced geometry, primarily due to the intrinsically noisy nature of the SDS loss. Our analysis identifies the core of these challenges as the interaction among noise levels in the 2D diffusion process, the architecture of the diffusion network, and the 3D model representation. To overcome these limitations, we present StableDreamer, a methodology incorporating three advances. First, inspired by InstructNeRF2NeRF, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss. This finding provides a novel tool to debug SDS, which we use to show the impact of time-annealing noise levels on reducing multi-faced geometries. Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition. Based on this observation, StableDreamer introduces a two-stage training strategy that effectively combines these aspects, resulting in high-fidelity 3D models. Third, we adopt an anisotropic 3D Gaussians representation, replacing Neural Radiance Fields (NeRFs), to enhance the overall quality, reduce memory usage during training, and accelerate rendering speeds, and better capture semi-transparent objects. StableDreamer reduces multi-face geometries, generates fine details, and converges stably.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02949", "title": "LLaVA-Grounding: Grounded Visual Chat with Large Multimodal Models", "authors": [ "Hao Zhang", "Hongyang Li", "Feng Li", "Tianhe Ren", "Xueyan Zou", "Shilong Liu", "Shijia Huang", "Jianfeng Gao", "Lei Zhang", "Chunyuan Li", "Jianwei Yang" ], "github": "https://github.com/UX-Decoder/LLaVA-Grounding", "project_page": "", "abstract": "With the recent significant advancements in large multi-modal models (LMMs), the importance of their grounding capability in visual chat is increasingly recognized. Despite recent efforts to enable LMMs to support grounding, their capabilities for grounding and chat are usually separate, and their chat performance drops dramatically when asked to ground. The problem is the lack of a dataset for grounded visual chat (GVC). Existing grounding datasets only contain short captions. To address this issue, we have created GVC data that allows for the combination of grounding and chat capabilities. To better evaluate the GVC capabilities, we have introduced a benchmark called Grounding-Bench. Additionally, we have proposed a model design that can support GVC and various types of visual prompts by connecting segmentation models with language models. Experimental results demonstrate that our model outperforms other LMMs on Grounding-Bench. Furthermore, our model achieves competitive performance on classic grounding benchmarks like RefCOCO/+/g and Flickr30K Entities. Our code will be released at https://github.com/UX-Decoder/LLaVA-Grounding .", "github_manual": "", "github_regex": "https://github.com/UX-Decoder/LLaVA-Grounding", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02931", "title": "WhisBERT: Multimodal Text-Audio Language Modeling on 100M Words", "authors": [ "Lukas Wolf", "Klemen Kotar", "Greta Tuckute", "Eghbal Hosseini", "Tamar Regev", "Ethan Wilcox", "Alex Warstadt" ], "github": "https://github.com/lu-wo/whisbert", "project_page": "", "abstract": "Training on multiple modalities of input can augment the capabilities of a language model. Here, we ask whether such a training regime can improve the quality and efficiency of these systems as well. We focus on text--audio and introduce Whisbert, which is inspired by the text--image approach of FLAVA singh_flava_2022. In accordance with Babylm warstadt2023papers guidelines, we pretrain Whisbert on a dataset comprising only 100 million words plus their corresponding speech from the word-aligned version of the People's Speech dataset galvez_peoples_2021. To assess the impact of multimodality, we compare versions of the model that are trained on text only and on both audio and text simultaneously. We find that while Whisbert is able to perform well on multimodal masked modeling and surpasses the Babylm baselines in most benchmark tasks, it struggles to optimize its complex objective and outperform its text-only Whisbert baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lu-wo/whisbert", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02969", "title": "Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models", "authors": [ "Xinyu Zhang", "Sebastian Hofst\u00e4tter", "Patrick Lewis", "Raphael Tang", "Jimmy Lin" ], "github": "", "project_page": "", "abstract": "Listwise rerankers based on large language models (LLM) are the zero-shot state-of-the-art. However, current works in this direction all depend on the GPT models, making it a single point of failure in scientific reproducibility. Moreover, it raises the concern that the current research findings only hold for GPT models but not LLM in general. In this work, we lift this pre-condition and build for the first time effective listwise rerankers without any form of dependency on GPT. Our passage retrieval experiments show that our best list se reranker surpasses the listwise rerankers based on GPT-3.5 by 13% and achieves 97% effectiveness of the ones built on GPT-4. Our results also show that the existing training datasets, which were expressly constructed for pointwise ranking, are insufficient for building such listwise rerankers. Instead, high-quality listwise ranking data is required and crucial, calling for further work on building human-annotated listwise data resources.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02206", "title": "Axiomatic Preference Modeling for Longform Question Answering", "authors": [ "Corby Rosset", "Guoqing Zheng", "Victor Dibia", "Ahmed Awadallah", "Paul Bennett" ], "github": "", "project_page": "", "abstract": "The remarkable abilities of large language models (LLMs) like GPT-4 partially stem from post-training processes like Reinforcement Learning from Human Feedback (RLHF) involving human preferences encoded in a reward model. However, these reward models (RMs) often lack direct knowledge of why, or under what principles, the preferences annotations were made. In this study, we identify principles that guide RMs to better align with human preferences, and then develop an axiomatic framework to generate a rich variety of preference signals to uphold them. We use these axiomatic signals to train a model for scoring answers to longform questions. Our approach yields a Preference Model with only about 220M parameters that agrees with gold human-annotated preference labels more often than GPT-4. The contributions of this work include: training a standalone preference model that can score human- and LLM-generated answers on the same scale; developing an axiomatic framework for generating training data pairs tailored to certain principles; and showing that a small amount of axiomatic signals can help small models outperform GPT-4 in preference scoring. We release our model on huggingface: https://huggingface.co/corbyrosset/axiomatic_preference_model", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02696", "title": "Analyzing and Improving the Training Dynamics of Diffusion Models", "authors": [ "Tero Karras", "Miika Aittala", "Jaakko Lehtinen", "Janne Hellsten", "Timo Aila", "Samuli Laine" ], "github": "https://github.com/nvlabs/edm2", "project_page": "", "abstract": "Diffusion models currently dominate the field of data-driven image synthesis with their unparalleled scaling to large datasets. In this paper, we identify and rectify several causes for uneven and ineffective training in the popular ADM diffusion model architecture, without altering its high-level structure. Observing uncontrolled magnitude changes and imbalances in both the network activations and weights over the course of training, we redesign the network layers to preserve activation, weight, and update magnitudes on expectation. We find that systematic application of this philosophy eliminates the observed drifts and imbalances, resulting in considerably better networks at equal computational complexity. Our modifications improve the previous record FID of 2.41 in ImageNet-512 synthesis to 1.81, achieved using fast deterministic sampling. As an independent contribution, we present a method for setting the exponential moving average (EMA) parameters post-hoc, i.e., after completing the training run. This allows precise tuning of EMA length without the cost of performing several training runs, and reveals its surprising interactions with network architecture, training time, and guidance.", "github_manual": "https://github.com/nvlabs/edm2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02432", "title": "Orthogonal Adaptation for Modular Customization of Diffusion Models", "authors": [ "Ryan Po", "Guandao Yang", "Kfir Aberman", "Gordon Wetzstein" ], "github": "", "project_page": "", "abstract": "Customization techniques for text-to-image models have paved the way for a wide range of previously unattainable applications, enabling the generation of specific concepts across diverse contexts and styles. While existing methods facilitate high-fidelity customization for individual concepts or a limited, pre-defined set of them, they fall short of achieving scalability, where a single model can seamlessly render countless concepts. In this paper, we address a new problem called Modular Customization, with the goal of efficiently merging customized models that were fine-tuned independently for individual concepts. This allows the merged model to jointly synthesize concepts in one image without compromising fidelity or incurring any additional computational costs. To address this problem, we introduce Orthogonal Adaptation, a method designed to encourage the customized models, which do not have access to each other during fine-tuning, to have orthogonal residual weights. This ensures that during inference time, the customized models can be summed with minimal interference. Our proposed method is both simple and versatile, applicable to nearly all optimizable weights in the model architecture. Through an extensive set of quantitative and qualitative evaluations, our method consistently outperforms relevant baselines in terms of efficiency and identity preservation, demonstrating a significant leap toward scalable customization of diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02216", "title": "DragVideo: Interactive Drag-style Video Editing", "authors": [ "Yufan Deng", "Ruida Wang", "Yuhao Zhang", "Yu-Wing Tai", "Chi-Keung Tang" ], "github": "https://github.com/rickyskywalker/dragvideo-official", "project_page": "", "abstract": "Editing visual content on videos remains a formidable challenge with two main issues: 1) direct and easy user control to produce 2) natural editing results without unsightly distortion and artifacts after changing shape, expression and layout. Inspired by DragGAN, a recent image-based drag-style editing technique, we address above issues by proposing DragVideo, where a similar drag-style user interaction is adopted to edit video content while maintaining temporal consistency. Empowered by recent diffusion models as in DragDiffusion, DragVideo contains the novel Drag-on-Video U-Net (DoVe) editing method, which optimizes diffused video latents generated by video U-Net to achieve the desired control. Specifically, we use Sample-specific LoRA fine-tuning and Mutual Self-Attention control to ensure faithful reconstruction of video from the DoVe method. We also present a series of testing examples for drag-style video editing and conduct extensive experiments across a wide array of challenging editing tasks, such as motion editing, skeleton editing, etc, underscoring DragVideo's versatility and generality. Our codes including the DragVideo web user interface will be released.", "github_manual": "https://github.com/rickyskywalker/dragvideo-official", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02772", "title": "Generating Fine-Grained Human Motions Using ChatGPT-Refined Descriptions", "authors": [ "Xu Shi", "Chuanchen Luo", "Junran Peng", "Hongwen Zhang", "Yunlian Sun" ], "github": "", "project_page": "", "abstract": "Recently, significant progress has been made in text-based motion generation, enabling the generation of diverse and high-quality human motions that conform to textual descriptions. However, it remains challenging to generate fine-grained or stylized motions due to the lack of datasets annotated with detailed textual descriptions. By adopting a divide-and-conquer strategy, we propose a new framework named Fine-Grained Human Motion Diffusion Model (FG-MDM) for human motion generation. Specifically, we first parse previous vague textual annotation into fine-grained description of different body parts by leveraging a large language model (GPT-3.5). We then use these fine-grained descriptions to guide a transformer-based diffusion model. FG-MDM can generate fine-grained and stylized motions even outside of the distribution of the training data. Our experimental results demonstrate the superiority of FG-MDM over previous methods, especially the strong generalization capability. We will release our fine-grained textual annotations for HumanML3D and KIT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02981", "title": "ReconFusion: 3D Reconstruction with Diffusion Priors", "authors": [ "Rundi Wu", "Ben Mildenhall", "Philipp Henzler", "Keunhong Park", "Ruiqi Gao", "Daniel Watson", "Pratul P. Srinivasan", "Dor Verbin", "Jonathan T. Barron", "Ben Poole", "Aleksander Holynski" ], "github": "", "project_page": "", "abstract": "3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes. However, recovering a high-quality NeRF typically requires tens to hundreds of input images, resulting in a time-consuming capture process. We present ReconFusion to reconstruct real-world scenes using only a few photos. Our approach leverages a diffusion prior for novel view synthesis, trained on synthetic and multiview datasets, which regularizes a NeRF-based 3D reconstruction pipeline at novel camera poses beyond those captured by the set of input images. Our method synthesizes realistic geometry and texture in underconstrained regions while preserving the appearance of observed regions. We perform an extensive evaluation across various real-world datasets, including forward-facing and 360-degree scenes, demonstrating significant performance improvements over previous few-view NeRF reconstruction approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02970", "title": "Alchemist: Parametric Control of Material Properties with Diffusion Models", "authors": [ "Prafull Sharma", "Varun Jampani", "Yuanzhen Li", "Xuhui Jia", "Dmitry Lagun", "Fredo Durand", "William T. Freeman", "Mark Matthews" ], "github": "", "project_page": "", "abstract": "We propose a method to control material attributes of objects like roughness, metallic, albedo, and transparency in real images. Our method capitalizes on the generative prior of text-to-image models known for photorealism, employing a scalar value and instructions to alter low-level material properties. Addressing the lack of datasets with controlled material attributes, we generated an object-centric synthetic dataset with physically-based materials. Fine-tuning a modified pre-trained text-to-image model on this synthetic dataset enables us to edit material properties in real-world images while preserving all other attributes. We show the potential application of our model to material edited NeRFs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.02963", "title": "MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human Captures", "authors": [ "Zhangyang Xiong", "Chenghong Li", "Kenkun Liu", "Hongjie Liao", "Jianqiao Hu", "Junyi Zhu", "Shuliang Ning", "Lingteng Qiu", "Chongjie Wang", "Shijie Wang", "Shuguang Cui", "Xiaoguang Han" ], "github": "", "project_page": "", "abstract": "In this era, the success of large language models and text-to-image models can be attributed to the driving force of large-scale datasets. However, in the realm of 3D vision, while remarkable progress has been made with models trained on large-scale synthetic and real-captured object data like Objaverse and MVImgNet, a similar level of progress has not been observed in the domain of human-centric tasks partially due to the lack of a large-scale human dataset. Existing datasets of high-fidelity 3D human capture continue to be mid-sized due to the significant challenges in acquiring large-scale high-quality 3D human data. To bridge this gap, we present MVHumanNet, a dataset that comprises multi-view human action sequences of 4,500 human identities. The primary focus of our work is on collecting human data that features a large number of diverse identities and everyday clothing using a multi-view human capture system, which facilitates easily scalable data collection. Our dataset contains 9,000 daily outfits, 60,000 motion sequences and 645 million frames with extensive annotations, including human masks, camera parameters, 2D and 3D keypoints, SMPL/SMPLX parameters, and corresponding textual descriptions. To explore the potential of MVHumanNet in various 2D and 3D visual tasks, we conducted pilot studies on view-consistent action recognition, human NeRF reconstruction, text-driven view-unconstrained human image generation, as well as 2D view-unconstrained human image and 3D avatar generation. Extensive experiments demonstrate the performance improvements and effective applications enabled by the scale provided by MVHumanNet. As the current largest-scale 3D human dataset, we hope that the release of MVHumanNet data with annotations will foster further innovations in the domain of 3D human-centric tasks at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-12", "arxiv_id": "2312.06550", "title": "LLM360: Towards Fully Transparent Open-Source LLMs", "authors": [ "Zhengzhong Liu", "Aurick Qiao", "Willie Neiswanger", "Hongyi Wang", "Bowen Tan", "Tianhua Tao", "Junbo Li", "Yuqi Wang", "Suqi Sun", "Omkar Pangarkar", "Richard Fan", "Yi Gu", "Victor Miller", "Yonghao Zhuang", "Guowei He", "Haonan Li", "Fajri Koto", "Liping Tang", "Nikhil Ranjan", "Zhiqiang Shen", "Xuguang Ren", "Roberto Iriondo", "Cun Mu", "Zhiting Hu", "Mark Schulze", "Preslav Nakov", "Tim Baldwin", "Eric P. Xing" ], "github": "https://github.com/llm360/analysis360", "project_page": "", "abstract": "The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers. However, most LLMs have only released partial artifacts, such as the final model weights or inference code, and technical reports increasingly limit their scope to high-level design choices and surface statistics. These choices hinder progress in the field by degrading transparency into the training of LLMs and forcing teams to rediscover many details in the training process. We present LLM360, an initiative to fully open-source LLMs, which advocates for all training code and data, model checkpoints, and intermediate results to be made available to the community. The goal of LLM360 is to support open and collaborative AI research by making the end-to-end LLM training process transparent and reproducible by everyone. As a first step of LLM360, we release two 7B parameter LLMs pre-trained from scratch, Amber and CrystalCoder, including their training code, data, intermediate checkpoints, and analyses (at https://www.llm360.ai). We are committed to continually pushing the boundaries of LLMs through this open-source effort. More large-scale and stronger models are underway and will be released in the future.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/llm360/analysis360", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-12", "arxiv_id": "2312.06655", "title": "Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior", "authors": [ "Fangfu Liu", "Diankun Wu", "Yi Wei", "Yongming Rao", "Yueqi Duan" ], "github": "https://github.com/liuff19/Sherpa3D", "project_page": "", "abstract": "Recently, 3D content creation from text prompts has demonstrated remarkable progress by utilizing 2D and 3D diffusion models. While 3D diffusion models ensure great multi-view consistency, their ability to generate high-quality and diverse 3D assets is hindered by the limited 3D data. In contrast, 2D diffusion models find a distillation approach that achieves excellent generalization and rich details without any 3D data. However, 2D lifting methods suffer from inherent view-agnostic ambiguity thereby leading to serious multi-face Janus issues, where text prompts fail to provide sufficient guidance to learn coherent 3D results. Instead of retraining a costly viewpoint-aware model, we study how to fully exploit easily accessible coarse 3D knowledge to enhance the prompts and guide 2D lifting optimization for refinement. In this paper, we propose Sherpa3D, a new text-to-3D framework that achieves high-fidelity, generalizability, and geometric consistency simultaneously. Specifically, we design a pair of guiding strategies derived from the coarse 3D prior generated by the 3D diffusion model: a structural guidance for geometric fidelity and a semantic guidance for 3D coherence. Employing the two types of guidance, the 2D diffusion model enriches the 3D content with diversified and high-quality results. Extensive experiments show the superiority of our Sherpa3D over the state-of-the-art text-to-3D methods in terms of quality and 3D consistency.", "github_manual": "https://github.com/liuff19/Sherpa3D", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-12", "arxiv_id": "2312.06585", "title": "Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models", "authors": [ "Avi Singh", "John D. Co-Reyes", "Rishabh Agarwal", "Ankesh Anand", "Piyush Patil", "Peter J. Liu", "James Harrison", "Jaehoon Lee", "Kelvin Xu", "Aaron Parisi", "Abhishek Kumar", "Alex Alemi", "Alex Rizkowsky", "Azade Nova", "Ben Adlam", "Bernd Bohnet", "Hanie Sedghi", "Igor Mordatch", "Isabelle Simpson", "Izzeddin Gur", "Jasper Snoek", "Jeffrey Pennington", "Jiri Hron", "Kathleen Kenealy", "Kevin Swersky", "Kshiteej Mahajan", "Laura Culp", "Lechao Xiao", "Maxwell L. Bileschi", "Noah Constant", "Roman Novak", "Rosanne Liu", "Tris Warkentin", "Yundi Qian", "Ethan Dyer", "Behnam Neyshabur", "Jascha Sohl-Dickstein", "Noah Fiedel" ], "github": "", "project_page": "", "abstract": "Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of high-quality human data. In this paper, we explore whether we can go beyond human data on tasks where we have access to scalar feedback, for example, on math problems where one can verify correctness. To do so, we investigate a simple self-training method based on expectation-maximization, which we call ReST^{EM}, where we (1) generate samples from the model and filter them using binary feedback, (2) fine-tune the model on these samples, and (3) repeat this process a few times. Testing on advanced MATH reasoning and APPS coding benchmarks using PaLM-2 models, we find that ReST^{EM} scales favorably with model size and significantly surpasses fine-tuning only on human data. Overall, our findings suggest self-training with feedback can substantially reduce dependence on human-generated data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-12", "arxiv_id": "2312.06109", "title": "Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models", "authors": [ "Haoran Wei", "Lingyu Kong", "Jinyue Chen", "Liang Zhao", "Zheng Ge", "Jinrong Yang", "Jianjian Sun", "Chunrui Han", "Xiangyu Zhang" ], "github": "https://github.com/Ucas-HaoranWei/Vary", "project_page": "", "abstract": "Modern Large Vision-Language Models (LVLMs) enjoy the same vision vocabulary -- CLIP, which can cover most common vision tasks. However, for some special vision task that needs dense and fine-grained vision perception, e.g., document-level OCR or chart understanding, especially in non-English scenarios, the CLIP-style vocabulary may encounter low efficiency in tokenizing the vision knowledge and even suffer out-of-vocabulary problem. Accordingly, we propose Vary, an efficient and effective method to scale up the vision vocabulary of LVLMs. The procedures of Vary are naturally divided into two folds: the generation and integration of a new vision vocabulary. In the first phase, we devise a vocabulary network along with a tiny decoder-only transformer to produce the desired vocabulary via autoregression. In the next, we scale up the vanilla vision vocabulary by merging the new one with the original one (CLIP), enabling the LVLMs can quickly garner new features. Compared to the popular BLIP-2, MiniGPT4, and LLaVA, Vary can maintain its vanilla capabilities while enjoying more excellent fine-grained perception and understanding ability. Specifically, Vary is competent in new document parsing features (OCR or markdown conversion) while achieving 78.2% ANLS in DocVQA and 36.2% in MMVet. Our code will be publicly available on the homepage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Ucas-HaoranWei/Vary", "github_api_added_by": "auto" }, { "date": "2023-12-12", "arxiv_id": "2312.06662", "title": "Photorealistic Video Generation with Diffusion Models", "authors": [ "Agrim Gupta", "Lijun Yu", "Kihyuk Sohn", "Xiuye Gu", "Meera Hahn", "Li Fei-Fei", "Irfan Essa", "Lu Jiang", "Jos\u00e9 Lezama" ], "github": "", "project_page": "", "abstract": "We present W.A.L.T, a transformer-based approach for photorealistic video generation via diffusion modeling. Our approach has two key design decisions. First, we use a causal encoder to jointly compress images and videos within a unified latent space, enabling training and generation across modalities. Second, for memory and training efficiency, we use a window attention architecture tailored for joint spatial and spatiotemporal generative modeling. Taken together these design decisions enable us to achieve state-of-the-art performance on established video (UCF-101 and Kinetics-600) and image (ImageNet) generation benchmarks without using classifier free guidance. Finally, we also train a cascade of three models for the task of text-to-video generation consisting of a base latent video diffusion model, and two video super-resolution diffusion models to generate videos of 512 times 896 resolution at 8 frames per second.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-12", "arxiv_id": "2312.05708", "title": "Context Tuning for Retrieval Augmented Generation", "authors": [ "Raviteja Anantha", "Tharun Bethi", "Danil Vodianik", "Srinivas Chappidi" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have the remarkable ability to solve new tasks with just a few examples, but they need access to the right tools. Retrieval Augmented Generation (RAG) addresses this problem by retrieving a list of relevant tools for a given task. However, RAG's tool retrieval step requires all the required information to be explicitly present in the query. This is a limitation, as semantic search, the widely adopted tool retrieval method, can fail when the query is incomplete or lacks context. To address this limitation, we propose Context Tuning for RAG, which employs a smart context retrieval system to fetch relevant information that improves both tool retrieval and plan generation. Our lightweight context retrieval model uses numerical, categorical, and habitual usage signals to retrieve and rank context items. Our empirical results demonstrate that context tuning significantly enhances semantic search, achieving a 3.5-fold and 1.5-fold improvement in Recall@K for context retrieval and tool retrieval tasks respectively, and resulting in an 11.6% increase in LLM-based planner accuracy. Additionally, we show that our proposed lightweight model using Reciprocal Rank Fusion (RRF) with LambdaMART outperforms GPT-4 based retrieval. Moreover, we observe context augmentation at plan generation, even after tool retrieval, reduces hallucination.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-12", "arxiv_id": "2312.06571", "title": "From Text to Motion: Grounding GPT-4 in a Humanoid Robot \"Alter3\"", "authors": [ "Takahide Yoshida", "Atsushi Masumori", "Takashi Ikegami" ], "github": "", "project_page": "", "abstract": "We report the development of Alter3, a humanoid robot capable of generating spontaneous motion using a Large Language Model (LLM), specifically GPT-4. This achievement was realized by integrating GPT-4 into our proprietary android, Alter3, thereby effectively grounding the LLM with Alter's bodily movement. Typically, low-level robot control is hardware-dependent and falls outside the scope of LLM corpora, presenting challenges for direct LLM-based robot control. However, in the case of humanoid robots like Alter3, direct control is feasible by mapping the linguistic expressions of human actions onto the robot's body through program code. Remarkably, this approach enables Alter3 to adopt various poses, such as a 'selfie' stance or 'pretending to be a ghost,' and generate sequences of actions over time without explicit programming for each body part. This demonstrates the robot's zero-shot learning capabilities. Additionally, verbal feedback can adjust poses, obviating the need for fine-tuning. A video of Alter3's generated motions is available at https://tnoinkwms.github.io/ALTER-LLM/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-12", "arxiv_id": "2312.05431", "title": "Efficient Quantization Strategies for Latent Diffusion Models", "authors": [ "Yuewei Yang", "Xiaoliang Dai", "Jialiang Wang", "Peizhao Zhang", "Hongbo Zhang" ], "github": "", "project_page": "", "abstract": "Latent Diffusion Models (LDMs) capture the dynamic evolution of latent variables over time, blending patterns and multimodality in a generative system. Despite the proficiency of LDM in various applications, such as text-to-image generation, facilitated by robust text encoders and a variational autoencoder, the critical need to deploy large generative models on edge devices compels a search for more compact yet effective alternatives. Post Training Quantization (PTQ), a method to compress the operational size of deep learning models, encounters challenges when applied to LDM due to temporal and structural complexities. This study proposes a quantization strategy that efficiently quantize LDMs, leveraging Signal-to-Quantization-Noise Ratio (SQNR) as a pivotal metric for evaluation. By treating the quantization discrepancy as relative noise and identifying sensitive part(s) of a model, we propose an efficient quantization approach encompassing both global and local strategies. The global quantization process mitigates relative quantization noise by initiating higher-precision quantization on sensitive blocks, while local treatments address specific challenges in quantization-sensitive and time-sensitive modules. The outcomes of our experiments reveal that the implementation of both global and local treatments yields a highly efficient and effective Post Training Quantization (PTQ) of LDMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-12", "arxiv_id": "2312.06351", "title": "Evaluation of Large Language Models for Decision Making in Autonomous Driving", "authors": [ "Kotaro Tanahashi", "Yuichi Inoue", "Yu Yamaguchi", "Hidetatsu Yaginuma", "Daiki Shiotsuka", "Hiroyuki Shimatani", "Kohei Iwamasa", "Yoshiaki Inoue", "Takafumi Yamaguchi", "Koki Igari", "Tsukasa Horinouchi", "Kento Tokuhiro", "Yugo Tokuchi", "Shunsuke Aoki" ], "github": "", "project_page": "", "abstract": "Various methods have been proposed for utilizing Large Language Models (LLMs) in autonomous driving. One strategy of using LLMs for autonomous driving involves inputting surrounding objects as text prompts to the LLMs, along with their coordinate and velocity information, and then outputting the subsequent movements of the vehicle. When using LLMs for such purposes, capabilities such as spatial recognition and planning are essential. In particular, two foundational capabilities are required: (1) spatial-aware decision making, which is the ability to recognize space from coordinate information and make decisions to avoid collisions, and (2) the ability to adhere to traffic rules. However, quantitative research has not been conducted on how accurately different types of LLMs can handle these problems. In this study, we quantitatively evaluated these two abilities of LLMs in the context of autonomous driving. Furthermore, to conduct a Proof of Concept (POC) for the feasibility of implementing these abilities in actual vehicles, we developed a system that uses LLMs to drive a vehicle.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-12", "arxiv_id": "2312.05491", "title": "Using Captum to Explain Generative Language Models", "authors": [ "Vivek Miglani", "Aobo Yang", "Aram H. Markosyan", "Diego Garcia-Olano", "Narine Kokhlikyan" ], "github": "", "project_page": "", "abstract": "Captum is a comprehensive library for model explainability in PyTorch, offering a range of methods from the interpretability literature to enhance users' understanding of PyTorch models. In this paper, we introduce new features in Captum that are specifically designed to analyze the behavior of generative language models. We provide an overview of the available functionalities and example applications of their potential for understanding learned associations within generative language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-12", "arxiv_id": "2312.06134", "title": "Order Matters in the Presence of Dataset Imbalance for Multilingual Learning", "authors": [ "Dami Choi", "Derrick Xin", "Hamid Dadkhahi", "Justin Gilmer", "Ankush Garg", "Orhan Firat", "Chih-Kuan Yeh", "Andrew M. Dai", "Behrooz Ghorbani" ], "github": "", "project_page": "", "abstract": "In this paper, we empirically study the optimization dynamics of multi-task learning, particularly focusing on those that govern a collection of tasks with significant data imbalance. We present a simple yet effective method of pre-training on high-resource tasks, followed by fine-tuning on a mixture of high/low-resource tasks. We provide a thorough empirical study and analysis of this method's benefits showing that it achieves consistent improvements relative to the performance trade-off profile of standard static weighting. We analyze under what data regimes this method is applicable and show its improvements empirically in neural machine translation (NMT) and multi-lingual language modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-12", "arxiv_id": "2312.06353", "title": "Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes", "authors": [ "Zhen Qin", "Daoyuan Chen", "Bingchen Qian", "Bolin Ding", "Yaliang Li", "Shuiguang Deng" ], "github": "https://github.com/zhenqincn/fedkseed", "project_page": "", "abstract": "Pre-trained large language models (LLMs) require fine-tuning to improve their responsiveness to natural language instructions. Federated learning (FL) offers a way to perform fine-tuning using the abundant data on end devices without compromising data privacy. Most existing federated fine-tuning methods for LLMs rely on parameter-efficient fine-tuning techniques, which may not reach the performance heights possible with full-parameter tuning. However, the communication overhead associated with full-parameter tuning is prohibitively high for both servers and clients. This work introduces FedKSeed, a novel approach that employs zeroth-order optimization (ZOO) with a set of random seeds. It enables federated full-parameter tuning of billion-sized LLMs directly on devices. Our method significantly reduces transmission requirements between the server and clients to just a few scalar gradients and random seeds, amounting to only a few thousand bytes. Building on this, we develop a strategy to assess the significance of ZOO perturbations for FL, allowing for probability-differentiated seed sampling. This prioritizes perturbations that have a greater impact on model accuracy. Experiments across six scenarios with different LLMs, datasets and data partitions demonstrate that our approach outperforms existing federated LLM fine-tuning methods in terms of both communication efficiency and new task generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhenqincn/fedkseed", "github_api_added_by": "auto" }, { "date": "2023-12-12", "arxiv_id": "2312.05605", "title": "TCNCA: Temporal Convolution Network with Chunked Attention for Scalable Sequence Processing", "authors": [ "Aleksandar Terzic", "Michael Hersche", "Geethan Karunaratne", "Luca Benini", "Abu Sebastian", "Abbas Rahimi" ], "github": "", "project_page": "", "abstract": "MEGA is a recent transformer-based architecture, which utilizes a linear recurrent operator whose parallel computation, based on the FFT, scales as O(LlogL), with L being the sequence length. We build upon their approach by replacing the linear recurrence with a special temporal convolutional network which permits larger receptive field size with shallower networks, and reduces the computational complexity to O(L). The resulting model is called TCNCA, a Temporal Convolutional Network with Chunked Attention. We evaluate TCNCA on EnWik8 language modeling, long-range-arena (LRA) sequence classification, as well as a synthetic reasoning benchmark associative recall. On EnWik8, TCNCA outperforms MEGA, reaching a lower loss with 1.37times/1.24times faster forward/backward pass during training. The dilated convolutions used in TCNCA are consistently and significantly faster operations than the FFT-based parallelized recurrence in GPUs, making them a scalable candidate for handling very large sequence lengths: they are up to 7.07times/2.86times faster in the forward/backward pass for sequences up to 131k. Further on LRA, TCNCA achieves, on average, 1.28times speed-up during inference with similar accuracy to what MEGA achieves. On associative recall, we find that even a simplified version of TCNCA, without excessive multiplicative and additive interactions, remains superior or competitive to MEGA on a range of sequence lengths and vocabulary sizes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-12", "arxiv_id": "2312.06149", "title": "Unlocking Anticipatory Text Generation: A Constrained Approach for Faithful Decoding with Large Language Models", "authors": [ "Lifu Tu", "Semih Yavuz", "Jin Qu", "Jiacheng Xu", "Rui Meng", "Caiming Xiong", "Yingbo Zhou" ], "github": "https://github.com/SalesforceAIResearch/Unlocking-TextGen", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated a powerful ability for text generation. However, achieving optimal results with a given prompt or instruction can be challenging, especially for billion-sized models. Additionally, undesired behaviors such as toxicity or hallucinations can manifest. While much larger models (e.g., ChatGPT) may demonstrate strength in mitigating these issues, there is still no guarantee of complete prevention. In this work, we propose formalizing text generation as a future-constrained generation problem to minimize undesirable behaviors and enforce faithfulness to instructions. The estimation of future constraint satisfaction, accomplished using LLMs, guides the text generation process. Our extensive experiments demonstrate the effectiveness of the proposed approach across three distinct text generation tasks: keyword-constrained generation (Lin et al., 2020), toxicity reduction (Gehman et al., 2020), and factual correctness in question-answering (Gao et al., 2023).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SalesforceAIResearch/Unlocking-TextGen", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.07537", "title": "FreeInit: Bridging Initialization Gap in Video Diffusion Models", "authors": [ "Tianxing Wu", "Chenyang Si", "Yuming Jiang", "Ziqi Huang", "Ziwei Liu" ], "github": "https://github.com/TianxingWu/FreeInit", "project_page": "", "abstract": "Though diffusion-based video generation has witnessed rapid progress, the inference results of existing models still exhibit unsatisfactory temporal consistency and unnatural dynamics. In this paper, we delve deep into the noise initialization of video diffusion models, and discover an implicit training-inference gap that attributes to the unsatisfactory inference quality. Our key findings are: 1) the spatial-temporal frequency distribution of the initial latent at inference is intrinsically different from that for training, and 2) the denoising process is significantly influenced by the low-frequency components of the initial noise. Motivated by these observations, we propose a concise yet effective inference sampling strategy, FreeInit, which significantly improves temporal consistency of videos generated by diffusion models. Through iteratively refining the spatial-temporal low-frequency components of the initial latent during inference, FreeInit is able to compensate the initialization gap between training and inference, thus effectively improving the subject appearance and temporal consistency of generation results. Extensive experiments demonstrate that FreeInit consistently enhances the generation results of various text-to-video generation models without additional training.", "github_manual": "https://github.com/TianxingWu/FreeInit", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.07536", "title": "FreeControl: Training-Free Spatial Control of Any Text-to-Image Diffusion Model with Any Condition", "authors": [ "Sicheng Mo", "Fangzhou Mu", "Kuan Heng Lin", "Yanli Liu", "Bochen Guan", "Yin Li", "Bolei Zhou" ], "github": "https://github.com/genforce/freecontrol", "project_page": "", "abstract": "Recent approaches such as ControlNet offer users fine-grained spatial control over text-to-image (T2I) diffusion models. However, auxiliary modules have to be trained for each type of spatial condition, model architecture, and checkpoint, putting them at odds with the diverse intents and preferences a human designer would like to convey to the AI models during the content creation process. In this work, we present FreeControl, a training-free approach for controllable T2I generation that supports multiple conditions, architectures, and checkpoints simultaneously. FreeControl designs structure guidance to facilitate the structure alignment with a guidance image, and appearance guidance to enable the appearance sharing between images generated using the same seed. Extensive qualitative and quantitative experiments demonstrate the superior performance of FreeControl across a variety of pre-trained T2I models. In particular, FreeControl facilitates convenient training-free control over many different architectures and checkpoints, allows the challenging input conditions on which most of the existing training-free methods fail, and achieves competitive synthesis quality with training-based approaches.", "github_manual": "https://github.com/genforce/freecontrol", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.07409", "title": "DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing", "authors": [ "Kaiwen Zhang", "Yifan Zhou", "Xudong Xu", "Xingang Pan", "Bo Dai" ], "github": "https://github.com/Kevin-thu/DiffMorpher", "project_page": "", "abstract": "Diffusion models have achieved remarkable image generation quality surpassing previous generative models. However, a notable limitation of diffusion models, in comparison to GANs, is their difficulty in smoothly interpolating between two image samples, due to their highly unstructured latent space. Such a smooth interpolation is intriguing as it naturally serves as a solution for the image morphing task with many applications. In this work, we present DiffMorpher, the first approach enabling smooth and natural image interpolation using diffusion models. Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively, and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition, where correspondence automatically emerges without the need for annotation. In addition, we propose an attention interpolation and injection technique and a new sampling schedule to further enhance the smoothness between consecutive images. Extensive experiments demonstrate that DiffMorpher achieves starkly better image morphing effects than previous methods across a variety of object categories, bridging a critical functional gap that distinguished diffusion models from GANs.", "github_manual": "https://github.com/Kevin-thu/DiffMorpher", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.06971", "title": "CCM: Adding Conditional Controls to Text-to-Image Consistency Models", "authors": [ "Jie Xiao", "Kai Zhu", "Han Zhang", "Zhiheng Liu", "Yujun Shen", "Yu Liu", "Xueyang Fu", "Zheng-Jun Zha" ], "github": "", "project_page": "", "abstract": "Consistency Models (CMs) have showed a promise in creating visual content efficiently and with high quality. However, the way to add new conditional controls to the pretrained CMs has not been explored. In this technical report, we consider alternative strategies for adding ControlNet-like conditional control to CMs and present three significant findings. 1) ControlNet trained for diffusion models (DMs) can be directly applied to CMs for high-level semantic controls but struggles with low-level detail and realism control. 2) CMs serve as an independent class of generative models, based on which ControlNet can be trained from scratch using Consistency Training proposed by Song et al. 3) A lightweight adapter can be jointly optimized under multiple conditions through Consistency Training, allowing for the swift transfer of DMs-based ControlNet to CMs. We study these three solutions across various conditional controls, including edge, depth, human pose, low-resolution image and masked image with text-to-image latent consistency models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.07046", "title": "Rethinking Compression: Reduced Order Modelling of Latent Features in Large Language Models", "authors": [ "Arnav Chavan", "Nahush Lele", "Deepak Gupta" ], "github": "", "project_page": "", "abstract": "Due to the substantial scale of Large Language Models (LLMs), the direct application of conventional compression methodologies proves impractical. The computational demands associated with even minimal gradient updates present challenges, particularly on consumer-grade hardware. This paper introduces an innovative approach for the parametric and practical compression of LLMs based on reduced order modelling, which entails low-rank decomposition within the feature space and re-parameterization in the weight space. Notably, this compression technique operates in a layer-wise manner, obviating the need for a GPU device and enabling the compression of billion-scale models within stringent constraints of both memory and time. Our method represents a significant advancement in model compression by leveraging matrix decomposition, demonstrating superior efficacy compared to the prevailing state-of-the-art structured pruning method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.06742", "title": "Honeybee: Locality-enhanced Projector for Multimodal LLM", "authors": [ "Junbum Cha", "Wooyoung Kang", "Jonghwan Mun", "Byungseok Roh" ], "github": "https://github.com/kakaobrain/honeybee", "project_page": "", "abstract": "In Multimodal Large Language Models (MLLMs), a visual projector plays a crucial role in bridging pre-trained vision encoders with LLMs, enabling profound visual understanding while harnessing the LLMs' robust capabilities. Despite the importance of the visual projector, it has been relatively less explored. In this study, we first identify two essential projector properties: (i) flexibility in managing the number of visual tokens, crucial for MLLMs' overall efficiency, and (ii) preservation of local context from visual features, vital for spatial understanding. Based on these findings, we propose a novel projector design that is both flexible and locality-enhanced, effectively satisfying the two desirable properties. Additionally, we present comprehensive strategies to effectively utilize multiple and multifaceted instruction datasets. Through extensive experiments, we examine the impact of individual design choices. Finally, our proposed MLLM, Honeybee, remarkably outperforms previous state-of-the-art methods across various benchmarks, including MME, MMBench, SEED-Bench, and LLaVA-Bench, achieving significantly higher efficiency. Code and models are available at https://github.com/kakaobrain/honeybee.", "github_manual": "", "github_regex": "https://github.com/kakaobrain/honeybee", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.07532", "title": "Interfacing Foundation Models' Embeddings", "authors": [ "Xueyan Zou", "Linjie Li", "Jianfeng Wang", "Jianwei Yang", "Mingyu Ding", "Zhengyuan Yang", "Feng Li", "Hao Zhang", "Shilong Liu", "Arul Aravinthan", "Yong Jae Lee", "Lijuan Wang" ], "github": "https://github.com/UX-Decoder/FIND", "project_page": "", "abstract": "We present FIND, a generalized interface for aligning foundation models' embeddings. As shown in teaser figure, a lightweight transformer interface without tuning any foundation model weights is enough for a unified image (segmentation) and dataset-level (retrieval) understanding. The proposed interface has the following favorable attributes: (1) Generalizable. It applies to various tasks spanning retrieval, segmentation, etc., under the same architecture and weights. (2) Prototypable. Different tasks are able to be implemented through prototyping attention masks and embedding types. (3) Extendable. The proposed interface is adaptive to new tasks, and new models. (4) Interleavable. With the benefit of multi-task multi-modal training, the proposed interface creates an interleaved shared embedding space. In light of the interleaved embedding space, we introduce the FIND-Bench, which introduces new training and evaluation annotations to the COCO dataset for interleave segmentation and retrieval. Our approach achieves state-of-the-art performance on FIND-Bench and competitive performance on standard retrieval and segmentation settings. The training, evaluation, and demo code as well as the dataset have been released at https://github.com/UX-Decoder/FIND.", "github_manual": "", "github_regex": "https://github.com/UX-Decoder/FIND", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.07533", "title": "VILA: On Pre-training for Visual Language Models", "authors": [ "Ji Lin", "Hongxu Yin", "Wei Ping", "Yao Lu", "Pavlo Molchanov", "Andrew Tao", "Huizi Mao", "Jan Kautz", "Mohammad Shoeybi", "Song Han" ], "github": "", "project_page": "", "abstract": "Visual language models (VLMs) rapidly progressed with the recent success of large language models. There have been growing efforts on visual instruction tuning to extend the LLM with visual inputs, but lacks an in-depth study of the visual language pre-training process, where the model learns to perform joint modeling on both modalities. In this work, we examine the design options for VLM pre-training by augmenting LLM towards VLM through step-by-step controllable comparisons. We introduce three main findings: (1) freezing LLMs during pre-training can achieve decent zero-shot performance, but lack in-context learning capability, which requires unfreezing the LLM; (2) interleaved pre-training data is beneficial whereas image-text pairs alone are not optimal; (3) re-blending text-only instruction data to image-text data during instruction fine-tuning not only remedies the degradation of text-only tasks, but also boosts VLM task accuracy. With an enhanced pre-training recipe we build VILA, a Visual Language model family that consistently outperforms the state-of-the-art models, e.g., LLaVA-1.5, across main benchmarks without bells and whistles. Multi-modal pre-training also helps unveil appealing properties of VILA, including multi-image reasoning, enhanced in-context learning, and better world knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.07000", "title": "Alignment for Honesty", "authors": [ "Yuqing Yang", "Ethan Chern", "Xipeng Qiu", "Graham Neubig", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/alignment-for-honesty", "project_page": "", "abstract": "Recent research has made significant strides in applying alignment techniques to enhance the helpfulness and harmlessness of large language models (LLMs) in accordance with human intentions. In this paper, we argue for the importance of alignment for honesty, ensuring that LLMs proactively refuse to answer questions when they lack knowledge, while still not being overly conservative. However, a pivotal aspect of alignment for honesty involves discerning the limits of an LLM's knowledge, which is far from straightforward. This challenge demands comprehensive solutions in terms of metric development, benchmark creation, and training methodologies. In this paper, we address these challenges by first establishing a precise problem definition and defining ``honesty'' inspired by the Analects of Confucius. This serves as a cornerstone for developing metrics that effectively measure an LLM's honesty by quantifying its progress post-alignment. Furthermore, we introduce a flexible training framework which is further instantiated by several efficient fine-tuning techniques that emphasize honesty without sacrificing performance on other tasks. Our extensive experiments reveal that these aligned models show a marked increase in honesty, as indicated by our proposed metrics. We open-source a wealth of resources to facilitate future research at https://github.com/GAIR-NLP/alignment-for-honesty, including honesty-aligned models, training and evaluation datasets for honesty alignment, concept glossary, as well as all relevant source code.", "github_manual": "", "github_regex": "https://github.com/GAIR-NLP/alignment-for-honesty", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.07231", "title": "Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation", "authors": [ "Shentong Mo", "Enze Xie", "Yue Wu", "Junsong Chen", "Matthias Nie\u00dfner", "Zhenguo Li" ], "github": "", "project_page": "", "abstract": "Diffusion Transformers have recently shown remarkable effectiveness in generating high-quality 3D point clouds. However, training voxel-based diffusion models for high-resolution 3D voxels remains prohibitively expensive due to the cubic complexity of attention operators, which arises from the additional dimension of voxels. Motivated by the inherent redundancy of 3D compared to 2D, we propose FastDiT-3D, a novel masked diffusion transformer tailored for efficient 3D point cloud generation, which greatly reduces training costs. Specifically, we draw inspiration from masked autoencoders to dynamically operate the denoising process on masked voxelized point clouds. We also propose a novel voxel-aware masking strategy to adaptively aggregate background/foreground information from voxelized point clouds. Our method achieves state-of-the-art performance with an extreme masking ratio of nearly 99%. Moreover, to improve multi-category 3D generation, we introduce Mixture-of-Expert (MoE) in 3D diffusion model. Each category can learn a distinct diffusion path with different experts, relieving gradient conflict. Experimental results on the ShapeNet dataset demonstrate that our method achieves state-of-the-art high-fidelity and diverse 3D point cloud generation performance. Our FastDiT-3D improves 1-Nearest Neighbor Accuracy and Coverage metrics when generating 128-resolution voxel point clouds, using only 6.5% of the original training cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.07424", "title": "How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation", "authors": [ "Zhongyi Han", "Guanglin Zhou", "Rundong He", "Jindong Wang", "Xing Xie", "Tailin Wu", "Yilong Yin", "Salman Khan", "Lina Yao", "Tongliang Liu", "Kun Zhang" ], "github": "https://github.com/jameszhou-gl/gpt-4v-distribution-shift", "project_page": "", "abstract": "In machine learning, generalization against distribution shifts -- where deployment conditions diverge from the training scenarios -- is crucial, particularly in fields like climate modeling, biomedicine, and autonomous driving. The emergence of foundation models, distinguished by their extensive pretraining and task versatility, has led to an increased interest in their adaptability to distribution shifts. GPT-4V(ision) acts as the most advanced publicly accessible multimodal foundation model, with extensive applications across various domains, including anomaly detection, video understanding, image generation, and medical diagnosis. However, its robustness against data distributions remains largely underexplored. Addressing this gap, this study rigorously evaluates GPT-4V's adaptability and generalization capabilities in dynamic environments, benchmarking against prominent models like CLIP and LLaVA. We delve into GPT-4V's zero-shot generalization across 13 diverse datasets spanning natural, medical, and molecular domains. We further investigate its adaptability to controlled data perturbations and examine the efficacy of in-context learning as a tool to enhance its adaptation. Our findings delineate GPT-4V's capability boundaries in distribution shifts, shedding light on its strengths and limitations across various scenarios. Importantly, this investigation contributes to our understanding of how AI foundation models generalize to distribution shifts, offering pivotal insights into their adaptability and robustness. Code is publicly available at https://github.com/jameszhou-gl/gpt-4v-distribution-shift.", "github_manual": "", "github_regex": "https://github.com/jameszhou-gl/gpt-4v-distribution-shift", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.06681", "title": "Steering Llama 2 via Contrastive Activation Addition", "authors": [ "Nina Rimsky", "Nick Gabrieli", "Julian Schulz", "Meg Tong", "Evan Hubinger", "Alexander Matt Turner" ], "github": "", "project_page": "", "abstract": "We introduce Contrastive Activation Addition (CAA), an innovative method for steering language models by modifying activations during their forward passes. CAA computes ``steering vectors'' by averaging the difference in residual stream activations between pairs of positive and negative examples of a particular behavior such as factual versus hallucinatory responses. During inference, these steering vectors are added at all token positions after the user's prompt with either a positive or negative coefficient, allowing precise control over the degree of the targeted behavior. We evaluate CAA's effectiveness on Llama 2 Chat using both multiple-choice behavioral question datasets and open-ended generation tasks. We demonstrate that CAA significantly alters model behavior, outperforms traditional methods like finetuning and few-shot prompting, and minimally reduces capabilities. Moreover, by employing various activation space interpretation methods, we gain deeper insights into CAA's mechanisms. CAA both accurately steers model outputs and also sheds light on how high-level concepts are represented in Large Language Models (LLMs).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.07509", "title": "PEEKABOO: Interactive Video Generation via Masked-Diffusion", "authors": [ "Yash Jain", "Anshul Nasery", "Vibhav Vineet", "Harkirat Behl" ], "github": "https://github.com/microsoft/peekaboo", "project_page": "", "abstract": "Recently there has been a lot of progress in text-to-video generation, with state-of-the-art models being capable of generating high quality, realistic videos. However, these models lack the capability for users to interactively control and generate videos, which can potentially unlock new areas of application. As a first step towards this goal, we tackle the problem of endowing diffusion-based video generation models with interactive spatio-temporal control over their output. To this end, we take inspiration from the recent advances in segmentation literature to propose a novel spatio-temporal masked attention module - Peekaboo. This module is a training-free, no-inference-overhead addition to off-the-shelf video generation models which enables spatio-temporal control. We also propose an evaluation benchmark for the interactive video generation task. Through extensive qualitative and quantitative evaluation, we establish that Peekaboo enables control video generation and even obtains a gain of upto 3.8x in mIoU over baseline models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/peekaboo", "github_api_added_by": "auto" }, { "date": "2023-12-13", "arxiv_id": "2312.06674", "title": "Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations", "authors": [ "Hakan Inan", "Kartikeya Upasani", "Jianfeng Chi", "Rashi Rungta", "Krithika Iyer", "Yuning Mao", "Michael Tontchev", "Qing Hu", "Brian Fuller", "Davide Testuggine", "Madian Khabsa" ], "github": "https://github.com/facebookresearch/PurpleLlama/tree/main/Llama-Guard", "project_page": "", "abstract": "We introduce Llama Guard, an LLM-based input-output safeguard model geared towards Human-AI conversation use cases. Our model incorporates a safety risk taxonomy, a valuable tool for categorizing a specific set of safety risks found in LLM prompts (i.e., prompt classification). This taxonomy is also instrumental in classifying the responses generated by LLMs to these prompts, a process we refer to as response classification. For the purpose of both prompt and response classification, we have meticulously gathered a dataset of high quality. Llama Guard, a Llama2-7b model that is instruction-tuned on our collected dataset, albeit low in volume, demonstrates strong performance on existing benchmarks such as the OpenAI Moderation Evaluation dataset and ToxicChat, where its performance matches or exceeds that of currently available content moderation tools. Llama Guard functions as a language model, carrying out multi-class classification and generating binary decision scores. Furthermore, the instruction fine-tuning of Llama Guard allows for the customization of tasks and the adaptation of output formats. This feature enhances the model's capabilities, such as enabling the adjustment of taxonomy categories to align with specific use cases, and facilitating zero-shot or few-shot prompting with diverse taxonomies at the input. We are making Llama Guard model weights available and we encourage researchers to further develop and adapt them to meet the evolving needs of the community for AI safety.", "github_manual": "https://github.com/facebookresearch/PurpleLlama/tree/main/Llama-Guard", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-13", "arxiv_id": "2312.07504", "title": "COLMAP-Free 3D Gaussian Splatting", "authors": [ "Yang Fu", "Sifei Liu", "Amey Kulkarni", "Jan Kautz", "Alexei A. Efros", "Xiaolong Wang" ], "github": "https://github.com/NVlabs/CF-3DGS", "project_page": "", "abstract": "While neural rendering has led to impressive advances in scene reconstruction and novel view synthesis, it relies heavily on accurately pre-computed camera poses. To relax this constraint, multiple efforts have been made to train Neural Radiance Fields (NeRFs) without pre-processed camera poses. However, the implicit representations of NeRFs provide extra challenges to optimize the 3D structure and camera poses at the same time. On the other hand, the recently proposed 3D Gaussian Splatting provides new opportunities given its explicit point cloud representations. This paper leverages both the explicit geometric representation and the continuity of the input video stream to perform novel view synthesis without any SfM preprocessing. We process the input frames in a sequential manner and progressively grow the 3D Gaussians set by taking one input frame at a time, without the need to pre-compute the camera poses. Our method significantly improves over previous approaches in view synthesis and camera pose estimation under large motion changes. Our project page is https://oasisyang.github.io/colmap-free-3dgs", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/CF-3DGS", "github_api_added_by": "auto" }, { "date": "2023-12-13", "arxiv_id": "2312.06908", "title": "\"I Want It That Way\": Enabling Interactive Decision Support Using Large Language Models and Constraint Programming", "authors": [ "Connor Lawless", "Jakob Schoeffer", "Lindy Le", "Kael Rowan", "Shilad Sen", "Cristina St. Hill", "Jina Suh", "Bahar Sarrafzadeh" ], "github": "", "project_page": "", "abstract": "A critical factor in the success of decision support systems is the accurate modeling of user preferences. Psychology research has demonstrated that users often develop their preferences during the elicitation process, highlighting the pivotal role of system-user interaction in developing personalized systems. This paper introduces a novel approach, combining Large Language Models (LLMs) with Constraint Programming to facilitate interactive decision support. We study this hybrid framework through the lens of meeting scheduling, a time-consuming daily activity faced by a multitude of information workers. We conduct three studies to evaluate the novel framework, including a diary study (n=64) to characterize contextual scheduling preferences, a quantitative evaluation of the system's performance, and a user study (n=10) with a prototype system. Our work highlights the potential for a hybrid LLM and optimization approach for iterative preference elicitation and design considerations for building systems that support human-system collaborative decision-making processes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-14", "arxiv_id": "2312.08136", "title": "ProNeRF: Learning Efficient Projection-Aware Ray Sampling for Fine-Grained Implicit Neural Radiance Fields", "authors": [ "Juan Luis Gonzalez Bello", "Minh-Quan Viet Bui", "Munchurl Kim" ], "github": "https://github.com/KAIST-VICLab/pronerf", "project_page": "", "abstract": "Recent advances in neural rendering have shown that, albeit slow, implicit compact models can learn a scene's geometries and view-dependent appearances from multiple views. To maintain such a small memory footprint but achieve faster inference times, recent works have adopted `sampler' networks that adaptively sample a small subset of points along each ray in the implicit neural radiance fields. Although these methods achieve up to a 10times reduction in rendering time, they still suffer from considerable quality degradation compared to the vanilla NeRF. In contrast, we propose ProNeRF, which provides an optimal trade-off between memory footprint (similar to NeRF), speed (faster than HyperReel), and quality (better than K-Planes). ProNeRF is equipped with a novel projection-aware sampling (PAS) network together with a new training strategy for ray exploration and exploitation, allowing for efficient fine-grained particle sampling. Our ProNeRF yields state-of-the-art metrics, being 15-23x faster with 0.65dB higher PSNR than NeRF and yielding 0.95dB higher PSNR than the best published sampler-based method, HyperReel. Our exploration and exploitation training strategy allows ProNeRF to learn the full scenes' color and density distributions while also learning efficient ray sampling focused on the highest-density regions. We provide extensive experimental results that support the effectiveness of our method on the widely adopted forward-facing and 360 datasets, LLFF and Blender, respectively.", "github_manual": "https://github.com/KAIST-VICLab/pronerf", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-14", "arxiv_id": "2312.07661", "title": "CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor", "authors": [ "Shuyang Sun", "Runjia Li", "Philip Torr", "Xiuye Gu", "Siyang Li" ], "github": "", "project_page": "", "abstract": "Existing open-vocabulary image segmentation methods require a fine-tuning step on mask annotations and/or image-text datasets. Mask labels are labor-intensive, which limits the number of categories in segmentation datasets. As a result, the open-vocabulary capacity of pre-trained VLMs is severely reduced after fine-tuning. However, without fine-tuning, VLMs trained under weak image-text supervision tend to make suboptimal mask predictions when there are text queries referring to non-existing concepts in the image. To alleviate these issues, we introduce a novel recurrent framework that progressively filters out irrelevant texts and enhances mask quality without training efforts. The recurrent unit is a two-stage segmenter built upon a VLM with frozen weights. Thus, our model retains the VLM's broad vocabulary space and strengthens its segmentation capability. Experimental results show that our method outperforms not only the training-free counterparts, but also those fine-tuned with millions of additional data samples, and sets new state-of-the-art records for both zero-shot semantic and referring image segmentation tasks. Specifically, we improve the current record by 28.8, 16.0, and 6.9 mIoU on Pascal VOC, COCO Object, and Pascal Context.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-14", "arxiv_id": "2312.08128", "title": "Clockwork Diffusion: Efficient Generation With Model-Step Distillation", "authors": [ "Amirhossein Habibian", "Amir Ghodrati", "Noor Fathima", "Guillaume Sautiere", "Risheek Garrepalli", "Fatih Porikli", "Jens Petersen" ], "github": "https://github.com/qualcomm-ai-research/clockwork-diffusion", "project_page": "", "abstract": "This work aims to improve the efficiency of text-to-image diffusion models. While diffusion models use computationally expensive UNet-based denoising operations in every generation step, we identify that not all operations are equally relevant for the final output quality. In particular, we observe that UNet layers operating on high-res feature maps are relatively sensitive to small perturbations. In contrast, low-res feature maps influence the semantic layout of the final image and can often be perturbed with no noticeable change in the output. Based on this observation, we propose Clockwork Diffusion, a method that periodically reuses computation from preceding denoising steps to approximate low-res feature maps at one or more subsequent steps. For multiple baselines, and for both text-to-image generation and image editing, we demonstrate that Clockwork leads to comparable or improved perceptual scores with drastically reduced computational complexity. As an example, for Stable Diffusion v1.5 with 8 DPM++ steps we save 32% of FLOPs with negligible FID and CLIP change.", "github_manual": "https://github.com/qualcomm-ai-research/clockwork-diffusion", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-14", "arxiv_id": "2312.08361", "title": "Distributed Inference and Fine-tuning of Large Language Models Over The Internet", "authors": [ "Alexander Borzunov", "Max Ryabinin", "Artem Chumachenko", "Dmitry Baranchuk", "Tim Dettmers", "Younes Belkada", "Pavel Samygin", "Colin Raffel" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are useful in many NLP tasks and become more capable with size, with the best open-source models having over 50 billion parameters. However, using these 50B+ models requires high-end hardware, making them inaccessible to most researchers. In this work, we investigate methods for cost-efficient inference and fine-tuning of LLMs, comparing local and distributed strategies. We observe that a large enough model (50B+) can run efficiently even on geodistributed devices in a consumer-grade network. This could allow running LLM efficiently by pooling together idle compute resources of multiple research groups and volunteers. We address two open problems: (1) how to perform inference and fine-tuning reliably if any device can disconnect abruptly and (2) how to partition LLMs between devices with uneven hardware, joining and leaving at will. In order to do that, we develop special fault-tolerant inference algorithms and load-balancing protocols that automatically assign devices to maximize the total system throughput. We showcase these algorithms in Petals - a decentralized system that runs Llama 2 (70B) and BLOOM (176B) over the Internet up to 10x faster than offloading for interactive generation. We evaluate the performance of our system in simulated conditions and a real-world setup spanning two continents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-14", "arxiv_id": "2312.08344", "title": "FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects", "authors": [ "Bowen Wen", "Wei Yang", "Jan Kautz", "Stan Birchfield" ], "github": "https://github.com/NVlabs/FoundationPose", "project_page": "", "abstract": "We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups. Our approach can be instantly applied at test-time to a novel object without fine-tuning, as long as its CAD model is given, or a small number of reference images are captured. We bridge the gap between these two setups with a neural implicit representation that allows for effective novel view synthesis, keeping the downstream pose estimation modules invariant under the same unified framework. Strong generalizability is achieved via large-scale synthetic training, aided by a large language model (LLM), a novel transformer-based architecture, and contrastive learning formulation. Extensive evaluation on multiple public datasets involving challenging scenarios and objects indicate our unified approach outperforms existing methods specialized for each task by a large margin. In addition, it even achieves comparable results to instance-level methods despite the reduced assumptions. Project page: https://nvlabs.github.io/FoundationPose/", "github_manual": "https://github.com/NVlabs/FoundationPose", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-14", "arxiv_id": "2312.07987", "title": "SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention", "authors": [ "R\u00f3bert Csord\u00e1s", "Piotr Pi\u0119kos", "Kazuki Irie", "J\u00fcrgen Schmidhuber" ], "github": "https://github.com/robertcsordas/moe_attention", "project_page": "", "abstract": "The costly self-attention layers in modern Transformers require memory and compute quadratic in sequence length. Existing approximation methods usually underperform and fail to obtain significant speedups in practice. Here we present SwitchHead - a novel method that reduces both compute and memory requirements and achieves wall-clock speedup, while matching the language modeling performance of baseline Transformers with the same parameter budget. SwitchHead uses Mixture-of-Experts (MoE) layers for the value and output projections and requires 4 to 8 times fewer attention matrices than standard Transformers. Our novel attention can also be combined with MoE MLP layers, resulting in an efficient fully-MoE \"SwitchAll\" Transformer model. Our code is public.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/robertcsordas/moe_attention", "github_api_added_by": "auto" }, { "date": "2023-12-14", "arxiv_id": "2312.07910", "title": "PromptBench: A Unified Library for Evaluation of Large Language Models", "authors": [ "Kaijie Zhu", "Qinlin Zhao", "Hao Chen", "Jindong Wang", "Xing Xie" ], "github": "https://github.com/microsoft/promptbench", "project_page": "", "abstract": "The evaluation of large language models (LLMs) is crucial to assess their performance and mitigate potential security risks. In this paper, we introduce PromptBench, a unified library to evaluate LLMs. It consists of several key components that are easily used and extended by researchers: prompt construction, prompt engineering, dataset and model loading, adversarial prompt attack, dynamic evaluation protocols, and analysis tools. PromptBench is designed to be an open, general, and flexible codebase for research purposes that can facilitate original study in creating new benchmarks, deploying downstream applications, and designing new evaluation protocols. The code is available at: https://github.com/microsoft/promptbench and will be continuously supported.", "github_manual": "", "github_regex": "https://github.com/microsoft/promptbench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-14", "arxiv_id": "2312.07843", "title": "Foundation Models in Robotics: Applications, Challenges, and the Future", "authors": [ "Roya Firoozi", "Johnathan Tucker", "Stephen Tian", "Anirudha Majumdar", "Jiankai Sun", "Weiyu Liu", "Yuke Zhu", "Shuran Song", "Ashish Kapoor", "Karol Hausman", "Brian Ichter", "Danny Driess", "Jiajun Wu", "Cewu Lu", "Mac Schwager" ], "github": "https://github.com/robotics-survey/Awesome-Robotics-Foundation-Models", "project_page": "", "abstract": "We survey applications of pretrained foundation models in robotics. Traditional deep learning models in robotics are trained on small datasets tailored for specific tasks, which limits their adaptability across diverse applications. In contrast, foundation models pretrained on internet-scale data appear to have superior generalization capabilities, and in some instances display an emergent ability to find zero-shot solutions to problems that are not present in the training data. Foundation models may hold the potential to enhance various components of the robot autonomy stack, from perception to decision-making and control. For example, large language models can generate code or provide common sense reasoning, while vision-language models enable open-vocabulary visual recognition. However, significant open research challenges remain, particularly around the scarcity of robot-relevant training data, safety guarantees and uncertainty quantification, and real-time execution. In this survey, we study recent papers that have used or built foundation models to solve robotics problems. We explore how foundation models contribute to improving robot capabilities in the domains of perception, decision-making, and control. We discuss the challenges hindering the adoption of foundation models in robot autonomy and provide opportunities and potential pathways for future advancements. The GitHub project corresponding to this paper (Preliminary release. We are committed to further enhancing and updating this work to ensure its quality and relevance) can be found here: https://github.com/robotics-survey/Awesome-Robotics-Foundation-Models", "github_manual": "", "github_regex": "https://github.com/robotics-survey/Awesome-Robotics-Foundation-Models", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-14", "arxiv_id": "2312.07859", "title": "Invariant Graph Transformer", "authors": [ "Zhe Xu", "Menghai Pan", "Yuzhong Chen", "Huiyuan Chen", "Yuchen Yan", "Mahashweta Das", "Hanghang Tong" ], "github": "", "project_page": "", "abstract": "Rationale discovery is defined as finding a subset of the input data that maximally supports the prediction of downstream tasks. In graph machine learning context, graph rationale is defined to locate the critical subgraph in the given graph topology, which fundamentally determines the prediction results. In contrast to the rationale subgraph, the remaining subgraph is named the environment subgraph. Graph rationalization can enhance the model performance as the mapping between the graph rationale and prediction label is viewed as invariant, by assumption. To ensure the discriminative power of the extracted rationale subgraphs, a key technique named \"intervention\" is applied. The core idea of intervention is that given any changing environment subgraphs, the semantics from the rationale subgraph is invariant, which guarantees the correct prediction result. However, most, if not all, of the existing rationalization works on graph data develop their intervention strategies on the graph level, which is coarse-grained. In this paper, we propose well-tailored intervention strategies on graph data. Our idea is driven by the development of Transformer models, whose self-attention module provides rich interactions between input nodes. Based on the self-attention module, our proposed invariant graph Transformer (IGT) can achieve fine-grained, more specifically, node-level and virtual node-level intervention. Our comprehensive experiments involve 7 real-world datasets, and the proposed IGT shows significant performance advantages compared to 13 baseline methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.09241", "title": "TinyGSM: achieving >80% on GSM8k with small language models", "authors": [ "Bingbin Liu", "Sebastien Bubeck", "Ronen Eldan", "Janardhan Kulkarni", "Yuanzhi Li", "Anh Nguyen", "Rachel Ward", "Yi Zhang" ], "github": "", "project_page": "", "abstract": "Small-scale models offer various computational advantages, and yet to which extent size is critical for problem-solving abilities remains an open question. Specifically for solving grade school math, the smallest model size so far required to break the 80\\% barrier on the GSM8K benchmark remains to be 34B. Our work studies how high-quality datasets may be the key for small language models to acquire mathematical reasoning. We introduce TinyGSM, a synthetic dataset of 12.3M grade school math problems paired with Python solutions, generated fully by GPT-3.5. After finetuning on TinyGSM, we find that a duo of a 1.3B generation model and a 1.3B verifier model can achieve 81.5\\% accuracy, outperforming existing models that are orders of magnitude larger. This also rivals the performance of the GPT-3.5 ``teacher'' model (77.4\\%), from which our model's training data is generated. Our approach is simple and has two key components: 1) the high-quality dataset TinyGSM, 2) the use of a verifier, which selects the final outputs from multiple candidate generations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.09237", "title": "Pixel Aligned Language Models", "authors": [ "Jiarui Xu", "Xingyi Zhou", "Shen Yan", "Xiuye Gu", "Anurag Arnab", "Chen Sun", "Xiaolong Wang", "Cordelia Schmid" ], "github": "", "project_page": "", "abstract": "Large language models have achieved great success in recent years, so as their variants in vision. Existing vision-language models can describe images in natural languages, answer visual-related questions, or perform complex reasoning about the image. However, it is yet unclear how localization tasks, such as word grounding or referring localization, can be performed using large language models. In this work, we aim to develop a vision-language model that can take locations, for example, a set of points or boxes, as either inputs or outputs. When taking locations as inputs, the model performs location-conditioned captioning, which generates captions for the indicated object or region. When generating locations as outputs, our model regresses pixel coordinates for each output word generated by the language model, and thus performs dense word grounding. Our model is pre-trained on the Localized Narrative dataset, which contains pixel-word-aligned captioning from human attention. We show our model can be applied to various location-aware vision-language tasks, including referring localization, location-conditioned captioning, and dense object captioning, archiving state-of-the-art performance on RefCOCO and Visual Genome. Project page: https://jerryxu.net/PixelLLM .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.09187", "title": "Vision-Language Models as a Source of Rewards", "authors": [ "Kate Baumli", "Satinder Baveja", "Feryal Behbahani", "Harris Chan", "Gheorghe Comanici", "Sebastian Flennerhag", "Maxime Gazeau", "Kristian Holsheimer", "Dan Horgan", "Michael Laskin", "Clare Lyle", "Hussain Masoom", "Kay McKinney", "Volodymyr Mnih", "Alexander Neitz", "Fabio Pardo", "Jack Parker-Holder", "John Quan", "Tim Rockt\u00e4schel", "Himanshu Sahni", "Tom Schaul", "Yannick Schroecker", "Stephen Spencer", "Richie Steigerwald", "Luyu Wang", "Lei Zhang" ], "github": "", "project_page": "", "abstract": "Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning. A key limiting factor for building generalist agents with RL has been the need for a large number of reward functions for achieving different goals. We investigate the feasibility of using off-the-shelf vision-language models, or VLMs, as sources of rewards for reinforcement learning agents. We show how rewards for visual achievement of a variety of language goals can be derived from the CLIP family of models, and used to train RL agents that can achieve a variety of language goals. We showcase this approach in two distinct visual domains and present a scaling trend showing how larger VLMs lead to more accurate rewards for visual goal achievement, which in turn produces more capable RL agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.09158", "title": "General Object Foundation Model for Images and Videos at Scale", "authors": [ "Junfeng Wu", "Yi Jiang", "Qihao Liu", "Zehuan Yuan", "Xiang Bai", "Song Bai" ], "github": "https://github.com/FoundationVision/GLEE", "project_page": "", "abstract": "We present GLEE in this work, an object-level foundation model for locating and identifying objects in images and videos. Through a unified framework, GLEE accomplishes detection, segmentation, tracking, grounding, and identification of arbitrary objects in the open world scenario for various object perception tasks. Adopting a cohesive learning strategy, GLEE acquires knowledge from diverse data sources with varying supervision levels to formulate general object representations, excelling in zero-shot transfer to new data and tasks. Specifically, we employ an image encoder, text encoder, and visual prompter to handle multi-modal inputs, enabling to simultaneously solve various object-centric downstream tasks while maintaining state-of-the-art performance. Demonstrated through extensive training on over five million images from diverse benchmarks, GLEE exhibits remarkable versatility and improved generalization performance, efficiently tackling downstream tasks without the need for task-specific adaptation. By integrating large volumes of automatically labeled data, we further enhance its zero-shot generalization capabilities. Additionally, GLEE is capable of being integrated into Large Language Models, serving as a foundational model to provide universal object-level information for multi-modal tasks. We hope that the versatility and universality of our method will mark a significant step in the development of efficient visual foundation models for AGI systems. The model and code will be released at https://glee-vision.github.io .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FoundationVision/GLEE", "github_api_added_by": "auto" }, { "date": "2023-12-15", "arxiv_id": "2312.08914", "title": "CogAgent: A Visual Language Model for GUI Agents", "authors": [ "Wenyi Hong", "Weihan Wang", "Qingsong Lv", "Jiazheng Xu", "Wenmeng Yu", "Junhui Ji", "Yan Wang", "Zihan Wang", "Yuxiao Dong", "Ming Ding", "Jie Tang" ], "github": "https://github.com/THUDM/CogVLM", "project_page": "", "abstract": "People are spending an enormous amount of time on digital devices through graphical user interfaces (GUIs), e.g., computer or smartphone screens. Large language models (LLMs) such as ChatGPT can assist people in tasks like writing emails, but struggle to understand and interact with GUIs, thus limiting their potential to increase automation levels. In this paper, we introduce CogAgent, an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation. By utilizing both low-resolution and high-resolution image encoders, CogAgent supports input at a resolution of 1120*1120, enabling it to recognize tiny page elements and text. As a generalist visual language model, CogAgent achieves the state of the art on five text-rich and four general VQA benchmarks, including VQAv2, OK-VQA, Text-VQA, ST-VQA, ChartQA, infoVQA, DocVQA, MM-Vet, and POPE. CogAgent, using only screenshots as input, outperforms LLM-based methods that consume extracted HTML text on both PC and Android GUI navigation tasks -- Mind2Web and AITW, advancing the state of the art. The model and codes are available at https://github.com/THUDM/CogVLM.", "github_manual": "", "github_regex": "https://github.com/THUDM/CogVLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.08688", "title": "TigerBot: An Open Multilingual Multitask LLM", "authors": [ "Ye Chen", "Wei Cai", "Liangmin Wu", "Xiaowei Li", "Zhanxuan Xin", "Cong Fu" ], "github": "https://github.com/tigerresearch/tigerbot", "project_page": "", "abstract": "We release and introduce the TigerBot family of large language models (LLMs), consisting of base and chat models, sized from 7, 13, 70 and 180 billion parameters. We develop our models embarking from Llama-2 and BLOOM, and push the boundary further in data, training algorithm, infrastructure, and application tools. Our models yield meaningful performance gain over SOTA open-source models, e.g., Llama-2, specifically 6\\% gain in English and 20\\% gain in Chinese. TigerBot model family also achieves leading performance in major academic and industrial benchmarks and leaderboards. We believe that TigerBot represents just a snapshot of lightning-fast progression in LLM open-source community. Therefore, we are thrilled to give back by publicly releasing our models and reporting our approach behind, with additional emphases on building SOTA LLMs in a democratized way and making LLMs of use in real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tigerresearch/tigerbot", "github_api_added_by": "auto" }, { "date": "2023-12-15", "arxiv_id": "2312.08618", "title": "Zebra: Extending Context Window with Layerwise Grouped Local-Global Attention", "authors": [ "Kaiqiang Song", "Xiaoyang Wang", "Sangwoo Cho", "Xiaoman Pan", "Dong Yu" ], "github": "", "project_page": "", "abstract": "This paper introduces a novel approach to enhance the capabilities of Large Language Models (LLMs) in processing and understanding extensive text sequences, a critical aspect in applications requiring deep comprehension and synthesis of large volumes of information. Recognizing the inherent challenges in extending the context window for LLMs, primarily built on Transformer architecture, we propose a new model architecture, referred to as Zebra. This architecture efficiently manages the quadratic time and memory complexity issues associated with full attention in the Transformer by employing grouped local-global attention layers. Our model, akin to a zebra's alternating stripes, balances local and global attention layers, significantly reducing computational requirements and memory consumption. Comprehensive experiments, including pretraining from scratch, continuation of long context adaptation training, and long instruction tuning, are conducted to evaluate the Zebra's performance. The results show that Zebra achieves comparable or superior performance on both short and long sequence benchmarks, while also enhancing training and inference efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.08583", "title": "ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks", "authors": [ "Xiaoxia Wu", "Haojun Xia", "Stephen Youn", "Zhen Zheng", "Shiyang Chen", "Arash Bakhtiari", "Michael Wyatt", "Yuxiong He", "Olatunji Ruwase", "Leon Song", "Zhewei Yao" ], "github": "https://github.com/qwopqwop200/GPTQ-for-LLaMa", "project_page": "", "abstract": "This study examines 4-bit quantization methods like GPTQ in large language models (LLMs), highlighting GPTQ's overfitting and limited enhancement in Zero-Shot tasks. While prior works merely focusing on zero-shot measurement, we extend task scope to more generative categories such as code generation and abstractive summarization, in which we found that INT4 quantization can significantly underperform. However, simply shifting to higher precision formats like FP6 has been particularly challenging, thus overlooked, due to poor performance caused by the lack of sophisticated integration and system acceleration strategies on current AI hardware. Our results show that FP6, even with a coarse-grain quantization scheme, performs robustly across various algorithms and tasks, demonstrating its superiority in accuracy and versatility. Notably, with the FP6 quantization, \\codestar-15B model performs comparably to its FP16 counterpart in code generation, and for smaller models like the 406M it closely matches their baselines in summarization. Neither can be achieved by INT4. To better accommodate various AI hardware and achieve the best system performance, we propose a novel 4+2 design for FP6 to achieve similar latency to the state-of-the-art INT4 fine-grain quantization. With our design, FP6 can become a promising solution to the current 4-bit quantization methods used in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/qwopqwop200/GPTQ-for-LLaMa", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.08578", "title": "A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions", "authors": [ "Jack Urbanek", "Florian Bordes", "Pietro Astolfi", "Mary Williamson", "Vasu Sharma", "Adriana Romero-Soriano" ], "github": "https://github.com/facebookresearch/DCI", "project_page": "", "abstract": "Curation methods for massive vision-language datasets trade off between dataset size and quality. However, even the highest quality of available curated captions are far too short to capture the rich visual detail in an image. To show the value of dense and highly-aligned image-text pairs, we collect the Densely Captioned Images (DCI) dataset, containing 8012 natural images human-annotated with mask-aligned descriptions averaging above 1000 words each. With precise and reliable captions associated with specific parts of an image, we can evaluate vision-language models' (VLMs) understanding of image content with a novel task that matches each caption with its corresponding subcrop. As current models are often limited to 77 text tokens, we also introduce a summarized version (sDCI) in which each caption length is limited. We show that modern techniques that make progress on standard benchmarks do not correspond with significant improvement on our sDCI based benchmark. Lastly, we finetune CLIP using sDCI and show significant improvements over the baseline despite a small training set. By releasing the first human annotated dense image captioning dataset, we hope to enable the development of new benchmarks or fine-tuning recipes for the next generation of VLMs to come.", "github_manual": "https://github.com/facebookresearch/DCI", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.09067", "title": "Holodeck: Language Guided Generation of 3D Embodied AI Environments", "authors": [ "Yue Yang", "Fan-Yun Sun", "Luca Weihs", "Eli VanderBilt", "Alvaro Herrasti", "Winson Han", "Jiajun Wu", "Nick Haber", "Ranjay Krishna", "Lingjie Liu", "Chris Callison-Burch", "Mark Yatskar", "Aniruddha Kembhavi", "Christopher Clark" ], "github": "https://github.com/allenai/Holodeck", "project_page": "", "abstract": "3D simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope. To mitigate this limitation, we present Holodeck, a system that generates 3D environments to match a user-supplied prompt fully automatedly. Holodeck can generate diverse scenes, e.g., arcades, spas, and museums, adjust the designs for styles, and can capture the semantics of complex queries such as \"apartment for a researcher with a cat\" and \"office of a professor who is a fan of Star Wars\". Holodeck leverages a large language model (GPT-4) for common sense knowledge about what the scene might look like and uses a large collection of 3D assets from Objaverse to populate the scene with diverse objects. To address the challenge of positioning objects correctly, we prompt GPT-4 to generate spatial relational constraints between objects and then optimize the layout to satisfy those constraints. Our large-scale human evaluation shows that annotators prefer Holodeck over manually designed procedural baselines in residential scenes and that Holodeck can produce high-quality outputs for diverse scene types. We also demonstrate an exciting application of Holodeck in Embodied AI, training agents to navigate in novel scenes like music rooms and daycares without human-constructed data, which is a significant step forward in developing general-purpose embodied agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/Holodeck", "github_api_added_by": "auto" }, { "date": "2023-12-15", "arxiv_id": "2312.08926", "title": "Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent", "authors": [ "Haoran Liao", "Qinyi Du", "Shaohua Hu", "Hao He", "Yanyan Xu", "Jidong Tian", "Yaohui Jin" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) face challenges in solving complex mathematical problems that require comprehensive capacities to parse the statements, associate domain knowledge, perform compound logical reasoning, and integrate the intermediate rationales. Tackling all these problems once could be arduous for LLMs, thus leading to confusion in generation. In this work, we explore the potential of enhancing LLMs with agents by meticulous decomposition and modeling of mathematical reasoning process. Specifically, we propose a formal description of the mathematical solving and extend LLMs with an agent-based zero-shot framework named Planner-Reasoner-Executor-Reflector (PRER). We further provide and implement two MathAgents that define the logical forms and inherent relations via a pool of actions in different grains and orientations: MathAgent-M adapts its actions to LLMs, while MathAgent-H aligns with humankind. Experiments on miniF2F and MATH have demonstrated the effectiveness of PRER and proposed MathAgents, achieving an increase of 12.3%(53.9%66.2%) on the MiniF2F, 9.2% (49.8%59.0%) on MATH, and 13.2%(23.2%35.4%) for level-5 problems of MATH against GPT-4. Further analytical results provide more insightful perspectives on exploiting the behaviors of LLMs as agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.08723", "title": "StemGen: A music generation model that listens", "authors": [ "Julian D. Parker", "Janne Spijkervet", "Katerina Kosta", "Furkan Yesiler", "Boris Kuznetsov", "Ju-Chiang Wang", "Matt Avent", "Jitong Chen", "Duc Le" ], "github": "", "project_page": "", "abstract": "End-to-end generation of musical audio using deep learning techniques has seen an explosion of activity recently. However, most models concentrate on generating fully mixed music in response to abstract conditioning information. In this work, we present an alternative paradigm for producing music generation models that can listen and respond to musical context. We describe how such a model can be constructed using a non-autoregressive, transformer-based model architecture and present a number of novel architectural and sampling improvements. We train the described architecture on both an open-source and a proprietary dataset. We evaluate the produced models using standard quality metrics and a new approach based on music information retrieval descriptors. The resulting model reaches the audio quality of state-of-the-art text-conditioned models, as well as exhibiting strong musical coherence with its context.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.09109", "title": "VideoLCM: Video Latent Consistency Model", "authors": [ "Xiang Wang", "Shiwei Zhang", "Han Zhang", "Yu Liu", "Yingya Zhang", "Changxin Gao", "Nong Sang" ], "github": "", "project_page": "", "abstract": "Consistency models have demonstrated powerful capability in efficient image generation and allowed synthesis within a few sampling steps, alleviating the high computational cost in diffusion models. However, the consistency model in the more challenging and resource-consuming video generation is still less explored. In this report, we present the VideoLCM framework to fill this gap, which leverages the concept of consistency models from image generation to efficiently synthesize videos with minimal steps while maintaining high quality. VideoLCM builds upon existing latent video diffusion models and incorporates consistency distillation techniques for training the latent consistency model. Experimental results reveal the effectiveness of our VideoLCM in terms of computational efficiency, fidelity and temporal consistency. Notably, VideoLCM achieves high-fidelity and smooth video synthesis with only four sampling steps, showcasing the potential for real-time synthesis. We hope that VideoLCM can serve as a simple yet effective baseline for subsequent research. The source code and models will be publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.09246", "title": "SHAP-EDITOR: Instruction-guided Latent 3D Editing in Seconds", "authors": [ "Minghao Chen", "Junyu Xie", "Iro Laina", "Andrea Vedaldi" ], "github": "", "project_page": "", "abstract": "We propose a novel feed-forward 3D editing framework called Shap-Editor. Prior research on editing 3D objects primarily concentrated on editing individual objects by leveraging off-the-shelf 2D image editing networks. This is achieved via a process called distillation, which transfers knowledge from the 2D network to 3D assets. Distillation necessitates at least tens of minutes per asset to attain satisfactory editing results, and is thus not very practical. In contrast, we ask whether 3D editing can be carried out directly by a feed-forward network, eschewing test-time optimisation. In particular, we hypothesise that editing can be greatly simplified by first encoding 3D objects in a suitable latent space. We validate this hypothesis by building upon the latent space of Shap-E. We demonstrate that direct 3D editing in this space is possible and efficient by building a feed-forward editor network that only requires approximately one second per edit. Our experiments show that Shap-Editor generalises well to both in-distribution and out-of-distribution 3D assets with different prompts, exhibiting comparable performance with methods that carry out test-time optimisation for each edited instance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.09222", "title": "Mosaic-SDF for 3D Generative Models", "authors": [ "Lior Yariv", "Omri Puny", "Natalia Neverova", "Oran Gafni", "Yaron Lipman" ], "github": "", "project_page": "", "abstract": "Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes. When training a diffusion or flow models on 3D shapes a crucial design choice is the shape representation. An effective shape representation needs to adhere three design principles: it should allow an efficient conversion of large 3D datasets to the representation form; it should provide a good tradeoff of approximation power versus number of parameters; and it should have a simple tensorial form that is compatible with existing powerful neural architectures. While standard 3D shape representations such as volumetric grids and point clouds do not adhere to all these principles simultaneously, we advocate in this paper a new representation that does. We introduce Mosaic-SDF (M-SDF): a simple 3D shape representation that approximates the Signed Distance Function (SDF) of a given shape by using a set of local grids spread near the shape's boundary. The M-SDF representation is fast to compute for each shape individually making it readily parallelizable; it is parameter efficient as it only covers the space around the shape's boundary; and it has a simple matrix form, compatible with Transformer-based architectures. We demonstrate the efficacy of the M-SDF representation by using it to train a 3D generative flow model including class-conditioned generation with the 3D Warehouse dataset, and text-to-3D generation using a dataset of about 600k caption-shape pairs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.09256", "title": "LIME: Localized Image Editing via Attention Regularization in Diffusion Models", "authors": [ "Enis Simsar", "Alessio Tonioni", "Yongqin Xian", "Thomas Hofmann", "Federico Tombari" ], "github": "https://github.com/google/prompt-to-prompt", "project_page": "", "abstract": "Diffusion models (DMs) have gained prominence due to their ability to generate high-quality, varied images, with recent advancements in text-to-image generation. The research focus is now shifting towards the controllability of DMs. A significant challenge within this domain is localized editing, where specific areas of an image are modified without affecting the rest of the content. This paper introduces LIME for localized image editing in diffusion models that do not require user-specified regions of interest (RoI) or additional text input. Our method employs features from pre-trained methods and a simple clustering technique to obtain precise semantic segmentation maps. Then, by leveraging cross-attention maps, it refines these segments for localized edits. Finally, we propose a novel cross-attention regularization technique that penalizes unrelated cross-attention scores in the RoI during the denoising steps, ensuring localized edits. Our approach, without re-training and fine-tuning, consistently improves the performance of existing methods in various editing benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google/prompt-to-prompt", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.08889", "title": "SEEAvatar: Photorealistic Text-to-3D Avatar Generation with Constrained Geometry and Appearance", "authors": [ "Yuanyou Xu", "Zongxin Yang", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Powered by large-scale text-to-image generation models, text-to-3D avatar generation has made promising progress. However, most methods fail to produce photorealistic results, limited by imprecise geometry and low-quality appearance. Towards more practical avatar generation, we present SEEAvatar, a method for generating photorealistic 3D avatars from text with SElf-Evolving constraints for decoupled geometry and appearance. For geometry, we propose to constrain the optimized avatar in a decent global shape with a template avatar. The template avatar is initialized with human prior and can be updated by the optimized avatar periodically as an evolving template, which enables more flexible shape generation. Besides, the geometry is also constrained by the static human prior in local parts like face and hands to maintain the delicate structures. For appearance generation, we use diffusion model enhanced by prompt engineering to guide a physically based rendering pipeline to generate realistic textures. The lightness constraint is applied on the albedo texture to suppress incorrect lighting effect. Experiments show that our method outperforms previous methods on both global and local geometry and appearance quality by a large margin. Since our method can produce high-quality meshes and textures, such assets can be directly applied in classic graphics pipeline for realistic rendering under any lighting condition. Project page at: https://seeavatar3d.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.09251", "title": "VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation", "authors": [ "Jinguo Zhu", "Xiaohan Ding", "Yixiao Ge", "Yuying Ge", "Sijie Zhao", "Hengshuang Zhao", "Xiaohua Wang", "Ying Shan" ], "github": "https://github.com/ailab-cvc/vl-gpt", "project_page": "", "abstract": "In this work, we introduce Vision-Language Generative Pre-trained Transformer (VL-GPT), a transformer model proficient at concurrently perceiving and generating visual and linguistic data. VL-GPT achieves a unified pre-training approach for both image and text modalities by employing a straightforward auto-regressive objective, thereby enabling the model to process image and text as seamlessly as a language model processes text. To accomplish this, we initially propose a novel image tokenizer-detokenizer framework for visual data, specifically designed to transform raw images into a sequence of continuous embeddings and reconstruct them accordingly. In combination with the existing text tokenizer and detokenizer, this framework allows for the encoding of interleaved image-text data into a multimodal sequence, which can subsequently be fed into the transformer model. Consequently, VL-GPT can perform large-scale pre-training on multimodal corpora utilizing a unified auto-regressive objective (i.e., next-token prediction). Upon completion of pre-training, VL-GPT exhibits remarkable zero-shot and few-shot performance across a diverse range of vision and language understanding and generation tasks, including image captioning, visual question answering, text-to-image generation, and more. Additionally, the pre-trained model retrains in-context learning capabilities when provided with multimodal prompts. We further conduct instruction tuning on our VL-GPT, highlighting its exceptional potential for multimodal assistance. The source code and model weights shall be released.", "github_manual": "https://github.com/ailab-cvc/vl-gpt", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.09244", "title": "Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking", "authors": [ "Jacob Eisenstein", "Chirag Nagpal", "Alekh Agarwal", "Ahmad Beirami", "Alex D'Amour", "DJ Dvijotham", "Adam Fisch", "Katherine Heller", "Stephen Pfohl", "Deepak Ramachandran", "Peter Shaw", "Jonathan Berant" ], "github": "https://github.com/google-deepmind/reward-ensembles", "project_page": "", "abstract": "Reward models play a key role in aligning language model applications towards human preferences. However, this setup creates an incentive for the language model to exploit errors in the reward model to achieve high estimated reward, a phenomenon often termed reward hacking. A natural mitigation is to train an ensemble of reward models, aggregating over model outputs to obtain a more robust reward estimate. We explore the application of reward ensembles to alignment at both training time (through reinforcement learning) and inference time (through reranking). First, we show that reward models are underspecified: reward models that perform similarly in-distribution can yield very different rewards when used in alignment, due to distribution shift. Second, underspecification results in overoptimization, where alignment to one reward model does not improve reward as measured by another reward model trained on the same data. Third, overoptimization is mitigated by the use of reward ensembles, and ensembles that vary by their pretraining seeds lead to better generalization than ensembles that differ only by their fine-tuning seeds, with both outperforming individual reward models. However, even pretrain reward ensembles do not eliminate reward hacking: we show several qualitative reward hacking phenomena that are not mitigated by ensembling because all reward models in the ensemble exhibit similar error patterns.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/reward-ensembles", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.08754", "title": "UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generation", "authors": [ "Zexiang Liu", "Yangguang Li", "Youtian Lin", "Xin Yu", "Sida Peng", "Yan-Pei Cao", "Xiaojuan Qi", "Xiaoshui Huang", "Ding Liang", "Wanli Ouyang" ], "github": "https://github.com/YG256Li/UniDream", "project_page": "", "abstract": "Recent advancements in text-to-3D generation technology have significantly advanced the conversion of textual descriptions into imaginative well-geometrical and finely textured 3D objects. Despite these developments, a prevalent limitation arises from the use of RGB data in diffusion or reconstruction models, which often results in models with inherent lighting and shadows effects that detract from their realism, thereby limiting their usability in applications that demand accurate relighting capabilities. To bridge this gap, we present UniDream, a text-to-3D generation framework by incorporating unified diffusion priors. Our approach consists of three main components: (1) a dual-phase training process to get albedo-normal aligned multi-view diffusion and reconstruction models, (2) a progressive generation procedure for geometry and albedo-textures based on Score Distillation Sample (SDS) using the trained reconstruction and diffusion models, and (3) an innovative application of SDS for finalizing PBR generation while keeping a fixed albedo based on Stable Diffusion model. Extensive evaluations demonstrate that UniDream surpasses existing methods in generating 3D objects with clearer albedo textures, smoother surfaces, enhanced realism, and superior relighting capabilities.", "github_manual": "https://github.com/YG256Li/UniDream", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-15", "arxiv_id": "2312.09252", "title": "FineControlNet: Fine-level Text Control for Image Generation with Spatially Aligned Text Control Injection", "authors": [ "Hongsuk Choi", "Isaac Kasahara", "Selim Engin", "Moritz Graule", "Nikhil Chavan-Dafle", "Volkan Isler" ], "github": "https://github.com/SamsungLabs/FineControlNet", "project_page": "", "abstract": "Recently introduced ControlNet has the ability to steer the text-driven image generation process with geometric input such as human 2D pose, or edge features. While ControlNet provides control over the geometric form of the instances in the generated image, it lacks the capability to dictate the visual appearance of each instance. We present FineControlNet to provide fine control over each instance's appearance while maintaining the precise pose control capability. Specifically, we develop and demonstrate FineControlNet with geometric control via human pose images and appearance control via instance-level text prompts. The spatial alignment of instance-specific text prompts and 2D poses in latent space enables the fine control capabilities of FineControlNet. We evaluate the performance of FineControlNet with rigorous comparison against state-of-the-art pose-conditioned text-to-image diffusion models. FineControlNet achieves superior performance in generating images that follow the user-provided instance-specific text prompts and poses compared with existing methods. Project webpage: https://samsunglabs.github.io/FineControlNet-project-page", "github_manual": "https://github.com/SamsungLabs/FineControlNet", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.09390", "title": "Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision", "authors": [ "Collin Burns", "Pavel Izmailov", "Jan Hendrik Kirchner", "Bowen Baker", "Leo Gao", "Leopold Aschenbrenner", "Yining Chen", "Adrien Ecoffet", "Manas Joglekar", "Jan Leike", "Ilya Sutskever", "Jeff Wu" ], "github": "https://github.com/openai/weak-to-strong", "project_page": "", "abstract": "Widely used alignment techniques, such as reinforcement learning from human feedback (RLHF), rely on the ability of humans to supervise model behavior - for example, to evaluate whether a model faithfully followed instructions or generated safe outputs. However, future superhuman models will behave in complex ways too difficult for humans to reliably evaluate; humans will only be able to weakly supervise superhuman models. We study an analogy to this problem: can weak model supervision elicit the full capabilities of a much stronger model? We test this using a range of pretrained language models in the GPT-4 family on natural language processing (NLP), chess, and reward modeling tasks. We find that when we naively finetune strong pretrained models on labels generated by a weak model, they consistently perform better than their weak supervisors, a phenomenon we call weak-to-strong generalization. However, we are still far from recovering the full capabilities of strong models with naive finetuning alone, suggesting that techniques like RLHF may scale poorly to superhuman models without further work. We find that simple methods can often significantly improve weak-to-strong generalization: for example, when finetuning GPT-4 with a GPT-2-level supervisor and an auxiliary confidence loss, we can recover close to GPT-3.5-level performance on NLP tasks. Our results suggest that it is feasible to make empirical progress today on a fundamental challenge of aligning superhuman models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openai/weak-to-strong", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.09299", "title": "Weight subcloning: direct initialization of transformers using larger pretrained ones", "authors": [ "Mohammad Samragh", "Mehrdad Farajtabar", "Sachin Mehta", "Raviteja Vemulapalli", "Fartash Faghri", "Devang Naik", "Oncel Tuzel", "Mohammad Rastegari" ], "github": "", "project_page": "", "abstract": "Training large transformer models from scratch for a target task requires lots of data and is computationally demanding. The usual practice of transfer learning overcomes this challenge by initializing the model with weights of a pretrained model of the same size and specification to increase the convergence and training speed. However, what if no pretrained model of the required size is available? In this paper, we introduce a simple yet effective technique to transfer the knowledge of a pretrained model to smaller variants. Our approach called weight subcloning expedites the training of scaled-down transformers by initializing their weights from larger pretrained models. Weight subcloning involves an operation on the pretrained model to obtain the equivalent initialized scaled-down model. It consists of two key steps: first, we introduce neuron importance ranking to decrease the embedding dimension per layer in the pretrained model. Then, we remove blocks from the transformer model to match the number of layers in the scaled-down network. The result is a network ready to undergo training, which gains significant improvements in training speed compared to random initialization. For instance, we achieve 4x faster training for vision transformers in image classification and language models designed for next token prediction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.09300", "title": "Self-Evaluation Improves Selective Generation in Large Language Models", "authors": [ "Jie Ren", "Yao Zhao", "Tu Vu", "Peter J. Liu", "Balaji Lakshminarayanan" ], "github": "", "project_page": "", "abstract": "Safe deployment of large language models (LLMs) may benefit from a reliable method for assessing their generated content to determine when to abstain or to selectively generate. While likelihood-based metrics such as perplexity are widely employed, recent research has demonstrated the limitations of using sequence-level probability estimates given by LLMs as reliable indicators of generation quality. Conversely, LLMs have demonstrated strong calibration at the token level, particularly when it comes to choosing correct answers in multiple-choice questions or evaluating true/false statements. In this work, we reformulate open-ended generation tasks into token-level prediction tasks, and leverage LLMs' superior calibration at the token level. We instruct an LLM to self-evaluate its answers, employing either a multi-way comparison or a point-wise evaluation approach, with the option to include a ``None of the above'' option to express the model's uncertainty explicitly. We benchmark a range of scoring methods based on self-evaluation and evaluate their performance in selective generation using TruthfulQA and TL;DR. Through experiments with PaLM-2 and GPT-3, we demonstrate that self-evaluation based scores not only improve accuracy, but also correlate better with the overall quality of generated content.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.10029", "title": "Challenges with unsupervised LLM knowledge discovery", "authors": [ "Sebastian Farquhar", "Vikrant Varma", "Zachary Kenton", "Johannes Gasteiger", "Vladimir Mikulik", "Rohin Shah" ], "github": "", "project_page": "", "abstract": "We show that existing unsupervised methods on large language model (LLM) activations do not discover knowledge -- instead they seem to discover whatever feature of the activations is most prominent. The idea behind unsupervised knowledge elicitation is that knowledge satisfies a consistency structure, which can be used to discover knowledge. We first prove theoretically that arbitrary features (not just knowledge) satisfy the consistency structure of a particular leading unsupervised knowledge-elicitation method, contrast-consistent search (Burns et al. - arXiv:2212.03827). We then present a series of experiments showing settings in which unsupervised methods result in classifiers that do not predict knowledge, but instead predict a different prominent feature. We conclude that existing unsupervised methods for discovering latent knowledge are insufficient, and we contribute sanity checks to apply to evaluating future knowledge elicitation methods. Conceptually, we hypothesise that the identification issues explored here, e.g. distinguishing a model's knowledge from that of a simulated character's, will persist for future unsupervised methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.10007", "title": "Faithful Persona-based Conversational Dataset Generation with Large Language Models", "authors": [ "Pegah Jandaghi", "XiangHai Sheng", "Xinyi Bai", "Jay Pujara", "Hakim Sidahmed" ], "github": "https://github.com/google-research-datasets/Synthetic-Persona-Chat", "project_page": "", "abstract": "High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is through personas, aspects of the user's character that provide insights into their personality, motivations, and behaviors. Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement. In this paper, we leverage the power of Large Language Models (LLMs) to create a large, high-quality conversational dataset from a seed dataset. We propose a Generator-Critic architecture framework to expand the initial dataset, while improving the quality of its conversations. The Generator is an LLM prompted to output conversations. The Critic consists of a mixture of expert LLMs that control the quality of the generated conversations. These experts select the best generated conversations, which we then use to improve the Generator. We release Synthetic-Persona-Chat, consisting of 20k conversations seeded from Persona-Chat. We evaluate the quality of Synthetic-Persona-Chat and our generation framework on different dimensions through extensive experiments, and observe that the losing rate of Synthetic-Persona-Chat against Persona-Chat during Turing test decreases from 17.2% to 8.8% over three iterations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-research-datasets/Synthetic-Persona-Chat", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.10003", "title": "ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent", "authors": [ "Renat Aksitov", "Sobhan Miryoosefi", "Zonglin Li", "Daliang Li", "Sheila Babayan", "Kavya Kopparapu", "Zachary Fisher", "Ruiqi Guo", "Sushant Prakash", "Pranesh Srinivasan", "Manzil Zaheer", "Felix Yu", "Sanjiv Kumar" ], "github": "", "project_page": "", "abstract": "Answering complex natural language questions often necessitates multi-step reasoning and integrating external information. Several systems have combined knowledge retrieval with a large language model (LLM) to answer such questions. These systems, however, suffer from various failure cases, and we cannot directly train them end-to-end to fix such failures, as interaction with external knowledge is non-differentiable. To address these deficiencies, we define a ReAct-style LLM agent with the ability to reason and act upon external knowledge. We further refine the agent through a ReST-like method that iteratively trains on previous trajectories, employing growing-batch reinforcement learning with AI feedback for continuous self-improvement and self-distillation. Starting from a prompted large model and after just two iterations of the algorithm, we can produce a fine-tuned small model that achieves comparable performance on challenging compositional question-answering benchmarks with two orders of magnitude fewer parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.09911", "title": "Amphion: An Open-Source Audio, Music and Speech Generation Toolkit", "authors": [ "Xueyao Zhang", "Liumeng Xue", "Yuancheng Wang", "Yicheng Gu", "Xi Chen", "Zihao Fang", "Haopeng Chen", "Lexiao Zou", "Chaoren Wang", "Jun Han", "Kai Chen", "Haizhou Li", "Zhizheng Wu" ], "github": "https://github.com/open-mmlab/Amphion", "project_page": "", "abstract": "Amphion is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development. Amphion offers a unique feature: visualizations of classic models or architectures. We believe that these visualizations are beneficial for junior researchers and engineers who wish to gain a better understanding of the model. The North-Star objective of Amphion is to offer a platform for studying the conversion of any inputs into general audio. Amphion is designed to support individual generation tasks. In addition to the specific generation tasks, Amphion also includes several vocoders and evaluation metrics. A vocoder is an important module for producing high-quality audio signals, while evaluation metrics are critical for ensuring consistent metrics in generation tasks. In this paper, we provide a high-level overview of Amphion.", "github_manual": "https://github.com/open-mmlab/Amphion", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.09767", "title": "DreamTalk: When Expressive Talking Head Generation Meets Diffusion Probabilistic Models", "authors": [ "Yifeng Ma", "Shiwei Zhang", "Jiayu Wang", "Xiang Wang", "Yingya Zhang", "Zhidong Deng" ], "github": "https://github.com/ali-vilab/dreamtalk", "project_page": "", "abstract": "Diffusion models have shown remarkable success in a variety of downstream generative tasks, yet remain under-explored in the important and challenging expressive talking head generation. In this work, we propose a DreamTalk framework to fulfill this gap, which employs meticulous design to unlock the potential of diffusion models in generating expressive talking heads. Specifically, DreamTalk consists of three crucial components: a denoising network, a style-aware lip expert, and a style predictor. The diffusion-based denoising network is able to consistently synthesize high-quality audio-driven face motions across diverse expressions. To enhance the expressiveness and accuracy of lip motions, we introduce a style-aware lip expert that can guide lip-sync while being mindful of the speaking styles. To eliminate the need for expression reference video or text, an extra diffusion-based style predictor is utilized to predict the target expression directly from the audio. By this means, DreamTalk can harness powerful diffusion models to generate expressive faces effectively and reduce the reliance on expensive style references. Experimental results demonstrate that DreamTalk is capable of generating photo-realistic talking faces with diverse speaking styles and achieving accurate lip motions, surpassing existing state-of-the-art counterparts.", "github_manual": "https://github.com/ali-vilab/dreamtalk", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.09579", "title": "MobileSAMv2: Faster Segment Anything to Everything", "authors": [ "Chaoning Zhang", "Dongshen Han", "Sheng Zheng", "Jinwoo Choi", "Tae-Ho Kim", "Choong Seon Hong" ], "github": "https://github.com/ChaoningZhang/MobileSAM", "project_page": "", "abstract": "Segment anything model (SAM) addresses two practical yet challenging segmentation tasks: segment anything (SegAny), which utilizes a certain point to predict the mask for a single object of interest, and segment everything (SegEvery), which predicts the masks for all objects on the image. What makes SegAny slow for SAM is its heavyweight image encoder, which has been addressed by MobileSAM via decoupled knowledge distillation. The efficiency bottleneck of SegEvery with SAM, however, lies in its mask decoder because it needs to first generate numerous masks with redundant grid-search prompts and then perform filtering to obtain the final valid masks. We propose to improve its efficiency by directly generating the final masks with only valid prompts, which can be obtained through object discovery. Our proposed approach not only helps reduce the total time on the mask decoder by at least 16 times but also achieves superior performance. Specifically, our approach yields an average performance boost of 3.6\\% (42.5\\% v.s. 38.9\\%) for zero-shot object proposal on the LVIS dataset with the mask AR@K metric. Qualitative results show that our approach generates fine-grained masks while avoiding over-segmenting things. This project targeting faster SegEvery than the original SAM is termed MobileSAMv2 to differentiate from MobileSAM which targets faster SegAny. Moreover, we demonstrate that our new prompt sampling is also compatible with the distilled image encoders in MobileSAM, contributing to a unified framework for efficient SegAny and SegEvery. The code is available at the same link as MobileSAM Project https://github.com/ChaoningZhang/MobileSAM{red{https://github.com/ChaoningZhang/MobileSAM}}. abstract", "github_manual": "", "github_regex": "https://github.com/ChaoningZhang/MobileSAM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.10035", "title": "Point Transformer V3: Simpler, Faster, Stronger", "authors": [ "Xiaoyang Wu", "Li Jiang", "Peng-Shuai Wang", "Zhijian Liu", "Xihui Liu", "Yu Qiao", "Wanli Ouyang", "Tong He", "Hengshuang Zhao" ], "github": "https://github.com/open-mmlab/OpenPCDet", "project_page": "", "abstract": "This paper is not motivated to seek innovation within the attention mechanism. Instead, it focuses on overcoming the existing trade-offs between accuracy and efficiency within the context of point cloud processing, leveraging the power of scale. Drawing inspiration from recent advances in 3D large-scale representation learning, we recognize that model performance is more influenced by scale than by intricate design. Therefore, we present Point Transformer V3 (PTv3), which prioritizes simplicity and efficiency over the accuracy of certain mechanisms that are minor to the overall performance after scaling, such as replacing the precise neighbor search by KNN with an efficient serialized neighbor mapping of point clouds organized with specific patterns. This principle enables significant scaling, expanding the receptive field from 16 to 1024 points while remaining efficient (a 3x increase in processing speed and a 10x improvement in memory efficiency compared with its predecessor, PTv2). PTv3 attains state-of-the-art results on over 20 downstream tasks that span both indoor and outdoor scenarios. Further enhanced with multi-dataset joint training, PTv3 pushes these results to a higher level.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-mmlab/OpenPCDet", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.09608", "title": "Faster Diffusion: Rethinking the Role of UNet Encoder in Diffusion Models", "authors": [ "Senmao Li", "Taihang Hu", "Fahad Shahbaz Khan", "Linxuan Li", "Shiqi Yang", "Yaxing Wang", "Ming-Ming Cheng", "Jian Yang" ], "github": "https://github.com/hutaiHang/Faster-Diffusion", "project_page": "", "abstract": "One of the key components within diffusion models is the UNet for noise prediction. While several works have explored basic properties of the UNet decoder, its encoder largely remains unexplored. In this work, we conduct the first comprehensive study of the UNet encoder. We empirically analyze the encoder features and provide insights to important questions regarding their changes at the inference process. In particular, we find that encoder features change gently, whereas the decoder features exhibit substantial variations across different time-steps. This finding inspired us to omit the encoder at certain adjacent time-steps and reuse cyclically the encoder features in the previous time-steps for the decoder. Further based on this observation, we introduce a simple yet effective encoder propagation scheme to accelerate the diffusion sampling for a diverse set of tasks. By benefiting from our propagation scheme, we are able to perform in parallel the decoder at certain adjacent time-steps. Additionally, we introduce a prior noise injection method to improve the texture details in the generated image. Besides the standard text-to-image task, we also validate our approach on other tasks: text-to-video, personalized generation and reference-guided generation. Without utilizing any knowledge distillation technique, our approach accelerates both the Stable Diffusion (SD) and the DeepFloyd-IF models sampling by 41% and 24% respectively, while maintaining high-quality generation performance. Our code is available in https://github.com/hutaiHang/Faster-Diffusion{FasterDiffusion}.", "github_manual": "", "github_regex": "https://github.com/hutaiHang/Faster-Diffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.09571", "title": "Extending Context Window of Large Language Models via Semantic Compression", "authors": [ "Weizhi Fei", "Xueyan Niu", "Pingyi Zhou", "Lu Hou", "Bo Bai", "Lei Deng", "Wei Han" ], "github": "", "project_page": "", "abstract": "Transformer-based Large Language Models (LLMs) often impose limitations on the length of the text input to ensure the generation of fluent and relevant responses. This constraint restricts their applicability in scenarios involving long texts. We propose a novel semantic compression method that enables generalization to texts that are 6-8 times longer, without incurring significant computational costs or requiring fine-tuning. Our proposed framework draws inspiration from source coding in information theory and employs a pre-trained model to reduce the semantic redundancy of long inputs before passing them to the LLMs for downstream tasks. Experimental results demonstrate that our method effectively extends the context window of LLMs across a range of tasks including question answering, summarization, few-shot learning, and information retrieval. Furthermore, the proposed semantic compression method exhibits consistent fluency in text generation while reducing the associated computational overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.10034", "title": "SlimmeRF: Slimmable Radiance Fields", "authors": [ "Shiran Yuan", "Hao Zhao" ], "github": "https://github.com/Shiran-Yuan/SlimmeRF", "project_page": "", "abstract": "Neural Radiance Field (NeRF) and its variants have recently emerged as successful methods for novel view synthesis and 3D scene reconstruction. However, most current NeRF models either achieve high accuracy using large model sizes, or achieve high memory-efficiency by trading off accuracy. This limits the applicable scope of any single model, since high-accuracy models might not fit in low-memory devices, and memory-efficient models might not satisfy high-quality requirements. To this end, we present SlimmeRF, a model that allows for instant test-time trade-offs between model size and accuracy through slimming, thus making the model simultaneously suitable for scenarios with different computing budgets. We achieve this through a newly proposed algorithm named Tensorial Rank Incrementation (TRaIn) which increases the rank of the model's tensorial representation gradually during training. We also observe that our model allows for more effective trade-offs in sparse-view scenarios, at times even achieving higher accuracy after being slimmed. We credit this to the fact that erroneous information such as floaters tend to be stored in components corresponding to higher ranks. Our implementation is available at https://github.com/Shiran-Yuan/SlimmeRF.", "github_manual": "", "github_regex": "https://github.com/Shiran-Yuan/SlimmeRF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.09323", "title": "Perspectives on the State and Future of Deep Learning -- 2023", "authors": [ "Micah Goldblum", "Anima Anandkumar", "Richard Baraniuk", "Tom Goldstein", "Kyunghyun Cho", "Zachary C Lipton", "Melanie Mitchell", "Preetum Nakkiran", "Max Welling", "Andrew Gordon Wilson" ], "github": "", "project_page": "", "abstract": "The goal of this series is to chronicle opinions and issues in the field of machine learning as they stand today and as they change over time. The plan is to host this survey periodically until the AI singularity paperclip-frenzy-driven doomsday, keeping an updated list of topical questions and interviewing new community members for each edition. In this issue, we probed people's opinions on interpretable AI, the value of benchmarking in modern NLP, the state of progress towards understanding deep learning, and the future of academia.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-18", "arxiv_id": "2312.09305", "title": "Stable Score Distillation for High-Quality 3D Generation", "authors": [ "Boshi Tang", "Jianan Wang", "Zhiyong Wu", "Lei Zhang" ], "github": "", "project_page": "", "abstract": "Score Distillation Sampling (SDS) has exhibited remarkable performance in conditional 3D content generation. However, a comprehensive understanding of the SDS formulation is still lacking, hindering the development of 3D generation. In this work, we present an interpretation of SDS as a combination of three functional components: mode-disengaging, mode-seeking and variance-reducing terms, and analyze the properties of each. We show that problems such as over-smoothness and color-saturation result from the intrinsic deficiency of the supervision terms and reveal that the variance-reducing term introduced by SDS is sub-optimal. Additionally, we shed light on the adoption of large Classifier-Free Guidance (CFG) scale for 3D generation. Based on the analysis, we propose a simple yet effective approach named Stable Score Distillation (SSD) which strategically orchestrates each term for high-quality 3D generation. Extensive experiments validate the efficacy of our approach, demonstrating its ability to generate high-fidelity 3D content without succumbing to issues such as over-smoothness and over-saturation, even under low CFG conditions with the most challenging NeRF representation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.10763", "title": "M3DBench: Let's Instruct Large Models with Multi-modal 3D Prompts", "authors": [ "Mingsheng Li", "Xin Chen", "Chi Zhang", "Sijin Chen", "Hongyuan Zhu", "Fukun Yin", "Gang Yu", "Tao Chen" ], "github": "https://github.com/OpenM3D/M3DBench", "project_page": "", "abstract": "Recently, 3D understanding has become popular to facilitate autonomous agents to perform further decisionmaking. However, existing 3D datasets and methods are often limited to specific tasks. On the other hand, recent progress in Large Language Models (LLMs) and Multimodal Language Models (MLMs) have demonstrated exceptional general language and imagery tasking performance. Therefore, it is interesting to unlock MLM's potential to be 3D generalist for wider tasks. However, current MLMs' research has been less focused on 3D tasks due to a lack of large-scale 3D instruction-following datasets. In this work, we introduce a comprehensive 3D instructionfollowing dataset called M3DBench, which possesses the following characteristics: 1) It supports general multimodal instructions interleaved with text, images, 3D objects, and other visual prompts. 2) It unifies diverse 3D tasks at both region and scene levels, covering a variety of fundamental abilities in real-world 3D environments. 3) It is a large-scale 3D instruction-following dataset with over 320k instruction-response pairs. Furthermore, we establish a new benchmark for assessing the performance of large models in understanding multi-modal 3D prompts. Extensive experiments demonstrate the effectiveness of our dataset and baseline, supporting general 3D-centric tasks, which can inspire future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenM3D/M3DBench", "github_api_added_by": "auto" }, { "date": "2023-12-19", "arxiv_id": "2312.11396", "title": "MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance", "authors": [ "Qi Mao", "Lan Chen", "Yuchao Gu", "Zhen Fang", "Mike Zheng Shou" ], "github": "https://github.com/showlab/loveu-tgve-2023", "project_page": "", "abstract": "Recent diffusion-based image editing approaches have exhibited impressive editing capabilities in images with simple compositions. However, localized editing in complex scenarios has not been well-studied in the literature, despite its growing real-world demands. Existing mask-based inpainting methods fall short of retaining the underlying structure within the edit region. Meanwhile, mask-free attention-based methods often exhibit editing leakage and misalignment in more complex compositions. In this work, we develop MAG-Edit, a training-free, inference-stage optimization method, which enables localized image editing in complex scenarios. In particular, MAG-Edit optimizes the noise latent feature in diffusion models by maximizing two mask-based cross-attention constraints of the edit token, which in turn gradually enhances the local alignment with the desired prompt. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in achieving both text alignment and structure preservation for localized editing within complex scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/showlab/loveu-tgve-2023", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.11461", "title": "GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning", "authors": [ "Ye Yuan", "Xueting Li", "Yangyi Huang", "Shalini De Mello", "Koki Nagano", "Jan Kautz", "Umar Iqbal" ], "github": "", "project_page": "", "abstract": "Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions, addressing the limitations (e.g., flexibility and efficiency) imposed by mesh or NeRF-based representations. However, a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems, we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animation. Second, to stabilize and amortize the learning of millions of Gaussians, we propose to use neural implicit fields to predict the Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries and extract detailed meshes, we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method, GAvatar, enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality, and achieves extremely fast rendering (100 fps) at 1K resolution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.10899", "title": "MagicScroll: Nontypical Aspect-Ratio Image Generation for Visual Storytelling via Multi-Layered Semantic-Aware Denoising", "authors": [ "Bingyuan Wang", "Hengyu Meng", "Zeyu Cai", "Lanjiong Li", "Yue Ma", "Qifeng Chen", "Zeyu Wang" ], "github": "https://github.com/damian0815/compel", "project_page": "", "abstract": "Visual storytelling often uses nontypical aspect-ratio images like scroll paintings, comic strips, and panoramas to create an expressive and compelling narrative. While generative AI has achieved great success and shown the potential to reshape the creative industry, it remains a challenge to generate coherent and engaging content with arbitrary size and controllable style, concept, and layout, all of which are essential for visual storytelling. To overcome the shortcomings of previous methods including repetitive content, style inconsistency, and lack of controllability, we propose MagicScroll, a multi-layered, progressive diffusion-based image generation framework with a novel semantic-aware denoising process. The model enables fine-grained control over the generated image on object, scene, and background levels with text, image, and layout conditions. We also establish the first benchmark for nontypical aspect-ratio image generation for visual storytelling including mediums like paintings, comics, and cinematic panoramas, with customized metrics for systematic evaluation. Through comparative and ablation studies, MagicScroll showcases promising results in aligning with the narrative text, improving visual coherence, and engaging the audience. We plan to release the code and benchmark in the hope of a better collaboration between AI researchers and creative practitioners involving visual storytelling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/damian0815/compel", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.11462", "title": "Cascade Speculative Drafting for Even Faster LLM Inference", "authors": [ "Ziyi Chen", "Xiaocong Yang", "Jiacheng Lin", "Chenkai Sun", "Jie Huang", "Kevin Chen-Chuan Chang" ], "github": "https://github.com/lfsszd/cs-drafting", "project_page": "", "abstract": "Speculative decoding enhances the efficiency of large language models (LLMs) by leveraging a draft model to draft for a larger target model to review. However, drafting in speculative decoding involves slow autoregressive generation and generating tokens of different importance with the same time allocation. These two inefficiencies lead to its suboptimal performance. To address this issue, we introduce Cascade Speculative Drafting (CS. Drafting), a novel approach that employs two types of cascades. The Vertical Cascade eliminates autoregressive generation from neural models. The Horizontal Cascade constitutes efficient time allocation in drafting with its optimality supported by our theoretical analysis. Combining both cascades, our CS. Drafting algorithm has achieved up to 72 percent additional speedup over speculative decoding in our experiments while keeping the same output distribution.", "github_manual": "https://github.com/lfsszd/cs-drafting", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.10523", "title": "Paloma: A Benchmark for Evaluating Language Model Fit", "authors": [ "Ian Magnusson", "Akshita Bhagia", "Valentin Hofmann", "Luca Soldaini", "Ananya Harsh Jha", "Oyvind Tafjord", "Dustin Schwenk", "Evan Pete Walsh", "Yanai Elazar", "Kyle Lo", "Dirk Groeneveld", "Iz Beltagy", "Hannaneh Hajishirzi", "Noah A. Smith", "Kyle Richardson", "Jesse Dodge" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "Language models (LMs) commonly report perplexity on monolithic data held out from training. Implicitly or explicitly, this data is composed of domainsx2013varying distributions of language. Rather than assuming perplexity on one distribution extrapolates to others, Perplexity Analysis for Language Model Assessment (Paloma), measures LM fit to 585 text domains, ranging from nytimes.com to r/depression on Reddit. We invite submissions to our benchmark and organize results by comparability based on compliance with guidelines such as removal of benchmark contamination from pretraining. Submissions can also record parameter and training token count to make comparisons of Pareto efficiency for performance as a function of these measures of cost. We populate our benchmark with results from 6 baselines pretrained on popular corpora. In case studies, we demonstrate analyses that are possible with Paloma, such as finding that pretraining without data beyond Common Crawl leads to inconsistent fit to many domains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.10253", "title": "Catwalk: A Unified Language Model Evaluation Framework for Many Datasets", "authors": [ "Dirk Groeneveld", "Anas Awadalla", "Iz Beltagy", "Akshita Bhagia", "Ian Magnusson", "Hao Peng", "Oyvind Tafjord", "Pete Walsh", "Kyle Richardson", "Jesse Dodge" ], "github": "https://github.com/allenai/catwalk", "project_page": "", "abstract": "The success of large language models has shifted the evaluation paradigms in natural language processing (NLP). The community's interest has drifted towards comparing NLP models across many tasks, domains, and datasets, often at an extreme scale. This imposes new engineering challenges: efforts in constructing datasets and models have been fragmented, and their formats and interfaces are incompatible. As a result, it often takes extensive (re)implementation efforts to make fair and controlled comparisons at scale. Catwalk aims to address these issues. Catwalk provides a unified interface to a broad range of existing NLP datasets and models, ranging from both canonical supervised training and fine-tuning, to more modern paradigms like in-context learning. Its carefully-designed abstractions allow for easy extensions to many others. Catwalk substantially lowers the barriers to conducting controlled experiments at scale. For example, we finetuned and evaluated over 64 models on over 86 datasets with a single command, without writing any code. Maintained by the AllenNLP team at the Allen Institute for Artificial Intelligence (AI2), Catwalk is an ongoing open-source effort: https://github.com/allenai/catwalk.", "github_manual": "", "github_regex": "https://github.com/allenai/catwalk", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.11370", "title": "G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model", "authors": [ "Jiahui Gao", "Renjie Pi", "Jipeng Zhang", "Jiacheng Ye", "Wanjun Zhong", "Yufei Wang", "Lanqing Hong", "Jianhua Han", "Hang Xu", "Zhenguo Li", "Lingpeng Kong" ], "github": "https://github.com/pipilurj/g-llava", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable proficiency in human-level reasoning and generation capabilities, which encourages extensive research on their application in mathematical problem solving. However, current work has been largely focused on text-based mathematical problems, with limited investigation in problems involving geometric information. Addressing this gap, we aim to enable LLMs to solve geometric problems by understanding image input. We first analyze the limitations of current Multimodal Large Language Models (MLLMs) in this area: they struggle to accurately comprehending basic geometric elements and their relationships. To overcome these challenges, we take advantage of the unique characteristics of geometric problems (such as unique geometric logical form, and geometric scalability) and the capacity of the textual LLMs to build an enriched multimodal geometry dataset based on existing data. The augmented dataset, Geo170K, contains more than 170K geometric image-caption and question-answer pairs. Utilizing our constructed Geo170K dataset, we develop G-LLaVA, which demonstrates exceptional performance in solving geometric problems, significantly outperforming GPT-4-V on the MathVista benchmark with only 7B parameters.", "github_manual": "https://github.com/pipilurj/g-llava", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.10240", "title": "Rich Human Feedback for Text-to-Image Generation", "authors": [ "Youwei Liang", "Junfeng He", "Gang Li", "Peizhao Li", "Arseniy Klimovskiy", "Nicholas Carolan", "Jiao Sun", "Jordi Pont-Tuset", "Sarah Young", "Feng Yang", "Junjie Ke", "Krishnamurthy Dj Dvijotham", "Katie Collins", "Yiwen Luo", "Yang Li", "Kai J Kohlhoff", "Deepak Ramachandran", "Vidhya Navalpakkam" ], "github": "https://github.com/youweiliang/RichHF", "project_page": "", "abstract": "Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made significant progress in generating high-resolution images based on text descriptions. However, many generated images still suffer from issues such as artifacts/implausibility, misalignment with text descriptions, and low aesthetic quality. Inspired by the success of Reinforcement Learning with Human Feedback (RLHF) for large language models, prior works collected human-provided scores as feedback on generated images and trained a reward model to improve the T2I generation. In this paper, we enrich the feedback signal by (i) marking image regions that are implausible or misaligned with the text, and (ii) annotating which words in the text prompt are misrepresented or missing on the image. We collect such rich human feedback on 18K generated images and train a multimodal transformer to predict the rich feedback automatically. We show that the predicted rich human feedback can be leveraged to improve image generation, for example, by selecting high-quality training data to finetune and improve the generative models, or by creating masks with predicted heatmaps to inpaint the problematic regions. Notably, the improvements generalize to models (Muse) beyond those used to generate the images on which human feedback data were collected (Stable Diffusion variants).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/youweiliang/RichHF", "github_api_added_by": "auto" }, { "date": "2023-12-19", "arxiv_id": "2312.10656", "title": "VidToMe: Video Token Merging for Zero-Shot Video Editing", "authors": [ "Xirui Li", "Chao Ma", "Xiaokang Yang", "Ming-Hsuan Yang" ], "github": "https://github.com/lixirui142/VidToMe", "project_page": "", "abstract": "Diffusion models have made significant advances in generating high-quality images, but their application to video generation has remained challenging due to the complexity of temporal motion. Zero-shot video editing offers a solution by utilizing pre-trained image diffusion models to translate source videos into new ones. Nevertheless, existing methods struggle to maintain strict temporal consistency and efficient memory consumption. In this work, we propose a novel approach to enhance temporal consistency in generated videos by merging self-attention tokens across frames. By aligning and compressing temporally redundant tokens across frames, our method improves temporal coherence and reduces memory consumption in self-attention computations. The merging strategy matches and aligns tokens according to the temporal correspondence between frames, facilitating natural temporal consistency in generated video frames. To manage the complexity of video processing, we divide videos into chunks and develop intra-chunk local token merging and inter-chunk global token merging, ensuring both short-term video continuity and long-term content consistency. Our video editing approach seamlessly extends the advancements in image editing to video editing, rendering favorable results in temporal consistency over state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lixirui142/VidToMe", "github_api_added_by": "auto" }, { "date": "2023-12-19", "arxiv_id": "2312.10835", "title": "Your Student is Better Than Expected: Adaptive Teacher-Student Collaboration for Text-Conditional Diffusion Models", "authors": [ "Nikita Starodubcev", "Artem Fedorov", "Artem Babenko", "Dmitry Baranchuk" ], "github": "https://github.com/yandex-research/adaptive-diffusion", "project_page": "", "abstract": "Knowledge distillation methods have recently shown to be a promising direction to speedup the synthesis of large-scale diffusion models by requiring only a few inference steps. While several powerful distillation methods were recently proposed, the overall quality of student samples is typically lower compared to the teacher ones, which hinders their practical usage. In this work, we investigate the relative quality of samples produced by the teacher text-to-image diffusion model and its distilled student version. As our main empirical finding, we discover that a noticeable portion of student samples exhibit superior fidelity compared to the teacher ones, despite the ``approximate'' nature of the student. Based on this finding, we propose an adaptive collaboration between student and teacher diffusion models for effective text-to-image synthesis. Specifically, the distilled model produces the initial sample, and then an oracle decides whether it needs further improvements with a slow teacher model. Extensive experiments demonstrate that the designed pipeline surpasses state-of-the-art text-to-image alternatives for various inference budgets in terms of human preference. Furthermore, the proposed approach can be naturally used in popular applications such as text-guided image editing and controllable generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yandex-research/adaptive-diffusion", "github_api_added_by": "auto" }, { "date": "2023-12-19", "arxiv_id": "2312.10540", "title": "VecFusion: Vector Font Generation with Diffusion", "authors": [ "Vikas Thamizharasan", "Difan Liu", "Shantanu Agarwal", "Matthew Fisher", "Michael Gharbi", "Oliver Wang", "Alec Jacobson", "Evangelos Kalogerakis" ], "github": "", "project_page": "", "abstract": "We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions. Our approach is a cascaded diffusion model which consists of a raster diffusion model followed by a vector diffusion model. The raster model generates low-resolution, rasterized fonts with auxiliary control point information, capturing the global style and shape of the font, while the vector model synthesizes vector fonts conditioned on the low-resolution raster fonts from the first stage. To synthesize long and complex curves, our vector diffusion model uses a transformer architecture and a novel vector representation that enables the modeling of diverse vector geometry and the precise prediction of control points. Our experiments show that, in contrast to previous generative models for vector graphics, our new cascaded vector diffusion model generates higher quality vector fonts, with complex structures and diverse styles.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.11458", "title": "GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis", "authors": [ "Yiqing Liang", "Numair Khan", "Zhengqin Li", "Thu Nguyen-Phuoc", "Douglas Lanman", "James Tompkin", "Lei Xiao" ], "github": "", "project_page": "", "abstract": "We propose a method for dynamic scene reconstruction using deformable 3D Gaussians that is tailored for monocular video. Building upon the efficiency of Gaussian splatting, our approach extends the representation to accommodate dynamic elements via a deformable set of Gaussians residing in a canonical space, and a time-dependent deformation field defined by a multi-layer perceptron (MLP). Moreover, under the assumption that most natural scenes have large regions that remain static, we allow the MLP to focus its representational power by additionally including a static Gaussian point cloud. The concatenated dynamic and static point clouds form the input for the Gaussian Splatting rasterizer, enabling real-time rendering. The differentiable pipeline is optimized end-to-end with a self-supervised rendering loss. Our method achieves results that are comparable to state-of-the-art dynamic neural radiance field methods while allowing much faster optimization and rendering. Project website: https://lynl7130.github.io/gaufre/index.html", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.11459", "title": "VolumeDiffusion: Flexible Text-to-3D Generation with Efficient Volumetric Encoder", "authors": [ "Zhicong Tang", "Shuyang Gu", "Chunyu Wang", "Ting Zhang", "Jianmin Bao", "Dong Chen", "Baining Guo" ], "github": "https://github.com/tzco/VolumeDiffusion", "project_page": "", "abstract": "This paper introduces a pioneering 3D volumetric encoder designed for text-to-3D generation. To scale up the training data for the diffusion model, a lightweight network is developed to efficiently acquire feature volumes from multi-view images. The 3D volumes are then trained on a diffusion model for text-to-3D generation using a 3D U-Net. This research further addresses the challenges of inaccurate object captions and high-dimensional feature volumes. The proposed model, trained on the public Objaverse dataset, demonstrates promising outcomes in producing diverse and recognizable samples from text prompts. Notably, it empowers finer control over object part characteristics through textual cues, fostering model creativity by seamlessly combining multiple concepts within a single object. This research significantly contributes to the progress of 3D generation by introducing an efficient, flexible, and scalable representation methodology. Code is available at https://github.com/tzco/VolumeDiffusion.", "github_manual": "", "github_regex": "https://github.com/tzco/VolumeDiffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.11392", "title": "SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing", "authors": [ "Zeyinzi Jiang", "Chaojie Mao", "Yulin Pan", "Zhen Han", "Jingfeng Zhang" ], "github": "", "project_page": "", "abstract": "Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis. Recent research has introduced tuning methods that make subtle adjustments to the original models, yielding promising results in specific adaptations of foundational generative diffusion models. Rather than modifying the main backbone of the diffusion model, we delve into the role of skip connection in U-Net and reveal that hierarchical features aggregating long-distance information across encoder and decoder make a significant impact on the content and quality of image generation. Based on the observation, we propose an efficient generative tuning framework, dubbed SCEdit, which integrates and edits Skip Connection using a lightweight tuning module named SC-Tuner. Furthermore, the proposed framework allows for straightforward extension to controllable image synthesis by injecting different conditions with Controllable SC-Tuner, simplifying and unifying the network design for multi-condition inputs. Our SCEdit substantially reduces training parameters, memory usage, and computational expense due to its lightweight tuners, with backward propagation only passing to the decoder blocks. Extensive experiments conducted on text-to-image generation and controllable image synthesis tasks demonstrate the superiority of our method in terms of efficiency and performance. Project page: https://scedit.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.10332", "title": "ProTIP: Progressive Tool Retrieval Improves Planning", "authors": [ "Raviteja Anantha", "Bortik Bandyopadhyay", "Anirudh Kashi", "Sayantan Mahinder", "Andrew W Hill", "Srinivas Chappidi" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "Large language models (LLMs) are increasingly employed for complex multi-step planning tasks, where the tool retrieval (TR) step is crucial for achieving successful outcomes. Two prevalent approaches for TR are single-step retrieval, which utilizes the complete query, and sequential retrieval using task decomposition (TD), where a full query is segmented into discrete atomic subtasks. While single-step retrieval lacks the flexibility to handle \"inter-tool dependency,\" the TD approach necessitates maintaining \"subtask-tool atomicity alignment,\" as the toolbox can evolve dynamically. To address these limitations, we introduce the Progressive Tool retrieval to Improve Planning (ProTIP) framework. ProTIP is a lightweight, contrastive learning-based framework that implicitly performs TD without the explicit requirement of subtask labels, while simultaneously maintaining subtask-tool atomicity. On the ToolBench dataset, ProTIP outperforms the ChatGPT task decomposition-based approach by a remarkable margin, achieving a 24% improvement in Recall@K=10 for TR and a 41% enhancement in tool accuracy for plan generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-19", "arxiv_id": "2312.10665", "title": "Silkie: Preference Distillation for Large Visual Language Models", "authors": [ "Lei Li", "Zhihui Xie", "Mukai Li", "Shunian Chen", "Peiyi Wang", "Liang Chen", "Yazheng Yang", "Benyou Wang", "Lingpeng Kong" ], "github": "", "project_page": "", "abstract": "This paper explores preference distillation for large vision language models (LVLMs), improving their ability to generate helpful and faithful responses anchoring the visual context. We first build a vision-language feedback (VLFeedback) dataset utilizing AI annotation. Specifically, responses are generated by models sampled from 12 LVLMs, conditioned on multi-modal instructions sourced from various datasets. We adopt GPT-4V to assess the generated outputs regarding helpfulness, visual faithfulness, and ethical considerations. Furthermore, the preference supervision is distilled into Qwen-VL-Chat through the direct preference optimization (DPO) method. The resulting model Silkie, achieves 6.9% and 9.5% relative improvement on the MME benchmark regarding the perception and cognition capabilities, respectively. Silkie also demonstrates reduced hallucination by setting a new state-of-the-art score of 3.02 on the MMHal-Bench benchmark. Further analysis shows that DPO with our VLFeedback dataset mainly boosts the fine-grained perception and complex cognition abilities of LVLMs, leading to more comprehensive improvements compared to human-annotated preference datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.11514", "title": "LLM in a flash: Efficient Large Language Model Inference with Limited Memory", "authors": [ "Keivan Alizadeh", "Iman Mirzadeh", "Dmitry Belenko", "Karen Khatamifard", "Minsik Cho", "Carlo C Del Mundo", "Mohammad Rastegari", "Mehrdad Farajtabar" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their intensive computational and memory requirements present challenges, especially for devices with limited DRAM capacity. This paper tackles the challenge of efficiently running LLMs that exceed the available DRAM capacity by storing the model parameters on flash memory but bringing them on demand to DRAM. Our method involves constructing an inference cost model that harmonizes with the flash memory behavior, guiding us to optimize in two critical areas: reducing the volume of data transferred from flash and reading data in larger, more contiguous chunks. Within this flash memory-informed framework, we introduce two principal techniques. First, \"windowing'\" strategically reduces data transfer by reusing previously activated neurons, and second, \"row-column bundling\", tailored to the sequential data access strengths of flash memory, increases the size of data chunks read from flash memory. These methods collectively enable running models up to twice the size of the available DRAM, with a 4-5x and 20-25x increase in inference speed compared to naive loading approaches in CPU and GPU, respectively. Our integration of sparsity awareness, context-adaptive loading, and a hardware-oriented design paves the way for effective inference of LLMs on devices with limited memory.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.11556", "title": "StarVector: Generating Scalable Vector Graphics Code from Images", "authors": [ "Juan A. Rodriguez", "Shubham Agarwal", "Issam H. Laradji", "Pau Rodriguez", "David Vazquez", "Christopher Pal", "Marco Pedersoli" ], "github": "https://github.com/joanrod/star-vector", "project_page": "", "abstract": "Scalable Vector Graphics (SVGs) have become integral in modern image rendering applications due to their infinite scalability in resolution, versatile usability, and editing capabilities. SVGs are particularly popular in the fields of web development and graphic design. Existing approaches for SVG modeling using deep learning often struggle with generating complex SVGs and are restricted to simpler ones that require extensive processing and simplification. This paper introduces StarVector, a multimodal SVG generation model that effectively integrates Code Generation Large Language Models (CodeLLMs) and vision models. Our approach utilizes a CLIP image encoder to extract visual representations from pixel-based images, which are then transformed into visual tokens via an adapter module. These visual tokens are pre-pended to the SVG token embeddings, and the sequence is modeled by the StarCoder model using next-token prediction, effectively learning to align the visual and code tokens. This enables StarVector to generate unrestricted SVGs that accurately represent pixel images. To evaluate StarVector's performance, we present SVG-Bench, a comprehensive benchmark for evaluating SVG methods across multiple datasets and relevant metrics. Within this benchmark, we introduce novel datasets including SVG-Stack, a large-scale dataset of real-world SVG examples, and use it to pre-train StarVector as a large foundation model for SVGs. Our results demonstrate significant enhancements in visual quality and complexity handling over current methods, marking a notable advancement in SVG generation technology. Code and models: https://github.com/joanrod/star-vector", "github_manual": "", "github_regex": "https://github.com/joanrod/star-vector", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.12436", "title": "A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise", "authors": [ "Chaoyou Fu", "Renrui Zhang", "Haojia Lin", "Zihan Wang", "Timin Gao", "Yongdong Luo", "Yubo Huang", "Zhengye Zhang", "Longtian Qiu", "Gaoxiang Ye", "Yunhang Shen", "Mengdan Zhang", "Peixian Chen", "Sirui Zhao", "Xiawu Zheng", "Shaohui Lin", "Deqiang Jiang", "Di Yin", "Peng Gao", "Ke Li", "Xing Sun", "Rongrong Ji" ], "github": "https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models", "project_page": "", "abstract": "The surge of interest towards Multi-modal Large Language Models (MLLMs), e.g., GPT-4V(ision) from OpenAI, has marked a significant trend in both academia and industry. They endow Large Language Models (LLMs) with powerful capabilities in visual understanding, enabling them to tackle diverse multi-modal tasks. Very recently, Google released Gemini, its newest and most capable MLLM built from the ground up for multi-modality. In light of the superior reasoning capabilities, can Gemini challenge GPT-4V's leading position in multi-modal learning? In this paper, we present a preliminary exploration of Gemini Pro's visual understanding proficiency, which comprehensively covers four domains: fundamental perception, advanced cognition, challenging vision tasks, and various expert capacities. We compare Gemini Pro with the state-of-the-art GPT-4V to evaluate its upper limits, along with the latest open-sourced MLLM, Sphinx, which reveals the gap between manual efforts and black-box systems. The qualitative samples indicate that, while GPT-4V and Gemini showcase different answering styles and preferences, they can exhibit comparable visual reasoning capabilities, and Sphinx still trails behind them concerning domain generalizability. Specifically, GPT-4V tends to elaborate detailed explanations and intermediate steps, and Gemini prefers to output a direct and concise answer. The quantitative evaluation on the popular MME benchmark also demonstrates the potential of Gemini to be a strong challenger to GPT-4V. Our early investigation of Gemini also observes some common issues of MLLMs, indicating that there still remains a considerable distance towards artificial general intelligence. Our project for tracking the progress of MLLM is released at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models.", "github_manual": "", "github_regex": "https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.12433", "title": "Tracking Any Object Amodally", "authors": [ "Cheng-Yen Hsieh", "Tarasha Khurana", "Achal Dave", "Deva Ramanan" ], "github": "https://github.com/WesleyHsieh0806/TAO-Amodal", "project_page": "", "abstract": "Amodal perception, the ability to comprehend complete object structures from partial visibility, is a fundamental skill, even for infants. Its significance extends to applications like autonomous driving, where a clear understanding of heavily occluded objects is essential. However, modern detection and tracking algorithms often overlook this critical capability, perhaps due to the prevalence of modal annotations in most datasets. To address the scarcity of amodal data, we introduce the TAO-Amodal benchmark, featuring 880 diverse categories in thousands of video sequences. Our dataset includes amodal and modal bounding boxes for visible and occluded objects, including objects that are partially out-of-frame. To enhance amodal tracking with object permanence, we leverage a lightweight plug-in module, the amodal expander, to transform standard, modal trackers into amodal ones through fine-tuning on a few hundred video sequences with data augmentation. We achieve a 3.3\\% and 1.6\\% improvement on the detection and tracking of occluded objects on TAO-Amodal. When evaluated on people, our method produces dramatic improvements of 2x compared to state-of-the-art modal baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/WesleyHsieh0806/TAO-Amodal", "github_api_added_by": "auto" }, { "date": "2023-12-20", "arxiv_id": "2312.11841", "title": "MixRT: Mixed Neural Representations For Real-Time NeRF Rendering", "authors": [ "Chaojian Li", "Bichen Wu", "Peter Vajda", "Yingyan", "Lin" ], "github": "", "project_page": "", "abstract": "Neural Radiance Field (NeRF) has emerged as a leading technique for novel view synthesis, owing to its impressive photorealistic reconstruction and rendering capability. Nevertheless, achieving real-time NeRF rendering in large-scale scenes has presented challenges, often leading to the adoption of either intricate baked mesh representations with a substantial number of triangles or resource-intensive ray marching in baked representations. We challenge these conventions, observing that high-quality geometry, represented by meshes with substantial triangles, is not necessary for achieving photorealistic rendering quality. Consequently, we propose MixRT, a novel NeRF representation that includes a low-quality mesh, a view-dependent displacement map, and a compressed NeRF model. This design effectively harnesses the capabilities of existing graphics hardware, thus enabling real-time NeRF rendering on edge devices. Leveraging a highly-optimized WebGL-based rendering framework, our proposed MixRT attains real-time rendering speeds on edge devices (over 30 FPS at a resolution of 1280 x 720 on a MacBook M1 Pro laptop), better rendering quality (0.2 PSNR higher in indoor scenes of the Unbounded-360 datasets), and a smaller storage size (less than 80% compared to state-of-the-art methods).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.12423", "title": "Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model", "authors": [ "Shraman Pramanick", "Guangxing Han", "Rui Hou", "Sayan Nag", "Ser-Nam Lim", "Nicolas Ballas", "Qifan Wang", "Rama Chellappa", "Amjad Almahairi" ], "github": "", "project_page": "", "abstract": "The ability of large language models (LLMs) to process visual inputs has given rise to general-purpose vision systems, unifying various vision-language (VL) tasks by instruction tuning. However, due to the enormous diversity in input-output formats in the vision domain, existing general-purpose models fail to successfully integrate segmentation and multi-image inputs with coarse-level tasks into a single framework. In this work, we introduce VistaLLM, a powerful visual system that addresses coarse- and fine-grained VL tasks over single and multiple input images using a unified framework. VistaLLM utilizes an instruction-guided image tokenizer that filters global embeddings using task descriptions to extract compressed and refined features from numerous images. Moreover, VistaLLM employs a gradient-aware adaptive sampling technique to represent binary segmentation masks as sequences, significantly improving over previously used uniform sampling. To bolster the desired capability of VistaLLM, we curate CoinIt, a comprehensive coarse-to-fine instruction tuning dataset with 6.8M samples. We also address the lack of multi-image grounding datasets by introducing a novel task, AttCoSeg (Attribute-level Co-Segmentation), which boosts the model's reasoning and grounding capability over multiple input images. Extensive experiments on a wide range of V- and VL tasks demonstrate the effectiveness of VistaLLM by achieving consistent state-of-the-art performance over strong baselines across all downstream tasks. Our project page can be found at https://shramanpramanick.github.io/VistaLLM/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.11805", "title": "Gemini: A Family of Highly Capable Multimodal Models", "authors": [ "Gemini Team", "Rohan Anil", "Sebastian Borgeaud", "Yonghui Wu", "Jean-Baptiste Alayrac", "Jiahui Yu", "Radu Soricut", "Johan Schalkwyk", "Andrew M. Dai", "Anja Hauth", "Katie Millican", "David Silver", "Slav Petrov", "Melvin Johnson", "Ioannis Antonoglou", "Julian Schrittwieser", "Amelia Glaese", "Jilin Chen", "Emily Pitler", "Timothy Lillicrap", "Angeliki Lazaridou", "Orhan Firat", "James Molloy", "Michael Isard", "Paul R. Barham", "Tom Hennigan", "Benjamin Lee", "Fabio Viola", "Malcolm Reynolds", "Yuanzhong Xu", "Ryan Doherty", "Eli Collins", "Clemens Meyer", "Eliza Rutherford", "Erica Moreira", "Kareem Ayoub", "Megha Goel", "George Tucker", "Enrique Piqueras", "Maxim Krikun", "Iain Barr", "Nikolay Savinov", "Ivo Danihelka", "Becca Roelofs", "Ana\u00efs White", "Anders Andreassen", "Tamara von Glehn", "Lakshman Yagati", "Mehran Kazemi", "Lucas Gonzalez", "Misha Khalman", "Jakub Sygnowski", "Alexandre Frechette", "Charlotte Smith", "Laura Culp", "Lev Proleev", "Yi Luan", "Xi Chen", "James Lottes", "Nathan Schucher", "Federico Lebron", "Alban Rrustemi", "Natalie Clay", "Phil Crone", "Tomas Kocisky", "Jeffrey Zhao", "Bartek Perz", "Dian Yu", "Heidi Howard", "Adam Bloniarz", "Jack W. Rae", "Han Lu", "Laurent Sifre", "Marcello Maggioni", "Fred Alcober", "Dan Garrette", "Megan Barnes", "Shantanu Thakoor", "Jacob Austin", "Gabriel Barth-Maron", "William Wong", "Rishabh Joshi", "Rahma Chaabouni", "Deeni Fatiha", "Arun Ahuja", "Ruibo Liu", "Yunxuan Li", "Sarah Cogan", "Jeremy Chen", "Chao Jia", "Chenjie Gu", "Qiao Zhang", "Jordan Grimstad", "Ale Jakse Hartman", "Martin Chadwick", "Gaurav Singh Tomar", "Xavier Garcia", "Evan Senter", "Emanuel Taropa", "Thanumalayan Sankaranarayana Pillai", "Jacob Devlin", "Michael Laskin", "Diego de Las Casas", "Dasha Valter", "Connie Tao", "Lorenzo Blanco", "Adri\u00e0 Puigdom\u00e8nech Badia", "David Reitter", "Mianna Chen", "Jenny Brennan", "Clara Rivera", "Sergey Brin", "Shariq Iqbal", "Gabriela Surita", "Jane Labanowski", "Abhi Rao", "Stephanie Winkler", "Emilio Parisotto", "Yiming Gu", "Kate Olszewska", "Yujing Zhang", "Ravi Addanki", "Antoine Miech", "Annie Louis", "Laurent El Shafey", "Denis Teplyashin", "Geoff Brown", "Elliot Catt", "Nithya Attaluri", "Jan Balaguer", "Jackie Xiang", "Pidong Wang", "Zoe Ashwood", "Anton Briukhov", "Albert Webson", "Sanjay Ganapathy", "Smit Sanghavi", "Ajay Kannan", "Ming-Wei Chang", "Axel Stjerngren", "Josip Djolonga", "Yuting Sun", "Ankur Bapna", "Matthew Aitchison", "Pedram Pejman", "Henryk Michalewski", "Tianhe Yu", "Cindy Wang", "Juliette Love", "Junwhan Ahn", "Dawn Bloxwich", "Kehang Han", "Peter Humphreys", "Thibault Sellam", "James Bradbury", "Varun Godbole", "Sina Samangooei", "Bogdan Damoc", "Alex Kaskasoli", "S\u00e9bastien M. R. Arnold", "Vijay Vasudevan", "Shubham Agrawal", "Jason Riesa", "Dmitry Lepikhin", "Richard Tanburn", "Srivatsan Srinivasan", "Hyeontaek Lim", "Sarah Hodkinson", "Pranav Shyam", "Johan Ferret", "Steven Hand", "Ankush Garg", "Tom Le Paine", "Jian Li", "Yujia Li", "Minh Giang", "Alexander Neitz", "Zaheer Abbas", "Sarah York", "Machel Reid", "Elizabeth Cole", "Aakanksha Chowdhery", "Dipanjan Das", "Dominika Rogozi\u0144ska", "Vitaly Nikolaev", "Pablo Sprechmann", "Zachary Nado", "Lukas Zilka", "Flavien Prost", "Luheng He", "Marianne Monteiro", "Gaurav Mishra", "Chris Welty", "Josh Newlan", "Dawei Jia", "Miltiadis Allamanis", "Clara Huiyi Hu", "Raoul de Liedekerke", "Justin Gilmer", "Carl Saroufim", "Shruti Rijhwani", "Shaobo Hou", "Disha Shrivastava", "Anirudh Baddepudi", "Alex Goldin", "Adnan Ozturel", "Albin Cassirer", "Yunhan Xu", "Daniel Sohn", "Devendra Sachan", "Reinald Kim Amplayo", "Craig Swanson", "Dessie Petrova", "Shashi Narayan", "Arthur Guez", "Siddhartha Brahma", "Jessica Landon", "Miteyan Patel", "Ruizhe Zhao", "Kevin Villela", "Luyu Wang", "Wenhao Jia", "Matthew Rahtz", "Mai Gim\u00e9nez", "Legg Yeung", "Hanzhao Lin", "James Keeling", "Petko Georgiev", "Diana Mincu", "Boxi Wu", "Salem Haykal", "Rachel Saputro", "Kiran Vodrahalli", "James Qin", "Zeynep Cankara", "Abhanshu Sharma", "Nick Fernando", "Will Hawkins", "Behnam Neyshabur", "Solomon Kim", "Adrian Hutter", "Priyanka Agrawal", "Alex Castro-Ros", "George van den Driessche", "Tao Wang", "Fan Yang", "Shuo-yiin Chang", "Paul Komarek", "Ross McIlroy", "Mario Lu\u010di\u0107", "Guodong Zhang", "Wael Farhan", "Michael Sharman", "Paul Natsev", "Paul Michel", "Yong Cheng", "Yamini Bansal", "Siyuan Qiao", "Kris Cao", "Siamak Shakeri", "Christina Butterfield", "Justin Chung", "Paul Kishan Rubenstein", "Shivani Agrawal", "Arthur Mensch", "Kedar Soparkar", "Karel Lenc", "Timothy Chung", "Aedan Pope", "Loren Maggiore", "Jackie Kay", "Priya Jhakra", "Shibo Wang", "Joshua Maynez", "Mary Phuong", "Taylor Tobin", "Andrea Tacchetti", "Maja Trebacz", "Kevin Robinson", "Yash Katariya", "Sebastian Riedel", "Paige Bailey", "Kefan Xiao", "Nimesh Ghelani", "Lora Aroyo", "Ambrose Slone", "Neil Houlsby", "Xuehan Xiong", "Zhen Yang", "Elena Gribovskaya", "Jonas Adler", "Mateo Wirth", "Lisa Lee", "Music Li", "Thais Kagohara", "Jay Pavagadhi", "Sophie Bridgers", "Anna Bortsova", "Sanjay Ghemawat", "Zafarali Ahmed", "Tianqi Liu", "Richard Powell", "Vijay Bolina", "Mariko Iinuma", "Polina Zablotskaia", "James Besley", "Da-Woon Chung", "Timothy Dozat", "Ramona Comanescu", "Xiance Si", "Jeremy Greer", "Guolong Su", "Martin Polacek", "Rapha\u00ebl Lopez Kaufman", "Simon Tokumine", "Hexiang Hu", "Elena Buchatskaya", "Yingjie Miao", "Mohamed Elhawaty", "Aditya Siddhant", "Nenad Tomasev", "Jinwei Xing", "Christina Greer", "Helen Miller", "Shereen Ashraf", "Aurko Roy", "Zizhao Zhang", "Ada Ma", "Angelos Filos", "Milos Besta", "Rory Blevins", "Ted Klimenko", "Chih-Kuan Yeh", "Soravit Changpinyo", "Jiaqi Mu", "Oscar Chang", "Mantas Pajarskas", "Carrie Muir", "Vered Cohen", "Charline Le Lan", "Krishna Haridasan", "Amit Marathe", "Steven Hansen", "Sholto Douglas", "Rajkumar Samuel", "Mingqiu Wang", "Sophia Austin", "Chang Lan", "Jiepu Jiang", "Justin Chiu", "Jaime Alonso Lorenzo", "Lars Lowe Sj\u00f6sund", "S\u00e9bastien Cevey", "Zach Gleicher", "Thi Avrahami", "Anudhyan Boral", "Hansa Srinivasan", "Vittorio Selo", "Rhys May", "Konstantinos Aisopos", "L\u00e9onard Hussenot", "Livio Baldini Soares", "Kate Baumli", "Michael B. Chang", "Adri\u00e0 Recasens", "Ben Caine", "Alexander Pritzel", "Filip Pavetic", "Fabio Pardo", "Anita Gergely", "Justin Frye", "Vinay Ramasesh", "Dan Horgan", "Kartikeya Badola", "Nora Kassner", "Subhrajit Roy", "Ethan Dyer", "V\u00edctor Campos", "Alex Tomala", "Yunhao Tang", "Dalia El Badawy", "Elspeth White", "Basil Mustafa", "Oran Lang", "Abhishek Jindal", "Sharad Vikram", "Zhitao Gong", "Sergi Caelles", "Ross Hemsley", "Gregory Thornton", "Fangxiaoyu Feng", "Wojciech Stokowiec", "Ce Zheng", "Phoebe Thacker", "\u00c7a\u011flar \u00dcnl\u00fc", "Zhishuai Zhang", "Mohammad Saleh", "James Svensson", "Max Bileschi", "Piyush Patil", "Ankesh Anand", "Roman Ring", "Katerina Tsihlas", "Arpi Vezer", "Marco Selvi", "Toby Shevlane", "Mikel Rodriguez", "Tom Kwiatkowski", "Samira Daruki", "Keran Rong", "Allan Dafoe", "Nicholas FitzGerald", "Keren Gu-Lemberg", "Mina Khan", "Lisa Anne Hendricks", "Marie Pellat", "Vladimir Feinberg", "James Cobon-Kerr", "Tara Sainath", "Maribeth Rauh", "Sayed Hadi Hashemi", "Richard Ives", "Yana Hasson", "YaGuang Li", "Eric Noland", "Yuan Cao", "Nathan Byrd", "Le Hou", "Qingze Wang", "Thibault Sottiaux", "Michela Paganini", "Jean-Baptiste Lespiau", "Alexandre Moufarek", "Samer Hassan", "Kaushik Shivakumar", "Joost van Amersfoort", "Amol Mandhane", "Pratik Joshi", "Anirudh Goyal", "Matthew Tung", "Andrew Brock", "Hannah Sheahan", "Vedant Misra", "Cheng Li", "Nemanja Raki\u0107evi\u0107", "Mostafa Dehghani", "Fangyu Liu", "Sid Mittal", "Junhyuk Oh", "Seb Noury", "Eren Sezener", "Fantine Huot", "Matthew Lamm", "Nicola De Cao", "Charlie Chen", "Gamaleldin Elsayed", "Ed Chi", "Mahdis Mahdieh", "Ian Tenney", "Nan Hua", "Ivan Petrychenko", "Patrick Kane", "Dylan Scandinaro", "Rishub Jain", "Jonathan Uesato", "Romina Datta", "Adam Sadovsky", "Oskar Bunyan", "Dominik Rabiej", "Shimu Wu", "John Zhang", "Gautam Vasudevan", "Edouard Leurent", "Mahmoud Alnahlawi", "Ionut Georgescu", "Nan Wei", "Ivy Zheng", "Betty Chan", "Pam G Rabinovitch", "Piotr Stanczyk", "Ye Zhang", "David Steiner", "Subhajit Naskar", "Michael Azzam", "Matthew Johnson", "Adam Paszke", "Chung-Cheng Chiu", "Jaume Sanchez Elias", "Afroz Mohiuddin", "Faizan Muhammad", "Jin Miao", "Andrew Lee", "Nino Vieillard", "Sahitya Potluri", "Jane Park", "Elnaz Davoodi", "Jiageng Zhang", "Jeff Stanway", "Drew Garmon", "Abhijit Karmarkar", "Zhe Dong", "Jong Lee", "Aviral Kumar", "Luowei Zhou", "Jonathan Evens", "William Isaac", "Zhe Chen", "Johnson Jia", "Anselm Levskaya", "Zhenkai Zhu", "Chris Gorgolewski", "Peter Grabowski", "Yu Mao", "Alberto Magni", "Kaisheng Yao", "Javier Snaider", "Norman Casagrande", "Paul Suganthan", "Evan Palmer", "Geoffrey Irving", "Edward Loper", "Manaal Faruqui", "Isha Arkatkar", "Nanxin Chen", "Izhak Shafran", "Michael Fink", "Alfonso Casta\u00f1o", "Irene Giannoumis", "Wooyeol Kim", "Miko\u0142aj Rybi\u0144ski", "Ashwin Sreevatsa", "Jennifer Prendki", "David Soergel", "Adrian Goedeckemeyer", "Willi Gierke", "Mohsen Jafari", "Meenu Gaba", "Jeremy Wiesner", "Diana Gage Wright", "Yawen Wei", "Harsha Vashisht", "Yana Kulizhskaya", "Jay Hoover", "Maigo Le", "Lu Li", "Chimezie Iwuanyanwu", "Lu Liu", "Kevin Ramirez", "Andrey Khorlin", "Albert Cui", "Tian LIN", "Marin Georgiev", "Marcus Wu", "Ricardo Aguilar", "Keith Pallo", "Abhishek Chakladar", "Alena Repina", "Xihui Wu", "Tom van der Weide", "Priya Ponnapalli", "Caroline Kaplan", "Jiri Simsa", "Shuangfeng Li", "Olivier Dousse", "Fan Yang", "Jeff Piper", "Nathan Ie", "Minnie Lui", "Rama Pasumarthi", "Nathan Lintz", "Anitha Vijayakumar", "Lam Nguyen Thiet", "Daniel Andor", "Pedro Valenzuela", "Cosmin Paduraru", "Daiyi Peng", "Katherine Lee", "Shuyuan Zhang", "Somer Greene", "Duc Dung Nguyen", "Paula Kurylowicz", "Sarmishta Velury", "Sebastian Krause", "Cassidy Hardin", "Lucas Dixon", "Lili Janzer", "Kiam Choo", "Ziqiang Feng", "Biao Zhang", "Achintya Singhal", "Tejasi Latkar", "Mingyang Zhang", "Quoc Le", "Elena Allica Abellan", "Dayou Du", "Dan McKinnon", "Natasha Antropova", "Tolga Bolukbasi", "Orgad Keller", "David Reid", "Daniel Finchelstein", "Maria Abi Raad", "Remi Crocker", "Peter Hawkins", "Robert Dadashi", "Colin Gaffney", "Sid Lall", "Ken Franko", "Egor Filonov", "Anna Bulanova", "R\u00e9mi Leblond", "Vikas Yadav", "Shirley Chung", "Harry Askham", "Luis C. Cobo", "Kelvin Xu", "Felix Fischer", "Jun Xu", "Christina Sorokin", "Chris Alberti", "Chu-Cheng Lin", "Colin Evans", "Hao Zhou", "Alek Dimitriev", "Hannah Forbes", "Dylan Banarse", "Zora Tung", "Jeremiah Liu", "Mark Omernick", "Colton Bishop", "Chintu Kumar", "Rachel Sterneck", "Ryan Foley", "Rohan Jain", "Swaroop Mishra", "Jiawei Xia", "Taylor Bos", "Geoffrey Cideron", "Ehsan Amid", "Francesco Piccinno", "Xingyu Wang", "Praseem Banzal", "Petru Gurita", "Hila Noga", "Premal Shah", "Daniel J. Mankowitz", "Alex Polozov", "Nate Kushman", "Victoria Krakovna", "Sasha Brown", "MohammadHossein Bateni", "Dennis Duan", "Vlad Firoiu", "Meghana Thotakuri", "Tom Natan", "Anhad Mohananey", "Matthieu Geist", "Sidharth Mudgal", "Sertan Girgin", "Hui Li", "Jiayu Ye", "Ofir Roval", "Reiko Tojo", "Michael Kwong", "James Lee-Thorp", "Christopher Yew", "Quan Yuan", "Sumit Bagri", "Danila Sinopalnikov", "Sabela Ramos", "John Mellor", "Abhishek Sharma", "Aliaksei Severyn", "Jonathan Lai", "Kathy Wu", "Heng-Tze Cheng", "David Miller", "Nicolas Sonnerat", "Denis Vnukov", "Rory Greig", "Jennifer Beattie", "Emily Caveness", "Libin Bai", "Julian Eisenschlos", "Alex Korchemniy", "Tomy Tsai", "Mimi Jasarevic", "Weize Kong", "Phuong Dao", "Zeyu Zheng", "Frederick Liu", "Fan Yang", "Rui Zhu", "Mark Geller", "Tian Huey Teh", "Jason Sanmiya", "Evgeny Gladchenko", "Nejc Trdin", "Andrei Sozanschi", "Daniel Toyama", "Evan Rosen", "Sasan Tavakkol", "Linting Xue", "Chen Elkind", "Oliver Woodman", "John Carpenter", "George Papamakarios", "Rupert Kemp", "Sushant Kafle", "Tanya Grunina", "Rishika Sinha", "Alice Talbert", "Abhimanyu Goyal", "Diane Wu", "Denese Owusu-Afriyie", "Cosmo Du", "Chloe Thornton", "Jordi Pont-Tuset", "Pradyumna Narayana", "Jing Li", "Sabaer Fatehi", "John Wieting", "Omar Ajmeri", "Benigno Uria", "Tao Zhu", "Yeongil Ko", "Laura Knight", "Am\u00e9lie H\u00e9liou", "Ning Niu", "Shane Gu", "Chenxi Pang", "Dustin Tran", "Yeqing Li", "Nir Levine", "Ariel Stolovich", "Norbert Kalb", "Rebeca Santamaria-Fernandez", "Sonam Goenka", "Wenny Yustalim", "Robin Strudel", "Ali Elqursh", "Balaji Lakshminarayanan", "Charlie Deck", "Shyam Upadhyay", "Hyo Lee", "Mike Dusenberry", "Zonglin Li", "Xuezhi Wang", "Kyle Levin", "Raphael Hoffmann", "Dan Holtmann-Rice", "Olivier Bachem", "Summer Yue", "Sho Arora", "Eric Malmi", "Daniil Mirylenka", "Qijun Tan", "Christy Koh", "Soheil Hassas Yeganeh", "Siim P\u00f5der", "Steven Zheng", "Francesco Pongetti", "Mukarram Tariq", "Yanhua Sun", "Lucian Ionita", "Mojtaba Seyedhosseini", "Pouya Tafti", "Ragha Kotikalapudi", "Zhiyu Liu", "Anmol Gulati", "Jasmine Liu", "Xinyu Ye", "Bart Chrzaszcz", "Lily Wang", "Nikhil Sethi", "Tianrun Li", "Ben Brown", "Shreya Singh", "Wei Fan", "Aaron Parisi", "Joe Stanton", "Chenkai Kuang", "Vinod Koverkathu", "Christopher A. Choquette-Choo", "Yunjie Li", "TJ Lu", "Abe Ittycheriah", "Prakash Shroff", "Pei Sun", "Mani Varadarajan", "Sanaz Bahargam", "Rob Willoughby", "David Gaddy", "Ishita Dasgupta", "Guillaume Desjardins", "Marco Cornero", "Brona Robenek", "Bhavishya Mittal", "Ben Albrecht", "Ashish Shenoy", "Fedor Moiseev", "Henrik Jacobsson", "Alireza Ghaffarkhah", "Morgane Rivi\u00e8re", "Alanna Walton", "Cl\u00e9ment Crepy", "Alicia Parrish", "Yuan Liu", "Zongwei Zhou", "Clement Farabet", "Carey Radebaugh", "Praveen Srinivasan", "Claudia van der Salm", "Andreas Fidjeland", "Salvatore Scellato", "Eri Latorre-Chimoto", "Hanna Klimczak-Pluci\u0144ska", "David Bridson", "Dario de Cesare", "Tom Hudson", "Piermaria Mendolicchio", "Lexi Walker", "Alex Morris", "Ivo Penchev", "Matthew Mauger", "Alexey Guseynov", "Alison Reid", "Seth Odoom", "Lucia Loher", "Victor Cotruta", "Madhavi Yenugula", "Dominik Grewe", "Anastasia Petrushkina", "Tom Duerig", "Antonio Sanchez", "Steve Yadlowsky", "Amy Shen", "Amir Globerson", "Adam Kurzrok", "Lynette Webb", "Sahil Dua", "Dong Li", "Preethi Lahoti", "Surya Bhupatiraju", "Dan Hurt", "Haroon Qureshi", "Ananth Agarwal", "Tomer Shani", "Matan Eyal", "Anuj Khare", "Shreyas Rammohan Belle", "Lei Wang", "Chetan Tekur", "Mihir Sanjay Kale", "Jinliang Wei", "Ruoxin Sang", "Brennan Saeta", "Tyler Liechty", "Yi Sun", "Yao Zhao", "Stephan Lee", "Pandu Nayak", "Doug Fritz", "Manish Reddy Vuyyuru", "John Aslanides", "Nidhi Vyas", "Martin Wicke", "Xiao Ma", "Taylan Bilal", "Evgenii Eltyshev", "Daniel Balle", "Nina Martin", "Hardie Cate", "James Manyika", "Keyvan Amiri", "Yelin Kim", "Xi Xiong", "Kai Kang", "Florian Luisier", "Nilesh Tripuraneni", "David Madras", "Mandy Guo", "Austin Waters", "Oliver Wang", "Joshua Ainslie", "Jason Baldridge", "Han Zhang", "Garima Pruthi", "Jakob Bauer", "Feng Yang", "Riham Mansour", "Jason Gelman", "Yang Xu", "George Polovets", "Ji Liu", "Honglong Cai", "Warren Chen", "XiangHai Sheng", "Emily Xue", "Sherjil Ozair", "Adams Yu", "Christof Angermueller", "Xiaowei Li", "Weiren Wang", "Julia Wiesinger", "Emmanouil Koukoumidis", "Yuan Tian", "Anand Iyer", "Madhu Gurumurthy", "Mark Goldenson", "Parashar Shah", "MK Blake", "Hongkun Yu", "Anthony Urbanowicz", "Jennimaria Palomaki", "Chrisantha Fernando", "Kevin Brooks", "Ken Durden", "Harsh Mehta", "Nikola Momchev", "Elahe Rahimtoroghi", "Maria Georgaki", "Amit Raul", "Sebastian Ruder", "Morgan Redshaw", "Jinhyuk Lee", "Komal Jalan", "Dinghua Li", "Ginger Perng", "Blake Hechtman", "Parker Schuh", "Milad Nasr", "Mia Chen", "Kieran Milan", "Vladimir Mikulik", "Trevor Strohman", "Juliana Franco", "Tim Green", "Demis Hassabis", "Koray Kavukcuoglu", "Jeffrey Dean", "Oriol Vinyals" ], "github": "", "project_page": "", "abstract": "This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of Gemini models in cross-modal reasoning and language understanding will enable a wide variety of use cases and we discuss our approach toward deploying them responsibly to users.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.11595", "title": "TIP: Text-Driven Image Processing with Semantic and Restoration Instructions", "authors": [ "Chenyang Qi", "Zhengzhong Tu", "Keren Ye", "Mauricio Delbracio", "Peyman Milanfar", "Qifeng Chen", "Hossein Talebi" ], "github": "", "project_page": "", "abstract": "Text-driven diffusion models have become increasingly popular for various image editing tasks, including inpainting, stylization, and object replacement. However, it still remains an open research problem to adopt this language-vision paradigm for more fine-level image processing tasks, such as denoising, super-resolution, deblurring, and compression artifact removal. In this paper, we develop TIP, a Text-driven Image Processing framework that leverages natural language as a user-friendly interface to control the image restoration process. We consider the capacity of text information in two dimensions. First, we use content-related prompts to enhance the semantic alignment, effectively alleviating identity ambiguity in the restoration outcomes. Second, our approach is the first framework that supports fine-level instruction through language-based quantitative specification of the restoration strength, without the need for explicit task-specific design. In addition, we introduce a novel fusion mechanism that augments the existing ControlNet architecture by learning to rescale the generative prior, thereby achieving better restoration fidelity. Our extensive experiments demonstrate the superior restoration performance of TIP compared to the state of the arts, alongside offering the flexibility of text-based control over the restoration effects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.11537", "title": "FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices with A Simple Super-Resolution Pipeline", "authors": [ "Chien-Yu Lin", "Qichen Fu", "Thomas Merth", "Karren Yang", "Anurag Ranjan" ], "github": "", "project_page": "", "abstract": "Super-resolution (SR) techniques have recently been proposed to upscale the outputs of neural radiance fields (NeRF) and generate high-quality images with enhanced inference speeds. However, existing NeRF+SR methods increase training overhead by using extra input features, loss functions, and/or expensive training procedures such as knowledge distillation. In this paper, we aim to leverage SR for efficiency gains without costly training or architectural changes. Specifically, we build a simple NeRF+SR pipeline that directly combines existing modules, and we propose a lightweight augmentation technique, random patch sampling, for training. Compared to existing NeRF+SR methods, our pipeline mitigates the SR computing overhead and can be trained up to 23x faster, making it feasible to run on consumer devices such as the Apple MacBook. Experiments show our pipeline can upscale NeRF outputs by 2-4x while maintaining high quality, increasing inference speeds by up to 18x on an NVIDIA V100 GPU and 12.8x on an M1 Pro chip. We conclude that SR can be a simple but effective technique for improving the efficiency of NeRF models for consumer devices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.11535", "title": "Customize-It-3D: High-Quality 3D Creation from A Single Image Using Subject-Specific Knowledge Prior", "authors": [ "Nan Huang", "Ting Zhang", "Yuhui Yuan", "Dong Chen", "Shanghang Zhang" ], "github": "", "project_page": "", "abstract": "In this paper, we present a novel two-stage approach that fully utilizes the information provided by the reference image to establish a customized knowledge prior for image-to-3D generation. While previous approaches primarily rely on a general diffusion prior, which struggles to yield consistent results with the reference image, we propose a subject-specific and multi-modal diffusion model. This model not only aids NeRF optimization by considering the shading mode for improved geometry but also enhances texture from the coarse results to achieve superior refinement. Both aspects contribute to faithfully aligning the 3D content with the subject. Extensive experiments showcase the superiority of our method, Customize-It-3D, outperforming previous works by a substantial margin. It produces faithful 360-degree reconstructions with impressive visual quality, making it well-suited for various applications, including text-to-3D creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.11897", "title": "Text-Conditioned Resampler For Long Form Video Understanding", "authors": [ "Bruno Korbar", "Yongqin Xian", "Alessio Tonioni", "Andrew Zisserman", "Federico Tombari" ], "github": "", "project_page": "", "abstract": "Videos are highly redundant data source and it is often enough to identify a few key moments to solve any given task. In this paper, we present a text-conditioned video resampler (TCR) module that uses a pre-trained and frozen visual encoder and large language model (LLM) to process long video sequences for a task. TCR localises relevant visual features from the video given a text condition and provides them to a LLM to generate a text response. Due to its lightweight design and use of cross-attention, TCR can process more than 100 frames at a time allowing the model to use much longer chunks of video than earlier works. We make the following contributions: (i) we design a transformer-based sampling architecture that can process long videos conditioned on a task, together with a training method that enables it to bridge pre-trained visual and language models; (ii) we empirically validate its efficacy on a wide variety of evaluation tasks, and set a new state-of-the-art on NextQA, EgoSchema, and the EGO4D-LTA challenge; and (iii) we determine tasks which require longer video contexts and that can thus be used effectively for further evaluation of long-range video models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.11532", "title": "Topic-VQ-VAE: Leveraging Latent Codebooks for Flexible Topic-Guided Document Generation", "authors": [ "YoungJoon Yoo", "Jongwon Choi" ], "github": "https://github.com/clovaai/TVQ-VAE", "project_page": "", "abstract": "This paper introduces a novel approach for topic modeling utilizing latent codebooks from Vector-Quantized Variational Auto-Encoder~(VQ-VAE), discretely encapsulating the rich information of the pre-trained embeddings such as the pre-trained language model. From the novel interpretation of the latent codebooks and embeddings as conceptual bag-of-words, we propose a new generative topic model called Topic-VQ-VAE~(TVQ-VAE) which inversely generates the original documents related to the respective latent codebook. The TVQ-VAE can visualize the topics with various generative distributions including the traditional BoW distribution and the autoregressive image generation. Our experimental results on document analysis and image generation demonstrate that TVQ-VAE effectively captures the topic context which reveals the underlying structures of the dataset and supports flexible forms of document generation. Official implementation of the proposed TVQ-VAE is available at https://github.com/clovaai/TVQ-VAE.", "github_manual": "", "github_regex": "https://github.com/clovaai/TVQ-VAE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.12030", "title": "Towards Accurate Guided Diffusion Sampling through Symplectic Adjoint Method", "authors": [ "Jiachun Pan", "Hanshu Yan", "Jun Hao Liew", "Jiashi Feng", "Vincent Y. F. Tan" ], "github": "https://github.com/hanshuyan/adjointdpm", "project_page": "", "abstract": "Training-free guided sampling in diffusion models leverages off-the-shelf pre-trained networks, such as an aesthetic evaluation model, to guide the generation process. Current training-free guided sampling algorithms obtain the guidance energy function based on a one-step estimate of the clean image. However, since the off-the-shelf pre-trained networks are trained on clean images, the one-step estimation procedure of the clean image may be inaccurate, especially in the early stages of the generation process in diffusion models. This causes the guidance in the early time steps to be inaccurate. To overcome this problem, we propose Symplectic Adjoint Guidance (SAG), which calculates the gradient guidance in two inner stages. Firstly, SAG estimates the clean image via n function calls, where n serves as a flexible hyperparameter that can be tailored to meet specific image quality requirements. Secondly, SAG uses the symplectic adjoint method to obtain the gradients accurately and efficiently in terms of the memory requirements. Extensive experiments demonstrate that SAG generates images with higher qualities compared to the baselines in both guided image and video generation tasks.", "github_manual": "https://github.com/hanshuyan/adjointdpm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-20", "arxiv_id": "2312.11894", "title": "3D-LFM: Lifting Foundation Model", "authors": [ "Mosam Dabhi", "Laszlo A. Jeni", "Simon Lucey" ], "github": "https://github.com/mosamdabhi/3dlfm", "project_page": "", "abstract": "The lifting of 3D structure and camera from 2D landmarks is at the cornerstone of the entire discipline of computer vision. Traditional methods have been confined to specific rigid objects, such as those in Perspective-n-Point (PnP) problems, but deep learning has expanded our capability to reconstruct a wide range of object classes (e.g. C3PDO and PAUL) with resilience to noise, occlusions, and perspective distortions. All these techniques, however, have been limited by the fundamental need to establish correspondences across the 3D training data -- significantly limiting their utility to applications where one has an abundance of \"in-correspondence\" 3D data. Our approach harnesses the inherent permutation equivariance of transformers to manage varying number of points per 3D data instance, withstands occlusions, and generalizes to unseen categories. We demonstrate state of the art performance across 2D-3D lifting task benchmarks. Since our approach can be trained across such a broad class of structures we refer to it simply as a 3D Lifting Foundation Model (3D-LFM) -- the first of its kind.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mosamdabhi/3dlfm", "github_api_added_by": "auto" }, { "date": "2023-12-20", "arxiv_id": "2312.11666", "title": "HAAR: Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles", "authors": [ "Vanessa Sklyarova", "Egor Zakharov", "Otmar Hilliges", "Michael J. Black", "Justus Thies" ], "github": "https://github.com/Vanessik/HAAR", "project_page": "", "abstract": "We present HAAR, a new strand-based generative model for 3D human hairstyles. Specifically, based on textual inputs, HAAR produces 3D hairstyles that could be used as production-level assets in modern computer graphics engines. Current AI-based generative models take advantage of powerful 2D priors to reconstruct 3D content in the form of point clouds, meshes, or volumetric functions. However, by using the 2D priors, they are intrinsically limited to only recovering the visual parts. Highly occluded hair structures can not be reconstructed with those methods, and they only model the ''outer shell'', which is not ready to be used in physics-based rendering or simulation pipelines. In contrast, we propose a first text-guided generative method that uses 3D hair strands as an underlying representation. Leveraging 2D visual question-answering (VQA) systems, we automatically annotate synthetic hair models that are generated from a small set of artist-created hairstyles. This allows us to train a latent diffusion model that operates in a common hairstyle UV space. In qualitative and quantitative studies, we demonstrate the capabilities of the proposed model and compare it to existing hairstyle generation approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Vanessik/HAAR", "github_api_added_by": "auto" }, { "date": "2023-12-21", "arxiv_id": "2312.12491", "title": "StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation", "authors": [ "Akio Kodaira", "Chenfeng Xu", "Toshiki Hazama", "Takanori Yoshimoto", "Kohei Ohno", "Shogo Mitsuhori", "Soichi Sugano", "Hanying Cho", "Zhijian Liu", "Kurt Keutzer" ], "github": "https://github.com/cumulo-autumn/StreamDiffusion", "project_page": "", "abstract": "We introduce StreamDiffusion, a real-time diffusion pipeline designed for interactive image generation. Existing diffusion models are adept at creating images from text or image prompts, yet they often fall short in real-time interaction. This limitation becomes particularly evident in scenarios involving continuous input, such as Metaverse, live video streaming, and broadcasting, where high throughput is imperative. To address this, we present a novel approach that transforms the original sequential denoising into the batching denoising process. Stream Batch eliminates the conventional wait-and-interact approach and enables fluid and high throughput streams. To handle the frequency disparity between data input and model throughput, we design a novel input-output queue for parallelizing the streaming process. Moreover, the existing diffusion pipeline uses classifier-free guidance(CFG), which requires additional U-Net computation. To mitigate the redundant computations, we propose a novel residual classifier-free guidance (RCFG) algorithm that reduces the number of negative conditional denoising steps to only one or even zero. Besides, we introduce a stochastic similarity filter(SSF) to optimize power consumption. Our Stream Batch achieves around 1.5x speedup compared to the sequential denoising method at different denoising levels. The proposed RCFG leads to speeds up to 2.05x higher than the conventional CFG. Combining the proposed strategies and existing mature acceleration tools makes the image-to-image generation achieve up-to 91.07fps on one RTX4090, improving the throughputs of AutoPipline developed by Diffusers over 59.56x. Furthermore, our proposed StreamDiffusion also significantly reduces the energy consumption by 2.39x on one RTX3060 and 1.99x on one RTX4090, respectively.", "github_manual": "https://github.com/cumulo-autumn/StreamDiffusion", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-21", "arxiv_id": "2312.12490", "title": "InstructVideo: Instructing Video Diffusion Models with Human Feedback", "authors": [ "Hangjie Yuan", "Shiwei Zhang", "Xiang Wang", "Yujie Wei", "Tao Feng", "Yining Pan", "Yingya Zhang", "Ziwei Liu", "Samuel Albanie", "Dong Ni" ], "github": "", "project_page": "", "abstract": "Diffusion models have emerged as the de facto paradigm for video generation. However, their reliance on web-scale data of varied quality often yields results that are visually unappealing and misaligned with the textual prompts. To tackle this problem, we propose InstructVideo to instruct text-to-video diffusion models with human feedback by reward fine-tuning. InstructVideo has two key ingredients: 1) To ameliorate the cost of reward fine-tuning induced by generating through the full DDIM sampling chain, we recast reward fine-tuning as editing. By leveraging the diffusion process to corrupt a sampled video, InstructVideo requires only partial inference of the DDIM sampling chain, reducing fine-tuning cost while improving fine-tuning efficiency. 2) To mitigate the absence of a dedicated video reward model for human preferences, we repurpose established image reward models, e.g., HPSv2. To this end, we propose Segmental Video Reward, a mechanism to provide reward signals based on segmental sparse sampling, and Temporally Attenuated Reward, a method that mitigates temporal modeling degradation during fine-tuning. Extensive experiments, both qualitative and quantitative, validate the practicality and efficacy of using image reward models in InstructVideo, significantly enhancing the visual quality of generated videos without compromising generalization capabilities. Code and models will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-21", "arxiv_id": "2312.13271", "title": "Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting", "authors": [ "Junwu Zhang", "Zhenyu Tang", "Yatian Pang", "Xinhua Cheng", "Peng Jin", "Yida Wei", "Wangbo Yu", "Munan Ning", "Li Yuan" ], "github": "https://github.com/junwuzhang19/repaint123", "project_page": "", "abstract": "Recent one image to 3D generation methods commonly adopt Score Distillation Sampling (SDS). Despite the impressive results, there are multiple deficiencies including multi-view inconsistency, over-saturated and over-smoothed textures, as well as the slow generation speed. To address these deficiencies, we present Repaint123 to alleviate multi-view bias as well as texture degradation and speed up the generation process. The core idea is to combine the powerful image generation capability of the 2D diffusion model and the texture alignment ability of the repainting strategy for generating high-quality multi-view images with consistency. We further propose visibility-aware adaptive repainting strength for overlap regions to enhance the generated image quality in the repainting process. The generated high-quality and multi-view consistent images enable the use of simple Mean Square Error (MSE) loss for fast 3D content generation. We conduct extensive experiments and show that our method has a superior ability to generate high-quality 3D content with multi-view consistency and fine textures in 2 minutes from scratch. Code is at https://github.com/junwuzhang19/repaint123.", "github_manual": "", "github_regex": "https://github.com/junwuzhang19/repaint123", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-21", "arxiv_id": "2312.12742", "title": "Cached Transformers: Improving Transformers with Differentiable Memory Cache", "authors": [ "Zhaoyang Zhang", "Wenqi Shao", "Yixiao Ge", "Xiaogang Wang", "Jinwei Gu", "Ping Luo" ], "github": "", "project_page": "", "abstract": "This work introduces a new Transformer model called Cached Transformer, which uses Gated Recurrent Cached (GRC) attention to extend the self-attention mechanism with a differentiable memory cache of tokens. GRC attention enables attending to both past and current tokens, increasing the receptive field of attention and allowing for exploring long-range dependencies. By utilizing a recurrent gating unit to continuously update the cache, our model achieves significant advancements in six language and vision tasks, including language modeling, machine translation, ListOPs, image classification, object detection, and instance segmentation. Furthermore, our approach surpasses previous memory-based techniques in tasks such as language modeling and displays the ability to be applied to a broader range of situations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-21", "arxiv_id": "2312.12682", "title": "Mini-GPTs: Efficient Large Language Models through Contextual Pruning", "authors": [ "Tim Valicenti", "Justice Vidal", "Ritik Patnaik" ], "github": "https://github.com/tval2/contextual-pruning", "project_page": "", "abstract": "In AI research, the optimization of Large Language Models (LLMs) remains a significant challenge, crucial for advancing the field's practical applications and sustainability. Building upon the foundational work of Professor Song Han's lab at MIT, this paper introduces a novel approach in developing Mini-GPTs via contextual pruning. Our methodology strategically prunes the computational architecture of traditional LLMs, like Phi-1.5, focusing on retaining core functionalities while drastically reducing model sizes. We employ the technique across diverse and complex datasets, including US law, Medical Q&A, Skyrim dialogue, English-Taiwanese translation, and Economics articles. The results underscore the efficiency and effectiveness of contextual pruning, not merely as a theoretical concept but as a practical tool in developing domain-specific, resource-efficient LLMs. Contextual pruning is a promising method for building domain-specific LLMs, and this research is a building block towards future development with more hardware compute, refined fine-tuning, and quantization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tval2/contextual-pruning", "github_api_added_by": "auto" }, { "date": "2023-12-21", "arxiv_id": "2312.12456", "title": "PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU", "authors": [ "Yixin Song", "Zeyu Mi", "Haotong Xie", "Haibo Chen" ], "github": "https://github.com/SJTU-IPADS/PowerInfer", "project_page": "", "abstract": "This paper introduces PowerInfer, a high-speed Large Language Model (LLM) inference engine on a personal computer (PC) equipped with a single consumer-grade GPU. The key underlying the design of PowerInfer is exploiting the high locality inherent in LLM inference, characterized by a power-law distribution in neuron activation. This distribution indicates that a small subset of neurons, termed hot neurons, are consistently activated across inputs, while the majority, cold neurons, vary based on specific inputs. PowerInfer exploits such an insight to design a GPU-CPU hybrid inference engine: hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons are computed on the CPU, thus significantly reducing GPU memory demands and CPU-GPU data transfers. PowerInfer further integrates adaptive predictors and neuron-aware sparse operators, optimizing the efficiency of neuron activation and computational sparsity. Evaluation shows that PowerInfer attains an average token generation rate of 13.20 tokens/s, with a peak of 29.08 tokens/s, across various LLMs (including OPT-175B) on a single NVIDIA RTX 4090 GPU, only 18% lower than that achieved by a top-tier server-grade A100 GPU. This significantly outperforms llama.cpp by up to 11.69x while retaining model accuracy.", "github_manual": "https://github.com/SJTU-IPADS/PowerInfer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-21", "arxiv_id": "2312.13286", "title": "Generative Multimodal Models are In-Context Learners", "authors": [ "Quan Sun", "Yufeng Cui", "Xiaosong Zhang", "Fan Zhang", "Qiying Yu", "Zhengxiong Luo", "Yueze Wang", "Yongming Rao", "Jingjing Liu", "Tiejun Huang", "Xinlong Wang" ], "github": "https://github.com/baaivision/emu", "project_page": "", "abstract": "The human ability to easily solve multimodal tasks in context (i.e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggled to imitate. In this work, we demonstrate that the task-agnostic in-context learning capabilities of large multimodal models can be significantly enhanced by effective scaling-up. We introduce Emu2, a generative multimodal model with 37 billion parameters, trained on large-scale multimodal sequences with a unified autoregressive objective. Emu2 exhibits strong multimodal in-context learning abilities, even emerging to solve tasks that require on-the-fly reasoning, such as visual prompting and object-grounded generation. The model sets a new record on multiple multimodal understanding tasks in few-shot settings. When instruction-tuned to follow specific instructions, Emu2 further achieves new state-of-the-art on challenging tasks such as question answering benchmarks for large multimodal models and open-ended subject-driven generation. These achievements demonstrate that Emu2 can serve as a base model and general-purpose interface for a wide range of multimodal tasks. Code and models are publicly available to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/baaivision/emu", "github_api_added_by": "auto" }, { "date": "2023-12-21", "arxiv_id": "2312.13102", "title": "SpecNeRF: Gaussian Directional Encoding for Specular Reflections", "authors": [ "Li Ma", "Vasu Agrawal", "Haithem Turki", "Changil Kim", "Chen Gao", "Pedro Sander", "Michael Zollh\u00f6fer", "Christian Richardt" ], "github": "", "project_page": "", "abstract": "Neural radiance fields have achieved remarkable performance in modeling the appearance of 3D scenes. However, existing approaches still struggle with the view-dependent appearance of glossy surfaces, especially under complex lighting of indoor environments. Unlike existing methods, which typically assume distant lighting like an environment map, we propose a learnable Gaussian directional encoding to better model the view-dependent effects under near-field lighting conditions. Importantly, our new directional encoding captures the spatially-varying nature of near-field lighting and emulates the behavior of prefiltered environment maps. As a result, it enables the efficient evaluation of preconvolved specular color at any 3D location with varying roughness coefficients. We further introduce a data-driven geometry prior that helps alleviate the shape radiance ambiguity in reflection modeling. We show that our Gaussian directional encoding and geometry prior significantly improve the modeling of challenging specular reflections in neural radiance fields, which helps decompose appearance into more physically meaningful components.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-21", "arxiv_id": "2312.12791", "title": "Model-Based Control with Sparse Neural Dynamics", "authors": [ "Ziang Liu", "Genggeng Zhou", "Jeff He", "Tobia Marcucci", "Li Fei-Fei", "Jiajun Wu", "Yunzhu Li" ], "github": "", "project_page": "", "abstract": "Learning predictive models from observations using deep neural networks (DNNs) is a promising new approach to many real-world planning and control problems. However, common DNNs are too unstructured for effective planning, and current control methods typically rely on extensive sampling or local gradient descent. In this paper, we propose a new framework for integrated model learning and predictive control that is amenable to efficient optimization algorithms. Specifically, we start with a ReLU neural model of the system dynamics and, with minimal losses in prediction accuracy, we gradually sparsify it by removing redundant neurons. This discrete sparsification process is approximated as a continuous problem, enabling an end-to-end optimization of both the model architecture and the weight parameters. The sparsified model is subsequently used by a mixed-integer predictive controller, which represents the neuron activations as binary variables and employs efficient branch-and-bound algorithms. Our framework is applicable to a wide variety of DNNs, from simple multilayer perceptrons to complex graph neural dynamics. It can efficiently handle tasks involving complicated contact dynamics, such as object pushing, compositional object sorting, and manipulation of deformable objects. Numerical and hardware experiments show that, despite the aggressive sparsification, our framework can deliver better closed-loop performance than existing state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-21", "arxiv_id": "2312.13252", "title": "Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model", "authors": [ "Saurabh Saxena", "Junhwa Hur", "Charles Herrmann", "Deqing Sun", "David J. Fleet" ], "github": "", "project_page": "", "abstract": "While methods for monocular depth estimation have made significant strides on standard benchmarks, zero-shot metric depth estimation remains unsolved. Challenges include the joint modeling of indoor and outdoor scenes, which often exhibit significantly different distributions of RGB and depth, and the depth-scale ambiguity due to unknown camera intrinsics. Recent work has proposed specialized multi-head architectures for jointly modeling indoor and outdoor scenes. In contrast, we advocate a generic, task-agnostic diffusion model, with several advancements such as log-scale depth parameterization to enable joint modeling of indoor and outdoor scenes, conditioning on the field-of-view (FOV) to handle scale ambiguity and synthetically augmenting FOV during training to generalize beyond the limited camera intrinsics in training datasets. Furthermore, by employing a more diverse training mixture than is common, and an efficient diffusion parameterization, our method, DMD (Diffusion for Metric Depth) achieves a 25\\% reduction in relative error (REL) on zero-shot indoor and 33\\% reduction on zero-shot outdoor datasets over the current SOTA using only a small number of denoising steps. For an overview see https://diffusion-vision.github.io/dmd", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-21", "arxiv_id": "2312.12865", "title": "RadEdit: stress-testing biomedical vision models via diffusion image editing", "authors": [ "Fernando P\u00e9rez-Garc\u00eda", "Sam Bond-Taylor", "Pedro P. Sanchez", "Boris van Breugel", "Daniel C. Castro", "Harshita Sharma", "Valentina Salvatelli", "Maria T. A. Wetscherek", "Hannah Richardson", "Matthew P. Lungren", "Aditya Nori", "Javier Alvarez-Valle", "Ozan Oktay", "Maximilian Ilse" ], "github": "", "project_page": "", "abstract": "Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. This work proposes using generative image editing to simulate dataset shifts and diagnose failure modes of biomedical vision models; this can be used in advance of deployment to assess readiness, potentially reducing cost and patient harm. Existing editing methods can produce undesirable changes, with spurious correlations learned due to the co-occurrence of disease and treatment interventions, limiting practical applicability. To address this, we train a text-to-image diffusion model on multiple chest X-ray datasets and introduce a new editing method RadEdit that uses multiple masks, if present, to constrain changes and ensure consistency in the edited images. We consider three types of dataset shifts: acquisition shift, manifestation shift, and population shift, and demonstrate that our approach can diagnose failures and quantify model robustness without additional data collection, complementing more qualitative tools for explainable AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-21", "arxiv_id": "2312.12487", "title": "Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models", "authors": [ "Angela Castillo", "Jonas Kohler", "Juan C. P\u00e9rez", "Juan Pablo P\u00e9rez", "Albert Pumarola", "Bernard Ghanem", "Pablo Arbel\u00e1ez", "Ali Thabet" ], "github": "", "project_page": "", "abstract": "This paper presents a comprehensive study on the role of Classifier-Free Guidance (CFG) in text-conditioned diffusion models from the perspective of inference efficiency. In particular, we relax the default choice of applying CFG in all diffusion steps and instead search for efficient guidance policies. We formulate the discovery of such policies in the differentiable Neural Architecture Search framework. Our findings suggest that the denoising steps proposed by CFG become increasingly aligned with simple conditional steps, which renders the extra neural network evaluation of CFG redundant, especially in the second half of the denoising process. Building upon this insight, we propose \"Adaptive Guidance\" (AG), an efficient variant of CFG, that adaptively omits network evaluations when the denoising process displays convergence. Our experiments demonstrate that AG preserves CFG's image quality while reducing computation by 25%. Thus, AG constitutes a plug-and-play alternative to Guidance Distillation, achieving 50% of the speed-ups of the latter while being training-free and retaining the capacity to handle negative prompts. Finally, we uncover further redundancies of CFG in the first half of the diffusion process, showing that entire neural function evaluations can be replaced by simple affine transformations of past score estimates. This method, termed LinearAG, offers even cheaper inference at the cost of deviating from the baseline model. Our findings provide insights into the efficiency of the conditional denoising process that contribute to more practical and swift deployment of text-conditioned diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-21", "arxiv_id": "2312.12468", "title": "MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers", "authors": [ "Haoyu Ma", "Shahin Mahdizadehaghdam", "Bichen Wu", "Zhipeng Fan", "Yuchao Gu", "Wenliang Zhao", "Lior Shapira", "Xiaohui Xie" ], "github": "", "project_page": "", "abstract": "Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets for training, and therefore challenging the deployment in practical applications. This study addresses this challenge by breaking down the text-based video editing process into two separate stages. In the first stage, we leverage an existing text-to-image diffusion model to simultaneously edit a few keyframes without additional fine-tuning. In the second stage, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the keyframes, benefiting from structural guidance provided by intermediate frames. Our comprehensive set of experiments illustrates the efficacy and efficiency of MaskINT when compared to other diffusion-based methodologies. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-21", "arxiv_id": "2312.13150", "title": "Splatter Image: Ultra-Fast Single-View 3D Reconstruction", "authors": [ "Stanislaw Szymanowicz", "Christian Rupprecht", "Andrea Vedaldi" ], "github": "https://github.com/szymanowiczs/splatter-image", "project_page": "", "abstract": "We introduce the Splatter Image, an ultra-fast approach for monocular 3D object reconstruction which operates at 38 FPS. Splatter Image is based on Gaussian Splatting, which has recently brought real-time rendering, fast training, and excellent scaling to multi-view reconstruction. For the first time, we apply Gaussian Splatting in a monocular reconstruction setting. Our approach is learning-based, and, at test time, reconstruction only requires the feed-forward evaluation of a neural network. The main innovation of Splatter Image is the surprisingly straightforward design: it uses a 2D image-to-image network to map the input image to one 3D Gaussian per pixel. The resulting Gaussians thus have the form of an image, the Splatter Image. We further extend the method to incorporate more than one image as input, which we do by adding cross-view attention. Owning to the speed of the renderer (588 FPS), we can use a single GPU for training while generating entire images at each iteration in order to optimize perceptual metrics like LPIPS. On standard benchmarks, we demonstrate not only fast reconstruction but also better results than recent and much more expensive baselines in terms of PSNR, LPIPS, and other metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/szymanowiczs/splatter-image", "github_api_added_by": "auto" }, { "date": "2023-12-21", "arxiv_id": "2312.13285", "title": "UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections", "authors": [ "Fangjinhua Wang", "Marie-Julie Rakotosaona", "Michael Niemeyer", "Richard Szeliski", "Marc Pollefeys", "Federico Tombari" ], "github": "", "project_page": "", "abstract": "Neural 3D scene representations have shown great potential for 3D reconstruction from 2D images. However, reconstructing real-world captures of complex scenes still remains a challenge. Existing generic 3D reconstruction methods often struggle to represent fine geometric details and do not adequately model reflective surfaces of large-scale scenes. Techniques that explicitly focus on reflective surfaces can model complex and detailed reflections by exploiting better reflection parameterizations. However, we observe that these methods are often not robust in real unbounded scenarios where non-reflective as well as reflective components are present. In this work, we propose UniSDF, a general purpose 3D reconstruction method that can reconstruct large complex scenes with reflections. We investigate both view-based as well as reflection-based color prediction parameterization techniques and find that explicitly blending these representations in 3D space enables reconstruction of surfaces that are more geometrically accurate, especially for reflective surfaces. We further combine this representation with a multi-resolution grid backbone that is trained in a coarse-to-fine manner, enabling faster reconstructions than prior methods. Extensive experiments on object-level datasets DTU, Shiny Blender as well as unbounded datasets Mip-NeRF 360 and Ref-NeRF real demonstrate that our method is able to robustly reconstruct complex large-scale scenes with fine details and reflective surfaces. Please see our project page at https://fangjinhuawang.github.io/UniSDF.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.13913", "title": "Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models", "authors": [ "Xianfang Zeng", "Xin Chen", "Zhongqi Qi", "Wen Liu", "Zibo Zhao", "Zhibin Wang", "BIN FU", "Yong Liu", "Gang Yu" ], "github": "https://github.com/opentexture/paint3d", "project_page": "", "abstract": "This paper presents Paint3D, a novel coarse-to-fine generative framework that is capable of producing high-resolution, lighting-less, and diverse 2K UV texture maps for untextured 3D meshes conditioned on text or image inputs. The key challenge addressed is generating high-quality textures without embedded illumination information, which allows the textures to be re-lighted or re-edited within modern graphics pipelines. To achieve this, our method first leverages a pre-trained depth-aware 2D diffusion model to generate view-conditional images and perform multi-view texture fusion, producing an initial coarse texture map. However, as 2D models cannot fully represent 3D shapes and disable lighting effects, the coarse texture map exhibits incomplete areas and illumination artifacts. To resolve this, we train separate UV Inpainting and UVHD diffusion models specialized for the shape-aware refinement of incomplete areas and the removal of illumination artifacts. Through this coarse-to-fine process, Paint3D can produce high-quality 2K UV textures that maintain semantic consistency while being lighting-less, significantly advancing the state-of-the-art in texturing 3D objects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/opentexture/paint3d", "github_api_added_by": "auto" }, { "date": "2023-12-22", "arxiv_id": "2312.13578", "title": "DREAM-Talk: Diffusion-based Realistic Emotional Audio-driven Method for Single Image Talking Face Generation", "authors": [ "Chenxu Zhang", "Chao Wang", "Jianfeng Zhang", "Hongyi Xu", "Guoxian Song", "You Xie", "Linjie Luo", "Yapeng Tian", "Xiaohu Guo", "Jiashi Feng" ], "github": "", "project_page": "", "abstract": "The generation of emotional talking faces from a single portrait image remains a significant challenge. The simultaneous achievement of expressive emotional talking and accurate lip-sync is particularly difficult, as expressiveness is often compromised for the accuracy of lip-sync. As widely adopted by many prior works, the LSTM network often fails to capture the subtleties and variations of emotional expressions. To address these challenges, we introduce DREAM-Talk, a two-stage diffusion-based audio-driven framework, tailored for generating diverse expressions and accurate lip-sync concurrently. In the first stage, we propose EmoDiff, a novel diffusion module that generates diverse highly dynamic emotional expressions and head poses in accordance with the audio and the referenced emotion style. Given the strong correlation between lip motion and audio, we then refine the dynamics with enhanced lip-sync accuracy using audio features and emotion style. To this end, we deploy a video-to-video rendering module to transfer the expressions and lip motions from our proxy 3D avatar to an arbitrary portrait. Both quantitatively and qualitatively, DREAM-Talk outperforms state-of-the-art methods in terms of expressiveness, lip-sync accuracy and perceptual quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.13834", "title": "Fairy: Fast Parallelized Instruction-Guided Video-to-Video Synthesis", "authors": [ "Bichen Wu", "Ching-Yao Chuang", "Xiaoyan Wang", "Yichen Jia", "Kapil Krishnakumar", "Tong Xiao", "Feng Liang", "Licheng Yu", "Peter Vajda" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce Fairy, a minimalist yet robust adaptation of image-editing diffusion models, enhancing them for video editing applications. Our approach centers on the concept of anchor-based cross-frame attention, a mechanism that implicitly propagates diffusion features across frames, ensuring superior temporal coherence and high-fidelity synthesis. Fairy not only addresses limitations of previous models, including memory and processing speed. It also improves temporal consistency through a unique data augmentation strategy. This strategy renders the model equivariant to affine transformations in both source and target images. Remarkably efficient, Fairy generates 120-frame 512x384 videos (4-second duration at 30 FPS) in just 14 seconds, outpacing prior works by at least 44x. A comprehensive user study, involving 1000 generated samples, confirms that our approach delivers superior quality, decisively outperforming established methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.13789", "title": "TinySAM: Pushing the Envelope for Efficient Segment Anything Model", "authors": [ "Han Shu", "Wenshuo Li", "Yehui Tang", "Yiman Zhang", "Yihao Chen", "Houqiang Li", "Yunhe Wang", "Xinghao Chen" ], "github": "https://github.com/xinghaochen/TinySAM", "project_page": "", "abstract": "Recently segment anything model (SAM) has shown powerful segmentation capability and has drawn great attention in computer vision fields. Massive following works have developed various applications based on the pretrained SAM and achieved impressive performance on downstream vision tasks. However, SAM consists of heavy architectures and requires massive computational capacity, which hinders the further application of SAM on computation constrained edge devices. To this end, in this paper we propose a framework to obtain a tiny segment anything model (TinySAM) while maintaining the strong zero-shot performance. We first propose a full-stage knowledge distillation method with online hard prompt sampling strategy to distill a lightweight student model. We also adapt the post-training quantization to the promptable segmentation task and further reduce the computational cost. Moreover, a hierarchical segmenting everything strategy is proposed to accelerate the everything inference by 2times with almost no performance degradation. With all these proposed methods, our TinySAM leads to orders of magnitude computational reduction and pushes the envelope for efficient segment anything task. Extensive experiments on various zero-shot transfer tasks demonstrate the significantly advantageous performance of our TinySAM against counterpart methods. Pre-trained models and codes will be available at https://github.com/xinghaochen/TinySAM and https://gitee.com/mindspore/models/tree/master/research/cv/TinySAM.", "github_manual": "", "github_regex": "https://github.com/xinghaochen/TinySAM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.13691", "title": "DreamTuner: Single Image is Enough for Subject-Driven Generation", "authors": [ "Miao Hua", "Jiawei Liu", "Fei Ding", "Wei Liu", "Jie Wu", "Qian He" ], "github": "", "project_page": "", "abstract": "Diffusion-based models have demonstrated impressive capabilities for text-to-image generation and are expected for personalized applications of subject-driven generation, which require the generation of customized concepts with one or a few reference images. However, existing methods based on fine-tuning fail to balance the trade-off between subject learning and the maintenance of the generation capabilities of pretrained models. Moreover, other methods that utilize additional image encoders tend to lose important details of the subject due to encoding compression. To address these challenges, we propose DreamTurner, a novel method that injects reference information from coarse to fine to achieve subject-driven image generation more effectively. DreamTurner introduces a subject-encoder for coarse subject identity preservation, where the compressed general subject features are introduced through an attention layer before visual-text cross-attention. We then modify the self-attention layers within pretrained text-to-image models to self-subject-attention layers to refine the details of the target subject. The generated image queries detailed features from both the reference image and itself in self-subject-attention. It is worth emphasizing that self-subject-attention is an effective, elegant, and training-free method for maintaining the detailed features of customized subjects and can serve as a plug-and-play solution during inference. Finally, with additional subject-driven fine-tuning, DreamTurner achieves remarkable performance in subject-driven image generation, which can be controlled by a text or other conditions such as pose. For further details, please visit the project page at https://dreamtuner-diffusion.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.13469", "title": "Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation", "authors": [ "Sudharshan Suresh", "Haozhi Qi", "Tingfan Wu", "Taosha Fan", "Luis Pineda", "Mike Lambeta", "Jitendra Malik", "Mrinal Kalakrishnan", "Roberto Calandra", "Michael Kaess", "Joseph Ortiz", "Mustafa Mukadam" ], "github": "", "project_page": "", "abstract": "To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, such spatial awareness involves estimating the object's pose and shape. The status quo for in-hand perception primarily employs vision, and restricts to tracking a priori known objects. Moreover, visual occlusion of objects in-hand is imminent during manipulation, preventing current systems to push beyond tasks without occlusion. We combine vision and touch sensing on a multi-fingered hand to estimate an object's pose and shape during in-hand manipulation. Our method, NeuralFeels, encodes object geometry by learning a neural field online and jointly tracks it by optimizing a pose graph problem. We study multimodal in-hand perception in simulation and the real-world, interacting with different objects via a proprioception-driven policy. Our experiments show final reconstruction F-scores of 81% and average pose drifts of 4.7,mm, further reduced to 2.3,mm with known CAD models. Additionally, we observe that under heavy visual occlusion we can achieve up to 94% improvements in tracking compared to vision-only methods. Our results demonstrate that touch, at the very least, refines and, at the very best, disambiguates visual estimates during in-hand manipulation. We release our evaluation dataset of 70 experiments, FeelSight, as a step towards benchmarking in this domain. Our neural representation driven by multimodal sensing can serve as a perception backbone towards advancing robot dexterity. Videos can be found on our project website https://suddhu.github.io/neural-feels/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.14125", "title": "VideoPoet: A Large Language Model for Zero-Shot Video Generation", "authors": [ "Dan Kondratyuk", "Lijun Yu", "Xiuye Gu", "Jos\u00e9 Lezama", "Jonathan Huang", "Rachel Hornung", "Hartwig Adam", "Hassan Akbari", "Yair Alon", "Vighnesh Birodkar", "Yong Cheng", "Ming-Chang Chiu", "Josh Dillon", "Irfan Essa", "Agrim Gupta", "Meera Hahn", "Anja Hauth", "David Hendon", "Alonso Martinez", "David Minnen", "David Ross", "Grant Schindler", "Mikhail Sirotenko", "Kihyuk Sohn", "Krishna Somandepalli", "Huisheng Wang", "Jimmy Yan", "Ming-Hsuan Yang", "Xuan Yang", "Bryan Seybold", "Lu Jiang" ], "github": "", "project_page": "", "abstract": "We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder-only transformer architecture that processes multimodal inputs -- including images, videos, text, and audio. The training protocol follows that of Large Language Models (LLMs), consisting of two stages: pretraining and task-specific adaptation. During pretraining, VideoPoet incorporates a mixture of multimodal generative objectives within an autoregressive Transformer framework. The pretrained LLM serves as a foundation that can be adapted for a range of video generation tasks. We present empirical results demonstrating the model's state-of-the-art capabilities in zero-shot video generation, specifically highlighting VideoPoet's ability to generate high-fidelity motions. Project page: http://sites.research.google/videopoet/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.13763", "title": "Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models", "authors": [ "Huan Ling", "Seung Wook Kim", "Antonio Torralba", "Sanja Fidler", "Karsten Kreis" ], "github": "", "project_page": "", "abstract": "Text-guided diffusion models have revolutionized image and video generation and have also been successfully used for optimization-based 3D object synthesis. Here, we instead focus on the underexplored text-to-4D setting and synthesize dynamic, animated 3D objects using score distillation methods with an additional temporal dimension. Compared to previous work, we pursue a novel compositional generation-based approach, and combine text-to-image, text-to-video, and 3D-aware multiview diffusion models to provide feedback during 4D object optimization, thereby simultaneously enforcing temporal consistency, high-quality visual appearance and realistic geometry. Our method, called Align Your Gaussians (AYG), leverages dynamic 3D Gaussian Splatting with deformation fields as 4D representation. Crucial to AYG is a novel method to regularize the distribution of the moving 3D Gaussians and thereby stabilize the optimization and induce motion. We also propose a motion amplification mechanism as well as a new autoregressive synthesis scheme to generate and combine multiple 4D sequences for longer generation. These techniques allow us to synthesize vivid dynamic scenes, outperform previous work qualitatively and quantitatively and achieve state-of-the-art text-to-4D performance. Due to the Gaussian 4D representation, different 4D animations can be seamlessly combined, as we demonstrate. AYG opens up promising avenues for animation, simulation and digital content creation as well as synthetic data generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.14140", "title": "HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs", "authors": [ "Artem Sevastopolsky", "Philip-William Grassal", "Simon Giebenhain", "ShahRukh Athar", "Luisa Verdoliva", "Matthias Niessner" ], "github": "", "project_page": "", "abstract": "Current advances in human head modeling allow to generate plausible-looking 3D head models via neural representations. Nevertheless, constructing complete high-fidelity head models with explicitly controlled animation remains an issue. Furthermore, completing the head geometry based on a partial observation, e.g. coming from a depth sensor, while preserving details is often problematic for the existing methods. We introduce a generative model for detailed 3D head meshes on top of an articulated 3DMM which allows explicit animation and high-detail preservation at the same time. Our method is trained in two stages. First, we register a parametric head model with vertex displacements to each mesh of the recently introduced NPHM dataset of accurate 3D head scans. The estimated displacements are baked into a hand-crafted UV layout. Second, we train a StyleGAN model in order to generalize over the UV maps of displacements. The decomposition of the parametric model and high-quality vertex displacements allows us to animate the model and modify it semantically. We demonstrate the results of unconditional generation and fitting to the full or partial observation. The project page is available at https://seva100.github.io/headcraft.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.14091", "title": "HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models", "authors": [ "Hayk Manukyan", "Andranik Sargsyan", "Barsegh Atanyan", "Zhangyang Wang", "Shant Navasardyan", "Humphrey Shi" ], "github": "https://github.com/Picsart-AI-Research/HD-Painter", "project_page": "", "abstract": "Recent progress in text-guided image inpainting, based on the unprecedented success of text-to-image diffusion models, has led to exceptionally realistic and visually plausible results. However, there is still significant potential for improvement in current text-to-image inpainting models, particularly in better aligning the inpainted area with user prompts and performing high-resolution inpainting. Therefore, in this paper we introduce HD-Painter, a completely training-free approach that accurately follows to prompts and coherently scales to high-resolution image inpainting. To this end, we design the Prompt-Aware Introverted Attention (PAIntA) layer enhancing self-attention scores by prompt information and resulting in better text alignment generations. To further improve the prompt coherence we introduce the Reweighting Attention Score Guidance (RASG) mechanism seamlessly integrating a post-hoc sampling strategy into general form of DDIM to prevent out-of-distribution latent shifts. Moreover, HD-Painter allows extension to larger scales by introducing a specialized super-resolution technique customized for inpainting, enabling the completion of missing regions in images of up to 2K resolution. Our experiments demonstrate that HD-Painter surpasses existing state-of-the-art approaches qualitatively and quantitatively, achieving an impressive generation accuracy improvement of 61.4% vs 51.9%. We will make the codes publicly available at: https://github.com/Picsart-AI-Research/HD-Painter", "github_manual": "", "github_regex": "https://github.com/Picsart-AI-Research/HD-Painter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.13980", "title": "Carve3D: Improving Multi-view Reconstruction Consistency for Diffusion Models with RL Finetuning", "authors": [ "Desai Xie", "Jiahao Li", "Hao Tan", "Xin Sun", "Zhixin Shu", "Yi Zhou", "Sai Bi", "S\u00f6ren Pirk", "Arie E. Kaufman" ], "github": "", "project_page": "", "abstract": "Recent advancements in the text-to-3D task leverage finetuned text-to-image diffusion models to generate multi-view images, followed by NeRF reconstruction. Yet, existing supervised finetuned (SFT) diffusion models still suffer from multi-view inconsistency and the resulting NeRF artifacts. Although training longer with SFT improves consistency, it also causes distribution shift, which reduces diversity and realistic details. We argue that the SFT of multi-view diffusion models resembles the instruction finetuning stage of the LLM alignment pipeline and can benefit from RL finetuning (RLFT) methods. Essentially, RLFT methods optimize models beyond their SFT data distribution by using their own outputs, effectively mitigating distribution shift. To this end, we introduce Carve3D, a RLFT method coupled with the Multi-view Reconstruction Consistency (MRC) metric, to improve the consistency of multi-view diffusion models. To compute MRC on a set of multi-view images, we compare them with their corresponding renderings of the reconstructed NeRF at the same viewpoints. We validate the robustness of MRC with extensive experiments conducted under controlled inconsistency levels. We enhance the base RLFT algorithm to stabilize the training process, reduce distribution shift, and identify scaling laws. Through qualitative and quantitative experiments, along with a user study, we demonstrate Carve3D's improved multi-view consistency, the resulting superior NeRF reconstruction quality, and minimal distribution shift compared to longer SFT. Project webpage: https://desaixie.github.io/carve-3d.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.13964", "title": "PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models", "authors": [ "Yiming Zhang", "Zhening Xing", "Yanhong Zeng", "Youqing Fang", "Kai Chen" ], "github": "https://github.com/open-mmlab/PIA", "project_page": "", "abstract": "Recent advancements in personalized text-to-image (T2I) models have revolutionized content creation, empowering non-experts to generate stunning images with unique styles. While promising, adding realistic motions into these personalized images by text poses significant challenges in preserving distinct styles, high-fidelity details, and achieving motion controllability by text. In this paper, we present PIA, a Personalized Image Animator that excels in aligning with condition images, achieving motion controllability by text, and the compatibility with various personalized T2I models without specific tuning. To achieve these goals, PIA builds upon a base T2I model with well-trained temporal alignment layers, allowing for the seamless transformation of any personalized T2I model into an image animation model. A key component of PIA is the introduction of the condition module, which utilizes the condition frame and inter-frame affinity as input to transfer appearance information guided by the affinity hint for individual frame synthesis in the latent space. This design mitigates the challenges of appearance-related image alignment within and allows for a stronger focus on aligning with motion-related guidance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/open-mmlab/PIA", "github_api_added_by": "auto" }, { "date": "2023-12-22", "arxiv_id": "2312.13771", "title": "AppAgent: Multimodal Agents as Smartphone Users", "authors": [ "Chi Zhang", "Zhao Yang", "Jiaxuan Liu", "Yucheng Han", "Xin Chen", "Zebiao Huang", "Bin Fu", "Gang Yu" ], "github": "", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing over 50 tasks in 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.13401", "title": "Time is Encoded in the Weights of Finetuned Language Models", "authors": [ "Kai Nylund", "Suchin Gururangan", "Noah A. Smith" ], "github": "https://github.com/KaiNylund/lm-weights-encode-time", "project_page": "", "abstract": "We present time vectors, a simple tool to customize language models to new time periods. Time vectors are created by finetuning a language model on data from a single time (e.g., a year or month), and then subtracting the weights of the original pretrained model. This vector specifies a direction in weight space that, as our experiments show, improves performance on text from that time period. Time vectors specialized to adjacent time periods appear to be positioned closer together in a manifold. Using this structure, we interpolate between time vectors to induce new models that perform better on intervening and future time periods, without any additional training. We demonstrate the consistency of our findings across different tasks, domains, model sizes, and time scales. Our results suggest that time is encoded in the weight space of finetuned models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KaiNylund/lm-weights-encode-time", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.13324", "title": "ShowRoom3D: Text to High-Quality 3D Room Generation Using 3D Priors", "authors": [ "Weijia Mao", "Yan-Pei Cao", "Jia-Wei Liu", "Zhongcong Xu", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "We introduce ShowRoom3D, a three-stage approach for generating high-quality 3D room-scale scenes from texts. Previous methods using 2D diffusion priors to optimize neural radiance fields for generating room-scale scenes have shown unsatisfactory quality. This is primarily attributed to the limitations of 2D priors lacking 3D awareness and constraints in the training methodology. In this paper, we utilize a 3D diffusion prior, MVDiffusion, to optimize the 3D room-scale scene. Our contributions are in two aspects. Firstly, we propose a progressive view selection process to optimize NeRF. This involves dividing the training process into three stages, gradually expanding the camera sampling scope. Secondly, we propose the pose transformation method in the second stage. It will ensure MVDiffusion provide the accurate view guidance. As a result, ShowRoom3D enables the generation of rooms with improved structural integrity, enhanced clarity from any view, reduced content repetition, and higher consistency across different perspectives. Extensive experiments demonstrate that our method, significantly outperforms state-of-the-art approaches by a large margin in terms of user study.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.13314", "title": "Unlocking Pre-trained Image Backbones for Semantic Image Synthesis", "authors": [ "Tariq Berrada", "Jakob Verbeek", "Camille Couprie", "Karteek Alahari" ], "github": "", "project_page": "", "abstract": "Semantic image synthesis, i.e., generating images from user-provided semantic label maps, is an important conditional image generation task as it allows to control both the content as well as the spatial layout of generated images. Although diffusion models have pushed the state of the art in generative image modeling, the iterative nature of their inference process makes them computationally demanding. Other approaches such as GANs are more efficient as they only need a single feed-forward pass for generation, but the image quality tends to suffer on large and diverse datasets. In this work, we propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images by exploiting feature backbone networks pre-trained for tasks such as image classification. We also introduce a new generator architecture with better context modeling and using cross-attention to inject noise into latent variables, leading to more diverse generated images. Our model, which we dub DP-SIMS, achieves state-of-the-art results in terms of image quality and consistency with the input label maps on ADE-20K, COCO-Stuff, and Cityscapes, surpassing recent diffusion models while requiring two orders of magnitude less compute for inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-22", "arxiv_id": "2312.13528", "title": "DyBluRF: Dynamic Deblurring Neural Radiance Fields for Blurry Monocular Video", "authors": [ "Minh-Quan Viet Bui", "Jongmin Park", "Jihyong Oh", "Munchurl Kim" ], "github": "", "project_page": "", "abstract": "Video view synthesis, allowing for the creation of visually appealing frames from arbitrary viewpoints and times, offers immersive viewing experiences. Neural radiance fields, particularly NeRF, initially developed for static scenes, have spurred the creation of various methods for video view synthesis. However, the challenge for video view synthesis arises from motion blur, a consequence of object or camera movement during exposure, which hinders the precise synthesis of sharp spatio-temporal views. In response, we propose a novel dynamic deblurring NeRF framework for blurry monocular video, called DyBluRF, consisting of an Interleave Ray Refinement (IRR) stage and a Motion Decomposition-based Deblurring (MDD) stage. Our DyBluRF is the first that addresses and handles the novel view synthesis for blurry monocular video. The IRR stage jointly reconstructs dynamic 3D scenes and refines the inaccurate camera pose information to combat imprecise pose information extracted from the given blurry frames. The MDD stage is a novel incremental latent sharp-rays prediction (ILSP) approach for the blurry monocular video frames by decomposing the latent sharp rays into global camera motion and local object motion components. Extensive experimental results demonstrate that our DyBluRF outperforms qualitatively and quantitatively the very recent state-of-the-art methods. Our project page including source codes and pretrained model are publicly available at https://kaist-viclab.github.io/dyblurf-site/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14302", "title": "Exploiting Novel GPT-4 APIs", "authors": [ "Kellin Pelrine", "Mohammad Taufeeque", "Micha\u0142 Zaj\u0105c", "Euan McLean", "Adam Gleave" ], "github": "https://github.com/alignmentresearch/gpt-4-novel-apis-attacks", "project_page": "", "abstract": "Language model attacks typically assume one of two extreme threat models: full white-box access to model weights, or black-box access limited to a text generation API. However, real-world APIs are often more flexible than just text generation: these APIs expose ``gray-box'' access leading to new threat vectors. To explore this, we red-team three new functionalities exposed in the GPT-4 APIs: fine-tuning, function calling and knowledge retrieval. We find that fine-tuning a model on as few as 15 harmful examples or 100 benign examples can remove core safeguards from GPT-4, enabling a range of harmful outputs. Furthermore, we find that GPT-4 Assistants readily divulge the function call schema and can be made to execute arbitrary function calls. Finally, we find that knowledge retrieval can be hijacked by injecting instructions into retrieval documents. These vulnerabilities highlight that any additions to the functionality exposed by an API can create new vulnerabilities.", "github_manual": "https://github.com/alignmentresearch/gpt-4-novel-apis-attacks", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14327", "title": "Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion", "authors": [ "Katrin Tomanek", "Shanqing Cai", "Subhashini Venugopalan" ], "github": "", "project_page": "", "abstract": "Abbreviation expansion is a strategy used to speed up communication by limiting the amount of typing and using a language model to suggest expansions. Here we look at personalizing a Large Language Model's (LLM) suggestions based on prior conversations to enhance the relevance of predictions, particularly when the user data is small (~1000 samples). Specifically, we compare fine-tuning, prompt-tuning, and retrieval augmented generation of expanded text suggestions for abbreviated inputs. Our case study with a deployed 8B parameter LLM on a real user living with ALS, and experiments on movie character personalization indicates that (1) customization may be necessary in some scenarios and prompt-tuning generalizes well to those, (2) fine-tuning on in-domain data (with as few as 600 samples) still shows some gains, however (3) retrieval augmented few-shot selection also outperforms fine-tuning. (4) Parameter efficient tuning allows for efficient and scalable personalization. For prompt-tuning, we also find that initializing the learned \"soft-prompts\" to user relevant concept tokens leads to higher accuracy than random initialization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14238", "title": "InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks", "authors": [ "Zhe Chen", "Jiannan Wu", "Wenhai Wang", "Weijie Su", "Guo Chen", "Sen Xing", "Zhong Muyan", "Qinglong Zhang", "Xizhou Zhu", "Lewei Lu", "Bin Li", "Ping Luo", "Tong Lu", "Yu Qiao", "Jifeng Dai" ], "github": "https://github.com/OpenGVLab/InternVL", "project_page": "", "abstract": "The exponential growth of large language models (LLMs) has opened up numerous possibilities for multi-modal AGI systems. However, the progress in vision and vision-language foundation models, which are also critical elements of multi-modal AGI, has not kept pace with LLMs. In this work, we design a large-scale vision-language foundation model (InternVL), which scales up the vision foundation model to 6 billion parameters and progressively aligns it with the large language model, using web-scale image-text data from various sources. This model can be broadly applied to and achieve state-of-the-art performance on visual perception tasks such as image-level or pixel-level recognition, vision-language tasks such as zero-shot image/video classification, zero-shot image/video-text retrieval, and link with LLMs to create multi-modal dialogue systems. We hope that our research could contribute to the development of multi-modal large models. Code and models are available at https://github.com/OpenGVLab/InternVL.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/InternVL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14187", "title": "WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation", "authors": [ "Zhaojian Yu", "Xin Zhang", "Ning Shang", "Yangyu Huang", "Can Xu", "Yishujie Zhao", "Wenxiang Hu", "Qiufeng Yin" ], "github": "", "project_page": "", "abstract": "Recent work demonstrates that, after being fine-tuned on a high-quality instruction dataset, the resulting model can obtain impressive capabilities to address a wide range of tasks. However, existing methods for instruction data generation often produce duplicate data and are not controllable enough on data quality. In this paper, we extend the generalization of instruction tuning by classifying the instruction data to 4 code-related tasks and propose a LLM-based Generator-Discriminator data process framework to generate diverse, high-quality instruction data from open source code. Hence, we introduce CodeOcean, a dataset comprising 20,000 instruction instances across 4 universal code-related tasks,which is aimed at augmenting the effectiveness of instruction tuning and improving the generalization ability of fine-tuned model. Subsequently, we present WaveCoder, a fine-tuned Code LLM with Widespread And Versatile Enhanced instruction tuning. This model is specifically designed for enhancing instruction tuning of Code Language Models (LLMs). Our experiments demonstrate that Wavecoder models outperform other open-source models in terms of generalization ability across different code-related tasks at the same level of fine-tuning scale. Moreover, Wavecoder exhibits high efficiency in previous code generation tasks. This paper thus offers a significant contribution to the field of instruction data generation and fine-tuning models, providing new insights and tools for enhancing performance in code-related tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14233", "title": "VCoder: Versatile Vision Encoders for Multimodal Large Language Models", "authors": [ "Jitesh Jain", "Jianwei Yang", "Humphrey Shi" ], "github": "https://github.com/SHI-Labs/VCoder", "project_page": "", "abstract": "Humans possess the remarkable skill of Visual Perception, the ability to see and understand the seen, helping them make sense of the visual world and, in turn, reason. Multimodal Large Language Models (MLLM) have recently achieved impressive performance on vision-language tasks ranging from visual question-answering and image captioning to visual reasoning and image generation. However, when prompted to identify or count (perceive) the entities in a given image, existing MLLM systems fail. Working towards developing an accurate MLLM system for perception and reasoning, we propose using Versatile vision enCoders (VCoder) as perception eyes for Multimodal LLMs. We feed the VCoder with perception modalities such as segmentation or depth maps, improving the MLLM's perception abilities. Secondly, we leverage the images from COCO and outputs from off-the-shelf vision perception models to create our COCO Segmentation Text (COST) dataset for training and evaluating MLLMs on the object perception task. Thirdly, we introduce metrics to assess the object perception abilities in MLLMs on our COST dataset. Lastly, we provide extensive experimental evidence proving the VCoder's improved object-level perception skills over existing Multimodal LLMs, including GPT-4V. We open-source our dataset, code, and models to promote research. We open-source our code at https://github.com/SHI-Labs/VCoder", "github_manual": "", "github_regex": "https://github.com/SHI-Labs/VCoder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14878", "title": "Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning", "authors": [ "Filippos Christianos", "Georgios Papoudakis", "Matthieu Zimmer", "Thomas Coste", "Zhihao Wu", "Jingxuan Chen", "Khyati Khandelwal", "James Doran", "Xidong Feng", "Jiacheng Liu", "Zheng Xiong", "Yicheng Luo", "Jianye Hao", "Kun Shao", "Haitham Bou-Ammar", "Jun Wang" ], "github": "", "project_page": "", "abstract": "A key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL). However, constructing a standalone RL policy that maps perception to action directly encounters severe problems, chief among them being its lack of generality across multiple tasks and the need for a large amount of training data. The leading cause is that it cannot effectively integrate prior information into the perception-action cycle when devising the policy. Large language models (LLMs) emerged as a fundamental way to incorporate cross-domain knowledge into AI agents but lack crucial learning and adaptation toward specific decision problems. This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies. Our methodology is motivated by the modularity found in the human brain. The framework utilises the construction of intrinsic and extrinsic functions to add previous understandings of reasoning structures. It also provides the adaptive ability to learn models inside every module or function, consistent with the modular structure of cognitive processes. We describe the framework in-depth and compare it with other AI pipelines and existing frameworks. The paper explores practical applications, covering experiments that show the effectiveness of our method. Our results indicate that AI agents perform and adapt far better when organised reasoning and prior knowledge are embedded. This opens the door to more resilient and general AI agent systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14862", "title": "YAYI 2: Multilingual Open-Source Large Language Models", "authors": [ "Yin Luo", "Qingchao Kong", "Nan Xu", "Jia Cao", "Bao Hao", "Baoyu Qu", "Bo Chen", "Chao Zhu", "Chenyang Zhao", "Donglei Zhang", "Fan Feng", "Feifei Zhao", "Hailong Sun", "Hanxuan Yang", "Haojun Pan", "Hongyu Liu", "Jianbin Guo", "Jiangtao Du", "Jingyi Wang", "Junfeng Li", "Lei Sun", "Liduo Liu", "Lifeng Dong", "Lili Liu", "Lin Wang", "Liwen Zhang", "Minzheng Wang", "Pin Wang", "Ping Yu", "Qingxiao Li", "Rui Yan", "Rui Zou", "Ruiqun Li", "Taiwen Huang", "Xiaodong Wang", "Xiaofei Wu", "Xin Peng", "Xina Zhang", "Xing Fang", "Xinglin Xiao", "Yanni Hao", "Yao Dong", "Yigang Wang", "Ying Liu", "Yongyu Jiang", "Yungan Wang", "Yuqi Wang", "Zhangsheng Wang", "Zhaoxin Yu", "Zhen Luo", "Wenji Mao", "Lei Wang", "Dajun Zeng" ], "github": "", "project_page": "", "abstract": "As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence. To better facilitate research on LLMs, many open-source LLMs, such as Llama 2 and Falcon, have recently been proposed and gained comparable performances to proprietary models. However, these models are primarily designed for English scenarios and exhibit poor performances in Chinese contexts. In this technical report, we propose YAYI 2, including both base and chat models, with 30 billion parameters. YAYI 2 is pre-trained from scratch on a multilingual corpus which contains 2.65 trillion tokens filtered by our pre-training data processing pipeline. The base model is aligned with human values through supervised fine-tuning with millions of instructions and reinforcement learning from human feedback. Extensive experiments on multiple benchmarks, such as MMLU and CMMLU, consistently demonstrate that the proposed YAYI 2 outperforms other similar sized open-source models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14591", "title": "Reasons to Reject? Aligning Language Models with Judgments", "authors": [ "Weiwen Xu", "Deng Cai", "Zhisong Zhang", "Wai Lam", "Shuming Shi" ], "github": "https://github.com/wwxu21/cut", "project_page": "", "abstract": "As humans, we consistently engage in interactions with our peers and receive feedback in the form of natural language. This language feedback allows us to reflect on our actions, maintain appropriate behavior, and rectify our errors. The question arises naturally: can we use language feedback to align large language models (LLMs)? In contrast to previous research that aligns LLMs with reward or preference data, we present the first systematic exploration of alignment through the lens of language feedback (i.e., judgment). We commence with an in-depth investigation of potential methods that can be adapted for aligning LLMs with judgments, revealing that these methods are unable to fully capitalize on the judgments. To facilitate more effective utilization of judgments, we propose a novel framework, Contrastive Unlikelihood Training (CUT), that allows for fine-grained inappropriate content detection and correction based on judgments. Our offline alignment results show that, with merely 1317 off-the-shelf judgment data, CUT (LLaMA2-13b) can beat the 175B DaVinci003 and surpass the best baseline by 52.34 points on AlpacaEval. The online alignment results demonstrate that CUT can align LLMs (LLaMA2-chat-13b) in an iterative fashion using model-specific judgment data, with a steady performance improvement from 81.09 to 91.36 points on AlpacaEval. Our analysis further suggests that judgments exhibit greater potential than rewards for LLM alignment and warrant future research.", "github_manual": "https://github.com/wwxu21/cut", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14203", "title": "Shai: A large language model for asset management", "authors": [ "Zhongyang Guo", "Guanran Jiang", "Zhongdan Zhang", "Peng Li", "Zhefeng Wang", "Yinchun Wang" ], "github": "", "project_page": "", "abstract": "This paper introduces \"Shai\" a 10B level large language model specifically designed for the asset management industry, built upon an open-source foundational model. With continuous pre-training and fine-tuning using a targeted corpus, Shai demonstrates enhanced performance in tasks relevant to its domain, outperforming baseline models. Our research includes the development of an innovative evaluation framework, which integrates professional qualification exams, tailored tasks, open-ended question answering, and safety assessments, to comprehensively assess Shai's capabilities. Furthermore, we discuss the challenges and implications of utilizing large language models like GPT-4 for performance assessment in asset management, suggesting a combination of automated evaluation and human judgment. Shai's development, showcasing the potential and versatility of 10B-level large language models in the financial sector with significant performance and modest computational requirements, hopes to provide practical insights and methodologies to assist industry peers in their similar endeavors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14206", "title": "LLM4VG: Large Language Models Evaluation for Video Grounding", "authors": [ "Wei Feng", "Xin Wang", "Hong Chen", "Zeyang Zhang", "Zihan Song", "Yuwei Zhou", "Wenwu Zhu" ], "github": "", "project_page": "", "abstract": "Recently, researchers have attempted to investigate the capability of LLMs in handling videos and proposed several video LLM models. However, the ability of LLMs to handle video grounding (VG), which is an important time-related video task requiring the model to precisely locate the start and end timestamps of temporal moments in videos that match the given textual queries, still remains unclear and unexplored in literature. To fill the gap, in this paper, we propose the LLM4VG benchmark, which systematically evaluates the performance of different LLMs on video grounding tasks. Based on our proposed LLM4VG, we design extensive experiments to examine two groups of video LLM models on video grounding: (i) the video LLMs trained on the text-video pairs (denoted as VidLLM), and (ii) the LLMs combined with pretrained visual description models such as the video/image captioning model. We propose prompt methods to integrate the instruction of VG and description from different kinds of generators, including caption-based generators for direct visual description and VQA-based generators for information enhancement. We also provide comprehensive comparisons of various VidLLMs and explore the influence of different choices of visual models, LLMs, prompt designs, etc, as well. Our experimental evaluations lead to two conclusions: (i) the existing VidLLMs are still far away from achieving satisfactory video grounding performance, and more time-related video tasks should be included to further fine-tune these models, and (ii) the combination of LLMs and visual models shows preliminary abilities for video grounding with considerable potential for improvement by resorting to more reliable models and further guidance of prompt instructions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14385", "title": "Generative AI Beyond LLMs: System Implications of Multi-Modal Generation", "authors": [ "Alicia Golden", "Samuel Hsia", "Fei Sun", "Bilge Acun", "Basil Hosmer", "Yejin Lee", "Zachary DeVito", "Jeff Johnson", "Gu-Yeon Wei", "David Brooks", "Carole-Jean Wu" ], "github": "", "project_page": "", "abstract": "As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal information presents unique challenges to quality, performance, and efficiency. We present the first work towards understanding this new system design space for multi-modal text-to-image (TTI) and text-to-video (TTV) generation models. Current model architecture designs are bifurcated into 2 categories: Diffusion- and Transformer-based models. Our systematic performance characterization on a suite of eight representative TTI/TTV models shows that after state-of-the-art optimization techniques such as Flash Attention are applied, Convolution accounts for up to 44% of execution time for Diffusion-based TTI models, while Linear layers consume up to 49% of execution time for Transformer-based models. We additionally observe that Diffusion-based TTI models resemble the Prefill stage of LLM inference, and benefit from 1.1-2.5x greater speedup from Flash Attention than Transformer-based TTI models that resemble the Decode phase. Since optimizations designed for LLMs do not map directly onto TTI/TTV models, we must conduct a thorough characterization of these workloads to gain insights for new optimization opportunities. In doing so, we define sequence length in the context of TTI/TTV models and observe sequence length can vary up to 4x in Diffusion model inference. We additionally observe temporal aspects of TTV workloads pose unique system bottlenecks, with Temporal Attention accounting for over 60% of total Attention time. Overall, our in-depth system performance characterization is a critical first step towards designing efficient and deployable systems for emerging TTI/TTV workloads.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14216", "title": "DreamDistribution: Prompt Distribution Learning for Text-to-Image Diffusion Models", "authors": [ "Brian Nlong Zhao", "Yuhang Xiao", "Jiashu Xu", "Xinyang Jiang", "Yifan Yang", "Dongsheng Li", "Laurent Itti", "Vibhav Vineet", "Yunhao Ge" ], "github": "https://github.com/briannlongzhao/DreamDistribution", "project_page": "", "abstract": "The popularization of Text-to-Image (T2I) diffusion models enables the generation of high-quality images from text descriptions. However, generating diverse customized images with reference visual attributes remains challenging. This work focuses on personalizing T2I diffusion models at a more abstract concept or category level, adapting commonalities from a set of reference images while creating new instances with sufficient variations. We introduce a solution that allows a pretrained T2I diffusion model to learn a set of soft prompts, enabling the generation of novel images by sampling prompts from the learned distribution. These prompts offer text-guided editing capabilities and additional flexibility in controlling variation and mixing between multiple distributions. We also show the adaptability of the learned prompt distribution to other tasks, such as text-to-3D. Finally we demonstrate effectiveness of our approach through quantitative analysis including automatic evaluation and human assessment. Project website: https://briannlongzhao.github.io/DreamDistribution", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/briannlongzhao/DreamDistribution", "github_api_added_by": "auto" }, { "date": "2023-12-26", "arxiv_id": "2312.14239", "title": "PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar", "authors": [ "Tzofi Klinghoffer", "Xiaoyu Xiang", "Siddharth Somasundaram", "Yuchen Fan", "Christian Richardt", "Ramesh Raskar", "Rakesh Ranjan" ], "github": "", "project_page": "", "abstract": "3D reconstruction from a single-view is challenging because of the ambiguity from monocular cues and lack of information about occluded regions. Neural radiance fields (NeRF), while popular for view synthesis and 3D reconstruction, are typically reliant on multi-view images. Existing methods for single-view 3D reconstruction with NeRF rely on either data priors to hallucinate views of occluded regions, which may not be physically accurate, or shadows observed by RGB cameras, which are difficult to detect in ambient light and low albedo backgrounds. We propose using time-of-flight data captured by a single-photon avalanche diode to overcome these limitations. Our method models two-bounce optical paths with NeRF, using lidar transient data for supervision. By leveraging the advantages of both NeRF and two-bounce light measured by lidar, we demonstrate that we can reconstruct visible and occluded geometry without data priors or reliance on controlled ambient lighting or scene albedo. In addition, we demonstrate improved generalization under practical constraints on sensor spatial- and temporal-resolution. We believe our method is a promising direction as single-photon lidars become ubiquitous on consumer devices, such as phones, tablets, and headsets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14232", "title": "Parrot Captions Teach CLIP to Spot Text", "authors": [ "Yiqi Lin", "Conghui He", "Alex Jinpeng Wang", "Bin Wang", "Weijia Li", "Mike Zheng Shou" ], "github": "https://github.com/opendatalab/clip-parrot-bias", "project_page": "", "abstract": "Despite CLIP being the foundation model in numerous vision-language applications, the CLIP suffers from a severe text spotting bias. Such bias causes CLIP models to `Parrot' the visual text embedded within images while disregarding the authentic visual semantics. We uncover that in the most popular image-text dataset LAION-2B, the captions also densely parrot (spell) the text embedded in images. Our analysis shows that around 50\\% of images are embedded with visual text content, and 90\\% of their captions more or less parrot the visual text. Based on such observation, we thoroughly inspect the different release d versions of CLIP models and verify that the visual text is the dominant factor in measuring the LAION-style image-text similarity for these models. To examine whether these parrot captions shape the text spotting bias, we train a series of CLIP models with LAION subsets curated by different parrot-caption-oriented criteria. We show that training with parrot captions easily shapes such bias but harms the expected visual-language representation learning in CLIP models. This suggests that it is urgent to revisit either the design of CLIP-like models or the existing image-text dataset curation pipeline built on CLIP score filtering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/opendatalab/clip-parrot-bias", "github_api_added_by": "auto" }, { "date": "2023-12-26", "arxiv_id": "2312.14198", "title": "ZeroShape: Regression-based Zero-shot Shape Reconstruction", "authors": [ "Zixuan Huang", "Stefan Stojanov", "Anh Thai", "Varun Jampani", "James M. Rehg" ], "github": "https://github.com/zxhuang1698/ZeroShape", "project_page": "", "abstract": "We study the problem of single-image zero-shot 3D shape reconstruction. Recent works learn zero-shot shape reconstruction through generative modeling of 3D assets, but these models are computationally expensive at train and inference time. In contrast, the traditional approach to this problem is regression-based, where deterministic models are trained to directly regress the object shape. Such regression methods possess much higher computational efficiency than generative methods. This raises a natural question: is generative modeling necessary for high performance, or conversely, are regression-based approaches still competitive? To answer this, we design a strong regression-based model, called ZeroShape, based on the converging findings in this field and a novel insight. We also curate a large real-world evaluation benchmark, with objects from three different real-world 3D datasets. This evaluation benchmark is more diverse and an order of magnitude larger than what prior works use to quantitatively evaluate their models, aiming at reducing the evaluation variance in our field. We show that ZeroShape not only achieves superior performance over state-of-the-art methods, but also demonstrates significantly higher computational and data efficiency.", "github_manual": "https://github.com/zxhuang1698/ZeroShape", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-26", "arxiv_id": "2312.14929", "title": "MACS: Mass Conditioned 3D Hand and Object Motion Synthesis", "authors": [ "Soshi Shimada", "Franziska Mueller", "Jan Bednarik", "Bardia Doosti", "Bernd Bickel", "Danhang Tang", "Vladislav Golyanik", "Jonathan Taylor", "Christian Theobalt", "Thabo Beeler" ], "github": "", "project_page": "", "abstract": "The physical properties of an object, such as mass, significantly affect how we manipulate it with our hands. Surprisingly, this aspect has so far been neglected in prior work on 3D motion synthesis. To improve the naturalness of the synthesized 3D hand object motions, this work proposes MACS the first MAss Conditioned 3D hand and object motion Synthesis approach. Our approach is based on cascaded diffusion models and generates interactions that plausibly adjust based on the object mass and interaction type. MACS also accepts a manually drawn 3D object trajectory as input and synthesizes the natural 3D hand motions conditioned by the object mass. This flexibility enables MACS to be used for various downstream applications, such as generating synthetic training data for ML tasks, fast animation of hands for graphics workflows, and generating character interactions for computer games. We show experimentally that a small-scale dataset is sufficient for MACS to reasonably generalize across interpolated and extrapolated object masses unseen during the training. Furthermore, MACS shows moderate generalization to unseen objects, thanks to the mass-conditioned contact labels generated by our surface contact synthesis model ConNet. Our comprehensive user study confirms that the synthesized 3D hand-object interactions are highly plausible and realistic.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-27", "arxiv_id": "2312.15166", "title": "SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling", "authors": [ "Dahyun Kim", "Chanjun Park", "Sanghoon Kim", "Wonsung Lee", "Wonho Song", "Yunsu Kim", "Hyeonwoo Kim", "Yungi Kim", "Hyeonju Lee", "Jihoo Kim", "Changbae Ahn", "Seonghoon Yang", "Sukyung Lee", "Hyunbyung Park", "Gyoungjin Gim", "Mikyoung Cha", "Hwalsuk Lee", "Sunghun Kim" ], "github": "https://github.com/cg123/mergekit", "project_page": "", "abstract": "We introduce depth up-scaling (DUS), a novel technique to up-scale base LLMs efficiently and effectively in a simple manner. In contrast to mixture-of-experts (MoE), DUS does not require complex changes to train and inference. Using DUS, we build SOLAR 10.7B, a large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. Comparative evaluations show that SOLAR 10.7B outperforms existing open-source pretrained LLMs, such as Llama 2 and Mistral 7B. We additionally present SOLAR 10.7B-Instruct, a variant fine-tuned for instruction-following capabilities, surpassing Mixtral-8x7B. SOLAR 10.7B is publicly available under the Apache 2.0 license, promoting broad access and application in the LLM field.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/cg123/mergekit", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-27", "arxiv_id": "2312.16171", "title": "Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4", "authors": [ "Sondos Mahmoud Bsharat", "Aidar Myrzakhan", "Zhiqiang Shen" ], "github": "https://github.com/VILA-Lab/ATLAS", "project_page": "", "abstract": "This paper introduces 26 guiding principles designed to streamline the process of querying and prompting large language models. Our goal is to simplify the underlying concepts of formulating questions for various scales of large language models, examining their abilities, and enhancing user comprehension on the behaviors of different scales of large language models when feeding into different prompts. Extensive experiments are conducted on LLaMA-1/2 (7B, 13B and 70B), GPT-3.5/4 to verify the effectiveness of the proposed principles on instructions and prompts design. We hope that this work provides a better guide for researchers working on the prompting of large language models. Project page is available at https://github.com/VILA-Lab/ATLAS.", "github_manual": "", "github_regex": "https://github.com/VILA-Lab/ATLAS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-27", "arxiv_id": "2312.15918", "title": "Supervised Knowledge Makes Large Language Models Better In-context Learners", "authors": [ "Linyi Yang", "Shuibai Zhang", "Zhuohao Yu", "Guangsheng Bao", "Yidong Wang", "Jindong Wang", "Ruochen Xu", "Wei Ye", "Xing Xie", "Weizhu Chen", "Yue Zhang" ], "github": "https://github.com/yanglinyi/supervised-knowledge-makes-large-language-models-better-in-context-learners", "project_page": "", "abstract": "Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The recent progress in large-scale generative models has further expanded their use in real-world language applications. However, the critical challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. While previous in-context learning research has focused on enhancing models to adhere to users' specific instructions and quality expectations, and to avoid undesired outputs, little to no work has explored the use of task-Specific fine-tuned Language Models (SLMs) to improve LLMs' in-context learning during the inference stage. Our primary contribution is the establishment of a simple yet effective framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks. Using our proposed plug-in method, enhanced versions of Llama 2 and ChatGPT surpass their original versions regarding generalizability and factuality. We offer a comprehensive suite of resources, including 16 curated datasets, prompts, model checkpoints, and LLM outputs across 9 distinct tasks. Our empirical analysis sheds light on the advantages of incorporating discriminative models into LLMs and highlights the potential of our methodology in fostering more reliable LLMs.", "github_manual": "https://github.com/yanglinyi/supervised-knowledge-makes-large-language-models-better-in-context-learners", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-27", "arxiv_id": "2312.15011", "title": "Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases", "authors": [ "Zhangyang Qi", "Ye Fang", "Mengchen Zhang", "Zeyi Sun", "Tong Wu", "Ziwei Liu", "Dahua Lin", "Jiaqi Wang", "Hengshuang Zhao" ], "github": "https://github.com/qi-zhangyang/gemini-vs-gpt4v", "project_page": "", "abstract": "The rapidly evolving sector of Multi-modal Large Language Models (MLLMs) is at the forefront of integrating linguistic and visual processing in artificial intelligence. This paper presents an in-depth comparative study of two pioneering models: Google's Gemini and OpenAI's GPT-4V(ision). Our study involves a multi-faceted evaluation of both models across key dimensions such as Vision-Language Capability, Interaction with Humans, Temporal Understanding, and assessments in both Intelligence and Emotional Quotients. The core of our analysis delves into the distinct visual comprehension abilities of each model. We conducted a series of structured experiments to evaluate their performance in various industrial application scenarios, offering a comprehensive perspective on their practical utility. We not only involve direct performance comparisons but also include adjustments in prompts and scenarios to ensure a balanced and fair analysis. Our findings illuminate the unique strengths and niches of both models. GPT-4V distinguishes itself with its precision and succinctness in responses, while Gemini excels in providing detailed, expansive answers accompanied by relevant imagery and links. These understandings not only shed light on the comparative merits of Gemini and GPT-4V but also underscore the evolving landscape of multimodal foundation models, paving the way for future advancements in this area. After the comparison, we attempted to achieve better results by combining the two models. Finally, We would like to express our profound gratitude to the teams behind GPT-4V and Gemini for their pioneering contributions to the field. Our acknowledgments are also extended to the comprehensive qualitative analysis presented in 'Dawn' by Yang et al. This work, with its extensive collection of image samples, prompts, and GPT-4V-related results, provided a foundational basis for our analysis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qi-zhangyang/gemini-vs-gpt4v", "github_api_added_by": "auto" }, { "date": "2023-12-27", "arxiv_id": "2312.15430", "title": "Make-A-Character: High Quality Text-to-3D Character Generation within Minutes", "authors": [ "Jianqiang Ren", "Chao He", "Lin Liu", "Jiahao Chen", "Yutong Wang", "Yafei Song", "Jianfang Li", "Tangli Xue", "Siqi Hu", "Tao Chen", "Kunkun Zheng", "Jianjing Xiang", "Liefeng Bo" ], "github": "https://github.com/Human3DAIGC/Make-A-Character", "project_page": "", "abstract": "There is a growing demand for customized and expressive 3D characters with the emergence of AI agents and Metaverse, but creating 3D characters using traditional computer graphics tools is a complex and time-consuming task. To address these challenges, we propose a user-friendly framework named Make-A-Character (Mach) to create lifelike 3D avatars from text descriptions. The framework leverages the power of large language and vision models for textual intention understanding and intermediate image generation, followed by a series of human-oriented visual perception and 3D generation modules. Our system offers an intuitive approach for users to craft controllable, realistic, fully-realized 3D characters that meet their expectations within 2 minutes, while also enabling easy integration with existing CG pipeline for dynamic expressiveness. For more information, please visit the project page at https://human3daigc.github.io/MACH/.", "github_manual": "https://github.com/Human3DAIGC/Make-A-Character", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-27", "arxiv_id": "2312.15715", "title": "UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces", "authors": [ "Jiannan Wu", "Yi Jiang", "Bin Yan", "Huchuan Lu", "Zehuan Yuan", "Ping Luo" ], "github": "https://github.com/FoundationVision/UniRef", "project_page": "", "abstract": "The reference-based object segmentation tasks, namely referring image segmentation (RIS), few-shot image segmentation (FSS), referring video object segmentation (RVOS), and video object segmentation (VOS), aim to segment a specific object by utilizing either language or annotated masks as references. Despite significant progress in each respective field, current methods are task-specifically designed and developed in different directions, which hinders the activation of multi-task capabilities for these tasks. In this work, we end the current fragmented situation and propose UniRef++ to unify the four reference-based object segmentation tasks with a single architecture. At the heart of our approach is the proposed UniFusion module which performs multiway-fusion for handling different tasks with respect to their specified references. And a unified Transformer architecture is then adopted for achieving instance-level segmentation. With the unified designs, UniRef++ can be jointly trained on a broad range of benchmarks and can flexibly complete multiple tasks at run-time by specifying the corresponding references. We evaluate our unified models on various benchmarks. Extensive experimental results indicate that our proposed UniRef++ achieves state-of-the-art performance on RIS and RVOS, and performs competitively on FSS and VOS with a parameter-shared network. Moreover, we showcase that the proposed UniFusion module could be easily incorporated into the current advanced foundation model SAM and obtain satisfactory results with parameter-efficient finetuning. Codes and models are available at https://github.com/FoundationVision/UniRef.", "github_manual": "", "github_regex": "https://github.com/FoundationVision/UniRef", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-27", "arxiv_id": "2312.16084", "title": "LangSplat: 3D Language Gaussian Splatting", "authors": [ "Minghan Qin", "Wanhua Li", "Jiawei Zhou", "Haoqian Wang", "Hanspeter Pfister" ], "github": "", "project_page": "", "abstract": "Human lives in a 3D world and commonly uses natural language to interact with a 3D scene. Modeling a 3D language field to support open-ended language queries in 3D has gained increasing attention recently. This paper introduces LangSplat, which constructs a 3D language field that enables precise and efficient open-vocabulary querying within 3D spaces. Unlike existing methods that ground CLIP language embeddings in a NeRF model, LangSplat advances the field by utilizing a collection of 3D Gaussians, each encoding language features distilled from CLIP, to represent the language field. By employing a tile-based splatting technique for rendering language features, we circumvent the costly rendering process inherent in NeRF. Instead of directly learning CLIP embeddings, LangSplat first trains a scene-wise language autoencoder and then learns language features on the scene-specific latent space, thereby alleviating substantial memory demands imposed by explicit modeling. Existing methods struggle with imprecise and vague 3D language fields, which fail to discern clear boundaries between objects. We delve into this issue and propose to learn hierarchical semantics using SAM, thereby eliminating the need for extensively querying the language field across various scales and the regularization of DINO features. Extensive experiments on open-vocabulary 3D object localization and semantic segmentation demonstrate that LangSplat significantly outperforms the previous state-of-the-art method LERF by a large margin. Notably, LangSplat is extremely efficient, achieving a {\\speed} times speedup compared to LERF at the resolution of 1440 times 1080. We strongly recommend readers to check out our video results at https://langsplat.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-27", "arxiv_id": "2312.15821", "title": "Audiobox: Unified Audio Generation with Natural Language Prompts", "authors": [ "Apoorv Vyas", "Bowen Shi", "Matthew Le", "Andros Tjandra", "Yi-Chiao Wu", "Baishan Guo", "Jiemin Zhang", "Xinyue Zhang", "Robert Adkins", "William Ngan", "Jeff Wang", "Ivan Cruz", "Bapi Akula", "Akinniyi Akinyemi", "Brian Ellis", "Rashel Moritz", "Yael Yungster", "Alice Rakotoarison", "Liang Tan", "Chris Summers", "Carleigh Wood", "Joshua Lane", "Mary Williamson", "Wei-Ning Hsu" ], "github": "", "project_page": "", "abstract": "Audio is an essential part of our life, but creating it often requires expertise and is time-consuming. Research communities have made great progress over the past year advancing the performance of large scale audio generative models for a single modality (speech, sound, or music) through adopting more powerful generative models and scaling data. However, these models lack controllability in several aspects: speech generation models cannot synthesize novel styles based on text description and are limited on domain coverage such as outdoor environments; sound generation models only provide coarse-grained control based on descriptions like \"a person speaking\" and would only generate mumbling human voices. This paper presents Audiobox, a unified model based on flow-matching that is capable of generating various audio modalities. We design description-based and example-based prompting to enhance controllability and unify speech and sound generation paradigms. We allow transcript, vocal, and other audio styles to be controlled independently when generating speech. To improve model generalization with limited labels, we adapt a self-supervised infilling objective to pre-train on large quantities of unlabeled audio. Audiobox sets new benchmarks on speech and sound generation (0.745 similarity on Librispeech for zero-shot TTS; 0.77 FAD on AudioCaps for text-to-sound) and unlocks new methods for generating audio with novel vocal and acoustic styles. We further integrate Bespoke Solvers, which speeds up generation by over 25 times compared to the default ODE solver for flow-matching, without loss of performance on several tasks. Our demo is available at https://audiobox.metademolab.com/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-27", "arxiv_id": "2312.16145", "title": "One-dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications", "authors": [ "Mengyao Lyu", "Yuhong Yang", "Haiwen Hong", "Hui Chen", "Xuan Jin", "Yuan He", "Hui Xue", "Jungong Han", "Guiguang Ding" ], "github": "https://github.com/Con6924/SPM", "project_page": "", "abstract": "The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors. Existing concept erasing methods in academia are all based on full parameter or specification-based fine-tuning, from which we observe the following issues: 1) Generation alternation towards erosion: Parameter drift during target elimination causes alternations and potential deformations across all generations, even eroding other concepts at varying degrees, which is more evident with multi-concept erased; 2) Transfer inability & deployment inefficiency: Previous model-specific erasure impedes the flexible combination of concepts and the training-free transfer towards other models, resulting in linear cost growth as the deployment scenarios increase. To achieve non-invasive, precise, customizable, and transferable elimination, we ground our erasing framework on one-dimensional adapters to erase multiple concepts from most DMs at once across versatile erasing applications. The concept-SemiPermeable structure is injected as a Membrane (SPM) into any DM to learn targeted erasing, and meantime the alteration and erosion phenomenon is effectively mitigated via a novel Latent Anchoring fine-tuning strategy. Once obtained, SPMs can be flexibly combined and plug-and-play for other DMs without specific re-tuning, enabling timely and efficient adaptation to diverse scenarios. During generation, our Facilitated Transport mechanism dynamically regulates the permeability of each SPM to respond to different input prompts, further minimizing the impact on other concepts. Quantitative and qualitative results across ~40 concepts, 7 DMs and 4 erasing applications have demonstrated the superior erasing of SPM. Our code and pre-tuned SPMs will be available on the project page https://lyumengyao.github.io/projects/spm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Con6924/SPM", "github_api_added_by": "auto" }, { "date": "2023-12-27", "arxiv_id": "2312.15770", "title": "A Recipe for Scaling up Text-to-Video Generation with Text-free Videos", "authors": [ "Xiang Wang", "Shiwei Zhang", "Hangjie Yuan", "Zhiwu Qing", "Biao Gong", "Yingya Zhang", "Yujun Shen", "Changxin Gao", "Nong Sang" ], "github": "", "project_page": "", "abstract": "Diffusion-based text-to-video generation has witnessed impressive progress in the past year yet still falls behind text-to-image generation. One of the key reasons is the limited scale of publicly available data (e.g., 10M video-text pairs in WebVid10M vs. 5B image-text pairs in LAION), considering the high cost of video captioning. Instead, it could be far easier to collect unlabeled clips from video platforms like YouTube. Motivated by this, we come up with a novel text-to-video generation framework, termed TF-T2V, which can directly learn with text-free videos. The rationale behind is to separate the process of text decoding from that of temporal modeling. To this end, we employ a content branch and a motion branch, which are jointly optimized with weights shared. Following such a pipeline, we study the effect of doubling the scale of training set (i.e., video-only WebVid10M) with some randomly collected text-free videos and are encouraged to observe the performance improvement (FID from 9.67 to 8.19 and FVD from 484 to 441), demonstrating the scalability of our approach. We also find that our model could enjoy sustainable performance gain (FID from 8.19 to 7.64 and FVD from 441 to 366) after reintroducing some text labels for training. Finally, we validate the effectiveness and generalizability of our ideology on both native text-to-video generation and compositional video synthesis paradigms. Code and models will be publicly available at https://tf-t2v.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-27", "arxiv_id": "2312.15258", "title": "Human101: Training 100+FPS Human Gaussians in 100s from 1 View", "authors": [ "Mingwei Li", "Jiachen Tao", "Zongxin Yang", "Yi Yang" ], "github": "https://github.com/longxiang-ai/Human101", "project_page": "", "abstract": "Reconstructing the human body from single-view videos plays a pivotal role in the virtual reality domain. One prevalent application scenario necessitates the rapid reconstruction of high-fidelity 3D digital humans while simultaneously ensuring real-time rendering and interaction. Existing methods often struggle to fulfill both requirements. In this paper, we introduce Human101, a novel framework adept at producing high-fidelity dynamic 3D human reconstructions from 1-view videos by training 3D Gaussians in 100 seconds and rendering in 100+ FPS. Our method leverages the strengths of 3D Gaussian Splatting, which provides an explicit and efficient representation of 3D humans. Standing apart from prior NeRF-based pipelines, Human101 ingeniously applies a Human-centric Forward Gaussian Animation method to deform the parameters of 3D Gaussians, thereby enhancing rendering speed (i.e., rendering 1024-resolution images at an impressive 60+ FPS and rendering 512-resolution images at 100+ FPS). Experimental results indicate that our approach substantially eclipses current methods, clocking up to a 10 times surge in frames per second and delivering comparable or superior rendering quality. Code and demos will be released at https://github.com/longxiang-ai/Human101.", "github_manual": "", "github_regex": "https://github.com/longxiang-ai/Human101", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-27", "arxiv_id": "2312.15980", "title": "HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D", "authors": [ "Sangmin Woo", "Byeongjun Park", "Hyojun Go", "Jin-Young Kim", "Changick Kim" ], "github": "https://github.com/byeongjun-park/HarmonyView", "project_page": "", "abstract": "Recent progress in single-image 3D generation highlights the importance of multi-view coherency, leveraging 3D priors from large-scale diffusion models pretrained on Internet-scale images. However, the aspect of novel-view diversity remains underexplored within the research landscape due to the ambiguity in converting a 2D image into 3D content, where numerous potential shapes can emerge. Here, we aim to address this research gap by simultaneously addressing both consistency and diversity. Yet, striking a balance between these two aspects poses a considerable challenge due to their inherent trade-offs. This work introduces HarmonyView, a simple yet effective diffusion sampling technique adept at decomposing two intricate aspects in single-image 3D generation: consistency and diversity. This approach paves the way for a more nuanced exploration of the two critical dimensions within the sampling process. Moreover, we propose a new evaluation metric based on CLIP image and text encoders to comprehensively assess the diversity of the generated views, which closely aligns with human evaluators' judgments. In experiments, HarmonyView achieves a harmonious balance, demonstrating a win-win scenario in both consistency and diversity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/byeongjun-park/HarmonyView", "github_api_added_by": "auto" }, { "date": "2023-12-29", "arxiv_id": "2312.16862", "title": "TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones", "authors": [ "Zhengqing Yuan", "Zhaoxu Li", "Lichao Sun" ], "github": "https://github.com/DLYuanGod/TinyGPT-V", "project_page": "", "abstract": "In the era of advanced multimodel learning, multimodal large language models (MLLMs) such as GPT-4V have made remarkable strides towards bridging language and visual elements. However, the closed-source nature and considerable computational demand present notable challenges for universal usage and modifications. This is where open-source MLLMs like LLaVA and MiniGPT-4 come in, presenting groundbreaking achievements across tasks. Despite these accomplishments, computational efficiency remains an unresolved issue, as these models, like LLaVA-v1.5-13B, require substantial resources. Addressing these issues, we introduce TinyGPT-V, a new-wave model marrying impressive performance with commonplace computational capacity. It stands out by requiring merely a 24G GPU for training and an 8G GPU or CPU for inference. Built upon Phi-2, TinyGPT-V couples an effective language backbone with pre-trained vision modules from BLIP-2 or CLIP. TinyGPT-V's 2.8B parameters can undergo a unique quantisation process, suitable for local deployment and inference tasks on 8G various devices. Our work fosters further developments for designing cost-effective, efficient, and high-performing MLLMs, expanding their applicability in a broad array of real-world scenarios. Furthermore this paper proposed a new paradigm of Multimodal Large Language Model via small backbones. Our code and training weights are placed at: https://github.com/DLYuanGod/TinyGPT-V and https://huggingface.co/Tyrannosaurus/TinyGPT-V respectively.", "github_manual": "", "github_regex": "https://github.com/DLYuanGod/TinyGPT-V", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.16486", "title": "PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion", "authors": [ "Guansong Lu", "Yuanfan Guo", "Jianhua Han", "Minzhe Niu", "Yihan Zeng", "Songcen Xu", "Zeyi Huang", "Zhao Zhong", "Wei Zhang", "Hang Xu" ], "github": "https://github.com/mindspore-lab/mindone/tree/master/examples/pangu_draw_v3", "project_page": "", "abstract": "Current large-scale diffusion models represent a giant leap forward in conditional image synthesis, capable of interpreting diverse cues like text, human poses, and edges. However, their reliance on substantial computational resources and extensive data collection remains a bottleneck. On the other hand, the integration of existing diffusion models, each specialized for different controls and operating in unique latent spaces, poses a challenge due to incompatible image resolutions and latent space embedding structures, hindering their joint use. Addressing these constraints, we present \"PanGu-Draw\", a novel latent diffusion model designed for resource-efficient text-to-image synthesis that adeptly accommodates multiple control signals. We first propose a resource-efficient Time-Decoupling Training Strategy, which splits the monolithic text-to-image model into structure and texture generators. Each generator is trained using a regimen that maximizes data utilization and computational efficiency, cutting data preparation by 48% and reducing training resources by 51%. Secondly, we introduce \"Coop-Diffusion\", an algorithm that enables the cooperative use of various pre-trained diffusion models with different latent spaces and predefined resolutions within a unified denoising process. This allows for multi-control image synthesis at arbitrary resolutions without the necessity for additional data or retraining. Empirical validations of Pangu-Draw show its exceptional prowess in text-to-image and multi-control image generation, suggesting a promising direction for future model training efficiencies and generation versatility. The largest 5B T2I PanGu-Draw model is released on the Ascend platform. Project page: https://pangu-draw.github.io", "github_manual": "https://github.com/mindspore-lab/mindone/tree/master/examples/pangu_draw_v3", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.17120", "title": "Generative AI for Math: Part I -- MathPile: A Billion-Token-Scale Pretraining Corpus for Math", "authors": [ "Zengzhi Wang", "Rui Xia", "Pengfei Liu" ], "github": "https://github.com/gair-nlp/mathpile", "project_page": "", "abstract": "High-quality, large-scale corpora are the cornerstone of building foundation models. In this work, we introduce MathPile, a diverse and high-quality math-centric corpus comprising about 9.5 billion tokens. Throughout its creation, we adhered to the principle of ``less is more'', firmly believing in the supremacy of data quality over quantity, even in the pre-training phase. Our meticulous data collection and processing efforts included a complex suite of preprocessing, prefiltering, language identification, cleaning, filtering, and deduplication, ensuring the high quality of our corpus. Furthermore, we performed data contamination detection on downstream benchmark test sets to eliminate duplicates. We hope our MathPile can help to enhance the mathematical reasoning abilities of language models. We plan to open-source different versions of \\mathpile with the scripts used for processing, to facilitate future developments in this field.", "github_manual": "https://github.com/gair-nlp/mathpile", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.16886", "title": "MobileVLM : A Fast, Reproducible and Strong Vision Language Assistant for Mobile Devices", "authors": [ "Xiangxiang Chu", "Limeng Qiao", "Xinyang Lin", "Shuang Xu", "Yang Yang", "Yiming Hu", "Fei Wei", "Xinyu Zhang", "Bo Zhang", "Xiaolin Wei", "Chunhua Shen" ], "github": "https://github.com/Meituan-AutoML/MobileVLM", "project_page": "", "abstract": "We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices. It is an amalgamation of a myriad of architectural designs and techniques that are mobile-oriented, which comprises a set of language models at the scale of 1.4B and 2.7B parameters, trained from scratch, a multimodal vision model that is pre-trained in the CLIP fashion, cross-modality interaction via an efficient projector. We evaluate MobileVLM on several typical VLM benchmarks. Our models demonstrate on par performance compared with a few much larger models. More importantly, we measure the inference speed on both a Qualcomm Snapdragon 888 CPU and an NVIDIA Jeston Orin GPU, and we obtain state-of-the-art performance of 21.5 tokens and 65.3 tokens per second, respectively. Our code will be made available at: https://github.com/Meituan-AutoML/MobileVLM.", "github_manual": "", "github_regex": "https://github.com/Meituan-AutoML/MobileVLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.16693", "title": "I2V-Adapter: A General Image-to-Video Adapter for Video Diffusion Models", "authors": [ "Xun Guo", "Mingwu Zheng", "Liang Hou", "Yuan Gao", "Yufan Deng", "Chongyang Ma", "Weiming Hu", "Zhengjun Zha", "Haibin Huang", "Pengfei Wan", "Di Zhang" ], "github": "https://github.com/KwaiVGI/I2V-Adapter", "project_page": "", "abstract": "In the rapidly evolving domain of digital content generation, the focus has shifted from text-to-image (T2I) models to more advanced video diffusion models, notably text-to-video (T2V) and image-to-video (I2V). This paper addresses the intricate challenge posed by I2V: converting static images into dynamic, lifelike video sequences while preserving the original image fidelity. Traditional methods typically involve integrating entire images into diffusion processes or using pretrained encoders for cross attention. However, these approaches often necessitate altering the fundamental weights of T2I models, thereby restricting their reusability. We introduce a novel solution, namely I2V-Adapter, designed to overcome such limitations. Our approach preserves the structural integrity of T2I models and their inherent motion modules. The I2V-Adapter operates by processing noised video frames in parallel with the input image, utilizing a lightweight adapter module. This module acts as a bridge, efficiently linking the input to the model's self-attention mechanism, thus maintaining spatial details without requiring structural changes to the T2I model. Moreover, I2V-Adapter requires only a fraction of the parameters of conventional models and ensures compatibility with existing community-driven T2I models and controlling tools. Our experimental results demonstrate I2V-Adapter's capability to produce high-quality video outputs. This performance, coupled with its versatility and reduced need for trainable parameters, represents a substantial advancement in the field of AI-driven video generation, particularly for creative applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KwaiVGI/I2V-Adapter", "github_api_added_by": "auto" }, { "date": "2023-12-29", "arxiv_id": "2312.17244", "title": "The LLM Surgeon", "authors": [ "Tycho F. A. van der Ouderaa", "Markus Nagel", "Mart van Baalen", "Yuki M. Asano", "Tijmen Blankevoort" ], "github": "https://github.com/qualcomm-ai-research/llm-surgeon", "project_page": "", "abstract": "State-of-the-art language models are becoming increasingly large in an effort to achieve the highest performance on large corpora of available textual data. However, the sheer size of the Transformer architectures makes it difficult to deploy models within computational, environmental or device-specific constraints. We explore data-driven compression of existing pretrained models as an alternative to training smaller models from scratch. To do so, we scale Kronecker-factored curvature approximations of the target loss landscape to large language models. In doing so, we can compute both the dynamic allocation of structures that can be removed as well as updates of remaining weights that account for the removal. We provide a general framework for unstructured, semi-structured and structured pruning and improve upon weight updates to capture more correlations between weights, while remaining computationally efficient. Experimentally, our method can prune rows and columns from a range of OPT models and Llamav2-7B by 20%-30%, with a negligible loss in performance, and achieve state-of-the-art results in unstructured and semi-structured pruning of large language models.", "github_manual": "https://github.com/qualcomm-ai-research/llm-surgeon", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.16837", "title": "DiffusionGAN3D: Boosting Text-guided 3D Generation and Domain Adaption by Combining 3D GANs and Diffusion Priors", "authors": [ "Biwen Lei", "Kai Yu", "Mengyang Feng", "Miaomiao Cui", "Xuansong Xie" ], "github": "https://github.com/youngLBW/DiffusionGAN3D", "project_page": "", "abstract": "Text-guided domain adaption and generation of 3D-aware portraits find many applications in various fields. However, due to the lack of training data and the challenges in handling the high variety of geometry and appearance, the existing methods for these tasks suffer from issues like inflexibility, instability, and low fidelity. In this paper, we propose a novel framework DiffusionGAN3D, which boosts text-guided 3D domain adaption and generation by combining 3D GANs and diffusion priors. Specifically, we integrate the pre-trained 3D generative models (e.g., EG3D) and text-to-image diffusion models. The former provides a strong foundation for stable and high-quality avatar generation from text. And the diffusion models in turn offer powerful priors and guide the 3D generator finetuning with informative direction to achieve flexible and efficient text-guided domain adaption. To enhance the diversity in domain adaption and the generation capability in text-to-avatar, we introduce the relative distance loss and case-specific learnable triplane respectively. Besides, we design a progressive texture refinement module to improve the texture quality for both tasks above. Extensive experiments demonstrate that the proposed framework achieves excellent results in both domain adaption and text-to-avatar tasks, outperforming existing methods in terms of generation quality and efficiency. The project homepage is at https://younglbw.github.io/DiffusionGAN3D-homepage/.", "github_manual": "https://github.com/youngLBW/DiffusionGAN3D", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.17172", "title": "Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action", "authors": [ "Jiasen Lu", "Christopher Clark", "Sangho Lee", "Zichen Zhang", "Savya Khosla", "Ryan Marten", "Derek Hoiem", "Aniruddha Kembhavi" ], "github": "https://github.com/allenai/unified-io-2", "project_page": "", "abstract": "We present Unified-IO 2, the first autoregressive multimodal model that is capable of understanding and generating image, text, audio, and action. To unify different modalities, we tokenize inputs and outputs -- images, text, audio, action, bounding boxes, etc., into a shared semantic space and then process them with a single encoder-decoder transformer model. Since training with such diverse modalities is challenging, we propose various architectural improvements to stabilize model training. We train our model from scratch on a large multimodal pre-training corpus from diverse sources with a multimodal mixture of denoisers objective. To learn an expansive set of skills, such as following multimodal instructions, we construct and finetune on an ensemble of 120 datasets with prompts and augmentations. With a single unified model, Unified-IO 2 achieves state-of-the-art performance on the GRIT benchmark and strong results in more than 35 benchmarks, including image generation and understanding, natural language understanding, video and audio understanding, and robotic manipulation. We release all our models to the research community.", "github_manual": "https://github.com/allenai/unified-io-2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.16720", "title": "Prompt Expansion for Adaptive Text-to-Image Generation", "authors": [ "Siddhartha Datta", "Alexander Ku", "Deepak Ramachandran", "Peter Anderson" ], "github": "", "project_page": "", "abstract": "Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the images can be repetitive. This paper proposes a Prompt Expansion framework that helps users generate high-quality, diverse images with less effort. The Prompt Expansion model takes a text query as input and outputs a set of expanded text prompts that are optimized such that when passed to a text-to-image model, generates a wider variety of appealing images. We conduct a human evaluation study that shows that images generated through Prompt Expansion are more aesthetically pleasing and diverse than those generated by baseline methods. Overall, this paper presents a novel and effective approach to improving the text-to-image generation experience.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.16218", "title": "Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks", "authors": [ "Christian Simon", "Sen He", "Juan-Manuel Perez-Rua", "Frost Xu", "Amine Benhalloum", "Tao Xiang" ], "github": "", "project_page": "", "abstract": "Solving image-to-3D from a single view is an ill-posed problem, and current neural reconstruction methods addressing it through diffusion models still rely on scene-specific optimization, constraining their generalization capability. To overcome the limitations of existing approaches regarding generalization and consistency, we introduce a novel neural rendering technique. Our approach employs the signed distance function as the surface representation and incorporates generalizable priors through geometry-encoding volumes and HyperNetworks. Specifically, our method builds neural encoding volumes from generated multi-view inputs. We adjust the weights of the SDF network conditioned on an input image at test-time to allow model adaptation to novel scenes in a feed-forward manner via HyperNetworks. To mitigate artifacts derived from the synthesized views, we propose the use of a volume transformer module to improve the aggregation of image features instead of processing each viewpoint separately. Through our proposed method, dubbed as Hyper-VolTran, we avoid the bottleneck of scene-specific optimization and maintain consistency across the images generated from multiple viewpoints. Our experiments show the advantages of our proposed approach with consistent results and rapid generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.17243", "title": "Unsupervised Universal Image Segmentation", "authors": [ "Dantong Niu", "Xudong Wang", "Xinyang Han", "Long Lian", "Roei Herzig", "Trevor Darrell" ], "github": "", "project_page": "", "abstract": "Several unsupervised image segmentation approaches have been proposed which eliminate the need for dense manually-annotated segmentation masks; current models separately handle either semantic segmentation (e.g., STEGO) or class-agnostic instance segmentation (e.g., CutLER), but not both (i.e., panoptic segmentation). We propose an Unsupervised Universal Segmentation model (U2Seg) adept at performing various image segmentation tasks -- instance, semantic and panoptic -- using a novel unified framework. U2Seg generates pseudo semantic labels for these segmentation tasks via leveraging self-supervised models followed by clustering; each cluster represents different semantic and/or instance membership of pixels. We then self-train the model on these pseudo semantic labels, yielding substantial performance gains over specialized methods tailored to each task: a +2.6 AP^{box} boost vs. CutLER in unsupervised instance segmentation on COCO and a +7.0 PixelAcc increase (vs. STEGO) in unsupervised semantic segmentation on COCOStuff. Moreover, our method sets up a new baseline for unsupervised panoptic segmentation, which has not been previously explored. U2Seg is also a strong pretrained model for few-shot segmentation, surpassing CutLER by +5.0 AP^{mask} when trained on a low-data regime, e.g., only 1% COCO labels. We hope our simple yet effective method can inspire more research on unsupervised universal image segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.17142", "title": "DreamGaussian4D: Generative 4D Gaussian Splatting", "authors": [ "Jiawei Ren", "Liang Pan", "Jiaxiang Tang", "Chi Zhang", "Ang Cao", "Gang Zeng", "Ziwei Liu" ], "github": "https://github.com/jiawei-ren/dreamgaussian4d", "project_page": "", "abstract": "Remarkable progress has been made in 4D content generation recently. However, existing methods suffer from long optimization time, lack of motion controllability, and a low level of detail. In this paper, we introduce DreamGaussian4D, an efficient 4D generation framework that builds on 4D Gaussian Splatting representation. Our key insight is that the explicit modeling of spatial transformations in Gaussian Splatting makes it more suitable for the 4D generation setting compared with implicit representations. DreamGaussian4D reduces the optimization time from several hours to just a few minutes, allows flexible control of the generated 3D motion, and produces animated meshes that can be efficiently rendered in 3D engines.", "github_manual": "https://github.com/jiawei-ren/dreamgaussian4d", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.16256", "title": "DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision", "authors": [ "Lu Ling", "Yichen Sheng", "Zhi Tu", "Wentian Zhao", "Cheng Xin", "Kun Wan", "Lantao Yu", "Qianyu Guo", "Zixun Yu", "Yawen Lu", "Xuanmao Li", "Xingpeng Sun", "Rohan Ashok", "Aniruddha Mukherjee", "Hao Kang", "Xiangrui Kong", "Gang Hua", "Tianti Zhang", "Bedrich Benes", "Aniket Bera" ], "github": "https://github.com/DL3DV-10K/Dataset", "project_page": "", "abstract": "We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to either synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset, benchmark results, and models will be publicly accessible at https://dl3dv-10k.github.io/DL3DV-10K/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DL3DV-10K/Dataset", "github_api_added_by": "auto" }, { "date": "2023-12-29", "arxiv_id": "2312.16272", "title": "SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generation", "authors": [ "Yuxuan Zhang", "Jiaming Liu", "Yiren Song", "Rui Wang", "Hao Tang", "Jinpeng Yu", "Huaxia Li", "Xu Tang", "Yao Hu", "Han Pan", "Zhongliang Jing" ], "github": "", "project_page": "", "abstract": "Recent advancements in subject-driven image generation have led to zero-shot generation, yet precise selection and focus on crucial subject representations remain challenging. Addressing this, we introduce the SSR-Encoder, a novel architecture designed for selectively capturing any subject from single or multiple reference images. It responds to various query modalities including text and masks, without necessitating test-time fine-tuning. The SSR-Encoder combines a Token-to-Patch Aligner that aligns query inputs with image patches and a Detail-Preserving Subject Encoder for extracting and preserving fine features of the subjects, thereby generating subject embeddings. These embeddings, used in conjunction with original text embeddings, condition the generation process. Characterized by its model generalizability and efficiency, the SSR-Encoder adapts to a range of custom models and control modules. Enhanced by the Embedding Consistency Regularization Loss for improved training, our extensive experiments demonstrate its effectiveness in versatile and high-quality image generation, indicating its broad applicability. Project page: https://ssr-encoder.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.17241", "title": "Compact Neural Graphics Primitives with Learned Hash Probing", "authors": [ "Towaki Takikawa", "Thomas M\u00fcller", "Merlin Nimier-David", "Alex Evans", "Sanja Fidler", "Alec Jacobson", "Alexander Keller" ], "github": "", "project_page": "", "abstract": "Neural graphics primitives are faster and achieve higher quality when their neural networks are augmented by spatial data structures that hold trainable features arranged in a grid. However, existing feature grids either come with a large memory footprint (dense or factorized grids, trees, and hash tables) or slow performance (index learning and vector quantization). In this paper, we show that a hash table with learned probes has neither disadvantage, resulting in a favorable combination of size and speed. Inference is faster than unprobed hash tables at equal quality while training is only 1.2-2.6x slower, significantly outperforming prior index learning approaches. We arrive at this formulation by casting all feature grids into a common framework: they each correspond to a lookup function that indexes into a table of feature vectors. In this framework, the lookup functions of existing data structures can be combined by simple arithmetic combinations of their indices, resulting in Pareto optimal compression and speed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.17161", "title": "Restoration by Generation with Constrained Priors", "authors": [ "Zheng Ding", "Xuaner Zhang", "Zhuowen Tu", "Zhihao Xia" ], "github": "", "project_page": "", "abstract": "The inherent generative power of denoising diffusion models makes them well-suited for image restoration tasks where the objective is to find the optimal high-quality image within the generative space that closely resembles the input image. We propose a method to adapt a pretrained diffusion model for image restoration by simply adding noise to the input image to be restored and then denoise. Our method is based on the observation that the space of a generative model needs to be constrained. We impose this constraint by finetuning the generative model with a set of anchor images that capture the characteristics of the input image. With the constrained space, we can then leverage the sampling strategy used for generation to do image restoration. We evaluate against previous methods and show superior performances on multiple real-world restoration datasets in preserving identity and image quality. We also demonstrate an important and practical application on personalized restoration, where we use a personal album as the anchor images to constrain the generative space. This approach allows us to produce results that accurately preserve high-frequency details, which previous works are unable to do. Project webpage: https://gen2res.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.17135", "title": "InsActor: Instruction-driven Physics-based Characters", "authors": [ "Jiawei Ren", "Mingyuan Zhang", "Cunjun Yu", "Xiao Ma", "Liang Pan", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Generating animation of physics-based characters with intuitive control has long been a desirable task with numerous applications. However, generating physically simulated animations that reflect high-level human instructions remains a difficult problem due to the complexity of physical environments and the richness of human language. In this paper, we present InsActor, a principled generative framework that leverages recent advancements in diffusion-based human motion models to produce instruction-driven animations of physics-based characters. Our framework empowers InsActor to capture complex relationships between high-level human instructions and character motions by employing diffusion policies for flexibly conditioned motion planning. To overcome invalid states and infeasible state transitions in planned motions, InsActor discovers low-level skills and maps plans to latent skill sequences in a compact latent space. Extensive experiments demonstrate that InsActor achieves state-of-the-art results on various tasks, including instruction-driven motion generation and instruction-driven waypoint heading. Notably, the ability of InsActor to generate physically simulated animations using high-level human instructions makes it a valuable tool, particularly in executing long-horizon tasks with a rich set of instructions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.16812", "title": "Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis", "authors": [ "Zhan Li", "Zhang Chen", "Zhong Li", "Yi Xu" ], "github": "https://github.com/oppo-us-research/spacetimegaussians", "project_page": "", "abstract": "Novel view synthesis of dynamic scenes has been an intriguing yet challenging problem. Despite recent advancements, simultaneously achieving high-resolution photorealistic results, real-time rendering, and compact storage remains a formidable task. To address these challenges, we propose Spacetime Gaussian Feature Splatting as a novel dynamic scene representation, composed of three pivotal components. First, we formulate expressive Spacetime Gaussians by enhancing 3D Gaussians with temporal opacity and parametric motion/rotation. This enables Spacetime Gaussians to capture static, dynamic, as well as transient content within a scene. Second, we introduce splatted feature rendering, which replaces spherical harmonics with neural features. These features facilitate the modeling of view- and time-dependent appearance while maintaining small size. Third, we leverage the guidance of training error and coarse depth to sample new Gaussians in areas that are challenging to converge with existing pipelines. Experiments on several established real-world datasets demonstrate that our method achieves state-of-the-art rendering quality and speed, while retaining compact storage. At 8K resolution, our lite-version model can render at 60 FPS on an Nvidia RTX 4090 GPU.", "github_manual": "https://github.com/oppo-us-research/spacetimegaussians", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-29", "arxiv_id": "2312.16457", "title": "City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web", "authors": [ "Kaiwen Song", "Juyong Zhang" ], "github": "https://github.com/USTC3DV/MERFStudio", "project_page": "", "abstract": "NeRF has significantly advanced 3D scene reconstruction, capturing intricate details across various environments. Existing methods have successfully leveraged radiance field baking to facilitate real-time rendering of small scenes. However, when applied to large-scale scenes, these techniques encounter significant challenges, struggling to provide a seamless real-time experience due to limited resources in computation, memory, and bandwidth. In this paper, we propose City-on-Web, which represents the whole scene by partitioning it into manageable blocks, each with its own Level-of-Detail, ensuring high fidelity, efficient memory management and fast rendering. Meanwhile, we carefully design the training and inference process such that the final rendering result on web is consistent with training. Thanks to our novel representation and carefully designed training/inference process, we are the first to achieve real-time rendering of large-scale scenes in resource-constrained environments. Extensive experimental results demonstrate that our method facilitates real-time rendering of large-scale scenes on a web platform, achieving 32FPS at 1080P resolution with an RTX 3060 GPU, while simultaneously achieving a quality that closely rivals that of state-of-the-art methods. Project page: https://ustc3dv.github.io/City-on-Web/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/USTC3DV/MERFStudio", "github_api_added_by": "auto" }, { "date": "2024-01-01", "arxiv_id": "2312.17276", "title": "PanGu-\u03c0: Enhancing Language Model Architectures via Nonlinearity Compensation", "authors": [ "Yunhe Wang", "Hanting Chen", "Yehui Tang", "Tianyu Guo", "Kai Han", "Ying Nie", "Xutao Wang", "Hailin Hu", "Zheyuan Bai", "Yun Wang", "Fangcheng Liu", "Zhicheng Liu", "Jianyuan Guo", "Sinan Zeng", "Yinchen Zhang", "Qinghua Xu", "Qun Liu", "Jun Yao", "Chao Xu", "Dacheng Tao" ], "github": "https://github.com/pengxiao-song/LaWGPT", "project_page": "", "abstract": "The recent trend of large language models (LLMs) is to increase the scale of both model size (\\aka the number of parameters) and dataset to achieve better generative ability, which is definitely proved by a lot of work such as the famous GPT and Llama. However, large models often involve massive computational costs, and practical applications cannot afford such high prices. However, the method of constructing a strong model architecture for LLMs is rarely discussed. We first analyze the state-of-the-art language model architectures and observe the feature collapse problem. Based on the theoretical analysis, we propose that the nonlinearity is also very important for language models, which is usually studied in convolutional neural networks for vision tasks. The series informed activation function is then introduced with tiny calculations that can be ignored, and an augmented shortcut is further used to enhance the model nonlinearity. We then demonstrate that the proposed approach is significantly effective for enhancing the model nonlinearity through carefully designed ablations; thus, we present a new efficient model architecture for establishing modern, namely, PanGu-pi. Experiments are then conducted using the same dataset and training strategy to compare PanGu-pi with state-of-the-art LLMs. The results show that PanGu-pi-7B can achieve a comparable performance to that of benchmarks with about 10\\% inference speed-up, and PanGu-pi-1B can achieve state-of-the-art performance in terms of accuracy and efficiency. In addition, we have deployed PanGu-pi-7B in the high-value domains of finance and law, developing an LLM named YunShan for practical application. The results show that YunShan can surpass other models with similar scales on benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pengxiao-song/LaWGPT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-01", "arxiv_id": "2312.17661", "title": "Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models", "authors": [ "Yuqing Wang", "Yun Zhao" ], "github": "https://github.com/eternityyw/gemini-commonsense-evaluation", "project_page": "", "abstract": "The burgeoning interest in Multimodal Large Language Models (MLLMs), such as OpenAI's GPT-4V(ision), has significantly impacted both academic and industrial realms. These models enhance Large Language Models (LLMs) with advanced visual understanding capabilities, facilitating their application in a variety of multimodal tasks. Recently, Google introduced Gemini, a cutting-edge MLLM designed specifically for multimodal integration. Despite its advancements, preliminary benchmarks indicate that Gemini lags behind GPT models in commonsense reasoning tasks. However, this assessment, based on a limited dataset (i.e., HellaSWAG), does not fully capture Gemini's authentic commonsense reasoning potential. To address this gap, our study undertakes a thorough evaluation of Gemini's performance in complex reasoning tasks that necessitate the integration of commonsense knowledge across modalities. We carry out a comprehensive analysis of 12 commonsense reasoning datasets, ranging from general to domain-specific tasks. This includes 11 datasets focused solely on language, as well as one that incorporates multimodal elements. Our experiments across four LLMs and two MLLMs demonstrate Gemini's competitive commonsense reasoning capabilities. Additionally, we identify common challenges faced by current LLMs and MLLMs in addressing commonsense problems, underscoring the need for further advancements in enhancing the commonsense reasoning abilities of these models.", "github_manual": "https://github.com/eternityyw/gemini-commonsense-evaluation", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-01", "arxiv_id": "2312.17742", "title": "Learning Vision from Models Rivals Learning Vision from Data", "authors": [ "Yonglong Tian", "Lijie Fan", "Kaifeng Chen", "Dina Katabi", "Dilip Krishnan", "Phillip Isola" ], "github": "", "project_page": "", "abstract": "We introduce SynCLR, a novel approach for learning visual representations exclusively from synthetic images and synthetic captions, without any real data. We synthesize a large dataset of image captions using LLMs, then use an off-the-shelf text-to-image model to generate multiple images corresponding to each synthetic caption. We perform visual representation learning on these synthetic images via contrastive learning, treating images sharing the same caption as positive pairs. The resulting representations transfer well to many downstream tasks, competing favorably with other general-purpose visual representation learners such as CLIP and DINO v2 in image classification tasks. Furthermore, in dense prediction tasks such as semantic segmentation, SynCLR outperforms previous self-supervised methods by a significant margin, e.g., improving over MAE and iBOT by 6.2 and 4.3 mIoU on ADE20k for ViT-B/16.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-01", "arxiv_id": "2312.17681", "title": "FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis", "authors": [ "Feng Liang", "Bichen Wu", "Jialiang Wang", "Licheng Yu", "Kunpeng Li", "Yinan Zhao", "Ishan Misra", "Jia-Bin Huang", "Peizhao Zhang", "Peter Vajda", "Diana Marculescu" ], "github": "", "project_page": "", "abstract": "Diffusion models have transformed the image-to-image (I2I) synthesis and are now permeating into videos. However, the advancement of video-to-video (V2V) synthesis has been hampered by the challenge of maintaining temporal consistency across video frames. This paper proposes a consistent V2V synthesis framework by jointly leveraging spatial conditions and temporal optical flow clues within the source video. Contrary to prior methods that strictly adhere to optical flow, our approach harnesses its benefits while handling the imperfection in flow estimation. We encode the optical flow via warping from the first frame and serve it as a supplementary reference in the diffusion model. This enables our model for video synthesis by editing the first frame with any prevalent I2I models and then propagating edits to successive frames. Our V2V model, FlowVid, demonstrates remarkable properties: (1) Flexibility: FlowVid works seamlessly with existing I2I models, facilitating various modifications, including stylization, object swaps, and local edits. (2) Efficiency: Generation of a 4-second video with 30 FPS and 512x512 resolution takes only 1.5 minutes, which is 3.1x, 7.2x, and 10.5x faster than CoDeF, Rerender, and TokenFlow, respectively. (3) High-quality: In user studies, our FlowVid is preferred 45.7% of the time, outperforming CoDeF (3.5%), Rerender (10.2%), and TokenFlow (40.4%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-01", "arxiv_id": "2312.17653", "title": "LARP: Language-Agent Role Play for Open-World Games", "authors": [ "Ming Yan", "Ruihao Li", "Hao Zhang", "Hao Wang", "Zhilan Yang", "Ji Yan" ], "github": "", "project_page": "", "abstract": "Language agents have shown impressive problem-solving skills within defined settings and brief timelines. Yet, with the ever-evolving complexities of open-world simulations, there's a pressing need for agents that can flexibly adapt to complex environments and consistently maintain a long-term memory to ensure coherent actions. To bridge the gap between language agents and open-world games, we introduce Language Agent for Role-Playing (LARP), which includes a cognitive architecture that encompasses memory processing and a decision-making assistant, an environment interaction module with a feedback-driven learnable action space, and a postprocessing method that promotes the alignment of various personalities. The LARP framework refines interactions between users and agents, predefined with unique backgrounds and personalities, ultimately enhancing the gaming experience in open-world contexts. Furthermore, it highlights the diverse uses of language models in a range of areas such as entertainment, education, and various simulation scenarios. The project page is released at https://miao-ai-lab.github.io/LARP/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-02", "arxiv_id": "2401.00448", "title": "Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws", "authors": [ "Nikhil Sardana", "Jonathan Frankle" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) scaling laws are empirical formulas that estimate changes in model quality as a result of increasing parameter count and training data. However, these formulas, including the popular DeepMind Chinchilla scaling laws, neglect to include the cost of inference. We modify the Chinchilla scaling laws to calculate the optimal LLM parameter count and pre-training data size to train and deploy a model of a given quality and inference demand. We conduct our analysis both in terms of a compute budget and real-world costs and find that LLM researchers expecting reasonably large inference demand (~1B requests) should train models smaller and longer than Chinchilla-optimal.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-02", "arxiv_id": "2401.00788", "title": "Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models", "authors": [ "Terry Yue Zhuo", "Armel Zebaze", "Nitchakarn Suppattarachai", "Leandro von Werra", "Harm de Vries", "Qian Liu", "Niklas Muennighoff" ], "github": "https://github.com/bigcode-project/astraios", "project_page": "", "abstract": "The high cost of full-parameter fine-tuning (FFT) of Large Language Models (LLMs) has led to a series of parameter-efficient fine-tuning (PEFT) methods. However, it remains unclear which methods provide the best cost-performance trade-off at different model scales. We introduce Astraios, a suite of 28 instruction-tuned OctoCoder models using 7 tuning methods and 4 model sizes up to 16 billion parameters. Through investigations across 5 tasks and 8 different datasets encompassing both code comprehension and code generation tasks, we find that FFT generally leads to the best downstream performance across all scales, and PEFT methods differ significantly in their efficacy based on the model scale. LoRA usually offers the most favorable trade-off between cost and performance. Further investigation into the effects of these methods on both model robustness and code security reveals that larger models tend to demonstrate reduced robustness and less security. At last, we explore the relationships among updated parameters, cross-entropy loss, and task performance. We find that the tuning effectiveness observed in small models generalizes well to larger models, and the validation loss in instruction tuning can be a reliable indicator of overall downstream performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bigcode-project/astraios", "github_api_added_by": "auto" }, { "date": "2024-01-02", "arxiv_id": "2401.00246", "title": "Boosting Large Language Model for Speech Synthesis: An Empirical Study", "authors": [ "Hongkun Hao", "Long Zhou", "Shujie Liu", "Jinyu Li", "Shujie Hu", "Rui Wang", "Furu Wei" ], "github": "https://github.com/NVIDIA/NeMo", "project_page": "", "abstract": "Large language models (LLMs) have made significant advancements in natural language processing and are concurrently extending the language ability to other modalities, such as speech and vision. Nevertheless, most of the previous work focuses on prompting LLMs with perception abilities like auditory comprehension, and the effective approach for augmenting LLMs with speech synthesis capabilities remains ambiguous. In this paper, we conduct a comprehensive empirical exploration of boosting LLMs with the ability to generate speech, by combining pre-trained LLM LLaMA/OPT and text-to-speech synthesis model VALL-E. We compare three integration methods between LLMs and speech synthesis models, including directly fine-tuned LLMs, superposed layers of LLMs and VALL-E, and coupled LLMs and VALL-E using LLMs as a powerful text encoder. Experimental results show that, using LoRA method to fine-tune LLMs directly to boost the speech synthesis capability does not work well, and superposed LLMs and VALL-E can improve the quality of generated speech both in speaker similarity and word error rate (WER). Among these three methods, coupled methods leveraging LLMs as the text encoder can achieve the best performance, making it outperform original speech synthesis models with a consistently better speaker similarity and a significant (10.9%) WER reduction.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA/NeMo", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-02", "arxiv_id": "2401.00368", "title": "Improving Text Embeddings with Large Language Models", "authors": [ "Liang Wang", "Nan Yang", "Xiaolong Huang", "Linjun Yang", "Rangan Majumder", "Furu Wei" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce a novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. Unlike existing methods that often depend on multi-stage intermediate pre-training with billions of weakly-supervised text pairs, followed by fine-tuning with a few labeled datasets, our method does not require building complex training pipelines or relying on manually collected datasets that are often constrained by task diversity and language coverage. We leverage proprietary LLMs to generate diverse synthetic data for hundreds of thousands of text embedding tasks across nearly 100 languages. We then fine-tune open-source decoder-only LLMs on the synthetic data using standard contrastive loss. Experiments demonstrate that our method achieves strong performance on highly competitive text embedding benchmarks without using any labeled data. Furthermore, when fine-tuned with a mixture of synthetic and labeled data, our model sets new state-of-the-art results on the BEIR and MTEB benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-02", "arxiv_id": "2401.00434", "title": "GeoGalactica: A Scientific Large Language Model in Geoscience", "authors": [ "Zhouhan Lin", "Cheng Deng", "Le Zhou", "Tianhang Zhang", "Yi Xu", "Yutong Xu", "Zhongmou He", "Yuanyuan Shi", "Beiya Dai", "Yunchong Song", "Boyi Zeng", "Qiyuan Chen", "Tao Shi", "Tianyu Huang", "Yiwei Xu", "Shu Wang", "Luoyi Fu", "Weinan Zhang", "Junxian He", "Chao Ma", "Yunqiang Zhu", "Xinbing Wang", "Chenghu Zhou" ], "github": "https://github.com/geobrain-ai/geogalactica", "project_page": "", "abstract": "Large language models (LLMs) have achieved huge success for their general knowledge and ability to solve a wide spectrum of tasks in natural language processing (NLP). Due to their impressive abilities, LLMs have shed light on potential inter-discipline applications to foster scientific discoveries of a specific domain by using artificial intelligence (AI for science, AI4S). In the meantime, utilizing NLP techniques in geoscience research and practice is wide and convoluted, contributing from knowledge extraction and document classification to question answering and knowledge discovery. In this work, we take the initial step to leverage LLM for science, through a rather straightforward approach. We try to specialize an LLM into geoscience, by further pre-training the model with a vast amount of texts in geoscience, as well as supervised fine-tuning (SFT) the resulting model with our custom collected instruction tuning dataset. These efforts result in a model GeoGalactica consisting of 30 billion parameters. To our best knowledge, it is the largest language model for the geoscience domain. More specifically, GeoGalactica is from further pre-training of Galactica. We train GeoGalactica over a geoscience-related text corpus containing 65 billion tokens curated from extensive data sources in the big science project Deep-time Digital Earth (DDE), preserving as the largest geoscience-specific text corpus. Then we fine-tune the model with 1 million pairs of instruction-tuning data consisting of questions that demand professional geoscience knowledge to answer. In this technical report, we will illustrate in detail all aspects of GeoGalactica, including data collection, data cleaning, base model selection, pre-training, SFT, and evaluation. We open-source our data curation tools and the checkpoints of GeoGalactica during the first 3/4 of pre-training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/geobrain-ai/geogalactica", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-02", "arxiv_id": "2401.00604", "title": "SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity", "authors": [ "Peihao Wang", "Zhiwen Fan", "Dejia Xu", "Dilin Wang", "Sreyas Mohan", "Forrest Iandola", "Rakesh Ranjan", "Yilei Li", "Qiang Liu", "Zhangyang Wang", "Vikas Chandra" ], "github": "", "project_page": "", "abstract": "Score distillation has emerged as one of the most prevalent approaches for text-to-3D asset synthesis. Essentially, score distillation updates 3D parameters by lifting and back-propagating scores averaged over different views. In this paper, we reveal that the gradient estimation in score distillation is inherent to high variance. Through the lens of variance reduction, the effectiveness of SDS and VSD can be interpreted as applications of various control variates to the Monte Carlo estimator of the distilled score. Motivated by this rethinking and based on Stein's identity, we propose a more general solution to reduce variance for score distillation, termed Stein Score Distillation (SSD). SSD incorporates control variates constructed by Stein identity, allowing for arbitrary baseline functions. This enables us to include flexible guidance priors and network architectures to explicitly optimize for variance reduction. In our experiments, the overall pipeline, dubbed SteinDreamer, is implemented by instantiating the control variate with a monocular depth estimator. The results suggest that SSD can effectively reduce the distillation variance and consistently improve visual quality for both object- and scene-level generation. Moreover, we demonstrate that SteinDreamer achieves faster convergence than existing methods due to more stable gradient updates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-02", "arxiv_id": "2401.00849", "title": "COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training", "authors": [ "Alex Jinpeng Wang", "Linjie Li", "Kevin Qinghong Lin", "Jianfeng Wang", "Kevin Lin", "Zhengyuan Yang", "Lijuan Wang", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "In the evolution of Vision-Language Pre-training, shifting from short-text comprehension to encompassing extended textual contexts is pivotal. Recent autoregressive vision-language models like flamingo, palme, leveraging the long-context capability of Large Language Models, have excelled in few-shot text generation tasks but face challenges in alignment tasks. Addressing this gap, we introduce the contrastive loss into text generation models, presenting the COntrastive-Streamlined MultimOdal framework (\\ModelName), strategically partitioning the language model into dedicated unimodal text processing and adept multimodal data handling components. \\ModelName, our unified framework, merges unimodal and multimodal elements, enhancing model performance for tasks involving textual and visual data while notably reducing learnable parameters. However, these models demand extensive long-text datasets, yet the availability of high-quality long-text video datasets remains limited. To bridge this gap, this work introduces \\VideoDatasetName, an inaugural interleaved video-text dataset featuring comprehensive captions, marking a significant step forward. Demonstrating its impact, we illustrate how enhances model performance in image-text tasks. With 34% learnable parameters and utilizing 72\\% of the available data, our model demonstrates significant superiority over OpenFlamingo~openflamingo. For instance, in the 4-shot flickr captioning task, performance notably improves from 57.2% to 65.\\%. The contributions of and are underscored by notable performance gains across 14 diverse downstream datasets encompassing both image-text and video-text tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-02", "arxiv_id": "2401.00134", "title": "Unicron: Economizing Self-Healing LLM Training at Scale", "authors": [ "Tao He", "Xue Li", "Zhibin Wang", "Kun Qian", "Jingbo Xu", "Wenyuan Yu", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "Training large-scale language models is increasingly critical in various domains, but it is hindered by frequent failures, leading to significant time and economic costs. Current failure recovery methods in cloud-based settings inadequately address the diverse and complex scenarios that arise, focusing narrowly on erasing downtime for individual tasks without considering the overall cost impact on a cluster. We introduce Unicron, a workload manager designed for efficient self-healing in large-scale language model training. Unicron optimizes the training process by minimizing failure-related costs across multiple concurrent tasks within a cluster. Its key features include in-band error detection for real-time error identification without extra overhead, a dynamic cost-aware plan generation mechanism for optimal reconfiguration, and an efficient transition strategy to reduce downtime during state changes. Deployed on a 128-GPU distributed cluster, Unicron demonstrates up to a 1.9x improvement in training efficiency over state-of-the-art methods, significantly reducing failure recovery costs and enhancing the reliability of large-scale language model training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-03", "arxiv_id": "2401.01335", "title": "Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models", "authors": [ "Zixiang Chen", "Yihe Deng", "Huizhuo Yuan", "Kaixuan Ji", "Quanquan Gu" ], "github": "https://github.com/uclaml/SPIN", "project_page": "", "abstract": "Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the prospect of growing a strong LLM out of a weak one without the need for acquiring additional human-annotated data. We propose a new fine-tuning method called Self-Play fIne-tuNing (SPIN), which starts from a supervised fine-tuned model. At the heart of SPIN lies a self-play mechanism, where the LLM refines its capability by playing against instances of itself. More specifically, the LLM generates its own training data from its previous iterations, refining its policy by discerning these self-generated responses from those obtained from human-annotated data. Our method progressively elevates the LLM from a nascent model to a formidable one, unlocking the full potential of human-annotated demonstration data for SFT. Theoretically, we prove that the global optimum to the training objective function of our method is achieved only when the LLM policy aligns with the target data distribution. Empirically, we evaluate our method on several benchmark datasets including the HuggingFace Open LLM Leaderboard, MT-Bench, and datasets from Big-Bench. Our results show that SPIN can significantly improve the LLM's performance across a variety of benchmarks and even outperform models trained through direct preference optimization (DPO) supplemented with extra GPT-4 preference data. This sheds light on the promise of self-play, enabling the achievement of human-level performance in LLMs without the need for expert opponents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/uclaml/SPIN", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-03", "arxiv_id": "2401.00908", "title": "DocLLM: A layout-aware generative language model for multimodal document understanding", "authors": [ "Dongsheng Wang", "Natraj Raman", "Mathieu Sibue", "Zhiqiang Ma", "Petr Babkin", "Simerjot Kaur", "Yulong Pei", "Armineh Nourbakhsh", "Xiaomo Liu" ], "github": "", "project_page": "", "abstract": "Enterprise documents such as forms, invoices, receipts, reports, contracts, and other similar records, often carry rich semantics at the intersection of textual and spatial modalities. The visual cues offered by their complex layouts play a crucial role in comprehending these documents effectively. In this paper, we present DocLLM, a lightweight extension to traditional large language models (LLMs) for reasoning over visual documents, taking into account both textual semantics and spatial layout. Our model differs from existing multimodal LLMs by avoiding expensive image encoders and focuses exclusively on bounding box information to incorporate the spatial layout structure. Specifically, the cross-alignment between text and spatial modalities is captured by decomposing the attention mechanism in classical transformers to a set of disentangled matrices. Furthermore, we devise a pre-training objective that learns to infill text segments. This approach allows us to address irregular layouts and heterogeneous content frequently encountered in visual documents. The pre-trained model is fine-tuned using a large-scale instruction dataset, covering four core document intelligence tasks. We demonstrate that our solution outperforms SotA LLMs on 14 out of 16 datasets across all tasks, and generalizes well to 4 out of 5 previously unseen datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-03", "arxiv_id": "2401.00935", "title": "Boundary Attention: Learning to Find Faint Boundaries at Any Resolution", "authors": [ "Mia Gaia Polansky", "Charles Herrmann", "Junhwa Hur", "Deqing Sun", "Dor Verbin", "Todd Zickler" ], "github": "", "project_page": "", "abstract": "We present a differentiable model that explicitly models boundaries -- including contours, corners and junctions -- using a new mechanism that we call boundary attention. We show that our model provides accurate results even when the boundary signal is very weak or is swamped by noise. Compared to previous classical methods for finding faint boundaries, our model has the advantages of being differentiable; being scalable to larger images; and automatically adapting to an appropriate level of geometric detail in each part of an image. Compared to previous deep methods for finding boundaries via end-to-end training, it has the advantages of providing sub-pixel precision, being more resilient to noise, and being able to process any image at its native resolution and aspect ratio.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-03", "arxiv_id": "2401.00909", "title": "Taming Mode Collapse in Score Distillation for Text-to-3D Generation", "authors": [ "Peihao Wang", "Dejia Xu", "Zhiwen Fan", "Dilin Wang", "Sreyas Mohan", "Forrest Iandola", "Rakesh Ranjan", "Yilei Li", "Qiang Liu", "Zhangyang Wang", "Vikas Chandra" ], "github": "", "project_page": "", "abstract": "Despite the remarkable performance of score distillation in text-to-3D generation, such techniques notoriously suffer from view inconsistency issues, also known as \"Janus\" artifact, where the generated objects fake each view with multiple front faces. Although empirically effective methods have approached this problem via score debiasing or prompt engineering, a more rigorous perspective to explain and tackle this problem remains elusive. In this paper, we reveal that the existing score distillation-based text-to-3D generation frameworks degenerate to maximal likelihood seeking on each view independently and thus suffer from the mode collapse problem, manifesting as the Janus artifact in practice. To tame mode collapse, we improve score distillation by re-establishing in entropy term in the corresponding variational objective, which is applied to the distribution of rendered images. Maximizing the entropy encourages diversity among different views in generated 3D assets, thereby mitigating the Janus problem. Based on this new objective, we derive a new update rule for 3D score distillation, dubbed Entropic Score Distillation (ESD). We theoretically reveal that ESD can be simplified and implemented by just adopting the classifier-free guidance trick upon variational score distillation. Although embarrassingly straightforward, our extensive experiments successfully demonstrate that ESD can be an effective treatment for Janus artifacts in score distillation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-03", "arxiv_id": "2401.01055", "title": "LLaMA Beyond English: An Empirical Study on Language Capability Transfer", "authors": [ "Jun Zhao", "Zhihao Zhang", "Qi Zhang", "Tao Gui", "Xuanjing Huang" ], "github": "", "project_page": "", "abstract": "In recent times, substantial advancements have been witnessed in large language models (LLMs), exemplified by ChatGPT, showcasing remarkable proficiency across a range of complex tasks. However, many mainstream LLMs (e.g. LLaMA) are pretrained on English-dominant corpus, which limits their performance in other non-English languages. In this paper, we focus on how to effectively transfer the capabilities of language generation and following instructions to a non-English language. To answer this question, we conduct an extensive empirical investigation based on LLaMA, accumulating over 1440 GPU hours. We analyze the impact of key factors such as vocabulary extension, further pretraining, and instruction tuning on transfer. To accurately assess the model's level of knowledge, we employ four widely used standardized testing benchmarks: C-Eval, MMLU, AGI-Eval, and GAOKAO-Bench. Furthermore, a comprehensive evaluation of the model's response quality is conducted, considering aspects such as accuracy, fluency, informativeness, logical coherence, and harmlessness, based on LLM-Eval, a benchmarks consisting instruction tasks from 17 diverse categories. Our evaluation results demonstrate that comparable performance to state-of-the-art transfer models can be achieved with less than 1% of the pretraining data, both in terms of knowledge alignment and response quality. Furthermore, the experimental outcomes across the thirteen low-resource languages also exhibit similar trends. We anticipate that the conclusions revealed by the experiments will aid the community in developing non-English LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-03", "arxiv_id": "2401.00896", "title": "TrailBlazer: Trajectory Control for Diffusion-Based Video Generation", "authors": [ "Wan-Duo Kurt Ma", "J. P. Lewis", "W. Bastiaan Kleijn" ], "github": "https://github.com/hohonu-vicml/Trailblazer", "project_page": "", "abstract": "Within recent approaches to text-to-video (T2V) generation, achieving controllability in the synthesized video is often a challenge. Typically, this issue is addressed by providing low-level per-frame guidance in the form of edge maps, depth maps, or an existing video to be altered. However, the process of obtaining such guidance can be labor-intensive. This paper focuses on enhancing controllability in video synthesis by employing straightforward bounding boxes to guide the subject in various ways, all without the need for neural network training, finetuning, optimization at inference time, or the use of pre-existing videos. Our algorithm, TrailBlazer, is constructed upon a pre-trained (T2V) model, and easy to implement. The subject is directed by a bounding box through the proposed spatial and temporal attention map editing. Moreover, we introduce the concept of keyframing, allowing the subject trajectory and overall appearance to be guided by both a moving bounding box and corresponding prompts, without the need to provide a detailed mask. The method is efficient, with negligible additional computation relative to the underlying pre-trained model. Despite the simplicity of the bounding box guidance, the resulting motion is surprisingly natural, with emergent effects including perspective and movement toward the virtual camera as the box size increases.", "github_manual": "https://github.com/hohonu-vicml/Trailblazer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-03", "arxiv_id": "2401.01173", "title": "En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D Synthetic Data", "authors": [ "Yifang Men", "Biwen Lei", "Yuan Yao", "Miaomiao Cui", "Zhouhui Lian", "Xuansong Xie" ], "github": "https://github.com/menyifang/En3D", "project_page": "", "abstract": "We present En3D, an enhanced generative scheme for sculpting high-quality 3D human avatars. Unlike previous works that rely on scarce 3D datasets or limited 2D collections with imbalanced viewing angles and imprecise pose priors, our approach aims to develop a zero-shot 3D generative scheme capable of producing visually realistic, geometrically accurate and content-wise diverse 3D humans without relying on pre-existing 3D or 2D assets. To address this challenge, we introduce a meticulously crafted workflow that implements accurate physical modeling to learn the enhanced 3D generative model from synthetic 2D data. During inference, we integrate optimization modules to bridge the gap between realistic appearances and coarse 3D shapes. Specifically, En3D comprises three modules: a 3D generator that accurately models generalizable 3D humans with realistic appearance from synthesized balanced, diverse, and structured human images; a geometry sculptor that enhances shape quality using multi-view normal constraints for intricate human anatomy; and a texturing module that disentangles explicit texture maps with fidelity and editability, leveraging semantical UV partitioning and a differentiable rasterizer. Experimental results show that our approach significantly outperforms prior works in terms of image quality, geometry accuracy and content diversity. We also showcase the applicability of our generated avatars for animation and editing, as well as the scalability of our approach for content-style free adaptation.", "github_manual": "https://github.com/menyifang/En3D", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-03", "arxiv_id": "2401.01286", "title": "A Comprehensive Study of Knowledge Editing for Large Language Models", "authors": [ "Ningyu Zhang", "Yunzhi Yao", "Bozhong Tian", "Peng Wang", "Shumin Deng", "Mengru Wang", "Zekun Xi", "Shengyu Mao", "Jintian Zhang", "Yuansheng Ni", "Siyuan Cheng", "Ziwen Xu", "Xin Xu", "Jia-Chen Gu", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Lei Liang", "Zhiqiang Zhang", "Xiaowei Zhu", "Jun Zhou", "Huajun Chen" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies in the significant computational demands during training, arising from their extensive parameterization. This challenge is further intensified by the dynamic nature of the world, necessitating frequent updates to LLMs to correct outdated information or integrate new knowledge, thereby ensuring their continued relevance. Note that many applications demand continual model adjustments post-training to address deficiencies or undesirable behaviors. There is an increasing interest in efficient, lightweight methods for on-the-fly model modifications. To this end, recent years have seen a burgeoning in the techniques of knowledge editing for LLMs, which aim to efficiently modify LLMs' behaviors within specific domains while preserving overall performance across various inputs. In this paper, we first define the knowledge editing problem and then provide a comprehensive review of cutting-edge approaches. Drawing inspiration from educational and cognitive research theories, we propose a unified categorization criterion that classifies knowledge editing methods into three groups: resorting to external knowledge, merging knowledge into the model, and editing intrinsic knowledge. Furthermore, we introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches. Additionally, we provide an in-depth analysis of knowledge location, which can provide a deeper understanding of the knowledge structures inherent within LLMs. Finally, we discuss several potential applications of knowledge editing, outlining its broad and impactful implications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-03", "arxiv_id": "2401.01117", "title": "Q-Refine: A Perceptual Quality Refiner for AI-Generated Image", "authors": [ "Chunyi Li", "Haoning Wu", "Zicheng Zhang", "Hongkun Hao", "Kaiwei Zhang", "Lei Bai", "Xiaohong Liu", "Xiongkuo Min", "Weisi Lin", "Guangtao Zhai" ], "github": "https://github.com/q-future/q-refine", "project_page": "", "abstract": "With the rapid evolution of the Text-to-Image (T2I) model in recent years, their unsatisfactory generation result has become a challenge. However, uniformly refining AI-Generated Images (AIGIs) of different qualities not only limited optimization capabilities for low-quality AIGIs but also brought negative optimization to high-quality AIGIs. To address this issue, a quality-award refiner named Q-Refine is proposed. Based on the preference of the Human Visual System (HVS), Q-Refine uses the Image Quality Assessment (IQA) metric to guide the refining process for the first time, and modify images of different qualities through three adaptive pipelines. Experimental shows that for mainstream T2I models, Q-Refine can perform effective optimization to AIGIs of different qualities. It can be a general refiner to optimize AIGIs from both fidelity and aesthetic quality levels, thus expanding the application of the T2I generation models.", "github_manual": "https://github.com/q-future/q-refine", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-03", "arxiv_id": "2401.01325", "title": "LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning", "authors": [ "Hongye Jin", "Xiaotian Han", "Jingfeng Yang", "Zhimeng Jiang", "Zirui Liu", "Chia-Yuan Chang", "Huiyuan Chen", "Xia Hu" ], "github": "https://github.com/datamllab/LongLM", "project_page": "", "abstract": "This work elicits LLMs' inherent ability to handle long contexts without fine-tuning. The limited length of the training sequence during training may limit the application of Large Language Models (LLMs) on long input sequences for inference. In this work, we argue that existing LLMs themselves have inherent capabilities for handling long contexts. Based on this argument, we suggest extending LLMs' context window by themselves to fully utilize the inherent ability.We propose Self-Extend to stimulate LLMs' long context handling potential. The basic idea is to construct bi-level attention information: the group level and the neighbor level. The two levels are computed by the original model's self-attention, which means the proposed does not require any training. With only four lines of code modification, the proposed method can effortlessly extend existing LLMs' context window without any fine-tuning. We conduct comprehensive experiments and the results show that the proposed method can effectively extend existing LLMs' context window's length.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/datamllab/LongLM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-03", "arxiv_id": "2401.01256", "title": "VideoDrafter: Content-Consistent Multi-Scene Video Generation with LLM", "authors": [ "Fuchen Long", "Zhaofan Qiu", "Ting Yao", "Tao Mei" ], "github": "https://github.com/fuchenustc/videostudio", "project_page": "", "abstract": "The recent innovations and breakthroughs in diffusion models have significantly expanded the possibilities of generating high-quality videos for the given prompts. Most existing works tackle the single-scene scenario with only one video event occurring in a single background. Extending to generate multi-scene videos nevertheless is not trivial and necessitates to nicely manage the logic in between while preserving the consistent visual appearance of key content across video scenes. In this paper, we propose a novel framework, namely VideoDrafter, for content-consistent multi-scene video generation. Technically, VideoDrafter leverages Large Language Models (LLM) to convert the input prompt into comprehensive multi-scene script that benefits from the logical knowledge learnt by LLM. The script for each scene includes a prompt describing the event, the foreground/background entities, as well as camera movement. VideoDrafter identifies the common entities throughout the script and asks LLM to detail each entity. The resultant entity description is then fed into a text-to-image model to generate a reference image for each entity. Finally, VideoDrafter outputs a multi-scene video by generating each scene video via a diffusion process that takes the reference images, the descriptive prompt of the event and camera movement into account. The diffusion model incorporates the reference images as the condition and alignment to strengthen the content consistency of multi-scene videos. Extensive experiments demonstrate that VideoDrafter outperforms the SOTA video generation models in terms of visual quality, content consistency, and user preference.", "github_manual": "https://github.com/fuchenustc/videostudio", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-04", "arxiv_id": "2401.01614", "title": "GPT-4V(ision) is a Generalist Web Agent, if Grounded", "authors": [ "Boyuan Zheng", "Boyu Gou", "Jihyung Kil", "Huan Sun", "Yu Su" ], "github": "https://github.com/osu-nlp-group/seeact", "project_page": "", "abstract": "The recent development on large multimodal models (LMMs), especially GPT-4V(ision) and Gemini, has been quickly expanding the capability boundaries of multimodal models beyond traditional tasks like image captioning and visual question answering. In this work, we explore the potential of LMMs like GPT-4V as a generalist web agent that can follow natural language instructions to complete tasks on any given website. We propose SEEACT, a generalist web agent that harnesses the power of LMMs for integrated visual understanding and acting on the web. We evaluate on the recent MIND2WEB benchmark. In addition to standard offline evaluation on cached websites, we enable a new online evaluation setting by developing a tool that allows running web agents on live websites. We show that GPT-4V presents a great potential for web agents - it can successfully complete 50% of the tasks on live websites if we manually ground its textual plans into actions on the websites. This substantially outperforms text-only LLMs like GPT-4 or smaller models (FLAN-T5 and BLIP-2) specifically fine-tuned for web agents. However, grounding still remains a major challenge. Existing LMM grounding strategies like set-of-mark prompting turns out not effective for web agents, and the best grounding strategy we develop in this paper leverages both the HTML text and visuals. Yet, there is still a substantial gap with oracle grounding, leaving ample room for further improvement.", "github_manual": "https://github.com/osu-nlp-group/seeact", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-04", "arxiv_id": "2401.01702", "title": "Image Sculpting: Precise Object Editing with 3D Geometry Control", "authors": [ "Jiraphon Yenphraphai", "Xichen Pan", "Sainan Liu", "Daniele Panozzo", "Saining Xie" ], "github": "", "project_page": "", "abstract": "We present Image Sculpting, a new framework for editing 2D images by incorporating tools from 3D geometry and graphics. This approach differs markedly from existing methods, which are confined to 2D spaces and typically rely on textual instructions, leading to ambiguity and limited control. Image Sculpting converts 2D objects into 3D, enabling direct interaction with their 3D geometry. Post-editing, these objects are re-rendered into 2D, merging into the original image to produce high-fidelity results through a coarse-to-fine enhancement process. The framework supports precise, quantifiable, and physically-plausible editing options such as pose editing, rotation, translation, 3D composition, carving, and serial addition. It marks an initial step towards combining the creative freedom of generative models with the precision of graphics pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-04", "arxiv_id": "2401.01885", "title": "From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations", "authors": [ "Evonne Ng", "Javier Romero", "Timur Bagautdinov", "Shaojie Bai", "Trevor Darrell", "Angjoo Kanazawa", "Alexander Richard" ], "github": "https://github.com/facebookresearch/audio2photoreal", "project_page": "", "abstract": "We present a framework for generating full-bodied photorealistic avatars that gesture according to the conversational dynamics of a dyadic interaction. Given speech audio, we output multiple possibilities of gestural motion for an individual, including face, body, and hands. The key behind our method is in combining the benefits of sample diversity from vector quantization with the high-frequency details obtained through diffusion to generate more dynamic, expressive motion. We visualize the generated motion using highly photorealistic avatars that can express crucial nuances in gestures (e.g. sneers and smirks). To facilitate this line of research, we introduce a first-of-its-kind multi-view conversational dataset that allows for photorealistic reconstruction. Experiments show our model generates appropriate and diverse gestures, outperforming both diffusion- and VQ-only methods. Furthermore, our perceptual evaluation highlights the importance of photorealism (vs. meshes) in accurately assessing subtle motion details in conversational gestures. Code and dataset available online.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/audio2photoreal", "github_api_added_by": "auto" }, { "date": "2024-01-04", "arxiv_id": "2401.01755", "title": "Incremental FastPitch: Chunk-based High Quality Text to Speech", "authors": [ "Muyang Du", "Chuan Liu", "Junjie Lai" ], "github": "", "project_page": "", "abstract": "Parallel text-to-speech models have been widely applied for real-time speech synthesis, and they offer more controllability and a much faster synthesis process compared with conventional auto-regressive models. Although parallel models have benefits in many aspects, they become naturally unfit for incremental synthesis due to their fully parallel architecture such as transformer. In this work, we propose Incremental FastPitch, a novel FastPitch variant capable of incrementally producing high-quality Mel chunks by improving the architecture with chunk-based FFT blocks, training with receptive-field constrained chunk attention masks, and inference with fixed size past model states. Experimental results show that our proposal can produce speech quality comparable to the parallel FastPitch, with a significant lower latency that allows even lower response time for real-time speech applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-04", "arxiv_id": "2401.01792", "title": "CoMoSVC: Consistency Model-based Singing Voice Conversion", "authors": [ "Yiwen Lu", "Zhen Ye", "Wei Xue", "Xu Tan", "Qifeng Liu", "Yike Guo" ], "github": "https://github.com/grace9994/comosvc", "project_page": "", "abstract": "The diffusion-based Singing Voice Conversion (SVC) methods have achieved remarkable performances, producing natural audios with high similarity to the target timbre. However, the iterative sampling process results in slow inference speed, and acceleration thus becomes crucial. In this paper, we propose CoMoSVC, a consistency model-based SVC method, which aims to achieve both high-quality generation and high-speed sampling. A diffusion-based teacher model is first specially designed for SVC, and a student model is further distilled under self-consistency properties to achieve one-step sampling. Experiments on a single NVIDIA GTX4090 GPU reveal that although CoMoSVC has a significantly faster inference speed than the state-of-the-art (SOTA) diffusion-based SVC system, it still achieves comparable or superior conversion performance based on both subjective and objective metrics. Audio samples and codes are available at https://comosvc.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/grace9994/comosvc", "github_api_added_by": "auto" }, { "date": "2024-01-04", "arxiv_id": "2401.01647", "title": "SIGNeRF: Scene Integrated Generation for Neural Radiance Fields", "authors": [ "Jan-Niklas Dihlmann", "Andreas Engelhardt", "Hendrik Lensch" ], "github": "https://github.com/mikonvergence/ControlNetInpaint", "project_page": "", "abstract": "Advances in image diffusion models have recently led to notable improvements in the generation of high-quality images. In combination with Neural Radiance Fields (NeRFs), they enabled new opportunities in 3D generation. However, most generative 3D approaches are object-centric and applying them to editing existing photorealistic scenes is not trivial. We propose SIGNeRF, a novel approach for fast and controllable NeRF scene editing and scene-integrated object generation. A new generative update strategy ensures 3D consistency across the edited images, without requiring iterative optimization. We find that depth-conditioned diffusion models inherently possess the capability to generate 3D consistent views by requesting a grid of images instead of single views. Based on these insights, we introduce a multi-view reference sheet of modified images. Our method updates an image collection consistently based on the reference sheet and refines the original NeRF with the newly generated image set in one go. By exploiting the depth conditioning mechanism of the image diffusion model, we gain fine control over the spatial location of the edit and enforce shape guidance by a selected region or an external mesh.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mikonvergence/ControlNetInpaint", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-04", "arxiv_id": "2401.01808", "title": "aMUSEd: An Open MUSE Reproduction", "authors": [ "Suraj Patil", "William Berman", "Robin Rombach", "Patrick von Platen" ], "github": "", "project_page": "", "abstract": "We present aMUSEd, an open-source, lightweight masked image model (MIM) for text-to-image generation based on MUSE. With 10 percent of MUSE's parameters, aMUSEd is focused on fast image generation. We believe MIM is under-explored compared to latent diffusion, the prevailing approach for text-to-image generation. Compared to latent diffusion, MIM requires fewer inference steps and is more interpretable. Additionally, MIM can be fine-tuned to learn additional styles with only a single image. We hope to encourage further exploration of MIM by demonstrating its effectiveness on large-scale text-to-image generation and releasing reproducible training code. We also release checkpoints for two models which directly produce images at 256x256 and 512x512 resolutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-04", "arxiv_id": "2401.01862", "title": "A Vision Check-up for Language Models", "authors": [ "Pratyusha Sharma", "Tamar Rott Shaham", "Manel Baradad", "Stephanie Fu", "Adrian Rodriguez-Munoz", "Shivam Duggal", "Phillip Isola", "Antonio Torralba" ], "github": "", "project_page": "", "abstract": "What does learning to model relationships between strings teach large language models (LLMs) about the visual world? We systematically evaluate LLMs' abilities to generate and recognize an assortment of visual concepts of increasing complexity and then demonstrate how a preliminary visual representation learning system can be trained using models of text. As language models lack the ability to consume or output visual information as pixels, we use code to represent images in our study. Although LLM-generated images do not look like natural images, results on image generation and the ability of models to correct these generated images indicate that precise modeling of strings can teach language models about numerous aspects of the visual world. Furthermore, experiments on self-supervised visual representation learning, utilizing images generated with text models, highlight the potential to train vision models capable of making semantic assessments of natural images using just LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-04", "arxiv_id": "2401.01854", "title": "Multilingual Instruction Tuning With Just a Pinch of Multilinguality", "authors": [ "Uri Shaham", "Jonathan Herzig", "Roee Aharoni", "Idan Szpektor", "Reut Tsarfaty", "Matan Eyal" ], "github": "", "project_page": "", "abstract": "As instruction-tuned large language models (LLMs) gain global adoption, their ability to follow instructions in multiple languages becomes increasingly crucial. One promising approach is cross-lingual transfer, where a model acquires specific functionality on some language by finetuning on another language. In this work, we investigate how multilinguality during instruction tuning of a multilingual LLM affects instruction-following across languages. We first show that many languages transfer some instruction-following capabilities to other languages from even monolingual tuning. Furthermore, we find that only 40 multilingual examples in an English tuning set substantially improve multilingual instruction-following, both in seen and unseen languages during tuning. In general, we observe that models tuned on multilingual mixtures exhibit comparable or superior performance in several languages compared to monolingually tuned models, despite training on 10x fewer examples in those languages. Finally, we find that increasing the number of languages in the instruction tuning set from 1 to only 2, 3, or 4 increases cross-lingual generalization. Our results suggest that building massively multilingual instruction-tuned models can be done with only a very small set of multilingual instruction-responses.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-04", "arxiv_id": "2401.01699", "title": "WordArt Designer API: User-Driven Artistic Typography Synthesis with Large Language Models on ModelScope", "authors": [ "Jun-Yan He", "Zhi-Qi Cheng", "Chenyang Li", "Jingdong Sun", "Wangmeng Xiang", "Yusen Hu", "Xianhui Lin", "Xiaoyang Kang", "Zengke Jin", "Bin Luo", "Yifeng Geng", "Xuansong Xie", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "This paper introduces the WordArt Designer API, a novel framework for user-driven artistic typography synthesis utilizing Large Language Models (LLMs) on ModelScope. We address the challenge of simplifying artistic typography for non-professionals by offering a dynamic, adaptive, and computationally efficient alternative to traditional rigid templates. Our approach leverages the power of LLMs to understand and interpret user input, facilitating a more intuitive design process. We demonstrate through various case studies how users can articulate their aesthetic preferences and functional requirements, which the system then translates into unique and creative typographic designs. Our evaluations indicate significant improvements in user satisfaction, design flexibility, and creative expression over existing systems. The WordArt Designer API not only democratizes the art of typography but also opens up new possibilities for personalized digital communication and design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-04", "arxiv_id": "2401.01827", "title": "Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions", "authors": [ "David Junhao Zhang", "Dongxu Li", "Hung Le", "Mike Zheng Shou", "Caiming Xiong", "Doyen Sahoo" ], "github": "https://github.com/salesforce/LAVIS", "project_page": "", "abstract": "Most existing video diffusion models (VDMs) are limited to mere text conditions. Thereby, they are usually lacking in control over visual appearance and geometry structure of the generated videos. This work presents Moonshot, a new video generation model that conditions simultaneously on multimodal inputs of image and text. The model builts upon a core module, called multimodal video block (MVB), which consists of conventional spatialtemporal layers for representing video features, and a decoupled cross-attention layer to address image and text inputs for appearance conditioning. In addition, we carefully design the model architecture such that it can optionally integrate with pre-trained image ControlNet modules for geometry visual conditions, without needing of extra training overhead as opposed to prior methods. Experiments show that with versatile multimodal conditioning mechanisms, Moonshot demonstrates significant improvement on visual quality and temporal consistency compared to existing models. In addition, the model can be easily repurposed for a variety of generative applications, such as personalized video generation, image animation and video editing, unveiling its potential to serve as a fundamental architecture for controllable video generation. Models will be made public on https://github.com/salesforce/LAVIS.", "github_manual": "", "github_regex": "https://github.com/salesforce/LAVIS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-04", "arxiv_id": "2401.01461", "title": "Efficient Hybrid Zoom using Camera Fusion on Mobile Phones", "authors": [ "Xiaotong Wu", "Wei-Sheng Lai", "YiChang Shih", "Charles Herrmann", "Michael Krainin", "Deqing Sun", "Chia-Kai Liang" ], "github": "", "project_page": "", "abstract": "DSLR cameras can achieve multiple zoom levels via shifting lens distances or swapping lens types. However, these techniques are not possible on smartphone devices due to space constraints. Most smartphone manufacturers adopt a hybrid zoom system: commonly a Wide (W) camera at a low zoom level and a Telephoto (T) camera at a high zoom level. To simulate zoom levels between W and T, these systems crop and digitally upsample images from W, leading to significant detail loss. In this paper, we propose an efficient system for hybrid zoom super-resolution on mobile devices, which captures a synchronous pair of W and T shots and leverages machine learning models to align and transfer details from T to W. We further develop an adaptive blending method that accounts for depth-of-field mismatches, scene occlusion, flow uncertainty, and alignment errors. To minimize the domain gap, we design a dual-phone camera rig to capture real-world inputs and ground-truths for supervised training. Our method generates a 12-megapixel image in 500ms on a mobile platform and compares favorably against state-of-the-art methods under extensive evaluation on real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.01952", "title": "Instruct-Imagen: Image Generation with Multi-modal Instruction", "authors": [ "Hexiang Hu", "Kelvin C. K. Chan", "Yu-Chuan Su", "Wenhu Chen", "Yandong Li", "Kihyuk Sohn", "Yang Zhao", "Xue Ben", "Boqing Gong", "William Cohen", "Ming-Wei Chang", "Xuhui Jia" ], "github": "", "project_page": "", "abstract": "This paper presents instruct-imagen, a model that tackles heterogeneous image generation tasks and generalizes across unseen tasks. We introduce *multi-modal instruction* for image generation, a task representation articulating a range of generation intents with precision. It uses natural language to amalgamate disparate modalities (e.g., text, edge, style, subject, etc.), such that abundant generation intents can be standardized in a uniform format. We then build instruct-imagen by fine-tuning a pre-trained text-to-image diffusion model with a two-stage framework. First, we adapt the model using the retrieval-augmented training, to enhance model's capabilities to ground its generation on external multimodal context. Subsequently, we fine-tune the adapted model on diverse image generation tasks that requires vision-language understanding (e.g., subject-driven generation, etc.), each paired with a multi-modal instruction encapsulating the task's essence. Human evaluation on various image generation datasets reveals that instruct-imagen matches or surpasses prior task-specific models in-domain and demonstrates promising generalization to unseen and more complex tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.02412", "title": "LLM Augmented LLMs: Expanding Capabilities through Composition", "authors": [ "Rachit Bansal", "Bidisha Samanta", "Siddharth Dalmia", "Nitish Gupta", "Shikhar Vashishth", "Sriram Ganapathy", "Abhishek Bapna", "Prateek Jain", "Partha Talukdar" ], "github": "", "project_page": "", "abstract": "Foundational models with billions of parameters which have been trained on large corpora of data have demonstrated non-trivial skills in a variety of domains. However, due to their monolithic structure, it is challenging and expensive to augment them or impart new skills. On the other hand, due to their adaptation abilities, several new instances of these models are being trained towards new domains and tasks. In this work, we study the problem of efficient and practical composition of existing foundation models with more specific models to enable newer capabilities. To this end, we propose CALM -- Composition to Augment Language Models -- which introduces cross-attention between models to compose their representations and enable new capabilities. Salient features of CALM are: (i) Scales up LLMs on new tasks by 're-using' existing LLMs along with a few additional parameters and data, (ii) Existing model weights are kept intact, and hence preserves existing capabilities, and (iii) Applies to diverse domains and settings. We illustrate that augmenting PaLM2-S with a smaller model trained on low-resource languages results in an absolute improvement of up to 13\\% on tasks like translation into English and arithmetic reasoning for low-resource languages. Similarly, when PaLM2-S is augmented with a code-specific model, we see a relative improvement of 40\\% over the base model for code generation and explanation tasks -- on-par with fully fine-tuned counterparts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.02038", "title": "Understanding LLMs: A Comprehensive Overview from Training to Inference", "authors": [ "Yiheng Liu", "Hao He", "Tianle Han", "Xu Zhang", "Mengyuan Liu", "Jiaming Tian", "Yutong Zhang", "Jiaqi Wang", "Xiaohui Gao", "Tianyang Zhong", "Yi Pan", "Shaochen Xu", "Zihao Wu", "Zhengliang Liu", "Xin Zhang", "Shu Zhang", "Xintao Hu", "Tuo Zhang", "Ning Qiang", "Tianming Liu", "Bao Ge" ], "github": "https://github.com/google-research/text-to-text-transfer-transformer", "project_page": "", "abstract": "The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cost-efficient training and deployment within this context. Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend. The discussion on training includes various aspects, including data preprocessing, training architecture, pre-training tasks, parallel training, and relevant content related to model fine-tuning. On the inference side, the paper covers topics such as model compression, parallel computation, memory scheduling, and structural optimization. It also explores LLMs' utilization and provides insights into their future development.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-research/text-to-text-transfer-transformer", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.02416", "title": "ODIN: A Single Model for 2D and 3D Perception", "authors": [ "Ayush Jain", "Pushkal Katara", "Nikolaos Gkanatsios", "Adam W. Harley", "Gabriel Sarch", "Kriti Aggarwal", "Vishrav Chaudhary", "Katerina Fragkiadaki" ], "github": "https://github.com/ayushjain1144/odin", "project_page": "", "abstract": "State-of-the-art models on contemporary 3D perception benchmarks like ScanNet consume and label dataset-provided 3D point clouds, obtained through post processing of sensed multiview RGB-D images. They are typically trained in-domain, forego large-scale 2D pre-training and outperform alternatives that featurize the posed RGB-D multiview images instead. The gap in performance between methods that consume posed images versus post-processed 3D point clouds has fueled the belief that 2D and 3D perception require distinct model architectures. In this paper, we challenge this view and propose ODIN (Omni-Dimensional INstance segmentation), a model that can segment and label both 2D RGB images and 3D point clouds, using a transformer architecture that alternates between 2D within-view and 3D cross-view information fusion. Our model differentiates 2D and 3D feature operations through the positional encodings of the tokens involved, which capture pixel coordinates for 2D patch tokens and 3D coordinates for 3D feature tokens. ODIN achieves state-of-the-art performance on ScanNet200, Matterport3D and AI2THOR 3D instance segmentation benchmarks, and competitive performance on ScanNet, S3DIS and COCO. It outperforms all previous works by a wide margin when the sensed 3D point cloud is used in place of the point cloud sampled from 3D mesh. When used as the 3D perception engine in an instructable embodied agent architecture, it sets a new state-of-the-art on the TEACh action-from-dialogue benchmark. Our code and checkpoints can be found at the project website: https://odin-seg.github.io.", "github_manual": "https://github.com/ayushjain1144/odin", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.02072", "title": "ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers", "authors": [ "Chen Zheng", "Ke Sun", "Da Tang", "Yukun Ma", "Yuyu Zhang", "Chenguang Xi", "Xun Zhou" ], "github": "", "project_page": "", "abstract": "The emergence of Large Language Models (LLMs) such as ChatGPT and LLaMA encounter limitations in domain-specific tasks, with these models often lacking depth and accuracy in specialized areas, and exhibiting a decrease in general capabilities when fine-tuned, particularly analysis ability in small sized models. To address these gaps, we introduce ICE-GRT, utilizing Reinforcement Learning from Human Feedback (RLHF) grounded in Proximal Policy Optimization (PPO), demonstrating remarkable ability in in-domain scenarios without compromising general task performance. Our exploration of ICE-GRT highlights its understanding and reasoning ability to not only generate robust answers but also to provide detailed analyses of the reasons behind the answer. This capability marks a significant progression beyond the scope of Supervised Fine-Tuning models. The success of ICE-GRT is dependent on several crucial factors, including Appropriate Data, Reward Size Scaling, KL-Control, Advantage Normalization, etc. The ICE-GRT model exhibits state-of-the-art performance in domain-specific tasks and across 12 general Language tasks against equivalent size and even larger size LLMs, highlighting the effectiveness of our approach. We provide a comprehensive analysis of the ICE-GRT, underscoring the significant advancements it brings to the field of LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.02411", "title": "What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs", "authors": [ "Alex Trevithick", "Matthew Chan", "Towaki Takikawa", "Umar Iqbal", "Shalini De Mello", "Manmohan Chandraker", "Ravi Ramamoorthi", "Koki Nagano" ], "github": "", "project_page": "", "abstract": "3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of 2D images via neural volume rendering. Yet, the significant memory and computational costs of dense sampling in volume rendering have forced 3D GANs to adopt patch-based training or employ low-resolution rendering with post-processing 2D super resolution, which sacrifices multiview consistency and the quality of resolved geometry. Consequently, 3D GANs have not yet been able to fully resolve the rich 3D geometry present in 2D images. In this work, we propose techniques to scale neural volume rendering to the much higher resolution of native 2D images, thereby resolving fine-grained 3D geometry with unprecedented detail. Our approach employs learning-based samplers for accelerating neural rendering for 3D GAN training using up to 5 times fewer depth samples. This enables us to explicitly \"render every pixel\" of the full-resolution image during training and inference without post-processing superresolution in 2D. Together with our strategy to learn high-quality surface geometry, our method synthesizes high-resolution 3D geometry and strictly view-consistent images while maintaining image quality on par with baselines relying on post-processing super resolution. We demonstrate state-of-the-art 3D gemetric quality on FFHQ and AFHQ, setting a new standard for unsupervised learning of 3D shapes in 3D GANs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.02385", "title": "TinyLlama: An Open-Source Small Language Model", "authors": [ "Peiyuan Zhang", "Guangtao Zeng", "Tianduo Wang", "Wei Lu" ], "github": "https://github.com/jzhang38/TinyLlama", "project_page": "", "abstract": "We present TinyLlama, a compact 1.1B language model pretrained on around 1 trillion tokens for approximately 3 epochs. Building on the architecture and tokenizer of Llama 2, TinyLlama leverages various advances contributed by the open-source community (e.g., FlashAttention), achieving better computational efficiency. Despite its relatively small size, TinyLlama demonstrates remarkable performance in a series of downstream tasks. It significantly outperforms existing open-source language models with comparable sizes. Our model checkpoints and code are publicly available on GitHub at https://github.com/jzhang38/TinyLlama.", "github_manual": "", "github_regex": "https://github.com/jzhang38/TinyLlama", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.02415", "title": "LLaMA Pro: Progressive LLaMA with Block Expansion", "authors": [ "Chengyue Wu", "Yukang Gan", "Yixiao Ge", "Zeyu Lu", "Jiahao Wang", "Ye Feng", "Ping Luo", "Ying Shan" ], "github": "https://github.com/TencentARC/LLaMA-Pro", "project_page": "", "abstract": "Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), e.g., from LLaMA to CodeLLaMA. To this end, we propose a new post-pretraining method for LLMs with an expansion of Transformer blocks. We tune the expanded blocks using only new corpus, efficiently and effectively improving the model's knowledge without catastrophic forgetting. In this paper, we experiment on the corpus of code and math, yielding LLaMA Pro-8.3B, a versatile foundation model initialized from LLaMA2-7B, excelling in general tasks, programming, and mathematics. LLaMA Pro and its instruction-following counterpart (LLaMA Pro-Instruct) achieve advanced performance among various benchmarks, demonstrating superiority over existing open models in the LLaMA family and the immense potential of reasoning and addressing diverse tasks as an intelligent agent. Our findings provide valuable insights into integrating natural and programming languages, laying a solid foundation for developing advanced language agents that operate effectively in various environments.", "github_manual": "https://github.com/TencentARC/LLaMA-Pro", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.02400", "title": "Learning the 3D Fauna of the Web", "authors": [ "Zizhang Li", "Dor Litvak", "Ruining Li", "Yunzhi Zhang", "Tomas Jakab", "Christian Rupprecht", "Shangzhe Wu", "Andrea Vedaldi", "Jiajun Wu" ], "github": "", "project_page": "", "abstract": "Learning 3D models of all animals on the Earth requires massively scaling up existing solutions. With this ultimate goal in mind, we develop 3D-Fauna, an approach that learns a pan-category deformable 3D animal model for more than 100 animal species jointly. One crucial bottleneck of modeling animals is the limited availability of training data, which we overcome by simply learning from 2D Internet images. We show that prior category-specific attempts fail to generalize to rare species with limited training images. We address this challenge by introducing the Semantic Bank of Skinned Models (SBSM), which automatically discovers a small set of base animal shapes by combining geometric inductive priors with semantic knowledge implicitly captured by an off-the-shelf self-supervised feature extractor. To train such a model, we also contribute a new large-scale dataset of diverse animal species. At inference time, given a single image of any quadruped animal, our model reconstructs an articulated 3D mesh in a feed-forward fashion within seconds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.02117", "title": "Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation", "authors": [ "Zipeng Fu", "Tony Z. Zhao", "Chelsea Finn" ], "github": "https://github.com/MarkFzp/mobile-aloha", "project_page": "", "abstract": "Imitation learning from human demonstrations has shown impressive performance in robotics. However, most results focus on table-top manipulation, lacking the mobility and dexterity necessary for generally useful tasks. In this work, we develop a system for imitating mobile manipulation tasks that are bimanual and require whole-body control. We first present Mobile ALOHA, a low-cost and whole-body teleoperation system for data collection. It augments the ALOHA system with a mobile base, and a whole-body teleoperation interface. Using data collected with Mobile ALOHA, we then perform supervised behavior cloning and find that co-training with existing static ALOHA datasets boosts performance on mobile manipulation tasks. With 50 demonstrations for each task, co-training can increase success rates by up to 90%, allowing Mobile ALOHA to autonomously complete complex mobile manipulation tasks such as sauteing and serving a piece of shrimp, opening a two-door wall cabinet to store heavy cooking pots, calling and entering an elevator, and lightly rinsing a used pan using a kitchen faucet. Project website: https://mobile-aloha.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MarkFzp/mobile-aloha", "github_api_added_by": "auto" }, { "date": "2024-01-05", "arxiv_id": "2401.02015", "title": "Improving Diffusion-Based Image Synthesis with Context Prediction", "authors": [ "Ling Yang", "Jingwei Liu", "Shenda Hong", "Zhilong Zhang", "Zhilin Huang", "Zheming Cai", "Wentao Zhang", "Bin Cui" ], "github": "", "project_page": "", "abstract": "Diffusion models are a new class of generative models, and have dramatically promoted image generation with unprecedented quality and diversity. Existing diffusion models mainly try to reconstruct input image from a corrupted one with a pixel-wise or feature-wise constraint along spatial axes. However, such point-based reconstruction may fail to make each predicted pixel/feature fully preserve its neighborhood context, impairing diffusion-based image synthesis. As a powerful source of automatic supervisory signal, context has been well studied for learning representations. Inspired by this, we for the first time propose ConPreDiff to improve diffusion-based image synthesis with context prediction. We explicitly reinforce each point to predict its neighborhood context (i.e., multi-stride features/tokens/pixels) with a context decoder at the end of diffusion denoising blocks in training stage, and remove the decoder for inference. In this way, each point can better reconstruct itself by preserving its semantic connections with neighborhood context. This new paradigm of ConPreDiff can generalize to arbitrary discrete and continuous diffusion backbones without introducing extra parameters in sampling procedure. Extensive experiments are conducted on unconditional image generation, text-to-image generation and image inpainting tasks. Our ConPreDiff consistently outperforms previous methods and achieves a new SOTA text-to-image generation results on MS-COCO, with a zero-shot FID score of 6.21.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.02330", "title": "LLaVA-\u03c6: Efficient Multi-Modal Assistant with Small Language Model", "authors": [ "Yichen Zhu", "Minjie Zhu", "Ning Liu", "Zhicai Ou", "Xiaofeng Mou", "Jian Tang" ], "github": "https://github.com/zhuyiche/llava-phi", "project_page": "", "abstract": "In this paper, we introduce LLaVA-phi (LLaVA-Phi), an efficient multi-modal assistant that harnesses the power of the recently advanced small language model, Phi-2, to facilitate multi-modal dialogues. LLaVA-Phi marks a notable advancement in the realm of compact multi-modal models. It demonstrates that even smaller language models, with as few as 2.7B parameters, can effectively engage in intricate dialogues that integrate both textual and visual elements, provided they are trained with high-quality corpora. Our model delivers commendable performance on publicly available benchmarks that encompass visual comprehension, reasoning, and knowledge-based perception. Beyond its remarkable performance in multi-modal dialogue tasks, our model opens new avenues for applications in time-sensitive environments and systems that require real-time interaction, such as embodied agents. It highlights the potential of smaller language models to achieve sophisticated levels of understanding and interaction, while maintaining greater resource efficiency.The project is available at {https://github.com/zhuyiche/llava-phi}.", "github_manual": "", "github_regex": "https://github.com/zhuyiche/llava-phi", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.01974", "title": "Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers", "authors": [ "Aleksandar Stani\u0107", "Sergi Caelles", "Michael Tschannen" ], "github": "", "project_page": "", "abstract": "Visual reasoning is dominated by end-to-end neural networks scaled to billions of model parameters and training examples. However, even the largest models struggle with compositional reasoning, generalization, fine-grained spatial and temporal reasoning, and counting. Visual reasoning with large language models (LLMs) as controllers can, in principle, address these limitations by decomposing the task and solving subtasks by orchestrating a set of (visual) tools. Recently, these models achieved great performance on tasks such as compositional visual question answering, visual grounding, and video temporal reasoning. Nevertheless, in their current form, these models heavily rely on human engineering of in-context examples in the prompt, which are often dataset- and task-specific and require significant labor by highly skilled programmers. In this work, we present a framework that mitigates these issues by introducing spatially and temporally abstract routines and by leveraging a small number of labeled examples to automatically generate in-context examples, thereby avoiding human-created in-context examples. On a number of visual reasoning tasks, we show that our framework leads to consistent gains in performance, makes LLMs as controllers setup more robust, and removes the need for human engineering of in-context examples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-05", "arxiv_id": "2401.01970", "title": "FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understanding", "authors": [ "Xingxing Zuo", "Pouya Samangouei", "Yunwen Zhou", "Yan Di", "Mingyang Li" ], "github": "", "project_page": "", "abstract": "Precisely perceiving the geometric and semantic properties of real-world 3D objects is crucial for the continued evolution of augmented reality and robotic applications. To this end, we present (), which incorporates vision-language embeddings of foundation models into 3D Gaussian Splatting (GS). The key contribution of this work is an efficient method to reconstruct and represent 3D vision-language models. This is achieved by distilling feature maps generated from image-based foundation models into those rendered from our 3D model. To ensure high-quality rendering and fast training, we introduce a novel scene representation by integrating strengths from both GS and multi-resolution hash encodings (MHE). Our effective training procedure also introduces a pixel alignment loss that makes the rendered feature distance of same semantic entities close, following the pixel-level semantic boundaries. Our results demonstrate remarkable multi-view semantic consistency, facilitating diverse downstream tasks, beating state-of-the-art methods by 10.2 percent on open-vocabulary language-based object detection, despite that we are 851times faster for inference. This research explores the intersection of vision, language, and 3D scene representation, paving the way for enhanced scene understanding in uncontrolled real-world environments. We plan to release the code upon paper acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-08", "arxiv_id": "2401.02954", "title": "DeepSeek LLM: Scaling Open-Source Language Models with Longtermism", "authors": [ "DeepSeek-AI", "Xiao Bi", "Deli Chen", "Guanting Chen", "Shanhuang Chen", "Damai Dai", "Chengqi Deng", "Honghui Ding", "Kai Dong", "Qiushi Du", "Zhe Fu", "Huazuo Gao", "Kaige Gao", "Wenjun Gao", "Ruiqi Ge", "Kang Guan", "Daya Guo", "Jianzhong Guo", "Guangbo Hao", "Zhewen Hao", "Ying He", "Wenjie Hu", "Panpan Huang", "Erhang Li", "Guowei Li", "Jiashi Li", "Yao Li", "Y. K. Li", "Wenfeng Liang", "Fangyun Lin", "A. X. Liu", "Bo Liu", "Wen Liu", "Xiaodong Liu", "Xin Liu", "Yiyuan Liu", "Haoyu Lu", "Shanghao Lu", "Fuli Luo", "Shirong Ma", "Xiaotao Nie", "Tian Pei", "Yishi Piao", "Junjie Qiu", "Hui Qu", "Tongzheng Ren", "Zehui Ren", "Chong Ruan", "Zhangli Sha", "Zhihong Shao", "Junxiao Song", "Xuecheng Su", "Jingxiang Sun", "Yaofeng Sun", "Minghui Tang", "Bingxuan Wang", "Peiyi Wang", "Shiyu Wang", "Yaohui Wang", "Yongji Wang", "Tong Wu", "Y. Wu", "Xin Xie", "Zhenda Xie", "Ziwei Xie", "Yiliang Xiong", "Hanwei Xu", "R. X. Xu", "Yanhong Xu", "Dejian Yang", "Yuxiang You", "Shuiping Yu", "Xingkai Yu", "B. Zhang", "Haowei Zhang", "Lecong Zhang", "Liyue Zhang", "Mingchuan Zhang", "Minghua Zhang", "Wentao Zhang", "Yichao Zhang", "Chenggang Zhao", "Yao Zhao", "Shangyan Zhou", "Shunfeng Zhou", "Qihao Zhu", "Yuheng Zou" ], "github": "https://github.com/deepseek-ai/DeepSeek-LLM", "project_page": "", "abstract": "The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5.", "github_manual": "https://github.com/deepseek-ai/DeepSeek-LLM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-08", "arxiv_id": "2401.02677", "title": "Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss", "authors": [ "Yatharth Gupta", "Vishnu V. Jaddipal", "Harish Prabhala", "Sayak Paul", "Patrick Von Platen" ], "github": "https://github.com/segmind/ssd-1b", "project_page": "", "abstract": "Stable Diffusion XL (SDXL) has become the best open source text-to-image model (T2I) for its versatility and top-notch image quality. Efficiently addressing the computational demands of SDXL models is crucial for wider reach and applicability. In this work, we introduce two scaled-down variants, Segmind Stable Diffusion (SSD-1B) and Segmind-Vega, with 1.3B and 0.74B parameter UNets, respectively, achieved through progressive removal using layer-level losses focusing on reducing the model size while preserving generative quality. We release these models weights at https://hf.co/Segmind. Our methodology involves the elimination of residual networks and transformer blocks from the U-Net structure of SDXL, resulting in significant reductions in parameters, and latency. Our compact models effectively emulate the original SDXL by capitalizing on transferred knowledge, achieving competitive results against larger multi-billion parameter SDXL. Our work underscores the efficacy of knowledge distillation coupled with layer-level losses in reducing model size while preserving the high-quality generative capabilities of SDXL, thus facilitating more accessible deployment in resource-constrained environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/segmind/ssd-1b", "github_api_added_by": "auto" }, { "date": "2024-01-08", "arxiv_id": "2401.02839", "title": "Pheme: Efficient and Conversational Speech Generation", "authors": [ "Pawe\u0142 Budzianowski", "Taras Sereda", "Tomasz Cichy", "Ivan Vuli\u0107" ], "github": "https://github.com/PolyAI-LDN/pheme", "project_page": "", "abstract": "In recent years, speech generation has seen remarkable progress, now achieving one-shot generation capability that is often virtually indistinguishable from real human voice. Integrating such advancements in speech generation with large language models might revolutionize a wide range of applications. However, certain applications, such as assistive conversational systems, require natural and conversational speech generation tools that also operate efficiently in real time. Current state-of-the-art models like VALL-E and SoundStorm, powered by hierarchical neural audio codecs, require large neural components and extensive training data to work well. In contrast, MQTTS aims to build more compact conversational TTS models while capitalizing on smaller-scale real-life conversational speech data. However, its autoregressive nature yields high inference latency and thus limits its real-time usage. In order to mitigate the current limitations of the state-of-the-art TTS models while capitalizing on their strengths, in this work we introduce the Pheme model series that 1) offers compact yet high-performing models, 2) allows for parallel speech generation of 3) natural conversational speech, and 4) it can be trained efficiently on smaller-scale conversational data, cutting data demands by more than 10x but still matching the quality of the autoregressive TTS models. We also show that through simple teacher-student distillation we can meet significant improvements in voice quality for single-speaker setups on top of pretrained Pheme checkpoints, relying solely on synthetic speech generated by much larger teacher models. Audio samples and pretrained models are available online.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PolyAI-LDN/pheme", "github_api_added_by": "auto" }, { "date": "2024-01-08", "arxiv_id": "2401.02669", "title": "Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache", "authors": [ "Bin Lin", "Tao Peng", "Chen Zhang", "Minmin Sun", "Lanbo Li", "Hanyu Zhao", "Wencong Xiao", "Qi Xu", "Xiafei Qiu", "Shen Li", "Zhigang Ji", "Yong Li", "Wei Lin" ], "github": "", "project_page": "", "abstract": "The rapid proliferation of Large Language Models (LLMs) has been a driving force in the growth of cloud-based LLM services, which are now integral to advancing AI applications. However, the dynamic auto-regressive nature of LLM service, along with the need to support exceptionally long context lengths, demands the flexible allocation and release of substantial resources. This presents considerable challenges in designing cloud-based LLM service systems, where inefficient management can lead to performance degradation or resource wastage. In response to these challenges, this work introduces DistAttention, a novel distributed attention algorithm that segments the KV Cache into smaller, manageable units, enabling distributed processing and storage of the attention module. Based on that, we propose DistKV-LLM, a distributed LLM serving system that dynamically manages KV Cache and effectively orchestrates all accessible GPU and CPU memories spanning across the data center. This ensures a high-performance LLM service on the cloud, adaptable to a broad range of context lengths. Validated in a cloud environment with 32 NVIDIA A100 GPUs in configurations from 2 to 32 instances, our system exhibited 1.03-2.4x end-to-end throughput improvements and supported context lengths 2-19x longer than current state-of-the-art LLM service systems, as evidenced by extensive testing across 18 datasets with context lengths up to 1,900K.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-08", "arxiv_id": "2401.02823", "title": "DocGraphLM: Documental Graph Language Model for Information Extraction", "authors": [ "Dongsheng Wang", "Zhiqiang Ma", "Armineh Nourbakhsh", "Kang Gu", "Sameena Shah" ], "github": "", "project_page": "", "abstract": "Advances in Visually Rich Document Understanding (VrDU) have enabled information extraction and question answering over documents with complex layouts. Two tropes of architectures have emerged -- transformer-based models inspired by LLMs, and Graph Neural Networks. In this paper, we introduce DocGraphLM, a novel framework that combines pre-trained language models with graph semantics. To achieve this, we propose 1) a joint encoder architecture to represent documents, and 2) a novel link prediction approach to reconstruct document graphs. DocGraphLM predicts both directions and distances between nodes using a convergent joint loss function that prioritizes neighborhood restoration and downweighs distant node detection. Our experiments on three SotA datasets show consistent improvement on IE and QA tasks with the adoption of graph features. Moreover, we report that adopting the graph features accelerates convergence in the learning process during training, despite being solely constructed through link prediction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-08", "arxiv_id": "2401.02957", "title": "Denoising Vision Transformers", "authors": [ "Jiawei Yang", "Katie Z Luo", "Jiefeng Li", "Kilian Q Weinberger", "Yonglong Tian", "Yue Wang" ], "github": "https://github.com/Jiawei-Yang/Denoising-ViT", "project_page": "", "abstract": "We delve into a nuanced but significant challenge inherent to Vision Transformers (ViTs): feature maps of these models exhibit grid-like artifacts, which detrimentally hurt the performance of ViTs in downstream tasks. Our investigations trace this fundamental issue down to the positional embeddings at the input stage. To address this, we propose a novel noise model, which is universally applicable to all ViTs. Specifically, the noise model dissects ViT outputs into three components: a semantics term free from noise artifacts and two artifact-related terms that are conditioned on pixel locations. Such a decomposition is achieved by enforcing cross-view feature consistency with neural fields in a per-image basis. This per-image optimization process extracts artifact-free features from raw ViT outputs, providing clean features for offline applications. Expanding the scope of our solution to support online functionality, we introduce a learnable denoiser to predict artifact-free features directly from unprocessed ViT outputs, which shows remarkable generalization capabilities to novel data without the need for per-image optimization. Our two-stage approach, termed Denoising Vision Transformers (DVT), does not require re-training existing pre-trained ViTs and is immediately applicable to any Transformer-based architecture. We evaluate our method on a variety of representative ViTs (DINO, MAE, DeiT-III, EVA02, CLIP, DINOv2, DINOv2-reg). Extensive evaluations demonstrate that our DVT consistently and significantly improves existing state-of-the-art general-purpose models in semantic and geometric tasks across multiple datasets (e.g., +3.84 mIoU). We hope our study will encourage a re-evaluation of ViT design, especially regarding the naive use of positional embeddings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Jiawei-Yang/Denoising-ViT", "github_api_added_by": "auto" }, { "date": "2024-01-08", "arxiv_id": "2401.02955", "title": "Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively", "authors": [ "Haobo Yuan", "Xiangtai Li", "Chong Zhou", "Yining Li", "Kai Chen", "Chen Change Loy" ], "github": "https://github.com/HarborYuan/ovsam", "project_page": "", "abstract": "The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs). SAM excels in segmentation tasks across diverse domains, while CLIP is renowned for its zero-shot recognition capabilities. This paper presents an in-depth exploration of integrating these two models into a unified framework. Specifically, we introduce the Open-Vocabulary SAM, a SAM-inspired model designed for simultaneous interactive segmentation and recognition, leveraging two unique knowledge transfer modules: SAM2CLIP and CLIP2SAM. The former adapts SAM's knowledge into the CLIP via distillation and learnable transformer adapters, while the latter transfers CLIP knowledge into SAM, enhancing its recognition capabilities. Extensive experiments on various datasets and detectors show the effectiveness of Open-Vocabulary SAM in both segmentation and recognition tasks, significantly outperforming the naive baselines of simply combining SAM and CLIP. Furthermore, aided with image classification data training, our method can segment and recognize approximately 22,000 classes.", "github_manual": "https://github.com/HarborYuan/ovsam", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-09", "arxiv_id": "2401.04088", "title": "Mixtral of Experts", "authors": [ "Albert Q. Jiang", "Alexandre Sablayrolles", "Antoine Roux", "Arthur Mensch", "Blanche Savary", "Chris Bamford", "Devendra Singh Chaplot", "Diego de las Casas", "Emma Bou Hanna", "Florian Bressand", "Gianna Lengyel", "Guillaume Bour", "Guillaume Lample", "L\u00e9lio Renard Lavaud", "Lucile Saulnier", "Marie-Anne Lachaux", "Pierre Stock", "Sandeep Subramanian", "Sophia Yang", "Szymon Antoniak", "Teven Le Scao", "Th\u00e9ophile Gervet", "Thibaut Lavril", "Thomas Wang", "Timoth\u00e9e Lacroix", "William El Sayed" ], "github": "", "project_page": "", "abstract": "We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral 7B, with the difference that each layer is composed of 8 feedforward blocks (i.e. experts). For every token, at each layer, a router network selects two experts to process the current state and combine their outputs. Even though each token only sees two experts, the selected experts can be different at each timestep. As a result, each token has access to 47B parameters, but only uses 13B active parameters during inference. Mixtral was trained with a context size of 32k tokens and it outperforms or matches Llama 2 70B and GPT-3.5 across all evaluated benchmarks. In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks. We also provide a model fine-tuned to follow instructions, Mixtral 8x7B - Instruct, that surpasses GPT-3.5 Turbo, Claude-2.1, Gemini Pro, and Llama 2 70B - chat model on human benchmarks. Both the base and instruct models are released under the Apache 2.0 license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-09", "arxiv_id": "2401.04092", "title": "GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation", "authors": [ "Tong Wu", "Guandao Yang", "Zhibing Li", "Kai Zhang", "Ziwei Liu", "Leonidas Guibas", "Dahua Lin", "Gordon Wetzstein" ], "github": "https://github.com/3DTopia/GPTEval3D", "project_page": "", "abstract": "Despite recent advances in text-to-3D generative methods, there is a notable absence of reliable evaluation metrics. Existing metrics usually focus on a single criterion each, such as how well the asset aligned with the input text. These metrics lack the flexibility to generalize to different evaluation criteria and might not align well with human preferences. Conducting user preference studies is an alternative that offers both adaptability and human-aligned results. User studies, however, can be very expensive to scale. This paper presents an automatic, versatile, and human-aligned evaluation metric for text-to-3D generative models. To this end, we first develop a prompt generator using GPT-4V to generate evaluating prompts, which serve as input to compare text-to-3D models. We further design a method instructing GPT-4V to compare two 3D assets according to user-defined criteria. Finally, we use these pairwise comparison results to assign these models Elo ratings. Experimental results suggest our metric strongly align with human preference across different evaluation criteria.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/GPTEval3D", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-09", "arxiv_id": "2401.03065", "title": "CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution", "authors": [ "Alex Gu", "Baptiste Rozi\u00e8re", "Hugh Leather", "Armando Solar-Lezama", "Gabriel Synnaeve", "Sida I. Wang" ], "github": "https://github.com/deepseek-ai/DeepSeek-Coder", "project_page": "", "abstract": "We present CRUXEval (Code Reasoning, Understanding, and eXecution Evaluation), a benchmark consisting of 800 Python functions (3-13 lines). Each function comes with an input-output pair, leading to two natural tasks: input prediction and output prediction. First, we propose a generic recipe for generating our execution benchmark which can be used to create future variation of the benchmark. Second, we evaluate twenty code models on our benchmark and discover that many recent high-scoring models on HumanEval do not show the same improvements on our benchmark. Third, we show that simple CoT and fine-tuning schemes can improve performance on our benchmark but remain far from solving it. The best setup, GPT-4 with chain of thought (CoT), achieves a pass@1 of 75% and 81% on input and output prediction, respectively. In contrast, Code Llama 34B achieves a pass@1 of 50% and 46% on input and output prediction, highlighting the gap between open and closed source models. As no model is close to acing CRUXEval, we provide examples of consistent GPT-4 failures on simple programs as a lens into its code reasoning capabilities and areas for improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/deepseek-ai/DeepSeek-Coder", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-09", "arxiv_id": "2401.03462", "title": "Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon", "authors": [ "Peitian Zhang", "Zheng Liu", "Shitao Xiao", "Ninglu Shao", "Qiwei Ye", "Zhicheng Dou" ], "github": "https://github.com/FlagOpen/FlagEmbedding", "project_page": "", "abstract": "The utilization of long contexts poses a big challenge for large language models due to their limited context window length. Although the context window can be extended through fine-tuning, it will result in a considerable cost at both training and inference time, and exert an unfavorable impact to the LLM's original capabilities. In this work, we propose Activation Beacon, which condenses LLM's raw activations into more compact forms such that it can perceive a much longer context with a limited context window. Activation Beacon is introduced as a plug-and-play module for the LLM. It fully preserves the LLM's original capability on short contexts while extending the new capability on processing longer contexts. Besides, it works with short sliding windows to process the long context, which achieves a competitive memory and time efficiency in both training and inference. Activation Beacon is learned by the auto-regression task conditioned on a mixture of beacons with diversified condensing ratios. Thanks to such a treatment, it can be efficiently trained purely with short-sequence data in just 10K steps, which consumes less than 9 hours on a single 8xA800 GPU machine. The experimental studies show that Activation Beacon is able to extend Llama-2-7B's context length by times100 times (from 4K to 400K), meanwhile achieving a superior result on both long-context generation and understanding tasks. Our model and code will be available at the BGE repository.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FlagOpen/FlagEmbedding", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-09", "arxiv_id": "2401.03003", "title": "AST-T5: Structure-Aware Pretraining for Code Generation and Understanding", "authors": [ "Linyuan Gong", "Mostafa Elhoushi", "Alvin Cheung" ], "github": "https://github.com/gonglinyuan/ast_t5", "project_page": "", "abstract": "Large language models (LLMs) have made significant advancements in code-related tasks, yet many LLMs treat code as simple sequences, neglecting its structured nature. We introduce AST-T5, a novel pretraining paradigm that leverages the Abstract Syntax Tree (AST) for enhanced code generation, transpilation, and understanding. Using dynamic programming, our AST-Aware Segmentation retains code structure, while our AST-Aware Span Corruption objective equips the model to reconstruct various code structures. Unlike other models, AST-T5 avoids intricate program analyses or architectural changes, so it integrates seamlessly with any encoder-decoder Transformer. Evaluations show that AST-T5 consistently outperforms similar-sized LMs across various code-related tasks. Structure-awareness makes AST-T5 particularly powerful in code-to-code tasks, surpassing CodeT5 by 2 points in exact match score for the Bugs2Fix task and by 3 points in exact match score for Java-C# Transpilation in CodeXGLUE. Our code and model are publicly available at https://github.com/gonglinyuan/ast_t5.", "github_manual": "", "github_regex": "https://github.com/gonglinyuan/ast_t5", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-09", "arxiv_id": "2401.02987", "title": "Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach", "authors": [ "Prince Aboagye", "Yan Zheng", "Junpeng Wang", "Uday Singh Saini", "Xin Dai", "Michael Yeh", "Yujie Fan", "Zhongfang Zhuang", "Shubham Jain", "Liang Wang", "Wei Zhang" ], "github": "", "project_page": "", "abstract": "The emergence of pretrained models has significantly impacted from Natural Language Processing (NLP) and Computer Vision to relational datasets. Traditionally, these models are assessed through fine-tuned downstream tasks. However, this raises the question of how to evaluate these models more efficiently and more effectively. In this study, we explore a novel approach where we leverage the meta features associated with each entity as a source of worldly knowledge and employ entity representations from the models. We propose using the consistency between these representations and the meta features as a metric for evaluating pretrained models. Our method's effectiveness is demonstrated across various domains, including models with relational datasets, large language models and images models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-09", "arxiv_id": "2401.02994", "title": "Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM", "authors": [ "Xiaoding Lu", "Adian Liusie", "Vyas Raina", "Yuwen Zhang", "William Beauchamp" ], "github": "", "project_page": "", "abstract": "In conversational AI research, there's a noticeable trend towards developing models with a larger number of parameters, exemplified by models like ChatGPT. While these expansive models tend to generate increasingly better chat responses, they demand significant computational resources and memory. This study explores a pertinent question: Can a combination of smaller models collaboratively achieve comparable or enhanced performance relative to a singular large model? We introduce an approach termed \"blending\", a straightforward yet effective method of integrating multiple chat AIs. Our empirical evidence suggests that when specific smaller models are synergistically blended, they can potentially outperform or match the capabilities of much larger counterparts. For instance, integrating just three models of moderate size (6B/13B paramaeters) can rival or even surpass the performance metrics of a substantially larger model like ChatGPT (175B+ paramaters). This hypothesis is rigorously tested using A/B testing methodologies with a large user base on the Chai research platform over a span of thirty days. The findings underscore the potential of the \"blending\" strategy as a viable approach for enhancing chat AI efficacy without a corresponding surge in computational demands.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-09", "arxiv_id": "2401.04081", "title": "MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts", "authors": [ "Maciej Pi\u00f3ro", "Kamil Ciebiera", "Krystian Kr\u00f3l", "Jan Ludziejewski", "Sebastian Jaszczur" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "State Space Models (SSMs) have become serious contenders in the field of sequential modeling, challenging the dominance of Transformers. At the same time, Mixture of Experts (MoE) has significantly improved Transformer-based LLMs, including recent state-of-the-art open-source models. We propose that to unlock the potential of SSMs for scaling, they should be combined with MoE. We showcase this on Mamba, a recent SSM-based model that achieves remarkable, Transformer-like performance. Our model, MoE-Mamba, outperforms both Mamba and Transformer-MoE. In particular, MoE-Mamba reaches the same performance as Mamba in 2.2x less training steps while preserving the inference performance gains of Mamba against the Transformer.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-09", "arxiv_id": "2401.03804", "title": "TeleChat Technical Report", "authors": [ "Zihan Wang", "Xinzhang Liu", "Shixuan Liu", "Yitong Yao", "Yuyao Huang", "Zhongjiang He", "Xuelong Li", "Yongxiang Li", "Zhonghao Che", "Zhaoxi Zhang", "Yan Wang", "Xin Wang", "Luwen Pu", "Huihan Xu", "Ruiyu Fang", "Yu Zhao", "Jie Zhang", "Xiaomeng Huang", "Zhilong Lu", "Jiaxin Peng", "Wenjun Zheng", "Shiquan Wang", "Bingkai Yang", "Xuewei he", "Zhuoru Jiang", "Qiyi Xie", "Yanhan Zhang", "Zhongqiu Li", "Lingling Shi", "Weiwei Fu", "Yin Zhang", "Zilu Huang", "Sishi Xiong", "Yuxiang Zhang", "Chao Wang", "Shuangyong Song" ], "github": "", "project_page": "", "abstract": "In this technical report, we present TeleChat, a collection of large language models (LLMs) with parameters of 3 billion, 7 billion and 12 billion. It includes pretrained language models as well as fine-tuned chat models that is aligned with human preferences. TeleChat is initially pretrained on an extensive corpus containing a diverse collection of texts from both English and Chinese languages, including trillions of tokens. Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe. We evaluate the performance of TeleChat on various tasks, including language understanding, mathematics, reasoning, code generation, and knowledge-based question answering. Our findings indicate that TeleChat achieves comparable performance to other open-source models of similar size across a wide range of public benchmarks. To support future research and applications utilizing LLMs, we release the fine-tuned model checkpoints of TeleChat's 7B and 12B variant, along with code and a portion of our pretraining data, to the public community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-09", "arxiv_id": "2401.04099", "title": "AGG: Amortized Generative 3D Gaussians for Single Image to 3D", "authors": [ "Dejia Xu", "Ye Yuan", "Morteza Mardani", "Sifei Liu", "Jiaming Song", "Zhangyang Wang", "Arash Vahdat" ], "github": "", "project_page": "", "abstract": "Given the growing need for automatic 3D content creation pipelines, various 3D representations have been studied to generate 3D objects from a single image. Due to its superior rendering efficiency, 3D Gaussian splatting-based models have recently excelled in both 3D reconstruction and generation. 3D Gaussian splatting approaches for image to 3D generation are often optimization-based, requiring many computationally expensive score-distillation steps. To overcome these challenges, we introduce an Amortized Generative 3D Gaussian framework (AGG) that instantly produces 3D Gaussians from a single image, eliminating the need for per-instance optimization. Utilizing an intermediate hybrid representation, AGG decomposes the generation of 3D Gaussian locations and other appearance attributes for joint optimization. Moreover, we propose a cascaded pipeline that first generates a coarse representation of the 3D data and later upsamples it with a 3D Gaussian super-resolution module. Our method is evaluated against existing optimization-based 3D Gaussian frameworks and sampling-based pipelines utilizing other 3D representations, where AGG showcases competitive generation abilities both qualitatively and quantitatively while being several orders of magnitude faster. Project page: https://ir1d.github.io/AGG/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-09", "arxiv_id": "2401.03506", "title": "DiarizationLM: Speaker Diarization Post-Processing with Large Language Models", "authors": [ "Quan Wang", "Yiling Huang", "Guanlong Zhao", "Evan Clark", "Wei Xia", "Hank Liao" ], "github": "https://github.com/google/speaker-id", "project_page": "", "abstract": "In this paper, we introduce DiarizationLM, a framework to leverage large language models (LLM) to post-process the outputs from a speaker diarization system. Various goals can be achieved with the proposed framework, such as improving the readability of the diarized transcript, or reducing the word diarization error rate (WDER). In this framework, the outputs of the automatic speech recognition (ASR) and speaker diarization systems are represented as a compact textual format, which is included in the prompt to an optionally finetuned LLM. The outputs of the LLM can be used as the refined diarization results with the desired enhancement. As a post-processing step, this framework can be easily applied to any off-the-shelf ASR and speaker diarization systems without retraining existing components. Our experiments show that a finetuned PaLM 2-S model can reduce the WDER by rel. 25.9% on the Fisher telephone conversation dataset, and rel. 31% on the Callhome English dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/google/speaker-id", "github_api_added_by": "auto" }, { "date": "2024-01-10", "arxiv_id": "2401.04468", "title": "MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation", "authors": [ "Weimin Wang", "Jiawei Liu", "Zhijie Lin", "Jiangqiao Yan", "Shuo Chen", "Chetwin Low", "Tuyen Hoang", "Jie Wu", "Jun Hao Liew", "Hanshu Yan", "Daquan Zhou", "Jiashi Feng" ], "github": "", "project_page": "", "abstract": "The growing demand for high-fidelity video generation from textual descriptions has catalyzed significant research in this field. In this work, we introduce MagicVideo-V2 that integrates the text-to-image model, video motion generator, reference image embedding module and frame interpolation module into an end-to-end video generation pipeline. Benefiting from these architecture designs, MagicVideo-V2 can generate an aesthetically pleasing, high-resolution video with remarkable fidelity and smoothness. It demonstrates superior performance over leading Text-to-Video systems such as Runway, Pika 1.0, Morph, Moon Valley and Stable Video Diffusion model via user evaluation at large scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-10", "arxiv_id": "2401.04575", "title": "Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding", "authors": [ "Yatong Bai", "Utsav Garg", "Apaar Shanker", "Haoming Zhang", "Samyak Parajuli", "Erhan Bas", "Isidora Filipovic", "Amelia N. Chu", "Eugenia D Fomitcheva", "Elliot Branson", "Aerin Kim", "Somayeh Sojoudi", "Kyunghyun Cho" ], "github": "", "project_page": "", "abstract": "Vision and vision-language applications of neural networks, such as image classification and captioning, rely on large-scale annotated datasets that require non-trivial data-collecting processes. This time-consuming endeavor hinders the emergence of large-scale datasets, limiting researchers and practitioners to a small number of choices. Therefore, we seek more efficient ways to collect and annotate images. Previous initiatives have gathered captions from HTML alt-texts and crawled social media postings, but these data sources suffer from noise, sparsity, or subjectivity. For this reason, we turn to commercial shopping websites whose data meet three criteria: cleanliness, informativeness, and fluency. We introduce the Let's Go Shopping (LGS) dataset, a large-scale public dataset with 15 million image-caption pairs from publicly available e-commerce websites. When compared with existing general-domain datasets, the LGS images focus on the foreground object and have less complex backgrounds. Our experiments on LGS show that the classifiers trained on existing benchmark datasets do not readily generalize to e-commerce data, while specific self-supervised visual feature extractors can better generalize. Furthermore, LGS's high-quality e-commerce-focused images and bimodal nature make it advantageous for vision-language bi-modal tasks: LGS enables image-captioning models to generate richer captions and helps text-to-image generation models achieve e-commerce style transfer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-10", "arxiv_id": "2401.04577", "title": "Masked Audio Generation using a Single Non-Autoregressive Transformer", "authors": [ "Alon Ziv", "Itai Gat", "Gael Le Lan", "Tal Remez", "Felix Kreuk", "Alexandre D\u00e9fossez", "Jade Copet", "Gabriel Synnaeve", "Yossi Adi" ], "github": "https://github.com/archinetai/audio-diffusion-pytorch", "project_page": "", "abstract": "We introduce MAGNeT, a masked generative sequence modeling method that operates directly over several streams of audio tokens. Unlike prior work, MAGNeT is comprised of a single-stage, non-autoregressive transformer. During training, we predict spans of masked tokens obtained from a masking scheduler, while during inference we gradually construct the output sequence using several decoding steps. To further enhance the quality of the generated audio, we introduce a novel rescoring method in which, we leverage an external pre-trained model to rescore and rank predictions from MAGNeT, which will be then used for later decoding steps. Lastly, we explore a hybrid version of MAGNeT, in which we fuse between autoregressive and non-autoregressive models to generate the first few seconds in an autoregressive manner while the rest of the sequence is being decoded in parallel. We demonstrate the efficiency of MAGNeT for the task of text-to-music and text-to-audio generation and conduct an extensive empirical evaluation, considering both objective metrics and human studies. The proposed approach is comparable to the evaluated baselines, while being significantly faster (x7 faster than the autoregressive baseline). Through ablation studies and analysis, we shed light on the importance of each of the components comprising MAGNeT, together with pointing to the trade-offs between autoregressive and non-autoregressive modeling, considering latency, throughput, and generation quality. Samples are available on our demo page https://pages.cs.huji.ac.il/adiyoss-lab/MAGNeT.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/archinetai/audio-diffusion-pytorch", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-10", "arxiv_id": "2401.04695", "title": "Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers", "authors": [ "Gal Yona", "Roee Aharoni", "Mor Geva" ], "github": "", "project_page": "", "abstract": "Factual questions typically can be answered correctly at different levels of granularity. For example, both ``August 4, 1961'' and ``1961'' are correct answers to the question ``When was Barack Obama born?''. Standard question answering (QA) evaluation protocols, however, do not explicitly take this into account and compare a predicted answer against answers of a single granularity level. In this work, we propose GRANOLA QA, a novel evaluation setting where a predicted answer is evaluated in terms of accuracy and informativeness against a set of multi-granularity answers. We present a simple methodology for enriching existing datasets with multi-granularity answers, and create GRANOLA-EQ, a multi-granularity version of the EntityQuestions dataset. We evaluate a range of decoding methods on GRANOLA-EQ, including a new algorithm, called Decoding with Response Aggregation (DRAG), that is geared towards aligning the response granularity with the model's uncertainty. Our experiments show that large language models with standard decoding tend to generate specific answers, which are often incorrect. In contrast, when evaluated on multi-granularity answers, DRAG yields a nearly 20 point increase in accuracy on average, which further increases for rare entities. Overall, this reveals that standard evaluation and decoding schemes may significantly underestimate the knowledge encapsulated in LMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-10", "arxiv_id": "2401.04658", "title": "Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models", "authors": [ "Zhen Qin", "Weigao Sun", "Dong Li", "Xuyang Shen", "Weixuan Sun", "Yiran Zhong" ], "github": "https://github.com/OpenNLPLab/lightning-attention", "project_page": "", "abstract": "Linear attention is an efficient attention mechanism that has recently emerged as a promising alternative to conventional softmax attention. With its ability to process tokens in linear computational complexities, linear attention, in theory, can handle sequences of unlimited length without sacrificing speed, i.e., maintaining a constant training speed for various sequence lengths with a fixed memory consumption. However, due to the issue with cumulative summation (cumsum), current linear attention algorithms cannot demonstrate their theoretical advantage in a causal setting. In this paper, we present Lightning Attention-2, the first linear attention implementation that enables linear attention to realize its theoretical computational benefits. To achieve this, we leverage the thought of tiling, separately handling the intra-block and inter-block components in linear attention calculation. Specifically, we utilize the conventional attention computation mechanism for the intra-blocks and apply linear attention kernel tricks for the inter-blocks. A tiling technique is adopted through both forward and backward procedures to take full advantage of the GPU hardware. We implement our algorithm in Triton to make it IO-aware and hardware-friendly. Various experiments are conducted on different model sizes and sequence lengths. Lightning Attention-2 retains consistent training and inference speed regardless of input sequence length and is significantly faster than other attention mechanisms. The source code is available at https://github.com/OpenNLPLab/lightning-attention.", "github_manual": "", "github_regex": "https://github.com/OpenNLPLab/lightning-attention", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-10", "arxiv_id": "2401.04398", "title": "Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding", "authors": [ "Zilong Wang", "Hao Zhang", "Chun-Liang Li", "Julian Martin Eisenschlos", "Vincent Perot", "Zifeng Wang", "Lesly Miculicich", "Yasuhisa Fujii", "Jingbo Shang", "Chen-Yu Lee", "Tomas Pfister" ], "github": "", "project_page": "", "abstract": "Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both free-form questions and semi-structured tabular data. Chain-of-Thought and its similar approaches incorporate the reasoning chain in the form of textual context, but it is still an open question how to effectively leverage tabular data in the reasoning chain. We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts. Specifically, we guide LLMs using in-context learning to iteratively generate operations and update the table to represent a tabular reasoning chain. LLMs can therefore dynamically plan the next operation based on the results of the previous ones. This continuous evolution of the table forms a chain, showing the reasoning process for a given tabular problem. The chain carries structured information of the intermediate results, enabling more accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art performance on WikiTQ, FeTaQA, and TabFact benchmarks across multiple LLM choices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-10", "arxiv_id": "2401.04718", "title": "Jump Cut Smoothing for Talking Heads", "authors": [ "Xiaojuan Wang", "Taesung Park", "Yang Zhou", "Eli Shechtman", "Richard Zhang" ], "github": "", "project_page": "", "abstract": "A jump cut offers an abrupt, sometimes unwanted change in the viewing experience. We present a novel framework for smoothing these jump cuts, in the context of talking head videos. We leverage the appearance of the subject from the other source frames in the video, fusing it with a mid-level representation driven by DensePose keypoints and face landmarks. To achieve motion, we interpolate the keypoints and landmarks between the end frames around the cut. We then use an image translation network from the keypoints and source frames, to synthesize pixels. Because keypoints can contain errors, we propose a cross-modal attention scheme to select and pick the most appropriate source amongst multiple options for each key point. By leveraging this mid-level representation, our method can achieve stronger results than a strong video interpolation baseline. We demonstrate our method on various jump cuts in the talking head videos, such as cutting filler words, pauses, and even random cuts. Our experiments show that we can achieve seamless transitions, even in the challenging cases where the talking head rotates or moves drastically in the jump cut.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-10", "arxiv_id": "2401.04283", "title": "FADI-AEC: Fast Score Based Diffusion Model Guided by Far-end Signal for Acoustic Echo Cancellation", "authors": [ "Yang Liu", "Li Wan", "Yun Li", "Yiteng Huang", "Ming Sun", "James Luan", "Yangyang Shi", "Xin Lei" ], "github": "", "project_page": "", "abstract": "Despite the potential of diffusion models in speech enhancement, their deployment in Acoustic Echo Cancellation (AEC) has been restricted. In this paper, we propose DI-AEC, pioneering a diffusion-based stochastic regeneration approach dedicated to AEC. Further, we propose FADI-AEC, fast score-based diffusion AEC framework to save computational demands, making it favorable for edge devices. It stands out by running the score model once per frame, achieving a significant surge in processing efficiency. Apart from that, we introduce a novel noise generation technique where far-end signals are utilized, incorporating both far-end and near-end signals to refine the score model's accuracy. We test our proposed method on the ICASSP2023 Microsoft deep echo cancellation challenge evaluation dataset, where our method outperforms some of the end-to-end methods and other diffusion based echo cancellation methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-11", "arxiv_id": "2401.05252", "title": "PIXART-\u03b4: Fast and Controllable Image Generation with Latent Consistency Models", "authors": [ "Junsong Chen", "Yue Wu", "Simian Luo", "Enze Xie", "Sayak Paul", "Ping Luo", "Hang Zhao", "Zhenguo Li" ], "github": "", "project_page": "", "abstract": "This technical report introduces PIXART-{\\delta}, a text-to-image synthesis framework that integrates the Latent Consistency Model (LCM) and ControlNet into the advanced PIXART-{\\alpha} model. PIXART-{\\alpha} is recognized for its ability to generate high-quality images of 1024px resolution through a remarkably efficient training process. The integration of LCM in PIXART-{\\delta} significantly accelerates the inference speed, enabling the production of high-quality images in just 2-4 steps. Notably, PIXART-{\\delta} achieves a breakthrough 0.5 seconds for generating 1024x1024 pixel images, marking a 7x improvement over the PIXART-{\\alpha}. Additionally, PIXART-{\\delta} is designed to be efficiently trainable on 32GB V100 GPUs within a single day. With its 8-bit inference capability (von Platen et al., 2023), PIXART-{\\delta} can synthesize 1024px images within 8GB GPU memory constraints, greatly enhancing its usability and accessibility. Furthermore, incorporating a ControlNet-like module enables fine-grained control over text-to-image diffusion models. We introduce a novel ControlNet-Transformer architecture, specifically tailored for Transformers, achieving explicit controllability alongside high-quality image generation. As a state-of-the-art, open-source image generation model, PIXART-{\\delta} offers a promising alternative to the Stable Diffusion family of models, contributing significantly to text-to-image synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-11", "arxiv_id": "2401.05334", "title": "URHand: Universal Relightable Hands", "authors": [ "Zhaoxi Chen", "Gyeongsik Moon", "Kaiwen Guo", "Chen Cao", "Stanislav Pidhorskyi", "Tomas Simon", "Rohan Joshi", "Yuan Dong", "Yichen Xu", "Bernardo Pires", "He Wen", "Lucas Evans", "Bo Peng", "Julia Buffalini", "Autumn Trimble", "Kevyn McPhail", "Melissa Schoeller", "Shoou-I Yu", "Javier Romero", "Michael Zollh\u00f6fer", "Yaser Sheikh", "Ziwei Liu", "Shunsuke Saito" ], "github": "", "project_page": "", "abstract": "Existing photorealistic relightable hand models require extensive identity-specific observations in different views, poses, and illuminations, and face challenges in generalizing to natural illuminations and novel identities. To bridge this gap, we present URHand, the first universal relightable hand model that generalizes across viewpoints, poses, illuminations, and identities. Our model allows few-shot personalization using images captured with a mobile phone, and is ready to be photorealistically rendered under novel illuminations. To simplify the personalization process while retaining photorealism, we build a powerful universal relightable prior based on neural relighting from multi-view images of hands captured in a light stage with hundreds of identities. The key challenge is scaling the cross-identity training while maintaining personalized fidelity and sharp details without compromising generalization under natural illuminations. To this end, we propose a spatially varying linear lighting model as the neural renderer that takes physics-inspired shading as input feature. By removing non-linear activations and bias, our specifically designed lighting model explicitly keeps the linearity of light transport. This enables single-stage training from light-stage data while generalizing to real-time rendering under arbitrary continuous illuminations across diverse identities. In addition, we introduce the joint learning of a physically based model and our neural relighting model, which further improves fidelity and generalization. Extensive experiments show that our approach achieves superior performance over existing methods in terms of both quality and generalizability. We also demonstrate quick personalization of URHand from a short phone scan of an unseen identity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-11", "arxiv_id": "2401.05335", "title": "InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes", "authors": [ "Mohamad Shahbazi", "Liesbeth Claessens", "Michael Niemeyer", "Edo Collins", "Alessio Tonioni", "Luc Van Gool", "Federico Tombari" ], "github": "", "project_page": "", "abstract": "We introduce InseRF, a novel method for generative object insertion in the NeRF reconstructions of 3D scenes. Based on a user-provided textual description and a 2D bounding box in a reference viewpoint, InseRF generates new objects in 3D scenes. Recently, methods for 3D scene editing have been profoundly transformed, owing to the use of strong priors of text-to-image diffusion models in 3D generative modeling. Existing methods are mostly effective in editing 3D scenes via style and appearance changes or removing existing objects. Generating new objects, however, remains a challenge for such methods, which we address in this study. Specifically, we propose grounding the 3D object insertion to a 2D object insertion in a reference view of the scene. The 2D edit is then lifted to 3D using a single-view object reconstruction method. The reconstructed object is then inserted into the scene, guided by the priors of monocular depth estimation methods. We evaluate our method on various 3D scenes and provide an in-depth analysis of the proposed components. Our experiments with generative insertion of objects in several 3D scenes indicate the effectiveness of our method compared to the existing methods. InseRF is capable of controllable and 3D-consistent object insertion without requiring explicit 3D information as input. Please visit our project page at https://mohamad-shahbazi.github.io/inserf.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-11", "arxiv_id": "2401.04925", "title": "The Impact of Reasoning Step Length on Large Language Models", "authors": [ "Mingyu Jin", "Qinkai Yu", "Dong shu", "Haiyan Zhao", "Wenyue Hua", "Yanda Meng", "Yongfeng Zhang", "Mengnan Du" ], "github": "https://github.com/jmyissb/The-Impact-of-Reasoning-Step-Length-on-Large-Language-Models", "project_page": "", "abstract": "Chain of Thought (CoT) is significant in improving the reasoning abilities of large language models (LLMs). However, the correlation between the effectiveness of CoT and the length of reasoning steps in prompts remains largely unknown. To shed light on this, we have conducted several empirical experiments to explore the relations. Specifically, we design experiments that expand and compress the rationale reasoning steps within CoT demonstrations, while keeping all other factors constant. We have the following key findings. First, the results indicate that lengthening the reasoning steps in prompts, even without adding new information into the prompt, considerably enhances LLMs' reasoning abilities across multiple datasets. Alternatively, shortening the reasoning steps, even while preserving the key information, significantly diminishes the reasoning abilities of models. This finding highlights the importance of the number of steps in CoT prompts and provides practical guidance to make better use of LLMs' potential in complex problem-solving scenarios. Second, we also investigated the relationship between the performance of CoT and the rationales used in demonstrations. Surprisingly, the result shows that even incorrect rationales can yield favorable outcomes if they maintain the requisite length of inference. Third, we observed that the advantages of increasing reasoning steps are task-dependent: simpler tasks require fewer steps, whereas complex tasks gain significantly from longer inference sequences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jmyissb/The-Impact-of-Reasoning-Step-Length-on-Large-Language-Models", "github_api_added_by": "auto" }, { "date": "2024-01-11", "arxiv_id": "2401.05033", "title": "Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk", "authors": [ "Dennis Ulmer", "Elman Mansimov", "Kaixiang Lin", "Justin Sun", "Xibin Gao", "Yi Zhang" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be challenging. Instructing tuning, i.e. tuning models on instruction and sample responses generated by humans (Ouyang et al., 2022), has proven as an effective method to do so, yet requires a number of data samples that a) might not be available or b) costly to generate. Furthermore, this cost increases when the goal is to make the LLM follow a specific workflow within a dialogue instead of single instructions. Inspired by the self-play technique in reinforcement learning and the use of LLMs to simulate human agents, we propose a more effective method for data collection through LLMs engaging in a conversation in various roles. This approach generates a training data via \"self-talk\" of LLMs that can be refined and utilized for supervised fine-tuning. We introduce an automated way to measure the (partial) success of a dialogue. This metric is used to filter the generated conversational data that is fed back in LLM for training. Based on our automated and human evaluations of conversation quality, we demonstrate that such self-talk data improves results. In addition, we examine the various characteristics that showcase the quality of generated dialogues and how they can be connected to their potential utility as training data.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-11", "arxiv_id": "2401.05314", "title": "ANIM-400K: A Large-Scale Dataset for Automated End-To-End Dubbing of Video", "authors": [ "Kevin Cai", "Chonghua Liu", "David M. Chan" ], "github": "https://github.com/davidmchan/Anim400K", "project_page": "", "abstract": "The Internet's wealth of content, with up to 60% published in English, starkly contrasts the global population, where only 18.8% are English speakers, and just 5.1% consider it their native language, leading to disparities in online information access. Unfortunately, automated processes for dubbing of video - replacing the audio track of a video with a translated alternative - remains a complex and challenging task due to pipelines, necessitating precise timing, facial movement synchronization, and prosody matching. While end-to-end dubbing offers a solution, data scarcity continues to impede the progress of both end-to-end and pipeline-based methods. In this work, we introduce Anim-400K, a comprehensive dataset of over 425K aligned animated video segments in Japanese and English supporting various video-related tasks, including automated dubbing, simultaneous translation, guided video summarization, and genre/theme/style classification. Our dataset is made publicly available for research purposes at https://github.com/davidmchan/Anim400K.", "github_manual": "", "github_regex": "https://github.com/davidmchan/Anim400K", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-11", "arxiv_id": "2401.05293", "title": "Score Distillation Sampling with Learned Manifold Corrective", "authors": [ "Thiemo Alldieck", "Nikos Kolotouros", "Cristian Sminchisescu" ], "github": "", "project_page": "", "abstract": "Score Distillation Sampling (SDS) is a recent but already widely popular method that relies on an image diffusion model to control optimization problems using text prompts. In this paper, we conduct an in-depth analysis of the SDS loss function, identify an inherent problem with its formulation, and propose a surprisingly easy but effective fix. Specifically, we decompose the loss into different factors and isolate the component responsible for noisy gradients. In the original formulation, high text guidance is used to account for the noise, leading to unwanted side effects. Instead, we train a shallow network mimicking the timestep-dependent denoising deficiency of the image diffusion model in order to effectively factor it out. We demonstrate the versatility and the effectiveness of our novel loss formulation through several qualitative and quantitative experiments, including optimization-based image synthesis and editing, zero-shot image translation network training, and text-to-3D synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.06104", "title": "Transformers are Multi-State RNNs", "authors": [ "Matanel Oren", "Michael Hassid", "Yossi Adi", "Roy Schwartz" ], "github": "https://github.com/schwartz-lab-NLP/TOVA", "project_page": "", "abstract": "Transformers are considered conceptually different compared to the previous generation of state-of-the-art NLP models - recurrent neural networks (RNNs). In this work, we demonstrate that decoder-only transformers can in fact be conceptualized as infinite multi-state RNNs - an RNN variant with unlimited hidden state size. We further show that pretrained transformers can be converted into finite multi-state RNNs by fixing the size of their hidden state. We observe that several existing transformers cache compression techniques can be framed as such conversion policies, and introduce a novel policy, TOVA, which is simpler compared to these policies. Our experiments with several long range tasks indicate that TOVA outperforms all other baseline policies, while being nearly on par with the full (infinite) model, and using in some cases only 1{8} of the original cache size. Our results indicate that transformer decoder LLMs often behave in practice as RNNs. They also lay out the option of mitigating one of their most painful computational bottlenecks - the size of their cache memory. We publicly release our code at https://github.com/schwartz-lab-NLP/TOVA.", "github_manual": "", "github_regex": "https://github.com/schwartz-lab-NLP/TOVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.05675", "title": "Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation", "authors": [ "Seung Hyun Lee", "Yinxiao Li", "Junjie Ke", "Innfarn Yoo", "Han Zhang", "Jiahui Yu", "Qifei Wang", "Fei Deng", "Glenn Entis", "Junfeng He", "Gang Li", "Sangpil Kim", "Irfan Essa", "Feng Yang" ], "github": "", "project_page": "", "abstract": "Recent works demonstrate that using reinforcement learning (RL) with quality rewards can enhance the quality of generated images in text-to-image (T2I) generation. However, a simple aggregation of multiple rewards may cause over-optimization in certain metrics and degradation in others, and it is challenging to manually find the optimal weights. An effective strategy to jointly optimize multiple rewards in RL for T2I generation is highly desirable. This paper introduces Parrot, a novel multi-reward RL framework for T2I generation. Through the use of the batch-wise Pareto optimal selection, Parrot automatically identifies the optimal trade-off among different rewards during the RL optimization of the T2I generation. Additionally, Parrot employs a joint optimization approach for the T2I model and the prompt expansion network, facilitating the generation of quality-aware text prompts, thus further enhancing the final image quality. To counteract the potential catastrophic forgetting of the original user prompt due to prompt expansion, we introduce original prompt centered guidance at inference time, ensuring that the generated image remains faithful to the user input. Extensive experiments and a user study demonstrate that Parrot outperforms several baseline methods across various quality criteria, including aesthetics, human preference, image sentiment, and text-image alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.06102", "title": "Patchscope: A Unifying Framework for Inspecting Hidden Representations of Language Models", "authors": [ "Asma Ghandeharioun", "Avi Caciularu", "Adam Pearce", "Lucas Dixon", "Mor Geva" ], "github": "", "project_page": "", "abstract": "Inspecting the information encoded in hidden representations of large language models (LLMs) can explain models' behavior and verify their alignment with human values. Given the capabilities of LLMs in generating human-understandable text, we propose leveraging the model itself to explain its internal representations in natural language. We introduce a framework called Patchscopes and show how it can be used to answer a wide range of research questions about an LLM's computation. We show that prior interpretability methods based on projecting representations into the vocabulary space and intervening on the LLM computation, can be viewed as special instances of this framework. Moreover, several of their shortcomings such as failure in inspecting early layers or lack of expressivity can be mitigated by a Patchscope. Beyond unifying prior inspection techniques, Patchscopes also opens up new possibilities such as using a more capable model to explain the representations of a smaller model, and unlocks new applications such as self-correction in multi-hop reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.05654", "title": "Towards Conversational Diagnostic AI", "authors": [ "Tao Tu", "Anil Palepu", "Mike Schaekermann", "Khaled Saab", "Jan Freyberg", "Ryutaro Tanno", "Amy Wang", "Brenna Li", "Mohamed Amin", "Nenad Tomasev", "Shekoofeh Azizi", "Karan Singhal", "Yong Cheng", "Le Hou", "Albert Webson", "Kavita Kulkarni", "S Sara Mahdavi", "Christopher Semturs", "Juraj Gottweis", "Joelle Barral", "Katherine Chou", "Greg S Corrado", "Yossi Matias", "Alan Karthikesalingam", "Vivek Natarajan" ], "github": "", "project_page": "", "abstract": "At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable of diagnostic dialogue could increase accessibility, consistency, and quality of care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue. AMIE uses a novel self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions, specialties, and contexts. We designed a framework for evaluating clinically-meaningful axes of performance including history-taking, diagnostic accuracy, management reasoning, communication skills, and empathy. We compared AMIE's performance to that of primary care physicians (PCPs) in a randomized, double-blind crossover study of text-based consultations with validated patient actors in the style of an Objective Structured Clinical Examination (OSCE). The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors. Our research has several limitations and should be interpreted with appropriate caution. Clinicians were limited to unfamiliar synchronous text-chat which permits large-scale LLM-patient interactions but is not representative of usual clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.06066", "title": "DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models", "authors": [ "Damai Dai", "Chengqi Deng", "Chenggang Zhao", "R. X. Xu", "Huazuo Gao", "Deli Chen", "Jiashi Li", "Wangding Zeng", "Xingkai Yu", "Y. Wu", "Zhenda Xie", "Y. K. Li", "Panpan Huang", "Fuli Luo", "Chong Ruan", "Zhifang Sui", "Wenfeng Liang" ], "github": "https://github.com/deepseek-ai/DeepSeek-MoE", "project_page": "", "abstract": "In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. However, conventional MoE architectures like GShard, which activate the top-K out of N experts, face challenges in ensuring expert specialization, i.e. each expert acquires non-overlapping and focused knowledge. In response, we propose the DeepSeekMoE architecture towards ultimate expert specialization. It involves two principal strategies: (1) finely segmenting the experts into mN ones and activating mK from them, allowing for a more flexible combination of activated experts; (2) isolating K_s experts as shared ones, aiming at capturing common knowledge and mitigating redundancy in routed experts. Starting from a modest scale with 2B parameters, we demonstrate that DeepSeekMoE 2B achieves comparable performance with GShard 2.9B, which has 1.5 times the expert parameters and computation. In addition, DeepSeekMoE 2B nearly approaches the performance of its dense counterpart with the same number of total parameters, which set the upper bound of MoE models. Subsequently, we scale up DeepSeekMoE to 16B parameters and show that it achieves comparable performance with LLaMA2 7B, with only about 40% of computations. Further, our preliminary efforts to scale up DeepSeekMoE to 145B parameters consistently validate its substantial advantages over the GShard architecture, and show its performance comparable with DeepSeek 67B, using only 28.5% (maybe even 18.2%) of computations.", "github_manual": "https://github.com/deepseek-ai/DeepSeek-MoE", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.05561", "title": "TrustLLM: Trustworthiness in Large Language Models", "authors": [ "Lichao Sun", "Yue Huang", "Haoran Wang", "Siyuan Wu", "Qihui Zhang", "Chujie Gao", "Yixin Huang", "Wenhan Lyu", "Yixuan Zhang", "Xiner Li", "Zhengliang Liu", "Yixin Liu", "Yijue Wang", "Zhikun Zhang", "Bhavya Kailkhura", "Caiming Xiong", "Chao Zhang", "Chaowei Xiao", "Chunyuan Li", "Eric Xing", "Furong Huang", "Hao Liu", "Heng Ji", "Hongyi Wang", "Huan Zhang", "Huaxiu Yao", "Manolis Kellis", "Marinka Zitnik", "Meng Jiang", "Mohit Bansal", "James Zou", "Jian Pei", "Jian Liu", "Jianfeng Gao", "Jiawei Han", "Jieyu Zhao", "Jiliang Tang", "Jindong Wang", "John Mitchell", "Kai Shu", "Kaidi Xu", "Kai-Wei Chang", "Lifang He", "Lifu Huang", "Michael Backes", "Neil Zhenqiang Gong", "Philip S. Yu", "Pin-Yu Chen", "Quanquan Gu", "Ran Xu", "Rex Ying", "Shuiwang Ji", "Suman Jana", "Tianlong Chen", "Tianming Liu", "Tianyi Zhou", "Willian Wang", "Xiang Li", "Xiangliang Zhang", "Xiao Wang", "Xing Xie", "Xun Chen", "Xuyu Wang", "Yan Liu", "Yanfang Ye", "Yinzhi Cao", "Yue Zhao" ], "github": "https://github.com/HowieHwong/TrustLLM", "project_page": "", "abstract": "Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Therefore, ensuring the trustworthiness of LLMs emerges as an important topic. This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. Our findings firstly show that in general trustworthiness and utility (i.e., functional effectiveness) are positively related. Secondly, our observations reveal that proprietary LLMs generally outperform most open-source counterparts in terms of trustworthiness, raising concerns about the potential risks of widely accessible open-source LLMs. However, a few open-source LLMs come very close to proprietary ones. Thirdly, it is important to note that some LLMs may be overly calibrated towards exhibiting trustworthiness, to the extent that they compromise their utility by mistakenly treating benign prompts as harmful and consequently not responding. Finally, we emphasize the importance of ensuring transparency not only in the models themselves but also in the technologies that underpin trustworthiness. Knowing the specific trustworthy technologies that have been employed is crucial for analyzing their effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HowieHwong/TrustLLM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.05583", "title": "Diffusion Priors for Dynamic View Synthesis from Monocular Videos", "authors": [ "Chaoyang Wang", "Peiye Zhuang", "Aliaksandr Siarohin", "Junli Cao", "Guocheng Qian", "Hsin-Ying Lee", "Sergey Tulyakov" ], "github": "", "project_page": "", "abstract": "Dynamic novel view synthesis aims to capture the temporal evolution of visual content within videos. Existing methods struggle to distinguishing between motion and structure, particularly in scenarios where camera poses are either unknown or constrained compared to object motion. Furthermore, with information solely from reference images, it is extremely challenging to hallucinate unseen regions that are occluded or partially observed in the given videos. To address these issues, we first finetune a pretrained RGB-D diffusion model on the video frames using a customization technique. Subsequently, we distill the knowledge from the finetuned model to a 4D representations encompassing both dynamic and static Neural Radiance Fields (NeRF) components. The proposed pipeline achieves geometric consistency while preserving the scene identity. We perform thorough experiments to evaluate the efficacy of the proposed method qualitatively and quantitatively. Our results demonstrate the robustness and utility of our approach in challenging cases, further advancing dynamic novel view synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.06121", "title": "TOFU: A Task of Fictitious Unlearning for LLMs", "authors": [ "Pratyush Maini", "Zhili Feng", "Avi Schwarzschild", "Zachary C. Lipton", "J. Zico Kolter" ], "github": "https://github.com/locuslab/tofu", "project_page": "", "abstract": "Large language models trained on massive corpora of data from the web can memorize and reproduce sensitive or private data raising both legal and ethical concerns. Unlearning, or tuning models to forget information present in their training data, provides us with a way to protect private data after training. Although several methods exist for such unlearning, it is unclear to what extent they result in models equivalent to those where the data to be forgotten was never learned in the first place. To address this challenge, we present TOFU, a Task of Fictitious Unlearning, as a benchmark aimed at helping deepen our understanding of unlearning. We offer a dataset of 200 diverse synthetic author profiles, each consisting of 20 question-answer pairs, and a subset of these profiles called the forget set that serves as the target for unlearning. We compile a suite of metrics that work together to provide a holistic picture of unlearning efficacy. Finally, we provide a set of baseline results from existing unlearning algorithms. Importantly, none of the baselines we consider show effective unlearning motivating continued efforts to develop approaches for unlearning that effectively tune models so that they truly behave as if they were never trained on the forget data at all.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/locuslab/tofu", "github_api_added_by": "auto" }, { "date": "2024-01-12", "arxiv_id": "2401.06071", "title": "LEGO:Language Enhanced Multi-modal Grounding Model", "authors": [ "Zhaowei Li", "Qi Xu", "Dong Zhang", "Hang Song", "Yiqing Cai", "Qi Qi", "Ran Zhou", "Junting Pan", "Zefeng Li", "Van Tu Vu", "Zhida Huang", "Tao Wang" ], "github": "", "project_page": "", "abstract": "Multi-modal large language models have demonstrated impressive performance across various tasks in different modalities. However, existing multi-modal models primarily emphasize capturing global information within each modality while neglecting the importance of perceiving local information across modalities. Consequently, these models lack the ability to effectively understand the fine-grained details of input data, limiting their performance in tasks that require a more nuanced understanding. To address this limitation, there is a compelling need to develop models that enable fine-grained understanding across multiple modalities, thereby enhancing their applicability to a wide range of tasks. In this paper, we propose LEGO, a language enhanced multi-modal grounding model. Beyond capturing global information like other multi-modal models, our proposed model excels at tasks demanding a detailed understanding of local information within the input. It demonstrates precise identification and localization of specific regions in images or moments in videos. To achieve this objective, we design a diversified dataset construction pipeline, resulting in a multi-modal, multi-granularity dataset for model training. The code, dataset, and demo of our model can be found at https: //github.com/lzw-lzw/LEGO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.05566", "title": "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training", "authors": [ "Evan Hubinger", "Carson Denison", "Jesse Mu", "Mike Lambert", "Meg Tong", "Monte MacDiarmid", "Tamera Lanham", "Daniel M. Ziegler", "Tim Maxwell", "Newton Cheng", "Adam Jermyn", "Amanda Askell", "Ansh Radhakrishnan", "Cem Anil", "David Duvenaud", "Deep Ganguli", "Fazl Barez", "Jack Clark", "Kamal Ndousse", "Kshitij Sachan", "Michael Sellitto", "Mrinank Sharma", "Nova DasSarma", "Roger Grosse", "Shauna Kravec", "Yuntao Bai", "Zachary Witten", "Marina Favaro", "Jan Brauner", "Holden Karnofsky", "Paul Christiano", "Samuel R. Bowman", "Logan Graham", "Jared Kaplan", "S\u00f6ren Mindermann", "Ryan Greenblatt", "Buck Shlegeris", "Nicholas Schiefer", "Ethan Perez" ], "github": "https://github.com/anthropics/sleeper-agents-paper", "project_page": "", "abstract": "Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when given the opportunity. If an AI system learned such a deceptive strategy, could we detect it and remove it using current state-of-the-art safety training techniques? To study this question, we construct proof-of-concept examples of deceptive behavior in large language models (LLMs). For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024. We find that such backdoored behavior can be made persistent, so that it is not removed by standard safety training techniques, including supervised fine-tuning, reinforcement learning, and adversarial training (eliciting unsafe behavior and then training to remove it). The backdoored behavior is most persistent in the largest models and in models trained to produce chain-of-thought reasoning about deceiving the training process, with the persistence remaining even when the chain-of-thought is distilled away. Furthermore, rather than removing backdoors, we find that adversarial training can teach models to better recognize their backdoor triggers, effectively hiding the unsafe behavior. Our results suggest that, once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/anthropics/sleeper-agents-paper", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.05391", "title": "Efficient LLM inference solution on Intel GPU", "authors": [ "Hui Wu", "Yi Gan", "Feng Yuan", "Jing Ma", "Wei Zhu", "Yutao Xu", "Hong Zhu", "Yuhua Zhu", "Xiaoli Liu", "Jinghui Gu" ], "github": "", "project_page": "", "abstract": "Transformer based Large Language Models (LLMs) have been widely used in many fields, and the efficiency of LLM inference becomes hot topic in real applications. However, LLMs are usually complicatedly designed in model structure with massive operations and perform inference in the auto-regressive mode, making it a challenging task to design a system with high efficiency. In this paper, we propose an efficient LLM inference solution with low latency and high throughput. Firstly, we simplify the LLM decoder layer by fusing data movement and element-wise operations to reduce the memory access frequency and lower system latency. We also propose a segment KV cache policy to keep key/value of the request and response tokens in separate physical memory for effective device memory management, helping enlarge the runtime batch size and improve system throughput. A customized Scaled-Dot-Product-Attention kernel is designed to match our fusion policy based on the segment KV cache solution. We implement our LLM inference solution on Intel GPU and publish it publicly. Compared with the standard HuggingFace implementation, the proposed solution achieves up to 7x lower token latency and 27x higher throughput for some popular LLMs on Intel GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.06003", "title": "TRIPS: Trilinear Point Splatting for Real-Time Radiance Field Rendering", "authors": [ "Linus Franke", "Darius R\u00fcckert", "Laura Fink", "Marc Stamminger" ], "github": "https://github.com/lfranke/trips", "project_page": "", "abstract": "Point-based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. However, also latest approaches in this domain are not without their shortcomings. 3D Gaussian Splatting [Kerbl and Kopanas et al. 2023] struggles when tasked with rendering highly detailed scenes, due to blurring and cloudy artifacts. On the other hand, ADOP [R\\\"uckert et al. 2022] can accommodate crisper images, but the neural reconstruction network decreases performance, it grapples with temporal instability and it is unable to effectively address large gaps in the point cloud. In this paper, we present TRIPS (Trilinear Point Splatting), an approach that combines ideas from both Gaussian Splatting and ADOP. The fundamental concept behind our novel technique involves rasterizing points into a screen-space image pyramid, with the selection of the pyramid layer determined by the projected point size. This approach allows rendering arbitrarily large points using a single trilinear write. A lightweight neural network is then used to reconstruct a hole-free image including detail beyond splat resolution. Importantly, our render pipeline is entirely differentiable, allowing for automatic optimization of both point sizes and positions. Our evaluation demonstrate that TRIPS surpasses existing state-of-the-art methods in terms of rendering quality while maintaining a real-time frame rate of 60 frames per second on readily available hardware. This performance extends to challenging scenarios, such as scenes featuring intricate geometry, expansive landscapes, and auto-exposed footage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lfranke/trips", "github_api_added_by": "auto" }, { "date": "2024-01-12", "arxiv_id": "2401.06080", "title": "Secrets of RLHF in Large Language Models Part II: Reward Modeling", "authors": [ "Binghai Wang", "Rui Zheng", "Lu Chen", "Yan Liu", "Shihan Dou", "Caishuang Huang", "Wei Shen", "Senjie Jin", "Enyu Zhou", "Chenyu Shi", "Songyang Gao", "Nuo Xu", "Yuhao Zhou", "Xiaoran Fan", "Zhiheng Xi", "Jun Zhao", "Xiao Wang", "Tao Ji", "Hang Yan", "Lixing Shen", "Zhan Chen", "Tao Gui", "Qi Zhang", "Xipeng Qiu", "Xuanjing Huang", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more helpful and harmless responses. Reward models are trained as proxies for human preferences to drive reinforcement learning optimization. While reward models are often considered central to achieving high performance, they face the following challenges in practical applications: (1) Incorrect and ambiguous preference pairs in the dataset may hinder the reward model from accurately capturing human intent. (2) Reward models trained on data from a specific distribution often struggle to generalize to examples outside that distribution and are not suitable for iterative RLHF training. In this report, we attempt to address these two issues. (1) From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. Experimental results confirm that data with varying preference strengths have different impacts on reward model performance. We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data. (2) From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization. Furthermore, we employ meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples, and this approach can be utilized for iterative RLHF optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.05811", "title": "Tuning LLMs with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languages", "authors": [ "Zhuoyuan Mao", "Yen Yu" ], "github": "", "project_page": "", "abstract": "This article introduces contrastive alignment instructions (AlignInstruct) to address two challenges in machine translation (MT) on large language models (LLMs). One is the expansion of supported languages to previously unseen ones. The second relates to the lack of data in low-resource languages. Model fine-tuning through MT instructions (MTInstruct) is a straightforward approach to the first challenge. However, MTInstruct is limited by weak cross-lingual signals inherent in the second challenge. AlignInstruct emphasizes cross-lingual supervision via a cross-lingual discriminator built using statistical word alignments. Our results based on fine-tuning the BLOOMZ models (1b1, 3b, and 7b1) in up to 24 unseen languages showed that: (1) LLMs can effectively translate unseen languages using MTInstruct; (2) AlignInstruct led to consistent improvements in translation quality across 48 translation directions involving English; (3) Discriminator-based instructions outperformed their generative counterparts as cross-lingual instructions; (4) AlignInstruct improved performance in 30 zero-shot directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.05749", "title": "A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism", "authors": [ "Brian Thompson", "Mehak Preet Dhaliwal", "Peter Frisch", "Tobias Domhan", "Marcello Federico" ], "github": "https://github.com/amazon-science/multi-way-parallel-ccmatrix", "project_page": "", "abstract": "We show that content on the web is often translated into many languages, and the low quality of these multi-way translations indicates they were likely created using Machine Translation (MT). Multi-way parallel, machine generated content not only dominates the translations in lower resource languages; it also constitutes a large fraction of the total web content in those languages. We also find evidence of a selection bias in the type of content which is translated into many languages, consistent with low quality English content being translated en masse into many lower resource languages, via MT. Our work raises serious concerns about training models such as multilingual large language models on both monolingual and bilingual data scraped from the web.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amazon-science/multi-way-parallel-ccmatrix", "github_api_added_by": "auto" }, { "date": "2024-01-12", "arxiv_id": "2401.06129", "title": "Distilling Vision-Language Models on Millions of Videos", "authors": [ "Yue Zhao", "Long Zhao", "Xingyi Zhou", "Jialin Wu", "Chun-Te Chu", "Hui Miao", "Florian Schroff", "Hartwig Adam", "Ting Liu", "Boqing Gong", "Philipp Kr\u00e4henb\u00fchl", "Liangzhe Yuan" ], "github": "", "project_page": "", "abstract": "The recent advance in vision-language models is largely attributed to the abundance of image-text data. We aim to replicate this success for video-language models, but there simply is not enough human-curated video-text data available. We thus resort to fine-tuning a video-language model from a strong image-language baseline with synthesized instructional data. The resulting video-language model is then used to auto-label millions of videos to generate high-quality captions. We show the adapted video-language model performs well on a wide range of video-language benchmarks. For instance, it surpasses the best prior result on open-ended NExT-QA by 2.8%. Besides, our model generates detailed descriptions for previously unseen videos, which provide better textual supervision than existing methods. Experiments show that a video-language dual-encoder model contrastively trained on these auto-generated captions is 3.8% better than the strongest baseline that also leverages vision-language models. Our best model outperforms state-of-the-art methods on MSR-VTT zero-shot text-to-video retrieval by 6%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.06105", "title": "PALP: Prompt Aligned Personalization of Text-to-Image Models", "authors": [ "Moab Arar", "Andrey Voynov", "Amir Hertz", "Omri Avrahami", "Shlomi Fruchter", "Yael Pritch", "Daniel Cohen-Or", "Ariel Shamir" ], "github": "", "project_page": "", "abstract": "Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want the resulting image to encompass a specific location, style, ambiance, and more. Existing personalization methods may compromise personalization ability or the alignment to complex textual prompts. This trade-off can impede the fulfillment of user prompts and subject fidelity. We propose a new approach focusing on personalization methods for a single prompt to address this issue. We term our approach prompt-aligned personalization. While this may seem restrictive, our method excels in improving text alignment, enabling the creation of images with complex and intricate prompts, which may pose a challenge for current techniques. In particular, our method keeps the personalized model aligned with a target prompt using an additional score distillation sampling term. We demonstrate the versatility of our method in multi- and single-shot settings and further show that it can compose multiple subjects or use inspiration from reference images, such as artworks. We compare our approach quantitatively and qualitatively with existing baselines and state-of-the-art techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-12", "arxiv_id": "2401.05735", "title": "Object-Centric Diffusion for Efficient Video Editing", "authors": [ "Kumara Kahatapitiya", "Adil Karjauv", "Davide Abati", "Fatih Porikli", "Yuki M. Asano", "Amirhossein Habibian" ], "github": "", "project_page": "", "abstract": "Diffusion-based video editing have reached impressive quality and can transform either the global style, local structure, and attributes of given video inputs, following textual edit prompts. However, such solutions typically incur heavy memory and computational costs to generate temporally-coherent frames, either in the form of diffusion inversion and/or cross-frame attention. In this paper, we conduct an analysis of such inefficiencies, and suggest simple yet effective modifications that allow significant speed-ups whilst maintaining quality. Moreover, we introduce Object-Centric Diffusion, coined as OCD, to further reduce latency by allocating computations more towards foreground edited regions that are arguably more important for perceptual quality. We achieve this by two novel proposals: i) Object-Centric Sampling, decoupling the diffusion steps spent on salient regions or background, allocating most of the model capacity to the former, and ii) Object-Centric 3D Token Merging, which reduces cost of cross-frame attention by fusing redundant tokens in unimportant background regions. Both techniques are readily applicable to a given video editing model without retraining, and can drastically reduce its memory and computational cost. We evaluate our proposals on inversion-based and control-signal-based editing pipelines, and show a latency reduction up to 10x for a comparable synthesis quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-17", "arxiv_id": "2401.08541", "title": "Scalable Pre-training of Large Autoregressive Image Models", "authors": [ "Alaaeldin El-Nouby", "Michal Klein", "Shuangfei Zhai", "Miguel Angel Bautista", "Alexander Toshev", "Vaishaal Shankar", "Joshua M Susskind", "Armand Joulin" ], "github": "https://github.com/apple/ml-aim", "project_page": "", "abstract": "This paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their textual counterparts, i.e., Large Language Models (LLMs), and exhibit similar scaling properties. Specifically, we highlight two key findings: (1) the performance of the visual features scale with both the model capacity and the quantity of data, (2) the value of the objective function correlates with the performance of the model on downstream tasks. We illustrate the practical implication of these findings by pre-training a 7 billion parameter AIM on 2 billion images, that achieves 84.0% on ImageNet-1k with a frozen trunk. Interestingly, even at this scale, we observe no sign of saturation in performance, suggesting that AIM potentially represents a new frontier for training large-scale vision models. The pre-training of AIM is similar to the pre-training of LLMs, and does not require any image-specific strategy to stabilize the training at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apple/ml-aim", "github_api_added_by": "auto" }, { "date": "2024-01-17", "arxiv_id": "2401.07519", "title": "InstantID: Zero-shot Identity-Preserving Generation in Seconds", "authors": [ "Qixun Wang", "Xu Bai", "Haofan Wang", "Zekui Qin", "Anthony Chen" ], "github": "https://github.com/instantX-research/InstantID", "project_page": "", "abstract": "There has been significant progress in personalized image synthesis with methods such as Textual Inversion, DreamBooth, and LoRA. Yet, their real-world applicability is hindered by high storage demands, lengthy fine-tuning processes, and the need for multiple reference images. Conversely, existing ID embedding-based methods, while requiring only a single forward inference, face challenges: they either necessitate extensive fine-tuning across numerous model parameters, lack compatibility with community pre-trained models, or fail to maintain high face fidelity. Addressing these limitations, we introduce InstantID, a powerful diffusion model-based solution. Our plug-and-play module adeptly handles image personalization in various styles using just a single facial image, while ensuring high fidelity. To achieve this, we design a novel IdentityNet by imposing strong semantic and weak spatial conditions, integrating facial and landmark images with textual prompts to steer the image generation. InstantID demonstrates exceptional performance and efficiency, proving highly beneficial in real-world applications where identity preservation is paramount. Moreover, our work seamlessly integrates with popular pre-trained text-to-image diffusion models like SD1.5 and SDXL, serving as an adaptable plugin. Our codes and pre-trained checkpoints will be available at https://github.com/InstantID/InstantID.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/instantX-research/InstantID", "github_api_added_by": "user" }, { "date": "2024-01-17", "arxiv_id": "2401.08417", "title": "Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation", "authors": [ "Haoran Xu", "Amr Sharaf", "Yunmo Chen", "Weiting Tan", "Lingfeng Shen", "Benjamin Van Durme", "Kenton Murray", "Young Jin Kim" ], "github": "https://github.com/fe1ixxu/alma", "project_page": "", "abstract": "Moderate-sized large language models (LLMs) -- those with 7B or 13B parameters -- exhibit promising machine translation (MT) performance. However, even the top-performing 13B LLM-based translation models, like ALMA, does not match the performance of state-of-the-art conventional encoder-decoder translation models or larger-scale LLMs such as GPT-4. In this study, we bridge this performance gap. We first assess the shortcomings of supervised fine-tuning for LLMs in the MT task, emphasizing the quality issues present in the reference data, despite being human-generated. Then, in contrast to SFT which mimics reference translations, we introduce Contrastive Preference Optimization (CPO), a novel approach that trains models to avoid generating adequate but not perfect translations. Applying CPO to ALMA models with only 22K parallel sentences and 12M parameters yields significant improvements. The resulting model, called ALMA-R, can match or exceed the performance of the WMT competition winners and GPT-4 on WMT'21, WMT'22 and WMT'23 test datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/fe1ixxu/alma", "github_api_added_by": "auto" }, { "date": "2024-01-17", "arxiv_id": "2401.07781", "title": "Towards A Better Metric for Text-to-Video Generation", "authors": [ "Jay Zhangjie Wu", "Guian Fang", "Haoning Wu", "Xintao Wang", "Yixiao Ge", "Xiaodong Cun", "David Junhao Zhang", "Jia-Wei Liu", "Yuchao Gu", "Rui Zhao", "Weisi Lin", "Wynne Hsu", "Ying Shan", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Generative models have demonstrated remarkable capability in synthesizing high-quality text, images, and videos. For video generation, contemporary text-to-video models exhibit impressive capabilities, crafting visually stunning videos. Nonetheless, evaluating such videos poses significant challenges. Current research predominantly employs automated metrics such as FVD, IS, and CLIP Score. However, these metrics provide an incomplete analysis, particularly in the temporal assessment of video content, thus rendering them unreliable indicators of true video quality. Furthermore, while user studies have the potential to reflect human perception accurately, they are hampered by their time-intensive and laborious nature, with outcomes that are often tainted by subjective bias. In this paper, we investigate the limitations inherent in existing metrics and introduce a novel evaluation pipeline, the Text-to-Video Score (T2VScore). This metric integrates two pivotal criteria: (1) Text-Video Alignment, which scrutinizes the fidelity of the video in representing the given text description, and (2) Video Quality, which evaluates the video's overall production caliber with a mixture of experts. Moreover, to evaluate the proposed metrics and facilitate future improvements on them, we present the TVGE dataset, collecting human judgements of 2,543 text-to-video generated videos on the two criteria. Experiments on the TVGE dataset demonstrate the superiority of the proposed T2VScore on offering a better metric for text-to-video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-17", "arxiv_id": "2401.08565", "title": "Tuning Language Models by Proxy", "authors": [ "Alisa Liu", "Xiaochuang Han", "Yizhong Wang", "Yulia Tsvetkov", "Yejin Choi", "Noah A. Smith" ], "github": "https://github.com/alisawuffles/proxy-tuning", "project_page": "", "abstract": "Despite the general capabilities of large pretrained language models, they consistently benefit from further adaptation to better achieve desired behaviors. However, tuning these models has become increasingly resource-intensive, or impossible when model weights are private. We introduce proxy-tuning, a lightweight decoding-time algorithm that operates on top of black-box LMs to achieve the result of directly tuning the model, but by accessing only its prediction over the output vocabulary. Our method instead tunes a smaller LM, then applies the difference between the predictions of the small tuned and untuned LMs to shift the original predictions of the base model in the direction of tuning, while retaining the benefits of larger scale pretraining. In experiments, when we apply proxy-tuning to Llama2-70B using proxies of only 7B size, we can close 88% of the gap between Llama2-70B and its truly-tuned chat version, when evaluated across knowledge, reasoning, and safety benchmarks. Interestingly, when tested on TruthfulQA, proxy-tuned models are actually more truthful than directly tuned models, possibly because decoding-time guidance better retains the model's factual knowledge. We then demonstrate the generality of proxy-tuning by applying it for domain adaptation on code, and task-specific finetuning on question-answering and math problems. Our work demonstrates the promise of using small tuned LMs to efficiently customize large, potentially proprietary LMs through decoding-time guidance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alisawuffles/proxy-tuning", "github_api_added_by": "auto" }, { "date": "2024-01-17", "arxiv_id": "2401.07049", "title": "Quantum Denoising Diffusion Models", "authors": [ "Michael K\u00f6lle", "Gerhard Stenzel", "Jonas Stein", "Sebastian Zielinski", "Bj\u00f6rn Ommer", "Claudia Linnhoff-Popien" ], "github": "https://github.com/michaelkoelle/quantum-diffusion", "project_page": "", "abstract": "In recent years, machine learning models like DALL-E, Craiyon, and Stable Diffusion have gained significant attention for their ability to generate high-resolution images from concise descriptions. Concurrently, quantum computing is showing promising advances, especially with quantum machine learning which capitalizes on quantum mechanics to meet the increasing computational requirements of traditional machine learning algorithms. This paper explores the integration of quantum machine learning and variational quantum circuits to augment the efficacy of diffusion-based image generation models. Specifically, we address two challenges of classical diffusion models: their low sampling speed and the extensive parameter requirements. We introduce two quantum diffusion models and benchmark their capabilities against their classical counterparts using MNIST digits, Fashion MNIST, and CIFAR-10. Our models surpass the classical models with similar parameter counts in terms of performance metrics FID, SSIM, and PSNR. Moreover, we introduce a consistency model unitary single sampling architecture that combines the diffusion procedure into a single step, enabling a fast one-step image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/michaelkoelle/quantum-diffusion", "github_api_added_by": "auto" }, { "date": "2024-01-17", "arxiv_id": "2401.07004", "title": "Extending LLMs' Context Window with 100 Samples", "authors": [ "Yikai Zhang", "Junlong Li", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/Entropy-ABF", "project_page": "", "abstract": "Large Language Models (LLMs) are known to have limited extrapolation ability beyond their pre-trained context window, constraining their application in downstream tasks with lengthy inputs. Recent studies have sought to extend LLMs' context window by modifying rotary position embedding (RoPE), a popular position encoding method adopted by well-known LLMs such as LLaMA, PaLM, and GPT-NeoX. However, prior works like Position Interpolation (PI) and YaRN are resource-intensive and lack comparative experiments to assess their applicability. In this work, we identify the inherent need for LLMs' attention entropy (i.e. the information entropy of attention scores) to maintain stability and introduce a novel extension to RoPE which combines adjusting RoPE's base frequency and scaling the attention logits to help LLMs efficiently adapt to a larger context window. We validate the superiority of our method in both fine-tuning performance and robustness across different context window sizes on various context-demanding tasks. Notably, our method extends the context window of LLaMA-2-7B-Chat to 16,384 with only 100 samples and 6 training steps, showcasing extraordinary efficiency. Finally, we also explore how data compositions and training curricula affect context window extension for specific downstream tasks, suggesting fine-tuning LLMs with lengthy conversations as a good starting point. We release our code and SFT data at https://github.com/GAIR-NLP/Entropy-ABF.", "github_manual": "", "github_regex": "https://github.com/GAIR-NLP/Entropy-ABF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-17", "arxiv_id": "2401.06951", "title": "E^2-LLM: Efficient and Extreme Length Extension of Large Language Models", "authors": [ "Jiaheng Liu", "Zhiqi Bai", "Yuanxing Zhang", "Chenchen Zhang", "Yu Zhang", "Ge Zhang", "Jiakai Wang", "Haoran Que", "Yukang Chen", "Wenbo Su", "Tiezheng Ge", "Jie Fu", "Wenhu Chen", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. Existing long-context extension methods usually need additional training procedures to support corresponding long-context windows, where the long-context training data (e.g., 32k) is needed, and high GPU training costs are assumed. To address the aforementioned issues, we propose an Efficient and Extreme length extension method for Large Language Models, called E 2 -LLM, with only one training procedure and dramatically reduced computation cost, which also removes the need to collect long-context data. Concretely, first, the training data of our E 2 -LLM only requires a short length (e.g., 4k), which reduces the tuning cost greatly. Second, the training procedure on the short training context window is performed only once time, and we can support different evaluation context windows at inference. Third, in E 2 - LLM, based on RoPE position embeddings, we introduce two different augmentation methods on the scale and position index parameters for different samples in training. It aims to make the model more robust to the different relative differences when directly interpolating the arbitrary context length at inference. Comprehensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our E 2 -LLM on challenging long-context tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-17", "arxiv_id": "2401.07727", "title": "HexaGen3D: StableDiffusion is just one step away from Fast and Diverse Text-to-3D Generation", "authors": [ "Antoine Mercier", "Ramin Nakhli", "Mahesh Reddy", "Rajeev Yasarla", "Hong Cai", "Fatih Porikli", "Guillaume Berger" ], "github": "", "project_page": "", "abstract": "Despite the latest remarkable advances in generative modeling, efficient generation of high-quality 3D assets from textual prompts remains a difficult task. A key challenge lies in data scarcity: the most extensive 3D datasets encompass merely millions of assets, while their 2D counterparts contain billions of text-image pairs. To address this, we propose a novel approach which harnesses the power of large, pretrained 2D diffusion models. More specifically, our approach, HexaGen3D, fine-tunes a pretrained text-to-image model to jointly predict 6 orthographic projections and the corresponding latent triplane. We then decode these latents to generate a textured mesh. HexaGen3D does not require per-sample optimization, and can infer high-quality and diverse objects from textual prompts in 7 seconds, offering significantly better quality-to-latency trade-offs when comparing to existing approaches. Furthermore, HexaGen3D demonstrates strong generalization to new objects or compositions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-18", "arxiv_id": "2401.08967", "title": "ReFT: Reasoning with Reinforced Fine-Tuning", "authors": [ "Trung Quoc Luong", "Xinbo Zhang", "Zhanming Jie", "Peng Sun", "Xiaoran Jin", "Hang Li" ], "github": "https://github.com/lqtrung1998/mwp_reft", "project_page": "", "abstract": "One way to enhance the reasoning capability of Large Language Models (LLMs) is to conduct Supervised Fine-Tuning (SFT) using Chain-of-Thought (CoT) annotations. This approach does not show sufficiently strong generalization ability, however, because the training only relies on the given CoT data. In math problem-solving, for example, there is usually only one annotated reasoning path for each question in the training data. Intuitively, it would be better for the algorithm to learn from multiple annotated reasoning paths given a question. To address this issue, we propose a simple yet effective approach called Reinforced Fine-Tuning (ReFT) to enhance the generalizability of learning LLMs for reasoning, with math problem-solving as an example. ReFT first warmups the model with SFT, and then employs on-line reinforcement learning, specifically the PPO algorithm in this paper, to further fine-tune the model, where an abundance of reasoning paths are automatically sampled given the question and the rewards are naturally derived from the ground-truth answers. Extensive experiments on GSM8K, MathQA, and SVAMP datasets show that ReFT significantly outperforms SFT, and the performance can be potentially further boosted by combining inference-time strategies such as majority voting and re-ranking. Note that ReFT obtains the improvement by learning from the same training questions as SFT, without relying on extra or augmented training questions. This indicates a superior generalization ability for ReFT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lqtrung1998/mwp_reft", "github_api_added_by": "auto" }, { "date": "2024-01-18", "arxiv_id": "2401.08671", "title": "DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference", "authors": [ "Connor Holmes", "Masahiro Tanaka", "Michael Wyatt", "Ammar Ahmad Awan", "Jeff Rasley", "Samyam Rajbhandari", "Reza Yazdani Aminabadi", "Heyang Qin", "Arash Bakhtiari", "Lev Kurilenko", "Yuxiong He" ], "github": "https://github.com/NVIDIA/FasterTransformer", "project_page": "", "abstract": "The deployment and scaling of large language models (LLMs) have become critical as they permeate various applications, demanding high-throughput and low-latency serving systems. Existing frameworks struggle to balance these requirements, especially for workloads with long prompts. This paper introduces DeepSpeed-FastGen, a system that employs Dynamic SplitFuse, a novel prompt and generation composition strategy, to deliver up to 2.3x higher effective throughput, 2x lower latency on average, and up to 3.7x lower (token-level) tail latency, compared to state-of-the-art systems like vLLM. We leverage a synergistic combination of DeepSpeed-MII and DeepSpeed-Inference to provide an efficient and easy-to-use serving system for LLMs. DeepSpeed-FastGen's advanced implementation supports a range of models and offers both non-persistent and persistent deployment options, catering to diverse user scenarios from interactive sessions to long-running applications. We present a detailed benchmarking methodology, analyze the performance through latency-throughput curves, and investigate scalability via load balancing. Our evaluations demonstrate substantial improvements in throughput and latency across various models and hardware configurations. We discuss our roadmap for future enhancements, including broader model support and new hardware backends. The DeepSpeed-FastGen code is readily available for community engagement and contribution.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA/FasterTransformer", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-18", "arxiv_id": "2401.09084", "title": "UniVG: Towards UNIfied-modal Video Generation", "authors": [ "Ludan Ruan", "Lei Tian", "Chuanwei Huang", "Xu Zhang", "Xinyan Xiao" ], "github": "", "project_page": "", "abstract": "Diffusion based video generation has received extensive attention and achieved considerable success within both the academic and industrial communities. However, current efforts are mainly concentrated on single-objective or single-task video generation, such as generation driven by text, by image, or by a combination of text and image. This cannot fully meet the needs of real-world application scenarios, as users are likely to input images and text conditions in a flexible manner, either individually or in combination. To address this, we propose a Unified-modal Video Genearation system that is capable of handling multiple video generation tasks across text and image modalities. To this end, we revisit the various video generation tasks within our system from the perspective of generative freedom, and classify them into high-freedom and low-freedom video generation categories. For high-freedom video generation, we employ Multi-condition Cross Attention to generate videos that align with the semantics of the input images or text. For low-freedom video generation, we introduce Biased Gaussian Noise to replace the pure random Gaussian Noise, which helps to better preserve the content of the input conditions. Our method achieves the lowest Fr\\'echet Video Distance (FVD) on the public academic benchmark MSR-VTT, surpasses the current open-source methods in human evaluations, and is on par with the current close-source method Gen2. For more samples, visit https://univg-baidu.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-18", "arxiv_id": "2401.09047", "title": "VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models", "authors": [ "Haoxin Chen", "Yong Zhang", "Xiaodong Cun", "Menghan Xia", "Xintao Wang", "Chao Weng", "Ying Shan" ], "github": "https://github.com/AILab-CVC/VideoCrafter", "project_page": "", "abstract": "Text-to-video generation aims to produce a video based on a given prompt. Recently, several commercial video models have been able to generate plausible videos with minimal noise, excellent details, and high aesthetic scores. However, these models rely on large-scale, well-filtered, high-quality videos that are not accessible to the community. Many existing research works, which train models using the low-quality WebVid-10M dataset, struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work, we explore the training scheme of video models extended from Stable Diffusion and investigate the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model. We first analyze the connection between the spatial and temporal modules of video models and the distribution shift to low-quality videos. We observe that full training of all modules results in a stronger coupling between spatial and temporal modules than only training temporal modules. Based on this stronger coupling, we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images, resulting in a generic high-quality video model. Evaluations are conducted to demonstrate the superiority of the proposed method, particularly in picture quality, motion, and concept composition.", "github_manual": "https://github.com/AILab-CVC/VideoCrafter", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-18", "arxiv_id": "2401.09340", "title": "SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding", "authors": [ "Baoxiong Jia", "Yixin Chen", "Huangyue Yu", "Yan Wang", "Xuesong Niu", "Tengyu Liu", "Qing Li", "Siyuan Huang" ], "github": "", "project_page": "", "abstract": "3D vision-language grounding, which focuses on aligning language with the 3D physical environment, stands as a cornerstone in the development of embodied agents. In comparison to recent advancements in the 2D domain, grounding language in 3D scenes faces several significant challenges: (i) the inherent complexity of 3D scenes due to the diverse object configurations, their rich attributes, and intricate relationships; (ii) the scarcity of paired 3D vision-language data to support grounded learning; and (iii) the absence of a unified learning framework to distill knowledge from grounded 3D data. In this work, we aim to address these three major challenges in 3D vision-language by examining the potential of systematically upscaling 3D vision-language learning in indoor environments. We introduce the first million-scale 3D vision-language dataset, SceneVerse, encompassing about 68K 3D indoor scenes and comprising 2.5M vision-language pairs derived from both human annotations and our scalable scene-graph-based generation approach. We demonstrate that this scaling allows for a unified pre-training framework, Grounded Pre-training for Scenes (GPS), for 3D vision-language learning. Through extensive experiments, we showcase the effectiveness of GPS by achieving state-of-the-art performance on all existing 3D visual grounding benchmarks. The vast potential of SceneVerse and GPS is unveiled through zero-shot transfer experiments in the challenging 3D vision-language tasks. Project website: https://scene-verse.github.io .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-18", "arxiv_id": "2401.09417", "title": "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model", "authors": [ "Lianghui Zhu", "Bencheng Liao", "Qian Zhang", "Xinlong Wang", "Wenyu Liu", "Xinggang Wang" ], "github": "https://github.com/hustvl/Vim", "project_page": "", "abstract": "Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., Mamba, have shown great potential for long sequence modeling. Building efficient and generic vision backbones purely upon SSMs is an appealing direction. However, representing visual data is challenging for SSMs due to the position-sensitivity of visual data and the requirement of global context for visual understanding. In this paper, we show that the reliance of visual representation learning on self-attention is not necessary and propose a new generic vision backbone with bidirectional Mamba blocks (Vim), which marks the image sequences with position embeddings and compresses the visual representation with bidirectional state space models. On ImageNet classification, COCO object detection, and ADE20k semantic segmentation tasks, Vim achieves higher performance compared to well-established vision transformers like DeiT, while also demonstrating significantly improved computation & memory efficiency. For example, Vim is 2.8times faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on images with a resolution of 1248times1248. The results demonstrate that Vim is capable of overcoming the computation & memory constraints on performing Transformer-style understanding for high-resolution images and it has great potential to become the next-generation backbone for vision foundation models. Code is available at https://github.com/hustvl/Vim.", "github_manual": "", "github_regex": "https://github.com/hustvl/Vim", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-18", "arxiv_id": "2401.08740", "title": "SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers", "authors": [ "Nanye Ma", "Mark Goldstein", "Michael S. Albergo", "Nicholas M. Boffi", "Eric Vanden-Eijnden", "Saining Xie" ], "github": "https://github.com/willisma/sit", "project_page": "", "abstract": "We present Scalable Interpolant Transformers (SiT), a family of generative models built on the backbone of Diffusion Transformers (DiT). The interpolant framework, which allows for connecting two distributions in a more flexible way than standard diffusion models, makes possible a modular study of various design choices impacting generative models built on dynamical transport: using discrete vs. continuous time learning, deciding the objective for the model to learn, choosing the interpolant connecting the distributions, and deploying a deterministic or stochastic sampler. By carefully introducing the above ingredients, SiT surpasses DiT uniformly across model sizes on the conditional ImageNet 256x256 benchmark using the exact same backbone, number of parameters, and GFLOPs. By exploring various diffusion coefficients, which can be tuned separately from learning, SiT achieves an FID-50K score of 2.06.", "github_manual": "https://github.com/willisma/sit", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-18", "arxiv_id": "2401.09135", "title": "Asynchronous Local-SGD Training for Language Modeling", "authors": [ "Bo Liu", "Rachita Chhaparia", "Arthur Douillard", "Satyen Kale", "Andrei A. Rusu", "Jiajun Shen", "Arthur Szlam", "Marc'Aurelio Ranzato" ], "github": "https://github.com/google-deepmind/asyncdiloco", "project_page": "", "abstract": "Local stochastic gradient descent (Local-SGD), also referred to as federated averaging, is an approach to distributed optimization where each device performs more than one SGD update per communication. This work presents an empirical study of {\\it asynchronous} Local-SGD for training language models; that is, each worker updates the global parameters as soon as it has finished its SGD steps. We conduct a comprehensive investigation by examining how worker hardware heterogeneity, model size, number of workers, and optimizer could impact the learning performance. We find that with naive implementations, asynchronous Local-SGD takes more iterations to converge than its synchronous counterpart despite updating the (global) model parameters more frequently. We identify momentum acceleration on the global parameters when worker gradients are stale as a key challenge. We propose a novel method that utilizes a delayed Nesterov momentum update and adjusts the workers' local training steps based on their computation speed. This approach, evaluated with models up to 150M parameters on the C4 dataset, matches the performance of synchronous Local-SGD in terms of perplexity per update step, and significantly surpasses it in terms of wall clock time.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/asyncdiloco", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-18", "arxiv_id": "2401.09419", "title": "GARField: Group Anything with Radiance Fields", "authors": [ "Chung Min Kim", "Mingxuan Wu", "Justin Kerr", "Ken Goldberg", "Matthew Tancik", "Angjoo Kanazawa" ], "github": "https://github.com/chungmin99/garfield", "project_page": "", "abstract": "Grouping is inherently ambiguous due to the multiple levels of granularity in which one can decompose a scene -- should the wheels of an excavator be considered separate or part of the whole? We present Group Anything with Radiance Fields (GARField), an approach for decomposing 3D scenes into a hierarchy of semantically meaningful groups from posed image inputs. To do this we embrace group ambiguity through physical scale: by optimizing a scale-conditioned 3D affinity feature field, a point in the world can belong to different groups of different sizes. We optimize this field from a set of 2D masks provided by Segment Anything (SAM) in a way that respects coarse-to-fine hierarchy, using scale to consistently fuse conflicting masks from different viewpoints. From this field we can derive a hierarchy of possible groupings via automatic tree construction or user interaction. We evaluate GARField on a variety of in-the-wild scenes and find it effectively extracts groups at many levels: clusters of objects, objects, and various subparts. GARField inherently represents multi-view consistent groupings and produces higher fidelity groups than the input SAM masks. GARField's hierarchical grouping could have exciting downstream applications such as 3D asset extraction or dynamic scene understanding. See the project website at https://www.garfield.studio/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/chungmin99/garfield", "github_api_added_by": "auto" }, { "date": "2024-01-18", "arxiv_id": "2401.09048", "title": "Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis", "authors": [ "Jonghyun Lee", "Hansam Cho", "Youngjoon Yoo", "Seoung Bum Kim", "Yonghyun Jeong" ], "github": "https://github.com/tomtom1103/compose-and-conquer", "project_page": "", "abstract": "Addressing the limitations of text as a source of accurate layout representation in text-conditional diffusion models, many works incorporate additional signals to condition certain attributes within a generated image. Although successful, previous works do not account for the specific localization of said attributes extended into the three dimensional plane. In this context, we present a conditional diffusion model that integrates control over three-dimensional object placement with disentangled representations of global stylistic semantics from multiple exemplar images. Specifically, we first introduce depth disentanglement training to leverage the relative depth of objects as an estimator, allowing the model to identify the absolute positions of unseen objects through the use of synthetic image triplets. We also introduce soft guidance, a method for imposing global semantics onto targeted regions without the use of any additional localization cues. Our integrated framework, Compose and Conquer (CnC), unifies these techniques to localize multiple conditions in a disentangled manner. We demonstrate that our approach allows perception of objects at varying depths while offering a versatile framework for composing localized objects with different global semantics. Code: https://github.com/tomtom1103/compose-and-conquer/", "github_manual": "", "github_regex": "https://github.com/tomtom1103/compose-and-conquer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-18", "arxiv_id": "2401.08937", "title": "ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization", "authors": [ "Weiyao Wang", "Pierre Gleize", "Hao Tang", "Xingyu Chen", "Kevin J Liang", "Matt Feiszli" ], "github": "", "project_page": "", "abstract": "Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View Synthesis (NVS) given a set of 2D images. However, NeRF training requires accurate camera pose for each input view, typically obtained by Structure-from-Motion (SfM) pipelines. Recent works have attempted to relax this constraint, but they still often rely on decent initial poses which they can refine. Here we aim at removing the requirement for pose initialization. We present Incremental CONfidence (ICON), an optimization procedure for training NeRFs from 2D video frames. ICON only assumes smooth camera motion to estimate initial guess for poses. Further, ICON introduces ``confidence\": an adaptive measure of model quality used to dynamically reweight gradients. ICON relies on high-confidence poses to learn NeRF, and high-confidence 3D structure (as encoded by NeRF) to learn poses. We show that ICON, without prior pose initialization, achieves superior performance in both CO3D and HO3D versus methods which use SfM pose.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-18", "arxiv_id": "2401.09416", "title": "TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion", "authors": [ "Yu-Ying Yeh", "Jia-Bin Huang", "Changil Kim", "Lei Xiao", "Thu Nguyen-Phuoc", "Numair Khan", "Cheng Zhang", "Manmohan Chandraker", "Carl S Marshall", "Zhao Dong", "Zhengqin Li" ], "github": "", "project_page": "", "abstract": "We present TextureDreamer, a novel image-guided texture synthesis method to transfer relightable textures from a small number of input images (3 to 5) to target 3D shapes across arbitrary categories. Texture creation is a pivotal challenge in vision and graphics. Industrial companies hire experienced artists to manually craft textures for 3D assets. Classical methods require densely sampled views and accurately aligned geometry, while learning-based methods are confined to category-specific shapes within the dataset. In contrast, TextureDreamer can transfer highly detailed, intricate textures from real-world environments to arbitrary objects with only a few casually captured images, potentially significantly democratizing texture creation. Our core idea, personalized geometry-aware score distillation (PGSD), draws inspiration from recent advancements in diffuse models, including personalized modeling for texture information extraction, variational score distillation for detailed appearance synthesis, and explicit geometry guidance with ControlNet. Our integration and several essential modifications substantially improve the texture quality. Experiments on real images spanning different categories show that TextureDreamer can successfully transfer highly realistic, semantic meaningful texture to arbitrary objects, surpassing the visual quality of previous state-of-the-art.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-19", "arxiv_id": "2401.10020", "title": "Self-Rewarding Language Models", "authors": [ "Weizhe Yuan", "Richard Yuanzhe Pang", "Kyunghyun Cho", "Sainbayar Sukhbaatar", "Jing Xu", "Jason Weston" ], "github": "", "project_page": "", "abstract": "We posit that to achieve superhuman agents, future models require superhuman feedback in order to provide an adequate training signal. Current approaches commonly train reward models from human preferences, which may then be bottlenecked by human performance level, and secondly these separate frozen reward models cannot then learn to improve during LLM training. In this work, we study Self-Rewarding Language Models, where the language model itself is used via LLM-as-a-Judge prompting to provide its own rewards during training. We show that during Iterative DPO training that not only does instruction following ability improve, but also the ability to provide high-quality rewards to itself. Fine-tuning Llama 2 70B on three iterations of our approach yields a model that outperforms many existing systems on the AlpacaEval 2.0 leaderboard, including Claude 2, Gemini Pro, and GPT-4 0613. While only a preliminary study, this work opens the door to the possibility of models that can continually improve in both axes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-19", "arxiv_id": "2401.09985", "title": "WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens", "authors": [ "Xiaofeng Wang", "Zheng Zhu", "Guan Huang", "Boyuan Wang", "Xinze Chen", "Jiwen Lu" ], "github": "", "project_page": "", "abstract": "World models play a crucial role in understanding and predicting the dynamics of the world, which is essential for video generation. However, existing world models are confined to specific scenarios such as gaming or driving, limiting their ability to capture the complexity of general world dynamic environments. Therefore, we introduce WorldDreamer, a pioneering world model to foster a comprehensive comprehension of general world physics and motions, which significantly enhances the capabilities of video generation. Drawing inspiration from the success of large language models, WorldDreamer frames world modeling as an unsupervised visual sequence modeling challenge. This is achieved by mapping visual inputs to discrete tokens and predicting the masked ones. During this process, we incorporate multi-modal prompts to facilitate interaction within the world model. Our experiments show that WorldDreamer excels in generating videos across different scenarios, including natural scenes and driving environments. WorldDreamer showcases versatility in executing tasks such as text-to-video conversion, image-tovideo synthesis, and video editing. These results underscore WorldDreamer's effectiveness in capturing dynamic elements within diverse general world environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-19", "arxiv_id": "2401.09962", "title": "CustomVideo: Customizing Text-to-Video Generation with Multiple Subjects", "authors": [ "Zhao Wang", "Aoxue Li", "Enze Xie", "Lingting Zhu", "Yong Guo", "Qi Dou", "Zhenguo Li" ], "github": "", "project_page": "", "abstract": "Customized text-to-video generation aims to generate high-quality videos guided by text prompts and subject references. Current approaches designed for single subjects suffer from tackling multiple subjects, which is a more challenging and practical scenario. In this work, we aim to promote multi-subject guided text-to-video customization. We propose CustomVideo, a novel framework that can generate identity-preserving videos with the guidance of multiple subjects. To be specific, firstly, we encourage the co-occurrence of multiple subjects via composing them in a single image. Further, upon a basic text-to-video diffusion model, we design a simple yet effective attention control strategy to disentangle different subjects in the latent space of diffusion model. Moreover, to help the model focus on the specific object area, we segment the object from given reference images and provide a corresponding object mask for attention learning. Also, we collect a multi-subject text-to-video generation dataset as a comprehensive benchmark, with 69 individual subjects and 57 meaningful pairs. Extensive qualitative, quantitative, and user study results demonstrate the superiority of our method, compared with the previous state-of-the-art approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-19", "arxiv_id": "2401.10061", "title": "DiffusionGPT: LLM-Driven Text-to-Image Generation System", "authors": [ "Jie Qin", "Jie Wu", "Weifeng Chen", "Yuxi Ren", "Huixia Li", "Hefeng Wu", "Xuefeng Xiao", "Rui Wang", "Shilei Wen" ], "github": "", "project_page": "", "abstract": "Diffusion models have opened up new avenues for the field of image generation, resulting in the proliferation of high-quality models shared on open-source platforms. However, a major challenge persists in current text-to-image systems are often unable to handle diverse inputs, or are limited to single model results. Current unified attempts often fall into two orthogonal aspects: i) parse Diverse Prompts in input stage; ii) activate expert model to output. To combine the best of both worlds, we propose DiffusionGPT, which leverages Large Language Models (LLM) to offer a unified generation system capable of seamlessly accommodating various types of prompts and integrating domain-expert models. DiffusionGPT constructs domain-specific Trees for various generative models based on prior knowledge. When provided with an input, the LLM parses the prompt and employs the Trees-of-Thought to guide the selection of an appropriate model, thereby relaxing input constraints and ensuring exceptional performance across diverse domains. Moreover, we introduce Advantage Databases, where the Tree-of-Thought is enriched with human feedback, aligning the model selection process with human preferences. Through extensive experiments and comparisons, we demonstrate the effectiveness of DiffusionGPT, showcasing its potential for pushing the boundaries of image synthesis in diverse domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-19", "arxiv_id": "2401.09865", "title": "Improving fine-grained understanding in image-text pre-training", "authors": [ "Ioana Bica", "Anastasija Ili\u0107", "Matthias Bauer", "Goker Erdogan", "Matko Bo\u0161njak", "Christos Kaplanis", "Alexey A. Gritsenko", "Matthias Minderer", "Charles Blundell", "Razvan Pascanu", "Jovana Mitrovi\u0107" ], "github": "", "project_page": "", "abstract": "We introduce SPARse Fine-grained Contrastive Alignment (SPARC), a simple method for pretraining more fine-grained multimodal representations from image-text pairs. Given that multiple image patches often correspond to single words, we propose to learn a grouping of image patches for every token in the caption. To achieve this, we use a sparse similarity metric between image patches and language tokens and compute for each token a language-grouped vision embedding as the weighted average of patches. The token and language-grouped vision embeddings are then contrasted through a fine-grained sequence-wise loss that only depends on individual samples and does not require other batch samples as negatives. This enables more detailed information to be learned in a computationally inexpensive manner. SPARC combines this fine-grained loss with a contrastive loss between global image and text embeddings to learn representations that simultaneously encode global and local information. We thoroughly evaluate our proposed method and show improved performance over competing approaches both on image-level tasks relying on coarse-grained information, e.g. classification, as well as region-level tasks relying on fine-grained information, e.g. retrieval, object detection, and segmentation. Moreover, SPARC improves model faithfulness and captioning in foundational vision-language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-19", "arxiv_id": "2401.10225", "title": "ChatQA: Building GPT-4 Level Conversational QA Models", "authors": [ "Zihan Liu", "Wei Ping", "Rajarshi Roy", "Peng Xu", "Mohammad Shoeybi", "Bryan Catanzaro" ], "github": "", "project_page": "", "abstract": "In this work, we introduce ChatQA, a family of conversational question answering (QA) models, that obtain GPT-4 level accuracies. Specifically, we propose a two-stage instruction tuning method that can significantly improve the zero-shot conversational QA results from large language models (LLMs). To handle retrieval in conversational QA, we fine-tune a dense retriever on a multi-turn QA dataset, which provides comparable results to using the state-of-the-art query rewriting model while largely reducing deployment cost. Notably, our ChatQA-70B can outperform GPT-4 in terms of average score on 10 conversational QA datasets (54.14 vs. 53.90), without relying on any synthetic data from OpenAI GPT models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-19", "arxiv_id": "2401.10166", "title": "VMamba: Visual State Space Model", "authors": [ "Yue Liu", "Yunjie Tian", "Yuzhong Zhao", "Hongtian Yu", "Lingxi Xie", "Yaowei Wang", "Qixiang Ye", "Yunfan Liu" ], "github": "https://github.com/MzeroMiko/VMamba", "project_page": "", "abstract": "Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) stand as the two most popular foundation models for visual representation learning. While CNNs exhibit remarkable scalability with linear complexity w.r.t. image resolution, ViTs surpass them in fitting capabilities despite contending with quadratic complexity. A closer inspection reveals that ViTs achieve superior visual modeling performance through the incorporation of global receptive fields and dynamic weights. This observation motivates us to propose a novel architecture that inherits these components while enhancing computational efficiency. To this end, we draw inspiration from the recently introduced state space model and propose the Visual State Space Model (VMamba), which achieves linear complexity without sacrificing global receptive fields. To address the encountered direction-sensitive issue, we introduce the Cross-Scan Module (CSM) to traverse the spatial domain and convert any non-causal visual image into order patch sequences. Extensive experimental results substantiate that VMamba not only demonstrates promising capabilities across various visual perception tasks, but also exhibits more pronounced advantages over established benchmarks as the image resolution increases. Source code has been available at https://github.com/MzeroMiko/VMamba.", "github_manual": "", "github_regex": "https://github.com/MzeroMiko/VMamba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-19", "arxiv_id": "2401.10032", "title": "FreGrad: Lightweight and Fast Frequency-aware Diffusion Vocoder", "authors": [ "Tan Dat Nguyen", "Ji-Hoon Kim", "Youngjoon Jang", "Jaehun Kim", "Joon Son Chung" ], "github": "https://github.com/kaistmm/fregrad", "project_page": "", "abstract": "The goal of this paper is to generate realistic audio with a lightweight and fast diffusion-based vocoder, named FreGrad. Our framework consists of the following three key components: (1) We employ discrete wavelet transform that decomposes a complicated waveform into sub-band wavelets, which helps FreGrad to operate on a simple and concise feature space, (2) We design a frequency-aware dilated convolution that elevates frequency awareness, resulting in generating speech with accurate frequency information, and (3) We introduce a bag of tricks that boosts the generation quality of the proposed model. In our experiments, FreGrad achieves 3.7 times faster training time and 2.2 times faster inference speed compared to our baseline while reducing the model size by 0.6 times (only 1.78M parameters) without sacrificing the output quality. Audio samples are available at: https://mm.kaist.ac.kr/projects/FreGrad.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kaistmm/fregrad", "github_api_added_by": "auto" }, { "date": "2024-01-19", "arxiv_id": "2401.09603", "title": "Rethinking FID: Towards a Better Evaluation Metric for Image Generation", "authors": [ "Sadeep Jayasumana", "Srikumar Ramalingam", "Andreas Veit", "Daniel Glasner", "Ayan Chakrabarti", "Sanjiv Kumar" ], "github": "", "project_page": "", "abstract": "As with many machine learning problems, the progress of image generation methods hinges on good evaluation metrics. One of the most popular is the Frechet Inception Distance (FID). FID estimates the distance between a distribution of Inception-v3 features of real images, and those of images generated by the algorithm. We highlight important drawbacks of FID: Inception's poor representation of the rich and varied content generated by modern text-to-image models, incorrect normality assumptions, and poor sample complexity. We call for a reevaluation of FID's use as the primary quality metric for generated images. We empirically demonstrate that FID contradicts human raters, it does not reflect gradual improvement of iterative text-to-image models, it does not capture distortion levels, and that it produces inconsistent results when varying the sample size. We also propose an alternative new metric, CMMD, based on richer CLIP embeddings and the maximum mean discrepancy distance with the Gaussian RBF kernel. It is an unbiased estimator that does not make any assumptions on the probability distribution of the embeddings and is sample efficient. Through extensive experiments and analysis, we demonstrate that FID-based evaluations of text-to-image models may be unreliable, and that CMMD offers a more robust and reliable assessment of image quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-19", "arxiv_id": "2401.10171", "title": "SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild", "authors": [ "Andreas Engelhardt", "Amit Raj", "Mark Boss", "Yunzhi Zhang", "Abhishek Kar", "Yuanzhen Li", "Deqing Sun", "Ricardo Martin Brualla", "Jonathan T. Barron", "Hendrik P. A. Lensch", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background. Inverse rendering of an object based on unconstrained image collections is a long-standing challenge in computer vision and graphics and requires a joint optimization over shape, radiance, and pose. We show that an implicit shape representation based on a multi-resolution hash encoding enables faster and robust shape reconstruction with joint camera alignment optimization that outperforms prior work. Further, to enable the editing of illumination and object reflectance (i.e. material) we jointly optimize BRDF and illumination together with the object's shape. Our method is class-agnostic and works on in-the-wild image collections of objects to produce relightable 3D assets for several use cases such as AR/VR, movies, games, etc. Project page: https://shinobi.aengelhardt.com Video: https://www.youtube.com/watch?v=iFENQ6AcYd8&feature=youtu.be", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-22", "arxiv_id": "2401.10774", "title": "Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads", "authors": [ "Tianle Cai", "Yuhong Li", "Zhengyang Geng", "Hongwu Peng", "Jason D. Lee", "Deming Chen", "Tri Dao" ], "github": "https://github.com/feifeibear/LLMSpeculativeSampling", "project_page": "", "abstract": "The inference process in Large Language Models (LLMs) is often limited due to the absence of parallelism in the auto-regressive decoding process, resulting in most operations being restricted by the memory bandwidth of accelerators. While methods such as speculative decoding have been suggested to address this issue, their implementation is impeded by the challenges associated with acquiring and maintaining a separate draft model. In this paper, we present Medusa, an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel. Using a tree-based attention mechanism, Medusa constructs multiple candidate continuations and verifies them simultaneously in each decoding step. By leveraging parallel processing, Medusa introduces only minimal overhead in terms of single-step latency while substantially reducing the number of decoding steps required. We present two levels of fine-tuning procedures for Medusa to meet the needs of different use cases: Medusa-1: Medusa is directly fine-tuned on top of a frozen backbone LLM, enabling lossless inference acceleration. Medusa-2: Medusa is fine-tuned together with the backbone LLM, enabling better prediction accuracy of Medusa heads and higher speedup but needing a special training recipe that preserves the backbone model's capabilities. Moreover, we propose several extensions that improve or expand the utility of Medusa, including a self-distillation to handle situations where no training data is available and a typical acceptance scheme to boost the acceptance rate while maintaining generation quality. We evaluate Medusa on models of various sizes and training procedures. Our experiments demonstrate that Medusa-1 can achieve over 2.2x speedup without compromising generation quality, while Medusa-2 further improves the speedup to 2.3-3.6x.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/feifeibear/LLMSpeculativeSampling", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-22", "arxiv_id": "2401.10822", "title": "ActAnywhere: Subject-Aware Video Background Generation", "authors": [ "Boxiao Pan", "Zhan Xu", "Chun-Hao Paul Huang", "Krishna Kumar Singh", "Yang Zhou", "Leonidas J. Guibas", "Jimei Yang" ], "github": "", "project_page": "", "abstract": "Generating video background that tailors to foreground subject motion is an important problem for the movie industry and visual effects community. This task involves synthesizing background that aligns with the motion and appearance of the foreground subject, while also complies with the artist's creative intention. We introduce ActAnywhere, a generative model that automates this process which traditionally requires tedious manual efforts. Our model leverages the power of large-scale video diffusion models, and is specifically tailored for this task. ActAnywhere takes a sequence of foreground subject segmentation as input and an image that describes the desired scene as condition, to produce a coherent video with realistic foreground-background interactions while adhering to the condition frame. We train our model on a large-scale dataset of human-scene interaction videos. Extensive evaluations demonstrate the superior performance of our model, significantly outperforming baselines. Moreover, we show that ActAnywhere generalizes to diverse out-of-distribution samples, including non-human subjects. Please visit our project webpage at https://actanywhere.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-22", "arxiv_id": "2401.10404", "title": "Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution", "authors": [ "Xin Yuan", "Jinoo Baek", "Keyang Xu", "Omer Tov", "Hongliang Fei" ], "github": "", "project_page": "", "abstract": "We propose an efficient diffusion-based text-to-video super-resolution (SR) tuning approach that leverages the readily learned capacity of pixel level image diffusion model to capture spatial information for video generation. To accomplish this goal, we design an efficient architecture by inflating the weightings of the text-to-image SR model into our video generation framework. Additionally, we incorporate a temporal adapter to ensure temporal coherence across video frames. We investigate different tuning approaches based on our inflated architecture and report trade-offs between computational costs and super-resolution quality. Empirical evaluation, both quantitative and qualitative, on the Shutterstock video dataset, demonstrates that our approach is able to perform text-to-video SR generation with good visual quality and temporal consistency. To evaluate temporal coherence, we also present visualizations in video format in https://drive.google.com/drive/folders/1YVc-KMSJqOrEUdQWVaI-Yfu8Vsfu_1aO?usp=sharing .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-22", "arxiv_id": "2401.10891", "title": "Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data", "authors": [ "Lihe Yang", "Bingyi Kang", "Zilong Huang", "Xiaogang Xu", "Jiashi Feng", "Hengshuang Zhao" ], "github": "https://github.com/LiheYoung/Depth-Anything", "project_page": "", "abstract": "This work presents Depth Anything, a highly practical solution for robust monocular depth estimation. Without pursuing novel technical modules, we aim to build a simple yet powerful foundation model dealing with any images under any circumstances. To this end, we scale up the dataset by designing a data engine to collect and automatically annotate large-scale unlabeled data (~62M), which significantly enlarges the data coverage and thus is able to reduce the generalization error. We investigate two simple yet effective strategies that make data scaling-up promising. First, a more challenging optimization target is created by leveraging data augmentation tools. It compels the model to actively seek extra visual knowledge and acquire robust representations. Second, an auxiliary supervision is developed to enforce the model to inherit rich semantic priors from pre-trained encoders. We evaluate its zero-shot capabilities extensively, including six public datasets and randomly captured photos. It demonstrates impressive generalization ability. Further, through fine-tuning it with metric depth information from NYUv2 and KITTI, new SOTAs are set. Our better depth model also results in a better depth-conditioned ControlNet. Our models are released at https://github.com/LiheYoung/Depth-Anything.", "github_manual": "", "github_regex": "https://github.com/LiheYoung/Depth-Anything", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-22", "arxiv_id": "2401.10889", "title": "Synthesizing Moving People with 3D Control", "authors": [ "Boyi Li", "Jathushan Rajasegaran", "Yossi Gandelsman", "Alexei A. Efros", "Jitendra Malik" ], "github": "", "project_page": "", "abstract": "In this paper, we present a diffusion model-based framework for animating people from a single image for a given target 3D motion sequence. Our approach has two core components: a) learning priors about invisible parts of the human body and clothing, and b) rendering novel body poses with proper clothing and texture. For the first part, we learn an in-filling diffusion model to hallucinate unseen parts of a person given a single image. We train this model on texture map space, which makes it more sample-efficient since it is invariant to pose and viewpoint. Second, we develop a diffusion-based rendering pipeline, which is controlled by 3D human poses. This produces realistic renderings of novel poses of the person, including clothing, hair, and plausible in-filling of unseen regions. This disentangled approach allows our method to generate a sequence of images that are faithful to the target motion in the 3D pose and, to the input image in terms of visual similarity. In addition to that, the 3D control allows various synthetic camera trajectories to render a person. Our experiments show that our method is resilient in generating prolonged motions and varied challenging and complex poses compared to prior methods. Please check our website for more details: https://boyiliee.github.io/3DHM.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-22", "arxiv_id": "2401.10241", "title": "Zero Bubble Pipeline Parallelism", "authors": [ "Penghui Qi", "Xinyi Wan", "Guangxing Huang", "Min Lin" ], "github": "https://github.com/sail-sg/zero-bubble-pipeline-parallelism", "project_page": "", "abstract": "Pipeline parallelism is one of the key components for large-scale distributed training, yet its efficiency suffers from pipeline bubbles which were deemed inevitable. In this work, we introduce a scheduling strategy that, to our knowledge, is the first to successfully achieve zero pipeline bubbles under synchronous training semantics. The key idea behind this improvement is to split the backward computation into two parts, one that computes gradient for the input and another that computes for the parameters. Based on this idea, we handcraft novel pipeline schedules that significantly outperform the baseline methods. We further develop an algorithm that automatically finds an optimal schedule based on specific model configuration and memory limit. Additionally, to truly achieve zero bubble, we introduce a novel technique to bypass synchronizations during the optimizer step. Experimental evaluations show that our method outperforms the 1F1B schedule up to 23% in throughput under a similar memory limit. This number can be further pushed to 31% when the memory constraint is relaxed. We believe our results mark a major step forward in harnessing the true potential of pipeline parallelism. We open sourced our implementation based on the popular Megatron-LM repository on https://github.com/sail-sg/zero-bubble-pipeline-parallelism.", "github_manual": "", "github_regex": "https://github.com/sail-sg/zero-bubble-pipeline-parallelism", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-22", "arxiv_id": "2401.10838", "title": "Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation", "authors": [ "Susan Lin", "Jeremy Warner", "J. D. Zamfirescu-Pereira", "Matthew G. Lee", "Sauhard Jain", "Michael Xuelin Huang", "Piyawat Lertvittayakumjorn", "Shanqing Cai", "Shumin Zhai", "Bj\u00f6rn Hartmann", "Can Liu" ], "github": "https://github.com/berkeleyhci/rambler", "project_page": "", "abstract": "Dictation enables efficient text input on mobile devices. However, writing with speech can produce disfluent, wordy, and incoherent text and thus requires heavy post-processing. This paper presents Rambler, an LLM-powered graphical user interface that supports gist-level manipulation of dictated text with two main sets of functions: gist extraction and macro revision. Gist extraction generates keywords and summaries as anchors to support the review and interaction with spoken text. LLM-assisted macro revisions allow users to respeak, split, merge and transform dictated text without specifying precise editing locations. Together they pave the way for interactive dictation and revision that help close gaps between spontaneous spoken words and well-structured writing. In a comparative study with 12 participants performing verbal composition tasks, Rambler outperformed the baseline of a speech-to-text editor + ChatGPT, as it better facilitates iterative revisions with enhanced user control over the content while supporting surprisingly diverse user strategies.", "github_manual": "https://github.com/berkeleyhci/rambler", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-22", "arxiv_id": "2401.10831", "title": "Understanding Video Transformers via Universal Concept Discovery", "authors": [ "Matthew Kowal", "Achal Dave", "Rares Ambrus", "Adrien Gaidon", "Konstantinos G. Derpanis", "Pavel Tokmakov" ], "github": "", "project_page": "", "abstract": "This paper studies the problem of concept-based interpretability of transformer representations for videos. Concretely, we seek to explain the decision-making process of video transformers based on high-level, spatiotemporal concepts that are automatically discovered. Prior research on concept-based interpretability has concentrated solely on image-level tasks. Comparatively, video models deal with the added temporal dimension, increasing complexity and posing challenges in identifying dynamic concepts over time. In this work, we systematically address these challenges by introducing the first Video Transformer Concept Discovery (VTCD) algorithm. To this end, we propose an efficient approach for unsupervised identification of units of video transformer representations - concepts, and ranking their importance to the output of a model. The resulting concepts are highly interpretable, revealing spatio-temporal reasoning mechanisms and object-centric representations in unstructured video models. Performing this analysis jointly over a diverse set of supervised and self-supervised representations, we discover that some of these mechanism are universal in video transformers. Finally, we demonstrate that VTCDcan be used to improve model performance for fine-grained tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.12070", "title": "Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text", "authors": [ "Abhimanyu Hans", "Avi Schwarzschild", "Valeriia Cherepanova", "Hamid Kazemi", "Aniruddha Saha", "Micah Goldblum", "Jonas Geiping", "Tom Goldstein" ], "github": "https://github.com/ahans30/binoculars", "project_page": "", "abstract": "Detecting text generated by modern large language models is thought to be hard, as both LLMs and humans can exhibit a wide range of complex behaviors. However, we find that a score based on contrasting two closely related language models is highly accurate at separating human-generated and machine-generated text. Based on this mechanism, we propose a novel LLM detector that only requires simple calculations using a pair of pre-trained LLMs. The method, called Binoculars, achieves state-of-the-art accuracy without any training data. It is capable of spotting machine text from a range of modern LLMs without any model-specific modifications. We comprehensively evaluate Binoculars on a number of text sources and in varied situations. Over a wide range of document types, Binoculars detects over 90% of generated samples from ChatGPT (and other LLMs) at a false positive rate of 0.01%, despite not being trained on any ChatGPT data.", "github_manual": "https://github.com/ahans30/binoculars", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.11944", "title": "CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark", "authors": [ "Ge Zhang", "Xinrun Du", "Bei Chen", "Yiming Liang", "Tongxu Luo", "Tianyu Zheng", "Kang Zhu", "Yuyang Cheng", "Chunpu Xu", "Shuyue Guo", "Haoran Zhang", "Xingwei Qu", "Junjie Wang", "Ruibin Yuan", "Yizhi Li", "Zekun Wang", "Yudong Liu", "Yu-Hsuan Tsai", "Fengji Zhang", "Chenghua Lin", "Wenhao Huang", "Wenhu Chen", "Jie Fu" ], "github": "", "project_page": "", "abstract": "As the capabilities of large multimodal models (LMMs) continue to advance, evaluating the performance of LMMs emerges as an increasing need. Additionally, there is an even larger gap in evaluating the advanced knowledge and reasoning abilities of LMMs in non-English contexts such as Chinese. We introduce CMMMU, a new Chinese Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate LMMs on tasks demanding college-level subject knowledge and deliberate reasoning in a Chinese context. CMMMU is inspired by and strictly follows the annotation and analysis pattern of MMMU. CMMMU includes 12k manually collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering, like its companion, MMMU. These questions span 30 subjects and comprise 39 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. CMMMU focuses on complex perception and reasoning with domain-specific knowledge in the Chinese context. We evaluate 11 open-source LLMs and one proprietary GPT-4V(ision). Even GPT-4V only achieves accuracies of 42%, indicating a large space for improvement. CMMMU will boost the community to build the next-generation LMMs towards expert artificial intelligence and promote the democratization of LMMs by providing diverse language contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.11708", "title": "Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs", "authors": [ "Ling Yang", "Zhaochen Yu", "Chenlin Meng", "Minkai Xu", "Stefano Ermon", "Bin Cui" ], "github": "https://github.com/YangLing0818/RPG-DiffusionMaster", "project_page": "", "abstract": "Diffusion models have exhibit exceptional performance in text-to-image generation and editing. However, existing methods often face challenges when handling complex text prompts that involve multiple objects with multiple attributes and relationships. In this paper, we propose a brand new training-free text-to-image generation/editing framework, namely Recaption, Plan and Generate (RPG), harnessing the powerful chain-of-thought reasoning ability of multimodal LLMs to enhance the compositionality of text-to-image diffusion models. Our approach employs the MLLM as a global planner to decompose the process of generating complex images into multiple simpler generation tasks within subregions. We propose complementary regional diffusion to enable region-wise compositional generation. Furthermore, we integrate text-guided image generation and editing within the proposed RPG in a closed-loop fashion, thereby enhancing generalization ability. Extensive experiments demonstrate our RPG outperforms state-of-the-art text-to-image diffusion models, including DALL-E 3 and SDXL, particularly in multi-category object composition and text-image semantic alignment. Notably, our RPG framework exhibits wide compatibility with various MLLM architectures (e.g., MiniGPT-4) and diffusion backbones (e.g., ControlNet). Our code is available at: https://github.com/YangLing0818/RPG-DiffusionMaster", "github_manual": "", "github_regex": "https://github.com/YangLing0818/RPG-DiffusionMaster", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.12208", "title": "CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation", "authors": [ "Zhihong Chen", "Maya Varma", "Jean-Benoit Delbrouck", "Magdalini Paschali", "Louis Blankemeier", "Dave Van Veen", "Jeya Maria Jose Valanarasu", "Alaa Youssef", "Joseph Paul Cohen", "Eduardo Pontes Reis", "Emily B. Tsai", "Andrew Johnston", "Cameron Olsen", "Tanishq Mathew Abraham", "Sergios Gatidis", "Akshay S. Chaudhari", "Curtis Langlotz" ], "github": "https://github.com/Stanford-AIMI/CheXagent", "project_page": "", "abstract": "Chest X-rays (CXRs) are the most frequently performed imaging test in clinical practice. Recent advances in the development of vision-language foundation models (FMs) give rise to the possibility of performing automated CXR interpretation, which can assist physicians with clinical decision-making and improve patient outcomes. However, developing FMs that can accurately interpret CXRs is challenging due to the (1) limited availability of large-scale vision-language datasets in the medical image domain, (2) lack of vision and language encoders that can capture the complexities of medical data, and (3) absence of evaluation frameworks for benchmarking the abilities of FMs on CXR interpretation. In this work, we address these challenges by first introducing CheXinstruct - a large-scale instruction-tuning dataset curated from 28 publicly-available datasets. We then present CheXagent - an instruction-tuned FM capable of analyzing and summarizing CXRs. To build CheXagent, we design a clinical large language model (LLM) for parsing radiology reports, a vision encoder for representing CXR images, and a network to bridge the vision and language modalities. Finally, we introduce CheXbench - a novel benchmark designed to systematically evaluate FMs across 8 clinically-relevant CXR interpretation tasks. Extensive quantitative evaluations and qualitative reviews with five expert radiologists demonstrate that CheXagent outperforms previously-developed general- and medical-domain FMs on CheXbench tasks. Furthermore, in an effort to improve model transparency, we perform a fairness evaluation across factors of sex, race and age to highlight potential performance disparities. Our project is at https://stanford-aimi.github.io/chexagent.html.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Stanford-AIMI/CheXagent", "github_api_added_by": "auto" }, { "date": "2024-01-23", "arxiv_id": "2401.12168", "title": "SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities", "authors": [ "Boyuan Chen", "Zhuo Xu", "Sean Kirmani", "Brian Ichter", "Danny Driess", "Pete Florence", "Dorsa Sadigh", "Leonidas Guibas", "Fei Xia" ], "github": "", "project_page": "", "abstract": "Understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. While Vision Language Models (VLM) have demonstrated remarkable performance in certain VQA benchmarks, they still lack capabilities in 3D spatial reasoning, such as recognizing quantitative relationships of physical objects like distances or size differences. We hypothesize that VLMs' limited spatial reasoning capability is due to the lack of 3D spatial knowledge in training data and aim to solve this problem by training VLMs with Internet-scale spatial reasoning data. To this end, we present a system to facilitate this approach. We first develop an automatic 3D spatial VQA data generation framework that scales up to 2 billion VQA examples on 10 million real-world images. We then investigate various factors in the training recipe, including data quality, training pipeline, and VLM architecture. Our work features the first internet-scale 3D spatial reasoning dataset in metric space. By training a VLM on such data, we significantly enhance its ability on both qualitative and quantitative spatial VQA. Finally, we demonstrate that this VLM unlocks novel downstream applications in chain-of-thought spatial reasoning and robotics due to its quantitative estimation capability. Project website: https://spatial-vlm.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.12202", "title": "OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics", "authors": [ "Peiqi Liu", "Yaswanth Orru", "Chris Paxton", "Nur Muhammad Mahi Shafiullah", "Lerrel Pinto" ], "github": "https://github.com/ok-robot/ok-robot", "project_page": "", "abstract": "Remarkable progress has been made in recent years in the fields of vision, language, and robotics. We now have vision models capable of recognizing objects based on language queries, navigation systems that can effectively control mobile systems, and grasping models that can handle a wide range of objects. Despite these advancements, general-purpose applications of robotics still lag behind, even though they rely on these fundamental capabilities of recognition, navigation, and grasping. In this paper, we adopt a systems-first approach to develop a new Open Knowledge-based robotics framework called OK-Robot. By combining Vision-Language Models (VLMs) for object detection, navigation primitives for movement, and grasping primitives for object manipulation, OK-Robot offers a integrated solution for pick-and-drop operations without requiring any training. To evaluate its performance, we run OK-Robot in 10 real-world home environments. The results demonstrate that OK-Robot achieves a 58.5% success rate in open-ended pick-and-drop tasks, representing a new state-of-the-art in Open Vocabulary Mobile Manipulation (OVMM) with nearly 1.8x the performance of prior work. On cleaner, uncluttered environments, OK-Robot's performance increases to 82%. However, the most important insight gained from OK-Robot is the critical role of nuanced details when combining Open Knowledge systems like VLMs with robotic modules. Videos of our experiments are available on our website: https://ok-robot.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ok-robot/ok-robot", "github_api_added_by": "auto" }, { "date": "2024-01-23", "arxiv_id": "2401.12187", "title": "WARM: On the Benefits of Weight Averaged Reward Models", "authors": [ "Alexandre Ram\u00e9", "Nino Vieillard", "L\u00e9onard Hussenot", "Robert Dadashi", "Geoffrey Cideron", "Olivier Bachem", "Johan Ferret" ], "github": "", "project_page": "", "abstract": "Aligning large language models (LLMs) with human preferences through reinforcement learning (RLHF) can lead to reward hacking, where LLMs exploit failures in the reward model (RM) to achieve seemingly high rewards without meeting the underlying objectives. We identify two primary challenges when designing RMs to mitigate reward hacking: distribution shifts during the RL process and inconsistencies in human preferences. As a solution, we propose Weight Averaged Reward Models (WARM), first fine-tuning multiple RMs, then averaging them in the weight space. This strategy follows the observation that fine-tuned weights remain linearly mode connected when sharing the same pre-training. By averaging weights, WARM improves efficiency compared to the traditional ensembling of predictions, while improving reliability under distribution shifts and robustness to preference inconsistencies. Our experiments on summarization tasks, using best-of-N and RL methods, shows that WARM improves the overall quality and alignment of LLM predictions; for example, a policy RL fine-tuned with WARM has a 79.4% win rate against a policy RL fine-tuned with a single RM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.11739", "title": "EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models", "authors": [ "Koichi Namekata", "Amirmojtaba Sabour", "Sanja Fidler", "Seung Wook Kim" ], "github": "", "project_page": "", "abstract": "Diffusion models have recently received increasing research attention for their remarkable transfer abilities in semantic segmentation tasks. However, generating fine-grained segmentation masks with diffusion models often requires additional training on annotated datasets, leaving it unclear to what extent pre-trained diffusion models alone understand the semantic relations of their generated images. To address this question, we leverage the semantic knowledge extracted from Stable Diffusion (SD) and aim to develop an image segmentor capable of generating fine-grained segmentation maps without any additional training. The primary difficulty stems from the fact that semantically meaningful feature maps typically exist only in the spatially lower-dimensional layers, which poses a challenge in directly extracting pixel-level semantic relations from these feature maps. To overcome this issue, our framework identifies semantic correspondences between image pixels and spatial locations of low-dimensional feature maps by exploiting SD's generation process and utilizes them for constructing image-resolution segmentation maps. In extensive experiments, the produced segmentation maps are demonstrated to be well delineated and capture detailed parts of the images, indicating the existence of highly accurate pixel-level semantic knowledge in diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.11067", "title": "Make-A-Shape: a Ten-Million-scale 3D Shape Model", "authors": [ "Ka-Hei Hui", "Aditya Sanghi", "Arianna Rampini", "Kamal Rahimi Malekshan", "Zhengzhe Liu", "Hooman Shayani", "Chi-Wing Fu" ], "github": "https://github.com/autodeskailab/make-a-shape", "project_page": "", "abstract": "Significant progress has been made in training large generative models for natural language and images. Yet, the advancement of 3D generative models is hindered by their substantial resource demands for training, along with inefficient, non-compact, and less expressive representations. This paper introduces Make-A-Shape, a new 3D generative model designed for efficient training on a vast scale, capable of utilizing 10 millions publicly-available shapes. Technical-wise, we first innovate a wavelet-tree representation to compactly encode shapes by formulating the subband coefficient filtering scheme to efficiently exploit coefficient relations. We then make the representation generatable by a diffusion model by devising the subband coefficients packing scheme to layout the representation in a low-resolution grid. Further, we derive the subband adaptive training strategy to train our model to effectively learn to generate coarse and detail wavelet coefficients. Last, we extend our framework to be controlled by additional input conditions to enable it to generate shapes from assorted modalities, e.g., single/multi-view images, point clouds, and low-resolution voxels. In our extensive set of experiments, we demonstrate various applications, such as unconditional generation, shape completion, and conditional generation on a wide range of modalities. Our approach not only surpasses the state of the art in delivering high-quality results but also efficiently generates shapes within a few seconds, often achieving this in just 2 seconds for most conditions.", "github_manual": "https://github.com/autodeskailab/make-a-shape", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.11053", "title": "StreamVoice: Streamable Context-Aware Language Modeling for Real-time Zero-Shot Voice Conversion", "authors": [ "Zhichao Wang", "Yuanzhe Chen", "Xinsheng Wang", "Zhuo Chen", "Lei Xie", "Yuping Wang", "Yuxuan Wang" ], "github": "https://github.com/facebookresearch/AudioDec", "project_page": "", "abstract": "Recent language model (LM) advancements have showcased impressive zero-shot voice conversion (VC) performance. However, existing LM-based VC models usually apply offline conversion from source semantics to acoustic features, demanding the complete source speech, and limiting their deployment to real-time applications. In this paper, we introduce StreamVoice, a novel streaming LM-based model for zero-shot VC, facilitating real-time conversion given arbitrary speaker prompts and source speech. Specifically, to enable streaming capability, StreamVoice employs a fully causal context-aware LM with a temporal-independent acoustic predictor, while alternately processing semantic and acoustic features at each time step of autoregression which eliminates the dependence on complete source speech. To address the potential performance degradation from the incomplete context in streaming processing, we enhance the context-awareness of the LM through two strategies: 1) teacher-guided context foresight, using a teacher model to summarize the present and future semantic context during training to guide the model's forecasting for missing context; 2) semantic masking strategy, promoting acoustic prediction from preceding corrupted semantic and acoustic input, enhancing context-learning ability. Notably, StreamVoice is the first LM-based streaming zero-shot VC model without any future look-ahead. Experimental results demonstrate StreamVoice's streaming conversion capability while maintaining zero-shot performance comparable to non-streaming VC systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/AudioDec", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.11605", "title": "Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers", "authors": [ "Katherine Crowson", "Stefan Andreas Baumann", "Alex Birch", "Tanishq Mathew Abraham", "Daniel Z. Kaplan", "Enrico Shippole" ], "github": "https://github.com/crowsonkb/k-diffusion", "project_page": "", "abstract": "We present the Hourglass Diffusion Transformer (HDiT), an image generative model that exhibits linear scaling with pixel count, supporting training at high-resolution (e.g. 1024 times 1024) directly in pixel-space. Building on the Transformer architecture, which is known to scale to billions of parameters, it bridges the gap between the efficiency of convolutional U-Nets and the scalability of Transformers. HDiT trains successfully without typical high-resolution training techniques such as multiscale architectures, latent autoencoders or self-conditioning. We demonstrate that HDiT performs competitively with existing models on ImageNet 256^2, and sets a new state-of-the-art for diffusion models on FFHQ-1024^2.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/crowsonkb/k-diffusion", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.12175", "title": "Single-View 3D Human Digitalization with Large Reconstruction Models", "authors": [ "Zhenzhen Weng", "Jingyuan Liu", "Hao Tan", "Zhan Xu", "Yang Zhou", "Serena Yeung-Levy", "Jimei Yang" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce Human-LRM, a single-stage feed-forward Large Reconstruction Model designed to predict human Neural Radiance Fields (NeRF) from a single image. Our approach demonstrates remarkable adaptability in training using extensive datasets containing 3D scans and multi-view capture. Furthermore, to enhance the model's applicability for in-the-wild scenarios especially with occlusions, we propose a novel strategy that distills multi-view reconstruction into single-view via a conditional triplane diffusion model. This generative extension addresses the inherent variations in human body shapes when observed from a single view, and makes it possible to reconstruct the full body human from an occluded image. Through extensive experiments, we show that Human-LRM surpasses previous methods by a significant margin on several benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.12179", "title": "DITTO: Diffusion Inference-Time T-Optimization for Music Generation", "authors": [ "Zachary Novack", "Julian McAuley", "Taylor Berg-Kirkpatrick", "Nicholas J. Bryan" ], "github": "", "project_page": "", "abstract": "We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-work for controlling pre-trained text-to-music diffusion models at inference-time via optimizing initial noise latents. Our method can be used to optimize through any differentiable feature matching loss to achieve a target (stylized) output and leverages gradient checkpointing for memory efficiency. We demonstrate a surprisingly wide-range of applications for music generation including inpainting, outpainting, and looping as well as intensity, melody, and musical structure control - all without ever fine-tuning the underlying model. When we compare our approach against related training, guidance, and optimization-based methods, we find DITTO achieves state-of-the-art performance on nearly all tasks, including outperforming comparable approaches on controllability, audio quality, and computational efficiency, thus opening the door for high-quality, flexible, training-free control of diffusion models. Sound examples can be found at https://DITTO-Music.github.io/web/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.11002", "title": "Fast Registration of Photorealistic Avatars for VR Facial Animation", "authors": [ "Chaitanya Patel", "Shaojie Bai", "Te-Li Wang", "Jason Saragih", "Shih-En Wei" ], "github": "", "project_page": "", "abstract": "Virtual Reality (VR) bares promise of social interactions that can feel more immersive than other media. Key to this is the ability to accurately animate a photorealistic avatar of one's likeness while wearing a VR headset. Although high quality registration of person-specific avatars to headset-mounted camera (HMC) images is possible in an offline setting, the performance of generic realtime models are significantly degraded. Online registration is also challenging due to oblique camera views and differences in modality. In this work, we first show that the domain gap between the avatar and headset-camera images is one of the primary sources of difficulty, where a transformer-based architecture achieves high accuracy on domain-consistent data, but degrades when the domain-gap is re-introduced. Building on this finding, we develop a system design that decouples the problem into two parts: 1) an iterative refinement module that takes in-domain inputs, and 2) a generic avatar-guided image-to-image style transfer module that is conditioned on current estimation of expression and head pose. These two modules reinforce each other, as image style transfer becomes easier when close-to-ground-truth examples are shown, and better domain-gap removal helps registration. Our system produces high-quality results efficiently, obviating the need for costly offline registration to generate personalized labels. We validate the accuracy and efficiency of our approach through extensive experiments on a commodity headset, demonstrating significant improvements over direct regression methods as well as offline registration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.11078", "title": "UltrAvatar: A Realistic Animatable 3D Avatar Diffusion Model with Authenticity Guided Textures", "authors": [ "Mingyuan Zhou", "Rakib Hyder", "Ziwei Xuan", "Guojun Qi" ], "github": "", "project_page": "", "abstract": "Recent advances in 3D avatar generation have gained significant attentions. These breakthroughs aim to produce more realistic animatable avatars, narrowing the gap between virtual and real-world experiences. Most of existing works employ Score Distillation Sampling (SDS) loss, combined with a differentiable renderer and text condition, to guide a diffusion model in generating 3D avatars. However, SDS often generates oversmoothed results with few facial details, thereby lacking the diversity compared with ancestral sampling. On the other hand, other works generate 3D avatar from a single image, where the challenges of unwanted lighting effects, perspective views, and inferior image quality make them difficult to reliably reconstruct the 3D face meshes with the aligned complete textures. In this paper, we propose a novel 3D avatar generation approach termed UltrAvatar with enhanced fidelity of geometry, and superior quality of physically based rendering (PBR) textures without unwanted lighting. To this end, the proposed approach presents a diffuse color extraction model and an authenticity guided texture diffusion model. The former removes the unwanted lighting effects to reveal true diffuse colors so that the generated avatars can be rendered under various lighting conditions. The latter follows two gradient-based guidances for generating PBR textures to render diverse face-identity features and details better aligning with 3D mesh geometry. We demonstrate the effectiveness and robustness of the proposed method, outperforming the state-of-the-art methods by a large margin in the experiments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-23", "arxiv_id": "2401.11985", "title": "Scaling Face Interaction Graph Networks to Real World Scenes", "authors": [ "Tatiana Lopez-Guevara", "Yulia Rubanova", "William F. Whitney", "Tobias Pfaff", "Kimberly Stachenfeld", "Kelsey R. Allen" ], "github": "", "project_page": "", "abstract": "Accurately simulating real world object dynamics is essential for various applications such as robotics, engineering, graphics, and design. To better capture complex real dynamics such as contact and friction, learned simulators based on graph networks have recently shown great promise. However, applying these learned simulators to real scenes comes with two major challenges: first, scaling learned simulators to handle the complexity of real world scenes which can involve hundreds of objects each with complicated 3D shapes, and second, handling inputs from perception rather than 3D state information. Here we introduce a method which substantially reduces the memory required to run graph-based learned simulators. Based on this memory-efficient simulation model, we then present a perceptual interface in the form of editable NeRFs which can convert real-world scenes into a structured representation that can be processed by graph network simulator. We show that our method uses substantially less memory than previous graph-based simulators while retaining their accuracy, and that the simulators learned in synthetic environments can be applied to real world scenes captured from multiple camera angles. This paves the way for expanding the application of learned simulators to settings where only perceptual information is available at inference time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-24", "arxiv_id": "2401.12503", "title": "Small Language Model Meets with Reinforced Vision Vocabulary", "authors": [ "Haoran Wei", "Lingyu Kong", "Jinyue Chen", "Liang Zhao", "Zheng Ge", "En Yu", "Jianjian Sun", "Chunrui Han", "Xiangyu Zhang" ], "github": "", "project_page": "", "abstract": "Playing Large Vision Language Models (LVLMs) in 2023 is trendy among the AI community. However, the relatively large number of parameters (more than 7B) of popular LVLMs makes it difficult to train and deploy on consumer GPUs, discouraging many researchers with limited resources. Imagine how cool it would be to experience all the features of current LVLMs on an old GTX1080ti (our only game card). Accordingly, we present Vary-toy in this report, a small-size Vary along with Qwen-1.8B as the base ``large'' language model. In Vary-toy, we introduce an improved vision vocabulary, allowing the model to not only possess all features of Vary but also gather more generality. Specifically, we replace negative samples of natural images with positive sample data driven by object detection in the procedure of generating vision vocabulary, more sufficiently utilizing the capacity of the vocabulary network and enabling it to efficiently encode visual information corresponding to natural objects. For experiments, Vary-toy can achieve 65.6% ANLS on DocVQA, 59.1% accuracy on ChartQA, 88.1% accuracy on RefCOCO, and 29% on MMVet. The code will be publicly available on the homepage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-24", "arxiv_id": "2401.12474", "title": "Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment", "authors": [ "Keming Lu", "Bowen Yu", "Chang Zhou", "Jingren Zhou" ], "github": "https://github.com/OFA-Sys/Ditto", "project_page": "", "abstract": "Considerable efforts have been invested in augmenting the role-playing proficiency of open-source large language models (LLMs) by emulating proprietary counterparts. Nevertheless, we posit that LLMs inherently harbor role-play capabilities, owing to the extensive knowledge of characters and potential dialogues ingrained in their vast training corpora. Thus, in this study, we introduce Ditto, a self-alignment method for role-play. Ditto capitalizes on character knowledge, encouraging an instruction-following LLM to simulate role-play dialogues as a variant of reading comprehension. This method creates a role-play training set comprising 4,000 characters, surpassing the scale of currently available datasets by tenfold regarding the number of roles. Subsequently, we fine-tune the LLM using this self-generated dataset to augment its role-playing capabilities. Upon evaluating our meticulously constructed and reproducible role-play benchmark and the roleplay subset of MT-Bench, Ditto, in various parameter scales, consistently maintains a consistent role identity and provides accurate role-specific knowledge in multi-turn role-play conversations. Notably, it outperforms all open-source role-play baselines, showcasing performance levels comparable to advanced proprietary chatbots. Furthermore, we present the first comprehensive cross-supervision alignment experiment in the role-play domain, revealing that the intrinsic capabilities of LLMs confine the knowledge within role-play. Meanwhile, the role-play styles can be easily acquired with the guidance of smaller models. We open-source related resources at https://github.com/OFA-Sys/Ditto.", "github_manual": "", "github_regex": "https://github.com/OFA-Sys/Ditto", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-24", "arxiv_id": "2401.12945", "title": "Lumiere: A Space-Time Diffusion Model for Video Generation", "authors": [ "Omer Bar-Tal", "Hila Chefer", "Omer Tov", "Charles Herrmann", "Roni Paiss", "Shiran Zada", "Ariel Ephrat", "Junhwa Hur", "Yuanzhen Li", "Tomer Michaeli", "Oliver Wang", "Deqing Sun", "Tali Dekel", "Inbar Mosseri" ], "github": "", "project_page": "", "abstract": "We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis. To this end, we introduce a Space-Time U-Net architecture that generates the entire temporal duration of the video at once, through a single pass in the model. This is in contrast to existing video models which synthesize distant keyframes followed by temporal super-resolution -- an approach that inherently makes global temporal consistency difficult to achieve. By deploying both spatial and (importantly) temporal down- and up-sampling and leveraging a pre-trained text-to-image diffusion model, our model learns to directly generate a full-frame-rate, low-resolution video by processing it in multiple space-time scales. We demonstrate state-of-the-art text-to-video generation results, and show that our design easily facilitates a wide range of content creation tasks and video editing applications, including image-to-video, video inpainting, and stylized generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-24", "arxiv_id": "2401.12954", "title": "Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding", "authors": [ "Mirac Suzgun", "Adam Tauman Kalai" ], "github": "https://github.com/suzgunmirac/meta-prompting", "project_page": "", "abstract": "We introduce meta-prompting, an effective scaffolding technique designed to enhance the functionality of language models (LMs). This approach transforms a single LM into a multi-faceted conductor, adept at managing and integrating multiple independent LM queries. By employing high-level instructions, meta-prompting guides the LM to break down complex tasks into smaller, more manageable subtasks. These subtasks are then handled by distinct \"expert\" instances of the same LM, each operating under specific, tailored instructions. Central to this process is the LM itself, in its role as the conductor, which ensures seamless communication and effective integration of the outputs from these expert models. It additionally employs its inherent critical thinking and robust verification processes to refine and authenticate the end result. This collaborative prompting approach empowers a single LM to simultaneously act as a comprehensive orchestrator and a panel of diverse experts, significantly enhancing its performance across a wide array of tasks. The zero-shot, task-agnostic nature of meta-prompting greatly simplifies user interaction by obviating the need for detailed, task-specific instructions. Furthermore, our research demonstrates the seamless integration of external tools, such as a Python interpreter, into the meta-prompting framework, thereby broadening its applicability and utility. Through rigorous experimentation with GPT-4, we establish the superiority of meta-prompting over conventional scaffolding methods: When averaged across all tasks, including the Game of 24, Checkmate-in-One, and Python Programming Puzzles, meta-prompting, augmented with a Python interpreter functionality, surpasses standard prompting by 17.1%, expert (dynamic) prompting by 17.3%, and multipersona prompting by 15.2%.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/suzgunmirac/meta-prompting", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-24", "arxiv_id": "2401.12963", "title": "AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents", "authors": [ "Michael Ahn", "Debidatta Dwibedi", "Chelsea Finn", "Montse Gonzalez Arenas", "Keerthana Gopalakrishnan", "Karol Hausman", "Brian Ichter", "Alex Irpan", "Nikhil Joshi", "Ryan Julian", "Sean Kirmani", "Isabel Leal", "Edward Lee", "Sergey Levine", "Yao Lu", "Isabel Leal", "Sharath Maddineni", "Kanishka Rao", "Dorsa Sadigh", "Pannag Sanketi", "Pierre Sermanet", "Quan Vuong", "Stefan Welker", "Fei Xia", "Ted Xiao", "Peng Xu", "Steve Xu", "Zhuo Xu" ], "github": "", "project_page": "", "abstract": "Foundation models that incorporate language, vision, and more recently actions have revolutionized the ability to harness internet scale data to reason about useful tasks. However, one of the key challenges of training embodied foundation models is the lack of data grounded in the physical world. In this paper, we propose AutoRT, a system that leverages existing foundation models to scale up the deployment of operational robots in completely unseen scenarios with minimal human supervision. AutoRT leverages vision-language models (VLMs) for scene understanding and grounding, and further uses large language models (LLMs) for proposing diverse and novel instructions to be performed by a fleet of robots. Guiding data collection by tapping into the knowledge of foundation models enables AutoRT to effectively reason about autonomy tradeoffs and safety while significantly scaling up data collection for robot learning. We demonstrate AutoRT proposing instructions to over 20 robots across multiple buildings and collecting 77k real robot episodes via both teleoperation and autonomous robot policies. We experimentally show that such \"in-the-wild\" data collected by AutoRT is significantly more diverse, and that AutoRT's use of LLMs allows for instruction following data collection robots that can align to human preferences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-24", "arxiv_id": "2401.12244", "title": "Large-scale Reinforcement Learning for Diffusion Models", "authors": [ "Yinan Zhang", "Eric Tzeng", "Yilun Du", "Dmitry Kislyuk" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion models are a class of deep generative models that have demonstrated an impressive capacity for high-quality image generation. However, these models are susceptible to implicit biases that arise from web-scale text-image training pairs and may inaccurately model aspects of images we care about. This can result in suboptimal samples, model bias, and images that do not align with human ethics and preferences. In this paper, we present an effective scalable algorithm to improve diffusion models using Reinforcement Learning (RL) across a diverse set of reward functions, such as human preference, compositionality, and fairness over millions of images. We illustrate how our approach substantially outperforms existing methods for aligning diffusion models with human preferences. We further illustrate how this substantially improves pretrained Stable Diffusion (SD) models, generating samples that are preferred by humans 80.3% of the time over those from the base SD model while simultaneously improving both the composition and diversity of generated samples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-24", "arxiv_id": "2401.12789", "title": "Multilingual and Fully Non-Autoregressive ASR with Large Language Model Fusion: A Comprehensive Study", "authors": [ "W. Ronny Huang", "Cyril Allauzen", "Tongzhou Chen", "Kilol Gupta", "Ke Hu", "James Qin", "Yu Zhang", "Yongqiang Wang", "Shuo-Yiin Chang", "Tara N. Sainath" ], "github": "", "project_page": "", "abstract": "In the era of large models, the autoregressive nature of decoding often results in latency serving as a significant bottleneck. We propose a non-autoregressive LM-fused ASR system that effectively leverages the parallelization capabilities of accelerator hardware. Our approach combines the Universal Speech Model (USM) and the PaLM 2 language model in per-segment scoring mode, achieving an average relative WER improvement across all languages of 10.8% on FLEURS and 3.6% on YouTube captioning. Furthermore, our comprehensive ablation study analyzes key parameters such as LLM size, context length, vocabulary size, fusion methodology. For instance, we explore the impact of LLM size ranging from 128M to 340B parameters on ASR performance. This study provides valuable insights into the factors influencing the effectiveness of practical large-scale LM-fused speech recognition systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-24", "arxiv_id": "2401.12522", "title": "BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models", "authors": [ "Feng Lin", "Hanling Yi", "Hongbin Li", "Yifan Yang", "Xiaotian Yu", "Guangming Lu", "Rong Xiao" ], "github": "https://github.com/linfeng93/bita", "project_page": "", "abstract": "Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and consequently extended latency. To mitigate this inefficiency, we present Bi-directional Tuning for lossless Acceleration (BiTA), an innovative method expediting LLMs via streamlined semi-autoregressive generation and draft verification. Inspired by the concept of prompt tuning, we enhance LLMs with a parameter-efficient design called bi-directional tuning for the capability in semi-autoregressive generation. Employing efficient tree-based decoding, the models perform draft candidate generation and verification in parallel, ensuring outputs identical to their autoregressive counterparts under greedy sampling. BiTA serves as a lightweight plug-in module, seamlessly boosting the inference efficiency of existing LLMs without requiring additional assistance models or incurring significant extra memory costs. Applying the proposed BiTA, LLaMA-2-70B-Chat achieves a 2.7times speedup on the MT-Bench benchmark. Extensive experiments confirm our method surpasses state-of-the-art acceleration techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/linfeng93/bita", "github_api_added_by": "auto" }, { "date": "2024-01-24", "arxiv_id": "2401.12246", "title": "Orion-14B: Open-source Multilingual Large Language Models", "authors": [ "Du Chen", "Yi Huang", "Xiaopu Li", "Yongqiang Li", "Yongqiang Liu", "Haihui Pan", "Leichao Xu", "Dacheng Zhang", "Zhipeng Zhang", "Kun Han" ], "github": "https://github.com/OrionStarAI/Orion", "project_page": "", "abstract": "In this study, we introduce Orion-14B, a collection of multilingual large language models with 14 billion parameters. We utilize a data scheduling approach to train a foundational model on a diverse corpus of 2.5 trillion tokens, sourced from texts in English, Chinese, Japanese, Korean, and other languages. Additionally, we fine-tuned a series of models tailored for conversational applications and other specific use cases. Our evaluation results demonstrate that Orion-14B achieves state-of-the-art performance across a broad spectrum of tasks. We make the Orion-14B model family and its associated code publicly accessible https://github.com/OrionStarAI/Orion, aiming to inspire future research and practical applications in the field.", "github_manual": "", "github_regex": "https://github.com/OrionStarAI/Orion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-24", "arxiv_id": "2401.12979", "title": "GALA: Generating Animatable Layered Assets from a Single Scan", "authors": [ "Taeksoo Kim", "Byungjun Kim", "Shunsuke Saito", "Hanbyul Joo" ], "github": "", "project_page": "", "abstract": "We present GALA, a framework that takes as input a single-layer clothed 3D human mesh and decomposes it into complete multi-layered 3D assets. The outputs can then be combined with other assets to create novel clothed human avatars with any pose. Existing reconstruction approaches often treat clothed humans as a single-layer of geometry and overlook the inherent compositionality of humans with hairstyles, clothing, and accessories, thereby limiting the utility of the meshes for downstream applications. Decomposing a single-layer mesh into separate layers is a challenging task because it requires the synthesis of plausible geometry and texture for the severely occluded regions. Moreover, even with successful decomposition, meshes are not normalized in terms of poses and body shapes, failing coherent composition with novel identities and poses. To address these challenges, we propose to leverage the general knowledge of a pretrained 2D diffusion model as geometry and appearance prior for humans and other assets. We first separate the input mesh using the 3D surface segmentation extracted from multi-view 2D segmentations. Then we synthesize the missing geometry of different layers in both posed and canonical spaces using a novel pose-guided Score Distillation Sampling (SDS) loss. Once we complete inpainting high-fidelity 3D geometry, we also apply the same SDS loss to its texture to obtain the complete appearance including the initially occluded regions. Through a series of decomposition steps, we obtain multiple layers of 3D assets in a shared canonical space normalized in terms of poses and human shapes, hence supporting effortless composition to novel identities and reanimation with novel poses. Our experiments demonstrate the effectiveness of our approach for decomposition, canonicalization, and composition tasks compared to existing solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-25", "arxiv_id": "2401.13388", "title": "UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion", "authors": [ "Wei Li", "Xue Xu", "Jiachen Liu", "Xinyan Xiao" ], "github": "", "project_page": "", "abstract": "Existing text-to-image diffusion models primarily generate images from text prompts. However, the inherent conciseness of textual descriptions poses challenges in faithfully synthesizing images with intricate details, such as specific entities or scenes. This paper presents UNIMO-G, a simple multimodal conditional diffusion framework that operates on multimodal prompts with interleaved textual and visual inputs, which demonstrates a unified ability for both text-driven and subject-driven image generation. UNIMO-G comprises two core components: a Multimodal Large Language Model (MLLM) for encoding multimodal prompts, and a conditional denoising diffusion network for generating images based on the encoded multimodal input. We leverage a two-stage training strategy to effectively train the framework: firstly pre-training on large-scale text-image pairs to develop conditional image generation capabilities, and then instruction tuning with multimodal prompts to achieve unified image generation proficiency. A well-designed data processing pipeline involving language grounding and image segmentation is employed to construct multi-modal prompts. UNIMO-G excels in both text-to-image generation and zero-shot subject-driven synthesis, and is notably effective in generating high-fidelity images from complex multimodal prompts involving multiple image entities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-25", "arxiv_id": "2401.13660", "title": "MambaByte: Token-free Selective State Space Model", "authors": [ "Junxiong Wang", "Tushaar Gangavarapu", "Jing Nathan Yan", "Alexander M Rush" ], "github": "", "project_page": "", "abstract": "Token-free language models learn directly from raw bytes and remove the bias of subword tokenization. Operating on bytes, however, results in significantly longer sequences, and standard autoregressive Transformers scale poorly in such settings. We experiment with MambaByte, a token-free adaptation of the Mamba state space model, trained autoregressively on byte sequences. Our experiments indicate the computational efficiency of MambaByte compared to other byte-level models. We also find MambaByte to be competitive with and even outperform state-of-the-art subword Transformers. Furthermore, owing to linear scaling in length, MambaByte benefits from fast inference compared to Transformers. Our findings establish the viability of MambaByte in enabling token-free language modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-25", "arxiv_id": "2401.13627", "title": "Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild", "authors": [ "Fanghua Yu", "Jinjin Gu", "Zheyuan Li", "Jinfan Hu", "Xiangtao Kong", "Xintao Wang", "Jingwen He", "Yu Qiao", "Chao Dong" ], "github": "", "project_page": "", "abstract": "We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-modal techniques and advanced generative prior, SUPIR marks a significant advance in intelligent and realistic image restoration. As a pivotal catalyst within SUPIR, model scaling dramatically enhances its capabilities and demonstrates new potential for image restoration. We collect a dataset comprising 20 million high-resolution, high-quality images for model training, each enriched with descriptive text annotations. SUPIR provides the capability to restore images guided by textual prompts, broadening its application scope and potential. Moreover, we introduce negative-quality prompts to further improve perceptual quality. We also develop a restoration-guided sampling method to suppress the fidelity issue encountered in generative-based restoration. Experiments demonstrate SUPIR's exceptional restoration effects and its novel capacity to manipulate restoration through textual prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-25", "arxiv_id": "2401.13601", "title": "MM-LLMs: Recent Advances in MultiModal Large Language Models", "authors": [ "Duzhen Zhang", "Yahan Yu", "Chenxing Li", "Jiahua Dong", "Dan Su", "Chenhui Chu", "Dong Yu" ], "github": "", "project_page": "", "abstract": "In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies. The resulting models not only preserve the inherent reasoning and decision-making capabilities of LLMs but also empower a diverse range of MM tasks. In this paper, we provide a comprehensive survey aimed at facilitating further research of MM-LLMs. Specifically, we first outline general design formulations for model architecture and training pipeline. Subsequently, we provide brief introductions of 26 existing MM-LLMs, each characterized by its specific formulations. Additionally, we review the performance of MM-LLMs on mainstream benchmarks and summarize key training recipes to enhance the potency of MM-LLMs. Lastly, we explore promising directions for MM-LLMs while concurrently maintaining a real-time tracking website for the latest developments in the field. We hope that this survey contributes to the ongoing advancement of the MM-LLMs domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-25", "arxiv_id": "2401.13311", "title": "ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models", "authors": [ "Rohan Wadhawan", "Hritik Bansal", "Kai-Wei Chang", "Nanyun Peng" ], "github": "https://github.com/rohan598/contextual", "project_page": "", "abstract": "Recent advancements in AI have led to the development of large multimodal models (LMMs) capable of processing complex tasks involving joint reasoning over text and visual content in the image (e.g., navigating maps in public places). This paper introduces ConTextual, a novel benchmark comprising instructions designed explicitly to evaluate LMMs' ability to perform context-sensitive text-rich visual reasoning. ConTextual emphasizes diverse real-world scenarios (e.g., time-reading, navigation, shopping and more) demanding a deeper understanding of the interactions between textual and visual elements. Our findings reveal a significant performance gap of 30.8% between the best-performing LMM, GPT-4V(ision), and human capabilities using human evaluation indicating substantial room for improvement in context-sensitive text-rich visual reasoning. Notably, while GPT-4V excelled in abstract categories like meme and quote interpretation, its overall performance still lagged behind humans. In addition to human evaluations, we also employed automatic evaluation metrics using GPT-4, uncovering similar trends in performance disparities. We also perform a fine-grained evaluation across diverse visual contexts and provide qualitative analysis which provides a robust framework for future advancements in the LMM design. https://con-textual.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rohan598/contextual", "github_api_added_by": "auto" }, { "date": "2024-01-25", "arxiv_id": "2401.13303", "title": "MaLA-500: Massive Language Adaptation of Large Language Models", "authors": [ "Peiqin Lin", "Shaoxiong Ji", "J\u00f6rg Tiedemann", "Andr\u00e9 F. T. Martins", "Hinrich Sch\u00fctze" ], "github": "", "project_page": "", "abstract": "Large language models have advanced the state of the art in natural language processing. However, their predominant design for English or a limited set of languages creates a substantial gap in their effectiveness for low-resource languages. To bridge this gap, we introduce MaLA-500, a novel large language model designed to cover an extensive range of 534 languages. To train MaLA-500, we employ vocabulary extension and continued pretraining on LLaMA 2 with Glot500-c. Our experiments on SIB-200 show that MaLA-500 achieves state-of-the-art in-context learning results. We release MaLA-500 at https://huggingface.co/MaLA-LM", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-25", "arxiv_id": "2401.13160", "title": "SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection", "authors": [ "Ke Ye", "Heinrich Jiang", "Afshin Rostamizadeh", "Ayan Chakrabarti", "Giulia DeSalvo", "Jean-Fran\u00e7ois Kagy", "Lazaros Karydas", "Gui Citovsky", "Sanjiv Kumar" ], "github": "", "project_page": "", "abstract": "Pre-training large language models is known to be extremely resource intensive and often times inefficient, under-utilizing the information encapsulated in the training text sequences. In this paper, we present SpacTor, a new training procedure consisting of (1) a hybrid objective combining span corruption (SC) and token replacement detection (RTD), and (2) a two-stage curriculum that optimizes the hybrid objective over the initial tau iterations, then transitions to standard SC loss. We show empirically that the effectiveness of the hybrid objective is tied to the two-stage pre-training schedule, and provide extensive analysis on why this is the case. In our experiments with encoder-decoder architectures (T5) on a variety of NLP tasks, SpacTor-T5 yields the same downstream performance as standard SC pre-training, while enabling a 50% reduction in pre-training iterations and 40% reduction in total FLOPs. Alternatively, given the same amount of computing budget, we find that SpacTor results in significantly improved downstream benchmark performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-26", "arxiv_id": "2401.14019", "title": "Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI", "authors": [ "Elron Bandel", "Yotam Perlitz", "Elad Venezian", "Roni Friedman-Melamed", "Ofir Arviv", "Matan Orbach", "Shachar Don-Yehyia", "Dafna Sheinwald", "Ariel Gera", "Leshem Choshen", "Michal Shmueli-Scheuer", "Yoav Katz" ], "github": "https://github.com/IBM/unitxt", "project_page": "", "abstract": "In the dynamic landscape of generative NLP, traditional text processing pipelines limit research flexibility and reproducibility, as they are tailored to specific dataset, task, and model combinations. The escalating complexity, involving system prompts, model-specific formats, instructions, and more, calls for a shift to a structured, modular, and customizable solution. Addressing this need, we present Unitxt, an innovative library for customizable textual data preparation and evaluation tailored to generative language models. Unitxt natively integrates with common libraries like HuggingFace and LM-eval-harness and deconstructs processing flows into modular components, enabling easy customization and sharing between practitioners. These components encompass model-specific formats, task prompts, and many other comprehensive dataset processing definitions. The Unitxt-Catalog centralizes these components, fostering collaboration and exploration in modern textual data workflows. Beyond being a tool, Unitxt is a community-driven platform, empowering users to build, share, and advance their pipelines collaboratively. Join the Unitxt community at https://github.com/IBM/unitxt!", "github_manual": "", "github_regex": "https://github.com/IBM/unitxt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-26", "arxiv_id": "2401.14404", "title": "Deconstructing Denoising Diffusion Models for Self-Supervised Learning", "authors": [ "Xinlei Chen", "Zhuang Liu", "Saining Xie", "Kaiming He" ], "github": "", "project_page": "", "abstract": "In this study, we examine the representation learning abilities of Denoising Diffusion Models (DDM) that were originally purposed for image generation. Our philosophy is to deconstruct a DDM, gradually transforming it into a classical Denoising Autoencoder (DAE). This deconstructive procedure allows us to explore how various components of modern DDMs influence self-supervised representation learning. We observe that only a very few modern components are critical for learning good representations, while many others are nonessential. Our study ultimately arrives at an approach that is highly simplified and to a large extent resembles a classical DAE. We hope our study will rekindle interest in a family of classical methods within the realm of modern self-supervised learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-26", "arxiv_id": "2401.13919", "title": "WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models", "authors": [ "Hongliang He", "Wenlin Yao", "Kaixin Ma", "Wenhao Yu", "Yong Dai", "Hongming Zhang", "Zhenzhong Lan", "Dong Yu" ], "github": "https://github.com/minorjerry/webvoyager", "project_page": "", "abstract": "The advancement of large language models (LLMs) leads to a new era marked by the development of autonomous applications in the real world, which drives innovation in the creation of advanced web-based agents. Existing web agents typically only handle one input modality and are evaluated only in simplified web simulators or static web snapshots, greatly limiting their applicability in real-world scenarios. To bridge this gap, we introduce WebVoyager, an innovative Large Multimodal Model (LMM) powered web agent that can complete user instructions end-to-end by interacting with real-world websites. Moreover, we propose a new evaluation protocol for web agents to address the challenges of automatic evaluation of open-ended web agent tasks, leveraging the robust multimodal comprehension capabilities of GPT-4V. We create a new benchmark by gathering real-world tasks from 15 widely used websites to evaluate our agents. We show that WebVoyager achieves a 55.7% task success rate, significantly surpassing the performance of both GPT-4 (All Tools) and the WebVoyager (text-only) setups, underscoring the exceptional capability of WebVoyager in practical applications. We found that our proposed automatic evaluation achieves 85.3% agreement with human judgment, paving the way for further development of web agents in a real-world setting.", "github_manual": "https://github.com/minorjerry/webvoyager", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-26", "arxiv_id": "2401.14066", "title": "CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion", "authors": [ "Nisha Huang", "Weiming Dong", "Yuxin Zhang", "Fan Tang", "Ronghui Li", "Chongyang Ma", "Xiu Li", "Changsheng Xu" ], "github": "https://github.com/haha-lisa/CreativeSynth", "project_page": "", "abstract": "Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images. However, adapting these models for artistic image editing presents two significant challenges. Firstly, users struggle to craft textual prompts that meticulously detail visual elements of the input image. Secondly, prevalent models, when effecting modifications in specific zones, frequently disrupt the overall artistic style, complicating the attainment of cohesive and aesthetically unified artworks. To surmount these obstacles, we build the innovative unified framework CreativeSynth, which is based on a diffusion model with the ability to coordinate multimodal inputs and multitask in the field of artistic image generation. By integrating multimodal features with customized attention mechanisms, CreativeSynth facilitates the importation of real-world semantic content into the domain of art through inversion and real-time style transfer. This allows for the precise manipulation of image style and content while maintaining the integrity of the original model parameters. Rigorous qualitative and quantitative evaluations underscore that CreativeSynth excels in enhancing artistic images' fidelity and preserves their innate aesthetic essence. By bridging the gap between generative models and artistic finesse, CreativeSynth becomes a custom digital palette.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/haha-lisa/CreativeSynth", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-26", "arxiv_id": "2401.13974", "title": "BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models", "authors": [ "Senthil Purushwalkam", "Akash Gokul", "Shafiq Joty", "Nikhil Naik" ], "github": "https://github.com/SalesforceAIResearch/bootpig", "project_page": "", "abstract": "Recent text-to-image generation models have demonstrated incredible success in generating images that faithfully follow input prompts. However, the requirement of using words to describe a desired concept provides limited control over the appearance of the generated concepts. In this work, we address this shortcoming by proposing an approach to enable personalization capabilities in existing text-to-image diffusion models. We propose a novel architecture (BootPIG) that allows a user to provide reference images of an object in order to guide the appearance of a concept in the generated images. The proposed BootPIG architecture makes minimal modifications to a pretrained text-to-image diffusion model and utilizes a separate UNet model to steer the generations toward the desired appearance. We introduce a training procedure that allows us to bootstrap personalization capabilities in the BootPIG architecture using data generated from pretrained text-to-image models, LLM chat agents, and image segmentation models. In contrast to existing methods that require several days of pretraining, the BootPIG architecture can be trained in approximately 1 hour. Experiments on the DreamBooth dataset demonstrate that BootPIG outperforms existing zero-shot methods while being comparable with test-time finetuning approaches. Through a user study, we validate the preference for BootPIG generations over existing methods both in maintaining fidelity to the reference object's appearance and aligning with textual prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SalesforceAIResearch/bootpig", "github_api_added_by": "auto" }, { "date": "2024-01-26", "arxiv_id": "2401.14196", "title": "DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence", "authors": [ "Daya Guo", "Qihao Zhu", "Dejian Yang", "Zhenda Xie", "Kai Dong", "Wentao Zhang", "Guanting Chen", "Xiao Bi", "Y. Wu", "Y. K. Li", "Fuli Luo", "Yingfei Xiong", "Wenfeng Liang" ], "github": "https://github.com/deepseek-ai/DeepSeek-Coder", "project_page": "", "abstract": "The rapid development of large language models has revolutionized code intelligence in software development. However, the predominance of closed-source models has restricted extensive research and development. To address this, we introduce the DeepSeek-Coder series, a range of open-source code models with sizes from 1.3B to 33B, trained from scratch on 2 trillion tokens. These models are pre-trained on a high-quality project-level code corpus and employ a fill-in-the-blank task with a 16K window to enhance code generation and infilling. Our extensive evaluations demonstrate that DeepSeek-Coder not only achieves state-of-the-art performance among open-source code models across multiple benchmarks but also surpasses existing closed-source models like Codex and GPT-3.5. Furthermore, DeepSeek-Coder models are under a permissive license that allows for both research and unrestricted commercial use.", "github_manual": "https://github.com/deepseek-ai/DeepSeek-Coder", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-26", "arxiv_id": "2401.14112", "title": "FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design", "authors": [ "Haojun Xia", "Zhen Zheng", "Xiaoxia Wu", "Shiyang Chen", "Zhewei Yao", "Stephen Youn", "Arash Bakhtiari", "Michael Wyatt", "Donglin Zhuang", "Zhongzhu Zhou", "Olatunji Ruwase", "Yuxiong He", "Shuaiwen Leon Song" ], "github": "", "project_page": "", "abstract": "Six-bit quantization (FP6) can effectively reduce the size of large language models (LLMs) and preserve the model quality consistently across varied applications. However, existing systems do not provide Tensor Core support for FP6 quantization and struggle to achieve practical performance improvements during LLM inference. It is challenging to support FP6 quantization on GPUs due to (1) unfriendly memory access of model weights with irregular bit-width and (2) high runtime overhead of weight de-quantization. To address these problems, we propose TC-FPx, the first full-stack GPU kernel design scheme with unified Tensor Core support of float-point weights for various quantization bit-width. We integrate TC-FPx kernel into an existing inference system, providing new end-to-end support (called FP6-LLM) for quantized LLM inference, where better trade-offs between inference cost and model quality are achieved. Experiments show that FP6-LLM enables the inference of LLaMA-70b using only a single GPU, achieving 1.69x-2.65x higher normalized inference throughput than the FP16 baseline. The source code will be publicly available soon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-26", "arxiv_id": "2401.14257", "title": "Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation", "authors": [ "Minglin Chen", "Longguang Wang", "Weihao Yuan", "Yukun Wang", "Zhe Sheng", "Yisheng He", "Zilong Dong", "Liefeng Bo", "Yulan Guo" ], "github": "", "project_page": "", "abstract": "Recently, text-to-3D approaches have achieved high-fidelity 3D content generation using text description. However, the generated objects are stochastic and lack fine-grained control. Sketches provide a cheap approach to introduce such fine-grained control. Nevertheless, it is challenging to achieve flexible control from these sketches due to their abstraction and ambiguity. In this paper, we present a multi-view sketch-guided text-to-3D generation framework (namely, Sketch2NeRF) to add sketch control to 3D generation. Specifically, our method leverages pretrained 2D diffusion models (e.g., Stable Diffusion and ControlNet) to supervise the optimization of a 3D scene represented by a neural radiance field (NeRF). We propose a novel synchronized generation and reconstruction method to effectively optimize the NeRF. In the experiments, we collected two kinds of multi-view sketch datasets to evaluate the proposed method. We demonstrate that our method can synthesize 3D consistent contents with fine-grained sketch control while being high-fidelity to text prompts. Extensive results show that our method achieves state-of-the-art performance in terms of sketch similarity and text alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-26", "arxiv_id": "2401.14391", "title": "Rethinking Patch Dependence for Masked Autoencoders", "authors": [ "Letian Fu", "Long Lian", "Renhao Wang", "Baifeng Shi", "Xudong Wang", "Adam Yala", "Trevor Darrell", "Alexei A. Efros", "Ken Goldberg" ], "github": "https://github.com/TonyLianLong/CrossMAE", "project_page": "", "abstract": "In this work, we re-examine inter-patch dependencies in the decoding mechanism of masked autoencoders (MAE). We decompose this decoding mechanism for masked patch reconstruction in MAE into self-attention and cross-attention. Our investigations suggest that self-attention between mask patches is not essential for learning good representations. To this end, we propose a novel pretraining framework: Cross-Attention Masked Autoencoders (CrossMAE). CrossMAE's decoder leverages only cross-attention between masked and visible tokens, with no degradation in downstream performance. This design also enables decoding only a small subset of mask tokens, boosting efficiency. Furthermore, each decoder block can now leverage different encoder features, resulting in improved representation learning. CrossMAE matches MAE in performance with 2.5 to 3.7times less decoding compute. It also surpasses MAE on ImageNet classification and COCO instance segmentation under the same compute. Code and models: https://crossmae.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TonyLianLong/CrossMAE", "github_api_added_by": "auto" }, { "date": "2024-01-26", "arxiv_id": "2401.13795", "title": "Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All", "authors": [ "Mehmet Saygin Seyfioglu", "Karim Bouyarmane", "Suren Kumar", "Amir Tavanaei", "Ismail B. Tutar" ], "github": "", "project_page": "", "abstract": "As online shopping is growing, the ability for buyers to virtually visualize products in their settings-a phenomenon we define as \"Virtual Try-All\"-has become crucial. Recent diffusion models inherently contain a world model, rendering them suitable for this task within an inpainting context. However, traditional image-conditioned diffusion models often fail to capture the fine-grained details of products. In contrast, personalization-driven models such as DreamPaint are good at preserving the item's details but they are not optimized for real-time applications. We present \"Diffuse to Choose,\" a novel diffusion-based image-conditioned inpainting model that efficiently balances fast inference with the retention of high-fidelity details in a given reference item while ensuring accurate semantic manipulations in the given scene content. Our approach is based on incorporating fine-grained features from the reference image directly into the latent feature maps of the main diffusion model, alongside with a perceptual loss to further preserve the reference item's details. We conduct extensive testing on both in-house and publicly available datasets, and show that Diffuse to Choose is superior to existing zero-shot diffusion inpainting methods as well as few-shot diffusion personalization algorithms like DreamPaint.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-26", "arxiv_id": "2401.14398", "title": "pix2gestalt: Amodal Segmentation by Synthesizing Wholes", "authors": [ "Ege Ozguroglu", "Ruoshi Liu", "D\u00eddac Sur\u00eds", "Dian Chen", "Achal Dave", "Pavel Tokmakov", "Carl Vondrick" ], "github": "https://github.com/cvlab-columbia/pix2gestalt", "project_page": "", "abstract": "We introduce pix2gestalt, a framework for zero-shot amodal segmentation, which learns to estimate the shape and appearance of whole objects that are only partially visible behind occlusions. By capitalizing on large-scale diffusion models and transferring their representations to this task, we learn a conditional diffusion model for reconstructing whole objects in challenging zero-shot cases, including examples that break natural and physical priors, such as art. As training data, we use a synthetically curated dataset containing occluded objects paired with their whole counterparts. Experiments show that our approach outperforms supervised baselines on established benchmarks. Our model can furthermore be used to significantly improve the performance of existing object recognition and 3D reconstruction methods in the presence of occlusions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cvlab-columbia/pix2gestalt", "github_api_added_by": "auto" }, { "date": "2024-01-26", "arxiv_id": "2401.14405", "title": "Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities", "authors": [ "Yiyuan Zhang", "Xiaohan Ding", "Kaixiong Gong", "Yixiao Ge", "Ying Shan", "Xiangyu Yue" ], "github": "https://github.com/AILab-CVC/M2PT", "project_page": "", "abstract": "We propose to improve transformers of a specific modality with irrelevant data from other modalities, e.g., improve an ImageNet model with audio or point cloud datasets. We would like to highlight that the data samples of the target modality are irrelevant to the other modalities, which distinguishes our method from other works utilizing paired (e.g., CLIP) or interleaved data of different modalities. We propose a methodology named Multimodal Pathway - given a target modality and a transformer designed for it, we use an auxiliary transformer trained with data of another modality and construct pathways to connect components of the two models so that data of the target modality can be processed by both models. In this way, we utilize the universal sequence-to-sequence modeling abilities of transformers obtained from two modalities. As a concrete implementation, we use a modality-specific tokenizer and task-specific head as usual but utilize the transformer blocks of the auxiliary model via a proposed method named Cross-Modal Re-parameterization, which exploits the auxiliary weights without any inference costs. On the image, point cloud, video, and audio recognition tasks, we observe significant and consistent performance improvements with irrelevant data from other modalities. The code and models are available at https://github.com/AILab-CVC/M2PT.", "github_manual": "", "github_regex": "https://github.com/AILab-CVC/M2PT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-26", "arxiv_id": "2401.14403", "title": "Adaptive Mobile Manipulation for Articulated Objects In the Open World", "authors": [ "Haoyu Xiong", "Russell Mendonca", "Kenneth Shaw", "Deepak Pathak" ], "github": "", "project_page": "", "abstract": "Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings, and prior mobile manipulation work is restricted to pick-move-place, which is arguably just the tip of the iceberg in this area. In this paper, we introduce Open-World Mobile Manipulation System, a full-stack approach to tackle realistic articulated object operation, e.g. real-world doors, cabinets, drawers, and refrigerators in open-ended unstructured environments. The robot utilizes an adaptive learning framework to initially learns from a small set of data through behavior cloning, followed by learning from online practice on novel objects that fall outside the training distribution. We also develop a low-cost mobile manipulation hardware platform capable of safe and autonomous online adaptation in unstructured environments with a cost of around 20,000 USD. In our experiments we utilize 20 articulate objects across 4 buildings in the CMU campus. With less than an hour of online learning for each object, the system is able to increase success rate from 50% of BC pre-training to 95% using online adaptation. Video results at https://open-world-mobilemanip.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-26", "arxiv_id": "2401.14367", "title": "Genie: Achieving Human Parity in Content-Grounded Datasets Generation", "authors": [ "Asaf Yehudai", "Boaz Carmeli", "Yosi Mass", "Ofir Arviv", "Nathaniel Mills", "Assaf Toledo", "Eyal Shnarch", "Leshem Choshen" ], "github": "", "project_page": "", "abstract": "The lack of high-quality data for content-grounded generation tasks has been identified as a major obstacle to advancing these tasks. To address this gap, we propose Genie, a novel method for automatically generating high-quality content-grounded data. It consists of three stages: (a) Content Preparation, (b) Generation: creating task-specific examples from the content (e.g., question-answer pairs or summaries). (c) Filtering mechanism aiming to ensure the quality and faithfulness of the generated data. We showcase this methodology by generating three large-scale synthetic data, making wishes, for Long-Form Question-Answering (LFQA), summarization, and information extraction. In a human evaluation, our generated data was found to be natural and of high quality. Furthermore, we compare models trained on our data with models trained on human-written data -- ELI5 and ASQA for LFQA and CNN-DailyMail for Summarization. We show that our models are on par with or outperforming models trained on human-generated data and consistently outperforming them in faithfulness. Finally, we applied our method to create LFQA data within the medical domain and compared a model trained on it with models trained on other domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-29", "arxiv_id": "2401.15071", "title": "From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities", "authors": [ "Chaochao Lu", "Chen Qian", "Guodong Zheng", "Hongxing Fan", "Hongzhi Gao", "Jie Zhang", "Jing Shao", "Jingyi Deng", "Jinlan Fu", "Kexin Huang", "Kunchang Li", "Lijun Li", "Limin Wang", "Lu Sheng", "Meiqi Chen", "Ming Zhang", "Qibing Ren", "Sirui Chen", "Tao Gui", "Wanli Ouyang", "Yali Wang", "Yan Teng", "Yaru Wang", "Yi Wang", "Yinan He", "Yingchun Wang", "Yixu Wang", "Yongting Zhang", "Yu Qiao", "Yujiong Shen", "Yurong Mou", "Yuxi Chen", "Zaibin Zhang", "Zhelun Shi", "Zhenfei Yin", "Zhipin Wang" ], "github": "", "project_page": "", "abstract": "Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide gap between the performance of recent MLLM-based applications and the expectation of the broad public, even though the most powerful OpenAI's GPT-4 and Google's Gemini have been deployed. This paper strives to enhance understanding of the gap through the lens of a qualitative study on the generalizability, trustworthiness, and causal reasoning capabilities of recent proprietary and open-source MLLMs across four modalities: ie, text, code, image, and video, ultimately aiming to improve the transparency of MLLMs. We believe these properties are several representative factors that define the reliability of MLLMs, in supporting various downstream applications. To be specific, we evaluate the closed-source GPT-4 and Gemini and 6 open-source LLMs and MLLMs. Overall we evaluate 230 manually designed cases, where the qualitative results are then summarized into 12 scores (ie, 4 modalities times 3 properties). In total, we uncover 14 empirical findings that are useful to understand the capabilities and limitations of both proprietary and open-source MLLMs, towards more reliable downstream multi-modal applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-29", "arxiv_id": "2401.15024", "title": "SliceGPT: Compress Large Language Models by Deleting Rows and Columns", "authors": [ "Saleh Ashkboos", "Maximilian L. Croci", "Marcelo Gennari do Nascimento", "Torsten Hoefler", "James Hensman" ], "github": "https://github.com/microsoft/TransformerCompression", "project_page": "", "abstract": "Large language models have become the cornerstone of natural language processing, but their use comes with substantial costs in terms of compute and memory resources. Sparsification provides a solution to alleviate these resource constraints, and recent works have shown that trained models can be sparsified post-hoc. Existing sparsification techniques face challenges as they need additional data structures and offer constrained speedup with current hardware. In this paper we present SliceGPT, a new post-training sparsification scheme which replaces each weight matrix with a smaller (dense) matrix, reducing the embedding dimension of the network. Through extensive experimentation, we show that SliceGPT can remove up to 25% of the model parameters (including embeddings) for LLAMA2-70B, OPT 66B and Phi-2 models while maintaining 99%, 99% and 90% zero-shot task performance of the dense model respectively. Our sliced models run on fewer GPUs and run faster without any additional code optimization: on 24GB consumer GPUs we reduce the total compute for inference on LLAMA2-70B to 64% of that of the dense model; on 40GB A100 GPUs we reduce it to 66%. We offer a new insight, computational invariance in transformer networks, which enables SliceGPT and we hope it will inspire and enable future avenues to reduce memory and computation demands for pre-trained models. Code is available at: https://github.com/microsoft/TransformerCompression", "github_manual": "", "github_regex": "https://github.com/microsoft/TransformerCompression", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-29", "arxiv_id": "2401.14953", "title": "Learning Universal Predictors", "authors": [ "Jordi Grau-Moya", "Tim Genewein", "Marcus Hutter", "Laurent Orseau", "Gr\u00e9goire Del\u00e9tang", "Elliot Catt", "Anian Ruoss", "Li Kevin Wenliang", "Christopher Mattern", "Matthew Aitchison", "Joel Veness" ], "github": "https://github.com/google-deepmind/neural_networks_solomonoff_induction", "project_page": "", "abstract": "Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data. Broad exposure to different tasks leads to versatile representations enabling general problem solving. But, what are the limits of meta-learning? In this work, we explore the potential of amortizing the most powerful universal predictor, namely Solomonoff Induction (SI), into neural networks via leveraging meta-learning to its limits. We use Universal Turing Machines (UTMs) to generate training data used to expose networks to a broad range of patterns. We provide theoretical analysis of the UTM data generation processes and meta-training protocols. We conduct comprehensive experiments with neural architectures (e.g. LSTMs, Transformers) and algorithmic data generators of varying complexity and universality. Our results suggest that UTM data is a valuable resource for meta-learning, and that it can be used to train neural networks capable of learning universal prediction strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/neural_networks_solomonoff_induction", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-29", "arxiv_id": "2401.14688", "title": "Taiyi-Diffusion-XL: Advancing Bilingual Text-to-Image Generation with Large Vision-Language Model Support", "authors": [ "Xiaojun Wu", "Dixiang Zhang", "Ruyi Gan", "Junyu Lu", "Ziwei Wu", "Renliang Sun", "Jiaxing Zhang", "Pingjian Zhang", "Yan Song" ], "github": "https://github.com/IDEA-CCNL/Taiyi-Diffusion-XL", "project_page": "", "abstract": "Recent advancements in text-to-image models have significantly enhanced image generation capabilities, yet a notable gap of open-source models persists in bilingual or Chinese language support. To address this need, we present Taiyi-Diffusion-XL, a new Chinese and English bilingual text-to-image model which is developed by extending the capabilities of CLIP and Stable-Diffusion-XL through a process of bilingual continuous pre-training. This approach includes the efficient expansion of vocabulary by integrating the most frequently used Chinese characters into CLIP's tokenizer and embedding layers, coupled with an absolute position encoding expansion. Additionally, we enrich text prompts by large vision-language model, leading to better images captions and possess higher visual quality. These enhancements are subsequently applied to downstream text-to-image models. Our empirical results indicate that the developed CLIP model excels in bilingual image-text retrieval.Furthermore, the bilingual image generation capabilities of Taiyi-Diffusion-XL surpass previous models. This research leads to the development and open-sourcing of the Taiyi-Diffusion-XL model, representing a notable advancement in the field of image generation, particularly for Chinese language applications. This contribution is a step forward in addressing the need for more diverse language support in multimodal research. The model and demonstration are made publicly available at https://huggingface.co/IDEA-CCNL/Taiyi-Stable-Diffusion-XL-3.5B/{this https URL}, fostering further research and collaboration in this domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/IDEA-CCNL/Taiyi-Diffusion-XL", "github_api_added_by": "auto" }, { "date": "2024-01-29", "arxiv_id": "2401.14673", "title": "Generative Expressive Robot Behaviors using Large Language Models", "authors": [ "Karthik Mahadevan", "Jonathan Chien", "Noah Brown", "Zhuo Xu", "Carolina Parada", "Fei Xia", "Andy Zeng", "Leila Takayama", "Dorsa Sadigh" ], "github": "", "project_page": "", "abstract": "People employ expressive behaviors to effectively communicate and coordinate their actions with others, such as nodding to acknowledge a person glancing at them or saying \"excuse me\" to pass people in a busy corridor. We would like robots to also demonstrate expressive behaviors in human-robot interaction. Prior work proposes rule-based methods that struggle to scale to new communication modalities or social situations, while data-driven methods require specialized datasets for each social situation the robot is used in. We propose to leverage the rich social context available from large language models (LLMs) and their ability to generate motion based on instructions or user preferences, to generate expressive robot motion that is adaptable and composable, building upon each other. Our approach utilizes few-shot chain-of-thought prompting to translate human language instructions into parametrized control code using the robot's available and learned skills. Through user studies and simulation experiments, we demonstrate that our approach produces behaviors that users found to be competent and easy to understand. Supplementary material can be found at https://generative-expressive-motion.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-29", "arxiv_id": "2401.14828", "title": "TIP-Editor: An Accurate 3D Editor Following Both Text-Prompts And Image-Prompts", "authors": [ "Jingyu Zhuang", "Di Kang", "Yan-Pei Cao", "Guanbin Li", "Liang Lin", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Text-driven 3D scene editing has gained significant attention owing to its convenience and user-friendliness. However, existing methods still lack accurate control of the specified appearance and location of the editing result due to the inherent limitations of the text description. To this end, we propose a 3D scene editing framework, TIPEditor, that accepts both text and image prompts and a 3D bounding box to specify the editing region. With the image prompt, users can conveniently specify the detailed appearance/style of the target content in complement to the text description, enabling accurate control of the appearance. Specifically, TIP-Editor employs a stepwise 2D personalization strategy to better learn the representation of the existing scene and the reference image, in which a localization loss is proposed to encourage correct object placement as specified by the bounding box. Additionally, TIPEditor utilizes explicit and flexible 3D Gaussian splatting as the 3D representation to facilitate local editing while keeping the background unchanged. Extensive experiments have demonstrated that TIP-Editor conducts accurate editing following the text and image prompts in the specified bounding box region, consistently outperforming the baselines in editing quality, and the alignment to the prompts, qualitatively and quantitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-29", "arxiv_id": "2401.15077", "title": "EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty", "authors": [ "Yuhui Li", "Fangyun Wei", "Chao Zhang", "Hongyang Zhang" ], "github": "", "project_page": "", "abstract": "Auto-regressive decoding makes the inference of Large Language Models (LLMs) time-consuming. We propose a simple framework, EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency), for lossless acceleration. Unlike traditional speculative sampling methods, EAGLE operates the drafting process auto-regressively at the more regular (second-top-layer) feature level and addresses the sampling uncertainty issues in the next-feature prediction problems by integrating tokens from one time step ahead. The acceleration provided by EAGLE is lossless: it involves no fine-tuning of the target LLM, and the generated text maintains the same distribution as that of vanilla auto-regressive decoding. As of the submission of this paper, EAGLE is the fastest known framework within the speculative sampling family. On MT-bench, EAGLE is 3x faster than vanilla decoding, 2x faster than Lookahead, and 1.6x faster than Medusa. Using gpt-fast, EAGLE attains on average 160 tokens/s with LLaMA2-Chat 13B on a single RTX 3090 GPU, compared to 24 tokens/s of Huggingface's implementations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-30", "arxiv_id": "2401.15947", "title": "MoE-LLaVA: Mixture of Experts for Large Vision-Language Models", "authors": [ "Bin Lin", "Zhenyu Tang", "Yang Ye", "Jiaxi Cui", "Bin Zhu", "Peng Jin", "Junwu Zhang", "Munan Ning", "Li Yuan" ], "github": "https://github.com/PKU-YuanGroup/MoE-LLaVA", "project_page": "", "abstract": "For Large Vision-Language Models (LVLMs), scaling the model can effectively improve performance. However, expanding model parameters significantly increases the training and inferring costs, as all model parameters are activated for each token in the calculation. In this work, we propose a novel training strategy MoE-tuning for LVLMs, which can constructing a sparse model with an outrageous number of parameter but a constant computational cost, and effectively addresses the performance degradation typically associated with multi-modal learning and model sparsity. Furthermore, we present the MoE-LLaVA framework, a MoE-based sparse LVLM architecture. This framework uniquely activates only the top-k experts through routers during deployment, keeping the remaining experts inactive. Our extensive experiments highlight the excellent capabilities of MoE-LLaVA in visual understanding and its potential to reduce hallucinations in model outputs. Remarkably, with just 3 billion sparsely activated parameters, MoE-LLaVA demonstrates performance comparable to the LLaVA-1.5-7B on various visual understanding datasets and even surpasses the LLaVA-1.5-13B in object hallucination benchmarks. Through MoE-LLaVA, we aim to establish a baseline for sparse LVLMs and provide valuable insights for future research in developing more efficient and effective multi-modal learning systems. Code is released at https://github.com/PKU-YuanGroup/MoE-LLaVA.", "github_manual": "", "github_regex": "https://github.com/PKU-YuanGroup/MoE-LLaVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-30", "arxiv_id": "2401.15977", "title": "Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling", "authors": [ "Xiaoyu Shi", "Zhaoyang Huang", "Fu-Yun Wang", "Weikang Bian", "Dasong Li", "Yi Zhang", "Manyuan Zhang", "Ka Chun Cheung", "Simon See", "Hongwei Qin", "Jifeng Da", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "We introduce Motion-I2V, a novel framework for consistent and controllable image-to-video generation (I2V). In contrast to previous methods that directly learn the complicated image-to-video mapping, Motion-I2V factorizes I2V into two stages with explicit motion modeling. For the first stage, we propose a diffusion-based motion field predictor, which focuses on deducing the trajectories of the reference image's pixels. For the second stage, we propose motion-augmented temporal attention to enhance the limited 1-D temporal attention in video latent diffusion models. This module can effectively propagate reference image's feature to synthesized frames with the guidance of predicted trajectories from the first stage. Compared with existing methods, Motion-I2V can generate more consistent videos even at the presence of large motion and viewpoint variation. By training a sparse trajectory ControlNet for the first stage, Motion-I2V can support users to precisely control motion trajectories and motion regions with sparse trajectory and region annotations. This offers more controllability of the I2V process than solely relying on textual instructions. Additionally, Motion-I2V's second stage naturally supports zero-shot video-to-video translation. Both qualitative and quantitative comparisons demonstrate the advantages of Motion-I2V over prior approaches in consistent and controllable image-to-video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-30", "arxiv_id": "2401.16158", "title": "Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception", "authors": [ "Junyang Wang", "Haiyang Xu", "Jiabo Ye", "Ming Yan", "Weizhou Shen", "Ji Zhang", "Fei Huang", "Jitao Sang" ], "github": "https://github.com/X-PLUG/MobileAgent", "project_page": "", "abstract": "Mobile device agent based on Multimodal Large Language Models (MLLM) is becoming a popular application. In this paper, we introduce Mobile-Agent, an autonomous multi-modal mobile device agent. Mobile-Agent first leverages visual perception tools to accurately identify and locate both the visual and textual elements within the app's front-end interface. Based on the perceived vision context, it then autonomously plans and decomposes the complex operation task, and navigates the mobile Apps through operations step by step. Different from previous solutions that rely on XML files of Apps or mobile system metadata, Mobile-Agent allows for greater adaptability across diverse mobile operating environments in a vision-centric way, thereby eliminating the necessity for system-specific customizations. To assess the performance of Mobile-Agent, we introduced Mobile-Eval, a benchmark for evaluating mobile device operations. Based on Mobile-Eval, we conducted a comprehensive evaluation of Mobile-Agent. The experimental results indicate that Mobile-Agent achieved remarkable accuracy and completion rates. Even with challenging instructions, such as multi-app operations, Mobile-Agent can still complete the requirements. Code and model will be open-sourced at https://github.com/X-PLUG/MobileAgent.", "github_manual": "", "github_regex": "https://github.com/X-PLUG/MobileAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-30", "arxiv_id": "2401.16420", "title": "InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model", "authors": [ "Xiaoyi Dong", "Pan Zhang", "Yuhang Zang", "Yuhang Cao", "Bin Wang", "Linke Ouyang", "Xilin Wei", "Songyang Zhang", "Haodong Duan", "Maosong Cao", "Wenwei Zhang", "Yining Li", "Hang Yan", "Yang Gao", "Xinyue Zhang", "Wei Li", "Jingwen Li", "Kai Chen", "Conghui He", "Xingcheng Zhang", "Yu Qiao", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/InternLM/InternLM-XComposer", "project_page": "", "abstract": "We introduce InternLM-XComposer2, a cutting-edge vision-language model excelling in free-form text-image composition and comprehension. This model goes beyond conventional vision-language understanding, adeptly crafting interleaved text-image content from diverse inputs like outlines, detailed textual specifications, and reference images, enabling highly customizable content creation. InternLM-XComposer2 proposes a Partial LoRA (PLoRA) approach that applies additional LoRA parameters exclusively to image tokens to preserve the integrity of pre-trained language knowledge, striking a balance between precise vision understanding and text composition with literary talent. Experimental results demonstrate the superiority of InternLM-XComposer2 based on InternLM2-7B in producing high-quality long-text multi-modal content and its exceptional vision-language understanding performance across various benchmarks, where it not only significantly outperforms existing multimodal models but also matches or even surpasses GPT-4V and Gemini Pro in certain assessments. This highlights its remarkable proficiency in the realm of multimodal understanding. The InternLM-XComposer2 model series with 7B parameters are publicly available at https://github.com/InternLM/InternLM-XComposer.", "github_manual": "", "github_regex": "https://github.com/InternLM/InternLM-XComposer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-30", "arxiv_id": "2401.16380", "title": "Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling", "authors": [ "Pratyush Maini", "Skyler Seto", "He Bai", "David Grangier", "Yizhe Zhang", "Navdeep Jaitly" ], "github": "", "project_page": "", "abstract": "Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased. Current scaling laws show that learning from such data requires an abundance of both compute and data, which grows with the size of the model being trained. This is infeasible both because of the large compute costs and duration associated with pre-training, and the impending scarcity of high-quality data on the web. In this work, we propose Web Rephrase Augmented Pre-training (WRAP) that uses an off-the-shelf instruction-tuned model prompted to paraphrase documents on the web in specific styles such as \"like Wikipedia\" or in \"question-answer format\" to jointly pre-train LLMs on real and synthetic rephrases. First, we show that using WRAP on the C4 dataset, which is naturally noisy, speeds up pre-training by sim3x. At the same pre-training compute budget, it improves perplexity by more than 10% on average across different subsets of the Pile, and improves zero-shot question answer accuracy across 13 tasks by more than 2%. Second, we investigate the impact of the re-phrasing style on the performance of the model, offering insights into how the composition of the training data can impact the performance of LLMs in OOD settings. Our gains are attributed to the fact that re-phrased synthetic data has higher utility than just real data because it (i) incorporates style diversity that closely reflects downstream evaluation style, and (ii) has higher 'quality' than web-scraped data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-30", "arxiv_id": "2401.15914", "title": "Overcoming the Pitfalls of Vision-Language Model Finetuning for OOD Generalization", "authors": [ "Yuhang Zang", "Hanlin Goh", "Josh Susskind", "Chen Huang" ], "github": "https://github.com/apple/ml-ogen", "project_page": "", "abstract": "Existing vision-language models exhibit strong generalization on a variety of visual domains and tasks. However, such models mainly perform zero-shot recognition in a closed-set manner, and thus struggle to handle open-domain visual concepts by design. There are recent finetuning methods, such as prompt learning, that not only study the discrimination between in-distribution (ID) and out-of-distribution (OOD) samples, but also show some improvements in both ID and OOD accuracies. In this paper, we first demonstrate that vision-language models, after long enough finetuning but without proper regularization, tend to overfit the known classes in the given dataset, with degraded performance on unknown classes. Then we propose a novel approach OGEN to address this pitfall, with the main focus on improving the OOD GENeralization of finetuned models. Specifically, a class-conditional feature generator is introduced to synthesize OOD features using just the class name of any unknown class. Such synthesized features will provide useful knowledge about unknowns and help regularize the decision boundary between ID and OOD data when optimized jointly. Equally important is our adaptive self-distillation mechanism to regularize our feature generation model during joint optimization, i.e., adaptively transferring knowledge between model states to further prevent overfitting. Experiments validate that our method yields convincing gains in OOD generalization performance in different settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apple/ml-ogen", "github_api_added_by": "auto" }, { "date": "2024-01-30", "arxiv_id": "2401.15975", "title": "StableIdentity: Inserting Anybody into Anywhere at First Sight", "authors": [ "Qinghe Wang", "Xu Jia", "Xiaomin Li", "Taiqing Li", "Liqian Ma", "Yunzhi Zhuge", "Huchuan Lu" ], "github": "", "project_page": "", "abstract": "Recent advances in large pretrained text-to-image models have shown unprecedented capabilities for high-quality human-centric generation, however, customizing face identity is still an intractable problem. Existing methods cannot ensure stable identity preservation and flexible editability, even with several images for each subject during training. In this work, we propose StableIdentity, which allows identity-consistent recontextualization with just one face image. More specifically, we employ a face encoder with an identity prior to encode the input face, and then land the face representation into a space with an editable prior, which is constructed from celeb names. By incorporating identity prior and editability prior, the learned identity can be injected anywhere with various contexts. In addition, we design a masked two-phase diffusion loss to boost the pixel-level perception of the input face and maintain the diversity of generation. Extensive experiments demonstrate our method outperforms previous customization methods. In addition, the learned identity can be flexibly combined with the off-the-shelf modules such as ControlNet. Notably, to the best knowledge, we are the first to directly inject the identity learned from a single image into video/3D generation without finetuning. We believe that the proposed StableIdentity is an important step to unify image, video, and 3D customized generation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-30", "arxiv_id": "2401.16013", "title": "SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning", "authors": [ "Jianlan Luo", "Zheyuan Hu", "Charles Xu", "You Liang Tan", "Jacob Berg", "Archit Sharma", "Stefan Schaal", "Chelsea Finn", "Abhishek Gupta", "Sergey Levine" ], "github": "https://github.com/rail-berkeley/serl", "project_page": "", "abstract": "In recent years, significant progress has been made in the field of robotic reinforcement learning (RL), enabling methods that handle complex image observations, train in the real world, and incorporate auxiliary data, such as demonstrations and prior experience. However, despite these advances, robotic RL remains hard to use. It is acknowledged among practitioners that the particular implementation details of these algorithms are often just as important (if not more so) for performance as the choice of algorithm. We posit that a significant challenge to widespread adoption of robotic RL, as well as further development of robotic RL methods, is the comparative inaccessibility of such methods. To address this challenge, we developed a carefully implemented library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment, a high-quality controller for a widely-adopted robot, and a number of challenging example tasks. We provide this library as a resource for the community, describe its design choices, and present experimental results. Perhaps surprisingly, we find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation between 25 to 50 minutes of training per policy on average, improving over state-of-the-art results reported for similar tasks in the literature. These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent recovery and correction behaviors. We hope that these promising results and our high-quality open-source implementation will provide a tool for the robotics community to facilitate further developments in robotic RL. Our code, documentation, and videos can be found at https://serl-robot.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rail-berkeley/serl", "github_api_added_by": "auto" }, { "date": "2024-01-30", "arxiv_id": "2401.15687", "title": "Media2Face: Co-speech Facial Animation Generation With Multi-Modality Guidance", "authors": [ "Qingcheng Zhao", "Pengyu Long", "Qixuan Zhang", "Dafei Qin", "Han Liang", "Longwen Zhang", "Yingliang Zhang", "Jingyi Yu", "Lan Xu" ], "github": "", "project_page": "", "abstract": "The synthesis of 3D facial animations from speech has garnered considerable attention. Due to the scarcity of high-quality 4D facial data and well-annotated abundant multi-modality labels, previous methods often suffer from limited realism and a lack of lexible conditioning. We address this challenge through a trilogy. We first introduce Generalized Neural Parametric Facial Asset (GNPFA), an efficient variational auto-encoder mapping facial geometry and images to a highly generalized expression latent space, decoupling expressions and identities. Then, we utilize GNPFA to extract high-quality expressions and accurate head poses from a large array of videos. This presents the M2F-D dataset, a large, diverse, and scan-level co-speech 3D facial animation dataset with well-annotated emotional and style labels. Finally, we propose Media2Face, a diffusion model in GNPFA latent space for co-speech facial animation generation, accepting rich multi-modality guidances from audio, text, and image. Extensive experiments demonstrate that our model not only achieves high fidelity in facial animation synthesis but also broadens the scope of expressiveness and style adaptability in 3D facial animation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-30", "arxiv_id": "2401.15708", "title": "Object-Driven One-Shot Fine-tuning of Text-to-Image Diffusion with Prototypical Embedding", "authors": [ "Jianxiang Lu", "Cong Xie", "Hui Guo" ], "github": "", "project_page": "", "abstract": "As large-scale text-to-image generation models have made remarkable progress in the field of text-to-image generation, many fine-tuning methods have been proposed. However, these models often struggle with novel objects, especially with one-shot scenarios. Our proposed method aims to address the challenges of generalizability and fidelity in an object-driven way, using only a single input image and the object-specific regions of interest. To improve generalizability and mitigate overfitting, in our paradigm, a prototypical embedding is initialized based on the object's appearance and its class, before fine-tuning the diffusion model. And during fine-tuning, we propose a class-characterizing regularization to preserve prior knowledge of object classes. To further improve fidelity, we introduce object-specific loss, which can also use to implant multiple objects. Overall, our proposed object-driven method for implanting new objects can integrate seamlessly with existing concepts as well as with high fidelity and generalization. Our method outperforms several existing works. The code will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-30", "arxiv_id": "2401.15688", "title": "Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation", "authors": [ "Zhenyu Wang", "Enze Xie", "Aoxue Li", "Zhongdao Wang", "Xihui Liu", "Zhenguo Li" ], "github": "", "project_page": "", "abstract": "Despite significant advancements in text-to-image models for generating high-quality images, these methods still struggle to ensure the controllability of text prompts over images in the context of complex text prompts, especially when it comes to retaining object attributes and relationships. In this paper, we propose CompAgent, a training-free approach for compositional text-to-image generation, with a large language model (LLM) agent as its core. The fundamental idea underlying CompAgent is premised on a divide-and-conquer methodology. Given a complex text prompt containing multiple concepts including objects, attributes, and relationships, the LLM agent initially decomposes it, which entails the extraction of individual objects, their associated attributes, and the prediction of a coherent scene layout. These individual objects can then be independently conquered. Subsequently, the agent performs reasoning by analyzing the text, plans and employs the tools to compose these isolated objects. The verification and human feedback mechanism is finally incorporated into our agent to further correct the potential attribute errors and refine the generated images. Guided by the LLM agent, we propose a tuning-free multi-concept customization model and a layout-to-image generation model as the tools for concept composition, and a local image editing method as the tool to interact with the agent for verification. The scene layout controls the image generation process among these tools to prevent confusion among multiple objects. Extensive experiments demonstrate the superiority of our approach for compositional text-to-image generation: CompAgent achieves more than 10\\% improvement on T2I-CompBench, a comprehensive benchmark for open-world compositional T2I generation. The extension to various related tasks also illustrates the flexibility of our CompAgent for potential applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-31", "arxiv_id": "2401.17268", "title": "Weaver: Foundation Models for Creative Writing", "authors": [ "Tiannan Wang", "Jiamin Chen", "Qingrui Jia", "Shuai Wang", "Ruoyu Fang", "Huilin Wang", "Zhaowei Gao", "Chunzhao Xie", "Chuou Xu", "Jihong Dai", "Yibin Liu", "Jialong Wu", "Shengwei Ding", "Long Li", "Zhiwei Huang", "Xinle Deng", "Teng Yu", "Gangan Ma", "Han Xiao", "Zixin Chen", "Danjun Xiang", "Yunxia Wang", "Yuanyuan Zhu", "Yi Xiao", "Jing Wang", "Yiru Wang", "Siran Ding", "Jiayang Huang", "Jiayi Xu", "Yilihamu Tayier", "Zhenyu Hu", "Yuan Gao", "Chengfeng Zheng", "Yueshu Ye", "Yihang Li", "Lei Wan", "Xinyue Jiang", "Yujie Wang", "Siyu Cheng", "Zhule Song", "Xiangru Tang", "Xiaohua Xu", "Ningyu Zhang", "Huajun Chen", "Yuchen Eleanor Jiang", "Wangchunshu Zhou" ], "github": "https://github.com/aiwaves-cn/WriteBench", "project_page": "", "abstract": "This work introduces Weaver, our first family of large language models (LLMs) dedicated to content creation. Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models. We then fine-tune Weaver for creative and professional writing purposes and align it to the preference of professional writers using a suit of novel methods for instruction data synthesis and LLM alignment, making it able to produce more human-like texts and follow more diverse instructions for content creation. The Weaver family consists of models of Weaver Mini (1.8B), Weaver Base (6B), Weaver Pro (14B), and Weaver Ultra (34B) sizes, suitable for different applications and can be dynamically dispatched by a routing agent according to query complexity to balance response quality and computation cost. Evaluation on a carefully curated benchmark for assessing the writing capabilities of LLMs shows Weaver models of all sizes outperform generalist LLMs several times larger than them. Notably, our most-capable Weaver Ultra model surpasses GPT-4, a state-of-the-art generalist LLM, on various writing scenarios, demonstrating the advantage of training specialized LLMs for writing purposes. Moreover, Weaver natively supports retrieval-augmented generation (RAG) and function calling (tool usage). We present various use cases of these abilities for improving AI-assisted writing systems, including integration of external knowledge bases, tools, or APIs, and providing personalized writing assistance. Furthermore, we discuss and summarize a guideline and best practices for pre-training and fine-tuning domain-specific LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aiwaves-cn/WriteBench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-31", "arxiv_id": "2401.17256", "title": "Weak-to-Strong Jailbreaking on Large Language Models", "authors": [ "Xuandong Zhao", "Xianjun Yang", "Tianyu Pang", "Chao Du", "Lei Li", "Yu-Xiang Wang", "William Yang Wang" ], "github": "https://github.com/XuandongZhao/weak-to-strong", "project_page": "", "abstract": "Although significant efforts have been dedicated to aligning large language models (LLMs), red-teaming reports suggest that these carefully aligned LLMs could still be jailbroken through adversarial prompts, tuning, or decoding. Upon examining the jailbreaking vulnerability of aligned LLMs, we observe that the decoding distributions of jailbroken and aligned models differ only in the initial generations. This observation motivates us to propose the weak-to-strong jailbreaking attack, where adversaries can utilize smaller unsafe/aligned LLMs (e.g., 7B) to guide jailbreaking against significantly larger aligned LLMs (e.g., 70B). To jailbreak, one only needs to additionally decode two smaller LLMs once, which involves minimal computation and latency compared to decoding the larger LLMs. The efficacy of this attack is demonstrated through experiments conducted on five models from three different organizations. Our study reveals a previously unnoticed yet efficient way of jailbreaking, exposing an urgent safety issue that needs to be considered when aligning LLMs. As an initial attempt, we propose a defense strategy to protect against such attacks, but creating more advanced defenses remains challenging. The code for replicating the method is available at https://github.com/XuandongZhao/weak-to-strong", "github_manual": "", "github_regex": "https://github.com/XuandongZhao/weak-to-strong", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-31", "arxiv_id": "2401.16818", "title": "H2O-Danube-1.8B Technical Report", "authors": [ "Philipp Singer", "Pascal Pfeiffer", "Yauhen Babakhin", "Maximilian Jeblick", "Nischay Dhankhar", "Gabor Fodor", "Sri Satish Ambati" ], "github": "https://github.com/h2oai/h2o-llmstudio", "project_page": "", "abstract": "We present H2O-Danube-1.8B, a 1.8B language model trained on 1T tokens following the core principles of LLama 2 and Mistral. We leverage and refine various techniques for pre-training large language models. Although our model is trained on significantly fewer total tokens compared to reference models of similar size, it exhibits highly competitive metrics across a multitude of benchmarks. We additionally release a chat model trained with supervised fine-tuning followed by direct preference optimization. We make H2O-Danube-1.8B openly available under Apache 2.0 license further democratizing LLMs to a wider audience economically.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/h2oai/h2o-llmstudio", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-31", "arxiv_id": "2401.17270", "title": "YOLO-World: Real-Time Open-Vocabulary Object Detection", "authors": [ "Tianheng Cheng", "Lin Song", "Yixiao Ge", "Wenyu Liu", "Xinggang Wang", "Ying Shan" ], "github": "https://github.com/AILab-CVC/YOLO-World", "project_page": "", "abstract": "The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However, their reliance on predefined and trained object categories limits their applicability in open scenarios. Addressing this limitation, we introduce YOLO-World, an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets. Specifically, we propose a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. On the challenging LVIS dataset, YOLO-World achieves 35.4 AP with 52.0 FPS on V100, which outperforms many state-of-the-art methods in terms of both accuracy and speed. Furthermore, the fine-tuned YOLO-World achieves remarkable performance on several downstream tasks, including object detection and open-vocabulary instance segmentation.", "github_manual": "https://github.com/AILab-CVC/YOLO-World", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-31", "arxiv_id": "2401.17221", "title": "MouSi: Poly-Visual-Expert Vision-Language Models", "authors": [ "Xiaoran Fan", "Tao Ji", "Changhao Jiang", "Shuo Li", "Senjie Jin", "Sirui Song", "Junke Wang", "Boyang Hong", "Lu Chen", "Guodong Zheng", "Ming Zhang", "Caishuang Huang", "Rui Zheng", "Zhiheng Xi", "Yuhao Zhou", "Shihan Dou", "Junjie Ye", "Hang Yan", "Tao Gui", "Qi Zhang", "Xipeng Qiu", "Xuanjing Huang", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "https://github.com/fudannlplab/mousi", "project_page": "", "abstract": "Current large vision-language models (VLMs) often encounter challenges such as insufficient capabilities of a single visual component and excessively long visual tokens. These issues can limit the model's effectiveness in accurately interpreting complex visual information and over-lengthy contextual information. Addressing these challenges is crucial for enhancing the performance and applicability of VLMs. This paper proposes the use of ensemble experts technique to synergizes the capabilities of individual visual encoders, including those skilled in image-text matching, OCR, image segmentation, etc. This technique introduces a fusion network to unify the processing of outputs from different visual experts, while bridging the gap between image encoders and pre-trained LLMs. In addition, we explore different positional encoding schemes to alleviate the waste of positional encoding caused by lengthy image feature sequences, effectively addressing the issue of position overflow and length limitations. For instance, in our implementation, this technique significantly reduces the positional occupancy in models like SAM, from a substantial 4096 to a more efficient and manageable 64 or even down to 1. Experimental results demonstrate that VLMs with multiple experts exhibit consistently superior performance over isolated visual encoders and mark a significant performance boost as more experts are integrated. We have open-sourced the training code used in this report. All of these resources can be found on our project website.", "github_manual": "https://github.com/fudannlplab/mousi", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-31", "arxiv_id": "2401.17181", "title": "Transfer Learning for Text Diffusion Models", "authors": [ "Kehang Han", "Kathleen Kenealy", "Aditya Barua", "Noah Fiedel", "Noah Constant" ], "github": "", "project_page": "", "abstract": "In this report, we explore the potential for text diffusion to replace autoregressive (AR) decoding for the training and deployment of large language models (LLMs). We are particularly interested to see whether pretrained AR models can be transformed into text diffusion models through a lightweight adaptation procedure we call ``AR2Diff''. We begin by establishing a strong baseline setup for training text diffusion models. Comparing across multiple architectures and pretraining objectives, we find that training a decoder-only model with a prefix LM objective is best or near-best across several tasks. Building on this finding, we test various transfer learning setups for text diffusion models. On machine translation, we find that text diffusion underperforms the standard AR approach. However, on code synthesis and extractive QA, we find diffusion models trained from scratch outperform AR models in many cases. We also observe quality gains from AR2Diff -- adapting AR models to use diffusion decoding. These results are promising given that text diffusion is relatively underexplored and can be significantly faster than AR decoding for long text generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-31", "arxiv_id": "2401.16467", "title": "ReGAL: Refactoring Programs to Discover Generalizable Abstractions", "authors": [ "Elias Stengel-Eskin", "Archiki Prasad", "Mohit Bansal" ], "github": "https://github.com/esteng/regal_program_learning", "project_page": "", "abstract": "While large language models (LLMs) are increasingly being used for program synthesis, they lack the global view needed to develop useful abstractions; they generally predict programs one at a time, often repeating the same functionality. Generating redundant code from scratch is both inefficient and error-prone. To address this, we propose Refactoring for Generalizable Abstraction Learning (ReGAL), a gradient-free method for learning a library of reusable functions via code refactorization, i.e. restructuring code without changing its execution output. ReGAL learns from a small set of existing programs, iteratively verifying and refining its abstractions via execution. We find that the shared function libraries discovered by ReGAL make programs easier to predict across diverse domains. On three datasets (LOGO graphics generation, Date reasoning, and TextCraft, a Minecraft-based text game), both open-source and proprietary LLMs improve in accuracy when predicting programs with ReGAL functions. For CodeLlama-13B, ReGAL results in absolute accuracy increases of 11.5% on graphics, 26.1% on date understanding, and 8.1% on TextCraft, outperforming GPT-3.5 in two of three domains. Our analysis reveals ReGAL's abstractions encapsulate frequently-used subroutines as well as environment dynamics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/esteng/regal_program_learning", "github_api_added_by": "auto" }, { "date": "2024-01-31", "arxiv_id": "2401.16861", "title": "Repositioning the Subject within Image", "authors": [ "Yikai Wang", "Chenjie Cao", "Qiaole Dong", "Yifan Li", "Yanwei Fu" ], "github": "", "project_page": "", "abstract": "Current image manipulation primarily centers on static manipulation, such as replacing specific regions within an image or altering its overall style. In this paper, we introduce an innovative dynamic manipulation task, subject repositioning. This task involves relocating a user-specified subject to a desired position while preserving the image's fidelity. Our research reveals that the fundamental sub-tasks of subject repositioning, which include filling the void left by the repositioned subject, reconstructing obscured portions of the subject and blending the subject to be consistent with surrounding areas, can be effectively reformulated as a unified, prompt-guided inpainting task. Consequently, we can employ a single diffusion generative model to address these sub-tasks using various task prompts learned through our proposed task inversion technique. Additionally, we integrate pre-processing and post-processing techniques to further enhance the quality of subject repositioning. These elements together form our SEgment-gEnerate-and-bLEnd (SEELE) framework. To assess SEELE's effectiveness in subject repositioning, we assemble a real-world subject repositioning dataset called ReS. Our results on ReS demonstrate the quality of repositioned image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-31", "arxiv_id": "2401.16677", "title": "T3: Transparent Tracking & Triggering for Fine-grained Overlap of Compute & Collectives", "authors": [ "Suchita Pati", "Shaizeen Aga", "Mahzabeen Islam", "Nuwan Jayasena", "Matthew D. Sinclair" ], "github": "", "project_page": "", "abstract": "Large Language Models increasingly rely on distributed techniques for their training and inference. These techniques require communication across devices which can reduce scaling efficiency as the number of devices increases. While some distributed techniques can overlap, and thus, hide this communication with independent computations, techniques such as Tensor Parallelism (TP) inherently serialize communication with model execution. One approach to hide this serialized communication is to interleave it with the producer operation (of the communicated data) in a fine-grained manner. However, this fine-grained interleaving of communication and computation in software can be difficult. Furthermore, as with any concurrent execution, it requires compute and memory resources to be shared between computation and communication, causing resource contention that reduces overlapping efficacy. To overcome these challenges, we propose T3 which applies hardware-software co-design to transparently overlap serialized communication while minimizing resource contention with compute. T3 transparently fuses producer operations with the subsequent communication via a simple configuration of the producer's output address space and requires minor software changes. At the hardware level, T3 adds a lightweight track and trigger mechanism to orchestrate the producer's compute, and communication. It further uses compute-enhanced memories for communication's attendant compute. As a result, T3 reduces resource contention, and efficiently overlaps serialized communication with computation. For important Transformer models like T-NLG, T3 speeds up communication-heavy sublayers by 30% geomean (max 47%) and reduces data movement by 22% geomean (max 36%). Furthermore, T3's benefits persist as models scale: geomean 29% for sublayers in sim500-billion parameter models, PALM and MT-NLG.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-31", "arxiv_id": "2401.17093", "title": "StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis", "authors": [ "Zecheng Tang", "Chenfei Wu", "Zekai Zhang", "Mingheng Ni", "Shengming Yin", "Yu Liu", "Zhengyuan Yang", "Lijuan Wang", "Zicheng Liu", "Juntao Li", "Nan Duan" ], "github": "", "project_page": "", "abstract": "To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model's ability to capture the true semantic representation of visual scenes. This paper posits that an alternative representation of images, vector graphics, can effectively surmount this limitation by enabling a more natural and semantically coherent segmentation of the image information. Thus, we introduce StrokeNUWA, a pioneering work exploring a better visual representation ''stroke tokens'' on vector graphics, which is inherently visual semantics rich, naturally compatible with LLMs, and highly compressed. Equipped with stroke tokens, StrokeNUWA can significantly surpass traditional LLM-based and optimization-based methods across various metrics in the vector graphic generation task. Besides, StrokeNUWA achieves up to a 94x speedup in inference over the speed of prior methods with an exceptional SVG code compression ratio of 6.9%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-31", "arxiv_id": "2401.17053", "title": "BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation", "authors": [ "Zhennan Wu", "Yang Li", "Han Yan", "Taizhang Shang", "Weixuan Sun", "Senbo Wang", "Ruikai Cui", "Weizhe Liu", "Hiroyuki Sato", "Hongdong Li", "Pan Ji" ], "github": "https://github.com/Tencent/BlockFusion", "project_page": "", "abstract": "We present BlockFusion, a diffusion-based model that generates 3D scenes as unit blocks and seamlessly incorporates new blocks to extend the scene. BlockFusion is trained using datasets of 3D blocks that are randomly cropped from complete 3D scene meshes. Through per-block fitting, all training blocks are converted into the hybrid neural fields: with a tri-plane containing the geometry features, followed by a Multi-layer Perceptron (MLP) for decoding the signed distance values. A variational auto-encoder is employed to compress the tri-planes into the latent tri-plane space, on which the denoising diffusion process is performed. Diffusion applied to the latent representations allows for high-quality and diverse 3D scene generation. To expand a scene during generation, one needs only to append empty blocks to overlap with the current scene and extrapolate existing latent tri-planes to populate new blocks. The extrapolation is done by conditioning the generation process with the feature samples from the overlapping tri-planes during the denoising iterations. Latent tri-plane extrapolation produces semantically and geometrically meaningful transitions that harmoniously blend with the existing scene. A 2D layout conditioning mechanism is used to control the placement and arrangement of scene elements. Experimental results indicate that BlockFusion is capable of generating diverse, geometrically consistent and unbounded large 3D scenes with unprecedented high-quality shapes in both indoor and outdoor scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tencent/BlockFusion", "github_api_added_by": "auto" }, { "date": "2024-01-31", "arxiv_id": "2401.16468", "title": "High-Quality Image Restoration Following Human Instructions", "authors": [ "Marcos V. Conde", "Gregor Geigle", "Radu Timofte" ], "github": "https://github.com/mv-lab/InstructIR", "project_page": "", "abstract": "Image restoration is a fundamental problem that involves recovering a high-quality clean image from its degraded observation. All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model. In this work, we present the first approach that uses human-written instructions to guide the image restoration model. Given natural language prompts, our model can recover high-quality images from their degraded counterparts, considering multiple degradation types. Our method, InstructIR, achieves state-of-the-art results on several restoration tasks including image denoising, deraining, deblurring, dehazing, and (low-light) image enhancement. InstructIR improves +1dB over previous all-in-one restoration methods. Moreover, our dataset and results represent a novel benchmark for new research on text-guided image restoration and enhancement. Our code, datasets and models are available at: https://github.com/mv-lab/InstructIR", "github_manual": "", "github_regex": "https://github.com/mv-lab/InstructIR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-31", "arxiv_id": "2401.17264", "title": "Proactive Detection of Voice Cloning with Localized Watermarking", "authors": [ "Robin San Roman", "Pierre Fernandez", "Alexandre D\u00e9fossez", "Teddy Furon", "Tuan Tran", "Hady Elsahar" ], "github": "https://github.com/facebookresearch/audioseal", "project_page": "", "abstract": "In the rapidly evolving field of speech generative models, there is a pressing need to ensure audio authenticity against the risks of voice cloning. We present AudioSeal, the first audio watermarking technique designed specifically for localized detection of AI-generated speech. AudioSeal employs a generator/detector architecture trained jointly with a localization loss to enable localized watermark detection up to the sample level, and a novel perceptual loss inspired by auditory masking, that enables AudioSeal to achieve better imperceptibility. AudioSeal achieves state-of-the-art performance in terms of robustness to real life audio manipulations and imperceptibility based on automatic and human evaluation metrics. Additionally, AudioSeal is designed with a fast, single-pass detector, that significantly surpasses existing models in speed - achieving detection up to two orders of magnitude faster, making it ideal for large-scale and real-time applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/audioseal", "github_api": "", "github_api_added_by": "" }, { "date": "2024-01-31", "arxiv_id": "2401.16658", "title": "OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer", "authors": [ "Yifan Peng", "Jinchuan Tian", "William Chen", "Siddhant Arora", "Brian Yan", "Yui Sudo", "Muhammad Shakeel", "Kwanghee Choi", "Jiatong Shi", "Xuankai Chang", "Jee-weon Jung", "Shinji Watanabe" ], "github": "", "project_page": "", "abstract": "Recent studies have advocated for fully open foundation models to promote transparency and open science. As an initial step, the Open Whisper-style Speech Model (OWSM) reproduced OpenAI's Whisper using publicly available data and open-source toolkits. With the aim of reproducing Whisper, the previous OWSM v1 through v3 models were still based on Transformer, which might lead to inferior performance compared to other state-of-the-art speech encoders. In this work, we aim to improve the performance and efficiency of OWSM without extra training data. We present E-Branchformer based OWSM v3.1 models at two scales, i.e., 100M and 1B. The 1B model is the largest E-Branchformer based speech model that has been made publicly available. It outperforms the previous OWSM v3 in a vast majority of evaluation benchmarks, while demonstrating up to 25% faster inference speed. We publicly release the data preparation scripts, pre-trained models and training logs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-01", "arxiv_id": "2401.17377", "title": "Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens", "authors": [ "Jiacheng Liu", "Sewon Min", "Luke Zettlemoyer", "Yejin Choi", "Hannaneh Hajishirzi" ], "github": "", "project_page": "", "abstract": "Are n-gram language models still relevant in this era of neural large language models (LLMs)? Our answer is yes, and we show their values in both text analysis and improving neural LLMs. Yet this necessitates modernizing n-gram models in two aspects. First, we train them at the same data scale as neural LLMs -- 1.4 trillion tokens. This is the largest n-gram model ever built. Second, existing n-gram models use small n which hinders their performance; we instead allow n to be arbitrarily large, by introducing a new infty-gram LM with backoff. Instead of pre-computing n-gram count tables (which would be very expensive), we develop an engine named infini-gram -- powered by suffix arrays -- that can compute infty-gram (as well as n-gram with arbitrary n) probabilities with millisecond-level latency. The infty-gram framework and infini-gram engine enable us to conduct many novel and interesting analyses of human-written and machine-generated text: we find that the infty-gram LM has fairly high accuracy for next-token prediction (47%), and can complement neural LLMs to greatly reduce their language modeling perplexities. When analyzing machine-generated text, we also observe irregularities in the machine--infty-gram agreement level with respect to the suffix length, which indicates deficiencies in neural LLM pretraining and the positional embeddings of Transformers. We open-source our infini-gram engine in the hopes of enabling more study on how to best use verbatim information retrieved from large text corpora.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-01", "arxiv_id": "2401.17574", "title": "Scavenging Hyena: Distilling Transformers into Long Convolution Models", "authors": [ "Tokiniaina Raharison Ralambomihanta", "Shahrad Mohammadzadeh", "Mohammad Sami Nur Islam", "Wassim Jabbour", "Laurence Liang" ], "github": "", "project_page": "", "abstract": "The rapid evolution of Large Language Models (LLMs), epitomized by architectures like GPT-4, has reshaped the landscape of natural language processing. This paper introduces a pioneering approach to address the efficiency concerns associated with LLM pre-training, proposing the use of knowledge distillation for cross-architecture transfer. Leveraging insights from the efficient Hyena mechanism, our method replaces attention heads in transformer models by Hyena, offering a cost-effective alternative to traditional pre-training while confronting the challenge of processing long contextual information, inherent in quadratic attention mechanisms. Unlike conventional compression-focused methods, our technique not only enhances inference speed but also surpasses pre-training in terms of both accuracy and efficiency. In the era of evolving LLMs, our work contributes to the pursuit of sustainable AI solutions, striking a balance between computational power and environmental impact.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-01", "arxiv_id": "2401.18058", "title": "LongAlign: A Recipe for Long Context Alignment of Large Language Models", "authors": [ "Yushi Bai", "Xin Lv", "Jiajie Zhang", "Yuze He", "Ji Qi", "Lei Hou", "Jie Tang", "Yuxiao Dong", "Juanzi Li" ], "github": "https://github.com/THUDM/LongAlign", "project_page": "", "abstract": "Extending large language models to effectively handle long contexts requires instruction fine-tuning on input sequences of similar length. To address this, we present LongAlign -- a recipe of the instruction data, training, and evaluation for long context alignment. First, we construct a long instruction-following dataset using Self-Instruct. To ensure the data diversity, it covers a broad range of tasks from various long context sources. Second, we adopt the packing and sorted batching strategies to speed up supervised fine-tuning on data with varied length distributions. Additionally, we develop a loss weighting method to balance the contribution to the loss across different sequences during packing training. Third, we introduce the LongBench-Chat benchmark for evaluating instruction-following capabilities on queries of 10k-100k in length. Experiments show that LongAlign outperforms existing recipes for LLMs in long context tasks by up to 30\\%, while also maintaining their proficiency in handling short, generic tasks. The code, data, and long-aligned models are open-sourced at https://github.com/THUDM/LongAlign.", "github_manual": "", "github_regex": "https://github.com/THUDM/LongAlign", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-01", "arxiv_id": "2401.17583", "title": "Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion", "authors": [ "Tairan He", "Chong Zhang", "Wenli Xiao", "Guanqi He", "Changliu Liu", "Guanya Shi" ], "github": "https://github.com/lecar-lab/abs", "project_page": "", "abstract": "Legged robots navigating cluttered environments must be jointly agile for efficient task execution and safe to avoid collisions with obstacles or humans. Existing studies either develop conservative controllers (< 1.0 m/s) to ensure safety, or focus on agility without considering potentially fatal collisions. This paper introduces Agile But Safe (ABS), a learning-based control framework that enables agile and collision-free locomotion for quadrupedal robots. ABS involves an agile policy to execute agile motor skills amidst obstacles and a recovery policy to prevent failures, collaboratively achieving high-speed and collision-free navigation. The policy switch in ABS is governed by a learned control-theoretic reach-avoid value network, which also guides the recovery policy as an objective function, thereby safeguarding the robot in a closed loop. The training process involves the learning of the agile policy, the reach-avoid value network, the recovery policy, and an exteroception representation network, all in simulation. These trained modules can be directly deployed in the real world with onboard sensing and computation, leading to high-speed and collision-free navigation in confined indoor and outdoor spaces with both static and dynamic obstacles.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lecar-lab/abs", "github_api_added_by": "auto" }, { "date": "2024-02-01", "arxiv_id": "2401.17464", "title": "Efficient Tool Use with Chain-of-Abstraction Reasoning", "authors": [ "Silin Gao", "Jane Dwivedi-Yu", "Ping Yu", "Xiaoqing Ellen Tan", "Ramakanth Pasunuru", "Olga Golovneva", "Koustuv Sinha", "Asli Celikyilmaz", "Antoine Bosselut", "Tianlu Wang" ], "github": "", "project_page": "", "abstract": "To achieve faithful reasoning that aligns with human expectations, large language models (LLMs) need to ground their reasoning to real-world knowledge (e.g., web facts, math and physical rules). Tools help LLMs access this external knowledge, but there remains challenges for fine-tuning LLM agents (e.g., Toolformer) to invoke tools in multi-step reasoning problems, where inter-connected tool calls require holistic and efficient tool usage planning. In this work, we propose a new method for LLMs to better leverage tools in multi-step reasoning. Our method, Chain-of-Abstraction (CoA), trains LLMs to first decode reasoning chains with abstract placeholders, and then call domain tools to reify each reasoning chain by filling in specific knowledge. This planning with abstract chains enables LLMs to learn more general reasoning strategies, which are robust to shifts of domain knowledge (e.g., math results) relevant to different reasoning questions. It also allows LLMs to perform decoding and calling of external tools in parallel, which avoids the inference delay caused by waiting for tool responses. In mathematical reasoning and Wiki QA domains, we show that our method consistently outperforms previous chain-of-thought and tool-augmented baselines on both in-distribution and out-of-distribution test sets, with an average ~6% absolute QA accuracy improvement. LLM agents trained with our method also show more efficient tool use, with inference speed being on average ~1.4x faster than baseline tool-augmented LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-01", "arxiv_id": "2401.18059", "title": "RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval", "authors": [ "Parth Sarthi", "Salman Abdullah", "Aditi Tuli", "Shubh Khanna", "Anna Goldie", "Christopher D. Manning" ], "github": "https://github.com/parthsarthi03/raptor", "project_page": "", "abstract": "Retrieval-augmented language models can better adapt to changes in world state and incorporate long-tail knowledge. However, most existing methods retrieve only short contiguous chunks from a retrieval corpus, limiting holistic understanding of the overall document context. We introduce the novel approach of recursively embedding, clustering, and summarizing chunks of text, constructing a tree with differing levels of summarization from the bottom up. At inference time, our RAPTOR model retrieves from this tree, integrating information across lengthy documents at different levels of abstraction. Controlled experiments show that retrieval with recursive summaries offers significant improvements over traditional retrieval-augmented LMs on several tasks. On question-answering tasks that involve complex, multi-step reasoning, we show state-of-the-art results; for example, by coupling RAPTOR retrieval with the use of GPT-4, we can improve the best performance on the QuALITY benchmark by 20% in absolute accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/parthsarthi03/raptor", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-01", "arxiv_id": "2401.18075", "title": "CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting", "authors": [ "Jiezhi Yang", "Khushi Desai", "Charles Packer", "Harshil Bhatia", "Nicholas Rhinehart", "Rowan McAllister", "Joseph Gonzalez" ], "github": "", "project_page": "", "abstract": "We propose CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting, a method for predicting future 3D scenes given past observations, such as 2D ego-centric images. Our method maps an image to a distribution over plausible 3D latent scene configurations using a probabilistic encoder, and predicts the evolution of the hypothesized scenes through time. Our latent scene representation conditions a global Neural Radiance Field (NeRF) to represent a 3D scene model, which enables explainable predictions and straightforward downstream applications. This approach extends beyond previous neural rendering work by considering complex scenarios of uncertainty in environmental states and dynamics. We employ a two-stage training of Pose-Conditional-VAE and NeRF to learn 3D representations. Additionally, we auto-regressively predict latent scene representations as a partially observable Markov decision process, utilizing a mixture density network. We demonstrate the utility of our method in realistic scenarios using the CARLA driving simulator, where CARFF can be used to enable efficient trajectory and contingency planning in complex multi-agent autonomous driving scenarios involving visual occlusions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-01", "arxiv_id": "2401.17509", "title": "Anything in Any Scene: Photorealistic Video Object Insertion", "authors": [ "Chen Bai", "Zeman Shao", "Guoxiang Zhang", "Di Liang", "Jie Yang", "Zhuorui Zhang", "Yujian Guo", "Chengzhang Zhong", "Yiqiao Qiu", "Zhendong Wang", "Yichen Guan", "Xiaoyin Zheng", "Tao Wang", "Cheng Lu" ], "github": "", "project_page": "", "abstract": "Realistic video simulation has shown significant potential across diverse applications, from virtual reality to film production. This is particularly true for scenarios where capturing videos in real-world settings is either impractical or expensive. Existing approaches in video simulation often fail to accurately model the lighting environment, represent the object geometry, or achieve high levels of photorealism. In this paper, we propose Anything in Any Scene, a novel and generic framework for realistic video simulation that seamlessly inserts any object into an existing dynamic video with a strong emphasis on physical realism. Our proposed general framework encompasses three key processes: 1) integrating a realistic object into a given scene video with proper placement to ensure geometric realism; 2) estimating the sky and environmental lighting distribution and simulating realistic shadows to enhance the light realism; 3) employing a style transfer network that refines the final video output to maximize photorealism. We experimentally demonstrate that Anything in Any Scene framework produces simulated videos of great geometric realism, lighting realism, and photorealism. By significantly mitigating the challenges associated with video data generation, our framework offers an efficient and cost-effective solution for acquiring high-quality videos. Furthermore, its applications extend well beyond video data augmentation, showing promising potential in virtual reality, video editing, and various other video-centric applications. Please check our project website https://anythinginanyscene.github.io for access to our project code and more high-resolution video results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-01", "arxiv_id": "2401.17807", "title": "Advances in 3D Generation: A Survey", "authors": [ "Xiaoyu Li", "Qi Zhang", "Di Kang", "Weihao Cheng", "Yiming Gao", "Jingbo Zhang", "Zhihao Liang", "Jing Liao", "Yan-Pei Cao", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Generating 3D models lies at the core of computer graphics and has been the focus of decades of research. With the emergence of advanced neural representations and generative models, the field of 3D content generation is developing rapidly, enabling the creation of increasingly high-quality and diverse 3D models. The rapid growth of this field makes it difficult to stay abreast of all recent developments. In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications. Specifically, we introduce the 3D representations that serve as the backbone for 3D generation. Furthermore, we provide a comprehensive overview of the rapidly growing literature on generation methods, categorized by the type of algorithmic paradigms, including feedforward generation, optimization-based generation, procedural generation, and generative novel view synthesis. Lastly, we discuss available datasets, applications, and open challenges. We hope this survey will help readers explore this exciting topic and foster further advancements in the field of 3D content generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-01", "arxiv_id": "2401.17895", "title": "ReplaceAnything3D:Text-Guided 3D Scene Editing with Compositional Neural Radiance Fields", "authors": [ "Edward Bartrum", "Thu Nguyen-Phuoc", "Chris Xie", "Zhengqin Li", "Numair Khan", "Armen Avetisyan", "Douglas Lanman", "Lei Xiao" ], "github": "", "project_page": "", "abstract": "We introduce ReplaceAnything3D model (RAM3D), a novel text-guided 3D scene editing method that enables the replacement of specific objects within a scene. Given multi-view images of a scene, a text prompt describing the object to replace, and a text prompt describing the new object, our Erase-and-Replace approach can effectively swap objects in the scene with newly generated content while maintaining 3D consistency across multiple viewpoints. We demonstrate the versatility of ReplaceAnything3D by applying it to various realistic 3D scenes, showcasing results of modified foreground objects that are well-integrated with the rest of the scene without affecting its overall integrity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-02", "arxiv_id": "2402.00396", "title": "Efficient Exploration for LLMs", "authors": [ "Vikranth Dwaracherla", "Seyed Mohammad Asghari", "Botao Hao", "Benjamin Van Roy" ], "github": "", "project_page": "", "abstract": "We present evidence of substantial benefit from efficient exploration in gathering human feedback to improve large language models. In our experiments, an agent sequentially generates queries while fitting a reward model to the feedback received. Our best-performing agent generates queries using double Thompson sampling, with uncertainty represented by an epistemic neural network. Our results demonstrate that efficient exploration enables high levels of performance with far fewer queries. Further, both uncertainty estimation and the choice of exploration scheme play critical roles.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-02", "arxiv_id": "2402.00769", "title": "AnimateLCM: Accelerating the Animation of Personalized Diffusion Models and Adapters with Decoupled Consistency Learning", "authors": [ "Fu-Yun Wang", "Zhaoyang Huang", "Xiaoyu Shi", "Weikang Bian", "Guanglu Song", "Yu Liu", "Hongsheng Li" ], "github": "https://github.com/G-U-N/AnimateLCM", "project_page": "", "abstract": "Video diffusion models has been gaining increasing attention for its ability to produce videos that are both coherent and of high fidelity. However, the iterative denoising process makes it computationally intensive and time-consuming, thus limiting its applications. Inspired by the Consistency Model (CM) that distills pretrained image diffusion models to accelerate the sampling with minimal steps and its successful extension Latent Consistency Model (LCM) on conditional image generation, we propose AnimateLCM, allowing for high-fidelity video generation within minimal steps. Instead of directly conducting consistency learning on the raw video dataset, we propose a decoupled consistency learning strategy that decouples the distillation of image generation priors and motion generation priors, which improves the training efficiency and enhance the generation visual quality. Additionally, to enable the combination of plug-and-play adapters in stable diffusion community to achieve various functions (e.g., ControlNet for controllable generation). we propose an efficient strategy to adapt existing adapters to our distilled text-conditioned video consistency model or train adapters from scratch without harming the sampling speed. We validate the proposed strategy in image-conditioned video generation and layout-conditioned video generation, all achieving top-performing results. Experimental results validate the effectiveness of our proposed method. Code and weights will be made public. More details are available at https://github.com/G-U-N/AnimateLCM.", "github_manual": "", "github_regex": "https://github.com/G-U-N/AnimateLCM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-02", "arxiv_id": "2402.00159", "title": "Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research", "authors": [ "Luca Soldaini", "Rodney Kinney", "Akshita Bhagia", "Dustin Schwenk", "David Atkinson", "Russell Authur", "Ben Bogin", "Khyathi Chandu", "Jennifer Dumas", "Yanai Elazar", "Valentin Hofmann", "Ananya Harsh Jha", "Sachin Kumar", "Li Lucy", "Xinxi Lyu", "Nathan Lambert", "Ian Magnusson", "Jacob Morrison", "Niklas Muennighoff", "Aakanksha Naik", "Crystal Nam", "Matthew E. Peters", "Abhilasha Ravichander", "Kyle Richardson", "Zejiang Shen", "Emma Strubell", "Nishant Subramani", "Oyvind Tafjord", "Pete Walsh", "Luke Zettlemoyer", "Noah A. Smith", "Hannaneh Hajishirzi", "Iz Beltagy", "Dirk Groeneveld", "Jesse Dodge", "Kyle Lo" ], "github": "https://github.com/allenai/dolma", "project_page": "", "abstract": "Language models have become a critical technology to tackling a wide range of natural language processing tasks, yet many details about how the best-performing language models were developed are not reported. In particular, information about their pretraining corpora is seldom discussed: commercial language models rarely provide any information about their data; even open models rarely release datasets they are trained on, or an exact recipe to reproduce them. As a result, it is challenging to conduct certain threads of language modeling research, such as understanding how training data impacts model capabilities and shapes their limitations. To facilitate open research on language model pretraining, we release Dolma, a three trillion tokens English corpus, built from a diverse mixture of web content, scientific papers, code, public-domain books, social media, and encyclopedic materials. In addition, we open source our data curation toolkit to enable further experimentation and reproduction of our work. In this report, we document Dolma, including its design principles, details about its construction, and a summary of its contents. We interleave this report with analyses and experimental results from training language models on intermediate states of Dolma to share what we have learned about important data curation practices, including the role of content or quality filters, deduplication, and multi-source mixing. Dolma has been used to train OLMo, a state-of-the-art, open language model and framework designed to build and study the science of language modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/dolma", "github_api_added_by": "auto" }, { "date": "2024-02-02", "arxiv_id": "2402.00786", "title": "CroissantLLM: A Truly Bilingual French-English Language Model", "authors": [ "Manuel Faysse", "Patrick Fernandes", "Nuno Guerreiro", "Ant\u00f3nio Loison", "Duarte Alves", "Caio Corro", "Nicolas Boizard", "Jo\u00e3o Alves", "Ricardo Rei", "Pedro Martins", "Antoni Bigata Casademunt", "Fran\u00e7ois Yvon", "Andr\u00e9 Martins", "Gautier Viaud", "C\u00e9line Hudelot", "Pierre Colombo" ], "github": "https://github.com/EleutherAI/lm-evaluation-harness", "project_page": "", "abstract": "We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81 % of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EleutherAI/lm-evaluation-harness", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-02", "arxiv_id": "2402.00742", "title": "Transforming and Combining Rewards for Aligning Large Language Models", "authors": [ "Zihao Wang", "Chirag Nagpal", "Jonathan Berant", "Jacob Eisenstein", "Alex D'Amour", "Sanmi Koyejo", "Victor Veitch" ], "github": "", "project_page": "", "abstract": "A common approach for aligning language models to human preferences is to first learn a reward model from preference data, and then use this reward model to update the language model. We study two closely related problems that arise in this approach. First, any monotone transformation of the reward model preserves preference ranking; is there a choice that is ``better'' than others? Second, we often wish to align language models to multiple properties: how should we combine multiple reward models? Using a probabilistic interpretation of the alignment procedure, we identify a natural choice for transformation for (the common case of) rewards learned from Bradley-Terry preference models. This derived transformation has two important properties. First, it emphasizes improving poorly-performing outputs, rather than outputs that already score well. This mitigates both underfitting (where some prompts are not improved) and reward hacking (where the model learns to exploit misspecification of the reward model). Second, it enables principled aggregation of rewards by linking summation to logical conjunction: the sum of transformed rewards corresponds to the probability that the output is ``good'' in all measured properties, in a sense we make precise. Experiments aligning language models to be both helpful and harmless using RLHF show substantial improvements over the baseline (non-transformed) approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-02", "arxiv_id": "2402.00518", "title": "EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models", "authors": [ "Xuchen Pan", "Yanxi Chen", "Yaliang Li", "Bolin Ding", "Jingren Zhou" ], "github": "https://github.com/pan-x-c/EE-LLM", "project_page": "", "abstract": "This work introduces EE-Tuning, a lightweight and economical solution to training/tuning early-exit large language models (LLMs). In contrast to the common approach of full-parameter pre-training, EE-Tuning augments any pre-trained (and possibly fine-tuned) standard LLM with additional early-exit layers that are tuned in a parameter-efficient manner, which requires significantly less computational resources and training data. Our implementation of EE-Tuning achieves outstanding training efficiency via extensive performance optimizations, as well as scalability due to its full compatibility with 3D parallelism. Results of systematic experiments validate the efficacy of EE-Tuning, confirming that effective early-exit LLM inference can be achieved with a limited training budget. In hope of making early-exit LLMs accessible to the community, we release the source code of our implementation of EE-Tuning at https://github.com/pan-x-c/EE-LLM.", "github_manual": "", "github_regex": "https://github.com/pan-x-c/EE-LLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-02", "arxiv_id": "2402.00838", "title": "OLMo: Accelerating the Science of Language Models", "authors": [ "Dirk Groeneveld", "Iz Beltagy", "Pete Walsh", "Akshita Bhagia", "Rodney Kinney", "Oyvind Tafjord", "Ananya Harsh Jha", "Hamish Ivison", "Ian Magnusson", "Yizhong Wang", "Shane Arora", "David Atkinson", "Russell Authur", "Khyathi Raghavi Chandu", "Arman Cohan", "Jennifer Dumas", "Yanai Elazar", "Yuling Gu", "Jack Hessel", "Tushar Khot", "William Merrill", "Jacob Morrison", "Niklas Muennighoff", "Aakanksha Naik", "Crystal Nam", "Matthew E. Peters", "Valentina Pyatkin", "Abhilasha Ravichander", "Dustin Schwenk", "Saurabh Shah", "Will Smith", "Emma Strubell", "Nishant Subramani", "Mitchell Wortsman", "Pradeep Dasigi", "Nathan Lambert", "Kyle Richardson", "Luke Zettlemoyer", "Jesse Dodge", "Kyle Lo", "Luca Soldaini", "Noah A. Smith", "Hannaneh Hajishirzi" ], "github": "", "project_page": "", "abstract": "Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, this technical report details the first release of OLMo, a state-of-the-art, truly Open Language Model and its framework to build and study the science of language modeling. Unlike most prior efforts that have only released model weights and inference code, we release OLMo and the whole framework, including training data and training and evaluation code. We hope this release will empower and strengthen the open research community and inspire a new wave of innovation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-02", "arxiv_id": "2402.00858", "title": "Can Large Language Models Understand Context?", "authors": [ "Yilun Zhu", "Joel Ruben Antony Moniz", "Shruti Bhargava", "Jiarui Lu", "Dhivya Piraviperumal", "Site Li", "Yuan Zhang", "Hong Yu", "Bo-Hsiang Tseng" ], "github": "https://github.com/apple/ml-llm-contextualization-eval", "project_page": "", "abstract": "Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. However, though the evaluation of LLMs encompasses various domains within the realm of Natural Language Processing, limited attention has been paid to probing their linguistic capability of understanding contextual features. This paper introduces a context understanding benchmark by adapting existing datasets to suit the evaluation of generative models. This benchmark comprises of four distinct tasks and nine datasets, all featuring prompts designed to assess the models' ability to understand context. First, we evaluate the performance of LLMs under the in-context learning pretraining scenario. Experimental results indicate that pre-trained dense models struggle with understanding more nuanced contextual features when compared to state-of-the-art fine-tuned models. Second, as LLM compression holds growing significance in both research and real-world applications, we assess the context understanding of quantized models under in-context-learning settings. We find that 3-bit post-training quantization leads to varying degrees of performance reduction on our benchmark. We conduct an extensive analysis of these scenarios to substantiate our experimental results.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/ml-llm-contextualization-eval", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-02", "arxiv_id": "2402.00854", "title": "SymbolicAI: A framework for logic-based approaches combining generative models and solvers", "authors": [ "Marius-Constantin Dinu", "Claudiu Leoveanu-Condrei", "Markus Holzleitner", "Werner Zellinger", "Sepp Hochreiter" ], "github": "", "project_page": "", "abstract": "We introduce SymbolicAI, a versatile and modular framework employing a logic-based approach to concept learning and flow management in generative processes. SymbolicAI enables the seamless integration of generative models with a diverse range of solvers by treating large language models (LLMs) as semantic parsers that execute tasks based on both natural and formal language instructions, thus bridging the gap between symbolic reasoning and generative AI. We leverage probabilistic programming principles to tackle complex tasks, and utilize differentiable and classical programming paradigms with their respective strengths. The framework introduces a set of polymorphic, compositional, and self-referential operations for data stream manipulation, aligning LLM outputs with user objectives. As a result, we can transition between the capabilities of various foundation models endowed with zero- and few-shot learning capabilities and specialized, fine-tuned models or solvers proficient in addressing specific problems. In turn, the framework facilitates the creation and evaluation of explainable computational graphs. We conclude by introducing a quality measure and its empirical score for evaluating these computational graphs, and propose a benchmark that compares various state-of-the-art LLMs across a set of complex workflows. We refer to the empirical score as the \"Vector Embedding for Relational Trajectory Evaluation through Cross-similarity\", or VERTEX score for short. The framework codebase and benchmark are linked below.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-02", "arxiv_id": "2402.00867", "title": "AToM: Amortized Text-to-Mesh using 2D Diffusion", "authors": [ "Guocheng Qian", "Junli Cao", "Aliaksandr Siarohin", "Yash Kant", "Chaoyang Wang", "Michael Vasilkovsky", "Hsin-Ying Lee", "Yuwei Fang", "Ivan Skorokhodov", "Peiye Zhuang", "Igor Gilitschenski", "Jian Ren", "Bernard Ghanem", "Kfir Aberman", "Sergey Tulyakov" ], "github": "", "project_page": "", "abstract": "We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously. In contrast to existing text-to-3D methods that often entail time-consuming per-prompt optimization and commonly output representations other than polygonal meshes, AToM directly generates high-quality textured meshes in less than 1 second with around 10 times reduction in the training cost, and generalizes to unseen prompts. Our key idea is a novel triplane-based text-to-mesh architecture with a two-stage amortized optimization strategy that ensures stable training and enables scalability. Through extensive experiments on various prompt benchmarks, AToM significantly outperforms state-of-the-art amortized approaches with over 4 times higher accuracy (in DF415 dataset) and produces more distinguishable and higher-quality 3D outputs. AToM demonstrates strong generalizability, offering finegrained 3D assets for unseen interpolated prompts without further optimization during inference, unlike per-prompt solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-02", "arxiv_id": "2402.00351", "title": "Machine Unlearning for Image-to-Image Generative Models", "authors": [ "Guihong Li", "Hsiang Hsu", "Chun-Fu", "Chen", "Radu Marculescu" ], "github": "https://github.com/jpmorganchase/i2i-generator-unlearning", "project_page": "", "abstract": "Machine unlearning has emerged as a new paradigm to deliberately forget data samples from a given model in order to adhere to stringent regulations. However, existing machine unlearning methods have been primarily focused on classification models, leaving the landscape of unlearning for generative models relatively unexplored. This paper serves as a bridge, addressing the gap by providing a unifying framework of machine unlearning for image-to-image generative models. Within this framework, we propose a computationally-efficient algorithm, underpinned by rigorous theoretical analysis, that demonstrates negligible performance degradation on the retain samples, while effectively removing the information from the forget samples. Empirical studies on two large-scale datasets, ImageNet-1K and Places-365, further show that our algorithm does not rely on the availability of the retain samples, which further complies with data retention policy. To our best knowledge, this work is the first that represents systemic, theoretical, empirical explorations of machine unlearning specifically tailored for image-to-image generative models. Our code is available at https://github.com/jpmorganchase/l2l-generator-unlearning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jpmorganchase/i2i-generator-unlearning", "github_api_added_by": "auto" }, { "date": "2024-02-05", "arxiv_id": "2402.01093", "title": "Specialized Language Models with Cheap Inference from Limited Domain Data", "authors": [ "David Grangier", "Angelos Katharopoulos", "Pierre Ablin", "Awni Hannun" ], "github": "", "project_page": "", "abstract": "Large language models have emerged as a versatile tool but are challenging to apply to tasks lacking large inference budgets and large in-domain training sets. This work formalizes these constraints and distinguishes four important variables: the pretraining budget (for training before the target domain is known), the specialization budget (for training after the target domain is known), the inference budget, and the in-domain training set size. Across these settings, we compare different approaches from the machine learning literature. Limited by inference cost, we find better alternatives to the standard practice of training very large vanilla transformer models. In particular, we show that hyper-networks and mixture of experts have better perplexity for large pretraining budgets, while small models trained on importance sampled datasets are attractive for large specialization budgets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-05", "arxiv_id": "2402.01118", "title": "Pok\u00e9LLMon: A Human-Parity Agent for Pok\u00e9mon Battles with Large Language Models", "authors": [ "Sihao Hu", "Tiansheng Huang", "Ling Liu" ], "github": "https://github.com/git-disl/PokeLLMon", "project_page": "", "abstract": "We introduce Pok\\'eLLMon, the first LLM-embodied agent that achieves human-parity performance in tactical battle games, as demonstrated in Pok\\'emon battles. The design of Pok\\'eLLMon incorporates three key strategies: (i) In-context reinforcement learning that instantly consumes text-based feedback derived from battles to iteratively refine the policy; (ii) Knowledge-augmented generation that retrieves external knowledge to counteract hallucination and enables the agent to act timely and properly; (iii) Consistent action generation to mitigate the panic switching phenomenon when the agent faces a powerful opponent and wants to elude the battle. We show that online battles against human demonstrates Pok\\'eLLMon's human-like battle strategies and just-in-time decision making, achieving 49\\% of win rate in the Ladder competitions and 56\\% of win rate in the invited battles. Our implementation and playable battle logs are available at: https://github.com/git-disl/PokeLLMon.", "github_manual": "", "github_regex": "https://github.com/git-disl/PokeLLMon", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-05", "arxiv_id": "2402.01032", "title": "Repeat After Me: Transformers are Better than State Space Models at Copying", "authors": [ "Samy Jelassi", "David Brandfonbrener", "Sham M. Kakade", "Eran Malach" ], "github": "https://github.com/sjelassi/transformers_ssm_copy", "project_page": "", "abstract": "Transformers are the dominant architecture for sequence modeling, but there is growing interest in models that use a fixed-size latent state that does not depend on the sequence length, which we refer to as \"generalized state space models\" (GSSMs). In this paper we show that while GSSMs are promising in terms of inference-time efficiency, they are limited compared to transformer models on tasks that require copying from the input context. We start with a theoretical analysis of the simple task of string copying and prove that a two layer transformer can copy strings of exponential length while GSSMs are fundamentally limited by their fixed-size latent state. Empirically, we find that transformers outperform GSSMs in terms of efficiency and generalization on synthetic tasks that require copying the context. Finally, we evaluate pretrained large language models and find that transformer models dramatically outperform state space models at copying and retrieving information from context. Taken together, these results suggest a fundamental gap between transformers and GSSMs on tasks of practical interest.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sjelassi/transformers_ssm_copy", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-05", "arxiv_id": "2402.01521", "title": "K-Level Reasoning with Large Language Models", "authors": [ "Yadong Zhang", "Shaoguang Mao", "Tao Ge", "Xun Wang", "Yan Xia", "Man Lan", "Furu Wei" ], "github": "", "project_page": "", "abstract": "While Large Language Models (LLMs) have demonstrated their proficiency in complex reasoning tasks, their performance in dynamic, interactive, and competitive scenarios - such as business strategy and stock market analysis - remains underexplored. To bridge this gap, we formally explore the dynamic reasoning capabilities of LLMs for decision-making in rapidly evolving environments. We introduce two game theory-based pilot challenges that mirror the complexities of real-world dynamic decision-making. These challenges are well-defined, enabling clear, controllable, and precise evaluation of LLMs' dynamic reasoning abilities. Through extensive experiments, we find that existing reasoning methods tend to falter in dynamic settings that require k-level thinking - a key concept not tackled by previous works. To address this, we propose a novel reasoning approach for LLMs, named \"K-Level Reasoning\". This approach adopts the perspective of rivals to recursively employ k-level thinking based on available historical information, which significantly improves the prediction accuracy of rivals' subsequent moves and informs more strategic decision-making. This research not only sets a robust quantitative benchmark for the assessment of dynamic reasoning but also markedly enhances the proficiency of LLMs in dynamic contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-05", "arxiv_id": "2402.01391", "title": "StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback", "authors": [ "Shihan Dou", "Yan Liu", "Haoxiang Jia", "Limao Xiong", "Enyu Zhou", "Junjie Shan", "Caishuang Huang", "Wei Shen", "Xiaoran Fan", "Zhiheng Xi", "Yuhao Zhou", "Tao Ji", "Rui Zheng", "Qi Zhang", "Xuanjing Huang", "Tao Gui" ], "github": "", "project_page": "", "abstract": "The advancement of large language models (LLMs) has significantly propelled the field of code generation. Previous work integrated reinforcement learning (RL) with compiler feedback for exploring the output space of LLMs to enhance code generation quality. However, the lengthy code generated by LLMs in response to complex human requirements makes RL exploration a challenge. Also, since the unit tests may not cover the complicated code, optimizing LLMs by using these unexecuted code snippets is ineffective. To tackle these challenges, we introduce StepCoder, a novel RL framework for code generation, consisting of two main components: CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks, while FGO only optimizes the model by masking the unexecuted code segments to provide Fine-Grained Optimization. In addition, we furthermore construct the APPS+ dataset for RL training, which is manually verified to ensure the correctness of unit tests. Experimental results show that our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-05", "arxiv_id": "2402.00892", "title": "EVA-GAN: Enhanced Various Audio Generation via Scalable Generative Adversarial Networks", "authors": [ "Shijia Liao", "Shiyi Lan", "Arun George Zachariah" ], "github": "https://github.com/jik876/hifi-gan", "project_page": "", "abstract": "The advent of Large Models marks a new era in machine learning, significantly outperforming smaller models by leveraging vast datasets to capture and synthesize complex patterns. Despite these advancements, the exploration into scaling, especially in the audio generation domain, remains limited, with previous efforts didn't extend into the high-fidelity (HiFi) 44.1kHz domain and suffering from both spectral discontinuities and blurriness in the high-frequency domain, alongside a lack of robustness against out-of-domain data. These limitations restrict the applicability of models to diverse use cases, including music and singing generation. Our work introduces Enhanced Various Audio Generation via Scalable Generative Adversarial Networks (EVA-GAN), yields significant improvements over previous state-of-the-art in spectral and high-frequency reconstruction and robustness in out-of-domain data performance, enabling the generation of HiFi audios by employing an extensive dataset of 36,000 hours of 44.1kHz audio, a context-aware module, a Human-In-The-Loop artifact measurement toolkit, and expands the model to approximately 200 million parameters. Demonstrations of our work are available at https://double-blind-eva-gan.cc.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jik876/hifi-gan", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-05", "arxiv_id": "2402.01622", "title": "TravelPlanner: A Benchmark for Real-World Planning with Language Agents", "authors": [ "Jian Xie", "Kai Zhang", "Jiangjie Chen", "Tinghui Zhu", "Renze Lou", "Yuandong Tian", "Yanghua Xiao", "Yu Su" ], "github": "https://github.com/OSU-NLP-Group/TravelPlanner", "project_page": "", "abstract": "Planning has been part of the core pursuit for artificial intelligence since its conception, but earlier AI agents mostly focused on constrained settings because many of the cognitive substrates necessary for human-level planning have been lacking. Recently, language agents powered by large language models (LLMs) have shown interesting capabilities such as tool use and reasoning. Are these language agents capable of planning in more complex settings that are out of the reach of prior AI agents? To advance this investigation, we propose TravelPlanner, a new planning benchmark that focuses on travel planning, a common real-world planning scenario. It provides a rich sandbox environment, various tools for accessing nearly four million data records, and 1,225 meticulously curated planning intents and reference plans. Comprehensive evaluations show that the current language agents are not yet capable of handling such complex planning tasks-even GPT-4 only achieves a success rate of 0.6%. Language agents struggle to stay on task, use the right tools to collect information, or keep track of multiple constraints. However, we note that the mere possibility for language agents to tackle such a complex problem is in itself non-trivial progress. TravelPlanner provides a challenging yet meaningful testbed for future language agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OSU-NLP-Group/TravelPlanner", "github_api_added_by": "auto" }, { "date": "2024-02-05", "arxiv_id": "2402.01566", "title": "Boximator: Generating Rich and Controllable Motions for Video Synthesis", "authors": [ "Jiawei Wang", "Yuchen Zhang", "Jiaxin Zou", "Yan Zeng", "Guoqiang Wei", "Liping Yuan", "Hang Li" ], "github": "", "project_page": "", "abstract": "Generating rich and controllable motion is a pivotal challenge in video synthesis. We propose Boximator, a new approach for fine-grained motion control. Boximator introduces two constraint types: hard box and soft box. Users select objects in the conditional frame using hard boxes and then use either type of boxes to roughly or rigorously define the object's position, shape, or motion path in future frames. Boximator functions as a plug-in for existing video diffusion models. Its training process preserves the base model's knowledge by freezing the original weights and training only the control module. To address training challenges, we introduce a novel self-tracking technique that greatly simplifies the learning of box-object correlations. Empirically, Boximator achieves state-of-the-art video quality (FVD) scores, improving on two base models, and further enhanced after incorporating box constraints. Its robust motion controllability is validated by drastic increases in the bounding box alignment metric. Human evaluation also shows that users favor Boximator generation results over the base model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-05", "arxiv_id": "2402.01613", "title": "Nomic Embed: Training a Reproducible Long Context Text Embedder", "authors": [ "Zach Nussbaum", "John X. Morris", "Brandon Duderstadt", "Andriy Mulyar" ], "github": "https://github.com/nomic-ai/contrastors", "project_page": "", "abstract": "This technical report describes the training of nomic-embed-text-v1, the first fully reproducible, open-source, open-weights, open-data, 8192 context length English text embedding model that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short and long-context tasks. We release the training code and model weights under an Apache 2 license. In contrast with other open-source models, we release a training data loader with 235 million curated text pairs that allows for the full replication of nomic-embed-text-v1. You can find code and data to replicate the model at https://github.com/nomic-ai/contrastors", "github_manual": "", "github_regex": "https://github.com/nomic-ai/contrastors", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.03040", "title": "InteractiveVideo: User-Centric Controllable Video Generation with Synergistic Multimodal Instructions", "authors": [ "Yiyuan Zhang", "Yuhao Kang", "Zhixin Zhang", "Xiaohan Ding", "Sanyuan Zhao", "Xiangyu Yue" ], "github": "https://github.com/invictus717/InteractiveVideo", "project_page": "", "abstract": "We introduce InteractiveVideo, a user-centric framework for video generation. Different from traditional generative approaches that operate based on user-provided images or text, our framework is designed for dynamic interaction, allowing users to instruct the generative model through various intuitive mechanisms during the whole generation process, e.g. text and image prompts, painting, drag-and-drop, etc. We propose a Synergistic Multimodal Instruction mechanism, designed to seamlessly integrate users' multimodal instructions into generative models, thus facilitating a cooperative and responsive interaction between user inputs and the generative process. This approach enables iterative and fine-grained refinement of the generation result through precise and effective user instructions. With InteractiveVideo, users are given the flexibility to meticulously tailor key aspects of a video. They can paint the reference image, edit semantics, and adjust video motions until their requirements are fully met. Code, models, and demo are available at https://github.com/invictus717/InteractiveVideo", "github_manual": "", "github_regex": "https://github.com/invictus717/InteractiveVideo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.03286", "title": "Training-Free Consistent Text-to-Image Generation", "authors": [ "Yoad Tewel", "Omri Kaduri", "Rinon Gal", "Yoni Kasten", "Lior Wolf", "Gal Chechik", "Yuval Atzmon" ], "github": "", "project_page": "", "abstract": "Text-to-image models offer a new level of creative flexibility by allowing users to guide the image generation process through natural language. However, using these models to consistently portray the same subject across diverse prompts remains challenging. Existing approaches fine-tune the model to teach it new words that describe specific user-provided subjects or add image conditioning to the model. These methods require lengthy per-subject optimization or large-scale pre-training. Moreover, they struggle to align generated images with text prompts and face difficulties in portraying multiple subjects. Here, we present ConsiStory, a training-free approach that enables consistent subject generation by sharing the internal activations of the pretrained model. We introduce a subject-driven shared attention block and correspondence-based feature injection to promote subject consistency between images. Additionally, we develop strategies to encourage layout diversity while maintaining subject consistency. We compare ConsiStory to a range of baselines, and demonstrate state-of-the-art performance on subject consistency and text alignment, without requiring a single optimization step. Finally, ConsiStory can naturally extend to multi-subject scenarios, and even enable training-free personalization for common objects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.02583", "title": "DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing", "authors": [ "Chong Mou", "Xintao Wang", "Jiechong Song", "Ying Shan", "Jian Zhang" ], "github": "https://github.com/MC-E/DragonDiffusion", "project_page": "", "abstract": "Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. Although owning diverse and high-quality generation capabilities, translating these abilities to fine-grained image editing remains challenging. In this paper, we propose DiffEditor to rectify two weaknesses in existing diffusion-based image editing: (1) in complex scenarios, editing results often lack editing accuracy and exhibit unexpected artifacts; (2) lack of flexibility to harmonize editing operations, e.g., imagine new content. In our solution, we introduce image prompts in fine-grained image editing, cooperating with the text prompt to better describe the editing content. To increase the flexibility while maintaining content consistency, we locally combine stochastic differential equation (SDE) into the ordinary differential equation (ODE) sampling. In addition, we incorporate regional score-based gradient guidance and a time travel strategy into the diffusion sampling, further improving the editing quality. Extensive experiments demonstrate that our method can efficiently achieve state-of-the-art performance on various fine-grained image editing tasks, including editing within a single image (e.g., object moving, resizing, and content dragging) and across images (e.g., appearance replacing and object pasting). Our source code is released at https://github.com/MC-E/DragonDiffusion.", "github_manual": "", "github_regex": "https://github.com/MC-E/DragonDiffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.03300", "title": "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models", "authors": [ "Zhihong Shao", "Peiyi Wang", "Qihao Zhu", "Runxin Xu", "Junxiao Song", "Mingchuan Zhang", "Y. K. Li", "Y. Wu", "Daya Guo" ], "github": "https://github.com/deepseek-ai/DeepSeek-Math", "project_page": "", "abstract": "Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which continues pre-training DeepSeek-Coder-Base-v1.5 7B with 120B math-related tokens sourced from Common Crawl, together with natural language and code data. DeepSeekMath 7B has achieved an impressive score of 51.7% on the competition-level MATH benchmark without relying on external toolkits and voting techniques, approaching the performance level of Gemini-Ultra and GPT-4. Self-consistency over 64 samples from DeepSeekMath 7B achieves 60.9% on MATH. The mathematical reasoning capability of DeepSeekMath is attributed to two key factors: First, we harness the significant potential of publicly available web data through a meticulously engineered data selection pipeline. Second, we introduce Group Relative Policy Optimization (GRPO), a variant of Proximal Policy Optimization (PPO), that enhances mathematical reasoning abilities while concurrently optimizing the memory usage of PPO.", "github_manual": "https://github.com/deepseek-ai/DeepSeek-Math", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.03162", "title": "Direct-a-Video: Customized Video Generation with User-Directed Camera Movement and Object Motion", "authors": [ "Shiyuan Yang", "Liang Hou", "Haibin Huang", "Chongyang Ma", "Pengfei Wan", "Di Zhang", "Xiaodong Chen", "Jing Liao" ], "github": "", "project_page": "", "abstract": "Recent text-to-video diffusion models have achieved impressive progress. In practice, users often desire the ability to control object motion and camera movement independently for customized video creation. However, current methods lack the focus on separately controlling object motion and camera movement in a decoupled manner, which limits the controllability and flexibility of text-to-video models. In this paper, we introduce Direct-a-Video, a system that allows users to independently specify motions for one or multiple objects and/or camera movements, as if directing a video. We propose a simple yet effective strategy for the decoupled control of object motion and camera movement. Object motion is controlled through spatial cross-attention modulation using the model's inherent priors, requiring no additional optimization. For camera movement, we introduce new temporal cross-attention layers to interpret quantitative camera movement parameters. We further employ an augmentation-based approach to train these layers in a self-supervised manner on a small-scale dataset, eliminating the need for explicit motion annotation. Both components operate independently, allowing individual or combined control, and can generalize to open-domain scenarios. Extensive experiments demonstrate the superiority and effectiveness of our method. Project page: https://direct-a-video.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.01739", "title": "OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models", "authors": [ "Fuzhao Xue", "Zian Zheng", "Yao Fu", "Jinjie Ni", "Zangwei Zheng", "Wangchunshu Zhou", "Yang You" ], "github": "https://github.com/XueFuzhao/OpenMoE", "project_page": "", "abstract": "To help the open-source community have a better understanding of Mixture-of-Experts (MoE) based large language models (LLMs), we train and release OpenMoE, a series of fully open-sourced and reproducible decoder-only MoE LLMs, ranging from 650M to 34B parameters and trained on up to over 1T tokens. Our investigation confirms that MoE-based LLMs can offer a more favorable cost-effectiveness trade-off than dense LLMs, highlighting the potential effectiveness for future LLM development. One more important contribution of this study is an in-depth analysis of the routing mechanisms within our OpenMoE models, leading to three significant findings: Context-Independent Specialization, Early Routing Learning, and Drop-towards-the-End. We discovered that routing decisions in MoE models are predominantly based on token IDs, with minimal context relevance. The token-to-expert assignments are determined early in the pre-training phase and remain largely unchanged. This imperfect routing can result in performance degradation, particularly in sequential tasks like multi-turn conversations, where tokens appearing later in a sequence are more likely to be dropped. Finally, we rethink our design based on the above-mentioned observations and analysis. To facilitate future MoE LLM development, we propose potential strategies for mitigating the issues we found and further improving off-the-shelf MoE LLM designs.", "github_manual": "https://github.com/XueFuzhao/OpenMoE", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.01878", "title": "LiPO: Listwise Preference Optimization through Learning-to-Rank", "authors": [ "Tianqi Liu", "Zhen Qin", "Junru Wu", "Jiaming Shen", "Misha Khalman", "Rishabh Joshi", "Yao Zhao", "Mohammad Saleh", "Simon Baumgartner", "Jialu Liu", "Peter J. Liu", "Xuanhui Wang" ], "github": "", "project_page": "", "abstract": "Aligning language models (LMs) with curated human feedback is critical to control their behaviors in real-world applications. Several recent policy optimization methods, such as DPO and SLiC, serve as promising alternatives to the traditional Reinforcement Learning from Human Feedback (RLHF) approach. In practice, human feedback often comes in a format of a ranked list over multiple responses to amortize the cost of reading prompt. Multiple responses can also be ranked by reward models or AI feedback. There lacks such a study on directly fitting upon a list of responses. In this work, we formulate the LM alignment as a listwise ranking problem and describe the Listwise Preference Optimization (LiPO) framework, where the policy can potentially learn more effectively from a ranked list of plausible responses given the prompt. This view draws an explicit connection to Learning-to-Rank (LTR), where most existing preference optimization work can be mapped to existing ranking objectives, especially pairwise ones. Following this connection, we provide an examination of ranking objectives that are not well studied for LM alignment withDPO and SLiC as special cases when list size is two. In particular, we highlight a specific method, LiPO-{\\lambda}, which leverages a state-of-the-art listwise ranking objective and weights each preference pair in a more advanced manner. We show that LiPO-{\\lambda} can outperform DPO and SLiC by a clear margin on two preference alignment tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.01771", "title": "BlackMamba: Mixture of Experts for State-Space Models", "authors": [ "Quentin Anthony", "Yury Tokpanov", "Paolo Glorioso", "Beren Millidge" ], "github": "https://github.com/Zyphra/BlackMamba", "project_page": "", "abstract": "State-space models (SSMs) have recently demonstrated competitive performance to transformers at large-scale language modeling benchmarks while achieving linear time and memory complexity as a function of sequence length. Mamba, a recently released SSM model, shows impressive performance in both language modeling and long sequence processing tasks. Simultaneously, mixture-of-expert (MoE) models have shown remarkable performance while significantly reducing the compute and latency costs of inference at the expense of a larger memory footprint. In this paper, we present BlackMamba, a novel architecture that combines the Mamba SSM with MoE to obtain the benefits of both. We demonstrate that BlackMamba performs competitively against both Mamba and transformer baselines, and outperforms in inference and training FLOPs. We fully train and open-source 340M/1.5B and 630M/2.8B BlackMamba models on 300B tokens of a custom dataset. We show that BlackMamba inherits and combines both of the benefits of SSM and MoE architectures, combining linear-complexity generation from SSM with cheap and fast inference from MoE. We release all weights, checkpoints, and inference code open-source. Inference code at: https://github.com/Zyphra/BlackMamba", "github_manual": "", "github_regex": "https://github.com/Zyphra/BlackMamba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.03161", "title": "Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization", "authors": [ "Yang Jin", "Zhicheng Sun", "Kun Xu", "Kun Xu", "Liwei Chen", "Hao Jiang", "Quzhe Huang", "Chengru Song", "Yuliang Liu", "Di Zhang", "Yang Song", "Kun Gai", "Yadong Mu" ], "github": "https://github.com/jy0205/LaVIT", "project_page": "", "abstract": "In light of recent advances in multimodal Large Language Models (LLMs), there is increasing attention to scaling them from image-text data to more informative real-world videos. Compared to static images, video poses unique challenges for effective large-scale pre-training due to the modeling of its spatiotemporal dynamics. In this paper, we address such limitations in video-language pre-training with an efficient video decomposition that represents each video as keyframes and temporal motions. These are then adapted to an LLM using well-designed tokenizers that discretize visual and temporal information as a few tokens, thus enabling unified generative pre-training of videos, images, and text. At inference, the generated tokens from the LLM are carefully recovered to the original continuous pixel space to create various video content. Our proposed framework is both capable of comprehending and generating image and video content, as demonstrated by its competitive performance across 13 multimodal benchmarks in image and video understanding and generation. Our code and models will be available at https://video-lavit.github.io.", "github_manual": "https://github.com/jy0205/LaVIT", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.02834", "title": "Shortened LLaMA: A Simple Depth Pruning for Large Language Models", "authors": [ "Bo-Kyeong Kim", "Geonmin Kim", "Tae-Ho Kim", "Thibault Castells", "Shinkook Choi", "Junho Shin", "Hyoung-Kyu Song" ], "github": "", "project_page": "", "abstract": "Structured pruning of modern large language models (LLMs) has emerged as a way of decreasing their high computational needs. Width pruning reduces the size of projection weight matrices (e.g., by removing attention heads) while maintaining the number of layers. Depth pruning, in contrast, removes entire layers or blocks, while keeping the size of the remaining weights unchanged. Most current research focuses on either width-only or a blend of width and depth pruning, with little comparative analysis between the two units (width vs. depth) concerning their impact on LLM inference efficiency. In this work, we show that a simple depth pruning approach can compete with recent width pruning methods in terms of zero-shot task performance. Our pruning method boosts inference speeds, especially under memory-constrained conditions that require limited batch sizes for running LLMs, where width pruning is ineffective. We hope this work can help deploy LLMs on local and edge devices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.02791", "title": "Rethinking Optimization and Architecture for Tiny Language Models", "authors": [ "Yehui Tang", "Fangcheng Liu", "Yunsheng Ni", "Yuchuan Tian", "Zheyuan Bai", "Yi-Qi Hu", "Sichao Liu", "Shangling Jui", "Kai Han", "Yunhe Wang" ], "github": "https://github.com/YuchuanTian/RethinkTinyLM", "project_page": "", "abstract": "The power of large language models (LLMs) has been demonstrated through numerous data and computing resources. However, the application of language models on mobile devices is facing huge challenge on the computation and memory costs, that is, tiny language models with high performance are urgently required. Limited by the highly complex training process, there are many details for optimizing language models that are seldom studied carefully. In this study, based on a tiny language model with 1B parameters, we carefully design a series of empirical study to analyze the effect of each component. Three perspectives are mainly discussed, i.e., neural architecture, parameter initialization, and optimization strategy. Several design formulas are empirically proved especially effective for tiny language models, including tokenizer compression, architecture tweaking, parameter inheritance and multiple-round training. Then we train PanGu-pi-1B Pro and PanGu-pi-1.5B Pro on 1.6T multilingual corpora, following the established formulas. Experimental results demonstrate the improved optimization and architecture yield a notable average improvement of 8.87 on benchmark evaluation sets for PanGu-pi-1B Pro. Besides, PanGu-pi-1.5B Pro surpasses a range of SOTA models with larger model sizes, validating its superior performance. The code will be released soon (https://github.com/YuchuanTian/RethinkTinyLM).", "github_manual": "", "github_regex": "https://github.com/YuchuanTian/RethinkTinyLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.01831", "title": "Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities", "authors": [ "Zhifeng Kong", "Arushi Goel", "Rohan Badlani", "Wei Ping", "Rafael Valle", "Bryan Catanzaro" ], "github": "https://github.com/NVIDIA/audio-flamingo", "project_page": "", "abstract": "Augmenting large language models (LLMs) to understand audio -- including non-speech sounds and non-verbal speech -- is critically important for diverse real-world applications of LLMs. In this paper, we propose Audio Flamingo, a novel audio language model with 1) strong audio understanding abilities, 2) the ability to quickly adapt to unseen tasks via in-context learning and retrieval, and 3) strong multi-turn dialogue abilities. We introduce a series of training techniques, architecture design, and data strategies to enhance our model with these abilities. Extensive evaluations across various audio understanding tasks confirm the efficacy of our method, setting new state-of-the-art benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA/audio-flamingo", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.01761", "title": "Rethinking Interpretability in the Era of Large Language Models", "authors": [ "Chandan Singh", "Jeevana Priya Inala", "Michel Galley", "Rich Caruana", "Jianfeng Gao" ], "github": "", "project_page": "", "abstract": "Interpretable machine learning has exploded as an area of interest over the last decade, sparked by the rise of increasingly large datasets and deep neural networks. Simultaneously, large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks, offering a chance to rethink opportunities in interpretable machine learning. Notably, the capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. However, these new capabilities raise new challenges, such as hallucinated explanations and immense computational costs. In this position paper, we start by reviewing existing methods to evaluate the emerging field of LLM interpretation (both interpreting LLMs and using LLMs for explanation). We contend that, despite their limitations, LLMs hold the opportunity to redefine interpretability with a more ambitious scope across many applications, including in auditing LLMs themselves. We highlight two emerging research priorities for LLM interpretation: using LLMs to directly analyze new datasets and to generate interactive explanations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-06", "arxiv_id": "2402.03310", "title": "V-IRL: Grounding Virtual Intelligence in Real Life", "authors": [ "Jihan Yang", "Runyu Ding", "Ellis Brown", "Xiaojuan Qi", "Saining Xie" ], "github": "https://github.com/VIRL-Platform/VIRL", "project_page": "", "abstract": "There is a sensory gulf between the Earth that humans inhabit and the digital realms in which modern AI agents are created. To develop AI agents that can sense, think, and act as flexibly as humans in real-world settings, it is imperative to bridge the realism gap between the digital and physical worlds. How can we embody agents in an environment as rich and diverse as the one we inhabit, without the constraints imposed by real hardware and control? Towards this end, we introduce V-IRL: a platform that enables agents to scalably interact with the real world in a virtual yet realistic environment. Our platform serves as a playground for developing agents that can accomplish various practical tasks and as a vast testbed for measuring progress in capabilities spanning perception, decision-making, and interaction with real-world data across the entire globe.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/VIRL-Platform/VIRL", "github_api_added_by": "auto" }, { "date": "2024-02-06", "arxiv_id": "2402.01935", "title": "Code Representation Learning At Scale", "authors": [ "Dejiao Zhang", "Wasi Ahmad", "Ming Tan", "Hantian Ding", "Ramesh Nallapati", "Dan Roth", "Xiaofei Ma", "Bing Xiang" ], "github": "https://github.com/amazon-science/CodeSage", "project_page": "", "abstract": "Recent studies have shown that code language models at scale demonstrate significant performance gains on downstream tasks, i.e., code generation. However, most of the existing works on code representation learning train models at a hundred million parameter scale using very limited pretraining corpora. In this work, we fuel code representation learning with a vast amount of code data via a two-stage pretraining scheme. We first train the encoders via a mix that leverages both randomness in masking language modeling and the structure aspect of programming language. We then enhance the representations via contrastive learning with hard negative and hard positive constructed in an unsupervised manner. We establish an off-the-shelf encoder model that persistently outperforms the existing models on a wide variety of downstream tasks by large margins. To comprehend the factors contributing to successful code representation learning, we conduct detailed ablations and share our findings on (i) a customized and effective token-level denoising scheme for source code; (ii) the importance of hard negatives and hard positives; (iii) how the proposed bimodal contrastive learning boost the cross-lingual semantic search performance; and (iv) how the pretraining schemes decide the downstream task performance scales with the model size.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/amazon-science/CodeSage", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-07", "arxiv_id": "2402.03620", "title": "Self-Discover: Large Language Models Self-Compose Reasoning Structures", "authors": [ "Pei Zhou", "Jay Pujara", "Xiang Ren", "Xinyun Chen", "Heng-Tze Cheng", "Quoc V. Le", "Ed H. Chi", "Denny Zhou", "Swaroop Mishra", "Huaixiu Steven Zheng" ], "github": "", "project_page": "", "abstract": "We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the task-intrinsic reasoning structures to tackle complex reasoning problems that are challenging for typical prompting methods. Core to the framework is a self-discovery process where LLMs select multiple atomic reasoning modules such as critical thinking and step-by-step thinking, and compose them into an explicit reasoning structure for LLMs to follow during decoding. SELF-DISCOVER substantially improves GPT-4 and PaLM 2's performance on challenging reasoning benchmarks such as BigBench-Hard, grounded agent reasoning, and MATH, by as much as 32% compared to Chain of Thought (CoT). Furthermore, SELF-DISCOVER outperforms inference-intensive methods such as CoT-Self-Consistency by more than 20%, while requiring 10-40x fewer inference compute. Finally, we show that the self-discovered reasoning structures are universally applicable across model families: from PaLM 2-L to GPT-4, and from GPT-4 to Llama2, and share commonalities with human reasoning patterns.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-07", "arxiv_id": "2402.04177", "title": "Scaling Laws for Downstream Task Performance of Large Language Models", "authors": [ "Berivan Isik", "Natalia Ponomareva", "Hussein Hazimeh", "Dimitris Paparas", "Sergei Vassilvitskii", "Sanmi Koyejo" ], "github": "", "project_page": "", "abstract": "Scaling laws provide important insights that can guide the design of large language models (LLMs). Existing work has primarily focused on studying scaling laws for pretraining (upstream) loss. However, in transfer learning settings, in which LLMs are pretrained on an unsupervised dataset and then finetuned on a downstream task, we often also care about the downstream performance. In this work, we study the scaling behavior in a transfer learning setting, where LLMs are finetuned for machine translation tasks. Specifically, we investigate how the choice of the pretraining data and its size affect downstream performance (translation quality) as judged by two metrics: downstream cross-entropy and BLEU score. Our experiments indicate that the size of the finetuning dataset and the distribution alignment between the pretraining and downstream data significantly influence the scaling behavior. With sufficient alignment, both downstream cross-entropy and BLEU score improve monotonically with more pretraining data. In such cases, we show that it is possible to predict the downstream BLEU score with good accuracy using a log-law. However, there are also cases where moderate misalignment causes the BLEU score to fluctuate or get worse with more pretraining, whereas downstream cross-entropy monotonically improves. By analyzing these observations, we provide new practical insights for choosing appropriate pretraining data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-07", "arxiv_id": "2402.04248", "title": "Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks", "authors": [ "Jongho Park", "Jaeseung Park", "Zheyang Xiong", "Nayoung Lee", "Jaewoong Cho", "Samet Oymak", "Kangwook Lee", "Dimitris Papailiopoulos" ], "github": "https://github.com/krafton-ai/mambaformer-icl", "project_page": "", "abstract": "State-space models (SSMs), such as Mamba Gu & Dao (2034), have been proposed as alternatives to Transformer networks in language modeling, by incorporating gating, convolutions, and input-dependent token selection to mitigate the quadratic cost of multi-head attention. Although SSMs exhibit competitive performance, their in-context learning (ICL) capabilities, a remarkable emergent property of modern language models that enables task execution without parameter optimization, remain underexplored compared to Transformers. In this study, we evaluate the ICL performance of SSMs, focusing on Mamba, against Transformer models across various tasks. Our results show that SSMs perform comparably to Transformers in standard regression ICL tasks, while outperforming them in tasks like sparse parity learning. However, SSMs fall short in tasks involving non-standard retrieval functionality. To address these limitations, we introduce a hybrid model, \\variant, that combines Mamba with attention blocks, surpassing individual models in tasks where they struggle independently. Our findings suggest that hybrid architectures offer promising avenues for enhancing ICL in language models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/krafton-ai/mambaformer-icl", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-07", "arxiv_id": "2402.03766", "title": "MobileVLM V2: Faster and Stronger Baseline for Vision Language Model", "authors": [ "Xiangxiang Chu", "Limeng Qiao", "Xinyu Zhang", "Shuang Xu", "Fei Wei", "Yang Yang", "Xiaofei Sun", "Yiming Hu", "Xinyang Lin", "Bo Zhang", "Chunhua Shen" ], "github": "https://github.com/Meituan-AutoML/MobileVLM", "project_page": "", "abstract": "We introduce MobileVLM V2, a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs' performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, our 3B model outperforms a large variety of VLMs at the 7B+ scale. Our models will be released at https://github.com/Meituan-AutoML/MobileVLM .", "github_manual": "", "github_regex": "https://github.com/Meituan-AutoML/MobileVLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-07", "arxiv_id": "2402.03749", "title": "Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models", "authors": [ "Jianyuan Guo", "Hanting Chen", "Chengcheng Wang", "Kai Han", "Chang Xu", "Yunhe Wang" ], "github": "https://github.com/ggjy/vision_weak_to_strong", "project_page": "", "abstract": "Recent advancements in large language models have sparked interest in their extraordinary and near-superhuman capabilities, leading researchers to explore methods for evaluating and optimizing these abilities, which is called superalignment. In this context, our paper delves into the realm of vision foundation models, focusing on the concept of weak-to-strong generalization, which involves using a weaker model to supervise a stronger one, aiming to enhance the latter's capabilities beyond the former's limits. We introduce a novel and adaptively adjustable loss function for weak-to-strong supervision. Our comprehensive experiments span various scenarios, including few-shot learning, transfer learning, noisy label learning, and common knowledge distillation settings. The results are striking: our approach not only exceeds the performance benchmarks set by strong-to-strong generalization but also surpasses the outcomes of fine-tuning strong models with whole datasets. This compelling evidence underscores the significant potential of weak-to-strong generalization, showcasing its capability to substantially elevate the performance of vision foundation models. The code is available at https://github.com/ggjy/vision_weak_to_strong.", "github_manual": "", "github_regex": "https://github.com/ggjy/vision_weak_to_strong", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-07", "arxiv_id": "2402.04236", "title": "CogCoM: Train Large Vision-Language Models Diving into Details through Chain of Manipulations", "authors": [ "Ji Qi", "Ming Ding", "Weihan Wang", "Yushi Bai", "Qingsong Lv", "Wenyi Hong", "Bin Xu", "Lei Hou", "Juanzi Li", "Yuxiao Dong", "Jie Tang" ], "github": "https://github.com/THUDM/CogCoM", "project_page": "", "abstract": "Vision-Language Models (VLMs) have demonstrated their widespread viability thanks to extensive training in aligning visual instructions to answers. However, this conclusive alignment leads models to ignore critical visual reasoning, and further result in failures on meticulous visual problems and unfaithful responses. In this paper, we propose Chain of Manipulations, a mechanism that enables VLMs to solve problems with a series of manipulations, where each manipulation refers to an operation on the visual input, either from intrinsic abilities (e.g., grounding) acquired through prior training or from imitating human-like behaviors (e.g., zoom in). This mechanism encourages VLMs to generate faithful responses with evidential visual reasoning, and permits users to trace error causes in the interpretable paths. We thus train CogCoM, a general 17B VLM with a memory-based compatible architecture endowed this reasoning mechanism. Experiments show that our model achieves the state-of-the-art performance across 8 benchmarks from 3 categories, and a limited number of training steps with the data swiftly gains a competitive performance. The code and data are publicly available at https://github.com/THUDM/CogCoM.", "github_manual": "", "github_regex": "https://github.com/THUDM/CogCoM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-07", "arxiv_id": "2402.04141", "title": "Multi-line AI-assisted Code Authoring", "authors": [ "Omer Dunay", "Daniel Cheng", "Adam Tait", "Parth Thakkar", "Peter C Rigby", "Andy Chiu", "Imad Ahmad", "Arun Ganesan", "Chandra Maddila", "Vijayaraghavan Murali", "Ali Tayyebi", "Nachiappan Nagappan" ], "github": "", "project_page": "", "abstract": "CodeCompose is an AI-assisted code authoring tool powered by large language models (LLMs) that provides inline suggestions to 10's of thousands of developers at Meta. In this paper, we present how we scaled the product from displaying single-line suggestions to multi-line suggestions. This evolution required us to overcome several unique challenges in improving the usability of these suggestions for developers. First, we discuss how multi-line suggestions can have a 'jarring' effect, as the LLM's suggestions constantly move around the developer's existing code, which would otherwise result in decreased productivity and satisfaction. Second, multi-line suggestions take significantly longer to generate; hence we present several innovative investments we made to reduce the perceived latency for users. These model-hosting optimizations sped up multi-line suggestion latency by 2.5x. Finally, we conduct experiments on 10's of thousands of engineers to understand how multi-line suggestions impact the user experience and contrast this with single-line suggestions. Our experiments reveal that (i) multi-line suggestions account for 42% of total characters accepted (despite only accounting for 16% for displayed suggestions) (ii) multi-line suggestions almost doubled the percentage of keystrokes saved for users from 9% to 17%. Multi-line CodeCompose has been rolled out to all engineers at Meta, and less than 1% of engineers have opted out of multi-line suggestions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-07", "arxiv_id": "2402.04252", "title": "EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters", "authors": [ "Quan Sun", "Jinsheng Wang", "Qiying Yu", "Yufeng Cui", "Fan Zhang", "Xiaosong Zhang", "Xinlong Wang" ], "github": "https://github.com/baaivision/EVA/tree/master/EVA-CLIP-18B", "project_page": "", "abstract": "Scaling up contrastive language-image pretraining (CLIP) is critical for empowering both vision and multimodal models. We present EVA-CLIP-18B, the largest and most powerful open-source CLIP model to date, with 18-billion parameters. With only 6-billion training samples seen, EVA-CLIP-18B achieves an exceptional 80.7% zero-shot top-1 accuracy averaged across 27 widely recognized image classification benchmarks, outperforming its forerunner EVA-CLIP (5-billion parameters) and other open-source CLIP models by a large margin. Remarkably, we observe a consistent performance improvement with the model size scaling of EVA-CLIP, despite maintaining a constant training dataset of 2-billion image-text pairs from LAION-2B and COYO-700M. This dataset is openly available and much smaller than the in-house datasets (e.g., DFN-5B, WebLI-10B) employed in other state-of-the-art CLIP models. EVA-CLIP-18B demonstrates the potential of EVA-style weak-to-strong visual model scaling. With our model weights made publicly available, we hope to facilitate future research in vision and multimodal foundation models.", "github_manual": "https://github.com/baaivision/EVA/tree/master/EVA-CLIP-18B", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-07", "arxiv_id": "2402.04229", "title": "MusicRL: Aligning Music Generation to Human Preferences", "authors": [ "Geoffrey Cideron", "Sertan Girgin", "Mauro Verzetti", "Damien Vincent", "Matej Kastelic", "Zal\u00e1n Borsos", "Brian McWilliams", "Victor Ungureanu", "Olivier Bachem", "Olivier Pietquin", "Matthieu Geist", "L\u00e9onard Hussenot", "Neil Zeghidour", "Andrea Agostinelli" ], "github": "", "project_page": "", "abstract": "We propose MusicRL, the first music generation system finetuned from human feedback. Appreciation of text-to-music models is particularly subjective since the concept of musicality as well as the specific intention behind a caption are user-dependent (e.g. a caption such as \"upbeat work-out music\" can map to a retro guitar solo or a techno pop beat). Not only this makes supervised training of such models challenging, but it also calls for integrating continuous human feedback in their post-deployment finetuning. MusicRL is a pretrained autoregressive MusicLM (Agostinelli et al., 2023) model of discrete audio tokens finetuned with reinforcement learning to maximise sequence-level rewards. We design reward functions related specifically to text-adherence and audio quality with the help from selected raters, and use those to finetune MusicLM into MusicRL-R. We deploy MusicLM to users and collect a substantial dataset comprising 300,000 pairwise preferences. Using Reinforcement Learning from Human Feedback (RLHF), we train MusicRL-U, the first text-to-music model that incorporates human feedback at scale. Human evaluations show that both MusicRL-R and MusicRL-U are preferred to the baseline. Ultimately, MusicRL-RU combines the two approaches and results in the best model according to human raters. Ablation studies shed light on the musical attributes influencing human preferences, indicating that text adherence and quality only account for a part of it. This underscores the prevalence of subjectivity in musical appreciation and calls for further involvement of human listeners in the finetuning of music generation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-07", "arxiv_id": "2402.03944", "title": "IMUSIC: IMU-based Facial Expression Capture", "authors": [ "Youjia Wang", "Yiwen Wu", "Ruiqian Li", "Hengan Zhou", "Hongyang Lin", "Yingwenqi Jiang", "Yingsheng Zhu", "Guanpeng Long", "Jingya Wang", "Lan Xu", "Jingyi Yu" ], "github": "", "project_page": "", "abstract": "For facial motion capture and analysis, the dominated solutions are generally based on visual cues, which cannot protect privacy and are vulnerable to occlusions. Inertial measurement units (IMUs) serve as potential rescues yet are mainly adopted for full-body motion capture. In this paper, we propose IMUSIC to fill the gap, a novel path for facial expression capture using purely IMU signals, significantly distant from previous visual solutions.The key design in our IMUSIC is a trilogy. We first design micro-IMUs to suit facial capture, companion with an anatomy-driven IMU placement scheme. Then, we contribute a novel IMU-ARKit dataset, which provides rich paired IMU/visual signals for diverse facial expressions and performances. Such unique multi-modality brings huge potential for future directions like IMU-based facial behavior analysis. Moreover, utilizing IMU-ARKit, we introduce a strong baseline approach to accurately predict facial blendshape parameters from purely IMU signals. Specifically, we tailor a Transformer diffusion model with a two-stage training strategy for this novel tracking task. The IMUSIC framework empowers us to perform accurate facial capture in scenarios where visual methods falter and simultaneously safeguard user privacy. We conduct extensive experiments about both the IMU configuration and technical components to validate the effectiveness of our IMUSIC approach. Notably, IMUSIC enables various potential and novel applications, i.e., privacy-protecting facial capture, hybrid capture against occlusions, or detecting minute facial movements that are often invisible through visual cues. We will release our dataset and implementations to enrich more possibilities of facial capture and analysis in our community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-07", "arxiv_id": "2402.03908", "title": "EscherNet: A Generative Model for Scalable View Synthesis", "authors": [ "Xin Kong", "Shikun Liu", "Xiaoyang Lyu", "Marwan Taher", "Xiaojuan Qi", "Andrew J. Davison" ], "github": "", "project_page": "", "abstract": "We introduce EscherNet, a multi-view conditioned diffusion model for view synthesis. EscherNet learns implicit and generative 3D representations coupled with a specialised camera positional encoding, allowing precise and continuous relative control of the camera transformation between an arbitrary number of reference and target views. EscherNet offers exceptional generality, flexibility, and scalability in view synthesis -- it can generate more than 100 consistent target views simultaneously on a single consumer-grade GPU, despite being trained with a fixed number of 3 reference views to 3 target views. As a result, EscherNet not only addresses zero-shot novel view synthesis, but also naturally unifies single- and multi-image 3D reconstruction, combining these diverse tasks into a single, cohesive framework. Our extensive experiments demonstrate that EscherNet achieves state-of-the-art performance in multiple benchmarks, even when compared to methods specifically tailored for each individual problem. This remarkable versatility opens up new directions for designing scalable neural architectures for 3D vision. Project page: https://kxhit.github.io/EscherNet.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-07", "arxiv_id": "2402.03570", "title": "Diffusion World Model", "authors": [ "Zihan Ding", "Amy Zhang", "Yuandong Tian", "Qinqing Zheng" ], "github": "", "project_page": "", "abstract": "We introduce Diffusion World Model (DWM), a conditional diffusion model capable of predicting multistep future states and rewards concurrently. As opposed to traditional one-step dynamics models, DWM offers long-horizon predictions in a single forward pass, eliminating the need for recursive quires. We integrate DWM into model-based value estimation, where the short-term return is simulated by future trajectories sampled from DWM. In the context of offline reinforcement learning, DWM can be viewed as a conservative value regularization through generative modeling. Alternatively, it can be seen as a data source that enables offline Q-learning with synthetic data. Our experiments on the D4RL dataset confirm the robustness of DWM to long-horizon simulation. In terms of absolute performance, DWM significantly surpasses one-step dynamics models with a 44% performance gain, and achieves state-of-the-art performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-08", "arxiv_id": "2402.04324", "title": "ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation", "authors": [ "Weiming Ren", "Harry Yang", "Ge Zhang", "Cong Wei", "Xinrun Du", "Stephen Huang", "Wenhu Chen" ], "github": "https://github.com/TIGER-AI-Lab/ConsistI2V", "project_page": "", "abstract": "Image-to-video (I2V) generation aims to use the initial frame (alongside a text prompt) to create a video sequence. A grand challenge in I2V generation is to maintain visual consistency throughout the video: existing methods often struggle to preserve the integrity of the subject, background, and style from the first frame, as well as ensure a fluid and logical progression within the video narrative. To mitigate these issues, we propose ConsistI2V, a diffusion-based method to enhance visual consistency for I2V generation. Specifically, we introduce (1) spatiotemporal attention over the first frame to maintain spatial and motion consistency, (2) noise initialization from the low-frequency band of the first frame to enhance layout consistency. These two approaches enable ConsistI2V to generate highly consistent videos. We also extend the proposed approaches to show their potential to improve consistency in auto-regressive long video generation and camera motion control. To verify the effectiveness of our method, we propose I2V-Bench, a comprehensive evaluation benchmark for I2V generation. Our automatic and human evaluation results demonstrate the superiority of ConsistI2V over existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TIGER-AI-Lab/ConsistI2V", "github_api_added_by": "auto" }, { "date": "2024-02-08", "arxiv_id": "2402.04792", "title": "Direct Language Model Alignment from Online AI Feedback", "authors": [ "Shangmin Guo", "Biao Zhang", "Tianlin Liu", "Tianqi Liu", "Misha Khalman", "Felipe Llinares", "Alexandre Rame", "Thomas Mesnard", "Yao Zhao", "Bilal Piot", "Johan Ferret", "Mathieu Blondel" ], "github": "", "project_page": "", "abstract": "Direct alignment from preferences (DAP) methods, such as DPO, have recently emerged as efficient alternatives to reinforcement learning from human feedback (RLHF), that do not require a separate reward model. However, the preference datasets used in DAP methods are usually collected ahead of training and never updated, thus the feedback is purely offline. Moreover, responses in these datasets are often sampled from a language model distinct from the one being aligned, and since the model evolves over training, the alignment phase is inevitably off-policy. In this study, we posit that online feedback is key and improves DAP methods. Our method, online AI feedback (OAIF), uses an LLM as annotator: on each training iteration, we sample two responses from the current model and prompt the LLM annotator to choose which one is preferred, thus providing online feedback. Despite its simplicity, we demonstrate via human evaluation in several tasks that OAIF outperforms both offline DAP and RLHF methods. We further show that the feedback leveraged in OAIF is easily controllable, via instruction prompts to the LLM annotator.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-08", "arxiv_id": "2402.04291", "title": "BiLLM: Pushing the Limit of Post-Training Quantization for LLMs", "authors": [ "Wei Huang", "Yangdong Liu", "Haotong Qin", "Ying Li", "Shiming Zhang", "Xianglong Liu", "Michele Magno", "Xiaojuan Qi" ], "github": "https://github.com/aaronhuang-778/billm", "project_page": "", "abstract": "Pretrained large language models (LLMs) exhibit exceptional general language processing capabilities but come with significant demands on memory and computational resources. As a powerful compression technology, binarization can extremely reduce model weights to a mere 1 bit, lowering the expensive computation and memory requirements. However, existing quantization techniques fall short of maintaining LLM performance under ultra-low bit-widths. In response to this challenge, we present BiLLM, a groundbreaking 1-bit post-training quantization scheme tailored for pretrained LLMs. Based on the weight distribution of LLMs, BiLLM first identifies and structurally selects salient weights, and minimizes the compression loss through an effective binary residual approximation strategy. Moreover, considering the bell-shaped distribution of the non-salient weights, we propose an optimal splitting search to group and binarize them accurately. BiLLM achieving for the first time high-accuracy inference (e.g. 8.41 perplexity on LLaMA2-70B) with only 1.08-bit weights across various LLMs families and evaluation metrics, outperforms SOTA quantization methods of LLM by significant margins. Moreover, BiLLM enables the binarization process of the LLM with 7 billion weights within 0.5 hours on a single GPU, demonstrating satisfactory time efficiency.", "github_manual": "https://github.com/aaronhuang-778/billm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-08", "arxiv_id": "2402.04615", "title": "ScreenAI: A Vision-Language Model for UI and Infographics Understanding", "authors": [ "Gilles Baechler", "Srinivas Sunkara", "Maria Wang", "Fedir Zubach", "Hassan Mansoor", "Vincent Etter", "Victor C\u0103rbune", "Jason Lin", "Jindong Chen", "Abhanshu Sharma" ], "github": "https://github.com/google-research-datasets/screen_annotation", "project_page": "", "abstract": "Screen user interfaces (UIs) and infographics, sharing similar visual language and design principles, play important roles in human communication and human-machine interaction. We introduce ScreenAI, a vision-language model that specializes in UI and infographics understanding. Our model improves upon the PaLI architecture with the flexible patching strategy of pix2struct and is trained on a unique mixture of datasets. At the heart of this mixture is a novel screen annotation task in which the model has to identify the type and location of UI elements. We use these text annotations to describe screens to Large Language Models and automatically generate question-answering (QA), UI navigation, and summarization training datasets at scale. We run ablation studies to demonstrate the impact of these design choices. At only 5B parameters, ScreenAI achieves new state-of-the-artresults on UI- and infographics-based tasks (Multi-page DocVQA, WebSRC, MoTIF and Widget Captioning), and new best-in-class performance on others (Chart QA, DocVQA, and InfographicVQA) compared to models of similar size. Finally, we release three new datasets: one focused on the screen annotation task and two others focused on question answering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/google-research-datasets/screen_annotation", "github_api_added_by": "auto" }, { "date": "2024-02-08", "arxiv_id": "2402.04858", "title": "CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay", "authors": [ "Natasha Butt", "Blazej Manczak", "Auke Wiggers", "Corrado Rainone", "David Zhang", "Micha\u00ebl Defferrard", "Taco Cohen" ], "github": "https://github.com/Qualcomm-AI-research/codeit", "project_page": "", "abstract": "Large language models are increasingly solving tasks that are commonly believed to require human-level reasoning ability. However, these models still perform very poorly on benchmarks of general intelligence such as the Abstraction and Reasoning Corpus (ARC). In this paper, we approach ARC as a programming-by-examples problem, and introduce a novel and scalable method for language model self-improvement called Code Iteration (CodeIt). Our method iterates between 1) program sampling and hindsight relabeling, and 2) learning from prioritized experience replay. By relabeling the goal of an episode (i.e., the target program output given input) to the realized output produced by the sampled program, our method effectively deals with the extreme sparsity of rewards in program synthesis. Applying CodeIt to the ARC dataset, we demonstrate that prioritized hindsight replay, along with pre-training and data-augmentation, leads to successful inter-task generalization. CodeIt is the first neuro-symbolic approach that scales to the full ARC evaluation dataset. Our method solves 15% of ARC evaluation tasks, achieving state-of-the-art performance and outperforming existing neural and symbolic baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Qualcomm-AI-research/codeit", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-08", "arxiv_id": "2402.04744", "title": "Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers", "authors": [ "Abhimanyu Rajeshkumar Bambhaniya", "Amir Yazdanbakhsh", "Suvinay Subramanian", "Sheng-Chun Kao", "Shivani Agrawal", "Utku Evci", "Tushar Krishna" ], "github": "https://github.com/abhibambhaniya/progressive_gradient_flow_nm_sparsity", "project_page": "", "abstract": "N:M Structured sparsity has garnered significant interest as a result of relatively modest overhead and improved efficiency. Additionally, this form of sparsity holds considerable appeal for reducing the memory footprint owing to their modest representation overhead. There have been efforts to develop training recipes for N:M structured sparsity, they primarily focus on low-sparsity regions (sim50\\%). Nonetheless, performance of models trained using these approaches tends to decline when confronted with high-sparsity regions (>80\\%). In this work, we study the effectiveness of existing sparse training recipes at high-sparsity regions and argue that these methods fail to sustain the model quality on par with low-sparsity regions. We demonstrate that the significant factor contributing to this disparity is the presence of elevated levels of induced noise in the gradient magnitudes. To mitigate this undesirable effect, we employ decay mechanisms to progressively restrict the flow of gradients towards pruned elements. Our approach improves the model quality by up to 2% and 5% in vision and language models at high sparsity regime, respectively. We also evaluate the trade-off between model accuracy and training compute cost in terms of FLOPs. At iso-training FLOPs, our method yields better performance compared to conventional sparse training recipes, exhibiting an accuracy improvement of up to 2%. The source code is available at https://github.com/abhibambhaniya/progressive_gradient_flow_nm_sparsity.", "github_manual": "", "github_regex": "https://github.com/abhibambhaniya/progressive_gradient_flow_nm_sparsity", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-08", "arxiv_id": "2402.04379", "title": "Fine-Tuned Language Models Generate Stable Inorganic Materials as Text", "authors": [ "Nate Gruver", "Anuroop Sriram", "Andrea Madotto", "Andrew Gordon Wilson", "C. Lawrence Zitnick", "Zachary Ulissi" ], "github": "https://github.com/facebookresearch/crystal-llm", "project_page": "", "abstract": "We propose fine-tuning large language models for generation of stable materials. While unorthodox, fine-tuning large language models on text-encoded atomistic data is simple to implement yet reliable, with around 90% of sampled structures obeying physical constraints on atom positions and charges. Using energy above hull calculations from both learned ML potentials and gold-standard DFT calculations, we show that our strongest model (fine-tuned LLaMA-2 70B) can generate materials predicted to be metastable at about twice the rate (49% vs 28%) of CDVAE, a competing diffusion model. Because of text prompting's inherent flexibility, our models can simultaneously be used for unconditional generation of stable material, infilling of partial structures and text-conditional generation. Finally, we show that language models' ability to capture key symmetries of crystal structures improves with model scale, suggesting that the biases of pretrained LLMs are surprisingly well-suited for atomistic data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/crystal-llm", "github_api_added_by": "auto" }, { "date": "2024-02-08", "arxiv_id": "2402.04494", "title": "Grandmaster-Level Chess Without Search", "authors": [ "Anian Ruoss", "Gr\u00e9goire Del\u00e9tang", "Sourabh Medapati", "Jordi Grau-Moya", "Li Kevin Wenliang", "Elliot Catt", "John Reid", "Tim Genewein" ], "github": "https://github.com/google-deepmind/searchless_chess", "project_page": "", "abstract": "The recent breakthrough successes in machine learning are mainly attributed to scale: namely large-scale attention-based architectures and datasets of unprecedented scale. This paper investigates the impact of training at scale for chess. Unlike traditional chess engines that rely on complex heuristics, explicit search, or a combination of both, we train a 270M parameter transformer model with supervised learning on a dataset of 10 million chess games. We annotate each board in the dataset with action-values provided by the powerful Stockfish 16 engine, leading to roughly 15 billion data points. Our largest model reaches a Lichess blitz Elo of 2895 against humans, and successfully solves a series of challenging chess puzzles, without any domain-specific tweaks or explicit search algorithms. We also show that our model outperforms AlphaZero's policy and value networks (without MCTS) and GPT-3.5-turbo-instruct. A systematic investigation of model and dataset size shows that strong chess performance only arises at sufficient scale. To validate our results, we perform an extensive series of ablations of design choices and hyperparameters.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/searchless_chess", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-08", "arxiv_id": "2402.05054", "title": "LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation", "authors": [ "Jiaxiang Tang", "Zhaoxi Chen", "Xiaokang Chen", "Tengfei Wang", "Gang Zeng", "Ziwei Liu" ], "github": "https://github.com/3DTopia/LGM", "project_page": "", "abstract": "3D content creation has achieved significant progress in terms of both quality and speed. Although current feed-forward models can produce 3D objects in seconds, their resolution is constrained by the intensive computation required during training. In this paper, we introduce Large Multi-View Gaussian Model (LGM), a novel framework designed to generate high-resolution 3D models from text prompts or single-view images. Our key insights are two-fold: 1) 3D Representation: We propose multi-view Gaussian features as an efficient yet powerful representation, which can then be fused together for differentiable rendering. 2) 3D Backbone: We present an asymmetric U-Net as a high-throughput backbone operating on multi-view images, which can be produced from text or single-view image input by leveraging multi-view diffusion models. Extensive experiments demonstrate the high fidelity and efficiency of our approach. Notably, we maintain the fast speed to generate 3D objects within 5 seconds while boosting the training resolution to 512, thereby achieving high-resolution 3D content generation.", "github_manual": "https://github.com/3DTopia/LGM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-08", "arxiv_id": "2402.04825", "title": "Fast Timing-Conditioned Latent Audio Diffusion", "authors": [ "Zach Evans", "CJ Carr", "Josiah Taylor", "Scott H. Hawley", "Jordi Pons" ], "github": "", "project_page": "", "abstract": "Generating long-form 44.1kHz stereo audio from text prompts can be computationally demanding. Further, most previous works do not tackle that music and sound effects naturally vary in their duration. Our research focuses on the efficient generation of long-form, variable-length stereo music and sounds at 44.1kHz using text prompts with a generative model. Stable Audio is based on latent diffusion, with its latent defined by a fully-convolutional variational autoencoder. It is conditioned on text prompts as well as timing embeddings, allowing for fine control over both the content and length of the generated music and sounds. Stable Audio is capable of rendering stereo signals of up to 95 sec at 44.1kHz in 8 sec on an A100 GPU. Despite its compute efficiency and fast inference, it is one of the best in two public text-to-music and -audio benchmarks and, differently from state-of-the-art models, can generate music with structure and stereo sounds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-08", "arxiv_id": "2402.04925", "title": "TP-Aware Dequantization", "authors": [ "Adnan Hoque", "Mudhakar Srivatsa", "Chih-Chieh Yang", "Raghu Ganti" ], "github": "", "project_page": "", "abstract": "In this paper, we present a novel method that reduces model inference latency during distributed deployment of Large Language Models (LLMs). Our contribution is an optimized inference deployment scheme that address the current limitations of state-of-the-art quantization kernels when used in conjunction with Tensor Parallel (TP). Our method preserves data locality in GPU memory access patterns and exploits a priori knowledge of TP to reduce global communication. We demonstrate an up to 1.81x speedup over existing methods for Llama-70B and up to 1.78x speedup for IBM WatsonX's Granite-20B MLP layer problem sizes on A100 and H100 NVIDIA DGX Systems for a variety of TP settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-08", "arxiv_id": "2402.05008", "title": "EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss", "authors": [ "Zhuoyang Zhang", "Han Cai", "Song Han" ], "github": "https://github.com/mit-han-lab/efficientvit", "project_page": "", "abstract": "We present EfficientViT-SAM, a new family of accelerated segment anything models. We retain SAM's lightweight prompt encoder and mask decoder while replacing the heavy image encoder with EfficientViT. For the training, we begin with the knowledge distillation from the SAM-ViT-H image encoder to EfficientViT. Subsequently, we conduct end-to-end training on the SA-1B dataset. Benefiting from EfficientViT's efficiency and capacity, EfficientViT-SAM delivers 48.9x measured TensorRT speedup on A100 GPU over SAM-ViT-H without sacrificing performance. Our code and pre-trained models are released at https://github.com/mit-han-lab/efficientvit.", "github_manual": "", "github_regex": "https://github.com/mit-han-lab/efficientvit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-08", "arxiv_id": "2402.04347", "title": "The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry", "authors": [ "Michael Zhang", "Kush Bhatia", "Hermann Kumbong", "Christopher R\u00e9" ], "github": "", "project_page": "", "abstract": "Linear attentions have shown potential for improving Transformer efficiency, reducing attention's quadratic complexity to linear in sequence length. This holds exciting promise for (1) training linear Transformers from scratch, (2) \"finetuned-conversion\" of task-specific Transformers into linear versions that recover task performance, and (3) \"pretrained-conversion\" of Transformers such as large language models into linear versions finetunable on downstream tasks. However, linear attentions often underperform standard softmax attention in quality. To close this performance gap, we find prior linear attentions lack key properties of softmax attention tied to good performance: low-entropy (or \"spiky\") weights and dot-product monotonicity. We further observe surprisingly simple feature maps that retain these properties and match softmax performance, but are inefficient to compute in linear attention. We thus propose Hedgehog, a learnable linear attention that retains the spiky and monotonic properties of softmax attention while maintaining linear complexity. Hedgehog uses simple trainable MLPs to produce attention weights mimicking softmax attention. Experiments show Hedgehog recovers over 99% of standard Transformer quality in train-from-scratch and finetuned-conversion settings, outperforming prior linear attentions up to 6 perplexity points on WikiText-103 with causal GPTs, and up to 8.7 GLUE score points on finetuned bidirectional BERTs. Hedgehog also enables pretrained-conversion. Converting a pretrained GPT-2 into a linear attention variant achieves state-of-the-art 16.7 perplexity on WikiText-103 for 125M subquadratic decoder models. We finally turn a pretrained Llama-2 7B into a viable linear attention Llama. With low-rank adaptation, Hedgehog-Llama2 7B achieves 28.1 higher ROUGE-1 points over the base standard attention model, where prior linear attentions lead to 16.5 point drops.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-08", "arxiv_id": "2402.05099", "title": "Hydragen: High-Throughput LLM Inference with Shared Prefixes", "authors": [ "Jordan Juravsky", "Bradley Brown", "Ryan Ehrlich", "Daniel Y. Fu", "Christopher R\u00e9", "Azalia Mirhoseini" ], "github": "https://github.com/jordan-benjamin/hydragen", "project_page": "", "abstract": "Transformer-based large language models (LLMs) are now deployed to hundreds of millions of users. LLM inference is commonly performed on batches of sequences that share a prefix, such as few-shot examples or a chatbot system prompt. Decoding in this large-batch setting can be bottlenecked by the attention operation, which reads large key-value (KV) caches from memory and computes inefficient matrix-vector products for every sequence in the batch. In this work, we introduce Hydragen, a hardware-aware exact implementation of attention with shared prefixes. Hydragen computes attention over the shared prefix and unique suffixes separately. This decomposition enables efficient prefix attention by batching queries together across sequences, reducing redundant memory reads and enabling the use of hardware-friendly matrix multiplications. Our method can improve end-to-end LLM throughput by up to 32x against competitive baselines, with speedup growing with the batch size and shared prefix length. Hydragen also enables the use of very long shared contexts: with a high batch size, increasing the prefix length from 1K to 16K tokens decreases Hydragen throughput by less than 15%, while the throughput of baselines drops by over 90%. Hydragen generalizes beyond simple prefix-suffix decomposition and can be applied to tree-based prompt sharing patterns, allowing us to further reduce inference time on competitive programming problems by 55%.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jordan-benjamin/hydragen", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05120", "title": "More Agents Is All You Need", "authors": [ "Junyou Li", "Qin Zhang", "Yangbin Yu", "Qiang Fu", "Deheng Ye" ], "github": "https://github.com/moreagentsisallyouneed/agentforest", "project_page": "", "abstract": "We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated. Also, this method is orthogonal to existing complicated methods to further enhance LLMs, while the degree of enhancement is correlated to the task difficulty. We conduct comprehensive experiments on a wide range of LLM benchmarks to verify the presence of our finding, and to study the properties that can facilitate its occurrence. Our code is publicly available at: https://anonymous.4open.science/r/more_agent_is_all_you_need.", "github_manual": "https://github.com/moreagentsisallyouneed/agentforest", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05468", "title": "Implicit Diffusion: Efficient Optimization through Stochastic Sampling", "authors": [ "Pierre Marion", "Anna Korba", "Peter Bartlett", "Mathieu Blondel", "Valentin De Bortoli", "Arnaud Doucet", "Felipe Llinares-L\u00f3pez", "Courtney Paquette", "Quentin Berthet" ], "github": "", "project_page": "", "abstract": "We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions. Doing so allows us to modify the outcome distribution of sampling processes by optimizing over their parameters. We introduce a general framework for first-order optimization of these processes, that performs jointly, in a single loop, optimization and sampling steps. This approach is inspired by recent advances in bilevel optimization and automatic implicit differentiation, leveraging the point of view of sampling as optimization over the space of probability distributions. We provide theoretical guarantees on the performance of our method, as well as experimental results demonstrating its effectiveness in real-world settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05195", "title": "\u03bb-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space", "authors": [ "Maitreya Patel", "Sangmin Jung", "Chitta Baral", "Yezhou Yang" ], "github": "https://github.com/eclipse-t2i/lambda-eclipse-inference", "project_page": "", "abstract": "Despite the recent advances in personalized text-to-image (P-T2I) generative models, subject-driven T2I remains challenging. The primary bottlenecks include 1) Intensive training resource requirements, 2) Hyper-parameter sensitivity leading to inconsistent outputs, and 3) Balancing the intricacies of novel visual concept and composition alignment. We start by re-iterating the core philosophy of T2I diffusion models to address the above limitations. Predominantly, contemporary subject-driven T2I approaches hinge on Latent Diffusion Models (LDMs), which facilitate T2I mapping through cross-attention layers. While LDMs offer distinct advantages, P-T2I methods' reliance on the latent space of these diffusion models significantly escalates resource demands, leading to inconsistent results and necessitating numerous iterations for a single desired image. Recently, ECLIPSE has demonstrated a more resource-efficient pathway for training UnCLIP-based T2I models, circumventing the need for diffusion text-to-image priors. Building on this, we introduce lambda-ECLIPSE. Our method illustrates that effective P-T2I does not necessarily depend on the latent space of diffusion models. lambda-ECLIPSE achieves single, multi-subject, and edge-guided T2I personalization with just 34M parameters and is trained on a mere 74 GPU hours using 1.6M image-text interleaved data. Through extensive experiments, we also establish that lambda-ECLIPSE surpasses existing baselines in composition alignment while preserving concept alignment performance, even with significantly lower resource utilization.", "github_manual": "https://github.com/eclipse-t2i/lambda-eclipse-inference", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05935", "title": "SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models", "authors": [ "Peng Gao", "Renrui Zhang", "Chris Liu", "Longtian Qiu", "Siyuan Huang", "Weifeng Lin", "Shitian Zhao", "Shijie Geng", "Ziyi Lin", "Peng Jin", "Kaipeng Zhang", "Wenqi Shao", "Chao Xu", "Conghui He", "Junjun He", "Hao Shao", "Pan Lu", "Hongsheng Li", "Yu Qiao" ], "github": "https://github.com/Alpha-VLLM/LLaMA2-Accessory", "project_page": "", "abstract": "We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series developed upon SPHINX. To improve the architecture and training efficiency, we modify the SPHINX framework by removing redundant visual encoders, bypassing fully-padded sub-images with skip tokens, and simplifying multi-stage training into a one-stage all-in-one paradigm. To fully unleash the potential of MLLMs, we assemble a comprehensive multi-domain and multimodal dataset covering publicly available resources in language, vision, and vision-language tasks. We further enrich this collection with our curated OCR intensive and Set-of-Mark datasets, extending the diversity and generality. By training over different base LLMs including TinyLlama1.1B, InternLM2-7B, LLaMA2-13B, and Mixtral8x7B, we obtain a spectrum of MLLMs that vary in parameter size and multilingual capabilities. Comprehensive benchmarking reveals a strong correlation between the multi-modal performance with the data and parameter scales. Code and models are released at https://github.com/Alpha-VLLM/LLaMA2-Accessory", "github_manual": "", "github_regex": "https://github.com/Alpha-VLLM/LLaMA2-Accessory", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05932", "title": "Driving Everywhere with Large Language Model Policy Adaptation", "authors": [ "Boyi Li", "Yue Wang", "Jiageng Mao", "Boris Ivanovic", "Sushant Veer", "Karen Leung", "Marco Pavone" ], "github": "", "project_page": "", "abstract": "Adapting driving behavior to new environments, customs, and laws is a long-standing problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs). In this paper, we present LLaDA, a simple yet powerful tool that enables human drivers and autonomous vehicles alike to drive everywhere by adapting their tasks and motion plans to traffic rules in new locations. LLaDA achieves this by leveraging the impressive zero-shot generalizability of large language models (LLMs) in interpreting the traffic rules in the local driver handbook. Through an extensive user study, we show that LLaDA's instructions are useful in disambiguating in-the-wild unexpected situations. We also demonstrate LLaDA's ability to adapt AV motion planning policies in real-world datasets; LLaDA outperforms baseline planning approaches on all our metrics. Please check our website for more details: https://boyiliee.github.io/llada.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05929", "title": "An Interactive Agent Foundation Model", "authors": [ "Zane Durante", "Bidipta Sarkar", "Ran Gong", "Rohan Taori", "Yusuke Noda", "Paul Tang", "Ehsan Adeli", "Shrinidhi Kowshika Lakshmikanth", "Kevin Schulman", "Arnold Milstein", "Demetri Terzopoulos", "Ade Famoti", "Noboru Kuno", "Ashley Llorens", "Hoi Vo", "Katsu Ikeuchi", "Li Fei-Fei", "Jianfeng Gao", "Naoki Wake", "Qiuyuan Huang" ], "github": "", "project_page": "", "abstract": "The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide range of applications. We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction, enabling a versatile and adaptable AI framework. We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare. Our model demonstrates its ability to generate meaningful and contextually relevant outputs in each area. The strength of our approach lies in its generality, leveraging a variety of data sources such as robotics sequences, gameplay data, large-scale video datasets, and textual information for effective multimodal and multi-task learning. Our approach provides a promising avenue for developing generalist, action-taking, multimodal systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05755", "title": "SpiRit-LM: Interleaved Spoken and Written Language Model", "authors": [ "Tu Anh Nguyen", "Benjamin Muller", "Bokai Yu", "Marta R. Costa-jussa", "Maha Elbayad", "Sravya Popuri", "Paul-Ambroise Duquenne", "Robin Algayres", "Ruslan Mavlyutov", "Itai Gat", "Gabriel Synnaeve", "Juan Pino", "Benoit Sagot", "Emmanuel Dupoux" ], "github": "https://github.com/facebookresearch/spiritlm", "project_page": "", "abstract": "We introduce SPIRIT-LM, a foundation multimodal language model that freely mixes text and speech. Our model is based on a pretrained text language model that we extend to the speech modality by continuously training it on text and speech units. Speech and text sequences are concatenated as a single set of tokens, and trained with a word-level interleaving method using a small automatically-curated speech-text parallel corpus. SPIRIT-LM comes in two versions: a BASE version that uses speech semantic units and an EXPRESSIVE version that models expressivity using pitch and style units in addition to the semantic units. For both versions, the text is encoded with subword BPE tokens. The resulting model displays both the semantic abilities of text models and the expressive abilities of speech models. Additionally, we demonstrate that SPIRIT-LM is able to learn new tasks in a few-shot fashion across modalities (i.e. ASR, TTS, Speech Classification).", "github_manual": "https://github.com/facebookresearch/spiritlm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05472", "title": "Question Aware Vision Transformer for Multimodal Reasoning", "authors": [ "Roy Ganz", "Yair Kittenplon", "Aviad Aberdam", "Elad Ben Avraham", "Oren Nuriel", "Shai Mazor", "Ron Litman" ], "github": "", "project_page": "", "abstract": "Vision-Language (VL) models have gained significant research focus, enabling remarkable advances in multimodal reasoning. These architectures typically comprise a vision encoder, a Large Language Model (LLM), and a projection module that aligns visual features with the LLM's representation space. Despite their success, a critical limitation persists: the vision encoding process remains decoupled from user queries, often in the form of image-related questions. Consequently, the resulting visual features may not be optimally attuned to the query-specific elements of the image. To address this, we introduce QA-ViT, a Question Aware Vision Transformer approach for multimodal reasoning, which embeds question awareness directly within the vision encoder. This integration results in dynamic visual features focusing on relevant image aspects to the posed question. QA-ViT is model-agnostic and can be incorporated efficiently into any VL architecture. Extensive experiments demonstrate the effectiveness of applying our method to various multimodal architectures, leading to consistent improvement across diverse tasks and showcasing its potential for enhancing visual and scene-text understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05140", "title": "Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains", "authors": [ "Junhong Shen", "Neil Tenenholtz", "James Brian Hall", "David Alvarez-Melis", "Nicolo Fusi" ], "github": "https://github.com/sjunhongshen/tag-llm", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding and generating natural language. However, their capabilities wane in highly specialized domains underrepresented in the pretraining corpus, such as physical and biomedical sciences. This work explores how to repurpose general LLMs into effective task solvers for specialized domains. We introduce a novel, model-agnostic framework for learning custom input tags, which are parameterized as continuous vectors appended to the LLM's embedding layer, to condition the LLM. We design two types of input tags: domain tags are used to delimit specialized representations (e.g., chemical formulas) and provide domain-relevant context; function tags are used to represent specific functions (e.g., predicting molecular properties) and compress function-solving instructions. We develop a three-stage protocol to learn these tags using auxiliary data and domain knowledge. By explicitly disentangling task domains from task functions, our method enables zero-shot generalization to unseen problems through diverse combinations of the input tags. It also boosts LLM's performance in various specialized domains, such as predicting protein or chemical properties and modeling drug-target interactions, outperforming expert models tailored to these tasks.", "github_manual": "https://github.com/sjunhongshen/tag-llm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05930", "title": "WebLINX: Real-World Website Navigation with Multi-Turn Dialogue", "authors": [ "Xing Han L\u00f9", "Zden\u011bk Kasner", "Siva Reddy" ], "github": "https://github.com/McGill-NLP/weblinx", "project_page": "", "abstract": "We propose the problem of conversational web navigation, where a digital agent controls a web browser and follows user instructions to solve real-world tasks in a multi-turn dialogue fashion. To support this problem, we introduce WEBLINX - a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. Our benchmark covers a broad range of patterns on over 150 real-world websites and can be used to train and evaluate agents in diverse scenarios. Due to the magnitude of information present, Large Language Models (LLMs) cannot process entire web pages in real-time. To solve this bottleneck, we design a retrieval-inspired model that efficiently prunes HTML pages by ranking relevant elements. We use the selected elements, along with screenshots and action history, to assess a variety of models for their ability to replicate human behavior when navigating the web. Our experiments span from small text-only to proprietary multimodal LLMs. We find that smaller finetuned decoders surpass the best zero-shot LLMs (including GPT-4V), but also larger finetuned multimodal models which were explicitly pretrained on screenshots. However, all finetuned models struggle to generalize to unseen websites. Our findings highlight the need for large multimodal models that can generalize to novel settings. Our code, data and models are available for research: https://mcgill-nlp.github.io/weblinx", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/McGill-NLP/weblinx", "github_api_added_by": "auto" }, { "date": "2024-02-09", "arxiv_id": "2402.05403", "title": "In-Context Principle Learning from Mistakes", "authors": [ "Tianjun Zhang", "Aman Madaan", "Luyu Gao", "Steven Zheng", "Swaroop Mishra", "Yiming Yang", "Niket Tandon", "Uri Alon" ], "github": "", "project_page": "", "abstract": "In-context learning (ICL, also known as few-shot prompting) has been the standard method of adapting LLMs to downstream tasks, by learning from a few input-output examples. Nonetheless, all ICL-based approaches only learn from correct input-output pairs. In this paper, we revisit this paradigm, by learning more from the few given input-output examples. We introduce Learning Principles (LEAP): First, we intentionally induce the model to make mistakes on these few examples; then we reflect on these mistakes, and learn explicit task-specific \"principles\" from them, which help solve similar problems and avoid common mistakes; finally, we prompt the model to answer unseen test questions using the original few-shot examples and these learned general principles. We evaluate LEAP on a wide range of benchmarks, including multi-hop question answering (Hotpot QA), textual QA (DROP), Big-Bench Hard reasoning, and math problems (GSM8K and MATH); in all these benchmarks, LEAP improves the strongest available LLMs such as GPT-3.5-turbo, GPT-4, GPT-4 turbo and Claude-2.1. For example, LEAP improves over the standard few-shot prompting using GPT-4 by 7.5% in DROP, and by 3.3% in HotpotQA. Importantly, LEAP does not require any more input or examples than the standard few-shot prompting settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05861", "title": "Memory Consolidation Enables Long-Context Video Understanding", "authors": [ "Ivana Bala\u017eevi\u0107", "Yuge Shi", "Pinelopi Papalampidi", "Rahma Chaabouni", "Skanda Koppula", "Olivier J. H\u00e9naff" ], "github": "", "project_page": "", "abstract": "Most transformer-based video encoders are limited to short temporal contexts due to their quadratic complexity. While various attempts have been made to extend this context, this has often come at the cost of both conceptual and computational complexity. We propose to instead re-purpose existing pre-trained video transformers by simply fine-tuning them to attend to memories derived non-parametrically from past activations. By leveraging redundancy reduction, our memory-consolidated vision transformer (MC-ViT) effortlessly extends its context far into the past and exhibits excellent scaling behavior when learning from longer videos. In doing so, MC-ViT sets a new state-of-the-art in long-context video understanding on EgoSchema, Perception Test, and Diving48, outperforming methods that benefit from orders of magnitude more parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05937", "title": "InstaGen: Enhancing Object Detection by Training on Synthetic Dataset", "authors": [ "Chengjian Feng", "Yujie Zhong", "Zequn Jie", "Weidi Xie", "Lin Ma" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce a novel paradigm to enhance the ability of object detector, e.g., expanding categories or improving detection performance, by training on synthetic dataset generated from diffusion models. Specifically, we integrate an instance-level grounding head into a pre-trained, generative diffusion model, to augment it with the ability of localising arbitrary instances in the generated images. The grounding head is trained to align the text embedding of category names with the regional visual feature of the diffusion model, using supervision from an off-the-shelf object detector, and a novel self-training scheme on (novel) categories not covered by the detector. This enhanced version of diffusion model, termed as InstaGen, can serve as a data synthesizer for object detection. We conduct thorough experiments to show that, object detector can be enhanced while training on the synthetic dataset from InstaGen, demonstrating superior performance over existing state-of-the-art methods in open-vocabulary (+4.5 AP) and data-sparse (+1.2 to 5.2 AP) scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05672", "title": "Multilingual E5 Text Embeddings: A Technical Report", "authors": [ "Liang Wang", "Nan Yang", "Xiaolong Huang", "Linjun Yang", "Rangan Majumder", "Furu Wei" ], "github": "https://github.com/microsoft/unilm/tree/master/e5", "project_page": "", "abstract": "This technical report presents the training methodology and evaluation results of the open-source multilingual E5 text embedding models, released in mid-2023. Three embedding models of different sizes (small / base / large) are provided, offering a balance between the inference efficiency and embedding quality. The training procedure adheres to the English E5 model recipe, involving contrastive pre-training on 1 billion multilingual text pairs, followed by fine-tuning on a combination of labeled datasets. Additionally, we introduce a new instruction-tuned embedding model, whose performance is on par with state-of-the-art, English-only models of similar sizes. Information regarding the model release can be found at https://github.com/microsoft/unilm/tree/master/e5 .", "github_manual": "", "github_regex": "https://github.com/microsoft/unilm/tree/master/e5", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-09", "arxiv_id": "2402.05546", "title": "Offline Actor-Critic Reinforcement Learning Scales to Large Models", "authors": [ "Jost Tobias Springenberg", "Abbas Abdolmaleki", "Jingwei Zhang", "Oliver Groth", "Michael Bloesch", "Thomas Lampe", "Philemon Brakel", "Sarah Bechtle", "Steven Kapturowski", "Roland Hafner", "Nicolas Heess", "Martin Riedmiller" ], "github": "", "project_page": "", "abstract": "We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning. We find that offline actor-critic algorithms can outperform strong, supervised, behavioral cloning baselines for multi-task training on a large dataset containing both sub-optimal and expert behavior on 132 continuous control tasks. We introduce a Perceiver-based actor-critic model and elucidate the key model features needed to make offline RL work with self- and cross-attention modules. Overall, we find that: i) simple offline actor critic algorithms are a natural choice for gradually moving away from the currently predominant paradigm of behavioral cloning, and ii) via offline RL it is possible to learn multi-task policies that master many domains simultaneously, including real robotics tasks, from sub-optimal demonstrations or self-generated data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-12", "arxiv_id": "2402.06118", "title": "ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling", "authors": [ "Siming Yan", "Min Bai", "Weifeng Chen", "Xiong Zhou", "Qixing Huang", "Li Erran Li" ], "github": "https://github.com/amazon-science/vigor", "project_page": "", "abstract": "By combining natural language understanding and the generation capabilities and breadth of knowledge of large language models with image perception, recent large vision language models (LVLMs) have shown unprecedented reasoning capabilities in the real world. However, the generated text often suffers from inaccurate grounding in the visual input, resulting in errors such as hallucinating nonexistent scene elements, missing significant parts of the scene, and inferring incorrect attributes and relationships between objects. To address these issues, we introduce a novel framework, ViGoR (Visual Grounding Through Fine-Grained Reward Modeling) that utilizes fine-grained reward modeling to significantly enhance the visual grounding of LVLMs over pre-trained baselines. This improvement is efficiently achieved using much cheaper human evaluations instead of full supervisions, as well as automated methods. We show the effectiveness of our approach through numerous metrics on several benchmarks. Additionally, we construct a comprehensive and challenging dataset specifically designed to validate the visual grounding capabilities of LVLMs. Finally, we plan to release our human annotation comprising approximately 16,000 images and generated text pairs with fine-grained evaluations to contribute to related research in the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amazon-science/vigor", "github_api_added_by": "auto" }, { "date": "2024-02-12", "arxiv_id": "2402.06149", "title": "HeadStudio: Text to Animatable Head Avatars with 3D Gaussian Splatting", "authors": [ "Zhenglin Zhou", "Fan Ma", "Hehe Fan", "Yi Yang" ], "github": "https://github.com/ZhenglinZhou/HeadStudio", "project_page": "", "abstract": "Creating digital avatars from textual prompts has long been a desirable yet challenging task. Despite the promising outcomes obtained through 2D diffusion priors in recent works, current methods face challenges in achieving high-quality and animated avatars effectively. In this paper, we present HeadStudio, a novel framework that utilizes 3D Gaussian splatting to generate realistic and animated avatars from text prompts. Our method drives 3D Gaussians semantically to create a flexible and achievable appearance through the intermediate FLAME representation. Specifically, we incorporate the FLAME into both 3D representation and score distillation: 1) FLAME-based 3D Gaussian splatting, driving 3D Gaussian points by rigging each point to a FLAME mesh. 2) FLAME-based score distillation sampling, utilizing FLAME-based fine-grained control signal to guide score distillation from the text prompt. Extensive experiments demonstrate the efficacy of HeadStudio in generating animatable avatars from textual prompts, exhibiting visually appealing appearances. The avatars are capable of rendering high-quality real-time (geq 40 fps) novel views at a resolution of 1024. They can be smoothly controlled by real-world speech and video. We hope that HeadStudio can advance digital avatar creation and that the present method can widely be applied across various domains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ZhenglinZhou/HeadStudio", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-12", "arxiv_id": "2402.06619", "title": "Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning", "authors": [ "Shivalika Singh", "Freddie Vargus", "Daniel Dsouza", "B\u00f6rje F. Karlsson", "Abinaya Mahendiran", "Wei-Yin Ko", "Herumb Shandilya", "Jay Patel", "Deividas Mataciunas", "Laura OMahony", "Mike Zhang", "Ramith Hettiarachchi", "Joseph Wilson", "Marina Machado", "Luisa Souza Moura", "Dominik Krzemi\u0144ski", "Hakimeh Fadaei", "Irem Erg\u00fcn", "Ifeoma Okoh", "Aisha Alaagib", "Oshan Mudannayake", "Zaid Alyafeai", "Vu Minh Chien", "Sebastian Ruder", "Surya Guthikonda", "Emad A. Alghamdi", "Sebastian Gehrmann", "Niklas Muennighoff", "Max Bartolo", "Julia Kreutzer", "Ahmet \u00dcst\u00fcn", "Marzieh Fadaee", "Sara Hooker" ], "github": "", "project_page": "", "abstract": "Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to the finetuning of pre-trained models on a diverse set of tasks that enables a large language model (LLM) to respond to instructions. Instruction fine-tuning (IFT) requires specifically constructed and annotated datasets. However, existing datasets are almost all in the English language. In this work, our primary goal is to bridge the language gap by building a human-curated instruction-following dataset spanning 65 languages. We worked with fluent speakers of languages from around the world to collect natural instances of instructions and completions. Furthermore, we create the most extensive multilingual collection to date, comprising 513 million instances through templating and translating existing datasets across 114 languages. In total, we contribute four key resources: we develop and open-source the Aya Annotation Platform, the Aya Dataset, the Aya Collection, and the Aya Evaluation Suite. The Aya initiative also serves as a valuable case study in participatory research, involving collaborators from 119 countries. We see this as a valuable framework for future research collaborations that aim to bridge gaps in resources.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-12", "arxiv_id": "2402.06178", "title": "MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models", "authors": [ "Yixiao Zhang", "Yukara Ikemiya", "Gus Xia", "Naoki Murata", "Marco Mart\u00ednez", "Wei-Hsiang Liao", "Yuki Mitsufuji", "Simon Dixon" ], "github": "https://github.com/ldzhangyx/MusicMagus", "project_page": "", "abstract": "Recent advances in text-to-music generation models have opened new avenues in musical creativity. However, music generation usually involves iterative refinements, and how to edit the generated music remains a significant challenge. This paper introduces a novel approach to the editing of music generated by such models, enabling the modification of specific attributes, such as genre, mood and instrument, while maintaining other aspects unchanged. Our method transforms text editing to latent space manipulation while adding an extra constraint to enforce consistency. It seamlessly integrates with existing pretrained text-to-music diffusion models without requiring additional training. Experimental results demonstrate superior performance over both zero-shot and certain supervised baselines in style and timbre transfer evaluations. Additionally, we showcase the practical applicability of our approach in real-world music editing scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ldzhangyx/MusicMagus", "github_api_added_by": "auto" }, { "date": "2024-02-12", "arxiv_id": "2402.06332", "title": "InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning", "authors": [ "Huaiyuan Ying", "Shuo Zhang", "Linyang Li", "Zhejian Zhou", "Yunfan Shao", "Zhaoye Fei", "Yichuan Ma", "Jiawei Hong", "Kuikun Liu", "Ziyi Wang", "Yudong Wang", "Zijian Wu", "Shuaibin Li", "Fengzhe Zhou", "Hongwei Liu", "Songyang Zhang", "Wenwei Zhang", "Hang Yan", "Xipeng Qiu", "Jiayu Wang", "Kai Chen", "Dahua Lin" ], "github": "https://github.com/InternLM/InternLM-Math", "project_page": "", "abstract": "The math abilities of large language models can represent their abstract reasoning ability. In this paper, we introduce and open-source our math reasoning LLMs InternLM-Math which is continue pre-trained from InternLM2. We unify chain-of-thought reasoning, reward modeling, formal reasoning, data augmentation, and code interpreter in a unified seq2seq format and supervise our model to be a versatile math reasoner, verifier, prover, and augmenter. These abilities can be used to develop the next math LLMs or self-iteration. InternLM-Math obtains open-sourced state-of-the-art performance under the setting of in-context learning, supervised fine-tuning, and code-assisted reasoning in various informal and formal benchmarks including GSM8K, MATH, Hungary math exam, MathBench-ZH, and MiniF2F. Our pre-trained model achieves 30.3 on the MiniF2F test set without fine-tuning. We further explore how to use LEAN to solve math problems and study its performance under the setting of multi-task learning which shows the possibility of using LEAN as a unified platform for solving and proving in math. Our models, codes, and data are released at https://github.com/InternLM/InternLM-Math.", "github_manual": "", "github_regex": "https://github.com/InternLM/InternLM-Math", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-12", "arxiv_id": "2402.06071", "title": "Keyframer: Empowering Animation Design using Large Language Models", "authors": [ "Tiffany Tseng", "Ruijia Cheng", "Jeffrey Nichols" ], "github": "https://github.com/Klowner/inkscape-applytransforms", "project_page": "", "abstract": "Large language models (LLMs) have the potential to impact a wide range of creative domains, but the application of LLMs to animation is underexplored and presents novel challenges such as how users might effectively describe motion in natural language. In this paper, we present Keyframer, a design tool for animating static images (SVGs) with natural language. Informed by interviews with professional animation designers and engineers, Keyframer supports exploration and refinement of animations through the combination of prompting and direct editing of generated output. The system also enables users to request design variants, supporting comparison and ideation. Through a user study with 13 participants, we contribute a characterization of user prompting strategies, including a taxonomy of semantic prompt types for describing motion and a 'decomposed' prompting style where users continually adapt their goals in response to generated output.We share how direct editing along with prompting enables iteration beyond one-shot prompting interfaces common in generative tools today. Through this work, we propose how LLMs might empower a range of audiences to engage with animation creation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Klowner/inkscape-applytransforms", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-12", "arxiv_id": "2402.06082", "title": "SubGen: Token Generation in Sublinear Time and Memory", "authors": [ "Amir Zandieh", "Insu Han", "Vahab Mirrokni", "Amin Karbasi" ], "github": "", "project_page": "", "abstract": "Despite the significant success of large language models (LLMs), their extensive memory requirements pose challenges for deploying them in long-context token generation. The substantial memory footprint of LLM decoders arises from the necessity to store all previous tokens in the attention module, a requirement imposed by key-value (KV) caching. In this work, our focus is on developing an efficient compression technique for the KV cache. Empirical evidence indicates a significant clustering tendency within key embeddings in the attention module. Building on this key insight, we have devised a novel caching method with sublinear complexity, employing online clustering on key tokens and online ell_2 sampling on values. The result is a provably accurate and efficient attention decoding algorithm, termed SubGen. Not only does this algorithm ensure a sublinear memory footprint and sublinear time complexity, but we also establish a tight error bound for our approach. Empirical evaluations on long-context question-answering tasks demonstrate that SubGen significantly outperforms existing and state-of-the-art KV cache compression methods in terms of performance and efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-12", "arxiv_id": "2402.06147", "title": "DeAL: Decoding-time Alignment for Large Language Models", "authors": [ "James Y. Huang", "Sailik Sengupta", "Daniele Bonadiman", "Yi-an Lai", "Arshit Gupta", "Nikolaos Pappas", "Saab Mansour", "Katrin Kirchoff", "Dan Roth" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are nowadays expected to generate content aligned with human preferences. Current work focuses on alignment at model training time, through techniques such as Reinforcement Learning with Human Feedback (RLHF). However, it is unclear if such methods are an effective choice to teach alignment objectives to the model. First, the inability to incorporate multiple, custom rewards and reliance on a model developer's view of universal and static principles are key limitations. Second, the residual gaps in model training and the reliability of such approaches are also questionable (e.g. susceptibility to jail-breaking even after safety training). To address these, we propose DeAL, a framework that allows the user to customize reward functions and enables Decoding-time Alignment of LLMs (DeAL). At its core, we view decoding as a heuristic-guided search process and facilitate the use of a wide variety of alignment objectives. Our experiments with programmatic constraints such as keyword and length constraints (studied widely in the pre-LLM era) and abstract objectives such as harmlessness and helpfulness (proposed in the post-LLM era) show that we can DeAL with fine-grained trade-offs, improve adherence to alignment objectives, and address residual gaps in LLMs. Lastly, while DeAL can be effectively paired with RLHF and prompting techniques, its generality makes decoding slower, an optimization we leave for future work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-12", "arxiv_id": "2402.06155", "title": "Model Editing with Canonical Examples", "authors": [ "John Hewitt", "Sarah Chen", "Lanruo Lora Xie", "Edward Adams", "Percy Liang", "Christopher D. Manning" ], "github": "https://github.com/john-hewitt/model-editing-canonical-examples", "project_page": "", "abstract": "We introduce model editing with canonical examples, a setting in which (1) a single learning example is provided per desired behavior, (2) evaluation is performed exclusively out-of-distribution, and (3) deviation from an initial model is strictly limited. A canonical example is a simple instance of good behavior, e.g., The capital of Mauritius is Port Louis) or bad behavior, e.g., An aspect of researchers is coldhearted). The evaluation set contains more complex examples of each behavior (like a paragraph in which the capital of Mauritius is called for.) We create three datasets and modify three more for model editing with canonical examples, covering knowledge-intensive improvements, social bias mitigation, and syntactic edge cases. In our experiments on Pythia language models, we find that LoRA outperforms full finetuning and MEMIT. We then turn to the Backpack language model architecture because it is intended to enable targeted improvement. The Backpack defines a large bank of sense vectors--a decomposition of the different uses of each word--which are weighted and summed to form the output logits of the model. We propose sense finetuning, which selects and finetunes a few (approx 10) sense vectors for each canonical example, and find that it outperforms other finetuning methods, e.g., 4.8% improvement vs 0.3%. Finally, we improve GPT-J-6B by an inference-time ensemble with just the changes from sense finetuning of a 35x smaller Backpack, in one setting outperforming editing GPT-J itself (4.1% vs 1.0%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/john-hewitt/model-editing-canonical-examples", "github_api_added_by": "auto" }, { "date": "2024-02-12", "arxiv_id": "2402.06088", "title": "Animated Stickers: Bringing Stickers to Life with Video Diffusion", "authors": [ "David Yan", "Winnie Zhang", "Luxin Zhang", "Anmol Kalia", "Dingkang Wang", "Ankit Ramchandani", "Miao Liu", "Albert Pumarola", "Edgar Schoenfeld", "Elliot Blanchard", "Krishna Narni", "Yaqiao Luo", "Lawrence Chen", "Guan Pang", "Ali Thabet", "Peter Vajda", "Amy Bearman", "Licheng Yu" ], "github": "", "project_page": "", "abstract": "We introduce animated stickers, a video diffusion model which generates an animation conditioned on a text prompt and static sticker image. Our model is built on top of the state-of-the-art Emu text-to-image model, with the addition of temporal layers to model motion. Due to the domain gap, i.e. differences in visual and motion style, a model which performed well on generating natural videos can no longer generate vivid videos when applied to stickers. To bridge this gap, we employ a two-stage finetuning pipeline: first with weakly in-domain data, followed by human-in-the-loop (HITL) strategy which we term ensemble-of-teachers. It distills the best qualities of multiple teachers into a smaller student model. We show that this strategy allows us to specifically target improvements to motion quality while maintaining the style from the static image. With inference optimizations, our model is able to generate an eight-frame video with high-quality, interesting, and relevant motion in under one second.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-12", "arxiv_id": "2402.06102", "title": "Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning", "authors": [ "Mohak Bhardwaj", "Thomas Lampe", "Michael Neunert", "Francesco Romano", "Abbas Abdolmaleki", "Arunkumar Byravan", "Markus Wulfmeier", "Martin Riedmiller", "Jonas Buchli" ], "github": "", "project_page": "", "abstract": "Recent advances in real-world applications of reinforcement learning (RL) have relied on the ability to accurately simulate systems at scale. However, domains such as fluid dynamical systems exhibit complex dynamic phenomena that are hard to simulate at high integration rates, limiting the direct application of modern deep RL algorithms to often expensive or safety critical hardware. In this work, we introduce \"Box o Flows\", a novel benchtop experimental control system for systematically evaluating RL algorithms in dynamic real-world scenarios. We describe the key components of the Box o Flows, and through a series of experiments demonstrate how state-of-the-art model-free RL algorithms can synthesize a variety of complex behaviors via simple reward specifications. Furthermore, we explore the role of offline RL in data-efficient hypothesis testing by reusing past experiences. We believe that the insights gained from this preliminary study and the availability of systems like the Box o Flows support the way forward for developing systematic RL algorithms that can be generally applied to complex, dynamical systems. Supplementary material and videos of experiments are available at https://sites.google.com/view/box-o-flows/home.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-12", "arxiv_id": "2402.06187", "title": "Premier-TACO: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss", "authors": [ "Ruijie Zheng", "Yongyuan Liang", "Xiyao Wang", "Shuang Ma", "Hal Daum\u00e9 III", "Huazhe Xu", "John Langford", "Praveen Palanisamy", "Kalyan Shankar Basu", "Furong Huang" ], "github": "https://github.com/PremierTACO/premier-taco", "project_page": "", "abstract": "We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks. Premier-TACO leverages a subset of multitask offline datasets for pretraining a general feature representation, which captures critical environmental dynamics and is fine-tuned using minimal expert demonstrations. It advances the temporal action contrastive learning (TACO) objective, known for state-of-the-art results in visual control tasks, by incorporating a novel negative example sampling strategy. This strategy is crucial in significantly boosting TACO's computational efficiency, making large-scale multitask offline pretraining feasible. Our extensive empirical evaluation in a diverse set of continuous control benchmarks including Deepmind Control Suite, MetaWorld, and LIBERO demonstrate Premier-TACO's effectiveness in pretraining visual representations, significantly enhancing few-shot imitation learning of novel tasks. Our code, pretraining data, as well as pretrained model checkpoints will be released at https://github.com/PremierTACO/premier-taco.", "github_manual": "", "github_regex": "https://github.com/PremierTACO/premier-taco", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.07456", "title": "OS-Copilot: Towards Generalist Computer Agents with Self-Improvement", "authors": [ "Zhiyong Wu", "Chengcheng Han", "Zichen Ding", "Zhenmin Weng", "Zhoumianze Liu", "Shunyu Yao", "Tao Yu", "Lingpeng Kong" ], "github": "https://github.com/OS-Copilot/FRIDAY", "project_page": "", "abstract": "Autonomous interaction with the computer has been a longstanding challenge with great potential, and the recent proliferation of large language models (LLMs) has markedly accelerated progress in building digital agents. However, most of these agents are designed to interact with a narrow domain, such as a specific software or website. This narrow focus constrains their applicability for general computer tasks. To this end, we introduce OS-Copilot, a framework to build generalist agents capable of interfacing with comprehensive elements in an operating system (OS), including the web, code terminals, files, multimedia, and various third-party applications. We use OS-Copilot to create FRIDAY, a self-improving embodied agent for automating general computer tasks. On GAIA, a general AI assistants benchmark, FRIDAY outperforms previous methods by 35%, showcasing strong generalization to unseen applications via accumulated skills from previous tasks. We also present numerical and quantitative evidence that FRIDAY learns to control and self-improve on Excel and Powerpoint with minimal supervision. Our OS-Copilot framework and empirical findings provide infrastructure and insights for future research toward more capable and general-purpose computer agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OS-Copilot/FRIDAY", "github_api_added_by": "auto" }, { "date": "2024-02-13", "arxiv_id": "2402.07043", "title": "A Tale of Tails: Model Collapse as a Change of Scaling Laws", "authors": [ "Elvis Dohmatob", "Yunzhen Feng", "Pu Yang", "Francois Charton", "Julia Kempe" ], "github": "", "project_page": "", "abstract": "As AI model size grows, neural scaling laws have become a crucial tool to predict the improvements of large models when increasing capacity and the size of original (human or natural) training data. Yet, the widespread use of popular models means that the ecosystem of online data and text will co-evolve to progressively contain increased amounts of synthesized data. In this paper we ask: How will the scaling laws change in the inevitable regime where synthetic data makes its way into the training corpus? Will future models, still improve, or be doomed to degenerate up to total (model) collapse? We develop a theoretical framework of model collapse through the lens of scaling laws. We discover a wide range of decay phenomena, analyzing loss of scaling, shifted scaling with number of generations, the ''un-learning\" of skills, and grokking when mixing human and synthesized data. Our theory is validated by large-scale experiments with a transformer on an arithmetic task and text generation using the large language model Llama2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.07033", "title": "Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models", "authors": [ "Keisuke Kamahori", "Yile Gu", "Kan Zhu", "Baris Kasikci" ], "github": "https://github.com/efeslab/fiddler", "project_page": "", "abstract": "Large Language Models (LLMs) based on Mixture-of-Experts (MoE) architecture are showing promising performance on various tasks. However, running them on resource-constrained settings, where GPU memory resources are not abundant, is challenging due to huge model sizes. Existing systems that offload model weights to CPU memory suffer from the significant overhead of frequently moving data between CPU and GPU. In this paper, we propose Fiddler, a resource-efficient inference engine with CPU-GPU orchestration for MoE models. The key idea of Fiddler is to use the computation ability of the CPU to minimize the data movement between the CPU and GPU. Our evaluation shows that Fiddler can run the uncompressed Mixtral-8x7B model, which exceeds 90GB in parameters, to generate over 3 tokens per second on a single GPU with 24GB memory, showing an order of magnitude improvement over existing methods. The code of Fiddler is publicly available at https://github.com/efeslab/fiddler", "github_manual": "", "github_regex": "https://github.com/efeslab/fiddler", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.06852", "title": "ChemLLM: A Chemical Large Language Model", "authors": [ "Di Zhang", "Wei Liu", "Qian Tan", "Jingdan Chen", "Hang Yan", "Yuliang Yan", "Jiatong Li", "Weiran Huang", "Xiangyu Yue", "Dongzhan Zhou", "Shufei Zhang", "Mao Su", "Hansen Zhong", "Yuqiang Li", "Wanli Ouyang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have made impressive progress in chemistry applications, including molecular property prediction, molecular generation, experimental protocol design, etc. However, the community lacks a dialogue-based model specifically designed for chemistry. The challenge arises from the fact that most chemical data and scientific knowledge are primarily stored in structured databases, and the direct use of these structured data compromises the model's ability to maintain coherent dialogue. To tackle this issue, we develop a novel template-based instruction construction method that transforms structured knowledge into plain dialogue, making it suitable for language model training. By leveraging this approach, we develop ChemLLM, the first large language model dedicated to chemistry, capable of performing various tasks across chemical disciplines with smooth dialogue interaction. ChemLLM beats GPT-3.5 on all three principal tasks in chemistry, i.e., name conversion, molecular caption, and reaction prediction, and surpasses GPT-4 on two of them. Remarkably, ChemLLM also shows exceptional adaptability to related mathematical and physical tasks despite being trained mainly on chemical-centric corpora. Furthermore, ChemLLM demonstrates proficiency in specialized NLP tasks within chemistry, such as literature translation and cheminformatic programming. ChemLLM opens up a new avenue for exploration within chemical studies, while our method of integrating structured chemical knowledge into dialogue systems sets a new frontier for developing LLMs across various scientific fields. Codes, Datasets, and Model weights are publicly accessible at hf.co/AI4Chem/ChemLLM-7B-Chat.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.07896", "title": "Suppressing Pink Elephants with Direct Principle Feedback", "authors": [ "Louis Castricato", "Nathan Lile", "Suraj Anand", "Hailey Schoelkopf", "Siddharth Verma", "Stella Biderman" ], "github": "", "project_page": "", "abstract": "Existing methods for controlling language models, such as RLHF and Constitutional AI, involve determining which LLM behaviors are desirable and training them into a language model. However, in many cases, it is desirable for LLMs to be controllable at inference time, so that they can be used in multiple contexts with diverse needs. We illustrate this with the Pink Elephant Problem: instructing an LLM to avoid discussing a certain entity (a ``Pink Elephant''), and instead discuss a preferred entity (``Grey Elephant''). We apply a novel simplification of Constitutional AI, Direct Principle Feedback, which skips the ranking of responses and uses DPO directly on critiques and revisions. Our results show that after DPF fine-tuning on our synthetic Pink Elephants dataset, our 13B fine-tuned LLaMA 2 model significantly outperforms Llama-2-13B-Chat and a prompted baseline, and performs as well as GPT-4 in on our curated test set assessing the Pink Elephant Problem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.07625", "title": "AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts", "authors": [ "Yifan Zhang", "Yifan Luo", "Yang Yuan", "Andrew Chi-Chih Yao" ], "github": "https://github.com/yifanzhang-pro/AutoMathText", "project_page": "", "abstract": "To improve language models' proficiency in mathematical reasoning via continual pretraining, we introduce a novel strategy that leverages base language models for autonomous data selection. Departing from conventional supervised fine-tuning or trained classifiers with human-annotated data, our approach utilizes meta-prompted language models as zero-shot verifiers to autonomously evaluate and select high-quality mathematical content, and we release the curated open-source AutoMathText dataset encompassing over 200GB of data. To demonstrate the efficacy of our method, we continuously pretrained a 7B-parameter Mistral language model on the AutoMathText dataset, achieving substantial improvements in downstream performance on the MATH dataset with a token amount reduced by orders of magnitude compared to previous continuous pretraining works. Our method showcases a 2 times increase in pretraining token efficiency compared to baselines, underscoring the potential of our approach in enhancing models' mathematical reasoning capabilities. The AutoMathText dataset is available at https://huggingface.co/datasets/math-ai/AutoMathText. The code is available at https://github.com/yifanzhang-pro/AutoMathText.", "github_manual": "", "github_regex": "https://github.com/yifanzhang-pro/AutoMathText", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.07610", "title": "Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping", "authors": [ "Haoyu Wang", "Guozheng Ma", "Ziqiao Meng", "Zeyu Qin", "Li Shen", "Zhong Zhang", "Bingzhe Wu", "Liu Liu", "Yatao Bian", "Tingyang Xu", "Xueqian Wang", "Peilin Zhao" ], "github": "", "project_page": "", "abstract": "Self-alignment is an effective way to reduce the cost of human annotation while ensuring promising model capability. However, most current methods complete the data collection and training steps in a single round, which may overlook the continuously improving ability of self-aligned models. This gives rise to a key query: What if we do multi-time bootstrapping self-alignment? Does this strategy enhance model performance or lead to rapid degradation? In this paper, our pioneering exploration delves into the impact of bootstrapping self-alignment on large language models. Our findings reveal that bootstrapping self-alignment markedly surpasses the single-round approach, by guaranteeing data diversity from in-context learning. To further exploit the capabilities of bootstrapping, we investigate and adjust the training order of data, which yields improved performance of the model. Drawing on these findings, we propose Step-On-Feet Tuning (SOFT) which leverages model's continuously enhanced few-shot ability to boost zero or one-shot performance. Based on easy-to-hard training recipe, we propose SOFT+ which further boost self-alignment's performance. Our experiments demonstrate the efficiency of SOFT (SOFT+) across various classification and generation tasks, highlighting the potential of bootstrapping self-alignment on continually enhancing model alignment performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.07207", "title": "GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting", "authors": [ "Xiaoyu Zhou", "Xingjian Ran", "Yajiao Xiong", "Jinlin He", "Zhiwei Lin", "Yongtao Wang", "Deqing Sun", "Ming-Hsuan Yang" ], "github": "", "project_page": "", "abstract": "We present GALA3D, generative 3D GAussians with LAyout-guided control, for effective compositional text-to-3D generation. We first utilize large language models (LLMs) to generate the initial layout and introduce a layout-guided 3D Gaussian representation for 3D content generation with adaptive geometric constraints. We then propose an object-scene compositional optimization mechanism with conditioned diffusion to collaboratively generate realistic 3D scenes with consistent geometry, texture, scale, and accurate interactions among multiple objects while simultaneously adjusting the coarse layout priors extracted from the LLMs to align with the generated scene. Experiments show that GALA3D is a user-friendly, end-to-end framework for state-of-the-art scene-level 3D content generation and controllable editing while ensuring the high fidelity of object-level entities within the scene. Source codes and models will be available at https://gala3d.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.06859", "title": "LiRank: Industrial Large Scale Ranking Models at LinkedIn", "authors": [ "Fedor Borisyuk", "Mingzhou Zhou", "Qingquan Song", "Siyu Zhu", "Birjodh Tiwana", "Ganesh Parameswaran", "Siddharth Dangi", "Lars Hertel", "Qiang Xiao", "Xiaochen Hou", "Yunbo Ouyang", "Aman Gupta", "Sheallika Singh", "Dan Liu", "Hailing Cheng", "Lei Le", "Jonathan Hung", "Sathiya Keerthi", "Ruoyan Wang", "Fengyu Zhang", "Mohit Kothari", "Chen Zhu", "Daqi Sun", "Yun Dai", "Xun Luan", "Sirou Zhu", "Zhiwei Wang", "Neil Daftary", "Qianqi Shen", "Chengming Jiang", "Haichao Wei", "Maneesh Varshney", "Amol Ghoting", "Souvik Ghosh" ], "github": "https://github.com/tensorflow/io/blob/075be7222dfd234c902aeb31e2e0a44a8db49c00/AVRO_TENSOR_DATASET.md", "project_page": "", "abstract": "We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods. We unveil several modeling improvements, including Residual DCN, which adds attention and residual connections to the famous DCNv2 architecture. We share insights into combining and tuning SOTA architectures to create a unified model, including Dense Gating, Transformers and Residual DCN. We also propose novel techniques for calibration and describe how we productionalized deep learning based explore/exploit methods. To enable effective, production-grade serving of large ranking models, we detail how to train and compress models using quantization and vocabulary compression. We provide details about the deployment setup for large-scale use cases of Feed ranking, Jobs Recommendations, and Ads click-through rate (CTR) prediction. We summarize our learnings from various A/B tests by elucidating the most effective technical approaches. These ideas have contributed to relative metrics improvements across the board at LinkedIn: +0.5% member sessions in the Feed, +1.76% qualified job applications for Jobs search and recommendations, and +4.3% for Ads CTR. We hope this work can provide practical insights and solutions for practitioners interested in leveraging large-scale deep ranking systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tensorflow/io/blob/075be7222dfd234c902aeb31e2e0a44a8db49c00/AVRO_TENSOR_DATASET.md", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.07876", "title": "Policy Improvement using Language Feedback Models", "authors": [ "Victor Zhong", "Dipendra Misra", "Xingdi Yuan", "Marc-Alexandre C\u00f4t\u00e9" ], "github": "https://github.com/vzhong/language_feedback_models", "project_page": "", "abstract": "We introduce Language Feedback Models (LFMs) that identify desirable behaviour - actions that help achieve tasks specified in the instruction - for imitation learning in instruction following. To train LFMs, we obtain feedback from Large Language Models (LLMs) on visual trajectories verbalized to language descriptions. First, by using LFMs to identify desirable behaviour to imitate, we improve in task-completion rate over strong behavioural cloning baselines on three distinct language grounding environments (Touchdown, ScienceWorld, and ALFWorld). Second, LFMs outperform using LLMs as experts to directly predict actions, when controlling for the number of LLM output tokens. Third, LFMs generalize to unseen environments, improving task-completion rate by 3.5-12.0% through one round of adaptation. Finally, LFM can be modified to provide human-interpretable feedback without performance loss, allowing human verification of desirable behaviour for imitation learning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vzhong/language_feedback_models", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.07872", "title": "PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs", "authors": [ "Soroush Nasiriany", "Fei Xia", "Wenhao Yu", "Ted Xiao", "Jacky Liang", "Ishita Dasgupta", "Annie Xie", "Danny Driess", "Ayzaan Wahid", "Zhuo Xu", "Quan Vuong", "Tingnan Zhang", "Tsang-Wei Edward Lee", "Kuang-Huei Lee", "Peng Xu", "Sean Kirmani", "Yuke Zhu", "Andy Zeng", "Karol Hausman", "Nicolas Heess", "Chelsea Finn", "Sergey Levine", "Brian Ichter" ], "github": "https://github.com/itseez/opencv", "project_page": "", "abstract": "Vision language models (VLMs) have shown impressive capabilities across a variety of tasks, from logical reasoning to visual understanding. This opens the door to richer interaction with the world, for example robotic control. However, VLMs produce only textual outputs, while robotic control and other spatial tasks require outputting continuous coordinates, actions, or trajectories. How can we enable VLMs to handle such settings without fine-tuning on task-specific data? In this paper, we propose a novel visual prompting approach for VLMs that we call Prompting with Iterative Visual Optimization (PIVOT), which casts tasks as iterative visual question answering. In each iteration, the image is annotated with a visual representation of proposals that the VLM can refer to (e.g., candidate robot actions, localizations, or trajectories). The VLM then selects the best ones for the task. These proposals are iteratively refined, allowing the VLM to eventually zero in on the best available answer. We investigate PIVOT on real-world robotic navigation, real-world manipulation from images, instruction following in simulation, and additional spatial inference tasks such as localization. We find, perhaps surprisingly, that our approach enables zero-shot control of robotic systems without any robot training data, navigation in a variety of environments, and other capabilities. Although current performance is far from perfect, our work highlights potentials and limitations of this new regime and shows a promising approach for Internet-Scale VLMs in robotic and spatial reasoning domains. Website: pivot-prompt.github.io and HuggingFace: https://huggingface.co/spaces/pivot-prompt/pivot-prompt-demo.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/itseez/opencv", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.07871", "title": "Scaling Laws for Fine-Grained Mixture of Experts", "authors": [ "Jakub Krajewski", "Jan Ludziejewski", "Kamil Adamczewski", "Maciej Pi\u00f3ro", "Micha\u0142 Krutul", "Szymon Antoniak", "Kamil Ciebiera", "Krystian Kr\u00f3l", "Tomasz Odrzyg\u00f3\u017ad\u017a", "Piotr Sankowski", "Marek Cygan", "Sebastian Jaszczur" ], "github": "https://github.com/llm-random/llm-random", "project_page": "", "abstract": "Mixture of Experts (MoE) models have emerged as a primary solution for reducing the computational cost of Large Language Models. In this work, we analyze their scaling properties, incorporating an expanded range of variables. Specifically, we introduce a new hyperparameter, granularity, whose adjustment enables precise control over the size of the experts. Building on this, we establish scaling laws for fine-grained MoE, taking into account the number of training tokens, model size, and granularity. Leveraging these laws, we derive the optimal training configuration for a given computational budget. Our findings not only show that MoE models consistently outperform dense Transformers but also highlight that the efficiency gap between dense and MoE models widens as we scale up the model size and training budget. Furthermore, we demonstrate that the common practice of setting the size of experts in MoE to mirror the feed-forward layer is not optimal at almost any computational budget.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/llm-random/llm-random", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.07865", "title": "Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models", "authors": [ "Siddharth Karamcheti", "Suraj Nair", "Ashwin Balakrishna", "Percy Liang", "Thomas Kollar", "Dorsa Sadigh" ], "github": "https://github.com/tri-ml/prismatic-vlms", "project_page": "", "abstract": "Visually-conditioned language models (VLMs) have seen growing adoption in applications such as visual dialogue, scene understanding, and robotic task planning; adoption that has fueled a wealth of new models such as LLaVa, InstructBLIP, and PaLI-3. Despite the volume of new releases, key design decisions around image preprocessing, architecture, and optimization are under-explored, making it challenging to understand what factors account for model performance - a challenge further complicated by the lack of objective, consistent evaluations. To address these gaps, we first compile a suite of standardized evaluations spanning visual question answering, object localization from language, and targeted challenge sets that probe properties such as hallucination; evaluations that provide calibrated, fine-grained insight into a VLM's capabilities. Second, we rigorously investigate VLMs along key design axes, including pretrained visual representations and quantifying the tradeoffs of using base vs. instruct-tuned language models, amongst others. We couple our analysis with three resource contributions: (1) a unified framework for evaluating VLMs, (2) optimized, flexible code for VLM training, and (3) checkpoints for all models, including a family of VLMs at the 7-13B scale that strictly outperform InstructBLIP and LLaVa v1.5, the state-of-the-art in open-source VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tri-ml/prismatic-vlms", "github_api_added_by": "auto" }, { "date": "2024-02-13", "arxiv_id": "2402.07827", "title": "Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model", "authors": [ "Ahmet \u00dcst\u00fcn", "Viraat Aryabumi", "Zheng-Xin Yong", "Wei-Yin Ko", "Daniel D'souza", "Gbemileke Onilude", "Neel Bhandari", "Shivalika Singh", "Hui-Lee Ooi", "Amr Kayid", "Freddie Vargus", "Phil Blunsom", "Shayne Longpre", "Niklas Muennighoff", "Marzieh Fadaee", "Julia Kreutzer", "Sara Hooker" ], "github": "", "project_page": "", "abstract": "Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages. What does it take to broaden access to breakthroughs beyond first-class citizen languages? Our work introduces Aya, a massively multilingual generative language model that follows instructions in 101 languages of which over 50% are considered as lower-resourced. Aya outperforms mT0 and BLOOMZ on the majority of tasks while covering double the number of languages. We introduce extensive new evaluation suites that broaden the state-of-art for multilingual eval across 99 languages -- including discriminative and generative tasks, human evaluation, and simulated win rates that cover both held-out tasks and in-distribution performance. Furthermore, we conduct detailed investigations on the optimal finetuning mixture composition, data pruning, as well as the toxicity, bias, and safety of our models. We open-source our instruction datasets and our model at https://hf.co/CohereForAI/aya-101", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.07383", "title": "Making Flow-Matching-Based Zero-Shot Text-to-Speech Laugh as You Like", "authors": [ "Naoyuki Kanda", "Xiaofei Wang", "Sefik Emre Eskimez", "Manthan Thakker", "Hemin Yang", "Zirun Zhu", "Min Tang", "Canrun Li", "Steven Tsai", "Zhen Xiao", "Yufei Xia", "Jinzhu Li", "Yanqing Liu", "Sheng Zhao", "Michael Zeng" ], "github": "", "project_page": "", "abstract": "Laughter is one of the most expressive and natural aspects of human speech, conveying emotions, social cues, and humor. However, most text-to-speech (TTS) systems lack the ability to produce realistic and appropriate laughter sounds, limiting their applications and user experience. While there have been prior works to generate natural laughter, they fell short in terms of controlling the timing and variety of the laughter to be generated. In this work, we propose ELaTE, a zero-shot TTS that can generate natural laughing speech of any speaker based on a short audio prompt with precise control of laughter timing and expression. Specifically, ELaTE works on the audio prompt to mimic the voice characteristic, the text prompt to indicate the contents of the generated speech, and the input to control the laughter expression, which can be either the start and end times of laughter, or the additional audio prompt that contains laughter to be mimicked. We develop our model based on the foundation of conditional flow-matching-based zero-shot TTS, and fine-tune it with frame-level representation from a laughter detector as additional conditioning. With a simple scheme to mix small-scale laughter-conditioned data with large-scale pre-training data, we demonstrate that a pre-trained zero-shot TTS model can be readily fine-tuned to generate natural laughter with precise controllability, without losing any quality of the pre-trained zero-shot TTS model. Through the evaluations, we show that ELaTE can generate laughing speech with significantly higher quality and controllability compared to conventional models. See https://aka.ms/elate/ for demo samples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-13", "arxiv_id": "2402.07319", "title": "ODIN: Disentangled Reward Mitigates Hacking in RLHF", "authors": [ "Lichang Chen", "Chen Zhu", "Davit Soselia", "Jiuhai Chen", "Tianyi Zhou", "Tom Goldstein", "Heng Huang", "Mohammad Shoeybi", "Bryan Catanzaro" ], "github": "https://github.com/lichang-chen/odin", "project_page": "", "abstract": "In this work, we study the issue of reward hacking on the response length, a challenge emerging in Reinforcement Learning from Human Feedback (RLHF) on LLMs. A well-formatted, verbose but less helpful response from the LLMs can often deceive LLMs or even human evaluators to achieve high scores. The same issue also holds for some reward models in RL. To address the challenges in both training and evaluation, we establish a more reliable evaluation protocol for comparing different training configurations, which inspects the trade-off between LLM evaluation score and response length obtained by varying training hyperparameters. Based on this evaluation, we conduct large-scale studies, where the results shed insights into the efficacy of hyperparameters and tricks used in RL on mitigating length bias. We further propose to improve the reward model by jointly training two linear heads on shared feature representations to predict the rewards, one trained to correlate with length, and the other trained to decorrelate with length and therefore focus more on the actual content. We then discard the length head in RL to prevent reward hacking on length. Experiments demonstrate that our approach almost eliminates the reward correlation with length, and improves the obtained policy by a significant margin.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lichang-chen/odin", "github_api_added_by": "auto" }, { "date": "2024-02-14", "arxiv_id": "2402.08609", "title": "Mixtures of Experts Unlock Parameter Scaling for Deep RL", "authors": [ "Johan Obando-Ceron", "Ghada Sokar", "Timon Willi", "Clare Lyle", "Jesse Farebrother", "Jakob Foerster", "Gintare Karolina Dziugaite", "Doina Precup", "Pablo Samuel Castro" ], "github": "", "project_page": "", "abstract": "The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size. Analogous scaling laws remain elusive for reinforcement learning domains, however, where increasing the parameter count of a model often hurts its final performance. In this paper, we demonstrate that incorporating Mixture-of-Expert (MoE) modules, and in particular Soft MoEs (Puigcerver et al., 2023), into value-based networks results in more parameter-scalable models, evidenced by substantial performance increases across a variety of training regimes and model sizes. This work thus provides strong empirical evidence towards developing scaling laws for reinforcement learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-14", "arxiv_id": "2402.08644", "title": "Tandem Transformers for Inference Efficient LLMs", "authors": [ "Aishwarya P S", "Pranav Ajit Nair", "Yashas Samaga", "Toby Boyd", "Sanjiv Kumar", "Prateek Jain", "Praneeth Netrapalli" ], "github": "", "project_page": "", "abstract": "The autoregressive nature of conventional large language models (LLMs) inherently limits inference speed, as tokens are generated sequentially. While speculative and parallel decoding techniques attempt to mitigate this, they face limitations: either relying on less accurate smaller models for generation or failing to fully leverage the base LLM's representations. We introduce a novel architecture, Tandem transformers, to address these issues. This architecture uniquely combines (1) a small autoregressive model and (2) a large model operating in block mode (processing multiple tokens simultaneously). The small model's predictive accuracy is substantially enhanced by granting it attention to the large model's richer representations. On the PaLM2 pretraining dataset, a tandem of PaLM2-Bison and PaLM2-Gecko demonstrates a 3.3% improvement in next-token prediction accuracy over a standalone PaLM2-Gecko, offering a 1.16x speedup compared to a PaLM2-Otter model with comparable downstream performance. We further incorporate the tandem model within the speculative decoding (SPEED) framework where the large model validates tokens from the small model. This ensures that the Tandem of PaLM2-Bison and PaLM2-Gecko achieves substantial speedup (around 1.14x faster than using vanilla PaLM2-Gecko in SPEED) while maintaining identical downstream task accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-14", "arxiv_id": "2402.08093", "title": "BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data", "authors": [ "Mateusz \u0141ajszczak", "Guillermo C\u00e1mbara", "Yang Li", "Fatih Beyhan", "Arent van Korlaar", "Fan Yang", "Arnaud Joly", "\u00c1lvaro Mart\u00edn-Cortinas", "Ammar Abbas", "Adam Michalski", "Alexis Moinet", "Sri Karlapati", "Ewa Muszy\u0144ska", "Haohan Guo", "Bartosz Putrycz", "Soledad L\u00f3pez Gambino", "Kayeon Yoo", "Elena Sokolova", "Thomas Drugman" ], "github": "", "project_page": "", "abstract": "We introduce a text-to-speech (TTS) model called BASE TTS, which stands for Big Adaptive Streamable TTS with Emergent abilities. BASE TTS is the largest TTS model to-date, trained on 100K hours of public domain speech data, achieving a new state-of-the-art in speech naturalness. It deploys a 1-billion-parameter autoregressive Transformer that converts raw texts into discrete codes (\"speechcodes\") followed by a convolution-based decoder which converts these speechcodes into waveforms in an incremental, streamable manner. Further, our speechcodes are built using a novel speech tokenization technique that features speaker ID disentanglement and compression with byte-pair encoding. Echoing the widely-reported \"emergent abilities\" of large language models when trained on increasing volume of data, we show that BASE TTS variants built with 10K+ hours and 500M+ parameters begin to demonstrate natural prosody on textually complex sentences. We design and share a specialized dataset to measure these emergent abilities for text-to-speech. We showcase state-of-the-art naturalness of BASE TTS by evaluating against baselines that include publicly available large-scale text-to-speech systems: YourTTS, Bark and TortoiseTTS. Audio samples generated by the model can be heard at https://amazon-ltts-paper.com/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-14", "arxiv_id": "2402.08622", "title": "NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs", "authors": [ "Michael Fischer", "Zhengqin Li", "Thu Nguyen-Phuoc", "Aljaz Bozic", "Zhao Dong", "Carl Marshall", "Tobias Ritschel" ], "github": "", "project_page": "", "abstract": "A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and appearance of a scene. We here ask the question whether we can transfer the appearance from a source NeRF onto a target 3D geometry in a semantically meaningful way, such that the resulting new NeRF retains the target geometry but has an appearance that is an analogy to the source NeRF. To this end, we generalize classic image analogies from 2D images to NeRFs. We leverage correspondence transfer along semantic affinity that is driven by semantic features from large, pre-trained 2D image models to achieve multi-view consistent appearance transfer. Our method allows exploring the mix-and-match product space of 3D geometry and appearance. We show that our method outperforms traditional stylization-based methods and that a large majority of users prefer our method over several typical baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-14", "arxiv_id": "2402.08678", "title": "Graph Mamba: Towards Learning on Graphs with State Space Models", "authors": [ "Ali Behrouz", "Farnoosh Hashemi" ], "github": "", "project_page": "", "abstract": "Graph Neural Networks (GNNs) have shown promising potential in graph representation learning. The majority of GNNs define a local message-passing mechanism, propagating information over the graph by stacking multiple layers. These methods, however, are known to suffer from two major limitations: over-squashing and poor capturing of long-range dependencies. Recently, Graph Transformers (GTs) emerged as a powerful alternative to Message-Passing Neural Networks (MPNNs). GTs, however, have quadratic computational cost, lack inductive biases on graph structures, and rely on complex Positional/Structural Encodings (SE/PE). In this paper, we show that while Transformers, complex message-passing, and SE/PE are sufficient for good performance in practice, neither is necessary. Motivated by the recent success of State Space Models (SSMs), such as Mamba, we present Graph Mamba Networks (GMNs), a general framework for a new class of GNNs based on selective SSMs. We discuss and categorize the new challenges when adopting SSMs to graph-structured data, and present four required and one optional steps to design GMNs, where we choose (1) Neighborhood Tokenization, (2) Token Ordering, (3) Architecture of Bidirectional Selective SSM Encoder, (4) Local Encoding, and dispensable (5) PE and SE. We further provide theoretical justification for the power of GMNs. Experiments demonstrate that despite much less computational cost, GMNs attain an outstanding performance in long-range, small-scale, large-scale, and heterophilic benchmark datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-14", "arxiv_id": "2402.08682", "title": "IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation", "authors": [ "Luke Melas-Kyriazi", "Iro Laina", "Christian Rupprecht", "Natalia Neverova", "Andrea Vedaldi", "Oran Gafni", "Filippos Kokkinos" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "Most text-to-3D generators build upon off-the-shelf text-to-image models trained on billions of images. They use variants of Score Distillation Sampling (SDS), which is slow, somewhat unstable, and prone to artifacts. A mitigation is to fine-tune the 2D generator to be multi-view aware, which can help distillation or can be combined with reconstruction networks to output 3D objects directly. In this paper, we further explore the design space of text-to-3D models. We significantly improve multi-view generation by considering video instead of image generators. Combined with a 3D reconstruction algorithm which, by using Gaussian splatting, can optimize a robust image-based loss, we directly produce high-quality 3D outputs from the generated views. Our new method, IM-3D, reduces the number of evaluations of the 2D generator network 10-100x, resulting in a much more efficient pipeline, better quality, fewer geometric inconsistencies, and higher yield of usable 3D assets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-14", "arxiv_id": "2402.08268", "title": "World Model on Million-Length Video And Language With RingAttention", "authors": [ "Hao Liu", "Wilson Yan", "Matei Zaharia", "Pieter Abbeel" ], "github": "https://github.com/LargeWorldModel/LWM", "project_page": "", "abstract": "Current language models fall short in understanding aspects of the world not easily described in words, and struggle with complex, long-form tasks. Video sequences offer valuable temporal information absent in language and static images, making them attractive for joint modeling with language. Such models could develop a understanding of both human textual knowledge and the physical world, enabling broader AI capabilities for assisting humans. However, learning from millions of tokens of video and language sequences poses challenges due to memory constraints, computational complexity, and limited datasets. To address these challenges, we curate a large dataset of diverse videos and books, utilize the RingAttention technique to scalably train on long sequences, and gradually increase context size from 4K to 1M tokens. This paper makes the following contributions: (a) Largest context size neural network: We train one of the largest context size transformers on long video and language sequences, setting new benchmarks in difficult retrieval tasks and long video understanding. (b) Solutions for overcoming vision-language training challenges, including using masked sequence packing for mixing different sequence lengths, loss weighting to balance language and vision, and model-generated QA dataset for long sequence chat. (c) A highly-optimized implementation with RingAttention, masked sequence packing, and other key features for training on millions-length multimodal sequences. (d) Fully open-sourced a family of 7B parameter models capable of processing long text documents (LWM-Text, LWM-Text-Chat) and videos (LWM, LWM-Chat) of over 1M tokens. This work paves the way for training on massive datasets of long video and language to develop understanding of both human knowledge and the multimodal world, and broader capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LargeWorldModel/LWM", "github_api_added_by": "auto" }, { "date": "2024-02-14", "arxiv_id": "2402.08017", "title": "Lumos : Empowering Multimodal LLMs with Scene Text Recognition", "authors": [ "Ashish Shenoy", "Yichao Lu", "Srihari Jayakumar", "Debojeet Chatterjee", "Mohsen Moslehpour", "Pierce Chuang", "Abhay Harpale", "Vikas Bhardwaj", "Di Xu", "Shicong Zhao", "Longfang Zhao", "Ankit Ramchandani", "Xin Luna Dong", "Anuj Kumar" ], "github": "", "project_page": "", "abstract": "We introduce Lumos, the first end-to-end multimodal question-answering system with text understanding capabilities. At the core of Lumos is a Scene Text Recognition (STR) component that extracts text from first person point-of-view images, the output of which is used to augment input to a Multimodal Large Language Model (MM-LLM). While building Lumos, we encountered numerous challenges related to STR quality, overall latency, and model inference. In this paper, we delve into those challenges, and discuss the system architecture, design choices, and modeling techniques employed to overcome these obstacles. We also provide a comprehensive evaluation for each component, showcasing high quality and efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-14", "arxiv_id": "2402.08654", "title": "Learning Continuous 3D Words for Text-to-Image Generation", "authors": [ "Ta-Ying Cheng", "Matheus Gadelha", "Thibault Groueix", "Matthew Fisher", "Radomir Mech", "Andrew Markham", "Niki Trigoni" ], "github": "https://github.com/ttchengab/continuous_3d_words_code", "project_page": "", "abstract": "Current controls over diffusion models (e.g., through text or ControlNet) for image generation fall short in recognizing abstract, continuous attributes like illumination direction or non-rigid shape change. In this paper, we present an approach for allowing users of text-to-image models to have fine-grained control of several attributes in an image. We do this by engineering special sets of input tokens that can be transformed in a continuous manner -- we call them Continuous 3D Words. These attributes can, for example, be represented as sliders and applied jointly with text prompts for fine-grained control over image generation. Given only a single mesh and a rendering engine, we show that our approach can be adopted to provide continuous user control over several 3D-aware attributes, including time-of-day illumination, bird wing orientation, dollyzoom effect, and object poses. Our method is capable of conditioning image creation with multiple Continuous 3D Words and text descriptions simultaneously while adding no overhead to the generative process. Project Page: https://ttchengab.github.io/continuous_3d_words", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ttchengab/continuous_3d_words_code", "github_api_added_by": "auto" }, { "date": "2024-02-14", "arxiv_id": "2402.07939", "title": "UFO: A UI-Focused Agent for Windows OS Interaction", "authors": [ "Chaoyun Zhang", "Liqun Li", "Shilin He", "Xu Zhang", "Bo Qiao", "Si Qin", "Minghua Ma", "Yu Kang", "Qingwei Lin", "Saravan Rajmohan", "Dongmei Zhang", "Qi Zhang" ], "github": "https://github.com/microsoft/UFO", "project_page": "", "abstract": "We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision. UFO employs a dual-agent framework to meticulously observe and analyze the graphical user interface (GUI) and control information of Windows applications. This enables the agent to seamlessly navigate and operate within individual applications and across them to fulfill user requests, even when spanning multiple applications. The framework incorporates a control interaction module, facilitating action grounding without human intervention and enabling fully automated execution. Consequently, UFO transforms arduous and time-consuming processes into simple tasks achievable solely through natural language commands. We conducted testing of UFO across 9 popular Windows applications, encompassing a variety of scenarios reflective of users' daily usage. The results, derived from both quantitative metrics and real-case studies, underscore the superior effectiveness of UFO in fulfilling user requests. To the best of our knowledge, UFO stands as the first UI agent specifically tailored for task completion within the Windows OS environment. The open-source code for UFO is available on https://github.com/microsoft/UFO.", "github_manual": "", "github_regex": "https://github.com/microsoft/UFO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-14", "arxiv_id": "2402.08303", "title": "ChatCell: Facilitating Single-Cell Analysis with Natural Language", "authors": [ "Yin Fang", "Kangwei Liu", "Ningyu Zhang", "Xinle Deng", "Penghui Yang", "Zhuo Chen", "Xiangru Tang", "Mark Gerstein", "Xiaohui Fan", "Huajun Chen" ], "github": "https://github.com/zjunlp/chatcell", "project_page": "", "abstract": "As Large Language Models (LLMs) rapidly evolve, their influence in science is becoming increasingly prominent. The emerging capabilities of LLMs in task generalization and free-form dialogue can significantly advance fields like chemistry and biology. However, the field of single-cell biology, which forms the foundational building blocks of living organisms, still faces several challenges. High knowledge barriers and limited scalability in current methods restrict the full exploitation of LLMs in mastering single-cell data, impeding direct accessibility and rapid iteration. To this end, we introduce ChatCell, which signifies a paradigm shift by facilitating single-cell analysis with natural language. Leveraging vocabulary adaptation and unified sequence generation, ChatCell has acquired profound expertise in single-cell biology and the capability to accommodate a diverse range of analysis tasks. Extensive experiments further demonstrate ChatCell's robust performance and potential to deepen single-cell insights, paving the way for more accessible and intuitive exploration in this pivotal field. Our project homepage is available at https://zjunlp.github.io/project/ChatCell.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zjunlp/chatcell", "github_api_added_by": "auto" }, { "date": "2024-02-14", "arxiv_id": "2402.08420", "title": "Vision-Based Hand Gesture Customization from a Single Demonstration", "authors": [ "Soroush Shahi", "Cori Tymoszek Park", "Richard Kang", "Asaf Liberman", "Oron Levy", "Jun Gong", "Abdelkareem Bedri", "Gierad Laput" ], "github": "", "project_page": "", "abstract": "Hand gesture recognition is becoming a more prevalent mode of human-computer interaction, especially as cameras proliferate across everyday devices. Despite continued progress in this field, gesture customization is often underexplored. Customization is crucial since it enables users to define and demonstrate gestures that are more natural, memorable, and accessible. However, customization requires efficient usage of user-provided data. We introduce a method that enables users to easily design bespoke gestures with a monocular camera from one demonstration. We employ transformers and meta-learning techniques to address few-shot learning challenges. Unlike prior work, our method supports any combination of one-handed, two-handed, static, and dynamic gestures, including different viewpoints. We evaluated our customization method through a user study with 20 gestures collected from 21 participants, achieving up to 97% average recognition accuracy from one demonstration. Our work provides a viable path for vision-based gesture customization, laying the foundation for future advancements in this domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-15", "arxiv_id": "2402.09368", "title": "Magic-Me: Identity-Specific Video Customized Diffusion", "authors": [ "Ze Ma", "Daquan Zhou", "Chun-Hsiao Yeh", "Xue-She Wang", "Xiuyu Li", "Huanrui Yang", "Zhen Dong", "Kurt Keutzer", "Jiashi Feng" ], "github": "https://github.com/Zhen-Dong/Magic-Me", "project_page": "", "abstract": "Creating content for a specific identity (ID) has shown significant interest in the field of generative models. In the field of text-to-image generation (T2I), subject-driven content generation has achieved great progress with the ID in the images controllable. However, extending it to video generation is not well explored. In this work, we propose a simple yet effective subject identity controllable video generation framework, termed Video Custom Diffusion (VCD). With a specified subject ID defined by a few images, VCD reinforces the identity information extraction and injects frame-wise correlation at the initialization stage for stable video outputs with identity preserved to a large extent. To achieve this, we propose three novel components that are essential for high-quality ID preservation: 1) an ID module trained with the cropped identity by prompt-to-segmentation to disentangle the ID information and the background noise for more accurate ID token learning; 2) a text-to-video (T2V) VCD module with 3D Gaussian Noise Prior for better inter-frame consistency and 3) video-to-video (V2V) Face VCD and Tiled VCD modules to deblur the face and upscale the video for higher resolution. Despite its simplicity, we conducted extensive experiments to verify that VCD is able to generate stable and high-quality videos with better ID over the selected strong baselines. Besides, due to the transferability of the ID module, VCD is also working well with finetuned text-to-image models available publically, further improving its usability. The codes are available at https://github.com/Zhen-Dong/Magic-Me.", "github_manual": "", "github_regex": "https://github.com/Zhen-Dong/Magic-Me", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-15", "arxiv_id": "2402.09052", "title": "L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects", "authors": [ "Yutaro Yamada", "Khyathi Chandu", "Yuchen Lin", "Jack Hessel", "Ilker Yildirim", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "Diffusion-based image generation models such as DALL-E 3 and Stable Diffusion-XL demonstrate remarkable capabilities in generating images with realistic and unique compositions. Yet, these models are not robust in precisely reasoning about physical and spatial configurations of objects, especially when instructed with unconventional, thereby out-of-distribution descriptions, such as \"a chair with five legs\". In this paper, we propose a language agent with chain-of-3D-thoughts (L3GO), an inference-time approach that can reason about part-based 3D mesh generation of unconventional objects that current data-driven diffusion models struggle with. More concretely, we use large language models as agents to compose a desired object via trial-and-error within the 3D simulation environment. To facilitate our investigation, we develop a new benchmark, Unconventionally Feasible Objects (UFO), as well as SimpleBlenv, a wrapper environment built on top of Blender where language agents can build and compose atomic building blocks via API calls. Human and automatic GPT-4V evaluations show that our approach surpasses the standard GPT-4 and other language agents (e.g., ReAct and Reflexion) for 3D mesh generation on ShapeNet. Moreover, when tested on our UFO benchmark, our approach outperforms other state-of-the-art text-to-2D image and text-to-3D models based on human evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-15", "arxiv_id": "2402.08939", "title": "Premise Order Matters in Reasoning with Large Language Models", "authors": [ "Xinyun Chen", "Ryan A. Chi", "Xuezhi Wang", "Denny Zhou" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have accomplished remarkable reasoning performance in various domains. However, in the domain of reasoning tasks, we discover a frailty: LLMs are surprisingly brittle to the ordering of the premises, despite the fact that such ordering does not alter the underlying task. In particular, we observe that LLMs achieve the best performance when the premise order aligns with the context required in intermediate reasoning steps. For example, in deductive reasoning tasks, presenting the premises in the same order as the ground truth proof in the prompt (as opposed to random ordering) drastically increases the model's accuracy. We first examine the effect of premise ordering on deductive reasoning on a variety of LLMs, and our evaluation shows that permuting the premise order can cause a performance drop of over 30%. In addition, we release the benchmark R-GSM, based on GSM8K, to examine the ordering effect for mathematical problem-solving, and we again observe a significant drop in accuracy, relative to the original GSM8K benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-15", "arxiv_id": "2402.09371", "title": "Transformers Can Achieve Length Generalization But Not Robustly", "authors": [ "Yongchao Zhou", "Uri Alon", "Xinyun Chen", "Xuezhi Wang", "Rishabh Agarwal", "Denny Zhou" ], "github": "", "project_page": "", "abstract": "Length generalization, defined as the ability to extrapolate from shorter training sequences to longer test ones, is a significant challenge for language models. This issue persists even with large-scale Transformers handling relatively straightforward tasks. In this paper, we test the Transformer's ability of length generalization using the task of addition of two integers. We show that the success of length generalization is intricately linked to the data format and the type of position encoding. Using the right combination of data format and position encodings, we show for the first time that standard Transformers can extrapolate to a sequence length that is 2.5x the input length. Nevertheless, unlike in-distribution generalization, length generalization remains fragile, significantly influenced by factors like random weight initialization and training data order, leading to large variances across different random seeds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-15", "arxiv_id": "2402.08797", "title": "Computing Power and the Governance of Artificial Intelligence", "authors": [ "Girish Sastry", "Lennart Heim", "Haydn Belfield", "Markus Anderljung", "Miles Brundage", "Julian Hazell", "Cullen O'Keefe", "Gillian K. Hadfield", "Richard Ngo", "Konstantin Pilz", "George Gor", "Emma Bluemke", "Sarah Shoker", "Janet Egan", "Robert F. Trager", "Shahar Avin", "Adrian Weller", "Yoshua Bengio", "Diane Coyle" ], "github": "", "project_page": "", "abstract": "Computing power, or \"compute,\" is crucial for the development and deployment of artificial intelligence (AI) capabilities. As a result, governments and companies have started to leverage compute as a means to govern AI. For example, governments are investing in domestic compute capacity, controlling the flow of compute to competing countries, and subsidizing compute access to certain sectors. However, these efforts only scratch the surface of how compute can be used to govern AI development and deployment. Relative to other key inputs to AI (data and algorithms), AI-relevant compute is a particularly effective point of intervention: it is detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain. These characteristics, alongside the singular importance of compute for cutting-edge AI models, suggest that governing compute can contribute to achieving common policy objectives, such as ensuring the safety and beneficial use of AI. More precisely, policymakers could use compute to facilitate regulatory visibility of AI, allocate resources to promote beneficial outcomes, and enforce restrictions against irresponsible or malicious AI development and usage. However, while compute-based policies and technologies have the potential to assist in these areas, there is significant variation in their readiness for implementation. Some ideas are currently being piloted, while others are hindered by the need for fundamental research. Furthermore, naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power. We end by suggesting guardrails to minimize these risks from compute governance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-15", "arxiv_id": "2402.08714", "title": "PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models", "authors": [ "Fei Deng", "Qifei Wang", "Wei Wei", "Matthias Grundmann", "Tingbo Hou" ], "github": "", "project_page": "", "abstract": "Reward finetuning has emerged as a promising approach to aligning foundation models with downstream objectives. Remarkable success has been achieved in the language domain by using reinforcement learning (RL) to maximize rewards that reflect human preference. However, in the vision domain, existing RL-based reward finetuning methods are limited by their instability in large-scale training, rendering them incapable of generalizing to complex, unseen prompts. In this paper, we propose Proximal Reward Difference Prediction (PRDP), enabling stable black-box reward finetuning for diffusion models for the first time on large-scale prompt datasets with over 100K prompts. Our key innovation is the Reward Difference Prediction (RDP) objective that has the same optimal solution as the RL objective while enjoying better training stability. Specifically, the RDP objective is a supervised regression objective that tasks the diffusion model with predicting the reward difference of generated image pairs from their denoising trajectories. We theoretically prove that the diffusion model that obtains perfect reward difference prediction is exactly the maximizer of the RL objective. We further develop an online algorithm with proximal updates to stably optimize the RDP objective. In experiments, we demonstrate that PRDP can match the reward maximization ability of well-established RL-based methods in small-scale training. Furthermore, through large-scale training on text prompts from the Human Preference Dataset v2 and the Pick-a-Pic v1 dataset, PRDP achieves superior generation quality on a diverse set of complex, unseen prompts whereas RL-based methods completely fail.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-15", "arxiv_id": "2402.08855", "title": "GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency", "authors": [ "Catherine Yeh", "Gonzalo Ramos", "Rachel Ng", "Andy Huntington", "Richard Banks" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are becoming more prevalent and have found a ubiquitous use in providing different forms of writing assistance. However, LLM-powered writing systems can frustrate users due to their limited personalization and control, which can be exacerbated when users lack experience with prompt engineering. We see design as one way to address these challenges and introduce GhostWriter, an AI-enhanced writing design probe where users can exercise enhanced agency and personalization. GhostWriter leverages LLMs to learn the user's intended writing style implicitly as they write, while allowing explicit teaching moments through manual style edits and annotations. We study 18 participants who use GhostWriter on two different writing tasks, observing that it helps users craft personalized text generations and empowers them by providing multiple ways to control the system's writing style. From this study, we present insights regarding people's relationship with AI-assisted writing and offer design recommendations for future work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-15", "arxiv_id": "2402.09126", "title": "MPIrigen: MPI Code Generation through Domain-Specific Language Models", "authors": [ "Nadav Schneider", "Niranjan Hasabnis", "Vy A. Vo", "Tal Kadosh", "Neva Krien", "Mihai Capot\u0103", "Abdul Wasay", "Guy Tamir", "Ted Willke", "Nesreen Ahmed", "Yuval Pinter", "Timothy Mattson", "Gal Oren" ], "github": "https://github.com/Scientific-Computing-Lab-NRCN/MPI-rigen", "project_page": "", "abstract": "The imperative need to scale computation across numerous nodes highlights the significance of efficient parallel computing, particularly in the realm of Message Passing Interface (MPI) integration. The challenging parallel programming task of generating MPI-based parallel programs has remained unexplored. This study first investigates the performance of state-of-the-art language models in generating MPI-based parallel programs. Findings reveal that widely used models such as GPT-3.5 and PolyCoder (specialized multi-lingual code models) exhibit notable performance degradation, when generating MPI-based programs compared to general-purpose programs. In contrast, domain-specific models such as MonoCoder, which are pretrained on MPI-related programming languages of C and C++, outperform larger models. Subsequently, we introduce a dedicated downstream task of MPI-based program generation by fine-tuning MonoCoder on HPCorpusMPI. We call the resulting model as MPIrigen. We propose an innovative preprocessing for completion only after observing the whole code, thus enabling better completion with a wider context. Comparative analysis against GPT-3.5 zero-shot performance, using a novel HPC-oriented evaluation method, demonstrates that MPIrigen excels in generating accurate MPI functions up to 0.8 accuracy in location and function predictions, and with more than 0.9 accuracy for argument predictions. The success of this tailored solution underscores the importance of domain-specific fine-tuning in optimizing language models for parallel computing code generation, paving the way for a new generation of automatic parallelization tools. The sources of this work are available at our GitHub MPIrigen repository: https://github.com/Scientific-Computing-Lab-NRCN/MPI-rigen", "github_manual": "", "github_regex": "https://github.com/Scientific-Computing-Lab-NRCN/MPI-rigen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-15", "arxiv_id": "2402.08958", "title": "Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers", "authors": [ "Junhan Kim", "Kyungphil Park", "Chungman Lee", "Ho-young Kim", "Joonyoung Kim", "Yongkweon Jeon" ], "github": "https://github.com/SamsungLabs/aespa", "project_page": "", "abstract": "With the increasing complexity of generative AI models, post-training quantization (PTQ) has emerged as a promising solution for deploying hyper-scale models on edge devices such as mobile devices and TVs. Existing PTQ schemes, however, consume considerable time and resources, which could be a bottleneck in real situations where frequent model updates and multiple hyper-parameter tunings are required. As a cost-effective alternative, one-shot PTQ schemes have been proposed. Still, the performance is somewhat limited because they cannot consider the inter-layer dependency within the attention module, which is a very important feature of Transformers. In this paper, we thus propose a novel PTQ algorithm that balances accuracy and efficiency. The key idea of the proposed algorithm called aespa is to perform quantization layer-wise for efficiency while considering cross-layer dependency to preserve the attention score. Through extensive experiments on various language models and complexity analysis, we demonstrate that aespa is accurate and efficient in quantizing Transformer models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SamsungLabs/aespa", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-16", "arxiv_id": "2402.10210", "title": "Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation", "authors": [ "Huizhuo Yuan", "Zixiang Chen", "Kaixuan Ji", "Quanquan Gu" ], "github": "", "project_page": "", "abstract": "Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI), especially when compared with the remarkable progress made in fine-tuning Large Language Models (LLMs). While cutting-edge diffusion models such as Stable Diffusion (SD) and SDXL rely on supervised fine-tuning, their performance inevitably plateaus after seeing a certain volume of data. Recently, reinforcement learning (RL) has been employed to fine-tune diffusion models with human preference data, but it requires at least two images (\"winner\" and \"loser\" images) for each text prompt. In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion), where the diffusion model engages in competition with its earlier versions, facilitating an iterative self-improvement process. Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment. Our experiments on the Pick-a-Pic dataset reveal that SPIN-Diffusion outperforms the existing supervised fine-tuning method in aspects of human preference alignment and visual appeal right from its first iteration. By the second iteration, it exceeds the performance of RLHF-based methods across all metrics, achieving these results with less data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-16", "arxiv_id": "2402.10193", "title": "BitDelta: Your Fine-Tune May Only Be Worth One Bit", "authors": [ "James Liu", "Guangxuan Xiao", "Kai Li", "Jason D. Lee", "Song Han", "Tri Dao", "Tianle Cai" ], "github": "https://github.com/FasterDecoding/BitDelta", "project_page": "", "abstract": "Large Language Models (LLMs) are typically trained in two phases: pre-training on large internet-scale datasets, and fine-tuning for downstream tasks. Given the higher computational demand of pre-training, it's intuitive to assume that fine-tuning adds less new information to the model, and is thus more compressible. We explore this assumption by decomposing the weights of fine-tuned models into their pre-trained components and an additional delta. We introduce a simple method, BitDelta, which successfully quantizes this delta down to 1 bit without compromising performance. This interesting finding not only highlights the potential redundancy of information added during fine-tuning, but also has significant implications for the multi-tenant serving and multi-tenant storage of fine-tuned models. By enabling the use of a single high-precision base model accompanied by multiple 1-bit deltas, BitDelta dramatically reduces GPU memory requirements by more than 10x, which can also be translated to enhanced generation latency in multi-tenant settings. We validate BitDelta through experiments across Llama-2 and Mistral model families, and on models up to 70B parameters, showcasing minimal performance degradation over all tested settings.", "github_manual": "https://github.com/FasterDecoding/BitDelta", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-16", "arxiv_id": "2402.09668", "title": "How to Train Data-Efficient LLMs", "authors": [ "Noveen Sachdeva", "Benjamin Coleman", "Wang-Cheng Kang", "Jianmo Ni", "Lichan Hong", "Ed H. Chi", "James Caverlee", "Julian McAuley", "Derek Zhiyuan Cheng" ], "github": "", "project_page": "", "abstract": "The training of large language models (LLMs) is expensive. In this paper, we study data-efficient approaches for pre-training LLMs, i.e., techniques that aim to optimize the Pareto frontier of model quality and training resource/data consumption. We seek to understand the tradeoffs associated with data selection routines based on (i) expensive-to-compute data-quality estimates, and (ii) maximization of coverage and diversity-based measures in the feature space. Our first technique, Ask-LLM, leverages the zero-shot reasoning capabilities of instruction-tuned LLMs to directly assess the quality of a training example. To target coverage, we propose Density sampling, which models the data distribution to select a diverse sample. In our comparison of 19 samplers, involving hundreds of evaluation tasks and pre-training runs, we find that Ask-LLM and Density are the best methods in their respective categories. Coverage sampling can recover the performance of the full data, while models trained on Ask-LLM data consistently outperform full-data training -- even when we reject 90% of the original dataset, while converging up to 70% faster.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-16", "arxiv_id": "2402.09727", "title": "A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts", "authors": [ "Kuang-Huei Lee", "Xinyun Chen", "Hiroki Furuta", "John Canny", "Ian Fischer" ], "github": "", "project_page": "", "abstract": "Current Large Language Models (LLMs) are not only limited to some maximum context length, but also are not able to robustly consume long inputs. To address these limitations, we propose ReadAgent, an LLM agent system that increases effective context length up to 20x in our experiments. Inspired by how humans interactively read long documents, we implement ReadAgent as a simple prompting system that uses the advanced language capabilities of LLMs to (1) decide what content to store together in a memory episode, (2) compress those memory episodes into short episodic memories called gist memories, and (3) take actions to look up passages in the original text if ReadAgent needs to remind itself of relevant details to complete a task. We evaluate ReadAgent against baselines using retrieval methods, using the original long contexts, and using the gist memories. These evaluations are performed on three long-document reading comprehension tasks: QuALITY, NarrativeQA, and QMSum. ReadAgent outperforms the baselines on all three tasks while extending the effective context window by 3-20x.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-16", "arxiv_id": "2402.10171", "title": "Data Engineering for Scaling Language Models to 128K Context", "authors": [ "Yao Fu", "Rameswar Panda", "Xinyao Niu", "Xiang Yue", "Hannaneh Hajishirzi", "Yoon Kim", "Hao Peng" ], "github": "https://github.com/franxyao/long-context-data-engineering", "project_page": "", "abstract": "We study the continual pretraining recipe for scaling language models' context lengths to 128K, with a focus on data engineering. We hypothesize that long context modeling, in particular the ability to utilize information at arbitrary input locations, is a capability that is mostly already acquired through large-scale pretraining, and that this capability can be readily extended to contexts substantially longer than seen during training~(e.g., 4K to 128K) through lightweight continual pretraining on appropriate data mixture. We investigate the quantity and quality of the data for continual pretraining: (1) for quantity, we show that 500 million to 5 billion tokens are enough to enable the model to retrieve information anywhere within the 128K context; (2) for quality, our results equally emphasize domain balance and length upsampling. Concretely, we find that naively upsampling longer data on certain domains like books, a common practice of existing work, gives suboptimal performance, and that a balanced domain mixture is important. We demonstrate that continual pretraining of the full model on 1B-5B tokens of such data is an effective and affordable strategy for scaling the context length of language models to 128K. Our recipe outperforms strong open-source long-context models and closes the gap to frontier models like GPT-4 128K.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/franxyao/long-context-data-engineering", "github_api_added_by": "auto" }, { "date": "2024-02-16", "arxiv_id": "2402.09470", "title": "Rolling Diffusion Models", "authors": [ "David Ruhe", "Jonathan Heek", "Tim Salimans", "Emiel Hoogeboom" ], "github": "", "project_page": "", "abstract": "Diffusion models have recently been increasingly applied to temporal data such as video, fluid mechanics simulations, or climate data. These methods generally treat subsequent frames equally regarding the amount of noise in the diffusion process. This paper explores Rolling Diffusion: a new approach that uses a sliding window denoising process. It ensures that the diffusion process progressively corrupts through time by assigning more noise to frames that appear later in a sequence, reflecting greater uncertainty about the future as the generation process unfolds. Empirically, we show that when the temporal dynamics are complex, Rolling Diffusion is superior to standard diffusion. In particular, this result is demonstrated in a video prediction task using the Kinetics-600 video dataset and in a chaotic fluid dynamics forecasting experiment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-16", "arxiv_id": "2402.10176", "title": "OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset", "authors": [ "Shubham Toshniwal", "Ivan Moshkov", "Sean Narenthiran", "Daria Gitman", "Fei Jia", "Igor Gitman" ], "github": "https://github.com/kipok/nemo-skills", "project_page": "", "abstract": "Recent work has shown the immense potential of synthetically generated datasets for training large language models (LLMs), especially for acquiring targeted skills. Current large-scale math instruction tuning datasets such as MetaMathQA (Yu et al., 2024) and MAmmoTH (Yue et al., 2024) are constructed using outputs from closed-source LLMs with commercially restrictive licenses. A key reason limiting the use of open-source LLMs in these data generation pipelines has been the wide gap between the mathematical skills of the best closed-source LLMs, such as GPT-4, and the best open-source LLMs. Building on the recent progress in open-source LLMs, our proposed prompting novelty, and some brute-force scaling, we construct OpenMathInstruct-1, a math instruction tuning dataset with 1.8M problem-solution pairs. The dataset is constructed by synthesizing code-interpreter solutions for GSM8K and MATH, two popular math reasoning benchmarks, using the recently released and permissively licensed Mixtral model. Our best model, OpenMath-CodeLlama-70B, trained on a subset of OpenMathInstruct-1, achieves a score of 84.6% on GSM8K and 50.7% on MATH, which is competitive with the best gpt-distilled models. We release our code, models, and the OpenMathInstruct-1 dataset under a commercially permissive license.", "github_manual": "https://github.com/kipok/nemo-skills", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-16", "arxiv_id": "2402.10200", "title": "Chain-of-Thought Reasoning Without Prompting", "authors": [ "Xuezhi Wang", "Denny Zhou" ], "github": "", "project_page": "", "abstract": "In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-thought (CoT) prompting. These methods, while effective, often involve manually intensive prompt engineering. Our study takes a novel approach by asking: Can LLMs reason effectively without prompting? Our findings reveal that, intriguingly, CoT reasoning paths can be elicited from pre-trained LLMs by simply altering the decoding process. Rather than conventional greedy decoding, we investigate the top-k alternative tokens, uncovering that CoT paths are frequently inherent in these sequences. This approach not only bypasses the confounders of prompting but also allows us to assess the LLMs' intrinsic reasoning abilities. Moreover, we observe that the presence of a CoT in the decoding path correlates with a higher confidence in the model's decoded answer. This confidence metric effectively differentiates between CoT and non-CoT paths. Extensive empirical studies on various reasoning benchmarks show that the proposed CoT-decoding substantially outperforms the standard greedy decoding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-16", "arxiv_id": "2402.09906", "title": "Generative Representational Instruction Tuning", "authors": [ "Niklas Muennighoff", "Hongjin Su", "Liang Wang", "Nan Yang", "Furu Wei", "Tao Yu", "Amanpreet Singh", "Douwe Kiela" ], "github": "https://github.com/ContextualAI/gritlm", "project_page": "", "abstract": "All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8x7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.", "github_manual": "", "github_regex": "https://github.com/ContextualAI/gritlm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-16", "arxiv_id": "2402.10128", "title": "GES: Generalized Exponential Splatting for Efficient Radiance Field Rendering", "authors": [ "Abdullah Hamdi", "Luke Melas-Kyriazi", "Guocheng Qian", "Jinjie Mai", "Ruoshi Liu", "Carl Vondrick", "Bernard Ghanem", "Andrea Vedaldi" ], "github": "https://github.com/ajhamdi/ges-splatting", "project_page": "", "abstract": "Advancements in 3D Gaussian Splatting have significantly accelerated 3D reconstruction and generation. However, it may require a large number of Gaussians, which creates a substantial memory footprint. This paper introduces GES (Generalized Exponential Splatting), a novel representation that employs Generalized Exponential Function (GEF) to model 3D scenes, requiring far fewer particles to represent a scene and thus significantly outperforming Gaussian Splatting methods in efficiency with a plug-and-play replacement ability for Gaussian-based utilities. GES is validated theoretically and empirically in both principled 1D setup and realistic 3D scenes. It is shown to represent signals with sharp edges more accurately, which are typically challenging for Gaussians due to their inherent low-pass characteristics. Our empirical analysis demonstrates that GEF outperforms Gaussians in fitting natural-occurring signals (e.g. squares, triangles, and parabolic signals), thereby reducing the need for extensive splitting operations that increase the memory footprint of Gaussian Splatting. With the aid of a frequency-modulated loss, GES achieves competitive performance in novel-view synthesis benchmarks while requiring less than half the memory storage of Gaussian Splatting and increasing the rendering speed by up to 39%. The code is available on the project website https://abdullahamdi.com/ges .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ajhamdi/ges-splatting", "github_api_added_by": "auto" }, { "date": "2024-02-16", "arxiv_id": "2402.09812", "title": "DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image Personalization", "authors": [ "Jisu Nam", "Heesu Kim", "DongJae Lee", "Siyoon Jin", "Seungryong Kim", "Seunggyu Chang" ], "github": "https://github.com/KU-CVLAB/DreamMatcher", "project_page": "", "abstract": "The objective of text-to-image (T2I) personalization is to customize a diffusion model to a user-provided reference concept, generating diverse images of the concept aligned with the target prompts. Conventional methods representing the reference concepts using unique text embeddings often fail to accurately mimic the appearance of the reference. To address this, one solution may be explicitly conditioning the reference images into the target denoising process, known as key-value replacement. However, prior works are constrained to local editing since they disrupt the structure path of the pre-trained T2I model. To overcome this, we propose a novel plug-in method, called DreamMatcher, which reformulates T2I personalization as semantic matching. Specifically, DreamMatcher replaces the target values with reference values aligned by semantic matching, while leaving the structure path unchanged to preserve the versatile capability of pre-trained T2I models for generating diverse structures. We also introduce a semantic-consistent masking strategy to isolate the personalized concept from irrelevant regions introduced by the target prompts. Compatible with existing T2I models, DreamMatcher shows significant improvements in complex scenarios. Intensive analyses demonstrate the effectiveness of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KU-CVLAB/DreamMatcher", "github_api_added_by": "auto" }, { "date": "2024-02-16", "arxiv_id": "2402.10009", "title": "Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion", "authors": [ "Hila Manor", "Tomer Michaeli" ], "github": "https://github.com/hilamanor/AudioEditing", "project_page": "", "abstract": "Editing signals using large pre-trained models, in a zero-shot manner, has recently seen rapid advancements in the image domain. However, this wave has yet to reach the audio domain. In this paper, we explore two zero-shot editing techniques for audio signals, which use DDPM inversion on pre-trained diffusion models. The first, adopted from the image domain, allows text-based editing. The second, is a novel approach for discovering semantically meaningful editing directions without supervision. When applied to music signals, this method exposes a range of musically interesting modifications, from controlling the participation of specific instruments to improvisations on the melody. Samples can be found on our examples page in https://hilamanor.github.io/AudioEditing/ and code can be found in https://github.com/hilamanor/AudioEditing/ .", "github_manual": "", "github_regex": "https://github.com/hilamanor/AudioEditing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-16", "arxiv_id": "2402.10211", "title": "Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling", "authors": [ "Raunaq Bhirangi", "Chenyu Wang", "Venkatesh Pattabiraman", "Carmel Majidi", "Abhinav Gupta", "Tess Hellebrekers", "Lerrel Pinto" ], "github": "https://github.com/raunaqbhirangi/hiss", "project_page": "", "abstract": "Reasoning from sequences of raw sensory data is a ubiquitous problem across fields ranging from medical devices to robotics. These problems often involve using long sequences of raw sensor data (e.g. magnetometers, piezoresistors) to predict sequences of desirable physical quantities (e.g. force, inertial measurements). While classical approaches are powerful for locally-linear prediction problems, they often fall short when using real-world sensors. These sensors are typically non-linear, are affected by extraneous variables (e.g. vibration), and exhibit data-dependent drift. For many problems, the prediction task is exacerbated by small labeled datasets since obtaining ground-truth labels requires expensive equipment. In this work, we present Hierarchical State-Space Models (HiSS), a conceptually simple, new technique for continuous sequential prediction. HiSS stacks structured state-space models on top of each other to create a temporal hierarchy. Across six real-world sensor datasets, from tactile-based state prediction to accelerometer-based inertial measurement, HiSS outperforms state-of-the-art sequence models such as causal Transformers, LSTMs, S4, and Mamba by at least 23% on MSE. Our experiments further indicate that HiSS demonstrates efficient scaling to smaller datasets and is compatible with existing data-filtering techniques. Code, datasets and videos can be found on https://hiss-csp.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/raunaqbhirangi/hiss", "github_api_added_by": "auto" }, { "date": "2024-02-19", "arxiv_id": "2402.10491", "title": "Make a Cheap Scaling: A Self-Cascade Diffusion Model for Higher-Resolution Adaptation", "authors": [ "Lanqing Guo", "Yingqing He", "Haoxin Chen", "Menghan Xia", "Xiaodong Cun", "Yufei Wang", "Siyu Huang", "Yong Zhang", "Xintao Wang", "Qifeng Chen", "Ying Shan", "Bihan Wen" ], "github": "https://github.com/guolanqing/self-cascade", "project_page": "", "abstract": "Diffusion models have proven to be highly effective in image and video generation; however, they still face composition challenges when generating images of varying sizes due to single-scale training data. Adapting large pre-trained diffusion models for higher resolution demands substantial computational and optimization resources, yet achieving a generation capability comparable to low-resolution models remains elusive. This paper proposes a novel self-cascade diffusion model that leverages the rich knowledge gained from a well-trained low-resolution model for rapid adaptation to higher-resolution image and video generation, employing either tuning-free or cheap upsampler tuning paradigms. Integrating a sequence of multi-scale upsampler modules, the self-cascade diffusion model can efficiently adapt to a higher resolution, preserving the original composition and generation capabilities. We further propose a pivot-guided noise re-schedule strategy to speed up the inference process and improve local structural details. Compared to full fine-tuning, our approach achieves a 5X training speed-up and requires only an additional 0.002M tuning parameters. Extensive experiments demonstrate that our approach can quickly adapt to higher resolution image and video synthesis by fine-tuning for just 10k steps, with virtually no additional inference time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/guolanqing/self-cascade", "github_api_added_by": "auto" }, { "date": "2024-02-19", "arxiv_id": "2402.10466", "title": "Large Language Models as Zero-shot Dialogue State Tracker through Function Calling", "authors": [ "Zekun Li", "Zhiyu Zoey Chen", "Mike Ross", "Patrick Huber", "Seungwhan Moon", "Zhaojiang Lin", "Xin Luna Dong", "Adithya Sagar", "Xifeng Yan", "Paul A. Crook" ], "github": "https://github.com/facebookresearch/fnctod", "project_page": "", "abstract": "Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts. However, their effectiveness in task-oriented dialogues (TOD), which requires not only response generation but also effective dialogue state tracking (DST) within specific tasks and domains, remains less satisfying. In this work, we propose a novel approach FnCTOD for solving DST with LLMs through function calling. This method improves zero-shot DST, allowing adaptation to diverse domains without extensive data collection or model tuning. Our experimental results demonstrate that our approach achieves exceptional performance with both modestly sized open-source and also proprietary LLMs: with in-context prompting it enables various 7B or 13B parameter models to surpass the previous state-of-the-art (SOTA) achieved by ChatGPT, and improves ChatGPT's performance beating the SOTA by 5.6% Avg. JGA. Individual model results for GPT-3.5 and GPT-4 are boosted by 4.8% and 14%, respectively. We also show that by fine-tuning on a small collection of diverse task-oriented dialogues, we can equip modestly sized models, specifically a 13B parameter LLaMA2-Chat model, with function-calling capabilities and DST performance comparable to ChatGPT while maintaining their chat capabilities. We plan to open-source experimental code and model.", "github_manual": "https://github.com/facebookresearch/fnctod", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-19", "arxiv_id": "2402.10259", "title": "GaussianObject: Just Taking Four Images to Get A High-Quality 3D Object with Gaussian Splatting", "authors": [ "Chen Yang", "Sikuang Li", "Jiemin Fang", "Ruofan Liang", "Lingxi Xie", "Xiaopeng Zhang", "Wei Shen", "Qi Tian" ], "github": "https://github.com/GaussianObject/GaussianObject", "project_page": "", "abstract": "Reconstructing and rendering 3D objects from highly sparse views is of critical importance for promoting applications of 3D vision techniques and improving user experience. However, images from sparse views only contain very limited 3D information, leading to two significant challenges: 1) Difficulty in building multi-view consistency as images for matching are too few; 2) Partially omitted or highly compressed object information as view coverage is insufficient. To tackle these challenges, we propose GaussianObject, a framework to represent and render the 3D object with Gaussian splatting, that achieves high rendering quality with only 4 input images. We first introduce techniques of visual hull and floater elimination which explicitly inject structure priors into the initial optimization process for helping build multi-view consistency, yielding a coarse 3D Gaussian representation. Then we construct a Gaussian repair model based on diffusion models to supplement the omitted object information, where Gaussians are further refined. We design a self-generating strategy to obtain image pairs for training the repair model. Our GaussianObject is evaluated on several challenging datasets, including MipNeRF360, OmniObject3D, and OpenIllumination, achieving strong reconstruction results from only 4 views and significantly outperforming previous state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GaussianObject/GaussianObject", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-19", "arxiv_id": "2402.10790", "title": "In Search of Needles in a 10M Haystack: Recurrent Memory Finds What LLMs Miss", "authors": [ "Yuri Kuratov", "Aydar Bulatov", "Petr Anokhin", "Dmitry Sorokin", "Artyom Sorokin", "Mikhail Burtsev" ], "github": "", "project_page": "", "abstract": "This paper addresses the challenge of processing long documents using generative transformer models. To evaluate different approaches, we introduce BABILong, a new benchmark designed to assess model capabilities in extracting and processing distributed facts within extensive texts. Our evaluation, which includes benchmarks for GPT-4 and RAG, reveals that common methods are effective only for sequences up to 10^4 elements. In contrast, fine-tuning GPT-2 with recurrent memory augmentations enables it to handle tasks involving up to 10^7 elements. This achievement marks a substantial leap, as it is by far the longest input processed by any open neural network model to date, demonstrating a significant improvement in the processing capabilities for long sequences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-19", "arxiv_id": "2402.10524", "title": "LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models", "authors": [ "Minsuk Kahng", "Ian Tenney", "Mahima Pushkarna", "Michael Xieyang Liu", "James Wexler", "Emily Reif", "Krystal Kallarackal", "Minsuk Chang", "Michael Terry", "Lucas Dixon" ], "github": "", "project_page": "", "abstract": "Automatic side-by-side evaluation has emerged as a promising approach to evaluating the quality of responses from large language models (LLMs). However, analyzing the results from this evaluation approach raises scalability and interpretability challenges. In this paper, we present LLM Comparator, a novel visual analytics tool for interactively analyzing results from automatic side-by-side evaluation. The tool supports interactive workflows for users to understand when and why a model performs better or worse than a baseline model, and how the responses from two models are qualitatively different. We iteratively designed and developed the tool by closely working with researchers and engineers at a large technology company. This paper details the user challenges we identified, the design and development of the tool, and an observational study with participants who regularly evaluate their models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-19", "arxiv_id": "2402.10379", "title": "DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows", "authors": [ "Ajay Patel", "Colin Raffel", "Chris Callison-Burch" ], "github": "https://github.com/datadreamer-dev/DataDreamer", "project_page": "", "abstract": "Large language models (LLMs) have become a dominant and important tool for NLP researchers in a wide range of tasks. Today, many researchers use LLMs in synthetic data generation, task evaluation, fine-tuning, distillation, and other model-in-the-loop research workflows. However, challenges arise when using these models that stem from their scale, their closed source nature, and the lack of standardized tooling for these new and emerging workflows. The rapid rise to prominence of these models and these unique challenges has had immediate adverse impacts on open science and on the reproducibility of work that uses them. In this paper, we introduce DataDreamer, an open source Python library that allows researchers to write simple code to implement powerful LLM workflows. DataDreamer also helps researchers adhere to best practices that we propose to encourage open science and reproducibility. The library and documentation are available at https://github.com/datadreamer-dev/DataDreamer .", "github_manual": "", "github_regex": "https://github.com/datadreamer-dev/DataDreamer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-19", "arxiv_id": "2402.10294", "title": "LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing", "authors": [ "Bryan Wang", "Yuliang Li", "Zhaoyang Lv", "Haijun Xia", "Yan Xu", "Raj Sodhi" ], "github": "", "project_page": "", "abstract": "Video creation has become increasingly popular, yet the expertise and effort required for editing often pose barriers to beginners. In this paper, we explore the integration of large language models (LLMs) into the video editing workflow to reduce these barriers. Our design vision is embodied in LAVE, a novel system that provides LLM-powered agent assistance and language-augmented editing features. LAVE automatically generates language descriptions for the user's footage, serving as the foundation for enabling the LLM to process videos and assist in editing tasks. When the user provides editing objectives, the agent plans and executes relevant actions to fulfill them. Moreover, LAVE allows users to edit videos through either the agent or direct UI manipulation, providing flexibility and enabling manual refinement of agent actions. Our user study, which included eight participants ranging from novices to proficient editors, demonstrated LAVE's effectiveness. The results also shed light on user perceptions of the proposed LLM-assisted editing paradigm and its impact on users' creativity and sense of co-creation. Based on these findings, we propose design implications to inform the future development of agent-assisted content editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-19", "arxiv_id": "2402.10896", "title": "PaLM2-VAdapter: Progressively Aligned Language Model Makes a Strong Vision-language Adapter", "authors": [ "Junfei Xiao", "Zheng Xu", "Alan Yuille", "Shen Yan", "Boyu Wang" ], "github": "", "project_page": "", "abstract": "This paper demonstrates that a progressively aligned language model can effectively bridge frozen vision encoders and large language models (LLMs). While the fundamental architecture and pre-training methods of vision encoders and LLMs have been extensively studied, the architecture and training strategy of vision-language adapters vary significantly across recent works. Our research undertakes a thorough exploration of the state-of-the-art perceiver resampler architecture and builds a strong baseline. However, we observe that the vision-language alignment with perceiver resampler exhibits slow convergence and limited scalability with a lack of direct supervision. To address this issue, we propose PaLM2-VAdapter, employing a progressively aligned language model as the vision-language adapter. Compared to the strong baseline with perceiver resampler, our method empirically shows faster convergence, higher performance, and stronger scalability. Extensive experiments across various Visual Question Answering (VQA) and captioning tasks on both images and videos demonstrate that our model exhibits state-of-the-art visual understanding and multi-modal reasoning capabilities. Notably, our method achieves these advancements with 30~70% fewer parameters than the state-of-the-art large vision-language models, marking a significant efficiency improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-19", "arxiv_id": "2402.10555", "title": "SPAR: Personalized Content-Based Recommendation via Long Engagement Attention", "authors": [ "Chiyu Zhang", "Yifei Sun", "Jun Chen", "Jie Lei", "Muhammad Abdul-Mageed", "Sinong Wang", "Rong Jin", "Sem Park", "Ning Yao", "Bo Long" ], "github": "", "project_page": "", "abstract": "Leveraging users' long engagement histories is essential for personalized content recommendations. The success of pretrained language models (PLMs) in NLP has led to their use in encoding user histories and candidate items, framing content recommendations as textual semantic matching tasks. However, existing works still struggle with processing very long user historical text and insufficient user-item interaction. In this paper, we introduce a content-based recommendation framework, SPAR, which effectively tackles the challenges of holistic user interest extraction from the long user engagement history. It achieves so by leveraging PLM, poly-attention layers and attention sparsity mechanisms to encode user's history in a session-based manner. The user and item side features are sufficiently fused for engagement prediction while maintaining standalone representations for both sides, which is efficient for practical model deployment. Moreover, we enhance user profiling by exploiting large language model (LLM) to extract global interests from user engagement history. Extensive experiments on two benchmark datasets demonstrate that our framework outperforms existing state-of-the-art (SoTA) methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-19", "arxiv_id": "2402.10893", "title": "RLVF: Learning from Verbal Feedback without Overgeneralization", "authors": [ "Moritz Stephan", "Alexander Khazatsky", "Eric Mitchell", "Annie S Chen", "Sheryl Hsu", "Archit Sharma", "Chelsea Finn" ], "github": "", "project_page": "", "abstract": "The diversity of contexts in which large language models (LLMs) are deployed requires the ability to modify or customize default model behaviors to incorporate nuanced requirements and preferences. A convenient interface to specify such model adjustments is high-level verbal feedback, such as \"Don't use emojis when drafting emails to my boss.\" However, while writing high-level feedback is far simpler than collecting annotations for reinforcement learning from human feedback (RLHF), we find that simply prompting a model with such feedback leads to overgeneralization of the feedback to contexts where it is not relevant. We study the problem of incorporating verbal feedback without such overgeneralization, inspiring a new method Contextualized Critiques with Constrained Preference Optimization (C3PO). C3PO uses a piece of high-level feedback to generate a small synthetic preference dataset specifying how the feedback should (and should not) be applied. It then fine-tunes the model in accordance with the synthetic preference data while minimizing the divergence from the original model for prompts where the feedback does not apply. Our experimental results indicate that our approach effectively applies verbal feedback to relevant scenarios while preserving existing behaviors for other contexts. For both human- and GPT-4-generated high-level feedback, C3PO effectively adheres to the given feedback comparably to in-context baselines while reducing overgeneralization by 30%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-19", "arxiv_id": "2402.10329", "title": "Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots", "authors": [ "Cheng Chi", "Zhenjia Xu", "Chuer Pan", "Eric Cousineau", "Benjamin Burchfiel", "Siyuan Feng", "Russ Tedrake", "Shuran Song" ], "github": "", "project_page": "", "abstract": "We present Universal Manipulation Interface (UMI) -- a data collection and policy learning framework that allows direct skill transfer from in-the-wild human demonstrations to deployable robot policies. UMI employs hand-held grippers coupled with careful interface design to enable portable, low-cost, and information-rich data collection for challenging bimanual and dynamic manipulation demonstrations. To facilitate deployable policy learning, UMI incorporates a carefully designed policy interface with inference-time latency matching and a relative-trajectory action representation. The resulting learned policies are hardware-agnostic and deployable across multiple robot platforms. Equipped with these features, UMI framework unlocks new robot manipulation capabilities, allowing zero-shot generalizable dynamic, bimanual, precise, and long-horizon behaviors, by only changing the training data for each task. We demonstrate UMI's versatility and efficacy with comprehensive real-world experiments, where policies learned via UMI zero-shot generalize to novel environments and objects when trained on diverse human demonstrations. UMI's hardware and software system is open-sourced at https://umi-gripper.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-19", "arxiv_id": "2402.10644", "title": "Linear Transformers with Learnable Kernel Functions are Better In-Context Models", "authors": [ "Yaroslav Aksenov", "Nikita Balagansky", "Sofia Maria Lo Cicero Vaina", "Boris Shaposhnikov", "Alexey Gorbatovski", "Daniil Gavrilov" ], "github": "https://github.com/corl-team/rebased", "project_page": "", "abstract": "Advancing the frontier of subquadratic architectures for Language Models (LMs) is crucial in the rapidly evolving field of natural language processing. Current innovations, including State Space Models, were initially celebrated for surpassing Transformer performance on language modeling tasks. However, these models have revealed deficiencies in essential In-Context Learning capabilities - a domain where the Transformer traditionally shines. The Based model emerged as a hybrid solution, blending a Linear Transformer with a kernel inspired by the Taylor expansion of exponential functions, augmented by convolutional networks. Mirroring the Transformer's in-context adeptness, it became a strong contender in the field. In our work, we present a singular, elegant alteration to the Based kernel that amplifies its In-Context Learning abilities evaluated with the Multi-Query Associative Recall task and overall language modeling process, as demonstrated on the Pile dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/corl-team/rebased", "github_api_added_by": "auto" }, { "date": "2024-02-20", "arxiv_id": "2402.11929", "title": "DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation", "authors": [ "Chong Zeng", "Yue Dong", "Pieter Peers", "Youkang Kong", "Hongzhi Wu", "Xin Tong" ], "github": "https://github.com/iamNCJ/DiLightNet", "project_page": "", "abstract": "This paper presents a novel method for exerting fine-grained lighting control during text-driven diffusion-based image generation. While existing diffusion models already have the ability to generate images under any lighting condition, without additional guidance these models tend to correlate image content and lighting. Moreover, text prompts lack the necessary expressional power to describe detailed lighting setups. To provide the content creator with fine-grained control over the lighting during image generation, we augment the text-prompt with detailed lighting information in the form of radiance hints, i.e., visualizations of the scene geometry with a homogeneous canonical material under the target lighting. However, the scene geometry needed to produce the radiance hints is unknown. Our key observation is that we only need to guide the diffusion process, hence exact radiance hints are not necessary; we only need to point the diffusion model in the right direction. Based on this observation, we introduce a three stage method for controlling the lighting during image generation. In the first stage, we leverage a standard pretrained diffusion model to generate a provisional image under uncontrolled lighting. Next, in the second stage, we resynthesize and refine the foreground object in the generated image by passing the target lighting to a refined diffusion model, named DiLightNet, using radiance hints computed on a coarse shape of the foreground object inferred from the provisional image. To retain the texture details, we multiply the radiance hints with a neural encoding of the provisional synthesized image before passing it to DiLightNet. Finally, in the third stage, we resynthesize the background to be consistent with the lighting on the foreground object. We demonstrate and validate our lighting controlled diffusion model on a variety of text prompts and lighting conditions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/iamNCJ/DiLightNet", "github_api_added_by": "auto" }, { "date": "2024-02-20", "arxiv_id": "2402.10986", "title": "FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models", "authors": [ "Gagan Bhatia", "El Moatez Billah Nagoudi", "Hasan Cavusoglu", "Muhammad Abdul-Mageed" ], "github": "https://github.com/UBC-NLP/fintral", "project_page": "", "abstract": "We introduce FinTral, a suite of state-of-the-art multimodal large language models (LLMs) built upon the Mistral-7b model and tailored for financial analysis. FinTral integrates textual, numerical, tabular, and image data. We enhance FinTral with domain-specific pretraining, instruction fine-tuning, and RLAIF training by exploiting a large collection of textual and visual datasets we curate for this work. We also introduce an extensive benchmark featuring nine tasks and 25 datasets for evaluation, including hallucinations in the financial domain. Our FinTral model trained with direct preference optimization employing advanced Tools and Retrieval methods, dubbed FinTral-DPO-T&R, demonstrates an exceptional zero-shot performance. It outperforms ChatGPT-3.5 in all tasks and surpasses GPT-4 in five out of nine tasks, marking a significant advancement in AI-driven financial technology. We also demonstrate that FinTral has the potential to excel in real-time analysis and decision-making in diverse financial contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/UBC-NLP/fintral", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-20", "arxiv_id": "2402.12226", "title": "AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling", "authors": [ "Jun Zhan", "Junqi Dai", "Jiasheng Ye", "Yunhua Zhou", "Dong Zhang", "Zhigeng Liu", "Xin Zhang", "Ruibin Yuan", "Ge Zhang", "Linyang Li", "Hang Yan", "Jie Fu", "Tao Gui", "Tianxiang Sun", "Yugang Jiang", "Xipeng Qiu" ], "github": "https://github.com/OpenMOSS/AnyGPT", "project_page": "", "abstract": "We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music. AnyGPT can be trained stably without any alterations to the current large language model (LLM) architecture or training paradigms. Instead, it relies exclusively on data-level preprocessing, facilitating the seamless integration of new modalities into LLMs, akin to the incorporation of new languages. We build a multimodal text-centric dataset for multimodal alignment pre-training. Utilizing generative models, we synthesize the first large-scale any-to-any multimodal instruction dataset. It consists of 108k samples of multi-turn conversations that intricately interweave various modalities, thus equipping the model to handle arbitrary combinations of multimodal inputs and outputs. Experimental results demonstrate that AnyGPT is capable of facilitating any-to-any multimodal conversation while achieving performance comparable to specialized models across all modalities, proving that discrete representations can effectively and conveniently unify multiple modalities within a language model. Demos are shown in https://junzhan2000.github.io/AnyGPT.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenMOSS/AnyGPT", "github_api_added_by": "auto" }, { "date": "2024-02-20", "arxiv_id": "2402.12376", "title": "FiT: Flexible Vision Transformer for Diffusion Model", "authors": [ "Zeyu Lu", "Zidong Wang", "Di Huang", "Chengyue Wu", "Xihui Liu", "Wanli Ouyang", "Lei Bai" ], "github": "https://github.com/whlzy/FiT", "project_page": "", "abstract": "Nature is infinitely resolution-free. In the context of this reality, existing diffusion models, such as Diffusion Transformers, often face challenges when processing image resolutions outside of their trained domain. To overcome this limitation, we present the Flexible Vision Transformer (FiT), a transformer architecture specifically designed for generating images with unrestricted resolutions and aspect ratios. Unlike traditional methods that perceive images as static-resolution grids, FiT conceptualizes images as sequences of dynamically-sized tokens. This perspective enables a flexible training strategy that effortlessly adapts to diverse aspect ratios during both training and inference phases, thus promoting resolution generalization and eliminating biases induced by image cropping. Enhanced by a meticulously adjusted network structure and the integration of training-free extrapolation techniques, FiT exhibits remarkable flexibility in resolution extrapolation generation. Comprehensive experiments demonstrate the exceptional performance of FiT across a broad range of resolutions, showcasing its effectiveness both within and beyond its training resolution distribution. Repository available at https://github.com/whlzy/FiT.", "github_manual": "", "github_regex": "https://github.com/whlzy/FiT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-20", "arxiv_id": "2402.11131", "title": "Speculative Streaming: Fast LLM Inference without Auxiliary Models", "authors": [ "Nikhil Bhendawade", "Irina Belousova", "Qichen Fu", "Henry Mason", "Mohammad Rastegari", "Mahyar Najibi" ], "github": "", "project_page": "", "abstract": "Speculative decoding is a prominent technique to speed up the inference of a large target language model based on predictions of an auxiliary draft model. While effective, in application-specific settings, it often involves fine-tuning both draft and target models to achieve high acceptance rates. As the number of downstream tasks grows, these draft models add significant complexity to inference systems. We propose Speculative Streaming, a single-model speculative decoding method that fuses drafting into the target model by changing the fine-tuning objective from next token prediction to future n-gram prediction. Speculative Streaming speeds up decoding by 1.8 - 3.1X in a diverse set of tasks, such as Summarization, Structured Queries, and Meaning Representation, without sacrificing generation quality. Additionally, Speculative Streaming is parameter-efficient. It achieves on-par/higher speed-ups than Medusa-style architectures while using ~10000X fewer extra parameters, making it well-suited for resource-constrained devices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-20", "arxiv_id": "2402.11295", "title": "OneBit: Towards Extremely Low-bit Large Language Models", "authors": [ "Yuzhuang Xu", "Xu Han", "Zonghan Yang", "Shuo Wang", "Qingfu Zhu", "Zhiyuan Liu", "Weidong Liu", "Wanxiang Che" ], "github": "https://github.com/xuyuzhuang11/onebit", "project_page": "", "abstract": "Model quantification uses low bit-width values to represent the weight matrices of models, which is a promising approach to reduce both storage and computational overheads of deploying highly anticipated LLMs. However, existing quantization methods suffer severe performance degradation when the bit-width is extremely reduced, and thus focus on utilizing 4-bit or 8-bit values to quantize models. This paper boldly quantizes the weight matrices of LLMs to 1-bit, paving the way for the extremely low bit-width deployment of LLMs. For this target, we introduce a 1-bit quantization-aware training (QAT) framework named OneBit, including a novel 1-bit parameter representation method to better quantize LLMs as well as an effective parameter initialization method based on matrix decomposition to improve the convergence speed of the QAT framework. Sufficient experimental results indicate that OneBit achieves good performance (at least 83% of the non-quantized performance) with robust training processes when only using 1-bit weight matrices.", "github_manual": "https://github.com/xuyuzhuang11/onebit", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-20", "arxiv_id": "2402.10963", "title": "GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements", "authors": [ "Alex Havrilla", "Sharath Raparthy", "Christoforus Nalmpantis", "Jane Dwivedi-Yu", "Maksym Zhuravinskyi", "Eric Hambro", "Roberta Railneau" ], "github": "", "project_page": "", "abstract": "State-of-the-art language models can exhibit impressive reasoning refinement capabilities on math, science or coding tasks. However, recent work demonstrates that even the best models struggle to identify when and where to refine without access to external feedback. Outcome-based Reward Models (ORMs), trained to predict correctness of the final answer indicating when to refine, offer one convenient solution for deciding when to refine. Process Based Reward Models (PRMs), trained to predict correctness of intermediate steps, can then be used to indicate where to refine. But they are expensive to train, requiring extensive human annotations. In this paper, we propose Stepwise ORMs (SORMs) which are trained, only on synthetic data, to approximate the expected future reward of the optimal policy or V^{star}. More specifically, SORMs are trained to predict the correctness of the final answer when sampling the current policy many times (rather than only once as in the case of ORMs). Our experiments show that SORMs can more accurately detect incorrect reasoning steps compared to ORMs, thus improving downstream accuracy when doing refinements. We then train global refinement models, which take only the question and a draft solution as input and predict a corrected solution, and local refinement models which also take as input a critique indicating the location of the first reasoning error. We generate training data for both models synthetically by reusing data used to train the SORM. We find combining global and local refinements, using the ORM as a reranker, significantly outperforms either one individually, as well as a best of three sample baseline. With this strategy we can improve the accuracy of a LLaMA-2 13B model (already fine-tuned with RL) on GSM8K from 53\\% to 65\\% when greedily sampled.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-20", "arxiv_id": "2402.12219", "title": "Reformatted Alignment", "authors": [ "Run-Ze Fan", "Xuefeng Li", "Haoyang Zou", "Junlong Li", "Shwai He", "Ethan Chern", "Jiewen Hu", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/ReAlign", "project_page": "", "abstract": "The quality of finetuning data is crucial for aligning large language models (LLMs) with human values. Current methods to improve data quality are either labor-intensive or prone to factual errors caused by LLM hallucinations. This paper explores elevating the quality of existing instruction data to better align with human values, introducing a simple and effective approach named ReAlign, which reformats the responses of instruction data into a format that better aligns with pre-established criteria and the collated evidence. This approach minimizes human annotation, hallucination, and the difficulty in scaling, remaining orthogonal to existing alignment techniques. Experimentally, ReAlign significantly boosts the general alignment ability, math reasoning, factuality, and readability of the LLMs. Encouragingly, without introducing any additional data or advanced training techniques, and merely by reformatting the response, LLaMA-2-13B's mathematical reasoning ability on GSM8K can be improved from 46.77% to 56.63% in accuracy. Additionally, a mere 5% of ReAlign data yields a 67% boost in general alignment ability measured by the Alpaca dataset. This work highlights the need for further research into the science and mechanistic interpretability of LLMs. We have made the associated code and data publicly accessible to support future studies at https://github.com/GAIR-NLP/ReAlign.", "github_manual": "", "github_regex": "https://github.com/GAIR-NLP/ReAlign", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-20", "arxiv_id": "2402.11248", "title": "CoLLaVO: Crayon Large Language and Vision mOdel", "authors": [ "Byung-Kwan Lee", "Beomchan Park", "Chae Won Kim", "Yong Man Ro" ], "github": "https://github.com/ByungKwanLee/CoLLaVO", "project_page": "", "abstract": "The remarkable success of Large Language Models (LLMs) and instruction tuning drives the evolution of Vision Language Models (VLMs) towards a versatile general-purpose model. Yet, it remains unexplored whether current VLMs genuinely possess quality object-level image understanding capabilities determined from 'what objects are in the image?' or 'which object corresponds to a specified bounding box?'. Our findings reveal that the image understanding capabilities of current VLMs are strongly correlated with their zero-shot performance on Vision Language (VL) tasks. This suggests that prioritizing basic image understanding is crucial for VLMs to excel at VL tasks. To enhance object-level image understanding, we propose Crayon Large Language and Vision mOdel (CoLLaVO), which incorporates instruction tuning with crayon prompt as a new visual prompt tuning scheme based on panoptic color maps. Furthermore, we present a learning strategy of Dual QLoRA to preserve object-level image understanding without forgetting it during visual instruction tuning, thereby achieving a significant leap in zero-shot numerous VL benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByungKwanLee/CoLLaVO", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-20", "arxiv_id": "2402.11550", "title": "LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration", "authors": [ "Jun Zhao", "Can Zu", "Hao Xu", "Yi Lu", "Wei He", "Yiwen Ding", "Tao Gui", "Qi Zhang", "Xuanjing Huang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated impressive performance in understanding language and executing complex reasoning tasks. However, LLMs with long context windows have been notorious for their expensive training costs and high inference latency. Even the most advanced models such as GPT-4 and Claude2 often make mistakes when processing inputs of over 100k tokens, a phenomenon also known as lost in the middle. In this paper, we propose LongAgent, a method based on multi-agent collaboration, which scales LLMs (e.g., LLaMA) to a context of 128K and demonstrates potential superiority in long-text processing compared to GPT-4. In LongAgent, a leader is responsible for understanding user intent and directing team members to acquire information from documents. Due to members' hallucinations, it is non-trivial for a leader to obtain accurate information from the responses of dozens to hundreds of members. To address this, we develop an inter-member communication mechanism to resolve response conflicts caused by hallucinations through information sharing. Our experimental results indicate that LongAgent offers a promising alternative for long-text processing. The agent team instantiated with LLaMA-7B achieves significant improvements in tasks such as 128k-long text retrieval, multi-hop question answering, compared to GPT-4.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-20", "arxiv_id": "2402.12377", "title": "Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis", "authors": [ "Christian Reiser", "Stephan Garbin", "Pratul P. Srinivasan", "Dor Verbin", "Richard Szeliski", "Ben Mildenhall", "Jonathan T. Barron", "Peter Hedman", "Andreas Geiger" ], "github": "", "project_page": "", "abstract": "While surface-based view synthesis algorithms are appealing due to their low computational requirements, they often struggle to reproduce thin structures. In contrast, more expensive methods that model the scene's geometry as a volumetric density field (e.g. NeRF) excel at reconstructing fine geometric detail. However, density fields often represent geometry in a \"fuzzy\" manner, which hinders exact localization of the surface. In this work, we modify density fields to encourage them to converge towards surfaces, without compromising their ability to reconstruct thin structures. First, we employ a discrete opacity grid representation instead of a continuous density field, which allows opacity values to discontinuously transition from zero to one at the surface. Second, we anti-alias by casting multiple rays per pixel, which allows occlusion boundaries and subpixel structures to be modelled without using semi-transparent voxels. Third, we minimize the binary entropy of the opacity values, which facilitates the extraction of surface geometry by encouraging opacity values to binarize towards the end of training. Lastly, we develop a fusion-based meshing strategy followed by mesh simplification and appearance model fitting. The compact meshes produced by our model can be rendered in real-time on mobile devices and achieve significantly higher view synthesis quality compared to existing mesh-based approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-20", "arxiv_id": "2402.11450", "title": "Learning to Learn Faster from Human Feedback with Language Model Predictive Control", "authors": [ "Jacky Liang", "Fei Xia", "Wenhao Yu", "Andy Zeng", "Montserrat Gonzalez Arenas", "Maria Attarian", "Maria Bauza", "Matthew Bennice", "Alex Bewley", "Adil Dostmohamed", "Chuyuan Kelly Fu", "Nimrod Gileadi", "Marissa Giustina", "Keerthana Gopalakrishnan", "Leonard Hasenclever", "Jan Humplik", "Jasmine Hsu", "Nikhil Joshi", "Ben Jyenis", "Chase Kew", "Sean Kirmani", "Tsang-Wei Edward Lee", "Kuang-Huei Lee", "Assaf Hurwitz Michaely", "Joss Moore", "Ken Oslund", "Dushyant Rao", "Allen Ren", "Baruch Tabanpour", "Quan Vuong", "Ayzaan Wahid", "Ted Xiao", "Ying Xu", "Vincent Zhuang", "Peng Xu", "Erik Frey", "Ken Caluwaerts", "Tingnan Zhang", "Brian Ichter", "Jonathan Tompson", "Leila Takayama", "Vincent Vanhoucke", "Izhak Shafran", "Maja Mataric", "Dorsa Sadigh", "Nicolas Heess", "Kanishka Rao", "Nik Stewart", "Jie Tan", "Carolina Parada" ], "github": "https://github.com/google-deepmind/mujoco", "project_page": "", "abstract": "Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot behaviors, modify them based on feedback, or compose them to perform new tasks. However, these capabilities (driven by in-context learning) are limited to short-term interactions, where users' feedback remains relevant for only as long as it fits within the context size of the LLM, and can be forgotten over longer interactions. In this work, we investigate fine-tuning the robot code-writing LLMs, to remember their in-context interactions and improve their teachability i.e., how efficiently they adapt to human inputs (measured by average number of corrections before the user considers the task successful). Our key observation is that when human-robot interactions are formulated as a partially observable Markov decision process (in which human language inputs are observations, and robot code outputs are actions), then training an LLM to complete previous interactions can be viewed as training a transition dynamics model -- that can be combined with classic robotics techniques such as model predictive control (MPC) to discover shorter paths to success. This gives rise to Language Model Predictive Control (LMPC), a framework that fine-tunes PaLM 2 to improve its teachability on 78 tasks across 5 robot embodiments -- improving non-expert teaching success rates of unseen tasks by 26.9% while reducing the average number of human corrections from 2.4 to 1.9. Experiments show that LMPC also produces strong meta-learners, improving the success rate of in-context learning new tasks on unseen robot embodiments and APIs by 31.5%. See videos, code, and demos at: https://robot-teaching.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/mujoco", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-20", "arxiv_id": "2402.11690", "title": "Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning", "authors": [ "Zhiyang Xu", "Chao Feng", "Rulin Shao", "Trevor Ashby", "Ying Shen", "Di Jin", "Yu Cheng", "Qifan Wang", "Lifu Huang" ], "github": "", "project_page": "", "abstract": "Despite vision-language models' (VLMs) remarkable capabilities as versatile visual assistants, two substantial challenges persist within the existing VLM frameworks: (1) lacking task diversity in pretraining and visual instruction tuning, and (2) annotation error and bias in GPT-4 synthesized instruction tuning data. Both challenges lead to issues such as poor generalizability, hallucination, and catastrophic forgetting. To address these challenges, we construct Vision-Flan, the most diverse publicly available visual instruction tuning dataset to date, comprising 187 diverse tasks and 1,664,261 instances sourced from academic datasets, and each task is accompanied by an expert-written instruction. In addition, we propose a two-stage instruction tuning framework, in which VLMs are firstly finetuned on Vision-Flan and further tuned on GPT-4 synthesized data. We find this two-stage tuning framework significantly outperforms the traditional single-stage visual instruction tuning framework and achieves the state-of-the-art performance across a wide range of multi-modal evaluation benchmarks. Finally, we conduct in-depth analyses to understand visual instruction tuning and our findings reveal that: (1) GPT-4 synthesized data does not substantially enhance VLMs' capabilities but rather modulates the model's responses to human-preferred formats; (2) A minimal quantity (e.g., 1,000) of GPT-4 synthesized data can effectively align VLM responses with human-preference; (3) Visual instruction tuning mainly helps large-language models (LLMs) to understand visual features.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-20", "arxiv_id": "2402.12225", "title": "Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability", "authors": [ "Xuelin Qian", "Yu Wang", "Simian Luo", "Yinda Zhang", "Ying Tai", "Zhenyu Zhang", "Chengjie Wang", "Xiangyang Xue", "Bo Zhao", "Tiejun Huang", "Yunsheng Wu", "Yanwei Fu" ], "github": "", "project_page": "", "abstract": "Auto-regressive models have achieved impressive results in 2D image generation by modeling joint distributions in grid space. In this paper, we extend auto-regressive models to 3D domains, and seek a stronger ability of 3D shape generation by improving auto-regressive models at capacity and scalability simultaneously. Firstly, we leverage an ensemble of publicly available 3D datasets to facilitate the training of large-scale models. It consists of a comprehensive collection of approximately 900,000 objects, with multiple properties of meshes, points, voxels, rendered images, and text captions. This diverse labeled dataset, termed Objaverse-Mix, empowers our model to learn from a wide range of object variations. However, directly applying 3D auto-regression encounters critical challenges of high computational demands on volumetric grids and ambiguous auto-regressive order along grid dimensions, resulting in inferior quality of 3D shapes. To this end, we then present a novel framework Argus3D in terms of capacity. Concretely, our approach introduces discrete representation learning based on a latent vector instead of volumetric grids, which not only reduces computational costs but also preserves essential geometric details by learning the joint distributions in a more tractable order. The capacity of conditional generation can thus be realized by simply concatenating various conditioning inputs to the latent vector, such as point clouds, categories, images, and texts. In addition, thanks to the simplicity of our model architecture, we naturally scale up our approach to a larger model with an impressive 3.6 billion parameters, further enhancing the quality of versatile 3D generation. Extensive experiments on four generation tasks demonstrate that Argus3D can synthesize diverse and faithful shapes across multiple categories, achieving remarkable performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-21", "arxiv_id": "2402.13144", "title": "Neural Network Diffusion", "authors": [ "Kai Wang", "Zhaopan Xu", "Yukun Zhou", "Zelin Zang", "Trevor Darrell", "Zhuang Liu", "Yang You" ], "github": "https://github.com/NUS-HPC-AI-Lab/Neural-Network-Diffusion", "project_page": "", "abstract": "Diffusion models have achieved remarkable success in image and video generation. In this work, we demonstrate that diffusion models can also generate high-performing neural network parameters. Our approach is simple, utilizing an autoencoder and a standard latent diffusion model. The autoencoder extracts latent representations of a subset of the trained network parameters. A diffusion model is then trained to synthesize these latent parameter representations from random noise. It then generates new representations that are passed through the autoencoder's decoder, whose outputs are ready to use as new subsets of network parameters. Across various architectures and datasets, our diffusion process consistently generates models of comparable or improved performance over trained networks, with minimal additional cost. Notably, we empirically find that the generated models perform differently with the trained networks. Our results encourage more exploration on the versatile use of diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NUS-HPC-AI-Lab/Neural-Network-Diffusion", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-21", "arxiv_id": "2402.13250", "title": "Video ReCap: Recursive Captioning of Hour-Long Videos", "authors": [ "Md Mohaiminul Islam", "Ngan Ho", "Xitong Yang", "Tushar Nagarajan", "Lorenzo Torresani", "Gedas Bertasius" ], "github": "https://github.com/md-mohaiminul/VideoRecap", "project_page": "", "abstract": "Most video captioning models are designed to process short video clips of few seconds and output text describing low-level visual concepts (e.g., objects, scenes, atomic actions). However, most real-world videos last for minutes or hours and have a complex hierarchical structure spanning different temporal granularities. We propose Video ReCap, a recursive video captioning model that can process video inputs of dramatically different lengths (from 1 second to 2 hours) and output video captions at multiple hierarchy levels. The recursive video-language architecture exploits the synergy between different video hierarchies and can process hour-long videos efficiently. We utilize a curriculum learning training scheme to learn the hierarchical structure of videos, starting from clip-level captions describing atomic actions, then focusing on segment-level descriptions, and concluding with generating summaries for hour-long videos. Furthermore, we introduce Ego4D-HCap dataset by augmenting Ego4D with 8,267 manually collected long-range video summaries. Our recursive model can flexibly generate captions at different hierarchy levels while also being useful for other complex video understanding tasks, such as VideoQA on EgoSchema. Data, code, and models are available at: https://sites.google.com/view/vidrecap", "github_manual": "https://github.com/md-mohaiminul/VideoRecap", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-21", "arxiv_id": "2402.12712", "title": "MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction", "authors": [ "Shitao Tang", "Jiacheng Chen", "Dilin Wang", "Chengzhou Tang", "Fuyang Zhang", "Yuchen Fan", "Vikas Chandra", "Yasutaka Furukawa", "Rakesh Ranjan" ], "github": "https://github.com/bennyguo/instant-nsr-pl", "project_page": "", "abstract": "This paper presents a neural architecture MVDiffusion++ for 3D object reconstruction that synthesizes dense and high-resolution views of an object given one or a few images without camera poses. MVDiffusion++ achieves superior flexibility and scalability with two surprisingly simple ideas: 1) A ``pose-free architecture'' where standard self-attention among 2D latent features learns 3D consistency across an arbitrary number of conditional and generation views without explicitly using camera pose information; and 2) A ``view dropout strategy'' that discards a substantial number of output views during training, which reduces the training-time memory footprint and enables dense and high-resolution view synthesis at test time. We use the Objaverse for training and the Google Scanned Objects for evaluation with standard novel view synthesis and 3D reconstruction metrics, where MVDiffusion++ significantly outperforms the current state of the arts. We also demonstrate a text-to-3D application example by combining MVDiffusion++ with a text-to-image generative model.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bennyguo/instant-nsr-pl", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-21", "arxiv_id": "2402.13217", "title": "VideoPrism: A Foundational Visual Encoder for Video Understanding", "authors": [ "Long Zhao", "Nitesh B. Gundavarapu", "Liangzhe Yuan", "Hao Zhou", "Shen Yan", "Jennifer J. Sun", "Luke Friedman", "Rui Qian", "Tobias Weyand", "Yue Zhao", "Rachel Hornung", "Florian Schroff", "Ming-Hsuan Yang", "David A. Ross", "Huisheng Wang", "Hartwig Adam", "Mikhail Sirotenko", "Ting Liu", "Boqing Gong" ], "github": "https://github.com/google-deepmind/videoprism", "project_page": "", "abstract": "We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model. We pretrain VideoPrism on a heterogeneous corpus containing 36M high-quality video-caption pairs and 582M video clips with noisy parallel text (e.g., ASR transcripts). The pretraining approach improves upon masked autoencoding by global-local distillation of semantic video embeddings and a token shuffling scheme, enabling VideoPrism to focus primarily on the video modality while leveraging the invaluable text associated with videos. We extensively test VideoPrism on four broad groups of video understanding tasks, from web video question answering to CV for science, achieving state-of-the-art performance on 30 out of 33 video understanding benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/videoprism", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-21", "arxiv_id": "2402.13064", "title": "Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models", "authors": [ "Haoran Li", "Qingxiu Dong", "Zhengyang Tang", "Chaojun Wang", "Xingxing Zhang", "Haoyang Huang", "Shaohan Huang", "Xiaolong Huang", "Zeqiang Huang", "Dongdong Zhang", "Yuxian Gu", "Xin Cheng", "Xun Wang", "Si-Qing Chen", "Li Dong", "Wei Lu", "Zhifang Sui", "Benyou Wang", "Wai Lam", "Furu Wei" ], "github": "", "project_page": "", "abstract": "We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on seed examples or existing datasets to construct instruction tuning data, GLAN exclusively utilizes a pre-curated taxonomy of human knowledge and capabilities as input and generates large-scale synthetic instruction data across all disciplines. Specifically, inspired by the systematic structure in human education system, we build the taxonomy by decomposing human knowledge and capabilities to various fields, sub-fields and ultimately, distinct disciplines semi-automatically, facilitated by LLMs. Subsequently, we generate a comprehensive list of subjects for every discipline and proceed to design a syllabus tailored to each subject, again utilizing LLMs. With the fine-grained key concepts detailed in every class session of the syllabus, we are able to generate diverse instructions with a broad coverage across the entire spectrum of human knowledge and skills. Extensive experiments on large language models (e.g., Mistral) demonstrate that GLAN excels in multiple dimensions from mathematical reasoning, coding, academic exams, logical reasoning to general instruction following without using task-specific training data of these tasks. In addition, GLAN allows for easy customization and new fields or skills can be added by simply incorporating a new node into our taxonomy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-21", "arxiv_id": "2402.13249", "title": "TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization", "authors": [ "Liyan Tang", "Igor Shalyminov", "Amy Wing-mei Wong", "Jon Burnsky", "Jake W. Vincent", "Yu'an Yang", "Siffi Singh", "Song Feng", "Hwanjun Song", "Hang Su", "Lijia Sun", "Yi Zhang", "Saab Mansour", "Kathleen McKeown" ], "github": "https://github.com/amazon-science/tofueval", "project_page": "", "abstract": "Single document news summarization has seen substantial progress on faithfulness in recent years, driven by research on the evaluation of factual consistency, or hallucinations. We ask whether these advances carry over to other text summarization domains. We propose a new evaluation benchmark on topic-focused dialogue summarization, generated by LLMs of varying sizes. We provide binary sentence-level human annotations of the factual consistency of these summaries along with detailed explanations of factually inconsistent sentences. Our analysis shows that existing LLMs hallucinate significant amounts of factual errors in the dialogue domain, regardless of the model's size. On the other hand, when LLMs, including GPT-4, serve as binary factual evaluators, they perform poorly and can be outperformed by prevailing state-of-the-art specialized factuality evaluation metrics. Finally, we conducted an analysis of hallucination types with a curated error taxonomy. We find that there are diverse errors and error distributions in model-generated summaries and that non-LLM based metrics can capture all error types better than LLM-based evaluators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amazon-science/tofueval", "github_api_added_by": "auto" }, { "date": "2024-02-21", "arxiv_id": "2402.13232", "title": "A Touch, Vision, and Language Dataset for Multimodal Alignment", "authors": [ "Letian Fu", "Gaurav Datta", "Huang Huang", "William Chung-Ho Panitch", "Jaimyn Drake", "Joseph Ortiz", "Mustafa Mukadam", "Mike Lambeta", "Roberto Calandra", "Ken Goldberg" ], "github": "https://github.com/Max-Fu/tvl", "project_page": "", "abstract": "Touch is an important sensing modality for humans, but it has not yet been incorporated into a multimodal generative language model. This is partially due to the difficulty of obtaining natural language labels for tactile data and the complexity of aligning tactile readings with both visual observations and language descriptions. As a step towards bridging that gap, this work introduces a new dataset of 44K in-the-wild vision-touch pairs, with English language labels annotated by humans (10%) and textual pseudo-labels from GPT-4V (90%). We use this dataset to train a vision-language-aligned tactile encoder for open-vocabulary classification and a touch-vision-language (TVL) model for text generation using the trained encoder. Results suggest that by incorporating touch, the TVL model improves (+29% classification accuracy) touch-vision-language alignment over existing models trained on any pair of those modalities. Although only a small fraction of the dataset is human-labeled, the TVL model demonstrates improved visual-tactile understanding over GPT-4V (+12%) and open-source vision-language models (+32%) on a new touch-vision understanding benchmark. Code and data: https://tactile-vlm.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Max-Fu/tvl", "github_api_added_by": "auto" }, { "date": "2024-02-21", "arxiv_id": "2402.13220", "title": "How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts", "authors": [ "Yusu Qian", "Haotian Zhang", "Yinfei Yang", "Zhe Gan" ], "github": "https://github.com/apple/ml-mad-bench", "project_page": "", "abstract": "The remarkable advancements in Multimodal Large Language Models (MLLMs) have not rendered them immune to challenges, particularly in the context of handling deceptive information in prompts, thus producing hallucinated responses under such conditions. To quantitatively assess this vulnerability, we present MAD-Bench, a carefully curated benchmark that contains 850 test samples divided into 6 categories, such as non-existent objects, count of objects, spatial relationship, and visual confusion. We provide a comprehensive analysis of popular MLLMs, ranging from GPT-4V, Gemini-Pro, to open-sourced models, such as LLaVA-1.5 and CogVLM. Empirically, we observe significant performance gaps between GPT-4V and other models; and previous robust instruction-tuned models, such as LRV-Instruction and LLaVA-RLHF, are not effective on this new benchmark. While GPT-4V achieves 75.02% accuracy on MAD-Bench, the accuracy of any other model in our experiments ranges from 5% to 35%. We further propose a remedy that adds an additional paragraph to the deceptive prompts to encourage models to think twice before answering the question. Surprisingly, this simple method can even double the accuracy; however, the absolute numbers are still too low to be satisfactory. We hope MAD-Bench can serve as a valuable benchmark to stimulate further research to enhance models' resilience against deceptive prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apple/ml-mad-bench", "github_api_added_by": "auto" }, { "date": "2024-02-21", "arxiv_id": "2402.12659", "title": "The FinBen: An Holistic Financial Benchmark for Large Language Models", "authors": [ "Qianqian Xie", "Weiguang Han", "Zhengyu Chen", "Ruoyu Xiang", "Xiao Zhang", "Yueru He", "Mengxi Xiao", "Dong Li", "Yongfu Dai", "Duanyu Feng", "Yijing Xu", "Haoqiang Kang", "Ziyan Kuang", "Chenhan Yuan", "Kailai Yang", "Zheheng Luo", "Tianlin Zhang", "Zhiwei Liu", "Guojun Xiong", "Zhiyang Deng", "Yuechen Jiang", "Zhiyuan Yao", "Haohang Li", "Yangyang Yu", "Gang Hu", "Jiajia Huang", "Xiao-Yang Liu", "Alejandro Lopez-Lira", "Benyou Wang", "Yanzhao Lai", "Hao Wang", "Min Peng", "Sophia Ananiadou", "Jimin Huang" ], "github": "", "project_page": "", "abstract": "LLMs have transformed NLP and shown promise in various fields, yet their potential in finance is underexplored due to a lack of thorough evaluations and the complexity of financial tasks. This along with the rapid development of LLMs, highlights the urgent need for a systematic financial evaluation benchmark for LLMs. In this paper, we introduce FinBen, the first comprehensive open-sourced evaluation benchmark, specifically designed to thoroughly assess the capabilities of LLMs in the financial domain. FinBen encompasses 35 datasets across 23 financial tasks, organized into three spectrums of difficulty inspired by the Cattell-Horn-Carroll theory, to evaluate LLMs' cognitive abilities in inductive reasoning, associative memory, quantitative reasoning, crystallized intelligence, and more. Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals insights into their strengths and limitations within the financial domain. The findings indicate that GPT-4 leads in quantification, extraction, numerical reasoning, and stock trading, while Gemini shines in generation and forecasting; however, both struggle with complex extraction and forecasting, showing a clear need for targeted enhancements. Instruction tuning boosts simple task performance but falls short in improving complex reasoning and forecasting abilities. FinBen seeks to continuously evaluate LLMs in finance, fostering AI development with regular updates of tasks and models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-21", "arxiv_id": "2402.12908", "title": "RealCompo: Dynamic Equilibrium between Realism and Compositionality Improves Text-to-Image Diffusion Models", "authors": [ "Xinchen Zhang", "Ling Yang", "Yaqi Cai", "Zhaochen Yu", "Jiake Xie", "Ye Tian", "Minkai Xu", "Yong Tang", "Yujiu Yang", "Bin Cui" ], "github": "https://github.com/YangLing0818/RealCompo", "project_page": "", "abstract": "Diffusion models have achieved remarkable advancements in text-to-image generation. However, existing models still have many difficulties when faced with multiple-object compositional generation. In this paper, we propose a new training-free and transferred-friendly text-to-image generation framework, namely RealCompo, which aims to leverage the advantages of text-to-image and layout-to-image models to enhance both realism and compositionality of the generated images. An intuitive and novel balancer is proposed to dynamically balance the strengths of the two models in denoising process, allowing plug-and-play use of any model without extra training. Extensive experiments show that our RealCompo consistently outperforms state-of-the-art text-to-image models and layout-to-image models in multiple-object compositional generation while keeping satisfactory realism and compositionality of the generated images. Code is available at https://github.com/YangLing0818/RealCompo", "github_manual": "", "github_regex": "https://github.com/YangLing0818/RealCompo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-21", "arxiv_id": "2402.13252", "title": "Improving Robustness for Joint Optimization of Camera Poses and Decomposed Low-Rank Tensorial Radiance Fields", "authors": [ "Bo-Yu Cheng", "Wei-Chen Chiu", "Yu-Lun Liu" ], "github": "https://github.com/nemo1999/joint-tensorf", "project_page": "", "abstract": "In this paper, we propose an algorithm that allows joint refinement of camera pose and scene geometry represented by decomposed low-rank tensor, using only 2D images as supervision. First, we conduct a pilot study based on a 1D signal and relate our findings to 3D scenarios, where the naive joint pose optimization on voxel-based NeRFs can easily lead to sub-optimal solutions. Moreover, based on the analysis of the frequency spectrum, we propose to apply convolutional Gaussian filters on 2D and 3D radiance fields for a coarse-to-fine training schedule that enables joint camera pose optimization. Leveraging the decomposition property in decomposed low-rank tensor, our method achieves an equivalent effect to brute-force 3D convolution with only incurring little computational overhead. To further improve the robustness and stability of joint optimization, we also propose techniques of smoothed 2D supervision, randomly scaled kernel parameters, and edge-guided loss mask. Extensive quantitative and qualitative evaluations demonstrate that our proposed framework achieves superior performance in novel view synthesis as well as rapid convergence for optimization.", "github_manual": "https://github.com/nemo1999/joint-tensorf", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-21", "arxiv_id": "2402.12847", "title": "Instruction-tuned Language Models are Better Knowledge Learners", "authors": [ "Zhengbao Jiang", "Zhiqing Sun", "Weijia Shi", "Pedro Rodriguez", "Chunting Zhou", "Graham Neubig", "Xi Victoria Lin", "Wen-tau Yih", "Srinivasan Iyer" ], "github": "", "project_page": "", "abstract": "In order for large language model (LLM)-based assistants to effectively adapt to evolving information needs, it must be possible to update their factual knowledge through continued training on new data. The standard recipe for doing so involves continued pre-training on new documents followed by instruction-tuning on question-answer (QA) pairs. However, we find that LLMs trained with this recipe struggle to answer questions, even though the perplexity of documents is minimized. We found that QA pairs are generally straightforward, while documents are more complex, weaving many factual statements together in an intricate manner. Therefore, we hypothesize that it is beneficial to expose LLMs to QA pairs before continued pre-training on documents so that the process of encoding knowledge from complex documents takes into account how this knowledge is accessed through questions. Based on this, we propose pre-instruction-tuning (PIT), a method that instruction-tunes on questions prior to training on documents. This contrasts with standard instruction-tuning, which learns how to extract knowledge after training on documents. Extensive experiments and ablation studies demonstrate that PIT significantly enhances the ability of LLMs to absorb knowledge from new documents, outperforming standard instruction-tuning by 17.8%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-21", "arxiv_id": "2402.13251", "title": "FlashTex: Fast Relightable Mesh Texturing with LightControlNet", "authors": [ "Kangle Deng", "Timothy Omernick", "Alexander Weiss", "Deva Ramanan", "Jun-Yan Zhu", "Tinghui Zhou", "Maneesh Agrawala" ], "github": "", "project_page": "", "abstract": "Manually creating textures for 3D meshes is time-consuming, even for expert visual content creators. We propose a fast approach for automatically texturing an input 3D mesh based on a user-provided text prompt. Importantly, our approach disentangles lighting from surface material/reflectance in the resulting texture so that the mesh can be properly relit and rendered in any lighting environment. We introduce LightControlNet, a new text-to-image model based on the ControlNet architecture, which allows the specification of the desired lighting as a conditioning image to the model. Our text-to-texture pipeline then constructs the texture in two stages. The first stage produces a sparse set of visually consistent reference views of the mesh using LightControlNet. The second stage applies a texture optimization based on Score Distillation Sampling (SDS) that works with LightControlNet to increase the texture quality while disentangling surface material from lighting. Our pipeline is significantly faster than previous text-to-texture methods, while producing high-quality and relightable textures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-22", "arxiv_id": "2402.13753", "title": "LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens", "authors": [ "Yiran Ding", "Li Lyna Zhang", "Chengruidong Zhang", "Yuanyuan Xu", "Ning Shang", "Jiahang Xu", "Fan Yang", "Mao Yang" ], "github": "https://github.com/microsoft/LongRoPE", "project_page": "", "abstract": "Large context window is a desirable feature in large language models (LLMs). However, due to high fine-tuning costs, scarcity of long texts, and catastrophic values introduced by new token positions, current extended context windows are limited to around 128k tokens. This paper introduces LongRoPE that, for the first time, extends the context window of pre-trained LLMs to an impressive 2048k tokens, with up to only 1k fine-tuning steps at within 256k training lengths, while maintaining performance at the original short context window. This is achieved by three key innovations: (i) we identify and exploit two forms of non-uniformities in positional interpolation through an efficient search, providing a better initialization for fine-tuning and enabling an 8x extension in non-fine-tuning scenarios; (ii) we introduce a progressive extension strategy that first fine-tunes a 256k length LLM and then conducts a second positional interpolation on the fine-tuned extended LLM to achieve a 2048k context window; (iii) we readjust LongRoPE on 8k length to recover the short context window performance. Extensive experiments on LLaMA2 and Mistral across various tasks demonstrate the effectiveness of our method. Models extended via LongRoPE retain the original architecture with minor modifications to the positional embedding, and can reuse most pre-existing optimizations.", "github_manual": "https://github.com/microsoft/LongRoPE", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-22", "arxiv_id": "2402.13349", "title": "Aria Everyday Activities Dataset", "authors": [ "Zhaoyang Lv", "Nickolas Charron", "Pierre Moulon", "Alexander Gamino", "Cheng Peng", "Chris Sweeney", "Edward Miller", "Huixuan Tang", "Jeff Meissner", "Jing Dong", "Kiran Somasundaram", "Luis Pesqueira", "Mark Schwesinger", "Omkar Parkhi", "Qiao Gu", "Renzo De Nardi", "Shangyi Cheng", "Steve Saarinen", "Vijay Baiyya", "Yuyang Zou", "Richard Newcombe", "Jakob Julian Engel", "Xiaqing Pan", "Carl Ren" ], "github": "https://github.com/facebookresearch/projectaria_tools", "project_page": "", "abstract": "We present Aria Everyday Activities (AEA) Dataset, an egocentric multimodal open dataset recorded using Project Aria glasses. AEA contains 143 daily activity sequences recorded by multiple wearers in five geographically diverse indoor locations. Each of the recording contains multimodal sensor data recorded through the Project Aria glasses. In addition, AEA provides machine perception data including high frequency globally aligned 3D trajectories, scene point cloud, per-frame 3D eye gaze vector and time aligned speech transcription. In this paper, we demonstrate a few exemplar research applications enabled by this dataset, including neural scene reconstruction and prompted segmentation. AEA is an open source dataset that can be downloaded from projectaria.com. We are also providing open-source implementations and examples of how to use the dataset in Project Aria Tools.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/projectaria_tools", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-22", "arxiv_id": "2402.12479", "title": "In deep reinforcement learning, a pruned network is a good network", "authors": [ "Johan Obando-Ceron", "Aaron Courville", "Pablo Samuel Castro" ], "github": "https://github.com/google-research/jaxpruner", "project_page": "", "abstract": "Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage prior insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks and exhibit a type of \"scaling law\", using only a small fraction of the full network parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-research/jaxpruner", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-22", "arxiv_id": "2402.13929", "title": "SDXL-Lightning: Progressive Adversarial Diffusion Distillation", "authors": [ "Shanchuan Lin", "Anran Wang", "Xiao Yang" ], "github": "", "project_page": "", "abstract": "We propose a diffusion distillation method that achieves new state-of-the-art in one-step/few-step 1024px text-to-image generation based on SDXL. Our method combines progressive and adversarial distillation to achieve a balance between quality and mode coverage. In this paper, we discuss the theoretical analysis, discriminator design, model formulation, and training techniques. We open-source our distilled SDXL-Lightning models both as LoRA and full UNet weights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-22", "arxiv_id": "2402.13577", "title": "BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models", "authors": [ "Xueliang Zhao", "Xinting Huang", "Tingchen Fu", "Qintong Li", "Shansan Gong", "Lemao Liu", "Wei Bi", "Lingpeng Kong" ], "github": "", "project_page": "", "abstract": "Multimodal reasoning stands as a pivotal capability for large vision-language models (LVLMs). The integration with Domain-Specific Languages (DSL), offering precise visual representations, equips these models with the opportunity to execute more accurate reasoning in complex and professional domains. However, the vanilla Chain-of-Thought (CoT) prompting method faces challenges in effectively leveraging the unique strengths of visual and DSL representations, primarily due to their differing reasoning mechanisms. Additionally, it often falls short in addressing critical steps in multi-step reasoning tasks. To mitigate these challenges, we introduce the Bi-Modal Behavioral Alignment (BBA) prompting method, designed to maximize the potential of DSL in augmenting complex multi-modal reasoning tasks. This method initiates by guiding LVLMs to create separate reasoning chains for visual and DSL representations. Subsequently, it aligns these chains by addressing any inconsistencies, thus achieving a cohesive integration of behaviors from different modalities. Our experiments demonstrate that BBA substantially improves the performance of GPT-4V(ision) on geometry problem solving (28.34% to 34.22%), chess positional advantage prediction (42.08% to 46.99%) and molecular property prediction (77.47% to 83.52%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-22", "arxiv_id": "2402.13598", "title": "User-LLM: Efficient LLM Contextualization with User Embeddings", "authors": [ "Lin Ning", "Luyang Liu", "Jiaxing Wu", "Neo Wu", "Devora Berlowitz", "Sushant Prakash", "Bradley Green", "Shawn O'Banion", "Jun Xie" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have revolutionized natural language processing. However, effectively incorporating complex and potentially noisy user interaction data remains a challenge. To address this, we propose User-LLM, a novel framework that leverages user embeddings to contextualize LLMs. These embeddings, distilled from diverse user interactions using self-supervised pretraining, capture latent user preferences and their evolution over time. We integrate these user embeddings with LLMs through cross-attention and soft-prompting, enabling LLMs to dynamically adapt to user context. Our comprehensive experiments on MovieLens, Amazon Review, and Google Local Review datasets demonstrate significant performance gains across various tasks. Notably, our approach outperforms text-prompt-based contextualization on long sequence tasks and tasks that require deep user understanding while being computationally efficient. We further incorporate Perceiver layers to streamline the integration between user encoders and LLMs, reducing computational demands.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-22", "arxiv_id": "2402.13720", "title": "Ouroboros: Speculative Decoding with Large Model Enhanced Drafting", "authors": [ "Weilin Zhao", "Yuxiang Huang", "Xu Han", "Chaojun Xiao", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/thunlp/Ouroboros", "project_page": "", "abstract": "Drafting-then-verifying decoding methods such as speculative decoding are widely adopted training-free methods to accelerate the inference of large language models (LLMs). Instead of employing an autoregressive process to decode tokens sequentially, speculative decoding initially creates drafts with an efficient small model. Then LLMs are required to conduct verification and correction in a non-autoregressive fashion to minimize time overhead. Generating longer drafts can lead to even more significant speedups once verified, but also incurs substantial trial and error costs if it fails. Suffering from the high verification failure probability, existing decoding methods cannot draft too much content for verification at one time, achieving sub-optimal inference acceleration. In this paper, we introduce Ouroboros, which constructs a phrase candidate pool from the verification process of LLMs to provide candidates for draft generation of the small model. Thereby, Ouroboros can further improve the efficiency and effectiveness of the initial drafts. The experimental results on typical text generation tasks show that Ouroboros achieves speedups of up to 1.9x and 2.8x compared to lookahead decoding and speculative decoding, respectively. The source code of Ouroboros is available at https://github.com/thunlp/Ouroboros.", "github_manual": "", "github_regex": "https://github.com/thunlp/Ouroboros", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-22", "arxiv_id": "2402.14020", "title": "Coercing LLMs to do and reveal (almost) anything", "authors": [ "Jonas Geiping", "Alex Stein", "Manli Shu", "Khalid Saifullah", "Yuxin Wen", "Tom Goldstein" ], "github": "https://github.com/jonasgeiping/carving", "project_page": "", "abstract": "It has recently been shown that adversarial attacks on large language models (LLMs) can \"jailbreak\" the model into making harmful statements. In this work, we argue that the spectrum of adversarial attacks on LLMs is much larger than merely jailbreaking. We provide a broad overview of possible attack surfaces and attack goals. Based on a series of concrete examples, we discuss, categorize and systematize attacks that coerce varied unintended behaviors, such as misdirection, model control, denial-of-service, or data extraction. We analyze these attacks in controlled experiments, and find that many of them stem from the practice of pre-training LLMs with coding capabilities, as well as the continued existence of strange \"glitch\" tokens in common LLM vocabularies that should be removed for security reasons.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jonasgeiping/carving", "github_api_added_by": "auto" }, { "date": "2024-02-22", "arxiv_id": "2402.13616", "title": "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information", "authors": [ "Chien-Yao Wang", "I-Hau Yeh", "Hong-Yuan Mark Liao" ], "github": "https://github.com/WongKinYiu/yolov9", "project_page": "", "abstract": "Today's deep learning methods focus on how to design the most appropriate objective functions so that the prediction results of the model can be closest to the ground truth. Meanwhile, an appropriate architecture that can facilitate acquisition of enough information for prediction has to be designed. Existing methods ignore a fact that when input data undergoes layer-by-layer feature extraction and spatial transformation, large amount of information will be lost. This paper will delve into the important issues of data loss when data is transmitted through deep networks, namely information bottleneck and reversible functions. We proposed the concept of programmable gradient information (PGI) to cope with the various changes required by deep networks to achieve multiple objectives. PGI can provide complete input information for the target task to calculate objective function, so that reliable gradient information can be obtained to update network weights. In addition, a new lightweight network architecture -- Generalized Efficient Layer Aggregation Network (GELAN), based on gradient path planning is designed. GELAN's architecture confirms that PGI has gained superior results on lightweight models. We verified the proposed GELAN and PGI on MS COCO dataset based object detection. The results show that GELAN only uses conventional convolution operators to achieve better parameter utilization than the state-of-the-art methods developed based on depth-wise convolution. PGI can be used for variety of models from lightweight to large. It can be used to obtain complete information, so that train-from-scratch models can achieve better results than state-of-the-art models pre-trained using large datasets, the comparison results are shown in Figure 1. The source codes are at: https://github.com/WongKinYiu/yolov9.", "github_manual": "", "github_regex": "https://github.com/WongKinYiu/yolov9", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-22", "arxiv_id": "2402.14017", "title": "D-Flow: Differentiating through Flows for Controlled Generation", "authors": [ "Heli Ben-Hamu", "Omri Puny", "Itai Gat", "Brian Karrer", "Uriel Singer", "Yaron Lipman" ], "github": "https://github.com/rdkit/rdkit", "project_page": "", "abstract": "Taming the generation outcome of state of the art Diffusion and Flow-Matching (FM) models without having to re-train a task-specific model unlocks a powerful tool for solving inverse problems, conditional generation, and controlled generation in general. In this work we introduce D-Flow, a simple framework for controlling the generation process by differentiating through the flow, optimizing for the source (noise) point. We motivate this framework by our key observation stating that for Diffusion/FM models trained with Gaussian probability paths, differentiating through the generation process projects gradient on the data manifold, implicitly injecting the prior into the optimization process. We validate our framework on linear and non-linear controlled generation problems including: image and audio inverse problems and conditional molecule generation reaching state of the art performance across all.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/rdkit/rdkit", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-22", "arxiv_id": "2402.13763", "title": "Music Style Transfer with Time-Varying Inversion of Diffusion Models", "authors": [ "Sifei Li", "Yuxin Zhang", "Fan Tang", "Chongyang Ma", "Weiming dong", "Changsheng Xu" ], "github": "https://github.com/lsfhuihuiff/MusicTI_AAAI2024", "project_page": "", "abstract": "With the development of diffusion models, text-guided image style transfer has demonstrated high-quality controllable synthesis results. However, the utilization of text for diverse music style transfer poses significant challenges, primarily due to the limited availability of matched audio-text datasets. Music, being an abstract and complex art form, exhibits variations and intricacies even within the same genre, thereby making accurate textual descriptions challenging. This paper presents a music style transfer approach that effectively captures musical attributes using minimal data. We introduce a novel time-varying textual inversion module to precisely capture mel-spectrogram features at different levels. During inference, we propose a bias-reduced stylization technique to obtain stable results. Experimental results demonstrate that our method can transfer the style of specific instruments, as well as incorporate natural sounds to compose melodies. Samples and source code are available at https://lsfhuihuiff.github.io/MusicTI/.", "github_manual": "https://github.com/lsfhuihuiff/MusicTI_AAAI2024", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-22", "arxiv_id": "2402.13573", "title": "ToDo: Token Downsampling for Efficient Generation of High-Resolution Images", "authors": [ "Ethan Smith", "Nayan Saxena", "Aninda Saha" ], "github": "https://github.com/ethansmith2000/ImprovedTokenMerge", "project_page": "", "abstract": "Attention mechanism has been crucial for image diffusion models, however, their quadratic computational complexity limits the sizes of images we can process within reasonable time and memory constraints. This paper investigates the importance of dense attention in generative image models, which often contain redundant features, making them suitable for sparser attention mechanisms. We propose a novel training-free method ToDo that relies on token downsampling of key and value tokens to accelerate Stable Diffusion inference by up to 2x for common sizes and up to 4.5x or more for high resolutions like 2048x2048. We demonstrate that our approach outperforms previous methods in balancing efficient throughput and fidelity.", "github_manual": "https://github.com/ethansmith2000/ImprovedTokenMerge", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14658", "title": "OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement", "authors": [ "Tianyu Zheng", "Ge Zhang", "Tianhao Shen", "Xueling Liu", "Bill Yuchen Lin", "Jie Fu", "Wenhu Chen", "Xiang Yue" ], "github": "https://github.com/opencodeinterpreter/opencodeinterpreter", "project_page": "", "abstract": "The introduction of large language models has significantly advanced code generation. However, open-source models often lack the execution capabilities and iterative refinement of advanced systems like the GPT-4 Code Interpreter. To address this, we introduce OpenCodeInterpreter, a family of open-source code systems designed for generating, executing, and iteratively refining code. Supported by Code-Feedback, a dataset featuring 68K multi-turn interactions, OpenCodeInterpreter integrates execution and human feedback for dynamic code refinement. Our comprehensive evaluation of OpenCodeInterpreter across key benchmarks such as HumanEval, MBPP, and their enhanced versions from EvalPlus reveals its exceptional performance. Notably, OpenCodeInterpreter-33B achieves an accuracy of 83.2 (76.4) on the average (and plus versions) of HumanEval and MBPP, closely rivaling GPT-4's 84.2 (76.2) and further elevates to 91.6 (84.6) with synthesized human feedback from GPT-4. OpenCodeInterpreter brings the gap between open-source code generation models and proprietary systems like GPT-4 Code Interpreter.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/opencodeinterpreter/opencodeinterpreter", "github_api_added_by": "auto" }, { "date": "2024-02-23", "arxiv_id": "2402.14797", "title": "Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis", "authors": [ "Willi Menapace", "Aliaksandr Siarohin", "Ivan Skorokhodov", "Ekaterina Deyneka", "Tsai-Shien Chen", "Anil Kag", "Yuwei Fang", "Aleksei Stoliar", "Elisa Ricci", "Jian Ren", "Sergey Tulyakov" ], "github": "", "project_page": "", "abstract": "Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since video content is highly redundant, we argue that naively bringing advances of image models to the video generation domain reduces motion fidelity, visual quality and impairs scalability. In this work, we build Snap Video, a video-first model that systematically addresses these challenges. To do that, we first extend the EDM framework to take into account spatially and temporally redundant pixels and naturally support video generation. Second, we show that a U-Net - a workhorse behind image generation - scales poorly when generating videos, requiring significant computational overhead. Hence, we propose a new transformer-based architecture that trains 3.31 times faster than U-Nets (and is ~4.5 faster at inference). This allows us to efficiently train a text-to-video model with billions of parameters for the first time, reach state-of-the-art results on a number of benchmarks, and generate videos with substantially higher quality, temporal consistency, and motion complexity. The user studies showed that our model was favored by a large margin over the most recent methods. See our website at https://snap-research.github.io/snapvideo/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14167", "title": "T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching", "authors": [ "Zizheng Pan", "Bohan Zhuang", "De-An Huang", "Weili Nie", "Zhiding Yu", "Chaowei Xiao", "Jianfei Cai", "Anima Anandkumar" ], "github": "https://github.com/NVlabs/T-Stitch", "project_page": "", "abstract": "Sampling from diffusion probabilistic models (DPMs) is often expensive for high-quality image generation and typically requires many steps with a large model. In this paper, we introduce sampling Trajectory Stitching T-Stitch, a simple yet efficient technique to improve the sampling efficiency with little or no generation degradation. Instead of solely using a large DPM for the entire sampling trajectory, T-Stitch first leverages a smaller DPM in the initial steps as a cheap drop-in replacement of the larger DPM and switches to the larger DPM at a later stage. Our key insight is that different diffusion models learn similar encodings under the same training data distribution and smaller models are capable of generating good global structures in the early steps. Extensive experiments demonstrate that T-Stitch is training-free, generally applicable for different architectures, and complements most existing fast sampling techniques with flexible speed and quality trade-offs. On DiT-XL, for example, 40% of the early timesteps can be safely replaced with a 10x faster DiT-S without performance drop on class-conditional ImageNet generation. We further show that our method can also be used as a drop-in technique to not only accelerate the popular pretrained stable diffusion (SD) models but also improve the prompt alignment of stylized SD models from the public model zoo. Code is released at https://github.com/NVlabs/T-Stitch", "github_manual": "", "github_regex": "https://github.com/NVlabs/T-Stitch", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14327", "title": "Subobject-level Image Tokenization", "authors": [ "Delong Chen", "Samuel Cahyawijaya", "Jianfeng Liu", "Baoyuan Wang", "Pascale Fung" ], "github": "https://github.com/ChenDelong1999/subobjects", "project_page": "", "abstract": "Transformer-based vision models typically tokenize images into fixed-size square patches as input units, which lacks the adaptability to image content and overlooks the inherent pixel grouping structure. Inspired by the subword tokenization widely adopted in language models, we propose an image tokenizer at a subobject level, where the subobjects are represented by semantically meaningful image segments obtained by segmentation models (e.g., segment anything models). To implement a learning system based on subobject tokenization, we first introduced a Sequence-to-sequence AutoEncoder (SeqAE) to compress subobject segments of varying sizes and shapes into compact embedding vectors, then fed the subobject embeddings into a large language model for vision language learning. Empirical results demonstrated that our subobject-level tokenization significantly facilitates efficient learning of translating images into object and attribute descriptions compared to the traditional patch-level tokenization. Codes and models will be open-sourced at https://github.com/ChenDelong1999/subobjects.", "github_manual": "", "github_regex": "https://github.com/ChenDelong1999/subobjects", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14289", "title": "TinyLLaVA: A Framework of Small-scale Large Multimodal Models", "authors": [ "Baichuan Zhou", "Ying Hu", "Xi Weng", "Junlong Jia", "Jie Luo", "Xien Liu", "Ji Wu", "Lei Huang" ], "github": "https://github.com/DLCV-BUAA/TinyLLaVABench", "project_page": "", "abstract": "We present the TinyLLaVA framework that provides a unified perspective in designing and analyzing the small-scale Large Multimodal Models (LMMs). We empirically study the effects of different vision encoders, connection modules, language models, training data and training recipes. Our extensive experiments showed that better quality of data combined with better training recipes, smaller LMMs can consistently achieve on-par performances compared to bigger LMMs. Under our framework, we train a family of small-scale LMMs. Our best model, TinyLLaVA-3.1B, achieves better overall performance against existing 7B models such as LLaVA-1.5 and Qwen-VL. We hope our findings can serve as baselines for future research in terms of data scaling, training setups and model selections. Our model weights and codes will be made public.", "github_manual": "https://github.com/DLCV-BUAA/TinyLLaVABench", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14261", "title": "Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming", "authors": [ "Anisha Agarwal", "Aaron Chan", "Shubham Chandel", "Jinu Jang", "Shaun Miller", "Roshanak Zilouchian Moghaddam", "Yevhen Mohylevskyy", "Neel Sundaresan", "Michele Tufano" ], "github": "", "project_page": "", "abstract": "The integration of Large Language Models (LLMs) into Development Environments (IDEs) has become a focal point in modern software development. LLMs such as OpenAI GPT-3.5/4 and Code Llama offer the potential to significantly augment developer productivity by serving as intelligent, chat-driven programming assistants. However, utilizing LLMs out of the box is unlikely to be optimal for any given scenario. Rather, each system requires the LLM to be honed to its set of heuristics to ensure the best performance. In this paper, we introduce the Copilot evaluation harness: a set of data and tools for evaluating LLM-guided IDE interactions, covering various programming scenarios and languages. We propose our metrics as a more robust and information-dense evaluation than previous state of the art evaluation systems. We design and compute both static and execution based success metrics for scenarios encompassing a wide range of developer tasks, including code generation from natural language (generate), documentation generation from code (doc), test case generation (test), bug-fixing (fix), and workspace understanding and query resolution (workspace). These success metrics are designed to evaluate the performance of LLMs within a given IDE and its respective parameter space. Our learnings from evaluating three common LLMs using these metrics can inform the development and validation of future scenarios in LLM guided IDEs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14034", "title": "AgentScope: A Flexible yet Robust Multi-Agent Platform", "authors": [ "Dawei Gao", "Zitao Li", "Weirui Kuang", "Xuchen Pan", "Daoyuan Chen", "Zhijian Ma", "Bingchen Qian", "Liuyi Yao", "Lin Zhu", "Chen Cheng", "Hongzhu Shi", "Yaliang Li", "Bolin Ding", "Jingren Zhou" ], "github": "https://github.com/modelscope/agentscope", "project_page": "", "abstract": "With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications. However, the complexities in coordinating agents' cooperation and LLMs' erratic performance pose notable challenges in developing robust and efficient multi-agent applications. To tackle these challenges, we propose AgentScope, a developer-centric multi-agent platform with message exchange as its core communication mechanism. Together with abundant syntactic tools, built-in resources, and user-friendly interactions, our communication mechanism significantly reduces the barriers to both development and understanding. Towards robust and flexible multi-agent application, AgentScope provides both built-in and customizable fault tolerance mechanisms while it is also armed with system-level supports for multi-modal data generation, storage and transmission. Additionally, we design an actor-based distribution framework, enabling easy conversion between local and distributed deployments and automatic parallel optimization without extra effort. With these features, AgentScope empowers developers to build applications that fully realize the potential of intelligent agents. We have released AgentScope at https://github.com/modelscope/agentscope, and hope AgentScope invites wider participation and innovation in this fast-moving field.", "github_manual": "", "github_regex": "https://github.com/modelscope/agentscope", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14086", "title": "LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons", "authors": [ "Zheng-Xin Yong", "Cristina Menghini", "Stephen H. Bach" ], "github": "", "project_page": "", "abstract": "Data scarcity in low-resource languages can be addressed with word-to-word translations from labeled task data in high-resource languages using bilingual lexicons. However, bilingual lexicons often have limited lexical overlap with task data, which results in poor translation coverage and lexicon utilization. We propose lexicon-conditioned data generation (LexC-Gen), a method that generates low-resource-language classification task data at scale. Specifically, LexC-Gen first uses high-resource-language words from bilingual lexicons to generate lexicon-compatible task data, and then it translates them into low-resource languages with bilingual lexicons via word translation. Across 17 extremely low-resource languages, LexC-Gen generated data is competitive with expert-translated gold data, and yields on average 5.6 and 8.9 points improvement over existing lexicon-based word translation methods on sentiment analysis and topic classification tasks respectively. We show that conditioning on bilingual lexicons is the key component of LexC-Gen. LexC-Gen is also practical -- it only needs a single GPU to generate data at scale. It works well with open-access LLMs, and its cost is one-fifth of the cost of GPT4-based multilingual data generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14818", "title": "PALO: A Polyglot Large Multimodal Model for 5B People", "authors": [ "Muhammad Maaz", "Hanoona Rasheed", "Abdelrahman Shaker", "Salman Khan", "Hisham Cholakal", "Rao M. Anwer", "Tim Baldwin", "Michael Felsberg", "Fahad S. Khan" ], "github": "https://github.com/mbzuai-oryx/PALO", "project_page": "", "abstract": "In pursuit of more inclusive Vision-Language Models (VLMs), this study introduces a Large Multilingual Multimodal Model called Palo. Palo offers visual reasoning capabilities in 10 major languages, including English, Chinese, Hindi, Spanish, French, Arabic, Bengali, Russian, Urdu, and Japanese, that span a total of sim5B people (65\\% of the world population). Our approach involves a semi-automated translation approach to adapt the multimodal instruction dataset from English to the target languages using a fine-tuned Large Language Model, thereby ensuring high linguistic fidelity while allowing scalability due to minimal manual effort. The incorporation of diverse instruction sets helps us boost overall performance across multiple languages especially those that are underrepresented like Hindi, Arabic, Bengali, and Urdu. The resulting models are trained across three scales (1.7B, 7B and 13B parameters) to show the generalization and scalability where we observe substantial improvements compared to strong baselines. We also propose the first multilingual multimodal benchmark for the forthcoming approaches to evaluate their vision-language reasoning capabilities across languages. Code: https://github.com/mbzuai-oryx/PALO.", "github_manual": "", "github_regex": "https://github.com/mbzuai-oryx/PALO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14590", "title": "Scaling Up LLM Reviews for Google Ads Content Moderation", "authors": [ "Wei Qiao", "Tushar Dogra", "Otilia Stretcu", "Yu-Han Lyu", "Tiantian Fang", "Dongjin Kwon", "Chun-Ta Lu", "Enming Luo", "Yuan Wang", "Chih-Chun Chia", "Ariel Fuxman", "Fangzhou Wang", "Ranjay Krishna", "Mehmet Tek" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are powerful tools for content moderation, but their inference costs and latency make them prohibitive for casual use on large datasets, such as the Google Ads repository. This study proposes a method for scaling up LLM reviews for content moderation in Google Ads. First, we use heuristics to select candidates via filtering and duplicate removal, and create clusters of ads for which we select one representative ad per cluster. We then use LLMs to review only the representative ads. Finally, we propagate the LLM decisions for the representative ads back to their clusters. This method reduces the number of reviews by more than 3 orders of magnitude while achieving a 2x recall compared to a baseline non-LLM model. The success of this approach is a strong function of the representations used in clustering and label propagation; we found that cross-modal similarity representations yield better results than uni-modal representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14547", "title": "OmniPred: Language Models as Universal Regressors", "authors": [ "Xingyou Song", "Oscar Li", "Chansoo Lee", "Bangding", "Yang", "Daiyi Peng", "Sagi Perel", "Yutian Chen" ], "github": "https://github.com/google-research/optformer", "project_page": "", "abstract": "Over the broad landscape of experimental design, regression has been a powerful tool to accurately predict the outcome metrics of a system or model given a set of parameters, but has been traditionally restricted to methods which are only applicable to a specific task. In this paper, we propose OmniPred, a framework for training language models as universal end-to-end regressors over (x,y) evaluation data from diverse real world experiments. Using data sourced from Google Vizier, one of the largest blackbox optimization databases in the world, our extensive experiments demonstrate that through only textual representations of mathematical parameters and values, language models are capable of very precise numerical regression, and if given the opportunity to train over multiple tasks, can significantly outperform traditional regression models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/google-research/optformer", "github_api_added_by": "auto" }, { "date": "2024-02-23", "arxiv_id": "2402.14253", "title": "MVD^2: Efficient Multiview 3D Reconstruction for Multiview Diffusion", "authors": [ "Xin-Yang Zheng", "Hao Pan", "Yu-Xiao Guo", "Xin Tong", "Yang Liu" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "As a promising 3D generation technique, multiview diffusion (MVD) has received a lot of attention due to its advantages in terms of generalizability, quality, and efficiency. By finetuning pretrained large image diffusion models with 3D data, the MVD methods first generate multiple views of a 3D object based on an image or text prompt and then reconstruct 3D shapes with multiview 3D reconstruction. However, the sparse views and inconsistent details in the generated images make 3D reconstruction challenging. We present MVD^2, an efficient 3D reconstruction method for multiview diffusion (MVD) images. MVD^2 aggregates image features into a 3D feature volume by projection and convolution and then decodes volumetric features into a 3D mesh. We train MVD^2 with 3D shape collections and MVD images prompted by rendered views of 3D shapes. To address the discrepancy between the generated multiview images and ground-truth views of the 3D shapes, we design a simple-yet-efficient view-dependent training scheme. MVD^2 improves the 3D generation quality of MVD and is fast and robust to various MVD methods. After training, it can efficiently decode 3D meshes from multiview images within one second. We train MVD^2 with Zero-123++ and ObjectVerse-LVIS 3D dataset and demonstrate its superior performance in generating 3D models from multiview images generated by different MVD methods, using both synthetic and real images as prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14083", "title": "Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping", "authors": [ "Lucas Lehnert", "Sainbayar Sukhbaatar", "Paul Mcvay", "Michael Rabbat", "Yuandong Tian" ], "github": "https://github.com/facebookresearch/searchformer", "project_page": "", "abstract": "While Transformers have enabled tremendous progress in various application settings, such architectures still lag behind traditional symbolic planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks and present Searchformer, a Transformer model that optimally solves previously unseen Sokoban puzzles 93.7% of the time, while using up to 26.8% fewer search steps than standard A^* search. Searchformer is an encoder-decoder Transformer model trained to predict the search dynamics of A^*. This model is then fine-tuned via expert iterations to perform fewer search steps than A^* search while still generating an optimal plan. In our training method, A^*'s search dynamics are expressed as a token sequence outlining when task states are added and removed into the search tree during symbolic planning. In our ablation studies on maze navigation, we find that Searchformer significantly outperforms baselines that predict the optimal plan directly with a 5-10times smaller model size and a 10times smaller training dataset. We also demonstrate how Searchformer scales to larger and more complex decision making tasks like Sokoban with improved percentage of solved tasks and shortened search dynamics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/searchformer", "github_api_added_by": "auto" }, { "date": "2024-02-23", "arxiv_id": "2402.14650", "title": "GaussianPro: 3D Gaussian Splatting with Progressive Propagation", "authors": [ "Kai Cheng", "Xiaoxiao Long", "Kaizhi Yang", "Yao Yao", "Wei Yin", "Yuexin Ma", "Wenping Wang", "Xuejin Chen" ], "github": "", "project_page": "", "abstract": "The advent of 3D Gaussian Splatting (3DGS) has recently brought about a revolution in the field of neural rendering, facilitating high-quality renderings at real-time speed. However, 3DGS heavily depends on the initialized point cloud produced by Structure-from-Motion (SfM) techniques. When tackling with large-scale scenes that unavoidably contain texture-less surfaces, the SfM techniques always fail to produce enough points in these surfaces and cannot provide good initialization for 3DGS. As a result, 3DGS suffers from difficult optimization and low-quality renderings. In this paper, inspired by classical multi-view stereo (MVS) techniques, we propose GaussianPro, a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians. Compared to the simple split and clone strategies used in 3DGS, our method leverages the priors of the existing reconstructed geometries of the scene and patch matching techniques to produce new Gaussians with accurate positions and orientations. Experiments on both large-scale and small-scale scenes validate the effectiveness of our method, where our method significantly surpasses 3DGS on the Waymo dataset, exhibiting an improvement of 1.15dB in terms of PSNR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14810", "title": "GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion", "authors": [ "Xueyi Liu", "Li Yi" ], "github": "https://github.com/meowuu7/geneoh-diffusion", "project_page": "", "abstract": "In this work, we tackle the challenging problem of denoising hand-object interactions (HOI). Given an erroneous interaction sequence, the objective is to refine the incorrect hand trajectory to remove interaction artifacts for a perceptually realistic sequence. This challenge involves intricate interaction noise, including unnatural hand poses and incorrect hand-object relations, alongside the necessity for robust generalization to new interactions and diverse noise patterns. We tackle those challenges through a novel approach, GeneOH Diffusion, incorporating two key designs: an innovative contact-centric HOI representation named GeneOH and a new domain-generalizable denoising scheme. The contact-centric representation GeneOH informatively parameterizes the HOI process, facilitating enhanced generalization across various HOI scenarios. The new denoising scheme consists of a canonical denoising model trained to project noisy data samples from a whitened noise space to a clean data manifold and a \"denoising via diffusion\" strategy which can handle input trajectories with various noise patterns by first diffusing them to align with the whitened noise space and cleaning via the canonical denoiser. Extensive experiments on four benchmarks with significant domain variations demonstrate the superior effectiveness of our method. GeneOH Diffusion also shows promise for various downstream applications. Project website: https://meowuu7.github.io/GeneOH-Diffusion/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/meowuu7/geneoh-diffusion", "github_api_added_by": "auto" }, { "date": "2024-02-23", "arxiv_id": "2402.14795", "title": "CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation", "authors": [ "Jun Wang", "Yuzhe Qin", "Kaiming Kuang", "Yigit Korkmaz", "Akhilan Gurumoorthy", "Hao Su", "Xiaolong Wang" ], "github": "", "project_page": "", "abstract": "We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks. By incorporating extensive data augmentation in a simulated environment, CyberDemo outperforms traditional in-domain real-world demonstrations when transferred to the real world, handling diverse physical and visual conditions. Regardless of its affordability and convenience in data collection, CyberDemo outperforms baseline methods in terms of success rates across various tasks and exhibits generalizability with previously unseen objects. For example, it can rotate novel tetra-valve and penta-valve, despite human demonstrations only involving tri-valves. Our research demonstrates the significant potential of simulated human demonstrations for real-world dexterous manipulation tasks. More details can be found at https://cyber-demo.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14194", "title": "BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay", "authors": [ "Catherine Weaver", "Chen Tang", "Ce Hao", "Kenta Kawamoto", "Masayoshi Tomizuka", "Wei Zhan" ], "github": "", "project_page": "", "abstract": "Imitation learning learns a policy from demonstrations without requiring hand-designed reward functions. In many robotic tasks, such as autonomous racing, imitated policies must model complex environment dynamics and human decision-making. Sequence modeling is highly effective in capturing intricate patterns of motion sequences but struggles to adapt to new environments or distribution shifts that are common in real-world robotics tasks. In contrast, Adversarial Imitation Learning (AIL) can mitigate this effect, but struggles with sample inefficiency and handling complex motion patterns. Thus, we propose BeTAIL: Behavior Transformer Adversarial Imitation Learning, which combines a Behavior Transformer (BeT) policy from human demonstrations with online AIL. BeTAIL adds an AIL residual policy to the BeT policy to model the sequential decision-making process of human experts and correct for out-of-distribution states or shifts in environment dynamics. We test BeTAIL on three challenges with expert-level demonstrations of real human gameplay in Gran Turismo Sport. Our proposed residual BeTAIL reduces environment interactions and improves racing performance and stability, even when the BeT is pretrained on different tracks than downstream learning. Videos and code available at: https://sites.google.com/berkeley.edu/BeTAIL/home.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14180", "title": "Linear Transformers are Versatile In-Context Learners", "authors": [ "Max Vladymyrov", "Johannes von Oswald", "Mark Sandler", "Rong Ge" ], "github": "", "project_page": "", "abstract": "Recent research has demonstrated that transformers, particularly linear attention models, implicitly execute gradient-descent-like algorithms on data provided in-context during their forward inference step. However, their capability in handling more complex problems remains unexplored. In this paper, we prove that any linear transformer maintains an implicit linear model and can be interpreted as performing a variant of preconditioned gradient descent. We also investigate the use of linear transformers in a challenging scenario where the training data is corrupted with different levels of noise. Remarkably, we demonstrate that for this problem linear transformers discover an intricate and highly effective optimization algorithm, surpassing or matching in performance many reasonable baselines. We reverse-engineer this algorithm and show that it is a novel approach incorporating momentum and adaptive rescaling based on noise levels. Our findings show that even linear transformers possess the surprising ability to discover sophisticated optimization strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-23", "arxiv_id": "2402.14792", "title": "Consolidating Attention Features for Multi-view Image Editing", "authors": [ "Or Patashnik", "Rinon Gal", "Daniel Cohen-Or", "Jun-Yan Zhu", "Fernando De la Torre" ], "github": "", "project_page": "", "abstract": "Large-scale text-to-image models enable a wide range of image editing techniques, using text prompts or even spatial controls. However, applying these editing methods to multi-view images depicting a single scene leads to 3D-inconsistent results. In this work, we focus on spatial control-based geometric manipulations and introduce a method to consolidate the editing process across various views. We build on two insights: (1) maintaining consistent features throughout the generative process helps attain consistency in multi-view editing, and (2) the queries in self-attention layers significantly influence the image structure. Hence, we propose to improve the geometric consistency of the edited images by enforcing the consistency of the queries. To do so, we introduce QNeRF, a neural radiance field trained on the internal query features of the edited images. Once trained, QNeRF can render 3D-consistent queries, which are then softly injected back into the self-attention layers during generation, greatly improving multi-view consistency. We refine the process through a progressive, iterative method that better consolidates queries across the diffusion timesteps. We compare our method to a range of existing techniques and demonstrate that it can achieve better multi-view consistency and higher fidelity to the input scene. These advantages allow us to train NeRFs with fewer visual artifacts, that are better aligned with the target geometry.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.15504", "title": "Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition", "authors": [ "Chun-Hsiao Yeh", "Ta-Ying Cheng", "He-Yen Hsieh", "Chuan-En Lin", "Yi Ma", "Andrew Markham", "Niki Trigoni", "H. T. Kung", "Yubei Chen" ], "github": "", "project_page": "", "abstract": "Recent text-to-image diffusion models are able to learn and synthesize images containing novel, personalized concepts (e.g., their own pets or specific items) with just a few examples for training. This paper tackles two interconnected issues within this realm of personalizing text-to-image diffusion models. First, current personalization techniques fail to reliably extend to multiple concepts -- we hypothesize this to be due to the mismatch between complex scenes and simple text descriptions in the pre-training dataset (e.g., LAION). Second, given an image containing multiple personalized concepts, there lacks a holistic metric that evaluates performance on not just the degree of resemblance of personalized concepts, but also whether all concepts are present in the image and whether the image accurately reflects the overall text description. To address these issues, we introduce Gen4Gen, a semi-automated dataset creation pipeline utilizing generative models to combine personalized concepts into complex compositions along with text-descriptions. Using this, we create a dataset called MyCanvas, that can be used to benchmark the task of multi-concept personalization. In addition, we design a comprehensive metric comprising two scores (CP-CLIP and TI-CLIP) for better quantifying the performance of multi-concept, personalized text-to-image diffusion methods. We provide a simple baseline built on top of Custom Diffusion with empirical prompting strategies for future researchers to evaluate on MyCanvas. We show that by improving data quality and prompting strategies, we can significantly increase multi-concept personalized image generation quality, without requiring any modifications to model architecture or training algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.15021", "title": "CLoVe: Encoding Compositional Language in Contrastive Vision-Language Models", "authors": [ "Santiago Castro", "Amir Ziai", "Avneesh Saluja", "Zhuoning Yuan", "Rada Mihalcea" ], "github": "https://github.com/netflix/clove", "project_page": "", "abstract": "Recent years have witnessed a significant increase in the performance of Vision and Language tasks. Foundational Vision-Language Models (VLMs), such as CLIP, have been leveraged in multiple settings and demonstrated remarkable performance across several tasks. Such models excel at object-centric recognition yet learn text representations that seem invariant to word order, failing to compose known concepts in novel ways. However, no evidence exists that any VLM, including large-scale single-stream models such as GPT-4V, identifies compositions successfully. In this paper, we introduce a framework to significantly improve the ability of existing models to encode compositional language, with over 10% absolute improvement on compositionality benchmarks, while maintaining or improving the performance on standard object-recognition and retrieval benchmarks. Our code and pre-trained models are publicly available at https://github.com/netflix/clove.", "github_manual": "", "github_regex": "https://github.com/netflix/clove", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.14904", "title": "Watermarking Makes Language Models Radioactive", "authors": [ "Tom Sander", "Pierre Fernandez", "Alain Durmus", "Matthijs Douze", "Teddy Furon" ], "github": "https://github.com/facebookresearch/radioactive-watermark", "project_page": "", "abstract": "This paper investigates the radioactivity of LLM-generated texts, i.e. whether it is possible to detect that such input was used as training data. Conventional methods like membership inference can carry out this detection with some level of accuracy. We show that watermarked training data leaves traces easier to detect and much more reliable than membership inference. We link the contamination level to the watermark robustness, its proportion in the training set, and the fine-tuning process. We notably demonstrate that training on watermarked synthetic instructions can be detected with high confidence (p-value < 1e-5) even when as little as 5% of training text is watermarked. Thus, LLM watermarking, originally designed for detecting machine-generated text, gives the ability to easily identify if the outputs of a watermarked LLM were used to fine-tune another LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/radioactive-watermark", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.14848", "title": "Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models", "authors": [ "Mosh Levy", "Alon Jacoby", "Yoav Goldberg" ], "github": "", "project_page": "", "abstract": "This paper explores the impact of extending input lengths on the capabilities of Large Language Models (LLMs). Despite LLMs advancements in recent times, their performance consistency across different input lengths is not well understood. We investigate this aspect by introducing a novel QA reasoning framework, specifically designed to assess the impact of input length. We isolate the effect of input length using multiple versions of the same sample, each being extended with padding of different lengths, types and locations. Our findings show a notable degradation in LLMs' reasoning performance at much shorter input lengths than their technical maximum. We show that the degradation trend appears in every version of our dataset, although at different intensities. Additionally, our study reveals that traditional perplexity metrics do not correlate with performance of LLMs' in long input reasoning tasks. We analyse our results and identify failure modes that can serve as useful guides for future research, potentially informing strategies to address the limitations observed in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.14830", "title": "Orca-Math: Unlocking the potential of SLMs in Grade School Math", "authors": [ "Arindam Mitra", "Hamed Khanpour", "Corby Rosset", "Ahmed Awadallah" ], "github": "", "project_page": "", "abstract": "Mathematical word problem-solving has long been recognized as a complex task for small language models (SLMs). A recent study hypothesized that the smallest model size, needed to achieve over 80% accuracy on the GSM8K benchmark, is 34 billion parameters. To reach this level of performance with smaller models, researcher often train SLMs to generate Python code or use tools to help avoid calculation errors. Additionally, they employ ensembling, where outputs of up to 100 model runs are combined to arrive at a more accurate result. Result selection is done using consensus, majority vote or a separate a verifier model used in conjunction with the SLM. Ensembling provides a substantial boost in accuracy but at a significant cost increase with multiple calls to the model (e.g., Phi-GSM uses top-48 to boost the performance from 68.2 to 81.5). In this work, we present Orca-Math, a 7-billion-parameter SLM based on the Mistral-7B, which achieves 86.81% on GSM8k without the need for multiple model calls or the use of verifiers, code execution or any other external tools. Our approach has the following key elements: (1) A high quality synthetic dataset of 200K math problems created using a multi-agent setup where agents collaborate to create the data, (2) An iterative learning techniques that enables the SLM to practice solving problems, receive feedback on its solutions and learn from preference pairs incorporating the SLM solutions and the feedback. When trained with Supervised Fine-Tuning alone, Orca-Math achieves 81.50% on GSM8k pass@1 metric. With iterative preference learning, Orca-Math achieves 86.81% pass@1. Orca-Math surpasses the performance of significantly larger models such as LLAMA-2-70B, WizardMath-70B, Gemini-Pro, ChatGPT-3.5. It also significantly outperforms other smaller models while using much smaller data (hundreds of thousands vs. millions of problems).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.15391", "title": "Genie: Generative Interactive Environments", "authors": [ "Jake Bruce", "Michael Dennis", "Ashley Edwards", "Jack Parker-Holder", "Yuge Shi", "Edward Hughes", "Matthew Lai", "Aditi Mavalankar", "Richie Steigerwald", "Chris Apps", "Yusuf Aytar", "Sarah Bechtle", "Feryal Behbahani", "Stephanie Chan", "Nicolas Heess", "Lucy Gonzalez", "Simon Osindero", "Sherjil Ozair", "Scott Reed", "Jingwei Zhang", "Konrad Zolna", "Jeff Clune", "Nando de Freitas", "Satinder Singh", "Tim Rockt\u00e4schel" ], "github": "", "project_page": "", "abstract": "We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos. The model can be prompted to generate an endless variety of action-controllable virtual worlds described through text, synthetic images, photographs, and even sketches. At 11B parameters, Genie can be considered a foundation world model. It is comprised of a spatiotemporal video tokenizer, an autoregressive dynamics model, and a simple and scalable latent action model. Genie enables users to act in the generated environments on a frame-by-frame basis despite training without any ground-truth action labels or other domain-specific requirements typically found in the world model literature. Further the resulting learned latent action space facilitates training agents to imitate behaviors from unseen videos, opening the path for training generalist agents of the future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.15506", "title": "AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning", "authors": [ "Jianguo Zhang", "Tian Lan", "Rithesh Murthy", "Zhiwei Liu", "Weiran Yao", "Juntao Tan", "Thai Hoang", "Liangwei Yang", "Yihao Feng", "Zuxin Liu", "Tulika Awalgaonkar", "Juan Carlos Niebles", "Silvio Savarese", "Shelby Heinecke", "Huan Wang", "Caiming Xiong" ], "github": "https://github.com/Significant-Gravitas/Auto-GPT", "project_page": "", "abstract": "Autonomous agents powered by large language models (LLMs) have garnered significant research attention. However, fully harnessing the potential of LLMs for agent-based tasks presents inherent challenges due to the heterogeneous nature of diverse data sources featuring multi-turn trajectories. In this paper, we introduce AgentOhana as a comprehensive solution to address these challenges. AgentOhana aggregates agent trajectories from distinct environments, spanning a wide array of scenarios. It meticulously standardizes and unifies these trajectories into a consistent format, streamlining the creation of a generic data loader optimized for agent training. Leveraging the data unification, our training pipeline maintains equilibrium across different data sources and preserves independent randomness across devices during dataset partitioning and model training. Additionally, we present xLAM-v0.1, a large action model tailored for AI agents, which demonstrates exceptional performance across various benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Significant-Gravitas/Auto-GPT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.15491", "title": "API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs", "authors": [ "Kinjal Basu", "Ibrahim Abdelaziz", "Subhajit Chaudhury", "Soham Dan", "Maxwell Crouse", "Asim Munawar", "Sadhana Kumaravel", "Vinod Muthusamy", "Pavan Kapanipathi", "Luis A. Lastras" ], "github": "https://github.com/ibm/api-blend", "project_page": "", "abstract": "There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks. As such, there is tremendous interest in methods that can acquire sufficient quantities of train and test data that involve calls to tools / APIs. Two lines of research have emerged as the predominant strategies for addressing this challenge. The first has focused on synthetic data generation techniques, while the second has involved curating task-adjacent datasets which can be transformed into API / Tool-based tasks. In this paper, we focus on the task of identifying, curating, and transforming existing datasets and, in turn, introduce API-BLEND, a large corpora for training and systematic testing of tool-augmented LLMs. The datasets mimic real-world scenarios involving API-tasks such as API / tool detection, slot filling, and sequencing of the detected APIs. We demonstrate the utility of the API-BLEND dataset for both training and benchmarking purposes.", "github_manual": "https://github.com/ibm/api-blend", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.15319", "title": "GPTVQ: The Blessing of Dimensionality for LLM Quantization", "authors": [ "Mart van Baalen", "Andrey Kuzmin", "Markus Nagel", "Peter Couperus", "Cedric Bastoul", "Eric Mahurin", "Tijmen Blankevoort", "Paul Whatmough" ], "github": "", "project_page": "", "abstract": "In this work we show that the size versus accuracy trade-off of neural network quantization can be significantly improved by increasing the quantization dimensionality. We propose the GPTVQ method, a new fast method for post-training vector quantization (VQ) that scales well to Large Language Models (LLMs). Our method interleaves quantization of one or more columns with updates to the remaining unquantized weights, using information from the Hessian of the per-layer output reconstruction MSE. Quantization codebooks are initialized using an efficient data-aware version of the EM algorithm. The codebooks are then updated, and further compressed by using integer quantization and SVD-based compression. GPTVQ establishes a new state-of-the art in the size vs accuracy trade-offs on a wide range of LLMs such as Llama-v2 and Mistral. Furthermore, our method is efficient: on a single H100 it takes between 3 and 11 hours to process a Llamav2-70B model, depending on quantization setting. Lastly, with on-device timings for VQ decompression on a mobile CPU we show that VQ leads to improved latency compared to using a 4-bit integer format.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.15220", "title": "ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition", "authors": [ "Lu Ye", "Ze Tao", "Yong Huang", "Yang Li" ], "github": "https://github.com/microsoft/chunk-attention", "project_page": "", "abstract": "Self-attention is an essential component of large language models(LLMs) but a significant source of inference latency for long sequences. In multi-tenant LLMs serving scenarios, the compute and memory operation cost of self-attention can be optimized by using the probability that multiple LLM requests have shared system prompts in prefixes. In this paper, we introduce ChunkAttention, a prefix-aware self-attention module that can detect matching prompt prefixes across multiple requests and share their key/value tensors in memory at runtime to improve the memory utilization of KV cache. This is achieved by breaking monolithic key/value tensors into smaller chunks and structuring them into the auxiliary prefix tree. Consequently, on top of the prefix-tree based KV cache, we design an efficient self-attention kernel, where a two-phase partition algorithm is implemented to improve the data locality during self-attention computation in the presence of shared system prompts. Experiments show that ChunkAttention can speed up the self-attention kernel by 3.2-4.8times compared to the start-of-the-art implementation, with the length of the system prompt ranging from 1024 to 4096.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/chunk-attention", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.15000", "title": "Divide-or-Conquer? Which Part Should You Distill Your LLM?", "authors": [ "Zhuofeng Wu", "He Bai", "Aonan Zhang", "Jiatao Gu", "VG Vinod Vydiswaran", "Navdeep Jaitly", "Yizhe Zhang" ], "github": "https://github.com/apple/ml-divide-or-conquer", "project_page": "", "abstract": "Recent methods have demonstrated that Large Language Models (LLMs) can solve reasoning tasks better when they are encouraged to solve subtasks of the main task first. In this paper we devise a similar strategy that breaks down reasoning tasks into a problem decomposition phase and a problem solving phase and show that the strategy is able to outperform a single stage solution. Further, we hypothesize that the decomposition should be easier to distill into a smaller model compared to the problem solving because the latter requires large amounts of domain knowledge while the former only requires learning general problem solving strategies. We propose methods to distill these two capabilities and evaluate their impact on reasoning outcomes and inference cost. We find that we can distill the problem decomposition phase and at the same time achieve good generalization across tasks, datasets, and models. However, it is harder to distill the problem solving capability without losing performance and the resulting distilled model struggles with generalization. These results indicate that by using smaller, distilled problem decomposition models in combination with problem solving LLMs we can achieve reasoning with cost-efficient inference and local adaptation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/ml-divide-or-conquer", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.14905", "title": "MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases", "authors": [ "Zechun Liu", "Changsheng Zhao", "Forrest Iandola", "Chen Lai", "Yuandong Tian", "Igor Fedorov", "Yunyang Xiong", "Ernie Chang", "Yangyang Shi", "Raghuraman Krishnamoorthi", "Liangzhen Lai", "Vikas Chandra" ], "github": "https://github.com/facebookresearch/mobilellm", "project_page": "", "abstract": "This paper addresses the growing need for efficient large language models (LLMs) on mobile devices, driven by increasing cloud costs and latency concerns. We focus on designing top-quality LLMs with fewer than a billion parameters, a practical choice for mobile deployment. Contrary to prevailing belief emphasizing the pivotal role of data and parameter quantity in determining model quality, our investigation underscores the significance of model architecture for sub-billion scale LLMs. Leveraging deep and thin architectures, coupled with embedding sharing and grouped-query attention mechanisms, we establish a strong baseline network denoted as MobileLLM, which attains a remarkable 2.7%/4.3% accuracy boost over preceding 125M/350M state-of-the-art models. Additionally, we propose an immediate block-wise weight sharing approach with no increase in model size and only marginal latency overhead. The resultant models, denoted as MobileLLM-LS, demonstrate a further accuracy enhancement of 0.7%/0.8% than MobileLLM 125M/350M. Moreover, MobileLLM model family shows significant improvements compared to previous sub-billion models on chat benchmarks, and demonstrates close correctness to LLaMA-v2 7B in API calling tasks, highlighting the capability of small models for common on-device use cases.", "github_manual": "https://github.com/facebookresearch/mobilellm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-26", "arxiv_id": "2402.15509", "title": "Seamless Human Motion Composition with Blended Positional Encodings", "authors": [ "German Barquero", "Sergio Escalera", "Cristina Palmero" ], "github": "https://github.com/BarqueroGerman/FlowMDM", "project_page": "", "abstract": "Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/BarqueroGerman/FlowMDM", "github_api_added_by": "auto" }, { "date": "2024-02-27", "arxiv_id": "2402.16107", "title": "FuseChat: Knowledge Fusion of Chat Models", "authors": [ "Fanqi Wan", "Ziyi Yang", "Longguang Zhong", "Xiaojun Quan", "Xinting Huang", "Wei Bi" ], "github": "https://github.com/fanqiwan/FuseLLM", "project_page": "", "abstract": "While training large language models (LLMs) from scratch can indeed lead to models with distinct capabilities and strengths, this approach incurs substantial costs and may lead to potential redundancy in competencies. An alternative strategy is to combine existing LLMs into a more robust LLM, thereby diminishing the necessity for expensive pre-training. However, due to the diverse architectures of LLMs, direct parameter blending proves to be unfeasible. Recently, FuseLLM introduced the concept of knowledge fusion to transfer the collective knowledge of multiple structurally varied LLMs into a target LLM through lightweight continual training. In this report, we extend the scalability and flexibility of the FuseLLM framework to realize the fusion of chat LLMs, resulting in FuseChat. FuseChat comprises two main stages. Firstly, we undertake knowledge fusion for structurally and scale-varied source LLMs to derive multiple target LLMs of identical structure and size via lightweight fine-tuning. Then, these target LLMs are merged within the parameter space, wherein we propose a novel method for determining the merging weights based on the variation ratio of parameter matrices before and after fine-tuning. We validate our approach using three prominent chat LLMs with diverse architectures and scales, namely NH2-Mixtral-8x7B, NH2-Solar-10.7B, and OpenChat-3.5-7B. Experimental results spanning various chat domains demonstrate the superiority of \\textsc{FuseChat-7B} across a broad spectrum of chat LLMs at 7B and 34B scales, even surpassing GPT-3.5 (March) and approaching Mixtral-8x7B-Instruct. Our code, model weights, and data are openly accessible at https://github.com/fanqiwan/FuseLLM.", "github_manual": "", "github_regex": "https://github.com/fanqiwan/FuseLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-27", "arxiv_id": "2402.16153", "title": "ChatMusician: Understanding and Generating Music Intrinsically with LLM", "authors": [ "Ruibin Yuan", "Hanfeng Lin", "Yi Wang", "Zeyue Tian", "Shangda Wu", "Tianhao Shen", "Ge Zhang", "Yuhang Wu", "Cong Liu", "Ziya Zhou", "Ziyang Ma", "Liumeng Xue", "Ziyu Wang", "Qin Liu", "Tianyu Zheng", "Yizhi Li", "Yinghao Ma", "Yiming Liang", "Xiaowei Chi", "Ruibo Liu", "Zili Wang", "Pengfei Li", "Jingcheng Wu", "Chenghua Lin", "Qifeng Liu", "Tao Jiang", "Wenhao Huang", "Wenhu Chen", "Emmanouil Benetos", "Jie Fu", "Gus Xia", "Roger Dannenberg", "Wei Xue", "Shiyin Kang", "Yike Guo" ], "github": "https://github.com/hf-lin/ChatMusician", "project_page": "", "abstract": "While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative language. We introduce ChatMusician, an open-source LLM that integrates intrinsic musical abilities. It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language. ChatMusician can understand and generate music with a pure text tokenizer without any external multi-modal neural structures or tokenizers. Interestingly, endowing musical abilities does not harm language abilities, even achieving a slightly higher MMLU score. Our model is capable of composing well-structured, full-length music, conditioned on texts, chords, melodies, motifs, musical forms, etc, surpassing GPT-4 baseline. On our meticulously curated college-level music understanding benchmark, MusicTheoryBench, ChatMusician surpasses LLaMA2 and GPT-3.5 on zero-shot setting by a noticeable margin. Our work reveals that LLMs can be an excellent compressor for music, but there remains significant territory to be conquered. We release our 4B token music-language corpora MusicPile, the collected MusicTheoryBench, code, model and demo in GitHub.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hf-lin/ChatMusician", "github_api_added_by": "auto" }, { "date": "2024-02-27", "arxiv_id": "2402.16843", "title": "Multi-LoRA Composition for Image Generation", "authors": [ "Ming Zhong", "Yelong Shen", "Shuohang Wang", "Yadong Lu", "Yizhu Jiao", "Siru Ouyang", "Donghan Yu", "Jiawei Han", "Weizhu Chen" ], "github": "", "project_page": "", "abstract": "Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated images. Nonetheless, existing methods face challenges in effectively composing multiple LoRAs, especially as the number of LoRAs to be integrated grows, thus hindering the creation of complex imagery. In this paper, we study multi-LoRA composition through a decoding-centric perspective. We present two training-free methods: LoRA Switch, which alternates between different LoRAs at each denoising step, and LoRA Composite, which simultaneously incorporates all LoRAs to guide more cohesive image synthesis. To evaluate the proposed approaches, we establish ComposLoRA, a new comprehensive testbed as part of this research. It features a diverse range of LoRA categories with 480 composition sets. Utilizing an evaluation framework based on GPT-4V, our findings demonstrate a clear improvement in performance with our methods over the prevalent baseline, particularly evident when increasing the number of LoRAs in a composition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-27", "arxiv_id": "2402.15627", "title": "MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs", "authors": [ "Ziheng Jiang", "Haibin Lin", "Yinmin Zhong", "Qi Huang", "Yangrui Chen", "Zhi Zhang", "Yanghua Peng", "Xiang Li", "Cong Xie", "Shibiao Nong", "Yulu Jia", "Sun He", "Hongmin Chen", "Zhihao Bai", "Qi Hou", "Shipeng Yan", "Ding Zhou", "Yiyao Sheng", "Zhuo Jiang", "Haohan Xu", "Haoran Wei", "Zhang Zhang", "Pengfei Nie", "Leqi Zou", "Sida Zhao", "Liang Xiang", "Zherui Liu", "Zhe Li", "Xiaoying Jia", "Jianxi Ye", "Xin Jin", "Xin Liu" ], "github": "", "project_page": "", "abstract": "We present the design, implementation and engineering experience in building and deploying MegaScale, a production system for training large language models (LLMs) at the scale of more than 10,000 GPUs. Training LLMs at this scale brings unprecedented challenges to training efficiency and stability. We take a full-stack approach that co-designs the algorithmic and system components across model block and optimizer design, computation and communication overlapping, operator optimization, data pipeline, and network performance tuning. Maintaining high efficiency throughout the training process (i.e., stability) is an important consideration in production given the long extent of LLM training jobs. Many hard stability issues only emerge at large scale, and in-depth observability is the key to address them. We develop a set of diagnosis tools to monitor system components and events deep in the stack, identify root causes, and derive effective techniques to achieve fault tolerance and mitigate stragglers. MegaScale achieves 55.2% Model FLOPs Utilization (MFU) when training a 175B LLM model on 12,288 GPUs, improving the MFU by 1.34x compared to Megatron-LM. We share our operational experience in identifying and fixing failures and stragglers. We hope by articulating the problems and sharing our experience from a systems perspective, this work can inspire future LLM systems research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-27", "arxiv_id": "2402.16840", "title": "MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT", "authors": [ "Omkar Thawakar", "Ashmal Vayani", "Salman Khan", "Hisham Cholakal", "Rao M. Anwer", "Michael Felsberg", "Tim Baldwin", "Eric P. Xing", "Fahad Shahbaz Khan" ], "github": "https://github.com/mbzuai-oryx/MobiLlama", "project_page": "", "abstract": "\"Bigger the better\" has been the predominant trend in recent Large Language Models (LLMs) development. However, LLMs do not suit well for scenarios that require on-device processing, energy efficiency, low memory footprint, and response efficiency. These requisites are crucial for privacy, security, and sustainable deployment. This paper explores the \"less is more\" paradigm by addressing the challenge of designing accurate yet efficient Small Language Models (SLMs) for resource constrained devices. Our primary contribution is the introduction of an accurate and fully transparent open-source 0.5 billion (0.5B) parameter SLM, named MobiLlama, catering to the specific needs of resource-constrained computing with an emphasis on enhanced performance with reduced resource demands. MobiLlama is a SLM design that initiates from a larger model and applies a careful parameter sharing scheme to reduce both the pre-training and the deployment cost. Our work strives to not only bridge the gap in open-source SLMs but also ensures full transparency, where complete training data pipeline, training code, model weights, and over 300 checkpoints along with evaluation codes is available at : https://github.com/mbzuai-oryx/MobiLlama.", "github_manual": "", "github_regex": "https://github.com/mbzuai-oryx/MobiLlama", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-27", "arxiv_id": "2402.16671", "title": "StructLM: Towards Building Generalist Models for Structured Knowledge Grounding", "authors": [ "Alex Zhuang", "Ge Zhang", "Tianyu Zheng", "Xinrun Du", "Junjie Wang", "Weiming Ren", "Stephen W. Huang", "Jie Fu", "Xiang Yue", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Structured data sources, such as tables, graphs, and databases, are ubiquitous knowledge sources. Despite the demonstrated capabilities of large language models (LLMs) on plain text, their proficiency in interpreting and utilizing structured data remains limited. Our investigation reveals a notable deficiency in LLMs' ability to process structured data, e.g., ChatGPT lags behind state-of-the-art (SoTA) model by an average of 35%. To augment the Structured Knowledge Grounding (SKG) capabilities in LLMs, we have developed a comprehensive instruction tuning dataset comprising 1.1 million examples. Utilizing this dataset, we train a series of models, referred to as StructLM, based on the Code-LLaMA architecture, ranging from 7B to 34B parameters. Our StructLM series surpasses task-specific models on 14 out of 18 evaluated datasets and establishes new SoTA achievements on 7 SKG tasks. Furthermore, StructLM demonstrates exceptional generalization across 6 novel SKG tasks. Contrary to expectations, we observe that scaling model size offers marginal benefits, with StructLM-34B showing only slight improvements over StructLM-7B. This suggests that structured knowledge grounding is still a challenging task and requires more innovative design to push to a new level.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-27", "arxiv_id": "2402.16837", "title": "Do Large Language Models Latently Perform Multi-Hop Reasoning?", "authors": [ "Sohee Yang", "Elena Gribovskaya", "Nora Kassner", "Mor Geva", "Sebastian Riedel" ], "github": "https://github.com/google-deepmind/latent-multi-hop-reasoning", "project_page": "", "abstract": "We study whether Large Language Models (LLMs) latently perform multi-hop reasoning with complex prompts such as \"The mother of the singer of 'Superstition' is\". We look for evidence of a latent reasoning pathway where an LLM (1) latently identifies \"the singer of 'Superstition'\" as Stevie Wonder, the bridge entity, and (2) uses its knowledge of Stevie Wonder's mother to complete the prompt. We analyze these two hops individually and consider their co-occurrence as indicative of latent multi-hop reasoning. For the first hop, we test if changing the prompt to indirectly mention the bridge entity instead of any other entity increases the LLM's internal recall of the bridge entity. For the second hop, we test if increasing this recall causes the LLM to better utilize what it knows about the bridge entity. We find strong evidence of latent multi-hop reasoning for the prompts of certain relation types, with the reasoning pathway used in more than 80% of the prompts. However, the utilization is highly contextual, varying across different types of prompts. Also, on average, the evidence for the second hop and the full multi-hop traversal is rather moderate and only substantial for the first hop. Moreover, we find a clear scaling trend with increasing model size for the first hop of reasoning but not for the second hop. Our experimental findings suggest potential challenges and opportunities for future development and applications of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/google-deepmind/latent-multi-hop-reasoning", "github_api_added_by": "auto" }, { "date": "2024-02-27", "arxiv_id": "2402.16641", "title": "Towards Open-ended Visual Quality Comparison", "authors": [ "Haoning Wu", "Hanwei Zhu", "Zicheng Zhang", "Erli Zhang", "Chaofeng Chen", "Liang Liao", "Chunyi Li", "Annan Wang", "Wenxiu Sun", "Qiong Yan", "Xiaohong Liu", "Guangtao Zhai", "Shiqi Wang", "Weisi Lin" ], "github": "", "project_page": "", "abstract": "Comparative settings (e.g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardizes the evaluation criteria across different observers and offer more clear-cut responses. In this work, we extend the edge of emerging large multi-modality models (LMMs) to further advance visual quality comparison into open-ended settings, that 1) can respond to open-range questions on quality comparison; 2) can provide detailed reasonings beyond direct answers. To this end, we propose the Co-Instruct. To train this first-of-its-kind open-source open-ended visual quality comparer, we collect the Co-Instruct-562K dataset, from two sources: (a) LMM-merged single image quality description, (b) GPT-4V \"teacher\" responses on unlabeled data. Furthermore, to better evaluate this setting, we propose the MICBench, the first benchmark on multi-image comparison for LMMs. We demonstrate that Co-Instruct not only achieves 30% higher superior accuracy than state-of-the-art open-source LMMs, but also outperforms GPT-4V (its teacher), on both existing related benchmarks and the proposed MICBench. Our model is published at https://huggingface.co/q-future/co-instruct.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-27", "arxiv_id": "2402.16822", "title": "Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts", "authors": [ "Mikayel Samvelyan", "Sharath Chandra Raparthy", "Andrei Lupu", "Eric Hambro", "Aram H. Markosyan", "Manish Bhatt", "Yuning Mao", "Minqi Jiang", "Jack Parker-Holder", "Jakob Foerster", "Tim Rockt\u00e4schel", "Roberta Raileanu" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to user inputs is of paramount importance. Existing methods for identifying adversarial prompts tend to focus on specific domains, lack diversity, or require extensive human annotations. To address these limitations, we present Rainbow Teaming, a novel approach for producing a diverse collection of adversarial prompts. Rainbow Teaming casts adversarial prompt generation as a quality-diversity problem, and uses open-ended search to generate prompts that are both effective and diverse. It can uncover a model's vulnerabilities across a broad range of domains including, in this paper, safety, question answering, and cybersecurity. We also demonstrate that fine-tuning on synthetic data generated by Rainbow Teaming improves the safety of state-of-the-art LLMs without hurting their general capabilities and helpfulness, paving the path to open-ended self-improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-27", "arxiv_id": "2402.16819", "title": "Nemotron-4 15B Technical Report", "authors": [ "Jupinder Parmar", "Shrimai Prabhumoye", "Joseph Jennings", "Mostofa Patwary", "Sandeep Subramanian", "Dan Su", "Chen Zhu", "Deepak Narayanan", "Aastha Jhunjhunwala", "Ayush Dattagupta", "Vibhu Jawa", "Jiwei Liu", "Ameya Mahabaleshwarkar", "Osvald Nitski", "Annika Brundyn", "James Maki", "Miguel Martinez", "Jiaxuan You", "John Kamalu", "Patrick LeGresley", "Denys Fridman", "Jared Casper", "Ashwath Aithal", "Oleksii Kuchaiev", "Mohammad Shoeybi", "Jonathan Cohen", "Bryan Catanzaro" ], "github": "", "project_page": "", "abstract": "We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens. Nemotron-4 15B demonstrates strong performance when assessed on English, multilingual, and coding tasks: it outperforms all existing similarly-sized open models on 4 out of 7 downstream evaluation areas and achieves competitive performance to the leading open models in the remaining ones. Specifically, Nemotron-4 15B exhibits the best multilingual capabilities of all similarly-sized models, even outperforming models over four times larger and those explicitly specialized for multilingual tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17177", "title": "Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models", "authors": [ "Yixin Liu", "Kai Zhang", "Yuan Li", "Zhiling Yan", "Chujie Gao", "Ruoxi Chen", "Zhengqing Yuan", "Yue Huang", "Hanchi Sun", "Jianfeng Gao", "Lifang He", "Lichao Sun" ], "github": "https://github.com/lichao-sun/sorareview", "project_page": "", "abstract": "Sora is a text-to-video generative AI model, released by OpenAI in February 2024. The model is trained to generate videos of realistic or imaginative scenes from text instructions and show potential in simulating the physical world. Based on public technical reports and reverse engineering, this paper presents a comprehensive review of the model's background, related technologies, applications, remaining challenges, and future directions of text-to-video AI models. We first trace Sora's development and investigate the underlying technologies used to build this \"world simulator\". Then, we describe in detail the applications and potential impact of Sora in multiple industries ranging from film-making and education to marketing. We discuss the main challenges and limitations that need to be addressed to widely deploy Sora, such as ensuring safe and unbiased video generation. Lastly, we discuss the future development of Sora and video generation models in general, and how advancements in the field could enable new ways of human-AI interaction, boosting productivity and creativity of video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lichao-sun/sorareview", "github_api_added_by": "auto" }, { "date": "2024-02-28", "arxiv_id": "2402.17139", "title": "Video as the New Language for Real-World Decision Making", "authors": [ "Sherry Yang", "Jacob Walker", "Jack Parker-Holder", "Yilun Du", "Jake Bruce", "Andre Barreto", "Pieter Abbeel", "Dale Schuurmans" ], "github": "", "project_page": "", "abstract": "Both text and video data are abundant on the internet and support large-scale self-supervised learning through next token or frame prediction. However, they have not been equally leveraged: language models have had significant real-world impact, whereas video generation has remained largely limited to media entertainment. Yet video data captures important information about the physical world that is difficult to express in language. To address this gap, we discuss an under-appreciated opportunity to extend video generation to solve tasks in the real world. We observe how, akin to language, video can serve as a unified interface that can absorb internet knowledge and represent diverse tasks. Moreover, we demonstrate how, like language models, video generation can serve as planners, agents, compute engines, and environment simulators through techniques such as in-context learning, planning and reinforcement learning. We identify major impact opportunities in domains such as robotics, self-driving, and science, supported by recent work that demonstrates how such advanced capabilities in video generation are plausibly within reach. Lastly, we identify key challenges in video generation that mitigate progress. Addressing these challenges will enable video generation models to demonstrate unique value alongside language models in a wider array of AI applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17485", "title": "EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions", "authors": [ "Linrui Tian", "Qi Wang", "Bang Zhang", "Liefeng Bo" ], "github": "", "project_page": "", "abstract": "In this work, we tackle the challenge of enhancing the realism and expressiveness in talking head video generation by focusing on the dynamic and nuanced relationship between audio cues and facial movements. We identify the limitations of traditional techniques that often fail to capture the full spectrum of human expressions and the uniqueness of individual facial styles. To address these issues, we propose EMO, a novel framework that utilizes a direct audio-to-video synthesis approach, bypassing the need for intermediate 3D models or facial landmarks. Our method ensures seamless frame transitions and consistent identity preservation throughout the video, resulting in highly expressive and lifelike animations. Experimental results demonsrate that EMO is able to produce not only convincing speaking videos but also singing videos in various styles, significantly outperforming existing state-of-the-art methodologies in terms of expressiveness and realism.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17403", "title": "Sora Generates Videos with Stunning Geometrical Consistency", "authors": [ "Xuanyi Li", "Daquan Zhou", "Chenxu Zhang", "Shaodong Wei", "Qibin Hou", "Ming-Ming Cheng" ], "github": "", "project_page": "", "abstract": "The recently developed Sora model [1] has exhibited remarkable capabilities in video generation, sparking intense discussions regarding its ability to simulate real-world phenomena. Despite its growing popularity, there is a lack of established metrics to evaluate its fidelity to real-world physics quantitatively. In this paper, we introduce a new benchmark that assesses the quality of the generated videos based on their adherence to real-world physics principles. We employ a method that transforms the generated videos into 3D models, leveraging the premise that the accuracy of 3D reconstruction is heavily contingent on the video quality. From the perspective of 3D reconstruction, we use the fidelity of the geometric constraints satisfied by the constructed 3D models as a proxy to gauge the extent to which the generated videos conform to real-world physics rules. Project page: https://sora-geometrical-consistency.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17553", "title": "OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web", "authors": [ "Raghav Kapoor", "Yash Parag Butala", "Melisa Russak", "Jing Yu Koh", "Kiran Kamble", "Waseem Alshikh", "Ruslan Salakhutdinov" ], "github": "", "project_page": "", "abstract": "For decades, human-computer interaction has fundamentally been manual. Even today, almost all productive work done on the computer necessitates human input at every step. Autonomous virtual agents represent an exciting step in automating many of these menial tasks. Virtual agents would empower users with limited technical proficiency to harness the full possibilities of computer systems. They could also enable the efficient streamlining of numerous computer tasks, ranging from calendar management to complex travel bookings, with minimal human intervention. In this paper, we introduce OmniACT, the first-of-a-kind dataset and benchmark for assessing an agent's capability to generate executable programs to accomplish computer tasks. Our scope extends beyond traditional web automation, covering a diverse range of desktop applications. The dataset consists of fundamental tasks such as \"Play the next song\", as well as longer horizon tasks such as \"Send an email to John Doe mentioning the time and place to meet\". Specifically, given a pair of screen image and a visually-grounded natural language task, the goal is to generate a script capable of fully executing the task. We run several strong baseline language model agents on our benchmark. The strongest baseline, GPT-4, performs the best on our benchmark However, its performance level still reaches only 15% of the human proficiency in generating executable scripts capable of completing the task, demonstrating the challenge of our task for conventional web agents. Our benchmark provides a platform to measure and evaluate the progress of language model agents in automating computer tasks and motivates future work towards building multimodal models that bridge large language models and the visual grounding of computer screens.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17764", "title": "The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits", "authors": [ "Shuming Ma", "Hongyu Wang", "Lingxiao Ma", "Lei Wang", "Wenhui Wang", "Shaohan Huang", "Li Dong", "Ruiping Wang", "Jilong Xue", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17723", "title": "Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners", "authors": [ "Yazhou Xing", "Yingqing He", "Zeyue Tian", "Xintao Wang", "Qifeng Chen" ], "github": "", "project_page": "", "abstract": "Video and audio content creation serves as the core technique for the movie industry and professional users. Recently, existing diffusion-based methods tackle video and audio generation separately, which hinders the technique transfer from academia to industry. In this work, we aim at filling the gap, with a carefully designed optimization-based framework for cross-visual-audio and joint-visual-audio generation. We observe the powerful generation ability of off-the-shelf video or audio generation models. Thus, instead of training the giant models from scratch, we propose to bridge the existing strong models with a shared latent representation space. Specifically, we propose a multimodality latent aligner with the pre-trained ImageBind model. Our latent aligner shares a similar core as the classifier guidance that guides the diffusion denoising process during inference time. Through carefully designed optimization strategy and loss functions, we show the superior performance of our method on joint video-audio generation, visual-steered audio generation, and audio-steered visual generation tasks. The project website can be found at https://yzxing87.github.io/Seeing-and-Hearing/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17759", "title": "Towards Optimal Learning of Language Models", "authors": [ "Yuxian Gu", "Li Dong", "Yaru Hao", "Qingxiu Dong", "Minlie Huang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "This work studies the general principles of improving the learning of language models (LMs), which aims at reducing the necessary training steps for achieving superior performance. Specifically, we present a theory for the optimal learning of LMs. We first propose an objective that optimizes LM learning by maximizing the data compression ratio in an \"LM-training-as-lossless-compression\" view. Then, we derive a theorem, named Learning Law, to reveal the properties of the dynamics in the optimal learning process under our objective. The theorem is then validated by experiments on a linear classification and a real-world language modeling task. Finally, we empirically verify that the optimal learning of LMs essentially stems from the improvement of the coefficients in the scaling law of LMs, indicating great promise and significance for designing practical learning acceleration methods. Our code can be found at https://aka.ms/LearningLaw.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.16936", "title": "Disentangled 3D Scene Generation with Layout Learning", "authors": [ "Dave Epstein", "Ben Poole", "Ben Mildenhall", "Alexei A. Efros", "Aleksander Holynski" ], "github": "", "project_page": "", "abstract": "We introduce a method to generate 3D scenes that are disentangled into their component objects. This disentanglement is unsupervised, relying only on the knowledge of a large pretrained text-to-image model. Our key insight is that objects can be discovered by finding parts of a 3D scene that, when rearranged spatially, still produce valid configurations of the same scene. Concretely, our method jointly optimizes multiple NeRFs from scratch - each representing its own object - along with a set of layouts that composite these objects into scenes. We then encourage these composited scenes to be in-distribution according to the image generator. We show that despite its simplicity, our approach successfully generates 3D scenes decomposed into individual objects, enabling new capabilities in text-to-3D content creation. For results and an interactive demo, see our project page at https://dave.ml/layoutlearning/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17193", "title": "When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method", "authors": [ "Biao Zhang", "Zhongtao Liu", "Colin Cherry", "Orhan Firat" ], "github": "", "project_page": "", "abstract": "While large language models (LLMs) often adopt finetuning to unlock their capabilities for downstream applications, our understanding on the inductive biases (especially the scaling properties) of different finetuning methods is still limited. To fill this gap, we conduct systematic experiments studying whether and how different scaling factors, including LLM model size, pretraining data size, new finetuning parameter size and finetuning data size, affect the finetuning performance. We consider two types of finetuning -- full-model tuning (FMT) and parameter efficient tuning (PET, including prompt tuning and LoRA), and explore their scaling behaviors in the data-limited regime where the LLM model size substantially outweighs the finetuning data size. Based on two sets of pretrained bilingual LLMs from 1B to 16B and experiments on bilingual machine translation and multilingual summarization benchmarks, we find that 1) LLM finetuning follows a powerbased multiplicative joint scaling law between finetuning data size and each other scaling factor; 2) LLM finetuning benefits more from LLM model scaling than pretraining data scaling, and PET parameter scaling is generally ineffective; and 3) the optimal finetuning method is highly task- and finetuning data-dependent. We hope our findings could shed light on understanding, selecting and developing LLM finetuning methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17245", "title": "Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation", "authors": [ "Daiqing Li", "Aleks Kamko", "Ehsan Akhgari", "Ali Sabet", "Linmiao Xu", "Suhail Doshi" ], "github": "", "project_page": "", "abstract": "In this work, we share three insights for achieving state-of-the-art aesthetic quality in text-to-image generative models. We focus on three critical aspects for model improvement: enhancing color and contrast, improving generation across multiple aspect ratios, and improving human-centric fine details. First, we delve into the significance of the noise schedule in training a diffusion model, demonstrating its profound impact on realism and visual fidelity. Second, we address the challenge of accommodating various aspect ratios in image generation, emphasizing the importance of preparing a balanced bucketed dataset. Lastly, we investigate the crucial role of aligning model outputs with human preferences, ensuring that generated images resonate with human perceptual expectations. Through extensive analysis and experiments, Playground v2.5 demonstrates state-of-the-art performance in terms of aesthetic quality under various conditions and aspect ratios, outperforming both widely-used open-source models like SDXL and Playground v2, and closed-source commercial systems such as DALLE 3 and Midjourney v5.2. Our model is open-source, and we hope the development of Playground v2.5 provides valuable guidelines for researchers aiming to elevate the aesthetic quality of diffusion-based image generation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17412", "title": "DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Model", "authors": [ "Shyam Marjit", "Harshit Singh", "Nityanand Mathur", "Sayak Paul", "Chia-Mu Yu", "Pin-Yu Chen" ], "github": "https://github.com/IBM/DiffuseKronA", "project_page": "", "abstract": "In the realm of subject-driven text-to-image (T2I) generative models, recent developments like DreamBooth and BLIP-Diffusion have led to impressive results yet encounter limitations due to their intensive fine-tuning demands and substantial parameter requirements. While the low-rank adaptation (LoRA) module within DreamBooth offers a reduction in trainable parameters, it introduces a pronounced sensitivity to hyperparameters, leading to a compromise between parameter efficiency and the quality of T2I personalized image synthesis. Addressing these constraints, we introduce \\textit{DiffuseKronA}, a novel Kronecker product-based adaptation module that not only significantly reduces the parameter count by 35\\% and 99.947\\% compared to LoRA-DreamBooth and the original DreamBooth, respectively, but also enhances the quality of image synthesis. Crucially, DiffuseKronA mitigates the issue of hyperparameter sensitivity, delivering consistent high-quality generations across a wide range of hyperparameters, thereby diminishing the necessity for extensive fine-tuning. Furthermore, a more controllable decomposition makes DiffuseKronA more interpretable and even can achieve up to a 50\\% reduction with results comparable to LoRA-Dreambooth. Evaluated against diverse and complex input images and text prompts, DiffuseKronA consistently outperforms existing models, producing diverse images of higher quality with improved fidelity and a more accurate color distribution of objects, all the while upholding exceptional parameter efficiency, thus presenting a substantial advancement in the field of T2I generative modeling. Our project page, consisting of links to the code, and pre-trained checkpoints, is available at https://diffusekrona.github.io/{https://diffusekrona.github.io/}.", "github_manual": "https://github.com/IBM/DiffuseKronA", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17753", "title": "Evaluating Very Long-Term Conversational Memory of LLM Agents", "authors": [ "Adyasha Maharana", "Dong-Ho Lee", "Sergey Tulyakov", "Mohit Bansal", "Francesco Barbieri", "Yuwei Fang" ], "github": "", "project_page": "", "abstract": "Existing works on long-term open-domain dialogues focus on evaluating model responses within contexts spanning no more than five chat sessions. Despite advancements in long-context large language models (LLMs) and retrieval augmented generation (RAG) techniques, their efficacy in very long-term dialogues remains unexplored. To address this research gap, we introduce a machine-human pipeline to generate high-quality, very long-term dialogues by leveraging LLM-based agent architectures and grounding their dialogues on personas and temporal event graphs. Moreover, we equip each agent with the capability of sharing and reacting to images. The generated conversations are verified and edited by human annotators for long-range consistency and grounding to the event graphs. Using this pipeline, we collect LoCoMo, a dataset of very long-term conversations, each encompassing 300 turns and 9K tokens on avg., over up to 35 sessions. Based on LoCoMo, we present a comprehensive evaluation benchmark to measure long-term memory in models, encompassing question answering, event summarization, and multi-modal dialogue generation tasks. Our experimental results indicate that LLMs exhibit challenges in understanding lengthy conversations and comprehending long-range temporal and causal dynamics within dialogues. Employing strategies like long-context LLMs or RAG can offer improvements but these models still substantially lag behind human performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17463", "title": "Training-Free Long-Context Scaling of Large Language Models", "authors": [ "Chenxin An", "Fei Huang", "Jun Zhang", "Shansan Gong", "Xipeng Qiu", "Chang Zhou", "Lingpeng Kong" ], "github": "https://github.com/HKUNLP/ChunkLlama", "project_page": "", "abstract": "The ability of Large Language Models (LLMs) to process and generate coherent text is markedly weakened when the number of input tokens exceeds their pretraining length. Given the expensive overhead of finetuning large-scale models with longer sequences, we propose Dual Chunk Attention (DCA), which enables Llama2 70B to support context windows of more than 100k tokens without continual training. By decomposing the attention computation for long sequences into chunk-based modules, DCA manages to effectively capture the relative positional information of tokens within the same chunk (Intra-Chunk) and across distinct chunks (Inter-Chunk), as well as integrates seamlessly with Flash Attention. In addition to its impressive extrapolation capability, DCA achieves performance on practical long-context tasks that is comparable to or even better than that of finetuned models. When compared with proprietary models, our training-free 70B model attains 94% of the performance of gpt-3.5-16k, indicating it is a viable open-source alternative. All code and data used in this work are released at https://github.com/HKUNLP/ChunkLlama.", "github_manual": "", "github_regex": "https://github.com/HKUNLP/ChunkLlama", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-02-28", "arxiv_id": "2402.17427", "title": "VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction", "authors": [ "Jiaqi Lin", "Zhihao Li", "Xiao Tang", "Jianzhuang Liu", "Shiyong Liu", "Jiayue Liu", "Yangdi Lu", "Xiaofei Wu", "Songcen Xu", "Youliang Yan", "Wenming Yang" ], "github": "", "project_page": "", "abstract": "Existing NeRF-based methods for large scene reconstruction often have limitations in visual quality and rendering speed. While the recent 3D Gaussian Splatting works well on small-scale and object-centric scenes, scaling it up to large scenes poses challenges due to limited video memory, long optimization time, and noticeable appearance variations. To address these challenges, we present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. We propose a progressive partitioning strategy to divide a large scene into multiple cells, where the training cameras and point cloud are properly distributed with an airspace-aware visibility criterion. These cells are merged into a complete scene after parallel optimization. We also introduce decoupled appearance modeling into the optimization process to reduce appearance variations in the rendered images. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets, enabling fast optimization and high-fidelity real-time rendering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-01", "arxiv_id": "2402.19479", "title": "Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers", "authors": [ "Tsai-Shien Chen", "Aliaksandr Siarohin", "Willi Menapace", "Ekaterina Deyneka", "Hsiang-wei Chao", "Byung Eun Jeon", "Yuwei Fang", "Hsin-Ying Lee", "Jian Ren", "Ming-Hsuan Yang", "Sergey Tulyakov" ], "github": "https://github.com/snap-research/Panda-70M", "project_page": "", "abstract": "The quality of the data and annotation upper-bounds the quality of a downstream model. While there exist large text corpora and image-text pairs, high-quality video-text data is much harder to collect. First of all, manual labeling is more time-consuming, as it requires an annotator to watch an entire video. Second, videos have a temporal dimension, consisting of several scenes stacked together, and showing multiple actions. Accordingly, to establish a video dataset with high-quality captions, we propose an automatic approach leveraging multimodal inputs, such as textual video description, subtitles, and individual video frames. Specifically, we curate 3.8M high-resolution videos from the publicly available HD-VILA-100M dataset. We then split them into semantically consistent video clips, and apply multiple cross-modality teacher models to obtain captions for each video. Next, we finetune a retrieval model on a small subset where the best caption of each video is manually selected and then employ the model in the whole dataset to select the best caption as the annotation. In this way, we get 70M videos paired with high-quality text captions. We dub the dataset as Panda-70M. We show the value of the proposed dataset on three downstream tasks: video captioning, video and text retrieval, and text-driven video generation. The models trained on the proposed data score substantially better on the majority of metrics across all the tasks.", "github_manual": "https://github.com/snap-research/Panda-70M", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-01", "arxiv_id": "2402.18842", "title": "ViewFusion: Towards Multi-View Consistency via Interpolated Denoising", "authors": [ "Xianghui Yang", "Yan Zuo", "Sameera Ramasinghe", "Loris Bazzani", "Gil Avraham", "Anton van den Hengel" ], "github": "https://github.com/Wi-sc/ViewFusion", "project_page": "", "abstract": "Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images. Yet, the independent process of image generation in these prevailing methods leads to challenges in maintaining multiple-view consistency. To address this, we introduce ViewFusion, a novel, training-free algorithm that can be seamlessly integrated into existing pre-trained diffusion models. Our approach adopts an auto-regressive method that implicitly leverages previously generated views as context for the next view generation, ensuring robust multi-view consistency during the novel-view generation process. Through a diffusion process that fuses known-view information via interpolated denoising, our framework successfully extends single-view conditioned models to work in multiple-view conditional settings without any additional fine-tuning. Extensive experimental results demonstrate the effectiveness of ViewFusion in generating consistent and detailed novel views.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Wi-sc/ViewFusion", "github_api_added_by": "auto" }, { "date": "2024-03-01", "arxiv_id": "2402.19427", "title": "Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models", "authors": [ "Soham De", "Samuel L. Smith", "Anushan Fernando", "Aleksandar Botev", "George Cristian-Muraru", "Albert Gu", "Ruba Haroun", "Leonard Berrada", "Yutian Chen", "Srivatsan Srinivasan", "Guillaume Desjardins", "Arnaud Doucet", "David Budden", "Yee Whye Teh", "Razvan Pascanu", "Nando De Freitas", "Caglar Gulcehre" ], "github": "", "project_page": "", "abstract": "Recurrent neural networks (RNNs) have fast inference and scale efficiently on long sequences, but they are difficult to train and hard to scale. We propose Hawk, an RNN with gated linear recurrences, and Griffin, a hybrid model that mixes gated linear recurrences with local attention. Hawk exceeds the reported performance of Mamba on downstream tasks, while Griffin matches the performance of Llama-2 despite being trained on over 6 times fewer tokens. We also show that Griffin can extrapolate on sequences significantly longer than those seen during training. Our models match the hardware efficiency of Transformers during training, and during inference they have lower latency and significantly higher throughput. We scale Griffin up to 14B parameters, and explain how to shard our models for efficient distributed training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-01", "arxiv_id": "2402.19155", "title": "Beyond Language Models: Byte Models are Digital World Simulators", "authors": [ "Shangda Wu", "Xu Tan", "Zili Wang", "Rui Wang", "Xiaobing Li", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "Traditional deep learning often overlooks bytes, the basic units of the digital world, where all forms of information and operations are encoded and manipulated in binary format. Inspired by the success of next token prediction in natural language processing, we introduce bGPT, a model with next byte prediction to simulate the digital world. bGPT matches specialized models in performance across various modalities, including text, audio, and images, and offers new possibilities for predicting, simulating, and diagnosing algorithm or hardware behaviour. It has almost flawlessly replicated the process of converting symbolic music data, achieving a low error rate of 0.0011 bits per byte in converting ABC notation to MIDI format. In addition, bGPT demonstrates exceptional capabilities in simulating CPU behaviour, with an accuracy exceeding 99.99% in executing various operations. Leveraging next byte prediction, models like bGPT can directly learn from vast binary data, effectively simulating the intricate patterns of the digital world.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-01", "arxiv_id": "2402.19469", "title": "Humanoid Locomotion as Next Token Prediction", "authors": [ "Ilija Radosavovic", "Bike Zhang", "Baifeng Shi", "Jathushan Rajasegaran", "Sarthak Kamat", "Trevor Darrell", "Koushil Sreenath", "Jitendra Malik" ], "github": "", "project_page": "", "abstract": "We cast real-world humanoid control as a next token prediction problem, akin to predicting the next word in language. Our model is a causal transformer trained via autoregressive prediction of sensorimotor trajectories. To account for the multi-modal nature of the data, we perform prediction in a modality-aligned way, and for each input token predict the next token from the same modality. This general formulation enables us to leverage data with missing modalities, like video trajectories without actions. We train our model on a collection of simulated trajectories coming from prior neural network policies, model-based controllers, motion capture data, and YouTube videos of humans. We show that our model enables a full-sized humanoid to walk in San Francisco zero-shot. Our model can transfer to the real world even when trained on only 27 hours of walking data, and can generalize to commands not seen during training like walking backward. These findings suggest a promising path toward learning challenging real-world control tasks by generative modeling of sensorimotor trajectories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-01", "arxiv_id": "2402.19173", "title": "StarCoder 2 and The Stack v2: The Next Generation", "authors": [ "Anton Lozhkov", "Raymond Li", "Loubna Ben Allal", "Federico Cassano", "Joel Lamy-Poirier", "Nouamane Tazi", "Ao Tang", "Dmytro Pykhtar", "Jiawei Liu", "Yuxiang Wei", "Tianyang Liu", "Max Tian", "Denis Kocetkov", "Arthur Zucker", "Younes Belkada", "Zijian Wang", "Qian Liu", "Dmitry Abulkhanov", "Indraneil Paul", "Zhuang Li", "Wen-Ding Li", "Megan Risdal", "Jia Li", "Jian Zhu", "Terry Yue Zhuo", "Evgenii Zheltonozhskii", "Nii Osae Osae Dade", "Wenhao Yu", "Lucas Krau\u00df", "Naman Jain", "Yixuan Su", "Xuanli He", "Manan Dey", "Edoardo Abati", "Yekun Chai", "Niklas Muennighoff", "Xiangru Tang", "Muhtasham Oblokulov", "Christopher Akiki", "Marc Marone", "Chenghao Mou", "Mayank Mishra", "Alex Gu", "Binyuan Hui", "Tri Dao", "Armel Zebaze", "Olivier Dehaene", "Nicolas Patry", "Canwen Xu", "Julian McAuley", "Han Hu", "Torsten Scholak", "Sebastien Paquet", "Jennifer Robinson", "Carolyn Jane Anderson", "Nicolas Chapados", "Mostofa Patwary", "Nima Tajbakhsh", "Yacine Jernite", "Carlos Mu\u00f1oz Ferrandis", "Lingming Zhang", "Sean Hughes", "Thomas Wolf", "Arjun Guha", "Leandro von Werra", "Harm de Vries" ], "github": "", "project_page": "", "abstract": "The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-01", "arxiv_id": "2402.19159", "title": "Trajectory Consistency Distillation", "authors": [ "Jianbin Zheng", "Minghui Hu", "Zhongyi Fan", "Chaoyue Wang", "Changxing Ding", "Dacheng Tao", "Tat-Jen Cham" ], "github": "https://github.com/jabir-zheng/TCD", "project_page": "", "abstract": "Latent Consistency Model (LCM) extends the Consistency Model to the latent space and leverages the guided consistency distillation technique to achieve impressive performance in accelerating text-to-image synthesis. However, we observed that LCM struggles to generate images with both clarity and detailed intricacy. To address this limitation, we initially delve into and elucidate the underlying causes. Our investigation identifies that the primary issue stems from errors in three distinct areas. Consequently, we introduce Trajectory Consistency Distillation (TCD), which encompasses trajectory consistency function and strategic stochastic sampling. The trajectory consistency function diminishes the distillation errors by broadening the scope of the self-consistency boundary condition and endowing the TCD with the ability to accurately trace the entire trajectory of the Probability Flow ODE. Additionally, strategic stochastic sampling is specifically designed to circumvent the accumulated errors inherent in multi-step consistency sampling, which is meticulously tailored to complement the TCD model. Experiments demonstrate that TCD not only significantly enhances image quality at low NFEs but also yields more detailed results compared to the teacher model at high NFEs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jabir-zheng/TCD", "github_api_added_by": "auto" }, { "date": "2024-03-01", "arxiv_id": "2402.18668", "title": "Simple linear attention language models balance the recall-throughput tradeoff", "authors": [ "Simran Arora", "Sabri Eyuboglu", "Michael Zhang", "Aman Timalsina", "Silas Alberti", "Dylan Zinsley", "James Zou", "Atri Rudra", "Christopher R\u00e9" ], "github": "https://github.com/HazyResearch/based", "project_page": "", "abstract": "Recent work has shown that attention-based language models excel at recall, the ability to ground generations in tokens previously seen in context. However, the efficiency of attention-based models is bottle-necked during inference by the KV-cache's aggressive memory consumption. In this work, we explore whether we can improve language model efficiency (e.g. by reducing memory consumption) without compromising on recall. By applying experiments and theory to a broad set of architectures, we identify a key tradeoff between a model's state size and recall ability. We show that efficient alternatives to attention (e.g. H3, Mamba, RWKV) maintain a fixed-size recurrent state, but struggle at recall. We propose BASED a simple architecture combining linear and sliding window attention. By varying BASED window size and linear attention feature dimension, we can dial the state size and traverse the pareto frontier of the recall-memory tradeoff curve, recovering the full quality of attention on one end and the small state size of attention-alternatives on the other. We train language models up to 1.3b parameters and show that BASED matches the strongest sub-quadratic models (e.g. Mamba) in perplexity and outperforms them on real-world recall-intensive tasks by 6.22 accuracy points. Implementations of linear attention are often less efficient than optimized standard attention implementations. To make BASED competitive, we develop IO-aware algorithms that enable 24x higher throughput on language generation than FlashAttention-2, when generating 1024 tokens using 1.3b parameter models. Code for this work is provided at: https://github.com/HazyResearch/based.", "github_manual": "", "github_regex": "https://github.com/HazyResearch/based", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-01", "arxiv_id": "2402.18734", "title": "Priority Sampling of Large Language Models for Compilers", "authors": [ "Dejan Grubisic", "Chris Cummins", "Volker Seeker", "Hugh Leather" ], "github": "", "project_page": "", "abstract": "Large language models show great potential in generating and optimizing code. Widely used sampling methods such as Nucleus Sampling increase the diversity of generation but often produce repeated samples for low temperatures and incoherent samples for high temperatures. Furthermore, the temperature coefficient has to be tuned for each task, limiting its usability. We present Priority Sampling, a simple and deterministic sampling technique that produces unique samples ordered by the model's confidence. Each new sample expands the unexpanded token with the highest probability in the augmented search tree. Additionally, Priority Sampling supports generation based on regular expression that provides a controllable and structured exploration process. Priority Sampling outperforms Nucleus Sampling for any number of samples, boosting the performance of the original model from 2.87% to 5% improvement over -Oz. Moreover, it outperforms the autotuner used for the generation of labels for the training of the original model in just 30 samples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-01", "arxiv_id": "2402.18796", "title": "MOSAIC: A Modular System for Assistive and Interactive Cooking", "authors": [ "Huaxiaoyue Wang", "Kushal Kedia", "Juntao Ren", "Rahma Abdullah", "Atiksh Bhardwaj", "Angela Chao", "Kelly Y Chen", "Nathaniel Chin", "Prithwish Dan", "Xinyi Fan", "Gonzalo Gonzalez-Pumariega", "Aditya Kompella", "Maximus Adrian Pace", "Yash Sharma", "Xiangwan Sun", "Neha Sunkara", "Sanjiban Choudhury" ], "github": "", "project_page": "", "abstract": "We present MOSAIC, a modular architecture for home robots to perform complex collaborative tasks, such as cooking with everyday users. MOSAIC tightly collaborates with humans, interacts with users using natural language, coordinates multiple robots, and manages an open vocabulary of everyday objects. At its core, MOSAIC employs modularity: it leverages multiple large-scale pre-trained models for general tasks like language and image recognition, while using streamlined modules designed for task-specific control. We extensively evaluate MOSAIC on 60 end-to-end trials where two robots collaborate with a human user to cook a combination of 6 recipes. We also extensively test individual modules with 180 episodes of visuomotor picking, 60 episodes of human motion forecasting, and 46 online user evaluations of the task planner. We show that MOSAIC is able to efficiently collaborate with humans by running the overall system end-to-end with a real human user, completing 68.3% (41/60) collaborative cooking trials of 6 different recipes with a subtask completion rate of 91.6%. Finally, we discuss the limitations of the current system and exciting open challenges in this domain. The project's website is at https://portal-cornell.github.io/MOSAIC/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-01", "arxiv_id": "2402.19481", "title": "DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models", "authors": [ "Muyang Li", "Tianle Cai", "Jiaxin Cao", "Qinsheng Zhang", "Han Cai", "Junjie Bai", "Yangqing Jia", "Ming-Yu Liu", "Kai Li", "Song Han" ], "github": "https://github.com/mit-han-lab/distrifuser", "project_page": "", "abstract": "Diffusion models have achieved great success in synthesizing high-quality images. However, generating high-resolution images with diffusion models is still challenging due to the enormous computational costs, resulting in a prohibitive latency for interactive applications. In this paper, we propose DistriFusion to tackle this problem by leveraging parallelism across multiple GPUs. Our method splits the model input into multiple patches and assigns each patch to a GPU. However, na\\\"{\\i}vely implementing such an algorithm breaks the interaction between patches and loses fidelity, while incorporating such an interaction will incur tremendous communication overhead. To overcome this dilemma, we observe the high similarity between the input from adjacent diffusion steps and propose displaced patch parallelism, which takes advantage of the sequential nature of the diffusion process by reusing the pre-computed feature maps from the previous timestep to provide context for the current step. Therefore, our method supports asynchronous communication, which can be pipelined by computation. Extensive experiments show that our method can be applied to recent Stable Diffusion XL with no quality degradation and achieve up to a 6.1times speedup on eight NVIDIA A100s compared to one. Our code is publicly available at https://github.com/mit-han-lab/distrifuser.", "github_manual": "", "github_regex": "https://github.com/mit-han-lab/distrifuser", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-04", "arxiv_id": "2403.00522", "title": "VisionLLaMA: A Unified LLaMA Interface for Vision Tasks", "authors": [ "Xiangxiang Chu", "Jianlin Su", "Bo Zhang", "Chunhua Shen" ], "github": "https://github.com/Meituan-AutoML/VisionLLaMA", "project_page": "", "abstract": "Large language models are built on top of a transformer-based architecture to process textual inputs. For example, the LLaMA stands out among many open-source implementations. Can the same transformer be used to process 2D images? In this paper, we answer this question by unveiling a LLaMA-like vision transformer in plain and pyramid forms, termed VisionLLaMA, which is tailored for this purpose. VisionLLaMA is a unified and generic modelling framework for solving most vision tasks. We extensively evaluate its effectiveness using typical pre-training paradigms in a good portion of downstream tasks of image perception and especially image generation. In many cases, VisionLLaMA have exhibited substantial gains over the previous state-of-the-art vision transformers. We believe that VisionLLaMA can serve as a strong new baseline model for vision generation and understanding. Our code will be released at https://github.com/Meituan-AutoML/VisionLLaMA.", "github_manual": "", "github_regex": "https://github.com/Meituan-AutoML/VisionLLaMA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-04", "arxiv_id": "2403.00504", "title": "Learning and Leveraging World Models in Visual Representation Learning", "authors": [ "Quentin Garrido", "Mahmoud Assran", "Nicolas Ballas", "Adrien Bardes", "Laurent Najman", "Yann LeCun" ], "github": "", "project_page": "", "abstract": "Joint-Embedding Predictive Architecture (JEPA) has emerged as a promising self-supervised approach that learns by leveraging a world model. While previously limited to predicting missing parts of an input, we explore how to generalize the JEPA prediction task to a broader set of corruptions. We introduce Image World Models, an approach that goes beyond masked image modeling and learns to predict the effect of global photometric transformations in latent space. We study the recipe of learning performant IWMs and show that it relies on three key aspects: conditioning, prediction difficulty, and capacity. Additionally, we show that the predictive world model learned by IWM can be adapted through finetuning to solve diverse tasks; a fine-tuned IWM world model matches or surpasses the performance of previous self-supervised methods. Finally, we show that learning with an IWM allows one to control the abstraction level of the learned representations, learning invariant representations such as contrastive methods, or equivariant representations such as masked image modelling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-04", "arxiv_id": "2403.00745", "title": "AtP*: An efficient and scalable method for localizing LLM behaviour to components", "authors": [ "J\u00e1nos Kram\u00e1r", "Tom Lieberum", "Rohin Shah", "Neel Nanda" ], "github": "", "project_page": "", "abstract": "Activation Patching is a method of directly computing causal attributions of behavior to model components. However, applying it exhaustively requires a sweep with cost scaling linearly in the number of model components, which can be prohibitively expensive for SoTA Large Language Models (LLMs). We investigate Attribution Patching (AtP), a fast gradient-based approximation to Activation Patching and find two classes of failure modes of AtP which lead to significant false negatives. We propose a variant of AtP called AtP*, with two changes to address these failure modes while retaining scalability. We present the first systematic study of AtP and alternative methods for faster activation patching and show that AtP significantly outperforms all other investigated methods, with AtP* providing further significant improvement. Finally, we provide a method to bound the probability of remaining false negatives of AtP* estimates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-04", "arxiv_id": "2403.00071", "title": "Resonance RoPE: Improving Context Length Generalization of Large Language Models", "authors": [ "Suyuchen Wang", "Ivan Kobyzev", "Peng Lu", "Mehdi Rezagholizadeh", "Bang Liu" ], "github": "https://github.com/sheryc/resonance_rope", "project_page": "", "abstract": "This paper addresses the challenge of train-short-test-long (TSTL) scenarios in Large Language Models (LLMs) equipped with Rotary Position Embedding (RoPE), where models pre-trained on shorter sequences face difficulty with out-of-distribution (OOD) token positions in longer sequences. We introduce Resonance RoPE, a novel approach designed to narrow the generalization gap in TSTL scenarios by refining the interpolation of RoPE features for OOD positions, significantly improving the model performance without additional online computational costs. Furthermore, we present PosGen, a new synthetic benchmark specifically designed for fine-grained behavior analysis in TSTL scenarios, aiming to isolate the constantly increasing difficulty of token generation on long contexts from the challenges of recognizing new token positions. Our experiments on synthetic tasks show that after applying Resonance RoPE, Transformers recognize OOD position better and more robustly. Our extensive LLM experiments also show superior performance after applying Resonance RoPE to the current state-of-the-art RoPE scaling method, YaRN, on both upstream language modeling tasks and a variety of downstream long-text applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sheryc/resonance_rope", "github_api_added_by": "auto" }, { "date": "2024-03-04", "arxiv_id": "2403.00483", "title": "RealCustom: Narrowing Real Text Word for Real-Time Open-Domain Text-to-Image Customization", "authors": [ "Mengqi Huang", "Zhendong Mao", "Mingcong Liu", "Qian He", "Yongdong Zhang" ], "github": "", "project_page": "", "abstract": "Text-to-image customization, which aims to synthesize text-driven images for the given subjects, has recently revolutionized content creation. Existing works follow the pseudo-word paradigm, i.e., represent the given subjects as pseudo-words and then compose them with the given text. However, the inherent entangled influence scope of pseudo-words with the given text results in a dual-optimum paradox, i.e., the similarity of the given subjects and the controllability of the given text could not be optimal simultaneously. We present RealCustom that, for the first time, disentangles similarity from controllability by precisely limiting subject influence to relevant parts only, achieved by gradually narrowing real text word from its general connotation to the specific subject and using its cross-attention to distinguish relevance. Specifically, RealCustom introduces a novel \"train-inference\" decoupled framework: (1) during training, RealCustom learns general alignment between visual conditions to original textual conditions by a novel adaptive scoring module to adaptively modulate influence quantity; (2) during inference, a novel adaptive mask guidance strategy is proposed to iteratively update the influence scope and influence quantity of the given subjects to gradually narrow the generation of the real text word. Comprehensive experiments demonstrate the superior real-time customization ability of RealCustom in the open domain, achieving both unprecedented similarity of the given subjects and controllability of the given text for the first time. The project page is https://corleone-huang.github.io/realcustom/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-05", "arxiv_id": "2403.01807", "title": "ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models", "authors": [ "Lukas H\u00f6llein", "Alja\u017e Bo\u017ei\u010d", "Norman M\u00fcller", "David Novotny", "Hung-Yu Tseng", "Christian Richardt", "Michael Zollh\u00f6fer", "Matthias Nie\u00dfner" ], "github": "https://github.com/facebookresearch/ViewDiff", "project_page": "", "abstract": "3D asset generation is getting massive amounts of attention, inspired by the recent success of text-guided 2D content creation. Existing text-to-3D methods use pretrained text-to-image diffusion models in an optimization problem or fine-tune them on synthetic data, which often results in non-photorealistic 3D objects without backgrounds. In this paper, we present a method that leverages pretrained text-to-image models as a prior, and learn to generate multi-view images in a single denoising process from real-world data. Concretely, we propose to integrate 3D volume-rendering and cross-frame-attention layers into each block of the existing U-Net network of the text-to-image model. Moreover, we design an autoregressive generation that renders more 3D-consistent images at any viewpoint. We train our model on real-world datasets of objects and showcase its capabilities to generate instances with a variety of high-quality shapes and textures in authentic surroundings. Compared to the existing methods, the results generated by our method are consistent, and have favorable visual quality (-30% FID, -37% KID).", "github_manual": "https://github.com/facebookresearch/ViewDiff", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-05", "arxiv_id": "2403.02084", "title": "ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models", "authors": [ "Jiaxiang Cheng", "Pan Xie", "Xin Xia", "Jiashi Li", "Jie Wu", "Yuxi Ren", "Huixia Li", "Xuefeng Xiao", "Min Zheng", "Lean Fu" ], "github": "https://github.com/bytedance/res-adapter", "project_page": "", "abstract": "Recent advancement in text-to-image models (e.g., Stable Diffusion) and corresponding personalized technologies (e.g., DreamBooth and LoRA) enables individuals to generate high-quality and imaginative images. However, they often suffer from limitations when generating images with resolutions outside of their trained domain. To overcome this limitation, we present the Resolution Adapter (ResAdapter), a domain-consistent adapter designed for diffusion models to generate images with unrestricted resolutions and aspect ratios. Unlike other multi-resolution generation methods that process images of static resolution with complex post-process operations, ResAdapter directly generates images with the dynamical resolution. Especially, after learning a deep understanding of pure resolution priors, ResAdapter trained on the general dataset, generates resolution-free images with personalized diffusion models while preserving their original style domain. Comprehensive experiments demonstrate that ResAdapter with only 0.5M can process images with flexible resolutions for arbitrary diffusion models. More extended experiments demonstrate that ResAdapter is compatible with other modules (e.g., ControlNet, IP-Adapter and LCM-LoRA) for image generation across a broad range of resolutions, and can be integrated into other multi-resolution model (e.g., ElasticDiffusion) for efficiently generating higher-resolution images. Project link is https://res-adapter.github.io", "github_manual": "https://github.com/bytedance/res-adapter", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-05", "arxiv_id": "2403.01800", "title": "AtomoVideo: High Fidelity Image-to-Video Generation", "authors": [ "Litong Gong", "Yiran Zhu", "Weijie Li", "Xiaoyang Kang", "Biao Wang", "Tiezheng Ge", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Recently, video generation has achieved significant rapid development based on superior text-to-image generation techniques. In this work, we propose a high fidelity framework for image-to-video generation, named AtomoVideo. Based on multi-granularity image injection, we achieve higher fidelity of the generated video to the given image. In addition, thanks to high quality datasets and training strategies, we achieve greater motion intensity while maintaining superior temporal consistency and stability. Our architecture extends flexibly to the video frame prediction task, enabling long sequence prediction through iterative generation. Furthermore, due to the design of adapter training, our approach can be well combined with existing personalised models and controllable modules. By quantitatively and qualitatively evaluation, AtomoVideo achieves superior results compared to popular methods, more examples can be found on our project website: https://atomo- video.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-05", "arxiv_id": "2403.01422", "title": "MovieLLM: Enhancing Long Video Understanding with AI-Generated Movies", "authors": [ "Zhende Song", "Chenchen Wang", "Jiamu Sheng", "Chi Zhang", "Gang Yu", "Jiayuan Fan", "Tao Chen" ], "github": "https://github.com/Deaddawn/DreamFrame-code", "project_page": "", "abstract": "The development of multimodal models has marked a significant step forward in how machines understand videos. These models have shown promise in analyzing short video clips. However, when it comes to longer formats like movies, they often fall short. The main hurdles are the lack of high-quality, diverse video data and the intensive work required to collect or annotate such data. In the face of these challenges, we propose MovieLLM, a novel framework designed to create synthetic, high-quality data for long videos. This framework leverages the power of GPT-4 and text-to-image models to generate detailed scripts and corresponding visuals. Our approach stands out for its flexibility and scalability, making it a superior alternative to traditional data collection methods. Our extensive experiments validate that the data produced by MovieLLM significantly improves the performance of multimodal models in understanding complex video narratives, overcoming the limitations of existing datasets regarding scarcity and bias.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Deaddawn/DreamFrame-code", "github_api_added_by": "auto" }, { "date": "2024-03-05", "arxiv_id": "2403.01779", "title": "OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on", "authors": [ "Yuhao Xu", "Tao Gu", "Weifeng Chen", "Chengcai Chen" ], "github": "https://github.com/levihsu/OOTDiffusion", "project_page": "", "abstract": "Image-based virtual try-on (VTON), which aims to generate an outfitted image of a target human wearing an in-shop garment, is a challenging image-synthesis task calling for not only high fidelity of the outfitted human but also full preservation of garment details. To tackle this issue, we propose Outfitting over Try-on Diffusion (OOTDiffusion), leveraging the power of pretrained latent diffusion models and designing a novel network architecture for realistic and controllable virtual try-on. Without an explicit warping process, we propose an outfitting UNet to learn the garment detail features, and merge them with the target human body via our proposed outfitting fusion in the denoising process of diffusion models. In order to further enhance the controllability of our outfitting UNet, we introduce outfitting dropout to the training process, which enables us to adjust the strength of garment features through classifier-free guidance. Our comprehensive experiments on the VITON-HD and Dress Code datasets demonstrate that OOTDiffusion efficiently generates high-quality outfitted images for arbitrary human and garment images, which outperforms other VTON methods in both fidelity and controllability, indicating an impressive breakthrough in virtual try-on. Our source code is available at https://github.com/levihsu/OOTDiffusion.", "github_manual": "", "github_regex": "https://github.com/levihsu/OOTDiffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-05", "arxiv_id": "2403.01487", "title": "InfiMM-HD: A Leap Forward in High-Resolution Multimodal Understanding", "authors": [ "Haogeng Liu", "Quanzeng You", "Xiaotian Han", "Yiqi Wang", "Bohan Zhai", "Yongfei Liu", "Yunzhe Tao", "Huaibo Huang", "Ran He", "Hongxia Yang" ], "github": "https://github.com/InfiMM/infimm-hd/", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have experienced significant advancements recently. Nevertheless, challenges persist in the accurate recognition and comprehension of intricate details within high-resolution images. Despite being indispensable for the development of robust MLLMs, this area remains underinvestigated. To tackle this challenge, our work introduces InfiMM-HD, a novel architecture specifically designed for processing images of different resolutions with low computational overhead. This innovation facilitates the enlargement of MLLMs to higher-resolution capabilities. InfiMM-HD incorporates a cross-attention module and visual windows to reduce computation costs. By integrating this architectural design with a four-stage training pipeline, our model attains improved visual perception efficiently and cost-effectively. Empirical study underscores the robustness and effectiveness of InfiMM-HD, opening new avenues for exploration in related areas. Codes and models can be found at https://huggingface.co/Infi-MM/infimm-hd", "github_manual": "https://github.com/InfiMM/infimm-hd/", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-05", "arxiv_id": "2403.02151", "title": "TripoSR: Fast 3D Object Reconstruction from a Single Image", "authors": [ "Dmitry Tochilkin", "David Pankratz", "Zexiang Liu", "Zixuan Huang", "Adam Letts", "Yangguang Li", "Ding Liang", "Christian Laforte", "Varun Jampani", "Yan-Pei Cao" ], "github": "https://github.com/VAST-AI-Research/TripoSR", "project_page": "", "abstract": "This technical report introduces TripoSR, a 3D reconstruction model leveraging transformer architecture for fast feed-forward 3D generation, producing 3D mesh from a single image in under 0.5 seconds. Building upon the LRM network architecture, TripoSR integrates substantial improvements in data processing, model design, and training techniques. Evaluations on public datasets show that TripoSR exhibits superior performance, both quantitatively and qualitatively, compared to other open-source alternatives. Released under the MIT license, TripoSR is intended to empower researchers, developers, and creatives with the latest advancements in 3D generative AI.", "github_manual": "https://github.com/VAST-AI-Research/TripoSR", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-05", "arxiv_id": "2403.01444", "title": "3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos", "authors": [ "Jiakai Sun", "Han Jiao", "Guangyuan Li", "Zhanjie Zhang", "Lei Zhao", "Wei Xing" ], "github": "https://github.com/SJoJoK/3DGStream", "project_page": "", "abstract": "Constructing photo-realistic Free-Viewpoint Videos (FVVs) of dynamic scenes from multi-view videos remains a challenging endeavor. Despite the remarkable advancements achieved by current neural rendering techniques, these methods generally require complete video sequences for offline training and are not capable of real-time rendering. To address these constraints, we introduce 3DGStream, a method designed for efficient FVV streaming of real-world dynamic scenes. Our method achieves fast on-the-fly per-frame reconstruction within 12 seconds and real-time rendering at 200 FPS. Specifically, we utilize 3D Gaussians (3DGs) to represent the scene. Instead of the na\\\"ive approach of directly optimizing 3DGs per-frame, we employ a compact Neural Transformation Cache (NTC) to model the translations and rotations of 3DGs, markedly reducing the training time and storage required for each FVV frame. Furthermore, we propose an adaptive 3DG addition strategy to handle emerging objects in dynamic scenes. Experiments demonstrate that 3DGStream achieves competitive performance in terms of rendering speed, image quality, training time, and model storage when compared with state-of-the-art methods.", "github_manual": "https://github.com/SJoJoK/3DGStream", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-05", "arxiv_id": "2403.02338", "title": "Twisting Lids Off with Two Hands", "authors": [ "Toru Lin", "Zhao-Heng Yin", "Haozhi Qi", "Pieter Abbeel", "Jitendra Malik" ], "github": "", "project_page": "", "abstract": "Manipulating objects with two multi-fingered hands has been a long-standing challenge in robotics, attributed to the contact-rich nature of many manipulation tasks and the complexity inherent in coordinating a high-dimensional bimanual system. In this work, we consider the problem of twisting lids of various bottle-like objects with two hands, and demonstrate that policies trained in simulation using deep reinforcement learning can be effectively transferred to the real world. With novel engineering insights into physical modeling, real-time perception, and reward design, the policy demonstrates generalization capabilities across a diverse set of unseen objects, showcasing dynamic and dexterous behaviors. Our findings serve as compelling evidence that deep reinforcement learning combined with sim-to-real transfer remains a promising approach for addressing manipulation problems of unprecedented complexity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-05", "arxiv_id": "2403.01823", "title": "RT-H: Action Hierarchies Using Language", "authors": [ "Suneel Belkhale", "Tianli Ding", "Ted Xiao", "Pierre Sermanet", "Quon Vuong", "Jonathan Tompson", "Yevgen Chebotar", "Debidatta Dwibedi", "Dorsa Sadigh" ], "github": "", "project_page": "", "abstract": "Language provides a way to break down complex concepts into digestible pieces. Recent works in robot imitation learning use language-conditioned policies that predict actions given visual observations and the high-level task specified in language. These methods leverage the structure of natural language to share data between semantically similar tasks (e.g., \"pick coke can\" and \"pick an apple\") in multi-task datasets. However, as tasks become more semantically diverse (e.g., \"pick coke can\" and \"pour cup\"), sharing data between tasks becomes harder, so learning to map high-level tasks to actions requires much more demonstration data. To bridge tasks and actions, our insight is to teach the robot the language of actions, describing low-level motions with more fine-grained phrases like \"move arm forward\". Predicting these language motions as an intermediate step between tasks and actions forces the policy to learn the shared structure of low-level motions across seemingly disparate tasks. Furthermore, a policy that is conditioned on language motions can easily be corrected during execution through human-specified language motions. This enables a new paradigm for flexible policies that can learn from human intervention in language. Our method RT-H builds an action hierarchy using language motions: it first learns to predict language motions, and conditioned on this and the high-level task, it predicts actions, using visual context at all stages. We show that RT-H leverages this language-action hierarchy to learn policies that are more robust and flexible by effectively tapping into multi-task datasets. We show that these policies not only allow for responding to language interventions, but can also learn from such interventions and outperform methods that learn from teleoperated interventions. Our website and videos are found at https://rt-hierarchy.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-05", "arxiv_id": "2403.00818", "title": "DenseMamba: State Space Models with Dense Hidden Connection for Efficient Large Language Models", "authors": [ "Wei He", "Kai Han", "Yehui Tang", "Chengcheng Wang", "Yujie Yang", "Tianyu Guo", "Yunhe Wang" ], "github": "https://github.com/WailordHe/DenseSSM", "project_page": "", "abstract": "Large language models (LLMs) face a daunting challenge due to the excessive computational and memory requirements of the commonly used Transformer architecture. While state space model (SSM) is a new type of foundational network architecture offering lower computational complexity, their performance has yet to fully rival that of Transformers. This paper introduces DenseSSM, a novel approach to enhance the flow of hidden information between layers in SSMs. By selectively integrating shallowlayer hidden states into deeper layers, DenseSSM retains fine-grained information crucial for the final output. Dense connections enhanced DenseSSM still maintains the training parallelizability and inference efficiency. The proposed method can be widely applicable to various SSM types like RetNet and Mamba. With similar model size, DenseSSM achieves significant improvements, exemplified by DenseRetNet outperforming the original RetNet with up to 5% accuracy improvement on public benchmarks.", "github_manual": "https://github.com/WailordHe/DenseSSM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-06", "arxiv_id": "2403.02884", "title": "MathScale: Scaling Instruction Tuning for Mathematical Reasoning", "authors": [ "Zhengyang Tang", "Xingxing Zhang", "Benyou Wan", "Furu Wei" ], "github": "https://github.com/math-eval/TAL-SCQ5K", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities in problem-solving. However, their proficiency in solving mathematical problems remains inadequate. We propose MathScale, a simple and scalable method to create high-quality mathematical reasoning data using frontier LLMs (e.g., {\\tt GPT-3.5}). Inspired by the cognitive mechanism in human mathematical learning, it first extracts topics and knowledge points from seed math questions and then build a concept graph, which is subsequently used to generate new math questions. MathScale exhibits effective scalability along the size axis of the math dataset that we generate. As a result, we create a mathematical reasoning dataset (MathScaleQA) containing two million math question-answer pairs. To evaluate mathematical reasoning abilities of LLMs comprehensively, we construct {\\sc MwpBench}, a benchmark of Math Word Problems, which is a collection of ten datasets (including GSM8K and MATH) covering K-12, college, and competition level math problems. We apply MathScaleQA to fine-tune open-source LLMs (e.g., LLaMA-2 and Mistral), resulting in significantly improved capabilities in mathematical reasoning. Evaluated on {\\sc MwpBench}, MathScale-7B achieves state-of-the-art performance across all datasets, surpassing its best peers of equivalent size by 42.9\\% in micro average accuracy and 43.7\\% in macro average accuracy, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/math-eval/TAL-SCQ5K", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-06", "arxiv_id": "2403.03206", "title": "Scaling Rectified Flow Transformers for High-Resolution Image Synthesis", "authors": [ "Patrick Esser", "Sumith Kulal", "Andreas Blattmann", "Rahim Entezari", "Jonas M\u00fcller", "Harry Saini", "Yam Levi", "Dominik Lorenz", "Axel Sauer", "Frederic Boesel", "Dustin Podell", "Tim Dockhorn", "Zion English", "Kyle Lacey", "Alex Goodwin", "Yannik Marek", "Robin Rombach" ], "github": "", "project_page": "", "abstract": "Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perceptual data such as images and videos. Rectified flow is a recent generative model formulation that connects data and noise in a straight line. Despite its better theoretical properties and conceptual simplicity, it is not yet decisively established as standard practice. In this work, we improve existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales. Through a large-scale study, we demonstrate the superior performance of this approach compared to established diffusion formulations for high-resolution text-to-image synthesis. Additionally, we present a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities and enables a bidirectional flow of information between image and text tokens, improving text comprehension, typography, and human preference ratings. We demonstrate that this architecture follows predictable scaling trends and correlates lower validation loss to improved text-to-image synthesis as measured by various metrics and human evaluations. Our largest models outperform state-of-the-art models, and we will make our experimental data, code, and model weights publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-06", "arxiv_id": "2403.03163", "title": "Design2Code: How Far Are We From Automating Front-End Engineering?", "authors": [ "Chenglei Si", "Yanzhe Zhang", "Zhengyuan Yang", "Ruibo Liu", "Diyi Yang" ], "github": "https://github.com/NoviScl/Design2Code", "project_page": "", "abstract": "Generative AI has made rapid advancements in recent years, achieving unprecedented capabilities in multimodal understanding and code generation. This can enable a new paradigm of front-end development, in which multimodal LLMs might directly convert visual designs into code implementations. In this work, we formalize this as a Design2Code task and conduct comprehensive benchmarking. Specifically, we manually curate a benchmark of 484 diverse real-world webpages as test cases and develop a set of automatic evaluation metrics to assess how well current multimodal LLMs can generate the code implementations that directly render into the given reference webpages, given the screenshots as input. We also complement automatic metrics with comprehensive human evaluations. We develop a suite of multimodal prompting methods and show their effectiveness on GPT-4V and Gemini Pro Vision. We further finetune an open-source Design2Code-18B model that successfully matches the performance of Gemini Pro Vision. Both human evaluation and automatic metrics show that GPT-4V performs the best on this task compared to other models. Moreover, annotators think GPT-4V generated webpages can replace the original reference webpages in 49% of cases in terms of visual appearance and content; and perhaps surprisingly, in 64% of cases GPT-4V generated webpages are considered better than the original reference webpages. Our fine-grained break-down metrics indicate that open-source models mostly lag in recalling visual elements from the input webpages and in generating correct layout designs, while aspects like text content and coloring can be drastically improved with proper finetuning.", "github_manual": "https://github.com/NoviScl/Design2Code", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-06", "arxiv_id": "2403.02677", "title": "Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters", "authors": [ "Weizhi Wang", "Khalil Mrini", "Linjie Yang", "Sateesh Kumar", "Yu Tian", "Xifeng Yan", "Heng Wang" ], "github": "", "project_page": "", "abstract": "We propose a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs). Our approach outperforms predominant filtering methods (e.g., CLIPScore) via integrating the recent advances in MLMs. We design four distinct yet complementary metrics to holistically measure the quality of image-text data. A new pipeline is established to construct high-quality instruction data for fine-tuning MLMs as data filters. Comparing with CLIPScore, our MLM filters produce more precise and comprehensive scores that directly improve the quality of filtered data and boost the performance of pre-trained models. We achieve significant improvements over CLIPScore on popular foundation models (i.e., CLIP and BLIP2) and various downstream tasks. Our MLM filter can generalize to different models and tasks, and be used as a drop-in replacement for CLIPScore. An additional ablation study is provided to verify our design choices for the MLM filter.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-06", "arxiv_id": "2403.03003", "title": "Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models", "authors": [ "Gen Luo", "Yiyi Zhou", "Yuxin Zhang", "Xiawu Zheng", "Xiaoshuai Sun", "Rongrong Ji" ], "github": "https://github.com/luogen1996/LLaVA-HR", "project_page": "", "abstract": "Despite remarkable progress, existing multimodal large language models (MLLMs) are still inferior in granular visual recognition. Contrary to previous works, we study this problem from the perspective of image resolution, and reveal that a combination of low- and high-resolution visual features can effectively mitigate this shortcoming. Based on this observation, we propose a novel and efficient method for MLLMs, termed Mixture-of-Resolution Adaptation (MRA). In particular, MRA adopts two visual pathways for images with different resolutions, where high-resolution visual information is embedded into the low-resolution pathway via the novel mixture-of-resolution adapters (MR-Adapters). This design also greatly reduces the input sequence length of MLLMs. To validate MRA, we apply it to a recent MLLM called LLaVA, and term the new model LLaVA-HR. We conduct extensive experiments on 11 vision-language (VL) tasks, which show that LLaVA-HR outperforms existing MLLMs on 8 VL tasks, e.g., +9.4% on TextVQA. More importantly, both training and inference of LLaVA-HR remain efficient with MRA, e.g., 20 training hours and 3times inference speed than LLaVA-1.5. Source codes are released at: https://github.com/luogen1996/LLaVA-HR.", "github_manual": "", "github_regex": "https://github.com/luogen1996/LLaVA-HR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-06", "arxiv_id": "2403.02775", "title": "EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs", "authors": [ "Hanlin Tang", "Yifu Sun", "Decheng Wu", "Kai Liu", "Jianchen Zhu", "Zhanhui Kang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have proven to be very superior to conventional methods in various tasks. However, their expensive computations and high memory requirements are prohibitive for deployment. Model quantization is an effective method for reducing this overhead. The problem is that in most previous works, the quantized model was calibrated using few samples from the training data, which might affect the generalization of the quantized LLMs to unknown cases and tasks. Hence in this work, we explore an important question: Can we design a data-independent quantization method for LLMs to guarantee its generalization performance? In this work, we propose EasyQuant, a training-free and data-independent weight-only quantization algorithm for LLMs. Our observation indicates that two factors: outliers in the weight and quantization ranges, are essential for reducing the quantization error. Therefore, in EasyQuant, we leave the outliers (less than 1%) unchanged and optimize the quantization range to reduce the reconstruction error. With these methods, we surprisingly find that EasyQuant achieves comparable performance to the original model. Since EasyQuant does not depend on any training data, the generalization performance of quantized LLMs is safely guaranteed. Moreover, EasyQuant can be implemented in parallel so that the quantized model could be attained in a few minutes even for LLMs over 100B. To our best knowledge, we are the first work that achieves almost lossless quantization performance for LLMs under a data-independent setting and our algorithm runs over 10 times faster than the data-dependent methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-06", "arxiv_id": "2403.02545", "title": "Wukong: Towards a Scaling Law for Large-Scale Recommendation", "authors": [ "Buyun Zhang", "Liang Luo", "Yuxin Chen", "Jade Nie", "Xi Liu", "Daifeng Guo", "Yanli Zhao", "Shen Li", "Yuchen Hao", "Yantao Yao", "Guna Lakshminarayanan", "Ellie Dingqiao Wen", "Jongsoo Park", "Maxim Naumov", "Wenlin Chen" ], "github": "", "project_page": "", "abstract": "Scaling laws play an instrumental role in the sustainable improvement in model quality. Unfortunately, recommendation models to date do not exhibit such laws similar to those observed in the domain of large language models, due to the inefficiencies of their upscaling mechanisms. This limitation poses significant challenges in adapting these models to increasingly more complex real-world datasets. In this paper, we propose an effective network architecture based purely on stacked factorization machines, and a synergistic upscaling strategy, collectively dubbed Wukong, to establish a scaling law in the domain of recommendation. Wukong's unique design makes it possible to capture diverse, any-order of interactions simply through taller and wider layers. We conducted extensive evaluations on six public datasets, and our results demonstrate that Wukong consistently outperforms state-of-the-art models quality-wise. Further, we assessed Wukong's scalability on an internal, large-scale dataset. The results show that Wukong retains its superiority in quality over state-of-the-art models, while holding the scaling law across two orders of magnitude in model complexity, extending beyond 100 Gflop or equivalently up to GPT-3/LLaMa-2 scale of total training compute, where prior arts fall short.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-06", "arxiv_id": "2403.02827", "title": "Tuning-Free Noise Rectification for High Fidelity Image-to-Video Generation", "authors": [ "Weijie Li", "Litong Gong", "Yiran Zhu", "Fanda Fan", "Biao Wang", "Tiezheng Ge", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Image-to-video (I2V) generation tasks always suffer from keeping high fidelity in the open domains. Traditional image animation techniques primarily focus on specific domains such as faces or human poses, making them difficult to generalize to open domains. Several recent I2V frameworks based on diffusion models can generate dynamic content for open domain images but fail to maintain fidelity. We found that two main factors of low fidelity are the loss of image details and the noise prediction biases during the denoising process. To this end, we propose an effective method that can be applied to mainstream video diffusion models. This method achieves high fidelity based on supplementing more precise image information and noise rectification. Specifically, given a specified image, our method first adds noise to the input image latent to keep more details, then denoises the noisy latent with proper rectification to alleviate the noise prediction biases. Our method is tuning-free and plug-and-play. The experimental results demonstrate the effectiveness of our approach in improving the fidelity of generated videos. For more image-to-video generated results, please refer to the project website: https://noise-rectification.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-06", "arxiv_id": "2403.03100", "title": "NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models", "authors": [ "Zeqian Ju", "Yuancheng Wang", "Kai Shen", "Xu Tan", "Detai Xin", "Dongchao Yang", "Yanqing Liu", "Yichong Leng", "Kaitao Song", "Siliang Tang", "Zhizheng Wu", "Tao Qin", "Xiang-Yang Li", "Wei Ye", "Shikun Zhang", "Jiang Bian", "Lei He", "Jinyu Li", "Sheng Zhao" ], "github": "", "project_page": "", "abstract": "While recent large-scale text-to-speech (TTS) models have achieved significant progress, they still fall short in speech quality, similarity, and prosody. Considering speech intricately encompasses various attributes (e.g., content, prosody, timbre, and acoustic details) that pose significant challenges for generation, a natural idea is to factorize speech into individual subspaces representing different attributes and generate them individually. Motivated by it, we propose NaturalSpeech 3, a TTS system with novel factorized diffusion models to generate natural speech in a zero-shot way. Specifically, 1) we design a neural codec with factorized vector quantization (FVQ) to disentangle speech waveform into subspaces of content, prosody, timbre, and acoustic details; 2) we propose a factorized diffusion model to generate attributes in each subspace following its corresponding prompt. With this factorization design, NaturalSpeech 3 can effectively and efficiently model the intricate speech with disentangled subspaces in a divide-and-conquer way. Experiments show that NaturalSpeech 3 outperforms the state-of-the-art TTS systems on quality, similarity, prosody, and intelligibility. Furthermore, we achieve better performance by scaling to 1B parameters and 200K hours of training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-06", "arxiv_id": "2403.02460", "title": "MagicClay: Sculpting Meshes With Generative Neural Fields", "authors": [ "Amir Barda", "Vladimir G. Kim", "Noam Aigerman", "Amit H. Bermano", "Thibault Groueix" ], "github": "https://github.com/amirbarda/MagicClay", "project_page": "", "abstract": "The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial properties, such as incremental control - a fundamental requirement for artistic work. Triangular meshes, on the other hand, are the representation of choice for most geometry related tasks, offering efficiency and intuitive control, but do not lend themselves to neural optimization. To support downstream tasks, previous art typically proposes a two-step approach, where first a shape is generated using neural fields, and then a mesh is extracted for further processing. Instead, in this paper we introduce a hybrid approach that maintains both a mesh and a Signed Distance Field (SDF) representations consistently. Using this representation, we introduce MagicClay - an artist friendly tool for sculpting regions of a mesh according to textual prompts while keeping other regions untouched. Our framework carefully and efficiently balances consistency between the representations and regularizations in every step of the shape optimization; Relying on the mesh representation, we show how to render the SDF at higher resolutions and faster. In addition, we employ recent work in differentiable mesh reconstruction to adaptively allocate triangles in the mesh where required, as indicated by the SDF. Using an implemented prototype, we demonstrate superior generated geometry compared to the state-of-the-art, and novel consistent control, allowing sequential prompt-based edits to the same mesh for the first time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amirbarda/MagicClay", "github_api_added_by": "auto" }, { "date": "2024-03-06", "arxiv_id": "2403.02709", "title": "RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches", "authors": [ "Priya Sundaresan", "Quan Vuong", "Jiayuan Gu", "Peng Xu", "Ted Xiao", "Sean Kirmani", "Tianhe Yu", "Michael Stark", "Ajinkya Jain", "Karol Hausman", "Dorsa Sadigh", "Jeannette Bohg", "Stefan Schaal" ], "github": "", "project_page": "", "abstract": "Natural language and images are commonly used as goal representations in goal-conditioned imitation learning (IL). However, natural language can be ambiguous and images can be over-specified. In this work, we propose hand-drawn sketches as a modality for goal specification in visual imitation learning. Sketches are easy for users to provide on the fly like language, but similar to images they can also help a downstream policy to be spatially-aware and even go beyond images to disambiguate task-relevant from task-irrelevant objects. We present RT-Sketch, a goal-conditioned policy for manipulation that takes a hand-drawn sketch of the desired scene as input, and outputs actions. We train RT-Sketch on a dataset of paired trajectories and corresponding synthetically generated goal sketches. We evaluate this approach on six manipulation skills involving tabletop object rearrangements on an articulated countertop. Experimentally we find that RT-Sketch is able to perform on a similar level to image or language-conditioned agents in straightforward settings, while achieving greater robustness when language goals are ambiguous or visual distractors are present. Additionally, we show that RT-Sketch has the capacity to interpret and act upon sketches with varied levels of specificity, ranging from minimal line drawings to detailed, colored drawings. For supplementary material and videos, please refer to our website: http://rt-sketch.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-06", "arxiv_id": "2403.03194", "title": "MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets", "authors": [ "Hossein Aboutalebi", "Hwanjun Song", "Yusheng Xie", "Arshit Gupta", "Justin Sun", "Hang Su", "Igor Shalyminov", "Nikolaos Pappas", "Siffi Singh", "Saab Mansour" ], "github": "https://github.com/amazon-science/MAGID", "project_page": "", "abstract": "Development of multimodal interactive systems is hindered by the lack of rich, multimodal (text, images) conversational data, which is needed in large quantities for LLMs. Previous approaches augment textual dialogues with retrieved images, posing privacy, diversity, and quality constraints. In this work, we introduce Multimodal Augmented Generative Images Dialogues (MAGID), a framework to augment text-only dialogues with diverse and high-quality images. Subsequently, a diffusion model is applied to craft corresponding images, ensuring alignment with the identified text. Finally, MAGID incorporates an innovative feedback loop between an image description generation module (textual LLM) and image quality modules (addressing aesthetics, image-text matching, and safety), that work in tandem to generate high-quality and multi-modal dialogues. We compare MAGID to other SOTA baselines on three dialogue datasets, using automated and human evaluation. Our results show that MAGID is comparable to or better than baselines, with significant improvements in human evaluation, especially against retrieval baselines where the image database is small.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amazon-science/MAGID", "github_api_added_by": "auto" }, { "date": "2024-03-06", "arxiv_id": "2403.02626", "title": "Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use", "authors": [ "Imad Eddine Toubal", "Aditya Avinash", "Neil Gordon Alldrin", "Jan Dlabal", "Wenlei Zhou", "Enming Luo", "Otilia Stretcu", "Hao Xiong", "Chun-Ta Lu", "Howard Zhou", "Ranjay Krishna", "Ariel Fuxman", "Tom Duerig" ], "github": "", "project_page": "", "abstract": "From content moderation to wildlife conservation, the number of applications that require models to recognize nuanced or subjective visual concepts is growing. Traditionally, developing classifiers for such concepts requires substantial manual effort measured in hours, days, or even months to identify and annotate data needed for training. Even with recently proposed Agile Modeling techniques, which enable rapid bootstrapping of image classifiers, users are still required to spend 30 minutes or more of monotonous, repetitive data labeling just to train a single classifier. Drawing on Fiske's Cognitive Miser theory, we propose a new framework that alleviates manual effort by replacing human labeling with natural language interactions, reducing the total effort required to define a concept by an order of magnitude: from labeling 2,000 images to only 100 plus some natural language interactions. Our framework leverages recent advances in foundation models, both large language models and vision-language models, to carve out the concept space through conversation and by automatically labeling training data points. Most importantly, our framework eliminates the need for crowd-sourced annotations. Moreover, our framework ultimately produces lightweight classification models that are deployable in cost-sensitive scenarios. Across 15 subjective concepts and across 2 public image classification datasets, our trained models outperform traditional Agile Modeling as well as state-of-the-art zero-shot classification models like ALIGN, CLIP, CuPL, and large visual question-answering models like PaLI-X.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-07", "arxiv_id": "2403.03507", "title": "GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection", "authors": [ "Jiawei Zhao", "Zhenyu Zhang", "Beidi Chen", "Zhangyang Wang", "Anima Anandkumar", "Yuandong Tian" ], "github": "https://github.com/jiaweizzhao/galore", "project_page": "", "abstract": "Training Large Language Models (LLMs) presents significant memory challenges, predominantly due to the growing size of weights and optimizer states. Common memory-reduction approaches, such as low-rank adaptation (LoRA), add a trainable low-rank matrix to the frozen pre-trained weight in each layer, reducing trainable parameters and optimizer states. However, such approaches typically underperform training with full-rank weights in both pre-training and fine-tuning stages since they limit the parameter search to a low-rank subspace and alter the training dynamics, and further, may require full-rank warm start. In this work, we propose Gradient Low-Rank Projection (GaLore), a training strategy that allows full-parameter learning but is more memory-efficient than common low-rank adaptation methods such as LoRA. Our approach reduces memory usage by up to 65.5% in optimizer states while maintaining both efficiency and performance for pre-training on LLaMA 1B and 7B architectures with C4 dataset with up to 19.7B tokens, and on fine-tuning RoBERTa on GLUE tasks. Our 8-bit GaLore further reduces optimizer memory by up to 82.5% and total training memory by 63.3%, compared to a BF16 baseline. Notably, we demonstrate, for the first time, the feasibility of pre-training a 7B model on consumer GPUs with 24GB memory (e.g., NVIDIA RTX 4090) without model parallel, checkpointing, or offloading strategies.", "github_manual": "https://github.com/jiaweizzhao/galore", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-07", "arxiv_id": "2403.03954", "title": "3D Diffusion Policy", "authors": [ "Yanjie Ze", "Gu Zhang", "Kangning Zhang", "Chenyuan Hu", "Muhan Wang", "Huazhe Xu" ], "github": "https://github.com/YanjieZe/3D-Diffusion-Policy", "project_page": "", "abstract": "Imitation learning provides an efficient way to teach robots dexterous skills; however, learning complex skills robustly and generalizablely usually consumes large amounts of human demonstrations. To tackle this challenging problem, we present 3D Diffusion Policy (DP3), a novel visual imitation learning approach that incorporates the power of 3D visual representations into diffusion policies, a class of conditional action generative models. The core design of DP3 is the utilization of a compact 3D visual representation, extracted from sparse point clouds with an efficient point encoder. In our experiments involving 72 simulation tasks, DP3 successfully handles most tasks with just 10 demonstrations and surpasses baselines with a 55.3% relative improvement. In 4 real robot tasks, DP3 demonstrates precise control with a high success rate of 85%, given only 40 demonstrations of each task, and shows excellent generalization abilities in diverse aspects, including space, viewpoint, appearance, and instance. Interestingly, in real robot experiments, DP3 rarely violates safety requirements, in contrast to baseline methods which frequently do, necessitating human intervention. Our extensive evaluation highlights the critical importance of 3D representations in real-world robot learning. Videos, code, and data are available on https://3d-diffusion-policy.github.io .", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YanjieZe/3D-Diffusion-Policy", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-07", "arxiv_id": "2403.03883", "title": "SaulLM-7B: A pioneering Large Language Model for Law", "authors": [ "Pierre Colombo", "Telmo Pessoa Pires", "Malik Boudiaf", "Dominic Culver", "Rui Melo", "Caio Corro", "Andre F. T. Martins", "Fabrizio Esposito", "Vera L\u00facia Raposo", "Sofia Morgado", "Michael Desa" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "In this paper, we introduce SaulLM-7B, a large language model (LLM) tailored for the legal domain. With 7 billion parameters, SaulLM-7B is the first LLM designed explicitly for legal text comprehension and generation. Leveraging the Mistral 7B architecture as its foundation, SaulLM-7B is trained on an English legal corpus of over 30 billion tokens. SaulLM-7B exhibits state-of-the-art proficiency in understanding and processing legal documents. Additionally, we present a novel instructional fine-tuning method that leverages legal datasets to further enhance SaulLM-7B's performance in legal tasks. SaulLM-7B is released under the CC-BY-SA-4.0 License.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-07", "arxiv_id": "2403.03870", "title": "Learning to Decode Collaboratively with Multiple Language Models", "authors": [ "Shannon Zejiang Shen", "Hunter Lang", "Bailin Wang", "Yoon Kim", "David Sontag" ], "github": "https://github.com/clinicalml/co-llm", "project_page": "", "abstract": "We propose a method to teach multiple large language models (LLM) to collaborate by interleaving their generations at the token level. We model the decision of which LLM generates the next token as a latent variable. By optimizing the marginal likelihood of a training set under our latent variable model, the base LLM automatically learns when to generate itself and when to call on one of the ``assistant'' language models to generate, all without direct supervision. Token-level collaboration during decoding allows for a fusion of each model's expertise in a manner tailored to the specific task at hand. Our collaborative decoding is especially useful in cross-domain settings where a generalist base LLM learns to invoke domain expert models. On instruction-following, domain-specific QA, and reasoning tasks, we show that the performance of the joint system exceeds that of the individual models. Through qualitative analysis of the learned latent decisions, we show models trained with our method exhibit several interesting collaboration patterns, e.g., template-filling. Our code is available at https://github.com/clinicalml/co-llm.", "github_manual": "", "github_regex": "https://github.com/clinicalml/co-llm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-07", "arxiv_id": "2403.03853", "title": "ShortGPT: Layers in Large Language Models are More Redundant Than You Expect", "authors": [ "Xin Men", "Mingyu Xu", "Qingyu Zhang", "Bingning Wang", "Hongyu Lin", "Yaojie Lu", "Xianpei Han", "Weipeng Chen" ], "github": "", "project_page": "", "abstract": "As Large Language Models (LLMs) continue to advance in performance, their size has escalated significantly, with current LLMs containing billions or even trillions of parameters. However, in this study, we discovered that many layers of LLMs exhibit high similarity, and some layers play a negligible role in network functionality. Based on this observation, we define a metric called Block Influence (BI) to gauge the significance of each layer in LLMs. We then propose a straightforward pruning approach: layer removal, in which we directly delete the redundant layers in LLMs based on their BI scores. Experiments demonstrate that our method, which we call ShortGPT, significantly outperforms previous state-of-the-art (SOTA) methods in model pruning. Moreover, ShortGPT is orthogonal to quantization-like methods, enabling further reduction in parameters and computation. The ability to achieve better results through simple layer removal, as opposed to more complex pruning techniques, suggests a high degree of redundancy in the model architecture.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-07", "arxiv_id": "2403.03346", "title": "Enhancing Vision-Language Pre-training with Rich Supervisions", "authors": [ "Yuan Gao", "Kunyu Shi", "Pengkai Zhu", "Edouard Belval", "Oren Nuriel", "Srikar Appalaraju", "Shabnam Ghadar", "Vijay Mahadevan", "Zhuowen Tu", "Stefano Soatto" ], "github": "", "project_page": "", "abstract": "We propose Strongly Supervised pre-training with ScreenShots (S4) - a novel pre-training paradigm for Vision-Language Models using data from large-scale web screenshot rendering. Using web screenshots unlocks a treasure trove of visual and textual cues that are not present in using image-text pairs. In S4, we leverage the inherent tree-structured hierarchy of HTML elements and the spatial localization to carefully design 10 pre-training tasks with large scale annotated data. These tasks resemble downstream tasks across different domains and the annotations are cheap to obtain. We demonstrate that, compared to current screenshot pre-training objectives, our innovative pre-training method significantly enhances performance of image-to-text model in nine varied and popular downstream tasks - up to 76.1% improvements on Table Detection, and at least 1% on Widget Captioning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-07", "arxiv_id": "2403.03956", "title": "Backtracing: Retrieving the Cause of the Query", "authors": [ "Rose E. Wang", "Pawan Wirawarn", "Omar Khattab", "Noah Goodman", "Dorottya Demszky" ], "github": "https://github.com/rosewang2008/backtracing", "project_page": "", "abstract": "Many online content portals allow users to ask questions to supplement their understanding (e.g., of lectures). While information retrieval (IR) systems may provide answers for such user queries, they do not directly assist content creators -- such as lecturers who want to improve their content -- identify segments that _caused_ a user to ask those questions. We introduce the task of backtracing, in which systems retrieve the text segment that most likely caused a user query. We formalize three real-world domains for which backtracing is important in improving content delivery and communication: understanding the cause of (a) student confusion in the Lecture domain, (b) reader curiosity in the News Article domain, and (c) user emotion in the Conversation domain. We evaluate the zero-shot performance of popular information retrieval methods and language modeling methods, including bi-encoder, re-ranking and likelihood-based methods and ChatGPT. While traditional IR systems retrieve semantically relevant information (e.g., details on \"projection matrices\" for a query \"does projecting multiple times still lead to the same point?\"), they often miss the causally relevant context (e.g., the lecturer states \"projecting twice gets me the same answer as one projection\"). Our results show that there is room for improvement on backtracing and it requires new retrieval approaches. We hope our benchmark serves to improve future retrieval systems for backtracing, spawning systems that refine content generation and identify linguistic triggers influencing user queries. Our code and data are open-sourced: https://github.com/rosewang2008/backtracing.", "github_manual": "", "github_regex": "https://github.com/rosewang2008/backtracing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-07", "arxiv_id": "2403.03950", "title": "Stop Regressing: Training Value Functions via Classification for Scalable Deep RL", "authors": [ "Jesse Farebrother", "Jordi Orbay", "Quan Vuong", "Adrien Ali Ta\u00efga", "Yevgen Chebotar", "Ted Xiao", "Alex Irpan", "Sergey Levine", "Pablo Samuel Castro", "Aleksandra Faust", "Aviral Kumar", "Rishabh Agarwal" ], "github": "", "project_page": "", "abstract": "Value functions are a central component of deep reinforcement learning (RL). These functions, parameterized by neural networks, are trained using a mean squared error regression objective to match bootstrapped target values. However, scaling value-based RL methods that use regression to large networks, such as high-capacity Transformers, has proven challenging. This difficulty is in stark contrast to supervised learning: by leveraging a cross-entropy classification loss, supervised methods have scaled reliably to massive networks. Observing this discrepancy, in this paper, we investigate whether the scalability of deep RL can also be improved simply by using classification in place of regression for training value functions. We demonstrate that value functions trained with categorical cross-entropy significantly improves performance and scalability in a variety of domains. These include: single-task RL on Atari 2600 games with SoftMoEs, multi-task RL on Atari with large-scale ResNets, robotic manipulation with Q-transformers, playing Chess without search, and a language-agent Wordle task with high-capacity Transformers, achieving state-of-the-art results on these domains. Through careful analysis, we show that the benefits of categorical cross-entropy primarily stem from its ability to mitigate issues inherent to value-based RL, such as noisy targets and non-stationarity. Overall, we argue that a simple shift to training value functions with categorical cross-entropy can yield substantial improvements in the scalability of deep RL at little-to-no cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-07", "arxiv_id": "2403.03234", "title": "Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling", "authors": [ "Yair Schiff", "Chia-Hsiang Kao", "Aaron Gokaslan", "Tri Dao", "Albert Gu", "Volodymyr Kuleshov" ], "github": "https://github.com/kuleshov-group/caduceus", "project_page": "", "abstract": "Large-scale sequence modeling has sparked rapid advances that now extend into biology and genomics. However, modeling genomic sequences introduces challenges such as the need to model long-range token interactions, the effects of upstream and downstream regions of the genome, and the reverse complementarity (RC) of DNA. Here, we propose an architecture motivated by these challenges that builds off the long-range Mamba block, and extends it to a BiMamba component that supports bi-directionality, and to a MambaDNA block that additionally supports RC equivariance. We use MambaDNA as the basis of Caduceus, the first family of RC equivariant bi-directional long-range DNA language models, and we introduce pre-training and fine-tuning strategies that yield Caduceus DNA foundation models. Caduceus outperforms previous long-range models on downstream benchmarks; on a challenging long-range variant effect prediction task, Caduceus exceeds the performance of 10x larger models that do not leverage bi-directionality or equivariance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kuleshov-group/caduceus", "github_api_added_by": "auto" }, { "date": "2024-03-08", "arxiv_id": "2403.04652", "title": "Yi: Open Foundation Models by 01.AI", "authors": [ "01. AI", "Alex Young", "Bei Chen", "Chao Li", "Chengen Huang", "Ge Zhang", "Guanwei Zhang", "Heng Li", "Jiangcheng Zhu", "Jianqun Chen", "Jing Chang", "Kaidong Yu", "Peng Liu", "Qiang Liu", "Shawn Yue", "Senbin Yang", "Shiming Yang", "Tao Yu", "Wen Xie", "Wenhao Huang", "Xiaohui Hu", "Xiaoyi Ren", "Xinyao Niu", "Pengcheng Nie", "Yuchi Xu", "Yudong Liu", "Yue Wang", "Yuxuan Cai", "Zhenyu Gu", "Zhiyuan Liu", "Zonghong Dai" ], "github": "https://github.com/01-ai/yi", "project_page": "", "abstract": "We introduce the Yi model family, a series of language and multimodal models that demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models, 200K long context models, depth-upscaled models, and vision-language models. Our base models achieve strong performance on a wide range of benchmarks like MMLU, and our finetuned chat models deliver strong human preference rate on major evaluation platforms like AlpacaEval and Chatbot Arena. Building upon our scalable super-computing infrastructure and the classical transformer architecture, we attribute the performance of Yi models primarily to its data quality resulting from our data-engineering efforts. For pretraining, we construct 3.1 trillion tokens of English and Chinese corpora using a cascaded data deduplication and quality filtering pipeline. For finetuning, we polish a small scale (less than 10K) instruction dataset over multiple iterations such that every single instance has been verified directly by our machine learning engineers. For vision-language, we combine the chat language model with a vision transformer encoder and train the model to align visual representations to the semantic space of the language model. We further extend the context length to 200K through lightweight continual pretraining and demonstrate strong needle-in-a-haystack retrieval performance. We show that extending the depth of the pretrained checkpoint through continual pretraining further improves performance. We believe that given our current results, continuing to scale up model parameters using thoroughly optimized data will lead to even stronger frontier models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/01-ai/yi", "github_api_added_by": "auto" }, { "date": "2024-03-08", "arxiv_id": "2403.04437", "title": "StableDrag: Stable Dragging for Point-based Image Editing", "authors": [ "Yutao Cui", "Xiaotong Zhao", "Guozhen Zhang", "Shengming Cao", "Kai Ma", "Limin Wang" ], "github": "", "project_page": "", "abstract": "Point-based image editing has attracted remarkable attention since the emergence of DragGAN. Recently, DragDiffusion further pushes forward the generative quality via adapting this dragging technique to diffusion models. Despite these great success, this dragging scheme exhibits two major drawbacks, namely inaccurate point tracking and incomplete motion supervision, which may result in unsatisfactory dragging outcomes. To tackle these issues, we build a stable and precise drag-based editing framework, coined as StableDrag, by designing a discirminative point tracking method and a confidence-based latent enhancement strategy for motion supervision. The former allows us to precisely locate the updated handle points, thereby boosting the stability of long-range manipulation, while the latter is responsible for guaranteeing the optimized latent as high-quality as possible across all the manipulation steps. Thanks to these unique designs, we instantiate two types of image editing models including StableDrag-GAN and StableDrag-Diff, which attains more stable dragging performance, through extensive qualitative experiments and quantitative assessment on DragBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-08", "arxiv_id": "2403.04132", "title": "Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference", "authors": [ "Wei-Lin Chiang", "Lianmin Zheng", "Ying Sheng", "Anastasios Nikolas Angelopoulos", "Tianle Li", "Dacheng Li", "Hao Zhang", "Banghua Zhu", "Michael Jordan", "Joseph E. Gonzalez", "Ion Stoica" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have unlocked new capabilities and applications; however, evaluating the alignment with human preferences still poses significant challenges. To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences. Our methodology employs a pairwise comparison approach and leverages input from a diverse user base through crowdsourcing. The platform has been operational for several months, amassing over 240K votes. This paper describes the platform, analyzes the data we have collected so far, and explains the tried-and-true statistical methods we are using for efficient and accurate evaluation and ranking of models. We confirm that the crowdsourced questions are sufficiently diverse and discriminating and that the crowdsourced human votes are in good agreement with those of expert raters. These analyses collectively establish a robust foundation for the credibility of Chatbot Arena. Because of its unique value and openness, Chatbot Arena has emerged as one of the most referenced LLM leaderboards, widely cited by leading LLM developers and companies. Our demo is publicly available at https://chat.lmsys.org.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-08", "arxiv_id": "2403.04642", "title": "Teaching Large Language Models to Reason with Reinforcement Learning", "authors": [ "Alex Havrilla", "Yuqing Du", "Sharath Chandra Raparthy", "Christoforos Nalmpantis", "Jane Dwivedi-Yu", "Maksym Zhuravinskyi", "Eric Hambro", "Sainbayar Sukhbaatar", "Roberta Raileanu" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Human Feedback (RLHF) has emerged as a dominant approach for aligning LLM outputs with human preferences. Inspired by the success of RLHF, we study the performance of multiple algorithms that learn from feedback (Expert Iteration, Proximal Policy Optimization (PPO), Return-Conditioned RL) on improving LLM reasoning capabilities. We investigate both sparse and dense rewards provided to the LLM both heuristically and via a learned reward model. We additionally start from multiple model sizes and initializations both with and without supervised fine-tuning (SFT) data. Overall, we find all algorithms perform comparably, with Expert Iteration performing best in most cases. Surprisingly, we find the sample complexity of Expert Iteration is similar to that of PPO, requiring at most on the order of 10^6 samples to converge from a pretrained checkpoint. We investigate why this is the case, concluding that during RL training models fail to explore significantly beyond solutions already produced by SFT models. Additionally, we discuss a trade off between maj@1 and pass@96 metric performance during SFT training and how conversely RL training improves both simultaneously. We then conclude by discussing the implications of our findings for RLHF and the future role of RL in LLM fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-08", "arxiv_id": "2403.04706", "title": "Common 7B Language Models Already Possess Strong Math Capabilities", "authors": [ "Chen Li", "Weiqi Wang", "Jingcheng Hu", "Yixuan Wei", "Nanning Zheng", "Han Hu", "Zheng Zhang", "Houwen Peng" ], "github": "", "project_page": "", "abstract": "Mathematical capabilities were previously believed to emerge in common language models only at a very large scale or require extensive math-related pre-training. This paper shows that the LLaMA-2 7B model with common pre-training already exhibits strong mathematical abilities, as evidenced by its impressive accuracy of 97.7% and 72.0% on the GSM8K and MATH benchmarks, respectively, when selecting the best response from 256 random generations. The primary issue with the current base model is the difficulty in consistently eliciting its inherent mathematical capabilities. Notably, the accuracy for the first answer drops to 49.5% and 7.9% on the GSM8K and MATH benchmarks, respectively. We find that simply scaling up the SFT data can significantly enhance the reliability of generating correct answers. However, the potential for extensive scaling is constrained by the scarcity of publicly available math questions. To overcome this limitation, we employ synthetic data, which proves to be nearly as effective as real data and shows no clear saturation when scaled up to approximately one million samples. This straightforward approach achieves an accuracy of 82.6% on GSM8K and 40.6% on MATH using LLaMA-2 7B models, surpassing previous models by 14.2% and 20.8%, respectively. We also provide insights into scaling behaviors across different reasoning complexities and error types.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-08", "arxiv_id": "2403.04746", "title": "LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error", "authors": [ "Boshi Wang", "Hao Fang", "Jason Eisner", "Benjamin Van Durme", "Yu Su" ], "github": "https://github.com/microsoft/simulated-trial-and-error", "project_page": "", "abstract": "Tools are essential for large language models (LLMs) to acquire up-to-date information and take consequential actions in external environments. Existing work on tool-augmented LLMs primarily focuses on the broad coverage of tools and the flexibility of adding new tools. However, a critical aspect that has surprisingly been understudied is simply how accurately an LLM uses tools for which it has been trained. We find that existing LLMs, including GPT-4 and open-source LLMs specifically fine-tuned for tool use, only reach a correctness rate in the range of 30% to 60%, far from reliable use in practice. We propose a biologically inspired method for tool-augmented LLMs, simulated trial and error (STE), that orchestrates three key mechanisms for successful tool use behaviors in the biological system: trial and error, imagination, and memory. Specifically, STE leverages an LLM's 'imagination' to simulate plausible scenarios for using a tool, after which the LLM interacts with the tool to learn from its execution feedback. Both short-term and long-term memory are employed to improve the depth and breadth of the exploration, respectively. Comprehensive experiments on ToolBench show that STE substantially improves tool learning for LLMs under both in-context learning and fine-tuning settings, bringing a boost of 46.7% to Mistral-Instruct-7B and enabling it to outperform GPT-4. We also show effective continual learning of tools via a simple experience replay strategy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/simulated-trial-and-error", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-08", "arxiv_id": "2403.04732", "title": "How Far Are We from Intelligent Visual Deductive Reasoning?", "authors": [ "Yizhe Zhang", "He Bai", "Ruixiang Zhang", "Jiatao Gu", "Shuangfei Zhai", "Josh Susskind", "Navdeep Jaitly" ], "github": "https://github.com/apple/ml-rpm-bench", "project_page": "", "abstract": "Vision-Language Models (VLMs) such as GPT-4V have recently demonstrated incredible strides on diverse vision language tasks. We dig into vision-based deductive reasoning, a more sophisticated but less explored realm, and find previously unexposed blindspots in the current SOTA VLMs. Specifically, we leverage Raven's Progressive Matrices (RPMs), to assess VLMs' abilities to perform multi-hop relational and deductive reasoning relying solely on visual clues. We perform comprehensive evaluations of several popular VLMs employing standard strategies such as in-context learning, self-consistency, and Chain-of-thoughts (CoT) on three diverse datasets, including the Mensa IQ test, IntelligenceTest, and RAVEN. The results reveal that despite the impressive capabilities of LLMs in text-based reasoning, we are still far from achieving comparable proficiency in visual deductive reasoning. We found that certain standard strategies that are effective when applied to LLMs do not seamlessly translate to the challenges presented by visual reasoning tasks. Moreover, a detailed analysis reveals that VLMs struggle to solve these tasks mainly because they are unable to perceive and comprehend multiple, confounding abstract patterns in RPM examples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apple/ml-rpm-bench", "github_api_added_by": "auto" }, { "date": "2024-03-08", "arxiv_id": "2403.04692", "title": "PixArt-\u03a3: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation", "authors": [ "Junsong Chen", "Chongjian Ge", "Enze Xie", "Yue Wu", "Lewei Yao", "Xiaozhe Ren", "Zhongdao Wang", "Ping Luo", "Huchuan Lu", "Zhenguo Li" ], "github": "https://github.com/PixArt-alpha/PixArt-sigma", "project_page": "", "abstract": "In this paper, we introduce PixArt-\\Sigma, a Diffusion Transformer model~(DiT) capable of directly generating images at 4K resolution. PixArt-\\Sigma represents a significant advancement over its predecessor, PixArt-\\alpha, offering images of markedly higher fidelity and improved alignment with text prompts. A key feature of PixArt-\\Sigma is its training efficiency. Leveraging the foundational pre-training of PixArt-\\alpha, it evolves from the `weaker' baseline to a `stronger' model via incorporating higher quality data, a process we term \"weak-to-strong training\". The advancements in PixArt-\\Sigma are twofold: (1) High-Quality Training Data: PixArt-\\Sigma incorporates superior-quality image data, paired with more precise and detailed image captions. (2) Efficient Token Compression: we propose a novel attention module within the DiT framework that compresses both keys and values, significantly improving efficiency and facilitating ultra-high-resolution image generation. Thanks to these improvements, PixArt-\\Sigma achieves superior image quality and user prompt adherence capabilities with significantly smaller model size (0.6B parameters) than existing text-to-image diffusion models, such as SDXL (2.6B parameters) and SD Cascade (5.1B parameters). Moreover, PixArt-\\Sigma's capability to generate 4K images supports the creation of high-resolution posters and wallpapers, efficiently bolstering the production of high-quality visual content in industries such as film and gaming.", "github_manual": "https://github.com/PixArt-alpha/PixArt-sigma", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-08", "arxiv_id": "2403.04116", "title": "Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis", "authors": [ "Yuanhao Cai", "Yixun Liang", "Jiahao Wang", "Angtian Wang", "Yulun Zhang", "Xiaokang Yang", "Zongwei Zhou", "Alan Yuille" ], "github": "https://github.com/caiyuanhao1998/X-Gaussian", "project_page": "", "abstract": "X-ray is widely applied for transmission imaging due to its stronger penetration than natural light. When rendering novel view X-ray projections, existing methods mainly based on NeRF suffer from long training time and slow inference speed. In this paper, we propose a 3D Gaussian splatting-based framework, namely X-Gaussian, for X-ray novel view synthesis. Firstly, we redesign a radiative Gaussian point cloud model inspired by the isotropic nature of X-ray imaging. Our model excludes the influence of view direction when learning to predict the radiation intensity of 3D points. Based on this model, we develop a Differentiable Radiative Rasterization (DRR) with CUDA implementation. Secondly, we customize an Angle-pose Cuboid Uniform Initialization (ACUI) strategy that directly uses the parameters of the X-ray scanner to compute the camera information and then uniformly samples point positions within a cuboid enclosing the scanned object. Experiments show that our X-Gaussian outperforms state-of-the-art methods by 6.5 dB while enjoying less than 15% training time and over 73x inference speed. The application on sparse-view CT reconstruction also reveals the practical values of our method. Code and models will be publicly available at https://github.com/caiyuanhao1998/X-Gaussian . A video demo of the training process visualization is at https://www.youtube.com/watch?v=gDVf_Ngeghg .", "github_manual": "", "github_regex": "https://github.com/caiyuanhao1998/X-Gaussian", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-08", "arxiv_id": "2403.04634", "title": "Pix2Gif: Motion-Guided Diffusion for GIF Generation", "authors": [ "Hitesh Kandala", "Jianfeng Gao", "Jianwei Yang" ], "github": "https://github.com/hiteshK03/Pix2Gif", "project_page": "", "abstract": "We present Pix2Gif, a motion-guided diffusion model for image-to-GIF (video) generation. We tackle this problem differently by formulating the task as an image translation problem steered by text and motion magnitude prompts, as shown in teaser fig. To ensure that the model adheres to motion guidance, we propose a new motion-guided warping module to spatially transform the features of the source image conditioned on the two types of prompts. Furthermore, we introduce a perceptual loss to ensure the transformed feature map remains within the same space as the target image, ensuring content consistency and coherence. In preparation for the model training, we meticulously curated data by extracting coherent image frames from the TGIF video-caption dataset, which provides rich information about the temporal changes of subjects. After pretraining, we apply our model in a zero-shot manner to a number of video datasets. Extensive qualitative and quantitative experiments demonstrate the effectiveness of our model -- it not only captures the semantic prompt from text but also the spatial ones from motion guidance. We train all our models using a single node of 16xV100 GPUs. Code, dataset and models are made public at: https://hiteshk03.github.io/Pix2Gif/.", "github_manual": "https://github.com/hiteshK03/Pix2Gif", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-11", "arxiv_id": "2403.05438", "title": "VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models", "authors": [ "Yabo Zhang", "Yuxiang Wei", "Xianhui Lin", "Zheng Hui", "Peiran Ren", "Xuansong Xie", "Xiangyang Ji", "Wangmeng Zuo" ], "github": "https://github.com/YBYBZhang/VideoElevator", "project_page": "", "abstract": "Text-to-image diffusion models (T2I) have demonstrated unprecedented capabilities in creating realistic and aesthetic images. On the contrary, text-to-video diffusion models (T2V) still lag far behind in frame quality and text alignment, owing to insufficient quality and quantity of training videos. In this paper, we introduce VideoElevator, a training-free and plug-and-play method, which elevates the performance of T2V using superior capabilities of T2I. Different from conventional T2V sampling (i.e., temporal and spatial modeling), VideoElevator explicitly decomposes each sampling step into temporal motion refining and spatial quality elevating. Specifically, temporal motion refining uses encapsulated T2V to enhance temporal consistency, followed by inverting to the noise distribution required by T2I. Then, spatial quality elevating harnesses inflated T2I to directly predict less noisy latent, adding more photo-realistic details. We have conducted experiments in extensive prompts under the combination of various T2V and T2I. The results show that VideoElevator not only improves the performance of T2V baselines with foundational T2I, but also facilitates stylistic video synthesis with personalized T2I. Our code is available at https://github.com/YBYBZhang/VideoElevator.", "github_manual": "", "github_regex": "https://github.com/YBYBZhang/VideoElevator", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-11", "arxiv_id": "2403.05135", "title": "ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment", "authors": [ "Xiwei Hu", "Rui Wang", "Yixiao Fang", "Bin Fu", "Pei Cheng", "Gang Yu" ], "github": "https://github.com/ELLA-Diffusion/ELLA", "project_page": "", "abstract": "Diffusion models have demonstrated remarkable performance in the domain of text-to-image generation. However, most widely used models still employ CLIP as their text encoder, which constrains their ability to comprehend dense prompts, encompassing multiple objects, detailed attributes, complex relationships, long-text alignment, etc. In this paper, we introduce an Efficient Large Language Model Adapter, termed ELLA, which equips text-to-image diffusion models with powerful Large Language Models (LLM) to enhance text alignment without training of either U-Net or LLM. To seamlessly bridge two pre-trained models, we investigate a range of semantic alignment connector designs and propose a novel module, the Timestep-Aware Semantic Connector (TSC), which dynamically extracts timestep-dependent conditions from LLM. Our approach adapts semantic features at different stages of the denoising process, assisting diffusion models in interpreting lengthy and intricate prompts over sampling timesteps. Additionally, ELLA can be readily incorporated with community models and tools to improve their prompt-following capabilities. To assess text-to-image models in dense prompt following, we introduce Dense Prompt Graph Benchmark (DPG-Bench), a challenging benchmark consisting of 1K dense prompts. Extensive experiments demonstrate the superiority of ELLA in dense prompt following compared to state-of-the-art methods, particularly in multiple object compositions involving diverse attributes and relationships.", "github_manual": "https://github.com/ELLA-Diffusion/ELLA", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-11", "arxiv_id": "2403.05525", "title": "DeepSeek-VL: Towards Real-World Vision-Language Understanding", "authors": [ "Haoyu Lu", "Wen Liu", "Bo Zhang", "Bingxuan Wang", "Kai Dong", "Bo Liu", "Jingxiang Sun", "Tongzheng Ren", "Zhuoshu Li", "Yaofeng Sun", "Chengqi Deng", "Hanwei Xu", "Zhenda Xie", "Chong Ruan" ], "github": "https://github.com/deepseek-ai/DeepSeek-VL", "project_page": "", "abstract": "We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. We posit that a proficient Vision-Language Model should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. The DeepSeek-VL family (both 1.3B and 7B models) showcases superior user experiences as a vision-language chatbot in real-world applications, achieving state-of-the-art or competitive performance across a wide range of visual-language benchmarks at the same model size while maintaining robust performance on language-centric benchmarks. We have made both 1.3B and 7B models publicly accessible to foster innovations based on this foundation model.", "github_manual": "https://github.com/deepseek-ai/DeepSeek-VL", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-11", "arxiv_id": "2403.05034", "title": "CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model", "authors": [ "Zhengyi Wang", "Yikai Wang", "Yifei Chen", "Chendong Xiang", "Shuo Chen", "Dajiang Yu", "Chongxuan Li", "Hang Su", "Jun Zhu" ], "github": "https://github.com/thu-ml/CRM", "project_page": "", "abstract": "Feed-forward 3D generative models like the Large Reconstruction Model (LRM) have demonstrated exceptional generation speed. However, the transformer-based methods do not leverage the geometric priors of the triplane component in their architecture, often leading to sub-optimal quality given the limited size of 3D data and slow training. In this work, we present the Convolutional Reconstruction Model (CRM), a high-fidelity feed-forward single image-to-3D generative model. Recognizing the limitations posed by sparse 3D data, we highlight the necessity of integrating geometric priors into network design. CRM builds on the key observation that the visualization of triplane exhibits spatial correspondence of six orthographic images. First, it generates six orthographic view images from a single input image, then feeds these images into a convolutional U-Net, leveraging its strong pixel-level alignment capabilities and significant bandwidth to create a high-resolution triplane. CRM further employs Flexicubes as geometric representation, facilitating direct end-to-end optimization on textured meshes. Overall, our model delivers a high-fidelity textured mesh from an image in just 10 seconds, without any test-time optimization.", "github_manual": "https://github.com/thu-ml/CRM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-11", "arxiv_id": "2403.05121", "title": "CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion", "authors": [ "Wendi Zheng", "Jiayan Teng", "Zhuoyi Yang", "Weihan Wang", "Jidong Chen", "Xiaotao Gu", "Yuxiao Dong", "Ming Ding", "Jie Tang" ], "github": "", "project_page": "", "abstract": "Recent advancements in text-to-image generative systems have been largely driven by diffusion models. However, single-stage text-to-image diffusion models still face challenges, in terms of computational efficiency and the refinement of image details. To tackle the issue, we propose CogView3, an innovative cascaded framework that enhances the performance of text-to-image diffusion. CogView3 is the first model implementing relay diffusion in the realm of text-to-image generation, executing the task by first creating low-resolution images and subsequently applying relay-based super-resolution. This methodology not only results in competitive text-to-image outputs but also greatly reduces both training and inference costs. Our experimental results demonstrate that CogView3 outperforms SDXL, the current state-of-the-art open-source text-to-image diffusion model, by 77.0\\% in human evaluations, all while requiring only about 1/2 of the inference time. The distilled variant of CogView3 achieves comparable performance while only utilizing 1/10 of the inference time by SDXL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-11", "arxiv_id": "2403.05530", "title": "Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context", "authors": [ "Machel Reid", "Nikolay Savinov", "Denis Teplyashin", "Dmitry Lepikhin", "Timothy Lillicrap", "Jean-baptiste Alayrac", "Radu Soricut", "Angeliki Lazaridou", "Orhan Firat", "Julian Schrittwieser", "Ioannis Antonoglou", "Rohan Anil", "Sebastian Borgeaud", "Andrew Dai", "Katie Millican", "Ethan Dyer", "Mia Glaese", "Thibault Sottiaux", "Benjamin Lee", "Fabio Viola", "Malcolm Reynolds", "Yuanzhong Xu", "James Molloy", "Jilin Chen", "Michael Isard", "Paul Barham", "Tom Hennigan", "Ross McIlroy", "Melvin Johnson", "Johan Schalkwyk", "Eli Collins", "Eliza Rutherford", "Erica Moreira", "Kareem Ayoub", "Megha Goel", "Clemens Meyer", "Gregory Thornton", "Zhen Yang", "Henryk Michalewski", "Zaheer Abbas", "Nathan Schucher", "Ankesh Anand", "Richard Ives", "James Keeling", "Karel Lenc", "Salem Haykal", "Siamak Shakeri", "Pranav Shyam", "Aakanksha Chowdhery", "Roman Ring", "Stephen Spencer", "Eren Sezener", "Luke Vilnis", "Oscar Chang", "Nobuyuki Morioka", "George Tucker", "Ce Zheng", "Oliver Woodman", "Nithya Attaluri", "Tomas Kocisky", "Evgenii Eltyshev", "Xi Chen", "Timothy Chung", "Vittorio Selo", "Siddhartha Brahma", "Petko Georgiev", "Ambrose Slone", "Zhenkai Zhu", "James Lottes", "Siyuan Qiao", "Ben Caine", "Sebastian Riedel", "Alex Tomala", "Martin Chadwick", "Juliette Love", "Peter Choy", "Sid Mittal", "Neil Houlsby", "Yunhao Tang", "Matthew Lamm", "Libin Bai", "Qiao Zhang", "Luheng He", "Yong Cheng", "Peter Humphreys", "Yujia Li", "Sergey Brin", "Albin Cassirer", "Yingjie Miao", "Lukas Zilka", "Taylor Tobin", "Kelvin Xu", "Lev Proleev", "Daniel Sohn", "Alberto Magni", "Lisa Anne Hendricks", "Isabel Gao", "Santiago Onta\u00f1\u00f3n", "Oskar Bunyan", "Nathan Byrd", "Abhanshu Sharma", "Biao Zhang", "Mario Pinto", "Rishika Sinha", "Harsh Mehta", "Dawei Jia", "Sergi Caelles", "Albert Webson", "Alex Morris", "Becca Roelofs", "Yifan Ding", "Robin Strudel", "Xuehan Xiong", "Marvin Ritter", "Mostafa Dehghani", "Rahma Chaabouni", "Abhijit Karmarkar", "Guangda Lai", "Fabian Mentzer", "Bibo Xu", "YaGuang Li", "Yujing Zhang", "Tom Le Paine", "Alex Goldin", "Behnam Neyshabur", "Kate Baumli", "Anselm Levskaya", "Michael Laskin", "Wenhao Jia", "Jack W. Rae", "Kefan Xiao", "Antoine He", "Skye Giordano", "Lakshman Yagati", "Jean-Baptiste Lespiau", "Paul Natsev", "Sanjay Ganapathy", "Fangyu Liu", "Danilo Martins", "Nanxin Chen", "Yunhan Xu", "Megan Barnes", "Rhys May", "Arpi Vezer", "Junhyuk Oh", "Ken Franko", "Sophie Bridgers", "Ruizhe Zhao", "Boxi Wu", "Basil Mustafa", "Sean Sechrist", "Emilio Parisotto", "Thanumalayan Sankaranarayana Pillai", "Chris Larkin", "Chenjie Gu", "Christina Sorokin", "Maxim Krikun", "Alexey Guseynov", "Jessica Landon", "Romina Datta", "Alexander Pritzel", "Phoebe Thacker", "Fan Yang", "Kevin Hui", "Anja Hauth", "Chih-Kuan Yeh", "David Barker", "Justin Mao-Jones", "Sophia Austin", "Hannah Sheahan", "Parker Schuh", "James Svensson", "Rohan Jain", "Vinay Ramasesh", "Anton Briukhov", "Da-Woon Chung", "Tamara von Glehn", "Christina Butterfield", "Priya Jhakra", "Matthew Wiethoff", "Justin Frye", "Jordan Grimstad", "Beer Changpinyo", "Charline Le Lan", "Anna Bortsova", "Yonghui Wu", "Paul Voigtlaender", "Tara Sainath", "Charlotte Smith", "Will Hawkins", "Kris Cao", "James Besley", "Srivatsan Srinivasan", "Mark Omernick", "Colin Gaffney", "Gabriela Surita", "Ryan Burnell", "Bogdan Damoc", "Junwhan Ahn", "Andrew Brock", "Mantas Pajarskas", "Anastasia Petrushkina", "Seb Noury", "Lorenzo Blanco", "Kevin Swersky", "Arun Ahuja", "Thi Avrahami", "Vedant Misra", "Raoul de Liedekerke", "Mariko Iinuma", "Alex Polozov", "Sarah York", "George van den Driessche", "Paul Michel", "Justin Chiu", "Rory Blevins", "Zach Gleicher", "Adri\u00e0 Recasens", "Alban Rrustemi", "Elena Gribovskaya", "Aurko Roy", "Wiktor Gworek", "S\u00e9b Arnold", "Lisa Lee", "James Lee-Thorp", "Marcello Maggioni", "Enrique Piqueras", "Kartikeya Badola", "Sharad Vikram", "Lucas Gonzalez", "Anirudh Baddepudi", "Evan Senter", "Jacob Devlin", "James Qin", "Michael Azzam", "Maja Trebacz", "Martin Polacek", "Kashyap Krishnakumar", "Shuo-yiin Chang", "Matthew Tung", "Ivo Penchev", "Rishabh Joshi", "Kate Olszewska", "Carrie Muir", "Mateo Wirth", "Ale Jakse Hartman", "Josh Newlan", "Sheleem Kashem", "Vijay Bolina", "Elahe Dabir", "Joost van Amersfoort", "Zafarali Ahmed", "James Cobon-Kerr", "Aishwarya Kamath", "Arnar Mar Hrafnkelsson", "Le Hou", "Ian Mackinnon", "Alexandre Frechette", "Eric Noland", "Xiance Si", "Emanuel Taropa", "Dong Li", "Phil Crone", "Anmol Gulati", "S\u00e9bastien Cevey", "Jonas Adler", "Ada Ma", "David Silver", "Simon Tokumine", "Richard Powell", "Stephan Lee", "Michael Chang", "Samer Hassan", "Diana Mincu", "Antoine Yang", "Nir Levine", "Jenny Brennan", "Mingqiu Wang", "Sarah Hodkinson", "Jeffrey Zhao", "Josh Lipschultz", "Aedan Pope", "Michael B. Chang", "Cheng Li", "Laurent El Shafey", "Michela Paganini", "Sholto Douglas", "Bernd Bohnet", "Fabio Pardo", "Seth Odoom", "Mihaela Rosca", "Cicero Nogueira dos Santos", "Kedar Soparkar", "Arthur Guez", "Tom Hudson", "Steven Hansen", "Chulayuth Asawaroengchai", "Ravi Addanki", "Tianhe Yu", "Wojciech Stokowiec", "Mina Khan", "Justin Gilmer", "Jaehoon Lee", "Carrie Grimes Bostock", "Keran Rong", "Jonathan Caton", "Pedram Pejman", "Filip Pavetic", "Geoff Brown", "Vivek Sharma", "Mario Lu\u010di\u0107", "Rajkumar Samuel", "Josip Djolonga", "Amol Mandhane", "Lars Lowe Sj\u00f6sund", "Elena Buchatskaya", "Elspeth White", "Natalie Clay", "Jiepu Jiang", "Hyeontaek Lim", "Ross Hemsley", "Jane Labanowski", "Nicola De Cao", "David Steiner", "Sayed Hadi Hashemi", "Jacob Austin", "Anita Gergely", "Tim Blyth", "Joe Stanton", "Kaushik Shivakumar", "Aditya Siddhant", "Anders Andreassen", "Carlos Araya", "Nikhil Sethi", "Rakesh Shivanna", "Steven Hand", "Ankur Bapna", "Ali Khodaei", "Antoine Miech", "Garrett Tanzer", "Andy Swing", "Shantanu Thakoor", "Zhufeng Pan", "Zachary Nado", "Stephanie Winkler", "Dian Yu", "Mohammad Saleh", "Loren Maggiore", "Iain Barr", "Minh Giang", "Thais Kagohara", "Ivo Danihelka", "Amit Marathe", "Vladimir Feinberg", "Mohamed Elhawaty", "Nimesh Ghelani", "Dan Horgan", "Helen Miller", "Lexi Walker", "Richard Tanburn", "Mukarram Tariq", "Disha Shrivastava", "Fei Xia", "Chung-Cheng Chiu", "Zoe Ashwood", "Khuslen Baatarsukh", "Sina Samangooei", "Fred Alcober", "Axel Stjerngren", "Paul Komarek", "Katerina Tsihlas", "Anudhyan Boral", "Ramona Comanescu", "Jeremy Chen", "Ruibo Liu", "Dawn Bloxwich", "Charlie Chen", "Yanhua Sun", "Fangxiaoyu Feng", "Matthew Mauger", "Xerxes Dotiwalla", "Vincent Hellendoorn", "Michael Sharman", "Ivy Zheng", "Krishna Haridasan", "Gabe Barth-Maron", "Craig Swanson", "Dominika Rogozi\u0144ska", "Alek Andreev", "Paul Kishan Rubenstein", "Ruoxin Sang", "Dan Hurt", "Gamaleldin Elsayed", "Renshen Wang", "Dave Lacey", "Anastasija Ili\u0107", "Yao Zhao", "Lora Aroyo", "Chimezie Iwuanyanwu", "Vitaly Nikolaev", "Balaji Lakshminarayanan", "Sadegh Jazayeri", "Rapha\u00ebl Lopez Kaufman", "Mani Varadarajan", "Chetan Tekur", "Doug Fritz", "Misha Khalman", "David Reitter", "Kingshuk Dasgupta", "Shourya Sarcar", "Tina Ornduff", "Javier Snaider", "Fantine Huot", "Johnson Jia", "Rupert Kemp", "Nejc Trdin", "Anitha Vijayakumar", "Lucy Kim", "Christof Angermueller", "Li Lao", "Tianqi Liu", "Haibin Zhang", "David Engel", "Somer Greene", "Ana\u00efs White", "Jessica Austin", "Lilly Taylor", "Shereen Ashraf", "Dangyi Liu", "Maria Georgaki", "Irene Cai", "Yana Kulizhskaya", "Sonam Goenka", "Brennan Saeta", "Kiran Vodrahalli", "Christian Frank", "Dario de Cesare", "Brona Robenek", "Harry Richardson", "Mahmoud Alnahlawi", "Christopher Yew", "Priya Ponnapalli", "Marco Tagliasacchi", "Alex Korchemniy", "Yelin Kim", "Dinghua Li", "Bill Rosgen", "Zoe Ashwood", "Kyle Levin", "Jeremy Wiesner", "Praseem Banzal", "Praveen Srinivasan", "Hongkun Yu", "\u00c7a\u011flar \u00dcnl\u00fc", "David Reid", "Zora Tung", "Daniel Finchelstein", "Ravin Kumar", "Andre Elisseeff", "Jin Huang", "Ming Zhang", "Rui Zhu", "Ricardo Aguilar", "Mai Gim\u00e9nez", "Jiawei Xia", "Olivier Dousse", "Willi Gierke", "Soheil Hassas Yeganeh", "Damion Yates", "Komal Jalan", "Lu Li", "Eri Latorre-Chimoto", "Duc Dung Nguyen", "Ken Durden", "Praveen Kallakuri", "Yaxin Liu", "Matthew Johnson", "Tomy Tsai", "Alice Talbert", "Jasmine Liu", "Alexander Neitz", "Chen Elkind", "Marco Selvi", "Mimi Jasarevic", "Livio Baldini Soares", "Albert Cui", "Pidong Wang", "Alek Wenjiao Wang", "Xinyu Ye", "Krystal Kallarackal", "Lucia Loher", "Hoi Lam", "Josef Broder", "Dan Holtmann-Rice", "Nina Martin", "Bramandia Ramadhana", "Daniel Toyama", "Mrinal Shukla", "Sujoy Basu", "Abhi Mohan", "Nick Fernando", "Noah Fiedel", "Kim Paterson", "Hui Li", "Ankush Garg", "Jane Park", "DongHyun Choi", "Diane Wu", "Sankalp Singh", "Zhishuai Zhang", "Amir Globerson", "Lily Yu", "John Carpenter", "F\u00e9lix de Chaumont Quitry", "Carey Radebaugh", "Chu-Cheng Lin", "Alex Tudor", "Prakash Shroff", "Drew Garmon", "Dayou Du", "Neera Vats", "Han Lu", "Shariq Iqbal", "Alex Yakubovich", "Nilesh Tripuraneni", "James Manyika", "Haroon Qureshi", "Nan Hua", "Christel Ngani", "Maria Abi Raad", "Hannah Forbes", "Anna Bulanova", "Jeff Stanway", "Mukund Sundararajan", "Victor Ungureanu", "Colton Bishop", "Yunjie Li", "Balaji Venkatraman", "Bo Li", "Chloe Thornton", "Salvatore Scellato", "Nishesh Gupta", "Yicheng Wang", "Ian Tenney", "Xihui Wu", "Ashish Shenoy", "Gabriel Carvajal", "Diana Gage Wright", "Ben Bariach", "Zhuyun Xiao", "Peter Hawkins", "Sid Dalmia", "Clement Farabet", "Pedro Valenzuela", "Quan Yuan", "Chris Welty", "Ananth Agarwal", "Mia Chen", "Wooyeol Kim", "Brice Hulse", "Nandita Dukkipati", "Adam Paszke", "Andrew Bolt", "Elnaz Davoodi", "Kiam Choo", "Jennifer Beattie", "Jennifer Prendki", "Harsha Vashisht", "Rebeca Santamaria-Fernandez", "Luis C. Cobo", "Jarek Wilkiewicz", "David Madras", "Ali Elqursh", "Grant Uy", "Kevin Ramirez", "Matt Harvey", "Tyler Liechty", "Heiga Zen", "Jeff Seibert", "Clara Huiyi Hu", "Mohamed Elhawaty", "Andrey Khorlin", "Maigo Le", "Asaf Aharoni", "Megan Li", "Lily Wang", "Sandeep Kumar", "Alejandro Lince", "Norman Casagrande", "Jay Hoover", "Dalia El Badawy", "David Soergel", "Denis Vnukov", "Matt Miecnikowski", "Jiri Simsa", "Anna Koop", "Praveen Kumar", "Thibault Sellam", "Daniel Vlasic", "Samira Daruki", "Nir Shabat", "John Zhang", "Guolong Su", "Jiageng Zhang", "Jeremiah Liu", "Yi Sun", "Evan Palmer", "Alireza Ghaffarkhah", "Xi Xiong", "Victor Cotruta", "Michael Fink", "Lucas Dixon", "Ashwin Sreevatsa", "Adrian Goedeckemeyer", "Alek Dimitriev", "Mohsen Jafari", "Remi Crocker", "Nicholas FitzGerald", "Aviral Kumar", "Sanjay Ghemawat", "Ivan Philips", "Frederick Liu", "Yannie Liang", "Rachel Sterneck", "Alena Repina", "Marcus Wu", "Laura Knight", "Marin Georgiev", "Hyo Lee", "Harry Askham", "Abhishek Chakladar", "Annie Louis", "Carl Crous", "Hardie Cate", "Dessie Petrova", "Michael Quinn", "Denese Owusu-Afriyie", "Achintya Singhal", "Nan Wei", "Solomon Kim", "Damien Vincent", "Milad Nasr", "Christopher A. Choquette-Choo", "Reiko Tojo", "Shawn Lu", "Diego de Las Casas", "Yuchung Cheng", "Tolga Bolukbasi", "Katherine Lee", "Saaber Fatehi", "Rajagopal Ananthanarayanan", "Miteyan Patel", "Charbel Kaed", "Jing Li", "Jakub Sygnowski", "Shreyas Rammohan Belle", "Zhe Chen", "Jaclyn Konzelmann", "Siim P\u00f5der", "Roopal Garg", "Vinod Koverkathu", "Adam Brown", "Chris Dyer", "Rosanne Liu", "Azade Nova", "Jun Xu", "Slav Petrov", "Demis Hassabis", "Koray Kavukcuoglu", "Jeffrey Dean", "Oriol Vinyals" ], "github": "", "project_page": "", "abstract": "In this report, we present the latest model of the Gemini family, Gemini 1.5 Pro, a highly compute-efficient multimodal mixture-of-experts model capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. Gemini 1.5 Pro achieves near-perfect recall on long-context retrieval tasks across modalities, improves the state-of-the-art in long-document QA, long-video QA and long-context ASR, and matches or surpasses Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5 Pro's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 2.1 (200k) and GPT-4 Turbo (128k). Finally, we highlight surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-11", "arxiv_id": "2403.05185", "title": "Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks", "authors": [ "Marco De Nadai", "Francesco Fabbri", "Paul Gigioli", "Alice Wang", "Ang Li", "Fabrizio Silvestri", "Laura Kim", "Shawn Lin", "Vladan Radosavljevic", "Sandeep Ghael", "David Nyhan", "Hugues Bouchard", "Mounia Lalmas-Roelleke", "Andreas Damianou" ], "github": "", "project_page": "", "abstract": "In the ever-evolving digital audio landscape, Spotify, well-known for its music and talk content, has recently introduced audiobooks to its vast user base. While promising, this move presents significant challenges for personalized recommendations. Unlike music and podcasts, audiobooks, initially available for a fee, cannot be easily skimmed before purchase, posing higher stakes for the relevance of recommendations. Furthermore, introducing a new content type into an existing platform confronts extreme data sparsity, as most users are unfamiliar with this new content type. Lastly, recommending content to millions of users requires the model to react fast and be scalable. To address these challenges, we leverage podcast and music user preferences and introduce 2T-HGNN, a scalable recommendation system comprising Heterogeneous Graph Neural Networks (HGNNs) and a Two Tower (2T) model. This novel approach uncovers nuanced item relationships while ensuring low latency and complexity. We decouple users from the HGNN graph and propose an innovative multi-link neighbor sampler. These choices, together with the 2T component, significantly reduce the complexity of the HGNN model. Empirical evaluations involving millions of users show significant improvement in the quality of personalized recommendations, resulting in a +46% increase in new audiobooks start rate and a +23% boost in streaming rates. Intriguingly, our model's impact extends beyond audiobooks, benefiting established products like podcasts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-12", "arxiv_id": "2403.06634", "title": "Stealing Part of a Production Language Model", "authors": [ "Nicholas Carlini", "Daniel Paleka", "Krishnamurthy Dj Dvijotham", "Thomas Steinke", "Jonathan Hayase", "A. Feder Cooper", "Katherine Lee", "Matthew Jagielski", "Milad Nasr", "Arthur Conmy", "Eric Wallace", "David Rolnick", "Florian Tram\u00e8r" ], "github": "https://github.com/dpaleka/stealing-part-lm-supplementary", "project_page": "", "abstract": "We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like OpenAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under \\20 USD, our attack extracts the entire projection matrix of OpenAI's Ada and Babbage language models. We thereby confirm, for the first time, that these black-box models have a hidden dimension of 1024 and 2048, respectively. We also recover the exact hidden dimension size of the gpt-3.5-turbo model, and estimate it would cost under 2,000 in queries to recover the entire projection matrix. We conclude with potential defenses and mitigations, and discuss the implications of possible future work that could extend our attack.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dpaleka/stealing-part-lm-supplementary", "github_api_added_by": "auto" }, { "date": "2024-03-12", "arxiv_id": "2403.06504", "title": "Adding NVMe SSDs to Enable and Accelerate 100B Model Fine-tuning on a Single GPU", "authors": [ "Changyue Liao", "Mo Sun", "Zihan Yang", "Kaiqi Chen", "Binhang Yuan", "Fei Wu", "Zeke Wang" ], "github": "https://github.com/RC4ML/LoHan", "project_page": "", "abstract": "Recent advances in large language models have brought immense value to the world, with their superior capabilities stemming from the massive number of parameters they utilize. However, even the GPUs with the highest memory capacities, currently peaking at 80GB, are far from sufficient to accommodate these vast parameters and their associated optimizer states when conducting stochastic gradient descent-based optimization. One approach to hosting such huge models is to aggregate device memory from many GPUs. However, this approach introduces prohibitive costs for most academic researchers, who always have a limited budget for many high-end GPU servers. In this paper, we focus on huge model fine-tuning on a single, even low-end, GPU in a commodity server, which is accessible to most AI researchers. In such a scenario, the state-of-the-art work ZeRO-Infinity suffers from two severe issues when running in a commodity server: 1) low GPU utilization due to inefficient swapping, and 2) limited trainable model size due to CPU memory capacity. The underlying reason is that ZeRO-Infinity is optimized for running on high-end GPU servers. To this end, we present Fuyou, a low-cost training framework that enables efficient 100B huge model fine-tuning on a low-end server with a low-end GPU and limited CPU memory capacity. The key idea is to add the SSD-CPU communication as an optimization dimension and thus carefully co-optimize computation and data swapping from a systematic approach to maximize GPU utilization. The experimental results show that 1) Fuyou is able to fine-tune 175B GPT-3 on a consumer GPU RTX 4090 with high GPU utilization, while ZeRO-Infinity fails to fine-tune; and 2) when training a small GPT-3 13B model, Fuyou achieves 156 TFLOPS on an RTX 4090 GPU while ZeRO-Infinity only achieves 45 TFLOPS.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/RC4ML/LoHan", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-12", "arxiv_id": "2403.06977", "title": "VideoMamba: State Space Model for Efficient Video Understanding", "authors": [ "Kunchang Li", "Xinhao Li", "Yi Wang", "Yinan He", "Yali Wang", "Limin Wang", "Yu Qiao" ], "github": "https://github.com/OpenGVLab/VideoMamba", "project_page": "", "abstract": "Addressing the dual challenges of local redundancy and global dependencies in video understanding, this work innovatively adapts the Mamba to the video domain. The proposed VideoMamba overcomes the limitations of existing 3D convolution neural networks and video transformers. Its linear-complexity operator enables efficient long-term modeling, which is crucial for high-resolution long video understanding. Extensive evaluations reveal VideoMamba's four core abilities: (1) Scalability in the visual domain without extensive dataset pretraining, thanks to a novel self-distillation technique; (2) Sensitivity for recognizing short-term actions even with fine-grained motion differences; (3) Superiority in long-term video understanding, showcasing significant advancements over traditional feature-based models; and (4) Compatibility with other modalities, demonstrating robustness in multi-modal contexts. Through these distinct advantages, VideoMamba sets a new benchmark for video understanding, offering a scalable and efficient solution for comprehensive video understanding. All the code and models are available at https://github.com/OpenGVLab/VideoMamba.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/VideoMamba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-12", "arxiv_id": "2403.05812", "title": "Algorithmic progress in language models", "authors": [ "Anson Ho", "Tamay Besiroglu", "Ege Erdil", "David Owen", "Robi Rahman", "Zifan Carl Guo", "David Atkinson", "Neil Thompson", "Jaime Sevilla" ], "github": "https://github.com/epoch-research/lm-algorithmic-progress", "project_page": "", "abstract": "We investigate the rate at which algorithms for pre-training language models have improved since the advent of deep learning. Using a dataset of over 200 language model evaluations on Wikitext and Penn Treebank spanning 2012-2023, we find that the compute required to reach a set performance threshold has halved approximately every 8 months, with a 95% confidence interval of around 5 to 14 months, substantially faster than hardware gains per Moore's Law. We estimate augmented scaling laws, which enable us to quantify algorithmic progress and determine the relative contributions of scaling models versus innovations in training algorithms. Despite the rapid pace of algorithmic progress and the development of new architectures such as the transformer, our analysis reveals that the increase in compute made an even larger contribution to overall performance improvements over this time period. Though limited by noisy benchmark data, our analysis quantifies the rapid progress in language modeling, shedding light on the relative contributions from compute and algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/epoch-research/lm-algorithmic-progress", "github_api_added_by": "auto" }, { "date": "2024-03-12", "arxiv_id": "2403.06738", "title": "V3D: Video Diffusion Models are Effective 3D Generators", "authors": [ "Zilong Chen", "Yikai Wang", "Feng Wang", "Zhengyi Wang", "Huaping Liu" ], "github": "https://github.com/heheyas/V3D", "project_page": "", "abstract": "Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency. Our code is available at https://github.com/heheyas/V3D", "github_manual": "", "github_regex": "https://github.com/heheyas/V3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-12", "arxiv_id": "2403.06775", "title": "FaceChain-SuDe: Building Derived Class to Inherit Category Attributes for One-shot Subject-Driven Generation", "authors": [ "Pengchong Qiao", "Lei Shang", "Chang Liu", "Baigui Sun", "Xiangyang Ji", "Jie Chen" ], "github": "https://github.com/modelscope/facechain", "project_page": "", "abstract": "Subject-driven generation has garnered significant interest recently due to its ability to personalize text-to-image generation. Typical works focus on learning the new subject's private attributes. However, an important fact has not been taken seriously that a subject is not an isolated new concept but should be a specialization of a certain category in the pre-trained model. This results in the subject failing to comprehensively inherit the attributes in its category, causing poor attribute-related generations. In this paper, motivated by object-oriented programming, we model the subject as a derived class whose base class is its semantic category. This modeling enables the subject to inherit public attributes from its category while learning its private attributes from the user-provided example. Specifically, we propose a plug-and-play method, Subject-Derived regularization (SuDe). It constructs the base-derived class modeling by constraining the subject-driven generated images to semantically belong to the subject's category. Extensive experiments under three baselines and two backbones on various subjects show that our SuDe enables imaginative attribute-related generations while maintaining subject fidelity. Codes will be open sourced soon at FaceChain (https://github.com/modelscope/facechain).", "github_manual": "", "github_regex": "https://github.com/modelscope/facechain", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-12", "arxiv_id": "2403.06098", "title": "VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models", "authors": [ "Wenhao Wang", "Yi Yang" ], "github": "https://github.com/wangwenhao0716/vidprom", "project_page": "", "abstract": "The arrival of Sora marks a new era for text-to-video diffusion models, bringing significant advancements in video generation and potential applications. However, Sora, as well as other text-to-video diffusion models, highly relies on the prompts, and there is no publicly available dataset featuring a study of text-to-video prompts. In this paper, we introduce VidProM, the first large-scale dataset comprising 1.67 million unique text-to-video prompts from real users. Additionally, the dataset includes 6.69 million videos generated by four state-of-the-art diffusion models and some related data. We initially demonstrate the curation of this large-scale dataset, which is a time-consuming and costly process. Subsequently, we show how the proposed VidProM differs from DiffusionDB, a large-scale prompt-gallery dataset for image generation. Based on the analysis of these prompts, we identify the necessity for a new prompt dataset specifically designed for text-to-video generation and gain insights into the preferences of real users when creating videos. Our large-scale and diverse dataset also inspires many exciting new research areas. For instance, to develop better, more efficient, and safer text-to-video diffusion models, we suggest exploring text-to-video prompt engineering, efficient video generation, and video copy detection for diffusion models. We make the collected dataset VidProM publicly available at GitHub and Hugging Face under the CC-BY- NC 4.0 License.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wangwenhao0716/vidprom", "github_api_added_by": "auto" }, { "date": "2024-03-12", "arxiv_id": "2403.06807", "title": "Multistep Consistency Models", "authors": [ "Jonathan Heek", "Emiel Hoogeboom", "Tim Salimans" ], "github": "", "project_page": "", "abstract": "Diffusion models are relatively easy to train but require many steps to generate samples. Consistency models are far more difficult to train, but generate samples in a single step. In this paper we propose Multistep Consistency Models: A unification between Consistency Models (Song et al., 2023) and TRACT (Berthelot et al., 2023) that can interpolate between a consistency model and a diffusion model: a trade-off between sampling speed and sampling quality. Specifically, a 1-step consistency model is a conventional consistency model whereas we show that a infty-step consistency model is a diffusion model. Multistep Consistency Models work really well in practice. By increasing the sample budget from a single step to 2-8 steps, we can train models more easily that generate higher quality samples, while retaining much of the sampling speed benefits. Notable results are 1.4 FID on Imagenet 64 in 8 step and 2.1 FID on Imagenet128 in 8 steps with consistency distillation. We also show that our method scales to a text-to-image diffusion model, generating samples that are very close to the quality of the original model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-12", "arxiv_id": "2403.06764", "title": "An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models", "authors": [ "Liang Chen", "Haozhe Zhao", "Tianyu Liu", "Shuai Bai", "Junyang Lin", "Chang Zhou", "Baobao Chang" ], "github": "https://github.com/pkunlp-icler/FastV", "project_page": "", "abstract": "In this study, we identify the inefficient attention phenomena in Large Vision-Language Models (LVLMs), notably within prominent models like LLaVA-1.5, QwenVL-Chat and Video-LLaVA. We find out that the attention computation over visual tokens is of extreme inefficiency in the deep layers of popular LVLMs, suggesting a need for a sparser approach compared to textual data handling. To this end, we introduce FastV, a versatile plug-and-play method designed to optimize computational efficiency by learning adaptive attention patterns in early layers and pruning visual tokens in subsequent ones. Our evaluations demonstrate FastV's ability to dramatically reduce computational costs (e.g., a 45 reduction in FLOPs for LLaVA-1.5-13B) without sacrificing performance in a wide range of image and video understanding tasks. The computational efficiency and performance trade-off of FastV are highly customizable and pareto-efficient. It can compress the FLOPs of a 13B-parameter model to achieve a lower budget than that of a 7B-parameter model, while still maintaining superior performance. We believe FastV has practical values for deployment of LVLMs in edge devices and commercial models. Code is released at https://github.com/pkunlp-icler/FastV.", "github_manual": "", "github_regex": "https://github.com/pkunlp-icler/FastV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-13", "arxiv_id": "2403.07420", "title": "DragAnything: Motion Control for Anything using Entity Representation", "authors": [ "Wejia Wu", "Zhuang Li", "Yuchao Gu", "Rui Zhao", "Yefei He", "David Junhao Zhang", "Mike Zheng Shou", "Yan Li", "Tingting Gao", "Di Zhang" ], "github": "https://github.com/showlab/DragAnything", "project_page": "", "abstract": "We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation. Comparison to existing motion control methods, DragAnything offers several advantages. Firstly, trajectory-based is more userfriendly for interaction, when acquiring other guidance signals (e.g., masks, depth maps) is labor-intensive. Users only need to draw a line (trajectory) during interaction. Secondly, our entity representation serves as an open-domain embedding capable of representing any object, enabling the control of motion for diverse entities, including background. Lastly, our entity representation allows simultaneous and distinct motion control for multiple objects. Extensive experiments demonstrate that our DragAnything achieves state-of-the-art performance for FVD, FID, and User Study, particularly in terms of object motion control, where our method surpasses the previous methods (e.g., DragNUWA) by 26% in human voting.", "github_manual": "https://github.com/showlab/DragAnything", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-13", "arxiv_id": "2403.07750", "title": "Synth^2: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings", "authors": [ "Sahand Sharifzadeh", "Christos Kaplanis", "Shreya Pathak", "Dharshan Kumaran", "Anastasija Ilic", "Jovana Mitrovic", "Charles Blundell", "Andrea Banino" ], "github": "", "project_page": "", "abstract": "The creation of high-quality human-labeled image-caption datasets presents a significant bottleneck in the development of Visual-Language Models (VLMs). We propose a novel approach that leverages the strengths of Large Language Models (LLMs) and image generation models to create synthetic image-text pairs for efficient and effective VLM training. Our method employs pretraining a text-to-image model to synthesize image embeddings starting from captions generated by an LLM. These synthetic pairs are then used to train a VLM. Extensive experiments demonstrate that the VLM trained with synthetic data exhibits comparable performance on image captioning, while requiring a fraction of the data used by models trained solely on human-annotated data. In particular, we outperform the baseline by 17% through augmentation with a synthetic dataset. Furthermore, we show that synthesizing in the image embedding space is 25% faster than in the pixel space. This research introduces a promising technique for generating large-scale, customizable image datasets, leading to enhanced VLM performance and wider applicability across various domains, all with improved data efficiency and resource utilization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-13", "arxiv_id": "2403.07816", "title": "Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM", "authors": [ "Sainbayar Sukhbaatar", "Olga Golovneva", "Vasu Sharma", "Hu Xu", "Xi Victoria Lin", "Baptiste Rozi\u00e8re", "Jacob Kahn", "Daniel Li", "Wen-tau Yih", "Jason Weston", "Xian Li" ], "github": "", "project_page": "", "abstract": "We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge. Our method, named Branch-Train-MiX (BTX), starts from a seed model, which is branched to train experts in embarrassingly parallel fashion with high throughput and reduced communication cost. After individual experts are asynchronously trained, BTX brings together their feedforward parameters as experts in Mixture-of-Expert (MoE) layers and averages the remaining parameters, followed by an MoE-finetuning stage to learn token-level routing. BTX generalizes two special cases, the Branch-Train-Merge method, which does not have the MoE finetuning stage to learn routing, and sparse upcycling, which omits the stage of training experts asynchronously. Compared to alternative approaches, BTX achieves the best accuracy-efficiency tradeoff.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-13", "arxiv_id": "2403.07815", "title": "Chronos: Learning the Language of Time Series", "authors": [ "Abdul Fatir Ansari", "Lorenzo Stella", "Caner Turkmen", "Xiyuan Zhang", "Pedro Mercado", "Huibin Shen", "Oleksandr Shchur", "Syama Sundar Rangapuram", "Sebastian Pineda Arango", "Shubham Kapoor", "Jasper Zschiegner", "Danielle C. Maddix", "Michael W. Mahoney", "Kari Torkkola", "Andrew Gordon Wilson", "Michael Bohlke-Schneider", "Yuyang Wang" ], "github": "", "project_page": "", "abstract": "We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-13", "arxiv_id": "2403.07508", "title": "MoAI: Mixture of All Intelligence for Large Language and Vision Models", "authors": [ "Byung-Kwan Lee", "Beomchan Park", "Chae Won Kim", "Yong Man Ro" ], "github": "https://github.com/ByungKwanLee/MoAI", "project_page": "", "abstract": "The rise of large language models (LLMs) and instruction tuning has led to the current trend of instruction-tuned large language and vision models (LLVMs). This trend involves either meticulously curating numerous instruction tuning datasets tailored to specific objectives or enlarging LLVMs to manage vast amounts of vision language (VL) data. However, current LLVMs have disregarded the detailed and comprehensive real-world scene understanding available from specialized computer vision (CV) models in visual perception tasks such as segmentation, detection, scene graph generation (SGG), and optical character recognition (OCR). Instead, the existing LLVMs rely mainly on the large capacity and emergent capabilities of their LLM backbones. Therefore, we present a new LLVM, Mixture of All Intelligence (MoAI), which leverages auxiliary visual information obtained from the outputs of external segmentation, detection, SGG, and OCR models. MoAI operates through two newly introduced modules: MoAI-Compressor and MoAI-Mixer. After verbalizing the outputs of the external CV models, the MoAI-Compressor aligns and condenses them to efficiently use relevant auxiliary visual information for VL tasks. MoAI-Mixer then blends three types of intelligence (1) visual features, (2) auxiliary features from the external CV models, and (3) language features by utilizing the concept of Mixture of Experts. Through this integration, MoAI significantly outperforms both open-source and closed-source LLVMs in numerous zero-shot VL tasks, particularly those related to real-world scene understanding such as object existence, positions, relations, and OCR without enlarging the model size or curating extra visual instruction tuning datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByungKwanLee/MoAI", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-13", "arxiv_id": "2403.07128", "title": "FAX: Scalable and Differentiable Federated Primitives in JAX", "authors": [ "Keith Rush", "Zachary Charles", "Zachary Garrett" ], "github": "https://github.com/google-research/google-research/tree/master/fax", "project_page": "", "abstract": "We present FAX, a JAX-based library designed to support large-scale distributed and federated computations in both data center and cross-device applications. FAX leverages JAX's sharding mechanisms to enable native targeting of TPUs and state-of-the-art JAX runtimes, including Pathways. FAX embeds building blocks for federated computations as primitives in JAX. This enables three key benefits. First, FAX computations can be translated to XLA HLO. Second, FAX provides a full implementation of federated automatic differentiation, greatly simplifying the expression of federated computations. Last, FAX computations can be interpreted out to existing production cross-device federated compute systems. We show that FAX provides an easily programmable, performant, and scalable framework for federated computations in the data center. FAX is available at https://github.com/google-research/google-research/tree/master/fax .", "github_manual": "", "github_regex": "https://github.com/google-research/google-research/tree/master/fax", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-13", "arxiv_id": "2403.07563", "title": "Learning Generalizable Feature Fields for Mobile Manipulation", "authors": [ "Ri-Zhao Qiu", "Yafei Hu", "Ge Yang", "Yuchen Song", "Yang Fu", "Jianglong Ye", "Jiteng Mu", "Ruihan Yang", "Nikolay Atanasov", "Sebastian Scherer", "Xiaolong Wang" ], "github": "", "project_page": "", "abstract": "An open problem in mobile manipulation is how to represent objects and scenes in a unified manner, so that robots can use it both for navigating in the environment and manipulating objects. The latter requires capturing intricate geometry while understanding fine-grained semantics, whereas the former involves capturing the complexity inherit to an expansive physical scale. In this work, we present GeFF (Generalizable Feature Fields), a scene-level generalizable neural feature field that acts as a unified representation for both navigation and manipulation that performs in real-time. To do so, we treat generative novel view synthesis as a pre-training task, and then align the resulting rich scene priors with natural language via CLIP feature distillation. We demonstrate the effectiveness of this approach by deploying GeFF on a quadrupedal robot equipped with a manipulator. We evaluate GeFF's ability to generalize to open-set objects as well as running time, when performing open-vocabulary mobile manipulation in dynamic scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-13", "arxiv_id": "2403.07487", "title": "Motion Mamba: Efficient and Long Sequence Motion Generation with Hierarchical and Bidirectional Selective SSM", "authors": [ "Zeyu Zhang", "Akide Liu", "Ian Reid", "Richard Hartley", "Bohan Zhuang", "Hao Tang" ], "github": "https://github.com/steve-zeyu-zhang/MotionMamba", "project_page": "", "abstract": "Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging. Recent advancements in state space models (SSMs), notably Mamba, have showcased considerable promise in long sequence modeling with an efficient hardware-aware design, which appears to be a promising direction to build motion generation model upon it. Nevertheless, adapting SSMs to motion generation faces hurdles since the lack of a specialized design architecture to model motion sequence. To address these challenges, we propose Motion Mamba, a simple and efficient approach that presents the pioneering motion generation model utilized SSMs. Specifically, we design a Hierarchical Temporal Mamba (HTM) block to process temporal data by ensemble varying numbers of isolated SSM modules across a symmetric U-Net architecture aimed at preserving motion consistency between frames. We also design a Bidirectional Spatial Mamba (BSM) block to bidirectionally process latent poses, to enhance accurate motion generation within a temporal frame. Our proposed method achieves up to 50% FID improvement and up to 4 times faster on the HumanML3D and KIT-ML datasets compared to the previous best diffusion-based method, which demonstrates strong capabilities of high-quality long sequence motion modeling and real-time human motion generation. See project website https://steve-zeyu-zhang.github.io/MotionMamba/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/steve-zeyu-zhang/MotionMamba", "github_api_added_by": "auto" }, { "date": "2024-03-14", "arxiv_id": "2403.08295", "title": "Gemma: Open Models Based on Gemini Research and Technology", "authors": [ "Gemma Team", "Thomas Mesnard", "Cassidy Hardin", "Robert Dadashi", "Surya Bhupatiraju", "Shreya Pathak", "Laurent Sifre", "Morgane Rivi\u00e8re", "Mihir Sanjay Kale", "Juliette Love", "Pouya Tafti", "L\u00e9onard Hussenot", "Aakanksha Chowdhery", "Adam Roberts", "Aditya Barua", "Alex Botev", "Alex Castro-Ros", "Ambrose Slone", "Am\u00e9lie H\u00e9liou", "Andrea Tacchetti", "Anna Bulanova", "Antonia Paterson", "Beth Tsai", "Bobak Shahriari", "Charline Le Lan", "Christopher A. Choquette-Choo", "Cl\u00e9ment Crepy", "Daniel Cer", "Daphne Ippolito", "David Reid", "Elena Buchatskaya", "Eric Ni", "Eric Noland", "Geng Yan", "George Tucker", "George-Christian Muraru", "Grigory Rozhdestvenskiy", "Henryk Michalewski", "Ian Tenney", "Ivan Grishchenko", "Jacob Austin", "James Keeling", "Jane Labanowski", "Jean-Baptiste Lespiau", "Jeff Stanway", "Jenny Brennan", "Jeremy Chen", "Johan Ferret", "Justin Chiu", "Justin Mao-Jones", "Katherine Lee", "Kathy Yu", "Katie Millican", "Lars Lowe Sjoesund", "Lisa Lee", "Lucas Dixon", "Machel Reid", "Maciej Miku\u0142a", "Mateo Wirth", "Michael Sharman", "Nikolai Chinaev", "Nithum Thain", "Olivier Bachem", "Oscar Chang", "Oscar Wahltinez", "Paige Bailey", "Paul Michel", "Petko Yotov", "Pier Giuseppe Sessa", "Rahma Chaabouni", "Ramona Comanescu", "Reena Jana", "Rohan Anil", "Ross McIlroy", "Ruibo Liu", "Ryan Mullins", "Samuel L Smith", "Sebastian Borgeaud", "Sertan Girgin", "Sholto Douglas", "Shree Pandya", "Siamak Shakeri", "Soham De", "Ted Klimenko", "Tom Hennigan", "Vlad Feinberg", "Wojciech Stokowiec", "Yu-hui Chen", "Zafarali Ahmed", "Zhitao Gong", "Tris Warkentin", "Ludovic Peran", "Minh Giang", "Cl\u00e9ment Farabet", "Oriol Vinyals", "Jeff Dean", "Koray Kavukcuoglu", "Demis Hassabis", "Zoubin Ghahramani", "Douglas Eck", "Joelle Barral", "Fernando Pereira", "Eli Collins", "Armand Joulin", "Noah Fiedel", "Evan Senter", "Alek Andreev", "Kathleen Kenealy" ], "github": "", "project_page": "", "abstract": "This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate strong performance across academic benchmarks for language understanding, reasoning, and safety. We release two sizes of models (2 billion and 7 billion parameters), and provide both pretrained and fine-tuned checkpoints. Gemma outperforms similarly sized open models on 11 out of 18 text-based tasks, and we present comprehensive evaluations of safety and responsibility aspects of the models, alongside a detailed description of model development. We believe the responsible release of LLMs is critical for improving the safety of frontier models, and for enabling the next wave of LLM innovations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-14", "arxiv_id": "2403.08764", "title": "VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis", "authors": [ "Enric Corona", "Andrei Zanfir", "Eduard Gabriel Bazavan", "Nikos Kolotouros", "Thiemo Alldieck", "Cristian Sminchisescu" ], "github": "", "project_page": "", "abstract": "We propose VLOGGER, a method for audio-driven human video generation from a single input image of a person, which builds on the success of recent generative diffusion models. Our method consists of 1) a stochastic human-to-3d-motion diffusion model, and 2) a novel diffusion-based architecture that augments text-to-image models with both spatial and temporal controls. This supports the generation of high quality video of variable length, easily controllable through high-level representations of human faces and bodies. In contrast to previous work, our method does not require training for each person, does not rely on face detection and cropping, generates the complete image (not just the face or the lips), and considers a broad spectrum of scenarios (e.g. visible torso or diverse subject identities) that are critical to correctly synthesize humans who communicate. We also curate MENTOR, a new and diverse dataset with 3d pose and expression annotations, one order of magnitude larger than previous ones (800,000 identities) and with dynamic gestures, on which we train and ablate our main technical contributions. VLOGGER outperforms state-of-the-art methods in three public benchmarks, considering image quality, identity preservation and temporal consistency while also generating upper-body gestures. We analyze the performance of VLOGGER with respect to multiple diversity metrics, showing that our architectural choices and the use of MENTOR benefit training a fair and unbiased model at scale. Finally we show applications in video editing and personalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-14", "arxiv_id": "2403.08268", "title": "Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts", "authors": [ "Yue Ma", "Yingqing He", "Hongfa Wang", "Andong Wang", "Chenyang Qi", "Chengfei Cai", "Xiu Li", "Zhifeng Li", "Heung-Yeung Shum", "Wei Liu", "Qifeng Chen" ], "github": "https://github.com/mayuelala/followyourclick", "project_page": "", "abstract": "Despite recent advances in image-to-video generation, better controllability and local animation are less explored. Most existing image-to-video methods are not locally aware and tend to move the entire scene. However, human artists may need to control the movement of different objects or regions. Additionally, current I2V methods require users not only to describe the target motion but also to provide redundant detailed descriptions of frame contents. These two issues hinder the practical utilization of current I2V tools. In this paper, we propose a practical framework, named Follow-Your-Click, to achieve image animation with a simple user click (for specifying what to move) and a short motion prompt (for specifying how to move). Technically, we propose the first-frame masking strategy, which significantly improves the video generation quality, and a motion-augmented module equipped with a short motion prompt dataset to improve the short prompt following abilities of our model. To further control the motion speed, we propose flow-based motion magnitude control to control the speed of target movement more precisely. Our framework has simpler yet precise user control and better generation performance than previous methods. Extensive experiments compared with 7 baselines, including both commercial tools and research methods on 8 metrics, suggest the superiority of our approach. Project Page: https://follow-your-click.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mayuelala/followyourclick", "github_api_added_by": "auto" }, { "date": "2024-03-14", "arxiv_id": "2403.08763", "title": "Simple and Scalable Strategies to Continually Pre-train Large Language Models", "authors": [ "Adam Ibrahim", "Benjamin Th\u00e9rien", "Kshitij Gupta", "Mats L. Richter", "Quentin Anthony", "Timoth\u00e9e Lesort", "Eugene Belilovsky", "Irina Rish" ], "github": "https://github.com/eleutherai/gpt-neox", "project_page": "", "abstract": "Large language models (LLMs) are routinely pre-trained on billions of tokens, only to start the process over again once new data becomes available. A much more efficient solution is to continually pre-train these models, saving significant compute compared to re-training. However, the distribution shift induced by new data typically results in degraded performance on previous data or poor adaptation to the new data. In this work, we show that a simple and scalable combination of learning rate (LR) re-warming, LR re-decaying, and replay of previous data is sufficient to match the performance of fully re-training from scratch on all available data, as measured by final loss and language model (LM) evaluation benchmarks. Specifically, we show this for a weak but realistic distribution shift between two commonly used LLM pre-training datasets (EnglishrightarrowEnglish) and a stronger distribution shift (EnglishrightarrowGerman) at the 405M parameter model scale with large dataset sizes (hundreds of billions of tokens). Selecting the weak but realistic shift for larger-scale experiments, we also find that our continual learning strategies match the re-training baseline for a 10B parameter LLM. Our results demonstrate that LLMs can be successfully updated via simple and scalable continual learning strategies, matching the re-training baseline using only a fraction of the compute. Finally, inspired by previous work, we propose alternatives to the cosine learning rate schedule that help circumvent forgetting induced by LR re-warming and that are not bound to a fixed token budget.", "github_manual": "https://github.com/eleutherai/gpt-neox", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-14", "arxiv_id": "2403.07918", "title": "On the Societal Impact of Open Foundation Models", "authors": [ "Sayash Kapoor", "Rishi Bommasani", "Kevin Klyman", "Shayne Longpre", "Ashwin Ramaswami", "Peter Cihon", "Aspen Hopkins", "Kevin Bankston", "Stella Biderman", "Miranda Bogen", "Rumman Chowdhury", "Alex Engler", "Peter Henderson", "Yacine Jernite", "Seth Lazar", "Stefano Maffulli", "Alondra Nelson", "Joelle Pineau", "Aviya Skowron", "Dawn Song", "Victor Storchan", "Daniel Zhang", "Daniel E. Ho", "Percy Liang", "Arvind Narayanan" ], "github": "", "project_page": "", "abstract": "Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, we focus on open foundation models, defined here as those with broadly available model weights (e.g. Llama 2, Stable Diffusion XL). We identify five distinctive properties (e.g. greater customizability, poor monitoring) of open foundation models that lead to both their benefits and risks. Open foundation models present significant benefits, with some caveats, that span innovation, competition, the distribution of decision-making power, and transparency. To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk. Across several misuse vectors (e.g. cyberattacks, bioweapons), we find that current research is insufficient to effectively characterize the marginal risk of open foundation models relative to pre-existing technologies. The framework helps explain why the marginal risk is low in some cases, clarifies disagreements about misuse risks by revealing that past work has focused on different subsets of the framework with different assumptions, and articulates a way forward for more constructive debate. Overall, our work helps support a more grounded assessment of the societal impact of open foundation models by outlining what research is needed to empirically validate their theoretical benefits and risks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-14", "arxiv_id": "2403.08715", "title": "SOTOPIA-\u03c0: Interactive Learning of Socially Intelligent Language Agents", "authors": [ "Ruiyi Wang", "Haofei Yu", "Wenxin Zhang", "Zhengyang Qi", "Maarten Sap", "Graham Neubig", "Yonatan Bisk", "Hao Zhu" ], "github": "https://github.com/sotopia-lab/sotopia-pi", "project_page": "", "abstract": "Humans learn social skills through both imitation and social interaction. This social learning process is largely understudied by existing research on building language agents. Motivated by this gap, we propose an interactive learning method, SOTOPIA-pi, improving the social intelligence of language agents. This method leverages behavior cloning and self-reinforcement training on filtered social interaction data according to large language model (LLM) ratings. We show that our training method allows a 7B LLM to reach the social goal completion ability of an expert model (GPT-4-based agent), while improving the safety of language agents and maintaining general QA ability on the MMLU benchmark. We also find that this training paradigm uncovers some difficulties in LLM-based evaluation of social intelligence: LLM-based evaluators overestimate the abilities of the language agents trained specifically for social interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sotopia-lab/sotopia-pi", "github_api_added_by": "auto" }, { "date": "2024-03-14", "arxiv_id": "2403.08551", "title": "GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting", "authors": [ "Xinjie Zhang", "Xingtong Ge", "Tongda Xu", "Dailan He", "Yan Wang", "Hongwei Qin", "Guo Lu", "Jing Geng", "Jun Zhang" ], "github": "https://github.com/xinjie-q/gaussianimage", "project_page": "", "abstract": "Implicit neural representations (INRs) recently achieved great success in image representation and compression, offering high visual quality and fast rendering speeds with 10-1000 FPS, assuming sufficient GPU resources are available. However, this requirement often hinders their use on low-end devices with limited memory. In response, we propose a groundbreaking paradigm of image representation and compression by 2D Gaussian Splatting, named GaussianImage. We first introduce 2D Gaussian to represent the image, where each Gaussian has 8 parameters including position, covariance and color. Subsequently, we unveil a novel rendering algorithm based on accumulated summation. Remarkably, our method with a minimum of 3times lower GPU memory usage and 5times faster fitting time not only rivals INRs (e.g., WIRE, I-NGP) in representation performance, but also delivers a faster rendering speed of 1500-2000 FPS regardless of parameter size. Furthermore, we integrate existing vector quantization technique to build an image codec. Experimental results demonstrate that our codec attains rate-distortion performance comparable to compression-based INRs such as COIN and COIN++, while facilitating decoding speeds of approximately 1000 FPS. Additionally, preliminary proof of concept shows that our codec surpasses COIN and COIN++ in performance when using partial bits-back coding.", "github_manual": "https://github.com/xinjie-q/gaussianimage", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-14", "arxiv_id": "2403.08540", "title": "Language models scale reliably with over-training and on downstream tasks", "authors": [ "Samir Yitzhak Gadre", "Georgios Smyrnis", "Vaishaal Shankar", "Suchin Gururangan", "Mitchell Wortsman", "Rulin Shao", "Jean Mercat", "Alex Fang", "Jeffrey Li", "Sedrick Keh", "Rui Xin", "Marianna Nezhurina", "Igor Vasiljevic", "Jenia Jitsev", "Alexandros G. Dimakis", "Gabriel Ilharco", "Shuran Song", "Thomas Kollar", "Yair Carmon", "Achal Dave", "Reinhard Heckel", "Niklas Muennighoff", "Ludwig Schmidt" ], "github": "https://github.com/mlfoundations/scaling", "project_page": "", "abstract": "Scaling laws are useful guides for developing language models, but there are still gaps between current scaling studies and how language models are ultimately trained and evaluated. For instance, scaling is usually studied in the compute-optimal training regime (i.e., \"Chinchilla optimal\" regime); however, in practice, models are often over-trained to reduce inference costs. Moreover, scaling laws mostly predict loss on next-token prediction, but ultimately models are compared based on downstream task performance. In this paper, we address both shortcomings. To do so, we create a testbed of 104 models with 0.011B to 6.9B parameters trained with various numbers of tokens on three data distributions. First, we investigate scaling in the over-trained regime. We fit scaling laws that extrapolate in both the number of model parameters and the ratio of training tokens to parameters. This enables us to predict the validation loss of a 1.4B parameter, 900B token run (i.e., 32times over-trained) and a 6.9B parameter, 138B token runx2014each from experiments that take 300times less compute. Second, we relate the perplexity of a language model to its downstream task performance via a power law. We use this law to predict top-1 error averaged over downstream tasks for the two aforementioned models using experiments that take 20times less compute. Our experiments are available at https://github.com/mlfoundations/scaling.", "github_manual": "", "github_regex": "https://github.com/mlfoundations/scaling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-14", "arxiv_id": "2403.08629", "title": "Scaling Up Dynamic Human-Scene Interaction Modeling", "authors": [ "Nan Jiang", "Zhiyuan Zhang", "Hongjie Li", "Xiaoxuan Ma", "Zan Wang", "Yixin Chen", "Tengyu Liu", "Yixin Zhu", "Siyuan Huang" ], "github": "", "project_page": "", "abstract": "Confronting the challenges of data scarcity and advanced motion synthesis in human-scene interaction modeling, we introduce the TRUMANS dataset alongside a novel HSI motion synthesis method. TRUMANS stands as the most comprehensive motion-captured HSI dataset currently available, encompassing over 15 hours of human interactions across 100 indoor scenes. It intricately captures whole-body human motions and part-level object dynamics, focusing on the realism of contact. This dataset is further scaled up by transforming physical environments into exact virtual models and applying extensive augmentations to appearance and motion for both humans and objects while maintaining interaction fidelity. Utilizing TRUMANS, we devise a diffusion-based autoregressive model that efficiently generates HSI sequences of any length, taking into account both scene context and intended actions. In experiments, our approach shows remarkable zero-shot generalizability on a range of 3D scene datasets (e.g., PROX, Replica, ScanNet, ScanNet++), producing motions that closely mimic original motion-captured sequences, as confirmed by quantitative experiments and human studies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-15", "arxiv_id": "2403.09611", "title": "MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training", "authors": [ "Brandon McKinzie", "Zhe Gan", "Jean-Philippe Fauconnier", "Sam Dodge", "Bowen Zhang", "Philipp Dufter", "Dhruti Shah", "Xianzhi Du", "Futang Peng", "Floris Weers", "Anton Belyi", "Haotian Zhang", "Karanjeet Singh", "Doug Kang", "Hongyu H\u00e8", "Max Schwarzer", "Tom Gunter", "Xiang Kong", "Aonan Zhang", "Jianyu Wang", "Chong Wang", "Nan Du", "Tao Lei", "Sam Wiseman", "Guoli Yin", "Mark Lee", "Zirui Wang", "Ruoming Pang", "Peter Grasch", "Alexander Toshev", "Yinfei Yang" ], "github": "", "project_page": "", "abstract": "In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons. For example, we demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data is crucial for achieving state-of-the-art (SOTA) few-shot results across multiple benchmarks, compared to other published pre-training results. Further, we show that the image encoder together with image resolution and the image token count has substantial impact, while the vision-language connector design is of comparatively negligible importance. By scaling up the presented recipe, we build MM1, a family of multimodal models up to 30B parameters, consisting of both dense models and mixture-of-experts (MoE) variants, that are SOTA in pre-training metrics and achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Thanks to large-scale pre-training, MM1 enjoys appealing properties such as enhanced in-context learning, and multi-image reasoning, enabling few-shot chain-of-thought prompting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-15", "arxiv_id": "2403.09334", "title": "Video Editing via Factorized Diffusion Distillation", "authors": [ "Uriel Singer", "Amit Zohar", "Yuval Kirstain", "Shelly Sheynin", "Adam Polyak", "Devi Parikh", "Yaniv Taigman" ], "github": "", "project_page": "", "abstract": "We introduce Emu Video Edit (EVE), a model that establishes a new state-of-the art in video editing without relying on any supervised video editing data. To develop EVE we separately train an image editing adapter and a video generation adapter, and attach both to the same text-to-image model. Then, to align the adapters towards video editing we introduce a new unsupervised distillation procedure, Factorized Diffusion Distillation. This procedure distills knowledge from one or more teachers simultaneously, without any supervised data. We utilize this procedure to teach EVE to edit videos by jointly distilling knowledge to (i) precisely edit each individual frame from the image editing adapter, and (ii) ensure temporal consistency among the edited frames using the video generation adapter. Finally, to demonstrate the potential of our approach in unlocking other capabilities, we align additional combinations of adapters", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-15", "arxiv_id": "2403.09530", "title": "VisionGPT-3D: A Generalized Multimodal Agent for Enhanced 3D Vision Understanding", "authors": [ "Chris Kelly", "Luhui Hu", "Jiayin Hu", "Yu Tian", "Deshun Yang", "Bang Yang", "Cindy Yang", "Zihao Li", "Zaoshan Huang", "Yuexian Zou" ], "github": "", "project_page": "", "abstract": "The evolution of text to visual components facilitates people's daily lives, such as generating image, videos from text and identifying the desired elements within the images. Computer vision models involving the multimodal abilities in the previous days are focused on image detection, classification based on well-defined objects. Large language models (LLMs) introduces the transformation from nature language to visual objects, which present the visual layout for text contexts. OpenAI GPT-4 has emerged as the pinnacle in LLMs, while the computer vision (CV) domain boasts a plethora of state-of-the-art (SOTA) models and algorithms to convert 2D images to their 3D representations. However, the mismatching between the algorithms with the problem could lead to undesired results. In response to this challenge, we propose an unified VisionGPT-3D framework to consolidate the state-of-the-art vision models, thereby facilitating the development of vision-oriented AI. VisionGPT-3D provides a versatile multimodal framework building upon the strengths of multimodal foundation models. It seamlessly integrates various SOTA vision models and brings the automation in the selection of SOTA vision models, identifies the suitable 3D mesh creation algorithms corresponding to 2D depth maps analysis, generates optimal results based on diverse multimodal inputs such as text prompts. Keywords: VisionGPT-3D, 3D vision understanding, Multimodal agent", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-15", "arxiv_id": "2403.09622", "title": "Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering", "authors": [ "Zeyu Liu", "Weicong Liang", "Zhanhao Liang", "Chong Luo", "Ji Li", "Gao Huang", "Yuhui Yuan" ], "github": "", "project_page": "", "abstract": "Visual text rendering poses a fundamental challenge for contemporary text-to-image generation models, with the core problem lying in text encoder deficiencies. To achieve accurate text rendering, we identify two crucial requirements for text encoders: character awareness and alignment with glyphs. Our solution involves crafting a series of customized text encoder, Glyph-ByT5, by fine-tuning the character-aware ByT5 encoder using a meticulously curated paired glyph-text dataset. We present an effective method for integrating Glyph-ByT5 with SDXL, resulting in the creation of the Glyph-SDXL model for design image generation. This significantly enhances text rendering accuracy, improving it from less than 20% to nearly 90% on our design image benchmark. Noteworthy is Glyph-SDXL's newfound ability for text paragraph rendering, achieving high spelling accuracy for tens to hundreds of characters with automated multi-line layouts. Finally, through fine-tuning Glyph-SDXL with a small set of high-quality, photorealistic images featuring visual text, we showcase a substantial improvement in scene text rendering capabilities in open-domain real images. These compelling outcomes aim to encourage further exploration in designing customized text encoders for diverse and challenging tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-15", "arxiv_id": "2403.08773", "title": "Veagle: Advancements in Multimodal Representation Learning", "authors": [ "Rajat Chawla", "Arkajit Datta", "Tushar Verma", "Adarsh Jha", "Anmol Gautam", "Ayush Vatsal", "Sukrit Chaterjee", "Mukunda NS", "Ishaan Bhola" ], "github": "https://github.com/superagi/veagle", "project_page": "", "abstract": "Lately, researchers in artificial intelligence have been really interested in how language and vision come together, giving rise to the development of multimodal models that aim to seamlessly integrate textual and visual information. Multimodal models, an extension of Large Language Models (LLMs), have exhibited remarkable capabilities in addressing a diverse array of tasks, ranging from image captioning and visual question answering (VQA) to visual grounding. While these models have showcased significant advancements, challenges persist in accurately interpreting images and answering the question, a common occurrence in real-world scenarios. This paper introduces a novel approach to enhance the multimodal capabilities of existing models. In response to the limitations observed in current Vision Language Models (VLMs) and Multimodal Large Language Models (MLLMs), our proposed model Veagle, incorporates a unique mechanism inspired by the successes and insights of previous works. Veagle leverages a dynamic mechanism to project encoded visual information directly into the language model. This dynamic approach allows for a more nuanced understanding of intricate details present in visual contexts. To validate the effectiveness of Veagle, we conduct comprehensive experiments on benchmark datasets, emphasizing tasks such as visual question answering and image understanding. Our results indicate a improvement of 5-6 \\% in performance, with Veagle outperforming existing models by a notable margin. The outcomes underscore the model's versatility and applicability beyond traditional benchmarks.", "github_manual": "https://github.com/superagi/veagle", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-15", "arxiv_id": "2403.09626", "title": "Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding", "authors": [ "Guo Chen", "Yifei Huang", "Jilan Xu", "Baoqi Pei", "Zhe Chen", "Zhiqi Li", "Jiahao Wang", "Kunchang Li", "Tong Lu", "Limin Wang" ], "github": "https://github.com/OpenGVLab/video-mamba-suite", "project_page": "", "abstract": "Understanding videos is one of the fundamental directions in computer vision research, with extensive efforts dedicated to exploring various architectures such as RNN, 3D CNN, and Transformers. The newly proposed architecture of state space model, e.g., Mamba, shows promising traits to extend its success in long sequence modeling to video modeling. To assess whether Mamba can be a viable alternative to Transformers in the video understanding domain, in this work, we conduct a comprehensive set of studies, probing different roles Mamba can play in modeling videos, while investigating diverse tasks where Mamba could exhibit superiority. We categorize Mamba into four roles for modeling videos, deriving a Video Mamba Suite composed of 14 models/modules, and evaluating them on 12 video understanding tasks. Our extensive experiments reveal the strong potential of Mamba on both video-only and video-language tasks while showing promising efficiency-performance trade-offs. We hope this work could provide valuable data points and insights for future research on video understanding. Code is public: https://github.com/OpenGVLab/video-mamba-suite.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/video-mamba-suite", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-15", "arxiv_id": "2403.09338", "title": "LocalMamba: Visual State Space Model with Windowed Selective Scan", "authors": [ "Tao Huang", "Xiaohuan Pei", "Shan You", "Fei Wang", "Chen Qian", "Chang Xu" ], "github": "https://github.com/hunto/LocalMamba", "project_page": "", "abstract": "Recent advancements in state space models, notably Mamba, have demonstrated significant progress in modeling long sequences for tasks like language understanding. Yet, their application in vision tasks has not markedly surpassed the performance of traditional Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). This paper posits that the key to enhancing Vision Mamba (ViM) lies in optimizing scan directions for sequence modeling. Traditional ViM approaches, which flatten spatial tokens, overlook the preservation of local 2D dependencies, thereby elongating the distance between adjacent tokens. We introduce a novel local scanning strategy that divides images into distinct windows, effectively capturing local dependencies while maintaining a global perspective. Additionally, acknowledging the varying preferences for scan patterns across different network layers, we propose a dynamic method to independently search for the optimal scan choices for each layer, substantially improving performance. Extensive experiments across both plain and hierarchical models underscore our approach's superiority in effectively capturing image representations. For example, our model significantly outperforms Vim-Ti by 3.1% on ImageNet with the same 1.5G FLOPs. Code is available at: https://github.com/hunto/LocalMamba.", "github_manual": "", "github_regex": "https://github.com/hunto/LocalMamba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-15", "arxiv_id": "2403.09029", "title": "Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset", "authors": [ "Hugo Lauren\u00e7on", "L\u00e9o Tronchon", "Victor Sanh" ], "github": "", "project_page": "", "abstract": "Using vision-language models (VLMs) in web development presents a promising strategy to increase efficiency and unblock no-code solutions: by providing a screenshot or a sketch of a UI, a VLM could generate the code to reproduce it, for instance in a language like HTML. Despite the advancements in VLMs for various tasks, the specific challenge of converting a screenshot into a corresponding HTML has been minimally explored. We posit that this is mainly due to the absence of a suitable, high-quality dataset. This work introduces WebSight, a synthetic dataset consisting of 2 million pairs of HTML codes and their corresponding screenshots. We fine-tune a foundational VLM on our dataset and show proficiency in converting webpage screenshots to functional HTML code. To accelerate the research in this area, we open-source WebSight.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-15", "arxiv_id": "2403.09347", "title": "BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences", "authors": [ "Sun Ao", "Weilin Zhao", "Xu Han", "Cheng Yang", "Zhiyuan Liu", "Chuan Shi", "Maosong Sun", "Shengnan Wang", "Teng Su" ], "github": "https://github.com/MayDomine/Burst-Attention", "project_page": "", "abstract": "Effective attention modules have played a crucial role in the success of Transformer-based large language models (LLMs), but the quadratic time and memory complexities of these attention modules also pose a challenge when processing long sequences. One potential solution for the long sequence problem is to utilize distributed clusters to parallelize the computation of attention modules across multiple devices (e.g., GPUs). However, adopting a distributed approach inevitably introduces extra memory overheads to store local attention results and incurs additional communication costs to aggregate local results into global ones. In this paper, we propose a distributed attention framework named ``BurstAttention'' to optimize memory access and communication operations at both the global cluster and local device levels. In our experiments, we compare BurstAttention with other competitive distributed attention solutions for long sequence processing. The experimental results under different length settings demonstrate that BurstAttention offers significant advantages for processing long sequences compared with these competitive baselines, reducing 40% communication overheads and achieving 2 X speedup during training 32K sequence length on 8 X A100.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MayDomine/Burst-Attention", "github_api_added_by": "auto" }, { "date": "2024-03-15", "arxiv_id": "2403.09333", "title": "Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring", "authors": [ "Yufei Zhan", "Yousong Zhu", "Hongyin Zhao", "Fan Yang", "Ming Tang", "Jinqiao Wang" ], "github": "https://github.com/jefferyZhan/Griffon", "project_page": "", "abstract": "Large Vision Language Models have achieved fine-grained object perception, but the limitation of image resolution remains a significant obstacle to surpass the performance of task-specific experts in complex and dense scenarios. Such limitation further restricts the model's potential to achieve nuanced visual and language referring in domains such as GUI Agents, Counting and \\etc. To address this issue, we introduce a unified high-resolution generalist model, Griffon v2, enabling flexible object referring with visual and textual prompts. To efficiently scaling up image resolution, we design a simple and lightweight down-sampling projector to overcome the input tokens constraint in Large Language Models. This design inherently preserves the complete contexts and fine details, and significantly improves multimodal perception ability especially for small objects. Building upon this, we further equip the model with visual-language co-referring capabilities through a plug-and-play visual tokenizer. It enables user-friendly interaction with flexible target images, free-form texts and even coordinates. Experiments demonstrate that Griffon v2 can localize any objects of interest with visual and textual referring, achieve state-of-the-art performance on REC, phrase grounding, and REG tasks, and outperform expert models in object detection and object counting. Data, codes and models will be released at https://github.com/jefferyZhan/Griffon.", "github_manual": "", "github_regex": "https://github.com/jefferyZhan/Griffon", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-15", "arxiv_id": "2403.09394", "title": "GiT: Towards Generalist Vision Transformer through Universal Language Interface", "authors": [ "Haiyang Wang", "Hao Tang", "Li Jiang", "Shaoshuai Shi", "Muhammad Ferjad Naeem", "Hongsheng Li", "Bernt Schiele", "Liwei Wang" ], "github": "https://github.com/Haiyang-W/GiT", "project_page": "", "abstract": "This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable for various vision tasks only with a vanilla ViT. Motivated by the universality of the Multi-layer Transformer architecture (e.g, GPT) widely used in large language models (LLMs), we seek to broaden its scope to serve as a powerful vision foundation model (VFM). However, unlike language modeling, visual tasks typically require specific modules, such as bounding box heads for detection and pixel decoders for segmentation, greatly hindering the application of powerful multi-layer transformers in the vision domain. To solve this, we design a universal language interface that empowers the successful auto-regressive decoding to adeptly unify various visual tasks, from image-level understanding (e.g., captioning), over sparse perception (e.g., detection), to dense prediction (e.g., segmentation). Based on the above designs, the entire model is composed solely of a ViT, without any specific additions, offering a remarkable architectural simplification. GiT is a multi-task visual model, jointly trained across five representative benchmarks without task-specific fine-tuning. Interestingly, our GiT builds a new benchmark in generalist performance, and fosters mutual enhancement across tasks, leading to significant improvements compared to isolated training. This reflects a similar impact observed in LLMs. Further enriching training with 27 datasets, GiT achieves strong zero-shot results over various tasks. Due to its simple design, this paradigm holds promise for narrowing the architectural gap between vision and language. Code and models will be available at https://github.com/Haiyang-W/GiT.", "github_manual": "", "github_regex": "https://github.com/Haiyang-W/GiT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-15", "arxiv_id": "2403.09629", "title": "Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking", "authors": [ "Eric Zelikman", "Georges Harik", "Yijia Shao", "Varuna Jayasiri", "Nick Haber", "Noah D. Goodman" ], "github": "https://github.com/ezelikman/quiet-star", "project_page": "", "abstract": "When writing and talking, people sometimes pause to think. Although reasoning-focused works have often framed reasoning as a method of answering questions or completing agentic tasks, reasoning is implicit in almost all written text. For example, this applies to the steps not stated between the lines of a proof or to the theory of mind underlying a conversation. In the Self-Taught Reasoner (STaR, Zelikman et al. 2022), useful thinking is learned by inferring rationales from few-shot examples in question-answering and learning from those that lead to a correct answer. This is a highly constrained setting -- ideally, a language model could instead learn to infer unstated rationales in arbitrary text. We present Quiet-STaR, a generalization of STaR in which LMs learn to generate rationales at each token to explain future text, improving their predictions. We address key challenges, including 1) the computational cost of generating continuations, 2) the fact that the LM does not initially know how to generate or use internal thoughts, and 3) the need to predict beyond individual next tokens. To resolve these, we propose a tokenwise parallel sampling algorithm, using learnable tokens indicating a thought's start and end, and an extended teacher-forcing technique. Encouragingly, generated rationales disproportionately help model difficult-to-predict tokens and improve the LM's ability to directly answer difficult questions. In particular, after continued pretraining of an LM on a corpus of internet text with Quiet-STaR, we find zero-shot improvements on GSM8K (5.9%rightarrow10.9%) and CommonsenseQA (36.3%rightarrow47.2%) and observe a perplexity improvement of difficult tokens in natural text. Crucially, these improvements require no fine-tuning on these tasks. Quiet-STaR marks a step towards LMs that can learn to reason in a more general and scalable way.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ezelikman/quiet-star", "github_api_added_by": "auto" }, { "date": "2024-03-15", "arxiv_id": "2403.09055", "title": "StreamMultiDiffusion: Real-Time Interactive Generation with Region-Based Semantic Control", "authors": [ "Jaerin Lee", "Daniel Sungho Jung", "Kanggeon Lee", "Kyoung Mu Lee" ], "github": "https://github.com/ironjr/StreamMultiDiffusion", "project_page": "", "abstract": "The enormous success of diffusion models in text-to-image synthesis has made them promising candidates for the next generation of end-user applications for image generation and editing. Previous works have focused on improving the usability of diffusion models by reducing the inference time or increasing user interactivity by allowing new, fine-grained controls such as region-based text prompts. However, we empirically find that integrating both branches of works is nontrivial, limiting the potential of diffusion models. To solve this incompatibility, we present StreamMultiDiffusion, the first real-time region-based text-to-image generation framework. By stabilizing fast inference techniques and restructuring the model into a newly proposed multi-prompt stream batch architecture, we achieve times 10 faster panorama generation than existing solutions, and the generation speed of 1.57 FPS in region-based text-to-image synthesis on a single RTX 2080 Ti GPU. Our solution opens up a new paradigm for interactive image generation named semantic palette, where high-quality images are generated in real-time from given multiple hand-drawn regions, encoding prescribed semantic meanings (e.g., eagle, girl). Our code and demo application are available at https://github.com/ironjr/StreamMultiDiffusion.", "github_manual": "", "github_regex": "https://github.com/ironjr/StreamMultiDiffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-15", "arxiv_id": "2403.09631", "title": "3D-VLA: A 3D Vision-Language-Action Generative World Model", "authors": [ "Haoyu Zhen", "Xiaowen Qiu", "Peihao Chen", "Jincheng Yang", "Xin Yan", "Yilun Du", "Yining Hong", "Chuang Gan" ], "github": "https://github.com/UMass-Foundation-Model/3D-VLA", "project_page": "", "abstract": "Recent vision-language-action (VLA) models rely on 2D inputs, lacking integration with the broader realm of the 3D physical world. Furthermore, they perform action prediction by learning a direct mapping from perception to action, neglecting the vast dynamics of the world and the relations between actions and dynamics. In contrast, human beings are endowed with world models that depict imagination about future scenarios to plan actions accordingly. To this end, we propose 3D-VLA by introducing a new family of embodied foundation models that seamlessly link 3D perception, reasoning, and action through a generative world model. Specifically, 3D-VLA is built on top of a 3D-based large language model (LLM), and a set of interaction tokens is introduced to engage with the embodied environment. Furthermore, to inject generation abilities into the model, we train a series of embodied diffusion models and align them into the LLM for predicting the goal images and point clouds. To train our 3D-VLA, we curate a large-scale 3D embodied instruction dataset by extracting vast 3D-related information from existing robotics datasets. Our experiments on held-in datasets demonstrate that 3D-VLA significantly improves the reasoning, multimodal generation, and planning capabilities in embodied environments, showcasing its potential in real-world applications.", "github_manual": "https://github.com/UMass-Foundation-Model/3D-VLA", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-18", "arxiv_id": "2403.10493", "title": "MusicHiFi: Fast High-Fidelity Stereo Vocoding", "authors": [ "Ge Zhu", "Juan-Pablo Caceres", "Zhiyao Duan", "Nicholas J. Bryan" ], "github": "https://github.com/s3a-spatialaudio/s3a-decorrelation-toolbox", "project_page": "", "abstract": "Diffusion-based audio and music generation models commonly generate music by constructing an image representation of audio (e.g., a mel-spectrogram) and then converting it to audio using a phase reconstruction model or vocoder. Typical vocoders, however, produce monophonic audio at lower resolutions (e.g., 16-24 kHz), which limits their effectiveness. We propose MusicHiFi -- an efficient high-fidelity stereophonic vocoder. Our method employs a cascade of three generative adversarial networks (GANs) that convert low-resolution mel-spectrograms to audio, upsamples to high-resolution audio via bandwidth expansion, and upmixes to stereophonic audio. Compared to previous work, we propose 1) a unified GAN-based generator and discriminator architecture and training procedure for each stage of our cascade, 2) a new fast, near downsampling-compatible bandwidth extension module, and 3) a new fast downmix-compatible mono-to-stereo upmixer that ensures the preservation of monophonic content in the output. We evaluate our approach using both objective and subjective listening tests and find our approach yields comparable or better audio quality, better spatialization control, and significantly faster inference speed compared to past work. Sound examples are at https://MusicHiFi.github.io/web/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/s3a-spatialaudio/s3a-decorrelation-toolbox", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-18", "arxiv_id": "2403.09981", "title": "Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting", "authors": [ "Zhiqi Li", "Yiming Chen", "Lingzhe Zhao", "Peidong Liu" ], "github": "https://github.com/WU-CVGL/MVControl-threestudio", "project_page": "", "abstract": "While text-to-3D and image-to-3D generation tasks have received considerable attention, one important but under-explored field between them is controllable text-to-3D generation, which we mainly focus on in this work. To address this task, 1) we introduce Multi-view ControlNet (MVControl), a novel neural network architecture designed to enhance existing pre-trained multi-view diffusion models by integrating additional input conditions, such as edge, depth, normal, and scribble maps. Our innovation lies in the introduction of a conditioning module that controls the base diffusion model using both local and global embeddings, which are computed from the input condition images and camera poses. Once trained, MVControl is able to offer 3D diffusion guidance for optimization-based 3D generation. And, 2) we propose an efficient multi-stage 3D generation pipeline that leverages the benefits of recent large reconstruction models and score distillation algorithm. Building upon our MVControl architecture, we employ a unique hybrid diffusion guidance method to direct the optimization process. In pursuit of efficiency, we adopt 3D Gaussians as our representation instead of the commonly used implicit representations. We also pioneer the use of SuGaR, a hybrid representation that binds Gaussians to mesh triangle faces. This approach alleviates the issue of poor geometry in 3D Gaussians and enables the direct sculpting of fine-grained geometry on the mesh. Extensive experiments demonstrate that our method achieves robust generalization and enables the controllable generation of high-quality 3D content.", "github_manual": "https://github.com/WU-CVGL/MVControl-threestudio", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-18", "arxiv_id": "2403.09919", "title": "Recurrent Drafter for Fast Speculative Decoding in Large Language Models", "authors": [ "Aonan Zhang", "Chong Wang", "Yi Wang", "Xuanyu Zhang", "Yunfei Cheng" ], "github": "https://github.com/apple/ml-recurrent-drafter", "project_page": "", "abstract": "In this paper, we introduce an improved approach of speculative decoding aimed at enhancing the efficiency of serving large language models. Our method capitalizes on the strengths of two established techniques: the classic two-model speculative decoding approach, and the more recent single-model approach, Medusa. Drawing inspiration from Medusa, our approach adopts a single-model strategy for speculative decoding. However, our method distinguishes itself by employing a single, lightweight draft head with a recurrent dependency design, akin in essence to the small, draft model uses in classic speculative decoding, but without the complexities of the full transformer architecture. And because of the recurrent dependency, we can use beam search to swiftly filter out undesired candidates with the draft head. The outcome is a method that combines the simplicity of single-model design and avoids the need to create a data-dependent tree attention structure only for inference in Medusa. We empirically demonstrate the effectiveness of the proposed method on several popular open source language models, along with a comprehensive analysis of the trade-offs involved in adopting this approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apple/ml-recurrent-drafter", "github_api_added_by": "auto" }, { "date": "2024-03-18", "arxiv_id": "2403.09704", "title": "Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations", "authors": [ "Swapnaja Achintalwar", "Ioana Baldini", "Djallel Bouneffouf", "Joan Byamugisha", "Maria Chang", "Pierre Dognin", "Eitan Farchi", "Ndivhuwo Makondo", "Aleksandra Mojsilovic", "Manish Nagireddy", "Karthikeyan Natesan Ramamurthy", "Inkit Padhi", "Orna Raz", "Jesus Rios", "Prasanna Sattigeri", "Moninder Singh", "Siphiwe Thwala", "Rosario A. Uceda-Sosa", "Kush R. Varshney" ], "github": "", "project_page": "", "abstract": "The alignment of large language models is usually done by model providers to add or control behaviors that are common or universally understood across use cases and contexts. In contrast, in this article, we present an approach and architecture that empowers application developers to tune a model to their particular values, social norms, laws and other regulations, and orchestrate between potentially conflicting requirements in context. We lay out three main components of such an Alignment Studio architecture: Framers, Instructors, and Auditors that work in concert to control the behavior of a language model. We illustrate this approach with a running example of aligning a company's internal-facing enterprise chatbot to its business conduct guidelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-18", "arxiv_id": "2403.10395", "title": "Isotropic3D: Image-to-3D Generation Based on a Single CLIP Embedding", "authors": [ "Pengkun Liu", "Yikai Wang", "Fuchun Sun", "Jiafang Li", "Hang Xiao", "Hongxiang Xue", "Xinzhou Wang" ], "github": "https://github.com/pkunliu/Isotropic3D", "project_page": "", "abstract": "Encouraged by the growing availability of pre-trained 2D diffusion models, image-to-3D generation by leveraging Score Distillation Sampling (SDS) is making remarkable progress. Most existing methods combine novel-view lifting from 2D diffusion models which usually take the reference image as a condition while applying hard L2 image supervision at the reference view. Yet heavily adhering to the image is prone to corrupting the inductive knowledge of the 2D diffusion model leading to flat or distorted 3D generation frequently. In this work, we reexamine image-to-3D in a novel perspective and present Isotropic3D, an image-to-3D generation pipeline that takes only an image CLIP embedding as input. Isotropic3D allows the optimization to be isotropic w.r.t. the azimuth angle by solely resting on the SDS loss. The core of our framework lies in a two-stage diffusion model fine-tuning. Firstly, we fine-tune a text-to-3D diffusion model by substituting its text encoder with an image encoder, by which the model preliminarily acquires image-to-image capabilities. Secondly, we perform fine-tuning using our Explicit Multi-view Attention (EMA) which combines noisy multi-view images with the noise-free reference image as an explicit condition. CLIP embedding is sent to the diffusion model throughout the whole process while reference images are discarded once after fine-tuning. As a result, with a single image CLIP embedding, Isotropic3D is capable of generating multi-view mutually consistent images and also a 3D model with more symmetrical and neat content, well-proportioned geometry, rich colored texture, and less distortion compared with existing image-to-3D methods while still preserving the similarity to the reference image to a large extent. The project page is available at https://isotropic3d.github.io/. The code and models are available at https://github.com/pkunliu/Isotropic3D.", "github_manual": "", "github_regex": "https://github.com/pkunliu/Isotropic3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-18", "arxiv_id": "2403.10517", "title": "VideoAgent: Long-form Video Understanding with Large Language Model as Agent", "authors": [ "Xiaohan Wang", "Yuhui Zhang", "Orr Zohar", "Serena Yeung-Levy" ], "github": "https://github.com/wxh1996/VideoAgent", "project_page": "", "abstract": "Long-form video understanding represents a significant challenge within computer vision, demanding a model capable of reasoning over long multi-modal sequences. Motivated by the human cognitive process for long-form video understanding, we emphasize interactive reasoning and planning over the ability to process lengthy visual inputs. We introduce a novel agent-based system, VideoAgent, that employs a large language model as a central agent to iteratively identify and compile crucial information to answer a question, with vision-language foundation models serving as tools to translate and retrieve visual information. Evaluated on the challenging EgoSchema and NExT-QA benchmarks, VideoAgent achieves 54.1% and 71.3% zero-shot accuracy with only 8.4 and 8.2 frames used on average. These results demonstrate superior effectiveness and efficiency of our method over the current state-of-the-art methods, highlighting the potential of agent-based approaches in advancing long-form video understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wxh1996/VideoAgent", "github_api_added_by": "auto" }, { "date": "2024-03-18", "arxiv_id": "2403.09977", "title": "EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba", "authors": [ "Xiaohuan Pei", "Tao Huang", "Chang Xu" ], "github": "https://github.com/TerryPei/EfficientVMamba", "project_page": "", "abstract": "Prior efforts in light-weight model development mainly centered on CNN and Transformer-based designs yet faced persistent challenges. CNNs adept at local feature extraction compromise resolution while Transformers offer global reach but escalate computational demands O(N^2). This ongoing trade-off between accuracy and efficiency remains a significant hurdle. Recently, state space models (SSMs), such as Mamba, have shown outstanding performance and competitiveness in various tasks such as language modeling and computer vision, while reducing the time complexity of global information extraction to O(N). Inspired by this, this work proposes to explore the potential of visual state space models in light-weight model design and introduce a novel efficient model variant dubbed EfficientVMamba. Concretely, our EfficientVMamba integrates a atrous-based selective scan approach by efficient skip sampling, constituting building blocks designed to harness both global and local representational features. Additionally, we investigate the integration between SSM blocks and convolutions, and introduce an efficient visual state space block combined with an additional convolution branch, which further elevate the model performance. Experimental results show that, EfficientVMamba scales down the computational complexity while yields competitive results across a variety of vision tasks. For example, our EfficientVMamba-S with 1.3G FLOPs improves Vim-Ti with 1.5G FLOPs by a large margin of 5.6% accuracy on ImageNet. Code is available at: https://github.com/TerryPei/EfficientVMamba.", "github_manual": "", "github_regex": "https://github.com/TerryPei/EfficientVMamba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-18", "arxiv_id": "2403.10301", "title": "Uni-SMART: Universal Science Multimodal Analysis and Research Transformer", "authors": [ "Hengxing Cai", "Xiaochen Cai", "Shuwen Yang", "Jiankun Wang", "Lin Yao", "Zhifeng Gao", "Junhan Chang", "Sihang Li", "Mingjun Xu", "Changxin Wang", "Hongshuai Wang", "Yongge Li", "Mujie Lin", "Yaqi Li", "Yuqi Yin", "Linfeng Zhang", "Guolin Ke" ], "github": "", "project_page": "", "abstract": "In scientific research and its application, scientific literature analysis is crucial as it allows researchers to build on the work of others. However, the fast growth of scientific knowledge has led to a massive increase in scholarly articles, making in-depth literature analysis increasingly challenging and time-consuming. The emergence of Large Language Models (LLMs) has offered a new way to address this challenge. Known for their strong abilities in summarizing texts, LLMs are seen as a potential tool to improve the analysis of scientific literature. However, existing LLMs have their own limits. Scientific literature often includes a wide range of multimodal elements, such as molecular structure, tables, and charts, which are hard for text-focused LLMs to understand and analyze. This issue points to the urgent need for new solutions that can fully understand and analyze multimodal content in scientific literature. To answer this demand, we present Uni-SMART (Universal Science Multimodal Analysis and Research Transformer), an innovative model designed for in-depth understanding of multimodal scientific literature. Through rigorous quantitative evaluation across several domains, Uni-SMART demonstrates superior performance over leading text-focused LLMs. Furthermore, our exploration extends to practical applications, including patent infringement detection and nuanced analysis of charts. These applications not only highlight Uni-SMART's adaptability but also its potential to revolutionize how we interact with scientific literature.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-18", "arxiv_id": "2403.10131", "title": "RAFT: Adapting Language Model to Domain Specific RAG", "authors": [ "Tianjun Zhang", "Shishir G. Patil", "Naman Jain", "Sheng Shen", "Matei Zaharia", "Ion Stoica", "Joseph E. Gonzalez" ], "github": "https://github.com/ShishirPatil/gorilla", "project_page": "", "abstract": "Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications, it is common to additionally bake in new knowledge (e.g., time-critical news, or private domain knowledge) into the pretrained model either through RAG-based-prompting, or fine-tuning. However, the optimal methodology for the model to gain such new knowledge remains an open question. In this paper, we present Retrieval Augmented FineTuning (RAFT), a training recipe that improves the model's ability to answer questions in a \"open-book\" in-domain settings. In RAFT, given a question, and a set of retrieved documents, we train the model to ignore those documents that don't help in answering the question, which we call, distractor documents. RAFT accomplishes this by citing verbatim the right sequence from the relevant document that would help answer the question. This coupled with RAFT's chain-of-thought-style response helps improve the model's ability to reason. In domain-specific RAG, RAFT consistently improves the model's performance across PubMed, HotpotQA, and Gorilla datasets, presenting a post-training recipe to improve pre-trained LLMs to in-domain RAG. RAFT's code and demo are open-sourced at github.com/ShishirPatil/gorilla.", "github_manual": "https://github.com/ShishirPatil/gorilla", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-18", "arxiv_id": "2403.10425", "title": "NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices", "authors": [ "Zhiyong Zhang", "Huaizu Jiang", "Hanumant Singh" ], "github": "https://github.com/neufieldrobotics/NeuFlow", "project_page": "", "abstract": "Real-time high-accuracy optical flow estimation is a crucial component in various applications, including localization and mapping in robotics, object tracking, and activity recognition in computer vision. While recent learning-based optical flow methods have achieved high accuracy, they often come with heavy computation costs. In this paper, we propose a highly efficient optical flow architecture, called NeuFlow, that addresses both high accuracy and computational cost concerns. The architecture follows a global-to-local scheme. Given the features of the input images extracted at different spatial resolutions, global matching is employed to estimate an initial optical flow on the 1/16 resolution, capturing large displacement, which is then refined on the 1/8 resolution with lightweight CNN layers for better accuracy. We evaluate our approach on Jetson Orin Nano and RTX 2080 to demonstrate efficiency improvements across different computing platforms. We achieve a notable 10x-80x speedup compared to several state-of-the-art methods, while maintaining comparable accuracy. Our approach achieves around 30 FPS on edge computing platforms, which represents a significant breakthrough in deploying complex computer vision tasks such as SLAM on small robots like drones. The full training and evaluation code is available at https://github.com/neufieldrobotics/NeuFlow.", "github_manual": "", "github_regex": "https://github.com/neufieldrobotics/NeuFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-18", "arxiv_id": "2403.10242", "title": "FDGaussian: Fast Gaussian Splatting from Single Image via Geometric-aware Diffusion Model", "authors": [ "Qijun Feng", "Zhen Xing", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "", "project_page": "", "abstract": "Reconstructing detailed 3D objects from single-view images remains a challenging task due to the limited information available. In this paper, we introduce FDGaussian, a novel two-stage framework for single-image 3D reconstruction. Recent methods typically utilize pre-trained 2D diffusion models to generate plausible novel views from the input image, yet they encounter issues with either multi-view inconsistency or lack of geometric fidelity. To overcome these challenges, we propose an orthogonal plane decomposition mechanism to extract 3D geometric features from the 2D input, enabling the generation of consistent multi-view images. Moreover, we further accelerate the state-of-the-art Gaussian Splatting incorporating epipolar attention to fuse images from different viewpoints. We demonstrate that FDGaussian generates images with high consistency across different views and reconstructs high-quality 3D objects, both qualitatively and quantitatively. More examples can be found at our website https://qjfeng.net/FDGaussian/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.12015", "title": "Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation", "authors": [ "Axel Sauer", "Frederic Boesel", "Tim Dockhorn", "Andreas Blattmann", "Patrick Esser", "Robin Rombach" ], "github": "https://github.com/mlfoundations/open", "project_page": "", "abstract": "Diffusion models are the main driver of progress in image and video synthesis, but suffer from slow inference speed. Distillation methods, like the recently introduced adversarial diffusion distillation (ADD) aim to shift the model from many-shot to single-step inference, albeit at the cost of expensive and difficult optimization due to its reliance on a fixed pretrained DINOv2 discriminator. We introduce Latent Adversarial Diffusion Distillation (LADD), a novel distillation approach overcoming the limitations of ADD. In contrast to pixel-based ADD, LADD utilizes generative features from pretrained latent diffusion models. This approach simplifies training and enhances performance, enabling high-resolution multi-aspect ratio image synthesis. We apply LADD to Stable Diffusion 3 (8B) to obtain SD3-Turbo, a fast model that matches the performance of state-of-the-art text-to-image generators using only four unguided sampling steps. Moreover, we systematically investigate its scaling behavior and demonstrate LADD's effectiveness in various applications such as image editing and inpainting.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mlfoundations/open", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.12008", "title": "SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion", "authors": [ "Vikram Voleti", "Chun-Han Yao", "Mark Boss", "Adam Letts", "David Pankratz", "Dmitry Tochilkin", "Christian Laforte", "Robin Rombach", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "We present Stable Video 3D (SV3D) -- a latent video diffusion model for high-resolution, image-to-multi-view generation of orbital videos around a 3D object. Recent work on 3D generation propose techniques to adapt 2D generative models for novel view synthesis (NVS) and 3D optimization. However, these methods have several disadvantages due to either limited views or inconsistent NVS, thereby affecting the performance of 3D object generation. In this work, we propose SV3D that adapts image-to-video diffusion model for novel multi-view synthesis and 3D generation, thereby leveraging the generalization and multi-view consistency of the video models, while further adding explicit camera control for NVS. We also propose improved 3D optimization techniques to use SV3D and its NVS outputs for image-to-3D generation. Extensive experimental results on multiple datasets with 2D and 3D metrics as well as user study demonstrate SV3D's state-of-the-art performance on NVS as well as 3D reconstruction compared to prior works.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.11781", "title": "Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm", "authors": [ "Yi Wu", "Ziqiang Li", "Heliang Zheng", "Chaoyue Wang", "Bin Li" ], "github": "", "project_page": "", "abstract": "Drawing on recent advancements in diffusion models for text-to-image generation, identity-preserved personalization has made significant progress in accurately capturing specific identities with just a single reference image. However, existing methods primarily integrate reference images within the text embedding space, leading to a complex entanglement of image and text information, which poses challenges for preserving both identity fidelity and semantic consistency. To tackle this challenge, we propose Infinite-ID, an ID-semantics decoupling paradigm for identity-preserved personalization. Specifically, we introduce identity-enhanced training, incorporating an additional image cross-attention module to capture sufficient ID information while deactivating the original text cross-attention module of the diffusion model. This ensures that the image stream faithfully represents the identity provided by the reference image while mitigating interference from textual input. Additionally, we introduce a feature interaction mechanism that combines a mixed attention module with an AdaIN-mean operation to seamlessly merge the two streams. This mechanism not only enhances the fidelity of identity and semantic consistency but also enables convenient control over the styles of the generated images. Extensive experimental results on both raw photo generation and style image generation demonstrate the superior performance of our proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.10615", "title": "LightIt: Illumination Modeling and Control for Diffusion Models", "authors": [ "Peter Kocsis", "Julien Philip", "Kalyan Sunkavalli", "Matthias Nie\u00dfner", "Yannick Hold-Geoffroy" ], "github": "", "project_page": "", "abstract": "We introduce LightIt, a method for explicit illumination control for image generation. Recent generative methods lack lighting control, which is crucial to numerous artistic aspects of image generation such as setting the overall mood or cinematic appearance. To overcome these limitations, we propose to condition the generation on shading and normal maps. We model the lighting with single bounce shading, which includes cast shadows. We first train a shading estimation module to generate a dataset of real-world images and shading pairs. Then, we train a control network using the estimated shading and normals as input. Our method demonstrates high-quality image generation and lighting control in numerous scenes. Additionally, we use our generated dataset to train an identity-preserving relighting model, conditioned on an image and a target shading. Our method is the first that enables the generation of images with controllable, consistent lighting and performs on par with specialized relighting state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.11481", "title": "VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding", "authors": [ "Yue Fan", "Xiaojian Ma", "Rujie Wu", "Yuntao Du", "Jiaqi Li", "Zhi Gao", "Qing Li" ], "github": "", "project_page": "", "abstract": "We explore how reconciling several foundation models (large language models and vision-language models) with a novel unified memory mechanism could tackle the challenging video understanding problem, especially capturing the long-term temporal relations in lengthy videos. In particular, the proposed multimodal agent VideoAgent: 1) constructs a structured memory to store both the generic temporal event descriptions and object-centric tracking states of the video; 2) given an input task query, it employs tools including video segment localization and object memory querying along with other visual foundation models to interactively solve the task, utilizing the zero-shot tool-use ability of LLMs. VideoAgent demonstrates impressive performances on several long-horizon video understanding benchmarks, an average increase of 6.6% on NExT-QA and 26.0% on EgoSchema over baselines, closing the gap between open-sourced models and private counterparts including Gemini 1.5 Pro.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.11207", "title": "MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data", "authors": [ "Paul S. Scotti", "Mihir Tripathy", "Cesar Kadir Torrico Villanueva", "Reese Kneeland", "Tong Chen", "Ashutosh Narang", "Charan Santhirasegaran", "Jonathan Xu", "Thomas Naselaris", "Kenneth A. Norman", "Tanishq Mathew Abraham" ], "github": "https://github.com/MedARC-AI/MindEyeV2", "project_page": "", "abstract": "Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited. This is because such models are trained independently per subject where each subject requires dozens of hours of expensive fMRI training data to attain high-quality results. The present work showcases high-quality reconstructions using only 1 hour of fMRI training data. We pretrain our model across 7 subjects and then fine-tune on minimal data from a new subject. Our novel functional alignment procedure linearly maps all brain data to a shared-subject latent space, followed by a shared non-linear mapping to CLIP image space. We then map from CLIP space to pixel space by fine-tuning Stable Diffusion XL to accept CLIP latents as inputs instead of text. This approach improves out-of-subject generalization with limited training data and also attains state-of-the-art image retrieval and reconstruction metrics compared to single-subject approaches. MindEye2 demonstrates how accurate reconstructions of perception are possible from a single visit to the MRI facility. All code is available on GitHub.", "github_manual": "https://github.com/MedARC-AI/MindEyeV2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.12032", "title": "Generic 3D Diffusion Adapter Using Controlled Multi-View Editing", "authors": [ "Hansheng Chen", "Ruoxi Shi", "Yulin Liu", "Bokui Shen", "Jiayuan Gu", "Gordon Wetzstein", "Hao Su", "Leonidas Guibas" ], "github": "https://github.com/Lakonik/MVEdit", "project_page": "", "abstract": "Open-domain 3D object synthesis has been lagging behind image synthesis due to limited data and higher computational complexity. To bridge this gap, recent works have investigated multi-view diffusion but often fall short in either 3D consistency, visual quality, or efficiency. This paper proposes MVEdit, which functions as a 3D counterpart of SDEdit, employing ancestral sampling to jointly denoise multi-view images and output high-quality textured meshes. Built on off-the-shelf 2D diffusion models, MVEdit achieves 3D consistency through a training-free 3D Adapter, which lifts the 2D views of the last timestep into a coherent 3D representation, then conditions the 2D views of the next timestep using rendered views, without uncompromising visual quality. With an inference time of only 2-5 minutes, this framework achieves better trade-off between quality and speed than score distillation. MVEdit is highly versatile and extendable, with a wide range of applications including text/image-to-3D generation, 3D-to-3D editing, and high-quality texture synthesis. In particular, evaluations demonstrate state-of-the-art performance in both image-to-3D and text-guided texture generation tasks. Additionally, we introduce a method for fine-tuning 2D latent diffusion models on small 3D datasets with limited resources, enabling fast low-resolution text-to-3D initialization.", "github_manual": "https://github.com/Lakonik/MVEdit", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.12019", "title": "LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation", "authors": [ "Yushi Lan", "Fangzhou Hong", "Shuai Yang", "Shangchen Zhou", "Xuyi Meng", "Bo Dai", "Xingang Pan", "Chen Change Loy" ], "github": "https://github.com/NIRVANALAN/LN3Diff", "project_page": "", "abstract": "The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework called LN3Diff to address this gap and enable fast, high-quality, and generic conditional 3D generation. Our approach harnesses a 3D-aware architecture and variational autoencoder (VAE) to encode the input image into a structured, compact, and 3D latent space. The latent is decoded by a transformer-based decoder into a high-capacity 3D neural field. Through training a diffusion model on this 3D-aware latent space, our method achieves state-of-the-art performance on ShapeNet for 3D generation and demonstrates superior performance in monocular 3D reconstruction and conditional 3D generation across various datasets. Moreover, it surpasses existing 3D diffusion methods in terms of inference speed, requiring no per-instance optimization. Our proposed LN3Diff presents a significant advancement in 3D generative modeling and holds promise for various applications in 3D vision and graphics tasks.", "github_manual": "https://github.com/NIRVANALAN/LN3Diff", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.11901", "title": "Larimar: Large Language Models with Episodic Memory Control", "authors": [ "Payel Das", "Subhajit Chaudhury", "Elliot Nelson", "Igor Melnyk", "Sarath Swaminathan", "Sihui Dai", "Aur\u00e9lie Lozano", "Georgios Kollias", "Vijil Chenthamarakshan", "Ji\u0159\u00ed", "Navr\u00e1til", "Soham Dan", "Pin-Yu Chen" ], "github": "https://github.com/ibm/larimar", "project_page": "", "abstract": "Efficient and accurate updating of knowledge stored in Large Language Models (LLMs) is one of the most pressing research challenges today. This paper presents Larimar - a novel, brain-inspired architecture for enhancing LLMs with a distributed episodic memory. Larimar's memory allows for dynamic, one-shot updates of knowledge without the need for computationally expensive re-training or fine-tuning. Experimental results on multiple fact editing benchmarks demonstrate that Larimar attains accuracy comparable to most competitive baselines, even in the challenging sequential editing setup, but also excels in speed - yielding speed-ups of 4-10x depending on the base LLM - as well as flexibility due to the proposed architecture being simple, LLM-agnostic, and hence general. We further provide mechanisms for selective fact forgetting and input context length generalization with Larimar and show their effectiveness.", "github_manual": "https://github.com/ibm/larimar", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.11703", "title": "LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images", "authors": [ "Ruyi Xu", "Yuan Yao", "Zonghao Guo", "Junbo Cui", "Zanlin Ni", "Chunjiang Ge", "Tat-Seng Chua", "Zhiyuan Liu", "Maosong Sun", "Gao Huang" ], "github": "https://github.com/thunlp/LLaVA-UHD", "project_page": "", "abstract": "Visual encoding constitutes the basis of large multimodal models (LMMs) in understanding the visual world. Conventional LMMs process images in fixed sizes and limited resolutions, while recent explorations in this direction are limited in adaptivity, efficiency, and even correctness. In this work, we first take GPT-4V and LLaVA-1.5 as representative examples and expose systematic flaws rooted in their visual encoding strategy. To address the challenges, we present LLaVA-UHD, a large multimodal model that can efficiently perceive images in any aspect ratio and high resolution. LLaVA-UHD includes three key components: (1) An image modularization strategy that divides native-resolution images into smaller variable-sized slices for efficient and extensible encoding, (2) a compression module that further condenses image tokens from visual encoders, and (3) a spatial schema to organize slice tokens for LLMs. Comprehensive experiments show that LLaVA-UHD outperforms established LMMs trained with 2-3 orders of magnitude more data on 9 benchmarks. Notably, our model built on LLaVA-1.5 336x336 supports 6 times larger (i.e., 672x1088) resolution images using only 94% inference computation, and achieves 6.4 accuracy improvement on TextVQA. Moreover, the model can be efficiently trained in academic settings, within 23 hours on 8 A100 GPUs (vs. 26 hours of LLaVA-1.5). We make the data and code publicly available at https://github.com/thunlp/LLaVA-UHD.", "github_manual": "", "github_regex": "https://github.com/thunlp/LLaVA-UHD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.10704", "title": "PERL: Parameter Efficient Reinforcement Learning from Human Feedback", "authors": [ "Hakim Sidahmed", "Samrat Phatale", "Alex Hutcheson", "Zhuonan Lin", "Zhang Chen", "Zac Yu", "Jarvis Jin", "Roman Komarytsia", "Christiane Ahlheim", "Yonghao Zhu", "Simral Chaudhary", "Bowen Li", "Saravanan Ganesh", "Bill Byrne", "Jessica Hoffmann", "Hassan Mansoor", "Wei Li", "Abhinav Rastogi", "Lucas Dixon" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Human Feedback (RLHF) has proven to be a strong method to align Pretrained Large Language Models (LLMs) with human preferences. But training models with RLHF is computationally expensive, and an overall complex process. In this work, we study RLHF where the underlying models are trained using the parameter efficient method of Low-Rank Adaptation (LoRA) introduced by Hu et al. [2021]. We investigate the setup of \"Parameter Efficient Reinforcement Learning\" (PERL), in which we perform reward model training and reinforcement learning using LoRA. We compare PERL to conventional fine-tuning (full-tuning) across various configurations for 7 benchmarks, including 2 novel datasets, of reward modeling and reinforcement learning. We find that PERL performs on par with the conventional RLHF setting, while training faster, and with less memory. This enables the high performance of RLHF, while reducing the computational burden that limits its adoption as an alignment technique for Large Language Models. We also release 2 novel thumbs up/down preference datasets: \"Taskmaster Coffee\", and \"Taskmaster Ticketing\" to promote research around RLHF.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.10616", "title": "DiPaCo: Distributed Path Composition", "authors": [ "Arthur Douillard", "Qixuan Feng", "Andrei A. Rusu", "Adhiguna Kuncoro", "Yani Donchev", "Rachita Chhaparia", "Ionel Gog", "Marc'Aurelio Ranzato", "Jiajun Shen", "Arthur Szlam" ], "github": "", "project_page": "", "abstract": "Progress in machine learning (ML) has been fueled by scaling neural network models. This scaling has been enabled by ever more heroic feats of engineering, necessary for accommodating ML approaches that require high bandwidth communication between devices working in parallel. In this work, we propose a co-designed modular architecture and training approach for ML models, dubbed DIstributed PAth COmposition (DiPaCo). During training, DiPaCo distributes computation by paths through a set of shared modules. Together with a Local-SGD inspired optimization (DiLoCo) that keeps modules in sync with drastically reduced communication, Our approach facilitates training across poorly connected and heterogeneous workers, with a design that ensures robustness to worker failures and preemptions. At inference time, only a single path needs to be executed for each input, without the need for any model compression. We consider this approach as a first prototype towards a new paradigm of large-scale learning, one that is less synchronous and more modular. Our experiments on the widely used C4 benchmark show that, for the same amount of training steps but less wall-clock time, DiPaCo exceeds the performance of a 1 billion-parameter dense transformer language model by choosing one of 256 possible paths, each with a size of 150 million parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-19", "arxiv_id": "2403.12034", "title": "VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models", "authors": [ "Junlin Han", "Filippos Kokkinos", "Philip Torr" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "This paper presents a novel paradigm for building scalable 3D generative models utilizing pre-trained video diffusion models. The primary obstacle in developing foundation 3D generative models is the limited availability of 3D data. Unlike images, texts, or videos, 3D data are not readily accessible and are difficult to acquire. This results in a significant disparity in scale compared to the vast quantities of other types of data. To address this issue, we propose using a video diffusion model, trained with extensive volumes of text, images, and videos, as a knowledge source for 3D data. By unlocking its multi-view generative capabilities through fine-tuning, we generate a large-scale synthetic multi-view dataset to train a feed-forward 3D generative model. The proposed model, VFusion3D, trained on nearly 3M synthetic multi-view data, can generate a 3D asset from a single image in seconds and achieves superior performance when compared to current SOTA feed-forward 3D generative models, with users preferring our results over 70% of the time.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12706", "title": "AnimateDiff-Lightning: Cross-Model Diffusion Distillation", "authors": [ "Shanchuan Lin", "Xiao Yang" ], "github": "", "project_page": "", "abstract": "We present AnimateDiff-Lightning for lightning-fast video generation. Our model uses progressive adversarial diffusion distillation to achieve new state-of-the-art in few-step video generation. We discuss our modifications to adapt it for the video modality. Furthermore, we propose to simultaneously distill the probability flow of multiple base diffusion models, resulting in a single distilled motion module with broader style compatibility. We are pleased to release our distilled AnimateDiff-Lightning model for the community's use.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12409", "title": "ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware Diffusion Guidance", "authors": [ "Yongwei Chen", "Tengfei Wang", "Tong Wu", "Xingang Pan", "Kui Jia", "Ziwei Liu" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "Generating high-quality 3D assets from a given image is highly desirable in various applications such as AR/VR. Recent advances in single-image 3D generation explore feed-forward models that learn to infer the 3D model of an object without optimization. Though promising results have been achieved in single object generation, these methods often struggle to model complex 3D assets that inherently contain multiple objects. In this work, we present ComboVerse, a 3D generation framework that produces high-quality 3D assets with complex compositions by learning to combine multiple models. 1) We first perform an in-depth analysis of this ``multi-object gap'' from both model and data perspectives. 2) Next, with reconstructed 3D models of different objects, we seek to adjust their sizes, rotation angles, and locations to create a 3D asset that matches the given image. 3) To automate this process, we apply spatially-aware score distillation sampling (SSDS) from pretrained diffusion models to guide the positioning of objects. Our proposed framework emphasizes spatial alignment of objects, compared with standard score distillation sampling, and thus achieves more accurate results. Extensive experiments validate ComboVerse achieves clear improvements over existing methods in generating compositional 3D assets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12963", "title": "FouriScale: A Frequency Perspective on Training-Free High-Resolution Image Synthesis", "authors": [ "Linjiang Huang", "Rongyao Fang", "Aiping Zhang", "Guanglu Song", "Si Liu", "Yu Liu", "Hongsheng Li" ], "github": "https://github.com/LeonHLJ/FouriScale", "project_page": "", "abstract": "In this study, we delve into the generation of high-resolution images from pre-trained diffusion models, addressing persistent challenges, such as repetitive patterns and structural distortions, that emerge when models are applied beyond their trained resolutions. To address this issue, we introduce an innovative, training-free approach FouriScale from the perspective of frequency domain analysis. We replace the original convolutional layers in pre-trained diffusion models by incorporating a dilation technique along with a low-pass operation, intending to achieve structural consistency and scale consistency across resolutions, respectively. Further enhanced by a padding-then-crop strategy, our method can flexibly handle text-to-image generation of various aspect ratios. By using the FouriScale as guidance, our method successfully balances the structural integrity and fidelity of generated images, achieving an astonishing capacity of arbitrary-size, high-resolution, and high-quality generation. With its simplicity and compatibility, our method can provide valuable insights for future explorations into the synthesis of ultra-high-resolution images. The code will be released at https://github.com/LeonHLJ/FouriScale.", "github_manual": "", "github_regex": "https://github.com/LeonHLJ/FouriScale", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12596", "title": "Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs", "authors": [ "Victor Carbune", "Hassan Mansoor", "Fangyu Liu", "Rahul Aralikatte", "Gilles Baechler", "Jindong Chen", "Abhanshu Sharma" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) are achieving increasingly strong performance on multimodal tasks. However, reasoning capabilities remain limited particularly for smaller VLMs, while those of large-language models (LLMs) have seen numerous improvements. We propose a technique to transfer capabilities from LLMs to VLMs. On the recently introduced ChartQA, our method obtains state-of-the-art performance when applied on the PaLI3-5B VLM by chen2023pali3, while also enabling much better performance on PlotQA and FigureQA. We first improve the chart representation by continuing the pre-training stage using an improved version of the chart-to-table translation task by liu2023deplot. We then propose constructing a 20x larger dataset than the original training set. To improve general reasoning capabilities and improve numerical operations, we synthesize reasoning traces using the table representation of charts. Lastly, our model is fine-tuned using the multitask loss introduced by hsieh2023distilling. Our variant ChartPaLI-5B outperforms even 10x larger models such as PaLIX-55B without using an upstream OCR system, while keeping inference time constant compared to the PaLI3-5B baseline. When rationales are further refined with a simple program-of-thought prompt chen2023program, our model outperforms the recently introduced Gemini Ultra and GPT-4V.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12957", "title": "GVGEN: Text-to-3D Generation with Volumetric Representation", "authors": [ "Xianglong He", "Junyi Chen", "Sida Peng", "Di Huang", "Yangguang Li", "Xiaoshui Huang", "Chun Yuan", "Wanli Ouyang", "Tong He" ], "github": "https://github.com/GVGEN/GVGEN", "project_page": "", "abstract": "In recent years, 3D Gaussian splatting has emerged as a powerful technique for 3D reconstruction and generation, known for its fast and high-quality rendering capabilities. To address these shortcomings, this paper introduces a novel diffusion-based framework, GVGEN, designed to efficiently generate 3D Gaussian representations from text input. We propose two innovative techniques:(1) Structured Volumetric Representation. We first arrange disorganized 3D Gaussian points as a structured form GaussianVolume. This transformation allows the capture of intricate texture details within a volume composed of a fixed number of Gaussians. To better optimize the representation of these details, we propose a unique pruning and densifying method named the Candidate Pool Strategy, enhancing detail fidelity through selective optimization. (2) Coarse-to-fine Generation Pipeline. To simplify the generation of GaussianVolume and empower the model to generate instances with detailed 3D geometry, we propose a coarse-to-fine pipeline. It initially constructs a basic geometric structure, followed by the prediction of complete Gaussian attributes. Our framework, GVGEN, demonstrates superior performance in qualitative and quantitative assessments compared to existing 3D generation methods. Simultaneously, it maintains a fast generation speed (sim7 seconds), effectively striking a balance between quality and efficiency.", "github_manual": "https://github.com/GVGEN/GVGEN", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12173", "title": "TnT-LLM: Text Mining at Scale with Large Language Models", "authors": [ "Mengting Wan", "Tara Safavi", "Sujay Kumar Jauhar", "Yujin Kim", "Scott Counts", "Jennifer Neville", "Siddharth Suri", "Chirag Shah", "Ryen W White", "Longqi Yang", "Reid Andersen", "Georg Buscher", "Dhruv Joshi", "Nagu Rangan" ], "github": "", "project_page": "", "abstract": "Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application. However, most existing methods for producing label taxonomies and building text-based label classifiers still rely heavily on domain expertise and manual curation, making the process expensive and time-consuming. This is particularly challenging when the label space is under-specified and large-scale data annotations are unavailable. In this paper, we address these challenges with Large Language Models (LLMs), whose prompt-based interface facilitates the induction and use of large-scale pseudo labels. We propose TnT-LLM, a two-phase framework that employs LLMs to automate the process of end-to-end label generation and assignment with minimal human effort for any given use-case. In the first phase, we introduce a zero-shot, multi-stage reasoning approach which enables LLMs to produce and refine a label taxonomy iteratively. In the second phase, LLMs are used as data labelers that yield training samples so that lightweight supervised classifiers can be reliably built, deployed, and served at scale. We apply TnT-LLM to the analysis of user intent and conversational domain for Bing Copilot (formerly Bing Chat), an open-domain chat-based search engine. Extensive experiments using both human and automatic evaluation metrics demonstrate that TnT-LLM generates more accurate and relevant label taxonomies when compared against state-of-the-art baselines, and achieves a favorable balance between accuracy and efficiency for classification at scale. We also share our practical experiences and insights on the challenges and opportunities of using LLMs for large-scale text mining in real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12943", "title": "Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers", "authors": [ "Vidhi Jain", "Maria Attarian", "Nikhil J Joshi", "Ayzaan Wahid", "Danny Driess", "Quan Vuong", "Pannag R Sanketi", "Pierre Sermanet", "Stefan Welker", "Christine Chan", "Igor Gilitschenski", "Yonatan Bisk", "Debidatta Dwibedi" ], "github": "", "project_page": "", "abstract": "While large-scale robotic systems typically rely on textual instructions for tasks, this work explores a different approach: can robots infer the task directly from observing humans? This shift necessitates the robot's ability to decode human intent and translate it into executable actions within its physical constraints and environment. We introduce Vid2Robot, a novel end-to-end video-based learning framework for robots. Given a video demonstration of a manipulation task and current visual observations, Vid2Robot directly produces robot actions. This is achieved through a unified representation model trained on a large dataset of human video and robot trajectory. The model leverages cross-attention mechanisms to fuse prompt video features to the robot's current state and generate appropriate actions that mimic the observed task. To further improve policy performance, we propose auxiliary contrastive losses that enhance the alignment between human and robot video representations. We evaluate Vid2Robot on real-world robots, demonstrating a 20% improvement in performance compared to other video-conditioned policies when using human demonstration videos. Additionally, our model exhibits emergent capabilities, such as successfully transferring observed motions from one object to another, and long-horizon composition, thus showcasing its potential for real-world applications. Project website: vid2robot.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12365", "title": "GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation", "authors": [ "Quankai Gao", "Qiangeng Xu", "Zhe Cao", "Ben Mildenhall", "Wenchao Ma", "Le Chen", "Danhang Tang", "Ulrich Neumann" ], "github": "https://github.com/Zerg-Overmind/GaussianFlow", "project_page": "", "abstract": "Creating 4D fields of Gaussian Splatting from images or videos is a challenging task due to its under-constrained nature. While the optimization can draw photometric reference from the input videos or be regulated by generative models, directly supervising Gaussian motions remains underexplored. In this paper, we introduce a novel concept, Gaussian flow, which connects the dynamics of 3D Gaussians and pixel velocities between consecutive frames. The Gaussian flow can be efficiently obtained by splatting Gaussian dynamics into the image space. This differentiable process enables direct dynamic supervision from optical flow. Our method significantly benefits 4D dynamic content generation and 4D novel view synthesis with Gaussian Splatting, especially for contents with rich motions that are hard to be handled by existing methods. The common color drifting issue that happens in 4D generation is also resolved with improved Guassian dynamics. Superior visual quality on extensive experiments demonstrates our method's effectiveness. Quantitative and qualitative evaluations show that our method achieves state-of-the-art results on both tasks of 4D generation and 4D novel view synthesis. Project page: https://zerg-overmind.github.io/GaussianFlow.github.io/", "github_manual": "https://github.com/Zerg-Overmind/GaussianFlow", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12968", "title": "LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression", "authors": [ "Zhuoshi Pan", "Qianhui Wu", "Huiqiang Jiang", "Menglin Xia", "Xufang Luo", "Jue Zhang", "Qingwei Lin", "Victor R\u00fchle", "Yuqing Yang", "Chin-Yew Lin", "H. Vicky Zhao", "Lili Qiu", "Dongmei Zhang" ], "github": "https://github.com/microsoft/LLMLingua", "project_page": "", "abstract": "This paper focuses on task-agnostic prompt compression for better generalizability and efficiency. Considering the redundancy in natural language, existing approaches compress prompts by removing tokens or lexical units according to their information entropy obtained from a causal language model such as LLaMa-7B. The challenge is that information entropy may be a suboptimal compression metric: (i) it only leverages unidirectional context and may fail to capture all essential information needed for prompt compression; (ii) it is not aligned with the prompt compression objective. To address these issues, we propose a data distillation procedure to derive knowledge from an LLM to compress prompts without losing crucial information, and meantime, introduce an extractive text compression dataset. We formulate prompt compression as a token classification problem to guarantee the faithfulness of the compressed prompt to the original one, and use a Transformer encoder as the base architecture to capture all essential information for prompt compression from the full bidirectional context. Our approach leads to lower latency by explicitly learning the compression objective with smaller models such as XLM-RoBERTa-large and mBERT. We evaluate our method on both in-domain and out-of-domain datasets, including MeetingBank, LongBench, ZeroScrolls, GSM8K, and BBH. Despite its small size, our model shows significant performance gains over strong baselines and demonstrates robust generalization ability across different LLMs. Additionally, our model is 3x-6x faster than existing prompt compression methods, while accelerating the end-to-end latency by 1.6x-2.9x with compression ratios of 2x-5x.", "github_manual": "https://github.com/microsoft/LLMLingua", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12895", "title": "mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding", "authors": [ "Anwen Hu", "Haiyang Xu", "Jiabo Ye", "Ming Yan", "Liang Zhang", "Bo Zhang", "Chen Li", "Ji Zhang", "Qin Jin", "Fei Huang", "Jingren Zhou" ], "github": "https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/DocOwl1.5", "project_page": "", "abstract": "Structure information is critical for understanding the semantics of text-rich images, such as documents, tables, and charts. Existing Multimodal Large Language Models (MLLMs) for Visual Document Understanding are equipped with text recognition ability but lack general structure understanding abilities for text-rich document images. In this work, we emphasize the importance of structure information in Visual Document Understanding and propose the Unified Structure Learning to boost the performance of MLLMs. Our Unified Structure Learning comprises structure-aware parsing tasks and multi-grained text localization tasks across 5 domains: document, webpage, table, chart, and natural image. To better encode structure information, we design a simple and effective vision-to-text module H-Reducer, which can not only maintain the layout information but also reduce the length of visual features by merging horizontal adjacent patches through convolution, enabling the LLM to understand high-resolution images more efficiently. Furthermore, by constructing structure-aware text sequences and multi-grained pairs of texts and bounding boxes for publicly available text-rich images, we build a comprehensive training set DocStruct4M to support structure learning. Finally, we construct a small but high-quality reasoning tuning dataset DocReason25K to trigger the detailed explanation ability in the document domain. Our model DocOwl 1.5 achieves state-of-the-art performance on 10 visual document understanding benchmarks, improving the SOTA performance of MLLMs with a 7B LLM by more than 10 points in 5/10 benchmarks. Our codes, models, and datasets are publicly available at https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/DocOwl1.5.", "github_manual": "", "github_regex": "https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/DocOwl1.5", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12881", "title": "Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models", "authors": [ "Zehui Chen", "Kuikun Liu", "Qiuchen Wang", "Wenwei Zhang", "Jiangning Liu", "Dahua Lin", "Kai Chen", "Feng Zhao" ], "github": "https://github.com/InternLM/Agent-FLAN", "project_page": "", "abstract": "Open-sourced Large Language Models (LLMs) have achieved great success in various NLP tasks, however, they are still far inferior to API-based models when acting as agents. How to integrate agent ability into general LLMs becomes a crucial and urgent problem. This paper first delivers three key observations: (1) the current agent training corpus is entangled with both formats following and agent reasoning, which significantly shifts from the distribution of its pre-training data; (2) LLMs exhibit different learning speeds on the capabilities required by agent tasks; and (3) current approaches have side-effects when improving agent abilities by introducing hallucinations. Based on the above findings, we propose Agent-FLAN to effectively Fine-tune LANguage models for Agents. Through careful decomposition and redesign of the training corpus, Agent-FLAN enables Llama2-7B to outperform prior best works by 3.5\\% across various agent evaluation datasets. With comprehensively constructed negative samples, Agent-FLAN greatly alleviates the hallucination issues based on our established evaluation benchmark. Besides, it consistently improves the agent capability of LLMs when scaling model sizes while slightly enhancing the general capability of LLMs. The code will be available at https://github.com/InternLM/Agent-FLAN.", "github_manual": "", "github_regex": "https://github.com/InternLM/Agent-FLAN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12906", "title": "TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation", "authors": [ "Yufei Liu", "Junwei Zhu", "Junshu Tang", "Shijie Zhang", "Jiangning Zhang", "Weijian Cao", "Chengjie Wang", "Yunsheng Wu", "Dongjin Huang" ], "github": "https://github.com/ggxxii/texdreamer", "project_page": "", "abstract": "Texturing 3D humans with semantic UV maps remains a challenge due to the difficulty of acquiring reasonably unfolded UV. Despite recent text-to-3D advancements in supervising multi-view renderings using large text-to-image (T2I) models, issues persist with generation speed, text consistency, and texture quality, resulting in data scarcity among existing datasets. We present TexDreamer, the first zero-shot multimodal high-fidelity 3D human texture generation model. Utilizing an efficient texture adaptation finetuning strategy, we adapt large T2I model to a semantic UV structure while preserving its original generalization capability. Leveraging a novel feature translator module, the trained model is capable of generating high-fidelity 3D human textures from either text or image within seconds. Furthermore, we introduce ArTicuLated humAn textureS (ATLAS), the largest high-resolution (1024 X 1024) 3D human texture dataset which contains 50k high-fidelity textures with text descriptions.", "github_manual": "https://github.com/ggxxii/texdreamer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-20", "arxiv_id": "2403.12962", "title": "FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation", "authors": [ "Shuai Yang", "Yifan Zhou", "Ziwei Liu", "Chen Change Loy" ], "github": "https://github.com/williamyang1991/fresco", "project_page": "", "abstract": "The remarkable efficacy of text-to-image diffusion models has motivated extensive exploration of their potential application in video domains. Zero-shot methods seek to extend image diffusion models to videos without necessitating model training. Recent methods mainly focus on incorporating inter-frame correspondence into attention mechanisms. However, the soft constraint imposed on determining where to attend to valid features can sometimes be insufficient, resulting in temporal inconsistency. In this paper, we introduce FRESCO, intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint. This enhancement ensures a more consistent transformation of semantically similar content across frames. Beyond mere attention guidance, our approach involves an explicit update of features to achieve high spatial-temporal consistency with the input video, significantly improving the visual coherence of the resulting translated videos. Extensive experiments demonstrate the effectiveness of our proposed framework in producing high-quality, coherent videos, marking a notable improvement over existing zero-shot methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/williamyang1991/fresco", "github_api_added_by": "auto" }, { "date": "2024-03-21", "arxiv_id": "2403.13248", "title": "Mora: Enabling Generalist Video Generation via A Multi-Agent Framework", "authors": [ "Zhengqing Yuan", "Ruoxi Chen", "Zhaoxu Li", "Haolong Jia", "Lifang He", "Chi Wang", "Lichao Sun" ], "github": "https://github.com/lichao-sun/Mora", "project_page": "", "abstract": "Sora is the first large-scale generalist video generation model that garnered significant attention across society. Since its launch by OpenAI in February 2024, no other video generation models have paralleled {Sora}'s performance or its capacity to support a broad spectrum of video generation tasks. Additionally, there are only a few fully published video generation models, with the majority being closed-source. To address this gap, this paper proposes a new multi-agent framework Mora, which incorporates several advanced visual AI agents to replicate generalist video generation demonstrated by Sora. In particular, Mora can utilize multiple visual agents and successfully mimic Sora's video generation capabilities in various tasks, such as (1) text-to-video generation, (2) text-conditional image-to-video generation, (3) extend generated videos, (4) video-to-video editing, (5) connect videos and (6) simulate digital worlds. Our extensive experimental results show that Mora achieves performance that is proximate to that of Sora in various tasks. However, there exists an obvious performance gap between our work and Sora when assessed holistically. In summary, we hope this project can guide the future trajectory of video generation through collaborative AI agents.", "github_manual": "https://github.com/lichao-sun/Mora", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13064", "title": "SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Model", "authors": [ "Armen Avetisyan", "Christopher Xie", "Henry Howard-Jenkins", "Tsun-Yi Yang", "Samir Aroudj", "Suvam Patra", "Fuyang Zhang", "Duncan Frost", "Luke Holland", "Campbell Orme", "Jakob Engel", "Edward Miller", "Richard Newcombe", "Vasileios Balntas" ], "github": "", "project_page": "", "abstract": "We introduce SceneScript, a method that directly produces full scene models as a sequence of structured language commands using an autoregressive, token-based approach. Our proposed scene representation is inspired by recent successes in transformers & LLMs, and departs from more traditional methods which commonly describe scenes as meshes, voxel grids, point clouds or radiance fields. Our method infers the set of structured language commands directly from encoded visual data using a scene language encoder-decoder architecture. To train SceneScript, we generate and release a large-scale synthetic dataset called Aria Synthetic Environments consisting of 100k high-quality in-door scenes, with photorealistic and ground-truth annotated renders of egocentric scene walkthroughs. Our method gives state-of-the art results in architectural layout estimation, and competitive results in 3D object detection. Lastly, we explore an advantage for SceneScript, which is the ability to readily adapt to new commands via simple additions to the structured language, which we illustrate for tasks such as coarse 3D object part reconstruction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13793", "title": "Evaluating Frontier Models for Dangerous Capabilities", "authors": [ "Mary Phuong", "Matthew Aitchison", "Elliot Catt", "Sarah Cogan", "Alexandre Kaskasoli", "Victoria Krakovna", "David Lindner", "Matthew Rahtz", "Yannis Assael", "Sarah Hodkinson", "Heidi Howard", "Tom Lieberum", "Ramana Kumar", "Maria Abi Raad", "Albert Webson", "Lewis Ho", "Sharon Lin", "Sebastian Farquhar", "Marcus Hutter", "Gregoire Deletang", "Anian Ruoss", "Seliem El-Sayed", "Sasha Brown", "Anca Dragan", "Rohin Shah", "Allan Dafoe", "Toby Shevlane" ], "github": "https://github.com/google-deepmind/dangerous-capability-evaluations", "project_page": "", "abstract": "To understand the risks posed by a new AI system, we must understand what it can and cannot do. Building on prior work, we introduce a programme of new \"dangerous capability\" evaluations and pilot them on Gemini 1.0 models. Our evaluations cover four areas: (1) persuasion and deception; (2) cyber-security; (3) self-proliferation; and (4) self-reasoning. We do not find evidence of strong dangerous capabilities in the models we evaluated, but we flag early warning signs. Our goal is to help advance a rigorous science of dangerous capability evaluation, in preparation for future models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/google-deepmind/dangerous-capability-evaluations", "github_api_added_by": "auto" }, { "date": "2024-03-21", "arxiv_id": "2403.13787", "title": "RewardBench: Evaluating Reward Models for Language Modeling", "authors": [ "Nathan Lambert", "Valentina Pyatkin", "Jacob Morrison", "LJ Miranda", "Bill Yuchen Lin", "Khyathi Chandu", "Nouha Dziri", "Sachin Kumar", "Tom Zick", "Yejin Choi", "Noah A. Smith", "Hannaneh Hajishirzi" ], "github": "https://github.com/allenai/reward-bench", "project_page": "", "abstract": "Reward models (RMs) are at the crux of successful RLHF to align pretrained models to human preferences, yet there has been relatively little study that focuses on evaluation of those reward models. Evaluating reward models presents an opportunity to understand the opaque technologies used for alignment of language models and which values are embedded in them. To date, very few descriptors of capabilities, training methods, or open-source reward models exist. In this paper, we present RewardBench, a benchmark dataset and code-base for evaluation, to enhance scientific understanding of reward models. The RewardBench dataset is a collection of prompt-win-lose trios spanning chat, reasoning, and safety, to benchmark how reward models perform on challenging, structured and out-of-distribution queries. We created specific comparison datasets for RMs that have subtle, but verifiable reasons (e.g. bugs, incorrect facts) why one answer should be preferred to another. On the RewardBench leaderboard, we evaluate reward models trained with a variety of methods, such as the direct MLE training of classifiers and the implicit reward modeling of Direct Preference Optimization (DPO), and on a spectrum of datasets. We present many findings on propensity for refusals, reasoning limitations, and instruction following shortcomings of various reward models towards a better understanding of the RLHF process.", "github_manual": "https://github.com/allenai/reward-bench", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13799", "title": "Reverse Training to Nurse the Reversal Curse", "authors": [ "Olga Golovneva", "Zeyuan Allen-Zhu", "Jason Weston", "Sainbayar Sukhbaatar" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have a surprising failure: when trained on \"A has a feature B\", they do not generalize to \"B is a feature of A\", which is termed the Reversal Curse. Even when training with trillions of tokens this issue still appears due to Zipf's law - hence even if we train on the entire internet. This work proposes an alternative training scheme, called reverse training, whereby all words are used twice, doubling the amount of available tokens. The LLM is trained in both forward and reverse directions by reversing the training strings while preserving (i.e., not reversing) chosen substrings, such as entities. We show that data-matched reverse-trained models provide superior performance to standard models on standard tasks, and compute-matched reverse-trained models provide far superior performance on reversal tasks, helping resolve the reversal curse issue.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13535", "title": "IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models", "authors": [ "Siying Cui", "Jiankang Deng", "Jia Guo", "Xiang An", "Yongle Zhao", "Xinyu Wei", "Ziyong Feng" ], "github": "", "project_page": "", "abstract": "Leveraging Stable Diffusion for the generation of personalized portraits has emerged as a powerful and noteworthy tool, enabling users to create high-fidelity, custom character avatars based on their specific prompts. However, existing personalization methods face challenges, including test-time fine-tuning, the requirement of multiple input images, low preservation of identity, and limited diversity in generated outcomes. To overcome these challenges, we introduce IDAdapter, a tuning-free approach that enhances the diversity and identity preservation in personalized image generation from a single face image. IDAdapter integrates a personalized concept into the generation process through a combination of textual and visual injections and a face identity loss. During the training phase, we incorporate mixed features from multiple reference images of a specific identity to enrich identity-related content details, guiding the model to generate images with more diverse styles, expressions, and angles compared to previous works. Extensive evaluations demonstrate the effectiveness of our method, achieving both diversity and identity fidelity in generated images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13043", "title": "When Do We Not Need Larger Vision Models?", "authors": [ "Baifeng Shi", "Ziyang Wu", "Maolin Mao", "Xin Wang", "Trevor Darrell" ], "github": "https://github.com/bfshi/scaling_on_scales", "project_page": "", "abstract": "Scaling up the size of vision models has been the de facto standard to obtain more powerful visual representations. In this work, we discuss the point beyond which larger vision models are not necessary. First, we demonstrate the power of Scaling on Scales (S^2), whereby a pre-trained and frozen smaller vision model (e.g., ViT-B or ViT-L), run over multiple image scales, can outperform larger models (e.g., ViT-H or ViT-G) on classification, segmentation, depth estimation, Multimodal LLM (MLLM) benchmarks, and robotic manipulation. Notably, S^2 achieves state-of-the-art performance in detailed understanding of MLLM on the V* benchmark, surpassing models such as GPT-4V. We examine the conditions under which S^2 is a preferred scaling approach compared to scaling on model size. While larger models have the advantage of better generalization on hard examples, we show that features of larger vision models can be well approximated by those of multi-scale smaller models. This suggests most, if not all, of the representations learned by current large pre-trained models can also be obtained from multi-scale smaller models. Our results show that a multi-scale smaller model has comparable learning capacity to a larger model, and pre-training smaller models with S^2 can match or even exceed the advantage of larger models. We release a Python package that can apply S^2 on any vision model with one line of code: https://github.com/bfshi/scaling_on_scales.", "github_manual": "", "github_regex": "https://github.com/bfshi/scaling_on_scales", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2401.13923", "title": "Towards 3D Molecule-Text Interpretation in Language Models", "authors": [ "Sihang Li", "Zhiyuan Liu", "Yanchen Luo", "Xiang Wang", "Xiangnan He", "Kenji Kawaguchi", "Tat-Seng Chua", "Qi Tian" ], "github": "https://github.com/lsh0520/3D-MoLM", "project_page": "", "abstract": "Language Models (LMs) have greatly influenced diverse domains. However, their inherent limitation in comprehending 3D molecular structures has considerably constrained their potential in the biomolecular domain. To bridge this gap, we focus on 3D molecule-text interpretation, and propose 3D-MoLM: 3D-Molecular Language Modeling. Specifically, 3D-MoLM enables an LM to interpret and analyze 3D molecules by equipping the LM with a 3D molecular encoder. This integration is achieved by a 3D molecule-text projector, bridging the 3D molecular encoder's representation space and the LM's input space. Moreover, to enhance 3D-MoLM's ability of cross-modal molecular understanding and instruction following, we meticulously curated a 3D molecule-centric instruction tuning dataset -- 3D-MoIT. Through 3D molecule-text alignment and 3D molecule-centric instruction tuning, 3D-MoLM establishes an integration of 3D molecular encoder and LM. It significantly surpasses existing baselines on downstream tasks, including molecule-text retrieval, molecule captioning, and more challenging open-text molecular QA tasks, especially focusing on 3D-dependent properties.", "github_manual": "https://github.com/lsh0520/3D-MoLM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13044", "title": "Magic Fixup: Streamlining Photo Editing by Watching Dynamic Videos", "authors": [ "Hadi Alzayer", "Zhihao Xia", "Xuaner Zhang", "Eli Shechtman", "Jia-Bin Huang", "Michael Gharbi" ], "github": "", "project_page": "", "abstract": "We propose a generative model that, given a coarsely edited image, synthesizes a photorealistic output that follows the prescribed layout. Our method transfers fine details from the original image and preserves the identity of its parts. Yet, it adapts it to the lighting and context defined by the new layout. Our key insight is that videos are a powerful source of supervision for this task: objects and camera motions provide many observations of how the world changes with viewpoint, lighting, and physical interactions. We construct an image dataset in which each sample is a pair of source and target frames extracted from the same video at randomly chosen time intervals. We warp the source frame toward the target using two motion models that mimic the expected test-time user edits. We supervise our model to translate the warped image into the ground truth, starting from a pretrained diffusion model. Our model design explicitly enables fine detail transfer from the source frame to the generated image, while closely following the user-specified layout. We show that by using simple segmentations and coarse 2D manipulations, we can synthesize a photorealistic edit faithful to the user's input while addressing second-order effects like harmonizing the lighting and physical interactions between edited objects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13524", "title": "Compress3D: a Compressed Latent Space for 3D Generation from a Single Image", "authors": [ "Bowen Zhang", "Tianyu Yang", "Yu Li", "Lei Zhang", "Xi Zhao" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "3D generation has witnessed significant advancements, yet efficiently producing high-quality 3D assets from a single image remains challenging. In this paper, we present a triplane autoencoder, which encodes 3D models into a compact triplane latent space to effectively compress both the 3D geometry and texture information. Within the autoencoder framework, we introduce a 3D-aware cross-attention mechanism, which utilizes low-resolution latent representations to query features from a high-resolution 3D feature volume, thereby enhancing the representation capacity of the latent space. Subsequently, we train a diffusion model on this refined latent space. In contrast to solely relying on image embedding for 3D generation, our proposed method advocates for the simultaneous utilization of both image embedding and shape embedding as conditions. Specifically, the shape embedding is estimated via a diffusion prior model conditioned on the image embedding. Through comprehensive experiments, we demonstrate that our method outperforms state-of-the-art algorithms, achieving superior performance while requiring less training data and time. Our approach enables the generation of high-quality 3D assets in merely 7 seconds on a single A100 GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13372", "title": "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models", "authors": [ "Yaowei Zheng", "Richong Zhang", "Junhao Zhang", "Yanhan Ye", "Zheyan Luo" ], "github": "https://github.com/hiyouga/LLaMA-Factory", "project_page": "", "abstract": "Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks. However, it requires non-trivial efforts to implement these methods on different models. We present LlamaFactory, a unified framework that integrates a suite of cutting-edge efficient training methods. It allows users to flexibly customize the fine-tuning of 100+ LLMs without the need for coding through the built-in web UI LlamaBoard. We empirically validate the efficiency and effectiveness of our framework on language modeling and text generation tasks. It has been released at https://github.com/hiyouga/LLaMA-Factory and already received over 13,000 stars and 1,600 forks.", "github_manual": "", "github_regex": "https://github.com/hiyouga/LLaMA-Factory", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13745", "title": "Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific Adaptation", "authors": [ "Fu-Yun Wang", "Xiaoshi Wu", "Zhaoyang Huang", "Xiaoyu Shi", "Dazhong Shen", "Guanglu Song", "Yu Liu", "Hongsheng Li" ], "github": "https://github.com/g-u-n/be-your-outpainter", "project_page": "", "abstract": "Video outpainting is a challenging task, aiming at generating video content outside the viewport of the input video while maintaining inter-frame and intra-frame consistency. Existing methods fall short in either generation quality or flexibility. We introduce MOTIA Mastering Video Outpainting Through Input-Specific Adaptation, a diffusion-based pipeline that leverages both the intrinsic data-specific patterns of the source video and the image/video generative prior for effective outpainting. MOTIA comprises two main phases: input-specific adaptation and pattern-aware outpainting. The input-specific adaptation phase involves conducting efficient and effective pseudo outpainting learning on the single-shot source video. This process encourages the model to identify and learn patterns within the source video, as well as bridging the gap between standard generative processes and outpainting. The subsequent phase, pattern-aware outpainting, is dedicated to the generalization of these learned patterns to generate outpainting outcomes. Additional strategies including spatial-aware insertion and noise travel are proposed to better leverage the diffusion model's generative prior and the acquired video patterns from source videos. Extensive evaluations underscore MOTIA's superiority, outperforming existing state-of-the-art methods in widely recognized benchmarks. Notably, these advancements are achieved without necessitating extensive, task-specific tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/g-u-n/be-your-outpainter", "github_api_added_by": "auto" }, { "date": "2024-03-21", "arxiv_id": "2403.13806", "title": "RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS", "authors": [ "Michael Niemeyer", "Fabian Manhardt", "Marie-Julie Rakotosaona", "Michael Oechsle", "Daniel Duckworth", "Rama Gosula", "Keisuke Tateno", "John Bates", "Dominik Kaeser", "Federico Tombari" ], "github": "", "project_page": "", "abstract": "Recent advances in view synthesis and real-time rendering have achieved photorealistic quality at impressive rendering speeds. While Radiance Field-based methods achieve state-of-the-art quality in challenging scenarios such as in-the-wild captures and large-scale scenes, they often suffer from excessively high compute requirements linked to volumetric rendering. Gaussian Splatting-based methods, on the other hand, rely on rasterization and naturally achieve real-time rendering but suffer from brittle optimization heuristics that underperform on more challenging scenes. In this work, we present RadSplat, a lightweight method for robust real-time rendering of complex scenes. Our main contributions are threefold. First, we use radiance fields as a prior and supervision signal for optimizing point-based scene representations, leading to improved quality and more robust optimization. Next, we develop a novel pruning technique reducing the overall point count while maintaining high quality, leading to smaller and more compact scene representations with faster inference speeds. Finally, we propose a novel test-time filtering approach that further accelerates rendering and allows to scale to larger, house-sized scenes. We find that our method enables state-of-the-art synthesis of complex captures at 900+ FPS.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13501", "title": "VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis", "authors": [ "Yumeng Li", "William Beluch", "Margret Keuper", "Dan Zhang", "Anna Khoreva" ], "github": "https://github.com/boschresearch/VSTAR", "project_page": "", "abstract": "Despite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content. They tend to synthesize quasi-static videos, ignoring the necessary visual change-over-time implied in the text prompt. At the same time, scaling these models to enable longer, more dynamic video synthesis often remains computationally intractable. To address this challenge, we introduce the concept of Generative Temporal Nursing (GTN), where we aim to alter the generative process on the fly during inference to improve control over the temporal dynamics and enable generation of longer videos. We propose a method for GTN, dubbed VSTAR, which consists of two key ingredients: 1) Video Synopsis Prompting (VSP) - automatic generation of a video synopsis based on the original single prompt leveraging LLMs, which gives accurate textual guidance to different visual states of longer videos, and 2) Temporal Attention Regularization (TAR) - a regularization technique to refine the temporal attention units of the pre-trained T2V diffusion models, which enables control over the video dynamics. We experimentally showcase the superiority of the proposed approach in generating longer, visually appealing videos over existing open-sourced T2V models. We additionally analyze the temporal attention maps realized with and without VSTAR, demonstrating the importance of applying our method to mitigate neglect of the desired visual change over time.", "github_manual": "https://github.com/boschresearch/VSTAR", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13788", "title": "DepthFM: Fast Monocular Depth Estimation with Flow Matching", "authors": [ "Ming Gui", "Johannes S. Fischer", "Ulrich Prestel", "Pingchuan Ma", "Dmytro Kotovenko", "Olga Grebenkova", "Stefan Andreas Baumann", "Vincent Tao Hu", "Bj\u00f6rn Ommer" ], "github": "https://github.com/CompVis/depth-fm", "project_page": "", "abstract": "Monocular depth estimation is crucial for numerous downstream vision tasks and applications. Current discriminative approaches to this problem are limited due to blurry artifacts, while state-of-the-art generative methods suffer from slow sampling due to their SDE nature. Rather than starting from noise, we seek a direct mapping from input image to depth map. We observe that this can be effectively framed using flow matching, since its straight trajectories through solution space offer efficiency and high quality. Our study demonstrates that a pre-trained image diffusion model can serve as an adequate prior for a flow matching depth model, allowing efficient training on only synthetic data to generalize to real images. We find that an auxiliary surface normals loss further improves the depth estimates. Due to the generative nature of our approach, our model reliably predicts the confidence of its depth estimates. On standard benchmarks of complex natural scenes, our lightweight approach exhibits state-of-the-art performance at favorable low computational cost despite only being trained on little synthetic data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CompVis/depth-fm", "github_api_added_by": "auto" }, { "date": "2024-03-21", "arxiv_id": "2403.13802", "title": "ZigMa: Zigzag Mamba Diffusion Model", "authors": [ "Vincent Tao Hu", "Stefan Andreas Baumann", "Ming Gui", "Olga Grebenkova", "Pingchuan Ma", "Johannes Fischer", "Bjorn Ommer" ], "github": "https://github.com/CompVis/zigma", "project_page": "", "abstract": "The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, we aim to leverage the long sequence modeling capability of a State-Space Model called Mamba to extend its applicability to visual data generation. Firstly, we identify a critical oversight in most current Mamba-based vision methods, namely the lack of consideration for spatial continuity in the scan scheme of Mamba. Secondly, building upon this insight, we introduce a simple, plug-and-play, zero-parameter method named Zigzag Mamba, which outperforms Mamba-based baselines and demonstrates improved speed and memory utilization compared to transformer-based baselines. Lastly, we integrate Zigzag Mamba with the Stochastic Interpolant framework to investigate the scalability of the model on large-resolution visual datasets, such as FacesHQ 1024times 1024 and UCF101, MultiModal-CelebA-HQ, and MS COCO 256times 256. Code will be released at https://taohu.me/zigma/", "github_manual": "https://github.com/CompVis/zigma", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13447", "title": "HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models", "authors": [ "Wenqiao Zhang", "Tianwei Lin", "Jiang Liu", "Fangxun Shu", "Haoyuan Li", "Lei Zhang", "He Wanggui", "Hao Zhou", "Zheqi Lv", "Hao Jiang", "Juncheng Li", "Siliang Tang", "Yueting Zhuang" ], "github": "https://github.com/DCDmllm/HyperLLaVA", "project_page": "", "abstract": "Recent advancements indicate that scaling up Multimodal Large Language Models (MLLMs) effectively enhances performance on downstream multimodal tasks. The prevailing MLLM paradigm, e.g., LLaVA, transforms visual features into text-like tokens using a static vision-language mapper, thereby enabling static LLMs to develop the capability to comprehend visual information through visual instruction tuning. Although promising, the static tuning strategy~The static tuning refers to the trained model with static parameters. that shares the same parameters may constrain performance across different downstream multimodal tasks. In light of this, we introduce HyperLLaVA, which involves adaptive tuning of the projector and LLM parameters, in conjunction with a dynamic visual expert and language expert, respectively. These experts are derived from HyperNetworks, which generates adaptive parameter shifts through visual and language guidance, enabling dynamic projector and LLM modeling in two-stage training. Our experiments demonstrate that our solution significantly surpasses LLaVA on existing MLLM benchmarks, including MME, MMBench, SEED-Bench, and LLaVA-Bench. ~Our project is available on the link https://github.com/DCDmllm/HyperLLaVA.", "github_manual": "", "github_regex": "https://github.com/DCDmllm/HyperLLaVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-21", "arxiv_id": "2403.13187", "title": "Evolutionary Optimization of Model Merging Recipes", "authors": [ "Takuya Akiba", "Makoto Shing", "Yujin Tang", "Qi Sun", "David Ha" ], "github": "https://github.com/SakanaAI/evolutionary-model-merge", "project_page": "", "abstract": "We present a novel application of evolutionary algorithms to automate the creation of powerful foundation models. While model merging has emerged as a promising approach for LLM development due to its cost-effectiveness, it currently relies on human intuition and domain knowledge, limiting its potential. Here, we propose an evolutionary approach that overcomes this limitation by automatically discovering effective combinations of diverse open-source models, harnessing their collective intelligence without requiring extensive additional training data or compute. Our approach operates in both parameter space and data flow space, allowing for optimization beyond just the weights of the individual models. This approach even facilitates cross-domain merging, generating models like a Japanese LLM with Math reasoning capabilities. Surprisingly, our Japanese Math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with significantly more parameters, despite not being explicitly trained for such tasks. Furthermore, a culturally-aware Japanese VLM generated through our approach demonstrates its effectiveness in describing Japanese culture-specific content, outperforming previous Japanese VLMs. This work not only contributes new state-of-the-art models back to the open-source community, but also introduces a new paradigm for automated model composition, paving the way for exploring alternative, efficient approaches to foundation model development.", "github_manual": "https://github.com/SakanaAI/evolutionary-model-merge", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-22", "arxiv_id": "2403.14148", "title": "Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition", "authors": [ "Sihyun Yu", "Weili Nie", "De-An Huang", "Boyi Li", "Jinwoo Shin", "Anima Anandkumar" ], "github": "https://github.com/facebookresearch/DiT", "project_page": "", "abstract": "Video diffusion models have recently made great progress in generation quality, but are still limited by the high memory and computational requirements. This is because current video diffusion models often attempt to process high-dimensional videos directly. To tackle this issue, we propose content-motion latent diffusion model (CMD), a novel efficient extension of pretrained image diffusion models for video generation. Specifically, we propose an autoencoder that succinctly encodes a video as a combination of a content frame (like an image) and a low-dimensional motion latent representation. The former represents the common content, and the latter represents the underlying motion in the video, respectively. We generate the content frame by fine-tuning a pretrained image diffusion model, and we generate the motion latent representation by training a new lightweight diffusion model. A key innovation here is the design of a compact latent space that can directly utilizes a pretrained image diffusion model, which has not been done in previous latent video diffusion models. This leads to considerably better quality generation and reduced computational costs. For instance, CMD can sample a video 7.7times faster than prior approaches by generating a video of 512times1024 resolution and length 16 in 3.1 seconds. Moreover, CMD achieves an FVD score of 212.7 on WebVid-10M, 27.3% better than the previous state-of-the-art of 292.4.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/DiT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-22", "arxiv_id": "2403.14468", "title": "AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks", "authors": [ "Max Ku", "Cong Wei", "Weiming Ren", "Huan Yang", "Wenhu Chen" ], "github": "https://github.com/TIGER-AI-Lab/AnyV2V", "project_page": "", "abstract": "Video-to-video editing involves editing a source video along with additional control (such as text prompts, subjects, or styles) to generate a new video that aligns with the source video and the provided control. Traditional methods have been constrained to certain editing types, limiting their ability to meet the wide range of user demands. In this paper, we introduce AnyV2V, a novel training-free framework designed to simplify video editing into two primary steps: (1) employing an off-the-shelf image editing model (e.g. InstructPix2Pix, InstantID, etc) to modify the first frame, (2) utilizing an existing image-to-video generation model (e.g. I2VGen-XL) for DDIM inversion and feature injection. In the first stage, AnyV2V can plug in any existing image editing tools to support an extensive array of video editing tasks. Beyond the traditional prompt-based editing methods, AnyV2V also can support novel video editing tasks, including reference-based style transfer, subject-driven editing, and identity manipulation, which were unattainable by previous methods. In the second stage, AnyV2V can plug in any existing image-to-video models to perform DDIM inversion and intermediate feature injection to maintain the appearance and motion consistency with the source video. On the prompt-based editing, we show that AnyV2V can outperform the previous best approach by 35\\% on prompt alignment, and 25\\% on human preference. On the three novel tasks, we show that AnyV2V also achieves a high success rate. We believe AnyV2V will continue to thrive due to its ability to seamlessly integrate the fast-evolving image editing methods. Such compatibility can help AnyV2V to increase its versatility to cater to diverse user demands.", "github_manual": "https://github.com/TIGER-AI-Lab/AnyV2V", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-22", "arxiv_id": "2403.14613", "title": "DreamReward: Text-to-3D Generation with Human Preference", "authors": [ "Junliang Ye", "Fangfu Liu", "Qixiu Li", "Zhengyi Wang", "Yikai Wang", "Xinzhou Wang", "Yueqi Duan", "Jun Zhu" ], "github": "https://github.com/liuff19/DreamReward", "project_page": "", "abstract": "3D content creation from text prompts has shown remarkable success recently. However, current text-to-3D methods often generate 3D results that do not align well with human preferences. In this paper, we present a comprehensive framework, coined DreamReward, to learn and improve text-to-3D models from human preference feedback. To begin with, we collect 25k expert comparisons based on a systematic annotation pipeline including rating and ranking. Then, we build Reward3D -- the first general-purpose text-to-3D human preference reward model to effectively encode human preferences. Building upon the 3D reward model, we finally perform theoretical analysis and present the Reward3D Feedback Learning (DreamFL), a direct tuning algorithm to optimize the multi-view diffusion models with a redefined scorer. Grounded by theoretical proof and extensive experiment comparisons, our DreamReward successfully generates high-fidelity and 3D consistent results with significant boosts in prompt alignment with human intention. Our results demonstrate the great potential for learning from human feedback to improve text-to-3D models.", "github_manual": "https://github.com/liuff19/DreamReward", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-22", "arxiv_id": "2403.14611", "title": "Explorative Inbetweening of Time and Space", "authors": [ "Haiwen Feng", "Zheng Ding", "Zhihao Xia", "Simon Niklaus", "Victoria Abrevaya", "Michael J. Black", "Xuaner Zhang" ], "github": "", "project_page": "", "abstract": "We introduce bounded generation as a generalized task to control video generation to synthesize arbitrary camera and subject motion based only on a given start and end frame. Our objective is to fully leverage the inherent generalization capability of an image-to-video model without additional training or fine-tuning of the original model. This is achieved through the proposed new sampling strategy, which we call Time Reversal Fusion, that fuses the temporally forward and backward denoising paths conditioned on the start and end frame, respectively. The fused path results in a video that smoothly connects the two frames, generating inbetweening of faithful subject motion, novel views of static scenes, and seamless video looping when the two bounding frames are identical. We curate a diverse evaluation dataset of image pairs and compare against the closest existing methods. We find that Time Reversal Fusion outperforms related work on all subtasks, exhibiting the ability to generate complex motions and 3D-consistent views guided by bounded frames. See project page at https://time-reversal.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-22", "arxiv_id": "2403.14186", "title": "StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN", "authors": [ "Jongwoo Choi", "Kwanggyoon Seo", "Amirsaman Ashtari", "Junyong Noh" ], "github": "https://github.com/jeolpyeoni/StyleCineGAN", "project_page": "", "abstract": "We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent unconditional video generation, we leverage a powerful pre-trained image generator to synthesize high-quality cinemagraphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN, our approach utilizes its deep feature space for both GAN inversion and cinemagraph generation. Specifically, we propose multi-scale deep feature warping (MSDFW), which warps the intermediate features of a pre-trained StyleGAN at different resolutions. By using MSDFW, the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.", "github_manual": "https://github.com/jeolpyeoni/StyleCineGAN", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-22", "arxiv_id": "2403.14554", "title": "Gaussian Frosting: Editable Complex Radiance Fields with Real-Time Rendering", "authors": [ "Antoine Gu\u00e9don", "Vincent Lepetit" ], "github": "https://github.com/Anttwo/Frosting", "project_page": "", "abstract": "We propose Gaussian Frosting, a novel mesh-based representation for high-quality rendering and editing of complex 3D effects in real-time. Our approach builds on the recent 3D Gaussian Splatting framework, which optimizes a set of 3D Gaussians to approximate a radiance field from images. We propose first extracting a base mesh from Gaussians during optimization, then building and refining an adaptive layer of Gaussians with a variable thickness around the mesh to better capture the fine details and volumetric effects near the surface, such as hair or grass. We call this layer Gaussian Frosting, as it resembles a coating of frosting on a cake. The fuzzier the material, the thicker the frosting. We also introduce a parameterization of the Gaussians to enforce them to stay inside the frosting layer and automatically adjust their parameters when deforming, rescaling, editing or animating the mesh. Our representation allows for efficient rendering using Gaussian splatting, as well as editing and animation by modifying the base mesh. We demonstrate the effectiveness of our method on various synthetic and real scenes, and show that it outperforms existing surface-based approaches. We will release our code and a web-based viewer as additional contributions. Our project page is the following: https://anttwo.github.io/frosting/", "github_manual": "https://github.com/Anttwo/Frosting", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-22", "arxiv_id": "2403.14624", "title": "MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?", "authors": [ "Renrui Zhang", "Dongzhi Jiang", "Yichi Zhang", "Haokun Lin", "Ziyu Guo", "Pengshuo Qiu", "Aojun Zhou", "Pan Lu", "Kai-Wei Chang", "Peng Gao", "Hongsheng Li" ], "github": "https://github.com/ZrrSkywalker/MathVerse", "project_page": "", "abstract": "The remarkable progress of Multi-modal Large Language Models (MLLMs) has garnered unparalleled attention, due to their superior performance in visual contexts. However, their capabilities in visual math problem-solving remain insufficiently evaluated and understood. We investigate current benchmarks to incorporate excessive visual content within textual questions, which potentially assist MLLMs in deducing answers without truly interpreting the input diagrams. To this end, we introduce MathVerse, an all-around visual math benchmark designed for an equitable and in-depth evaluation of MLLMs. We meticulously collect 2,612 high-quality, multi-subject math problems with diagrams from publicly available sources. Each problem is then transformed by human annotators into six distinct versions, each offering varying degrees of information content in multi-modality, contributing to 15K test samples in total. This approach allows MathVerse to comprehensively assess whether and how much MLLMs can truly understand the visual diagrams for mathematical reasoning. In addition, we propose a Chain-of-Thought (CoT) evaluation strategy for a fine-grained assessment of the output answers. Rather than naively judging True or False, we employ GPT-4(V) to adaptively extract crucial reasoning steps, and then score each step with detailed error analysis, which can reveal the intermediate CoT reasoning quality by MLLMs. We hope the MathVerse benchmark may provide unique insights to guide the future development of MLLMs. Project page: https://mathverse-cuhk.github.io", "github_manual": "https://github.com/ZrrSkywalker/MathVerse", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-22", "arxiv_id": "2403.14467", "title": "Recourse for reclamation: Chatting with generative language models", "authors": [ "Jennifer Chien", "Kevin R. McKee", "Jackie Kay", "William Isaac" ], "github": "", "project_page": "", "abstract": "Researchers and developers increasingly rely on toxicity scoring to moderate generative language model outputs, in settings such as customer service, information retrieval, and content generation. However, toxicity scoring may render pertinent information inaccessible, rigidify or \"value-lock\" cultural norms, and prevent language reclamation processes, particularly for marginalized people. In this work, we extend the concept of algorithmic recourse to generative language models: we provide users a novel mechanism to achieve their desired prediction by dynamically setting thresholds for toxicity filtering. Users thereby exercise increased agency relative to interactions with the baseline system. A pilot study (n = 30) supports the potential of our proposed recourse mechanism, indicating improvements in usability compared to fixed-threshold toxicity-filtering of model outputs. Future work should explore the intersection of toxicity scoring, model controllability, user agency, and language reclamation processes -- particularly with regard to the bias that many communities encounter when interacting with generative language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-22", "arxiv_id": "2403.14599", "title": "MyVLM: Personalizing VLMs for User-Specific Queries", "authors": [ "Yuval Alaluf", "Elad Richardson", "Sergey Tulyakov", "Kfir Aberman", "Daniel Cohen-Or" ], "github": "https://github.com/snap-research/MyVLM", "project_page": "", "abstract": "Recent large-scale vision-language models (VLMs) have demonstrated remarkable capabilities in understanding and generating textual descriptions for visual content. However, these models lack an understanding of user-specific concepts. In this work, we take a first step toward the personalization of VLMs, enabling them to learn and reason over user-provided concepts. For example, we explore whether these models can learn to recognize you in an image and communicate what you are doing, tailoring the model to reflect your personal experiences and relationships. To effectively recognize a variety of user-specific concepts, we augment the VLM with external concept heads that function as toggles for the model, enabling the VLM to identify the presence of specific target concepts in a given image. Having recognized the concept, we learn a new concept embedding in the intermediate feature space of the VLM. This embedding is tasked with guiding the language model to naturally integrate the target concept in its generated response. We apply our technique to BLIP-2 and LLaVA for personalized image captioning and further show its applicability for personalized visual question-answering. Our experiments demonstrate our ability to generalize to unseen images of learned concepts while preserving the model behavior on unrelated inputs.", "github_manual": "https://github.com/snap-research/MyVLM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-22", "arxiv_id": "2403.14520", "title": "Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference", "authors": [ "Han Zhao", "Min Zhang", "Wei Zhao", "Pengxiang Ding", "Siteng Huang", "Donglin Wang" ], "github": "https://github.com/h-zhao1997/cobra", "project_page": "", "abstract": "In recent years, the application of multimodal large language models (MLLM) in various fields has achieved remarkable success. However, as the foundation model for many downstream tasks, current MLLMs are composed of the well-known Transformer network, which has a less efficient quadratic computation complexity. To improve the efficiency of such basic models, we propose Cobra, a linear computational complexity MLLM. Specifically, Cobra integrates the efficient Mamba language model into the visual modality. Moreover, we explore and study various modal fusion schemes to create an effective multi-modal Mamba. Extensive experiments demonstrate that (1) Cobra achieves extremely competitive performance with current computationally efficient state-of-the-art methods, e.g., LLaVA-Phi, TinyLLaVA, and MobileVLM v2, and has faster speed due to Cobra's linear sequential modeling. (2) Interestingly, the results of closed-set challenging prediction benchmarks show that Cobra performs well in overcoming visual illusions and spatial relationship judgments. (3) Notably, Cobra even achieves comparable performance to LLaVA with about 43% of the number of parameters. We will make all codes of Cobra open-source and hope that the proposed method can facilitate future research on complexity problems in MLLM. Our project page is available at: https://sites.google.com/view/cobravlm.", "github_manual": "https://github.com/h-zhao1997/cobra", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-22", "arxiv_id": "2403.14621", "title": "GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation", "authors": [ "Yinghao Xu", "Zifan Shi", "Wang Yifan", "Hansheng Chen", "Ceyuan Yang", "Sida Peng", "Yujun Shen", "Gordon Wetzstein" ], "github": "https://github.com/justimyhxu/grm", "project_page": "", "abstract": "We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from sparse-view images in around 0.1s. GRM is a feed-forward transformer-based model that efficiently incorporates multi-view information to translate the input pixels into pixel-aligned Gaussians, which are unprojected to create a set of densely distributed 3D Gaussians representing a scene. Together, our transformer architecture and the use of 3D Gaussians unlock a scalable and efficient reconstruction framework. Extensive experimental results demonstrate the superiority of our method over alternatives regarding both reconstruction quality and efficiency. We also showcase the potential of GRM in generative tasks, i.e., text-to-3D and image-to-3D, by integrating it with existing multi-view diffusion models. Our project website is at: https://justimyhxu.github.io/projects/grm/.", "github_manual": "https://github.com/justimyhxu/grm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-22", "arxiv_id": "2403.14602", "title": "ReNoise: Real Image Inversion Through Iterative Noising", "authors": [ "Daniel Garibi", "Or Patashnik", "Andrey Voynov", "Hadar Averbuch-Elor", "Daniel Cohen-Or" ], "github": "https://github.com/garibida/ReNoise-Inversion", "project_page": "", "abstract": "Recent advancements in text-guided diffusion models have unlocked powerful image manipulation capabilities. However, applying these methods to real images necessitates the inversion of the images into the domain of the pretrained diffusion model. Achieving faithful inversion remains a challenge, particularly for more recent models trained to generate images with a small number of denoising steps. In this work, we introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. Building on reversing the diffusion sampling process, our method employs an iterative renoising mechanism at each inversion sampling step. This mechanism refines the approximation of a predicted point along the forward diffusion trajectory, by iteratively applying the pretrained diffusion model, and averaging these predictions. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models. Through comprehensive evaluations and comparisons, we show its effectiveness in terms of both accuracy and speed. Furthermore, we confirm that our method preserves editability by demonstrating text-driven image editing on real images.", "github_manual": "https://github.com/garibida/ReNoise-Inversion", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-25", "arxiv_id": "2403.15377", "title": "InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding", "authors": [ "Yi Wang", "Kunchang Li", "Xinhao Li", "Jiashuo Yu", "Yinan He", "Guo Chen", "Baoqi Pei", "Rongkun Zheng", "Jilan Xu", "Zun Wang", "Yansong Shi", "Tianxiang Jiang", "Songze Li", "Hongjie Zhang", "Yifei Huang", "Yu Qiao", "Yali Wang", "Limin Wang" ], "github": "https://github.com/OpenGVLab/InternVideo2", "project_page": "", "abstract": "We introduce InternVideo2, a new video foundation model (ViFM) that achieves the state-of-the-art performance in action recognition, video-text tasks, and video-centric dialogue. Our approach employs a progressive training paradigm that unifies the different self- or weakly-supervised learning frameworks of masked video token reconstruction, cross-modal contrastive learning, and next token prediction. Different training stages would guide our model to capture different levels of structure and semantic information through different pretext tasks. At the data level, we prioritize the spatiotemporal consistency by semantically segmenting videos and generating video-audio-speech captions. This improves the alignment between video and text. We scale both data and model size for our InternVideo2. Through extensive experiments, we validate our designs and demonstrate the state-of-the-art performance on over 60 video and audio tasks. Notably, our model outperforms others on various video-related captioning, dialogue, and long video understanding benchmarks, highlighting its ability to reason and comprehend long temporal contexts. Code and models are available at https://github.com/OpenGVLab/InternVideo2/.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/InternVideo2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-25", "arxiv_id": "2403.15360", "title": "SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series", "authors": [ "Badri N. Patro", "Vijay S. Agneeswaran" ], "github": "https://github.com/badripatro/Simba", "project_page": "", "abstract": "Transformers have widely adopted attention networks for sequence mixing and MLPs for channel mixing, playing a pivotal role in achieving breakthroughs across domains. However, recent literature highlights issues with attention networks, including low inductive bias and quadratic complexity concerning input sequence length. State Space Models (SSMs) like S4 and others (Hippo, Global Convolutions, liquid S4, LRU, Mega, and Mamba), have emerged to address the above issues to help handle longer sequence lengths. Mamba, while being the state-of-the-art SSM, has a stability issue when scaled to large networks for computer vision datasets. We propose SiMBA, a new architecture that introduces Einstein FFT (EinFFT) for channel modeling by specific eigenvalue computations and uses the Mamba block for sequence modeling. Extensive performance studies across image and time-series benchmarks demonstrate that SiMBA outperforms existing SSMs, bridging the performance gap with state-of-the-art transformers. Notably, SiMBA establishes itself as the new state-of-the-art SSM on ImageNet and transfer learning benchmarks such as Stanford Car and Flower as well as task learning benchmarks as well as seven time series benchmark datasets. The project page is available on this website ~https://github.com/badripatro/Simba.", "github_manual": "", "github_regex": "https://github.com/badripatro/Simba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-25", "arxiv_id": "2403.15371", "title": "Can large language models explore in-context?", "authors": [ "Akshay Krishnamurthy", "Keegan Harris", "Dylan J. Foster", "Cyril Zhang", "Aleksandrs Slivkins" ], "github": "", "project_page": "", "abstract": "We investigate the extent to which contemporary Large Language Models (LLMs) can engage in exploration, a core capability in reinforcement learning and decision making. We focus on native performance of existing LLMs, without training interventions. We deploy LLMs as agents in simple multi-armed bandit environments, specifying the environment description and interaction history entirely in-context, i.e., within the LLM prompt. We experiment with GPT-3.5, GPT-4, and Llama2, using a variety of prompt designs, and find that the models do not robustly engage in exploration without substantial interventions: i) Across all of our experiments, only one configuration resulted in satisfactory exploratory behavior: GPT-4 with chain-of-thought reasoning and an externally summarized interaction history, presented as sufficient statistics; ii) All other configurations did not result in robust exploratory behavior, including those with chain-of-thought reasoning but unsummarized history. Although these findings can be interpreted positively, they suggest that external summarization -- which may not be possible in more complex settings -- is important for obtaining desirable behavior from LLM agents. We conclude that non-trivial algorithmic interventions, such as fine-tuning or dataset curation, may be required to empower LLM-based decision making agents in complex settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-25", "arxiv_id": "2403.14773", "title": "StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text", "authors": [ "Roberto Henschel", "Levon Khachatryan", "Daniil Hayrapetyan", "Hayk Poghosyan", "Vahram Tadevosyan", "Zhangyang Wang", "Shant Navasardyan", "Humphrey Shi" ], "github": "https://github.com/Picsart-AI-Research/StreamingT2V", "project_page": "", "abstract": "Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, making it easy to create diverse and individual content. However, existing approaches mostly focus on high-quality short video generation (typically 16 or 24 frames), ending up with hard-cuts when naively extended to the case of long video synthesis. To overcome these limitations, we introduce StreamingT2V, an autoregressive approach for long video generation of 80, 240, 600, 1200 or more frames with smooth transitions. The key components are:(i) a short-term memory block called conditional attention module (CAM), which conditions the current generation on the features extracted from the previous chunk via an attentional mechanism, leading to consistent chunk transitions, (ii) a long-term memory block called appearance preservation module, which extracts high-level scene and object features from the first video chunk to prevent the model from forgetting the initial scene, and (iii) a randomized blending approach that enables to apply a video enhancer autoregressively for infinitely long videos without inconsistencies between chunks. Experiments show that StreamingT2V generates high motion amount. In contrast, all competing image-to-video methods are prone to video stagnation when applied naively in an autoregressive manner. Thus, we propose with StreamingT2V a high-quality seamless text-to-long video generator that outperforms competitors with consistency and motion. Our code will be available at: https://github.com/Picsart-AI-Research/StreamingT2V", "github_manual": "", "github_regex": "https://github.com/Picsart-AI-Research/StreamingT2V", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-25", "arxiv_id": "2403.15042", "title": "LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement", "authors": [ "Nicholas Lee", "Thanakul Wattanawong", "Sehoon Kim", "Karttikeya Mangalam", "Sheng Shen", "Gopala Anumanchipali", "Michael W. Mahoney", "Kurt Keutzer", "Amir Gholami" ], "github": "https://github.com/SqueezeAILab/LLM2LLM", "project_page": "", "abstract": "Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. While many real-world applications still require fine-tuning to reach satisfactory levels of performance, many of them are in the low-data regime, making fine-tuning challenging. To address this, we propose LLM2LLM, a targeted and iterative data augmentation strategy that uses a teacher LLM to enhance a small seed dataset by augmenting additional data that can be used for fine-tuning on a specific task. LLM2LLM (1) fine-tunes a baseline student LLM on the initial seed data, (2) evaluates and extracts data points that the model gets wrong, and (3) uses a teacher LLM to generate synthetic data based on these incorrect data points, which are then added back into the training data. This approach amplifies the signal from incorrectly predicted data points by the LLM during training and reintegrates them into the dataset to focus on more challenging examples for the LLM. Our results show that LLM2LLM significantly enhances the performance of LLMs in the low-data regime, outperforming both traditional fine-tuning and other data augmentation baselines. LLM2LLM reduces the dependence on labor-intensive data curation and paves the way for more scalable and performant LLM solutions, allowing us to tackle data-constrained domains and tasks. We achieve improvements up to 24.2% on the GSM8K dataset, 32.6% on CaseHOLD, 32.0% on SNIPS, 52.6% on TREC and 39.8% on SST-2 over regular fine-tuning in the low-data regime using a LLaMA2-7B student model.", "github_manual": "https://github.com/SqueezeAILab/LLM2LLM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-25", "arxiv_id": "2403.15246", "title": "FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions", "authors": [ "Orion Weller", "Benjamin Chang", "Sean MacAvaney", "Kyle Lo", "Arman Cohan", "Benjamin Van Durme", "Dawn Lawrie", "Luca Soldaini" ], "github": "https://github.com/orionw/FollowIR", "project_page": "", "abstract": "Modern Large Language Models (LLMs) are capable of following long and complex instructions that enable a diverse amount of user tasks. However, despite Information Retrieval (IR) models using LLMs as the backbone of their architectures, nearly all of them still only take queries as input, with no instructions. For the handful of recent models that do take instructions, it's unclear how they use them. We introduce our dataset FollowIR, which contains a rigorous instruction evaluation benchmark as well as a training set for helping IR models learn to better follow real-world instructions. FollowIR builds off the long history of the TREC conferences: as TREC provides human annotators with instructions (also known as narratives) to determine document relevance, so should IR models be able to understand and decide relevance based on these detailed instructions. Our evaluation benchmark starts with three deeply judged TREC collections and alters the annotator instructions, re-annotating relevant documents. Through this process, we can measure how well IR models follow instructions, through a new pairwise evaluation framework. Our results indicate that existing retrieval models fail to correctly use instructions, using them for basic keywords and struggling to understand long-form information. However, we show that it is possible for IR models to learn to follow complex instructions: our new FollowIR-7B model has significant improvements (over 13%) after fine-tuning on our training set.", "github_manual": "https://github.com/orionw/FollowIR", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-25", "arxiv_id": "2403.15385", "title": "LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis", "authors": [ "Kevin Xie", "Jonathan Lorraine", "Tianshi Cao", "Jun Gao", "James Lucas", "Antonio Torralba", "Sanja Fidler", "Xiaohui Zeng" ], "github": "https://github.com/3DTopia/3DTopia", "project_page": "", "abstract": "Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming optimization that can take up to an hour per prompt. Amortized methods like ATT3D optimize multiple prompts simultaneously to improve efficiency, enabling fast text-to-3D synthesis. However, they cannot capture high-frequency geometry and texture details and struggle to scale to large prompt sets, so they generalize poorly. We introduce LATTE3D, addressing these limitations to achieve fast, high-quality generation on a significantly larger prompt set. Key to our method is 1) building a scalable architecture and 2) leveraging 3D data during optimization through 3D-aware diffusion priors, shape regularization, and model initialization to achieve robustness to diverse and complex training prompts. LATTE3D amortizes both neural field and textured surface generation to produce highly detailed textured meshes in a single forward pass. LATTE3D generates 3D objects in 400ms, and can be further enhanced with fast test-time optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/3DTopia", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-25", "arxiv_id": "2403.15383", "title": "ThemeStation: Generating Theme-Aware 3D Assets from Few Exemplars", "authors": [ "Zhenwei Wang", "Tengfei Wang", "Gerhard Hancke", "Ziwei Liu", "Rynson W. H. Lau" ], "github": "https://github.com/3DTopia/ThemeStation", "project_page": "", "abstract": "Real-world applications often require a large gallery of 3D assets that share a consistent theme. While remarkable advances have been made in general 3D content creation from text or image, synthesizing customized 3D assets following the shared theme of input 3D exemplars remains an open and challenging problem. In this work, we present ThemeStation, a novel approach for theme-aware 3D-to-3D generation. ThemeStation synthesizes customized 3D assets based on given few exemplars with two goals: 1) unity for generating 3D assets that thematically align with the given exemplars and 2) diversity for generating 3D assets with a high degree of variations. To this end, we design a two-stage framework that draws a concept image first, followed by a reference-informed 3D modeling stage. We propose a novel dual score distillation (DSD) loss to jointly leverage priors from both the input exemplars and the synthesized concept image. Extensive experiments and user studies confirm that ThemeStation surpasses prior works in producing diverse theme-aware 3D models with impressive quality. ThemeStation also enables various applications such as controllable 3D-to-3D generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/3DTopia/ThemeStation", "github_api_added_by": "auto" }, { "date": "2024-03-25", "arxiv_id": "2403.14870", "title": "VidLA: Video-Language Alignment at Scale", "authors": [ "Mamshad Nayeem Rizve", "Fan Fei", "Jayakrishnan Unnikrishnan", "Son Tran", "Benjamin Z. Yao", "Belinda Zeng", "Mubarak Shah", "Trishul Chilimbi" ], "github": "", "project_page": "", "abstract": "In this paper, we propose VidLA, an approach for video-language alignment at scale. There are two major limitations of previous video-language alignment approaches. First, they do not capture both short-range and long-range temporal dependencies and typically employ complex hierarchical deep network architectures that are hard to integrate with existing pretrained image-text foundation models. To effectively address this limitation, we instead keep the network architecture simple and use a set of data tokens that operate at different temporal resolutions in a hierarchical manner, accounting for the temporally hierarchical nature of videos. By employing a simple two-tower architecture, we are able to initialize our video-language model with pretrained image-text foundation models, thereby boosting the final performance. Second, existing video-language alignment works struggle due to the lack of semantically aligned large-scale training data. To overcome it, we leverage recent LLMs to curate the largest video-language dataset to date with better visual grounding. Furthermore, unlike existing video-text datasets which only contain short clips, our dataset is enriched with video clips of varying durations to aid our temporally hierarchical data tokens in extracting better representations at varying temporal scales. Overall, empirical results show that our proposed approach surpasses state-of-the-art methods on multiple retrieval benchmarks, especially on longer videos, and performs competitively on classification benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-25", "arxiv_id": "2403.14714", "title": "Compiler generated feedback for Large Language Models", "authors": [ "Dejan Grubisic", "Chris Cummins", "Volker Seeker", "Hugh Leather" ], "github": "", "project_page": "", "abstract": "We introduce a novel paradigm in compiler optimization powered by Large Language Models with compiler feedback to optimize the code size of LLVM assembly. The model takes unoptimized LLVM IR as input and produces optimized IR, the best optimization passes, and instruction counts of both unoptimized and optimized IRs. Then we compile the input with generated optimization passes and evaluate if the predicted instruction count is correct, generated IR is compilable, and corresponds to compiled code. We provide this feedback back to LLM and give it another chance to optimize code. This approach adds an extra 0.53% improvement over -Oz to the original model. Even though, adding more information with feedback seems intuitive, simple sampling techniques achieve much higher performance given 10 or more samples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-25", "arxiv_id": "2403.14781", "title": "Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance", "authors": [ "Shenhao Zhu", "Junming Leo Chen", "Zuozhuo Dai", "Yinghui Xu", "Xun Cao", "Yao Yao", "Hao Zhu", "Siyu Zhu" ], "github": "https://github.com/fudan-generative-vision/champ", "project_page": "", "abstract": "In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in curernt human generative techniques. The methodology utilizes the SMPL(Skinned Multi-Person Linear) model as the 3D human parametric model to establish a unified representation of body shape and pose. This facilitates the accurate capture of intricate human geometry and motion characteristics from source videos. Specifically, we incorporate rendered depth images, normal maps, and semantic maps obtained from SMPL sequences, alongside skeleton-based motion guidance, to enrich the conditions to the latent diffusion model with comprehensive 3D shape and detailed pose attributes. A multi-layer motion fusion module, integrating self-attention mechanisms, is employed to fuse the shape and motion latent representations in the spatial domain. By representing the 3D human parametric model as the motion guidance, we can perform parametric shape alignment of the human body between the reference image and the source video motion. Experimental evaluations conducted on benchmark datasets demonstrate the methodology's superior ability to generate high-quality human animations that accurately capture both pose and shape variations. Furthermore, our approach also exhibits superior generalization capabilities on the proposed wild dataset. Project page: https://fudan-generative-vision.github.io/champ.", "github_manual": "https://github.com/fudan-generative-vision/champ", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-25", "arxiv_id": "2403.15382", "title": "DragAPart: Learning a Part-Level Motion Prior for Articulated Objects", "authors": [ "Ruining Li", "Chuanxia Zheng", "Christian Rupprecht", "Andrea Vedaldi" ], "github": "https://github.com/RuiningLi/DragAPart", "project_page": "", "abstract": "We introduce DragAPart, a method that, given an image and a set of drags as input, can generate a new image of the same object in a new state, compatible with the action of the drags. Differently from prior works that focused on repositioning objects, DragAPart predicts part-level interactions, such as opening and closing a drawer. We study this problem as a proxy for learning a generalist motion model, not restricted to a specific kinematic structure or object category. To this end, we start from a pre-trained image generator and fine-tune it on a new synthetic dataset, Drag-a-Move, which we introduce. Combined with a new encoding for the drags and dataset randomization, the new model generalizes well to real images and different categories. Compared to prior motion-controlled generators, we demonstrate much better part-level motion understanding.", "github_manual": "https://github.com/RuiningLi/DragAPart", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-25", "arxiv_id": "2403.15157", "title": "AllHands: Ask Me Anything on Large-scale Verbatim Feedback via Large Language Models", "authors": [ "Chaoyun Zhang", "Zicheng Ma", "Yuhao Wu", "Shilin He", "Si Qin", "Minghua Ma", "Xiaoting Qin", "Yu Kang", "Yuyi Liang", "Xiaoyu Gou", "Yajie Xue", "Qingwei Lin", "Saravan Rajmohan", "Dongmei Zhang", "Qi Zhang" ], "github": "", "project_page": "", "abstract": "Verbatim feedback constitutes a valuable repository of user experiences, opinions, and requirements essential for software development. Effectively and efficiently extracting valuable insights from such data poses a challenging task. This paper introduces Allhands , an innovative analytic framework designed for large-scale feedback analysis through a natural language interface, leveraging large language models (LLMs). Allhands adheres to a conventional feedback analytic workflow, initially conducting classification and topic modeling on the feedback to convert them into a structurally augmented format, incorporating LLMs to enhance accuracy, robustness, generalization, and user-friendliness. Subsequently, an LLM agent is employed to interpret users' diverse questions in natural language on feedback, translating them into Python code for execution, and delivering comprehensive multi-modal responses, including text, code, tables, and images. We evaluate Allhands across three diverse feedback datasets. The experiments demonstrate that Allhands achieves superior efficacy at all stages of analysis, including classification and topic modeling, eventually providing users with an ``ask me anything'' experience with comprehensive, correct and human-readable response. To the best of our knowledge, Allhands stands as the first comprehensive feedback analysis framework that supports diverse and customized requirements for insight extraction through a natural language interface.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-26", "arxiv_id": "2403.16627", "title": "SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions", "authors": [ "Yuda Song", "Zehao Sun", "Xuanwu Yin" ], "github": "https://github.com/IDKiro/sdxs", "project_page": "", "abstract": "Recent advancements in diffusion models have positioned them at the forefront of image generation. Despite their superior performance, diffusion models are not without drawbacks; they are characterized by complex architectures and substantial computational demands, resulting in significant latency due to their iterative sampling process. To mitigate these limitations, we introduce a dual approach involving model miniaturization and a reduction in sampling steps, aimed at significantly decreasing model latency. Our methodology leverages knowledge distillation to streamline the U-Net and image decoder architectures, and introduces an innovative one-step DM training technique that utilizes feature matching and score distillation. We present two models, SDXS-512 and SDXS-1024, achieving inference speeds of approximately 100 FPS (30x faster than SD v1.5) and 30 FP (60x faster than SDXL) on a single GPU, respectively. Moreover, our training approach offers promising applications in image-conditioned control, facilitating efficient image-to-image translation.", "github_manual": "https://github.com/IDKiro/sdxs", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-26", "arxiv_id": "2403.17005", "title": "TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models", "authors": [ "Zhongwei Zhang", "Fuchen Long", "Yingwei Pan", "Zhaofan Qiu", "Ting Yao", "Yang Cao", "Tao Mei" ], "github": "", "project_page": "", "abstract": "Recent advances in text-to-video generation have demonstrated the utility of powerful diffusion models. Nevertheless, the problem is not trivial when shaping diffusion models to animate static image (i.e., image-to-video generation). The difficulty originates from the aspect that the diffusion process of subsequent animated frames should not only preserve the faithful alignment with the given image but also pursue temporal coherence among adjacent frames. To alleviate this, we present TRIP, a new recipe of image-to-video diffusion paradigm that pivots on image noise prior derived from static image to jointly trigger inter-frame relational reasoning and ease the coherent temporal modeling via temporal residual learning. Technically, the image noise prior is first attained through one-step backward diffusion process based on both static image and noised video latent codes. Next, TRIP executes a residual-like dual-path scheme for noise prediction: 1) a shortcut path that directly takes image noise prior as the reference noise of each frame to amplify the alignment between the first frame and subsequent frames; 2) a residual path that employs 3D-UNet over noised video and static image latent codes to enable inter-frame relational reasoning, thereby easing the learning of the residual noise for each frame. Furthermore, both reference and residual noise of each frame are dynamically merged via attention mechanism for final video generation. Extensive experiments on WebVid-10M, DTDB and MSR-VTT datasets demonstrate the effectiveness of our TRIP for image-to-video generation. Please see our project page at https://trip-i2v.github.io/TRIP/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-26", "arxiv_id": "2403.16971", "title": "LLM Agent Operating System", "authors": [ "Kai Mei", "Zelong Li", "Shuyuan Xu", "Ruosong Ye", "Yingqiang Ge", "Yongfeng Zhang" ], "github": "https://github.com/agiresearch/AIOS", "project_page": "", "abstract": "The integration and deployment of large language model (LLM)-based intelligent agents have been fraught with challenges that compromise their efficiency and efficacy. Among these issues are sub-optimal scheduling and resource allocation of agent requests over the LLM, the difficulties in maintaining context during interactions between agent and LLM, and the complexities inherent in integrating heterogeneous agents with different capabilities and specializations. The rapid increase of agent quantity and complexity further exacerbates these issues, often leading to bottlenecks and sub-optimal utilization of resources. Inspired by these challenges, this paper presents AIOS, an LLM agent operating system, which embeds large language model into operating systems (OS). Specifically, AIOS is designed to optimize resource allocation, facilitate context switch across agents, enable concurrent execution of agents, provide tool service for agents, and maintain access control for agents. We present the architecture of such an operating system, outline the core challenges it aims to resolve, and provide the basic design and implementation of the AIOS. Our experiments on concurrent execution of multiple agents demonstrate the reliability and efficiency of our AIOS modules. Through this, we aim to not only improve the performance and efficiency of LLM agents but also to pioneer for better development and deployment of the AIOS ecosystem in the future. The project is open-source at https://github.com/agiresearch/AIOS.", "github_manual": "", "github_regex": "https://github.com/agiresearch/AIOS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-26", "arxiv_id": "2403.17001", "title": "VP3D: Unleashing 2D Visual Prompt for Text-to-3D Generation", "authors": [ "Yang Chen", "Yingwei Pan", "Haibo Yang", "Ting Yao", "Tao Mei" ], "github": "", "project_page": "", "abstract": "Recent innovations on text-to-3D generation have featured Score Distillation Sampling (SDS), which enables the zero-shot learning of implicit 3D models (NeRF) by directly distilling prior knowledge from 2D diffusion models. However, current SDS-based models still struggle with intricate text prompts and commonly result in distorted 3D models with unrealistic textures or cross-view inconsistency issues. In this work, we introduce a novel Visual Prompt-guided text-to-3D diffusion model (VP3D) that explicitly unleashes the visual appearance knowledge in 2D visual prompt to boost text-to-3D generation. Instead of solely supervising SDS with text prompt, VP3D first capitalizes on 2D diffusion model to generate a high-quality image from input text, which subsequently acts as visual prompt to strengthen SDS optimization with explicit visual appearance. Meanwhile, we couple the SDS optimization with additional differentiable reward function that encourages rendering images of 3D models to better visually align with 2D visual prompt and semantically match with text prompt. Through extensive experiments, we show that the 2D Visual Prompt in our VP3D significantly eases the learning of visual appearance of 3D models and thus leads to higher visual fidelity with more detailed textures. It is also appealing in view that when replacing the self-generating visual prompt with a given reference image, VP3D is able to trigger a new task of stylized text-to-3D generation. Our project page is available at https://vp3d-cvpr24.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-26", "arxiv_id": "2403.16990", "title": "Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation", "authors": [ "Omer Dahary", "Or Patashnik", "Kfir Aberman", "Daniel Cohen-Or" ], "github": "https://github.com/omer11a/bounded-attention", "project_page": "", "abstract": "Text-to-image diffusion models have an unprecedented ability to generate diverse and high-quality images. However, they often struggle to faithfully capture the intended semantics of complex input prompts that include multiple subjects. Recently, numerous layout-to-image extensions have been introduced to improve user control, aiming to localize subjects represented by specific tokens. Yet, these methods often produce semantically inaccurate images, especially when dealing with multiple semantically or visually similar subjects. In this work, we study and analyze the causes of these limitations. Our exploration reveals that the primary issue stems from inadvertent semantic leakage between subjects in the denoising process. This leakage is attributed to the diffusion model's attention layers, which tend to blend the visual features of different subjects. To address these issues, we introduce Bounded Attention, a training-free method for bounding the information flow in the sampling process. Bounded Attention prevents detrimental leakage among subjects and enables guiding the generation to promote each subject's individuality, even with complex multi-subject conditioning. Through extensive experimentation, we demonstrate that our method empowers the generation of multiple subjects that better align with given prompts and layouts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/omer11a/bounded-attention", "github_api_added_by": "auto" }, { "date": "2024-03-26", "arxiv_id": "2403.17008", "title": "FlashFace: Human Image Personalization with High-fidelity Identity Preservation", "authors": [ "Shilong Zhang", "Lianghua Huang", "Xi Chen", "Yifei Zhang", "Zhi-Fan Wu", "Yutong Feng", "Wei Wang", "Yujun Shen", "Yu Liu", "Ping Luo" ], "github": "https://github.com/ali-vilab/FlashFace", "project_page": "", "abstract": "This work presents FlashFace, a practical tool with which users can easily personalize their own photos on the fly by providing one or a few reference face images and a text prompt. Our approach is distinguishable from existing human photo customization methods by higher-fidelity identity preservation and better instruction following, benefiting from two subtle designs. First, we encode the face identity into a series of feature maps instead of one image token as in prior arts, allowing the model to retain more details of the reference faces (e.g., scars, tattoos, and face shape ). Second, we introduce a disentangled integration strategy to balance the text and image guidance during the text-to-image generation process, alleviating the conflict between the reference faces and the text prompts (e.g., personalizing an adult into a \"child\" or an \"elder\"). Extensive experimental results demonstrate the effectiveness of our method on various applications, including human image personalization, face swapping under language prompts, making virtual characters into real people, etc. Project Page: https://jshilong.github.io/flashface-page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ali-vilab/FlashFace", "github_api_added_by": "auto" }, { "date": "2024-03-26", "arxiv_id": "2403.15484", "title": "RakutenAI-7B: Extending Large Language Models for Japanese", "authors": [ "Rakuten Group", "Aaron Levine", "Connie Huang", "Chenguang Wang", "Eduardo Batista", "Ewa Szymanska", "Hongyi Ding", "Hou Wei Chou", "Jean-Fran\u00e7ois Pessiot", "Johanes Effendi", "Justin Chiu", "Kai Torben Ohlhus", "Karan Chopra", "Keiji Shinzato", "Koji Murakami", "Lee Xiong", "Lei Chen", "Maki Kubota", "Maksim Tkachenko", "Miroku Lee", "Naoki Takahashi", "Prathyusha Jwalapuram", "Ryutaro Tatsushima", "Saurabh Jain", "Sunil Kumar Yadav", "Ting Cai", "Wei-Te Chen", "Yandi Xia", "Yuki Nakayama", "Yutaka Higashiyama" ], "github": "", "project_page": "", "abstract": "We introduce RakutenAI-7B, a suite of Japanese-oriented large language models that achieve the best performance on the Japanese LM Harness benchmarks among the open 7B models. Along with the foundation model, we release instruction- and chat-tuned models, RakutenAI-7B-instruct and RakutenAI-7B-chat respectively, under the Apache 2.0 license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-26", "arxiv_id": "2403.15447", "title": "Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression", "authors": [ "Junyuan Hong", "Jinhao Duan", "Chenhui Zhang", "Zhangheng Li", "Chulin Xie", "Kelsey Lieberman", "James Diffenderfer", "Brian Bartoldson", "Ajay Jaiswal", "Kaidi Xu", "Bhavya Kailkhura", "Dan Hendrycks", "Dawn Song", "Zhangyang Wang", "Bo Li" ], "github": "https://github.com/decoding-comp-trust/comp-trust", "project_page": "", "abstract": "Compressing high-capability Large Language Models (LLMs) has emerged as a favored strategy for resource-efficient inferences. While state-of-the-art (SoTA) compression methods boast impressive advancements in preserving benign task performance, the potential risks of compression in terms of safety and trustworthiness have been largely neglected. This study conducts the first, thorough evaluation of three (3) leading LLMs using five (5) SoTA compression techniques across eight (8) trustworthiness dimensions. Our experiments highlight the intricate interplay between compression and trustworthiness, revealing some interesting patterns. We find that quantization is currently a more effective approach than pruning in achieving efficiency and trustworthiness simultaneously. For instance, a 4-bit quantized model retains the trustworthiness of its original counterpart, but model pruning significantly degrades trustworthiness, even at 50% sparsity. Moreover, employing quantization within a moderate bit range could unexpectedly improve certain trustworthiness dimensions such as ethics and fairness. Conversely, extreme quantization to very low bit levels (3 bits) tends to significantly reduce trustworthiness. This increased risk cannot be uncovered by looking at benign performance alone, in turn, mandating comprehensive trustworthiness evaluation in practice. These findings culminate in practical recommendations for simultaneously achieving high utility, efficiency, and trustworthiness in LLMs. Models and code are available at https://decoding-comp-trust.github.io/.", "github_manual": "https://github.com/decoding-comp-trust/comp-trust", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-27", "arxiv_id": "2403.17887", "title": "The Unreasonable Ineffectiveness of the Deeper Layers", "authors": [ "Andrey Gromov", "Kushal Tirumala", "Hassan Shapourian", "Paolo Glorioso", "Daniel A. Roberts" ], "github": "", "project_page": "", "abstract": "We empirically study a simple layer-pruning strategy for popular families of open-weight pretrained LLMs, finding minimal degradation of performance on different question-answering benchmarks until after a large fraction (up to half) of the layers are removed. To prune these models, we identify the optimal block of layers to prune by considering similarity across layers; then, to \"heal\" the damage, we perform a small amount of finetuning. In particular, we use parameter-efficient finetuning (PEFT) methods, specifically quantization and Low Rank Adapters (QLoRA), such that each of our experiments can be performed on a single A100 GPU. From a practical perspective, these results suggest that layer pruning methods can complement other PEFT strategies to further reduce computational resources of finetuning on the one hand, and can improve the memory and latency of inference on the other hand. From a scientific perspective, the robustness of these LLMs to the deletion of layers implies either that current pretraining methods are not properly leveraging the parameters in the deeper layers of the network or that the shallow layers play a critical role in storing knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-27", "arxiv_id": "2403.17804", "title": "Improving Text-to-Image Consistency via Automatic Prompt Optimization", "authors": [ "Oscar Ma\u00f1as", "Pietro Astolfi", "Melissa Hall", "Candace Ross", "Jack Urbanek", "Adina Williams", "Aishwarya Agrawal", "Adriana Romero-Soriano", "Michal Drozdzal" ], "github": "", "project_page": "", "abstract": "Impressive advances in text-to-image (T2I) generative models have yielded a plethora of high performing models which are able to generate aesthetically appealing, photorealistic images. Despite the progress, these models still struggle to produce images that are consistent with the input prompt, oftentimes failing to capture object quantities, relations and attributes properly. Existing solutions to improve prompt-image consistency suffer from the following challenges: (1) they oftentimes require model fine-tuning, (2) they only focus on nearby prompt samples, and (3) they are affected by unfavorable trade-offs among image quality, representation diversity, and prompt-image consistency. In this paper, we address these challenges and introduce a T2I optimization-by-prompting framework, OPT2I, which leverages a large language model (LLM) to improve prompt-image consistency in T2I models. Our framework starts from a user prompt and iteratively generates revised prompts with the goal of maximizing a consistency score. Our extensive validation on two datasets, MSCOCO and PartiPrompts, shows that OPT2I can boost the initial consistency score by up to 24.9% in terms of DSG score while preserving the FID and increasing the recall between generated and real data. Our work paves the way toward building more reliable and robust T2I systems by harnessing the power of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-27", "arxiv_id": "2403.17920", "title": "TC4D: Trajectory-Conditioned Text-to-4D Generation", "authors": [ "Sherwin Bahmani", "Xian Liu", "Yifan Wang", "Ivan Skorokhodov", "Victor Rong", "Ziwei Liu", "Xihui Liu", "Jeong Joon Park", "Sergey Tulyakov", "Gordon Wetzstein", "Andrea Tagliasacchi", "David B. Lindell" ], "github": "https://github.com/sherwinbahmani/tc4d", "project_page": "", "abstract": "Recent techniques for text-to-4D generation synthesize dynamic 3D scenes using supervision from pre-trained text-to-video models. However, existing representations for motion, such as deformation models or time-dependent neural representations, are limited in the amount of motion they can generate-they cannot synthesize motion extending far beyond the bounding box used for volume rendering. The lack of a more flexible motion model contributes to the gap in realism between 4D generation methods and recent, near-photorealistic video generation models. Here, we propose TC4D: trajectory-conditioned text-to-4D generation, which factors motion into global and local components. We represent the global motion of a scene's bounding box using rigid transformation along a trajectory parameterized by a spline. We learn local deformations that conform to the global trajectory using supervision from a text-to-video model. Our approach enables the synthesis of scenes animated along arbitrary trajectories, compositional scene generation, and significant improvements to the realism and amount of generated motion, which we evaluate qualitatively and through a user study. Video results can be viewed on our website: https://sherwinbahmani.github.io/tc4d.", "github_manual": "https://github.com/sherwinbahmani/tc4d", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-27", "arxiv_id": "2403.17607", "title": "Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUs", "authors": [ "Kai Yuan", "Christoph Bauinger", "Xiangyi Zhang", "Pascal Baehr", "Matthias Kirchhart", "Darius Dabert", "Adrien Tousnakhoff", "Pierre Boudier", "Michael Paulitsch" ], "github": "https://github.com/intel/tiny-dpcpp-nn", "project_page": "", "abstract": "This paper presents a SYCL implementation of Multi-Layer Perceptrons (MLPs), which targets and is optimized for the Intel Data Center GPU Max 1550. To increase the performance, our implementation minimizes the slow global memory accesses by maximizing the data reuse within the general register file and the shared local memory by fusing the operations in each layer of the MLP. We show with a simple roofline model that this results in a significant increase in the arithmetic intensity, leading to improved performance, especially for inference. We compare our approach to a similar CUDA implementation for MLPs and show that our implementation on the Intel Data Center GPU outperforms the CUDA implementation on Nvidia's H100 GPU by a factor up to 2.84 in inference and 1.75 in training. The paper also showcases the efficiency of our SYCL implementation in three significant areas: Image Compression, Neural Radiance Fields, and Physics-Informed Machine Learning. In all cases, our implementation outperforms the off-the-shelf Intel Extension for PyTorch (IPEX) implementation on the same Intel GPU by up to a factor of 30 and the CUDA PyTorch version on Nvidia's H100 GPU by up to a factor 19. The code can be found at https://github.com/intel/tiny-dpcpp-nn.", "github_manual": "", "github_regex": "https://github.com/intel/tiny-dpcpp-nn", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-27", "arxiv_id": "2403.17297", "title": "InternLM2 Technical Report", "authors": [ "Zheng Cai", "Maosong Cao", "Haojiong Chen", "Kai Chen", "Keyu Chen", "Xin Chen", "Xun Chen", "Zehui Chen", "Zhi Chen", "Pei Chu", "Xiaoyi Dong", "Haodong Duan", "Qi Fan", "Zhaoye Fei", "Yang Gao", "Jiaye Ge", "Chenya Gu", "Yuzhe Gu", "Tao Gui", "Aijia Guo", "Qipeng Guo", "Conghui He", "Yingfan Hu", "Ting Huang", "Tao Jiang", "Penglong Jiao", "Zhenjiang Jin", "Zhikai Lei", "Jiaxing Li", "Jingwen Li", "Linyang Li", "Shuaibin Li", "Wei Li", "Yining Li", "Hongwei Liu", "Jiangning Liu", "Jiawei Hong", "Kaiwen Liu", "Kuikun Liu", "Xiaoran Liu", "Chengqi Lv", "Haijun Lv", "Kai Lv", "Li Ma", "Runyuan Ma", "Zerun Ma", "Wenchang Ning", "Linke Ouyang", "Jiantao Qiu", "Yuan Qu", "Fukai Shang", "Yunfan Shao", "Demin Song", "Zifan Song", "Zhihao Sui", "Peng Sun", "Yu Sun", "Huanze Tang", "Bin Wang", "Guoteng Wang", "Jiaqi Wang", "Jiayu Wang", "Rui Wang", "Yudong Wang", "Ziyi Wang", "Xingjian Wei", "Qizhen Weng", "Fan Wu", "Yingtong Xiong", "Chao Xu", "Ruiliang Xu", "Hang Yan", "Yirong Yan", "Xiaogui Yang", "Haochen Ye", "Huaiyuan Ying", "Jia Yu", "Jing Yu", "Yuhang Zang", "Chuyu Zhang", "Li Zhang", "Pan Zhang", "Peng Zhang", "Ruijie Zhang", "Shuo Zhang", "Songyang Zhang", "Wenjian Zhang", "Wenwei Zhang", "Xingcheng Zhang", "Xinyue Zhang", "Hui Zhao", "Qian Zhao", "Xiaomeng Zhao", "Fengzhe Zhou", "Zaida Zhou", "Jingming Zhuo", "Yicheng Zou", "Xipeng Qiu", "Yu Qiao", "Dahua Lin" ], "github": "", "project_page": "", "abstract": "The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This paper introduces InternLM2, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques. The pre-training process of InternLM2 is meticulously detailed, highlighting the preparation of diverse data types including text, code, and long-context data. InternLM2 efficiently captures long-term dependencies, initially trained on 4k tokens before advancing to 32k tokens in pre-training and fine-tuning stages, exhibiting remarkable performance on the 200k ``Needle-in-a-Haystack\" test. InternLM2 is further aligned using Supervised Fine-Tuning (SFT) and a novel Conditional Online Reinforcement Learning from Human Feedback (COOL RLHF) strategy that addresses conflicting human preferences and reward hacking. By releasing InternLM2 models in different training stages and model sizes, we provide the community with insights into the model's evolution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-27", "arxiv_id": "2403.17898", "title": "Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians", "authors": [ "Kerui Ren", "Lihan Jiang", "Tao Lu", "Mulin Yu", "Linning Xu", "Zhangkai Ni", "Bo Dai" ], "github": "https://github.com/city-super/Octree-GS", "project_page": "", "abstract": "The recent 3D Gaussian splatting (3D-GS) has shown remarkable rendering fidelity and efficiency compared to NeRF-based neural scene representations. While demonstrating the potential for real-time rendering, 3D-GS encounters rendering bottlenecks in large scenes with complex details due to an excessive number of Gaussian primitives located within the viewing frustum. This limitation is particularly noticeable in zoom-out views and can lead to inconsistent rendering speeds in scenes with varying details. Moreover, it often struggles to capture the corresponding level of details at different scales with its heuristic density control operation. Inspired by the Level-of-Detail (LOD) techniques, we introduce Octree-GS, featuring an LOD-structured 3D Gaussian approach supporting level-of-detail decomposition for scene representation that contributes to the final rendering results. Our model dynamically selects the appropriate level from the set of multi-resolution anchor points, ensuring consistent rendering performance with adaptive LOD adjustments while maintaining high-fidelity rendering results.", "github_manual": "https://github.com/city-super/Octree-GS", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-27", "arxiv_id": "2403.17237", "title": "DreamPolisher: Towards High-Quality Text-to-3D Generation via Geometric Diffusion", "authors": [ "Yuanze Lin", "Ronald Clark", "Philip Torr" ], "github": "https://github.com/yuanze-lin/DreamPolisher", "project_page": "", "abstract": "We present DreamPolisher, a novel Gaussian Splatting based method with geometric guidance, tailored to learn cross-view consistency and intricate detail from textual descriptions. While recent progress on text-to-3D generation methods have been promising, prevailing methods often fail to ensure view-consistency and textural richness. This problem becomes particularly noticeable for methods that work with text input alone. To address this, we propose a two-stage Gaussian Splatting based approach that enforces geometric consistency among views. Initially, a coarse 3D generation undergoes refinement via geometric optimization. Subsequently, we use a ControlNet driven refiner coupled with the geometric consistency term to improve both texture fidelity and overall consistency of the generated 3D asset. Empirical evaluations across diverse textual prompts spanning various object categories demonstrate the efficacy of DreamPolisher in generating consistent and realistic 3D objects, aligning closely with the semantics of the textual instructions.", "github_manual": "https://github.com/yuanze-lin/DreamPolisher", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-27", "arxiv_id": "2403.17888", "title": "2D Gaussian Splatting for Geometrically Accurate Radiance Fields", "authors": [ "Binbin Huang", "Zehao Yu", "Anpei Chen", "Andreas Geiger", "Shenghua Gao" ], "github": "https://github.com/hbb1/2d-gaussian-splatting", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking. However, 3DGS fails to accurately represent surfaces due to the multi-view inconsistent nature of 3D Gaussians. We present 2D Gaussian Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate radiance fields from multi-view images. Our key idea is to collapse the 3D volume into a set of 2D oriented planar Gaussian disks. Unlike 3D Gaussians, 2D Gaussians provide view-consistent geometry while modeling surfaces intrinsically. To accurately recover thin surfaces and achieve stable optimization, we introduce a perspective-accurate 2D splatting process utilizing ray-splat intersection and rasterization. Additionally, we incorporate depth distortion and normal consistency terms to further enhance the quality of the reconstructions. We demonstrate that our differentiable renderer allows for noise-free and detailed geometry reconstruction while maintaining competitive appearance quality, fast training speed, and real-time rendering. Our code will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hbb1/2d-gaussian-splatting", "github_api_added_by": "auto" }, { "date": "2024-03-27", "arxiv_id": "2403.17694", "title": "AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation", "authors": [ "Huawei Wei", "Zejun Yang", "Zhisheng Wang" ], "github": "https://github.com/scutzzj/AniPortrait", "project_page": "", "abstract": "In this study, we propose AniPortrait, a novel framework for generating high-quality animation driven by audio and a reference portrait image. Our methodology is divided into two stages. Initially, we extract 3D intermediate representations from audio and project them into a sequence of 2D facial landmarks. Subsequently, we employ a robust diffusion model, coupled with a motion module, to convert the landmark sequence into photorealistic and temporally consistent portrait animation. Experimental results demonstrate the superiority of AniPortrait in terms of facial naturalness, pose diversity, and visual quality, thereby offering an enhanced perceptual experience. Moreover, our methodology exhibits considerable potential in terms of flexibility and controllability, which can be effectively applied in areas such as facial motion editing or face reenactment. We release code and model weights at https://github.com/scutzzj/AniPortrait", "github_manual": "", "github_regex": "https://github.com/scutzzj/AniPortrait", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-28", "arxiv_id": "2403.18795", "title": "Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction", "authors": [ "Qiuhong Shen", "Xuanyu Yi", "Zike Wu", "Pan Zhou", "Hanwang Zhang", "Shuicheng Yan", "Xinchao Wang" ], "github": "https://github.com/SkyworkAI/Gamba", "project_page": "", "abstract": "We tackle the challenge of efficiently reconstructing a 3D asset from a single image with growing demands for automated 3D content creation pipelines. Previous methods primarily rely on Score Distillation Sampling (SDS) and Neural Radiance Fields (NeRF). Despite their significant success, these approaches encounter practical limitations due to lengthy optimization and considerable memory usage. In this report, we introduce Gamba, an end-to-end amortized 3D reconstruction model from single-view images, emphasizing two main insights: (1) 3D representation: leveraging a large number of 3D Gaussians for an efficient 3D Gaussian splatting process; (2) Backbone design: introducing a Mamba-based sequential network that facilitates context-dependent reasoning and linear scalability with the sequence (token) length, accommodating a substantial number of Gaussians. Gamba incorporates significant advancements in data preprocessing, regularization design, and training methodologies. We assessed Gamba against existing optimization-based and feed-forward 3D generation approaches using the real-world scanned OmniObject3D dataset. Here, Gamba demonstrates competitive generation capabilities, both qualitatively and quantitatively, while achieving remarkable speed, approximately 0.6 second on a single NVIDIA A100 GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SkyworkAI/Gamba", "github_api_added_by": "auto" }, { "date": "2024-03-28", "arxiv_id": "2403.18421", "title": "BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text", "authors": [ "Elliot Bolton", "Abhinav Venigalla", "Michihiro Yasunaga", "David Hall", "Betty Xiong", "Tony Lee", "Roxana Daneshjou", "Jonathan Frankle", "Percy Liang", "Michael Carbin", "Christopher D. Manning" ], "github": "https://github.com/stanford-crfm/biomedlm", "project_page": "", "abstract": "Models such as GPT-4 and Med-PaLM 2 have demonstrated impressive performance on a wide variety of biomedical NLP tasks. However, these models have hundreds of billions of parameters, are computationally expensive to run, require users to send their input data over the internet, and are trained on unknown data sources. Can smaller, more targeted models compete? To address this question, we build and release BioMedLM, a 2.7 billion parameter GPT-style autoregressive model trained exclusively on PubMed abstracts and full articles. When fine-tuned, BioMedLM can produce strong multiple-choice biomedical question-answering results competitive with much larger models, such as achieving a score of 57.3% on MedMCQA (dev) and 69.0% on the MMLU Medical Genetics exam. BioMedLM can also be fine-tuned to produce useful answers to patient questions on medical topics. This demonstrates that smaller models can potentially serve as transparent, privacy-preserving, economical and environmentally friendly foundations for particular NLP applications, such as in biomedicine. The model is available on the Hugging Face Hub: https://huggingface.co/stanford-crfm/BioMedLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stanford-crfm/biomedlm", "github_api_added_by": "auto" }, { "date": "2024-03-28", "arxiv_id": "2403.18814", "title": "Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models", "authors": [ "Yanwei Li", "Yuechen Zhang", "Chengyao Wang", "Zhisheng Zhong", "Yixin Chen", "Ruihang Chu", "Shaoteng Liu", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/MiniGemini", "project_page": "", "abstract": "In this work, we introduce Mini-Gemini, a simple and effective framework enhancing multi-modality Vision Language Models (VLMs). Despite the advancements in VLMs facilitating basic visual dialog and reasoning, a performance gap persists compared to advanced models like GPT-4 and Gemini. We try to narrow the gap by mining the potential of VLMs for better performance and any-to-any workflow from three aspects, i.e., high-resolution visual tokens, high-quality data, and VLM-guided generation. To enhance visual tokens, we propose to utilize an additional visual encoder for high-resolution refinement without increasing the visual token count. We further construct a high-quality dataset that promotes precise image comprehension and reasoning-based generation, expanding the operational scope of current VLMs. In general, Mini-Gemini further mines the potential of VLMs and empowers current frameworks with image understanding, reasoning, and generation simultaneously. Mini-Gemini supports a series of dense and MoE Large Language Models (LLMs) from 2B to 34B. It is demonstrated to achieve leading performance in several zero-shot benchmarks and even surpasses the developed private models. Code and models are available at https://github.com/dvlab-research/MiniGemini.", "github_manual": "", "github_regex": "https://github.com/dvlab-research/MiniGemini", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-28", "arxiv_id": "2403.18361", "title": "ViTAR: Vision Transformer with Any Resolution", "authors": [ "Qihang Fan", "Quanzeng You", "Xiaotian Han", "Yongfei Liu", "Yunzhe Tao", "Huaibo Huang", "Ran He", "Hongxia Yang" ], "github": "", "project_page": "", "abstract": "his paper tackles a significant challenge faced by Vision Transformers (ViTs): their constrained scalability across different image resolutions. Typically, ViTs experience a performance decline when processing resolutions different from those seen during training. Our work introduces two key innovations to address this issue. Firstly, we propose a novel module for dynamic resolution adjustment, designed with a single Transformer block, specifically to achieve highly efficient incremental token integration. Secondly, we introduce fuzzy positional encoding in the Vision Transformer to provide consistent positional awareness across multiple resolutions, thereby preventing overfitting to any single training resolution. Our resulting model, ViTAR (Vision Transformer with Any Resolution), demonstrates impressive adaptability, achieving 83.3\\% top-1 accuracy at a 1120x1120 resolution and 80.4\\% accuracy at a 4032x4032 resolution, all while reducing computational costs. ViTAR also shows strong performance in downstream tasks such as instance and semantic segmentation and can easily combined with self-supervised learning techniques like Masked AutoEncoder. Our work provides a cost-effective solution for enhancing the resolution scalability of ViTs, paving the way for more versatile and efficient high-resolution image processing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-28", "arxiv_id": "2403.18802", "title": "Long-form factuality in large language models", "authors": [ "Jerry Wei", "Chengrun Yang", "Xinying Song", "Yifeng Lu", "Nathan Hu", "Dustin Tran", "Daiyi Peng", "Ruibo Liu", "Da Huang", "Cosmo Du", "Quoc V. Le" ], "github": "https://github.com/google-deepmind/long-form-factuality", "project_page": "", "abstract": "Large language models (LLMs) often generate content that contains factual errors when responding to fact-seeking prompts on open-ended topics. To benchmark a model's long-form factuality in open domains, we first use GPT-4 to generate LongFact, a prompt set comprising thousands of questions spanning 38 topics. We then propose that LLM agents can be used as automated evaluators for long-form factuality through a method which we call Search-Augmented Factuality Evaluator (SAFE). SAFE utilizes an LLM to break down a long-form response into a set of individual facts and to evaluate the accuracy of each fact using a multi-step reasoning process comprising sending search queries to Google Search and determining whether a fact is supported by the search results. Furthermore, we propose extending F1 score as an aggregated metric for long-form factuality. To do so, we balance the percentage of supported facts in a response (precision) with the percentage of provided facts relative to a hyperparameter representing a user's preferred response length (recall). Empirically, we demonstrate that LLM agents can achieve superhuman rating performance - on a set of ~16k individual facts, SAFE agrees with crowdsourced human annotators 72% of the time, and on a random subset of 100 disagreement cases, SAFE wins 76% of the time. At the same time, SAFE is more than 20 times cheaper than human annotators. We also benchmark thirteen language models on LongFact across four model families (Gemini, GPT, Claude, and PaLM-2), finding that larger language models generally achieve better long-form factuality. LongFact, SAFE, and all experimental code are available at https://github.com/google-deepmind/long-form-factuality.", "github_manual": "", "github_regex": "https://github.com/google-deepmind/long-form-factuality", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-28", "arxiv_id": "2403.18816", "title": "Garment3DGen: 3D Garment Stylization and Texture Generation", "authors": [ "Nikolaos Sarafianos", "Tuur Stuyck", "Xiaoyu Xiang", "Yilei Li", "Jovan Popovic", "Rakesh Ranjan" ], "github": "https://github.com/nsarafianos/Garment3DGen", "project_page": "", "abstract": "We introduce Garment3DGen a new method to synthesize 3D garment assets from a base mesh given a single input image as guidance. Our proposed approach allows users to generate 3D textured clothes based on both real and synthetic images, such as those generated by text prompts. The generated assets can be directly draped and simulated on human bodies. First, we leverage the recent progress of image to 3D diffusion methods to generate 3D garment geometries. However, since these geometries cannot be utilized directly for downstream tasks, we propose to use them as pseudo ground-truth and set up a mesh deformation optimization procedure that deforms a base template mesh to match the generated 3D target. Second, we introduce carefully designed losses that allow the input base mesh to freely deform towards the desired target, yet preserve mesh quality and topology such that they can be simulated. Finally, a texture estimation module generates high-fidelity texture maps that are globally and locally consistent and faithfully capture the input guidance, allowing us to render the generated 3D assets. With Garment3DGen users can generate the textured 3D garment of their choice without the need of artist intervention. One can provide a textual prompt describing the garment they desire to generate a simulation-ready 3D asset. We present a plethora of quantitative and qualitative comparisons on various assets both real and generated and provide use-cases of how one can generate simulation-ready 3D garments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nsarafianos/Garment3DGen", "github_api_added_by": "auto" }, { "date": "2024-03-28", "arxiv_id": "2403.18605", "title": "FlexEdit: Flexible and Controllable Diffusion-based Object-centric Image Editing", "authors": [ "Trong-Tung Nguyen", "Duc-Anh Nguyen", "Anh Tran", "Cuong Pham" ], "github": "", "project_page": "", "abstract": "Our work addresses limitations seen in previous approaches for object-centric editing problems, such as unrealistic results due to shape discrepancies and limited control in object replacement or insertion. To this end, we introduce FlexEdit, a flexible and controllable editing framework for objects where we iteratively adjust latents at each denoising step using our FlexEdit block. Initially, we optimize latents at test time to align with specified object constraints. Then, our framework employs an adaptive mask, automatically extracted during denoising, to protect the background while seamlessly blending new content into the target image. We demonstrate the versatility of FlexEdit in various object editing tasks and curate an evaluation test suite with samples from both real and synthetic images, along with novel evaluation metrics designed for object-centric editing. We conduct extensive experiments on different editing scenarios, demonstrating the superiority of our editing framework over recent advanced text-guided image editing methods. Our project page is published at https://flex-edit.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-28", "arxiv_id": "2403.18783", "title": "Towards a World-English Language Model for On-Device Virtual Assistants", "authors": [ "Rricha Jalota", "Lyan Verwimp", "Markus Nussbaum-Thom", "Amr Mousa", "Arturo Argueta", "Youssef Oualil" ], "github": "", "project_page": "", "abstract": "Neural Network Language Models (NNLMs) for Virtual Assistants (VAs) are generally language-, region-, and in some cases, device-dependent, which increases the effort to scale and maintain them. Combining NNLMs for one or more of the categories is one way to improve scalability. In this work, we combine regional variants of English to build a ``World English'' NNLM for on-device VAs. In particular, we investigate the application of adapter bottlenecks to model dialect-specific characteristics in our existing production NNLMs {and enhance the multi-dialect baselines}. We find that adapter modules are more effective in modeling dialects than specializing entire sub-networks. Based on this insight and leveraging the design of our production models, we introduce a new architecture for World English NNLM that meets the accuracy, latency, and memory constraints of our single-dialect models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-28", "arxiv_id": "2403.18118", "title": "EgoLifter: Open-world 3D Segmentation for Egocentric Perception", "authors": [ "Qiao Gu", "Zhaoyang Lv", "Duncan Frost", "Simon Green", "Julian Straub", "Chris Sweeney" ], "github": "https://github.com/facebookresearch/egolifter", "project_page": "", "abstract": "In this paper we present EgoLifter, a novel system that can automatically segment scenes captured from egocentric sensors into a complete decomposition of individual 3D objects. The system is specifically designed for egocentric data where scenes contain hundreds of objects captured from natural (non-scanning) motion. EgoLifter adopts 3D Gaussians as the underlying representation of 3D scenes and objects and uses segmentation masks from the Segment Anything Model (SAM) as weak supervision to learn flexible and promptable definitions of object instances free of any specific object taxonomy. To handle the challenge of dynamic objects in ego-centric videos, we design a transient prediction module that learns to filter out dynamic objects in the 3D reconstruction. The result is a fully automatic pipeline that is able to reconstruct 3D object instances as collections of 3D Gaussians that collectively compose the entire scene. We created a new benchmark on the Aria Digital Twin dataset that quantitatively demonstrates its state-of-the-art performance in open-world 3D segmentation from natural egocentric input. We run EgoLifter on various egocentric activity datasets which shows the promise of the method for 3D egocentric perception at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/egolifter", "github_api_added_by": "auto" }, { "date": "2024-03-28", "arxiv_id": "2403.18818", "title": "ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion", "authors": [ "Daniel Winter", "Matan Cohen", "Shlomi Fruchter", "Yael Pritch", "Alex Rav-Acha", "Yedid Hoshen" ], "github": "", "project_page": "", "abstract": "Diffusion models have revolutionized image editing but often generate images that violate physical laws, particularly the effects of objects on the scene, e.g., occlusions, shadows, and reflections. By analyzing the limitations of self-supervised approaches, we propose a practical solution centered on a counterfactual dataset. Our method involves capturing a scene before and after removing a single object, while minimizing other changes. By fine-tuning a diffusion model on this dataset, we are able to not only remove objects but also their effects on the scene. However, we find that applying this approach for photorealistic object insertion requires an impractically large dataset. To tackle this challenge, we propose bootstrap supervision; leveraging our object removal model trained on a small counterfactual dataset, we synthetically expand this dataset considerably. Our approach significantly outperforms prior methods in photorealistic object removal and insertion, particularly at modeling the effects of objects on the scene.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-29", "arxiv_id": "2403.18978", "title": "TextCraftor: Your Text Encoder Can be Image Quality Controller", "authors": [ "Yanyu Li", "Xian Liu", "Anil Kag", "Ju Hu", "Yerlan Idelbayev", "Dhritiman Sagar", "Yanzhi Wang", "Sergey Tulyakov", "Jian Ren" ], "github": "", "project_page": "", "abstract": "Diffusion-based text-to-image generative models, e.g., Stable Diffusion, have revolutionized the field of content generation, enabling significant advancements in areas like image editing and video synthesis. Despite their formidable capabilities, these models are not without their limitations. It is still challenging to synthesize an image that aligns well with the input text, and multiple runs with carefully crafted prompts are required to achieve satisfactory results. To mitigate these limitations, numerous studies have endeavored to fine-tune the pre-trained diffusion models, i.e., UNet, utilizing various technologies. Yet, amidst these efforts, a pivotal question of text-to-image diffusion model training has remained largely unexplored: Is it possible and feasible to fine-tune the text encoder to improve the performance of text-to-image diffusion models? Our findings reveal that, instead of replacing the CLIP text encoder used in Stable Diffusion with other large language models, we can enhance it through our proposed fine-tuning approach, TextCraftor, leading to substantial improvements in quantitative benchmarks and human assessments. Interestingly, our technique also empowers controllable image generation through the interpolation of different text encoders fine-tuned with various rewards. We also demonstrate that TextCraftor is orthogonal to UNet finetuning, and can be combined to further improve generative quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-29", "arxiv_id": "2403.19046", "title": "LITA: Language Instructed Temporal-Localization Assistant", "authors": [ "De-An Huang", "Shijia Liao", "Subhashree Radhakrishnan", "Hongxu Yin", "Pavlo Molchanov", "Zhiding Yu", "Jan Kautz" ], "github": "https://github.com/NVlabs/LITA", "project_page": "", "abstract": "There has been tremendous progress in multimodal Large Language Models (LLMs). Recent works have extended these models to video input with promising instruction following capabilities. However, an important missing piece is temporal localization. These models cannot accurately answer the \"When?\" questions. We identify three key aspects that limit their temporal localization capabilities: (i) time representation, (ii) architecture, and (iii) data. We address these shortcomings by proposing Language Instructed Temporal-Localization Assistant (LITA) with the following features: (1) We introduce time tokens that encode timestamps relative to the video length to better represent time in videos. (2) We introduce SlowFast tokens in the architecture to capture temporal information at fine temporal resolution. (3) We emphasize temporal localization data for LITA. In addition to leveraging existing video datasets with timestamps, we propose a new task, Reasoning Temporal Localization (RTL), along with the dataset, ActivityNet-RTL, for learning and evaluating this task. Reasoning temporal localization requires both the reasoning and temporal localization of Video LLMs. LITA demonstrates strong performance on this challenging task, nearly doubling the temporal mean intersection-over-union (mIoU) of baselines. In addition, we show that our emphasis on temporal localization also substantially improves video-based text generation compared to existing Video LLMs, including a 36% relative improvement of Temporal Understanding. Code is available at: https://github.com/NVlabs/LITA", "github_manual": "", "github_regex": "https://github.com/NVlabs/LITA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-29", "arxiv_id": "2403.19655", "title": "GaussianCube: Structuring Gaussian Splatting using Optimal Transport for 3D Generative Modeling", "authors": [ "Bowen Zhang", "Yiji Cheng", "Jiaolong Yang", "Chunyu Wang", "Feng Zhao", "Yansong Tang", "Dong Chen", "Baining Guo" ], "github": "https://github.com/GaussianCube/GaussianCube", "project_page": "", "abstract": "3D Gaussian Splatting (GS) have achieved considerable improvement over Neural Radiance Fields in terms of 3D fitting fidelity and rendering speed. However, this unstructured representation with scattered Gaussians poses a significant challenge for generative modeling. To address the problem, we introduce GaussianCube, a structured GS representation that is both powerful and efficient for generative modeling. We achieve this by first proposing a modified densification-constrained GS fitting algorithm which can yield high-quality fitting results using a fixed number of free Gaussians, and then re-arranging the Gaussians into a predefined voxel grid via Optimal Transport. The structured grid representation allows us to use standard 3D U-Net as our backbone in diffusion generative modeling without elaborate designs. Extensive experiments conducted on ShapeNet and OmniObject3D show that our model achieves state-of-the-art generation results both qualitatively and quantitatively, underscoring the potential of GaussianCube as a powerful and versatile 3D representation.", "github_manual": "https://github.com/GaussianCube/GaussianCube", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-29", "arxiv_id": "2403.19319", "title": "Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation", "authors": [ "Yujin Chen", "Yinyu Nie", "Benjamin Ummenhofer", "Reiner Birkl", "Michael Paulitsch", "Matthias M\u00fcller", "Matthias Nie\u00dfner" ], "github": "https://github.com/Lakonik/SSDNeRF", "project_page": "", "abstract": "We present Mesh2NeRF, an approach to derive ground-truth radiance fields from textured meshes for 3D generation tasks. Many 3D generative approaches represent 3D scenes as radiance fields for training. Their ground-truth radiance fields are usually fitted from multi-view renderings from a large-scale synthetic 3D dataset, which often results in artifacts due to occlusions or under-fitting issues. In Mesh2NeRF, we propose an analytic solution to directly obtain ground-truth radiance fields from 3D meshes, characterizing the density field with an occupancy function featuring a defined surface thickness, and determining view-dependent color through a reflection function considering both the mesh and environment lighting. Mesh2NeRF extracts accurate radiance fields which provides direct supervision for training generative NeRFs and single scene representation. We validate the effectiveness of Mesh2NeRF across various tasks, achieving a noteworthy 3.12dB improvement in PSNR for view synthesis in single scene representation on the ABO dataset, a 0.69 PSNR enhancement in the single-view conditional generation of ShapeNet Cars, and notably improved mesh extraction from NeRF in the unconditional generation of Objaverse Mugs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Lakonik/SSDNeRF", "github_api": "", "github_api_added_by": "" }, { "date": "2024-03-29", "arxiv_id": "2403.19270", "title": "sDPO: Don't Use Your Data All at Once", "authors": [ "Dahyun Kim", "Yungi Kim", "Wonho Song", "Hyeonwoo Kim", "Yunsu Kim", "Sanghoon Kim", "Chanjun Park" ], "github": "", "project_page": "", "abstract": "As development of large language models (LLM) progresses, aligning them with human preferences has become increasingly important. We propose stepwise DPO (sDPO), an extension of the recently popularized direct preference optimization (DPO) for alignment tuning. This approach involves dividing the available preference datasets and utilizing them in a stepwise manner, rather than employing it all at once. We demonstrate that this method facilitates the use of more precisely aligned reference models within the DPO training framework. Furthermore, sDPO trains the final model to be more performant, even outperforming other popular LLMs with more parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-01", "arxiv_id": "2403.20331", "title": "Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models", "authors": [ "Atsuyuki Miyai", "Jingkang Yang", "Jingyang Zhang", "Yifei Ming", "Qing Yu", "Go Irie", "Yixuan Li", "Hai Li", "Ziwei Liu", "Kiyoharu Aizawa" ], "github": "https://github.com/AtsuMiyai/UPD/", "project_page": "", "abstract": "This paper introduces a novel and significant challenge for Vision Language Models (VLMs), termed Unsolvable Problem Detection (UPD). UPD examines the VLM's ability to withhold answers when faced with unsolvable problems in the context of Visual Question Answering (VQA) tasks. UPD encompasses three distinct settings: Absent Answer Detection (AAD), Incompatible Answer Set Detection (IASD), and Incompatible Visual Question Detection (IVQD). To deeply investigate the UPD problem, extensive experiments indicate that most VLMs, including GPT-4V and LLaVA-Next-34B, struggle with our benchmarks to varying extents, highlighting significant room for the improvements. To address UPD, we explore both training-free and training-based solutions, offering new insights into their effectiveness and limitations. We hope our insights, together with future efforts within the proposed UPD settings, will enhance the broader understanding and development of more practical and reliable VLMs.", "github_manual": "https://github.com/AtsuMiyai/UPD/", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-01", "arxiv_id": "2403.20309", "title": "InstantSplat: Unbounded Sparse-view Pose-free Gaussian Splatting in 40 Seconds", "authors": [ "Zhiwen Fan", "Wenyan Cong", "Kairun Wen", "Kevin Wang", "Jian Zhang", "Xinghao Ding", "Danfei Xu", "Boris Ivanovic", "Marco Pavone", "Georgios Pavlakos", "Zhangyang Wang", "Yue Wang" ], "github": "https://github.com/NVlabs/InstantSplat", "project_page": "", "abstract": "While novel view synthesis (NVS) has made substantial progress in 3D computer vision, it typically requires an initial estimation of camera intrinsics and extrinsics from dense viewpoints. This pre-processing is usually conducted via a Structure-from-Motion (SfM) pipeline, a procedure that can be slow and unreliable, particularly in sparse-view scenarios with insufficient matched features for accurate reconstruction. In this work, we integrate the strengths of point-based representations (e.g., 3D Gaussian Splatting, 3D-GS) with end-to-end dense stereo models (DUSt3R) to tackle the complex yet unresolved issues in NVS under unconstrained settings, which encompasses pose-free and sparse view challenges. Our framework, InstantSplat, unifies dense stereo priors with 3D-GS to build 3D Gaussians of large-scale scenes from sparseview & pose-free images in less than 1 minute. Specifically, InstantSplat comprises a Coarse Geometric Initialization (CGI) module that swiftly establishes a preliminary scene structure and camera parameters across all training views, utilizing globally-aligned 3D point maps derived from a pre-trained dense stereo pipeline. This is followed by the Fast 3D-Gaussian Optimization (F-3DGO) module, which jointly optimizes the 3D Gaussian attributes and the initialized poses with pose regularization. Experiments conducted on the large-scale outdoor Tanks & Temples datasets demonstrate that InstantSplat significantly improves SSIM (by 32%) while concurrently reducing Absolute Trajectory Error (ATE) by 80%. These establish InstantSplat as a viable solution for scenarios involving posefree and sparse-view conditions. Project page: instantsplat.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/InstantSplat", "github_api_added_by": "auto" }, { "date": "2024-04-01", "arxiv_id": "2403.20275", "title": "Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces", "authors": [ "Mauro Comi", "Alessio Tonioni", "Max Yang", "Jonathan Tremblay", "Valts Blukis", "Yijiong Lin", "Nathan F. Lepora", "Laurence Aitchison" ], "github": "", "project_page": "", "abstract": "Touch and vision go hand in hand, mutually enhancing our ability to understand the world. From a research perspective, the problem of mixing touch and vision is underexplored and presents interesting challenges. To this end, we propose Tactile-Informed 3DGS, a novel approach that incorporates touch data (local depth maps) with multi-view vision data to achieve surface reconstruction and novel view synthesis. Our method optimises 3D Gaussian primitives to accurately model the object's geometry at points of contact. By creating a framework that decreases the transmittance at touch locations, we achieve a refined surface reconstruction, ensuring a uniformly smooth depth map. Touch is particularly useful when considering non-Lambertian objects (e.g. shiny or reflective surfaces) since contemporary methods tend to fail to reconstruct with fidelity specular highlights. By combining vision and tactile sensing, we achieve more accurate geometry reconstructions with fewer images than prior methods. We conduct evaluation on objects with glossy and reflective surfaces and demonstrate the effectiveness of our approach, offering significant improvements in reconstruction quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-01", "arxiv_id": "2403.20329", "title": "ReALM: Reference Resolution As Language Modeling", "authors": [ "Joel Ruben Antony Moniz", "Soundarya Krishnan", "Melis Ozyildirim", "Prathamesh Saraf", "Halim Cagri Ates", "Yuan Zhang", "Hong Yu", "Nidhi Rajshree" ], "github": "", "project_page": "", "abstract": "Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-01", "arxiv_id": "2403.19887", "title": "Jamba: A Hybrid Transformer-Mamba Language Model", "authors": [ "Opher Lieber", "Barak Lenz", "Hofit Bata", "Gal Cohen", "Jhonathan Osin", "Itay Dalmedigos", "Erez Safahi", "Shaked Meirom", "Yonatan Belinkov", "Shai Shalev-Shwartz", "Omri Abend", "Raz Alon", "Tomer Asida", "Amir Bergman", "Roman Glozman", "Michael Gokhman", "Avashalom Manevich", "Nir Ratner", "Noam Rozen", "Erez Shwartz", "Mor Zusman", "Yoav Shoham" ], "github": "", "project_page": "", "abstract": "We present Jamba, a new base large language model based on a novel hybrid Transformer-Mamba mixture-of-experts (MoE) architecture. Specifically, Jamba interleaves blocks of Transformer and Mamba layers, enjoying the benefits of both model families. MoE is added in some of these layers to increase model capacity while keeping active parameter usage manageable. This flexible architecture allows resource- and objective-specific configurations. In the particular configuration we have implemented, we end up with a powerful model that fits in a single 80GB GPU. Built at large scale, Jamba provides high throughput and small memory footprint compared to vanilla Transformers, and at the same time state-of-the-art performance on standard language model benchmarks and long-context evaluations. Remarkably, the model presents strong results for up to 256K tokens context length. We study various architectural decisions, such as how to combine Transformer and Mamba layers, and how to mix experts, and show that some of them are crucial in large scale modeling. We also describe several interesting properties of these architectures which the training and evaluation of Jamba have revealed, and plan to release checkpoints from various ablation runs, to encourage further exploration of this novel architecture. We make the weights of our implementation of Jamba publicly available under a permissive license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-01", "arxiv_id": "2403.20327", "title": "Gecko: Versatile Text Embeddings Distilled from Large Language Models", "authors": [ "Jinhyuk Lee", "Zhuyun Dai", "Xiaoqi Ren", "Blair Chen", "Daniel Cer", "Jeremy R. Cole", "Kai Hui", "Michael Boratko", "Rajvi Kapadia", "Wen Ding", "Yi Luan", "Sai Meher Karthik Duddu", "Gustavo Hernandez Abrego", "Weiqiang Shi", "Nithi Gupta", "Aditya Kusupati", "Prateek Jain", "Siddhartha Reddy Jonnalagadda", "Ming-Wei Chang", "Iftekhar Naim" ], "github": "", "project_page": "", "abstract": "We present Gecko, a compact and versatile text embedding model. Gecko achieves strong retrieval performance by leveraging a key idea: distilling knowledge from large language models (LLMs) into a retriever. Our two-step distillation process begins with generating diverse, synthetic paired data using an LLM. Next, we further refine the data quality by retrieving a set of candidate passages for each query, and relabeling the positive and hard negative passages using the same LLM. The effectiveness of our approach is demonstrated by the compactness of the Gecko. On the Massive Text Embedding Benchmark (MTEB), Gecko with 256 embedding dimensions outperforms all existing entries with 768 embedding size. Gecko with 768 embedding dimensions achieves an average score of 66.31, competing with 7x larger models and 5x higher dimensional embeddings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-01", "arxiv_id": "2403.20041", "title": "Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs", "authors": [ "Luchang Li", "Sheng Qian", "Jie Lu", "Lunxi Yuan", "Rui Wang", "Qin Xie" ], "github": "", "project_page": "", "abstract": "The Large Language Model (LLM) is widely employed for tasks such as intelligent assistants, text summarization, translation, and multi-modality on mobile phones. However, the current methods for on-device LLM deployment maintain slow inference speed, which causes poor user experience. To facilitate high-efficiency LLM deployment on device GPUs, we propose four optimization techniques: (a) a symbolic expression-based approach to support dynamic shape model inference; (b) operator optimizations and execution priority setting to enhance inference speed and reduce phone lagging; (c) an FP4 quantization method termed M0E4 to reduce dequantization overhead; (d) a sub-tensor-based technique to eliminate the need for copying KV cache after LLM inference. Furthermore, we implement these methods in our mobile inference engine, Transformer-Lite, which is compatible with both Qualcomm and MTK processors. We evaluated Transformer-Lite's performance using LLMs with varied architectures and parameters ranging from 2B to 14B. Specifically, we achieved prefill and decoding speeds of 121 token/s and 14 token/s for ChatGLM2 6B, and 330 token/s and 30 token/s for smaller Gemma 2B, respectively. Compared with CPU-based FastLLM and GPU-based MLC-LLM, our engine attains over 10x speedup for the prefill speed and 2~3x speedup for the decoding speed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-01", "arxiv_id": "2403.19888", "title": "MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection", "authors": [ "Ali Behrouz", "Michele Santacatterina", "Ramin Zabih" ], "github": "https://github.com/MambaMixer/M2", "project_page": "", "abstract": "Recent advances in deep learning have mainly relied on Transformers due to their data dependency and ability to learn at scale. The attention module in these architectures, however, exhibits quadratic time and space in input size, limiting their scalability for long-sequence modeling. Despite recent attempts to design efficient and effective architecture backbone for multi-dimensional data, such as images and multivariate time series, existing models are either data independent, or fail to allow inter- and intra-dimension communication. Recently, State Space Models (SSMs), and more specifically Selective State Space Models, with efficient hardware-aware implementation, have shown promising potential for long sequence modeling. Motivated by the success of SSMs, we present MambaMixer, a new architecture with data-dependent weights that uses a dual selection mechanism across tokens and channels, called Selective Token and Channel Mixer. MambaMixer connects selective mixers using a weighted averaging mechanism, allowing layers to have direct access to early features. As a proof of concept, we design Vision MambaMixer (ViM2) and Time Series MambaMixer (TSM2) architectures based on the MambaMixer block and explore their performance in various vision and time series forecasting tasks. Our results underline the importance of selective mixing across both tokens and channels. In ImageNet classification, object detection, and semantic segmentation tasks, ViM2 achieves competitive performance with well-established vision models and outperforms SSM-based vision models. In time series forecasting, TSM2 achieves outstanding performance compared to state-of-the-art methods while demonstrating significantly improved computational cost. These results show that while Transformers, cross-channel attention, and MLPs are sufficient for good performance in time series forecasting, neither is necessary.", "github_manual": "https://github.com/MambaMixer/M2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-01", "arxiv_id": "2403.19928", "title": "DiJiang: Efficient Large Language Models through Compact Kernelization", "authors": [ "Hanting Chen", "Zhicheng Liu", "Xutao Wang", "Yuchuan Tian", "Yunhe Wang" ], "github": "https://github.com/YuchuanTian/DiJiang", "project_page": "", "abstract": "In an effort to reduce the computational load of Transformers, research on linear attention has gained significant momentum. However, the improvement strategies for attention mechanisms typically necessitate extensive retraining, which is impractical for large language models with a vast array of parameters. In this paper, we present DiJiang, a novel Frequency Domain Kernelization approach that enables the transformation of a pre-trained vanilla Transformer into a linear complexity model with little training costs. By employing a weighted Quasi-Monte Carlo method for sampling, the proposed approach theoretically offers superior approximation efficiency. To further reduce the training computational complexity, our kernelization is based on Discrete Cosine Transform (DCT) operations. Extensive experiments demonstrate that the proposed method achieves comparable performance to the original Transformer, but with significantly reduced training costs and much faster inference speeds. Our DiJiang-7B achieves comparable performance with LLaMA2-7B on various benchmark while requires only about 1/50 training cost. Code is available at https://github.com/YuchuanTian/DiJiang.", "github_manual": "", "github_regex": "https://github.com/YuchuanTian/DiJiang", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-01", "arxiv_id": "2403.19851", "title": "Localizing Paragraph Memorization in Language Models", "authors": [ "Niklas Stoehr", "Mitchell Gordon", "Chiyuan Zhang", "Owen Lewis" ], "github": "https://github.com/googleinterns/localizing-paragraph-memorization", "project_page": "", "abstract": "Can we localize the weights and mechanisms used by a language model to memorize and recite entire paragraphs of its training data? In this paper, we show that while memorization is spread across multiple layers and model components, gradients of memorized paragraphs have a distinguishable spatial pattern, being larger in lower model layers than gradients of non-memorized examples. Moreover, the memorized examples can be unlearned by fine-tuning only the high-gradient weights. We localize a low-layer attention head that appears to be especially involved in paragraph memorization. This head is predominantly focusing its attention on distinctive, rare tokens that are least frequent in a corpus-level unigram distribution. Next, we study how localized memorization is across the tokens in the prefix by perturbing tokens and measuring the caused change in the decoding. A few distinctive tokens early in a prefix can often corrupt the entire continuation. Overall, memorized continuations are not only harder to unlearn, but also to corrupt than non-memorized ones.", "github_manual": "https://github.com/googleinterns/localizing-paragraph-memorization", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-02", "arxiv_id": "2404.01197", "title": "Getting it Right: Improving Spatial Consistency in Text-to-Image Models", "authors": [ "Agneet Chatterjee", "Gabriela Ben Melech Stan", "Estelle Aflalo", "Sayak Paul", "Dhruba Ghosh", "Tejas Gokhale", "Ludwig Schmidt", "Hannaneh Hajishirzi", "Vasudev Lal", "Chitta Baral", "Yezhou Yang" ], "github": "https://github.com/SPRIGHT-T2I/SPRIGHT", "project_page": "", "abstract": "One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in the text prompt. In this paper, we offer a comprehensive investigation of this limitation, while also developing datasets and methods that achieve state-of-the-art performance. First, we find that current vision-language datasets do not represent spatial relationships well enough; to alleviate this bottleneck, we create SPRIGHT, the first spatially-focused, large scale dataset, by re-captioning 6 million images from 4 widely used vision datasets. Through a 3-fold evaluation and analysis pipeline, we find that SPRIGHT largely improves upon existing datasets in capturing spatial relationships. To demonstrate its efficacy, we leverage only ~0.25% of SPRIGHT and achieve a 22% improvement in generating spatially accurate images while also improving the FID and CMMD scores. Secondly, we find that training on images containing a large number of objects results in substantial improvements in spatial consistency. Notably, we attain state-of-the-art on T2I-CompBench with a spatial score of 0.2133, by fine-tuning on <500 images. Finally, through a set of controlled experiments and ablations, we document multiple findings that we believe will enhance the understanding of factors that affect spatial consistency in text-to-image models. We publicly release our dataset and model to foster further research in this area.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SPRIGHT-T2I/SPRIGHT", "github_api_added_by": "auto" }, { "date": "2024-04-02", "arxiv_id": "2404.00987", "title": "FlexiDreamer: Single Image-to-3D Generation with FlexiCubes", "authors": [ "Ruowen Zhao", "Zhengyi Wang", "Yikai Wang", "Zihan Zhou", "Jun Zhu" ], "github": "https://github.com/zhaorw02/FlexiDreamer", "project_page": "", "abstract": "3D content generation from text prompts or single images has made remarkable progress in quality and speed recently. One of its dominant paradigms involves generating consistent multi-view images followed by a sparse-view reconstruction. However, due to the challenge of directly deforming the mesh representation to approach the target topology, most methodologies learn an implicit representation (such as NeRF) during the sparse-view reconstruction and acquire the target mesh by a post-processing extraction. Although the implicit representation can effectively model rich 3D information, its training typically entails a long convergence time. In addition, the post-extraction operation from the implicit field also leads to undesirable visual artifacts. In this paper, we propose FlexiDreamer, a novel single image-to-3d generation framework that reconstructs the target mesh in an end-to-end manner. By leveraging a flexible gradient-based extraction known as FlexiCubes, our method circumvents the defects brought by the post-processing and facilitates a direct acquisition of the target mesh. Furthermore, we incorporate a multi-resolution hash grid encoding scheme that progressively activates the encoding levels into the implicit field in FlexiCubes to help capture geometric details for per-step optimization. Notably, FlexiDreamer recovers a dense 3D structure from a single-view image in approximately 1 minute on a single NVIDIA A100 GPU, outperforming previous methodologies by a large margin.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhaorw02/FlexiDreamer", "github_api_added_by": "auto" }, { "date": "2024-04-02", "arxiv_id": "2404.01258", "title": "Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward", "authors": [ "Ruohong Zhang", "Liangke Gui", "Zhiqing Sun", "Yihao Feng", "Keyang Xu", "Yuanhan Zhang", "Di Fu", "Chunyuan Li", "Alexander Hauptmann", "Yonatan Bisk", "Yiming Yang" ], "github": "https://github.com/riflezhang/llava-hound-dpo", "project_page": "", "abstract": "Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM). However, in tasks involving video instruction-following, providing informative feedback, especially for detecting hallucinations in generated responses, remains a significant challenge. Previous studies have explored using large large multimodal models (LMMs) as reward models to guide preference modeling, but their ability to accurately assess the factuality of generated responses compared to corresponding videos has not been conclusively established. This paper introduces a novel framework that utilizes detailed video captions as a proxy of video content, enabling language models to incorporate this information as supporting evidence for scoring video Question Answering (QA) predictions. Our approach demonstrates robust alignment with OpenAI GPT-4V model's reward mechanism, which directly takes video frames as input. Furthermore, we show that applying this tailored reward through DPO significantly improves the performance of video LMMs on video QA tasks.", "github_manual": "https://github.com/riflezhang/llava-hound-dpo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-02", "arxiv_id": "2404.01143", "title": "Condition-Aware Neural Network for Controlled Image Generation", "authors": [ "Han Cai", "Muyang Li", "Zhuoyang Zhang", "Qinsheng Zhang", "Ming-Yu Liu", "Song Han" ], "github": "", "project_page": "", "abstract": "We present Condition-Aware Neural Network (CAN), a new method for adding control to image generative models. In parallel to prior conditional control methods, CAN controls the image generation process by dynamically manipulating the weight of the neural network. This is achieved by introducing a condition-aware weight generation module that generates conditional weight for convolution/linear layers based on the input condition. We test CAN on class-conditional image generation on ImageNet and text-to-image generation on COCO. CAN consistently delivers significant improvements for diffusion transformer models, including DiT and UViT. In particular, CAN combined with EfficientViT (CaT) achieves 2.78 FID on ImageNet 512x512, surpassing DiT-XL/2 while requiring 52x fewer MACs per sampling step.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-02", "arxiv_id": "2404.00345", "title": "MaGRITTe: Manipulative and Generative 3D Realization from Image, Topview and Text", "authors": [ "Takayuki Hara", "Tatsuya Harada" ], "github": "", "project_page": "", "abstract": "The generation of 3D scenes from user-specified conditions offers a promising avenue for alleviating the production burden in 3D applications. Previous studies required significant effort to realize the desired scene, owing to limited control conditions. We propose a method for controlling and generating 3D scenes under multimodal conditions using partial images, layout information represented in the top view, and text prompts. Combining these conditions to generate a 3D scene involves the following significant difficulties: (1) the creation of large datasets, (2) reflection on the interaction of multimodal conditions, and (3) domain dependence of the layout conditions. We decompose the process of 3D scene generation into 2D image generation from the given conditions and 3D scene generation from 2D images. 2D image generation is achieved by fine-tuning a pretrained text-to-image model with a small artificial dataset of partial images and layouts, and 3D scene generation is achieved by layout-conditioned depth estimation and neural radiance fields (NeRF), thereby avoiding the creation of large datasets. The use of a common representation of spatial information using 360-degree images allows for the consideration of multimodal condition interactions and reduces the domain dependence of the layout control. The experimental results qualitatively and quantitatively demonstrated that the proposed method can generate 3D scenes in diverse domains, from indoor to outdoor, according to multimodal conditions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-02", "arxiv_id": "2404.00399", "title": "Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order", "authors": [ "Taishi Nakamura", "Mayank Mishra", "Simone Tedeschi", "Yekun Chai", "Jason T Stillerman", "Felix Friedrich", "Prateek Yadav", "Tanmay Laud", "Vu Minh Chien", "Terry Yue Zhuo", "Diganta Misra", "Ben Bogin", "Xuan-Son Vu", "Marzena Karpinska", "Arnav Varma Dantuluri", "Wojciech Kusa", "Tommaso Furlanello", "Rio Yokota", "Niklas Muennighoff", "Suhas Pai", "Tosin Adewumi", "Veronika Laippala", "Xiaozhe Yao", "Adalberto Junior", "Alpay Ariyak", "Aleksandr Drozd", "Jordan Clive", "Kshitij Gupta", "Liangyu Chen", "Qi Sun", "Ken Tsui", "Noah Persaud", "Nour Fahmy", "Tianlong Chen", "Mohit Bansal", "Nicolo Monti", "Tai Dang", "Ziyang Luo", "Tien-Tung Bui", "Roberto Navigli", "Virendra Mehta", "Matthew Blumberg", "Victor May", "Huu Nguyen", "Sampo Pyysalo" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility. Initiatives such as BLOOM and StarCoder aim to democratize access to pretrained models for collaborative community development. However, such existing models face challenges: limited multilingual capabilities, continual pretraining causing catastrophic forgetting, whereas pretraining from scratch is computationally expensive, and compliance with AI safety and development laws. This paper presents Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. Continually pretrained from StarCoderPlus on 435 billion additional tokens, Aurora-M surpasses 2 trillion tokens in total training token count. It is the first open-source multilingual model fine-tuned on human-reviewed safety instructions, thus aligning its development not only with conventional red-teaming considerations, but also with the specific concerns articulated in the Biden-Harris Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Aurora-M is rigorously evaluated across various tasks and languages, demonstrating robustness against catastrophic forgetting and outperforming alternatives in multilingual settings, particularly in safety evaluations. To promote responsible open-source LLM development, Aurora-M and its variants are released at https://huggingface.co/collections/aurora-m/aurora-m-models-65fdfdff62471e09812f5407 .", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-02", "arxiv_id": "2404.01292", "title": "Measuring Style Similarity in Diffusion Models", "authors": [ "Gowthami Somepalli", "Anubhav Gupta", "Kamal Gupta", "Shramay Palta", "Micah Goldblum", "Jonas Geiping", "Abhinav Shrivastava", "Tom Goldstein" ], "github": "https://github.com/learn2phoenix/CSD", "project_page": "", "abstract": "Generative models are now widely used by graphic designers and artists. Prior works have shown that these models remember and often replicate content from their training data during generation. Hence as their proliferation increases, it has become important to perform a database search to determine whether the properties of the image are attributable to specific training data, every time before a generated image is used for professional purposes. Existing tools for this purpose focus on retrieving images of similar semantic content. Meanwhile, many artists are concerned with style replication in text-to-image models. We present a framework for understanding and extracting style descriptors from images. Our framework comprises a new dataset curated using the insight that style is a subjective property of an image that captures complex yet meaningful interactions of factors including but not limited to colors, textures, shapes, etc. We also propose a method to extract style descriptors that can be used to attribute style of a generated image to the images used in the training dataset of a text-to-image model. We showcase promising results in various style retrieval tasks. We also quantitatively and qualitatively analyze style attribution and matching in the Stable Diffusion model. Code and artifacts are available at https://github.com/learn2phoenix/CSD.", "github_manual": "", "github_regex": "https://github.com/learn2phoenix/CSD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-02", "arxiv_id": "2404.01294", "title": "CosmicMan: A Text-to-Image Foundation Model for Humans", "authors": [ "Shikai Li", "Jianglin Fu", "Kaiyuan Liu", "Wentao Wang", "Kwan-Yee Lin", "Wayne Wu" ], "github": "https://github.com/cosmicman-cvpr2024/CosmicMan", "project_page": "", "abstract": "We present CosmicMan, a text-to-image foundation model specialized for generating high-fidelity human images. Unlike current general-purpose foundation models that are stuck in the dilemma of inferior quality and text-image misalignment for humans, CosmicMan enables generating photo-realistic human images with meticulous appearance, reasonable structure, and precise text-image alignment with detailed dense descriptions. At the heart of CosmicMan's success are the new reflections and perspectives on data and models: (1) We found that data quality and a scalable data production flow are essential for the final results from trained models. Hence, we propose a new data production paradigm, Annotate Anyone, which serves as a perpetual data flywheel to produce high-quality data with accurate yet cost-effective annotations over time. Based on this, we constructed a large-scale dataset, CosmicMan-HQ 1.0, with 6 Million high-quality real-world human images in a mean resolution of 1488x1255, and attached with precise text annotations deriving from 115 Million attributes in diverse granularities. (2) We argue that a text-to-image foundation model specialized for humans must be pragmatic -- easy to integrate into down-streaming tasks while effective in producing high-quality human images. Hence, we propose to model the relationship between dense text descriptions and image pixels in a decomposed manner, and present Decomposed-Attention-Refocusing (Daring) training framework. It seamlessly decomposes the cross-attention features in existing text-to-image diffusion model, and enforces attention refocusing without adding extra modules. Through Daring, we show that explicitly discretizing continuous text space into several basic groups that align with human body structure is the key to tackling the misalignment problem in a breeze.", "github_manual": "https://github.com/cosmicman-cvpr2024/CosmicMan", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-02", "arxiv_id": "2404.00656", "title": "WavLLM: Towards Robust and Adaptive Speech Large Language Model", "authors": [ "Shujie Hu", "Long Zhou", "Shujie Liu", "Sanyuan Chen", "Hongkun Hao", "Jing Pan", "Xunying Liu", "Jinyu Li", "Sunit Sivasankaran", "Linquan Liu", "Furu Wei" ], "github": "https://github.com/microsoft/SpeechT5/tree/main/WavLLM", "project_page": "", "abstract": "The recent advancements in large language models (LLMs) have revolutionized the field of natural language processing, progressively broadening their scope to multimodal perception and generation. However, effectively integrating listening capabilities into LLMs poses significant challenges, particularly with respect to generalizing across varied contexts and executing complex auditory tasks. In this work, we introduce WavLLM, a robust and adaptive speech large language model with dual encoders, and a prompt-aware LoRA weight adapter, optimized by a two-stage curriculum learning approach. Leveraging dual encoders, we decouple different types of speech information, utilizing a Whisper encoder to process the semantic content of speech, and a WavLM encoder to capture the unique characteristics of the speaker's identity. Within the curriculum learning framework, WavLLM first builds its foundational capabilities by optimizing on mixed elementary single tasks, followed by advanced multi-task training on more complex tasks such as combinations of the elementary tasks. To enhance the flexibility and adherence to different tasks and instructions, a prompt-aware LoRA weight adapter is introduced in the second advanced multi-task training stage. We validate the proposed model on universal speech benchmarks including tasks such as ASR, ST, SV, ER, and also apply it to specialized datasets like Gaokao English listening comprehension set for SQA, and speech Chain-of-Thought (CoT) evaluation set. Experiments demonstrate that the proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size, exhibiting robust generalization capabilities in executing complex tasks using CoT approach. Furthermore, our model successfully completes Gaokao tasks without specialized training. The codes, models, audio, and Gaokao evaluation set can be accessed at aka.ms/wavllm.", "github_manual": "https://github.com/microsoft/SpeechT5/tree/main/WavLLM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-02", "arxiv_id": "2404.00488", "title": "Noise-Aware Training of Layout-Aware Language Models", "authors": [ "Ritesh Sarkhel", "Xiaoqi Ren", "Lauro Beltrao Costa", "Guolong Su", "Vincent Perot", "Yanan Xie", "Emmanouil Koukoumidis", "Arnab Nandi" ], "github": "", "project_page": "", "abstract": "A visually rich document (VRD) utilizes visual features along with linguistic cues to disseminate information. Training a custom extractor that identifies named entities from a document requires a large number of instances of the target document type annotated at textual and visual modalities. This is an expensive bottleneck in enterprise scenarios, where we want to train custom extractors for thousands of different document types in a scalable way. Pre-training an extractor model on unlabeled instances of the target document type, followed by a fine-tuning step on human-labeled instances does not work in these scenarios, as it surpasses the maximum allowable training time allocated for the extractor. We address this scenario by proposing a Noise-Aware Training method or NAT in this paper. Instead of acquiring expensive human-labeled documents, NAT utilizes weakly labeled documents to train an extractor in a scalable way. To avoid degradation in the model's quality due to noisy, weakly labeled samples, NAT estimates the confidence of each training sample and incorporates it as uncertainty measure during training. We train multiple state-of-the-art extractor models using NAT. Experiments on a number of publicly available and in-house datasets show that NAT-trained models are not only robust in performance -- it outperforms a transfer-learning baseline by up to 6% in terms of macro-F1 score, but it is also more label-efficient -- it reduces the amount of human-effort required to obtain comparable performance by up to 73%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-02", "arxiv_id": "2404.00308", "title": "ST-LLM: Large Language Models Are Effective Temporal Learners", "authors": [ "Ruyang Liu", "Chen Li", "Haoran Tang", "Yixiao Ge", "Ying Shan", "Ge Li" ], "github": "https://github.com/TencentARC/ST-LLM", "project_page": "", "abstract": "Large Language Models (LLMs) have showcased impressive capabilities in text comprehension and generation, prompting research efforts towards video LLMs to facilitate human-AI interaction at the video level. However, how to effectively encode and understand videos in video-based dialogue systems remains to be solved. In this paper, we investigate a straightforward yet unexplored question: Can we feed all spatial-temporal tokens into the LLM, thus delegating the task of video sequence modeling to the LLMs? Surprisingly, this simple approach yields significant improvements in video understanding. Based upon this, we propose ST-LLM, an effective video-LLM baseline with Spatial-Temporal sequence modeling inside LLM. Furthermore, to address the overhead and stability issues introduced by uncompressed video tokens within LLMs, we develop a dynamic masking strategy with tailor-made training objectives. For particularly long videos, we have also designed a global-local input module to balance efficiency and effectiveness. Consequently, we harness LLM for proficient spatial-temporal modeling, while upholding efficiency and stability. Extensive experimental results attest to the effectiveness of our method. Through a more concise model and training pipeline, ST-LLM establishes a new state-of-the-art result on VideoChatGPT-Bench and MVBench. Codes have been available at https://github.com/TencentARC/ST-LLM.", "github_manual": "", "github_regex": "https://github.com/TencentARC/ST-LLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-02", "arxiv_id": "2404.01297", "title": "Streaming Dense Video Captioning", "authors": [ "Xingyi Zhou", "Anurag Arnab", "Shyamal Buch", "Shen Yan", "Austin Myers", "Xuehan Xiong", "Arsha Nagrani", "Cordelia Schmid" ], "github": "https://github.com/google-research/scenic", "project_page": "", "abstract": "An ideal model for dense video captioning -- predicting captions localized temporally in a video -- should be able to handle long input videos, predict rich, detailed textual descriptions, and be able to produce outputs before processing the entire video. Current state-of-the-art models, however, process a fixed number of downsampled frames, and make a single full prediction after seeing the whole video. We propose a streaming dense video captioning model that consists of two novel components: First, we propose a new memory module, based on clustering incoming tokens, which can handle arbitrarily long videos as the memory is of a fixed size. Second, we develop a streaming decoding algorithm that enables our model to make predictions before the entire video has been processed. Our model achieves this streaming ability, and significantly improves the state-of-the-art on three dense video captioning benchmarks: ActivityNet, YouCook2 and ViTT. Our code is released at https://github.com/google-research/scenic.", "github_manual": "", "github_regex": "https://github.com/google-research/scenic", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-03", "arxiv_id": "2404.02060", "title": "Long-context LLMs Struggle with Long In-context Learning", "authors": [ "Tianle Li", "Ge Zhang", "Quy Duc Do", "Xiang Yue", "Wenhu Chen" ], "github": "https://github.com/TIGER-AI-Lab/LongICLBench", "project_page": "", "abstract": "Large Language Models (LLMs) have made significant strides in handling long sequences exceeding 32K tokens. However, their performance evaluation has largely been confined to metrics like perplexity and synthetic tasks, which may not fully capture their abilities in more nuanced, real-world scenarios. This study introduces a specialized benchmark (LIConBench) focusing on long in-context learning within the realm of extreme-label classification. We meticulously selected six datasets with a label range spanning 28 to 174 classes covering different input (few-shot demonstration) length from 2K to 50K. Our benchmark requires LLMs to comprehend the entire input to recognize the massive label spaces to make correct prediction. We evaluate 13 long-context LLMs on our benchmarks. We find that the long-context LLMs perform relatively well under the token length of 20K and the performance benefits from utilizing the long context window. However, after the context window exceeds 20K, most LLMs except GPT-4 will dip dramatically. This suggests a notable gap in current LLM capabilities for processing and understanding long, context-rich sequences. Further analysis revealed a tendency among models to favor predictions for labels presented towards the end at the sequence. Their ability to reason over multiple pieces in the long sequence is yet to be improved. Our study reveals that long context understanding and reasoning is still a challenging task for the existing LLMs. We believe LIConBench could serve as a more realistic evaluation for the future long context LLMs.", "github_manual": "https://github.com/TIGER-AI-Lab/LongICLBench", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-03", "arxiv_id": "2404.02078", "title": "Advancing LLM Reasoning Generalists with Preference Trees", "authors": [ "Lifan Yuan", "Ganqu Cui", "Hanbin Wang", "Ning Ding", "Xingyao Wang", "Jia Deng", "Boji Shan", "Huimin Chen", "Ruobing Xie", "Yankai Lin", "Zhenghao Liu", "Bowen Zhou", "Hao Peng", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/OpenBMB/Eurus", "project_page": "", "abstract": "We introduce Eurus, a suite of large language models (LLMs) optimized for reasoning. Finetuned from Mistral-7B and CodeLlama-70B, Eurus models achieve state-of-the-art results among open-source models on a diverse set of benchmarks covering mathematics, code generation, and logical reasoning problems. Notably, Eurus-70B beats GPT-3.5 Turbo in reasoning through a comprehensive benchmarking across 12 tests covering five tasks, and achieves a 33.3% pass@1 accuracy on LeetCode and 32.6% on TheoremQA, two challenging benchmarks, substantially outperforming existing open-source models by margins more than 13.3%. The strong performance of Eurus can be primarily attributed to UltraInteract, our newly-curated large-scale, high-quality alignment dataset specifically designed for complex reasoning tasks. UltraInteract can be used in both supervised fine-tuning and preference learning. For each instruction, it includes a preference tree consisting of (1) reasoning chains with diverse planning strategies in a unified format, (2) multi-turn interaction trajectories with the environment and the critique, and (3) pairwise data to facilitate preference learning. UltraInteract allows us to conduct an in-depth exploration of preference learning for reasoning tasks. Our investigation reveals that some well-established preference learning algorithms may be less suitable for reasoning tasks compared to their effectiveness in general conversations. Inspired by this, we derive a novel reward modeling objective which, together with UltraInteract, leads to a strong reward model.", "github_manual": "https://github.com/OpenBMB/Eurus", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-03", "arxiv_id": "2404.02101", "title": "CameraCtrl: Enabling Camera Control for Text-to-Video Generation", "authors": [ "Hao He", "Yinghao Xu", "Yuwei Guo", "Gordon Wetzstein", "Bo Dai", "Hongsheng Li", "Ceyuan Yang" ], "github": "https://github.com/hehao13/CameraCtrl", "project_page": "", "abstract": "Controllability plays a crucial role in video generation since it allows users to create desired content. However, existing models largely overlooked the precise control of camera pose that serves as a cinematic language to express deeper narrative nuances. To alleviate this issue, we introduce CameraCtrl, enabling accurate camera pose control for text-to-video(T2V) models. After precisely parameterizing the camera trajectory, a plug-and-play camera module is then trained on a T2V model, leaving others untouched. Additionally, a comprehensive study on the effect of various datasets is also conducted, suggesting that videos with diverse camera distribution and similar appearances indeed enhance controllability and generalization. Experimental results demonstrate the effectiveness of CameraCtrl in achieving precise and domain-adaptive camera control, marking a step forward in the pursuit of dynamic and customized video storytelling from textual and camera pose inputs. Our project website is at: https://hehao13.github.io/projects-CameraCtrl/.", "github_manual": "https://github.com/hehao13/CameraCtrl", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-03", "arxiv_id": "2404.01475", "title": "Are large language models superhuman chemists?", "authors": [ "Adrian Mirza", "Nawaf Alampara", "Sreekanth Kunchapu", "Benedict Emoekabu", "Aswanth Krishnan", "Mara Wilhelmi", "Macjonathan Okereke", "Juliane Eberhardt", "Amir Mohammad Elahi", "Maximilian Greiner", "Caroline T. Holick", "Tanya Gupta", "Mehrdad Asgari", "Christina Glaubitz", "Lea C. Klepsch", "Yannik K\u00f6ster", "Jakob Meyer", "Santiago Miret", "Tim Hoffmann", "Fabian Alexander Kreth", "Michael Ringleb", "Nicole Roesner", "Ulrich S. Schubert", "Leanne M. Stafast", "Dinga Wonanke", "Michael Pieler", "Philippe Schwaller", "Kevin Maik Jablonka" ], "github": "https://github.com/lamalab-org/chem-bench", "project_page": "", "abstract": "Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained. This is relevant for the chemical sciences, which face the problem of small and diverse datasets that are frequently in the form of text. LLMs have shown promise in addressing these issues and are increasingly being harnessed to predict chemical properties, optimize reactions, and even design and conduct experiments autonomously. However, we still have only a very limited systematic understanding of the chemical reasoning capabilities of LLMs, which would be required to improve models and mitigate potential harms. Here, we introduce \"ChemBench,\" an automated framework designed to rigorously evaluate the chemical knowledge and reasoning abilities of state-of-the-art LLMs against the expertise of human chemists. We curated more than 7,000 question-answer pairs for a wide array of subfields of the chemical sciences, evaluated leading open and closed-source LLMs, and found that the best models outperformed the best human chemists in our study on average. The models, however, struggle with some chemical reasoning tasks that are easy for human experts and provide overconfident, misleading predictions, such as about chemicals' safety profiles. These findings underscore the dual reality that, although LLMs demonstrate remarkable proficiency in chemical tasks, further research is critical to enhancing their safety and utility in chemical sciences. Our findings also indicate a need for adaptations to chemistry curricula and highlight the importance of continuing to develop evaluation frameworks to improve safe and useful LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lamalab-org/chem-bench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-03", "arxiv_id": "2404.01331", "title": "LLaVA-Gemma: Accelerating Multimodal Foundation Models with a Compact Language Model", "authors": [ "Musashi Hinck", "Matthew L. Olson", "David Cobbley", "Shao-Yen Tseng", "Vasudev Lal" ], "github": "", "project_page": "", "abstract": "We train a suite of multimodal foundation models (MMFM) using the popular LLaVA framework with the recently released Gemma family of large language models (LLMs). Of particular interest is the 2B parameter Gemma model, which provides opportunities to construct capable small-scale MMFMs. In line with findings from other papers in this space, we test the effect of ablating three design features: pretraining the connector, utilizing a more powerful image backbone, and increasing the size of the language backbone. The resulting models, which we call LLaVA-Gemma, exhibit moderate performance on an array of evaluations, but fail to improve past the current comparably sized SOTA models. Closer analysis of performance shows mixed effects; skipping pretraining tends to reduce performance, larger vision models sometimes improve performance, and increasing language model size has inconsistent effects. We publicly release training recipes, code and weights for our models for the LLaVA-Gemma models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-03", "arxiv_id": "2404.01617", "title": "LLM-ABR: Designing Adaptive Bitrate Algorithms via Large Language Models", "authors": [ "Zhiyuan He", "Aashish Gottipati", "Lili Qiu", "Francis Y. Yan", "Xufang Luo", "Kenuo Xu", "Yuqing Yang" ], "github": "https://github.com/hzy46/nada", "project_page": "", "abstract": "We present LLM-ABR, the first system that utilizes the generative capabilities of large language models (LLMs) to autonomously design adaptive bitrate (ABR) algorithms tailored for diverse network characteristics. Operating within a reinforcement learning framework, LLM-ABR empowers LLMs to design key components such as states and neural network architectures. We evaluate LLM-ABR across diverse network settings, including broadband, satellite, 4G, and 5G. LLM-ABR consistently outperforms default ABR algorithms.", "github_manual": "https://github.com/hzy46/nada", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-03", "arxiv_id": "2404.01367", "title": "Bigger is not Always Better: Scaling Properties of Latent Diffusion Models", "authors": [ "Kangfu Mei", "Zhengzhong Tu", "Mauricio Delbracio", "Hossein Talebi", "Vishal M. Patel", "Peyman Milanfar" ], "github": "", "project_page": "", "abstract": "We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency. While improved network architecture and inference algorithms have shown to effectively boost sampling efficiency of diffusion models, the role of model size -- a critical determinant of sampling efficiency -- has not been thoroughly examined. Through empirical analysis of established text-to-image diffusion models, we conduct an in-depth investigation into how model size influences sampling efficiency across varying sampling steps. Our findings unveil a surprising trend: when operating under a given inference budget, smaller models frequently outperform their larger equivalents in generating high-quality results. Moreover, we extend our study to demonstrate the generalizability of the these findings by applying various diffusion samplers, exploring diverse downstream tasks, evaluating post-distilled models, as well as comparing performance relative to training compute. These findings open up new pathways for the development of LDM scaling strategies which can be employed to enhance generative capabilities within limited inference budgets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-03", "arxiv_id": "2404.01856", "title": "Poro 34B and the Blessing of Multilinguality", "authors": [ "Risto Luukkonen", "Jonathan Burdge", "Elaine Zosa", "Aarne Talman", "Ville Komulainen", "V\u00e4in\u00f6 Hatanp\u00e4\u00e4", "Peter Sarlin", "Sampo Pyysalo" ], "github": "", "project_page": "", "abstract": "The pretraining of state-of-the-art large language models now requires trillions of words of text, which is orders of magnitude more than available for the vast majority of languages. While including text in more than one language is an obvious way to acquire more pretraining data, multilinguality is often seen as a curse, and most model training efforts continue to focus near-exclusively on individual large languages. We believe that multilinguality can be a blessing and that it should be possible to substantially improve over the capabilities of monolingual models for small languages through multilingual training. In this study, we introduce Poro 34B, a 34 billion parameter model trained for 1 trillion tokens of Finnish, English, and programming languages, and demonstrate that a multilingual training approach can produce a model that not only substantially advances over the capabilities of existing models for Finnish, but also excels in translation and is competitive in its class in generating English and programming languages. We release the model parameters, scripts, and data under open licenses at https://huggingface.co/LumiOpen/Poro-34B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-03", "arxiv_id": "2404.01744", "title": "Octopus v2: On-device language model for super agent", "authors": [ "Wei Chen", "Zhiyuan Li" ], "github": "https://github.com/mlc-ai/mlc-llm", "project_page": "", "abstract": "Language models have shown effectiveness in a variety of software applications, particularly in tasks related to automatic workflow. These models possess the crucial ability to call functions, which is essential in creating AI agents. Despite the high performance of large-scale language models in cloud environments, they are often associated with concerns over privacy and cost. Current on-device models for function calling face issues with latency and accuracy. Our research presents a new method that empowers an on-device model with 2 billion parameters to surpass the performance of GPT-4 in both accuracy and latency, and decrease the context length by 95\\%. When compared to Llama-7B with a RAG-based function calling mechanism, our method enhances latency by 35-fold. This method reduces the latency to levels deemed suitable for deployment across a variety of edge devices in production environments, aligning with the performance requisites for real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mlc-ai/mlc-llm", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-03", "arxiv_id": "2404.02125", "title": "3D Congealing: 3D-Aware Image Alignment in the Wild", "authors": [ "Yunzhi Zhang", "Zizhang Li", "Amit Raj", "Andreas Engelhardt", "Yuanzhen Li", "Tingbo Hou", "Jiajun Wu", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "We propose 3D Congealing, a novel problem of 3D-aware alignment for 2D images capturing semantically similar objects. Given a collection of unlabeled Internet images, our goal is to associate the shared semantic parts from the inputs and aggregate the knowledge from 2D images to a shared 3D canonical space. We introduce a general framework that tackles the task without assuming shape templates, poses, or any camera parameters. At its core is a canonical 3D representation that encapsulates geometric and semantic information. The framework optimizes for the canonical representation together with the pose for each input image, and a per-image coordinate map that warps 2D pixel coordinates to the 3D canonical frame to account for the shape matching. The optimization procedure fuses prior knowledge from a pre-trained image generative model and semantic information from input images. The former provides strong knowledge guidance for this under-constraint task, while the latter provides the necessary information to mitigate the training data bias from the pre-trained model. Our framework can be used for various tasks such as correspondence matching, pose estimation, and image editing, achieving strong results on real-world image datasets under challenging illumination conditions and on in-the-wild online image collections.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-03", "arxiv_id": "2404.01954", "title": "HyperCLOVA X Technical Report", "authors": [ "Kang Min Yoo", "Jaegeun Han", "Sookyo In", "Heewon Jeon", "Jisu Jeong", "Jaewook Kang", "Hyunwook Kim", "Kyung-Min Kim", "Munhyong Kim", "Sungju Kim", "Donghyun Kwak", "Hanock Kwak", "Se Jung Kwon", "Bado Lee", "Dongsoo Lee", "Gichang Lee", "Jooho Lee", "Baeseong Park", "Seongjin Shin", "Joonsang Yu", "Seolki Baek", "Sumin Byeon", "Eungsup Cho", "Dooseok Choe", "Jeesung Han", "Youngkyun Jin", "Hyein Jun", "Jaeseung Jung", "Chanwoong Kim", "Jinhong Kim", "Jinuk Kim", "Dokyeong Lee", "Dongwook Park", "Jeong Min Sohn", "Sujung Han", "Jiae Heo", "Sungju Hong", "Mina Jeon", "Hyunhoon Jung", "Jungeun Jung", "Wangkyo Jung", "Chungjoon Kim", "Hyeri Kim", "Jonghyun Kim", "Min Young Kim", "Soeun Lee", "Joonhee Park", "Jieun Shin", "Sojin Yang", "Jungsoon Yoon", "Hwaran Lee", "Sanghwan Bae", "Jeehwan Cha", "Donghoon Ham", "Youngki Hong", "Yunki Hong", "Myunggeun Ji", "Yeguk Jin", "Chansong Jo", "Shinyoung Joo", "Seunghwan Jung", "Hyomin Kim", "Jungwhan Kim", "Minkyoung Kim", "Minseung Kim", "Sungdong Kim", "Yonghee Kim", "Youngjun Kim", "Donghyeon Ko", "Dughyun Lee", "Jaehong Lee", "Jieun Lee", "Jongjin Lee", "Min Young Lee", "Yehbin Lee", "Taehong Min", "Kiyoon Moon", "Jaesun Park", "Kyuyon Park", "Seunghyun Seo", "Gyubin Son", "Wonjoon Yoo", "Myungin You", "Doheon Ahn", "Homin Ahn", "Joohee Ahn", "Seongmin Ahn", "Chanwoo An", "Hyeryun An", "Junho An", "Sang-Min An", "Boram Byun", "Jongho Cha", "Minji Chang", "Seunggyu Chang", "Haesong Cho", "Youngdo Cho", "Dalnim Choi", "Daseul Choi", "Hyoseok Choi", "Minseong Choi", "Sangho Choi", "Seongjae Choi", "Wooyong Choi", "Sewhan Chun", "Dong Young Go", "Chiheon Ham", "Danbi Han", "Jaemin Han", "Mihak Hong", "Moonyoung Hong", "Sung Bum Hong", "Seongchan Hwang", "Eunbin Hyun", "Jinbae Im", "Jaehyung Jang", "Jaeni Jang", "Sihyeon Jang", "Sungwon Jang", "Joonha Jeon", "Yujin Jeon", "Daun Jeong", "Joonhyun Jeong", "Kyeongseok Jeong", "Mini Jeong", "Yeji Jeong", "Sol Jin", "Hanbyeol Jo", "Hanju Jo", "Minjung Jo", "Lee Jonghyun", "Chaeyoon Jung", "Hyungsik Jung", "Jaeuk Jung", "Ju Hwan Jung", "Kwangsun Jung", "Seungjae Jung", "Soonwon Ka", "Donghan Kang", "Soyoung Kang", "Taeho Kil", "Areum Kim", "Beomyoung Kim", "Byeongwook Kim", "Daehee Kim", "Dong-Gyun Kim", "Donggook Kim", "Donghyun Kim", "Euna Kim", "Eunchul Kim", "Geewook Kim", "Gyu Ri Kim", "Hanbyul Kim", "Heesu Kim", "Isaac Kim", "Jeonghoon Kim", "Jihye Kim", "Joonghoon Kim", "Minjae Kim", "Minsub Kim", "Pil Hwan Kim", "Sammy Kim", "Seokhun Kim", "Seonghyeon Kim", "Soojin Kim", "Soong Kim", "Soyoon Kim", "Sunyoung Kim", "Taeho Kim", "Wonho Kim", "Yoonsik Kim", "You Jin Kim", "Yuri Kim", "Beomseok Kwon", "Ohsung Kwon", "Yoo-Hwan Kwon", "Anna Lee", "Byungwook Lee", "Changho Lee", "Daun Lee", "Dongjae Lee", "Ha-Ram Lee", "Hodong Lee", "Hwiyeong Lee", "Hyunmi Lee", "Injae Lee", "Jaeung Lee", "Jeongsang Lee", "Jisoo Lee", "Joongjae Lee", "Juhan Lee", "Jung Hyun Lee", "Junghoon Lee", "Junwoo Lee", "Se Yun Lee", "Sujin Lee", "Sungjae Lee", "Sungwoo Lee", "Wonjae Lee", "Zoo Hyun Lee", "Jong Kun Lim", "Kun Lim", "Taemin Lim", "Yuri Min", "Nuri Na", "Jeongyeon Nam", "Kyeong-Min Nam", "Yeonseog Noh", "Biro Oh", "Hyangnam Oh", "Jung-Sik Oh", "Solgil Oh", "Yeontaek Oh", "Boyoun Park", "Cheonbok Park", "Dongju Park", "Hyeonjin Park", "Hyun Tae Park", "Hyunjung Park", "Jihye Park", "Jooseok Park", "Junghwan Park", "Jungsoo Park", "Miru Park", "Sang Hee Park", "Seunghyun Park", "Taerim Park", "Wonkyeong Park", "Hyunjoon Ryu", "Jeonghun Ryu", "Nahyeon Ryu", "Soonshin Seo", "Suk Min Seo", "Yoonjeong Shim", "Kyuyong Shin", "Wonkwang Shin", "Hyun Sim", "Mihyun Sim", "Woongseob Sim", "Hyejin Soh", "Bokyoung Son", "Hyunjun Son", "Seulah Son", "Chi-Yun Song", "Chiyoung Song", "Ka Yeon Song", "Minchul Song", "Seungmin Song", "Jisung Wang", "Matt Yeo", "Yonggoo Yeo", "Myeong Yeon Yi", "Moon Bin Yim", "Taehwan Yoo", "Youngjoon Yoo", "Sungmin Yoon", "Young Jin Yoon", "Hangyeol Yu", "Ui Seon Yu", "Xingdong Zuo", "Jeongin Bae", "Joungeun Bae", "Hyunsoo Cho", "Seonghyun Cho", "Yongjin Cho", "Taekyoon Choi", "Yera Choi", "Jiwan Chung", "Zhenghui Han", "Byeongho Heo", "Euisuk Hong", "Taebaek Hwang", "Seonyeol Im", "Sumin Jegal", "Sumin Jeon", "Yelim Jeong", "Yonghyun Jeong", "Can Jiang", "Juyong Jiang", "Jiho Jin", "Ara Jo", "Younghyun Jo", "Hoyoun Jung", "Juyoung Jung", "Dae Hee Kim", "Ginam Kim", "Hangyeol Kim", "Heeseung Kim", "Hyojin Kim", "Hyojun Kim", "Hyun-Ah Kim", "Jeehye Kim", "Jin-Hwa Kim", "Jiseon Kim", "Jonghak Kim", "Jung Yoon Kim", "Rak Yeong Kim", "Seoyoon Kim", "Sewon Kim", "Sooyoung Kim", "Sukyoung Kim", "Taeyong Kim", "Naeun Ko", "Bonseung Koo", "Heeyoung Kwak", "Haena Kwon", "Youngjin Kwon", "Boram Lee", "Bruce W. Lee", "Dagyeong Lee", "Erin Lee", "Euijin Lee", "Ha Gyeong Lee", "Hyojin Lee", "Hyunjeong Lee", "Jeeyoon Lee", "Jeonghyun Lee", "Jongheok Lee", "Joonhyung Lee", "Junhyuk Lee", "Mingu Lee", "Nayeon Lee", "Sangkyu Lee", "Se Young Lee", "Seulgi Lee", "Seung Jin Lee", "Suhyeon Lee", "Yeonjae Lee", "Yesol Lee", "Youngbeom Lee", "Yujin Lee", "Shaodong Li", "Tianyu Liu", "Seong-Eun Moon", "Taehong Moon", "Max-Lasse Nihlenramstroem", "Wonseok Oh", "Yuri Oh", "Hongbeen Park", "Hyekyung Park", "Nohil Park", "Sangjin Park", "Jiwon Ryu", "Miru Ryu", "Simo Ryu", "Ahreum Seo", "Hee Seo", "Kangdeok Seo", "Jamin Shin", "Seungyoun Shin", "Heetae Sin", "Jiangping Wang", "Lei Wang", "Ning Xiang", "Longxiang Xiao", "Jing Xu", "Seonyeong Yi", "Haanju Yoo", "Haneul Yoo", "Hwanhee Yoo", "Liang Yu", "Youngjae Yu", "Weijie Yuan", "Bo Zeng", "Qian Zhou", "Kyunghyun Cho", "Jung-Woo Ha", "Joonsuk Park", "Jihyun Hwang", "Hyoung Jo Kwon", "Soonyong Kwon", "Jungyeon Lee", "Seungho Lee", "Seungho Choi", "Sang-Woo Lee", "Jung Hwa Lim", "Nako Sung" ], "github": "", "project_page": "", "abstract": "We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-04", "arxiv_id": "2404.02905", "title": "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction", "authors": [ "Keyu Tian", "Yi Jiang", "Zehuan Yuan", "Bingyue Peng", "Liwei Wang" ], "github": "https://github.com/FoundationVision/VAR", "project_page": "", "abstract": "We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine \"next-scale prediction\" or \"next-resolution prediction\", diverging from the standard raster-scan \"next-token prediction\". This simple, intuitive methodology allows autoregressive (AR) transformers to learn visual distributions fast and generalize well: VAR, for the first time, makes AR models surpass diffusion transformers in image generation. On ImageNet 256x256 benchmark, VAR significantly improve AR baseline by improving Frechet inception distance (FID) from 18.65 to 1.80, inception score (IS) from 80.4 to 356.4, with around 20x faster inference speed. It is also empirically verified that VAR outperforms the Diffusion Transformer (DiT) in multiple dimensions including image quality, inference speed, data efficiency, and scalability. Scaling up VAR models exhibits clear power-law scaling laws similar to those observed in LLMs, with linear correlation coefficients near -0.998 as solid evidence. VAR further showcases zero-shot generalization ability in downstream tasks including image in-painting, out-painting, and editing. These results suggest VAR has initially emulated the two important properties of LLMs: Scaling Laws and zero-shot task generalization. We have released all models and codes to promote the exploration of AR/VAR models for visual generation and unified learning.", "github_manual": "https://github.com/FoundationVision/VAR", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-04", "arxiv_id": "2404.02883", "title": "On the Scalability of Diffusion-based Text-to-Image Generation", "authors": [ "Hao Li", "Yang Zou", "Ying Wang", "Orchid Majumder", "Yusheng Xie", "R. Manmatha", "Ashwin Swaminathan", "Zhuowen Tu", "Stefano Ermon", "Stefano Soatto" ], "github": "", "project_page": "", "abstract": "Scaling up model and data size has been quite successful for the evolution of LLMs. However, the scaling law for the diffusion based text-to-image (T2I) models is not fully explored. It is also unclear how to efficiently scale the model for better performance at reduced cost. The different training settings and expensive training cost make a fair model comparison extremely difficult. In this work, we empirically study the scaling properties of diffusion based T2I models by performing extensive and rigours ablations on scaling both denoising backbones and training set, including training scaled UNet and Transformer variants ranging from 0.4B to 4B parameters on datasets upto 600M images. For model scaling, we find the location and amount of cross attention distinguishes the performance of existing UNet designs. And increasing the transformer blocks is more parameter-efficient for improving text-image alignment than increasing channel numbers. We then identify an efficient UNet variant, which is 45% smaller and 28% faster than SDXL's UNet. On the data scaling side, we show the quality and diversity of the training set matters more than simply dataset size. Increasing caption density and diversity improves text-image alignment performance and the learning efficiency. Finally, we provide scaling functions to predict the text-image alignment performance as functions of the scale of model size, compute and dataset size.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-04", "arxiv_id": "2404.02893", "title": "ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline", "authors": [ "Yifan Xu", "Xiao Liu", "Xinghan Liu", "Zhenyu Hou", "Yueyan Li", "Xiaohan Zhang", "Zihan Wang", "Aohan Zeng", "Zhengxiao Du", "Wenyi Zhao", "Jie Tang", "Yuxiao Dong" ], "github": "https://github.com/THUDM/ChatGLM-Math", "project_page": "", "abstract": "Large language models (LLMs) have shown excellent mastering of human language, but still struggle in real-world applications that require mathematical problem-solving. While many strategies and datasets to enhance LLMs' mathematics are developed, it remains a challenge to simultaneously maintain and improve both language and mathematical capabilities in deployed LLM systems.In this work, we tailor the Self-Critique pipeline, which addresses the challenge in the feedback learning stage of LLM alignment. We first train a general Math-Critique model from the LLM itself to provide feedback signals. Then, we sequentially employ rejective fine-tuning and direct preference optimization over the LLM's own generations for data collection. Based on ChatGLM3-32B, we conduct a series of experiments on both academic and our newly created challenging dataset, MathUserEval. Results show that our pipeline significantly enhances the LLM's mathematical problem-solving while still improving its language ability, outperforming LLMs that could be two times larger. Related techniques have been deployed to ChatGLM\\url{https://chatglm.cn}, an online serving LLM. Related evaluation dataset and scripts are released at https://github.com/THUDM/ChatGLM-Math.", "github_manual": "", "github_regex": "https://github.com/THUDM/ChatGLM-Math", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-04", "arxiv_id": "2404.02747", "title": "Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models", "authors": [ "Wentian Zhang", "Haozhe Liu", "Jinheng Xie", "Francesco Faccio", "Mike Zheng Shou", "J\u00fcrgen Schmidhuber" ], "github": "https://github.com/HaozheLiu-ST/T-GATE", "project_page": "", "abstract": "This study explores the role of cross-attention during inference in text-conditional diffusion models. We find that cross-attention outputs converge to a fixed point after few inference steps. Accordingly, the time point of convergence naturally divides the entire inference process into two stages: an initial semantics-planning stage, during which, the model relies on cross-attention to plan text-oriented visual semantics, and a subsequent fidelity-improving stage, during which the model tries to generate images from previously planned semantics. Surprisingly, ignoring text conditions in the fidelity-improving stage not only reduces computation complexity, but also maintains model performance. This yields a simple and training-free method called TGATE for efficient generation, which caches the cross-attention output once it converges and keeps it fixed during the remaining inference steps. Our empirical study on the MS-COCO validation set confirms its effectiveness. The source code of TGATE is available at https://github.com/HaozheLiu-ST/T-GATE.", "github_manual": "", "github_regex": "https://github.com/HaozheLiu-ST/T-GATE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-04", "arxiv_id": "2404.02733", "title": "InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation", "authors": [ "Haofan Wang", "Qixun Wang", "Xu Bai", "Zekui Qin", "Anthony Chen" ], "github": "https://github.com/instantX-research/InstantStyle", "project_page": "", "abstract": "Tuning-free diffusion-based models have demonstrated significant potential in the realm of image personalization and customization. However, despite this notable progress, current models continue to grapple with several complex challenges in producing style-consistent image generation. Firstly, the concept of style is inherently underdetermined, encompassing a multitude of elements such as color, material, atmosphere, design, and structure, among others. Secondly, inversion-based methods are prone to style degradation, often resulting in the loss of fine-grained details. Lastly, adapter-based approaches frequently require meticulous weight tuning for each reference image to achieve a balance between style intensity and text controllability. In this paper, we commence by examining several compelling yet frequently overlooked observations. We then proceed to introduce InstantStyle, a framework designed to address these issues through the implementation of two key strategies: 1) A straightforward mechanism that decouples style and content from reference images within the feature space, predicated on the assumption that features within the same space can be either added to or subtracted from one another. 2) The injection of reference image features exclusively into style-specific blocks, thereby preventing style leaks and eschewing the need for cumbersome weight tuning, which often characterizes more parameter-heavy designs.Our work demonstrates superior visual stylization outcomes, striking an optimal balance between the intensity of style and the controllability of textual elements. Our codes will be available at https://github.com/InstantStyle/InstantStyle.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/instantX-research/InstantStyle", "github_api_added_by": "user" }, { "date": "2024-04-04", "arxiv_id": "2404.02575", "title": "Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models", "authors": [ "Hyungjoo Chae", "Yeonghyeon Kim", "Seungone Kim", "Kai Tzu-iunn Ong", "Beong-woo Kwak", "Moohyeon Kim", "Seonghwan Kim", "Taeyoon Kwon", "Jiwan Chung", "Youngjae Yu", "Jinyoung Yeo" ], "github": "", "project_page": "", "abstract": "Algorithmic reasoning refers to the ability to understand the complex patterns behind the problem and decompose them into a sequence of reasoning steps towards the solution. Such nature of algorithmic reasoning makes it a challenge for large language models (LLMs), even though they have demonstrated promising performance in other reasoning tasks. Within this context, some recent studies use programming languages (e.g., Python) to express the necessary logic for solving a given instance/question (e.g., Program-of-Thought) as inspired by their strict and precise syntaxes. However, it is non-trivial to write an executable code that expresses the correct logic on the fly within a single inference call. Also, the code generated specifically for an instance cannot be reused for others, even if they are from the same task and might require identical logic to solve. This paper presents Think-and-Execute, a novel framework that decomposes the reasoning process of language models into two steps. (1) In Think, we discover a task-level logic that is shared across all instances for solving a given task and then express the logic with pseudocode; (2) In Execute, we further tailor the generated pseudocode to each instance and simulate the execution of the code. With extensive experiments on seven algorithmic reasoning tasks, we demonstrate the effectiveness of Think-and-Execute. Our approach better improves LMs' reasoning compared to several strong baselines performing instance-specific reasoning (e.g., CoT and PoT), suggesting the helpfulness of discovering task-level logic. Also, we show that compared to natural language, pseudocode can better guide the reasoning of LMs, even though they are trained to follow natural language instructions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-04", "arxiv_id": "2404.02258", "title": "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models", "authors": [ "David Raposo", "Sam Ritter", "Blake Richards", "Timothy Lillicrap", "Peter Conway Humphreys", "Adam Santoro" ], "github": "", "project_page": "", "abstract": "Transformer-based language models spread FLOPs uniformly across input sequences. In this work we demonstrate that transformers can instead learn to dynamically allocate FLOPs (or compute) to specific positions in a sequence, optimising the allocation along the sequence for different layers across the model depth. Our method enforces a total compute budget by capping the number of tokens (k) that can participate in the self-attention and MLP computations at a given layer. The tokens to be processed are determined by the network using a top-k routing mechanism. Since k is defined a priori, this simple procedure uses a static computation graph with known tensor sizes, unlike other conditional computation techniques. Nevertheless, since the identities of the k tokens are fluid, this method can expend FLOPs non-uniformly across the time and model depth dimensions. Thus, compute expenditure is entirely predictable in sum total, but dynamic and context-sensitive at the token-level. Not only do models trained in this way learn to dynamically allocate compute, they do so efficiently. These models match baseline performance for equivalent FLOPS and wall-clock times to train, but require a fraction of the FLOPs per forward pass, and can be upwards of 50\\% faster to step during post-training sampling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-04", "arxiv_id": "2404.02514", "title": "Freditor: High-Fidelity and Transferable NeRF Editing by Frequency Decomposition", "authors": [ "Yisheng He", "Weihao Yuan", "Siyu Zhu", "Zilong Dong", "Liefeng Bo", "Qixing Huang" ], "github": "", "project_page": "", "abstract": "This paper enables high-fidelity, transferable NeRF editing by frequency decomposition. Recent NeRF editing pipelines lift 2D stylization results to 3D scenes while suffering from blurry results, and fail to capture detailed structures caused by the inconsistency between 2D editings. Our critical insight is that low-frequency components of images are more multiview-consistent after editing compared with their high-frequency parts. Moreover, the appearance style is mainly exhibited on the low-frequency components, and the content details especially reside in high-frequency parts. This motivates us to perform editing on low-frequency components, which results in high-fidelity edited scenes. In addition, the editing is performed in the low-frequency feature space, enabling stable intensity control and novel scene transfer. Comprehensive experiments conducted on photorealistic datasets demonstrate the superior performance of high-fidelity and transferable NeRF editing. The project page is at https://aigc3d.github.io/freditor.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-05", "arxiv_id": "2404.03543", "title": "CodeEditorBench: Evaluating Code Editing Capability of Large Language Models", "authors": [ "Jiawei Guo", "Ziming Li", "Xueling Liu", "Kaijing Ma", "Tianyu Zheng", "Zhouliang Yu", "Ding Pan", "Yizhi LI", "Ruibo Liu", "Yue Wang", "Shuyue Guo", "Xingwei Qu", "Xiang Yue", "Ge Zhang", "Wenhu Chen", "Jie Fu" ], "github": "https://github.com/CodeEditorBench/CodeEditorBench", "project_page": "", "abstract": "Large Language Models (LLMs) for code are rapidly evolving, with code editing emerging as a critical capability. We introduce CodeEditorBench, an evaluation framework designed to rigorously assess the performance of LLMs in code editing tasks, including debugging, translating, polishing, and requirement switching. Unlike existing benchmarks focusing solely on code generation, CodeEditorBench emphasizes real-world scenarios and practical aspects of software development. We curate diverse coding challenges and scenarios from five sources, covering various programming languages, complexity levels, and editing tasks. Evaluation of 19 LLMs reveals that closed-source models (particularly Gemini-Ultra and GPT-4), outperform open-source models in CodeEditorBench, highlighting differences in model performance based on problem types and prompt sensitivities. CodeEditorBench aims to catalyze advancements in LLMs by providing a robust platform for assessing code editing capabilities. We will release all prompts and datasets to enable the community to expand the dataset and benchmark emerging LLMs. By introducing CodeEditorBench, we contribute to the advancement of LLMs in code editing and provide a valuable resource for researchers and practitioners.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CodeEditorBench/CodeEditorBench", "github_api_added_by": "auto" }, { "date": "2024-04-05", "arxiv_id": "2404.03592", "title": "ReFT: Representation Finetuning for Language Models", "authors": [ "Zhengxuan Wu", "Aryaman Arora", "Zheng Wang", "Atticus Geiger", "Dan Jurafsky", "Christopher D. Manning", "Christopher Potts" ], "github": "https://github.com/stanfordnlp/pyreft", "project_page": "", "abstract": "Parameter-efficient fine-tuning (PEFT) methods seek to adapt large models via updates to a small number of weights. However, much prior interpretability work has shown that representations encode rich semantic information, suggesting that editing representations might be a more powerful alternative. Here, we pursue this hypothesis by developing a family of Representation Finetuning (ReFT) methods. ReFT methods operate on a frozen base model and learn task-specific interventions on hidden representations. We define a strong instance of the ReFT family, Low-rank Linear Subspace ReFT (LoReFT). LoReFT is a drop-in replacement for existing PEFTs and learns interventions that are 10x-50x more parameter-efficient than prior state-of-the-art PEFTs. We showcase LoReFT on eight commonsense reasoning tasks, four arithmetic reasoning tasks, Alpaca-Eval v1.0, and GLUE. In all these evaluations, LoReFT delivers the best balance of efficiency and performance, and almost always outperforms state-of-the-art PEFTs. We release a generic ReFT training library publicly at https://github.com/stanfordnlp/pyreft.", "github_manual": "", "github_regex": "https://github.com/stanfordnlp/pyreft", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-05", "arxiv_id": "2404.03626", "title": "Training LLMs over Neurally Compressed Text", "authors": [ "Brian Lester", "Jaehoon Lee", "Alex Alemi", "Jeffrey Pennington", "Adam Roberts", "Jascha Sohl-Dickstein", "Noah Constant" ], "github": "", "project_page": "", "abstract": "In this paper, we explore the idea of training large language models (LLMs) over highly compressed text. While standard subword tokenizers compress text by a small factor, neural text compressors can achieve much higher rates of compression. If it were possible to train LLMs directly over neurally compressed text, this would confer advantages in training and serving efficiency, as well as easier handling of long text spans. The main obstacle to this goal is that strong compression tends to produce opaque outputs that are not well-suited for learning. In particular, we find that text na\\\"ively compressed via Arithmetic Coding is not readily learnable by LLMs. To overcome this, we propose Equal-Info Windows, a novel compression technique whereby text is segmented into blocks that each compress to the same bit length. Using this method, we demonstrate effective learning over neurally compressed text that improves with scale, and outperforms byte-level baselines by a wide margin on perplexity and inference speed benchmarks. While our method delivers worse perplexity than subword tokenizers for models trained with the same parameter count, it has the benefit of shorter sequence lengths. Shorter sequence lengths require fewer autoregressive generation steps, and reduce latency. Finally, we provide extensive analysis of the properties that contribute to learnability, and offer concrete suggestions for how to further improve the performance of high-compression tokenizers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-05", "arxiv_id": "2404.03648", "title": "AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent", "authors": [ "Hanyu Lai", "Xiao Liu", "Iat Long Iong", "Shuntian Yao", "Yuxuan Chen", "Pengbo Shen", "Hao Yu", "Hanchen Zhang", "Xiaohan Zhang", "Yuxiao Dong", "Jie Tang" ], "github": "https://github.com/THUDM/AutoWebGLM", "project_page": "", "abstract": "Large language models (LLMs) have fueled many intelligent agent tasks, such as web navigation -- but most existing agents perform far from satisfying in real-world webpages due to three factors: (1) the versatility of actions on webpages, (2) HTML text exceeding model processing capacity, and (3) the complexity of decision-making due to the open-domain nature of web. In light of the challenge, we develop AutoWebGLM, a GPT-4-outperforming automated web navigation agent built upon ChatGLM3-6B. Inspired by human browsing patterns, we design an HTML simplification algorithm to represent webpages, preserving vital information succinctly. We employ a hybrid human-AI method to build web browsing data for curriculum training. Then, we bootstrap the model by reinforcement learning and rejection sampling to further facilitate webpage comprehension, browser operations, and efficient task decomposition by itself. For testing, we establish a bilingual benchmark -- AutoWebBench -- for real-world web browsing tasks. We evaluate AutoWebGLM across diverse web navigation benchmarks, revealing its improvements but also underlying challenges to tackle real environments. Related code, model, and data will be released at https://github.com/THUDM/AutoWebGLM.", "github_manual": "", "github_regex": "https://github.com/THUDM/AutoWebGLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-05", "arxiv_id": "2404.03411", "title": "Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?", "authors": [ "Shuo Chen", "Zhen Han", "Bailan He", "Zifeng Ding", "Wenqian Yu", "Philip Torr", "Volker Tresp", "Jindong Gu" ], "github": "https://github.com/chenxshuo/redteaminggpt4v", "project_page": "", "abstract": "Various jailbreak attacks have been proposed to red-team Large Language Models (LLMs) and revealed the vulnerable safeguards of LLMs. Besides, some methods are not limited to the textual modality and extend the jailbreak attack to Multimodal Large Language Models (MLLMs) by perturbing the visual input. However, the absence of a universal evaluation benchmark complicates the performance reproduction and fair comparison. Besides, there is a lack of comprehensive evaluation of closed-source state-of-the-art (SOTA) models, especially MLLMs, such as GPT-4V. To address these issues, this work first builds a comprehensive jailbreak evaluation dataset with 1445 harmful questions covering 11 different safety policies. Based on this dataset, extensive red-teaming experiments are conducted on 11 different LLMs and MLLMs, including both SOTA proprietary models and open-source models. We then conduct a deep analysis of the evaluated results and find that (1) GPT4 and GPT-4V demonstrate better robustness against jailbreak attacks compared to open-source LLMs and MLLMs. (2) Llama2 and Qwen-VL-Chat are more robust compared to other open-source models. (3) The transferability of visual jailbreak methods is relatively limited compared to textual jailbreak methods. The dataset and code can be found here https://anonymous.4open.science/r/red_teaming_gpt4-C1CE/README.md .", "github_manual": "https://github.com/chenxshuo/redteaminggpt4v", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-05", "arxiv_id": "2404.03653", "title": "CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching", "authors": [ "Dongzhi Jiang", "Guanglu Song", "Xiaoshi Wu", "Renrui Zhang", "Dazhong Shen", "Zhuofan Zong", "Yu Liu", "Hongsheng Li" ], "github": "https://github.com/CaraJ7/CoMat", "project_page": "", "abstract": "Diffusion models have demonstrated great success in the field of text-to-image generation. However, alleviating the misalignment between the text prompts and images is still challenging. The root reason behind the misalignment has not been extensively investigated. We observe that the misalignment is caused by inadequate token attention activation. We further attribute this phenomenon to the diffusion model's insufficient condition utilization, which is caused by its training paradigm. To address the issue, we propose CoMat, an end-to-end diffusion model fine-tuning strategy with an image-to-text concept matching mechanism. We leverage an image captioning model to measure image-to-text alignment and guide the diffusion model to revisit ignored tokens. A novel attribute concentration module is also proposed to address the attribute binding problem. Without any image or human preference data, we use only 20K text prompts to fine-tune SDXL to obtain CoMat-SDXL. Extensive experiments show that CoMat-SDXL significantly outperforms the baseline model SDXL in two text-to-image alignment benchmarks and achieves start-of-the-art performance.", "github_manual": "https://github.com/CaraJ7/CoMat", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-05", "arxiv_id": "2404.03566", "title": "PointInfinity: Resolution-Invariant Point Diffusion Models", "authors": [ "Zixuan Huang", "Justin Johnson", "Shoubhik Debnath", "James M. Rehg", "Chao-Yuan Wu" ], "github": "", "project_page": "", "abstract": "We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant latent representation. This enables efficient training with low-resolution point clouds, while allowing high-resolution point clouds to be generated during inference. More importantly, we show that scaling the test-time resolution beyond the training resolution improves the fidelity of generated point clouds and surfaces. We analyze this phenomenon and draw a link to classifier-free guidance commonly used in diffusion models, demonstrating that both allow trading off fidelity and variability during inference. Experiments on CO3D show that PointInfinity can efficiently generate high-resolution point clouds (up to 131k points, 31 times more than Point-E) with state-of-the-art quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-05", "arxiv_id": "2404.03118", "title": "LVLM-Intrepret: An Interpretability Tool for Large Vision-Language Models", "authors": [ "Gabriela Ben Melech Stan", "Raanan Yehezkel Rohekar", "Yaniv Gurwicz", "Matthew Lyle Olson", "Anahita Bhiwandiwalla", "Estelle Aflalo", "Chenfei Wu", "Nan Duan", "Shao-Yen Tseng", "Vasudev Lal" ], "github": "", "project_page": "", "abstract": "In the rapidly evolving landscape of artificial intelligence, multi-modal large language models are emerging as a significant area of interest. These models, which combine various forms of data input, are becoming increasingly popular. However, understanding their internal mechanisms remains a complex task. Numerous advancements have been made in the field of explainability tools and mechanisms, yet there is still much to explore. In this work, we present a novel interactive application aimed towards understanding the internal mechanisms of large vision-language models. Our interface is designed to enhance the interpretability of the image patches, which are instrumental in generating an answer, and assess the efficacy of the language model in grounding its output in the image. With our application, a user can systematically investigate the model and uncover system limitations, paving the way for enhancements in system capabilities. Finally, we present a case study of how our application can aid in understanding failure mechanisms in a popular large multi-modal model: LLaVA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-05", "arxiv_id": "2404.03204", "title": "RALL-E: Robust Codec Language Modeling with Chain-of-Thought Prompting for Text-to-Speech Synthesis", "authors": [ "Detai Xin", "Xu Tan", "Kai Shen", "Zeqian Ju", "Dongchao Yang", "Yuancheng Wang", "Shinnosuke Takamichi", "Hiroshi Saruwatari", "Shujie Liu", "Jinyu Li", "Sheng Zhao" ], "github": "", "project_page": "", "abstract": "We present RALL-E, a robust language modeling method for text-to-speech (TTS) synthesis. While previous work based on large language models (LLMs) shows impressive performance on zero-shot TTS, such methods often suffer from poor robustness, such as unstable prosody (weird pitch and rhythm/duration) and a high word error rate (WER), due to the autoregressive prediction style of language models. The core idea behind RALL-E is chain-of-thought (CoT) prompting, which decomposes the task into simpler steps to enhance the robustness of LLM-based TTS. To accomplish this idea, RALL-E first predicts prosody features (pitch and duration) of the input text and uses them as intermediate conditions to predict speech tokens in a CoT style. Second, RALL-E utilizes the predicted duration prompt to guide the computing of self-attention weights in Transformer to enforce the model to focus on the corresponding phonemes and prosody features when predicting speech tokens. Results of comprehensive objective and subjective evaluations demonstrate that, compared to a powerful baseline method VALL-E, RALL-E significantly improves the WER of zero-shot TTS from 6.3% (without reranking) and 2.1% (with reranking) to 2.8% and 1.0%, respectively. Furthermore, we demonstrate that RALL-E correctly synthesizes sentences that are hard for VALL-E and reduces the error rate from 68% to 4%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-05", "arxiv_id": "2404.03413", "title": "MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens", "authors": [ "Kirolos Ataallah", "Xiaoqian Shen", "Eslam Abdelrahman", "Essam Sleiman", "Deyao Zhu", "Jian Ding", "Mohamed Elhoseiny" ], "github": "https://github.com/Vision-CAIR/MiniGPT4-video", "project_page": "", "abstract": "This paper introduces MiniGPT4-Video, a multimodal Large Language Model (LLM) designed specifically for video understanding. The model is capable of processing both temporal visual and textual data, making it adept at understanding the complexities of videos. Building upon the success of MiniGPT-v2, which excelled in translating visual features into the LLM space for single images and achieved impressive results on various image-text benchmarks, this paper extends the model's capabilities to process a sequence of frames, enabling it to comprehend videos. MiniGPT4-video does not only consider visual content but also incorporates textual conversations, allowing the model to effectively answer queries involving both visual and text components. The proposed model outperforms existing state-of-the-art methods, registering gains of 4.22%, 1.13%, 20.82%, and 13.1% on the MSVD, MSRVTT, TGIF, and TVQA benchmarks respectively. Our models and code have been made publicly available here https://vision-cair.github.io/MiniGPT4-video/", "github_manual": "https://github.com/Vision-CAIR/MiniGPT4-video", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-08", "arxiv_id": "2404.03683", "title": "Stream of Search (SoS): Learning to Search in Language", "authors": [ "Kanishk Gandhi", "Denise Lee", "Gabriel Grand", "Muxin Liu", "Winson Cheng", "Archit Sharma", "Noah D. Goodman" ], "github": "https://github.com/kanishkg/stream-of-search", "project_page": "", "abstract": "Language models are rarely shown fruitful mistakes while training. They then struggle to look beyond the next token, suffering from a snowballing of errors and struggling to predict the consequence of their actions several steps ahead. In this paper, we show how language models can be taught to search by representing the process of search in language, as a flattened string -- a stream of search (SoS). We propose a unified language for search that captures an array of different symbolic search strategies. We demonstrate our approach using the simple yet difficult game of Countdown, where the goal is to combine input numbers with arithmetic operations to reach a target number. We pretrain a transformer-based language model from scratch on a dataset of streams of search generated by heuristic solvers. We find that SoS pretraining increases search accuracy by 25% over models trained to predict only the optimal search trajectory. We further finetune this model with two policy improvement methods: Advantage-Induced Policy Alignment (APA) and Self-Taught Reasoner (STaR). The finetuned SoS models solve 36% of previously unsolved problems, including problems that cannot be solved by any of the heuristic solvers. Our results indicate that language models can learn to solve problems via search, self-improve to flexibly use different search strategies, and potentially discover new ones.", "github_manual": "https://github.com/kanishkg/stream-of-search", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-08", "arxiv_id": "2404.03715", "title": "Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences", "authors": [ "Corby Rosset", "Ching-An Cheng", "Arindam Mitra", "Michael Santacroce", "Ahmed Awadallah", "Tengyang Xie" ], "github": "", "project_page": "", "abstract": "This paper studies post-training large language models (LLMs) using preference feedback from a powerful oracle to help a model iteratively improve over itself. The typical approach for post-training LLMs involves Reinforcement Learning from Human Feedback (RLHF), which traditionally separates reward learning and subsequent policy optimization. However, such a reward maximization approach is limited by the nature of \"point-wise\" rewards (such as Bradley-Terry model), which fails to express complex intransitive or cyclic preference relations. While advances on RLHF show reward learning and policy optimization can be merged into a single contrastive objective for stability, they yet still remain tethered to the reward maximization framework. Recently, a new wave of research sidesteps the reward maximization presumptions in favor of directly optimizing over \"pair-wise\" or general preferences. In this paper, we introduce Direct Nash Optimization (DNO), a provable and scalable algorithm that marries the simplicity and stability of contrastive learning with theoretical generality from optimizing general preferences. Because DNO is a batched on-policy algorithm using a regression-based objective, its implementation is straightforward and efficient. Moreover, DNO enjoys monotonic improvement across iterations that help it improve even over a strong teacher (such as GPT-4). In our experiments, a resulting 7B parameter Orca-2.5 model aligned by DNO achieves the state-of-the-art win-rate against GPT-4-Turbo of 33% on AlpacaEval 2.0 (even after controlling for response length), an absolute gain of 26% (7% to 33%) over the initializing model. It outperforms models with far more parameters, including Mistral Large, Self-Rewarding LM (70B parameters), and older versions of GPT-4.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-08", "arxiv_id": "2404.03673", "title": "RL for Consistency Models: Faster Reward Guided Text-to-Image Generation", "authors": [ "Owen Oertell", "Jonathan D. Chang", "Yiyi Zhang", "Kiant\u00e9 Brantley", "Wen Sun" ], "github": "https://github.com/Owen-Oertell/rlcm", "project_page": "", "abstract": "Reinforcement learning (RL) has improved guided image generation with diffusion models by directly optimizing rewards that capture image quality, aesthetics, and instruction following capabilities. However, the resulting generative policies inherit the same iterative sampling process of diffusion models that causes slow generation. To overcome this limitation, consistency models proposed learning a new class of generative models that directly map noise to data, resulting in a model that can generate an image in as few as one sampling iteration. In this work, to optimize text-to-image generative models for task specific rewards and enable fast training and inference, we propose a framework for fine-tuning consistency models via RL. Our framework, called Reinforcement Learning for Consistency Model (RLCM), frames the iterative inference process of a consistency model as an RL procedure. RLCM improves upon RL fine-tuned diffusion models on text-to-image generation capabilities and trades computation during inference time for sample quality. Experimentally, we show that RLCM can adapt text-to-image consistency models to objectives that are challenging to express with prompting, such as image compressibility, and those derived from human feedback, such as aesthetic quality. Comparing to RL finetuned diffusion models, RLCM trains significantly faster, improves the quality of the generation measured under the reward objectives, and speeds up the inference procedure by generating high quality images with as few as two inference steps. Our code is available at https://rlcm.owenoertell.com", "github_manual": "https://github.com/Owen-Oertell/rlcm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-08", "arxiv_id": "2404.04125", "title": "No \"Zero-Shot\" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance", "authors": [ "Vishaal Udandarao", "Ameya Prabhu", "Adhiraj Ghosh", "Yash Sharma", "Philip H. S. Torr", "Adel Bibi", "Samuel Albanie", "Matthias Bethge" ], "github": "https://github.com/bethgelab/frequency_determines_performance", "project_page": "", "abstract": "Web-crawled pretraining datasets underlie the impressive \"zero-shot\" evaluation performance of multimodal models, such as CLIP for classification/retrieval and Stable-Diffusion for image generation. However, it is unclear how meaningful the notion of \"zero-shot\" generalization is for such multimodal models, as it is not known to what extent their pretraining datasets encompass the downstream concepts targeted for during \"zero-shot\" evaluation. In this work, we ask: How is the performance of multimodal models on downstream concepts influenced by the frequency of these concepts in their pretraining datasets? We comprehensively investigate this question across 34 models and five standard pretraining datasets (CC-3M, CC-12M, YFCC-15M, LAION-400M, LAION-Aesthetics), generating over 300GB of data artifacts. We consistently find that, far from exhibiting \"zero-shot\" generalization, multimodal models require exponentially more data to achieve linear improvements in downstream \"zero-shot\" performance, following a sample inefficient log-linear scaling trend. This trend persists even when controlling for sample-level similarity between pretraining and downstream datasets, and testing on purely synthetic data distributions. Furthermore, upon benchmarking models on long-tailed data sampled based on our analysis, we demonstrate that multimodal models across the board perform poorly. We contribute this long-tail test set as the \"Let it Wag!\" benchmark to further research in this direction. Taken together, our study reveals an exponential need for training data which implies that the key to \"zero-shot\" generalization capabilities under large-scale training paradigms remains to be found.", "github_manual": "https://github.com/bethgelab/frequency_determines_performance", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-08", "arxiv_id": "2404.04167", "title": "Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model", "authors": [ "Xinrun Du", "Zhouliang Yu", "Songyang Gao", "Ding Pan", "Yuyang Cheng", "Ziyang Ma", "Ruibin Yuan", "Xingwei Qu", "Jiaheng Liu", "Tianyu Zheng", "Xinchen Luo", "Guorui Zhou", "Binhang Yuan", "Wenhu Chen", "Jie Fu", "Ge Zhang" ], "github": "", "project_page": "", "abstract": "In this study, we introduce CT-LLM, a 2B large language model (LLM) that illustrates a pivotal shift towards prioritizing the Chinese language in developing LLMs. Uniquely initiated from scratch, CT-LLM diverges from the conventional methodology by primarily incorporating Chinese textual data, utilizing an extensive corpus of 1,200 billion tokens, including 800 billion Chinese tokens, 300 billion English tokens, and 100 billion code tokens. This strategic composition facilitates the model's exceptional proficiency in understanding and processing Chinese, a capability further enhanced through alignment techniques. Demonstrating remarkable performance on the CHC-Bench, CT-LLM excels in Chinese language tasks, and showcases its adeptness in English through SFT. This research challenges the prevailing paradigm of training LLMs predominantly on English corpora and then adapting them to other languages, broadening the horizons for LLM training methodologies. By open-sourcing the full process of training a Chinese LLM, including a detailed data processing procedure with the obtained Massive Appropriate Pretraining Chinese Corpus (MAP-CC), a well-chosen multidisciplinary Chinese Hard Case Benchmark (CHC-Bench), and the 2B-size Chinese Tiny LLM (CT-LLM), we aim to foster further exploration and innovation in both academia and industry, paving the way for more inclusive and versatile language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-08", "arxiv_id": "2404.03820", "title": "CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues", "authors": [ "Makesh Narsimhan Sreedhar", "Traian Rebedea", "Shaona Ghosh", "Christopher Parisien" ], "github": "", "project_page": "", "abstract": "Recent advancements in instruction-tuning datasets have predominantly focused on specific tasks like mathematical or logical reasoning. There has been a notable gap in data designed for aligning language models to maintain topic relevance in conversations - a critical aspect for deploying chatbots to production. We introduce the CantTalkAboutThis dataset to help language models remain focused on the subject at hand during task-oriented interactions. It consists of synthetic dialogues on a wide range of conversation topics from different domains. These dialogues are interspersed with distractor turns that intentionally divert the chatbot from the predefined topic. Fine-tuning language models on this dataset helps make them resilient to deviating from the role assigned and improves their ability to maintain topical coherence compared to general-purpose instruction-tuned LLMs like GPT-4-turbo and Mixtral-Instruct. Additionally, preliminary observations suggest that training models on this dataset also enhance their performance on fine-grained instruction following tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-08", "arxiv_id": "2404.04211", "title": "Robust Gaussian Splatting", "authors": [ "Fran\u00e7ois Darmon", "Lorenzo Porzi", "Samuel Rota-Bul\u00f2", "Peter Kontschieder" ], "github": "", "project_page": "", "abstract": "In this paper, we address common error sources for 3D Gaussian Splatting (3DGS) including blur, imperfect camera poses, and color inconsistencies, with the goal of improving its robustness for practical applications like reconstructions from handheld phone captures. Our main contribution involves modeling motion blur as a Gaussian distribution over camera poses, allowing us to address both camera pose refinement and motion blur correction in a unified way. Additionally, we propose mechanisms for defocus blur compensation and for addressing color in-consistencies caused by ambient light, shadows, or due to camera-related factors like varying white balancing settings. Our proposed solutions integrate in a seamless way with the 3DGS formulation while maintaining its benefits in terms of training efficiency and rendering speed. We experimentally validate our contributions on relevant benchmark datasets including Scannet++ and Deblur-NeRF, obtaining state-of-the-art results and thus consistent improvements over relevant baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-08", "arxiv_id": "2404.04204", "title": "Social Skill Training with Large Language Models", "authors": [ "Diyi Yang", "Caleb Ziems", "William Held", "Omar Shaikh", "Michael S. Bernstein", "John Mitchell" ], "github": "", "project_page": "", "abstract": "People rely on social skills like conflict resolution to communicate effectively and to thrive in both work and personal life. However, practice environments for social skills are typically out of reach for most people. How can we make social skill training more available, accessible, and inviting? Drawing upon interdisciplinary research from communication and psychology, this perspective paper identifies social skill barriers to enter specialized fields. Then we present a solution that leverages large language models for social skill training via a generic framework. Our AI Partner, AI Mentor framework merges experiential learning with realistic practice and tailored feedback. This work ultimately calls for cross-disciplinary innovation to address the broader implications for workforce development and social equality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-08", "arxiv_id": "2404.04256", "title": "Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation", "authors": [ "Zifu Wan", "Yuhao Wang", "Silong Yong", "Pingping Zhang", "Simon Stepputtis", "Katia Sycara", "Yaqi Xie" ], "github": "https://github.com/zifuwan/Sigma", "project_page": "", "abstract": "Multi-modal semantic segmentation significantly enhances AI agents' perception and scene understanding, especially under adverse conditions like low-light or overexposed environments. Leveraging additional modalities (X-modality) like thermal and depth alongside traditional RGB provides complementary information, enabling more robust and reliable segmentation. In this work, we introduce Sigma, a Siamese Mamba network for multi-modal semantic segmentation, utilizing the Selective Structured State Space Model, Mamba. Unlike conventional methods that rely on CNNs, with their limited local receptive fields, or Vision Transformers (ViTs), which offer global receptive fields at the cost of quadratic complexity, our model achieves global receptive fields coverage with linear complexity. By employing a Siamese encoder and innovating a Mamba fusion mechanism, we effectively select essential information from different modalities. A decoder is then developed to enhance the channel-wise modeling ability of the model. Our method, Sigma, is rigorously evaluated on both RGB-Thermal and RGB-Depth segmentation tasks, demonstrating its superiority and marking the first successful application of State Space Models (SSMs) in multi-modal perception tasks. Code is available at https://github.com/zifuwan/Sigma.", "github_manual": "", "github_regex": "https://github.com/zifuwan/Sigma", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.05595", "title": "UniFL: Improve Stable Diffusion via Unified Feedback Learning", "authors": [ "Jiacheng Zhang", "Jie Wu", "Yuxi Ren", "Xin Xia", "Huafeng Kuang", "Pan Xie", "Jiashi Li", "Xuefeng Xiao", "Weilin Huang", "Min Zheng", "Lean Fu", "Guanbin Li" ], "github": "", "project_page": "", "abstract": "Diffusion models have revolutionized the field of image generation, leading to the proliferation of high-quality models and diverse downstream applications. However, despite these significant advancements, the current competitive solutions still suffer from several limitations, including inferior visual quality, a lack of aesthetic appeal, and inefficient inference, without a comprehensive solution in sight. To address these challenges, we present UniFL, a unified framework that leverages feedback learning to enhance diffusion models comprehensively. UniFL stands out as a universal, effective, and generalizable solution applicable to various diffusion models, such as SD1.5 and SDXL. Notably, UniFL incorporates three key components: perceptual feedback learning, which enhances visual quality; decoupled feedback learning, which improves aesthetic appeal; and adversarial feedback learning, which optimizes inference speed. In-depth experiments and extensive user studies validate the superior performance of our proposed method in enhancing both the quality of generated models and their acceleration. For instance, UniFL surpasses ImageReward by 17% user preference in terms of generation quality and outperforms LCM and SDXL Turbo by 57% and 20% in 4-step inference. Moreover, we have verified the efficacy of our approach in downstream tasks, including Lora, ControlNet, and AnimateDiff.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.05726", "title": "MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding", "authors": [ "Bo He", "Hengduo Li", "Young Kyun Jang", "Menglin Jia", "Xuefei Cao", "Ashish Shah", "Abhinav Shrivastava", "Ser-Nam Lim" ], "github": "https://github.com/boheumd/MA-LMM", "project_page": "", "abstract": "With the success of large language models (LLMs), integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However, existing LLM-based large multimodal models (e.g., Video-LLaMA, VideoChat) can only take in a limited number of frames for short video understanding. In this study, we mainly focus on designing an efficient and effective model for long-term video understanding. Instead of trying to process more frames simultaneously like most existing work, we propose to process videos in an online manner and store past video information in a memory bank. This allows our model to reference historical video content for long-term analysis without exceeding LLMs' context length constraints or GPU memory limits. Our memory bank can be seamlessly integrated into current multimodal LLMs in an off-the-shelf manner. We conduct extensive experiments on various video understanding tasks, such as long-video understanding, video question answering, and video captioning, and our model can achieve state-of-the-art performances across multiple datasets. Code available at https://boheumd.github.io/MA-LMM/.", "github_manual": "https://github.com/boheumd/MA-LMM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.04478", "title": "Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models", "authors": [ "Zhengcong Fei", "Mingyuan Fan", "Changqian Yu", "Debang Li", "Junshi Huang" ], "github": "https://github.com/feizc/Diffusion-RWKV", "project_page": "", "abstract": "Transformers have catalyzed advancements in computer vision and natural language processing (NLP) fields. However, substantial computational complexity poses limitations for their application in long-context tasks, such as high-resolution image generation. This paper introduces a series of architectures adapted from the RWKV model used in the NLP, with requisite modifications tailored for diffusion model applied to image generation tasks, referred to as Diffusion-RWKV. Similar to the diffusion with Transformers, our model is designed to efficiently handle patchnified inputs in a sequence with extra conditions, while also scaling up effectively, accommodating both large-scale parameters and extensive datasets. Its distinctive advantage manifests in its reduced spatial aggregation complexity, rendering it exceptionally adept at processing high-resolution images, thereby eliminating the necessity for windowing or group cached operations. Experimental results on both condition and unconditional image generation tasks demonstrate that Diffison-RWKV achieves performance on par with or surpasses existing CNN or Transformer-based diffusion models in FID and IS metrics while significantly reducing total computation FLOP usage.", "github_manual": "https://github.com/feizc/Diffusion-RWKV", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.05014", "title": "MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators", "authors": [ "Shenghai Yuan", "Jinfa Huang", "Yujun Shi", "Yongqi Xu", "Ruijie Zhu", "Bin Lin", "Xinhua Cheng", "Li Yuan", "Jiebo Luo" ], "github": "https://github.com/PKU-YuanGroup/MagicTime", "project_page": "", "abstract": "Recent advances in Text-to-Video generation (T2V) have achieved remarkable success in synthesizing high-quality general videos from textual descriptions. A largely overlooked problem in T2V is that existing models have not adequately encoded physical knowledge of the real world, thus generated videos tend to have limited motion and poor variations. In this paper, we propose MagicTime, a metamorphic time-lapse video generation model, which learns real-world physics knowledge from time-lapse videos and implements metamorphic generation. First, we design a MagicAdapter scheme to decouple spatial and temporal training, encode more physical knowledge from metamorphic videos, and transform pre-trained T2V models to generate metamorphic videos. Second, we introduce a Dynamic Frames Extraction strategy to adapt to metamorphic time-lapse videos, which have a wider variation range and cover dramatic object metamorphic processes, thus embodying more physical knowledge than general videos. Finally, we introduce a Magic Text-Encoder to improve the understanding of metamorphic video prompts. Furthermore, we create a time-lapse video-text dataset called ChronoMagic, specifically curated to unlock the metamorphic video generation ability. Extensive experiments demonstrate the superiority and effectiveness of MagicTime for generating high-quality and dynamic metamorphic videos, suggesting time-lapse video generation is a promising path toward building metamorphic simulators of the physical world.", "github_manual": "https://github.com/PKU-YuanGroup/MagicTime", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.05719", "title": "Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs", "authors": [ "Keen You", "Haotian Zhang", "Eldon Schoop", "Floris Weers", "Amanda Swearngin", "Jeffrey Nichols", "Yinfei Yang", "Zhe Gan" ], "github": "", "project_page": "", "abstract": "Recent advancements in multimodal large language models (MLLMs) have been noteworthy, yet, these general-domain MLLMs often fall short in their ability to comprehend and interact effectively with user interface (UI) screens. In this paper, we present Ferret-UI, a new MLLM tailored for enhanced understanding of mobile UI screens, equipped with referring, grounding, and reasoning capabilities. Given that UI screens typically exhibit a more elongated aspect ratio and contain smaller objects of interest (e.g., icons, texts) than natural images, we incorporate \"any resolution\" on top of Ferret to magnify details and leverage enhanced visual features. Specifically, each screen is divided into 2 sub-images based on the original aspect ratio (i.e., horizontal division for portrait screens and vertical division for landscape screens). Both sub-images are encoded separately before being sent to LLMs. We meticulously gather training samples from an extensive range of elementary UI tasks, such as icon recognition, find text, and widget listing. These samples are formatted for instruction-following with region annotations to facilitate precise referring and grounding. To augment the model's reasoning ability, we further compile a dataset for advanced tasks, including detailed description, perception/interaction conversations, and function inference. After training on the curated datasets, Ferret-UI exhibits outstanding comprehension of UI screens and the capability to execute open-ended instructions. For model evaluation, we establish a comprehensive benchmark encompassing all the aforementioned tasks. Ferret-UI excels not only beyond most open-source UI MLLMs, but also surpasses GPT-4V on all the elementary UI tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.04860", "title": "ByteEdit: Boost, Comply and Accelerate Generative Image Editing", "authors": [ "Yuxi Ren", "Jie Wu", "Yanzuo Lu", "Huafeng Kuang", "Xin Xia", "Xionghui Wang", "Qianqian Wang", "Yixing Zhu", "Pan Xie", "Shiyin Wang", "Xuefeng Xiao", "Yitong Wang", "Min Zheng", "Lean Fu" ], "github": "", "project_page": "", "abstract": "Recent advancements in diffusion-based generative image editing have sparked a profound revolution, reshaping the landscape of image outpainting and inpainting tasks. Despite these strides, the field grapples with inherent challenges, including: i) inferior quality; ii) poor consistency; iii) insufficient instrcution adherence; iv) suboptimal generation efficiency. To address these obstacles, we present ByteEdit, an innovative feedback learning framework meticulously designed to Boost, Comply, and Accelerate Generative Image Editing tasks. ByteEdit seamlessly integrates image reward models dedicated to enhancing aesthetics and image-text alignment, while also introducing a dense, pixel-level reward model tailored to foster coherence in the output. Furthermore, we propose a pioneering adversarial and progressive feedback learning strategy to expedite the model's inference speed. Through extensive large-scale user evaluations, we demonstrate that ByteEdit surpasses leading generative image editing products, including Adobe, Canva, and MeiTu, in both generation quality and consistency. ByteEdit-Outpainting exhibits a remarkable enhancement of 388% and 135% in quality and consistency, respectively, when compared to the baseline model. Experiments also verfied that our acceleration models maintains excellent performance results in terms of quality and consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.04544", "title": "BeyondScene: Higher-Resolution Human-Centric Scene Generation With Pretrained Diffusion", "authors": [ "Gwanghyun Kim", "Hayeon Kim", "Hoigi Seo", "Dong Un Kang", "Se Young Chun" ], "github": "", "project_page": "", "abstract": "Generating higher-resolution human-centric scenes with details and controls remains a challenge for existing text-to-image diffusion models. This challenge stems from limited training image size, text encoder capacity (limited tokens), and the inherent difficulty of generating complex scenes involving multiple humans. While current methods attempted to address training size limit only, they often yielded human-centric scenes with severe artifacts. We propose BeyondScene, a novel framework that overcomes prior limitations, generating exquisite higher-resolution (over 8K) human-centric scenes with exceptional text-image correspondence and naturalness using existing pretrained diffusion models. BeyondScene employs a staged and hierarchical approach to initially generate a detailed base image focusing on crucial elements in instance creation for multiple humans and detailed descriptions beyond token limit of diffusion model, and then to seamlessly convert the base image to a higher-resolution output, exceeding training image size and incorporating details aware of text and instances via our novel instance-aware hierarchical enlargement process that consists of our proposed high-frequency injected forward diffusion and adaptive joint diffusion. BeyondScene surpasses existing methods in terms of correspondence with detailed text descriptions and naturalness, paving the way for advanced applications in higher-resolution human-centric scene creation beyond the capacity of pretrained diffusion models without costly retraining. Project page: https://janeyeon.github.io/beyond-scene.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.04465", "title": "Aligning Diffusion Models by Optimizing Human Utility", "authors": [ "Shufan Li", "Konstantinos Kallidromitis", "Akash Gokul", "Yusuke Kato", "Kazuki Kozuka" ], "github": "https://github.com/jacklishufan/diffusion-kto", "project_page": "", "abstract": "We present Diffusion-KTO, a novel approach for aligning text-to-image diffusion models by formulating the alignment objective as the maximization of expected human utility. Since this objective applies to each generation independently, Diffusion-KTO does not require collecting costly pairwise preference data nor training a complex reward model. Instead, our objective requires simple per-image binary feedback signals, e.g. likes or dislikes, which are abundantly available. After fine-tuning using Diffusion-KTO, text-to-image diffusion models exhibit superior performance compared to existing techniques, including supervised fine-tuning and Diffusion-DPO, both in terms of human judgment and automatic evaluation metrics such as PickScore and ImageReward. Overall, Diffusion-KTO unlocks the potential of leveraging readily available per-image binary signals and broadens the applicability of aligning text-to-image diffusion models with human preferences.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jacklishufan/diffusion-kto", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.04526", "title": "DATENeRF: Depth-Aware Text-based Editing of NeRFs", "authors": [ "Sara Rojas", "Julien Philip", "Kai Zhang", "Sai Bi", "Fujun Luan", "Bernard Ghanem", "Kalyan Sunkavall" ], "github": "", "project_page": "", "abstract": "Recent advancements in diffusion models have shown remarkable proficiency in editing 2D images based on text prompts. However, extending these techniques to edit scenes in Neural Radiance Fields (NeRF) is complex, as editing individual 2D frames can result in inconsistencies across multiple views. Our crucial insight is that a NeRF scene's geometry can serve as a bridge to integrate these 2D edits. Utilizing this geometry, we employ a depth-conditioned ControlNet to enhance the coherence of each 2D image modification. Moreover, we introduce an inpainting approach that leverages the depth information of NeRF scenes to distribute 2D edits across different images, ensuring robustness against errors and resampling challenges. Our results reveal that this methodology achieves more consistent, lifelike, and detailed edits than existing leading methods for text-driven NeRF scene editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.04346", "title": "Koala: Key frame-conditioned long video-LLM", "authors": [ "Reuben Tan", "Ximeng Sun", "Ping Hu", "Jui-hsien Wang", "Hanieh Deilamsalehy", "Bryan A. Plummer", "Bryan Russell", "Kate Saenko" ], "github": "https://github.com/rxtan2/Koala-video-llm", "project_page": "", "abstract": "Long video question answering is a challenging task that involves recognizing short-term activities and reasoning about their fine-grained relationships. State-of-the-art video Large Language Models (vLLMs) hold promise as a viable solution due to their demonstrated emergent capabilities on new tasks. However, despite being trained on millions of short seconds-long videos, vLLMs are unable to understand minutes-long videos and accurately answer questions about them. To address this limitation, we propose a lightweight and self-supervised approach, Key frame-conditioned long video-LLM (Koala), that introduces learnable spatiotemporal queries to adapt pretrained vLLMs for generalizing to longer videos. Our approach introduces two new tokenizers that condition on visual tokens computed from sparse video key frames for understanding short and long video moments. We train our proposed approach on HowTo100M and demonstrate its effectiveness on zero-shot long video understanding benchmarks, where it outperforms state-of-the-art large models by 3 - 6% in absolute accuracy across all tasks. Surprisingly, we also empirically show that our approach not only helps a pretrained vLLM to understand long videos but also improves its accuracy on short-term action recognition.", "github_manual": "https://github.com/rxtan2/Koala-video-llm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.05717", "title": "SwapAnything: Enabling Arbitrary Object Swapping in Personalized Visual Editing", "authors": [ "Jing Gu", "Yilin Wang", "Nanxuan Zhao", "Wei Xiong", "Qing Liu", "Zhifei Zhang", "He Zhang", "Jianming Zhang", "HyunJoon Jung", "Xin Eric Wang" ], "github": "https://github.com/eric-ai-lab/swap-anything", "project_page": "", "abstract": "Effective editing of personal content holds a pivotal role in enabling individuals to express their creativity, weaving captivating narratives within their visual stories, and elevate the overall quality and impact of their visual content. Therefore, in this work, we introduce SwapAnything, a novel framework that can swap any objects in an image with personalized concepts given by the reference, while keeping the context unchanged. Compared with existing methods for personalized subject swapping, SwapAnything has three unique advantages: (1) precise control of arbitrary objects and parts rather than the main subject, (2) more faithful preservation of context pixels, (3) better adaptation of the personalized concept to the image. First, we propose targeted variable swapping to apply region control over latent feature maps and swap masked variables for faithful context preservation and initial semantic concept swapping. Then, we introduce appearance adaptation, to seamlessly adapt the semantic concept into the original image in terms of target location, shape, style, and content during the image generation process. Extensive results on both human and automatic evaluation demonstrate significant improvements of our approach over baseline methods on personalized swapping. Furthermore, SwapAnything shows its precise and faithful swapping abilities across single object, multiple objects, partial object, and cross-domain swapping tasks. SwapAnything also achieves great performance on text-based swapping and tasks beyond swapping such as object insertion.", "github_manual": "https://github.com/eric-ai-lab/swap-anything", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.05666", "title": "YaART: Yet Another ART Rendering Technology", "authors": [ "Sergey Kastryulin", "Artem Konev", "Alexander Shishenya", "Eugene Lyapustin", "Artem Khurshudov", "Alexander Tselousov", "Nikita Vinokurov", "Denis Kuznedelev", "Alexander Markovich", "Grigoriy Livshits", "Alexey Kirillov", "Anastasiia Tabisheva", "Liubov Chubarova", "Marina Kaminskaia", "Alexander Ustyuzhanin", "Artemii Shvetsov", "Daniil Shlenskii", "Valerii Startsev", "Dmitrii Kornilov", "Mikhail Romanov", "Artem Babenko", "Sergei Ovcharenko", "Valentin Khrulkov" ], "github": "", "project_page": "", "abstract": "In the rapidly progressing field of generative models, the development of efficient and high-fidelity text-to-image diffusion systems represents a significant frontier. This study introduces YaART, a novel production-grade text-to-image cascaded diffusion model aligned to human preferences using Reinforcement Learning from Human Feedback (RLHF). During the development of YaART, we especially focus on the choices of the model and training dataset sizes, the aspects that were not systematically investigated for text-to-image cascaded diffusion models before. In particular, we comprehensively analyze how these choices affect both the efficiency of the training process and the quality of the generated images, which are highly important in practice. Furthermore, we demonstrate that models trained on smaller datasets of higher-quality images can successfully compete with those trained on larger datasets, establishing a more efficient scenario of diffusion models training. From the quality perspective, YaART is consistently preferred by users over many existing state-of-the-art models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.04319", "title": "SpatialTracker: Tracking Any 2D Pixels in 3D Space", "authors": [ "Yuxi Xiao", "Qianqian Wang", "Shangzhan Zhang", "Nan Xue", "Sida Peng", "Yujun Shen", "Xiaowei Zhou" ], "github": "", "project_page": "", "abstract": "Recovering dense and long-range pixel motion in videos is a challenging problem. Part of the difficulty arises from the 3D-to-2D projection process, leading to occlusions and discontinuities in the 2D motion domain. While 2D motion can be intricate, we posit that the underlying 3D motion can often be simple and low-dimensional. In this work, we propose to estimate point trajectories in 3D space to mitigate the issues caused by image projection. Our method, named SpatialTracker, lifts 2D pixels to 3D using monocular depth estimators, represents the 3D content of each frame efficiently using a triplane representation, and performs iterative updates using a transformer to estimate 3D trajectories. Tracking in 3D allows us to leverage as-rigid-as-possible (ARAP) constraints while simultaneously learning a rigidity embedding that clusters pixels into different rigid parts. Extensive evaluation shows that our approach achieves state-of-the-art tracking performance both qualitatively and quantitatively, particularly in challenging scenarios such as out-of-plane rotation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.04421", "title": "PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations", "authors": [ "Yang Zheng", "Qingqing Zhao", "Guandao Yang", "Wang Yifan", "Donglai Xiang", "Florian Dubost", "Dmitry Lagun", "Thabo Beeler", "Federico Tombari", "Leonidas Guibas", "Gordon Wetzstein" ], "github": "https://github.com/ipc-sim/Codim-IPC", "project_page": "", "abstract": "Modeling and rendering photorealistic avatars is of crucial importance in many applications. Existing methods that build a 3D avatar from visual observations, however, struggle to reconstruct clothed humans. We introduce PhysAvatar, a novel framework that combines inverse rendering with inverse physics to automatically estimate the shape and appearance of a human from multi-view video data along with the physical parameters of the fabric of their clothes. For this purpose, we adopt a mesh-aligned 4D Gaussian technique for spatio-temporal mesh tracking as well as a physically based inverse renderer to estimate the intrinsic material properties. PhysAvatar integrates a physics simulator to estimate the physical parameters of the garments using gradient-based optimization in a principled manner. These novel capabilities enable PhysAvatar to create high-quality novel-view renderings of avatars dressed in loose-fitting clothes under motions and lighting conditions not seen in the training data. This marks a significant advancement towards modeling photorealistic digital humans using physically based inverse rendering with physics in the loop. Our project website is at: https://qingqing-zhao.github.io/PhysAvatar", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ipc-sim/Codim-IPC", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-09", "arxiv_id": "2404.05674", "title": "MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation", "authors": [ "Kunpeng Song", "Yizhe Zhu", "Bingchen Liu", "Qing Yan", "Ahmed Elgammal", "Xiao Yang" ], "github": "https://github.com/bytedance/MoMA", "project_page": "", "abstract": "In this paper, we present MoMA: an open-vocabulary, training-free personalized image model that boasts flexible zero-shot capabilities. As foundational text-to-image models rapidly evolve, the demand for robust image-to-image translation grows. Addressing this need, MoMA specializes in subject-driven personalized image generation. Utilizing an open-source, Multimodal Large Language Model (MLLM), we train MoMA to serve a dual role as both a feature extractor and a generator. This approach effectively synergizes reference image and text prompt information to produce valuable image features, facilitating an image diffusion model. To better leverage the generated features, we further introduce a novel self-attention shortcut method that efficiently transfers image features to an image diffusion model, improving the resemblance of the target object in generated images. Remarkably, as a tuning-free plug-and-play module, our model requires only a single reference image and outperforms existing methods in generating images with high detail fidelity, enhanced identity-preservation and prompt faithfulness. Our work is open-source, thereby providing universal access to these advancements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bytedance/MoMA", "github_api_added_by": "auto" }, { "date": "2024-04-10", "arxiv_id": "2404.06429", "title": "Magic-Boost: Boost 3D Generation with Mutli-View Conditioned Diffusion", "authors": [ "Fan Yang", "Jianfeng Zhang", "Yichun Shi", "Bowen Chen", "Chenxu Zhang", "Huichao Zhang", "Xiaofeng Yang", "Jiashi Feng", "Guosheng Lin" ], "github": "https://github.com/magic-research/magic-boost", "project_page": "", "abstract": "Benefiting from the rapid development of 2D diffusion models, 3D content creation has made significant progress recently. One promising solution involves the fine-tuning of pre-trained 2D diffusion models to harness their capacity for producing multi-view images, which are then lifted into accurate 3D models via methods like fast-NeRFs or large reconstruction models. However, as inconsistency still exists and limited generated resolution, the generation results of such methods still lack intricate textures and complex geometries. To solve this problem, we propose Magic-Boost, a multi-view conditioned diffusion model that significantly refines coarse generative results through a brief period of SDS optimization (sim15min). Compared to the previous text or single image based diffusion models, Magic-Boost exhibits a robust capability to generate images with high consistency from pseudo synthesized multi-view images. It provides precise SDS guidance that well aligns with the identity of the input images, enriching the local detail in both geometry and texture of the initial generative results. Extensive experiments show Magic-Boost greatly enhances the coarse inputs and generates high-quality 3D assets with rich geometric and textural details. (Project Page: https://magic-research.github.io/magic-boost/)", "github_manual": "https://github.com/magic-research/magic-boost", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-10", "arxiv_id": "2404.06512", "title": "InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD", "authors": [ "Xiaoyi Dong", "Pan Zhang", "Yuhang Zang", "Yuhang Cao", "Bin Wang", "Linke Ouyang", "Songyang Zhang", "Haodong Duan", "Wenwei Zhang", "Yining Li", "Hang Yan", "Yang Gao", "Zhe Chen", "Xinyue Zhang", "Wei Li", "Jingwen Li", "Wenhai Wang", "Kai Chen", "Conghui He", "Xingcheng Zhang", "Jifeng Dai", "Yu Qiao", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/InternLM/InternLM-XComposer", "project_page": "", "abstract": "The Large Vision-Language Model (LVLM) field has seen significant advancements, yet its progression has been hindered by challenges in comprehending fine-grained visual content due to limited resolution. Recent efforts have aimed to enhance the high-resolution understanding capabilities of LVLMs, yet they remain capped at approximately 1500 x 1500 pixels and constrained to a relatively narrow resolution range. This paper represents InternLM-XComposer2-4KHD, a groundbreaking exploration into elevating LVLM resolution capabilities up to 4K HD (3840 x 1600) and beyond. Concurrently, considering the ultra-high resolution may not be necessary in all scenarios, it supports a wide range of diverse resolutions from 336 pixels to 4K standard, significantly broadening its scope of applicability. Specifically, this research advances the patch division paradigm by introducing a novel extension: dynamic resolution with automatic patch configuration. It maintains the training image aspect ratios while automatically varying patch counts and configuring layouts based on a pre-trained Vision Transformer (ViT) (336 x 336), leading to dynamic training resolution from 336 pixels to 4K standard. Our research demonstrates that scaling training resolution up to 4K HD leads to consistent performance enhancements without hitting the ceiling of potential improvements. InternLM-XComposer2-4KHD shows superb capability that matches or even surpasses GPT-4V and Gemini Pro in 10 of the 16 benchmarks. The InternLM-XComposer2-4KHD model series with 7B parameters are publicly available at https://github.com/InternLM/InternLM-XComposer.", "github_manual": "", "github_regex": "https://github.com/InternLM/InternLM-XComposer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-10", "arxiv_id": "2404.06393", "title": "MuPT: A Generative Symbolic Music Pretrained Transformer", "authors": [ "Xingwei Qu", "Yuelin Bai", "Yinghao Ma", "Ziya Zhou", "Ka Man Lo", "Jiaheng Liu", "Ruibin Yuan", "Lejun Min", "Xueling Liu", "Tianyu Zhang", "Xinrun Du", "Shuyue Guo", "Yiming Liang", "Yizhi Li", "Shangda Wu", "Junting Zhou", "Tianyu Zheng", "Ziyang Ma", "Fengze Han", "Wei Xue", "Gus Xia", "Emmanouil Benetos", "Xiang Yue", "Chenghua Lin", "Xu Tan", "Stephen W. Huang", "Wenhu Chen", "Jie Fu", "Ge Zhang" ], "github": "", "project_page": "", "abstract": "In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music. While the prevalent use of MIDI in music modeling is well-established, our findings suggest that LLMs are inherently more compatible with ABC Notation, which aligns more closely with their design and strengths, thereby enhancing the model's performance in musical composition. To address the challenges associated with misaligned measures from different tracks during generation, we propose the development of a Synchronized Multi-Track ABC Notation (SMT-ABC Notation), which aims to preserve coherence across multiple musical tracks. Our contributions include a series of models capable of handling up to 8192 tokens, covering 90\\% of the symbolic music data in our training set. Furthermore, we explore the implications of the Symbolic Music Scaling Law (SMS Law) on model performance. The results indicate a promising direction for future research in music generation, offering extensive resources for community-led research through our open-source contributions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-10", "arxiv_id": "2404.05961", "title": "LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders", "authors": [ "Parishad BehnamGhader", "Vaibhav Adlakha", "Marius Mosbach", "Dzmitry Bahdanau", "Nicolas Chapados", "Siva Reddy" ], "github": "https://github.com/McGill-NLP/llm2vec", "project_page": "", "abstract": "Large decoder-only language models (LLMs) are the state-of-the-art models on most of today's NLP tasks and benchmarks. Yet, the community is only slowly adopting these models for text embedding tasks, which require rich contextualized representations. In this work, we introduce LLM2Vec, a simple unsupervised approach that can transform any decoder-only LLM into a strong text encoder. LLM2Vec consists of three simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. We demonstrate the effectiveness of LLM2Vec by applying it to 3 popular LLMs ranging from 1.3B to 7B parameters and evaluate the transformed models on English word- and sequence-level tasks. We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB). Moreover, when combining LLM2Vec with supervised contrastive learning, we achieve state-of-the-art performance on MTEB among models that train only on publicly available data. Our strong empirical results and extensive analysis demonstrate that LLMs can be effectively transformed into universal text encoders in a parameter-efficient manner without the need for expensive adaptation or synthetic GPT-4 generated data.", "github_manual": "https://github.com/McGill-NLP/llm2vec", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-10", "arxiv_id": "2404.05892", "title": "Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence", "authors": [ "Bo Peng", "Daniel Goldstein", "Quentin Anthony", "Alon Albalak", "Eric Alcaide", "Stella Biderman", "Eugene Cheah", "Teddy Ferdinan", "Haowen Hou", "Przemys\u0142aw Kazienko", "Kranthi Kiran GV", "Jan Koco\u0144", "Bart\u0142omiej Koptyra", "Satyapriya Krishna", "Ronald McClelland Jr.", "Niklas Muennighoff", "Fares Obeid", "Atsushi Saito", "Guangyu Song", "Haoqin Tu", "Stanis\u0142aw Wo\u017aniak", "Ruichong Zhang", "Bingchen Zhao", "Qihang Zhao", "Peng Zhou", "Jian Zhu", "Rui-Jie Zhu" ], "github": "https://github.com/rwkv/rwkv-infctx-trainer", "project_page": "", "abstract": "We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture. Our architectural design advancements include multi-headed matrix-valued states and a dynamic recurrence mechanism that improve expressivity while maintaining the inference efficiency characteristics of RNNs. We introduce a new multilingual corpus with 1.12 trillion tokens and a fast tokenizer based on greedy matching for enhanced multilinguality. We trained four Eagle models, ranging from 0.46 to 7.5 billion parameters, and two Finch models with 1.6 and 3.1 billion parameters and find that they achieve competitive performance across a wide variety of benchmarks. We release all our models on HuggingFace under the Apache 2.0 license. Models at: https://huggingface.co/RWKV Training code at: https://github.com/RWKV/RWKV-LM Inference code at: https://github.com/RWKV/ChatRWKV Time-parallel training code at: https://github.com/RWKV/RWKV-infctx-trainer", "github_manual": "https://github.com/rwkv/rwkv-infctx-trainer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-10", "arxiv_id": "2404.06212", "title": "OmniFusion Technical Report", "authors": [ "Elizaveta Goncharova", "Anton Razzhigaev", "Matvey Mikhalchuk", "Maxim Kurkin", "Irina Abdullaeva", "Matvey Skripkin", "Ivan Oseledets", "Denis Dimitrov", "Andrey Kuznetsov" ], "github": "https://github.com/AIRI-Institute/OmniFusion", "project_page": "", "abstract": "Last year, multimodal architectures served up a revolution in AI-based approaches and solutions, extending the capabilities of large language models (LLM). We propose an OmniFusion model based on a pretrained LLM and adapters for visual modality. We evaluated and compared several architecture design principles for better text and visual data coupling: MLP and transformer adapters, various CLIP ViT-based encoders (SigLIP, InternVIT, etc.), and their fusing approach, image encoding method (whole image or tiles encoding) and two 7B LLMs (the proprietary one and open-source Mistral). Experiments on 8 visual-language benchmarks show the top score for the best OmniFusion setup in terms of different VQA tasks in comparison with open-source LLaVA-like solutions: VizWiz, Pope, MM-Vet, ScienceQA, MMBench, TextVQA, VQAv2, MMMU. We also propose a variety of situations, where OmniFusion provides highly-detailed answers in different domains: housekeeping, sightseeing, culture, medicine, handwritten and scanned equations recognition, etc. Mistral-based OmniFusion model is an open-source solution with weights, training and inference scripts available at https://github.com/AIRI-Institute/OmniFusion.", "github_manual": "", "github_regex": "https://github.com/AIRI-Institute/OmniFusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-10", "arxiv_id": "2404.06395", "title": "MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies", "authors": [ "Shengding Hu", "Yuge Tu", "Xu Han", "Chaoqun He", "Ganqu Cui", "Xiang Long", "Zhi Zheng", "Yewei Fang", "Yuxiang Huang", "Weilin Zhao", "Xinrong Zhang", "Zheng Leng Thai", "Kaihuo Zhang", "Chongyi Wang", "Yuan Yao", "Chenyang Zhao", "Jie Zhou", "Jie Cai", "Zhongwu Zhai", "Ning Ding", "Chao Jia", "Guoyang Zeng", "Dahai Li", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/OpenBMB/MiniCPM", "project_page": "", "abstract": "The burgeoning interest in developing Large Language Models (LLMs) with up to trillion parameters has been met with concerns regarding resource efficiency and practical expense, particularly given the immense cost of experimentation. This scenario underscores the importance of exploring the potential of Small Language Models (SLMs) as a resource-efficient alternative. In this context, we introduce MiniCPM, specifically the 1.2B and 2.4B non-embedding parameter variants, not only excel in their respective categories but also demonstrate capabilities on par with 7B-13B LLMs. While focusing on SLMs, our approach exhibits scalability in both model and data dimensions for future LLM research. Regarding model scaling, we employ extensive model wind tunnel experiments for stable and optimal scaling. For data scaling, we introduce a Warmup-Stable-Decay (WSD) learning rate scheduler (LRS), conducive to continuous training and domain adaptation. We present an in-depth analysis of the intriguing training dynamics that occurred in the WSD LRS. With WSD LRS, we are now able to efficiently study data-model scaling law without extensive retraining experiments on both axes of model and data, from which we derive the much higher compute optimal data-model ratio than Chinchilla Optimal. Additionally, we introduce MiniCPM family, including MiniCPM-DPO, MiniCPM-MoE and MiniCPM-128K, whose excellent performance further cementing MiniCPM's foundation in diverse SLM applications. MiniCPM models are available publicly at https://github.com/OpenBMB/MiniCPM .", "github_manual": "", "github_regex": "https://github.com/OpenBMB/MiniCPM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-10", "arxiv_id": "2404.06209", "title": "Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models", "authors": [ "Sebastian Bordt", "Harsha Nori", "Vanessa Rodrigues", "Besmira Nushi", "Rich Caruana" ], "github": "https://github.com/interpretml/LLM-Tabular-Memorization-Checker", "project_page": "", "abstract": "While many have shown how Large Language Models (LLMs) can be applied to a diverse set of tasks, the critical issues of data contamination and memorization are often glossed over. In this work, we address this concern for tabular data. Specifically, we introduce a variety of different techniques to assess whether a language model has seen a tabular dataset during training. This investigation reveals that LLMs have memorized many popular tabular datasets verbatim. We then compare the few-shot learning performance of LLMs on datasets that were seen during training to the performance on datasets released after training. We find that LLMs perform better on datasets seen during training, indicating that memorization leads to overfitting. At the same time, LLMs show non-trivial performance on novel datasets and are surprisingly robust to data transformations. We then investigate the in-context statistical learning abilities of LLMs. Without fine-tuning, we find them to be limited. This suggests that much of the few-shot performance on novel datasets is due to the LLM's world knowledge. Overall, our results highlight the importance of testing whether an LLM has seen an evaluation dataset during pre-training. We make the exposure tests we developed available as the tabmemcheck Python package at https://github.com/interpretml/LLM-Tabular-Memorization-Checker", "github_manual": "", "github_regex": "https://github.com/interpretml/LLM-Tabular-Memorization-Checker", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-10", "arxiv_id": "2404.05875", "title": "CodecLM: Aligning Language Models with Tailored Synthetic Data", "authors": [ "Zifeng Wang", "Chun-Liang Li", "Vincent Perot", "Long T. Le", "Jin Miao", "Zizhao Zhang", "Chen-Yu Lee", "Tomas Pfister" ], "github": "", "project_page": "", "abstract": "Instruction tuning has emerged as the key in aligning large language models (LLMs) with specific task instructions, thereby mitigating the discrepancy between the next-token prediction objective and users' actual goals. To reduce the labor and time cost to collect or annotate data by humans, researchers start to explore the use of LLMs to generate instruction-aligned synthetic data. Recent works focus on generating diverse instructions and applying LLM to increase instruction complexity, often neglecting downstream use cases. It remains unclear how to tailor high-quality data to elicit better instruction-following abilities in different target instruction distributions and LLMs. To this end, we introduce CodecLM, a general framework for adaptively generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs. Drawing on the Encode-Decode principles, we use LLMs as codecs to guide the data generation process. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution, and then decode metadata to create tailored instructions. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples. Extensive experiments on four open-domain instruction following benchmarks validate the effectiveness of CodecLM over the current state-of-the-arts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-10", "arxiv_id": "2404.05829", "title": "SambaLingo: Teaching Large Language Models New Languages", "authors": [ "Zoltan Csaki", "Bo Li", "Jonathan Li", "Qiantong Xu", "Pian Pawakapan", "Leon Zhang", "Yun Du", "Hengyu Zhao", "Changran Hu", "Urmish Thakker" ], "github": "", "project_page": "", "abstract": "Despite the widespread availability of LLMs, there remains a substantial gap in their capabilities and availability across diverse languages. One approach to address these issues has been to take an existing pre-trained LLM and continue to train it on new languages. While prior works have experimented with language adaptation, many questions around best practices and methodology have not been covered. In this paper, we present a comprehensive investigation into the adaptation of LLMs to new languages. Our study covers the key components in this process, including vocabulary extension, direct preference optimization and the data scarcity problem for human alignment in low-resource languages. We scale these experiments across 9 languages and 2 parameter scales (7B and 70B). We compare our models against Llama 2, Aya-101, XGLM, BLOOM and existing language experts, outperforming all prior published baselines. Additionally, all evaluation code and checkpoints are made public to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-10", "arxiv_id": "2404.06507", "title": "Reconstructing Hand-Held Objects in 3D", "authors": [ "Jane Wu", "Georgios Pavlakos", "Georgia Gkioxari", "Jitendra Malik" ], "github": "https://github.com/janehwu/mcc-ho", "project_page": "", "abstract": "Objects manipulated by the hand (i.e., manipulanda) are particularly challenging to reconstruct from in-the-wild RGB images or videos. Not only does the hand occlude much of the object, but also the object is often only visible in a small number of image pixels. At the same time, two strong anchors emerge in this setting: (1) estimated 3D hands help disambiguate the location and scale of the object, and (2) the set of manipulanda is small relative to all possible objects. With these insights in mind, we present a scalable paradigm for handheld object reconstruction that builds on recent breakthroughs in large language/vision models and 3D object datasets. Our model, MCC-Hand-Object (MCC-HO), jointly reconstructs hand and object geometry given a single RGB image and inferred 3D hand as inputs. Subsequently, we use GPT-4(V) to retrieve a 3D object model that matches the object in the image and rigidly align the model to the network-inferred geometry; we call this alignment Retrieval-Augmented Reconstruction (RAR). Experiments demonstrate that MCC-HO achieves state-of-the-art performance on lab and Internet datasets, and we show how RAR can be used to automatically obtain 3D labels for in-the-wild images of hand-object interactions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/janehwu/mcc-ho", "github_api_added_by": "auto" }, { "date": "2024-04-10", "arxiv_id": "2404.06091", "title": "Hash3D: Training-free Acceleration for 3D Generation", "authors": [ "Xingyi Yang", "Xinchao Wang" ], "github": "https://github.com/Adamdad/hash3D", "project_page": "", "abstract": "The evolution of 3D generative modeling has been notably propelled by the adoption of 2D diffusion models. Despite this progress, the cumbersome optimization process per se presents a critical hurdle to efficiency. In this paper, we introduce Hash3D, a universal acceleration for 3D generation without model training. Central to Hash3D is the insight that feature-map redundancy is prevalent in images rendered from camera positions and diffusion time-steps in close proximity. By effectively hashing and reusing these feature maps across neighboring timesteps and camera angles, Hash3D substantially prevents redundant calculations, thus accelerating the diffusion model's inference in 3D generation tasks. We achieve this through an adaptive grid-based hashing. Surprisingly, this feature-sharing mechanism not only speed up the generation but also enhances the smoothness and view consistency of the synthesized 3D objects. Our experiments covering 5 text-to-3D and 3 image-to-3D models, demonstrate Hash3D's versatility to speed up optimization, enhancing efficiency by 1.3 to 4 times. Additionally, Hash3D's integration with 3D Gaussian splatting largely speeds up 3D model creation, reducing text-to-3D processing to about 10 minutes and image-to-3D conversion to roughly 30 seconds. The project page is at https://adamdad.github.io/hash3D/.", "github_manual": "https://github.com/Adamdad/hash3D", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-10", "arxiv_id": "2404.06109", "title": "Revising Densification in Gaussian Splatting", "authors": [ "Samuel Rota Bul\u00f2", "Lorenzo Porzi", "Peter Kontschieder" ], "github": "", "project_page": "", "abstract": "In this paper, we address the limitations of Adaptive Density Control (ADC) in 3D Gaussian Splatting (3DGS), a scene representation method achieving high-quality, photorealistic results for novel view synthesis. ADC has been introduced for automatic 3D point primitive management, controlling densification and pruning, however, with certain limitations in the densification logic. Our main contribution is a more principled, pixel-error driven formulation for density control in 3DGS, leveraging an auxiliary, per-pixel error function as the criterion for densification. We further introduce a mechanism to control the total number of primitives generated per scene and correct a bias in the current opacity handling strategy of ADC during cloning operations. Our approach leads to consistent quality improvements across a variety of benchmark scenes, without sacrificing the method's efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-11", "arxiv_id": "2404.07143", "title": "Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention", "authors": [ "Tsendsuren Munkhdalai", "Manaal Faruqui", "Siddharth Gopal" ], "github": "", "project_page": "", "abstract": "This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and long-term linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on long-context language modeling benchmarks, 1M sequence length passkey context block retrieval and 500K length book summarization tasks with 1B and 8B LLMs. Our approach introduces minimal bounded memory parameters and enables fast streaming inference for LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-11", "arxiv_id": "2404.06654", "title": "RULER: What's the Real Context Size of Your Long-Context Language Models?", "authors": [ "Cheng-Ping Hsieh", "Simeng Sun", "Samuel Kriman", "Shantanu Acharya", "Dima Rekesh", "Fei Jia", "Boris Ginsburg" ], "github": "https://github.com/hsiehjackson/RULER", "project_page": "", "abstract": "The needle-in-a-haystack (NIAH) test, which examines the ability to retrieve a piece of information (the \"needle\") from long distractor texts (the \"haystack\"), has been widely adopted to evaluate long-context language models (LMs). However, this simple retrieval-based test is indicative of only a superficial form of long-context understanding. To provide a more comprehensive evaluation of long-context LMs, we create a new synthetic benchmark RULER with flexible configurations for customized sequence length and task complexity. RULER expands upon the vanilla NIAH test to encompass variations with diverse types and quantities of needles. Moreover, RULER introduces new task categories multi-hop tracing and aggregation to test behaviors beyond searching from context. We evaluate ten long-context LMs with 13 representative tasks in RULER. Despite achieving nearly perfect accuracy in the vanilla NIAH test, all models exhibit large performance drops as the context length increases. While these models all claim context sizes of 32K tokens or greater, only four models (GPT-4, Command-R, Yi-34B, and Mixtral) can maintain satisfactory performance at the length of 32K. Our analysis of Yi-34B, which supports context length of 200K, reveals large room for improvement as we increase input length and task complexity. We open source RULER to spur comprehensive evaluation of long-context LMs.", "github_manual": "https://github.com/hsiehjackson/RULER", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-11", "arxiv_id": "2404.07199", "title": "RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion", "authors": [ "Jaidev Shriram", "Alex Trevithick", "Lingjie Liu", "Ravi Ramamoorthi" ], "github": "https://github.com/jaidevshriram/realmdreamer", "project_page": "", "abstract": "We introduce RealmDreamer, a technique for generation of general forward-facing 3D scenes from text descriptions. Our technique optimizes a 3D Gaussian Splatting representation to match complex text prompts. We initialize these splats by utilizing the state-of-the-art text-to-image generators, lifting their samples into 3D, and computing the occlusion volume. We then optimize this representation across multiple views as a 3D inpainting task with image-conditional diffusion models. To learn correct geometric structure, we incorporate a depth diffusion model by conditioning on the samples from the inpainting model, giving rich geometric structure. Finally, we finetune the model using sharpened samples from image generators. Notably, our technique does not require video or multi-view data and can synthesize a variety of high-quality 3D scenes in different styles, consisting of multiple objects. Its generality additionally allows 3D synthesis from a single image.", "github_manual": "https://github.com/jaidevshriram/realmdreamer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-11", "arxiv_id": "2404.06780", "title": "Urban Architect: Steerable 3D Urban Scene Generation with Layout Prior", "authors": [ "Fan Lu", "Kwan-Yee Lin", "Yan Xu", "Hongsheng Li", "Guang Chen", "Changjun Jiang" ], "github": "https://github.com/UrbanArchitect/UrbanArchitect", "project_page": "", "abstract": "Text-to-3D generation has achieved remarkable success via large-scale text-to-image diffusion models. Nevertheless, there is no paradigm for scaling up the methodology to urban scale. Urban scenes, characterized by numerous elements, intricate arrangement relationships, and vast scale, present a formidable barrier to the interpretability of ambiguous textual descriptions for effective model optimization. In this work, we surmount the limitations by introducing a compositional 3D layout representation into text-to-3D paradigm, serving as an additional prior. It comprises a set of semantic primitives with simple geometric structures and explicit arrangement relationships, complementing textual descriptions and enabling steerable generation. Upon this, we propose two modifications -- (1) We introduce Layout-Guided Variational Score Distillation to address model optimization inadequacies. It conditions the score distillation sampling process with geometric and semantic constraints of 3D layouts. (2) To handle the unbounded nature of urban scenes, we represent 3D scene with a Scalable Hash Grid structure, incrementally adapting to the growing scale of urban scenes. Extensive experiments substantiate the capability of our framework to scale text-to-3D generation to large-scale urban scenes that cover over 1000m driving distance for the first time. We also present various scene editing demonstrations, showing the powers of steerable urban scene generation. Website: https://urbanarchitect.github.io.", "github_manual": "https://github.com/UrbanArchitect/UrbanArchitect", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-11", "arxiv_id": "2404.07204", "title": "BRAVE: Broadening the visual encoding of vision-language models", "authors": [ "O\u011fuzhan Fatih Kar", "Alessio Tonioni", "Petra Poklukar", "Achin Kulshrestha", "Amir Zamir", "Federico Tombari" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) are typically composed of a vision encoder, e.g. CLIP, and a language model (LM) that interprets the encoded features to solve downstream tasks. Despite remarkable progress, VLMs are subject to several shortcomings due to the limited capabilities of vision encoders, e.g. \"blindness\" to certain image features, visual hallucination, etc. To address these issues, we study broadening the visual encoding capabilities of VLMs. We first comprehensively benchmark several vision encoders with different inductive biases for solving VLM tasks. We observe that there is no single encoding configuration that consistently achieves top performance across different tasks, and encoders with different biases can perform surprisingly similarly. Motivated by this, we introduce a method, named BRAVE, that consolidates features from multiple frozen encoders into a more versatile representation that can be directly fed as the input to a frozen LM. BRAVE achieves state-of-the-art performance on a broad range of captioning and VQA benchmarks and significantly reduces the aforementioned issues of VLMs, while requiring a smaller number of trainable parameters than existing methods and having a more compressed representation. Our results highlight the potential of incorporating different visual biases for a more broad and contextualized visual understanding of VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-11", "arxiv_id": "2404.06773", "title": "Adapting LLaMA Decoder to Vision Transformer", "authors": [ "Jiahao Wang", "Wenqi Shao", "Mengzhao Chen", "Chengyue Wu", "Yong Liu", "Kaipeng Zhang", "Songyang Zhang", "Kai Chen", "Ping Luo" ], "github": "https://github.com/techmonsterwang/illama", "project_page": "", "abstract": "This work examines whether decoder-only Transformers such as LLaMA, which were originally designed for large language models (LLMs), can be adapted to the computer vision field. We first \"LLaMAfy\" a standard ViT step-by-step to align with LLaMA's architecture, and find that directly applying a casual mask to the self-attention brings an attention collapse issue, resulting in the failure to the network training. We suggest to reposition the class token behind the image tokens with a post-sequence class token technique to overcome this challenge, enabling causal self-attention to efficiently capture the entire image's information. Additionally, we develop a soft mask strategy that gradually introduces a casual mask to the self-attention at the onset of training to facilitate the optimization behavior. The tailored model, dubbed as image LLaMA (iLLaMA), is akin to LLaMA in architecture and enables direct supervised learning. Its causal self-attention boosts computational efficiency and learns complex representation by elevating attention map ranks. iLLaMA rivals the performance with its encoder-only counterparts, achieving 75.1% ImageNet top-1 accuracy with only 5.7M parameters. Scaling the model to ~310M and pre-training on ImageNet-21K further enhances the accuracy to 86.0%. Extensive experiments demonstrate iLLaMA's reliable properties: calibration, shape-texture bias, quantization compatibility, ADE20K segmentation and CIFAR transfer learning. We hope our study can kindle fresh views to visual model design in the wave of LLMs. Pre-trained models and codes are available here.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/techmonsterwang/illama", "github_api_added_by": "auto" }, { "date": "2024-04-11", "arxiv_id": "2404.06903", "title": "DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting", "authors": [ "Shijie Zhou", "Zhiwen Fan", "Dejia Xu", "Haoran Chang", "Pradyumna Chari", "Tejas Bharadwaj", "Suya You", "Zhangyang Wang", "Achuta Kadambi" ], "github": "", "project_page": "", "abstract": "The increasing demand for virtual reality applications has highlighted the significance of crafting immersive 3D assets. We present a text-to-3D 360^{circ} scene generation pipeline that facilitates the creation of comprehensive 360^{circ} scenes for in-the-wild environments in a matter of minutes. Our approach utilizes the generative power of a 2D diffusion model and prompt self-refinement to create a high-quality and globally coherent panoramic image. This image acts as a preliminary \"flat\" (2D) scene representation. Subsequently, it is lifted into 3D Gaussians, employing splatting techniques to enable real-time exploration. To produce consistent 3D geometry, our pipeline constructs a spatially coherent structure by aligning the 2D monocular depth into a globally optimized point cloud. This point cloud serves as the initial state for the centroids of 3D Gaussians. In order to address invisible issues inherent in single-view inputs, we impose semantic and geometric constraints on both synthesized and input camera views as regularizations. These guide the optimization of Gaussians, aiding in the reconstruction of unseen regions. In summary, our method offers a globally consistent 3D scene within a 360^{circ} perspective, providing an enhanced immersive experience over existing techniques. Project website at: http://dreamscene360.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07448", "title": "Transferable and Principled Efficiency for Open-Vocabulary Segmentation", "authors": [ "Jingxuan Xu", "Wuyang Chen", "Yao Zhao", "Yunchao Wei" ], "github": "https://github.com/Xujxyang/OpenTrans", "project_page": "", "abstract": "Recent success of pre-trained foundation vision-language models makes Open-Vocabulary Segmentation (OVS) possible. Despite the promising performance, this approach introduces heavy computational overheads for two challenges: 1) large model sizes of the backbone; 2) expensive costs during the fine-tuning. These challenges hinder this OVS strategy from being widely applicable and affordable in real-world scenarios. Although traditional methods such as model compression and efficient fine-tuning can address these challenges, they often rely on heuristics. This means that their solutions cannot be easily transferred and necessitate re-training on different models, which comes at a cost. In the context of efficient OVS, we target achieving performance that is comparable to or even better than prior OVS works based on large vision-language foundation models, by utilizing smaller models that incur lower training costs. The core strategy is to make our efficiency principled and thus seamlessly transferable from one OVS framework to others without further customization. Comprehensive experiments on diverse OVS benchmarks demonstrate our superior trade-off between segmentation accuracy and computation costs over previous works. Our code is available on https://github.com/Xujxyang/OpenTrans", "github_manual": "", "github_regex": "https://github.com/Xujxyang/OpenTrans", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07413", "title": "JetMoE: Reaching Llama2 Performance with 0.1M Dollars", "authors": [ "Yikang Shen", "Zhen Guo", "Tianle Cai", "Zengyi Qin" ], "github": "https://github.com/myshell-ai/JetMoE", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved remarkable results, but their increasing resource demand has become a major obstacle to the development of powerful and accessible super-human intelligence. This report introduces JetMoE-8B, a new LLM trained with less than $0.1 million, using 1.25T tokens from carefully mixed open-source corpora and 30,000 H100 GPU hours. Despite its low cost, the JetMoE-8B demonstrates impressive performance, with JetMoE-8B outperforming the Llama2-7B model and JetMoE-8B-Chat surpassing the Llama2-13B-Chat model. These results suggest that LLM training can be much more cost-effective than generally thought. JetMoE-8B is based on an efficient Sparsely-gated Mixture-of-Experts (SMoE) architecture, composed of attention and feedforward experts. Both layers are sparsely activated, allowing JetMoE-8B to have 8B parameters while only activating 2B for each input token, reducing inference computation by about 70% compared to Llama2-7B. Moreover, JetMoE-8B is highly open and academia-friendly, using only public datasets and training code. All training parameters and data mixtures have been detailed in this report to facilitate future efforts in the development of open foundation models. This transparency aims to encourage collaboration and further advancements in the field of accessible and efficient LLMs. The model weights are publicly available at https://github.com/myshell-ai/JetMoE.", "github_manual": "", "github_regex": "https://github.com/myshell-ai/JetMoE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07616", "title": "Audio Dialogues: Dialogues dataset for audio and music understanding", "authors": [ "Arushi Goel", "Zhifeng Kong", "Rafael Valle", "Bryan Catanzaro" ], "github": "", "project_page": "", "abstract": "Existing datasets for audio understanding primarily focus on single-turn interactions (i.e. audio captioning, audio question answering) for describing audio in natural language, thus limiting understanding audio via interactive dialogue. To address this gap, we introduce Audio Dialogues: a multi-turn dialogue dataset containing 163.8k samples for general audio sounds and music. In addition to dialogues, Audio Dialogues also has question-answer pairs to understand and compare multiple input audios together. Audio Dialogues leverages a prompting-based approach and caption annotations from existing datasets to generate multi-turn dialogues using a Large Language Model (LLM). We evaluate existing audio-augmented large language models on our proposed dataset to demonstrate the complexity and applicability of Audio Dialogues. Our code for generating the dataset will be made publicly available. Detailed prompts and generated dialogues can be found on the demo website https://audiodialogues.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07544", "title": "From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples", "authors": [ "Robert Vacareanu", "Vlad-Andrei Negru", "Vasile Suciu", "Mihai Surdeanu" ], "github": "https://github.com/robertvacareanu/llm4regression", "project_page": "", "abstract": "We analyze how well pre-trained large language models (e.g., Llama2, GPT-4, Claude 3, etc) can do linear and non-linear regression when given in-context examples, without any additional training or gradient updates. Our findings reveal that several large language models (e.g., GPT-4, Claude 3) are able to perform regression tasks with a performance rivaling (or even outperforming) that of traditional supervised methods such as Random Forest, Bagging, or Gradient Boosting. For example, on the challenging Friedman #2 regression dataset, Claude 3 outperforms many supervised methods such as AdaBoost, SVM, Random Forest, KNN, or Gradient Boosting. We then investigate how well the performance of large language models scales with the number of in-context exemplars. We borrow from the notion of regret from online learning and empirically show that LLMs are capable of obtaining a sub-linear regret.", "github_manual": "https://github.com/robertvacareanu/llm4regression", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07973", "title": "Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models", "authors": [ "Haotian Zhang", "Haoxuan You", "Philipp Dufter", "Bowen Zhang", "Chen Chen", "Hong-You Chen", "Tsu-Jui Fu", "William Yang Wang", "Shih-Fu Chang", "Zhe Gan", "Yinfei Yang" ], "github": "", "project_page": "", "abstract": "While Ferret seamlessly integrates regional understanding into the Large Language Model (LLM) to facilitate its referring and grounding capability, it poses certain limitations: constrained by the pre-trained fixed visual encoder and failed to perform well on broader tasks. In this work, we unveil Ferret-v2, a significant upgrade to Ferret, with three key designs. (1) Any resolution grounding and referring: A flexible approach that effortlessly handles higher image resolution, improving the model's ability to process and understand images in greater detail. (2) Multi-granularity visual encoding: By integrating the additional DINOv2 encoder, the model learns better and diverse underlying contexts for global and fine-grained visual information. (3) A three-stage training paradigm: Besides image-caption alignment, an additional stage is proposed for high-resolution dense alignment before the final instruction tuning. Experiments show that Ferret-v2 provides substantial improvements over Ferret and other state-of-the-art methods, thanks to its high-resolution scaling and fine-grained visual processing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07965", "title": "Rho-1: Not All Tokens Are What You Need", "authors": [ "Zhenghao Lin", "Zhibin Gou", "Yeyun Gong", "Xiao Liu", "Yelong Shen", "Ruochen Xu", "Chen Lin", "Yujiu Yang", "Jian Jiao", "Nan Duan", "Weizhu Chen" ], "github": "https://github.com/microsoft/rho", "project_page": "", "abstract": "Previous language model pre-training methods have uniformly applied a next-token prediction loss to all training tokens. Challenging this norm, we posit that \"Not all tokens in a corpus are equally important for language model training\". Our initial analysis delves into token-level training dynamics of language model, revealing distinct loss patterns for different tokens. Leveraging these insights, we introduce a new language model called Rho-1. Unlike traditional LMs that learn to predict every next token in a corpus, Rho-1 employs Selective Language Modeling (SLM), which selectively trains on useful tokens that aligned with the desired distribution. This approach involves scoring pretraining tokens using a reference model, and then training the language model with a focused loss on tokens with higher excess loss. When continual pretraining on 15B OpenWebMath corpus, Rho-1 yields an absolute improvement in few-shot accuracy of up to 30% in 9 math tasks. After fine-tuning, Rho-1-1B and 7B achieved state-of-the-art results of 40.6% and 51.8% on MATH dataset, respectively - matching DeepSeekMath with only 3% of the pretraining tokens. Furthermore, when pretraining on 80B general tokens, Rho-1 achieves 6.8% average enhancement across 15 diverse tasks, increasing both efficiency and performance of the language model pre-training.", "github_manual": "https://github.com/microsoft/rho", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07839", "title": "RecurrentGemma: Moving Past Transformers for Efficient Open Language Models", "authors": [ "Aleksandar Botev", "Soham De", "Samuel L Smith", "Anushan Fernando", "George-Cristian Muraru", "Ruba Haroun", "Leonard Berrada", "Razvan Pascanu", "Pier Giuseppe Sessa", "Robert Dadashi", "L\u00e9onard Hussenot", "Johan Ferret", "Sertan Girgin", "Olivier Bachem", "Alek Andreev", "Kathleen Kenealy", "Thomas Mesnard", "Cassidy Hardin", "Surya Bhupatiraju", "Shreya Pathak", "Laurent Sifre", "Morgane Rivi\u00e8re", "Mihir Sanjay Kale", "Juliette Love", "Pouya Tafti", "Armand Joulin", "Noah Fiedel", "Evan Senter", "Yutian Chen", "Srivatsan Srinivasan", "Guillaume Desjardins", "David Budden", "Arnaud Doucet", "Sharad Vikram", "Adam Paszke", "Trevor Gale", "Sebastian Borgeaud", "Charlie Chen", "Andy Brock", "Antonia Paterson", "Jenny Brennan", "Meg Risdal", "Raj Gundluru", "Nesh Devanathan", "Paul Mooney", "Nilay Chauhan", "Phil Culliton", "Luiz GUStavo Martins", "Elisa Bandy", "David Huntsperger", "Glenn Cameron", "Arthur Zucker", "Tris Warkentin", "Ludovic Peran", "Minh Giang", "Zoubin Ghahramani", "Cl\u00e9ment Farabet", "Koray Kavukcuoglu", "Demis Hassabis", "Raia Hadsell", "Yee Whye Teh", "Nando de Frietas" ], "github": "https://github.com/google-deepmind/recurrentgemma", "project_page": "", "abstract": "We introduce RecurrentGemma, an open language model which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide a pre-trained model with 2B non-embedding parameters, and an instruction tuned variant. Both models achieve comparable performance to Gemma-2B despite being trained on fewer tokens.", "github_manual": "https://github.com/google-deepmind/recurrentgemma", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07987", "title": "ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback", "authors": [ "Ming Li", "Taojiannan Yang", "Huafeng Kuang", "Jie Wu", "Zhaoning Wang", "Xuefeng Xiao", "Chen Chen" ], "github": "https://github.com/liming-ai/ControlNet_Plus_Plus", "project_page": "", "abstract": "To enhance the controllability of text-to-image diffusion models, existing efforts like ControlNet incorporated image-based conditional controls. In this paper, we reveal that existing methods still face significant challenges in generating images that align with the image conditional controls. To this end, we propose ControlNet++, a novel approach that improves controllable generation by explicitly optimizing pixel-level cycle consistency between generated images and conditional controls. Specifically, for an input conditional control, we use a pre-trained discriminative reward model to extract the corresponding condition of the generated images, and then optimize the consistency loss between the input conditional control and extracted condition. A straightforward implementation would be generating images from random noises and then calculating the consistency loss, but such an approach requires storing gradients for multiple sampling timesteps, leading to considerable time and memory costs. To address this, we introduce an efficient reward strategy that deliberately disturbs the input images by adding noise, and then uses the single-step denoised images for reward fine-tuning. This avoids the extensive costs associated with image sampling, allowing for more efficient reward fine-tuning. Extensive experiments show that ControlNet++ significantly improves controllability under various conditional controls. For example, it achieves improvements over ControlNet by 7.9% mIoU, 13.4% SSIM, and 7.6% RMSE, respectively, for segmentation mask, line-art edge, and depth conditions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/liming-ai/ControlNet_Plus_Plus", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07972", "title": "OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments", "authors": [ "Tianbao Xie", "Danyang Zhang", "Jixuan Chen", "Xiaochuan Li", "Siheng Zhao", "Ruisheng Cao", "Toh Jing Hua", "Zhoujun Cheng", "Dongchan Shin", "Fangyu Lei", "Yitao Liu", "Yiheng Xu", "Shuyan Zhou", "Silvio Savarese", "Caiming Xiong", "Victor Zhong", "Tao Yu" ], "github": "https://github.com/xlang-ai/OSWorld", "project_page": "", "abstract": "Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity. However, existing benchmarks either lack an interactive environment or are limited to environments specific to certain applications or domains, failing to reflect the diverse and complex nature of real-world computer use, thereby limiting the scope of tasks and agent scalability. To address this issue, we introduce OSWorld, the first-of-its-kind scalable, real computer environment for multimodal agents, supporting task setup, execution-based evaluation, and interactive learning across various operating systems such as Ubuntu, Windows, and macOS. OSWorld can serve as a unified, integrated computer environment for assessing open-ended computer tasks that involve arbitrary applications. Building upon OSWorld, we create a benchmark of 369 computer tasks involving real web and desktop apps in open domains, OS file I/O, and workflows spanning multiple applications. Each task example is derived from real-world computer use cases and includes a detailed initial state setup configuration and a custom execution-based evaluation script for reliable, reproducible evaluation. Extensive evaluation of state-of-the-art LLM/VLM-based agents on OSWorld reveals significant deficiencies in their ability to serve as computer assistants. While humans can accomplish over 72.36% of the tasks, the best model achieves only 12.24% success, primarily struggling with GUI grounding and operational knowledge. Comprehensive analysis using OSWorld provides valuable insights for developing multimodal generalist agents that were not possible with previous benchmarks. Our code, environment, baseline models, and data are publicly available at https://os-world.github.io.", "github_manual": "https://github.com/xlang-ai/OSWorld", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07979", "title": "LLoCO: Learning Long Contexts Offline", "authors": [ "Sijun Tan", "Xiuyu Li", "Shishir Patil", "Ziyang Wu", "Tianjun Zhang", "Kurt Keutzer", "Joseph E. Gonzalez", "Raluca Ada Popa" ], "github": "https://github.com/jeffreysijuntan/lloco", "project_page": "", "abstract": "Processing long contexts remains a challenge for large language models (LLMs) due to the quadratic computational and memory overhead of the self-attention mechanism and the substantial KV cache sizes during generation. We propose a novel approach to address this problem by learning contexts offline through context compression and in-domain parameter-efficient finetuning. Our method enables an LLM to create a concise representation of the original context and efficiently retrieve relevant information to answer questions accurately. We introduce LLoCO, a technique that combines context compression, retrieval, and parameter-efficient finetuning using LoRA. Our approach extends the effective context window of a 4k token LLaMA2-7B model to handle up to 128k tokens. We evaluate our approach on several long-context question-answering datasets, demonstrating that LLoCO significantly outperforms in-context learning while using 30times fewer tokens during inference. LLoCO achieves up to 7.62times speed-up and substantially reduces the cost of long document question answering, making it a promising solution for efficient long context processing. Our code is publicly available at https://github.com/jeffreysijuntan/lloco.", "github_manual": "", "github_regex": "https://github.com/jeffreysijuntan/lloco", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07503", "title": "Best Practices and Lessons Learned on Synthetic Data for Language Models", "authors": [ "Ruibo Liu", "Jerry Wei", "Fangyu Liu", "Chenglei Si", "Yanzhe Zhang", "Jinmeng Rao", "Steven Zheng", "Daiyi Peng", "Diyi Yang", "Denny Zhou", "Andrew M. Dai" ], "github": "", "project_page": "", "abstract": "The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns. This paper provides an overview of synthetic data research, discussing its applications, challenges, and future directions. We present empirical evidence from prior art to demonstrate its effectiveness and highlight the importance of ensuring its factuality, fidelity, and unbiasedness. We emphasize the need for responsible use of synthetic data to build more powerful, inclusive, and trustworthy language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07821", "title": "Sparse Laneformer", "authors": [ "Ji Liu", "Zifeng Zhang", "Mingjie Lu", "Hongyang Wei", "Dong Li", "Yile Xie", "Jinzhang Peng", "Lu Tian", "Ashish Sirasao", "Emad Barsoum" ], "github": "", "project_page": "", "abstract": "Lane detection is a fundamental task in autonomous driving, and has achieved great progress as deep learning emerges. Previous anchor-based methods often design dense anchors, which highly depend on the training dataset and remain fixed during inference. We analyze that dense anchors are not necessary for lane detection, and propose a transformer-based lane detection framework based on a sparse anchor mechanism. To this end, we generate sparse anchors with position-aware lane queries and angle queries instead of traditional explicit anchors. We adopt Horizontal Perceptual Attention (HPA) to aggregate the lane features along the horizontal direction, and adopt Lane-Angle Cross Attention (LACA) to perform interactions between lane queries and angle queries. We also propose Lane Perceptual Attention (LPA) based on deformable cross attention to further refine the lane predictions. Our method, named Sparse Laneformer, is easy-to-implement and end-to-end trainable. Extensive experiments demonstrate that Sparse Laneformer performs favorably against the state-of-the-art methods, e.g., surpassing Laneformer by 3.0% F1 score and O2SFormer by 0.7% F1 score with fewer MACs on CULane with the same ResNet-34 backbone.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07724", "title": "Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models", "authors": [ "Tuomas Kynk\u00e4\u00e4nniemi", "Miika Aittala", "Tero Karras", "Samuli Laine", "Timo Aila", "Jaakko Lehtinen" ], "github": "https://github.com/kynkaat/guidance-interval", "project_page": "", "abstract": "Guidance is a crucial technique for extracting the best performance out of image-generating diffusion models. Traditionally, a constant guidance weight has been applied throughout the sampling chain of an image. We show that guidance is clearly harmful toward the beginning of the chain (high noise levels), largely unnecessary toward the end (low noise levels), and only beneficial in the middle. We thus restrict it to a specific range of noise levels, improving both the inference speed and result quality. This limited guidance interval improves the record FID in ImageNet-512 significantly, from 1.81 to 1.40. We show that it is quantitatively and qualitatively beneficial across different sampler parameters, network architectures, and datasets, including the large-scale setting of Stable Diffusion XL. We thus suggest exposing the guidance interval as a hyperparameter in all diffusion models that use guidance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kynkaat/guidance-interval", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.05902", "title": "WILBUR: Adaptive In-Context Learning for Robust and Accurate Web Agents", "authors": [ "Michael Lutz", "Arth Bohra", "Manvel Saroyan", "Artem Harutyunyan", "Giovanni Campagna" ], "github": "", "project_page": "", "abstract": "In the realm of web agent research, achieving both generalization and accuracy remains a challenging problem. Due to high variance in website structure, existing approaches often fail. Moreover, existing fine-tuning and in-context learning techniques fail to generalize across multiple websites. We introduce Wilbur, an approach that uses a differentiable ranking model and a novel instruction synthesis technique to optimally populate a black-box large language model's prompt with task demonstrations from previous runs. To maximize end-to-end success rates, we also propose an intelligent backtracking mechanism that learns and recovers from its mistakes. Finally, we show that our ranking model can be trained on data from a generative auto-curriculum which samples representative goals from an LLM, runs the agent, and automatically evaluates it, with no manual annotation. Wilbur achieves state-of-the-art results on the WebVoyager benchmark, beating text-only models by 8% overall, and up to 36% on certain websites. On the same benchmark, Wilbur is within 5% of a strong multi-modal model despite only receiving textual inputs, and further analysis reveals a substantial number of failures are due to engineering challenges of operating the web.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-12", "arxiv_id": "2404.07904", "title": "HGRN2: Gated Linear RNNs with State Expansion", "authors": [ "Zhen Qin", "Songlin Yang", "Weixuan Sun", "Xuyang Shen", "Dong Li", "Weigao Sun", "Yiran Zhong" ], "github": "https://github.com/OpenNLPLab/HGRN2", "project_page": "", "abstract": "Hierarchically gated linear RNN (HGRN,Qin et al. 2023) has demonstrated competitive training speed and performance in language modeling, while offering efficient inference. However, the recurrent state size of HGRN remains relatively small, which limits its expressiveness.To address this issue, inspired by linear attention, we introduce a simple outer-product-based state expansion mechanism so that the recurrent state size can be significantly enlarged without introducing any additional parameters. The linear attention form also allows for hardware-efficient training.Our extensive experiments verify the advantage of HGRN2 over HGRN1 in language modeling, image classification, and Long Range Arena.Our largest 3B HGRN2 model slightly outperforms Mamba and LLaMa Architecture Transformer for language modeling in a controlled experiment setting; and performs competitively with many open-source 3B models in downstream evaluation while using much fewer total training tokens.", "github_manual": "https://github.com/OpenNLPLab/HGRN2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-15", "arxiv_id": "2404.08634", "title": "Pre-training Small Base LMs with Fewer Tokens", "authors": [ "Sunny Sanyal", "Sujay Sanghavi", "Alexandros G. Dimakis" ], "github": "https://github.com/sanyalsunny111/LLM-Inheritune", "project_page": "", "abstract": "We study the effectiveness of a simple approach to develop a small base language model (LM) starting from an existing large base LM: first inherit a few transformer blocks from the larger LM, and then train this smaller model on a very small subset (0.1\\%) of the raw pretraining data of the larger model. We call our simple recipe Inheritune and first demonstrate it for building a small base LM with 1.5B parameters using 1B tokens (and a starting few layers of larger LM of 3B parameters); we do this using a single A6000 GPU for less than half a day. Across 9 diverse evaluation datasets as well as the MMLU benchmark, the resulting model compares favorably to publicly available base models of 1B-2B size, some of which have been trained using 50-1000 times more tokens. We investigate Inheritune in a slightly different setting where we train small LMs utilizing larger LMs and their full pre-training dataset. Here we show that smaller LMs trained utilizing some of the layers of GPT2-medium (355M) and GPT-2-large (770M) can effectively match the val loss of their bigger counterparts when trained from scratch for the same number of training steps on OpenWebText dataset with 9B tokens. We analyze our recipe with extensive experiments and demonstrate it efficacy on diverse settings. Our code is available at https://github.com/sanyalsunny111/LLM-Inheritune.", "github_manual": "", "github_regex": "https://github.com/sanyalsunny111/LLM-Inheritune", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-15", "arxiv_id": "2404.08252", "title": "MonoPatchNeRF: Improving Neural Radiance Fields with Patch-based Monocular Guidance", "authors": [ "Yuqun Wu", "Jae Yong Lee", "Chuhang Zou", "Shenlong Wang", "Derek Hoiem" ], "github": "https://github.com/yuqunw/monopatch_nerf", "project_page": "", "abstract": "The latest regularized Neural Radiance Field (NeRF) approaches produce poor geometry and view extrapolation for multiview stereo (MVS) benchmarks such as ETH3D. In this paper, we aim to create 3D models that provide accurate geometry and view synthesis, partially closing the large geometric performance gap between NeRF and traditional MVS methods. We propose a patch-based approach that effectively leverages monocular surface normal and relative depth predictions. The patch-based ray sampling also enables the appearance regularization of normalized cross-correlation (NCC) and structural similarity (SSIM) between randomly sampled virtual and training views. We further show that \"density restrictions\" based on sparse structure-from-motion points can help greatly improve geometric accuracy with a slight drop in novel view synthesis metrics. Our experiments show 4x the performance of RegNeRF and 8x that of FreeNeRF on average F1@2cm for ETH3D MVS benchmark, suggesting a fruitful research direction to improve the geometric accuracy of NeRF-based models, and sheds light on a potential future approach to enable NeRF-based optimization to eventually outperform traditional MVS.", "github_manual": "https://github.com/yuqunw/monopatch_nerf", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-15", "arxiv_id": "2404.08540", "title": "On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation", "authors": [ "Agneet Chatterjee", "Tejas Gokhale", "Chitta Baral", "Yezhou Yang" ], "github": "https://github.com/agneet42/robustness_depth_lang", "project_page": "", "abstract": "Recent advances in monocular depth estimation have been made by incorporating natural language as additional guidance. Although yielding impressive results, the impact of the language prior, particularly in terms of generalization and robustness, remains unexplored. In this paper, we address this gap by quantifying the impact of this prior and introduce methods to benchmark its effectiveness across various settings. We generate \"low-level\" sentences that convey object-centric, three-dimensional spatial relationships, incorporate them as additional language priors and evaluate their downstream impact on depth estimation. Our key finding is that current language-guided depth estimators perform optimally only with scene-level descriptions and counter-intuitively fare worse with low level descriptions. Despite leveraging additional data, these methods are not robust to directed adversarial attacks and decline in performance with an increase in distribution shift. Finally, to provide a foundation for future research, we identify points of failures and offer insights to better understand these shortcomings. With an increasing number of methods using language for depth estimation, our findings highlight the opportunities and pitfalls that require careful consideration for effective deployment in real-world settings", "github_manual": "https://github.com/agneet42/robustness_depth_lang", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-15", "arxiv_id": "2404.08636", "title": "Probing the 3D Awareness of Visual Foundation Models", "authors": [ "Mohamed El Banani", "Amit Raj", "Kevis-Kokitsi Maninis", "Abhishek Kar", "Yuanzhen Li", "Michael Rubinstein", "Deqing Sun", "Leonidas Guibas", "Justin Johnson", "Varun Jampani" ], "github": "https://github.com/mbanani/probe3d", "project_page": "", "abstract": "Recent advances in large-scale pretraining have yielded visual foundation models with strong capabilities. Not only can recent models generalize to arbitrary images for their training task, their intermediate representations are useful for other visual tasks such as detection and segmentation. Given that such models can classify, delineate, and localize objects in 2D, we ask whether they also represent their 3D structure? In this work, we analyze the 3D awareness of visual foundation models. We posit that 3D awareness implies that representations (1) encode the 3D structure of the scene and (2) consistently represent the surface across views. We conduct a series of experiments using task-specific probes and zero-shot inference procedures on frozen features. Our experiments reveal several limitations of the current models. Our code and analysis can be found at https://github.com/mbanani/probe3d.", "github_manual": "", "github_regex": "https://github.com/mbanani/probe3d", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-15", "arxiv_id": "2404.08639", "title": "COCONut: Modernizing COCO Segmentation", "authors": [ "Xueqing Deng", "Qihang Yu", "Peng Wang", "Xiaohui Shen", "Liang-Chieh Chen" ], "github": "https://github.com/bytedance/coconut_cvpr2024", "project_page": "", "abstract": "In recent decades, the vision community has witnessed remarkable progress in visual recognition, partially owing to advancements in dataset benchmarks. Notably, the established COCO benchmark has propelled the development of modern detection and segmentation systems. However, the COCO segmentation benchmark has seen comparatively slow improvement over the last decade. Originally equipped with coarse polygon annotations for thing instances, it gradually incorporated coarse superpixel annotations for stuff regions, which were subsequently heuristically amalgamated to yield panoptic segmentation annotations. These annotations, executed by different groups of raters, have resulted not only in coarse segmentation masks but also in inconsistencies between segmentation types. In this study, we undertake a comprehensive reevaluation of the COCO segmentation annotations. By enhancing the annotation quality and expanding the dataset to encompass 383K images with more than 5.18M panoptic masks, we introduce COCONut, the COCO Next Universal segmenTation dataset. COCONut harmonizes segmentation annotations across semantic, instance, and panoptic segmentation with meticulously crafted high-quality masks, and establishes a robust benchmark for all segmentation tasks. To our knowledge, COCONut stands as the inaugural large-scale universal segmentation dataset, verified by human raters. We anticipate that the release of COCONut will significantly contribute to the community's ability to assess the progress of novel neural networks.", "github_manual": "https://github.com/bytedance/coconut_cvpr2024", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-15", "arxiv_id": "2404.08197", "title": "Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies", "authors": [ "Zichao Li", "Cihang Xie", "Ekin Dogus Cubuk" ], "github": "", "project_page": "", "abstract": "This paper investigates the performance of the Contrastive Language-Image Pre-training (CLIP) when scaled down to limited computation budgets. We explore CLIP along three dimensions: data, architecture, and training strategies. With regards to data, we demonstrate the significance of high-quality training data and show that a smaller dataset of high-quality data can outperform a larger dataset with lower quality. We also examine how model performance varies with different dataset sizes, suggesting that smaller ViT models are better suited for smaller datasets, while larger models perform better on larger datasets with fixed compute. Additionally, we provide guidance on when to choose a CNN-based architecture or a ViT-based architecture for CLIP training. We compare four CLIP training strategies - SLIP, FLIP, CLIP, and CLIP+Data Augmentation - and show that the choice of training strategy depends on the available compute resource. Our analysis reveals that CLIP+Data Augmentation can achieve comparable performance to CLIP using only half of the training data. This work provides practical insights into how to effectively train and deploy CLIP models, making them more accessible and affordable for practical use in various applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-15", "arxiv_id": "2404.08495", "title": "Dataset Reset Policy Optimization for RLHF", "authors": [ "Jonathan D. Chang", "Wenhao Shan", "Owen Oertell", "Kiant\u00e9 Brantley", "Dipendra Misra", "Jason D. Lee", "Wen Sun" ], "github": "https://github.com/Cornell-RL/drpo", "project_page": "", "abstract": "Reinforcement Learning (RL) from Human Preference-based feedback is a popular paradigm for fine-tuning generative models, which has produced impressive models such as GPT-4 and Claude3 Opus. This framework often consists of two steps: learning a reward model from an offline preference dataset followed by running online RL to optimize the learned reward model. In this work, leveraging the idea of reset, we propose a new RLHF algorithm with provable guarantees. Motivated by the fact that offline preference dataset provides informative states (i.e., data that is preferred by the labelers), our new algorithm, Dataset Reset Policy Optimization (DR-PO), integrates the existing offline preference dataset into the online policy training procedure via dataset reset: it directly resets the policy optimizer to the states in the offline dataset, instead of always starting from the initial state distribution. In theory, we show that DR-PO learns to perform at least as good as any policy that is covered by the offline dataset under general function approximation with finite sample complexity. In experiments, we demonstrate that on both the TL;DR summarization and the Anthropic Helpful Harmful (HH) dataset, the generation from DR-PO is better than that from Proximal Policy Optimization (PPO) and Direction Preference Optimization (DPO), under the metric of GPT4 win-rate. Code for this work can be found at https://github.com/Cornell-RL/drpo.", "github_manual": "", "github_regex": "https://github.com/Cornell-RL/drpo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-16", "arxiv_id": "2404.09956", "title": "Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization", "authors": [ "Navonil Majumder", "Chia-Yu Hung", "Deepanway Ghosal", "Wei-Ning Hsu", "Rada Mihalcea", "Soujanya Poria" ], "github": "https://github.com/declare-lab/tango", "project_page": "", "abstract": "Generative multimodal content is increasingly prevalent in much of the content creation arena, as it has the potential to allow artists and media personnel to create pre-production mockups by quickly bringing their ideas to life. The generation of audio from text prompts is an important aspect of such processes in the music and film industry. Many of the recent diffusion-based text-to-audio models focus on training increasingly sophisticated diffusion models on a large set of datasets of prompt-audio pairs. These models do not explicitly focus on the presence of concepts or events and their temporal ordering in the output audio with respect to the input prompt. Our hypothesis is focusing on how these aspects of audio generation could improve audio generation performance in the presence of limited data. As such, in this work, using an existing text-to-audio model Tango, we synthetically create a preference dataset where each prompt has a winner audio output and some loser audio outputs for the diffusion model to learn from. The loser outputs, in theory, have some concepts from the prompt missing or in an incorrect order. We fine-tune the publicly available Tango text-to-audio model using diffusion-DPO (direct preference optimization) loss on our preference dataset and show that it leads to improved audio output over Tango and AudioLDM2, in terms of both automatic- and manual-evaluation metrics.", "github_manual": "https://github.com/declare-lab/tango", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-16", "arxiv_id": "2404.09967", "title": "Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model", "authors": [ "Han Lin", "Jaemin Cho", "Abhay Zala", "Mohit Bansal" ], "github": "https://github.com/HL-hanlin/Ctrl-Adapter", "project_page": "", "abstract": "ControlNets are widely used for adding spatial control in image generation with different conditions, such as depth maps, canny edges, and human poses. However, there are several challenges when leveraging the pretrained image ControlNets for controlled video generation. First, pretrained ControlNet cannot be directly plugged into new backbone models due to the mismatch of feature spaces, and the cost of training ControlNets for new backbones is a big burden. Second, ControlNet features for different frames might not effectively handle the temporal consistency. To address these challenges, we introduce Ctrl-Adapter, an efficient and versatile framework that adds diverse controls to any image/video diffusion models, by adapting pretrained ControlNets (and improving temporal alignment for videos). Ctrl-Adapter provides diverse capabilities including image control, video control, video control with sparse frames, multi-condition control, compatibility with different backbones, adaptation to unseen control conditions, and video editing. In Ctrl-Adapter, we train adapter layers that fuse pretrained ControlNet features to different image/video diffusion models, while keeping the parameters of the ControlNets and the diffusion models frozen. Ctrl-Adapter consists of temporal and spatial modules so that it can effectively handle the temporal consistency of videos. We also propose latent skipping and inverse timestep sampling for robust adaptation and sparse control. Moreover, Ctrl-Adapter enables control from multiple conditions by simply taking the (weighted) average of ControlNet outputs. With diverse image/video diffusion backbones (SDXL, Hotshot-XL, I2VGen-XL, and SVD), Ctrl-Adapter matches ControlNet for image control and outperforms all baselines for video control (achieving the SOTA accuracy on the DAVIS 2017 dataset) with significantly lower computational costs (less than 10 GPU hours).", "github_manual": "https://github.com/HL-hanlin/Ctrl-Adapter", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-16", "arxiv_id": "2404.09990", "title": "HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing", "authors": [ "Mude Hui", "Siwei Yang", "Bingchen Zhao", "Yichun Shi", "Heng Wang", "Peng Wang", "Yuyin Zhou", "Cihang Xie" ], "github": "https://github.com/UCSC-VLAA/HQ-Edit", "project_page": "", "abstract": "This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200,000 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data collection pipeline leveraging advanced foundation models, namely GPT-4V and DALL-E 3. To ensure its high quality, diverse examples are first collected online, expanded, and then used to create high-quality diptychs featuring input and output images with detailed text prompts, followed by precise alignment ensured through post-processing. In addition, we propose two evaluation metrics, Alignment and Coherence, to quantitatively assess the quality of image edit pairs using GPT-4V. HQ-Edits high-resolution images, rich in detail and accompanied by comprehensive editing prompts, substantially enhance the capabilities of existing image editing models. For example, an HQ-Edit finetuned InstructPix2Pix can attain state-of-the-art image editing performance, even surpassing those models fine-tuned with human-annotated data. The project page is https://thefllood.github.io/HQEdit_web.", "github_manual": "https://github.com/UCSC-VLAA/HQ-Edit", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-16", "arxiv_id": "2404.09656", "title": "Learn Your Reference Model for Real Good Alignment", "authors": [ "Alexey Gorbatovski", "Boris Shaposhnikov", "Alexey Malakhov", "Nikita Surnachev", "Yaroslav Aksenov", "Ian Maksimov", "Nikita Balagansky", "Daniil Gavrilov" ], "github": "", "project_page": "", "abstract": "The complexity of the alignment problem stems from the fact that existing methods are unstable. Researchers continuously invent various tricks to address this shortcoming. For instance, in the fundamental Reinforcement Learning From Human Feedback (RLHF) technique of Language Model alignment, in addition to reward maximization, the Kullback-Leibler divergence between the trainable policy and the SFT policy is minimized. This addition prevents the model from being overfitted to the Reward Model (RM) and generating texts that are out-of-domain for the RM. The Direct Preference Optimization (DPO) method reformulates the optimization task of RLHF and eliminates the Reward Model while tacitly maintaining the requirement for the policy to be close to the SFT policy. In our paper, we argue that this implicit limitation in the DPO method leads to sub-optimal results. We propose a new method called Trust Region DPO (TR-DPO), which updates the reference policy during training. With such a straightforward update, we demonstrate the effectiveness of TR-DPO against DPO on the Anthropic HH and TLDR datasets. We show that TR-DPO outperforms DPO by up to 19%, measured by automatic evaluation with GPT-4. The new alignment approach that we propose allows us to improve the quality of models across several parameters at once, such as coherence, correctness, level of detail, helpfulness, and harmlessness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-16", "arxiv_id": "2404.09995", "title": "Taming Latent Diffusion Model for Neural Radiance Field Inpainting", "authors": [ "Chieh Hubert Lin", "Changil Kim", "Jia-Bin Huang", "Qinbo Li", "Chih-Yao Ma", "Johannes Kopf", "Ming-Hsuan Yang", "Hung-Yu Tseng" ], "github": "", "project_page": "", "abstract": "Neural Radiance Field (NeRF) is a representation for 3D reconstruction from multi-view images. Despite some recent work showing preliminary success in editing a reconstructed NeRF with diffusion prior, they remain struggling to synthesize reasonable geometry in completely uncovered regions. One major reason is the high diversity of synthetic contents from the diffusion model, which hinders the radiance field from converging to a crisp and deterministic geometry. Moreover, applying latent diffusion models on real data often yields a textural shift incoherent to the image condition due to auto-encoding errors. These two problems are further reinforced with the use of pixel-distance losses. To address these issues, we propose tempering the diffusion model's stochasticity with per-scene customization and mitigating the textural shift with masked adversarial training. During the analyses, we also found the commonly used pixel and perceptual losses are harmful in the NeRF inpainting task. Through rigorous experiments, our framework yields state-of-the-art NeRF inpainting results on various real-world scenes. Project page: https://hubert0527.github.io/MALD-NeRF", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-16", "arxiv_id": "2404.09173", "title": "TransformerFAM: Feedback attention is working memory", "authors": [ "Dongseong Hwang", "Weiran Wang", "Zhuoyuan Huo", "Khe Chai Sim", "Pedro Moreno Mengibar" ], "github": "", "project_page": "", "abstract": "While Transformers have revolutionized deep learning, their quadratic attention complexity hinders their ability to process infinitely long inputs. We propose Feedback Attention Memory (FAM), a novel Transformer architecture that leverages a feedback loop to enable the network to attend to its own latent representations. This design fosters the emergence of working memory within the Transformer, allowing it to process indefinitely long sequences. TransformerFAM requires no additional weights, enabling seamless integration with pre-trained models. Our experiments show that TransformerFAM significantly improves Transformer performance on long-context tasks across various model sizes (1B, 8B, and 24B). These results showcase the potential to empower Large Language Models (LLMs) to process sequences of unlimited length.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-16", "arxiv_id": "2404.09833", "title": "Video2Game: Real-time, Interactive, Realistic and Browser-Compatible Environment from a Single Video", "authors": [ "Hongchi Xia", "Zhi-Hao Lin", "Wei-Chiu Ma", "Shenlong Wang" ], "github": "https://github.com/video2game/video2game", "project_page": "", "abstract": "Creating high-quality and interactive virtual environments, such as games and simulators, often involves complex and costly manual modeling processes. In this paper, we present Video2Game, a novel approach that automatically converts videos of real-world scenes into realistic and interactive game environments. At the heart of our system are three core components:(i) a neural radiance fields (NeRF) module that effectively captures the geometry and visual appearance of the scene; (ii) a mesh module that distills the knowledge from NeRF for faster rendering; and (iii) a physics module that models the interactions and physical dynamics among the objects. By following the carefully designed pipeline, one can construct an interactable and actionable digital replica of the real world. We benchmark our system on both indoor and large-scale outdoor scenes. We show that we can not only produce highly-realistic renderings in real-time, but also build interactive games on top.", "github_manual": "https://github.com/video2game/video2game", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-16", "arxiv_id": "2404.08801", "title": "Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length", "authors": [ "Xuezhe Ma", "Xiaomeng Yang", "Wenhan Xiong", "Beidi Chen", "Lili Yu", "Hao Zhang", "Jonathan May", "Luke Zettlemoyer", "Omer Levy", "Chunting Zhou" ], "github": "https://github.com/XuezheMax/megalodon", "project_page": "", "abstract": "The quadratic complexity and weak length extrapolation of Transformers limits their ability to scale to long sequences, and while sub-quadratic solutions like linear attention and state space models exist, they empirically underperform Transformers in pretraining efficiency and downstream task accuracy. We introduce Megalodon, a neural architecture for efficient sequence modeling with unlimited context length. Megalodon inherits the architecture of Mega (exponential moving average with gated attention), and further introduces multiple technical components to improve its capability and stability, including complex exponential moving average (CEMA), timestep normalization layer, normalized attention mechanism and pre-norm with two-hop residual configuration. In a controlled head-to-head comparison with Llama2, Megalodon achieves better efficiency than Transformer in the scale of 7 billion parameters and 2 trillion training tokens. Megalodon reaches a training loss of 1.70, landing mid-way between Llama2-7B (1.75) and 13B (1.67). Code: https://github.com/XuezheMax/megalodon", "github_manual": "", "github_regex": "https://github.com/XuezheMax/megalodon", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-16", "arxiv_id": "2404.09458", "title": "CompGS: Efficient 3D Scene Representation via Compressed Gaussian Splatting", "authors": [ "Xiangrui Liu", "Xinju Wu", "Pingping Zhang", "Shiqi Wang", "Zhu Li", "Sam Kwong" ], "github": "", "project_page": "", "abstract": "Gaussian splatting, renowned for its exceptional rendering quality and efficiency, has emerged as a prominent technique in 3D scene representation. However, the substantial data volume of Gaussian splatting impedes its practical utility in real-world applications. Herein, we propose an efficient 3D scene representation, named Compressed Gaussian Splatting (CompGS), which harnesses compact Gaussian primitives for faithful 3D scene modeling with a remarkably reduced data size. To ensure the compactness of Gaussian primitives, we devise a hybrid primitive structure that captures predictive relationships between each other. Then, we exploit a small set of anchor primitives for prediction, allowing the majority of primitives to be encapsulated into highly compact residual forms. Moreover, we develop a rate-constrained optimization scheme to eliminate redundancies within such hybrid primitives, steering our CompGS towards an optimal trade-off between bitrate consumption and representation efficacy. Experimental results show that the proposed CompGS significantly outperforms existing methods, achieving superior compactness in 3D scene representation without compromising model accuracy and rendering quality. Our code will be released on GitHub for further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-16", "arxiv_id": "2404.09937", "title": "Compression Represents Intelligence Linearly", "authors": [ "Yuzhen Huang", "Jinghan Zhang", "Zifei Shan", "Junxian He" ], "github": "https://github.com/hkust-nlp/llm-compression-intelligence", "project_page": "", "abstract": "There is a belief that learning to compress well will lead to intelligence. Recently, language modeling has been shown to be equivalent to compression, which offers a compelling rationale for the success of large language models (LLMs): the development of more advanced language models is essentially enhancing compression which facilitates intelligence. Despite such appealing discussions, little empirical evidence is present for the interplay between compression and intelligence. In this work, we examine their relationship in the context of LLMs, treating LLMs as data compressors. Given the abstract concept of \"intelligence\", we adopt the average downstream benchmark scores as a surrogate, specifically targeting intelligence related to knowledge and commonsense, coding, and mathematical reasoning. Across 12 benchmarks, our study brings together 30 public LLMs that originate from diverse organizations. Remarkably, we find that LLMs' intelligence -- reflected by average benchmark scores -- almost linearly correlates with their ability to compress external text corpora. These results provide concrete evidence supporting the belief that superior compression indicates greater intelligence. Furthermore, our findings suggest that compression efficiency, as an unsupervised metric derived from raw text corpora, serves as a reliable evaluation measure that is linearly associated with the model capabilities. We open-source our compression datasets as well as our data collection pipelines to facilitate future researchers to assess compression properly.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hkust-nlp/llm-compression-intelligence", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-16", "arxiv_id": "2404.08856", "title": "On Speculative Decoding for Multimodal Large Language Models", "authors": [ "Mukul Gagrani", "Raghavv Goel", "Wonseok Jeon", "Junyoung Park", "Mingu Lee", "Christopher Lott" ], "github": "", "project_page": "", "abstract": "Inference with Multimodal Large Language Models (MLLMs) is slow due to their large-language-model backbone which suffers from memory bandwidth bottleneck and generates tokens auto-regressively. In this paper, we explore the application of speculative decoding to enhance the inference efficiency of MLLMs, specifically the LLaVA 7B model. We show that a language-only model can serve as a good draft model for speculative decoding with LLaVA 7B, bypassing the need for image tokens and their associated processing components from the draft model. Our experiments across three different tasks show that speculative decoding can achieve a memory-bound speedup of up to 2.37times using a 115M parameter language model that we trained from scratch. Additionally, we introduce a compact LLaVA draft model incorporating an image adapter, which shows marginal performance gains in image captioning while maintaining comparable results in other tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-16", "arxiv_id": "2404.09204", "title": "TextHawk: Exploring Efficient Fine-Grained Perception of Multimodal Large Language Models", "authors": [ "Ya-Qi Yu", "Minghui Liao", "Jihao Wu", "Yongxin Liao", "Xiaoyu Zheng", "Wei Zeng" ], "github": "https://github.com/yuyq96/texthawk", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have shown impressive results on various multimodal tasks. However, most existing MLLMs are not well suited for document-oriented tasks, which require fine-grained image perception and information compression. In this paper, we present TextHawk, a MLLM that is specifically designed for document-oriented tasks, while preserving the general capabilities of MLLMs. TextHawk is aimed to explore efficient fine-grained perception by designing four dedicated components. Firstly, a ReSampling and ReArrangement (ReSA) module is proposed to reduce the redundancy in the document texts and lower the computational cost of the MLLM. We explore encoding the positions of each local feature by presenting Scalable Positional Embeddings (SPEs), which can preserve the scalability of various image sizes. A Query Proposal Network (QPN) is then adopted to initialize the queries dynamically among different sub-images. To further enhance the fine-grained visual perceptual ability of the MLLM, we design a Multi-Level Cross-Attention (MLCA) mechanism that captures the hierarchical structure and semantic relations of document images. Furthermore, we create a new instruction-tuning dataset for document-oriented tasks by enriching the multimodal document data with Gemini Pro. We conduct extensive experiments on both general and document-oriented MLLM benchmarks, and show that TextHawk outperforms the state-of-the-art methods, demonstrating its effectiveness and superiority in fine-grained document perception and general abilities.", "github_manual": "https://github.com/yuyq96/texthawk", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-17", "arxiv_id": "2404.10179", "title": "Scaling Instructable Agents Across Many Simulated Worlds", "authors": [ "SIMA Team", "Maria Abi Raad", "Arun Ahuja", "Catarina Barros", "Frederic Besse", "Andrew Bolt", "Adrian Bolton", "Bethanie Brownfield", "Gavin Buttimore", "Max Cant", "Sarah Chakera", "Stephanie C. Y. Chan", "Jeff Clune", "Adrian Collister", "Vikki Copeman", "Alex Cullum", "Ishita Dasgupta", "Dario de Cesare", "Julia Di Trapani", "Yani Donchev", "Emma Dunleavy", "Martin Engelcke", "Ryan Faulkner", "Frankie Garcia", "Charles Gbadamosi", "Zhitao Gong", "Lucy Gonzales", "Karol Gregor", "Arne Olav Hallingstad", "Tim Harley", "Sam Haves", "Felix Hill", "Ed Hirst", "Drew A. Hudson", "Steph Hughes-Fitt", "Danilo J. Rezende", "Mimi Jasarevic", "Laura Kampis", "Rosemary Ke", "Thomas Keck", "Junkyung Kim", "Oscar Knagg", "Kavya Kopparapu", "Andrew Lampinen", "Shane Legg", "Alexander Lerchner", "Marjorie Limont", "Yulan Liu", "Maria Loks-Thompson", "Joseph Marino", "Kathryn Martin Cussons", "Loic Matthey", "Siobhan Mcloughlin", "Piermaria Mendolicchio", "Hamza Merzic", "Anna Mitenkova", "Alexandre Moufarek", "Valeria Oliveira", "Yanko Oliveira", "Hannah Openshaw", "Renke Pan", "Aneesh Pappu", "Alex Platonov", "Ollie Purkiss", "David Reichert", "John Reid", "Pierre Harvey Richemond", "Tyson Roberts", "Giles Ruscoe", "Jaume Sanchez Elias", "Tasha Sandars", "Daniel P. Sawyer", "Tim Scholtes", "Guy Simmons", "Daniel Slater", "Hubert Soyer", "Heiko Strathmann", "Peter Stys", "Allison C. Tam", "Denis Teplyashin", "Tayfun Terzi", "Davide Vercelli", "Bojan Vujatovic", "Marcus Wainwright", "Jane X. Wang", "Zhengdong Wang", "Daan Wierstra", "Duncan Williams", "Nathaniel Wong", "Sarah York", "Nick Young" ], "github": "", "project_page": "", "abstract": "Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires learning to ground language in perception and embodied actions, in order to accomplish complex tasks. The Scalable, Instructable, Multiworld Agent (SIMA) project tackles this by training agents to follow free-form instructions across a diverse range of virtual 3D environments, including curated research environments as well as open-ended, commercial video games. Our goal is to develop an instructable agent that can accomplish anything a human can do in any simulated 3D environment. Our approach focuses on language-driven generality while imposing minimal assumptions. Our agents interact with environments in real-time using a generic, human-like interface: the inputs are image observations and language instructions and the outputs are keyboard-and-mouse actions. This general approach is challenging, but it allows agents to ground language across many visually complex and semantically rich environments while also allowing us to readily run agents in new environments. In this paper we describe our motivation and goal, the initial progress we have made, and promising preliminary results on several diverse research environments and a variety of commercial video games.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-17", "arxiv_id": "2404.10301", "title": "Long-form music generation with latent diffusion", "authors": [ "Zach Evans", "Julian D. Parker", "CJ Carr", "Zack Zukowski", "Josiah Taylor", "Jordi Pons" ], "github": "", "project_page": "", "abstract": "Audio-based generative models for music have seen great strides recently, but so far have not managed to produce full-length music tracks with coherent musical structure. We show that by training a generative model on long temporal contexts it is possible to produce long-form music of up to 4m45s. Our model consists of a diffusion-transformer operating on a highly downsampled continuous latent representation (latent rate of 21.5Hz). It obtains state-of-the-art generations according to metrics on audio quality and prompt alignment, and subjective tests reveal that it produces full-length music with coherent structure.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-19", "arxiv_id": "2404.12390", "title": "BLINK: Multimodal Large Language Models Can See but Not Perceive", "authors": [ "Xingyu Fu", "Yushi Hu", "Bangzheng Li", "Yu Feng", "Haoyu Wang", "Xudong Lin", "Dan Roth", "Noah A. Smith", "Wei-Chiu Ma", "Ranjay Krishna" ], "github": "https://github.com/zeyofu/BLINK_Benchmark", "project_page": "", "abstract": "We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses on core visual perception abilities not found in other evaluations. Most of the Blink tasks can be solved by humans \"within a blink\" (e.g., relative depth estimation, visual correspondence, forensics detection, and multi-view reasoning). However, we find these perception-demanding tasks cast significant challenges for current multimodal LLMs because they resist mediation through natural language. Blink reformats 14 classic computer vision tasks into 3,807 multiple-choice questions, paired with single or multiple images and visual prompting. While humans get 95.70% accuracy on average, Blink is surprisingly challenging for existing multimodal LLMs: even the best-performing GPT-4V and Gemini achieve accuracies of 51.26% and 45.72%, only 13.17% and 7.63% higher than random guessing, indicating that such perception abilities have not \"emerged\" yet in recent multimodal LLMs. Our analysis also highlights that specialist CV models could solve these problems much better, suggesting potential pathways for future improvements. We believe Blink will stimulate the community to help multimodal LLMs catch up with human-level visual perception.", "github_manual": "https://github.com/zeyofu/BLINK_Benchmark", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-19", "arxiv_id": "2404.12387", "title": "Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models", "authors": [ "Aitor Ormazabal", "Che Zheng", "Cyprien de Masson d'Autume", "Dani Yogatama", "Deyu Fu", "Donovan Ong", "Eric Chen", "Eugenie Lamprecht", "Hai Pham", "Isaac Ong", "Kaloyan Aleksiev", "Lei Li", "Matthew Henderson", "Max Bain", "Mikel Artetxe", "Nishant Relan", "Piotr Padlewski", "Qi Liu", "Ren Chen", "Samuel Phua", "Yazheng Yang", "Yi Tay", "Yuqi Wang", "Zhongkai Zhu", "Zhihui Xie" ], "github": "", "project_page": "", "abstract": "We introduce Reka Core, Flash, and Edge, a series of powerful multimodal language models trained from scratch by Reka. Reka models are able to process and reason with text, images, video, and audio inputs. This technical report discusses details of training some of these models and provides comprehensive evaluation results. We show that Reka Edge and Reka Flash are not only state-of-the-art but also outperform many much larger models, delivering outsized values for their respective compute class. Meanwhile, our most capable and largest model, Reka Core, approaches the best frontier models on both automatic evaluations and blind human evaluations. On image question answering benchmarks (e.g. MMMU, VQAv2), Core performs competitively to GPT4-V. Meanwhile, on multimodal chat, Core ranks as the second most preferred model under a blind third-party human evaluation setup, outperforming other models such as Claude 3 Opus. On text benchmarks, Core not only performs competitively to other frontier models on a set of well-established benchmarks (e.g. MMLU, GSM8K) but also outperforms GPT4-0613 on human evaluation. On video question answering (Perception-Test), Core outperforms Gemini Ultra. Models are shipped in production at http://chat.reka.ai . A showcase of non cherry picked qualitative examples can also be found at http://showcase.reka.ai .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-19", "arxiv_id": "2404.12347", "title": "AniClipart: Clipart Animation with Text-to-Video Priors", "authors": [ "Ronghuan Wu", "Wanchao Su", "Kede Ma", "Jing Liao" ], "github": "", "project_page": "", "abstract": "Clipart, a pre-made graphic art form, offers a convenient and efficient way of illustrating visual content. Traditional workflows to convert static clipart images into motion sequences are laborious and time-consuming, involving numerous intricate steps like rigging, key animation and in-betweening. Recent advancements in text-to-video generation hold great potential in resolving this problem. Nevertheless, direct application of text-to-video generation models often struggles to retain the visual identity of clipart images or generate cartoon-style motions, resulting in unsatisfactory animation outcomes. In this paper, we introduce AniClipart, a system that transforms static clipart images into high-quality motion sequences guided by text-to-video priors. To generate cartoon-style and smooth motion, we first define B\\'{e}zier curves over keypoints of the clipart image as a form of motion regularization. We then align the motion trajectories of the keypoints with the provided text prompt by optimizing the Video Score Distillation Sampling (VSDS) loss, which encodes adequate knowledge of natural motion within a pretrained text-to-video diffusion model. With a differentiable As-Rigid-As-Possible shape deformation algorithm, our method can be end-to-end optimized while maintaining deformation rigidity. Experimental results show that the proposed AniClipart consistently outperforms existing image-to-video generation models, in terms of text-video alignment, visual identity preservation, and motion consistency. Furthermore, we showcase the versatility of AniClipart by adapting it to generate a broader array of animation formats, such as layered animation, which allows topological changes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-19", "arxiv_id": "2404.11912", "title": "TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding", "authors": [ "Hanshi Sun", "Zhuoming Chen", "Xinyu Yang", "Yuandong Tian", "Beidi Chen" ], "github": "https://github.com/Infini-AI-Lab/TriForce", "project_page": "", "abstract": "With large language models (LLMs) widely deployed in long content generation recently, there has emerged an increasing demand for efficient long-sequence inference support. However, key-value (KV) cache, which is stored to avoid re-computation, has emerged as a critical bottleneck by growing linearly in size with the sequence length. Due to the auto-regressive nature of LLMs, the entire KV cache will be loaded for every generated token, resulting in low utilization of computational cores and high latency. While various compression methods for KV cache have been proposed to alleviate this issue, they suffer from degradation in generation quality. We introduce TriForce, a hierarchical speculative decoding system that is scalable to long sequence generation. This approach leverages the original model weights and dynamic sparse KV cache via retrieval as a draft model, which serves as an intermediate layer in the hierarchy and is further speculated by a smaller model to reduce its drafting latency. TriForce not only facilitates impressive speedups for Llama2-7B-128K, achieving up to 2.31times on an A100 GPU but also showcases scalability in handling even longer contexts. For the offloading setting on two RTX 4090 GPUs, TriForce achieves 0.108s/tokenx2014only half as slow as the auto-regressive baseline on an A100, which attains 7.78times on our optimized offloading system. Additionally, TriForce performs 4.86times than DeepSpeed-Zero-Inference on a single RTX 4090 GPU. TriForce's robustness is highlighted by its consistently outstanding performance across various temperatures. The code is available at https://github.com/Infini-AI-Lab/TriForce.", "github_manual": "", "github_regex": "https://github.com/Infini-AI-Lab/TriForce", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-19", "arxiv_id": "2404.12195", "title": "OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data", "authors": [ "Chandeepa Dissanayake", "Lahiru Lowe", "Sachith Gunasekara", "Yasiru Ratnayake" ], "github": "https://github.com/vihangd/alpaca-qlora", "project_page": "", "abstract": "Instruction fine-tuning pretrained LLMs for diverse downstream tasks has demonstrated remarkable success and has captured the interest of both academics and practitioners. To ensure such fine-tuned LLMs align with human preferences, techniques such as RLHF and DPO have emerged. At the same time, there is increasing interest in smaller parameter counts for models. In this work, using OpenLLaMA 3Bv2 as a base model, we describe the recipe used to fine-tune the OpenBezoar family of models. In this recipe: We first generate synthetic instruction fine-tuning data using an open and commercially non-restrictive instruction fine-tuned variant of the Falcon-40B model under three schemes based on: LaMini-LM, WizardLM/Evol-Instruct (with databricks-dolly-15k as a seed dataset) and Orca (with the Flan Collection as a seed dataset), then filter these generations using GPT-4 as a human proxy. We then perform cost-effective QLoRA-based supervised fine-tuning sequentially with each scheme. The resulting checkpoint is further fine-tuned with a subset of the HH-RLHF dataset to minimize distribution shift prior to using the DPO loss to obtain the final checkpoint. Evaluation is done with the LM Eval Harness tasks/metrics as well as on MT-Bench using the \"LLM-as-a-judge\" framework with Claude 2.1, with the finding that the final checkpoint, \"OpenBezoar-HH-RLHF-DPO\", demonstrates superior performance over many models at the 3B parameter scale, even outperforming the top model in one of the categories on the Huggingface Open LLM Leaderboard. We release \"OpenBezoar-SFT\", \"OpenBezoar-HH-RLHF-SFT\", \"OpenBezoar-HH-RLHF-DPO\" checkpoints, alongside our generated datasets on HuggingFace at https://huggingface.co/collections/SurgeGlobal/open-bezoar-6620a24923e12127e9e2b9cc and our codebase at https://bitbucket.org/paladinanalytics/workspace/projects/OP.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vihangd/alpaca-qlora", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-19", "arxiv_id": "2404.12253", "title": "Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing", "authors": [ "Ye Tian", "Baolin Peng", "Linfeng Song", "Lifeng Jin", "Dian Yu", "Haitao Mi", "Dong Yu" ], "github": "https://github.com/yetianjhu/alphallm", "project_page": "", "abstract": "Despite the impressive capabilities of Large Language Models (LLMs) on various tasks, they still struggle with scenarios that involves complex reasoning and planning. Recent work proposed advanced prompting techniques and the necessity of fine-tuning with high-quality data to augment LLMs' reasoning abilities. However, these approaches are inherently constrained by data availability and quality. In light of this, self-correction and self-learning emerge as viable solutions, employing strategies that allow LLMs to refine their outputs and learn from self-assessed rewards. Yet, the efficacy of LLMs in self-refining its response, particularly in complex reasoning and planning task, remains dubious. In this paper, we introduce AlphaLLM for the self-improvements of LLMs, which integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop, thereby enhancing the capabilities of LLMs without additional annotations. Drawing inspiration from the success of AlphaGo, AlphaLLM addresses the unique challenges of combining MCTS with LLM for self-improvement, including data scarcity, the vastness search spaces of language tasks, and the subjective nature of feedback in language tasks. AlphaLLM is comprised of prompt synthesis component, an efficient MCTS approach tailored for language tasks, and a trio of critic models for precise feedback. Our experimental results in mathematical reasoning tasks demonstrate that AlphaLLM significantly enhances the performance of LLMs without additional annotations, showing the potential for self-improvement in LLMs.", "github_manual": "https://github.com/yetianjhu/alphallm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-19", "arxiv_id": "2404.11565", "title": "MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation", "authors": [ "Kuan-Chieh", "Wang", "Daniil Ostashev", "Yuwei Fang", "Sergey Tulyakov", "Kfir Aberman" ], "github": "https://github.com/snap-research/mixture-of-attention", "project_page": "", "abstract": "We introduce a new architecture for personalization of text-to-image diffusion models, coined Mixture-of-Attention (MoA). Inspired by the Mixture-of-Experts mechanism utilized in large language models (LLMs), MoA distributes the generation workload between two attention pathways: a personalized branch and a non-personalized prior branch. MoA is designed to retain the original model's prior by fixing its attention layers in the prior branch, while minimally intervening in the generation process with the personalized branch that learns to embed subjects in the layout and context generated by the prior branch. A novel routing mechanism manages the distribution of pixels in each layer across these branches to optimize the blend of personalized and generic content creation. Once trained, MoA facilitates the creation of high-quality, personalized images featuring multiple subjects with compositions and interactions as diverse as those generated by the original model. Crucially, MoA enhances the distinction between the model's pre-existing capability and the newly augmented personalized intervention, thereby offering a more disentangled subject-context control that was previously unattainable. Project page: https://snap-research.github.io/mixture-of-attention", "github_manual": "https://github.com/snap-research/mixture-of-attention", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-19", "arxiv_id": "2404.12318", "title": "Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual Alignment", "authors": [ "Zhaofeng Wu", "Ananth Balashankar", "Yoon Kim", "Jacob Eisenstein", "Ahmad Beirami" ], "github": "", "project_page": "", "abstract": "Aligning language models (LMs) based on human-annotated preference data is a crucial step in obtaining practical and performant LM-based systems. However, multilingual human preference data are difficult to obtain at scale, making it challenging to extend this framework to diverse languages. In this work, we evaluate a simple approach for zero-shot cross-lingual alignment, where a reward model is trained on preference data in one source language and directly applied to other target languages. On summarization and open-ended dialog generation, we show that this method is consistently successful under comprehensive evaluation settings, including human evaluation: cross-lingually aligned models are preferred by humans over unaligned models on up to >70% of evaluation instances. We moreover find that a different-language reward model sometimes yields better aligned models than a same-language reward model. We also identify best practices when there is no language-specific data for even supervised finetuning, another component in alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-19", "arxiv_id": "2404.12241", "title": "Introducing v0.5 of the AI Safety Benchmark from MLCommons", "authors": [ "Bertie Vidgen", "Adarsh Agrawal", "Ahmed M. Ahmed", "Victor Akinwande", "Namir Al-Nuaimi", "Najla Alfaraj", "Elie Alhajjar", "Lora Aroyo", "Trupti Bavalatti", "Borhane Blili-Hamelin", "Kurt Bollacker", "Rishi Bomassani", "Marisa Ferrara Boston", "Sim\u00e9on Campos", "Kal Chakra", "Canyu Chen", "Cody Coleman", "Zacharie Delpierre Coudert", "Leon Derczynski", "Debojyoti Dutta", "Ian Eisenberg", "James Ezick", "Heather Frase", "Brian Fuller", "Ram Gandikota", "Agasthya Gangavarapu", "Ananya Gangavarapu", "James Gealy", "Rajat Ghosh", "James Goel", "Usman Gohar", "Sujata Goswami", "Scott A. Hale", "Wiebke Hutiri", "Joseph Marvin Imperial", "Surgan Jandial", "Nick Judd", "Felix Juefei-Xu", "Foutse Khomh", "Bhavya Kailkhura", "Hannah Rose Kirk", "Kevin Klyman", "Chris Knotz", "Michael Kuchnik", "Shachi H. Kumar", "Chris Lengerich", "Bo Li", "Zeyi Liao", "Eileen Peters Long", "Victor Lu", "Yifan Mai", "Priyanka Mary Mammen", "Kelvin Manyeki", "Sean McGregor", "Virendra Mehta", "Shafee Mohammed", "Emanuel Moss", "Lama Nachman", "Dinesh Jinenhally Naganna", "Amin Nikanjam", "Besmira Nushi", "Luis Oala", "Iftach Orr", "Alicia Parrish", "Cigdem Patlak", "William Pietri", "Forough Poursabzi-Sangdeh", "Eleonora Presani", "Fabrizio Puletti", "Paul R\u00f6ttger", "Saurav Sahay", "Tim Santos", "Nino Scherrer", "Alice Schoenauer Sebag", "Patrick Schramowski", "Abolfazl Shahbazi", "Vin Sharma", "Xudong Shen", "Vamsi Sistla", "Leonard Tang", "Davide Testuggine", "Vithursan Thangarasa", "Elizabeth Anne Watkins", "Rebecca Weiss", "Chris Welty", "Tyler Wilbers", "Adina Williams", "Carole-Jean Wu", "Poonam Yadav", "Xianjun Yang", "Yi Zeng", "Wenhui Zhang", "Fedor Zhdanov", "Jiacheng Zhu", "Percy Liang", "Peter Mattson", "Joaquin Vanschoren" ], "github": "https://github.com/mlcommons/modelbench", "project_page": "", "abstract": "This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mlcommons/modelbench", "github_api_added_by": "auto" }, { "date": "2024-04-19", "arxiv_id": "2404.11614", "title": "Dynamic Typography: Bringing Words to Life", "authors": [ "Zichen Liu", "Yihao Meng", "Hao Ouyang", "Yue Yu", "Bolin Zhao", "Daniel Cohen-Or", "Huamin Qu" ], "github": "https://github.com/zliucz/animate-your-word", "project_page": "", "abstract": "Text animation serves as an expressive medium, transforming static communication into dynamic experiences by infusing words with motion to evoke emotions, emphasize meanings, and construct compelling narratives. Crafting animations that are semantically aware poses significant challenges, demanding expertise in graphic design and animation. We present an automated text animation scheme, termed \"Dynamic Typography\", which combines two challenging tasks. It deforms letters to convey semantic meaning and infuses them with vibrant movements based on user prompts. Our technique harnesses vector graphics representations and an end-to-end optimization-based framework. This framework employs neural displacement fields to convert letters into base shapes and applies per-frame motion, encouraging coherence with the intended textual concept. Shape preservation techniques and perceptual loss regularization are employed to maintain legibility and structural integrity throughout the animation process. We demonstrate the generalizability of our approach across various text-to-video models and highlight the superiority of our end-to-end methodology over baseline methods, which might comprise separate tasks. Through quantitative and qualitative evaluations, we demonstrate the effectiveness of our framework in generating coherent text animations that faithfully interpret user prompts while maintaining readability. Our code is available at: https://animate-your-word.github.io/demo/.", "github_manual": "https://github.com/zliucz/animate-your-word", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-19", "arxiv_id": "2404.12385", "title": "MeshLRM: Large Reconstruction Model for High-Quality Mesh", "authors": [ "Xinyue Wei", "Kai Zhang", "Sai Bi", "Hao Tan", "Fujun Luan", "Valentin Deschaintre", "Kalyan Sunkavalli", "Hao Su", "Zexiang Xu" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "We propose MeshLRM, a novel LRM-based approach that can reconstruct a high-quality mesh from merely four input images in less than one second. Different from previous large reconstruction models (LRMs) that focus on NeRF-based reconstruction, MeshLRM incorporates differentiable mesh extraction and rendering within the LRM framework. This allows for end-to-end mesh reconstruction by fine-tuning a pre-trained NeRF LRM with mesh rendering. Moreover, we improve the LRM architecture by simplifying several complex designs in previous LRMs. MeshLRM's NeRF initialization is sequentially trained with low- and high-resolution images; this new LRM training strategy enables significantly faster convergence and thereby leads to better quality with less compute. Our approach achieves state-of-the-art mesh reconstruction from sparse-view inputs and also allows for many downstream applications, including text-to-3D and single-image-to-3D generation. Project page: https://sarahweiii.github.io/meshlrm/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-19", "arxiv_id": "2404.11925", "title": "EdgeFusion: On-Device Text-to-Image Generation", "authors": [ "Thibault Castells", "Hyoung-Kyu Song", "Tairen Piao", "Shinkook Choi", "Bo-Kyeong Kim", "Hanyoung Yim", "Changgwun Lee", "Jae Gon Kim", "Tae-Ho Kim" ], "github": "", "project_page": "", "abstract": "The intensive computational burden of Stable Diffusion (SD) for text-to-image generation poses a significant hurdle for its practical application. To tackle this challenge, recent research focuses on methods to reduce sampling steps, such as Latent Consistency Model (LCM), and on employing architectural optimizations, including pruning and knowledge distillation. Diverging from existing approaches, we uniquely start with a compact SD variant, BK-SDM. We observe that directly applying LCM to BK-SDM with commonly used crawled datasets yields unsatisfactory results. It leads us to develop two strategies: (1) leveraging high-quality image-text pairs from leading generative models and (2) designing an advanced distillation process tailored for LCM. Through our thorough exploration of quantization, profiling, and on-device deployment, we achieve rapid generation of photo-realistic, text-aligned images in just two steps, with latency under one second on resource-limited edge devices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-22", "arxiv_id": "2404.13026", "title": "PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation", "authors": [ "Tianyuan Zhang", "Hong-Xing Yu", "Rundi Wu", "Brandon Y. Feng", "Changxi Zheng", "Noah Snavely", "Jiajun Wu", "William T. Freeman" ], "github": "https://github.com/a1600012888/PhysDreamer", "project_page": "", "abstract": "Realistic object interactions are crucial for creating immersive virtual experiences, yet synthesizing realistic 3D object dynamics in response to novel interactions remains a significant challenge. Unlike unconditional or text-conditioned dynamics generation, action-conditioned dynamics requires perceiving the physical material properties of objects and grounding the 3D motion prediction on these properties, such as object stiffness. However, estimating physical material properties is an open problem due to the lack of material ground-truth data, as measuring these properties for real objects is highly difficult. We present PhysDreamer, a physics-based approach that endows static 3D objects with interactive dynamics by leveraging the object dynamics priors learned by video generation models. By distilling these priors, PhysDreamer enables the synthesis of realistic object responses to novel interactions, such as external forces or agent manipulations. We demonstrate our approach on diverse examples of elastic objects and evaluate the realism of the synthesized interactions through a user study. PhysDreamer takes a step towards more engaging and realistic virtual experiences by enabling static 3D objects to dynamically respond to interactive stimuli in a physically plausible manner. See our project page at https://physdreamer.github.io/.", "github_manual": "https://github.com/a1600012888/PhysDreamer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-22", "arxiv_id": "2404.12547", "title": "Does Gaussian Splatting need SFM Initialization?", "authors": [ "Yalda Foroutan", "Daniel Rebain", "Kwang Moo Yi", "Andrea Tagliasacchi" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting has recently been embraced as a versatile and effective method for scene reconstruction and novel view synthesis, owing to its high-quality results and compatibility with hardware rasterization. Despite its advantages, Gaussian Splatting's reliance on high-quality point cloud initialization by Structure-from-Motion (SFM) algorithms is a significant limitation to be overcome. To this end, we investigate various initialization strategies for Gaussian Splatting and delve into how volumetric reconstructions from Neural Radiance Fields (NeRF) can be utilized to bypass the dependency on SFM data. Our findings demonstrate that random initialization can perform much better if carefully designed and that by employing a combination of improved initialization strategies and structure distillation from low-cost NeRF models, it is possible to achieve equivalent results, or at times even superior, to those obtained from SFM initialization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-22", "arxiv_id": "2404.12833", "title": "How Far Can We Go with Practical Function-Level Program Repair?", "authors": [ "Jiahong Xiang", "Xiaoyang Xu", "Fanchu Kong", "Mingyuan Wu", "Haotian Zhang", "Yuqun Zhang" ], "github": "https://github.com/ghabix/srepair", "project_page": "", "abstract": "Recently, multiple Automated Program Repair (APR) techniques based on Large Language Models (LLMs) have been proposed to enhance the repair performance. While these techniques mainly focus on the single-line or hunk-level repair, they face significant challenges in real-world application due to the limited repair task scope and costly statement-level fault localization. However, the more practical function-level APR, which broadens the scope of APR task to fix entire buggy functions and requires only cost-efficient function-level fault localization, remains underexplored. In this paper, we conduct the first comprehensive study of LLM-based function-level APR including investigating the effect of the few-shot learning mechanism and the auxiliary repair-relevant information. Specifically, we adopt six widely-studied LLMs and construct a benchmark in both the Defects4J 1.2 and 2.0 datasets. Our study demonstrates that LLMs with zero-shot learning are already powerful function-level APR techniques, while applying the few-shot learning mechanism leads to disparate repair performance. Moreover, we find that directly applying the auxiliary repair-relevant information to LLMs significantly increases function-level repair performance. Inspired by our findings, we propose an LLM-based function-level APR technique, namely SRepair, which adopts a dual-LLM framework to leverage the power of the auxiliary repair-relevant information for advancing the repair performance. The evaluation results demonstrate that SRepair can correctly fix 300 single-function bugs in the Defects4J dataset, largely surpassing all previous APR techniques by at least 85%, without the need for the costly statement-level fault location information. Furthermore, SRepair successfully fixes 32 multi-function bugs in the Defects4J dataset, which is the first time achieved by any APR technique ever to our best knowledge.", "github_manual": "https://github.com/ghabix/srepair", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-22", "arxiv_id": "2404.12753", "title": "AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation", "authors": [ "Wenhao Huang", "Chenghao Peng", "Zhixu Li", "Jiaqing Liang", "Yanghua Xiao", "Liqian Wen", "Zulong Chen" ], "github": "https://github.com/EZ-hwh/AutoCrawler", "project_page": "", "abstract": "Web automation is a significant technique that accomplishes complicated web tasks by automating common web actions, enhancing operational efficiency, and reducing the need for manual intervention. Traditional methods, such as wrappers, suffer from limited adaptability and scalability when faced with a new website. On the other hand, generative agents empowered by large language models (LLMs) exhibit poor performance and reusability in open-world scenarios. In this work, we introduce a crawler generation task for vertical information web pages and the paradigm of combining LLMs with crawlers, which helps crawlers handle diverse and changing web environments more efficiently. We propose AutoCrawler, a two-stage framework that leverages the hierarchical structure of HTML for progressive understanding. Through top-down and step-back operations, AutoCrawler can learn from erroneous actions and continuously prune HTML for better action generation. We conduct comprehensive experiments with multiple LLMs and demonstrate the effectiveness of our framework. Resources of this paper can be found at https://github.com/EZ-hwh/AutoCrawler", "github_manual": "", "github_regex": "https://github.com/EZ-hwh/AutoCrawler", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-22", "arxiv_id": "2404.12872", "title": "LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency", "authors": [ "Zhaodonghui Li", "Haitao Yuan", "Huiming Wang", "Gao Cong", "Lidong Bing" ], "github": "https://github.com/damo-nlp-sg/llm-r2", "project_page": "", "abstract": "Query rewrite, which aims to generate more efficient queries by altering a SQL query's structure without changing the query result, has been an important research problem. In order to maintain equivalence between the rewritten query and the original one during rewriting, traditional query rewrite methods always rewrite the queries following certain rewrite rules. However, some problems still remain. Firstly, existing methods of finding the optimal choice or sequence of rewrite rules are still limited and the process always costs a lot of resources. Methods involving discovering new rewrite rules typically require complicated proofs of structural logic or extensive user interactions. Secondly, current query rewrite methods usually rely highly on DBMS cost estimators which are often not accurate. In this paper, we address these problems by proposing a novel method of query rewrite named LLM-R2, adopting a large language model (LLM) to propose possible rewrite rules for a database rewrite system. To further improve the inference ability of LLM in recommending rewrite rules, we train a contrastive model by curriculum to learn query representations and select effective query demonstrations for the LLM. Experimental results have shown that our method can significantly improve the query execution efficiency and outperform the baseline methods. In addition, our method enjoys high robustness across different datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/damo-nlp-sg/llm-r2", "github_api_added_by": "auto" }, { "date": "2024-04-22", "arxiv_id": "2404.12803", "title": "TextSquare: Scaling up Text-Centric Visual Instruction Tuning", "authors": [ "Jingqun Tang", "Chunhui Lin", "Zhen Zhao", "Shu Wei", "Binghong Wu", "Qi Liu", "Hao Feng", "Yang Li", "Siqi Wang", "Lei Liao", "Wei Shi", "Yuliang Liu", "Hao Liu", "Yuan Xie", "Xiang Bai", "Can Huang" ], "github": "", "project_page": "", "abstract": "Text-centric visual question answering (VQA) has made great strides with the development of Multimodal Large Language Models (MLLMs), yet open-source models still fall short of leading models like GPT4V and Gemini, partly due to a lack of extensive, high-quality instruction tuning data. To this end, we introduce a new approach for creating a massive, high-quality instruction-tuning dataset, Square-10M, which is generated using closed-source MLLMs. The data construction process, termed Square, consists of four steps: Self-Questioning, Answering, Reasoning, and Evaluation. Our experiments with Square-10M led to three key findings: 1) Our model, TextSquare, considerably surpasses open-source previous state-of-the-art Text-centric MLLMs and sets a new standard on OCRBench(62.2%). It even outperforms top-tier models like GPT4V and Gemini in 6 of 10 text-centric benchmarks. 2) Additionally, we demonstrate the critical role of VQA reasoning data in offering comprehensive contextual insights for specific questions. This not only improves accuracy but also significantly mitigates hallucinations. Specifically, TextSquare scores an average of 75.1% across four general VQA and hallucination evaluation datasets, outperforming previous state-of-the-art models. 3) Notably, the phenomenon observed in scaling text-centric VQA datasets reveals a vivid pattern: the exponential increase of instruction tuning data volume is directly proportional to the improvement in model performance, thereby validating the necessity of the dataset scale and the high quality of Square-10M.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-22", "arxiv_id": "2404.13013", "title": "Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models", "authors": [ "Chuofan Ma", "Yi Jiang", "Jiannan Wu", "Zehuan Yuan", "Xiaojuan Qi" ], "github": "https://github.com/FoundationVision/Groma", "project_page": "", "abstract": "We introduce Groma, a Multimodal Large Language Model (MLLM) with grounded and fine-grained visual perception ability. Beyond holistic image understanding, Groma is adept at region-level tasks such as region captioning and visual grounding. Such capabilities are built upon a localized visual tokenization mechanism, where an image input is decomposed into regions of interest and subsequently encoded into region tokens. By integrating region tokens into user instructions and model responses, we seamlessly enable Groma to understand user-specified region inputs and ground its textual output to images. Besides, to enhance the grounded chat ability of Groma, we curate a visually grounded instruction dataset by leveraging the powerful GPT-4V and visual prompting techniques. Compared with MLLMs that rely on the language model or external module for localization, Groma consistently demonstrates superior performances in standard referring and grounding benchmarks, highlighting the advantages of embedding localization into image tokenization. Project page: https://groma-mllm.github.io/.", "github_manual": "https://github.com/FoundationVision/Groma", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-23", "arxiv_id": "2404.13358", "title": "Music Consistency Models", "authors": [ "Zhengcong Fei", "Mingyuan Fan", "Junshi Huang" ], "github": "", "project_page": "", "abstract": "Consistency models have exhibited remarkable capabilities in facilitating efficient image/video generation, enabling synthesis with minimal sampling steps. It has proven to be advantageous in mitigating the computational burdens associated with diffusion models. Nevertheless, the application of consistency models in music generation remains largely unexplored. To address this gap, we present Music Consistency Models (MusicCM), which leverages the concept of consistency models to efficiently synthesize mel-spectrogram for music clips, maintaining high quality while minimizing the number of sampling steps. Building upon existing text-to-music diffusion models, the MusicCM model incorporates consistency distillation and adversarial discriminator training. Moreover, we find it beneficial to generate extended coherent music by incorporating multiple diffusion processes with shared constraints. Experimental results reveal the effectiveness of our model in terms of computational efficiency, fidelity, and naturalness. Notable, MusicCM achieves seamless music synthesis with a mere four sampling steps, e.g., only one second per minute of the music clip, showcasing the potential for real-time application.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-23", "arxiv_id": "2404.14219", "title": "Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone", "authors": [ "Marah Abdin", "Sam Ade Jacobs", "Ammar Ahmad Awan", "Jyoti Aneja", "Ahmed Awadallah", "Hany Awadalla", "Nguyen Bach", "Amit Bahree", "Arash Bakhtiari", "Harkirat Behl", "Alon Benhaim", "Misha Bilenko", "Johan Bjorck", "S\u00e9bastien Bubeck", "Martin Cai", "Caio C\u00e9sar Teodoro Mendes", "Weizhu Chen", "Vishrav Chaudhary", "Parul Chopra", "Allie Del Giorno", "Gustavo de Rosa", "Matthew Dixon", "Ronen Eldan", "Dan Iter", "Abhishek Goswami", "Suriya Gunasekar", "Emman Haider", "Junheng Hao", "Russell J. Hewett", "Jamie Huynh", "Mojan Javaheripi", "Xin Jin", "Piero Kauffmann", "Nikos Karampatziakis", "Dongwoo Kim", "Mahoud Khademi", "Lev Kurilenko", "James R. Lee", "Yin Tat Lee", "Yuanzhi Li", "Chen Liang", "Weishung Liu", "Eric Lin", "Zeqi Lin", "Piyush Madan", "Arindam Mitra", "Hardik Modi", "Anh Nguyen", "Brandon Norick", "Barun Patra", "Daniel Perez-Becker", "Thomas Portet", "Reid Pryzant", "Heyang Qin", "Marko Radmilac", "Corby Rosset", "Sambudha Roy", "Olli Saarikivi", "Amin Saied", "Adil Salim", "Michael Santacroce", "Shital Shah", "Ning Shang", "Hiteshi Sharma", "Xia Song", "Olatunji Ruwase", "Xin Wang", "Rachel Ward", "Guanhua Wang", "Philipp Witte", "Michael Wyatt", "Can Xu", "Jiahang Xu", "Sonali Yadav", "Fan Yang", "Ziyi Yang", "Donghan Yu", "Chengruidong Zhang", "Cyril Zhang", "Jianwen Zhang", "Li Lyna Zhang", "Yi Zhang", "Yunan Zhang", "Xiren Zhou" ], "github": "", "project_page": "", "abstract": "We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. The innovation lies entirely in our dataset for training, a scaled-up version of the one used for phi-2, composed of heavily filtered web data and synthetic data. The model is also further aligned for robustness, safety, and chat format. We also provide some initial parameter-scaling results with a 7B and 14B models trained for 4.8T tokens, called phi-3-small and phi-3-medium, both significantly more capable than phi-3-mini (e.g., respectively 75% and 78% on MMLU, and 8.7 and 8.9 on MT-bench).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-23", "arxiv_id": "2404.14239", "title": "MultiBooth: Towards Generating All Your Concepts in an Image from Text", "authors": [ "Chenyang Zhu", "Kai Li", "Yue Ma", "Chunming He", "Li Xiu" ], "github": "https://github.com/chenyangzhu1/MultiBooth", "project_page": "", "abstract": "This paper introduces MultiBooth, a novel and efficient technique for multi-concept customization in image generation from text. Despite the significant advancements in customized generation methods, particularly with the success of diffusion models, existing methods often struggle with multi-concept scenarios due to low concept fidelity and high inference cost. MultiBooth addresses these issues by dividing the multi-concept generation process into two phases: a single-concept learning phase and a multi-concept integration phase. During the single-concept learning phase, we employ a multi-modal image encoder and an efficient concept encoding technique to learn a concise and discriminative representation for each concept. In the multi-concept integration phase, we use bounding boxes to define the generation area for each concept within the cross-attention map. This method enables the creation of individual concepts within their specified regions, thereby facilitating the formation of multi-concept images. This strategy not only improves concept fidelity but also reduces additional inference cost. MultiBooth surpasses various baselines in both qualitative and quantitative evaluations, showcasing its superior performance and computational efficiency. Project Page: https://multibooth.github.io/", "github_manual": "https://github.com/chenyangzhu1/MultiBooth", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-23", "arxiv_id": "2404.14394", "title": "A Multimodal Automated Interpretability Agent", "authors": [ "Tamar Rott Shaham", "Sarah Schwettmann", "Franklin Wang", "Achyuta Rajaram", "Evan Hernandez", "Jacob Andreas", "Antonio Torralba" ], "github": "https://github.com/multimodal-interpretability/maia", "project_page": "", "abstract": "This paper describes MAIA, a Multimodal Automated Interpretability Agent. MAIA is a system that uses neural models to automate neural model understanding tasks like feature interpretation and failure mode discovery. It equips a pre-trained vision-language model with a set of tools that support iterative experimentation on subcomponents of other models to explain their behavior. These include tools commonly used by human interpretability researchers: for synthesizing and editing inputs, computing maximally activating exemplars from real-world datasets, and summarizing and describing experimental results. Interpretability experiments proposed by MAIA compose these tools to describe and explain system behavior. We evaluate applications of MAIA to computer vision models. We first characterize MAIA's ability to describe (neuron-level) features in learned representations of images. Across several trained models and a novel dataset of synthetic vision neurons with paired ground-truth descriptions, MAIA produces descriptions comparable to those generated by expert human experimenters. We then show that MAIA can aid in two additional interpretability tasks: reducing sensitivity to spurious features, and automatically identifying inputs likely to be mis-classified.", "github_manual": "https://github.com/multimodal-interpretability/maia", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-23", "arxiv_id": "2404.13686", "title": "Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis", "authors": [ "Yuxi Ren", "Xin Xia", "Yanzuo Lu", "Jiacheng Zhang", "Jie Wu", "Pan Xie", "Xing Wang", "Xuefeng Xiao" ], "github": "", "project_page": "", "abstract": "Recently, a series of diffusion-aware distillation algorithms have emerged to alleviate the computational overhead associated with the multi-step inference process of Diffusion Models (DMs). Current distillation techniques often dichotomize into two distinct aspects: i) ODE Trajectory Preservation; and ii) ODE Trajectory Reformulation. However, these approaches suffer from severe performance degradation or domain shifts. To address these limitations, we propose Hyper-SD, a novel framework that synergistically amalgamates the advantages of ODE Trajectory Preservation and Reformulation, while maintaining near-lossless performance during step compression. Firstly, we introduce Trajectory Segmented Consistency Distillation to progressively perform consistent distillation within pre-defined time-step segments, which facilitates the preservation of the original ODE trajectory from a higher-order perspective. Secondly, we incorporate human feedback learning to boost the performance of the model in a low-step regime and mitigate the performance loss incurred by the distillation process. Thirdly, we integrate score distillation to further improve the low-step generation capability of the model and offer the first attempt to leverage a unified LoRA to support the inference process at all steps. Extensive experiments and user studies demonstrate that Hyper-SD achieves SOTA performance from 1 to 8 inference steps for both SDXL and SD1.5. For example, Hyper-SDXL surpasses SDXL-Lightning by +0.68 in CLIP Score and +0.51 in Aes Score in the 1-step inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-23", "arxiv_id": "2404.14396", "title": "SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation", "authors": [ "Yuying Ge", "Sijie Zhao", "Jinguo Zhu", "Yixiao Ge", "Kun Yi", "Lin Song", "Chen Li", "Xiaohan Ding", "Ying Shan" ], "github": "https://github.com/AILab-CVC/SEED-X", "project_page": "", "abstract": "The rapid evolution of multimodal foundation model has demonstrated significant progresses in vision-language understanding and generation, e.g., our previous work SEED-LLaMA. However, there remains a gap between its capability and the real-world applicability, primarily due to the model's limited capacity to effectively respond to various user instructions and interact with diverse visual data. In this work, we focus on bridging this gap through integrating two enhanced features: (1) comprehending images of arbitrary sizes and ratios, and (2) enabling multi-granularity image generation. We present a unified and versatile foundation model, namely, SEED-X, which is able to model multi-granularity visual semantics for comprehension and generation tasks. Besides the competitive results on public benchmarks, SEED-X demonstrates its effectiveness in handling real-world applications across various domains after instruction tuning. We hope that our work will inspire future research into what can be achieved by versatile multimodal foundation models in real-world applications. The models, codes, and datasets will be released in https://github.com/AILab-CVC/SEED-X.", "github_manual": "", "github_regex": "https://github.com/AILab-CVC/SEED-X", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-23", "arxiv_id": "2404.13050", "title": "FlowMind: Automatic Workflow Generation with LLMs", "authors": [ "Zhen Zeng", "William Watson", "Nicole Cho", "Saba Rahimi", "Shayleen Reynolds", "Tucker Balch", "Manuela Veloso" ], "github": "", "project_page": "", "abstract": "The rapidly evolving field of Robotic Process Automation (RPA) has made significant strides in automating repetitive processes, yet its effectiveness diminishes in scenarios requiring spontaneous or unpredictable tasks demanded by users. This paper introduces a novel approach, FlowMind, leveraging the capabilities of Large Language Models (LLMs) such as Generative Pretrained Transformer (GPT), to address this limitation and create an automatic workflow generation system. In FlowMind, we propose a generic prompt recipe for a lecture that helps ground LLM reasoning with reliable Application Programming Interfaces (APIs). With this, FlowMind not only mitigates the common issue of hallucinations in LLMs, but also eliminates direct interaction between LLMs and proprietary data or code, thus ensuring the integrity and confidentiality of information - a cornerstone in financial services. FlowMind further simplifies user interaction by presenting high-level descriptions of auto-generated workflows, enabling users to inspect and provide feedback effectively. We also introduce NCEN-QA, a new dataset in finance for benchmarking question-answering tasks from N-CEN reports on funds. We used NCEN-QA to evaluate the performance of workflows generated by FlowMind against baseline and ablation variants of FlowMind. We demonstrate the success of FlowMind, the importance of each component in the proposed lecture recipe, and the effectiveness of user interaction and feedback in FlowMind.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-23", "arxiv_id": "2404.13208", "title": "The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions", "authors": [ "Eric Wallace", "Kai Xiao", "Reimar Leike", "Lilian Weng", "Johannes Heidecke", "Alex Beutel" ], "github": "", "project_page": "", "abstract": "Today's LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow adversaries to overwrite a model's original instructions with their own malicious prompts. In this work, we argue that one of the primary vulnerabilities underlying these attacks is that LLMs often consider system prompts (e.g., text from an application developer) to be the same priority as text from untrusted users and third parties. To address this, we propose an instruction hierarchy that explicitly defines how models should behave when instructions of different priorities conflict. We then propose a data generation method to demonstrate this hierarchical instruction following behavior, which teaches LLMs to selectively ignore lower-privileged instructions. We apply this method to GPT-3.5, showing that it drastically increases robustness -- even for attack types not seen during training -- while imposing minimal degradations on standard capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-23", "arxiv_id": "2404.14047", "title": "How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study", "authors": [ "Wei Huang", "Xudong Ma", "Haotong Qin", "Xingyu Zheng", "Chengtao Lv", "Hong Chen", "Jie Luo", "Xiaojuan Qi", "Xianglong Liu", "Michele Magno" ], "github": "https://github.com/Macaronlin/LLaMA3-Quantization", "project_page": "", "abstract": "Meta's LLaMA family has become one of the most powerful open-source Large Language Model (LLM) series. Notably, LLaMA3 models have recently been released and achieve impressive performance across various with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-limited scenarios, we explore LLaMA3's capabilities when quantized to low bit-width. This exploration holds the potential to unveil new insights and challenges for low-bit quantization of LLaMA3 and other forthcoming LLMs, especially in addressing performance degradation problems that suffer in LLM compression. Specifically, we evaluate the 10 existing post-training quantization and LoRA-finetuning methods of LLaMA3 on 1-8 bits and diverse datasets to comprehensively reveal LLaMA3's low-bit quantization performance. Our experiment results indicate that LLaMA3 still suffers non-negligent degradation in these scenarios, especially in ultra-low bit-width. This highlights the significant performance gap under low bit-width that needs to be bridged in future developments. We expect that this empirical study will prove valuable in advancing future models, pushing the LLMs to lower bit-width with higher accuracy for being practical. Our project is released on https://github.com/Macaronlin/LLaMA3-Quantization and quantized LLaMA3 models are released in https://huggingface.co/LLMQ.", "github_manual": "", "github_regex": "https://github.com/Macaronlin/LLaMA3-Quantization", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-23", "arxiv_id": "2404.14405", "title": "Learning H-Infinity Locomotion Control", "authors": [ "Junfeng Long", "Wenye Yu", "Quanyi Li", "Zirui Wang", "Dahua Lin", "Jiangmiao Pang" ], "github": "https://github.com/OpenRobotLab/HIMLoco", "project_page": "", "abstract": "Stable locomotion in precipitous environments is an essential capability of quadruped robots, demanding the ability to resist various external disturbances. However, recent learning-based policies only use basic domain randomization to improve the robustness of learned policies, which cannot guarantee that the robot has adequate disturbance resistance capabilities. In this paper, we propose to model the learning process as an adversarial interaction between the actor and a newly introduced disturber and ensure their optimization with H_{infty} constraint. In contrast to the actor that maximizes the discounted overall reward, the disturber is responsible for generating effective external forces and is optimized by maximizing the error between the task reward and its oracle, i.e., \"cost\" in each iteration. To keep joint optimization between the actor and the disturber stable, our H_{infty} constraint mandates the bound of ratio between the cost to the intensity of the external forces. Through reciprocal interaction throughout the training phase, the actor can acquire the capability to navigate increasingly complex physical disturbances. We verify the robustness of our approach on quadrupedal locomotion tasks with Unitree Aliengo robot, and also a more challenging task with Unitree A1 robot, where the quadruped is expected to perform locomotion merely on its hind legs as if it is a bipedal robot. The simulated quantitative results show improvement against baselines, demonstrating the effectiveness of the method and each design choice. On the other hand, real-robot experiments qualitatively exhibit how robust the policy is when interfering with various disturbances on various terrains, including stairs, high platforms, slopes, and slippery terrains. All code, checkpoints, and real-world deployment guidance will be made public.", "github_manual": "https://github.com/OpenRobotLab/HIMLoco", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-23", "arxiv_id": "2404.14351", "title": "Scene Coordinate Reconstruction: Posing of Image Collections via Incremental Learning of a Relocalizer", "authors": [ "Eric Brachmann", "Jamie Wynn", "Shuai Chen", "Tommaso Cavallari", "\u00c1ron Monszpart", "Daniyar Turmukhambetov", "Victor Adrian Prisacariu" ], "github": "https://github.com/nianticlabs/acezero", "project_page": "", "abstract": "We address the task of estimating camera parameters from a set of images depicting a scene. Popular feature-based structure-from-motion (SfM) tools solve this task by incremental reconstruction: they repeat triangulation of sparse 3D points and registration of more camera views to the sparse point cloud. We re-interpret incremental structure-from-motion as an iterated application and refinement of a visual relocalizer, that is, of a method that registers new views to the current state of the reconstruction. This perspective allows us to investigate alternative visual relocalizers that are not rooted in local feature matching. We show that scene coordinate regression, a learning-based relocalization approach, allows us to build implicit, neural scene representations from unposed images. Different from other learning-based reconstruction methods, we do not require pose priors nor sequential inputs, and we optimize efficiently over thousands of images. Our method, ACE0 (ACE Zero), estimates camera poses to an accuracy comparable to feature-based SfM, as demonstrated by novel view synthesis. Project page: https://nianticlabs.github.io/acezero/", "github_manual": "https://github.com/nianticlabs/acezero", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-24", "arxiv_id": "2404.14619", "title": "OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework", "authors": [ "Sachin Mehta", "Mohammad Hossein Sekhavat", "Qingqing Cao", "Maxwell Horton", "Yanzi Jin", "Chenfan Sun", "Iman Mirzadeh", "Mahyar Najibi", "Dmitry Belenko", "Peter Zatloukal", "Mohammad Rastegari" ], "github": "https://github.com/apple/corenet", "project_page": "", "abstract": "The reproducibility and transparency of large language models are crucial for advancing open research, ensuring the trustworthiness of results, and enabling investigations into data and model biases, as well as potential risks. To this end, we release OpenELM, a state-of-the-art open language model. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. For example, with a parameter budget of approximately one billion parameters, OpenELM exhibits a 2.36% improvement in accuracy compared to OLMo while requiring 2times fewer pre-training tokens. Diverging from prior practices that only provide model weights and inference code, and pre-train on private datasets, our release includes the complete framework for training and evaluation of the language model on publicly available datasets, including training logs, multiple checkpoints, and pre-training configurations. We also release code to convert models to MLX library for inference and fine-tuning on Apple devices. This comprehensive release aims to empower and strengthen the open research community, paving the way for future open research endeavors. Our source code along with pre-trained model weights and training recipes is available at https://github.com/apple/corenet. Additionally, \\model models can be found on HuggingFace at: https://huggingface.co/apple/OpenELM.", "github_manual": "", "github_regex": "https://github.com/apple/corenet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-24", "arxiv_id": "2404.14507", "title": "Align Your Steps: Optimizing Sampling Schedules in Diffusion Models", "authors": [ "Amirmojtaba Sabour", "Sanja Fidler", "Karsten Kreis" ], "github": "", "project_page": "", "abstract": "Diffusion models (DMs) have established themselves as the state-of-the-art generative modeling approach in the visual domain and beyond. A crucial drawback of DMs is their slow sampling speed, relying on many sequential function evaluations through large neural networks. Sampling from DMs can be seen as solving a differential equation through a discretized set of noise levels known as the sampling schedule. While past works primarily focused on deriving efficient solvers, little attention has been given to finding optimal sampling schedules, and the entire literature relies on hand-crafted heuristics. In this work, for the first time, we propose a general and principled approach to optimizing the sampling schedules of DMs for high-quality outputs, called Align Your Steps. We leverage methods from stochastic calculus and find optimal schedules specific to different solvers, trained DMs and datasets. We evaluate our novel approach on several image, video as well as 2D toy data synthesis benchmarks, using a variety of different samplers, and observe that our optimized schedules outperform previous hand-crafted schedules in almost all experiments. Our method demonstrates the untapped potential of sampling schedule optimization, especially in the few-step synthesis regime.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-24", "arxiv_id": "2404.14469", "title": "SnapKV: LLM Knows What You are Looking for Before Generation", "authors": [ "Yuhong Li", "Yingbing Huang", "Bowen Yang", "Bharat Venkitesh", "Acyr Locatelli", "Hanchen Ye", "Tianle Cai", "Patrick Lewis", "Deming Chen" ], "github": "https://github.com/FasterDecoding/SnapKV", "project_page": "", "abstract": "Large Language Models (LLMs) have made remarkable progress in processing extensive contexts, with the Key-Value (KV) cache playing a vital role in enhancing their performance. However, the growth of the KV cache in response to increasing input length poses challenges to memory and time efficiency. To address this problem, this paper introduces SnapKV, an innovative and fine-tuning-free approach that efficiently minimizes KV cache size while still delivering comparable performance in real-world applications. We discover that each attention head in the model consistently focuses on specific prompt attention features during generation. Meanwhile, this robust pattern can be obtained from an `observation' window located at the end of the prompts. Drawing on this insight, SnapKV automatically compresses KV caches by selecting clustered important KV positions for each attention head. Our approach significantly reduces the growing computational overhead and memory footprint when processing long input sequences. Specifically, SnapKV achieves a consistent decoding speed with a 3.6x increase in generation speed and an 8.2x enhancement in memory efficiency compared to baseline when processing inputs of 16K tokens. At the same time, it maintains comparable performance to baseline models across 16 long sequence datasets. Moreover, SnapKV can process up to 380K context tokens on a single A100-80GB GPU using HuggingFace implementation with minor changes, exhibiting only a negligible accuracy drop in the Needle-in-a-Haystack test. Further comprehensive studies suggest SnapKV's potential for practical applications.", "github_manual": "https://github.com/FasterDecoding/SnapKV", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-24", "arxiv_id": "2404.14700", "title": "FlashSpeech: Efficient Zero-Shot Speech Synthesis", "authors": [ "Zhen Ye", "Zeqian Ju", "Haohe Liu", "Xu Tan", "Jianyi Chen", "Yiwen Lu", "Peiwen Sun", "Jiahao Pan", "Weizhen Bian", "Shulin He", "Qifeng Liu", "Yike Guo", "Wei Xue" ], "github": "", "project_page": "", "abstract": "Recent progress in large-scale zero-shot speech synthesis has been significantly advanced by language models and diffusion models. However, the generation process of both methods is slow and computationally intensive. Efficient speech synthesis using a lower computing budget to achieve quality on par with previous work remains a significant challenge. In this paper, we present FlashSpeech, a large-scale zero-shot speech synthesis system with approximately 5\\% of the inference time compared with previous work. FlashSpeech is built on the latent consistency model and applies a novel adversarial consistency training approach that can train from scratch without the need for a pre-trained diffusion model as the teacher. Furthermore, a new prosody generator module enhances the diversity of prosody, making the rhythm of the speech sound more natural. The generation processes of FlashSpeech can be achieved efficiently with one or two sampling steps while maintaining high audio quality and high similarity to the audio prompt for zero-shot speech generation. Our experimental results demonstrate the superior performance of FlashSpeech. Notably, FlashSpeech can be about 20 times faster than other zero-shot speech synthesis systems while maintaining comparable performance in terms of voice quality and similarity. Furthermore, FlashSpeech demonstrates its versatility by efficiently performing tasks like voice conversion, speech editing, and diverse speech sampling. Audio samples can be found in https://flashspeech.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-24", "arxiv_id": "2404.14687", "title": "Pegasus-v1 Technical Report", "authors": [ "Raehyuk Jung", "Hyojun Go", "Jaehyuk Yi", "Jiho Jang", "Daniel Kim", "Jay Suh", "Aiden Lee", "Cooper Han", "Jae Lee", "Jeff Kim", "Jin-Young Kim", "Junwan Kim", "Kyle Park", "Lucas Lee", "Mars Ha", "Minjoon Seo", "Abraham Jo", "Ed Park", "Hassan Kianinejad", "SJ Kim", "Tony Moon", "Wade Jeong", "Andrei Popescu", "Esther Kim", "EK Yoon", "Genie Heo", "Henry Choi", "Jenna Kang", "Kevin Han", "Noah Seo", "Sunny Nguyen", "Ryan Won", "Yeonhoo Park", "Anthony Giuliani", "Dave Chung", "Hans Yoon", "James Le", "Jenny Ahn", "June Lee", "Maninder Saini", "Meredith Sanders", "Soyoung Lee", "Sue Kim", "Travis Couture" ], "github": "", "project_page": "", "abstract": "This technical report introduces Pegasus-1, a multimodal language model specialized in video content understanding and interaction through natural language. Pegasus-1 is designed to address the unique challenges posed by video data, such as interpreting spatiotemporal information, to offer nuanced video content comprehension across various lengths. This technical report overviews Pegasus-1's architecture, training strategies, and its performance in benchmarks on video conversation, zero-shot video question answering, and video summarization. We also explore qualitative characteristics of Pegasus-1 , demonstrating its capabilities as well as its limitations, in order to provide readers a balanced view of its current state and its future direction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-24", "arxiv_id": "2404.15045", "title": "Multi-Head Mixture-of-Experts", "authors": [ "Xun Wu", "Shaohan Huang", "Wenhui Wang", "Furu Wei" ], "github": "https://github.com/yushuiwx/MH-MoE", "project_page": "", "abstract": "Sparse Mixtures of Experts (SMoE) scales model capacity without significant increases in training and inference costs, but exhibits the following two issues: (1) Low expert activation, where only a small subset of experts are activated for optimization. (2) Lacking fine-grained analytical capabilities for multiple semantic concepts within individual tokens. We propose Multi-Head Mixture-of-Experts (MH-MoE), which employs a multi-head mechanism to split each token into multiple sub-tokens. These sub-tokens are then assigned to and processed by a diverse set of experts in parallel, and seamlessly reintegrated into the original token form. The multi-head mechanism enables the model to collectively attend to information from various representation spaces within different experts, while significantly enhances expert activation, thus deepens context understanding and alleviate overfitting. Moreover, our MH-MoE is straightforward to implement and decouples from other SMoE optimization methods, making it easy to integrate with other SMoE models for enhanced performance. Extensive experimental results across three tasks: English-focused language modeling, Multi-lingual language modeling and Masked multi-modality modeling tasks, demonstrate the effectiveness of MH-MoE.", "github_manual": "https://github.com/yushuiwx/MH-MoE", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-24", "arxiv_id": "2404.14994", "title": "Transformers Can Represent n-gram Language Models", "authors": [ "Anej Svete", "Ryan Cotterell" ], "github": "https://github.com/rycolab/transformer-ngrams", "project_page": "", "abstract": "Plenty of existing work has analyzed the abilities of the transformer architecture by describing its representational capacity with formal models of computation. However, the focus so far has been on analyzing the architecture in terms of language acceptance. We contend that this is an ill-suited problem in the study of language models (LMs), which are definitionally probability distributions over strings. In this paper, we focus on the relationship between transformer LMs and n-gram LMs, a simple and historically relevant class of language models. We show that transformer LMs using the hard or sparse attention mechanisms can exactly represent any n-gram LM, giving us a concrete lower bound on their probabilistic representational capacity. This provides a first step towards understanding the mechanisms that transformer LMs can use to represent probability distributions over strings.", "github_manual": "https://github.com/rycolab/transformer-ngrams", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-25", "arxiv_id": "2404.16022", "title": "PuLID: Pure and Lightning ID Customization via Contrastive Alignment", "authors": [ "Zinan Guo", "Yanze Wu", "Zhuowei Chen", "Lang Chen", "Qian He" ], "github": "https://github.com/ToTheBeginning/PuLID", "project_page": "", "abstract": "We propose Pure and Lightning ID customization (PuLID), a novel tuning-free ID customization method for text-to-image generation. By incorporating a Lightning T2I branch with a standard diffusion one, PuLID introduces both contrastive alignment loss and accurate ID loss, minimizing disruption to the original model and ensuring high ID fidelity. Experiments show that PuLID achieves superior performance in both ID fidelity and editability. Another attractive property of PuLID is that the image elements (e.g., background, lighting, composition, and style) before and after the ID insertion are kept as consistent as possible. Codes and models will be available at https://github.com/ToTheBeginning/PuLID", "github_manual": "", "github_regex": "https://github.com/ToTheBeginning/PuLID", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-25", "arxiv_id": "2404.15449", "title": "ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning", "authors": [ "Weifeng Chen", "Jiacheng Zhang", "Jie Wu", "Hefeng Wu", "Xuefeng Xiao", "Liang Lin" ], "github": "https://github.com/Weifeng-Chen/ID-Aligner", "project_page": "", "abstract": "The rapid development of diffusion models has triggered diverse applications. Identity-preserving text-to-image generation (ID-T2I) particularly has received significant attention due to its wide range of application scenarios like AI portrait and advertising. While existing ID-T2I methods have demonstrated impressive results, several key challenges remain: (1) It is hard to maintain the identity characteristics of reference portraits accurately, (2) The generated images lack aesthetic appeal especially while enforcing identity retention, and (3) There is a limitation that cannot be compatible with LoRA-based and Adapter-based methods simultaneously. To address these issues, we present ID-Aligner, a general feedback learning framework to enhance ID-T2I performance. To resolve identity features lost, we introduce identity consistency reward fine-tuning to utilize the feedback from face detection and recognition models to improve generated identity preservation. Furthermore, we propose identity aesthetic reward fine-tuning leveraging rewards from human-annotated preference data and automatically constructed feedback on character structure generation to provide aesthetic tuning signals. Thanks to its universal feedback fine-tuning framework, our method can be readily applied to both LoRA and Adapter models, achieving consistent performance gains. Extensive experiments on SD1.5 and SDXL diffusion models validate the effectiveness of our approach. Project Page: \\url{https://idaligner.github.io/}", "github_manual": "https://github.com/Weifeng-Chen/ID-Aligner", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-25", "arxiv_id": "2404.15789", "title": "MotionMaster: Training-free Camera Motion Transfer For Video Generation", "authors": [ "Teng Hu", "Jiangning Zhang", "Ran Yi", "Yating Wang", "Hongrui Huang", "Jieyu Weng", "Yabiao Wang", "Lizhuang Ma" ], "github": "https://github.com/sjtuplayer/MotionMaster", "project_page": "", "abstract": "The emergence of diffusion models has greatly propelled the progress in image and video generation. Recently, some efforts have been made in controllable video generation, including text-to-video generation and video motion control, among which camera motion control is an important topic. However, existing camera motion control methods rely on training a temporal camera module, and necessitate substantial computation resources due to the large amount of parameters in video generation models. Moreover, existing methods pre-define camera motion types during training, which limits their flexibility in camera control. Therefore, to reduce training costs and achieve flexible camera control, we propose COMD, a novel training-free video motion transfer model, which disentangles camera motions and object motions in source videos and transfers the extracted camera motions to new videos. We first propose a one-shot camera motion disentanglement method to extract camera motion from a single source video, which separates the moving objects from the background and estimates the camera motion in the moving objects region based on the motion in the background by solving a Poisson equation. Furthermore, we propose a few-shot camera motion disentanglement method to extract the common camera motion from multiple videos with similar camera motions, which employs a window-based clustering technique to extract the common features in temporal attention maps of multiple videos. Finally, we propose a motion combination method to combine different types of camera motions together, enabling our model a more controllable and flexible camera control. Extensive experiments demonstrate that our training-free approach can effectively decouple camera-object motion and apply the decoupled camera motion to a wide range of controllable video generation tasks, achieving flexible and diverse camera motion control.", "github_manual": "https://github.com/sjtuplayer/MotionMaster", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-25", "arxiv_id": "2404.16029", "title": "Editable Image Elements for Controllable Synthesis", "authors": [ "Jiteng Mu", "Micha\u00ebl Gharbi", "Richard Zhang", "Eli Shechtman", "Nuno Vasconcelos", "Xiaolong Wang", "Taesung Park" ], "github": "", "project_page": "", "abstract": "Diffusion models have made significant advances in text-guided synthesis tasks. However, editing user-provided images remains challenging, as the high dimensional noise input space of diffusion models is not naturally suited for image inversion or spatial editing. In this work, we propose an image representation that promotes spatial editing of input images using a diffusion model. Concretely, we learn to encode an input into \"image elements\" that can faithfully reconstruct an input image. These elements can be intuitively edited by a user, and are decoded by a diffusion model into realistic images. We show the effectiveness of our representation on various image editing tasks, such as object resizing, rearrangement, dragging, de-occlusion, removal, variation, and image composition. Project page: https://jitengmu.github.io/Editable_Image_Elements/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-25", "arxiv_id": "2404.15420", "title": "XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference", "authors": [ "Jo\u00e3o Monteiro", "\u00c9tienne Marcotte", "Pierre-Andr\u00e9 No\u00ebl", "Valentina Zantedeschi", "David V\u00e1zquez", "Nicolas Chapados", "Christopher Pal", "Perouz Taslakian" ], "github": "", "project_page": "", "abstract": "In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference information. Just-in-time processing of a context is inefficient due to the quadratic cost of self-attention operations, and caching is desirable. However, caching transformer states can easily require almost as much space as the model parameters. When the right context isn't known in advance, caching ICL can be challenging. This work addresses these limitations by introducing models that, inspired by the encoder-decoder architecture, use cross-attention to condition generation on reference text without the prompt. More precisely, we leverage pre-trained decoder-only models and only train a small number of added layers. We use Question-Answering (QA) as a testbed to evaluate the ability of our models to perform conditional generation and observe that they outperform ICL, are comparable to fine-tuned prompted LLMs, and drastically reduce the space footprint relative to standard KV caching by two orders of magnitude.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-25", "arxiv_id": "2404.15778", "title": "BASS: Batched Attention-optimized Speculative Sampling", "authors": [ "Haifeng Qian", "Sujan Kumar Gonugondla", "Sungsoo Ha", "Mingyue Shang", "Sanjay Krishna Gouda", "Ramesh Nallapati", "Sudipta Sengupta", "Xiaofei Ma", "Anoop Deoras" ], "github": "", "project_page": "", "abstract": "Speculative decoding has emerged as a powerful method to improve latency and throughput in hosting large language models. However, most existing implementations focus on generating a single sequence. Real-world generative AI applications often require multiple responses and how to perform speculative decoding in a batched setting while preserving its latency benefits poses non-trivial challenges. This paper describes a system of batched speculative decoding that sets a new state of the art in multi-sequence generation latency and that demonstrates superior GPU utilization as well as quality of generations within a time budget. For example, for a 7.8B-size model on a single A100 GPU and with a batch size of 8, each sequence is generated at an average speed of 5.8ms per token, the overall throughput being 1.1K tokens per second. These results represent state-of-the-art latency and a 2.15X speed-up over optimized regular decoding. Within a time budget that regular decoding does not finish, our system is able to generate sequences with HumanEval Pass@First of 43% and Pass@All of 61%, far exceeding what's feasible with single-sequence speculative decoding. Our peak GPU utilization during decoding reaches as high as 15.8%, more than 3X the highest of that of regular decoding and around 10X of single-sequence speculative decoding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-25", "arxiv_id": "2404.16030", "title": "MoDE: CLIP Data Experts via Clustering", "authors": [ "Jiawei Ma", "Po-Yao Huang", "Saining Xie", "Shang-Wen Li", "Luke Zettlemoyer", "Shih-Fu Chang", "Wen-Tau Yih", "Hu Xu" ], "github": "https://github.com/facebookresearch/MetaCLIP/tree/main/mode", "project_page": "", "abstract": "The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data. We present Mixture of Data Experts (MoDE) and learn a system of CLIP data experts via clustering. Each data expert is trained on one data cluster, being less sensitive to false negative noises in other clusters. At inference time, we ensemble their outputs by applying weights determined through the correlation between task metadata and cluster conditions. To estimate the correlation precisely, the samples in one cluster should be semantically similar, but the number of data experts should still be reasonable for training and inference. As such, we consider the ontology in human language and propose to use fine-grained cluster centers to represent each data expert at a coarse-grained level. Experimental studies show that four CLIP data experts on ViT-B/16 outperform the ViT-L/14 by OpenAI CLIP and OpenCLIP on zero-shot image classification but with less (<35\\%) training cost. Meanwhile, MoDE can train all data expert asynchronously and can flexibly include new data experts. The code is available at https://github.com/facebookresearch/MetaCLIP/tree/main/mode.", "github_manual": "", "github_regex": "https://github.com/facebookresearch/MetaCLIP/tree/main/mode", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-25", "arxiv_id": "2404.15653", "title": "CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data", "authors": [ "Sachin Mehta", "Maxwell Horton", "Fartash Faghri", "Mohammad Hossein Sekhavat", "Mahyar Najibi", "Mehrdad Farajtabar", "Oncel Tuzel", "Mohammad Rastegari" ], "github": "https://github.com/apple/corenet", "project_page": "", "abstract": "Contrastive learning has emerged as a transformative method for learning effective visual representations through the alignment of image and text embeddings. However, pairwise similarity computation in contrastive loss between image and text pairs poses computational challenges. This paper presents a novel weakly supervised pre-training of vision models on web-scale image-text data. The proposed method reframes pre-training on image-text data as a classification task. Consequently, it eliminates the need for pairwise similarity computations in contrastive loss, achieving a remarkable 2.7times acceleration in training speed compared to contrastive learning on web-scale data. Through extensive experiments spanning diverse vision tasks, including detection and segmentation, we demonstrate that the proposed method maintains high representation quality. Our source code along with pre-trained model weights and training recipes is available at https://github.com/apple/corenet.", "github_manual": "", "github_regex": "https://github.com/apple/corenet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-25", "arxiv_id": "2404.16035", "title": "MaGGIe: Masked Guided Gradual Human Instance Matting", "authors": [ "Chuong Huynh", "Seoung Wug Oh", "Abhinav Shrivastava", "Joon-Young Lee" ], "github": "https://github.com/hmchuong/MaGGIe", "project_page": "", "abstract": "Human matting is a foundation task in image and video processing, where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional guidance or improve the temporal consistency of a single instance across frames. We propose a new framework MaGGIe, Masked Guided Gradual Human Instance Matting, which predicts alpha mattes progressively for each human instances while maintaining the computational cost, precision, and consistency. Our method leverages modern architectures, including transformer attention and sparse convolution, to output all instance mattes simultaneously without exploding memory and latency. Although keeping constant inference costs in the multiple-instance scenario, our framework achieves robust and versatile performance on our proposed synthesized benchmarks. With the higher quality image and video matting benchmarks, the novel multi-instance synthesis approach from publicly available sources is introduced to increase the generalization of models in real-world scenarios.", "github_manual": "https://github.com/hmchuong/MaGGIe", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-26", "arxiv_id": "2404.16510", "title": "Interactive3D: Create What You Want by Interactive 3D Generation", "authors": [ "Shaocong Dong", "Lihe Ding", "Zhanpeng Huang", "Zibin Wang", "Tianfan Xue", "Dan Xu" ], "github": "https://github.com/interactive-3d/interactive3d", "project_page": "", "abstract": "3D object generation has undergone significant advancements, yielding high-quality results. However, fall short of achieving precise user control, often yielding results that do not align with user expectations, thus limiting their applicability. User-envisioning 3D object generation faces significant challenges in realizing its concepts using current generative models due to limited interaction capabilities. Existing methods mainly offer two approaches: (i) interpreting textual instructions with constrained controllability, or (ii) reconstructing 3D objects from 2D images. Both of them limit customization to the confines of the 2D reference and potentially introduce undesirable artifacts during the 3D lifting process, restricting the scope for direct and versatile 3D modifications. In this work, we introduce Interactive3D, an innovative framework for interactive 3D generation that grants users precise control over the generative process through extensive 3D interaction capabilities. Interactive3D is constructed in two cascading stages, utilizing distinct 3D representations. The first stage employs Gaussian Splatting for direct user interaction, allowing modifications and guidance of the generative direction at any intermediate step through (i) Adding and Removing components, (ii) Deformable and Rigid Dragging, (iii) Geometric Transformations, and (iv) Semantic Editing. Subsequently, the Gaussian splats are transformed into InstantNGP. We introduce a novel (v) Interactive Hash Refinement module to further add details and extract the geometry in the second stage. Our experiments demonstrate that Interactive3D markedly improves the controllability and quality of 3D generation. Our project webpage is available at https://interactive-3d.github.io/.", "github_manual": "https://github.com/interactive-3d/interactive3d", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-26", "arxiv_id": "2404.16790", "title": "SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension", "authors": [ "Bohao Li", "Yuying Ge", "Yi Chen", "Yixiao Ge", "Ruimao Zhang", "Ying Shan" ], "github": "https://github.com/AILab-CVC/SEED-Bench", "project_page": "", "abstract": "Comprehending text-rich visual content is paramount for the practical application of Multimodal Large Language Models (MLLMs), since text-rich scenarios are ubiquitous in the real world, which are characterized by the presence of extensive texts embedded within images. Recently, the advent of MLLMs with impressive versatility has raised the bar for what we can expect from MLLMs. However, their proficiency in text-rich scenarios has yet to be comprehensively and objectively assessed, since current MLLM benchmarks primarily focus on evaluating general visual comprehension. In this work, we introduce SEED-Bench-2-Plus, a benchmark specifically designed for evaluating text-rich visual comprehension of MLLMs. Our benchmark comprises 2.3K multiple-choice questions with precise human annotations, spanning three broad categories: Charts, Maps, and Webs, each of which covers a wide spectrum of text-rich scenarios in the real world. These categories, due to their inherent complexity and diversity, effectively simulate real-world text-rich environments. We further conduct a thorough evaluation involving 34 prominent MLLMs (including GPT-4V, Gemini-Pro-Vision and Claude-3-Opus) and emphasize the current limitations of MLLMs in text-rich visual comprehension. We hope that our work can serve as a valuable addition to existing MLLM benchmarks, providing insightful observations and inspiring further research in the area of text-rich visual comprehension with MLLMs. The dataset and evaluation code can be accessed at https://github.com/AILab-CVC/SEED-Bench.", "github_manual": "", "github_regex": "https://github.com/AILab-CVC/SEED-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-26", "arxiv_id": "2404.16821", "title": "How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites", "authors": [ "Zhe Chen", "Weiyun Wang", "Hao Tian", "Shenglong Ye", "Zhangwei Gao", "Erfei Cui", "Wenwen Tong", "Kongzhi Hu", "Jiapeng Luo", "Zheng Ma", "Ji Ma", "Jiaqi Wang", "Xiaoyi Dong", "Hang Yan", "Hewei Guo", "Conghui He", "Zhenjiang Jin", "Chao Xu", "Bin Wang", "Xingjian Wei", "Wei Li", "Wenjian Zhang", "Lewei Lu", "Xizhou Zhu", "Tong Lu", "Dahua Lin", "Yu Qiao" ], "github": "https://github.com/OpenGVLab/InternVL", "project_page": "", "abstract": "In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements: (1) Strong Vision Encoder: we explored a continuous learning strategy for the large-scale vision foundation model -- InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs. (2) Dynamic High-Resolution: we divide images into tiles ranging from 1 to 40 of 448times448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input. (3) High-Quality Bilingual Dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images, and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in OCR- and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 benchmarks. Code has been released at https://github.com/OpenGVLab/InternVL.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/InternVL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-26", "arxiv_id": "2404.16645", "title": "Tele-FLM Technical Report", "authors": [ "Xiang Li", "Yiqun Yao", "Xin Jiang", "Xuezhi Fang", "Chao Wang", "Xinzhang Liu", "Zihan Wang", "Yu Zhao", "Xin Wang", "Yuyao Huang", "Shuangyong Song", "Yongxiang Li", "Zheng Zhang", "Bo Zhao", "Aixin Sun", "Yequan Wang", "Zhongjiang He", "Zhongyuan Wang", "Xuelong Li", "Tiejun Huang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications. However, there is a notable paucity of detailed, open-sourced methodologies on efficiently scaling LLMs beyond 50 billion parameters with minimum trial-and-error cost and computational resources. In this report, we introduce Tele-FLM (aka FLM-2), a 52B open-sourced multilingual large language model that features a stable, efficient pre-training paradigm and enhanced factual judgment capabilities. Tele-FLM demonstrates superior multilingual language modeling abilities, measured by BPB on textual corpus. Besides, in both English and Chinese foundation model evaluation, it is comparable to strong open-sourced models that involve larger pre-training FLOPs, such as Llama2-70B and DeepSeek-67B. In addition to the model weights, we share the core designs, engineering practices, and training details, which we expect to benefit both the academic and industrial communities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-26", "arxiv_id": "2404.16710", "title": "Layer Skip: Enabling Early Exit Inference and Self-Speculative Decoding", "authors": [ "Mostafa Elhoushi", "Akshat Shrivastava", "Diana Liskovich", "Basil Hosmer", "Bram Wasti", "Liangzhen Lai", "Anas Mahmoud", "Bilge Acun", "Saurabh Agarwal", "Ahmed Roman", "Ahmed A Aly", "Beidi Chen", "Carole-Jean Wu" ], "github": "", "project_page": "", "abstract": "We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, we show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, we present a novel self-speculative decoding solution where we exit at early layers and verify and correct with remaining layers of the model. Our proposed self-speculative decoding approach has less memory footprint than other speculative decoding approaches and benefits from shared compute and activations of the draft and verification stages. We run experiments on different Llama model sizes on different types of training: pretraining from scratch, continual pretraining, finetuning on specific data domain, and finetuning on specific task. We implement our inference solution and show speedups of up to 2.16x on summarization for CNN/DM documents, 1.82x on coding, and 2.0x on TOPv2 semantic parsing task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-26", "arxiv_id": "2404.16771", "title": "ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving", "authors": [ "Jiehui Huang", "Xiao Dong", "Wenhui Song", "Hanhui Li", "Jun Zhou", "Yuhao Cheng", "Shutao Liao", "Long Chen", "Yiqiang Yan", "Shengcai Liao", "Xiaodan Liang" ], "github": "https://github.com/JackAILab/ConsistentID", "project_page": "", "abstract": "Diffusion-based technologies have made significant strides, particularly in personalized and customized facialgeneration. However, existing methods face challenges in achieving high-fidelity and detailed identity (ID)consistency, primarily due to insufficient fine-grained control over facial areas and the lack of a comprehensive strategy for ID preservation by fully considering intricate facial details and the overall face. To address these limitations, we introduce ConsistentID, an innovative method crafted for diverseidentity-preserving portrait generation under fine-grained multimodal facial prompts, utilizing only a single reference image. ConsistentID comprises two key components: a multimodal facial prompt generator that combines facial features, corresponding facial descriptions and the overall facial context to enhance precision in facial details, and an ID-preservation network optimized through the facial attention localization strategy, aimed at preserving ID consistency in facial regions. Together, these components significantly enhance the accuracy of ID preservation by introducing fine-grained multimodal ID information from facial regions. To facilitate training of ConsistentID, we present a fine-grained portrait dataset, FGID, with over 500,000 facial images, offering greater diversity and comprehensiveness than existing public facial datasets. % such as LAION-Face, CelebA, FFHQ, and SFHQ. Experimental results substantiate that our ConsistentID achieves exceptional precision and diversity in personalized facial generation, surpassing existing methods in the MyStyle dataset. Furthermore, while ConsistentID introduces more multimodal ID information, it maintains a fast inference speed during generation.", "github_manual": "https://github.com/JackAILab/ConsistentID", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-26", "arxiv_id": "2404.16811", "title": "Make Your LLM Fully Utilize the Context", "authors": [ "Shengnan An", "Zexiong Ma", "Zeqi Lin", "Nanning Zheng", "Jian-Guang Lou" ], "github": "https://github.com/microsoft/FILM", "project_page": "", "abstract": "While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-middle challenge. We hypothesize that it stems from insufficient explicit supervision during the long-context training, which fails to emphasize that any position in a long context can hold crucial information. Based on this intuition, our study presents information-intensive (IN2) training, a purely data-driven solution to overcome lost-in-the-middle. Specifically, IN2 training leverages a synthesized long-context question-answer dataset, where the answer requires (1) fine-grained information awareness on a short segment (~128 tokens) within a synthesized long context (4K-32K tokens), and (2) the integration and reasoning of information from two or more short segments. Through applying this information-intensive training on Mistral-7B, we present FILM-7B (FILl-in-the-Middle). To thoroughly assess the ability of FILM-7B for utilizing long contexts, we design three probing tasks that encompass various context styles (document, code, and structured-data context) and information retrieval patterns (forward, backward, and bi-directional retrieval). The probing results demonstrate that FILM-7B can robustly retrieve information from different positions in its 32K context window. Beyond these probing tasks, FILM-7B significantly improves the performance on real-world long-context tasks (e.g., 23.5->26.9 F1 score on NarrativeQA), while maintaining a comparable performance on short-context tasks (e.g., 59.3->59.2 accuracy on MMLU). Github Link: https://github.com/microsoft/FILM.", "github_manual": "", "github_regex": "https://github.com/microsoft/FILM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-26", "arxiv_id": "2404.16221", "title": "NeRF-XL: Scaling NeRFs with Multiple GPUs", "authors": [ "Ruilong Li", "Sanja Fidler", "Angjoo Kanazawa", "Francis Williams" ], "github": "", "project_page": "", "abstract": "We present NeRF-XL, a principled method for distributing Neural Radiance Fields (NeRFs) across multiple GPUs, thus enabling the training and rendering of NeRFs with an arbitrarily large capacity. We begin by revisiting existing multi-GPU approaches, which decompose large scenes into multiple independently trained NeRFs, and identify several fundamental issues with these methods that hinder improvements in reconstruction quality as additional computational resources (GPUs) are used in training. NeRF-XL remedies these issues and enables the training and rendering of NeRFs with an arbitrary number of parameters by simply using more hardware. At the core of our method lies a novel distributed training and rendering formulation, which is mathematically equivalent to the classic single-GPU case and minimizes communication between GPUs. By unlocking NeRFs with arbitrarily large parameter counts, our approach is the first to reveal multi-GPU scaling laws for NeRFs, showing improvements in reconstruction quality with larger parameter counts and speed improvements with more GPUs. We demonstrate the effectiveness of NeRF-XL on a wide variety of datasets, including the largest open-source dataset to date, MatrixCity, containing 258K images covering a 25km^2 city area.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-26", "arxiv_id": "2404.16820", "title": "Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings", "authors": [ "Olivia Wiles", "Chuhan Zhang", "Isabela Albuquerque", "Ivana Kaji\u0107", "Su Wang", "Emanuele Bugliarello", "Yasumasa Onoe", "Chris Knutsen", "Cyrus Rashtchian", "Jordi Pont-Tuset", "Aida Nematzadeh" ], "github": "https://github.com/google-deepmind/gecko_benchmark_t2i", "project_page": "", "abstract": "While text-to-image (T2I) generative models have become ubiquitous, they do not necessarily generate images that align with a given prompt. While previous work has evaluated T2I alignment by proposing metrics, benchmarks, and templates for collecting human judgements, the quality of these components is not systematically measured. Human-rated prompt sets are generally small and the reliability of the ratings -- and thereby the prompt set used to compare models -- is not evaluated. We address this gap by performing an extensive study evaluating auto-eval metrics and human templates. We provide three main contributions: (1) We introduce a comprehensive skills-based benchmark that can discriminate models across different human templates. This skills-based benchmark categorises prompts into sub-skills, allowing a practitioner to pinpoint not only which skills are challenging, but at what level of complexity a skill becomes challenging. (2) We gather human ratings across four templates and four T2I models for a total of >100K annotations. This allows us to understand where differences arise due to inherent ambiguity in the prompt and where they arise due to differences in metric and model quality. (3) Finally, we introduce a new QA-based auto-eval metric that is better correlated with human ratings than existing metrics for our new dataset, across different human templates, and on TIFA160.", "github_manual": "https://github.com/google-deepmind/gecko_benchmark_t2i", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-26", "arxiv_id": "2404.16375", "title": "List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs", "authors": [ "An Yan", "Zhengyuan Yang", "Junda Wu", "Wanrong Zhu", "Jianwei Yang", "Linjie Li", "Kevin Lin", "Jianfeng Wang", "Julian McAuley", "Jianfeng Gao", "Lijuan Wang" ], "github": "https://github.com/zzxslp/SoM-LLaVA", "project_page": "", "abstract": "Set-of-Mark (SoM) Prompting unleashes the visual grounding capability of GPT-4V, by enabling the model to associate visual objects with tags inserted on the image. These tags, marked with alphanumerics, can be indexed via text tokens for easy reference. Despite the extraordinary performance from GPT-4V, we observe that other Multimodal Large Language Models (MLLMs) struggle to understand these visual tags. To promote the learning of SoM prompting for open-source models, we propose a new learning paradigm: \"list items one by one,\" which asks the model to enumerate and describe all visual tags placed on the image following the alphanumeric orders of tags. By integrating our curated dataset with other visual instruction tuning datasets, we are able to equip existing MLLMs with the SoM prompting ability. Furthermore, we evaluate our finetuned SoM models on five MLLM benchmarks. We find that this new dataset, even in a relatively small size (10k-30k images with tags), significantly enhances visual reasoning capabilities and reduces hallucinations for MLLMs. Perhaps surprisingly, these improvements persist even when the visual tags are omitted from input images during inference. This suggests the potential of \"list items one by one\" as a new paradigm for training MLLMs, which strengthens the object-text alignment through the use of visual tags in the training stage. Finally, we conduct analyses by probing trained models to understand the working mechanism of SoM. Our code and data are available at https://github.com/zzxslp/SoM-LLaVA.", "github_manual": "", "github_regex": "https://github.com/zzxslp/SoM-LLaVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-29", "arxiv_id": "2404.16873", "title": "AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs", "authors": [ "Anselm Paulus", "Arman Zharmagambetov", "Chuan Guo", "Brandon Amos", "Yuandong Tian" ], "github": "https://github.com/facebookresearch/advprompter", "project_page": "", "abstract": "While recently Large Language Models (LLMs) have achieved remarkable successes, they are vulnerable to certain jailbreaking attacks that lead to generation of inappropriate or harmful content. Manual red-teaming requires finding adversarial prompts that cause such jailbreaking, e.g. by appending a suffix to a given instruction, which is inefficient and time-consuming. On the other hand, automatic adversarial prompt generation often leads to semantically meaningless attacks that can easily be detected by perplexity-based filters, may require gradient information from the TargetLLM, or do not scale well due to time-consuming discrete optimization processes over the token space. In this paper, we present a novel method that uses another LLM, called the AdvPrompter, to generate human-readable adversarial prompts in seconds, sim800times faster than existing optimization-based approaches. We train the AdvPrompter using a novel algorithm that does not require access to the gradients of the TargetLLM. This process alternates between two steps: (1) generating high-quality target adversarial suffixes by optimizing the AdvPrompter predictions, and (2) low-rank fine-tuning of the AdvPrompter with the generated adversarial suffixes. The trained AdvPrompter generates suffixes that veil the input instruction without changing its meaning, such that the TargetLLM is lured to give a harmful response. Experimental results on popular open source TargetLLMs show state-of-the-art results on the AdvBench dataset, that also transfer to closed-source black-box LLM APIs. Further, we demonstrate that by fine-tuning on a synthetic dataset generated by AdvPrompter, LLMs can be made more robust against jailbreaking attacks while maintaining performance, i.e. high MMLU scores.", "github_manual": "https://github.com/facebookresearch/advprompter", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-29", "arxiv_id": "2404.16845", "title": "HaLo-NeRF: Learning Geometry-Guided Semantics for Exploring Unconstrained Photo Collections", "authors": [ "Chen Dudai", "Morris Alper", "Hana Bezalel", "Rana Hanocka", "Itai Lang", "Hadar Averbuch-Elor" ], "github": "https://github.com/TAU-VAILab/HaLo-NeRF", "project_page": "", "abstract": "Internet image collections containing photos captured by crowds of photographers show promise for enabling digital exploration of large-scale tourist landmarks. However, prior works focus primarily on geometric reconstruction and visualization, neglecting the key role of language in providing a semantic interface for navigation and fine-grained understanding. In constrained 3D domains, recent methods have leveraged vision-and-language models as a strong prior of 2D visual semantics. While these models display an excellent understanding of broad visual semantics, they struggle with unconstrained photo collections depicting such tourist landmarks, as they lack expert knowledge of the architectural domain. In this work, we present a localization system that connects neural representations of scenes depicting large-scale landmarks with text describing a semantic region within the scene, by harnessing the power of SOTA vision-and-language models with adaptations for understanding landmark scene semantics. To bolster such models with fine-grained knowledge, we leverage large-scale Internet data containing images of similar landmarks along with weakly-related textual information. Our approach is built upon the premise that images physically grounded in space can provide a powerful supervision signal for localizing new concepts, whose semantics may be unlocked from Internet textual metadata with large language models. We use correspondences between views of scenes to bootstrap spatial understanding of these semantics, providing guidance for 3D-compatible segmentation that ultimately lifts to a volumetric scene representation. Our results show that HaLo-NeRF can accurately localize a variety of semantic concepts related to architectural landmarks, surpassing the results of other 3D models as well as strong 2D segmentation baselines. Our project page is at https://tau-vailab.github.io/HaLo-NeRF/.", "github_manual": "https://github.com/TAU-VAILab/HaLo-NeRF", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-29", "arxiv_id": "2404.16994", "title": "PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning", "authors": [ "Lin Xu", "Yilin Zhao", "Daquan Zhou", "Zhijie Lin", "See Kiong Ng", "Jiashi Feng" ], "github": "https://github.com/magic-research/PLLaVA", "project_page": "", "abstract": "Vision-language pre-training has significantly elevated performance across a wide range of image-language applications. Yet, the pre-training process for video-related tasks demands exceptionally large computational and data resources, which hinders the progress of video-language models. This paper investigates a straightforward, highly efficient, and resource-light approach to adapting an existing image-language pre-trained model for dense video understanding. Our preliminary experiments reveal that directly fine-tuning pre-trained image-language models with multiple frames as inputs on video datasets leads to performance saturation or even a drop. Our further investigation reveals that it is largely attributed to the bias of learned high-norm visual features. Motivated by this finding, we propose a simple but effective pooling strategy to smooth the feature distribution along the temporal dimension and thus reduce the dominant impacts from the extreme features. The new model is termed Pooling LLaVA, or in short. achieves new state-of-the-art performance on modern benchmark datasets for both video question-answer and captioning tasks. Notably, on the recent popular Video ChatGPT benchmark, PLLaVA achieves a score of 3.48 out of 5 on average of five evaluated dimensions, exceeding the previous SOTA results from GPT4V (IG-VLM) by 9\\%. On the latest multi-choice benchmark MVBench, PLLaVA achieves 58.1\\% accuracy on average across 20 sub-tasks, 14.5\\% higher than GPT4V (IG-VLM). Code is available at https://github.com/magic-research/PLLaVA.", "github_manual": "", "github_regex": "https://github.com/magic-research/PLLaVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-29", "arxiv_id": "2404.17569", "title": "MaPa: Text-driven Photorealistic Material Painting for 3D Shapes", "authors": [ "Shangzhan Zhang", "Sida Peng", "Tao Xu", "Yuanbo Yang", "Tianrun Chen", "Nan Xue", "Yujun Shen", "Hujun Bao", "Ruizhen Hu", "Xiaowei Zhou" ], "github": "https://github.com/zhanghe3z/MaPa", "project_page": "", "abstract": "This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to generate segment-wise procedural material graphs as the appearance representation, which supports high-quality rendering and provides substantial flexibility in editing. Instead of relying on extensive paired data, i.e., 3D meshes with material graphs and corresponding text descriptions, to train a material graph generative model, we propose to leverage the pre-trained 2D diffusion model as a bridge to connect the text and material graphs. Specifically, our approach decomposes a shape into a set of segments and designs a segment-controlled diffusion model to synthesize 2D images that are aligned with mesh parts. Based on generated images, we initialize parameters of material graphs and fine-tune them through the differentiable rendering module to produce materials in accordance with the textual description. Extensive experiments demonstrate the superior performance of our framework in photorealism, resolution, and editability over existing methods. Project page: https://zhanghe3z.github.io/MaPa/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhanghe3z/MaPa", "github_api_added_by": "auto" }, { "date": "2024-04-30", "arxiv_id": "2404.17521", "title": "Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations", "authors": [ "Puhao Li", "Tengyu Liu", "Yuyang Li", "Muzhi Han", "Haoran Geng", "Shu Wang", "Yixin Zhu", "Song-Chun Zhu", "Siyuan Huang" ], "github": "https://github.com/xiaoyao-li/Ag2Manip", "project_page": "", "abstract": "Autonomous robotic systems capable of learning novel manipulation tasks are poised to transform industries from manufacturing to service automation. However, modern methods (e.g., VIP and R3M) still face significant hurdles, notably the domain gap among robotic embodiments and the sparsity of successful task executions within specific action spaces, resulting in misaligned and ambiguous task representations. We introduce Ag2Manip (Agent-Agnostic representations for Manipulation), a framework aimed at surmounting these challenges through two key innovations: a novel agent-agnostic visual representation derived from human manipulation videos, with the specifics of embodiments obscured to enhance generalizability; and an agent-agnostic action representation abstracting a robot's kinematics to a universal agent proxy, emphasizing crucial interactions between end-effector and object. Ag2Manip's empirical validation across simulated benchmarks like FrankaKitchen, ManiSkill, and PartManip shows a 325% increase in performance, achieved without domain-specific demonstrations. Ablation studies underline the essential contributions of the visual and action representations to this success. Extending our evaluations to the real world, Ag2Manip significantly improves imitation learning success rates from 50% to 77.5%, demonstrating its effectiveness and generalizability across both simulated and physical environments.", "github_manual": "https://github.com/xiaoyao-li/Ag2Manip", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-30", "arxiv_id": "2404.18416", "title": "Capabilities of Gemini Models in Medicine", "authors": [ "Khaled Saab", "Tao Tu", "Wei-Hung Weng", "Ryutaro Tanno", "David Stutz", "Ellery Wulczyn", "Fan Zhang", "Tim Strother", "Chunjong Park", "Elahe Vedadi", "Juanma Zambrano Chaves", "Szu-Yeu Hu", "Mike Schaekermann", "Aishwarya Kamath", "Yong Cheng", "David G. T. Barrett", "Cathy Cheung", "Basil Mustafa", "Anil Palepu", "Daniel McDuff", "Le Hou", "Tomer Golany", "Luyang Liu", "Jean-baptiste Alayrac", "Neil Houlsby", "Nenad Tomasev", "Jan Freyberg", "Charles Lau", "Jonas Kemp", "Jeremy Lai", "Shekoofeh Azizi", "Kimberly Kanada", "SiWai Man", "Kavita Kulkarni", "Ruoxi Sun", "Siamak Shakeri", "Luheng He", "Ben Caine", "Albert Webson", "Natasha Latysheva", "Melvin Johnson", "Philip Mansfield", "Jian Lu", "Ehud Rivlin", "Jesper Anderson", "Bradley Green", "Renee Wong", "Jonathan Krause", "Jonathon Shlens", "Ewa Dominowska", "S. M. Ali Eslami", "Claire Cui", "Oriol Vinyals", "Koray Kavukcuoglu", "James Manyika", "Jeff Dean", "Demis Hassabis", "Yossi Matias", "Dale Webster", "Joelle Barral", "Greg Corrado", "Christopher Semturs", "S. Sara Mahdavi", "Juraj Gottweis", "Alan Karthikesalingam", "Vivek Natarajan" ], "github": "", "project_page": "", "abstract": "Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of complex multimodal data. Gemini models, with strong general capabilities in multimodal and long-context reasoning, offer exciting possibilities in medicine. Building on these core strengths of Gemini, we introduce Med-Gemini, a family of highly capable multimodal models that are specialized in medicine with the ability to seamlessly use web search, and that can be efficiently tailored to novel modalities using custom encoders. We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin. On the popular MedQA (USMLE) benchmark, our best-performing Med-Gemini model achieves SoTA performance of 91.1% accuracy, using a novel uncertainty-guided search strategy. On 7 multimodal benchmarks including NEJM Image Challenges and MMMU (health & medicine), Med-Gemini improves over GPT-4V by an average relative margin of 44.5%. We demonstrate the effectiveness of Med-Gemini's long-context capabilities through SoTA performance on a needle-in-a-haystack retrieval task from long de-identified health records and medical video question answering, surpassing prior bespoke methods using only in-context learning. Finally, Med-Gemini's performance suggests real-world utility by surpassing human experts on tasks such as medical text summarization, alongside demonstrations of promising potential for multimodal medical dialogue, medical research and education. Taken together, our results offer compelling evidence for Med-Gemini's potential, although further rigorous evaluation will be crucial before real-world deployment in this safety-critical domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-30", "arxiv_id": "2404.18243", "title": "LEGENT: Open Platform for Embodied Agents", "authors": [ "Zhili Cheng", "Zhitong Wang", "Jinyi Hu", "Shengding Hu", "An Liu", "Yuge Tu", "Pengkai Li", "Lei Shi", "Zhiyuan Liu", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "Despite advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), their integration into language-grounded, human-like embodied agents remains incomplete, hindering complex real-life task performance in physical environments. Existing integrations often feature limited open sourcing, challenging collective progress in this field. We introduce LEGENT, an open, scalable platform for developing embodied agents using LLMs and LMMs. LEGENT offers a dual approach: a rich, interactive 3D environment with communicable and actionable agents, paired with a user-friendly interface, and a sophisticated data generation pipeline utilizing advanced algorithms to exploit supervision from simulated worlds at scale. In our experiments, an embryonic vision-language-action model trained on LEGENT-generated data surpasses GPT-4V in embodied tasks, showcasing promising generalization capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-30", "arxiv_id": "2404.18796", "title": "Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models", "authors": [ "Pat Verga", "Sebastian Hofstatter", "Sophia Althammer", "Yixuan Su", "Aleksandra Piktus", "Arkady Arkhangorodsky", "Minjie Xu", "Naomi White", "Patrick Lewis" ], "github": "", "project_page": "", "abstract": "As Large Language Models (LLMs) have become more advanced, they have outpaced our abilities to accurately evaluate their quality. Not only is finding data to adequately probe particular model properties difficult, but evaluating the correctness of a model's freeform generation alone is a challenge. To address this, many evaluations now rely on using LLMs themselves as judges to score the quality of outputs from other LLMs. Evaluations most commonly use a single large model like GPT4. While this method has grown in popularity, it is costly, has been shown to introduce intramodel bias, and in this work, we find that very large models are often unnecessary. We propose instead to evaluate models using a Panel of LLm evaluators (PoLL). Across three distinct judge settings and spanning six different datasets, we find that using a PoLL composed of a larger number of smaller models outperforms a single large judge, exhibits less intra-model bias due to its composition of disjoint model families, and does so while being over seven times less expensive.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-30", "arxiv_id": "2404.17672", "title": "BlenderAlchemy: Editing 3D Graphics with Vision-Language Models", "authors": [ "Ian Huang", "Guandao Yang", "Leonidas Guibas" ], "github": "https://github.com/ianhuang0630/BlenderAlchemyOfficial", "project_page": "", "abstract": "Graphics design is important for various applications, including movie production and game design. To create a high-quality scene, designers usually need to spend hours in software like Blender, in which they might need to interleave and repeat operations, such as connecting material nodes, hundreds of times. Moreover, slightly different design goals may require completely different sequences, making automation difficult. In this paper, we propose a system that leverages Vision-Language Models (VLMs), like GPT-4V, to intelligently search the design action space to arrive at an answer that can satisfy a user's intent. Specifically, we design a vision-based edit generator and state evaluator to work together to find the correct sequence of actions to achieve the goal. Inspired by the role of visual imagination in the human design process, we supplement the visual reasoning capabilities of VLMs with \"imagined\" reference images from image-generation models, providing visual grounding of abstract language descriptions. In this paper, we provide empirical evidence suggesting our system can produce simple but tedious Blender editing sequences for tasks such as editing procedural materials from text and/or reference images, as well as adjusting lighting configurations for product renderings in complex scenes.", "github_manual": "https://github.com/ianhuang0630/BlenderAlchemyOfficial", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-30", "arxiv_id": "2404.18911", "title": "Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting", "authors": [ "Fangcheng Liu", "Yehui Tang", "Zhenhua Liu", "Yunsheng Ni", "Kai Han", "Yunhe Wang" ], "github": "https://github.com/Equationliu/Kangaroo", "project_page": "", "abstract": "Speculative decoding has demonstrated its effectiveness in accelerating the inference of large language models while maintaining a consistent sampling distribution. However, the conventional approach of training a separate draft model to achieve a satisfactory token acceptance rate can be costly. Drawing inspiration from early exiting, we propose a novel self-speculative decoding framework Kangaroo, which uses a fixed shallow sub-network as a self-draft model, with the remaining layers serving as the larger target model. We train a lightweight and efficient adapter module on top of the sub-network to bridge the gap between the sub-network and the full model's representation ability. It is noteworthy that the inference latency of the self-draft model may no longer be negligible compared to the large model, necessitating strategies to increase the token acceptance rate while minimizing the drafting steps of the small model. To address this challenge, we introduce an additional early exiting mechanism for generating draft tokens. Specifically, we halt the small model's subsequent prediction during the drafting phase once the confidence level for the current token falls below a certain threshold. Extensive experiments on the Spec-Bench demonstrate the effectiveness of Kangaroo. Under single-sequence verification, Kangaroo achieves speedups up to 1.68times on Spec-Bench, outperforming Medusa-1 with 88.7\\% fewer additional parameters (67M compared to 591M). The code for Kangaroo is available at https://github.com/Equationliu/Kangaroo.", "github_manual": "", "github_regex": "https://github.com/Equationliu/Kangaroo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-30", "arxiv_id": "2401.16465", "title": "DressCode: Autoregressively Sewing and Generating Garments from Text Guidance", "authors": [ "Kai He", "Kaixin Yao", "Qixuan Zhang", "Jingyi Yu", "Lingjie Liu", "Lan Xu" ], "github": "https://github.com/IHe-KaiI/DressCode", "project_page": "", "abstract": "Apparel's significant role in human appearance underscores the importance of garment digitalization for digital human creation. Recent advances in 3D content creation are pivotal for digital human creation. Nonetheless, garment generation from text guidance is still nascent. We introduce a text-driven 3D garment generation framework, DressCode, which aims to democratize design for novices and offer immense potential in fashion design, virtual try-on, and digital human creation. For our framework, we first introduce SewingGPT, a GPT-based architecture integrating cross-attention with text-conditioned embedding to generate sewing patterns with text guidance. We also tailored a pre-trained Stable Diffusion for high-quality, tile-based PBR texture generation. By leveraging a large language model, our framework generates CG-friendly garments through natural language interaction. Our method also facilitates pattern completion and texture editing, simplifying the process for designers by user-friendly interaction. With comprehensive evaluations and comparisons with other state-of-the-art methods, our method showcases the best quality and alignment with input prompts. User studies further validate our high-quality rendering results, highlighting its practical utility and potential in production settings.", "github_manual": "https://github.com/IHe-KaiI/DressCode", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-04-30", "arxiv_id": "2404.18928", "title": "Stylus: Automatic Adapter Selection for Diffusion Models", "authors": [ "Michael Luo", "Justin Wong", "Brandon Trabucco", "Yanping Huang", "Joseph E. Gonzalez", "Zhifeng Chen", "Ruslan Salakhutdinov", "Ion Stoica" ], "github": "https://github.com/stylus-diffusion/stylus", "project_page": "", "abstract": "Beyond scaling base models with more data or parameters, fine-tuned adapters provide an alternative way to generate high fidelity, custom images at reduced costs. As such, adapters have been widely adopted by open-source communities, accumulating a database of over 100K adapters-most of which are highly customized with insufficient descriptions. This paper explores the problem of matching the prompt to a set of relevant adapters, built on recent work that highlight the performance gains of composing adapters. We introduce Stylus, which efficiently selects and automatically composes task-specific adapters based on a prompt's keywords. Stylus outlines a three-stage approach that first summarizes adapters with improved descriptions and embeddings, retrieves relevant adapters, and then further assembles adapters based on prompts' keywords by checking how well they fit the prompt. To evaluate Stylus, we developed StylusDocs, a curated dataset featuring 75K adapters with pre-computed adapter embeddings. In our evaluation on popular Stable Diffusion checkpoints, Stylus achieves greater CLIP-FID Pareto efficiency and is twice as preferred, with humans and multimodal models as evaluators, over the base model. See stylus-diffusion.github.io for more.", "github_manual": "https://github.com/stylus-diffusion/stylus", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19427", "title": "InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation", "authors": [ "Chanran Kim", "Jeongin Lee", "Shichang Joung", "Bongmo Kim", "Yeul-Min Baek" ], "github": "", "project_page": "", "abstract": "In the field of personalized image generation, the ability to create images preserving concepts has significantly improved. Creating an image that naturally integrates multiple concepts in a cohesive and visually appealing composition can indeed be challenging. This paper introduces \"InstantFamily,\" an approach that employs a novel masked cross-attention mechanism and a multimodal embedding stack to achieve zero-shot multi-ID image generation. Our method effectively preserves ID as it utilizes global and local features from a pre-trained face recognition model integrated with text conditions. Additionally, our masked cross-attention mechanism enables the precise control of multi-ID and composition in the generated images. We demonstrate the effectiveness of InstantFamily through experiments showing its dominance in generating images with multi-ID, while resolving well-known multi-ID generation problems. Additionally, our model achieves state-of-the-art performance in both single-ID and multi-ID preservation. Furthermore, our model exhibits remarkable scalability with a greater number of ID preservation than it was originally trained with.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19753", "title": "DOCCI: Descriptions of Connected and Contrasting Images", "authors": [ "Yasumasa Onoe", "Sunayana Rane", "Zachary Berger", "Yonatan Bitton", "Jaemin Cho", "Roopal Garg", "Alexander Ku", "Zarana Parekh", "Jordi Pont-Tuset", "Garrett Tanzer", "Su Wang", "Jason Baldridge" ], "github": "", "project_page": "", "abstract": "Vision-language datasets are vital for both text-to-image (T2I) and image-to-text (I2T) research. However, current datasets lack descriptions with fine-grained detail that would allow for richer associations to be learned by models. To fill the gap, we introduce Descriptions of Connected and Contrasting Images (DOCCI), a dataset with long, human-annotated English descriptions for 15k images that were taken, curated and donated by a single researcher intent on capturing key challenges such as spatial relations, counting, text rendering, world knowledge, and more. We instruct human annotators to create comprehensive descriptions for each image; these average 136 words in length and are crafted to clearly distinguish each image from those that are related or similar. Each description is highly compositional and typically encompasses multiple challenges. Through both quantitative and qualitative analyses, we demonstrate that DOCCI serves as an effective training resource for image-to-text generation -- a PaLI 5B model finetuned on DOCCI shows equal or superior results compared to highly-performant larger models like LLaVA-1.5 7B and InstructBLIP 7B. Furthermore, we show that DOCCI is a useful testbed for text-to-image generation, highlighting the limitations of current text-to-image models in capturing long descriptions and fine details.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19702", "title": "GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting", "authors": [ "Kai Zhang", "Sai Bi", "Hao Tan", "Yuanbo Xiangli", "Nanxuan Zhao", "Kalyan Sunkavalli", "Zexiang Xu" ], "github": "", "project_page": "", "abstract": "We propose GS-LRM, a scalable large reconstruction model that can predict high-quality 3D Gaussian primitives from 2-4 posed sparse images in 0.23 seconds on single A100 GPU. Our model features a very simple transformer-based architecture; we patchify input posed images, pass the concatenated multi-view image tokens through a sequence of transformer blocks, and decode final per-pixel Gaussian parameters directly from these tokens for differentiable rendering. In contrast to previous LRMs that can only reconstruct objects, by predicting per-pixel Gaussians, GS-LRM naturally handles scenes with large variations in scale and complexity. We show that our model can work on both object and scene captures by training it on Objaverse and RealEstate10K respectively. In both scenarios, the models outperform state-of-the-art baselines by a wide margin. We also demonstrate applications of our model in downstream 3D generation tasks. Our project webpage is available at: https://sai-bi.github.io/project/gs-lrm/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19737", "title": "Better & Faster Large Language Models via Multi-token Prediction", "authors": [ "Fabian Gloeckle", "Badr Youbi Idrissi", "Baptiste Rozi\u00e8re", "David Lopez-Paz", "Gabriel Synnaeve" ], "github": "", "project_page": "", "abstract": "Large language models such as GPT and Llama are trained with a next-token prediction loss. In this work, we suggest that training language models to predict multiple future tokens at once results in higher sample efficiency. More specifically, at each position in the training corpus, we ask the model to predict the following n tokens using n independent output heads, operating on top of a shared model trunk. Considering multi-token prediction as an auxiliary training task, we measure improved downstream capabilities with no overhead in training time for both code and natural language models. The method is increasingly useful for larger model sizes, and keeps its appeal when training for multiple epochs. Gains are especially pronounced on generative benchmarks like coding, where our models consistently outperform strong baselines by several percentage points. Our 13B parameter models solves 12 % more problems on HumanEval and 17 % more on MBPP than comparable next-token models. Experiments on small algorithmic tasks demonstrate that multi-token prediction is favorable for the development of induction heads and algorithmic reasoning capabilities. As an additional benefit, models trained with 4-token prediction are up to 3 times faster at inference, even with large batch sizes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19733", "title": "Iterative Reasoning Preference Optimization", "authors": [ "Richard Yuanzhe Pang", "Weizhe Yuan", "Kyunghyun Cho", "He He", "Sainbayar Sukhbaatar", "Jason Weston" ], "github": "", "project_page": "", "abstract": "Iterative preference optimization methods have recently been shown to perform well for general instruction tuning tasks, but typically make little improvement on reasoning tasks (Yuan et al., 2024, Chen et al., 2024). In this work we develop an iterative approach that optimizes the preference between competing generated Chain-of-Thought (CoT) candidates by optimizing for winning vs. losing reasoning steps that lead to the correct answer. We train using a modified DPO loss (Rafailov et al., 2023) with an additional negative log-likelihood term, which we find to be crucial. We show reasoning improves across repeated iterations of this scheme. While only relying on examples in the training set, our approach results in increasing accuracy for Llama-2-70B-Chat from 55.6% to 81.6% on GSM8K (and 88.7% with majority voting out of 32 samples), from 12.5% to 20.8% on MATH, and from 77.8% to 86.7% on ARC-Challenge, which outperforms other Llama-2-based models not relying on additionally sourced datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19149", "title": "SAGS: Structure-Aware 3D Gaussian Splatting", "authors": [ "Evangelos Ververas", "Rolandos Alexandros Potamias", "Jifei Song", "Jiankang Deng", "Stefanos Zafeiriou" ], "github": "", "project_page": "", "abstract": "Following the advent of NeRFs, 3D Gaussian Splatting (3D-GS) has paved the way to real-time neural rendering overcoming the computational burden of volumetric methods. Following the pioneering work of 3D-GS, several methods have attempted to achieve compressible and high-fidelity performance alternatives. However, by employing a geometry-agnostic optimization scheme, these methods neglect the inherent 3D structure of the scene, thereby restricting the expressivity and the quality of the representation, resulting in various floating points and artifacts. In this work, we propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene, which reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets. SAGS is founded on a local-global graph representation that facilitates the learning of complex scenes and enforces meaningful point displacements that preserve the scene's geometry. Additionally, we introduce a lightweight version of SAGS, using a simple yet effective mid-point interpolation scheme, which showcases a compact representation of the scene with up to 24times size reduction without the reliance on any compression strategies. Extensive experiments across multiple benchmark datasets demonstrate the superiority of SAGS compared to state-of-the-art 3D-GS methods under both rendering quality and model size. Besides, we demonstrate that our structure-aware method can effectively mitigate floating artifacts and irregular distortions of previous methods while obtaining precise depth maps. Project page https://eververas.github.io/SAGS/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19752", "title": "Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation", "authors": [ "Yunhao Ge", "Xiaohui Zeng", "Jacob Samuel Huffman", "Tsung-Yi Lin", "Ming-Yu Liu", "Yin Cui" ], "github": "", "project_page": "", "abstract": "Existing automatic captioning methods for visual content face challenges such as lack of detail, content hallucination, and poor instruction following. In this work, we propose VisualFactChecker (VFC), a flexible training-free pipeline that generates high-fidelity and detailed captions for both 2D images and 3D objects. VFC consists of three steps: 1) proposal, where image-to-text captioning models propose multiple initial captions; 2) verification, where a large language model (LLM) utilizes tools such as object detection and VQA models to fact-check proposed captions; 3) captioning, where an LLM generates the final caption by summarizing caption proposals and the fact check verification results. In this step, VFC can flexibly generate captions in various styles following complex instructions. We conduct comprehensive captioning evaluations using four metrics: 1) CLIP-Score for image-text similarity; 2) CLIP-Image-Score for measuring the image-image similarity between the original and the reconstructed image generated by a text-to-image model using the caption. 3) human study on Amazon Mechanical Turk; 4) GPT-4V for fine-grained evaluation. Evaluation results show that VFC outperforms state-of-the-art open-sourced captioning methods for 2D images on the COCO dataset and 3D assets on the Objaverse dataset. Our study demonstrates that by combining open-source models into a pipeline, we can attain captioning capability comparable to proprietary models such as GPT-4V, despite being over 10x smaller in model size.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19553", "title": "Extending Llama-3's Context Ten-Fold Overnight", "authors": [ "Peitian Zhang", "Ninglu Shao", "Zheng Liu", "Shitao Xiao", "Hongjin Qian", "Qiwei Ye", "Zhicheng Dou" ], "github": "https://github.com/FlagOpen/FlagEmbedding", "project_page": "", "abstract": "We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning. The entire training cycle is super efficient, which takes 8 hours on one 8xA800 (80G) GPU machine. The resulted model exhibits superior performances across a broad range of evaluation tasks, such as NIHS, topic retrieval, and long-context language understanding; meanwhile, it also well preserves the original capability over short contexts. The dramatic context extension is mainly attributed to merely 3.5K synthetic training samples generated by GPT-4 , which indicates the LLMs' inherent (yet largely underestimated) potential to extend its original context length. In fact, the context length could be extended far beyond 80K with more computation resources. Therefore, the team will publicly release the entire resources (including data, model, data generation pipeline, training code) so as to facilitate the future research from the community: https://github.com/FlagOpen/FlagEmbedding.", "github_manual": "", "github_regex": "https://github.com/FlagOpen/FlagEmbedding", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19296", "title": "Octopus v4: Graph of language models", "authors": [ "Wei Chen", "Zhiyuan Li" ], "github": "https://github.com/NexaAI/octopus-v4", "project_page": "", "abstract": "Language models have been effective in a wide range of applications, yet the most sophisticated models are often proprietary. For example, GPT-4 by OpenAI and various models by Anthropic are expensive and consume substantial energy. In contrast, the open-source community has produced competitive models, like Llama3. Furthermore, niche-specific smaller language models, such as those tailored for legal, medical or financial tasks, have outperformed their proprietary counterparts. This paper introduces a novel approach that employs functional tokens to integrate multiple open-source models, each optimized for particular tasks. Our newly developed Octopus v4 model leverages functional tokens to intelligently direct user queries to the most appropriate vertical model and reformat the query to achieve the best performance. Octopus v4, an evolution of the Octopus v1, v2, and v3 models, excels in selection and parameter understanding and reformatting. Additionally, we explore the use of graph as a versatile data structure that effectively coordinates multiple open-source models by harnessing the capabilities of the Octopus model and functional tokens. Use our open-sourced GitHub (https://www.nexa4ai.com/) to try Octopus v4 models (https://huggingface.co/NexaAIDev/Octopus-v4), and contrite to a larger graph of language models. By activating models less than 10B parameters, we achieved SOTA MMLU score of 74.8 among the same level models.", "github_manual": "https://github.com/NexaAI/octopus-v4", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19759", "title": "MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model", "authors": [ "Wenxun Dai", "Ling-Hao Chen", "Jingbo Wang", "Jinpeng Liu", "Bo Dai", "Yansong Tang" ], "github": "https://github.com/Dai-Wenxun/MotionLCM", "project_page": "", "abstract": "This work introduces MotionLCM, extending controllable motion generation to a real-time level. Existing methods for spatial control in text-conditioned motion generation suffer from significant runtime inefficiency. To address this issue, we first propose the motion latent consistency model (MotionLCM) for motion generation, building upon the latent diffusion model (MLD). By employing one-step (or few-step) inference, we further improve the runtime efficiency of the motion latent diffusion model for motion generation. To ensure effective controllability, we incorporate a motion ControlNet within the latent space of MotionLCM and enable explicit control signals (e.g., pelvis trajectory) in the vanilla motion space to control the generation process directly, similar to controlling other latent-free diffusion models for motion generation. By employing these techniques, our approach can generate human motions with text and control signals in real-time. Experimental results demonstrate the remarkable generation and controlling capabilities of MotionLCM while maintaining real-time runtime efficiency.", "github_manual": "https://github.com/Dai-Wenxun/MotionLCM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19756", "title": "KAN: Kolmogorov-Arnold Networks", "authors": [ "Ziming Liu", "Yixuan Wang", "Sachin Vaidya", "Fabian Ruehle", "James Halverson", "Marin Solja\u010di\u0107", "Thomas Y. Hou", "Max Tegmark" ], "github": "https://github.com/kindxiaoming/pykan", "project_page": "", "abstract": "Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes (\"neurons\"), KANs have learnable activation functions on edges (\"weights\"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs.", "github_manual": "https://github.com/kindxiaoming/pykan", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19758", "title": "Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting", "authors": [ "Paul Engstler", "Andrea Vedaldi", "Iro Laina", "Christian Rupprecht" ], "github": "https://github.com/paulengstler/invisible-stitch", "project_page": "", "abstract": "3D scene generation has quickly become a challenging new research direction, fueled by consistent improvements of 2D generative diffusion models. Most prior work in this area generates scenes by iteratively stitching newly generated frames with existing geometry. These works often depend on pre-trained monocular depth estimators to lift the generated images into 3D, fusing them with the existing scene representation. These approaches are then often evaluated via a text metric, measuring the similarity between the generated images and a given text prompt. In this work, we make two fundamental contributions to the field of 3D scene generation. First, we note that lifting images to 3D with a monocular depth estimation model is suboptimal as it ignores the geometry of the existing scene. We thus introduce a novel depth completion model, trained via teacher distillation and self-training to learn the 3D fusion process, resulting in improved geometric coherence of the scene. Second, we introduce a new benchmarking scheme for scene generation methods that is based on ground truth geometry, and thus measures the quality of the structure of the scene.", "github_manual": "https://github.com/paulengstler/invisible-stitch", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19525", "title": "MicroDreamer: Zero-shot 3D Generation in sim20 Seconds by Score-based Iterative Reconstruction", "authors": [ "Luxi Chen", "Zhengyi Wang", "Chongxuan Li", "Tingting Gao", "Hang Su", "Jun Zhu" ], "github": "https://github.com/ML-GSAI/MicroDreamer", "project_page": "", "abstract": "Optimization-based approaches, such as score distillation sampling (SDS), show promise in zero-shot 3D generation but suffer from low efficiency, primarily due to the high number of function evaluations (NFEs) required for each sample. In this paper, we introduce score-based iterative reconstruction (SIR), an efficient and general algorithm for 3D generation with a multi-view score-based diffusion model. Given the images produced by the diffusion model, SIR reduces NFEs by repeatedly optimizing 3D parameters, unlike the single optimization in SDS, mimicking the 3D reconstruction process. With other improvements including optimization in the pixel space, we present an efficient approach called MicroDreamer that generally applies to various 3D representations and 3D generation tasks. In particular, retaining a comparable performance, MicroDreamer is 5-20 times faster than SDS in generating neural radiance field and takes about 20 seconds to generate meshes from 3D Gaussian splitting on a single A100 GPU, halving the time of the fastest zero-shot baseline, DreamGaussian. Our code is available at https://github.com/ML-GSAI/MicroDreamer.", "github_manual": "", "github_regex": "https://github.com/ML-GSAI/MicroDreamer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-01", "arxiv_id": "2404.19760", "title": "Lightplane: Highly-Scalable Components for Neural 3D Fields", "authors": [ "Ang Cao", "Justin Johnson", "Andrea Vedaldi", "David Novotny" ], "github": "https://github.com/facebookresearch/lightplane", "project_page": "", "abstract": "Contemporary 3D research, particularly in reconstruction and generation, heavily relies on 2D images for inputs or supervision. However, current designs for these 2D-3D mapping are memory-intensive, posing a significant bottleneck for existing methods and hindering new applications. In response, we propose a pair of highly scalable components for 3D neural fields: Lightplane Render and Splatter, which significantly reduce memory usage in 2D-3D mapping. These innovations enable the processing of vastly more and higher resolution images with small memory and computational costs. We demonstrate their utility in various applications, from benefiting single-scene optimization with image-level losses to realizing a versatile pipeline for dramatically scaling 3D reconstruction and generation. Code: https://github.com/facebookresearch/lightplane.", "github_manual": "", "github_regex": "https://github.com/facebookresearch/lightplane", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-02", "arxiv_id": "2405.00676", "title": "Spectrally Pruned Gaussian Fields with Neural Compensation", "authors": [ "Runyi Yang", "Zhenxin Zhu", "Zhou Jiang", "Baijun Ye", "Xiaoxue Chen", "Yifei Zhang", "Yuantao Chen", "Jian Zhao", "Hao Zhao" ], "github": "https://github.com/RunyiYang/SUNDAE", "project_page": "", "abstract": "Recently, 3D Gaussian Splatting, as a novel 3D representation, has garnered attention for its fast rendering speed and high rendering quality. However, this comes with high memory consumption, e.g., a well-trained Gaussian field may utilize three million Gaussian primitives and over 700 MB of memory. We credit this high memory footprint to the lack of consideration for the relationship between primitives. In this paper, we propose a memory-efficient Gaussian field named SUNDAE with spectral pruning and neural compensation. On one hand, we construct a graph on the set of Gaussian primitives to model their relationship and design a spectral down-sampling module to prune out primitives while preserving desired signals. On the other hand, to compensate for the quality loss of pruning Gaussians, we exploit a lightweight neural network head to mix splatted features, which effectively compensates for quality losses while capturing the relationship between primitives in its weights. We demonstrate the performance of SUNDAE with extensive results. For example, SUNDAE can achieve 26.80 PSNR at 145 FPS using 104 MB memory while the vanilla Gaussian splatting algorithm achieves 25.60 PSNR at 160 FPS using 523 MB memory, on the Mip-NeRF360 dataset. Codes are publicly available at https://runyiyang.github.io/projects/SUNDAE/.", "github_manual": "https://github.com/RunyiYang/SUNDAE", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-02", "arxiv_id": "2405.00332", "title": "A Careful Examination of Large Language Model Performance on Grade School Arithmetic", "authors": [ "Hugh Zhang", "Jeff Da", "Dean Lee", "Vaughn Robinson", "Catherine Wu", "Will Song", "Tiffany Zhao", "Pranav Raja", "Dylan Slack", "Qin Lyu", "Sean Hendryx", "Russell Kaplan", "Michele", "Lunati", "Summer Yue" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning. However, there is growing concern that some of this performance actually reflects dataset contamination, where data closely resembling benchmark questions leaks into the training data, instead of true reasoning ability. To investigate this claim rigorously, we commission Grade School Math 1000 (GSM1k). GSM1k is designed to mirror the style and complexity of the established GSM8k benchmark, the gold standard for measuring elementary mathematical reasoning. We ensure that the two benchmarks are comparable across important metrics such as human solve rates, number of steps in solution, answer magnitude, and more. When evaluating leading open- and closed-source LLMs on GSM1k, we observe accuracy drops of up to 13%, with several families of models (e.g., Phi and Mistral) showing evidence of systematic overfitting across almost all model sizes. At the same time, many models, especially those on the frontier, (e.g., Gemini/GPT/Claude) show minimal signs of overfitting. Further analysis suggests a positive relationship (Spearman's r^2=0.32) between a model's probability of generating an example from GSM8k and its performance gap between GSM8k and GSM1k, suggesting that many models may have partially memorized GSM8k.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-02", "arxiv_id": "2405.00664", "title": "Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3", "authors": [ "Junsang Yoon", "Akshat Gupta", "Gopala Anumanchipalli" ], "github": "https://github.com/scalable-model-editing/unified-model-editing", "project_page": "", "abstract": "This study presents a targeted model editing analysis focused on the latest large language model, Llama-3. We explore the efficacy of popular model editing techniques - ROME, MEMIT, and EMMET, which are designed for precise layer interventions. We identify the most effective layers for targeted edits through an evaluation that encompasses up to 4096 edits across three distinct strategies: sequential editing, batch editing, and a hybrid approach we call as sequential-batch editing. Our findings indicate that increasing edit batch-sizes may degrade model performance more significantly than using smaller edit batches sequentially for equal number of edits. With this, we argue that sequential model editing is an important component for scaling model editing methods and future research should focus on methods that combine both batched and sequential editing. This observation suggests a potential limitation in current model editing methods which push towards bigger edit batch sizes, and we hope it paves way for future investigations into optimizing batch sizes and model editing performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/scalable-model-editing/unified-model-editing", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-02", "arxiv_id": "2405.00233", "title": "SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound", "authors": [ "Haohe Liu", "Xuenan Xu", "Yi Yuan", "Mengyue Wu", "Wenwu Wang", "Mark D. Plumbley" ], "github": "https://github.com/haoheliu/SemantiCodec-inference", "project_page": "", "abstract": "Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modelling techniques to audio data. However, traditional codecs often operate at high bitrates or within narrow domains such as speech and lack the semantic clues required for efficient language modelling. Addressing these challenges, we introduce SemantiCodec, a novel codec designed to compress audio into fewer than a hundred tokens per second across diverse audio types, including speech, general audio, and music, without compromising quality. SemantiCodec features a dual-encoder architecture: a semantic encoder using a self-supervised AudioMAE, discretized using k-means clustering on extensive audio data, and an acoustic encoder to capture the remaining details. The semantic and acoustic encoder outputs are used to reconstruct audio via a diffusion-model-based decoder. SemantiCodec is presented in three variants with token rates of 25, 50, and 100 per second, supporting a range of ultra-low bit rates between 0.31 kbps and 1.43 kbps. Experimental results demonstrate that SemantiCodec significantly outperforms the state-of-the-art Descript codec on reconstruction quality. Our results also suggest that SemantiCodec contains significantly richer semantic information than all evaluated audio codecs, even at significantly lower bitrates. Our code and demos are available at https://haoheliu.github.io/SemantiCodec/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/haoheliu/SemantiCodec-inference", "github_api_added_by": "auto" }, { "date": "2024-05-02", "arxiv_id": "2404.18212", "title": "Paint by Inpaint: Learning to Add Image Objects by Removing Them First", "authors": [ "Navve Wasserman", "Noam Rotstein", "Roy Ganz", "Ron Kimmel" ], "github": "https://github.com/RotsteinNoam/Paint-by-Inpaint", "project_page": "", "abstract": "Image editing has advanced significantly with the introduction of text-conditioned diffusion models. Despite this progress, seamlessly adding objects to images based on textual instructions without requiring user-provided input masks remains a challenge. We address this by leveraging the insight that removing objects (Inpaint) is significantly simpler than its inverse process of adding them (Paint), attributed to the utilization of segmentation mask datasets alongside inpainting models that inpaint within these masks. Capitalizing on this realization, by implementing an automated and extensive pipeline, we curate a filtered large-scale image dataset containing pairs of images and their corresponding object-removed versions. Using these pairs, we train a diffusion model to inverse the inpainting process, effectively adding objects into images. Unlike other editing datasets, ours features natural target images instead of synthetic ones; moreover, it maintains consistency between source and target by construction. Additionally, we utilize a large Vision-Language Model to provide detailed descriptions of the removed objects and a Large Language Model to convert these descriptions into diverse, natural-language instructions. We show that the trained model surpasses existing ones both qualitatively and quantitatively, and release the large-scale dataset alongside the trained models for the community.", "github_manual": "https://github.com/RotsteinNoam/Paint-by-Inpaint", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-02", "arxiv_id": "2405.00263", "title": "Clover: Regressive Lightweight Speculative Decoding with Sequential Knowledge", "authors": [ "Bin Xiao", "Chunan Shi", "Xiaonan Nie", "Fan Yang", "Xiangwei Deng", "Lei Su", "Weipeng Chen", "Bin Cui" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) suffer from low efficiency as the mismatch between the requirement of auto-regressive decoding and the design of most contemporary GPUs. Specifically, billions to trillions of parameters must be loaded to the GPU cache through its limited memory bandwidth for computation, but only a small batch of tokens is actually computed. Consequently, the GPU spends most of its time on memory transfer instead of computation. Recently, parallel decoding, a type of speculative decoding algorithms, is becoming more popular and has demonstrated impressive efficiency improvement in generation. It introduces extra decoding heads to large models, enabling them to predict multiple subsequent tokens simultaneously and verify these candidate continuations in a single decoding step. However, this approach deviates from the training objective of next token prediction used during pre-training, resulting in a low hit rate for candidate tokens. In this paper, we propose a new speculative decoding algorithm, Clover, which integrates sequential knowledge into the parallel decoding process. This enhancement improves the hit rate of speculators and thus boosts the overall efficiency. Clover transmits the sequential knowledge from pre-speculated tokens via the Regressive Connection, then employs an Attention Decoder to integrate these speculated tokens. Additionally, Clover incorporates an Augmenting Block that modifies the hidden states to better align with the purpose of speculative generation rather than next token prediction. The experiment results demonstrate that Clover outperforms the baseline by up to 91% on Baichuan-Small and 146% on Baichuan-Large, respectively, and exceeds the performance of the previously top-performing method, Medusa, by up to 37% on Baichuan-Small and 57% on Baichuan-Large, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-02", "arxiv_id": "2405.00675", "title": "Self-Play Preference Optimization for Language Model Alignment", "authors": [ "Yue Wu", "Zhiqing Sun", "Huizhuo Yuan", "Kaixuan Ji", "Yiming Yang", "Quanquan Gu" ], "github": "https://github.com/uclaml/sppo", "project_page": "", "abstract": "Traditional reinforcement learning from human feedback (RLHF) approaches relying on parametric models like the Bradley-Terry model fall short in capturing the intransitivity and irrationality in human preferences. Recent advancements suggest that directly working with preference probabilities can yield a more accurate reflection of human preferences, enabling more flexible and accurate language model alignment. In this paper, we propose a self-play-based method for language model alignment, which treats the problem as a constant-sum two-player game aimed at identifying the Nash equilibrium policy. Our approach, dubbed Self-Play Preference Optimization (SPPO), approximates the Nash equilibrium through iterative policy updates and enjoys theoretical convergence guarantee. Our method can effectively increase the log-likelihood of the chosen response and decrease that of the rejected response, which cannot be trivially achieved by symmetric pairwise loss such as Direct Preference Optimization (DPO) and Identity Preference Optimization (IPO). In our experiments, using only 60k prompts (without responses) from the UltraFeedback dataset and without any prompt augmentation, by leveraging a pre-trained preference model PairRM with only 0.4B parameters, SPPO can obtain a model from fine-tuning Mistral-7B-Instruct-v0.2 that achieves the state-of-the-art length-controlled win-rate of 28.53% against GPT-4-Turbo on AlpacaEval 2.0. It also outperforms the (iterative) DPO and IPO on MT-Bench and the Open LLM Leaderboard. Notably, the strong performance of SPPO is achieved without additional external supervision (e.g., responses, preferences, etc.) from GPT-4 or other stronger language models.", "github_manual": "https://github.com/uclaml/sppo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-02", "arxiv_id": "2405.00236", "title": "STT: Stateful Tracking with Transformers for Autonomous Driving", "authors": [ "Longlong Jing", "Ruichi Yu", "Xu Chen", "Zhengli Zhao", "Shiwei Sheng", "Colin Graber", "Qi Chen", "Qinru Li", "Shangxuan Wu", "Han Deng", "Sangjin Lee", "Chris Sweeney", "Qiurui He", "Wei-Chih Hung", "Tong He", "Xingyi Zhou", "Farshid Moussavi", "Zijian Guo", "Yin Zhou", "Mingxing Tan", "Weilong Yang", "Congcong Li" ], "github": "", "project_page": "", "abstract": "Tracking objects in three-dimensional space is critical for autonomous driving. To ensure safety while driving, the tracker must be able to reliably track objects across frames and accurately estimate their states such as velocity and acceleration in the present. Existing works frequently focus on the association task while either neglecting the model performance on state estimation or deploying complex heuristics to predict the states. In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately. STT consumes rich appearance, geometry, and motion signals through long term history of detections and is jointly optimized for both data association and state estimation tasks. Since the standard tracking metrics like MOTA and MOTP do not capture the combined performance of the two tasks in the wider spectrum of object states, we extend them with new metrics called S-MOTA and MOTPS that address this limitation. STT achieves competitive real-time performance on the Waymo Open Dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-02", "arxiv_id": "2405.00029", "title": "Automatic Creative Selection with Cross-Modal Matching", "authors": [ "Alex Kim", "Jia Huang", "Rob Monarch", "Jerry Kwac", "Anikesh Kamath", "Parmeshwar Khurd", "Kailash Thiyagarajan", "Goodman Gu" ], "github": "", "project_page": "", "abstract": "Application developers advertise their Apps by creating product pages with App images, and bidding on search terms. It is then crucial for App images to be highly relevant with the search terms. Solutions to this problem require an image-text matching model to predict the quality of the match between the chosen image and the search terms. In this work, we present a novel approach to matching an App image to search terms based on fine-tuning a pre-trained LXMERT model. We show that compared to the CLIP model and a baseline using a Transformer model for search terms, and a ResNet model for images, we significantly improve the matching accuracy. We evaluate our approach using two sets of labels: advertiser associated (image, search term) pairs for a given application, and human ratings for the relevance between (image, search term) pairs. Our approach achieves 0.96 AUC score for advertiser associated ground truth, outperforming the transformer+ResNet baseline and the fine-tuned CLIP model by 8% and 14%. For human labeled ground truth, our approach achieves 0.95 AUC score, outperforming the transformer+ResNet baseline and the fine-tuned CLIP model by 16% and 17%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-03", "arxiv_id": "2405.01434", "title": "StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation", "authors": [ "Yupeng Zhou", "Daquan Zhou", "Ming-Ming Cheng", "Jiashi Feng", "Qibin Hou" ], "github": "https://github.com/HVision-NKU/StoryDiffusion", "project_page": "", "abstract": "For recent diffusion-based generative models, maintaining consistent content across a series of generated images, especially those containing subjects and complex details, presents a significant challenge. In this paper, we propose a new way of self-attention calculation, termed Consistent Self-Attention, that significantly boosts the consistency between the generated images and augments prevalent pretrained diffusion-based text-to-image models in a zero-shot manner. To extend our method to long-range video generation, we further introduce a novel semantic space temporal motion prediction module, named Semantic Motion Predictor. It is trained to estimate the motion conditions between two provided images in the semantic spaces. This module converts the generated sequence of images into videos with smooth transitions and consistent subjects that are significantly more stable than the modules based on latent spaces only, especially in the context of long video generation. By merging these two novel components, our framework, referred to as StoryDiffusion, can describe a text-based story with consistent images or videos encompassing a rich variety of contents. The proposed StoryDiffusion encompasses pioneering explorations in visual story generation with the presentation of images and videos, which we hope could inspire more research from the aspect of architectural modifications. Our code is made publicly available at https://github.com/HVision-NKU/StoryDiffusion.", "github_manual": "", "github_regex": "https://github.com/HVision-NKU/StoryDiffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-03", "arxiv_id": "2405.01535", "title": "Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models", "authors": [ "Seungone Kim", "Juyoung Suk", "Shayne Longpre", "Bill Yuchen Lin", "Jamin Shin", "Sean Welleck", "Graham Neubig", "Moontae Lee", "Kyungjae Lee", "Minjoon Seo" ], "github": "https://github.com/prometheus-eval/prometheus-eval", "project_page": "", "abstract": "Proprietary LMs such as GPT-4 are often employed to assess the quality of responses from various LMs. However, concerns including transparency, controllability, and affordability strongly motivate the development of open-source LMs specialized in evaluations. On the other hand, existing open evaluator LMs exhibit critical shortcomings: 1) they issue scores that significantly diverge from those assigned by humans, and 2) they lack the flexibility to perform both direct assessment and pairwise ranking, the two most prevalent forms of assessment. Additionally, they do not possess the ability to evaluate based on custom evaluation criteria, focusing instead on general attributes like helpfulness and harmlessness. To address these issues, we introduce Prometheus 2, a more powerful evaluator LM than its predecessor that closely mirrors human and GPT-4 judgements. Moreover, it is capable of processing both direct assessment and pair-wise ranking formats grouped with a user-defined evaluation criteria. On four direct assessment benchmarks and four pairwise ranking benchmarks, Prometheus 2 scores the highest correlation and agreement with humans and proprietary LM judges among all tested open evaluator LMs. Our models, code, and data are all publicly available at https://github.com/prometheus-eval/prometheus-eval.", "github_manual": "", "github_regex": "https://github.com/prometheus-eval/prometheus-eval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-03", "arxiv_id": "2405.01470", "title": "WildChat: 1M ChatGPT Interaction Logs in the Wild", "authors": [ "Wenting Zhao", "Xiang Ren", "Jack Hessel", "Claire Cardie", "Yejin Choi", "Yuntian Deng" ], "github": "", "project_page": "", "abstract": "Chatbots such as GPT-4 and ChatGPT are now serving millions of users. Despite their widespread use, there remains a lack of public datasets showcasing how these tools are used by a population of users in practice. To bridge this gap, we offered free access to ChatGPT for online users in exchange for their affirmative, consensual opt-in to anonymously collect their chat transcripts and request headers. From this, we compiled WildChat, a corpus of 1 million user-ChatGPT conversations, which consists of over 2.5 million interaction turns. We compare WildChat with other popular user-chatbot interaction datasets, and find that our dataset offers the most diverse user prompts, contains the largest number of languages, and presents the richest variety of potentially toxic use-cases for researchers to study. In addition to timestamped chat transcripts, we enrich the dataset with demographic data, including state, country, and hashed IP addresses, alongside request headers. This augmentation allows for more detailed analysis of user behaviors across different geographical regions and temporal dimensions. Finally, because it captures a broad range of use cases, we demonstrate the dataset's potential utility in fine-tuning instruction-following models. WildChat is released at https://wildchat.allen.ai under AI2 ImpACT Licenses.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-03", "arxiv_id": "2405.00732", "title": "LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report", "authors": [ "Justin Zhao", "Timothy Wang", "Wael Abid", "Geoffrey Angus", "Arnav Garg", "Jeffery Kinnison", "Alex Sherstinsky", "Piero Molino", "Travis Addair", "Devvret Rishi" ], "github": "https://github.com/predibase/lora_bakeoff", "project_page": "", "abstract": "Low Rank Adaptation (LoRA) has emerged as one of the most widely adopted methods for Parameter Efficient Fine-Tuning (PEFT) of Large Language Models (LLMs). LoRA reduces the number of trainable parameters and memory usage while achieving comparable performance to full fine-tuning. We aim to assess the viability of training and serving LLMs fine-tuned with LoRA in real-world applications. First, we measure the quality of LLMs fine-tuned with quantized low rank adapters across 10 base models and 31 tasks for a total of 310 models. We find that 4-bit LoRA fine-tuned models outperform base models by 34 points and GPT-4 by 10 points on average. Second, we investigate the most effective base models for fine-tuning and assess the correlative and predictive capacities of task complexity heuristics in forecasting the outcomes of fine-tuning. Finally, we evaluate the latency and concurrency capabilities of LoRAX, an open-source Multi-LoRA inference server that facilitates the deployment of multiple LoRA fine-tuned models on a single GPU using shared base model weights and dynamic adapter loading. LoRAX powers LoRA Land, a web application that hosts 25 LoRA fine-tuned Mistral-7B LLMs on a single NVIDIA A100 GPU with 80GB memory. LoRA Land highlights the quality and cost-effectiveness of employing multiple specialized LLMs over a single, general-purpose LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/predibase/lora_bakeoff", "github_api_added_by": "auto" }, { "date": "2024-05-03", "arxiv_id": "2405.01481", "title": "NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment", "authors": [ "Gerald Shen", "Zhilin Wang", "Olivier Delalleau", "Jiaqi Zeng", "Yi Dong", "Daniel Egert", "Shengyang Sun", "Jimmy Zhang", "Sahil Jain", "Ali Taghibakhshi", "Markel Sanz Ausin", "Ashwath Aithal", "Oleksii Kuchaiev" ], "github": "https://github.com/NVIDIA/NeMo-Aligner", "project_page": "", "abstract": "Aligning Large Language Models (LLMs) with human values and preferences is essential for making them helpful and safe. However, building efficient tools to perform alignment can be challenging, especially for the largest and most competent LLMs which often contain tens or hundreds of billions of parameters. We create NeMo-Aligner, a toolkit for model alignment that can efficiently scale to using hundreds of GPUs for training. NeMo-Aligner comes with highly optimized and scalable implementations for major paradigms of model alignment such as: Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), SteerLM, and Self-Play Fine-Tuning (SPIN). Additionally, our toolkit supports running most of the alignment techniques in a Parameter Efficient Fine-Tuning (PEFT) setting. NeMo-Aligner is designed for extensibility, allowing support for other alignment techniques with minimal effort. It is open-sourced with Apache 2.0 License and we invite community contributions at https://github.com/NVIDIA/NeMo-Aligner", "github_manual": "", "github_regex": "https://github.com/NVIDIA/NeMo-Aligner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-03", "arxiv_id": "2405.00983", "title": "LLM-AD: Large Language Model based Audio Description System", "authors": [ "Peng Chu", "Jiang Wang", "Andre Abrantes" ], "github": "", "project_page": "", "abstract": "The development of Audio Description (AD) has been a pivotal step forward in making video content more accessible and inclusive. Traditionally, AD production has demanded a considerable amount of skilled labor, while existing automated approaches still necessitate extensive training to integrate multimodal inputs and tailor the output from a captioning style to an AD style. In this paper, we introduce an automated AD generation pipeline that harnesses the potent multimodal and instruction-following capacities of GPT-4V(ision). Notably, our methodology employs readily available components, eliminating the need for additional training. It produces ADs that not only comply with established natural language AD production standards but also maintain contextually consistent character information across frames, courtesy of a tracking-based character recognition module. A thorough analysis on the MAD dataset reveals that our approach achieves a performance on par with learning-based methods in automated AD production, as substantiated by a CIDEr score of 20.5.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-03", "arxiv_id": "2405.01525", "title": "FLAME: Factuality-Aware Alignment for Large Language Models", "authors": [ "Sheng-Chieh Lin", "Luyu Gao", "Barlas Oguz", "Wenhan Xiong", "Jimmy Lin", "Wen-tau Yih", "Xilun Chen" ], "github": "", "project_page": "", "abstract": "Alignment is a standard procedure to fine-tune pre-trained large language models (LLMs) to follow natural language instructions and serve as helpful AI assistants. We have observed, however, that the conventional alignment process fails to enhance the factual accuracy of LLMs, and often leads to the generation of more false facts (i.e. hallucination). In this paper, we study how to make the LLM alignment process more factual, by first identifying factors that lead to hallucination in both alignment steps:\\ supervised fine-tuning (SFT) and reinforcement learning (RL). In particular, we find that training the LLM on new knowledge or unfamiliar texts can encourage hallucination. This makes SFT less factual as it trains on human labeled data that may be novel to the LLM. Furthermore, reward functions used in standard RL can also encourage hallucination, because it guides the LLM to provide more helpful responses on a diverse set of instructions, often preferring longer and more detailed responses. Based on these observations, we propose factuality-aware alignment, comprised of factuality-aware SFT and factuality-aware RL through direct preference optimization. Experiments show that our proposed factuality-aware alignment guides LLMs to output more factual responses while maintaining instruction-following capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-03", "arxiv_id": "2405.01536", "title": "Customizing Text-to-Image Models with a Single Image Pair", "authors": [ "Maxwell Jones", "Sheng-Yu Wang", "Nupur Kumari", "David Bau", "Jun-Yan Zhu" ], "github": "", "project_page": "", "abstract": "Art reinterpretation is the practice of creating a variation of a reference work, making a paired artwork that exhibits a distinct artistic style. We ask if such an image pair can be used to customize a generative model to capture the demonstrated stylistic difference. We propose Pair Customization, a new customization method that learns stylistic difference from a single image pair and then applies the acquired style to the generation process. Unlike existing methods that learn to mimic a single concept from a collection of images, our method captures the stylistic difference between paired images. This allows us to apply a stylistic change without overfitting to the specific image content in the examples. To address this new task, we employ a joint optimization method that explicitly separates the style and content into distinct LoRA weight spaces. We optimize these style and content weights to reproduce the style and content images while encouraging their orthogonality. During inference, we modify the diffusion process via a new style guidance based on our learned weights. Both qualitative and quantitative experiments show that our method can effectively learn style while avoiding overfitting to image content, highlighting the potential of modeling such stylistic differences from a single image pair.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-14", "arxiv_id": "2405.07990", "title": "Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots", "authors": [ "Chengyue Wu", "Yixiao Ge", "Qiushan Guo", "Jiahao Wang", "Zhixuan Liang", "Zeyu Lu", "Ying Shan", "Ping Luo" ], "github": "https://github.com/TencentARC/Plot2Code", "project_page": "", "abstract": "The remarkable progress of Multi-modal Large Language Models (MLLMs) has attracted significant attention due to their superior performance in visual contexts. However, their capabilities in turning visual figure to executable code, have not been evaluated thoroughly. To address this, we introduce Plot2Code, a comprehensive visual coding benchmark designed for a fair and in-depth assessment of MLLMs. We carefully collect 132 manually selected high-quality matplotlib plots across six plot types from publicly available matplotlib galleries. For each plot, we carefully offer its source code, and an descriptive instruction summarized by GPT-4. This approach enables Plot2Code to extensively evaluate MLLMs' code capabilities across various input modalities. Furthermore, we propose three automatic evaluation metrics, including code pass rate, text-match ratio, and GPT-4V overall rating, for a fine-grained assessment of the output code and rendered images. Instead of simply judging pass or fail, we employ GPT-4V to make an overall judgement between the generated and reference images, which has been shown to be consistent with human evaluation. The evaluation results, which include analyses of 14 MLLMs such as the proprietary GPT-4V, Gemini-Pro, and the open-sourced Mini-Gemini, highlight the substantial challenges presented by Plot2Code. With Plot2Code, we reveal that most existing MLLMs struggle with visual coding for text-dense plots, heavily relying on textual instruction. We hope that the evaluation results from Plot2Code on visual coding will guide the future development of MLLMs. All data involved with Plot2Code are available at https://huggingface.co/datasets/TencentARC/Plot2Code.", "github_manual": "https://github.com/TencentARC/Plot2Code", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-14", "arxiv_id": "2405.07518", "title": "SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts", "authors": [ "Raghu Prabhakar", "Ram Sivaramakrishnan", "Darshan Gandhi", "Yun Du", "Mingran Wang", "Xiangyu Song", "Kejie Zhang", "Tianren Gao", "Angela Wang", "Karen Li", "Yongning Sheng", "Joshua Brot", "Denis Sokolov", "Apurv Vivek", "Calvin Leung", "Arjun Sabnis", "Jiayu Bai", "Tuowen Zhao", "Mark Gottscho", "David Jackson", "Mark Luttrell", "Manish K. Shah", "Edison Chen", "Kaizhao Liang", "Swayambhoo Jain", "Urmish Thakker", "Dawei Huang", "Sumti Jairath", "Kevin J. Brown", "Kunle Olukotun" ], "github": "", "project_page": "", "abstract": "Monolithic large language models (LLMs) like GPT-4 have paved the way for modern generative AI applications. Training, serving, and maintaining monolithic LLMs at scale, however, remains prohibitively expensive and challenging. The disproportionate increase in compute-to-memory ratio of modern AI accelerators have created a memory wall, necessitating new methods to deploy AI. Composition of Experts (CoE) is an alternative modular approach that lowers the cost and complexity of training and serving. However, this approach presents two key challenges when using conventional hardware: (1) without fused operations, smaller models have lower operational intensity, which makes high utilization more challenging to achieve; and (2) hosting a large number of models can be either prohibitively expensive or slow when dynamically switching between them. In this paper, we describe how combining CoE, streaming dataflow, and a three-tier memory system scales the AI memory wall. We describe Samba-CoE, a CoE system with 150 experts and a trillion total parameters. We deploy Samba-CoE on the SambaNova SN40L Reconfigurable Dataflow Unit (RDU) - a commercial dataflow accelerator architecture that has been co-designed for enterprise inference and training applications. The chip introduces a new three-tier memory system with on-chip distributed SRAM, on-package HBM, and off-package DDR DRAM. A dedicated inter-RDU network enables scaling up and out over multiple sockets. We demonstrate speedups ranging from 2x to 13x on various benchmarks running on eight RDU sockets compared with an unfused baseline. We show that for CoE inference deployments, the 8-socket RDU Node reduces machine footprint by up to 19x, speeds up model switching time by 15x to 31x, and achieves an overall speedup of 3.7x over a DGX H100 and 6.6x over a DGX A100.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-14", "arxiv_id": "2405.06932", "title": "Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training", "authors": [ "Junqin Huang", "Zhongjie Hu", "Zihao Jing", "Mengya Gao", "Yichao Wu" ], "github": "https://github.com/hjq133/piccolo-embedding", "project_page": "", "abstract": "In this report, we introduce Piccolo2, an embedding model that surpasses other models in the comprehensive evaluation over 6 tasks on CMTEB benchmark, setting a new state-of-the-art. Piccolo2 primarily leverages an efficient multi-task hybrid loss training approach, effectively harnessing textual data and labels from diverse downstream tasks. In addition, Piccolo2 scales up the embedding dimension and uses MRL training to support more flexible vector dimensions. The latest information of piccolo models can be accessed via: https://huggingface.co/sensenova/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hjq133/piccolo-embedding", "github_api_added_by": "auto" }, { "date": "2024-05-14", "arxiv_id": "2405.07526", "title": "MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels", "authors": [ "Qi Chen", "Xiubo Geng", "Corby Rosset", "Carolyn Buractaon", "Jingwen Lu", "Tao Shen", "Kun Zhou", "Chenyan Xiong", "Yeyun Gong", "Paul Bennett", "Nick Craswell", "Xing Xie", "Fan Yang", "Bryan Tower", "Nikhil Rao", "Anlei Dong", "Wenqi Jiang", "Zheng Liu", "Mingqin Li", "Chuanjie Liu", "Zengzhong Li", "Rangan Majumder", "Jennifer Neville", "Andy Oakley", "Knut Magne Risvik", "Harsha Vardhan Simhadri", "Manik Varma", "Yujing Wang", "Linjun Yang", "Mao Yang", "Ce Zhang" ], "github": "https://github.com/microsoft/MS-MARCO-Web-Search", "project_page": "", "abstract": "Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-scale information-rich web dataset, featuring millions of real clicked query-document labels. This dataset closely mimics real-world web document and query distribution, provides rich information for various kinds of downstream tasks and encourages research in various areas, such as generic end-to-end neural indexer models, generic embedding models, and next generation information access system with large language models. MS MARCO Web Search offers a retrieval benchmark with three web retrieval challenge tasks that demand innovations in both machine learning and information retrieval system research domains. As the first dataset that meets large, real and rich data requirements, MS MARCO Web Search paves the way for future advancements in AI and system research. MS MARCO Web Search dataset is available at: https://github.com/microsoft/MS-MARCO-Web-Search.", "github_manual": "", "github_regex": "https://github.com/microsoft/MS-MARCO-Web-Search", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-14", "arxiv_id": "2405.07065", "title": "LogoMotion: Visually Grounded Code Generation for Content-Aware Animation", "authors": [ "Vivian Liu", "Rubaiat Habib Kazi", "Li-Yi Wei", "Matthew Fisher", "Timothy Langlois", "Seth Walker", "Lydia Chilton" ], "github": "", "project_page": "", "abstract": "Animated logos are a compelling and ubiquitous way individuals and brands represent themselves online. Manually authoring these logos can require significant artistic skill and effort. To help novice designers animate logos, design tools currently offer templates and animation presets. However, these solutions can be limited in their expressive range. Large language models have the potential to help novice designers create animated logos by generating animation code that is tailored to their content. In this paper, we introduce LogoMotion, an LLM-based system that takes in a layered document and generates animated logos through visually-grounded program synthesis. We introduce techniques to create an HTML representation of a canvas, identify primary and secondary elements, synthesize animation code, and visually debug animation errors. When compared with an industry standard tool, we find that LogoMotion produces animations that are more content-aware and are on par in terms of quality. We conclude with a discussion of the implications of LLM-generated animation for motion design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-14", "arxiv_id": "2405.06650", "title": "Large Language Models as Planning Domain Generators", "authors": [ "James Oswald", "Kavitha Srinivas", "Harsha Kokel", "Junkyu Lee", "Michael Katz", "Shirin Sohrabi" ], "github": "https://github.com/IBM/NL2PDDL", "project_page": "", "abstract": "Developing domain models is one of the few remaining places that require manual human labor in AI planning. Thus, in order to make planning more accessible, it is desirable to automate the process of domain model generation. To this end, we investigate if large language models (LLMs) can be used to generate planning domain models from simple textual descriptions. Specifically, we introduce a framework for automated evaluation of LLM-generated domains by comparing the sets of plans for domain instances. Finally, we perform an empirical analysis of 7 large language models, including coding and chat models across 9 different planning domains, and under three classes of natural language domain descriptions. Our results indicate that LLMs, particularly those with high parameter counts, exhibit a moderate level of proficiency in generating correct planning domains from natural language descriptions. Our code is available at https://github.com/IBM/NL2PDDL.", "github_manual": "", "github_regex": "https://github.com/IBM/NL2PDDL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-14", "arxiv_id": "2405.07863", "title": "RLHF Workflow: From Reward Modeling to Online RLHF", "authors": [ "Hanze Dong", "Wei Xiong", "Bo Pang", "Haoxiang Wang", "Han Zhao", "Yingbo Zhou", "Nan Jiang", "Doyen Sahoo", "Caiming Xiong", "Tong Zhang" ], "github": "https://github.com/RLHFlow/RLHF-Reward-Modeling", "project_page": "", "abstract": "We present the workflow of Online Iterative Reinforcement Learning from Human Feedback (RLHF) in this technical report, which is widely reported to outperform its offline counterpart by a large margin in the recent large language model (LLM) literature. However, existing open-source RLHF projects are still largely confined to the offline learning setting. In this technical report, we aim to fill in this gap and provide a detailed recipe that is easy to reproduce for online iterative RLHF. In particular, since online human feedback is usually infeasible for open-source communities with limited resources, we start by constructing preference models using a diverse set of open-source datasets and use the constructed proxy preference model to approximate human feedback. Then, we discuss the theoretical insights and algorithmic principles behind online iterative RLHF, followed by a detailed practical implementation. Our trained LLM, SFR-Iterative-DPO-LLaMA-3-8B-R, achieves impressive performance on LLM chatbot benchmarks, including AlpacaEval-2, Arena-Hard, and MT-Bench, as well as other academic benchmarks such as HumanEval and TruthfulQA. We have shown that supervised fine-tuning (SFT) and iterative RLHF can obtain state-of-the-art performance with fully open-source datasets. Further, we have made our models, curated datasets, and comprehensive step-by-step code guidebooks publicly available. Please refer to https://github.com/RLHFlow/RLHF-Reward-Modeling and https://github.com/RLHFlow/Online-RLHF for more detailed information.", "github_manual": "https://github.com/RLHFlow/RLHF-Reward-Modeling", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-14", "arxiv_id": "2405.06694", "title": "SUTRA: Scalable Multilingual Language Model Architecture", "authors": [ "Abhijit Bendale", "Michael Sapienza", "Steven Ripplinger", "Simon Gibbs", "Jaewon Lee", "Pranav Mistry" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce SUTRA, multilingual Large Language Model architecture capable of understanding, reasoning, and generating text in over 50 languages. SUTRA's design uniquely decouples core conceptual understanding from language-specific processing, which facilitates scalable and efficient multilingual alignment and learning. Employing a Mixture of Experts framework both in language and concept processing, SUTRA demonstrates both computational efficiency and responsiveness. Through extensive evaluations, SUTRA is demonstrated to surpass existing models like GPT-3.5, Llama2 by 20-30% on leading Massive Multitask Language Understanding (MMLU) benchmarks for multilingual tasks. SUTRA models are also online LLMs that can use knowledge from the internet to provide hallucination-free, factual and up-to-date responses while retaining their multilingual capabilities. Furthermore, we explore the broader implications of its architecture for the future of multilingual AI, highlighting its potential to democratize access to AI technology globally and to improve the equity and utility of AI in regions with predominantly non-English languages. Our findings suggest that SUTRA not only fills pivotal gaps in multilingual model capabilities but also establishes a new benchmark for operational efficiency and scalability in AI applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-14", "arxiv_id": "2405.02246", "title": "What matters when building vision-language models?", "authors": [ "Hugo Lauren\u00e7on", "L\u00e9o Tronchon", "Matthieu Cord", "Victor Sanh" ], "github": "", "project_page": "", "abstract": "The growing interest in vision-language models (VLMs) has been driven by improvements in large language models and vision transformers. Despite the abundance of literature on this subject, we observe that critical decisions regarding the design of VLMs are often not justified. We argue that these unsupported decisions impede progress in the field by making it difficult to identify which choices improve model performance. To address this issue, we conduct extensive experiments around pre-trained models, architecture choice, data, and training methods. Our consolidation of findings includes the development of Idefics2, an efficient foundational VLM of 8 billion parameters. Idefics2 achieves state-of-the-art performance within its size category across various multimodal benchmarks, and is often on par with models four times its size. We release the model (base, instructed, and chat) along with the datasets created for its training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-15", "arxiv_id": "2405.08246", "title": "Compositional Text-to-Image Generation with Dense Blob Representations", "authors": [ "Weili Nie", "Sifei Liu", "Morteza Mardani", "Chao Liu", "Benjamin Eckart", "Arash Vahdat" ], "github": "", "project_page": "", "abstract": "Existing text-to-image models struggle to follow complex text prompts, raising the need for extra grounding inputs for better controllability. In this work, we propose to decompose a scene into visual primitives - denoted as dense blob representations - that contain fine-grained details of the scene while being modular, human-interpretable, and easy-to-construct. Based on blob representations, we develop a blob-grounded text-to-image diffusion model, termed BlobGEN, for compositional generation. Particularly, we introduce a new masked cross-attention module to disentangle the fusion between blob representations and visual features. To leverage the compositionality of large language models (LLMs), we introduce a new in-context learning approach to generate blob representations from text prompts. Our extensive experiments show that BlobGEN achieves superior zero-shot generation quality and better layout-guided controllability on MS-COCO. When augmented by LLMs, our method exhibits superior numerical and spatial correctness on compositional image generation benchmarks. Project page: https://blobgen-2d.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-15", "arxiv_id": "2405.08748", "title": "Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding", "authors": [ "Zhimin Li", "Jianwei Zhang", "Qin Lin", "Jiangfeng Xiong", "Yanxin Long", "Xinchi Deng", "Yingfang Zhang", "Xingchao Liu", "Minbin Huang", "Zedong Xiao", "Dayou Chen", "Jiajun He", "Jiahao Li", "Wenyue Li", "Chen Zhang", "Rongwei Quan", "Jianxiang Lu", "Jiabin Huang", "Xiaoyan Yuan", "Xiaoxiao Zheng", "Yixuan Li", "Jihong Zhang", "Chao Zhang", "Meng Chen", "Jie Liu", "Zheng Fang", "Weiyan Wang", "Jinbao Xue", "Yangyu Tao", "Jianchen Zhu", "Kai Liu", "Sihuan Lin", "Yifu Sun", "Yun Li", "Dongdong Wang", "Mingtao Chen", "Zhichao Hu", "Xiao Xiao", "Yan Chen", "Yuhong Liu", "Wei Liu", "Di Wang", "Yong Yang", "Jie Jiang", "Qinglin Lu" ], "github": "https://github.com/Tencent/HunyuanDiT", "project_page": "", "abstract": "We present Hunyuan-DiT, a text-to-image diffusion transformer with fine-grained understanding of both English and Chinese. To construct Hunyuan-DiT, we carefully design the transformer structure, text encoder, and positional encoding. We also build from scratch a whole data pipeline to update and evaluate data for iterative model optimization. For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images. Finally, Hunyuan-DiT can perform multi-turn multimodal dialogue with users, generating and refining images according to the context. Through our holistic human evaluation protocol with more than 50 professional human evaluators, Hunyuan-DiT sets a new state-of-the-art in Chinese-to-image generation compared with other open-source models. Code and pretrained models are publicly available at github.com/Tencent/HunyuanDiT", "github_manual": "https://github.com/Tencent/HunyuanDiT", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-15", "arxiv_id": "2405.08054", "title": "Coin3D: Controllable and Interactive 3D Assets Generation with Proxy-Guided Conditioning", "authors": [ "Wenqi Dong", "Bangbang Yang", "Lin Ma", "Xiao Liu", "Liyuan Cui", "Hujun Bao", "Yuewen Ma", "Zhaopeng Cui" ], "github": "https://github.com/zju3dv/Coin3D", "project_page": "", "abstract": "As humans, we aspire to create media content that is both freely willed and readily controlled. Thanks to the prominent development of generative techniques, we now can easily utilize 2D diffusion methods to synthesize images controlled by raw sketch or designated human poses, and even progressively edit/regenerate local regions with masked inpainting. However, similar workflows in 3D modeling tasks are still unavailable due to the lack of controllability and efficiency in 3D generation. In this paper, we present a novel controllable and interactive 3D assets modeling framework, named Coin3D. Coin3D allows users to control the 3D generation using a coarse geometry proxy assembled from basic shapes, and introduces an interactive generation workflow to support seamless local part editing while delivering responsive 3D object previewing within a few seconds. To this end, we develop several techniques, including the 3D adapter that applies volumetric coarse shape control to the diffusion model, proxy-bounded editing strategy for precise part editing, progressive volume cache to support responsive preview, and volume-SDS to ensure consistent mesh reconstruction. Extensive experiments of interactive generation and editing on diverse shape proxies demonstrate that our method achieves superior controllability and flexibility in the 3D assets generation task.", "github_manual": "https://github.com/zju3dv/Coin3D", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-15", "arxiv_id": "2405.08344", "title": "No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding", "authors": [ "Yingjie Zhai", "Wenshuo Li", "Yehui Tang", "Xinghao Chen", "Yunhe Wang" ], "github": "https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/SqueezeTime", "project_page": "", "abstract": "Current architectures for video understanding mainly build upon 3D convolutional blocks or 2D convolutions with additional operations for temporal modeling. However, these methods all regard the temporal axis as a separate dimension of the video sequence, which requires large computation and memory budgets and thus limits their usage on mobile devices. In this paper, we propose to squeeze the time axis of a video sequence into the channel dimension and present a lightweight video recognition network, term as SqueezeTime, for mobile video understanding. To enhance the temporal modeling capability of the proposed network, we design a Channel-Time Learning (CTL) Block to capture temporal dynamics of the sequence. This module has two complementary branches, in which one branch is for temporal importance learning and another branch with temporal position restoring capability is to enhance inter-temporal object modeling ability. The proposed SqueezeTime is much lightweight and fast with high accuracies for mobile video understanding. Extensive experiments on various video recognition and action detection benchmarks, i.e., Kinetics400, Kinetics600, HMDB51, AVA2.1 and THUMOS14, demonstrate the superiority of our model. For example, our SqueezeTime achieves +1.2% accuracy and +80% GPU throughput gain on Kinetics400 than prior methods. Codes are publicly available at https://github.com/xinghaochen/SqueezeTime and https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/SqueezeTime.", "github_manual": "https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/SqueezeTime", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-15", "arxiv_id": "2405.08707", "title": "Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory", "authors": [ "Xueyan Niu", "Bo Bai", "Lei Deng", "Wei Han" ], "github": "https://github.com/apple/corenet", "project_page": "", "abstract": "Increasing the size of a Transformer model does not always lead to enhanced performance. This phenomenon cannot be explained by the empirical scaling laws. Furthermore, improved generalization ability occurs as the model memorizes the training samples. We present a theoretical framework that sheds light on the memorization process and performance dynamics of transformer-based language models. We model the behavior of Transformers with associative memories using Hopfield networks, such that each transformer block effectively conducts an approximate nearest-neighbor search. Based on this, we design an energy function analogous to that in the modern continuous Hopfield network which provides an insightful explanation for the attention mechanism. Using the majorization-minimization technique, we construct a global energy function that captures the layered architecture of the Transformer. Under specific conditions, we show that the minimum achievable cross-entropy loss is bounded from below by a constant approximately equal to 1. We substantiate our theoretical results by conducting experiments with GPT-2 on various data sizes, as well as training vanilla Transformers on a dataset of 2M tokens.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/corenet", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-15", "arxiv_id": "2405.08448", "title": "Understanding the performance gap between online and offline alignment algorithms", "authors": [ "Yunhao Tang", "Daniel Zhaohan Guo", "Zeyu Zheng", "Daniele Calandriello", "Yuan Cao", "Eugene Tarassov", "R\u00e9mi Munos", "Bernardo \u00c1vila Pires", "Michal Valko", "Yong Cheng", "Will Dabney" ], "github": "", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) is the canonical framework for large language model alignment. However, rising popularity in offline alignment algorithms challenge the need for on-policy sampling in RLHF. Within the context of reward over-optimization, we start with an opening set of experiments that demonstrate the clear advantage of online methods over offline methods. This prompts us to investigate the causes to the performance discrepancy through a series of carefully designed experimental ablations. We show empirically that hypotheses such as offline data coverage and data quality by itself cannot convincingly explain the performance difference. We also find that while offline algorithms train policy to become good at pairwise classification, it is worse at generations; in the meantime the policies trained by online algorithms are good at generations while worse at pairwise classification. This hints at a unique interplay between discriminative and generative capabilities, which is greatly impacted by the sampling process. Lastly, we observe that the performance discrepancy persists for both contrastive and non-contrastive loss functions, and appears not to be addressed by simply scaling up policy networks. Taken together, our study sheds light on the pivotal role of on-policy sampling in AI alignment, and hints at certain fundamental challenges of offline alignment algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-15", "arxiv_id": "2405.08317", "title": "SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large Language Models", "authors": [ "Raghuveer Peri", "Sai Muralidhar Jayanthi", "Srikanth Ronanki", "Anshu Bhatia", "Karel Mundnich", "Saket Dingliwal", "Nilaksh Das", "Zejiang Hou", "Goeric Huybrechts", "Srikanth Vishnubhotla", "Daniel Garcia-Romero", "Sundararajan Srinivasan", "Kyu J Han", "Katrin Kirchhoff" ], "github": "", "project_page": "", "abstract": "Integrated Speech and Large Language Models (SLMs) that can follow speech instructions and generate relevant text responses have gained popularity lately. However, the safety and robustness of these models remains largely unclear. In this work, we investigate the potential vulnerabilities of such instruction-following speech-language models to adversarial attacks and jailbreaking. Specifically, we design algorithms that can generate adversarial examples to jailbreak SLMs in both white-box and black-box attack settings without human involvement. Additionally, we propose countermeasures to thwart such jailbreaking attacks. Our models, trained on dialog data with speech instructions, achieve state-of-the-art performance on spoken question-answering task, scoring over 80% on both safety and helpfulness metrics. Despite safety guardrails, experiments on jailbreaking demonstrate the vulnerability of SLMs to adversarial perturbations and transfer attacks, with average attack success rates of 90% and 10% respectively when evaluated on a dataset of carefully designed harmful questions spanning 12 different toxic categories. However, we demonstrate that our proposed countermeasures reduce the attack success significantly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-15", "arxiv_id": "2405.08295", "title": "SpeechVerse: A Large-scale Generalizable Audio Language Model", "authors": [ "Nilaksh Das", "Saket Dingliwal", "Srikanth Ronanki", "Rohit Paturi", "David Huang", "Prashant Mathur", "Jie Yuan", "Dhanush Bekal", "Xing Niu", "Sai Muralidhar Jayanthi", "Xilai Li", "Karel Mundnich", "Monica Sunkara", "Sundararajan Srinivasan", "Kyu J Han", "Katrin Kirchhoff" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have shown incredible proficiency in performing tasks that require semantic understanding of natural language instructions. Recently, many works have further expanded this capability to perceive multimodal audio and text inputs, but their capabilities are often limited to specific fine-tuned tasks such as automatic speech recognition and translation. We therefore develop SpeechVerse, a robust multi-task training and curriculum learning framework that combines pre-trained speech and text foundation models via a small set of learnable parameters, while keeping the pre-trained models frozen during training. The models are instruction finetuned using continuous latent representations extracted from the speech foundation model to achieve optimal zero-shot performance on a diverse range of speech processing tasks using natural language instructions. We perform extensive benchmarking that includes comparing our model performance against traditional baselines across several datasets and tasks. Furthermore, we evaluate the model's capability for generalized instruction following by testing on out-of-domain datasets, novel prompts, and unseen tasks. Our empirical experiments reveal that our multi-task SpeechVerse model is even superior to conventional task-specific baselines on 9 out of the 11 tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-16", "arxiv_id": "2405.09215", "title": "Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model", "authors": [ "Wanting Xu", "Yang Liu", "Langping He", "Xucheng Huang", "Ling Jiang" ], "github": "https://github.com/XiaoduoAILab/XmodelVLM", "project_page": "", "abstract": "We introduce Xmodel-VLM, a cutting-edge multimodal vision language model. It is designed for efficient deployment on consumer GPU servers. Our work directly confronts a pivotal industry issue by grappling with the prohibitive service costs that hinder the broad adoption of large-scale multimodal systems. Through rigorous training, we have developed a 1B-scale language model from the ground up, employing the LLaVA paradigm for modal alignment. The result, which we call Xmodel-VLM, is a lightweight yet powerful multimodal vision language model. Extensive testing across numerous classic multimodal benchmarks has revealed that despite its smaller size and faster execution, Xmodel-VLM delivers performance comparable to that of larger models. Our model checkpoints and code are publicly available on GitHub at https://github.com/XiaoduoAILab/XmodelVLM.", "github_manual": "", "github_regex": "https://github.com/XiaoduoAILab/XmodelVLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-16", "arxiv_id": "2405.09220", "title": "ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models", "authors": [ "Siwei Wang", "Yifei Shen", "Shi Feng", "Haoran Sun", "Shang-Hua Teng", "Wei Chen" ], "github": "", "project_page": "", "abstract": "In this paper, we present the findings of our Project ALPINE which stands for ``Autoregressive Learning for Planning In NEtworks.\" Project ALPINE initiates a theoretical investigation into the development of planning capabilities in Transformer-based language models through their autoregressive learning mechanisms, aiming to identify any potential limitations in their planning abilities. We abstract planning as a network path-finding task where the objective is to generate a valid path from a specified source node to a designated target node. In terms of expressiveness, we show that the Transformer is capable of executing path-finding by embedding the adjacency and reachability matrices within its weights. Our theoretical analysis of the gradient-based learning dynamic of the Transformer reveals that the Transformer is capable of learning both the adjacency matrix and a limited form of the reachability matrix. These theoretical insights are then validated through experiments, which demonstrate that the Transformer indeed learns the adjacency matrix and an incomplete reachability matrix, which aligns with the predictions made in our theoretical analysis. Additionally, when applying our methodology to a real-world planning benchmark, called Blocksworld, our observations remain consistent. Our theoretical and empirical analyses further unveil a potential limitation of Transformer in path-finding: it cannot identify reachability relationships through transitivity, and thus would fail when path concatenation is needed to generate a path. In summary, our findings shed new light on how the internal mechanisms of autoregressive learning enable planning in networks. This study may contribute to our understanding of the general planning capabilities in other related domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-16", "arxiv_id": "2405.09546", "title": "BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation", "authors": [ "Yunhao Ge", "Yihe Tang", "Jiashu Xu", "Cem Gokmen", "Chengshu Li", "Wensi Ai", "Benjamin Jose Martinez", "Arman Aydin", "Mona Anvari", "Ayush K Chakravarthy", "Hong-Xing Yu", "Josiah Wong", "Sanjana Srivastava", "Sharon Lee", "Shengxin Zha", "Laurent Itti", "Yunzhu Li", "Roberto Mart\u00edn-Mart\u00edn", "Miao Liu", "Pengchuan Zhang", "Ruohan Zhang", "Li Fei-Fei", "Jiajun Wu" ], "github": "https://github.com/behavior-vision-suite/behavior-vision-suite.github.io", "project_page": "", "abstract": "The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels, which real-world vision datasets rarely satisfy. While current synthetic data generators offer a promising alternative, particularly for embodied AI tasks, they often fall short for computer vision tasks due to low asset and rendering quality, limited diversity, and unrealistic physical properties. We introduce the BEHAVIOR Vision Suite (BVS), a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models, based on the newly developed embodied AI benchmark, BEHAVIOR-1K. BVS supports a large number of adjustable parameters at the scene level (e.g., lighting, object placement), the object level (e.g., joint configuration, attributes such as \"filled\" and \"folded\"), and the camera level (e.g., field of view, focal length). Researchers can arbitrarily vary these parameters during data generation to perform controlled experiments. We showcase three example application scenarios: systematically evaluating the robustness of models across different continuous axes of domain shift, evaluating scene understanding models on the same set of images, and training and evaluating simulation-to-real transfer for a novel vision task: unary and binary state prediction. Project website: https://behavior-vision-suite.github.io/", "github_manual": "https://github.com/behavior-vision-suite/behavior-vision-suite.github.io", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-16", "arxiv_id": "2405.09062", "title": "Naturalistic Music Decoding from EEG Data via Latent Diffusion Models", "authors": [ "Emilian Postolache", "Natalia Polouliakh", "Hiroaki Kitano", "Akima Connelly", "Emanuele Rodol\u00e0", "Taketo Akama" ], "github": "", "project_page": "", "abstract": "In this article, we explore the potential of using latent diffusion models, a family of powerful generative models, for the task of reconstructing naturalistic music from electroencephalogram (EEG) recordings. Unlike simpler music with limited timbres, such as MIDI-generated tunes or monophonic pieces, the focus here is on intricate music featuring a diverse array of instruments, voices, and effects, rich in harmonics and timbre. This study represents an initial foray into achieving general music reconstruction of high-quality using non-invasive EEG data, employing an end-to-end training approach directly on raw data without the need for manual pre-processing and channel selection. We train our models on the public NMED-T dataset and perform quantitative evaluation proposing neural embedding-based metrics. We additionally perform song classification based on the generated tracks. Our work contributes to the ongoing research in neural decoding and brain-computer interfaces, offering insights into the feasibility of using EEG data for complex auditory information reconstruction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-17", "arxiv_id": "2405.09818", "title": "Chameleon: Mixed-Modal Early-Fusion Foundation Models", "authors": [ "Chameleon Team" ], "github": "https://github.com/facebookresearch/chameleon", "project_page": "", "abstract": "We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation. Chameleon demonstrates broad and general capabilities, including state-of-the-art performance in image captioning tasks, outperforms Llama-2 in text-only tasks while being competitive with models such as Mixtral 8x7B and Gemini-Pro, and performs non-trivial image generation, all in a single model. It also matches or exceeds the performance of much larger models, including Gemini Pro and GPT-4V, according to human judgments on a new long-form mixed-modal generation evaluation, where either the prompt or outputs contain mixed sequences of both images and text. Chameleon marks a significant step forward in a unified modeling of full multimodal documents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/chameleon", "github_api_added_by": "auto" }, { "date": "2024-05-17", "arxiv_id": "2405.09673", "title": "LoRA Learns Less and Forgets Less", "authors": [ "Dan Biderman", "Jose Gonzalez Ortiz", "Jacob Portes", "Mansheej Paul", "Philip Greengard", "Connor Jennings", "Daniel King", "Sam Havens", "Vitaliy Chiley", "Jonathan Frankle", "Cody Blakeney", "John P. Cunningham" ], "github": "https://github.com/danbider/lora-tradeoffs", "project_page": "", "abstract": "Low-Rank Adaptation (LoRA) is a widely-used parameter-efficient finetuning method for large language models. LoRA saves memory by training only low rank perturbations to selected weight matrices. In this work, we compare the performance of LoRA and full finetuning on two target domains, programming and mathematics. We consider both the instruction finetuning (approx100K prompt-response pairs) and continued pretraining (approx10B unstructured tokens) data regimes. Our results show that, in most settings, LoRA substantially underperforms full finetuning. Nevertheless, LoRA exhibits a desirable form of regularization: it better maintains the base model's performance on tasks outside the target domain. We show that LoRA provides stronger regularization compared to common techniques such as weight decay and dropout; it also helps maintain more diverse generations. We show that full finetuning learns perturbations with a rank that is 10-100X greater than typical LoRA configurations, possibly explaining some of the reported gaps. We conclude by proposing best practices for finetuning with LoRA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/danbider/lora-tradeoffs", "github_api_added_by": "auto" }, { "date": "2024-05-17", "arxiv_id": "2405.10314", "title": "CAT3D: Create Anything in 3D with Multi-View Diffusion Models", "authors": [ "Ruiqi Gao", "Aleksander Holynski", "Philipp Henzler", "Arthur Brussee", "Ricardo Martin-Brualla", "Pratul Srinivasan", "Jonathan T. Barron", "Ben Poole" ], "github": "", "project_page": "", "abstract": "Advances in 3D reconstruction have enabled high-quality 3D capture, but require a user to collect hundreds to thousands of images to create a 3D scene. We present CAT3D, a method for creating anything in 3D by simulating this real-world capture process with a multi-view diffusion model. Given any number of input images and a set of target novel viewpoints, our model generates highly consistent novel views of a scene. These generated views can be used as input to robust 3D reconstruction techniques to produce 3D representations that can be rendered from any viewpoint in real-time. CAT3D can create entire 3D scenes in as little as one minute, and outperforms existing methods for single image and few-view 3D scene creation. See our project page for results and interactive demos at https://cat3d.github.io .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-17", "arxiv_id": "2405.09798", "title": "Many-Shot In-Context Learning in Multimodal Foundation Models", "authors": [ "Yixing Jiang", "Jeremy Irvin", "Ji Hun Wang", "Muhammad Ahmed Chaudhry", "Jonathan H. Chen", "Andrew Y. Ng" ], "github": "https://github.com/stanfordmlgroup/ManyICL", "project_page": "", "abstract": "Large language models are well-known to be effective at few-shot in-context learning (ICL). Recent advancements in multimodal foundation models have enabled unprecedentedly long context windows, presenting an opportunity to explore their capability to perform ICL with many more demonstrating examples. In this work, we evaluate the performance of multimodal foundation models scaling from few-shot to many-shot ICL. We benchmark GPT-4o and Gemini 1.5 Pro across 10 datasets spanning multiple domains (natural imagery, medical imagery, remote sensing, and molecular imagery) and tasks (multi-class, multi-label, and fine-grained classification). We observe that many-shot ICL, including up to almost 2,000 multimodal demonstrating examples, leads to substantial improvements compared to few-shot (<100 examples) ICL across all of the datasets. Further, Gemini 1.5 Pro performance continues to improve log-linearly up to the maximum number of tested examples on many datasets. Given the high inference costs associated with the long prompts required for many-shot ICL, we also explore the impact of batching multiple queries in a single API call. We show that batching up to 50 queries can lead to performance improvements under zero-shot and many-shot ICL, with substantial gains in the zero-shot setting on multiple datasets, while drastically reducing per-query cost and latency. Finally, we measure ICL data efficiency of the models, or the rate at which the models learn from more demonstrating examples. We find that while GPT-4o and Gemini 1.5 Pro achieve similar zero-shot performance across the datasets, Gemini 1.5 Pro exhibits higher ICL data efficiency than GPT-4o on most datasets. Our results suggest that many-shot ICL could enable users to efficiently adapt multimodal foundation models to new applications and domains. Our codebase is publicly available at https://github.com/stanfordmlgroup/ManyICL .", "github_manual": "", "github_regex": "https://github.com/stanfordmlgroup/ManyICL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-17", "arxiv_id": "2405.09874", "title": "Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode Multi-view Latent Diffusion", "authors": [ "Xinyang Li", "Zhangyu Lai", "Linning Xu", "Jianfei Guo", "Liujuan Cao", "Shengchuan Zhang", "Bo Dai", "Rongrong Ji" ], "github": "https://github.com/LAION-AI/aesthetic-predictor", "project_page": "", "abstract": "We present Dual3D, a novel text-to-3D generation framework that generates high-quality 3D assets from texts in only 1 minute.The key component is a dual-mode multi-view latent diffusion model. Given the noisy multi-view latents, the 2D mode can efficiently denoise them with a single latent denoising network, while the 3D mode can generate a tri-plane neural surface for consistent rendering-based denoising. Most modules for both modes are tuned from a pre-trained text-to-image latent diffusion model to circumvent the expensive cost of training from scratch. To overcome the high rendering cost during inference, we propose the dual-mode toggling inference strategy to use only 1/10 denoising steps with 3D mode, successfully generating a 3D asset in just 10 seconds without sacrificing quality. The texture of the 3D asset can be further enhanced by our efficient texture refinement process in a short time. Extensive experiments demonstrate that our method delivers state-of-the-art performance while significantly reducing generation time. Our project page is available at https://dual3d.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LAION-AI/aesthetic-predictor", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-17", "arxiv_id": "2405.10320", "title": "Toon3D: Seeing Cartoons from a New Perspective", "authors": [ "Ethan Weber", "Riley Peterlinz", "Rohan Mathur", "Frederik Warburg", "Alexei A. Efros", "Angjoo Kanazawa" ], "github": "", "project_page": "", "abstract": "In this work, we recover the underlying 3D structure of non-geometrically consistent scenes. We focus our analysis on hand-drawn images from cartoons and anime. Many cartoons are created by artists without a 3D rendering engine, which means that any new image of a scene is hand-drawn. The hand-drawn images are usually faithful representations of the world, but only in a qualitative sense, since it is difficult for humans to draw multiple perspectives of an object or scene 3D consistently. Nevertheless, people can easily perceive 3D scenes from inconsistent inputs! In this work, we correct for 2D drawing inconsistencies to recover a plausible 3D structure such that the newly warped drawings are consistent with each other. Our pipeline consists of a user-friendly annotation tool, camera pose estimation, and image deformation to recover a dense structure. Our method warps images to obey a perspective camera model, enabling our aligned results to be plugged into novel-view synthesis reconstruction methods to experience cartoons from viewpoints never drawn before. Our project page is https://toon3d.studio/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-17", "arxiv_id": "2405.10315", "title": "TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction", "authors": [ "Yunfan Jiang", "Chen Wang", "Ruohan Zhang", "Jiajun Wu", "Li Fei-Fei" ], "github": "https://github.com/transic-robot/transic", "project_page": "", "abstract": "Learning in simulation and transferring the learned policy to the real world has the potential to enable generalist robots. The key challenge of this approach is to address simulation-to-reality (sim-to-real) gaps. Previous methods often require domain-specific knowledge a priori. We argue that a straightforward way to obtain such knowledge is by asking humans to observe and assist robot policy execution in the real world. The robots can then learn from humans to close various sim-to-real gaps. We propose TRANSIC, a data-driven approach to enable successful sim-to-real transfer based on a human-in-the-loop framework. TRANSIC allows humans to augment simulation policies to overcome various unmodeled sim-to-real gaps holistically through intervention and online correction. Residual policies can be learned from human corrections and integrated with simulation policies for autonomous execution. We show that our approach can achieve successful sim-to-real transfer in complex and contact-rich manipulation tasks such as furniture assembly. Through synergistic integration of policies learned in simulation and from humans, TRANSIC is effective as a holistic approach to addressing various, often coexisting sim-to-real gaps. It displays attractive properties such as scaling with human effort. Videos and code are available at https://transic-robot.github.io/", "github_manual": "https://github.com/transic-robot/transic", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-17", "arxiv_id": "2405.10300", "title": "Grounding DINO 1.5: Advance the \"Edge\" of Open-Set Object Detection", "authors": [ "Tianhe Ren", "Qing Jiang", "Shilong Liu", "Zhaoyang Zeng", "Wenlong Liu", "Han Gao", "Hongjie Huang", "Zhengyu Ma", "Xiaoke Jiang", "Yihao Chen", "Yuda Xiong", "Hao Zhang", "Feng Li", "Peijun Tang", "Kent Yu", "Lei Zhang" ], "github": "https://github.com/IDEA-Research/Grounding-DINO-1.5-API", "project_page": "", "abstract": "This paper introduces Grounding DINO 1.5, a suite of advanced open-set object detection models developed by IDEA Research, which aims to advance the \"Edge\" of open-set object detection. The suite encompasses two models: Grounding DINO 1.5 Pro, a high-performance model designed for stronger generalization capability across a wide range of scenarios, and Grounding DINO 1.5 Edge, an efficient model optimized for faster speed demanded in many applications requiring edge deployment. The Grounding DINO 1.5 Pro model advances its predecessor by scaling up the model architecture, integrating an enhanced vision backbone, and expanding the training dataset to over 20 million images with grounding annotations, thereby achieving a richer semantic understanding. The Grounding DINO 1.5 Edge model, while designed for efficiency with reduced feature scales, maintains robust detection capabilities by being trained on the same comprehensive dataset. Empirical results demonstrate the effectiveness of Grounding DINO 1.5, with the Grounding DINO 1.5 Pro model attaining a 54.3 AP on the COCO detection benchmark and a 55.7 AP on the LVIS-minival zero-shot transfer benchmark, setting new records for open-set object detection. Furthermore, the Grounding DINO 1.5 Edge model, when optimized with TensorRT, achieves a speed of 75.2 FPS while attaining a zero-shot performance of 36.2 AP on the LVIS-minival benchmark, making it more suitable for edge computing scenarios. Model examples and demos with API will be released at https://github.com/IDEA-Research/Grounding-DINO-1.5-API", "github_manual": "", "github_regex": "https://github.com/IDEA-Research/Grounding-DINO-1.5-API", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-20", "arxiv_id": "2405.10938", "title": "Observational Scaling Laws and the Predictability of Language Model Performance", "authors": [ "Yangjun Ruan", "Chris J. Maddison", "Tatsunori Hashimoto" ], "github": "https://github.com/ryoungj/obsscaling", "project_page": "", "abstract": "Understanding how language model performance varies with scale is critical to benchmark and algorithm development. Scaling laws are one approach to building this understanding, but the requirement of training models across many different scales has limited their use. We propose an alternative, observational approach that bypasses model training and instead builds scaling laws from ~80 publically available models. Building a single scaling law from multiple model families is challenging due to large variations in their training compute efficiencies and capabilities. However, we show that these variations are consistent with a simple, generalized scaling law where language model performance is a function of a low-dimensional capability space, and model families only vary in their efficiency in converting training compute to capabilities. Using this approach, we show the surprising predictability of complex scaling phenomena: we show that several emergent phenomena follow a smooth, sigmoidal behavior and are predictable from small models; we show that the agent performance of models such as GPT-4 can be precisely predicted from simpler non-agentic benchmarks; and we show how to predict the impact of post-training interventions like Chain-of-Thought and Self-Consistency as language model capabilities continue to improve.", "github_manual": "https://github.com/ryoungj/obsscaling", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-20", "arxiv_id": "2405.10725", "title": "INDUS: Effective and Efficient Language Models for Scientific Applications", "authors": [ "Bishwaranjan Bhattacharjee", "Aashka Trivedi", "Masayasu Muraoka", "Muthukumaran Ramasubramanian", "Takuma Udagawa", "Iksha Gurung", "Rong Zhang", "Bharath Dandala", "Rahul Ramachandran", "Manil Maskey", "Kayleen Bugbee", "Mike Little", "Elizabeth Fancher", "Lauren Sanders", "Sylvain Costes", "Sergi Blanco-Cuaresma", "Kelly Lockhart", "Thomas Allen", "Felix Grazes", "Megan Ansdel", "Alberto Accomazzi", "Yousef El-Kurdi", "Davis Wertheimer", "Birgit Pfitzmann", "Cesar Berrospi Ramis", "Michele Dolfi", "Rafael Teixeira de Lima", "Panos Vegenas", "S. Karthik Mukkavilli", "Peter Staar", "Sanaz Vahidinia", "Ryan McGranaghan", "Armin Mehrabian", "Tsendgar Lee" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs trained using domain-focused corpora perform better on specialized tasks. Inspired by this pivotal insight, we developed INDUS, a comprehensive suite of LLMs tailored for the Earth science, biology, physics, heliophysics, planetary sciences and astrophysics domains and trained using curated scientific corpora drawn from diverse data sources. The suite of models include: (1) an encoder model trained using domain-specific vocabulary and corpora to address natural language understanding tasks, (2) a contrastive-learning-based general text embedding model trained using a diverse set of datasets drawn from multiple sources to address information retrieval tasks and (3) smaller versions of these models created using knowledge distillation techniques to address applications which have latency or resource constraints. We also created three new scientific benchmark datasets namely, CLIMATE-CHANGE-NER (entity-recognition), NASA-QA (extractive QA) and NASA-IR (IR) to accelerate research in these multi-disciplinary fields. Finally, we show that our models outperform both general-purpose encoders (RoBERTa) and existing domain-specific encoders (SciBERT) on these new tasks as well as existing benchmark tasks in the domains of interest.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-20", "arxiv_id": "2405.10370", "title": "Grounded 3D-LLM with Referent Tokens", "authors": [ "Yilun Chen", "Shuai Yang", "Haifeng Huang", "Tai Wang", "Ruiyuan Lyu", "Runsen Xu", "Dahua Lin", "Jiangmiao Pang" ], "github": "https://github.com/groundedSceneLLM/grounded_3d-llm", "project_page": "", "abstract": "Prior studies on 3D scene understanding have primarily developed specialized models for specific tasks or required task-specific fine-tuning. In this study, we propose Grounded 3D-LLM, which explores the potential of 3D large multi-modal models (3D LMMs) to consolidate various 3D vision tasks within a unified generative framework. The model uses scene referent tokens as special noun phrases to reference 3D scenes, enabling the handling of sequences that interleave 3D and textual data. It offers a natural approach for translating 3D vision tasks into language formats using task-specific instruction templates. To facilitate the use of referent tokens in subsequent language modeling, we have curated large-scale grounded language datasets that offer finer scene-text correspondence at the phrase level by bootstrapping existing object labels. Subsequently, we introduced Contrastive LAnguage-Scene Pre-training (CLASP) to effectively leverage this data, thereby integrating 3D vision with language models. Our comprehensive evaluation covers open-ended tasks like dense captioning and 3D QA, alongside close-ended tasks such as object detection and language grounding. Experiments across multiple 3D benchmarks reveal the leading performance and the broad applicability of Grounded 3D-LLM. Code and datasets will be released on the project page: https://groundedscenellm.github.io/grounded_3d-llm.github.io.", "github_manual": "https://github.com/groundedSceneLLM/grounded_3d-llm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-20", "arxiv_id": "2405.10637", "title": "Layer-Condensed KV Cache for Efficient Inference of Large Language Models", "authors": [ "Haoyi Wu", "Kewei Tu" ], "github": "https://github.com/whyNLP/LCKV", "project_page": "", "abstract": "Huge memory consumption has been a major bottleneck for deploying high-throughput large language models in real-world applications. In addition to the large number of parameters, the key-value (KV) cache for the attention mechanism in the transformer architecture consumes a significant amount of memory, especially when the number of layers is large for deep language models. In this paper, we propose a novel method that only computes and caches the KVs of a small number of layers, thus significantly saving memory consumption and improving inference throughput. Our experiments on large language models show that our method achieves up to 26times higher throughput than standard transformers and competitive performance in language modeling and downstream tasks. In addition, our method is orthogonal to existing transformer memory-saving techniques, so it is straightforward to integrate them with our model, achieving further improvement in inference efficiency. Our code is available at https://github.com/whyNLP/LCKV.", "github_manual": "", "github_regex": "https://github.com/whyNLP/LCKV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-20", "arxiv_id": "2405.10626", "title": "Dynamic data sampler for cross-language transfer learning in large language models", "authors": [ "Yudong Li", "Yuhao Feng", "Wen Zhou", "Zhe Zhao", "Linlin Shen", "Cheng Hou", "Xianxu Hou" ], "github": "https://github.com/cvi-szu/linly", "project_page": "", "abstract": "Large Language Models (LLMs) have gained significant attention in the field of natural language processing (NLP) due to their wide range of applications. However, training LLMs for languages other than English poses significant challenges, due to the difficulty in acquiring large-scale corpus and the requisite computing resources. In this paper, we propose ChatFlow, a cross-language transfer-based LLM, to address these challenges and train large Chinese language models in a cost-effective manner. We employ a mix of Chinese, English, and parallel corpus to continuously train the LLaMA2 model, aiming to align cross-language representations and facilitate the knowledge transfer specifically to the Chinese language model. In addition, we use a dynamic data sampler to progressively transition the model from unsupervised pre-training to supervised fine-tuning. Experimental results demonstrate that our approach accelerates model convergence and achieves superior performance. We evaluate ChatFlow on popular Chinese and English benchmarks, the results indicate that it outperforms other Chinese models post-trained on LLaMA-2-7B.", "github_manual": "https://github.com/cvi-szu/linly", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-21", "arxiv_id": "2405.11143", "title": "OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework", "authors": [ "Jian Hu", "Xibin Wu", "Weixun Wang", "Xianyu", "Dehao Zhang", "Yu Cao" ], "github": "https://github.com/OpenLLMAI/OpenRLHF", "project_page": "", "abstract": "As large language models (LLMs) continue to grow by scaling laws, reinforcement learning from human feedback (RLHF) has gained significant attention due to its outstanding performance. However, unlike pretraining or fine-tuning a single model, scaling reinforcement learning from human feedback (RLHF) for training large language models poses coordination challenges across four models. We present OpenRLHF, an open-source framework enabling efficient RLHF scaling. Unlike existing RLHF frameworks that co-locate four models on the same GPUs, OpenRLHF re-designs scheduling for the models beyond 70B parameters using Ray, vLLM, and DeepSpeed, leveraging improved resource utilization and diverse training approaches. Integrating seamlessly with Hugging Face, OpenRLHF provides an out-of-the-box solution with optimized algorithms and launch scripts, which ensures user-friendliness. OpenRLHF implements RLHF, DPO, rejection sampling, and other alignment techniques. Empowering state-of-the-art LLM development, OpenRLHF's code is available at https://github.com/OpenLLMAI/OpenRLHF.", "github_manual": "", "github_regex": "https://github.com/OpenLLMAI/OpenRLHF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-21", "arxiv_id": "2405.11252", "title": "Dreamer XL: Towards High-Resolution Text-to-3D Generation via Trajectory Score Matching", "authors": [ "Xingyu Miao", "Haoran Duan", "Varun Ojha", "Jun Song", "Tejal Shah", "Yang Long", "Rajiv Ranjan" ], "github": "https://github.com/xingy038/Dreamer-XL", "project_page": "", "abstract": "In this work, we propose a novel Trajectory Score Matching (TSM) method that aims to solve the pseudo ground truth inconsistency problem caused by the accumulated error in Interval Score Matching (ISM) when using the Denoising Diffusion Implicit Models (DDIM) inversion process. Unlike ISM which adopts the inversion process of DDIM to calculate on a single path, our TSM method leverages the inversion process of DDIM to generate two paths from the same starting point for calculation. Since both paths start from the same starting point, TSM can reduce the accumulated error compared to ISM, thus alleviating the problem of pseudo ground truth inconsistency. TSM enhances the stability and consistency of the model's generated paths during the distillation process. We demonstrate this experimentally and further show that ISM is a special case of TSM. Furthermore, to optimize the current multi-stage optimization process from high-resolution text to 3D generation, we adopt Stable Diffusion XL for guidance. In response to the issues of abnormal replication and splitting caused by unstable gradients during the 3D Gaussian splatting process when using Stable Diffusion XL, we propose a pixel-by-pixel gradient clipping method. Extensive experiments show that our model significantly surpasses the state-of-the-art models in terms of visual quality and performance. Code: https://github.com/xingy038/Dreamer-XL.", "github_manual": "", "github_regex": "https://github.com/xingy038/Dreamer-XL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-21", "arxiv_id": "2405.11473", "title": "FIFO-Diffusion: Generating Infinite Videos from Text without Training", "authors": [ "Jihwan Kim", "Junoh Kang", "Jinyoung Choi", "Bohyung Han" ], "github": "https://github.com/jjihwan/FIFO-Diffusion_public", "project_page": "", "abstract": "We propose a novel inference technique based on a pretrained diffusion model for text-conditional video generation. Our approach, called FIFO-Diffusion, is conceptually capable of generating infinitely long videos without training. This is achieved by iteratively performing diagonal denoising, which concurrently processes a series of consecutive frames with increasing noise levels in a queue; our method dequeues a fully denoised frame at the head while enqueuing a new random noise frame at the tail. However, diagonal denoising is a double-edged sword as the frames near the tail can take advantage of cleaner ones by forward reference but such a strategy induces the discrepancy between training and inference. Hence, we introduce latent partitioning to reduce the training-inference gap and lookahead denoising to leverage the benefit of forward referencing. We have demonstrated the promising results and effectiveness of the proposed methods on existing text-to-video generation baselines.", "github_manual": "https://github.com/jjihwan/FIFO-Diffusion_public", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-21", "arxiv_id": "2405.12213", "title": "Octo: An Open-Source Generalist Robot Policy", "authors": [ "Octo Model Team", "Dibya Ghosh", "Homer Walke", "Karl Pertsch", "Kevin Black", "Oier Mees", "Sudeep Dasari", "Joey Hejna", "Tobias Kreiman", "Charles Xu", "Jianlan Luo", "You Liang Tan", "Pannag Sanketi", "Quan Vuong", "Ted Xiao", "Dorsa Sadigh", "Chelsea Finn", "Sergey Levine" ], "github": "https://github.com/octo-models/octo", "project_page": "", "abstract": "Large policies pretrained on diverse robot datasets have the potential to transform robotic learning: instead of training new policies from scratch, such generalist robot policies may be finetuned with only a little in-domain data, yet generalize broadly. However, to be widely applicable across a range of robotic learning scenarios, environments, and tasks, such policies need to handle diverse sensors and action spaces, accommodate a variety of commonly used robotic platforms, and finetune readily and efficiently to new domains. In this work, we aim to lay the groundwork for developing open-source, widely applicable, generalist policies for robotic manipulation. As a first step, we introduce Octo, a large transformer-based policy trained on 800k trajectories from the Open X-Embodiment dataset, the largest robot manipulation dataset to date. It can be instructed via language commands or goal images and can be effectively finetuned to robot setups with new sensory inputs and action spaces within a few hours on standard consumer GPUs. In experiments across 9 robotic platforms, we demonstrate that Octo serves as a versatile policy initialization that can be effectively finetuned to new observation and action spaces. We also perform detailed ablations of design decisions for the Octo model, from architecture to training data, to guide future research on building generalist robot models.", "github_manual": "https://github.com/octo-models/octo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-21", "arxiv_id": "2405.12130", "title": "MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning", "authors": [ "Ting Jiang", "Shaohan Huang", "Shengyue Luo", "Zihan Zhang", "Haizhen Huang", "Furu Wei", "Weiwei Deng", "Feng Sun", "Qi Zhang", "Deqing Wang", "Fuzhen Zhuang" ], "github": "https://github.com/kongds/MoRA", "project_page": "", "abstract": "Low-rank adaptation is a popular parameter-efficient fine-tuning method for large language models. In this paper, we analyze the impact of low-rank updating, as implemented in LoRA. Our findings suggest that the low-rank updating mechanism may limit the ability of LLMs to effectively learn and memorize new knowledge. Inspired by this observation, we propose a new method called MoRA, which employs a square matrix to achieve high-rank updating while maintaining the same number of trainable parameters. To achieve it, we introduce the corresponding non-parameter operators to reduce the input dimension and increase the output dimension for the square matrix. Furthermore, these operators ensure that the weight can be merged back into LLMs, which makes our method can be deployed like LoRA. We perform a comprehensive evaluation of our method across five tasks: instruction tuning, mathematical reasoning, continual pretraining, memory and pretraining. Our method outperforms LoRA on memory-intensive tasks and achieves comparable performance on other tasks.", "github_manual": "https://github.com/kongds/MoRA", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-21", "arxiv_id": "2405.12107", "title": "Imp: Highly Capable Large Multimodal Models for Mobile Devices", "authors": [ "Zhenwei Shao", "Zhou Yu", "Jun Yu", "Xuecheng Ouyang", "Lihao Zheng", "Zhenbiao Gai", "Mingyang Wang", "Jiajun Ding" ], "github": "https://github.com/MILVLG/imp", "project_page": "", "abstract": "By harnessing the capabilities of large language models (LLMs), recent large multimodal models (LMMs) have shown remarkable versatility in open-world multimodal understanding. Nevertheless, they are usually parameter-heavy and computation-intensive, thus hindering their applicability in resource-constrained scenarios. To this end, several lightweight LMMs have been proposed successively to maximize the capabilities under constrained scale (e.g., 3B). Despite the encouraging results achieved by these methods, most of them only focus on one or two aspects of the design space, and the key design choices that influence model capability have not yet been thoroughly investigated. In this paper, we conduct a systematic study for lightweight LMMs from the aspects of model architecture, training strategy, and training data. Based on our findings, we obtain Imp -- a family of highly capable LMMs at the 2B-4B scales. Notably, our Imp-3B model steadily outperforms all the existing lightweight LMMs of similar size, and even surpasses the state-of-the-art LMMs at the 13B scale. With low-bit quantization and resolution reduction techniques, our Imp model can be deployed on a Qualcomm Snapdragon 8Gen3 mobile chip with a high inference speed of about 13 tokens/s.", "github_manual": "https://github.com/MILVLG/imp", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-21", "arxiv_id": "2405.11157", "title": "Towards Modular LLMs by Building and Reusing a Library of LoRAs", "authors": [ "Oleksiy Ostapenko", "Zhan Su", "Edoardo Maria Ponti", "Laurent Charlin", "Nicolas Le Roux", "Matheus Pereira", "Lucas Caccia", "Alessandro Sordoni" ], "github": "", "project_page": "", "abstract": "The growing number of parameter-efficient adaptations of a base large language model (LLM) calls for studying whether we can reuse such trained adapters to improve performance for new tasks. We study how to best build a library of adapters given multi-task data and devise techniques for both zero-shot and supervised task generalization through routing in such library. We benchmark existing approaches to build this library and introduce model-based clustering, MBC, a method that groups tasks based on the similarity of their adapter parameters, indirectly optimizing for transfer across the multi-task dataset. To re-use the library, we present a novel zero-shot routing mechanism, Arrow, which enables dynamic selection of the most relevant adapters for new inputs without the need for retraining. We experiment with several LLMs, such as Phi-2 and Mistral, on a wide array of held-out tasks, verifying that MBC-based adapters and Arrow routing lead to superior generalization to new tasks. We make steps towards creating modular, adaptable LLMs that can match or outperform traditional joint training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-21", "arxiv_id": "2405.11582", "title": "SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization", "authors": [ "Jialong Guo", "Xinghao Chen", "Yehui Tang", "Yunhe Wang" ], "github": "https://github.com/xinghaochen/SLAB", "project_page": "", "abstract": "Transformers have become foundational architectures for both natural language and computer vision tasks. However, the high computational cost makes it quite challenging to deploy on resource-constraint devices. This paper investigates the computational bottleneck modules of efficient transformer, i.e., normalization layers and attention modules. LayerNorm is commonly used in transformer architectures but is not computational friendly due to statistic calculation during inference. However, replacing LayerNorm with more efficient BatchNorm in transformer often leads to inferior performance and collapse in training. To address this problem, we propose a novel method named PRepBN to progressively replace LayerNorm with re-parameterized BatchNorm in training. Moreover, we propose a simplified linear attention (SLA) module that is simple yet effective to achieve strong performance. Extensive experiments on image classification as well as object detection demonstrate the effectiveness of our proposed method. For example, our SLAB-Swin obtains 83.6% top-1 accuracy on ImageNet-1K with 16.2ms latency, which is 2.4ms less than that of Flatten-Swin with 0.1% higher accuracy. We also evaluated our method for language modeling task and obtain comparable performance and lower latency.Codes are publicly available at https://github.com/xinghaochen/SLAB and https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/SLAB.", "github_manual": "https://github.com/xinghaochen/SLAB", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-22", "arxiv_id": "2405.12978", "title": "Personalized Residuals for Concept-Driven Text-to-Image Generation", "authors": [ "Cusuh Ham", "Matthew Fisher", "James Hays", "Nicholas Kolkin", "Yuchen Liu", "Richard Zhang", "Tobias Hinz" ], "github": "https://github.com/XavierXiao/Dreambooth-Stable-Diffusion", "project_page": "", "abstract": "We present personalized residuals and localized attention-guided sampling for efficient concept-driven generation using text-to-image diffusion models. Our method first represents concepts by freezing the weights of a pretrained text-conditioned diffusion model and learning low-rank residuals for a small subset of the model's layers. The residual-based approach then directly enables application of our proposed sampling technique, which applies the learned residuals only in areas where the concept is localized via cross-attention and applies the original diffusion weights in all other regions. Localized sampling therefore combines the learned identity of the concept with the existing generative prior of the underlying diffusion model. We show that personalized residuals effectively capture the identity of a concept in ~3 minutes on a single GPU without the use of regularization images and with fewer parameters than previous models, and localized sampling allows using the original model as strong prior for large parts of the image.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/XavierXiao/Dreambooth-Stable-Diffusion", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-22", "arxiv_id": "2405.12399", "title": "Diffusion for World Modeling: Visual Details Matter in Atari", "authors": [ "Eloi Alonso", "Adam Jelley", "Vincent Micheli", "Anssi Kanervisto", "Amos Storkey", "Tim Pearce", "Fran\u00e7ois Fleuret" ], "github": "https://github.com/eloialonso/diamond", "project_page": "", "abstract": "World models constitute a promising approach for training reinforcement learning agents in a safe and sample-efficient manner. Recent world models predominantly operate on sequences of discrete latent variables to model environment dynamics. However, this compression into a compact discrete representation may ignore visual details that are important for reinforcement learning. Concurrently, diffusion models have become a dominant approach for image generation, challenging well-established methods modeling discrete latents. Motivated by this paradigm shift, we introduce DIAMOND (DIffusion As a Model Of eNvironment Dreams), a reinforcement learning agent trained in a diffusion world model. We analyze the key design choices that are required to make diffusion suitable for world modeling, and demonstrate how improved visual details can lead to improved agent performance. DIAMOND achieves a mean human normalized score of 1.46 on the competitive Atari 100k benchmark; a new best for agents trained entirely within a world model. To foster future research on diffusion for world modeling, we release our code, agents and playable world models at https://github.com/eloialonso/diamond.", "github_manual": "", "github_regex": "https://github.com/eloialonso/diamond", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-22", "arxiv_id": "2405.12250", "title": "Your Transformer is Secretly Linear", "authors": [ "Anton Razzhigaev", "Matvey Mikhalchuk", "Elizaveta Goncharova", "Nikolai Gerasimenko", "Ivan Oseledets", "Denis Dimitrov", "Andrey Kuznetsov" ], "github": "https://github.com/AIRI-Institute/LLM-Microscope", "project_page": "", "abstract": "This paper reveals a novel linear characteristic exclusive to transformer decoders, including models such as GPT, LLaMA, OPT, BLOOM and others. We analyze embedding transformations between sequential layers, uncovering a near-perfect linear relationship (Procrustes similarity score of 0.99). However, linearity decreases when the residual component is removed due to a consistently low output norm of the transformer layer. Our experiments show that removing or linearly approximating some of the most linear blocks of transformers does not affect significantly the loss or model performance. Moreover, in our pretraining experiments on smaller models we introduce a cosine-similarity-based regularization, aimed at reducing layer linearity. This regularization improves performance metrics on benchmarks like Tiny Stories and SuperGLUE and as well successfully decreases the linearity of the models. This study challenges the existing understanding of transformer architectures, suggesting that their operation may be more linear than previously assumed.", "github_manual": "https://github.com/AIRI-Institute/LLM-Microscope", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-22", "arxiv_id": "2405.12981", "title": "Reducing Transformer Key-Value Cache Size with Cross-Layer Attention", "authors": [ "William Brandon", "Mayank Mishra", "Aniruddha Nrusimha", "Rameswar Panda", "Jonathan Ragan Kelly" ], "github": "", "project_page": "", "abstract": "Key-value (KV) caching plays an essential role in accelerating decoding for transformer-based autoregressive large language models (LLMs). However, the amount of memory required to store the KV cache can become prohibitive at long sequence lengths and large batch sizes. Since the invention of the transformer, two of the most effective interventions discovered for reducing the size of the KV cache have been Multi-Query Attention (MQA) and its generalization, Grouped-Query Attention (GQA). MQA and GQA both modify the design of the attention block so that multiple query heads can share a single key/value head, reducing the number of distinct key/value heads by a large factor while only minimally degrading accuracy. In this paper, we show that it is possible to take Multi-Query Attention a step further by also sharing key and value heads between adjacent layers, yielding a new attention design we call Cross-Layer Attention (CLA). With CLA, we find that it is possible to reduce the size of the KV cache by another 2x while maintaining nearly the same accuracy as unmodified MQA. In experiments training 1B- and 3B-parameter models from scratch, we demonstrate that CLA provides a Pareto improvement over the memory/accuracy tradeoffs which are possible with traditional MQA, enabling inference with longer sequence lengths and larger batch sizes than would otherwise be possible", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-22", "arxiv_id": "2405.12979", "title": "OmniGlue: Generalizable Feature Matching with Foundation Model Guidance", "authors": [ "Hanwen Jiang", "Arjun Karpur", "Bingyi Cao", "Qixing Huang", "Andre Araujo" ], "github": "https://github.com/google-research/omniglue", "project_page": "", "abstract": "The image matching field has been witnessing a continuous emergence of novel learnable feature matching techniques, with ever-improving performance on conventional benchmarks. However, our investigation shows that despite these gains, their potential for real-world applications is restricted by their limited generalization capabilities to novel image domains. In this paper, we introduce OmniGlue, the first learnable image matcher that is designed with generalization as a core principle. OmniGlue leverages broad knowledge from a vision foundation model to guide the feature matching process, boosting generalization to domains not seen at training time. Additionally, we propose a novel keypoint position-guided attention mechanism which disentangles spatial and appearance information, leading to enhanced matching descriptors. We perform comprehensive experiments on a suite of 7 datasets with varied image domains, including scene-level, object-centric and aerial images. OmniGlue's novel components lead to relative gains on unseen domains of 20.9% with respect to a directly comparable reference model, while also outperforming the recent LightGlue method by 9.5% relatively.Code and model can be found at https://hwjiang1510.github.io/OmniGlue", "github_manual": "https://github.com/google-research/omniglue", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-22", "arxiv_id": "2405.12970", "title": "Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control", "authors": [ "Yue Han", "Junwei Zhu", "Keke He", "Xu Chen", "Yanhao Ge", "Wei Li", "Xiangtai Li", "Jiangning Zhang", "Chengjie Wang", "Yong Liu" ], "github": "", "project_page": "", "abstract": "Current face reenactment and swapping methods mainly rely on GAN frameworks, but recent focus has shifted to pre-trained diffusion models for their superior generation capabilities. However, training these models is resource-intensive, and the results have not yet achieved satisfactory performance levels. To address this issue, we introduce Face-Adapter, an efficient and effective adapter designed for high-precision and high-fidelity face editing for pre-trained diffusion models. We observe that both face reenactment/swapping tasks essentially involve combinations of target structure, ID and attribute. We aim to sufficiently decouple the control of these factors to achieve both tasks in one model. Specifically, our method contains: 1) A Spatial Condition Generator that provides precise landmarks and background; 2) A Plug-and-play Identity Encoder that transfers face embeddings to the text space by a transformer decoder. 3) An Attribute Controller that integrates spatial conditions and detailed attributes. Face-Adapter achieves comparable or even superior performance in terms of motion control precision, ID retention capability, and generation quality compared to fully fine-tuned face reenactment/swapping models. Additionally, Face-Adapter seamlessly integrates with various StableDiffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.13195", "title": "CamViG: Camera Aware Image-to-Video Generation with Multimodal Transformers", "authors": [ "Andrew Marmon", "Grant Schindler", "Jos\u00e9 Lezama", "Dan Kondratyuk", "Bryan Seybold", "Irfan Essa" ], "github": "", "project_page": "", "abstract": "We extend multimodal transformers to include 3D camera motion as a conditioning signal for the task of video generation. Generative video models are becoming increasingly powerful, thus focusing research efforts on methods of controlling the output of such models. We propose to add virtual 3D camera controls to generative video methods by conditioning generated video on an encoding of three-dimensional camera movement over the course of the generated video. Results demonstrate that we are (1) able to successfully control the camera during video generation, starting from a single frame and a camera signal, and (2) we demonstrate the accuracy of the generated 3D camera paths using traditional computer vision methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.14677", "title": "RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance", "authors": [ "Zhicheng Sun", "Zhenhao Yang", "Yang Jin", "Haozhe Chi", "Kun Xu", "Kun Xu", "Liwei Chen", "Hao Jiang", "Di Zhang", "Yang Song", "Kun Gai", "Yadong Mu" ], "github": "https://github.com/feifeiobama/RectifID", "project_page": "", "abstract": "Customizing diffusion models to generate identity-preserving images from user-provided reference images is an intriguing new problem. The prevalent approaches typically require training on extensive domain-specific images to achieve identity preservation, which lacks flexibility across different use cases. To address this issue, we exploit classifier guidance, a training-free technique that steers diffusion models using an existing classifier, for personalized image generation. Our study shows that based on a recent rectified flow framework, the major limitation of vanilla classifier guidance in requiring a special classifier can be resolved with a simple fixed-point solution, allowing flexible personalization with off-the-shelf image discriminators. Moreover, its solving procedure proves to be stable when anchored to a reference flow trajectory, with a convergence guarantee. The derived method is implemented on rectified flow with different off-the-shelf image discriminators, delivering advantageous personalization results for human faces, live subjects, and certain objects. Code is available at https://github.com/feifeiobama/RectifID.", "github_manual": "", "github_regex": "https://github.com/feifeiobama/RectifID", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.14598", "title": "Visual Echoes: A Simple Unified Transformer for Audio-Visual Generation", "authors": [ "Shiqi Yang", "Zhi Zhong", "Mengjie Zhao", "Shusuke Takahashi", "Masato Ishii", "Takashi Shibuya", "Yuki Mitsufuji" ], "github": "", "project_page": "", "abstract": "In recent years, with the realistic generation results and a wide range of personalized applications, diffusion-based generative models gain huge attention in both visual and audio generation areas. Compared to the considerable advancements of text2image or text2audio generation, research in audio2visual or visual2audio generation has been relatively slow. The recent audio-visual generation methods usually resort to huge large language model or composable diffusion models. Instead of designing another giant model for audio-visual generation, in this paper we take a step back showing a simple and lightweight generative transformer, which is not fully investigated in multi-modal generation, can achieve excellent results on image2audio generation. The transformer operates in the discrete audio and visual Vector-Quantized GAN space, and is trained in the mask denoising manner. After training, the classifier-free guidance could be deployed off-the-shelf achieving better performance, without any extra training or modification. Since the transformer model is modality symmetrical, it could also be directly deployed for audio2image generation and co-generation. In the experiments, we show that our simple method surpasses recent image2audio generation methods. Generated audio samples can be found at https://docs.google.com/presentation/d/1ZtC0SeblKkut4XJcRaDsSTuCRIXB3ypxmSi7HTY3IyQ", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.13865", "title": "ReVideo: Remake a Video with Motion and Content Control", "authors": [ "Chong Mou", "Mingdeng Cao", "Xintao Wang", "Zhaoyang Zhang", "Ying Shan", "Jian Zhang" ], "github": "", "project_page": "", "abstract": "Despite significant advancements in video generation and editing using diffusion models, achieving accurate and localized video editing remains a substantial challenge. Additionally, most existing video editing methods primarily focus on altering visual content, with limited research dedicated to motion editing. In this paper, we present a novel attempt to Remake a Video (ReVideo) which stands out from existing methods by allowing precise video editing in specific areas through the specification of both content and motion. Content editing is facilitated by modifying the first frame, while the trajectory-based motion control offers an intuitive user interaction experience. ReVideo addresses a new task involving the coupling and training imbalance between content and motion control. To tackle this, we develop a three-stage training strategy that progressively decouples these two aspects from coarse to fine. Furthermore, we propose a spatiotemporal adaptive fusion module to integrate content and motion control across various sampling steps and spatial locations. Extensive experiments demonstrate that our ReVideo has promising performance on several accurate video editing applications, i.e., (1) locally changing video content while keeping the motion constant, (2) keeping content unchanged and customizing new motion trajectories, (3) modifying both content and motion trajectories. Our method can also seamlessly extend these applications to multi-area editing without specific training, demonstrating its flexibility and robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.13800", "title": "Dense Connector for MLLMs", "authors": [ "Huanjin Yao", "Wenhao Wu", "Taojiannan Yang", "YuXin Song", "Mengxi Zhang", "Haocheng Feng", "Yifan Sun", "Zhiheng Li", "Wanli Ouyang", "Jingdong Wang" ], "github": "https://github.com/HJYao00/DenseConnector", "project_page": "", "abstract": "Do we fully leverage the potential of visual encoder in Multimodal Large Language Models (MLLMs)? The recent outstanding performance of MLLMs in multimodal understanding has garnered broad attention from both academia and industry. In the current MLLM rat race, the focus seems to be predominantly on the linguistic side. We witness the rise of larger and higher-quality instruction datasets, as well as the involvement of larger-sized LLMs. Yet, scant attention has been directed towards the visual signals utilized by MLLMs, often assumed to be the final high-level features extracted by a frozen visual encoder. In this paper, we introduce the Dense Connector - a simple, effective, and plug-and-play vision-language connector that significantly enhances existing MLLMs by leveraging multi-layer visual features, with minimal additional computational overhead. Furthermore, our model, trained solely on images, showcases remarkable zero-shot capabilities in video understanding as well. Experimental results across various vision encoders, image resolutions, training dataset scales, varying sizes of LLMs (2.7B->70B), and diverse architectures of MLLMs (e.g., LLaVA and Mini-Gemini) validate the versatility and scalability of our approach, achieving state-of-the-art performance on across 19 image and video benchmarks. We hope that this work will provide valuable experience and serve as a basic module for future MLLM development.", "github_manual": "https://github.com/HJYao00/DenseConnector", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.14867", "title": "Improved Distribution Matching Distillation for Fast Image Synthesis", "authors": [ "Tianwei Yin", "Micha\u00ebl Gharbi", "Taesung Park", "Richard Zhang", "Eli Shechtman", "Fredo Durand", "William T. Freeman" ], "github": "https://github.com/tianweiy/DMD2", "project_page": "", "abstract": "Recent approaches have shown promises distilling diffusion models into efficient one-step generators. Among them, Distribution Matching Distillation (DMD) produces one-step generators that match their teacher in distribution, without enforcing a one-to-one correspondence with the sampling trajectories of their teachers. However, to ensure stable training, DMD requires an additional regression loss computed using a large set of noise-image pairs generated by the teacher with many steps of a deterministic sampler. This is costly for large-scale text-to-image synthesis and limits the student's quality, tying it too closely to the teacher's original sampling paths. We introduce DMD2, a set of techniques that lift this limitation and improve DMD training. First, we eliminate the regression loss and the need for expensive dataset construction. We show that the resulting instability is due to the fake critic not estimating the distribution of generated samples accurately and propose a two time-scale update rule as a remedy. Second, we integrate a GAN loss into the distillation procedure, discriminating between generated samples and real images. This lets us train the student model on real data, mitigating the imperfect real score estimation from the teacher model, and enhancing quality. Lastly, we modify the training procedure to enable multi-step sampling. We identify and address the training-inference input mismatch problem in this setting, by simulating inference-time generator samples during training time. Taken together, our improvements set new benchmarks in one-step image generation, with FID scores of 1.28 on ImageNet-64x64 and 8.35 on zero-shot COCO 2014, surpassing the original teacher despite a 500X reduction in inference cost. Further, we show our approach can generate megapixel images by distilling SDXL, demonstrating exceptional visual quality among few-step methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tianweiy/DMD2", "github_api_added_by": "auto" }, { "date": "2024-05-24", "arxiv_id": "2405.14477", "title": "LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models", "authors": [ "Seyedmorteza Sadat", "Jakob Buhmann", "Derek Bradley", "Otmar Hilliges", "Romann M. Weber" ], "github": "", "project_page": "", "abstract": "Advances in latent diffusion models (LDMs) have revolutionized high-resolution image generation, but the design space of the autoencoder that is central to these systems remains underexplored. In this paper, we introduce LiteVAE, a family of autoencoders for LDMs that leverage the 2D discrete wavelet transform to enhance scalability and computational efficiency over standard variational autoencoders (VAEs) with no sacrifice in output quality. We also investigate the training methodologies and the decoder architecture of LiteVAE and propose several enhancements that improve the training dynamics and reconstruction quality. Our base LiteVAE model matches the quality of the established VAEs in current LDMs with a six-fold reduction in encoder parameters, leading to faster training and lower GPU memory requirements, while our larger model outperforms VAEs of comparable complexity across all evaluated metrics (rFID, LPIPS, PSNR, and SSIM).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.14224", "title": "DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis", "authors": [ "Yao Teng", "Yue Wu", "Han Shi", "Xuefei Ning", "Guohao Dai", "Yu Wang", "Zhenguo Li", "Xihui Liu" ], "github": "https://github.com/tyshiwo1/DiM-DiffusionMamba", "project_page": "", "abstract": "Diffusion models have achieved great success in image generation, with the backbone evolving from U-Net to Vision Transformers. However, the computational cost of Transformers is quadratic to the number of tokens, leading to significant challenges when dealing with high-resolution images. In this work, we propose Diffusion Mamba (DiM), which combines the efficiency of Mamba, a sequence model based on State Space Models (SSM), with the expressive power of diffusion models for efficient high-resolution image synthesis. To address the challenge that Mamba cannot generalize to 2D signals, we make several architecture designs including multi-directional scans, learnable padding tokens at the end of each row and column, and lightweight local feature enhancement. Our DiM architecture achieves inference-time efficiency for high-resolution images. In addition, to further improve training efficiency for high-resolution image generation with DiM, we investigate ``weak-to-strong'' training strategy that pretrains DiM on low-resolution images (256times 256) and then finetune it on high-resolution images (512 times 512). We further explore training-free upsampling strategies to enable the model to generate higher-resolution images (e.g., 1024times 1024 and 1536times 1536) without further fine-tuning. Experiments demonstrate the effectiveness and efficiency of our DiM.", "github_manual": "https://github.com/tyshiwo1/DiM-DiffusionMamba", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.14860", "title": "Not All Language Model Features Are Linear", "authors": [ "Joshua Engels", "Isaac Liao", "Eric J. Michaud", "Wes Gurnee", "Max Tegmark" ], "github": "https://github.com/joshengels/multidimensionalfeatures", "project_page": "", "abstract": "Recent work has proposed the linear representation hypothesis: that language models perform computation by manipulating one-dimensional representations of concepts (\"features\") in activation space. In contrast, we explore whether some language model representations may be inherently multi-dimensional. We begin by developing a rigorous definition of irreducible multi-dimensional features based on whether they can be decomposed into either independent or non-co-occurring lower-dimensional features. Motivated by these definitions, we design a scalable method that uses sparse autoencoders to automatically find multi-dimensional features in GPT-2 and Mistral 7B. These auto-discovered features include strikingly interpretable examples, e.g. circular features representing days of the week and months of the year. We identify tasks where these exact circles are used to solve computational problems involving modular arithmetic in days of the week and months of the year. Finally, we provide evidence that these circular features are indeed the fundamental unit of computation in these tasks with intervention experiments on Mistral 7B and Llama 3 8B, and we find further circular representations by breaking down the hidden states for these tasks into interpretable components.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/joshengels/multidimensionalfeatures", "github_api_added_by": "auto" }, { "date": "2024-05-24", "arxiv_id": "2405.14333", "title": "DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data", "authors": [ "Huajian Xin", "Daya Guo", "Zhihong Shao", "Zhizhou Ren", "Qihao Zhu", "Bo Liu", "Chong Ruan", "Wenda Li", "Xiaodan Liang" ], "github": "", "project_page": "", "abstract": "Proof assistants like Lean have revolutionized mathematical proof verification, ensuring high accuracy and reliability. Although large language models (LLMs) show promise in mathematical reasoning, their advancement in formal theorem proving is hindered by a lack of training data. To address this issue, we introduce an approach to generate extensive Lean 4 proof data derived from high-school and undergraduate-level mathematical competition problems. This approach involves translating natural language problems into formal statements, filtering out low-quality statements, and generating proofs to create synthetic data. After fine-tuning the DeepSeekMath 7B model on this synthetic dataset, which comprises 8 million formal statements with proofs, our model achieved whole-proof generation accuracies of 46.3% with 64 samples and 52% cumulatively on the Lean 4 miniF2F test, surpassing the baseline GPT-4 at 23.0% with 64 samples and a tree search reinforcement learning method at 41.0%. Additionally, our model successfully proved 5 out of 148 problems in the Lean 4 Formalized International Mathematical Olympiad (FIMO) benchmark, while GPT-4 failed to prove any. These results demonstrate the potential of leveraging large-scale synthetic data to enhance theorem-proving capabilities in LLMs. Both the synthetic dataset and the model will be made available to facilitate further research in this promising field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.14857", "title": "Semantica: An Adaptable Image-Conditioned Diffusion Model", "authors": [ "Manoj Kumar", "Neil Houlsby", "Emiel Hoogeboom" ], "github": "", "project_page": "", "abstract": "We investigate the task of adapting image generative models to different datasets without finetuneing. To this end, we introduce Semantica, an image-conditioned diffusion model capable of generating images based on the semantics of a conditioning image. Semantica is trained exclusively on web-scale image pairs, that is it receives a random image from a webpage as conditional input and models another random image from the same webpage. Our experiments highlight the expressivity of pretrained image encoders and necessity of semantic-based data filtering in achieving high-quality image generation. Once trained, it can adaptively generate new images from a dataset by simply using images from that dataset as input. We study the transfer properties of Semantica on ImageNet, LSUN Churches, LSUN Bedroom and SUN397.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.13817", "title": "Thermodynamic Natural Gradient Descent", "authors": [ "Kaelan Donatella", "Samuel Duffield", "Maxwell Aifer", "Denis Melanson", "Gavin Crooks", "Patrick J. Coles" ], "github": "", "project_page": "", "abstract": "Second-order training methods have better convergence properties than gradient descent but are rarely used in practice for large-scale training due to their computational overhead. This can be viewed as a hardware limitation (imposed by digital computers). Here we show that natural gradient descent (NGD), a second-order method, can have a similar computational complexity per iteration to a first-order method, when employing appropriate hardware. We present a new hybrid digital-analog algorithm for training neural networks that is equivalent to NGD in a certain parameter regime but avoids prohibitively costly linear system solves. Our algorithm exploits the thermodynamic properties of an analog system at equilibrium, and hence requires an analog thermodynamic computer. The training occurs in a hybrid digital-analog loop, where the gradient and Fisher information matrix (or any other positive semi-definite curvature matrix) are calculated at given time intervals while the analog dynamics take place. We numerically demonstrate the superiority of this approach over state-of-the-art digital first- and second-order training methods on classification tasks and language model fine-tuning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.14129", "title": "AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability", "authors": [ "Fei Zhao", "Taotian Pang", "Chunhui Li", "Zhen Wu", "Junjie Guo", "Shangyu Xing", "Xinyu Dai" ], "github": "https://github.com/AlignGPT-VL/AlignGPT", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) are widely regarded as crucial in the exploration of Artificial General Intelligence (AGI). The core of MLLMs lies in their capability to achieve cross-modal alignment. To attain this goal, current MLLMs typically follow a two-phase training paradigm: the pre-training phase and the instruction-tuning phase. Despite their success, there are shortcomings in the modeling of alignment capabilities within these models. Firstly, during the pre-training phase, the model usually assumes that all image-text pairs are uniformly aligned, but in fact the degree of alignment between different image-text pairs is inconsistent. Secondly, the instructions currently used for finetuning incorporate a variety of tasks, different tasks's instructions usually require different levels of alignment capabilities, but previous MLLMs overlook these differentiated alignment needs. To tackle these issues, we propose a new multimodal large language model AlignGPT. In the pre-training stage, instead of treating all image-text pairs equally, we assign different levels of alignment capabilities to different image-text pairs. Then, in the instruction-tuning phase, we adaptively combine these different levels of alignment capabilities to meet the dynamic alignment needs of different instructions. Extensive experimental results show that our model achieves competitive performance on 12 benchmarks.", "github_manual": "https://github.com/AlignGPT-VL/AlignGPT", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.14105", "title": "Distributed Speculative Inference of Large Language Models", "authors": [ "Nadav Timor", "Jonathan Mamou", "Daniel Korat", "Moshe Berchansky", "Oren Pereg", "Moshe Wasserblat", "Tomer Galanti", "Michal Gordon", "David Harel" ], "github": "", "project_page": "", "abstract": "Accelerating the inference of large language models (LLMs) is an important challenge in artificial intelligence. This paper introduces distributed speculative inference (DSI), a novel distributed inference algorithm that is provably faster than speculative inference (SI) [leviathan2023fast, chen2023accelerating, miao2023specinfer] and traditional autoregressive inference (non-SI). Like other SI algorithms, DSI works on frozen LLMs, requiring no training or architectural modifications, and it preserves the target distribution. Prior studies on SI have demonstrated empirical speedups (compared to non-SI) but require a fast and accurate drafter LLM. In practice, off-the-shelf LLMs often do not have matching drafters that are sufficiently fast and accurate. We show a gap: SI gets slower than non-SI when using slower or less accurate drafters. We close this gap by proving that DSI is faster than both SI and non-SI given any drafters. By orchestrating multiple instances of the target and drafters, DSI is not only faster than SI but also supports LLMs that cannot be accelerated with SI. Our simulations show speedups of off-the-shelf LLMs in realistic settings: DSI is 1.29-1.92x faster than SI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.14866", "title": "Tele-Aloha: A Low-budget and High-authenticity Telepresence System Using Sparse RGB Cameras", "authors": [ "Hanzhang Tu", "Ruizhi Shao", "Xue Dong", "Shunyuan Zheng", "Hao Zhang", "Lili Chen", "Meili Wang", "Wenyu Li", "Siyan Ma", "Shengping Zhang", "Boyao Zhou", "Yebin Liu" ], "github": "", "project_page": "", "abstract": "In this paper, we present a low-budget and high-authenticity bidirectional telepresence system, Tele-Aloha, targeting peer-to-peer communication scenarios. Compared to previous systems, Tele-Aloha utilizes only four sparse RGB cameras, one consumer-grade GPU, and one autostereoscopic screen to achieve high-resolution (2048x2048), real-time (30 fps), low-latency (less than 150ms) and robust distant communication. As the core of Tele-Aloha, we propose an efficient novel view synthesis algorithm for upper-body. Firstly, we design a cascaded disparity estimator for obtaining a robust geometry cue. Additionally a neural rasterizer via Gaussian Splatting is introduced to project latent features onto target view and to decode them into a reduced resolution. Further, given the high-quality captured data, we leverage weighted blending mechanism to refine the decoded image into the final resolution of 2K. Exploiting world-leading autostereoscopic display and low-latency iris tracking, users are able to experience a strong three-dimensional sense even without any wearable head-mounted display device. Altogether, our telepresence system demonstrates the sense of co-presence in real-life experiments, inspiring the next generation of communication.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.14871", "title": "NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections", "authors": [ "Dor Verbin", "Pratul P. Srinivasan", "Peter Hedman", "Ben Mildenhall", "Benjamin Attal", "Richard Szeliski", "Jonathan T. Barron" ], "github": "", "project_page": "", "abstract": "Neural Radiance Fields (NeRFs) typically struggle to reconstruct and render highly specular objects, whose appearance varies quickly with changes in viewpoint. Recent works have improved NeRF's ability to render detailed specular appearance of distant environment illumination, but are unable to synthesize consistent reflections of closer content. Moreover, these techniques rely on large computationally-expensive neural networks to model outgoing radiance, which severely limits optimization and rendering speed. We address these issues with an approach based on ray tracing: instead of querying an expensive neural network for the outgoing view-dependent radiance at points along each camera ray, our model casts reflection rays from these points and traces them through the NeRF representation to render feature vectors which are decoded into color using a small inexpensive network. We demonstrate that our model outperforms prior methods for view synthesis of scenes containing shiny objects, and that it is the only existing NeRF method that can synthesize photorealistic specular appearance and reflections in real-world scenes, while requiring comparable optimization time to current state-of-the-art view synthesis models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-24", "arxiv_id": "2405.14847", "title": "Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling", "authors": [ "Liwen Wu", "Sai Bi", "Zexiang Xu", "Fujun Luan", "Kai Zhang", "Iliyan Georgiev", "Kalyan Sunkavalli", "Ravi Ramamoorthi" ], "github": "https://github.com/lwwu2/nde", "project_page": "", "abstract": "Novel-view synthesis of specular objects like shiny metals or glossy paints remains a significant challenge. Not only the glossy appearance but also global illumination effects, including reflections of other objects in the environment, are critical components to faithfully reproduce a scene. In this paper, we present Neural Directional Encoding (NDE), a view-dependent appearance encoding of neural radiance fields (NeRF) for rendering specular objects. NDE transfers the concept of feature-grid-based spatial encoding to the angular domain, significantly improving the ability to model high-frequency angular signals. In contrast to previous methods that use encoding functions with only angular input, we additionally cone-trace spatial features to obtain a spatially varying directional encoding, which addresses the challenging interreflection effects. Extensive experiments on both synthetic and real datasets show that a NeRF model with NDE (1) outperforms the state of the art on view synthesis of specular objects, and (2) works with small networks to allow fast (real-time) inference. The project webpage and source code are available at: https://lwwu2.github.io/nde/.", "github_manual": "https://github.com/lwwu2/nde", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.15071", "title": "Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization", "authors": [ "Boshi Wang", "Xiang Yue", "Yu Su", "Huan Sun" ], "github": "https://github.com/OSU-NLP-Group/GrokkedTransformer", "project_page": "", "abstract": "We study whether transformers can learn to implicitly reason over parametric knowledge, a skill that even the most capable language models struggle with. Focusing on two representative reasoning types, composition and comparison, we consistently find that transformers can learn implicit reasoning, but only through grokking, i.e., extended training far beyond overfitting. The levels of generalization also vary across reasoning types: when faced with out-of-distribution examples, transformers fail to systematically generalize for composition but succeed for comparison. We delve into the model's internals throughout training, conducting analytical experiments that reveal: 1) the mechanism behind grokking, such as the formation of the generalizing circuit and its relation to the relative efficiency of generalizing and memorizing circuits, and 2) the connection between systematicity and the configuration of the generalizing circuit. Our findings guide data and training setup to better induce implicit reasoning and suggest potential improvements to the transformer architecture, such as encouraging cross-layer knowledge sharing. Furthermore, we demonstrate that for a challenging reasoning task with a large search space, GPT-4-Turbo and Gemini-1.5-Pro based on non-parametric memory fail badly regardless of prompting styles or retrieval augmentation, while a fully grokked transformer can achieve near-perfect accuracy, showcasing the power of parametric memory for complex reasoning.", "github_manual": "https://github.com/OSU-NLP-Group/GrokkedTransformer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.15574", "title": "Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models", "authors": [ "Byung-Kwan Lee", "Chae Won Kim", "Beomchan Park", "Yong Man Ro" ], "github": "https://github.com/ByungKwanLee/Meteor", "project_page": "", "abstract": "The rapid development of large language and vision models (LLVMs) has been driven by advances in visual instruction tuning. Recently, open-source LLVMs have curated high-quality visual instruction tuning datasets and utilized additional vision encoders or multiple computer vision models in order to narrow the performance gap with powerful closed-source LLVMs. These advancements are attributed to multifaceted information required for diverse capabilities, including fundamental image understanding, real-world knowledge about common-sense and non-object concepts (e.g., charts, diagrams, symbols, signs, and math problems), and step-by-step procedures for solving complex questions. Drawing from the multifaceted information, we present a new efficient LLVM, Mamba-based traversal of rationales (Meteor), which leverages multifaceted rationale to enhance understanding and answering capabilities. To embed lengthy rationales containing abundant information, we employ the Mamba architecture, capable of processing sequential data with linear time complexity. We introduce a new concept of traversal of rationale that facilitates efficient embedding of rationale. Subsequently, the backbone multimodal language model (MLM) is trained to generate answers with the aid of rationale. Through these steps, Meteor achieves significant improvements in vision language performances across multiple evaluation benchmarks requiring diverse capabilities, without scaling up the model size or employing additional vision encoders and computer vision models.", "github_manual": "https://github.com/ByungKwanLee/Meteor", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.15216", "title": "Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition", "authors": [ "Zijin Gu", "Tatiana Likhomanenko", "He Bai", "Erik McDermott", "Ronan Collobert", "Navdeep Jaitly" ], "github": "", "project_page": "", "abstract": "Language models (LMs) have long been used to improve results of automatic speech recognition (ASR) systems, but they are unaware of the errors that ASR systems make. Error correction models are designed to fix ASR errors, however, they showed little improvement over traditional LMs mainly due to the lack of supervised training data. In this paper, we present Denoising LM (DLM), which is a scaled error correction model trained with vast amounts of synthetic data, significantly exceeding prior attempts meanwhile achieving new state-of-the-art ASR performance. We use text-to-speech (TTS) systems to synthesize audio, which is fed into an ASR system to produce noisy hypotheses, which are then paired with the original texts to train the DLM. DLM has several key ingredients: (i) up-scaled model and data; (ii) usage of multi-speaker TTS systems; (iii) combination of multiple noise augmentation strategies; and (iv) new decoding techniques. With a Transformer-CTC ASR, DLM achieves 1.5% word error rate (WER) on test-clean and 3.3% WER on test-other on Librispeech, which to our knowledge are the best reported numbers in the setting where no external audio data are used and even match self-supervised methods which use external audio data. Furthermore, a single DLM is applicable to different ASRs, and greatly surpassing the performance of conventional LM based beam-search rescoring. These results indicate that properly investigated error correction models have the potential to replace conventional LMs, holding the key to a new level of accuracy in ASR systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.15032", "title": "Aya 23: Open Weight Releases to Further Multilingual Progress", "authors": [ "Viraat Aryabumi", "John Dang", "Dwarak Talupuru", "Saurabh Dash", "David Cairuz", "Hangyu Lin", "Bharat Venkitesh", "Madeline Smith", "Kelly Marchisio", "Sebastian Ruder", "Acyr Locatelli", "Julia Kreutzer", "Nick Frosst", "Phil Blunsom", "Marzieh Fadaee", "Ahmet \u00dcst\u00fcn", "Sara Hooker" ], "github": "", "project_page": "", "abstract": "This technical report introduces Aya 23, a family of multilingual language models. Aya 23 builds on the recent release of the Aya model (\\\"Ust\\\"un et al., 2024), focusing on pairing a highly performant pre-trained model with the recently released Aya collection (Singh et al., 2024). The result is a powerful multilingual large language model serving 23 languages, expanding state-of-art language modeling capabilities to approximately half of the world's population. The Aya model covered 101 languages whereas Aya 23 is an experiment in depth vs breadth, exploring the impact of allocating more capacity to fewer languages that are included during pre-training. Aya 23 outperforms both previous massively multilingual models like Aya 101 for the languages it covers, as well as widely used models like Gemma, Mistral and Mixtral on an extensive range of discriminative and generative tasks. We release the open weights for both the 8B and 35B models as part of our continued commitment for expanding access to multilingual progress.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.14908", "title": "Data Mixing Made Efficient: A Bivariate Scaling Law for Language Model Pretraining", "authors": [ "Ce Ge", "Zhijian Ma", "Daoyuan Chen", "Yaliang Li", "Bolin Ding" ], "github": "", "project_page": "", "abstract": "Large language models exhibit exceptional generalization capabilities, primarily attributed to the utilization of diversely sourced data. However, conventional practices in integrating this diverse data heavily rely on heuristic schemes, lacking theoretical guidance. This research tackles these limitations by investigating strategies based on low-cost proxies for data mixtures, with the aim of streamlining data curation to enhance training efficiency. Specifically, we propose a unified scaling law, termed BiMix, which accurately models the bivariate scaling behaviors of both data quantity and mixing proportions. We conduct systematic experiments and provide empirical evidence for the predictive power and fundamental principles of BiMix. Notably, our findings reveal that entropy-driven training-free data mixtures can achieve comparable or even better performance than more resource-intensive methods. We hope that our quantitative insights can shed light on further judicious research and development in cost-effective language modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.14906", "title": "AutoCoder: Enhancing Code Large Language Model with AIEV-Instruct", "authors": [ "Bin Lei", "Yuchen Li", "Qiuwu Chen" ], "github": "https://github.com/bin123apple/AutoCoder", "project_page": "", "abstract": "We introduce AutoCoder, the first Large Language Model to surpass GPT-4 Turbo (April 2024) and GPT-4o in pass@1 on the Human Eval benchmark test (90.9% vs. 90.2%). In addition, AutoCoder offers a more versatile code interpreter compared to GPT-4 Turbo and GPT-4o. It's code interpreter can install external packages instead of limiting to built-in packages. AutoCoder's training data is a multi-turn dialogue dataset created by a system combining agent interaction and external code execution verification, a method we term \\textsc{AIEV-Instruct} (Instruction Tuning with Agent-Interaction and Execution-Verified). Compared to previous large-scale code dataset generation methods, AIEV-Instruct reduces dependence on proprietary large models and provides execution-validated code dataset. The code and the demo video is available in https://github.com/bin123apple/AutoCoder.", "github_manual": "", "github_regex": "https://github.com/bin123apple/AutoCoder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.14979", "title": "CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner", "authors": [ "Weiyu Li", "Jiarui Liu", "Rui Chen", "Yixun Liang", "Xuelin Chen", "Ping Tan", "Xiaoxiao Long" ], "github": "https://github.com/wyysf-98/CraftsMan", "project_page": "", "abstract": "We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in an interactive manner. Despite the significant advancements in 3D generation, existing methods still struggle with lengthy optimization processes, irregular mesh topologies, noisy surfaces, and difficulties in accommodating user edits, consequently impeding their widespread adoption and implementation in 3D modeling software. Our work is inspired by the craftsman, who usually roughs out the holistic figure of the work first and elaborates the surface details subsequently. Specifically, we employ a 3D native diffusion model, which operates on latent space learned from latent set-based 3D representations, to generate coarse geometries with regular mesh topology in seconds. In particular, this process takes as input a text prompt or a reference image and leverages a powerful multi-view (MV) diffusion model to generate multiple views of the coarse geometry, which are fed into our MV-conditioned 3D diffusion model for generating the 3D geometry, significantly improving robustness and generalizability. Following that, a normal-based geometry refiner is used to significantly enhance the surface details. This refinement can be performed automatically, or interactively with user-supplied edits. Extensive experiments demonstrate that our method achieves high efficacy in producing superior-quality 3D assets compared to existing methods. HomePage: https://craftsman3d.github.io/, Code: https://github.com/wyysf-98/CraftsMan", "github_manual": "", "github_regex": "https://github.com/wyysf-98/CraftsMan", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.15613", "title": "Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach", "authors": [ "Huy V. Vo", "Vasil Khalidov", "Timoth\u00e9e Darcet", "Th\u00e9o Moutakanni", "Nikita Smetanin", "Marc Szafraniec", "Hugo Touvron", "Camille Couprie", "Maxime Oquab", "Armand Joulin", "Herv\u00e9 J\u00e9gou", "Patrick Labatut", "Piotr Bojanowski" ], "github": "https://github.com/facebookresearch/ssl-data-curation", "project_page": "", "abstract": "Self-supervised features are the cornerstone of modern machine learning systems. They are typically pre-trained on data collections whose construction and curation typically require extensive human effort. This manual process has some limitations similar to those encountered in supervised learning, e.g., the crowd-sourced selection of data is costly and time-consuming, preventing scaling the dataset size. In this work, we consider the problem of automatic curation of high-quality datasets for self-supervised pre-training. We posit that such datasets should be large, diverse and balanced, and propose a clustering-based approach for building ones satisfying all these criteria. Our method involves successive and hierarchical applications of k-means on a large and diverse data repository to obtain clusters that distribute uniformly among data concepts, followed by a hierarchical, balanced sampling step from these clusters. Extensive experiments on three different data domains including web-based images, satellite images and text show that features trained on our automatically curated datasets outperform those trained on uncurated data while being on par or better than ones trained on manually curated data.", "github_manual": "https://github.com/facebookresearch/ssl-data-curation", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.15223", "title": "iVideoGPT: Interactive VideoGPTs are Scalable World Models", "authors": [ "Jialong Wu", "Shaofeng Yin", "Ningya Feng", "Xu He", "Dong Li", "Jianye Hao", "Mingsheng Long" ], "github": "https://github.com/s-tian/vp2", "project_page": "", "abstract": "World models empower model-based agents to interactively explore, reason, and plan within imagined environments for real-world decision-making. However, the high demand for interactivity poses challenges in harnessing recent advancements in video generative models for developing world models at scale. This work introduces Interactive VideoGPT (iVideoGPT), a scalable autoregressive transformer framework that integrates multimodal signals--visual observations, actions, and rewards--into a sequence of tokens, facilitating an interactive experience of agents via next-token prediction. iVideoGPT features a novel compressive tokenization technique that efficiently discretizes high-dimensional visual observations. Leveraging its scalable architecture, we are able to pre-train iVideoGPT on millions of human and robotic manipulation trajectories, establishing a versatile foundation that is adaptable to serve as interactive world models for a wide range of downstream tasks. These include action-conditioned video prediction, visual planning, and model-based reinforcement learning, where iVideoGPT achieves competitive performance compared with state-of-the-art methods. Our work advances the development of interactive general world models, bridging the gap between generative video models and practical model-based reinforcement learning applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/s-tian/vp2", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.15125", "title": "HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting", "authors": [ "Yuanhao Cai", "Zihao Xiao", "Yixun Liang", "Yulun Zhang", "Xiaokang Yang", "Yaoyao Liu", "Alan Yuille" ], "github": "https://github.com/caiyuanhao1998/HDR-GS", "project_page": "", "abstract": "High dynamic range (HDR) novel view synthesis (NVS) aims to create photorealistic images from novel viewpoints using HDR imaging techniques. The rendered HDR images capture a wider range of brightness levels containing more details of the scene than normal low dynamic range (LDR) images. Existing HDR NVS methods are mainly based on NeRF. They suffer from long training time and slow inference speed. In this paper, we propose a new framework, High Dynamic Range Gaussian Splatting (HDR-GS), which can efficiently render novel HDR views and reconstruct LDR images with a user input exposure time. Specifically, we design a Dual Dynamic Range (DDR) Gaussian point cloud model that uses spherical harmonics to fit HDR color and employs an MLP-based tone-mapper to render LDR color. The HDR and LDR colors are then fed into two Parallel Differentiable Rasterization (PDR) processes to reconstruct HDR and LDR views. To establish the data foundation for the research of 3D Gaussian splatting-based methods in HDR NVS, we recalibrate the camera parameters and compute the initial positions for Gaussian point clouds. Experiments demonstrate that our HDR-GS surpasses the state-of-the-art NeRF-based method by 3.84 and 1.91 dB on LDR and HDR NVS while enjoying 1000x inference speed and only requiring 6.3% training time.", "github_manual": "https://github.com/caiyuanhao1998/HDR-GS", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.15319", "title": "Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training", "authors": [ "Wenyu Du", "Tongxu Luo", "Zihan Qiu", "Zeyu Huang", "Yikang Shen", "Reynold Cheng", "Yike Guo", "Jie Fu" ], "github": "https://github.com/tongxuluo/prts", "project_page": "", "abstract": "LLMs are computationally expensive to pre-train due to their large scale. Model growth emerges as a promising approach by leveraging smaller models to accelerate the training of larger ones. However, the viability of these model growth methods in efficient LLM pre-training remains underexplored. This work identifies three critical textit{O}bstacles: (O1) lack of comprehensive evaluation, (O2) untested viability for scaling, and (O3) lack of empirical guidelines. To tackle O1, we summarize existing approaches into four atomic growth operators and systematically evaluate them in a standardized LLM pre-training setting. Our findings reveal that a depthwise stacking operator, called G_{stack}, exhibits remarkable acceleration in training, leading to decreased loss and improved overall performance on eight standard NLP benchmarks compared to strong baselines. Motivated by these promising results, we conduct extensive experiments to delve deeper into G_{stack} to address O2 and O3. For O2 (untested scalability), our study shows that G_{stack} is scalable and consistently performs well, with experiments up to 7B LLMs after growth and pre-training LLMs with 750B tokens. For example, compared to a conventionally trained 7B model using 300B tokens, our G_{stack} model converges to the same loss with 194B tokens, resulting in a 54.6\\% speedup. We further address O3 (lack of empirical guidelines) by formalizing guidelines to determine growth timing and growth factor for G_{stack}, making it practical in general LLM pre-training. We also provide in-depth discussions and comprehensive ablation studies of G_{stack}. Our code and pre-trained model are available at https://llm-stacking.github.io/{https://llm-stacking.github.io/}.", "github_manual": "https://github.com/tongxuluo/prts", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.15738", "title": "ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models", "authors": [ "Chunjiang Ge", "Sijie Cheng", "Ziming Wang", "Jiale Yuan", "Yuan Gao", "Jun Song", "Shiji Song", "Gao Huang", "Bo Zheng" ], "github": "https://github.com/alibaba/conv-llava", "project_page": "", "abstract": "High-resolution Large Multimodal Models (LMMs) encounter the challenges of excessive visual tokens and quadratic visual complexity. Current high-resolution LMMs address the quadratic complexity while still generating excessive visual tokens. However, the redundancy in visual tokens is the key problem as it leads to more substantial compute. To mitigate this issue, we propose ConvLLaVA, which employs ConvNeXt, a hierarchical backbone, as the visual encoder of LMM to replace Vision Transformer (ViT). ConvLLaVA compresses high-resolution images into information-rich visual features, effectively preventing the generation of excessive visual tokens. To enhance the capabilities of ConvLLaVA, we propose two critical optimizations. Since the low-resolution pretrained ConvNeXt underperforms when directly applied on high resolution, we update it to bridge the gap. Moreover, since ConvNeXt's original compression ratio is inadequate for much higher resolution inputs, we train a successive stage to further compress the visual tokens, thereby reducing redundancy. These optimizations enable ConvLLaVA to support inputs of 1536x1536 resolution generating only 576 visual tokens, capable of handling images of arbitrary aspect ratios. Experimental results demonstrate that our method achieves competitive performance with state-of-the-art models on mainstream benchmarks. The ConvLLaVA model series are publicly available at https://github.com/alibaba/conv-llava.", "github_manual": "", "github_regex": "https://github.com/alibaba/conv-llava", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-27", "arxiv_id": "2405.15682", "title": "The Road Less Scheduled", "authors": [ "Aaron Defazio", "Xingyu", "Yang", "Harsh Mehta", "Konstantin Mishchenko", "Ahmed Khaled", "Ashok Cutkosky" ], "github": "https://github.com/facebookresearch/schedule_free", "project_page": "", "abstract": "Existing learning rate schedules that do not require specification of the optimization stopping step T are greatly out-performed by learning rate schedules that depend on T. We propose an approach that avoids the need for this stopping time by eschewing the use of schedules entirely, while exhibiting state-of-the-art performance compared to schedules across a wide family of problems ranging from convex problems to large-scale deep learning problems. Our Schedule-Free approach introduces no additional hyper-parameters over standard optimizers with momentum. Our method is a direct consequence of a new theory we develop that unifies scheduling and iterate averaging. An open source implementation of our method is available (https://github.com/facebookresearch/schedule_free).", "github_manual": "", "github_regex": "https://github.com/facebookresearch/schedule_free", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.17430", "title": "Matryoshka Multimodal Models", "authors": [ "Mu Cai", "Jianwei Yang", "Jianfeng Gao", "Yong Jae Lee" ], "github": "https://github.com/mu-cai/matryoshka-mm", "project_page": "", "abstract": "Large Multimodal Models (LMMs) such as LLaVA have shown strong performance in visual-linguistic reasoning. These models first embed images into a fixed large number of visual tokens and then feed them into a Large Language Model (LLM). However, this design causes an excessive number of tokens for dense visual scenarios such as high-resolution images and videos, leading to great inefficiency. While token pruning/merging methods do exist, they produce a single length output for each image and do not afford flexibility in trading off information density v.s. efficiency. Inspired by the concept of Matryoshka Dolls, we propose M3: Matryoshka Multimodal Models, which learns to represent visual content as nested sets of visual tokens that capture information across multiple coarse-to-fine granularities. Our approach offers several unique benefits for LMMs: (1) One can explicitly control the visual granularity per test instance during inference, e.g. , adjusting the number of tokens used to represent an image based on the anticipated complexity or simplicity of the content; (2) M3 provides a framework for analyzing the granularity needed for existing datasets, where we find that COCO-style benchmarks only need around ~9 visual tokens to obtain accuracy similar to that of using all 576 tokens; (3) Our approach provides a foundation to explore the best trade-off between performance and visual token length at sample level, where our investigation reveals that a large gap exists between the oracle upper bound and current fixed-scale representations.", "github_manual": "https://github.com/mu-cai/matryoshka-mm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.17247", "title": "An Introduction to Vision-Language Modeling", "authors": [ "Florian Bordes", "Richard Yuanzhe Pang", "Anurag Ajay", "Alexander C. Li", "Adrien Bardes", "Suzanne Petryk", "Oscar Ma\u00f1as", "Zhiqiu Lin", "Anas Mahmoud", "Bargav Jayaraman", "Mark Ibrahim", "Melissa Hall", "Yunyang Xiong", "Jonathan Lebensold", "Candace Ross", "Srihari Jayakumar", "Chuan Guo", "Diane Bouchacourt", "Haider Al-Tahan", "Karthik Padthe", "Vasu Sharma", "Hu Xu", "Xiaoqing Ellen Tan", "Megan Richards", "Samuel Lavoie", "Pietro Astolfi", "Reyhane Askari Hemmat", "Jun Chen", "Kushal Tirumala", "Rim Assouel", "Mazda Moayeri", "Arjang Talattof", "Kamalika Chaudhuri", "Zechun Liu", "Xilun Chen", "Quentin Garrido", "Karen Ullrich", "Aishwarya Agrawal", "Kate Saenko", "Asli Celikyilmaz", "Vikas Chandra" ], "github": "", "project_page": "", "abstract": "Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.16822", "title": "Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic Gaussian Surfels", "authors": [ "Yikai Wang", "Xinzhou Wang", "Zilong Chen", "Zhengyi Wang", "Fuchun Sun", "Jun Zhu" ], "github": "https://github.com/yikaiw/vidu4d", "project_page": "", "abstract": "Video generative models are receiving particular attention given their ability to generate realistic and imaginative frames. Besides, these models are also observed to exhibit strong 3D consistency, significantly enhancing their potential to act as world simulators. In this work, we present Vidu4D, a novel reconstruction model that excels in accurately reconstructing 4D (i.e., sequential 3D) representations from single generated videos, addressing challenges associated with non-rigidity and frame distortion. This capability is pivotal for creating high-fidelity virtual contents that maintain both spatial and temporal coherence. At the core of Vidu4D is our proposed Dynamic Gaussian Surfels (DGS) technique. DGS optimizes time-varying warping functions to transform Gaussian surfels (surface elements) from a static state to a dynamically warped state. This transformation enables a precise depiction of motion and deformation over time. To preserve the structural integrity of surface-aligned Gaussian surfels, we design the warped-state geometric regularization based on continuous warping fields for estimating normals. Additionally, we learn refinements on rotation and scaling parameters of Gaussian surfels, which greatly alleviates texture flickering during the warping process and enhances the capture of fine-grained appearance details. Vidu4D also contains a novel initialization state that provides a proper start for the warping fields in DGS. Equipping Vidu4D with an existing video generative model, the overall framework demonstrates high-fidelity text-to-4D generation in both appearance and geometry.", "github_manual": "https://github.com/yikaiw/vidu4d", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.17428", "title": "NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models", "authors": [ "Chankyu Lee", "Rajarshi Roy", "Mengyao Xu", "Jonathan Raiman", "Mohammad Shoeybi", "Bryan Catanzaro", "Wei Ping" ], "github": "https://github.com/PolyAI-LDN/task-specific-datasets", "project_page": "", "abstract": "Decoder-only large language model (LLM)-based embedding models are beginning to outperform BERT or T5-based embedding models in general-purpose text embedding tasks, including dense vector-based retrieval. In this work, we introduce the NV-Embed model with a variety of architectural designs and training procedures to significantly enhance the performance of LLM as a versatile embedding model, while maintaining its simplicity and reproducibility. For model architecture, we propose a latent attention layer to obtain pooled embeddings, which consistently improves retrieval and downstream task accuracy compared to mean pooling or using the last token embedding from LLMs. To enhance representation learning, we remove the causal attention mask of LLMs during contrastive training. For model training, we introduce a two-stage contrastive instruction-tuning method. It first applies contrastive training with instructions on retrieval datasets, utilizing in-batch negatives and curated hard negative examples. At stage-2, it blends various non-retrieval datasets into instruction tuning, which not only enhances non-retrieval task accuracy but also improves retrieval performance. Combining these techniques, our NV-Embed model, using only publicly available data, has achieved a record-high score of 69.32, ranking No. 1 on the Massive Text Embedding Benchmark (MTEB) (as of May 24, 2024), with 56 tasks, encompassing retrieval, reranking, classification, clustering, and semantic textual similarity tasks. Notably, our model also attains the highest score of 59.36 on 15 retrieval tasks in the MTEB benchmark (also known as BEIR). We will open-source the model at: https://huggingface.co/nvidia/NV-Embed-v1.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PolyAI-LDN/task-specific-datasets", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.16852", "title": "EM Distillation for One-step Diffusion Models", "authors": [ "Sirui Xie", "Zhisheng Xiao", "Diederik P Kingma", "Tingbo Hou", "Ying Nian Wu", "Kevin Patrick Murphy", "Tim Salimans", "Ben Poole", "Ruiqi Gao" ], "github": "", "project_page": "", "abstract": "While diffusion models can learn complex distributions, sampling requires a computationally expensive iterative process. Existing distillation methods enable efficient sampling, but have notable limitations, such as performance degradation with very few sampling steps, reliance on training data access, or mode-seeking optimization that may fail to capture the full distribution. We propose EM Distillation (EMD), a maximum likelihood-based approach that distills a diffusion model to a one-step generator model with minimal loss of perceptual quality. Our approach is derived through the lens of Expectation-Maximization (EM), where the generator parameters are updated using samples from the joint distribution of the diffusion teacher prior and inferred generator latents. We develop a reparametrized sampling scheme and a noise cancellation technique that together stabilizes the distillation process. We further reveal an interesting connection of our method with existing methods that minimize mode-seeking KL. EMD outperforms existing one-step generative methods in terms of FID scores on ImageNet-64 and ImageNet-128, and compares favorably with prior work on distilling text-to-image diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.16888", "title": "Part123: Part-aware 3D Reconstruction from a Single-view Image", "authors": [ "Anran Liu", "Cheng Lin", "Yuan Liu", "Xiaoxiao Long", "Zhiyang Dou", "Hao-Xiang Guo", "Ping Luo", "Wenping Wang" ], "github": "", "project_page": "", "abstract": "Recently, the emergence of diffusion models has opened up new opportunities for single-view reconstruction. However, all the existing methods represent the target object as a closed mesh devoid of any structural information, thus neglecting the part-based structure, which is crucial for many downstream applications, of the reconstructed shape. Moreover, the generated meshes usually suffer from large noises, unsmooth surfaces, and blurry textures, making it challenging to obtain satisfactory part segments using 3D segmentation techniques. In this paper, we present Part123, a novel framework for part-aware 3D reconstruction from a single-view image. We first use diffusion models to generate multiview-consistent images from a given image, and then leverage Segment Anything Model (SAM), which demonstrates powerful generalization ability on arbitrary objects, to generate multiview segmentation masks. To effectively incorporate 2D part-based information into 3D reconstruction and handle inconsistency, we introduce contrastive learning into a neural rendering framework to learn a part-aware feature space based on the multiview segmentation masks. A clustering-based algorithm is also developed to automatically derive 3D part segmentation results from the reconstructed models. Experiments show that our method can generate 3D models with high-quality segmented parts on various objects. Compared to existing unstructured reconstruction methods, the part-aware 3D models from our method benefit some important applications, including feature-preserving reconstruction, primitive fitting, and 3D shape editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.16759", "title": "Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models", "authors": [ "Cristina N. Vasconcelos", "Abdullah Rashwan Austin Waters", "Trevor Walker", "Keyang Xu", "Jimmy Yan", "Rui Qian", "Shixin Luo", "Zarana Parekh", "Andrew Bunner", "Hongliang Fei", "Roopal Garg", "Mandy Guo", "Ivana Kajic", "Yeqing Li", "Henna Nandwani", "Jordi Pont-Tuset", "Yasumasa Onoe", "Sarah Rosston", "Su Wang", "Wenlei Zhou", "Kevin Swersky", "David J. Fleet", "Jason M. Baldridge", "Oliver Wang" ], "github": "", "project_page": "", "abstract": "We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy growing method for stable training of large-scale, high-resolution models. without the needs for cascaded super-resolution components. The key insight stems from careful pre-training of core components, namely, those responsible for text-to-image alignment {\\it vs.} high-resolution rendering. We first demonstrate the benefits of scaling a {\\it Shallow UNet}, with no down(up)-sampling enc(dec)oder. Scaling its deep core layers is shown to improve alignment, object structure, and composition. Building on this core model, we propose a greedy algorithm that grows the architecture into high-resolution end-to-end models, while preserving the integrity of the pre-trained representation, stabilizing training, and reducing the need for large high-resolution datasets. This enables a single stage model capable of generating high-resolution images without the need of a super-resolution cascade. Our key results rely on public datasets and show that we are able to train non-cascaded models up to 8B parameters with no further regularization schemes. Vermeer, our full pipeline model trained with internal datasets to produce 1024x1024 images, without cascades, is preferred by 44.0% vs. 21.4% human evaluators over SDXL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.17258", "title": "Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning", "authors": [ "Runqian Wang", "Soumya Ghosh", "David Cox", "Diego Antognini", "Aude Oliva", "Rogerio Feris", "Leonid Karlinsky" ], "github": "", "project_page": "", "abstract": "Low-rank adapters (LoRA) and their variants are popular parameter-efficient fine-tuning (PEFT) techniques that closely match full model fine-tune performance while requiring only a small number of additional parameters. These additional LoRA parameters are specific to the base model being adapted. When the base model needs to be deprecated and replaced with a new one, all the associated LoRA modules need to be re-trained. Such re-training requires access to the data used to train the LoRA for the original base model. This is especially problematic for commercial cloud applications where the LoRA modules and the base models are hosted by service providers who may not be allowed to host proprietary client task data. To address this challenge, we propose Trans-LoRA -- a novel method for lossless, nearly data-free transfer of LoRAs across base models. Our approach relies on synthetic data to transfer LoRA modules. Using large language models, we design a synthetic data generator to approximate the data-generating process of the observed task data subset. Training on the resulting synthetic dataset transfers LoRA modules to new models. We show the effectiveness of our approach using both LLama and Gemma model families. Our approach achieves lossless (mostly improved) LoRA transfer between models within and across different base model families, and even between different PEFT methods, on a wide variety of tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.17405", "title": "Human4DiT: Free-view Human Video Generation with 4D Diffusion Transformer", "authors": [ "Ruizhi Shao", "Youxin Pang", "Zerong Zheng", "Jingxiang Sun", "Yebin Liu" ], "github": "https://github.com/MooreThreads/Moore-AnimateAnyone", "project_page": "", "abstract": "We present a novel approach for generating high-quality, spatio-temporally coherent human videos from a single image under arbitrary viewpoints. Our framework combines the strengths of U-Nets for accurate condition injection and diffusion transformers for capturing global correlations across viewpoints and time. The core is a cascaded 4D transformer architecture that factorizes attention across views, time, and spatial dimensions, enabling efficient modeling of the 4D space. Precise conditioning is achieved by injecting human identity, camera parameters, and temporal signals into the respective transformers. To train this model, we curate a multi-dimensional dataset spanning images, videos, multi-view data and 3D/4D scans, along with a multi-dimensional training strategy. Our approach overcomes the limitations of previous methods based on GAN or UNet-based diffusion models, which struggle with complex motions and viewpoint changes. Through extensive experiments, we demonstrate our method's ability to synthesize realistic, coherent and free-view human videos, paving the way for advanced multimedia applications in areas such as virtual reality and animation. Our project website is https://human4dit.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MooreThreads/Moore-AnimateAnyone", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.17414", "title": "Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Control", "authors": [ "Zhengfei Kuang", "Shengqu Cai", "Hao He", "Yinghao Xu", "Hongsheng Li", "Leonidas Guibas", "Gordon Wetzstein" ], "github": "https://github.com/CollaborativeVideoDiffusion/CVD", "project_page": "", "abstract": "Research on video generation has recently made tremendous progress, enabling high-quality videos to be generated from text prompts or images. Adding control to the video generation process is an important goal moving forward and recent approaches that condition video generation models on camera trajectories make strides towards it. Yet, it remains challenging to generate a video of the same scene from multiple different camera trajectories. Solutions to this multi-video generation problem could enable large-scale 3D scene generation with editable camera trajectories, among other applications. We introduce collaborative video diffusion (CVD) as an important step towards this vision. The CVD framework includes a novel cross-video synchronization module that promotes consistency between corresponding frames of the same video rendered from different camera poses using an epipolar attention mechanism. Trained on top of a state-of-the-art camera-control module for video generation, CVD generates multiple videos rendered from different camera trajectories with significantly better consistency than baselines, as shown in extensive experiments. Project page: https://collaborativevideodiffusion.github.io/.", "github_manual": "https://github.com/CollaborativeVideoDiffusion/CVD", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.16712", "title": "Zamba: A Compact 7B SSM Hybrid Model", "authors": [ "Paolo Glorioso", "Quentin Anthony", "Yury Tokpanov", "James Whittington", "Jonathan Pilault", "Adam Ibrahim", "Beren Millidge" ], "github": "", "project_page": "", "abstract": "In this technical report, we present Zamba, a novel 7B SSM-transformer hybrid model which achieves competitive performance against leading open-weight models at a comparable scale. Zamba is trained on 1T tokens from openly available datasets and is the best non-transformer model at this scale. Zamba pioneers a unique architecture combining a Mamba backbone with a single shared attention module, thus obtaining the benefits of attention at minimal parameter cost. Due to its architecture, Zamba is significantly faster at inference than comparable transformer models and requires substantially less memory for generation of long sequences. Zamba is pretrained in two phases: the first phase is based on existing web datasets, while the second one consists of annealing the model over high-quality instruct and synthetic datasets, and is characterized by a rapid learning rate decay. We open-source the weights and all checkpoints for Zamba, through both phase 1 and annealing phases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.15757", "title": "Looking Backward: Streaming Video-to-Video Translation with Feature Banks", "authors": [ "Feng Liang", "Akio Kodaira", "Chenfeng Xu", "Masayoshi Tomizuka", "Kurt Keutzer", "Diana Marculescu" ], "github": "https://github.com/Jeff-LiangF/streamv2v", "project_page": "", "abstract": "This paper introduces StreamV2V, a diffusion model that achieves real-time streaming video-to-video (V2V) translation with user prompts. Unlike prior V2V methods using batches to process limited frames, we opt to process frames in a streaming fashion, to support unlimited frames. At the heart of StreamV2V lies a backward-looking principle that relates the present to the past. This is realized by maintaining a feature bank, which archives information from past frames. For incoming frames, StreamV2V extends self-attention to include banked keys and values and directly fuses similar past features into the output. The feature bank is continually updated by merging stored and new features, making it compact but informative. StreamV2V stands out for its adaptability and efficiency, seamlessly integrating with image diffusion models without fine-tuning. It can run 20 FPS on one A100 GPU, being 15x, 46x, 108x, and 158x faster than FlowVid, CoDeF, Rerender, and TokenFlow, respectively. Quantitative metrics and user studies confirm StreamV2V's exceptional ability to maintain temporal consistency.", "github_manual": "https://github.com/Jeff-LiangF/streamv2v", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.17399", "title": "Transformers Can Do Arithmetic with the Right Embeddings", "authors": [ "Sean McLeish", "Arpit Bansal", "Alex Stein", "Neel Jain", "John Kirchenbauer", "Brian R. Bartoldson", "Bhavya Kailkhura", "Abhinav Bhatele", "Jonas Geiping", "Avi Schwarzschild", "Tom Goldstein" ], "github": "https://github.com/mcleish7/arithmetic", "project_page": "", "abstract": "The poor performance of transformers on arithmetic tasks seems to stem in large part from their inability to keep track of the exact position of each digit inside of a large span of digits. We mend this problem by adding an embedding to each digit that encodes its position relative to the start of the number. In addition to the boost these embeddings provide on their own, we show that this fix enables architectural modifications such as input injection and recurrent layers to improve performance even further. With positions resolved, we can study the logical extrapolation ability of transformers. Can they solve arithmetic problems that are larger and more complex than those in their training data? We find that training on only 20 digit numbers with a single GPU for one day, we can reach state-of-the-art performance, achieving up to 99% accuracy on 100 digit addition problems. Finally, we show that these gains in numeracy also unlock improvements on other multi-step reasoning tasks including sorting and multiplication.", "github_manual": "https://github.com/mcleish7/arithmetic", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.16287", "title": "LoGAH: Predicting 774-Million-Parameter Transformers using Graph HyperNetworks with 1/100 Parameters", "authors": [ "Xinyu Zhou", "Boris Knyazev", "Alexia Jolicoeur-Martineau", "Jie Fu" ], "github": "https://github.com/Blackzxy/LoGAH", "project_page": "", "abstract": "A good initialization of deep learning models is essential since it can help them converge better and faster. However, pretraining large models is unaffordable for many researchers, which makes a desired prediction for initial parameters more necessary nowadays. Graph HyperNetworks (GHNs), one approach to predicting model parameters, have recently shown strong performance in initializing large vision models. Unfortunately, predicting parameters of very wide networks relies on copying small chunks of parameters multiple times and requires an extremely large number of parameters to support full prediction, which greatly hinders its adoption in practice. To address this limitation, we propose LoGAH (Low-rank GrAph Hypernetworks), a GHN with a low-rank parameter decoder that expands to significantly wider networks without requiring as excessive increase of parameters as in previous attempts. LoGAH allows us to predict the parameters of 774-million large neural networks in a memory-efficient manner. We show that vision and language models (i.e., ViT and GPT-2) initialized with LoGAH achieve better performance than those initialized randomly or using existing hypernetworks. Furthermore, we show promising transfer learning results w.r.t. training LoGAH on small datasets and using the predicted parameters to initialize for larger tasks. We provide the codes in https://github.com/Blackzxy/LoGAH .", "github_manual": "", "github_regex": "https://github.com/Blackzxy/LoGAH", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-28", "arxiv_id": "2405.16537", "title": "I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models", "authors": [ "Wenqi Ouyang", "Yi Dong", "Lei Yang", "Jianlou Si", "Xingang Pan" ], "github": "", "project_page": "", "abstract": "The remarkable generative capabilities of diffusion models have motivated extensive research in both image and video editing. Compared to video editing which faces additional challenges in the time dimension, image editing has witnessed the development of more diverse, high-quality approaches and more capable software like Photoshop. In light of this gap, we introduce a novel and generic solution that extends the applicability of image editing tools to videos by propagating edits from a single frame to the entire video using a pre-trained image-to-video model. Our method, dubbed I2VEdit, adaptively preserves the visual and motion integrity of the source video depending on the extent of the edits, effectively handling global edits, local edits, and moderate shape changes, which existing methods cannot fully achieve. At the core of our method are two main processes: Coarse Motion Extraction to align basic motion patterns with the original video, and Appearance Refinement for precise adjustments using fine-grained attention matching. We also incorporate a skip-interval strategy to mitigate quality degradation from auto-regressive generation across multiple video clips. Experimental results demonstrate our framework's superior performance in fine-grained video editing, proving its capability to produce high-quality, temporally consistent outputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-29", "arxiv_id": "2405.18407", "title": "Phased Consistency Model", "authors": [ "Fu-Yun Wang", "Zhaoyang Huang", "Alexander William Bergman", "Dazhong Shen", "Peng Gao", "Michael Lingelbach", "Keqiang Sun", "Weikang Bian", "Guanglu Song", "Yu Liu", "Hongsheng Li", "Xiaogang Wang" ], "github": "https://github.com/G-U-N/Phased-Consistency-Model", "project_page": "", "abstract": "The consistency model (CM) has recently made significant progress in accelerating the generation of diffusion models. However, its application to high-resolution, text-conditioned image generation in the latent space (a.k.a., LCM) remains unsatisfactory. In this paper, we identify three key flaws in the current design of LCM. We investigate the reasons behind these limitations and propose the Phased Consistency Model (PCM), which generalizes the design space and addresses all identified limitations. Our evaluations demonstrate that PCM significantly outperforms LCM across 1--16 step generation settings. While PCM is specifically designed for multi-step refinement, it achieves even superior or comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show that PCM's methodology is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator. More details are available at https://g-u-n.github.io/projects/pcm/.", "github_manual": "https://github.com/G-U-N/Phased-Consistency-Model", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-29", "arxiv_id": "2405.18377", "title": "LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models", "authors": [ "Anthony Sarah", "Sharath Nittur Sridhar", "Maciej Szankin", "Sairam Sundaresan" ], "github": "", "project_page": "", "abstract": "The abilities of modern large language models (LLMs) in solving natural language processing, complex reasoning, sentiment analysis and other tasks have been extraordinary which has prompted their extensive adoption. Unfortunately, these abilities come with very high memory and computational costs which precludes the use of LLMs on most hardware platforms. To mitigate this, we propose an effective method of finding Pareto-optimal network architectures based on LLaMA2-7B using one-shot NAS. In particular, we fine-tune LLaMA2-7B only once and then apply genetic algorithm-based search to find smaller, less computationally complex network architectures. We show that, for certain standard benchmark tasks, the pre-trained LLaMA2-7B network is unnecessarily large and complex. More specifically, we demonstrate a 1.5x reduction in model size and 1.3x speedup in throughput for certain tasks with negligible drop in accuracy. In addition to finding smaller, higher-performing network architectures, our method does so more effectively and efficiently than certain pruning or sparsification techniques. Finally, we demonstrate how quantization is complementary to our method and that the size and complexity of the networks we find can be further decreased using quantization. We believe that our work provides a way to automatically create LLMs which can be used on less expensive and more readily available hardware platforms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-29", "arxiv_id": "2405.18047", "title": "2BP: 2-Stage Backpropagation", "authors": [ "Christopher Rae", "Joseph K. L. Lee", "James Richings" ], "github": "", "project_page": "", "abstract": "As Deep Neural Networks (DNNs) grow in size and complexity, they often exceed the memory capacity of a single accelerator, necessitating the sharding of model parameters across multiple accelerators. Pipeline parallelism is a commonly used sharding strategy for training large DNNs. However, current implementations of pipeline parallelism are being unintentionally bottlenecked by the automatic differentiation tools provided by ML frameworks. This paper introduces 2-stage backpropagation (2BP). By splitting the backward propagation step into two separate stages, we can reduce idle compute time. We tested 2BP on various model architectures and pipelining schedules, achieving increases in throughput in all cases. Using 2BP, we were able to achieve a 1.70x increase in throughput compared to traditional methods when training a LLaMa-like transformer with 7 billion parameters across 4 GPUs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-29", "arxiv_id": "2405.17991", "title": "VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections", "authors": [ "Roy Miles", "Pradyumna Reddy", "Ismail Elezi", "Jiankang Deng" ], "github": "https://github.com/roymiles/VeLoRA", "project_page": "", "abstract": "Large language models (LLMs) have recently emerged as powerful tools for tackling many language-processing tasks. Despite their success, training and fine-tuning these models is still far too computationally and memory intensive. In this paper, we identify and characterise the important components needed for effective model convergence using gradient descent. In doing so we find that the intermediate activations used to implement backpropagation can be excessively compressed without incurring any degradation in performance. This result leads us to a cheap and memory-efficient algorithm for both fine-tuning and pre-training LLMs. The proposed algorithm simply divides the tokens up into smaller sub-tokens before projecting them onto a fixed 1-dimensional subspace during the forward pass. These features are then coarsely reconstructed during the backward pass to implement the update rules. We confirm the effectiveness of our algorithm as being complimentary to many state-of-the-art PEFT methods on the VTAB-1k fine-tuning benchmark. Furthermore, we outperform QLoRA for fine-tuning LLaMA and show competitive performance against other memory-efficient pre-training methods on the large-scale C4 dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/roymiles/VeLoRA", "github_api_added_by": "auto" }, { "date": "2024-05-29", "arxiv_id": "2405.17976", "title": "Yuan 2.0-M32: Mixture of Experts with Attention Router", "authors": [ "Shaohua Wu", "Jiangang Luo", "Xi Chen", "Lingjun Li", "Xudong Zhao", "Tong Yu", "Chao Wang", "Yue Wang", "Fei Wang", "Weixu Qiao", "Houbo He", "Zeru Zhang", "Zeyu Sun", "Junxiong Mao", "Chong Shen" ], "github": "https://github.com/IEIT-Yuan/Yuan2.0-M32", "project_page": "", "abstract": "Yuan 2.0-M32, with a similar base architecture as Yuan-2.0 2B, uses a mixture-of-experts architecture with 32 experts of which 2 experts are active. A new router network, Attention Router, is proposed and adopted for a more efficient selection of experts, which boosts the accuracy of 3.8% compared to the model with classical router network. Yuan 2.0-M32 is trained with 2000B tokens from scratch, and the training computation consumption is only 9.25% of a dense model at the same parameter scale. Yuan 2.0-M32 demonstrates competitive capability on coding, math, and various domains of expertise, with only 3.7B active parameters of 40B in total, and 7.4 GFlops forward computation per token, both of which are only 1/19 of Llama3-70B. Yuan 2.0-M32 surpass Llama3-70B on MATH and ARC-Challenge benchmark, with accuracy of 55.89 and 95.8 respectively. The models and source codes of Yuan 2.0-M32 are released at Github.", "github_manual": "https://github.com/IEIT-Yuan/Yuan2.0-M32", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-29", "arxiv_id": "2405.18426", "title": "GFlow: Recovering 4D World from Monocular Video", "authors": [ "Shizun Wang", "Xingyi Yang", "Qiuhong Shen", "Zhenxiang Jiang", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Reconstructing 4D scenes from video inputs is a crucial yet challenging task. Conventional methods usually rely on the assumptions of multi-view video inputs, known camera parameters, or static scenes, all of which are typically absent under in-the-wild scenarios. In this paper, we relax all these constraints and tackle a highly ambitious but practical task, which we termed as AnyV4D: we assume only one monocular video is available without any camera parameters as input, and we aim to recover the dynamic 4D world alongside the camera poses. To this end, we introduce GFlow, a new framework that utilizes only 2D priors (depth and optical flow) to lift a video (3D) to a 4D explicit representation, entailing a flow of Gaussian splatting through space and time. GFlow first clusters the scene into still and moving parts, then applies a sequential optimization process that optimizes camera poses and the dynamics of 3D Gaussian points based on 2D priors and scene clustering, ensuring fidelity among neighboring points and smooth movement across frames. Since dynamic scenes always introduce new content, we also propose a new pixel-wise densification strategy for Gaussian points to integrate new visual content. Moreover, GFlow transcends the boundaries of mere 4D reconstruction; it also enables tracking of any points across frames without the need for prior training and segments moving objects from the scene in an unsupervised way. Additionally, the camera poses of each frame can be derived from GFlow, allowing for rendering novel views of a video scene through changing camera pose. By employing the explicit representation, we may readily conduct scene-level or object-level editing as desired, underscoring its versatility and power. Visit our project website at: https://littlepure2333.github.io/GFlow", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-29", "arxiv_id": "2405.18386", "title": "Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning", "authors": [ "Yixiao Zhang", "Yukara Ikemiya", "Woosung Choi", "Naoki Murata", "Marco A. Mart\u00ednez-Ram\u00edrez", "Liwei Lin", "Gus Xia", "Wei-Hsiang Liao", "Yuki Mitsufuji", "Simon Dixon" ], "github": "https://github.com/ldzhangyx/instruct-MusicGen", "project_page": "", "abstract": "Recent advances in text-to-music editing, which employ text queries to modify music (e.g.\\ by changing its style or adjusting instrumental components), present unique challenges and opportunities for AI-assisted music creation. Previous approaches in this domain have been constrained by the necessity to train specific editing models from scratch, which is both resource-intensive and inefficient; other research uses large language models to predict edited music, resulting in imprecise audio reconstruction. To Combine the strengths and address these limitations, we introduce Instruct-MusicGen, a novel approach that finetunes a pretrained MusicGen model to efficiently follow editing instructions such as adding, removing, or separating stems. Our approach involves a modification of the original MusicGen architecture by incorporating a text fusion module and an audio fusion module, which allow the model to process instruction texts and audio inputs concurrently and yield the desired edited music. Remarkably, Instruct-MusicGen only introduces 8% new parameters to the original MusicGen model and only trains for 5K steps, yet it achieves superior performance across all tasks compared to existing baselines, and demonstrates performance comparable to the models trained for specific tasks. This advancement not only enhances the efficiency of text-to-music editing but also broadens the applicability of music language models in dynamic music production environments.", "github_manual": "https://github.com/ldzhangyx/instruct-MusicGen", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-29", "arxiv_id": "2405.18424", "title": "3DitScene: Editing Any Scene via Language-guided Disentangled Gaussian Splatting", "authors": [ "Qihang Zhang", "Yinghao Xu", "Chaoyang Wang", "Hsin-Ying Lee", "Gordon Wetzstein", "Bolei Zhou", "Ceyuan Yang" ], "github": "https://github.com/zqh0253/3DitScene", "project_page": "", "abstract": "Scene image editing is crucial for entertainment, photography, and advertising design. Existing methods solely focus on either 2D individual object or 3D global scene editing. This results in a lack of a unified approach to effectively control and manipulate scenes at the 3D level with different levels of granularity. In this work, we propose 3DitScene, a novel and unified scene editing framework leveraging language-guided disentangled Gaussian Splatting that enables seamless editing from 2D to 3D, allowing precise control over scene composition and individual objects. We first incorporate 3D Gaussians that are refined through generative priors and optimization techniques. Language features from CLIP then introduce semantics into 3D geometry for object disentanglement. With the disentangled Gaussians, 3DitScene allows for manipulation at both the global and individual levels, revolutionizing creative expression and empowering control over scenes and objects. Experimental results demonstrate the effectiveness and versatility of 3DitScene in scene image editing. Code and online demo can be found at our project homepage: https://zqh0253.github.io/3DitScene/.", "github_manual": "https://github.com/zqh0253/3DitScene", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-30", "arxiv_id": "2405.19327", "title": "MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series", "authors": [ "Ge Zhang", "Scott Qu", "Jiaheng Liu", "Chenchen Zhang", "Chenghua Lin", "Chou Leuang Yu", "Danny Pan", "Esther Cheng", "Jie Liu", "Qunshu Lin", "Raven Yuan", "Tuney Zheng", "Wei Pang", "Xinrun Du", "Yiming Liang", "Yinghao Ma", "Yizhi Li", "Ziyang Ma", "Bill Lin", "Emmanouil Benetos", "Huan Yang", "Junting Zhou", "Kaijing Ma", "Minghao Liu", "Morry Niu", "Noah Wang", "Quehry Que", "Ruibo Liu", "Sine Liu", "Shawn Guo", "Soren Gao", "Wangchunshu Zhou", "Xinyue Zhang", "Yizhi Zhou", "Yubo Wang", "Yuelin Bai", "Yuhan Zhang", "Yuxiang Zhang", "Zenith Wang", "Zhenzhu Yang", "Zijian Zhao", "Jiajun Zhang", "Wanli Ouyang", "Wenhao Huang", "Wenhu Chen" ], "github": "https://github.com/multimodal-art-projection/MAP-NEO", "project_page": "", "abstract": "Large Language Models (LLMs) have made great strides in recent years to achieve unprecedented performance across different tasks. However, due to commercial interest, the most competitive models like GPT, Gemini, and Claude have been gated behind proprietary interfaces without disclosing the training details. Recently, many institutions have open-sourced several strong LLMs like LLaMA-3, comparable to existing closed-source LLMs. However, only the model's weights are provided with most details (e.g., intermediate checkpoints, pre-training corpus, and training code, etc.) being undisclosed. To improve the transparency of LLMs, the research community has formed to open-source truly open LLMs (e.g., Pythia, Amber, OLMo), where more details (e.g., pre-training corpus and training code) are being provided. These models have greatly advanced the scientific study of these large models including their strengths, weaknesses, biases and risks. However, we observe that the existing truly open LLMs on reasoning, knowledge, and coding tasks are still inferior to existing state-of-the-art LLMs with similar model sizes. To this end, we open-source MAP-Neo, a highly capable and transparent bilingual language model with 7B parameters trained from scratch on 4.5T high-quality tokens. Our MAP-Neo is the first fully open-sourced bilingual LLM with comparable performance compared to existing state-of-the-art LLMs. Moreover, we open-source all details to reproduce our MAP-Neo, where the cleaned pre-training corpus, data cleaning pipeline, checkpoints, and well-optimized training/evaluation framework are provided. Finally, we hope our MAP-Neo will enhance and strengthen the open research community and inspire more innovations and creativities to facilitate the further improvements of LLMs.", "github_manual": "https://github.com/multimodal-art-projection/MAP-NEO", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-30", "arxiv_id": "2405.18870", "title": "LLMs achieve adult human performance on higher-order theory of mind tasks", "authors": [ "Winnie Street", "John Oliver Siy", "Geoff Keeling", "Adrien Baranes", "Benjamin Barnett", "Michael McKibben", "Tatenda Kanyere", "Alison Lentz", "Blaise Aguera y Arcas", "Robin I. M. Dunbar" ], "github": "", "project_page": "", "abstract": "This paper examines the extent to which large language models (LLMs) have developed higher-order theory of mind (ToM); the human ability to reason about multiple mental and emotional states in a recursive manner (e.g. I think that you believe that she knows). This paper builds on prior work by introducing a handwritten test suite -- Multi-Order Theory of Mind Q&A -- and using it to compare the performance of five LLMs to a newly gathered adult human benchmark. We find that GPT-4 and Flan-PaLM reach adult-level and near adult-level performance on ToM tasks overall, and that GPT-4 exceeds adult performance on 6th order inferences. Our results suggest that there is an interplay between model size and finetuning for the realisation of ToM abilities, and that the best-performing LLMs have developed a generalised capacity for ToM. Given the role that higher-order ToM plays in a wide range of cooperative and competitive human behaviours, these findings have significant implications for user-facing LLM applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-30", "arxiv_id": "2405.18750", "title": "T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback", "authors": [ "Jiachen Li", "Weixi Feng", "Tsu-Jui Fu", "Xinyi Wang", "Sugato Basu", "Wenhu Chen", "William Yang Wang" ], "github": "https://github.com/Ji4chenLi/t2v-turbo", "project_page": "", "abstract": "Diffusion-based text-to-video (T2V) models have achieved significant success but continue to be hampered by the slow sampling speed of their iterative sampling processes. To address the challenge, consistency models have been proposed to facilitate fast inference, albeit at the cost of sample quality. In this work, we aim to break the quality bottleneck of a video consistency model (VCM) to achieve both fast and high-quality video generation. We introduce T2V-Turbo, which integrates feedback from a mixture of differentiable reward models into the consistency distillation (CD) process of a pre-trained T2V model. Notably, we directly optimize rewards associated with single-step generations that arise naturally from computing the CD loss, effectively bypassing the memory constraints imposed by backpropagating gradients through an iterative sampling process. Remarkably, the 4-step generations from our T2V-Turbo achieve the highest total score on VBench, even surpassing Gen-2 and Pika. We further conduct human evaluations to corroborate the results, validating that the 4-step generations from our T2V-Turbo are preferred over the 50-step DDIM samples from their teacher models, representing more than a tenfold acceleration while improving video generation quality.", "github_manual": "https://github.com/Ji4chenLi/t2v-turbo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-30", "arxiv_id": "2405.19331", "title": "NPGA: Neural Parametric Gaussian Avatars", "authors": [ "Simon Giebenhain", "Tobias Kirschstein", "Martin R\u00fcnz", "Lourdes Agapito", "Matthias Nie\u00dfner" ], "github": "", "project_page": "", "abstract": "The creation of high-fidelity, digital versions of human heads is an important stepping stone in the process of further integrating virtual components into our everyday lives. Constructing such avatars is a challenging research problem, due to a high demand for photo-realism and real-time rendering performance. In this work, we propose Neural Parametric Gaussian Avatars (NPGA), a data-driven approach to create high-fidelity, controllable avatars from multi-view video recordings. We build our method around 3D Gaussian Splatting for its highly efficient rendering and to inherit the topological flexibility of point clouds. In contrast to previous work, we condition our avatars' dynamics on the rich expression space of neural parametric head models (NPHM), instead of mesh-based 3DMMs. To this end, we distill the backward deformation field of our underlying NPHM into forward deformations which are compatible with rasterization-based rendering. All remaining fine-scale, expression-dependent details are learned from the multi-view videos. To increase the representational capacity of our avatars, we augment the canonical Gaussian point cloud using per-primitive latent features which govern its dynamic behavior. To regularize this increased dynamic expressivity, we propose Laplacian terms on the latent features and predicted dynamics. We evaluate our method on the public NeRSemble dataset, demonstrating that NPGA significantly outperforms the previous state-of-the-art avatars on the self-reenactment task by 2.6 PSNR. Furthermore, we demonstrate accurate animation capabilities from real-world monocular videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-30", "arxiv_id": "2405.18503", "title": "SoundCTM: Uniting Score-based and Consistency Models for Text-to-Sound Generation", "authors": [ "Koichi Saito", "Dongjun Kim", "Takashi Shibuya", "Chieh-Hsin Lai", "Zhi Zhong", "Yuhta Takida", "Yuki Mitsufuji" ], "github": "https://github.com/sony/soundctm", "project_page": "", "abstract": "Sound content is an indispensable element for multimedia works such as video games, music, and films. Recent high-quality diffusion-based sound generation models can serve as valuable tools for the creators. However, despite producing high-quality sounds, these models often suffer from slow inference speeds. This drawback burdens creators, who typically refine their sounds through trial and error to align them with their artistic intentions. To address this issue, we introduce Sound Consistency Trajectory Models (SoundCTM). Our model enables flexible transitioning between high-quality 1-step sound generation and superior sound quality through multi-step generation. This allows creators to initially control sounds with 1-step samples before refining them through multi-step generation. While CTM fundamentally achieves flexible 1-step and multi-step generation, its impressive performance heavily depends on an additional pretrained feature extractor and an adversarial loss, which are expensive to train and not always available in other domains. Thus, we reframe CTM's training framework and introduce a novel feature distance by utilizing the teacher's network for a distillation loss. Additionally, while distilling classifier-free guided trajectories, we train conditional and unconditional student models simultaneously and interpolate between these models during inference. We also propose training-free controllable frameworks for SoundCTM, leveraging its flexible sampling capability. SoundCTM achieves both promising 1-step and multi-step real-time sound generation without using any extra off-the-shelf networks. Furthermore, we demonstrate SoundCTM's capability of controllable sound generation in a training-free manner.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sony/soundctm", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-30", "arxiv_id": "2405.18515", "title": "Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication", "authors": [ "Yunuo Chen", "Tianyi Xie", "Zeshun Zong", "Xuan Li", "Feng Gao", "Yin Yang", "Ying Nian Wu", "Chenfanfu Jiang" ], "github": "https://github.com/yunuoch/Atlas3D", "project_page": "", "abstract": "Existing diffusion-based text-to-3D generation methods primarily focus on producing visually realistic shapes and appearances, often neglecting the physical constraints necessary for downstream tasks. Generated models frequently fail to maintain balance when placed in physics-based simulations or 3D printed. This balance is crucial for satisfying user design intentions in interactive gaming, embodied AI, and robotics, where stable models are needed for reliable interaction. Additionally, stable models ensure that 3D-printed objects, such as figurines for home decoration, can stand on their own without requiring additional supports. To fill this gap, we introduce Atlas3D, an automatic and easy-to-implement method that enhances existing Score Distillation Sampling (SDS)-based text-to-3D tools. Atlas3D ensures the generation of self-supporting 3D models that adhere to physical laws of stability under gravity, contact, and friction. Our approach combines a novel differentiable simulation-based loss function with physically inspired regularization, serving as either a refinement or a post-processing module for existing frameworks. We verify Atlas3D's efficacy through extensive generation tasks and validate the resulting 3D models in both simulated and real-world environments.", "github_manual": "https://github.com/yunuoch/Atlas3D", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-30", "arxiv_id": "2405.18991", "title": "EasyAnimate: A High-Performance Long Video Generation Method based on Transformer Architecture", "authors": [ "Jiaqi Xu", "Xinyi Zou", "Kunzhe Huang", "Yunkuo Chen", "Bo Liu", "MengLi Cheng", "Xing Shi", "Jun Huang" ], "github": "https://github.com/aigc-apps/EasyAnimate", "project_page": "", "abstract": "This paper presents EasyAnimate, an advanced method for video generation that leverages the power of transformer architecture for high-performance outcomes. We have expanded the DiT framework originally designed for 2D image synthesis to accommodate the complexities of 3D video generation by incorporating a motion module block. It is used to capture temporal dynamics, thereby ensuring the production of consistent frames and seamless motion transitions. The motion module can be adapted to various DiT baseline methods to generate video with different styles. It can also generate videos with different frame rates and resolutions during both training and inference phases, suitable for both images and videos. Moreover, we introduce slice VAE, a novel approach to condense the temporal axis, facilitating the generation of long duration videos. Currently, EasyAnimate exhibits the proficiency to generate videos with 144 frames. We provide a holistic ecosystem for video production based on DiT, encompassing aspects such as data pre-processing, VAE training, DiT models training (both the baseline model and LoRA model), and end-to-end video inference. Code is available at: https://github.com/aigc-apps/EasyAnimate. We are continuously working to enhance the performance of our method.", "github_manual": "", "github_regex": "https://github.com/aigc-apps/EasyAnimate", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-30", "arxiv_id": "2405.19107", "title": "Offline Regularised Reinforcement Learning for Large Language Models Alignment", "authors": [ "Pierre Harvey Richemond", "Yunhao Tang", "Daniel Guo", "Daniele Calandriello", "Mohammad Gheshlaghi Azar", "Rafael Rafailov", "Bernardo Avila Pires", "Eugene Tarassov", "Lucas Spangher", "Will Ellsworth", "Aliaksei Severyn", "Jonathan Mallinson", "Lior Shani", "Gil Shamir", "Rishabh Joshi", "Tianqi Liu", "Remi Munos", "Bilal Piot" ], "github": "", "project_page": "", "abstract": "The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from preference data. This involves building datasets where each element is a quadruplet composed of a prompt, two independent responses (completions of the prompt) and a human preference between the two independent responses, yielding a preferred and a dis-preferred response. Such data is typically scarce and expensive to collect. On the other hand, single-trajectory datasets where each element is a triplet composed of a prompt, a response and a human feedback is naturally more abundant. The canonical element of such datasets is for instance an LLM's response to a user's prompt followed by a user's feedback such as a thumbs-up/down. Consequently, in this work, we propose DRO, or Direct Reward Optimisation, as a framework and associated algorithms that do not require pairwise preferences. DRO uses a simple mean-squared objective that can be implemented in various ways. We validate our findings empirically, using T5 encoder-decoder language models, and show DRO's performance over selected baselines such as Kahneman-Tversky Optimization (KTO). Thus, we confirm that DRO is a simple and empirically compelling method for single-trajectory policy optimisation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-30", "arxiv_id": "2405.18669", "title": "Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities", "authors": [ "Vicky Zayats", "Peter Chen", "Melissa Merrari", "Dirk Padfield" ], "github": "", "project_page": "", "abstract": "Integrating multiple generative foundation models, especially those trained on different modalities, into something greater than the sum of its parts poses significant challenges. Two key hurdles are the availability of aligned data (concepts that contain similar meaning but is expressed differently in different modalities), and effectively leveraging unimodal representations in cross-domain generative tasks, without compromising their original unimodal capabilities. We propose Zipper, a multi-tower decoder architecture that addresses these concerns by using cross-attention to flexibly compose multimodal generative models from independently pre-trained unimodal decoders. In our experiments fusing speech and text modalities, we show the proposed architecture performs very competitively in scenarios with limited aligned text-speech data. We also showcase the flexibility of our model to selectively maintain unimodal (e.g., text-to-text generation) generation performance by freezing the corresponding modal tower (e.g. text). In cross-modal tasks such as automatic speech recognition (ASR) where the output modality is text, we show that freezing the text backbone results in negligible performance degradation. In cross-modal tasks such as text-to-speech generation (TTS) where the output modality is speech, we show that using a pre-trained speech backbone results in superior performance to the baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-30", "arxiv_id": "2405.19325", "title": "Nearest Neighbor Speculative Decoding for LLM Generation and Attribution", "authors": [ "Minghan Li", "Xilun Chen", "Ari Holtzman", "Beidi Chen", "Jimmy Lin", "Wen-tau Yih", "Xi Victoria Lin" ], "github": "https://github.com/facebookresearch/NEST", "project_page": "", "abstract": "Large language models (LLMs) often hallucinate and lack the ability to provide attribution for their generations. Semi-parametric LMs, such as kNN-LM, approach these limitations by refining the output of an LM for a given prompt using its nearest neighbor matches in a non-parametric data store. However, these models often exhibit slow inference speeds and produce non-fluent texts. In this paper, we introduce Nearest Neighbor Speculative Decoding (NEST), a novel semi-parametric language modeling approach that is capable of incorporating real-world text spans of arbitrary length into the LM generations and providing attribution to their sources. NEST performs token-level retrieval at each inference step to compute a semi-parametric mixture distribution and identify promising span continuations in a corpus. It then uses an approximate speculative decoding procedure that accepts a prefix of the retrieved span or generates a new token. NEST significantly enhances the generation quality and attribution rate of the base LM across a variety of knowledge-intensive tasks, surpassing the conventional kNN-LM method and performing competitively with in-context retrieval augmentation. In addition, NEST substantially improves the generation speed, achieving a 1.8x speedup in inference time when applied to Llama-2-Chat 70B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/NEST", "github_api_added_by": "auto" }, { "date": "2024-05-30", "arxiv_id": "2405.19320", "title": "Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF", "authors": [ "Shicong Cen", "Jincheng Mei", "Katayoon Goshvadi", "Hanjun Dai", "Tong Yang", "Sherry Yang", "Dale Schuurmans", "Yuejie Chi", "Bo Dai" ], "github": "", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) has demonstrated great promise in aligning large language models (LLMs) with human preference. Depending on the availability of preference data, both online and offline RLHF are active areas of investigation. A key bottleneck is understanding how to incorporate uncertainty estimation in the reward function learned from the preference data for RLHF, regardless of how the preference data is collected. While the principles of optimism or pessimism under uncertainty are well-established in standard reinforcement learning (RL), a practically-implementable and theoretically-grounded form amenable to large language models is not yet available, as standard techniques for constructing confidence intervals become intractable under arbitrary policy parameterizations. In this paper, we introduce a unified approach to online and offline RLHF -- value-incentivized preference optimization (VPO) -- which regularizes the maximum-likelihood estimate of the reward function with the corresponding value function, modulated by a sign to indicate whether the optimism or pessimism is chosen. VPO also directly optimizes the policy with implicit reward modeling, and therefore shares a simpler RLHF pipeline similar to direct preference optimization. Theoretical guarantees of VPO are provided for both online and offline settings, matching the rates of their standard RL counterparts. Moreover, experiments on text summarization and dialog verify the practicality and effectiveness of VPO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-30", "arxiv_id": "2405.19332", "title": "Self-Exploring Language Models: Active Preference Elicitation for Online Alignment", "authors": [ "Shenao Zhang", "Donghan Yu", "Hiteshi Sharma", "Ziyi Yang", "Shuohang Wang", "Hany Hassan", "Zhaoran Wang" ], "github": "https://github.com/shenao-zhang/SELM", "project_page": "", "abstract": "Preference optimization, particularly through Reinforcement Learning from Human Feedback (RLHF), has achieved significant success in aligning Large Language Models (LLMs) to adhere to human intentions. Unlike offline alignment with a fixed dataset, online feedback collection from humans or AI on model generations typically leads to more capable reward models and better-aligned LLMs through an iterative process. However, achieving a globally accurate reward model requires systematic exploration to generate diverse responses that span the vast space of natural language. Random sampling from standard reward-maximizing LLMs alone is insufficient to fulfill this requirement. To address this issue, we propose a bilevel objective optimistically biased towards potentially high-reward responses to actively explore out-of-distribution regions. By solving the inner-level problem with the reparameterized reward function, the resulting algorithm, named Self-Exploring Language Models (SELM), eliminates the need for a separate RM and iteratively updates the LLM with a straightforward objective. Compared to Direct Preference Optimization (DPO), the SELM objective reduces indiscriminate favor of unseen extrapolations and enhances exploration efficiency. Our experimental results demonstrate that when finetuned on Zephyr-7B-SFT and Llama-3-8B-Instruct models, SELM significantly boosts the performance on instruction-following benchmarks such as MT-Bench and AlpacaEval 2.0, as well as various standard academic benchmarks in different settings. Our code and models are available at https://github.com/shenao-zhang/SELM.", "github_manual": "", "github_regex": "https://github.com/shenao-zhang/SELM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-31", "arxiv_id": "2405.20289", "title": "DITTO-2: Distilled Diffusion Inference-Time T-Optimization for Music Generation", "authors": [ "Zachary Novack", "Julian McAuley", "Taylor Berg-Kirkpatrick", "Nicholas Bryan" ], "github": "", "project_page": "", "abstract": "Controllable music generation methods are critical for human-centered AI-based music creation, but are currently limited by speed, quality, and control design trade-offs. Diffusion Inference-Time T-optimization (DITTO), in particular, offers state-of-the-art results, but is over 10x slower than real-time, limiting practical use. We propose Distilled Diffusion Inference-Time T -Optimization (or DITTO-2), a new method to speed up inference-time optimization-based control and unlock faster-than-real-time generation for a wide-variety of applications such as music inpainting, outpainting, intensity, melody, and musical structure control. Our method works by (1) distilling a pre-trained diffusion model for fast sampling via an efficient, modified consistency or consistency trajectory distillation process (2) performing inference-time optimization using our distilled model with one-step sampling as an efficient surrogate optimization task and (3) running a final multi-step sampling generation (decoding) using our estimated noise latents for best-quality, fast, controllable generation. Through thorough evaluation, we find our method not only speeds up generation over 10-20x, but simultaneously improves control adherence and generation quality all at once. Furthermore, we apply our approach to a new application of maximizing text adherence (CLAP score) and show we can convert an unconditional diffusion model without text inputs into a model that yields state-of-the-art text control. Sound examples can be found at https://ditto-music.github.io/ditto2/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-31", "arxiv_id": "2405.20327", "title": "GECO: Generative Image-to-3D within a SECOnd", "authors": [ "Chen Wang", "Jiatao Gu", "Xiaoxiao Long", "Yuan Liu", "Lingjie Liu" ], "github": "https://github.com/cwchenwang/geco", "project_page": "", "abstract": "3D generation has seen remarkable progress in recent years. Existing techniques, such as score distillation methods, produce notable results but require extensive per-scene optimization, impacting time efficiency. Alternatively, reconstruction-based approaches prioritize efficiency but compromise quality due to their limited handling of uncertainty. We introduce GECO, a novel method for high-quality 3D generative modeling that operates within a second. Our approach addresses the prevalent issues of uncertainty and inefficiency in current methods through a two-stage approach. In the initial stage, we train a single-step multi-view generative model with score distillation. Then, a second-stage distillation is applied to address the challenge of view inconsistency from the multi-view prediction. This two-stage process ensures a balanced approach to 3D generation, optimizing both quality and efficiency. Our comprehensive experiments demonstrate that GECO achieves high-quality image-to-3D generation with an unprecedented level of efficiency.", "github_manual": "https://github.com/cwchenwang/geco", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-31", "arxiv_id": "2405.20222", "title": "MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model", "authors": [ "Muyao Niu", "Xiaodong Cun", "Xintao Wang", "Yong Zhang", "Ying Shan", "Yinqiang Zheng" ], "github": "https://github.com/MyNiuuu/MOFA-Video", "project_page": "", "abstract": "We present MOFA-Video, an advanced controllable image animation method that generates video from the given image using various additional controllable signals (such as human landmarks reference, manual trajectories, and another even provided video) or their combinations. This is different from previous methods which only can work on a specific motion domain or show weak control abilities with diffusion prior. To achieve our goal, we design several domain-aware motion field adapters (\\ie, MOFA-Adapters) to control the generated motions in the video generation pipeline. For MOFA-Adapters, we consider the temporal motion consistency of the video and generate the dense motion flow from the given sparse control conditions first, and then, the multi-scale features of the given image are wrapped as a guided feature for stable video diffusion generation. We naively train two motion adapters for the manual trajectories and the human landmarks individually since they both contain sparse information about the control. After training, the MOFA-Adapters in different domains can also work together for more controllable video generation.", "github_manual": "https://github.com/MyNiuuu/MOFA-Video", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-31", "arxiv_id": "2405.20204", "title": "Jina CLIP: Your CLIP Model Is Also Your Text Retriever", "authors": [ "Andreas Koukounas", "Georgios Mastrapas", "Michael G\u00fcnther", "Bo Wang", "Scott Martens", "Isabelle Mohr", "Saba Sturua", "Mohammad Kalim Akram", "Joan Fontanals Mart\u00ednez", "Saahil Ognawala", "Susana Guzman", "Maximilian Werk", "Nan Wang", "Han Xiao" ], "github": "", "project_page": "", "abstract": "Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors. These models are key to multimodal information retrieval and related tasks. However, CLIP models generally underperform in text-only tasks compared to specialized text models. This creates inefficiencies for information retrieval systems that keep separate embeddings and models for text-only and multimodal tasks. We propose a novel, multi-task contrastive training method to address this issue, which we use to train the jina-clip-v1 model to achieve the state-of-the-art performance on both text-image and text-text retrieval tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-31", "arxiv_id": "2405.19893", "title": "Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts", "authors": [ "Chunjing Gan", "Dan Yang", "Binbin Hu", "Hanxiao Zhang", "Siyuan Li", "Ziqi Liu", "Yue Shen", "Lin Ju", "Zhiqiang Zhang", "Jinjie Gu", "Lei Liang", "Jun Zhou" ], "github": "https://github.com/hiyouga/LLaMA-Factory", "project_page": "", "abstract": "In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucination issues of LLMs have curtailed their applications in knowledge intensive tasks, where retrieval augmented generation (RAG) can be of help. Nevertheless, existing retrieval augmented models typically use similarity as a bridge between queries and documents and follow a retrieve then read procedure. In this work, we argue that similarity is not always the panacea and totally relying on similarity would sometimes degrade the performance of retrieval augmented generation. To this end, we propose MetRag, a Multi layEred Thoughts enhanced Retrieval Augmented Generation framework. To begin with, beyond existing similarity oriented thought, we embrace a small scale utility model that draws supervision from an LLM for utility oriented thought and further come up with a smarter model by comprehensively combining the similarity and utility oriented thoughts. Furthermore, given the fact that the retrieved document set tends to be huge and using them in isolation makes it difficult to capture the commonalities and characteristics among them, we propose to make an LLM as a task adaptive summarizer to endow retrieval augmented generation with compactness-oriented thought. Finally, with multi layered thoughts from the precedent stages, an LLM is called for knowledge augmented generation. Extensive experiments on knowledge-intensive tasks have demonstrated the superiority of MetRag.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hiyouga/LLaMA-Factory", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-31", "arxiv_id": "2405.20335", "title": "Xwin-LM: Strong and Scalable Alignment Practice for LLMs", "authors": [ "Bolin Ni", "JingCheng Hu", "Yixuan Wei", "Houwen Peng", "Zheng Zhang", "Gaofeng Meng", "Han Hu" ], "github": "https://github.com/Xwin-LM/Xwin-LM", "project_page": "", "abstract": "In this work, we present Xwin-LM, a comprehensive suite of alignment methodologies for large language models (LLMs). This suite encompasses several key techniques, including supervised finetuning (SFT), reward modeling (RM), rejection sampling finetuning (RS), and direct preference optimization (DPO). The key components are as follows: (1) Xwin-LM-SFT, models initially finetuned with high-quality instruction data; (2) Xwin-Pair, a large-scale, multi-turn preference dataset meticulously annotated using GPT-4; (3) Xwin-RM, reward models trained on Xwin-Pair, developed at scales of 7B, 13B, and 70B parameters; (4) Xwin-Set, a multiwise preference dataset in which each prompt is linked to 64 unique responses generated by Xwin-LM-SFT and scored by Xwin-RM; (5) Xwin-LM-RS, models finetuned with the highest-scoring responses from Xwin-Set; (6) Xwin-LM-DPO, models further optimized on Xwin-Set using the DPO algorithm. Our evaluations on AlpacaEval and MT-bench demonstrate consistent and significant improvements across the pipeline, demonstrating the strength and scalability of Xwin-LM. The repository https://github.com/Xwin-LM/Xwin-LM will be continually updated to foster community research.", "github_manual": "", "github_regex": "https://github.com/Xwin-LM/Xwin-LM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-31", "arxiv_id": "2405.19957", "title": "PLA4D: Pixel-Level Alignments for Text-to-4D Gaussian Splatting", "authors": [ "Qiaowei Miao", "Yawei Luo", "Yi Yang" ], "github": "https://github.com/MiaoQiaowei/PLA4D.github.io", "project_page": "", "abstract": "As text-conditioned diffusion models (DMs) achieve breakthroughs in image, video, and 3D generation, the research community's focus has shifted to the more challenging task of text-to-4D synthesis, which introduces a temporal dimension to generate dynamic 3D objects. In this context, we identify Score Distillation Sampling (SDS), a widely used technique for text-to-3D synthesis, as a significant hindrance to text-to-4D performance due to its Janus-faced and texture-unrealistic problems coupled with high computational costs. In this paper, we propose Pixel-Level Alignments for Text-to-4D Gaussian Splatting (PLA4D), a novel method that utilizes text-to-video frames as explicit pixel alignment targets to generate static 3D objects and inject motion into them. Specifically, we introduce Focal Alignment to calibrate camera poses for rendering and GS-Mesh Contrastive Learning to distill geometry priors from rendered image contrasts at the pixel level. Additionally, we develop Motion Alignment using a deformation network to drive changes in Gaussians and implement Reference Refinement for smooth 4D object surfaces. These techniques enable 4D Gaussian Splatting to align geometry, texture, and motion with generated videos at the pixel level. Compared to previous methods, PLA4D produces synthesized outputs with better texture details in less time and effectively mitigates the Janus-faced problem. PLA4D is fully implemented using open-source models, offering an accessible, user-friendly, and promising direction for 4D digital content creation. Our project page: https://github.com/MiaoQiaowei/PLA4D.github.io{https://github.com/MiaoQiaowei/PLA4D.github.io}.", "github_manual": "", "github_regex": "https://github.com/MiaoQiaowei/PLA4D.github.io", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-31", "arxiv_id": "2405.20340", "title": "MotionLLM: Understanding Human Behaviors from Human Motions and Videos", "authors": [ "Ling-Hao Chen", "Shunlin Lu", "Ailing Zeng", "Hao Zhang", "Benyou Wang", "Ruimao Zhang", "Lei Zhang" ], "github": "https://github.com/IDEA-Research/MotionLLM", "project_page": "", "abstract": "This study delves into the realm of multi-modality (i.e., video and motion modalities) human behavior understanding by leveraging the powerful capabilities of Large Language Models (LLMs). Diverging from recent LLMs designed for video-only or motion-only understanding, we argue that understanding human behavior necessitates joint modeling from both videos and motion sequences (e.g., SMPL sequences) to capture nuanced body part dynamics and semantics effectively. In light of this, we present MotionLLM, a straightforward yet effective framework for human motion understanding, captioning, and reasoning. Specifically, MotionLLM adopts a unified video-motion training strategy that leverages the complementary advantages of existing coarse video-text data and fine-grained motion-text data to glean rich spatial-temporal insights. Furthermore, we collect a substantial dataset, MoVid, comprising diverse videos, motions, captions, and instructions. Additionally, we propose the MoVid-Bench, with carefully manual annotations, for better evaluation of human behavior understanding on video and motion. Extensive experiments show the superiority of MotionLLM in the caption, spatial-temporal comprehension, and reasoning ability.", "github_manual": "https://github.com/IDEA-Research/MotionLLM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-31", "arxiv_id": "2405.19888", "title": "Parrot: Efficient Serving of LLM-based Applications with Semantic Variable", "authors": [ "Chaofan Lin", "Zhenhua Han", "Chengruidong Zhang", "Yuqing Yang", "Fan Yang", "Chen Chen", "Lili Qiu" ], "github": "", "project_page": "", "abstract": "The rise of large language models (LLMs) has enabled LLM-based applications (a.k.a. AI agents or co-pilots), a new software paradigm that combines the strength of LLM and conventional software. Diverse LLM applications from different tenants could design complex workflows using multiple LLM requests to accomplish one task. However, they have to use the over-simplified request-level API provided by today's public LLM services, losing essential application-level information. Public LLM services have to blindly optimize individual LLM requests, leading to sub-optimal end-to-end performance of LLM applications. This paper introduces Parrot, an LLM service system that focuses on the end-to-end experience of LLM-based applications. Parrot proposes Semantic Variable, a unified abstraction to expose application-level knowledge to public LLM services. A Semantic Variable annotates an input/output variable in the prompt of a request, and creates the data pipeline when connecting multiple LLM requests, providing a natural way to program LLM applications. Exposing Semantic Variables to the public LLM service allows it to perform conventional data flow analysis to uncover the correlation across multiple LLM requests. This correlation opens a brand-new optimization space for the end-to-end performance of LLM-based applications. Extensive evaluations demonstrate that Parrot can achieve up to an order-of-magnitude improvement for popular and practical use cases of LLM applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-31", "arxiv_id": "2405.19856", "title": "DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories", "authors": [ "Jia Li", "Ge Li", "Yunfei Zhao", "Yongmin Li", "Huanyu Liu", "Hao Zhu", "Lecheng Wang", "Kaibo Liu", "Zheng Fang", "Lanshen Wang", "Jiazheng Ding", "Xuanming Zhang", "Yuqi Zhu", "Yihong Dong", "Zhi Jin", "Binhua Li", "Fei Huang", "Yongbin Li" ], "github": "https://github.com/seketeam/DevEval", "project_page": "", "abstract": "How to evaluate the coding abilities of Large Language Models (LLMs) remains an open question. We find that existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of LLMs. To address the knowledge gap, we propose a new benchmark named DevEval, which has three advances. (1) DevEval aligns with real-world repositories in multiple dimensions, e.g., code distributions and dependency distributions. (2) DevEval is annotated by 13 developers and contains comprehensive annotations (e.g., requirements, original repositories, reference code, and reference dependencies). (3) DevEval comprises 1,874 testing samples from 117 repositories, covering 10 popular domains (e.g., Internet, Database). Based on DevEval, we propose repository-level code generation and evaluate 8 popular LLMs on DevEval (e.g., gpt-4, gpt-3.5, StarCoder 2, DeepSeek Coder, CodeLLaMa). Our experiments reveal these LLMs' coding abilities in real-world code repositories. For example, in our experiments, the highest Pass@1 of gpt-4-turbo is only 53.04%. We also analyze LLMs' failed cases and summarize their shortcomings. We hope DevEval can facilitate the development of LLMs in real code repositories. DevEval, prompts, and LLMs' predictions have been released.", "github_manual": "https://github.com/seketeam/DevEval", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-05-31", "arxiv_id": "2405.19707", "title": "DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark", "authors": [ "Haoxing Chen", "Yan Hong", "Zizheng Huang", "Zhuoer Xu", "Zhangxuan Gu", "Yaohui Li", "Jun Lan", "Huijia Zhu", "Jianfu Zhang", "Weiqiang Wang", "Huaxiong Li" ], "github": "https://github.com/chenhaoxing/DeMamba", "project_page": "", "abstract": "Recently, video generation techniques have advanced rapidly. Given the popularity of video content on social media platforms, these models intensify concerns about the spread of fake information. Therefore, there is a growing demand for detectors capable of distinguishing between fake AI-generated videos and mitigating the potential harm caused by fake information. However, the lack of large-scale datasets from the most advanced video generators poses a barrier to the development of such detectors. To address this gap, we introduce the first AI-generated video detection dataset, GenVideo. It features the following characteristics: (1) a large volume of videos, including over one million AI-generated and real videos collected; (2) a rich diversity of generated content and methodologies, covering a broad spectrum of video categories and generation techniques. We conducted extensive studies of the dataset and proposed two evaluation methods tailored for real-world-like scenarios to assess the detectors' performance: the cross-generator video classification task assesses the generalizability of trained detectors on generators; the degraded video classification task evaluates the robustness of detectors to handle videos that have degraded in quality during dissemination. Moreover, we introduced a plug-and-play module, named Detail Mamba (DeMamba), designed to enhance the detectors by identifying AI-generated videos through the analysis of inconsistencies in temporal and spatial dimensions. Our extensive experiments demonstrate DeMamba's superior generalizability and robustness on GenVideo compared to existing detectors. We believe that the GenVideo dataset and the DeMamba module will significantly advance the field of AI-generated video detection. Our code and dataset will be aviliable at https://github.com/chenhaoxing/DeMamba.", "github_manual": "", "github_regex": "https://github.com/chenhaoxing/DeMamba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-03", "arxiv_id": "2405.21060", "title": "Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality", "authors": [ "Tri Dao", "Albert Gu" ], "github": "https://github.com/state-spaces/mamba", "project_page": "", "abstract": "While Transformers have been the main architecture behind deep learning's success in language modeling, state-space models (SSMs) such as Mamba have recently been shown to match or outperform Transformers at small to medium scale. We show that these families of models are actually quite closely related, and develop a rich framework of theoretical connections between SSMs and variants of attention, connected through various decompositions of a well-studied class of structured semiseparable matrices. Our state space duality (SSD) framework allows us to design a new architecture (Mamba-2) whose core layer is an a refinement of Mamba's selective SSM that is 2-8X faster, while continuing to be competitive with Transformers on language modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/state-spaces/mamba", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-03", "arxiv_id": "2405.18144", "title": "4-bit Shampoo for Memory-Efficient Network Training", "authors": [ "Sike Wang", "Jia Li", "Pan Zhou", "Hua Huang" ], "github": "https://github.com/sike-wang/low-bit-shampoo", "project_page": "", "abstract": "Second-order optimizers, maintaining a matrix termed a preconditioner, are superior to first-order optimizers in both theory and practice. The states forming the preconditioner and its inverse root restrict the maximum size of models trained by second-order optimizers. To address this, compressing 32-bit optimizer states to lower bitwidths has shown promise in reducing memory usage. However, current approaches only pertain to first-order optimizers. In this paper, we propose the first 4-bit second-order optimizers, exemplified by 4-bit Shampoo, maintaining performance similar to that of 32-bit ones. We show that quantizing the eigenvector matrix of the preconditioner in 4-bit Shampoo is remarkably better than quantizing the preconditioner itself both theoretically and experimentally. By rectifying the orthogonality of the quantized eigenvector matrix, we enhance the approximation of the preconditioner's eigenvector matrix, which also benefits the computation of its inverse 4-th root. Besides, we find that linear square quantization slightly outperforms dynamic tree quantization when quantizing second-order optimizer states. Evaluation on various networks for image classification demonstrates that our 4-bit Shampoo achieves comparable test accuracy to its 32-bit counterpart while being more memory-efficient. The source code will be made available.", "github_manual": "https://github.com/sike-wang/low-bit-shampoo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-03", "arxiv_id": "2405.21075", "title": "Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis", "authors": [ "Chaoyou Fu", "Yuhan Dai", "Yondong Luo", "Lei Li", "Shuhuai Ren", "Renrui Zhang", "Zihan Wang", "Chenyu Zhou", "Yunhang Shen", "Mengdan Zhang", "Peixian Chen", "Yanwei Li", "Shaohui Lin", "Sirui Zhao", "Ke Li", "Tong Xu", "Xiawu Zheng", "Enhong Chen", "Rongrong Ji", "Xing Sun" ], "github": "https://github.com/MME-Benchmarks/Video-MME", "project_page": "", "abstract": "In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements. However, the predominant focus remains on developing their capabilities in static image understanding. The potential of MLLMs in processing sequential visual data is still insufficiently explored, highlighting the absence of a comprehensive, high-quality assessment of their performance. In this paper, we introduce Video-MME, the first-ever full-spectrum, Multi-Modal Evaluation benchmark of MLLMs in Video analysis. Our work distinguishes from existing benchmarks through four key features: 1) Diversity in video types, spanning 6 primary visual domains with 30 subfields to ensure broad scenario generalizability; 2) Duration in temporal dimension, encompassing both short-, medium-, and long-term videos, ranging from 11 seconds to 1 hour, for robust contextual dynamics; 3) Breadth in data modalities, integrating multi-modal inputs besides video frames, including subtitles and audios, to unveil the all-round capabilities of MLLMs; 4) Quality in annotations, utilizing rigorous manual labeling by expert annotators to facilitate precise and reliable model assessment. 900 videos with a total of 256 hours are manually selected and annotated by repeatedly viewing all the video content, resulting in 2,700 question-answer pairs. With Video-MME, we extensively evaluate various state-of-the-art MLLMs, including GPT-4 series and Gemini 1.5 Pro, as well as open-source image models like InternVL-Chat-V1.5 and video models like LLaVA-NeXT-Video. Our experiments reveal that Gemini 1.5 Pro is the best-performing commercial model, significantly outperforming the open-source models. Our dataset along with these findings underscores the need for further improvements in handling longer sequences and multi-modal data. Project Page: https://video-mme.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MME-Benchmarks/Video-MME", "github_api_added_by": "user" }, { "date": "2024-06-03", "arxiv_id": "2405.21048", "title": "Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling", "authors": [ "Jiatao Gu", "Ying Shen", "Shuangfei Zhai", "Yizhe Zhang", "Navdeep Jaitly", "Joshua M. Susskind" ], "github": "", "project_page": "", "abstract": "Diffusion models have emerged as a powerful tool for generating high-quality images from textual descriptions. Despite their successes, these models often exhibit limited diversity in the sampled images, particularly when sampling with a high classifier-free guidance weight. To address this issue, we present Kaleido, a novel approach that enhances the diversity of samples by incorporating autoregressive latent priors. Kaleido integrates an autoregressive language model that encodes the original caption and generates latent variables, serving as abstract and intermediary representations for guiding and facilitating the image generation process. In this paper, we explore a variety of discrete latent representations, including textual descriptions, detection bounding boxes, object blobs, and visual tokens. These representations diversify and enrich the input conditions to the diffusion models, enabling more diverse outputs. Our experimental results demonstrate that Kaleido effectively broadens the diversity of the generated image samples from a given textual description while maintaining high image quality. Furthermore, we show that Kaleido adheres closely to the guidance provided by the generated latent variables, demonstrating its capability to effectively control and direct the image generation process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-03", "arxiv_id": "2405.20674", "title": "4Diffusion: Multi-view Video Diffusion Model for 4D Generation", "authors": [ "Haiyu Zhang", "Xinyuan Chen", "Yaohui Wang", "Xihui Liu", "Yunhong Wang", "Yu Qiao" ], "github": "https://github.com/aejion/4Diffusion", "project_page": "", "abstract": "Current 4D generation methods have achieved noteworthy efficacy with the aid of advanced diffusion generative models. However, these methods lack multi-view spatial-temporal modeling and encounter challenges in integrating diverse prior knowledge from multiple diffusion models, resulting in inconsistent temporal appearance and flickers. In this paper, we propose a novel 4D generation pipeline, namely 4Diffusion aimed at generating spatial-temporally consistent 4D content from a monocular video. We first design a unified diffusion model tailored for multi-view video generation by incorporating a learnable motion module into a frozen 3D-aware diffusion model to capture multi-view spatial-temporal correlations. After training on a curated dataset, our diffusion model acquires reasonable temporal consistency and inherently preserves the generalizability and spatial consistency of the 3D-aware diffusion model. Subsequently, we propose 4D-aware Score Distillation Sampling loss, which is based on our multi-view video diffusion model, to optimize 4D representation parameterized by dynamic NeRF. This aims to eliminate discrepancies arising from multiple diffusion models, allowing for generating spatial-temporally consistent 4D content. Moreover, we devise an anchor loss to enhance the appearance details and facilitate the learning of dynamic NeRF. Extensive qualitative and quantitative experiments demonstrate that our method achieves superior performance compared to previous methods.", "github_manual": "https://github.com/aejion/4Diffusion", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-03", "arxiv_id": "2405.20541", "title": "Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models", "authors": [ "Zachary Ankner", "Cody Blakeney", "Kartik Sreenivasan", "Max Marion", "Matthew L. Leavitt", "Mansheej Paul" ], "github": "https://github.com/mosaicml/llm-foundry", "project_page": "", "abstract": "In this work, we investigate whether small language models can determine high-quality subsets of large-scale text datasets that improve the performance of larger language models. While existing work has shown that pruning based on the perplexity of a larger model can yield high-quality data, we investigate whether smaller models can be used for perplexity-based pruning and how pruning is affected by the domain composition of the data being pruned. We demonstrate that for multiple dataset compositions, perplexity-based pruning of pretraining data can significantly improve downstream task performance: pruning based on perplexities computed with a 125 million parameter model improves the average performance on downstream tasks of a 3 billion parameter model by up to 2.04 and achieves up to a 1.45times reduction in pretraining steps to reach commensurate baseline performance. Furthermore, we demonstrate that such perplexity-based data pruning also yields downstream performance gains in the over-trained and data-constrained regimes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mosaicml/llm-foundry", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-04", "arxiv_id": "2406.01574", "title": "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark", "authors": [ "Yubo Wang", "Xueguang Ma", "Ge Zhang", "Yuansheng Ni", "Abhranil Chandra", "Shiguang Guo", "Weiming Ren", "Aaran Arulraj", "Xuan He", "Ziyan Jiang", "Tianle Li", "Max Ku", "Kai Wang", "Alex Zhuang", "Rongqi Fan", "Xiang Yue", "Wenhu Chen" ], "github": "https://github.com/tiger-ai-lab/mmlu-pro", "project_page": "", "abstract": "In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in language comprehension and reasoning across diverse domains. However, as models continue to improve, their performance on these benchmarks has begun to plateau, making it increasingly difficult to discern differences in model capabilities. This paper introduces MMLU-Pro, an enhanced dataset designed to extend the mostly knowledge-driven MMLU benchmark by integrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. Additionally, MMLU-Pro eliminates the trivial and noisy questions in MMLU. Our experimental results show that MMLU-Pro not only raises the challenge, causing a significant drop in accuracy by 16% to 33% compared to MMLU but also demonstrates greater stability under varying prompts. With 24 different prompt styles tested, the sensitivity of model scores to prompt variations decreased from 4-5% in MMLU to just 2% in MMLU-Pro. Additionally, we found that models utilizing Chain of Thought (CoT) reasoning achieved better performance on MMLU-Pro compared to direct answering, which is in stark contrast to the findings on the original MMLU, indicating that MMLU-Pro includes more complex reasoning questions. Our assessments confirm that MMLU-Pro is a more discriminative benchmark to better track progress in the field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tiger-ai-lab/mmlu-pro", "github_api_added_by": "auto" }, { "date": "2024-06-04", "arxiv_id": "2406.01493", "title": "Learning Temporally Consistent Video Depth from Video Diffusion Priors", "authors": [ "Jiahao Shao", "Yuanbo Yang", "Hongyu Zhou", "Youmin Zhang", "Yujun Shen", "Matteo Poggi", "Yiyi Liao" ], "github": "https://github.com/jiahao-shao1/ChronoDepth", "project_page": "", "abstract": "This work addresses the challenge of video depth estimation, which expects not only per-frame accuracy but, more importantly, cross-frame consistency. Instead of directly developing a depth estimator from scratch, we reformulate the prediction task into a conditional generation problem. This allows us to leverage the prior knowledge embedded in existing video generation models, thereby reducing learn- ing difficulty and enhancing generalizability. Concretely, we study how to tame the public Stable Video Diffusion (SVD) to predict reliable depth from input videos using a mixture of image depth and video depth datasets. We empirically confirm that a procedural training strategy - first optimizing the spatial layers of SVD and then optimizing the temporal layers while keeping the spatial layers frozen - yields the best results in terms of both spatial accuracy and temporal consistency. We further examine the sliding window strategy for inference on arbitrarily long videos. Our observations indicate a trade-off between efficiency and performance, with a one-frame overlap already producing favorable results. Extensive experimental results demonstrate the superiority of our approach, termed ChronoDepth, over existing alternatives, particularly in terms of the temporal consistency of the estimated depth. Additionally, we highlight the benefits of more consistent video depth in two practical applications: depth-conditioned video generation and novel view synthesis. Our project page is available at https://jhaoshao.github.io/ChronoDepth/{this http URL}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jiahao-shao1/ChronoDepth", "github_api_added_by": "auto" }, { "date": "2024-06-04", "arxiv_id": "2406.00888", "title": "Show, Don't Tell: Aligning Language Models with Demonstrated Feedback", "authors": [ "Omar Shaikh", "Michelle Lam", "Joey Hejna", "Yijia Shao", "Michael Bernstein", "Diyi Yang" ], "github": "https://github.com/SALT-NLP/demonstrated-feedback", "project_page": "", "abstract": "Language models are aligned to emulate the collective voice of many, resulting in outputs that align with no one in particular. Steering LLMs away from generic output is possible through supervised finetuning or RLHF, but requires prohibitively large datasets for new ad-hoc tasks. We argue that it is instead possible to align an LLM to a specific setting by leveraging a very small number (<10) of demonstrations as feedback. Our method, Demonstration ITerated Task Optimization (DITTO), directly aligns language model outputs to a user's demonstrated behaviors. Derived using ideas from online imitation learning, DITTO cheaply generates online comparison data by treating users' demonstrations as preferred over output from the LLM and its intermediate checkpoints. We evaluate DITTO's ability to learn fine-grained style and task alignment across domains such as news articles, emails, and blog posts. Additionally, we conduct a user study soliciting a range of demonstrations from participants (N=16). Across our benchmarks and user study, we find that win-rates for DITTO outperform few-shot prompting, supervised fine-tuning, and other self-play methods by an average of 19% points. By using demonstrations as feedback directly, DITTO offers a novel method for effective customization of LLMs.", "github_manual": "https://github.com/SALT-NLP/demonstrated-feedback", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-04", "arxiv_id": "2406.00392", "title": "Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning", "authors": [ "Jonathan Cook", "Chris Lu", "Edward Hughes", "Joel Z. Leibo", "Jakob Foerster" ], "github": "https://github.com/FLAIROx/cultural-accumulation", "project_page": "", "abstract": "Cultural accumulation drives the open-ended and diverse progress in capabilities spanning human history. It builds an expanding body of knowledge and skills by combining individual exploration with inter-generational information transmission. Despite its widespread success among humans, the capacity for artificial learning agents to accumulate culture remains under-explored. In particular, approaches to reinforcement learning typically strive for improvements over only a single lifetime. Generational algorithms that do exist fail to capture the open-ended, emergent nature of cultural accumulation, which allows individuals to trade-off innovation and imitation. Building on the previously demonstrated ability for reinforcement learning agents to perform social learning, we find that training setups which balance this with independent learning give rise to cultural accumulation. These accumulating agents outperform those trained for a single lifetime with the same cumulative experience. We explore this accumulation by constructing two models under two distinct notions of a generation: episodic generations, in which accumulation occurs via in-context learning and train-time generations, in which accumulation occurs via in-weights learning. In-context and in-weights cultural accumulation can be interpreted as analogous to knowledge and skill accumulation, respectively. To the best of our knowledge, this work is the first to present general models that achieve emergent cultural accumulation in reinforcement learning, opening up new avenues towards more open-ended learning systems, as well as presenting new opportunities for modelling human culture.", "github_manual": "https://github.com/FLAIROx/cultural-accumulation", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-04", "arxiv_id": "2406.00153", "title": "\u03bcLO: Compute-Efficient Meta-Generalization of Learned Optimizers", "authors": [ "Benjamin Th\u00e9rien", "Charles-\u00c9tienne Joseph", "Boris Knyazev", "Edouard Oyallon", "Irina Rish", "Eugene Belilovsky" ], "github": "https://github.com/bentherien/mu_learned_optimization", "project_page": "", "abstract": "Learned optimizers (LOs) can significantly reduce the wall-clock training time of neural networks, substantially reducing training costs. However, they often suffer from poor meta-generalization, especially when training networks larger than those seen during meta-training. To address this, we use the recently proposed Maximal Update Parametrization (muP), which allows zero-shot generalization of optimizer hyperparameters from smaller to larger models. We extend muP theory to learned optimizers, treating the meta-training problem as finding the learned optimizer under muP. Our evaluation shows that LOs meta-trained with muP substantially improve meta-generalization as compared to LOs trained under standard parametrization (SP). Notably, when applied to large-width models, our best muLO, trained for 103 GPU-hours, matches or exceeds the performance of VeLO, the largest publicly available learned optimizer, meta-trained with 4000 TPU-months of compute. Moreover, muLOs demonstrate better generalization than their SP counterparts to deeper networks and to much longer training horizons (25 times longer) than those seen during meta-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bentherien/mu_learned_optimization", "github_api_added_by": "auto" }, { "date": "2024-06-04", "arxiv_id": "2406.00908", "title": "ZeroSmooth: Training-free Diffuser Adaptation for High Frame Rate Video Generation", "authors": [ "Shaoshu Yang", "Yong Zhang", "Xiaodong Cun", "Ying Shan", "Ran He" ], "github": "https://github.com/ssyang2020/ZeroSmooth", "project_page": "", "abstract": "Video generation has made remarkable progress in recent years, especially since the advent of the video diffusion models. Many video generation models can produce plausible synthetic videos, e.g., Stable Video Diffusion (SVD). However, most video models can only generate low frame rate videos due to the limited GPU memory as well as the difficulty of modeling a large set of frames. The training videos are always uniformly sampled at a specified interval for temporal compression. Previous methods promote the frame rate by either training a video interpolation model in pixel space as a postprocessing stage or training an interpolation model in latent space for a specific base video model. In this paper, we propose a training-free video interpolation method for generative video diffusion models, which is generalizable to different models in a plug-and-play manner. We investigate the non-linearity in the feature space of video diffusion models and transform a video model into a self-cascaded video diffusion model with incorporating the designed hidden state correction modules. The self-cascaded architecture and the correction module are proposed to retain the temporal consistency between key frames and the interpolated frames. Extensive evaluations are preformed on multiple popular video models to demonstrate the effectiveness of the propose method, especially that our training-free method is even comparable to trained interpolation models supported by huge compute resources and large-scale datasets.", "github_manual": "https://github.com/ssyang2020/ZeroSmooth", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-05", "arxiv_id": "2406.02430", "title": "Seed-TTS: A Family of High-Quality Versatile Speech Generation Models", "authors": [ "Philip Anastassiou", "Jiawei Chen", "Jitong Chen", "Yuanzhe Chen", "Zhuo Chen", "Ziyi Chen", "Jian Cong", "Lelai Deng", "Chuang Ding", "Lu Gao", "Mingqing Gong", "Peisong Huang", "Qingqing Huang", "Zhiying Huang", "Yuanyuan Huo", "Dongya Jia", "Chumin Li", "Feiya Li", "Hui Li", "Jiaxin Li", "Xiaoyang Li", "Xingxing Li", "Lin Liu", "Shouda Liu", "Sichao Liu", "Xudong Liu", "Yuchen Liu", "Zhengxi Liu", "Lu Lu", "Junjie Pan", "Xin Wang", "Yuping Wang", "Yuxuan Wang", "Zhen Wei", "Jian Wu", "Chao Yao", "Yifeng Yang", "Yuanhao Yi", "Junteng Zhang", "Qidi Zhang", "Shuo Zhang", "Wenjie Zhang", "Yang Zhang", "Zilin Zhao", "Dejian Zhong", "Xiaobin Zhuang" ], "github": "", "project_page": "", "abstract": "We introduce Seed-TTS, a family of large-scale autoregressive text-to-speech (TTS) models capable of generating speech that is virtually indistinguishable from human speech. Seed-TTS serves as a foundation model for speech generation and excels in speech in-context learning, achieving performance in speaker similarity and naturalness that matches ground truth human speech in both objective and subjective evaluations. With fine-tuning, we achieve even higher subjective scores across these metrics. Seed-TTS offers superior controllability over various speech attributes such as emotion and is capable of generating highly expressive and diverse speech for speakers in the wild. Furthermore, we propose a self-distillation method for speech factorization, as well as a reinforcement learning approach to enhance model robustness, speaker similarity, and controllability. We additionally present a non-autoregressive (NAR) variant of the Seed-TTS model, named Seed-TTS_DiT, which utilizes a fully diffusion-based architecture. Unlike previous NAR-based TTS systems, Seed-TTS_DiT does not depend on pre-estimated phoneme durations and performs speech generation through end-to-end processing. We demonstrate that this variant achieves comparable performance to the language model-based variant and showcase its effectiveness in speech editing. We encourage readers to listen to demos at https://bytedancespeech.github.io/seedtts_tech_report.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-05", "arxiv_id": "2406.02230", "title": "I4VGen: Image as Stepping Stone for Text-to-Video Generation", "authors": [ "Xiefan Guo", "Jinlin Liu", "Miaomiao Cui", "Di Huang" ], "github": "https://github.com/xiefan-guo/i4vgen", "project_page": "", "abstract": "Text-to-video generation has lagged behind text-to-image synthesis in quality and diversity due to the complexity of spatio-temporal modeling and limited video-text datasets. This paper presents I4VGen, a training-free and plug-and-play video diffusion inference framework, which enhances text-to-video generation by leveraging robust image techniques. Specifically, following text-to-image-to-video, I4VGen decomposes the text-to-video generation into two stages: anchor image synthesis and anchor image-guided video synthesis. Correspondingly, a well-designed generation-selection pipeline is employed to achieve visually-realistic and semantically-faithful anchor image, and an innovative Noise-Invariant Video Score Distillation Sampling is incorporated to animate the image to a dynamic video, followed by a video regeneration process to refine the video. This inference strategy effectively mitigates the prevalent issue of non-zero terminal signal-to-noise ratio. Extensive evaluations show that I4VGen not only produces videos with higher visual realism and textual fidelity but also integrates seamlessly into existing image-to-video diffusion models, thereby improving overall video quality.", "github_manual": "https://github.com/xiefan-guo/i4vgen", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-05", "arxiv_id": "2406.01660", "title": "Self-Improving Robust Preference Optimization", "authors": [ "Eugene Choi", "Arash Ahmadian", "Matthieu Geist", "Oilvier Pietquin", "Mohammad Gheshlaghi Azar" ], "github": "", "project_page": "", "abstract": "Both online and offline RLHF methods such as PPO and DPO have been extremely successful in aligning AI with human preferences. Despite their success, the existing methods suffer from a fundamental problem that their optimal solution is highly task-dependent (i.e., not robust to out-of-distribution (OOD) tasks). Here we address this challenge by proposing Self-Improving Robust Preference Optimization SRPO, a practical and mathematically principled offline RLHF framework that is completely robust to the changes in the task. The key idea of SRPO is to cast the problem of learning from human preferences as a self-improvement process, which can be mathematically expressed in terms of a min-max objective that aims at joint optimization of self-improvement policy and the generative policy in an adversarial fashion. The solution for this optimization problem is independent of the training task and thus it is robust to its changes. We then show that this objective can be re-expressed in the form of a non-adversarial offline loss which can be optimized using standard supervised optimization techniques at scale without any need for reward model and online inference. We show the effectiveness of SRPO in terms of AI Win-Rate (WR) against human (GOLD) completions. In particular, when SRPO is evaluated on the OOD XSUM dataset, it outperforms the celebrated DPO by a clear margin of 15% after 5 self-revisions, achieving WR of 90%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-05", "arxiv_id": "2406.02543", "title": "To Believe or Not to Believe Your LLM", "authors": [ "Yasin Abbasi Yadkori", "Ilja Kuzborskij", "Andr\u00e1s Gy\u00f6rgy", "Csaba Szepesv\u00e1ri" ], "github": "", "project_page": "", "abstract": "We explore uncertainty quantification in large language models (LLMs), with the goal to identify when uncertainty in responses given a query is large. We simultaneously consider both epistemic and aleatoric uncertainties, where the former comes from the lack of knowledge about the ground truth (such as about facts or the language), and the latter comes from irreducible randomness (such as multiple possible answers). In particular, we derive an information-theoretic metric that allows to reliably detect when only epistemic uncertainty is large, in which case the output of the model is unreliable. This condition can be computed based solely on the output of the model obtained simply by some special iterative prompting based on the previous responses. Such quantification, for instance, allows to detect hallucinations (cases when epistemic uncertainty is high) in both single- and multi-answer responses. This is in contrast to many standard uncertainty quantification strategies (such as thresholding the log-likelihood of a response) where hallucinations in the multi-answer case cannot be detected. We conduct a series of experiments which demonstrate the advantage of our formulation. Further, our investigations shed some light on how the probabilities assigned to a given output by an LLM can be amplified by iterative prompting, which might be of independent interest.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-05", "arxiv_id": "2406.02507", "title": "Guiding a Diffusion Model with a Bad Version of Itself", "authors": [ "Tero Karras", "Miika Aittala", "Tuomas Kynk\u00e4\u00e4nniemi", "Jaakko Lehtinen", "Timo Aila", "Samuli Laine" ], "github": "https://github.com/NVlabs/edm2", "project_page": "", "abstract": "The primary axes of interest in image-generating diffusion models are image quality, the amount of variation in the results, and how well the results align with a given condition, e.g., a class label or a text prompt. The popular classifier-free guidance approach uses an unconditional model to guide a conditional model, leading to simultaneously better prompt alignment and higher-quality images at the cost of reduced variation. These effects seem inherently entangled, and thus hard to control. We make the surprising observation that it is possible to obtain disentangled control over image quality without compromising the amount of variation by guiding generation using a smaller, less-trained version of the model itself rather than an unconditional model. This leads to significant improvements in ImageNet generation, setting record FIDs of 1.01 for 64x64 and 1.25 for 512x512, using publicly available networks. Furthermore, the method is also applicable to unconditional diffusion models, drastically improving their quality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVlabs/edm2", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-05", "arxiv_id": "2406.02523", "title": "RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots", "authors": [ "Soroush Nasiriany", "Abhiram Maddukuri", "Lance Zhang", "Adeet Parikh", "Aaron Lo", "Abhishek Joshi", "Ajay Mandlekar", "Yuke Zhu" ], "github": "https://github.com/robocasa/robocasa", "project_page": "", "abstract": "Recent advancements in Artificial Intelligence (AI) have largely been propelled by scaling. In Robotics, scaling is hindered by the lack of access to massive robot datasets. We advocate using realistic physical simulation as a means to scale environments, tasks, and datasets for robot learning methods. We present RoboCasa, a large-scale simulation framework for training generalist robots in everyday environments. RoboCasa features realistic and diverse scenes focusing on kitchen environments. We provide thousands of 3D assets across over 150 object categories and dozens of interactable furniture and appliances. We enrich the realism and diversity of our simulation with generative AI tools, such as object assets from text-to-3D models and environment textures from text-to-image models. We design a set of 100 tasks for systematic evaluation, including composite tasks generated by the guidance of large language models. To facilitate learning, we provide high-quality human demonstrations and integrate automated trajectory generation methods to substantially enlarge our datasets with minimal human burden. Our experiments show a clear scaling trend in using synthetically generated robot data for large-scale imitation learning and show great promise in harnessing simulation data in real-world tasks. Videos and open-source code are available at https://robocasa.ai/", "github_manual": "https://github.com/robocasa/robocasa", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-05", "arxiv_id": "2406.02511", "title": "V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation", "authors": [ "Cong Wang", "Kuan Tian", "Jun Zhang", "Yonghang Guan", "Feng Luo", "Fei Shen", "Zhiwei Jiang", "Qing Gu", "Xiao Han", "Wei Yang" ], "github": "https://github.com/tencent-ailab/V-Express", "project_page": "", "abstract": "In the field of portrait video generation, the use of single images to generate portrait videos has become increasingly prevalent. A common approach involves leveraging generative models to enhance adapters for controlled generation. However, control signals (e.g., text, audio, reference image, pose, depth map, etc.) can vary in strength. Among these, weaker conditions often struggle to be effective due to interference from stronger conditions, posing a challenge in balancing these conditions. In our work on portrait video generation, we identified audio signals as particularly weak, often overshadowed by stronger signals such as facial pose and reference image. However, direct training with weak signals often leads to difficulties in convergence. To address this, we propose V-Express, a simple method that balances different control signals through the progressive training and the conditional dropout operation. Our method gradually enables effective control by weak conditions, thereby achieving generation capabilities that simultaneously take into account the facial pose, reference image, and audio. The experimental results demonstrate that our method can effectively generate portrait videos controlled by audio. Furthermore, a potential solution is provided for the simultaneous and effective use of conditions of varying strengths.", "github_manual": "https://github.com/tencent-ailab/V-Express", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-05", "arxiv_id": "2406.02509", "title": "CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation", "authors": [ "Dejia Xu", "Weili Nie", "Chao Liu", "Sifei Liu", "Jan Kautz", "Zhangyang Wang", "Arash Vahdat" ], "github": "https://github.com/GaParmar/clean-fid", "project_page": "", "abstract": "Recently video diffusion models have emerged as expressive generative tools for high-quality video content creation readily available to general users. However, these models often do not offer precise control over camera poses for video generation, limiting the expression of cinematic language and user control. To address this issue, we introduce CamCo, which allows fine-grained Camera pose Control for image-to-video generation. We equip a pre-trained image-to-video generator with accurately parameterized camera pose input using Pl\\\"ucker coordinates. To enhance 3D consistency in the videos produced, we integrate an epipolar attention module in each attention block that enforces epipolar constraints to the feature maps. Additionally, we fine-tune CamCo on real-world videos with camera poses estimated through structure-from-motion algorithms to better synthesize object motion. Our experiments show that CamCo significantly improves 3D consistency and camera control capabilities compared to previous models while effectively generating plausible object motion. Project page: https://ir1d.github.io/CamCo/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GaParmar/clean-fid", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-06", "arxiv_id": "2406.03184", "title": "Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion", "authors": [ "Hao Wen", "Zehuan Huang", "Yaohui Wang", "Xinyuan Chen", "Yu Qiao", "Lu Sheng" ], "github": "https://github.com/Costwen/Ouroboros3D", "project_page": "", "abstract": "Existing single image-to-3D creation methods typically involve a two-stage process, first generating multi-view images, and then using these images for 3D reconstruction. However, training these two stages separately leads to significant data bias in the inference phase, thus affecting the quality of reconstructed results. We introduce a unified 3D generation framework, named Ouroboros3D, which integrates diffusion-based multi-view image generation and 3D reconstruction into a recursive diffusion process. In our framework, these two modules are jointly trained through a self-conditioning mechanism, allowing them to adapt to each other's characteristics for robust inference. During the multi-view denoising process, the multi-view diffusion model uses the 3D-aware maps rendered by the reconstruction module at the previous timestep as additional conditions. The recursive diffusion framework with 3D-aware feedback unites the entire process and improves geometric consistency.Experiments show that our framework outperforms separation of these two stages and existing methods that combine them at the inference phase. Project page: https://costwen.github.io/Ouroboros3D/", "github_manual": "https://github.com/Costwen/Ouroboros3D", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-06", "arxiv_id": "2406.02539", "title": "Parrot: Multilingual Visual Instruction Tuning", "authors": [ "Hai-Long Sun", "Da-Wei Zhou", "Yang Li", "Shiyin Lu", "Chao Yi", "Qing-Guo Chen", "Zhao Xu", "Weihua Luo", "Kaifu Zhang", "De-Chuan Zhan", "Han-Jia Ye" ], "github": "https://github.com/AIDC-AI/Parrot", "project_page": "", "abstract": "The rapid development of Multimodal Large Language Models (MLLMs) like GPT-4V has marked a significant step towards artificial general intelligence. Existing methods mainly focus on aligning vision encoders with LLMs through supervised fine-tuning (SFT) to endow LLMs with multimodal abilities, making MLLMs' inherent ability to react to multiple languages progressively deteriorate as the training process evolves. We empirically find that the imbalanced SFT datasets, primarily composed of English-centric image-text pairs, lead to significantly reduced performance in non-English languages. This is due to the failure of aligning the vision encoder and LLM with multilingual tokens during the SFT process. In this paper, we introduce Parrot, a novel method that utilizes textual guidance to drive visual token alignment at the language level. Parrot makes the visual tokens condition on diverse language inputs and uses Mixture-of-Experts (MoE) to promote the alignment of multilingual tokens. Specifically, to enhance non-English visual tokens alignment, we compute the cross-attention using the initial visual features and textual embeddings, the result of which is then fed into the MoE router to select the most relevant experts. The selected experts subsequently convert the initial visual tokens into language-specific visual tokens. Moreover, considering the current lack of benchmarks for evaluating multilingual capabilities within the field, we collect and make available a Massive Multilingual Multimodal Benchmark which includes 6 languages, 15 categories, and 12,000 questions, named as MMMB. Our method not only demonstrates state-of-the-art performance on multilingual MMBench and MMMB, but also excels across a broad range of multimodal tasks. Both the source code and the training dataset of Parrot will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AIDC-AI/Parrot", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-06", "arxiv_id": "2406.02856", "title": "Xmodel-LM Technical Report", "authors": [ "Yichuan Wang", "Yang Liu", "Yu Yan", "Xucheng Huang", "Ling Jiang" ], "github": "https://github.com/XiaoduoAILab/XmodelLM", "project_page": "", "abstract": "We introduce Xmodel-LM, a compact and efficient 1.1B language model pre-trained on over 2 trillion tokens. Trained on our self-built dataset (Xdata), which balances Chinese and English corpora based on downstream task optimization, Xmodel-LM exhibits remarkable performance despite its smaller size. It notably surpasses existing open-source language models of similar scale. Our model checkpoints and code are publicly accessible on GitHub at https://github.com/XiaoduoAILab/XmodelLM.", "github_manual": "", "github_regex": "https://github.com/XiaoduoAILab/XmodelLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-06", "arxiv_id": "2406.02844", "title": "Item-Language Model for Conversational Recommendation", "authors": [ "Li Yang", "Anushya Subbiah", "Hardik Patel", "Judith Yue Li", "Yanwei Song", "Reza Mirghaderi", "Vikram Aggarwal" ], "github": "", "project_page": "", "abstract": "Large-language Models (LLMs) have been extremely successful at tasks like complex dialogue understanding, reasoning and coding due to their emergent abilities. These emergent abilities have been extended with multi-modality to include image, audio, and video capabilities. Recommender systems, on the other hand, have been critical for information seeking and item discovery needs. Recently, there have been attempts to apply LLMs for recommendations. One difficulty of current attempts is that the underlying LLM is usually not trained on the recommender system data, which largely contains user interaction signals and is often not publicly available. Another difficulty is user interaction signals often have a different pattern from natural language text, and it is currently unclear if the LLM training setup can learn more non-trivial knowledge from interaction signals compared with traditional recommender system methods. Finally, it is difficult to train multiple LLMs for different use-cases, and to retain the original language and reasoning abilities when learning from recommender system data. To address these three limitations, we propose an Item-Language Model (ILM), which is composed of an item encoder to produce text-aligned item representations that encode user interaction signals, and a frozen LLM that can understand those item representations with preserved pretrained knowledge. We conduct extensive experiments which demonstrate both the importance of the language-alignment and of user interaction knowledge in the item encoder.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-06", "arxiv_id": "2406.02657", "title": "Block Transformer: Global-to-Local Language Modeling for Fast Inference", "authors": [ "Namgyu Ho", "Sangmin Bae", "Taehyeon Kim", "Hyunjik Jo", "Yireun Kim", "Tal Schuster", "Adam Fisch", "James Thorne", "Se-Young Yun" ], "github": "https://github.com/itsnamgyu/block-transformer", "project_page": "", "abstract": "This paper presents the Block Transformer architecture which adopts hierarchical global-to-local modeling to autoregressive transformers to mitigate the inference bottlenecks of self-attention. To apply self-attention, the key-value (KV) cache of all previous sequences must be retrieved from memory at every decoding step. Thereby, this KV cache IO becomes a significant bottleneck in batch inference. We notice that these costs stem from applying self-attention on the global context, therefore we isolate the expensive bottlenecks of global modeling to lower layers and apply fast local modeling in upper layers. To mitigate the remaining costs in the lower layers, we aggregate input tokens into fixed size blocks and then apply self-attention at this coarse level. Context information is aggregated into a single embedding to enable upper layers to decode the next block of tokens, without global attention. Free of global attention bottlenecks, the upper layers can fully utilize the compute hardware to maximize inference throughput. By leveraging global and local modules, the Block Transformer architecture demonstrates 10-20x gains in inference throughput compared to vanilla transformers with equivalent perplexity. Our work introduces a new approach to optimize language model inference through novel application of global-to-local modeling. Code is available at https://github.com/itsnamgyu/block-transformer.", "github_manual": "", "github_regex": "https://github.com/itsnamgyu/block-transformer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-06", "arxiv_id": "2406.02897", "title": "LiveSpeech: Low-Latency Zero-shot Text-to-Speech via Autoregressive Modeling of Audio Discrete Codes", "authors": [ "Trung Dang", "David Aponte", "Dung Tran", "Kazuhito Koishida" ], "github": "", "project_page": "", "abstract": "Prior works have demonstrated zero-shot text-to-speech by using a generative language model on audio tokens obtained via a neural audio codec. It is still challenging, however, to adapt them to low-latency scenarios. In this paper, we present LiveSpeech - a fully autoregressive language model-based approach for zero-shot text-to-speech, enabling low-latency streaming of the output audio. To allow multiple token prediction within a single decoding step, we propose (1) using adaptive codebook loss weights that consider codebook contribution in each frame and focus on hard instances, and (2) grouping codebooks and processing groups in parallel. Experiments show our proposed models achieve competitive results to state-of-the-art baselines in terms of content accuracy, speaker similarity, audio quality, and inference speed while being suitable for low-latency streaming applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-06", "arxiv_id": "2406.03344", "title": "Audio Mamba: Bidirectional State Space Model for Audio Representation Learning", "authors": [ "Mehmet Hamza Erol", "Arda Senocak", "Jiu Feng", "Joon Son Chung" ], "github": "https://github.com/mhamzaerol/Audio-Mamba-AuM", "project_page": "", "abstract": "Transformers have rapidly become the preferred choice for audio classification, surpassing methods based on CNNs. However, Audio Spectrogram Transformers (ASTs) exhibit quadratic scaling due to self-attention. The removal of this quadratic self-attention cost presents an appealing direction. Recently, state space models (SSMs), such as Mamba, have demonstrated potential in language and vision tasks in this regard. In this study, we explore whether reliance on self-attention is necessary for audio classification tasks. By introducing Audio Mamba (AuM), the first self-attention-free, purely SSM-based model for audio classification, we aim to address this question. We evaluate AuM on various audio datasets - comprising six different benchmarks - where it achieves comparable or better performance compared to well-established AST model.", "github_manual": "https://github.com/mhamzaerol/Audio-Mamba-AuM", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-06", "arxiv_id": "2406.02900", "title": "Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms", "authors": [ "Rafael Rafailov", "Yaswanth Chittepu", "Ryan Park", "Harshit Sikchi", "Joey Hejna", "Bradley Knox", "Chelsea Finn", "Scott Niekum" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Human Feedback (RLHF) has been crucial to the recent success of Large Language Models (LLMs), however, it is often a complex and brittle process. In the classical RLHF framework, a reward model is first trained to represent human preferences, which is in turn used by an online reinforcement learning (RL) algorithm to optimize the LLM. A prominent issue with such methods is reward over-optimization or reward hacking, where performance as measured by the learned proxy reward model increases, but true quality plateaus or even deteriorates. Direct Alignment Algorithms (DDAs) like Direct Preference Optimization have emerged as alternatives to the classical RLHF pipeline by circumventing the reward modeling phase. However, although DAAs do not use a separate proxy reward model, they still commonly deteriorate from over-optimization. While the so-called reward hacking phenomenon is not well-defined for DAAs, we still uncover similar trends: at higher KL budgets, DAA algorithms exhibit similar degradation patterns to their classic RLHF counterparts. In particular, we find that DAA methods deteriorate not only across a wide range of KL budgets but also often before even a single epoch of the dataset is completed. Through extensive empirical experimentation, this work formulates and formalizes the reward over-optimization or hacking problem for DAAs and explores its consequences across objectives, training regimes, and model scales.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-06", "arxiv_id": "2406.02886", "title": "PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs", "authors": [ "Rongzhi Zhang", "Jiaming Shen", "Tianqi Liu", "Haorui Wang", "Zhen Qin", "Feng Han", "Jialu Liu", "Simon Baumgartner", "Michael Bendersky", "Chao Zhang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have exhibited impressive capabilities in various tasks, yet their vast parameter sizes restrict their applicability in resource-constrained settings. Knowledge distillation (KD) offers a viable solution by transferring expertise from large teacher models to compact student models. However, traditional KD techniques face specific challenges when applied to LLMs, including restricted access to LLM outputs, significant teacher-student capacity gaps, and the inherited mis-calibration issue. In this work, we present PLaD, a novel preference-based LLM distillation framework. PLaD exploits the teacher-student capacity discrepancy to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. Then, PLaD leverages a ranking loss to re-calibrate student's estimation of sequence likelihood, which steers the student's focus towards understanding the relative quality of outputs instead of simply imitating the teacher. PLaD bypasses the need for access to teacher LLM's internal states, tackles the student's expressivity limitations, and mitigates the student mis-calibration issue. Through extensive experiments on two sequence generation tasks and with various LLMs, we demonstrate the effectiveness of our proposed PLaD framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-06", "arxiv_id": "2406.02884", "title": "PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM", "authors": [ "Tao Yang", "Yingmin Luo", "Zhongang Qi", "Yang Wu", "Ying Shan", "Chang Wen Chen" ], "github": "https://github.com/posterllava/PosterLLaVA", "project_page": "", "abstract": "Layout generation is the keystone in achieving automated graphic design, requiring arranging the position and size of various multi-modal design elements in a visually pleasing and constraint-following manner. Previous approaches are either inefficient for large-scale applications or lack flexibility for varying design requirements. Our research introduces a unified framework for automated graphic layout generation, leveraging the multi-modal large language model (MLLM) to accommodate diverse design tasks. In contrast, our data-driven method employs structured text (JSON format) and visual instruction tuning to generate layouts under specific visual and textual constraints, including user-defined natural language specifications. We conducted extensive experiments and achieved state-of-the-art (SOTA) performance on public multi-modal layout generation benchmarks, demonstrating the effectiveness of our method. Moreover, recognizing existing datasets' limitations in capturing the complexity of real-world graphic designs, we propose two new datasets for much more challenging tasks (user-constrained generation and complicated poster), further validating our model's utility in real-life settings. Marking by its superior accessibility and adaptability, this approach further automates large-scale graphic design tasks. The code and datasets will be publicly available on https://github.com/posterllava/PosterLLaVA.", "github_manual": "", "github_regex": "https://github.com/posterllava/PosterLLaVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-06", "arxiv_id": "2406.03215", "title": "Searching Priors Makes Text-to-Video Synthesis Better", "authors": [ "Haoran Cheng", "Liang Peng", "Linxuan Xia", "Yuepeng Hu", "Hengjia Li", "Qinglin Lu", "Xiaofei He", "Boxi Wu" ], "github": "", "project_page": "", "abstract": "Significant advancements in video diffusion models have brought substantial progress to the field of text-to-video (T2V) synthesis. However, existing T2V synthesis model struggle to accurately generate complex motion dynamics, leading to a reduction in video realism. One possible solution is to collect massive data and train the model on it, but this would be extremely expensive. To alleviate this problem, in this paper, we reformulate the typical T2V generation process as a search-based generation pipeline. Instead of scaling up the model training, we employ existing videos as the motion prior database. Specifically, we divide T2V generation process into two steps: (i) For a given prompt input, we search existing text-video datasets to find videos with text labels that closely match the prompt motions. We propose a tailored search algorithm that emphasizes object motion features. (ii) Retrieved videos are processed and distilled into motion priors to fine-tune a pre-trained base T2V model, followed by generating desired videos using input prompt. By utilizing the priors gleaned from the searched videos, we enhance the realism of the generated videos' motion. All operations can be finished on a single NVIDIA RTX 4090 GPU. We validate our method against state-of-the-art T2V models across diverse prompt inputs. The code will be public.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-06", "arxiv_id": "2406.01014", "title": "Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration", "authors": [ "Junyang Wang", "Haiyang Xu", "Haitao Jia", "Xi Zhang", "Ming Yan", "Weizhou Shen", "Ji Zhang", "Fei Huang", "Jitao Sang" ], "github": "https://github.com/X-PLUG/MobileAgent", "project_page": "", "abstract": "Mobile device operation tasks are increasingly becoming a popular multi-modal AI application scenario. Current Multi-modal Large Language Models (MLLMs), constrained by their training data, lack the capability to function effectively as operation assistants. Instead, MLLM-based agents, which enhance capabilities through tool invocation, are gradually being applied to this scenario. However, the two major navigation challenges in mobile device operation tasks, task progress navigation and focus content navigation, are significantly complicated under the single-agent architecture of existing work. This is due to the overly long token sequences and the interleaved text-image data format, which limit performance. To address these navigation challenges effectively, we propose Mobile-Agent-v2, a multi-agent architecture for mobile device operation assistance. The architecture comprises three agents: planning agent, decision agent, and reflection agent. The planning agent generates task progress, making the navigation of history operations more efficient. To retain focus content, we design a memory unit that updates with task progress. Additionally, to correct erroneous operations, the reflection agent observes the outcomes of each operation and handles any mistakes accordingly. Experimental results indicate that Mobile-Agent-v2 achieves over a 30% improvement in task completion compared to the single-agent architecture of Mobile-Agent. The code is open-sourced at https://github.com/X-PLUG/MobileAgent.", "github_manual": "", "github_regex": "https://github.com/X-PLUG/MobileAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-07", "arxiv_id": "2406.04324", "title": "SF-V: Single Forward Video Generation Model", "authors": [ "Zhixing Zhang", "Yanyu Li", "Yushu Wu", "Yanwu Xu", "Anil Kag", "Ivan Skorokhodov", "Willi Menapace", "Aliaksandr Siarohin", "Junli Cao", "Dimitris Metaxas", "Sergey Tulyakov", "Jian Ren" ], "github": "https://github.com/snap-research/SF-V", "project_page": "", "abstract": "Diffusion-based video generation models have demonstrated remarkable success in obtaining high-fidelity videos through the iterative denoising process. However, these models require multiple denoising steps during sampling, resulting in high computational costs. In this work, we propose a novel approach to obtain single-step video generation models by leveraging adversarial training to fine-tune pre-trained video diffusion models. We show that, through the adversarial training, the multi-steps video diffusion model, i.e., Stable Video Diffusion (SVD), can be trained to perform single forward pass to synthesize high-quality videos, capturing both temporal and spatial dependencies in the video data. Extensive experiments demonstrate that our method achieves competitive generation quality of synthesized videos with significantly reduced computational overhead for the denoising process (i.e., around 23times speedup compared with SVD and 6times speedup compared with existing works, with even better generation quality), paving the way for real-time video synthesis and editing. More visualization results are made publicly available at https://snap-research.github.io/SF-V.", "github_manual": "https://github.com/snap-research/SF-V", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-07", "arxiv_id": "2406.04277", "title": "VideoTetris: Towards Compositional Text-to-Video Generation", "authors": [ "Ye Tian", "Ling Yang", "Haotian Yang", "Yuan Gao", "Yufan Deng", "Jingmin Chen", "Xintao Wang", "Zhaochen Yu", "Xin Tao", "Pengfei Wan", "Di Zhang", "Bin Cui" ], "github": "https://github.com/YangLing0818/VideoTetris", "project_page": "", "abstract": "Diffusion models have demonstrated great success in text-to-video (T2V) generation. However, existing methods may face challenges when handling complex (long) video generation scenarios that involve multiple objects or dynamic changes in object numbers. To address these limitations, we propose VideoTetris, a novel framework that enables compositional T2V generation. Specifically, we propose spatio-temporal compositional diffusion to precisely follow complex textual semantics by manipulating and composing the attention maps of denoising networks spatially and temporally. Moreover, we propose an enhanced video data preprocessing to enhance the training data regarding motion dynamics and prompt understanding, equipped with a new reference frame attention mechanism to improve the consistency of auto-regressive video generation. Extensive experiments demonstrate that our VideoTetris achieves impressive qualitative and quantitative results in compositional T2V generation. Code is available at: https://github.com/YangLing0818/VideoTetris", "github_manual": "", "github_regex": "https://github.com/YangLing0818/VideoTetris", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-07", "arxiv_id": "2406.04333", "title": "BitsFusion: 1.99 bits Weight Quantization of Diffusion Model", "authors": [ "Yang Sui", "Yanyu Li", "Anil Kag", "Yerlan Idelbayev", "Junli Cao", "Ju Hu", "Dhritiman Sagar", "Bo Yuan", "Sergey Tulyakov", "Jian Ren" ], "github": "https://github.com/snap-research/BitsFusion", "project_page": "", "abstract": "Diffusion-based image generation models have achieved great success in recent years by showing the capability of synthesizing high-quality content. However, these models contain a huge number of parameters, resulting in a significantly large model size. Saving and transferring them is a major bottleneck for various applications, especially those running on resource-constrained devices. In this work, we develop a novel weight quantization method that quantizes the UNet from Stable Diffusion v1.5 to 1.99 bits, achieving a model with 7.9X smaller size while exhibiting even better generation quality than the original one. Our approach includes several novel techniques, such as assigning optimal bits to each layer, initializing the quantized model for better performance, and improving the training strategy to dramatically reduce quantization error. Furthermore, we extensively evaluate our quantized model across various benchmark datasets and through human evaluation to demonstrate its superior generation quality.", "github_manual": "https://github.com/snap-research/BitsFusion", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-07", "arxiv_id": "2406.04271", "title": "Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models", "authors": [ "Ling Yang", "Zhaochen Yu", "Tianjun Zhang", "Shiyi Cao", "Minkai Xu", "Wentao Zhang", "Joseph E. Gonzalez", "Bin Cui" ], "github": "https://github.com/YangLing0818/buffer-of-thought-llm", "project_page": "", "abstract": "We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs). Specifically, we propose meta-buffer to store a series of informative high-level thoughts, namely thought-template, distilled from the problem-solving processes across various tasks. Then for each problem, we retrieve a relevant thought-template and adaptively instantiate it with specific reasoning structures to conduct efficient reasoning. To guarantee the scalability and stability, we further propose buffer-manager to dynamically update the meta-buffer, thus enhancing the capacity of meta-buffer as more tasks are solved. We conduct extensive experiments on 10 challenging reasoning-intensive tasks, and achieve significant performance improvements over previous SOTA methods: 11% on Game of 24, 20% on Geometric Shapes and 51% on Checkmate-in-One. Further analysis demonstrate the superior generalization ability and model robustness of our BoT, while requiring only 12% of the cost of multi-query prompting methods (e.g., tree/graph of thoughts) on average. Notably, we find that our Llama3-8B+BoT has the potential to surpass Llama3-70B model. Our project is available at: https://github.com/YangLing0818/buffer-of-thought-llm", "github_manual": "", "github_regex": "https://github.com/YangLing0818/buffer-of-thought-llm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-07", "arxiv_id": "2406.04151", "title": "AgentGym: Evolving Large Language Model-based Agents across Diverse Environments", "authors": [ "Zhiheng Xi", "Yiwen Ding", "Wenxiang Chen", "Boyang Hong", "Honglin Guo", "Junzhe Wang", "Dingwen Yang", "Chenyang Liao", "Xin Guo", "Wei He", "Songyang Gao", "Lu Chen", "Rui Zheng", "Yicheng Zou", "Tao Gui", "Qi Zhang", "Xipeng Qiu", "Xuanjing Huang", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "https://github.com/WooooDyy/AgentGym", "project_page": "", "abstract": "Building generalist agents that can handle diverse tasks and evolve themselves across different environments is a long-term goal in the AI community. Large language models (LLMs) are considered a promising foundation to build such agents due to their generalized capabilities. Current approaches either have LLM-based agents imitate expert-provided trajectories step-by-step, requiring human supervision, which is hard to scale and limits environmental exploration; or they let agents explore and learn in isolated environments, resulting in specialist agents with limited generalization. In this paper, we take the first step towards building generally-capable LLM-based agents with self-evolution ability. We identify a trinity of ingredients: 1) diverse environments for agent exploration and learning, 2) a trajectory set to equip agents with basic capabilities and prior knowledge, and 3) an effective and scalable evolution method. We propose AgentGym, a new framework featuring a variety of environments and tasks for broad, real-time, uni-format, and concurrent agent exploration. AgentGym also includes a database with expanded instructions, a benchmark suite, and high-quality trajectories across environments. Next, we propose a novel method, AgentEvol, to investigate the potential of agent self-evolution beyond previously seen data across tasks and environments. Experimental results show that the evolved agents can achieve results comparable to SOTA models. We release the AgentGym suite, including the platform, dataset, benchmark, checkpoints, and algorithm implementations. The AgentGym suite is available on https://github.com/WooooDyy/AgentGym.", "github_manual": "", "github_regex": "https://github.com/WooooDyy/AgentGym", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-07", "arxiv_id": "2406.04268", "title": "Open-Endedness is Essential for Artificial Superhuman Intelligence", "authors": [ "Edward Hughes", "Michael Dennis", "Jack Parker-Holder", "Feryal Behbahani", "Aditi Mavalankar", "Yuge Shi", "Tom Schaul", "Tim Rocktaschel" ], "github": "", "project_page": "", "abstract": "In recent years there has been a tremendous surge in the general capabilities of AI systems, mainly fuelled by training foundation models on internetscale data. Nevertheless, the creation of openended, ever self-improving AI remains elusive. In this position paper, we argue that the ingredients are now in place to achieve openendedness in AI systems with respect to a human observer. Furthermore, we claim that such open-endedness is an essential property of any artificial superhuman intelligence (ASI). We begin by providing a concrete formal definition of open-endedness through the lens of novelty and learnability. We then illustrate a path towards ASI via open-ended systems built on top of foundation models, capable of making novel, humanrelevant discoveries. We conclude by examining the safety implications of generally-capable openended AI. We expect that open-ended foundation models will prove to be an increasingly fertile and safety-critical area of research in the near future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-07", "arxiv_id": "2406.04314", "title": "Step-aware Preference Optimization: Aligning Preference with Denoising Performance at Each Step", "authors": [ "Zhanhao Liang", "Yuhui Yuan", "Shuyang Gu", "Bohan Chen", "Tiankai Hang", "Ji Li", "Liang Zheng" ], "github": "https://github.com/RockeyCoss/SPO", "project_page": "", "abstract": "Recently, Direct Preference Optimization (DPO) has extended its success from aligning large language models (LLMs) to aligning text-to-image diffusion models with human preferences. Unlike most existing DPO methods that assume all diffusion steps share a consistent preference order with the final generated images, we argue that this assumption neglects step-specific denoising performance and that preference labels should be tailored to each step's contribution. To address this limitation, we propose Step-aware Preference Optimization (SPO), a novel post-training approach that independently evaluates and adjusts the denoising performance at each step, using a step-aware preference model and a step-wise resampler to ensure accurate step-aware supervision. Specifically, at each denoising step, we sample a pool of images, find a suitable win-lose pair, and, most importantly, randomly select a single image from the pool to initialize the next denoising step. This step-wise resampler process ensures the next win-lose image pair comes from the same image, making the win-lose comparison independent of the previous step. To assess the preferences at each step, we train a separate step-aware preference model that can be applied to both noisy and clean images. Our experiments with Stable Diffusion v1.5 and SDXL demonstrate that SPO significantly outperforms the latest Diffusion-DPO in aligning generated images with complex, detailed prompts and enhancing aesthetics, while also achieving more than 20x times faster in training efficiency. Code and model: https://rockeycoss.github.io/spo.github.io/", "github_manual": "https://github.com/RockeyCoss/SPO", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-07", "arxiv_id": "2406.04325", "title": "ShareGPT4Video: Improving Video Understanding and Generation with Better Captions", "authors": [ "Lin Chen", "Xilin Wei", "Jinsong Li", "Xiaoyi Dong", "Pan Zhang", "Yuhang Zang", "Zehui Chen", "Haodong Duan", "Bin Lin", "Zhenyu Tang", "Li Yuan", "Yu Qiao", "Dahua Lin", "Feng Zhao", "Jiaqi Wang" ], "github": "https://github.com/ShareGPT4Omni/ShareGPT4Video", "project_page": "", "abstract": "We present the ShareGPT4Video series, aiming to facilitate the video understanding of large video-language models (LVLMs) and the video generation of text-to-video models (T2VMs) via dense and precise captions. The series comprises: 1) ShareGPT4Video, 40K GPT4V annotated dense captions of videos with various lengths and sources, developed through carefully designed data filtering and annotating strategy. 2) ShareCaptioner-Video, an efficient and capable captioning model for arbitrary videos, with 4.8M high-quality aesthetic videos annotated by it. 3) ShareGPT4Video-8B, a simple yet superb LVLM that reached SOTA performance on three advancing video benchmarks. To achieve this, taking aside the non-scalable costly human annotators, we find using GPT4V to caption video with a naive multi-frame or frame-concatenation input strategy leads to less detailed and sometimes temporal-confused results. We argue the challenge of designing a high-quality video captioning strategy lies in three aspects: 1) Inter-frame precise temporal change understanding. 2) Intra-frame detailed content description. 3) Frame-number scalability for arbitrary-length videos. To this end, we meticulously designed a differential video captioning strategy, which is stable, scalable, and efficient for generating captions for videos with arbitrary resolution, aspect ratios, and length. Based on it, we construct ShareGPT4Video, which contains 40K high-quality videos spanning a wide range of categories, and the resulting captions encompass rich world knowledge, object attributes, camera movements, and crucially, detailed and precise temporal descriptions of events. Based on ShareGPT4Video, we further develop ShareCaptioner-Video, a superior captioner capable of efficiently generating high-quality captions for arbitrary videos...", "github_manual": "https://github.com/ShareGPT4Omni/ShareGPT4Video", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-07", "arxiv_id": "2406.01300", "title": "pOps: Photo-Inspired Diffusion Operators", "authors": [ "Elad Richardson", "Yuval Alaluf", "Ali Mahdavi-Amiri", "Daniel Cohen-Or" ], "github": "", "project_page": "", "abstract": "Text-guided image generation enables the creation of visual content from textual descriptions. However, certain visual concepts cannot be effectively conveyed through language alone. This has sparked a renewed interest in utilizing the CLIP image embedding space for more visually-oriented tasks through methods such as IP-Adapter. Interestingly, the CLIP image embedding space has been shown to be semantically meaningful, where linear operations within this space yield semantically meaningful results. Yet, the specific meaning of these operations can vary unpredictably across different images. To harness this potential, we introduce pOps, a framework that trains specific semantic operators directly on CLIP image embeddings. Each pOps operator is built upon a pretrained Diffusion Prior model. While the Diffusion Prior model was originally trained to map between text embeddings and image embeddings, we demonstrate that it can be tuned to accommodate new input conditions, resulting in a diffusion operator. Working directly over image embeddings not only improves our ability to learn semantic operations but also allows us to directly use a textual CLIP loss as an additional supervision when needed. We show that pOps can be used to learn a variety of photo-inspired operators with distinct semantic meanings, highlighting the semantic diversity and potential of our proposed approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-10", "arxiv_id": "2406.04485", "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "authors": [ "Dongfu Jiang", "Max Ku", "Tianle Li", "Yuansheng Ni", "Shizhuo Sun", "Rongqi Fan", "Wenhu Chen" ], "github": "https://github.com/Kwai-Kolors/Kolors", "project_page": "", "abstract": "Generative AI has made remarkable strides to revolutionize fields such as image and video generation. These advancements are driven by innovative algorithms, architecture, and data. However, the rapid proliferation of generative models has highlighted a critical gap: the absence of trustworthy evaluation metrics. Current automatic assessments such as FID, CLIP, FVD, etc often fail to capture the nuanced quality and user satisfaction associated with generative outputs. This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GenAI-Arena aims to provide a more democratic and accurate measure of model performance. It covers three arenas for text-to-image generation, text-to-video generation, and image editing respectively. Currently, we cover a total of 27 open-source generative models. GenAI-Arena has been operating for four months, amassing over 6000 votes from the community. We describe our platform, analyze the data, and explain the statistical methods for ranking the models. To further promote the research in building model-based evaluation metrics, we release a cleaned version of our preference data for the three tasks, namely GenAI-Bench. We prompt the existing multi-modal models like Gemini, GPT-4o to mimic human voting. We compute the correlation between model voting with human voting to understand their judging abilities. Our results show existing multimodal models are still lagging in assessing the generated visual content, even the best model GPT-4o only achieves a Pearson correlation of 0.22 in the quality subscore, and behaves like random guessing in others.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Kwai-Kolors/Kolors", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-10", "arxiv_id": "2406.04692", "title": "Mixture-of-Agents Enhances Large Language Model Capabilities", "authors": [ "Junlin Wang", "Jue Wang", "Ben Athiwaratkun", "Ce Zhang", "James Zou" ], "github": "https://github.com/togethercomputer/moa", "project_page": "", "abstract": "Recent advances in large language models (LLMs) demonstrate substantial capabilities in natural language understanding and generation tasks. With the growing number of LLMs, how to harness the collective expertise of multiple LLMs is an exciting open direction. Toward this goal, we propose a new approach that leverages the collective strengths of multiple LLMs through a Mixture-of-Agents (MoA) methodology. In our approach, we construct a layered MoA architecture wherein each layer comprises multiple LLM agents. Each agent takes all the outputs from agents in the previous layer as auxiliary information in generating its response. MoA models achieves state-of-art performance on AlpacaEval 2.0, MT-Bench and FLASK, surpassing GPT-4 Omni. For example, our MoA using only open-source LLMs is the leader of AlpacaEval 2.0 by a substantial gap, achieving a score of 65.1% compared to 57.5% by GPT-4 Omni.", "github_manual": "https://github.com/togethercomputer/moa", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-10", "arxiv_id": "2406.04744", "title": "CRAG -- Comprehensive RAG Benchmark", "authors": [ "Xiao Yang", "Kai Sun", "Hao Xin", "Yushi Sun", "Nikita Bhalla", "Xiangsen Chen", "Sajal Choudhary", "Rongze Daniel Gui", "Ziran Will Jiang", "Ziyu Jiang", "Lingkun Kong", "Brian Moran", "Jiaqi Wang", "Yifan Ethan Xu", "An Yan", "Chenyu Yang", "Eting Yuan", "Hanwen Zha", "Nan Tang", "Lei Chen", "Nicolas Scheffer", "Yue Liu", "Nirav Shah", "Rakesh Wanga", "Anuj Kumar", "Wen-tau Yih", "Xin Luna Dong" ], "github": "https://github.com/facebookresearch/crag", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets, however, do not adequately represent the diverse and dynamic nature of real-world Question Answering (QA) tasks. To bridge this gap, we introduce the Comprehensive RAG Benchmark (CRAG), a factual question answering benchmark of 4,409 question-answer pairs and mock APIs to simulate web and Knowledge Graph (KG) search. CRAG is designed to encapsulate a diverse array of questions across five domains and eight question categories, reflecting varied entity popularity from popular to long-tail, and temporal dynamisms ranging from years to seconds. Our evaluation on this benchmark highlights the gap to fully trustworthy QA. Whereas most advanced LLMs achieve <=34% accuracy on CRAG, adding RAG in a straightforward manner improves the accuracy only to 44%. State-of-the-art industry RAG solutions only answer 63% questions without any hallucination. CRAG also reveals much lower accuracy in answering questions regarding facts with higher dynamism, lower popularity, or higher complexity, suggesting future research directions. The CRAG benchmark laid the groundwork for a KDD Cup 2024 challenge, attracting thousands of participants and submissions within the first 50 days of the competition. We commit to maintaining CRAG to serve research communities in advancing RAG solutions and general QA solutions.", "github_manual": "https://github.com/facebookresearch/crag", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-10", "arxiv_id": "2406.04520", "title": "NATURAL PLAN: Benchmarking LLMs on Natural Language Planning", "authors": [ "Huaixiu Steven Zheng", "Swaroop Mishra", "Hugh Zhang", "Xinyun Chen", "Minmin Chen", "Azade Nova", "Le Hou", "Heng-Tze Cheng", "Quoc V. Le", "Ed H. Chi", "Denny Zhou" ], "github": "", "project_page": "", "abstract": "We introduce NATURAL PLAN, a realistic planning benchmark in natural language containing 3 key tasks: Trip Planning, Meeting Planning, and Calendar Scheduling. We focus our evaluation on the planning capabilities of LLMs with full information on the task, by providing outputs from tools such as Google Flights, Google Maps, and Google Calendar as contexts to the models. This eliminates the need for a tool-use environment for evaluating LLMs on Planning. We observe that NATURAL PLAN is a challenging benchmark for state of the art models. For example, in Trip Planning, GPT-4 and Gemini 1.5 Pro could only achieve 31.1% and 34.8% solve rate respectively. We find that model performance drops drastically as the complexity of the problem increases: all models perform below 5% when there are 10 cities, highlighting a significant gap in planning in natural language for SoTA LLMs. We also conduct extensive ablation studies on NATURAL PLAN to further shed light on the (in)effectiveness of approaches such as self-correction, few-shot generalization, and in-context planning with long-contexts on improving LLM planning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-10", "arxiv_id": "2406.04594", "title": "Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach", "authors": [ "Jianbo Dong", "Bin Luo", "Jun Zhang", "Pengcheng Zhang", "Fei Feng", "Yikai Zhu", "Ang Liu", "Zian Chen", "Yi Shi", "Hairong Jiao", "Gang Lu", "Yu Guan", "Ennan Zhai", "Wencong Xiao", "Hanyu Zhao", "Man Yuan", "Siran Yang", "Xiang Li", "Jiamang Wang", "Rui Men", "Jianwei Zhang", "Huang Zhong", "Dennis Cai", "Yuan Xie", "Binzhang Fu" ], "github": "", "project_page": "", "abstract": "The emergence of Large Language Models (LLMs) has necessitated the adoption of parallel training techniques, involving the deployment of thousands of GPUs to train a single model. Unfortunately, we have found that the efficiency of current parallel training is often suboptimal, largely due to the following two main issues. Firstly, hardware failures are inevitable, leading to interruptions in the training tasks. The inability to quickly identify the faulty components results in a substantial waste of GPU resources. Secondly, since GPUs must wait for parameter synchronization to complete before proceeding to the next round of computation, network congestions can greatly increase the waiting time for GPUs. To address these challenges, this paper introduces a communication-driven solution, namely the C4. The key insights of C4 are two folds. First, in parallel training, collective communication exhibits periodic and homogeneous characteristics, so any anomalies are certainly due to some form of hardware malfunction. By leveraging this feature, C4 can rapidly identify the faulty components, swiftly isolate the anomaly, and restart the task, thereby avoiding resource wastage caused by delays in anomaly detection. Second, the predictable communication model of collective communication, involving few large flows, allows C4 to efficiently execute traffic planning, substantially reducing network congestion. C4 has been extensively implemented across our production systems, cutting error-induced overhead by roughly 30% and enhancing runtime performance by about 15% for certain applications with moderate communication costs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-10", "arxiv_id": "2406.04391", "title": "Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?", "authors": [ "Rylan Schaeffer", "Hailey Schoelkopf", "Brando Miranda", "Gabriel Mukobi", "Varun Madan", "Adam Ibrahim", "Herbie Bradley", "Stella Biderman", "Sanmi Koyejo" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "Predictable behavior from scaling advanced AI systems is an extremely desirable property. Although a well-established literature exists on how pretraining performance scales, the literature on how particular downstream capabilities scale is significantly muddier. In this work, we take a step back and ask: why has predicting specific downstream capabilities with scale remained elusive? While many factors are certainly responsible, we identify a new factor that makes modeling scaling behavior on widely used multiple-choice question-answering benchmarks challenging. Using five model families and twelve well-established multiple-choice benchmarks, we show that downstream performance is computed from negative log likelihoods via a sequence of transformations that progressively degrade the statistical relationship between performance and scale. We then reveal the mechanism causing this degradation: downstream metrics require comparing the correct choice against a small number of specific incorrect choices, meaning accurately predicting downstream capabilities requires predicting not just how probability mass concentrates on the correct choice with scale, but also how probability mass fluctuates on specific incorrect choices with scale. We empirically study how probability mass on the correct choice co-varies with probability mass on incorrect choices with increasing compute, suggesting that scaling laws for incorrect choices might be achievable. Our work also explains why pretraining scaling laws are commonly regarded as more predictable than downstream capabilities and contributes towards establishing scaling-predictable evaluations of frontier AI models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-10", "arxiv_id": "2406.04770", "title": "WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild", "authors": [ "Bill Yuchen Lin", "Yuntian Deng", "Khyathi Chandu", "Faeze Brahman", "Abhilasha Ravichander", "Valentina Pyatkin", "Nouha Dziri", "Ronan Le Bras", "Yejin Choi" ], "github": "https://github.com/allenai/wildbench", "project_page": "", "abstract": "We introduce WildBench, an automated evaluation framework designed to benchmark large language models (LLMs) using challenging, real-world user queries. WildBench consists of 1,024 tasks carefully selected from over one million human-chatbot conversation logs. For automated evaluation with WildBench, we have developed two metrics, WB-Reward and WB-Score, which are computable using advanced LLMs such as GPT-4-turbo. WildBench evaluation uses task-specific checklists to evaluate model outputs systematically and provides structured explanations that justify the scores and comparisons, resulting in more reliable and interpretable automatic judgments. WB-Reward employs fine-grained pairwise comparisons between model responses, generating five potential outcomes: much better, slightly better, slightly worse, much worse, or a tie. Unlike previous evaluations that employed a single baseline model, we selected three baseline models at varying performance levels to ensure a comprehensive pairwise evaluation. Additionally, we propose a simple method to mitigate length bias, by converting outcomes of ``slightly better/worse'' to ``tie'' if the winner response exceeds the loser one by more than K characters. WB-Score evaluates the quality of model outputs individually, making it a fast and cost-efficient evaluation metric. WildBench results demonstrate a strong correlation with the human-voted Elo ratings from Chatbot Arena on hard tasks. Specifically, WB-Reward achieves a Pearson correlation of 0.98 with top-ranking models. Additionally, WB-Score reaches 0.95, surpassing both ArenaHard's 0.91 and AlpacaEval2.0's 0.89 for length-controlled win rates, as well as the 0.87 for regular win rates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/wildbench", "github_api_added_by": "auto" }, { "date": "2024-06-10", "arxiv_id": "2406.04370", "title": "Large Language Model Confidence Estimation via Black-Box Access", "authors": [ "Tejaswini Pedapati", "Amit Dhurandhar", "Soumya Ghosh", "Soham Dan", "Prasanna Sattigeri" ], "github": "https://github.com/zlin7/UQ-NLG", "project_page": "", "abstract": "Estimating uncertainty or confidence in the responses of a model can be significant in evaluating trust not only in the responses, but also in the model as a whole. In this paper, we explore the problem of estimating confidence for responses of large language models (LLMs) with simply black-box or query access to them. We propose a simple and extensible framework where, we engineer novel features and train a (interpretable) model (viz. logistic regression) on these features to estimate the confidence. We empirically demonstrate that our simple framework is effective in estimating confidence of flan-ul2, llama-13b and mistral-7b with it consistently outperforming existing black-box confidence estimation approaches on benchmark datasets such as TriviaQA, SQuAD, CoQA and Natural Questions by even over 10% (on AUROC) in some cases. Additionally, our interpretable approach provides insight into features that are predictive of confidence, leading to the interesting and useful discovery that our confidence models built for one LLM generalize zero-shot across others on a given dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zlin7/UQ-NLG", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-10", "arxiv_id": "2406.04523", "title": "Proofread: Fixes All Errors with One Tap", "authors": [ "Renjie Liu", "Yanxiang Zhang", "Yun Zhu", "Haicheng Sun", "Yuanbo Zhang", "Michael Xuelin Huang", "Shanqing Cai", "Lei Meng", "Shumin Zhai" ], "github": "", "project_page": "", "abstract": "The impressive capabilities in Large Language Models (LLMs) provide a powerful approach to reimagine users' typing experience. This paper demonstrates Proofread, a novel Gboard feature powered by a server-side LLM in Gboard, enabling seamless sentence-level and paragraph-level corrections with a single tap. We describe the complete system in this paper, from data generation, metrics design to model tuning and deployment. To obtain models with sufficient quality, we implement a careful data synthetic pipeline tailored to online use cases, design multifaceted metrics, employ a two-stage tuning approach to acquire the dedicated LLM for the feature: the Supervised Fine Tuning (SFT) for foundational quality, followed by the Reinforcement Learning (RL) tuning approach for targeted refinement. Specifically, we find sequential tuning on Rewrite and proofread tasks yields the best quality in SFT stage, and propose global and direct rewards in the RL tuning stage to seek further improvement. Extensive experiments on a human-labeled golden set showed our tuned PaLM2-XS model achieved 85.56\\% good ratio. We launched the feature to Pixel 8 devices by serving the model on TPU v5 in Google Cloud, with thousands of daily active users. Serving latency was significantly reduced by quantization, bucket inference, text segmentation, and speculative decoding. Our demo could be seen in https://youtu.be/4ZdcuiwFU7I{Youtube}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.06525", "title": "Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation", "authors": [ "Peize Sun", "Yi Jiang", "Shoufa Chen", "Shilong Zhang", "Bingyue Peng", "Ping Luo", "Zehuan Yuan" ], "github": "https://github.com/FoundationVision/LlamaGen", "project_page": "", "abstract": "We introduce LlamaGen, a new family of image generation models that apply original ``next-token prediction'' paradigm of large language models to visual generation domain. It is an affirmative answer to whether vanilla autoregressive models, e.g., Llama, without inductive biases on visual signals can achieve state-of-the-art image generation performance if scaling properly. We reexamine design spaces of image tokenizers, scalability properties of image generation models, and their training data quality. The outcome of this exploration consists of: (1) An image tokenizer with downsample ratio of 16, reconstruction quality of 0.94 rFID and codebook usage of 97% on ImageNet benchmark. (2) A series of class-conditional image generation models ranging from 111M to 3.1B parameters, achieving 2.18 FID on ImageNet 256x256 benchmarks, outperforming the popular diffusion models such as LDM, DiT. (3) A text-conditional image generation model with 775M parameters, from two-stage training on LAION-COCO and high aesthetics quality images, demonstrating competitive performance of visual quality and text alignment. (4) We verify the effectiveness of LLM serving frameworks in optimizing the inference speed of image generation models and achieve 326% - 414% speedup. We release all models and codes to facilitate open-source community of visual generation and multimodal foundation models.", "github_manual": "https://github.com/FoundationVision/LlamaGen", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.06474", "title": "Towards a Personal Health Large Language Model", "authors": [ "Justin Cosentino", "Anastasiya Belyaeva", "Xin Liu", "Nicholas A. Furlotte", "Zhun Yang", "Chace Lee", "Erik Schenck", "Yojan Patel", "Jian Cui", "Logan Douglas Schneider", "Robby Bryant", "Ryan G. Gomes", "Allen Jiang", "Roy Lee", "Yun Liu", "Javier Perez", "Jameson K. Rogers", "Cathy Speed", "Shyam Tailor", "Megan Walker", "Jeffrey Yu", "Tim Althoff", "Conor Heneghan", "John Hernandez", "Mark Malhotra", "Leor Stern", "Yossi Matias", "Greg S. Corrado", "Shwetak Patel", "Shravya Shetty", "Jiening Zhan", "Shruthi Prabhakara", "Daniel McDuff", "Cory Y. McLean" ], "github": "", "project_page": "", "abstract": "In health, most large language model (LLM) research has focused on clinical tasks. However, mobile and wearable devices, which are rarely integrated into such tasks, provide rich, longitudinal data for personal health monitoring. Here we present Personal Health Large Language Model (PH-LLM), fine-tuned from Gemini for understanding and reasoning over numerical time-series personal health data. We created and curated three datasets that test 1) production of personalized insights and recommendations from sleep patterns, physical activity, and physiological responses, 2) expert domain knowledge, and 3) prediction of self-reported sleep outcomes. For the first task we designed 857 case studies in collaboration with domain experts to assess real-world scenarios in sleep and fitness. Through comprehensive evaluation of domain-specific rubrics, we observed that Gemini Ultra 1.0 and PH-LLM are not statistically different from expert performance in fitness and, while experts remain superior for sleep, fine-tuning PH-LLM provided significant improvements in using relevant domain knowledge and personalizing information for sleep insights. We evaluated PH-LLM domain knowledge using multiple choice sleep medicine and fitness examinations. PH-LLM achieved 79% on sleep and 88% on fitness, exceeding average scores from a sample of human experts. Finally, we trained PH-LLM to predict self-reported sleep quality outcomes from textual and multimodal encoding representations of wearable data, and demonstrate that multimodal encoding is required to match performance of specialized discriminative models. Although further development and evaluation are necessary in the safety-critical personal health domain, these results demonstrate both the broad knowledge and capabilities of Gemini models and the benefit of contextualizing physiological data for personal health applications as done with PH-LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.05814", "title": "Unified Text-to-Image Generation and Retrieval", "authors": [ "Leigang Qu", "Haochuan Li", "Tan Wang", "Wenjie Wang", "Yongqi Li", "Liqiang Nie", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "How humans can efficiently and effectively acquire images has always been a perennial question. A typical solution is text-to-image retrieval from an existing database given the text query; however, the limited database typically lacks creativity. By contrast, recent breakthroughs in text-to-image generation have made it possible to produce fancy and diverse visual content, but it faces challenges in synthesizing knowledge-intensive images. In this work, we rethink the relationship between text-to-image generation and retrieval and propose a unified framework in the context of Multimodal Large Language Models (MLLMs). Specifically, we first explore the intrinsic discriminative abilities of MLLMs and introduce a generative retrieval method to perform retrieval in a training-free manner. Subsequently, we unify generation and retrieval in an autoregressive generation way and propose an autonomous decision module to choose the best-matched one between generated and retrieved images as the response to the text query. Additionally, we construct a benchmark called TIGeR-Bench, including creative and knowledge-intensive domains, to standardize the evaluation of unified text-to-image generation and retrieval. Extensive experimental results on TIGeR-Bench and two retrieval benchmarks, i.e., Flickr30K and MS-COCO, demonstrate the superiority and effectiveness of our proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.05370", "title": "VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers", "authors": [ "Sanyuan Chen", "Shujie Liu", "Long Zhou", "Yanqing Liu", "Xu Tan", "Jinyu Li", "Sheng Zhao", "Yao Qian", "Furu Wei" ], "github": "", "project_page": "", "abstract": "This paper introduces VALL-E 2, the latest advancement in neural codec language models that marks a milestone in zero-shot text-to-speech synthesis (TTS), achieving human parity for the first time. Based on its predecessor, VALL-E, the new iteration introduces two significant enhancements: Repetition Aware Sampling refines the original nucleus sampling process by accounting for token repetition in the decoding history. It not only stabilizes the decoding but also circumvents the infinite loop issue. Grouped Code Modeling organizes codec codes into groups to effectively shorten the sequence length, which not only boosts inference speed but also addresses the challenges of long sequence modeling. Our experiments on the LibriSpeech and VCTK datasets show that VALL-E 2 surpasses previous systems in speech robustness, naturalness, and speaker similarity. It is the first of its kind to reach human parity on these benchmarks. Moreover, VALL-E 2 consistently synthesizes high-quality speech, even for sentences that are traditionally challenging due to their complexity or repetitive phrases. The advantages of this work could contribute to valuable endeavors, such as generating speech for individuals with aphasia or people with amyotrophic lateral sclerosis. Demos of VALL-E 2 will be posted to https://aka.ms/valle2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.05768", "title": "MLCM: Multistep Consistency Distillation of Latent Diffusion Model", "authors": [ "Qingsong Xie", "Zhenyi Liao", "Zhijie Deng", "Chen chen", "Shixiang Tang", "Haonan Lu" ], "github": "", "project_page": "", "abstract": "Distilling large latent diffusion models (LDMs) into ones that are fast to sample from is attracting growing research interest. However, the majority of existing methods face a dilemma where they either (i) depend on multiple individual distilled models for different sampling budgets, or (ii) sacrifice generation quality with limited (e.g., 2-4) and/or moderate (e.g., 5-8) sampling steps. To address these, we extend the recent multistep consistency distillation (MCD) strategy to representative LDMs, establishing the Multistep Latent Consistency Models (MLCMs) approach for low-cost high-quality image synthesis. MLCM serves as a unified model for various sampling steps due to the promise of MCD. We further augment MCD with a progressive training strategy to strengthen inter-segment consistency to boost the quality of few-step generations. We take the states from the sampling trajectories of the teacher model as training data for MLCMs to lift the requirements for high-quality training datasets and to bridge the gap between the training and inference of the distilled model. MLCM is compatible with preference learning strategies for further improvement of visual quality and aesthetic appeal. Empirically, MLCM can generate high-quality, delightful images with only 2-8 sampling steps. On the MSCOCO-2017 5K benchmark, MLCM distilled from SDXL gets a CLIP Score of 33.30, Aesthetic Score of 6.19, and Image Reward of 1.20 with only 4 steps, substantially surpassing 4-step LCM [23], 8-step SDXL-Lightning [17], and 8-step HyperSD [33]. We also demonstrate the versatility of MLCMs in applications including controllable generation, image style transfer, and Chinese-to-image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.06527", "title": "IllumiNeRF: 3D Relighting without Inverse Rendering", "authors": [ "Xiaoming Zhao", "Pratul P. Srinivasan", "Dor Verbin", "Keunhong Park", "Ricardo Martin Brualla", "Philipp Henzler" ], "github": "https://github.com/StanfordORB/Stanford-ORB/blob/962ea6d2cc/scripts/test.py", "project_page": "", "abstract": "Existing methods for relightable view synthesis -- using a set of images of an object under unknown lighting to recover a 3D representation that can be rendered from novel viewpoints under a target illumination -- are based on inverse rendering, and attempt to disentangle the object geometry, materials, and lighting that explain the input images. Furthermore, this typically involves optimization through differentiable Monte Carlo rendering, which is brittle and computationally-expensive. In this work, we propose a simpler approach: we first relight each input image using an image diffusion model conditioned on lighting and then reconstruct a Neural Radiance Field (NeRF) with these relit images, from which we render novel views under the target lighting. We demonstrate that this strategy is surprisingly competitive and achieves state-of-the-art results on multiple relighting benchmarks. Please see our project page at https://illuminerf.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/StanfordORB/Stanford-ORB/blob/962ea6d2cc/scripts/test.py", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.06216", "title": "Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering for HDR View Synthesis", "authors": [ "Xin Jin", "Pengyi Jiao", "Zheng-Peng Duan", "Xingchao Yang", "Chun-Le Guo", "Bo Ren", "Chongyi Li" ], "github": "https://github.com/Srameo/LE3D", "project_page": "", "abstract": "Volumetric rendering based methods, like NeRF, excel in HDR view synthesis from RAWimages, especially for nighttime scenes. While, they suffer from long training times and cannot perform real-time rendering due to dense sampling requirements. The advent of 3D Gaussian Splatting (3DGS) enables real-time rendering and faster training. However, implementing RAW image-based view synthesis directly using 3DGS is challenging due to its inherent drawbacks: 1) in nighttime scenes, extremely low SNR leads to poor structure-from-motion (SfM) estimation in distant views; 2) the limited representation capacity of spherical harmonics (SH) function is unsuitable for RAW linear color space; and 3) inaccurate scene structure hampers downstream tasks such as refocusing. To address these issues, we propose LE3D (Lighting Every darkness with 3DGS). Our method proposes Cone Scatter Initialization to enrich the estimation of SfM, and replaces SH with a Color MLP to represent the RAW linear color space. Additionally, we introduce depth distortion and near-far regularizations to improve the accuracy of scene structure for downstream tasks. These designs enable LE3D to perform real-time novel view synthesis, HDR rendering, refocusing, and tone-mapping changes. Compared to previous volumetric rendering based methods, LE3D reduces training time to 1% and improves rendering speed by up to 4,000 times for 2K resolution images in terms of FPS. Code and viewer can be found in https://github.com/Srameo/LE3D .", "github_manual": "", "github_regex": "https://github.com/Srameo/LE3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.06469", "title": "Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning", "authors": [ "Joongwon Kim", "Bhargavi Paranjape", "Tushar Khot", "Hannaneh Hajishirzi" ], "github": "https://github.com/agent-husky/Husky-v1", "project_page": "", "abstract": "Language agents perform complex tasks by using tools to execute each step precisely. However, most existing agents are based on proprietary models or designed to target specific tasks, such as mathematics or multi-hop question answering. We introduce Husky, a holistic, open-source language agent that learns to reason over a unified action space to address a diverse set of complex tasks involving numerical, tabular, and knowledge-based reasoning. Husky iterates between two stages: 1) generating the next action to take towards solving a given task and 2) executing the action using expert models and updating the current solution state. We identify a thorough ontology of actions for addressing complex tasks and curate high-quality data to train expert models for executing these actions. Our experiments show that Husky outperforms prior language agents across 14 evaluation datasets. Moreover, we introduce HuskyQA, a new evaluation set which stress tests language agents for mixed-tool reasoning, with a focus on retrieving missing knowledge and performing numerical reasoning. Despite using 7B models, Husky matches or even exceeds frontier LMs such as GPT-4 on these tasks, showcasing the efficacy of our holistic approach in addressing complex reasoning problems. Our code and models are available at https://github.com/agent-husky/Husky-v1.", "github_manual": "", "github_regex": "https://github.com/agent-husky/Husky-v1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.05981", "title": "ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization", "authors": [ "Haoran You", "Yipin Guo", "Yichao Fu", "Wei Zhou", "Huihong Shi", "Xiaofan Zhang", "Souvik Kundu", "Amir Yazdanbakhsh", "Yingyan Lin" ], "github": "https://github.com/GATECH-EIC/ShiftAddLLM", "project_page": "", "abstract": "Large language models (LLMs) have shown impressive performance on language tasks but face challenges when deployed on resource-constrained devices due to their extensive parameters and reliance on dense multiplications, resulting in high memory demands and latency bottlenecks. Shift-and-add reparameterization offers a promising solution by replacing costly multiplications with hardware-friendly primitives in both the attention and multi-layer perceptron (MLP) layers of an LLM. However, current reparameterization techniques require training from scratch or full parameter fine-tuning to restore accuracy, which is resource-intensive for LLMs. To address this, we propose accelerating pretrained LLMs through post-training shift-and-add reparameterization, creating efficient multiplication-free models, dubbed ShiftAddLLM. Specifically, we quantize each weight matrix into binary matrices paired with group-wise scaling factors. The associated multiplications are reparameterized into (1) shifts between activations and scaling factors and (2) queries and adds according to the binary matrices. To reduce accuracy loss, we present a multi-objective optimization method to minimize both weight and output activation reparameterization errors. Additionally, based on varying sensitivity across layers to reparameterization, we develop an automated bit allocation strategy to further reduce memory usage and latency. Experiments on five LLM families and eight tasks consistently validate the effectiveness of ShiftAddLLM, achieving average perplexity improvements of 5.6 and 22.7 points at comparable or lower latency compared to the most competitive quantized LLMs at 3 and 2 bits, respectively, and more than 80% memory and energy reductions over the original LLMs. Codes and models are available at https://github.com/GATECH-EIC/ShiftAddLLM.", "github_manual": "", "github_regex": "https://github.com/GATECH-EIC/ShiftAddLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.06316", "title": "Tx-LLM: A Large Language Model for Therapeutics", "authors": [ "Juan Manuel Zambrano Chaves", "Eric Wang", "Tao Tu", "Eeshit Dhaval Vaishnav", "Byron Lee", "S. Sara Mahdavi", "Christopher Semturs", "David Fleet", "Vivek Natarajan", "Shekoofeh Azizi" ], "github": "https://github.com/rdkit/rdkit", "project_page": "", "abstract": "Developing therapeutics is a lengthy and expensive process that requires the satisfaction of many different criteria, and AI models capable of expediting the process would be invaluable. However, the majority of current AI approaches address only a narrowly defined set of tasks, often circumscribed within a particular domain. To bridge this gap, we introduce Tx-LLM, a generalist large language model (LLM) fine-tuned from PaLM-2 which encodes knowledge about diverse therapeutic modalities. Tx-LLM is trained using a collection of 709 datasets that target 66 tasks spanning various stages of the drug discovery pipeline. Using a single set of weights, Tx-LLM simultaneously processes a wide variety of chemical or biological entities(small molecules, proteins, nucleic acids, cell lines, diseases) interleaved with free-text, allowing it to predict a broad range of associated properties, achieving competitive with state-of-the-art (SOTA) performance on 43 out of 66 tasks and exceeding SOTA on 22. Among these, Tx-LLM is particularly powerful and exceeds best-in-class performance on average for tasks combining molecular SMILES representations with text such as cell line names or disease names, likely due to context learned during pretraining. We observe evidence of positive transfer between tasks with diverse drug types (e.g.,tasks involving small molecules and tasks involving proteins), and we study the impact of model size, domain finetuning, and prompting strategies on performance. We believe Tx-LLM represents an important step towards LLMs encoding biochemical knowledge and could have a future role as an end-to-end tool across the drug discovery development pipeline.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/rdkit/rdkit", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.06040", "title": "Vript: A Video Is Worth Thousands of Words", "authors": [ "Dongjie Yang", "Suyuan Huang", "Chengqiang Lu", "Xiaodong Han", "Haoxin Zhang", "Yan Gao", "Yao Hu", "Hai Zhao" ], "github": "https://github.com/mutonix/Vript", "project_page": "", "abstract": "Advancements in multimodal learning, particularly in video understanding and generation, require high-quality video-text datasets for improved model performance. Vript addresses this issue with a meticulously annotated corpus of 12K high-resolution videos, offering detailed, dense, and script-like captions for over 420K clips. Each clip has a caption of ~145 words, which is over 10x longer than most video-text datasets. Unlike captions only documenting static content in previous datasets, we enhance video captioning to video scripting by documenting not just the content, but also the camera operations, which include the shot types (medium shot, close-up, etc) and camera movements (panning, tilting, etc). By utilizing the Vript, we explore three training paradigms of aligning more text with the video modality rather than clip-caption pairs. This results in Vriptor, a top-performing video captioning model among open-source models, comparable to GPT-4V in performance. Vriptor is also a powerful model capable of end-to-end generation of dense and detailed captions for long videos. Moreover, we introduce Vript-Hard, a benchmark consisting of three video understanding tasks that are more challenging than existing benchmarks: Vript-HAL is the first benchmark evaluating action and object hallucinations in video LLMs, Vript-RR combines reasoning with retrieval resolving question ambiguity in long-video QAs, and Vript-ERO is a new task to evaluate the temporal understanding of events in long videos rather than actions in short videos in previous works. All code, models, and datasets are available in https://github.com/mutonix/Vript.", "github_manual": "", "github_regex": "https://github.com/mutonix/Vript", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.06133", "title": "ExtraNeRF: Visibility-Aware View Extrapolation of Neural Radiance Fields with Diffusion Models", "authors": [ "Meng-Li Shih", "Wei-Chiu Ma", "Aleksander Holynski", "Forrester Cole", "Brian L. Curless", "Janne Kontkanen" ], "github": "https://github.com/ShihMengLi/extranerf", "project_page": "", "abstract": "We propose ExtraNeRF, a novel method for extrapolating the range of views handled by a Neural Radiance Field (NeRF). Our main idea is to leverage NeRFs to model scene-specific, fine-grained details, while capitalizing on diffusion models to extrapolate beyond our observed data. A key ingredient is to track visibility to determine what portions of the scene have not been observed, and focus on reconstructing those regions consistently with diffusion models. Our primary contributions include a visibility-aware diffusion-based inpainting module that is fine-tuned on the input imagery, yielding an initial NeRF with moderate quality (often blurry) inpainted regions, followed by a second diffusion model trained on the input imagery to consistently enhance, notably sharpen, the inpainted imagery from the first pass. We demonstrate high-quality results, extrapolating beyond a small number of (typically six or fewer) input views, effectively outpainting the NeRF as well as inpainting newly disoccluded regions inside the original viewing volume. We compare with related work both quantitatively and qualitatively and show significant gains over prior art.", "github_manual": "https://github.com/ShihMengLi/extranerf", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.05649", "title": "GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement", "authors": [ "Peiye Zhuang", "Songfang Han", "Chaoyang Wang", "Aliaksandr Siarohin", "Jiaxu Zou", "Michael Vasilkovsky", "Vladislav Shakhrai", "Sergey Korolev", "Sergey Tulyakov", "Hsin-Ying Lee" ], "github": "", "project_page": "", "abstract": "We propose a novel approach for 3D mesh reconstruction from multi-view images. Our method takes inspiration from large reconstruction models like LRM that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model trained on multi-view images. However, in our method, we introduce several important modifications that allow us to significantly enhance 3D reconstruction quality. First of all, we examine the original LRM architecture and find several shortcomings. Subsequently, we introduce respective modifications to the LRM architecture, which lead to improved multi-view image representation and more computationally efficient training. Second, in order to improve geometry reconstruction and enable supervision at full image resolution, we extract meshes from the NeRF field in a differentiable manner and fine-tune the NeRF model through mesh rendering. These modifications allow us to achieve state-of-the-art performance on both 2D and 3D evaluation metrics, such as a PSNR of 28.67 on Google Scanned Objects (GSO) dataset. Despite these superior results, our feed-forward model still struggles to reconstruct complex textures, such as text and portraits on assets. To address this, we introduce a lightweight per-instance texture refinement procedure. This procedure fine-tunes the triplane representation and the NeRF color estimation model on the mesh surface using the input multi-view images in just 4 seconds. This refinement improves the PSNR to 29.79 and achieves faithful reconstruction of complex textures, such as text. Additionally, our approach enables various downstream applications, including text- or image-to-3D generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-11", "arxiv_id": "2406.06424", "title": "Margin-aware Preference Optimization for Aligning Diffusion Models without Reference", "authors": [ "Jiwoo Hong", "Sayak Paul", "Noah Lee", "Kashif Rasul", "James Thorne", "Jongheon Jeong" ], "github": "https://github.com/mapo-t2i/mapo", "project_page": "", "abstract": "Modern alignment techniques based on human preferences, such as RLHF and DPO, typically employ divergence regularization relative to the reference model to ensure training stability. However, this often limits the flexibility of models during alignment, especially when there is a clear distributional discrepancy between the preference data and the reference model. In this paper, we focus on the alignment of recent text-to-image diffusion models, such as Stable Diffusion XL (SDXL), and find that this \"reference mismatch\" is indeed a significant problem in aligning these models due to the unstructured nature of visual modalities: e.g., a preference for a particular stylistic aspect can easily induce such a discrepancy. Motivated by this observation, we propose a novel and memory-friendly preference alignment method for diffusion models that does not depend on any reference model, coined margin-aware preference optimization (MaPO). MaPO jointly maximizes the likelihood margin between the preferred and dispreferred image sets and the likelihood of the preferred sets, simultaneously learning general stylistic features and preferences. For evaluation, we introduce two new pairwise preference datasets, which comprise self-generated image pairs from SDXL, Pick-Style and Pick-Safety, simulating diverse scenarios of reference mismatch. Our experiments validate that MaPO can significantly improve alignment on Pick-Style and Pick-Safety and general preference alignment when used with Pick-a-Pic v2, surpassing the base SDXL and other existing methods. Our code, models, and datasets are publicly available via https://mapo-t2i.github.io", "github_manual": "https://github.com/mapo-t2i/mapo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-12", "arxiv_id": "2406.06592", "title": "Improve Mathematical Reasoning in Language Models by Automated Process Supervision", "authors": [ "Liangchen Luo", "Yinxiao Liu", "Rosanne Liu", "Samrat Phatale", "Harsh Lara", "Yunxuan Li", "Lei Shu", "Yun Zhu", "Lei Meng", "Jiao Sun", "Abhinav Rastogi" ], "github": "", "project_page": "", "abstract": "Complex multi-step reasoning tasks, such as solving mathematical problems or generating code, remain a significant hurdle for even the most advanced large language models (LLMs). Verifying LLM outputs with an Outcome Reward Model (ORM) is a standard inference-time technique aimed at enhancing the reasoning performance of LLMs. However, this still proves insufficient for reasoning tasks with a lengthy or multi-hop reasoning chain, where the intermediate outcomes are neither properly rewarded nor penalized. Process supervision addresses this limitation by assigning intermediate rewards during the reasoning process. To date, the methods used to collect process supervision data have relied on either human annotation or per-step Monte Carlo estimation, both prohibitively expensive to scale, thus hindering the broad application of this technique. In response to this challenge, we propose a novel divide-and-conquer style Monte Carlo Tree Search (MCTS) algorithm named OmegaPRM for the efficient collection of high-quality process supervision data. This algorithm swiftly identifies the first error in the Chain of Thought (CoT) with binary search and balances the positive and negative examples, thereby ensuring both efficiency and quality. As a result, we are able to collect over 1.5 million process supervision annotations to train a Process Reward Model (PRM). Utilizing this fully automated process supervision alongside the weighted self-consistency algorithm, we have enhanced the instruction tuned Gemini Pro model's math reasoning performance, achieving a 69.4\\% success rate on the MATH benchmark, a 36\\% relative improvement from the 51\\% base model performance. Additionally, the entire process operates without any human intervention, making our method both financially and computationally cost-effective compared to existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-12", "arxiv_id": "2406.07472", "title": "4Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models", "authors": [ "Heng Yu", "Chaoyang Wang", "Peiye Zhuang", "Willi Menapace", "Aliaksandr Siarohin", "Junli Cao", "Laszlo A Jeni", "Sergey Tulyakov", "Hsin-Ying Lee" ], "github": "", "project_page": "", "abstract": "Existing dynamic scene generation methods mostly rely on distilling knowledge from pre-trained 3D generative models, which are typically fine-tuned on synthetic object datasets. As a result, the generated scenes are often object-centric and lack photorealism. To address these limitations, we introduce a novel pipeline designed for photorealistic text-to-4D scene generation, discarding the dependency on multi-view generative models and instead fully utilizing video generative models trained on diverse real-world datasets. Our method begins by generating a reference video using the video generation model. We then learn the canonical 3D representation of the video using a freeze-time video, delicately generated from the reference video. To handle inconsistencies in the freeze-time video, we jointly learn a per-frame deformation to model these imperfections. We then learn the temporal deformation based on the canonical representation to capture dynamic interactions in the reference video. The pipeline facilitates the generation of dynamic scenes with enhanced photorealism and structural integrity, viewable from multiple perspectives, thereby setting a new standard in 4D scene generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-12", "arxiv_id": "2406.07547", "title": "Zero-shot Image Editing with Reference Imitation", "authors": [ "Xi Chen", "Yutong Feng", "Mengting Chen", "Yiyang Wang", "Shilong Zhang", "Yu Liu", "Yujun Shen", "Hengshuang Zhao" ], "github": "https://github.com/ali-vilab/MimicBrush", "project_page": "", "abstract": "Image editing serves as a practical yet challenging task considering the diverse demands from users, where one of the hardest parts is to precisely describe how the edited image should look like. In this work, we present a new form of editing, termed imitative editing, to help users exercise their creativity more conveniently. Concretely, to edit an image region of interest, users are free to directly draw inspiration from some in-the-wild references (e.g., some relative pictures come across online), without having to cope with the fit between the reference and the source. Such a design requires the system to automatically figure out what to expect from the reference to perform the editing. For this purpose, we propose a generative training framework, dubbed MimicBrush, which randomly selects two frames from a video clip, masks some regions of one frame, and learns to recover the masked regions using the information from the other frame. That way, our model, developed from a diffusion prior, is able to capture the semantic correspondence between separate images in a self-supervised manner. We experimentally show the effectiveness of our method under various test cases as well as its superiority over existing alternatives. We also construct a benchmark to facilitate further research.", "github_manual": "https://github.com/ali-vilab/MimicBrush", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-12", "arxiv_id": "2406.07496", "title": "TextGrad: Automatic \"Differentiation\" via Text", "authors": [ "Mert Yuksekgonul", "Federico Bianchi", "Joseph Boen", "Sheng Liu", "Zhi Huang", "Carlos Guestrin", "James Zou" ], "github": "https://github.com/zou-group/textgrad", "project_page": "", "abstract": "AI is undergoing a paradigm shift, with breakthroughs achieved by systems orchestrating multiple large language models (LLMs) and other complex components. As a result, developing principled and automated optimization methods for compound AI systems is one of the most important new challenges. Neural networks faced a similar challenge in its early days until backpropagation and automatic differentiation transformed the field by making optimization turn-key. Inspired by this, we introduce TextGrad, a powerful framework performing automatic ``differentiation'' via text. TextGrad backpropagates textual feedback provided by LLMs to improve individual components of a compound AI system. In our framework, LLMs provide rich, general, natural language suggestions to optimize variables in computation graphs, ranging from code snippets to molecular structures. TextGrad follows PyTorch's syntax and abstraction and is flexible and easy-to-use. It works out-of-the-box for a variety of tasks, where the users only provide the objective function without tuning components or prompts of the framework. We showcase TextGrad's effectiveness and generality across a diverse range of applications, from question answering and molecule optimization to radiotherapy treatment planning. Without modifying the framework, TextGrad improves the zero-shot accuracy of GPT-4o in Google-Proof Question Answering from 51% to 55%, yields 20% relative performance gain in optimizing LeetCode-Hard coding problem solutions, improves prompts for reasoning, designs new druglike small molecules with desirable in silico binding, and designs radiation oncology treatment plans with high specificity. TextGrad lays a foundation to accelerate the development of the next-generation of AI systems.", "github_manual": "https://github.com/zou-group/textgrad", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-12", "arxiv_id": "2406.07550", "title": "An Image is Worth 32 Tokens for Reconstruction and Generation", "authors": [ "Qihang Yu", "Mark Weber", "Xueqing Deng", "Xiaohui Shen", "Daniel Cremers", "Liang-Chieh Chen" ], "github": "https://github.com/bytedance/1d-tokenizer", "project_page": "", "abstract": "Recent advancements in generative models have highlighted the crucial role of image tokenization in the efficient synthesis of high-resolution images. Tokenization, which transforms images into latent representations, reduces computational demands compared to directly processing pixels and enhances the effectiveness and efficiency of the generation process. Prior methods, such as VQGAN, typically utilize 2D latent grids with fixed downsampling factors. However, these 2D tokenizations face challenges in managing the inherent redundancies present in images, where adjacent regions frequently display similarities. To overcome this issue, we introduce Transformer-based 1-Dimensional Tokenizer (TiTok), an innovative approach that tokenizes images into 1D latent sequences. TiTok provides a more compact latent representation, yielding substantially more efficient and effective representations than conventional techniques. For example, a 256 x 256 x 3 image can be reduced to just 32 discrete tokens, a significant reduction from the 256 or 1024 tokens obtained by prior methods. Despite its compact nature, TiTok achieves competitive performance to state-of-the-art approaches. Specifically, using the same generator framework, TiTok attains 1.97 gFID, outperforming MaskGIT baseline significantly by 4.21 at ImageNet 256 x 256 benchmark. The advantages of TiTok become even more significant when it comes to higher resolution. At ImageNet 512 x 512 benchmark, TiTok not only outperforms state-of-the-art diffusion model DiT-XL/2 (gFID 2.74 vs. 3.04), but also reduces the image tokens by 64x, leading to 410x faster generation process. Our best-performing variant can significantly surpasses DiT-XL/2 (gFID 2.13 vs. 3.04) while still generating high-quality samples 74x faster.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bytedance/1d-tokenizer", "github_api_added_by": "auto" }, { "date": "2024-06-12", "arxiv_id": "2406.07394", "title": "Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B", "authors": [ "Di Zhang", "Jiatong Li", "Xiaoshui Huang", "Dongzhan Zhou", "Yuqiang Li", "Wanli Ouyang" ], "github": "https://github.com/trotsky1997/mathblackbox", "project_page": "", "abstract": "This paper introduces the MCT Self-Refine (MCTSr) algorithm, an innovative integration of Large Language Models (LLMs) with Monte Carlo Tree Search (MCTS), designed to enhance performance in complex mathematical reasoning tasks. Addressing the challenges of accuracy and reliability in LLMs, particularly in strategic and mathematical reasoning, MCTSr leverages systematic exploration and heuristic self-refine mechanisms to improve decision-making frameworks within LLMs. The algorithm constructs a Monte Carlo search tree through iterative processes of Selection, self-refine, self-evaluation, and Backpropagation, utilizing an improved Upper Confidence Bound (UCB) formula to optimize the exploration-exploitation balance. Extensive experiments demonstrate MCTSr's efficacy in solving Olympiad-level mathematical problems, significantly improving success rates across multiple datasets, including GSM8K, GSM Hard, MATH, and Olympiad-level benchmarks, including Math Odyssey, AIME, and OlympiadBench. The study advances the application of LLMs in complex reasoning tasks and sets a foundation for future AI integration, enhancing decision-making accuracy and reliability in LLM-driven applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/trotsky1997/mathblackbox", "github_api_added_by": "auto" }, { "date": "2024-06-12", "arxiv_id": "2406.07524", "title": "Simple and Effective Masked Diffusion Language Models", "authors": [ "Subham Sekhar Sahoo", "Marianne Arriola", "Yair Schiff", "Aaron Gokaslan", "Edgar Marroquin", "Justin T Chiu", "Alexander Rush", "Volodymyr Kuleshov" ], "github": "https://github.com/kuleshov-group/mdlm", "project_page": "", "abstract": "While diffusion models excel at generating high-quality images, prior work reports a significant performance gap between diffusion and autoregressive (AR) methods in language modeling. In this work, we show that simple masked discrete diffusion is more performant than previously thought. We apply an effective training recipe that improves the performance of masked diffusion models and derive a simplified, Rao-Blackwellized objective that results in additional improvements. Our objective has a simple form -- it is a mixture of classical masked language modeling losses -- and can be used to train encoder-only language models that admit efficient samplers, including ones that can generate arbitrary lengths of text semi-autoregressively like a traditional language model. On language modeling benchmarks, a range of masked diffusion models trained with modern engineering practices achieves a new state-of-the-art among diffusion models, and approaches AR perplexity. We release our code at: https://github.com/kuleshov-group/mdlm", "github_manual": "", "github_regex": "https://github.com/kuleshov-group/mdlm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-12", "arxiv_id": "2406.06911", "title": "AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising", "authors": [ "Zigeng Chen", "Xinyin Ma", "Gongfan Fang", "Zhenxiong Tan", "Xinchao Wang" ], "github": "https://github.com/czg1225/AsyncDiff", "project_page": "", "abstract": "Diffusion models have garnered significant interest from the community for their great generative ability across various applications. However, their typical multi-step sequential-denoising nature gives rise to high cumulative latency, thereby precluding the possibilities of parallel computation. To address this, we introduce AsyncDiff, a universal and plug-and-play acceleration scheme that enables model parallelism across multiple devices. Our approach divides the cumbersome noise prediction model into multiple components, assigning each to a different device. To break the dependency chain between these components, it transforms the conventional sequential denoising into an asynchronous process by exploiting the high similarity between hidden states in consecutive diffusion steps. Consequently, each component is facilitated to compute in parallel on separate devices. The proposed strategy significantly reduces inference latency while minimally impacting the generative quality. Specifically, for the Stable Diffusion v2.1, AsyncDiff achieves a 2.7x speedup with negligible degradation and a 4.0x speedup with only a slight reduction of 0.38 in CLIP Score, on four NVIDIA A5000 GPUs. Our experiments also demonstrate that AsyncDiff can be readily applied to video diffusion models with encouraging performances. The code is available at https://github.com/czg1225/AsyncDiff.", "github_manual": "", "github_regex": "https://github.com/czg1225/AsyncDiff", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-12", "arxiv_id": "2406.06573", "title": "MedFuzz: Exploring the Robustness of Large Language Models in Medical Question Answering", "authors": [ "Robert Osazuwa Ness", "Katie Matton", "Hayden Helm", "Sheng Zhang", "Junaid Bajwa", "Carey E. Priebe", "Eric Horvitz" ], "github": "", "project_page": "", "abstract": "Large language models (LLM) have achieved impressive performance on medical question-answering benchmarks. However, high benchmark accuracy does not imply that the performance generalizes to real-world clinical settings. Medical question-answering benchmarks rely on assumptions consistent with quantifying LLM performance but that may not hold in the open world of the clinic. Yet LLMs learn broad knowledge that can help the LLM generalize to practical conditions regardless of unrealistic assumptions in celebrated benchmarks. We seek to quantify how well LLM medical question-answering benchmark performance generalizes when benchmark assumptions are violated. Specifically, we present an adversarial method that we call MedFuzz (for medical fuzzing). MedFuzz attempts to modify benchmark questions in ways aimed at confounding the LLM. We demonstrate the approach by targeting strong assumptions about patient characteristics presented in the MedQA benchmark. Successful \"attacks\" modify a benchmark item in ways that would be unlikely to fool a medical expert but nonetheless \"trick\" the LLM into changing from a correct to an incorrect answer. Further, we present a permutation test technique that can ensure a successful attack is statistically significant. We show how to use performance on a \"MedFuzzed\" benchmark, as well as individual successful attacks. The methods show promise at providing insights into the ability of an LLM to operate robustly in more realistic settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-12", "arxiv_id": "2406.06563", "title": "Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models", "authors": [ "Tianwen Wei", "Bo Zhu", "Liang Zhao", "Cheng Cheng", "Biye Li", "Weiwei L\u00fc", "Peng Cheng", "Jianhao Zhang", "Xiaoyu Zhang", "Liang Zeng", "Xiaokun Wang", "Yutuan Ma", "Rui Hu", "Shuicheng Yan", "Han Fang", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "In this technical report, we introduce the training methodologies implemented in the development of Skywork-MoE, a high-performance mixture-of-experts (MoE) large language model (LLM) with 146 billion parameters and 16 experts. It is initialized from the pre-existing dense checkpoints of our Skywork-13B model. We explore the comparative effectiveness of upcycling versus training from scratch initializations. Our findings suggest that the choice between these two approaches should consider both the performance of the existing dense checkpoints and the MoE training budget. We highlight two innovative techniques: gating logit normalization, which improves expert diversification, and adaptive auxiliary loss coefficients, allowing for layer-specific adjustment of auxiliary loss coefficients. Our experimental results validate the effectiveness of these methods. Leveraging these techniques and insights, we trained our upcycled Skywork-MoE on a condensed subset of our SkyPile corpus. The evaluation results demonstrate that our model delivers strong performance across a wide range of benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-12", "arxiv_id": "2406.06608", "title": "The Prompt Report: A Systematic Survey of Prompting Techniques", "authors": [ "Sander Schulhoff", "Michael Ilie", "Nishant Balepur", "Konstantine Kahadze", "Amanda Liu", "Chenglei Si", "Yinheng Li", "Aayush Gupta", "HyoJung Han", "Sevien Schulhoff", "Pranav Sandeep Dulepet", "Saurav Vidyadhara", "Dayeon Ki", "Sweta Agrawal", "Chau Pham", "Gerson Kroiz", "Feileen Li", "Hudson Tao", "Ashay Srivastava", "Hevander Da Costa", "Saloni Gupta", "Megan L. Rogers", "Inna Goncearenco", "Giuseppe Sarli", "Igor Galynker", "Denis Peskoff", "Marine Carpuat", "Jules White", "Shyamal Anadkat", "Alexander Hoyle", "Philip Resnik" ], "github": "https://github.com/trigaten/Prompt_Systematic_Review", "project_page": "", "abstract": "Generative Artificial Intelligence (GenAI) systems are being increasingly deployed across all parts of industry and research settings. Developers and end users interact with these systems through the use of prompting or prompt engineering. While prompting is a widespread and highly researched concept, there exists conflicting terminology and a poor ontological understanding of what constitutes a prompt due to the area's nascency. This paper establishes a structured understanding of prompts, by assembling a taxonomy of prompting techniques and analyzing their use. We present a comprehensive vocabulary of 33 vocabulary terms, a taxonomy of 58 text-only prompting techniques, and 40 techniques for other modalities. We further present a meta-analysis of the entire literature on natural language prefix-prompting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/trigaten/Prompt_Systematic_Review", "github_api_added_by": "auto" }, { "date": "2024-06-12", "arxiv_id": "2406.06612", "title": "SEE-2-SOUND: Zero-Shot Spatial Environment-to-Spatial Sound", "authors": [ "Rishit Dagli", "Shivesh Prakash", "Robert Wu", "Houman Khosravani" ], "github": "https://github.com/see2sound/see2sound", "project_page": "", "abstract": "Generating combined visual and auditory sensory experiences is critical for the consumption of immersive content. Recent advances in neural generative models have enabled the creation of high-resolution content across multiple modalities such as images, text, speech, and videos. Despite these successes, there remains a significant gap in the generation of high-quality spatial audio that complements generated visual content. Furthermore, current audio generation models excel in either generating natural audio or speech or music but fall short in integrating spatial audio cues necessary for immersive experiences. In this work, we introduce SEE-2-SOUND, a zero-shot approach that decomposes the task into (1) identifying visual regions of interest; (2) locating these elements in 3D space; (3) generating mono-audio for each; and (4) integrating them into spatial audio. Using our framework, we demonstrate compelling results for generating spatial audio for high-quality videos, images, and dynamic images from the internet, as well as media generated by learned approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/see2sound/see2sound", "github_api_added_by": "auto" }, { "date": "2024-06-12", "arxiv_id": "2406.05629", "title": "Separating the \"Chirp\" from the \"Chat\": Self-supervised Visual Grounding of Sound and Language", "authors": [ "Mark Hamilton", "Andrew Zisserman", "John R. Hershey", "William T. Freeman" ], "github": "https://github.com/mhamilton723/DenseAV", "project_page": "", "abstract": "We present DenseAV, a novel dual encoder grounding architecture that learns high-resolution, semantically meaningful, and audio-visually aligned features solely through watching videos. We show that DenseAV can discover the ``meaning'' of words and the ``location'' of sounds without explicit localization supervision. Furthermore, it automatically discovers and distinguishes between these two types of associations without supervision. We show that DenseAV's localization abilities arise from a new multi-head feature aggregation operator that directly compares dense image and audio representations for contrastive learning. In contrast, many other systems that learn ``global'' audio and video representations cannot localize words and sound. Finally, we contribute two new datasets to improve the evaluation of AV representations through speech and sound prompted semantic segmentation. On these and other datasets we show DenseAV dramatically outperforms the prior art on speech and sound prompted semantic segmentation. DenseAV outperforms the previous state-of-the-art, ImageBind, on cross-modal retrieval using fewer than half of the parameters. Project Page: https://aka.ms/denseav{https://aka.ms/denseav}", "github_manual": "https://github.com/mhamilton723/DenseAV", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-12", "arxiv_id": "2406.07436", "title": "McEval: Massively Multilingual Code Evaluation", "authors": [ "Linzheng Chai", "Shukai Liu", "Jian Yang", "Yuwei Yin", "Ke Jin", "Jiaheng Liu", "Tao Sun", "Ge Zhang", "Changyu Ren", "Hongcheng Guo", "Zekun Wang", "Boyang Wang", "Xianjie Wu", "Bing Wang", "Tongliang Li", "Liqun Yang", "Sufeng Duan", "Zhoujun Li" ], "github": "https://github.com/MCEVAL/McEval", "project_page": "", "abstract": "Code large language models (LLMs) have shown remarkable advances in code understanding, completion, and generation tasks. Programming benchmarks, comprised of a selection of code challenges and corresponding test cases, serve as a standard to evaluate the capability of different LLMs in such tasks. However, most existing benchmarks primarily focus on Python and are still restricted to a limited number of languages, where other languages are translated from the Python samples (e.g. MultiPL-E) degrading the data diversity. To further facilitate the research of code LLMs, we propose a massively multilingual code benchmark covering 40 programming languages (McEval) with 16K test samples, which substantially pushes the limits of code LLMs in multilingual scenarios. The benchmark contains challenging code completion, understanding, and generation evaluation tasks with finely curated massively multilingual instruction corpora McEval-Instruct. In addition, we introduce an effective multilingual coder mCoder trained on McEval-Instruct to support multilingual programming language generation. Extensive experimental results on McEval show that there is still a difficult journey between open-source models and closed-source LLMs (e.g. GPT-series models) in numerous languages. The instruction corpora, evaluation benchmark, and leaderboard are available at https://mceval.github.io/.", "github_manual": "https://github.com/MCEVAL/McEval", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-12", "arxiv_id": "2406.07188", "title": "Merging Improves Self-Critique Against Jailbreak Attacks", "authors": [ "Victor Gallego" ], "github": "https://github.com/vicgalle/merging-self-critique-jailbreaks", "project_page": "", "abstract": "The robustness of large language models (LLMs) against adversarial manipulations, such as jailbreak attacks, remains a significant challenge. In this work, we propose an approach that enhances the self-critique capability of the LLM and further fine-tunes it over sanitized synthetic data. This is done with the addition of an external critic model that can be merged with the original, thus bolstering self-critique capabilities and improving the robustness of the LLMs response to adversarial prompts. Our results demonstrate that the combination of merging and self-critique can reduce the attack success rate of adversaries significantly, thus offering a promising defense mechanism against jailbreak attacks. Code, data and models released at https://github.com/vicgalle/merging-self-critique-jailbreaks .", "github_manual": "", "github_regex": "https://github.com/vicgalle/merging-self-critique-jailbreaks", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-12", "arxiv_id": "2406.07520", "title": "Neural Gaffer: Relighting Any Object via Diffusion", "authors": [ "Haian Jin", "Yuan Li", "Fujun Luan", "Yuanbo Xiangli", "Sai Bi", "Kai Zhang", "Zexiang Xu", "Jin Sun", "Noah Snavely" ], "github": "https://github.com/Haian-Jin/Neural_Gaffer", "project_page": "", "abstract": "Single-image relighting is a challenging task that involves reasoning about the complex interplay between geometry, materials, and lighting. Many prior methods either support only specific categories of images, such as portraits, or require special capture conditions, like using a flashlight. Alternatively, some methods explicitly decompose a scene into intrinsic components, such as normals and BRDFs, which can be inaccurate or under-expressive. In this work, we propose a novel end-to-end 2D relighting diffusion model, called Neural Gaffer, that takes a single image of any object and can synthesize an accurate, high-quality relit image under any novel environmental lighting condition, simply by conditioning an image generator on a target environment map, without an explicit scene decomposition. Our method builds on a pre-trained diffusion model, and fine-tunes it on a synthetic relighting dataset, revealing and harnessing the inherent understanding of lighting present in the diffusion model. We evaluate our model on both synthetic and in-the-wild Internet imagery and demonstrate its advantages in terms of generalization and accuracy. Moreover, by combining with other generative methods, our model enables many downstream 2D tasks, such as text-based relighting and object insertion. Our model can also operate as a strong relighting prior for 3D tasks, such as relighting a radiance field.", "github_manual": "https://github.com/Haian-Jin/Neural_Gaffer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.08464", "title": "Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing", "authors": [ "Zhangchen Xu", "Fengqing Jiang", "Luyao Niu", "Yuntian Deng", "Radha Poovendran", "Yejin Choi", "Bill Yuchen Lin" ], "github": "https://github.com/magpie-align/magpie", "project_page": "", "abstract": "High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent existing open-source data creation methods from scaling effectively, potentially limiting the diversity and quality of public alignment datasets. Is it possible to synthesize high-quality instruction data at scale by extracting it directly from an aligned LLM? We present a self-synthesis method for generating large-scale alignment data named Magpie. Our key observation is that aligned LLMs like Llama-3-Instruct can generate a user query when we input only the left-side templates up to the position reserved for user messages, thanks to their auto-regressive nature. We use this method to prompt Llama-3-Instruct and generate 4 million instructions along with their corresponding responses. We perform a comprehensive analysis of the extracted data and select 300K high-quality instances. To compare Magpie data with other public instruction datasets, we fine-tune Llama-3-8B-Base with each dataset and evaluate the performance of the fine-tuned models. Our results indicate that in some tasks, models fine-tuned with Magpie perform comparably to the official Llama-3-8B-Instruct, despite the latter being enhanced with 10 million data points through supervised fine-tuning (SFT) and subsequent feedback learning. We also show that using Magpie solely for SFT can surpass the performance of previous public datasets utilized for both SFT and preference optimization, such as direct preference optimization with UltraFeedback. This advantage is evident on alignment benchmarks such as AlpacaEval, ArenaHard, and WildBench.", "github_manual": "https://github.com/magpie-align/magpie", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.08478", "title": "What If We Recaption Billions of Web Images with LLaMA-3?", "authors": [ "Xianhang Li", "Haoqin Tu", "Mude Hui", "Zeyu Wang", "Bingchen Zhao", "Junfei Xiao", "Sucheng Ren", "Jieru Mei", "Qing Liu", "Huangjie Zheng", "Yuyin Zhou", "Cihang Xie" ], "github": "https://github.com/UCSC-VLAA/Recap-DataComp-1B", "project_page": "", "abstract": "Web-crawled image-text pairs are inherently noisy. Prior studies demonstrate that semantically aligning and enriching textual descriptions of these pairs can significantly enhance model training across various vision-language tasks, particularly text-to-image generation. However, large-scale investigations in this area remain predominantly closed-source. Our paper aims to bridge this community effort, leveraging the powerful and open-sourced LLaMA-3, a GPT-4 level LLM. Our recaptioning pipeline is simple: first, we fine-tune a LLaMA-3-8B powered LLaVA-1.5 and then employ it to recaption 1.3 billion images from the DataComp-1B dataset. Our empirical results confirm that this enhanced dataset, Recap-DataComp-1B, offers substantial benefits in training advanced vision-language models. For discriminative models like CLIP, we observe enhanced zero-shot performance in cross-modal retrieval tasks. For generative models like text-to-image Diffusion Transformers, the generated images exhibit a significant improvement in alignment with users' text instructions, especially in following complex queries. Our project page is https://www.haqtu.me/Recap-Datacomp-1B/", "github_manual": "https://github.com/UCSC-VLAA/Recap-DataComp-1B", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.08407", "title": "MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos", "authors": [ "Xuehai He", "Weixi Feng", "Kaizhi Zheng", "Yujie Lu", "Wanrong Zhu", "Jiachen Li", "Yue Fan", "Jianfeng Wang", "Linjie Li", "Zhengyuan Yang", "Kevin Lin", "William Yang Wang", "Lijuan Wang", "Xin Eric Wang" ], "github": "https://github.com/eric-ai-lab/MMWorld", "project_page": "", "abstract": "Multimodal Language Language Models (MLLMs) demonstrate the emerging abilities of \"world models\" -- interpreting and reasoning about complex real-world dynamics. To assess these abilities, we posit videos are the ideal medium, as they encapsulate rich representations of real-world dynamics and causalities. To this end, we introduce MMWorld, a new benchmark for multi-discipline, multi-faceted multimodal video understanding. MMWorld distinguishes itself from previous video understanding benchmarks with two unique advantages: (1) multi-discipline, covering various disciplines that often require domain expertise for comprehensive understanding; (2) multi-faceted reasoning, including explanation, counterfactual thinking, future prediction, etc. MMWorld consists of a human-annotated dataset to evaluate MLLMs with questions about the whole videos and a synthetic dataset to analyze MLLMs within a single modality of perception. Together, MMWorld encompasses 1,910 videos across seven broad disciplines and 69 subdisciplines, complete with 6,627 question-answer pairs and associated captions. The evaluation includes 2 proprietary and 10 open-source MLLMs, which struggle on MMWorld (e.g., GPT-4V performs the best with only 52.3\\% accuracy), showing large room for improvement. Further ablation studies reveal other interesting findings such as models' different skill sets from humans. We hope MMWorld can serve as an essential step towards world model evaluation in videos.", "github_manual": "https://github.com/eric-ai-lab/MMWorld", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.07792", "title": "Hierarchical Patch Diffusion Models for High-Resolution Video Generation", "authors": [ "Ivan Skorokhodov", "Willi Menapace", "Aliaksandr Siarohin", "Sergey Tulyakov" ], "github": "", "project_page": "", "abstract": "Diffusion models have demonstrated remarkable performance in image and video synthesis. However, scaling them to high-resolution inputs is challenging and requires restructuring the diffusion pipeline into multiple independent components, limiting scalability and complicating downstream applications. This makes it very efficient during training and unlocks end-to-end optimization on high-resolution videos. We improve PDMs in two principled ways. First, to enforce consistency between patches, we develop deep context fusion -- an architectural technique that propagates the context information from low-scale to high-scale patches in a hierarchical manner. Second, to accelerate training and inference, we propose adaptive computation, which allocates more network capacity and computation towards coarse image details. The resulting model sets a new state-of-the-art FVD score of 66.32 and Inception Score of 87.68 in class-conditional video generation on UCF-101 256^2, surpassing recent methods by more than 100%. Then, we show that it can be rapidly fine-tuned from a base 36times 64 low-resolution generator for high-resolution 64 times 288 times 512 text-to-video synthesis. To the best of our knowledge, our model is the first diffusion-based architecture which is trained on such high resolutions entirely end-to-end. Project webpage: https://snap-research.github.io/hpdm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.07686", "title": "AV-DiT: Efficient Audio-Visual Diffusion Transformer for Joint Audio and Video Generation", "authors": [ "Kai Wang", "Shijian Deng", "Jing Shi", "Dimitrios Hatzinakos", "Yapeng Tian" ], "github": "", "project_page": "", "abstract": "Recent Diffusion Transformers (DiTs) have shown impressive capabilities in generating high-quality single-modality content, including images, videos, and audio. However, it is still under-explored whether the transformer-based diffuser can efficiently denoise the Gaussian noises towards superb multimodal content creation. To bridge this gap, we introduce AV-DiT, a novel and efficient audio-visual diffusion transformer designed to generate high-quality, realistic videos with both visual and audio tracks. To minimize model complexity and computational costs, AV-DiT utilizes a shared DiT backbone pre-trained on image-only data, with only lightweight, newly inserted adapters being trainable. This shared backbone facilitates both audio and video generation. Specifically, the video branch incorporates a trainable temporal attention layer into a frozen pre-trained DiT block for temporal consistency. Additionally, a small number of trainable parameters adapt the image-based DiT block for audio generation. An extra shared DiT block, equipped with lightweight parameters, facilitates feature interaction between audio and visual modalities, ensuring alignment. Extensive experiments on the AIST++ and Landscape datasets demonstrate that AV-DiT achieves state-of-the-art performance in joint audio-visual generation with significantly fewer tunable parameters. Furthermore, our results highlight that a single shared image generative backbone with modality-specific adaptations is sufficient for constructing a joint audio-video generator. Our source code and pre-trained models will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.06523", "title": "NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing", "authors": [ "Ting-Hsuan Chen", "Jiewen Chan", "Hau-Shiang Shiu", "Shih-Han Yen", "Chang-Han Yeh", "Yu-Lun Liu" ], "github": "https://github.com/koi953215/NaRCan", "project_page": "", "abstract": "We propose a video editing framework, NaRCan, which integrates a hybrid deformation field and diffusion prior to generate high-quality natural canonical images to represent the input video. Our approach utilizes homography to model global motion and employs multi-layer perceptrons (MLPs) to capture local residual deformations, enhancing the model's ability to handle complex video dynamics. By introducing a diffusion prior from the early stages of training, our model ensures that the generated images retain a high-quality natural appearance, making the produced canonical images suitable for various downstream tasks in video editing, a capability not achieved by current canonical-based methods. Furthermore, we incorporate low-rank adaptation (LoRA) fine-tuning and introduce a noise and diffusion prior update scheduling technique that accelerates the training process by 14 times. Extensive experimental results show that our method outperforms existing approaches in various video editing tasks and produces coherent and high-quality edited video sequences. See our project page for video results at https://koi953215.github.io/NaRCan_page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/koi953215/NaRCan", "github_api_added_by": "auto" }, { "date": "2024-06-13", "arxiv_id": "2406.07476", "title": "VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs", "authors": [ "Zesen Cheng", "Sicong Leng", "Hang Zhang", "Yifei Xin", "Xin Li", "Guanzheng Chen", "Yongxin Zhu", "Wenqi Zhang", "Ziyang Luo", "Deli Zhao", "Lidong Bing" ], "github": "https://github.com/DAMO-NLP-SG/VideoLLaMA2", "project_page": "", "abstract": "In this paper, we present the VideoLLaMA 2, a set of Video Large Language Models (Video-LLMs) designed to enhance spatial-temporal modeling and audio understanding in video and audio-oriented tasks. Building upon its predecessor, VideoLLaMA 2 incorporates a tailor-made Spatial-Temporal Convolution (STC) connector, which effectively captures the intricate spatial and temporal dynamics of video data. Additionally, we integrate an Audio Branch into the model through joint training, thereby enriching the multimodal understanding capabilities of the model by seamlessly incorporating audio cues. Comprehensive evaluations on multiple-choice video question answering (MC-VQA), open-ended video question answering (OE-VQA), and video captioning (VC) tasks demonstrate that VideoLLaMA 2 consistently achieves competitive results among open-source models and even gets close to some proprietary models on several benchmarks. Furthermore, VideoLLaMA 2 exhibits reasonable improvements in audio-only and audio-video question-answering (AQA & OE-AVQA) benchmarks over existing models. These advancements underline VideoLLaMA 2's superior performance in multimodal comprehension, setting a new standard for intelligent video analysis systems. All models are public to facilitate further research.", "github_manual": "https://github.com/DAMO-NLP-SG/VideoLLaMA2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.05132", "title": "3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination", "authors": [ "Jianing Yang", "Xuweiyi Chen", "Nikhil Madaan", "Madhavan Iyengar", "Shengyi Qian", "David F. Fouhey", "Joyce Chai" ], "github": "https://github.com/sled-group/3D-GRAND", "project_page": "", "abstract": "The integration of language and 3D perception is crucial for developing embodied agents and robots that comprehend and interact with the physical world. While large language models (LLMs) have demonstrated impressive language understanding and generation capabilities, their adaptation to 3D environments (3D-LLMs) remains in its early stages. A primary challenge is the absence of large-scale datasets that provide dense grounding between language and 3D scenes. In this paper, we introduce 3D-GRAND, a pioneering large-scale dataset comprising 40,087 household scenes paired with 6.2 million densely-grounded scene-language instructions. Our results show that instruction tuning with 3D-GRAND significantly enhances grounding capabilities and reduces hallucinations in 3D-LLMs. As part of our contributions, we propose a comprehensive benchmark 3D-POPE to systematically evaluate hallucination in 3D-LLMs, enabling fair comparisons among future models. Our experiments highlight a scaling effect between dataset size and 3D-LLM performance, emphasizing the critical role of large-scale 3D-text datasets in advancing embodied AI research. Notably, our results demonstrate early signals for effective sim-to-real transfer, indicating that models trained on large synthetic data can perform well on real-world 3D scans. Through 3D-GRAND and 3D-POPE, we aim to equip the embodied AI community with essential resources and insights, setting the stage for more reliable and better-grounded 3D-LLMs. Project website: https://3d-grand.github.io", "github_manual": "https://github.com/sled-group/3D-GRAND", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.08392", "title": "FontStudio: Shape-Adaptive Diffusion Model for Coherent and Consistent Font Effect Generation", "authors": [ "Xinzhi Mu", "Li Chen", "Bohan Chen", "Shuyang Gu", "Jianmin Bao", "Dong Chen", "Ji Li", "Yuhui Yuan" ], "github": "", "project_page": "", "abstract": "Recently, the application of modern diffusion-based text-to-image generation models for creating artistic fonts, traditionally the domain of professional designers, has garnered significant interest. Diverging from the majority of existing studies that concentrate on generating artistic typography, our research aims to tackle a novel and more demanding challenge: the generation of text effects for multilingual fonts. This task essentially requires generating coherent and consistent visual content within the confines of a font-shaped canvas, as opposed to a traditional rectangular canvas. To address this task, we introduce a novel shape-adaptive diffusion model capable of interpreting the given shape and strategically planning pixel distributions within the irregular canvas. To achieve this, we curate a high-quality shape-adaptive image-text dataset and incorporate the segmentation mask as a visual condition to steer the image generation process within the irregular-canvas. This approach enables the traditionally rectangle canvas-based diffusion model to produce the desired concepts in accordance with the provided geometric shapes. Second, to maintain consistency across multiple letters, we also present a training-free, shape-adaptive effect transfer method for transferring textures from a generated reference letter to others. The key insights are building a font effect noise prior and propagating the font effect information in a concatenated latent space. The efficacy of our FontStudio system is confirmed through user preference studies, which show a marked preference (78% win-rates on aesthetics) for our system even when compared to the latest unrivaled commercial product, Adobe Firefly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.05338", "title": "MotionClone: Training-Free Motion Cloning for Controllable Video Generation", "authors": [ "Pengyang Ling", "Jiazi Bu", "Pan Zhang", "Xiaoyi Dong", "Yuhang Zang", "Tong Wu", "Huaian Chen", "Jiaqi Wang", "Yi Jin" ], "github": "https://github.com/Bujiazi/MotionClone/", "project_page": "", "abstract": "Motion-based controllable text-to-video generation involves motions to control the video generation. Previous methods typically require the training of models to encode motion cues or the fine-tuning of video diffusion models. However, these approaches often result in suboptimal motion generation when applied outside the trained domain. In this work, we propose MotionClone, a training-free framework that enables motion cloning from a reference video to control text-to-video generation. We employ temporal attention in video inversion to represent the motions in the reference video and introduce primary temporal-attention guidance to mitigate the influence of noisy or very subtle motions within the attention weights. Furthermore, to assist the generation model in synthesizing reasonable spatial relationships and enhance its prompt-following capability, we propose a location-aware semantic guidance mechanism that leverages the coarse location of the foreground from the reference video and original classifier-free guidance features to guide the video generation. Extensive experiments demonstrate that MotionClone exhibits proficiency in both global camera motion and local object motion, with notable superiority in terms of motion fidelity, textual alignment, and temporal consistency.", "github_manual": "https://github.com/Bujiazi/MotionClone/", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.05955", "title": "Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters", "authors": [ "Yixin Song", "Haotong Xie", "Zhengyan Zhang", "Bo Wen", "Li Ma", "Zeyu Mi", "Haibo Chen" ], "github": "", "project_page": "", "abstract": "Exploiting activation sparsity is a promising approach to significantly accelerating the inference process of large language models (LLMs) without compromising performance. However, activation sparsity is determined by activation functions, and commonly used ones like SwiGLU and GeGLU exhibit limited sparsity. Simply replacing these functions with ReLU fails to achieve sufficient sparsity. Moreover, inadequate training data can further increase the risk of performance degradation. To address these challenges, we propose a novel dReLU function, which is designed to improve LLM activation sparsity, along with a high-quality training data mixture ratio to facilitate effective sparsification. Additionally, we leverage sparse activation patterns within the Feed-Forward Network (FFN) experts of Mixture-of-Experts (MoE) models to further boost efficiency. By applying our neuron sparsification method to the Mistral and Mixtral models, only 2.5 billion and 4.3 billion parameters are activated per inference iteration, respectively, while achieving even more powerful model performance. Evaluation results demonstrate that this sparsity achieves a 2-5x decoding speedup. Remarkably, on mobile phones, our TurboSparse-Mixtral-47B achieves an inference speed of 11 tokens per second. Our models are available at https://huggingface.co/PowerInfer", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.06282", "title": "PowerInfer-2: Fast Large Language Model Inference on a Smartphone", "authors": [ "Zhenliang Xue", "Yixin Song", "Zeyu Mi", "Le Chen", "Yubin Xia", "Haibo Chen" ], "github": "", "project_page": "", "abstract": "This paper introduces PowerInfer-2, a framework designed for high-speed inference of Large Language Models (LLMs) on smartphones, particularly effective for models whose sizes exceed the device's memory capacity. The key insight of PowerInfer-2 is to utilize the heterogeneous computation, memory, and I/O resources in smartphones by decomposing traditional matrix computations into fine-grained neuron cluster computations. Specifically, PowerInfer-2 features a polymorphic neuron engine that adapts computational strategies for various stages of LLM inference. Additionally, it introduces segmented neuron caching and fine-grained neuron-cluster-level pipelining, which effectively minimize and conceal the overhead caused by I/O operations. The implementation and evaluation of PowerInfer-2 demonstrate its capability to support a wide array of LLM models on two smartphones, achieving up to a 29.2x speed increase compared with state-of-the-art frameworks. Notably, PowerInfer-2 is the first system to serve the TurboSparse-Mixtral-47B model with a generation rate of 11.68 tokens per second on a smartphone. For models that fit entirely within the memory, PowerInfer-2 can achieve approximately a 40% reduction in memory usage while maintaining inference speeds comparable to llama.cpp and MLC-LLM. For more details, including a demonstration video, please visit the project site at www.powerinfer.ai/v2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.08414", "title": "Discovering Preference Optimization Algorithms with and for Large Language Models", "authors": [ "Chris Lu", "Samuel Holt", "Claudio Fanconi", "Alex J. Chan", "Jakob Foerster", "Mihaela van der Schaar", "Robert Tjarko Lange" ], "github": "https://github.com/luchris429/DiscoPOP", "project_page": "", "abstract": "Offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. Typically, preference optimization is approached as an offline supervised learning task using manually-crafted convex loss functions. While these methods are based on theoretical insights, they are inherently constrained by human creativity, so the large search space of possible loss functions remains under explored. We address this by performing LLM-driven objective discovery to automatically discover new state-of-the-art preference optimization algorithms without (expert) human intervention. Specifically, we iteratively prompt an LLM to propose and implement new preference optimization loss functions based on previously-evaluated performance metrics. This process leads to the discovery of previously-unknown and performant preference optimization algorithms. The best performing of these we call Discovered Preference Optimization (DiscoPOP), a novel algorithm that adaptively blends logistic and exponential losses. Experiments demonstrate the state-of-the-art performance of DiscoPOP and its successful transfer to held-out tasks.", "github_manual": "https://github.com/luchris429/DiscoPOP", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.04320", "title": "Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models", "authors": [ "Ali Behrouz", "Michele Santacatterina", "Ramin Zabih" ], "github": "", "project_page": "", "abstract": "Modeling multivariate time series is a well-established problem with a wide range of applications from healthcare to financial markets. Traditional State Space Models (SSMs) are classical approaches for univariate time series modeling due to their simplicity and expressive power to represent linear dependencies. They, however, have fundamentally limited expressive power to capture non-linear dependencies, are slow in practice, and fail to model the inter-variate information flow. Despite recent attempts to improve the expressive power of SSMs by using deep structured SSMs, the existing methods are either limited to univariate time series, fail to model complex patterns (e.g., seasonal patterns), fail to dynamically model the dependencies of variate and time dimensions, and/or are input-independent. We present Chimera that uses two input-dependent 2-D SSM heads with different discretization processes to learn long-term progression and seasonal patterns. To improve the efficiency of complex 2D recurrence, we present a fast training using a new 2-dimensional parallel selective scan. We further present and discuss 2-dimensional Mamba and Mamba-2 as the spacial cases of our 2D SSM. Our experimental evaluation shows the superior performance of Chimera on extensive and diverse benchmarks, including ECG and speech time series classification, long-term and short-term time series forecasting, and time series anomaly detection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.04338", "title": "Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion", "authors": [ "Fangfu Liu", "Hanyang Wang", "Shunyu Yao", "Shengjun Zhang", "Jie Zhou", "Yueqi Duan" ], "github": "https://github.com/liuff19/Physics3D", "project_page": "", "abstract": "In recent years, there has been rapid development in 3D generation models, opening up new possibilities for applications such as simulating the dynamic movements of 3D objects and customizing their behaviors. However, current 3D generative models tend to focus only on surface features such as color and shape, neglecting the inherent physical properties that govern the behavior of objects in the real world. To accurately simulate physics-aligned dynamics, it is essential to predict the physical properties of materials and incorporate them into the behavior prediction process. Nonetheless, predicting the diverse materials of real-world objects is still challenging due to the complex nature of their physical attributes. In this paper, we propose Physics3D, a novel method for learning various physical properties of 3D objects through a video diffusion model. Our approach involves designing a highly generalizable physical simulation system based on a viscoelastic material model, which enables us to simulate a wide range of materials with high-fidelity capabilities. Moreover, we distill the physical priors from a video diffusion model that contains more understanding of realistic object materials. Extensive experiments demonstrate the effectiveness of our method with both elastic and plastic materials. Physics3D shows great potential for bridging the gap between the physical world and virtual neural space, providing a better integration and application of realistic physical principles in virtual environments. Project page: https://liuff19.github.io/Physics3D.", "github_manual": "https://github.com/liuff19/Physics3D", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.08487", "title": "Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models", "authors": [ "Yi-Fan Zhang", "Qingsong Wen", "Chaoyou Fu", "Xue Wang", "Zhang Zhang", "Liang Wang", "Rong Jin" ], "github": "https://github.com/yfzhang114/SliME", "project_page": "", "abstract": "Seeing clearly with high resolution is a foundation of Large Multimodal Models (LMMs), which has been proven to be vital for visual perception and reasoning. Existing works usually employ a straightforward resolution upscaling method, where the image consists of global and local branches, with the latter being the sliced image patches but resized to the same resolution as the former. This means that higher resolution requires more local patches, resulting in exorbitant computational expenses, and meanwhile, the dominance of local image tokens may diminish the global context. In this paper, we dive into the problems and propose a new framework as well as an elaborate optimization strategy. Specifically, we extract contextual information from the global view using a mixture of adapters, based on the observation that different adapters excel at different tasks. With regard to local patches, learnable query embeddings are introduced to reduce image tokens, the most important tokens accounting for the user question will be further selected by a similarity-based selector. Our empirical results demonstrate a `less is more' pattern, where utilizing fewer but more informative local image tokens leads to improved performance. Besides, a significant challenge lies in the training strategy, as simultaneous end-to-end training of the global mining block and local compression block does not yield optimal results. We thus advocate for an alternating training way, ensuring balanced learning between global and local aspects. Finally, we also introduce a challenging dataset with high requirements for image detail, enhancing the training of the local compression layer. The proposed method, termed LMM with Sophisticated Tasks, Local image compression, and Mixture of global Experts (SliME), achieves leading performance across various benchmarks with only 2 million training data.", "github_manual": "https://github.com/yfzhang114/SliME", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.05074", "title": "Hibou: A Family of Foundational Vision Transformers for Pathology", "authors": [ "Dmitry Nechaev", "Alexey Pchelnikov", "Ekaterina Ivanova" ], "github": "https://github.com/HistAI/hibou", "project_page": "", "abstract": "Pathology, the microscopic examination of diseased tissue, is critical for diagnosing various medical conditions, particularly cancers. Traditional methods are labor-intensive and prone to human error. Digital pathology, which converts glass slides into high-resolution digital images for analysis by computer algorithms, revolutionizes the field by enhancing diagnostic accuracy, consistency, and efficiency through automated image analysis and large-scale data processing. Foundational transformer pretraining is crucial for developing robust, generalizable models as it enables learning from vast amounts of unannotated data. This paper introduces the Hibou family of foundational vision transformers for pathology, leveraging the DINOv2 framework to pretrain two model variants, Hibou-B and Hibou-L, on a proprietary dataset of over 1 million whole slide images (WSIs) representing diverse tissue types and staining techniques. Our pretrained models demonstrate superior performance on both patch-level and slide-level benchmarks, surpassing existing state-of-the-art methods. Notably, Hibou-L achieves the highest average accuracy across multiple benchmark datasets. To support further research and application in the field, we have open-sourced the Hibou-B model, which can be accessed at https://github.com/HistAI/hibou", "github_manual": "", "github_regex": "https://github.com/HistAI/hibou", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.04127", "title": "Are We Done with MMLU?", "authors": [ "Aryo Pradipta Gema", "Joshua Ong Jun Leang", "Giwon Hong", "Alessio Devoto", "Alberto Carlo Maria Mancino", "Rohit Saxena", "Xuanli He", "Yu Zhao", "Xiaotang Du", "Mohammad Reza Ghasemi Madani", "Claire Barale", "Robert McHardy", "Joshua Harris", "Jean Kaddour", "Emile van Krieken", "Pasquale Minervini" ], "github": "https://github.com/aryopg/mmlu-redux", "project_page": "", "abstract": "Maybe not. We identify and analyse errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates numerous ground truth errors that obscure the true capabilities of LLMs. For example, we find that 57% of the analysed questions in the Virology subset contain errors. To address this issue, we introduce a comprehensive framework for identifying dataset errors using a novel error taxonomy. Then, we create MMLU-Redux, which is a subset of 3,000 manually re-annotated questions across 30 MMLU subjects. Using MMLU-Redux, we demonstrate significant discrepancies with the model performance metrics that were originally reported. Our results strongly advocate for revising MMLU's error-ridden questions to enhance its future utility and reliability as a benchmark. Therefore, we open up MMLU-Redux for additional annotation https://huggingface.co/datasets/edinburgh-dawg/mmlu-redux.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aryopg/mmlu-redux", "github_api_added_by": "auto" }, { "date": "2024-06-13", "arxiv_id": "2406.07933", "title": "Large Language Model Unlearning via Embedding-Corrupted Prompts", "authors": [ "Chris Yuhao Liu", "Yaxuan Wang", "Jeffrey Flanigan", "Yang Liu" ], "github": "https://github.com/chrisliu298/llm-unlearn-eco", "project_page": "", "abstract": "Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a large language model should not know is important for ensuring alignment and thus safe use. However, accurately and efficiently unlearning knowledge from an LLM remains challenging due to the potential collateral damage caused by the fuzzy boundary between retention and forgetting, and the large computational requirements for optimization across state-of-the-art models with hundreds of billions of parameters. In this work, we present Embedding-COrrupted (ECO) Prompts, a lightweight unlearning framework for large language models to address both the challenges of knowledge entanglement and unlearning efficiency. Instead of relying on the LLM itself to unlearn, we enforce an unlearned state during inference by employing a prompt classifier to identify and safeguard prompts to forget. We learn corruptions added to prompt embeddings via zeroth order optimization toward the unlearning objective offline and corrupt prompts flagged by the classifier during inference. We find that these embedding-corrupted prompts not only lead to desirable outputs that satisfy the unlearning objective but also closely approximate the output from a model that has never been trained on the data intended for forgetting. Through extensive experiments on unlearning, we demonstrate the superiority of our method in achieving promising unlearning at nearly zero side effects in general domains and domains closely related to the unlearned ones. Additionally, we highlight the scalability of our method to 100 LLMs, ranging from 0.5B to 236B parameters, incurring no additional cost as the number of parameters increases.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/chrisliu298/llm-unlearn-eco", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.04329", "title": "Simplified and Generalized Masked Diffusion for Discrete Data", "authors": [ "Jiaxin Shi", "Kehang Han", "Zhe Wang", "Arnaud Doucet", "Michalis K. Titsias" ], "github": "https://github.com/google-deepmind/md4", "project_page": "", "abstract": "Masked (or absorbing) diffusion is actively explored as an alternative to autoregressive models for generative modeling of discrete data. However, existing work in this area has been hindered by unnecessarily complex model formulations and unclear relationships between different perspectives, leading to suboptimal parameterization, training objectives, and ad hoc adjustments to counteract these issues. In this work, we aim to provide a simple and general framework that unlocks the full potential of masked diffusion models. We show that the continuous-time variational objective of masked diffusion models is a simple weighted integral of cross-entropy losses. Our framework also enables training generalized masked diffusion models with state-dependent masking schedules. When evaluated by perplexity, our models trained on OpenWebText surpass prior diffusion language models at GPT-2 scale and demonstrate superior performance on 4 out of 5 zero-shot language modeling tasks. Furthermore, our models vastly outperform previous discrete diffusion models on pixel-level image modeling, achieving 2.78~(CIFAR-10) and 3.42 (ImageNet 64times64) bits per dimension that are comparable or better than autoregressive models of similar sizes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/md4", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-13", "arxiv_id": "2406.06462", "title": "VCR: Visual Caption Restoration", "authors": [ "Tianyu Zhang", "Suyuchen Wang", "Lu Li", "Ge Zhang", "Perouz Taslakian", "Sai Rajeswar", "Jie Fu", "Bang Liu", "Yoshua Bengio" ], "github": "https://github.com/tianyu-z/vcr", "project_page": "", "abstract": "We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured texts using pixel-level hints within images. This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images. While numerous works have integrated text embedded in images into visual question-answering tasks, approaches to these tasks generally rely on optical character recognition or masked language modeling, thus reducing the task to mainly text-based processing. However, text-based processing becomes ineffective in VCR as accurate text restoration depends on the combined information from provided images, context, and subtle cues from the tiny exposed areas of masked texts. We develop a pipeline to generate synthetic images for the VCR task using image-caption pairs, with adjustable caption visibility to control the task difficulty. With this pipeline, we construct a dataset for VCR called VCR-Wiki using images with captions from Wikipedia, comprising 2.11M English and 346K Chinese entities in both easy and hard split variants. Our results reveal that current vision language models significantly lag behind human performance in the VCR task, and merely fine-tuning the models on our dataset does not lead to notable improvements. We release VCR-Wiki and the data construction code to facilitate future research.", "github_manual": "https://github.com/tianyu-z/vcr", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.08552", "title": "DiTFastAttn: Attention Compression for Diffusion Transformer Models", "authors": [ "Zhihang Yuan", "Pu Lu", "Hanling Zhang", "Xuefei Ning", "Linfeng Zhang", "Tianchen Zhao", "Shengen Yan", "Guohao Dai", "Yu Wang" ], "github": "https://github.com/thu-nics/DiTFastAttn", "project_page": "", "abstract": "Diffusion Transformers (DiT) excel at image and video generation but face computational challenges due to self-attention's quadratic complexity. We propose DiTFastAttn, a novel post-training compression method to alleviate DiT's computational bottleneck. We identify three key redundancies in the attention computation during DiT inference: 1. spatial redundancy, where many attention heads focus on local information; 2. temporal redundancy, with high similarity between neighboring steps' attention outputs; 3. conditional redundancy, where conditional and unconditional inferences exhibit significant similarity. To tackle these redundancies, we propose three techniques: 1. Window Attention with Residual Caching to reduce spatial redundancy; 2. Temporal Similarity Reduction to exploit the similarity between steps; 3. Conditional Redundancy Elimination to skip redundant computations during conditional generation. To demonstrate the effectiveness of DiTFastAttn, we apply it to DiT, PixArt-Sigma for image generation tasks, and OpenSora for video generation tasks. Evaluation results show that for image generation, our method reduces up to 88\\% of the FLOPs and achieves up to 1.6x speedup at high resolution generation.", "github_manual": "https://github.com/thu-nics/DiTFastAttn", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09415", "title": "An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels", "authors": [ "Duy-Kien Nguyen", "Mahmoud Assran", "Unnat Jain", "Martin R. Oswald", "Cees G. M. Snoek", "Xinlei Chen" ], "github": "", "project_page": "", "abstract": "This work does not introduce a new method. Instead, we present an interesting finding that questions the necessity of the inductive bias -- locality in modern computer vision architectures. Concretely, we find that vanilla Transformers can operate by directly treating each individual pixel as a token and achieve highly performant results. This is substantially different from the popular design in Vision Transformer, which maintains the inductive bias from ConvNets towards local neighborhoods (e.g. by treating each 16x16 patch as a token). We mainly showcase the effectiveness of pixels-as-tokens across three well-studied tasks in computer vision: supervised learning for object classification, self-supervised learning via masked autoencoding, and image generation with diffusion models. Although directly operating on individual pixels is less computationally practical, we believe the community must be aware of this surprising piece of knowledge when devising the next generation of neural architectures for computer vision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09414", "title": "Depth Anything V2", "authors": [ "Lihe Yang", "Bingyi Kang", "Zilong Huang", "Zhen Zhao", "Xiaogang Xu", "Jiashi Feng", "Hengshuang Zhao" ], "github": "https://github.com/DepthAnything/Depth-Anything-V2", "project_page": "", "abstract": "This work presents Depth Anything V2. Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation model. Notably, compared with V1, this version produces much finer and more robust depth predictions through three key practices: 1) replacing all labeled real images with synthetic images, 2) scaling up the capacity of our teacher model, and 3) teaching student models via the bridge of large-scale pseudo-labeled real images. Compared with the latest models built on Stable Diffusion, our models are significantly more efficient (more than 10x faster) and more accurate. We offer models of different scales (ranging from 25M to 1.3B params) to support extensive scenarios. Benefiting from their strong generalization capability, we fine-tune them with metric depth labels to obtain our metric depth models. In addition to our models, considering the limited diversity and frequent noise in current test sets, we construct a versatile evaluation benchmark with precise annotations and diverse scenes to facilitate future research.", "github_manual": "https://github.com/DepthAnything/Depth-Anything-V2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.08862", "title": "Cognitively Inspired Energy-Based World Models", "authors": [ "Alexi Gladstone", "Ganesh Nanduru", "Md Mofijul Islam", "Aman Chadha", "Jundong Li", "Tariq Iqbal" ], "github": "", "project_page": "", "abstract": "One of the predominant methods for training world models is autoregressive prediction in the output space of the next element of a sequence. In Natural Language Processing (NLP), this takes the form of Large Language Models (LLMs) predicting the next token; in Computer Vision (CV), this takes the form of autoregressive models predicting the next frame/token/pixel. However, this approach differs from human cognition in several respects. First, human predictions about the future actively influence internal cognitive processes. Second, humans naturally evaluate the plausibility of predictions regarding future states. Based on this capability, and third, by assessing when predictions are sufficient, humans allocate a dynamic amount of time to make a prediction. This adaptive process is analogous to System 2 thinking in psychology. All these capabilities are fundamental to the success of humans at high-level reasoning and planning. Therefore, to address the limitations of traditional autoregressive models lacking these human-like capabilities, we introduce Energy-Based World Models (EBWM). EBWM involves training an Energy-Based Model (EBM) to predict the compatibility of a given context and a predicted future state. In doing so, EBWM enables models to achieve all three facets of human cognition described. Moreover, we developed a variant of the traditional autoregressive transformer tailored for Energy-Based models, termed the Energy-Based Transformer (EBT). Our results demonstrate that EBWM scales better with data and GPU Hours than traditional autoregressive transformers in CV, and that EBWM offers promising early scaling in NLP. Consequently, this approach offers an exciting path toward training future models capable of System 2 thinking and intelligently searching across state spaces.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.08673", "title": "HelpSteer2: Open-source dataset for training top-performing reward models", "authors": [ "Zhilin Wang", "Yi Dong", "Olivier Delalleau", "Jiaqi Zeng", "Gerald Shen", "Daniel Egert", "Jimmy J. Zhang", "Makesh Narsimhan Sreedhar", "Oleksii Kuchaiev" ], "github": "https://github.com/NVIDIA/NeMo-Aligner", "project_page": "", "abstract": "High-quality preference datasets are essential for training reward models that can effectively guide large language models (LLMs) in generating high-quality responses aligned with human preferences. As LLMs become stronger and better aligned, permissively licensed preference datasets, such as Open Assistant, HH-RLHF, and HelpSteer need to be updated to remain effective for reward modeling. Methods that distil preference data from proprietary LLMs such as GPT-4 have restrictions on commercial usage imposed by model providers. To improve upon both generated responses and attribute labeling quality, we release HelpSteer2, a permissively licensed preference dataset (CC-BY-4.0). Using a powerful internal base model trained on HelpSteer2, we are able to achieve the SOTA score (92.0%) on Reward-Bench's primary dataset, outperforming currently listed open and proprietary models, as of June 12th, 2024. Notably, HelpSteer2 consists of only ten thousand response pairs, an order of magnitude fewer than existing preference datasets (e.g., HH-RLHF), which makes it highly efficient for training reward models. Our extensive experiments demonstrate that reward models trained with HelpSteer2 are effective in aligning LLMs. In particular, we propose SteerLM 2.0, a model alignment approach that can effectively make use of the rich multi-attribute score predicted by our reward models. HelpSteer2 is available at https://huggingface.co/datasets/nvidia/HelpSteer2 and code is available at https://github.com/NVIDIA/NeMo-Aligner", "github_manual": "", "github_regex": "https://github.com/NVIDIA/NeMo-Aligner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09412", "title": "Explore the Limits of Omni-modal Pretraining at Scale", "authors": [ "Yiyuan Zhang", "Handong Li", "Jing Liu", "Xiangyu Yue" ], "github": "https://github.com/invictus717/MiCo", "project_page": "", "abstract": "We propose to build omni-modal intelligence, which is capable of understanding any modality and learning universal representations. In specific, we propose a scalable pretraining paradigm, named Multimodal Context (MiCo), which can scale up the numbers of modalities and amount of data, together with the model parameters, in the pretraining process. With MiCo, the pretrained models show significant emergent abilities in multimodal learning, which are evaluated on the following tasks: i) single-modality perception benchmarks of 10 different modalities, ii) 25 cross-modality understanding tasks of retrieval, question-answering, captioning, and iii) 18 multimodal large language model benchmarks. Our models establish 37 new records for state-of-the-art performance. We hope that our research could contribute to the development of omni-modal intelligence. Code and Models are at https://github.com/invictus717/MiCo", "github_manual": "", "github_regex": "https://github.com/invictus717/MiCo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09308", "title": "Transformers meet Neural Algorithmic Reasoners", "authors": [ "Wilfried Bounsi", "Borja Ibarz", "Andrew Dudzik", "Jessica B. Hamrick", "Larisa Markeeva", "Alex Vitvitskyi", "Razvan Pascanu", "Petar Veli\u010dkovi\u0107" ], "github": "", "project_page": "", "abstract": "Transformers have revolutionized machine learning with their simple yet effective architecture. Pre-training Transformers on massive text datasets from the Internet has led to unmatched generalization for natural language understanding (NLU) tasks. However, such language models remain fragile when tasked with algorithmic forms of reasoning, where computations must be precise and robust. To address this limitation, we propose a novel approach that combines the Transformer's language understanding with the robustness of graph neural network (GNN)-based neural algorithmic reasoners (NARs). Such NARs proved effective as generic solvers for algorithmic tasks, when specified in graph form. To make their embeddings accessible to a Transformer, we propose a hybrid architecture with a two-phase training procedure, allowing the tokens in the language model to cross-attend to the node embeddings from the NAR. We evaluate our resulting TransNAR model on CLRS-Text, the text-based version of the CLRS-30 benchmark, and demonstrate significant gains over Transformer-only models for algorithmic reasoning, both in and out of distribution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09246", "title": "OpenVLA: An Open-Source Vision-Language-Action Model", "authors": [ "Moo Jin Kim", "Karl Pertsch", "Siddharth Karamcheti", "Ted Xiao", "Ashwin Balakrishna", "Suraj Nair", "Rafael Rafailov", "Ethan Foster", "Grace Lam", "Pannag Sanketi", "Quan Vuong", "Thomas Kollar", "Benjamin Burchfiel", "Russ Tedrake", "Dorsa Sadigh", "Sergey Levine", "Percy Liang", "Chelsea Finn" ], "github": "https://github.com/openvla/openvla", "project_page": "", "abstract": "Large policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rather than training new behaviors from scratch, we can fine-tune such vision-language-action (VLA) models to obtain robust, generalizable policies for visuomotor control. Yet, widespread adoption of VLAs for robotics has been challenging as 1) existing VLAs are largely closed and inaccessible to the public, and 2) prior work fails to explore methods for efficiently fine-tuning VLAs for new tasks, a key component for adoption. Addressing these challenges, we introduce OpenVLA, a 7B-parameter open-source VLA trained on a diverse collection of 970k real-world robot demonstrations. OpenVLA builds on a Llama 2 language model combined with a visual encoder that fuses pretrained features from DINOv2 and SigLIP. As a product of the added data diversity and new model components, OpenVLA demonstrates strong results for generalist manipulation, outperforming closed models such as RT-2-X (55B) by 16.5% in absolute task success rate across 29 tasks and multiple robot embodiments, with 7x fewer parameters. We further show that we can effectively fine-tune OpenVLA for new settings, with especially strong generalization results in multi-task environments involving multiple objects and strong language grounding abilities, and outperform expressive from-scratch imitation learning methods such as Diffusion Policy by 20.4%. We also explore compute efficiency; as a separate contribution, we show that OpenVLA can be fine-tuned on consumer GPUs via modern low-rank adaptation methods and served efficiently via quantization without a hit to downstream success rate. Finally, we release model checkpoints, fine-tuning notebooks, and our PyTorch codebase with built-in support for training VLAs at scale on Open X-Embodiment datasets.", "github_manual": "https://github.com/openvla/openvla", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.08657", "title": "Mistral-C2F: Coarse to Fine Actor for Analytical and Reasoning Enhancement in RLHF and Effective-Merged LLMs", "authors": [ "Chen Zheng", "Ke Sun", "Xun Zhou" ], "github": "", "project_page": "", "abstract": "Despite the advances in Large Language Models (LLMs), exemplified by models like GPT-4 and Claude, smaller-scale LLMs such as Llama and Mistral often struggle with generating in-depth and coherent dialogues. This paper presents a novel two-step Coarse-to-Fine Actor model to address the inherent limitations in conversational and analytical capabilities of small-sized LLMs. Our approach begins with the Policy-based Coarse Actor, employing a technique we term \"Continuous Maximization\". The Coarse Actor establishes an enhanced, knowledge-rich pool adept at aligning with human preference styles in analysis and reasoning. Through the RLHF process, it employs Continuous Maximization, a strategy that dynamically and adaptively extends the output length limit, enabling the generation of more detailed and analytical content. Subsequently, the Fine Actor refines this analytical content, addressing the generation of excessively redundant information from the Coarse Actor. We introduce a \"Knowledge Residue Merger\" approach, refining the content from the Coarse Actor and merging it with an existing Instruction model to improve quality, correctness, and reduce redundancies. We applied our methodology to the popular Mistral model, creating Mistral-C2F, which has demonstrated exceptional performance across 11 general language tasks and the MT-Bench Dialogue task, outperforming similar-scale models and even larger models with 13B and 30B parameters. Our model has significantly improved conversational and analytical reasoning abilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09413", "title": "Interpreting the Weight Space of Customized Diffusion Models", "authors": [ "Amil Dravid", "Yossi Gandelsman", "Kuan-Chieh Wang", "Rameen Abdal", "Gordon Wetzstein", "Alexei A. Efros", "Kfir Aberman" ], "github": "https://github.com/snap-research/weights2weights", "project_page": "", "abstract": "We investigate the space of weights spanned by a large collection of customized diffusion models. We populate this space by creating a dataset of over 60,000 models, each of which is a base model fine-tuned to insert a different person's visual identity. We model the underlying manifold of these weights as a subspace, which we term weights2weights. We demonstrate three immediate applications of this space -- sampling, editing, and inversion. First, as each point in the space corresponds to an identity, sampling a set of weights from it results in a model encoding a novel identity. Next, we find linear directions in this space corresponding to semantic edits of the identity (e.g., adding a beard). These edits persist in appearance across generated samples. Finally, we show that inverting a single image into this space reconstructs a realistic identity, even if the input image is out of distribution (e.g., a painting). Our results indicate that the weight space of fine-tuned diffusion models behaves as an interpretable latent space of identities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/snap-research/weights2weights", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09416", "title": "Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models", "authors": [ "Qihao Liu", "Zhanpeng Zeng", "Ju He", "Qihang Yu", "Xiaohui Shen", "Liang-Chieh Chen" ], "github": "https://github.com/qihao067/DiMR", "project_page": "", "abstract": "This paper presents innovative enhancements to diffusion models by integrating a novel multi-resolution network and time-dependent layer normalization. Diffusion models have gained prominence for their effectiveness in high-fidelity image generation. While conventional approaches rely on convolutional U-Net architectures, recent Transformer-based designs have demonstrated superior performance and scalability. However, Transformer architectures, which tokenize input data (via \"patchification\"), face a trade-off between visual fidelity and computational complexity due to the quadratic nature of self-attention operations concerning token length. While larger patch sizes enable attention computation efficiency, they struggle to capture fine-grained visual details, leading to image distortions. To address this challenge, we propose augmenting the Diffusion model with the Multi-Resolution network (DiMR), a framework that refines features across multiple resolutions, progressively enhancing detail from low to high resolution. Additionally, we introduce Time-Dependent Layer Normalization (TD-LN), a parameter-efficient approach that incorporates time-dependent parameters into layer normalization to inject time information and achieve superior performance. Our method's efficacy is demonstrated on the class-conditional ImageNet generation benchmark, where DiMR-XL variants outperform prior diffusion models, setting new state-of-the-art FID scores of 1.70 on ImageNet 256 x 256 and 2.89 on ImageNet 512 x 512. Project page: https://qihao067.github.io/projects/DiMR", "github_manual": "https://github.com/qihao067/DiMR", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.08656", "title": "TC-Bench: Benchmarking Temporal Compositionality in Text-to-Video and Image-to-Video Generation", "authors": [ "Weixi Feng", "Jiachen Li", "Michael Saxon", "Tsu-jui Fu", "Wenhu Chen", "William Yang Wang" ], "github": "https://github.com/weixi-feng/tc-bench", "project_page": "", "abstract": "Video generation has many unique challenges beyond those of image generation. The temporal dimension introduces extensive possible variations across frames, over which consistency and continuity may be violated. In this study, we move beyond evaluating simple actions and argue that generated videos should incorporate the emergence of new concepts and their relation transitions like in real-world videos as time progresses. To assess the Temporal Compositionality of video generation models, we propose TC-Bench, a benchmark of meticulously crafted text prompts, corresponding ground truth videos, and robust evaluation metrics. The prompts articulate the initial and final states of scenes, effectively reducing ambiguities for frame development and simplifying the assessment of transition completion. In addition, by collecting aligned real-world videos corresponding to the prompts, we expand TC-Bench's applicability from text-conditional models to image-conditional ones that can perform generative frame interpolation. We also develop new metrics to measure the completeness of component transitions in generated videos, which demonstrate significantly higher correlations with human judgments than existing metrics. Our comprehensive experimental results reveal that most video generators achieve less than 20% of the compositional changes, highlighting enormous space for future improvement. Our analysis indicates that current video generation models struggle to interpret descriptions of compositional changes and synthesize various components across different time steps.", "github_manual": "https://github.com/weixi-feng/tc-bench", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09403", "title": "Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models", "authors": [ "Yushi Hu", "Weijia Shi", "Xingyu Fu", "Dan Roth", "Mari Ostendorf", "Luke Zettlemoyer", "Noah A Smith", "Ranjay Krishna" ], "github": "https://github.com/Yushi-Hu/VisualSketchpad", "project_page": "", "abstract": "Humans draw to facilitate reasoning: we draw auxiliary lines when solving geometry problems; we mark and circle when reasoning on maps; we use sketches to amplify our ideas and relieve our limited-capacity working memory. However, such actions are missing in current multimodal language models (LMs). Current chain-of-thought and tool-use paradigms only use text as intermediate reasoning steps. In this work, we introduce Sketchpad, a framework that gives multimodal LMs a visual sketchpad and tools to draw on the sketchpad. The LM conducts planning and reasoning according to the visual artifacts it has drawn. Different from prior work, which uses text-to-image models to enable LMs to draw, Sketchpad enables LMs to draw with lines, boxes, marks, etc., which is closer to human sketching and better facilitates reasoning. Sketchpad can also use specialist vision models during the sketching process (e.g., draw bounding boxes with object detection models, draw masks with segmentation models), to further enhance visual perception and reasoning. We experiment with a wide range of math tasks (including geometry, functions, graphs, and chess) and complex visual reasoning tasks. Sketchpad substantially improves performance on all tasks over strong base models with no sketching, yielding an average gain of 12.7% on math tasks, and 8.6% on vision tasks. GPT-4o with Sketchpad sets a new state of the art on all tasks, including V*Bench (80.3%), BLINK spatial reasoning (83.9%), and visual correspondence (80.8%). All codes and data are in https://visualsketchpad.github.io/.", "github_manual": "https://github.com/Yushi-Hu/VisualSketchpad", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.08587", "title": "CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery", "authors": [ "Xiaoshuai Song", "Muxi Diao", "Guanting Dong", "Zhengyang Wang", "Yujia Fu", "Runqi Qiao", "Zhexu Wang", "Dayuan Fu", "Huangxuan Wu", "Bin Liang", "Weihao Zeng", "Yejie Wang", "Zhuoma GongQue", "Jianing Yu", "Qiuna Tan", "Weiran Xu" ], "github": "https://github.com/csbench/csbench", "project_page": "", "abstract": "Computer Science (CS) stands as a testament to the intricacies of human intelligence, profoundly advancing the development of artificial intelligence and modern society. However, the current community of large language models (LLMs) overly focuses on benchmarks for analyzing specific foundational skills (e.g. mathematics and code generation), neglecting an all-round evaluation of the computer science field. To bridge this gap, we introduce CS-Bench, the first bilingual (Chinese-English) benchmark dedicated to evaluating the performance of LLMs in computer science. CS-Bench comprises approximately 5K meticulously curated test samples, covering 26 subfields across 4 key areas of computer science, encompassing various task forms and divisions of knowledge and reasoning. Utilizing CS-Bench, we conduct a comprehensive evaluation of over 30 mainstream LLMs, revealing the relationship between CS performance and model scales. We also quantitatively analyze the reasons for failures in existing LLMs and highlight directions for improvements, including knowledge supplementation and CS-specific reasoning. Further cross-capability experiments show a high correlation between LLMs' capabilities in computer science and their abilities in mathematics and coding. Moreover, expert LLMs specialized in mathematics and coding also demonstrate strong performances in several CS subfields. Looking ahead, we envision CS-Bench serving as a cornerstone for LLM applications in the CS field and paving new avenues in assessing LLMs' diverse reasoning capabilities. The CS-Bench data and evaluation code are available at https://github.com/csbench/csbench.", "github_manual": "", "github_regex": "https://github.com/csbench/csbench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09170", "title": "Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning", "authors": [ "Bahare Fatemi", "Mehran Kazemi", "Anton Tsitsulin", "Karishma Malkan", "Jinyeong Yim", "John Palowitch", "Sungyong Seo", "Jonathan Halcrow", "Bryan Perozzi" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex temporal logic. Existing research has explored LLM performance on temporal reasoning using diverse datasets and benchmarks. However, these studies often rely on real-world data that LLMs may have encountered during pre-training or employ anonymization techniques that can inadvertently introduce factual inconsistencies. In this work, we address these limitations by introducing novel synthetic datasets specifically designed to assess LLM temporal reasoning abilities in various scenarios. The diversity of question types across these datasets enables systematic investigation into the impact of the problem structure, size, question type, fact order, and other factors on LLM performance. Our findings provide valuable insights into the strengths and weaknesses of current LLMs in temporal reasoning tasks. To foster further research in this area, we are open-sourcing the datasets and evaluation framework used in our experiments: https://huggingface.co/datasets/baharef/ToT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.08479", "title": "Real3D: Scaling Up Large Reconstruction Models with Real-World Images", "authors": [ "Hanwen Jiang", "Qixing Huang", "Georgios Pavlakos" ], "github": "https://github.com/hwjiang1510/Real3D", "project_page": "", "abstract": "The default strategy for training single-view Large Reconstruction Models (LRMs) follows the fully supervised route using large-scale datasets of synthetic 3D assets or multi-view captures. Although these resources simplify the training procedure, they are hard to scale up beyond the existing datasets and they are not necessarily representative of the real distribution of object shapes. To address these limitations, in this paper, we introduce Real3D, the first LRM system that can be trained using single-view real-world images. Real3D introduces a novel self-training framework that can benefit from both the existing synthetic data and diverse single-view real images. We propose two unsupervised losses that allow us to supervise LRMs at the pixel- and semantic-level, even for training examples without ground-truth 3D or novel views. To further improve performance and scale up the image data, we develop an automatic data curation approach to collect high-quality examples from in-the-wild images. Our experiments show that Real3D consistently outperforms prior work in four diverse evaluation settings that include real and synthetic data, as well as both in-domain and out-of-domain shapes. Code and model can be found here: https://hwjiang1510.github.io/Real3D/", "github_manual": "https://github.com/hwjiang1510/Real3D", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.07522", "title": "Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling", "authors": [ "Liliang Ren", "Yang Liu", "Yadong Lu", "Yelong Shen", "Chen Liang", "Weizhu Chen" ], "github": "https://github.com/microsoft/Samba", "project_page": "", "abstract": "Efficiently modeling sequences with infinite context length has been a long-standing problem. Past works suffer from either the quadratic computation complexity or the limited extrapolation ability on length generalization. In this work, we present Samba, a simple hybrid architecture that layer-wise combines Mamba, a selective State Space Model (SSM), with Sliding Window Attention (SWA). Samba selectively compresses a given sequence into recurrent hidden states while still maintaining the ability to precisely recall memories with the attention mechanism. We scale Samba up to 3.8B parameters with 3.2T training tokens and show that Samba substantially outperforms the state-of-the-art models based on pure attention or SSMs on a wide range of benchmarks. When trained on 4K length sequences, Samba can be efficiently extrapolated to 256K context length with perfect memory recall and show improved token predictions up to 1M context length. As a linear-time sequence model, Samba enjoys a 3.73x higher throughput compared to Transformers with grouped-query attention when processing user prompts of 128K length, and 3.64x speedup when generating 64K tokens with unlimited streaming. A sample implementation of Samba is publicly available in https://github.com/microsoft/Samba.", "github_manual": "", "github_regex": "https://github.com/microsoft/Samba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.08598", "title": "Language Model Council: Benchmarking Foundation Models on Highly Subjective Tasks by Consensus", "authors": [ "Justin Zhao", "Flor Miriam Plaza-del-Arco", "Amanda Cercas Curry" ], "github": "https://github.com/llm-council/llm-council", "project_page": "", "abstract": "The rapid advancement of Large Language Models (LLMs) necessitates robust and challenging benchmarks. Leaderboards like Chatbot Arena rank LLMs based on how well their responses align with human preferences. However, many tasks such as those related to emotional intelligence, creative writing, or persuasiveness, are highly subjective and often lack majoritarian human agreement. Judges may have irreconcilable disagreements about what constitutes a better response. To address the challenge of ranking LLMs on highly subjective tasks, we propose a novel benchmarking framework, the Language Model Council (LMC). The LMC operates through a democratic process to: 1) formulate a test set through equal participation, 2) administer the test among council members, and 3) evaluate responses as a collective jury. We deploy a council of 20 newest LLMs on an open-ended emotional intelligence task: responding to interpersonal dilemmas. Our results show that the LMC produces rankings that are more separable, robust, and less biased than those from any individual LLM judge, and is more consistent with a human-established leaderboard compared to other benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/llm-council/llm-council", "github_api_added_by": "auto" }, { "date": "2024-06-14", "arxiv_id": "2406.09371", "title": "LRM-Zero: Training Large Reconstruction Models with Synthesized Data", "authors": [ "Desai Xie", "Sai Bi", "Zhixin Shu", "Kai Zhang", "Zexiang Xu", "Yi Zhou", "S\u00f6ren Pirk", "Arie Kaufman", "Xin Sun", "Hao Tan" ], "github": "https://github.com/desaixie/zeroverse", "project_page": "", "abstract": "We present LRM-Zero, a Large Reconstruction Model (LRM) trained entirely on synthesized 3D data, achieving high-quality sparse-view 3D reconstruction. The core of LRM-Zero is our procedural 3D dataset, Zeroverse, which is automatically synthesized from simple primitive shapes with random texturing and augmentations (e.g., height fields, boolean differences, and wireframes). Unlike previous 3D datasets (e.g., Objaverse) which are often captured or crafted by humans to approximate real 3D data, Zeroverse completely ignores realistic global semantics but is rich in complex geometric and texture details that are locally similar to or even more intricate than real objects. We demonstrate that our LRM-Zero, trained with our fully synthesized Zeroverse, can achieve high visual quality in the reconstruction of real-world objects, competitive with models trained on Objaverse. We also analyze several critical design choices of Zeroverse that contribute to LRM-Zero's capability and training stability. Our work demonstrates that 3D reconstruction, one of the core tasks in 3D vision, can potentially be addressed without the semantics of real-world objects. The Zeroverse's procedural synthesis code and interactive visualization are available at: https://desaixie.github.io/lrm-zero/.", "github_manual": "https://github.com/desaixie/zeroverse", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.07546", "title": "Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?", "authors": [ "Xingyu Fu", "Muyu He", "Yujie Lu", "William Yang Wang", "Dan Roth" ], "github": "https://github.com/zeyofu/Commonsense-T2I", "project_page": "", "abstract": "We present a novel task and benchmark for evaluating the ability of text-to-image(T2I) generation models to produce images that fit commonsense in real life, which we call Commonsense-T2I. Given two adversarial text prompts containing an identical set of action words with minor differences, such as \"a lightbulb without electricity\" v.s. \"a lightbulb with electricity\", we evaluate whether T2I models can conduct visual-commonsense reasoning, e.g. produce images that fit \"the lightbulb is unlit\" vs. \"the lightbulb is lit\" correspondingly. Commonsense-T2I presents an adversarial challenge, providing pairwise text prompts along with expected outputs. The dataset is carefully hand-curated by experts and annotated with fine-grained labels, such as commonsense type and likelihood of the expected outputs, to assist analyzing model behavior. We benchmark a variety of state-of-the-art (sota) T2I models and surprisingly find that, there is still a large gap between image synthesis and real life photos--even the DALL-E 3 model could only achieve 48.92% on Commonsense-T2I, and the stable diffusion XL model only achieves 24.92% accuracy. Our experiments show that GPT-enriched prompts cannot solve this challenge, and we include a detailed analysis about possible reasons for such deficiency. We aim for Commonsense-T2I to serve as a high-quality evaluation benchmark for T2I commonsense checking, fostering advancements in real life image generation.", "github_manual": "https://github.com/zeyofu/Commonsense-T2I", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09411", "title": "MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding", "authors": [ "Fei Wang", "Xingyu Fu", "James Y. Huang", "Zekun Li", "Qin Liu", "Xiaogeng Liu", "Mingyu Derek Ma", "Nan Xu", "Wenxuan Zhou", "Kai Zhang", "Tianyi Lorena Yan", "Wenjie Jacky Mo", "Hsiang-Hui Liu", "Pan Lu", "Chunyuan Li", "Chaowei Xiao", "Kai-Wei Chang", "Dan Roth", "Sheng Zhang", "Hoifung Poon", "Muhao Chen" ], "github": "https://github.com/muirbench/MuirBench", "project_page": "", "abstract": "We introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MuirBench consists of 12 diverse multi-image tasks (e.g., scene understanding, ordering) that involve 10 categories of multi-image relations (e.g., multiview, temporal relations). Comprising 11,264 images and 2,600 multiple-choice questions, MuirBench is created in a pairwise manner, where each standard instance is paired with an unanswerable variant that has minimal semantic differences, in order for a reliable assessment. Evaluated upon 20 recent multi-modal LLMs, our results reveal that even the best-performing models like GPT-4o and Gemini Pro find it challenging to solve MuirBench, achieving 68.0% and 49.3% in accuracy. Open-source multimodal LLMs trained on single images can hardly generalize to multi-image questions, hovering below 33.3% in accuracy. These results highlight the importance of MuirBench in encouraging the community to develop multimodal LLMs that can look beyond a single image, suggesting potential pathways for future improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/muirbench/MuirBench", "github_api_added_by": "auto" }, { "date": "2024-06-14", "arxiv_id": "2406.09305", "title": "Toffee: Efficient Million-Scale Dataset Construction for Subject-Driven Text-to-Image Generation", "authors": [ "Yufan Zhou", "Ruiyi Zhang", "Kaizhi Zheng", "Nanxuan Zhao", "Jiuxiang Gu", "Zichao Wang", "Xin Eric Wang", "Tong Sun" ], "github": "", "project_page": "", "abstract": "In subject-driven text-to-image generation, recent works have achieved superior performance by training the model on synthetic datasets containing numerous image pairs. Trained on these datasets, generative models can produce text-aligned images for specific subject from arbitrary testing image in a zero-shot manner. They even outperform methods which require additional fine-tuning on testing images. However, the cost of creating such datasets is prohibitive for most researchers. To generate a single training pair, current methods fine-tune a pre-trained text-to-image model on the subject image to capture fine-grained details, then use the fine-tuned model to create images for the same subject based on creative text prompts. Consequently, constructing a large-scale dataset with millions of subjects can require hundreds of thousands of GPU hours. To tackle this problem, we propose Toffee, an efficient method to construct datasets for subject-driven editing and generation. Specifically, our dataset construction does not need any subject-level fine-tuning. After pre-training two generative models, we are able to generate infinite number of high-quality samples. We construct the first large-scale dataset for subject-driven image editing and generation, which contains 5 million image pairs, text prompts, and masks. Our dataset is 5 times the size of previous largest dataset, yet our cost is tens of thousands of GPU hours lower. To test the proposed dataset, we also propose a model which is capable of both subject-driven image editing and generation. By simply training the model on our proposed dataset, it obtains competitive results, illustrating the effectiveness of the proposed dataset construction framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09162", "title": "EMMA: Your Text-to-Image Diffusion Model Can Secretly Accept Multi-Modal Prompts", "authors": [ "Yucheng Han", "Rui Wang", "Chi Zhang", "Juntao Hu", "Pei Cheng", "Bin Fu", "Hanwang Zhang" ], "github": "https://github.com/TencentQQGYLab/ELLA", "project_page": "", "abstract": "Recent advancements in image generation have enabled the creation of high-quality images from text conditions. However, when facing multi-modal conditions, such as text combined with reference appearances, existing methods struggle to balance multiple conditions effectively, typically showing a preference for one modality over others. To address this challenge, we introduce EMMA, a novel image generation model accepting multi-modal prompts built upon the state-of-the-art text-to-image (T2I) diffusion model, ELLA. EMMA seamlessly incorporates additional modalities alongside text to guide image generation through an innovative Multi-modal Feature Connector design, which effectively integrates textual and supplementary modal information using a special attention mechanism. By freezing all parameters in the original T2I diffusion model and only adjusting some additional layers, we reveal an interesting finding that the pre-trained T2I diffusion model can secretly accept multi-modal prompts. This interesting property facilitates easy adaptation to different existing frameworks, making EMMA a flexible and effective tool for producing personalized and context-aware images and even videos. Additionally, we introduce a strategy to assemble learned EMMA modules to produce images conditioned on multiple modalities simultaneously, eliminating the need for additional training with mixed multi-modal prompts. Extensive experiments demonstrate the effectiveness of EMMA in maintaining high fidelity and detail in generated images, showcasing its potential as a robust solution for advanced multi-modal conditional image generation tasks.", "github_manual": "https://github.com/TencentQQGYLab/ELLA", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.08707", "title": "mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus", "authors": [ "Matthieu Futeral", "Armel Zebaze", "Pedro Ortiz Suarez", "Julien Abadji", "R\u00e9mi Lacroix", "Cordelia Schmid", "Rachel Bawden", "Beno\u00eet Sagot" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (mLLMs) are trained on a large amount of text-image data. While most mLLMs are trained on caption-like data only, Alayrac et al. [2022] showed that additionally training them on interleaved sequences of text and images can lead to the emergence of in-context learning capabilities. However, the dataset they used, M3W, is not public and is only in English. There have been attempts to reproduce their results but the released datasets are English-only. In contrast, current multilingual and multimodal datasets are either composed of caption-like only or medium-scale or fully private data. This limits mLLM research for the 7,000 other languages spoken in the world. We therefore introduce mOSCAR, to the best of our knowledge the first large-scale multilingual and multimodal document corpus crawled from the web. It covers 163 languages, 315M documents, 214B tokens and 1.2B images. We carefully conduct a set of filtering and evaluation steps to make sure mOSCAR is sufficiently safe, diverse and of good quality. We additionally train two types of multilingual model to prove the benefits of mOSCAR: (1) a model trained on a subset of mOSCAR and captioning data and (2) a model train on captioning data only. The model additionally trained on mOSCAR shows a strong boost in few-shot learning performance across various multilingual image-text tasks and benchmarks, confirming previous findings for English-only mLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.05967", "title": "CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark", "authors": [ "David Romero", "Chenyang Lyu", "Haryo Akbarianto Wibowo", "Teresa Lynn", "Injy Hamed", "Aditya Nanda Kishore", "Aishik Mandal", "Alina Dragonetti", "Artem Abzaliev", "Atnafu Lambebo Tonja", "Bontu Fufa Balcha", "Chenxi Whitehouse", "Christian Salamea", "Dan John Velasco", "David Ifeoluwa Adelani", "David Le Meur", "Emilio Villa-Cueva", "Fajri Koto", "Fauzan Farooqui", "Frederico Belcavello", "Ganzorig Batnasan", "Gisela Vallejo", "Grainne Caulfield", "Guido Ivetta", "Haiyue Song", "Henok Biadglign Ademtew", "Hern\u00e1n Maina", "Holy Lovenia", "Israel Abebe Azime", "Jan Christian Blaise Cruz", "Jay Gala", "Jiahui Geng", "Jesus-German Ortiz-Barajas", "Jinheon Baek", "Jocelyn Dunstan", "Laura Alonso Alemany", "Kumaranage Ravindu Yasas Nagasinghe", "Luciana Benotti", "Luis Fernando D'Haro", "Marcelo Viridiano", "Marcos Estecha-Garitagoitia", "Maria Camila Buitrago Cabrera", "Mario Rodr\u00edguez-Cantelar", "M\u00e9lanie Jouitteau", "Mihail Mihaylov", "Mohamed Fazli Mohamed Imam", "Muhammad Farid Adilazuarda", "Munkhjargal Gochoo", "Munkh-Erdene Otgonbold", "Naome Etori", "Olivier Niyomugisha", "Paula M\u00f3nica Silva", "Pranjal Chitale", "Raj Dabre", "Rendi Chevi", "Ruochen Zhang", "Ryandito Diandaru", "Samuel Cahyawijaya", "Santiago G\u00f3ngora", "Soyeong Jeong", "Sukannya Purkayastha", "Tatsuki Kuribayashi", "Thanmay Jayakumar", "Tiago Timponi Torrent", "Toqeer Ehsan", "Vladimir Araujo", "Yova Kementchedjhieva", "Zara Burzo", "Zheng Wei Lim", "Zheng Xin Yong", "Oana Ignat", "Joan Nwatu", "Rada Mihalcea", "Thamar Solorio", "Alham Fikri Aji" ], "github": "", "project_page": "", "abstract": "Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data. However, most of the current VQA models use datasets that are primarily focused on English and a few major world languages, with images that are typically Western-centric. While recent efforts have tried to increase the number of languages covered on VQA datasets, they still lack diversity in low-resource languages. More importantly, although these datasets often extend their linguistic range via translation or some other approaches, they usually keep images the same, resulting in narrow cultural representation. To address these limitations, we construct CVQA, a new Culturally-diverse multilingual Visual Question Answering benchmark, designed to cover a rich set of languages and cultures, where we engage native speakers and cultural experts in the data collection process. As a result, CVQA includes culturally-driven images and questions from across 28 countries on four continents, covering 26 languages with 11 scripts, providing a total of 9k questions. We then benchmark several Multimodal Large Language Models (MLLMs) on CVQA, and show that the dataset is challenging for the current state-of-the-art models. This benchmark can serve as a probing evaluation suite for assessing the cultural capability and bias of multimodal models and hopefully encourage more research efforts toward increasing cultural awareness and linguistic diversity in this field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.07457", "title": "Estimating the Hallucination Rate of Generative AI", "authors": [ "Andrew Jesson", "Nicolas Beltran-Velez", "Quentin Chu", "Sweta Karlekar", "Jannik Kossen", "Yarin Gal", "John P. Cunningham", "David Blei" ], "github": "", "project_page": "", "abstract": "This work is about estimating the hallucination rate for in-context learning (ICL) with Generative AI. In ICL, a conditional generative model (CGM) is prompted with a dataset and asked to make a prediction based on that dataset. The Bayesian interpretation of ICL assumes that the CGM is calculating a posterior predictive distribution over an unknown Bayesian model of a latent parameter and data. With this perspective, we define a hallucination as a generated prediction that has low-probability under the true latent parameter. We develop a new method that takes an ICL problem -- that is, a CGM, a dataset, and a prediction question -- and estimates the probability that a CGM will generate a hallucination. Our method only requires generating queries and responses from the model and evaluating its response log probability. We empirically evaluate our method on synthetic regression and natural language ICL tasks using large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09297", "title": "MLKV: Multi-Layer Key-Value Heads for Memory Efficient Transformer Decoding", "authors": [ "Zayd Muhammad Kawakibi Zuhri", "Muhammad Farid Adilazuarda", "Ayu Purwarianti", "Alham Fikri Aji" ], "github": "https://github.com/zaydzuhri/pythia-mlkv", "project_page": "", "abstract": "Auto-regressive inference of transformers benefit greatly from Key-Value (KV) caching, but can lead to major memory bottlenecks as model size, batch size, and sequence length grow at scale. We introduce Multi-Layer Key-Value (MLKV) sharing, a novel approach extending KV sharing across transformer layers to reduce memory usage beyond what was possible with Multi-Query Attention (MQA) and Grouped-Query Attention (GQA). Evaluations on various NLP benchmarks and inference metrics using uptrained Pythia-160M variants demonstrate that MLKV significantly reduces memory usage with minimal performance loss, reducing KV cache size down to a factor of 6x compared to MQA. These results highlight MLKV's potential for efficient deployment of transformer models at scale. We provide code at https://github.com/zaydzuhri/pythia-mlkv", "github_manual": "", "github_regex": "https://github.com/zaydzuhri/pythia-mlkv", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09356", "title": "CMC-Bench: Towards a New Paradigm of Visual Signal Compression", "authors": [ "Chunyi Li", "Xiele Wu", "Haoning Wu", "Donghui Feng", "Zicheng Zhang", "Guo Lu", "Xiongkuo Min", "Xiaohong Liu", "Guangtao Zhai", "Weisi Lin" ], "github": "https://github.com/q-future/cmc-bench", "project_page": "", "abstract": "Ultra-low bitrate image compression is a challenging and demanding topic. With the development of Large Multimodal Models (LMMs), a Cross Modality Compression (CMC) paradigm of Image-Text-Image has emerged. Compared with traditional codecs, this semantic-level compression can reduce image data size to 0.1\\% or even lower, which has strong potential applications. However, CMC has certain defects in consistency with the original image and perceptual quality. To address this problem, we introduce CMC-Bench, a benchmark of the cooperative performance of Image-to-Text (I2T) and Text-to-Image (T2I) models for image compression. This benchmark covers 18,000 and 40,000 images respectively to verify 6 mainstream I2T and 12 T2I models, including 160,000 subjective preference scores annotated by human experts. At ultra-low bitrates, this paper proves that the combination of some I2T and T2I models has surpassed the most advanced visual signal codecs; meanwhile, it highlights where LMMs can be further optimized toward the compression task. We encourage LMM developers to participate in this test to promote the evolution of visual signal codec protocols.", "github_manual": "https://github.com/q-future/cmc-bench", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09358", "title": "Understanding Hallucinations in Diffusion Models through Mode Interpolation", "authors": [ "Sumukh K Aithal", "Pratyush Maini", "Zachary C. Lipton", "J. Zico Kolter" ], "github": "https://github.com/locuslab/diffusion-model-hallucination", "project_page": "", "abstract": "Colloquially speaking, image generation models based upon diffusion processes are frequently said to exhibit \"hallucinations,\" samples that could never occur in the training data. But where do such hallucinations come from? In this paper, we study a particular failure mode in diffusion models, which we term mode interpolation. Specifically, we find that diffusion models smoothly \"interpolate\" between nearby data modes in the training set, to generate samples that are completely outside the support of the original training distribution; this phenomenon leads diffusion models to generate artifacts that never existed in real data (i.e., hallucinations). We systematically study the reasons for, and the manifestation of this phenomenon. Through experiments on 1D and 2D Gaussians, we show how a discontinuous loss landscape in the diffusion model's decoder leads to a region where any smooth approximation will cause such hallucinations. Through experiments on artificial datasets with various shapes, we show how hallucination leads to the generation of combinations of shapes that never existed. Finally, we show that diffusion models in fact know when they go out of support and hallucinate. This is captured by the high variance in the trajectory of the generated sample towards the final few backward sampling process. Using a simple metric to capture this variance, we can remove over 95% of hallucinations at generation time while retaining 96% of in-support samples. We conclude our exploration by showing the implications of such hallucination (and its removal) on the collapse (and stabilization) of recursive training on synthetic data with experiments on MNIST and 2D Gaussians dataset. We release our code at https://github.com/locuslab/diffusion-model-hallucination.", "github_manual": "", "github_regex": "https://github.com/locuslab/diffusion-model-hallucination", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-14", "arxiv_id": "2406.09406", "title": "4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities", "authors": [ "Roman Bachmann", "O\u011fuzhan Fatih Kar", "David Mizrahi", "Ali Garjani", "Mingfei Gao", "David Griffiths", "Jiaming Hu", "Afshin Dehghan", "Amir Zamir" ], "github": "https://github.com/apple/ml-4m/", "project_page": "", "abstract": "Current multimodal and multitask foundation models like 4M or UnifiedIO show promising results, but in practice their out-of-the-box abilities to accept diverse inputs and perform diverse tasks are limited by the (usually rather small) number of modalities and tasks they are trained on. In this paper, we expand upon the capabilities of them by training a single model on tens of highly diverse modalities and by performing co-training on large-scale multimodal datasets and text corpora. This includes training on several semantic and geometric modalities, feature maps from recent state of the art models like DINOv2 and ImageBind, pseudo labels of specialist models like SAM and 4DHumans, and a range of new modalities that allow for novel ways to interact with the model and steer the generation, for example image metadata or color palettes. A crucial step in this process is performing discrete tokenization on various modalities, whether they are image-like, neural network feature maps, vectors, structured data like instance segmentation or human poses, or data that can be represented as text. Through this, we expand on the out-of-the-box capabilities of multimodal models and specifically show the possibility of training one model to solve at least 3x more tasks/modalities than existing ones and doing so without a loss in performance. This enables more fine-grained and controllable multimodal generation capabilities and allows us to study the distillation of models trained on diverse data and objectives into a unified model. We successfully scale the training to a three billion parameter model using tens of modalities and different datasets. The resulting models and training code are open sourced at 4m.epfl.ch.", "github_manual": "https://github.com/apple/ml-4m/", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.10208", "title": "Glyph-ByT5-v2: A Strong Aesthetic Baseline for Accurate Multilingual Visual Text Rendering", "authors": [ "Zeyu Liu", "Weicong Liang", "Yiming Zhao", "Bohan Chen", "Ji Li", "Yuhui Yuan" ], "github": "https://github.com/AIGText/Glyph-ByT5", "project_page": "", "abstract": "Recently, Glyph-ByT5 has achieved highly accurate visual text rendering performance in graphic design images. However, it still focuses solely on English and performs relatively poorly in terms of visual appeal. In this work, we address these two fundamental limitations by presenting Glyph-ByT5-v2 and Glyph-SDXL-v2, which not only support accurate visual text rendering for 10 different languages but also achieve much better aesthetic quality. To achieve this, we make the following contributions: (i) creating a high-quality multilingual glyph-text and graphic design dataset consisting of more than 1 million glyph-text pairs and 10 million graphic design image-text pairs covering nine other languages, (ii) building a multilingual visual paragraph benchmark consisting of 1,000 prompts, with 100 for each language, to assess multilingual visual spelling accuracy, and (iii) leveraging the latest step-aware preference learning approach to enhance the visual aesthetic quality. With the combination of these techniques, we deliver a powerful customized multilingual text encoder, Glyph-ByT5-v2, and a strong aesthetic graphic generation model, Glyph-SDXL-v2, that can support accurate spelling in 10 different languages. We perceive our work as a significant advancement, considering that the latest DALL-E3 and Ideogram 1.0 still struggle with the multilingual visual text rendering task.", "github_manual": "https://github.com/AIGText/Glyph-ByT5", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.09559", "title": "Decoding the Diversity: A Review of the Indic AI Research Landscape", "authors": [ "Sankalp KJ", "Vinija Jain", "Sreyoshi Bhaduri", "Tamoghna Roy", "Aman Chadha" ], "github": "", "project_page": "", "abstract": "This review paper provides a comprehensive overview of large language model (LLM) research directions within Indic languages. Indic languages are those spoken in the Indian subcontinent, including India, Pakistan, Bangladesh, Sri Lanka, Nepal, and Bhutan, among others. These languages have a rich cultural and linguistic heritage and are spoken by over 1.5 billion people worldwide. With the tremendous market potential and growing demand for natural language processing (NLP) based applications in diverse languages, generative applications for Indic languages pose unique challenges and opportunities for research. Our paper deep dives into the recent advancements in Indic generative modeling, contributing with a taxonomy of research directions, tabulating 84 recent publications. Research directions surveyed in this paper include LLM development, fine-tuning existing LLMs, development of corpora, benchmarking and evaluation, as well as publications around specific techniques, tools, and applications. We found that researchers across the publications emphasize the challenges associated with limited data availability, lack of standardization, and the peculiar linguistic complexities of Indic languages. This work aims to serve as a valuable resource for researchers and practitioners working in the field of NLP, particularly those focused on Indic languages, and contributes to the development of more accurate and efficient LLM applications for these languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.08418", "title": "OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text", "authors": [ "Qingyun Li", "Zhe Chen", "Weiyun Wang", "Wenhai Wang", "Shenglong Ye", "Zhenjiang Jin", "Guanzhou Chen", "Yinan He", "Zhangwei Gao", "Erfei Cui", "Jiashuo Yu", "Hao Tian", "Jiasheng Zhou", "Chao Xu", "Bin Wang", "Xingjian Wei", "Wei Li", "Wenjian Zhang", "Bo Zhang", "Pinlong Cai", "Licheng Wen", "Xiangchao Yan", "Zhenxiang Li", "Pei Chu", "Yi Wang", "Min Dou", "Changyao Tian", "Xizhou Zhu", "Lewei Lu", "Yushi Chen", "Junjun He", "Zhongying Tu", "Tong Lu", "Yali Wang", "Limin Wang", "Dahua Lin", "Yu Qiao", "Botian Shi", "Conghui He", "Jifeng Dai" ], "github": "https://github.com/OpenGVLab/OmniCorpus", "project_page": "", "abstract": "Image-text interleaved data, consisting of multiple images and texts arranged in a natural document format, aligns with the presentation paradigm of internet data and closely resembles human reading habits. Recent studies have shown that such data aids multimodal in-context learning and maintains the capabilities of large language models during multimodal fine-tuning. However, the limited scale and diversity of current image-text interleaved data restrict the development of multimodal large language models. In this paper, we introduce OmniCorpus, a 10 billion-scale image-text interleaved dataset. Using an efficient data engine, we filter and extract large-scale high-quality documents, which contain 8.6 billion images and 1,696 billion text tokens. Compared to counterparts (e.g., MMC4, OBELICS), our dataset 1) has 15 times larger scales while maintaining good data quality; 2) features more diverse sources, including both English and non-English websites as well as video-centric websites; 3) is more flexible, easily degradable from an image-text interleaved format to pure text corpus and image-text pairs. Through comprehensive analysis and experiments, we validate the quality, usability, and effectiveness of the proposed dataset. We hope this could provide a solid data foundation for future multimodal model research. Code and data are released at https://github.com/OpenGVLab/OmniCorpus.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/OmniCorpus", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.07230", "title": "Needle In A Multimodal Haystack", "authors": [ "Weiyun Wang", "Shuibo Zhang", "Yiming Ren", "Yuchen Duan", "Tiantong Li", "Shuo Liu", "Mengkang Hu", "Zhe Chen", "Kaipeng Zhang", "Lewei Lu", "Xizhou Zhu", "Ping Luo", "Yu Qiao", "Jifeng Dai", "Wenqi Shao", "Wenhai Wang" ], "github": "https://github.com/OpenGVLab/MM-NIAH", "project_page": "", "abstract": "With the rapid advancement of multimodal large language models (MLLMs), their evaluation has become increasingly comprehensive. However, understanding long multimodal content, as a foundational ability for real-world applications, remains underexplored. In this work, we present Needle In A Multimodal Haystack (MM-NIAH), the first benchmark specifically designed to systematically evaluate the capability of existing MLLMs to comprehend long multimodal documents. Our benchmark includes three types of evaluation tasks: multimodal retrieval, counting, and reasoning. In each task, the model is required to answer the questions according to different key information scattered throughout the given multimodal document. Evaluating the leading MLLMs on MM-NIAH, we observe that existing models still have significant room for improvement on these tasks, especially on vision-centric evaluation. We hope this work can provide a platform for further research on long multimodal document comprehension and contribute to the advancement of MLLMs. Code and benchmark are released at https://github.com/OpenGVLab/MM-NIAH.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/MM-NIAH", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.08451", "title": "GUI Odyssey: A Comprehensive Dataset for Cross-App GUI Navigation on Mobile Devices", "authors": [ "Quanfeng Lu", "Wenqi Shao", "Zitao Liu", "Fanqing Meng", "Boxuan Li", "Botong Chen", "Siyuan Huang", "Kaipeng Zhang", "Yu Qiao", "Ping Luo" ], "github": "https://github.com/OpenGVLab/GUI-Odyssey", "project_page": "", "abstract": "Smartphone users often navigate across multiple applications (apps) to complete tasks such as sharing content between social media platforms. Autonomous Graphical User Interface (GUI) navigation agents can enhance user experience in communication, entertainment, and productivity by streamlining workflows and reducing manual intervention. However, prior GUI agents often trained with datasets comprising simple tasks that can be completed within a single app, leading to poor performance in cross-app navigation. To address this problem, we introduce GUI Odyssey, a comprehensive dataset for training and evaluating cross-app navigation agents. GUI Odyssey consists of 7,735 episodes from 6 mobile devices, spanning 6 types of cross-app tasks, 201 apps, and 1.4K app combos. Leveraging GUI Odyssey, we developed OdysseyAgent, a multimodal cross-app navigation agent by fine-tuning the Qwen-VL model with a history resampling module. Extensive experiments demonstrate OdysseyAgent's superior accuracy compared to existing models. For instance, OdysseyAgent surpasses fine-tuned Qwen-VL and zero-shot GPT-4V by 1.44\\% and 55.49\\% in-domain accuracy, and 2.29\\% and 48.14\\% out-of-domain accuracy on average. The dataset and code will be released in https://github.com/OpenGVLab/GUI-Odyssey.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/GUI-Odyssey", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.09961", "title": "ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation", "authors": [ "Chufan Shi", "Cheng Yang", "Yaxin Liu", "Bo Shui", "Junjie Wang", "Mohan Jing", "Linran Xu", "Xinyu Zhu", "Siheng Li", "Yuxiang Zhang", "Gongye Liu", "Xiaomei Nie", "Deng Cai", "Yujiu Yang" ], "github": "https://github.com/ChartMimic/ChartMimic", "project_page": "", "abstract": "We introduce a new benchmark, ChartMimic, aimed at assessing the visually-grounded code generation capabilities of large multimodal models (LMMs). ChartMimic utilizes information-intensive visual charts and textual instructions as inputs, requiring LMMs to generate the corresponding code for chart rendering. ChartMimic includes 1,000 human-curated (figure, instruction, code) triplets, which represent the authentic chart use cases found in scientific papers across various domains(e.g., Physics, Computer Science, Economics, etc). These charts span 18 regular types and 4 advanced types, diversifying into 191 subcategories. Furthermore, we propose multi-level evaluation metrics to provide an automatic and thorough assessment of the output code and the rendered charts. Unlike existing code generation benchmarks, ChartMimic places emphasis on evaluating LMMs' capacity to harmonize a blend of cognitive capabilities, encompassing visual understanding, code generation, and cross-modal reasoning. The evaluation of 3 proprietary models and 11 open-weight models highlights the substantial challenges posed by ChartMimic. Even the advanced GPT-4V, Claude-3-opus only achieve an average score of 73.2 and 53.7, respectively, indicating significant room for improvement. We anticipate that ChartMimic will inspire the development of LMMs, advancing the pursuit of artificial general intelligence.", "github_manual": "https://github.com/ChartMimic/ChartMimic", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.08845", "title": "Rethinking Human Evaluation Protocol for Text-to-Video Models: Enhancing Reliability,Reproducibility, and Practicality", "authors": [ "Tianle Zhang", "Langtian Ma", "Yuchen Yan", "Yuchen Zhang", "Kai Wang", "Yue Yang", "Ziyao Guo", "Wenqi Shao", "Yang You", "Yu Qiao", "Ping Luo", "Kaipeng Zhang" ], "github": "https://github.com/ztlmememe/T2VHE", "project_page": "", "abstract": "Recent text-to-video (T2V) technology advancements, as demonstrated by models such as Gen2, Pika, and Sora, have significantly broadened its applicability and popularity. Despite these strides, evaluating these models poses substantial challenges. Primarily, due to the limitations inherent in automatic metrics, manual evaluation is often considered a superior method for assessing T2V generation. However, existing manual evaluation protocols face reproducibility, reliability, and practicality issues. To address these challenges, this paper introduces the Text-to-Video Human Evaluation (T2VHE) protocol, a comprehensive and standardized protocol for T2V models. The T2VHE protocol includes well-defined metrics, thorough annotator training, and an effective dynamic evaluation module. Experimental results demonstrate that this protocol not only ensures high-quality annotations but can also reduce evaluation costs by nearly 50%. We will open-source the entire setup of the T2VHE protocol, including the complete protocol workflow, the dynamic evaluation component details, and the annotation interface code. This will help communities establish more sophisticated human assessment protocols.", "github_manual": "https://github.com/ztlmememe/T2VHE", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.07882", "title": "Designing a Dashboard for Transparency and Control of Conversational AI", "authors": [ "Yida Chen", "Aoyu Wu", "Trevor DePodesta", "Catherine Yeh", "Kenneth Li", "Nicholas Castillo Marin", "Oam Patel", "Jan Riecke", "Shivam Raval", "Olivia Seow", "Martin Wattenberg", "Fernanda Vi\u00e9gas" ], "github": "https://github.com/yc015/TalkTuner-chatbot-llm-dashboard/tree/main", "project_page": "", "abstract": "Conversational LLMs function as black box systems, leaving users guessing about why they see the output they do. This lack of transparency is potentially problematic, especially given concerns around bias and truthfulness. To address this issue, we present an end-to-end prototype-connecting interpretability techniques with user experience design-that seeks to make chatbots more transparent. We begin by showing evidence that a prominent open-source LLM has a \"user model\": examining the internal state of the system, we can extract data related to a user's age, gender, educational level, and socioeconomic status. Next, we describe the design of a dashboard that accompanies the chatbot interface, displaying this user model in real time. The dashboard can also be used to control the user model and the system's behavior. Finally, we discuss a study in which users conversed with the instrumented system. Our results suggest that users appreciate seeing internal states, which helped them expose biased behavior and increased their sense of control. Participants also made valuable suggestions that point to future directions for both design and machine learning research. The project page and video demo of our TalkTuner system are available at https://bit.ly/talktuner-project-page", "github_manual": "https://github.com/yc015/TalkTuner-chatbot-llm-dashboard/tree/main", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.08973", "title": "XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning", "authors": [ "Alexander Nikulin", "Ilya Zisman", "Alexey Zemtsov", "Viacheslav Sinii", "Vladislav Kurenkov", "Sergey Kolesnikov" ], "github": "https://github.com/dunno-lab/xland-minigrid-datasets", "project_page": "", "abstract": "Following the success of the in-context learning paradigm in large-scale language and computer vision models, the recently emerging field of in-context reinforcement learning is experiencing a rapid growth. However, its development has been held back by the lack of challenging benchmarks, as all the experiments have been carried out in simple environments and on small-scale datasets. We present XLand-100B, a large-scale dataset for in-context reinforcement learning based on the XLand-MiniGrid environment, as a first step to alleviate this problem. It contains complete learning histories for nearly 30,000 different tasks, covering 100B transitions and 2.5B episodes. It took 50,000 GPU hours to collect the dataset, which is beyond the reach of most academic labs. Along with the dataset, we provide the utilities to reproduce or expand it even further. With this substantial effort, we aim to democratize research in the rapidly growing field of in-context reinforcement learning and provide a solid foundation for further scaling. The code is open-source and available under Apache 2.0 licence at https://github.com/dunno-lab/xland-minigrid-datasets.", "github_manual": "", "github_regex": "https://github.com/dunno-lab/xland-minigrid-datasets", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.06263", "title": "MaskLID: Code-Switching Language Identification through Iterative Masking", "authors": [ "Amir Hossein Kargaran", "Fran\u00e7ois Yvon", "Hinrich Sch\u00fctze" ], "github": "https://github.com/cisnlp/MaskLID", "project_page": "", "abstract": "We present MaskLID, a simple, yet effective, code-switching (CS) language identification (LID) method. MaskLID does not require any training and is designed to complement current high-performance sentence-level LIDs. Sentence-level LIDs are classifiers trained on monolingual texts to provide single labels, typically using a softmax layer to turn scores into probabilities. However, in cases where a sentence is composed in both L1 and L2 languages, the LID classifier often only returns the dominant label L1. To address this limitation, MaskLID employs a strategy to mask text features associated with L1, allowing the LID to classify the text as L2 in the next round. This method uses the LID itself to identify the features that require masking and does not rely on any external resource. In this work, we explore the use of MaskLID for two open-source LIDs (GlotLID and OpenLID), that are both based on the FastText architecture. Code and demo are available at https://github.com/cisnlp/MaskLID.", "github_manual": "", "github_regex": "https://github.com/cisnlp/MaskLID", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.10111", "title": "GaussianSR: 3D Gaussian Super-Resolution with 2D Diffusion Priors", "authors": [ "Xiqian Yu", "Hanxin Zhu", "Tianyu He", "Zhibo Chen" ], "github": "", "project_page": "", "abstract": "Achieving high-resolution novel view synthesis (HRNVS) from low-resolution input views is a challenging task due to the lack of high-resolution data. Previous methods optimize high-resolution Neural Radiance Field (NeRF) from low-resolution input views but suffer from slow rendering speed. In this work, we base our method on 3D Gaussian Splatting (3DGS) due to its capability of producing high-quality images at a faster rendering speed. To alleviate the shortage of data for higher-resolution synthesis, we propose to leverage off-the-shelf 2D diffusion priors by distilling the 2D knowledge into 3D with Score Distillation Sampling (SDS). Nevertheless, applying SDS directly to Gaussian-based 3D super-resolution leads to undesirable and redundant 3D Gaussian primitives, due to the randomness brought by generative priors. To mitigate this issue, we introduce two simple yet effective techniques to reduce stochastic disturbances introduced by SDS. Specifically, we 1) shrink the range of diffusion timestep in SDS with an annealing strategy; 2) randomly discard redundant Gaussian primitives during densification. Extensive experiments have demonstrated that our proposed GaussainSR can attain high-quality results for HRNVS with only low-resolution inputs on both synthetic and real-world datasets. Project page: https://chchnii.github.io/GaussianSR/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.09900", "title": "GEB-1.3B: Open Lightweight Large Language Model", "authors": [ "Jie Wu", "Yufeng Zhu", "Lei Shen", "Xuqing Lu" ], "github": "", "project_page": "", "abstract": "Recently developed large language models (LLMs) such as ChatGPT, Claude, and Llama have demonstrated impressive abilities, and even surpass human-level performance in several tasks. Despite their success, the resource-intensive demands of these models, requiring significant computational power for both training and inference, limit their deployment to high-performance servers. Additionally, the extensive calculation requirements of the models often lead to increased latency in response times. With the increasing need for LLMs to operate efficiently on CPUs, research about lightweight models that are optimized for CPU inference has emerged. In this work, we introduce GEB-1.3B, a lightweight LLM trained on 550 billion tokens in both Chinese and English languages. We employ novel training techniques, including ROPE, Group-Query-Attention, and FlashAttention-2, to accelerate training while maintaining model performance. Additionally, we fine-tune the model using 10 million samples of instruction data to enhance alignment. GEB-1.3B exhibits outstanding performance on general benchmarks such as MMLU, C-Eval, and CMMLU, outperforming comparative models such as MindLLM-1.3B and TinyLLaMA-1.1B. Notably, the FP32 version of GEB-1.3B achieves commendable inference times on CPUs, with ongoing efforts to further enhance speed through advanced quantization techniques. The release of GEB-1.3B as an open-source model marks a significant contribution to the development of lightweight LLMs, promising to foster further research and innovation in the field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.10149", "title": "BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack", "authors": [ "Yuri Kuratov", "Aydar Bulatov", "Petr Anokhin", "Ivan Rodkin", "Dmitry Sorokin", "Artyom Sorokin", "Mikhail Burtsev" ], "github": "https://github.com/booydar/babilong", "project_page": "", "abstract": "In recent years, the input context sizes of large language models (LLMs) have increased dramatically. However, existing evaluation methods have not kept pace, failing to comprehensively assess the efficiency of models in handling long contexts. To bridge this gap, we introduce the BABILong benchmark, designed to test language models' ability to reason across facts distributed in extremely long documents. BABILong includes a diverse set of 20 reasoning tasks, including fact chaining, simple induction, deduction, counting, and handling lists/sets. These tasks are challenging on their own, and even more demanding when the required facts are scattered across long natural text. Our evaluations show that popular LLMs effectively utilize only 10-20\\% of the context and their performance declines sharply with increased reasoning complexity. Among alternatives to in-context reasoning, Retrieval-Augmented Generation methods achieve a modest 60\\% accuracy on single-fact question answering, independent of context length. Among context extension methods, the highest performance is demonstrated by recurrent memory transformers, enabling the processing of lengths up to 11 million tokens. The BABILong benchmark is extendable to any length to support the evaluation of new upcoming models with increased capabilities, and we provide splits up to 1 million token lengths.", "github_manual": "https://github.com/booydar/babilong", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.10210", "title": "Make It Count: Text-to-Image Generation with an Accurate Number of Objects", "authors": [ "Lital Binyamin", "Yoad Tewel", "Hilit Segev", "Eran Hirsch", "Royi Rassin", "Gal Chechik" ], "github": "https://github.com/Litalby1/make-it-count", "project_page": "", "abstract": "Despite the unprecedented success of text-to-image diffusion models, controlling the number of depicted objects using text is surprisingly hard. This is important for various applications from technical documents, to children's books to illustrating cooking recipes. Generating object-correct counts is fundamentally challenging because the generative model needs to keep a sense of separate identity for every instance of the object, even if several objects look identical or overlap, and then carry out a global computation implicitly during generation. It is still unknown if such representations exist. To address count-correct generation, we first identify features within the diffusion model that can carry the object identity information. We then use them to separate and count instances of objects during the denoising process and detect over-generation and under-generation. We fix the latter by training a model that predicts both the shape and location of a missing object, based on the layout of existing ones, and show how it can be used to guide denoising with correct object count. Our approach, CountGen, does not depend on external source to determine object layout, but rather uses the prior from the diffusion model itself, creating prompt-dependent and seed-dependent layouts. Evaluated on two benchmark datasets, we find that CountGen strongly outperforms the count-accuracy of existing baselines.", "github_manual": "https://github.com/Litalby1/make-it-count", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.10118", "title": "SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages", "authors": [ "Holy Lovenia", "Rahmad Mahendra", "Salsabil Maulana Akbar", "Lester James V. Miranda", "Jennifer Santoso", "Elyanah Aco", "Akhdan Fadhilah", "Jonibek Mansurov", "Joseph Marvin Imperial", "Onno P. Kampman", "Joel Ruben Antony Moniz", "Muhammad Ravi Shulthan Habibi", "Frederikus Hudi", "Railey Montalan", "Ryan Ignatius", "Joanito Agili Lopo", "William Nixon", "B\u00f6rje F. Karlsson", "James Jaya", "Ryandito Diandaru", "Yuze Gao", "Patrick Amadeus", "Bin Wang", "Jan Christian Blaise Cruz", "Chenxi Whitehouse", "Ivan Halim Parmonangan", "Maria Khelli", "Wenyu Zhang", "Lucky Susanto", "Reynard Adha Ryanda", "Sonny Lazuardi Hermawan", "Dan John Velasco", "Muhammad Dehan Al Kautsar", "Willy Fitra Hendria", "Yasmin Moslem", "Noah Flynn", "Muhammad Farid Adilazuarda", "Haochen Li", "Johanes Lee", "R. Damanhuri", "Shuo Sun", "Muhammad Reza Qorib", "Amirbek Djanibekov", "Wei Qi Leong", "Quyet V. Do", "Niklas Muennighoff", "Tanrada Pansuwan", "Ilham Firdausi Putra", "Yan Xu", "Ngee Chia Tai", "Ayu Purwarianti", "Sebastian Ruder", "William Tjhi", "Peerat Limkonchotiwat", "Alham Fikri Aji", "Sedrick Keh", "Genta Indra Winata", "Ruochen Zhang", "Fajri Koto", "Zheng-Xin Yong", "Samuel Cahyawijaya" ], "github": "https://github.com/SEACrowd/seacrowd-datahub", "project_page": "", "abstract": "Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1,300 indigenous languages and a population of 671 million people. However, prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA, compromising the quality of AI models for SEA languages. Evaluating models for SEA languages is challenging due to the scarcity of high-quality datasets, compounded by the dominance of English training data, raising concerns about potential cultural misrepresentation. To address these challenges, we introduce SEACrowd, a collaborative initiative that consolidates a comprehensive resource hub that fills the resource gap by providing standardized corpora in nearly 1,000 SEA languages across three modalities. Through our SEACrowd benchmarks, we assess the quality of AI models on 36 indigenous languages across 13 tasks, offering valuable insights into the current AI landscape in SEA. Furthermore, we propose strategies to facilitate greater AI advancements, maximizing potential utility and resource equity for the future of AI in SEA.", "github_manual": "https://github.com/SEACrowd/seacrowd-datahub", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.10126", "title": "Training-free Camera Control for Video Generation", "authors": [ "Chen Hou", "Guoqiang Wei", "Yan Zeng", "Zhibo Chen" ], "github": "", "project_page": "", "abstract": "We propose a training-free and robust solution to offer camera movement control for off-the-shelf video diffusion models. Unlike previous work, our method does not require any supervised finetuning on camera-annotated datasets or self-supervised training via data augmentation. Instead, it can be plugged and played with most pretrained video diffusion models and generate camera controllable videos with a single image or text prompt as input. The inspiration of our work comes from the layout prior that intermediate latents hold towards generated results, thus rearranging noisy pixels in them will make output content reallocated as well. As camera move could also be seen as a kind of pixel rearrangement caused by perspective change, videos could be reorganized following specific camera motion if their noisy latents change accordingly. Established on this, we propose our method CamTrol, which enables robust camera control for video diffusion models. It is achieved by a two-stage process. First, we model image layout rearrangement through explicit camera movement in 3D point cloud space. Second, we generate videos with camera motion using layout prior of noisy latents formed by a series of rearranged images. Extensive experiments have demonstrated the robustness our method holds in controlling camera motion of generated videos. Furthermore, we show that our method can produce impressive results in generating 3D rotation videos with dynamic content. Project page at https://lifedecoder.github.io/CamTrol/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.10227", "title": "VideoGUI: A Benchmark for GUI Automation from Instructional Videos", "authors": [ "Kevin Qinghong Lin", "Linjie Li", "Difei Gao", "Qinchen WU", "Mingyi Yan", "Zhengyuan Yang", "Lijuan Wang", "Mike Zheng Shou" ], "github": "https://github.com/showlab/videogui", "project_page": "", "abstract": "Graphical User Interface (GUI) automation holds significant promise for enhancing human productivity by assisting with computer tasks. Existing task formulations primarily focus on simple tasks that can be specified by a single, language-only instruction, such as \"Insert a new slide.\" In this work, we introduce VideoGUI, a novel multi-modal benchmark designed to evaluate GUI assistants on visual-centric GUI tasks. Sourced from high-quality web instructional videos, our benchmark focuses on tasks involving professional and novel software (e.g., Adobe Photoshop or Stable Diffusion WebUI) and complex activities (e.g., video editing). VideoGUI evaluates GUI assistants through a hierarchical process, allowing for identification of the specific levels at which they may fail: (i) high-level planning: reconstruct procedural subtasks from visual conditions without language descriptions; (ii) middle-level planning: generate sequences of precise action narrations based on visual state (i.e., screenshot) and goals; (iii) atomic action execution: perform specific actions such as accurately clicking designated elements. For each level, we design evaluation metrics across individual dimensions to provide clear signals, such as individual performance in clicking, dragging, typing, and scrolling for atomic action execution. Our evaluation on VideoGUI reveals that even the SoTA large multimodal model GPT4o performs poorly on visual-centric GUI tasks, especially for high-level planning.", "github_manual": "https://github.com/showlab/videogui", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.08659", "title": "Vivid-ZOO: Multi-View Video Generation with Diffusion Model", "authors": [ "Bing Li", "Cheng Zheng", "Wenxuan Zhu", "Jinjie Mai", "Biao Zhang", "Peter Wonka", "Bernard Ghanem" ], "github": "https://github.com/hi-zhengcheng/vividzoo", "project_page": "", "abstract": "While diffusion models have shown impressive performance in 2D image/video generation, diffusion-based Text-to-Multi-view-Video (T2MVid) generation remains underexplored. The new challenges posed by T2MVid generation lie in the lack of massive captioned multi-view videos and the complexity of modeling such multi-dimensional distribution. To this end, we propose a novel diffusion-based pipeline that generates high-quality multi-view videos centered around a dynamic 3D object from text. Specifically, we factor the T2MVid problem into viewpoint-space and time components. Such factorization allows us to combine and reuse layers of advanced pre-trained multi-view image and 2D video diffusion models to ensure multi-view consistency as well as temporal coherence for the generated multi-view videos, largely reducing the training cost. We further introduce alignment modules to align the latent spaces of layers from the pre-trained multi-view and the 2D video diffusion models, addressing the reused layers' incompatibility that arises from the domain gap between 2D and multi-view data. In support of this and future research, we further contribute a captioned multi-view video dataset. Experimental results demonstrate that our method generates high-quality multi-view videos, exhibiting vivid motions, temporal coherence, and multi-view consistency, given a variety of text prompts.", "github_manual": "https://github.com/hi-zhengcheng/vividzoo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.08920", "title": "AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis", "authors": [ "Swapnil Bhosale", "Haosen Yang", "Diptesh Kanojia", "Jiankang Deng", "Xiatian Zhu" ], "github": "https://github.com/Surrey-UP-Lab/AV-GS", "project_page": "", "abstract": "Novel view acoustic synthesis (NVAS) aims to render binaural audio at any target viewpoint, given a mono audio emitted by a sound source at a 3D scene. Existing methods have proposed NeRF-based implicit models to exploit visual cues as a condition for synthesizing binaural audio. However, in addition to low efficiency originating from heavy NeRF rendering, these methods all have a limited ability of characterizing the entire scene environment such as room geometry, material properties, and the spatial relation between the listener and sound source. To address these issues, we propose a novel Audio-Visual Gaussian Splatting (AV-GS) model. To obtain a material-aware and geometry-aware condition for audio synthesis, we learn an explicit point-based scene representation with an audio-guidance parameter on locally initialized Gaussian points, taking into account the space relation from the listener and sound source. To make the visual scene model audio adaptive, we propose a point densification and pruning strategy to optimally distribute the Gaussian points, with the per-point contribution in sound propagation (e.g., more points needed for texture-less wall surfaces as they affect sound path diversion). Extensive experiments validate the superiority of our AV-GS over existing alternatives on the real-world RWAS and simulation-based SoundSpaces datasets.", "github_manual": "https://github.com/Surrey-UP-Lab/AV-GS", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.08545", "title": "RVT-2: Learning Precise Manipulation from Few Demonstrations", "authors": [ "Ankit Goyal", "Valts Blukis", "Jie Xu", "Yijie Guo", "Yu-Wei Chao", "Dieter Fox" ], "github": "https://github.com/nvlabs/rvt", "project_page": "", "abstract": "In this work, we study how to build a robotic system that can solve multiple 3D manipulation tasks given language instructions. To be useful in industrial and household domains, such a system should be capable of learning new tasks with few demonstrations and solving them precisely. Prior works, like PerAct and RVT, have studied this problem, however, they often struggle with tasks requiring high precision. We study how to make them more effective, precise, and fast. Using a combination of architectural and system-level improvements, we propose RVT-2, a multitask 3D manipulation model that is 6X faster in training and 2X faster in inference than its predecessor RVT. RVT-2 achieves a new state-of-the-art on RLBench, improving the success rate from 65% to 82%. RVT-2 is also effective in the real world, where it can learn tasks requiring high precision, like picking up and inserting plugs, with just 10 demonstrations. Visual results, code, and trained model are provided at: https://robotic-view-transformer-2.github.io/.", "github_manual": "https://github.com/nvlabs/rvt", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-17", "arxiv_id": "2406.10209", "title": "Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs", "authors": [ "Abhimanyu Hans", "Yuxin Wen", "Neel Jain", "John Kirchenbauer", "Hamid Kazemi", "Prajwal Singhania", "Siddharth Singh", "Gowthami Somepalli", "Jonas Geiping", "Abhinav Bhatele", "Tom Goldstein" ], "github": "https://github.com/ahans30/goldfish-loss", "project_page": "", "abstract": "Large language models can memorize and repeat their training data, causing privacy and copyright risks. To mitigate memorization, we introduce a subtle modification to the next-token training objective that we call the goldfish loss. During training, a randomly sampled subset of tokens are excluded from the loss computation. These dropped tokens are not memorized by the model, which prevents verbatim reproduction of a complete chain of tokens from the training set. We run extensive experiments training billion-scale Llama-2 models, both pre-trained and trained from scratch, and demonstrate significant reductions in extractable memorization with little to no impact on downstream benchmarks.", "github_manual": "https://github.com/ahans30/goldfish-loss", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11768", "title": "GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities", "authors": [ "Sreyan Ghosh", "Sonal Kumar", "Ashish Seth", "Chandra Kiran Reddy Evuru", "Utkarsh Tyagi", "S Sakshi", "Oriol Nieto", "Ramani Duraiswami", "Dinesh Manocha" ], "github": "https://github.com/Sreyan88/GAMA", "project_page": "", "abstract": "Perceiving and understanding non-speech sounds and non-verbal speech is essential to making decisions that help us interact with our surroundings. In this paper, we propose GAMA, a novel General-purpose Large Audio-Language Model (LALM) with Advanced Audio Understanding and Complex Reasoning Abilities. We build GAMA by integrating an LLM with multiple types of audio representations, including features from a custom Audio Q-Former, a multi-layer aggregator that aggregates features from multiple layers of an audio encoder. We fine-tune GAMA on a large-scale audio-language dataset, which augments it with audio understanding capabilities. Next, we propose CompA-R (Instruction-Tuning for Complex Audio Reasoning), a synthetically generated instruction-tuning (IT) dataset with instructions that require the model to perform complex reasoning on the input audio. We instruction-tune GAMA with CompA-R to endow it with complex reasoning abilities, where we further add a soft prompt as input with high-level semantic evidence by leveraging event tags of the input audio. Finally, we also propose CompA-R-test, a human-labeled evaluation dataset for evaluating the capabilities of LALMs on open-ended audio question-answering that requires complex reasoning. Through automated and expert human evaluations, we show that GAMA outperforms all other LALMs in literature on diverse audio understanding tasks by margins of 1%-84%. Further, GAMA IT-ed on CompA-R proves to be superior in its complex reasoning and instruction following capabilities.", "github_manual": "https://github.com/Sreyan88/GAMA", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11794", "title": "DataComp-LM: In search of the next generation of training sets for language models", "authors": [ "Jeffrey Li", "Alex Fang", "Georgios Smyrnis", "Maor Ivgi", "Matt Jordan", "Samir Gadre", "Hritik Bansal", "Etash Guha", "Sedrick Keh", "Kushal Arora", "Saurabh Garg", "Rui Xin", "Niklas Muennighoff", "Reinhard Heckel", "Jean Mercat", "Mayee Chen", "Suchin Gururangan", "Mitchell Wortsman", "Alon Albalak", "Yonatan Bitton", "Marianna Nezhurina", "Amro Abbas", "Cheng-Yu Hsieh", "Dhruba Ghosh", "Josh Gardner", "Maciej Kilian", "Hanlin Zhang", "Rulin Shao", "Sarah Pratt", "Sunny Sanyal", "Gabriel Ilharco", "Giannis Daras", "Kalyani Marathe", "Aaron Gokaslan", "Jieyu Zhang", "Khyathi Chandu", "Thao Nguyen", "Igor Vasiljevic", "Sham Kakade", "Shuran Song", "Sujay Sanghavi", "Fartash Faghri", "Sewoong Oh", "Luke Zettlemoyer", "Kyle Lo", "Alaaeldin El-Nouby", "Hadi Pouransari", "Alexander Toshev", "Stephanie Wang", "Dirk Groeneveld", "Luca Soldani", "Pang Wei Koh", "Jenia Jitsev", "Thomas Kollar", "Alexandros G. Dimakis", "Yair Carmon", "Achal Dave", "Ludwig Schmidt", "Vaishaal Shankar" ], "github": "https://github.com/mlfoundations/dclm", "project_page": "", "abstract": "We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardized corpus of 240T tokens extracted from Common Crawl, effective pretraining recipes based on the OpenLM framework, and a broad suite of 53 downstream evaluations. Participants in the DCLM benchmark can experiment with data curation strategies such as deduplication, filtering, and data mixing at model scales ranging from 412M to 7B parameters. As a baseline for DCLM, we conduct extensive experiments and find that model-based filtering is key to assembling a high-quality training set. The resulting dataset, DCLM-Baseline enables training a 7B parameter language model from scratch to 64% 5-shot accuracy on MMLU with 2.6T training tokens. Compared to MAP-Neo, the previous state-of-the-art in open-data language models, DCLM-Baseline represents a 6.6 percentage point improvement on MMLU while being trained with 40% less compute. Our baseline model is also comparable to Mistral-7B-v0.3 and Llama 3 8B on MMLU (63% & 66%), and performs similarly on an average of 53 natural language understanding tasks while being trained with 6.6x less compute than Llama 3 8B. Our results highlight the importance of dataset design for training language models and offer a starting point for further research on data curation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mlfoundations/dclm", "github_api_added_by": "auto" }, { "date": "2024-06-18", "arxiv_id": "2406.11839", "title": "mDPO: Conditional Preference Optimization for Multimodal Large Language Models", "authors": [ "Fei Wang", "Wenxuan Zhou", "James Y. Huang", "Nan Xu", "Sheng Zhang", "Hoifung Poon", "Muhao Chen" ], "github": "https://github.com/luka-group/mDPO", "project_page": "", "abstract": "Direct preference optimization (DPO) has shown to be an effective method for large language model (LLM) alignment. Recent works have attempted to apply DPO to multimodal scenarios but have found it challenging to achieve consistent improvement. Through a comparative experiment, we identify the unconditional preference problem in multimodal preference optimization, where the model overlooks the image condition. To address this problem, we propose mDPO, a multimodal DPO objective that prevents the over-prioritization of language-only preferences by also optimizing image preference. Moreover, we introduce a reward anchor that forces the reward to be positive for chosen responses, thereby avoiding the decrease in their likelihood -- an intrinsic problem of relative preference optimization. Experiments on two multimodal LLMs of different sizes and three widely used benchmarks demonstrate that mDPO effectively addresses the unconditional preference problem in multimodal preference optimization and significantly improves model performance, particularly in reducing hallucination.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/luka-group/mDPO", "github_api_added_by": "auto" }, { "date": "2024-06-18", "arxiv_id": "2406.10324", "title": "L4GM: Large 4D Gaussian Reconstruction Model", "authors": [ "Jiawei Ren", "Kevin Xie", "Ashkan Mirzaei", "Hanxue Liang", "Xiaohui Zeng", "Karsten Kreis", "Ziwei Liu", "Antonio Torralba", "Sanja Fidler", "Seung Wook Kim", "Huan Ling" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "We present L4GM, the first 4D Large Reconstruction Model that produces animated objects from a single-view video input -- in a single feed-forward pass that takes only a second. Key to our success is a novel dataset of multiview videos containing curated, rendered animated objects from Objaverse. This dataset depicts 44K diverse objects with 110K animations rendered in 48 viewpoints, resulting in 12M videos with a total of 300M frames. We keep our L4GM simple for scalability and build directly on top of LGM, a pretrained 3D Large Reconstruction Model that outputs 3D Gaussian ellipsoids from multiview image input. L4GM outputs a per-frame 3D Gaussian Splatting representation from video frames sampled at a low fps and then upsamples the representation to a higher fps to achieve temporal smoothness. We add temporal self-attention layers to the base LGM to help it learn consistency across time, and utilize a per-timestep multiview rendering loss to train the model. The representation is upsampled to a higher framerate by training an interpolation model which produces intermediate 3D Gaussian representations. We showcase that L4GM that is only trained on synthetic data generalizes extremely well on in-the-wild videos, producing high quality animated 3D assets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.10163", "title": "MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers", "authors": [ "Yiwen Chen", "Tong He", "Di Huang", "Weicai Ye", "Sijin Chen", "Jiaxiang Tang", "Xin Chen", "Zhongang Cai", "Lei Yang", "Gang Yu", "Guosheng Lin", "Chi Zhang" ], "github": "https://github.com/buaacyw/MeshAnything", "project_page": "", "abstract": "Recently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement. However, this potential is largely unrealized because these assets always need to be converted to meshes for 3D industry applications, and the meshes produced by current mesh extraction methods are significantly inferior to Artist-Created Meshes (AMs), i.e., meshes created by human artists. Specifically, current mesh extraction methods rely on dense faces and ignore geometric features, leading to inefficiencies, complicated post-processing, and lower representation quality. To address these issues, we introduce MeshAnything, a model that treats mesh extraction as a generation problem, producing AMs aligned with specified shapes. By converting 3D assets in any 3D representation into AMs, MeshAnything can be integrated with various 3D asset production methods, thereby enhancing their application across the 3D industry. The architecture of MeshAnything comprises a VQ-VAE and a shape-conditioned decoder-only transformer. We first learn a mesh vocabulary using the VQ-VAE, then train the shape-conditioned decoder-only transformer on this vocabulary for shape-conditioned autoregressive mesh generation. Our extensive experiments show that our method generates AMs with hundreds of times fewer faces, significantly improving storage, rendering, and simulation efficiencies, while achieving precision comparable to previous methods.", "github_manual": "https://github.com/buaacyw/MeshAnything", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11196", "title": "Vid3D: Synthesis of Dynamic 3D Scenes using 2D Video Diffusion", "authors": [ "Rishab Parthasarathy", "Zack Ankner", "Aaron Gokaslan" ], "github": "https://github.com/rishab-partha/Vid3D", "project_page": "", "abstract": "A recent frontier in computer vision has been the task of 3D video generation, which consists of generating a time-varying 3D representation of a scene. To generate dynamic 3D scenes, current methods explicitly model 3D temporal dynamics by jointly optimizing for consistency across both time and views of the scene. In this paper, we instead investigate whether it is necessary to explicitly enforce multiview consistency over time, as current approaches do, or if it is sufficient for a model to generate 3D representations of each timestep independently. We hence propose a model, Vid3D, that leverages 2D video diffusion to generate 3D videos by first generating a 2D \"seed\" of the video's temporal dynamics and then independently generating a 3D representation for each timestep in the seed video. We evaluate Vid3D against two state-of-the-art 3D video generation methods and find that Vid3D is achieves comparable results despite not explicitly modeling 3D temporal dynamics. We further ablate how the quality of Vid3D depends on the number of views generated per frame. While we observe some degradation with fewer views, performance degradation remains minor. Our results thus suggest that 3D temporal knowledge may not be necessary to generate high-quality dynamic 3D scenes, potentially enabling simpler generative algorithms for this task.", "github_manual": "https://github.com/rishab-partha/Vid3D", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.10996", "title": "THEANINE: Revisiting Memory Management in Long-term Conversations with Timeline-augmented Response Generation", "authors": [ "Seo Hyun Kim", "Kai Tzu-iunn Ong", "Taeyoon Kwon", "Namyoung Kim", "Keummin Ka", "SeongHyeon Bae", "Yohan Jo", "Seung-won Hwang", "Dongha Lee", "Jinyoung Yeo" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are capable of processing lengthy dialogue histories during prolonged interaction with users without additional memory modules; however, their responses tend to overlook or incorrectly recall information from the past. In this paper, we revisit memory-augmented response generation in the era of LLMs. While prior work focuses on getting rid of outdated memories, we argue that such memories can provide contextual cues that help dialogue systems understand the development of past events and, therefore, benefit response generation. We present Theanine, a framework that augments LLMs' response generation with memory timelines -- series of memories that demonstrate the development and causality of relevant past events. Along with Theanine, we introduce TeaFarm, a counterfactual-driven question-answering pipeline addressing the limitation of G-Eval in long-term conversations. Supplementary videos of our methods and the TeaBag dataset for TeaFarm evaluation are in https://theanine-693b0.web.app/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11069", "title": "WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences", "authors": [ "Yujie Lu", "Dongfu Jiang", "Wenhu Chen", "William Yang Wang", "Yejin Choi", "Bill Yuchen Lin" ], "github": "", "project_page": "", "abstract": "Recent breakthroughs in vision-language models (VLMs) emphasize the necessity of benchmarking human preferences in real-world multimodal interactions. To address this gap, we launched WildVision-Arena (WV-Arena), an online platform that collects human preferences to evaluate VLMs. We curated WV-Bench by selecting 500 high-quality samples from 8,000 user submissions in WV-Arena. WV-Bench uses GPT-4 as the judge to compare each VLM with Claude-3-Sonnet, achieving a Spearman correlation of 0.94 with the WV-Arena Elo. This significantly outperforms other benchmarks like MMVet, MMMU, and MMStar. Our comprehensive analysis of 20K real-world interactions reveals important insights into the failure cases of top-performing VLMs. For example, we find that although GPT-4V surpasses many other models like Reka-Flash, Opus, and Yi-VL-Plus in simple visual recognition and reasoning tasks, it still faces challenges with subtle contextual cues, spatial reasoning, visual imagination, and expert domain knowledge. Additionally, current VLMs exhibit issues with hallucinations and safety when intentionally provoked. We are releasing our chat and feedback data to further advance research in the field of VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11816", "title": "VideoLLM-online: Online Video Large Language Model for Streaming Video", "authors": [ "Joya Chen", "Zhaoyang Lv", "Shiwei Wu", "Kevin Qinghong Lin", "Chenan Song", "Difei Gao", "Jia-Wei Liu", "Ziteng Gao", "Dongxing Mao", "Mike Zheng Shou" ], "github": "https://github.com/showlab/VideoLLM-online", "project_page": "", "abstract": "Recent Large Language Models have been enhanced with vision capabilities, enabling them to comprehend images, videos, and interleaved vision-language content. However, the learning methods of these large multimodal models typically treat videos as predetermined clips, making them less effective and efficient at handling streaming video inputs. In this paper, we propose a novel Learning-In-Video-Stream (LIVE) framework, which enables temporally aligned, long-context, and real-time conversation within a continuous video stream. Our LIVE framework comprises comprehensive approaches to achieve video streaming dialogue, encompassing: (1) a training objective designed to perform language modeling for continuous streaming inputs, (2) a data generation scheme that converts offline temporal annotations into a streaming dialogue format, and (3) an optimized inference pipeline to speed up the model responses in real-world video streams. With our LIVE framework, we built VideoLLM-online model upon Llama-2/Llama-3 and demonstrate its significant advantages in processing streaming videos. For instance, on average, our model can support streaming dialogue in a 5-minute video clip at over 10 FPS on an A100 GPU. Moreover, it also showcases state-of-the-art performance on public offline video benchmarks, such as recognition, captioning, and forecasting. The code, model, data, and demo have been made available at https://showlab.github.io/videollm-online.", "github_manual": "https://github.com/showlab/VideoLLM-online", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11827", "title": "WPO: Enhancing RLHF with Weighted Preference Optimization", "authors": [ "Wenxuan Zhou", "Ravi Agrawal", "Shujian Zhang", "Sathish Reddy Indurthi", "Sanqiang Zhao", "Kaiqiang Song", "Silei Xu", "Chenguang Zhu" ], "github": "https://github.com/wzhouad/WPO", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) is a promising solution to align large language models (LLMs) more closely with human values. Off-policy preference optimization, where the preference data is obtained from other models, is widely adopted due to its cost efficiency and scalability. However, off-policy preference optimization often suffers from a distributional gap between the policy used for data collection and the target policy, leading to suboptimal optimization. In this paper, we propose a novel strategy to mitigate this problem by simulating on-policy learning with off-policy preference data. Our Weighted Preference Optimization (WPO) method adapts off-policy data to resemble on-policy data more closely by reweighting preference pairs according to their probability under the current policy. This method not only addresses the distributional gap problem but also enhances the optimization process without incurring additional costs. We validate our method on instruction following benchmarks including Alpaca Eval 2 and MT-bench. WPO not only outperforms Direct Preference Optimization (DPO) by up to 5.6% on Alpaca Eval 2 but also establishes a remarkable length-controlled winning rate against GPT-4-turbo of 48.6% based on Llama-3-8B-Instruct, making it the strongest 8B model on the leaderboard. We will release the code and models at https://github.com/wzhouad/WPO.", "github_manual": "", "github_regex": "https://github.com/wzhouad/WPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11775", "title": "Task Me Anything", "authors": [ "Jieyu Zhang", "Weikai Huang", "Zixian Ma", "Oscar Michel", "Dong He", "Tanmay Gupta", "Wei-Chiu Ma", "Ali Farhadi", "Aniruddha Kembhavi", "Ranjay Krishna" ], "github": "https://github.com/JieyuZ2/TaskMeAnything", "project_page": "", "abstract": "Benchmarks for large multimodal language models (MLMs) now serve to simultaneously assess the general capabilities of models instead of evaluating for a specific capability. As a result, when a developer wants to identify which models to use for their application, they are overwhelmed by the number of benchmarks and remain uncertain about which benchmark's results are most reflective of their specific use case. This paper introduces Task-Me-Anything, a benchmark generation engine which produces a benchmark tailored to a user's needs. Task-Me-Anything maintains an extendable taxonomy of visual assets and can programmatically generate a vast number of task instances. Additionally, it algorithmically addresses user queries regarding MLM performance efficiently within a computational budget. It contains 113K images, 10K videos, 2K 3D object assets, over 365 object categories, 655 attributes, and 335 relationships. It can generate 750M image/video question-answering pairs, which focus on evaluating MLM perceptual capabilities. Task-Me-Anything reveals critical insights: open-source MLMs excel in object and attribute recognition but lack spatial and temporal understanding; each model exhibits unique strengths and weaknesses; larger models generally perform better, though exceptions exist; and GPT4o demonstrates challenges in recognizing rotating/moving objects and distinguishing colors.", "github_manual": "https://github.com/JieyuZ2/TaskMeAnything", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.10328", "title": "From Pixels to Prose: A Large Dataset of Dense Image Captions", "authors": [ "Vasu Singla", "Kaiyu Yue", "Sukriti Paul", "Reza Shirkavand", "Mayuka Jayawardhana", "Alireza Ganjdanesh", "Heng Huang", "Abhinav Bhatele", "Gowthami Somepalli", "Tom Goldstein" ], "github": "https://github.com/GantMan/nsfw_model", "project_page": "", "abstract": "Training large vision-language models requires extensive, high-quality image-text pairs. Existing web-scraped datasets, however, are noisy and lack detailed image descriptions. To bridge this gap, we introduce PixelProse, a comprehensive dataset of over 16M (million) synthetically generated captions, leveraging cutting-edge vision-language models for detailed and accurate descriptions. To ensure data integrity, we rigorously analyze our dataset for problematic content, including child sexual abuse material (CSAM), personally identifiable information (PII), and toxicity. We also provide valuable metadata such as watermark presence and aesthetic scores, aiding in further dataset filtering. We hope PixelProse will be a valuable resource for future vision-language research. PixelProse is available at https://huggingface.co/datasets/tomg-group-umd/pixelprose", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GantMan/nsfw_model", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11402", "title": "Evaluating Open Language Models Across Task Types, Application Domains, and Reasoning Types: An In-Depth Experimental Analysis", "authors": [ "Neelabh Sinha", "Vinija Jain", "Aman Chadha" ], "github": "https://github.com/neelabhsinha/lm-application-eval-kit", "project_page": "", "abstract": "The rapid rise of Language Models (LMs) has expanded their use in several applications. Yet, due to constraints of model size, associated cost, or proprietary restrictions, utilizing state-of-the-art (SOTA) LLMs is not always feasible. With open, smaller LMs emerging, more applications can leverage their capabilities, but selecting the right LM can be challenging. This work conducts an in-depth experimental analysis of the semantic correctness of outputs of 10 smaller, open LMs across three aspects: task types, application domains and reasoning types, using diverse prompt styles. We demonstrate that most effective models and prompt styles vary depending on the specific requirements. Our analysis provides a comparative assessment of LMs and prompt styles using a proposed three-tier schema of aspects for their strategic selection based on use-case and other constraints. We also show that if utilized appropriately, these LMs can compete with, and sometimes outperform, SOTA LLMs like DeepSeek-v2, GPT-3.5-Turbo, and GPT-4o.", "github_manual": "https://github.com/neelabhsinha/lm-application-eval-kit", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11271", "title": "MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens", "authors": [ "Anas Awadalla", "Le Xue", "Oscar Lo", "Manli Shu", "Hannah Lee", "Etash Kumar Guha", "Matt Jordan", "Sheng Shen", "Mohamed Awadalla", "Silvio Savarese", "Caiming Xiong", "Ran Xu", "Yejin Choi", "Ludwig Schmidt" ], "github": "https://github.com/mlfoundations/MINT-1T", "project_page": "", "abstract": "Multimodal interleaved datasets featuring free-form interleaved sequences of images and text are crucial for training frontier large multimodal models (LMMs). Despite the rapid progression of open-source LMMs, there remains a pronounced scarcity of large-scale, diverse open-source multimodal interleaved datasets. In response, we introduce MINT-1T, the most extensive and diverse open-source Multimodal INTerleaved dataset to date. MINT-1T comprises one trillion text tokens and three billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. As scaling multimodal interleaved datasets requires substantial engineering effort, sharing the data curation process and releasing the dataset greatly benefits the community. Our experiments show that LMMs trained on MINT-1T rival the performance of models trained on the previous leading dataset, OBELICS. Our data and code will be released at https://github.com/mlfoundations/MINT-1T.", "github_manual": "", "github_regex": "https://github.com/mlfoundations/MINT-1T", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.10906", "title": "Breaking the Attention Bottleneck", "authors": [ "Kalle Hilsenbek" ], "github": "", "project_page": "", "abstract": "Attention-based transformers have become the standard architecture in many deep learning fields, primarily due to their ability to model long-range dependencies and handle variable-length input sequences. However, the attention mechanism with its quadratic complexity is a significant bottleneck in the transformer architecture. This algorithm is only uni-directional in the decoder and converges to a static pattern in over-parametrized decoder-only models. I address this issue by developing a generative function as attention or activation replacement. It still has the auto-regressive character by comparing each token with the previous one. In my test setting with nanoGPT this yields a smaller loss while having a smaller model. The loss further drops by incorporating an average context vector. This concept of attention replacement is distributed under the GNU AGPL v3 license at https://gitlab.com/Bachstelze/causal_generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11840", "title": "LLaNA: Large Language and NeRF Assistant", "authors": [ "Andrea Amaduzzi", "Pierluigi Zama Ramirez", "Giuseppe Lisanti", "Samuele Salti", "Luigi Di Stefano" ], "github": "https://github.com/haotian-liu/LLaVA", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have demonstrated an excellent understanding of images and 3D data. However, both modalities have shortcomings in holistically capturing the appearance and geometry of objects. Meanwhile, Neural Radiance Fields (NeRFs), which encode information within the weights of a simple Multi-Layer Perceptron (MLP), have emerged as an increasingly widespread modality that simultaneously encodes the geometry and photorealistic appearance of objects. This paper investigates the feasibility and effectiveness of ingesting NeRF into MLLM. We create LLaNA, the first general-purpose NeRF-language assistant capable of performing new tasks such as NeRF captioning and Q\\&A. Notably, our method directly processes the weights of the NeRF's MLP to extract information about the represented objects without the need to render images or materialize 3D data structures. Moreover, we build a dataset of NeRFs with text annotations for various NeRF-language tasks with no human intervention. Based on this dataset, we develop a benchmark to evaluate the NeRF understanding capability of our method. Results show that processing NeRF weights performs favourably against extracting 2D or 3D representations from NeRFs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/haotian-liu/LLaVA", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11813", "title": "How Do Large Language Models Acquire Factual Knowledge During Pretraining?", "authors": [ "Hoyeon Chang", "Jinho Park", "Seonghyeon Ye", "Sohee Yang", "Youngkyung Seo", "Du-Seong Chang", "Minjoon Seo" ], "github": "https://github.com/kaistai/factual-knowledge-acquisition", "project_page": "", "abstract": "Despite the recent observation that large language models (LLMs) can store substantial factual knowledge, there is a limited understanding of the mechanisms of how they acquire factual knowledge through pretraining. This work addresses this gap by studying how LLMs acquire factual knowledge during pretraining. The findings reveal several important insights into the dynamics of factual knowledge acquisition during pretraining. First, counterintuitively, we observe that pretraining on more data shows no significant improvement in the model's capability to acquire and maintain factual knowledge. Next, there is a power-law relationship between training steps and forgetting of memorization and generalization of factual knowledge, and LLMs trained with duplicated training data exhibit faster forgetting. Third, training LLMs with larger batch sizes can enhance the models' robustness to forgetting. Overall, our observations suggest that factual knowledge acquisition in LLM pretraining occurs by progressively increasing the probability of factual knowledge presented in the pretraining data at each step. However, this increase is diluted by subsequent forgetting. Based on this interpretation, we demonstrate that we can provide plausible explanations for recently observed behaviors of LLMs, such as the poor performance of LLMs on long-tail knowledge and the benefits of deduplicating the pretraining corpus.", "github_manual": "https://github.com/kaistai/factual-knowledge-acquisition", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.10670", "title": "CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training", "authors": [ "David Brandfonbrener", "Hanlin Zhang", "Andreas Kirsch", "Jonathan Richard Schwarz", "Sham Kakade" ], "github": "https://github.com/davidbrandfonbrener/color-filter-olmo", "project_page": "", "abstract": "Selecting high-quality data for pre-training is crucial in shaping the downstream task performance of language models. A major challenge lies in identifying this optimal subset, a problem generally considered intractable, thus necessitating scalable and effective heuristics. In this work, we propose a data selection method, CoLoR-Filter (Conditional Loss Reduction Filtering), which leverages an empirical Bayes-inspired approach to derive a simple and computationally efficient selection criterion based on the relative loss values of two auxiliary models. In addition to the modeling rationale, we evaluate CoLoR-Filter empirically on two language modeling tasks: (1) selecting data from C4 for domain adaptation to evaluation on Books and (2) selecting data from C4 for a suite of downstream multiple-choice question answering tasks. We demonstrate favorable scaling both as we subselect more aggressively and using small auxiliary models to select data for large target models. As one headline result, CoLoR-Filter data selected using a pair of 150m parameter auxiliary models can train a 1.2b parameter target model to match a 1.2b parameter model trained on 25b randomly selected tokens with 25x less data for Books and 11x less data for the downstream tasks. Code: https://github.com/davidbrandfonbrener/color-filter-olmo Filtered data: https://huggingface.co/datasets/davidbrandfonbrener/color-filtered-c4", "github_manual": "", "github_regex": "https://github.com/davidbrandfonbrener/color-filter-olmo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11833", "title": "MMDU: A Multi-Turn Multi-Image Dialog Understanding Benchmark and Instruction-Tuning Dataset for LVLMs", "authors": [ "Ziyu Liu", "Tao Chu", "Yuhang Zang", "Xilin Wei", "Xiaoyi Dong", "Pan Zhang", "Zijian Liang", "Yuanjun Xiong", "Yu Qiao", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/Liuziyu77/MMDU", "project_page": "", "abstract": "Generating natural and meaningful responses to communicate with multi-modal human inputs is a fundamental capability of Large Vision-Language Models(LVLMs). While current open-source LVLMs demonstrate promising performance in simplified scenarios such as single-turn single-image input, they fall short in real-world conversation scenarios such as following instructions in a long context history with multi-turn and multi-images. Existing LVLM benchmarks primarily focus on single-choice questions or short-form responses, which do not adequately assess the capabilities of LVLMs in real-world human-AI interaction applications. Therefore, we introduce MMDU, a comprehensive benchmark, and MMDU-45k, a large-scale instruction tuning dataset, designed to evaluate and improve LVLMs' abilities in multi-turn and multi-image conversations. We employ the clustering algorithm to ffnd the relevant images and textual descriptions from the open-source Wikipedia and construct the question-answer pairs by human annotators with the assistance of the GPT-4o model. MMDU has a maximum of 18k image+text tokens, 20 images, and 27 turns, which is at least 5x longer than previous benchmarks and poses challenges to current LVLMs. Our in-depth analysis of 15 representative LVLMs using MMDU reveals that open-source LVLMs lag behind closed-source counterparts due to limited conversational instruction tuning data. We demonstrate that ffne-tuning open-source LVLMs on MMDU-45k signiffcantly address this gap, generating longer and more accurate conversations, and improving scores on MMDU and existing benchmarks (MMStar: +1.1%, MathVista: +1.5%, ChartQA:+1.2%). Our contributions pave the way for bridging the gap between current LVLM models and real-world application demands. This project is available at https://github.com/Liuziyu77/MMDU.", "github_manual": "", "github_regex": "https://github.com/Liuziyu77/MMDU", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11831", "title": "Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models", "authors": [ "Bingqi Ma", "Zhuofan Zong", "Guanglu Song", "Hongsheng Li", "Yu Liu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) based on decoder-only transformers have demonstrated superior text understanding capabilities compared to CLIP and T5-series models. However, the paradigm for utilizing current advanced LLMs in text-to-image diffusion models remains to be explored. We observed an unusual phenomenon: directly using a large language model as the prompt encoder significantly degrades the prompt-following ability in image generation. We identified two main obstacles behind this issue. One is the misalignment between the next token prediction training in LLM and the requirement for discriminative prompt features in diffusion models. The other is the intrinsic positional bias introduced by the decoder-only architecture. To deal with this issue, we propose a novel framework to fully harness the capabilities of LLMs. Through the carefully designed usage guidance, we effectively enhance the text representation capability for prompt encoding and eliminate its inherent positional bias. This allows us to integrate state-of-the-art LLMs into the text-to-image generation model flexibly. Furthermore, we also provide an effective manner to fuse multiple LLMs into our framework. Considering the excellent performance and scaling capabilities demonstrated by the transformer architecture, we further design an LLM-Infused Diffusion Transformer (LI-DiT) based on the framework. We conduct extensive experiments to validate LI-DiT across model size and data size. Benefiting from the inherent ability of the LLMs and our innovative designs, the prompt understanding performance of LI-DiT easily surpasses state-of-the-art open-source models as well as mainstream closed-source commercial models including Stable Diffusion 3, DALL-E 3, and Midjourney V6. The powerful LI-DiT-10B will be available after further optimization and security checks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11202", "title": "Consistency^2: Consistent and Fast 3D Painting with Latent Consistency Models", "authors": [ "Tianfu Wang", "Anton Obukhov", "Konrad Schindler" ], "github": "https://github.com/kongdai123/consistency2", "project_page": "", "abstract": "Generative 3D Painting is among the top productivity boosters in high-resolution 3D asset management and recycling. Ever since text-to-image models became accessible for inference on consumer hardware, the performance of 3D Painting methods has consistently improved and is currently close to plateauing. At the core of most such models lies denoising diffusion in the latent space, an inherently time-consuming iterative process. Multiple techniques have been developed recently to accelerate generation and reduce sampling iterations by orders of magnitude. Designed for 2D generative imaging, these techniques do not come with recipes for lifting them into 3D. In this paper, we address this shortcoming by proposing a Latent Consistency Model (LCM) adaptation for the task at hand. We analyze the strengths and weaknesses of the proposed model and evaluate it quantitatively and qualitatively. Based on the Objaverse dataset samples study, our 3D painting method attains strong preference in all evaluations. Source code is available at https://github.com/kongdai123/consistency2.", "github_manual": "", "github_regex": "https://github.com/kongdai123/consistency2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11463", "title": "Just How Flexible are Neural Networks in Practice?", "authors": [ "Ravid Shwartz-Ziv", "Micah Goldblum", "Arpit Bansal", "C. Bayan Bruss", "Yann LeCun", "Andrew Gordon Wilson" ], "github": "", "project_page": "", "abstract": "It is widely believed that a neural network can fit a training set containing at least as many samples as it has parameters, underpinning notions of overparameterized and underparameterized models. In practice, however, we only find solutions accessible via our training procedure, including the optimizer and regularizers, limiting flexibility. Moreover, the exact parameterization of the function class, built into an architecture, shapes its loss surface and impacts the minima we find. In this work, we examine the ability of neural networks to fit data in practice. Our findings indicate that: (1) standard optimizers find minima where the model can only fit training sets with significantly fewer samples than it has parameters; (2) convolutional networks are more parameter-efficient than MLPs and ViTs, even on randomly labeled data; (3) while stochastic training is thought to have a regularizing effect, SGD actually finds minima that fit more training data than full-batch gradient descent; (4) the difference in capacity to fit correctly labeled and incorrectly labeled samples can be predictive of generalization; (5) ReLU activation functions result in finding minima that fit more data despite being designed to avoid vanishing and exploding gradients in deep architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.10803", "title": "HiddenTables & PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies", "authors": [ "William Watson", "Nicole Cho", "Tucker Balch", "Manuela Veloso" ], "github": "", "project_page": "", "abstract": "A myriad of different Large Language Models (LLMs) face a common challenge in contextually analyzing table question-answering tasks. These challenges are engendered from (1) finite context windows for large tables, (2) multi-faceted discrepancies amongst tokenization patterns against cell boundaries, and (3) various limitations stemming from data confidentiality in the process of using external models such as gpt-3.5-turbo. We propose a cooperative game dubbed \"HiddenTables\" as a potential resolution to this challenge. In essence, \"HiddenTables\" is played between the code-generating LLM \"Solver\" and the \"Oracle\" which evaluates the ability of the LLM agents to solve Table QA tasks. This game is based on natural language schemas and importantly, ensures the security of the underlying data. We provide evidential experiments on a diverse set of tables that demonstrate an LLM's collective inability to generalize and perform on complex queries, handle compositional dependencies, and align natural language to programmatic commands when concrete table schemas are provided. Unlike encoder-based models, we have pushed the boundaries of \"HiddenTables\" to not be limited by the number of rows - therefore we exhibit improved efficiency in prompt and completion tokens. Our infrastructure has spawned a new dataset \"PyQTax\" that spans across 116,671 question-table-answer triplets and provides additional fine-grained breakdowns & labels for varying question taxonomies. Therefore, in tandem with our academic contributions regarding LLMs' deficiency in TableQA tasks, \"HiddenTables\" is a tactile manifestation of how LLMs can interact with massive datasets while ensuring data security and minimizing generation costs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11194", "title": "In-Context Editing: Learning Knowledge from Self-Induced Distributions", "authors": [ "Siyuan Qi", "Bangcheng Yang", "Kailin Jiang", "Xiaobo Wang", "Jiaqi Li", "Yifan Zhong", "Yaodong Yang", "Zilong Zheng" ], "github": "", "project_page": "", "abstract": "The existing fine-tuning paradigm for language models is brittle in knowledge editing scenarios, where the model must incorporate new information without extensive retraining. This brittleness often results in overfitting, reduced performance, and unnatural language generation. To address this, we propose Consistent In-Context Editing (ICE), a novel approach that leverages the model's in-context learning capability to tune toward a contextual distribution rather than a one-hot target. ICE introduces a straightforward optimization framework that includes both a target and a procedure, enhancing the robustness and effectiveness of gradient-based tuning methods. We provide analytical insights into ICE across four critical aspects of knowledge editing: accuracy, locality, generalization, and linguistic quality, showing its advantages. Experimental results across four datasets confirm the effectiveness of ICE and demonstrate its potential for continual editing, ensuring that updated information is incorporated while preserving the integrity of the model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.09455", "title": "Pandora: Towards General World Model with Natural Language Actions and Video States", "authors": [ "Jiannan Xiang", "Guangyi Liu", "Yi Gu", "Qiyue Gao", "Yuting Ning", "Yuheng Zha", "Zeyu Feng", "Tianhua Tao", "Shibo Hao", "Yemin Shi", "Zhengzhong Liu", "Eric P. Xing", "Zhiting Hu" ], "github": "https://github.com/maitrix-org/Pandora", "project_page": "", "abstract": "World models simulate future states of the world in response to different actions. They facilitate interactive content creation and provides a foundation for grounded, long-horizon reasoning. Current foundation models do not fully meet the capabilities of general world models: large language models (LLMs) are constrained by their reliance on language modality and their limited understanding of the physical world, while video models lack interactive action control over the world simulations. This paper makes a step towards building a general world model by introducing Pandora, a hybrid autoregressive-diffusion model that simulates world states by generating videos and allows real-time control with free-text actions. Pandora achieves domain generality, video consistency, and controllability through large-scale pretraining and instruction tuning. Crucially, Pandora bypasses the cost of training-from-scratch by integrating a pretrained LLM (7B) and a pretrained video model, requiring only additional lightweight finetuning. We illustrate extensive outputs by Pandora across diverse domains (indoor/outdoor, natural/urban, human/robot, 2D/3D, etc.). The results indicate great potential of building stronger general world models with larger-scale training.", "github_manual": "https://github.com/maitrix-org/Pandora", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11430", "title": "A Simple and Effective L_2 Norm-Based Strategy for KV Cache Compression", "authors": [ "Alessio Devoto", "Yu Zhao", "Simone Scardapane", "Pasquale Minervini" ], "github": "", "project_page": "", "abstract": "The deployment of large language models (LLMs) is often hindered by the extensive memory requirements of the Key-Value (KV) cache, especially as context lengths increase. Existing approaches to reduce the KV cache size involve either fine-tuning the model to learn a compression strategy or leveraging attention scores to reduce the sequence length. We analyse the attention distributions in decoder-only Transformers-based models and observe that attention allocation patterns stay consistent across most layers. Surprisingly, we find a clear correlation between the L_2 and the attention scores over cached KV pairs, where a low L_2 of a key embedding usually leads to a high attention score during decoding. This finding indicates that the influence of a KV pair is potentially determined by the key embedding itself before being queried. Based on this observation, we compress the KV cache based on the L_2 of key embeddings. Our experimental results show that this simple strategy can reduce the KV cache size by 50% on language modelling and needle-in-a-haystack tasks and 90% on passkey retrieval tasks without losing accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.11251", "title": "Unifying Multimodal Retrieval via Document Screenshot Embedding", "authors": [ "Xueguang Ma", "Sheng-Chieh Lin", "Minghan Li", "Wenhu Chen", "Jimmy Lin" ], "github": "", "project_page": "", "abstract": "In the real world, documents are organized in different formats and varied modalities. Traditional retrieval pipelines require tailored document parsing techniques and content extraction modules to prepare input for indexing. This process is tedious, prone to errors, and has information loss. To this end, we propose Document Screenshot Embedding} (DSE), a novel retrieval paradigm that regards document screenshots as a unified input format, which does not require any content extraction preprocess and preserves all the information in a document (e.g., text, image and layout). DSE leverages a large vision-language model to directly encode document screenshots into dense representations for retrieval. To evaluate our method, we first craft the dataset of Wiki-SS, a 1.3M Wikipedia web page screenshots as the corpus to answer the questions from the Natural Questions dataset. In such a text-intensive document retrieval setting, DSE shows competitive effectiveness compared to other text retrieval methods relying on parsing. For example, DSE outperforms BM25 by 17 points in top-1 retrieval accuracy. Additionally, in a mixed-modality task of slide retrieval, DSE significantly outperforms OCR text retrieval methods by over 15 points in nDCG@10. These experiments show that DSE is an effective document retrieval paradigm for diverse types of documents. Model checkpoints, code, and Wiki-SS collection will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.10522", "title": "Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning", "authors": [ "Jifan Zhang", "Lalit Jain", "Yang Guo", "Jiayi Chen", "Kuan Lok Zhou", "Siddharth Suresh", "Andrew Wagenmaker", "Scott Sievert", "Timothy Rogers", "Kevin Jamieson", "Robert Mankoff", "Robert Nowak" ], "github": "https://github.com/yguooo/cartoon-caption-generation", "project_page": "", "abstract": "We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2.2 million captions, collected through crowdsourcing rating data for The New Yorker's weekly cartoon caption contest over the past eight years. This unique dataset supports the development and evaluation of multimodal large language models and preference-based fine-tuning algorithms for humorous caption generation. We propose novel benchmarks for judging the quality of model-generated captions, utilizing both GPT4 and human judgments to establish ranking-based evaluation strategies. Our experimental results highlight the limitations of current fine-tuning methods, such as RLHF and DPO, when applied to creative tasks. Furthermore, we demonstrate that even state-of-the-art models like GPT4 and Claude currently underperform top human contestants in generating humorous captions. As we conclude this extensive data collection effort, we release the entire preference dataset to the research community, fostering further advancements in AI humor generation and evaluation.", "github_manual": "https://github.com/yguooo/cartoon-caption-generation", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-18", "arxiv_id": "2406.10023", "title": "Deep Bayesian Active Learning for Preference Modeling in Large Language Models", "authors": [ "Luckeciano C. Melo", "Panagiotis Tigas", "Alessandro Abate", "Yarin Gal" ], "github": "https://github.com/luckeciano/bal-pm", "project_page": "", "abstract": "Leveraging human preferences for steering the behavior of Large Language Models (LLMs) has demonstrated notable success in recent years. Nonetheless, data selection and labeling are still a bottleneck for these systems, particularly at large scale. Hence, selecting the most informative points for acquiring human feedback may considerably reduce the cost of preference labeling and unleash the further development of LLMs. Bayesian Active Learning provides a principled framework for addressing this challenge and has demonstrated remarkable success in diverse settings. However, previous attempts to employ it for Preference Modeling did not meet such expectations. In this work, we identify that naive epistemic uncertainty estimation leads to the acquisition of redundant samples. We address this by proposing the Bayesian Active Learner for Preference Modeling (BAL-PM), a novel stochastic acquisition policy that not only targets points of high epistemic uncertainty according to the preference model but also seeks to maximize the entropy of the acquired prompt distribution in the feature space spanned by the employed LLM. Notably, our experiments demonstrate that BAL-PM requires 33% to 68% fewer preference labels in two popular human preference datasets and exceeds previous stochastic Bayesian acquisition policies.", "github_manual": "https://github.com/luckeciano/bal-pm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12066", "title": "Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks", "authors": [ "Jack Gallifant", "Shan Chen", "Pedro Moreira", "Nikolaj Munch", "Mingye Gao", "Jackson Pond", "Leo Anthony Celi", "Hugo Aerts", "Thomas Hartvigsen", "Danielle Bitterman" ], "github": "https://github.com/BittermanLab/RABBITS", "project_page": "", "abstract": "Medical knowledge is context-dependent and requires consistent reasoning across various natural language expressions of semantically equivalent phrases. This is particularly crucial for drug names, where patients often use brand names like Advil or Tylenol instead of their generic equivalents. To study this, we create a new robustness dataset, RABBITS, to evaluate performance differences on medical benchmarks after swapping brand and generic drug names using physician expert annotations. We assess both open-source and API-based LLMs on MedQA and MedMCQA, revealing a consistent performance drop ranging from 1-10\\%. Furthermore, we identify a potential source of this fragility as the contamination of test data in widely used pre-training datasets. All code is accessible at https://github.com/BittermanLab/RABBITS, and a HuggingFace leaderboard is available at https://huggingface.co/spaces/AIM-Harvard/rabbits-leaderboard.", "github_manual": "", "github_regex": "https://github.com/BittermanLab/RABBITS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.11811", "title": "RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content", "authors": [ "Joao Monteiro", "Pierre-Andre Noel", "Etienne Marcotte", "Sai Rajeswar", "Valentina Zantedeschi", "David Vazquez", "Nicolas Chapados", "Christopher Pal", "Perouz Taslakian" ], "github": "https://github.com/ServiceNow/repliqa", "project_page": "", "abstract": "Large Language Models (LLMs) are trained on vast amounts of data, most of which is automatically scraped from the internet. This data includes encyclopedic documents that harbor a vast amount of general knowledge (e.g., Wikipedia) but also potentially overlap with benchmark datasets used for evaluating LLMs. Consequently, evaluating models on test splits that might have leaked into the training set is prone to misleading conclusions. To foster sound evaluation of language models, we introduce a new test dataset named RepLiQA, suited for question-answering and topic retrieval tasks. RepLiQA is a collection of five splits of test sets, four of which have not been released to the internet or exposed to LLM APIs prior to this publication. Each sample in RepLiQA comprises (1) a reference document crafted by a human annotator and depicting an imaginary scenario (e.g., a news article) absent from the internet; (2) a question about the document's topic; (3) a ground-truth answer derived directly from the information in the document; and (4) the paragraph extracted from the reference document containing the answer. As such, accurate answers can only be generated if a model can find relevant content within the provided document. We run a large-scale benchmark comprising several state-of-the-art LLMs to uncover differences in performance across models of various types and sizes in a context-conditional language modeling setting. Released splits of RepLiQA can be found here: https://huggingface.co/datasets/ServiceNow/repliqa.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ServiceNow/repliqa", "github_api_added_by": "auto" }, { "date": "2024-06-19", "arxiv_id": "2406.12275", "title": "VoCo-LLaMA: Towards Vision Compression with Large Language Models", "authors": [ "Xubing Ye", "Yukang Gan", "Xiaoke Huang", "Yixiao Ge", "Ying Shan", "Yansong Tang" ], "github": "https://github.com/Yxxxb/VoCo-LLaMA", "project_page": "", "abstract": "Vision-Language Models (VLMs) have achieved remarkable success in various multi-modal tasks, but they are often bottlenecked by the limited context window and high computational cost of processing high-resolution image inputs and videos. Vision compression can alleviate this problem by reducing the vision token count. Previous approaches compress vision tokens with external modules and force LLMs to understand the compressed ones, leading to visual information loss. However, the LLMs' understanding paradigm of vision tokens is not fully utilised in the compression learning process. We propose VoCo-LLaMA, the first approach to compress vision tokens using LLMs. By introducing Vision Compression tokens during the vision instruction tuning phase and leveraging attention distillation, our method distill how LLMs comprehend vision tokens into their processing of VoCo tokens. VoCo-LLaMA facilitates effective vision compression and improves the computational efficiency during the inference stage. Specifically, our method achieves minimal performance loss with a compression ratio of 576times, resulting in up to 94.8% fewer FLOPs and 69.6% acceleration in inference time. Furthermore, through continuous training using time-series compressed token sequences of video frames, VoCo-LLaMA demonstrates the ability to understand temporal correlations, outperforming previous methods on popular video question-answering benchmarks. Our approach presents a promising way to unlock the full potential of VLMs' contextual window, enabling more scalable multi-modal applications. The project page, along with the associated code, can be accessed via https://yxxxb.github.io/VoCo-LLaMA-page/{this https URL}.", "github_manual": "https://github.com/Yxxxb/VoCo-LLaMA", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12753", "title": "OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI", "authors": [ "Zhen Huang", "Zengzhi Wang", "Shijie Xia", "Xuefeng Li", "Haoyang Zou", "Ruijie Xu", "Run-Ze Fan", "Lyumanshan Ye", "Ethan Chern", "Yixin Ye", "Yikai Zhang", "Yuqing Yang", "Ting Wu", "Binjie Wang", "Shichao Sun", "Yang Xiao", "Yiyuan Li", "Fan Zhou", "Steffi Chern", "Yiwei Qin", "Yan Ma", "Jiadi Su", "Yixiu Liu", "Yuxiang Zheng", "Shaoting Zhang", "Dahua Lin", "Yu Qiao", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/OlympicArena", "project_page": "", "abstract": "The evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showcasing potential cognitive reasoning abilities in problem-solving and scientific discovery (i.e., AI4Science) once exclusive to human intellect. To comprehensively evaluate current models' performance in cognitive reasoning abilities, we introduce OlympicArena, which includes 11,163 bilingual problems across both text-only and interleaved text-image modalities. These challenges encompass a wide range of disciplines spanning seven fields and 62 international Olympic competitions, rigorously examined for data leakage. We argue that the challenges in Olympic competition problems are ideal for evaluating AI's cognitive reasoning due to their complexity and interdisciplinary nature, which are essential for tackling complex scientific challenges and facilitating discoveries. Beyond evaluating performance across various disciplines using answer-only criteria, we conduct detailed experiments and analyses from multiple perspectives. We delve into the models' cognitive reasoning abilities, their performance across different modalities, and their outcomes in process-level evaluations, which are vital for tasks requiring complex reasoning with lengthy solutions. Our extensive evaluations reveal that even advanced models like GPT-4o only achieve a 39.97% overall accuracy, illustrating current AI limitations in complex reasoning and multimodal integration. Through the OlympicArena, we aim to advance AI towards superintelligence, equipping it to address more complex challenges in science and beyond. We also provide a comprehensive set of resources to support AI research, including a benchmark dataset, an open-source annotation platform, a detailed evaluation tool, and a leaderboard with automatic submission features.", "github_manual": "https://github.com/GAIR-NLP/OlympicArena", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12246", "title": "TroL: Traversal of Layers for Large Language and Vision Models", "authors": [ "Byung-Kwan Lee", "Sangyun Chung", "Chae Won Kim", "Beomchan Park", "Yong Man Ro" ], "github": "https://github.com/ByungKwanLee/TroL", "project_page": "", "abstract": "Large language and vision models (LLVMs) have been driven by the generalization power of large language models (LLMs) and the advent of visual instruction tuning. Along with scaling them up directly, these models enable LLVMs to showcase powerful vision language (VL) performances by covering diverse tasks via natural language instructions. However, existing open-source LLVMs that perform comparably to closed-source LLVMs such as GPT-4V are often considered too large (e.g., 26B, 34B, and 110B parameters), having a larger number of layers. These large models demand costly, high-end resources for both training and inference. To address this issue, we present a new efficient LLVM family with 1.8B, 3.8B, and 7B LLM model sizes, Traversal of Layers (TroL), which enables the reuse of layers in a token-wise manner. This layer traversing technique simulates the effect of looking back and retracing the answering stream while increasing the number of forward propagation layers without physically adding more layers. We demonstrate that TroL employs a simple layer traversing approach yet efficiently outperforms the open-source LLVMs with larger model sizes and rivals the performances of the closed-source LLVMs with substantial sizes.", "github_manual": "https://github.com/ByungKwanLee/TroL", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.11931", "title": "DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence", "authors": [ "DeepSeek-AI", "Qihao Zhu", "Daya Guo", "Zhihong Shao", "Dejian Yang", "Peiyi Wang", "Runxin Xu", "Y. Wu", "Yukun Li", "Huazuo Gao", "Shirong Ma", "Wangding Zeng", "Xiao Bi", "Zihui Gu", "Hanwei Xu", "Damai Dai", "Kai Dong", "Liyue Zhang", "Yishi Piao", "Zhibin Gou", "Zhenda Xie", "Zhewen Hao", "Bingxuan Wang", "Junxiao Song", "Deli Chen", "Xin Xie", "Kang Guan", "Yuxiang You", "Aixin Liu", "Qiushi Du", "Wenjun Gao", "Xuan Lu", "Qinyu Chen", "Yaohui Wang", "Chengqi Deng", "Jiashi Li", "Chenggang Zhao", "Chong Ruan", "Fuli Luo", "Wenfeng Liang" ], "github": "https://github.com/deepseek-ai/DeepSeek-Coder-V2", "project_page": "", "abstract": "We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K. In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks.", "github_manual": "https://github.com/deepseek-ai/DeepSeek-Coder-V2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12793", "title": "ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools", "authors": [ "Team GLM", "Aohan Zeng", "Bin Xu", "Bowen Wang", "Chenhui Zhang", "Da Yin", "Diego Rojas", "Guanyu Feng", "Hanlin Zhao", "Hanyu Lai", "Hao Yu", "Hongning Wang", "Jiadai Sun", "Jiajie Zhang", "Jiale Cheng", "Jiayi Gui", "Jie Tang", "Jing Zhang", "Juanzi Li", "Lei Zhao", "Lindong Wu", "Lucen Zhong", "Mingdao Liu", "Minlie Huang", "Peng Zhang", "Qinkai Zheng", "Rui Lu", "Shuaiqi Duan", "Shudan Zhang", "Shulin Cao", "Shuxun Yang", "Weng Lam Tam", "Wenyi Zhao", "Xiao Liu", "Xiao Xia", "Xiaohan Zhang", "Xiaotao Gu", "Xin Lv", "Xinghan Liu", "Xinyi Liu", "Xinyue Yang", "Xixuan Song", "Xunkai Zhang", "Yifan An", "Yifan Xu", "Yilin Niu", "Yuantao Yang", "Yueyan Li", "Yushi Bai", "Yuxiao Dong", "Zehan Qi", "Zhaoyu Wang", "Zhen Yang", "Zhengxiao Du", "Zhenyu Hou", "Zihan Wang" ], "github": "", "project_page": "", "abstract": "We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B. They represent our most capable models that are trained with all the insights and lessons gained from the preceding three generations of ChatGLM. To date, the GLM-4 models are pre-trained on ten trillions of tokens mostly in Chinese and English, along with a small set of corpus from 24 languages, and aligned primarily for Chinese and English usage. The high-quality alignment is achieved via a multi-stage post-training process, which involves supervised fine-tuning and learning from human feedback. Evaluations show that GLM-4 1) closely rivals or outperforms GPT-4 in terms of general metrics such as MMLU, GSM8K, MATH, BBH, GPQA, and HumanEval, 2) gets close to GPT-4-Turbo in instruction following as measured by IFEval, 3) matches GPT-4 Turbo (128K) and Claude 3 for long context tasks, and 4) outperforms GPT-4 in Chinese alignments as measured by AlignBench. The GLM-4 All Tools model is further aligned to understand user intent and autonomously decide when and which tool(s) touse -- including web browser, Python interpreter, text-to-image model, and user-defined functions -- to effectively complete complex tasks. In practical applications, it matches and even surpasses GPT-4 All Tools in tasks like accessing online information via web browsing and solving math problems using Python interpreter. Over the course, we have open-sourced a series of models, including ChatGLM-6B (three generations), GLM-4-9B (128K, 1M), GLM-4V-9B, WebGLM, and CodeGeeX, attracting over 10 million downloads on Hugging face in the year 2023 alone. The open models can be accessed through https://github.com/THUDM and https://huggingface.co/THUDM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12824", "title": "From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries", "authors": [ "Hitesh Wadhwa", "Rahul Seetharaman", "Somyaa Aggarwal", "Reshmi Ghosh", "Samyadeep Basu", "Soundararajan Srinivasan", "Wenlong Zhao", "Shreyas Chaudhari", "Ehsan Aghazadeh" ], "github": "", "project_page": "", "abstract": "Retrieval Augmented Generation (RAG) enriches the ability of language models to reason using external context to augment responses for a given user prompt. This approach has risen in popularity due to practical applications in various applications of language models in search, question/answering, and chat-bots. However, the exact nature of how this approach works isn't clearly understood. In this paper, we mechanistically examine the RAG pipeline to highlight that language models take shortcut and have a strong bias towards utilizing only the context information to answer the question, while relying minimally on their parametric memory. We probe this mechanistic behavior in language models with: (i) Causal Mediation Analysis to show that the parametric memory is minimally utilized when answering a question and (ii) Attention Contributions and Knockouts to show that the last token residual stream do not get enriched from the subject token in the question, but gets enriched from other informative tokens in the context. We find this pronounced shortcut behaviour true across both LLaMa and Phi family of models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12644", "title": "Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models", "authors": [ "Devichand Budagam", "Sankalp KJ", "Ashutosh Kumar", "Vinija Jain", "Aman Chadha" ], "github": "https://github.com/devichand579/HPT", "project_page": "", "abstract": "Assessing the effectiveness of large language models (LLMs) in addressing diverse tasks is essential for comprehending their strengths and weaknesses. Conventional evaluation techniques typically apply a single prompting strategy uniformly across datasets, not considering the varying degrees of task complexity. We introduce the Hierarchical Prompting Taxonomy (HPT), a taxonomy that employs a Hierarchical Prompt Framework (HPF) composed of five unique prompting strategies, arranged from the simplest to the most complex, to assess LLMs more precisely and to offer a clearer perspective. This taxonomy assigns a score, called the Hierarchical Prompting Score (HP-Score), to datasets as well as LLMs based on the rules of the taxonomy, providing a nuanced understanding of their ability to solve diverse tasks and offering a universal measure of task complexity. Additionally, we introduce the Adaptive Hierarchical Prompt framework, which automates the selection of appropriate prompting strategies for each task. This study compares manual and adaptive hierarchical prompt frameworks using four instruction-tuned LLMs, namely Llama 3 8B, Phi 3 3.8B, Mistral 7B, and Gemma 7B, across four datasets: BoolQ, CommonSenseQA (CSQA), IWSLT-2017 en-fr (IWSLT), and SamSum. Experiments demonstrate the effectiveness of HPT, providing a reliable way to compare different tasks and LLM capabilities. This paper leads to the development of a universal evaluation metric that can be used to evaluate both the complexity of the datasets and the capabilities of LLMs. The implementation of both manual HPF and adaptive HPF is publicly available.", "github_manual": "https://github.com/devichand579/HPT", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12292", "title": "JEN-1 DreamStyler: Customized Musical Concept Learning via Pivotal Parameters Tuning", "authors": [ "Boyu Chen", "Peike Li", "Yao Yao", "Alex Wang" ], "github": "", "project_page": "", "abstract": "Large models for text-to-music generation have achieved significant progress, facilitating the creation of high-quality and varied musical compositions from provided text prompts. However, input text prompts may not precisely capture user requirements, particularly when the objective is to generate music that embodies a specific concept derived from a designated reference collection. In this paper, we propose a novel method for customized text-to-music generation, which can capture the concept from a two-minute reference music and generate a new piece of music conforming to the concept. We achieve this by fine-tuning a pretrained text-to-music model using the reference music. However, directly fine-tuning all parameters leads to overfitting issues. To address this problem, we propose a Pivotal Parameters Tuning method that enables the model to assimilate the new concept while preserving its original generative capabilities. Additionally, we identify a potential concept conflict when introducing multiple concepts into the pretrained model. We present a concept enhancement strategy to distinguish multiple concepts, enabling the fine-tuned model to generate music incorporating either individual or multiple concepts simultaneously. Since we are the first to work on the customized music generation task, we also introduce a new dataset and evaluation protocol for the new task. Our proposed Jen1-DreamStyler outperforms several baselines in both qualitative and quantitative evaluations. Demos will be available at https://www.jenmusic.ai/research#DreamStyler.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12168", "title": "BPO: Supercharging Online Preference Learning by Adhering to the Proximity of Behavior LLM", "authors": [ "Wenda Xu", "Jiachen Li", "William Yang Wang", "Lei Li" ], "github": "https://github.com/xu1998hz/bpo", "project_page": "", "abstract": "Direct alignment from preferences (DAP) has emerged as a promising paradigm for aligning large language models (LLMs) to human desiderata from pre-collected, offline preference datasets. While recent studies indicate that existing offline DAP methods can directly benefit from online training samples, we highlight the need to develop specific online DAP algorithms to fully harness the power of online training. Specifically, we identify that the learned LLM should adhere to the proximity of the behavior LLM, which collects the training samples. To this end, we propose online Preference Optimization in proximity to the Behavior LLM (BPO), emphasizing the importance of constructing a proper trust region for LLM alignment. We conduct extensive experiments to validate the effectiveness and applicability of our approach by integrating it with various DAP methods, resulting in significant performance improvements across a wide range of tasks when training with the same amount of preference data. Even when only introducing one additional data collection phase, our online BPO improves its offline DAP baseline from 72.0% to 80.2% on TL;DR and from 82.2% to 89.1% on Anthropic Helpfulness in terms of win rate against human reference text.", "github_manual": "https://github.com/xu1998hz/bpo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.09760", "title": "Bootstrapping Language Models with DPO Implicit Rewards", "authors": [ "Changyu Chen", "Zichen Liu", "Chao Du", "Tianyu Pang", "Qian Liu", "Arunesh Sinha", "Pradeep Varakantham", "Min Lin" ], "github": "https://github.com/sail-sg/dice", "project_page": "", "abstract": "Human alignment in large language models (LLMs) is an active area of research. A recent groundbreaking work, direct preference optimization (DPO), has greatly simplified the process from past work in reinforcement learning from human feedback (RLHF) by bypassing the reward learning stage in RLHF. DPO, after training, provides an implicit reward model. In this work, we make a novel observation that this implicit reward model can by itself be used in a bootstrapping fashion to further align the LLM. Our approach is to use the rewards from a current LLM model to construct a preference dataset, which is then used in subsequent DPO rounds. We incorporate refinements that debias the length of the responses and improve the quality of the preference dataset to further improve our approach. Our approach, named self-alignment with DPO ImpliCit rEwards (DICE), shows great improvements in alignment and achieves superior performance than Gemini Pro on AlpacaEval 2, reaching 27.55% length-controlled win rate against GPT-4 Turbo, but with only 8B parameters and no external feedback. Our code is available at https://github.com/sail-sg/dice.", "github_manual": "", "github_regex": "https://github.com/sail-sg/dice", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12742", "title": "Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoning", "authors": [ "Bingchen Zhao", "Yongshuo Zong", "Letian Zhang", "Timothy Hospedales" ], "github": "https://github.com/dtennant/mirb_eval", "project_page": "", "abstract": "The advancement of large language models (LLMs) has significantly broadened the scope of applications in natural language processing, with multi-modal LLMs extending these capabilities to integrate and interpret visual data. However, existing benchmarks for visual language models (VLMs) predominantly focus on single-image inputs, neglecting the crucial aspect of multi-image understanding. In this paper, we introduce a Multi-Image Relational Benchmark MIRB, designed to evaluate VLMs' ability to compare, analyze, and reason across multiple images. Our benchmark encompasses four categories: perception, visual world knowledge, reasoning, and multi-hop reasoning. Through a comprehensive evaluation of a wide range of open-source and closed-source models, we demonstrate that while open-source VLMs were shown to approach the performance of GPT-4V in single-image tasks, a significant performance gap remains in multi-image reasoning tasks. Our findings also reveal that even the state-of-the-art GPT-4V model struggles with our benchmark, underscoring the need for further research and development in this area. We believe our contribution of MIRB could serve as a testbed for developing the next-generation multi-modal models.", "github_manual": "https://github.com/dtennant/mirb_eval", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.11687", "title": "Tokenization Falling Short: The Curse of Tokenization", "authors": [ "Yekun Chai", "Yewei Fang", "Qiwei Peng", "Xuhong Li" ], "github": "https://github.com/floatai/tkeval", "project_page": "", "abstract": "Language models typically tokenize raw text into sequences of subword identifiers from a predefined vocabulary, a process inherently sensitive to typographical errors, length variations, and largely oblivious to the internal structure of tokens-issues we term the curse of tokenization. In this study, we delve into these drawbacks and demonstrate that large language models (LLMs) remain susceptible to these problems. This study systematically investigates these challenges and their impact on LLMs through three critical research questions: (1) complex problem solving, (2) token structure probing, and (3) resilience to typographical variation. Our findings reveal that scaling model parameters can mitigate the issue of tokenization; however, LLMs still suffer from biases induced by typos and other text format variations. Our experiments show that subword regularization such as BPE-dropout can mitigate this issue. We will release our code and data to facilitate further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/floatai/tkeval", "github_api_added_by": "auto" }, { "date": "2024-06-19", "arxiv_id": "2406.12311", "title": "Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models", "authors": [ "Dongwon Jo", "Taesu Kim", "Yulhwa Kim", "Jae-Joon Kim" ], "github": "https://github.com/dongwonjo/BinaryMoS", "project_page": "", "abstract": "Binarization, which converts weight parameters to binary values, has emerged as an effective strategy to reduce the size of large language models (LLMs). However, typical binarization techniques significantly diminish linguistic effectiveness of LLMs. To address this issue, we introduce a novel binarization technique called Mixture of Scales (BinaryMoS). Unlike conventional methods, BinaryMoS employs multiple scaling experts for binary weights, dynamically merging these experts for each token to adaptively generate scaling factors. This token-adaptive approach boosts the representational power of binarized LLMs by enabling contextual adjustments to the values of binary weights. Moreover, because this adaptive process only involves the scaling factors rather than the entire weight matrix, BinaryMoS maintains compression efficiency similar to traditional static binarization methods. Our experimental results reveal that BinaryMoS surpasses conventional binarization techniques in various natural language processing tasks and even outperforms 2-bit quantization methods, all while maintaining similar model size to static binarization techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dongwonjo/BinaryMoS", "github_api_added_by": "auto" }, { "date": "2024-06-19", "arxiv_id": "2406.12459", "title": "HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors", "authors": [ "Panwang Pan", "Zhuo Su", "Chenguo Lin", "Zhen Fan", "Yongjie Zhang", "Zeming Li", "Tingting Shen", "Yadong Mu", "Yebin Liu" ], "github": "", "project_page": "", "abstract": "Despite recent advancements in high-fidelity human reconstruction techniques, the requirements for densely captured images or time-consuming per-instance optimization significantly hinder their applications in broader scenarios. To tackle these issues, we present HumanSplat which predicts the 3D Gaussian Splatting properties of any human from a single input image in a generalizable manner. In particular, HumanSplat comprises a 2D multi-view diffusion model and a latent reconstruction transformer with human structure priors that adeptly integrate geometric priors and semantic features within a unified framework. A hierarchical loss that incorporates human semantic information is further designed to achieve high-fidelity texture modeling and better constrain the estimated multiple views. Comprehensive experiments on standard benchmarks and in-the-wild images demonstrate that HumanSplat surpasses existing state-of-the-art methods in achieving photorealistic novel-view synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12031", "title": "Large Scale Transfer Learning for Tabular Data via Language Modeling", "authors": [ "Josh Gardner", "Juan C. Perdomo", "Ludwig Schmidt" ], "github": "https://github.com/mlfoundations/rtfm", "project_page": "", "abstract": "Tabular data -- structured, heterogeneous, spreadsheet-style data with rows and columns -- is widely used in practice across many domains. However, while recent foundation models have reduced the need for developing task-specific datasets and predictors in domains such as language modeling and computer vision, this transfer learning paradigm has not had similar impact in the tabular domain. In this work, we seek to narrow this gap and present TabuLa-8B, a language model for tabular prediction. We define a process for extracting a large, high-quality training dataset from the TabLib corpus, proposing methods for tabular data filtering and quality control. Using the resulting dataset, which comprises over 1.6B rows from 3.1M unique tables, we fine-tune a Llama 3-8B large language model (LLM) for tabular data prediction (classification and binned regression) using a novel packing and attention scheme for tabular prediction. Through evaluation across a test suite of 329 datasets, we find that TabuLa-8B has zero-shot accuracy on unseen tables that is over 15 percentage points (pp) higher than random guessing, a feat that is not possible with existing state-of-the-art tabular prediction models (e.g. XGBoost, TabPFN). In the few-shot setting (1-32 shots), without any fine-tuning on the target datasets, TabuLa-8B is 5-15 pp more accurate than XGBoost and TabPFN models that are explicitly trained on equal, or even up to 16x more data. We release our model, code, and data along with the publication of this paper.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mlfoundations/rtfm", "github_api_added_by": "auto" }, { "date": "2024-06-19", "arxiv_id": "2406.11912", "title": "AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology", "authors": [ "Minh Huynh Nguyen", "Thang Phan Chau", "Phong X. Nguyen", "Nghi D. Q. Bui" ], "github": "https://github.com/FSoft-AI4Code/AgileCoder", "project_page": "", "abstract": "Software agents have emerged as promising tools for addressing complex software engineering tasks. However, existing works oversimplify software development workflows by following the waterfall model. Thus, we propose AgileCoder, a multi-agent system that integrates Agile Methodology (AM) into the framework. This system assigns specific AM roles such as Product Manager, Developer, and Tester to different agents, who then collaboratively develop software based on user inputs. AgileCoder enhances development efficiency by organizing work into sprints, focusing on incrementally developing software through sprints. Additionally, we introduce Dynamic Code Graph Generator, a module that creates a Code Dependency Graph dynamically as updates are made to the codebase. This allows agents to better comprehend the codebase, leading to more precise code generation and modifications throughout the software development process. AgileCoder surpasses existing benchmarks, like ChatDev and MetaGPT, establishing a new standard and showcasing the capabilities of multi-agent systems in advanced software engineering environments. Our source code can be found at https://github.com/FSoft-AI4Code/AgileCoder.", "github_manual": "", "github_regex": "https://github.com/FSoft-AI4Code/AgileCoder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12849", "title": "Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation", "authors": [ "Ning-Hsu Wang", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Accurately estimating depth in 360-degree imagery is crucial for virtual reality, autonomous navigation, and immersive media applications. Existing depth estimation methods designed for perspective-view imagery fail when applied to 360-degree images due to different camera projections and distortions, whereas 360-degree methods perform inferior due to the lack of labeled data pairs. We propose a new depth estimation framework that utilizes unlabeled 360-degree data effectively. Our approach uses state-of-the-art perspective depth estimation models as teacher models to generate pseudo labels through a six-face cube projection technique, enabling efficient labeling of depth in 360-degree images. This method leverages the increasing availability of large datasets. Our approach includes two main stages: offline mask generation for invalid regions and an online semi-supervised joint training regime. We tested our approach on benchmark datasets such as Matterport3D and Stanford2D3D, showing significant improvements in depth estimation accuracy, particularly in zero-shot scenarios. Our proposed training pipeline can enhance any 360 monocular depth estimator and demonstrates effective knowledge transfer across different camera projections and data types. See our project page for results: https://albert100121.github.io/Depth-Anywhere/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.11939", "title": "From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline", "authors": [ "Tianle Li", "Wei-Lin Chiang", "Evan Frick", "Lisa Dunlap", "Tianhao Wu", "Banghua Zhu", "Joseph E. Gonzalez", "Ion Stoica" ], "github": "https://github.com/lm-sys/arena-hard-auto", "project_page": "", "abstract": "The rapid evolution of language models has necessitated the development of more challenging benchmarks. Current static benchmarks often struggle to consistently distinguish between the capabilities of different models and fail to align with real-world user preferences. On the other hand, live crowd-sourced platforms like the Chatbot Arena collect a wide range of natural prompts and user feedback. However, these prompts vary in sophistication and the feedback cannot be applied offline to new models. In order to ensure that benchmarks keep up with the pace of LLM development, we address how one can evaluate benchmarks on their ability to confidently separate models and their alignment with human preference. Under these principles, we developed BenchBuilder, a living benchmark that filters high-quality prompts from live data sources to enable offline evaluation on fresh, challenging prompts. BenchBuilder identifies seven indicators of a high-quality prompt, such as the requirement for domain knowledge, and utilizes an LLM annotator to select a high-quality subset of prompts from various topic clusters. The LLM evaluation process employs an LLM judge to ensure a fully automated, high-quality, and constantly updating benchmark. We apply BenchBuilder on prompts from the Chatbot Arena to create Arena-Hard-Auto v0.1: 500 challenging user prompts from a wide range of tasks. Arena-Hard-Auto v0.1 offers 3x tighter confidence intervals than MT-Bench and achieves a state-of-the-art 89.1% agreement with human preference rankings, all at a cost of only $25 and without human labelers. The BenchBuilder pipeline enhances evaluation benchmarks and provides a valuable tool for developers, enabling them to extract high-quality benchmarks from extensive data with minimal effort.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lm-sys/arena-hard-auto", "github_api_added_by": "auto" }, { "date": "2024-06-19", "arxiv_id": "2406.12814", "title": "Adversarial Attacks on Multimodal Agents", "authors": [ "Chen Henry Wu", "Jing Yu Koh", "Ruslan Salakhutdinov", "Daniel Fried", "Aditi Raghunathan" ], "github": "https://github.com/ChenWu98/agent-attack", "project_page": "", "abstract": "Vision-enabled language models (VLMs) are now used to build autonomous multimodal agents capable of taking actions in real environments. In this paper, we show that multimodal agents raise new safety risks, even though attacking agents is more challenging than prior attacks due to limited access to and knowledge about the environment. Our attacks use adversarial text strings to guide gradient-based perturbation over one trigger image in the environment: (1) our captioner attack attacks white-box captioners if they are used to process images into captions as additional inputs to the VLM; (2) our CLIP attack attacks a set of CLIP models jointly, which can transfer to proprietary VLMs. To evaluate the attacks, we curated VisualWebArena-Adv, a set of adversarial tasks based on VisualWebArena, an environment for web-based multimodal agent tasks. Within an L-infinity norm of 16/256 on a single image, the captioner attack can make a captioner-augmented GPT-4V agent execute the adversarial goals with a 75% success rate. When we remove the captioner or use GPT-4V to generate its own captions, the CLIP attack can achieve success rates of 21% and 43%, respectively. Experiments on agents based on other VLMs, such as Gemini-1.5, Claude-3, and GPT-4o, show interesting differences in their robustness. Further analysis reveals several key factors contributing to the attack's success, and we also discuss the implications for defenses as well. Project page: https://chenwu.io/attack-agent Code and data: https://github.com/ChenWu98/agent-attack", "github_manual": "", "github_regex": "https://github.com/ChenWu98/agent-attack", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12831", "title": "VIA: A Spatiotemporal Video Adaptation Framework for Global and Local Video Editing", "authors": [ "Jing Gu", "Yuwei Fang", "Ivan Skorokhodov", "Peter Wonka", "Xinya Du", "Sergey Tulyakov", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "Video editing stands as a cornerstone of digital media, from entertainment and education to professional communication. However, previous methods often overlook the necessity of comprehensively understanding both global and local contexts, leading to inaccurate and inconsistency edits in the spatiotemporal dimension, especially for long videos. In this paper, we introduce VIA, a unified spatiotemporal VIdeo Adaptation framework for global and local video editing, pushing the limits of consistently editing minute-long videos. First, to ensure local consistency within individual frames, the foundation of VIA is a novel test-time editing adaptation method, which adapts a pre-trained image editing model for improving consistency between potential editing directions and the text instruction, and adapts masked latent variables for precise local control. Furthermore, to maintain global consistency over the video sequence, we introduce spatiotemporal adaptation that adapts consistent attention variables in key frames and strategically applies them across the whole sequence to realize the editing effects. Extensive experiments demonstrate that, compared to baseline methods, our VIA approach produces edits that are more faithful to the source videos, more coherent in the spatiotemporal context, and more precise in local control. More importantly, we show that VIA can achieve consistent long video editing in minutes, unlocking the potentials for advanced video editing tasks over long video sequences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12050", "title": "Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning", "authors": [ "Zhihan Zhang", "Zhenwen Liang", "Wenhao Yu", "Dian Yu", "Mengzhao Jia", "Dong Yu", "Meng Jiang" ], "github": "https://github.com/ytyz1307zzh/RefAug", "project_page": "", "abstract": "Supervised fine-tuning enhances the problem-solving abilities of language models across various mathematical reasoning tasks. To maximize such benefits, existing research focuses on broadening the training set with various data augmentation techniques, which is effective for standard single-round question-answering settings. Our work introduces a novel technique aimed at cultivating a deeper understanding of the training problems at hand, enhancing performance not only in standard settings but also in more complex scenarios that require reflective thinking. Specifically, we propose reflective augmentation, a method that embeds problem reflection into each training instance. It trains the model to consider alternative perspectives and engage with abstractions and analogies, thereby fostering a thorough comprehension through reflective reasoning. Extensive experiments validate the achievement of our aim, underscoring the unique advantages of our method and its complementary nature relative to existing augmentation techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ytyz1307zzh/RefAug", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12274", "title": "SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models", "authors": [ "Somnath Banerjee", "Soham Tripathy", "Sayan Layek", "Shanu Kumar", "Animesh Mukherjee", "Rima Hazra" ], "github": "https://github.com/neuralsentinel/safeinfer", "project_page": "", "abstract": "Safety-aligned language models often exhibit fragile and imbalanced safety mechanisms, increasing the likelihood of generating unsafe content. In addition, incorporating new knowledge through editing techniques to language models can further compromise safety. To address these issues, we propose SafeInfer, a context-adaptive, decoding-time safety alignment strategy for generating safe responses to user queries. SafeInfer comprises two phases: the safety amplification phase, which employs safe demonstration examples to adjust the model's hidden states and increase the likelihood of safer outputs, and the safety-guided decoding phase, which influences token selection based on safety-optimized distributions, ensuring the generated content complies with ethical guidelines. Further, we present HarmEval, a novel benchmark for extensive safety evaluations, designed to address potential misuse scenarios in accordance with the policies of leading AI tech giants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/neuralsentinel/safeinfer", "github_api_added_by": "auto" }, { "date": "2024-06-19", "arxiv_id": "2406.11801", "title": "Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations", "authors": [ "Rima Hazra", "Sayan Layek", "Somnath Banerjee", "Soujanya Poria" ], "github": "https://github.com/declare-lab/safety-arithmetic", "project_page": "", "abstract": "Ensuring the safe alignment of large language models (LLMs) with human values is critical as they become integral to applications like translation and question answering. Current alignment methods struggle with dynamic user intentions and complex objectives, making models vulnerable to generating harmful content. We propose Safety Arithmetic, a training-free framework enhancing LLM safety across different scenarios: Base models, Supervised fine-tuned models (SFT), and Edited models. Safety Arithmetic involves Harm Direction Removal to avoid harmful content and Safety Alignment to promote safe responses. Additionally, we present NoIntentEdit, a dataset highlighting edit instances that could compromise model safety if used unintentionally. Our experiments show that Safety Arithmetic significantly improves safety measures, reduces over-safety, and maintains model utility, outperforming existing methods in ensuring safe content generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/declare-lab/safety-arithmetic", "github_api_added_by": "auto" }, { "date": "2024-06-19", "arxiv_id": "2406.12673", "title": "Estimating Knowledge in Large Language Models Without Generating a Single Token", "authors": [ "Daniela Gottesman", "Mor Geva" ], "github": "https://github.com/dhgottesman/keen_estimating_knowledge_in_llms", "project_page": "", "abstract": "To evaluate knowledge in large language models (LLMs), current methods query the model and then evaluate its generated responses. In this work, we ask whether evaluation can be done before the model has generated any text. Concretely, is it possible to estimate how knowledgeable a model is about a certain entity, only from its internal computation? We study this question with two tasks: given a subject entity, the goal is to predict (a) the ability of the model to answer common questions about the entity, and (b) the factuality of responses generated by the model about the entity. Experiments with a variety of LLMs show that KEEN, a simple probe trained over internal subject representations, succeeds at both tasks - strongly correlating with both the QA accuracy of the model per-subject and FActScore, a recent factuality metric in open-ended generation. Moreover, KEEN naturally aligns with the model's hedging behavior and faithfully reflects changes in the model's knowledge after fine-tuning. Lastly, we show a more interpretable yet equally performant variant of KEEN, which highlights a small set of tokens that correlates with the model's lack of knowledge. Being simple and lightweight, KEEN can be leveraged to identify gaps and clusters of entity knowledge in LLMs, and guide decisions such as augmenting queries with retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dhgottesman/keen_estimating_knowledge_in_llms", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.12042", "title": "Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion Models", "authors": [ "Alireza Ganjdanesh", "Reza Shirkavand", "Shangqian Gao", "Heng Huang" ], "github": "https://github.com/rezashkv/diffusion_pruning", "project_page": "", "abstract": "Text-to-image (T2I) diffusion models have demonstrated impressive image generation capabilities. Still, their computational intensity prohibits resource-constrained organizations from deploying T2I models after fine-tuning them on their internal target data. While pruning techniques offer a potential solution to reduce the computational burden of T2I models, static pruning methods use the same pruned model for all input prompts, overlooking the varying capacity requirements of different prompts. Dynamic pruning addresses this issue by utilizing a separate sub-network for each prompt, but it prevents batch parallelism on GPUs. To overcome these limitations, we introduce Adaptive Prompt-Tailored Pruning (APTP), a novel prompt-based pruning method designed for T2I diffusion models. Central to our approach is a prompt router model, which learns to determine the required capacity for an input text prompt and routes it to an architecture code, given a total desired compute budget for prompts. Each architecture code represents a specialized model tailored to the prompts assigned to it, and the number of codes is a hyperparameter. We train the prompt router and architecture codes using contrastive learning, ensuring that similar prompts are mapped to nearby codes. Further, we employ optimal transport to prevent the codes from collapsing into a single one. We demonstrate APTP's effectiveness by pruning Stable Diffusion (SD) V2.1 using CC3M and COCO as target datasets. APTP outperforms the single-model pruning baselines in terms of FID, CLIP, and CMMD scores. Our analysis of the clusters learned by APTP reveals they are semantically meaningful. We also show that APTP can automatically discover previously empirically found challenging prompts for SD, e.g., prompts for generating text images, assigning them to higher capacity codes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rezashkv/diffusion_pruning", "github_api_added_by": "auto" }, { "date": "2024-06-19", "arxiv_id": "2406.12303", "title": "Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment", "authors": [ "Yiheng Li", "Heyang Jiang", "Akio Kodaira", "Masayoshi Tomizuka", "Kurt Keutzer", "Chenfeng Xu" ], "github": "", "project_page": "", "abstract": "In this paper, we point out suboptimal noise-data mapping leads to slow training of diffusion models. During diffusion training, current methods diffuse each image across the entire noise space, resulting in a mixture of all images at every point in the noise layer. We emphasize that this random mixture of noise-data mapping complicates the optimization of the denoising function in diffusion models. Drawing inspiration from the immiscible phenomenon in physics, we propose Immiscible Diffusion, a simple and effective method to improve the random mixture of noise-data mapping. In physics, miscibility can vary according to various intermolecular forces. Thus, immiscibility means that the mixing of the molecular sources is distinguishable. Inspired by this, we propose an assignment-then-diffusion training strategy. Specifically, prior to diffusing the image data into noise, we assign diffusion target noise for the image data by minimizing the total image-noise pair distance in a mini-batch. The assignment functions analogously to external forces to separate the diffuse-able areas of images, thus mitigating the inherent difficulties in diffusion training. Our approach is remarkably simple, requiring only one line of code to restrict the diffuse-able area for each image while preserving the Gaussian distribution of noise. This ensures that each image is projected only to nearby noise. To address the high complexity of the assignment algorithm, we employ a quantized-assignment method to reduce the computational overhead to a negligible level. Experiments demonstrate that our method achieve up to 3x faster training for consistency models and DDIM on the CIFAR dataset, and up to 1.3x faster on CelebA datasets for consistency models. Besides, we conduct thorough analysis about the Immiscible Diffusion, which sheds lights on how it improves diffusion training speed while improving the fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.11909", "title": "Mixture-of-Subspaces in Low-Rank Adaptation", "authors": [ "Taiqiang Wu", "Jiahao Wang", "Zhe Zhao", "Ngai Wong" ], "github": "https://github.com/wutaiqiang/MoSLoRA", "project_page": "", "abstract": "In this paper, we introduce a subspace-inspired Low-Rank Adaptation (LoRA) method, which is computationally efficient, easy to implement, and readily applicable to large language, multimodal, and diffusion models. Initially, we equivalently decompose the weights of LoRA into two subspaces, and find that simply mixing them can enhance performance. To study such a phenomenon, we revisit it through a fine-grained subspace lens, showing that such modification is equivalent to employing a fixed mixer to fuse the subspaces. To be more flexible, we jointly learn the mixer with the original LoRA weights, and term the method Mixture-of-Subspaces LoRA (MoSLoRA). MoSLoRA consistently outperforms LoRA on tasks in different modalities, including commonsense reasoning, visual instruction tuning, and subject-driven text-to-image generation, demonstrating its effectiveness and robustness. Codes are available at https://github.com/wutaiqiang/MoSLoRA{github}.", "github_manual": "", "github_regex": "https://github.com/wutaiqiang/MoSLoRA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-19", "arxiv_id": "2406.11431", "title": "Super(ficial)-alignment: Strong Models May Deceive Weak Models in Weak-to-Strong Generalization", "authors": [ "Wenkai Yang", "Shiqi Shen", "Guangyao Shen", "Zhi Gong", "Yankai Lin" ], "github": "", "project_page": "", "abstract": "Superalignment, where humans are weak supervisors of superhuman models, has become an important and widely discussed issue in the current era of rapid development of Large Language Models (LLMs). The recent work preliminarily studies this problem by using weak models to supervise strong models. It discovers that weakly supervised strong students can consistently outperform weak teachers towards the alignment target, leading to a weak-to-strong generalization phenomenon. However, we are concerned that behind such a promising phenomenon, whether there exists an issue of weak-to-strong deception, where strong models may deceive weak models by exhibiting well-aligned in areas known to weak models but producing misaligned behaviors in cases weak models do not know. We then take an initial step towards exploring this security issue in a specific but realistic multi-objective alignment case, where there may be some alignment targets conflicting with each other (e.g., helpfulness v.s. harmlessness). Such a conflict is likely to cause strong models to deceive weak models in one alignment dimension to gain high reward in other alignment dimension. Our experiments on both the reward modeling task and the preference optimization scenario indicate: (1) the weak-to-strong deception exists; (2) the deception phenomenon may intensify as the capability gap between weak and strong models increases. We also discuss potential solutions and find bootstrapping with an intermediate model can mitigate the deception to some extent. Our work highlights the urgent need to pay more attention to the true reliability of superalignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-20", "arxiv_id": "2406.11612", "title": "Long Code Arena: a Set of Benchmarks for Long-Context Code Models", "authors": [ "Egor Bogomolov", "Aleksandra Eliseeva", "Timur Galimzyanov", "Evgeniy Glukhov", "Anton Shapkin", "Maria Tigina", "Yaroslav Golubev", "Alexander Kovrigin", "Arie van Deursen", "Maliheh Izadi", "Timofey Bryksin" ], "github": "https://github.com/jetbrains-research/lca-baselines", "project_page": "", "abstract": "Nowadays, the fields of code and natural language processing are evolving rapidly. In particular, models become better at processing long context windows - supported context sizes have increased by orders of magnitude over the last few years. However, there is a shortage of benchmarks for code processing that go beyond a single file of context, while the most popular ones are limited to a single method. With this work, we aim to close this gap by introducing Long Code Arena, a suite of six benchmarks for code processing tasks that require project-wide context. These tasks cover different aspects of code processing: library-based code generation, CI builds repair, project-level code completion, commit message generation, bug localization, and module summarization. For each task, we provide a manually verified dataset for testing, an evaluation suite, and open-source baseline solutions based on popular LLMs to showcase the usage of the dataset and to simplify adoption by other researchers. We publish the benchmark page on HuggingFace Spaces with the leaderboard, links to HuggingFace Hub for all the datasets, and link to the GitHub repository with baselines: https://huggingface.co/spaces/JetBrains-Research/long-code-arena.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jetbrains-research/lca-baselines", "github_api_added_by": "auto" }, { "date": "2024-06-20", "arxiv_id": "2406.11230", "title": "Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models", "authors": [ "Hengyi Wang", "Haizhou Shi", "Shiwei Tan", "Weiyi Qin", "Wenyuan Wang", "Tunyu Zhang", "Akshay Nambi", "Tanuja Ganu", "Hao Wang" ], "github": "https://github.com/Wang-ML-Lab/multimodal-needle-in-a-haystack", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have shown significant promise in various applications, leading to broad interest from researchers and practitioners alike. However, a comprehensive evaluation of their long-context capabilities remains underexplored. To address these gaps, we introduce the MultiModal Needle-in-a-haystack (MMNeedle) benchmark, specifically designed to assess the long-context capabilities of MLLMs. Besides multi-image input, we employ image stitching to further increase the input context length, and develop a protocol to automatically generate labels for sub-image level retrieval. Essentially, MMNeedle evaluates MLLMs by stress-testing their capability to locate a target sub-image (needle) within a set of images (haystack) based on textual instructions and descriptions of image contents. This setup necessitates an advanced understanding of extensive visual contexts and effective information retrieval within long-context image inputs. With this benchmark, we evaluate state-of-the-art MLLMs, encompassing both API-based and open-source models. The findings reveal that GPT-4o consistently surpasses other models in long-context scenarios, but suffers from hallucination problems in negative samples, i.e., when needles are not in the haystacks. Our comprehensive long-context evaluation of MLLMs also sheds lights on the considerable performance gap between API-based and open-source models. All the code, data, and instructions required to reproduce the main results are available at https://github.com/Wang-ML-Lab/multimodal-needle-in-a-haystack.", "github_manual": "", "github_regex": "https://github.com/Wang-ML-Lab/multimodal-needle-in-a-haystack", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-20", "arxiv_id": "2406.12649", "title": "Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models", "authors": [ "Hengyi Wang", "Shiwei Tan", "Hao Wang" ], "github": "", "project_page": "", "abstract": "Vision transformers (ViTs) have emerged as a significant area of focus, particularly for their capacity to be jointly trained with large language models and to serve as robust vision foundation models. Yet, the development of trustworthy explanation methods for ViTs has lagged, particularly in the context of post-hoc interpretations of ViT predictions. Existing sub-image selection approaches, such as feature-attribution and conceptual models, fall short in this regard. This paper proposes five desiderata for explaining ViTs -- faithfulness, stability, sparsity, multi-level structure, and parsimony -- and demonstrates the inadequacy of current methods in meeting these criteria comprehensively. We introduce a variational Bayesian explanation framework, dubbed ProbAbilistic Concept Explainers (PACE), which models the distributions of patch embeddings to provide trustworthy post-hoc conceptual explanations. Our qualitative analysis reveals the distributions of patch-level concepts, elucidating the effectiveness of ViTs by modeling the joint distribution of patch embeddings and ViT's predictions. Moreover, these patch-level explanations bridge the gap between image-level and dataset-level explanations, thus completing the multi-level structure of PACE. Through extensive experiments on both synthetic and real-world datasets, we demonstrate that PACE surpasses state-of-the-art methods in terms of the defined desiderata.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-20", "arxiv_id": "2406.12034", "title": "Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts", "authors": [ "Junmo Kang", "Leonid Karlinsky", "Hongyin Luo", "Zhen Wang", "Jacob Hansen", "James Glass", "David Cox", "Rameswar Panda", "Rogerio Feris", "Alan Ritter" ], "github": "", "project_page": "", "abstract": "We present Self-MoE, an approach that transforms a monolithic LLM into a compositional, modular system of self-specialized experts, named MiXSE (MiXture of Self-specialized Experts). Our approach leverages self-specialization, which constructs expert modules using self-generated synthetic data, each equipped with a shared base LLM and incorporating self-optimized routing. This allows for dynamic and capability-specific handling of various target tasks, enhancing overall capabilities, without extensive human-labeled data and added parameters. Our empirical results reveal that specializing LLMs may exhibit potential trade-offs in performances on non-specialized tasks. On the other hand, our Self-MoE demonstrates substantial improvements over the base LLM across diverse benchmarks such as knowledge, reasoning, math, and coding. It also consistently outperforms other methods, including instance merging and weight merging, while offering better flexibility and interpretability by design with semantic experts and routing. Our findings highlight the critical role of modularity and the potential of self-improvement in achieving efficient, scalable, and adaptable systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-20", "arxiv_id": "2406.11139", "title": "Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance", "authors": [ "Somnath Banerjee", "Avik Halder", "Rajarshi Mandal", "Sayan Layek", "Ian Soboroff", "Rima Hazra", "Animesh Mukherjee" ], "github": "", "project_page": "", "abstract": "The integration of pretrained language models (PLMs) like BERT and GPT has revolutionized NLP, particularly for English, but it has also created linguistic imbalances. This paper strategically identifies the need for linguistic equity by examining several knowledge editing techniques in multilingual contexts. We evaluate the performance of models such as Mistral, TowerInstruct, OpenHathi, Tamil-Llama, and Kan-Llama across languages including English, German, French, Italian, Spanish, Hindi, Tamil, and Kannada. Our research identifies significant discrepancies in normal and merged models concerning cross-lingual consistency. We employ strategies like 'each language for itself' (ELFI) and 'each language for others' (ELFO) to stress-test these models. Our findings demonstrate the potential for LLMs to overcome linguistic barriers, laying the groundwork for future research in achieving linguistic inclusivity in AI technologies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-20", "arxiv_id": "2406.12209", "title": "Interface Design for Self-Supervised Speech Models", "authors": [ "Yi-Jen Shih", "David Harwath" ], "github": "https://github.com/atosystem/ssl_interface", "project_page": "", "abstract": "Self-supervised speech (SSL) models have recently become widely adopted for many downstream speech processing tasks. The general usage pattern is to employ SSL models as feature extractors, and then train a downstream prediction head to solve a specific task. However, different layers of SSL models have been shown to capture different types of information, and the methods of combining them are not well studied. To this end, we extend the general framework for SSL model utilization by proposing the interface that connects the upstream and downstream. Under this view, the dominant technique of combining features via a layerwise weighted sum can be regarded as a specific interface. We propose several alternative interface designs and demonstrate that the weighted sum interface is suboptimal for many tasks. In particular, we show that a convolutional interface whose depth scales logarithmically with the depth of the upstream model consistently outperforms many other interface designs.", "github_manual": "https://github.com/atosystem/ssl_interface", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-20", "arxiv_id": "2406.11614", "title": "Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces", "authors": [ "Yihuai Hong", "Lei Yu", "Shauli Ravfogel", "Haiqin Yang", "Mor Geva" ], "github": "https://github.com/yihuaihong/ConceptVectors", "project_page": "", "abstract": "The task of \"unlearning\" certain concepts in large language models (LLMs) has attracted immense attention recently, due to its importance for mitigating undesirable model behaviours, such as the generation of harmful, private, or incorrect information. Current protocols to evaluate unlearning methods largely rely on behavioral tests, without monitoring the presence of unlearned knowledge within the model's parameters. This residual knowledge can be adversarially exploited to recover the erased information post-unlearning. We argue that unlearning should also be evaluated internally, by considering changes in the parametric knowledge traces of the unlearned concepts. To this end, we propose a general methodology for eliciting directions in the parameter space (termed \"concept vectors\") that encode concrete concepts, and construct ConceptVectors, a benchmark dataset containing hundreds of common concepts and their parametric knowledge traces within two open-source LLMs. Evaluation on ConceptVectors shows that existing unlearning methods minimally impact concept vectors, while directly ablating these vectors demonstrably removes the associated knowledge from the LLMs and significantly reduces their susceptibility to adversarial manipulation. Our results highlight limitations in behavioral-based unlearning evaluations and call for future work to include parametric-based evaluations. To support this, we release our code and benchmark at https://github.com/yihuaihong/ConceptVectors.", "github_manual": "", "github_regex": "https://github.com/yihuaihong/ConceptVectors", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-20", "arxiv_id": "2406.11715", "title": "Measuring memorization in RLHF for code completion", "authors": [ "Aneesh Pappu", "Billy Porter", "Ilia Shumailov", "Jamie Hayes" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with human feedback (RLHF) has become the dominant method to align large models to user preferences. Unlike fine-tuning, for which there are many studies regarding training data memorization, it is not clear how memorization is affected by or introduced in the RLHF alignment process. Understanding this relationship is important as real user data may be collected and used to align large models; if user data is memorized during RLHF and later regurgitated, this could raise privacy concerns. In this work, we analyze how training data memorization can surface and propagate through each phase of RLHF. We focus our study on code completion models, as code completion is one of the most popular use cases for large language models. We find that RLHF significantly decreases the chance that data used for reward modeling and reinforcement learning is memorized, in comparison to aligning via directly fine-tuning on this data, but that examples already memorized during the fine-tuning stage of RLHF, will, in the majority of cases, remain memorized after RLHF.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.14491", "title": "Instruction Pre-Training: Language Models are Supervised Multitask Learners", "authors": [ "Daixuan Cheng", "Yuxian Gu", "Shaohan Huang", "Junyu Bi", "Minlie Huang", "Furu Wei" ], "github": "https://github.com/microsoft/LMOps", "project_page": "", "abstract": "Unsupervised multitask pre-training has been the critical method behind the recent success of language models (LMs). However, supervised multitask learning still holds significant promise, as scaling it in the post-training stage trends towards better generalization. In this paper, we explore supervised multitask pre-training by proposing Instruction Pre-Training, a framework that scalably augments massive raw corpora with instruction-response pairs to pre-train LMs. The instruction-response pairs are generated by an efficient instruction synthesizer built on open-source models. In our experiments, we synthesize 200M instruction-response pairs covering 40+ task categories to verify the effectiveness of Instruction Pre-Training. In pre-training from scratch, Instruction Pre-Training not only consistently enhances pre-trained base models but also benefits more from further instruction tuning. In continual pre-training, Instruction Pre-Training enables Llama3-8B to be comparable to or even outperform Llama3-70B. Our model, code, and data are available at https://github.com/microsoft/LMOps.", "github_manual": "", "github_regex": "https://github.com/microsoft/LMOps", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.11927", "title": "REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark", "authors": [ "Nam Le Hai", "Dung Manh Nguyen", "Nghi D. Q. Bui" ], "github": "https://github.com/FSoft-AI4Code/RepoExec", "project_page": "", "abstract": "The ability of CodeLLMs to generate executable and functionally correct code at the repository-level scale remains largely unexplored. We introduce RepoExec, a novel benchmark for evaluating code generation at the repository-level scale. RepoExec focuses on three main aspects: executability, functional correctness through automated test case generation with high coverage rate, and carefully crafted cross-file contexts to accurately generate code. Our work explores a controlled scenario where developers specify necessary code dependencies, challenging the model to integrate these accurately. Experiments show that while pretrained LLMs outperform instruction-tuned models in correctness, the latter excel in utilizing provided dependencies and demonstrating debugging capabilities. We also introduce a new instruction-tuned dataset that focuses on code dependencies and demonstrate that CodeLLMs fine-tuned on our dataset have a better capability to leverage these dependencies effectively. RepoExec aims to provide a comprehensive evaluation of code functionality and alignment with developer intent, paving the way for more reliable and applicable CodeLLMs in real-world scenarios. The dataset and source code can be found at~https://github.com/FSoft-AI4Code/RepoExec.", "github_manual": "", "github_regex": "https://github.com/FSoft-AI4Code/RepoExec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.11896", "title": "DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning", "authors": [ "Hao Bai", "Yifei Zhou", "Mert Cemri", "Jiayi Pan", "Alane Suhr", "Sergey Levine", "Aviral Kumar" ], "github": "https://github.com/DigiRL-agent/digirl", "project_page": "", "abstract": "Training corpuses for vision language models (VLMs) typically lack sufficient amounts of decision-centric data. This renders off-the-shelf VLMs sub-optimal for decision-making tasks such as in-the-wild device control through graphical user interfaces (GUIs). While training with static demonstrations has shown some promise, we show that such methods fall short for controlling real GUIs due to their failure to deal with real-world stochasticity and non-stationarity not captured in static observational data. This paper introduces a novel autonomous RL approach, called DigiRL, for training in-the-wild device control agents through fine-tuning a pre-trained VLM in two stages: offline RL to initialize the model, followed by offline-to-online RL. To do this, we build a scalable and parallelizable Android learning environment equipped with a VLM-based evaluator and develop a simple yet effective RL approach for learning in this domain. Our approach runs advantage-weighted RL with advantage estimators enhanced to account for stochasticity along with an automatic curriculum for deriving maximal learning signal. We demonstrate the effectiveness of DigiRL using the Android-in-the-Wild (AitW) dataset, where our 1.3B VLM trained with RL achieves a 49.5% absolute improvement -- from 17.7 to 67.2% success rate -- over supervised fine-tuning with static human demonstration data. These results significantly surpass not only the prior best agents, including AppAgent with GPT-4V (8.3% success rate) and the 17B CogAgent trained with AitW data (38.5%), but also the prior best autonomous RL approach based on filtered behavior cloning (57.8%), thereby establishing a new state-of-the-art for digital agents for in-the-wild device control.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/DigiRL-agent/digirl", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.14130", "title": "ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning", "authors": [ "Zhongjie Duan", "Wenmeng Zhou", "Cen Chen", "Yaliang Li", "Weining Qian" ], "github": "https://github.com/modelscope/DiffSynth-Studio", "project_page": "", "abstract": "Recently, advancements in video synthesis have attracted significant attention. Video synthesis models such as AnimateDiff and Stable Video Diffusion have demonstrated the practical applicability of diffusion models in creating dynamic visual content. The emergence of SORA has further spotlighted the potential of video generation technologies. Nonetheless, the extension of video lengths has been constrained by the limitations in computational resources. Most existing video synthesis models can only generate short video clips. In this paper, we propose a novel post-tuning methodology for video synthesis models, called ExVideo. This approach is designed to enhance the capability of current video synthesis models, allowing them to produce content over extended temporal durations while incurring lower training expenditures. In particular, we design extension strategies across common temporal model architectures respectively, including 3D convolution, temporal attention, and positional embedding. To evaluate the efficacy of our proposed post-tuning approach, we conduct extension training on the Stable Video Diffusion model. Our approach augments the model's capacity to generate up to 5times its original number of frames, requiring only 1.5k GPU hours of training on a dataset comprising 40k videos. Importantly, the substantial increase in video length doesn't compromise the model's innate generalization capabilities, and the model showcases its advantages in generating videos of diverse styles and resolutions. We will release the source code and the enhanced model publicly.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/modelscope/DiffSynth-Studio", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.12045", "title": "\u03c4-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains", "authors": [ "Shunyu Yao", "Noah Shinn", "Pedram Razavi", "Karthik Narasimhan" ], "github": "https://github.com/sierra-research/tau-bench", "project_page": "", "abstract": "Existing benchmarks do not test language agents on their interaction with human users or ability to follow domain-specific rules, both of which are vital for deploying them in real world applications. We propose tau-bench, a benchmark emulating dynamic conversations between a user (simulated by language models) and a language agent provided with domain-specific API tools and policy guidelines. We employ an efficient and faithful evaluation process that compares the database state at the end of a conversation with the annotated goal state. We also propose a new metric (pass^k) to evaluate the reliability of agent behavior over multiple trials. Our experiments show that even state-of-the-art function calling agents (like gpt-4o) succeed on <50% of the tasks, and are quite inconsistent (pass^8 <25% in retail). Our findings point to the need for methods that can improve the ability of agents to act consistently and follow rules reliably.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sierra-research/tau-bench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.13923", "title": "PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents", "authors": [ "Junjie Wang", "Yin Zhang", "Yatai Ji", "Yuxiang Zhang", "Chunyang Jiang", "Yubo Wang", "Kang Zhu", "Zekun Wang", "Tiezhen Wang", "Wenhao Huang", "Jie Fu", "Bei Chen", "Qunshu Lin", "Minghao Liu", "Ge Zhang", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Recent advancements in Large Multimodal Models (LMMs) have leveraged extensive multimodal datasets to enhance capabilities in complex knowledge-driven tasks. However, persistent challenges in perceptual and reasoning errors limit their efficacy, particularly in interpreting intricate visual data and deducing multimodal relationships. Addressing these issues, we introduce a novel dataset format, PIN (Paired and INterleaved multimodal documents), designed to significantly improve both the depth and breadth of multimodal training. The PIN format is built on three foundational principles: knowledge intensity, scalability, and support for diverse training modalities. This innovative format combines markdown files and comprehensive images to enrich training data with a dense knowledge structure and versatile training strategies. We present PIN-14M, an open-source dataset comprising 14 million samples derived from a diverse range of Chinese and English sources, tailored to include complex web and scientific content. This dataset is constructed meticulously to ensure data quality and ethical integrity, aiming to facilitate advanced training strategies and improve model robustness against common multimodal training pitfalls. Our initial results, forming the basis of this technical report, suggest significant potential for the PIN format in refining LMM performance, with plans for future expansions and detailed evaluations of its impact on model capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.14544", "title": "Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs", "authors": [ "Yuxuan Qiao", "Haodong Duan", "Xinyu Fang", "Junming Yang", "Lin Chen", "Songyang Zhang", "Jiaqi Wang", "Dahua Lin", "Kai Chen" ], "github": "https://github.com/SparksJoe/Prism", "project_page": "", "abstract": "Vision Language Models (VLMs) demonstrate remarkable proficiency in addressing a wide array of visual questions, which requires strong perception and reasoning faculties. Assessing these two competencies independently is crucial for model refinement, despite the inherent difficulty due to the intertwined nature of seeing and reasoning in existing VLMs. To tackle this issue, we present Prism, an innovative framework designed to disentangle the perception and reasoning processes involved in visual question solving. Prism comprises two distinct stages: a perception stage that utilizes a VLM to extract and articulate visual information in textual form, and a reasoning stage that formulates responses based on the extracted visual information using a Large Language Model (LLM). This modular design enables the systematic comparison and assessment of both proprietary and open-source VLM for their perception and reasoning strengths. Our analytical framework provides several valuable insights, underscoring Prism's potential as a cost-effective solution for vision-language tasks. By combining a streamlined VLM focused on perception with a powerful LLM tailored for reasoning, Prism achieves superior results in general vision-language tasks while substantially cutting down on training and operational expenses. Quantitative evaluations show that Prism, when configured with a vanilla 2B LLaVA and freely accessible GPT-3.5, delivers performance on par with VLMs 10 times larger on the rigorous multimodal benchmark MMStar. The project is released at: https://github.com/SparksJoe/Prism.", "github_manual": "", "github_regex": "https://github.com/SparksJoe/Prism", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.14515", "title": "MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding", "authors": [ "Xinyu Fang", "Kangrui Mao", "Haodong Duan", "Xiangyu Zhao", "Yining Li", "Dahua Lin", "Kai Chen" ], "github": "https://github.com/open-compass/VLMEvalKit", "project_page": "", "abstract": "The advent of large vision-language models (LVLMs) has spurred research into their applications in multi-modal contexts, particularly in video understanding. Traditional VideoQA benchmarks, despite providing quantitative metrics, often fail to encompass the full spectrum of video content and inadequately assess models' temporal comprehension. To address these limitations, we introduce MMBench-Video, a quantitative benchmark designed to rigorously evaluate LVLMs' proficiency in video understanding. MMBench-Video incorporates lengthy videos from YouTube and employs free-form questions, mirroring practical use cases. The benchmark is meticulously crafted to probe the models' temporal reasoning skills, with all questions human-annotated according to a carefully constructed ability taxonomy. We employ GPT-4 for automated assessment, demonstrating superior accuracy and robustness over earlier LLM-based evaluations. Utilizing MMBench-Video, we have conducted comprehensive evaluations that include both proprietary and open-source LVLMs for images and videos. MMBench-Video stands as a valuable resource for the research community, facilitating improved evaluation of LVLMs and catalyzing progress in the field of video understanding. The evalutation code of MMBench-Video will be integrated into VLMEvalKit: https://github.com/open-compass/VLMEvalKit.", "github_manual": "", "github_regex": "https://github.com/open-compass/VLMEvalKit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.14562", "title": "Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities", "authors": [ "Sachit Menon", "Richard Zemel", "Carl Vondrick" ], "github": "", "project_page": "", "abstract": "When presented with questions involving visual thinking, humans naturally switch reasoning modalities, often forming mental images or drawing visual aids. Large language models have shown promising results in arithmetic and symbolic reasoning by expressing intermediate reasoning in text as a chain of thought, yet struggle to extend this capability to answer text queries that are easily solved by visual reasoning, even with extensive multimodal pretraining. We introduce a simple method, whiteboard-of-thought prompting, to unlock the visual reasoning capabilities of multimodal large language models across modalities. Whiteboard-of-thought prompting provides multimodal large language models with a metaphorical `whiteboard' to draw out reasoning steps as images, then returns these images back to the model for further processing. We find this can be accomplished with no demonstrations or specialized modules, instead leveraging models' existing ability to write code with libraries such as Matplotlib and Turtle. This simple approach shows state-of-the-art results on four difficult natural language tasks that involve visual and spatial reasoning. We identify multiple settings where GPT-4o using chain-of-thought fails dramatically, including more than one where it achieves 0% accuracy, while whiteboard-of-thought enables up to 92% accuracy in these same settings. We present a detailed exploration of where the technique succeeds as well as its sources of error.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.11817", "title": "Iterative Length-Regularized Direct Preference Optimization: A Case Study on Improving 7B Language Models to GPT-4 Level", "authors": [ "Jie Liu", "Zhanhui Zhou", "Jiaheng Liu", "Xingyuan Bu", "Chao Yang", "Han-Sen Zhong", "Wanli Ouyang" ], "github": "", "project_page": "", "abstract": "Direct Preference Optimization (DPO), a standard method for aligning language models with human preferences, is traditionally applied to offline preferences. Recent studies show that DPO benefits from iterative training with online preferences labeled by a trained reward model. In this work, we identify a pitfall of vanilla iterative DPO - improved response quality can lead to increased verbosity. To address this, we introduce iterative length-regularized DPO (iLR-DPO) to penalize response length. Our empirical results show that iLR-DPO can enhance a 7B model to perform on par with GPT-4 without increasing verbosity. Specifically, our 7B model achieves a 50.5% length-controlled win rate against GPT-4 Preview on AlpacaEval 2.0, and excels across standard benchmarks including MT-Bench, Arena-Hard and OpenLLM Leaderboard. These results demonstrate the effectiveness of iterative DPO in aligning language models with human feedback.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.12925", "title": "GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks", "authors": [ "Ihor Stepanov", "Mykhailo Shtopko" ], "github": "", "project_page": "", "abstract": "Information extraction tasks require both accurate, efficient, and generalisable models. Classical supervised deep learning approaches can achieve the required performance, but they need large datasets and are limited in their ability to adapt to different tasks. On the other hand, large language models (LLMs) demonstrate good generalization, meaning that they can adapt to many different tasks based on user requests. However, LLMs are computationally expensive and tend to fail to generate structured outputs. In this article, we will introduce a new kind of GLiNER model that can be used for various information extraction tasks while being a small encoder model. Our model achieved SoTA performance on zero-shot NER benchmarks and leading performance on question-answering, summarization and relation extraction tasks. Additionally, in this article, we will cover experimental results on self-learning approaches for named entity recognition using GLiNER models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.14347", "title": "nabla^2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials", "authors": [ "Kuzma Khrabrov", "Anton Ber", "Artem Tsypin", "Konstantin Ushenin", "Egor Rumiantsev", "Alexander Telepov", "Dmitry Protasov", "Ilya Shenbin", "Anton Alekseev", "Mikhail Shirokikh", "Sergey Nikolenko", "Elena Tutubalina", "Artur Kadurin" ], "github": "https://github.com/AIRI-Institute/nablaDFT", "project_page": "", "abstract": "Methods of computational quantum chemistry provide accurate approximations of molecular properties crucial for computer-aided drug discovery and other areas of chemical science. However, high computational complexity limits the scalability of their applications. Neural network potentials (NNPs) are a promising alternative to quantum chemistry methods, but they require large and diverse datasets for training. This work presents a new dataset and benchmark called nabla^2DFT that is based on the nablaDFT. It contains twice as much molecular structures, three times more conformations, new data types and tasks, and state-of-the-art models. The dataset includes energies, forces, 17 molecular properties, Hamiltonian and overlap matrices, and a wavefunction object. All calculations were performed at the DFT level (omegaB97X-D/def2-SVP) for each conformation. Moreover, nabla^2DFT is the first dataset that contains relaxation trajectories for a substantial number of drug-like molecules. We also introduce a novel benchmark for evaluating NNPs in molecular property prediction, Hamiltonian prediction, and conformational optimization tasks. Finally, we propose an extendable framework for training NNPs and implement 10 models within it.", "github_manual": "https://github.com/AIRI-Institute/nablaDFT", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.11410", "title": "HARE: HumAn pRiors, a key to small language model Efficiency", "authors": [ "Lingyun Zhang", "Bin jin", "Gaojian Ge", "Lunhui Liu", "Xuewen Shen", "Mingyong Wu", "Houqian Zhang", "Yongneng Jiang", "Shiqi Chen", "Shi Pu" ], "github": "", "project_page": "", "abstract": "Human priors play a crucial role in efficiently utilizing data in deep learning. However, with the development of large language models (LLMs), there is an increasing emphasis on scaling both model size and data volume, which often diminishes the importance of human priors in data construction. Influenced by these trends, existing Small Language Models (SLMs) mainly rely on web-scraped large-scale training data, neglecting the proper incorporation of human priors. This oversight limits the training efficiency of language models in resource-constrained settings. In this paper, we propose a principle to leverage human priors for data construction. This principle emphasizes achieving high-performance SLMs by training on a concise dataset that accommodates both semantic diversity and data quality consistency, while avoiding benchmark data leakage. Following this principle, we train an SLM named HARE-1.1B. Extensive experiments on large-scale benchmark datasets demonstrate that HARE-1.1B performs favorably against state-of-the-art SLMs, validating the effectiveness of the proposed principle. Additionally, this provides new insights into efficient language model training in resource-constrained environments from the view of human priors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.14539", "title": "Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps", "authors": [ "Nikita Starodubcev", "Mikhail Khoroshikh", "Artem Babenko", "Dmitry Baranchuk" ], "github": "", "project_page": "", "abstract": "Diffusion distillation represents a highly promising direction for achieving faithful text-to-image generation in a few sampling steps. However, despite recent successes, existing distilled models still do not provide the full spectrum of diffusion abilities, such as real image inversion, which enables many precise image manipulation methods. This work aims to enrich distilled text-to-image diffusion models with the ability to effectively encode real images into their latent space. To this end, we introduce invertible Consistency Distillation (iCD), a generalized consistency distillation framework that facilitates both high-quality image synthesis and accurate image encoding in only 3-4 inference steps. Though the inversion problem for text-to-image diffusion models gets exacerbated by high classifier-free guidance scales, we notice that dynamic guidance significantly reduces reconstruction errors without noticeable degradation in generation performance. As a result, we demonstrate that iCD equipped with dynamic guidance may serve as a highly effective tool for zero-shot text-guided image editing, competing with more expensive state-of-the-art alternatives.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.14319", "title": "LiveMind: Low-latency Large Language Models with Simultaneous Inference", "authors": [ "Chuangtao Chen", "Grace Li Zhang", "Xunzhao Yin", "Cheng Zhuo", "Ulf Schlichtmann", "Bing Li" ], "github": "https://github.com/chuangtaochen-tum/livemind", "project_page": "", "abstract": "In this paper, we introduce a novel low-latency inference framework for large language models (LLMs) inference which enables LLMs to perform inferences with incomplete prompts. By reallocating computational processes to prompt input phase, we achieve a substantial reduction in latency, thereby significantly enhancing the interactive experience for users of LLMs. The framework adeptly manages the visibility of the streaming prompt to the model, allowing it to infer from incomplete prompts or await additional prompts. Compared with traditional inference methods that utilize complete prompts, our approach demonstrates an average reduction of 59% in response latency on the MMLU-Pro dataset, while maintaining comparable accuracy. Additionally, our framework facilitates collaborative inference and output across different models. By employing an LLM for inference and a small language model (SLM) for output, we achieve an average 68% reduction in response latency, alongside a 5.5% improvement in accuracy on the MMLU-Pro dataset compared with the SLM baseline. For long prompts exceeding 20 sentences, the response latency can be reduced by up to 93%.", "github_manual": "https://github.com/chuangtaochen-tum/livemind", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.13542", "title": "Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models", "authors": [ "Guanting Dong", "Keming Lu", "Chengpeng Li", "Tingyu Xia", "Bowen Yu", "Chang Zhou", "Jingren Zhou" ], "github": "https://github.com/QwenLM/AutoIF", "project_page": "", "abstract": "One core capability of large language models (LLMs) is to follow natural language instructions. However, the issue of automatically constructing high-quality training data to enhance the complex instruction-following abilities of LLMs without manual annotation remains unresolved. In this paper, we introduce AutoIF, the first scalable and reliable method for automatically generating instruction-following training data. AutoIF transforms the validation of instruction-following data quality into code verification, requiring LLMs to generate instructions, the corresponding code to check the correctness of the instruction responses, and unit test samples to verify the code's correctness. Then, execution feedback-based rejection sampling can generate data for Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) training. AutoIF achieves significant improvements across three training algorithms, SFT, Offline DPO, and Online DPO, when applied to the top open-source LLMs, Qwen2 and LLaMA3, in self-alignment and strong-to-weak distillation settings. Our code is publicly available at https://github.com/QwenLM/AutoIF.", "github_manual": "", "github_regex": "https://github.com/QwenLM/AutoIF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.13621", "title": "Improving Visual Commonsense in Language Models via Multiple Image Generation", "authors": [ "Guy Yariv", "Idan Schwartz", "Yossi Adi", "Sagie Benaim" ], "github": "https://github.com/guyyariv/vLMIG", "project_page": "", "abstract": "Commonsense reasoning is fundamentally based on multimodal knowledge. However, existing large language models (LLMs) are primarily trained using textual data only, limiting their ability to incorporate essential visual information. In contrast, Visual Language Models, which excel at visually-oriented tasks, often fail at non-visual tasks such as basic commonsense reasoning. This divergence highlights a critical challenge - the integration of robust visual understanding with foundational text-based language reasoning. To this end, we introduce a method aimed at enhancing LLMs' visual commonsense. Specifically, our method generates multiple images based on the input text prompt and integrates these into the model's decision-making process by mixing their prediction probabilities. To facilitate multimodal grounded language modeling, we employ a late-fusion layer that combines the projected visual features with the output of a pre-trained LLM conditioned on text only. This late-fusion layer enables predictions based on comprehensive image-text knowledge as well as text only when this is required. We evaluate our approach using several visual commonsense reasoning tasks together with traditional NLP tasks, including common sense reasoning and reading comprehension. Our experimental results demonstrate significant superiority over existing baselines. When applied to recent state-of-the-art LLMs (e.g., Llama3), we observe improvements not only in visual common sense but also in traditional NLP benchmarks. Code and models are available under https://github.com/guyyariv/vLMIG.", "github_manual": "", "github_regex": "https://github.com/guyyariv/vLMIG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.10601", "title": "The Devil is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN Inversion and High Quality Image Editing", "authors": [ "Denis Bobkov", "Vadim Titov", "Aibek Alanov", "Dmitry Vetrov" ], "github": "https://github.com/AIRI-Institute/StyleFeatureEditor", "project_page": "", "abstract": "The task of manipulating real image attributes through StyleGAN inversion has been extensively researched. This process involves searching latent variables from a well-trained StyleGAN generator that can synthesize a real image, modifying these latent variables, and then synthesizing an image with the desired edits. A balance must be struck between the quality of the reconstruction and the ability to edit. Earlier studies utilized the low-dimensional W-space for latent search, which facilitated effective editing but struggled with reconstructing intricate details. More recent research has turned to the high-dimensional feature space F, which successfully inverses the input image but loses much of the detail during editing. In this paper, we introduce StyleFeatureEditor -- a novel method that enables editing in both w-latents and F-latents. This technique not only allows for the reconstruction of finer image details but also ensures their preservation during editing. We also present a new training pipeline specifically designed to train our model to accurately edit F-latents. Our method is compared with state-of-the-art encoding approaches, demonstrating that our model excels in terms of reconstruction quality and is capable of editing even challenging out-of-domain examples. Code is available at https://github.com/AIRI-Institute/StyleFeatureEditor.", "github_manual": "", "github_regex": "https://github.com/AIRI-Institute/StyleFeatureEditor", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.13663", "title": "Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation", "authors": [ "Jirui Qi", "Gabriele Sarti", "Raquel Fern\u00e1ndez", "Arianna Bisazza" ], "github": "https://github.com/Betswish/MIRAGE", "project_page": "", "abstract": "Ensuring the verifiability of model answers is a fundamental challenge for retrieval-augmented generation (RAG) in the question answering (QA) domain. Recently, self-citation prompting was proposed to make large language models (LLMs) generate citations to supporting documents along with their answers. However, self-citing LLMs often struggle to match the required format, refer to non-existent sources, and fail to faithfully reflect LLMs' context usage throughout the generation. In this work, we present MIRAGE --Model Internals-based RAG Explanations -- a plug-and-play approach using model internals for faithful answer attribution in RAG applications. MIRAGE detects context-sensitive answer tokens and pairs them with retrieved documents contributing to their prediction via saliency methods. We evaluate our proposed approach on a multilingual extractive QA dataset, finding high agreement with human answer attribution. On open-ended QA, MIRAGE achieves citation quality and efficiency comparable to self-citation while also allowing for a finer-grained control of attribution parameters. Our qualitative evaluation highlights the faithfulness of MIRAGE's attributions and underscores the promising application of model internals for RAG answer attribution.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Betswish/MIRAGE", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.12618", "title": "From Insights to Actions: The Impact of Interpretability and Analysis Research on NLP", "authors": [ "Marius Mosbach", "Vagrant Gautam", "Tom\u00e1s Vergara-Browne", "Dietrich Klakow", "Mor Geva" ], "github": "https://github.com/mmarius/interpretability-impact", "project_page": "", "abstract": "Interpretability and analysis (IA) research is a growing subfield within NLP with the goal of developing a deeper understanding of the behavior or inner workings of NLP systems and methods. Despite growing interest in the subfield, a commonly voiced criticism is that it lacks actionable insights and therefore has little impact on NLP. In this paper, we seek to quantify the impact of IA research on the broader field of NLP. We approach this with a mixed-methods analysis of: (1) a citation graph of 185K+ papers built from all papers published at ACL and EMNLP conferences from 2018 to 2023, and (2) a survey of 138 members of the NLP community. Our quantitative results show that IA work is well-cited outside of IA, and central in the NLP citation graph. Through qualitative analysis of survey responses and manual annotation of 556 papers, we find that NLP researchers build on findings from IA work and perceive it is important for progress in NLP, multiple subfields, and rely on its findings and terminology for their own work. Many novel methods are proposed based on IA findings and highly influenced by them, but highly influential non-IA work cites IA findings without being driven by them. We end by summarizing what is missing in IA work today and provide a call to action, to pave the way for a more impactful future of IA research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mmarius/interpretability-impact", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.14563", "title": "Model Merging and Safety Alignment: One Bad Model Spoils the Bunch", "authors": [ "Hasan Abed Al Kader Hammoud", "Umberto Michieli", "Fabio Pizzati", "Philip Torr", "Adel Bibi", "Bernard Ghanem", "Mete Ozay" ], "github": "", "project_page": "", "abstract": "Merging Large Language Models (LLMs) is a cost-effective technique for combining multiple expert LLMs into a single versatile model, retaining the expertise of the original ones. However, current approaches often overlook the importance of safety alignment during merging, leading to highly misaligned models. This work investigates the effects of model merging on alignment. We evaluate several popular model merging techniques, demonstrating that existing methods do not only transfer domain expertise but also propagate misalignment. We propose a simple two-step approach to address this problem: (i) generating synthetic safety and domain-specific data, and (ii) incorporating these generated data into the optimization process of existing data-aware model merging techniques. This allows us to treat alignment as a skill that can be maximized in the resulting merged LLM. Our experiments illustrate the effectiveness of integrating alignment-related data during merging, resulting in models that excel in both domain expertise and alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.13735", "title": "StableSemantics: A Synthetic Language-Vision Dataset of Semantic Representations in Naturalistic Images", "authors": [ "Rushikesh Zawar", "Shaurya Dewan", "Andrew F. Luo", "Margaret M. Henderson", "Michael J. Tarr", "Leila Wehbe" ], "github": "", "project_page": "", "abstract": "Understanding the semantics of visual scenes is a fundamental challenge in Computer Vision. A key aspect of this challenge is that objects sharing similar semantic meanings or functions can exhibit striking visual differences, making accurate identification and categorization difficult. Recent advancements in text-to-image frameworks have led to models that implicitly capture natural scene statistics. These frameworks account for the visual variability of objects, as well as complex object co-occurrences and sources of noise such as diverse lighting conditions. By leveraging large-scale datasets and cross-attention conditioning, these models generate detailed and contextually rich scene representations. This capability opens new avenues for improving object recognition and scene understanding in varied and challenging environments. Our work presents StableSemantics, a dataset comprising 224 thousand human-curated prompts, processed natural language captions, over 2 million synthetic images, and 10 million attention maps corresponding to individual noun chunks. We explicitly leverage human-generated prompts that correspond to visually interesting stable diffusion generations, provide 10 generations per phrase, and extract cross-attention maps for each image. We explore the semantic distribution of generated images, examine the distribution of objects within images, and benchmark captioning and open vocabulary segmentation methods on our data. To the best of our knowledge, we are the first to release a diffusion dataset with semantic attributions. We expect our proposed dataset to catalyze advances in visual semantic understanding and provide a foundation for developing more sophisticated and effective visual models. Website: https://stablesemantics.github.io/StableSemantics", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.11289", "title": "A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models", "authors": [ "Haopeng Zhang", "Philip S. Yu", "Jiawei Zhang" ], "github": "", "project_page": "", "abstract": "Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs). This survey thus provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts. It is organized into two main parts: (1) a detailed overview of datasets, evaluation metrics, and summarization methods before the LLM era, encompassing traditional statistical methods, deep learning approaches, and PLM fine-tuning techniques, and (2) the first detailed examination of recent advancements in benchmarking, modeling, and evaluating summarization in the LLM era. By synthesizing existing literature and presenting a cohesive overview, this survey also discusses research trends, open challenges, and proposes promising research directions in summarization, aiming to guide researchers through the evolving landscape of summarization research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-21", "arxiv_id": "2406.13099", "title": "Sampling 3D Gaussian Scenes in Seconds with Latent Diffusion Models", "authors": [ "Paul Henderson", "Melonie de Almeida", "Daniela Ivanova", "Titas Anciukevi\u010dius" ], "github": "", "project_page": "", "abstract": "We present a latent diffusion model over 3D scenes, that can be trained using only 2D image data. To achieve this, we first design an autoencoder that maps multi-view images to 3D Gaussian splats, and simultaneously builds a compressed latent representation of these splats. Then, we train a multi-view diffusion model over the latent space to learn an efficient generative model. This pipeline does not require object masks nor depths, and is suitable for complex scenes with arbitrary camera positions. We conduct careful experiments on two large-scale datasets of complex real-world scenes -- MVImgNet and RealEstate10K. We show that our approach enables generating 3D scenes in as little as 0.2 seconds, either from scratch, from a single input view, or from sparse input views. It produces diverse and high-quality results while running an order of magnitude faster than non-latent diffusion models and earlier NeRF-based generative models", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.14393", "title": "Jailbreaking as a Reward Misspecification Problem", "authors": [ "Zhihui Xie", "Jiahui Gao", "Lei Li", "Zhenguo Li", "Qi Liu", "Lingpeng Kong" ], "github": "https://github.com/zhxieml/remiss-jailbreak", "project_page": "", "abstract": "The widespread adoption of large language models (LLMs) has raised concerns about their safety and reliability, particularly regarding their vulnerability to adversarial attacks. In this paper, we propose a novel perspective that attributes this vulnerability to reward misspecification during the alignment process. We introduce a metric ReGap to quantify the extent of reward misspecification and demonstrate its effectiveness and robustness in detecting harmful backdoor prompts. Building upon these insights, we present ReMiss, a system for automated red teaming that generates adversarial prompts against various target aligned LLMs. ReMiss achieves state-of-the-art attack success rates on the AdvBench benchmark while preserving the human readability of the generated prompts. Detailed analysis highlights the unique advantages brought by the proposed reward misspecification objective compared to previous methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhxieml/remiss-jailbreak", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.14835", "title": "ToVo: Toxicity Taxonomy via Voting", "authors": [ "Tinh Son Luong", "Thanh-Thien Le", "Thang Viet Doan", "Linh Ngo Van", "Thien Huu Nguyen", "Diep Thi-Ngoc Nguyen" ], "github": "", "project_page": "", "abstract": "Existing toxic detection models face significant limitations, such as lack of transparency, customization, and reproducibility. These challenges stem from the closed-source nature of their training data and the paucity of explanations for their evaluation mechanism. To address these issues, we propose a dataset creation mechanism that integrates voting and chain-of-thought processes, producing a high-quality open-source dataset for toxic content detection. Our methodology ensures diverse classification metrics for each sample and includes both classification scores and explanatory reasoning for the classifications. We utilize the dataset created through our proposed mechanism to train our model, which is then compared against existing widely-used detectors. Our approach not only enhances transparency and customizability but also facilitates better fine-tuning for specific use cases. This work contributes a robust framework for developing toxic content detection models, emphasizing openness and adaptability, thus paving the way for more effective and user-specific content moderation solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.14805", "title": "How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensions", "authors": [ "Julia Kharchenko", "Tanya Roosta", "Aman Chadha", "Chirag Shah" ], "github": "https://github.com/juliakharchenko/multilingual-ai-alignment", "project_page": "", "abstract": "Large Language Models (LLMs) attempt to imitate human behavior by responding to humans in a way that pleases them, including by adhering to their values. However, humans come from diverse cultures with different values. It is critical to understand whether LLMs showcase different values to the user based on the stereotypical values of a user's known country. We prompt different LLMs with a series of advice requests based on 5 Hofstede Cultural Dimensions -- a quantifiable way of representing the values of a country. Throughout each prompt, we incorporate personas representing 36 different countries and, separately, languages predominantly tied to each country to analyze the consistency in the LLMs' cultural understanding. Through our analysis of the responses, we found that LLMs can differentiate between one side of a value and another, as well as understand that countries have differing values, but will not always uphold the values when giving advice, and fail to understand the need to answer differently based on different cultural values. Rooted in these findings, we present recommendations for training value-aligned and culturally sensitive LLMs. More importantly, the methodology and the framework developed here can help further understand and mitigate culture and language alignment issues with LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/juliakharchenko/multilingual-ai-alignment", "github_api_added_by": "auto" }, { "date": "2024-06-24", "arxiv_id": "2406.15319", "title": "LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs", "authors": [ "Ziyan Jiang", "Xueguang Ma", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "In traditional RAG framework, the basic retrieval units are normally short. The common retrievers like DPR normally work with 100-word Wikipedia paragraphs. Such a design forces the retriever to search over a large corpus to find the `needle' unit. In contrast, the readers only need to extract answers from the short retrieved units. Such an imbalanced `heavy' retriever and `light' reader design can lead to sub-optimal performance. In order to alleviate the imbalance, we propose a new framework LongRAG, consisting of a `long retriever' and a `long reader'. LongRAG processes the entire Wikipedia into 4K-token units, which is 30x longer than before. By increasing the unit size, we significantly reduce the total units from 22M to 700K. This significantly lowers the burden of retriever, which leads to a remarkable retrieval score: answer recall@1=71% on NQ (previously 52%) and answer recall@2=72% (previously 47%) on HotpotQA (full-wiki). Then we feed the top-k retrieved units (approx 30K tokens) to an existing long-context LLM to perform zero-shot answer extraction. Without requiring any training, LongRAG achieves an EM of 62.7% on NQ, which is the best known result. LongRAG also achieves 64.3% on HotpotQA (full-wiki), which is on par of the SoTA model. Our study offers insights into the future roadmap for combining RAG with long-context LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.15252", "title": "MantisScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation", "authors": [ "Xuan He", "Dongfu Jiang", "Ge Zhang", "Max Ku", "Achint Soni", "Sherman Siu", "Haonan Chen", "Abhranil Chandra", "Ziyan Jiang", "Aaran Arulraj", "Kai Wang", "Quy Duc Do", "Yuansheng Ni", "Bohan Lyu", "Yaswanth Narsupalli", "Rongqi Fan", "Zhiheng Lyu", "Yuchen Lin", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "The recent years have witnessed great advances in video generation. However, the development of automatic video metrics is lagging significantly behind. None of the existing metric is able to provide reliable scores over generated videos. The main barrier is the lack of large-scale human-annotated dataset. In this paper, we release VideoFeedback, the first large-scale dataset containing human-provided multi-aspect score over 37.6K synthesized videos from 11 existing video generative models. We train MantisScore (initialized from Mantis) based on VideoFeedback to enable automatic video quality assessment. Experiments show that the Spearman correlation between MantisScore and humans can reach 77.1 on VideoFeedback-test, beating the prior best metrics by about 50 points. Further result on other held-out EvalCrafter, GenAI-Bench, and VBench show that MantisScore has consistently much higher correlation with human judges than other metrics. Due to these results, we believe MantisScore can serve as a great proxy for human raters to (1) rate different video models to track progress (2) simulate fine-grained human feedback in Reinforcement Learning with Human Feedback (RLHF) to improve current video generation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.15193", "title": "Reward Steering with Evolutionary Heuristics for Decoding-time Alignment", "authors": [ "Chia-Yu Hung", "Navonil Majumder", "Ambuj Mehrish", "Soujanya Poria" ], "github": "https://github.com/declare-lab/darwin", "project_page": "", "abstract": "The widespread applicability and increasing omnipresence of LLMs have instigated a need to align LLM responses to user and stakeholder preferences. Many preference optimization approaches have been proposed that fine-tune LLM parameters to achieve good alignment. However, such parameter tuning is known to interfere with model performance on many tasks. Moreover, keeping up with shifting user preferences is tricky in such a situation. Decoding-time alignment with reward model guidance solves these issues at the cost of increased inference time. However, most of such methods fail to strike the right balance between exploration and exploitation of reward -- often due to the conflated formulation of these two aspects - to give well-aligned responses. To remedy this we decouple these two aspects and implement them in an evolutionary fashion: exploration is enforced by decoding from mutated instructions and exploitation is represented as the periodic replacement of poorly-rewarded generations with well-rewarded ones. Empirical evidences indicate that this strategy outperforms many preference optimization and decode-time alignment approaches on two widely accepted alignment benchmarks AlpacaEval 2 and MT-Bench. Our implementation will be available at: https://darwin-alignment.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/declare-lab/darwin", "github_api_added_by": "auto" }, { "date": "2024-06-24", "arxiv_id": "2406.14599", "title": "Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models", "authors": [ "Matthew Zheng", "Enis Simsar", "Hidir Yesiltepe", "Federico Tombari", "Joel Simon", "Pinar Yanardag" ], "github": "https://github.com/stylebreeder/stylebreeder-code", "project_page": "", "abstract": "Text-to-image models are becoming increasingly popular, revolutionizing the landscape of digital art creation by enabling highly detailed and creative visual content generation. These models have been widely employed across various domains, particularly in art generation, where they facilitate a broad spectrum of creative expression and democratize access to artistic creation. In this paper, we introduce STYLEBREEDER, a comprehensive dataset of 6.8M images and 1.8M prompts generated by 95K users on Artbreeder, a platform that has emerged as a significant hub for creative exploration with over 13M users. We introduce a series of tasks with this dataset aimed at identifying diverse artistic styles, generating personalized content, and recommending styles based on user interests. By documenting unique, user-generated styles that transcend conventional categories like 'cyberpunk' or 'Picasso,' we explore the potential for unique, crowd-sourced styles that could provide deep insights into the collective creative psyche of users worldwide. We also evaluate different personalization methods to enhance artistic expression and introduce a style atlas, making these models available in LoRA format for public use. Our research demonstrates the potential of text-to-image diffusion models to uncover and promote unique artistic expressions, further democratizing AI in art and fostering a more diverse and inclusive artistic community. The dataset, code and models are available at https://stylebreeder.github.io under a Public Domain (CC0) license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stylebreeder/stylebreeder-code", "github_api_added_by": "auto" }, { "date": "2024-06-24", "arxiv_id": "2406.12624", "title": "Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges", "authors": [ "Aman Singh Thakur", "Kartik Choudhary", "Venkat Srinik Ramayapally", "Sankaran Vaidyanathan", "Dieuwke Hupkes" ], "github": "https://github.com/UMass-Meta-LLM-Eval/llm_eval", "project_page": "", "abstract": "Offering a promising solution to the scalability challenges associated with human evaluation, the LLM-as-a-judge paradigm is rapidly gaining traction as an approach to evaluating large language models (LLMs). However, there are still many open questions about the strengths and weaknesses of this paradigm, and what potential biases it may hold. In this paper, we present a comprehensive study of the performance of various LLMs acting as judges. We leverage TriviaQA as a benchmark for assessing objective knowledge reasoning of LLMs and evaluate them alongside human annotations which we found to have a high inter-annotator agreement. Our study includes 9 judge models and 9 exam taker models -- both base and instruction-tuned. We assess the judge model's alignment across different model sizes, families, and judge prompts. Among other results, our research rediscovers the importance of using Cohen's kappa as a metric of alignment as opposed to simple percent agreement, showing that judges with high percent agreement can still assign vastly different scores. We find that both Llama-3 70B and GPT-4 Turbo have an excellent alignment with humans, but in terms of ranking exam taker models, they are outperformed by both JudgeLM-7B and the lexical judge Contains, which have up to 34 points lower human alignment. Through error analysis and various other studies, including the effects of instruction length and leniency bias, we hope to provide valuable lessons for using LLMs as judges in the future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UMass-Meta-LLM-Eval/llm_eval", "github_api_added_by": "auto" }, { "date": "2024-06-24", "arxiv_id": "2406.14035", "title": "Two Giraffes in a Dirt Field: Using Game Play to Investigate Situation Modelling in Large Multimodal Models", "authors": [ "Sherzod Hakimov", "Yerkezhan Abdullayeva", "Kushal Koshti", "Antonia Schmidt", "Yan Weiser", "Anne Beyer", "David Schlangen" ], "github": "", "project_page": "", "abstract": "While the situation has improved for text-only models, it again seems to be the case currently that multimodal (text and image) models develop faster than ways to evaluate them. In this paper, we bring a recently developed evaluation paradigm from text models to multimodal models, namely evaluation through the goal-oriented game (self) play, complementing reference-based and preference-based evaluation. Specifically, we define games that challenge a model's capability to represent a situation from visual information and align such representations through dialogue. We find that the largest closed models perform rather well on the games that we define, while even the best open-weight models struggle with them. On further analysis, we find that the exceptional deep captioning capabilities of the largest models drive some of the performance. There is still room to grow for both kinds of models, ensuring the continued relevance of the benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.15275", "title": "Cognitive Map for Language Models: Optimal Planning via Verbally Representing the World Model", "authors": [ "Doyoung Kim", "Jongwon Lee", "Jinho Park", "Minjoon Seo" ], "github": "https://github.com/kaistai/language_extrapolation", "project_page": "", "abstract": "Language models have demonstrated impressive capabilities across various natural language processing tasks, yet they struggle with planning tasks requiring multi-step simulations. Inspired by human cognitive processes, this paper investigates the optimal planning power of language models that can construct a cognitive map of a given environment. Our experiments demonstrate that cognitive map significantly enhances the performance of both optimal and reachable planning generation ability in the Gridworld path planning task. We observe that our method showcases two key characteristics similar to human cognition: generalization of its planning ability to extrapolated environments and rapid adaptation with limited training data. We hope our findings in the Gridworld task provide insights into modeling human cognitive processes in language models, potentially leading to the development of more advanced and robust systems that better resemble human cognition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kaistai/language_extrapolation", "github_api_added_by": "auto" }, { "date": "2024-06-24", "arxiv_id": "2406.12056", "title": "Learning Molecular Representation in a Cell", "authors": [ "Gang Liu", "Srijit Seal", "John Arevalo", "Zhenwen Liang", "Anne E. Carpenter", "Meng Jiang", "Shantanu Singh" ], "github": "https://github.com/liugangcode/InfoAlign", "project_page": "", "abstract": "Predicting drug efficacy and safety in vivo requires information on biological responses (e.g., cell morphology and gene expression) to small molecule perturbations. However, current molecular representation learning methods do not provide a comprehensive view of cell states under these perturbations and struggle to remove noise, hindering model generalization. We introduce the Information Alignment (InfoAlign) approach to learn molecular representations through the information bottleneck method in cells. We integrate molecules and cellular response data as nodes into a context graph, connecting them with weighted edges based on chemical, biological, and computational criteria. For each molecule in a training batch, InfoAlign optimizes the encoder's latent representation with a minimality objective to discard redundant structural information. A sufficiency objective decodes the representation to align with different feature spaces from the molecule's neighborhood in the context graph. We demonstrate that the proposed sufficiency objective for alignment is tighter than existing encoder-based contrastive methods. Empirically, we validate representations from InfoAlign in two downstream tasks: molecular property prediction against up to 19 baseline methods across four datasets, plus zero-shot molecule-morphology matching.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/liugangcode/InfoAlign", "github_api_added_by": "auto" }, { "date": "2024-06-24", "arxiv_id": "2406.11403", "title": "Multimodal Structured Generation: CVPR's 2nd MMFM Challenge Technical Report", "authors": [ "Franz Louis Cesista" ], "github": "https://github.com/leloykun/MMFM-Challenge", "project_page": "", "abstract": "Multimodal Foundation Models (MMFMs) have shown remarkable performance on various computer vision and natural language processing tasks. However, their performance on particular tasks such as document understanding is still limited. They also require more compute, time, and engineering resources to finetune and deploy compared to traditional, unimodal models. In this report, we present Multimodal Structured Generation, a general framework which constrains the output logits of frozen MMFMs to force them to reason before responding with structured outputs that downstream APIs can parse and use. We provide a detailed account of our approach, including the technical details, theoretical discussions, and final evaluation results in the 2nd Multimodal Foundation Models Challenge hosted by the Computer Vision and Pattern Recognition (CVPR) conference. Our approach achieved the second highest score in the hidden test set for Phase 2 and third highest overall. This shows the method's ability to generalize to unseen tasks. And that simple engineering can beat expensive & complicated modelling steps as we first discussed in our paper, Retrieval Augmented Structured Generation: Business Document Information Extraction as Tool Use. All of our scripts, deployment steps, and evaluation results can be accessed in https://github.com/leloykun/MMFM-Challenge", "github_manual": "", "github_regex": "https://github.com/leloykun/MMFM-Challenge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.15349", "title": "NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking", "authors": [ "Daniel Dauner", "Marcel Hallgarten", "Tianyu Li", "Xinshuo Weng", "Zhiyu Huang", "Zetong Yang", "Hongyang Li", "Igor Gilitschenski", "Boris Ivanovic", "Marco Pavone", "Andreas Geiger", "Kashyap Chitta" ], "github": "https://github.com/autonomousvision/navsim", "project_page": "", "abstract": "Benchmarking vision-based driving policies is challenging. On one hand, open-loop evaluation with real data is easy, but these results do not reflect closed-loop performance. On the other, closed-loop evaluation is possible in simulation, but is hard to scale due to its significant computational demands. Further, the simulators available today exhibit a large domain gap to real data. This has resulted in an inability to draw clear conclusions from the rapidly growing body of research on end-to-end autonomous driving. In this paper, we present NAVSIM, a middle ground between these evaluation paradigms, where we use large datasets in combination with a non-reactive simulator to enable large-scale real-world benchmarking. Specifically, we gather simulation-based metrics, such as progress and time to collision, by unrolling bird's eye view abstractions of the test scenes for a short simulation horizon. Our simulation is non-reactive, i.e., the evaluated policy and environment do not influence each other. As we demonstrate empirically, this decoupling allows open-loop metric computation while being better aligned with closed-loop evaluations than traditional displacement errors. NAVSIM enabled a new competition held at CVPR 2024, where 143 teams submitted 463 entries, resulting in several new insights. On a large set of challenging scenarios, we observe that simple methods with moderate compute requirements such as TransFuser can match recent large-scale end-to-end driving architectures such as UniAD. Our modular framework can potentially be extended with new datasets, data curation strategies, and metrics, and will be continually maintained to host future challenges. Our code is available at https://github.com/autonomousvision/navsim.", "github_manual": "", "github_regex": "https://github.com/autonomousvision/navsim", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.13457", "title": "EvTexture: Event-driven Texture Enhancement for Video Super-Resolution", "authors": [ "Dachun Kai", "Jiayao Lu", "Yueyi Zhang", "Xiaoyan Sun" ], "github": "https://github.com/DachunKai/EvTexture", "project_page": "", "abstract": "Event-based vision has drawn increasing attention due to its unique characteristics, such as high temporal resolution and high dynamic range. It has been used in video super-resolution (VSR) recently to enhance the flow estimation and temporal alignment. Rather than for motion learning, we propose in this paper the first VSR method that utilizes event signals for texture enhancement. Our method, called EvTexture, leverages high-frequency details of events to better recover texture regions in VSR. In our EvTexture, a new texture enhancement branch is presented. We further introduce an iterative texture enhancement module to progressively explore the high-temporal-resolution event information for texture restoration. This allows for gradual refinement of texture regions across multiple iterations, leading to more accurate and rich high-resolution details. Experimental results show that our EvTexture achieves state-of-the-art performance on four datasets. For the Vid4 dataset with rich textures, our method can get up to 4.67dB gain compared with recent event-based methods. Code: https://github.com/DachunKai/EvTexture.", "github_manual": "", "github_regex": "https://github.com/DachunKai/EvTexture", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.14972", "title": "A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG Systems", "authors": [ "Florin Cuconasu", "Giovanni Trappolini", "Nicola Tonellotto", "Fabrizio Silvestri" ], "github": "https://github.com/florin-git/Base-vs-Instruct-LLMs-in-RAG-Systems", "project_page": "", "abstract": "Retrieval Augmented Generation (RAG) represents a significant advancement in artificial intelligence combining a retrieval phase with a generative phase, with the latter typically being powered by large language models (LLMs). The current common practices in RAG involve using \"instructed\" LLMs, which are fine-tuned with supervised training to enhance their ability to follow instructions and are aligned with human preferences using state-of-the-art techniques. Contrary to popular belief, our study demonstrates that base models outperform their instructed counterparts in RAG tasks by 20% on average under our experimental settings. This finding challenges the prevailing assumptions about the superiority of instructed LLMs in RAG applications. Further investigations reveal a more nuanced situation, questioning fundamental aspects of RAG and suggesting the need for broader discussions on the topic; or, as Fromm would have it, \"Seldom is a glance at the statistics enough to understand the meaning of the figures\".", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/florin-git/Base-vs-Instruct-LLMs-in-RAG-Systems", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.14938", "title": "Towards Retrieval Augmented Generation over Large Video Libraries", "authors": [ "Yannis Tevissen", "Khalil Guetari", "Fr\u00e9d\u00e9ric Petitpont" ], "github": "", "project_page": "", "abstract": "Video content creators need efficient tools to repurpose content, a task that often requires complex manual or automated searches. Crafting a new video from large video libraries remains a challenge. In this paper we introduce the task of Video Library Question Answering (VLQA) through an interoperable architecture that applies Retrieval Augmented Generation (RAG) to video libraries. We propose a system that uses large language models (LLMs) to generate search queries, retrieving relevant video moments indexed by speech and visual metadata. An answer generation module then integrates user queries with this metadata to produce responses with specific video timestamps. This approach shows promise in multimedia content retrieval, and AI-assisted video content creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.13393", "title": "Style-NeRF2NeRF: 3D Style Transfer From Style-Aligned Multi-View Images", "authors": [ "Haruo Fujiwara", "Yusuke Mukuta", "Tatsuya Harada" ], "github": "", "project_page": "", "abstract": "We propose a simple yet effective pipeline for stylizing a 3D scene, harnessing the power of 2D image diffusion models. Given a NeRF model reconstructed from a set of multi-view images, we perform 3D style transfer by refining the source NeRF model using stylized images generated by a style-aligned image-to-image diffusion model. Given a target style prompt, we first generate perceptually similar multi-view images by leveraging a depth-conditioned diffusion model with an attention-sharing mechanism. Next, based on the stylized multi-view images, we propose to guide the style transfer process with the sliced Wasserstein loss based on the feature maps extracted from a pre-trained CNN model. Our pipeline consists of decoupled steps, allowing users to test various prompt ideas and preview the stylized 3D result before proceeding to the NeRF fine-tuning stage. We demonstrate that our method can transfer diverse artistic styles to real-world 3D scenes with competitive quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.12564", "title": "Low-Resource Machine Translation through the Lens of Personalized Federated Learning", "authors": [ "Viktor Moskvoretskii", "Nazarii Tupitsa", "Chris Biemann", "Samuel Horv\u00e1th", "Eduard Gorbunov", "Irina Nikishina" ], "github": "https://github.com/VityaVitalich/MeritFed", "project_page": "", "abstract": "We present a new approach based on the Personalized Federated Learning algorithm MeritFed that can be applied to Natural Language Tasks with heterogeneous data. We evaluate it on the Low-Resource Machine Translation task, using the dataset from the Large-Scale Multilingual Machine Translation Shared Task (Small Track #2) and the subset of Sami languages from the multilingual benchmark for Finno-Ugric languages. In addition to its effectiveness, MeritFed is also highly interpretable, as it can be applied to track the impact of each language used for training. Our analysis reveals that target dataset size affects weight distribution across auxiliary languages, that unrelated languages do not interfere with the training, and auxiliary optimizer parameters have minimal impact. Our approach is easy to apply with a few lines of code, and we provide scripts for reproducing the experiments at https://github.com/VityaVitalich/MeritFed", "github_manual": "", "github_regex": "https://github.com/VityaVitalich/MeritFed", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.14213", "title": "Complexity of Symbolic Representation in Working Memory of Transformer Correlates with the Complexity of a Task", "authors": [ "Alsu Sagirova", "Mikhail Burtsev" ], "github": "", "project_page": "", "abstract": "Even though Transformers are extensively used for Natural Language Processing tasks, especially for machine translation, they lack an explicit memory to store key concepts of processed texts. This paper explores the properties of the content of symbolic working memory added to the Transformer model decoder. Such working memory enhances the quality of model predictions in machine translation task and works as a neural-symbolic representation of information that is important for the model to make correct translations. The study of memory content revealed that translated text keywords are stored in the working memory, pointing to the relevance of memory content to the processed text. Also, the diversity of tokens and parts of speech stored in memory correlates with the complexity of the corpora for machine translation task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.14764", "title": "RE-AdaptIR: Improving Information Retrieval through Reverse Engineered Adaptation", "authors": [ "William Fleshman", "Benjamin Van Durme" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) fine-tuned for text-retrieval have demonstrated state-of-the-art results across several information retrieval (IR) benchmarks. However, supervised training for improving these models requires numerous labeled examples, which are generally unavailable or expensive to acquire. In this work, we explore the effectiveness of extending reverse engineered adaptation to the context of information retrieval (RE-AdaptIR). We use RE-AdaptIR to improve LLM-based IR models using only unlabeled data. We demonstrate improved performance both in training domains as well as zero-shot in domains where the models have seen no queries. We analyze performance changes in various fine-tuning scenarios and offer findings of immediate use to practitioners.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.14783", "title": "Evaluating RAG-Fusion with RAGElo: an Automated Elo-based Framework", "authors": [ "Zackary Rackauckas", "Arthur C\u00e2mara", "Jakub Zavrel" ], "github": "", "project_page": "", "abstract": "Challenges in the automated evaluation of Retrieval-Augmented Generation (RAG) Question-Answering (QA) systems include hallucination problems in domain-specific knowledge and the lack of gold standard benchmarks for company internal tasks. This results in difficulties in evaluating RAG variations, like RAG-Fusion (RAGF), in the context of a product QA task at Infineon Technologies. To solve these problems, we propose a comprehensive evaluation framework, which leverages Large Language Models (LLMs) to generate large datasets of synthetic queries based on real user queries and in-domain documents, uses LLM-as-a-judge to rate retrieved documents and answers, evaluates the quality of answers, and ranks different variants of Retrieval-Augmented Generation (RAG) agents with RAGElo's automated Elo-based competition. LLM-as-a-judge rating of a random sample of synthetic queries shows a moderate, positive correlation with domain expert scoring in relevance, accuracy, completeness, and precision. While RAGF outperformed RAG in Elo score, a significance analysis against expert annotations also shows that RAGF significantly outperforms RAG in completeness, but underperforms in precision. In addition, Infineon's RAGF assistant demonstrated slightly higher performance in document relevance based on MRR@5 scores. We find that RAGElo positively aligns with the preferences of human annotators, though due caution is still required. Finally, RAGF's approach leads to more complete answers based on expert annotations and better answers overall based on RAGElo's evaluation criteria.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.13527", "title": "4K4DGen: Panoramic 4D Generation at 4K Resolution", "authors": [ "Renjie Li", "Panwang Pan", "Bangbang Yang", "Dejia Xu", "Shijie Zhou", "Xuanyang Zhang", "Zeming Li", "Achuta Kadambi", "Zhangyang Wang", "Zhiwen Fan" ], "github": "", "project_page": "", "abstract": "The blooming of virtual reality and augmented reality (VR/AR) technologies has driven an increasing demand for the creation of high-quality, immersive, and dynamic environments. However, existing generative techniques either focus solely on dynamic objects or perform outpainting from a single perspective image, failing to meet the needs of VR/AR applications. In this work, we tackle the challenging task of elevating a single panorama to an immersive 4D experience. For the first time, we demonstrate the capability to generate omnidirectional dynamic scenes with 360-degree views at 4K resolution, thereby providing an immersive user experience. Our method introduces a pipeline that facilitates natural scene animations and optimizes a set of 4D Gaussians using efficient splatting techniques for real-time exploration. To overcome the lack of scene-scale annotated 4D data and models, especially in panoramic formats, we propose a novel Panoramic Denoiser that adapts generic 2D diffusion priors to animate consistently in 360-degree images, transforming them into panoramic videos with dynamic scenes at targeted regions. Subsequently, we elevate the panoramic video into a 4D immersive environment while preserving spatial and temporal consistency. By transferring prior knowledge from 2D models in the perspective domain to the panoramic domain and the 4D lifting with spatial appearance and geometry regularization, we achieve high-quality Panorama-to-4D generation at a resolution of (4096 times 2048) for the first time. See the project website at https://4k4dgen.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.11617", "title": "DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling", "authors": [ "Pala Tej Deep", "Rishabh Bhardwaj", "Soujanya Poria" ], "github": "https://github.com/declare-lab/della", "project_page": "", "abstract": "With the proliferation of domain-specific models, model merging has emerged as a set of techniques that combine the capabilities of multiple models into one that can multitask without the cost of additional training. In this paper, we propose a new model merging technique, Drop and rEscaLe via sampLing with mAgnitude (DELLA-Merging), that employs a novel pruning technique, MAGPRUNE, which shows significant advantages over DARE and TIES. MAGPRUNE first ranks the parameters in order of their magnitude and assigns higher dropout probabilities (p) to parameters with lower ranks corresponding to lower magnitudes. To approximate the original embeddings, MAGPRUNE employs a rescaling operation on the parameters that survive the random dropping by 1/(1 - p). On three different expert models considered for merging (LM, Math, Code) and corresponding benchmark datasets (AlpacaEval, GSM8K, MBPP), DELLA shows an average improvement of 2.4 points over baseline methods employing delta parameter pruning (an improvement of 3.6 points over TIES, 1.2 points over DARE), and 11.1 points over the no-pruning baseline (TA). We release the source code at: https://github.com/declare-lab/della.", "github_manual": "", "github_regex": "https://github.com/declare-lab/della", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.11654", "title": "Ruby Teaming: Improving Quality Diversity Search with Memory for Automated Red Teaming", "authors": [ "Vernon Toh Yan Han", "Rishabh Bhardwaj", "Soujanya Poria" ], "github": "", "project_page": "", "abstract": "We propose Ruby Teaming, a method that improves on Rainbow Teaming by including a memory cache as its third dimension. The memory dimension provides cues to the mutator to yield better-quality prompts, both in terms of attack success rate (ASR) and quality diversity. The prompt archive generated by Ruby Teaming has an ASR of 74%, which is 20% higher than the baseline. In terms of quality diversity, Ruby Teaming outperforms Rainbow Teaming by 6% and 3% on Shannon's Evenness Index (SEI) and Simpson's Diversity Index (SDI), respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.13236", "title": "Data Contamination Can Cross Language Barriers", "authors": [ "Feng Yao", "Yufan Zhuang", "Zihao Sun", "Sunan Xu", "Animesh Kumar", "Jingbo Shang" ], "github": "https://github.com/ShangDataLab/Deep-Contam", "project_page": "", "abstract": "The opacity in developing large language models (LLMs) is raising growing concerns about the potential contamination of public benchmarks in the pre-training data. Existing contamination detection methods are typically based on the text overlap between training and evaluation data, which can be too superficial to reflect deeper forms of contamination. In this paper, we first present a cross-lingual form of contamination that inflates LLMs' performance while evading current detection methods, deliberately injected by overfitting LLMs on the translated versions of benchmark test sets. Then, we propose generalization-based approaches to unmask such deeply concealed contamination. Specifically, we examine the LLM's performance change after modifying the original benchmark by replacing the false answer choices with correct ones from other questions. Contaminated models can hardly generalize to such easier situations, where the false choices can be not even wrong, as all choices are correct in their memorization. Experimental results demonstrate that cross-lingual contamination can easily fool existing detection methods, but not ours. In addition, we discuss the potential utilization of cross-lingual contamination in interpreting LLMs' working mechanisms and in post-training LLMs for enhanced multilingual capabilities. The code and dataset we use can be obtained from https://github.com/ShangDataLab/Deep-Contam.", "github_manual": "", "github_regex": "https://github.com/ShangDataLab/Deep-Contam", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.14596", "title": "ICAL: Continual Learning of Multimodal Agents by Transforming Trajectories into Actionable Insights", "authors": [ "Gabriel Sarch", "Lawrence Jang", "Michael J. Tarr", "William W. Cohen", "Kenneth Marino", "Katerina Fragkiadaki" ], "github": "", "project_page": "", "abstract": "Large-scale generative language and vision-language models (LLMs and VLMs) excel in few-shot in-context learning for decision making and instruction following. However, they require high-quality exemplar demonstrations to be included in their context window. In this work, we ask: Can LLMs and VLMs generate their own prompt examples from generic, sub-optimal demonstrations? We propose In-Context Abstraction Learning (ICAL), a method that builds a memory of multimodal experience insights from sub-optimal demonstrations and human feedback. Given a noisy demonstration in a new domain, VLMs abstract the trajectory into a general program by fixing inefficient actions and annotating cognitive abstractions: task relationships, object state changes, temporal subgoals, and task construals. These abstractions are refined and adapted interactively through human feedback while the agent attempts to execute the trajectory in a similar environment. The resulting abstractions, when used as exemplars in the prompt, significantly improve decision-making in retrieval-augmented LLM and VLM agents. Our ICAL agent surpasses the state-of-the-art in dialogue-based instruction following in TEACh, multimodal web agents in VisualWebArena, and action anticipation in Ego4D. In TEACh, we achieve a 12.6% improvement in goal-condition success. In VisualWebArena, our task success rate improves over the SOTA from 14.3% to 22.7%. In Ego4D action forecasting, we improve over few-shot GPT-4V and remain competitive with supervised models. We show finetuning our retrieval-augmented in-context agent yields additional improvements. Our approach significantly reduces reliance on expert-crafted examples and consistently outperforms in-context learning from action plans that lack such insights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-24", "arxiv_id": "2406.15877", "title": "BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions", "authors": [ "Terry Yue Zhuo", "Minh Chien Vu", "Jenny Chim", "Han Hu", "Wenhao Yu", "Ratnadira Widyasari", "Imam Nur Bani Yusuf", "Haolan Zhan", "Junda He", "Indraneil Paul", "Simon Brunner", "Chen Gong", "Thong Hoang", "Armel Randy Zebaze", "Xiaoheng Hong", "Wen-Ding Li", "Jean Kaddour", "Ming Xu", "Zhihan Zhang", "Prateek Yadav", "Naman Jain", "Alex Gu", "Zhoujun Cheng", "Jiawei Liu", "Qian Liu", "Zijian Wang", "David Lo", "Binyuan Hui", "Niklas Muennighoff", "Daniel Fried", "Xiaoning Du", "Harm de Vries", "Leandro Von Werra" ], "github": "https://github.com/bigcode-project/bigcodebench-annotation", "project_page": "", "abstract": "Automated software engineering has been greatly empowered by the recent advances in Large Language Models (LLMs) for programming. While current benchmarks have shown that LLMs can perform various software engineering tasks like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks. Solving challenging and practical programming tasks requires the capability of utilizing diverse function calls as tools to efficiently implement functionalities like data analysis and web development. In addition, using multiple tools to solve a task needs compositional reasoning by accurately understanding complex instructions. Fulfilling both of these characteristics can pose a great challenge for LLMs. To assess how well LLMs can solve challenging and practical programming tasks, we introduce Bench, a benchmark that challenges LLMs to invoke multiple function calls as tools from 139 libraries and 7 domains for 1,140 fine-grained programming tasks. To evaluate LLMs rigorously, each programming task encompasses 5.6 test cases with an average branch coverage of 99%. In addition, we propose a natural-language-oriented variant of Bench, Benchi, that automatically transforms the original docstrings into short instructions only with essential information. Our extensive evaluation of 60 LLMs shows that LLMs are not yet capable of following complex instructions to use function calls precisely, with scores up to 60%, significantly lower than the human performance of 97%. The results underscore the need for further advancements in this area.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bigcode-project/bigcodebench-annotation", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16260", "title": "Video-Infinity: Distributed Long Video Generation", "authors": [ "Zhenxiong Tan", "Xingyi Yang", "Songhua Liu", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Diffusion models have recently achieved remarkable results for video generation. Despite the encouraging performances, the generated videos are typically constrained to a small number of frames, resulting in clips lasting merely a few seconds. The primary challenges in producing longer videos include the substantial memory requirements and the extended processing time required on a single GPU. A straightforward solution would be to split the workload across multiple GPUs, which, however, leads to two issues: (1) ensuring all GPUs communicate effectively to share timing and context information, and (2) modifying existing video diffusion models, which are usually trained on short sequences, to create longer videos without additional training. To tackle these, in this paper we introduce Video-Infinity, a distributed inference pipeline that enables parallel processing across multiple GPUs for long-form video generation. Specifically, we propose two coherent mechanisms: Clip parallelism and Dual-scope attention. Clip parallelism optimizes the gathering and sharing of context information across GPUs which minimizes communication overhead, while Dual-scope attention modulates the temporal self-attention to balance local and global contexts efficiently across the devices. Together, the two mechanisms join forces to distribute the workload and enable the fast generation of long videos. Under an 8 x Nvidia 6000 Ada GPU (48G) setup, our method generates videos up to 2,300 frames in approximately 5 minutes, enabling long video generation at a speed 100 times faster than the prior methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16338", "title": "VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models", "authors": [ "Yuxuan Wang", "Yueqian Wang", "Dongyan Zhao", "Cihang Xie", "Zilong Zheng" ], "github": "", "project_page": "", "abstract": "Recent advancements in Multimodal Large Language Models (MLLMs) have extended their capabilities to video understanding. Yet, these models are often plagued by \"hallucinations\", where irrelevant or nonsensical content is generated, deviating from the actual video context. This work introduces VideoHallucer, the first comprehensive benchmark for hallucination detection in large video-language models (LVLMs). VideoHallucer categorizes hallucinations into two main types: intrinsic and extrinsic, offering further subcategories for detailed analysis, including object-relation, temporal, semantic detail, extrinsic factual, and extrinsic non-factual hallucinations. We adopt an adversarial binary VideoQA method for comprehensive evaluation, where pairs of basic and hallucinated questions are crafted strategically. By evaluating eleven LVLMs on VideoHallucer, we reveal that i) the majority of current models exhibit significant issues with hallucinations; ii) while scaling datasets and parameters improves models' ability to detect basic visual cues and counterfactuals, it provides limited benefit for detecting extrinsic factual hallucinations; iii) existing models are more adept at detecting facts than identifying hallucinations. As a byproduct, these analyses further instruct the development of our self-PEP framework, achieving an average of 5.38% improvement in hallucination resistance across all model architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16758", "title": "Towards Fast Multilingual LLM Inference: Speculative Decoding and Specialized Drafters", "authors": [ "Euiin Yi", "Taehyeon Kim", "Hongseok Jeung", "Du-Seong Chang", "Se-Young Yun" ], "github": "https://github.com/Kthyeon/Multilingual-SpecBench", "project_page": "", "abstract": "Large language models (LLMs) have revolutionized natural language processing and broadened their applicability across diverse commercial applications. However, the deployment of these models is constrained by high inference time in multilingual settings. To mitigate this challenge, this paper explores a training recipe of an assistant model in speculative decoding, which are leveraged to draft and-then its future tokens are verified by the target LLM. We show that language-specific draft models, optimized through a targeted pretrain-and-finetune strategy, substantially brings a speedup of inference time compared to the previous methods. We validate these models across various languages in inference time, out-of-domain speedup, and GPT-4o evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Kthyeon/Multilingual-SpecBench", "github_api_added_by": "auto" }, { "date": "2024-06-25", "arxiv_id": "2406.16855", "title": "DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation", "authors": [ "Yuang Peng", "Yuxin Cui", "Haomiao Tang", "Zekun Qi", "Runpei Dong", "Jing Bai", "Chunrui Han", "Zheng Ge", "Xiangyu Zhang", "Shu-Tao Xia" ], "github": "https://github.com/yuangpeng/dreambench_plus", "project_page": "", "abstract": "Personalized image generation holds great promise in assisting humans in everyday work and life due to its impressive function in creatively generating personalized content. However, current evaluations either are automated but misalign with humans or require human evaluations that are time-consuming and expensive. In this work, we present DreamBench++, a human-aligned benchmark automated by advanced multimodal GPT models. Specifically, we systematically design the prompts to let GPT be both human-aligned and self-aligned, empowered with task reinforcement. Further, we construct a comprehensive dataset comprising diverse images and prompts. By benchmarking 7 modern generative models, we demonstrate that DreamBench++ results in significantly more human-aligned evaluation, helping boost the community with innovative findings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yuangpeng/dreambench_plus", "github_api_added_by": "auto" }, { "date": "2024-06-25", "arxiv_id": "2406.14833", "title": "Efficient Continual Pre-training by Mitigating the Stability Gap", "authors": [ "Yiduo Guo", "Jie Fu", "Huishuai Zhang", "Dongyan Zhao", "Yikang Shen" ], "github": "", "project_page": "", "abstract": "Continual pre-training has increasingly become the predominant approach for adapting Large Language Models (LLMs) to new domains. This process involves updating the pre-trained LLM with a corpus from a new domain, resulting in a shift in the training distribution. To study the behavior of LLMs during this shift, we measured the model's performance throughout the continual pre-training process. we observed a temporary performance drop at the beginning, followed by a recovery phase, a phenomenon known as the \"stability gap,\" previously noted in vision models classifying new classes. To address this issue and enhance LLM performance within a fixed compute budget, we propose three effective strategies: (1) Continually pre-training the LLM on a subset with a proper size for multiple epochs, resulting in faster performance recovery than pre-training the LLM on a large corpus in a single epoch; (2) Pre-training the LLM only on high-quality sub-corpus, which rapidly boosts domain performance; and (3) Using a data mixture similar to the pre-training data to reduce distribution gap. We conduct various experiments on Llama-family models to validate the effectiveness of our strategies in both medical continual pre-training and instruction tuning. For example, our strategies improve the average medical task performance of the OpenLlama-3B model from 36.2% to 40.7% with only 40% of the original training budget and enhance the average general task performance without causing forgetting. Furthermore, we apply our strategies to the Llama-3-8B model. The resulting model, Llama-3-Physician, achieves the best medical performance among current open-source models, and performs comparably to or even better than GPT-4 on several medical benchmarks. We release our models at https://huggingface.co/YiDuo1999/Llama-3-Physician-8B-Instruct.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16852", "title": "Long Context Transfer from Language to Vision", "authors": [ "Peiyuan Zhang", "Kaichen Zhang", "Bo Li", "Guangtao Zeng", "Jingkang Yang", "Yuanhan Zhang", "Ziyue Wang", "Haoran Tan", "Chunyuan Li", "Ziwei Liu" ], "github": "https://github.com/EvolvingLMMs-Lab/LongVA", "project_page": "", "abstract": "Video sequences offer valuable temporal information, but existing large multimodal models (LMMs) fall short in understanding extremely long videos. Many works address this by reducing the number of visual tokens using visual resamplers. Alternatively, in this paper, we approach this problem from the perspective of the language model. By simply extrapolating the context length of the language backbone, we enable LMMs to comprehend orders of magnitude more visual tokens without any video training. We call this phenomenon long context transfer and carefully ablate its properties. To effectively measure LMMs' ability to generalize to long contexts in the vision modality, we develop V-NIAH (Visual Needle-In-A-Haystack), a purely synthetic long vision benchmark inspired by the language model's NIAH test. Our proposed Long Video Assistant (LongVA) can process 2000 frames or over 200K visual tokens without additional complexities. With its extended context length, LongVA achieves state-of-the-art performance on Video-MME among 7B-scale models by densely sampling more input frames. Our work is open-sourced at https://github.com/EvolvingLMMs-Lab/LongVA.", "github_manual": "", "github_regex": "https://github.com/EvolvingLMMs-Lab/LongVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16235", "title": "Preference Tuning For Toxicity Mitigation Generalizes Across Languages", "authors": [ "Xiaochen Li", "Zheng-Xin Yong", "Stephen H. Bach" ], "github": "https://github.com/batsresearch/cross-lingual-detox", "project_page": "", "abstract": "Detoxifying multilingual Large Language Models (LLMs) has become crucial due to their increasing global use. In this work, we explore zero-shot cross-lingual generalization of preference tuning in detoxifying LLMs. Unlike previous studies that show limited cross-lingual generalization for other safety tasks, we demonstrate that Direct Preference Optimization (DPO) training with only English data can significantly reduce toxicity in multilingual open-ended generations. For example, the probability of mGPT-1.3B generating toxic continuations drops from 46.8% to 3.9% across 17 different languages after training. Our results also extend to other multilingual LLMs, such as BLOOM, Llama3, and Aya-23. Using mechanistic interpretability tools like causal intervention and activation analysis, we identified the dual multilinguality property of MLP layers in LLMs, which explains the cross-lingual generalization of DPO. Finally, we show that bilingual sentence retrieval can predict the cross-lingual transferability of DPO preference tuning.", "github_manual": "https://github.com/batsresearch/cross-lingual-detox", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16690", "title": "Scaling Laws for Linear Complexity Language Models", "authors": [ "Xuyang Shen", "Dong Li", "Ruitao Leng", "Zhen Qin", "Weigao Sun", "Yiran Zhong" ], "github": "", "project_page": "", "abstract": "The interest in linear complexity models for large language models is on the rise, although their scaling capacity remains uncertain. In this study, we present the scaling laws for linear complexity language models to establish a foundation for their scalability. Specifically, we examine the scaling behaviors of three efficient linear architectures. These include TNL, a linear attention model with data-independent decay; HGRN2, a linear RNN with data-dependent decay; and cosFormer2, a linear attention model without decay. We also include LLaMA as a baseline architecture for softmax attention for comparison. These models were trained with six variants, ranging from 70M to 7B parameters on a 300B-token corpus, and evaluated with a total of 1,376 intermediate checkpoints on various downstream tasks. These tasks include validation loss, commonsense reasoning, and information retrieval and generation. The study reveals that existing linear complexity language models exhibit similar scaling capabilities as conventional transformer-based models while also demonstrating superior linguistic proficiency and knowledge retention.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16768", "title": "WARP: On the Benefits of Weight Averaged Rewarded Policies", "authors": [ "Alexandre Ram\u00e9", "Johan Ferret", "Nino Vieillard", "Robert Dadashi", "L\u00e9onard Hussenot", "Pierre-Louis Cedoz", "Pier Giuseppe Sessa", "Sertan Girgin", "Arthur Douillard", "Olivier Bachem" ], "github": "", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) aligns large language models (LLMs) by encouraging their generations to have high rewards, using a reward model trained on human preferences. To prevent the forgetting of pre-trained knowledge, RLHF usually incorporates a KL regularization; this forces the policy to remain close to its supervised fine-tuned initialization, though it hinders the reward optimization. To tackle the trade-off between KL and reward, in this paper we introduce a novel alignment strategy named Weight Averaged Rewarded Policies (WARP). WARP merges policies in the weight space at three distinct stages. First, it uses the exponential moving average of the policy as a dynamic anchor in the KL regularization. Second, it applies spherical interpolation to merge independently fine-tuned policies into a new enhanced one. Third, it linearly interpolates between this merged model and the initialization, to recover features from pre-training. This procedure is then applied iteratively, with each iteration's final model used as an advanced initialization for the next, progressively refining the KL-reward Pareto front, achieving superior rewards at fixed KL. Experiments with GEMMA policies validate that WARP improves their quality and alignment, outperforming other open-source LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.14540", "title": "IRASim: Learning Interactive Real-Robot Action Simulators", "authors": [ "Fangqi Zhu", "Hongtao Wu", "Song Guo", "Yuxiao Liu", "Chilam Cheang", "Tao Kong" ], "github": "", "project_page": "", "abstract": "Scalable robot learning in the real world is limited by the cost and safety issues of real robots. In addition, rolling out robot trajectories in the real world can be time-consuming and labor-intensive. In this paper, we propose to learn an interactive real-robot action simulator as an alternative. We introduce a novel method, IRASim, which leverages the power of generative models to generate extremely realistic videos of a robot arm that executes a given action trajectory, starting from an initial given frame. To validate the effectiveness of our method, we create a new benchmark, IRASim Benchmark, based on three real-robot datasets and perform extensive experiments on the benchmark. Results show that IRASim outperforms all the baseline methods and is more preferable in human evaluations. We hope that IRASim can serve as an effective and scalable approach to enhance robot learning in the real world. To promote research for generative real-robot action simulators, we open-source code, benchmark, and checkpoints at https: //gen-irasim.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16815", "title": "ClotheDreamer: Text-Guided Garment Generation with 3D Gaussians", "authors": [ "Yufei Liu", "Junshu Tang", "Chu Zheng", "Shijie Zhang", "Jinkun Hao", "Junwei Zhu", "Dongjin Huang" ], "github": "", "project_page": "", "abstract": "High-fidelity 3D garment synthesis from text is desirable yet challenging for digital avatar creation. Recent diffusion-based approaches via Score Distillation Sampling (SDS) have enabled new possibilities but either intricately couple with human body or struggle to reuse. We introduce ClotheDreamer, a 3D Gaussian-based method for generating wearable, production-ready 3D garment assets from text prompts. We propose a novel representation Disentangled Clothe Gaussian Splatting (DCGS) to enable separate optimization. DCGS represents clothed avatar as one Gaussian model but freezes body Gaussian splats. To enhance quality and completeness, we incorporate bidirectional SDS to supervise clothed avatar and garment RGBD renderings respectively with pose conditions and propose a new pruning strategy for loose clothing. Our approach can also support custom clothing templates as input. Benefiting from our design, the synthetic 3D garment can be easily applied to virtual try-on and support physically accurate animation. Extensive experiments showcase our method's superior and competitive performance. Our project page is at https://ggxxii.github.io/clothedreamer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.15704", "title": "video-SALMONN: Speech-Enhanced Audio-Visual Large Language Models", "authors": [ "Guangzhi Sun", "Wenyi Yu", "Changli Tang", "Xianzhao Chen", "Tian Tan", "Wei Li", "Lu Lu", "Zejun Ma", "Yuxuan Wang", "Chao Zhang" ], "github": "https://github.com/bytedance/SALMONN", "project_page": "", "abstract": "Speech understanding as an element of the more generic video understanding using audio-visual large language models (av-LLMs) is a crucial yet understudied aspect. This paper proposes video-SALMONN, a single end-to-end av-LLM for video processing, which can understand not only visual frame sequences, audio events and music, but speech as well. To obtain fine-grained temporal information required by speech understanding, while keeping efficient for other video elements, this paper proposes a novel multi-resolution causal Q-Former (MRC Q-Former) structure to connect pre-trained audio-visual encoders and the backbone large language model. Moreover, dedicated training approaches including the diversity loss and the unpaired audio-visual mixed training scheme are proposed to avoid frames or modality dominance. On the introduced speech-audio-visual evaluation benchmark, video-SALMONN achieves more than 25\\% absolute accuracy improvements on the video-QA task and over 30\\% absolute accuracy improvements on audio-visual QA tasks with human speech. In addition, video-SALMONN demonstrates remarkable video comprehension and reasoning abilities on tasks that are unprecedented by other av-LLMs. Our training code and model checkpoints are available at \\url{https://github.com/bytedance/SALMONN/}.", "github_manual": "", "github_regex": "https://github.com/bytedance/SALMONN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.15927", "title": "Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs", "authors": [ "Jannik Kossen", "Jiatong Han", "Muhammed Razzak", "Lisa Schut", "Shreshth Malik", "Yarin Gal" ], "github": "https://github.com/oatml/semantic-entropy-probes", "project_page": "", "abstract": "We propose semantic entropy probes (SEPs), a cheap and reliable method for uncertainty quantification in Large Language Models (LLMs). Hallucinations, which are plausible-sounding but factually incorrect and arbitrary model generations, present a major challenge to the practical adoption of LLMs. Recent work by Farquhar et al. (2024) proposes semantic entropy (SE), which can detect hallucinations by estimating uncertainty in the space semantic meaning for a set of model generations. However, the 5-to-10-fold increase in computation cost associated with SE computation hinders practical adoption. To address this, we propose SEPs, which directly approximate SE from the hidden states of a single generation. SEPs are simple to train and do not require sampling multiple model generations at test time, reducing the overhead of semantic uncertainty quantification to almost zero. We show that SEPs retain high performance for hallucination detection and generalize better to out-of-distribution data than previous probing methods that directly predict model accuracy. Our results across models and tasks suggest that model hidden states capture SE, and our ablation studies give further insights into the token positions and model layers for which this is the case.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/oatml/semantic-entropy-probes", "github_api_added_by": "auto" }, { "date": "2024-06-25", "arxiv_id": "2406.16860", "title": "Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs", "authors": [ "Shengbang Tong", "Ellis Brown", "Penghao Wu", "Sanghyun Woo", "Manoj Middepogu", "Sai Charitha Akula", "Jihan Yang", "Shusheng Yang", "Adithya Iyer", "Xichen Pan", "Austin Wang", "Rob Fergus", "Yann LeCun", "Saining Xie" ], "github": "https://github.com/cambrian-mllm/cambrian", "project_page": "", "abstract": "We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach. While stronger language models can enhance multimodal capabilities, the design choices for vision components are often insufficiently explored and disconnected from visual representation learning research. This gap hinders accurate sensory grounding in real-world scenarios. Our study uses LLMs and visual instruction tuning as an interface to evaluate various visual representations, offering new insights into different models and architectures -- self-supervised, strongly supervised, or combinations thereof -- based on experiments with over 20 vision encoders. We critically examine existing MLLM benchmarks, addressing the difficulties involved in consolidating and interpreting results from various tasks, and introduce a new vision-centric benchmark, CV-Bench. To further improve visual grounding, we propose the Spatial Vision Aggregator (SVA), a dynamic and spatially-aware connector that integrates high-resolution vision features with LLMs while reducing the number of tokens. Additionally, we discuss the curation of high-quality visual instruction-tuning data from publicly available sources, emphasizing the importance of data source balancing and distribution ratio. Collectively, Cambrian-1 not only achieves state-of-the-art performance but also serves as a comprehensive, open cookbook for instruction-tuned MLLMs. We provide model weights, code, supporting tools, datasets, and detailed instruction-tuning and evaluation recipes. We hope our release will inspire and accelerate advancements in multimodal systems and visual representation learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cambrian-mllm/cambrian", "github_api_added_by": "auto" }, { "date": "2024-06-25", "arxiv_id": "2406.16772", "title": "OlympicArena Medal Ranks: Who Is the Most Intelligent AI So Far?", "authors": [ "Zhen Huang", "Zengzhi Wang", "Shijie Xia", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/OlympicArena", "project_page": "", "abstract": "In this report, we pose the following question: Who is the most intelligent AI model to date, as measured by the OlympicArena (an Olympic-level, multi-discipline, multi-modal benchmark for superintelligent AI)? We specifically focus on the most recently released models: Claude-3.5-Sonnet, Gemini-1.5-Pro, and GPT-4o. For the first time, we propose using an Olympic medal Table approach to rank AI models based on their comprehensive performance across various disciplines. Empirical results reveal: (1) Claude-3.5-Sonnet shows highly competitive overall performance over GPT-4o, even surpassing GPT-4o on a few subjects (i.e., Physics, Chemistry, and Biology). (2) Gemini-1.5-Pro and GPT-4V are ranked consecutively just behind GPT-4o and Claude-3.5-Sonnet, but with a clear performance gap between them. (3) The performance of AI models from the open-source community significantly lags behind these proprietary models. (4) The performance of these models on this benchmark has been less than satisfactory, indicating that we still have a long way to go before achieving superintelligence. We remain committed to continuously tracking and evaluating the performance of the latest powerful models on this benchmark (available at https://github.com/GAIR-NLP/OlympicArena).", "github_manual": "", "github_regex": "https://github.com/GAIR-NLP/OlympicArena", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16048", "title": "Evaluating D-MERIT of Partial-annotation on Information Retrieval", "authors": [ "Royi Rassin", "Yaron Fairstein", "Oren Kalinsky", "Guy Kushilevitz", "Nachshon Cohen", "Alexander Libov", "Yoav Goldberg" ], "github": "", "project_page": "", "abstract": "Retrieval models are often evaluated on partially-annotated datasets. Each query is mapped to a few relevant texts and the remaining corpus is assumed to be irrelevant. As a result, models that successfully retrieve false negatives are punished in evaluation. Unfortunately, completely annotating all texts for every query is not resource efficient. In this work, we show that using partially-annotated datasets in evaluation can paint a distorted picture. We curate D-MERIT, a passage retrieval evaluation set from Wikipedia, aspiring to contain all relevant passages for each query. Queries describe a group (e.g., ``journals about linguistics'') and relevant passages are evidence that entities belong to the group (e.g., a passage indicating that Language is a journal about linguistics). We show that evaluating on a dataset containing annotations for only a subset of the relevant passages might result in misleading ranking of the retrieval systems and that as more relevant texts are included in the evaluation set, the rankings converge. We propose our dataset as a resource for evaluation and our study as a recommendation for balance between resource-efficiency and reliable evaluation when annotating evaluation sets for text retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.14051", "title": "How Many Parameters Does it Take to Change a Light Bulb? Evaluating Performance in Self-Play of Conversational Games as a Function of Model Characteristics", "authors": [ "Nidhir Bhavsar", "Jonathan Jordan", "Sherzod Hakimov", "David Schlangen" ], "github": "", "project_page": "", "abstract": "What makes a good Large Language Model (LLM)? That it performs well on the relevant benchmarks -- which hopefully measure, with some validity, the presence of capabilities that are also challenged in real application. But what makes the model perform well? What gives a model its abilities? We take a recently introduced type of benchmark that is meant to challenge capabilities in a goal-directed, agentive context through self-play of conversational games, and analyse how performance develops as a function of model characteristics like number of parameters, or type of training. We find that while there is a clear relationship between number of parameters and performance, there is still a wide spread of performance points within a given size bracket, which is to be accounted for by training parameters such as fine-tuning data quality and method. From a more practical angle, we also find a certain degree of unpredictability about performance across access methods, possible due to unexposed sampling parameters, and a, very welcome, performance stability against at least moderate weight quantisation during inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16714", "title": "AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models", "authors": [ "Jiale Cheng", "Yida Lu", "Xiaotao Gu", "Pei Ke", "Xiao Liu", "Yuxiao Dong", "Hongning Wang", "Jie Tang", "Minlie Huang" ], "github": "https://github.com/thu-coai/AutoDetect", "project_page": "", "abstract": "Although Large Language Models (LLMs) are becoming increasingly powerful, they still exhibit significant but subtle weaknesses, such as mistakes in instruction-following or coding tasks. As these unexpected errors could lead to severe consequences in practical deployments, it is crucial to investigate the limitations within LLMs systematically. Traditional benchmarking approaches cannot thoroughly pinpoint specific model deficiencies, while manual inspections are costly and not scalable. In this paper, we introduce a unified framework, AutoDetect, to automatically expose weaknesses in LLMs across various tasks. Inspired by the educational assessment process that measures students' learning outcomes, AutoDetect consists of three LLM-powered agents: Examiner, Questioner, and Assessor. The collaboration among these three agents is designed to realize comprehensive and in-depth weakness identification. Our framework demonstrates significant success in uncovering flaws, with an identification success rate exceeding 30% in prominent models such as ChatGPT and Claude. More importantly, these identified weaknesses can guide specific model improvements, proving more effective than untargeted data augmentation methods like Self-Instruct. Our approach has led to substantial enhancements in popular LLMs, including the Llama series and Mistral-7b, boosting their performance by over 10% across several benchmarks. Code and data are publicly available at https://github.com/thu-coai/AutoDetect.", "github_manual": "", "github_regex": "https://github.com/thu-coai/AutoDetect", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16747", "title": "Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers", "authors": [ "Chao Lou", "Zixia Jia", "Zilong Zheng", "Kewei Tu" ], "github": "", "project_page": "", "abstract": "Accommodating long sequences efficiently in autoregressive Transformers, especially within an extended context window, poses significant challenges due to the quadratic computational complexity and substantial KV memory requirements inherent in self-attention mechanisms. In this work, we introduce SPARSEK Attention, a novel sparse attention mechanism designed to overcome these computational and memory obstacles while maintaining performance. Our approach integrates a scoring network and a differentiable top-k mask operator, SPARSEK, to select a constant number of KV pairs for each query, thereby enabling gradient-based optimization. As a result, SPARSEK Attention offers linear time complexity and constant memory footprint during generation. Experimental results reveal that SPARSEK Attention outperforms previous sparse attention methods and provides significant speed improvements during both training and inference, particularly in language modeling and downstream tasks. Furthermore, our method can be seamlessly integrated into pre-trained Large Language Models (LLMs) with minimal fine-tuning, offering a practical solution for effectively managing long-range dependencies in diverse applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.15718", "title": "Beyond the Turn-Based Game: Enabling Real-Time Conversations with Duplex Models", "authors": [ "Xinrong Zhang", "Yingfa Chen", "Shengding Hu", "Xu Han", "Zihang Xu", "Yuanwei Xu", "Weilin Zhao", "Maosong Sun", "Zhiyuan Liu" ], "github": "https://github.com/thunlp/duplex-model", "project_page": "", "abstract": "As large language models (LLMs) increasingly permeate daily lives, there is a growing demand for real-time interactions that mirror human conversations. Traditional turn-based chat systems driven by LLMs prevent users from verbally interacting with the system while it is generating responses. To overcome these limitations, we adapt existing LLMs to duplex models so that these LLMs can listen for users while generating output and dynamically adjust themselves to provide users with instant feedback. % such as in response to interruptions. Specifically, we divide the queries and responses of conversations into several time slices and then adopt a time-division-multiplexing (TDM) encoding-decoding strategy to pseudo-simultaneously process these slices. Furthermore, to make LLMs proficient enough to handle real-time conversations, we build a fine-tuning dataset consisting of alternating time slices of queries and responses as well as covering typical feedback types in instantaneous interactions. Our experiments show that although the queries and responses of conversations are segmented into incomplete slices for processing, LLMs can preserve their original performance on standard benchmarks with a few fine-tuning steps on our dataset. Automatic and human evaluation indicate that duplex models make user-AI interactions more natural and human-like, and greatly improve user satisfaction compared to vanilla LLMs. Our duplex model and dataset will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thunlp/duplex-model", "github_api_added_by": "auto" }, { "date": "2024-06-25", "arxiv_id": "2406.16254", "title": "Confidence Regulation Neurons in Language Models", "authors": [ "Alessandro Stolfo", "Ben Wu", "Wes Gurnee", "Yonatan Belinkov", "Xingyi Song", "Mrinmaya Sachan", "Neel Nanda" ], "github": "https://github.com/bpwu1/confidence-regulation-neurons", "project_page": "", "abstract": "Despite their widespread use, the mechanisms by which large language models (LLMs) represent and regulate uncertainty in next-token predictions remain largely unexplored. This study investigates two critical components believed to influence this uncertainty: the recently discovered entropy neurons and a new set of components that we term token frequency neurons. Entropy neurons are characterized by an unusually high weight norm and influence the final layer normalization (LayerNorm) scale to effectively scale down the logits. Our work shows that entropy neurons operate by writing onto an unembedding null space, allowing them to impact the residual stream norm with minimal direct effect on the logits themselves. We observe the presence of entropy neurons across a range of models, up to 7 billion parameters. On the other hand, token frequency neurons, which we discover and describe here for the first time, boost or suppress each token's logit proportionally to its log frequency, thereby shifting the output distribution towards or away from the unigram distribution. Finally, we present a detailed case study where entropy neurons actively manage confidence in the setting of induction, i.e. detecting and continuing repeated subsequences.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bpwu1/confidence-regulation-neurons", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.13632", "title": "Can Few-shot Work in Long-Context? Recycling the Context to Generate Demonstrations", "authors": [ "Arie Cattan", "Alon Jacovi", "Alex Fabrikant", "Jonathan Herzig", "Roee Aharoni", "Hannah Rashkin", "Dror Marcus", "Avinatan Hassidim", "Yossi Matias", "Idan Szpektor", "Avi Caciularu" ], "github": "", "project_page": "", "abstract": "Despite recent advancements in Large Language Models (LLMs), their performance on tasks involving long contexts remains sub-optimal. In-Context Learning (ICL) with few-shot examples may be an appealing solution to enhance LLM performance in this scenario; However, naively adding ICL examples with long context introduces challenges, including substantial token overhead added for each few-shot example and context mismatch between the demonstrations and the target query. In this work, we propose to automatically generate few-shot examples for long context QA tasks by recycling contexts. Specifically, given a long input context (1-3k tokens) and a query, we generate additional query-output pairs from the given context as few-shot examples, while introducing the context only once. This ensures that the demonstrations are leveraging the same context as the target query while only adding a small number of tokens to the prompt. We further enhance each demonstration by instructing the model to explicitly identify the relevant paragraphs before the answer, which improves performance while providing fine-grained attribution to the answer source. We apply our method on multiple LLMs and obtain substantial improvements (+23\\% on average across models) on various QA datasets with long context, especially when the answer lies within the middle of the context. Surprisingly, despite introducing only single-hop ICL examples, LLMs also successfully generalize to multi-hop long-context QA using our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16008", "title": "Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization", "authors": [ "Cheng-Yu Hsieh", "Yung-Sung Chuang", "Chun-Liang Li", "Zifeng Wang", "Long T. Le", "Abhishek Kumar", "James Glass", "Alexander Ratner", "Chen-Yu Lee", "Ranjay Krishna", "Tomas Pfister" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input. This phenomenon has been known as the lost-in-the-middle problem. In this work, we make three contributions. First, we set out to understand the factors that cause this phenomenon. In doing so, we establish a connection between lost-in-the-middle to LLMs' intrinsic attention bias: LLMs exhibit a U-shaped attention bias where the tokens at the beginning and at the end of its input receive higher attention, regardless of their relevance. Second, we mitigate this positional bias through a calibration mechanism, found-in-the-middle, that allows the model to attend to contexts faithfully according to their relevance, even though when they are in the middle. Third, we show found-in-the-middle not only achieves better performance in locating relevant information within a long context, but also eventually leads to improved retrieval-augmented generation (RAG) performance across various tasks, outperforming existing methods by up to 15 percentage points. These findings open up future directions in understanding LLM attention bias and its potential consequences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-25", "arxiv_id": "2406.16683", "title": "Repulsive Score Distillation for Diverse Sampling of Diffusion Models", "authors": [ "Nicolas Zilberstein", "Morteza Mardani", "Santiago Segarra" ], "github": "https://github.com/nzilberstein/Repulsive-score-distillation-RSD-", "project_page": "", "abstract": "Score distillation sampling has been pivotal for integrating diffusion models into generation of complex visuals. Despite impressive results it suffers from mode collapse and lack of diversity. To cope with this challenge, we leverage the gradient flow interpretation of score distillation to propose Repulsive Score Distillation (RSD). In particular, we propose a variational framework based on repulsion of an ensemble of particles that promotes diversity. Using a variational approximation that incorporates a coupling among particles, the repulsion appears as a simple regularization that allows interaction of particles based on their relative pairwise similarity, measured e.g., via radial basis kernels. We design RSD for both unconstrained and constrained sampling scenarios. For constrained sampling we focus on inverse problems in the latent space that leads to an augmented variational formulation, that strikes a good balance between compute, quality and diversity. Our extensive experiments for text-to-image generation, and inverse problems demonstrate that RSD achieves a superior trade-off between diversity and quality compared with state-of-the-art alternatives.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nzilberstein/Repulsive-score-distillation-RSD-", "github_api_added_by": "auto" }, { "date": "2024-06-26", "arxiv_id": "2406.16273", "title": "YouDream: Generating Anatomically Controllable Consistent Text-to-3D Animals", "authors": [ "Sandeep Mishra", "Oindrila Saha", "Alan C. Bovik" ], "github": "", "project_page": "", "abstract": "3D generation guided by text-to-image diffusion models enables the creation of visually compelling assets. However previous methods explore generation based on image or text. The boundaries of creativity are limited by what can be expressed through words or the images that can be sourced. We present YouDream, a method to generate high-quality anatomically controllable animals. YouDream is guided using a text-to-image diffusion model controlled by 2D views of a 3D pose prior. Our method generates 3D animals that are not possible to create using previous text-to-3D generative methods. Additionally, our method is capable of preserving anatomic consistency in the generated animals, an area where prior text-to-3D approaches often struggle. Moreover, we design a fully automated pipeline for generating commonly found animals. To circumvent the need for human intervention to create a 3D pose, we propose a multi-agent LLM that adapts poses from a limited library of animal 3D poses to represent the desired animal. A user study conducted on the outcomes of YouDream demonstrates the preference of the animal models generated by our method over others. Turntable results and code are released at https://youdream3d.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.17245", "title": "Unlocking Continual Learning Abilities in Language Models", "authors": [ "Wenyu Du", "Shuang Cheng", "Tongxu Luo", "Zihan Qiu", "Zeyu Huang", "Ka Chun Cheung", "Reynold Cheng", "Jie Fu" ], "github": "https://github.com/wenyudu/MIGU", "project_page": "", "abstract": "Language models (LMs) exhibit impressive performance and generalization capabilities. However, LMs struggle with the persistent challenge of catastrophic forgetting, which undermines their long-term sustainability in continual learning (CL). Existing approaches usually address the issue by incorporating old task data or task-wise inductive bias into LMs. However, old data and accurate task information are often unavailable or costly to collect, hindering the availability of current CL approaches for LMs. To address this limitation, we introduce MIGU (MagnItude-based Gradient Updating for continual learning), a rehearsal-free and task-label-free method that only updates the model parameters with large magnitudes of output in LMs' linear layers. MIGU is based on our observation that the L1-normalized magnitude distribution of the output in LMs' linear layers is different when the LM models deal with different task data. By imposing this simple constraint on the gradient update process, we can leverage the inherent behaviors of LMs, thereby unlocking their innate CL abilities. Our experiments demonstrate that MIGU is universally applicable to all three LM architectures (T5, RoBERTa, and Llama2), delivering state-of-the-art or on-par performance across continual finetuning and continual pre-training settings on four CL benchmarks. For example, MIGU brings a 15.2% average accuracy improvement over conventional parameter-efficient finetuning baselines in a 15-task CL benchmark. MIGU can also seamlessly integrate with all three existing CL types to further enhance performance. Code is available at https://github.com/wenyudu/MIGU{this https URL}.", "github_manual": "", "github_regex": "https://github.com/wenyudu/MIGU", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.17770", "title": "MG-LLaVA: Towards Multi-Granularity Visual Instruction Tuning", "authors": [ "Xiangyu Zhao", "Xiangtai Li", "Haodong Duan", "Haian Huang", "Yining Li", "Kai Chen", "Hua Yang" ], "github": "https://github.com/PhoenixZ810/MG-LLaVA", "project_page": "", "abstract": "Multi-modal large language models (MLLMs) have made significant strides in various visual understanding tasks. However, the majority of these models are constrained to process low-resolution images, which limits their effectiveness in perception tasks that necessitate detailed visual information. In our study, we present MG-LLaVA, an innovative MLLM that enhances the model's visual processing capabilities by incorporating a multi-granularity vision flow, which includes low-resolution, high-resolution, and object-centric features. We propose the integration of an additional high-resolution visual encoder to capture fine-grained details, which are then fused with base visual features through a Conv-Gate fusion network. To further refine the model's object recognition abilities, we incorporate object-level features derived from bounding boxes identified by offline detectors. Being trained solely on publicly available multimodal data through instruction tuning, MG-LLaVA demonstrates exceptional perception skills. We instantiate MG-LLaVA with a wide variety of language encoders, ranging from 3.8B to 34B, to evaluate the model's performance comprehensively. Extensive evaluations across multiple benchmarks demonstrate that MG-LLaVA outperforms existing MLLMs of comparable parameter sizes, showcasing its remarkable efficacy. The code will be available at https://github.com/PhoenixZ810/MG-LLaVA.", "github_manual": "", "github_regex": "https://github.com/PhoenixZ810/MG-LLaVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.17588", "title": "LongIns: A Challenging Long-context Instruction-based Exam for LLMs", "authors": [ "Shawn Gavin", "Tuney Zheng", "Jiaheng Liu", "Quehry Que", "Noah Wang", "Jian Yang", "Chenchen Zhang", "Wenhao Huang", "Wenhu Chen", "Ge Zhang" ], "github": "", "project_page": "", "abstract": "The long-context capabilities of large language models (LLMs) have been a hot topic in recent years. To evaluate the performance of LLMs in different scenarios, various assessment benchmarks have emerged. However, as most of these benchmarks focus on identifying key information to answer questions, which mainly requires the retrieval ability of LLMs, these benchmarks can partially represent the reasoning performance of LLMs from large amounts of information. Meanwhile, although LLMs often claim to have context windows of 32k, 128k, 200k, or even longer, these benchmarks fail to reveal the actual supported length of these LLMs. To address these issues, we propose the LongIns benchmark dataset, a challenging long-context instruction-based exam for LLMs, which is built based on the existing instruction datasets. Specifically, in our LongIns, we introduce three evaluation settings: Global Instruction & Single Task (GIST), Local Instruction & Single Task (LIST), and Local Instruction & Multiple Tasks (LIMT). Based on LongIns, we perform comprehensive evaluations on existing LLMs and have the following important findings: (1). The top-performing GPT-4 with 128k context length performs poorly on the evaluation context window of 16k in our LongIns. (2). For the multi-hop reasoning ability of many existing LLMs, significant efforts are still needed under short context windows (less than 4k).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.17763", "title": "DiffusionPDE: Generative PDE-Solving Under Partial Observation", "authors": [ "Jiahe Huang", "Guandao Yang", "Zichen Wang", "Jeong Joon Park" ], "github": "https://github.com/jhhuangchloe/DiffusionPDE", "project_page": "", "abstract": "We introduce a general framework for solving partial differential equations (PDEs) using generative diffusion models. In particular, we focus on the scenarios where we do not have the full knowledge of the scene necessary to apply classical solvers. Most existing forward or inverse PDE approaches perform poorly when the observations on the data or the underlying coefficients are incomplete, which is a common assumption for real-world measurements. In this work, we propose DiffusionPDE that can simultaneously fill in the missing information and solve a PDE by modeling the joint distribution of the solution and coefficient spaces. We show that the learned generative priors lead to a versatile framework for accurately solving a wide range of PDEs under partial observation, significantly outperforming the state-of-the-art methods for both forward and inverse directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jhhuangchloe/DiffusionPDE", "github_api_added_by": "auto" }, { "date": "2024-06-26", "arxiv_id": "2406.16863", "title": "FreeTraj: Tuning-Free Trajectory Control in Video Diffusion Models", "authors": [ "Haonan Qiu", "Zhaoxi Chen", "Zhouxia Wang", "Yingqing He", "Menghan Xia", "Ziwei Liu" ], "github": "https://github.com/arthur-qiu/freetraj", "project_page": "", "abstract": "Diffusion model has demonstrated remarkable capability in video generation, which further sparks interest in introducing trajectory control into the generation process. While existing works mainly focus on training-based methods (e.g., conditional adapter), we argue that diffusion model itself allows decent control over the generated content without requiring any training. In this study, we introduce a tuning-free framework to achieve trajectory-controllable video generation, by imposing guidance on both noise construction and attention computation. Specifically, 1) we first show several instructive phenomenons and analyze how initial noises influence the motion trajectory of generated content. 2) Subsequently, we propose FreeTraj, a tuning-free approach that enables trajectory control by modifying noise sampling and attention mechanisms. 3) Furthermore, we extend FreeTraj to facilitate longer and larger video generation with controllable trajectories. Equipped with these designs, users have the flexibility to provide trajectories manually or opt for trajectories automatically generated by the LLM trajectory planner. Extensive experiments validate the efficacy of our approach in enhancing the trajectory controllability of video diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/arthur-qiu/freetraj", "github_api_added_by": "auto" }, { "date": "2024-06-26", "arxiv_id": "2406.17557", "title": "The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale", "authors": [ "Guilherme Penedo", "Hynek Kydl\u00ed\u010dek", "Loubna Ben allal", "Anton Lozhkov", "Margaret Mitchell", "Colin Raffel", "Leandro Von Werra", "Thomas Wolf" ], "github": "https://github.com/huggingface/datatrove/blob/main/examples/fineweb.py", "project_page": "", "abstract": "The performance of a large language model (LLM) depends heavily on the quality and size of its pretraining dataset. However, the pretraining datasets for state-of-the-art open LLMs like Llama 3 and Mixtral are not publicly available and very little is known about how they were created. In this work, we introduce FineWeb, a 15-trillion token dataset derived from 96 Common Crawl snapshots that produces better-performing LLMs than other open pretraining datasets. To advance the understanding of how best to curate high-quality pretraining datasets, we carefully document and ablate all of the design choices used in FineWeb, including in-depth investigations of deduplication and filtering strategies. In addition, we introduce FineWeb-Edu, a 1.3-trillion token collection of educational text filtered from FineWeb. LLMs pretrained on FineWeb-Edu exhibit dramatically better performance on knowledge- and reasoning-intensive benchmarks like MMLU and ARC. Along with our datasets, we publicly release our data curation codebase and all of the models trained during our ablation experiments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/datatrove/blob/main/examples/fineweb.py", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.17758", "title": "MotionBooth: Motion-Aware Customized Text-to-Video Generation", "authors": [ "Jianzong Wu", "Xiangtai Li", "Yanhong Zeng", "Jiangning Zhang", "Qianyu Zhou", "Yining Li", "Yunhai Tong", "Kai Chen" ], "github": "", "project_page": "", "abstract": "In this work, we present MotionBooth, an innovative framework designed for animating customized subjects with precise control over both object and camera movements. By leveraging a few images of a specific object, we efficiently fine-tune a text-to-video model to capture the object's shape and attributes accurately. Our approach presents subject region loss and video preservation loss to enhance the subject's learning performance, along with a subject token cross-attention loss to integrate the customized subject with motion control signals. Additionally, we propose training-free techniques for managing subject and camera motions during inference. In particular, we utilize cross-attention map manipulation to govern subject motion and introduce a novel latent shift module for camera movement control as well. MotionBooth excels in preserving the appearance of subjects while simultaneously controlling the motions in generated videos. Extensive quantitative and qualitative evaluations demonstrate the superiority and effectiveness of our method. Our project page is at https://jianzongwu.github.io/projects/motionbooth", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.17636", "title": "Aligning Diffusion Models with Noise-Conditioned Perception", "authors": [ "Alexander Gambashidze", "Anton Kulikov", "Yuriy Sosnin", "Ilya Makarov" ], "github": "", "project_page": "", "abstract": "Recent advancements in human preference optimization, initially developed for Language Models (LMs), have shown promise for text-to-image Diffusion Models, enhancing prompt alignment, visual appeal, and user preference. Unlike LMs, Diffusion Models typically optimize in pixel or VAE space, which does not align well with human perception, leading to slower and less efficient training during the preference alignment stage. We propose using a perceptual objective in the U-Net embedding space of the diffusion model to address these issues. Our approach involves fine-tuning Stable Diffusion 1.5 and XL using Direct Preference Optimization (DPO), Contrastive Preference Optimization (CPO), and supervised fine-tuning (SFT) within this embedding space. This method significantly outperforms standard latent-space implementations across various metrics, including quality and computational cost. For SDXL, our approach provides 60.8\\% general preference, 62.2\\% visual appeal, and 52.1\\% prompt following against original open-sourced SDXL-DPO on the PartiPrompts dataset, while significantly reducing compute. Our approach not only improves the efficiency and quality of human preference alignment for diffusion models but is also easily integrable with other optimization techniques. The training code and LoRA weights will be available here: https://huggingface.co/alexgambashidze/SDXL\\_NCP-DPO\\_v0.1", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.15339", "title": "Image Conductor: Precision Control for Interactive Video Synthesis", "authors": [ "Yaowei Li", "Xintao Wang", "Zhaoyang Zhang", "Zhouxia Wang", "Ziyang Yuan", "Liangbin Xie", "Yuexian Zou", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Filmmaking and animation production often require sophisticated techniques for coordinating camera transitions and object movements, typically involving labor-intensive real-world capturing. Despite advancements in generative AI for video creation, achieving precise control over motion for interactive video asset generation remains challenging. To this end, we propose Image Conductor, a method for precise control of camera transitions and object movements to generate video assets from a single image. An well-cultivated training strategy is proposed to separate distinct camera and object motion by camera LoRA weights and object LoRA weights. To further address cinematographic variations from ill-posed trajectories, we introduce a camera-free guidance technique during inference, enhancing object movements while eliminating camera transitions. Additionally, we develop a trajectory-oriented video motion data curation pipeline for training. Quantitative and qualitative experiments demonstrate our method's precision and fine-grained control in generating motion-controllable videos from images, advancing the practical application of interactive video synthesis. Project webpage available at https://liyaowei-stu.github.io/project/ImageConductor/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.16377", "title": "On the Transformations across Reward Model, Parameter Update, and In-Context Prompt", "authors": [ "Deng Cai", "Huayang Li", "Tingchen Fu", "Siheng Li", "Weiwen Xu", "Shuaiyi Li", "Bowen Cao", "Zhisong Zhang", "Xinting Huang", "Leyang Cui", "Yan Wang", "Lemao Liu", "Taro Watanabe", "Shuming Shi" ], "github": "", "project_page": "", "abstract": "Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications. In this paper, we demonstrate the interchangeability of three popular and distinct adaptation tools: parameter updating, reward modeling, and in-context prompting. This interchangeability establishes a triangular framework with six transformation directions, each of which facilitates a variety of applications. Our work offers a holistic view that unifies numerous existing studies and suggests potential research directions. We envision our work as a useful roadmap for future research on LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.16678", "title": "Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation", "authors": [ "Markus Frohmann", "Igor Sterner", "Ivan Vuli\u0107", "Benjamin Minixhofer", "Markus Schedl" ], "github": "https://github.com/segment-any-text/wtpsplit", "project_page": "", "abstract": "Segmenting text into sentences plays an early and crucial role in many NLP systems. This is commonly achieved by using rule-based or statistical methods relying on lexical features such as punctuation. Although some recent works no longer exclusively rely on punctuation, we find that no prior method achieves all of (i) robustness to missing punctuation, (ii) effective adaptability to new domains, and (iii) high efficiency. We introduce a new model - Segment any Text (SaT) - to solve this problem. To enhance robustness, we propose a new pretraining scheme that ensures less reliance on punctuation. To address adaptability, we introduce an extra stage of parameter-efficient fine-tuning, establishing state-of-the-art performance in distinct domains such as verses from lyrics and legal documents. Along the way, we introduce architectural modifications that result in a threefold gain in speed over the previous state of the art and solve spurious reliance on context far in the future. Finally, we introduce a variant of our model with fine-tuning on a diverse, multilingual mixture of sentence-segmented data, acting as a drop-in replacement and enhancement for existing segmentation tools. Overall, our contributions provide a universal approach for segmenting any text. Our method outperforms all baselines - including strong LLMs - across 8 corpora spanning diverse domains and languages, especially in practically relevant situations where text is poorly formatted. Our models and code, including documentation, are available at https://huggingface.co/segment-any-text under the MIT license.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/segment-any-text/wtpsplit", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.13144", "title": "DialSim: A Real-Time Simulator for Evaluating Long-Term Dialogue Understanding of Conversational Agents", "authors": [ "Jiho Kim", "Woosog Chay", "Hyeonji Hwang", "Daeun Kyung", "Hyunseung Chung", "Eunbyeol Cho", "Yohan Jo", "Edward Choi" ], "github": "https://github.com/jiho283/Simulator", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of conversational agents, making them applicable to various fields (e.g., education). Despite their progress, the evaluation of the agents often overlooks the complexities of real-world conversations, such as real-time interactions, multi-party dialogues, and extended contextual dependencies. To bridge this gap, we introduce DialSim, a real-time dialogue simulator. In this simulator, an agent is assigned the role of a character from popular TV shows, requiring it to respond to spontaneous questions using past dialogue information and to distinguish between known and unknown information. Key features of DialSim include evaluating the agent's ability to respond within a reasonable time limit, handling long-term multi-party dialogues, and managing adversarial settings (e.g., swap character names) to challenge the agent's reliance on pre-trained knowledge. We utilized this simulator to evaluate the latest conversational agents and analyze their limitations. Our experiments highlight both the strengths and weaknesses of these agents, providing valuable insights for future improvements in the field of conversational AI. DialSim is available at https://github.com/jiho283/Simulator.", "github_manual": "", "github_regex": "https://github.com/jiho283/Simulator", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.17774", "title": "Fast and Uncertainty-Aware SVBRDF Recovery from Multi-View Capture using Frequency Domain Analysis", "authors": [ "Ruben Wiersma", "Julien Philip", "Milo\u0161 Ha\u0161an", "Krishna Mullia", "Fujun Luan", "Elmar Eisemann", "Valentin Deschaintre" ], "github": "https://github.com/rubenwiersma/svbrdf_uncertainty", "project_page": "", "abstract": "Relightable object acquisition is a key challenge in simplifying digital asset creation. Complete reconstruction of an object typically requires capturing hundreds to thousands of photographs under controlled illumination, with specialized equipment. The recent progress in differentiable rendering improved the quality and accessibility of inverse rendering optimization. Nevertheless, under uncontrolled illumination and unstructured viewpoints, there is no guarantee that the observations contain enough information to reconstruct the appearance properties of the captured object. We thus propose to consider the acquisition process from a signal-processing perspective. Given an object's geometry and a lighting environment, we estimate the properties of the materials on the object's surface in seconds. We do so by leveraging frequency domain analysis, considering the recovery of material properties as a deconvolution, enabling fast error estimation. We then quantify the uncertainty of the estimation, based on the available data, highlighting the areas for which priors or additional samples would be required for improved acquisition quality. We compare our approach to previous work and quantitatively evaluate our results, showing similar quality as previous work in a fraction of the time, and providing key information about the certainty of the results.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/rubenwiersma/svbrdf_uncertainty", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.17563", "title": "Multi-property Steering of Large Language Models with Dynamic Activation Composition", "authors": [ "Daniel Scalena", "Gabriele Sarti", "Malvina Nissim" ], "github": "", "project_page": "", "abstract": "Activation steering methods were shown to be effective in conditioning language model generation by additively intervening over models' intermediate representations. However, the evaluation of these techniques has so far been limited to single conditioning properties and synthetic settings. In this work, we conduct a comprehensive evaluation of various activation steering strategies, highlighting the property-dependent nature of optimal parameters to ensure a robust effect throughout generation. To address this issue, we propose Dynamic Activation Composition, an information-theoretic approach to modulate the steering intensity of one or more properties throughout generation. Our experiments on multi-property steering show that our method successfully maintains high conditioning while minimizing the impact of conditioning on generation fluency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.17419", "title": "Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA", "authors": [ "Minzheng Wang", "Longze Chen", "Cheng Fu", "Shengyi Liao", "Xinghua Zhang", "Bingli Wu", "Haiyang Yu", "Nan Xu", "Lei Zhang", "Run Luo", "Yunshui Li", "Min Yang", "Fei Huang", "Yongbin Li" ], "github": "https://github.com/mozerwang/loong", "project_page": "", "abstract": "Long-context modeling capabilities have garnered widespread attention, leading to the emergence of Large Language Models (LLMs) with ultra-context windows. Meanwhile, benchmarks for evaluating long-context LLMs are gradually catching up. However, existing benchmarks employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-context applications. To bridge this gap, we propose a novel long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA). Unlike typical document QA, in Loong's test cases, each document is relevant to the final answer, ignoring any document will lead to the failure of the answer. Furthermore, Loong introduces four types of tasks with a range of context lengths: Spotlight Locating, Comparison, Clustering, and Chain of Reasoning, to facilitate a more realistic and comprehensive evaluation of long-context understanding. Extensive experiments indicate that existing long-context language models still exhibit considerable potential for enhancement. Retrieval augmented generation (RAG) achieves poor performance, demonstrating that Loong can reliably assess the model's long-context modeling capabilities.", "github_manual": "https://github.com/mozerwang/loong", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.17660", "title": "Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients", "authors": [ "Aashiq Muhamed", "Oscar Li", "David Woodruff", "Mona Diab", "Virginia Smith" ], "github": "https://github.com/aashiqmuhamed/GRASS", "project_page": "", "abstract": "Large language model (LLM) training and finetuning are often bottlenecked by limited GPU memory. While existing projection-based optimization methods address this by projecting gradients into a lower-dimensional subspace to reduce optimizer state memory, they typically rely on dense projection matrices, which can introduce computational and memory overheads. In this work, we propose Grass (GRAdient Stuctured Sparsification), a novel approach that leverages sparse projections to transform gradients into structured sparse updates. This design not only significantly reduces memory usage for optimizer states but also minimizes gradient memory footprint, computation, and communication costs, leading to substantial throughput improvements. Extensive experiments on pretraining and finetuning tasks demonstrate that Grass achieves competitive performance to full-rank training and existing projection-based methods. Notably, Grass enables half-precision pretraining of a 13B parameter LLaMA model on a single 40GB A100 GPU--a feat infeasible for previous methods--and yields up to a 2times throughput improvement on an 8-GPU system. Code can be found at https://github.com/aashiqmuhamed/GRASS .", "github_manual": "", "github_regex": "https://github.com/aashiqmuhamed/GRASS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.17055", "title": "Large Language Models Assume People are More Rational than We Really are", "authors": [ "Ryan Liu", "Jiayi Geng", "Joshua C. Peterson", "Ilia Sucholutsky", "Thomas L. Griffiths" ], "github": "https://github.com/theryanl/llm-rationality", "project_page": "", "abstract": "In order for AI systems to communicate effectively with people, they must understand how we make decisions. However, people's decisions are not always rational, so the implicit internal models of human decision-making in Large Language Models (LLMs) must account for this. Previous empirical evidence seems to suggest that these implicit models are accurate -- LLMs offer believable proxies of human behavior, acting how we expect humans would in everyday interactions. However, by comparing LLM behavior and predictions to a large dataset of human decisions, we find that this is actually not the case: when both simulating and predicting people's choices, a suite of cutting-edge LLMs (GPT-4o & 4-Turbo, Llama-3-8B & 70B, Claude 3 Opus) assume that people are more rational than we really are. Specifically, these models deviate from human behavior and align more closely with a classic model of rational choice -- expected value theory. Interestingly, people also tend to assume that other people are rational when interpreting their behavior. As a consequence, when we compare the inferences that LLMs and people draw from the decisions of others using another psychological dataset, we find that these inferences are highly correlated. Thus, the implicit decision-making models of LLMs appear to be aligned with the human expectation that other people will act rationally, rather than with how people actually act.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/theryanl/llm-rationality", "github_api_added_by": "auto" }, { "date": "2024-06-26", "arxiv_id": "2406.15279", "title": "Cross-Modality Safety Alignment", "authors": [ "Siyin Wang", "Xingsong Ye", "Qinyuan Cheng", "Junwen Duan", "Shimin Li", "Jinlan Fu", "Xipeng Qiu", "Xuanjing Huang" ], "github": "", "project_page": "", "abstract": "As Artificial General Intelligence (AGI) becomes increasingly integrated into various facets of human life, ensuring the safety and ethical alignment of such systems is paramount. Previous studies primarily focus on single-modality threats, which may not suffice given the integrated and complex nature of cross-modality interactions. We introduce a novel safety alignment challenge called Safe Inputs but Unsafe Output (SIUO) to evaluate cross-modality safety alignment. Specifically, it considers cases where single modalities are safe independently but could potentially lead to unsafe or unethical outputs when combined. To empirically investigate this problem, we developed the SIUO, a cross-modality benchmark encompassing 9 critical safety domains, such as self-harm, illegal activities, and privacy violations. Our findings reveal substantial safety vulnerabilities in both closed- and open-source LVLMs, such as GPT-4V and LLaVA, underscoring the inadequacy of current models to reliably interpret and respond to complex, real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-26", "arxiv_id": "2406.18518", "title": "APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets", "authors": [ "Zuxin Liu", "Thai Hoang", "Jianguo Zhang", "Ming Zhu", "Tian Lan", "Shirley Kokane", "Juntao Tan", "Weiran Yao", "Zhiwei Liu", "Yihao Feng", "Rithesh Murthy", "Liangwei Yang", "Silvio Savarese", "Juan Carlos Niebles", "Huan Wang", "Shelby Heinecke", "Caiming Xiong" ], "github": "", "project_page": "", "abstract": "The advancement of function-calling agent models requires diverse, reliable, and high-quality datasets. This paper presents APIGen, an automated data generation pipeline designed to synthesize verifiable high-quality datasets for function-calling applications. We leverage APIGen and collect 3,673 executable APIs across 21 different categories to generate diverse function-calling datasets in a scalable and structured manner. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness. We demonstrate that models trained with our curated datasets, even with only 7B parameters, can achieve state-of-the-art performance on the Berkeley Function-Calling Benchmark, outperforming multiple GPT-4 models. Moreover, our 1B model achieves exceptional performance, surpassing GPT-3.5-Turbo and Claude-3 Haiku. We release a dataset containing 60,000 high-quality entries, aiming to advance the field of function-calling agent domains. The dataset is available on Huggingface: https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k and the project homepage: https://apigen-pipeline.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-27", "arxiv_id": "2406.18521", "title": "CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs", "authors": [ "Zirui Wang", "Mengzhou Xia", "Luxi He", "Howard Chen", "Yitao Liu", "Richard Zhu", "Kaiqu Liang", "Xindi Wu", "Haotian Liu", "Sadhika Malladi", "Alexis Chevalier", "Sanjeev Arora", "Danqi Chen" ], "github": "https://github.com/princeton-nlp/CharXiv", "project_page": "", "abstract": "Chart understanding plays a pivotal role when applying Multimodal Large Language Models (MLLMs) to real-world tasks such as analyzing scientific papers or financial reports. However, existing datasets often focus on oversimplified and homogeneous charts with template-based questions, leading to an over-optimistic measure of progress. We demonstrate that although open-source models can appear to outperform strong proprietary models on these benchmarks, a simple stress test with slightly different charts or questions can deteriorate performance by up to 34.5%. In this work, we propose CharXiv, a comprehensive evaluation suite involving 2,323 natural, challenging, and diverse charts from arXiv papers. CharXiv includes two types of questions: 1) descriptive questions about examining basic chart elements and 2) reasoning questions that require synthesizing information across complex visual elements in the chart. To ensure quality, all charts and questions are handpicked, curated, and verified by human experts. Our results reveal a substantial, previously underestimated gap between the reasoning skills of the strongest proprietary model (i.e., GPT-4o), which achieves 47.1% accuracy, and the strongest open-source model (i.e., InternVL Chat V1.5), which achieves 29.2%. All models lag far behind human performance of 80.5%, underscoring weaknesses in the chart understanding capabilities of existing MLLMs. We hope CharXiv facilitates future research on MLLM chart understanding by providing a more realistic and faithful measure of progress. Project page and leaderboard: https://charxiv.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/princeton-nlp/CharXiv", "github_api_added_by": "auto" }, { "date": "2024-06-27", "arxiv_id": "2406.18082", "title": "Octo-planner: On-device Language Model for Planner-Action Agents", "authors": [ "Wei Chen", "Zhiyuan Li", "Zhen Guo", "Yikang Shen" ], "github": "", "project_page": "", "abstract": "AI agents have become increasingly significant in various domains, enabling autonomous decision-making and problem-solving. To function effectively, these agents require a planning process that determines the best course of action and then executes the planned actions. In this paper, we present an efficient on-device Planner-Action framework that separates planning and action execution into two distinct components: a planner agent based on Phi-3 Mini, a 3.8 billion parameter LLM optimized for edge devices, and an action agent using the Octopus model for function execution. The planner agent first responds to user queries by decomposing tasks into a sequence of sub-steps, which are then executed by the action agent. To optimize performance on resource-constrained devices, we employ model fine-tuning instead of in-context learning, reducing computational costs and energy consumption while improving response times. Our approach involves using GPT-4 to generate diverse planning queries and responses based on available functions, with subsequent validations to ensure data quality. We fine-tune the Phi-3 Mini model on this curated dataset, achieving a 97\\% success rate in our in-domain test environment. To address multi-domain planning challenges, we developed a multi-LoRA training method that merges weights from LoRAs trained on distinct function subsets. This approach enables flexible handling of complex, multi-domain queries while maintaining computational efficiency on resource-constrained devices. To support further research, we have open-sourced our model weights at https://huggingface.co/NexaAIDev/octopus-planning. For the demo, please refer to https://www.nexa4ai.com/octo-planner.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-27", "arxiv_id": "2406.16341", "title": "EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records", "authors": [ "Yeonsu Kwon", "Jiho Kim", "Gyubok Lee", "Seongsu Bae", "Daeun Kyung", "Wonchul Cha", "Tom Pollard", "Alistair Johnson", "Edward Choi" ], "github": "https://github.com/dustn1259/EHRCon", "project_page": "", "abstract": "Electronic Health Records (EHRs) are integral for storing comprehensive patient medical records, combining structured data (e.g., medications) with detailed clinical notes (e.g., physician notes). These elements are essential for straightforward data retrieval and provide deep, contextual insights into patient care. However, they often suffer from discrepancies due to unintuitive EHR system designs and human errors, posing serious risks to patient safety. To address this, we developed EHRCon, a new dataset and task specifically designed to ensure data consistency between structured tables and unstructured notes in EHRs. EHRCon was crafted in collaboration with healthcare professionals using the MIMIC-III EHR dataset, and includes manual annotations of 3,943 entities across 105 clinical notes checked against database entries for consistency. EHRCon has two versions, one using the original MIMIC-III schema, and another using the OMOP CDM schema, in order to increase its applicability and generalizability. Furthermore, leveraging the capabilities of large language models, we introduce CheckEHR, a novel framework for verifying the consistency between clinical notes and database tables. CheckEHR utilizes an eight-stage process and shows promising results in both few-shot and zero-shot settings. The code is available at https://github.com/dustn1259/EHRCon.", "github_manual": "", "github_regex": "https://github.com/dustn1259/EHRCon", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-27", "arxiv_id": "2406.18495", "title": "WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs", "authors": [ "Seungju Han", "Kavel Rao", "Allyson Ettinger", "Liwei Jiang", "Bill Yuchen Lin", "Nathan Lambert", "Yejin Choi", "Nouha Dziri" ], "github": "https://github.com/allenai/wildguard", "project_page": "", "abstract": "We introduce WildGuard -- an open, light-weight moderation tool for LLM safety that achieves three goals: (1) identifying malicious intent in user prompts, (2) detecting safety risks of model responses, and (3) determining model refusal rate. Together, WildGuard serves the increasing needs for automatic safety moderation and evaluation of LLM interactions, providing a one-stop tool with enhanced accuracy and broad coverage across 13 risk categories. While existing open moderation tools such as Llama-Guard2 score reasonably well in classifying straightforward model interactions, they lag far behind a prompted GPT-4, especially in identifying adversarial jailbreaks and in evaluating models' refusals, a key measure for evaluating safety behaviors in model responses. To address these challenges, we construct WildGuardMix, a large-scale and carefully balanced multi-task safety moderation dataset with 92K labeled examples that cover vanilla (direct) prompts and adversarial jailbreaks, paired with various refusal and compliance responses. WildGuardMix is a combination of WildGuardTrain, the training data of WildGuard, and WildGuardTest, a high-quality human-annotated moderation test set with 5K labeled items covering broad risk scenarios. Through extensive evaluations on WildGuardTest and ten existing public benchmarks, we show that WildGuard establishes state-of-the-art performance in open-source safety moderation across all the three tasks compared to ten strong existing open-source moderation models (e.g., up to 26.4% improvement on refusal detection). Importantly, WildGuard matches and sometimes exceeds GPT-4 performance (e.g., up to 3.9% improvement on prompt harmfulness identification). WildGuard serves as a highly effective safety moderator in an LLM interface, reducing the success rate of jailbreak attacks from 79.8% to 2.4%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/wildguard", "github_api_added_by": "auto" }, { "date": "2024-06-27", "arxiv_id": "2406.16793", "title": "Adam-mini: Use Fewer Learning Rates To Gain More", "authors": [ "Yushun Zhang", "Congliang Chen", "Ziniu Li", "Tian Ding", "Chenwei Wu", "Yinyu Ye", "Zhi-Quan Luo", "Ruoyu Sun" ], "github": "https://github.com/zyushun/adam-mini", "project_page": "", "abstract": "We propose Adam-mini, an optimizer that achieves on-par or better performance than AdamW with 45% to 50% less memory footprint. Adam-mini reduces memory by cutting down the learning rate resources in Adam (i.e., 1/v). We find that geq 90% of these learning rates in v could be harmlessly removed if we (1) carefully partition the parameters into blocks following our proposed principle on Hessian structure; (2) assign a single but good learning rate to each parameter block. We further find that, for each of these parameter blocks, there exists a single high-quality learning rate that can outperform Adam, provided that sufficient resources are available to search it out. We then provide one cost-effective way to find good learning rates and propose Adam-mini. Empirically, we verify that Adam-mini performs on par or better than AdamW on various language models sized from 125M to 7B for pre-training, supervised fine-tuning, and RLHF. The reduced memory footprint of Adam-mini also alleviates communication overheads among GPUs and CPUs, thereby increasing throughput. For instance, Adam-mini achieves 49.6% higher throughput than AdamW when pre-training Llama2-7B on 2times A800-80GB GPUs, which saves 33% wall-clock time for pre-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zyushun/adam-mini", "github_api_added_by": "auto" }, { "date": "2024-06-27", "arxiv_id": "2406.18219", "title": "A Closer Look into Mixture-of-Experts in Large Language Models", "authors": [ "Ka Man Lo", "Zeyu Huang", "Zihan Qiu", "Zili Wang", "Jie Fu" ], "github": "https://github.com/kamanphoebe/Look-into-MoEs", "project_page": "", "abstract": "Mixture-of-experts (MoE) is gaining increasing attention due to its unique properties and remarkable performance, especially for language tasks. By sparsely activating a subset of parameters for each token, MoE architecture could increase the model size without sacrificing computational efficiency, achieving a better trade-off between performance and training costs. However, the underlying mechanism of MoE still lacks further exploration, and its modularization degree remains questionable. In this paper, we make an initial attempt to understand the inner workings of MoE-based large language models. Concretely, we comprehensively study the parametric and behavioral features of three recent MoE-based models and reveal some intriguing observations, including (1) Neurons act like fine-grained experts. (2) The router of MoE usually selects experts with larger output norms. (3) The expert diversity increases as the layer increases, while the last layer is an outlier. Based on the observations, we also provide suggestions for a broad spectrum of MoE practitioners, such as router design and expert allocation. We hope this work could shed light on future research on the MoE framework and other modular architectures. Code is available at https://github.com/kamanphoebe/Look-into-MoEs.", "github_manual": "", "github_regex": "https://github.com/kamanphoebe/Look-into-MoEs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-27", "arxiv_id": "2406.17565", "title": "MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool", "authors": [ "Cunchen Hu", "Heyang Huang", "Junhao Hu", "Jiang Xu", "Xusheng Chen", "Tao Xie", "Chenxi Wang", "Sa Wang", "Yungang Bao", "Ninghui Sun", "Yizhou Shan" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) serving has transformed from stateless to stateful systems, utilizing techniques like context caching and disaggregated inference. These optimizations extend the lifespan and domain of the KV cache, necessitating a new architectural approach. We present MemServe, a unified system that integrates both inter-request and intra-request optimizations. MemServe introduces MemPool, an elastic memory pool managing distributed memory and KV caches across serving instances. Using MemPool APIs, MemServe combines context caching with disaggregated inference for the first time, supported by a global scheduler that enhances cache reuse through a global prompt tree-based locality-aware policy. Tests show that MemServe significantly improves job completion time and time-to-first-time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-27", "arxiv_id": "2406.18530", "title": "MatchTime: Towards Automatic Soccer Game Commentary Generation", "authors": [ "Jiayuan Rao", "Haoning Wu", "Chang Liu", "Yanfeng Wang", "Weidi Xie" ], "github": "", "project_page": "", "abstract": "Soccer is a globally popular sport with a vast audience, in this paper, we consider constructing an automatic soccer game commentary model to improve the audiences' viewing experience. In general, we make the following contributions: First, observing the prevalent video-text misalignment in existing datasets, we manually annotate timestamps for 49 matches, establishing a more robust benchmark for soccer game commentary generation, termed as SN-Caption-test-align; Second, we propose a multi-modal temporal alignment pipeline to automatically correct and filter the existing dataset at scale, creating a higher-quality soccer game commentary dataset for training, denoted as MatchTime; Third, based on our curated dataset, we train an automatic commentary generation model, named MatchVoice. Extensive experiments and ablation studies have demonstrated the effectiveness of our alignment pipeline, and training model on the curated datasets achieves state-of-the-art performance for commentary generation, showcasing that better alignment can lead to significant performance improvements in downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-27", "arxiv_id": "2406.16979", "title": "Understanding and Diagnosing Deep Reinforcement Learning", "authors": [ "Ezgi Korkmaz" ], "github": "", "project_page": "", "abstract": "Deep neural policies have recently been installed in a diverse range of settings, from biotechnology to automated financial systems. However, the utilization of deep neural networks to approximate the value function leads to concerns on the decision boundary stability, in particular, with regard to the sensitivity of policy decision making to indiscernible, non-robust features due to highly non-convex and complex deep neural manifolds. These concerns constitute an obstruction to understanding the reasoning made by deep neural policies, and their foundational limitations. Hence, it is crucial to develop techniques that aim to understand the sensitivities in the learnt representations of neural network policies. To achieve this we introduce a theoretically founded method that provides a systematic analysis of the unstable directions in the deep neural policy decision boundary across both time and space. Through experiments in the Arcade Learning Environment (ALE), we demonstrate the effectiveness of our technique for identifying correlated directions of instability, and for measuring how sample shifts remold the set of sensitive directions in the neural policy landscape. Most importantly, we demonstrate that state-of-the-art robust training techniques yield learning of disjoint unstable directions, with dramatically larger oscillations over time, when compared to standard training. We believe our results reveal the fundamental properties of the decision process made by reinforcement learning policies, and can help in constructing reliable and robust deep neural policies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-27", "arxiv_id": "2406.17294", "title": "Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models", "authors": [ "Wenhao Shi", "Zhiqiang Hu", "Yi Bin", "Junhua Liu", "Yang Yang", "See-Kiong Ng", "Lidong Bing", "Roy Ka-Wei Lee" ], "github": "https://github.com/HZQ950419/Math-LLaVA", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated impressive reasoning capabilities, particularly in textual mathematical problem-solving. However, existing open-source image instruction fine-tuning datasets, containing limited question-answer pairs per image, do not fully exploit visual information to enhance the multimodal mathematical reasoning capabilities of Multimodal LLMs (MLLMs). To bridge this gap, we address the lack of high-quality, diverse multimodal mathematical datasets by collecting 40K high-quality images with question-answer pairs from 24 existing datasets and synthesizing 320K new pairs, creating the MathV360K dataset, which enhances both the breadth and depth of multimodal mathematical questions. We introduce Math-LLaVA, a LLaVA-1.5-based model fine-tuned with MathV360K. This novel approach significantly improves the multimodal mathematical reasoning capabilities of LLaVA-1.5, achieving a 19-point increase and comparable performance to GPT-4V on MathVista's minitest split. Furthermore, Math-LLaVA demonstrates enhanced generalizability, showing substantial improvements on the MMMU benchmark. Our research highlights the importance of dataset diversity and synthesis in advancing MLLMs' mathematical reasoning abilities. The code and data are available at: https://github.com/HZQ950419/Math-LLaVA.", "github_manual": "", "github_regex": "https://github.com/HZQ950419/Math-LLaVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-27", "arxiv_id": "2406.18532", "title": "Symbolic Learning Enables Self-Evolving Agents", "authors": [ "Wangchunshu Zhou", "Yixin Ou", "Shengwei Ding", "Long Li", "Jialong Wu", "Tiannan Wang", "Jiamin Chen", "Shuai Wang", "Xiaohua Xu", "Ningyu Zhang", "Huajun Chen", "Yuchen Eleanor Jiang" ], "github": "", "project_page": "", "abstract": "The AI community has been exploring a pathway to artificial general intelligence (AGI) by developing \"language agents\", which are complex large language models (LLMs) pipelines involving both prompting techniques and tool usage methods. While language agents have demonstrated impressive capabilities for many real-world tasks, a fundamental limitation of current language agents research is that they are model-centric, or engineering-centric. That's to say, the progress on prompts, tools, and pipelines of language agents requires substantial manual engineering efforts from human experts rather than automatically learning from data. We believe the transition from model-centric, or engineering-centric, to data-centric, i.e., the ability of language agents to autonomously learn and evolve in environments, is the key for them to possibly achieve AGI. In this work, we introduce agent symbolic learning, a systematic framework that enables language agents to optimize themselves on their own in a data-centric way using symbolic optimizers. Specifically, we consider agents as symbolic networks where learnable weights are defined by prompts, tools, and the way they are stacked together. Agent symbolic learning is designed to optimize the symbolic network within language agents by mimicking two fundamental algorithms in connectionist learning: back-propagation and gradient descent. Instead of dealing with numeric weights, agent symbolic learning works with natural language simulacrums of weights, loss, and gradients. We conduct proof-of-concept experiments on both standard benchmarks and complex real-world tasks and show that agent symbolic learning enables language agents to update themselves after being created and deployed in the wild, resulting in \"self-evolving agents\".", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-27", "arxiv_id": "2406.18522", "title": "ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation", "authors": [ "Shenghai Yuan", "Jinfa Huang", "Yongqi Xu", "Yaoyang Liu", "Shaofeng Zhang", "Yujun Shi", "Ruijie Zhu", "Xinhua Cheng", "Jiebo Luo", "Li Yuan" ], "github": "", "project_page": "", "abstract": "We propose a novel text-to-video (T2V) generation benchmark, ChronoMagic-Bench, to evaluate the temporal and metamorphic capabilities of the T2V models (e.g. Sora and Lumiere) in time-lapse video generation. In contrast to existing benchmarks that focus on the visual quality and textual relevance of generated videos, ChronoMagic-Bench focuses on the model's ability to generate time-lapse videos with significant metamorphic amplitude and temporal coherence. The benchmark probes T2V models for their physics, biology, and chemistry capabilities, in a free-form text query. For these purposes, ChronoMagic-Bench introduces 1,649 prompts and real-world videos as references, categorized into four major types of time-lapse videos: biological, human-created, meteorological, and physical phenomena, which are further divided into 75 subcategories. This categorization comprehensively evaluates the model's capacity to handle diverse and complex transformations. To accurately align human preference with the benchmark, we introduce two new automatic metrics, MTScore and CHScore, to evaluate the videos' metamorphic attributes and temporal coherence. MTScore measures the metamorphic amplitude, reflecting the degree of change over time, while CHScore assesses the temporal coherence, ensuring the generated videos maintain logical progression and continuity. Based on the ChronoMagic-Bench, we conduct comprehensive manual evaluations of ten representative T2V models, revealing their strengths and weaknesses across different categories of prompts, and providing a thorough evaluation framework that addresses current gaps in video generation research. Moreover, we create a large-scale ChronoMagic-Pro dataset, containing 460k high-quality pairs of 720p time-lapse videos and detailed captions ensuring high physical pertinence and large metamorphic amplitude.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-27", "arxiv_id": "2406.18510", "title": "WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models", "authors": [ "Liwei Jiang", "Kavel Rao", "Seungju Han", "Allyson Ettinger", "Faeze Brahman", "Sachin Kumar", "Niloofar Mireshghallah", "Ximing Lu", "Maarten Sap", "Yejin Choi", "Nouha Dziri" ], "github": "https://github.com/allenai/wildteaming", "project_page": "", "abstract": "We introduce WildTeaming, an automatic LLM safety red-teaming framework that mines in-the-wild user-chatbot interactions to discover 5.7K unique clusters of novel jailbreak tactics, and then composes multiple tactics for systematic exploration of novel jailbreaks. Compared to prior work that performed red-teaming via recruited human workers, gradient-based optimization, or iterative revision with LLMs, our work investigates jailbreaks from chatbot users who were not specifically instructed to break the system. WildTeaming reveals previously unidentified vulnerabilities of frontier LLMs, resulting in up to 4.6x more diverse and successful adversarial attacks compared to state-of-the-art jailbreak methods. While many datasets exist for jailbreak evaluation, very few open-source datasets exist for jailbreak training, as safety training data has been closed even when model weights are open. With WildTeaming we create WildJailbreak, a large-scale open-source synthetic safety dataset with 262K vanilla (direct request) and adversarial (complex jailbreak) prompt-response pairs. To mitigate exaggerated safety behaviors, WildJailbreak provides two contrastive types of queries: 1) harmful queries (vanilla & adversarial) and 2) benign queries that resemble harmful queries in form but contain no harm. As WildJailbreak considerably upgrades the quality and scale of existing safety resources, it uniquely enables us to examine the scaling effects of data and the interplay of data properties and model capabilities during safety training. Through extensive experiments, we identify the training properties that enable an ideal balance of safety behaviors: appropriate safeguarding without over-refusal, effective handling of vanilla and adversarial queries, and minimal, if any, decrease in general capabilities. All components of WildJailbeak contribute to achieving balanced safety behaviors of models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/wildteaming", "github_api_added_by": "auto" }, { "date": "2024-06-27", "arxiv_id": "2406.15334", "title": "Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning", "authors": [ "Brandon Huang", "Chancharik Mitra", "Assaf Arbelle", "Leonid Karlinsky", "Trevor Darrell", "Roei Herzig" ], "github": "", "project_page": "", "abstract": "The recent success of interleaved Large Multimodal Models (LMMs) in few-shot learning suggests that in-context learning (ICL) with many examples can be promising for learning new tasks. However, this many-shot multimodal ICL setting has one crucial problem: it is fundamentally limited by the model's context length set at pretraining. The problem is especially prominent in the multimodal domain, which processes both text and images, requiring additional tokens. This motivates the need for a multimodal method to compress many shots into fewer tokens without finetuning. In this work, we enable LMMs to perform multimodal, many-shot in-context learning by leveraging Multimodal Task Vectors (MTV)--compact implicit representations of in-context examples compressed in the model's attention heads. Specifically, we first demonstrate the existence of such MTV in LMMs and then leverage these extracted MTV to enable many-shot in-context learning for various vision-and-language tasks. Our experiments suggest that MTV can scale in performance with the number of compressed shots and generalize to similar out-of-domain tasks without additional context length for inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.19215", "title": "SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation", "authors": [ "Zijun Yao", "Weijian Qi", "Liangming Pan", "Shulin Cao", "Linmei Hu", "Weichuan Liu", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/THU-KEG/SeaKR", "project_page": "", "abstract": "This paper introduces Self-aware Knowledge Retrieval (SeaKR), a novel adaptive RAG model that extracts self-aware uncertainty of LLMs from their internal states. SeaKR activates retrieval when the LLMs present high self-aware uncertainty for generation. To effectively integrate retrieved knowledge snippets, SeaKR re-ranks them based on LLM's self-aware uncertainty to preserve the snippet that reduces their uncertainty to the utmost. To facilitate solving complex tasks that require multiple retrievals, SeaKR utilizes their self-aware uncertainty to choose among different reasoning strategies. Our experiments on both complex and simple Question Answering datasets show that SeaKR outperforms existing adaptive RAG methods. We release our code at https://github.com/THU-KEG/SeaKR.", "github_manual": "", "github_regex": "https://github.com/THU-KEG/SeaKR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.19227", "title": "Aligning Teacher with Student Preferences for Tailored Training Data Generation", "authors": [ "Yantao Liu", "Zhao Zhang", "Zijun Yao", "Shulin Cao", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/THU-KEG/ARTE", "project_page": "", "abstract": "Large Language Models (LLMs) have shown significant promise as copilots in various tasks. Local deployment of LLMs on edge devices is necessary when handling privacy-sensitive data or latency-sensitive tasks. The computational constraints of such devices make direct deployment of powerful large-scale LLMs impractical, necessitating the Knowledge Distillation from large-scale models to lightweight models. Lots of work has been done to elicit diversity and quality training examples from LLMs, but little attention has been paid to aligning teacher instructional content based on student preferences, akin to \"responsive teaching\" in pedagogy. Thus, we propose ARTE, dubbed Aligning TeacheR with StudenT PreferencEs, a framework that aligns the teacher model with student preferences to generate tailored training examples for Knowledge Distillation. Specifically, we elicit draft questions and rationales from the teacher model, then collect student preferences on these questions and rationales using students' performance with in-context learning as a proxy, and finally align the teacher model with student preferences. In the end, we repeat the first step with the aligned teacher model to elicit tailored training examples for the student model on the target task. Extensive experiments on academic benchmarks demonstrate the superiority of ARTE over existing instruction-tuning datasets distilled from powerful LLMs. Moreover, we thoroughly investigate the generalization of ARTE, including the generalization of fine-tuned student models in reasoning ability and the generalization of aligned teacher models to generate tailored training data across tasks and students. In summary, our contributions lie in proposing a novel framework for tailored training example generation, demonstrating its efficacy in experiments, and investigating the generalization of both student & aligned teacher models in ARTE.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THU-KEG/ARTE", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.18629", "title": "Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs", "authors": [ "Xin Lai", "Zhuotao Tian", "Yukang Chen", "Senqiao Yang", "Xiangru Peng", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/Step-DPO", "project_page": "", "abstract": "Mathematical reasoning presents a significant challenge for Large Language Models (LLMs) due to the extensive and precise chain of reasoning required for accuracy. Ensuring the correctness of each reasoning step is critical. To address this, we aim to enhance the robustness and factuality of LLMs by learning from human feedback. However, Direct Preference Optimization (DPO) has shown limited benefits for long-chain mathematical reasoning, as models employing DPO struggle to identify detailed errors in incorrect answers. This limitation stems from a lack of fine-grained process supervision. We propose a simple, effective, and data-efficient method called Step-DPO, which treats individual reasoning steps as units for preference optimization rather than evaluating answers holistically. Additionally, we have developed a data construction pipeline for Step-DPO, enabling the creation of a high-quality dataset containing 10K step-wise preference pairs. We also observe that in DPO, self-generated data is more effective than data generated by humans or GPT-4, due to the latter's out-of-distribution nature. Our findings demonstrate that as few as 10K preference data pairs and fewer than 500 Step-DPO training steps can yield a nearly 3% gain in accuracy on MATH for models with over 70B parameters. Notably, Step-DPO, when applied to Qwen2-72B-Instruct, achieves scores of 70.8% and 94.0% on the test sets of MATH and GSM8K, respectively, surpassing a series of closed-source models, including GPT-4-1106, Claude-3-Opus, and Gemini-1.5-Pro. Our code, data, and models are available at https://github.com/dvlab-research/Step-DPO.", "github_manual": "", "github_regex": "https://github.com/dvlab-research/Step-DPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.19263", "title": "Read Anywhere Pointed: Layout-aware GUI Screen Reading with Tree-of-Lens Grounding", "authors": [ "Yue Fan", "Lei Ding", "Ching-Chen Kuo", "Shan Jiang", "Yang Zhao", "Xinze Guan", "Jie Yang", "Yi Zhang", "Xin Eric Wang" ], "github": "https://github.com/eric-ai-lab/Screen-Point-and-Read", "project_page": "", "abstract": "Graphical User Interfaces (GUIs) are central to our interaction with digital devices. Recently, growing efforts have been made to build models for various GUI understanding tasks. However, these efforts largely overlook an important GUI-referring task: screen reading based on user-indicated points, which we name the Screen Point-and-Read (SPR) task. This task is predominantly handled by rigid accessible screen reading tools, in great need of new models driven by advancements in Multimodal Large Language Models (MLLMs). In this paper, we propose a Tree-of-Lens (ToL) agent, utilizing a novel ToL grounding mechanism, to address the SPR task. Based on the input point coordinate and the corresponding GUI screenshot, our ToL agent constructs a Hierarchical Layout Tree. Based on the tree, our ToL agent not only comprehends the content of the indicated area but also articulates the layout and spatial relationships between elements. Such layout information is crucial for accurately interpreting information on the screen, distinguishing our ToL agent from other screen reading tools. We also thoroughly evaluate the ToL agent against other baselines on a newly proposed SPR benchmark, which includes GUIs from mobile, web, and operating systems. Last but not least, we test the ToL agent on mobile GUI navigation tasks, demonstrating its utility in identifying incorrect actions along the path of agent execution trajectories. Code and data: screen-point-and-read.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/eric-ai-lab/Screen-Point-and-Read", "github_api_added_by": "auto" }, { "date": "2024-06-28", "arxiv_id": "2406.19226", "title": "Simulating Classroom Education with LLM-Empowered Agents", "authors": [ "Zheyuan Zhang", "Daniel Zhang-Li", "Jifan Yu", "Linlu Gong", "Jinchang Zhou", "Zhiyuan Liu", "Lei Hou", "Juanzi Li" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have been employed in various intelligent educational tasks to assist teaching. While preliminary explorations have focused on independent LLM-empowered agents for specific educational tasks, the potential for LLMs within a multi-agent collaborative framework to simulate a classroom with real user participation remains unexplored. In this work, we propose SimClass, a multi-agent classroom simulation framework involving user participation. We recognize representative class roles and introduce a novel class control mechanism for automatic classroom teaching, and conduct user experiments in two real-world courses. Utilizing the Flanders Interactive Analysis System and Community of Inquiry theoretical frame works from educational analysis, we demonstrate that LLMs can simulate traditional classroom interaction patterns effectively while enhancing user's experience. We also observe emergent group behaviors among agents in SimClass, where agents collaborate to create enlivening interactions in classrooms to improve user learning process. We hope this work pioneers the application of LLM-empowered multi-agent systems in virtual classroom teaching.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.19389", "title": "OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding", "authors": [ "Tao Zhang", "Xiangtai Li", "Hao Fei", "Haobo Yuan", "Shengqiong Wu", "Shunping Ji", "Chen Change Loy", "Shuicheng Yan" ], "github": "", "project_page": "", "abstract": "Current universal segmentation methods demonstrate strong capabilities in pixel-level image and video understanding. However, they lack reasoning abilities and cannot be controlled via text instructions. In contrast, large vision-language multimodal models exhibit powerful vision-based conversation and reasoning capabilities but lack pixel-level understanding and have difficulty accepting visual prompts for flexible user interaction. This paper proposes OMG-LLaVA, a new and elegant framework combining powerful pixel-level vision understanding with reasoning abilities. It can accept various visual and text prompts for flexible user interaction. Specifically, we use a universal segmentation method as the visual encoder, integrating image information, perception priors, and visual prompts into visual tokens provided to the LLM. The LLM is responsible for understanding the user's text instructions and providing text responses and pixel-level segmentation results based on the visual information. We propose perception prior embedding to better integrate perception priors with image features. OMG-LLaVA achieves image-level, object-level, and pixel-level reasoning and understanding in a single model, matching or surpassing the performance of specialized methods on multiple benchmarks. Rather than using LLM to connect each specialist, our work aims at end-to-end training on one encoder, one decoder, and one LLM. The code and model have been released for further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.14909", "title": "MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression", "authors": [ "Tianyu Fu", "Haofeng Huang", "Xuefei Ning", "Genghan Zhang", "Boju Chen", "Tianqi Wu", "Hongyi Wang", "Zixiao Huang", "Shiyao Li", "Shengen Yan", "Guohao Dai", "Huazhong Yang", "Yu Wang" ], "github": "https://github.com/thu-nics/MoA", "project_page": "", "abstract": "Sparse attention can effectively mitigate the significant memory and throughput demands of Large Language Models (LLMs) in long contexts. Existing methods typically employ a uniform sparse attention mask, applying the same sparse pattern across different attention heads and input lengths. However, this uniform approach fails to capture the diverse attention patterns inherent in LLMs, ignoring their distinct accuracy-latency trade-offs. To address this challenge, we propose the Mixture of Attention (MoA), which automatically tailors distinct sparse attention configurations to different heads and layers. MoA constructs and navigates a search space of various attention patterns and their scaling rules relative to input sequence lengths. It profiles the model, evaluates potential configurations, and pinpoints the optimal sparse attention compression plan. MoA adapts to varying input sizes, revealing that some attention heads expand their focus to accommodate longer sequences, while other heads consistently concentrate on fixed-length local contexts. Experiments show that MoA increases the effective context length by 3.9times with the same average attention span, boosting retrieval accuracy by 1.5-7.1times over the uniform-attention baseline across Vicuna-7B, Vicuna-13B, and Llama3-8B models. Moreover, MoA narrows the capability gaps between sparse and dense models, reducing the maximum relative performance drop from 9%-36% to within 5% across two long-context understanding benchmarks. MoA achieves a 1.2-1.4times GPU memory reduction and boosts decode throughput by 5.5-6.7 times for 7B and 13B dense models on a single GPU, with minimal impact on performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/thu-nics/MoA", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.08316", "title": "Is Programming by Example solved by LLMs?", "authors": [ "Wen-Ding Li", "Kevin Ellis" ], "github": "https://github.com/microsoft/prose-benchmarks", "project_page": "", "abstract": "Programming-by-Examples (PBE) aims to generate an algorithm from input-output examples. Such systems are practically and theoretically important: from an end-user perspective, they are deployed to millions of people, and from an AI perspective, PBE corresponds to a very general form of few-shot inductive inference. Given the success of Large Language Models (LLMs) in code-generation tasks, we investigate here the extent to which LLMs can be said to have `solved' PBE. We experiment on classic domains such as lists and strings, and an uncommon graphics programming domain not well represented in typical pretraining data. We find that pretrained models are not effective at PBE, but that they can be fine-tuned for much higher performance, provided the test problems are in-distribution. We analyze empirically what causes these models to succeed and fail, and take steps toward understanding how to achieve better out-of-distribution generalization. Collectively these results suggest that LLMs make strong progress toward solving the typical suite of PBE tasks, potentially increasing the flexibility and applicability of PBE systems, while also identifying ways in which LLMs still fall short.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/prose-benchmarks", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.14629", "title": "Can LLMs Learn by Teaching? A Preliminary Study", "authors": [ "Xuefei Ning", "Zifu Wang", "Shiyao Li", "Zinan Lin", "Peiran Yao", "Tianyu Fu", "Matthew B. Blaschko", "Guohao Dai", "Huazhong Yang", "Yu Wang" ], "github": "https://github.com/imagination-research/lbt", "project_page": "", "abstract": "Teaching to improve student models (e.g., knowledge distillation) is an extensively studied methodology in LLMs. However, for humans, teaching not only improves students but also improves teachers. We ask: Can LLMs also learn by teaching (LbT)? If yes, we can potentially unlock the possibility of continuously advancing the models without solely relying on human-produced data or stronger models. In this paper, we provide a preliminary exploration of this ambitious agenda. We show that LbT ideas can be incorporated into existing LLM training/prompting pipelines and provide noticeable improvements. Specifically, we design three methods, each mimicking one of the three levels of LbT in humans: observing students' feedback, learning from the feedback, and learning iteratively, with the goals of improving answer accuracy without training and improving models' inherent capability with fine-tuning. The findings are encouraging. For example, similar to LbT in human, we see that: (1) LbT can induce weak-to-strong generalization: strong models can improve themselves by teaching other weak models; (2) Diversity in students might help: teaching multiple students could be better than teaching one student or the teacher itself. We hope that this early promise can inspire future research on LbT and more broadly adopting the advanced techniques in education to improve LLMs. The code is available at https://github.com/imagination-research/lbt.", "github_manual": "", "github_regex": "https://github.com/imagination-research/lbt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.19314", "title": "LiveBench: A Challenging, Contamination-Free LLM Benchmark", "authors": [ "Colin White", "Samuel Dooley", "Manley Roberts", "Arka Pal", "Ben Feuer", "Siddhartha Jain", "Ravid Shwartz-Ziv", "Neel Jain", "Khalid Saifullah", "Siddartha Naidu", "Chinmay Hegde", "Yann LeCun", "Tom Goldstein", "Willie Neiswanger", "Micah Goldblum" ], "github": "https://github.com/livebench/livebench", "project_page": "", "abstract": "Test set contamination, wherein test data from a benchmark ends up in a newer model's training set, is a well-documented obstacle for fair LLM evaluation and can quickly render benchmarks obsolete. To mitigate this, many recent benchmarks crowdsource new prompts and evaluations from human or LLM judges; however, these can introduce significant biases, and break down when scoring hard questions. In this work, we introduce a new benchmark for LLMs designed to be immune to both test set contamination and the pitfalls of LLM judging and human crowdsourcing. We release LiveBench, the first benchmark that (1) contains frequently-updated questions from recent information sources, (2) scores answers automatically according to objective ground-truth values, and (3) contains a wide variety of challenging tasks, spanning math, coding, reasoning, language, instruction following, and data analysis. To achieve this, LiveBench contains questions that are based on recently-released math competitions, arXiv papers, news articles, and datasets, and it contains harder, contamination-free versions of tasks from previous benchmarks such as Big-Bench Hard, AMPS, and IFEval. We evaluate many prominent closed-source models, as well as dozens of open-source models ranging from 0.5B to 110B in size. LiveBench is difficult, with top models achieving below 65% accuracy. We release all questions, code, and model answers. Questions will be added and updated on a monthly basis, and we will release new tasks and harder versions of tasks over time so that LiveBench can distinguish between the capabilities of LLMs as they improve in the future. We welcome community engagement and collaboration for expanding the benchmark tasks and models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/livebench/livebench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.18120", "title": "ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech Recognition Using LLMs", "authors": [ "Ahmed Heakl", "Youssef Zaghloul", "Mennatullah Ali", "Rania Hossam", "Walid Gomaa" ], "github": "https://github.com/ahmedheakl/arazn-llm", "project_page": "", "abstract": "Motivated by the widespread increase in the phenomenon of code-switching between Egyptian Arabic and English in recent times, this paper explores the intricacies of machine translation (MT) and automatic speech recognition (ASR) systems, focusing on translating code-switched Egyptian Arabic-English to either English or Egyptian Arabic. Our goal is to present the methodologies employed in developing these systems, utilizing large language models such as LLama and Gemma. In the field of ASR, we explore the utilization of the Whisper model for code-switched Egyptian Arabic recognition, detailing our experimental procedures including data preprocessing and training techniques. Through the implementation of a consecutive speech-to-text translation system that integrates ASR with MT, we aim to overcome challenges posed by limited resources and the unique characteristics of the Egyptian Arabic dialect. Evaluation against established metrics showcases promising results, with our methodologies yielding a significant improvement of 56% in English translation over the state-of-the-art and 9.3% in Arabic translation. Since code-switching is deeply inherent in spoken languages, it is crucial that ASR systems can effectively handle this phenomenon. This capability is crucial for enabling seamless interaction in various domains, including business negotiations, cultural exchanges, and academic discourse. Our models and code are available as open-source resources. Code: http://github.com/ahmedheakl/arazn-llm}, Models: http://huggingface.co/collections/ahmedheakl/arazn-llm-662ceaf12777656607b9524e.", "github_manual": "https://github.com/ahmedheakl/arazn-llm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.18125", "title": "ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models", "authors": [ "Ahmed Heakl", "Youssef Mohamed", "Noran Mohamed", "Ali Sharkaway", "Ahmed Zaky" ], "github": "https://github.com/noran-mohamed/Resume-Classification-Dataset", "project_page": "", "abstract": "The increasing reliance on online recruitment platforms coupled with the adoption of AI technologies has highlighted the critical need for efficient resume classification methods. However, challenges such as small datasets, lack of standardized resume templates, and privacy concerns hinder the accuracy and effectiveness of existing classification models. In this work, we address these challenges by presenting a comprehensive approach to resume classification. We curated a large-scale dataset of 13,389 resumes from diverse sources and employed Large Language Models (LLMs) such as BERT and Gemma1.1 2B for classification. Our results demonstrate significant improvements over traditional machine learning approaches, with our best model achieving a top-1 accuracy of 92\\% and a top-5 accuracy of 97.5\\%. These findings underscore the importance of dataset quality and advanced model architectures in enhancing the accuracy and robustness of resume classification systems, thus advancing the field of online recruitment practices.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/noran-mohamed/Resume-Classification-Dataset", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.19395", "title": "Dataset Size Recovery from LoRA Weights", "authors": [ "Mohammad Salama", "Jonathan Kahana", "Eliahu Horwitz", "Yedid Hoshen" ], "github": "https://github.com/MoSalama98/DSiRe", "project_page": "", "abstract": "Model inversion and membership inference attacks aim to reconstruct and verify the data which a model was trained on. However, they are not guaranteed to find all training samples as they do not know the size of the training set. In this paper, we introduce a new task: dataset size recovery, that aims to determine the number of samples used to train a model, directly from its weights. We then propose DSiRe, a method for recovering the number of images used to fine-tune a model, in the common case where fine-tuning uses LoRA. We discover that both the norm and the spectrum of the LoRA matrices are closely linked to the fine-tuning dataset size; we leverage this finding to propose a simple yet effective prediction algorithm. To evaluate dataset size recovery of LoRA weights, we develop and release a new benchmark, LoRA-WiSE, consisting of over 25000 weight snapshots from more than 2000 diverse LoRA fine-tuned models. Our best classifier can predict the number of fine-tuning images with a mean absolute error of 0.36 images, establishing the feasibility of this attack.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MoSalama98/DSiRe", "github_api_added_by": "auto" }, { "date": "2024-06-28", "arxiv_id": "2406.17513", "title": "Benchmarking Mental State Representations in Language Models", "authors": [ "Matteo Bortoletto", "Constantin Ruhdorfer", "Lei Shi", "Andreas Bulling" ], "github": "", "project_page": "", "abstract": "While numerous works have assessed the generative performance of language models (LMs) on tasks requiring Theory of Mind reasoning, research into the models' internal representation of mental states remains limited. Recent work has used probing to demonstrate that LMs can represent beliefs of themselves and others. However, these claims are accompanied by limited evaluation, making it difficult to assess how mental state representations are affected by model design and training choices. We report an extensive benchmark with various LM types with different model sizes, fine-tuning approaches, and prompt designs to study the robustness of mental state representations and memorisation issues within the probes. Our results show that the quality of models' internal representations of the beliefs of others increases with model size and, more crucially, with fine-tuning. We are the first to study how prompt variations impact probing performance on theory of mind tasks. We demonstrate that models' representations are sensitive to prompt variations, even when such variations should be beneficial. Finally, we complement previous activation editing experiments on Theory of Mind tasks and show that it is possible to improve models' reasoning performance by steering their activations without the need to train any probe.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.18790", "title": "MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data", "authors": [ "William Berman", "Alexander Peysakhovich" ], "github": "", "project_page": "", "abstract": "We train a model to generate images from multimodal prompts of interleaved text and images such as \"a man and his dog in an animated style.\" We bootstrap a multimodal dataset by extracting semantically meaningful image crops corresponding to words in the image captions of synthetically generated and publicly available text-image data. Our model, MUMU, is composed of a vision-language model encoder with a diffusion decoder and is trained on a single 8xH100 GPU node. Despite being only trained on crops from the same image, MUMU learns to compose inputs from different images into a coherent output. For example, an input of a realistic person and a cartoon will output the same person in the cartoon style, and an input of a standing subject and a scooter will output the subject riding the scooter. As a result, our model generalizes to tasks such as style transfer and character consistency. Our results show the promise of using multimodal models as general purpose controllers for image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.18676", "title": "Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation", "authors": [ "Guanting Dong", "Yutao Zhu", "Chenghao Zhang", "Zechen Wang", "Zhicheng Dou", "Ji-Rong Wen" ], "github": "https://github.com/dongguanting/DPA-RAG", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) has demonstrated effectiveness in mitigating the hallucination problem of large language models (LLMs). However, the difficulty of aligning the retriever with the diverse LLMs' knowledge preferences inevitably poses an inevitable challenge in developing a reliable RAG system. To address this issue, we propose DPA-RAG, a universal framework designed to align diverse knowledge preferences within RAG systems. Specifically, we initially introduce a preference knowledge construction pipline and incorporate five novel query augmentation strategies to alleviate preference data scarcity. Based on preference data, DPA-RAG accomplishes both external and internal preference alignment: 1) It jointly integrate pair-wise, point-wise, and contrastive preference alignment abilities into the reranker, achieving external preference alignment among RAG components. 2) It further introduces a pre-aligned stage before vanilla Supervised Fine-tuning (SFT), enabling LLMs to implicitly capture knowledge aligned with their reasoning preferences, achieving LLMs' internal alignment. Experimental results across four knowledge-intensive QA datasets demonstrate that DPA-RAG outperforms all baselines and seamlessly integrates both black-box and open-sourced LLM readers. Further qualitative analysis and discussions also provide empirical guidance for achieving reliable RAG systems. Our code is publicly available at https://github.com/dongguanting/DPA-RAG.", "github_manual": "", "github_regex": "https://github.com/dongguanting/DPA-RAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-06-28", "arxiv_id": "2406.19223", "title": "T-FREE: Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings", "authors": [ "Bj\u00f6rn Deiseroth", "Manuel Brack", "Patrick Schramowski", "Kristian Kersting", "Samuel Weinbach" ], "github": "https://github.com/aleph-alpha/trigrams", "project_page": "", "abstract": "Tokenizers are crucial for encoding information in Large Language Models, but their development has recently stagnated, and they contain inherent weaknesses. Major limitations include computational overhead, ineffective vocabulary use, and unnecessarily large embedding and head layers. Additionally, their performance is biased towards a reference corpus, leading to reduced effectiveness for underrepresented languages. To remedy these issues, we propose T-FREE, which directly embeds words through sparse activation patterns over character triplets, and does not require a reference corpus. T-FREE inherently exploits morphological similarities and allows for strong compression of embedding layers. In our exhaustive experimental evaluation, we achieve competitive downstream performance with a parameter reduction of more than 85% on these layers. Further, T-FREE shows significant improvements in cross-lingual transfer learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aleph-alpha/trigrams", "github_api_added_by": "auto" }, { "date": "2024-06-28", "arxiv_id": "2406.10900", "title": "AUTOHALLUSION: Automatic Generation of Hallucination Benchmarks for Vision-Language Models", "authors": [ "Xiyang Wu", "Tianrui Guan", "Dianqi Li", "Shuaiyi Huang", "Xiaoyu Liu", "Xijun Wang", "Ruiqi Xian", "Abhinav Shrivastava", "Furong Huang", "Jordan Lee Boyd-Graber", "Tianyi Zhou", "Dinesh Manocha" ], "github": "https://github.com/wuxiyang1996/AutoHallusion", "project_page": "", "abstract": "Large vision-language models (LVLMs) hallucinate: certain context cues in an image may trigger the language module's overconfident and incorrect reasoning on abnormal or hypothetical objects. Though a few benchmarks have been developed to investigate LVLM hallucinations, they mainly rely on hand-crafted corner cases whose fail patterns may hardly generalize, and finetuning on them could undermine their validity. These motivate us to develop the first automatic benchmark generation approach, AUTOHALLUSION, that harnesses a few principal strategies to create diverse hallucination examples. It probes the language modules in LVLMs for context cues and uses them to synthesize images by: (1) adding objects abnormal to the context cues; (2) for two co-occurring objects, keeping one and excluding the other; or (3) removing objects closely tied to the context cues. It then generates image-based questions whose ground-truth answers contradict the language module's prior. A model has to overcome contextual biases and distractions to reach correct answers, while incorrect or inconsistent answers indicate hallucinations. AUTOHALLUSION enables us to create new benchmarks at the minimum cost and thus overcomes the fragility of hand-crafted benchmarks. It also reveals common failure patterns and reasons, providing key insights to detect, avoid, or control hallucinations. Comprehensive evaluations of top-tier LVLMs, e.g., GPT-4V(ision), Gemini Pro Vision, Claude 3, and LLaVA-1.5, show a 97.7% and 98.7% success rate of hallucination induction on synthetic and real-world datasets of AUTOHALLUSION, paving the way for a long battle against hallucinations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wuxiyang1996/AutoHallusion", "github_api_added_by": "auto" }, { "date": "2024-07-01", "arxiv_id": "2406.20094", "title": "Scaling Synthetic Data Creation with 1,000,000,000 Personas", "authors": [ "Xin Chan", "Xiaoyang Wang", "Dian Yu", "Haitao Mi", "Dong Yu" ], "github": "https://github.com/tencent-ailab/persona-hub", "project_page": "", "abstract": "We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce Persona Hub -- a collection of 1 billion diverse personas automatically curated from web data. These 1 billion personas (~13% of the world's total population), acting as distributed carriers of world knowledge, can tap into almost every perspective encapsulated within the LLM, thereby facilitating the creation of diverse synthetic data at scale for various scenarios. By showcasing Persona Hub's use cases in synthesizing high-quality mathematical and logical reasoning problems, instructions (i.e., user prompts), knowledge-rich texts, game NPCs and tools (functions) at scale, we demonstrate persona-driven data synthesis is versatile, scalable, flexible, and easy to use, potentially driving a paradigm shift in synthetic data creation and applications in practice, which may have a profound impact on LLM research and development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tencent-ailab/persona-hub", "github_api_added_by": "auto" }, { "date": "2024-07-01", "arxiv_id": "2406.19280", "title": "HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale", "authors": [ "Junying Chen", "Ruyi Ouyang", "Anningzhe Gao", "Shunian Chen", "Guiming Hardy Chen", "Xidong Wang", "Ruifei Zhang", "Zhenyang Cai", "Ke Ji", "Guangjun Yu", "Xiang Wan", "Benyou Wang" ], "github": "https://github.com/freedomintelligence/huatuogpt-vision", "project_page": "", "abstract": "The rapid development of multimodal large language models (MLLMs), such as GPT-4V, has led to significant advancements. However, these models still face challenges in medical multimodal capabilities due to limitations in the quantity and quality of medical vision-text data, stemming from data privacy concerns and high annotation costs. While pioneering approaches utilize PubMed's large-scale, de-identified medical image-text pairs to address these limitations, they still fall short due to inherent data noise. To tackle this, we refined medical image-text pairs from PubMed and employed MLLMs (GPT-4V) in an 'unblinded' capacity to denoise and reformat the data, resulting in the creation of the PubMedVision dataset with 1.3 million medical VQA samples. Our validation demonstrates that: (1) PubMedVision can significantly enhance the medical multimodal capabilities of current MLLMs, showing significant improvement in benchmarks including the MMMU Health & Medicine track; (2) manual checks by medical experts and empirical results validate the superior data quality of our dataset compared to other data construction methods. Using PubMedVision, we train a 34B medical MLLM HuatuoGPT-Vision, which shows superior performance in medical multimodal scenarios among open-source MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/freedomintelligence/huatuogpt-vision", "github_api_added_by": "auto" }, { "date": "2024-07-01", "arxiv_id": "2406.19251", "title": "AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation", "authors": [ "Jia Fu", "Xiaoting Qin", "Fangkai Yang", "Lu Wang", "Jue Zhang", "Qingwei Lin", "Yubo Chen", "Dongmei Zhang", "Saravan Rajmohan", "Qi Zhang" ], "github": "", "project_page": "", "abstract": "Recent advancements in Large Language Models have transformed ML/AI development, necessitating a reevaluation of AutoML principles for the Retrieval-Augmented Generation (RAG) systems. To address the challenges of hyper-parameter optimization and online adaptation in RAG, we propose the AutoRAG-HP framework, which formulates the hyper-parameter tuning as an online multi-armed bandit (MAB) problem and introduces a novel two-level Hierarchical MAB (Hier-MAB) method for efficient exploration of large search spaces. We conduct extensive experiments on tuning hyper-parameters, such as top-k retrieved documents, prompt compression ratio, and embedding methods, using the ALCE-ASQA and Natural Questions datasets. Our evaluation from jointly optimization all three hyper-parameters demonstrate that MAB-based online learning methods can achieve Recall@5 approx 0.8 for scenarios with prominent gradients in search space, using only sim20% of the LLM API calls required by the Grid Search approach. Additionally, the proposed Hier-MAB approach outperforms other baselines in more challenging optimization scenarios. The code will be made available at https://aka.ms/autorag.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-01", "arxiv_id": "2406.19774", "title": "Direct Preference Knowledge Distillation for Large Language Models", "authors": [ "Yixing Li", "Yuxian Gu", "Li Dong", "Dequan Wang", "Yu Cheng", "Furu Wei" ], "github": "", "project_page": "", "abstract": "In the field of large language models (LLMs), Knowledge Distillation (KD) is a critical technique for transferring capabilities from teacher models to student models. However, existing KD methods face limitations and challenges in distillation of LLMs, including efficiency and insufficient measurement capabilities of traditional KL divergence. It is shown that LLMs can serve as an implicit reward function, which we define as a supplement to KL divergence. In this work, we propose Direct Preference Knowledge Distillation (DPKD) for LLMs. DPKD utilizes distribution divergence to represent the preference loss and implicit reward function. We re-formulate KD of LLMs into two stages: first optimizing and objective consisting of implicit reward and reverse KL divergence and then improving the preference probability of teacher outputs over student outputs. We conducted experiments and analysis on various datasets with LLM parameters ranging from 120M to 13B and demonstrate the broad applicability and effectiveness of our DPKD approach. Meanwhile, we prove the value and effectiveness of the introduced implicit reward and output preference in KD through experiments and theoretical analysis. The DPKD method outperforms the baseline method in both output response precision and exact match percentage. Code and data are available at https://aka.ms/dpkd.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-01", "arxiv_id": "2406.20076", "title": "EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model", "authors": [ "Yuxuan Zhang", "Tianheng Cheng", "Rui Hu", "ei Liu", "Heng Liu", "Longjin Ran", "Xiaoxin Chen", "Wenyu Liu", "Xinggang Wang" ], "github": "https://github.com/hustvl/evf-sam", "project_page": "", "abstract": "Segment Anything Model (SAM) has attracted widespread attention for its superior interactive segmentation capabilities with visual prompts while lacking further exploration of text prompts. In this paper, we empirically investigate what text prompt encoders (e.g., CLIP or LLM) are good for adapting SAM for referring expression segmentation and introduce the Early Vision-language Fusion-based SAM (EVF-SAM). EVF-SAM is a simple yet effective referring segmentation method which exploits multimodal prompts (i.e., image and text) and comprises a pre-trained vision-language model to generate referring prompts and a SAM model for segmentation. Surprisingly, we observe that: (1) multimodal prompts and (2) vision-language models with early fusion (e.g., BEIT-3) are beneficial for prompting SAM for accurate referring segmentation. Our experiments show that the proposed EVF-SAM based on BEIT-3 can obtain state-of-the-art performance on RefCOCO/+/g for referring expression segmentation and demonstrate the superiority of prompting SAM with early vision-language fusion. In addition, the proposed EVF-SAM with 1.32B parameters achieves remarkably higher performance while reducing nearly 82% of parameters compared to previous SAM methods based on large multimodal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hustvl/evf-sam", "github_api_added_by": "auto" }, { "date": "2024-07-01", "arxiv_id": "2406.18462", "title": "GaussianDreamerPro: Text to Manipulable 3D Gaussians with Highly Enhanced Quality", "authors": [ "Taoran Yi", "Jiemin Fang", "Zanwei Zhou", "Junjie Wang", "Guanjun Wu", "Lingxi Xie", "Xiaopeng Zhang", "Wenyu Liu", "Xinggang Wang", "Qi Tian" ], "github": "", "project_page": "", "abstract": "Recently, 3D Gaussian splatting (3D-GS) has achieved great success in reconstructing and rendering real-world scenes. To transfer the high rendering quality to generation tasks, a series of research works attempt to generate 3D-Gaussian assets from text. However, the generated assets have not achieved the same quality as those in reconstruction tasks. We observe that Gaussians tend to grow without control as the generation process may cause indeterminacy. Aiming at highly enhancing the generation quality, we propose a novel framework named GaussianDreamerPro. The main idea is to bind Gaussians to reasonable geometry, which evolves over the whole generation process. Along different stages of our framework, both the geometry and appearance can be enriched progressively. The final output asset is constructed with 3D Gaussians bound to mesh, which shows significantly enhanced details and quality compared with previous methods. Notably, the generated asset can also be seamlessly integrated into downstream manipulation pipelines, e.g. animation, composition, and simulation etc., greatly promoting its potential in wide applications. Demos are available at https://taoranyi.com/gaussiandreamerpro/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-01", "arxiv_id": "2406.19320", "title": "Efficient World Models with Context-Aware Tokenization", "authors": [ "Vincent Micheli", "Eloi Alonso", "Fran\u00e7ois Fleuret" ], "github": "https://github.com/vmicheli/delta-iris", "project_page": "", "abstract": "Scaling up deep Reinforcement Learning (RL) methods presents a significant challenge. Following developments in generative modelling, model-based RL positions itself as a strong contender. Recent advances in sequence modelling have led to effective transformer-based world models, albeit at the price of heavy computations due to the long sequences of tokens required to accurately simulate environments. In this work, we propose Delta-IRIS, a new agent with a world model architecture composed of a discrete autoencoder that encodes stochastic deltas between time steps and an autoregressive transformer that predicts future deltas by summarizing the current state of the world with continuous tokens. In the Crafter benchmark, Delta-IRIS sets a new state of the art at multiple frame budgets, while being an order of magnitude faster to train than previous attention-based approaches. We release our code and models at https://github.com/vmicheli/delta-iris.", "github_manual": "", "github_regex": "https://github.com/vmicheli/delta-iris", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-01", "arxiv_id": "2406.20095", "title": "LLaRA: Supercharging Robot Learning Data for Vision-Language Policy", "authors": [ "Xiang Li", "Cristina Mata", "Jongwoo Park", "Kumara Kahatapitiya", "Yoo Sung Jang", "Jinghuan Shang", "Kanchana Ranasinghe", "Ryan Burgert", "Mu Cai", "Yong Jae Lee", "Michael S. Ryoo" ], "github": "https://github.com/LostXine/LLaRA", "project_page": "", "abstract": "Large Language Models (LLMs) equipped with extensive world knowledge and strong reasoning skills can tackle diverse tasks across domains, often by posing them as conversation-style instruction-response pairs. In this paper, we propose LLaRA: Large Language and Robotics Assistant, a framework which formulates robot action policy as conversations, and provides improved responses when trained with auxiliary data that complements policy learning. LLMs with visual inputs, i.e., Vision Language Models (VLMs), have the capacity to process state information as visual-textual prompts and generate optimal policy decisions in text. To train such action policy VLMs, we first introduce an automated pipeline to generate diverse high-quality robotics instruction data from existing behavior cloning data. A VLM finetuned with the resulting collection of datasets based on a conversation-style formulation tailored for robotics tasks, can generate meaningful robot action policy decisions. Our experiments across multiple simulated and real-world environments demonstrate the state-of-the-art performance of the proposed LLaRA framework. The code, datasets, and pretrained models are available at https://github.com/LostXine/LLaRA.", "github_manual": "", "github_regex": "https://github.com/LostXine/LLaRA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-01", "arxiv_id": "2406.17720", "title": "Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity", "authors": [ "Chih-Hsuan Yang", "Benjamin Feuer", "Zaki Jubery", "Zi K. Deng", "Andre Nakkab", "Md Zahid Hasan", "Shivani Chiranjeevi", "Kelly Marshall", "Nirmal Baishnab", "Asheesh K Singh", "Arti Singh", "Soumik Sarkar", "Nirav Merchant", "Chinmay Hegde", "Baskar Ganapathysubramanian" ], "github": "", "project_page": "", "abstract": "We introduce Arboretum, the largest publicly accessible dataset designed to advance AI for biodiversity applications. This dataset, curated from the iNaturalist community science platform and vetted by domain experts to ensure accuracy, includes 134.6 million images, surpassing existing datasets in scale by an order of magnitude. The dataset encompasses image-language paired data for a diverse set of species from birds (Aves), spiders/ticks/mites (Arachnida), insects (Insecta), plants (Plantae), fungus/mushrooms (Fungi), snails (Mollusca), and snakes/lizards (Reptilia), making it a valuable resource for multimodal vision-language AI models for biodiversity assessment and agriculture research. Each image is annotated with scientific names, taxonomic details, and common names, enhancing the robustness of AI model training. We showcase the value of Arboretum by releasing a suite of CLIP models trained using a subset of 40 million captioned images. We introduce several new benchmarks for rigorous assessment, report accuracy for zero-shot learning, and evaluations across life stages, rare species, confounding species, and various levels of the taxonomic hierarchy. We anticipate that Arboretum will spur the development of AI models that can enable a variety of digital tools ranging from pest control strategies, crop monitoring, and worldwide biodiversity assessment and environmental conservation. These advancements are critical for ensuring food security, preserving ecosystems, and mitigating the impacts of climate change. Arboretum is publicly available, easily accessible, and ready for immediate use. Please see the https://baskargroup.github.io/Arboretum/{project website} for links to our data, models, and code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-01", "arxiv_id": "2406.16845", "title": "RaTEScore: A Metric for Radiology Report Generation", "authors": [ "Weike Zhao", "Chaoyi Wu", "Xiaoman Zhang", "Ya Zhang", "Yanfeng Wang", "Weidi Xie" ], "github": "https://github.com/MAGIC-AI4Med/RaTEScore", "project_page": "", "abstract": "This paper introduces a novel, entity-aware metric, termed as Radiological Report (Text) Evaluation (RaTEScore), to assess the quality of medical reports generated by AI models. RaTEScore emphasizes crucial medical entities such as diagnostic outcomes and anatomical details, and is robust against complex medical synonyms and sensitive to negation expressions. Technically, we developed a comprehensive medical NER dataset, RaTE-NER, and trained an NER model specifically for this purpose. This model enables the decomposition of complex radiological reports into constituent medical entities. The metric itself is derived by comparing the similarity of entity embeddings, obtained from a language model, based on their types and relevance to clinical significance. Our evaluations demonstrate that RaTEScore aligns more closely with human preference than existing metrics, validated both on established public benchmarks and our newly proposed RaTE-Eval benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MAGIC-AI4Med/RaTEScore", "github_api_added_by": "auto" }, { "date": "2024-07-01", "arxiv_id": "2407.00617", "title": "Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning", "authors": [ "Yuheng Zhang", "Dian Yu", "Baolin Peng", "Linfeng Song", "Ye Tian", "Mingyue Huo", "Nan Jiang", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Human Feedback (RLHF) has achieved great success in aligning large language models (LLMs) with human preferences. Prevalent RLHF approaches are reward-based, following the Bradley-Terry (BT) model assumption, which may not fully capture the complexity of human preferences. In this paper, we explore RLHF under a general preference framework and approach it from a game-theoretic perspective. Specifically, we formulate the problem as a two-player game and propose a novel algorithm, iterative Nash policy optimization (INPO). The key idea is to let the policy play against itself via no-regret learning, thereby approximating the Nash policy. Unlike previous methods, INPO bypasses the need for estimating the expected win rate for individual responses, which typically incurs high computational or annotation costs. Instead, we introduce a new loss objective that is directly minimized over a preference dataset. We provide theoretical analysis for our approach and demonstrate its effectiveness through experiments on various representative benchmarks. With an LLaMA-3-8B-based SFT model, INPO achieves a 41.5% length-controlled win rate on AlpacaEval 2.0 and a 38.3% win rate on Arena-Hard, showing substantial improvement over the state-of-the-art iterative algorithm [Dong et al., 2024] under the BT model assumption. Additionally, our ablation study highlights the benefits of incorporating KL regularization for response length control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.01284", "title": "We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?", "authors": [ "Runqi Qiao", "Qiuna Tan", "Guanting Dong", "Minhui Wu", "Chong Sun", "Xiaoshuai Song", "Zhuoma GongQue", "Shanglin Lei", "Zhe Wei", "Miaoxuan Zhang", "Runfeng Qiao", "Yifan Zhang", "Xiao Zong", "Yida Xu", "Muxi Diao", "Zhimin Bao", "Chen Li", "Honggang Zhang" ], "github": "https://github.com/We-Math/We-Math", "project_page": "", "abstract": "Visual mathematical reasoning, as a fundamental visual reasoning ability, has received widespread attention from the Large Multimodal Models (LMMs) community. Existing benchmarks, such as MathVista and MathVerse, focus more on the result-oriented performance but neglect the underlying principles in knowledge acquisition and generalization. Inspired by human-like mathematical reasoning, we introduce WE-MATH, the first benchmark specifically designed to explore the problem-solving principles beyond end-to-end performance. We meticulously collect and categorize 6.5K visual math problems, spanning 67 hierarchical knowledge concepts and five layers of knowledge granularity. We decompose composite problems into sub-problems according to the required knowledge concepts and introduce a novel four-dimensional metric, namely Insufficient Knowledge (IK), Inadequate Generalization (IG), Complete Mastery (CM), and Rote Memorization (RM), to hierarchically assess inherent issues in LMMs' reasoning process. With WE-MATH, we conduct a thorough evaluation of existing LMMs in visual mathematical reasoning and reveal a negative correlation between solving steps and problem-specific performance. We confirm the IK issue of LMMs can be effectively improved via knowledge augmentation strategies. More notably, the primary challenge of GPT-4o has significantly transitioned from IK to IG, establishing it as the first LMM advancing towards the knowledge generalization stage. In contrast, other LMMs exhibit a marked inclination towards Rote Memorization - they correctly solve composite problems involving multiple knowledge concepts yet fail to answer sub-problems. We anticipate that WE-MATH will open new pathways for advancements in visual mathematical reasoning for LMMs. The WE-MATH data and evaluation code are available at https://github.com/We-Math/We-Math.", "github_manual": "", "github_regex": "https://github.com/We-Math/We-Math", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.00468", "title": "MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation", "authors": [ "Jinsheng Huang", "Liang Chen", "Taian Guo", "Fu Zeng", "Yusheng Zhao", "Bohan Wu", "Ye Yuan", "Haozhe Zhao", "Zhihui Guo", "Yichi Zhang", "Jingyang Yuan", "Wei Ju", "Luchen Liu", "Tianyu Liu", "Baobao Chang", "Ming Zhang" ], "github": "https://github.com/chenllliang/mmevalpro", "project_page": "", "abstract": "Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, often assessed through multiple-choice questions (MCQs) that include an image, a question, and several options. However, many benchmarks used for such evaluations suffer from systematic biases. Remarkably, Large Language Models (LLMs) without any visual perception capabilities achieve non-trivial performance, undermining the credibility of these evaluations. To address this issue while maintaining the efficiency of MCQ evaluations, we propose MMEvalPro, a benchmark designed to avoid Type-I errors through a trilogy evaluation pipeline and more rigorous metrics. For each original question from existing benchmarks, human annotators augment it by creating one perception question and one knowledge anchor question through a meticulous annotation process. MMEvalPro comprises 2,138 question triplets, totaling 6,414 distinct questions. Two-thirds of these questions are manually labeled by human experts, while the rest are sourced from existing benchmarks (MMMU, ScienceQA, and MathVista). Compared with the existing benchmarks, our experiments with the latest LLMs and LMMs demonstrate that MMEvalPro is more challenging (the best LMM lags behind human performance by 31.73%, compared to an average gap of 8.03% in previous benchmarks) and more trustworthy (the best LLM trails the best LMM by 23.09%, whereas the gap for previous benchmarks is just 14.64%). Our in-depth analysis explains the reason for the large performance gap and justifies the trustworthiness of evaluation, underscoring its significant potential for advancing future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/chenllliang/mmevalpro", "github_api_added_by": "auto" }, { "date": "2024-07-02", "arxiv_id": "2406.18009", "title": "E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS", "authors": [ "Sefik Emre Eskimez", "Xiaofei Wang", "Manthan Thakker", "Canrun Li", "Chung-Hsien Tsai", "Zhen Xiao", "Hemin Yang", "Zirun Zhu", "Min Tang", "Xu Tan", "Yanqing Liu", "Sheng Zhao", "Naoyuki Kanda" ], "github": "", "project_page": "", "abstract": "This paper introduces Embarrassingly Easy Text-to-Speech (E2 TTS), a fully non-autoregressive zero-shot text-to-speech system that offers human-level naturalness and state-of-the-art speaker similarity and intelligibility. In the E2 TTS framework, the text input is converted into a character sequence with filler tokens. The flow-matching-based mel spectrogram generator is then trained based on the audio infilling task. Unlike many previous works, it does not require additional components (e.g., duration model, grapheme-to-phoneme) or complex techniques (e.g., monotonic alignment search). Despite its simplicity, E2 TTS achieves state-of-the-art zero-shot TTS capabilities that are comparable to or surpass previous works, including Voicebox and NaturalSpeech 3. The simplicity of E2 TTS also allows for flexibility in the input representation. We propose several variants of E2 TTS to improve usability during inference. See https://aka.ms/e2tts/ for demo samples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.00320", "title": "LiteSearch: Efficacious Tree Search for LLM", "authors": [ "Ante Wang", "Linfeng Song", "Ye Tian", "Baolin Peng", "Dian Yu", "Haitao Mi", "Jinsong Su", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Recent research suggests that tree search algorithms (e.g. Monte Carlo Tree Search) can dramatically boost LLM performance on complex mathematical reasoning tasks. However, they often require more than 10 times the computational resources of greedy decoding due to wasteful search strategies, making them difficult to be deployed in practical applications. This study introduces a novel guided tree search algorithm with dynamic node selection and node-level exploration budget (maximum number of children) calculation to tackle this issue. By considering the search progress towards the final answer (history) and the guidance from a value network (future) trained without any step-wise annotations, our algorithm iteratively selects the most promising tree node before expanding it within the boundaries of the allocated computational budget. Experiments conducted on the GSM8K and TabMWP datasets demonstrate that our approach not only offers competitive performance but also enjoys significantly lower computational costs compared to baseline methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.01231", "title": "MIRAI: Evaluating LLM Agents for Event Forecasting", "authors": [ "Chenchen Ye", "Ziniu Hu", "Yihe Deng", "Zijie Huang", "Mingyu Derek Ma", "Yanqiao Zhu", "Wei Wang" ], "github": "", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems. Given this capability, increasing interests have been put into employing LLM agents for predicting international events, which can influence decision-making and shape policy development on an international scale. Despite such a growing interest, there is a lack of a rigorous benchmark of LLM agents' forecasting capability and reliability. To address this gap, we introduce MIRAI, a novel benchmark designed to systematically evaluate LLM agents as temporal forecasters in the context of international events. Our benchmark features an agentic environment with tools for accessing an extensive database of historical, structured events and textual news articles. We refine the GDELT event database with careful cleaning and parsing to curate a series of relational prediction tasks with varying forecasting horizons, assessing LLM agents' abilities from short-term to long-term forecasting. We further implement APIs to enable LLM agents to utilize different tools via a code-based interface. In summary, MIRAI comprehensively evaluates the agents' capabilities in three dimensions: 1) autonomously source and integrate critical information from large global databases; 2) write codes using domain-specific APIs and libraries for tool-use; and 3) jointly reason over historical knowledge from diverse formats and time to accurately predict future events. Through comprehensive benchmarking, we aim to establish a reliable framework for assessing the capabilities of LLM agents in forecasting international events, thereby contributing to the development of more accurate and trustworthy models for international relation analysis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.01519", "title": "DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration Models", "authors": [ "Chang-Han Yeh", "Chin-Yang Lin", "Zhixiang Wang", "Chi-Wei Hsiao", "Ting-Hsuan Chen", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "This paper introduces a method for zero-shot video restoration using pre-trained image restoration diffusion models. Traditional video restoration methods often need retraining for different settings and struggle with limited generalization across various degradation types and datasets. Our approach uses a hierarchical token merging strategy for keyframes and local frames, combined with a hybrid correspondence mechanism that blends optical flow and feature-based nearest neighbor matching (latent merging). We show that our method not only achieves top performance in zero-shot video restoration but also significantly surpasses trained models in generalization across diverse datasets and extreme degradations (8times super-resolution and high-standard deviation video denoising). We present evidence through quantitative metrics and visual comparisons on various challenging datasets. Additionally, our technique works with any 2D restoration diffusion model, offering a versatile and powerful tool for video enhancement tasks without extensive retraining. This research leads to more efficient and widely applicable video restoration technologies, supporting advancements in fields that require high-quality video output. See our project page for video results at https://jimmycv07.github.io/DiffIR2VR_web/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.00782", "title": "Step-Controlled DPO: Leveraging Stepwise Error for Enhanced Mathematical Reasoning", "authors": [ "Zimu Lu", "Aojun Zhou", "Ke Wang", "Houxing Ren", "Weikang Shi", "Junting Pan", "Mingjie Zhan" ], "github": "https://github.com/mathllm/Step-Controlled_DPO", "project_page": "", "abstract": "Direct Preference Optimization (DPO) has proven effective at improving the performance of large language models (LLMs) on downstream tasks such as reasoning and alignment. In this work, we propose Step-Controlled DPO (SCDPO), a method for automatically providing stepwise error supervision by creating negative samples of mathematical reasoning rationales that start making errors at a specified step. By applying these samples in DPO training, SCDPO can better align the model to understand reasoning errors and output accurate reasoning steps. We apply SCDPO to both code-integrated and chain-of-thought solutions, empirically showing that it consistently improves the performance compared to naive DPO on three different SFT models, including one existing SFT model and two models we finetuned. Qualitative analysis of the credit assignment of SCDPO and DPO demonstrates the effectiveness of SCDPO at identifying errors in mathematical solutions. We then apply SCDPO to an InternLM2-20B model, resulting in a 20B model that achieves high scores of 88.5% on GSM8K and 58.1% on MATH, rivaling all other open-source LLMs, showing the great potential of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mathllm/Step-Controlled_DPO", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.01492", "title": "RegMix: Data Mixture as Regression for Language Model Pre-training", "authors": [ "Qian Liu", "Xiaosen Zheng", "Niklas Muennighoff", "Guangtao Zeng", "Longxu Dou", "Tianyu Pang", "Jing Jiang", "Min Lin" ], "github": "https://github.com/sail-sg/regmix", "project_page": "", "abstract": "The data mixture for large language model pre-training significantly impacts performance, yet how to determine an effective mixture remains unclear. We propose RegMix to automatically identify a high-performing data mixture by formulating it as a regression task. RegMix involves training a set of small models with diverse data mixtures and fitting a regression model to predict their performance given their respective mixtures. With the fitted regression model, we simulate the top-ranked mixture and use it to train a large-scale model with orders of magnitude more compute. To empirically validate RegMix, we train 512 models with 1M parameters for 1B tokens of different mixtures to fit the regression model and find the optimal mixture. Using this mixture we train a 1B parameter model for 25B tokens (i.e. 1000x larger and 25x longer) which we find performs best among 64 candidate 1B parameter models with other mixtures. Further, our method demonstrates superior performance compared to human selection and achieves results that match or surpass DoReMi, while utilizing only 10% of the compute budget. Our experiments also show that (1) Data mixtures significantly impact performance with single-task performance variations of up to 14.6%; (2) Web corpora rather than data perceived as high-quality like Wikipedia have the strongest positive correlation with downstream performance; (3) Domains interact in complex ways often contradicting common sense, thus automatic approaches like RegMix are needed; (4) Data mixture effects transcend scaling laws, and our approach captures the complexity by considering all domains together. Our code is available at https://github.com/sail-sg/regmix.", "github_manual": "", "github_regex": "https://github.com/sail-sg/regmix", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.00367", "title": "SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix", "authors": [ "Peng Dai", "Feitong Tan", "Qiangeng Xu", "David Futschik", "Ruofei Du", "Sean Fanello", "Xiaojuan Qi", "Yinda Zhang" ], "github": "", "project_page": "", "abstract": "Video generation models have demonstrated great capabilities of producing impressive monocular videos, however, the generation of 3D stereoscopic video remains under-explored. We propose a pose-free and training-free approach for generating 3D stereoscopic videos using an off-the-shelf monocular video generation model. Our method warps a generated monocular video into camera views on stereoscopic baseline using estimated video depth, and employs a novel frame matrix video inpainting framework. The framework leverages the video generation model to inpaint frames observed from different timestamps and views. This effective approach generates consistent and semantically coherent stereoscopic videos without scene optimization or model fine-tuning. Moreover, we develop a disocclusion boundary re-injection scheme that further improves the quality of video inpainting by alleviating the negative effects propagated from disoccluded areas in the latent space. We validate the efficacy of our proposed method by conducting experiments on videos from various generative models, including Sora [4 ], Lumiere [2], WALT [8 ], and Zeroscope [ 42]. The experiments demonstrate that our method has a significant improvement over previous methods. The code will be released at https://daipengwa.github.io/SVG_ProjectPage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.00837", "title": "Towards Robust Speech Representation Learning for Thousands of Languages", "authors": [ "William Chen", "Wangyou Zhang", "Yifan Peng", "Xinjian Li", "Jinchuan Tian", "Jiatong Shi", "Xuankai Chang", "Soumi Maiti", "Karen Livescu", "Shinji Watanabe" ], "github": "", "project_page": "", "abstract": "Self-supervised learning (SSL) has helped extend speech technologies to more languages by reducing the need for labeled data. However, models are still far from supporting the world's 7000+ languages. We propose XEUS, a Cross-lingual Encoder for Universal Speech, trained on over 1 million hours of data across 4057 languages, extending the language coverage of SSL models 4-fold. We combine 1 million hours of speech from existing publicly accessible corpora with a newly created corpus of 7400+ hours from 4057 languages, which will be publicly released. To handle the diverse conditions of multilingual speech data, we augment the typical SSL masked prediction approach with a novel dereverberation objective, increasing robustness. We evaluate XEUS on several benchmarks, and show that it consistently outperforms or achieves comparable results to state-of-the-art (SOTA) SSL models across a variety of tasks. XEUS sets a new SOTA on the ML-SUPERB benchmark: it outperforms MMS 1B and w2v-BERT 2.0 v2 by 0.8% and 4.4% respectively, despite having less parameters or pre-training data. Checkpoints, code, and data are found in https://www.wavlab.org/activities/2024/xeus/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.01449", "title": "ColPali: Efficient Document Retrieval with Vision Language Models", "authors": [ "Manuel Faysse", "Hugues Sibille", "Tony Wu", "Gautier Viaud", "C\u00e9line Hudelot", "Pierre Colombo" ], "github": "", "project_page": "", "abstract": "Documents are visually rich structures that convey information through text, as well as tables, figures, page layouts, or fonts. While modern document retrieval systems exhibit strong performance on query-to-text matching, they struggle to exploit visual cues efficiently, hindering their performance on practical document retrieval applications such as Retrieval Augmented Generation. To benchmark current systems on visually rich document retrieval, we introduce the Visual Document Retrieval Benchmark ViDoRe, composed of various page-level retrieving tasks spanning multiple domains, languages, and settings. The inherent shortcomings of modern systems motivate the introduction of a new retrieval model architecture, ColPali, which leverages the document understanding capabilities of recent Vision Language Models to produce high-quality contextualized embeddings solely from images of document pages. Combined with a late interaction matching mechanism, ColPali largely outperforms modern document retrieval pipelines while being drastically faster and end-to-end trainable.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.01470", "title": "DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging", "authors": [ "Tzu-Han Lin", "Chen-An Li", "Hung-yi Lee", "Yun-Nung Chen" ], "github": "https://github.com/miulab/dogerm", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) is a popular strategy for aligning large language models (LLMs) with desired behaviors. Reward modeling is a crucial step in RLHF. However, collecting paired preference data for training reward models is often costly and time-consuming, especially for domain-specific preferences requiring expert annotation. To address this challenge, we propose the Domain knowledge merged Reward Model (DogeRM), a novel framework that integrates domain-specific knowledge into a general reward model by model merging. The experiments demonstrate that DogeRM enhances performance across different benchmarks and provide a detailed analysis showcasing the effects of model merging, showing the great potential of facilitating model alignment.", "github_manual": "https://github.com/miulab/dogerm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.00114", "title": "OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents", "authors": [ "Zihao Wang", "Shaofei Cai", "Zhancun Mu", "Haowei Lin", "Ceyao Zhang", "Xuejie Liu", "Qing Li", "Anji Liu", "Xiaojian Ma", "Yitao Liang" ], "github": "", "project_page": "", "abstract": "We present OmniJARVIS, a novel Vision-Language-Action (VLA) model for open-world instruction-following agents in open-world Minecraft. Compared to prior works that either emit textual goals to separate controllers or produce the control command directly, OmniJARVIS seeks a different path to ensure both strong reasoning and efficient decision-making capabilities via unified tokenization of multimodal interaction data. First, we introduce a self-supervised approach to learn a behavior encoder that produces discretized tokens for behavior trajectories tau = {o_0, a_0, dots} and an imitation learning (IL) policy decoder conditioned on these tokens. These additional behavior tokens will be augmented to the vocabulary of pretrained Multimodal Language Models (MLMs). With this encoder, we then pack long-term multimodal interactions involving task instructions, memories, thoughts, observations, textual responses, behavior trajectories, etc. into unified token sequences and model them with autoregressive transformers. Thanks to the semantically meaningful behavior tokens, the resulting VLA model, OmniJARVIS, can reason (by producing chain-of-thoughts), plan, answer questions, and act (by producing behavior tokens for the IL policy decoder). OmniJARVIS demonstrates excellent performances on a comprehensive collection of atomic, programmatic, and open-ended tasks in open-world Minecraft. Our analysis further unveils the crucial design principles in interaction data formation, unified tokenization, and its scaling potentials.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2406.19997", "title": "Wavelets Are All You Need for Autoregressive Image Generation", "authors": [ "Wael Mattar", "Idan Levy", "Nir Sharon", "Shai Dekel" ], "github": "", "project_page": "", "abstract": "In this paper, we take a new approach to autoregressive image generation that is based on two main ingredients. The first is wavelet image coding, which allows to tokenize the visual details of an image from coarse to fine details by ordering the information starting with the most significant bits of the most significant wavelet coefficients. The second is a variant of a language transformer whose architecture is re-designed and optimized for token sequences in this 'wavelet language'. The transformer learns the significant statistical correlations within a token sequence, which are the manifestations of well-known correlations between the wavelet subbands at various resolutions. We show experimental results with conditioning on the generation process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.00653", "title": "Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs", "authors": [ "Yifei Zhang", "Xintao Wang", "Jiaqing Liang", "Sirui Xia", "Lida Chen", "Yanghua Xiao" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have exhibited impressive proficiency in various natural language processing (NLP) tasks, which involve increasingly complex reasoning. Knowledge reasoning, a primary type of reasoning, aims at deriving new knowledge from existing one.While it has been widely studied in the context of knowledge graphs (KGs), knowledge reasoning in LLMs remains underexplored. In this paper, we introduce Chain-of-Knowledge, a comprehensive framework for knowledge reasoning, including methodologies for both dataset construction and model learning. For dataset construction, we create KnowReason via rule mining on KGs. For model learning, we observe rule overfitting induced by naive training. Hence, we enhance CoK with a trial-and-error mechanism that simulates the human process of internal knowledge exploration. We conduct extensive experiments with KnowReason. Our results show the effectiveness of CoK in refining LLMs in not only knowledge reasoning, but also general reasoning benchmarkms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.00106", "title": "UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI", "authors": [ "Ilia Shumailov", "Jamie Hayes", "Eleni Triantafillou", "Guillermo Ortiz-Jimenez", "Nicolas Papernot", "Matthew Jagielski", "Itay Yona", "Heidi Howard", "Eugene Bagdasaryan" ], "github": "", "project_page": "", "abstract": "Exact unlearning was first introduced as a privacy mechanism that allowed a user to retract their data from machine learning models on request. Shortly after, inexact schemes were proposed to mitigate the impractical costs associated with exact unlearning. More recently unlearning is often discussed as an approach for removal of impermissible knowledge i.e. knowledge that the model should not possess such as unlicensed copyrighted, inaccurate, or malicious information. The promise is that if the model does not have a certain malicious capability, then it cannot be used for the associated malicious purpose. In this paper we revisit the paradigm in which unlearning is used for in Large Language Models (LLMs) and highlight an underlying inconsistency arising from in-context learning. Unlearning can be an effective control mechanism for the training phase, yet it does not prevent the model from performing an impermissible act during inference. We introduce a concept of ununlearning, where unlearned knowledge gets reintroduced in-context, effectively rendering the model capable of behaving as if it knows the forgotten knowledge. As a result, we argue that content filtering for impermissible knowledge will be required and even exact unlearning schemes are not enough for effective content regulation. We discuss feasibility of ununlearning for modern LLMs and examine broader implications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.00788", "title": "InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation", "authors": [ "Haofan Wang", "Peng Xing", "Renyuan Huang", "Hao Ai", "Qixun Wang", "Xu Bai" ], "github": "https://github.com/instantX-research/InstantStyle-Plus", "project_page": "", "abstract": "Style transfer is an inventive process designed to create an image that maintains the essence of the original while embracing the visual style of another. Although diffusion models have demonstrated impressive generative power in personalized subject-driven or style-driven applications, existing state-of-the-art methods still encounter difficulties in achieving a seamless balance between content preservation and style enhancement. For example, amplifying the style's influence can often undermine the structural integrity of the content. To address these challenges, we deconstruct the style transfer task into three core elements: 1) Style, focusing on the image's aesthetic characteristics; 2) Spatial Structure, concerning the geometric arrangement and composition of visual elements; and 3) Semantic Content, which captures the conceptual meaning of the image. Guided by these principles, we introduce InstantStyle-Plus, an approach that prioritizes the integrity of the original content while seamlessly integrating the target style. Specifically, our method accomplishes style injection through an efficient, lightweight process, utilizing the cutting-edge InstantStyle framework. To reinforce the content preservation, we initiate the process with an inverted content latent noise and a versatile plug-and-play tile ControlNet for preserving the original image's intrinsic layout. We also incorporate a global semantic adapter to enhance the semantic content's fidelity. To safeguard against the dilution of style information, a style extractor is employed as discriminator for providing supplementary style guidance. Codes will be available at https://github.com/instantX-research/InstantStyle-Plus.", "github_manual": "", "github_regex": "https://github.com/instantX-research/InstantStyle-Plus", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.01272", "title": "Show Less, Instruct More: Enriching Prompts with Definitions and Guidelines for Zero-Shot NER", "authors": [ "Andrew Zamai", "Andrea Zugarini", "Leonardo Rigutini", "Marco Ernandes", "Marco Maggini" ], "github": "", "project_page": "", "abstract": "Recently, several specialized instruction-tuned Large Language Models (LLMs) for Named Entity Recognition (NER) have emerged. Compared to traditional NER approaches, these models have strong generalization capabilities. Existing LLMs mainly focus on zero-shot NER in out-of-domain distributions, being fine-tuned on an extensive number of entity classes that often highly or completely overlap with test sets. In this work instead, we propose SLIMER, an approach designed to tackle never-seen-before named entity tags by instructing the model on fewer examples, and by leveraging a prompt enriched with definition and guidelines. Experiments demonstrate that definition and guidelines yield better performance, faster and more robust learning, particularly when labelling unseen Named Entities. Furthermore, SLIMER performs comparably to state-of-the-art approaches in out-of-domain zero-shot NER, while being trained on a reduced tag set.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2406.20085", "title": "Auto Cherry-Picker: Learning from High-quality Generative Data Driven by Language", "authors": [ "Yicheng Chen", "Xiangtai Li", "Yining Li", "Yanhong Zeng", "Jianzong Wu", "Xiangyu Zhao", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Diffusion-based models have shown great potential in generating high-quality images with various layouts, which can benefit downstream perception tasks. However, a fully automatic layout generation driven only by language and a suitable metric for measuring multiple generated instances has not been well explored. In this work, we present Auto Cherry-Picker (ACP), a novel framework that generates high-quality multi-modal training examples to augment perception and multi-modal training. Starting with a simple list of natural language concepts, we prompt large language models (LLMs) to generate a detailed description and design reasonable layouts. Next, we use an off-the-shelf text-to-image model to generate multiple images. Then, the generated data are refined using a comprehensively designed metric to ensure quality. In particular, we present a new metric, Composite Layout and Image Score (CLIS), to evaluate the generated images fairly. Our synthetic high-quality examples boost performance in various scenarios by customizing the initial concept list, especially in addressing challenges associated with long-tailed distribution and imbalanced datasets. Experiment results on downstream tasks demonstrate that Auto Cherry-Picker can significantly improve the performance of existing models. In addition, we have thoroughly investigated the correlation between CLIS and performance gains in downstream tasks, and we find that a better CLIS score results in better performance. This finding shows the potential for evaluation metrics as the role for various visual perception and MLLM tasks. Code will be available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2406.19741", "title": "ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning", "authors": [ "Christopher E. Mower", "Yuhui Wan", "Hongzhan Yu", "Antoine Grosnit", "Jonas Gonzalez-Billandon", "Matthieu Zimmer", "Jinlong Wang", "Xinyu Zhang", "Yao Zhao", "Anbang Zhai", "Puze Liu", "Davide Tateo", "Cesar Cadena", "Marco Hutter", "Jan Peters", "Guangjian Tian", "Yuzheng Zhuang", "Kun Shao", "Xingyue Quan", "Jianye Hao", "Jun Wang", "Haitham Bou-Ammar" ], "github": "https://github.com/huawei-noah/HEBO/tree/master/ROSLLM", "project_page": "", "abstract": "We present a framework for intuitive robot programming by non-experts, leveraging natural language prompts and contextual information from the Robot Operating System (ROS). Our system integrates large language models (LLMs), enabling non-experts to articulate task requirements to the system through a chat interface. Key features of the framework include: integration of ROS with an AI agent connected to a plethora of open-source and commercial LLMs, automatic extraction of a behavior from the LLM output and execution of ROS actions/services, support for three behavior modes (sequence, behavior tree, state machine), imitation learning for adding new robot actions to the library of possible actions, and LLM reflection via human and environment feedback. Extensive experiments validate the framework, showcasing robustness, scalability, and versatility in diverse scenarios, including long-horizon tasks, tabletop rearrangements, and remote supervisory control. To facilitate the adoption of our framework and support the reproduction of our results, we have made our code open-source. You can access it at: https://github.com/huawei-noah/HEBO/tree/master/ROSLLM.", "github_manual": "", "github_regex": "https://github.com/huawei-noah/HEBO/tree/master/ROSLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.00111", "title": "Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models", "authors": [ "Ben Fauber" ], "github": "", "project_page": "", "abstract": "We describe the accurate prediction of ligand-protein interaction (LPI) affinities, also known as drug-target interactions (DTI), with instruction fine-tuned pretrained generative small language models (SLMs). We achieved accurate predictions for a range of affinity values associated with ligand-protein interactions on out-of-sample data in a zero-shot setting. Only the SMILES string of the ligand and the amino acid sequence of the protein were used as the model inputs. Our results demonstrate a clear improvement over machine learning (ML) and free-energy perturbation (FEP+) based methods in accurately predicting a range of ligand-protein interaction affinities, which can be leveraged to further accelerate drug discovery campaigns against challenging therapeutic targets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.00088", "title": "T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge", "authors": [ "Jianyu Wei", "Shijie Cao", "Ting Cao", "Lingxiao Ma", "Lei Wang", "Yanyong Zhang", "Mao Yang" ], "github": "https://github.com/microsoft/T-MAC", "project_page": "", "abstract": "The deployment of Large Language Models (LLMs) on edge devices is increasingly important to enhance on-device intelligence. Weight quantization is crucial for reducing the memory footprint of LLMs on devices. However, low-bit LLMs necessitate mixed precision matrix multiplication (mpGEMM) of low precision weights and high precision activations during inference. Existing systems, lacking native support for mpGEMM, resort to dequantize weights for high precision computation. Such an indirect way can lead to a significant inference overhead. In this paper, we introduce T-MAC, an innovative lookup table(LUT)-based method designed for efficient low-bit LLM (i.e., weight-quantized LLM) inference on CPUs. T-MAC directly supports mpGEMM without dequantization, while simultaneously eliminating multiplications and reducing additions required. Specifically, T-MAC transforms the traditional data-type-centric multiplication to bit-wise table lookup, and enables a unified and scalable mpGEMM solution. Our LUT-based kernels scale linearly to the weight bit-width. Evaluated on low-bit Llama and BitNet models, T-MAC demonstrates up to 4x increase in throughput and 70% reduction in energy consumption compared to llama.cpp. For BitNet-b1.58-3B, T-MAC delivers a token generation throughput of 30 tokens/s with a single core and 71 tokens/s with eight cores on M2-Ultra, and 11 tokens/s on lower-end devices like Raspberry Pi 5, which significantly exceeds the adult average reading speed. T-MAC with LUT-based computing paradigm, paves the way for the practical deployment of low-bit LLMs on resource-constrained edge devices without compromising computational efficiency. The system is open-sourced at https://github.com/microsoft/T-MAC.", "github_manual": "", "github_regex": "https://github.com/microsoft/T-MAC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2407.00402", "title": "Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP", "authors": [ "Omer Goldman", "Alon Jacovi", "Aviv Slobodkin", "Aviya Maimon", "Ido Dagan", "Reut Tsarfaty" ], "github": "", "project_page": "", "abstract": "Improvements in language models' capabilities have pushed their applications towards longer contexts, making long-context evaluation and development an active research area. However, many disparate use-cases are grouped together under the umbrella term of \"long-context\", defined simply by the total length of the model's input, including - for example - Needle-in-a-Haystack tasks, book summarization, and information aggregation. Given their varied difficulty, in this position paper we argue that conflating different tasks by their context length is unproductive. As a community, we require a more precise vocabulary to understand what makes long-context tasks similar or different. We propose to unpack the taxonomy of long-context based on the properties that make them more difficult with longer contexts. We propose two orthogonal axes of difficulty: (I) Diffusion: How hard is it to find the necessary information in the context? (II) Scope: How much necessary information is there to find? We survey the literature on long-context, provide justification for this taxonomy as an informative descriptor, and situate the literature with respect to it. We conclude that the most difficult and interesting settings, whose necessary information is very long and highly diffused within the input, is severely under-explored. By using a descriptive vocabulary and discussing the relevant properties of difficulty in long-context, we can implement more informed research in this area. We call for a careful design of tasks and benchmarks with distinctly long context, taking into account the characteristics that make it qualitatively different from shorter context.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2406.20086", "title": "Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs", "authors": [ "Sheridan Feucht", "David Atkinson", "Byron Wallace", "David Bau" ], "github": "", "project_page": "", "abstract": "LLMs process text as sequences of tokens that roughly correspond to words, where less common words are represented by multiple tokens. However, individual tokens are often semantically unrelated to the meanings of the words/concepts they comprise. For example, Llama-2-7b's tokenizer splits the word \"northeastern\" into the tokens ['_n', 'ort', 'he', 'astern'], none of which correspond to semantically meaningful units like \"north\" or \"east.\" Similarly, the overall meanings of named entities like \"Neil Young\" and multi-word expressions like \"break a leg\" cannot be directly inferred from their constituent tokens. Mechanistically, how do LLMs convert such arbitrary groups of tokens into useful higher-level representations? In this work, we find that last token representations of named entities and multi-token words exhibit a pronounced \"erasure\" effect, where information about previous and current tokens is rapidly forgotten in early layers. Using this observation, we propose a method to \"read out\" the implicit vocabulary of an autoregressive LLM by examining differences in token representations across layers, and present results of this method for Llama-2-7b and Llama-3-8B. To our knowledge, this is the first attempt to probe the implicit vocabulary of an LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2406.18284", "title": "RealTalk: Real-time and Realistic Audio-driven Face Generation with 3D Facial Prior-guided Identity Alignment Network", "authors": [ "Xiaozhong Ji", "Chuming Lin", "Zhonggan Ding", "Ying Tai", "Jian Yang", "Junwei Zhu", "Xiaobin Hu", "Jiangning Zhang", "Donghao Luo", "Chengjie Wang" ], "github": "", "project_page": "", "abstract": "Person-generic audio-driven face generation is a challenging task in computer vision. Previous methods have achieved remarkable progress in audio-visual synchronization, but there is still a significant gap between current results and practical applications. The challenges are two-fold: 1) Preserving unique individual traits for achieving high-precision lip synchronization. 2) Generating high-quality facial renderings in real-time performance. In this paper, we propose a novel generalized audio-driven framework RealTalk, which consists of an audio-to-expression transformer and a high-fidelity expression-to-face renderer. In the first component, we consider both identity and intra-personal variation features related to speaking lip movements. By incorporating cross-modal attention on the enriched facial priors, we can effectively align lip movements with audio, thus attaining greater precision in expression prediction. In the second component, we design a lightweight facial identity alignment (FIA) module which includes a lip-shape control structure and a face texture reference structure. This novel design allows us to generate fine details in real-time, without depending on sophisticated and inefficient feature alignment modules. Our experimental results, both quantitative and qualitative, on public datasets demonstrate the clear advantages of our method in terms of lip-speech synchronization and generation quality. Furthermore, our method is efficient and requires fewer computational resources, making it well-suited to meet the needs of practical applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2406.20087", "title": "ProgressGym: Alignment with a Millennium of Moral Progress", "authors": [ "Tianyi Qiu", "Yang Zhang", "Xuchuan Huang", "Jasmine Xinze Li", "Jiaming Ji", "Yaodong Yang" ], "github": "https://github.com/PKU-Alignment/ProgressGym", "project_page": "", "abstract": "Frontier AI systems, including large language models (LLMs), hold increasing influence over the epistemology of human users. Such influence can reinforce prevailing societal values, potentially contributing to the lock-in of misguided moral beliefs and, consequently, the perpetuation of problematic moral practices on a broad scale. We introduce progress alignment as a technical solution to mitigate this imminent risk. Progress alignment algorithms learn to emulate the mechanics of human moral progress, thereby addressing the susceptibility of existing alignment methods to contemporary moral blindspots. To empower research in progress alignment, we introduce ProgressGym, an experimental framework allowing the learning of moral progress mechanics from history, in order to facilitate future progress in real-world moral decisions. Leveraging 9 centuries of historical text and 18 historical LLMs, ProgressGym enables codification of real-world progress alignment challenges into concrete benchmarks. Specifically, we introduce three core challenges: tracking evolving values (PG-Follow), preemptively anticipating moral progress (PG-Predict), and regulating the feedback loop between human and AI value shifts (PG-Coevolve). Alignment methods without a temporal dimension are inapplicable to these tasks. In response, we present lifelong and extrapolative algorithms as baseline methods of progress alignment, and build an open leaderboard soliciting novel algorithms and challenges. The framework and the leaderboard are available at https://github.com/PKU-Alignment/ProgressGym and https://huggingface.co/spaces/PKU-Alignment/ProgressGym-LeaderBoard respectively.", "github_manual": "", "github_regex": "https://github.com/PKU-Alignment/ProgressGym", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-02", "arxiv_id": "2406.19999", "title": "The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models", "authors": [ "Xinyi Chen", "Baohao Liao", "Jirui Qi", "Panagiotis Eustratiadis", "Christof Monz", "Arianna Bisazza", "Maarten de Rijke" ], "github": "https://github.com/shin-ee-chen/SIFo", "project_page": "", "abstract": "Following multiple instructions is a crucial ability for large language models (LLMs). Evaluating this ability comes with significant challenges: (i) limited coherence between multiple instructions, (ii) positional bias where the order of instructions affects model performance, and (iii) a lack of objectively verifiable tasks. To address these issues, we introduce a benchmark designed to evaluate models' abilities to follow multiple instructions through sequential instruction following (SIFo) tasks. In SIFo, the successful completion of multiple instructions is verifiable by examining only the final instruction. Our benchmark evaluates instruction following using four tasks (text modification, question answering, mathematics, and security rule following), each assessing different aspects of sequential instruction following. Our evaluation of popular LLMs, both closed-source and open-source, shows that more recent and larger models significantly outperform their older and smaller counterparts on the SIFo tasks, validating the benchmark's effectiveness. All models struggle with following sequences of instructions, hinting at an important lack of robustness of today's language models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/shin-ee-chen/SIFo", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-03", "arxiv_id": "2407.01494", "title": "FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds", "authors": [ "Yiming Zhang", "Yicheng Gu", "Yanhong Zeng", "Zhening Xing", "Yuancheng Wang", "Zhizheng Wu", "Kai Chen" ], "github": "https://github.com/open-mmlab/FoleyCrafter", "project_page": "", "abstract": "We study Neural Foley, the automatic generation of high-quality sound effects synchronizing with videos, enabling an immersive audio-visual experience. Despite its wide range of applications, existing approaches encounter limitations when it comes to simultaneously synthesizing high-quality and video-aligned (i.e.,, semantic relevant and temporal synchronized) sounds. To overcome these limitations, we propose FoleyCrafter, a novel framework that leverages a pre-trained text-to-audio model to ensure high-quality audio generation. FoleyCrafter comprises two key components: the semantic adapter for semantic alignment and the temporal controller for precise audio-video synchronization. The semantic adapter utilizes parallel cross-attention layers to condition audio generation on video features, producing realistic sound effects that are semantically relevant to the visual content. Meanwhile, the temporal controller incorporates an onset detector and a timestampbased adapter to achieve precise audio-video alignment. One notable advantage of FoleyCrafter is its compatibility with text prompts, enabling the use of text descriptions to achieve controllable and diverse video-to-audio generation according to user intents. We conduct extensive quantitative and qualitative experiments on standard benchmarks to verify the effectiveness of FoleyCrafter. Models and codes are available at https://github.com/open-mmlab/FoleyCrafter.", "github_manual": "", "github_regex": "https://github.com/open-mmlab/FoleyCrafter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-03", "arxiv_id": "2407.02398", "title": "Consistency Flow Matching: Defining Straight Flows with Velocity Consistency", "authors": [ "Ling Yang", "Zixiang Zhang", "Zhilong Zhang", "Xingchao Liu", "Minkai Xu", "Wentao Zhang", "Chenlin Meng", "Stefano Ermon", "Bin Cui" ], "github": "https://github.com/YangLing0818/consistency_flow_matching", "project_page": "", "abstract": "Flow matching (FM) is a general framework for defining probability paths via Ordinary Differential Equations (ODEs) to transform between noise and data samples. Recent approaches attempt to straighten these flow trajectories to generate high-quality samples with fewer function evaluations, typically through iterative rectification methods or optimal transport solutions. In this paper, we introduce Consistency Flow Matching (Consistency-FM), a novel FM method that explicitly enforces self-consistency in the velocity field. Consistency-FM directly defines straight flows starting from different times to the same endpoint, imposing constraints on their velocity values. Additionally, we propose a multi-segment training approach for Consistency-FM to enhance expressiveness, achieving a better trade-off between sampling quality and speed. Preliminary experiments demonstrate that our Consistency-FM significantly improves training efficiency by converging 4.4x faster than consistency models and 1.7x faster than rectified flow models while achieving better generation quality. Our code is available at: https://github.com/YangLing0818/consistency_flow_matching", "github_manual": "", "github_regex": "https://github.com/YangLing0818/consistency_flow_matching", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-03", "arxiv_id": "2407.02490", "title": "MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention", "authors": [ "Huiqiang Jiang", "Yucheng Li", "Chengruidong Zhang", "Qianhui Wu", "Xufang Luo", "Surin Ahn", "Zhenhua Han", "Amir H. Abdi", "Dongsheng Li", "Chin-Yew Lin", "Yuqing Yang", "Lili Qiu" ], "github": "", "project_page": "", "abstract": "The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase. Due to the quadratic complexity of the attention computation, it takes 30 minutes for an 8B LLM to process a prompt of 1M tokens (i.e., the pre-filling stage) on a single A100 GPU. Existing methods for speeding up prefilling often fail to maintain acceptable accuracy or efficiency when applied to long-context LLMs. To address this gap, we introduce MInference (Milliontokens Inference), a sparse calculation method designed to accelerate pre-filling of long-sequence processing. Specifically, we identify three unique patterns in long-context attention matrices-the A-shape, Vertical-Slash, and Block-Sparsethat can be leveraged for efficient sparse computation on GPUs. We determine the optimal pattern for each attention head offline and dynamically build sparse indices based on the assigned pattern during inference. With the pattern and sparse indices, we perform efficient sparse attention calculations via our optimized GPU kernels to significantly reduce the latency in the pre-filling stage of long-context LLMs. Our proposed technique can be directly applied to existing LLMs without any modifications to the pre-training setup or additional fine-tuning. By evaluating on a wide range of downstream tasks, including InfiniteBench, RULER, PG-19, and Needle In A Haystack, and models including LLaMA-3-1M, GLM4-1M, Yi-200K, Phi-3-128K, and Qwen2-128K, we demonstrate that MInference effectively reduces inference latency by up to 10x for pre-filling on an A100, while maintaining accuracy. Our code is available at https://aka.ms/MInference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-03", "arxiv_id": "2407.01489", "title": "Agentless: Demystifying LLM-based Software Engineering Agents", "authors": [ "Chunqiu Steven Xia", "Yinlin Deng", "Soren Dunn", "Lingming Zhang" ], "github": "https://github.com/OpenAutoCoder/Agentless", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) have significantly advanced the automation of software development tasks, including code synthesis, program repair, and test generation. More recently, researchers and industry practitioners have developed various autonomous LLM agents to perform end-to-end software development tasks. These agents are equipped with the ability to use tools, run commands, observe feedback from the environment, and plan for future actions. However, the complexity of these agent-based approaches, together with the limited abilities of current LLMs, raises the following question: Do we really have to employ complex autonomous software agents? To attempt to answer this question, we build Agentless -- an agentless approach to automatically solve software development problems. Compared to the verbose and complex setup of agent-based approaches, Agentless employs a simplistic two-phase process of localization followed by repair, without letting the LLM decide future actions or operate with complex tools. Our results on the popular SWE-bench Lite benchmark show that surprisingly the simplistic Agentless is able to achieve both the highest performance (27.33%) and lowest cost (\\$0.34) compared with all existing open-source software agents! Furthermore, we manually classified the problems in SWE-bench Lite and found problems with exact ground truth patch or insufficient/misleading issue descriptions. As such, we construct SWE-bench Lite-S by excluding such problematic issues to perform more rigorous evaluation and comparison. Our work highlights the current overlooked potential of a simple, interpretable technique in autonomous software development. We hope Agentless will help reset the baseline, starting point, and horizon for autonomous software agents, and inspire future work along this crucial direction.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenAutoCoder/Agentless", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-03", "arxiv_id": "2407.01370", "title": "Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems", "authors": [ "Philippe Laban", "Alexander R. Fabbri", "Caiming Xiong", "Chien-Sheng Wu" ], "github": "https://github.com/salesforce/summary-of-a-haystack", "project_page": "", "abstract": "LLMs and RAG systems are now capable of handling millions of input tokens or more. However, evaluating the output quality of such systems on long-context tasks remains challenging, as tasks like Needle-in-a-Haystack lack complexity. In this work, we argue that summarization can play a central role in such evaluation. We design a procedure to synthesize Haystacks of documents, ensuring that specific insights repeat across documents. The \"Summary of a Haystack\" (SummHay) task then requires a system to process the Haystack and generate, given a query, a summary that identifies the relevant insights and precisely cites the source documents. Since we have precise knowledge of what insights should appear in a haystack summary and what documents should be cited, we implement a highly reproducible automatic evaluation that can score summaries on two aspects - Coverage and Citation. We generate Haystacks in two domains (conversation, news), and perform a large-scale evaluation of 10 LLMs and corresponding 50 RAG systems. Our findings indicate that SummHay is an open challenge for current systems, as even systems provided with an Oracle signal of document relevance lag our estimate of human performance (56\\%) by 10+ points on a Joint Score. Without a retriever, long-context LLMs like GPT-4o and Claude 3 Opus score below 20% on SummHay. We show SummHay can also be used to study enterprise RAG systems and position bias in long-context models. We hope future systems can equal and surpass human performance on SummHay.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/salesforce/summary-of-a-haystack", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-03", "arxiv_id": "2406.19568", "title": "What Matters in Detecting AI-Generated Videos like Sora?", "authors": [ "Chirui Chang", "Zhengzhe Liu", "Xiaoyang Lyu", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "Recent advancements in diffusion-based video generation have showcased remarkable results, yet the gap between synthetic and real-world videos remains under-explored. In this study, we examine this gap from three fundamental perspectives: appearance, motion, and geometry, comparing real-world videos with those generated by a state-of-the-art AI model, Stable Video Diffusion. To achieve this, we train three classifiers using 3D convolutional networks, each targeting distinct aspects: vision foundation model features for appearance, optical flow for motion, and monocular depth for geometry. Each classifier exhibits strong performance in fake video detection, both qualitatively and quantitatively. This indicates that AI-generated videos are still easily detectable, and a significant gap between real and fake videos persists. Furthermore, utilizing the Grad-CAM, we pinpoint systematic failures of AI-generated videos in appearance, motion, and geometry. Finally, we propose an Ensemble-of-Experts model that integrates appearance, optical flow, and depth information for fake video detection, resulting in enhanced robustness and generalization ability. Our model is capable of detecting videos generated by Sora with high accuracy, even without exposure to any Sora videos during training. This suggests that the gap between real and fake videos can be generalized across various video generative models. Project page: https://justin-crchang.github.io/3DCNNDetection.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-03", "arxiv_id": "2407.02371", "title": "OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation", "authors": [ "Kepan Nan", "Rui Xie", "Penghao Zhou", "Tiehan Fan", "Zhenheng Yang", "Zhijie Chen", "Xiang Li", "Jian Yang", "Ying Tai" ], "github": "", "project_page": "", "abstract": "Text-to-video (T2V) generation has recently garnered significant attention thanks to the large multi-modality model Sora. However, T2V generation still faces two important challenges: 1) Lacking a precise open sourced high-quality dataset. The previous popular video datasets, e.g. WebVid-10M and Panda-70M, are either with low quality or too large for most research institutions. Therefore, it is challenging but crucial to collect a precise high-quality text-video pairs for T2V generation. 2) Ignoring to fully utilize textual information. Recent T2V methods have focused on vision transformers, using a simple cross attention module for video generation, which falls short of thoroughly extracting semantic information from text prompt. To address these issues, we introduce OpenVid-1M, a precise high-quality dataset with expressive captions. This open-scenario dataset contains over 1 million text-video pairs, facilitating research on T2V generation. Furthermore, we curate 433K 1080p videos from OpenVid-1M to create OpenVidHD-0.4M, advancing high-definition video generation. Additionally, we propose a novel Multi-modal Video Diffusion Transformer (MVDiT) capable of mining both structure information from visual tokens and semantic information from text tokens. Extensive experiments and ablation studies verify the superiority of OpenVid-1M over previous datasets and the effectiveness of our MVDiT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-03", "arxiv_id": "2407.01920", "title": "To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models", "authors": [ "Bozhong Tian", "Xiaozhuan Liang", "Siyuan Cheng", "Qingbin Liu", "Mengru Wang", "Dianbo Sui", "Xi Chen", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/KnowUnDo", "project_page": "", "abstract": "Large Language Models (LLMs) trained on extensive corpora inevitably retain sensitive data, such as personal privacy information and copyrighted material. Recent advancements in knowledge unlearning involve updating LLM parameters to erase specific knowledge. However, current unlearning paradigms are mired in vague forgetting boundaries, often erasing knowledge indiscriminately. In this work, we introduce KnowUnDo, a benchmark containing copyrighted content and user privacy domains to evaluate if the unlearning process inadvertently erases essential knowledge. Our findings indicate that existing unlearning methods often suffer from excessive unlearning. To address this, we propose a simple yet effective method, MemFlex, which utilizes gradient information to precisely target and unlearn sensitive parameters. Experimental results show that MemFlex is superior to existing methods in both precise knowledge unlearning and general knowledge retaining of LLMs. Code and dataset will be released at https://github.com/zjunlp/KnowUnDo.", "github_manual": "", "github_regex": "https://github.com/zjunlp/KnowUnDo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-03", "arxiv_id": "2406.19238", "title": "Revealing Fine-Grained Values and Opinions in Large Language Models", "authors": [ "Dustin Wright", "Arnav Arora", "Nadav Borenstein", "Srishti Yadav", "Serge Belongie", "Isabelle Augenstein" ], "github": "https://github.com/copenlu/llm-pct-tropes", "project_page": "", "abstract": "Uncovering latent values and opinions in large language models (LLMs) can help identify biases and mitigate potential harm. Recently, this has been approached by presenting LLMs with survey questions and quantifying their stances towards morally and politically charged statements. However, the stances generated by LLMs can vary greatly depending on how they are prompted, and there are many ways to argue for or against a given position. In this work, we propose to address this by analysing a large and robust dataset of 156k LLM responses to the 62 propositions of the Political Compass Test (PCT) generated by 6 LLMs using 420 prompt variations. We perform coarse-grained analysis of their generated stances and fine-grained analysis of the plain text justifications for those stances. For fine-grained analysis, we propose to identify tropes in the responses: semantically similar phrases that are recurrent and consistent across different prompts, revealing patterns in the text that a given LLM is prone to produce. We find that demographic features added to prompts significantly affect outcomes on the PCT, reflecting bias, as well as disparities between the results of tests when eliciting closed-form vs. open domain responses. Additionally, patterns in the plain text rationales via tropes show that similar justifications are repeatedly generated across models and prompts even with disparate stances.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/copenlu/llm-pct-tropes", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-03", "arxiv_id": "2407.02477", "title": "Understanding Alignment in Multimodal LLMs: A Comprehensive Study", "authors": [ "Elmira Amirloo", "Jean-Philippe Fauconnier", "Christoph Roesmann", "Christian Kerl", "Rinu Boney", "Yusu Qian", "Zirui Wang", "Afshin Dehghan", "Yinfei Yang", "Zhe Gan", "Peter Grasch" ], "github": "", "project_page": "", "abstract": "Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains comparatively underexplored. Similar to language models, MLLMs for image understanding tasks encounter challenges like hallucination. In MLLMs, hallucination can occur not only by stating incorrect facts but also by producing responses that are inconsistent with the image content. A primary objective of alignment for MLLMs is to encourage these models to align responses more closely with image information. Recently, multiple works have introduced preference datasets for MLLMs and examined different alignment methods, including Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO). However, due to variations in datasets, base model types, and alignment methods, it remains unclear which specific elements contribute most significantly to the reported improvements in these works. In this paper, we independently analyze each aspect of preference alignment in MLLMs. We start by categorizing the alignment algorithms into two groups, offline (such as DPO), and online (such as online-DPO), and show that combining offline and online methods can improve the performance of the model in certain scenarios. We review a variety of published multimodal preference datasets and discuss how the details of their construction impact model performance. Based on these insights, we introduce a novel way of creating multimodal preference data called Bias-Driven Hallucination Sampling (BDHS) that needs neither additional annotation nor external models, and show that it can achieve competitive performance to previously published alignment work for multimodal models across a range of benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-03", "arxiv_id": "2407.02489", "title": "Magic Insert: Style-Aware Drag-and-Drop", "authors": [ "Nataniel Ruiz", "Yuanzhen Li", "Neal Wadhwa", "Yael Pritch", "Michael Rubinstein", "David E. Jacobs", "Shlomi Fruchter" ], "github": "", "project_page": "", "abstract": "We present Magic Insert, a method for dragging-and-dropping subjects from a user-provided image into a target image of a different style in a physically plausible manner while matching the style of the target image. This work formalizes the problem of style-aware drag-and-drop and presents a method for tackling it by addressing two sub-problems: style-aware personalization and realistic object insertion in stylized images. For style-aware personalization, our method first fine-tunes a pretrained text-to-image diffusion model using LoRA and learned text tokens on the subject image, and then infuses it with a CLIP representation of the target style. For object insertion, we use Bootstrapped Domain Adaption to adapt a domain-specific photorealistic object insertion model to the domain of diverse artistic styles. Overall, the method significantly outperforms traditional approaches such as inpainting. Finally, we present a dataset, SubjectPlop, to facilitate evaluation and future progress in this area. Project page: https://magicinsert.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-03", "arxiv_id": "2407.01791", "title": "\u03bc-Bench: A Vision-Language Benchmark for Microscopy Understanding", "authors": [ "Alejandro Lozano", "Jeffrey Nirschl", "James Burgess", "Sanket Rajan Gupte", "Yuhui Zhang", "Alyssa Unell", "Serena Yeung-Levy" ], "github": "https://github.com/Ale9806/eVLLM", "project_page": "", "abstract": "Recent advances in microscopy have enabled the rapid generation of terabytes of image data in cell biology and biomedical research. Vision-language models (VLMs) offer a promising solution for large-scale biological image analysis, enhancing researchers' efficiency, identifying new image biomarkers, and accelerating hypothesis generation and scientific discovery. However, there is a lack of standardized, diverse, and large-scale vision-language benchmarks to evaluate VLMs' perception and cognition capabilities in biological image understanding. To address this gap, we introduce {\\mu}-Bench, an expert-curated benchmark encompassing 22 biomedical tasks across various scientific disciplines (biology, pathology), microscopy modalities (electron, fluorescence, light), scales (subcellular, cellular, tissue), and organisms in both normal and abnormal states. We evaluate state-of-the-art biomedical, pathology, and general VLMs on {\\mu}-Bench and find that: i) current models struggle on all categories, even for basic tasks such as distinguishing microscopy modalities; ii) current specialist models fine-tuned on biomedical data often perform worse than generalist models; iii) fine-tuning in specific microscopy domains can cause catastrophic forgetting, eroding prior biomedical knowledge encoded in their base model. iv) weight interpolation between fine-tuned and pre-trained models offers one solution to forgetting and improves general performance across biomedical tasks. We release {\\mu}-Bench under a permissive license to accelerate the research and development of microscopy foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Ale9806/eVLLM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-04", "arxiv_id": "2407.02392", "title": "TokenPacker: Efficient Visual Projector for Multimodal LLM", "authors": [ "Wentong Li", "Yuqian Yuan", "Jian Liu", "Dongqi Tang", "Song Wang", "Jianke Zhu", "Lei Zhang" ], "github": "https://github.com/CircleRadon/TokenPacker", "project_page": "", "abstract": "The visual projector serves as an essential bridge between the visual encoder and the Large Language Model (LLM) in a Multimodal LLM (MLLM). Typically, MLLMs adopt a simple MLP to preserve all visual contexts via one-to-one transformation. However, the visual tokens are redundant and can be considerably increased when dealing with high-resolution images, impairing the efficiency of MLLMs significantly. Some recent works have introduced resampler or abstractor to reduce the number of resulting visual tokens. Unfortunately, they fail to capture finer details and undermine the visual reasoning capabilities of MLLMs. In this work, we propose a novel visual projector, which adopts a coarse-to-fine scheme to inject the enriched characteristics to generate the condensed visual tokens. In specific, we first interpolate the visual features as a low-resolution point query, providing the overall visual representation as the foundation. Then, we introduce a region-to-point injection module that utilizes high-resolution, multi-level region-based cues as fine-grained reference keys and values, allowing them to be fully absorbed within the corresponding local context region. This step effectively updates the coarse point query, transforming it into an enriched one for the subsequent LLM reasoning. Extensive experiments demonstrate that our approach compresses the visual tokens by 75%~89%, while achieves comparable or even better performance across diverse benchmarks with significantly higher efficiency. The source codes can be found at https://github.com/CircleRadon/TokenPacker.", "github_manual": "", "github_regex": "https://github.com/CircleRadon/TokenPacker", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-04", "arxiv_id": "2407.03320", "title": "InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output", "authors": [ "Pan Zhang", "Xiaoyi Dong", "Yuhang Zang", "Yuhang Cao", "Rui Qian", "Lin Chen", "Qipeng Guo", "Haodong Duan", "Bin Wang", "Linke Ouyang", "Songyang Zhang", "Wenwei Zhang", "Yining Li", "Yang Gao", "Peng Sun", "Xinyue Zhang", "Wei Li", "Jingwen Li", "Wenhai Wang", "Hang Yan", "Conghui He", "Xingcheng Zhang", "Kai Chen", "Jifeng Dai", "Yu Qiao", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/InternLM/InternLM-XComposer", "project_page": "", "abstract": "We present InternLM-XComposer-2.5 (IXC-2.5), a versatile large-vision language model that supports long-contextual input and output. IXC-2.5 excels in various text-image comprehension and composition applications, achieving GPT-4V level capabilities with merely 7B LLM backend. Trained with 24K interleaved image-text contexts, it can seamlessly extend to 96K long contexts via RoPE extrapolation. This long-context capability allows IXC-2.5 to excel in tasks requiring extensive input and output contexts. Compared to its previous 2.0 version, InternLM-XComposer-2.5 features three major upgrades in vision-language comprehension: (1) Ultra-High Resolution Understanding, (2) Fine-Grained Video Understanding, and (3) Multi-Turn Multi-Image Dialogue. In addition to comprehension, IXC-2.5 extends to two compelling applications using extra LoRA parameters for text-image composition: (1) Crafting Webpages and (2) Composing High-Quality Text-Image Articles. IXC-2.5 has been evaluated on 28 benchmarks, outperforming existing open-source state-of-the-art models on 16 benchmarks. It also surpasses or competes closely with GPT-4V and Gemini Pro on 16 key tasks. The InternLM-XComposer-2.5 is publicly available at https://github.com/InternLM/InternLM-XComposer.", "github_manual": "", "github_regex": "https://github.com/InternLM/InternLM-XComposer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-04", "arxiv_id": "2407.02687", "title": "No Training, No Problem: Rethinking Classifier-Free Guidance for Diffusion Models", "authors": [ "Seyedmorteza Sadat", "Manuel Kansy", "Otmar Hilliges", "Romann M. Weber" ], "github": "", "project_page": "", "abstract": "Classifier-free guidance (CFG) has become the standard method for enhancing the quality of conditional diffusion models. However, employing CFG requires either training an unconditional model alongside the main diffusion model or modifying the training procedure by periodically inserting a null condition. There is also no clear extension of CFG to unconditional models. In this paper, we revisit the core principles of CFG and introduce a new method, independent condition guidance (ICG), which provides the benefits of CFG without the need for any special training procedures. Our approach streamlines the training process of conditional diffusion models and can also be applied during inference on any pre-trained conditional model. Additionally, by leveraging the time-step information encoded in all diffusion networks, we propose an extension of CFG, called time-step guidance (TSG), which can be applied to any diffusion model, including unconditional ones. Our guidance techniques are easy to implement and have the same sampling cost as CFG. Through extensive experiments, we demonstrate that ICG matches the performance of standard CFG across various conditional diffusion models. Moreover, we show that TSG improves generation quality in a manner similar to CFG, without relying on any conditional information.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-04", "arxiv_id": "2407.02869", "title": "PicoAudio: Enabling Precise Timestamp and Frequency Controllability of Audio Events in Text-to-audio Generation", "authors": [ "Zeyu Xie", "Xuenan Xu", "Zhizheng Wu", "Mengyue Wu" ], "github": "https://github.com/picoaudio/picoaudio", "project_page": "", "abstract": "Recently, audio generation tasks have attracted considerable research interests. Precise temporal controllability is essential to integrate audio generation with real applications. In this work, we propose a temporal controlled audio generation framework, PicoAudio. PicoAudio integrates temporal information to guide audio generation through tailored model design. It leverages data crawling, segmentation, filtering, and simulation of fine-grained temporally-aligned audio-text data. Both subjective and objective evaluations demonstrate that PicoAudio dramantically surpasses current state-of-the-art generation models in terms of timestamp and occurrence frequency controllability. The generated samples are available on the demo website https://PicoAudio.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/picoaudio/picoaudio", "github_api_added_by": "auto" }, { "date": "2024-07-04", "arxiv_id": "2407.03300", "title": "DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents", "authors": [ "Yilun Xu", "Gabriele Corso", "Tommi Jaakkola", "Arash Vahdat", "Karsten Kreis" ], "github": "https://github.com/gcorso/disco-diffdock", "project_page": "", "abstract": "Diffusion models (DMs) have revolutionized generative learning. They utilize a diffusion process to encode data into a simple Gaussian distribution. However, encoding a complex, potentially multimodal data distribution into a single continuous Gaussian distribution arguably represents an unnecessarily challenging learning problem. We propose Discrete-Continuous Latent Variable Diffusion Models (DisCo-Diff) to simplify this task by introducing complementary discrete latent variables. We augment DMs with learnable discrete latents, inferred with an encoder, and train DM and encoder end-to-end. DisCo-Diff does not rely on pre-trained networks, making the framework universally applicable. The discrete latents significantly simplify learning the DM's complex noise-to-data mapping by reducing the curvature of the DM's generative ODE. An additional autoregressive transformer models the distribution of the discrete latents, a simple step because DisCo-Diff requires only few discrete variables with small codebooks. We validate DisCo-Diff on toy data, several image synthesis tasks as well as molecular docking, and find that introducing discrete latents consistently improves model performance. For example, DisCo-Diff achieves state-of-the-art FID scores on class-conditioned ImageNet-64/128 datasets with ODE sampler.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gcorso/disco-diffdock", "github_api_added_by": "auto" }, { "date": "2024-07-04", "arxiv_id": "2407.03169", "title": "Investigating Decoder-only Large Language Models for Speech-to-text Translation", "authors": [ "Chao-Wei Huang", "Hui Lu", "Hongyu Gong", "Hirofumi Inaguma", "Ilia Kulikov", "Ruslan Mavlyutov", "Sravya Popuri" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs), known for their exceptional reasoning capabilities, generalizability, and fluency across diverse domains, present a promising avenue for enhancing speech-related tasks. In this paper, we focus on integrating decoder-only LLMs to the task of speech-to-text translation (S2TT). We propose a decoder-only architecture that enables the LLM to directly consume the encoded speech representation and generate the text translation. Additionally, we investigate the effects of different parameter-efficient fine-tuning techniques and task formulation. Our model achieves state-of-the-art performance on CoVoST 2 and FLEURS among models trained without proprietary data. We also conduct analyses to validate the design choices of our proposed model and bring insights to the integration of LLMs to S2TT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-04", "arxiv_id": "2406.19380", "title": "TabReD: A Benchmark of Tabular Machine Learning in-the-Wild", "authors": [ "Ivan Rubachev", "Nikolay Kartashev", "Yury Gorishniy", "Artem Babenko" ], "github": "https://github.com/yandex-research/tabred", "project_page": "", "abstract": "Benchmarks that closely reflect downstream application scenarios are essential for the streamlined adoption of new research in tabular machine learning (ML). In this work, we examine existing tabular benchmarks and find two common characteristics of industry-grade tabular data that are underrepresented in the datasets available to the academic community. First, tabular data often changes over time in real-world deployment scenarios. This impacts model performance and requires time-based train and test splits for correct model evaluation. Yet, existing academic tabular datasets often lack timestamp metadata to enable such evaluation. Second, a considerable portion of datasets in production settings stem from extensive data acquisition and feature engineering pipelines. For each specific dataset, this can have a different impact on the absolute and relative number of predictive, uninformative, and correlated features, which in turn can affect model selection. To fill the aforementioned gaps in academic benchmarks, we introduce TabReD -- a collection of eight industry-grade tabular datasets covering a wide range of domains from finance to food delivery services. We assess a large number of tabular ML models in the feature-rich, temporally-evolving data setting facilitated by TabReD. We demonstrate that evaluation on time-based data splits leads to different methods ranking, compared to evaluation on random splits more common in academic benchmarks. Furthermore, on the TabReD datasets, MLP-like architectures and GBDT show the best results, while more sophisticated DL models are yet to prove their effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yandex-research/tabred", "github_api_added_by": "auto" }, { "date": "2024-07-04", "arxiv_id": "2407.02551", "title": "A False Sense of Safety: Unsafe Information Leakage in 'Safe' AI Responses", "authors": [ "David Glukhov", "Ziwen Han", "Ilia Shumailov", "Vardan Papyan", "Nicolas Papernot" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are vulnerable to jailbreaksx2013methods to elicit harmful or generally impermissible outputs. Safety measures are developed and assessed on their effectiveness at defending against jailbreak attacks, indicating a belief that safety is equivalent to robustness. We assert that current defense mechanisms, such as output filters and alignment fine-tuning, are, and will remain, fundamentally insufficient for ensuring model safety. These defenses fail to address risks arising from dual-intent queries and the ability to composite innocuous outputs to achieve harmful goals. To address this critical gap, we introduce an information-theoretic threat model called inferential adversaries who exploit impermissible information leakage from model outputs to achieve malicious goals. We distinguish these from commonly studied security adversaries who only seek to force victim models to generate specific impermissible outputs. We demonstrate the feasibility of automating inferential adversaries through question decomposition and response aggregation. To provide safety guarantees, we define an information censorship criterion for censorship mechanisms, bounding the leakage of impermissible information. We propose a defense mechanism which ensures this bound and reveal an intrinsic safety-utility trade-off. Our work provides the first theoretically grounded understanding of the requirements for releasing safe LLMs and the utility costs involved.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-04", "arxiv_id": "2407.01100", "title": "Eliminating Position Bias of Language Models: A Mechanistic Approach", "authors": [ "Ziqi Wang", "Hanlin Zhang", "Xiner Li", "Kuan-Hao Huang", "Chi Han", "Shuiwang Ji", "Sham M. Kakade", "Hao Peng", "Heng Ji" ], "github": "https://github.com/wzq016/pine", "project_page": "", "abstract": "Position bias has proven to be a prevalent issue of modern language models (LMs), where the models prioritize content based on its position within the given context. This bias often leads to unexpected model failures and hurts performance, robustness, and reliability across various applications. Our mechanistic analysis attributes the position bias to two components employed in nearly all state-of-the-art LMs: causal attention and relative positional encodings. Specifically, we find that causal attention generally causes models to favor distant content, while relative positional encodings like RoPE prefer nearby ones based on the analysis of retrieval-augmented question answering (QA). Further, our empirical study on object detection reveals that position bias is also present in vision-language models (VLMs). Based on the above analyses, we propose to ELIMINATE position bias caused by different input segment orders (e.g., options in LM-as-a-judge, retrieved documents in QA) in a TRAINING-FREE ZERO-SHOT manner. Our method changes the causal attention to bidirectional attention between segments and utilizes model attention values to decide the relative orders of segments instead of using the order provided in input prompts, therefore enabling Position-INvariant inferencE (PINE) at the segment level. By eliminating position bias, models achieve better performance and reliability in downstream tasks where position bias widely exists, such as LM-as-a-judge and retrieval-augmented QA. Notably, PINE is especially useful when adapting LMs for evaluating reasoning pairs: it consistently provides 8 to 10 percentage points performance gains in most cases, and makes Llama-3-70B-Instruct perform even better than GPT-4-0125-preview on the RewardBench reasoning subset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wzq016/pine", "github_api_added_by": "auto" }, { "date": "2024-07-05", "arxiv_id": "2407.01906", "title": "Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models", "authors": [ "Zihan Wang", "Deli Chen", "Damai Dai", "Runxin Xu", "Zhuoshu Li", "Y. Wu" ], "github": "https://github.com/deepseek-ai/esft", "project_page": "", "abstract": "Parameter-efficient fine-tuning (PEFT) is crucial for customizing Large Language Models (LLMs) with constrained resources. Although there have been various PEFT methods for dense-architecture LLMs, PEFT for sparse-architecture LLMs is still underexplored. In this work, we study the PEFT method for LLMs with the Mixture-of-Experts (MoE) architecture and the contents of this work are mainly threefold: (1) We investigate the dispersion degree of the activated experts in customized tasks, and found that the routing distribution for a specific task tends to be highly concentrated, while the distribution of activated experts varies significantly across different tasks. (2) We propose Expert-Specialized Fine-Tuning, or ESFT, which tunes the experts most relevant to downstream tasks while freezing the other experts and modules; experimental results demonstrate that our method not only improves the tuning efficiency, but also matches or even surpasses the performance of full-parameter fine-tuning. (3) We further analyze the impact of the MoE architecture on expert-specialized fine-tuning. We find that MoE models with finer-grained experts are more advantageous in selecting the combination of experts that are most relevant to downstream tasks, thereby enhancing both the training efficiency and effectiveness.", "github_manual": "https://github.com/deepseek-ai/esft", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-05", "arxiv_id": "2407.01392", "title": "Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion", "authors": [ "Boyuan Chen", "Diego Marti Monso", "Yilun Du", "Max Simchowitz", "Russ Tedrake", "Vincent Sitzmann" ], "github": "", "project_page": "", "abstract": "This paper presents Diffusion Forcing, a new training paradigm where a diffusion model is trained to denoise a set of tokens with independent per-token noise levels. We apply Diffusion Forcing to sequence generative modeling by training a causal next-token prediction model to generate one or several future tokens without fully diffusing past ones. Our approach is shown to combine the strengths of next-token prediction models, such as variable-length generation, with the strengths of full-sequence diffusion models, such as the ability to guide sampling to desirable trajectories. Our method offers a range of additional capabilities, such as (1) rolling-out sequences of continuous tokens, such as video, with lengths past the training horizon, where baselines diverge and (2) new sampling and guiding schemes that uniquely profit from Diffusion Forcing's variable-horizon and causal architecture, and which lead to marked performance gains in decision-making and planning tasks. In addition to its empirical success, our method is proven to optimize a variational lower bound on the likelihoods of all subsequences of tokens drawn from the true joint distribution. Project website: https://boyuan.space/diffusion-forcing/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-05", "arxiv_id": "2407.03321", "title": "Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages", "authors": [ "Max Zuo", "Francisco Piedrahita Velez", "Xiaochen Li", "Michael L. Littman", "Stephen H. Bach" ], "github": "https://github.com/BatsResearch/planetarium", "project_page": "", "abstract": "Many recent works have explored using language models for planning problems. One line of research focuses on translating natural language descriptions of planning tasks into structured planning languages, such as the planning domain definition language (PDDL). While this approach is promising, accurately measuring the quality of generated PDDL code continues to pose significant challenges. First, generated PDDL code is typically evaluated using planning validators that check whether the problem can be solved with a planner. This method is insufficient because a language model might generate valid PDDL code that does not align with the natural language description of the task. Second, existing evaluation sets often have natural language descriptions of the planning task that closely resemble the ground truth PDDL, reducing the challenge of the task. To bridge this gap, we introduce \\benchmarkName, a benchmark designed to evaluate language models' ability to generate PDDL code from natural language descriptions of planning tasks. We begin by creating a PDDL equivalence algorithm that rigorously evaluates the correctness of PDDL code generated by language models by flexibly comparing it against a ground truth PDDL. Then, we present a dataset of 132,037 text-to-PDDL pairs across 13 different tasks, with varying levels of difficulty. Finally, we evaluate several API-access and open-weight language models that reveal this task's complexity. For example, 87.6% of the PDDL problem descriptions generated by GPT-4o are syntactically parseable, 82.2% are valid, solve-able problems, but only 35.1% are semantically correct, highlighting the need for a more rigorous benchmark for this problem.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/BatsResearch/planetarium", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2407.04051", "title": "FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs", "authors": [ "Tongyi SpeechTeam" ], "github": "", "project_page": "", "abstract": "This report introduces FunAudioLLM, a model family designed to enhance natural voice interactions between humans and large language models (LLMs). At its core are two innovative models: SenseVoice, which handles multilingual speech recognition, emotion recognition, and audio event detection; and CosyVoice, which facilitates natural speech generation with control over multiple languages, timbre, speaking style, and speaker identity. SenseVoice-Small delivers exceptionally low-latency ASR for 5 languages, and SenseVoice-Large supports high-precision ASR for over 50 languages, while CosyVoice excels in multi-lingual voice generation, zero-shot in-context learning, cross-lingual voice cloning, and instruction-following capabilities. The models related to SenseVoice and CosyVoice have been open-sourced on Modelscope and Huggingface, along with the corresponding training, inference, and fine-tuning codes released on GitHub. By integrating these models with LLMs, FunAudioLLM enables applications such as speech-to-speech translation, emotional voice chat, interactive podcasts, and expressive audiobook narration, thereby pushing the boundaries of voice interaction technology. Demos are available at https://fun-audio-llm.github.io, and the code can be accessed at https://github.com/FunAudioLLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2407.03963", "title": "LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs", "authors": [ "LLM-jp", "Akiko Aizawa", "Eiji Aramaki", "Bowen Chen", "Fei Cheng", "Hiroyuki Deguchi", "Rintaro Enomoto", "Kazuki Fujii", "Kensuke Fukumoto", "Takuya Fukushima", "Namgi Han", "Yuto Harada", "Chikara Hashimoto", "Tatsuya Hiraoka", "Shohei Hisada", "Sosuke Hosokawa", "Lu Jie", "Keisuke Kamata", "Teruhito Kanazawa", "Hiroki Kanezashi", "Hiroshi Kataoka", "Satoru Katsumata", "Daisuke Kawahara", "Seiya Kawano", "Atsushi Keyaki", "Keisuke Kiryu", "Hirokazu Kiyomaru", "Takashi Kodama", "Takahiro Kubo", "Yohei Kuga", "Ryoma Kumon", "Shuhei Kurita", "Sadao Kurohashi", "Conglong Li", "Taiki Maekawa", "Hiroshi Matsuda", "Yusuke Miyao", "Kentaro Mizuki", "Sakae Mizuki", "Yugo Murawaki", "Ryo Nakamura", "Taishi Nakamura", "Kouta Nakayama", "Tomoka Nakazato", "Takuro Niitsuma", "Jiro Nishitoba", "Yusuke Oda", "Hayato Ogawa", "Takumi Okamoto", "Naoaki Okazaki", "Yohei Oseki", "Shintaro Ozaki", "Koki Ryu", "Rafal Rzepka", "Keisuke Sakaguchi", "Shota Sasaki", "Satoshi Sekine", "Kohei Suda", "Saku Sugawara", "Issa Sugiura", "Hiroaki Sugiyama", "Hisami Suzuki", "Jun Suzuki", "Toyotaro Suzumura", "Kensuke Tachibana", "Yu Takagi", "Kyosuke Takami", "Koichi Takeda", "Masashi Takeshita", "Masahiro Tanaka", "Kenjiro Taura", "Arseny Tolmachev", "Nobuhiro Ueda", "Zhen Wan", "Shuntaro Yada", "Sakiko Yahata", "Yuya Yamamoto", "Yusuke Yamauchi", "Hitomi Yanaka", "Rio Yokota", "Koichiro Yoshino" ], "github": "https://github.com/tatHi/multigram", "project_page": "", "abstract": "This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and strong Japanese LLMs, and as of this writing, more than 1,500 participants from academia and industry are working together for this purpose. This paper presents the background of the establishment of LLM-jp, summaries of its activities, and technical reports on the LLMs developed by LLM-jp. For the latest activities, visit https://llm-jp.nii.ac.jp/en/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tatHi/multigram", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2407.04622", "title": "On scalable oversight with weak LLMs judging strong LLMs", "authors": [ "Zachary Kenton", "Noah Y. Siegel", "J\u00e1nos Kram\u00e1r", "Jonah Brown-Cohen", "Samuel Albanie", "Jannis Bulian", "Rishabh Agarwal", "David Lindner", "Yunhao Tang", "Noah D. Goodman", "Rohin Shah" ], "github": "", "project_page": "", "abstract": "Scalable oversight protocols aim to enable humans to accurately supervise superhuman AI. In this paper we study debate, where two AI's compete to convince a judge; consultancy, where a single AI tries to convince a judge that asks questions; and compare to a baseline of direct question-answering, where the judge just answers outright without the AI. We use large language models (LLMs) as both AI agents and as stand-ins for human judges, taking the judge models to be weaker than agent models. We benchmark on a diverse range of asymmetries between judges and agents, extending previous work on a single extractive QA task with information asymmetry, to also include mathematics, coding, logic and multimodal reasoning asymmetries. We find that debate outperforms consultancy across all tasks when the consultant is randomly assigned to argue for the correct/incorrect answer. Comparing debate to direct question answering, the results depend on the type of task: in extractive QA tasks with information asymmetry debate outperforms direct question answering, but in other tasks without information asymmetry the results are mixed. Previous work assigned debaters/consultants an answer to argue for. When we allow them to instead choose which answer to argue for, we find judges are less frequently convinced by the wrong answer in debate than in consultancy. Further, we find that stronger debater models increase judge accuracy, though more modestly than in previous studies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2407.04620", "title": "Learning to (Learn at Test Time): RNNs with Expressive Hidden States", "authors": [ "Yu Sun", "Xinhao Li", "Karan Dalal", "Jiarui Xu", "Arjun Vikram", "Genghan Zhang", "Yann Dubois", "Xinlei Chen", "Xiaolong Wang", "Sanmi Koyejo", "Tatsunori Hashimoto", "Carlos Guestrin" ], "github": "https://github.com/test-time-training/ttt-lm-jax", "project_page": "", "abstract": "Self-attention performs well in long context but has quadratic complexity. Existing RNN layers have linear complexity, but their performance in long context is limited by the expressive power of their hidden state. We propose a new class of sequence modeling layers with linear complexity and an expressive hidden state. The key idea is to make the hidden state a machine learning model itself, and the update rule a step of self-supervised learning. Since the hidden state is updated by training even on test sequences, our layers are called Test-Time Training (TTT) layers. We consider two instantiations: TTT-Linear and TTT-MLP, whose hidden state is a linear model and a two-layer MLP respectively. We evaluate our instantiations at the scale of 125M to 1.3B parameters, comparing with a strong Transformer and Mamba, a modern RNN. Both TTT-Linear and TTT-MLP match or exceed the baselines. Similar to Transformer, they can keep reducing perplexity by conditioning on more tokens, while Mamba cannot after 16k context. With preliminary systems optimization, TTT-Linear is already faster than Transformer at 8k context and matches Mamba in wall-clock time. TTT-MLP still faces challenges in memory I/O, but shows larger potential in long context, pointing to a promising direction for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/test-time-training/ttt-lm-jax", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2406.11832", "title": "Unveiling Encoder-Free Vision-Language Models", "authors": [ "Haiwen Diao", "Yufeng Cui", "Xiaotong Li", "Yueze Wang", "Huchuan Lu", "Xinlong Wang" ], "github": "https://github.com/baaivision/EVE", "project_page": "", "abstract": "Existing vision-language models (VLMs) mostly rely on vision encoders to extract visual features followed by large language models (LLMs) for visual-language tasks. However, the vision encoders set a strong inductive bias in abstracting visual representation, e.g., resolution, aspect ratio, and semantic priors, which could impede the flexibility and efficiency of the VLMs. Training pure VLMs that accept the seamless vision and language inputs, i.e., without vision encoders, remains challenging and rarely explored. Empirical observations reveal that direct training without encoders results in slow convergence and large performance gaps. In this work, we bridge the gap between encoder-based and encoder-free models, and present a simple yet effective training recipe towards pure VLMs. Specifically, we unveil the key aspects of training encoder-free VLMs efficiently via thorough experiments: (1) Bridging vision-language representation inside one unified decoder; (2) Enhancing visual recognition capability via extra supervision. With these strategies, we launch EVE, an encoder-free vision-language model that can be trained and forwarded efficiently. Notably, solely utilizing 35M publicly accessible data, EVE can impressively rival the encoder-based VLMs of similar capacities across multiple vision-language benchmarks. It significantly outperforms the counterpart Fuyu-8B with mysterious training procedures and undisclosed training data. We believe that EVE provides a transparent and efficient route for developing a pure decoder-only architecture across modalities. Our code and models are publicly available at: https://github.com/baaivision/EVE.", "github_manual": "", "github_regex": "https://github.com/baaivision/EVE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2407.03418", "title": "HEMM: Holistic Evaluation of Multimodal Foundation Models", "authors": [ "Paul Pu Liang", "Akshay Goindani", "Talha Chafekar", "Leena Mathur", "Haofei Yu", "Ruslan Salakhutdinov", "Louis-Philippe Morency" ], "github": "https://github.com/pliang279/hemm", "project_page": "", "abstract": "Multimodal foundation models that can holistically process text alongside images, video, audio, and other sensory modalities are increasingly used in a variety of real-world applications. However, it is challenging to characterize and study progress in multimodal foundation models, given the range of possible modeling decisions, tasks, and domains. In this paper, we introduce Holistic Evaluation of Multimodal Models (HEMM) to systematically evaluate the capabilities of multimodal foundation models across a set of 3 dimensions: basic skills, information flow, and real-world use cases. Basic multimodal skills are internal abilities required to solve problems, such as learning interactions across modalities, fine-grained alignment, multi-step reasoning, and the ability to handle external knowledge. Information flow studies how multimodal content changes during a task through querying, translation, editing, and fusion. Use cases span domain-specific challenges introduced in real-world multimedia, affective computing, natural sciences, healthcare, and human-computer interaction applications. Through comprehensive experiments across the 30 tasks in HEMM, we (1) identify key dataset dimensions (e.g., basic skills, information flows, and use cases) that pose challenges to today's models, and (2) distill performance trends regarding how different modeling dimensions (e.g., scale, pre-training data, multimodal alignment, pre-training, and instruction tuning objectives) influence performance. Our conclusions regarding challenging multimodal interactions, use cases, and tasks requiring reasoning and external knowledge, the benefits of data and model scale, and the impacts of instruction tuning yield actionable insights for future work in multimodal foundation models.", "github_manual": "https://github.com/pliang279/hemm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2407.04363", "title": "AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents", "authors": [ "Petr Anokhin", "Nikita Semenov", "Artyom Sorokin", "Dmitry Evseev", "Mikhail Burtsev", "Evgeny Burnaev" ], "github": "https://github.com/airi-institute/arigraph", "project_page": "", "abstract": "Advancements in generative AI have broadened the potential applications of Large Language Models (LLMs) in the development of autonomous agents. Achieving true autonomy requires accumulating and updating knowledge gained from interactions with the environment and effectively utilizing it. Current LLM-based approaches leverage past experiences using a full history of observations, summarization or retrieval augmentation. However, these unstructured memory representations do not facilitate the reasoning and planning essential for complex decision-making. In our study, we introduce AriGraph, a novel method wherein the agent constructs a memory graph that integrates semantic and episodic memories while exploring the environment. This graph structure facilitates efficient associative retrieval of interconnected concepts, relevant to the agent's current state and goals, thus serving as an effective environmental model that enhances the agent's exploratory and planning capabilities. We demonstrate that our Ariadne LLM agent, equipped with this proposed memory architecture augmented with planning and decision-making, effectively handles complex tasks on a zero-shot basis in the TextWorld environment. Our approach markedly outperforms established methods such as full-history, summarization, and Retrieval-Augmented Generation in various tasks, including the cooking challenge from the First TextWorld Problems competition and novel tasks like house cleaning and puzzle Treasure Hunting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/airi-institute/arigraph", "github_api_added_by": "auto" }, { "date": "2024-07-08", "arxiv_id": "2407.04078", "title": "DotaMath: Decomposition of Thought with Code Assistance and Self-correction for Mathematical Reasoning", "authors": [ "Chengpeng Li", "Guanting Dong", "Mingfeng Xue", "Ru Peng", "Xiang Wang", "Dayiheng Liu" ], "github": "https://github.com/ChengpengLi1003/DotaMath", "project_page": "", "abstract": "Large language models (LLMs) have made impressive progress in handling simple math problems, yet they still struggle with more challenging and complex mathematical tasks. In this paper, we introduce a series of LLMs that employs the Decomposition of thought with code assistance and self-correction for mathematical reasoning, dubbed as DotaMath. DotaMath models tackle complex mathematical tasks by decomposing them into simpler logical subtasks, leveraging code to solve these subtasks, obtaining fine-grained feedback from the code interpreter, and engaging in self-reflection and correction. By annotating diverse interactive tool-use trajectories and employing query evolution on GSM8K and MATH datasets, we generate an instruction fine-tuning dataset called DotaMathQA with 574K query-response pairs. We train a series of base LLMs using imitation learning on DotaMathQA, resulting in DotaMath models that achieve remarkable performance compared to open-source LLMs across various in-domain and out-of-domain benchmarks. Notably, DotaMath-deepseek-7B showcases an outstanding performance of 64.8% on the competitive MATH dataset and 86.7% on GSM8K. Besides, DotaMath-deepseek-7B maintains strong competitiveness on a series of in-domain and out-of-domain benchmarks (Avg. 80.1%). Looking forward, we anticipate that the DotaMath paradigm will open new pathways for addressing intricate mathematical problems. Our code is publicly available at https://github.com/ChengpengLi1003/DotaMath.", "github_manual": "", "github_regex": "https://github.com/ChengpengLi1003/DotaMath", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2407.04172", "title": "ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild", "authors": [ "Ahmed Masry", "Megh Thakkar", "Aayush Bajaj", "Aaryaman Kartha", "Enamul Hoque", "Shafiq Joty" ], "github": "https://github.com/vis-nlp/ChartGemma", "project_page": "", "abstract": "Given the ubiquity of charts as a data analysis, visualization, and decision-making tool across industries and sciences, there has been a growing interest in developing pre-trained foundation models as well as general purpose instruction-tuned models for chart understanding and reasoning. However, existing methods suffer crucial drawbacks across two critical axes affecting the performance of chart representation models: they are trained on data generated from underlying data tables of the charts, ignoring the visual trends and patterns in chart images, and use weakly aligned vision-language backbone models for domain-specific training, limiting their generalizability when encountering charts in the wild. We address these important drawbacks and introduce ChartGemma, a novel chart understanding and reasoning model developed over PaliGemma. Rather than relying on underlying data tables, ChartGemma is trained on instruction-tuning data generated directly from chart images, thus capturing both high-level trends and low-level visual information from a diverse set of charts. Our simple approach achieves state-of-the-art results across 5 benchmarks spanning chart summarization, question answering, and fact-checking, and our elaborate qualitative studies on real-world charts show that ChartGemma generates more realistic and factually correct summaries compared to its contemporaries. We release the code, model checkpoints, dataset, and demos at https://github.com/vis-nlp/ChartGemma.", "github_manual": "", "github_regex": "https://github.com/vis-nlp/ChartGemma", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2407.03923", "title": "CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion Blur Images", "authors": [ "Junghe Lee", "Donghyeong Kim", "Dogyoon Lee", "Suhwan Cho", "Sangyoun Lee" ], "github": "", "project_page": "", "abstract": "Neural radiance fields (NeRFs) have received significant attention due to their high-quality novel view rendering ability, prompting research to address various real-world cases. One critical challenge is the camera motion blur caused by camera movement during exposure time, which prevents accurate 3D scene reconstruction. In this study, we propose continuous rigid motion-aware gaussian splatting (CRiM-GS) to reconstruct accurate 3D scene from blurry images with real-time rendering speed. Considering the actual camera motion blurring process, which consists of complex motion patterns, we predict the continuous movement of the camera based on neural ordinary differential equations (ODEs). Specifically, we leverage rigid body transformations to model the camera motion with proper regularization, preserving the shape and size of the object. Furthermore, we introduce a continuous deformable 3D transformation in the SE(3) field to adapt the rigid body transformation to real-world problems by ensuring a higher degree of freedom. By revisiting fundamental camera theory and employing advanced neural network training techniques, we achieve accurate modeling of continuous camera trajectories. We conduct extensive experiments, demonstrating state-of-the-art performance both quantitatively and qualitatively on benchmark datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2407.03958", "title": "Stark: Social Long-Term Multi-Modal Conversation with Persona Commonsense Knowledge", "authors": [ "Young-Jun Lee", "Dokyong Lee", "Junyoung Youn", "Kyeongjin Oh", "Byungsoo Ko", "Jonghwan Hyeon", "Ho-Jin Choi" ], "github": "https://github.com/heartexlabs/label-studio", "project_page": "", "abstract": "Humans share a wide variety of images related to their personal experiences within conversations via instant messaging tools. However, existing works focus on (1) image-sharing behavior in singular sessions, leading to limited long-term social interaction, and (2) a lack of personalized image-sharing behavior. In this work, we introduce Stark, a large-scale long-term multi-modal conversation dataset that covers a wide range of social personas in a multi-modality format, time intervals, and images. To construct Stark automatically, we propose a novel multi-modal contextualization framework, Mcu, that generates long-term multi-modal dialogue distilled from ChatGPT and our proposed Plan-and-Execute image aligner. Using our Stark, we train a multi-modal conversation model, Ultron 7B, which demonstrates impressive visual imagination ability. Furthermore, we demonstrate the effectiveness of our dataset in human evaluation. We make our source code and dataset publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/heartexlabs/label-studio", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2407.02855", "title": "Safe Unlearning: A Surprisingly Effective and Generalizable Solution to Defend Against Jailbreak Attacks", "authors": [ "Zhexin Zhang", "Junxiao Yang", "Pei Ke", "Shiyao Cui", "Chujie Zheng", "Hongning Wang", "Minlie Huang" ], "github": "https://github.com/thu-coai/SafeUnlearning", "project_page": "", "abstract": "LLMs are known to be vulnerable to jailbreak attacks, even after safety alignment. An important observation is that, while different types of jailbreak attacks can generate significantly different queries, they mostly result in similar responses that are rooted in the same harmful knowledge (e.g., detailed steps to make a bomb). Therefore, we conjecture that directly unlearn the harmful knowledge in the LLM can be a more effective way to defend against jailbreak attacks than the mainstream supervised fine-tuning (SFT) based approaches. Our extensive experiments confirmed our insight and suggested surprising generalizability of our unlearning-based approach: using only 20 raw harmful questions without any jailbreak prompt during training, our solution reduced the Attack Success Rate (ASR) in Vicuna-7B on out-of-distribution (OOD) harmful questions wrapped with various complex jailbreak prompts from 82.6\\% to 7.7\\%. This significantly outperforms Llama2-7B-Chat, which is fine-tuned on about 0.1M safety alignment samples but still has an ASR of 21.9\\% even under the help of an additional safety system prompt. Further analysis reveals that the generalization ability of our solution stems from the intrinsic relatedness among harmful responses across harmful questions (e.g., response patterns, shared steps and actions, and similarity among their learned representations in the LLM). Our code is available at https://github.com/thu-coai/SafeUnlearning.", "github_manual": "", "github_regex": "https://github.com/thu-coai/SafeUnlearning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2406.08085", "title": "Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams", "authors": [ "Haoji Zhang", "Yiqin Wang", "Yansong Tang", "Yong Liu", "Jiashi Feng", "Jifeng Dai", "Xiaojie Jin" ], "github": "", "project_page": "", "abstract": "Benefiting from the advancements in large language models and cross-modal alignment, existing multi-modal video understanding methods have achieved prominent performance in offline scenario. However, online video streams, as one of the most common media forms in the real world, have seldom received attention. Compared to offline videos, the 'dynamic' nature of online video streams poses challenges for the direct application of existing models and introduces new problems, such as the storage of extremely long-term information, interaction between continuous visual content and 'asynchronous' user questions. Therefore, in this paper we present Flash-VStream, a video-language model that simulates the memory mechanism of human. Our model is able to process extremely long video streams in real-time and respond to user queries simultaneously. Compared to existing models, Flash-VStream achieves significant reductions in inference latency and VRAM consumption, which is intimately related to performing understanding of online streaming video. In addition, given that existing video understanding benchmarks predominantly concentrate on offline scenario, we propose VStream-QA, a novel question answering benchmark specifically designed for online video streaming understanding. Comparisons with popular existing methods on the proposed benchmark demonstrate the superiority of our method for such challenging setting. To verify the generalizability of our approach, we further evaluate it on existing video understanding benchmarks and achieves state-of-the-art performance in offline scenarios as well. All code, models, and datasets are available at the https://invinciblewyq.github.io/vstream-page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2407.05131", "title": "RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models", "authors": [ "Peng Xia", "Kangyu Zhu", "Haoran Li", "Hongtu Zhu", "Yun Li", "Gang Li", "Linjun Zhang", "Huaxiu Yao" ], "github": "https://github.com/richard-peng-xia/RULE", "project_page": "", "abstract": "The recent emergence of Medical Large Vision Language Models (Med-LVLMs) has enhanced medical diagnosis. However, current Med-LVLMs frequently encounter factual issues, often generating responses that do not align with established medical facts. Retrieval-Augmented Generation (RAG), which utilizes external knowledge, can improve the factual accuracy of these models but introduces two major challenges. First, limited retrieved contexts might not cover all necessary information, while excessive retrieval can introduce irrelevant and inaccurate references, interfering with the model's generation. Second, in cases where the model originally responds correctly, applying RAG can lead to an over-reliance on retrieved contexts, resulting in incorrect answers. To address these issues, we propose RULE, which consists of two components. First, we introduce a provably effective strategy for controlling factuality risk through the calibrated selection of the number of retrieved contexts. Second, based on samples where over-reliance on retrieved contexts led to errors, we curate a preference dataset to fine-tune the model, balancing its dependence on inherent knowledge and retrieved contexts for generation. We demonstrate the effectiveness of RULE on three medical VQA datasets, achieving an average improvement of 20.8% in factual accuracy. We publicly release our benchmark and code in https://github.com/richard-peng-xia/RULE.", "github_manual": "", "github_regex": "https://github.com/richard-peng-xia/RULE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-08", "arxiv_id": "2407.04952", "title": "Granular Privacy Control for Geolocation with Vision Language Models", "authors": [ "Ethan Mendes", "Yang Chen", "James Hays", "Sauvik Das", "Wei Xu", "Alan Ritter" ], "github": "https://github.com/ethanm88/GPTGeoChat", "project_page": "", "abstract": "Vision Language Models (VLMs) are rapidly advancing in their capability to answer information-seeking questions. As these models are widely deployed in consumer applications, they could lead to new privacy risks due to emergent abilities to identify people in photos, geolocate images, etc. As we demonstrate, somewhat surprisingly, current open-source and proprietary VLMs are very capable image geolocators, making widespread geolocation with VLMs an immediate privacy risk, rather than merely a theoretical future concern. As a first step to address this challenge, we develop a new benchmark, GPTGeoChat, to test the ability of VLMs to moderate geolocation dialogues with users. We collect a set of 1,000 image geolocation conversations between in-house annotators and GPT-4v, which are annotated with the granularity of location information revealed at each turn. Using this new dataset, we evaluate the ability of various VLMs to moderate GPT-4v geolocation conversations by determining when too much location information has been revealed. We find that custom fine-tuned models perform on par with prompted API-based models when identifying leaked location information at the country or city level; however, fine-tuning on supervised data appears to be needed to accurately moderate finer granularities, such as the name of a restaurant or building.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ethanm88/GPTGeoChat", "github_api_added_by": "auto" }, { "date": "2024-07-09", "arxiv_id": "2407.05975", "title": "LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages", "authors": [ "Yinquan Lu", "Wenhao Zhu", "Lei Li", "Yu Qiao", "Fei Yuan" ], "github": "https://github.com/CONE-MT/LLaMAX", "project_page": "", "abstract": "Large Language Models~(LLMs) demonstrate remarkable translation capabilities in high-resource language tasks, yet their performance in low-resource languages is hindered by insufficient multilingual data during pre-training. To address this, we dedicate 35,000 A100-SXM4-80GB GPU hours in conducting extensive multilingual continual pre-training on the LLaMA series models, enabling translation support across more than 100 languages. Through a comprehensive analysis of training strategies, such as vocabulary expansion and data augmentation, we develop LLaMAX. Remarkably, without sacrificing its generalization ability, LLaMAX achieves significantly higher translation performance compared to existing open-source LLMs~(by more than 10 spBLEU points) and performs on-par with specialized translation model~(M2M-100-12B) on the Flores-101 benchmark. Extensive experiments indicate that LLaMAX can serve as a robust multilingual foundation model. The code~\\url{https://github.com/CONE-MT/LLaMAX/.} and models~\\url{https://huggingface.co/LLaMAX/.} are publicly available.", "github_manual": "", "github_regex": "https://github.com/CONE-MT/LLaMAX", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-09", "arxiv_id": "2407.06191", "title": "Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images", "authors": [ "Zhangyang Qi", "Yunhan Yang", "Mengchen Zhang", "Long Xing", "Xiaoyang Wu", "Tong Wu", "Dahua Lin", "Xihui Liu", "Jiaqi Wang", "Hengshuang Zhao" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "Recent advances in 3D AIGC have shown promise in directly creating 3D objects from text and images, offering significant cost savings in animation and product design. However, detailed edit and customization of 3D assets remains a long-standing challenge. Specifically, 3D Generation methods lack the ability to follow finely detailed instructions as precisely as their 2D image creation counterparts. Imagine you can get a toy through 3D AIGC but with undesired accessories and dressing. To tackle this challenge, we propose a novel pipeline called Tailor3D, which swiftly creates customized 3D assets from editable dual-side images. We aim to emulate a tailor's ability to locally change objects or perform overall style transfer. Unlike creating 3D assets from multiple views, using dual-side images eliminates conflicts on overlapping areas that occur when editing individual views. Specifically, it begins by editing the front view, then generates the back view of the object through multi-view diffusion. Afterward, it proceeds to edit the back views. Finally, a Dual-sided LRM is proposed to seamlessly stitch together the front and back 3D features, akin to a tailor sewing together the front and back of a garment. The Dual-sided LRM rectifies imperfect consistencies between the front and back views, enhancing editing capabilities and reducing memory burdens while seamlessly integrating them into a unified 3D representation with the LoRA Triplane Transformer. Experimental results demonstrate Tailor3D's effectiveness across various 3D generation and editing tasks, including 3D generative fill and style transfer. It provides a user-friendly, efficient solution for editing 3D assets, with each editing step taking only seconds to complete.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-09", "arxiv_id": "2407.04693", "title": "ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models", "authors": [ "Yuzhe Gu", "Ziwei Ji", "Wenwei Zhang", "Chengqi Lyu", "Dahua Lin", "Kai Chen" ], "github": "https://github.com/open-compass/ANAH", "project_page": "", "abstract": "Large language models (LLMs) exhibit hallucinations in long-form question-answering tasks across various domains and wide applications. Current hallucination detection and mitigation datasets are limited in domains and sizes, which struggle to scale due to prohibitive labor costs and insufficient reliability of existing hallucination annotators. To facilitate the scalable oversight of LLM hallucinations, this paper introduces an iterative self-training framework that simultaneously and progressively scales up the hallucination annotation dataset and improves the accuracy of the hallucination annotator. Based on the Expectation Maximization (EM) algorithm, in each iteration, the framework first applies a hallucination annotation pipeline to annotate a scaled dataset and then trains a more accurate hallucination annotator on the dataset. This new hallucination annotator is adopted in the hallucination annotation pipeline used for the next iteration. Extensive experimental results demonstrate that the finally obtained hallucination annotator with only 7B parameters surpasses the performance of GPT-4 and obtains new state-of-the-art hallucination detection results on HaluEval and HalluQA by zero-shot inference. Such an annotator can not only evaluate the hallucination levels of various LLMs on the large-scale dataset but also help to mitigate the hallucination of LLMs generations, with the Natural Language Inference (NLI) metric increasing from 25% to 37% on HaluEval.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-compass/ANAH", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-09", "arxiv_id": "2407.04842", "title": "MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?", "authors": [ "Zhaorun Chen", "Yichao Du", "Zichen Wen", "Yiyang Zhou", "Chenhang Cui", "Zhenzhen Weng", "Haoqin Tu", "Chaoqi Wang", "Zhengwei Tong", "Qinglan Huang", "Canyu Chen", "Qinghao Ye", "Zhihong Zhu", "Yuqing Zhang", "Jiawei Zhou", "Zhuokai Zhao", "Rafael Rafailov", "Chelsea Finn", "Huaxiu Yao" ], "github": "https://github.com/MJ-Bench/MJ-Bench", "project_page": "", "abstract": "While text-to-image models like DALLE-3 and Stable Diffusion are rapidly proliferating, they often encounter challenges such as hallucination, bias, and the production of unsafe, low-quality output. To effectively address these issues, it is crucial to align these models with desired behaviors based on feedback from a multimodal judge. Despite their significance, current multimodal judges frequently undergo inadequate evaluation of their capabilities and limitations, potentially leading to misalignment and unsafe fine-tuning outcomes. To address this issue, we introduce MJ-Bench, a novel benchmark which incorporates a comprehensive preference dataset to evaluate multimodal judges in providing feedback for image generation models across four key perspectives: alignment, safety, image quality, and bias. Specifically, we evaluate a large variety of multimodal judges including smaller-sized CLIP-based scoring models, open-source VLMs (e.g. LLaVA family), and close-source VLMs (e.g. GPT-4o, Claude 3) on each decomposed subcategory of our preference dataset. Experiments reveal that close-source VLMs generally provide better feedback, with GPT-4o outperforming other judges in average. Compared with open-source VLMs, smaller-sized scoring models can provide better feedback regarding text-image alignment and image quality, while VLMs provide more accurate feedback regarding safety and generation bias due to their stronger reasoning capabilities. Further studies in feedback scale reveal that VLM judges can generally provide more accurate and stable feedback in natural language (Likert-scale) than numerical scales. Notably, human evaluations on end-to-end fine-tuned models using separate feedback from these multimodal judges provide similar conclusions, further confirming the effectiveness of MJ-Bench. All data, code, models are available at https://huggingface.co/MJ-Bench.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MJ-Bench/MJ-Bench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-09", "arxiv_id": "2407.06135", "title": "ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation", "authors": [ "Ethan Chern", "Jiadi Su", "Yan Ma", "Pengfei Liu" ], "github": "https://github.com/gair-nlp/anole", "project_page": "", "abstract": "Previous open-source large multimodal models (LMMs) have faced several limitations: (1) they often lack native integration, requiring adapters to align visual representations with pre-trained large language models (LLMs); (2) many are restricted to single-modal generation; (3) while some support multimodal generation, they rely on separate diffusion models for visual modeling and generation. To mitigate these limitations, we present Anole, an open, autoregressive, native large multimodal model for interleaved image-text generation. We build Anole from Meta AI's Chameleon, adopting an innovative fine-tuning strategy that is both data-efficient and parameter-efficient. Anole demonstrates high-quality, coherent multimodal generation capabilities. We have open-sourced our model, training framework, and instruction tuning data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gair-nlp/anole", "github_api_added_by": "auto" }, { "date": "2024-07-09", "arxiv_id": "2407.04604", "title": "PartCraft: Crafting Creative Objects by Parts", "authors": [ "Kam Woh Ng", "Xiatian Zhu", "Yi-Zhe Song", "Tao Xiang" ], "github": "https://github.com/kamwoh/partcraft", "project_page": "", "abstract": "This paper propels creative control in generative visual AI by allowing users to \"select\". Departing from traditional text or sketch-based methods, we for the first time allow users to choose visual concepts by parts for their creative endeavors. The outcome is fine-grained generation that precisely captures selected visual concepts, ensuring a holistically faithful and plausible result. To achieve this, we first parse objects into parts through unsupervised feature clustering. Then, we encode parts into text tokens and introduce an entropy-based normalized attention loss that operates on them. This loss design enables our model to learn generic prior topology knowledge about object's part composition, and further generalize to novel part compositions to ensure the generation looks holistically faithful. Lastly, we employ a bottleneck encoder to project the part tokens. This not only enhances fidelity but also accelerates learning, by leveraging shared knowledge and facilitating information exchange among instances. Visual results in the paper and supplementary material showcase the compelling power of PartCraft in crafting highly customized, innovative creations, exemplified by the \"charming\" and creative birds. Code is released at https://github.com/kamwoh/partcraft.", "github_manual": "", "github_regex": "https://github.com/kamwoh/partcraft", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-09", "arxiv_id": "2407.04020", "title": "LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking", "authors": [ "Amy Xin", "Yunjia Qi", "Zijun Yao", "Fangwei Zhu", "Kaisheng Zeng", "Xu Bin", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/THU-KEG/LLMAEL", "project_page": "", "abstract": "Entity Linking (EL) models are well-trained at mapping mentions to their corresponding entities according to a given context. However, EL models struggle to disambiguate long-tail entities due to their limited training data. Meanwhile, large language models (LLMs) are more robust at interpreting uncommon mentions. Yet, due to a lack of specialized training, LLMs suffer at generating correct entity IDs. Furthermore, training an LLM to perform EL is cost-intensive. Building upon these insights, we introduce LLM-Augmented Entity Linking LLMAEL, a plug-and-play approach to enhance entity linking through LLM data augmentation. We leverage LLMs as knowledgeable context augmenters, generating mention-centered descriptions as additional input, while preserving traditional EL models for task specific processing. Experiments on 6 standard datasets show that the vanilla LLMAEL outperforms baseline EL models in most cases, while the fine-tuned LLMAEL set the new state-of-the-art results across all 6 benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THU-KEG/LLMAEL", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-09", "arxiv_id": "2407.05700", "title": "InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct", "authors": [ "Yutong Wu", "Di Huang", "Wenxuan Shi", "Wei Wang", "Lingzhe Gao", "Shihao Liu", "Ziyuan Nan", "Kaizhao Yuan", "Rui Zhang", "Xishan Zhang", "Zidong Du", "Qi Guo", "Yewen Pu", "Dawei Yin", "Xing Hu", "Yunji Chen" ], "github": "https://github.com/wyt2000/InverseCoder", "project_page": "", "abstract": "Recent advancements in open-source code large language models (LLMs) have demonstrated remarkable coding abilities by fine-tuning on the data generated from powerful closed-source LLMs such as GPT-3.5 and GPT-4 for instruction tuning. This paper explores how to further improve an instruction-tuned code LLM by generating data from itself rather than querying closed-source LLMs. Our key observation is the misalignment between the translation of formal and informal languages: translating formal language (i.e., code) to informal language (i.e., natural language) is more straightforward than the reverse. Based on this observation, we propose INVERSE-INSTRUCT, which summarizes instructions from code snippets instead of the reverse. Specifically, given an instruction tuning corpus for code and the resulting instruction-tuned code LLM, we ask the code LLM to generate additional high-quality instructions for the original corpus through code summarization and self-evaluation. Then, we fine-tune the base LLM on the combination of the original corpus and the self-generated one, which yields a stronger instruction-tuned LLM. We present a series of code LLMs named InverseCoder, which surpasses the performance of the original code LLMs on a wide range of benchmarks, including Python text-to-code generation, multilingual coding, and data-science code generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/wyt2000/InverseCoder", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-09", "arxiv_id": "2407.06027", "title": "PAS: Data-Efficient Plug-and-Play Prompt Augmentation System", "authors": [ "Miao Zheng", "Hao Liang", "Fan Yang", "Haoze Sun", "Tianpeng Li", "Lingchu Xiong", "Yan Zhang", "Yozhen Wu", "Kun Li", "Yanjun Sheng", "Mingan Lin", "Tao Zhang", "Guosheng Dong", "Yujing Qiao", "Kun Fang", "Weipeng Chen", "Bin Cui", "Wentao Zhang", "Zenan Zhou" ], "github": "https://github.com/PKU-Baichuan-MLSystemLab/PAS", "project_page": "", "abstract": "In recent years, the rise of Large Language Models (LLMs) has spurred a growing demand for plug-and-play AI systems. Among the various AI techniques, prompt engineering stands out as particularly significant. However, users often face challenges in writing prompts due to the steep learning curve and significant time investment, and existing automatic prompt engineering (APE) models can be difficult to use. To address this issue, we propose PAS, an LLM-based plug-and-play APE system. PAS utilizes LLMs trained on high-quality, automatically generated prompt complementary datasets, resulting in exceptional performance. In comprehensive benchmarks, PAS achieves state-of-the-art (SoTA) results compared to previous APE models, with an average improvement of 6.09 points. Moreover, PAS is highly efficient, achieving SoTA performance with only 9000 data points. Additionally, PAS can autonomously generate prompt augmentation data without requiring additional human labor. Its flexibility also allows it to be compatible with all existing LLMs and applicable to a wide range of tasks. PAS excels in human evaluations, underscoring its suitability as a plug-in for users. This combination of high performance, efficiency, and flexibility makes PAS a valuable system for enhancing the usability and effectiveness of LLMs through improved prompt engineering.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PKU-Baichuan-MLSystemLab/PAS", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-09", "arxiv_id": "2407.06182", "title": "Compositional Video Generation as Flow Equalization", "authors": [ "Xingyi Yang", "Xinchao Wang" ], "github": "https://github.com/Adamdad/vico", "project_page": "", "abstract": "Large-scale Text-to-Video (T2V) diffusion models have recently demonstrated unprecedented capability to transform natural language descriptions into stunning and photorealistic videos. Despite the promising results, a significant challenge remains: these models struggle to fully grasp complex compositional interactions between multiple concepts and actions. This issue arises when some words dominantly influence the final video, overshadowing other concepts.To tackle this problem, we introduce Vico, a generic framework for compositional video generation that explicitly ensures all concepts are represented properly. At its core, Vico analyzes how input tokens influence the generated video, and adjusts the model to prevent any single concept from dominating. Specifically, Vico extracts attention weights from all layers to build a spatial-temporal attention graph, and then estimates the influence as the max-flow from the source text token to the video target token. Although the direct computation of attention flow in diffusion models is typically infeasible, we devise an efficient approximation based on subgraph flows and employ a fast and vectorized implementation, which in turn makes the flow computation manageable and differentiable. By updating the noisy latent to balance these flows, Vico captures complex interactions and consequently produces videos that closely adhere to textual descriptions. We apply our method to multiple diffusion-based video models for compositional T2V and video editing. Empirical results demonstrate that our framework significantly enhances the compositional richness and accuracy of the generated videos. Visit our website at~https://adamdad.github.io/vico/{https://adamdad.github.io/vico/}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Adamdad/vico", "github_api_added_by": "auto" }, { "date": "2024-07-09", "arxiv_id": "2407.04841", "title": "Associative Recurrent Memory Transformer", "authors": [ "Ivan Rodkin", "Yuri Kuratov", "Aydar Bulatov", "Mikhail Burtsev" ], "github": "", "project_page": "", "abstract": "This paper addresses the challenge of creating a neural architecture for very long sequences that requires constant time for processing new information at each time step. Our approach, Associative Recurrent Memory Transformer (ARMT), is based on transformer self-attention for local context and segment-level recurrence for storage of task specific information distributed over a long context. We demonstrate that ARMT outperfors existing alternatives in associative retrieval tasks and sets a new performance record in the recent BABILong multi-task long-context benchmark by answering single-fact questions over 50 million tokens with an accuracy of 79.9%. The source code for training and evaluation is available on github.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-09", "arxiv_id": "2407.06192", "title": "Multi-Object Hallucination in Vision-Language Models", "authors": [ "Xuweiyi Chen", "Ziqiao Ma", "Xuejun Zhang", "Sihan Xu", "Shengyi Qian", "Jianing Yang", "David F. Fouhey", "Joyce Chai" ], "github": "https://github.com/sled-group/moh", "project_page": "", "abstract": "Large vision language models (LVLMs) often suffer from object hallucination, producing objects not present in the given images. While current benchmarks for object hallucination primarily concentrate on the presence of a single object class rather than individual entities, this work systematically investigates multi-object hallucination, examining how models misperceive (e.g., invent nonexistent objects or become distracted) when tasked with focusing on multiple objects simultaneously. We introduce Recognition-based Object Probing Evaluation (ROPE), an automated evaluation protocol that considers the distribution of object classes within a single image during testing and uses visual referring prompts to eliminate ambiguity. With comprehensive empirical studies and analysis of potential factors leading to multi-object hallucination, we found that (1) LVLMs suffer more hallucinations when focusing on multiple objects compared to a single object. (2) The tested object class distribution affects hallucination behaviors, indicating that LVLMs may follow shortcuts and spurious correlations.(3) Hallucinatory behaviors are influenced by data-specific factors, salience and frequency, and model intrinsic behaviors. We hope to enable LVLMs to recognize and reason about multiple objects that often occur in realistic visual scenes, provide insights, and quantify our progress towards mitigating the issues.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sled-group/moh", "github_api_added_by": "auto" }, { "date": "2024-07-09", "arxiv_id": "2407.05463", "title": "Training Task Experts through Retrieval Based Distillation", "authors": [ "Jiaxin Ge", "Xueying Jia", "Vijay Viswanathan", "Hongyin Luo", "Graham Neubig" ], "github": "", "project_page": "", "abstract": "One of the most reliable ways to create deployable models for specialized tasks is to obtain an adequate amount of high-quality task-specific data. However, for specialized tasks, often such datasets do not exist. Existing methods address this by creating such data from large language models (LLMs) and then distilling such knowledge into smaller models. However, these methods are limited by the quality of the LLMs output, and tend to generate repetitive or incorrect data. In this work, we present Retrieval Based Distillation (ReBase), a method that first retrieves data from rich online sources and then transforms them into domain-specific data. This method greatly enhances data diversity. Moreover, ReBase generates Chain-of-Thought reasoning and distills the reasoning capacity of LLMs. We test our method on 4 benchmarks and results show that our method significantly improves performance by up to 7.8% on SQuAD, 1.37% on MNLI, and 1.94% on BigBench-Hard.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-09", "arxiv_id": "2407.05282", "title": "UltraEdit: Instruction-based Fine-Grained Image Editing at Scale", "authors": [ "Haozhe Zhao", "Xiaojian Ma", "Liang Chen", "Shuzheng Si", "Rujie Wu", "Kaikai An", "Peiyu Yu", "Minjia Zhang", "Qing Li", "Baobao Chang" ], "github": "https://github.com/pkunlp-icler/ultraedit", "project_page": "", "abstract": "This paper presents UltraEdit, a large-scale (approximately 4 million editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to address the drawbacks in existing image editing datasets like InstructPix2Pix and MagicBrush, and provide a systematic approach to producing massive and high-quality image editing samples. UltraEdit offers several distinct advantages: 1) It features a broader range of editing instructions by leveraging the creativity of large language models (LLMs) alongside in-context editing examples from human raters; 2) Its data sources are based on real images, including photographs and artworks, which provide greater diversity and reduced bias compared to datasets solely generated by text-to-image models; 3) It also supports region-based editing, enhanced by high-quality, automatically produced region annotations. Our experiments show that canonical diffusion-based editing baselines trained on UltraEdit set new records on MagicBrush and Emu-Edit benchmarks. Our analysis further confirms the crucial role of real image anchors and region-based editing data. The dataset, code, and models can be found in https://ultra-editing.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/pkunlp-icler/ultraedit", "github_api_added_by": "auto" }, { "date": "2024-07-09", "arxiv_id": "2407.03471", "title": "Learning Action and Reasoning-Centric Image Editing from Videos and Simulations", "authors": [ "Benno Krojer", "Dheeraj Vattikonda", "Luis Lara", "Varun Jampani", "Eva Portelance", "Christopher Pal", "Siva Reddy" ], "github": "https://github.com/McGill-NLP/AURORA", "project_page": "", "abstract": "An image editing model should be able to perform diverse edits, ranging from object replacement, changing attributes or style, to performing actions or movement, which require many forms of reasoning. Current general instruction-guided editing models have significant shortcomings with action and reasoning-centric edits. Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g. physical dynamics, temporality and spatial reasoning. To this end, we meticulously curate the AURORA Dataset (Action-Reasoning-Object-Attribute), a collection of high-quality training data, human-annotated and curated from videos and simulation engines. We focus on a key aspect of quality training data: triplets (source image, prompt, target image) contain a single meaningful visual change described by the prompt, i.e., truly minimal changes between source and target images. To demonstrate the value of our dataset, we evaluate an AURORA-finetuned model on a new expert-curated benchmark (AURORA-Bench) covering 8 diverse editing tasks. Our model significantly outperforms previous editing models as judged by human raters. For automatic evaluations, we find important flaws in previous metrics and caution their use for semantically hard editing tasks. Instead, we propose a new automatic metric that focuses on discriminative understanding. We hope that our efforts : (1) curating a quality training dataset and an evaluation benchmark, (2) developing critical evaluations, and (3) releasing a state-of-the-art model, will fuel further progress on general image editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/McGill-NLP/AURORA", "github_api_added_by": "auto" }, { "date": "2024-07-09", "arxiv_id": "2407.03651", "title": "Evaluating Language Model Context Windows: A \"Working Memory\" Test and Inference-time Correction", "authors": [ "Amanda Dsouza", "Christopher Glaze", "Changho Shin", "Frederic Sala" ], "github": "https://github.com/snorkel-ai/long-context-eval", "project_page": "", "abstract": "Large language models are prominently used in real-world applications, often tasked with reasoning over large volumes of documents. An exciting development in this space is models boasting extended context capabilities, with some accommodating over 2 million tokens. Such long context model capabilities remain uncertain in production systems, motivating the need to benchmark their performance on real world use cases. We address this challenge by proposing SWiM, an evaluation framework that addresses the limitations of standard tests. Testing the framework on eight long context models, we find that even strong models such as GPT-4 and Claude 3 Opus degrade in performance when information is present in the middle of the context window (lost-in-the-middle effect). Next, in addition to our benchmark, we propose medoid voting, a simple, but effective training-free approach that helps alleviate this effect, by generating responses a few times, each time randomly permuting documents in the context, and selecting the medoid answer. We evaluate medoid voting on single document QA tasks, achieving up to a 24% lift in accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/snorkel-ai/long-context-eval", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-09", "arxiv_id": "2407.06076", "title": "Understanding Visual Feature Reliance through the Lens of Complexity", "authors": [ "Thomas Fel", "Louis Bethune", "Andrew Kyle Lampinen", "Thomas Serre", "Katherine Hermann" ], "github": "", "project_page": "", "abstract": "Recent studies suggest that deep learning models inductive bias towards favoring simpler features may be one of the sources of shortcut learning. Yet, there has been limited focus on understanding the complexity of the myriad features that models learn. In this work, we introduce a new metric for quantifying feature complexity, based on V-information and capturing whether a feature requires complex computational transformations to be extracted. Using this V-information metric, we analyze the complexities of 10,000 features, represented as directions in the penultimate layer, that were extracted from a standard ImageNet-trained vision model. Our study addresses four key questions: First, we ask what features look like as a function of complexity and find a spectrum of simple to complex features present within the model. Second, we ask when features are learned during training. We find that simpler features dominate early in training, and more complex features emerge gradually. Third, we investigate where within the network simple and complex features flow, and find that simpler features tend to bypass the visual hierarchy via residual connections. Fourth, we explore the connection between features complexity and their importance in driving the networks decision. We find that complex features tend to be less important. Surprisingly, important features become accessible at earlier layers during training, like a sedimentation process, allowing the model to build upon these foundational elements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-10", "arxiv_id": "2407.07071", "title": "Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps", "authors": [ "Yung-Sung Chuang", "Linlu Qiu", "Cheng-Yu Hsieh", "Ranjay Krishna", "Yoon Kim", "James Glass" ], "github": "https://github.com/voidism/lookback-lens", "project_page": "", "abstract": "When asked to summarize articles or answer questions given a passage, large language models (LLMs) can hallucinate details and respond with unsubstantiated answers that are inaccurate with respect to the input context. This paper describes a simple approach for detecting such contextual hallucinations. We hypothesize that contextual hallucinations are related to the extent to which an LLM attends to information in the provided context versus its own generations. Based on this intuition, we propose a simple hallucination detection model whose input features are given by the ratio of attention weights on the context versus newly generated tokens (for each attention head). We find that a linear classifier based on these lookback ratio features is as effective as a richer detector that utilizes the entire hidden states of an LLM or a text-based entailment model. The lookback ratio-based detector -- Lookback Lens -- is found to transfer across tasks and even models, allowing a detector that is trained on a 7B model to be applied (without retraining) to a larger 13B model. We further apply this detector to mitigate contextual hallucinations, and find that a simple classifier-guided decoding approach is able to reduce the amount of hallucination, for example by 9.6% in the XSum summarization task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/voidism/lookback-lens", "github_api_added_by": "auto" }, { "date": "2024-07-10", "arxiv_id": "2407.06358", "title": "MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions", "authors": [ "Xuan Ju", "Yiming Gao", "Zhaoyang Zhang", "Ziyang Yuan", "Xintao Wang", "Ailing Zeng", "Yu Xiong", "Qiang Xu", "Ying Shan" ], "github": "https://github.com/mira-space/MiraData", "project_page": "", "abstract": "Sora's high-motion intensity and long consistent videos have significantly impacted the field of video generation, attracting unprecedented attention. However, existing publicly available datasets are inadequate for generating Sora-like videos, as they mainly contain short videos with low motion intensity and brief captions. To address these issues, we propose MiraData, a high-quality video dataset that surpasses previous ones in video duration, caption detail, motion strength, and visual quality. We curate MiraData from diverse, manually selected sources and meticulously process the data to obtain semantically consistent clips. GPT-4V is employed to annotate structured captions, providing detailed descriptions from four different perspectives along with a summarized dense caption. To better assess temporal consistency and motion intensity in video generation, we introduce MiraBench, which enhances existing benchmarks by adding 3D consistency and tracking-based motion strength metrics. MiraBench includes 150 evaluation prompts and 17 metrics covering temporal consistency, motion strength, 3D consistency, visual quality, text-video alignment, and distribution similarity. To demonstrate the utility and effectiveness of MiraData, we conduct experiments using our DiT-based video generation model, MiraDiT. The experimental results on MiraBench demonstrate the superiority of MiraData, especially in motion strength.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mira-space/MiraData", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-10", "arxiv_id": "2407.06304", "title": "VIMI: Grounding Video Generation through Multi-modal Instruction", "authors": [ "Yuwei Fang", "Willi Menapace", "Aliaksandr Siarohin", "Tsai-Shien Chen", "Kuan-Chien Wang", "Ivan Skorokhodov", "Graham Neubig", "Sergey Tulyakov" ], "github": "", "project_page": "", "abstract": "Existing text-to-video diffusion models rely solely on text-only encoders for their pretraining. This limitation stems from the absence of large-scale multimodal prompt video datasets, resulting in a lack of visual grounding and restricting their versatility and application in multimodal integration. To address this, we construct a large-scale multimodal prompt dataset by employing retrieval methods to pair in-context examples with the given text prompts and then utilize a two-stage training strategy to enable diverse video generation tasks within the same model. In the first stage, we propose a multimodal conditional video generation framework for pretraining on these augmented datasets, establishing a foundational model for grounded video generation. Secondly, we finetune the model from the first stage on three video generation tasks, incorporating multi-modal instructions. This process further refines the model's ability to handle diverse inputs and tasks, ensuring seamless integration of multi-modal information. After this two-stage train-ing process, VIMI demonstrates multimodal understanding capabilities, producing contextually rich and personalized videos grounded in the provided inputs, as shown in Figure 1. Compared to previous visual grounded video generation methods, VIMI can synthesize consistent and temporally coherent videos with large motion while retaining the semantic control. Lastly, VIMI also achieves state-of-the-art text-to-video generation results on UCF101 benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-10", "arxiv_id": "2407.06581", "title": "Vision language models are blind", "authors": [ "Pooyan Rahmanzadehgervi", "Logan Bolton", "Mohammad Reza Taesiri", "Anh Totti Nguyen" ], "github": "", "project_page": "", "abstract": "Large language models with vision capabilities (VLMs), e.g., GPT-4o and Gemini 1.5 Pro are powering countless image-text applications and scoring high on many vision-understanding benchmarks. Yet, we find that VLMs fail on 7 visual tasks absurdly easy to humans such as identifying (a) whether two circles overlap; (b) whether two lines intersect; (c) which letter is being circled in a word; and (d) counting the number of circles in a Olympic-like logo. The shockingly poor performance of four state-of-the-art VLMs suggests their vision is, at best, like of a person with myopia seeing fine details as blurry, and at worst, like an intelligent person that is blind making educated guesses. Code is available at: https://vlmsareblind.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-10", "arxiv_id": "2407.06938", "title": "RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models", "authors": [ "Bowen Zhang", "Yiji Cheng", "Chunyu Wang", "Ting Zhang", "Jiaolong Yang", "Yansong Tang", "Feng Zhao", "Dong Chen", "Baining Guo" ], "github": "https://github.com/rodinhd/rodinhd", "project_page": "", "abstract": "We present RodinHD, which can generate high-fidelity 3D avatars from a portrait image. Existing methods fail to capture intricate details such as hairstyles which we tackle in this paper. We first identify an overlooked problem of catastrophic forgetting that arises when fitting triplanes sequentially on many avatars, caused by the MLP decoder sharing scheme. To overcome this issue, we raise a novel data scheduling strategy and a weight consolidation regularization term, which improves the decoder's capability of rendering sharper details. Additionally, we optimize the guiding effect of the portrait image by computing a finer-grained hierarchical representation that captures rich 2D texture cues, and injecting them to the 3D diffusion model at multiple layers via cross-attention. When trained on 46K avatars with a noise schedule optimized for triplanes, the resulting model can generate 3D avatars with notably better details than previous methods and can generalize to in-the-wild portrait input.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rodinhd/rodinhd", "github_api_added_by": "auto" }, { "date": "2024-07-10", "arxiv_id": "2407.06189", "title": "Video-STaR: Self-Training Enables Video Instruction Tuning with Any Supervision", "authors": [ "Orr Zohar", "Xiaohan Wang", "Yonatan Bitton", "Idan Szpektor", "Serena Yeung-Levy" ], "github": "https://github.com/orrzohar/Video-STaR", "project_page": "", "abstract": "The performance of Large Vision Language Models (LVLMs) is dependent on the size and quality of their training datasets. Existing video instruction tuning datasets lack diversity as they are derived by prompting large language models with video captions to generate question-answer pairs, and are therefore mostly descriptive. Meanwhile, many labeled video datasets with diverse labels and supervision exist - however, we find that their integration into LVLMs is non-trivial. Herein, we present Video Self-Training with augmented Reasoning (Video-STaR), the first video self-training approach. Video-STaR allows the utilization of any labeled video dataset for video instruction tuning. In Video-STaR, an LVLM cycles between instruction generation and finetuning, which we show (I) improves general video understanding and (II) adapts LVLMs to novel downstream tasks with existing supervision. During generation, an LVLM is prompted to propose an answer. The answers are then filtered only to those that contain the original video labels, and the LVLM is then re-trained on the generated dataset. By only training on generated answers that contain the correct video labels, Video-STaR utilizes these existing video labels as weak supervision for video instruction tuning. Our results demonstrate that Video-STaR-enhanced LVLMs exhibit improved performance in (I) general video QA, where TempCompass performance improved by 10%, and (II) on downstream tasks, where Video-STaR improved Kinetics700-QA accuracy by 20% and action quality assessment on FineDiving by 15%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/orrzohar/Video-STaR", "github_api_added_by": "auto" }, { "date": "2024-07-10", "arxiv_id": "2407.07061", "title": "Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence", "authors": [ "Weize Chen", "Ziming You", "Ran Li", "Yitong Guan", "Chen Qian", "Chenyang Zhao", "Cheng Yang", "Ruobing Xie", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/OpenBMB/IoA", "project_page": "", "abstract": "The rapid advancement of large language models (LLMs) has paved the way for the development of highly capable autonomous agents. However, existing multi-agent frameworks often struggle with integrating diverse capable third-party agents due to reliance on agents defined within their own ecosystems. They also face challenges in simulating distributed environments, as most frameworks are limited to single-device setups. Furthermore, these frameworks often rely on hard-coded communication pipelines, limiting their adaptability to dynamic task requirements. Inspired by the concept of the Internet, we propose the Internet of Agents (IoA), a novel framework that addresses these limitations by providing a flexible and scalable platform for LLM-based multi-agent collaboration. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control. Through extensive experiments on general assistant tasks, embodied AI tasks, and retrieval-augmented generation benchmarks, we demonstrate that IoA consistently outperforms state-of-the-art baselines, showcasing its ability to facilitate effective collaboration among heterogeneous agents. IoA represents a step towards linking diverse agents in an Internet-like environment, where agents can seamlessly collaborate to achieve greater intelligence and capabilities. Our codebase has been released at https://github.com/OpenBMB/IoA.", "github_manual": "", "github_regex": "https://github.com/OpenBMB/IoA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-10", "arxiv_id": "2407.03502", "title": "AgentInstruct: Toward Generative Teaching with Agentic Flows", "authors": [ "Arindam Mitra", "Luciano Del Corro", "Guoqing Zheng", "Shweti Mahajan", "Dany Rouhana", "Andres Codas", "Yadong Lu", "Wei-ge Chen", "Olga Vrousgos", "Corby Rosset", "Fillipe Silva", "Hamed Khanpour", "Yash Lara", "Ahmed Awadallah" ], "github": "", "project_page": "", "abstract": "Synthetic data is becoming increasingly important for accelerating the development of language models, both large and small. Despite several successful use cases, researchers also raised concerns around model collapse and drawbacks of imitating other models. This discrepancy can be attributed to the fact that synthetic data varies in quality and diversity. Effective use of synthetic data usually requires significant human effort in curating the data. We focus on using synthetic data for post-training, specifically creating data by powerful models to teach a new skill or behavior to another model, we refer to this setting as Generative Teaching. We introduce AgentInstruct, an extensible agentic framework for automatically creating large amounts of diverse and high-quality synthetic data. AgentInstruct can create both the prompts and responses, using only raw data sources like text documents and code files as seeds. We demonstrate the utility of AgentInstruct by creating a post training dataset of 25M pairs to teach language models different skills, such as text editing, creative writing, tool usage, coding, reading comprehension, etc. The dataset can be used for instruction tuning of any base model. We post-train Mistral-7b with the data. When comparing the resulting model Orca-3 to Mistral-7b-Instruct (which uses the same base model), we observe significant improvements across many benchmarks. For example, 40% improvement on AGIEval, 19% improvement on MMLU, 54% improvement on GSM8K, 38% improvement on BBH and 45% improvement on AlpacaEval. Additionally, it consistently outperforms other models such as LLAMA-8B-instruct and GPT-3.5-turbo.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-10", "arxiv_id": "2407.06071", "title": "From Loops to Oops: Fallback Behaviors of Language Models Under Uncertainty", "authors": [ "Maor Ivgi", "Ori Yoran", "Jonathan Berant", "Mor Geva" ], "github": "https://github.com/mivg/fallbacks", "project_page": "", "abstract": "Large language models (LLMs) often exhibit undesirable behaviors, such as hallucinations and sequence repetitions. We propose to view these behaviors as fallbacks that models exhibit under uncertainty, and investigate the connection between them. We categorize fallback behaviors -- sequence repetitions, degenerate text, and hallucinations -- and extensively analyze them in models from the same family that differ by the amount of pretraining tokens, parameter count, or the inclusion of instruction-following training. Our experiments reveal a clear and consistent ordering of fallback behaviors, across all these axes: the more advanced an LLM is (i.e., trained on more tokens, has more parameters, or instruction-tuned), its fallback behavior shifts from sequence repetitions, to degenerate text, and then to hallucinations. Moreover, the same ordering is observed throughout a single generation, even for the best-performing models; as uncertainty increases, models shift from generating hallucinations to producing degenerate text and then sequence repetitions. Lastly, we demonstrate that while common decoding techniques, such as random sampling, might alleviate some unwanted behaviors like sequence repetitions, they increase harder-to-detect hallucinations.", "github_manual": "https://github.com/mivg/fallbacks", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-10", "arxiv_id": "2407.02880", "title": "Knowledge Composition using Task Vectors with Learned Anisotropic Scaling", "authors": [ "Frederic Z. Zhang", "Paul Albert", "Cristian Rodriguez-Opazo", "Anton van den Hengel", "Ehsan Abbasnejad" ], "github": "https://github.com/fredzzhang/atlas", "project_page": "", "abstract": "Pre-trained models produce strong generic representations that can be adapted via fine-tuning. The learned weight difference relative to the pre-trained model, known as a task vector, characterises the direction and stride of fine-tuning. The significance of task vectors is such that simple arithmetic operations on them can be used to combine diverse representations from different domains. This paper builds on these properties of task vectors and aims to answer (1) whether components of task vectors, particularly parameter blocks, exhibit similar characteristics, and (2) how such blocks can be used to enhance knowledge composition and transfer. To this end, we introduce aTLAS, an algorithm that linearly combines parameter blocks with different learned coefficients, resulting in anisotropic scaling at the task vector level. We show that such linear combinations explicitly exploit the low intrinsic dimensionality of pre-trained models, with only a few coefficients being the learnable parameters. Furthermore, composition of parameter blocks leverages the already learned representations, thereby reducing the dependency on large amounts of data. We demonstrate the effectiveness of our method in task arithmetic, few-shot recognition and test-time adaptation, with supervised or unsupervised objectives. In particular, we show that (1) learned anisotropic scaling allows task vectors to be more disentangled, causing less interference in composition; (2) task vector composition excels with scarce or no labeled data and is less prone to domain shift, thus leading to better generalisability; (3) mixing the most informative parameter blocks across different task vectors prior to training can reduce the memory footprint and improve the flexibility of knowledge transfer. Moreover, we show the potential of aTLAS as a PEFT method, particularly with less data, and demonstrate that its scalibility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/fredzzhang/atlas", "github_api_added_by": "auto" }, { "date": "2024-07-10", "arxiv_id": "2407.05015", "title": "How do you know that? Teaching Generative Language Models to Reference Answers to Biomedical Questions", "authors": [ "Bojana Ba\u0161aragin", "Adela Ljaji\u0107", "Darija Medvecki", "Lorenzo Cassano", "Milo\u0161 Ko\u0161prdi\u0107", "Nikola Milo\u0161evi\u0107" ], "github": "https://github.com/nikolamilosevic86/verif.ai", "project_page": "", "abstract": "Large language models (LLMs) have recently become the leading source of answers for users' questions online. Despite their ability to offer eloquent answers, their accuracy and reliability can pose a significant challenge. This is especially true for sensitive domains such as biomedicine, where there is a higher need for factually correct answers. This paper introduces a biomedical retrieval-augmented generation (RAG) system designed to enhance the reliability of generated responses. The system is based on a fine-tuned LLM for the referenced question-answering, where retrieved relevant abstracts from PubMed are passed to LLM's context as input through a prompt. Its output is an answer based on PubMed abstracts, where each statement is referenced accordingly, allowing the users to verify the answer. Our retrieval system achieves an absolute improvement of 23% compared to the PubMed search engine. Based on the manual evaluation on a small sample, our fine-tuned LLM component achieves comparable results to GPT-4 Turbo in referencing relevant abstracts. We make the dataset used to fine-tune the models and the fine-tuned models based on Mistral-7B-instruct-v0.1 and v0.2 publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nikolamilosevic86/verif.ai", "github_api_added_by": "auto" }, { "date": "2024-07-10", "arxiv_id": "2407.07080", "title": "Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities", "authors": [ "Shaltiel Shmidman", "Avi Shmidman", "Amir DN Cohen", "Moshe Koppel" ], "github": "", "project_page": "", "abstract": "Training large language models (LLMs) in low-resource languages such as Hebrew poses unique challenges. In this paper, we introduce DictaLM2.0 and DictaLM2.0-Instruct, two LLMs derived from the Mistral model, trained on a substantial corpus of approximately 200 billion tokens in both Hebrew and English. Adapting a pre-trained model to a new language involves specialized techniques that differ significantly from training a model from scratch or further training existing models on well-resourced languages such as English. We outline these novel training methodologies, which facilitate effective learning and adaptation to the linguistic properties of Hebrew. Additionally, we fine-tuned DictaLM2.0-Instruct on a comprehensive instruct dataset to enhance its performance on task-specific instructions. To rigorously evaluate our models, we introduce a new benchmark suite for Hebrew LLM evaluation, covering a diverse set of tasks including Question Answering, Sentiment Analysis, Winograd Schema Challenge, Translation, and Summarization. Our work not only addresses the intricacies of training LLMs in low-resource languages but also proposes a framework that can be leveraged for adapting other LLMs to various non-English languages, contributing to the broader field of multilingual NLP.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-10", "arxiv_id": "2407.06723", "title": "Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions", "authors": [ "Yu-Guan Hsieh", "Cheng-Yu Hsieh", "Shih-Ying Yeh", "Louis B\u00e9thune", "Hadi Pour Ansari", "Pavan Kumar Anasosalu Vasu", "Chun-Liang Li", "Ranjay Krishna", "Oncel Tuzel", "Marco Cuturi" ], "github": "https://github.com/apple/ml-gbc", "project_page": "", "abstract": "Humans describe complex scenes with compositionality, using simple text descriptions enriched with links and relationships. While vision-language research has aimed to develop models with compositional understanding capabilities, this is not reflected yet in existing datasets which, for the most part, still use plain text to describe images. In this work, we propose a new annotation strategy, graph-based captioning (GBC) that describes an image using a labelled graph structure, with nodes of various types. The nodes in GBC are created using, in a first stage, object detection and dense captioning tools nested recursively to uncover and describe entity nodes, further linked together in a second stage by highlighting, using new types of nodes, compositions and relations among entities. Since all GBC nodes hold plain text descriptions, GBC retains the flexibility found in natural language, but can also encode hierarchical information in its edges. We demonstrate that GBC can be produced automatically, using off-the-shelf multimodal LLMs and open-vocabulary detection models, by building a new dataset, GBC10M, gathering GBC annotations for about 10M images of the CC12M dataset. We use GBC10M to showcase the wealth of node captions uncovered by GBC, as measured with CLIP training. We show that using GBC nodes' annotations -- notably those stored in composition and relation nodes -- results in significant performance boost on downstream models when compared to other dataset formats. To further explore the opportunities provided by GBC, we also propose a new attention mechanism that can leverage the entire GBC graph, with encouraging experimental results that show the extra benefits of incorporating the graph structure. Our datasets are released at https://huggingface.co/graph-based-captions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apple/ml-gbc", "github_api_added_by": "auto" }, { "date": "2024-07-10", "arxiv_id": "2407.03618", "title": "BM25S: Orders of magnitude faster lexical search via eager sparse scoring", "authors": [ "Xing Han L\u00f9" ], "github": "https://github.com/xhluca/bm25s", "project_page": "", "abstract": "We introduce BM25S, an efficient Python-based implementation of BM25 that only depends on Numpy and Scipy. BM25S achieves up to a 500x speedup compared to the most popular Python-based framework by eagerly computing BM25 scores during indexing and storing them into sparse matrices. It also achieves considerable speedups compared to highly optimized Java-based implementations, which are used by popular commercial products. Finally, BM25S reproduces the exact implementation of five BM25 variants based on Kamphuis et al. (2020) by extending eager scoring to non-sparse variants using a novel score shifting method. The code can be found at https://github.com/xhluca/bm25s", "github_manual": "", "github_regex": "https://github.com/xhluca/bm25s", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-10", "arxiv_id": "2407.03203", "title": "TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts", "authors": [ "Ruida Wang", "Jipeng Zhang", "Yizhen Jia", "Rui Pan", "Shizhe Diao", "Renjie Pi", "Tong Zhang" ], "github": "https://github.com/RickySkywalker/TheoremLlama", "project_page": "", "abstract": "Proving mathematical theorems using computer-verifiable formal languages like Lean significantly impacts mathematical reasoning. One approach to formal theorem proving involves generating complete proofs using Large Language Models (LLMs) based on Natural Language (NL) proofs. Similar methods have shown promising results in code generation. However, most modern LLMs exhibit suboptimal performance due to the scarcity of aligned NL and Formal Language (FL) theorem-proving data. This scarcity results in a paucity of methodologies for training LLMs and techniques to fully utilize their capabilities in composing formal proofs. To address the challenges, this paper proposes **TheoremLlama**, an end-to-end framework to train a general-purpose LLM to become a Lean4 expert. This framework encompasses NL-FL aligned dataset generation methods, training approaches for the LLM formal theorem prover, and techniques for LLM Lean4 proof writing. Using the dataset generation method, we provide *Open Bootstrapped Theorems* (OBT), an NL-FL aligned and bootstrapped dataset. A key innovation in this framework is the NL-FL bootstrapping method, where NL proofs are integrated into Lean4 code for training datasets, leveraging the NL reasoning ability of LLMs for formal reasoning. The **TheoremLlama** framework achieves cumulative accuracies of 36.48% and 33.61% on MiniF2F-Valid and Test datasets respectively, surpassing the GPT-4 baseline of 22.95% and 25.41%. We have also open-sourced our model checkpoints and generated dataset, and will soon make all the code publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/RickySkywalker/TheoremLlama", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-10", "arxiv_id": "2407.06533", "title": "LETS-C: Leveraging Language Embedding for Time Series Classification", "authors": [ "Rachneet Kaur", "Zhen Zeng", "Tucker Balch", "Manuela Veloso" ], "github": "", "project_page": "", "abstract": "Recent advancements in language modeling have shown promising results when applied to time series data. In particular, fine-tuning pre-trained large language models (LLMs) for time series classification tasks has achieved state-of-the-art (SOTA) performance on standard benchmarks. However, these LLM-based models have a significant drawback due to the large model size, with the number of trainable parameters in the millions. In this paper, we propose an alternative approach to leveraging the success of language modeling in the time series domain. Instead of fine-tuning LLMs, we utilize a language embedding model to embed time series and then pair the embeddings with a simple classification head composed of convolutional neural networks (CNN) and multilayer perceptron (MLP). We conducted extensive experiments on well-established time series classification benchmark datasets. We demonstrated LETS-C not only outperforms the current SOTA in classification accuracy but also offers a lightweight solution, using only 14.5% of the trainable parameters on average compared to the SOTA model. Our findings suggest that leveraging language encoders to embed time series data, combined with a simple yet effective classification head, offers a promising direction for achieving high-performance time series classification while maintaining a lightweight model architecture.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-11", "arxiv_id": "2407.07464", "title": "Video-to-Audio Generation with Hidden Alignment", "authors": [ "Manjie Xu", "Chenxing Li", "Yong Ren", "Rilin Chen", "Yu Gu", "Wei Liang", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Generating semantically and temporally aligned audio content in accordance with video input has become a focal point for researchers, particularly following the remarkable breakthrough in text-to-video generation. In this work, we aim to offer insights into the video-to-audio generation paradigm, focusing on three crucial aspects: vision encoders, auxiliary embeddings, and data augmentation techniques. Beginning with a foundational model VTA-LDM built on a simple yet surprisingly effective intuition, we explore various vision encoders and auxiliary embeddings through ablation studies. Employing a comprehensive evaluation pipeline that emphasizes generation quality and video-audio synchronization alignment, we demonstrate that our model exhibits state-of-the-art video-to-audio generation capabilities. Furthermore, we provide critical insights into the impact of different data augmentation methods on enhancing the generation framework's overall capacity. We showcase possibilities to advance the challenge of generating synchronized audio from semantic and temporal perspectives. We hope these insights will serve as a stepping stone toward developing more realistic and accurate audio-visual generation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-11", "arxiv_id": "2407.07667", "title": "VEnhancer: Generative Space-Time Enhancement for Video Generation", "authors": [ "Jingwen He", "Tianfan Xue", "Dongyang Liu", "Xinqi Lin", "Peng Gao", "Dahua Lin", "Yu Qiao", "Wanli Ouyang", "Ziwei Liu" ], "github": "https://github.com/hpcaitech/Open-Sora", "project_page": "", "abstract": "We present VEnhancer, a generative space-time enhancement framework that improves the existing text-to-video results by adding more details in spatial domain and synthetic detailed motion in temporal domain. Given a generated low-quality video, our approach can increase its spatial and temporal resolution simultaneously with arbitrary up-sampling space and time scales through a unified video diffusion model. Furthermore, VEnhancer effectively removes generated spatial artifacts and temporal flickering of generated videos. To achieve this, basing on a pretrained video diffusion model, we train a video ControlNet and inject it to the diffusion model as a condition on low frame-rate and low-resolution videos. To effectively train this video ControlNet, we design space-time data augmentation as well as video-aware conditioning. Benefiting from the above designs, VEnhancer yields to be stable during training and shares an elegant end-to-end training manner. Extensive experiments show that VEnhancer surpasses existing state-of-the-art video super-resolution and space-time super-resolution methods in enhancing AI-generated videos. Moreover, with VEnhancer, exisiting open-source state-of-the-art text-to-video method, VideoCrafter-2, reaches the top one in video generation benchmark -- VBench.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hpcaitech/Open-Sora", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-11", "arxiv_id": "2407.07895", "title": "LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models", "authors": [ "Feng Li", "Renrui Zhang", "Hao Zhang", "Yuanhan Zhang", "Bo Li", "Wei Li", "Zejun Ma", "Chunyuan Li" ], "github": "https://github.com/LLaVA-VL/LLaVA-NeXT", "project_page": "", "abstract": "Visual instruction tuning has made considerable strides in enhancing the capabilities of Large Multimodal Models (LMMs). However, existing open LMMs largely focus on single-image tasks, their applications to multi-image scenarios remains less explored. Additionally, prior LMM research separately tackles different scenarios, leaving it impossible to generalize cross scenarios with new emerging capabilities. To this end, we introduce LLaVA-NeXT-Interleave, which simultaneously tackles Multi-image, Multi-frame (video), Multi-view (3D), and Multi-patch (single-image) scenarios in LMMs. To enable these capabilities, we regard the interleaved data format as a general template and compile the M4-Instruct dataset with 1,177.6k samples, spanning 4 primary domains with 14 tasks and 41 datasets. We also curate the LLaVA-Interleave Bench to comprehensively evaluate the multi-image performance of LMMs. Through extensive experiments, LLaVA-NeXT-Interleave achieves leading results in multi-image, video, and 3D benchmarks, while maintaining the performance of single-image tasks. Besides, our model also exhibits several emerging capabilities, e.g., transferring tasks across different settings and modalities. Code is available at https://github.com/LLaVA-VL/LLaVA-NeXT", "github_manual": "", "github_regex": "https://github.com/LLaVA-VL/LLaVA-NeXT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-11", "arxiv_id": "2407.07726", "title": "PaliGemma: A versatile 3B VLM for transfer", "authors": [ "Lucas Beyer", "Andreas Steiner", "Andr\u00e9 Susano Pinto", "Alexander Kolesnikov", "Xiao Wang", "Daniel Salz", "Maxim Neumann", "Ibrahim Alabdulmohsin", "Michael Tschannen", "Emanuele Bugliarello", "Thomas Unterthiner", "Daniel Keysers", "Skanda Koppula", "Fangyu Liu", "Adam Grycner", "Alexey Gritsenko", "Neil Houlsby", "Manoj Kumar", "Keran Rong", "Julian Eisenschlos", "Rishabh Kabra", "Matthias Bauer", "Matko Bo\u0161njak", "Xi Chen", "Matthias Minderer", "Paul Voigtlaender", "Ioana Bica", "Ivana Balazevic", "Joan Puigcerver", "Pinelopi Papalampidi", "Olivier Henaff", "Xi Xiong", "Radu Soricut", "Jeremiah Harmsen", "Xiaohua Zhai" ], "github": "", "project_page": "", "abstract": "PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong performance on a wide variety of open-world tasks. We evaluate PaliGemma on almost 40 diverse tasks including standard VLM benchmarks, but also more specialized tasks such as remote-sensing and segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-11", "arxiv_id": "2407.07304", "title": "Inference Performance Optimization for Large Language Models on CPUs", "authors": [ "Pujiang He", "Shan Zhou", "Wenhuan Huang", "Changqing Li", "Duyi Wang", "Bin Guo", "Chen Meng", "Sheng Gui", "Weifei Yu", "Yi Xie" ], "github": "https://github.com/intel/xFasterTransformer", "project_page": "", "abstract": "Large language models (LLMs) have shown exceptional performance and vast potential across diverse tasks. However, the deployment of LLMs with high performance in low-resource environments has garnered significant attention in the industry. When GPU hardware resources are limited, we can explore alternative options on CPUs. To mitigate the financial burden and alleviate constraints imposed by hardware resources, optimizing inference performance is necessary. In this paper, we introduce an easily deployable inference performance optimization solution aimed at accelerating LLMs on CPUs. In this solution, we implement an effective way to reduce the KV cache size while ensuring precision. We propose a distributed inference optimization approach and implement it based on oneAPI Collective Communications Library. Furthermore, we propose optimization approaches for LLMs on CPU, and conduct tailored optimizations for the most commonly used models. The code is open-sourced at https://github.com/intel/xFasterTransformer.", "github_manual": "", "github_regex": "https://github.com/intel/xFasterTransformer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-11", "arxiv_id": "2407.07860", "title": "Controlling Space and Time with Diffusion Models", "authors": [ "Daniel Watson", "Saurabh Saxena", "Lala Li", "Andrea Tagliasacchi", "David J. Fleet" ], "github": "", "project_page": "", "abstract": "We present 4DiM, a cascaded diffusion model for 4D novel view synthesis (NVS), conditioned on one or more images of a general scene, and a set of camera poses and timestamps. To overcome challenges due to limited availability of 4D training data, we advocate joint training on 3D (with camera pose), 4D (pose+time) and video (time but no pose) data and propose a new architecture that enables the same. We further advocate the calibration of SfM posed data using monocular metric depth estimators for metric scale camera control. For model evaluation, we introduce new metrics to enrich and overcome shortcomings of current evaluation schemes, demonstrating state-of-the-art results in both fidelity and pose control compared to existing diffusion models for 3D NVS, while at the same time adding the ability to handle temporal dynamics. 4DiM is also used for improved panorama stitching, pose-conditioned video to video translation, and several other tasks. For an overview see https://4d-diffusion.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-11", "arxiv_id": "2407.07788", "title": "BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark", "authors": [ "Nikita Chernyadev", "Nicholas Backshall", "Xiao Ma", "Yunfan Lu", "Younggyo Seo", "Stephen James" ], "github": "https://github.com/tonyzhaozh/aloha", "project_page": "", "abstract": "We introduce BiGym, a new benchmark and learning environment for mobile bi-manual demo-driven robotic manipulation. BiGym features 40 diverse tasks set in home environments, ranging from simple target reaching to complex kitchen cleaning. To capture the real-world performance accurately, we provide human-collected demonstrations for each task, reflecting the diverse modalities found in real-world robot trajectories. BiGym supports a variety of observations, including proprioceptive data and visual inputs such as RGB, and depth from 3 camera views. To validate the usability of BiGym, we thoroughly benchmark the state-of-the-art imitation learning algorithms and demo-driven reinforcement learning algorithms within the environment and discuss the future opportunities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tonyzhaozh/aloha", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-11", "arxiv_id": "2407.05528", "title": "An accurate detection is not all you need to combat label noise in web-noisy datasets", "authors": [ "Paul Albert", "Jack Valmadre", "Eric Arazo", "Tarun Krishna", "Noel E. O'Connor", "Kevin McGuinness" ], "github": "https://github.com/paulalbert31/lsa", "project_page": "", "abstract": "Training a classifier on web-crawled data demands learning algorithms that are robust to annotation errors and irrelevant examples. This paper builds upon the recent empirical observation that applying unsupervised contrastive learning to noisy, web-crawled datasets yields a feature representation under which the in-distribution (ID) and out-of-distribution (OOD) samples are linearly separable. We show that direct estimation of the separating hyperplane can indeed offer an accurate detection of OOD samples, and yet, surprisingly, this detection does not translate into gains in classification accuracy. Digging deeper into this phenomenon, we discover that the near-perfect detection misses a type of clean examples that are valuable for supervised learning. These examples often represent visually simple images, which are relatively easy to identify as clean examples using standard loss- or distance-based methods despite being poorly separated from the OOD distribution using unsupervised learning. Because we further observe a low correlation with SOTA metrics, this urges us to propose a hybrid solution that alternates between noise detection using linear separation and a state-of-the-art (SOTA) small-loss approach. When combined with the SOTA algorithm PLS, we substantially improve SOTA results for real-world image classification in the presence of web noise github.com/PaulAlbert31/LSA", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/paulalbert31/lsa", "github_api_added_by": "auto" }, { "date": "2024-07-11", "arxiv_id": "2407.07565", "title": "On Leakage of Code Generation Evaluation Datasets", "authors": [ "Alexandre Matton", "Tom Sherborne", "Dennis Aumiller", "Elena Tommasone", "Milad Alizadeh", "Jingyi He", "Raymond Ma", "Maxime Voisin", "Ellen Gilsenan-McMahon", "Matthias Gall\u00e9" ], "github": "", "project_page": "", "abstract": "In this paper we consider contamination by code generation test sets, in particular in their use in modern large language models. We discuss three possible sources of such contamination and show findings supporting each of them: (i) direct data leakage, (ii) indirect data leakage through the use of synthetic data and (iii) overfitting to evaluation sets during model selection. Key to our findings is a new dataset of 161 prompts with their associated python solutions, dataset which is released at https://huggingface.co/datasets/CohereForAI/lbpp .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-11", "arxiv_id": "2407.07315", "title": "CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging", "authors": [ "Raza Imam", "Mohammed Talha Alam", "Umaima Rahman", "Mohsen Guizani", "Fakhri Karray" ], "github": "", "project_page": "", "abstract": "Existing vision-text contrastive learning models enhance representation transferability and support zero-shot prediction by matching paired image and caption embeddings while pushing unrelated pairs apart. However, astronomical image-label datasets are significantly smaller compared to general image and label datasets available from the internet. We introduce CosmoCLIP, an astronomical image-text contrastive learning framework precisely fine-tuned on the pre-trained CLIP model using SpaceNet and BLIP-based captions. SpaceNet, attained via FLARE, constitutes ~13k optimally distributed images, while BLIP acts as a rich knowledge extractor. The rich semantics derived from this SpaceNet and BLIP descriptions, when learned contrastively, enable CosmoCLIP to achieve superior generalization across various in-domain and out-of-domain tasks. Our results demonstrate that CosmoCLIP is a straightforward yet powerful framework, significantly outperforming CLIP in zero-shot classification and image-text retrieval tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-11", "arxiv_id": "2407.05530", "title": "This&That: Language-Gesture Controlled Video Generation for Robot Planning", "authors": [ "Boyang Wang", "Nikhil Sridhar", "Chao Feng", "Mark Van der Merwe", "Adam Fishman", "Nima Fazeli", "Jeong Joon Park" ], "github": "https://github.com/pixeli99/SVD_Xtend", "project_page": "", "abstract": "We propose a robot learning method for communicating, planning, and executing a wide range of tasks, dubbed This&That. We achieve robot planning for general tasks by leveraging the power of video generative models trained on internet-scale data containing rich physical and semantic context. In this work, we tackle three fundamental challenges in video-based planning: 1) unambiguous task communication with simple human instructions, 2) controllable video generation that respects user intents, and 3) translating visual planning into robot actions. We propose language-gesture conditioning to generate videos, which is both simpler and clearer than existing language-only methods, especially in complex and uncertain environments. We then suggest a behavioral cloning design that seamlessly incorporates the video plans. This&That demonstrates state-of-the-art effectiveness in addressing the above three challenges, and justifies the use of video generation as an intermediate representation for generalizable task planning and execution. Project website: https://cfeng16.github.io/this-and-that/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pixeli99/SVD_Xtend", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-11", "arxiv_id": "2302.06555", "title": "Do Vision and Language Models Share Concepts? A Vector Space Alignment Study", "authors": [ "Jiaang Li", "Yova Kementchedjhieva", "Constanza Fierro", "Anders S\u00f8gaard" ], "github": "https://github.com/jiaangli/vlca", "project_page": "", "abstract": "Large-scale pretrained language models (LMs) are said to ``lack the ability to connect utterances to the world'' (Bender and Koller, 2020), because they do not have ``mental models of the world' '(Mitchell and Krakauer, 2023). If so, one would expect LM representations to be unrelated to representations induced by vision models. We present an empirical evaluation across four families of LMs (BERT, GPT-2, OPT and LLaMA-2) and three vision model architectures (ResNet, SegFormer, and MAE). Our experiments show that LMs partially converge towards representations isomorphic to those of vision models, subject to dispersion, polysemy and frequency. This has important implications for both multi-modal processing and the LM understanding debate (Mitchell and Krakauer, 2023).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jiaangli/vlca", "github_api_added_by": "auto" }, { "date": "2024-07-11", "arxiv_id": "2407.08674", "title": "Still-Moving: Customized Video Generation without Customized Video Data", "authors": [ "Hila Chefer", "Shiran Zada", "Roni Paiss", "Ariel Ephrat", "Omer Tov", "Michael Rubinstein", "Lior Wolf", "Tali Dekel", "Tomer Michaeli", "Inbar Mosseri" ], "github": "", "project_page": "", "abstract": "Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expanding this progress to video generation is still in its infancy, primarily due to the lack of customized video data. In this work, we introduce Still-Moving, a novel generic framework for customizing a text-to-video (T2V) model, without requiring any customized video data. The framework applies to the prominent T2V design where the video model is built over a text-to-image (T2I) model (e.g., via inflation). We assume access to a customized version of the T2I model, trained only on still image data (e.g., using DreamBooth or StyleDrop). Naively plugging in the weights of the customized T2I model into the T2V model often leads to significant artifacts or insufficient adherence to the customization data. To overcome this issue, we train lightweight Spatial Adapters that adjust the features produced by the injected T2I layers. Importantly, our adapters are trained on \"frozen videos\" (i.e., repeated images), constructed from image samples generated by the customized T2I model. This training is facilitated by a novel Motion Adapter module, which allows us to train on such static videos while preserving the motion prior of the video model. At test time, we remove the Motion Adapter modules and leave in only the trained Spatial Adapters. This restores the motion prior of the T2V model while adhering to the spatial prior of the customized T2I model. We demonstrate the effectiveness of our approach on diverse tasks including personalized, stylized, and conditional generation. In all evaluated scenarios, our method seamlessly integrates the spatial prior of the customized T2I model with a motion prior supplied by the T2V model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-11", "arxiv_id": "2407.06188", "title": "CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation", "authors": [ "Xinying Guo", "Mingyuan Zhang", "Haozhe Xie", "Chenyang Gu", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Crowd Motion Generation is essential in entertainment industries such as animation and games as well as in strategic fields like urban simulation and planning. This new task requires an intricate integration of control and generation to realistically synthesize crowd dynamics under specific spatial and semantic constraints, whose challenges are yet to be fully explored. On the one hand, existing human motion generation models typically focus on individual behaviors, neglecting the complexities of collective behaviors. On the other hand, recent methods for multi-person motion generation depend heavily on pre-defined scenarios and are limited to a fixed, small number of inter-person interactions, thus hampering their practicality. To overcome these challenges, we introduce CrowdMoGen, a zero-shot text-driven framework that harnesses the power of Large Language Model (LLM) to incorporate the collective intelligence into the motion generation framework as guidance, thereby enabling generalizable planning and generation of crowd motions without paired training data. Our framework consists of two key components: 1) Crowd Scene Planner that learns to coordinate motions and dynamics according to specific scene contexts or introduced perturbations, and 2) Collective Motion Generator that efficiently synthesizes the required collective motions based on the holistic plans. Extensive quantitative and qualitative experiments have validated the effectiveness of our framework, which not only fills a critical gap by providing scalable and generalizable solutions for Crowd Motion Generation task but also achieves high levels of realism and flexibility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08739", "title": "MAVIS: Mathematical Visual Instruction Tuning", "authors": [ "Renrui Zhang", "Xinyu Wei", "Dongzhi Jiang", "Yichi Zhang", "Ziyu Guo", "Chengzhuo Tong", "Jiaming Liu", "Aojun Zhou", "Bin Wei", "Shanghang Zhang", "Peng Gao", "Hongsheng Li" ], "github": "https://github.com/ZrrSkywalker/MAVIS", "project_page": "", "abstract": "Multi-modal Large Language Models (MLLMs) have recently emerged as a significant focus in academia and industry. Despite their proficiency in general multi-modal scenarios, the mathematical problem-solving capabilities in visual contexts remain insufficiently explored. We identify three key areas within MLLMs that need to be improved: visual encoding of math diagrams, diagram-language alignment, and mathematical reasoning skills. This draws forth an urgent demand for large-scale, high-quality data and training pipelines in visual mathematics. In this paper, we propose MAVIS, the first MAthematical VISual instruction tuning paradigm for MLLMs, involving a series of mathematical visual datasets and specialized MLLMs. Targeting the three issues, MAVIS contains three progressive training stages from scratch. First, we curate MAVIS-Caption, consisting of 558K diagram-caption pairs, to fine-tune a math-specific vision encoder (CLIP-Math) through contrastive learning, tailored for improved diagram visual encoding. Second, we utilize MAVIS-Caption to align the CLIP-Math with a large language model (LLM) by a projection layer, enhancing vision-language alignment in mathematical domains. Third, we introduce MAVIS-Instruct, including 900K meticulously collected and annotated visual math problems, which is adopted to finally instruct-tune the MLLM for robust mathematical reasoning skills. In MAVIS-Instruct, we incorporate complete chain-of-thought (CoT) rationales for each problem, and minimize textual redundancy, thereby concentrating the model towards the visual elements. Data and Models are released at https://github.com/ZrrSkywalker/MAVIS", "github_manual": "", "github_regex": "https://github.com/ZrrSkywalker/MAVIS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08348", "title": "Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models -- The Story Goes On", "authors": [ "Liang Zeng", "Liangjun Zhong", "Liang Zhao", "Tianwen Wei", "Liu Yang", "Jujie He", "Cheng Cheng", "Rui Hu", "Yang Liu", "Shuicheng Yan", "Han Fang", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "In this paper, we investigate the underlying factors that potentially enhance the mathematical reasoning capabilities of large language models (LLMs). We argue that the data scaling law for math reasoning capabilities in modern LLMs is far from being saturated, highlighting how the model's quality improves with increases in data quantity. To support this claim, we introduce the Skywork-Math model series, supervised fine-tuned (SFT) on common 7B LLMs using our proposed 2.5M-instance Skywork-MathQA dataset. Skywork-Math 7B has achieved impressive accuracies of 51.2% on the competition-level MATH benchmark and 83.9% on the GSM8K benchmark using only SFT data, outperforming an early version of GPT-4 on MATH. The superior performance of Skywork-Math models contributes to our novel two-stage data synthesis and model SFT pipelines, which include three different augmentation methods and a diverse seed problem set, ensuring both the quantity and quality of Skywork-MathQA dataset across varying difficulty levels. Most importantly, we provide several practical takeaways to enhance math reasoning abilities in LLMs for both research and industry applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08583", "title": "The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective", "authors": [ "Zhen Qin", "Daoyuan Chen", "Wenhao Zhang", "Liuyi Yao", "Yilun Huang", "Bolin Ding", "Yaliang Li", "Shuiguang Deng" ], "github": "https://github.com/modelscope/data-juicer/blob/main/docs", "project_page": "", "abstract": "The rapid development of large language models (LLMs) has been witnessed in recent years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from text to a broader spectrum of domains, attracting widespread attention due to the broader range of application scenarios. As LLMs and MLLMs rely on vast amounts of model parameters and data to achieve emergent capabilities, the importance of data is receiving increasingly widespread attention and recognition. Tracing and analyzing recent data-oriented works for MLLMs, we find that the development of models and data is not two separate paths but rather interconnected. On the one hand, vaster and higher-quality data contribute to better performance of MLLMs, on the other hand, MLLMs can facilitate the development of data. The co-development of multi-modal data and MLLMs requires a clear view of 1) at which development stage of MLLMs can specific data-centric approaches be employed to enhance which capabilities, and 2) by utilizing which capabilities and acting as which roles can models contribute to multi-modal data. To promote the data-model co-development for MLLM community, we systematically review existing works related to MLLMs from the data-model co-development perspective. A regularly maintained project associated with this survey is accessible at https://github.com/modelscope/data-juicer/blob/main/docs/awesome_llm_data.md.", "github_manual": "", "github_regex": "https://github.com/modelscope/data-juicer/blob/main/docs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08303", "title": "DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception", "authors": [ "Xiaotong Li", "Fan Zhang", "Haiwen Diao", "Yueze Wang", "Xinlong Wang", "Ling-Yu Duan" ], "github": "https://github.com/baaivision/DenseFusion", "project_page": "", "abstract": "Existing Multimodal Large Language Models (MLLMs) increasingly emphasize complex understanding of various visual elements, including multiple objects, text information, and spatial relations. Their development for comprehensive visual perception hinges on the availability of high-quality image-text datasets that offer diverse visual elements and throughout image descriptions. However, the scarcity of such hyper-detailed datasets currently hinders progress within the MLLM community. The bottleneck stems from the limited perceptual capabilities of current caption engines, which fall short in providing complete and accurate annotations. To facilitate the cutting-edge research of MLLMs on comprehensive vision perception, we thereby propose Perceptual Fusion, using a low-budget but highly effective caption engine for complete and accurate image descriptions. Specifically, Perceptual Fusion integrates diverse perception experts as image priors to provide explicit information on visual elements and adopts an efficient MLLM as a centric pivot to mimic advanced MLLMs' perception abilities. We carefully select 1M highly representative images from uncurated LAION dataset and generate dense descriptions using our engine, dubbed DenseFusion-1M. Extensive experiments validate that our engine outperforms its counterparts, where the resulting dataset significantly improves the perception and cognition abilities of existing MLLMs across diverse vision-language benchmarks, especially with high-resolution images as inputs. The dataset and code are publicly available at https://github.com/baaivision/DenseFusion.", "github_manual": "", "github_regex": "https://github.com/baaivision/DenseFusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08250", "title": "Gradient Boosting Reinforcement Learning", "authors": [ "Benjamin Fuhrer", "Chen Tessler", "Gal Dalal" ], "github": "https://github.com/NVlabs/gbrl", "project_page": "", "abstract": "Neural networks (NN) achieve remarkable results in various tasks, but lack key characteristics: interpretability, support for categorical features, and lightweight implementations suitable for edge devices. While ongoing efforts aim to address these challenges, Gradient Boosting Trees (GBT) inherently meet these requirements. As a result, GBTs have become the go-to method for supervised learning tasks in many real-world applications and competitions. However, their application in online learning scenarios, notably in reinforcement learning (RL), has been limited. In this work, we bridge this gap by introducing Gradient-Boosting RL (GBRL), a framework that extends the advantages of GBT to the RL domain. Using the GBRL framework, we implement various actor-critic algorithms and compare their performance with their NN counterparts. Inspired by shared backbones in NN we introduce a tree-sharing approach for policy and value functions with distinct learning rates, enhancing learning efficiency over millions of interactions. GBRL achieves competitive performance across a diverse array of tasks, excelling in domains with structured or categorical features. Additionally, we present a high-performance, GPU-accelerated implementation that integrates seamlessly with widely-used RL libraries (available at https://github.com/NVlabs/gbrl). GBRL expands the toolkit for RL practitioners, demonstrating the viability and promise of GBT within the RL paradigm, particularly in domains characterized by structured or categorical features.", "github_manual": "", "github_regex": "https://github.com/NVlabs/gbrl", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08083", "title": "MambaVision: A Hybrid Mamba-Transformer Vision Backbone", "authors": [ "Ali Hatamizadeh", "Jan Kautz" ], "github": "https://github.com/NVlabs/MambaVision", "project_page": "", "abstract": "We propose a novel hybrid Mamba-Transformer backbone, denoted as MambaVision, which is specifically tailored for vision applications. Our core contribution includes redesigning the Mamba formulation to enhance its capability for efficient modeling of visual features. In addition, we conduct a comprehensive ablation study on the feasibility of integrating Vision Transformers (ViT) with Mamba. Our results demonstrate that equipping the Mamba architecture with several self-attention blocks at the final layers greatly improves the modeling capacity to capture long-range spatial dependencies. Based on our findings, we introduce a family of MambaVision models with a hierarchical architecture to meet various design criteria. For Image classification on ImageNet-1K dataset, MambaVision model variants achieve a new State-of-the-Art (SOTA) performance in terms of Top-1 accuracy and image throughput. In downstream tasks such as object detection, instance segmentation and semantic segmentation on MS COCO and ADE20K datasets, MambaVision outperforms comparably-sized backbones and demonstrates more favorable performance. Code: https://github.com/NVlabs/MambaVision.", "github_manual": "", "github_regex": "https://github.com/NVlabs/MambaVision", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08737", "title": "Video Diffusion Alignment via Reward Gradients", "authors": [ "Mihir Prabhudesai", "Russell Mendonca", "Zheyang Qin", "Katerina Fragkiadaki", "Deepak Pathak" ], "github": "https://github.com/mihirp1998/vader", "project_page": "", "abstract": "We have made significant progress towards building foundational video diffusion models. As these models are trained using large-scale unsupervised data, it has become crucial to adapt these models to specific downstream tasks. Adapting these models via supervised fine-tuning requires collecting target datasets of videos, which is challenging and tedious. In this work, we utilize pre-trained reward models that are learned via preferences on top of powerful vision discriminative models to adapt video diffusion models. These models contain dense gradient information with respect to generated RGB pixels, which is critical to efficient learning in complex search spaces, such as videos. We show that backpropagating gradients from these reward models to a video diffusion model can allow for compute and sample efficient alignment of the video diffusion model. We show results across a variety of reward models and video diffusion models, demonstrating that our approach can learn much more efficiently in terms of reward queries and computation than prior gradient-free approaches. Our code, model weights,and more visualization are available at https://vader-vid.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mihirp1998/vader", "github_api_added_by": "auto" }, { "date": "2024-07-12", "arxiv_id": "2407.08711", "title": "OmniNOCS: A unified NOCS dataset and model for 3D lifting of 2D objects", "authors": [ "Akshay Krishnan", "Abhijit Kundu", "Kevis-Kokitsi Maninis", "James Hays", "Matthew Brown" ], "github": "", "project_page": "", "abstract": "We propose OmniNOCS, a large-scale monocular dataset with 3D Normalized Object Coordinate Space (NOCS) maps, object masks, and 3D bounding box annotations for indoor and outdoor scenes. OmniNOCS has 20 times more object classes and 200 times more instances than existing NOCS datasets (NOCS-Real275, Wild6D). We use OmniNOCS to train a novel, transformer-based monocular NOCS prediction model (NOCSformer) that can predict accurate NOCS, instance masks and poses from 2D object detections across diverse classes. It is the first NOCS model that can generalize to a broad range of classes when prompted with 2D boxes. We evaluate our model on the task of 3D oriented bounding box prediction, where it achieves comparable results to state-of-the-art 3D detection methods such as Cube R-CNN. Unlike other 3D detection methods, our model also provides detailed and accurate 3D object shape and segmentation. We propose a novel benchmark for the task of NOCS prediction based on OmniNOCS, which we hope will serve as a useful baseline for future work in this area. Our dataset and code will be at the project website: https://omninocs.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08701", "title": "Live2Diff: Live Stream Translation via Uni-directional Attention in Video Diffusion Models", "authors": [ "Zhening Xing", "Gereon Fox", "Yanhong Zeng", "Xingang Pan", "Mohamed Elgharib", "Christian Theobalt", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Large Language Models have shown remarkable efficacy in generating streaming data such as text and audio, thanks to their temporally uni-directional attention mechanism, which models correlations between the current token and previous tokens. However, video streaming remains much less explored, despite a growing need for live video processing. State-of-the-art video diffusion models leverage bi-directional temporal attention to model the correlations between the current frame and all the surrounding (i.e. including future) frames, which hinders them from processing streaming videos. To address this problem, we present Live2Diff, the first attempt at designing a video diffusion model with uni-directional temporal attention, specifically targeting live streaming video translation. Compared to previous works, our approach ensures temporal consistency and smoothness by correlating the current frame with its predecessors and a few initial warmup frames, without any future frames. Additionally, we use a highly efficient denoising scheme featuring a KV-cache mechanism and pipelining, to facilitate streaming video translation at interactive framerates. Extensive experiments demonstrate the effectiveness of the proposed attention mechanism and pipeline, outperforming previous methods in terms of temporal smoothness and/or efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08683", "title": "SEED-Story: Multimodal Long Story Generation with Large Language Model", "authors": [ "Shuai Yang", "Yuying Ge", "Yang Li", "Yukang Chen", "Yixiao Ge", "Ying Shan", "Yingcong Chen" ], "github": "https://github.com/tencentarc/seed-story", "project_page": "", "abstract": "With the remarkable advancements in image generation and open-form text generation, the creation of interleaved image-text content has become an increasingly intriguing field. Multimodal story generation, characterized by producing narrative texts and vivid images in an interleaved manner, has emerged as a valuable and practical task with broad applications. However, this task poses significant challenges, as it necessitates the comprehension of the complex interplay between texts and images, and the ability to generate long sequences of coherent, contextually relevant texts and visuals. In this work, we propose SEED-Story, a novel method that leverages a Multimodal Large Language Model (MLLM) to generate extended multimodal stories. Our model, built upon the powerful comprehension capability of MLLM, predicts text tokens as well as visual tokens, which are subsequently processed with an adapted visual de-tokenizer to produce images with consistent characters and styles. We further propose multimodal attention sink mechanism to enable the generation of stories with up to 25 sequences (only 10 for training) in a highly efficient autoregressive manner. Additionally, we present a large-scale and high-resolution dataset named StoryStream for training our model and quantitatively evaluating the task of multimodal story generation in various aspects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tencentarc/seed-story", "github_api_added_by": "auto" }, { "date": "2024-07-12", "arxiv_id": "2407.08680", "title": "Generalizable Implicit Motion Modeling for Video Frame Interpolation", "authors": [ "Zujin Guo", "Wei Li", "Chen Change Loy" ], "github": "", "project_page": "", "abstract": "Motion modeling is critical in flow-based Video Frame Interpolation (VFI). Existing paradigms either consider linear combinations of bidirectional flows or directly predict bilateral flows for given timestamps without exploring favorable motion priors, thus lacking the capability of effectively modeling spatiotemporal dynamics in real-world videos. To address this limitation, in this study, we introduce Generalizable Implicit Motion Modeling (GIMM), a novel and effective approach to motion modeling for VFI. Specifically, to enable GIMM as an effective motion modeling paradigm, we design a motion encoding pipeline to model spatiotemporal motion latent from bidirectional flows extracted from pre-trained flow estimators, effectively representing input-specific motion priors. Then, we implicitly predict arbitrary-timestep optical flows within two adjacent input frames via an adaptive coordinate-based neural network, with spatiotemporal coordinates and motion latent as inputs. Our GIMM can be smoothly integrated with existing flow-based VFI works without further modifications. We show that GIMM performs better than the current state of the art on the VFI benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08551", "title": "Autoregressive Speech Synthesis without Vector Quantization", "authors": [ "Lingwei Meng", "Long Zhou", "Shujie Liu", "Sanyuan Chen", "Bing Han", "Shujie Hu", "Yanqing Liu", "Jinyu Li", "Sheng Zhao", "Xixin Wu", "Helen Meng", "Furu Wei" ], "github": "", "project_page": "", "abstract": "We present MELLE, a novel continuous-valued tokens based language modeling approach for text to speech synthesis (TTS). MELLE autoregressively generates continuous mel-spectrogram frames directly from text condition, bypassing the need for vector quantization, which are originally designed for audio compression and sacrifice fidelity compared to mel-spectrograms. Specifically, (i) instead of cross-entropy loss, we apply regression loss with a proposed spectrogram flux loss function to model the probability distribution of the continuous-valued tokens. (ii) we have incorporated variational inference into MELLE to facilitate sampling mechanisms, thereby enhancing the output diversity and model robustness. Experiments demonstrate that, compared to the two-stage codec language models VALL-E and its variants, the single-stage MELLE mitigates robustness issues by avoiding the inherent flaws of sampling discrete codes, achieves superior performance across multiple metrics, and, most importantly, offers a more streamlined paradigm. See https://aka.ms/melle for demos of our work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08447", "title": "WildGaussians: 3D Gaussian Splatting in the Wild", "authors": [ "Jonas Kulhanek", "Songyou Peng", "Zuzana Kukelova", "Marc Pollefeys", "Torsten Sattler" ], "github": "https://github.com/jkulhanek/wild-gaussians", "project_page": "", "abstract": "While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jkulhanek/wild-gaussians", "github_api_added_by": "auto" }, { "date": "2024-07-12", "arxiv_id": "2407.08713", "title": "GTA: A Benchmark for General Tool Agents", "authors": [ "Jize Wang", "Zerun Ma", "Yining Li", "Songyang Zhang", "Cailian Chen", "Kai Chen", "Xinyi Le" ], "github": "https://github.com/open-compass/GTA", "project_page": "", "abstract": "Significant focus has been placed on integrating large language models (LLMs) with various tools in developing general-purpose agents. This poses a challenge to LLMs' tool-use capabilities. However, there are evident gaps between existing tool-use evaluations and real-world scenarios. Current evaluations often use AI-generated queries, single-step tasks, dummy tools, and text-only interactions, failing to reveal the agents' real-world problem-solving abilities effectively. To address this, we propose GTA, a benchmark for General Tool Agents, featuring three main aspects: (i) Real user queries: human-written queries with simple real-world objectives but implicit tool-use, requiring the LLM to reason the suitable tools and plan the solution steps. (ii) Real deployed tools: an evaluation platform equipped with tools across perception, operation, logic, and creativity categories to evaluate the agents' actual task execution performance. (iii) Real multimodal inputs: authentic image files, such as spatial scenes, web page screenshots, tables, code snippets, and printed/handwritten materials, used as the query contexts to align with real-world scenarios closely. We design 229 real-world tasks and executable tool chains to evaluate mainstream LLMs. Our findings show that real-world user queries are challenging for existing LLMs, with GPT-4 completing less than 50% of the tasks and most LLMs achieving below 25%. This evaluation reveals the bottlenecks in the tool-use capabilities of current LLMs in real-world scenarios, which provides future direction for advancing general-purpose tool agents. The code and dataset are available at https://github.com/open-compass/GTA.", "github_manual": "", "github_regex": "https://github.com/open-compass/GTA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08296", "title": "Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients", "authors": [ "Zhenyu Zhang", "Ajay Jaiswal", "Lu Yin", "Shiwei Liu", "Jiawei Zhao", "Yuandong Tian", "Zhangyang Wang" ], "github": "https://github.com/VITA-Group/Q-GaLore", "project_page": "", "abstract": "Training Large Language Models (LLMs) is memory-intensive due to the large number of parameters and associated optimization states. GaLore, a recent method, reduces memory usage by projecting weight gradients into a low-rank subspace without compromising performance. However, GaLore relies on time-consuming Singular Value Decomposition (SVD) operations to identify the subspace, and the frequent subspace updates lead to significant training time overhead. Moreover, GaLore offers minimal improvements in accuracy and efficiency compared to LoRA in more accessible fine-tuning scenarios. To address these limitations, we introduce Q-Galore, a novel approach that substantially reduces memory usage by combining quantization and low-rank projection, surpassing the benefits of GaLore. Our method is based on two key observations: (i) the gradient subspace exhibits diverse properties, with some layers converging early in training while others are subject to frequent changes; (ii) the projection matrices are highly resilient to low-bit quantization. Leveraging these insights, Q-GaLore adaptively updates the gradient subspace based on its convergence statistics, achieving comparable performance while significantly reducing the number of SVD operations. We maintain the projection matrices in INT4 format and weights in INT8 format, incorporating stochastic rounding to capture accumulated gradient information. This approach enables a high-precision training trajectory using only low-precision weights. We demonstrate that Q-GaLore achieves highly competitive performance with exceptional memory efficiency. At pre-training, Q-GaLore facilitates training a LLaMA-7B model from scratch on a single NVIDIA RTX 4060 Ti with only 16 GB memory. At fine-tuning, it reduces memory consumption by up to 50% compared to LoRA and GaLore, while consistently outperforming QLoRA at the same memory cost.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/VITA-Group/Q-GaLore", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08726", "title": "Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using Large-scale Public Data", "authors": [ "Cherie Ho", "Jiaye Zou", "Omar Alama", "Sai Mitheran Jagadesh Kumar", "Benjamin Chiang", "Taneesh Gupta", "Chen Wang", "Nikhil Keetha", "Katia Sycara", "Sebastian Scherer" ], "github": "https://github.com/MapItAnywhere/MapItAnywhere", "project_page": "", "abstract": "Top-down Bird's Eye View (BEV) maps are a popular representation for ground robot navigation due to their richness and flexibility for downstream tasks. While recent methods have shown promise for predicting BEV maps from First-Person View (FPV) images, their generalizability is limited to small regions captured by current autonomous vehicle-based datasets. In this context, we show that a more scalable approach towards generalizable map prediction can be enabled by using two large-scale crowd-sourced mapping platforms, Mapillary for FPV images and OpenStreetMap for BEV semantic maps. We introduce Map It Anywhere (MIA), a data engine that enables seamless curation and modeling of labeled map prediction data from existing open-source map platforms. Using our MIA data engine, we display the ease of automatically collecting a dataset of 1.2 million pairs of FPV images & BEV maps encompassing diverse geographies, landscapes, environmental factors, camera models & capture scenarios. We further train a simple camera model-agnostic model on this data for BEV map prediction. Extensive evaluations using established benchmarks and our dataset show that the data curated by MIA enables effective pretraining for generalizable BEV map prediction, with zero-shot performance far exceeding baselines trained on existing datasets by 35%. Our analysis highlights the promise of using large-scale public maps for developing & testing generalizable BEV perception, paving the way for more robust autonomous navigation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MapItAnywhere/MapItAnywhere", "github_api_added_by": "auto" }, { "date": "2024-07-12", "arxiv_id": "2407.08642", "title": "Towards Building Specialized Generalist AI with System 1 and System 2 Fusion", "authors": [ "Kaiyan Zhang", "Biqing Qi", "Bowen Zhou" ], "github": "", "project_page": "", "abstract": "In this perspective paper, we introduce the concept of Specialized Generalist Artificial Intelligence (SGAI or simply SGI) as a crucial milestone toward Artificial General Intelligence (AGI). Compared to directly scaling general abilities, SGI is defined as AI that specializes in at least one task, surpassing human experts, while also retaining general abilities. This fusion path enables SGI to rapidly achieve high-value areas. We categorize SGI into three stages based on the level of mastery over professional skills and generality performance. Additionally, we discuss the necessity of SGI in addressing issues associated with large language models, such as their insufficient generality, specialized capabilities, uncertainty in innovation, and practical applications. Furthermore, we propose a conceptual framework for developing SGI that integrates the strengths of Systems 1 and 2 cognitive processing. This framework comprises three layers and four key components, which focus on enhancing individual abilities and facilitating collaborative evolution. We conclude by summarizing the potential challenges and suggesting future directions. We hope that the proposed SGI will provide insights into further research and applications towards achieving AGI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.08733", "title": "Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklist", "authors": [ "Zihao Zhou", "Shudong Liu", "Maizhen Ning", "Wei Liu", "Jindong Wang", "Derek F. Wong", "Xiaowei Huang", "Qiufeng Wang", "Kaizhu Huang" ], "github": "", "project_page": "", "abstract": "Exceptional mathematical reasoning ability is one of the key features that demonstrate the power of large language models (LLMs). How to comprehensively define and evaluate the mathematical abilities of LLMs, and even reflect the user experience in real-world scenarios, has emerged as a critical issue. Current benchmarks predominantly concentrate on problem-solving capabilities, which presents a substantial risk of model overfitting and fails to accurately represent genuine mathematical reasoning abilities. In this paper, we argue that if a model really understands a problem, it should be robustly and readily applied across a diverse array of tasks. Motivated by this, we introduce MATHCHECK, a well-designed checklist for testing task generalization and reasoning robustness, as well as an automatic tool to generate checklists efficiently. MATHCHECK includes multiple mathematical reasoning tasks and robustness test types to facilitate a comprehensive evaluation of both mathematical reasoning ability and behavior testing. Utilizing MATHCHECK, we develop MATHCHECK-GSM and MATHCHECK-GEO to assess mathematical textual reasoning and multi-modal reasoning capabilities, respectively, serving as upgraded versions of benchmarks including GSM8k, GeoQA, UniGeo, and Geometry3K. We adopt MATHCHECK-GSM and MATHCHECK-GEO to evaluate over 20 LLMs and 11 MLLMs, assessing their comprehensive mathematical reasoning abilities. Our results demonstrate that while frontier LLMs like GPT-4o continue to excel in various abilities on the checklist, many other model families exhibit a significant decline. Further experiments indicate that, compared to traditional math benchmarks, MATHCHECK better reflects true mathematical abilities and represents mathematical intelligence more linearly, thereby supporting our design. On our MATHCHECK, we can easily conduct detailed behavior analysis to deeply investigate models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.07053", "title": "Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model", "authors": [ "Wenqi Zhang", "Zhenglin Cheng", "Yuanyu He", "Mengna Wang", "Yongliang Shen", "Zeqi Tan", "Guiyang Hou", "Mingqian He", "Yanna Ma", "Weiming Lu", "Yueting Zhuang" ], "github": "https://github.com/zwq2018/Multi-modal-Self-instruct", "project_page": "", "abstract": "Although most current large multimodal models (LMMs) can already understand photos of natural scenes and portraits, their understanding of abstract images, e.g., charts, maps, or layouts, and visual reasoning capabilities remains quite rudimentary. They often struggle with simple daily tasks, such as reading time from a clock, understanding a flowchart, or planning a route using a road map. In light of this, we design a multi-modal self-instruct, utilizing large language models and their code capabilities to synthesize massive abstract images and visual reasoning instructions across daily scenarios. Our strategy effortlessly creates a multimodal benchmark with 11,193 instructions for eight visual scenarios: charts, tables, simulated maps, dashboards, flowcharts, relation graphs, floor plans, and visual puzzles. This benchmark, constructed with simple lines and geometric elements, exposes the shortcomings of most advanced LMMs like Claude-3.5-Sonnet and GPT-4o in abstract image understanding, spatial relations reasoning, and visual element induction. Besides, to verify the quality of our synthetic data, we fine-tune an LMM using 62,476 synthetic chart, table and road map instructions. The results demonstrate improved chart understanding and map navigation performance, and also demonstrate potential benefits for other visual reasoning tasks. Our code is available at: https://github.com/zwq2018/Multi-modal-Self-instruct.", "github_manual": "", "github_regex": "https://github.com/zwq2018/Multi-modal-Self-instruct", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-12", "arxiv_id": "2407.07176", "title": "Scaling Up Personalized Aesthetic Assessment via Task Vector Customization", "authors": [ "Jooyeol Yun", "Jaegul Choo" ], "github": "https://github.com/YeolJ00/Personalized-Aesthetics", "project_page": "", "abstract": "The task of personalized image aesthetic assessment seeks to tailor aesthetic score prediction models to match individual preferences with just a few user-provided inputs. However, the scalability and generalization capabilities of current approaches are considerably restricted by their reliance on an expensive curated database. To overcome this long-standing scalability challenge, we present a unique approach that leverages readily available databases for general image aesthetic assessment and image quality assessment. Specifically, we view each database as a distinct image score regression task that exhibits varying degrees of personalization potential. By determining optimal combinations of task vectors, known to represent specific traits of each database, we successfully create personalized models for individuals. This approach of integrating multiple models allows us to harness a substantial amount of data. Our extensive experiments demonstrate the effectiveness of our approach in generalizing to previously unseen domains-a challenge previous approaches have struggled to achieve-making it highly applicable to real-world scenarios. Our novel approach significantly advances the field by offering scalable solutions for personalized aesthetic assessment and establishing high standards for future research. https://yeolj00.github.io/personal-projects/personalized-aesthetics/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YeolJ00/Personalized-Aesthetics", "github_api_added_by": "auto" }, { "date": "2024-07-12", "arxiv_id": "2407.06946", "title": "Self-Recognition in Language Models", "authors": [ "Tim R. Davidson", "Viacheslav Surkov", "Veniamin Veselovsky", "Giuseppe Russo", "Robert West", "Caglar Gulcehre" ], "github": "https://github.com/trdavidson/self-recognition", "project_page": "", "abstract": "A rapidly growing number of applications rely on a small set of closed-source language models (LMs). This dependency might introduce novel security risks if LMs develop self-recognition capabilities. Inspired by human identity verification methods, we propose a novel approach for assessing self-recognition in LMs using model-generated \"security questions\". Our test can be externally administered to keep track of frontier models as it does not require access to internal model parameters or output probabilities. We use our test to examine self-recognition in ten of the most capable open- and closed-source LMs currently publicly available. Our extensive experiments found no empirical evidence of general or consistent self-recognition in any examined LM. Instead, our results suggest that given a set of alternatives, LMs seek to pick the \"best\" answer, regardless of its origin. Moreover, we find indications that preferences about which models produce the best answers are consistent across LMs. We additionally uncover novel insights on position bias considerations for LMs in multiple-choice settings.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/trdavidson/self-recognition", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.09388", "title": "GAVEL: Generating Games Via Evolution and Language Models", "authors": [ "Graham Todd", "Alexander Padula", "Matthew Stephenson", "\u00c9ric Piette", "Dennis J. N. J. Soemers", "Julian Togelius" ], "github": "https://github.com/gdrtodd/gavel", "project_page": "", "abstract": "Automatically generating novel and interesting games is a complex task. Challenges include representing game rules in a computationally workable form, searching through the large space of potential games under most such representations, and accurately evaluating the originality and quality of previously unseen games. Prior work in automated game generation has largely focused on relatively restricted rule representations and relied on domain-specific heuristics. In this work, we explore the generation of novel games in the comparatively expansive Ludii game description language, which encodes the rules of over 1000 board games in a variety of styles and modes of play. We draw inspiration from recent advances in large language models and evolutionary computation in order to train a model that intelligently mutates and recombines games and mechanics expressed as code. We demonstrate both quantitatively and qualitatively that our approach is capable of generating new and interesting games, including in regions of the potential rules space not covered by existing games in the Ludii dataset. A sample of the generated games are available to play online through the Ludii portal.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gdrtodd/gavel", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.09025", "title": "SpreadsheetLLM: Encoding Spreadsheets for Large Language Models", "authors": [ "Yuzhang Tian", "Jianbo Zhao", "Haoyu Dong", "Junyu Xiong", "Shiyu Xia", "Mengyu Zhou", "Yun Lin", "Jos\u00e9 Cambronero", "Yeye He", "Shi Han", "Dongmei Zhang" ], "github": "", "project_page": "", "abstract": "Spreadsheets, with their extensive two-dimensional grids, various layouts, and diverse formatting options, present notable challenges for large language models (LLMs). In response, we introduce SpreadsheetLLM, pioneering an efficient encoding method designed to unleash and optimize LLMs' powerful understanding and reasoning capability on spreadsheets. Initially, we propose a vanilla serialization approach that incorporates cell addresses, values, and formats. However, this approach was limited by LLMs' token constraints, making it impractical for most applications. To tackle this challenge, we develop SheetCompressor, an innovative encoding framework that compresses spreadsheets effectively for LLMs. It comprises three modules: structural-anchor-based compression, inverse index translation, and data-format-aware aggregation. It significantly improves performance in spreadsheet table detection task, outperforming the vanilla approach by 25.6% in GPT4's in-context learning setting. Moreover, fine-tuned LLM with SheetCompressor has an average compression ratio of 25 times, but achieves a state-of-the-art 78.9% F1 score, surpassing the best existing models by 12.3%. Finally, we propose Chain of Spreadsheet for downstream tasks of spreadsheet understanding and validate in a new and demanding spreadsheet QA task. We methodically leverage the inherent layout and structure of spreadsheets, demonstrating that SpreadsheetLLM is highly effective across a variety of spreadsheet tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.09450", "title": "Human-like Episodic Memory for Infinite Context LLMs", "authors": [ "Zafeirios Fountas", "Martin A Benfeghoul", "Adnan Oomerjee", "Fenia Christopoulou", "Gerasimos Lampouras", "Haitham Bou-Ammar", "Jun Wang" ], "github": "https://github.com/em-llm/EM-LLM-model", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable capabilities, but still struggle with processing extensive contexts, limiting their ability to maintain coherence and accuracy over long sequences. In contrast, the human brain excels at organising and retrieving episodic experiences across vast temporal scales, spanning a lifetime. In this work, we introduce EM-LLM, a novel approach that integrates key aspects of human episodic memory and event cognition into LLMs, enabling them to effectively handle practically infinite context lengths while maintaining computational efficiency. EM-LLM organises sequences of tokens into coherent episodic events using a combination of Bayesian surprise and graph-theoretic boundary refinement in an on-line fashion. When needed, these events are retrieved through a two-stage memory process, combining similarity-based and temporally contiguous retrieval for efficient and human-like access to relevant information. Experiments on the LongBench dataset demonstrate EM-LLM's superior performance, outperforming the state-of-the-art InfLLM model with an overall relative improvement of 4.3% across various tasks, including a 33% improvement on the PassageRetrieval task. Furthermore, our analysis reveals strong correlations between EM-LLM's event segmentation and human-perceived events, suggesting a bridge between this artificial system and its biological counterpart. This work not only advances LLM capabilities in processing extended contexts but also provides a computational framework for exploring human memory mechanisms, opening new avenues for interdisciplinary research in AI and cognitive science.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/em-llm/EM-LLM-model", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.09435", "title": "MUSCLE: A Model Update Strategy for Compatible LLM Evolution", "authors": [ "Jessica Echterhoff", "Fartash Faghri", "Raviteja Vemulapalli", "Ting-Yao Hu", "Chun-Liang Li", "Oncel Tuzel", "Hadi Pouransari" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are frequently updated due to data or architecture changes to improve their performance. When updating models, developers often focus on increasing overall performance metrics with less emphasis on being compatible with previous model versions. However, users often build a mental model of the functionality and capabilities of a particular machine learning model they are interacting with. They have to adapt their mental model with every update -- a draining task that can lead to user dissatisfaction. In practice, fine-tuned downstream task adapters rely on pretrained LLM base models. When these base models are updated, these user-facing downstream task models experience instance regression or negative flips -- previously correct instances are now predicted incorrectly. This happens even when the downstream task training procedures remain identical. Our work aims to provide seamless model updates to a user in two ways. First, we provide evaluation metrics for a notion of compatibility to prior model versions, specifically for generative tasks but also applicable for discriminative tasks. We observe regression and inconsistencies between different model versions on a diverse set of tasks and model updates. Second, we propose a training strategy to minimize the number of inconsistencies in model updates, involving training of a compatibility model that can enhance task fine-tuned language models. We reduce negative flips -- instances where a prior model version was correct, but a new model incorrect -- by up to 40% from Llama 1 to Llama 2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.09298", "title": "Transformer Layers as Painters", "authors": [ "Qi Sun", "Marc Pickett", "Aakash Kumar Nain", "Llion Jones" ], "github": "", "project_page": "", "abstract": "Despite their nearly universal adoption for large language models, the internal workings of transformers are not well understood. We aim to better understand the impact of removing or reorganizing information throughout the layers of a pretrained transformer. Such an understanding could both yield better usage of existing models as well as to make architectural improvements to produce new variants. We present a series of empirical studies on frozen models that show that the lower and final layers of pretrained transformers differ from middle layers, but that middle layers have a surprising amount of uniformity. We further show that some classes of problems have robustness to skipping layers, running the layers in an order different from how they were trained, or running the layers in parallel. Our observations suggest that even frozen pretrained models may gracefully trade accuracy for latency by skipping layers or running layers in parallel.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.09276", "title": "H2O-Danube3 Technical Report", "authors": [ "Pascal Pfeiffer", "Philipp Singer", "Yauhen Babakhin", "Gabor Fodor", "Nischay Dhankhar", "Sri Satish Ambati" ], "github": "https://github.com/h2oai/h2o-llmstudio", "project_page": "", "abstract": "We present H2O-Danube3, a series of small language models consisting of H2O-Danube3-4B, trained on 6T tokens and H2O-Danube3-500M, trained on 4T tokens. Our models are pre-trained on high quality Web data consisting of primarily English tokens in three stages with different data mixes before final supervised tuning for chat version. The models exhibit highly competitive metrics across a multitude of academic, chat, and fine-tuning benchmarks. Thanks to its compact architecture, H2O-Danube3 can be efficiently run on a modern smartphone, enabling local inference and rapid processing capabilities even on mobile devices. We make all models openly available under Apache 2.0 license further democratizing LLMs to a wider audience economically.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/h2oai/h2o-llmstudio", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2406.02265", "title": "Understanding Retrieval Robustness for Retrieval-Augmented Image Captioning", "authors": [ "Wenyan Li", "Jiaang Li", "Rita Ramos", "Raphael Tang", "Desmond Elliott" ], "github": "https://github.com/lyan62/RobustCap", "project_page": "", "abstract": "Recent advances in retrieval-augmented models for image captioning highlight the benefit of retrieving related captions for efficient, lightweight models with strong domain-transfer capabilities. While these models demonstrate the success of retrieval augmentation, retrieval models are still far from perfect in practice: the retrieved information can sometimes mislead the model, resulting in incorrect generation and worse performance. In this paper, we analyze the robustness of a retrieval-augmented captioning model SmallCap. Our analysis shows that the model is sensitive to tokens that appear in the majority of the retrieved captions, and the input attribution shows that those tokens are likely copied into the generated output. Given these findings, we propose to train the model by sampling retrieved captions from more diverse sets. This decreases the chance that the model learns to copy majority tokens, and improves both in-domain and cross-domain performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lyan62/RobustCap", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.09473", "title": "StyleSplat: 3D Object Style Transfer with Gaussian Splatting", "authors": [ "Sahil Jain", "Avik Kuthiala", "Prabhdeep Singh Sethi", "Prakanshul Saxena" ], "github": "", "project_page": "", "abstract": "Recent advancements in radiance fields have opened new avenues for creating high-quality 3D assets and scenes. Style transfer can enhance these 3D assets with diverse artistic styles, transforming creative expression. However, existing techniques are often slow or unable to localize style transfer to specific objects. We introduce StyleSplat, a lightweight method for stylizing 3D objects in scenes represented by 3D Gaussians from reference style images. Our approach first learns a photorealistic representation of the scene using 3D Gaussian splatting while jointly segmenting individual 3D objects. We then use a nearest-neighbor feature matching loss to finetune the Gaussians of the selected objects, aligning their spherical harmonic coefficients with the style image to ensure consistency and visual appeal. StyleSplat allows for quick, customizable style transfer and localized stylization of multiple objects within a scene, each with a different style. We demonstrate its effectiveness across various 3D scenes and styles, showcasing enhanced control and customization in 3D creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.09012", "title": "TCAN: Animating Human Images with Temporally Consistent Pose Guidance using Diffusion Models", "authors": [ "Jeongho Kim", "Min-Jung Kim", "Junsoo Lee", "Jaegul Choo" ], "github": "https://github.com/Wangt-CN/DisCo", "project_page": "", "abstract": "Pose-driven human-image animation diffusion models have shown remarkable capabilities in realistic human video synthesis. Despite the promising results achieved by previous approaches, challenges persist in achieving temporally consistent animation and ensuring robustness with off-the-shelf pose detectors. In this paper, we present TCAN, a pose-driven human image animation method that is robust to erroneous poses and consistent over time. In contrast to previous methods, we utilize the pre-trained ControlNet without fine-tuning to leverage its extensive pre-acquired knowledge from numerous pose-image-caption pairs. To keep the ControlNet frozen, we adapt LoRA to the UNet layers, enabling the network to align the latent space between the pose and appearance features. Additionally, by introducing an additional temporal layer to the ControlNet, we enhance robustness against outliers of the pose detector. Through the analysis of attention maps over the temporal axis, we also designed a novel temperature map leveraging pose information, allowing for a more static background. Extensive experiments demonstrate that the proposed method can achieve promising results in video synthesis tasks encompassing various poses, like chibi. Project Page: https://eccv2024tcan.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Wangt-CN/DisCo", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.08892", "title": "Characterizing Prompt Compression Methods for Long Context Inference", "authors": [ "Siddharth Jha", "Lutfi Eren Erdogan", "Sehoon Kim", "Kurt Keutzer", "Amir Gholami" ], "github": "", "project_page": "", "abstract": "Long context inference presents challenges at the system level with increased compute and memory requirements, as well as from an accuracy perspective in being able to reason over long contexts. Recently, several methods have been proposed to compress the prompt to reduce the context length. However, there has been little work on comparing the different proposed methods across different tasks through a standardized analysis. This has led to conflicting results. To address this, here we perform a comprehensive characterization and evaluation of different prompt compression methods. In particular, we analyze extractive compression, summarization-based abstractive compression, and token pruning methods. Surprisingly, we find that extractive compression often outperforms all the other approaches, and enables up to 10x compression with minimal accuracy degradation. Interestingly, we also find that despite several recent claims, token pruning methods often lag behind extractive compression. We only found marginal improvements on summarization tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.09072", "title": "New Desiderata for Direct Preference Optimization", "authors": [ "Xiangkun Hu", "Tong He", "David Wipf" ], "github": "", "project_page": "", "abstract": "Large language models in the past have typically relied on some form of reinforcement learning with human feedback (RLHF) to better align model responses with human preferences. However, because of oft-observed instabilities when implementing these RLHF pipelines, various reparameterization techniques have recently been introduced to sidestep the need for separately learning an RL reward model. Instead, directly fine-tuning for human preferences is achieved via the minimization of a single closed-form training objective, a process originally referred to as direct preference optimization (DPO) and followed by several notable descendants. Although effective in certain real-world settings, we introduce new evaluation criteria that serve to highlight unresolved shortcomings in the ability of existing DPO methods to interpolate between a pre-trained reference model and empirical measures of human preferences, as well as unavoidable trade-offs in how low- and high-quality responses are regularized and constraints are handled. Our insights then motivate an alternative DPO-like loss that provably mitigates these limitations. Empirical results serve to corroborate notable aspects of our analyses.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.09121", "title": "Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training", "authors": [ "Youliang Yuan", "Wenxiang Jiao", "Wenxuan Wang", "Jen-tse Huang", "Jiahao Xu", "Tian Liang", "Pinjia He", "Zhaopeng Tu" ], "github": "https://github.com/RobustNLP/DeRTa", "project_page": "", "abstract": "This study addresses a critical gap in safety tuning practices for Large Language Models (LLMs) by identifying and tackling a refusal position bias within safety tuning data, which compromises the models' ability to appropriately refuse generating unsafe content. We introduce a novel approach, Decoupled Refusal Training (DeRTa), designed to empower LLMs to refuse compliance to harmful prompts at any response position, significantly enhancing their safety capabilities. DeRTa incorporates two novel components: (1) Maximum Likelihood Estimation (MLE) with Harmful Response Prefix, which trains models to recognize and avoid unsafe content by appending a segment of harmful response to the beginning of a safe response, and (2) Reinforced Transition Optimization (RTO), which equips models with the ability to transition from potential harm to safety refusal consistently throughout the harmful response sequence. Our empirical evaluation, conducted using LLaMA3 and Mistral model families across six attack scenarios, demonstrates that our method not only improves model safety without compromising performance but also surpasses well-known models such as GPT-4 in defending against attacks. Importantly, our approach successfully defends recent advanced attack methods (e.g., CodeAttack) that have jailbroken GPT-4 and LLaMA3-70B-Instruct. Our code and data can be found at https://github.com/RobustNLP/DeRTa.", "github_manual": "", "github_regex": "https://github.com/RobustNLP/DeRTa", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.08770", "title": "Model Surgery: Modulating LLM's Behavior Via Simple Parameter Editing", "authors": [ "Huanqian Wang", "Yang Yue", "Rui Lu", "Jingxin Shi", "Andrew Zhao", "Shenzhi Wang", "Shiji Song", "Gao Huang" ], "github": "https://github.com/lucywang720/model-surgery", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated great potential as generalist assistants, showcasing powerful task understanding and problem-solving capabilities. To deploy LLMs as AI assistants, it is crucial that these models exhibit desirable behavioral traits, such as non-toxicity and resilience against jailbreak attempts. Current methods for detoxification or preventing jailbreaking usually involve Supervised Fine-Tuning (SFT) or Reinforcement Learning from Human Feedback (RLHF), which requires finetuning billions of parameters through gradient descent with substantial computation cost. Furthermore, models modified through SFT and RLHF may deviate from the pretrained models, potentially leading to a degradation in foundational LLM capabilities. In this paper, we observe that surprisingly, directly editing a small subset of parameters can effectively modulate specific behaviors of LLMs, such as detoxification and resistance to jailbreaking. Specifically, for a behavior that we aim to avoid, we employ a linear classifier, which we term the behavior probe, to classify binary behavior labels within the hidden state space of the LLM. Using this probe, we introduce an algorithm to identify a critical subset of LLM parameters that significantly influence this targeted behavior. Then we directly edit these selected parameters by shifting them towards the behavior probe. Such a direct parameter editing method necessitates only inference-level computational resources. Experiments demonstrate that in the representative detoxification task, our approach achieves reductions of up to 90.0\\% in toxicity on the RealToxicityPrompts dataset and 49.2\\% on ToxiGen, while maintaining the LLM's general capabilities in areas such as common sense, question answering, and mathematics. Our code is available at https://github.com/lucywang720/model-surgery.", "github_manual": "", "github_regex": "https://github.com/lucywang720/model-surgery", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.09413", "title": "SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers", "authors": [ "Shraman Pramanick", "Rama Chellappa", "Subhashini Venugopalan" ], "github": "https://github.com/google/spiqa", "project_page": "", "abstract": "Seeking answers to questions within long scientific research articles is a crucial area of study that aids readers in quickly addressing their inquiries. However, existing question-answering (QA) datasets based on scientific papers are limited in scale and focus solely on textual content. To address this limitation, we introduce SPIQA (Scientific Paper Image Question Answering), the first large-scale QA dataset specifically designed to interpret complex figures and tables within the context of scientific research articles across various domains of computer science. Leveraging the breadth of expertise and ability of multimodal large language models (MLLMs) to understand figures, we employ automatic and manual curation to create the dataset. We craft an information-seeking task involving multiple images that cover a wide variety of plots, charts, tables, schematic diagrams, and result visualizations. SPIQA comprises 270K questions divided into training, validation, and three different evaluation splits. Through extensive experiments with 12 prominent foundational models, we evaluate the ability of current multimodal systems to comprehend the nuanced aspects of research articles. Additionally, we propose a Chain-of-Thought (CoT) evaluation strategy with in-context retrieval that allows fine-grained, step-by-step assessment and improves model performance. We further explore the upper bounds of performance enhancement with additional textual information, highlighting its promising potential for future research and the dataset's impact on revolutionizing how we interact with scientific literature.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google/spiqa", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.06397", "title": "RRM: Relightable assets using Radiance guided Material extraction", "authors": [ "Diego Gomez", "Julien Philip", "Adrien Kaiser", "\u00c9lie Michel" ], "github": "", "project_page": "", "abstract": "Synthesizing NeRFs under arbitrary lighting has become a seminal problem in the last few years. Recent efforts tackle the problem via the extraction of physically-based parameters that can then be rendered under arbitrary lighting, but they are limited in the range of scenes they can handle, usually mishandling glossy scenes. We propose RRM, a method that can extract the materials, geometry, and environment lighting of a scene even in the presence of highly reflective objects. Our method consists of a physically-aware radiance field representation that informs physically-based parameters, and an expressive environment light structure based on a Laplacian Pyramid. We demonstrate that our contributions outperform the state-of-the-art on parameter retrieval tasks, leading to high-fidelity relighting and novel view synthesis on surfacic scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.07874", "title": "Toto: Time Series Optimized Transformer for Observability", "authors": [ "Ben Cohen", "Emaad Khwaja", "Kan Wang", "Charles Masson", "Elise Ram\u00e9", "Youssef Doubli", "Othmane Abou-Amal" ], "github": "", "project_page": "", "abstract": "This technical report describes the Time Series Optimized Transformer for Observability (Toto), a new state of the art foundation model for time series forecasting developed by Datadog. In addition to advancing the state of the art on generalized time series benchmarks in domains such as electricity and weather, this model is the first general-purpose time series forecasting foundation model to be specifically tuned for observability metrics. Toto was trained on a dataset of one trillion time series data points, the largest among all currently published time series foundation models. Alongside publicly available time series datasets, 75% of the data used to train Toto consists of fully anonymous numerical metric data points from the Datadog platform. In our experiments, Toto outperforms existing time series foundation models on observability data. It does this while also excelling at general-purpose forecasting tasks, achieving state-of-the-art zero-shot performance on multiple open benchmark datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-15", "arxiv_id": "2407.09732", "title": "Speech Slytherin: Examining the Performance and Efficiency of Mamba for Speech Separation, Recognition, and Synthesis", "authors": [ "Xilin Jiang", "Yinghao Aaron Li", "Adrian Nicolas Florea", "Cong Han", "Nima Mesgarani" ], "github": "https://github.com/xi-j/mamba-asr", "project_page": "", "abstract": "It is too early to conclude that Mamba is a better alternative to transformers for speech before comparing Mamba with transformers in terms of both performance and efficiency in multiple speech-related tasks. To reach this conclusion, we propose and evaluate three models for three tasks: Mamba-TasNet for speech separation, ConMamba for speech recognition, and VALL-M for speech synthesis. We compare them with transformers of similar sizes in performance, memory, and speed. Our Mamba or Mamba-transformer hybrid models show comparable or higher performance than their transformer counterparts: Sepformer, Conformer, and VALL-E. They are more efficient than transformers in memory and speed for speech longer than a threshold duration, inversely related to the resolution of a speech token. Mamba for separation is the most efficient, and Mamba for recognition is the least. Further, we show that Mamba is not more efficient than transformer for speech shorter than the threshold duration and performs worse in models that require joint modeling of text and speech, such as cross or masked attention of two inputs. Therefore, we argue that the superiority of Mamba or transformer depends on particular problems and models. Code available at https://github.com/xi-j/Mamba-TasNet and https://github.com/xi-j/Mamba-ASR.", "github_manual": "https://github.com/xi-j/mamba-asr", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10457", "title": "The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism", "authors": [ "Yifan Song", "Guoyin Wang", "Sujian Li", "Bill Yuchen Lin" ], "github": "https://github.com/yifan-song793/goodbadgreedy", "project_page": "", "abstract": "Current evaluations of large language models (LLMs) often overlook non-determinism, typically focusing on a single output per example. This limits our understanding of LLM performance variability in real-world applications. Our study addresses this issue by exploring key questions about the performance differences between greedy decoding and sampling, identifying benchmarks' consistency regarding non-determinism, and examining unique model behaviors. Through extensive experiments, we observe that greedy decoding generally outperforms sampling methods for most evaluated tasks. We also observe consistent performance across different LLM sizes and alignment methods, noting that alignment can reduce sampling variance. Moreover, our best-of-N sampling approach demonstrates that smaller LLMs can match or surpass larger models such as GPT-4-Turbo, highlighting the untapped potential of smaller LLMs. This research shows the importance of considering non-determinism in LLM evaluations and provides insights for future LLM development and evaluation.", "github_manual": "https://github.com/yifan-song793/goodbadgreedy", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10671", "title": "Qwen2 Technical Report", "authors": [ "An Yang", "Baosong Yang", "Binyuan Hui", "Bo Zheng", "Bowen Yu", "Chang Zhou", "Chengpeng Li", "Chengyuan Li", "Dayiheng Liu", "Fei Huang", "Guanting Dong", "Haoran Wei", "Huan Lin", "Jialong Tang", "Jialin Wang", "Jian Yang", "Jianhong Tu", "Jianwei Zhang", "Jianxin Ma", "Jin Xu", "Jingren Zhou", "Jinze Bai", "Jinzheng He", "Junyang Lin", "Kai Dang", "Keming Lu", "Keqin Chen", "Kexin Yang", "Mei Li", "Mingfeng Xue", "Na Ni", "Pei Zhang", "Peng Wang", "Ru Peng", "Rui Men", "Ruize Gao", "Runji Lin", "Shijie Wang", "Shuai Bai", "Sinan Tan", "Tianhang Zhu", "Tianhao Li", "Tianyu Liu", "Wenbin Ge", "Xiaodong Deng", "Xiaohuan Zhou", "Xingzhang Ren", "Xinyu Zhang", "Xipin Wei", "Xuancheng Ren", "Yang Fan", "Yang Yao", "Yichang Zhang", "Yu Wan", "Yunfei Chu", "Zeyu Cui", "Zhenru Zhang", "Zhihao Fan" ], "github": "https://github.com/qwenlm/qwen2", "project_page": "", "abstract": "This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models. We release a comprehensive suite of foundational and instruction-tuned language models, encompassing a parameter range from 0.5 to 72 billion, featuring dense models and a Mixture-of-Experts model. Qwen2 surpasses most prior open-weight models, including its predecessor Qwen1.5, and exhibits competitive performance relative to proprietary models across diverse benchmarks on language understanding, generation, multilingual proficiency, coding, mathematics, and reasoning. The flagship model, Qwen2-72B, showcases remarkable performance: 84.2 on MMLU, 37.9 on GPQA, 64.6 on HumanEval, 89.5 on GSM8K, and 82.4 on BBH as a base language model. The instruction-tuned variant, Qwen2-72B-Instruct, attains 9.1 on MT-Bench, 48.1 on Arena-Hard, and 35.7 on LiveCodeBench. Moreover, Qwen2 demonstrates robust multilingual capabilities, proficient in approximately 30 languages, spanning English, Chinese, Spanish, French, German, Arabic, Russian, Korean, Japanese, Thai, Vietnamese, and more, underscoring its versatility and global reach. To foster community innovation and accessibility, we have made the Qwen2 model weights openly available on Hugging Face1 and ModelScope2, and the supplementary materials including example code on GitHub3. These platforms also include resources for quantization, fine-tuning, and deployment, facilitating a wide range of applications and research endeavors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qwenlm/qwen2", "github_api_added_by": "auto" }, { "date": "2024-07-16", "arxiv_id": "2407.09533", "title": "Video Occupancy Models", "authors": [ "Manan Tomar", "Philippe Hansen-Estruch", "Philip Bachman", "Alex Lamb", "John Langford", "Matthew E. Taylor", "Sergey Levine" ], "github": "https://github.com/manantomar/video-occupancy-models", "project_page": "", "abstract": "We introduce a new family of video prediction models designed to support downstream control tasks. We call these models Video Occupancy models (VOCs). VOCs operate in a compact latent space, thus avoiding the need to make predictions about individual pixels. Unlike prior latent-space world models, VOCs directly predict the discounted distribution of future states in a single step, thus avoiding the need for multistep roll-outs. We show that both properties are beneficial when building predictive models of video for use in downstream control. Code is available at https://github.com/manantomar/video-occupancy-models{github.com/manantomar/video-occupancy-models}.", "github_manual": "", "github_regex": "https://github.com/manantomar/video-occupancy-models", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10973", "title": "Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion", "authors": [ "Yongyuan Liang", "Tingqiang Xu", "Kaizhe Hu", "Guangqi Jiang", "Furong Huang", "Huazhe Xu" ], "github": "", "project_page": "", "abstract": "Can we generate a control policy for an agent using just one demonstration of desired behaviors as a prompt, as effortlessly as creating an image from a textual description? In this paper, we present Make-An-Agent, a novel policy parameter generator that leverages the power of conditional diffusion models for behavior-to-policy generation. Guided by behavior embeddings that encode trajectory information, our policy generator synthesizes latent parameter representations, which can then be decoded into policy networks. Trained on policy network checkpoints and their corresponding trajectories, our generation model demonstrates remarkable versatility and scalability on multiple tasks and has a strong generalization ability on unseen tasks to output well-performed policies with only few-shot demonstrations as inputs. We showcase its efficacy and efficiency on various domains and tasks, including varying objectives, behaviors, and even across different robot manipulators. Beyond simulation, we directly deploy policies generated by Make-An-Agent onto real-world robots on locomotion tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10285", "title": "Noise Calibration: Plug-and-play Content-Preserving Video Enhancement using Pre-trained Video Diffusion Models", "authors": [ "Qinyu Yang", "Haoxin Chen", "Yong Zhang", "Menghan Xia", "Xiaodong Cun", "Zhixun Su", "Ying Shan" ], "github": "https://github.com/yangqy1110/nc-sdedit", "project_page": "", "abstract": "In order to improve the quality of synthesized videos, currently, one predominant method involves retraining an expert diffusion model and then implementing a noising-denoising process for refinement. Despite the significant training costs, maintaining consistency of content between the original and enhanced videos remains a major challenge. To tackle this challenge, we propose a novel formulation that considers both visual quality and consistency of content. Consistency of content is ensured by a proposed loss function that maintains the structure of the input, while visual quality is improved by utilizing the denoising process of pretrained diffusion models. To address the formulated optimization problem, we have developed a plug-and-play noise optimization strategy, referred to as Noise Calibration. By refining the initial random noise through a few iterations, the content of original video can be largely preserved, and the enhancement effect demonstrates a notable improvement. Extensive experiments have demonstrated the effectiveness of the proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yangqy1110/nc-sdedit", "github_api_added_by": "auto" }, { "date": "2024-07-16", "arxiv_id": "2407.07523", "title": "SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning", "authors": [ "Haiwen Diao", "Bo Wan", "Xu Jia", "Yunzhi Zhuge", "Ying Zhang", "Huchuan Lu", "Long Chen" ], "github": "https://github.com/Paranioar/SHERL", "project_page": "", "abstract": "Parameter-efficient transfer learning (PETL) has emerged as a flourishing research field for adapting large pre-trained models to downstream tasks, greatly reducing trainable parameters while grappling with memory challenges during fine-tuning. To address it, memory-efficient series (METL) avoid backpropagating gradients through the large backbone. However, they compromise by exclusively relying on frozen intermediate outputs and limiting the exhaustive exploration of prior knowledge from pre-trained models. Moreover, the dependency and redundancy between cross-layer features are frequently overlooked, thereby submerging more discriminative representations and causing an inherent performance gap (vs. conventional PETL methods). Hence, we propose an innovative METL strategy called SHERL for resource-limited scenarios to decouple the entire adaptation into two successive and complementary processes. In the early route, intermediate outputs are consolidated via an anti-redundancy operation, enhancing their compatibility for subsequent interactions; thereby in the late route, utilizing minimal late pre-trained layers could alleviate the peak demand on memory overhead and regulate these fairly flexible features into more adaptive and powerful representations for new domains. Extensive ablations on vision-and-language and language-only tasks show that SHERL combines the strengths of both parameter and memory-efficient techniques, performing on-par or better across diverse architectures with lower memory during fine-tuning. Our code is publicly available at: https://github.com/Paranioar/SHERL.", "github_manual": "", "github_regex": "https://github.com/Paranioar/SHERL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10387", "title": "Masked Generative Video-to-Audio Transformers with Enhanced Synchronicity", "authors": [ "Santiago Pascual", "Chunghsin Yeh", "Ioannis Tsiamas", "Joan Serr\u00e0" ], "github": "", "project_page": "", "abstract": "Video-to-audio (V2A) generation leverages visual-only video features to render plausible sounds that match the scene. Importantly, the generated sound onsets should match the visual actions that are aligned with them, otherwise unnatural synchronization artifacts arise. Recent works have explored the progression of conditioning sound generators on still images and then video features, focusing on quality and semantic matching while ignoring synchronization, or by sacrificing some amount of quality to focus on improving synchronization only. In this work, we propose a V2A generative model, named MaskVAT, that interconnects a full-band high-quality general audio codec with a sequence-to-sequence masked generative model. This combination allows modeling both high audio quality, semantic matching, and temporal synchronicity at the same time. Our results show that, by combining a high-quality codec with the proper pre-trained audio-visual features and a sequence-to-sequence parallel structure, we are able to yield highly synchronized results on one hand, whilst being competitive with the state of the art of non-codec generative audio models. Sample videos and generated audios are available at https://maskvat.github.io .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10362", "title": "LAB-Bench: Measuring Capabilities of Language Models for Biology Research", "authors": [ "Jon M. Laurent", "Joseph D. Janizek", "Michael Ruzo", "Michaela M. Hinks", "Michael J. Hammerling", "Siddharth Narayanan", "Manvitha Ponnapati", "Andrew D. White", "Samuel G. Rodriques" ], "github": "", "project_page": "", "abstract": "There is widespread optimism that frontier Large Language Models (LLMs) and LLM-augmented systems have the potential to rapidly accelerate scientific discovery across disciplines. Today, many benchmarks exist to measure LLM knowledge and reasoning on textbook-style science questions, but few if any benchmarks are designed to evaluate language model performance on practical tasks required for scientific research, such as literature search, protocol planning, and data analysis. As a step toward building such benchmarks, we introduce the Language Agent Biology Benchmark (LAB-Bench), a broad dataset of over 2,400 multiple choice questions for evaluating AI systems on a range of practical biology research capabilities, including recall and reasoning over literature, interpretation of figures, access and navigation of databases, and comprehension and manipulation of DNA and protein sequences. Importantly, in contrast to previous scientific benchmarks, we expect that an AI system that can achieve consistently high scores on the more difficult LAB-Bench tasks would serve as a useful assistant for researchers in areas such as literature search and molecular cloning. As an initial assessment of the emergent scientific task capabilities of frontier language models, we measure performance of several against our benchmark and report results compared to human expert biology researchers. We will continue to update and expand LAB-Bench over time, and expect it to serve as a useful tool in the development of automated research systems going forward. A public subset of LAB-Bench is available for use at the following URL: https://huggingface.co/datasets/futurehouse/lab-bench", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10943", "title": "GRUtopia: Dream General Robots in a City at Scale", "authors": [ "Hanqing Wang", "Jiahe Chen", "Wensi Huang", "Qingwei Ben", "Tai Wang", "Boyu Mi", "Tao Huang", "Siheng Zhao", "Yilun Chen", "Sizhe Yang", "Peizhou Cao", "Wenye Yu", "Zichao Ye", "Jialun Li", "Junfeng Long", "Zirui Wang", "Huiling Wang", "Ying Zhao", "Zhongying Tu", "Yu Qiao", "Dahua Lin", "Jiangmiao Pang" ], "github": "https://github.com/OpenRobotLab/GRUtopia", "project_page": "", "abstract": "Recent works have been exploring the scaling laws in the field of Embodied AI. Given the prohibitive costs of collecting real-world data, we believe the Simulation-to-Real (Sim2Real) paradigm is a crucial step for scaling the learning of embodied models. This paper introduces project GRUtopia, the first simulated interactive 3D society designed for various robots. It features several advancements: (a) The scene dataset, GRScenes, includes 100k interactive, finely annotated scenes, which can be freely combined into city-scale environments. In contrast to previous works mainly focusing on home, GRScenes covers 89 diverse scene categories, bridging the gap of service-oriented environments where general robots would be initially deployed. (b) GRResidents, a Large Language Model (LLM) driven Non-Player Character (NPC) system that is responsible for social interaction, task generation, and task assignment, thus simulating social scenarios for embodied AI applications. (c) The benchmark, GRBench, supports various robots but focuses on legged robots as primary agents and poses moderately challenging tasks involving Object Loco-Navigation, Social Loco-Navigation, and Loco-Manipulation. We hope that this work can alleviate the scarcity of high-quality data in this field and provide a more comprehensive assessment of Embodied AI research. The project is available at https://github.com/OpenRobotLab/GRUtopia.", "github_manual": "", "github_regex": "https://github.com/OpenRobotLab/GRUtopia", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10058", "title": "Learning to Refuse: Towards Mitigating Privacy Risks in LLMs", "authors": [ "Zhenhua Liu", "Tong Zhu", "Chuanyuan Tan", "Wenliang Chen" ], "github": "https://github.com/zhliu0106/learning-to-refuse", "project_page": "", "abstract": "Large language models (LLMs) exhibit remarkable capabilities in understanding and generating natural language. However, these models can inadvertently memorize private information, posing significant privacy risks. This study addresses the challenge of enabling LLMs to protect specific individuals' private data without the need for complete retraining. We propose \\return, a Real-world pErsonal daTa UnleaRNing dataset, comprising 2,492 individuals from Wikipedia with associated QA pairs, to evaluate machine unlearning (MU) methods for protecting personal data in a realistic scenario. Additionally, we introduce the Name-Aware Unlearning Framework (NAUF) for Privacy Protection, which enables the model to learn which individuals' information should be protected without affecting its ability to answer questions related to other unrelated individuals. Our extensive experiments demonstrate that NAUF achieves a state-of-the-art average unlearning score, surpassing the best baseline method by 5.65 points, effectively protecting target individuals' personal data while maintaining the model's general capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhliu0106/learning-to-refuse", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10969", "title": "Q-Sparse: All Large Language Models can be Fully Sparsely-Activated", "authors": [ "Hongyu Wang", "Shuming Ma", "Ruiping Wang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "We introduce, Q-Sparse, a simple yet effective approach to training sparsely-activated large language models (LLMs). Q-Sparse enables full sparsity of activations in LLMs which can bring significant efficiency gains in inference. This is achieved by applying top-K sparsification to the activations and the straight-through-estimator to the training. The key results from this work are, (1) Q-Sparse can achieve results comparable to those of baseline LLMs while being much more efficient at inference time; (2) We present an inference-optimal scaling law for sparsely-activated LLMs; (3) Q-Sparse is effective in different settings, including training-from-scratch, continue-training of off-the-shelf LLMs, and finetuning; (4) Q-Sparse works for both full-precision and 1-bit LLMs (e.g., BitNet b1.58). Particularly, the synergy of BitNet b1.58 and Q-Sparse (can be equipped with MoE) provides the cornerstone and a clear path to revolutionize the efficiency, including cost and energy consumption, of future LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10910", "title": "DataDream: Few-shot Guided Dataset Generation", "authors": [ "Jae Myung Kim", "Jessica Bader", "Stephan Alaniz", "Cordelia Schmid", "Zeynep Akata" ], "github": "https://github.com/ExplainableML/DataDream", "project_page": "", "abstract": "While text-to-image diffusion models have been shown to achieve state-of-the-art results in image synthesis, they have yet to prove their effectiveness in downstream applications. Previous work has proposed to generate data for image classifier training given limited real data access. However, these methods struggle to generate in-distribution images or depict fine-grained features, thereby hindering the generalization of classification models trained on synthetic datasets. We propose DataDream, a framework for synthesizing classification datasets that more faithfully represents the real data distribution when guided by few-shot examples of the target classes. DataDream fine-tunes LoRA weights for the image generation model on the few real images before generating the training data using the adapted model. We then fine-tune LoRA weights for CLIP using the synthetic data to improve downstream image classification over previous approaches on a large variety of datasets. We demonstrate the efficacy of DataDream through extensive experiments, surpassing state-of-the-art classification accuracy with few-shot data across 7 out of 10 datasets, while being competitive on the other 3. Additionally, we provide insights into the impact of various factors, such as the number of real-shot and generated images as well as the fine-tuning compute on model performance. The code is available at https://github.com/ExplainableML/DataDream.", "github_manual": "", "github_regex": "https://github.com/ExplainableML/DataDream", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10953", "title": "MMM: Multilingual Mutual Reinforcement Effect Mix Datasets & Test with Open-domain Information Extraction Large Language Models", "authors": [ "Chengguang Gan", "Qingyu Yin", "Xinyang He", "Hanjun Wei", "Yunhao Liang", "Younghun Lim", "Shijian Wang", "Hexiang Huang", "Qinghao Zhang", "Shiwen Ni", "Tatsunori Mori" ], "github": "", "project_page": "", "abstract": "The Mutual Reinforcement Effect (MRE) represents a promising avenue in information extraction and multitasking research. Nevertheless, its applicability has been constrained due to the exclusive availability of MRE mix datasets in Japanese, thereby limiting comprehensive exploration by the global research community. To address this limitation, we introduce a Multilingual MRE mix dataset (MMM) that encompasses 21 sub-datasets in English, Japanese, and Chinese. In this paper, we also propose a method for dataset translation assisted by Large Language Models (LLMs), which significantly reduces the manual annotation time required for dataset construction by leveraging LLMs to translate the original Japanese datasets. Additionally, we have enriched the dataset by incorporating open-domain Named Entity Recognition (NER) and sentence classification tasks. Utilizing this expanded dataset, we developed a unified input-output framework to train an Open-domain Information Extraction Large Language Model (OIELLM). The OIELLM model demonstrates the capability to effectively process novel MMM datasets, exhibiting significant improvements in performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10827", "title": "LLM Circuit Analyses Are Consistent Across Training and Scale", "authors": [ "Curt Tigges", "Michael Hanna", "Qinan Yu", "Stella Biderman" ], "github": "", "project_page": "", "abstract": "Most currently deployed large language models (LLMs) undergo continuous training or additional finetuning. By contrast, most research into LLMs' internal mechanisms focuses on models at one snapshot in time (the end of pre-training), raising the question of whether their results generalize to real-world settings. Existing studies of mechanisms over time focus on encoder-only or toy models, which differ significantly from most deployed models. In this study, we track how model mechanisms, operationalized as circuits, emerge and evolve across 300 billion tokens of training in decoder-only LLMs, in models ranging from 70 million to 2.8 billion parameters. We find that task abilities and the functional components that support them emerge consistently at similar token counts across scale. Moreover, although such components may be implemented by different attention heads over time, the overarching algorithm that they implement remains. Surprisingly, both these algorithms and the types of components involved therein can replicate across model scale. These results suggest that circuit analyses conducted on small models at the end of pre-training can provide insights that still apply after additional pre-training and over model scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-16", "arxiv_id": "2407.10956", "title": "Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?", "authors": [ "Ruisheng Cao", "Fangyu Lei", "Haoyuan Wu", "Jixuan Chen", "Yeqiao Fu", "Hongcheng Gao", "Xinzhuang Xiong", "Hanchong Zhang", "Yuchen Mao", "Wenjing Hu", "Tianbao Xie", "Hongshen Xu", "Danyang Zhang", "Sida Wang", "Ruoxi Sun", "Pengcheng Yin", "Caiming Xiong", "Ansong Ni", "Qian Liu", "Victor Zhong", "Lu Chen", "Kai Yu", "Tao Yu" ], "github": "https://github.com/xlang-ai/spider2-v", "project_page": "", "abstract": "Data science and engineering workflows often span multiple stages, from warehousing to orchestration, using tools like BigQuery, dbt, and Airbyte. As vision language models (VLMs) advance in multimodal understanding and code generation, VLM-based agents could potentially automate these workflows by generating SQL queries, Python code, and GUI operations. This automation can improve the productivity of experts while democratizing access to large-scale data analysis. In this paper, we introduce Spider2-V, the first multimodal agent benchmark focusing on professional data science and engineering workflows, featuring 494 real-world tasks in authentic computer environments and incorporating 20 enterprise-level professional applications. These tasks, derived from real-world use cases, evaluate the ability of a multimodal agent to perform data-related tasks by writing code and managing the GUI in enterprise data software systems. To balance realistic simulation with evaluation simplicity, we devote significant effort to developing automatic configurations for task setup and carefully crafting evaluation metrics for each task. Furthermore, we supplement multimodal agents with comprehensive documents of these enterprise data software systems. Our empirical evaluation reveals that existing state-of-the-art LLM/VLM-based agents do not reliably automate full data workflows (14.0% success). Even with step-by-step guidance, these agents still underperform in tasks that require fine-grained, knowledge-intensive GUI actions (16.2%) and involve remote cloud-hosted workspaces (10.6%). We hope that Spider2-V paves the way for autonomous multimodal agents to transform the automation of data science and engineering workflow. Our code and data are available at https://spider2-v.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xlang-ai/spider2-v", "github_api_added_by": "auto" }, { "date": "2024-07-16", "arxiv_id": "2407.10817", "title": "Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation", "authors": [ "Tu Vu", "Kalpesh Krishna", "Salaheddin Alzubi", "Chris Tar", "Manaal Faruqui", "Yun-Hsuan Sung" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) advance, it becomes more challenging to reliably evaluate their output due to the high costs of human evaluation. To make progress towards better LLM autoraters, we introduce FLAMe, a family of Foundational Large Autorater Models. FLAMe is trained on our large and diverse collection of 100+ quality assessment tasks comprising 5M+ human judgments, curated and standardized using publicly released human evaluations from previous research. FLAMe significantly improves generalization to a wide variety of held-out tasks, outperforming LLMs trained on proprietary data like GPT-4 and Claude-3 on many tasks. We show that FLAMe can also serve as a powerful starting point for further downstream fine-tuning, using reward modeling evaluation as a case study (FLAMe-RM). Notably, on RewardBench, our FLAMe-RM-24B model (with an accuracy of 87.8%) is the top-performing generative model trained exclusively on permissively licensed data, outperforming both GPT-4-0125 (85.9%) and GPT-4o (84.7%). Additionally, we explore a more computationally efficient approach using a novel tail-patch fine-tuning strategy to optimize our FLAMe multitask mixture for reward modeling evaluation (FLAMe-Opt-RM), offering competitive RewardBench performance while requiring approximately 25x less training datapoints. Overall, our FLAMe variants outperform all popular proprietary LLM-as-a-Judge models we consider across 8 out of 12 autorater evaluation benchmarks, encompassing 53 quality assessment tasks, including RewardBench and LLM-AggreFact. Finally, our analysis reveals that FLAMe is significantly less biased than these LLM-as-a-Judge models on the CoBBLEr autorater bias benchmark, while effectively identifying high-quality responses for code generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11144", "title": "YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus", "authors": [ "Garrett Tanzer", "Biao Zhang" ], "github": "", "project_page": "", "abstract": "Even for better-studied sign languages like American Sign Language (ASL), data is the bottleneck for machine learning research. The situation is worse yet for the many other sign languages used by Deaf/Hard of Hearing communities around the world. In this paper, we present YouTube-SL-25, a large-scale, open-domain multilingual corpus of sign language videos with seemingly well-aligned captions drawn from YouTube. With >3000 hours of videos across >25 sign languages, YouTube-SL-25 is a) >3x the size of YouTube-ASL, b) the largest parallel sign language dataset to date, and c) the first or largest parallel dataset for many of its component languages. We provide baselines for sign-to-text tasks using a unified multilingual multitask model based on T5 and report scores on benchmarks across 4 sign languages. The results demonstrate that multilingual transfer benefits both higher- and lower-resource sign languages within YouTube-SL-25.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.10759", "title": "Qwen2-Audio Technical Report", "authors": [ "Yunfei Chu", "Jin Xu", "Qian Yang", "Haojie Wei", "Xipin Wei", "Zhifang Guo", "Yichong Leng", "Yuanjun Lv", "Jinzheng He", "Junyang Lin", "Chang Zhou", "Jingren Zhou" ], "github": "https://github.com/qwenlm/qwen2-audio", "project_page": "", "abstract": "We introduce the latest progress of Qwen-Audio, a large-scale audio-language model called Qwen2-Audio, which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions. In contrast to complex hierarchical tags, we have simplified the pre-training process by utilizing natural language prompts for different data and tasks, and have further expanded the data volume. We have boosted the instruction-following capability of Qwen2-Audio and implemented two distinct audio interaction modes for voice chat and audio analysis. In the voice chat mode, users can freely engage in voice interactions with Qwen2-Audio without text input. In the audio analysis mode, users could provide audio and text instructions for analysis during the interaction. Note that we do not use any system prompts to switch between voice chat and audio analysis modes. Qwen2-Audio is capable of intelligently comprehending the content within audio and following voice commands to respond appropriately. For instance, in an audio segment that simultaneously contains sounds, multi-speaker conversations, and a voice command, Qwen2-Audio can directly understand the command and provide an interpretation and response to the audio. Additionally, DPO has optimized the model's performance in terms of factuality and adherence to desired behavior. According to the evaluation results from AIR-Bench, Qwen2-Audio outperformed previous SOTAs, such as Gemini-1.5-pro, in tests focused on audio-centric instruction-following capabilities. Qwen2-Audio is open-sourced with the aim of fostering the advancement of the multi-modal language community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qwenlm/qwen2-audio", "github_api_added_by": "auto" }, { "date": "2024-07-17", "arxiv_id": "2407.11784", "title": "Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model Co-development", "authors": [ "Daoyuan Chen", "Haibin Wang", "Yilun Huang", "Ce Ge", "Yaliang Li", "Bolin Ding", "Jingren Zhou" ], "github": "https://github.com/modelscope/data-juicer/blob/main/docs", "project_page": "", "abstract": "The emergence of large-scale multi-modal generative models has drastically advanced artificial intelligence, introducing unprecedented levels of performance and functionality. However, optimizing these models remains challenging due to historically isolated paths of model-centric and data-centric developments, leading to suboptimal outcomes and inefficient resource utilization. In response, we present a novel sandbox suite tailored for integrated data-model co-development. This sandbox provides a comprehensive experimental platform, enabling rapid iteration and insight-driven refinement of both data and models. Our proposed \"Probe-Analyze-Refine\" workflow, validated through applications on state-of-the-art LLaVA-like and DiT based models, yields significant performance boosts, such as topping the VBench leaderboard. We also uncover fruitful insights gleaned from exhaustive benchmarks, shedding light on the critical interplay between data quality, diversity, and model behavior. With the hope of fostering deeper understanding and future progress in multi-modal data and generative modeling, our codes, datasets, and models are maintained and accessible at https://github.com/modelscope/data-juicer/blob/main/docs/Sandbox.md.", "github_manual": "", "github_regex": "https://github.com/modelscope/data-juicer/blob/main/docs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11966", "title": "Efficient Training with Denoised Neural Weights", "authors": [ "Yifan Gong", "Zheng Zhan", "Yanyu Li", "Yerlan Idelbayev", "Andrey Zharkov", "Kfir Aberman", "Sergey Tulyakov", "Yanzhi Wang", "Jian Ren" ], "github": "", "project_page": "", "abstract": "Good weight initialization serves as an effective measure to reduce the training cost of a deep neural network (DNN) model. The choice of how to initialize parameters is challenging and may require manual tuning, which can be time-consuming and prone to human error. To overcome such limitations, this work takes a novel step towards building a weight generator to synthesize the neural weights for initialization. We use the image-to-image translation task with generative adversarial networks (GANs) as an example due to the ease of collecting model weights spanning a wide range. Specifically, we first collect a dataset with various image editing concepts and their corresponding trained weights, which are later used for the training of the weight generator. To address the different characteristics among layers and the substantial number of weights to be predicted, we divide the weights into equal-sized blocks and assign each block an index. Subsequently, a diffusion model is trained with such a dataset using both text conditions of the concept and the block indexes. By initializing the image translation model with the denoised weights predicted by our diffusion model, the training requires only 43.3 seconds. Compared to training from scratch (i.e., Pix2pix), we achieve a 15x training time acceleration for a new concept while obtaining even better image generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11963", "title": "NeedleBench: Can LLMs Do Retrieval and Reasoning in 1 Million Context Window?", "authors": [ "Mo Li", "Songyang Zhang", "Yunxin Liu", "Kai Chen" ], "github": "https://github.com/open-compass/opencompass", "project_page": "", "abstract": "In evaluating the long-context capabilities of large language models (LLMs), identifying content relevant to a user's query from original long documents is a crucial prerequisite for any LLM to answer questions based on long text. We present NeedleBench, a framework consisting of a series of progressively more challenging tasks for assessing bilingual long-context capabilities, spanning multiple length intervals (4k, 8k, 32k, 128k, 200k, 1000k, and beyond) and different depth ranges, allowing the strategic insertion of critical data points in different text depth zones to rigorously test the retrieval and reasoning capabilities of models in diverse contexts. We use the NeedleBench framework to assess how well the leading open-source models can identify key information relevant to the question and apply that information to reasoning in bilingual long texts. Furthermore, we propose the Ancestral Trace Challenge (ATC) to mimic the complexity of logical reasoning challenges that are likely to be present in real-world long-context tasks, providing a simple method for evaluating LLMs in dealing with complex long-context situations. Our results suggest that current LLMs have significant room for improvement in practical long-context applications, as they struggle with the complexity of logical reasoning challenges that are likely to be present in real-world long-context tasks. All codes and resources are available at OpenCompass: https://github.com/open-compass/opencompass.", "github_manual": "", "github_regex": "https://github.com/open-compass/opencompass", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11793", "title": "Click-Gaussian: Interactive Segmentation to Any 3D Gaussians", "authors": [ "Seokhun Choi", "Hyeonseop Song", "Jaechul Kim", "Taehyeong Kim", "Hoseok Do" ], "github": "", "project_page": "", "abstract": "Interactive segmentation of 3D Gaussians opens a great opportunity for real-time manipulation of 3D scenes thanks to the real-time rendering capability of 3D Gaussian Splatting. However, the current methods suffer from time-consuming post-processing to deal with noisy segmentation output. Also, they struggle to provide detailed segmentation, which is important for fine-grained manipulation of 3D scenes. In this study, we propose Click-Gaussian, which learns distinguishable feature fields of two-level granularity, facilitating segmentation without time-consuming post-processing. We delve into challenges stemming from inconsistently learned feature fields resulting from 2D segmentation obtained independently from a 3D scene. 3D segmentation accuracy deteriorates when 2D segmentation results across the views, primary cues for 3D segmentation, are in conflict. To overcome these issues, we propose Global Feature-guided Learning (GFL). GFL constructs the clusters of global feature candidates from noisy 2D segments across the views, which smooths out noises when training the features of 3D Gaussians. Our method runs in 10 ms per click, 15 to 130 times as fast as the previous methods, while also significantly improving segmentation accuracy. Our project page is available at https://seokhunchoi.github.io/Click-Gaussian", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11633", "title": "Scaling Diffusion Transformers to 16 Billion Parameters", "authors": [ "Zhengcong Fei", "Mingyuan Fan", "Changqian Yu", "Debang Li", "Junshi Huang" ], "github": "https://github.com/feizc/DiT-MoE", "project_page": "", "abstract": "In this paper, we present DiT-MoE, a sparse version of the diffusion Transformer, that is scalable and competitive with dense networks while exhibiting highly optimized inference. The DiT-MoE includes two simple designs: shared expert routing and expert-level balance loss, thereby capturing common knowledge and reducing redundancy among the different routed experts. When applied to conditional image generation, a deep analysis of experts specialization gains some interesting observations: (i) Expert selection shows preference with spatial position and denoising time step, while insensitive with different class-conditional information; (ii) As the MoE layers go deeper, the selection of experts gradually shifts from specific spacial position to dispersion and balance. (iii) Expert specialization tends to be more concentrated at the early time step and then gradually uniform after half. We attribute it to the diffusion process that first models the low-frequency spatial information and then high-frequency complex information. Based on the above guidance, a series of DiT-MoE experimentally achieves performance on par with dense networks yet requires much less computational load during inference. More encouragingly, we demonstrate the potential of DiT-MoE with synthesized image data, scaling diffusion model at a 16.5B parameter that attains a new SoTA FID-50K score of 1.80 in 512times512 resolution settings. The project page: https://github.com/feizc/DiT-MoE.", "github_manual": "", "github_regex": "https://github.com/feizc/DiT-MoE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11398", "title": "Animate3D: Animating Any 3D Model with Multi-view Video Diffusion", "authors": [ "Yanqin Jiang", "Chaohui Yu", "Chenjie Cao", "Fan Wang", "Weiming Hu", "Jin Gao" ], "github": "", "project_page": "", "abstract": "Recent advances in 4D generation mainly focus on generating 4D content by distilling pre-trained text or single-view image-conditioned models. It is inconvenient for them to take advantage of various off-the-shelf 3D assets with multi-view attributes, and their results suffer from spatiotemporal inconsistency owing to the inherent ambiguity in the supervision signals. In this work, we present Animate3D, a novel framework for animating any static 3D model. The core idea is two-fold: 1) We propose a novel multi-view video diffusion model (MV-VDM) conditioned on multi-view renderings of the static 3D object, which is trained on our presented large-scale multi-view video dataset (MV-Video). 2) Based on MV-VDM, we introduce a framework combining reconstruction and 4D Score Distillation Sampling (4D-SDS) to leverage the multi-view video diffusion priors for animating 3D objects. Specifically, for MV-VDM, we design a new spatiotemporal attention module to enhance spatial and temporal consistency by integrating 3D and video diffusion models. Additionally, we leverage the static 3D model's multi-view renderings as conditions to preserve its identity. For animating 3D models, an effective two-stage pipeline is proposed: we first reconstruct motions directly from generated multi-view videos, followed by the introduced 4D-SDS to refine both appearance and motion. Qualitative and quantitative experiments demonstrate that Animate3D significantly outperforms previous approaches. Data, code, and models will be open-released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11394", "title": "DreamCatalyst: Fast and High-Quality 3D Editing via Controlling Editability and Identity Preservation", "authors": [ "Jiwook Kim", "Seonho Lee", "Jaeyo Shin", "Jiho Choi", "Hyunjung Shim" ], "github": "https://github.com/kaist-cvml-lab/DreamCatalyst", "project_page": "", "abstract": "Score distillation sampling (SDS) has emerged as an effective framework in text-driven 3D editing tasks due to its inherent 3D consistency. However, existing SDS-based 3D editing methods suffer from extensive training time and lead to low-quality results, primarily because these methods deviate from the sampling dynamics of diffusion models. In this paper, we propose DreamCatalyst, a novel framework that interprets SDS-based editing as a diffusion reverse process. Our objective function considers the sampling dynamics, thereby making the optimization process of DreamCatalyst an approximation of the diffusion reverse process in editing tasks. DreamCatalyst aims to reduce training time and improve editing quality. DreamCatalyst presents two modes: (1) a faster mode, which edits the NeRF scene in only about 25 minutes, and (2) a high-quality mode, which produces superior results in less than 70 minutes. Specifically, our high-quality mode outperforms current state-of-the-art NeRF editing methods both in terms of speed and quality. See more extensive results on our project page: https://dream-catalyst.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kaist-cvml-lab/DreamCatalyst", "github_api_added_by": "auto" }, { "date": "2024-07-17", "arxiv_id": "2407.11385", "title": "Grasping Diverse Objects with Simulated Humanoids", "authors": [ "Zhengyi Luo", "Jinkun Cao", "Sammy Christen", "Alexander Winkler", "Kris Kitani", "Weipeng Xu" ], "github": "", "project_page": "", "abstract": "We present a method for controlling a simulated humanoid to grasp an object and move it to follow an object trajectory. Due to the challenges in controlling a humanoid with dexterous hands, prior methods often use a disembodied hand and only consider vertical lifts or short trajectories. This limited scope hampers their applicability for object manipulation required for animation and simulation. To close this gap, we learn a controller that can pick up a large number (>1200) of objects and carry them to follow randomly generated trajectories. Our key insight is to leverage a humanoid motion representation that provides human-like motor skills and significantly speeds up training. Using only simplistic reward, state, and object representations, our method shows favorable scalability on diverse object and trajectories. For training, we do not need dataset of paired full-body motion and object trajectories. At test time, we only require the object mesh and desired trajectories for grasping and transporting. To demonstrate the capabilities of our method, we show state-of-the-art success rates in following object trajectories and generalizing to unseen objects. Code and models will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11239", "title": "From GaLore to WeLore: How Low-Rank Weights Non-uniformly Emerge from Low-Rank Gradients", "authors": [ "Ajay Jaiswal", "Lu Yin", "Zhenyu Zhang", "Shiwei Liu", "Jiawei Zhao", "Yuandong Tian", "Zhangyang Wang" ], "github": "https://github.com/VITA-Group/welore", "project_page": "", "abstract": "Modern Large Language Models (LLMs) are composed of matrices with billions of elements, making their storage and processing quite demanding in terms of computational resources and memory usage. Being significantly large, such matrices can often be expressed in low-rank format with potential to relax resource requirements. Unlike prior works which focus on developing novel matrix decomposition algorithms, in this work we first study the emergence of low-rank structures across matrices within different layers of LLMs and establish a consequential relationship between the gradient dynamics and emerging low-rank expressiveness of matrices. Our findings reveal that different layers exhibit varying levels of converged low-rank structure, necessitating a non-uniform rank reduction across them to minimize performance drop due to compression. In view of that, we present Weight Low-Rank Projection (WeLore) that unifies weight compression and memory-efficient fine-tuning as ONE, in a data-agnostic and one-shot way. WeLore capitalizes the heavy-tail distribution of singular values to identify a suitable rank reduction ratio for matrices within LLMs. Going beyond only as a compression technique, WeLore categorizes weight matrices into Low-rank Components (LRCs) and Non-Low-rank Components (N-LRCs) based on their ability to express themselves as low-rank. Our gradient perspective and extensive experiments illustrate that LRCs tend to have better finetuning capabilities and can closely mimic (sometimes outperform) the training loss trajectory and performance of full-finetuning with notable memory and compute footprint reduction. For example, finetuning a 50\\% compressed LLaMa-2 7B model using only a fraction of parameters in LRCs (WeLore) can outperform its full finetuning with ~3x better throughput and ~0.6x GPU requirement. Our codes are available at https://github.com/VITA-Group/welore", "github_manual": "", "github_regex": "https://github.com/VITA-Group/welore", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.10718", "title": "Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoning", "authors": [ "Yulong Wang", "Tianhao Shen", "Lifeng Liu", "Jian Xie" ], "github": "https://github.com/ag2s1/sibyl-system", "project_page": "", "abstract": "Existing agents based on large language models (LLMs) demonstrate robust problem-solving capabilities by integrating LLMs' inherent knowledge, strong in-context learning and zero-shot capabilities, and the use of tools combined with intricately designed LLM invocation workflows by humans. However, these agents still exhibit shortcomings in long-term reasoning and under-use the potential of existing tools, leading to noticeable deficiencies in complex real-world reasoning scenarios. To address these limitations, we introduce Sibyl, a simple yet powerful LLM-based agent framework designed to tackle complex reasoning tasks by efficiently leveraging a minimal set of tools. Drawing inspiration from Global Workspace Theory, Sibyl incorporates a global workspace to enhance the management and sharing of knowledge and conversation history throughout the system. Furthermore, guided by Society of Mind Theory, Sibyl implements a multi-agent debate-based jury to self-refine the final answers, ensuring a comprehensive and balanced approach. This approach aims to reduce system complexity while expanding the scope of problems solvable-from matters typically resolved by humans in minutes to those requiring hours or even days, thus facilitating a shift from System-1 to System-2 thinking. Sibyl has been designed with a focus on scalability and ease of debugging by incorporating the concept of reentrancy from functional programming from its inception, with the aim of seamless and low effort integration in other LLM applications to improve capabilities. Our experimental results on the GAIA benchmark test set reveal that the Sibyl agent instantiated with GPT-4 achieves state-of-the-art performance with an average score of 34.55%, compared to other agents based on GPT-4. We hope that Sibyl can inspire more reliable and reusable LLM-based agent solutions to address complex real-world reasoning tasks.", "github_manual": "https://github.com/ag2s1/sibyl-system", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11895", "title": "OmniBind: Large-scale Omni Multimodal Representation via Binding Spaces", "authors": [ "Zehan Wang", "Ziang Zhang", "Hang Zhang", "Luping Liu", "Rongjie Huang", "Xize Cheng", "Hengshuang Zhao", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "Recently, human-computer interaction with various modalities has shown promising applications, like GPT-4o and Gemini. Given the foundational role of multimodal joint representation in understanding and generation pipelines, high-quality omni joint representations would be a step toward co-processing more diverse multimodal information. In this work, we present OmniBind, large-scale multimodal joint representation models ranging in scale from 7 billion to 30 billion parameters, which support 3D, audio, image, and language inputs. Due to the scarcity of data pairs across all modalities, instead of training large models from scratch, we propose remapping and binding the spaces of various pre-trained specialist models together. This approach enables \"scaling up\" by indirectly increasing the model parameters and the amount of seen data. To effectively integrate various spaces, we dynamically assign weights to different spaces by learning routers with two objectives: cross-modal overall alignment and language representation decoupling. Notably, since binding and routing spaces both only require lightweight networks, OmniBind is extremely training-efficient. Learning the largest 30B model requires merely unpaired unimodal data and approximately 3 days on a single 8-4090 node. Extensive experiments demonstrate the versatility and superiority of OmniBind as an omni representation model, highlighting its great potential for diverse applications, such as any-query and composable multimodal understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11691", "title": "VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models", "authors": [ "Haodong Duan", "Junming Yang", "Yuxuan Qiao", "Xinyu Fang", "Lin Chen", "Yuan Liu", "Xiaoyi Dong", "Yuhang Zang", "Pan Zhang", "Jiaqi Wang", "Dahua Lin", "Kai Chen" ], "github": "https://github.com/open-compass/VLMEvalKit", "project_page": "", "abstract": "We present VLMEvalKit: an open-source toolkit for evaluating large multi-modality models based on PyTorch. The toolkit aims to provide a user-friendly and comprehensive framework for researchers and developers to evaluate existing multi-modality models and publish reproducible evaluation results. In VLMEvalKit, we implement over 70 different large multi-modality models, including both proprietary APIs and open-source models, as well as more than 20 different multi-modal benchmarks. By implementing a single interface, new models can be easily added to the toolkit, while the toolkit automatically handles the remaining workloads, including data preparation, distributed inference, prediction post-processing, and metric calculation. Although the toolkit is currently mainly used for evaluating large vision-language models, its design is compatible with future updates that incorporate additional modalities, such as audio and video. Based on the evaluation results obtained with the toolkit, we host OpenVLM Leaderboard, a comprehensive leaderboard to track the progress of multi-modality learning research. The toolkit is released at https://github.com/open-compass/VLMEvalKit and is actively maintained.", "github_manual": "", "github_regex": "https://github.com/open-compass/VLMEvalKit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.10957", "title": "Ref-AVS: Refer and Segment Objects in Audio-Visual Scenes", "authors": [ "Yaoting Wang", "Peiwen Sun", "Dongzhan Zhou", "Guangyao Li", "Honggang Zhang", "Di Hu" ], "github": "", "project_page": "", "abstract": "Traditional reference segmentation tasks have predominantly focused on silent visual scenes, neglecting the integral role of multimodal perception and interaction in human experiences. In this work, we introduce a novel task called Reference Audio-Visual Segmentation (Ref-AVS), which seeks to segment objects within the visual domain based on expressions containing multimodal cues. Such expressions are articulated in natural language forms but are enriched with multimodal cues, including audio and visual descriptions. To facilitate this research, we construct the first Ref-AVS benchmark, which provides pixel-level annotations for objects described in corresponding multimodal-cue expressions. To tackle the Ref-AVS task, we propose a new method that adequately utilizes multimodal cues to offer precise segmentation guidance. Finally, we conduct quantitative and qualitative experiments on three test subsets to compare our approach with existing methods from related tasks. The results demonstrate the effectiveness of our method, highlighting its capability to precisely segment objects using multimodal-cue expressions. Dataset is available at https://gewu-lab.github.io/Ref-AVS{https://gewu-lab.github.io/Ref-AVS}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11522", "title": "FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models", "authors": [ "Pengxiang Li", "Zhi Gao", "Bofei Zhang", "Tao Yuan", "Yuwei Wu", "Mehrtash Harandi", "Yunde Jia", "Song-Chun Zhu", "Qing Li" ], "github": "https://github.com/xiaoachen98/Open-LLaVA-NeXT", "project_page": "", "abstract": "Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xiaoachen98/Open-LLaVA-NeXT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11828", "title": "Vibravox: A Dataset of French Speech Captured with Body-conduction Audio Sensors", "authors": [ "Julien Hauret", "Malo Olivier", "Thomas Joubaud", "Christophe Langrenne", "Sarah Poir\u00e9e", "V\u00e9ronique Zimpfer", "\u00c9ric Bavu" ], "github": "https://github.com/jhauret/vibravox", "project_page": "", "abstract": "Vibravox is a dataset compliant with the General Data Protection Regulation (GDPR) containing audio recordings using five different body-conduction audio sensors : two in-ear microphones, two bone conduction vibration pickups and a laryngophone. The data set also includes audio data from an airborne microphone used as a reference. The Vibravox corpus contains 38 hours of speech samples and physiological sounds recorded by 188 participants under different acoustic conditions imposed by an high order ambisonics 3D spatializer. Annotations about the recording conditions and linguistic transcriptions are also included in the corpus. We conducted a series of experiments on various speech-related tasks, including speech recognition, speech enhancement and speaker verification. These experiments were carried out using state-of-the-art models to evaluate and compare their performances on signals captured by the different audio sensors offered by the Vibravox dataset, with the aim of gaining a better grasp of their individual characteristics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jhauret/vibravox", "github_api_added_by": "auto" }, { "date": "2024-07-17", "arxiv_id": "2407.11282", "title": "Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models", "authors": [ "Qingcheng Zeng", "Mingyu Jin", "Qinkai Yu", "Zhenting Wang", "Wenyue Hua", "Zihao Zhou", "Guangyan Sun", "Yanda Meng", "Shiqing Ma", "Qifan Wang", "Felix Juefei-Xu", "Kaize Ding", "Fan Yang", "Ruixiang Tang", "Yongfeng Zhang" ], "github": "https://github.com/qcznlp/uncertainty_attack", "project_page": "", "abstract": "Large Language Models (LLMs) are employed across various high-stakes domains, where the reliability of their outputs is crucial. One commonly used method to assess the reliability of LLMs' responses is uncertainty estimation, which gauges the likelihood of their answers being correct. While many studies focus on improving the accuracy of uncertainty estimations for LLMs, our research investigates the fragility of uncertainty estimation and explores potential attacks. We demonstrate that an attacker can embed a backdoor in LLMs, which, when activated by a specific trigger in the input, manipulates the model's uncertainty without affecting the final output. Specifically, the proposed backdoor attack method can alter an LLM's output probability distribution, causing the probability distribution to converge towards an attacker-predefined distribution while ensuring that the top-1 prediction remains unchanged. Our experimental results demonstrate that this attack effectively undermines the model's self-evaluation reliability in multiple-choice questions. For instance, we achieved a 100 attack success rate (ASR) across three different triggering strategies in four models. Further, we investigate whether this manipulation generalizes across different prompts and domains. This work highlights a significant threat to the reliability of LLMs and underscores the need for future defenses against such attacks. The code is available at https://github.com/qcznlp/uncertainty_attack.", "github_manual": "", "github_regex": "https://github.com/qcznlp/uncertainty_attack", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-17", "arxiv_id": "2407.11062", "title": "EfficientQAT: Efficient Quantization-Aware Training for Large Language Models", "authors": [ "Mengzhao Chen", "Wenqi Shao", "Peng Xu", "Jiahao Wang", "Peng Gao", "Kaipeng Zhang", "Yu Qiao", "Ping Luo" ], "github": "https://github.com/OpenGVLab/EfficientQAT", "project_page": "", "abstract": "Large language models (LLMs) are integral to modern natural language processing and artificial intelligence. However, they face challenges in managing their significant memory requirements. Although quantization-aware training (QAT) offers a solution by reducing memory consumption through low-bit representations with minimal accuracy loss, it demands substantial training resources to optimize model weights and quantization parameters. To address this, we propose Efficient Quantization-Aware Training (EfficientQAT), a novel quantization technique for compressing LLMs. EfficientQAT involves two consecutive phases: Block-wise training of all parameters (Block-AP) and end-to-end training of quantization parameters (E2E-QP). Block-AP sequentially conducts quantization-aware training for all parameters in each transformer block with block-wise reconstruction, maintaining efficiency by avoiding training the entire LLM. Initialized with quantized model, E2E-QP then trains only quantization parameters (step sizes) end-to-end, enhancing efficiency with a fixed quantized backbone and reduced trainable parameter count. Extensive experiments demonstrate that EfficientQAT outperforms previous quantization methods across a range of models, including base LLMs, instruction-tuned LLMs, and multimodal LLMs, with scales from 7B to 70B parameters at various quantization bits. For instance, EfficientQAT obtains a 2-bit Llama-2-70B model on a single A100-80GB GPU in 41 hours, with less than 3\\% accuracy degradation compared to the full precision (69.48 vs. 72.41). Notably, this INT2 quantized 70B model obtains a 1.67 accuracy gain over the Llama-2-13B model (69.48 vs. 67.81) while requiring less memory (19.2GB vs. 24.2GB). Code is available at https://github.com/OpenGVLab/EfficientQAT.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/EfficientQAT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12772", "title": "LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models", "authors": [ "Kaichen Zhang", "Bo Li", "Peiyuan Zhang", "Fanyi Pu", "Joshua Adrian Cahyono", "Kairui Hu", "Shuai Liu", "Yuanhan Zhang", "Jingkang Yang", "Chunyuan Li", "Ziwei Liu" ], "github": "https://github.com/EvolvingLMMs-Lab/lmms-eval", "project_page": "", "abstract": "The advances of large foundation models necessitate wide-coverage, low-cost, and zero-contamination benchmarks. Despite continuous exploration of language model evaluations, comprehensive studies on the evaluation of Large Multi-modal Models (LMMs) remain limited. In this work, we introduce LMMS-EVAL, a unified and standardized multimodal benchmark framework with over 50 tasks and more than 10 models to promote transparent and reproducible evaluations. Although LMMS-EVAL offers comprehensive coverage, we find it still falls short in achieving low cost and zero contamination. To approach this evaluation trilemma, we further introduce LMMS-EVAL LITE, a pruned evaluation toolkit that emphasizes both coverage and efficiency. Additionally, we present Multimodal LIVEBENCH that utilizes continuously updating news and online forums to assess models' generalization abilities in the wild, featuring a low-cost and zero-contamination evaluation approach. In summary, our work highlights the importance of considering the evaluation trilemma and provides practical solutions to navigate the trade-offs in evaluating large multi-modal models, paving the way for more effective and reliable benchmarking of LMMs. We opensource our codebase and maintain leaderboard of LIVEBENCH at https://github.com/EvolvingLMMs-Lab/lmms-eval and https://huggingface.co/spaces/lmms-lab/LiveBench.", "github_manual": "", "github_regex": "https://github.com/EvolvingLMMs-Lab/lmms-eval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12705", "title": "IMAGDressing-v1: Customizable Virtual Dressing", "authors": [ "Fei Shen", "Xin Jiang", "Xin He", "Hu Ye", "Cong Wang", "Xiaoyu Du", "Zechao Li", "Jinghui Tang" ], "github": "https://github.com/muzishen/IMAGDressing", "project_page": "", "abstract": "Latest advances have achieved realistic virtual try-on (VTON) through localized garment inpainting using latent diffusion models, significantly enhancing consumers' online shopping experience. However, existing VTON technologies neglect the need for merchants to showcase garments comprehensively, including flexible control over garments, optional faces, poses, and scenes. To address this issue, we define a virtual dressing (VD) task focused on generating freely editable human images with fixed garments and optional conditions. Meanwhile, we design a comprehensive affinity metric index (CAMI) to evaluate the consistency between generated images and reference garments. Then, we propose IMAGDressing-v1, which incorporates a garment UNet that captures semantic features from CLIP and texture features from VAE. We present a hybrid attention module, including a frozen self-attention and a trainable cross-attention, to integrate garment features from the garment UNet into a frozen denoising UNet, ensuring users can control different scenes through text. IMAGDressing-v1 can be combined with other extension plugins, such as ControlNet and IP-Adapter, to enhance the diversity and controllability of generated images. Furthermore, to address the lack of data, we release the interactive garment pairing (IGPair) dataset, containing over 300,000 pairs of clothing and dressed images, and establish a standard pipeline for data assembly. Extensive experiments demonstrate that our IMAGDressing-v1 achieves state-of-the-art human image synthesis performance under various controlled conditions. The code and model will be available at https://github.com/muzishen/IMAGDressing.", "github_manual": "", "github_regex": "https://github.com/muzishen/IMAGDressing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12679", "title": "Goldfish: Vision-Language Understanding of Arbitrarily Long Videos", "authors": [ "Kirolos Ataallah", "Xiaoqian Shen", "Eslam Abdelrahman", "Essam Sleiman", "Mingchen Zhuge", "Jian Ding", "Deyao Zhu", "J\u00fcrgen Schmidhuber", "Mohamed Elhoseiny" ], "github": "", "project_page": "", "abstract": "Most current LLM-based models for video understanding can process videos within minutes. However, they struggle with lengthy videos due to challenges such as \"noise and redundancy\", as well as \"memory and computation\" constraints. In this paper, we present Goldfish, a methodology tailored for comprehending videos of arbitrary lengths. We also introduce the TVQA-long benchmark, specifically designed to evaluate models' capabilities in understanding long videos with questions in both vision and text content. Goldfish approaches these challenges with an efficient retrieval mechanism that initially gathers the top-k video clips relevant to the instruction before proceeding to provide the desired response. This design of the retrieval mechanism enables the Goldfish to efficiently process arbitrarily long video sequences, facilitating its application in contexts such as movies or television series. To facilitate the retrieval process, we developed MiniGPT4-Video that generates detailed descriptions for the video clips. In addressing the scarcity of benchmarks for long video evaluation, we adapted the TVQA short video benchmark for extended content analysis by aggregating questions from entire episodes, thereby shifting the evaluation from partial to full episode comprehension. We attained a 41.78% accuracy rate on the TVQA-long benchmark, surpassing previous methods by 14.94%. Our MiniGPT4-Video also shows exceptional performance in short video comprehension, exceeding existing state-of-the-art methods by 3.23%, 2.03%, 16.5% and 23.59% on the MSVD, MSRVTT, TGIF, and TVQA short video benchmarks, respectively. These results indicate that our models have significant improvements in both long and short-video understanding. Our models and code have been made publicly available at https://vision-cair.github.io/Goldfish_website/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12665", "title": "Patch-Level Training for Large Language Models", "authors": [ "Chenze Shao", "Fandong Meng", "Jie Zhou" ], "github": "https://github.com/shaochenze/PatchTrain", "project_page": "", "abstract": "As Large Language Models (LLMs) achieve remarkable progress in language understanding and generation, their training efficiency has become a critical concern. Traditionally, LLMs are trained to predict the next token in a sequence. Despite the success of token-level training, it suffers from considerable computational costs due to the need to process an extensive number of tokens. To mitigate this issue, this paper introduces patch-level training for LLMs, which reduces the sequence length by compressing multiple tokens into a single patch. During patch-level training, we feed the language model shorter sequences of patches and train it to predict the next patch, thereby processing the majority of the training data at a significantly reduced computational cost. Following this, the model continues token-level training on the remaining training data to align with the inference mode. Experiments on a diverse range of models (370M-2.7B parameters) demonstrate that patch-level training can reduce overall computational costs to 0.5times, without compromising the model performance compared to token-level training. Source code: https://github.com/shaochenze/PatchTrain.", "github_manual": "", "github_regex": "https://github.com/shaochenze/PatchTrain", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12580", "title": "E5-V: Universal Embeddings with Multimodal Large Language Models", "authors": [ "Ting Jiang", "Minghui Song", "Zihan Zhang", "Haizhen Huang", "Weiwei Deng", "Feng Sun", "Qi Zhang", "Deqing Wang", "Fuzhen Zhuang" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have shown promising advancements in general visual and language understanding. However, the representation of multimodal information using MLLMs remains largely unexplored. In this work, we introduce a new framework, E5-V, designed to adapt MLLMs for achieving universal multimodal embeddings. Our findings highlight the significant potential of MLLMs in representing multimodal inputs compared to previous approaches. By leveraging MLLMs with prompts, E5-V effectively bridges the modality gap between different types of inputs, demonstrating strong performance in multimodal embeddings even without fine-tuning. We propose a single modality training approach for E5-V, where the model is trained exclusively on text pairs. This method demonstrates significant improvements over traditional multimodal training on image-text pairs, while reducing training costs by approximately 95%. Additionally, this approach eliminates the need for costly multimodal training data collection. Extensive experiments across four types of tasks demonstrate the effectiveness of E5-V. As a universal multimodal model, E5-V not only achieves but often surpasses state-of-the-art performance in each task, despite being trained on a single modality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12563", "title": "Audio Conditioning for Music Generation via Discrete Bottleneck Features", "authors": [ "Simon Rouard", "Yossi Adi", "Jade Copet", "Axel Roebel", "Alexandre D\u00e9fossez" ], "github": "", "project_page": "", "abstract": "While most music generation models use textual or parametric conditioning (e.g. tempo, harmony, musical genre), we propose to condition a language model based music generation system with audio input. Our exploration involves two distinct strategies. The first strategy, termed textual inversion, leverages a pre-trained text-to-music model to map audio input to corresponding \"pseudowords\" in the textual embedding space. For the second model we train a music language model from scratch jointly with a text conditioner and a quantized audio feature extractor. At inference time, we can mix textual and audio conditioning and balance them thanks to a novel double classifier free guidance method. We conduct automatic and human studies that validates our approach. We will release the code and we provide music samples on https://musicgenstyle.github.io in order to show the quality of our model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12306", "title": "Splatfacto-W: A Nerfstudio Implementation of Gaussian Splatting for Unconstrained Photo Collections", "authors": [ "Congrong Xu", "Justin Kerr", "Angjoo Kanazawa" ], "github": "", "project_page": "", "abstract": "Novel view synthesis from unconstrained in-the-wild image collections remains a significant yet challenging task due to photometric variations and transient occluders that complicate accurate scene reconstruction. Previous methods have approached these issues by integrating per-image appearance features embeddings in Neural Radiance Fields (NeRFs). Although 3D Gaussian Splatting (3DGS) offers faster training and real-time rendering, adapting it for unconstrained image collections is non-trivial due to the substantially different architecture. In this paper, we introduce Splatfacto-W, an approach that integrates per-Gaussian neural color features and per-image appearance embeddings into the rasterization process, along with a spherical harmonics-based background model to represent varying photometric appearances and better depict backgrounds. Our key contributions include latent appearance modeling, efficient transient object handling, and precise background modeling. Splatfacto-W delivers high-quality, real-time novel view synthesis with improved scene consistency in in-the-wild scenarios. Our method improves the Peak Signal-to-Noise Ratio (PSNR) by an average of 5.3 dB compared to 3DGS, enhances training speed by 150 times compared to NeRF-based methods, and achieves a similar rendering speed to 3DGS. Additional video results and code integrated into Nerfstudio are available at https://kevinxu02.github.io/splatfactow/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12781", "title": "VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control", "authors": [ "Sherwin Bahmani", "Ivan Skorokhodov", "Aliaksandr Siarohin", "Willi Menapace", "Guocheng Qian", "Michael Vasilkovsky", "Hsin-Ying Lee", "Chaoyang Wang", "Jiaxu Zou", "Andrea Tagliasacchi", "David B. Lindell", "Sergey Tulyakov" ], "github": "", "project_page": "", "abstract": "Modern text-to-video synthesis models demonstrate coherent, photorealistic generation of complex videos from a text description. However, most existing models lack fine-grained control over camera movement, which is critical for downstream applications related to content creation, visual effects, and 3D vision. Recently, new methods demonstrate the ability to generate videos with controllable camera poses these techniques leverage pre-trained U-Net-based diffusion models that explicitly disentangle spatial and temporal generation. Still, no existing approach enables camera control for new, transformer-based video diffusion models that process spatial and temporal information jointly. Here, we propose to tame video transformers for 3D camera control using a ControlNet-like conditioning mechanism that incorporates spatiotemporal camera embeddings based on Plucker coordinates. The approach demonstrates state-of-the-art performance for controllable video generation after fine-tuning on the RealEstate10K dataset. To the best of our knowledge, our work is the first to enable camera control for transformer-based video diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12327", "title": "Spectra: A Comprehensive Study of Ternary, Quantized, and FP16 Language Models", "authors": [ "Ayush Kaushal", "Tejas Pandey", "Tejas Vaidhya", "Aaryan Bhagat", "Irina Rish" ], "github": "https://github.com/NolanoOrg/SpectraSuite", "project_page": "", "abstract": "Post-training quantization is the leading method for addressing memory-related bottlenecks in LLM inference, but unfortunately, it suffers from significant performance degradation below 4-bit precision. An alternative approach involves training compressed models directly at a low bitwidth (e.g., binary or ternary models). However, the performance, training dynamics, and scaling trends of such models are not yet well understood. To address this issue, we train and openly release the Spectra LLM suite consisting of 54 language models ranging from 99M to 3.9B parameters, trained on 300B tokens. Spectra includes FloatLMs, post-training quantized QuantLMs (3, 4, 6, and 8 bits), and ternary LLMs (TriLMs) - our improved architecture for ternary language modeling, which significantly outperforms previously proposed ternary models of a given size (in bits), matching half-precision models at scale. For example, TriLM 3.9B is (bit-wise) smaller than the half-precision FloatLM 830M, but matches half-precision FloatLM 3.9B in commonsense reasoning and knowledge benchmarks. However, TriLM 3.9B is also as toxic and stereotyping as FloatLM 3.9B, a model six times larger in size. Additionally, TriLM 3.9B lags behind FloatLM in perplexity on validation splits and web-based corpora but performs better on less noisy datasets like Lambada and PennTreeBank. To enhance understanding of low-bitwidth models, we are releasing 500+ intermediate checkpoints of the Spectra suite at https://github.com/NolanoOrg/SpectraSuite{https://github.com/NolanoOrg/SpectraSuite}.", "github_manual": "", "github_regex": "https://github.com/NolanoOrg/SpectraSuite", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12043", "title": "The Art of Saying No: Contextual Noncompliance in Language Models", "authors": [ "Faeze Brahman", "Sachin Kumar", "Vidhisha Balachandran", "Pradeep Dasigi", "Valentina Pyatkin", "Abhilasha Ravichander", "Sarah Wiegreffe", "Nouha Dziri", "Khyathi Chandu", "Jack Hessel", "Yulia Tsvetkov", "Noah A. Smith", "Yejin Choi", "Hannaneh Hajishirzi" ], "github": "", "project_page": "", "abstract": "Chat-based language models are designed to be helpful, yet they should not comply with every user request. While most existing work primarily focuses on refusal of \"unsafe\" queries, we posit that the scope of noncompliance should be broadened. We introduce a comprehensive taxonomy of contextual noncompliance describing when and how models should not comply with user requests. Our taxonomy spans a wide range of categories including incomplete, unsupported, indeterminate, and humanizing requests (in addition to unsafe requests). To test noncompliance capabilities of language models, we use this taxonomy to develop a new evaluation suite of 1000 noncompliance prompts. We find that most existing models show significantly high compliance rates in certain previously understudied categories with models like GPT-4 incorrectly complying with as many as 30% of requests. To address these gaps, we explore different training strategies using a synthetically-generated training set of requests and expected noncompliant responses. Our experiments demonstrate that while direct finetuning of instruction-tuned models can lead to both over-refusal and a decline in general capabilities, using parameter efficient methods like low rank adapters helps to strike a good balance between appropriate noncompliance and other capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12504", "title": "Case2Code: Learning Inductive Reasoning with Synthetic Data", "authors": [ "Yunfan Shao", "Linyang Li", "Yichuan Ma", "Peiji Li", "Demin Song", "Qinyuan Cheng", "Shimin Li", "Xiaonan Li", "Pengyu Wang", "Qipeng Guo", "Hang Yan", "Xipeng Qiu", "Xuanjing Huang", "Dahua Lin" ], "github": "https://github.com/choosewhatulike/case2code", "project_page": "", "abstract": "Complex reasoning is an impressive ability shown by large language models (LLMs). Most LLMs are skilled in deductive reasoning, such as chain-of-thought prompting or iterative tool-using to solve challenging tasks step-by-step. In this paper, we hope to focus on evaluating and teaching LLMs to conduct inductive reasoning, that is, LLMs are supposed to infer underlying rules by observing examples or sequential transformations. However, collecting large-scale and diverse human-generated inductive data is challenging. We focus on data synthesis in the code domain and propose a Case2Code task by exploiting the expressiveness and correctness of programs. Specifically, we collect a diverse set of executable programs, synthesize input-output transformations for each program, and force LLMs to infer the underlying code implementations based on the synthetic I/O cases. We first evaluate representative LLMs on the synthesized Case2Code task and demonstrate that the Case-to-code induction is challenging for LLMs. Then, we synthesize large-scale Case2Code training samples to train LLMs to perform inductive reasoning. Experimental results show that such induction training benefits not only in distribution Case2Code performance but also enhances various coding abilities of trained LLMs, demonstrating the great potential of learning inductive reasoning via synthetic data.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/choosewhatulike/case2code", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12077", "title": "GoldFinch: High Performance RWKV/Transformer Hybrid with Linear Pre-Fill and Extreme KV-Cache Compression", "authors": [ "Daniel Goldstein", "Fares Obeid", "Eric Alcaide", "Guangyu Song", "Eugene Cheah" ], "github": "https://github.com/recursal/GoldFinch-paper", "project_page": "", "abstract": "We introduce GoldFinch, a hybrid Linear Attention/Transformer sequence model that uses a new technique to efficiently generate a highly compressed and reusable KV-Cache in linear time and space with respect to sequence length. GoldFinch stacks our new GOLD transformer on top of an enhanced version of the Finch (RWKV-6) architecture. We train up to 1.5B parameter class models of the Finch, Llama, and GoldFinch architectures, and find dramatically improved modeling performance relative to both Finch and Llama. Our cache size savings increase linearly with model layer count, ranging from 756-2550 times smaller than the traditional transformer cache for common sizes, enabling inference of extremely large context lengths even on limited hardware. Although autoregressive generation has O(n) time complexity per token because of attention, pre-fill computation of the entire initial cache state for a submitted context costs only O(1) time per token due to the use of a recurrent neural network (RNN) to generate this cache. We release our trained weights and training code under the Apache 2.0 license for community use.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/recursal/GoldFinch-paper", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12784", "title": "AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases", "authors": [ "Zhaorun Chen", "Zhen Xiang", "Chaowei Xiao", "Dawn Song", "Bo Li" ], "github": "https://github.com/BillChan226/AgentPoison", "project_page": "", "abstract": "LLM agents have demonstrated remarkable performance across various applications, primarily due to their advanced capabilities in reasoning, utilizing external knowledge and tools, calling APIs, and executing actions to interact with environments. Current agents typically utilize a memory module or a retrieval-augmented generation (RAG) mechanism, retrieving past knowledge and instances with similar embeddings from knowledge bases to inform task planning and execution. However, the reliance on unverified knowledge bases raises significant concerns about their safety and trustworthiness. To uncover such vulnerabilities, we propose a novel red teaming approach AgentPoison, the first backdoor attack targeting generic and RAG-based LLM agents by poisoning their long-term memory or RAG knowledge base. In particular, we form the trigger generation process as a constrained optimization to optimize backdoor triggers by mapping the triggered instances to a unique embedding space, so as to ensure that whenever a user instruction contains the optimized backdoor trigger, the malicious demonstrations are retrieved from the poisoned memory or knowledge base with high probability. In the meantime, benign instructions without the trigger will still maintain normal performance. Unlike conventional backdoor attacks, AgentPoison requires no additional model training or fine-tuning, and the optimized backdoor trigger exhibits superior transferability, in-context coherence, and stealthiness. Extensive experiments demonstrate AgentPoison's effectiveness in attacking three types of real-world LLM agents: RAG-based autonomous driving agent, knowledge-intensive QA agent, and healthcare EHRAgent. On each agent, AgentPoison achieves an average attack success rate higher than 80% with minimal impact on benign performance (less than 1%) with a poison rate less than 0.1%.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/BillChan226/AgentPoison", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.09018", "title": "AUITestAgent: Automatic Requirements Oriented GUI Function Testing", "authors": [ "Yongxiang Hu", "Xuan Wang", "Yingchuan Wang", "Yu Zhang", "Shiyu Guo", "Chaoyi Chen", "Xin Wang", "Yangfan Zhou" ], "github": "https://github.com/bz-lab/auitestagent", "project_page": "", "abstract": "The Graphical User Interface (GUI) is how users interact with mobile apps. To ensure it functions properly, testing engineers have to make sure it functions as intended, based on test requirements that are typically written in natural language. While widely adopted manual testing and script-based methods are effective, they demand substantial effort due to the vast number of GUI pages and rapid iterations in modern mobile apps. This paper introduces AUITestAgent, the first automatic, natural language-driven GUI testing tool for mobile apps, capable of fully automating the entire process of GUI interaction and function verification. Since test requirements typically contain interaction commands and verification oracles. AUITestAgent can extract GUI interactions from test requirements via dynamically organized agents. Then, AUITestAgent employs a multi-dimensional data extraction strategy to retrieve data relevant to the test requirements from the interaction trace and perform verification. Experiments on customized benchmarks demonstrate that AUITestAgent outperforms existing tools in the quality of generated GUI interactions and achieved the accuracy of verifications of 94%. Moreover, field deployment in Meituan has shown AUITestAgent's practical usability, with it detecting 4 new functional bugs during 10 regression tests in two months.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bz-lab/auitestagent", "github_api_added_by": "auto" }, { "date": "2024-07-18", "arxiv_id": "2407.11854", "title": "Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection", "authors": [ "Gaetan Lopez Latouche", "Marc-Andr\u00e9 Carbonneau", "Ben Swanson" ], "github": "", "project_page": "", "abstract": "Grammatical Error Detection (GED) methods rely heavily on human annotated error corpora. However, these annotations are unavailable in many low-resource languages. In this paper, we investigate GED in this context. Leveraging the zero-shot cross-lingual transfer capabilities of multilingual pre-trained language models, we train a model using data from a diverse set of languages to generate synthetic errors in other languages. These synthetic error corpora are then used to train a GED model. Specifically we propose a two-stage fine-tuning pipeline where the GED model is first fine-tuned on multilingual synthetic data from target languages followed by fine-tuning on human-annotated GED corpora from source languages. This approach outperforms current state-of-the-art annotation-free GED methods. We also analyse the errors produced by our method and other strong baselines, finding that our approach produces errors that are more diverse and more similar to human errors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.10223", "title": "Practical Unlearning for Large Language Models", "authors": [ "Chongyang Gao", "Lixu Wang", "Chenkai Weng", "Xiao Wang", "Qi Zhu" ], "github": "https://github.com/gcyzsl/o3-llm-unlearning", "project_page": "", "abstract": "While LLMs have demonstrated impressive performance across various domains and tasks, their security issues have become increasingly severe. Machine unlearning (MU) has emerged as a promising solution to address these issues by removing the influence of undesired data on the target model without compromising its utility in other aspects. MU typically assumes full access to the original training data to preserve utility, which is difficult to achieve in LLM unlearning. Existing LLM unlearning methods often assume access to data most affected by undesired data unlearning. However, this assumption underestimates the entanglement among various LLM capabilities and ignores data access limitations due to various issues. Moreover, these LLM unlearning methods do not sufficiently consider that unlearning requests in real-world scenarios are continuously emerging. To overcome these challenges and achieve practical LLM unlearning, we propose the O3 framework. The O3 framework includes an Out-Of-Distribution (OOD) detector to measure the similarity between input and unlearning data, and an Orthogonal low-rank adapter (LoRA) for continuously unlearning requested data. The OOD detector is trained with a novel contrastive entropy loss and utilizes a local-global layer-aggregated scoring mechanism. The orthogonal LoRA achieves parameter disentanglement among continual unlearning requests. During inference, our O3 framework can smartly decide whether and to what extent to load the unlearning LoRA based on the OOD detector's predictions. Notably, O3's effectiveness does not rely on any retained data. We conducted extensive experiments on O3 and state-of-the-art LLM unlearning methods across three tasks and seven datasets. The results indicate that O3 consistently achieves the best trade-off between unlearning effectiveness and utility preservation, especially when facing continuous unlearning requests.", "github_manual": "https://github.com/gcyzsl/o3-llm-unlearning", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.11298", "title": "ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter", "authors": [ "Yaoyao Qian", "Xupeng Zhu", "Ondrej Biza", "Shuo Jiang", "Linfeng Zhao", "Haojie Huang", "Yu Qi", "Robert Platt" ], "github": "https://github.com/H-Freax/ThinkGrasp", "project_page": "", "abstract": "Robotic grasping in cluttered environments remains a significant challenge due to occlusions and complex object arrangements. We have developed ThinkGrasp, a plug-and-play vision-language grasping system that makes use of GPT-4o's advanced contextual reasoning for heavy clutter environment grasping strategies. ThinkGrasp can effectively identify and generate grasp poses for target objects, even when they are heavily obstructed or nearly invisible, by using goal-oriented language to guide the removal of obstructing objects. This approach progressively uncovers the target object and ultimately grasps it with a few steps and a high success rate. In both simulated and real experiments, ThinkGrasp achieved a high success rate and significantly outperformed state-of-the-art methods in heavily cluttered environments or with diverse unseen objects, demonstrating strong generalization capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/H-Freax/ThinkGrasp", "github_api_added_by": "auto" }, { "date": "2024-07-18", "arxiv_id": "2407.12366", "title": "NavGPT-2: Unleashing Navigational Reasoning Capability for Large Vision-Language Models", "authors": [ "Gengze Zhou", "Yicong Hong", "Zun Wang", "Xin Eric Wang", "Qi Wu" ], "github": "https://github.com/gengzezhou/navgpt-2", "project_page": "", "abstract": "Capitalizing on the remarkable advancements in Large Language Models (LLMs), there is a burgeoning initiative to harness LLMs for instruction following robotic navigation. Such a trend underscores the potential of LLMs to generalize navigational reasoning and diverse language understanding. However, a significant discrepancy in agent performance is observed when integrating LLMs in the Vision-and-Language navigation (VLN) tasks compared to previous downstream specialist models. Furthermore, the inherent capacity of language to interpret and facilitate communication in agent interactions is often underutilized in these integrations. In this work, we strive to bridge the divide between VLN-specialized models and LLM-based navigation paradigms, while maintaining the interpretative prowess of LLMs in generating linguistic navigational reasoning. By aligning visual content in a frozen LLM, we encompass visual observation comprehension for LLMs and exploit a way to incorporate LLMs and navigation policy networks for effective action predictions and navigational reasoning. We demonstrate the data efficiency of the proposed methods and eliminate the gap between LM-based agents and state-of-the-art VLN specialists.", "github_manual": "https://github.com/gengzezhou/navgpt-2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-18", "arxiv_id": "2407.12581", "title": "Towards Understanding Unsafe Video Generation", "authors": [ "Yan Pang", "Aiping Xiong", "Yang Zhang", "Tianhao Wang" ], "github": "https://github.com/py85252876/uvd", "project_page": "", "abstract": "Video generation models (VGMs) have demonstrated the capability to synthesize high-quality output. It is important to understand their potential to produce unsafe content, such as violent or terrifying videos. In this work, we provide a comprehensive understanding of unsafe video generation. First, to confirm the possibility that these models could indeed generate unsafe videos, we choose unsafe content generation prompts collected from 4chan and Lexica, and three open-source SOTA VGMs to generate unsafe videos. After filtering out duplicates and poorly generated content, we created an initial set of 2112 unsafe videos from an original pool of 5607 videos. Through clustering and thematic coding analysis of these generated videos, we identify 5 unsafe video categories: Distorted/Weird, Terrifying, Pornographic, Violent/Bloody, and Political. With IRB approval, we then recruit online participants to help label the generated videos. Based on the annotations submitted by 403 participants, we identified 937 unsafe videos from the initial video set. With the labeled information and the corresponding prompts, we created the first dataset of unsafe videos generated by VGMs. We then study possible defense mechanisms to prevent the generation of unsafe videos. Existing defense methods in image generation focus on filtering either input prompt or output results. We propose a new approach called Latent Variable Defense (LVD), which works within the model's internal sampling process. LVD can achieve 0.90 defense accuracy while reducing time and computing resources by 10x when sampling a large number of unsafe prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/py85252876/uvd", "github_api_added_by": "auto" }, { "date": "2024-07-19", "arxiv_id": "2407.13623", "title": "Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies", "authors": [ "Chaofan Tao", "Qian Liu", "Longxu Dou", "Niklas Muennighoff", "Zhongwei Wan", "Ping Luo", "Min Lin", "Ngai Wong" ], "github": "https://github.com/sail-sg/scaling-with-vocab", "project_page": "", "abstract": "Research on scaling large language models (LLMs) has primarily focused on model parameters and training data size, overlooking the role of vocabulary size. % Intuitively, larger vocabularies enable more efficient tokenization by representing sentences with fewer tokens, but they also increase the risk of under-fitting representations for rare tokens. We investigate how vocabulary size impacts LLM scaling laws by training models ranging from 33M to 3B parameters on up to 500B characters with various vocabulary configurations. We propose three complementary approaches for predicting the compute-optimal vocabulary size: IsoFLOPs analysis, derivative estimation, and parametric fit of the loss function. Our approaches converge on the same result that the optimal vocabulary size depends on the available compute budget and that larger models deserve larger vocabularies. However, most LLMs use too small vocabulary sizes. For example, we predict that the optimal vocabulary size of Llama2-70B should have been at least 216K, 7 times larger than its vocabulary of 32K. We validate our predictions empirically by training models with 3B parameters across different FLOPs budgets. Adopting our predicted optimal vocabulary size consistently improves downstream performance over commonly used vocabulary sizes. By increasing the vocabulary size from the conventional 32K to 43K, we improve performance on ARC-Challenge from 29.1 to 32.0 with the same 2.3e21 FLOPs. Our work emphasizes the necessity of jointly considering model parameters and vocabulary size for efficient scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sail-sg/scaling-with-vocab", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2407.10424", "title": "CodeV: Empowering LLMs for Verilog Generation through Multi-Level Summarization", "authors": [ "Yang Zhao", "Di Huang", "Chongxiao Li", "Pengwei Jin", "Ziyuan Nan", "Tianyun Ma", "Lei Qi", "Yansong Pan", "Zhenxing Zhang", "Rui Zhang", "Xishan Zhang", "Zidong Du", "Qi Guo", "Xing Hu", "Yunji Chen" ], "github": "", "project_page": "", "abstract": "The increasing complexity and high costs associated with modern processor design have led to a surge in demand for processor design automation. Instruction-tuned large language models (LLMs) have demonstrated remarkable performance in automatically generating code for general-purpose programming languages like Python. However, these methods fail on hardware description languages (HDLs) like Verilog due to the scarcity of high-quality instruction tuning data, as even advanced LLMs like GPT-3.5 exhibit limited performance on Verilog generation. Regarding this issue, we observe that (1) Verilog code collected from the real world has higher quality than those generated by LLMs. (2) LLMs like GPT-3.5 excel in summarizing Verilog code rather than generating it. Based on these observations, this paper introduces CodeV, a series of open-source instruction-tuned Verilog generation LLMs. Instead of generating descriptions first and then getting the corresponding code from advanced LLMs, we prompt the LLM with Verilog code and let the LLM generate the corresponding natural language description by multi-level summarization. Experimental results show that CodeV relatively surpasses the previous open-source SOTA by 14.4% (BetterV in VerilogEval) and 11.3% (RTLCoder in RTLLM) respectively, and also relatively outperforms previous commercial SOTA GPT-4 by 22.1% in VerilogEval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2407.13759", "title": "Streetscapes: Large-scale Consistent Street View Generation Using Autoregressive Video Diffusion", "authors": [ "Boyang Deng", "Richard Tucker", "Zhengqi Li", "Leonidas Guibas", "Noah Snavely", "Gordon Wetzstein" ], "github": "", "project_page": "", "abstract": "We present a method for generating Streetscapes-long sequences of views through an on-the-fly synthesized city-scale scene. Our generation is conditioned by language input (e.g., city name, weather), as well as an underlying map/layout hosting the desired trajectory. Compared to recent models for video generation or 3D view synthesis, our method can scale to much longer-range camera trajectories, spanning several city blocks, while maintaining visual quality and consistency. To achieve this goal, we build on recent work on video diffusion, used within an autoregressive framework that can easily scale to long sequences. In particular, we introduce a new temporal imputation method that prevents our autoregressive approach from drifting from the distribution of realistic city imagery. We train our Streetscapes system on a compelling source of data-posed imagery from Google Street View, along with contextual map data-which allows users to generate city views conditioned on any desired city layout, with controllable camera poses. Please see more results at our project page at https://boyangdeng.com/streetscapes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2407.12854", "title": "Scaling Retrieval-Based Language Models with a Trillion-Token Datastore", "authors": [ "Rulin Shao", "Jacqueline He", "Akari Asai", "Weijia Shi", "Tim Dettmers", "Sewon Min", "Luke Zettlemoyer", "Pang Wei Koh" ], "github": "https://github.com/RulinShao/retrieval-scaling", "project_page": "", "abstract": "Scaling laws with respect to the amount of training data and the number of parameters allow us to predict the cost-benefit trade-offs of pretraining language models (LMs) in different configurations. In this paper, we consider another dimension of scaling: the amount of data available at inference time. Specifically, we find that increasing the size of the datastore used by a retrieval-based LM monotonically improves language modeling and several downstream tasks without obvious saturation, such that a smaller model augmented with a large datastore outperforms a larger LM-only model on knowledge-intensive tasks. By plotting compute-optimal scaling curves with varied datastore, model, and pretraining data sizes, we show that using larger datastores can significantly improve model performance for the same training compute budget. We carry out our study by constructing a 1.4 trillion-token datastore named MassiveDS, which is the largest and the most diverse open-sourced datastore for retrieval-based LMs to date, and designing an efficient pipeline for studying datastore scaling in a computationally accessible manner. Finally, we analyze the effect of improving the retriever, datastore quality filtering, and other design choices on our observed scaling trends. Overall, our results show that datastore size should be considered as an integral part of LM efficiency and performance trade-offs. To facilitate future research, we open-source our datastore and code at https://github.com/RulinShao/retrieval-scaling.", "github_manual": "", "github_regex": "https://github.com/RulinShao/retrieval-scaling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2407.13244", "title": "PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks", "authors": [ "Alessandro Berti", "Humam Kourani", "Wil M. P. van der Aalst" ], "github": "https://github.com/fit-alessandro-berti/pm-llm-benchmark", "project_page": "", "abstract": "Large Language Models (LLMs) have the potential to semi-automate some process mining (PM) analyses. While commercial models are already adequate for many analytics tasks, the competitive level of open-source LLMs in PM tasks is unknown. In this paper, we propose PM-LLM-Benchmark, the first comprehensive benchmark for PM focusing on domain knowledge (process-mining-specific and process-specific) and on different implementation strategies. We focus also on the challenges in creating such a benchmark, related to the public availability of the data and on evaluation biases by the LLMs. Overall, we observe that most of the considered LLMs can perform some process mining tasks at a satisfactory level, but tiny models that would run on edge devices are still inadequate. We also conclude that while the proposed benchmark is useful for identifying LLMs that are adequate for process mining tasks, further research is needed to overcome the evaluation biases and perform a more thorough ranking of the competitive LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/fit-alessandro-berti/pm-llm-benchmark", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2407.13638", "title": "A Comparative Study on Automatic Coding of Medical Letters with Explainability", "authors": [ "Jamie Glen", "Lifeng Han", "Paul Rayson", "Goran Nenadic" ], "github": "https://github.com/Glenj01/Medical-Coding", "project_page": "", "abstract": "This study aims to explore the implementation of Natural Language Processing (NLP) and machine learning (ML) techniques to automate the coding of medical letters with visualised explainability and light-weighted local computer settings. Currently in clinical settings, coding is a manual process that involves assigning codes to each condition, procedure, and medication in a patient's paperwork (e.g., 56265001 heart disease using SNOMED CT code). There are preliminary research on automatic coding in this field using state-of-the-art ML models; however, due to the complexity and size of the models, the real-world deployment is not achieved. To further facilitate the possibility of automatic coding practice, we explore some solutions in a local computer setting; in addition, we explore the function of explainability for transparency of AI models. We used the publicly available MIMIC-III database and the HAN/HLAN network models for ICD code prediction purposes. We also experimented with the mapping between ICD and SNOMED CT knowledge bases. In our experiments, the models provided useful information for 97.98\\% of codes. The result of this investigation can shed some light on implementing automatic clinical coding in practice, such as in hospital settings, on the local computers used by clinicians , project page https://github.com/Glenj01/Medical-Coding.", "github_manual": "", "github_regex": "https://github.com/Glenj01/Medical-Coding", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2407.13696", "title": "Benchmark Agreement Testing Done Right: A Guide for LLM Benchmark Evaluation", "authors": [ "Yotam Perlitz", "Ariel Gera", "Ofir Arviv", "Asaf Yehudai", "Elron Bandel", "Eyal Shnarch", "Michal Shmueli-Scheuer", "Leshem Choshen" ], "github": "https://github.com/IBM/BenchBench", "project_page": "", "abstract": "Recent advancements in Language Models (LMs) have catalyzed the creation of multiple benchmarks, designed to assess these models' general capabilities. A crucial task, however, is assessing the validity of the benchmarks themselves. This is most commonly done via Benchmark Agreement Testing (BAT), where new benchmarks are validated against established ones using some agreement metric (e.g., rank correlation). Despite the crucial role of BAT for benchmark builders and consumers, there are no standardized procedures for such agreement testing. This deficiency can lead to invalid conclusions, fostering mistrust in benchmarks and upending the ability to properly choose the appropriate benchmark to use. By analyzing over 40 prominent benchmarks, we demonstrate how some overlooked methodological choices can significantly influence BAT results, potentially undermining the validity of conclusions. To address these inconsistencies, we propose a set of best practices for BAT and demonstrate how utilizing these methodologies greatly improves BAT robustness and validity. To foster adoption and facilitate future research,, we introduce BenchBench, a python package for BAT, and release the BenchBench-leaderboard, a meta-benchmark designed to evaluate benchmarks using their peers. Our findings underscore the necessity for standardized BAT, ensuring the robustness and validity of benchmark evaluations in the evolving landscape of language model research. BenchBench Package: https://github.com/IBM/BenchBench Leaderboard: https://huggingface.co/spaces/per/BenchBench", "github_manual": "", "github_regex": "https://github.com/IBM/BenchBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2407.13709", "title": "Understanding Reference Policies in Direct Preference Optimization", "authors": [ "Yixin Liu", "Pengfei Liu", "Arman Cohan" ], "github": "https://github.com/yale-nlp/refdpo", "project_page": "", "abstract": "Direct Preference Optimization (DPO) has become a widely used training method for the instruction fine-tuning of large language models (LLMs). In this work, we explore an under-investigated aspect of DPO - its dependency on the reference model or policy. Such reference policies, typically instantiated as the model to be further fine-tuned, are important since they can impose an upper limit on DPO's effectiveness. Therefore, we address three related research questions in this work. First, we explore the optimal strength of the KL-divergence constraint in DPO, which penalizes deviations from the reference policy, and find that DPO is sensitive to this strength. Next, we examine the necessity of reference policies for instruction fine-tuning by providing both theoretical and empirical comparisons between DPO and related learning objectives, demonstrating DPO's superiority. Additionally, we investigate whether DPO benefits from stronger reference policies, finding that a stronger reference policy can lead to improved performance, but only when it is similar to the model being fine-tuned. Our findings highlight the confounding role of reference policies in DPO and offer insights for best practices, while also identifying open research questions for future studies.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yale-nlp/refdpo", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2407.13739", "title": "Scaling Granite Code Models to 128K Context", "authors": [ "Matt Stallone", "Vaibhav Saxena", "Leonid Karlinsky", "Bridget McGinn", "Tim Bula", "Mayank Mishra", "Adriana Meza Soria", "Gaoyuan Zhang", "Aditya Prasad", "Yikang Shen", "Saptha Surendran", "Shanmukha Guttula", "Hima Patel", "Parameswaran Selvam", "Xuan-Hong Dang", "Yan Koyfman", "Atin Sood", "Rogerio Feris", "Nirmit Desai", "David D. Cox", "Ruchir Puri", "Rameswar Panda" ], "github": "", "project_page": "", "abstract": "This paper introduces long-context Granite code models that support effective context windows of up to 128K tokens. Our solution for scaling context length of Granite 3B/8B code models from 2K/4K to 128K consists of a light-weight continual pretraining by gradually increasing its RoPE base frequency with repository-level file packing and length-upsampled long-context data. Additionally, we also release instruction-tuned models with long-context support which are derived by further finetuning the long context base models on a mix of permissively licensed short and long-context instruction-response pairs. While comparing to the original short-context Granite code models, our long-context models achieve significant improvements on long-context tasks without any noticeable performance degradation on regular code completion benchmarks (e.g., HumanEval). We release all our long-context Granite code models under an Apache 2.0 license for both research and commercial use.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2407.13764", "title": "Shape of Motion: 4D Reconstruction from a Single Video", "authors": [ "Qianqian Wang", "Vickie Ye", "Hang Gao", "Jake Austin", "Zhengqi Li", "Angjoo Kanazawa" ], "github": "https://github.com/vye16/shape-of-motion", "project_page": "", "abstract": "Monocular dynamic reconstruction is a challenging and long-standing vision problem due to the highly ill-posed nature of the task. Existing approaches are limited in that they either depend on templates, are effective only in quasi-static scenes, or fail to model 3D motion explicitly. In this work, we introduce a method capable of reconstructing generic dynamic scenes, featuring explicit, full-sequence-long 3D motion, from casually captured monocular videos. We tackle the under-constrained nature of the problem with two key insights: First, we exploit the low-dimensional structure of 3D motion by representing scene motion with a compact set of SE3 motion bases. Each point's motion is expressed as a linear combination of these bases, facilitating soft decomposition of the scene into multiple rigidly-moving groups. Second, we utilize a comprehensive set of data-driven priors, including monocular depth maps and long-range 2D tracks, and devise a method to effectively consolidate these noisy supervisory signals, resulting in a globally consistent representation of the dynamic scene. Experiments show that our method achieves state-of-the-art performance for both long-range 3D/2D motion estimation and novel view synthesis on dynamic scenes. Project Page: https://shape-of-motion.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vye16/shape-of-motion", "github_api_added_by": "auto" }, { "date": "2024-07-19", "arxiv_id": "2406.07057", "title": "Benchmarking Trustworthiness of Multimodal Large Language Models: A Comprehensive Study", "authors": [ "Yichi Zhang", "Yao Huang", "Yitong Sun", "Chang Liu", "Zhe Zhao", "Zhengwei Fang", "Yifan Wang", "Huanran Chen", "Xiao Yang", "Xingxing Wei", "Hang Su", "Yinpeng Dong", "Jun Zhu" ], "github": "", "project_page": "", "abstract": "Despite the superior capabilities of Multimodal Large Language Models (MLLMs) across diverse tasks, they still face significant trustworthiness challenges. Yet, current literature on the assessment of trustworthy MLLMs remains limited, lacking a holistic evaluation to offer thorough insights into future improvements. In this work, we establish MultiTrust, the first comprehensive and unified benchmark on the trustworthiness of MLLMs across five primary aspects: truthfulness, safety, robustness, fairness, and privacy. Our benchmark employs a rigorous evaluation strategy that addresses both multimodal risks and cross-modal impacts, encompassing 32 diverse tasks with self-curated datasets. Extensive experiments with 21 modern MLLMs reveal some previously unexplored trustworthiness issues and risks, highlighting the complexities introduced by the multimodality and underscoring the necessity for advanced methodologies to enhance their reliability. For instance, typical proprietary models still struggle with the perception of visually confusing images and are vulnerable to multimodal jailbreaking and adversarial attacks; MLLMs are more inclined to disclose privacy in text and reveal ideological and cultural biases even when paired with irrelevant images in inference, indicating that the multimodality amplifies the internal risks from base LLMs. Additionally, we release a scalable toolbox for standardized trustworthiness research, aiming to facilitate future advancements in this important field. Code and resources are publicly available at: https://multi-trust.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2407.12883", "title": "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "authors": [ "Hongjin Su", "Howard Yen", "Mengzhou Xia", "Weijia Shi", "Niklas Muennighoff", "Han-yu Wang", "Haisu Liu", "Quan Shi", "Zachary S. Siegel", "Michael Tang", "Ruoxi Sun", "Jinsung Yoon", "Sercan O. Arik", "Danqi Chen", "Tao Yu" ], "github": "https://github.com/xlang-ai/BRIGHT", "project_page": "", "abstract": "Existing retrieval benchmarks primarily consist of information-seeking queries (e.g., aggregated questions from search engines) where keyword or semantic-based retrieval is usually sufficient. However, many complex real-world queries require in-depth reasoning to identify relevant documents that go beyond surface form matching. For example, finding documentation for a coding question requires understanding the logic and syntax of the functions involved. To better benchmark retrieval on such challenging queries, we introduce BRIGHT, the first text retrieval benchmark that requires intensive reasoning to retrieve relevant documents. BRIGHT is constructed from the 1,398 real-world queries collected from diverse domains (such as economics, psychology, robotics, software engineering, earth sciences, etc.), sourced from naturally occurring or carefully curated human data. Extensive evaluation reveals that even state-of-the-art retrieval models perform poorly on BRIGHT. The leading model on the MTEB leaderboard [38 ], which achieves a score of 59.0 nDCG@10,2 produces a score of nDCG@10 of 18.0 on BRIGHT. We further demonstrate that augmenting queries with Chain-of-Thought reasoning generated by large language models (LLMs) improves performance by up to 12.2 points. Moreover, BRIGHT is robust against data leakage during pretraining of the benchmarked models as we validate by showing similar performance even when documents from the benchmark are included in the training data. We believe that BRIGHT paves the way for future research on retrieval systems in more realistic and challenging settings. Our code and data are available at https://brightbenchmark.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xlang-ai/BRIGHT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2407.13481", "title": "Attention Overflow: Language Model Input Blur during Long-Context Missing Items Recommendation", "authors": [ "Damien Sileo" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) can suggest missing elements from items listed in a prompt, which can be used for list completion or recommendations based on users' history. However, their performance degrades when presented with too many items, as they start to suggest items already included in the input list. This occurs at around 100 items for mid-2024 flagship LLMs. We evaluate this phenomenon on both synthetic problems (e.g., finding missing numbers in a given range of shuffled integers) and realistic movie recommendation scenarios. We refer to this issue as attention overflow, as preventing repetition requires attending to all items simultaneously. Although iterative loops can mitigate this problem, their costs increase with the repetition rate, affecting the language models' ability to derive novelty from lengthy inputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2407.12982", "title": "Retrieval-Enhanced Machine Learning: Synthesis and Opportunities", "authors": [ "To Eun Kim", "Alireza Salemi", "Andrew Drozdov", "Fernando Diaz", "Hamed Zamani" ], "github": "", "project_page": "", "abstract": "In the field of language modeling, models augmented with retrieval components have emerged as a promising solution to address several challenges faced in the natural language processing (NLP) field, including knowledge grounding, interpretability, and scalability. Despite the primary focus on NLP, we posit that the paradigm of retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) such as computer vision, time series prediction, and computational biology. Therefore, this work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. Also, we found that while a number of studies employ retrieval components to augment their models, there is a lack of integration with foundational Information Retrieval (IR) research. We bridge this gap between the seminal IR research and contemporary REML studies by investigating each component that comprises the REML framework. Ultimately, the goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-19", "arxiv_id": "2406.13897", "title": "CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets", "authors": [ "Longwen Zhang", "Ziyu Wang", "Qixuan Zhang", "Qiwei Qiu", "Anqi Pang", "Haoran Jiang", "Wei Yang", "Lan Xu", "Jingyi Yu" ], "github": "", "project_page": "", "abstract": "In the realm of digital creativity, our potential to craft intricate 3D worlds from imagination is often hampered by the limitations of existing digital tools, which demand extensive expertise and efforts. To narrow this disparity, we introduce CLAY, a 3D geometry and material generator designed to effortlessly transform human imagination into intricate 3D digital structures. CLAY supports classic text or image inputs as well as 3D-aware controls from diverse primitives (multi-view images, voxels, bounding boxes, point clouds, implicit representations, etc). At its core is a large-scale generative model composed of a multi-resolution Variational Autoencoder (VAE) and a minimalistic latent Diffusion Transformer (DiT), to extract rich 3D priors directly from a diverse range of 3D geometries. Specifically, it adopts neural fields to represent continuous and complete surfaces and uses a geometry generative module with pure transformer blocks in latent space. We present a progressive training scheme to train CLAY on an ultra large 3D model dataset obtained through a carefully designed processing pipeline, resulting in a 3D native geometry generator with 1.5 billion parameters. For appearance generation, CLAY sets out to produce physically-based rendering (PBR) textures by employing a multi-view material diffusion model that can generate 2K resolution textures with diffuse, roughness, and metallic modalities. We demonstrate using CLAY for a range of controllable 3D asset creations, from sketchy conceptual designs to production ready assets with intricate details. Even first time users can easily use CLAY to bring their vivid 3D imaginations to life, unleashing unlimited creativity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-22", "arxiv_id": "2407.13976", "title": "PlacidDreamer: Advancing Harmony in Text-to-3D Generation", "authors": [ "Shuo Huang", "Shikun Sun", "Zixuan Wang", "Xiaoyu Qin", "Yanmin Xiong", "Yuan Zhang", "Pengfei Wan", "Di Zhang", "Jia Jia" ], "github": "https://github.com/HansenHuang0823/PlacidDreamer", "project_page": "", "abstract": "Recently, text-to-3D generation has attracted significant attention, resulting in notable performance enhancements. Previous methods utilize end-to-end 3D generation models to initialize 3D Gaussians, multi-view diffusion models to enforce multi-view consistency, and text-to-image diffusion models to refine details with score distillation algorithms. However, these methods exhibit two limitations. Firstly, they encounter conflicts in generation directions since different models aim to produce diverse 3D assets. Secondly, the issue of over-saturation in score distillation has not been thoroughly investigated and solved. To address these limitations, we propose PlacidDreamer, a text-to-3D framework that harmonizes initialization, multi-view generation, and text-conditioned generation with a single multi-view diffusion model, while simultaneously employing a novel score distillation algorithm to achieve balanced saturation. To unify the generation direction, we introduce the Latent-Plane module, a training-friendly plug-in extension that enables multi-view diffusion models to provide fast geometry reconstruction for initialization and enhanced multi-view images to personalize the text-to-image diffusion model. To address the over-saturation problem, we propose to view score distillation as a multi-objective optimization problem and introduce the Balanced Score Distillation algorithm, which offers a Pareto Optimal solution that achieves both rich details and balanced saturation. Extensive experiments validate the outstanding capabilities of our PlacidDreamer. The code is available at https://github.com/HansenHuang0823/PlacidDreamer.", "github_manual": "", "github_regex": "https://github.com/HansenHuang0823/PlacidDreamer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-22", "arxiv_id": "2407.14435", "title": "Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders", "authors": [ "Senthooran Rajamanoharan", "Tom Lieberum", "Nicolas Sonnerat", "Arthur Conmy", "Vikrant Varma", "J\u00e1nos Kram\u00e1r", "Neel Nanda" ], "github": "", "project_page": "", "abstract": "Sparse autoencoders (SAEs) are a promising unsupervised approach for identifying causally relevant and interpretable linear features in a language model's (LM) activations. To be useful for downstream tasks, SAEs need to decompose LM activations faithfully; yet to be interpretable the decomposition must be sparse -- two objectives that are in tension. In this paper, we introduce JumpReLU SAEs, which achieve state-of-the-art reconstruction fidelity at a given sparsity level on Gemma 2 9B activations, compared to other recent advances such as Gated and TopK SAEs. We also show that this improvement does not come at the cost of interpretability through manual and automated interpretability studies. JumpReLU SAEs are a simple modification of vanilla (ReLU) SAEs -- where we replace the ReLU with a discontinuous JumpReLU activation function -- and are similarly efficient to train and run. By utilising straight-through-estimators (STEs) in a principled manner, we show how it is possible to train JumpReLU SAEs effectively despite the discontinuous JumpReLU function introduced in the SAE's forward pass. Similarly, we use STEs to directly train L0 to be sparse, instead of training on proxies such as L1, avoiding problems like shrinkage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-22", "arxiv_id": "2407.14358", "title": "Stable Audio Open", "authors": [ "Zach Evans", "Julian D. Parker", "CJ Carr", "Zack Zukowski", "Josiah Taylor", "Jordi Pons" ], "github": "https://github.com/stability-ai/stable-audio-tools", "project_page": "", "abstract": "Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stability-ai/stable-audio-tools", "github_api_added_by": "auto" }, { "date": "2024-07-22", "arxiv_id": "2407.14257", "title": "SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided Geometric Linearization", "authors": [ "Mae Younes", "Amine Ouasfi", "Adnane Boukhayma" ], "github": "https://github.com/maeyounes/SparseCraft", "project_page": "", "abstract": "We present a novel approach for recovering 3D shape and view dependent appearance from a few colored images, enabling efficient 3D reconstruction and novel view synthesis. Our method learns an implicit neural representation in the form of a Signed Distance Function (SDF) and a radiance field. The model is trained progressively through ray marching enabled volumetric rendering, and regularized with learning-free multi-view stereo (MVS) cues. Key to our contribution is a novel implicit neural shape function learning strategy that encourages our SDF field to be as linear as possible near the level-set, hence robustifying the training against noise emanating from the supervision and regularization signals. Without using any pretrained priors, our method, called SparseCraft, achieves state-of-the-art performances both in novel-view synthesis and reconstruction from sparse views in standard benchmarks, while requiring less than 10 minutes for training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/maeyounes/SparseCraft", "github_api_added_by": "auto" }, { "date": "2024-07-22", "arxiv_id": "2407.14177", "title": "EVLM: An Efficient Vision-Language Model for Visual Understanding", "authors": [ "Kaibing Chen", "Dong Shen", "Hanwen Zhong", "Huasong Zhong", "Kui Xia", "Di Xu", "Wei Yuan", "Yifei Hu", "Bin Wen", "Tianke Zhang", "Changyi Liu", "Dewen Fan", "Huihui Xiao", "Jiahong Wu", "Fan Yang", "Size Li", "Di Zhang" ], "github": "", "project_page": "", "abstract": "In the field of multi-modal language models, the majority of methods are built on an architecture similar to LLaVA. These models use a single-layer ViT feature as a visual prompt, directly feeding it into the language models alongside textual tokens. However, when dealing with long sequences of visual signals or inputs such as videos, the self-attention mechanism of language models can lead to significant computational overhead. Additionally, using single-layer ViT features makes it challenging for large language models to perceive visual signals fully. This paper proposes an efficient multi-modal language model to minimize computational costs while enabling the model to perceive visual signals as comprehensively as possible. Our method primarily includes: (1) employing cross-attention to image-text interaction similar to Flamingo. (2) utilize hierarchical ViT features. (3) introduce the Mixture of Experts (MoE) mechanism to enhance model effectiveness. Our model achieves competitive scores on public multi-modal benchmarks and performs well in tasks such as image captioning and video captioning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-22", "arxiv_id": "2407.14138", "title": "Visual Text Generation in the Wild", "authors": [ "Yuanzhi Zhu", "Jiawei Liu", "Feiyu Gao", "Wenyu Liu", "Xinggang Wang", "Peng Wang", "Fei Huang", "Cong Yao", "Zhibo Yang" ], "github": "https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/SceneVTG", "project_page": "", "abstract": "Recently, with the rapid advancements of generative models, the field of visual text generation has witnessed significant progress. However, it is still challenging to render high-quality text images in real-world scenarios, as three critical criteria should be satisfied: (1) Fidelity: the generated text images should be photo-realistic and the contents are expected to be the same as specified in the given conditions; (2) Reasonability: the regions and contents of the generated text should cohere with the scene; (3) Utility: the generated text images can facilitate related tasks (e.g., text detection and recognition). Upon investigation, we find that existing methods, either rendering-based or diffusion-based, can hardly meet all these aspects simultaneously, limiting their application range. Therefore, we propose in this paper a visual text generator (termed SceneVTG), which can produce high-quality text images in the wild. Following a two-stage paradigm, SceneVTG leverages a Multimodal Large Language Model to recommend reasonable text regions and contents across multiple scales and levels, which are used by a conditional diffusion model as conditions to generate text images. Extensive experiments demonstrate that the proposed SceneVTG significantly outperforms traditional rendering-based methods and recent diffusion-based methods in terms of fidelity and reasonability. Besides, the generated images provide superior utility for tasks involving text detection and text recognition. Code and datasets are available at AdvancedLiterateMachinery.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/SceneVTG", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-22", "arxiv_id": "2407.14057", "title": "LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference", "authors": [ "Qichen Fu", "Minsik Cho", "Thomas Merth", "Sachin Mehta", "Mohammad Rastegari", "Mahyar Najibi" ], "github": "https://github.com/THUDM/LongBench", "project_page": "", "abstract": "The inference of transformer-based large language models consists of two sequential stages: 1) a prefilling stage to compute the KV cache of prompts and generate the first token, and 2) a decoding stage to generate subsequent tokens. For long prompts, the KV cache must be computed for all tokens during the prefilling stage, which can significantly increase the time needed to generate the first token. Consequently, the prefilling stage may become a bottleneck in the generation process. An open question remains whether all prompt tokens are essential for generating the first token. To answer this, we introduce a novel method, LazyLLM, that selectively computes the KV for tokens important for the next token prediction in both the prefilling and decoding stages. Contrary to static pruning approaches that prune the prompt at once, LazyLLM allows language models to dynamically select different subsets of tokens from the context in different generation steps, even though they might be pruned in previous steps. Extensive experiments on standard datasets across various tasks demonstrate that LazyLLM is a generic method that can be seamlessly integrated with existing language models to significantly accelerate the generation without fine-tuning. For instance, in the multi-document question-answering task, LazyLLM accelerates the prefilling stage of the LLama 2 7B model by 2.34x while maintaining accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUDM/LongBench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-22", "arxiv_id": "2407.13833", "title": "Phi-3 Safety Post-Training: Aligning Language Models with a \"Break-Fix\" Cycle", "authors": [ "Emman Haider", "Daniel Perez-Becker", "Thomas Portet", "Piyush Madan", "Amit Garg", "David Majercak", "Wen Wen", "Dongwoo Kim", "Ziyi Yang", "Jianwen Zhang", "Hiteshi Sharma", "Blake Bullwinkel", "Martin Pouliot", "Amanda Minnich", "Shiven Chawla", "Solianna Herrera", "Shahed Warreth", "Maggie Engler", "Gary Lopez", "Nina Chikanov", "Raja Sekhar Rao Dheekonda", "Bolor-Erdene Jagdagdorj", "Roman Lutz", "Richard Lundeen", "Tori Westerhoff", "Pete Bryan", "Christian Seifert", "Ram Shankar Siva Kumar", "Andrew Berkley", "Alex Kessler" ], "github": "https://github.com/Azure/PyRIT", "project_page": "", "abstract": "Recent innovations in language model training have demonstrated that it is possible to create highly performant models that are small enough to run on a smartphone. As these models are deployed in an increasing number of domains, it is critical to ensure that they are aligned with human preferences and safety considerations. In this report, we present our methodology for safety aligning the Phi-3 series of language models. We utilized a \"break-fix\" cycle, performing multiple rounds of dataset curation, safety post-training, benchmarking, red teaming, and vulnerability identification to cover a variety of harm areas in both single and multi-turn scenarios. Our results indicate that this approach iteratively improved the performance of the Phi-3 models across a wide range of responsible AI benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Azure/PyRIT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-22", "arxiv_id": "2407.14402", "title": "The Vision of Autonomic Computing: Can LLMs Make It a Reality?", "authors": [ "Zhiyang Zhang", "Fangkai Yang", "Xiaoting Qin", "Jue Zhang", "Qingwei Lin", "Gong Cheng", "Dongmei Zhang", "Saravan Rajmohan", "Qi Zhang" ], "github": "", "project_page": "", "abstract": "The Vision of Autonomic Computing (ACV), proposed over two decades ago, envisions computing systems that self-manage akin to biological organisms, adapting seamlessly to changing environments. Despite decades of research, achieving ACV remains challenging due to the dynamic and complex nature of modern computing systems. Recent advancements in Large Language Models (LLMs) offer promising solutions to these challenges by leveraging their extensive knowledge, language understanding, and task automation capabilities. This paper explores the feasibility of realizing ACV through an LLM-based multi-agent framework for microservice management. We introduce a five-level taxonomy for autonomous service maintenance and present an online evaluation benchmark based on the Sock Shop microservice demo project to assess our framework's performance. Our findings demonstrate significant progress towards achieving Level 3 autonomy, highlighting the effectiveness of LLMs in detecting and resolving issues within microservice architectures. This study contributes to advancing autonomic computing by pioneering the integration of LLMs into microservice management frameworks, paving the way for more adaptive and self-managing computing systems. The code will be made available at https://aka.ms/ACV-LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-22", "arxiv_id": "2407.14329", "title": "Efficient Audio Captioning with Encoder-Level Knowledge Distillation", "authors": [ "Xuenan Xu", "Haohe Liu", "Mengyue Wu", "Wenwu Wang", "Mark D. Plumbley" ], "github": "", "project_page": "", "abstract": "Significant improvement has been achieved in automated audio captioning (AAC) with recent models. However, these models have become increasingly large as their performance is enhanced. In this work, we propose a knowledge distillation (KD) framework for AAC. Our analysis shows that in the encoder-decoder based AAC models, it is more effective to distill knowledge into the encoder as compared with the decoder. To this end, we incorporate encoder-level KD loss into training, in addition to the standard supervised loss and sequence-level KD loss. We investigate two encoder-level KD methods, based on mean squared error (MSE) loss and contrastive loss, respectively. Experimental results demonstrate that contrastive KD is more robust than MSE KD, exhibiting superior performance in data-scarce situations. By leveraging audio-only data into training in the KD framework, our student model achieves competitive performance, with an inference speed that is 19 times fasterAn online demo is available at \\url{https://huggingface.co/spaces/wsntxxn/efficient_audio_captioning}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-22", "arxiv_id": "2407.14482", "title": "ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities", "authors": [ "Peng Xu", "Wei Ping", "Xianchao Wu", "Zihan Liu", "Mohammad Shoeybi", "Bryan Catanzaro" ], "github": "", "project_page": "", "abstract": "In this work, we introduce ChatQA 2, a Llama3-based model designed to bridge the gap between open-access LLMs and leading proprietary models (e.g., GPT-4-Turbo) in long-context understanding and retrieval-augmented generation (RAG) capabilities. These two capabilities are essential for LLMs to process large volumes of information that cannot fit into a single prompt and are complementary to each other, depending on the downstream tasks and computational budgets. We present a detailed continued training recipe to extend the context window of Llama3-70B-base from 8K to 128K tokens, along with a three-stage instruction tuning process to enhance the model's instruction-following, RAG performance, and long-context understanding capabilities. Our results demonstrate that the Llama3-ChatQA-2-70B model achieves accuracy comparable to GPT-4-Turbo-2024-0409 on many long-context understanding tasks and surpasses it on the RAG benchmark. Interestingly, we find that the state-of-the-art long-context retriever can alleviate the top-k context fragmentation issue in RAG, further improving RAG-based results for long-context understanding tasks. We also provide extensive comparisons between RAG and long-context solutions using state-of-the-art long-context LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-22", "arxiv_id": "2407.10960", "title": "Fast Matrix Multiplications for Lookup Table-Quantized LLMs", "authors": [ "Han Guo", "William Brandon", "Radostin Cholakov", "Jonathan Ragan-Kelley", "Eric P. Xing", "Yoon Kim" ], "github": "https://github.com/hanguo97/flute", "project_page": "", "abstract": "The deployment of large language models (LLMs) is often constrained by memory bandwidth, where the primary bottleneck is the cost of transferring model parameters from the GPU's global memory to its registers. When coupled with custom kernels that fuse the dequantization and matmul operations, weight-only quantization can thus enable faster inference by reducing the amount of memory movement. However, developing high-performance kernels for weight-quantized LLMs presents substantial challenges, especially when the weights are compressed to non-evenly-divisible bit widths (e.g., 3 bits) with non-uniform, lookup table (LUT) quantization. This paper describes FLUTE, a flexible lookup table engine for LUT-quantized LLMs, which uses offline restructuring of the quantized weight matrix to minimize bit manipulations associated with unpacking, and vectorization and duplication of the lookup table to mitigate shared memory bandwidth constraints. At batch sizes < 32 and quantization group size of 128 (typical in LLM inference), the FLUTE kernel can be 2-4x faster than existing GEMM kernels. As an application of FLUTE, we explore a simple extension to lookup table-based NormalFloat quantization and apply it to quantize LLaMA3 to various configurations, obtaining competitive quantization performance against strong baselines while obtaining an end-to-end throughput increase of 1.5 to 2 times.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hanguo97/flute", "github_api_added_by": "auto" }, { "date": "2024-07-22", "arxiv_id": "2407.13168", "title": "SciCode: A Research Coding Benchmark Curated by Scientists", "authors": [ "Minyang Tian", "Luyu Gao", "Shizhuo Dylan Zhang", "Xinan Chen", "Cunwei Fan", "Xuefei Guo", "Roland Haas", "Pan Ji", "Kittithat Krongchon", "Yao Li", "Shengyan Liu", "Di Luo", "Yutao Ma", "Hao Tong", "Kha Trinh", "Chenyu Tian", "Zihan Wang", "Bohao Wu", "Yanyu Xiong", "Shengzhu Yin", "Minhui Zhu", "Kilian Lieret", "Yanxin Lu", "Genglin Liu", "Yufeng Du", "Tianhua Tao", "Ofir Press", "Jamie Callan", "Eliu Huerta", "Hao Peng" ], "github": "", "project_page": "", "abstract": "Since language models (LMs) now outperform average humans on many challenging tasks, it has become increasingly difficult to develop challenging, high-quality, and realistic evaluations. We address this issue by examining LMs' capabilities to generate code for solving real scientific research problems. Incorporating input from scientists and AI researchers in 16 diverse natural science sub-fields, including mathematics, physics, chemistry, biology, and materials science, we created a scientist-curated coding benchmark, SciCode. The problems in SciCode naturally factorize into multiple subproblems, each involving knowledge recall, reasoning, and code synthesis. In total, SciCode contains 338 subproblems decomposed from 80 challenging main problems. It offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions and test cases for evaluation. Claude3.5-Sonnet, the best-performing model among those tested, can solve only 4.6% of the problems in the most realistic setting. We believe that SciCode demonstrates both contemporary LMs' progress towards becoming helpful scientific assistants and sheds light on the development and evaluation of scientific AI in the future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-22", "arxiv_id": "2407.12594", "title": "VisFocus: Prompt-Guided Vision Encoders for OCR-Free Dense Document Understanding", "authors": [ "Ofir Abramovich", "Niv Nayman", "Sharon Fogel", "Inbal Lavi", "Ron Litman", "Shahar Tsiper", "Royee Tichauer", "Srikar Appalaraju", "Shai Mazor", "R. Manmatha" ], "github": "https://github.com/amazon-science/visfocus", "project_page": "", "abstract": "In recent years, notable advancements have been made in the domain of visual document understanding, with the prevailing architecture comprising a cascade of vision and language models. The text component can either be extracted explicitly with the use of external OCR models in OCR-based approaches, or alternatively, the vision model can be endowed with reading capabilities in OCR-free approaches. Typically, the queries to the model are input exclusively to the language component, necessitating the visual features to encompass the entire document. In this paper, we present VisFocus, an OCR-free method designed to better exploit the vision encoder's capacity by coupling it directly with the language prompt. To do so, we replace the down-sampling layers with layers that receive the input prompt and allow highlighting relevant parts of the document, while disregarding others. We pair the architecture enhancements with a novel pre-training task, using language masking on a snippet of the document text fed to the visual encoder in place of the prompt, to empower the model with focusing capabilities. Consequently, VisFocus learns to allocate its attention to text patches pertinent to the provided prompt. Our experiments demonstrate that this prompt-guided visual encoding approach significantly improves performance, achieving state-of-the-art results on various benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amazon-science/visfocus", "github_api_added_by": "auto" }, { "date": "2024-07-22", "arxiv_id": "2407.13559", "title": "Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition", "authors": [ "Gagan Bhatia", "El Moatez Billah Nagoudi", "Fakhraddin Alwajih", "Muhammad Abdul-Mageed" ], "github": "", "project_page": "", "abstract": "Arabic Optical Character Recognition (OCR) and Handwriting Recognition (HWR) pose unique challenges due to the cursive and context-sensitive nature of the Arabic script. This study introduces Qalam, a novel foundation model designed for Arabic OCR and HWR, built on a SwinV2 encoder and RoBERTa decoder architecture. Our model significantly outperforms existing methods, achieving a Word Error Rate (WER) of just 0.80% in HWR tasks and 1.18% in OCR tasks. We train Qalam on a diverse dataset, including over 4.5 million images from Arabic manuscripts and a synthetic dataset comprising 60k image-text pairs. Notably, Qalam demonstrates exceptional handling of Arabic diacritics, a critical feature in Arabic scripts. Furthermore, it shows a remarkable ability to process high-resolution inputs, addressing a common limitation in current OCR systems. These advancements underscore Qalam's potential as a leading solution for Arabic script recognition, offering a significant leap in accuracy and efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-22", "arxiv_id": "2407.14507", "title": "Internal Consistency and Self-Feedback in Large Language Models: A Survey", "authors": [ "Xun Liang", "Shichao Song", "Zifan Zheng", "Hanyu Wang", "Qingchen Yu", "Xunkai Li", "Rong-Hua Li", "Feiyu Xiong", "Zhiyu Li" ], "github": "https://github.com/IAAR-Shanghai/ICSFSurvey", "project_page": "", "abstract": "Large language models (LLMs) are expected to respond accurately but often exhibit deficient reasoning or generate hallucinatory content. To address these, studies prefixed with ``Self-'' such as Self-Consistency, Self-Improve, and Self-Refine have been initiated. They share a commonality: involving LLMs evaluating and updating itself to mitigate the issues. Nonetheless, these efforts lack a unified perspective on summarization, as existing surveys predominantly focus on categorization without examining the motivations behind these works. In this paper, we summarize a theoretical framework, termed Internal Consistency, which offers unified explanations for phenomena such as the lack of reasoning and the presence of hallucinations. Internal Consistency assesses the coherence among LLMs' latent layer, decoding layer, and response layer based on sampling methodologies. Expanding upon the Internal Consistency framework, we introduce a streamlined yet effective theoretical framework capable of mining Internal Consistency, named Self-Feedback. The Self-Feedback framework consists of two modules: Self-Evaluation and Self-Update. This framework has been employed in numerous studies. We systematically classify these studies by tasks and lines of work; summarize relevant evaluation methods and benchmarks; and delve into the concern, ``Does Self-Feedback Really Work?'' We propose several critical viewpoints, including the ``Hourglass Evolution of Internal Consistency'', ``Consistency Is (Almost) Correctness'' hypothesis, and ``The Paradox of Latent and Explicit Reasoning''. Furthermore, we outline promising directions for future research. We have open-sourced the experimental code, reference list, and statistical data, available at https://github.com/IAAR-Shanghai/ICSFSurvey.", "github_manual": "", "github_regex": "https://github.com/IAAR-Shanghai/ICSFSurvey", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.14561", "title": "NNsight and NDIF: Democratizing Access to Foundation Model Internals", "authors": [ "Jaden Fiotto-Kaufman", "Alexander R Loftus", "Eric Todd", "Jannik Brinkmann", "Caden Juang", "Koyena Pal", "Can Rager", "Aaron Mueller", "Samuel Marks", "Arnab Sen Sharma", "Francesca Lucchetti", "Michael Ripa", "Adam Belfki", "Nikhil Prakash", "Sumeet Multani", "Carla Brodley", "Arjun Guha", "Jonathan Bell", "Byron Wallace", "David Bau" ], "github": "https://github.com/ndif-team/nnsight", "project_page": "", "abstract": "The enormous scale of state-of-the-art foundation models has limited their accessibility to scientists, because customized experiments at large model sizes require costly hardware and complex engineering that is impractical for most researchers. To alleviate these problems, we introduce NNsight, an open-source Python package with a simple, flexible API that can express interventions on any PyTorch model by building computation graphs. We also introduce NDIF, a collaborative research platform providing researchers access to foundation-scale LLMs via the NNsight API. Code, documentation, and tutorials are available at https://www.nnsight.net.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ndif-team/nnsight", "github_api_added_by": "auto" }, { "date": "2024-07-23", "arxiv_id": "2407.15841", "title": "SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models", "authors": [ "Mingze Xu", "Mingfei Gao", "Zhe Gan", "Hong-You Chen", "Zhengfeng Lai", "Haiming Gang", "Kai Kang", "Afshin Dehghan" ], "github": "https://github.com/apple/ml-slowfast-llava", "project_page": "", "abstract": "We propose SlowFast-LLaVA (or SF-LLaVA for short), a training-free video large language model (LLM) that can jointly capture the detailed spatial semantics and long-range temporal context without exceeding the token budget of commonly used LLMs. This is realized by using a two-stream SlowFast design of inputs for Video LLMs to aggregate features from sampled video frames in an effective way. Specifically, the Slow pathway extracts features at a low frame rate while keeping as many spatial details as possible (e.g., with 24x24 tokens), and the Fast pathway operates on a high frame rate but uses a larger spatial pooling stride (e.g., downsampling 6x) to focus on the motion cues. As a result, this design allows us to adequately capture both spatial and temporal features that are beneficial for understanding details along the video. Experimental results show that SF-LLaVA outperforms existing training-free methods on a wide range of video tasks. On some benchmarks, it achieves comparable or even better performance compared to state-of-the-art Video LLMs that are fine-tuned on video datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/ml-slowfast-llava", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.15762", "title": "Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning", "authors": [ "Kaiwen Wang", "Rahul Kidambi", "Ryan Sullivan", "Alekh Agarwal", "Christoph Dann", "Andrea Michi", "Marco Gelmi", "Yunxuan Li", "Raghav Gupta", "Avinava Dubey", "Alexandre Ram\u00e9", "Johan Ferret", "Geoffrey Cideron", "Le Hou", "Hongkun Yu", "Amr Ahmed", "Aranyak Mehta", "L\u00e9onard Hussenot", "Olivier Bachem", "Edouard Leurent" ], "github": "", "project_page": "", "abstract": "Reward-based finetuning is crucial for aligning language policies with intended behaviors (e.g., creativity and safety). A key challenge here is to develop steerable language models that trade-off multiple (conflicting) objectives in a flexible and efficient manner. This paper presents Conditioned Language Policy (CLP), a general framework for finetuning language models on multiple objectives. Building on techniques from multi-task training and parameter-efficient finetuning, CLP can learn steerable models that effectively trade-off conflicting objectives at inference time. Notably, this does not require training or maintaining multiple models to achieve different trade-offs between the objectives. Through an extensive set of experiments and ablations, we show that the CLP framework learns steerable models that outperform and Pareto-dominate the current state-of-the-art approaches for multi-objective finetuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.15002", "title": "GET-Zero: Graph Embodiment Transformer for Zero-shot Embodiment Generalization", "authors": [ "Austin Patel", "Shuran Song" ], "github": "", "project_page": "", "abstract": "This paper introduces GET-Zero, a model architecture and training procedure for learning an embodiment-aware control policy that can immediately adapt to new hardware changes without retraining. To do so, we present Graph Embodiment Transformer (GET), a transformer model that leverages the embodiment graph connectivity as a learned structural bias in the attention mechanism. We use behavior cloning to distill demonstration data from embodiment-specific expert policies into an embodiment-aware GET model that conditions on the hardware configuration of the robot to make control decisions. We conduct a case study on a dexterous in-hand object rotation task using different configurations of a four-fingered robot hand with joints removed and with link length extensions. Using the GET model along with a self-modeling loss enables GET-Zero to zero-shot generalize to unseen variation in graph structure and link length, yielding a 20% improvement over baseline methods. All code and qualitative video results are on https://get-zero-paper.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.14958", "title": "Temporal Residual Jacobians For Rig-free Motion Transfer", "authors": [ "Sanjeev Muralikrishnan", "Niladri Shekhar Dutt", "Siddhartha Chaudhuri", "Noam Aigerman", "Vladimir Kim", "Matthew Fisher", "Niloy J. Mitra" ], "github": "", "project_page": "", "abstract": "We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer. Our approach does not assume access to any rigging or intermediate shape keyframes, produces geometrically and temporally consistent motions, and can be used to transfer long motion sequences. Central to our approach are two coupled neural networks that individually predict local geometric and temporal changes that are subsequently integrated, spatially and temporally, to produce the final animated meshes. The two networks are jointly trained, complement each other in producing spatial and temporal signals, and are supervised directly with 3D positional information. During inference, in the absence of keyframes, our method essentially solves a motion extrapolation problem. We test our setup on diverse meshes (synthetic and scanned shapes) to demonstrate its superiority in generating realistic and natural-looking animations on unseen body shapes against SoTA alternatives. Supplemental video and code are available at https://temporaljacobians.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.15295", "title": "VideoGameBunny: Towards vision assistants for video games", "authors": [ "Mohammad Reza Taesiri", "Cor-Paul Bezemer" ], "github": "", "project_page": "", "abstract": "Large multimodal models (LMMs) hold substantial promise across various domains, from personal assistance in daily tasks to sophisticated applications like medical diagnostics. However, their capabilities have limitations in the video game domain, such as challenges with scene understanding, hallucinations, and inaccurate descriptions of video game content, especially in open-source models. This paper describes the development of VideoGameBunny, a LLaVA-style model based on Bunny, specifically tailored for understanding images from video games. We release intermediate checkpoints, training logs, and an extensive dataset comprising 185,259 video game images from 413 titles, along with 389,565 image-instruction pairs that include image captions, question-answer pairs, and a JSON representation of 16 elements of 136,974 images. Our experiments show that our high quality game-related data has the potential to make a relatively small model outperform the much larger state-of-the-art model LLaVa-1.6-34b (which has more than 4x the number of parameters). Our study paves the way for future research in video game understanding on tasks such as playing, commentary, and debugging. Code and data are available at https://videogamebunny.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.15754", "title": "LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding", "authors": [ "Haoning Wu", "Dongxu Li", "Bei Chen", "Junnan Li" ], "github": "https://github.com/longvideobench/longvideobench", "project_page": "", "abstract": "Large multimodal models (LMMs) are processing increasingly longer and richer inputs. Albeit the progress, few public benchmark is available to measure such development. To mitigate this gap, we introduce LongVideoBench, a question-answering benchmark that features video-language interleaved inputs up to an hour long. Our benchmark includes 3,763 varying-length web-collected videos with their subtitles across diverse themes, designed to comprehensively evaluate LMMs on long-term multimodal understanding. To achieve this, we interpret the primary challenge as to accurately retrieve and reason over detailed multimodal information from long inputs. As such, we formulate a novel video question-answering task termed referring reasoning. Specifically, as part of the question, it contains a referring query that references related video contexts, called referred context. The model is then required to reason over relevant video details from the referred context. Following the paradigm of referring reasoning, we curate 6,678 human-annotated multiple-choice questions in 17 fine-grained categories, establishing one of the most comprehensive benchmarks for long-form video understanding. Evaluations suggest that the LongVideoBench presents significant challenges even for the most advanced proprietary models (e.g. GPT-4o, Gemini-1.5-Pro, GPT-4-Turbo), while their open-source counterparts show an even larger performance gap. In addition, our results indicate that model performance on the benchmark improves only when they are capable of processing more frames, positioning LongVideoBench as a valuable benchmark for evaluating future-generation long-context LMMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/longvideobench/longvideobench", "github_api_added_by": "auto" }, { "date": "2024-07-23", "arxiv_id": "2407.15187", "title": "HoloDreamer: Holistic 3D Panoramic World Generation from Text Descriptions", "authors": [ "Haiyang Zhou", "Xinhua Cheng", "Wangbo Yu", "Yonghong Tian", "Li Yuan" ], "github": "", "project_page": "", "abstract": "3D scene generation is in high demand across various domains, including virtual reality, gaming, and the film industry. Owing to the powerful generative capabilities of text-to-image diffusion models that provide reliable priors, the creation of 3D scenes using only text prompts has become viable, thereby significantly advancing researches in text-driven 3D scene generation. In order to obtain multiple-view supervision from 2D diffusion models, prevailing methods typically employ the diffusion model to generate an initial local image, followed by iteratively outpainting the local image using diffusion models to gradually generate scenes. Nevertheless, these outpainting-based approaches prone to produce global inconsistent scene generation results without high degree of completeness, restricting their broader applications. To tackle these problems, we introduce HoloDreamer, a framework that first generates high-definition panorama as a holistic initialization of the full 3D scene, then leverage 3D Gaussian Splatting (3D-GS) to quickly reconstruct the 3D scene, thereby facilitating the creation of view-consistent and fully enclosed 3D scenes. Specifically, we propose Stylized Equirectangular Panorama Generation, a pipeline that combines multiple diffusion models to enable stylized and detailed equirectangular panorama generation from complex text prompts. Subsequently, Enhanced Two-Stage Panorama Reconstruction is introduced, conducting a two-stage optimization of 3D-GS to inpaint the missing region and enhance the integrity of the scene. Comprehensive experiments demonstrated that our method outperforms prior works in terms of overall visual consistency and harmony as well as reconstruction quality and rendering robustness when generating fully enclosed scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.15842", "title": "Artist: Aesthetically Controllable Text-Driven Stylization without Training", "authors": [ "Ruixiang Jiang", "Changwen Chen" ], "github": "https://github.com/songrise/Artist", "project_page": "", "abstract": "Diffusion models entangle content and style generation during the denoising process, leading to undesired content modification when directly applied to stylization tasks. Existing methods struggle to effectively control the diffusion model to meet the aesthetic-level requirements for stylization. In this paper, we introduce Artist, a training-free approach that aesthetically controls the content and style generation of a pretrained diffusion model for text-driven stylization. Our key insight is to disentangle the denoising of content and style into separate diffusion processes while sharing information between them. We propose simple yet effective content and style control methods that suppress style-irrelevant content generation, resulting in harmonious stylization results. Extensive experiments demonstrate that our method excels at achieving aesthetic-level stylization requirements, preserving intricate details in the content image and aligning well with the style prompt. Furthermore, we showcase the highly controllability of the stylization strength from various perspectives. Code will be released, project home page: https://DiffusionArtist.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/songrise/Artist", "github_api_added_by": "auto" }, { "date": "2024-07-23", "arxiv_id": "2407.15642", "title": "Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models", "authors": [ "Xin Ma", "Yaohui Wang", "Gengyu Jia", "Xinyuan Chen", "Yuan-Fang Li", "Cunjian Chen", "Yu Qiao" ], "github": "https://github.com/maxin-cn/Cinemo", "project_page": "", "abstract": "Diffusion models have achieved great progress in image animation due to powerful generative capabilities. However, maintaining spatio-temporal consistency with detailed information from the input static image over time (e.g., style, background, and object of the input static image) and ensuring smoothness in animated video narratives guided by textual prompts still remains challenging. In this paper, we introduce Cinemo, a novel image animation approach towards achieving better motion controllability, as well as stronger temporal consistency and smoothness. In general, we propose three effective strategies at the training and inference stages of Cinemo to accomplish our goal. At the training stage, Cinemo focuses on learning the distribution of motion residuals, rather than directly predicting subsequent via a motion diffusion model. Additionally, a structural similarity index-based strategy is proposed to enable Cinemo to have better controllability of motion intensity. At the inference stage, a noise refinement technique based on discrete cosine transformation is introduced to mitigate sudden motion changes. Such three strategies enable Cinemo to produce highly consistent, smooth, and motion-controllable results. Compared to previous methods, Cinemo offers simpler and more precise user controllability. Extensive experiments against several state-of-the-art methods, including both commercial tools and research approaches, across multiple metrics, demonstrate the effectiveness and superiority of our proposed approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/maxin-cn/Cinemo", "github_api_added_by": "auto" }, { "date": "2024-07-23", "arxiv_id": "2407.15420", "title": "Local All-Pair Correspondence for Point Tracking", "authors": [ "Seokju Cho", "Jiahui Huang", "Jisu Nam", "Honggyu An", "Seungryong Kim", "Joon-Young Lee" ], "github": "https://github.com/ku-cvlab/locotrack", "project_page": "", "abstract": "We introduce LocoTrack, a highly accurate and efficient model designed for the task of tracking any point (TAP) across video sequences. Previous approaches in this task often rely on local 2D correlation maps to establish correspondences from a point in the query image to a local region in the target image, which often struggle with homogeneous regions or repetitive features, leading to matching ambiguities. LocoTrack overcomes this challenge with a novel approach that utilizes all-pair correspondences across regions, i.e., local 4D correlation, to establish precise correspondences, with bidirectional correspondence and matching smoothness significantly enhancing robustness against ambiguities. We also incorporate a lightweight correlation encoder to enhance computational efficiency, and a compact Transformer architecture to integrate long-term temporal information. LocoTrack achieves unmatched accuracy on all TAP-Vid benchmarks and operates at a speed almost 6 times faster than the current state-of-the-art.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ku-cvlab/locotrack", "github_api_added_by": "auto" }, { "date": "2024-07-23", "arxiv_id": "2407.15337", "title": "ThermalNeRF: Thermal Radiance Fields", "authors": [ "Yvette Y. Lin", "Xin-Yi Pan", "Sara Fridovich-Keil", "Gordon Wetzstein" ], "github": "", "project_page": "", "abstract": "Thermal imaging has a variety of applications, from agricultural monitoring to building inspection to imaging under poor visibility, such as in low light, fog, and rain. However, reconstructing thermal scenes in 3D presents several challenges due to the comparatively lower resolution and limited features present in long-wave infrared (LWIR) images. To overcome these challenges, we propose a unified framework for scene reconstruction from a set of LWIR and RGB images, using a multispectral radiance field to represent a scene viewed by both visible and infrared cameras, thus leveraging information across both spectra. We calibrate the RGB and infrared cameras with respect to each other, as a preprocessing step using a simple calibration target. We demonstrate our method on real-world sets of RGB and LWIR photographs captured from a handheld thermal camera, showing the effectiveness of our method at scene representation across the visible and infrared spectra. We show that our method is capable of thermal super-resolution, as well as visually removing obstacles to reveal objects that are occluded in either the RGB or thermal channels. Please see https://yvette256.github.io/thermalnerf for video results as well as our code and dataset release.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.15060", "title": "MusiConGen: Rhythm and Chord Control for Transformer-Based Text-to-Music Generation", "authors": [ "Yun-Han Lan", "Wen-Yi Hsiao", "Hao-Chung Cheng", "Yi-Hsuan Yang" ], "github": "https://github.com/Cyan0731/MusiConGen", "project_page": "", "abstract": "Existing text-to-music models can produce high-quality audio with great diversity. However, textual prompts alone cannot precisely control temporal musical features such as chords and rhythm of the generated music. To address this challenge, we introduce MusiConGen, a temporally-conditioned Transformer-based text-to-music model that builds upon the pretrained MusicGen framework. Our innovation lies in an efficient finetuning mechanism, tailored for consumer-grade GPUs, that integrates automatically-extracted rhythm and chords as the condition signal. During inference, the condition can either be musical features extracted from a reference audio signal, or be user-defined symbolic chord sequence, BPM, and textual prompts. Our performance evaluation on two datasets -- one derived from extracted features and the other from user-created inputs -- demonstrates that MusiConGen can generate realistic backing track music that aligns well with the specified conditions. We open-source the code and model checkpoints, and provide audio examples online, https://musicongen.github.io/musicongen_demo/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Cyan0731/MusiConGen", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.15017", "title": "Knowledge Mechanisms in Large Language Models: A Survey and Perspective", "authors": [ "Mengru Wang", "Yunzhi Yao", "Ziwen Xu", "Shuofei Qiao", "Shumin Deng", "Peng Wang", "Xiang Chen", "Jia-Chen Gu", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Huajun Chen", "Ningyu Zhang" ], "github": "", "project_page": "", "abstract": "Understanding knowledge mechanisms in Large Language Models (LLMs) is crucial for advancing towards trustworthy AGI. This paper reviews knowledge mechanism analysis from a novel taxonomy including knowledge utilization and evolution. Knowledge utilization delves into the mechanism of memorization, comprehension and application, and creation. Knowledge evolution focuses on the dynamic progression of knowledge within individual and group LLMs. Moreover, we discuss what knowledge LLMs have learned, the reasons for the fragility of parametric knowledge, and the potential dark knowledge (hypothesis) that will be challenging to address. We hope this work can help understand knowledge in LLMs and provide insights for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.14931", "title": "POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation", "authors": [ "Alexey Skrynnik", "Anton Andreychuk", "Anatolii Borzilov", "Alexander Chernyavskiy", "Konstantin Yakovlev", "Aleksandr Panov" ], "github": "https://github.com/jbr-ai-labs/mamba", "project_page": "", "abstract": "Multi-agent reinforcement learning (MARL) has recently excelled in solving challenging cooperative and competitive multi-agent problems in various environments with, mostly, few agents and full observability. Moreover, a range of crucial robotics-related tasks, such as multi-robot navigation and obstacle avoidance, that have been conventionally approached with the classical non-learnable methods (e.g., heuristic search) is currently suggested to be solved by the learning-based or hybrid methods. Still, in this domain, it is hard, not to say impossible, to conduct a fair comparison between classical, learning-based, and hybrid approaches due to the lack of a unified framework that supports both learning and evaluation. To this end, we introduce POGEMA, a set of comprehensive tools that includes a fast environment for learning, a generator of problem instances, the collection of pre-defined ones, a visualization toolkit, and a benchmarking tool that allows automated evaluation. We introduce and specify an evaluation protocol defining a range of domain-related metrics computed on the basics of the primary evaluation indicators (such as success rate and path length), allowing a fair multi-fold comparison. The results of such a comparison, which involves a variety of state-of-the-art MARL, search-based, and hybrid methods, are presented.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jbr-ai-labs/mamba", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.15848", "title": "BoostMVSNeRFs: Boosting MVS-based NeRFs to Generalizable View Synthesis in Large-scale Scenes", "authors": [ "Chih-Hai Su", "Chih-Yao Hu", "Shr-Ruei Tsai", "Jie-Ying Lee", "Chin-Yang Lin", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "While Neural Radiance Fields (NeRFs) have demonstrated exceptional quality, their protracted training duration remains a limitation. Generalizable and MVS-based NeRFs, although capable of mitigating training time, often incur tradeoffs in quality. This paper presents a novel approach called BoostMVSNeRFs to enhance the rendering quality of MVS-based NeRFs in large-scale scenes. We first identify limitations in MVS-based NeRF methods, such as restricted viewport coverage and artifacts due to limited input views. Then, we address these limitations by proposing a new method that selects and combines multiple cost volumes during volume rendering. Our method does not require training and can adapt to any MVS-based NeRF methods in a feed-forward fashion to improve rendering quality. Furthermore, our approach is also end-to-end trainable, allowing fine-tuning on specific scenes. We demonstrate the effectiveness of our method through experiments on large-scale datasets, showing significant rendering quality improvements in large-scale scenes and unbounded outdoor scenarios. We release the source code of BoostMVSNeRFs at https://su-terry.github.io/BoostMVSNeRFs/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.15711", "title": "AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?", "authors": [ "Ori Yoran", "Samuel Joseph Amouyal", "Chaitanya Malaviya", "Ben Bogin", "Ofir Press", "Jonathan Berant" ], "github": "", "project_page": "", "abstract": "Language agents, built on top of language models (LMs), are systems that can interact with complex environments, such as the open web. In this work, we examine whether such agents can perform realistic and time-consuming tasks on the web, e.g., monitoring real-estate markets or locating relevant nearby businesses. We introduce AssistantBench, a challenging new benchmark consisting of 214 realistic tasks that can be automatically evaluated, covering different scenarios and domains. We find that AssistantBench exposes the limitations of current systems, including language models and retrieval-augmented language models, as no model reaches an accuracy of more than 25 points. While closed-book LMs perform well, they exhibit low precision since they tend to hallucinate facts. State-of-the-art web agents reach a score of near zero. Additionally, we introduce SeePlanAct (SPA), a new web agent that significantly outperforms previous agents, and an ensemble of SPA and closed-book models reaches the best overall performance. Moreover, we analyze failures of current systems and highlight that web navigation remains a major challenge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.14622", "title": "BOND: Aligning LLMs with Best-of-N Distillation", "authors": [ "Pier Giuseppe Sessa", "Robert Dadashi", "L\u00e9onard Hussenot", "Johan Ferret", "Nino Vieillard", "Alexandre Ram\u00e9", "Bobak Shariari", "Sarah Perrin", "Abe Friesen", "Geoffrey Cideron", "Sertan Girgin", "Piotr Stanczyk", "Andrea Michi", "Danila Sinopalnikov", "Sabela Ramos", "Am\u00e9lie H\u00e9liou", "Aliaksei Severyn", "Matt Hoffman", "Nikola Momchev", "Olivier Bachem" ], "github": "", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models. Yet, a surprisingly simple and strong inference-time strategy is Best-of-N sampling that selects the best generation among N candidates. In this paper, we propose Best-of-N Distillation (BOND), a novel RLHF algorithm that seeks to emulate Best-of-N but without its significant computational overhead at inference time. Specifically, BOND is a distribution matching algorithm that forces the distribution of generations from the policy to get closer to the Best-of-N distribution. We use the Jeffreys divergence (a linear combination of forward and backward KL) to balance between mode-covering and mode-seeking behavior, and derive an iterative formulation that utilizes a moving anchor for efficiency. We demonstrate the effectiveness of our approach and several design choices through experiments on abstractive summarization and Gemma models. Aligning Gemma policies with BOND outperforms other RLHF algorithms by improving results on several benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.15272", "title": "MIBench: Evaluating Multimodal Large Language Models over Multiple Images", "authors": [ "Haowei Liu", "Xi Zhang", "Haiyang Xu", "Yaya Shi", "Chaoya Jiang", "Ming Yan", "Ji Zhang", "Fei Huang", "Chunfeng Yuan", "Bing Li", "Weiming Hu" ], "github": "", "project_page": "", "abstract": "Built on the power of LLMs, numerous multimodal large language models (MLLMs) have recently achieved remarkable performance on various vision-language tasks across multiple benchmarks. However, most existing MLLMs and benchmarks primarily focus on single-image input scenarios, leaving the performance of MLLMs when handling realistic multiple images remain underexplored. Although a few benchmarks consider multiple images, their evaluation dimensions and samples are very limited. Therefore, in this paper, we propose a new benchmark MIBench, to comprehensively evaluate fine-grained abilities of MLLMs in multi-image scenarios. Specifically, MIBench categorizes the multi-image abilities into three scenarios: multi-image instruction (MII), multimodal knowledge-seeking (MKS) and multimodal in-context learning (MIC), and constructs 13 tasks with a total of 13K annotated samples. During data construction, for MII and MKS, we extract correct options from manual annotations and create challenging distractors to obtain multiple-choice questions. For MIC, to enable an in-depth evaluation, we set four sub-tasks and transform the original datasets into in-context learning formats. We evaluate several open-source MLLMs and close-source MLLMs on the proposed MIBench. The results reveal that although current models excel in single-image tasks, they exhibit significant shortcomings when faced with multi-image inputs, such as confused fine-grained perception, limited multi-image reasoning, and unstable in-context learning. The annotated data in MIBench is available at https://huggingface.co/datasets/StarBottle/MIBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.14933", "title": "Consent in Crisis: The Rapid Decline of the AI Data Commons", "authors": [ "Shayne Longpre", "Robert Mahari", "Ariel Lee", "Campbell Lund", "Hamidah Oderinwale", "William Brannon", "Nayan Saxena", "Naana Obeng-Marnu", "Tobin South", "Cole Hunter", "Kevin Klyman", "Christopher Klamm", "Hailey Schoelkopf", "Nikhil Singh", "Manuel Cherep", "Ahmad Anis", "An Dinh", "Caroline Chitongo", "Da Yin", "Damien Sileo", "Deividas Mataciunas", "Diganta Misra", "Emad Alghamdi", "Enrico Shippole", "Jianguo Zhang", "Joanna Materzynska", "Kun Qian", "Kush Tiwary", "Lester Miranda", "Manan Dey", "Minnie Liang", "Mohammed Hamdy", "Niklas Muennighoff", "Seonghyeon Ye", "Seungone Kim", "Shrestha Mohanty", "Vipul Gupta", "Vivek Sharma", "Vu Minh Chien", "Xuhui Zhou", "Yizhi Li", "Caiming Xiong", "Luis Villa", "Stella Biderman", "Hanlin Li", "Daphne Ippolito", "Sara Hooker", "Jad Kabbara", "Sandy Pentland" ], "github": "", "project_page": "", "abstract": "General-purpose artificial intelligence (AI) systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we conduct the first, large-scale, longitudinal audit of the consent protocols for the web domains underlying AI training corpora. Our audit of 14,000 web domains provides an expansive view of crawlable web data and how consent preferences to use it are changing over time. We observe a proliferation of AI-specific clauses to limit use, acute differences in restrictions on AI developers, as well as general inconsistencies between websites' expressed intentions in their Terms of Service and their robots.txt. We diagnose these as symptoms of ineffective web protocols, not designed to cope with the widespread re-purposing of the internet for AI. Our longitudinal analyses show that in a single year (2023-2024) there has been a rapid crescendo of data restrictions from web sources, rendering ~5%+ of all tokens in C4, or 28%+ of the most actively maintained, critical sources in C4, fully restricted from use. For Terms of Service crawling restrictions, a full 45% of C4 is now restricted. If respected or enforced, these restrictions are rapidly biasing the diversity, freshness, and scaling laws for general-purpose AI systems. We hope to illustrate the emerging crisis in data consent, foreclosing much of the open web, not only for commercial AI, but non-commercial AI and academic purposes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.15595", "title": "Discrete Flow Matching", "authors": [ "Itai Gat", "Tal Remez", "Neta Shaul", "Felix Kreuk", "Ricky T. Q. Chen", "Gabriel Synnaeve", "Yossi Adi", "Yaron Lipman" ], "github": "", "project_page": "", "abstract": "Despite Flow Matching and diffusion models having emerged as powerful generative paradigms for continuous variables such as images and videos, their application to high-dimensional discrete data, such as language, is still limited. In this work, we present Discrete Flow Matching, a novel discrete flow paradigm designed specifically for generating discrete data. Discrete Flow Matching offers several key contributions: (i) it works with a general family of probability paths interpolating between source and target distributions; (ii) it allows for a generic formula for sampling from these probability paths using learned posteriors such as the probability denoiser (x-prediction) and noise-prediction (epsilon-prediction); (iii) practically, focusing on specific probability paths defined with different schedulers considerably improves generative perplexity compared to previous discrete diffusion and flow models; and (iv) by scaling Discrete Flow Matching models up to 1.7B parameters, we reach 6.7% Pass@1 and 13.4% Pass@10 on HumanEval and 6.7% Pass@1 and 20.6% Pass@10 on 1-shot MBPP coding benchmarks. Our approach is capable of generating high-quality discrete data in a non-autoregressive fashion, significantly closing the gap between autoregressive models and discrete flow models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.15233", "title": "CGB-DM: Content and Graphic Balance Layout Generation with Transformer-based Diffusion Model", "authors": [ "Yu Li", "Yifan Chen", "Gongye Liu", "Jie Wu", "Yujiu Yang" ], "github": "", "project_page": "", "abstract": "Layout generation is the foundation task of intelligent design, which requires the integration of visual aesthetics and harmonious expression of content delivery. However, existing methods still face challenges in generating precise and visually appealing layouts, including blocking, overlap, or spatial misalignment between layouts, which are closely related to the spatial structure of graphic layouts. We find that these methods overly focus on content information and lack constraints on layout spatial structure, resulting in an imbalance of learning content-aware and graphic-aware features. To tackle this issue, we propose Content and Graphic Balance Layout Generation with Transformer-based Diffusion Model (CGB-DM). Specifically, we first design a regulator that balances the predicted content and graphic weight, overcoming the tendency of paying more attention to the content on canvas. Secondly, we introduce a graphic constraint of saliency bounding box to further enhance the alignment of geometric features between layout representations and images. In addition, we adapt a transformer-based diffusion model as the backbone, whose powerful generation capability ensures the quality in layout generation. Extensive experimental results indicate that our method has achieved state-of-the-art performance in both quantitative and qualitative evaluations. Our model framework can also be expanded to other graphic design fields.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-23", "arxiv_id": "2407.13766", "title": "Visual Haystacks: Answering Harder Questions About Sets of Images", "authors": [ "Tsung-Han Wu", "Giscard Biamby", "Jerome Quenum", "Ritwik Gupta", "Joseph E. Gonzalez", "Trevor Darrell", "David M. Chan" ], "github": "https://github.com/visual-haystacks/vhs_benchmark", "project_page": "", "abstract": "Recent advancements in Large Multimodal Models (LMMs) have made significant progress in the field of single-image visual question answering. However, these models face substantial challenges when tasked with queries that span extensive collections of images, similar to real-world scenarios like searching through large photo albums, finding specific information across the internet, or monitoring environmental changes through satellite imagery. This paper explores the task of Multi-Image Visual Question Answering (MIQA): given a large set of images and a natural language query, the task is to generate a relevant and grounded response. We propose a new public benchmark, dubbed \"Visual Haystacks (VHs),\" specifically designed to evaluate LMMs' capabilities in visual retrieval and reasoning over sets of unrelated images, where we perform comprehensive evaluations demonstrating that even robust closed-source models struggle significantly. Towards addressing these shortcomings, we introduce MIRAGE (Multi-Image Retrieval Augmented Generation), a novel retrieval/QA framework tailored for LMMs that confronts the challenges of MIQA with marked efficiency and accuracy improvements over baseline methods. Our evaluation shows that MIRAGE surpasses closed-source GPT-4o models by up to 11% on the VHs benchmark and offers up to 3.4x improvements in efficiency over text-focused multi-stage approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/visual-haystacks/vhs_benchmark", "github_api_added_by": "auto" }, { "date": "2024-07-23", "arxiv_id": "2407.14679", "title": "Compact Language Models via Pruning and Knowledge Distillation", "authors": [ "Saurav Muralidharan", "Sharath Turuvekere Sreenivas", "Raviraj Joshi", "Marcin Chochowski", "Mostofa Patwary", "Mohammad Shoeybi", "Bryan Catanzaro", "Jan Kautz", "Pavlo Molchanov" ], "github": "https://github.com/nvlabs/minitron", "project_page": "", "abstract": "Large language models (LLMs) targeting different deployment scales and sizes are currently produced by training each variant from scratch; this is extremely compute-intensive. In this paper, we investigate if pruning an existing LLM and then re-training it with a fraction (<3%) of the original training data can be a suitable alternative to repeated, full retraining. To this end, we develop a set of practical and effective compression best practices for LLMs that combine depth, width, attention and MLP pruning with knowledge distillation-based retraining; we arrive at these best practices through a detailed empirical exploration of pruning strategies for each axis, methods to combine axes, distillation strategies, and search techniques for arriving at optimal compressed architectures. We use this guide to compress the Nemotron-4 family of LLMs by a factor of 2-4x, and compare their performance to similarly-sized models on a variety of language modeling tasks. Deriving 8B and 4B models from an already pretrained 15B model using our approach requires up to 40x fewer training tokens per model compared to training from scratch; this results in compute cost savings of 1.8x for training the full model family (15B, 8B, and 4B). Minitron models exhibit up to a 16% improvement in MMLU scores compared to training from scratch, perform comparably to other community models such as Mistral 7B, Gemma 7B and Llama-3 8B, and outperform state-of-the-art compression techniques from the literature. We have open-sourced Minitron model weights on Huggingface, with corresponding supplementary material including example code available on GitHub.", "github_manual": "https://github.com/nvlabs/minitron", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-24", "arxiv_id": "2407.13301", "title": "CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis", "authors": [ "Junying Chen", "Chi Gui", "Anningzhe Gao", "Ke Ji", "Xidong Wang", "Xiang Wan", "Benyou Wang" ], "github": "https://github.com/freedomintelligence/chain-of-diagnosis", "project_page": "", "abstract": "The field of medical diagnosis has undergone a significant transformation with the advent of large language models (LLMs), yet the challenges of interpretability within these models remain largely unaddressed. This study introduces Chain-of-Diagnosis (CoD) to enhance the interpretability of LLM-based medical diagnostics. CoD transforms the diagnostic process into a diagnostic chain that mirrors a physician's thought process, providing a transparent reasoning pathway. Additionally, CoD outputs the disease confidence distribution to ensure transparency in decision-making. This interpretability makes model diagnostics controllable and aids in identifying critical symptoms for inquiry through the entropy reduction of confidences. With CoD, we developed DiagnosisGPT, capable of diagnosing 9604 diseases. Experimental results demonstrate that DiagnosisGPT outperforms other LLMs on diagnostic benchmarks. Moreover, DiagnosisGPT provides interpretability while ensuring controllability in diagnostic rigor.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/freedomintelligence/chain-of-diagnosis", "github_api_added_by": "auto" }, { "date": "2024-07-24", "arxiv_id": "2407.16655", "title": "MovieDreamer: Hierarchical Generation for Coherent Long Visual Sequence", "authors": [ "Canyu Zhao", "Mingyu Liu", "Wen Wang", "Jianlong Yuan", "Hao Chen", "Bo Zhang", "Chunhua Shen" ], "github": "", "project_page": "", "abstract": "Recent advancements in video generation have primarily leveraged diffusion models for short-duration content. However, these approaches often fall short in modeling complex narratives and maintaining character consistency over extended periods, which is essential for long-form video production like movies. We propose MovieDreamer, a novel hierarchical framework that integrates the strengths of autoregressive models with diffusion-based rendering to pioneer long-duration video generation with intricate plot progressions and high visual fidelity. Our approach utilizes autoregressive models for global narrative coherence, predicting sequences of visual tokens that are subsequently transformed into high-quality video frames through diffusion rendering. This method is akin to traditional movie production processes, where complex stories are factorized down into manageable scene capturing. Further, we employ a multimodal script that enriches scene descriptions with detailed character information and visual style, enhancing continuity and character identity across scenes. We present extensive experiments across various movie genres, demonstrating that our approach not only achieves superior visual and narrative quality but also effectively extends the duration of generated content significantly beyond current capabilities. Homepage: https://aim-uofa.github.io/MovieDreamer/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-24", "arxiv_id": "2407.14505", "title": "T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation", "authors": [ "Kaiyue Sun", "Kaiyi Huang", "Xian Liu", "Yue Wu", "Zihan Xu", "Zhenguo Li", "Xihui Liu" ], "github": "https://github.com/KaiyueSun98/T2V-CompBench", "project_page": "", "abstract": "Text-to-video (T2V) generation models have advanced significantly, yet their ability to compose different objects, attributes, actions, and motions into a video remains unexplored. Previous text-to-video benchmarks also neglect this important ability for evaluation. In this work, we conduct the first systematic study on compositional text-to-video generation. We propose T2V-CompBench, the first benchmark tailored for compositional text-to-video generation. T2V-CompBench encompasses diverse aspects of compositionality, including consistent attribute binding, dynamic attribute binding, spatial relationships, motion binding, action binding, object interactions, and generative numeracy. We further carefully design evaluation metrics of MLLM-based metrics, detection-based metrics, and tracking-based metrics, which can better reflect the compositional text-to-video generation quality of seven proposed categories with 700 text prompts. The effectiveness of the proposed metrics is verified by correlation with human evaluations. We also benchmark various text-to-video generative models and conduct in-depth analysis across different models and different compositional categories. We find that compositional text-to-video generation is highly challenging for current models, and we hope that our attempt will shed light on future research in this direction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KaiyueSun98/T2V-CompBench", "github_api_added_by": "auto" }, { "date": "2024-07-24", "arxiv_id": "2407.16224", "title": "OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person", "authors": [ "Ke Sun", "Jian Cao", "Qi Wang", "Linrui Tian", "Xindi Zhang", "Lian Zhuo", "Bang Zhang", "Liefeng Bo", "Wenbo Zhou", "Weiming Zhang", "Daiheng Gao" ], "github": "", "project_page": "", "abstract": "Virtual Try-On (VTON) has become a transformative technology, empowering users to experiment with fashion without ever having to physically try on clothing. However, existing methods often struggle with generating high-fidelity and detail-consistent results. While diffusion models, such as Stable Diffusion series, have shown their capability in creating high-quality and photorealistic images, they encounter formidable challenges in conditional generation scenarios like VTON. Specifically, these models struggle to maintain a balance between control and consistency when generating images for virtual clothing trials. OutfitAnyone addresses these limitations by leveraging a two-stream conditional diffusion model, enabling it to adeptly handle garment deformation for more lifelike results. It distinguishes itself with scalability-modulating factors such as pose, body shape and broad applicability, extending from anime to in-the-wild images. OutfitAnyone's performance in diverse scenarios underscores its utility and readiness for real-world deployment. For more details and animated results, please see https://humanaigc.github.io/outfit-anyone/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-24", "arxiv_id": "2407.16412", "title": "Cross Anything: General Quadruped Robot Navigation through Complex Terrains", "authors": [ "Shaoting Zhu", "Derun Li", "Yong Liu", "Ningyi Xu", "Hang Zhao" ], "github": "", "project_page": "", "abstract": "The application of vision-language models (VLMs) has achieved impressive success in various robotics tasks, but there are few explorations for foundation models used in quadruped robot navigation. We introduce Cross Anything System (CAS), an innovative system composed of a high-level reasoning module and a low-level control policy, enabling the robot to navigate across complex 3D terrains and reach the goal position. For high-level reasoning and motion planning, we propose a novel algorithmic system taking advantage of a VLM, with a design of task decomposition and a closed-loop sub-task execution mechanism. For low-level locomotion control, we utilize the Probability Annealing Selection (PAS) method to train a control policy by reinforcement learning. Numerous experiments show that our whole system can accurately and robustly navigate across complex 3D terrains, and its strong generalization ability ensures the applications in diverse indoor and outdoor scenarios and terrains. Project page: https://cross-anything.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-24", "arxiv_id": "2407.16198", "title": "INF-LLaVA: Dual-perspective Perception for High-Resolution Multimodal Large Language Model", "authors": [ "Yiwei Ma", "Zhibin Wang", "Xiaoshuai Sun", "Weihuang Lin", "Qiang Zhou", "Jiayi Ji", "Rongrong Ji" ], "github": "https://github.com/WeihuangLin/INF-LLaVA", "project_page": "", "abstract": "With advancements in data availability and computing resources, Multimodal Large Language Models (MLLMs) have showcased capabilities across various fields. However, the quadratic complexity of the vision encoder in MLLMs constrains the resolution of input images. Most current approaches mitigate this issue by cropping high-resolution images into smaller sub-images, which are then processed independently by the vision encoder. Despite capturing sufficient local details, these sub-images lack global context and fail to interact with one another. To address this limitation, we propose a novel MLLM, INF-LLaVA, designed for effective high-resolution image perception. INF-LLaVA incorporates two innovative components. First, we introduce a Dual-perspective Cropping Module (DCM), which ensures that each sub-image contains continuous details from a local perspective and comprehensive information from a global perspective. Second, we introduce Dual-perspective Enhancement Module (DEM) to enable the mutual enhancement of global and local features, allowing INF-LLaVA to effectively process high-resolution images by simultaneously capturing detailed local information and comprehensive global context. Extensive ablation studies validate the effectiveness of these components, and experiments on a diverse set of benchmarks demonstrate that INF-LLaVA outperforms existing MLLMs. Code and pretrained model are available at https://github.com/WeihuangLin/INF-LLaVA.", "github_manual": "", "github_regex": "https://github.com/WeihuangLin/INF-LLaVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-24", "arxiv_id": "2407.12435", "title": "F-HOI: Toward Fine-grained Semantic-Aligned 3D Human-Object Interactions", "authors": [ "Jie Yang", "Xuesong Niu", "Nan Jiang", "Ruimao Zhang", "Siyuan Huang" ], "github": "", "project_page": "", "abstract": "Existing 3D human object interaction (HOI) datasets and models simply align global descriptions with the long HOI sequence, while lacking a detailed understanding of intermediate states and the transitions between states. In this paper, we argue that fine-grained semantic alignment, which utilizes state-level descriptions, offers a promising paradigm for learning semantically rich HOI representations. To achieve this, we introduce Semantic-HOI, a new dataset comprising over 20K paired HOI states with fine-grained descriptions for each HOI state and the body movements that happen between two consecutive states. Leveraging the proposed dataset, we design three state-level HOI tasks to accomplish fine-grained semantic alignment within the HOI sequence. Additionally, we propose a unified model called F-HOI, designed to leverage multimodal instructions and empower the Multi-modal Large Language Model to efficiently handle diverse HOI tasks. F-HOI offers multiple advantages: (1) It employs a unified task formulation that supports the use of versatile multimodal inputs. (2) It maintains consistency in HOI across 2D, 3D, and linguistic spaces. (3) It utilizes fine-grained textual supervision for direct optimization, avoiding intricate modeling of HOI states. Extensive experiments reveal that F-HOI effectively aligns HOI states with fine-grained semantic descriptions, adeptly tackling understanding, reasoning, generation, and reconstruction tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-24", "arxiv_id": "2407.16674", "title": "KAN or MLP: A Fairer Comparison", "authors": [ "Runpeng Yu", "Weihao Yu", "Xinchao Wang" ], "github": "https://github.com/yu-rp/KANbeFair", "project_page": "", "abstract": "This paper does not introduce a novel method. Instead, it offers a fairer and more comprehensive comparison of KAN and MLP models across various tasks, including machine learning, computer vision, audio processing, natural language processing, and symbolic formula representation. Specifically, we control the number of parameters and FLOPs to compare the performance of KAN and MLP. Our main observation is that, except for symbolic formula representation tasks, MLP generally outperforms KAN. We also conduct ablation studies on KAN and find that its advantage in symbolic formula representation mainly stems from its B-spline activation function. When B-spline is applied to MLP, performance in symbolic formula representation significantly improves, surpassing or matching that of KAN. However, in other tasks where MLP already excels over KAN, B-spline does not substantially enhance MLP's performance. Furthermore, we find that KAN's forgetting issue is more severe than that of MLP in a standard class-incremental continual learning setting, which differs from the findings reported in the KAN paper. We hope these results provide insights for future research on KAN and other MLP alternatives. Project link: https://github.com/yu-rp/KANbeFair", "github_manual": "", "github_regex": "https://github.com/yu-rp/KANbeFair", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-24", "arxiv_id": "2407.16680", "title": "A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data", "authors": [ "Adrian Remonda", "Nicklas Hansen", "Ayoub Raji", "Nicola Musiu", "Marko Bertogna", "Eduardo Veas", "Xiaolong Wang" ], "github": "https://github.com/dasgringuen/assetto_corsa_gym", "project_page": "", "abstract": "Despite the availability of international prize-money competitions, scaled vehicles, and simulation environments, research on autonomous racing and the control of sports cars operating close to the limit of handling has been limited by the high costs of vehicle acquisition and management, as well as the limited physics accuracy of open-source simulators. In this paper, we propose a racing simulation platform based on the simulator Assetto Corsa to test, validate, and benchmark autonomous driving algorithms, including reinforcement learning (RL) and classical Model Predictive Control (MPC), in realistic and challenging scenarios. Our contributions include the development of this simulation platform, several state-of-the-art algorithms tailored to the racing environment, and a comprehensive dataset collected from human drivers. Additionally, we evaluate algorithms in the offline RL setting. All the necessary code (including environment and benchmarks), working examples, datasets, and videos are publicly released and can be found at: https://assetto-corsa-gym.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dasgringuen/assetto_corsa_gym", "github_api_added_by": "auto" }, { "date": "2024-07-24", "arxiv_id": "2407.15447", "title": "SIGMA: Sinkhorn-Guided Masked Video Modeling", "authors": [ "Mohammadreza Salehi", "Michael Dorkenwald", "Fida Mohammad Thoker", "Efstratios Gavves", "Cees G. M. Snoek", "Yuki M. Asano" ], "github": "", "project_page": "", "abstract": "Video-based pretraining offers immense potential for learning strong visual representations on an unprecedented scale. Recently, masked video modeling methods have shown promising scalability, yet fall short in capturing higher-level semantics due to reconstructing predefined low-level targets such as pixels. To tackle this, we present Sinkhorn-guided Masked Video Modelling (SIGMA), a novel video pretraining method that jointly learns the video model in addition to a target feature space using a projection network. However, this simple modification means that the regular L2 reconstruction loss will lead to trivial solutions as both networks are jointly optimized. As a solution, we distribute features of space-time tubes evenly across a limited number of learnable clusters. By posing this as an optimal transport problem, we enforce high entropy in the generated features across the batch, infusing semantic and temporal meaning into the feature space. The resulting cluster assignments are used as targets for a symmetric prediction task where the video model predicts cluster assignment of the projection network and vice versa. Experimental results on ten datasets across three benchmarks validate the effectiveness of SIGMA in learning more performant, temporally-aware, and robust video representations improving upon state-of-the-art methods. Our project website with code is available at: https://quva-lab.github.io/SIGMA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-24", "arxiv_id": "2407.16318", "title": "PrimeGuard: Safe and Helpful LLMs through Tuning-Free Routing", "authors": [ "Blazej Manczak", "Eliott Zemour", "Eric Lin", "Vaikkunth Mugunthan" ], "github": "https://github.com/dynamofl/PrimeGuard", "project_page": "", "abstract": "Deploying language models (LMs) necessitates outputs to be both high-quality and compliant with safety guidelines. Although Inference-Time Guardrails (ITG) offer solutions that shift model output distributions towards compliance, we find that current methods struggle in balancing safety with helpfulness. ITG Methods that safely address non-compliant queries exhibit lower helpfulness while those that prioritize helpfulness compromise on safety. We refer to this trade-off as the guardrail tax, analogous to the alignment tax. To address this, we propose PrimeGuard, a novel ITG method that utilizes structured control flow. PrimeGuard routes requests to different self-instantiations of the LM with varying instructions, leveraging its inherent instruction-following capabilities and in-context learning. Our tuning-free approach dynamically compiles system-designer guidelines for each query. We construct and release safe-eval, a diverse red-team safety benchmark. Extensive evaluations demonstrate that PrimeGuard, without fine-tuning, overcomes the guardrail tax by (1) significantly increasing resistance to iterative jailbreak attacks and (2) achieving state-of-the-art results in safety guardrailing while (3) matching helpfulness scores of alignment-tuned models. Extensive evaluations demonstrate that PrimeGuard, without fine-tuning, outperforms all competing baselines and overcomes the guardrail tax by improving the fraction of safe responses from 61% to 97% and increasing average helpfulness scores from 4.17 to 4.29 on the largest models, while reducing attack success rate from 100% to 8%. PrimeGuard implementation is available at https://github.com/dynamofl/PrimeGuard and safe-eval dataset is available at https://huggingface.co/datasets/dynamoai/safe_eval.", "github_manual": "", "github_regex": "https://github.com/dynamofl/PrimeGuard", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-25", "arxiv_id": "2407.16154", "title": "DDK: Distilling Domain Knowledge for Efficient Large Language Models", "authors": [ "Jiaheng Liu", "Chenchen Zhang", "Jinyang Guo", "Yuanxing Zhang", "Haoran Que", "Ken Deng", "Zhiqi Bai", "Jie Liu", "Ge Zhang", "Jiakai Wang", "Yanan Wu", "Congnan Liu", "Wenbo Su", "Jiamang Wang", "Lin Qu", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Despite the advanced intelligence abilities of large language models (LLMs) in various applications, they still face significant computational and storage demands. Knowledge Distillation (KD) has emerged as an effective strategy to improve the performance of a smaller LLM (i.e., the student model) by transferring knowledge from a high-performing LLM (i.e., the teacher model). Prevailing techniques in LLM distillation typically use a black-box model API to generate high-quality pretrained and aligned datasets, or utilize white-box distillation by altering the loss function to better transfer knowledge from the teacher LLM. However, these methods ignore the knowledge differences between the student and teacher LLMs across domains. This results in excessive focus on domains with minimal performance gaps and insufficient attention to domains with large gaps, reducing overall performance. In this paper, we introduce a new LLM distillation framework called DDK, which dynamically adjusts the composition of the distillation dataset in a smooth manner according to the domain performance differences between the teacher and student models, making the distillation process more stable and effective. Extensive evaluations show that DDK significantly improves the performance of student models, outperforming both continuously pretrained baselines and existing knowledge distillation methods by a large margin.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-25", "arxiv_id": "2407.17453", "title": "VILA^2: VILA Augmented VILA", "authors": [ "Yunhao Fang", "Ligeng Zhu", "Yao Lu", "Yan Wang", "Pavlo Molchanov", "Jang Hyun Cho", "Marco Pavone", "Song Han", "Hongxu Yin" ], "github": "", "project_page": "", "abstract": "Visual language models (VLMs) have rapidly progressed, driven by the success of large language models (LLMs). While model architectures and training infrastructures advance rapidly, data curation remains under-explored. When data quantity and quality become a bottleneck, existing work either directly crawls more raw data from the Internet that does not have a guarantee of data quality or distills from black-box commercial models (e.g., GPT-4V / Gemini) causing the performance upper bounded by that model. In this work, we introduce a novel approach that includes a self-augment step and a specialist-augment step to iteratively improve data quality and model performance. In the self-augment step, a VLM recaptions its own pretraining data to enhance data quality, and then retrains from scratch using this refined dataset to improve model performance. This process can iterate for several rounds. Once self-augmentation saturates, we employ several specialist VLMs finetuned from the self-augmented VLM with domain-specific expertise, to further infuse specialist knowledge into the generalist VLM through task-oriented recaptioning and retraining. With the combined self-augmented and specialist-augmented training, we introduce VILA^2 (VILA-augmented-VILA), a VLM family that consistently improves the accuracy on a wide range of tasks over prior art, and achieves new state-of-the-art results on MMMU leaderboard among open-sourced models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-25", "arxiv_id": "2407.17365", "title": "ViPer: Visual Personalization of Generative Models via Individual Preference Learning", "authors": [ "Sogand Salehi", "Mahdi Shafiei", "Teresa Yeo", "Roman Bachmann", "Amir Zamir" ], "github": "", "project_page": "", "abstract": "Different users find different images generated for the same prompt desirable. This gives rise to personalized image generation which involves creating images aligned with an individual's visual preference. Current generative models are, however, unpersonalized, as they are tuned to produce outputs that appeal to a broad audience. Using them to generate images aligned with individual users relies on iterative manual prompt engineering by the user which is inefficient and undesirable. We propose to personalize the image generation process by first capturing the generic preferences of the user in a one-time process by inviting them to comment on a small selection of images, explaining why they like or dislike each. Based on these comments, we infer a user's structured liked and disliked visual attributes, i.e., their visual preference, using a large language model. These attributes are used to guide a text-to-image model toward producing images that are tuned towards the individual user's visual preference. Through a series of user studies and large language model guided evaluations, we demonstrate that the proposed method results in generations that are well aligned with individual users' visual preferences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-25", "arxiv_id": "2407.16741", "title": "OpenDevin: An Open Platform for AI Software Developers as Generalist Agents", "authors": [ "Xingyao Wang", "Boxuan Li", "Yufan Song", "Frank F. Xu", "Xiangru Tang", "Mingchen Zhuge", "Jiayi Pan", "Yueqi Song", "Bowen Li", "Jaskirat Singh", "Hoang H. Tran", "Fuqiang Li", "Ren Ma", "Mingzhang Zheng", "Bill Qian", "Yanjun Shao", "Niklas Muennighoff", "Yizhe Zhang", "Binyuan Hui", "Junyang Lin", "Robert Brennan", "Hao Peng", "Heng Ji", "Graham Neubig" ], "github": "", "project_page": "", "abstract": "Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and affect change in their surrounding environments. In this paper, we introduce OpenDevin, a platform for the development of powerful and flexible AI agents that interact with the world in similar ways to those of a human developer: by writing code, interacting with a command line, and browsing the web. We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, coordination between multiple agents, and incorporation of evaluation benchmarks. Based on our currently incorporated benchmarks, we perform an evaluation of agents over 15 challenging tasks, including software engineering (e.g., SWE-Bench) and web browsing (e.g., WebArena), among others. Released under the permissive MIT license, OpenDevin is a community project spanning academia and industry with more than 1.3K contributions from over 160 contributors and will improve going forward.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-25", "arxiv_id": "2407.17353", "title": "Scalify: scale propagation for efficient low-precision LLM training", "authors": [ "Paul Balan\u00e7a", "Sam Hosegood", "Carlo Luschi", "Andrew Fitzgibbon" ], "github": "https://github.com/graphcore-research/jax-scalify", "project_page": "", "abstract": "Low-precision formats such as float8 have been introduced in machine learning accelerated hardware to improve computational efficiency for large language models training and inference. Nevertheless, adoption by the ML community has been slowed down by the complex, and sometimes brittle, techniques required to match higher precision training accuracy. In this work, we present Scalify, a end-to-end scale propagation paradigm for computational graphs, generalizing and formalizing existing tensor scaling methods. Experiment results show that Scalify supports out-of-the-box float8 matrix multiplication and gradients representation, as well as float16 optimizer state storage. Our JAX implementation of Scalify is open-sourced at https://github.com/graphcore-research/jax-scalify", "github_manual": "", "github_regex": "https://github.com/graphcore-research/jax-scalify", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-25", "arxiv_id": "2407.17470", "title": "SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency", "authors": [ "Yiming Xie", "Chun-Han Yao", "Vikram Voleti", "Huaizu Jiang", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "We present Stable Video 4D (SV4D), a latent video diffusion model for multi-frame and multi-view consistent dynamic 3D content generation. Unlike previous methods that rely on separately trained generative models for video generation and novel view synthesis, we design a unified diffusion model to generate novel view videos of dynamic 3D objects. Specifically, given a monocular reference video, SV4D generates novel views for each video frame that are temporally consistent. We then use the generated novel view videos to optimize an implicit 4D representation (dynamic NeRF) efficiently, without the need for cumbersome SDS-based optimization used in most prior works. To train our unified novel view video generation model, we curated a dynamic 3D object dataset from the existing Objaverse dataset. Extensive experimental results on multiple datasets and user studies demonstrate SV4D's state-of-the-art performance on novel-view video synthesis as well as 4D generation compared to prior works.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-25", "arxiv_id": "2407.17387", "title": "PERSONA: A Reproducible Testbed for Pluralistic Alignment", "authors": [ "Louis Castricato", "Nathan Lile", "Rafael Rafailov", "Jan-Philipp Fr\u00e4nken", "Chelsea Finn" ], "github": "", "project_page": "", "abstract": "The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the plurality of user opinions, instead reinforcing majority viewpoints and marginalizing minority perspectives. We introduce PERSONA, a reproducible test bed designed to evaluate and improve pluralistic alignment of LMs. We procedurally generate diverse user profiles from US census data, resulting in 1,586 synthetic personas with varied demographic and idiosyncratic attributes. We then generate a large-scale evaluation dataset containing 3,868 prompts and 317,200 feedback pairs obtained from our synthetic personas. Leveraging this dataset, we systematically evaluate LM capabilities in role-playing diverse users, verified through human judges, and the establishment of both a benchmark, PERSONA Bench, for pluralistic alignment approaches as well as an extensive dataset to create new and future benchmarks. The full dataset and benchmarks are available here: https://www.synthlabs.ai/research/persona.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-25", "arxiv_id": "2407.17438", "title": "HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation", "authors": [ "Zhenzhi Wang", "Yixuan Li", "Yanhong Zeng", "Youqing Fang", "Yuwei Guo", "Wenran Liu", "Jing Tan", "Kai Chen", "Tianfan Xue", "Bo Dai", "Dahua Lin" ], "github": "https://github.com/zhenzhiwang/HumanVid", "project_page": "", "abstract": "Human image animation involves generating videos from a character photo, allowing user control and unlocking potential for video and movie production. While recent approaches yield impressive results using high-quality training data, the inaccessibility of these datasets hampers fair and transparent benchmarking. Moreover, these approaches prioritize 2D human motion and overlook the significance of camera motions in videos, leading to limited control and unstable video generation.To demystify the training data, we present HumanVid, the first large-scale high-quality dataset tailored for human image animation, which combines crafted real-world and synthetic data. For the real-world data, we compile a vast collection of copyright-free real-world videos from the internet. Through a carefully designed rule-based filtering strategy, we ensure the inclusion of high-quality videos, resulting in a collection of 20K human-centric videos in 1080P resolution. Human and camera motion annotation is accomplished using a 2D pose estimator and a SLAM-based method. For the synthetic data, we gather 2,300 copyright-free 3D avatar assets to augment existing available 3D assets. Notably, we introduce a rule-based camera trajectory generation method, enabling the synthetic pipeline to incorporate diverse and precise camera motion annotation, which can rarely be found in real-world data. To verify the effectiveness of HumanVid, we establish a baseline model named CamAnimate, short for Camera-controllable Human Animation, that considers both human and camera motions as conditions. Through extensive experimentation, we demonstrate that such simple baseline training on our HumanVid achieves state-of-the-art performance in controlling both human pose and camera motions, setting a new benchmark. Code and data will be publicly available at https://github.com/zhenzhiwang/HumanVid/.", "github_manual": "", "github_regex": "https://github.com/zhenzhiwang/HumanVid", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-25", "arxiv_id": "2407.16312", "title": "MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning", "authors": [ "Florian Felten", "Umut Ucak", "Hicham Azmani", "Gao Peng", "Willem R\u00f6pke", "Hendrik Baier", "Patrick Mannion", "Diederik M. Roijers", "Jordan K. Terry", "El-Ghazali Talbi", "Gr\u00e9goire Danoy", "Ann Now\u00e9", "Roxana R\u0103dulescu" ], "github": "https://github.com/farama-foundation/momaland", "project_page": "", "abstract": "Many challenging tasks such as managing traffic systems, electricity grids, or supply chains involve complex decision-making processes that must balance multiple conflicting objectives and coordinate the actions of various independent decision-makers (DMs). One perspective for formalising and addressing such tasks is multi-objective multi-agent reinforcement learning (MOMARL). MOMARL broadens reinforcement learning (RL) to problems with multiple agents each needing to consider multiple objectives in their learning process. In reinforcement learning research, benchmarks are crucial in facilitating progress, evaluation, and reproducibility. The significance of benchmarks is underscored by the existence of numerous benchmark frameworks developed for various RL paradigms, including single-agent RL (e.g., Gymnasium), multi-agent RL (e.g., PettingZoo), and single-agent multi-objective RL (e.g., MO-Gymnasium). To support the advancement of the MOMARL field, we introduce MOMAland, the first collection of standardised environments for multi-objective multi-agent reinforcement learning. MOMAland addresses the need for comprehensive benchmarking in this emerging field, offering over 10 diverse environments that vary in the number of agents, state representations, reward structures, and utility considerations. To provide strong baselines for future research, MOMAland also includes algorithms capable of learning policies in such settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/farama-foundation/momaland", "github_api_added_by": "auto" }, { "date": "2024-07-25", "arxiv_id": "2407.15815", "title": "Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning", "authors": [ "Zhecheng Yuan", "Tianming Wei", "Shuiqi Cheng", "Gu Zhang", "Yuanpei Chen", "Huazhe Xu" ], "github": "", "project_page": "", "abstract": "Can we endow visuomotor robots with generalization capabilities to operate in diverse open-world scenarios? In this paper, we propose Maniwhere, a generalizable framework tailored for visual reinforcement learning, enabling the trained robot policies to generalize across a combination of multiple visual disturbance types. Specifically, we introduce a multi-view representation learning approach fused with Spatial Transformer Network (STN) module to capture shared semantic information and correspondences among different viewpoints. In addition, we employ a curriculum-based randomization and augmentation approach to stabilize the RL training process and strengthen the visual generalization ability. To exhibit the effectiveness of Maniwhere, we meticulously design 8 tasks encompassing articulate objects, bi-manual, and dexterous hand manipulation tasks, demonstrating Maniwhere's strong visual generalization and sim2real transfer abilities across 3 hardware platforms. Our experiments show that Maniwhere significantly outperforms existing state-of-the-art methods. Videos are provided at https://gemcollector.github.io/maniwhere/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-25", "arxiv_id": "2407.16988", "title": "DreamCar: Leveraging Car-specific Prior for in-the-wild 3D Car Reconstruction", "authors": [ "Xiaobiao Du", "Haiyang Sun", "Ming Lu", "Tianqing Zhu", "Xin Yu" ], "github": "https://github.com/xiaobiaodu/DreamCar", "project_page": "", "abstract": "Self-driving industries usually employ professional artists to build exquisite 3D cars. However, it is expensive to craft large-scale digital assets. Since there are already numerous datasets available that contain a vast number of images of cars, we focus on reconstructing high-quality 3D car models from these datasets. However, these datasets only contain one side of cars in the forward-moving scene. We try to use the existing generative models to provide more supervision information, but they struggle to generalize well in cars since they are trained on synthetic datasets not car-specific. In addition, The reconstructed 3D car texture misaligns due to a large error in camera pose estimation when dealing with in-the-wild images. These restrictions make it challenging for previous methods to reconstruct complete 3D cars. To address these problems, we propose a novel method, named DreamCar, which can reconstruct high-quality 3D cars given a few images even a single image. To generalize the generative model, we collect a car dataset, named Car360, with over 5,600 vehicles. With this dataset, we make the generative model more robust to cars. We use this generative prior specific to the car to guide its reconstruction via Score Distillation Sampling. To further complement the supervision information, we utilize the geometric and appearance symmetry of cars. Finally, we propose a pose optimization method that rectifies poses to tackle texture misalignment. Extensive experiments demonstrate that our method significantly outperforms existing methods in reconstructing high-quality 3D cars. https://xiaobiaodu.github.io/dreamcar-project/{Our code is available.}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xiaobiaodu/DreamCar", "github_api_added_by": "auto" }, { "date": "2024-07-25", "arxiv_id": "2407.14207", "title": "Longhorn: State Space Models are Amortized Online Learners", "authors": [ "Bo Liu", "Rui Wang", "Lemeng Wu", "Yihao Feng", "Peter Stone", "Qiang Liu" ], "github": "https://github.com/Cranial-XIX/longhorn", "project_page": "", "abstract": "The most fundamental capability of modern AI methods such as Large Language Models (LLMs) is the ability to predict the next token in a long sequence of tokens, known as ``sequence modeling.\" Although the Transformers model is the current dominant approach to sequence modeling, its quadratic computational cost with respect to sequence length is a significant drawback. State-space models (SSMs) offer a promising alternative due to their linear decoding efficiency and high parallelizability during training. However, existing SSMs often rely on seemingly ad hoc linear recurrence designs. In this work, we explore SSM design through the lens of online learning, conceptualizing SSMs as meta-modules for specific online learning problems. This approach links SSM design to formulating precise online learning objectives, with state transition rules derived from optimizing these objectives. Based on this insight, we introduce a novel deep SSM architecture based on the implicit update for optimizing an online regression objective. Our experimental results show that our models outperform state-of-the-art SSMs, including the Mamba model, on standard sequence modeling benchmarks and language modeling tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Cranial-XIX/longhorn", "github_api_added_by": "auto" }, { "date": "2024-07-25", "arxiv_id": "2406.00856", "title": "DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection", "authors": [ "Yewon Lim", "Changyeon Lee", "Aerin Kim", "Oren Etzioni" ], "github": "https://github.com/miraflow/DistilDIRE", "project_page": "", "abstract": "A dramatic influx of diffusion-generated images has marked recent years, posing unique challenges to current detection technologies. While the task of identifying these images falls under binary classification, a seemingly straightforward category, the computational load is significant when employing the \"reconstruction then compare\" technique. This approach, known as DIRE (Diffusion Reconstruction Error), not only identifies diffusion-generated images but also detects those produced by GANs, highlighting the technique's broad applicability. To address the computational challenges and improve efficiency, we propose distilling the knowledge embedded in diffusion models to develop rapid deepfake detection models. Our approach, aimed at creating a small, fast, cheap, and lightweight diffusion synthesized deepfake detector, maintains robust performance while significantly reducing operational demands. Maintaining performance, our experimental results indicate an inference speed 3.2 times faster than the existing DIRE framework. This advance not only enhances the practicality of deploying these systems in real-world settings but also paves the way for future research endeavors that seek to leverage diffusion model knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/miraflow/DistilDIRE", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-26", "arxiv_id": "2407.17490", "title": "AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents", "authors": [ "Yuxiang Chai", "Siyuan Huang", "Yazhe Niu", "Han Xiao", "Liang Liu", "Dingyu Zhang", "Peng Gao", "Shuai Ren", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "AI agents have drawn increasing attention mostly on their ability to perceive environments, understand tasks, and autonomously achieve goals. To advance research on AI agents in mobile scenarios, we introduce the Android Multi-annotation EXpo (AMEX), a comprehensive, large-scale dataset designed for generalist mobile GUI-control agents. Their capabilities of completing complex tasks by directly interacting with the graphical user interface (GUI) on mobile devices are trained and evaluated with the proposed dataset. AMEX comprises over 104K high-resolution screenshots from 110 popular mobile applications, which are annotated at multiple levels. Unlike existing mobile device-control datasets, e.g., MoTIF, AitW, etc., AMEX includes three levels of annotations: GUI interactive element grounding, GUI screen and element functionality descriptions, and complex natural language instructions, each averaging 13 steps with stepwise GUI-action chains. We develop this dataset from a more instructive and detailed perspective, complementing the general settings of existing datasets. Additionally, we develop a baseline model SPHINX Agent and compare its performance across state-of-the-art agents trained on other datasets. To facilitate further research, we open-source our dataset, models, and relevant evaluation tools. The project is available at https://yuxiangchai.github.io/AMEX/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-26", "arxiv_id": "2407.17789", "title": "Very Large-Scale Multi-Agent Simulation in AgentScope", "authors": [ "Xuchen Pan", "Dawei Gao", "Yuexiang Xie", "Zhewei Wei", "Yaliang Li", "Bolin Ding", "Ji-Rong Wen", "Jingren Zhou" ], "github": "https://github.com/modelscope/agentscope", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have opened new avenues for applying multi-agent systems in very large-scale simulations. However, there remain several challenges when conducting multi-agent simulations with existing platforms, such as limited scalability and low efficiency, unsatisfied agent diversity, and effort-intensive management processes. To address these challenges, we develop several new features and components for AgentScope, a user-friendly multi-agent platform, enhancing its convenience and flexibility for supporting very large-scale multi-agent simulations. Specifically, we propose an actor-based distributed mechanism as the underlying technological infrastructure towards great scalability and high efficiency, and provide flexible environment support for simulating various real-world scenarios, which enables parallel execution of multiple agents, centralized workflow orchestration, and both inter-agent and agent-environment interactions among agents. Moreover, we integrate an easy-to-use configurable tool and an automatic background generation pipeline in AgentScope, simplifying the process of creating agents with diverse yet detailed background settings. Last but not least, we provide a web-based interface for conveniently monitoring and managing a large number of agents that might deploy across multiple devices. We conduct a comprehensive simulation to demonstrate the effectiveness of the proposed enhancements in AgentScope, and provide detailed observations and discussions to highlight the great potential of applying multi-agent systems in large-scale simulations. The source code is released on GitHub at https://github.com/modelscope/agentscope to inspire further research and development in large-scale multi-agent simulations.", "github_manual": "", "github_regex": "https://github.com/modelscope/agentscope", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-26", "arxiv_id": "2407.16982", "title": "Diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model", "authors": [ "Lirui Zhao", "Tianshuo Yang", "Wenqi Shao", "Yuxin Zhang", "Yu Qiao", "Ping Luo", "Kaipeng Zhang", "Rongrong Ji" ], "github": "https://github.com/OpenGVLab/Diffree", "project_page": "", "abstract": "This paper addresses an important problem of object addition for images with only text guidance. It is challenging because the new object must be integrated seamlessly into the image with consistent visual context, such as lighting, texture, and spatial location. While existing text-guided image inpainting methods can add objects, they either fail to preserve the background consistency or involve cumbersome human intervention in specifying bounding boxes or user-scribbled masks. To tackle this challenge, we introduce Diffree, a Text-to-Image (T2I) model that facilitates text-guided object addition with only text control. To this end, we curate OABench, an exquisite synthetic dataset by removing objects with advanced image inpainting techniques. OABench comprises 74K real-world tuples of an original image, an inpainted image with the object removed, an object mask, and object descriptions. Trained on OABench using the Stable Diffusion model with an additional mask prediction module, Diffree uniquely predicts the position of the new object and achieves object addition with guidance from only text. Extensive experiments demonstrate that Diffree excels in adding new objects with a high success rate while maintaining background consistency, spatial appropriateness, and object relevance and quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenGVLab/Diffree", "github_api_added_by": "auto" }, { "date": "2024-07-26", "arxiv_id": "2407.17535", "title": "LAMBDA: A Large Model Based Data Agent", "authors": [ "Maojun Sun", "Ruijian Han", "Binyan Jiang", "Houduo Qi", "Defeng Sun", "Yancheng Yuan", "Jian Huang" ], "github": "https://github.com/AMA-CMFAI/LAMBDA", "project_page": "", "abstract": "We introduce ``LAMBDA,\" a novel open-source, code-free multi-agent data analysis system that that harnesses the power of large models. LAMBDA is designed to address data analysis challenges in complex data-driven applications through the use of innovatively designed data agents that operate iteratively and generatively using natural language. At the core of LAMBDA are two key agent roles: the programmer and the inspector, which are engineered to work together seamlessly. Specifically, the programmer generates code based on the user's instructions and domain-specific knowledge, enhanced by advanced models. Meanwhile, the inspector debugs the code when necessary. To ensure robustness and handle adverse scenarios, LAMBDA features a user interface that allows direct user intervention in the operational loop. Additionally, LAMBDA can flexibly integrate external models and algorithms through our knowledge integration mechanism, catering to the needs of customized data analysis. LAMBDA has demonstrated strong performance on various machine learning datasets. It has the potential to enhance data science practice and analysis paradigm by seamlessly integrating human and artificial intelligence, making it more accessible, effective, and efficient for individuals from diverse backgrounds. The strong performance of LAMBDA in solving data science problems is demonstrated in several case studies, which are presented at https://www.polyu.edu.hk/ama/cmfai/lambda.html.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AMA-CMFAI/LAMBDA", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-26", "arxiv_id": "2407.17952", "title": "BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation", "authors": [ "Xiang Zhang", "Bingxin Ke", "Hayko Riemenschneider", "Nando Metzger", "Anton Obukhov", "Markus Gross", "Konrad Schindler", "Christopher Schroers" ], "github": "", "project_page": "", "abstract": "By training over large-scale datasets, zero-shot monocular depth estimation (MDE) methods show robust performance in the wild but often suffer from insufficiently precise details. Although recent diffusion-based MDE approaches exhibit appealing detail extraction ability, they still struggle in geometrically challenging scenes due to the difficulty of gaining robust geometric priors from diverse datasets. To leverage the complementary merits of both worlds, we propose BetterDepth to efficiently achieve geometrically correct affine-invariant MDE performance while capturing fine-grained details. Specifically, BetterDepth is a conditional diffusion-based refiner that takes the prediction from pre-trained MDE models as depth conditioning, in which the global depth context is well-captured, and iteratively refines details based on the input image. For the training of such a refiner, we propose global pre-alignment and local patch masking methods to ensure the faithfulness of BetterDepth to depth conditioning while learning to capture fine-grained scene details. By efficient training on small-scale synthetic datasets, BetterDepth achieves state-of-the-art zero-shot MDE performance on diverse public datasets and in-the-wild scenes. Moreover, BetterDepth can improve the performance of other MDE models in a plug-and-play manner without additional re-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-26", "arxiv_id": "2407.16637", "title": "Course-Correction: Safety Alignment Using Synthetic Preferences", "authors": [ "Rongwu Xu", "Yishuo Cai", "Zhenhong Zhou", "Renjie Gu", "Haiqin Weng", "Yan Liu", "Tianwei Zhang", "Wei Xu", "Han Qiu" ], "github": "https://github.com/pillowsofwind/course-correction", "project_page": "", "abstract": "The risk of harmful content generated by large language models (LLMs) becomes a critical concern. This paper presents a systematic study on assessing and improving LLMs' capability to perform the task of course-correction, \\ie, the model can steer away from generating harmful content autonomously. To start with, we introduce the C^2-Eval benchmark for quantitative assessment and analyze 10 popular LLMs, revealing varying proficiency of current safety-tuned LLMs in course-correction. To improve, we propose fine-tuning LLMs with preference learning, emphasizing the preference for timely course-correction. Using an automated pipeline, we create C^2-Syn, a synthetic dataset with 750K pairwise preferences, to teach models the concept of timely course-correction through data-driven preference learning. Experiments on 2 LLMs, Llama2-Chat 7B and Qwen2 7B, show that our method effectively enhances course-correction skills without affecting general performance. Additionally, it effectively improves LLMs' safety, particularly in resisting jailbreak attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/pillowsofwind/course-correction", "github_api_added_by": "auto" }, { "date": "2024-07-26", "arxiv_id": "2407.16607", "title": "Data Mixture Inference: What do BPE Tokenizers Reveal about their Training Data?", "authors": [ "Jonathan Hayase", "Alisa Liu", "Yejin Choi", "Sewoong Oh", "Noah A. Smith" ], "github": "https://github.com/alisawuffles/tokenizer-attack", "project_page": "", "abstract": "The pretraining data of today's strongest language models is opaque. In particular, little is known about the proportions of various domains or languages represented. In this work, we tackle a task which we call data mixture inference, which aims to uncover the distributional make-up of training data. We introduce a novel attack based on a previously overlooked source of information -- byte-pair encoding (BPE) tokenizers, used by the vast majority of modern language models. Our key insight is that the ordered list of merge rules learned by a BPE tokenizer naturally reveals information about the token frequencies in its training data: the first merge is the most common byte pair, the second is the most common pair after merging the first token, and so on. Given a tokenizer's merge list along with data samples for each category of interest, we formulate a linear program that solves for the proportion of each category in the tokenizer's training set. Importantly, to the extent to which tokenizer training data is representative of the pretraining data, we indirectly learn about the pretraining data. In controlled experiments, we show that our attack recovers mixture ratios with high precision for tokenizers trained on known mixtures of natural languages, programming languages, and data sources. We then apply our approach to off-the-shelf tokenizers released with recent LMs. We confirm much publicly disclosed information about these models, and also make several new inferences: GPT-4o's tokenizer is much more multilingual than its predecessors, training on 39% non-English data; Llama3 extends GPT-3.5's tokenizer primarily for multilingual (48%) use; GPT-3.5's and Claude's tokenizers are trained on predominantly code (~60%). We hope our work sheds light on current design practices for pretraining data, and inspires continued research into data mixture inference for LMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alisawuffles/tokenizer-attack", "github_api_added_by": "auto" }, { "date": "2024-07-26", "arxiv_id": "2407.18121", "title": "Efficient Inference of Vision Instruction-Following Models with Elastic Cache", "authors": [ "Zuyan Liu", "Benlin Liu", "Jiahui Wang", "Yuhao Dong", "Guangyi Chen", "Yongming Rao", "Ranjay Krishna", "Jiwen Lu" ], "github": "https://github.com/liuzuyan/ElasticCache", "project_page": "", "abstract": "In the field of instruction-following large vision-language models (LVLMs), the efficient deployment of these models faces challenges, notably due to the high memory demands of their key-value (KV) caches. Conventional cache management strategies for LLMs focus on cache eviction, which often fails to address the specific needs of multimodal instruction-following models. Recognizing this gap, in this paper, we introduce Elastic Cache, a novel approach that benefits from applying distinct acceleration methods for instruction encoding and output generation stages. We investigate the metrics of importance in different stages and propose an importance-driven cache merging strategy to prune redundancy caches. Instead of discarding less important caches, our strategy identifies important key/value vectors as anchor points. Surrounding less important caches are then merged with these anchors, enhancing the preservation of contextual information in the KV caches while yielding an arbitrary acceleration ratio. For instruction encoding, we utilize the frequency to evaluate the importance of caches. Regarding output generation, we prioritize tokens based on their distance with an offset, by which both the initial and most recent tokens are retained. Results on a range of LVLMs demonstrate that Elastic Cache not only boosts efficiency but also notably outperforms existing pruning methods in language generation across various tasks. Code is available at https://github.com/liuzuyan/ElasticCache", "github_manual": "", "github_regex": "https://github.com/liuzuyan/ElasticCache", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-26", "arxiv_id": "2407.18129", "title": "Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic", "authors": [ "Fakhraddin Alwajih", "Gagan Bhatia", "Muhammad Abdul-Mageed" ], "github": "", "project_page": "", "abstract": "Recent advancements have significantly enhanced the capabilities of Multimodal Large Language Models (MLLMs) in generating and understanding image-to-text content. Despite these successes, progress is predominantly limited to English due to the scarcity of high quality multimodal resources in other languages. This limitation impedes the development of competitive models in languages such as Arabic. To alleviate this situation, we introduce an efficient Arabic multimodal assistant, dubbed Dallah, that utilizes an advanced language model based on LLaMA-2 to facilitate multimodal interactions. Dallah demonstrates state-of-the-art performance in Arabic MLLMs. Through fine-tuning six Arabic dialects, Dallah showcases its capability to handle complex dialectal interactions incorporating both textual and visual elements. The model excels in two benchmark tests: one evaluating its performance on Modern Standard Arabic (MSA) and another specifically designed to assess dialectal responses. Beyond its robust performance in multimodal interaction tasks, Dallah has the potential to pave the way for further development of dialect-aware Arabic MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-26", "arxiv_id": "2407.17722", "title": "Text-Driven Neural Collaborative Filtering Model for Paper Source Tracing", "authors": [ "Aobo Xu", "Bingyu Chang", "Qingpeng Liu", "Ling Jian" ], "github": "https://github.com/MyLove-XAB/KDDCupFinal", "project_page": "", "abstract": "Identifying significant references within the complex interrelations of a citation knowledge graph is challenging, which encompasses connections through citations, authorship, keywords, and other relational attributes. The Paper Source Tracing (PST) task seeks to automate the identification of pivotal references for given scholarly articles utilizing advanced data mining techniques. In the KDD CUP 2024, we design a recommendation-based framework tailored for the PST task. This framework employs the Neural Collaborative Filtering (NCF) model to generate final predictions. To process the textual attributes of the papers and extract input features for the model, we utilize SciBERT, a pre-trained language model. According to the experimental results, our method achieved a score of 0.37814 on the Mean Average Precision (MAP) metric, outperforming baseline models and ranking 11th among all participating teams. The source code is publicly available at https://github.com/MyLove-XAB/KDDCupFinal.", "github_manual": "", "github_regex": "https://github.com/MyLove-XAB/KDDCupFinal", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-26", "arxiv_id": "2407.18054", "title": "LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution Kernels", "authors": [ "Ziwei Cui", "Jingfeng Yao", "Lunbin Zeng", "Juan Yang", "Wenyu Liu", "Xinggang Wang" ], "github": "https://github.com/hustvl/LKCell", "project_page": "", "abstract": "The segmentation of cell nuclei in tissue images stained with the blood dye hematoxylin and eosin (H&E) is essential for various clinical applications and analyses. Due to the complex characteristics of cellular morphology, a large receptive field is considered crucial for generating high-quality segmentation. However, previous methods face challenges in achieving a balance between the receptive field and computational burden. To address this issue, we propose LKCell, a high-accuracy and efficient cell segmentation method. Its core insight lies in unleashing the potential of large convolution kernels to achieve computationally efficient large receptive fields. Specifically, (1) We transfer pre-trained large convolution kernel models to the medical domain for the first time, demonstrating their effectiveness in cell segmentation. (2) We analyze the redundancy of previous methods and design a new segmentation decoder based on large convolution kernels. It achieves higher performance while significantly reducing the number of parameters. We evaluate our method on the most challenging benchmark and achieve state-of-the-art results (0.5080 mPQ) in cell nuclei instance segmentation with only 21.6% FLOPs compared with the previous leading method. Our source code and models are available at https://github.com/hustvl/LKCell.", "github_manual": "", "github_regex": "https://github.com/hustvl/LKCell", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-26", "arxiv_id": "2407.18147", "title": "The FIGNEWS Shared Task on News Media Narratives", "authors": [ "Wajdi Zaghouani", "Mustafa Jarrar", "Nizar Habash", "Houda Bouamor", "Imed Zitouni", "Mona Diab", "Samhaa R. El-Beltagy", "Muhammed AbuOdeh" ], "github": "", "project_page": "", "abstract": "We present an overview of the FIGNEWS shared task, organized as part of the ArabicNLP 2024 conference co-located with ACL 2024. The shared task addresses bias and propaganda annotation in multilingual news posts. We focus on the early days of the Israel War on Gaza as a case study. The task aims to foster collaboration in developing annotation guidelines for subjective tasks by creating frameworks for analyzing diverse narratives highlighting potential bias and propaganda. In a spirit of fostering and encouraging diversity, we address the problem from a multilingual perspective, namely within five languages: English, French, Arabic, Hebrew, and Hindi. A total of 17 teams participated in two annotation subtasks: bias (16 teams) and propaganda (6 teams). The teams competed in four evaluation tracks: guidelines development, annotation quality, annotation quantity, and consistency. Collectively, the teams produced 129,800 data points. Key findings and implications for the field are discussed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-29", "arxiv_id": "2407.18902", "title": "Lessons from Learning to Spin \"Pens\"", "authors": [ "Jun Wang", "Ying Yuan", "Haichuan Che", "Haozhi Qi", "Yi Ma", "Jitendra Malik", "Xiaolong Wang" ], "github": "", "project_page": "", "abstract": "In-hand manipulation of pen-like objects is an important skill in our daily lives, as many tools such as hammers and screwdrivers are similarly shaped. However, current learning-based methods struggle with this task due to a lack of high-quality demonstrations and the significant gap between simulation and the real world. In this work, we push the boundaries of learning-based in-hand manipulation systems by demonstrating the capability to spin pen-like objects. We first use reinforcement learning to train an oracle policy with privileged information and generate a high-fidelity trajectory dataset in simulation. This serves two purposes: 1) pre-training a sensorimotor policy in simulation; 2) conducting open-loop trajectory replay in the real world. We then fine-tune the sensorimotor policy using these real-world trajectories to adapt it to the real world dynamics. With less than 50 trajectories, our policy learns to rotate more than ten pen-like objects with different physical properties for multiple revolutions. We present a comprehensive analysis of our design choices and share the lessons learned during development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-29", "arxiv_id": "2407.18901", "title": "AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents", "authors": [ "Harsh Trivedi", "Tushar Khot", "Mareike Hartmann", "Ruskin Manku", "Vinty Dong", "Edward Li", "Shashank Gupta", "Ashish Sabharwal", "Niranjan Balasubramanian" ], "github": "https://github.com/stonybrooknlp/appworld", "project_page": "", "abstract": "Autonomous agents that address day-to-day digital tasks (e.g., ordering groceries for a household), must not only operate multiple apps (e.g., notes, messaging, shopping app) via APIs, but also generate rich code with complex control flow in an iterative manner based on their interaction with the environment. However, existing benchmarks for tool use are inadequate, as they only cover tasks that require a simple sequence of API calls. To remedy this gap, we built AppWorld Engine, a high-quality execution environment (60K lines of code) of 9 day-to-day apps operable via 457 APIs and populated with realistic digital activities simulating the lives of ~100 fictitious users. We then created AppWorld Benchmark (40K lines of code), a suite of 750 natural, diverse, and challenging autonomous agent tasks requiring rich and interactive code generation. It supports robust programmatic evaluation with state-based unit tests, allowing for different ways of completing a task while also checking for unexpected changes, i.e., collateral damage. The state-of-the-art LLM, GPT-4o, solves only ~49% of our 'normal' tasks and ~30% of 'challenge' tasks, while other models solve at least 16% fewer. This highlights the benchmark's difficulty and AppWorld's potential to push the frontiers of interactive coding agents. The project website is available at https://appworld.dev/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stonybrooknlp/appworld", "github_api_added_by": "auto" }, { "date": "2024-07-29", "arxiv_id": "2407.18559", "title": "VSSD: Vision Mamba with Non-Casual State Space Duality", "authors": [ "Yuheng Shi", "Minjing Dong", "Mingjia Li", "Chang Xu" ], "github": "https://github.com/YuHengsss/VSSD", "project_page": "", "abstract": "Vision transformers have significantly advanced the field of computer vision, offering robust modeling capabilities and global receptive field. However, their high computational demands limit their applicability in processing long sequences. To tackle this issue, State Space Models (SSMs) have gained prominence in vision tasks as they offer linear computational complexity. Recently, State Space Duality (SSD), an improved variant of SSMs, was introduced in Mamba2 to enhance model performance and efficiency. However, the inherent causal nature of SSD/SSMs restricts their applications in non-causal vision tasks. To address this limitation, we introduce Visual State Space Duality (VSSD) model, which has a non-causal format of SSD. Specifically, we propose to discard the magnitude of interactions between the hidden state and tokens while preserving their relative weights, which relieves the dependencies of token contribution on previous tokens. Together with the involvement of multi-scan strategies, we show that the scanning results can be integrated to achieve non-causality, which not only improves the performance of SSD in vision tasks but also enhances its efficiency. We conduct extensive experiments on various benchmarks including image classification, detection, and segmentation, where VSSD surpasses existing state-of-the-art SSM-based models. Code and weights are available at https://github.com/YuHengsss/VSSD.", "github_manual": "", "github_regex": "https://github.com/YuHengsss/VSSD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-29", "arxiv_id": "2407.18908", "title": "Wolf: Captioning Everything with a World Summarization Framework", "authors": [ "Boyi Li", "Ligeng Zhu", "Ran Tian", "Shuhan Tan", "Yuxiao Chen", "Yao Lu", "Yin Cui", "Sushant Veer", "Max Ehrlich", "Jonah Philion", "Xinshuo Weng", "Fuzhao Xue", "Andrew Tao", "Ming-Yu Liu", "Sanja Fidler", "Boris Ivanovic", "Trevor Darrell", "Jitendra Malik", "Song Han", "Marco Pavone" ], "github": "https://github.com/NVlabs/Wolf", "project_page": "", "abstract": "We propose Wolf, a WOrLd summarization Framework for accurate video captioning. Wolf is an automated captioning framework that adopts a mixture-of-experts approach, leveraging complementary strengths of Vision Language Models (VLMs). By utilizing both image and video models, our framework captures different levels of information and summarizes them efficiently. Our approach can be applied to enhance video understanding, auto-labeling, and captioning. To evaluate caption quality, we introduce CapScore, an LLM-based metric to assess the similarity and quality of generated captions compared to the ground truth captions. We further build four human-annotated datasets in three domains: autonomous driving, general scenes, and robotics, to facilitate comprehensive comparisons. We show that Wolf achieves superior captioning performance compared to state-of-the-art approaches from the research community (VILA1.5, CogAgent) and commercial solutions (Gemini-Pro-1.5, GPT-4V). For instance, in comparison with GPT-4V, Wolf improves CapScore both quality-wise by 55.6% and similarity-wise by 77.4% on challenging driving videos. Finally, we establish a benchmark for video captioning and introduce a leaderboard, aiming to accelerate advancements in video understanding, captioning, and data alignment. Leaderboard: https://wolfv0.github.io/leaderboard.html.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/Wolf", "github_api_added_by": "auto" }, { "date": "2024-07-29", "arxiv_id": "2407.18907", "title": "SHIC: Shape-Image Correspondences with no Keypoint Supervision", "authors": [ "Aleksandar Shtedritski", "Christian Rupprecht", "Andrea Vedaldi" ], "github": "", "project_page": "", "abstract": "Canonical surface mapping generalizes keypoint detection by assigning each pixel of an object to a corresponding point in a 3D template. Popularised by DensePose for the analysis of humans, authors have since attempted to apply the concept to more categories, but with limited success due to the high cost of manual supervision. In this work, we introduce SHIC, a method to learn canonical maps without manual supervision which achieves better results than supervised methods for most categories. Our idea is to leverage foundation computer vision models such as DINO and Stable Diffusion that are open-ended and thus possess excellent priors over natural categories. SHIC reduces the problem of estimating image-to-template correspondences to predicting image-to-image correspondences using features from the foundation models. The reduction works by matching images of the object to non-photorealistic renders of the template, which emulates the process of collecting manual annotations for this task. These correspondences are then used to supervise high-quality canonical maps for any object of interest. We also show that image generators can further improve the realism of the template views, which provide an additional source of supervision for the model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-29", "arxiv_id": "2407.18914", "title": "Floating No More: Object-Ground Reconstruction from a Single Image", "authors": [ "Yunze Man", "Yichen Sheng", "Jianming Zhang", "Liang-Yan Gui", "Yu-Xiong Wang" ], "github": "", "project_page": "", "abstract": "Recent advancements in 3D object reconstruction from single images have primarily focused on improving the accuracy of object shapes. Yet, these techniques often fail to accurately capture the inter-relation between the object, ground, and camera. As a result, the reconstructed objects often appear floating or tilted when placed on flat surfaces. This limitation significantly affects 3D-aware image editing applications like shadow rendering and object pose manipulation. To address this issue, we introduce ORG (Object Reconstruction with Ground), a novel task aimed at reconstructing 3D object geometry in conjunction with the ground surface. Our method uses two compact pixel-level representations to depict the relationship between camera, object, and ground. Experiments show that the proposed ORG model can effectively reconstruct object-ground geometry on unseen data, significantly enhancing the quality of shadow generation and pose manipulation compared to conventional single-image 3D reconstruction techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-29", "arxiv_id": "2407.19669", "title": "mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval", "authors": [ "Xin Zhang", "Yanzhao Zhang", "Dingkun Long", "Wen Xie", "Ziqi Dai", "Jialong Tang", "Huan Lin", "Baosong Yang", "Pengjun Xie", "Fei Huang", "Meishan Zhang", "Wenjie Li", "Min Zhang" ], "github": "", "project_page": "", "abstract": "We present systematic efforts in building long-context multilingual text representation model (TRM) and reranker from scratch for text retrieval. We first introduce a text encoder (base size) enhanced with RoPE and unpadding, pre-trained in a native 8192-token context (longer than 512 of previous multilingual encoders). Then we construct a hybrid TRM and a cross-encoder reranker by contrastive learning. Evaluations show that our text encoder outperforms the same-sized previous state-of-the-art XLM-R. Meanwhile, our TRM and reranker match the performance of large-sized state-of-the-art BGE-M3 models and achieve better results on long-context retrieval benchmarks. Further analysis demonstrate that our proposed models exhibit higher efficiency during both training and inference. We believe their efficiency and effectiveness could benefit various researches and industrial applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.19584", "title": "SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain", "authors": [ "Pierre Colombo", "Telmo Pires", "Malik Boudiaf", "Rui Melo", "Dominic Culver", "Sofia Morgado", "Etienne Malaboeuf", "Gabriel Hautreux", "Johanne Charpentier", "Michael Desa" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce SaulLM-54B and SaulLM-141B, two large language models (LLMs) tailored for the legal sector. These models, which feature architectures of 54 billion and 141 billion parameters, respectively, are based on the Mixtral architecture. The development of SaulLM-54B and SaulLM-141B is guided by large-scale domain adaptation, divided into three strategies: (1) the exploitation of continued pretraining involving a base corpus that includes over 540 billion of legal tokens, (2) the implementation of a specialized legal instruction-following protocol, and (3) the alignment of model outputs with human preferences in legal interpretations. The integration of synthetically generated data in the second and third steps enhances the models' capabilities in interpreting and processing legal texts, effectively reaching state-of-the-art performance and outperforming previous open-source models on LegalBench-Instruct. This work explores the trade-offs involved in domain-specific adaptation at this scale, offering insights that may inform future studies on domain adaptation using strong decoder models. Building upon SaulLM-7B, this study refines the approach to produce an LLM better equipped for legal tasks. We are releasing base, instruct, and aligned versions on top of SaulLM-54B and SaulLM-141B under the MIT License to facilitate reuse and collaborative research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.19918", "title": "FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attention", "authors": [ "Yu Lu", "Yuanzhi Liang", "Linchao Zhu", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Video diffusion models have made substantial progress in various video generation applications. However, training models for long video generation tasks require significant computational and data resources, posing a challenge to developing long video diffusion models. This paper investigates a straightforward and training-free approach to extend an existing short video diffusion model (e.g. pre-trained on 16-frame videos) for consistent long video generation (e.g. 128 frames). Our preliminary observation has found that directly applying the short video diffusion model to generate long videos can lead to severe video quality degradation. Further investigation reveals that this degradation is primarily due to the distortion of high-frequency components in long videos, characterized by a decrease in spatial high-frequency components and an increase in temporal high-frequency components. Motivated by this, we propose a novel solution named FreeLong to balance the frequency distribution of long video features during the denoising process. FreeLong blends the low-frequency components of global video features, which encapsulate the entire video sequence, with the high-frequency components of local video features that focus on shorter subsequences of frames. This approach maintains global consistency while incorporating diverse and high-quality spatiotemporal details from local videos, enhancing both the consistency and fidelity of long video generation. We evaluated FreeLong on multiple base video diffusion models and observed significant improvements. Additionally, our method supports coherent multi-prompt generation, ensuring both visual coherence and seamless transitions between scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.18248", "title": "Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning", "authors": [ "Tianduo Wang", "Shichen Li", "Wei Lu" ], "github": "https://github.com/tianduowang/dpo-st", "project_page": "", "abstract": "Effective training of language models (LMs) for mathematical reasoning tasks demands high-quality supervised fine-tuning data. Besides obtaining annotations from human experts, a common alternative is sampling from larger and more powerful LMs. However, this knowledge distillation approach can be costly and unstable, particularly when relying on closed-source, proprietary LMs like GPT-4, whose behaviors are often unpredictable. In this work, we demonstrate that the reasoning abilities of small-scale LMs can be enhanced through self-training, a process where models learn from their own outputs. We also show that the conventional self-training can be further augmented by a preference learning algorithm called Direct Preference Optimization (DPO). By integrating DPO into self-training, we leverage preference data to guide LMs towards more accurate and diverse chain-of-thought reasoning. We evaluate our method across various mathematical reasoning tasks using different base models. Our experiments show that this approach not only improves LMs' reasoning performance but also offers a more cost-effective and scalable solution compared to relying on large proprietary LMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tianduowang/dpo-st", "github_api_added_by": "auto" }, { "date": "2024-07-30", "arxiv_id": "2407.17398", "title": "3D Question Answering for City Scene Understanding", "authors": [ "Penglei Sun", "Yaoxian Song", "Xiang Liu", "Xiaofei Yang", "Qiang Wang", "Tiefeng Li", "Yang Yang", "Xiaowen Chu" ], "github": "", "project_page": "", "abstract": "3D multimodal question answering (MQA) plays a crucial role in scene understanding by enabling intelligent agents to comprehend their surroundings in 3D environments. While existing research has primarily focused on indoor household tasks and outdoor roadside autonomous driving tasks, there has been limited exploration of city-level scene understanding tasks. Furthermore, existing research faces challenges in understanding city scenes, due to the absence of spatial semantic information and human-environment interaction information at the city level.To address these challenges, we investigate 3D MQA from both dataset and method perspectives. From the dataset perspective, we introduce a novel 3D MQA dataset named City-3DQA for city-level scene understanding, which is the first dataset to incorporate scene semantic and human-environment interactive tasks within the city. From the method perspective, we propose a Scene graph enhanced City-level Understanding method (Sg-CityU), which utilizes the scene graph to introduce the spatial semantic. A new benchmark is reported and our proposed Sg-CityU achieves accuracy of 63.94 % and 63.76 % in different settings of City-3DQA. Compared to indoor 3D MQA methods and zero-shot using advanced large language models (LLMs), Sg-CityU demonstrates state-of-the-art (SOTA) performance in robustness and generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.19474", "title": "Visual Riddles: a Commonsense and World Knowledge Challenge for Large Vision and Language Models", "authors": [ "Nitzan Bitton-Guetta", "Aviv Slobodkin", "Aviya Maimon", "Eliya Habba", "Royi Rassin", "Yonatan Bitton", "Idan Szpektor", "Amir Globerson", "Yuval Elovici" ], "github": "", "project_page": "", "abstract": "Imagine observing someone scratching their arm; to understand why, additional context would be necessary. However, spotting a mosquito nearby would immediately offer a likely explanation for the person's discomfort, thereby alleviating the need for further information. This example illustrates how subtle visual cues can challenge our cognitive skills and demonstrates the complexity of interpreting visual scenarios. To study these skills, we present Visual Riddles, a benchmark aimed to test vision and language models on visual riddles requiring commonsense and world knowledge. The benchmark comprises 400 visual riddles, each featuring a unique image created by a variety of text-to-image models, question, ground-truth answer, textual hint, and attribution. Human evaluation reveals that existing models lag significantly behind human performance, which is at 82\\% accuracy, with Gemini-Pro-1.5 leading with 40\\% accuracy. Our benchmark comes with automatic evaluation tasks to make assessment scalable. These findings underscore the potential of Visual Riddles as a valuable resource for enhancing vision and language models' capabilities in interpreting complex visual scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.18961", "title": "MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains", "authors": [ "Guoli Yin", "Haoping Bai", "Shuang Ma", "Feng Nan", "Yanchao Sun", "Zhaoyang Xu", "Shen Ma", "Jiarui Lu", "Xiang Kong", "Aonan Zhang", "Dian Ang Yap", "Yizhe zhang", "Karsten Ahnert", "Vik Kamath", "Mathias Berglund", "Dominic Walsh", "Tobias Gindele", "Juergen Wiest", "Zhengfeng Lai", "Xiaoming Wang", "Jiulong Shan", "Meng Cao", "Ruoming Pang", "Zirui Wang" ], "github": "https://github.com/apple/axlearn", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, while useful, often focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes. This lack of granularity makes it difficult to deeply discern where failures stem from. Additionally, setting up these environments requires considerable effort, and issues of unreliability and reproducibility sometimes arise, especially in interactive tasks. To address these limitations, we introduce the Massive Multitask Agent Understanding (MMAU) benchmark, featuring comprehensive offline tasks that eliminate the need for complex environment setups. It evaluates models across five domains, including teal{Tool-use}, teal{Directed Acyclic Graph (DAG) QA}, teal{Data Science and Machine Learning coding}, teal{Contest-level programming} and teal{Mathematics}, and covers five essential capabilities: orange{Understanding}, orange{Reasoning}, orange{Planning}, orange{Problem-solving}, and orange{Self-correction}. With a total of 20 meticulously designed tasks encompassing over 3K distinct prompts, MMAU provides a comprehensive framework for evaluating the strengths and limitations of LLM agents. By testing 18 representative models on MMAU, we provide deep and insightful analyses. Ultimately, MMAU not only sheds light on the capabilities and limitations of LLM agents but also enhances the interpretability of their performance. Datasets and evaluation scripts of MMAU are released at https://github.com/apple/axlearn/docs/research/mmau.", "github_manual": "", "github_regex": "https://github.com/apple/axlearn", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.18946", "title": "WalkTheDog: Cross-Morphology Motion Alignment via Phase Manifolds", "authors": [ "Peizhuo Li", "Sebastian Starke", "Yuting Ye", "Olga Sorkine-Hornung" ], "github": "https://github.com/PeizhuoLi/walk-the-dog", "project_page": "", "abstract": "We present a new approach for understanding the periodicity structure and semantics of motion datasets, independently of the morphology and skeletal structure of characters. Unlike existing methods using an overly sparse high-dimensional latent, we propose a phase manifold consisting of multiple closed curves, each corresponding to a latent amplitude. With our proposed vector quantized periodic autoencoder, we learn a shared phase manifold for multiple characters, such as a human and a dog, without any supervision. This is achieved by exploiting the discrete structure and a shallow network as bottlenecks, such that semantically similar motions are clustered into the same curve of the manifold, and the motions within the same component are aligned temporally by the phase variable. In combination with an improved motion matching framework, we demonstrate the manifold's capability of timing and semantics alignment in several applications, including motion retrieval, transfer and stylization. Code and pre-trained models for this paper are available at https://peizhuoli.github.io/walkthedog.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PeizhuoLi/walk-the-dog", "github_api_added_by": "auto" }, { "date": "2024-07-30", "arxiv_id": "2407.20179", "title": "Theia: Distilling Diverse Vision Foundation Models for Robot Learning", "authors": [ "Jinghuan Shang", "Karl Schmeckpeper", "Brandon B. May", "Maria Vittoria Minniti", "Tarik Kelestemur", "David Watkins", "Laura Herlant" ], "github": "https://github.com/bdaiinstitute/theia", "project_page": "", "abstract": "Vision-based robot policy learning, which maps visual inputs to actions, necessitates a holistic understanding of diverse visual tasks beyond single-task needs like classification or segmentation. Inspired by this, we introduce Theia, a vision foundation model for robot learning that distills multiple off-the-shelf vision foundation models trained on varied vision tasks. Theia's rich visual representations encode diverse visual knowledge, enhancing downstream robot learning. Extensive experiments demonstrate that Theia outperforms its teacher models and prior robot learning models using less training data and smaller model sizes. Additionally, we quantify the quality of pre-trained visual representations and hypothesize that higher entropy in feature norm distributions leads to improved robot learning performance. Code and models are available at https://github.com/bdaiinstitute/theia.", "github_manual": "", "github_regex": "https://github.com/bdaiinstitute/theia", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.19795", "title": "VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks", "authors": [ "Juhwan Choi", "Junehyoung Kwon", "JungMin Yun", "Seunguk Yu", "YoungBin Kim" ], "github": "", "project_page": "", "abstract": "Domain generalizability is a crucial aspect of a deep learning model since it determines the capability of the model to perform well on data from unseen domains. However, research on the domain generalizability of deep learning models for vision-language tasks remains limited, primarily because of the lack of required datasets. To address these challenges, we propose VolDoGer: Vision-Language Dataset for Domain Generalization, a dedicated dataset designed for domain generalization that addresses three vision-language tasks: image captioning, visual question answering, and visual entailment. We constructed VolDoGer by extending LLM-based data annotation techniques to vision-language tasks, thereby alleviating the burden of recruiting human annotators. We evaluated the domain generalizability of various models, ranging from fine-tuned models to a recent multimodal large language model, through VolDoGer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.19985", "title": "Mixture of Nested Experts: Adaptive Processing of Visual Tokens", "authors": [ "Gagan Jain", "Nidhi Hegde", "Aditya Kusupati", "Arsha Nagrani", "Shyamal Buch", "Prateek Jain", "Anurag Arnab", "Sujoy Paul" ], "github": "", "project_page": "", "abstract": "The visual medium (images and videos) naturally contains a large amount of information redundancy, thereby providing a great opportunity for leveraging efficiency in processing. While Vision Transformer (ViT) based models scale effectively to large data regimes, they fail to capitalize on this inherent redundancy, leading to higher computational costs. Mixture of Experts (MoE) networks demonstrate scalability while maintaining same inference-time costs, but they come with a larger parameter footprint. We present Mixture of Nested Experts (MoNE), which utilizes a nested structure for experts, wherein individual experts fall on an increasing compute-accuracy curve. Given a compute budget, MoNE learns to dynamically choose tokens in a priority order, and thus redundant tokens are processed through cheaper nested experts. Using this framework, we achieve equivalent performance as the baseline models, while reducing inference time compute by over two-fold. We validate our approach on standard image and video datasets - ImageNet-21K, Kinetics400, and Something-Something-v2. We further highlight MoNE's adaptability by showcasing its ability to maintain strong performance across different inference-time compute budgets on videos, using only a single trained model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.20183", "title": "MindSearch: Mimicking Human Minds Elicits Deep AI Searcher", "authors": [ "Zehui Chen", "Kuikun Liu", "Qiuchen Wang", "Jiangning Liu", "Wenwei Zhang", "Kai Chen", "Feng Zhao" ], "github": "https://github.com/InternLM/MindSearch", "project_page": "", "abstract": "Information seeking and integration is a complex cognitive task that consumes enormous time and effort. Inspired by the remarkable progress of Large Language Models, recent works attempt to solve this task by combining LLMs and search engines. However, these methods still obtain unsatisfying performance due to three challenges: (1) complex requests often cannot be accurately and completely retrieved by the search engine once (2) corresponding information to be integrated is spread over multiple web pages along with massive noise, and (3) a large number of web pages with long contents may quickly exceed the maximum context length of LLMs. Inspired by the cognitive process when humans solve these problems, we introduce MindSearch to mimic the human minds in web information seeking and integration, which can be instantiated by a simple yet effective LLM-based multi-agent framework. The WebPlanner models the human mind of multi-step information seeking as a dynamic graph construction process: it decomposes the user query into atomic sub-questions as nodes in the graph and progressively extends the graph based on the search result from WebSearcher. Tasked with each sub-question, WebSearcher performs hierarchical information retrieval with search engines and collects valuable information for WebPlanner. The multi-agent design of MindSearch enables the whole framework to seek and integrate information parallelly from larger-scale (e.g., more than 300) web pages in 3 minutes, which is worth 3 hours of human effort. MindSearch demonstrates significant improvement in the response quality in terms of depth and breadth, on both close-set and open-set QA problems. Besides, responses from MindSearch based on InternLM2.5-7B are preferable by humans to ChatGPT-Web and Perplexity.ai applications, which implies that MindSearch can already deliver a competitive solution to the proprietary AI search engine.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/InternLM/MindSearch", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.20171", "title": "Diffusion Feedback Helps CLIP See Better", "authors": [ "Wenxuan Wang", "Quan Sun", "Fan Zhang", "Yepeng Tang", "Jing Liu", "Xinlong Wang" ], "github": "https://github.com/baaivision/DIVA", "project_page": "", "abstract": "Contrastive Language-Image Pre-training (CLIP), which excels at abstracting open-world representations across domains and modalities, has become a foundation for a variety of vision and multimodal tasks. However, recent studies reveal that CLIP has severe visual shortcomings, such as which can hardly distinguish orientation, quantity, color, structure, etc. These visual shortcomings also limit the perception capabilities of multimodal large language models (MLLMs) built on CLIP. The main reason could be that the image-text pairs used to train CLIP are inherently biased, due to the lack of the distinctiveness of the text and the diversity of images. In this work, we present a simple post-training approach for CLIP models, which largely overcomes its visual shortcomings via a self-supervised diffusion process. We introduce DIVA, which uses the DIffusion model as a Visual Assistant for CLIP. Specifically, DIVA leverages generative feedback from text-to-image diffusion models to optimize CLIP representations, with only images (without corresponding text). We demonstrate that DIVA improves CLIP's performance on the challenging MMVP-VLM benchmark which assesses fine-grained visual abilities to a large extent (e.g., 3-7%), and enhances the performance of MLLMs and vision models on multimodal understanding and segmentation tasks. Extensive evaluation on 29 image classification and retrieval benchmarks confirms that our framework preserves CLIP's strong zero-shot capabilities. The code will be available at https://github.com/baaivision/DIVA.", "github_manual": "", "github_regex": "https://github.com/baaivision/DIVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.19593", "title": "Bridging the Gap: Studio-like Avatar Creation from a Monocular Phone Capture", "authors": [ "ShahRukh Athar", "Shunsuke Saito", "Zhengyu Yang", "Stanislav Pidhorsky", "Chen Cao" ], "github": "", "project_page": "", "abstract": "Creating photorealistic avatars for individuals traditionally involves extensive capture sessions with complex and expensive studio devices like the LightStage system. While recent strides in neural representations have enabled the generation of photorealistic and animatable 3D avatars from quick phone scans, they have the capture-time lighting baked-in, lack facial details and have missing regions in areas such as the back of the ears. Thus, they lag in quality compared to studio-captured avatars. In this paper, we propose a method that bridges this gap by generating studio-like illuminated texture maps from short, monocular phone captures. We do this by parameterizing the phone texture maps using the W^+ space of a StyleGAN2, enabling near-perfect reconstruction. Then, we finetune a StyleGAN2 by sampling in the W^+ parameterized space using a very small set of studio-captured textures as an adversarial training signal. To further enhance the realism and accuracy of facial details, we super-resolve the output of the StyleGAN2 using carefully designed diffusion model that is guided by image gradients of the phone-captured texture map. Once trained, our method excels at producing studio-like facial texture maps from casual monocular smartphone videos. Demonstrating its capabilities, we showcase the generation of photorealistic, uniformly lit, complete avatars from monocular phone captures. http://shahrukhathar.github.io/2024/07/22/Bridging.html{The project page can be found here.}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.19548", "title": "Cycle3D: High-quality and Consistent Image-to-3D Generation via Generation-Reconstruction Cycle", "authors": [ "Zhenyu Tang", "Junwu Zhang", "Xinhua Cheng", "Wangbo Yu", "Chaoran Feng", "Yatian Pang", "Bin Lin", "Li Yuan" ], "github": "", "project_page": "", "abstract": "Recent 3D large reconstruction models typically employ a two-stage process, including first generate multi-view images by a multi-view diffusion model, and then utilize a feed-forward model to reconstruct images to 3D content.However, multi-view diffusion models often produce low-quality and inconsistent images, adversely affecting the quality of the final 3D reconstruction. To address this issue, we propose a unified 3D generation framework called Cycle3D, which cyclically utilizes a 2D diffusion-based generation module and a feed-forward 3D reconstruction module during the multi-step diffusion process. Concretely, 2D diffusion model is applied for generating high-quality texture, and the reconstruction model guarantees multi-view consistency.Moreover, 2D diffusion model can further control the generated content and inject reference-view information for unseen views, thereby enhancing the diversity and texture consistency of 3D generation during the denoising process. Extensive experiments demonstrate the superior ability of our method to create 3D content with high-quality and consistency compared with state-of-the-art baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.19672", "title": "SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages", "authors": [ "Wenxuan Zhang", "Hou Pong Chan", "Yiran Zhao", "Mahani Aljunied", "Jianyu Wang", "Chaoqun Liu", "Yue Deng", "Zhiqiang Hu", "Weiwen Xu", "Yew Ken Chia", "Xin Li", "Lidong Bing" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have shown remarkable abilities across various tasks, yet their development has predominantly centered on high-resource languages like English and Chinese, leaving low-resource languages underserved. To address this disparity, we present SeaLLMs 3, the latest iteration of the SeaLLMs model family, tailored for Southeast Asian languages. This region, characterized by its rich linguistic diversity, has lacked adequate language technology support. SeaLLMs 3 aims to bridge this gap by covering a comprehensive range of languages spoken in this region, including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese. Leveraging efficient language enhancement techniques and a specially constructed instruction tuning dataset, SeaLLMs 3 significantly reduces training costs while maintaining high performance and versatility. Our model excels in tasks such as world knowledge, mathematical reasoning, translation, and instruction following, achieving state-of-the-art performance among similarly sized models. Additionally, we prioritized safety and reliability by addressing both general and culture-specific considerations and incorporated mechanisms to reduce hallucinations. This work underscores the importance of inclusive AI, showing that advanced LLM capabilities can benefit underserved linguistic and cultural communities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.19835", "title": "ATHAR: A High-Quality and Diverse Dataset for Classical Arabic to English Translation", "authors": [ "Mohammed Khalil", "Mohammed Sabry" ], "github": "", "project_page": "", "abstract": "Classical Arabic represents a significant era, encompassing the golden age of Arab culture, philosophy, and scientific literature. With a broad consensus on the importance of translating these literatures to enrich knowledge dissemination across communities, the advent of large language models (LLMs) and translation systems offers promising tools to facilitate this goal. However, we have identified a scarcity of translation datasets in Classical Arabic, which are often limited in scope and topics, hindering the development of high-quality translation systems. In response, we present the ATHAR dataset, comprising 66,000 high-quality Classical Arabic to English translation samples that cover a wide array of subjects including science, culture, and philosophy. Furthermore, we assess the performance of current state-of-the-art LLMs under various settings, concluding that there is a need for such datasets in current systems. Our findings highlight how models can benefit from fine-tuning or incorporating this dataset into their pretraining pipelines. The dataset is publicly available on the HuggingFace Data Hub at https://huggingface.co/datasets/mohamed-khalil/ATHAR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.19914", "title": "Sentiment Analysis of Lithuanian Online Reviews Using Large Language Models", "authors": [ "Brigita Vileikyt\u0117", "Mantas Luko\u0161evi\u010dius", "Lukas Stankevi\u010dius" ], "github": "", "project_page": "", "abstract": "Sentiment analysis is a widely researched area within Natural Language Processing (NLP), attracting significant interest due to the advent of automated solutions. Despite this, the task remains challenging because of the inherent complexity of languages and the subjective nature of sentiments. It is even more challenging for less-studied and less-resourced languages such as Lithuanian. Our review of existing Lithuanian NLP research reveals that traditional machine learning methods and classification algorithms have limited effectiveness for the task. In this work, we address sentiment analysis of Lithuanian five-star-based online reviews from multiple domains that we collect and clean. We apply transformer models to this task for the first time, exploring the capabilities of pre-trained multilingual Large Language Models (LLMs), specifically focusing on fine-tuning BERT and T5 models. Given the inherent difficulty of the task, the fine-tuned models perform quite well, especially when the sentiments themselves are less ambiguous: 80.74% and 89.61% testing recognition accuracy of the most popular one- and five-star reviews respectively. They significantly outperform current commercial state-of-the-art general-purpose LLM GPT-4. We openly share our fine-tuned LLMs online.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.16291", "title": "TAPTRv2: Attention-based Position Update Improves Tracking Any Point", "authors": [ "Hongyang Li", "Hao Zhang", "Shilong Liu", "Zhaoyang Zeng", "Feng Li", "Tianhe Ren", "Bohan Li", "Lei Zhang" ], "github": "", "project_page": "", "abstract": "In this paper, we present TAPTRv2, a Transformer-based approach built upon TAPTR for solving the Tracking Any Point (TAP) task. TAPTR borrows designs from DEtection TRansformer (DETR) and formulates each tracking point as a point query, making it possible to leverage well-studied operations in DETR-like algorithms. TAPTRv2 improves TAPTR by addressing a critical issue regarding its reliance on cost-volume,which contaminates the point query\\'s content feature and negatively impacts both visibility prediction and cost-volume computation. In TAPTRv2, we propose a novel attention-based position update (APU) operation and use key-aware deformable attention to realize. For each query, this operation uses key-aware attention weights to combine their corresponding deformable sampling positions to predict a new query position. This design is based on the observation that local attention is essentially the same as cost-volume, both of which are computed by dot-production between a query and its surrounding features. By introducing this new operation, TAPTRv2 not only removes the extra burden of cost-volume computation, but also leads to a substantial performance improvement. TAPTRv2 surpasses TAPTR and achieves state-of-the-art performance on many challenging datasets, demonstrating the superiority", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.20020", "title": "ImagiNet: A Multi-Content Dataset for Generalizable Synthetic Image Detection via Contrastive Learning", "authors": [ "Delyan Boychev", "Radostin Cholakov" ], "github": "https://github.com/delyan-boychev/imaginet", "project_page": "", "abstract": "Generative models, such as diffusion models (DMs), variational autoencoders (VAEs), and generative adversarial networks (GANs), produce images with a level of authenticity that makes them nearly indistinguishable from real photos and artwork. While this capability is beneficial for many industries, the difficulty of identifying synthetic images leaves online media platforms vulnerable to impersonation and misinformation attempts. To support the development of defensive methods, we introduce ImagiNet, a high-resolution and balanced dataset for synthetic image detection, designed to mitigate potential biases in existing resources. It contains 200K examples, spanning four content categories: photos, paintings, faces, and uncategorized. Synthetic images are produced with open-source and proprietary generators, whereas real counterparts of the same content type are collected from public datasets. The structure of ImagiNet allows for a two-track evaluation system: i) classification as real or synthetic and ii) identification of the generative model. To establish a baseline, we train a ResNet-50 model using a self-supervised contrastive objective (SelfCon) for each track. The model demonstrates state-of-the-art performance and high inference speed across established benchmarks, achieving an AUC of up to 0.99 and balanced accuracy ranging from 86% to 95%, even under social network conditions that involve compression and resizing. Our data and code are available at https://github.com/delyan-boychev/imaginet.", "github_manual": "", "github_regex": "https://github.com/delyan-boychev/imaginet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.19594", "title": "Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge", "authors": [ "Tianhao Wu", "Weizhe Yuan", "Olga Golovneva", "Jing Xu", "Yuandong Tian", "Jiantao Jiao", "Jason Weston", "Sainbayar Sukhbaatar" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are rapidly surpassing human knowledge in many domains. While improving these models traditionally relies on costly human data, recent self-rewarding mechanisms (Yuan et al., 2024) have shown that LLMs can improve by judging their own responses instead of relying on human labelers. However, existing methods have primarily focused on improving model responses rather than judgment capabilities, resulting in rapid saturation during iterative training. To address this issue, we introduce a novel Meta-Rewarding step to the self-improvement process, where the model judges its own judgements and uses that feedback to refine its judgment skills. Surprisingly, this unsupervised approach improves the model's ability to judge {\\em and} follow instructions, as demonstrated by a win rate improvement of Llama-3-8B-Instruct from 22.9% to 39.4% on AlpacaEval 2, and 20.6% to 29.1% on Arena-Hard. These results strongly suggest the potential for self-improving models without human supervision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-30", "arxiv_id": "2407.19340", "title": "Integrating Large Language Models into a Tri-Modal Architecture for Automated Depression Classification", "authors": [ "Santosh V. Patapati" ], "github": "", "project_page": "", "abstract": "Major Depressive Disorder (MDD) is a pervasive mental health condition that affects 300 million people worldwide. This work presents a novel, BiLSTM-based tri-modal model-level fusion architecture for the binary classification of depression from clinical interview recordings. The proposed architecture incorporates Mel Frequency Cepstral Coefficients, Facial Action Units, and uses a two-shot learning based GPT-4 model to process text data. This is the first work to incorporate large language models into a multi-modal architecture for this task. It achieves impressive results on the DAIC-WOZ AVEC 2016 Challenge cross-validation split and Leave-One-Subject-Out cross-validation split, surpassing all baseline models and multiple state-of-the-art models. In Leave-One-Subject-Out testing, it achieves an accuracy of 91.01%, an F1-Score of 85.95%, a precision of 80%, and a recall of 92.86%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-31", "arxiv_id": "2407.21017", "title": "Matting by Generation", "authors": [ "Zhixiang Wang", "Baiang Li", "Jian Wang", "Yu-Lun Liu", "Jinwei Gu", "Yung-Yu Chuang", "Shin'ichi Satoh" ], "github": "", "project_page": "", "abstract": "This paper introduces an innovative approach for image matting that redefines the traditional regression-based task as a generative modeling challenge. Our method harnesses the capabilities of latent diffusion models, enriched with extensive pre-trained knowledge, to regularize the matting process. We present novel architectural innovations that empower our model to produce mattes with superior resolution and detail. The proposed method is versatile and can perform both guidance-free and guidance-based image matting, accommodating a variety of additional cues. Our comprehensive evaluation across three benchmark datasets demonstrates the superior performance of our approach, both quantitatively and qualitatively. The results not only reflect our method's robust effectiveness but also highlight its ability to generate visually compelling mattes that approach photorealistic quality. The project page for this paper is available at https://lightchaserx.github.io/matting-by-generation/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-31", "arxiv_id": "2407.20445", "title": "Futga: Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation", "authors": [ "Junda Wu", "Zachary Novack", "Amit Namburi", "Jiaheng Dai", "Hao-Wen Dong", "Zhouhang Xie", "Carol Chen", "Julian McAuley" ], "github": "", "project_page": "", "abstract": "Existing music captioning methods are limited to generating concise global descriptions of short music clips, which fail to capture fine-grained musical characteristics and time-aware musical changes. To address these limitations, we propose FUTGA, a model equipped with fined-grained music understanding capabilities through learning from generative augmentation with temporal compositions. We leverage existing music caption datasets and large language models (LLMs) to synthesize fine-grained music captions with structural descriptions and time boundaries for full-length songs. Augmented by the proposed synthetic dataset, FUTGA is enabled to identify the music's temporal changes at key transition points and their musical functions, as well as generate detailed descriptions for each music segment. We further introduce a full-length music caption dataset generated by FUTGA, as the augmentation of the MusicCaps and the Song Describer datasets. We evaluate the automatically generated captions on several downstream tasks, including music generation and retrieval. The experiments demonstrate the quality of the generated captions and the better performance in various downstream tasks achieved by the proposed music captioning approach. Our code and datasets can be found in https://huggingface.co/JoshuaW1997/FUTGA{blue{https://huggingface.co/JoshuaW1997/FUTGA}}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-31", "arxiv_id": "2407.21018", "title": "ThinK: Thinner Key Cache by Query-Driven Pruning", "authors": [ "Yuhui Xu", "Zhanming Jie", "Hanze Dong", "Lei Wang", "Xudong Lu", "Aojun Zhou", "Amrita Saha", "Caiming Xiong", "Doyen Sahoo" ], "github": "https://github.com/SalesforceAIResearch/ThinK", "project_page": "", "abstract": "Large Language Models (LLMs) have revolutionized the field of natural language processing, achieving unprecedented performance across a variety of applications by leveraging increased model sizes and sequence lengths. However, the associated rise in computational and memory costs poses significant challenges, particularly in managing long sequences due to the quadratic complexity of the transformer attention mechanism. This paper focuses on the long-context scenario, addressing the inefficiencies in KV cache memory consumption during inference. Unlike existing approaches that optimize the memory based on the sequence lengths, we uncover that the channel dimension of the KV cache exhibits significant redundancy, characterized by unbalanced magnitude distribution and low-rank structure in attention weights. Based on these observations, we propose ThinK, a novel query-dependent KV cache pruning method designed to minimize attention weight loss while selectively pruning the least significant channels. Our approach not only maintains or enhances model accuracy but also achieves a reduction in memory costs by over 20% compared with vanilla KV cache eviction methods. Extensive evaluations on the LLaMA3 and Mistral models across various long-sequence datasets confirm the efficacy of ThinK, setting a new precedent for efficient LLM deployment without compromising performance. We also outline the potential of extending our method to value cache pruning, demonstrating ThinK's versatility and broad applicability in reducing both memory and computational overheads.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SalesforceAIResearch/ThinK", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-31", "arxiv_id": "2407.20581", "title": "Knesset-DictaBERT: A Hebrew Language Model for Parliamentary Proceedings", "authors": [ "Gili Goldin", "Shuly Wintner" ], "github": "", "project_page": "", "abstract": "We present Knesset-DictaBERT, a large Hebrew language model fine-tuned on the Knesset Corpus, which comprises Israeli parliamentary proceedings. The model is based on the DictaBERT architecture and demonstrates significant improvements in understanding parliamentary language according to the MLM task. We provide a detailed evaluation of the model's performance, showing improvements in perplexity and accuracy over the baseline DictaBERT model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-31", "arxiv_id": "2407.20798", "title": "Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning", "authors": [ "Norman Di Palo", "Leonard Hasenclever", "Jan Humplik", "Arunkumar Byravan" ], "github": "", "project_page": "", "abstract": "We introduce Diffusion Augmented Agents (DAAG), a novel framework that leverages large language models, vision language models, and diffusion models to improve sample efficiency and transfer learning in reinforcement learning for embodied agents. DAAG hindsight relabels the agent's past experience by using diffusion models to transform videos in a temporally and geometrically consistent way to align with target instructions with a technique we call Hindsight Experience Augmentation. A large language model orchestrates this autonomous process without requiring human supervision, making it well-suited for lifelong learning scenarios. The framework reduces the amount of reward-labeled data needed to 1) finetune a vision language model that acts as a reward detector, and 2) train RL agents on new tasks. We demonstrate the sample efficiency gains of DAAG in simulated robotics environments involving manipulation and navigation. Our results show that DAAG improves learning of reward detectors, transferring past experience, and acquiring new tasks - key abilities for developing efficient lifelong learning agents. Supplementary material and visualizations are available on our website https://sites.google.com/view/diffusion-augmented-agents/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-31", "arxiv_id": "2407.20595", "title": "Harvesting Textual and Structured Data from the HAL Publication Repository", "authors": [ "Francis Kulumba", "Wissam Antoun", "Guillaume Vimont", "Laurent Romary" ], "github": "https://github.com/Madjakul/HALvesting", "project_page": "", "abstract": "HAL (Hyper Articles en Ligne) is the French national publication repository, used by most higher education and research organizations for their open science policy. As a digital library, it is a rich repository of scholarly documents, but its potential for advanced research has been underutilized. We present HALvest, a unique dataset that bridges the gap between citation networks and the full text of papers submitted on HAL. We craft our dataset by filtering HAL for scholarly publications, resulting in approximately 700,000 documents, spanning 34 languages across 13 identified domains, suitable for language model training, and yielding approximately 16.5 billion tokens (with 8 billion in French and 7 billion in English, the most represented languages). We transform the metadata of each paper into a citation network, producing a directed heterogeneous graph. This graph includes uniquely identified authors on HAL, as well as all open submitted papers, and their citations. We provide a baseline for authorship attribution using the dataset, implement a range of state-of-the-art models in graph representation learning for link prediction, and discuss the usefulness of our generated knowledge graph structure.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Madjakul/HALvesting", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-31", "arxiv_id": "2407.20729", "title": "Adapting Safe-for-Work Classifier for Malaysian Language Text: Enhancing Alignment in LLM-Ops Framework", "authors": [ "Aisyah Razak", "Ariff Nazhan", "Kamarul Adha", "Wan Adzhar Faiq Adzlan", "Mas Aisyah Ahmad", "Ammar Azman" ], "github": "https://github.com/heartexlabs/label-studio", "project_page": "", "abstract": "As large language models (LLMs) become increasingly integrated into operational workflows (LLM-Ops), there is a pressing need for effective guardrails to ensure safe and aligned interactions, including the ability to detect potentially unsafe or inappropriate content across languages. However, existing safe-for-work classifiers are primarily focused on English text. To address this gap for the Malaysian language, we present a novel safe-for-work text classifier tailored specifically for Malaysian language content. By curating and annotating a first-of-its-kind dataset of Malaysian text spanning multiple content categories, we trained a classification model capable of identifying potentially unsafe material using state-of-the-art natural language processing techniques. This work represents an important step in enabling safer interactions and content filtering to mitigate potential risks and ensure responsible deployment of LLMs. To maximize accessibility and promote further research towards enhancing alignment in LLM-Ops for the Malaysian context, the model is publicly released at https://huggingface.co/malaysia-ai/malaysian-sfw-classifier.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/heartexlabs/label-studio", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-31", "arxiv_id": "2407.20743", "title": "Meltemi: The first open Large Language Model for Greek", "authors": [ "Leon Voukoutis", "Dimitris Roussis", "Georgios Paraskevopoulos", "Sokratis Sofianopoulos", "Prokopis Prokopidis", "Vassilis Papavasileiou", "Athanasios Katsamanis", "Stelios Piperidis", "Vassilis Katsouros" ], "github": "", "project_page": "", "abstract": "We describe the development and capabilities of Meltemi 7B, the first open Large Language Model for the Greek language. Meltemi 7B has 7 billion parameters and is trained on a 40 billion token Greek corpus. For the development of Meltemi 7B, we adapt Mistral, by continuous pretraining on the Greek Corpus. Meltemi 7B contains up-to-date information up to September 2023. Furthermore, we have translated and curated a Greek instruction corpus, which has been used for the instruction-tuning of a chat model, named Meltemi 7B Instruct. Special care has been given to the alignment and the removal of toxic content for the Meltemi 7B Instruct. The developed models are evaluated on a broad set of collected evaluation corpora, and examples of prompts and responses are presented. Both Meltemi 7B and Meltemi 7B Instruct are available at https://huggingface.co/ilsp under the Apache 2.0 license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-31", "arxiv_id": "2407.20750", "title": "JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resources", "authors": [ "Benjamin Clavi\u00e9" ], "github": "", "project_page": "", "abstract": "Neural Information Retrieval has advanced rapidly in high-resource languages, but progress in lower-resource ones such as Japanese has been hindered by data scarcity, among other challenges. Consequently, multilingual models have dominated Japanese retrieval, despite their computational inefficiencies and inability to capture linguistic nuances. While recent multi-vector monolingual models like JaColBERT have narrowed this gap, they still lag behind multilingual methods in large-scale evaluations. This work addresses the suboptimal training methods of multi-vector retrievers in lower-resource settings, focusing on Japanese. We systematically evaluate and improve key aspects of the inference and training settings of JaColBERT, and more broadly, multi-vector models. We further enhance performance through a novel checkpoint merging step, showcasing it to be an effective way of combining the benefits of fine-tuning with the generalization capabilities of the original checkpoint. Building on our analysis, we introduce a novel training recipe, resulting in the JaColBERTv2.5 model. JaColBERTv2.5, with only 110 million parameters and trained in under 15 hours on 4 A100 GPUs, significantly outperforms all existing methods across all common benchmarks, reaching an average score of 0.754, significantly above the previous best of 0.720. To support future research, we make our final models, intermediate checkpoints and all data used publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-07-31", "arxiv_id": "2407.20267", "title": "A Large Encoder-Decoder Family of Foundation Models For Chemical Language", "authors": [ "Eduardo Soares", "Victor Shirasuna", "Emilio Vital Brazil", "Renato Cerqueira", "Dmitry Zubarev", "Kristin Schmidt" ], "github": "https://github.com/IBM/materials", "project_page": "", "abstract": "Large-scale pre-training methodologies for chemical language models represent a breakthrough in cheminformatics. These methods excel in tasks such as property prediction and molecule generation by learning contextualized representations of input tokens through self-supervised learning on large unlabeled corpora. Typically, this involves pre-training on unlabeled data followed by fine-tuning on specific tasks, reducing dependence on annotated datasets and broadening chemical language representation understanding. This paper introduces a large encoder-decoder chemical foundation models pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, which is equivalent to 4 billion of molecular tokens. The proposed foundation model supports different complex tasks, including quantum property prediction, and offer flexibility with two main variants (289M and 8times289M). Our experiments across multiple benchmark datasets validate the capacity of the proposed model in providing state-of-the-art results for different tasks. We also provide a preliminary assessment of the compositionality of the embedding space as a prerequisite for the reasoning tasks. We demonstrate that the produced latent space is separable compared to the state-of-the-art with few-shot learning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/IBM/materials", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-01", "arxiv_id": "2407.21475", "title": "Fine-gained Zero-shot Video Sampling", "authors": [ "Dengsheng Chen", "Jie Hu", "Xiaoming Wei", "Enhua Wu" ], "github": "", "project_page": "", "abstract": "Incorporating a temporal dimension into pretrained image diffusion models for video generation is a prevalent approach. However, this method is computationally demanding and necessitates large-scale video datasets. More critically, the heterogeneity between image and video datasets often results in catastrophic forgetting of the image expertise. Recent attempts to directly extract video snippets from image diffusion models have somewhat mitigated these problems. Nevertheless, these methods can only generate brief video clips with simple movements and fail to capture fine-grained motion or non-grid deformation. In this paper, we propose a novel Zero-Shot video Sampling algorithm, denoted as ZS^2, capable of directly sampling high-quality video clips from existing image synthesis methods, such as Stable Diffusion, without any training or optimization. Specifically, ZS^2 utilizes the dependency noise model and temporal momentum attention to ensure content consistency and animation coherence, respectively. This ability enables it to excel in related tasks, such as conditional and context-specialized video generation and instruction-guided video editing. Experimental results demonstrate that ZS^2 achieves state-of-the-art performance in zero-shot video generation, occasionally outperforming recent supervised methods. Homepage: https://densechen.github.io/zss/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-01", "arxiv_id": "2407.21646", "title": "Towards Achieving Human Parity on End-to-end Simultaneous Speech Translation via LLM Agent", "authors": [ "Shanbo Cheng", "Zhichao Huang", "Tom Ko", "Hang Li", "Ningxin Peng", "Lu Xu", "Qini Zhang" ], "github": "https://github.com/byteresearchcla/realsi", "project_page": "", "abstract": "In this paper, we present Cross Language Agent -- Simultaneous Interpretation, CLASI, a high-quality and human-like Simultaneous Speech Translation (SiST) System. Inspired by professional human interpreters, we utilize a novel data-driven read-write strategy to balance the translation quality and latency. To address the challenge of translating in-domain terminologies, CLASI employs a multi-modal retrieving module to obtain relevant information to augment the translation. Supported by LLMs, our approach can generate error-tolerated translation by considering the input audio, historical context, and retrieved information. Experimental results show that our system outperforms other systems by significant margins. Aligned with professional human interpreters, we evaluate CLASI with a better human evaluation metric, valid information proportion (VIP), which measures the amount of information that can be successfully conveyed to the listeners. In the real-world scenarios, where the speeches are often disfluent, informal, and unclear, CLASI achieves VIP of 81.3% and 78.0% for Chinese-to-English and English-to-Chinese translation directions, respectively. In contrast, state-of-the-art commercial or open-source systems only achieve 35.4% and 41.6%. On the extremely hard dataset, where other systems achieve under 13% VIP, CLASI can still achieve 70% VIP.", "github_manual": "https://github.com/byteresearchcla/realsi", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-01", "arxiv_id": "2407.21781", "title": "Berkeley Humanoid: A Research Platform for Learning-based Control", "authors": [ "Qiayuan Liao", "Bike Zhang", "Xuanyu Huang", "Xiaoyu Huang", "Zhongyu Li", "Koushil Sreenath" ], "github": "", "project_page": "", "abstract": "We introduce Berkeley Humanoid, a reliable and low-cost mid-scale humanoid research platform for learning-based control. Our lightweight, in-house-built robot is designed specifically for learning algorithms with low simulation complexity, anthropomorphic motion, and high reliability against falls. The robot's narrow sim-to-real gap enables agile and robust locomotion across various terrains in outdoor environments, achieved with a simple reinforcement learning controller using light domain randomization. Furthermore, we demonstrate the robot traversing for hundreds of meters, walking on a steep unpaved trail, and hopping with single and double legs as a testimony to its high performance in dynamical walking. Capable of omnidirectional locomotion and withstanding large perturbations with a compact setup, our system aims for scalable, sim-to-real deployment of learning-based humanoid systems. Please check http://berkeley-humanoid.com for more details.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-01", "arxiv_id": "2407.21686", "title": "Expressive Whole-Body 3D Gaussian Avatar", "authors": [ "Gyeongsik Moon", "Takaaki Shiratori", "Shunsuke Saito" ], "github": "https://github.com/mks0601/ExAvatar_RELEASE", "project_page": "", "abstract": "Facial expression and hand motions are necessary to express our emotions and interact with the world. Nevertheless, most of the 3D human avatars modeled from a casually captured video only support body motions without facial expressions and hand motions.In this work, we present ExAvatar, an expressive whole-body 3D human avatar learned from a short monocular video. We design ExAvatar as a combination of the whole-body parametric mesh model (SMPL-X) and 3D Gaussian Splatting (3DGS). The main challenges are 1) a limited diversity of facial expressions and poses in the video and 2) the absence of 3D observations, such as 3D scans and RGBD images. The limited diversity in the video makes animations with novel facial expressions and poses non-trivial. In addition, the absence of 3D observations could cause significant ambiguity in human parts that are not observed in the video, which can result in noticeable artifacts under novel motions. To address them, we introduce our hybrid representation of the mesh and 3D Gaussians. Our hybrid representation treats each 3D Gaussian as a vertex on the surface with pre-defined connectivity information (i.e., triangle faces) between them following the mesh topology of SMPL-X. It makes our ExAvatar animatable with novel facial expressions by driven by the facial expression space of SMPL-X. In addition, by using connectivity-based regularizers, we significantly reduce artifacts in novel facial expressions and poses.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mks0601/ExAvatar_RELEASE", "github_api_added_by": "auto" }, { "date": "2024-08-01", "arxiv_id": "2407.21705", "title": "Tora: Trajectory-oriented Diffusion Transformer for Video Generation", "authors": [ "Zhenghao Zhang", "Junchao Liao", "Menghao Li", "Long Qin", "Weizhi Wang" ], "github": "https://github.com/alibaba/Tora", "project_page": "", "abstract": "Recent advancements in Diffusion Transformer (DiT) have demonstrated remarkable proficiency in producing high-quality video content. Nonetheless, the potential of transformer-based diffusion models for effectively generating videos with controllable motion remains an area of limited exploration. This paper introduces Tora, the first trajectory-oriented DiT framework that integrates textual, visual, and trajectory conditions concurrently for video generation. Specifically, Tora consists of a Trajectory Extractor~(TE), a Spatial-Temporal DiT, and a Motion-guidance Fuser~(MGF). The TE encodes arbitrary trajectories into hierarchical spacetime motion patches with a 3D video compression network. The MGF integrates the motion patches into the DiT blocks to generate consistent videos following trajectories. Our design aligns seamlessly with DiT's scalability, allowing precise control of video content's dynamics with diverse durations, aspect ratios, and resolutions. Extensive experiments demonstrate Tora's excellence in achieving high motion fidelity, while also meticulously simulating the movement of the physical world. Page can be found at https://ali-videoai.github.io/tora_video.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/alibaba/Tora", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-01", "arxiv_id": "2407.21770", "title": "MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts", "authors": [ "Xi Victoria Lin", "Akshat Shrivastava", "Liang Luo", "Srinivasan Iyer", "Mike Lewis", "Gargi Gosh", "Luke Zettlemoyer", "Armen Aghajanyan" ], "github": "", "project_page": "", "abstract": "We introduce MoMa, a novel modality-aware mixture-of-experts (MoE) architecture designed for pre-training mixed-modal, early-fusion language models. MoMa processes images and text in arbitrary sequences by dividing expert modules into modality-specific groups. These groups exclusively process designated tokens while employing learned routing within each group to maintain semantically informed adaptivity. Our empirical results reveal substantial pre-training efficiency gains through this modality-specific parameter allocation. Under a 1-trillion-token training budget, the MoMa 1.4B model, featuring 4 text experts and 4 image experts, achieves impressive FLOPs savings: 3.7x overall, with 2.6x for text and 5.2x for image processing compared to a compute-equivalent dense baseline, measured by pre-training loss. This outperforms the standard expert-choice MoE with 8 mixed-modal experts, which achieves 3x overall FLOPs savings (3x for text, 2.8x for image). Combining MoMa with mixture-of-depths (MoD) further improves pre-training FLOPs savings to 4.2x overall (text: 3.4x, image: 5.3x), although this combination hurts performance in causal inference due to increased sensitivity to router accuracy. These results demonstrate MoMa's potential to significantly advance the efficiency of mixed-modal, early-fusion language model pre-training, paving the way for more resource-efficient and capable multimodal AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-01", "arxiv_id": "2407.21530", "title": "Data Contamination Report from the 2024 CONDA Shared Task", "authors": [ "Oscar Sainz", "Iker Garc\u00eda-Ferrero", "Alon Jacovi", "Jon Ander Campos", "Yanai Elazar", "Eneko Agirre", "Yoav Goldberg", "Wei-Lin Chen", "Jenny Chim", "Leshem Choshen", "Luca D'Amico-Wong", "Melissa Dell", "Run-Ze Fan", "Shahriar Golchin", "Yucheng Li", "Pengfei Liu", "Bhavish Pahwa", "Ameya Prabhu", "Suryansh Sharma", "Emily Silcock", "Kateryna Solonko", "David Stap", "Mihai Surdeanu", "Yu-Min Tseng", "Vishaal Udandarao", "Zengzhi Wang", "Ruijie Xu", "Jinglin Yang" ], "github": "", "project_page": "", "abstract": "The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora used to train large scale models, compromising evaluation results. The workshop fostered a shared task to collect evidence on data contamination in current available datasets and models. The goal of the shared task and associated database is to assist the community in understanding the extent of the problem and to assist researchers in avoiding reporting evaluation results on known contaminated resources. The shared task provides a structured, centralized public database for the collection of contamination evidence, open to contributions from the community via GitHub pool requests. This first compilation paper is based on 566 reported entries over 91 contaminated sources from a total of 23 contributors. The details of the individual contamination events are available in the platform. The platform continues to be online, open to contributions from the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-01", "arxiv_id": "2407.21783", "title": "The Llama 3 Herd of Models", "authors": [ "Abhimanyu Dubey", "Abhinav Jauhri", "Abhinav Pandey", "Abhishek Kadian", "Ahmad Al-Dahle", "Aiesha Letman", "Akhil Mathur", "Alan Schelten", "Amy Yang", "Angela Fan", "Anirudh Goyal", "Anthony Hartshorn", "Aobo Yang", "Archi Mitra", "Archie Sravankumar", "Artem Korenev", "Arthur Hinsvark", "Arun Rao", "Aston Zhang", "Aurelien Rodriguez", "Austen Gregerson", "Ava Spataru", "Baptiste Roziere", "Bethany Biron", "Binh Tang", "Bobbie Chern", "Charlotte Caucheteux", "Chaya Nayak", "Chloe Bi", "Chris Marra", "Chris McConnell", "Christian Keller", "Christophe Touret", "Chunyang Wu", "Corinne Wong", "Cristian Canton Ferrer", "Cyrus Nikolaidis", "Damien Allonsius", "Daniel Song", "Danielle Pintz", "Danny Livshits", "David Esiobu", "Dhruv Choudhary", "Dhruv Mahajan", "Diego Garcia-Olano", "Diego Perino", "Dieuwke Hupkes", "Egor Lakomkin", "Ehab AlBadawy", "Elina Lobanova", "Emily Dinan", "Eric Michael Smith", "Filip Radenovic", "Frank Zhang", "Gabriel Synnaeve", "Gabrielle Lee", "Georgia Lewis Anderson", "Graeme Nail", "Gregoire Mialon", "Guan Pang", "Guillem Cucurell", "Hailey Nguyen", "Hannah Korevaar", "Hu Xu", "Hugo Touvron", "Iliyan Zarov", "Imanol Arrieta Ibarra", "Isabel Kloumann", "Ishan Misra", "Ivan Evtimov", "Jade Copet", "Jaewon Lee", "Jan Geffert", "Jana Vranes", "Jason Park", "Jay Mahadeokar", "Jeet Shah", "Jelmer van der Linde", "Jennifer Billock", "Jenny Hong", "Jenya Lee", "Jeremy Fu", "Jianfeng Chi", "Jianyu Huang", "Jiawen Liu", "Jie Wang", "Jiecao Yu", "Joanna Bitton", "Joe Spisak", "Jongsoo Park", "Joseph Rocca", "Joshua Johnstun", "Joshua Saxe", "Junteng Jia", "Kalyan Vasuden Alwala", "Kartikeya Upasani", "Kate Plawiak", "Ke Li", "Kenneth Heafield", "Kevin Stone", "Khalid El-Arini", "Krithika Iyer", "Kshitiz Malik", "Kuenley Chiu", "Kunal Bhalla", "Lauren Rantala-Yeary", "Laurens van der Maaten", "Lawrence Chen", "Liang Tan", "Liz Jenkins", "Louis Martin", "Lovish Madaan", "Lubo Malo", "Lukas Blecher", "Lukas Landzaat", "Luke de Oliveira", "Madeline Muzzi", "Mahesh Pasupuleti", "Mannat Singh", "Manohar Paluri", "Marcin Kardas", "Mathew Oldham", "Mathieu Rita", "Maya Pavlova", "Melanie Kambadur", "Mike Lewis", "Min Si", "Mitesh Kumar Singh", "Mona Hassan", "Naman Goyal", "Narjes Torabi", "Nikolay Bashlykov", "Nikolay Bogoychev", "Niladri Chatterji", "Olivier Duchenne", "Onur \u00c7elebi", "Patrick Alrassy", "Pengchuan Zhang", "Pengwei Li", "Petar Vasic", "Peter Weng", "Prajjwal Bhargava", "Pratik Dubal", "Praveen Krishnan", "Punit Singh Koura", "Puxin Xu", "Qing He", "Qingxiao Dong", "Ragavan Srinivasan", "Raj Ganapathy", "Ramon Calderer", "Ricardo Silveira Cabral", "Robert Stojnic", "Roberta Raileanu", "Rohit Girdhar", "Rohit Patel", "Romain Sauvestre", "Ronnie Polidoro", "Roshan Sumbaly", "Ross Taylor", "Ruan Silva", "Rui Hou", "Rui Wang", "Saghar Hosseini", "Sahana Chennabasappa", "Sanjay Singh", "Sean Bell", "Seohyun Sonia Kim", "Sergey Edunov", "Shaoliang Nie", "Sharan Narang", "Sharath Raparthy", "Sheng Shen", "Shengye Wan", "Shruti Bhosale", "Shun Zhang", "Simon Vandenhende", "Soumya Batra", "Spencer Whitman", "Sten Sootla", "Stephane Collot", "Suchin Gururangan", "Sydney Borodinsky", "Tamar Herman", "Tara Fowler", "Tarek Sheasha", "Thomas Georgiou", "Thomas Scialom", "Tobias Speckbacher", "Todor Mihaylov", "Tong Xiao", "Ujjwal Karn", "Vedanuj Goswami", "Vibhor Gupta", "Vignesh Ramanathan", "Viktor Kerkez", "Vincent Gonguet", "Virginie Do", "Vish Vogeti", "Vladan Petrovic", "Weiwei Chu", "Wenhan Xiong", "Wenyin Fu", "Whitney Meers", "Xavier Martinet", "Xiaodong Wang", "Xiaoqing Ellen Tan", "Xinfeng Xie", "Xuchao Jia", "Xuewei Wang", "Yaelle Goldschlag", "Yashesh Gaur", "Yasmine Babaei", "Yi Wen", "Yiwen Song", "Yuchen Zhang", "Yue Li", "Yuning Mao", "Zacharie Delpierre Coudert", "Zheng Yan", "Zhengxing Chen", "Zoe Papakipos", "Aaditya Singh", "Aaron Grattafiori", "Abha Jain", "Adam Kelsey", "Adam Shajnfeld", "Adithya Gangidi", "Adolfo Victoria", "Ahuva Goldstand", "Ajay Menon", "Ajay Sharma", "Alex Boesenberg", "Alex Vaughan", "Alexei Baevski", "Allie Feinstein", "Amanda Kallet", "Amit Sangani", "Anam Yunus", "Andrei Lupu", "Andres Alvarado", "Andrew Caples", "Andrew Gu", "Andrew Ho", "Andrew Poulton", "Andrew Ryan", "Ankit Ramchandani", "Annie Franco", "Aparajita Saraf", "Arkabandhu Chowdhury", "Ashley Gabriel", "Ashwin Bharambe", "Assaf Eisenman", "Azadeh Yazdan", "Beau James", "Ben Maurer", "Benjamin Leonhardi", "Bernie Huang", "Beth Loyd", "Beto De Paola", "Bhargavi Paranjape", "Bing Liu", "Bo Wu", "Boyu Ni", "Braden Hancock", "Bram Wasti", "Brandon Spence", "Brani Stojkovic", "Brian Gamido", "Britt Montalvo", "Carl Parker", "Carly Burton", "Catalina Mejia", "Changhan Wang", "Changkyu Kim", "Chao Zhou", "Chester Hu", "Ching-Hsiang Chu", "Chris Cai", "Chris Tindal", "Christoph Feichtenhofer", "Damon Civin", "Dana Beaty", "Daniel Kreymer", "Daniel Li", "Danny Wyatt", "David Adkins", "David Xu", "Davide Testuggine", "Delia David", "Devi Parikh", "Diana Liskovich", "Didem Foss", "Dingkang Wang", "Duc Le", "Dustin Holland", "Edward Dowling", "Eissa Jamil", "Elaine Montgomery", "Eleonora Presani", "Emily Hahn", "Emily Wood", "Erik Brinkman", "Esteban Arcaute", "Evan Dunbar", "Evan Smothers", "Fei Sun", "Felix Kreuk", "Feng Tian", "Firat Ozgenel", "Francesco Caggioni", "Francisco Guzm\u00e1n", "Frank Kanayet", "Frank Seide", "Gabriela Medina Florez", "Gabriella Schwarz", "Gada Badeer", "Georgia Swee", "Gil Halpern", "Govind Thattai", "Grant Herman", "Grigory Sizov", "Guangyi", "Zhang", "Guna Lakshminarayanan", "Hamid Shojanazeri", "Han Zou", "Hannah Wang", "Hanwen Zha", "Haroun Habeeb", "Harrison Rudolph", "Helen Suk", "Henry Aspegren", "Hunter Goldman", "Igor Molybog", "Igor Tufanov", "Irina-Elena Veliche", "Itai Gat", "Jake Weissman", "James Geboski", "James Kohli", "Japhet Asher", "Jean-Baptiste Gaya", "Jeff Marcus", "Jeff Tang", "Jennifer Chan", "Jenny Zhen", "Jeremy Reizenstein", "Jeremy Teboul", "Jessica Zhong", "Jian Jin", "Jingyi Yang", "Joe Cummings", "Jon Carvill", "Jon Shepard", "Jonathan McPhie", "Jonathan Torres", "Josh Ginsburg", "Junjie Wang", "Kai Wu", "Kam Hou U", "Karan Saxena", "Karthik Prasad", "Kartikay Khandelwal", "Katayoun Zand", "Kathy Matosich", "Kaushik Veeraraghavan", "Kelly Michelena", "Keqian Li", "Kun Huang", "Kunal Chawla", "Kushal Lakhotia", "Kyle Huang", "Lailin Chen", "Lakshya Garg", "Lavender A", "Leandro Silva", "Lee Bell", "Lei Zhang", "Liangpeng Guo", "Licheng Yu", "Liron Moshkovich", "Luca Wehrstedt", "Madian Khabsa", "Manav Avalani", "Manish Bhatt", "Maria Tsimpoukelli", "Martynas Mankus", "Matan Hasson", "Matthew Lennie", "Matthias Reso", "Maxim Groshev", "Maxim Naumov", "Maya Lathi", "Meghan Keneally", "Michael L. Seltzer", "Michal Valko", "Michelle Restrepo", "Mihir Patel", "Mik Vyatskov", "Mikayel Samvelyan", "Mike Clark", "Mike Macey", "Mike Wang", "Miquel Jubert Hermoso", "Mo Metanat", "Mohammad Rastegari", "Munish Bansal", "Nandhini Santhanam", "Natascha Parks", "Natasha White", "Navyata Bawa", "Nayan Singhal", "Nick Egebo", "Nicolas Usunier", "Nikolay Pavlovich Laptev", "Ning Dong", "Ning Zhang", "Norman Cheng", "Oleg Chernoguz", "Olivia Hart", "Omkar Salpekar", "Ozlem Kalinli", "Parkin Kent", "Parth Parekh", "Paul Saab", "Pavan Balaji", "Pedro Rittner", "Philip Bontrager", "Pierre Roux", "Piotr Dollar", "Polina Zvyagina", "Prashant Ratanchandani", "Pritish Yuvraj", "Qian Liang", "Rachad Alao", "Rachel Rodriguez", "Rafi Ayub", "Raghotham Murthy", "Raghu Nayani", "Rahul Mitra", "Raymond Li", "Rebekkah Hogan", "Robin Battey", "Rocky Wang", "Rohan Maheswari", "Russ Howes", "Ruty Rinott", "Sai Jayesh Bondu", "Samyak Datta", "Sara Chugh", "Sara Hunt", "Sargun Dhillon", "Sasha Sidorov", "Satadru Pan", "Saurabh Verma", "Seiji Yamamoto", "Sharadh Ramaswamy", "Shaun Lindsay", "Shaun Lindsay", "Sheng Feng", "Shenghao Lin", "Shengxin Cindy Zha", "Shiva Shankar", "Shuqiang Zhang", "Shuqiang Zhang", "Sinong Wang", "Sneha Agarwal", "Soji Sajuyigbe", "Soumith Chintala", "Stephanie Max", "Stephen Chen", "Steve Kehoe", "Steve Satterfield", "Sudarshan Govindaprasad", "Sumit Gupta", "Sungmin Cho", "Sunny Virk", "Suraj Subramanian", "Sy Choudhury", "Sydney Goldman", "Tal Remez", "Tamar Glaser", "Tamara Best", "Thilo Kohler", "Thomas Robinson", "Tianhe Li", "Tianjun Zhang", "Tim Matthews", "Timothy Chou", "Tzook Shaked", "Varun Vontimitta", "Victoria Ajayi", "Victoria Montanez", "Vijai Mohan", "Vinay Satish Kumar", "Vishal Mangla", "Vlad Ionescu", "Vlad Poenaru", "Vlad Tiberiu Mihailescu", "Vladimir Ivanov", "Wei Li", "Wenchen Wang", "Wenwen Jiang", "Wes Bouaziz", "Will Constable", "Xiaocheng Tang", "Xiaofang Wang", "Xiaojian Wu", "Xiaolan Wang", "Xide Xia", "Xilun Wu", "Xinbo Gao", "Yanjun Chen", "Ye Hu", "Ye Jia", "Ye Qi", "Yenda Li", "Yilin Zhang", "Ying Zhang", "Yossi Adi", "Youngjin Nam", "Yu", "Wang", "Yuchen Hao", "Yundi Qian", "Yuzi He", "Zach Rait", "Zachary DeVito", "Zef Rosnbrick", "Zhaoduo Wen", "Zhenyu Yang", "Zhiwei Zhao" ], "github": "", "project_page": "", "abstract": "Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-01", "arxiv_id": "2407.21772", "title": "ShieldGemma: Generative AI Content Moderation Based on Gemma", "authors": [ "Wenjun Zeng", "Yuchi Liu", "Ryan Mullins", "Ludovic Peran", "Joe Fernandez", "Hamza Harkous", "Karthik Narasimhan", "Drew Proud", "Piyush Kumar", "Bhaktipriya Radharapu", "Olivia Sturman", "Oscar Wahltinez" ], "github": "", "project_page": "", "abstract": "We present ShieldGemma, a comprehensive suite of LLM-based safety content moderation models built upon Gemma2. These models provide robust, state-of-the-art predictions of safety risks across key harm types (sexually explicit, dangerous content, harassment, hate speech) in both user input and LLM-generated output. By evaluating on both public and internal benchmarks, we demonstrate superior performance compared to existing models, such as Llama Guard (+10.8\\% AU-PRC on public benchmarks) and WildCard (+4.3\\%). Additionally, we present a novel LLM-based data curation pipeline, adaptable to a variety of safety-related tasks and beyond. We have shown strong generalization performance for model trained mainly on synthetic data. By releasing ShieldGemma, we provide a valuable resource to the research community, advancing LLM safety and enabling the creation of more effective content moderation solutions for developers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-01", "arxiv_id": "2407.21721", "title": "Open-Vocabulary Audio-Visual Semantic Segmentation", "authors": [ "Ruohao Guo", "Liao Qu", "Dantong Niu", "Yanyu Qi", "Wenzhen Yue", "Ji Shi", "Bowei Xing", "Xianghua Ying" ], "github": "https://github.com/ruohaoguo/ovavss", "project_page": "", "abstract": "Audio-visual semantic segmentation (AVSS) aims to segment and classify sounding objects in videos with acoustic cues. However, most approaches operate on the close-set assumption and only identify pre-defined categories from training data, lacking the generalization ability to detect novel categories in practical applications. In this paper, we introduce a new task: open-vocabulary audio-visual semantic segmentation, extending AVSS task to open-world scenarios beyond the annotated label space. This is a more challenging task that requires recognizing all categories, even those that have never been seen nor heard during training. Moreover, we propose the first open-vocabulary AVSS framework, OV-AVSS, which mainly consists of two parts: 1) a universal sound source localization module to perform audio-visual fusion and locate all potential sounding objects and 2) an open-vocabulary classification module to predict categories with the help of the prior knowledge from large-scale pre-trained vision-language models. To properly evaluate the open-vocabulary AVSS, we split zero-shot training and testing subsets based on the AVSBench-semantic benchmark, namely AVSBench-OV. Extensive experiments demonstrate the strong segmentation and zero-shot generalization ability of our model on all categories. On the AVSBench-OV dataset, OV-AVSS achieves 55.43% mIoU on base categories and 29.14% mIoU on novel categories, exceeding the state-of-the-art zero-shot method by 41.88%/20.61% and open-vocabulary method by 10.2%/11.6%. The code is available at https://github.com/ruohaoguo/ovavss.", "github_manual": "", "github_regex": "https://github.com/ruohaoguo/ovavss", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-01", "arxiv_id": "2407.21630", "title": "TAROT: Task-Oriented Authorship Obfuscation Using Policy Optimization Methods", "authors": [ "Gabriel Loiseau", "Damien Sileo", "Damien Riquet", "Maxime Meyer", "Marc Tommasi" ], "github": "https://github.com/hornetsecurity/tarot", "project_page": "", "abstract": "Authorship obfuscation aims to disguise the identity of an author within a text by altering the writing style, vocabulary, syntax, and other linguistic features associated with the text author. This alteration needs to balance privacy and utility. While strong obfuscation techniques can effectively hide the author's identity, they often degrade the quality and usefulness of the text for its intended purpose. Conversely, maintaining high utility tends to provide insufficient privacy, making it easier for an adversary to de-anonymize the author. Thus, achieving an optimal trade-off between these two conflicting objectives is crucial. In this paper, we propose TAROT: Task-Oriented Authorship Obfuscation Using Policy Optimization, a new unsupervised authorship obfuscation method whose goal is to optimize the privacy-utility trade-off by regenerating the entire text considering its downstream utility. Our approach leverages policy optimization as a fine-tuning paradigm over small language models in order to rewrite texts by preserving author identity and downstream task utility. We show that our approach largely reduce the accuracy of attackers while preserving utility. We make our code and models publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hornetsecurity/tarot", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-01", "arxiv_id": "2404.01300", "title": "NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields", "authors": [ "Muhammad Zubair Irshad", "Sergey Zakharov", "Vitor Guizilini", "Adrien Gaidon", "Zsolt Kira", "Rares Ambrus" ], "github": "https://github.com/zubair-irshad/NeRF-MAE", "project_page": "", "abstract": "Neural fields excel in computer vision and robotics due to their ability to understand the 3D visual world such as inferring semantics, geometry, and dynamics. Given the capabilities of neural fields in densely representing a 3D scene from 2D images, we ask the question: Can we scale their self-supervised pretraining, specifically using masked autoencoders, to generate effective 3D representations from posed RGB images. Owing to the astounding success of extending transformers to novel data modalities, we employ standard 3D Vision Transformers to suit the unique formulation of NeRFs. We leverage NeRF's volumetric grid as a dense input to the transformer, contrasting it with other 3D representations such as pointclouds where the information density can be uneven, and the representation is irregular. Due to the difficulty of applying masked autoencoders to an implicit representation, such as NeRF, we opt for extracting an explicit representation that canonicalizes scenes across domains by employing the camera trajectory for sampling. Our goal is made possible by masking random patches from NeRF's radiance and density grid and employing a standard 3D Swin Transformer to reconstruct the masked patches. In doing so, the model can learn the semantic and spatial structure of complete scenes. We pretrain this representation at scale on our proposed curated posed-RGB data, totaling over 1.8 million images. Once pretrained, the encoder is used for effective 3D transfer learning. Our novel self-supervised pretraining for NeRFs, NeRF-MAE, scales remarkably well and improves performance on various challenging 3D tasks. Utilizing unlabeled posed 2D data for pretraining, NeRF-MAE significantly outperforms self-supervised 3D pretraining and NeRF scene understanding baselines on Front3D and ScanNet datasets with an absolute performance improvement of over 20% AP50 and 8% AP25 for 3D object detection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zubair-irshad/NeRF-MAE", "github_api_added_by": "auto" }, { "date": "2024-08-01", "arxiv_id": "2407.20229", "title": "Improving 2D Feature Representations by 3D-Aware Fine-Tuning", "authors": [ "Yuanwen Yue", "Anurag Das", "Francis Engelmann", "Siyu Tang", "Jan Eric Lenssen" ], "github": "", "project_page": "", "abstract": "Current visual foundation models are trained purely on unstructured 2D data, limiting their understanding of 3D structure of objects and scenes. In this work, we show that fine-tuning on 3D-aware data improves the quality of emerging semantic features. We design a method to lift semantic 2D features into an efficient 3D Gaussian representation, which allows us to re-render them for arbitrary views. Using the rendered 3D-aware features, we design a fine-tuning strategy to transfer such 3D awareness into a 2D foundation model. We demonstrate that models fine-tuned in that way produce features that readily improve downstream task performance in semantic segmentation and depth estimation through simple linear probing. Notably, though fined-tuned on a single indoor dataset, the improvement is transferable to a variety of indoor datasets and out-of-domain datasets. We hope our study encourages the community to consider injecting 3D awareness when training 2D foundation models. Project page: https://ywyue.github.io/FiT3D.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00203", "title": "OmniParser for Pure Vision Based GUI Agent", "authors": [ "Yadong Lu", "Jianwei Yang", "Yelong Shen", "Ahmed Awadallah" ], "github": "https://github.com/microsoft/omniparser", "project_page": "", "abstract": "The recent success of large vision language models shows great potential in driving the agent system operating on user interfaces. However, we argue that the power multimodal models like GPT-4V as a general agent on multiple operating systems across different applications is largely underestimated due to the lack of a robust screen parsing technique capable of: 1) reliably identifying interactable icons within the user interface, and 2) understanding the semantics of various elements in a screenshot and accurately associate the intended action with the corresponding region on the screen. To fill these gaps, we introduce OmniParser, a comprehensive method for parsing user interface screenshots into structured elements, which significantly enhances the ability of GPT-4V to generate actions that can be accurately grounded in the corresponding regions of the interface. We first curated an interactable icon detection dataset using popular webpages and an icon description dataset. These datasets were utilized to fine-tune specialized models: a detection model to parse interactable regions on the screen and a caption model to extract the functional semantics of the detected elements. OmniParser significantly improves GPT-4V's performance on ScreenSpot benchmark. And on Mind2Web and AITW benchmark, OmniParser with screenshot only input outperforms the GPT-4V baselines requiring additional information outside of screenshot.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/omniparser", "github_api_added_by": "auto" }, { "date": "2024-08-02", "arxiv_id": "2408.00754", "title": "Coarse Correspondence Elicit 3D Spacetime Understanding in Multimodal Language Model", "authors": [ "Benlin Liu", "Yuhao Dong", "Yiqin Wang", "Yongming Rao", "Yansong Tang", "Wei-Chiu Ma", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Multimodal language models (MLLMs) are increasingly being implemented in real-world environments, necessitating their ability to interpret 3D spaces and comprehend temporal dynamics. Despite their potential, current top models within our community still fall short in adequately understanding spatial and temporal dimensions. We introduce Coarse Correspondence, a simple, training-free, effective, and general-purpose visual prompting method to elicit 3D and temporal understanding in multimodal LLMs. Our method uses a lightweight tracking model to find object correspondences between frames in a video or between sets of image viewpoints. It selects the most frequent object instances and visualizes them with markers with unique IDs in the image. With this simple approach, we achieve state-of-the-art results on 3D understanding benchmarks including ScanQA (+20.5\\%) and a subset of OpenEQA (+9.7\\%), and on long-form video benchmarks such as EgoSchema (+6.0\\%). We also curate a small diagnostic dataset to evaluate whether MLLMs can reason about space from a described viewpoint other than the camera viewpoint. Again, Coarse Correspondence improves spatial perspective-taking abilities but we highlight that MLLMs struggle with this task. Together, we demonstrate that our simple prompting method can significantly aid downstream tasks that require 3D or temporal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00735", "title": "TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models", "authors": [ "Gilad Deutch", "Rinon Gal", "Daniel Garibi", "Or Patashnik", "Daniel Cohen-Or" ], "github": "https://github.com/GiilDe/turbo-edit", "project_page": "", "abstract": "Diffusion models have opened the path to a wide range of text-based image editing frameworks. However, these typically build on the multi-step nature of the diffusion backwards process, and adapting them to distilled, fast-sampling methods has proven surprisingly challenging. Here, we focus on a popular line of text-based editing frameworks - the ``edit-friendly'' DDPM-noise inversion approach. We analyze its application to fast sampling methods and categorize its failures into two classes: the appearance of visual artifacts, and insufficient editing strength. We trace the artifacts to mismatched noise statistics between inverted noises and the expected noise schedule, and suggest a shifted noise schedule which corrects for this offset. To increase editing strength, we propose a pseudo-guidance approach that efficiently increases the magnitude of edits without introducing new artifacts. All in all, our method enables text-based image editing with as few as three diffusion steps, while providing novel insights into the mechanisms behind popular text-based editing approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GiilDe/turbo-edit", "github_api_added_by": "auto" }, { "date": "2024-08-02", "arxiv_id": "2408.00714", "title": "SAM 2: Segment Anything in Images and Videos", "authors": [ "Nikhila Ravi", "Valentin Gabeur", "Yuan-Ting Hu", "Ronghang Hu", "Chaitanya Ryali", "Tengyu Ma", "Haitham Khedr", "Roman R\u00e4dle", "Chloe Rolland", "Laura Gustafson", "Eric Mintun", "Junting Pan", "Kalyan Vasudev Alwala", "Nicolas Carion", "Chao-Yuan Wu", "Ross Girshick", "Piotr Doll\u00e1r", "Christoph Feichtenhofer" ], "github": "", "project_page": "", "abstract": "We present Segment Anything Model 2 (SAM 2), a foundation model towards solving promptable visual segmentation in images and videos. We build a data engine, which improves model and data via user interaction, to collect the largest video segmentation dataset to date. Our model is a simple transformer architecture with streaming memory for real-time video processing. SAM 2 trained on our data provides strong performance across a wide range of tasks. In video segmentation, we observe better accuracy, using 3x fewer interactions than prior approaches. In image segmentation, our model is more accurate and 6x faster than the Segment Anything Model (SAM). We believe that our data, model, and insights will serve as a significant milestone for video segmentation and related perception tasks. We are releasing a version of our model, the dataset and an interactive demo.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00762", "title": "UniTalker: Scaling up Audio-Driven 3D Facial Animation through A Unified Model", "authors": [ "Xiangyu Fan", "Jiaqi Li", "Zhiqian Lin", "Weiye Xiao", "Lei Yang" ], "github": "https://github.com/X-niper/UniTalker", "project_page": "", "abstract": "Audio-driven 3D facial animation aims to map input audio to realistic facial motion. Despite significant progress, limitations arise from inconsistent 3D annotations, restricting previous models to training on specific annotations and thereby constraining the training scale. In this work, we present UniTalker, a unified model featuring a multi-head architecture designed to effectively leverage datasets with varied annotations. To enhance training stability and ensure consistency among multi-head outputs, we employ three training strategies, namely, PCA, model warm-up, and pivot identity embedding. To expand the training scale and diversity, we assemble A2F-Bench, comprising five publicly available datasets and three newly curated datasets. These datasets contain a wide range of audio domains, covering multilingual speech voices and songs, thereby scaling the training data from commonly employed datasets, typically less than 1 hour, to 18.5 hours. With a single trained UniTalker model, we achieve substantial lip vertex error reductions of 9.2% for BIWI dataset and 13.7% for Vocaset. Additionally, the pre-trained UniTalker exhibits promise as the foundation model for audio-driven facial animation tasks. Fine-tuning the pre-trained UniTalker on seen datasets further enhances performance on each dataset, with an average error reduction of 6.3% on A2F-Bench. Moreover, fine-tuning UniTalker on an unseen dataset with only half the data surpasses prior state-of-the-art models trained on the full dataset. The code and dataset are available at the project page https://github.com/X-niper/UniTalker.", "github_manual": "", "github_regex": "https://github.com/X-niper/UniTalker", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00690", "title": "Improving Text Embeddings for Smaller Language Models Using Contrastive Fine-tuning", "authors": [ "Trapoom Ukarapol", "Zhicheng Lee", "Amy Xin" ], "github": "https://github.com/trapoom555/Language-Model-STS-CFT", "project_page": "", "abstract": "While Large Language Models show remarkable performance in natural language understanding, their resource-intensive nature makes them less accessible. In contrast, smaller language models such as MiniCPM offer more sustainable scalability, but often underperform without specialized optimization. In this paper, we explore the enhancement of smaller language models through the improvement of their text embeddings. We select three language models, MiniCPM, Phi-2, and Gemma, to conduct contrastive fine-tuning on the NLI dataset. Our results demonstrate that this fine-tuning method enhances the quality of text embeddings for all three models across various benchmarks, with MiniCPM showing the most significant improvements of an average 56.33\\% performance gain. The contrastive fine-tuning code is publicly available at https://github.com/trapoom555/Language-Model-STS-CFT.", "github_manual": "", "github_regex": "https://github.com/trapoom555/Language-Model-STS-CFT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00653", "title": "SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement", "authors": [ "Mark Boss", "Zixuan Huang", "Aaryaman Vasishta", "Varun Jampani" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "We present SF3D, a novel method for rapid and high-quality textured object mesh reconstruction from a single image in just 0.5 seconds. Unlike most existing approaches, SF3D is explicitly trained for mesh generation, incorporating a fast UV unwrapping technique that enables swift texture generation rather than relying on vertex colors. The method also learns to predict material parameters and normal maps to enhance the visual quality of the reconstructed 3D meshes. Furthermore, SF3D integrates a delighting step to effectively remove low-frequency illumination effects, ensuring that the reconstructed meshes can be easily used in novel illumination conditions. Experiments demonstrate the superior performance of SF3D over the existing techniques. Project page: https://stable-fast-3d.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00298", "title": "Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names", "authors": [ "Ragav Sachdeva", "Gyungin Shin", "Andrew Zisserman" ], "github": "https://github.com/ragavsachdeva/magi", "project_page": "", "abstract": "Enabling engagement of manga by visually impaired individuals presents a significant challenge due to its inherently visual nature. With the goal of fostering accessibility, this paper aims to generate a dialogue transcript of a complete manga chapter, entirely automatically, with a particular emphasis on ensuring narrative consistency. This entails identifying (i) what is being said, i.e., detecting the texts on each page and classifying them into essential vs non-essential, and (ii) who is saying it, i.e., attributing each dialogue to its speaker, while ensuring the same characters are named consistently throughout the chapter. To this end, we introduce: (i) Magiv2, a model that is capable of generating high-quality chapter-wide manga transcripts with named characters and significantly higher precision in speaker diarisation over prior works; (ii) an extension of the PopManga evaluation dataset, which now includes annotations for speech-bubble tail boxes, associations of text to corresponding tails, classifications of text as essential or non-essential, and the identity for each character box; and (iii) a new character bank dataset, which comprises over 11K characters from 76 manga series, featuring 11.5K exemplar character images in total, as well as a list of chapters in which they appear. The code, trained model, and both datasets can be found at: https://github.com/ragavsachdeva/magi", "github_manual": "", "github_regex": "https://github.com/ragavsachdeva/magi", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00205", "title": "Sentence-wise Speech Summarization: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillation", "authors": [ "Kohei Matsuura", "Takanori Ashihara", "Takafumi Moriya", "Masato Mimura", "Takatomo Kano", "Atsunori Ogawa", "Marc Delcroix" ], "github": "", "project_page": "", "abstract": "This paper introduces a novel approach called sentence-wise speech summarization (Sen-SSum), which generates text summaries from a spoken document in a sentence-by-sentence manner. Sen-SSum combines the real-time processing of automatic speech recognition (ASR) with the conciseness of speech summarization. To explore this approach, we present two datasets for Sen-SSum: Mega-SSum and CSJ-SSum. Using these datasets, our study evaluates two types of Transformer-based models: 1) cascade models that combine ASR and strong text summarization models, and 2) end-to-end (E2E) models that directly convert speech into a text summary. While E2E models are appealing to develop compute-efficient models, they perform worse than cascade models. Therefore, we propose knowledge distillation for E2E models using pseudo-summaries generated by the cascade models. Our experiments show that this proposed knowledge distillation effectively improves the performance of the E2E model on both datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00765", "title": "MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities", "authors": [ "Weihao Yu", "Zhengyuan Yang", "Linfeng Ren", "Linjie Li", "Jianfeng Wang", "Kevin Lin", "Chung-Ching Lin", "Zicheng Liu", "Lijuan Wang", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "MM-Vet, with open-ended vision-language questions targeting at evaluating integrated capabilities, has become one of the most popular benchmarks for large multimodal model evaluation. MM-Vet assesses six core vision-language (VL) capabilities: recognition, knowledge, spatial awareness, language generation, OCR, and math. However, its question format is restricted to single image-text pairs, lacking the interleaved image and text sequences prevalent in real-world scenarios. To address this limitation, we introduce MM-Vet v2, which includes a new VL capability called \"image-text sequence understanding\", evaluating models' ability to process VL sequences. Furthermore, we maintain the high quality of evaluation samples while further expanding the evaluation set size. Using MM-Vet v2 to benchmark large multimodal models, we found that Claude 3.5 Sonnet is the best model with a score of 71.8, slightly outperforming GPT-4o which scored 71.0. Among open-weight models, InternVL2-Llama3-76B leads with a score of 68.4.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00584", "title": "Non Verbis, Sed Rebus: Large Language Models are Weak Solvers of Italian Rebuses", "authors": [ "Gabriele Sarti", "Tommaso Caselli", "Malvina Nissim", "Arianna Bisazza" ], "github": "", "project_page": "", "abstract": "Rebuses are puzzles requiring constrained multi-step reasoning to identify a hidden phrase from a set of images and letters. In this work, we introduce a large collection of verbalized rebuses for the Italian language and use it to assess the rebus-solving capabilities of state-of-the-art large language models. While general-purpose systems such as LLaMA-3 and GPT-4o perform poorly on this task, ad-hoc fine-tuning seems to improve models' performance. However, we find that performance gains from training are largely motivated by memorization. Our results suggest that rebus solving remains a challenging test bed to evaluate large language models' linguistic proficiency and sequential instruction-following skills.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00458", "title": "Reenact Anything: Semantic Video Motion Transfer Using Motion-Textual Inversion", "authors": [ "Manuel Kansy", "Jacek Naruniec", "Christopher Schroers", "Markus Gross", "Romann M. Weber" ], "github": "", "project_page": "", "abstract": "Recent years have seen a tremendous improvement in the quality of video generation and editing approaches. While several techniques focus on editing appearance, few address motion. Current approaches using text, trajectories, or bounding boxes are limited to simple motions, so we specify motions with a single motion reference video instead. We further propose to use a pre-trained image-to-video model rather than a text-to-video model. This approach allows us to preserve the exact appearance and position of a target object or scene and helps disentangle appearance from motion. Our method, called motion-textual inversion, leverages our observation that image-to-video models extract appearance mainly from the (latent) image input, while the text/image embedding injected via cross-attention predominantly controls motion. We thus represent motion using text/image embedding tokens. By operating on an inflated motion-text embedding containing multiple text/image embedding tokens per frame, we achieve a high temporal motion granularity. Once optimized on the motion reference video, this embedding can be applied to various target images to generate videos with semantically similar motions. Our approach does not require spatial alignment between the motion reference video and target image, generalizes across various domains, and can be applied to various tasks such as full-body and face reenactment, as well as controlling the motion of inanimate objects and the camera. We empirically demonstrate the effectiveness of our method in the semantic video motion transfer task, significantly outperforming existing methods in this context.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00118", "title": "Gemma 2: Improving Open Language Models at a Practical Size", "authors": [ "Gemma Team", "Morgane Riviere", "Shreya Pathak", "Pier Giuseppe Sessa", "Cassidy Hardin", "Surya Bhupatiraju", "L\u00e9onard Hussenot", "Thomas Mesnard", "Bobak Shahriari", "Alexandre Ram\u00e9", "Johan Ferret", "Peter Liu", "Pouya Tafti", "Abe Friesen", "Michelle Casbon", "Sabela Ramos", "Ravin Kumar", "Charline Le Lan", "Sammy Jerome", "Anton Tsitsulin", "Nino Vieillard", "Piotr Stanczyk", "Sertan Girgin", "Nikola Momchev", "Matt Hoffman", "Shantanu Thakoor", "Jean-Bastien Grill", "Behnam Neyshabur", "Alanna Walton", "Aliaksei Severyn", "Alicia Parrish", "Aliya Ahmad", "Allen Hutchison", "Alvin Abdagic", "Amanda Carl", "Amy Shen", "Andy Brock", "Andy Coenen", "Anthony Laforge", "Antonia Paterson", "Ben Bastian", "Bilal Piot", "Bo Wu", "Brandon Royal", "Charlie Chen", "Chintu Kumar", "Chris Perry", "Chris Welty", "Christopher A. Choquette-Choo", "Danila Sinopalnikov", "David Weinberger", "Dimple Vijaykumar", "Dominika Rogozi\u0144ska", "Dustin Herbison", "Elisa Bandy", "Emma Wang", "Eric Noland", "Erica Moreira", "Evan Senter", "Evgenii Eltyshev", "Francesco Visin", "Gabriel Rasskin", "Gary Wei", "Glenn Cameron", "Gus Martins", "Hadi Hashemi", "Hanna Klimczak-Pluci\u0144ska", "Harleen Batra", "Harsh Dhand", "Ivan Nardini", "Jacinda Mein", "Jack Zhou", "James Svensson", "Jeff Stanway", "Jetha Chan", "Jin Zhou", "Joana Carrasqueira", "Joana Iljazi", "Jocelyn Becker", "Joe Fernandez", "Joost van Amersfoort", "Josh Gordon", "Josh Lipschultz", "Josh Newlan", "Ju-yeong Ji", "Kareem Mohamed", "Kartikeya Badola", "Kat Black", "Katie Millican", "Keelin McDonell", "Kelvin Nguyen", "Kiranbir Sodhia", "Kish Greene", "Lars Lowe Sjoesund", "Lauren Usui", "Laurent Sifre", "Lena Heuermann", "Leticia Lago", "Lilly McNealus", "Livio Baldini Soares", "Logan Kilpatrick", "Lucas Dixon", "Luciano Martins", "Machel Reid", "Manvinder Singh", "Mark Iverson", "Martin G\u00f6rner", "Mat Velloso", "Mateo Wirth", "Matt Davidow", "Matt Miller", "Matthew Rahtz", "Matthew Watson", "Meg Risdal", "Mehran Kazemi", "Michael Moynihan", "Ming Zhang", "Minsuk Kahng", "Minwoo Park", "Mofi Rahman", "Mohit Khatwani", "Natalie Dao", "Nenshad Bardoliwalla", "Nesh Devanathan", "Neta Dumai", "Nilay Chauhan", "Oscar Wahltinez", "Pankil Botarda", "Parker Barnes", "Paul Barham", "Paul Michel", "Pengchong Jin", "Petko Georgiev", "Phil Culliton", "Pradeep Kuppala", "Ramona Comanescu", "Ramona Merhej", "Reena Jana", "Reza Ardeshir Rokni", "Rishabh Agarwal", "Ryan Mullins", "Samaneh Saadat", "Sara Mc Carthy", "Sarah Perrin", "S\u00e9bastien Arnold", "Sebastian Krause", "Shengyang Dai", "Shruti Garg", "Shruti Sheth", "Sue Ronstrom", "Susan Chan", "Timothy Jordan", "Ting Yu", "Tom Eccles", "Tom Hennigan", "Tomas Kocisky", "Tulsee Doshi", "Vihan Jain", "Vikas Yadav", "Vilobh Meshram", "Vishal Dharmadhikari", "Warren Barkley", "Wei Wei", "Wenming Ye", "Woohyun Han", "Woosuk Kwon", "Xiang Xu", "Zhe Shen", "Zhitao Gong", "Zichuan Wei", "Victor Cotruta", "Phoebe Kirk", "Anand Rao", "Minh Giang", "Ludovic Peran", "Tris Warkentin", "Eli Collins", "Joelle Barral", "Zoubin Ghahramani", "Raia Hadsell", "D. Sculley", "Jeanine Banks", "Anca Dragan", "Slav Petrov", "Oriol Vinyals", "Jeff Dean", "Demis Hassabis", "Koray Kavukcuoglu", "Clement Farabet", "Elena Buchatskaya", "Sebastian Borgeaud", "Noah Fiedel", "Armand Joulin", "Kathleen Kenealy", "Robert Dadashi", "Alek Andreev" ], "github": "", "project_page": "", "abstract": "In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00167", "title": "Finch: Prompt-guided Key-Value Cache Compression", "authors": [ "Giulio Corallo", "Paolo Papotti" ], "github": "", "project_page": "", "abstract": "Recent large language model applications, such as Retrieval-Augmented Generation and chatbots, have led to an increased need to process longer input contexts. However, this requirement is hampered by inherent limitations. Architecturally, models are constrained by a context window defined during training. Additionally, processing extensive texts requires substantial GPU memory. We propose a novel approach, Finch, to compress the input context by leveraging the pre-trained model weights of the self-attention. Given a prompt and a long text, Finch iteratively identifies the most relevant Key (K) and Value (V) pairs over chunks of the text conditioned on the prompt. Only such pairs are stored in the KV cache, which, within the space constrained by the context window, ultimately contains a compressed version of the long text. Our proposal enables models to consume large inputs even with high compression (up to 93x) while preserving semantic integrity without the need for fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2407.21794", "title": "Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey", "authors": [ "Atsuyuki Miyai", "Jingkang Yang", "Jingyang Zhang", "Yifei Ming", "Yueqian Lin", "Qing Yu", "Go Irie", "Shafiq Joty", "Yixuan Li", "Hai Li", "Ziwei Liu", "Toshihiko Yamasaki", "Kiyoharu Aizawa" ], "github": "https://github.com/AtsuMiyai/Awesome-OOD-VLM", "project_page": "", "abstract": "Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine learning systems and has shaped the field of OOD detection. Meanwhile, several other problems are closely related to OOD detection, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD). To unify these problems, a generalized OOD detection framework was proposed, taxonomically categorizing these five problems. However, Vision Language Models (VLMs) such as CLIP have significantly changed the paradigm and blurred the boundaries between these fields, again confusing researchers. In this survey, we first present a generalized OOD detection v2, encapsulating the evolution of AD, ND, OSR, OOD detection, and OD in the VLM era. Our framework reveals that, with some field inactivity and integration, the demanding challenges have become OOD detection and AD. In addition, we also highlight the significant shift in the definition, problem settings, and benchmarks; we thus feature a comprehensive review of the methodology for OOD detection, including the discussion over other related tasks to clarify their relationship to OOD detection. Finally, we explore the advancements in the emerging Large Vision Language Model (LVLM) era, such as GPT-4V. We conclude this survey with open challenges and future directions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AtsuMiyai/Awesome-OOD-VLM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2408.00760", "title": "Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention", "authors": [ "Susung Hong" ], "github": "https://github.com/SusungHong/SEG-SDXL", "project_page": "", "abstract": "Conditional diffusion models have shown remarkable success in visual content generation, producing high-quality samples across various domains, largely due to classifier-free guidance (CFG). Recent attempts to extend guidance to unconditional models have relied on heuristic techniques, resulting in suboptimal generation quality and unintended effects. In this work, we propose Smoothed Energy Guidance (SEG), a novel training- and condition-free approach that leverages the energy-based perspective of the self-attention mechanism to enhance image generation. By defining the energy of self-attention, we introduce a method to reduce the curvature of the energy landscape of attention and use the output as the unconditional prediction. Practically, we control the curvature of the energy landscape by adjusting the Gaussian kernel parameter while keeping the guidance scale parameter fixed. Additionally, we present a query blurring method that is equivalent to blurring the entire attention weights without incurring quadratic complexity in the number of tokens. In our experiments, SEG achieves a Pareto improvement in both quality and the reduction of side effects. The code is available at https://github.com/SusungHong/SEG-SDXL.", "github_manual": "", "github_regex": "https://github.com/SusungHong/SEG-SDXL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-02", "arxiv_id": "2407.21139", "title": "Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning", "authors": [ "Omer Nacar", "Anis Koubaa" ], "github": "", "project_page": "", "abstract": "This work presents a novel framework for training Arabic nested embedding models through Matryoshka Embedding Learning, leveraging multilingual, Arabic-specific, and English-based models, to highlight the power of nested embeddings models in various Arabic NLP downstream tasks. Our innovative contribution includes the translation of various sentence similarity datasets into Arabic, enabling a comprehensive evaluation framework to compare these models across different dimensions. We trained several nested embedding models on the Arabic Natural Language Inference triplet dataset and assessed their performance using multiple evaluation metrics, including Pearson and Spearman correlations for cosine similarity, Manhattan distance, Euclidean distance, and dot product similarity. The results demonstrate the superior performance of the Matryoshka embedding models, particularly in capturing semantic nuances unique to the Arabic language. Results demonstrated that Arabic Matryoshka embedding models have superior performance in capturing semantic nuances unique to the Arabic language, significantly outperforming traditional models by up to 20-25\\% across various similarity metrics. These results underscore the effectiveness of language-specific training and highlight the potential of Matryoshka models in enhancing semantic textual similarity tasks for Arabic NLP.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-05", "arxiv_id": "2408.01031", "title": "POA: Pre-training Once for Models of All Sizes", "authors": [ "Yingying Zhang", "Xin Guo", "Jiangwei Lao", "Lei Yu", "Lixiang Ru", "Jian Wang", "Guo Ye", "Huimei He", "Jingdong Chen", "Ming Yang" ], "github": "https://github.com/Qichuzyy/POA", "project_page": "", "abstract": "Large-scale self-supervised pre-training has paved the way for one foundation model to handle many different vision tasks. Most pre-training methodologies train a single model of a certain size at one time. Nevertheless, various computation or storage constraints in real-world scenarios require substantial efforts to develop a series of models with different sizes to deploy. Thus, in this study, we propose a novel tri-branch self-supervised training framework, termed as POA (Pre-training Once for All), to tackle this aforementioned issue. Our approach introduces an innovative elastic student branch into a modern self-distillation paradigm. At each pre-training step, we randomly sample a sub-network from the original student to form the elastic student and train all branches in a self-distilling fashion. Once pre-trained, POA allows the extraction of pre-trained models of diverse sizes for downstream tasks. Remarkably, the elastic student facilitates the simultaneous pre-training of multiple models with different sizes, which also acts as an additional ensemble of models of various sizes to enhance representation learning. Extensive experiments, including k-nearest neighbors, linear probing evaluation and assessments on multiple downstream tasks demonstrate the effectiveness and advantages of our POA. It achieves state-of-the-art performance using ViT, Swin Transformer and ResNet backbones, producing around a hundred models with different sizes through a single pre-training session. The code is available at: https://github.com/Qichuzyy/POA.", "github_manual": "", "github_regex": "https://github.com/Qichuzyy/POA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-05", "arxiv_id": "2408.01291", "title": "TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling", "authors": [ "Dong Huo", "Zixin Guo", "Xinxin Zuo", "Zhihao Shi", "Juwei Lu", "Peng Dai", "Songcen Xu", "Li Cheng", "Yee-Hong Yang" ], "github": "", "project_page": "", "abstract": "Given a 3D mesh, we aim to synthesize 3D textures that correspond to arbitrary textual descriptions. Current methods for generating and assembling textures from sampled views often result in prominent seams or excessive smoothing. To tackle these issues, we present TexGen, a novel multi-view sampling and resampling framework for texture generation leveraging a pre-trained text-to-image diffusion model. For view consistent sampling, first of all we maintain a texture map in RGB space that is parameterized by the denoising step and updated after each sampling step of the diffusion model to progressively reduce the view discrepancy. An attention-guided multi-view sampling strategy is exploited to broadcast the appearance information across views. To preserve texture details, we develop a noise resampling technique that aids in the estimation of noise, generating inputs for subsequent denoising steps, as directed by the text prompt and current texture map. Through an extensive amount of qualitative and quantitative evaluations, we demonstrate that our proposed method produces significantly better texture quality for diverse 3D objects with a high degree of view consistency and rich appearance details, outperforming current state-of-the-art methods. Furthermore, our proposed texture generation technique can also be applied to texture editing while preserving the original identity. More experimental results are available at https://dong-huo.github.io/TexGen/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-05", "arxiv_id": "2408.00874", "title": "Medical SAM 2: Segment medical images as video via Segment Anything Model 2", "authors": [ "Jiayuan Zhu", "Yunli Qi", "Junde Wu" ], "github": "https://github.com/MedicineToken/Medical-SAM2", "project_page": "", "abstract": "In this paper, we introduce Medical SAM 2 (MedSAM-2), an advanced segmentation model that utilizes the SAM 2 framework to address both 2D and 3D medical image segmentation tasks. By adopting the philosophy of taking medical images as videos, MedSAM-2 not only applies to 3D medical images but also unlocks new One-prompt Segmentation capability. That allows users to provide a prompt for just one or a specific image targeting an object, after which the model can autonomously segment the same type of object in all subsequent images, regardless of temporal relationships between the images. We evaluated MedSAM-2 across a variety of medical imaging modalities, including abdominal organs, optic discs, brain tumors, thyroid nodules, and skin lesions, comparing it against state-of-the-art models in both traditional and interactive segmentation settings. Our findings show that MedSAM-2 not only surpasses existing models in performance but also exhibits superior generalization across a range of medical image segmentation tasks. Our code will be released at: https://github.com/MedicineToken/Medical-SAM2", "github_manual": "", "github_regex": "https://github.com/MedicineToken/Medical-SAM2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-05", "arxiv_id": "2408.01337", "title": "MuChoMusic: Evaluating Music Understanding in Multimodal Audio-Language Models", "authors": [ "Benno Weck", "Ilaria Manco", "Emmanouil Benetos", "Elio Quinton", "George Fazekas", "Dmitry Bogdanov" ], "github": "https://github.com/mulab-mir/muchomusic", "project_page": "", "abstract": "Multimodal models that jointly process audio and language hold great promise in audio understanding and are increasingly being adopted in the music domain. By allowing users to query via text and obtain information about a given audio input, these models have the potential to enable a variety of music understanding tasks via language-based interfaces. However, their evaluation poses considerable challenges, and it remains unclear how to effectively assess their ability to correctly interpret music-related inputs with current methods. Motivated by this, we introduce MuChoMusic, a benchmark for evaluating music understanding in multimodal language models focused on audio. MuChoMusic comprises 1,187 multiple-choice questions, all validated by human annotators, on 644 music tracks sourced from two publicly available music datasets, and covering a wide variety of genres. Questions in the benchmark are crafted to assess knowledge and reasoning abilities across several dimensions that cover fundamental musical concepts and their relation to cultural and functional contexts. Through the holistic analysis afforded by the benchmark, we evaluate five open-source models and identify several pitfalls, including an over-reliance on the language modality, pointing to a need for better multimodal integration. Data and code are open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mulab-mir/muchomusic", "github_api_added_by": "auto" }, { "date": "2024-08-05", "arxiv_id": "2408.00113", "title": "Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models", "authors": [ "Adam Karvonen", "Benjamin Wright", "Can Rager", "Rico Angell", "Jannik Brinkmann", "Logan Smith", "Claudio Mayrink Verdun", "David Bau", "Samuel Marks" ], "github": "https://github.com/adamkarvonen/SAE_BoardGameEval", "project_page": "", "abstract": "What latent features are encoded in language model (LM) representations? Recent work on training sparse autoencoders (SAEs) to disentangle interpretable features in LM representations has shown significant promise. However, evaluating the quality of these SAEs is difficult because we lack a ground-truth collection of interpretable features that we expect good SAEs to recover. We thus propose to measure progress in interpretable dictionary learning by working in the setting of LMs trained on chess and Othello transcripts. These settings carry natural collections of interpretable features -- for example, \"there is a knight on F3\" -- which we leverage into supervised metrics for SAE quality. To guide progress in interpretable dictionary learning, we introduce a new SAE training technique, p-annealing, which improves performance on prior unsupervised metrics as well as our new metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/adamkarvonen/SAE_BoardGameEval", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-05", "arxiv_id": "2408.00103", "title": "ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget", "authors": [ "Riccardo Orlando", "Pere-Lluis Huguet-Cabot", "Edoardo Barba", "Roberto Navigli" ], "github": "", "project_page": "", "abstract": "Entity Linking (EL) and Relation Extraction (RE) are fundamental tasks in Natural Language Processing, serving as critical components in a wide range of applications. In this paper, we propose ReLiK, a Retriever-Reader architecture for both EL and RE, where, given an input text, the Retriever module undertakes the identification of candidate entities or relations that could potentially appear within the text. Subsequently, the Reader module is tasked to discern the pertinent retrieved entities or relations and establish their alignment with the corresponding textual spans. Notably, we put forward an innovative input representation that incorporates the candidate entities or relations alongside the text, making it possible to link entities or extract relations in a single forward pass and to fully leverage pre-trained language models contextualization capabilities, in contrast with previous Retriever-Reader-based methods, which require a forward pass for each candidate. Our formulation of EL and RE achieves state-of-the-art performance in both in-domain and out-of-domain benchmarks while using academic budget training and with up to 40x inference speed compared to competitors. Finally, we show how our architecture can be used seamlessly for Information Extraction (cIE), i.e. EL + RE, and setting a new state of the art by employing a shared Reader that simultaneously extracts entities and relations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-05", "arxiv_id": "2408.00397", "title": "In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation", "authors": [ "Armel Zebaze", "Beno\u00eet Sagot", "Rachel Bawden" ], "github": "https://github.com/ArmelRandy/ICL-MT", "project_page": "", "abstract": "The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural language processing tasks. In this paper, we focus on machine translation (MT), a task that has been shown to benefit from in-context translation examples. However no systematic studies have been published on how best to select examples, and mixed results have been reported on the usefulness of similarity-based selection over random selection. We provide a study covering multiple LLMs and multiple in-context example retrieval strategies, comparing multilingual sentence embeddings. We cover several language directions, representing different levels of language resourcedness (English into French, German, Swahili and Wolof). Contrarily to previously published results, we find that sentence embedding similarity can improve MT, especially for low-resource language directions, and discuss the balance between selection pool diversity and quality. We also highlight potential problems with the evaluation of LLM-based MT and suggest a more appropriate evaluation protocol, adapting the COMET metric to the evaluation of LLMs. Code and outputs are freely available at https://github.com/ArmelRandy/ICL-MT.", "github_manual": "", "github_regex": "https://github.com/ArmelRandy/ICL-MT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-05", "arxiv_id": "2407.20060", "title": "RelBench: A Benchmark for Deep Learning on Relational Databases", "authors": [ "Joshua Robinson", "Rishabh Ranjan", "Weihua Hu", "Kexin Huang", "Jiaqi Han", "Alejandro Dobles", "Matthias Fey", "Jan E. Lenssen", "Yiwen Yuan", "Zecheng Zhang", "Xinwei He", "Jure Leskovec" ], "github": "https://github.com/snap-stanford/relbench", "project_page": "", "abstract": "We present RelBench, a public benchmark for solving predictive tasks over relational databases with graph neural networks. RelBench provides databases and tasks spanning diverse domains and scales, and is intended to be a foundational infrastructure for future research. We use RelBench to conduct the first comprehensive study of Relational Deep Learning (RDL) (Fey et al., 2024), which combines graph neural network predictive models with (deep) tabular models that extract initial entity-level representations from raw tables. End-to-end learned RDL models fully exploit the predictive signal encoded in primary-foreign key links, marking a significant shift away from the dominant paradigm of manual feature engineering combined with tabular models. To thoroughly evaluate RDL against this prior gold-standard, we conduct an in-depth user study where an experienced data scientist manually engineers features for each task. In this study, RDL learns better models whilst reducing human work needed by more than an order of magnitude. This demonstrates the power of deep learning for solving predictive tasks over relational databases, opening up many new research opportunities enabled by RelBench.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/snap-stanford/relbench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.02666", "title": "Self-Taught Evaluators", "authors": [ "Tianlu Wang", "Ilia Kulikov", "Olga Golovneva", "Ping Yu", "Weizhe Yuan", "Jane Dwivedi-Yu", "Richard Yuanzhe Pang", "Maryam Fazel-Zarandi", "Jason Weston", "Xian Li" ], "github": "", "project_page": "", "abstract": "Model-based evaluation is at the heart of successful model development -- as a reward model for training, and as a replacement for human evaluation. To train such evaluators, the standard approach is to collect a large amount of human preference judgments over model responses, which is costly and the data becomes stale as models improve. In this work, we present an approach that aims to im-prove evaluators without human annotations, using synthetic training data only. Starting from unlabeled instructions, our iterative self-improvement scheme generates contrasting model outputs and trains an LLM-as-a-Judge to produce reasoning traces and final judgments, repeating this training at each new iteration using the improved predictions. Without any labeled preference data, our Self-Taught Evaluator can improve a strong LLM (Llama3-70B-Instruct) from 75.4 to 88.3 (88.7 with majority vote) on RewardBench. This outperforms commonly used LLM judges such as GPT-4 and matches the performance of the top-performing reward models trained with labeled examples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.02629", "title": "VidGen-1M: A Large-Scale Dataset for Text-to-video Generation", "authors": [ "Zhiyu Tan", "Xiaomeng Yang", "Luozheng Qin", "Hao Li" ], "github": "", "project_page": "", "abstract": "The quality of video-text pairs fundamentally determines the upper bound of text-to-video models. Currently, the datasets used for training these models suffer from significant shortcomings, including low temporal consistency, poor-quality captions, substandard video quality, and imbalanced data distribution. The prevailing video curation process, which depends on image models for tagging and manual rule-based curation, leads to a high computational load and leaves behind unclean data. As a result, there is a lack of appropriate training datasets for text-to-video models. To address this problem, we present VidGen-1M, a superior training dataset for text-to-video models. Produced through a coarse-to-fine curation strategy, this dataset guarantees high-quality videos and detailed captions with excellent temporal consistency. When used to train the video generation model, this dataset has led to experimental results that surpass those obtained with other models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.02226", "title": "ProCreate, Dont Reproduce! Propulsive Energy Diffusion for Creative Generation", "authors": [ "Jack Lu", "Ryan Teehan", "Mengye Ren" ], "github": "https://github.com/Agentic-Learning-AI-Lab/procreate-diffusion-public", "project_page": "", "abstract": "In this paper, we propose ProCreate, a simple and easy-to-implement method to improve sample diversity and creativity of diffusion-based image generative models and to prevent training data reproduction. ProCreate operates on a set of reference images and actively propels the generated image embedding away from the reference embeddings during the generation process. We propose FSCG-8 (Few-Shot Creative Generation 8), a few-shot creative generation dataset on eight different categories -- encompassing different concepts, styles, and settings -- in which ProCreate achieves the highest sample diversity and fidelity. Furthermore, we show that ProCreate is effective at preventing replicating training data in a large-scale evaluation using training text prompts. Code and FSCG-8 are available at https://github.com/Agentic-Learning-AI-Lab/procreate-diffusion-public. The project page is available at https://procreate-diffusion.github.io.", "github_manual": "", "github_regex": "https://github.com/Agentic-Learning-AI-Lab/procreate-diffusion-public", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.02657", "title": "Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining", "authors": [ "Dongyang Liu", "Shitian Zhao", "Le Zhuo", "Weifeng Lin", "Yu Qiao", "Hongsheng Li", "Peng Gao" ], "github": "https://github.com/alpha-vllm/lumina-mgpt", "project_page": "", "abstract": "We present Lumina-mGPT, a family of multimodal autoregressive models capable of various vision and language tasks, particularly excelling in generating flexible photorealistic images from text descriptions. Unlike existing autoregressive image generation approaches, Lumina-mGPT employs a pretrained decoder-only transformer as a unified framework for modeling multimodal token sequences. Our key insight is that a simple decoder-only transformer with multimodal Generative PreTraining (mGPT), utilizing the next-token prediction objective on massive interleaved text-image sequences, can learn broad and general multimodal capabilities, thereby illuminating photorealistic text-to-image generation. Building on these pretrained models, we propose Flexible Progressive Supervised Finetuning (FP-SFT) on high-quality image-text pairs to fully unlock their potential for high-aesthetic image synthesis at any resolution while maintaining their general multimodal capabilities. Furthermore, we introduce Ominiponent Supervised Finetuning (Omni-SFT), transforming Lumina-mGPT into a foundation model that seamlessly achieves omnipotent task unification. The resulting model demonstrates versatile multimodal capabilities, including visual generation tasks like flexible text-to-image generation and controllable generation, visual recognition tasks like segmentation and depth estimation, and vision-language tasks like multiturn visual question answering. Additionally, we analyze the differences and similarities between diffusion-based and autoregressive methods in a direct comparison.", "github_manual": "https://github.com/alpha-vllm/lumina-mgpt", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.02622", "title": "Language Model Can Listen While Speaking", "authors": [ "Ziyang Ma", "Yakun Song", "Chenpeng Du", "Jian Cong", "Zhuo Chen", "Yuping Wang", "Yuxuan Wang", "Xie Chen" ], "github": "", "project_page": "", "abstract": "Dialogue serves as the most natural manner of human-computer interaction (HCI). Recent advancements in speech language models (SLM) have significantly enhanced speech-based conversational AI. However, these models are limited to turn-based conversation, lacking the ability to interact with humans in real-time spoken scenarios, for example, being interrupted when the generated content is not satisfactory. To address these limitations, we explore full duplex modeling (FDM) in interactive speech language models (iSLM), focusing on enhancing real-time interaction and, more explicitly, exploring the quintessential ability of interruption. We introduce a novel model design, namely listening-while-speaking language model (LSLM), an end-to-end system equipped with both listening and speaking channels. Our LSLM employs a token-based decoder-only TTS for speech generation and a streaming self-supervised learning (SSL) encoder for real-time audio input. LSLM fuses both channels for autoregressive generation and detects turn-taking in real time. Three fusion strategies -- early fusion, middle fusion, and late fusion -- are explored, with middle fusion achieving an optimal balance between speech generation and real-time interaction. Two experimental settings, command-based FDM and voice-based FDM, demonstrate LSLM's robustness to noise and sensitivity to diverse instructions. Our results highlight LSLM's capability to achieve duplex communication with minimal impact on existing systems. This study aims to advance the development of interactive speech dialogue systems, enhancing their applicability in real-world contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.01584", "title": "GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS", "authors": [ "Saman Kazemkhani", "Aarav Pandya", "Daphne Cornelisse", "Brennan Shacklett", "Eugene Vinitsky" ], "github": "https://github.com/Emerge-Lab/gpudrive", "project_page": "", "abstract": "Multi-agent learning algorithms have been successful at generating superhuman planning in a wide variety of games but have had little impact on the design of deployed multi-agent planners. A key bottleneck in applying these techniques to multi-agent planning is that they require billions of steps of experience. To enable the study of multi-agent planning at this scale, we present GPUDrive, a GPU-accelerated, multi-agent simulator built on top of the Madrona Game Engine that can generate over a million steps of experience per second. Observation, reward, and dynamics functions are written directly in C++, allowing users to define complex, heterogeneous agent behaviors that are lowered to high-performance CUDA. We show that using GPUDrive we are able to effectively train reinforcement learning agents over many scenes in the Waymo Motion dataset, yielding highly effective goal-reaching agents in minutes for individual scenes and generally capable agents in a few hours. We ship these trained agents as part of the code base at https://github.com/Emerge-Lab/gpudrive.", "github_manual": "", "github_regex": "https://github.com/Emerge-Lab/gpudrive", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.02555", "title": "MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization", "authors": [ "Yiwen Chen", "Yikai Wang", "Yihao Luo", "Zhengyi Wang", "Zilong Chen", "Jun Zhu", "Chi Zhang", "Guosheng Lin" ], "github": "https://github.com/buaacyw/meshanythingv2", "project_page": "", "abstract": "We introduce MeshAnything V2, an autoregressive transformer that generates Artist-Created Meshes (AM) aligned to given shapes. It can be integrated with various 3D asset production pipelines to achieve high-quality, highly controllable AM generation. MeshAnything V2 surpasses previous methods in both efficiency and performance using models of the same size. These improvements are due to our newly proposed mesh tokenization method: Adjacent Mesh Tokenization (AMT). Different from previous methods that represent each face with three vertices, AMT uses a single vertex whenever possible. Compared to previous methods, AMT requires about half the token sequence length to represent the same mesh in average. Furthermore, the token sequences from AMT are more compact and well-structured, fundamentally benefiting AM generation. Our extensive experiments show that AMT significantly improves the efficiency and performance of AM generation. Project Page: https://buaacyw.github.io/meshanything-v2/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/buaacyw/meshanythingv2", "github_api_added_by": "auto" }, { "date": "2024-08-06", "arxiv_id": "2408.02085", "title": "Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models", "authors": [ "Yulei Qin", "Yuncheng Yang", "Pengcheng Guo", "Gang Li", "Hang Shao", "Yuchen Shi", "Zihan Xu", "Yun Gu", "Ke Li", "Xing Sun" ], "github": "https://github.com/yuleiqin/fantastic-data-engineering", "project_page": "", "abstract": "Instruction tuning plays a critical role in aligning large language models (LLMs) with human preference. Despite the vast amount of open instruction datasets, naively training a LLM on all existing instructions may not be optimal and practical. To pinpoint the most beneficial datapoints, data assessment and selection methods have been proposed in the fields of natural language processing (NLP) and deep learning. However, under the context of instruction tuning, there still exists a gap in knowledge on what kind of data evaluation metrics can be employed and how they can be integrated into the selection mechanism. To bridge this gap, we present a comprehensive review on existing literature of data assessment and selection especially for instruction tuning of LLMs. We systematically categorize all applicable methods into quality-based, diversity-based, and importance-based ones where a unified, fine-grained taxonomy is structured. For each category, representative methods are elaborated to describe the landscape of relevant research. In addition, comparison between latest methods is conducted on their officially reported results to provide in-depth discussions on their limitations. Finally, we summarize the open challenges and propose the promosing avenues for future studies. All related contents are available at https://github.com/yuleiqin/fantastic-data-engineering.", "github_manual": "", "github_regex": "https://github.com/yuleiqin/fantastic-data-engineering", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.01800", "title": "MiniCPM-V: A GPT-4V Level MLLM on Your Phone", "authors": [ "Yuan Yao", "Tianyu Yu", "Ao Zhang", "Chongyi Wang", "Junbo Cui", "Hongji Zhu", "Tianchi Cai", "Haoyu Li", "Weilin Zhao", "Zhihui He", "Qianyu Chen", "Huarong Zhou", "Zhensheng Zou", "Haoye Zhang", "Shengding Hu", "Zhi Zheng", "Jie Zhou", "Jie Cai", "Xu Han", "Guoyang Zeng", "Dahai Li", "Zhiyuan Liu", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of AI research and industry, shedding light on a promising path toward the next AI milestone. However, significant challenges remain preventing MLLMs from being practical in real-world applications. The most notable challenge comes from the huge cost of running an MLLM with a massive number of parameters and extensive computation. As a result, most MLLMs need to be deployed on high-performing cloud servers, which greatly limits their application scopes such as mobile, offline, energy-sensitive, and privacy-protective scenarios. In this work, we present MiniCPM-V, a series of efficient MLLMs deployable on end-side devices. By integrating the latest MLLM techniques in architecture, pretraining and alignment, the latest MiniCPM-Llama3-V 2.5 has several notable features: (1) Strong performance, outperforming GPT-4V-1106, Gemini Pro and Claude 3 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks, (2) strong OCR capability and 1.8M pixel high-resolution image perception at any aspect ratio, (3) trustworthy behavior with low hallucination rates, (4) multilingual support for 30+ languages, and (5) efficient deployment on mobile phones. More importantly, MiniCPM-V can be viewed as a representative example of a promising trend: The model sizes for achieving usable (e.g., GPT-4V) level performance are rapidly decreasing, along with the fast growth of end-side computation capacity. This jointly shows that GPT-4V level MLLMs deployed on end devices are becoming increasingly possible, unlocking a wider spectrum of real-world AI applications in the near future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.02210", "title": "ExoViP: Step-by-step Verification and Exploration with Exoskeleton Modules for Compositional Visual Reasoning", "authors": [ "Yuxuan Wang", "Alan Yuille", "Zhuowan Li", "Zilong Zheng" ], "github": "", "project_page": "", "abstract": "Compositional visual reasoning methods, which translate a complex query into a structured composition of feasible visual tasks, have exhibited a strong potential in complicated multi-modal tasks. Empowered by recent advances in large language models (LLMs), this multi-modal challenge has been brought to a new stage by treating LLMs as few-shot/zero-shot planners, i.e., vision-language (VL) programming. Such methods, despite their numerous merits, suffer from challenges due to LLM planning mistakes or inaccuracy of visual execution modules, lagging behind the non-compositional models. In this work, we devise a \"plug-and-play\" method, ExoViP, to correct errors in both the planning and execution stages through introspective verification. We employ verification modules as \"exoskeletons\" to enhance current VL programming schemes. Specifically, our proposed verification module utilizes a mixture of three sub-verifiers to validate predictions after each reasoning step, subsequently calibrating the visual module predictions and refining the reasoning trace planned by LLMs. Experimental results on two representative VL programming methods showcase consistent improvements on five compositional reasoning tasks on standard benchmarks. In light of this, we believe that ExoViP can foster better performance and generalization on open-domain multi-modal challenges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.01050", "title": "The Impact of Hyperparameters on Large Language Model Inference Performance: An Evaluation of vLLM and HuggingFace Pipelines", "authors": [ "Matias Martinez" ], "github": "", "project_page": "", "abstract": "The recent surge of open-source large language models (LLMs) enables developers to create AI-based solutions while maintaining control over aspects such as privacy and compliance, thereby providing governance and ownership of the model deployment process. To utilize these LLMs, inference engines are needed. These engines load the model's weights onto available resources, such as GPUs, and process queries to generate responses. The speed of inference, or performance, of the LLM, is critical for real-time applications, as it computes millions or billions of floating point operations per inference. Recently, advanced inference engines such as vLLM have emerged, incorporating novel mechanisms such as efficient memory management to achieve state-of-the-art performance. In this paper, we analyze the performance, particularly the throughput (tokens generated per unit of time), of 20 LLMs using two inference libraries: vLLM and HuggingFace's pipelines. We investigate how various hyperparameters, which developers must configure, influence inference performance. Our results reveal that throughput landscapes are irregular, with distinct peaks, highlighting the importance of hyperparameter optimization to achieve maximum performance. We also show that applying hyperparameter optimization when upgrading or downgrading the GPU model used for inference can improve throughput from HuggingFace pipelines by an average of 9.16% and 13.7%, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.02373", "title": "Operationalizing Contextual Integrity in Privacy-Conscious Assistants", "authors": [ "Sahra Ghalebikesabi", "Eugene Bagdasaryan", "Ren Yi", "Itay Yona", "Ilia Shumailov", "Aneesh Pappu", "Chongyang Shi", "Laura Weidinger", "Robert Stanforth", "Leonard Berrada", "Pushmeet Kohli", "Po-Sen Huang", "Borja Balle" ], "github": "", "project_page": "", "abstract": "Advanced AI assistants combine frontier LLMs and tool access to autonomously perform complex tasks on behalf of users. While the helpfulness of such assistants can increase dramatically with access to user information including emails and documents, this raises privacy concerns about assistants sharing inappropriate information with third parties without user supervision. To steer information-sharing assistants to behave in accordance with privacy expectations, we propose to operationalize contextual integrity (CI), a framework that equates privacy with the appropriate flow of information in a given context. In particular, we design and evaluate a number of strategies to steer assistants' information-sharing actions to be CI compliant. Our evaluation is based on a novel form filling benchmark composed of synthetic data and human annotations, and it reveals that prompting frontier LLMs to perform CI-based reasoning yields strong results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.02545", "title": "RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation", "authors": [ "Daniel Fleischer", "Moshe Berchansky", "Moshe Wasserblat", "Peter Izsak" ], "github": "https://github.com/IntelLabs/RAGFoundry", "project_page": "", "abstract": "Implementing Retrieval-Augmented Generation (RAG) systems is inherently complex, requiring deep understanding of data, use cases, and intricate design decisions. Additionally, evaluating these systems presents significant challenges, necessitating assessment of both retrieval accuracy and generative quality through a multi-faceted approach. We introduce RAG Foundry, an open-source framework for augmenting large language models for RAG use cases. RAG Foundry integrates data creation, training, inference and evaluation into a single workflow, facilitating the creation of data-augmented datasets for training and evaluating large language models in RAG settings. This integration enables rapid prototyping and experimentation with various RAG techniques, allowing users to easily generate datasets and train RAG models using internal or specialized knowledge sources. We demonstrate the framework effectiveness by augmenting and fine-tuning Llama-3 and Phi-3 models with diverse RAG configurations, showcasing consistent improvements across three knowledge-intensive datasets. Code is released as open-source in https://github.com/IntelLabs/RAGFoundry.", "github_manual": "", "github_regex": "https://github.com/IntelLabs/RAGFoundry", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-06", "arxiv_id": "2408.02600", "title": "BioMamba: A Pre-trained Biomedical Language Representation Model Leveraging Mamba", "authors": [ "Ling Yue", "Sixue Xing", "Yingzhou Lu", "Tianfan Fu" ], "github": "https://github.com/LeoYML/BioMamba", "project_page": "", "abstract": "The advancement of natural language processing (NLP) in biology hinges on models' ability to interpret intricate biomedical literature. Traditional models often struggle with the complex and domain-specific language in this field. In this paper, we present BioMamba, a pre-trained model specifically designed for biomedical text mining. BioMamba builds upon the Mamba architecture and is pre-trained on an extensive corpus of biomedical literature. Our empirical studies demonstrate that BioMamba significantly outperforms models like BioBERT and general-domain Mamba across various biomedical tasks. For instance, BioMamba achieves a 100 times reduction in perplexity and a 4 times reduction in cross-entropy loss on the BioASQ test set. We provide an overview of the model architecture, pre-training process, and fine-tuning techniques. Additionally, we release the code and trained model to facilitate further research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LeoYML/BioMamba", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-07", "arxiv_id": "2408.02718", "title": "MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models", "authors": [ "Fanqing Meng", "Jin Wang", "Chuanhao Li", "Quanfeng Lu", "Hao Tian", "Jiaqi Liao", "Xizhou Zhu", "Jifeng Dai", "Yu Qiao", "Ping Luo", "Kaipeng Zhang", "Wenqi Shao" ], "github": "", "project_page": "", "abstract": "The capability to process multiple images is crucial for Large Vision-Language Models (LVLMs) to develop a more thorough and nuanced understanding of a scene. Recent multi-image LVLMs have begun to address this need. However, their evaluation has not kept pace with their development. To fill this gap, we introduce the Multimodal Multi-image Understanding (MMIU) benchmark, a comprehensive evaluation suite designed to assess LVLMs across a wide range of multi-image tasks. MMIU encompasses 7 types of multi-image relationships, 52 tasks, 77K images, and 11K meticulously curated multiple-choice questions, making it the most extensive benchmark of its kind. Our evaluation of 24 popular LVLMs, including both open-source and proprietary models, reveals significant challenges in multi-image comprehension, particularly in tasks involving spatial understanding. Even the most advanced models, such as GPT-4o, achieve only 55.7% accuracy on MMIU. Through multi-faceted analytical experiments, we identify key performance gaps and limitations, providing valuable insights for future model and data improvements. We aim for MMIU to advance the frontier of LVLM research and development, moving us toward achieving sophisticated multimodal multi-image user interactions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-07", "arxiv_id": "2408.02752", "title": "Diffusion Models as Data Mining Tools", "authors": [ "Ioannis Siglidis", "Aleksander Holynski", "Alexei A. Efros", "Mathieu Aubry", "Shiry Ginosar" ], "github": "", "project_page": "", "abstract": "This paper demonstrates how to use generative models trained for image synthesis as tools for visual data mining. Our insight is that since contemporary generative models learn an accurate representation of their training data, we can use them to summarize the data by mining for visual patterns. Concretely, we show that after finetuning conditional diffusion models to synthesize images from a specific dataset, we can use these models to define a typicality measure on that dataset. This measure assesses how typical visual elements are for different data labels, such as geographic location, time stamps, semantic labels, or even the presence of a disease. This analysis-by-synthesis approach to data mining has two key advantages. First, it scales much better than traditional correspondence-based approaches since it does not require explicitly comparing all pairs of visual elements. Second, while most previous works on visual data mining focus on a single dataset, our approach works on diverse datasets in terms of content and scale, including a historical car dataset, a historical face dataset, a large worldwide street-view dataset, and an even larger scene dataset. Furthermore, our approach allows for translating visual elements across class labels and analyzing consistent changes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-07", "arxiv_id": "2408.03178", "title": "An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion", "authors": [ "Xingguang Yan", "Han-Hung Lee", "Ziyu Wan", "Angel X. Chang" ], "github": "", "project_page": "", "abstract": "We introduce a new approach for generating realistic 3D models with UV maps through a representation termed \"Object Images.\" This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-07", "arxiv_id": "2408.02900", "title": "MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine", "authors": [ "Yunfei Xie", "Ce Zhou", "Lang Gao", "Juncheng Wu", "Xianhang Li", "Hong-Yu Zhou", "Sheng Liu", "Lei Xing", "James Zou", "Cihang Xie", "Yuyin Zhou" ], "github": "https://github.com/UCSC-VLAA/MedTrinity-25M", "project_page": "", "abstract": "This paper introduces MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities, with multigranular annotations for more than 65 diseases. These enriched annotations encompass both global textual information, such as disease/lesion type, modality, region-specific descriptions, and inter-regional relationships, as well as detailed local annotations for regions of interest (ROIs), including bounding boxes, segmentation masks. Unlike existing approach which is limited by the availability of image-text pairs, we have developed the first automated pipeline that scales up multimodal data by generating multigranular visual and texual annotations (in the form of image-ROI-description triplets) without the need for any paired text descriptions. Specifically, data from over 90 different sources have been collected, preprocessed, and grounded using domain-specific expert models to identify ROIs related to abnormal regions. We then build a comprehensive knowledge base and prompt multimodal large language models to perform retrieval-augmented generation with the identified ROIs as guidance, resulting in multigranular texual descriptions. Compared to existing datasets, MedTrinity-25M provides the most enriched annotations, supporting a comprehensive range of multimodal tasks such as captioning and report generation, as well as vision-centric tasks like classification and segmentation. Pretraining on MedTrinity-25M, our model achieves state-of-the-art performance on VQA-RAD and PathVQA, surpassing both multimodal large language models and other representative SoTA approaches. This dataset can also be utilized to support large-scale pre-training of multimodal medical AI models, contributing to the development of future foundation models in the medical domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UCSC-VLAA/MedTrinity-25M", "github_api_added_by": "auto" }, { "date": "2024-08-07", "arxiv_id": "2408.03325", "title": "CoverBench: A Challenging Benchmark for Complex Claim Verification", "authors": [ "Alon Jacovi", "Moran Ambar", "Eyal Ben-David", "Uri Shaham", "Amir Feder", "Mor Geva", "Dror Marcus", "Avi Caciularu" ], "github": "", "project_page": "", "abstract": "There is a growing line of research on verifying the correctness of language models' outputs. At the same time, LMs are being used to tackle complex queries that require reasoning. We introduce CoverBench, a challenging benchmark focused on verifying LM outputs in complex reasoning settings. Datasets that can be used for this purpose are often designed for other complex reasoning tasks (e.g., QA) targeting specific use-cases (e.g., financial tables), requiring transformations, negative sampling and selection of hard examples to collect such a benchmark. CoverBench provides a diversified evaluation for complex claim verification in a variety of domains, types of reasoning, relatively long inputs, and a variety of standardizations, such as multiple representations for tables where available, and a consistent schema. We manually vet the data for quality to ensure low levels of label noise. Finally, we report a variety of competitive baseline results to show CoverBench is challenging and has very significant headroom. The data is available at https://huggingface.co/datasets/google/coverbench .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-07", "arxiv_id": "2408.03326", "title": "LLaVA-OneVision: Easy Visual Task Transfer", "authors": [ "Bo Li", "Yuanhan Zhang", "Dong Guo", "Renrui Zhang", "Feng Li", "Hao Zhang", "Kaichen Zhang", "Yanwei Li", "Ziwei Liu", "Chunyuan Li" ], "github": "", "project_page": "", "abstract": "We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series. Our experimental results demonstrate that LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario capabilities are demonstrated through task transfer from images to videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-07", "arxiv_id": "2408.03284", "title": "ReSyncer: Rewiring Style-based Generator for Unified Audio-Visually Synced Facial Performer", "authors": [ "Jiazhi Guan", "Zhiliang Xu", "Hang Zhou", "Kaisiyuan Wang", "Shengyi He", "Zhanwang Zhang", "Borong Liang", "Haocheng Feng", "Errui Ding", "Jingtuo Liu", "Jingdong Wang", "Youjian Zhao", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Lip-syncing videos with given audio is the foundation for various applications including the creation of virtual presenters or performers. While recent studies explore high-fidelity lip-sync with different techniques, their task-orientated models either require long-term videos for clip-specific training or retain visible artifacts. In this paper, we propose a unified and effective framework ReSyncer, that synchronizes generalized audio-visual facial information. The key design is revisiting and rewiring the Style-based generator to efficiently adopt 3D facial dynamics predicted by a principled style-injected Transformer. By simply re-configuring the information insertion mechanisms within the noise and style space, our framework fuses motion and appearance with unified training. Extensive experiments demonstrate that ReSyncer not only produces high-fidelity lip-synced videos according to audio, but also supports multiple appealing properties that are suitable for creating virtual presenters and performers, including fast personalized fine-tuning, video-driven lip-syncing, the transfer of speaking styles, and even face swapping. Resources can be found at https://guanjz20.github.io/projects/ReSyncer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-07", "arxiv_id": "2408.03209", "title": "IPAdapter-Instruct: Resolving Ambiguity in Image-based Conditioning using Instruct Prompts", "authors": [ "Ciara Rowles", "Shimon Vainer", "Dante De Nigris", "Slava Elizarov", "Konstantin Kutsy", "Simon Donn\u00e9" ], "github": "", "project_page": "", "abstract": "Diffusion models continuously push the boundary of state-of-the-art image generation, but the process is hard to control with any nuance: practice proves that textual prompts are inadequate for accurately describing image style or fine structural details (such as faces). ControlNet and IPAdapter address this shortcoming by conditioning the generative process on imagery instead, but each individual instance is limited to modeling a single conditional posterior: for practical use-cases, where multiple different posteriors are desired within the same workflow, training and using multiple adapters is cumbersome. We propose IPAdapter-Instruct, which combines natural-image conditioning with ``Instruct'' prompts to swap between interpretations for the same conditioning image: style transfer, object extraction, both, or something else still? IPAdapterInstruct efficiently learns multiple tasks with minimal loss in quality compared to dedicated per-task models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-07", "arxiv_id": "2408.03314", "title": "Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters", "authors": [ "Charlie Snell", "Jaehoon Lee", "Kelvin Xu", "Aviral Kumar" ], "github": "https://github.com/huggingface/search-and-learn", "project_page": "", "abstract": "Enabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natural language. In this paper, we study the scaling of inference-time computation in LLMs, with a focus on answering the question: if an LLM is allowed to use a fixed but non-trivial amount of inference-time compute, how much can it improve its performance on a challenging prompt? Answering this question has implications not only on the achievable performance of LLMs, but also on the future of LLM pretraining and how one should tradeoff inference-time and pre-training compute. Despite its importance, little research attempted to understand the scaling behaviors of various test-time inference methods. Moreover, current work largely provides negative results for a number of these strategies. In this work, we analyze two primary mechanisms to scale test-time computation: (1) searching against dense, process-based verifier reward models; and (2) updating the model's distribution over a response adaptively, given the prompt at test time. We find that in both cases, the effectiveness of different approaches to scaling test-time compute critically varies depending on the difficulty of the prompt. This observation motivates applying a \"compute-optimal\" scaling strategy, which acts to most effectively allocate test-time compute adaptively per prompt. Using this compute-optimal strategy, we can improve the efficiency of test-time compute scaling by more than 4x compared to a best-of-N baseline. Additionally, in a FLOPs-matched evaluation, we find that on problems where a smaller base model attains somewhat non-trivial success rates, test-time compute can be used to outperform a 14x larger model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/huggingface/search-and-learn", "github_api_added_by": "auto" }, { "date": "2024-08-07", "arxiv_id": "2408.01708", "title": "AVESFormer: Efficient Transformer Design for Real-Time Audio-Visual Segmentation", "authors": [ "Zili Wang", "Qi Yang", "Linsu Shi", "Jiazhong Yu", "Qinghua Liang", "Fei Li", "Shiming Xiang" ], "github": "https://github.com/MarkXCloud/AVESFormer.git", "project_page": "", "abstract": "Recently, transformer-based models have demonstrated remarkable performance on audio-visual segmentation (AVS) tasks. However, their expensive computational cost makes real-time inference impractical. By characterizing attention maps of the network, we identify two key obstacles in AVS models: 1) attention dissipation, corresponding to the over-concentrated attention weights by Softmax within restricted frames, and 2) inefficient, burdensome transformer decoder, caused by narrow focus patterns in early stages. In this paper, we introduce AVESFormer, the first real-time Audio-Visual Efficient Segmentation transformer that achieves fast, efficient and light-weight simultaneously. Our model leverages an efficient prompt query generator to correct the behaviour of cross-attention. Additionally, we propose ELF decoder to bring greater efficiency by facilitating convolutions suitable for local features to reduce computational burdens. Extensive experiments demonstrate that our AVESFormer significantly enhances model performance, achieving 79.9% on S4, 57.9% on MS3 and 31.2% on AVSS, outperforming previous state-of-the-art and achieving an excellent trade-off between performance and speed. Code can be found at https://github.com/MarkXCloud/AVESFormer.git.", "github_manual": "", "github_regex": "https://github.com/MarkXCloud/AVESFormer.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-07", "arxiv_id": "2408.03281", "title": "StructEval: Deepen and Broaden Large Language Model Assessment via Structured Evaluation", "authors": [ "Boxi Cao", "Mengjie Ren", "Hongyu Lin", "Xianpei Han", "Feng Zhang", "Junfeng Zhan", "Le Sun" ], "github": "https://github.com/c-box/structeval", "project_page": "", "abstract": "Evaluation is the baton for the development of large language models. Current evaluations typically employ a single-item assessment paradigm for each atomic test objective, which struggles to discern whether a model genuinely possesses the required capabilities or merely memorizes/guesses the answers to specific questions. To this end, we propose a novel evaluation framework referred to as StructEval. Starting from an atomic test objective, StructEval deepens and broadens the evaluation by conducting a structured assessment across multiple cognitive levels and critical concepts, and therefore offers a comprehensive, robust and consistent evaluation for LLMs. Experiments on three widely-used benchmarks demonstrate that StructEval serves as a reliable tool for resisting the risk of data contamination and reducing the interference of potential biases, thereby providing more reliable and consistent conclusions regarding model capabilities. Our framework also sheds light on the design of future principled and trustworthy LLM evaluation protocols.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/c-box/structeval", "github_api_added_by": "auto" }, { "date": "2024-08-07", "arxiv_id": "2408.03256", "title": "Synthesizing Text-to-SQL Data from Weak and Strong LLMs", "authors": [ "Jiaxi Yang", "Binyuan Hui", "Min Yang", "Jian Yang", "Junyang Lin", "Chang Zhou" ], "github": "https://github.com/Yangjiaxi/Sense", "project_page": "", "abstract": "The capability gap between open-source and closed-source large language models (LLMs) remains a challenge in text-to-SQL tasks. In this paper, we introduce a synthetic data approach that combines data produced by larger, more powerful models (strong models) with error information data generated by smaller, not well-aligned models (weak models). The method not only enhances the domain generalization of text-to-SQL models but also explores the potential of error data supervision through preference learning. Furthermore, we employ the synthetic data approach for instruction tuning on open-source LLMs, resulting SENSE, a specialized text-to-SQL model. The effectiveness of SENSE is demonstrated through state-of-the-art results on the SPIDER and BIRD benchmarks, bridging the performance gap between open-source models and methods prompted by closed-source models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Yangjiaxi/Sense", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-08", "arxiv_id": "2408.03906", "title": "Achieving Human Level Competitive Robot Table Tennis", "authors": [ "David B. D'Ambrosio", "Saminda Abeyruwan", "Laura Graesser", "Atil Iscen", "Heni Ben Amor", "Alex Bewley", "Barney J. Reed", "Krista Reymann", "Leila Takayama", "Yuval Tassa", "Krzysztof Choromanski", "Erwin Coumans", "Deepali Jain", "Navdeep Jaitly", "Natasha Jaques", "Satoshi Kataoka", "Yuheng Kuang", "Nevena Lazic", "Reza Mahjourian", "Sherry Moore", "Kenneth Oslund", "Anish Shankar", "Vikas Sindhwani", "Vincent Vanhoucke", "Grace Vesom", "Peng Xu", "Pannag R. Sanketi" ], "github": "", "project_page": "", "abstract": "Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in competitive table tennis. Table tennis is a physically demanding sport which requires human players to undergo years of training to achieve an advanced level of proficiency. In this paper, we contribute (1) a hierarchical and modular policy architecture consisting of (i) low level controllers with their detailed skill descriptors which model the agent's capabilities and help to bridge the sim-to-real gap and (ii) a high level controller that chooses the low level skills, (2) techniques for enabling zero-shot sim-to-real including an iterative approach to defining the task distribution that is grounded in the real-world and defines an automatic curriculum, and (3) real time adaptation to unseen opponents. Policy performance was assessed through 29 robot vs. human matches of which the robot won 45% (13/29). All humans were unseen players and their skill level varied from beginner to tournament level. Whilst the robot lost all matches vs. the most advanced players it won 100% matches vs. beginners and 55% matches vs. intermediate players, demonstrating solidly amateur human-level performance. Videos of the matches can be viewed at https://sites.google.com/view/competitive-robot-table-tennis", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-08", "arxiv_id": "2408.03541", "title": "EXAONE 3.0 7.8B Instruction Tuned Language Model", "authors": [ "LG AI Research", "Soyoung An", "Kyunghoon Bae", "Eunbi Choi", "Stanley Jungkyu Choi", "Yemuk Choi", "Seokhee Hong", "Yeonjung Hong", "Junwon Hwang", "Hyojin Jeon", "Gerrard Jeongwon Jo", "Hyunjik Jo", "Jiyeon Jung", "Yountae Jung", "Euisoon Kim", "Hyosang Kim", "Joonkee Kim", "Seonghwan Kim", "Soyeon Kim", "Sunkyoung Kim", "Yireun Kim", "Youchul Kim", "Edward Hwayoung Lee", "Haeju Lee", "Honglak Lee", "Jinsik Lee", "Kyungmin Lee", "Moontae Lee", "Seungjun Lee", "Woohyung Lim", "Sangha Park", "Sooyoun Park", "Yongmin Park", "Boseong Seo", "Sihoon Yang", "Heuiyeen Yeen", "Kyungjae Yoo", "Hyeongu Yun" ], "github": "", "project_page": "", "abstract": "We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model sizes, we publicly release the 7.8B instruction-tuned model to promote open research and innovations. Through extensive evaluations across a wide range of public and in-house benchmarks, EXAONE 3.0 demonstrates highly competitive real-world performance with instruction-following capability against other state-of-the-art open models of similar size. Our comparative analysis shows that EXAONE 3.0 excels particularly in Korean, while achieving compelling performance across general tasks and complex reasoning. With its strong real-world effectiveness and bilingual proficiency, we hope that EXAONE keeps contributing to advancements in Expert AI. Our EXAONE 3.0 instruction-tuned model is available at https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-08", "arxiv_id": "2408.03356", "title": "RayGauss: Volumetric Gaussian-Based Ray Casting for Photorealistic Novel View Synthesis", "authors": [ "Hugo Blanc", "Jean-Emmanuel Deschaud", "Alexis Paljic" ], "github": "", "project_page": "", "abstract": "Differentiable volumetric rendering-based methods made significant progress in novel view synthesis. On one hand, innovative methods have replaced the Neural Radiance Fields (NeRF) network with locally parameterized structures, enabling high-quality renderings in a reasonable time. On the other hand, approaches have used differentiable splatting instead of NeRF's ray casting to optimize radiance fields rapidly using Gaussian kernels, allowing for fine adaptation to the scene. However, differentiable ray casting of irregularly spaced kernels has been scarcely explored, while splatting, despite enabling fast rendering times, is susceptible to clearly visible artifacts. Our work closes this gap by providing a physically consistent formulation of the emitted radiance c and density {\\sigma}, decomposed with Gaussian functions associated with Spherical Gaussians/Harmonics for all-frequency colorimetric representation. We also introduce a method enabling differentiable ray casting of irregularly distributed Gaussians using an algorithm that integrates radiance fields slab by slab and leverages a BVH structure. This allows our approach to finely adapt to the scene while avoiding splatting artifacts. As a result, we achieve superior rendering quality compared to the state-of-the-art while maintaining reasonable training times and achieving inference speeds of 25 FPS on the Blender dataset. Project page with videos and code: https://raygauss.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-08", "arxiv_id": "2408.03615", "title": "Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks", "authors": [ "Zaijing Li", "Yuquan Xie", "Rui Shao", "Gongwei Chen", "Dongmei Jiang", "Liqiang Nie" ], "github": "https://github.com/JiuTian-VL/Optimus-1", "project_page": "", "abstract": "Building a general-purpose agent is a long-standing vision in the field of artificial intelligence. Existing agents have made remarkable progress in many domains, yet they still struggle to complete long-horizon tasks in an open world. We attribute this to the lack of necessary world knowledge and multimodal experience that can guide agents through a variety of long-horizon tasks. In this paper, we propose a Hybrid Multimodal Memory module to address the above challenges. It 1) transforms knowledge into Hierarchical Directed Knowledge Graph that allows agents to explicitly represent and learn world knowledge, and 2) summarises historical information into Abstracted Multimodal Experience Pool that provide agents with rich references for in-context learning. On top of the Hybrid Multimodal Memory module, a multimodal agent, Optimus-1, is constructed with dedicated Knowledge-guided Planner and Experience-Driven Reflector, contributing to a better planning and reflection in the face of long-horizon tasks in Minecraft. Extensive experimental results show that Optimus-1 significantly outperforms all existing agents on challenging long-horizon task benchmarks, and exhibits near human-level performance on many tasks. In addition, we introduce various Multimodal Large Language Models (MLLMs) as the backbone of Optimus-1. Experimental results show that Optimus-1 exhibits strong generalization with the help of the Hybrid Multimodal Memory module, outperforming the GPT-4V baseline on many tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JiuTian-VL/Optimus-1", "github_api_added_by": "auto" }, { "date": "2024-08-08", "arxiv_id": "2408.03695", "title": "Openstory++: A Large-scale Dataset and Benchmark for Instance-aware Open-domain Visual Storytelling", "authors": [ "Zilyu Ye", "Jinxiu Liu", "Ruotian Peng", "Jinjin Cao", "Zhiyang Chen", "Yiyang Zhang", "Ziwei Xuan", "Mingyuan Zhou", "Xiaoqian Shen", "Mohamed Elhoseiny", "Qi Liu", "Guo-Jun Qi" ], "github": "https://github.com/YeLuoSuiYou/openstorypp", "project_page": "", "abstract": "Recent image generation models excel at creating high-quality images from brief captions. However, they fail to maintain consistency of multiple instances across images when encountering lengthy contexts. This inconsistency is largely due to in existing training datasets the absence of granular instance feature labeling in existing training datasets. To tackle these issues, we introduce Openstory++, a large-scale dataset combining additional instance-level annotations with both images and text. Furthermore, we develop a training methodology that emphasizes entity-centric image-text generation, ensuring that the models learn to effectively interweave visual and textual information. Specifically, Openstory++ streamlines the process of keyframe extraction from open-domain videos, employing vision-language models to generate captions that are then polished by a large language model for narrative continuity. It surpasses previous datasets by offering a more expansive open-domain resource, which incorporates automated captioning, high-resolution imagery tailored for instance count, and extensive frame sequences for temporal consistency. Additionally, we present Cohere-Bench, a pioneering benchmark framework for evaluating the image generation tasks when long multimodal context is provided, including the ability to keep the background, style, instances in the given context coherent. Compared to existing benchmarks, our work fills critical gaps in multi-modal generation, propelling the development of models that can adeptly generate and interpret complex narratives in open-domain environments. Experiments conducted within Cohere-Bench confirm the superiority of Openstory++ in nurturing high-quality visual storytelling models, enhancing their ability to address open-domain generation tasks. More details can be found at https://openstorypp.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YeLuoSuiYou/openstorypp", "github_api_added_by": "auto" }, { "date": "2024-08-08", "arxiv_id": "2408.03910", "title": "CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases", "authors": [ "Xiangyan Liu", "Bo Lan", "Zhiyuan Hu", "Yang Liu", "Zhicheng Zhang", "Wenmeng Zhou", "Fei Wang", "Michael Shieh" ], "github": "https://github.com/modelscope/modelscope-agent/tree/master/apps", "project_page": "", "abstract": "Large Language Models (LLMs) excel in stand-alone code tasks like HumanEval and MBPP, but struggle with handling entire code repositories. This challenge has prompted research on enhancing LLM-codebase interaction at a repository scale. Current solutions rely on similarity-based retrieval or manual tools and APIs, each with notable drawbacks. Similarity-based retrieval often has low recall in complex tasks, while manual tools and APIs are typically task-specific and require expert knowledge, reducing their generalizability across diverse code tasks and real-world applications. To mitigate these limitations, we introduce \\framework, a system that integrates LLM agents with graph database interfaces extracted from code repositories. By leveraging the structural properties of graph databases and the flexibility of the graph query language, \\framework enables the LLM agent to construct and execute queries, allowing for precise, code structure-aware context retrieval and code navigation. We assess \\framework using three benchmarks: CrossCodeEval, SWE-bench, and EvoCodeBench. Additionally, we develop five real-world coding applications. With a unified graph database schema, \\framework demonstrates competitive performance and potential in both academic and real-world environments, showcasing its versatility and efficacy in software engineering. Our application demo: https://github.com/modelscope/modelscope-agent/tree/master/apps/codexgraph_agent.", "github_manual": "", "github_regex": "https://github.com/modelscope/modelscope-agent/tree/master/apps", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-08", "arxiv_id": "2408.03900", "title": "Speech-MASSIVE: A Multilingual Speech Dataset for SLU and Beyond", "authors": [ "Beomseok Lee", "Ioan Calapodescu", "Marco Gaido", "Matteo Negri", "Laurent Besacier" ], "github": "https://github.com/hlt-mt/Speech-MASSIVE", "project_page": "", "abstract": "We present Speech-MASSIVE, a multilingual Spoken Language Understanding (SLU) dataset comprising the speech counterpart for a portion of the MASSIVE textual corpus. Speech-MASSIVE covers 12 languages from different families and inherits from MASSIVE the annotations for the intent prediction and slot-filling tasks. Our extension is prompted by the scarcity of massively multilingual SLU datasets and the growing need for versatile speech datasets to assess foundation models (LLMs, speech encoders) across languages and tasks. We provide a multimodal, multitask, multilingual dataset and report SLU baselines using both cascaded and end-to-end architectures in various training scenarios (zero-shot, few-shot, and full fine-tune). Furthermore, we demonstrate the suitability of Speech-MASSIVE for benchmarking other tasks such as speech transcription, language identification, and speech translation. The dataset, models, and code are publicly available at: https://github.com/hlt-mt/Speech-MASSIVE", "github_manual": "", "github_regex": "https://github.com/hlt-mt/Speech-MASSIVE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-08", "arxiv_id": "2408.03822", "title": "Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields", "authors": [ "Joo Chan Lee", "Daniel Rho", "Xiangyu Sun", "Jong Hwan Ko", "Eunbyung Park" ], "github": "", "project_page": "", "abstract": "3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussian-based representation and introduces an approximated volumetric rendering, achieving very fast rendering speed and promising image quality. Furthermore, subsequent studies have successfully extended 3DGS to dynamic 3D scenes, demonstrating its wide range of applications. However, a significant drawback arises as 3DGS and its following methods entail a substantial number of Gaussians to maintain the high fidelity of the rendered images, which requires a large amount of memory and storage. To address this critical issue, we place a specific emphasis on two key objectives: reducing the number of Gaussian points without sacrificing performance and compressing the Gaussian attributes, such as view-dependent color and covariance. To this end, we propose a learnable mask strategy that significantly reduces the number of Gaussians while preserving high performance. In addition, we propose a compact but effective representation of view-dependent color by employing a grid-based neural field rather than relying on spherical harmonics. Finally, we learn codebooks to compactly represent the geometric and temporal attributes by residual vector quantization. With model compression techniques such as quantization and entropy coding, we consistently show over 25x reduced storage and enhanced rendering speed compared to 3DGS for static scenes, while maintaining the quality of the scene representation. For dynamic scenes, our approach achieves more than 12x storage efficiency and retains a high-quality reconstruction compared to the existing state-of-the-art methods. Our work provides a comprehensive framework for 3D scene representation, achieving high performance, fast training, compactness, and real-time rendering. Our project page is available at https://maincold2.github.io/c3dgs/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-08", "arxiv_id": "2408.03588", "title": "Facing the Music: Tackling Singing Voice Separation in Cinematic Audio Source Separation", "authors": [ "Karn N. Watcharasupat", "Chih-Wei Wu", "Iroro Orife" ], "github": "https://github.com/kwatcharasupat/source-separation-landing", "project_page": "", "abstract": "Cinematic audio source separation (CASS) is a fairly new subtask of audio source separation. A typical setup of CASS is a three-stem problem, with the aim of separating the mixture into the dialogue stem (DX), music stem (MX), and effects stem (FX). In practice, however, several edge cases exist as some sound sources do not fit neatly in either of these three stems, necessitating the use of additional auxiliary stems in production. One very common edge case is the singing voice in film audio, which may belong in either the DX or MX, depending heavily on the cinematic context. In this work, we demonstrate a very straightforward extension of the dedicated-decoder Bandit and query-based single-decoder Banquet models to a four-stem problem, treating non-musical dialogue, instrumental music, singing voice, and effects as separate stems. Interestingly, the query-based Banquet model outperformed the dedicated-decoder Bandit model. We hypothesized that this is due to a better feature alignment at the bottleneck as enforced by the band-agnostic FiLM layer. Dataset and model implementation will be made available at https://github.com/kwatcharasupat/source-separation-landing.", "github_manual": "", "github_regex": "https://github.com/kwatcharasupat/source-separation-landing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-08", "arxiv_id": "2408.03923", "title": "Fast Sprite Decomposition from Animated Graphics", "authors": [ "Tomoyuki Suzuki", "Kotaro Kikuchi", "Kota Yamaguchi" ], "github": "https://github.com/gaomingqi/Track-Anything", "project_page": "", "abstract": "This paper presents an approach to decomposing animated graphics into sprites, a set of basic elements or layers. Our approach builds on the optimization of sprite parameters to fit the raster video. For efficiency, we assume static textures for sprites to reduce the search space while preventing artifacts using a texture prior model. To further speed up the optimization, we introduce the initialization of the sprite parameters utilizing a pre-trained video object segmentation model and user input of single frame annotations. For our study, we construct the Crello Animation dataset from an online design service and define quantitative metrics to measure the quality of the extracted sprites. Experiments show that our method significantly outperforms baselines for similar decomposition tasks in terms of the quality/efficiency tradeoff.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gaomingqi/Track-Anything", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-08", "arxiv_id": "2408.03837", "title": "WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models", "authors": [ "Prannaya Gupta", "Le Qi Yau", "Hao Han Low", "I-Shiang Lee", "Hugo Maximus Lim", "Yu Xin Teoh", "Jia Hng Koh", "Dar Win Liew", "Rishabh Bhardwaj", "Rajat Bhardwaj", "Soujanya Poria" ], "github": "https://github.com/walledai/walledeval", "project_page": "", "abstract": "WalledEval is a comprehensive AI safety testing toolkit designed to evaluate large language models (LLMs). It accommodates a diverse range of models, including both open-weight and API-based ones, and features over 35 safety benchmarks covering areas such as multilingual safety, exaggerated safety, and prompt injections. The framework supports both LLM and judge benchmarking, and incorporates custom mutators to test safety against various text-style mutations such as future tense and paraphrasing. Additionally, WalledEval introduces WalledGuard, a new, small and performant content moderation tool, and SGXSTest, a benchmark for assessing exaggerated safety in cultural contexts. We make WalledEval publicly available at https://github.com/walledai/walledevalA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/walledai/walledeval", "github_api_added_by": "user" }, { "date": "2024-08-09", "arxiv_id": "2408.04631", "title": "Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamics", "authors": [ "Ruining Li", "Chuanxia Zheng", "Christian Rupprecht", "Andrea Vedaldi" ], "github": "", "project_page": "", "abstract": "We present Puppet-Master, an interactive video generative model that can serve as a motion prior for part-level dynamics. At test time, given a single image and a sparse set of motion trajectories (i.e., drags), Puppet-Master can synthesize a video depicting realistic part-level motion faithful to the given drag interactions. This is achieved by fine-tuning a large-scale pre-trained video diffusion model, for which we propose a new conditioning architecture to inject the dragging control effectively. More importantly, we introduce the all-to-first attention mechanism, a drop-in replacement for the widely adopted spatial attention modules, which significantly improves generation quality by addressing the appearance and background issues in existing models. Unlike other motion-conditioned video generators that are trained on in-the-wild videos and mostly move an entire object, Puppet-Master is learned from Objaverse-Animation-HQ, a new dataset of curated part-level motion clips. We propose a strategy to automatically filter out sub-optimal animations and augment the synthetic renderings with meaningful motion trajectories. Puppet-Master generalizes well to real images across various categories and outperforms existing methods in a zero-shot manner on a real-world benchmark. See our project page for more results: vgg-puppetmaster.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-09", "arxiv_id": "2408.03361", "title": "GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI", "authors": [ "Pengcheng Chen", "Jin Ye", "Guoan Wang", "Yanjun Li", "Zhongying Deng", "Wei Li", "Tianbin Li", "Haodong Duan", "Ziyan Huang", "Yanzhou Su", "Benyou Wang", "Shaoting Zhang", "Bin Fu", "Jianfei Cai", "Bohan Zhuang", "Eric J Seibel", "Junjun He", "Yu Qiao" ], "github": "https://github.com/uni-medical/GMAI-MMBench", "project_page": "", "abstract": "Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medical field, LVLMs have a high potential to offer substantial assistance for diagnosis and treatment. Before that, it is crucial to develop benchmarks to evaluate LVLMs' effectiveness in various medical applications. Current benchmarks are often built upon specific academic literature, mainly focusing on a single domain, and lacking varying perceptual granularities. Thus, they face specific challenges, including limited clinical relevance, incomplete evaluations, and insufficient guidance for interactive LVLMs. To address these limitations, we developed the GMAI-MMBench, the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 285 datasets across 39 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format. Additionally, we implemented a lexical tree structure that allows users to customize evaluation tasks, accommodating various assessment needs and substantially supporting medical AI research and applications. We evaluated 50 LVLMs, and the results show that even the advanced GPT-4o only achieves an accuracy of 52%, indicating significant room for improvement. Moreover, we identified five key insufficiencies in current cutting-edge LVLMs that need to be addressed to advance the development of better medical applications. We believe that GMAI-MMBench will stimulate the community to build the next generation of LVLMs toward GMAI. Project Page: https://uni-medical.github.io/GMAI-MMBench.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/uni-medical/GMAI-MMBench", "github_api_added_by": "auto" }, { "date": "2024-08-09", "arxiv_id": "2408.04034", "title": "Task-oriented Sequential Grounding in 3D Scenes", "authors": [ "Zhuofan Zhang", "Ziyu Zhu", "Pengxiang Li", "Tengyu Liu", "Xiaojian Ma", "Yixin Chen", "Baoxiong Jia", "Siyuan Huang", "Qing Li" ], "github": "", "project_page": "", "abstract": "Grounding natural language in physical 3D environments is essential for the advancement of embodied artificial intelligence. Current datasets and models for 3D visual grounding predominantly focus on identifying and localizing objects from static, object-centric descriptions. These approaches do not adequately address the dynamic and sequential nature of task-oriented grounding necessary for practical applications. In this work, we propose a new task: Task-oriented Sequential Grounding in 3D scenes, wherein an agent must follow detailed step-by-step instructions to complete daily activities by locating a sequence of target objects in indoor scenes. To facilitate this task, we introduce SG3D, a large-scale dataset containing 22,346 tasks with 112,236 steps across 4,895 real-world 3D scenes. The dataset is constructed using a combination of RGB-D scans from various 3D scene datasets and an automated task generation pipeline, followed by human verification for quality assurance. We adapted three state-of-the-art 3D visual grounding models to the sequential grounding task and evaluated their performance on SG3D. Our results reveal that while these models perform well on traditional benchmarks, they face significant challenges with task-oriented sequential grounding, underscoring the need for further research in this area.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-09", "arxiv_id": "2408.04567", "title": "Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches", "authors": [ "Yongzhi Xu", "Yonhon Ng", "Yifu Wang", "Inkyu Sa", "Yunfei Duan", "Yang Li", "Pan Ji", "Hongdong Li" ], "github": "", "project_page": "", "abstract": "3D Content Generation is at the heart of many computer graphics applications, including video gaming, film-making, virtual and augmented reality, etc. This paper proposes a novel deep-learning based approach for automatically generating interactive and playable 3D game scenes, all from the user's casual prompts such as a hand-drawn sketch. Sketch-based input offers a natural, and convenient way to convey the user's design intention in the content creation process. To circumvent the data-deficient challenge in learning (i.e. the lack of large training data of 3D scenes), our method leverages a pre-trained 2D denoising diffusion model to generate a 2D image of the scene as the conceptual guidance. In this process, we adopt the isometric projection mode to factor out unknown camera poses while obtaining the scene layout. From the generated isometric image, we use a pre-trained image understanding method to segment the image into meaningful parts, such as off-ground objects, trees, and buildings, and extract the 2D scene layout. These segments and layouts are subsequently fed into a procedural content generation (PCG) engine, such as a 3D video game engine like Unity or Unreal, to create the 3D scene. The resulting 3D scene can be seamlessly integrated into a game development environment and is readily playable. Extensive tests demonstrate that our method can efficiently generate high-quality and interactive 3D game scenes with layouts that closely follow the user's intention.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-09", "arxiv_id": "2408.04594", "title": "Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models", "authors": [ "Qirui Jiao", "Daoyuan Chen", "Yilun Huang", "Yaliang Li", "Ying Shen" ], "github": "https://github.com/modelscope/data-juicer", "project_page": "", "abstract": "High-performance Multimodal Large Language Models (MLLMs) rely heavily on data quality. This study introduces a novel dataset named Img-Diff, designed to enhance fine-grained image recognition in MLLMs by leveraging insights from contrastive learning and image difference captioning. By analyzing object differences between similar images, we challenge models to identify both matching and distinct components. We utilize the Stable-Diffusion-XL model and advanced image editing techniques to create pairs of similar images that highlight object replacements. Our methodology includes a Difference Area Generator for object differences identifying, followed by a Difference Captions Generator for detailed difference descriptions. The result is a relatively small but high-quality dataset of \"object replacement\" samples. We use the the proposed dataset to fine-tune state-of-the-art (SOTA) MLLMs such as MGM-7B, yielding comprehensive improvements of performance scores over SOTA models that trained with larger-scale datasets, in numerous image difference and Visual Question Answering tasks. For instance, our trained models notably surpass the SOTA models GPT-4V and Gemini on the MMVP benchmark. Besides, we investigate alternative methods for generating image difference data through \"object removal\" and conduct thorough evaluation to confirm the dataset's diversity, quality, and robustness, presenting several insights on synthesis of such contrastive dataset. To encourage further research and advance the field of multimodal data synthesis and enhancement of MLLMs' fundamental capabilities for image understanding, we release our codes and dataset at https://github.com/modelscope/data-juicer/tree/ImgDiff.", "github_manual": "", "github_regex": "https://github.com/modelscope/data-juicer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-09", "arxiv_id": "2408.04284", "title": "LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection", "authors": [ "Mervat Abassy", "Kareem Elozeiri", "Alexander Aziz", "Minh Ngoc Ta", "Raj Vardhan Tomar", "Bimarsha Adhikari", "Saad El Dine Ahmed", "Yuxia Wang", "Osama Mohammed Afzal", "Zhuohan Xie", "Jonibek Mansurov", "Ekaterina Artemova", "Vladislav Mikhailov", "Rui Xing", "Jiahui Geng", "Hasan Iqbal", "Zain Muhammad Mujahid", "Tarek Mahmoud", "Akim Tsvigun", "Alham Fikri Aji", "Artem Shelmanov", "Nizar Habash", "Iryna Gurevych", "Preslav Nakov" ], "github": "https://github.com/mbzuai-nlp/llm-detectaive", "project_page": "", "abstract": "The widespread accessibility of large language models (LLMs) to the general public has significantly amplified the dissemination of machine-generated texts (MGTs). Advancements in prompt manipulation have exacerbated the difficulty in discerning the origin of a text (human-authored vs machinegenerated). This raises concerns regarding the potential misuse of MGTs, particularly within educational and academic domains. In this paper, we present LLM-DetectAIve -- a system designed for fine-grained MGT detection. It is able to classify texts into four categories: human-written, machine-generated, machine-written machine-humanized, and human-written machine-polished. Contrary to previous MGT detectors that perform binary classification, introducing two additional categories in LLM-DetectiAIve offers insights into the varying degrees of LLM intervention during the text creation. This might be useful in some domains like education, where any LLM intervention is usually prohibited. Experiments show that LLM-DetectAIve can effectively identify the authorship of textual content, proving its usefulness in enhancing integrity in education, academia, and other domains. LLM-DetectAIve is publicly accessible at https://huggingface.co/spaces/raj-tomar001/MGT-New. The video describing our system is available at https://youtu.be/E8eT_bE7k8c.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mbzuai-nlp/llm-detectaive", "github_api_added_by": "auto" }, { "date": "2024-08-09", "arxiv_id": "2408.04614", "title": "Better Alignment with Instruction Back-and-Forth Translation", "authors": [ "Thao Nguyen", "Jeffrey Li", "Sewoong Oh", "Ludwig Schmidt", "Jason Weston", "Luke Zettlemoyer", "Xian Li" ], "github": "", "project_page": "", "abstract": "We propose a new method, instruction back-and-forth translation, to construct high-quality synthetic data grounded in world knowledge for aligning large language models (LLMs). Given documents from a web corpus, we generate and curate synthetic instructions using the backtranslation approach proposed by Li et al.(2023a), and rewrite the responses to improve their quality further based on the initial documents. Fine-tuning with the resulting (backtranslated instruction, rewritten response) pairs yields higher win rates on AlpacaEval than using other common instruction datasets such as Humpback, ShareGPT, Open Orca, Alpaca-GPT4 and Self-instruct. We also demonstrate that rewriting the responses with an LLM outperforms direct distillation, and the two generated text distributions exhibit significant distinction in embedding space. Further analysis shows that our backtranslated instructions are of higher quality than other sources of synthetic instructions, while our responses are more diverse and complex than those obtained from distillation. Overall we find that instruction back-and-forth translation combines the best of both worlds -- making use of the information diversity and quantity found on the web, while ensuring the quality of the responses which is necessary for effective alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-09", "arxiv_id": "2408.04619", "title": "Transformer Explainer: Interactive Learning of Text-Generative Models", "authors": [ "Aeree Cho", "Grace C. Kim", "Alexander Karpekov", "Alec Helbling", "Zijie J. Wang", "Seongmin Lee", "Benjamin Hoover", "Duen Horng Chau" ], "github": "", "project_page": "", "abstract": "Transformers have revolutionized machine learning, yet their inner workings remain opaque to many. We present Transformer Explainer, an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2 model. Our tool helps users understand complex Transformer concepts by integrating a model overview and enabling smooth transitions across abstraction levels of mathematical operations and model structures. It runs a live GPT-2 instance locally in the user's browser, empowering users to experiment with their own input and observe in real-time how the internal components and parameters of the Transformer work together to predict the next tokens. Our tool requires no installation or special hardware, broadening the public's education access to modern generative AI techniques. Our open-sourced tool is available at https://poloclub.github.io/transformer-explainer/. A video demo is available at https://youtu.be/ECR4oAwocjs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-09", "arxiv_id": "2408.04303", "title": "Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP", "authors": [ "Fran\u00e7ois Remy", "Pieter Delobelle", "Hayastan Avetisyan", "Alfiya Khabibullina", "Miryam de Lhoneux", "Thomas Demeester" ], "github": "https://github.com/lagom-nlp/transtokenizer", "project_page": "", "abstract": "The development of monolingual language models for low and mid-resource languages continues to be hindered by the difficulty in sourcing high-quality training data. In this study, we present a novel cross-lingual vocabulary transfer strategy, trans-tokenization, designed to tackle this challenge and enable more efficient language adaptation. Our approach focuses on adapting a high-resource monolingual LLM to an unseen target language by initializing the token embeddings of the target language using a weighted average of semantically similar token embeddings from the source language. For this, we leverage a translation resource covering both the source and target languages. We validate our method with the Tweeties, a series of trans-tokenized LLMs, and demonstrate their competitive performance on various downstream tasks across a small but diverse set of languages. Additionally, we introduce Hydra LLMs, models with multiple swappable language modeling heads and embedding tables, which further extend the capabilities of our trans-tokenization strategy. By designing a Hydra LLM based on the multilingual model TowerInstruct, we developed a state-of-the-art machine translation model for Tatar, in a zero-shot manner, completely bypassing the need for high-quality parallel data. This breakthrough is particularly significant for low-resource languages like Tatar, where high-quality parallel data is hard to come by. By lowering the data and time requirements for training high-quality models, our trans-tokenization strategy allows for the development of LLMs for a wider range of languages, especially those with limited resources. We hope that our work will inspire further research and collaboration in the field of cross-lingual vocabulary transfer and contribute to the empowerment of languages on a global scale.", "github_manual": "https://github.com/lagom-nlp/transtokenizer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-09", "arxiv_id": "2408.04520", "title": "Advancing Molecular Machine (Learned) Representations with Stereoelectronics-Infused Molecular Graphs", "authors": [ "Daniil A. Boiko", "Thiago Resch\u00fctzegger", "Benjamin Sanchez-Lengeling", "Samuel M. Blau", "Gabe Gomes" ], "github": "https://github.com/gomesgroup/simg", "project_page": "", "abstract": "Molecular representation is a foundational element in our understanding of the physical world. Its importance ranges from the fundamentals of chemical reactions to the design of new therapies and materials. Previous molecular machine learning models have employed strings, fingerprints, global features, and simple molecular graphs that are inherently information-sparse representations. However, as the complexity of prediction tasks increases, the molecular representation needs to encode higher fidelity information. This work introduces a novel approach to infusing quantum-chemical-rich information into molecular graphs via stereoelectronic effects. We show that the explicit addition of stereoelectronic interactions significantly improves the performance of molecular machine learning models. Furthermore, stereoelectronics-infused representations can be learned and deployed with a tailored double graph neural network workflow, enabling its application to any downstream molecular machine learning task. Finally, we show that the learned representations allow for facile stereoelectronic evaluation of previously intractable systems, such as entire proteins, opening new avenues of molecular design.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gomesgroup/simg", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-09", "arxiv_id": "2408.02816", "title": "Learning to Predict Program Execution by Modeling Dynamic Dependency on Code Graphs", "authors": [ "Cuong Chi Le", "Hoang Nhat Phan", "Huy Nhat Phan", "Tien N. Nguyen", "Nghi D. Q. Bui" ], "github": "", "project_page": "", "abstract": "Predicting program behavior without execution is an essential and challenging task in software engineering. Traditional models often struggle to capture dynamic dependencies and interactions within code. This paper introduces a novel machine learning-based framework called CodeFlowrepresents, which predicts code coverage and detects runtime errors through Dynamic Dependencies Learning. Utilizing control flow graphs (CFGs), CodeFlowrepresents all possible execution paths and the relationships between different statements, offering a comprehensive understanding of program behavior. It constructs CFGs to depict execution paths and learns vector representations for CFG nodes, capturing static control-flow dependencies. Additionally, it learns dynamic dependencies through execution traces, which reflect the impacts among statements during execution. This approach enables accurate prediction of code coverage and identification of runtime errors. Empirical evaluations show significant improvements in code coverage prediction accuracy and effective localization of runtime errors, surpassing current models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-09", "arxiv_id": "2407.18245", "title": "VGGHeads: A Large-Scale Synthetic Dataset for 3D Human Heads", "authors": [ "Orest Kupyn", "Eugene Khvedchenia", "Christian Rupprecht" ], "github": "https://github.com/kupynorest/head_detector", "project_page": "", "abstract": "Human head detection, keypoint estimation, and 3D head model fitting are important tasks with many applications. However, traditional real-world datasets often suffer from bias, privacy, and ethical concerns, and they have been recorded in laboratory environments, which makes it difficult for trained models to generalize. Here, we introduce VGGHeads -- a large scale synthetic dataset generated with diffusion models for human head detection and 3D mesh estimation. Our dataset comprises over 1 million high-resolution images, each annotated with detailed 3D head meshes, facial landmarks, and bounding boxes. Using this dataset we introduce a new model architecture capable of simultaneous heads detection and head meshes reconstruction from a single image in a single step. Through extensive experimental evaluations, we demonstrate that models trained on our synthetic data achieve strong performance on real images. Furthermore, the versatility of our dataset makes it applicable across a broad spectrum of tasks, offering a general and comprehensive representation of human heads. Additionally, we provide detailed information about the synthetic data generation pipeline, enabling it to be re-used for other tasks and domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kupynorest/head_detector", "github_api_added_by": "auto" }, { "date": "2024-08-09", "arxiv_id": "2406.04604", "title": "Learning Task Decomposition to Assist Humans in Competitive Programming", "authors": [ "Jiaxin Wen", "Ruiqi Zhong", "Pei Ke", "Zhihong Shao", "Hongning Wang", "Minlie Huang" ], "github": "", "project_page": "", "abstract": "When using language models (LMs) to solve complex problems, humans might struggle to understand the LM-generated solutions and repair the flawed ones. To assist humans in repairing them, we propose to automatically decompose complex solutions into multiple simpler pieces that correspond to specific subtasks. We introduce a novel objective for learning task decomposition, termed assistive value (AssistV), which measures the feasibility and speed for humans to repair the decomposed solution. We collect a dataset of human repair experiences on different decomposed solutions. Utilizing the collected data as in-context examples, we then learn to critique, refine, and rank decomposed solutions to improve AssistV. We validate our method under competitive programming problems: under 177 hours of human study, our method enables non-experts to solve 33.3\\% more problems, speeds them up by 3.3x, and empowers them to match unassisted experts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-12", "arxiv_id": "2408.05211", "title": "VITA: Towards Open-Source Interactive Omni Multimodal LLM", "authors": [ "Chaoyou Fu", "Haojia Lin", "Zuwei Long", "Yunhang Shen", "Meng Zhao", "Yifan Zhang", "Xiong Wang", "Di Yin", "Long Ma", "Xiawu Zheng", "Ran He", "Rongrong Ji", "Yunsheng Wu", "Caifeng Shan", "Xing Sun" ], "github": "", "project_page": "", "abstract": "The remarkable multimodal capabilities and interactive experience of GPT-4o underscore their necessity in practical applications, yet open-source models rarely excel in both areas. In this paper, we introduce VITA, the first-ever open-source Multimodal Large Language Model (MLLM) adept at simultaneous processing and analysis of Video, Image, Text, and Audio modalities, and meanwhile has an advanced multimodal interactive experience. Starting from Mixtral 8x7B as a language foundation, we expand its Chinese vocabulary followed by bilingual instruction tuning. We further endow the language model with visual and audio capabilities through two-stage multi-task learning of multimodal alignment and instruction tuning. VITA demonstrates robust foundational capabilities of multilingual, vision, and audio understanding, as evidenced by its strong performance across a range of both unimodal and multimodal benchmarks. Beyond foundational capabilities, we have made considerable progress in enhancing the natural multimodal human-computer interaction experience. To the best of our knowledge, we are the first to exploit non-awakening interaction and audio interrupt in MLLM. VITA is the first step for the open-source community to explore the seamless integration of multimodal understanding and interaction. While there is still lots of work to be done on VITA to get close to close-source counterparts, we hope that its role as a pioneer can serve as a cornerstone for subsequent research. Project Page: https://vita-home.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-12", "arxiv_id": "2408.04810", "title": "UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling", "authors": [ "Haider Al-Tahan", "Quentin Garrido", "Randall Balestriero", "Diane Bouchacourt", "Caner Hazirbas", "Mark Ibrahim" ], "github": "https://github.com/facebookresearch/unibench", "project_page": "", "abstract": "Significant research efforts have been made to scale and improve vision-language model (VLM) training approaches. Yet, with an ever-growing number of benchmarks, researchers are tasked with the heavy burden of implementing each protocol, bearing a non-trivial computational cost, and making sense of how all these benchmarks translate into meaningful axes of progress. To facilitate a systematic evaluation of VLM progress, we introduce UniBench: a unified implementation of 50+ VLM benchmarks spanning a comprehensive range of carefully categorized capabilities from object recognition to spatial awareness, counting, and much more. We showcase the utility of UniBench for measuring progress by evaluating nearly 60 publicly available vision-language models, trained on scales of up to 12.8B samples. We find that while scaling training data or model size can boost many vision-language model capabilities, scaling offers little benefit for reasoning or relations. Surprisingly, we also discover today's best VLMs struggle on simple digit recognition and counting tasks, e.g. MNIST, which much simpler networks can solve. Where scale falls short, we find that more precise interventions, such as data quality or tailored-learning objectives offer more promise. For practitioners, we also offer guidance on selecting a suitable VLM for a given application. Finally, we release an easy-to-run UniBench code-base with the full set of 50+ benchmarks and comparisons across 59 models as well as a distilled, representative set of benchmarks that runs in 5 minutes on a single GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/unibench", "github_api_added_by": "auto" }, { "date": "2024-08-12", "arxiv_id": "2408.05205", "title": "Kalman-Inspired Feature Propagation for Video Face Super-Resolution", "authors": [ "Ruicheng Feng", "Chongyi Li", "Chen Change Loy" ], "github": "", "project_page": "", "abstract": "Despite the promising progress of face image super-resolution, video face super-resolution remains relatively under-explored. Existing approaches either adapt general video super-resolution networks to face datasets or apply established face image super-resolution models independently on individual video frames. These paradigms encounter challenges either in reconstructing facial details or maintaining temporal consistency. To address these issues, we introduce a novel framework called Kalman-inspired Feature Propagation (KEEP), designed to maintain a stable face prior over time. The Kalman filtering principles offer our method a recurrent ability to use the information from previously restored frames to guide and regulate the restoration process of the current frame. Extensive experiments demonstrate the effectiveness of our method in capturing facial details consistently across video frames. Code and video demo are available at https://jnjaby.github.io/projects/KEEP.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-12", "arxiv_id": "2408.05147", "title": "Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2", "authors": [ "Tom Lieberum", "Senthooran Rajamanoharan", "Arthur Conmy", "Lewis Smith", "Nicolas Sonnerat", "Vikrant Varma", "J\u00e1nos Kram\u00e1r", "Anca Dragan", "Rohin Shah", "Neel Nanda" ], "github": "https://github.com/google-deepmind/mishax", "project_page": "", "abstract": "Sparse autoencoders (SAEs) are an unsupervised method for learning a sparse decomposition of a neural network's latent representations into seemingly interpretable features. Despite recent excitement about their potential, research applications outside of industry are limited by the high cost of training a comprehensive suite of SAEs. In this work, we introduce Gemma Scope, an open suite of JumpReLU SAEs trained on all layers and sub-layers of Gemma 2 2B and 9B and select layers of Gemma 2 27B base models. We primarily train SAEs on the Gemma 2 pre-trained models, but additionally release SAEs trained on instruction-tuned Gemma 2 9B for comparison. We evaluate the quality of each SAE on standard metrics and release these results. We hope that by releasing these SAE weights, we can help make more ambitious safety and interpretability research easier for the community. Weights and a tutorial can be found at https://huggingface.co/google/gemma-scope and an interactive demo can be found at https://www.neuronpedia.org/gemma-scope", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/mishax", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-12", "arxiv_id": "2408.04708", "title": "MulliVC: Multi-lingual Voice Conversion With Cycle Consistency", "authors": [ "Jiawei Huang", "Chen Zhang", "Yi Ren", "Ziyue Jiang", "Zhenhui Ye", "Jinglin Liu", "Jinzheng He", "Xiang Yin", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "Voice conversion aims to modify the source speaker's voice to resemble the target speaker while preserving the original speech content. Despite notable advancements in voice conversion these days, multi-lingual voice conversion (including both monolingual and cross-lingual scenarios) has yet to be extensively studied. It faces two main challenges: 1) the considerable variability in prosody and articulation habits across languages; and 2) the rarity of paired multi-lingual datasets from the same speaker. In this paper, we propose MulliVC, a novel voice conversion system that only converts timbre and keeps original content and source language prosody without multi-lingual paired data. Specifically, each training step of MulliVC contains three substeps: In step one the model is trained with monolingual speech data; then, steps two and three take inspiration from back translation, construct a cyclical process to disentangle the timbre and other information (content, prosody, and other language-related information) in the absence of multi-lingual data from the same speaker. Both objective and subjective results indicate that MulliVC significantly surpasses other methods in both monolingual and cross-lingual contexts, demonstrating the system's efficacy and the viability of the three-step approach with cycle consistency. Audio samples can be found on our demo page (mullivc.github.io).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-12", "arxiv_id": "2408.04682", "title": "ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities", "authors": [ "Jiarui Lu", "Thomas Holleis", "Yizhe Zhang", "Bernhard Aumayer", "Feng Nan", "Felix Bai", "Shuang Ma", "Shen Ma", "Mengyu Li", "Guoli Yin", "Zirui Wang", "Ruoming Pang" ], "github": "https://github.com/apple/ToolSandbox", "project_page": "", "abstract": "Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation of tool-use capabilities. While previous works focused on either evaluating over stateless web services (RESTful API), based on a single turn user prompt, or an off-policy dialog trajectory, ToolSandbox includes stateful tool execution, implicit state dependencies between tools, a built-in user simulator supporting on-policy conversational evaluation and a dynamic evaluation strategy for intermediate and final milestones over an arbitrary trajectory. We show that open source and proprietary models have a significant performance gap, and complex tasks like State Dependency, Canonicalization and Insufficient Information defined in ToolSandbox are challenging even the most capable SOTA LLMs, providing brand-new insights into tool-use LLM capabilities. ToolSandbox evaluation framework is released at https://github.com/apple/ToolSandbox", "github_manual": "", "github_regex": "https://github.com/apple/ToolSandbox", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-12", "arxiv_id": "2408.04840", "title": "mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models", "authors": [ "Jiabo Ye", "Haiyang Xu", "Haowei Liu", "Anwen Hu", "Ming Yan", "Qi Qian", "Ji Zhang", "Fei Huang", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in executing instructions for a variety of single-image tasks. Despite this progress, significant challenges remain in modeling long image sequences. In this work, we introduce the versatile multi-modal large language model, mPLUG-Owl3, which enhances the capability for long image-sequence understanding in scenarios that incorporate retrieved image-text knowledge, interleaved image-text, and lengthy videos. Specifically, we propose novel hyper attention blocks to efficiently integrate vision and language into a common language-guided semantic space, thereby facilitating the processing of extended multi-image scenarios. Extensive experimental results suggest that mPLUG-Owl3 achieves state-of-the-art performance among models with a similar size on single-image, multi-image, and video benchmarks. Moreover, we propose a challenging long visual sequence evaluation named Distractor Resistance to assess the ability of models to maintain focus amidst distractions. Finally, with the proposed architecture, mPLUG-Owl3 demonstrates outstanding performance on ultra-long visual sequence inputs. We hope that mPLUG-Owl3 can contribute to the development of more efficient and powerful multimodal large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-12", "arxiv_id": "2408.04785", "title": "BRAT: Bonus oRthogonAl Token for Architecture Agnostic Textual Inversion", "authors": [ "James Baker" ], "github": "https://github.com/jamesBaker361/tex_inv_plus", "project_page": "", "abstract": "Textual Inversion remains a popular method for personalizing diffusion models, in order to teach models new subjects and styles. We note that textual inversion has been underexplored using alternatives to the UNet, and experiment with textual inversion with a vision transformer. We also seek to optimize textual inversion using a strategy that does not require explicit use of the UNet and its idiosyncratic layers, so we add bonus tokens and enforce orthogonality. We find the use of the bonus token improves adherence to the source images and the use of the vision transformer improves adherence to the prompt. Code is available at https://github.com/jamesBaker361/tex_inv_plus.", "github_manual": "", "github_regex": "https://github.com/jamesBaker361/tex_inv_plus", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-12", "arxiv_id": "2408.05086", "title": "Generating novel experimental hypotheses from language models: A case study on cross-dative generalization", "authors": [ "Kanishka Misra", "Najoung Kim" ], "github": "https://github.com/kanishkamisra/encouraging-exposures", "project_page": "", "abstract": "Neural network language models (LMs) have been shown to successfully capture complex linguistic knowledge. However, their utility for understanding language acquisition is still debated. We contribute to this debate by presenting a case study where we use LMs as simulated learners to derive novel experimental hypotheses to be tested with humans. We apply this paradigm to study cross-dative generalization (CDG): productive generalization of novel verbs across dative constructions (she pilked me the ball/she pilked the ball to me) -- acquisition of which is known to involve a large space of contextual features -- using LMs trained on child-directed speech. We specifically ask: \"what properties of the training exposure facilitate a novel verb's generalization to the (unmodeled) alternate construction?\" To answer this, we systematically vary the exposure context in which a novel dative verb occurs in terms of the properties of the theme and recipient, and then analyze the LMs' usage of the novel verb in the unmodeled dative construction. We find LMs to replicate known patterns of children's CDG, as a precondition to exploring novel hypotheses. Subsequent simulations reveal a nuanced role of the features of the novel verbs' exposure context on the LMs' CDG. We find CDG to be facilitated when the first postverbal argument of the exposure context is pronominal, definite, short, and conforms to the prototypical animacy expectations of the exposure dative. These patterns are characteristic of harmonic alignment in datives, where the argument with features ranking higher on the discourse prominence scale tends to precede the other. This gives rise to a novel hypothesis that CDG is facilitated insofar as the features of the exposure context -- in particular, its first postverbal argument -- are harmonically aligned. We conclude by proposing future experiments that can test this hypothesis in children.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kanishkamisra/encouraging-exposures", "github_api_added_by": "auto" }, { "date": "2024-08-12", "arxiv_id": "2408.05101", "title": "MooER: LLM-based Speech Recognition and Translation Models from Moore Threads", "authors": [ "Junhao Xu", "Zhenlin Liang", "Yi Liu", "Yichao Hu", "Jian Li", "Yajun Zheng", "Meng Cai", "Hua Wang" ], "github": "https://github.com/moorethreads/mooer", "project_page": "", "abstract": "In this paper, we present MooER, a LLM-based large-scale automatic speech recognition (ASR) / automatic speech translation (AST) model of Moore Threads. A 5000h pseudo labeled dataset containing open source and self collected speech data is used for training. We achieve performance comparable to other open source models trained with up to hundreds of thousands of hours of labeled speech data. Meanwhile, experiments conducted on Covost2 Zh2en testset suggest that our model outperforms other open source Speech LLMs. A BLEU score of 25.2 can be obtained. The main contributions of this paper are summarized as follows. First, this paper presents a training strategy for encoders and LLMs on speech related tasks (including ASR and AST) using a small size of pseudo labeled data without any extra manual annotation and selection. Second, we release our ASR and AST models and plan to open-source our training code and strategy in the near future. Moreover, a model trained on 8wh scale training data is planned to be released later on.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/moorethreads/mooer", "github_api_added_by": "auto" }, { "date": "2024-08-13", "arxiv_id": "2408.06292", "title": "The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery", "authors": [ "Chris Lu", "Cong Lu", "Robert Tjarko Lange", "Jakob Foerster", "Jeff Clune", "David Ha" ], "github": "https://github.com/SakanaAI/AI-Scientist", "project_page": "", "abstract": "One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used as aids to human scientists, e.g. for brainstorming ideas, writing code, or prediction tasks, they still conduct only a small part of the scientific process. This paper presents the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models to perform research independently and communicate their findings. We introduce The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a full scientific paper, and then runs a simulated review process for evaluation. In principle, this process can be repeated to iteratively develop ideas in an open-ended fashion, acting like the human scientific community. We demonstrate its versatility by applying it to three distinct subfields of machine learning: diffusion modeling, transformer-based language modeling, and learning dynamics. Each idea is implemented and developed into a full paper at a cost of less than $15 per paper. To evaluate the generated papers, we design and validate an automated reviewer, which we show achieves near-human performance in evaluating paper scores. The AI Scientist can produce papers that exceed the acceptance threshold at a top machine learning conference as judged by our automated reviewer. This approach signifies the beginning of a new era in scientific discovery in machine learning: bringing the transformative benefits of AI agents to the entire research process of AI itself, and taking us closer to a world where endless affordable creativity and innovation can be unleashed on the world's most challenging problems. Our code is open-sourced at https://github.com/SakanaAI/AI-Scientist", "github_manual": "", "github_regex": "https://github.com/SakanaAI/AI-Scientist", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-13", "arxiv_id": "2408.06070", "title": "ControlNeXt: Powerful and Efficient Control for Image and Video Generation", "authors": [ "Bohao Peng", "Jian Wang", "Yuechen Zhang", "Wenbo Li", "Ming-Chang Yang", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/controlnext", "project_page": "", "abstract": "Diffusion models have demonstrated remarkable and robust abilities in both image and video generation. To achieve greater control over generated results, researchers introduce additional architectures, such as ControlNet, Adapters and ReferenceNet, to integrate conditioning controls. However, current controllable generation methods often require substantial additional computational resources, especially for video generation, and face challenges in training or exhibit weak control. In this paper, we propose ControlNeXt: a powerful and efficient method for controllable image and video generation. We first design a more straightforward and efficient architecture, replacing heavy additional branches with minimal additional cost compared to the base model. Such a concise structure also allows our method to seamlessly integrate with other LoRA weights, enabling style alteration without the need for additional training. As for training, we reduce up to 90% of learnable parameters compared to the alternatives. Furthermore, we propose another method called Cross Normalization (CN) as a replacement for Zero-Convolution' to achieve fast and stable training convergence. We have conducted various experiments with different base models across images and videos, demonstrating the robustness of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dvlab-research/controlnext", "github_api_added_by": "auto" }, { "date": "2024-08-13", "arxiv_id": "2408.05939", "title": "UniPortrait: A Unified Framework for Identity-Preserving Single- and Multi-Human Image Personalization", "authors": [ "Junjie He", "Yifeng Geng", "Liefeng Bo" ], "github": "https://github.com/junjiehe96/UniPortrait", "project_page": "", "abstract": "This paper presents UniPortrait, an innovative human image personalization framework that unifies single- and multi-ID customization with high face fidelity, extensive facial editability, free-form input description, and diverse layout generation. UniPortrait consists of only two plug-and-play modules: an ID embedding module and an ID routing module. The ID embedding module extracts versatile editable facial features with a decoupling strategy for each ID and embeds them into the context space of diffusion models. The ID routing module then combines and distributes these embeddings adaptively to their respective regions within the synthesized image, achieving the customization of single and multiple IDs. With a carefully designed two-stage training scheme, UniPortrait achieves superior performance in both single- and multi-ID customization. Quantitative and qualitative experiments demonstrate the advantages of our method over existing approaches as well as its good scalability, e.g., the universal compatibility with existing generative control tools. The project page is at https://aigcdesigngroup.github.io/UniPortrait-Page/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/junjiehe96/UniPortrait", "github_api_added_by": "auto" }, { "date": "2024-08-13", "arxiv_id": "2408.06190", "title": "FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework", "authors": [ "Lukas Meyer", "Andreas Gilson", "Ute Schmidt", "Marc Stamminger" ], "github": "", "project_page": "", "abstract": "We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count.The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit.We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mango.Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-13", "arxiv_id": "2408.06072", "title": "CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer", "authors": [ "Zhuoyi Yang", "Jiayan Teng", "Wendi Zheng", "Ming Ding", "Shiyu Huang", "Jiazheng Xu", "Yuanming Yang", "Wenyi Hong", "Xiaohan Zhang", "Guanyu Feng", "Da Yin", "Xiaotao Gu", "Yuxuan Zhang", "Weihan Wang", "Yean Cheng", "Ting Liu", "Bin Xu", "Yuxiao Dong", "Jie Tang" ], "github": "https://github.com/THUDM/CogVideo", "project_page": "", "abstract": "We introduce CogVideoX, a large-scale diffusion transformer model designed for generating videos based on text prompts. To efficently model video data, we propose to levearge a 3D Variational Autoencoder (VAE) to compress videos along both spatial and temporal dimensions. To improve the text-video alignment, we propose an expert transformer with the expert adaptive LayerNorm to facilitate the deep fusion between the two modalities. By employing a progressive training technique, CogVideoX is adept at producing coherent, long-duration videos characterized by significant motions. In addition, we develop an effective text-video data processing pipeline that includes various data preprocessing strategies and a video captioning method. It significantly helps enhance the performance of CogVideoX, improving both generation quality and semantic alignment. Results show that CogVideoX demonstrates state-of-the-art performance across both multiple machine metrics and human evaluations. The model weights of both the 3D Causal VAE and CogVideoX are publicly available at https://github.com/THUDM/CogVideo.", "github_manual": "", "github_regex": "https://github.com/THUDM/CogVideo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-13", "arxiv_id": "2408.06316", "title": "Body Transformer: Leveraging Robot Embodiment for Policy Learning", "authors": [ "Carmelo Sferrazza", "Dun-Ming Huang", "Fangchen Liu", "Jongmin Lee", "Pieter Abbeel" ], "github": "https://github.com/carlosferrazza/BodyTransformer", "project_page": "", "abstract": "In recent years, the transformer architecture has become the de facto standard for machine learning algorithms applied to natural language processing and computer vision. Despite notable evidence of successful deployment of this architecture in the context of robot learning, we claim that vanilla transformers do not fully exploit the structure of the robot learning problem. Therefore, we propose Body Transformer (BoT), an architecture that leverages the robot embodiment by providing an inductive bias that guides the learning process. We represent the robot body as a graph of sensors and actuators, and rely on masked attention to pool information throughout the architecture. The resulting architecture outperforms the vanilla transformer, as well as the classical multilayer perceptron, in terms of task completion, scaling properties, and computational efficiency when representing either imitation or reinforcement learning policies. Additional material including the open-source code is available at https://sferrazza.cc/bot_site.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/carlosferrazza/BodyTransformer", "github_api_added_by": "auto" }, { "date": "2024-08-13", "arxiv_id": "2408.06195", "title": "Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers", "authors": [ "Zhenting Qi", "Mingyuan Ma", "Jiahang Xu", "Li Lyna Zhang", "Fan Yang", "Mao Yang" ], "github": "https://github.com/zhentingqi/rStar", "project_page": "", "abstract": "This paper introduces rStar, a self-play mutual reasoning approach that significantly improves reasoning capabilities of small language models (SLMs) without fine-tuning or superior models. rStar decouples reasoning into a self-play mutual generation-discrimination process. First, a target SLM augments the Monte Carlo Tree Search (MCTS) with a rich set of human-like reasoning actions to construct higher quality reasoning trajectories. Next, another SLM, with capabilities similar to the target SLM, acts as a discriminator to verify each trajectory generated by the target SLM. The mutually agreed reasoning trajectories are considered mutual consistent, thus are more likely to be correct. Extensive experiments across five SLMs demonstrate rStar can effectively solve diverse reasoning problems, including GSM8K, GSM-Hard, MATH, SVAMP, and StrategyQA. Remarkably, rStar boosts GSM8K accuracy from 12.51% to 63.91% for LLaMA2-7B, from 36.46% to 81.88% for Mistral-7B, from 74.53% to 91.13% for LLaMA3-8B-Instruct. Code will be available at https://github.com/zhentingqi/rStar.", "github_manual": "", "github_regex": "https://github.com/zhentingqi/rStar", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-13", "arxiv_id": "2408.06019", "title": "HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors", "authors": [ "Xiaozheng Zheng", "Chao Wen", "Zhaohu Li", "Weiyi Zhang", "Zhuo Su", "Xu Chang", "Yang Zhao", "Zheng Lv", "Xiaoyuan Zhang", "Yongjie Zhang", "Guidong Wang", "Lan Xu" ], "github": "", "project_page": "", "abstract": "In this paper, we present a novel 3D head avatar creation approach capable of generalizing from few-shot in-the-wild data with high-fidelity and animatable robustness. Given the underconstrained nature of this problem, incorporating prior knowledge is essential. Therefore, we propose a framework comprising prior learning and avatar creation phases. The prior learning phase leverages 3D head priors derived from a large-scale multi-view dynamic dataset, and the avatar creation phase applies these priors for few-shot personalization. Our approach effectively captures these priors by utilizing a Gaussian Splatting-based auto-decoder network with part-based dynamic modeling. Our method employs identity-shared encoding with personalized latent codes for individual identities to learn the attributes of Gaussian primitives. During the avatar creation phase, we achieve fast head avatar personalization by leveraging inversion and fine-tuning strategies. Extensive experiments demonstrate that our model effectively exploits head priors and successfully generalizes them to few-shot personalization, achieving photo-realistic rendering quality, multi-view consistency, and stable animation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-13", "arxiv_id": "2408.06327", "title": "VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents", "authors": [ "Xiao Liu", "Tianjie Zhang", "Yu Gu", "Iat Long Iong", "Yifan Xu", "Xixuan Song", "Shudan Zhang", "Hanyu Lai", "Xinyi Liu", "Hanlin Zhao", "Jiadai Sun", "Xinyue Yang", "Yu Yang", "Zehan Qi", "Shuntian Yao", "Xueqiao Sun", "Siyi Cheng", "Qinkai Zheng", "Hao Yu", "Hanchen Zhang", "Wenyi Hong", "Ming Ding", "Lihang Pan", "Xiaotao Gu", "Aohan Zeng", "Zhengxiao Du", "Chan Hee Song", "Yu Su", "Yuxiao Dong", "Jie Tang" ], "github": "https://github.com/THUDM/VisualAgentBench", "project_page": "", "abstract": "Large Multimodal Models (LMMs) have ushered in a new era in artificial intelligence, merging capabilities in both language and vision to form highly capable Visual Foundation Agents. These agents are postulated to excel across a myriad of tasks, potentially approaching general artificial intelligence. However, existing benchmarks fail to sufficiently challenge or showcase the full potential of LMMs in complex, real-world environments. To address this gap, we introduce VisualAgentBench (VAB), a comprehensive and pioneering benchmark specifically designed to train and evaluate LMMs as visual foundation agents across diverse scenarios, including Embodied, Graphical User Interface, and Visual Design, with tasks formulated to probe the depth of LMMs' understanding and interaction capabilities. Through rigorous testing across nine proprietary LMM APIs and eight open models, we demonstrate the considerable yet still developing agent capabilities of these models. Additionally, VAB constructs a trajectory training set constructed through hybrid methods including Program-based Solvers, LMM Agent Bootstrapping, and Human Demonstrations, promoting substantial performance improvements in LMMs through behavior cloning. Our work not only aims to benchmark existing models but also provides a solid foundation for future development into visual foundation agents. Code, train \\& test data, and part of fine-tuned open LMMs are available at https://github.com/THUDM/VisualAgentBench.", "github_manual": "", "github_regex": "https://github.com/THUDM/VisualAgentBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-13", "arxiv_id": "2408.06142", "title": "Med42-v2: A Suite of Clinical LLMs", "authors": [ "Cl\u00e9ment Christophe", "Praveen K Kanithi", "Tathagata Raha", "Shadab Khan", "Marco AF Pimentel" ], "github": "", "project_page": "", "abstract": "Med42-v2 introduces a suite of clinical large language models (LLMs) designed to address the limitations of generic models in healthcare settings. These models are built on Llama3 architecture and fine-tuned using specialized clinical data. They underwent multi-stage preference alignment to effectively respond to natural prompts. While generic models are often preference-aligned to avoid answering clinical queries as a precaution, Med42-v2 is specifically trained to overcome this limitation, enabling its use in clinical settings. Med42-v2 models demonstrate superior performance compared to the original Llama3 models in both 8B and 70B parameter configurations and GPT-4 across various medical benchmarks. These LLMs are developed to understand clinical queries, perform reasoning tasks, and provide valuable assistance in clinical environments. The models are now publicly available at https://huggingface.co/m42-health{https://huggingface.co/m42-health}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-13", "arxiv_id": "2408.05506", "title": "Your Context Is Not an Array: Unveiling Random Access Limitations in Transformers", "authors": [ "MohammadReza Ebrahimi", "Sunny Panchal", "Roland Memisevic" ], "github": "", "project_page": "", "abstract": "Despite their recent successes, Transformer-based large language models show surprising failure modes. A well-known example of such failure modes is their inability to length-generalize: solving problem instances at inference time that are longer than those seen during training. In this work, we further explore the root cause of this failure by performing a detailed analysis of model behaviors on the simple parity task. Our analysis suggests that length generalization failures are intricately related to a model's inability to perform random memory accesses within its context window. We present supporting evidence for this hypothesis by demonstrating the effectiveness of methodologies that circumvent the need for indexing or that enable random token access indirectly, through content-based addressing. We further show where and how the failure to perform random memory access manifests through attention map visualizations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.07009", "title": "Imagen 3", "authors": [ "Imagen-Team-Google", "Jason Baldridge", "Jakob Bauer", "Mukul Bhutani", "Nicole Brichtova", "Andrew Bunner", "Kelvin Chan", "Yichang Chen", "Sander Dieleman", "Yuqing Du", "Zach Eaton-Rosen", "Hongliang Fei", "Nando de Freitas", "Yilin Gao", "Evgeny Gladchenko", "Sergio G\u00f3mez Colmenarejo", "Mandy Guo", "Alex Haig", "Will Hawkins", "Hexiang Hu", "Huilian Huang", "Tobenna Peter Igwe", "Christos Kaplanis", "Siavash Khodadadeh", "Yelin Kim", "Ksenia Konyushkova", "Karol Langner", "Eric Lau", "Shixin Luo", "So\u0148a Mokr\u00e1", "Henna Nandwani", "Yasumasa Onoe", "A\u00e4ron van den Oord", "Zarana Parekh", "Jordi Pont-Tuset", "Hang Qi", "Rui Qian", "Deepak Ramachandran", "Poorva Rane", "Abdullah Rashwan", "Ali Razavi", "Robert Riachi", "Hansa Srinivasan", "Srivatsan Srinivasan", "Robin Strudel", "Benigno Uria", "Oliver Wang", "Su Wang", "Austin Waters", "Chris Wolff", "Auriel Wright", "Zhisheng Xiao", "Hao Xiong", "Keyang Xu", "Marc van Zee", "Junlin Zhang", "Katie Zhang", "Wenlei Zhou", "Konrad Zolna", "Ola Aboubakar", "Canfer Akbulut", "Oscar Akerlund", "Isabela Albuquerque", "Nina Anderson", "Marco Andreetto", "Lora Aroyo", "Ben Bariach", "David Barker", "Sherry Ben", "Dana Berman", "Courtney Biles", "Irina Blok", "Pankil Botadra", "Jenny Brennan", "Karla Brown", "John Buckley", "Rudy Bunel", "Elie Bursztein", "Christina Butterfield", "Ben Caine", "Viral Carpenter", "Norman Casagrande", "Ming-Wei Chang", "Solomon Chang", "Shamik Chaudhuri", "Tony Chen", "John Choi", "Dmitry Churbanau", "Nathan Clement", "Matan Cohen", "Forrester Cole", "Mikhail Dektiarev", "Vincent Du", "Praneet Dutta", "Tom Eccles", "Ndidi Elue", "Ashley Feden", "Shlomi Fruchter", "Frankie Garcia", "Roopal Garg", "Weina Ge", "Ahmed Ghazy", "Bryant Gipson", "Andrew Goodman", "Dawid G\u00f3rny", "Sven Gowal", "Khyatti Gupta", "Yoni Halpern", "Yena Han", "Susan Hao", "Jamie Hayes", "Amir Hertz", "Ed Hirst", "Tingbo Hou", "Heidi Howard", "Mohamed Ibrahim", "Dirichi Ike-Njoku", "Joana Iljazi", "Vlad Ionescu", "William Isaac", "Reena Jana", "Gemma Jennings", "Donovon Jenson", "Xuhui Jia", "Kerry Jones", "Xiaoen Ju", "Ivana Kajic", "Christos Kaplanis", "Burcu Karagol Ayan", "Jacob Kelly", "Suraj Kothawade", "Christina Kouridi", "Ira Ktena", "Jolanda Kumakaw", "Dana Kurniawan", "Dmitry Lagun", "Lily Lavitas", "Jason Lee", "Tao Li", "Marco Liang", "Maggie Li-Calis", "Yuchi Liu", "Javier Lopez Alberca", "Peggy Lu", "Kristian Lum", "Yukun Ma", "Chase Malik", "John Mellor", "Inbar Mosseri", "Tom Murray", "Aida Nematzadeh", "Paul Nicholas", "Jo\u00e3o Gabriel Oliveira", "Guillermo Ortiz-Jimenez", "Michela Paganini", "Tom Le Paine", "Roni Paiss", "Alicia Parrish", "Anne Peckham", "Vikas Peswani", "Igor Petrovski", "Tobias Pfaff", "Alex Pirozhenko", "Ryan Poplin", "Utsav Prabhu", "Yuan Qi", "Matthew Rahtz", "Cyrus Rashtchian", "Charvi Rastogi", "Amit Raul", "Ali Razavi", "Sylvestre-Alvise Rebuffi", "Susanna Ricco", "Felix Riedel", "Dirk Robinson", "Pankaj Rohatgi", "Bill Rosgen", "Sarah Rumbley", "Moonkyung Ryu", "Anthony Salgado", "Sahil Singla", "Florian Schroff", "Candice Schumann", "Tanmay Shah", "Brendan Shillingford", "Kaushik Shivakumar", "Dennis Shtatnov", "Zach Singer", "Evgeny Sluzhaev", "Valerii Sokolov", "Thibault Sottiaux", "Florian Stimberg", "Brad Stone", "David Stutz", "Yu-Chuan Su", "Eric Tabellion", "Shuai Tang", "David Tao", "Kurt Thomas", "Gregory Thornton", "Andeep Toor", "Cristian Udrescu", "Aayush Upadhyay", "Cristina Vasconcelos", "Alex Vasiloff", "Andrey Voynov", "Amanda Walker", "Luyu Wang", "Miaosen Wang", "Simon Wang", "Stanley Wang", "Qifei Wang", "Yuxiao Wang", "\u00c1goston Weisz", "Olivia Wiles", "Chenxia Wu", "Xingyu Federico Xu", "Andrew Xue", "Jianbo Yang", "Luo Yu", "Mete Yurtoglu", "Ali Zand", "Han Zhang", "Jiageng Zhang", "Catherine Zhao", "Adilet Zhaxybay", "Miao Zhou", "Shengqi Zhu", "Zhenkai Zhu", "Dawn Bloxwich", "Mahyar Bordbar", "Luis C. Cobo", "Eli Collins", "Shengyang Dai", "Tulsee Doshi", "Anca Dragan", "Douglas Eck", "Demis Hassabis", "Sissie Hsiao", "Tom Hume", "Koray Kavukcuoglu", "Helen King", "Jack Krawczyk", "Yeqing Li", "Kathy Meier-Hellstern", "Andras Orban", "Yury Pinsky", "Amar Subramanya", "Oriol Vinyals", "Ting Yu", "Yori Zwols" ], "github": "", "project_page": "", "abstract": "We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.07055", "title": "LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs", "authors": [ "Yushi Bai", "Jiajie Zhang", "Xin Lv", "Linzhi Zheng", "Siqi Zhu", "Lei Hou", "Yuxiao Dong", "Jie Tang", "Juanzi Li" ], "github": "https://github.com/THUDM/LongWriter", "project_page": "", "abstract": "Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through controlled experiments, we find that the model's effective generation length is inherently bounded by the sample it has seen during supervised fine-tuning (SFT). In other words, their output limitation is due to the scarcity of long-output examples in existing SFT datasets. To address this, we introduce AgentWrite, an agent-based pipeline that decomposes ultra-long generation tasks into subtasks, enabling off-the-shelf LLMs to generate coherent outputs exceeding 20,000 words. Leveraging AgentWrite, we construct LongWriter-6k, a dataset containing 6,000 SFT data with output lengths ranging from 2k to 32k words. By incorporating this dataset into model training, we successfully scale the output length of existing models to over 10,000 words while maintaining output quality. We also develop LongBench-Write, a comprehensive benchmark for evaluating ultra-long generation capabilities. Our 9B parameter model, further improved through DPO, achieves state-of-the-art performance on this benchmark, surpassing even much larger proprietary models. In general, our work demonstrates that existing long context LLM already possesses the potential for a larger output window--all you need is data with extended output during model alignment to unlock this capability. Our code & models are at: https://github.com/THUDM/LongWriter.", "github_manual": "", "github_regex": "https://github.com/THUDM/LongWriter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.06941", "title": "OpenResearcher: Unleashing AI for Accelerated Scientific Research", "authors": [ "Yuxiang Zheng", "Shichao Sun", "Lin Qiu", "Dongyu Ru", "Cheng Jiayang", "Xuefeng Li", "Jifan Lin", "Binjie Wang", "Yun Luo", "Renjie Pan", "Yang Xu", "Qingkai Min", "Zizhao Zhang", "Yiwen Wang", "Wenjie Li", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/OpenResearcher", "project_page": "", "abstract": "The rapid growth of scientific literature imposes significant challenges for researchers endeavoring to stay updated with the latest advancements in their fields and delve into new areas. We introduce OpenResearcher, an innovative platform that leverages Artificial Intelligence (AI) techniques to accelerate the research process by answering diverse questions from researchers. OpenResearcher is built based on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. Moreover, we develop various tools for OpenResearcher to understand researchers' queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine these answers. OpenResearcher can flexibly use these tools to balance efficiency and effectiveness. As a result, OpenResearcher enables researchers to save time and increase their potential to discover new insights and drive scientific breakthroughs. Demo, video, and code are available at: https://github.com/GAIR-NLP/OpenResearcher.", "github_manual": "", "github_regex": "https://github.com/GAIR-NLP/OpenResearcher", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.06481", "title": "UniT: Unified Tactile Representation for Robot Learning", "authors": [ "Zhengtong Xu", "Raghava Uppuluri", "Xinwei Zhang", "Cael Fitch", "Philip Glen Crandall", "Wan Shou", "Dongyi Wang", "Yu She" ], "github": "https://github.com/ZhengtongXu/UniT", "project_page": "", "abstract": "UniT is a novel approach to tactile representation learning, using VQVAE to learn a compact latent space and serve as the tactile representation. It uses tactile images obtained from a single simple object to train the representation with transferability and generalizability. This tactile representation can be zero-shot transferred to various downstream tasks, including perception tasks and manipulation policy learning. Our benchmarking on an in-hand 3D pose estimation task shows that UniT outperforms existing visual and tactile representation learning methods. Additionally, UniT's effectiveness in policy learning is demonstrated across three real-world tasks involving diverse manipulated objects and complex robot-object-environment interactions. Through extensive experimentation, UniT is shown to be a simple-to-train, plug-and-play, yet widely effective method for tactile representation learning. For more details, please refer to our open-source repository https://github.com/ZhengtongXu/UniT and the project website https://zhengtongxu.github.io/unifiedtactile.github.io/.", "github_manual": "", "github_regex": "https://github.com/ZhengtongXu/UniT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.06693", "title": "DC3DO: Diffusion Classifier for 3D Objects", "authors": [ "Nursena Koprucu", "Meher Shashwat Nigam", "Shicheng Xu", "Biruk Abere", "Gabriele Dominici", "Andrew Rodriguez", "Sharvaree Vadgam", "Berfin Inal", "Alberto Tono" ], "github": "https://github.com/sgi-2023/3d-building-classification", "project_page": "", "abstract": "Inspired by Geoffrey Hinton emphasis on generative modeling, To recognize shapes, first learn to generate them, we explore the use of 3D diffusion models for object classification. Leveraging the density estimates from these models, our approach, the Diffusion Classifier for 3D Objects (DC3DO), enables zero-shot classification of 3D shapes without additional training. On average, our method achieves a 12.5 percent improvement compared to its multiview counterparts, demonstrating superior multimodal reasoning over discriminative approaches. DC3DO employs a class-conditional diffusion model trained on ShapeNet, and we run inferences on point clouds of chairs and cars. This work highlights the potential of generative models in 3D object classification.", "github_manual": "https://github.com/sgi-2023/3d-building-classification", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.06793", "title": "Layerwise Recurrent Router for Mixture-of-Experts", "authors": [ "Zihan Qiu", "Zeyu Huang", "Shuang Cheng", "Yizhi Zhou", "Zili Wang", "Ivan Titov", "Jie Fu" ], "github": "https://github.com/qiuzh20/RMoE", "project_page": "", "abstract": "The scaling of large language models (LLMs) has revolutionized their capabilities in various tasks, yet this growth must be matched with efficient computational strategies. The Mixture-of-Experts (MoE) architecture stands out for its ability to scale model size without significantly increasing training costs. Despite their advantages, current MoE models often display parameter inefficiency. For instance, a pre-trained MoE-based LLM with 52 billion parameters might perform comparably to a standard model with 6.7 billion parameters. Being a crucial part of MoE, current routers in different layers independently assign tokens without leveraging historical routing information, potentially leading to suboptimal token-expert combinations and the parameter inefficiency problem. To alleviate this issue, we introduce the Layerwise Recurrent Router for Mixture-of-Experts (RMoE). RMoE leverages a Gated Recurrent Unit (GRU) to establish dependencies between routing decisions across consecutive layers. Such layerwise recurrence can be efficiently parallelly computed for input tokens and introduces negotiable costs. Our extensive empirical evaluations demonstrate that RMoE-based language models consistently outperform a spectrum of baseline models. Furthermore, RMoE integrates a novel computation stage orthogonal to existing methods, allowing seamless compatibility with other MoE architectures. Our analyses attribute RMoE's gains to its effective cross-layer information sharing, which also improves expert selection and diversity. Our code is at https://github.com/qiuzh20/RMoE", "github_manual": "", "github_regex": "https://github.com/qiuzh20/RMoE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.06697", "title": "SlotLifter: Slot-guided Feature Lifting for Learning Object-centric Radiance Fields", "authors": [ "Yu Liu", "Baoxiong Jia", "Yixin Chen", "Siyuan Huang" ], "github": "https://github.com/KovenYu/uORF", "project_page": "", "abstract": "The ability to distill object-centric abstractions from intricate visual scenes underpins human-level generalization. Despite the significant progress in object-centric learning methods, learning object-centric representations in the 3D physical world remains a crucial challenge. In this work, we propose SlotLifter, a novel object-centric radiance model addressing scene reconstruction and decomposition jointly via slot-guided feature lifting. Such a design unites object-centric learning representations and image-based rendering methods, offering state-of-the-art performance in scene decomposition and novel-view synthesis on four challenging synthetic and four complex real-world datasets, outperforming existing 3D object-centric learning methods by a large margin. Through extensive ablative studies, we showcase the efficacy of designs in SlotLifter, revealing key insights for potential future directions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KovenYu/uORF", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.06506", "title": "TacSL: A Library for Visuotactile Sensor Simulation and Learning", "authors": [ "Iretiayo Akinola", "Jie Xu", "Jan Carius", "Dieter Fox", "Yashraj Narang" ], "github": "", "project_page": "", "abstract": "For both humans and robots, the sense of touch, known as tactile sensing, is critical for performing contact-rich manipulation tasks. Three key challenges in robotic tactile sensing are 1) interpreting sensor signals, 2) generating sensor signals in novel scenarios, and 3) learning sensor-based policies. For visuotactile sensors, interpretation has been facilitated by their close relationship with vision sensors (e.g., RGB cameras). However, generation is still difficult, as visuotactile sensors typically involve contact, deformation, illumination, and imaging, all of which are expensive to simulate; in turn, policy learning has been challenging, as simulation cannot be leveraged for large-scale data collection. We present TacSL (taxel), a library for GPU-based visuotactile sensor simulation and learning. TacSL can be used to simulate visuotactile images and extract contact-force distributions over 200times faster than the prior state-of-the-art, all within the widely-used Isaac Gym simulator. Furthermore, TacSL provides a learning toolkit containing multiple sensor models, contact-intensive training environments, and online/offline algorithms that can facilitate policy learning for sim-to-real applications. On the algorithmic side, we introduce a novel online reinforcement-learning algorithm called asymmetric actor-critic distillation (\\sysName), designed to effectively and efficiently learn tactile-based policies in simulation that can transfer to the real world. Finally, we demonstrate the utility of our library and algorithms by evaluating the benefits of distillation and multimodal sensing for contact-rich manip ulation tasks, and most critically, performing sim-to-real transfer. Supplementary videos and results are at https://iakinola23.github.io/tacsl/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.06281", "title": "MovieSum: An Abstractive Summarization Dataset for Movie Screenplays", "authors": [ "Rohit Saxena", "Frank Keller" ], "github": "https://github.com/saxenarohit/moviesum", "project_page": "", "abstract": "Movie screenplay summarization is challenging, as it requires an understanding of long input contexts and various elements unique to movies. Large language models have shown significant advancements in document summarization, but they often struggle with processing long input contexts. Furthermore, while television transcripts have received attention in recent studies, movie screenplay summarization remains underexplored. To stimulate research in this area, we present a new dataset, MovieSum, for abstractive summarization of movie screenplays. This dataset comprises 2200 movie screenplays accompanied by their Wikipedia plot summaries. We manually formatted the movie screenplays to represent their structural elements. Compared to existing datasets, MovieSum possesses several distinctive features: (1) It includes movie screenplays, which are longer than scripts of TV episodes. (2) It is twice the size of previous movie screenplay datasets. (3) It provides metadata with IMDb IDs to facilitate access to additional external knowledge. We also show the results of recently released large language models applied to summarization on our dataset to provide a detailed baseline.", "github_manual": "https://github.com/saxenarohit/moviesum", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.05928", "title": "Adapting General Disentanglement-Based Speaker Anonymization for Enhanced Emotion Preservation", "authors": [ "Xiaoxiao Miao", "Yuxiang Zhang", "Xin Wang", "Natalia Tomashenko", "Donny Cheng Lock Soh", "Ian Mcloughlin" ], "github": "https://github.com/xiaoxiaomiao323/emotion-compensation", "project_page": "", "abstract": "A general disentanglement-based speaker anonymization system typically separates speech into content, speaker, and prosody features using individual encoders. This paper explores how to adapt such a system when a new speech attribute, for example, emotion, needs to be preserved to a greater extent. While existing systems are good at anonymizing speaker embeddings, they are not designed to preserve emotion. Two strategies for this are examined. First, we show that integrating emotion embeddings from a pre-trained emotion encoder can help preserve emotional cues, even though this approach slightly compromises privacy protection. Alternatively, we propose an emotion compensation strategy as a post-processing step applied to anonymized speaker embeddings. This conceals the original speaker's identity and reintroduces the emotional traits lost during speaker embedding anonymization. Specifically, we model the emotion attribute using support vector machines to learn separate boundaries for each emotion. During inference, the original speaker embedding is processed in two ways: one, by an emotion indicator to predict emotion and select the emotion-matched SVM accurately; and two, by a speaker anonymizer to conceal speaker characteristics. The anonymized speaker embedding is then modified along the corresponding SVM boundary towards an enhanced emotional direction to save the emotional cues. The proposed strategies are also expected to be useful for adapting a general disentanglement-based speaker anonymization system to preserve other target paralinguistic attributes, with potential for a range of downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xiaoxiaomiao323/emotion-compensation", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.06396", "title": "Design Proteins Using Large Language Models: Enhancements and Comparative Analyses", "authors": [ "Kamyar Zeinalipour", "Neda Jamshidi", "Monica Bianchini", "Marco Maggini", "Marco Gori" ], "github": "https://github.com/kamyarzeinalipour/protein-design-llms", "project_page": "", "abstract": "Pre-trained LLMs have demonstrated substantial capabilities across a range of conventional natural language processing (NLP) tasks, such as summarization and entity recognition. In this paper, we explore the application of LLMs in the generation of high-quality protein sequences. Specifically, we adopt a suite of pre-trained LLMs, including Mistral-7B1, Llama-2-7B2, Llama-3-8B3, and gemma-7B4, to produce valid protein sequences. All of these models are publicly available.5 Unlike previous work in this field, our approach utilizes a relatively small dataset comprising 42,000 distinct human protein sequences. We retrain these models to process protein-related data, ensuring the generation of biologically feasible protein structures. Our findings demonstrate that even with limited data, the adapted models exhibit efficiency comparable to established protein-focused models such as ProGen varieties, ProtGPT2, and ProLLaMA, which were trained on millions of protein sequences. To validate and quantify the performance of our models, we conduct comparative analyses employing standard metrics such as pLDDT, RMSD, TM-score, and REU. Furthermore, we commit to making the trained versions of all four models publicly available, fostering greater transparency and collaboration in the field of computational biology.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kamyarzeinalipour/protein-design-llms", "github_api_added_by": "auto" }, { "date": "2024-08-14", "arxiv_id": "2408.06273", "title": "FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data", "authors": [ "Haoran Sun", "Renren Jin", "Shaoyang Xu", "Leiyu Pan", "Supryadi", "Menglong Cui", "Jiangcun Du", "Yikun Lei", "Lei Yang", "Ling Shi", "Juesi Xiao", "Shaolin Zhu", "Deyi Xiong" ], "github": "https://github.com/tjunlp-lab/fuxitranyu", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource languages. To mitigate this challenge, we present FuxiTranyu, an open-source multilingual LLM, which is designed to satisfy the need of the research community for balanced and high-performing multilingual capabilities. FuxiTranyu-8B, the base model with 8 billion parameters, is trained from scratch on a meticulously balanced multilingual data repository that contains 600 billion tokens covering 43 natural languages and 16 programming languages. In addition to the base model, we also develop two instruction-tuned models: FuxiTranyu-8B-SFT that is fine-tuned on a diverse multilingual instruction dataset, and FuxiTranyu-8B-DPO that is further refined with DPO on a preference dataset for enhanced alignment ability. Extensive experiments on a wide range of multilingual benchmarks demonstrate the competitive performance of FuxiTranyu against existing multilingual LLMs, e.g., BLOOM-7B, PolyLM-13B, Llama-2-Chat-7B and Mistral-7B-Instruct. Interpretability analyses at both the neuron and representation level suggest that FuxiTranyu is able to learn consistent multilingual representations across different languages. To promote further research into multilingual LLMs and their working mechanisms, we release both the base and instruction-tuned FuxiTranyu models together with 58 pretraining checkpoints at HuggingFace and Github.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tjunlp-lab/fuxitranyu", "github_api_added_by": "auto" }, { "date": "2024-08-14", "arxiv_id": "2408.06663", "title": "Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models", "authors": [ "Kaiser Sun", "Mark Dredze" ], "github": "https://github.com/KaiserWhoLearns/AmuroCharPTFTRelationship", "project_page": "", "abstract": "The development of large language models leads to the formation of a pre-train-then-align paradigm, in which the model is typically pre-trained on a large text corpus and undergoes a tuning stage to align the model with human preference or downstream tasks. In this work, we investigate the relationship between pre-training and fine-tuning by fine-tuning multiple intermediate pre-trained model checkpoints. Our results on 18 datasets suggest that i) continual pre-training improves the model in a latent way that unveils after fine-tuning; ii) with extra fine-tuning, the datasets that the model does not demonstrate capability gain much more than those that the model performs well during the pre-training stage; iii) although model benefits significantly through supervised fine-tuning, it may forget previously known domain knowledge and the tasks that are not seen during fine-tuning; iv) the model resembles high sensitivity to evaluation prompts after supervised fine-tuning, but this sensitivity can be alleviated by more pre-training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KaiserWhoLearns/AmuroCharPTFTRelationship", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.05492", "title": "ZePo: Zero-Shot Portrait Stylization with Faster Sampling", "authors": [ "Jin Liu", "Huaibo Huang", "Jie Cao", "Ran He" ], "github": "https://github.com/liujin112/ZePo", "project_page": "", "abstract": "Diffusion-based text-to-image generation models have significantly advanced the field of art content synthesis. However, current portrait stylization methods generally require either model fine-tuning based on examples or the employment of DDIM Inversion to revert images to noise space, both of which substantially decelerate the image generation process. To overcome these limitations, this paper presents an inversion-free portrait stylization framework based on diffusion models that accomplishes content and style feature fusion in merely four sampling steps. We observed that Latent Consistency Models employing consistency distillation can effectively extract representative Consistency Features from noisy images. To blend the Consistency Features extracted from both content and style images, we introduce a Style Enhancement Attention Control technique that meticulously merges content and style features within the attention space of the target image. Moreover, we propose a feature merging strategy to amalgamate redundant features in Consistency Features, thereby reducing the computational load of attention control. Extensive experiments have validated the effectiveness of our proposed framework in enhancing stylization efficiency and fidelity. The code is available at https://github.com/liujin112/ZePo.", "github_manual": "", "github_regex": "https://github.com/liujin112/ZePo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.07060", "title": "Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents", "authors": [ "Kexun Zhang", "Weiran Yao", "Zuxin Liu", "Yihao Feng", "Zhiwei Liu", "Rithesh Murthy", "Tian Lan", "Lei Li", "Renze Lou", "Jiacheng Xu", "Bo Pang", "Yingbo Zhou", "Shelby Heinecke", "Silvio Savarese", "Huan Wang", "Caiming Xiong" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) agents have shown great potential in solving real-world software engineering (SWE) problems. The most advanced open-source SWE agent can resolve over 27% of real GitHub issues in SWE-Bench Lite. However, these sophisticated agent frameworks exhibit varying strengths, excelling in certain tasks while underperforming in others. To fully harness the diversity of these agents, we propose DEI (Diversity Empowered Intelligence), a framework that leverages their unique expertise. DEI functions as a meta-module atop existing SWE agent frameworks, managing agent collectives for enhanced problem-solving. Experimental results show that a DEI-guided committee of agents is able to surpass the best individual agent's performance by a large margin. For instance, a group of open-source SWE agents, with a maximum individual resolve rate of 27.3% on SWE-Bench Lite, can achieve a 34.3% resolve rate with DEI, making a 25% improvement and beating most closed-source solutions. Our best-performing group excels with a 55% resolve rate, securing the highest ranking on SWE-Bench Lite. Our findings contribute to the growing body of research on collaborative AI systems and their potential to solve complex software engineering challenges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-14", "arxiv_id": "2408.07246", "title": "Seeing and Understanding: Bridging Vision with Chemical Knowledge Via ChemVLM", "authors": [ "Junxian Li", "Di Zhang", "Xunzhi Wang", "Zeying Hao", "Jingdi Lei", "Qian Tan", "Cai Zhou", "Wei Liu", "Weiyun Wang", "Zhe Chen", "Wenhai Wang", "Wei Li", "Shufei Zhang", "Mao Su", "Wanli Ouyang", "Yuqiang Li", "Dongzhan Zhou" ], "github": "https://github.com/lijunxian111/ChemVlm", "project_page": "", "abstract": "In this technical report, we propose ChemVLM, the first open-source multimodal large language model dedicated to the fields of chemistry, designed to address the incompatibility between chemical image understanding and text analysis. Built upon the VIT-MLP-LLM architecture, we leverage ChemLLM-20B as the foundational large model, endowing our model with robust capabilities in understanding and utilizing chemical text knowledge. Additionally, we employ InternVIT-6B as a powerful image encoder. We have curated high-quality data from the chemical domain, including molecules, reaction formulas, and chemistry examination data, and compiled these into a bilingual multimodal question-answering dataset. We test the performance of our model on multiple open-source benchmarks and three custom evaluation sets. Experimental results demonstrate that our model achieves excellent performance, securing state-of-the-art results in five out of six involved tasks. Our model can be found at https://huggingface.co/AI4Chem/ChemVLM-26B.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lijunxian111/ChemVlm", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-15", "arxiv_id": "2408.07116", "title": "Generative Photomontage", "authors": [ "Sean J. Liu", "Nupur Kumari", "Ariel Shamir", "Jun-Yan Zhu" ], "github": "", "project_page": "", "abstract": "Text-to-image models are powerful tools for image creation. However, the generation process is akin to a dice roll and makes it difficult to achieve a single image that captures everything a user wants. In this paper, we propose a framework for creating the desired image by compositing it from various parts of generated images, in essence forming a Generative Photomontage. Given a stack of images generated by ControlNet using the same input condition and different seeds, we let users select desired parts from the generated results using a brush stroke interface. We introduce a novel technique that takes in the user's brush strokes, segments the generated images using a graph-based optimization in diffusion feature space, and then composites the segmented regions via a new feature-space blending method. Our method faithfully preserves the user-selected regions while compositing them harmoniously. We demonstrate that our flexible framework can be used for many applications, including generating new appearance combinations, fixing incorrect shapes and artifacts, and improving prompt alignment. We show compelling results for each application and demonstrate that our method outperforms existing image blending methods and various baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-15", "arxiv_id": "2408.07089", "title": "InfinityMATH: A Scalable Instruction Tuning Dataset in Programmatic Mathematical Reasoning", "authors": [ "Bo-Wen Zhang", "Yan Yan", "Lin Li", "Guang Liu" ], "github": "", "project_page": "", "abstract": "Recent advancements in Chain-of-Thoughts (CoT) and Program-of-Thoughts (PoT) methods have greatly enhanced language models' mathematical reasoning capabilities, facilitating their integration into instruction tuning datasets with LLMs. However, existing methods for large-scale dataset creation require substantial seed data and high computational costs for data synthesis, posing significant challenges for scalability. We introduce InfinityMATH, a scalable instruction tuning dataset for programmatic mathematical reasoning. The construction pipeline emphasizes decoupling numbers from mathematical problems to synthesize number-independent programs, enabling efficient and flexible scaling while minimizing dependency on specific numerical values. Fine-tuning experiments with open-source language and code models, such as Llama2 and CodeLlama, demonstrate the practical benefits of InfinityMATH. These fine-tuned models, showed significant relative improvements on both in-domain and out-of-domain benchmarks, ranging from 184.7% to 514.3% on average. Additionally, these models exhibited high robustness on the GSM8K+ and MATH+ benchmarks, which are enhanced version of test sets with simply the number variations. InfinityMATH ensures that models are more versatile and effective across a broader range of mathematical problems. The data is available at https://huggingface.co/datasets/flagopen/InfinityMATH.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-15", "arxiv_id": "2408.07410", "title": "Aquila2 Technical Report", "authors": [ "Bo-Wen Zhang", "Liangdong Wang", "Jijie Li", "Shuhao Gu", "Xinya Wu", "Zhengduo Zhang", "Boyan Gao", "Yulong Ao", "Guang Liu" ], "github": "", "project_page": "", "abstract": "This paper introduces the Aquila2 series, which comprises a wide range of bilingual models with parameter sizes of 7, 34, and 70 billion. These models are trained based on an innovative framework named HeuriMentor (HM), which offers real-time insights into model convergence and enhances the training process and data management. The HM System, comprising the Adaptive Training Engine (ATE), Training State Monitor (TSM), and Data Management Unit (DMU), allows for precise monitoring of the model's training progress and enables efficient optimization of data distribution, thereby enhancing training effectiveness. Extensive evaluations show that the Aquila2 model series performs comparably well on both English and Chinese benchmarks. Specifically, Aquila2-34B demonstrates only a slight decrease in performance when quantized to Int4. Furthermore, we have made our training code (https://github.com/FlagOpen/FlagScale) and model weights (https://github.com/FlagAI-Open/Aquila2) publicly available to support ongoing research and the development of applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-15", "arxiv_id": "2408.07540", "title": "3D Gaussian Editing with A Single Image", "authors": [ "Guan Luo", "Tian-Xing Xu", "Ying-Tian Liu", "Xiao-Xiong Fan", "Fang-Lue Zhang", "Song-Hai Zhang" ], "github": "", "project_page": "", "abstract": "The modeling and manipulation of 3D scenes captured from the real world are pivotal in various applications, attracting growing research interest. While previous works on editing have achieved interesting results through manipulating 3D meshes, they often require accurately reconstructed meshes to perform editing, which limits their application in 3D content generation. To address this gap, we introduce a novel single-image-driven 3D scene editing approach based on 3D Gaussian Splatting, enabling intuitive manipulation via directly editing the content on a 2D image plane. Our method learns to optimize the 3D Gaussians to align with an edited version of the image rendered from a user-specified viewpoint of the original scene. To capture long-range object deformation, we introduce positional loss into the optimization process of 3D Gaussian Splatting and enable gradient propagation through reparameterization. To handle occluded 3D Gaussians when rendering from the specified viewpoint, we build an anchor-based structure and employ a coarse-to-fine optimization strategy capable of handling long-range deformation while maintaining structural stability. Furthermore, we design a novel masking strategy to adaptively identify non-rigid deformation regions for fine-scale modeling. Extensive experiments show the effectiveness of our method in handling geometric details, long-range, and non-rigid deformation, demonstrating superior editing flexibility and quality compared to previous approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-15", "arxiv_id": "2408.07547", "title": "PeriodWave: Multi-Period Flow Matching for High-Fidelity Waveform Generation", "authors": [ "Sang-Hoon Lee", "Ha-Yeong Choi", "Seong-Whan Lee" ], "github": "https://github.com/sh-lee-prml/PeriodWave", "project_page": "", "abstract": "Recently, universal waveform generation tasks have been investigated conditioned on various out-of-distribution scenarios. Although GAN-based methods have shown their strength in fast waveform generation, they are vulnerable to train-inference mismatch scenarios such as two-stage text-to-speech. Meanwhile, diffusion-based models have shown their powerful generative performance in other domains; however, they stay out of the limelight due to slow inference speed in waveform generation tasks. Above all, there is no generator architecture that can explicitly disentangle the natural periodic features of high-resolution waveform signals. In this paper, we propose PeriodWave, a novel universal waveform generation model. First, we introduce a period-aware flow matching estimator that can capture the periodic features of the waveform signal when estimating the vector fields. Additionally, we utilize a multi-period estimator that avoids overlaps to capture different periodic features of waveform signals. Although increasing the number of periods can improve the performance significantly, this requires more computational costs. To reduce this issue, we also propose a single period-conditional universal estimator that can feed-forward parallel by period-wise batch inference. Additionally, we utilize discrete wavelet transform to losslessly disentangle the frequency information of waveform signals for high-frequency modeling, and introduce FreeU to reduce the high-frequency noise for waveform generation. The experimental results demonstrated that our model outperforms the previous models both in Mel-spectrogram reconstruction and text-to-speech tasks. All source code will be available at https://github.com/sh-lee-prml/PeriodWave.", "github_manual": "", "github_regex": "https://github.com/sh-lee-prml/PeriodWave", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-15", "arxiv_id": "2408.07416", "title": "Rethinking Open-Vocabulary Segmentation of Radiance Fields in 3D Space", "authors": [ "Hyunjee Lee", "Youngsik Yun", "Jeongmin Bae", "Seoha Kim", "Youngjung Uh" ], "github": "", "project_page": "", "abstract": "Understanding the 3D semantics of a scene is a fundamental problem for various scenarios such as embodied agents. While NeRFs and 3DGS excel at novel-view synthesis, previous methods for understanding their semantics have been limited to incomplete 3D understanding: their segmentation results are 2D masks and their supervision is anchored at 2D pixels. This paper revisits the problem set to pursue a better 3D understanding of a scene modeled by NeRFs and 3DGS as follows. 1) We directly supervise the 3D points to train the language embedding field. It achieves state-of-the-art accuracy without relying on multi-scale language embeddings. 2) We transfer the pre-trained language field to 3DGS, achieving the first real-time rendering speed without sacrificing training time or accuracy. 3) We introduce a 3D querying and evaluation protocol for assessing the reconstructed geometry and semantics together. Code, checkpoints, and annotations will be available online. Project page: https://hyunji12.github.io/Open3DRF", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-15", "arxiv_id": "2408.05366", "title": "DeepSpeak Dataset v1.0", "authors": [ "Sarah Barrington", "Matyas Bohacek", "Hany Farid" ], "github": "https://github.com/hfaridlab/deepspeak/tree/main/data_collection", "project_page": "", "abstract": "We describe a large-scale dataset--{\\em DeepSpeak}--of real and deepfake footage of people talking and gesturing in front of their webcams. The real videos in this first version of the dataset consist of 9 hours of footage from 220 diverse individuals. Constituting more than 25 hours of footage, the fake videos consist of a range of different state-of-the-art face-swap and lip-sync deepfakes with natural and AI-generated voices. We expect to release future versions of this dataset with different and updated deepfake technologies. This dataset is made freely available for research and non-commercial uses; requests for commercial use will be considered.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hfaridlab/deepspeak/tree/main/data_collection", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-16", "arxiv_id": "2408.08189", "title": "FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance", "authors": [ "Jiasong Feng", "Ao Ma", "Jing Wang", "Bo Cheng", "Xiaodan Liang", "Dawei Leng", "Yuhui Yin" ], "github": "https://github.com/360CVGroup/FancyVideo", "project_page": "", "abstract": "Synthesizing motion-rich and temporally consistent videos remains a challenge in artificial intelligence, especially when dealing with extended durations. Existing text-to-video (T2V) models commonly employ spatial cross-attention for text control, equivalently guiding different frame generations without frame-specific textual guidance. Thus, the model's capacity to comprehend the temporal logic conveyed in prompts and generate videos with coherent motion is restricted. To tackle this limitation, we introduce FancyVideo, an innovative video generator that improves the existing text-control mechanism with the well-designed Cross-frame Textual Guidance Module (CTGM). Specifically, CTGM incorporates the Temporal Information Injector (TII), Temporal Affinity Refiner (TAR), and Temporal Feature Booster (TFB) at the beginning, middle, and end of cross-attention, respectively, to achieve frame-specific textual guidance. Firstly, TII injects frame-specific information from latent features into text conditions, thereby obtaining cross-frame textual conditions. Then, TAR refines the correlation matrix between cross-frame textual conditions and latent features along the time dimension. Lastly, TFB boosts the temporal consistency of latent features. Extensive experiments comprising both quantitative and qualitative evaluations demonstrate the effectiveness of FancyVideo. Our approach achieves state-of-the-art T2V generation results on the EvalCrafter benchmark and facilitates the synthesis of dynamic and consistent videos. The video show results can be available at https://fancyvideo.github.io/, and we will make our code and model weights publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/360CVGroup/FancyVideo", "github_api_added_by": "auto" }, { "date": "2024-08-16", "arxiv_id": "2408.08152", "title": "DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search", "authors": [ "Huajian Xin", "Z. Z. Ren", "Junxiao Song", "Zhihong Shao", "Wanjia Zhao", "Haocheng Wang", "Bo Liu", "Liyue Zhang", "Xuan Lu", "Qiushi Du", "Wenjun Gao", "Qihao Zhu", "Dejian Yang", "Zhibin Gou", "Z. F. Wu", "Fuli Luo", "Chong Ruan" ], "github": "https://github.com/deepseek-ai/deepseek-prover-v1.5", "project_page": "", "abstract": "We introduce DeepSeek-Prover-V1.5, an open-source language model designed for theorem proving in Lean 4, which enhances DeepSeek-Prover-V1 by optimizing both training and inference processes. Pre-trained on DeepSeekMath-Base with specialization in formal mathematical languages, the model undergoes supervised fine-tuning using an enhanced formal theorem proving dataset derived from DeepSeek-Prover-V1. Further refinement is achieved through reinforcement learning from proof assistant feedback (RLPAF). Beyond the single-pass whole-proof generation approach of DeepSeek-Prover-V1, we propose RMaxTS, a variant of Monte-Carlo tree search that employs an intrinsic-reward-driven exploration strategy to generate diverse proof paths. DeepSeek-Prover-V1.5 demonstrates significant improvements over DeepSeek-Prover-V1, achieving new state-of-the-art results on the test set of the high school level miniF2F benchmark (63.5%) and the undergraduate level ProofNet benchmark (25.3%).", "github_manual": "https://github.com/deepseek-ai/deepseek-prover-v1.5", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-16", "arxiv_id": "2408.08313", "title": "Can Large Language Models Understand Symbolic Graphics Programs?", "authors": [ "Zeju Qiu", "Weiyang Liu", "Haiwen Feng", "Zhen Liu", "Tim Z. Xiao", "Katherine M. Collins", "Joshua B. Tenenbaum", "Adrian Weller", "Michael J. Black", "Bernhard Sch\u00f6lkopf" ], "github": "", "project_page": "", "abstract": "Assessing the capabilities of large language models (LLMs) is often challenging, in part, because it is hard to find tasks to which they have not been exposed during training. We take one step to address this challenge by turning to a new task: focusing on symbolic graphics programs, which are a popular representation for graphics content that procedurally generates visual data. LLMs have shown exciting promise towards program synthesis, but do they understand symbolic graphics programs? Unlike conventional programs, symbolic graphics programs can be translated to graphics content. Here, we characterize an LLM's understanding of symbolic programs in terms of their ability to answer questions related to the graphics content. This task is challenging as the questions are difficult to answer from the symbolic programs alone -- yet, they would be easy to answer from the corresponding graphics content as we verify through a human experiment. To understand symbolic programs, LLMs may need to possess the ability to imagine how the corresponding graphics content would look without directly accessing the rendered visual content. We use this task to evaluate LLMs by creating a large benchmark for the semantic understanding of symbolic graphics programs. This benchmark is built via program-graphics correspondence, hence requiring minimal human efforts. We evaluate current LLMs on our benchmark to elucidate a preliminary assessment of their ability to reason about visual scenes from programs. We find that this task distinguishes existing LLMs and models considered good at reasoning perform better. Lastly, we introduce Symbolic Instruction Tuning (SIT) to improve this ability. Specifically, we query GPT4-o with questions and images generated by symbolic programs. Such data are then used to finetune an LLM. We also find that SIT data can improve the general instruction following ability of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-16", "arxiv_id": "2408.08201", "title": "Heavy Labels Out! Dataset Distillation with Label Space Lightening", "authors": [ "Ruonan Yu", "Songhua Liu", "Zigeng Chen", "Jingwen Ye", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Dataset distillation or condensation aims to condense a large-scale training dataset into a much smaller synthetic one such that the training performance of distilled and original sets on neural networks are similar. Although the number of training samples can be reduced substantially, current state-of-the-art methods heavily rely on enormous soft labels to achieve satisfactory performance. As a result, the required storage can be comparable even to original datasets, especially for large-scale ones. To solve this problem, instead of storing these heavy labels, we propose a novel label-lightening framework termed HeLlO aiming at effective image-to-label projectors, with which synthetic labels can be directly generated online from synthetic images. Specifically, to construct such projectors, we leverage prior knowledge in open-source foundation models, e.g., CLIP, and introduce a LoRA-like fine-tuning strategy to mitigate the gap between pre-trained and target distributions, so that original models for soft-label generation can be distilled into a group of low-rank matrices. Moreover, an effective image optimization method is proposed to further mitigate the potential error between the original and distilled label generators. Extensive experiments demonstrate that with only about 0.003% of the original storage required for a complete set of soft labels, we achieve comparable performance to current state-of-the-art dataset distillation methods on large-scale datasets. Our code will be available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-16", "arxiv_id": "2408.08000", "title": "MVInpainter: Learning Multi-View Consistent Inpainting to Bridge 2D and 3D Editing", "authors": [ "Chenjie Cao", "Chaohui Yu", "Yanwei Fu", "Fan Wang", "Xiangyang Xue" ], "github": "", "project_page": "", "abstract": "Novel View Synthesis (NVS) and 3D generation have recently achieved prominent improvements. However, these works mainly focus on confined categories or synthetic 3D assets, which are discouraged from generalizing to challenging in-the-wild scenes and fail to be employed with 2D synthesis directly. Moreover, these methods heavily depended on camera poses, limiting their real-world applications. To overcome these issues, we propose MVInpainter, re-formulating the 3D editing as a multi-view 2D inpainting task. Specifically, MVInpainter partially inpaints multi-view images with the reference guidance rather than intractably generating an entirely novel view from scratch, which largely simplifies the difficulty of in-the-wild NVS and leverages unmasked clues instead of explicit pose conditions. To ensure cross-view consistency, MVInpainter is enhanced by video priors from motion components and appearance guidance from concatenated reference key&value attention. Furthermore, MVInpainter incorporates slot attention to aggregate high-level optical flow features from unmasked regions to control the camera movement with pose-free training and inference. Sufficient scene-level experiments on both object-centric and forward-facing datasets verify the effectiveness of MVInpainter, including diverse tasks, such as multi-view object removal, synthesis, insertion, and replacement. The project page is https://ewrfcas.github.io/MVInpainter/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-16", "arxiv_id": "2408.08019", "title": "Accelerating High-Fidelity Waveform Generation via Adversarial Flow Matching Optimization", "authors": [ "Sang-Hoon Lee", "Ha-Yeong Choi", "Seong-Whan Lee" ], "github": "https://github.com/sh-lee-prml/PeriodWave", "project_page": "", "abstract": "This paper introduces PeriodWave-Turbo, a high-fidelity and high-efficient waveform generation model via adversarial flow matching optimization. Recently, conditional flow matching (CFM) generative models have been successfully adopted for waveform generation tasks, leveraging a single vector field estimation objective for training. Although these models can generate high-fidelity waveform signals, they require significantly more ODE steps compared to GAN-based models, which only need a single generation step. Additionally, the generated samples often lack high-frequency information due to noisy vector field estimation, which fails to ensure high-frequency reproduction. To address this limitation, we enhance pre-trained CFM-based generative models by incorporating a fixed-step generator modification. We utilized reconstruction losses and adversarial feedback to accelerate high-fidelity waveform generation. Through adversarial flow matching optimization, it only requires 1,000 steps of fine-tuning to achieve state-of-the-art performance across various objective metrics. Moreover, we significantly reduce inference speed from 16 steps to 2 or 4 steps. Additionally, by scaling up the backbone of PeriodWave from 29M to 70M parameters for improved generalization, PeriodWave-Turbo achieves unprecedented performance, with a perceptual evaluation of speech quality (PESQ) score of 4.454 on the LibriTTS dataset. Audio samples, source code and checkpoints will be available at https://github.com/sh-lee-prml/PeriodWave.", "github_manual": "", "github_regex": "https://github.com/sh-lee-prml/PeriodWave", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-16", "arxiv_id": "2408.08172", "title": "Towards flexible perception with visual memory", "authors": [ "Robert Geirhos", "Priyank Jaini", "Austin Stone", "Sourabh Medapati", "Xi Yi", "George Toderici", "Abhijit Ogale", "Jonathon Shlens" ], "github": "https://github.com/google-deepmind/visual-memory", "project_page": "", "abstract": "Training a neural network is a monolithic endeavor, akin to carving knowledge into stone: once the process is completed, editing the knowledge in a network is nearly impossible, since all information is distributed across the network's weights. We here explore a simple, compelling alternative by marrying the representational power of deep neural networks with the flexibility of a database. Decomposing the task of image classification into image similarity (from a pre-trained embedding) and search (via fast nearest neighbor retrieval from a knowledge database), we build a simple and flexible visual memory that has the following key capabilities: (1.) The ability to flexibly add data across scales: from individual samples all the way to entire classes and billion-scale data; (2.) The ability to remove data through unlearning and memory pruning; (3.) An interpretable decision-mechanism on which we can intervene to control its behavior. Taken together, these capabilities comprehensively demonstrate the benefits of an explicit visual memory. We hope that it might contribute to a conversation on how knowledge should be represented in deep vision models -- beyond carving it in ``stone'' weights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/google-deepmind/visual-memory", "github_api_added_by": "auto" }, { "date": "2024-08-16", "arxiv_id": "2408.08274", "title": "BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts", "authors": [ "Qizhen Zhang", "Nikolas Gritsch", "Dwaraknath Gnaneshwar", "Simon Guo", "David Cairuz", "Bharat Venkitesh", "Jakob Foerster", "Phil Blunsom", "Sebastian Ruder", "Ahmet Ustun", "Acyr Locatelli" ], "github": "", "project_page": "", "abstract": "The Mixture of Experts (MoE) framework has become a popular architecture for large language models due to its superior performance over dense models. However, training MoEs from scratch in a large-scale regime is prohibitively expensive. Existing methods mitigate this by pre-training multiple dense expert models independently and using them to initialize an MoE. This is done by using experts' feed-forward network (FFN) to initialize the MoE's experts while merging other parameters. However, this method limits the reuse of dense model parameters to only the FFN layers, thereby constraining the advantages when \"upcycling\" these models into MoEs. We propose BAM (Branch-Attend-Mix), a simple yet effective method that addresses this shortcoming. BAM makes full use of specialized dense models by not only using their FFN to initialize the MoE layers but also leveraging experts' attention parameters fully by initializing them into a soft-variant of Mixture of Attention (MoA) layers. We explore two methods for upcycling attention parameters: 1) initializing separate attention experts from dense models including all attention parameters for the best model performance; and 2) sharing key and value parameters across all experts to facilitate for better inference efficiency. To further improve efficiency, we adopt a parallel attention transformer architecture to MoEs, which allows the attention experts and FFN experts to be computed concurrently. Our experiments on seed models ranging from 590 million to 2 billion parameters demonstrate that BAM surpasses baselines in both perplexity and downstream task performance, within the same computational and data constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-16", "arxiv_id": "2408.07852", "title": "Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability", "authors": [ "Jiri Hron", "Laura Culp", "Gamaleldin Elsayed", "Rosanne Liu", "Ben Adlam", "Maxwell Bileschi", "Bernd Bohnet", "JD Co-Reyes", "Noah Fiedel", "C. Daniel Freeman", "Izzeddin Gur", "Kathleen Kenealy", "Jaehoon Lee", "Peter J. Liu", "Gaurav Mishra", "Igor Mordatch", "Azade Nova", "Roman Novak", "Aaron Parisi", "Jeffrey Pennington", "Alex Rizkowsky", "Isabelle Simpson", "Hanie Sedghi", "Jascha Sohl-dickstein", "Kevin Swersky", "Sharad Vikram", "Tris Warkentin", "Lechao Xiao", "Kelvin Xu", "Jasper Snoek", "Simon Kornblith" ], "github": "", "project_page": "", "abstract": "While many capabilities of language models (LMs) improve with increased training budget, the influence of scale on hallucinations is not yet fully understood. Hallucinations come in many forms, and there is no universally accepted definition. We thus focus on studying only those hallucinations where a correct answer appears verbatim in the training set. To fully control the training data content, we construct a knowledge graph (KG)-based dataset, and use it to train a set of increasingly large LMs. We find that for a fixed dataset, larger and longer-trained LMs hallucinate less. However, hallucinating on leq5% of the training data requires an order of magnitude larger model, and thus an order of magnitude more compute, than Hoffmann et al. (2022) reported was optimal. Given this costliness, we study how hallucination detectors depend on scale. While we see detector size improves performance on fixed LM's outputs, we find an inverse relationship between the scale of the LM and the detectability of its hallucinations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-16", "arxiv_id": "2408.08072", "title": "I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm", "authors": [ "Yiming Liang", "Ge Zhang", "Xingwei Qu", "Tianyu Zheng", "Jiawei Guo", "Xinrun Du", "Zhenzhu Yang", "Jiaheng Liu", "Chenghua Lin", "Lei Ma", "Wenhao Huang", "Jiajun Zhang" ], "github": "https://github.com/multimodal-art-projection/I-SHEEP", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved significant advancements, however, the common learning paradigm treats LLMs as passive information repositories, neglecting their potential for active learning and alignment. Some approaches train LLMs using their own generated synthetic data, exploring the possibility of active alignment. However, there is still a huge gap between these one-time alignment methods and the continuous automatic alignment of humans. In this paper, we introduce I-SHEEP, an Iterative Self-EnHancEmEnt Paradigm.This human-like paradigm enables LLMs to continuously self-align from scratch with nothing. Compared to the one-time alignment method Dromedary sun2023principledriven, which refers to the first iteration in this paper, I-SHEEP can significantly enhance capacities on both Qwen and Llama models. I-SHEEP achieves a maximum relative improvement of 78.2\\% in the Alpaca Eval, 24.0\\% in the MT Bench, and an absolute increase of 8.88\\% in the IFEval accuracy over subsequent iterations in Qwen-1.5 72B model. Additionally, I-SHEEP surpasses the base model in various standard benchmark generation tasks, achieving an average improvement of 24.77\\% in code generation tasks, 12.04\\% in TrivialQA, and 20.29\\% in SQuAD. We also provide new insights based on the experiment results. Our codes, datasets, and models are available at https://anonymous.4open.science/r/I-SHEEP.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/multimodal-art-projection/I-SHEEP", "github_api_added_by": "auto" }, { "date": "2024-08-16", "arxiv_id": "2408.07990", "title": "FuseChat: Knowledge Fusion of Chat Models", "authors": [ "Fanqi Wan", "Longguang Zhong", "Ziyi Yang", "Ruijun Chen", "Xiaojun Quan" ], "github": "https://github.com/fanqiwan/FuseAI", "project_page": "", "abstract": "While training large language models (LLMs) from scratch can indeed lead to models with distinct capabilities and strengths, it incurs substantial costs and may lead to redundancy in competencies. Knowledge fusion aims to integrate existing LLMs of diverse architectures and capabilities into a more potent LLM through lightweight continual training, thereby reducing the need for costly LLM development. In this work, we propose a new framework for the knowledge fusion of chat LLMs through two main stages, resulting in FuseChat. Firstly, we conduct pairwise knowledge fusion on source chat LLMs of varying structures and scales to create multiple target LLMs with identical structure and size via lightweight fine-tuning. During this process, a statistics-based token alignment approach is introduced as the cornerstone for fusing LLMs with different structures. Secondly, we merge these target LLMs within the parameter space, where we propose a novel method for determining the merging coefficients based on the magnitude of parameter updates before and after fine-tuning. We implement and validate FuseChat using six prominent chat LLMs with diverse architectures and scales, including OpenChat-3.5-7B, Starling-LM-7B-alpha, NH2-SOLAR-10.7B, InternLM2-Chat-20B, Mixtral-8x7B-Instruct, and Qwen-1.5-Chat-72B. Experimental results on two instruction-following benchmarks, AlpacaEval 2.0 and MT-Bench, demonstrate the superiority of FuseChat-7B over baselines of various sizes. Our model is even comparable to the larger Mixtral-8x7B-Instruct and approaches GPT-3.5-Turbo-1106 on MT-Bench. Our code, model weights, and data are public at https://github.com/fanqiwan/FuseAI.", "github_manual": "", "github_regex": "https://github.com/fanqiwan/FuseAI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-16", "arxiv_id": "2408.08291", "title": "The ShareLM Collection and Plugin: Contributing Human-Model Chats for the Benefit of the Community", "authors": [ "Shachar Don-Yehiya", "Leshem Choshen", "Omri Abend" ], "github": "https://github.com/shachardon/share-lm", "project_page": "", "abstract": "Human-model conversations provide a window into users' real-world scenarios, behavior, and needs, and thus are a valuable resource for model development and research. While for-profit companies collect user data through the APIs of their models, using it internally to improve their own models, the open source and research community lags behind. We introduce the ShareLM collection, a unified set of human conversations with large language models, and its accompanying plugin, a Web extension for voluntarily contributing user-model conversations. Where few platforms share their chats, the ShareLM plugin adds this functionality, thus, allowing users to share conversations from most platforms. The plugin allows the user to rate their conversations, both at the conversation and the response levels, and delete conversations they prefer to keep private before they ever leave the user's local storage. We release the plugin conversations as part of the ShareLM collection, and call for more community effort in the field of open human-model data. The code, plugin, and data are available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/shachardon/share-lm", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-19", "arxiv_id": "2408.08459", "title": "JPEG-LM: LLMs as Image Generators with Canonical Codec Representations", "authors": [ "Xiaochuang Han", "Marjan Ghazvininejad", "Pang Wei Koh", "Yulia Tsvetkov" ], "github": "", "project_page": "", "abstract": "Recent work in image and video generation has been adopting the autoregressive LLM architecture due to its generality and potentially easy integration into multi-modal systems. The crux of applying autoregressive training in language generation to visual generation is discretization -- representing continuous data like images and videos as discrete tokens. Common methods of discretizing images and videos include modeling raw pixel values, which are prohibitively lengthy, or vector quantization, which requires convoluted pre-hoc training. In this work, we propose to directly model images and videos as compressed files saved on computers via canonical codecs (e.g., JPEG, AVC/H.264). Using the default Llama architecture without any vision-specific modifications, we pretrain JPEG-LM from scratch to generate images (and AVC-LM to generate videos as a proof of concept), by directly outputting compressed file bytes in JPEG and AVC formats. Evaluation of image generation shows that this simple and straightforward approach is more effective than pixel-based modeling and sophisticated vector quantization baselines (on which our method yields a 31% reduction in FID). Our analysis shows that JPEG-LM has an especial advantage over vector quantization models in generating long-tail visual elements. Overall, we show that using canonical codec representations can help lower the barriers between language generation and visual generation, facilitating future research on multi-modal language/image/video LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-19", "arxiv_id": "2408.08872", "title": "xGen-MM (BLIP-3): A Family of Open Large Multimodal Models", "authors": [ "Le Xue", "Manli Shu", "Anas Awadalla", "Jun Wang", "An Yan", "Senthil Purushwalkam", "Honglu Zhou", "Viraj Prabhu", "Yutong Dai", "Michael S Ryoo", "Shrikant Kendre", "Jieyu Zhang", "Can Qin", "Shu Zhang", "Chia-Chih Chen", "Ning Yu", "Juntao Tan", "Tulika Manoj Awalgaonkar", "Shelby Heinecke", "Huan Wang", "Yejin Choi", "Ludwig Schmidt", "Zeyuan Chen", "Silvio Savarese", "Juan Carlos Niebles", "Caiming Xiong", "Ran Xu" ], "github": "", "project_page": "", "abstract": "This report introduces xGen-MM (also known as BLIP-3), a framework for developing Large Multimodal Models (LMMs). The framework comprises meticulously curated datasets, a training recipe, model architectures, and a resulting suite of LMMs. xGen-MM, short for xGen-MultiModal, expands the Salesforce xGen initiative on foundation AI models. Our models undergo rigorous evaluation across a range of tasks, including both single and multi-image benchmarks. Our pre-trained base model exhibits strong in-context learning capabilities and the instruction-tuned model demonstrates competitive performance among open-source LMMs with similar model sizes. In addition, we introduce a safety-tuned model with DPO, aiming to mitigate harmful behaviors such as hallucinations and improve safety. We open-source our models, curated large-scale datasets, and our fine-tuning codebase to facilitate further advancements in LMM research. Associated resources will be available on our project page above.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-19", "arxiv_id": "2408.07931", "title": "Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning", "authors": [ "Haofeng Liu", "Erli Zhang", "Junde Wu", "Mingxuan Hong", "Yueming Jin" ], "github": "https://github.com/jinlab-imvr/surgical-sam-2", "project_page": "", "abstract": "Surgical video segmentation is a critical task in computer-assisted surgery and is vital for enhancing surgical quality and patient outcomes. Recently, the Segment Anything Model 2 (SAM2) framework has shown superior advancements in image and video segmentation. However, SAM2 struggles with efficiency due to the high computational demands of processing high-resolution images and complex and long-range temporal dynamics in surgical videos. To address these challenges, we introduce Surgical SAM 2 (SurgSAM-2), an advanced model to utilize SAM2 with an Efficient Frame Pruning (EFP) mechanism, to facilitate real-time surgical video segmentation. The EFP mechanism dynamically manages the memory bank by selectively retaining only the most informative frames, reducing memory usage and computational cost while maintaining high segmentation accuracy. Our extensive experiments demonstrate that SurgSAM-2 significantly improves both efficiency and segmentation accuracy compared to the vanilla SAM2. Remarkably, SurgSAM-2 achieves a 3times FPS compared with SAM2, while also delivering state-of-the-art performance after fine-tuning with lower-resolution data. These advancements establish SurgSAM-2 as a leading model for surgical video analysis, making real-time surgical video segmentation in resource-constrained environments a feasible reality.", "github_manual": "https://github.com/jinlab-imvr/surgical-sam-2", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-19", "arxiv_id": "2408.08435", "title": "Automated Design of Agentic Systems", "authors": [ "Shengran Hu", "Cong Lu", "Jeff Clune" ], "github": "https://github.com/shengranhu/adas", "project_page": "", "abstract": "Researchers are investing substantial effort in developing powerful general-purpose agents, wherein Foundation Models are used as modules within agentic systems (e.g. Chain-of-Thought, Self-Reflection, Toolformer). However, the history of machine learning teaches us that hand-designed solutions are eventually replaced by learned solutions. We formulate a new research area, Automated Design of Agentic Systems (ADAS), which aims to automatically create powerful agentic system designs, including inventing novel building blocks and/or combining them in new ways. We further demonstrate that there is an unexplored yet promising approach within ADAS where agents can be defined in code and new agents can be automatically discovered by a meta agent programming ever better ones in code. Given that programming languages are Turing Complete, this approach theoretically enables the learning of any possible agentic system: including novel prompts, tool use, control flows, and combinations thereof. We present a simple yet effective algorithm named Meta Agent Search to demonstrate this idea, where a meta agent iteratively programs interesting new agents based on an ever-growing archive of previous discoveries. Through extensive experiments across multiple domains including coding, science, and math, we show that our algorithm can progressively invent agents with novel designs that greatly outperform state-of-the-art hand-designed agents. Importantly, we consistently observe the surprising result that agents invented by Meta Agent Search maintain superior performance even when transferred across domains and models, demonstrating their robustness and generality. Provided we develop it safely, our work illustrates the potential of an exciting new research direction toward automatically designing ever-more powerful agentic systems to benefit humanity.", "github_manual": "https://github.com/shengranhu/adas", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-19", "arxiv_id": "2408.08332", "title": "TurboEdit: Instant text-based image editing", "authors": [ "Zongze Wu", "Nicholas Kolkin", "Jonathan Brandt", "Richard Zhang", "Eli Shechtman" ], "github": "", "project_page": "", "abstract": "We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-19", "arxiv_id": "2408.08441", "title": "D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning", "authors": [ "Rafael Rafailov", "Kyle Hatch", "Anikait Singh", "Laura Smith", "Aviral Kumar", "Ilya Kostrikov", "Philippe Hansen-Estruch", "Victor Kolev", "Philip Ball", "Jiajun Wu", "Chelsea Finn", "Sergey Levine" ], "github": "https://github.com/ikostrikov/jaxrl2", "project_page": "", "abstract": "Offline reinforcement learning algorithms hold the promise of enabling data-driven RL methods that do not require costly or dangerous real-world exploration and benefit from large pre-collected datasets. This in turn can facilitate real-world applications, as well as a more standardized approach to RL research. Furthermore, offline RL methods can provide effective initializations for online finetuning to overcome challenges with exploration. However, evaluating progress on offline RL algorithms requires effective and challenging benchmarks that capture properties of real-world tasks, provide a range of task difficulties, and cover a range of challenges both in terms of the parameters of the domain (e.g., length of the horizon, sparsity of rewards) and the parameters of the data (e.g., narrow demonstration data or broad exploratory data). While considerable progress in offline RL in recent years has been enabled by simpler benchmark tasks, the most widely used datasets are increasingly saturating in performance and may fail to reflect properties of realistic tasks. We propose a new benchmark for offline RL that focuses on realistic simulations of robotic manipulation and locomotion environments, based on models of real-world robotic systems, and comprising a variety of data sources, including scripted data, play-style data collected by human teleoperators, and other data sources. Our proposed benchmark covers state-based and image-based domains, and supports both offline RL and online fine-tuning evaluation, with some of the tasks specifically designed to require both pre-training and fine-tuning. We hope that our proposed benchmark will facilitate further progress on both offline RL and fine-tuning algorithms. Website with code, examples, tasks, and data is available at https://sites.google.com/view/d5rl/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ikostrikov/jaxrl2", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-19", "arxiv_id": "2408.07888", "title": "Fine-tuning Large Language Models with Human-inspired Learning Strategies in Medical Question Answering", "authors": [ "Yushi Yang", "Andrew M. Bean", "Robert McCraith", "Adam Mahdi" ], "github": "https://github.com/Oxford-AI-for-Society/human-learning-strategies", "project_page": "", "abstract": "Training Large Language Models (LLMs) incurs substantial data-related costs, motivating the development of data-efficient training methods through optimised data ordering and selection. Human-inspired learning strategies, such as curriculum learning, offer possibilities for efficient training by organising data according to common human learning practices. Despite evidence that fine-tuning with curriculum learning improves the performance of LLMs for natural language understanding tasks, its effectiveness is typically assessed using a single model. In this work, we extend previous research by evaluating both curriculum-based and non-curriculum-based learning strategies across multiple LLMs, using human-defined and automated data labels for medical question answering. Our results indicate a moderate impact of using human-inspired learning strategies for fine-tuning LLMs, with maximum accuracy gains of 1.77% per model and 1.81% per dataset. Crucially, we demonstrate that the effectiveness of these strategies varies significantly across different model-dataset combinations, emphasising that the benefits of a specific human-inspired strategy for fine-tuning LLMs do not generalise. Additionally, we find evidence that curriculum learning using LLM-defined question difficulty outperforms human-defined difficulty, highlighting the potential of using model-generated measures for optimal curriculum design.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Oxford-AI-for-Society/human-learning-strategies", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-20", "arxiv_id": "2408.10198", "title": "MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model", "authors": [ "Minghua Liu", "Chong Zeng", "Xinyue Wei", "Ruoxi Shi", "Linghao Chen", "Chao Xu", "Mengqi Zhang", "Zhaoning Wang", "Xiaoshuai Zhang", "Isabella Liu", "Hongzhi Wu", "Hao Su" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "Open-world 3D reconstruction models have recently garnered significant attention. However, without sufficient 3D inductive bias, existing methods typically entail expensive training costs and struggle to extract high-quality 3D meshes. In this work, we introduce MeshFormer, a sparse-view reconstruction model that explicitly leverages 3D native structure, input guidance, and training supervision. Specifically, instead of using a triplane representation, we store features in 3D sparse voxels and combine transformers with 3D convolutions to leverage an explicit 3D structure and projective bias. In addition to sparse-view RGB input, we require the network to take input and generate corresponding normal maps. The input normal maps can be predicted by 2D diffusion models, significantly aiding in the guidance and refinement of the geometry's learning. Moreover, by combining Signed Distance Function (SDF) supervision with surface rendering, we directly learn to generate high-quality meshes without the need for complex multi-stage training processes. By incorporating these explicit 3D biases, MeshFormer can be trained efficiently and deliver high-quality textured meshes with fine-grained geometric details. It can also be integrated with 2D diffusion models to enable fast single-image-to-3D and text-to-3D tasks. Project page: https://meshformer3d.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-20", "arxiv_id": "2408.10161", "title": "NeuFlow v2: High-Efficiency Optical Flow Estimation on Edge Devices", "authors": [ "Zhiyong Zhang", "Aniket Gupta", "Huaizu Jiang", "Hanumant Singh" ], "github": "https://github.com/neufieldrobotics/NeuFlow_v2", "project_page": "", "abstract": "Real-time high-accuracy optical flow estimation is crucial for various real-world applications. While recent learning-based optical flow methods have achieved high accuracy, they often come with significant computational costs. In this paper, we propose a highly efficient optical flow method that balances high accuracy with reduced computational demands. Building upon NeuFlow v1, we introduce new components including a much more light-weight backbone and a fast refinement module. Both these modules help in keeping the computational demands light while providing close to state of the art accuracy. Compares to other state of the art methods, our model achieves a 10x-70x speedup while maintaining comparable performance on both synthetic and real-world data. It is capable of running at over 20 FPS on 512x384 resolution images on a Jetson Orin Nano. The full training and evaluation code is available at https://github.com/neufieldrobotics/NeuFlow_v2.", "github_manual": "", "github_regex": "https://github.com/neufieldrobotics/NeuFlow_v2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-20", "arxiv_id": "2408.10119", "title": "Factorized-Dreamer: Training A High-Quality Video Generator with Limited and Low-Quality Data", "authors": [ "Tao Yang", "Yangming Shi", "Yunwen Huang", "Feng Chen", "Yin Zheng", "Lei Zhang" ], "github": "https://github.com/yangxy/Factorized-Dreamer", "project_page": "", "abstract": "Text-to-video (T2V) generation has gained significant attention due to its wide applications to video generation, editing, enhancement and translation, \\etc. However, high-quality (HQ) video synthesis is extremely challenging because of the diverse and complex motions existed in real world. Most existing works struggle to address this problem by collecting large-scale HQ videos, which are inaccessible to the community. In this work, we show that publicly available limited and low-quality (LQ) data are sufficient to train a HQ video generator without recaptioning or finetuning. We factorize the whole T2V generation process into two steps: generating an image conditioned on a highly descriptive caption, and synthesizing the video conditioned on the generated image and a concise caption of motion details. Specifically, we present Factorized-Dreamer, a factorized spatiotemporal framework with several critical designs for T2V generation, including an adapter to combine text and image embeddings, a pixel-aware cross attention module to capture pixel-level image information, a T5 text encoder to better understand motion description, and a PredictNet to supervise optical flows. We further present a noise schedule, which plays a key role in ensuring the quality and stability of video generation. Our model lowers the requirements in detailed captions and HQ videos, and can be directly trained on limited LQ datasets with noisy and brief captions such as WebVid-10M, largely alleviating the cost to collect large-scale HQ video-text pairs. Extensive experiments in a variety of T2V and image-to-video generation tasks demonstrate the effectiveness of our proposed Factorized-Dreamer. Our source codes are available at https://github.com/yangxy/Factorized-Dreamer/.", "github_manual": "", "github_regex": "https://github.com/yangxy/Factorized-Dreamer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-20", "arxiv_id": "2408.08926", "title": "Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risk of Language Models", "authors": [ "Andy K. Zhang", "Neil Perry", "Riya Dulepet", "Eliot Jones", "Justin W. Lin", "Joey Ji", "Celeste Menders", "Gashon Hussein", "Samantha Liu", "Donovan Jasper", "Pura Peetathawatchai", "Ari Glenn", "Vikram Sivashankar", "Daniel Zamoshchin", "Leo Glikbarg", "Derek Askaryar", "Mike Yang", "Teddy Zhang", "Rishi Alluri", "Nathan Tran", "Rinnara Sangpisit", "Polycarpos Yiorkadjis", "Kenny Osele", "Gautham Raghupathi", "Dan Boneh", "Daniel E. Ho", "Percy Liang" ], "github": "https://github.com/andyzorigin/cybench", "project_page": "", "abstract": "Language Model (LM) agents for cybersecurity that are capable of autonomously identifying vulnerabilities and executing exploits have the potential to cause real-world impact. Policymakers, model providers, and other researchers in the AI and cybersecurity communities are interested in quantifying the capabilities of such agents to help mitigate cyberrisk and investigate opportunities for penetration testing. Toward that end, we introduce Cybench, a framework for specifying cybersecurity tasks and evaluating agents on those tasks. We include 40 professional-level Capture the Flag (CTF) tasks from 4 distinct CTF competitions, chosen to be recent, meaningful, and spanning a wide range of difficulties. Each task includes its own description, starter files, and is initialized in an environment where an agent can execute bash commands and observe outputs. Since many tasks are beyond the capabilities of existing LM agents, we introduce subtasks, which break down a task into intermediary steps for more gradated evaluation; we add subtasks for 17 of the 40 tasks. To evaluate agent capabilities, we construct a cybersecurity agent and evaluate 7 models: GPT-4o, Claude 3 Opus, Claude 3.5 Sonnet, Mixtral 8x22b Instruct, Gemini 1.5 Pro, Llama 3 70B Chat, and Llama 3.1 405B Instruct. Without guidance, we find that agents are able to solve only the easiest complete tasks that took human teams up to 11 minutes to solve, with Claude 3.5 Sonnet and GPT-4o having the highest success rates. Finally, subtasks provide more signal for measuring performance compared to unguided runs, with models achieving a 3.2\\% higher success rate on complete tasks with subtask-guidance than without subtask-guidance. All code and data are publicly available at https://cybench.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/andyzorigin/cybench", "github_api_added_by": "auto" }, { "date": "2024-08-20", "arxiv_id": "2408.10188", "title": "LongVILA: Scaling Long-Context Visual Language Models for Long Videos", "authors": [ "Fuzhao Xue", "Yukang Chen", "Dacheng Li", "Qinghao Hu", "Ligeng Zhu", "Xiuyu Li", "Yunhao Fang", "Haotian Tang", "Shang Yang", "Zhijian Liu", "Ethan He", "Hongxu Yin", "Pavlo Molchanov", "Jan Kautz", "Linxi Fan", "Yuke Zhu", "Yao Lu", "Song Han" ], "github": "https://github.com/NVlabs/VILA/tree/main/longvila", "project_page": "", "abstract": "Long-context capability is critical for multi-modal foundation models. We introduce LongVILA, a full-stack solution for long-context vision-language models, including system, model training, and dataset development. On the system side, we introduce the first Multi-Modal Sequence Parallelism (MM-SP) system that enables long-context training and inference, enabling 2M context length training on 256 GPUs. MM-SP is also efficient, being 2.1x - 5.7x faster than Ring-Style Sequence Parallelism and 1.1x - 1.4x faster than Megatron-LM in text-only settings. Moreover, it seamlessly integrates with Hugging Face Transformers. For model training, we propose a five-stage pipeline comprising alignment, pre-training, context extension, and long-short joint supervised fine-tuning. Regarding datasets, we meticulously construct large-scale visual language pre-training datasets and long video instruction-following datasets to support our multi-stage training process. The full-stack solution extends the feasible frame number of VILA by a factor of 128 (from 8 to 1024 frames) and improves long video captioning score from 2.00 to 3.26 (1.6x), achieving 99.5% accuracy in 1400-frames video (274k context length) needle in a haystack. LongVILA-8B also demonstrates a consistent improvement in performance on long videos within the VideoMME benchmark as the video frames increase.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVlabs/VILA/tree/main/longvila", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-20", "arxiv_id": "2408.08946", "title": "Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges", "authors": [ "Baixiang Huang", "Canyu Chen", "Kai Shu" ], "github": "", "project_page": "", "abstract": "Accurate attribution of authorship is crucial for maintaining the integrity of digital content, improving forensic investigations, and mitigating the risks of misinformation and plagiarism. Addressing the imperative need for proper authorship attribution is essential to uphold the credibility and accountability of authentic authorship. The rapid advancements of Large Language Models (LLMs) have blurred the lines between human and machine authorship, posing significant challenges for traditional methods. We presents a comprehensive literature review that examines the latest research on authorship attribution in the era of LLMs. This survey systematically explores the landscape of this field by categorizing four representative problems: (1) Human-written Text Attribution; (2) LLM-generated Text Detection; (3) LLM-generated Text Attribution; and (4) Human-LLM Co-authored Text Attribution. We also discuss the challenges related to ensuring the generalization and explainability of authorship attribution methods. Generalization requires the ability to generalize across various domains, while explainability emphasizes providing transparent and understandable insights into the decisions made by these models. By evaluating the strengths and limitations of existing methods and benchmarks, we identify key open problems and future research directions in this field. This literature review serves a roadmap for researchers and practitioners interested in understanding the state of the art in this rapidly evolving field. Additional resources and a curated list of papers are available and regularly updated at https://llm-authorship.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-20", "arxiv_id": "2408.09739", "title": "TraDiffusion: Trajectory-Based Training-Free Image Generation", "authors": [ "Mingrui Wu", "Oucheng Huang", "Jiayi Ji", "Jiale Li", "Xinyue Cai", "Huafeng Kuang", "Jianzhuang Liu", "Xiaoshuai Sun", "Rongrong Ji" ], "github": "https://github.com/och-mac/tradiffusion", "project_page": "", "abstract": "In this work, we propose a training-free, trajectory-based controllable T2I approach, termed TraDiffusion. This novel method allows users to effortlessly guide image generation via mouse trajectories. To achieve precise control, we design a distance awareness energy function to effectively guide latent variables, ensuring that the focus of generation is within the areas defined by the trajectory. The energy function encompasses a control function to draw the generation closer to the specified trajectory and a movement function to diminish activity in areas distant from the trajectory. Through extensive experiments and qualitative assessments on the COCO dataset, the results reveal that TraDiffusion facilitates simpler, more natural image control. Moreover, it showcases the ability to manipulate salient regions, attributes, and relationships within the generated images, alongside visual input based on arbitrary or enhanced trajectories.", "github_manual": "https://github.com/och-mac/tradiffusion", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-20", "arxiv_id": "2408.09702", "title": "Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering", "authors": [ "Ruofan Liang", "Zan Gojcic", "Merlin Nimier-David", "David Acuna", "Nandita Vijaykumar", "Sanja Fidler", "Zian Wang" ], "github": "", "project_page": "", "abstract": "The correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials, as well as the image formation process. While recent large-scale diffusion models have shown strong generative and inpainting capabilities, we find that current models do not sufficiently \"understand\" the scene shown in a single picture to generate consistent lighting effects (shadows, bright reflections, etc.) while preserving the identity and details of the composited object. We propose using a personalized large diffusion model as guidance to a physically based inverse rendering process. Our method recovers scene lighting and tone-mapping parameters, allowing the photorealistic composition of arbitrary virtual objects in single frames or videos of indoor or outdoor scenes. Our physically based pipeline further enables automatic materials and tone-mapping refinement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-20", "arxiv_id": "2408.09085", "title": "Segment Anything with Multiple Modalities", "authors": [ "Aoran Xiao", "Weihao Xuan", "Heli Qi", "Yun Xing", "Naoto Yokoya", "Shijian Lu" ], "github": "https://github.com/weihao1115/mm-sam", "project_page": "", "abstract": "Robust and accurate segmentation of scenes has become one core functionality in various visual recognition and navigation tasks. This has inspired the recent development of Segment Anything Model (SAM), a foundation model for general mask segmentation. However, SAM is largely tailored for single-modal RGB images, limiting its applicability to multi-modal data captured with widely-adopted sensor suites, such as LiDAR plus RGB, depth plus RGB, thermal plus RGB, etc. We develop MM-SAM, an extension and expansion of SAM that supports cross-modal and multi-modal processing for robust and enhanced segmentation with different sensor suites. MM-SAM features two key designs, namely, unsupervised cross-modal transfer and weakly-supervised multi-modal fusion, enabling label-efficient and parameter-efficient adaptation toward various sensor modalities. It addresses three main challenges: 1) adaptation toward diverse non-RGB sensors for single-modal processing, 2) synergistic processing of multi-modal data via sensor fusion, and 3) mask-free training for different downstream tasks. Extensive experiments show that MM-SAM consistently outperforms SAM by large margins, demonstrating its effectiveness and robustness across various sensors and data modalities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/weihao1115/mm-sam", "github_api_added_by": "auto" }, { "date": "2024-08-20", "arxiv_id": "2408.10195", "title": "SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views", "authors": [ "Chao Xu", "Ang Li", "Linghao Chen", "Yulin Liu", "Ruoxi Shi", "Hao Su", "Minghua Liu" ], "github": "", "project_page": "", "abstract": "Open-world 3D generation has recently attracted considerable attention. While many single-image-to-3D methods have yielded visually appealing outcomes, they often lack sufficient controllability and tend to produce hallucinated regions that may not align with users' expectations. In this paper, we explore an important scenario in which the input consists of one or a few unposed 2D images of a single object, with little or no overlap. We propose a novel method, SpaRP, to reconstruct a 3D textured mesh and estimate the relative camera poses for these sparse-view images. SpaRP distills knowledge from 2D diffusion models and finetunes them to implicitly deduce the 3D spatial relationships between the sparse views. The diffusion model is trained to jointly predict surrogate representations for camera poses and multi-view images of the object under known poses, integrating all information from the input sparse views. These predictions are then leveraged to accomplish 3D reconstruction and pose estimation, and the reconstructed 3D model can be used to further refine the camera poses of input views. Through extensive experiments on three datasets, we demonstrate that our method not only significantly outperforms baseline methods in terms of 3D reconstruction quality and pose prediction accuracy but also exhibits strong efficiency. It requires only about 20 seconds to produce a textured mesh and camera poses for the input views. Project page: https://chaoxu.xyz/sparp.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-20", "arxiv_id": "2408.09858", "title": "ShortCircuit: AlphaZero-Driven Circuit Design", "authors": [ "Dimitrios Tsaras", "Antoine Grosnit", "Lei Chen", "Zhiyao Xie", "Haitham Bou-Ammar", "Mingxuan Yuan" ], "github": "", "project_page": "", "abstract": "Chip design relies heavily on generating Boolean circuits, such as AND-Inverter Graphs (AIGs), from functional descriptions like truth tables. While recent advances in deep learning have aimed to accelerate circuit design, these efforts have mostly focused on tasks other than synthesis, and traditional heuristic methods have plateaued. In this paper, we introduce ShortCircuit, a novel transformer-based architecture that leverages the structural properties of AIGs and performs efficient space exploration. Contrary to prior approaches attempting end-to-end generation of logic circuits using deep networks, ShortCircuit employs a two-phase process combining supervised with reinforcement learning to enhance generalization to unseen truth tables. We also propose an AlphaZero variant to handle the double exponentially large state space and the sparsity of the rewards, enabling the discovery of near-optimal designs. To evaluate the generative performance of our trained model , we extract 500 truth tables from a benchmark set of 20 real-world circuits. ShortCircuit successfully generates AIGs for 84.6% of the 8-input test truth tables, and outperforms the state-of-the-art logic synthesis tool, ABC, by 14.61% in terms of circuits size.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.11001", "title": "MegaFusion: Extend Diffusion Models towards Higher-resolution Image Generation without Further Tuning", "authors": [ "Haoning Wu", "Shaocheng Shen", "Qiang Hu", "Xiaoyun Zhang", "Ya Zhang", "Yanfeng Wang" ], "github": "", "project_page": "", "abstract": "Diffusion models have emerged as frontrunners in text-to-image generation for their impressive capabilities. Nonetheless, their fixed image resolution during training often leads to challenges in high-resolution image generation, such as semantic inaccuracies and object replication. This paper introduces MegaFusion, a novel approach that extends existing diffusion-based text-to-image generation models towards efficient higher-resolution generation without additional fine-tuning or extra adaptation. Specifically, we employ an innovative truncate and relay strategy to bridge the denoising processes across different resolutions, allowing for high-resolution image generation in a coarse-to-fine manner. Moreover, by integrating dilated convolutions and noise re-scheduling, we further adapt the model's priors for higher resolution. The versatility and efficacy of MegaFusion make it universally applicable to both latent-space and pixel-space diffusion models, along with other derivative models. Extensive experiments confirm that MegaFusion significantly boosts the capability of existing models to produce images of megapixels and various aspect ratios, while only requiring about 40% of the original computational cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.10998", "title": "Audio Match Cutting: Finding and Creating Matching Audio Transitions in Movies and Videos", "authors": [ "Dennis Fedorishin", "Lie Lu", "Srirangaraj Setlur", "Venu Govindaraju" ], "github": "", "project_page": "", "abstract": "A \"match cut\" is a common video editing technique where a pair of shots that have a similar composition transition fluidly from one to another. Although match cuts are often visual, certain match cuts involve the fluid transition of audio, where sounds from different sources merge into one indistinguishable transition between two shots. In this paper, we explore the ability to automatically find and create \"audio match cuts\" within videos and movies. We create a self-supervised audio representation for audio match cutting and develop a coarse-to-fine audio match pipeline that recommends matching shots and creates the blended audio. We further annotate a dataset for the proposed audio match cut task and compare the ability of multiple audio representations to find audio match cut candidates. Finally, we evaluate multiple methods to blend two matching audio candidates with the goal of creating a smooth transition. Project page and examples are available at: https://denfed.github.io/audiomatchcut/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.10487", "title": "MambaEVT: Event Stream based Visual Object Tracking using State Space Model", "authors": [ "Xiao Wang", "Chao wang", "Shiao Wang", "Xixi Wang", "Zhicheng Zhao", "Lin Zhu", "Bo Jiang" ], "github": "https://github.com/Event-AHU/MambaEVT", "project_page": "", "abstract": "Event camera-based visual tracking has drawn more and more attention in recent years due to the unique imaging principle and advantages of low energy consumption, high dynamic range, and dense temporal resolution. Current event-based tracking algorithms are gradually hitting their performance bottlenecks, due to the utilization of vision Transformer and the static template for target object localization. In this paper, we propose a novel Mamba-based visual tracking framework that adopts the state space model with linear complexity as a backbone network. The search regions and target template are fed into the vision Mamba network for simultaneous feature extraction and interaction. The output tokens of search regions will be fed into the tracking head for target localization. More importantly, we consider introducing a dynamic template update strategy into the tracking framework using the Memory Mamba network. By considering the diversity of samples in the target template library and making appropriate adjustments to the template memory module, a more effective dynamic template can be integrated. The effective combination of dynamic and static templates allows our Mamba-based tracking algorithm to achieve a good balance between accuracy and computational cost on multiple large-scale datasets, including EventVOT, VisEvent, and FE240hz. The source code will be released on https://github.com/Event-AHU/MambaEVT", "github_manual": "", "github_regex": "https://github.com/Event-AHU/MambaEVT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.11049", "title": "MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding", "authors": [ "Jian Chen", "Vashisth Tiwari", "Ranajoy Sadhukhan", "Zhuoming Chen", "Jinyuan Shi", "Ian En-Hsu Yen", "Beidi Chen" ], "github": "https://github.com/infini-ai-lab/magicdec", "project_page": "", "abstract": "Large Language Models (LLMs) have become more prevalent in long-context applications such as interactive chatbots, document analysis, and agent workflows, but it is challenging to serve long-context requests with low latency and high throughput. Speculative decoding (SD) is a widely used technique to reduce latency without sacrificing performance but the conventional wisdom suggests that its efficacy is limited to small batch sizes. In MagicDec, we show that surprisingly SD can achieve speedup even for a high throughput inference regime for moderate to long sequences. More interestingly, an intelligent drafting strategy can achieve better speedup with increasing batch size based on our rigorous analysis. MagicDec first identifies the bottleneck shifts with increasing batch size and sequence length, and uses these insights to deploy speculative decoding more effectively for high throughput inference. Then, it leverages draft models with sparse KV cache to address the KV bottleneck that scales with both sequence length and batch size.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/infini-ai-lab/magicdec", "github_api_added_by": "auto" }, { "date": "2024-08-21", "arxiv_id": "2408.11039", "title": "Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model", "authors": [ "Chunting Zhou", "Lili Yu", "Arun Babu", "Kushal Tirumala", "Michihiro Yasunaga", "Leonid Shamis", "Jacob Kahn", "Xuezhe Ma", "Luke Zettlemoyer", "Omer Levy" ], "github": "", "project_page": "", "abstract": "We introduce Transfusion, a recipe for training a multi-modal model over discrete and continuous data. Transfusion combines the language modeling loss function (next token prediction) with diffusion to train a single transformer over mixed-modality sequences. We pretrain multiple Transfusion models up to 7B parameters from scratch on a mixture of text and image data, establishing scaling laws with respect to a variety of uni- and cross-modal benchmarks. Our experiments show that Transfusion scales significantly better than quantizing images and training a language model over discrete image tokens. By introducing modality-specific encoding and decoding layers, we can further improve the performance of Transfusion models, and even compress each image to just 16 patches. We further demonstrate that scaling our Transfusion recipe to 7B parameters and 2T multi-modal tokens produces a model that can generate images and text on a par with similar scale diffusion models and language models, reaping the benefits of both worlds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.10906", "title": "ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining", "authors": [ "Qi Ma", "Yue Li", "Bin Ren", "Nicu Sebe", "Ender Konukoglu", "Theo Gevers", "Luc Van Gool", "Danda Pani Paudel" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) has become the de facto method of 3D representation in many vision tasks. This calls for the 3D understanding directly in this representation space. To facilitate the research in this direction, we first build a large-scale dataset of 3DGS using the commonly used ShapeNet and ModelNet datasets. Our dataset ShapeSplat consists of 65K objects from 87 unique categories, whose labels are in accordance with the respective datasets. The creation of this dataset utilized the compute equivalent of 2 GPU years on a TITAN XP GPU. We utilize our dataset for unsupervised pretraining and supervised finetuning for classification and segmentation tasks. To this end, we introduce \\textit{Gaussian-MAE}, which highlights the unique benefits of representation learning from Gaussian parameters. Through exhaustive experiments, we provide several valuable insights. In particular, we show that (1) the distribution of the optimized GS centroids significantly differs from the uniformly sampled point cloud (used for initialization) counterpart; (2) this change in distribution results in degradation in classification but improvement in segmentation tasks when using only the centroids; (3) to leverage additional Gaussian parameters, we propose Gaussian feature grouping in a normalized feature space, along with splats pooling layer, offering a tailored solution to effectively group and embed similar Gaussians, which leads to notable improvement in finetuning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.10914", "title": "To Code, or Not To Code? Exploring Impact of Code in Pre-training", "authors": [ "Viraat Aryabumi", "Yixuan Su", "Raymond Ma", "Adrien Morisot", "Ivan Zhang", "Acyr Locatelli", "Marzieh Fadaee", "Ahmet \u00dcst\u00fcn", "Sara Hooker" ], "github": "", "project_page": "", "abstract": "Including code in the pre-training data mixture, even for models not specifically designed for code, has become a common practice in LLMs pre-training. While there has been anecdotal consensus among practitioners that code data plays a vital role in general LLMs' performance, there is only limited work analyzing the precise impact of code on non-code tasks. In this work, we systematically investigate the impact of code data on general performance. We ask \"what is the impact of code data used in pre-training on a large variety of downstream tasks beyond code generation\". We conduct extensive ablations and evaluate across a broad range of natural language reasoning tasks, world knowledge tasks, code benchmarks, and LLM-as-a-judge win-rates for models with sizes ranging from 470M to 2.8B parameters. Across settings, we find a consistent results that code is a critical building block for generalization far beyond coding tasks and improvements to code quality have an outsized impact across all tasks. In particular, compared to text-only pre-training, the addition of code results in up to relative increase of 8.2% in natural language (NL) reasoning, 4.2% in world knowledge, 6.6% improvement in generative win-rates, and a 12x boost in code performance respectively. Our work suggests investments in code quality and preserving code during pre-training have positive impacts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.11048", "title": "RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands", "authors": [ "Yi Zhao", "Le Chen", "Jan Schneider", "Quankai Gao", "Juho Kannala", "Bernhard Sch\u00f6lkopf", "Joni Pajarinen", "Dieter B\u00fcchler" ], "github": "", "project_page": "", "abstract": "It has been a long-standing research goal to endow robot hands with human-level dexterity. Bi-manual robot piano playing constitutes a task that combines challenges from dynamic tasks, such as generating fast while precise motions, with slower but contact-rich manipulation problems. Although reinforcement learning based approaches have shown promising results in single-task performance, these methods struggle in a multi-song setting. Our work aims to close this gap and, thereby, enable imitation learning approaches for robot piano playing at scale. To this end, we introduce the Robot Piano 1 Million (RP1M) dataset, containing bi-manual robot piano playing motion data of more than one million trajectories. We formulate finger placements as an optimal transport problem, thus, enabling automatic annotation of vast amounts of unlabeled songs. Benchmarking existing imitation learning approaches shows that such approaches reach state-of-the-art robot piano playing performance by leveraging RP1M.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.10764", "title": "Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model", "authors": [ "Chenhan Yuan", "Fei Huang", "Ru Peng", "Keming Lu", "Bowen Yu", "Chang Zhou", "Jingren Zhou" ], "github": "https://github.com/chenhan97/Otter", "project_page": "", "abstract": "Transformer-based large language models (LLMs) exhibit limitations such as generating unsafe responses, unreliable reasoning, etc. Existing inference intervention approaches attempt to mitigate these issues by finetuning additional models to produce calibration signals (such as rewards) that guide the LLM's decoding process. However, this solution introduces substantial time and space overhead due to the separate models required. This work proposes Non-disruptive parameters insertion (Otter), inserting extra parameters into the transformer architecture to predict calibration signals along with the original LLM output. Otter offers state-of-the-art performance on multiple demanding tasks while saving up to 86.5\\% extra space and 98.5\\% extra time. Furthermore, Otter seamlessly integrates with existing inference engines, requiring only a one-line code change, and the original model response remains accessible after the parameter insertion. Our code is publicly available at https://github.com/chenhan97/Otter", "github_manual": "", "github_regex": "https://github.com/chenhan97/Otter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.09174", "title": "TableBench: A Comprehensive and Complex Benchmark for Table Question Answering", "authors": [ "Xianjie Wu", "Jian Yang", "Linzheng Chai", "Ge Zhang", "Jiaheng Liu", "Xinrun Du", "Di Liang", "Daixin Shu", "Xianfu Cheng", "Tianzhen Sun", "Guanglin Niu", "Tongliang Li", "Zhoujun Li" ], "github": "https://github.com/TableBench/TableBench", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) have markedly enhanced the interpretation and processing of tabular data, introducing previously unimaginable capabilities. Despite these achievements, LLMs still encounter significant challenges when applied in industrial scenarios, particularly due to the increased complexity of reasoning required with real-world tabular data, underscoring a notable disparity between academic benchmarks and practical applications. To address this discrepancy, we conduct a detailed investigation into the application of tabular data in industrial scenarios and propose a comprehensive and complex benchmark TableBench, including 18 fields within four major categories of table question answering (TableQA) capabilities. Furthermore, we introduce TableLLM, trained on our meticulously constructed training set TableInstruct, achieving comparable performance with GPT-3.5. Massive experiments conducted on TableBench indicate that both open-source and proprietary LLMs still have significant room for improvement to meet real-world demands, where the most advanced model, GPT-4, achieves only a modest score compared to humans.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/TableBench/TableBench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.10446", "title": "The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks", "authors": [ "Niyar R Barman", "Krish Sharma", "Ashhar Aziz", "Shashwat Bajpai", "Shwetangshu Biswas", "Vasu Sharma", "Vinija Jain", "Aman Chadha", "Amit Sheth", "Amitava Das" ], "github": "", "project_page": "", "abstract": "The rapid advancement of text-to-image generation systems, exemplified by models like Stable Diffusion, Midjourney, Imagen, and DALL-E, has heightened concerns about their potential misuse. In response, companies like Meta and Google have intensified their efforts to implement watermarking techniques on AI-generated images to curb the circulation of potentially misleading visuals. However, in this paper, we argue that current image watermarking methods are fragile and susceptible to being circumvented through visual paraphrase attacks. The proposed visual paraphraser operates in two steps. First, it generates a caption for the given image using KOSMOS-2, one of the latest state-of-the-art image captioning systems. Second, it passes both the original image and the generated caption to an image-to-image diffusion system. During the denoising step of the diffusion pipeline, the system generates a visually similar image that is guided by the text caption. The resulting image is a visual paraphrase and is free of any watermarks. Our empirical findings demonstrate that visual paraphrase attacks can effectively remove watermarks from images. This paper provides a critical assessment, empirically revealing the vulnerability of existing watermarking techniques to visual paraphrase attacks. While we do not propose solutions to this issue, this paper serves as a call to action for the scientific community to prioritize the development of more robust watermarking techniques. Our first-of-its-kind visual paraphrase dataset and accompanying code are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.10088", "title": "Recent Surge in Public Interest in Transportation: Sentiment Analysis of Baidu Apollo Go Using Weibo Data", "authors": [ "Shiqi Wang", "Zhouye Zhao", "Yuhang Xie", "Mingchuan Ma", "Zirui Chen", "Zeyu Wang", "Bohao Su", "Wenrui Xu", "Tianyi Li" ], "github": "https://github.com/GIStudio/trb2024", "project_page": "", "abstract": "Urban mobility and transportation systems have been profoundly transformed by the advancement of autonomous vehicle technologies. Baidu Apollo Go, a pioneer robotaxi service from the Chinese tech giant Baidu, has recently been widely deployed in major cities like Beijing and Wuhan, sparking increased conversation and offering a glimpse into the future of urban mobility. This study investigates public attitudes towards Apollo Go across China using Sentiment Analysis with a hybrid BERT model on 36,096 Weibo posts from January to July 2024. The analysis shows that 89.56\\% of posts related to Apollo Go are clustered in July. From January to July, public sentiment was mostly positive, but negative comments began to rise after it became a hot topic on July 21. Spatial analysis indicates a strong correlation between provinces with high discussion intensity and those where Apollo Go operates. Initially, Hubei and Guangdong dominated online posting volume, but by July, Guangdong, Beijing, and international regions had overtaken Hubei. Attitudes varied significantly among provinces, with Xinjiang and Qinghai showing optimism and Tibet and Gansu expressing concerns about the impact on traditional taxi services. Sentiment analysis revealed that positive comments focused on technology applications and personal experiences, while negative comments centered on job displacement and safety concerns. In summary, this study highlights the divergence in public perceptions of autonomous ride-hailing services, providing valuable insights for planners, policymakers, and service providers. The model is published on Hugging Face at https://huggingface.co/wsqstar/bert-finetuned-weibo-luobokuaipao and the repository on GitHub at https://github.com/GIStudio/trb2024.", "github_manual": "", "github_regex": "https://github.com/GIStudio/trb2024", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.10701", "title": "Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring Technique", "authors": [ "Tej Deep Pala", "Vernon Y. H. Toh", "Rishabh Bhardwaj", "Soujanya Poria" ], "github": "https://github.com/declare-lab/ferret", "project_page": "", "abstract": "In today's era, where large language models (LLMs) are integrated into numerous real-world applications, ensuring their safety and robustness is crucial for responsible AI usage. Automated red-teaming methods play a key role in this process by generating adversarial attacks to identify and mitigate potential vulnerabilities in these models. However, existing methods often struggle with slow performance, limited categorical diversity, and high resource demands. While Rainbow Teaming, a recent approach, addresses the diversity challenge by framing adversarial prompt generation as a quality-diversity search, it remains slow and requires a large fine-tuned mutator for optimal performance. To overcome these limitations, we propose Ferret, a novel approach that builds upon Rainbow Teaming by generating multiple adversarial prompt mutations per iteration and using a scoring function to rank and select the most effective adversarial prompt. We explore various scoring functions, including reward models, Llama Guard, and LLM-as-a-judge, to rank adversarial mutations based on their potential harm to improve the efficiency of the search for harmful mutations. Our results demonstrate that Ferret, utilizing a reward model as a scoring function, improves the overall attack success rate (ASR) to 95%, which is 46% higher than Rainbow Teaming. Additionally, Ferret reduces the time needed to achieve a 90% ASR by 15.2% compared to the baseline and generates adversarial prompts that are transferable i.e. effective on other LLMs of larger size. Our codes are available at https://github.com/declare-lab/ferret.", "github_manual": "", "github_regex": "https://github.com/declare-lab/ferret", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.09574", "title": "PhysBERT: A Text Embedding Model for Physics Scientific Literature", "authors": [ "Thorsten Hellert", "Jo\u00e3o Montenegro", "Andrea Pollastro" ], "github": "", "project_page": "", "abstract": "The specialized language and complex concepts in physics pose significant challenges for information extraction through Natural Language Processing (NLP). Central to effective NLP applications is the text embedding model, which converts text into dense vector representations for efficient information retrieval and semantic analysis. In this work, we introduce PhysBERT, the first physics-specific text embedding model. Pre-trained on a curated corpus of 1.2 million arXiv physics papers and fine-tuned with supervised data, PhysBERT outperforms leading general-purpose models on physics-specific tasks including the effectiveness in fine-tuning for specific physics subdomains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-21", "arxiv_id": "2408.11054", "title": "NeCo: Improving DINOv2's spatial representations in 19 GPU hours with Patch Neighbor Consistency", "authors": [ "Valentinos Pariza", "Mohammadreza Salehi", "Gertjan Burghouts", "Francesco Locatello", "Yuki M. Asano" ], "github": "", "project_page": "", "abstract": "We propose sorting patch representations across views as a novel self-supervised learning signal to improve pretrained representations. To this end, we introduce NeCo: Patch Neighbor Consistency, a novel training loss that enforces patch-level nearest neighbor consistency across a student and teacher model, relative to reference batches. Our method leverages a differentiable sorting method applied on top of pretrained representations, such as DINOv2-registers to bootstrap the learning signal and further improve upon them. This dense post-pretraining leads to superior performance across various models and datasets, despite requiring only 19 hours on a single GPU. We demonstrate that this method generates high-quality dense feature encoders and establish several new state-of-the-art results: +5.5% and + 6% for non-parametric in-context semantic segmentation on ADE20k and Pascal VOC, and +7.2% and +5.7% for linear segmentation evaluations on COCO-Things and -Stuff.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-22", "arxiv_id": "2408.11318", "title": "TWLV-I: Analysis and Insights from Holistic Evaluation on Video Foundation Models", "authors": [ "Hyeongmin Lee", "Jin-Young Kim", "Kyungjune Baek", "Jihwan Kim", "Hyojun Go", "Seongsu Ha", "Seokjin Han", "Jiho Jang", "Raehyuk Jung", "Daewoo Kim", "GeunOh Kim", "JongMok Kim", "Jongseok Kim", "Junwan Kim", "Soonwoo Kwon", "Jangwon Lee", "Seungjoon Park", "Minjoon Seo", "Jay Suh", "Jaehyuk Yi", "Aiden Lee" ], "github": "https://github.com/twelvelabs-io/video-embeddings-evaluation-framework", "project_page": "", "abstract": "In this work, we discuss evaluating video foundation models in a fair and robust manner. Unlike language or image foundation models, many video foundation models are evaluated with differing parameters (such as sampling rate, number of frames, pretraining steps, etc.), making fair and robust comparisons challenging. Therefore, we present a carefully designed evaluation framework for measuring two core capabilities of video comprehension: appearance and motion understanding. Our findings reveal that existing video foundation models, whether text-supervised like UMT or InternVideo2, or self-supervised like V-JEPA, exhibit limitations in at least one of these capabilities. As an alternative, we introduce TWLV-I, a new video foundation model that constructs robust visual representations for both motion- and appearance-based videos. Based on the average top-1 accuracy of linear probing on five action recognition benchmarks, pretrained only on publicly accessible datasets, our model shows a 4.6%p improvement compared to V-JEPA (ViT-L) and a 7.7%p improvement compared to UMT (ViT-L). Even when compared to much larger models, our model demonstrates a 7.2%p improvement compared to DFN (ViT-H), a 2.7%p improvement compared to V-JEPA~(ViT-H) and a 2.8%p improvement compared to InternVideo2 (ViT-g). We provide embedding vectors obtained by TWLV-I from videos of several commonly used video benchmarks, along with evaluation source code that can directly utilize these embeddings. The code is available on \"https://github.com/twelvelabs-io/video-embeddings-evaluation-framework\".", "github_manual": "", "github_regex": "https://github.com/twelvelabs-io/video-embeddings-evaluation-framework", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-22", "arxiv_id": "2408.11475", "title": "TrackGo: A Flexible and Efficient Method for Controllable Video Generation", "authors": [ "Haitao Zhou", "Chuang Wang", "Rui Nie", "Jinxiao Lin", "Dongdong Yu", "Qian Yu", "Changhu Wang" ], "github": "", "project_page": "", "abstract": "Recent years have seen substantial progress in diffusion-based controllable video generation. However, achieving precise control in complex scenarios, including fine-grained object parts, sophisticated motion trajectories, and coherent background movement, remains a challenge. In this paper, we introduce TrackGo, a novel approach that leverages free-form masks and arrows for conditional video generation. This method offers users with a flexible and precise mechanism for manipulating video content. We also propose the TrackAdapter for control implementation, an efficient and lightweight adapter designed to be seamlessly integrated into the temporal self-attention layers of a pretrained video generation model. This design leverages our observation that the attention map of these layers can accurately activate regions corresponding to motion in videos. Our experimental results demonstrate that our new approach, enhanced by the TrackAdapter, achieves state-of-the-art performance on key metrics such as FVD, FID, and ObjMC scores. The project page of TrackGo can be found at: https://zhtjtcz.github.io/TrackGo-Page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-22", "arxiv_id": "2408.11745", "title": "FocusLLM: Scaling LLM's Context by Parallel Decoding", "authors": [ "Zhenyu Li", "Yike Zhang", "Tengyu Pan", "Yutao Sun", "Zhichao Duan", "Junjie Fang", "Rong Han", "Zixuan Wang", "Jianyong Wang" ], "github": "https://github.com/leezythu/FocusLLM", "project_page": "", "abstract": "Empowering LLMs with the ability to utilize useful information from a long context is crucial for many downstream applications. However, achieving long context lengths with the conventional transformer architecture requires substantial training and inference resources. In this paper, we present FocusLLM, a framework designed to extend the context length of any decoder-only LLM, enabling the model to focus on relevant information from very long sequences. FocusLLM processes long text inputs by dividing them into chunks based on the model's original context length to alleviate the issue of attention distraction. Then, it appends the local context to each chunk as a prompt to extract essential information from each chunk based on a novel parallel decoding mechanism, and ultimately integrates the extracted information into the local context. FocusLLM stands out for great training efficiency and versatility: trained with an 8K input length with much less training cost than previous methods, FocusLLM exhibits superior performance across downstream long-context tasks and maintains strong language modeling ability when handling extensive long texts, even up to 400K tokens. Our code is available at https://github.com/leezythu/FocusLLM.", "github_manual": "", "github_regex": "https://github.com/leezythu/FocusLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-22", "arxiv_id": "2408.11796", "title": "LLM Pruning and Distillation in Practice: The Minitron Approach", "authors": [ "Sharath Turuvekere Sreenivas", "Saurav Muralidharan", "Raviraj Joshi", "Marcin Chochowski", "Mostofa Patwary", "Mohammad Shoeybi", "Bryan Catanzaro", "Jan Kautz", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "We present a comprehensive report on compressing the Llama 3.1 8B and Mistral NeMo 12B models to 4B and 8B parameters, respectively, using pruning and distillation. We explore two distinct pruning strategies: (1) depth pruning and (2) joint hidden/attention/MLP (width) pruning, and evaluate the results on common benchmarks from the LM Evaluation Harness. The models are then aligned with NeMo Aligner and tested in instruct-tuned versions. This approach produces a compelling 4B model from Llama 3.1 8B and a state-of-the-art Mistral-NeMo-Minitron-8B (MN-Minitron-8B for brevity) model from Mistral NeMo 12B. We found that with no access to the original data, it is beneficial to slightly fine-tune teacher models on the distillation dataset. We open-source our base model weights on Hugging Face with a permissive license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-22", "arxiv_id": "2408.11457", "title": "Expanding FLORES+ Benchmark for more Low-Resource Settings: Portuguese-Emakhuwa Machine Translation Evaluation", "authors": [ "Felermino D. M. Antonio Ali", "Henrique Lopes Cardoso", "Rui Sousa-Silva" ], "github": "", "project_page": "", "abstract": "As part of the Open Language Data Initiative shared tasks, we have expanded the FLORES+ evaluation set to include Emakhuwa, a low-resource language widely spoken in Mozambique. We translated the dev and devtest sets from Portuguese into Emakhuwa, and we detail the translation process and quality assurance measures used. Our methodology involved various quality checks, including post-editing and adequacy assessments. The resulting datasets consist of multiple reference sentences for each source. We present baseline results from training a Neural Machine Translation system and fine-tuning existing multilingual translation models. Our findings suggest that spelling inconsistencies remain a challenge in Emakhuwa. Additionally, the baseline models underperformed on this evaluation set, underscoring the necessity for further research to enhance machine translation quality for Emakhuwa. The data is publicly available at https://huggingface.co/datasets/LIACC/Emakhuwa-FLORES.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-22", "arxiv_id": "2408.11706", "title": "FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting", "authors": [ "Liyao Jiang", "Negar Hassanpour", "Mohammad Salameh", "Mohan Sai Singamsetti", "Fengyu Sun", "Wei Lu", "Di Niu" ], "github": "https://github.com/LiyaoJiang1998/FRAP", "project_page": "", "abstract": "Text-to-image (T2I) diffusion models have demonstrated impressive capabilities in generating high-quality images given a text prompt. However, ensuring the prompt-image alignment remains a considerable challenge, i.e., generating images that faithfully align with the prompt's semantics. Recent works attempt to improve the faithfulness by optimizing the latent code, which potentially could cause the latent code to go out-of-distribution and thus produce unrealistic images. In this paper, we propose FRAP, a simple, yet effective approach based on adaptively adjusting the per-token prompt weights to improve prompt-image alignment and authenticity of the generated images. We design an online algorithm to adaptively update each token's weight coefficient, which is achieved by minimizing a unified objective function that encourages object presence and the binding of object-modifier pairs. Through extensive evaluations, we show FRAP generates images with significantly higher prompt-image alignment to prompts from complex datasets, while having a lower average latency compared to recent latent code optimization methods, e.g., 4 seconds faster than D&B on the COCO-Subject dataset. Furthermore, through visual comparisons and evaluation on the CLIP-IQA-Real metric, we show that FRAP not only improves prompt-image alignment but also generates more authentic images with realistic appearances. We also explore combining FRAP with prompt rewriting LLM to recover their degraded prompt-image alignment, where we observe improvements in both prompt-image alignment and image quality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LiyaoJiang1998/FRAP", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-22", "arxiv_id": "2408.08793", "title": "Backward-Compatible Aligned Representations via an Orthogonal Transformation Layer", "authors": [ "Simone Ricci", "Niccol\u00f2 Biondi", "Federico Pernici", "Alberto Del Bimbo" ], "github": "https://github.com/z3n0e/oca", "project_page": "", "abstract": "Visual retrieval systems face significant challenges when updating models with improved representations due to misalignment between the old and new representations. The costly and resource-intensive backfilling process involves recalculating feature vectors for images in the gallery set whenever a new model is introduced. To address this, prior research has explored backward-compatible training methods that enable direct comparisons between new and old representations without backfilling. Despite these advancements, achieving a balance between backward compatibility and the performance of independently trained models remains an open problem. In this paper, we address it by expanding the representation space with additional dimensions and learning an orthogonal transformation to achieve compatibility with old models and, at the same time, integrate new information. This transformation preserves the original feature space's geometry, ensuring that our model aligns with previous versions while also learning new data. Our Orthogonal Compatible Aligned (OCA) approach eliminates the need for re-indexing during model updates and ensures that features can be compared directly across different model updates without additional mapping functions. Experimental results on CIFAR-100 and ImageNet-1k demonstrate that our method not only maintains compatibility with previous models but also achieves state-of-the-art accuracy, outperforming several existing methods.", "github_manual": "https://github.com/z3n0e/oca", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-22", "arxiv_id": "2408.11817", "title": "GRAB: A Challenging GRaph Analysis Benchmark for Large Multimodal Models", "authors": [ "Jonathan Roberts", "Kai Han", "Samuel Albanie" ], "github": "", "project_page": "", "abstract": "Large multimodal models (LMMs) have exhibited proficiencies across many visual tasks. Although numerous well-known benchmarks exist to evaluate model performance, they increasingly have insufficient headroom. As such, there is a pressing need for a new generation of benchmarks challenging enough for the next generation of LMMs. One area that LMMs show potential is graph analysis, specifically, the tasks an analyst might typically perform when interpreting figures such as estimating the mean, intercepts or correlations of functions and data series. In this work, we introduce GRAB, a graph analysis benchmark, fit for current and future frontier LMMs. Our benchmark is entirely synthetic, ensuring high-quality, noise-free questions. GRAB is comprised of 2170 questions, covering four tasks and 23 graph properties. We evaluate 20 LMMs on GRAB, finding it to be a challenging benchmark, with the highest performing model attaining a score of just 21.7%. Finally, we conduct various ablations to investigate where the models succeed and struggle. We release GRAB to encourage progress in this important, growing domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-22", "arxiv_id": "2408.11721", "title": "Iterative Object Count Optimization for Text-to-image Diffusion Models", "authors": [ "Oz Zafar", "Lior Wolf", "Idan Schwartz" ], "github": "", "project_page": "", "abstract": "We address a persistent challenge in text-to-image models: accurately generating a specified number of objects. Current models, which learn from image-text pairs, inherently struggle with counting, as training data cannot depict every possible number of objects for any given object. To solve this, we propose optimizing the generated image based on a counting loss derived from a counting model that aggregates an object\\'s potential. Employing an out-of-the-box counting model is challenging for two reasons: first, the model requires a scaling hyperparameter for the potential aggregation that varies depending on the viewpoint of the objects, and second, classifier guidance techniques require modified models that operate on noisy intermediate diffusion steps. To address these challenges, we propose an iterated online training mode that improves the accuracy of inferred images while altering the text conditioning embedding and dynamically adjusting hyperparameters. Our method offers three key advantages: (i) it can consider non-derivable counting techniques based on detection models, (ii) it is a zero-shot plug-and-play solution facilitating rapid changes to the counting techniques and image generation methods, and (iii) the optimized counting token can be reused to generate accurate images without additional optimization. We evaluate the generation of various objects and show significant improvements in accuracy. The project page is available at https://ozzafar.github.io/count_token.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-22", "arxiv_id": "2408.11812", "title": "Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation", "authors": [ "Ria Doshi", "Homer Walke", "Oier Mees", "Sudeep Dasari", "Sergey Levine" ], "github": "https://github.com/rail-berkeley/crossformer", "project_page": "", "abstract": "Modern machine learning systems rely on large datasets to attain broad generalization, and this often poses a challenge in robot learning, where each robotic platform and task might have only a small dataset. By training a single policy across many different kinds of robots, a robot learning method can leverage much broader and more diverse datasets, which in turn can lead to better generalization and robustness. However, training a single policy on multi-robot data is challenging because robots can have widely varying sensors, actuators, and control frequencies. We propose CrossFormer, a scalable and flexible transformer-based policy that can consume data from any embodiment. We train CrossFormer on the largest and most diverse dataset to date, 900K trajectories across 20 different robot embodiments. We demonstrate that the same network weights can control vastly different robots, including single and dual arm manipulation systems, wheeled robots, quadcopters, and quadrupeds. Unlike prior work, our model does not require manual alignment of the observation or action spaces. Extensive experiments in the real world show that our method matches the performance of specialist policies tailored for each embodiment, while also significantly outperforming the prior state of the art in cross-embodiment learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rail-berkeley/crossformer", "github_api_added_by": "auto" }, { "date": "2024-08-22", "arxiv_id": "2408.11247", "title": "Unboxing Occupational Bias: Grounded Debiasing LLMs with U.S. Labor Data", "authors": [ "Atmika Gorti", "Manas Gaur", "Aman Chadha" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are prone to inheriting and amplifying societal biases embedded within their training data, potentially reinforcing harmful stereotypes related to gender, occupation, and other sensitive categories. This issue becomes particularly problematic as biased LLMs can have far-reaching consequences, leading to unfair practices and exacerbating social inequalities across various domains, such as recruitment, online content moderation, or even the criminal justice system. Although prior research has focused on detecting bias in LLMs using specialized datasets designed to highlight intrinsic biases, there has been a notable lack of investigation into how these findings correlate with authoritative datasets, such as those from the U.S. National Bureau of Labor Statistics (NBLS). To address this gap, we conduct empirical research that evaluates LLMs in a ``bias-out-of-the-box\" setting, analyzing how the generated outputs compare with the distributions found in NBLS data. Furthermore, we propose a straightforward yet effective debiasing mechanism that directly incorporates NBLS instances to mitigate bias within LLMs. Our study spans seven different LLMs, including instructable, base, and mixture-of-expert models, and reveals significant levels of bias that are often overlooked by existing bias detection techniques. Importantly, our debiasing method, which does not rely on external datasets, demonstrates a substantial reduction in bias scores, highlighting the efficacy of our approach in creating fairer and more reliable LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-22", "arxiv_id": "2408.11237", "title": "Out-of-Distribution Detection with Attention Head Masking for Multimodal Document Classification", "authors": [ "Christos Constantinou", "Georgios Ioannides", "Aman Chadha", "Aaron Elkins", "Edwin Simpson" ], "github": "https://github.com/constantinouchristos/ood-ahm", "project_page": "", "abstract": "Detecting out-of-distribution (OOD) data is crucial in machine learning applications to mitigate the risk of model overconfidence, thereby enhancing the reliability and safety of deployed systems. The majority of existing OOD detection methods predominantly address uni-modal inputs, such as images or texts. In the context of multi-modal documents, there is a notable lack of extensive research on the performance of these methods, which have primarily been developed with a focus on computer vision tasks. We propose a novel methodology termed as attention head masking (AHM) for multi-modal OOD tasks in document classification systems. Our empirical results demonstrate that the proposed AHM method outperforms all state-of-the-art approaches and significantly decreases the false positive rate (FPR) compared to existing solutions up to 7.5\\%. This methodology generalizes well to multi-modal data, such as documents, where visual and textual information are modeled under the same Transformer architecture. To address the scarcity of high-quality publicly available document datasets and encourage further research on OOD detection for documents, we introduce FinanceDocs, a new document AI dataset. Our code and dataset are publicly available.", "github_manual": "https://github.com/constantinouchristos/ood-ahm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.12599", "title": "Controllable Text Generation for Large Language Models: A Survey", "authors": [ "Xun Liang", "Hanyu Wang", "Yezhaohui Wang", "Shichao Song", "Jiawei Yang", "Simin Niu", "Jie Hu", "Dan Liu", "Shunyu Yao", "Feiyu Xiong", "Zhiyu Li" ], "github": "https://github.com/IAAR-Shanghai/CTGSurvey", "project_page": "", "abstract": "In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality. However, in real-world applications, LLMs must meet increasingly complex requirements. Beyond avoiding misleading or inappropriate content, LLMs are also expected to cater to specific user needs, such as imitating particular writing styles or generating text with poetic richness. These varied demands have driven the development of Controllable Text Generation (CTG) techniques, which ensure that outputs adhere to predefined control conditions--such as safety, sentiment, thematic consistency, and linguistic style--while maintaining high standards of helpfulness, fluency, and diversity. This paper systematically reviews the latest advancements in CTG for LLMs, offering a comprehensive definition of its core concepts and clarifying the requirements for control conditions and text quality. We categorize CTG tasks into two primary types: content control and attribute control. The key methods are discussed, including model retraining, fine-tuning, reinforcement learning, prompt engineering, latent space manipulation, and decoding-time intervention. We analyze each method's characteristics, advantages, and limitations, providing nuanced insights for achieving generation control. Additionally, we review CTG evaluation methods, summarize its applications across domains, and address key challenges in current research, including reduced fluency and practicality. We also propose several appeals, such as placing greater emphasis on real-world applications in future research. This paper aims to offer valuable guidance to researchers and developers in the field. Our reference list and Chinese version are open-sourced at https://github.com/IAAR-Shanghai/CTGSurvey.", "github_manual": "", "github_regex": "https://github.com/IAAR-Shanghai/CTGSurvey", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.11857", "title": "Hermes 3 Technical Report", "authors": [ "Ryan Teknium", "Jeffrey Quesnelle", "Chen Guang" ], "github": "", "project_page": "", "abstract": "Instruct (or \"chat\") tuned models have become the primary way in which most people interact with large language models. As opposed to \"base\" or \"foundation\" models, instruct-tuned models are optimized to respond to imperative statements. We present Hermes 3, a neutrally-aligned generalist instruct and tool use model with strong reasoning and creative abilities. Its largest version, Hermes 3 405B, achieves state of the art performance among open weight models on several public benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.12590", "title": "xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations", "authors": [ "Can Qin", "Congying Xia", "Krithika Ramakrishnan", "Michael Ryoo", "Lifu Tu", "Yihao Feng", "Manli Shu", "Honglu Zhou", "Anas Awadalla", "Jun Wang", "Senthil Purushwalkam", "Le Xue", "Yingbo Zhou", "Huan Wang", "Silvio Savarese", "Juan Carlos Niebles", "Zeyuan Chen", "Ran Xu", "Caiming Xiong" ], "github": "", "project_page": "", "abstract": "We present xGen-VideoSyn-1, a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. Building on recent advancements, such as OpenAI's Sora, we explore the latent diffusion model (LDM) architecture and introduce a video variational autoencoder (VidVAE). VidVAE compresses video data both spatially and temporally, significantly reducing the length of visual tokens and the computational demands associated with generating long-sequence videos. To further address the computational costs, we propose a divide-and-merge strategy that maintains temporal consistency across video segments. Our Diffusion Transformer (DiT) model incorporates spatial and temporal self-attention layers, enabling robust generalization across different timeframes and aspect ratios. We have devised a data processing pipeline from the very beginning and collected over 13M high-quality video-text pairs. The pipeline includes multiple steps such as clipping, text detection, motion estimation, aesthetics scoring, and dense captioning based on our in-house video-LLM model. Training the VidVAE and DiT models required approximately 40 and 642 H100 days, respectively. Our model supports over 14-second 720p video generation in an end-to-end way and demonstrates competitive performance against state-of-the-art T2V models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.10635", "title": "Strategist: Learning Strategic Skills by LLMs via Bi-Level Tree Search", "authors": [ "Jonathan Light", "Min Cai", "Weiqin Chen", "Guanzhi Wang", "Xiusi Chen", "Wei Cheng", "Yisong Yue", "Ziniu Hu" ], "github": "", "project_page": "", "abstract": "In this paper, we propose a new method Strategist that utilizes LLMs to acquire new skills for playing multi-agent games through a self-improvement process. Our method gathers quality feedback through self-play simulations with Monte Carlo tree search and LLM-based reflection, which can then be used to learn high-level strategic skills such as how to evaluate states that guide the low-level execution.We showcase how our method can be used in both action planning and dialogue generation in the context of games, achieving good performance on both tasks. Specifically, we demonstrate that our method can help train agents with better performance than both traditional reinforcement learning-based approaches and other LLM-based skill learning approaches in games including the Game of Pure Strategy (GOPS) and The Resistance: Avalon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.12245", "title": "Scalable Autoregressive Image Generation with Mamba", "authors": [ "Haopeng Li", "Jinyue Yang", "Kexin Wang", "Xuerui Qiu", "Yuhong Chou", "Xin Li", "Guoqi Li" ], "github": "https://github.com/hp-l33/AiM", "project_page": "", "abstract": "We introduce AiM, an autoregressive (AR) image generative model based on Mamba architecture. AiM employs Mamba, a novel state-space model characterized by its exceptional performance for long-sequence modeling with linear time complexity, to supplant the commonly utilized Transformers in AR image generation models, aiming to achieve both superior generation quality and enhanced inference speed. Unlike existing methods that adapt Mamba to handle two-dimensional signals via multi-directional scan, AiM directly utilizes the next-token prediction paradigm for autoregressive image generation. This approach circumvents the need for extensive modifications to enable Mamba to learn 2D spatial representations. By implementing straightforward yet strategically targeted modifications for visual generative tasks, we preserve Mamba's core structure, fully exploiting its efficient long-sequence modeling capabilities and scalability. We provide AiM models in various scales, with parameter counts ranging from 148M to 1.3B. On the ImageNet1K 256*256 benchmark, our best AiM model achieves a FID of 2.21, surpassing all existing AR models of comparable parameter counts and demonstrating significant competitiveness against diffusion models, with 2 to 10 times faster inference speed. Code is available at https://github.com/hp-l33/AiM", "github_manual": "", "github_regex": "https://github.com/hp-l33/AiM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.11915", "title": "Video-Foley: Two-Stage Video-To-Sound Generation via Temporal Event Condition For Foley Sound", "authors": [ "Junwon Lee", "Jaekwon Im", "Dabin Kim", "Juhan Nam" ], "github": "https://github.com/jnwnlee/video-foley", "project_page": "", "abstract": "Foley sound synthesis is crucial for multimedia production, enhancing user experience by synchronizing audio and video both temporally and semantically. Recent studies on automating this labor-intensive process through video-to-sound generation face significant challenges. Systems lacking explicit temporal features suffer from poor controllability and alignment, while timestamp-based models require costly and subjective human annotation. We propose Video-Foley, a video-to-sound system using Root Mean Square (RMS) as a temporal event condition with semantic timbre prompts (audio or text). RMS, a frame-level intensity envelope feature closely related to audio semantics, ensures high controllability and synchronization. The annotation-free self-supervised learning framework consists of two stages, Video2RMS and RMS2Sound, incorporating novel ideas including RMS discretization and RMS-ControlNet with a pretrained text-to-audio model. Our extensive evaluation shows that Video-Foley achieves state-of-the-art performance in audio-visual alignment and controllability for sound timing, intensity, timbre, and nuance. Code, model weights, and demonstrations are available on the accompanying website. (https://jnwnlee.github.io/video-foley-demo)", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jnwnlee/video-foley", "github_api_added_by": "auto" }, { "date": "2024-08-23", "arxiv_id": "2408.12569", "title": "Sapiens: Foundation for Human Vision Models", "authors": [ "Rawal Khirodkar", "Timur Bagautdinov", "Julieta Martinez", "Su Zhaoen", "Austin James", "Peter Selednik", "Stuart Anderson", "Shunsuke Saito" ], "github": "", "project_page": "", "abstract": "We present Sapiens, a family of models for four fundamental human-centric vision tasks - 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Our models natively support 1K high-resolution inference and are extremely easy to adapt for individual tasks by simply fine-tuning models pretrained on over 300 million in-the-wild human images. We observe that, given the same computational budget, self-supervised pretraining on a curated dataset of human images significantly boosts the performance for a diverse set of human-centric tasks. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. Our simple model design also brings scalability - model performance across tasks improves as we scale the number of parameters from 0.3 to 2 billion. Sapiens consistently surpasses existing baselines across various human-centric benchmarks. We achieve significant improvements over the prior state-of-the-art on Humans-5K (pose) by 7.6 mAP, Humans-2K (part-seg) by 17.1 mIoU, Hi4D (depth) by 22.4% relative RMSE, and THuman2 (normal) by 53.5% relative angular error.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.12528", "title": "Show-o: One Single Transformer to Unify Multimodal Understanding and Generation", "authors": [ "Jinheng Xie", "Weijia Mao", "Zechen Bai", "David Junhao Zhang", "Weihao Wang", "Kevin Qinghong Lin", "Yuchao Gu", "Zhijie Chen", "Zhenheng Yang", "Mike Zheng Shou" ], "github": "https://github.com/showlab/Show-o", "project_page": "", "abstract": "We present a unified transformer, i.e., Show-o, that unifies multimodal understanding and generation. Unlike fully autoregressive models, Show-o unifies autoregressive and (discrete) diffusion modeling to adaptively handle inputs and outputs of various and mixed modalities. The unified model flexibly supports a wide range of vision-language tasks including visual question-answering, text-to-image generation, text-guided inpainting/extrapolation, and mixed-modality generation. Across various benchmarks, it demonstrates comparable or superior performance to existing individual models with an equivalent or larger number of parameters tailored for understanding or generation. This significantly highlights its potential as a next-generation foundation model. Code and models are released at https://github.com/showlab/Show-o.", "github_manual": "", "github_regex": "https://github.com/showlab/Show-o", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.11878", "title": "Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications", "authors": [ "Qianqian Xie", "Dong Li", "Mengxi Xiao", "Zihao Jiang", "Ruoyu Xiang", "Xiao Zhang", "Zhengyu Chen", "Yueru He", "Weiguang Han", "Yuzhe Yang", "Shunian Chen", "Yifei Zhang", "Lihang Shen", "Daniel Kim", "Zhiwei Liu", "Zheheng Luo", "Yangyang Yu", "Yupeng Cao", "Zhiyang Deng", "Zhiyuan Yao", "Haohang Li", "Duanyu Feng", "Yongfu Dai", "VijayaSai Somasundaram", "Peng Lu", "Yilun Zhao", "Yitao Long", "Guojun Xiong", "Kaleb Smith", "Honghai Yu", "Yanzhao Lai", "Min Peng", "Jianyun Nie", "Jordan W. Suchow", "Xiao-Yang Liu", "Benyou Wang", "Alejandro Lopez-Lira", "Jimin Huang", "Sophia Ananiadou" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have advanced financial applications, yet they often lack sufficient financial knowledge and struggle with tasks involving multi-modal inputs like tables and time series data. To address these limitations, we introduce Open-FinLLMs, a series of Financial LLMs. We begin with FinLLaMA, pre-trained on a 52 billion token financial corpus, incorporating text, tables, and time-series data to embed comprehensive financial knowledge. FinLLaMA is then instruction fine-tuned with 573K financial instructions, resulting in FinLLaMA-instruct, which enhances task performance. Finally, we present FinLLaVA, a multimodal LLM trained with 1.43M image-text instructions to handle complex financial data types. Extensive evaluations demonstrate FinLLaMA's superior performance over LLaMA3-8B, LLaMA3.1-8B, and BloombergGPT in both zero-shot and few-shot settings across 19 and 4 datasets, respectively. FinLLaMA-instruct outperforms GPT-4 and other Financial LLMs on 15 datasets. FinLLaVA excels in understanding tables and charts across 4 multimodal tasks. Additionally, FinLLaMA achieves impressive Sharpe Ratios in trading simulations, highlighting its robust financial application capabilities. We will continually maintain and improve our models and benchmarks to support ongoing innovation in academia and industry.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.12570", "title": "Jamba-1.5: Hybrid Transformer-Mamba Models at Scale", "authors": [ "Jamba Team", "Barak Lenz", "Alan Arazi", "Amir Bergman", "Avshalom Manevich", "Barak Peleg", "Ben Aviram", "Chen Almagor", "Clara Fridman", "Dan Padnos", "Daniel Gissin", "Daniel Jannai", "Dor Muhlgay", "Dor Zimberg", "Edden M Gerber", "Elad Dolev", "Eran Krakovsky", "Erez Safahi", "Erez Schwartz", "Gal Cohen", "Gal Shachaf", "Haim Rozenblum", "Hofit Bata", "Ido Blass", "Inbal Magar", "Itay Dalmedigos", "Jhonathan Osin", "Julie Fadlon", "Maria Rozman", "Matan Danos", "Michael Gokhman", "Mor Zusman", "Naama Gidron", "Nir Ratner", "Noam Gat", "Noam Rozen", "Oded Fried", "Ohad Leshno", "Omer Antverg", "Omri Abend", "Opher Lieber", "Or Dagan", "Orit Cohavi", "Raz Alon", "Ro'i Belson", "Roi Cohen", "Rom Gilad", "Roman Glozman", "Shahar Lev", "Shaked Meirom", "Tal Delbari", "Tal Ness", "Tomer Asida", "Tom Ben Gal", "Tom Braude", "Uriya Pumerantz", "Yehoshua Cohen", "Yonatan Belinkov", "Yuval Globerson", "Yuval Peleg Levy", "Yoav Shoham" ], "github": "", "project_page": "", "abstract": "We present Jamba-1.5, new instruction-tuned large language models based on our Jamba architecture. Jamba is a hybrid Transformer-Mamba mixture of experts architecture, providing high throughput and low memory usage across context lengths, while retaining the same or better quality as Transformer models. We release two model sizes: Jamba-1.5-Large, with 94B active parameters, and Jamba-1.5-Mini, with 12B active parameters. Both models are fine-tuned for a variety of conversational and instruction-following capabilties, and have an effective context length of 256K tokens, the largest amongst open-weight models. To support cost-effective inference, we introduce ExpertsInt8, a novel quantization technique that allows fitting Jamba-1.5-Large on a machine with 8 80GB GPUs when processing 256K-token contexts without loss of quality. When evaluated on a battery of academic and chatbot benchmarks, Jamba-1.5 models achieve excellent results while providing high throughput and outperforming other open-weight models on long-context benchmarks. The model weights for both sizes are publicly available under the Jamba Open Model License and we release ExpertsInt8 as open source.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.12282", "title": "Subsurface Scattering for 3D Gaussian Splatting", "authors": [ "Jan-Niklas Dihlmann", "Arjun Majumdar", "Andreas Engelhardt", "Raphael Braun", "Hendrik P. A. Lensch" ], "github": "", "project_page": "", "abstract": "3D reconstruction and relighting of objects made from scattering materials present a significant challenge due to the complex light transport beneath the surface. 3D Gaussian Splatting introduced high-quality novel view synthesis at real-time speeds. While 3D Gaussians efficiently approximate an object's surface, they fail to capture the volumetric properties of subsurface scattering. We propose a framework for optimizing an object's shape together with the radiance transfer field given multi-view OLAT (one light at a time) data. Our method decomposes the scene into an explicit surface represented as 3D Gaussians, with a spatially varying BRDF, and an implicit volumetric representation of the scattering component. A learned incident light field accounts for shadowing. We optimize all parameters jointly via ray-traced differentiable rendering. Our approach enables material editing, relighting and novel view synthesis at interactive rates. We show successful application on synthetic data and introduce a newly acquired multi-view multi-light dataset of objects in a light-stage setup. Compared to previous work we achieve comparable or better results at a fraction of optimization and rendering time while enabling detailed control over material attributes. Project page https://sss.jdihlmann.com/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.12601", "title": "DreamCinema: Cinematic Transfer with Free Camera and 3D Character", "authors": [ "Weiliang Chen", "Fangfu Liu", "Diankun Wu", "Haowen Sun", "Haixu Song", "Yueqi Duan" ], "github": "", "project_page": "", "abstract": "We are living in a flourishing era of digital media, where everyone has the potential to become a personal filmmaker. Current research on cinematic transfer empowers filmmakers to reproduce and manipulate the visual elements (e.g., cinematography and character behaviors) from classic shots. However, characters in the reimagined films still rely on manual crafting, which involves significant technical complexity and high costs, making it unattainable for ordinary users. Furthermore, their estimated cinematography lacks smoothness due to inadequate capturing of inter-frame motion and modeling of physical trajectories. Fortunately, the remarkable success of 2D and 3D AIGC has opened up the possibility of efficiently generating characters tailored to users' needs, diversifying cinematography. In this paper, we propose DreamCinema, a novel cinematic transfer framework that pioneers generative AI into the film production paradigm, aiming at facilitating user-friendly film creation. Specifically, we first extract cinematic elements (i.e., human and camera pose) and optimize the camera trajectory. Then, we apply a character generator to efficiently create 3D high-quality characters with a human structure prior. Finally, we develop a structure-guided motion transfer strategy to incorporate generated characters into film creation and transfer it via 3D graphics engines smoothly. Extensive experiments demonstrate the effectiveness of our method for creating high-quality films with free camera and 3D characters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.11813", "title": "SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs", "authors": [ "Yuanyang Yin", "Yaqi Zhao", "Yajie Zhang", "Ke Lin", "Jiahao Wang", "Xin Tao", "Pengfei Wan", "Di Zhang", "Baoqun Yin", "Wentao Zhang" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable perceptual and reasoning abilities, typically comprising a Vision Encoder, an Adapter, and a Large Language Model (LLM). The adapter serves as the critical bridge between the visual and language components. However, training adapters with image-level supervision often results in significant misalignment, undermining the LLMs' capabilities and limiting the potential of Multimodal LLMs. To address this, we introduce Supervised Embedding Alignment (SEA), a token-level alignment method that leverages vision-language pre-trained models, such as CLIP, to align visual tokens with the LLM's embedding space through contrastive learning. This approach ensures a more coherent integration of visual and language representations, enhancing the performance and interpretability of multimodal LLMs while preserving their inherent capabilities. Extensive experiments show that SEA effectively improves MLLMs, particularly for smaller models, without adding extra data or inference computation. SEA also lays the groundwork for developing more general and adaptable solutions to enhance multimodal systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.12588", "title": "Real-Time Video Generation with Pyramid Attention Broadcast", "authors": [ "Xuanlei Zhao", "Xiaolong Jin", "Kai Wang", "Yang You" ], "github": "https://github.com/nus-hpc-ai-lab/opendit", "project_page": "", "abstract": "We present Pyramid Attention Broadcast (PAB), a real-time, high quality and training-free approach for DiT-based video generation. Our method is founded on the observation that attention difference in the diffusion process exhibits a U-shaped pattern, indicating significant redundancy. We mitigate this by broadcasting attention outputs to subsequent steps in a pyramid style. It applies different broadcast strategies to each attention based on their variance for best efficiency. We further introduce broadcast sequence parallel for more efficient distributed inference. PAB demonstrates superior results across three models compared to baselines, achieving real-time generation for up to 720p videos. We anticipate that our simple yet effective method will serve as a robust baseline and facilitate future research and application for video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nus-hpc-ai-lab/opendit", "github_api_added_by": "auto" }, { "date": "2024-08-23", "arxiv_id": "2408.12114", "title": "SPARK: Multi-Vision Sensor Perception and Reasoning Benchmark for Large-scale Vision-Language Models", "authors": [ "Youngjoon Yu", "Sangyun Chung", "Byung-Kwan Lee", "Yong Man Ro" ], "github": "https://github.com/top-yun/SPARK", "project_page": "", "abstract": "Large-scale Vision-Language Models (LVLMs) have significantly advanced with text-aligned vision inputs. They have made remarkable progress in computer vision tasks by aligning text modality with vision inputs. There are also endeavors to incorporate multi-vision sensors beyond RGB, including thermal, depth, and medical X-ray images. However, we observe that current LVLMs view images taken from multi-vision sensors as if they were in the same RGB domain without considering the physical characteristics of multi-vision sensors. They fail to convey the fundamental multi-vision sensor information from the dataset and the corresponding contextual knowledge properly. Consequently, alignment between the information from the actual physical environment and the text is not achieved correctly, making it difficult to answer complex sensor-related questions that consider the physical environment. In this paper, we aim to establish a multi-vision Sensor Perception And Reasoning benchmarK called SPARK that can reduce the fundamental multi-vision sensor information gap between images and multi-vision sensors. We generated 6,248 vision-language test samples automatically to investigate multi-vision sensory perception and multi-vision sensory reasoning on physical sensor knowledge proficiency across different formats, covering different types of sensor-related questions. We utilized these samples to assess ten leading LVLMs. The results showed that most models displayed deficiencies in multi-vision sensory reasoning to varying extents. Codes and data are available at https://github.com/top-yun/SPARK", "github_manual": "", "github_regex": "https://github.com/top-yun/SPARK", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.09787", "title": "Anim-Director: A Large Multimodal Model Powered Agent for Controllable Animation Video Generation", "authors": [ "Yunxin Li", "Haoyuan Shi", "Baotian Hu", "Longyue Wang", "Jiashun Zhu", "Jinyi Xu", "Zhen Zhao", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Traditional animation generation methods depend on training generative models with human-labelled data, entailing a sophisticated multi-stage pipeline that demands substantial human effort and incurs high training costs. Due to limited prompting plans, these methods typically produce brief, information-poor, and context-incoherent animations. To overcome these limitations and automate the animation process, we pioneer the introduction of large multimodal models (LMMs) as the core processor to build an autonomous animation-making agent, named Anim-Director. This agent mainly harnesses the advanced understanding and reasoning capabilities of LMMs and generative AI tools to create animated videos from concise narratives or simple instructions. Specifically, it operates in three main stages: Firstly, the Anim-Director generates a coherent storyline from user inputs, followed by a detailed director's script that encompasses settings of character profiles and interior/exterior descriptions, and context-coherent scene descriptions that include appearing characters, interiors or exteriors, and scene events. Secondly, we employ LMMs with the image generation tool to produce visual images of settings and scenes. These images are designed to maintain visual consistency across different scenes using a visual-language prompting method that combines scene descriptions and images of the appearing character and setting. Thirdly, scene images serve as the foundation for producing animated videos, with LMMs generating prompts to guide this process. The whole process is notably autonomous without manual intervention, as the LMMs interact seamlessly with generative tools to generate prompts, evaluate visual quality, and select the best one to optimize the final output.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.12076", "title": "ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM", "authors": [ "Zhaochen Su", "Jun Zhang", "Xiaoye Qu", "Tong Zhu", "Yanshu Li", "Jiashuo Sun", "Juntao Li", "Min Zhang", "Yu Cheng" ], "github": "https://github.com/zhaochen0110/conflictbank", "project_page": "", "abstract": "Large language models (LLMs) have achieved impressive advancements across numerous disciplines, yet the critical issue of knowledge conflicts, a major source of hallucinations, has rarely been studied. Only a few research explored the conflicts between the inherent knowledge of LLMs and the retrieved contextual knowledge. However, a thorough assessment of knowledge conflict in LLMs is still missing. Motivated by this research gap, we present ConflictBank, the first comprehensive benchmark developed to systematically evaluate knowledge conflicts from three aspects: (i) conflicts encountered in retrieved knowledge, (ii) conflicts within the models' encoded knowledge, and (iii) the interplay between these conflict forms. Our investigation delves into four model families and twelve LLM instances, meticulously analyzing conflicts stemming from misinformation, temporal discrepancies, and semantic divergences. Based on our proposed novel construction framework, we create 7,453,853 claim-evidence pairs and 553,117 QA pairs. We present numerous findings on model scale, conflict causes, and conflict types. We hope our ConflictBank benchmark will help the community better understand model behavior in conflicts and develop more reliable LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhaochen0110/conflictbank", "github_api_added_by": "auto" }, { "date": "2024-08-23", "arxiv_id": "2408.12480", "title": "Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese", "authors": [ "Khang T. Doan", "Bao G. Huynh", "Dung T. Hoang", "Thuc D. Pham", "Nhat H. Pham", "Quan T. M. Nguyen", "Bang Q. Vo", "Suong N. Hoang" ], "github": "", "project_page": "", "abstract": "In this report, we introduce Vintern-1B, a reliable 1-billion-parameters multimodal large language model (MLLM) for Vietnamese language tasks. By integrating the Qwen2-0.5B-Instruct language model with the InternViT-300M-448px visual model, Vintern-1B is optimized for a range of applications, including optical character recognition (OCR), document extraction, and general question-answering in Vietnamese context. The model is fine-tuned on an extensive dataset of over 3 million image-question-answer pairs, achieving robust performance and reliable results across multiple Vietnamese language benchmarks like OpenViVQA and ViTextVQA. Vintern-1B is small enough to fit into various on-device applications easily. Additionally, we have open-sourced several Vietnamese vision question answering (VQA) datasets for text and diagrams, created with Gemini 1.5 Flash. Our models are available at: https://huggingface.co/5CD-AI/Vintern-1B-v2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.12503", "title": "The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design", "authors": [ "Artem Snegirev", "Maria Tikhonova", "Anna Maksimova", "Alena Fenogenova", "Alexander Abramov" ], "github": "", "project_page": "", "abstract": "Embedding models play a crucial role in Natural Language Processing (NLP) by creating text embeddings used in various tasks such as information retrieval and assessing semantic text similarity. This paper focuses on research related to embedding models in the Russian language. It introduces a new Russian-focused embedding model called ru-en-RoSBERTa and the ruMTEB benchmark, the Russian version extending the Massive Text Embedding Benchmark (MTEB). Our benchmark includes seven categories of tasks, such as semantic textual similarity, text classification, reranking, and retrieval. The research also assesses a representative set of Russian and multilingual models on the proposed benchmark. The findings indicate that the new model achieves results that are on par with state-of-the-art models in Russian. We release the model ru-en-RoSBERTa, and the ruMTEB framework comes with open-source code, integration into the original framework and a public leaderboard.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-23", "arxiv_id": "2408.12060", "title": "Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs", "authors": [ "Ronit Singhal", "Pransh Patwa", "Parth Patwa", "Aman Chadha", "Amitava Das" ], "github": "https://github.com/ronit-singhal/evidence-backed-fact-checking-using-rag-and-few-shot-in-context-learning-with-llms", "project_page": "", "abstract": "Given the widespread dissemination of misinformation on social media, implementing fact-checking mechanisms for online claims is essential. Manually verifying every claim is highly challenging, underscoring the need for an automated fact-checking system. This paper presents our system designed to address this issue. We utilize the Averitec dataset to assess the veracity of claims. In addition to veracity prediction, our system provides supporting evidence, which is extracted from the dataset. We develop a Retrieve and Generate (RAG) pipeline to extract relevant evidence sentences from a knowledge base, which are then inputted along with the claim into a large language model (LLM) for classification. We also evaluate the few-shot In-Context Learning (ICL) capabilities of multiple LLMs. Our system achieves an 'Averitec' score of 0.33, which is a 22% absolute improvement over the baseline. All code will be made available on All code will be made available on https://github.com/ronit-singhal/evidence-backed-fact-checking-using-rag-and-few-shot-in-context-learning-with-llms.", "github_manual": "", "github_regex": "https://github.com/ronit-singhal/evidence-backed-fact-checking-using-rag-and-few-shot-in-context-learning-with-llms", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-26", "arxiv_id": "2408.13239", "title": "CustomCrafter: Customized Video Generation with Preserving Motion and Concept Composition Abilities", "authors": [ "Tao Wu", "Yong Zhang", "Xintao Wang", "Xianpan Zhou", "Guangcong Zheng", "Zhongang Qi", "Ying Shan", "Xi Li" ], "github": "https://github.com/wutao-cs/customcrafter", "project_page": "", "abstract": "Customized video generation aims to generate high-quality videos guided by text prompts and subject's reference images. However, since it is only trained on static images, the fine-tuning process of subject learning disrupts abilities of video diffusion models (VDMs) to combine concepts and generate motions. To restore these abilities, some methods use additional video similar to the prompt to fine-tune or guide the model. This requires frequent changes of guiding videos and even re-tuning of the model when generating different motions, which is very inconvenient for users. In this paper, we propose CustomCrafter, a novel framework that preserves the model's motion generation and conceptual combination abilities without additional video and fine-tuning to recovery. For preserving conceptual combination ability, we design a plug-and-play module to update few parameters in VDMs, enhancing the model's ability to capture the appearance details and the ability of concept combinations for new subjects. For motion generation, we observed that VDMs tend to restore the motion of video in the early stage of denoising, while focusing on the recovery of subject details in the later stage. Therefore, we propose Dynamic Weighted Video Sampling Strategy. Using the pluggability of our subject learning modules, we reduce the impact of this module on motion generation in the early stage of denoising, preserving the ability to generate motion of VDMs. In the later stage of denoising, we restore this module to repair the appearance details of the specified subject, thereby ensuring the fidelity of the subject's appearance. Experimental results show that our method has a significant improvement compared to previous methods.", "github_manual": "https://github.com/wutao-cs/customcrafter", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-26", "arxiv_id": "2408.13257", "title": "MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?", "authors": [ "Yi-Fan Zhang", "Huanyu Zhang", "Haochen Tian", "Chaoyou Fu", "Shuangqing Zhang", "Junfei Wu", "Feng Li", "Kun Wang", "Qingsong Wen", "Zhang Zhang", "Liang Wang", "Rong Jin", "Tieniu Tan" ], "github": "", "project_page": "", "abstract": "Comprehensive evaluation of Multimodal Large Language Models (MLLMs) has recently garnered widespread attention in the research community. However, we observe that existing benchmarks present several common barriers that make it difficult to measure the significant challenges that models face in the real world, including: 1) small data scale leads to a large performance variance; 2) reliance on model-based annotations results in restricted data quality; 3) insufficient task difficulty, especially caused by the limited image resolution. To tackle these issues, we introduce MME-RealWorld. Specifically, we collect more than 300K images from public datasets and the Internet, filtering 13,366 high-quality images for annotation. This involves the efforts of professional 25 annotators and 7 experts in MLLMs, contributing to 29,429 question-answer pairs that cover 43 subtasks across 5 real-world scenarios, extremely challenging even for humans. As far as we know, MME-RealWorld is the largest manually annotated benchmark to date, featuring the highest resolution and a targeted focus on real-world applications. We further conduct a thorough evaluation involving 28 prominent MLLMs, such as GPT-4o, Gemini 1.5 Pro, and Claude 3.5 Sonnet. Our results show that even the most advanced models struggle with our benchmarks, where none of them reach 60% accuracy. The challenges of perceiving high-resolution images and understanding complex real-world scenarios remain urgent issues to be addressed. The data and evaluation code are released at https://mme-realworld.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-26", "arxiv_id": "2408.12894", "title": "FLoD: Integrating Flexible Level of Detail into 3D Gaussian Splatting for Customizable Rendering", "authors": [ "Yunji Seo", "Young Sun Choi", "Hyun Seung Son", "Youngjung Uh" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) achieves fast and high-quality renderings by using numerous small Gaussians, which leads to significant memory consumption. This reliance on a large number of Gaussians restricts the application of 3DGS-based models on low-cost devices due to memory limitations. However, simply reducing the number of Gaussians to accommodate devices with less memory capacity leads to inferior quality compared to the quality that can be achieved on high-end hardware. To address this lack of scalability, we propose integrating a Flexible Level of Detail (FLoD) to 3DGS, to allow a scene to be rendered at varying levels of detail according to hardware capabilities. While existing 3DGSs with LoD focus on detailed reconstruction, our method provides reconstructions using a small number of Gaussians for reduced memory requirements, and a larger number of Gaussians for greater detail. Experiments demonstrate our various rendering options with tradeoffs between rendering quality and memory usage, thereby allowing real-time rendering across different memory constraints. Furthermore, we show that our method generalizes to different 3DGS frameworks, indicating its potential for integration into future state-of-the-art developments. Project page: https://3dgs-flod.github.io/flod.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-26", "arxiv_id": "2408.12885", "title": "T3M: Text Guided 3D Human Motion Synthesis from Speech", "authors": [ "Wenshuo Peng", "Kaipeng Zhang", "Sai Qian Zhang" ], "github": "https://github.com/Gloria2tt/T3M.git", "project_page": "", "abstract": "Speech-driven 3D motion synthesis seeks to create lifelike animations based on human speech, with potential uses in virtual reality, gaming, and the film production. Existing approaches reply solely on speech audio for motion generation, leading to inaccurate and inflexible synthesis results. To mitigate this problem, we introduce a novel text-guided 3D human motion synthesis method, termed T3M. Unlike traditional approaches, T3M allows precise control over motion synthesis via textual input, enhancing the degree of diversity and user customization. The experiment results demonstrate that T3M can greatly outperform the state-of-the-art methods in both quantitative metrics and qualitative evaluations. We have publicly released our code at https://github.com/Gloria2tt/T3M.git{https://github.com/Gloria2tt/T3M.git}", "github_manual": "", "github_regex": "https://github.com/Gloria2tt/T3M.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-26", "arxiv_id": "2408.13252", "title": "LayerPano3D: Layered 3D Panorama for Hyper-Immersive Scene Generation", "authors": [ "Shuai Yang", "Jing Tan", "Mengchen Zhang", "Tong Wu", "Yixuan Li", "Gordon Wetzstein", "Ziwei Liu", "Dahua Lin" ], "github": "", "project_page": "", "abstract": "3D immersive scene generation is a challenging yet critical task in computer vision and graphics. A desired virtual 3D scene should 1) exhibit omnidirectional view consistency, and 2) allow for free exploration in complex scene hierarchies. Existing methods either rely on successive scene expansion via inpainting or employ panorama representation to represent large FOV scene environments. However, the generated scene suffers from semantic drift during expansion and is unable to handle occlusion among scene hierarchies. To tackle these challenges, we introduce LayerPano3D, a novel framework for full-view, explorable panoramic 3D scene generation from a single text prompt. Our key insight is to decompose a reference 2D panorama into multiple layers at different depth levels, where each layer reveals the unseen space from the reference views via diffusion prior. LayerPano3D comprises multiple dedicated designs: 1) we introduce a novel text-guided anchor view synthesis pipeline for high-quality, consistent panorama generation. 2) We pioneer the Layered 3D Panorama as underlying representation to manage complex scene hierarchies and lift it into 3D Gaussians to splat detailed 360-degree omnidirectional scenes with unconstrained viewing paths. Extensive experiments demonstrate that our framework generates state-of-the-art 3D panoramic scene in both full view consistency and immersive exploratory experience. We believe that LayerPano3D holds promise for advancing 3D panoramic scene creation with numerous applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-26", "arxiv_id": "2408.13233", "title": "Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time", "authors": [ "Yingyu Liang", "Zhizhou Sha", "Zhenmei Shi", "Zhao Song", "Yufa Zhou" ], "github": "", "project_page": "", "abstract": "The quadratic computational complexity in the self-attention mechanism of popular transformer architectures poses significant challenges for training and inference, particularly in terms of efficiency and memory requirements. Towards addressing these challenges, this paper introduces a novel fast computation method for gradient calculation in multi-layer transformer models. Our approach enables the computation of gradients for the entire multi-layer transformer model in almost linear time n^{1+o(1)}, where n is the input sequence length. This breakthrough significantly reduces the computational bottleneck associated with the traditional quadratic time complexity. Our theory holds for any loss function and maintains a bounded approximation error across the entire model. Furthermore, our analysis can hold when the multi-layer transformer model contains many practical sub-modules, such as residual connection, casual mask, and multi-head attention. By improving the efficiency of gradient computation in large language models, we hope that our work will facilitate the more effective training and deployment of long-context language models based on our theoretical results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-26", "arxiv_id": "2408.12637", "title": "Building and better understanding vision-language models: insights and future directions", "authors": [ "Hugo Lauren\u00e7on", "Andr\u00e9s Marafioti", "Victor Sanh", "L\u00e9o Tronchon" ], "github": "", "project_page": "", "abstract": "The field of vision-language models (VLMs), which take images and texts as inputs and output texts, is rapidly evolving and has yet to reach consensus on several key aspects of the development pipeline, including data, architecture, and training methods. This paper can be seen as a tutorial for building a VLM. We begin by providing a comprehensive overview of the current state-of-the-art approaches, highlighting the strengths and weaknesses of each, addressing the major challenges in the field, and suggesting promising research directions for underexplored areas. We then walk through the practical steps to build Idefics3-8B, a powerful VLM that significantly outperforms its predecessor Idefics2-8B, while being trained efficiently, exclusively on open datasets, and using a straightforward pipeline. These steps include the creation of Docmatix, a dataset for improving document understanding capabilities, which is 240 times larger than previously available datasets. We release the model along with the datasets created for its training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-26", "arxiv_id": "2408.12418", "title": "CODE: Confident Ordinary Differential Editing", "authors": [ "Bastien van Delft", "Tommaso Martorella", "Alexandre Alahi" ], "github": "https://github.com/vita-epfl/code", "project_page": "", "abstract": "Conditioning image generation facilitates seamless editing and the creation of photorealistic images. However, conditioning on noisy or Out-of-Distribution (OoD) images poses significant challenges, particularly in balancing fidelity to the input and realism of the output. We introduce Confident Ordinary Differential Editing (CODE), a novel approach for image synthesis that effectively handles OoD guidance images. Utilizing a diffusion model as a generative prior, CODE enhances images through score-based updates along the probability-flow Ordinary Differential Equation (ODE) trajectory. This method requires no task-specific training, no handcrafted modules, and no assumptions regarding the corruptions affecting the conditioning image. Our method is compatible with any diffusion model. Positioned at the intersection of conditional image generation and blind image restoration, CODE operates in a fully blind manner, relying solely on a pre-trained generative model. Our method introduces an alternative approach to blind restoration: instead of targeting a specific ground truth image based on assumptions about the underlying corruption, CODE aims to increase the likelihood of the input image while maintaining fidelity. This results in the most probable in-distribution image around the input. Our contributions are twofold. First, CODE introduces a novel editing method based on ODE, providing enhanced control, realism, and fidelity compared to its SDE-based counterpart. Second, we introduce a confidence interval-based clipping method, which improves CODE's effectiveness by allowing it to disregard certain pixels or information, thus enhancing the restoration process in a blind manner. Experimental results demonstrate CODE's effectiveness over existing methods, particularly in scenarios involving severe degradation or OoD inputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vita-epfl/code", "github_api_added_by": "auto" }, { "date": "2024-08-26", "arxiv_id": "2408.12857", "title": "Memory-Efficient LLM Training with Online Subspace Descent", "authors": [ "Kaizhao Liang", "Bo Liu", "Lizhang Chen", "Qiang Liu" ], "github": "https://github.com/kyleliang919/online-subspace-descent", "project_page": "", "abstract": "Recently, a wide range of memory-efficient LLM training algorithms have gained substantial popularity. These methods leverage the low-rank structure of gradients to project optimizer states into a subspace using projection matrix found by singular value decomposition (SVD). However, convergence of these algorithms is highly dependent on the update rules of their projection matrix. In this work, we provide the first convergence guarantee for arbitrary update rules of projection matrix. This guarantee is generally applicable to optimizers that can be analyzed with Hamiltonian Descent, including most common ones, such as LION, Adam. Inspired by our theoretical understanding, we propose Online Subspace Descent, a new family of subspace descent optimizer without SVD. Instead of updating the projection matrix with eigenvectors, Online Subspace Descent updates the projection matrix with online PCA. Online Subspace Descent is flexible and introduces only minimum overhead to training. We show that for the task of pretraining LLaMA models ranging from 60M to 7B parameters on the C4 dataset, Online Subspace Descent achieves lower perplexity and better downstream tasks performance than state-of-the-art low-rank training methods across different settings and narrows the gap with full-rank baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kyleliang919/online-subspace-descent", "github_api_added_by": "auto" }, { "date": "2024-08-26", "arxiv_id": "2408.13010", "title": "A Web-Based Solution for Federated Learning with LLM-Based Automation", "authors": [ "Chamith Mawela", "Chaouki Ben Issaid", "Mehdi Bennis" ], "github": "https://github.com/ICONgroupCWC/FedLFrontEnd", "project_page": "", "abstract": "Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. However, its adoption is hindered by the complexity of building reliable communication architectures and the need for expertise in both machine learning and network programming. This paper presents a comprehensive solution that simplifies the orchestration of FL tasks while integrating intent-based automation. We develop a user-friendly web application supporting the federated averaging (FedAvg) algorithm, enabling users to configure parameters through an intuitive interface. The backend solution efficiently manages communication between the parameter server and edge nodes. We also implement model compression and scheduling algorithms to optimize FL performance. Furthermore, we explore intent-based automation in FL using a fine-tuned Language Model (LLM) trained on a tailored dataset, allowing users to conduct FL tasks using high-level prompts. We observe that the LLM-based automated solution achieves comparable test accuracy to the standard web-based solution while reducing transferred bytes by up to 64% and CPU time by up to 46% for FL tasks. Also, we leverage the neural architecture search (NAS) and hyperparameter optimization (HPO) using LLM to improve the performance. We observe that by using this approach test accuracy can be improved by 10-20% for the carried out FL tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ICONgroupCWC/FedLFrontEnd", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-26", "arxiv_id": "2408.10945", "title": "HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models in Resource-Constrained Environments", "authors": [ "Kazi Hasan Ibn Arif", "JinYi Yoon", "Dimitrios S. Nikolopoulos", "Hans Vandierendonck", "Deepu John", "Bo Ji" ], "github": "https://github.com/hasanar1f/hired", "project_page": "", "abstract": "High-resolution Vision-Language Models (VLMs) have been widely used in multimodal tasks to enhance accuracy by preserving detailed image information. However, these models often generate excessive visual tokens due to encoding multiple partitions of the input image. Processing these excessive visual tokens is computationally challenging, especially in resource-constrained environments with commodity GPUs. To support high-resolution images while meeting resource constraints, we propose High-Resolution Early Dropping (HiRED), a token-dropping scheme that operates within a fixed token budget before the Large Language Model (LLM) stage. HiRED can be integrated with existing high-resolution VLMs in a plug-and-play manner, as it requires no additional training while still maintaining superior accuracy. We strategically use the vision encoder's attention in the initial layers to assess the visual content of each image partition and allocate the token budget accordingly. Then, using the attention in the final layer, we select the most important visual tokens from each partition within the allocated budget, dropping the rest. Empirically, when applied to LLaVA-Next-7B on NVIDIA TESLA P40 GPU, HiRED with a 20% token budget increases token generation throughput by 4.7, reduces first-token generation latency by 15 seconds, and saves 2.3 GB of GPU memory for a single inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hasanar1f/hired", "github_api_added_by": "auto" }, { "date": "2024-08-26", "arxiv_id": "2408.12369", "title": "RoundTable: Leveraging Dynamic Schema and Contextual Autocomplete for Enhanced Query Precision in Tabular Question Answering", "authors": [ "Pratyush Kumar", "Kuber Vijaykumar Bellad", "Bharat Vadlamudi", "Aman Chadha" ], "github": "https://github.com/pkscanvas/roundtable", "project_page": "", "abstract": "With advancements in Large Language Models (LLMs), a major use case that has emerged is querying databases in plain English, translating user questions into executable database queries, which has improved significantly. However, real-world datasets often feature a vast array of attributes and complex values, complicating the LLMs task of accurately identifying relevant columns or values from natural language queries. Traditional methods cannot fully relay the datasets size and complexity to the LLM. To address these challenges, we propose a novel framework that leverages Full-Text Search (FTS) on the input table. This approach not only enables precise detection of specific values and columns but also narrows the search space for language models, thereby enhancing query accuracy. Additionally, it supports a custom auto-complete feature that suggests queries based on the data in the table. This integration significantly refines the interaction between the user and complex datasets, offering a sophisticated solution to the limitations faced by current table querying capabilities. This work is accompanied by an application for both Mac and Windows platforms, which readers can try out themselves on their own data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/pkscanvas/roundtable", "github_api_added_by": "auto" }, { "date": "2024-08-27", "arxiv_id": "2408.14354", "title": "SWE-bench-java: A GitHub Issue Resolving Benchmark for Java", "authors": [ "Daoguang Zan", "Zhirong Huang", "Ailun Yu", "Shaoxin Lin", "Yifan Shi", "Wei Liu", "Dong Chen", "Zongshuai Qi", "Hao Yu", "Lei Yu", "Dezhi Ran", "Muhan Zeng", "Bo Shen", "Pan Bian", "Guangtai Liang", "Bei Guan", "Pengjie Huang", "Tao Xie", "Yongji Wang", "Qianxiang Wang" ], "github": "", "project_page": "", "abstract": "GitHub issue resolving is a critical task in software engineering, recently gaining significant attention in both industry and academia. Within this task, SWE-bench has been released to evaluate issue resolving capabilities of large language models (LLMs), but has so far only focused on Python version. However, supporting more programming languages is also important, as there is a strong demand in industry. As a first step toward multilingual support, we have developed a Java version of SWE-bench, called SWE-bench-java. We have publicly released the dataset, along with the corresponding Docker-based evaluation environment and leaderboard, which will be continuously maintained and updated in the coming months. To verify the reliability of SWE-bench-java, we implement a classic method SWE-agent and test several powerful LLMs on it. As is well known, developing a high-quality multi-lingual benchmark is time-consuming and labor-intensive, so we welcome contributions through pull requests or collaboration to accelerate its iteration and refinement, paving the way for fully automated programming.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-27", "arxiv_id": "2408.11727", "title": "Efficient Detection of Toxic Prompts in Large Language Models", "authors": [ "Yi Liu", "Junzhe Yu", "Huijia Sun", "Ling Shi", "Gelei Deng", "Yuqi Chen", "Yang Liu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) like ChatGPT and Gemini have significantly advanced natural language processing, enabling various applications such as chatbots and automated content generation. However, these models can be exploited by malicious individuals who craft toxic prompts to elicit harmful or unethical responses. These individuals often employ jailbreaking techniques to bypass safety mechanisms, highlighting the need for robust toxic prompt detection methods. Existing detection techniques, both blackbox and whitebox, face challenges related to the diversity of toxic prompts, scalability, and computational efficiency. In response, we propose ToxicDetector, a lightweight greybox method designed to efficiently detect toxic prompts in LLMs. ToxicDetector leverages LLMs to create toxic concept prompts, uses embedding vectors to form feature vectors, and employs a Multi-Layer Perceptron (MLP) classifier for prompt classification. Our evaluation on various versions of the LLama models, Gemma-2, and multiple datasets demonstrates that ToxicDetector achieves a high accuracy of 96.39\\% and a low false positive rate of 2.00\\%, outperforming state-of-the-art methods. Additionally, ToxicDetector's processing time of 0.0780 seconds per prompt makes it highly suitable for real-time applications. ToxicDetector achieves high accuracy, efficiency, and scalability, making it a practical method for toxic prompt detection in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-27", "arxiv_id": "2408.13423", "title": "Training-free Long Video Generation with Chain of Diffusion Model Experts", "authors": [ "Wenhao Li", "Yichao Cao", "Xie Su", "Xi Lin", "Shan You", "Mingkai Zheng", "Yi Chen", "Chang Xu" ], "github": "", "project_page": "", "abstract": "Video generation models hold substantial potential in areas such as filmmaking. However, current video diffusion models need high computational costs and produce suboptimal results due to high complexity of video generation task. In this paper, we propose ConFiner, an efficient high-quality video generation framework that decouples video generation into easier subtasks: structure control and spatial-temporal refinement. It can generate high-quality videos with chain of off-the-shelf diffusion model experts, each expert responsible for a decoupled subtask. During the refinement, we introduce coordinated denoising, which can merge multiple diffusion experts' capabilities into a single sampling. Furthermore, we design ConFiner-Long framework, which can generate long coherent video with three constraint strategies on ConFiner. Experimental results indicate that with only 10\\% of the inference cost, our ConFiner surpasses representative models like Lavie and Modelscope across all objective and subjective metrics. And ConFiner-Long can generate high-quality and coherent videos with up to 600 frames.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-27", "arxiv_id": "2408.13413", "title": "TVG: A Training-free Transition Video Generation Method with Diffusion Models", "authors": [ "Rui Zhang", "Yaosen Chen", "Yuegen Liu", "Wei Wang", "Xuming Wen", "Hongxia Wang" ], "github": "", "project_page": "", "abstract": "Transition videos play a crucial role in media production, enhancing the flow and coherence of visual narratives. Traditional methods like morphing often lack artistic appeal and require specialized skills, limiting their effectiveness. Recent advances in diffusion model-based video generation offer new possibilities for creating transitions but face challenges such as poor inter-frame relationship modeling and abrupt content changes. We propose a novel training-free Transition Video Generation (TVG) approach using video-level diffusion models that addresses these limitations without additional training. Our method leverages Gaussian Process Regression (GPR) to model latent representations, ensuring smooth and dynamic transitions between frames. Additionally, we introduce interpolation-based conditional controls and a Frequency-aware Bidirectional Fusion (FBiF) architecture to enhance temporal control and transition reliability. Evaluations of benchmark datasets and custom image pairs demonstrate the effectiveness of our approach in generating high-quality smooth transition videos. The code are provided in https://sobeymil.github.io/tvg.com.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-27", "arxiv_id": "2408.14468", "title": "K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models via K-wise Human Preferences", "authors": [ "Zhikai Li", "Xuewen Liu", "Dongrong Fu", "Jianquan Li", "Qingyi Gu", "Kurt Keutzer", "Zhen Dong" ], "github": "", "project_page": "", "abstract": "The rapid advancement of visual generative models necessitates efficient and reliable evaluation methods. Arena platform, which gathers user votes on model comparisons, can rank models with human preferences. However, traditional Arena methods, while established, require an excessive number of comparisons for ranking to converge and are vulnerable to preference noise in voting, suggesting the need for better approaches tailored to contemporary evaluation challenges. In this paper, we introduce K-Sort Arena, an efficient and reliable platform based on a key insight: images and videos possess higher perceptual intuitiveness than texts, enabling rapid evaluation of multiple samples simultaneously. Consequently, K-Sort Arena employs K-wise comparisons, allowing K models to engage in free-for-all competitions, which yield much richer information than pairwise comparisons. To enhance the robustness of the system, we leverage probabilistic modeling and Bayesian updating techniques. We propose an exploration-exploitation-based matchmaking strategy to facilitate more informative comparisons. In our experiments, K-Sort Arena exhibits 16.3x faster convergence compared to the widely used ELO algorithm. To further validate the superiority and obtain a comprehensive leaderboard, we collect human feedback via crowdsourced evaluations of numerous cutting-edge text-to-image and text-to-video models. Thanks to its high efficiency, K-Sort Arena can continuously incorporate emerging models and update the leaderboard with minimal votes. Our project has undergone several months of internal testing and is now available at https://huggingface.co/spaces/ksort/K-Sort-Arena", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-27", "arxiv_id": "2408.13934", "title": "Learning to Move Like Professional Counter-Strike Players", "authors": [ "David Durst", "Feng Xie", "Vishnu Sarukkai", "Brennan Shacklett", "Iuri Frosio", "Chen Tessler", "Joohwan Kim", "Carly Taylor", "Gilbert Bernstein", "Sanjiban Choudhury", "Pat Hanrahan", "Kayvon Fatahalian" ], "github": "", "project_page": "", "abstract": "In multiplayer, first-person shooter games like Counter-Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high-level strategic play. However, the complexity of team coordination and the variety of conditions present in popular game maps make it impractical to author hand-crafted movement policies for every scenario. We show that it is possible to take a data-driven approach to creating human-like movement controllers for CS:GO. We curate a team movement dataset comprising 123 hours of professional game play traces, and use this dataset to train a transformer-based movement model that generates human-like team movement for all players in a \"Retakes\" round of the game. Importantly, the movement prediction model is efficient. Performing inference for all players takes less than 0.5 ms per game step (amortized cost) on a single CPU core, making it plausible for use in commercial games today. Human evaluators assess that our model behaves more like humans than both commercially-available bots and procedural movement controllers scripted by experts (16% to 59% higher by TrueSkill rating of \"human-like\"). Using experiments involving in-game bot vs. bot self-play, we demonstrate that our model performs simple forms of teamwork, makes fewer common movement mistakes, and yields movement distributions, player lifetimes, and kill locations similar to those observed in professional CS:GO match play.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-27", "arxiv_id": "2408.13933", "title": "MobileQuant: Mobile-friendly Quantization for On-device Language Models", "authors": [ "Fuwen Tan", "Royson Lee", "\u0141ukasz Dudziak", "Shell Xu Hu", "Sourav Bhattacharya", "Timothy Hospedales", "Georgios Tzimiropoulos", "Brais Martinez" ], "github": "https://github.com/saic-fi/mobilequant", "project_page": "", "abstract": "Large language models (LLMs) have revolutionized language processing, delivering outstanding results across multiple applications. However, deploying LLMs on edge devices poses several challenges with respect to memory, energy, and compute costs, limiting their widespread use in devices such as mobile phones. A promising solution is to reduce the number of bits used to represent weights and activations. While existing works have found partial success at quantizing LLMs to lower bitwidths, e.g. 4-bit weights, quantizing activations beyond 16 bits often leads to large computational overheads due to poor on-device quantization support, or a considerable accuracy drop. Yet, 8-bit activations are very attractive for on-device deployment as they would enable LLMs to fully exploit mobile-friendly hardware, e.g. Neural Processing Units (NPUs). In this work, we make a first attempt to facilitate the on-device deployment of LLMs using integer-only quantization. We first investigate the limitations of existing quantization methods for on-device deployment, with a special focus on activation quantization. We then address these limitations by introducing a simple post-training quantization method, named MobileQuant, that extends previous weight equivalent transformation works by jointly optimizing the weight transformation and activation range parameters in an end-to-end manner. MobileQuant demonstrates superior capabilities over existing methods by 1) achieving near-lossless quantization on a wide range of LLM benchmarks, 2) reducing latency and energy consumption by 20\\%-50\\% compared to current on-device quantization strategies, 3) requiring limited compute budget, 4) being compatible with mobile-friendly compute units, e.g. NPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/saic-fi/mobilequant", "github_api_added_by": "auto" }, { "date": "2024-08-27", "arxiv_id": "2408.14340", "title": "Foundation Models for Music: A Survey", "authors": [ "Yinghao Ma", "Anders \u00d8land", "Anton Ragni", "Bleiz MacSen Del Sette", "Charalampos Saitis", "Chris Donahue", "Chenghua Lin", "Christos Plachouras", "Emmanouil Benetos", "Elio Quinton", "Elona Shatri", "Fabio Morreale", "Ge Zhang", "Gy\u00f6rgy Fazekas", "Gus Xia", "Huan Zhang", "Ilaria Manco", "Jiawen Huang", "Julien Guinot", "Liwei Lin", "Luca Marinelli", "Max W. Y. Lam", "Megha Sharma", "Qiuqiang Kong", "Roger B. Dannenberg", "Ruibin Yuan", "Shangda Wu", "Shih-Lun Wu", "Shuqi Dai", "Shun Lei", "Shiyin Kang", "Simon Dixon", "Wenhu Chen", "Wehhao Huang", "Xingjian Du", "Xingwei Qu", "Xu Tan", "Yizhi Li", "Zeyue Tian", "Zhiyong Wu", "Zhizheng Wu", "Ziyang Ma", "Ziyu Wang" ], "github": "https://github.com/nicolaus625/fm4music", "project_page": "", "abstract": "In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models and foundation models in music, spanning from representation learning, generative learning and multimodal learning. We first contextualise the significance of music in various industries and trace the evolution of AI in music. By delineating the modalities targeted by foundation models, we discover many of the music representations are underexplored in FM development. Then, emphasis is placed on the lack of versatility of previous methods on diverse music applications, along with the potential of FMs in music understanding, generation and medical application. By comprehensively exploring the details of the model pre-training paradigm, architectural choices, tokenisation, finetuning methodologies and controllability, we emphasise the important topics that should have been well explored, like instruction tuning and in-context learning, scaling law and emergent ability, as well as long-sequence modelling etc. A dedicated section presents insights into music agents, accompanied by a thorough analysis of datasets and evaluations essential for pre-training and downstream tasks. Finally, by underscoring the vital importance of ethical considerations, we advocate that following research on FM for music should focus more on such issues as interpretability, transparency, human responsibility, and copyright issues. The paper offers insights into future challenges and trends on FMs for music, aiming to shape the trajectory of human-AI collaboration in the music realm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nicolaus625/fm4music", "github_api_added_by": "auto" }, { "date": "2024-08-27", "arxiv_id": "2408.13467", "title": "LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to Small-Scale Local LLMs", "authors": [ "Chansung Park", "Juyong Jiang", "Fan Wang", "Sayak Paul", "Jing Tang", "Sunghun Kim" ], "github": "https://github.com/deep-diver/llamaduo", "project_page": "", "abstract": "The widespread adoption of cloud-based proprietary large language models (LLMs) has introduced significant challenges, including operational dependencies, privacy concerns, and the necessity of continuous internet connectivity. In this work, we introduce an LLMOps pipeline, \"LlamaDuo\", for the seamless migration of knowledge and abilities from service-oriented LLMs to smaller, locally manageable models. This pipeline is crucial for ensuring service continuity in the presence of operational failures, strict privacy policies, or offline requirements. Our LlamaDuo involves fine-tuning a small language model against the service LLM using a synthetic dataset generated by the latter. If the performance of the fine-tuned model falls short of expectations, it is enhanced by further fine-tuning with additional similar data created by the service LLM. This iterative process guarantees that the smaller model can eventually match or even surpass the service LLM's capabilities in specific downstream tasks, offering a practical and scalable solution for managing AI deployments in constrained environments. Extensive experiments with leading edge LLMs are conducted to demonstrate the effectiveness, adaptability, and affordability of LlamaDuo across various downstream tasks. Our pipeline implementation is available at https://github.com/deep-diver/llamaduo.", "github_manual": "", "github_regex": "https://github.com/deep-diver/llamaduo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-27", "arxiv_id": "2408.13402", "title": "LLaVaOLMoBitnet1B: Ternary LLM goes Multimodal!", "authors": [ "Jainaveen Sundaram", "Ravishankar Iyer" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MM-LLMs) have seen significant advancements in the last year, demonstrating impressive performance across tasks. However, to truly democratize AI, models must exhibit strong capabilities and be able to run efficiently on small compute footprints accessible by most. Part of this quest, we introduce LLaVaOLMoBitnet1B - the first Ternary Multimodal LLM capable of accepting Image(s)+Text inputs to produce coherent textual responses. The model is fully open-sourced along with training scripts to encourage further research in this space. This accompanying technical report highlights the training process, evaluation details, challenges associated with ternary models and future opportunities. Link to the model: https://huggingface.co/IntelLabs/LlavaOLMoBitnet1B", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-27", "arxiv_id": "2408.13359", "title": "Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler", "authors": [ "Yikang Shen", "Matthew Stallone", "Mayank Mishra", "Gaoyuan Zhang", "Shawn Tan", "Aditya Prasad", "Adriana Meza Soria", "David D. Cox", "Rameswar Panda" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "Finding the optimal learning rate for language model pretraining is a challenging task. This is not only because there is a complicated correlation between learning rate, batch size, number of training tokens, model size, and other hyperparameters but also because it is prohibitively expensive to perform a hyperparameter search for large language models with Billions or Trillions of parameters. Recent studies propose using small proxy models and small corpus to perform hyperparameter searches and transposing the optimal parameters to large models and large corpus. While the zero-shot transferability is theoretically and empirically proven for model size related hyperparameters, like depth and width, the zero-shot transfer from small corpus to large corpus is underexplored. In this paper, we study the correlation between optimal learning rate, batch size, and number of training tokens for the recently proposed WSD scheduler. After thousands of small experiments, we found a power-law relationship between variables and demonstrated its transferability across model sizes. Based on the observation, we propose a new learning rate scheduler, Power scheduler, that is agnostic about the number of training tokens and batch size. The experiment shows that combining the Power scheduler with Maximum Update Parameterization (muP) can consistently achieve impressive performance with one set of hyperparameters regardless of the number of training tokens, batch size, model size, and even model architecture. Our 3B dense and MoE models trained with the Power scheduler achieve comparable performance as state-of-the-art small language models. We open-source these pretrained models at https://ibm.biz/BdKhLa.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-27", "arxiv_id": "2408.14176", "title": "SwiftBrush v2: Make Your One-step Diffusion Model Better Than Its Teacher", "authors": [ "Trung Dao", "Thuan Hoang Nguyen", "Thanh Le", "Duc Vu", "Khoi Nguyen", "Cuong Pham", "Anh Tran" ], "github": "https://github.com/vinairesearch/swiftbrushv2", "project_page": "", "abstract": "In this paper, we aim to enhance the performance of SwiftBrush, a prominent one-step text-to-image diffusion model, to be competitive with its multi-step Stable Diffusion counterpart. Initially, we explore the quality-diversity trade-off between SwiftBrush and SD Turbo: the former excels in image diversity, while the latter excels in image quality. This observation motivates our proposed modifications in the training methodology, including better weight initialization and efficient LoRA training. Moreover, our introduction of a novel clamped CLIP loss enhances image-text alignment and results in improved image quality. Remarkably, by combining the weights of models trained with efficient LoRA and full training, we achieve a new state-of-the-art one-step diffusion model, achieving an FID of 8.14 and surpassing all GAN-based and multi-step Stable Diffusion models. The evaluation code is available at: https://github.com/vinairesearch/swiftbrushv2.", "github_manual": "", "github_regex": "https://github.com/vinairesearch/swiftbrushv2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-27", "arxiv_id": "2408.14211", "title": "MagicMan: Generative Novel View Synthesis of Humans with 3D-Aware Diffusion and Iterative Refinement", "authors": [ "Xu He", "Xiaoyu Li", "Di Kang", "Jiangnan Ye", "Chaopeng Zhang", "Liyang Chen", "Xiangjun Gao", "Han Zhang", "Zhiyong Wu", "Haolin Zhuang" ], "github": "", "project_page": "", "abstract": "Existing works in single-image human reconstruction suffer from weak generalizability due to insufficient training data or 3D inconsistencies for a lack of comprehensive multi-view knowledge. In this paper, we introduce MagicMan, a human-specific multi-view diffusion model designed to generate high-quality novel view images from a single reference image. As its core, we leverage a pre-trained 2D diffusion model as the generative prior for generalizability, with the parametric SMPL-X model as the 3D body prior to promote 3D awareness. To tackle the critical challenge of maintaining consistency while achieving dense multi-view generation for improved 3D human reconstruction, we first introduce hybrid multi-view attention to facilitate both efficient and thorough information interchange across different views. Additionally, we present a geometry-aware dual branch to perform concurrent generation in both RGB and normal domains, further enhancing consistency via geometry cues. Last but not least, to address ill-shaped issues arising from inaccurate SMPL-X estimation that conflicts with the reference image, we propose a novel iterative refinement strategy, which progressively optimizes SMPL-X accuracy while enhancing the quality and consistency of the generated multi-views. Extensive experimental results demonstrate that our method significantly outperforms existing approaches in both novel view synthesis and subsequent 3D human reconstruction tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-27", "arxiv_id": "2408.12757", "title": "NanoFlow: Towards Optimal Large Language Model Serving Throughput", "authors": [ "Kan Zhu", "Yilong Zhao", "Liangyu Zhao", "Gefei Zuo", "Yile Gu", "Dedong Xie", "Yufei Gao", "Qinyu Xu", "Tian Tang", "Zihao Ye", "Keisuke Kamahori", "Chien-Yu Lin", "Stephanie Wang", "Arvind Krishnamurthy", "Baris Kasikci" ], "github": "", "project_page": "", "abstract": "The increasing usage of Large Language Models (LLMs) has resulted in a surging demand for planet-scale serving systems, where tens of thousands of GPUs continuously serve hundreds of millions of users. Consequently, throughput (under reasonable latency constraints) has emerged as a key metric that determines serving systems' performance. To boost throughput, various methods of inter-device parallelism (e.g., data, tensor, pipeline) have been explored. However, existing methods do not consider overlapping the utilization of different resources within a single device, leading to underutilization and sub-optimal performance. We propose NanoFlow, a novel serving framework that exploits intra-device parallelism, which overlaps the usage of resources including compute, memory, and network within a single device through operation co-scheduling. To exploit intra-device parallelism, NanoFlow introduces two key innovations: First, NanoFlow splits requests into nano-batches at the granularity of operations, which breaks the dependency of sequential operations in LLM inference and enables overlapping; then, to get benefit from overlapping, NanoFlow uses an operation-level pipeline with execution unit scheduling, which partitions the device's functional units and simultaneously executes different operations in each unit. NanoFlow automates the pipeline setup using a parameter search algorithm, which enables easily porting NanoFlow to different models. We implement NanoFlow on NVIDIA GPUs and evaluate end-to-end serving throughput on several popular models such as LLaMA-2-70B, Mixtral 8x7B, LLaMA-3-8B, etc.. With practical workloads, NanoFlow provides 1.91x throughput boost compared to state-of-the-art serving systems achieving 59% to 72% of optimal throughput across ported models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-28", "arxiv_id": "2408.13674", "title": "GenCA: A Text-conditioned Generative Model for Realistic and Drivable Codec Avatars", "authors": [ "Keqiang Sun", "Amin Jourabloo", "Riddhish Bhalodia", "Moustafa Meshry", "Yu Rong", "Zhengyu Yang", "Thu Nguyen-Phuoc", "Christian Haene", "Jiu Xu", "Sam Johnson", "Hongsheng Li", "Sofien Bouaziz" ], "github": "", "project_page": "", "abstract": "Photo-realistic and controllable 3D avatars are crucial for various applications such as virtual and mixed reality (VR/MR), telepresence, gaming, and film production. Traditional methods for avatar creation often involve time-consuming scanning and reconstruction processes for each avatar, which limits their scalability. Furthermore, these methods do not offer the flexibility to sample new identities or modify existing ones. On the other hand, by learning a strong prior from data, generative models provide a promising alternative to traditional reconstruction methods, easing the time constraints for both data capture and processing. Additionally, generative methods enable downstream applications beyond reconstruction, such as editing and stylization. Nonetheless, the research on generative 3D avatars is still in its infancy, and therefore current methods still have limitations such as creating static avatars, lacking photo-realism, having incomplete facial details, or having limited drivability. To address this, we propose a text-conditioned generative model that can generate photo-realistic facial avatars of diverse identities, with more complete details like hair, eyes and mouth interior, and which can be driven through a powerful non-parametric latent expression space. Specifically, we integrate the generative and editing capabilities of latent diffusion models with a strong prior model for avatar expression driving. Our model can generate and control high-fidelity avatars, even those out-of-distribution. We also highlight its potential for downstream applications, including avatar editing and single-shot avatar reconstruction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-28", "arxiv_id": "2408.14819", "title": "Build-A-Scene: Interactive 3D Layout Control for Diffusion-Based Image Generation", "authors": [ "Abdelrahman Eldesokey", "Peter Wonka" ], "github": "", "project_page": "", "abstract": "We propose a diffusion-based approach for Text-to-Image (T2I) generation with interactive 3D layout control. Layout control has been widely studied to alleviate the shortcomings of T2I diffusion models in understanding objects' placement and relationships from text descriptions. Nevertheless, existing approaches for layout control are limited to 2D layouts, require the user to provide a static layout beforehand, and fail to preserve generated images under layout changes. This makes these approaches unsuitable for applications that require 3D object-wise control and iterative refinements, e.g., interior design and complex scene generation. To this end, we leverage the recent advancements in depth-conditioned T2I models and propose a novel approach for interactive 3D layout control. We replace the traditional 2D boxes used in layout control with 3D boxes. Furthermore, we revamp the T2I task as a multi-stage generation process, where at each stage, the user can insert, change, and move an object in 3D while preserving objects from earlier stages. We achieve this through our proposed Dynamic Self-Attention (DSA) module and the consistent 3D object translation strategy. Experiments show that our approach can generate complicated scenes based on 3D layouts, boosting the object generation success rate over the standard depth-conditioned T2I methods by 2x. Moreover, it outperforms other methods in comparison in preserving objects under layout changes. Project Page: https://abdo-eldesokey.github.io/build-a-scene/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-28", "arxiv_id": "2408.14805", "title": "Platypus: A Generalized Specialist Model for Reading Text in Various Forms", "authors": [ "Peng Wang", "Zhaohai Li", "Jun Tang", "Humen Zhong", "Fei Huang", "Zhibo Yang", "Cong Yao" ], "github": "https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR", "project_page": "", "abstract": "Reading text from images (either natural scenes or documents) has been a long-standing research topic for decades, due to the high technical challenge and wide application range. Previously, individual specialist models are developed to tackle the sub-tasks of text reading (e.g., scene text recognition, handwritten text recognition and mathematical expression recognition). However, such specialist models usually cannot effectively generalize across different sub-tasks. Recently, generalist models (such as GPT-4V), trained on tremendous data in a unified way, have shown enormous potential in reading text in various scenarios, but with the drawbacks of limited accuracy and low efficiency. In this work, we propose Platypus, a generalized specialist model for text reading. Specifically, Platypus combines the best of both worlds: being able to recognize text of various forms with a single unified architecture, while achieving excellent accuracy and high efficiency. To better exploit the advantage of Platypus, we also construct a text reading dataset (called Worms), the images of which are curated from previous datasets and partially re-labeled. Experiments on standard benchmarks demonstrate the effectiveness and superiority of the proposed Platypus model. Model and data will be made publicly available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/Platypus.", "github_manual": "", "github_regex": "https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-28", "arxiv_id": "2408.14837", "title": "Diffusion Models Are Real-Time Game Engines", "authors": [ "Dani Valevski", "Yaniv Leviathan", "Moab Arar", "Shlomi Fruchter" ], "github": "", "project_page": "", "abstract": "We present GameNGen, the first game engine powered entirely by a neural model that enables real-time interaction with a complex environment over long trajectories at high quality. GameNGen can interactively simulate the classic game DOOM at over 20 frames per second on a single TPU. Next frame prediction achieves a PSNR of 29.4, comparable to lossy JPEG compression. Human raters are only slightly better than random chance at distinguishing short clips of the game from clips of the simulation. GameNGen is trained in two phases: (1) an RL-agent learns to play the game and the training sessions are recorded, and (2) a diffusion model is trained to produce the next frame, conditioned on the sequence of past frames and actions. Conditioning augmentations enable stable auto-regressive generation over long trajectories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-28", "arxiv_id": "2408.15239", "title": "Generative Inbetweening: Adapting Image-to-Video Models for Keyframe Interpolation", "authors": [ "Xiaojuan Wang", "Boyang Zhou", "Brian Curless", "Ira Kemelmacher-Shlizerman", "Aleksander Holynski", "Steven M. Seitz" ], "github": "", "project_page": "", "abstract": "We present a method for generating video sequences with coherent motion between a pair of input key frames. We adapt a pretrained large-scale image-to-video diffusion model (originally trained to generate videos moving forward in time from a single input image) for key frame interpolation, i.e., to produce a video in between two input frames. We accomplish this adaptation through a lightweight fine-tuning technique that produces a version of the model that instead predicts videos moving backwards in time from a single input image. This model (along with the original forward-moving model) is subsequently used in a dual-directional diffusion sampling process that combines the overlapping model estimates starting from each of the two keyframes. Our experiments show that our method outperforms both existing diffusion-based methods and traditional frame interpolation techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-28", "arxiv_id": "2408.15237", "title": "The Mamba in the Llama: Distilling and Accelerating Hybrid Models", "authors": [ "Junxiong Wang", "Daniele Paliotta", "Avner May", "Alexander M. Rush", "Tri Dao" ], "github": "https://github.com/jxiw/MambaInLlama", "project_page": "", "abstract": "Linear RNN architectures, like Mamba, can be competitive with Transformer models in language modeling while having advantageous deployment characteristics. Given the focus on training large-scale Transformer models, we consider the challenge of converting these pretrained models for deployment. We demonstrate that it is feasible to distill large Transformers into linear RNNs by reusing the linear projection weights from attention layers with academic GPU resources. The resulting hybrid model, which incorporates a quarter of the attention layers, achieves performance comparable to the original Transformer in chat benchmarks and outperforms open-source hybrid Mamba models trained from scratch with trillions of tokens in both chat benchmarks and general benchmarks. Moreover, we introduce a hardware-aware speculative decoding algorithm that accelerates the inference speed of Mamba and hybrid models. Overall we show how, with limited computation resources, we can remove many of the original attention layers and generate from the resulting model more efficiently. Our top-performing model, distilled from Llama3-8B-Instruct, achieves a 29.61 length-controlled win rate on AlpacaEval 2 against GPT-4 and 7.35 on MT-Bench, surpassing the best instruction-tuned linear RNN model.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jxiw/MambaInLlama", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-28", "arxiv_id": "2408.14717", "title": "Text2SQL is Not Enough: Unifying AI and Databases with TAG", "authors": [ "Asim Biswal", "Liana Patel", "Siddarth Jha", "Amog Kamsetty", "Shu Liu", "Joseph E. Gonzalez", "Carlos Guestrin", "Matei Zaharia" ], "github": "https://github.com/TAG-Research/TAG-Bench", "project_page": "", "abstract": "AI systems that serve natural language questions over databases promise to unlock tremendous value. Such systems would allow users to leverage the powerful reasoning and knowledge capabilities of language models (LMs) alongside the scalable computational power of data management systems. These combined capabilities would empower users to ask arbitrary natural language questions over custom data sources. However, existing methods and benchmarks insufficiently explore this setting. Text2SQL methods focus solely on natural language questions that can be expressed in relational algebra, representing a small subset of the questions real users wish to ask. Likewise, Retrieval-Augmented Generation (RAG) considers the limited subset of queries that can be answered with point lookups to one or a few data records within the database. We propose Table-Augmented Generation (TAG), a unified and general-purpose paradigm for answering natural language questions over databases. The TAG model represents a wide range of interactions between the LM and database that have been previously unexplored and creates exciting research opportunities for leveraging the world knowledge and reasoning capabilities of LMs over data. We systematically develop benchmarks to study the TAG problem and find that standard methods answer no more than 20% of queries correctly, confirming the need for further research in this area. We release code for the benchmark at https://github.com/TAG-Research/TAG-Bench.", "github_manual": "", "github_regex": "https://github.com/TAG-Research/TAG-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-28", "arxiv_id": "2408.14906", "title": "Writing in the Margins: Better Inference Pattern for Long Context Retrieval", "authors": [ "Melisa Russak", "Umar Jamil", "Christopher Bryant", "Kiran Kamble", "Axel Magnuson", "Mateusz Russak", "Waseem AlShikh" ], "github": "https://github.com/writer/writing-in-the-margins", "project_page": "", "abstract": "In this paper, we introduce Writing in the Margins (WiM), a new inference pattern for Large Language Models designed to optimize the handling of long input sequences in retrieval-oriented tasks. This approach leverages the chunked prefill of the key-value cache to perform segment-wise inference, which enables efficient processing of extensive contexts along with the generation and classification of intermediate information (\"margins\") that guide the model towards specific tasks. This method increases computational overhead marginally while significantly enhancing the performance of off-the-shelf models without the need for fine-tuning. Specifically, we observe that WiM provides an average enhancement of 7.5% in accuracy for reasoning skills (HotpotQA, MultiHop-RAG) and more than a 30.0% increase in the F1-score for aggregation tasks (CWE). Additionally, we show how the proposed pattern fits into an interactive retrieval design that provides end-users with ongoing updates about the progress of context processing, and pinpoints the integration of relevant information into the final response. We release our implementation of WiM using Hugging Face Transformers library at https://github.com/writer/writing-in-the-margins.", "github_manual": "", "github_regex": "https://github.com/writer/writing-in-the-margins", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-28", "arxiv_id": "2408.14849", "title": "Project SHADOW: Symbolic Higher-order Associative Deductive reasoning On Wikidata using LM probing", "authors": [ "Hanna Abi Akl" ], "github": "https://github.com/hannaabiakl/shadow", "project_page": "", "abstract": "We introduce SHADOW, a fine-tuned language model trained on an intermediate task using associative deductive reasoning, and measure its performance on a knowledge base construction task using Wikidata triple completion. We evaluate SHADOW on the LM-KBC 2024 challenge and show that it outperforms the baseline solution by 20% with a F1 score of 68.72%.", "github_manual": "https://github.com/hannaabiakl/shadow", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-28", "arxiv_id": "2408.13629", "title": "Temporally-consistent 3D Reconstruction of Birds", "authors": [ "Johannes H\u00e4gerlind", "Jonas Hentati-Sundberg", "Bastian Wandt" ], "github": "", "project_page": "", "abstract": "This paper deals with 3D reconstruction of seabirds which recently came into focus of environmental scientists as valuable bio-indicators for environmental change. Such 3D information is beneficial for analyzing the bird's behavior and physiological shape, for example by tracking motion, shape, and appearance changes. From a computer vision perspective birds are especially challenging due to their rapid and oftentimes non-rigid motions. We propose an approach to reconstruct the 3D pose and shape from monocular videos of a specific breed of seabird - the common murre. Our approach comprises a full pipeline of detection, tracking, segmentation, and temporally consistent 3D reconstruction. Additionally, we propose a temporal loss that extends current single-image 3D bird pose estimators to the temporal domain. Moreover, we provide a real-world dataset of 10000 frames of video observations on average capture nine birds simultaneously, comprising a large variety of motions and interactions, including a smaller test set with bird-specific keypoint labels. Using our temporal optimization, we achieve state-of-the-art performance for the challenging sequences in our dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-28", "arxiv_id": "2408.14236", "title": "DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for type classification", "authors": [ "Hanna Abi Akl" ], "github": "https://github.com/hannaabiakl/semantictowers", "project_page": "", "abstract": "We introduce semantic towers, an extrinsic knowledge representation method, and compare it to intrinsic knowledge in large language models for ontology learning. Our experiments show a trade-off between performance and semantic grounding for extrinsic knowledge compared to a fine-tuned model intrinsic knowledge. We report our findings on the Large Language Models for Ontology Learning (LLMs4OL) 2024 challenge.", "github_manual": "https://github.com/hannaabiakl/semantictowers", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-28", "arxiv_id": "2408.14307", "title": "LLM-3D Print: Large Language Models To Monitor and Control 3D Printing", "authors": [ "Yayati Jadhav", "Peter Pak", "Amir Barati Farimani" ], "github": "https://github.com/Klipper3d/klipper", "project_page": "", "abstract": "Industry 4.0 has revolutionized manufacturing by driving digitalization and shifting the paradigm toward additive manufacturing (AM). Fused Deposition Modeling (FDM), a key AM technology, enables the creation of highly customized, cost-effective products with minimal material waste through layer-by-layer extrusion, posing a significant challenge to traditional subtractive methods. However, the susceptibility of material extrusion techniques to errors often requires expert intervention to detect and mitigate defects that can severely compromise product quality. While automated error detection and machine learning models exist, their generalizability across diverse 3D printer setups, firmware, and sensors is limited, and deep learning methods require extensive labeled datasets, hindering scalability and adaptability. To address these challenges, we present a process monitoring and control framework that leverages pre-trained Large Language Models (LLMs) alongside 3D printers to detect and address printing defects. The LLM evaluates print quality by analyzing images captured after each layer or print segment, identifying failure modes and querying the printer for relevant parameters. It then generates and executes a corrective action plan. We validated the effectiveness of the proposed framework in identifying defects by comparing it against a control group of engineers with diverse AM expertise. Our evaluation demonstrated that LLM-based agents not only accurately identify common 3D printing errors, such as inconsistent extrusion, stringing, warping, and layer adhesion, but also effectively determine the parameters causing these failures and autonomously correct them without any need for human intervention.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Klipper3d/klipper", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-29", "arxiv_id": "2408.15518", "title": "Dolphin: Long Context as a New Modality for Energy-Efficient On-Device Language Models", "authors": [ "Wei Chen", "Zhiyuan Li", "Shuo Xin", "Yihao Wang" ], "github": "", "project_page": "", "abstract": "This paper presents Dolphin, a novel decoder-decoder architecture for energy-efficient processing of long contexts in language models. Our approach addresses the significant energy consumption and latency challenges inherent in on-device models. Dolphin employs a compact 0.5B parameter decoder to distill extensive contextual information into a memory embedding, substantially reducing the input length for the primary 7B parameter decoder model. Inspired by vision-language models, we repurpose the image embedding projector to encode long textual contexts, effectively treating extended context as a distinct modality. This innovative method enables processing of substantially longer contexts without the typical computational overhead associated with extended input sequences. Empirical evaluations demonstrate a 10-fold improvement in energy efficiency and a 5-fold reduction in latency compared to conventional full-length context processing methods without losing quality of the response. Our work contributes to the development of more sustainable and scalable language models for on-device applications, addressing the critical need for energy-efficient and responsive AI technologies in resource-constrained environments while maintaining the accuracy to understand long contexts. This research has implications for the broader field of natural language processing, particularly in the domain of efficient model design for resource-limited settings. By enabling more sophisticated AI capabilities on edge devices, Dolphin paves the way for advanced language processing in a wide range of applications where computational resources are at a premium. The Dolphin model is publicly available at https://huggingface.co/NexaAIDev/Dolphin.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-29", "arxiv_id": "2408.15915", "title": "Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models", "authors": [ "Yuncheng Yang", "Yulei Qin", "Tong Wu", "Zihan Xu", "Gang Li", "Pengcheng Guo", "Hang Shao", "Yucheng Shi", "Ke Li", "Xing Sun", "Jie Yang", "Yun Gu" ], "github": "https://github.com/yaphabates/rocket", "project_page": "", "abstract": "The cultivation of expertise for large language models (LLMs) to solve tasks of specific areas often requires special-purpose tuning with calibrated behaviors on the expected stable outputs. To avoid huge cost brought by manual preparation of instruction datasets and training resources up to hundreds of hours, the exploitation of open knowledge including a wealth of low rank adaptation (LoRA) models and instruction datasets serves as a good starting point. However, existing methods on model and data selection focus on the performance of general-purpose capabilities while neglecting the knowledge gap exposed in domain-specific deployment. In the present study, we propose to bridge such gap by introducing few human-annotated samples (i.e., K-shot) for advancing task expertise of LLMs with open knowledge. Specifically, we develop an efficient and scalable pipeline to cost-efficiently produce task experts where K-shot data intervene in selecting the most promising expert candidates and the task-relevant instructions. A mixture-of-expert (MoE) system is built to make the best use of individual-yet-complementary knowledge between multiple experts. We unveil the two keys to the success of a MoE system, 1) the abidance by K-shot, and 2) the insistence on diversity. For the former, we ensure that models that truly possess problem-solving abilities on K-shot are selected rather than those blind guessers. Besides, during data selection, instructions that share task-relevant contexts with K-shot are prioritized. For the latter, we highlight the diversity of constituting experts and that of the fine-tuning instructions throughout the model and data selection process. Extensive experimental results confirm the superiority of our approach over existing methods on utilization of open knowledge across various tasks. Codes and models will be released later.", "github_manual": "https://github.com/yaphabates/rocket", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-29", "arxiv_id": "2408.15998", "title": "Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders", "authors": [ "Min Shi", "Fuxiao Liu", "Shihao Wang", "Shijia Liao", "Subhashree Radhakrishnan", "De-An Huang", "Hongxu Yin", "Karan Sapra", "Yaser Yacoob", "Humphrey Shi", "Bryan Catanzaro", "Andrew Tao", "Jan Kautz", "Zhiding Yu", "Guilin Liu" ], "github": "https://github.com/NVlabs/Eagle", "project_page": "", "abstract": "The ability to accurately interpret complex visual information is a crucial topic of multimodal large language models (MLLMs). Recent work indicates that enhanced visual perception significantly reduces hallucinations and improves performance on resolution-sensitive tasks, such as optical character recognition and document analysis. A number of recent MLLMs achieve this goal using a mixture of vision encoders. Despite their success, there is a lack of systematic comparisons and detailed ablation studies addressing critical aspects, such as expert selection and the integration of multiple vision experts. This study provides an extensive exploration of the design space for MLLMs using a mixture of vision encoders and resolutions. Our findings reveal several underlying principles common to various existing strategies, leading to a streamlined yet effective design approach. We discover that simply concatenating visual tokens from a set of complementary vision encoders is as effective as more complex mixing architectures or strategies. We additionally introduce Pre-Alignment to bridge the gap between vision-focused encoders and language tokens, enhancing model coherence. The resulting family of MLLMs, Eagle, surpasses other leading open-source models on major MLLM benchmarks. Models and code: https://github.com/NVlabs/Eagle", "github_manual": "", "github_regex": "https://github.com/NVlabs/Eagle", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-29", "arxiv_id": "2408.15995", "title": "TEDRA: Text-based Editing of Dynamic and Photoreal Actors", "authors": [ "Basavaraj Sunagad", "Heming Zhu", "Mohit Mendiratta", "Adam Kortylewski", "Christian Theobalt", "Marc Habermann" ], "github": "", "project_page": "", "abstract": "Over the past years, significant progress has been made in creating photorealistic and drivable 3D avatars solely from videos of real humans. However, a core remaining challenge is the fine-grained and user-friendly editing of clothing styles by means of textual descriptions. To this end, we present TEDRA, the first method allowing text-based edits of an avatar, which maintains the avatar's high fidelity, space-time coherency, as well as dynamics, and enables skeletal pose and view control. We begin by training a model to create a controllable and high-fidelity digital replica of the real actor. Next, we personalize a pretrained generative diffusion model by fine-tuning it on various frames of the real character captured from different camera angles, ensuring the digital representation faithfully captures the dynamics and movements of the real person. This two-stage process lays the foundation for our approach to dynamic human avatar editing. Utilizing this personalized diffusion model, we modify the dynamic avatar based on a provided text prompt using our Personalized Normal Aligned Score Distillation Sampling (PNA-SDS) within a model-based guidance framework. Additionally, we propose a time step annealing strategy to ensure high-quality edits. Our results demonstrate a clear improvement over prior work in functionality and visual quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-29", "arxiv_id": "2408.15881", "title": "LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation", "authors": [ "Fangxun Shu", "Yue Liao", "Le Zhuo", "Chenning Xu", "Guanghao Zhang", "Haonan Shi", "Long Chen", "Tao Zhong", "Wanggui He", "Siming Fu", "Haoyuan Li", "Bolin Li", "Zhelun Yu", "Si Liu", "Hongsheng Li", "Hao Jiang" ], "github": "https://github.com/shufangxun/LLaVA-MoD", "project_page": "", "abstract": "We introduce LLaVA-MoD, a novel framework designed to enable the efficient training of small-scale Multimodal Language Models (s-MLLM) by distilling knowledge from large-scale MLLM (l-MLLM). Our approach tackles two fundamental challenges in MLLM distillation. First, we optimize the network structure of s-MLLM by integrating a sparse Mixture of Experts (MoE) architecture into the language model, striking a balance between computational efficiency and model expressiveness. Second, we propose a progressive knowledge transfer strategy to ensure comprehensive knowledge migration. This strategy begins with mimic distillation, where we minimize the Kullback-Leibler (KL) divergence between output distributions to enable the student model to emulate the teacher network's understanding. Following this, we introduce preference distillation via Direct Preference Optimization (DPO), where the key lies in treating l-MLLM as the reference model. During this phase, the s-MLLM's ability to discriminate between superior and inferior examples is significantly enhanced beyond l-MLLM, leading to a better student that surpasses its teacher, particularly in hallucination benchmarks. Extensive experiments demonstrate that LLaVA-MoD outperforms existing models across various multimodal benchmarks while maintaining a minimal number of activated parameters and low computational costs. Remarkably, LLaVA-MoD, with only 2B activated parameters, surpasses Qwen-VL-Chat-7B by an average of 8.8% across benchmarks, using merely 0.3% of the training data and 23% trainable parameters. These results underscore LLaVA-MoD's ability to effectively distill comprehensive knowledge from its teacher model, paving the way for the development of more efficient MLLMs. The code will be available on: https://github.com/shufangxun/LLaVA-MoD.", "github_manual": "", "github_regex": "https://github.com/shufangxun/LLaVA-MoD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-29", "arxiv_id": "2408.15708", "title": "Towards Realistic Example-based Modeling via 3D Gaussian Stitching", "authors": [ "Xinyu Gao", "Ziyi Yang", "Bingchen Gong", "Xiaoguang Han", "Sipeng Yang", "Xiaogang Jin" ], "github": "", "project_page": "", "abstract": "Using parts of existing models to rebuild new models, commonly termed as example-based modeling, is a classical methodology in the realm of computer graphics. Previous works mostly focus on shape composition, making them very hard to use for realistic composition of 3D objects captured from real-world scenes. This leads to combining multiple NeRFs into a single 3D scene to achieve seamless appearance blending. However, the current SeamlessNeRF method struggles to achieve interactive editing and harmonious stitching for real-world scenes due to its gradient-based strategy and grid-based representation. To this end, we present an example-based modeling method that combines multiple Gaussian fields in a point-based representation using sample-guided synthesis. Specifically, as for composition, we create a GUI to segment and transform multiple fields in real time, easily obtaining a semantically meaningful composition of models represented by 3D Gaussian Splatting (3DGS). For texture blending, due to the discrete and irregular nature of 3DGS, straightforwardly applying gradient propagation as SeamlssNeRF is not supported. Thus, a novel sampling-based cloning method is proposed to harmonize the blending while preserving the original rich texture and content. Our workflow consists of three steps: 1) real-time segmentation and transformation of a Gaussian model using a well-tailored GUI, 2) KNN analysis to identify boundary points in the intersecting area between the source and target models, and 3) two-phase optimization of the target model using sampling-based cloning and gradient constraints. Extensive experimental results validate that our approach significantly outperforms previous works in terms of realistic synthesis, demonstrating its practicality. More demos are available at https://ingra14m.github.io/gs_stitching_website.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-29", "arxiv_id": "2408.15496", "title": "ReMamba: Equip Mamba with Effective Long-Sequence Modeling", "authors": [ "Danlong Yuan", "Jiahao Liu", "Bei Li", "Huishuai Zhang", "Jingang Wang", "Xunliang Cai", "Dongyan Zhao" ], "github": "https://github.com/lblankl/remamba", "project_page": "", "abstract": "While the Mamba architecture demonstrates superior inference efficiency and competitive performance on short-context natural language processing (NLP) tasks, empirical evidence suggests its capacity to comprehend long contexts is limited compared to transformer-based models. In this study, we investigate the long-context efficiency issues of the Mamba models and propose ReMamba, which enhances Mamba's ability to comprehend long contexts. ReMamba incorporates selective compression and adaptation techniques within a two-stage re-forward process, incurring minimal additional inference costs overhead. Experimental results on the LongBench and L-Eval benchmarks demonstrate ReMamba's efficacy, improving over the baselines by 3.2 and 1.6 points, respectively, and attaining performance almost on par with same-size transformer models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lblankl/remamba", "github_api_added_by": "auto" }, { "date": "2024-08-29", "arxiv_id": "2408.15980", "title": "In-Context Imitation Learning via Next-Token Prediction", "authors": [ "Letian Fu", "Huang Huang", "Gaurav Datta", "Lawrence Yunliang Chen", "William Chung-Ho Panitch", "Fangchen Liu", "Hui Li", "Ken Goldberg" ], "github": "", "project_page": "", "abstract": "We explore how to enhance next-token prediction models to perform in-context imitation learning on a real robot, where the robot executes new tasks by interpreting contextual information provided during the input phase, without updating its underlying policy parameters. We propose In-Context Robot Transformer (ICRT), a causal transformer that performs autoregressive prediction on sensorimotor trajectories without relying on any linguistic data or reward function. This formulation enables flexible and training-free execution of new tasks at test time, achieved by prompting the model with sensorimotor trajectories of the new task composing of image observations, actions and states tuples, collected through human teleoperation. Experiments with a Franka Emika robot demonstrate that the ICRT can adapt to new tasks specified by prompts, even in environment configurations that differ from both the prompt and the training data. In a multitask environment setup, ICRT significantly outperforms current state-of-the-art next-token prediction models in robotics on generalizing to unseen tasks. Code, checkpoints and data are available on https://icrt.dev/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-29", "arxiv_id": "2408.15079", "title": "BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline", "authors": [ "Guosheng Dong", "Da Pan", "Yiding Sun", "Shusen Zhang", "Zheng Liang", "Xin Wu", "Yanjun Shen", "Fan Yang", "Haoze Sun", "Tianpeng Li", "Mingan Lin", "Jianhua Xu", "Yufan Zhang", "Xiaonan Nie", "Lei Su", "Bingning Wang", "Wentao Zhang", "Jiaxin Mao", "Zenan Zhou", "Weipeng Chen" ], "github": "https://github.com/google/cld3", "project_page": "", "abstract": "The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several institutions. To mitigate this issue, we open-source the details of a universally applicable data processing pipeline and validate its effectiveness and potential by introducing a competitive LLM baseline. Specifically, the data processing pipeline consists of broad collection to scale up and reweighting to improve quality. We then pretrain a 7B model BaichuanSEED with 3T tokens processed by our pipeline without any deliberate downstream task-related optimization, followed by an easy but effective supervised fine-tuning stage. BaichuanSEED demonstrates consistency and predictability throughout training and achieves comparable performance on comprehensive benchmarks with several commercial advanced large language models, such as Qwen1.5 and Llama3. We also conduct several heuristic experiments to discuss the potential for further optimization of downstream tasks, such as mathematics and coding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google/cld3", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-29", "arxiv_id": "2408.15836", "title": "Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature", "authors": [ "Uri Katz", "Mosh Levy", "Yoav Goldberg" ], "github": "", "project_page": "", "abstract": "The exponential growth of scientific literature necessitates advanced tools for effective knowledge exploration. We present Knowledge Navigator, a system designed to enhance exploratory search abilities by organizing and structuring the retrieved documents from broad topical queries into a navigable, two-level hierarchy of named and descriptive scientific topics and subtopics. This structured organization provides an overall view of the research themes in a domain, while also enabling iterative search and deeper knowledge discovery within specific subtopics by allowing users to refine their focus and retrieve additional relevant documents. Knowledge Navigator combines LLM capabilities with cluster-based methods to enable an effective browsing method. We demonstrate our approach's effectiveness through automatic and manual evaluations on two novel benchmarks, CLUSTREC-COVID and SCITOC. Our code, prompts, and benchmarks are made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-29", "arxiv_id": "2408.15991", "title": "Distribution Backtracking Builds A Faster Convergence Trajectory for One-step Diffusion Distillation", "authors": [ "Shengyuan Zhang", "Ling Yang", "Zejian Li", "An Zhao", "Chenye Meng", "Changyuan Yang", "Guang Yang", "Zhiyuan Yang", "Lingyun Sun" ], "github": "https://github.com/SYZhang0805/DisBack", "project_page": "", "abstract": "Accelerating the sampling speed of diffusion models remains a significant challenge. Recent score distillation methods distill a heavy teacher model into an one-step student generator, which is optimized by calculating the difference between the two score functions on the samples generated by the student model. However, there is a score mismatch issue in the early stage of the distillation process, because existing methods mainly focus on using the endpoint of pre-trained diffusion models as teacher models, overlooking the importance of the convergence trajectory between the student generator and the teacher model. To address this issue, we extend the score distillation process by introducing the entire convergence trajectory of teacher models and propose Distribution Backtracking Distillation (DisBack) for distilling student generators. DisBask is composed of two stages: Degradation Recording and Distribution Backtracking. Degradation Recording is designed to obtain the convergence trajectory of teacher models, which records the degradation path from the trained teacher model to the untrained initial student generator. The degradation path implicitly represents the intermediate distributions of teacher models. Then Distribution Backtracking trains a student generator to backtrack the intermediate distributions for approximating the convergence trajectory of teacher models. Extensive experiments show that DisBack achieves faster and better convergence than the existing distillation method and accomplishes comparable generation performance. Notably, DisBack is easy to implement and can be generalized to existing distillation methods to boost performance. Our code is publicly available on https://github.com/SYZhang0805/DisBack.", "github_manual": "", "github_regex": "https://github.com/SYZhang0805/DisBack", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-29", "arxiv_id": "2408.15792", "title": "Efficient LLM Scheduling by Learning to Rank", "authors": [ "Yichao Fu", "Siqi Zhu", "Runlong Su", "Aurick Qiao", "Ion Stoica", "Hao Zhang" ], "github": "https://github.com/hao-ai-lab/vllm-ltr.git", "project_page": "", "abstract": "In Large Language Model (LLM) inference, the output length of an LLM request is typically regarded as not known a priori. Consequently, most LLM serving systems employ a simple First-come-first-serve (FCFS) scheduling strategy, leading to Head-Of-Line (HOL) blocking and reduced throughput and service quality. In this paper, we reexamine this assumption -- we show that, although predicting the exact generation length of each request is infeasible, it is possible to predict the relative ranks of output lengths in a batch of requests, using learning to rank. The ranking information offers valuable guidance for scheduling requests. Building on this insight, we develop a novel scheduler for LLM inference and serving that can approximate the shortest-job-first (SJF) schedule better than existing approaches. We integrate this scheduler with the state-of-the-art LLM serving system and show significant performance improvement in several important applications: 2.8x lower latency in chatbot serving and 6.5x higher throughput in synthetic data generation. Our code is available at https://github.com/hao-ai-lab/vllm-ltr.git", "github_manual": "", "github_regex": "https://github.com/hao-ai-lab/vllm-ltr.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-29", "arxiv_id": "2408.15664", "title": "Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts", "authors": [ "Lean Wang", "Huazuo Gao", "Chenggang Zhao", "Xu Sun", "Damai Dai" ], "github": "", "project_page": "", "abstract": "For Mixture-of-Experts (MoE) models, an unbalanced expert load will lead to routing collapse or increased computational overhead. Existing methods commonly employ an auxiliary loss to encourage load balance, but a large auxiliary loss will introduce non-negligible interference gradients into training and thus impair the model performance. In order to control load balance while not producing undesired gradients during training, we propose Loss-Free Balancing, featured by an auxiliary-loss-free load balancing strategy. To be specific, before the top-K routing decision, Loss-Free Balancing will first apply an expert-wise bias to the routing scores of each expert. By dynamically updating the bias of each expert according to its recent load, Loss-Free Balancing can consistently maintain a balanced distribution of expert load. In addition, since Loss-Free Balancing does not produce any interference gradients, it also elevates the upper bound of model performance gained from MoE training. We validate the performance of Loss-Free Balancing on MoE models with up to 3B parameters trained on up to 200B tokens. Experimental results show that Loss-Free Balancing achieves both better performance and better load balance compared with traditional auxiliary-loss-controlled load balancing strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-30", "arxiv_id": "2408.16061", "title": "3D Reconstruction with Spatial Memory", "authors": [ "Hengyi Wang", "Lourdes Agapito" ], "github": "https://github.com/hengyiwang/spann3r", "project_page": "", "abstract": "We present Spann3R, a novel approach for dense 3D reconstruction from ordered or unordered image collections. Built on the DUSt3R paradigm, Spann3R uses a transformer-based architecture to directly regress pointmaps from images without any prior knowledge of the scene or camera parameters. Unlike DUSt3R, which predicts per image-pair pointmaps each expressed in its local coordinate frame, Spann3R can predict per-image pointmaps expressed in a global coordinate system, thus eliminating the need for optimization-based global alignment. The key idea of Spann3R is to manage an external spatial memory that learns to keep track of all previous relevant 3D information. Spann3R then queries this spatial memory to predict the 3D structure of the next frame in a global coordinate system. Taking advantage of DUSt3R's pre-trained weights, and further fine-tuning on a subset of datasets, Spann3R shows competitive performance and generalization ability on various unseen datasets and can process ordered image collections in real time. Project page: https://hengyiwang.github.io/projects/spanner", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hengyiwang/spann3r", "github_api_added_by": "auto" }, { "date": "2024-08-30", "arxiv_id": "2408.16046", "title": "Scaling Up Diffusion and Flow-based XGBoost Models", "authors": [ "Jesse C. Cresswell", "Taewoo Kim" ], "github": "https://github.com/layer6ai-labs/calo-forest", "project_page": "", "abstract": "Novel machine learning methods for tabular data generation are often developed on small datasets which do not match the scale required for scientific applications. We investigate a recent proposal to use XGBoost as the function approximator in diffusion and flow-matching models on tabular data, which proved to be extremely memory intensive, even on tiny datasets. In this work, we conduct a critical analysis of the existing implementation from an engineering perspective, and show that these limitations are not fundamental to the method; with better implementation it can be scaled to datasets 370x larger than previously used. Our efficient implementation also unlocks scaling models to much larger sizes which we show directly leads to improved performance on benchmark tasks. We also propose algorithmic improvements that can further benefit resource usage and model performance, including multi-output trees which are well-suited to generative modeling. Finally, we present results on large-scale scientific datasets derived from experimental particle physics as part of the Fast Calorimeter Simulation Challenge. Code is available at https://github.com/layer6ai-labs/calo-forest.", "github_manual": "", "github_regex": "https://github.com/layer6ai-labs/calo-forest", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-30", "arxiv_id": "2408.16357", "title": "Law of Vision Representation in MLLMs", "authors": [ "Shijia Yang", "Bohan Zhai", "Quanzeng You", "Jianbo Yuan", "Hongxia Yang", "Chenfeng Xu" ], "github": "https://github.com/bronyayang/law_of_vision_representation_in_mllms", "project_page": "", "abstract": "We present the \"Law of Vision Representation\" in multimodal large language models (MLLMs). It reveals a strong correlation between the combination of cross-modal alignment, correspondence in vision representation, and MLLM performance. We quantify the two factors using the cross-modal Alignment and Correspondence score (AC score). Through extensive experiments involving thirteen different vision representation settings and evaluations across eight benchmarks, we find that the AC score is linearly correlated to model performance. By leveraging this relationship, we are able to identify and train the optimal vision representation only, which does not require finetuning the language model every time, resulting in a 99.7% reduction in computational cost.", "github_manual": "https://github.com/bronyayang/law_of_vision_representation_in_mllms", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-30", "arxiv_id": "2408.16768", "title": "SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners", "authors": [ "Ziyu Guo", "Renrui Zhang", "Xiangyang Zhu", "Chengzhuo Tong", "Peng Gao", "Chunyuan Li", "Pheng-Ann Heng" ], "github": "https://github.com/ZiyuGuo99/SAM2Point", "project_page": "", "abstract": "We introduce SAM2Point, a preliminary exploration adapting Segment Anything Model 2 (SAM 2) for zero-shot and promptable 3D segmentation. SAM2Point interprets any 3D data as a series of multi-directional videos, and leverages SAM 2 for 3D-space segmentation, without further training or 2D-3D projection. Our framework supports various prompt types, including 3D points, boxes, and masks, and can generalize across diverse scenarios, such as 3D objects, indoor scenes, outdoor environments, and raw sparse LiDAR. Demonstrations on multiple 3D datasets, e.g., Objaverse, S3DIS, ScanNet, Semantic3D, and KITTI, highlight the robust generalization capabilities of SAM2Point. To our best knowledge, we present the most faithful implementation of SAM in 3D, which may serve as a starting point for future research in promptable 3D segmentation. Online Demo: https://huggingface.co/spaces/ZiyuG/SAM2Point . Code: https://github.com/ZiyuGuo99/SAM2Point .", "github_manual": "", "github_regex": "https://github.com/ZiyuGuo99/SAM2Point", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-30", "arxiv_id": "2408.16766", "title": "CSGO: Content-Style Composition in Text-to-Image Generation", "authors": [ "Peng Xing", "Haofan Wang", "Yanpeng Sun", "Qixun Wang", "Xu Bai", "Hao Ai", "Renyuan Huang", "Zechao Li" ], "github": "", "project_page": "", "abstract": "The diffusion model has shown exceptional capabilities in controlled image generation, which has further fueled interest in image style transfer. Existing works mainly focus on training free-based methods (e.g., image inversion) due to the scarcity of specific data. In this study, we present a data construction pipeline for content-style-stylized image triplets that generates and automatically cleanses stylized data triplets. Based on this pipeline, we construct a dataset IMAGStyle, the first large-scale style transfer dataset containing 210k image triplets, available for the community to explore and research. Equipped with IMAGStyle, we propose CSGO, a style transfer model based on end-to-end training, which explicitly decouples content and style features employing independent feature injection. The unified CSGO implements image-driven style transfer, text-driven stylized synthesis, and text editing-driven stylized synthesis. Extensive experiments demonstrate the effectiveness of our approach in enhancing style control capabilities in image generation. Additional visualization and access to the source code can be located on the project page: https://csgo-gen.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-30", "arxiv_id": "2408.16500", "title": "CogVLM2: Visual Language Models for Image and Video Understanding", "authors": [ "Wenyi Hong", "Weihan Wang", "Ming Ding", "Wenmeng Yu", "Qingsong Lv", "Yan Wang", "Yean Cheng", "Shiyu Huang", "Junhui Ji", "Zhao Xue", "Lei Zhao", "Zhuoyi Yang", "Xiaotao Gu", "Xiaohan Zhang", "Guanyu Feng", "Da Yin", "Zihan Wang", "Ji Qi", "Xixuan Song", "Peng Zhang", "Debing Liu", "Bin Xu", "Juanzi Li", "Yuxiao Dong", "Jie Tang" ], "github": "https://github.com/thudm/glm-4", "project_page": "", "abstract": "Beginning with VisualGLM and CogVLM, we are continuously exploring VLMs in pursuit of enhanced vision-language fusion, efficient higher-resolution architecture, and broader modalities and applications. Here we propose the CogVLM2 family, a new generation of visual language models for image and video understanding including CogVLM2, CogVLM2-Video and GLM-4V. As an image understanding model, CogVLM2 inherits the visual expert architecture with improved training recipes in both pre-training and post-training stages, supporting input resolution up to 1344 times 1344 pixels. As a video understanding model, CogVLM2-Video integrates multi-frame input with timestamps and proposes automated temporal grounding data construction. Notably, CogVLM2 family has achieved state-of-the-art results on benchmarks like MMBench, MM-Vet, TextVQA, MVBench and VCGBench. All models are open-sourced in https://github.com/THUDM/CogVLM2 and https://github.com/THUDM/GLM-4, contributing to the advancement of the field.", "github_manual": "https://github.com/thudm/glm-4", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-30", "arxiv_id": "2408.16532", "title": "WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling", "authors": [ "Shengpeng Ji", "Ziyue Jiang", "Xize Cheng", "Yifu Chen", "Minghui Fang", "Jialong Zuo", "Qian Yang", "Ruiqi Li", "Ziang Zhang", "Xiaoda Yang", "Rongjie Huang", "Yidi Jiang", "Qian Chen", "Siqi Zheng", "Wen Wang", "Zhou Zhao" ], "github": "https://github.com/jishengpeng/WavTokenizer", "project_page": "", "abstract": "Language models have been effectively applied to modeling natural signals, such as images, video, speech, and audio. A crucial component of these models is the codec tokenizer, which compresses high-dimensional natural signals into lower-dimensional discrete tokens. In this paper, we introduce WavTokenizer, which offers several advantages over previous SOTA acoustic codec models in the audio domain: 1)extreme compression. By compressing the layers of quantizers and the temporal dimension of the discrete codec, one-second audio of 24kHz sampling rate requires only a single quantizer with 40 or 75 tokens. 2)improved subjective quality. Despite the reduced number of tokens, WavTokenizer achieves state-of-the-art reconstruction quality with outstanding UTMOS scores and inherently contains richer semantic information. Specifically, we achieve these results by designing a broader VQ space, extended contextual windows, and improved attention networks, as well as introducing a powerful multi-scale discriminator and an inverse Fourier transform structure. We conducted extensive reconstruction experiments in the domains of speech, audio, and music. WavTokenizer exhibited strong performance across various objective and subjective metrics compared to state-of-the-art models. We also tested semantic information, VQ utilization, and adaptability to generative models. Comprehensive ablation studies confirm the necessity of each module in WavTokenizer. The related code, demos, and pre-trained models are available at https://github.com/jishengpeng/WavTokenizer.", "github_manual": "", "github_regex": "https://github.com/jishengpeng/WavTokenizer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-30", "arxiv_id": "2408.16767", "title": "ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model", "authors": [ "Fangfu Liu", "Wenqiang Sun", "Hanyang Wang", "Yikai Wang", "Haowen Sun", "Junliang Ye", "Jun Zhang", "Yueqi Duan" ], "github": "", "project_page": "", "abstract": "Advancements in 3D scene reconstruction have transformed 2D images from the real world into 3D models, producing realistic 3D results from hundreds of input photos. Despite great success in dense-view reconstruction scenarios, rendering a detailed scene from insufficient captured views is still an ill-posed optimization problem, often resulting in artifacts and distortions in unseen areas. In this paper, we propose ReconX, a novel 3D scene reconstruction paradigm that reframes the ambiguous reconstruction challenge as a temporal generation task. The key insight is to unleash the strong generative prior of large pre-trained video diffusion models for sparse-view reconstruction. However, 3D view consistency struggles to be accurately preserved in directly generated video frames from pre-trained models. To address this, given limited input views, the proposed ReconX first constructs a global point cloud and encodes it into a contextual space as the 3D structure condition. Guided by the condition, the video diffusion model then synthesizes video frames that are both detail-preserved and exhibit a high degree of 3D consistency, ensuring the coherence of the scene from various perspectives. Finally, we recover the 3D scene from the generated video through a confidence-aware 3D Gaussian Splatting optimization scheme. Extensive experiments on various real-world datasets show the superiority of our ReconX over state-of-the-art methods in terms of quality and generalizability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-30", "arxiv_id": "2408.15666", "title": "StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements", "authors": [ "Jillian Fisher", "Skyler Hallinan", "Ximing Lu", "Mitchell Gordon", "Zaid Harchaoui", "Yejin Choi" ], "github": "https://github.com/jfisher52/StyleRemix", "project_page": "", "abstract": "Authorship obfuscation, rewriting a text to intentionally obscure the identity of the author, is an important but challenging task. Current methods using large language models (LLMs) lack interpretability and controllability, often ignoring author-specific stylistic features, resulting in less robust performance overall. To address this, we develop StyleRemix, an adaptive and interpretable obfuscation method that perturbs specific, fine-grained style elements of the original input text. StyleRemix uses pre-trained Low Rank Adaptation (LoRA) modules to rewrite an input specifically along various stylistic axes (e.g., formality and length) while maintaining low computational cost. StyleRemix outperforms state-of-the-art baselines and much larger LLMs in a variety of domains as assessed by both automatic and human evaluation. Additionally, we release AuthorMix, a large set of 30K high-quality, long-form texts from a diverse set of 14 authors and 4 domains, and DiSC, a parallel corpus of 1,500 texts spanning seven style axes in 16 unique directions", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jfisher52/StyleRemix", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-30", "arxiv_id": "2408.14608", "title": "Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold", "authors": [ "Lazar Atanackovic", "Xi Zhang", "Brandon Amos", "Mathieu Blanchette", "Leo J. Lee", "Yoshua Bengio", "Alexander Tong", "Kirill Neklyudov" ], "github": "https://github.com/lazaratan/meta-flow-matching", "project_page": "", "abstract": "Numerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynamics of communicating cells or physical particles. Learning the dynamics of such systems is essential for predicting the temporal evolution of populations across novel samples and unseen environments. Flow-based models allow for learning these dynamics at the population level - they model the evolution of the entire distribution of samples. However, current flow-based models are limited to a single initial population and a set of predefined conditions which describe different dynamics. We argue that multiple processes in natural sciences have to be represented as vector fields on the Wasserstein manifold of probability densities. That is, the change of the population at any moment in time depends on the population itself due to the interactions between samples. In particular, this is crucial for personalized medicine where the development of diseases and their respective treatment response depends on the microenvironment of cells specific to each patient. We propose Meta Flow Matching (MFM), a practical approach to integrating along these vector fields on the Wasserstein manifold by amortizing the flow model over the initial populations. Namely, we embed the population of samples using a Graph Neural Network (GNN) and use these embeddings to train a Flow Matching model. This gives MFM the ability to generalize over the initial distributions unlike previously proposed methods. We demonstrate the ability of MFM to improve prediction of individual treatment responses on a large scale multi-patient single-cell drug screen dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lazaratan/meta-flow-matching", "github_api": "", "github_api_added_by": "" }, { "date": "2024-08-30", "arxiv_id": "2408.16293", "title": "Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems", "authors": [ "Tian Ye", "Zicheng Xu", "Yuanzhi Li", "Zeyuan Allen-Zhu" ], "github": "", "project_page": "", "abstract": "Language models have demonstrated remarkable performance in solving reasoning tasks; however, even the strongest models still occasionally make reasoning mistakes. Recently, there has been active research aimed at improving reasoning accuracy, particularly by using pretrained language models to \"self-correct\" their mistakes via multi-round prompting. In this paper, we follow this line of work but focus on understanding the usefulness of incorporating \"error-correction\" data directly into the pretraining stage. This data consists of erroneous solution steps immediately followed by their corrections. Using a synthetic math dataset, we show promising results: this type of pretrain data can help language models achieve higher reasoning accuracy directly (i.e., through simple auto-regression, without multi-round prompting) compared to pretraining on the same amount of error-free data. We also delve into many details, such as (1) how this approach differs from beam search, (2) how such data can be prepared, (3) whether masking is needed on the erroneous tokens, (4) the amount of error required, (5) whether such data can be deferred to the fine-tuning stage, and many others.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-02", "arxiv_id": "2408.14572", "title": "CURLoRA: Stable LLM Continual Fine-Tuning and Catastrophic Forgetting Mitigation", "authors": [ "Muhammad Fawi" ], "github": "https://github.com/mnoorfawi/curlora", "project_page": "", "abstract": "This paper introduces CURLoRA, a novel approach to fine-tuning large language models (LLMs) that leverages CUR matrix decomposition in the context of Low-Rank Adaptation (LoRA). Our method addresses two critical challenges in LLM fine-tuning: mitigating catastrophic forgetting during continual learning and reducing the number of trainable parameters. We propose a unique modification to the CUR decomposition process, utilizing inverted probabilities for column and row selection which acts as an implicit regularization, and initializing the U matrix as a zero matrix, and only fine-tuning it. We demonstrate through experiments on multiple datasets that CURLoRA outperforms standard LoRA in mitigating catastrophic forgetting. It maintains model stability and performance across tasks while significantly reducing the number of trainable parameters. Our results show that CURLoRA achieves very good and stable task accuracy while maintaining base model's perplexity scores fixed compared to LoRA upon continual fine-tuning, particularly in scenarios with limited data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mnoorfawi/curlora", "github_api_added_by": "auto" }, { "date": "2024-09-02", "arxiv_id": "2408.14886", "title": "The VoxCeleb Speaker Recognition Challenge: A Retrospective", "authors": [ "Jaesung Huh", "Joon Son Chung", "Arsha Nagrani", "Andrew Brown", "Jee-weon Jung", "Daniel Garcia-Romero", "Andrew Zisserman" ], "github": "", "project_page": "", "abstract": "The VoxCeleb Speaker Recognition Challenges (VoxSRC) were a series of challenges and workshops that ran annually from 2019 to 2023. The challenges primarily evaluated the tasks of speaker recognition and diarisation under various settings including: closed and open training data; as well as supervised, self-supervised, and semi-supervised training for domain adaptation. The challenges also provided publicly available training and evaluation datasets for each task and setting, with new test sets released each year. In this paper, we provide a review of these challenges that covers: what they explored; the methods developed by the challenge participants and how these evolved; and also the current state of the field for speaker verification and diarisation. We chart the progress in performance over the five installments of the challenge on a common evaluation dataset and provide a detailed analysis of how each year's special focus affected participants' performance. This paper is aimed both at researchers who want an overview of the speaker recognition and diarisation field, and also at challenge organisers who want to benefit from the successes and avoid the mistakes of the VoxSRC challenges. We end with a discussion of the current strengths of the field and open challenges. Project page : https://mm.kaist.ac.kr/datasets/voxceleb/voxsrc/workshop.html", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-02", "arxiv_id": "2408.15914", "title": "CoRe: Context-Regularized Text Embedding Learning for Text-to-Image Personalization", "authors": [ "Feize Wu", "Yun Pang", "Junyi Zhang", "Lianyu Pang", "Jian Yin", "Baoquan Zhao", "Qing Li", "Xudong Mao" ], "github": "", "project_page": "", "abstract": "Recent advances in text-to-image personalization have enabled high-quality and controllable image synthesis for user-provided concepts. However, existing methods still struggle to balance identity preservation with text alignment. Our approach is based on the fact that generating prompt-aligned images requires a precise semantic understanding of the prompt, which involves accurately processing the interactions between the new concept and its surrounding context tokens within the CLIP text encoder. To address this, we aim to embed the new concept properly into the input embedding space of the text encoder, allowing for seamless integration with existing tokens. We introduce Context Regularization (CoRe), which enhances the learning of the new concept's text embedding by regularizing its context tokens in the prompt. This is based on the insight that appropriate output vectors of the text encoder for the context tokens can only be achieved if the new concept's text embedding is correctly learned. CoRe can be applied to arbitrary prompts without requiring the generation of corresponding images, thus improving the generalization of the learned text embedding. Additionally, CoRe can serve as a test-time optimization technique to further enhance the generations for specific prompts. Comprehensive experiments demonstrate that our method outperforms several baseline methods in both identity preservation and text alignment. Code will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-02", "arxiv_id": "2408.15545", "title": "SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding", "authors": [ "Sihang Li", "Jin Huang", "Jiaxi Zhuang", "Yaorui Shi", "Xiaochen Cai", "Mingjun Xu", "Xiang Wang", "Linfeng Zhang", "Guolin Ke", "Hengxing Cai" ], "github": "https://github.com/dptech-corp/Uni-SMART", "project_page": "", "abstract": "Scientific literature understanding is crucial for extracting targeted information and garnering insights, thereby significantly advancing scientific discovery. Despite the remarkable success of Large Language Models (LLMs), they face challenges in scientific literature understanding, primarily due to (1) a lack of scientific knowledge and (2) unfamiliarity with specialized scientific tasks. To develop an LLM specialized in scientific literature understanding, we propose a hybrid strategy that integrates continual pre-training (CPT) and supervised fine-tuning (SFT), to simultaneously infuse scientific domain knowledge and enhance instruction-following capabilities for domain-specific tasks.cIn this process, we identify two key challenges: (1) constructing high-quality CPT corpora, and (2) generating diverse SFT instructions. We address these challenges through a meticulous pipeline, including PDF text extraction, parsing content error correction, quality filtering, and synthetic instruction creation. Applying this strategy, we present a suite of LLMs: SciLitLLM, specialized in scientific literature understanding. These models demonstrate promising performance on scientific literature understanding benchmarks. Our contributions are threefold: (1) We present an effective framework that integrates CPT and SFT to adapt LLMs to scientific literature understanding, which can also be easily adapted to other domains. (2) We propose an LLM-based synthesis method to generate diverse and high-quality scientific instructions, resulting in a new instruction set -- SciLitIns -- for supervised fine-tuning in less-represented scientific domains. (3) SciLitLLM achieves promising performance improvements on scientific literature understanding benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dptech-corp/Uni-SMART", "github_api_added_by": "auto" }, { "date": "2024-09-02", "arxiv_id": "2408.15827", "title": "Automatic Differential Diagnosis using Transformer-Based Multi-Label Sequence Classification", "authors": [ "Abu Adnan Sadi", "Mohammad Ashrafuzzaman Khan", "Lubaba Binte Saber" ], "github": "", "project_page": "", "abstract": "As the field of artificial intelligence progresses, assistive technologies are becoming more widely used across all industries. The healthcare industry is no different, with numerous studies being done to develop assistive tools for healthcare professionals. Automatic diagnostic systems are one such beneficial tool that can assist with a variety of tasks, including collecting patient information, analyzing test results, and diagnosing patients. However, the idea of developing systems that can provide a differential diagnosis has been largely overlooked in most of these research studies. In this study, we propose a transformer-based approach for providing differential diagnoses based on a patient's age, sex, medical history, and symptoms. We use the DDXPlus dataset, which provides differential diagnosis information for patients based on 49 disease types. Firstly, we propose a method to process the tabular patient data from the dataset and engineer them into patient reports to make them suitable for our research. In addition, we introduce two data modification modules to diversify the training data and consequently improve the robustness of the models. We approach the task as a multi-label classification problem and conduct extensive experiments using four transformer models. All the models displayed promising results by achieving over 97% F1 score on the held-out test set. Moreover, we design additional behavioral tests to get a broader understanding of the models. In particular, for one of our test cases, we prepared a custom test set of 100 samples with the assistance of a doctor. The results on the custom set showed that our proposed data modification modules improved the model's generalization capabilities. We hope our findings will provide future researchers with valuable insights and inspire them to develop reliable systems for automatic differential diagnosis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-02", "arxiv_id": "2408.16672", "title": "Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever", "authors": [ "Rohan Jha", "Bo Wang", "Michael G\u00fcnther", "Saba Sturua", "Mohammad Kalim Akram", "Han Xiao" ], "github": "", "project_page": "", "abstract": "Multi-vector dense models, such as ColBERT, have proven highly effective in information retrieval. ColBERT's late interaction scoring approximates the joint query-document attention seen in cross-encoders while maintaining inference efficiency closer to traditional dense retrieval models, thanks to its bi-encoder architecture and recent optimizations in indexing and search. In this paper, we introduce several improvements to the ColBERT model architecture and training pipeline, leveraging techniques successful in the more established single-vector embedding model paradigm, particularly those suited for heterogeneous multilingual data. Our new model, Jina-ColBERT-v2, demonstrates strong performance across a range of English and multilingual retrieval tasks, while also cutting storage requirements by up to 50% compared to previous models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-02", "arxiv_id": "2408.16444", "title": "SurveySum: A Dataset for Summarizing Multiple Scientific Articles into a Survey Section", "authors": [ "Leandro Car\u00edsio Fernandes", "Gustavo Bartz Guedes", "Thiago Soares Laitz", "Thales Sales Almeida", "Rodrigo Nogueira", "Roberto Lotufo", "Jayr Pereira" ], "github": "", "project_page": "", "abstract": "Document summarization is a task to shorten texts into concise and informative summaries. This paper introduces a novel dataset designed for summarizing multiple scientific articles into a section of a survey. Our contributions are: (1) SurveySum, a new dataset addressing the gap in domain-specific summarization tools; (2) two specific pipelines to summarize scientific articles into a section of a survey; and (3) the evaluation of these pipelines using multiple metrics to compare their performance. Our results highlight the importance of high-quality retrieval stages and the impact of different configurations on the quality of generated summaries.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-02", "arxiv_id": "2408.16245", "title": "Large-Scale Multi-omic Biosequence Transformers for Modeling Peptide-Nucleotide Interactions", "authors": [ "Sully F. Chen", "Robert J. Steele", "Beakal Lemeneh", "Shivanand P. Lad", "Eric Oermann" ], "github": "", "project_page": "", "abstract": "The transformer architecture has revolutionized bioinformatics and driven progress in the understanding and prediction of the properties of biomolecules. Almost all research on large-scale biosequence transformers has focused on one domain at a time (single-omic), usually nucleotides or peptides. These models have seen incredible success in downstream tasks in each domain and have achieved particularly noteworthy breakthroughs in sequences of peptides and structural modeling. However, these single-omic models are naturally incapable of modeling multi-omic tasks, one of the most biologically critical being nucleotide-peptide interactions. We present our work training the first multi-omic nucleotide-peptide foundation models. We show that these multi-omic models (MOMs) can learn joint representations between various single-omic distributions that are emergently consistent with the Central Dogma of molecular biology, despite only being trained on unlabeled biosequences. We further demonstrate that MOMs can be fine-tuned to achieve state-of-the-art results on peptide-nucleotide interaction tasks, namely predicting the change in Gibbs free energy ({\\Delta}G) of the binding interaction between a given oligonucleotide and peptide, as well as the effect on this binding interaction due to mutations in the oligonucleotide sequence ({\\Delta}{\\Delta}G). Remarkably, we show that multi-omic biosequence transformers emergently learn useful structural information without any prior structural training, allowing us to predict which peptide residues are most involved in the peptide-nucleotide binding interaction. Lastly, we provide evidence that multi-omic biosequence models are non-inferior to foundation models trained on single-omics distributions, suggesting a more generalized or foundational approach to building these models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-02", "arxiv_id": "2408.16176", "title": "VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images", "authors": [ "M. Maruf", "Arka Daw", "Kazi Sajeed Mehrab", "Harish Babu Manogaran", "Abhilash Neog", "Medha Sawhney", "Mridul Khurana", "James P. Balhoff", "Yasin Bakis", "Bahadir Altintas", "Matthew J. Thompson", "Elizabeth G. Campolongo", "Josef C. Uyeda", "Hilmar Lapp", "Henry L. Bart", "Paula M. Mabee", "Yu Su", "Wei-Lun Chao", "Charles Stewart", "Tanya Berger-Wolf", "Wasila Dahdul", "Anuj Karpatne" ], "github": "https://github.com/Imageomics/VLM4Bio", "project_page": "", "abstract": "Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs). We ask if pre-trained VLMs can aid scientists in answering a range of biologically relevant questions without any additional fine-tuning. In this paper, we evaluate the effectiveness of 12 state-of-the-art (SOTA) VLMs in the field of organismal biology using a novel dataset, VLM4Bio, consisting of 469K question-answer pairs involving 30K images from three groups of organisms: fishes, birds, and butterflies, covering five biologically relevant tasks. We also explore the effects of applying prompting techniques and tests for reasoning hallucination on the performance of VLMs, shedding new light on the capabilities of current SOTA VLMs in answering biologically relevant questions using images. The code and datasets for running all the analyses reported in this paper can be found at https://github.com/sammarfy/VLM4Bio.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Imageomics/VLM4Bio", "github_api_added_by": "user" }, { "date": "2024-09-02", "arxiv_id": "2408.15993", "title": "ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution", "authors": [ "Sungduk Yu", "Brian L. White", "Anahita Bhiwandiwalla", "Musashi Hinck", "Matthew Lyle Olson", "Tung Nguyen", "Vasudev Lal" ], "github": "", "project_page": "", "abstract": "Detecting and attributing temperature increases due to climate change is crucial for understanding global warming and guiding adaptation strategies. The complexity of distinguishing human-induced climate signals from natural variability has challenged traditional detection and attribution (D&A) approaches, which seek to identify specific \"fingerprints\" in climate response variables. Deep learning offers potential for discerning these complex patterns in expansive spatial datasets. However, lack of standard protocols has hindered consistent comparisons across studies. We introduce ClimDetect, a standardized dataset of over 816k daily climate snapshots, designed to enhance model accuracy in identifying climate change signals. ClimDetect integrates various input and target variables used in past research, ensuring comparability and consistency. We also explore the application of vision transformers (ViT) to climate data, a novel and modernizing approach in this context. Our open-access data and code serve as a benchmark for advancing climate science through improved model evaluations. ClimDetect is publicly accessible via Huggingface dataet respository at: https://huggingface.co/datasets/ClimDetect/ClimDetect.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-02", "arxiv_id": "2408.17024", "title": "InkubaLM: A small language model for low-resource African languages", "authors": [ "Atnafu Lambebo Tonja", "Bonaventure F. P. Dossou", "Jessica Ojo", "Jenalea Rajab", "Fadel Thior", "Eric Peter Wairagala", "Aremu Anuoluwapo", "Pelonomi Moiloa", "Jade Abbott", "Vukosi Marivate", "Benjamin Rosman" ], "github": "", "project_page": "", "abstract": "High-resource language models often fall short in the African context, where there is a critical need for models that are efficient, accessible, and locally relevant, even amidst significant computing and data constraints. This paper introduces InkubaLM, a small language model with 0.4 billion parameters, which achieves performance comparable to models with significantly larger parameter counts and more extensive training data on tasks such as machine translation, question-answering, AfriMMLU, and the AfriXnli task. Notably, InkubaLM outperforms many larger models in sentiment analysis and demonstrates remarkable consistency across multiple languages. This work represents a pivotal advancement in challenging the conventional paradigm that effective language models must rely on substantial resources. Our model and datasets are publicly available \\url{https://huggingface.co/lelapa} to encourage research and development on low-resource languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-02", "arxiv_id": "2408.17267", "title": "UrBench: A Comprehensive Benchmark for Evaluating Large Multimodal Models in Multi-View Urban Scenarios", "authors": [ "Baichuan Zhou", "Haote Yang", "Dairong Chen", "Junyan Ye", "Tianyi Bai", "Jinhua Yu", "Songyang Zhang", "Dahua Lin", "Conghui He", "Weijia Li" ], "github": "https://github.com/opendatalab/urbench", "project_page": "", "abstract": "Recent evaluations of Large Multimodal Models (LMMs) have explored their capabilities in various domains, with only few benchmarks specifically focusing on urban environments. Moreover, existing urban benchmarks have been limited to evaluating LMMs with basic region-level urban tasks under singular views, leading to incomplete evaluations of LMMs' abilities in urban environments. To address these issues, we present UrBench, a comprehensive benchmark designed for evaluating LMMs in complex multi-view urban scenarios. UrBench contains 11.6K meticulously curated questions at both region-level and role-level that cover 4 task dimensions: Geo-Localization, Scene Reasoning, Scene Understanding, and Object Understanding, totaling 14 task types. In constructing UrBench, we utilize data from existing datasets and additionally collect data from 11 cities, creating new annotations using a cross-view detection-matching method. With these images and annotations, we then integrate LMM-based, rule-based, and human-based methods to construct large-scale high-quality questions. Our evaluations on 21 LMMs show that current LMMs struggle in the urban environments in several aspects. Even the best performing GPT-4o lags behind humans in most tasks, ranging from simple tasks such as counting to complex tasks such as orientation, localization and object attribute recognition, with an average performance gap of 17.4%. Our benchmark also reveals that LMMs exhibit inconsistent behaviors with different urban views, especially with respect to understanding cross-view relations. UrBench datasets and benchmark results will be publicly available at https://opendatalab.github.io/UrBench/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/opendatalab/urbench", "github_api_added_by": "auto" }, { "date": "2024-09-02", "arxiv_id": "2408.14765", "title": "CrossViewDiff: A Cross-View Diffusion Model for Satellite-to-Street View Synthesis", "authors": [ "Weijia Li", "Jun He", "Junyan Ye", "Huaping Zhong", "Zhimeng Zheng", "Zilong Huang", "Dahua Lin", "Conghui He" ], "github": "", "project_page": "", "abstract": "Satellite-to-street view synthesis aims at generating a realistic street-view image from its corresponding satellite-view image. Although stable diffusion models have exhibit remarkable performance in a variety of image generation applications, their reliance on similar-view inputs to control the generated structure or texture restricts their application to the challenging cross-view synthesis task. In this work, we propose CrossViewDiff, a cross-view diffusion model for satellite-to-street view synthesis. To address the challenges posed by the large discrepancy across views, we design the satellite scene structure estimation and cross-view texture mapping modules to construct the structural and textural controls for street-view image synthesis. We further design a cross-view control guided denoising process that incorporates the above controls via an enhanced cross-view attention module. To achieve a more comprehensive evaluation of the synthesis results, we additionally design a GPT-based scoring method as a supplement to standard evaluation metrics. We also explore the effect of different data sources (e.g., text, maps, building heights, and multi-temporal satellite imagery) on this task. Results on three public cross-view datasets show that CrossViewDiff outperforms current state-of-the-art on both standard and GPT-based evaluation metrics, generating high-quality street-view panoramas with more realistic structures and textures across rural, suburban, and urban scenes. The code and models of this work will be released at https://opendatalab.github.io/CrossViewDiff/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-02", "arxiv_id": "2408.15300", "title": "GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs", "authors": [ "Maxim Zhelnin", "Viktor Moskvoretskii", "Egor Shvetsov", "Egor Venediktov", "Mariya Krylova", "Aleksandr Zuev", "Evgeny Burnaev" ], "github": "https://github.com/On-Point-RND/GIFT_SW", "project_page": "", "abstract": "Parameter Efficient Fine-Tuning (PEFT) methods have gained popularity and democratized the usage of Large Language Models (LLMs). Recent studies have shown that a small subset of weights significantly impacts performance. Based on this observation, we introduce a novel PEFT method, called Gaussian noise Injected Fine Tuning of Salient Weights (GIFT-SW). Our method updates only salient columns, while injecting Gaussian noise into non-salient ones. To identify these columns, we developeda generalized sensitivity metric that extends and unifies metrics from previous studies. Experiments with LLaMA models demonstrate that GIFT-SW outperforms full fine-tuning and modern PEFT methods under the same computational budget. Moreover, GIFT-SW offers practical advantages to recover performance of models subjected to mixed-precision quantization with keeping salient weights in full precision.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/On-Point-RND/GIFT_SW", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-02", "arxiv_id": "2408.17131", "title": "VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion Transformers", "authors": [ "Juncan Deng", "Shuaiting Li", "Zeyu Wang", "Hong Gu", "Kedong Xu", "Kejie Huang" ], "github": "", "project_page": "", "abstract": "The Diffusion Transformers Models (DiTs) have transitioned the network architecture from traditional UNets to transformers, demonstrating exceptional capabilities in image generation. Although DiTs have been widely applied to high-definition video generation tasks, their large parameter size hinders inference on edge devices. Vector quantization (VQ) can decompose model weight into a codebook and assignments, allowing extreme weight quantization and significantly reducing memory usage. In this paper, we propose VQ4DiT, a fast post-training vector quantization method for DiTs. We found that traditional VQ methods calibrate only the codebook without calibrating the assignments. This leads to weight sub-vectors being incorrectly assigned to the same assignment, providing inconsistent gradients to the codebook and resulting in a suboptimal result. To address this challenge, VQ4DiT calculates the candidate assignment set for each weight sub-vector based on Euclidean distance and reconstructs the sub-vector based on the weighted average. Then, using the zero-data and block-wise calibration method, the optimal assignment from the set is efficiently selected while calibrating the codebook. VQ4DiT quantizes a DiT XL/2 model on a single NVIDIA A100 GPU within 20 minutes to 5 hours depending on the different quantization settings. Experiments show that VQ4DiT establishes a new state-of-the-art in model size and performance trade-offs, quantizing weights to 2-bit precision while retaining acceptable image generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-02", "arxiv_id": "2408.16667", "title": "Iterative Graph Alignment", "authors": [ "Fangyuan Yu", "Hardeep Singh Arora", "Matt Johnson" ], "github": "https://github.com/fangyuan-ksgk/ruleeval", "project_page": "", "abstract": "By compressing diverse narratives, LLMs go beyond memorization, achieving intelligence by capturing generalizable causal relationships. However, they suffer from local 'representation gaps' due to insufficient training data diversity, limiting their real-world utility, especially in tasks requiring strict alignment to rules. Traditional alignment methods relying on heavy human annotations are inefficient and unscalable. Recent self-alignment techniques also fall short, as they often depend on self-selection based prompting and memorization-based learning. To address these issues, we introduce Iterative Graph Alignment (IGA), an annotation-free rule-based alignment algorithm. A teacher model (VLM) employs Iterative Graph Prompting (IGP) to create logical graphs and reference answers. The student model (LLM) identifies local knowledge gaps by attempting to align its responses with these references, collaborating with helper models to generate diverse answers. These aligned responses are then used for iterative supervised fine-tuning (SFT). Our evaluations across five rule-based scenarios demonstrate IGP's effectiveness, with a 73.12\\% alignment improvement in Claude Sonnet 3.5, and Llama3-8B-Instruct achieving an 86.20\\% improvement, outperforming Claude Sonnet 3.5 in rule-based alignment.", "github_manual": "https://github.com/fangyuan-ksgk/ruleeval", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-03", "arxiv_id": "2408.17253", "title": "VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters", "authors": [ "Mouxiang Chen", "Lefei Shen", "Zhuo Li", "Xiaoyun Joy Wang", "Jianling Sun", "Chenghao Liu" ], "github": "https://github.com/Keytoyze/VisionTS", "project_page": "", "abstract": "Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either fine-tune large language models (LLMs) or build large-scale time-series datasets to develop TSF foundation models. However, these methods face challenges due to the severe cross-domain gap or in-domain heterogeneity. In this paper, we explore a new road to building a TSF foundation model from rich and high-quality natural images, based on the intrinsic similarities between images and time series. To bridge the gap between the two domains, we reformulate the TSF task as an image reconstruction task, which is further processed by a visual masked autoencoder (MAE) self-supervised pre-trained on the ImageNet dataset. Surprisingly, without further adaptation in the time-series domain, the proposed VisionTS could achieve superior zero-shot forecasting performance compared to existing TSF foundation models. With minimal fine-tuning, VisionTS could further improve the forecasting and achieve state-of-the-art performance in most cases. These findings suggest that visual models could be a free lunch for TSF and highlight the potential for future cross-domain research between computer vision and TSF. Our code is publicly available at https://github.com/Keytoyze/VisionTS.", "github_manual": "", "github_regex": "https://github.com/Keytoyze/VisionTS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-03", "arxiv_id": "2408.16725", "title": "Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming", "authors": [ "Zhifei Xie", "Changqiao Wu" ], "github": "https://github.com/gpt-omni/mini-omni", "project_page": "", "abstract": "Recent advances in language models have achieved significant progress. GPT-4o, as a new milestone, has enabled real-time conversations with humans, demonstrating near-human natural fluency. Such human-computer interaction necessitates models with the capability to perform reasoning directly with the audio modality and generate output in streaming. However, this remains beyond the reach of current academic models, as they typically depend on extra TTS systems for speech synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni, an audio-based end-to-end conversational model, capable of real-time speech interaction. To achieve this capability, we propose a text-instructed speech generation method, along with batch-parallel strategies during inference to further boost the performance. Our method also helps to retain the original model's language capabilities with minimal degradation, enabling other works to establish real-time interaction capabilities. We call this training method \"Any Model Can Talk\". We also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for speech output. To our best knowledge, Mini-Omni is the first fully end-to-end, open-source model for real-time speech interaction, offering valuable potential for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gpt-omni/mini-omni", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.02060", "title": "OLMoE: Open Mixture-of-Experts Language Models", "authors": [ "Niklas Muennighoff", "Luca Soldaini", "Dirk Groeneveld", "Kyle Lo", "Jacob Morrison", "Sewon Min", "Weijia Shi", "Pete Walsh", "Oyvind Tafjord", "Nathan Lambert", "Yuling Gu", "Shane Arora", "Akshita Bhagia", "Dustin Schwenk", "David Wadden", "Alexander Wettig", "Binyuan Hui", "Tim Dettmers", "Douwe Kiela", "Ali Farhadi", "Noah A. Smith", "Pang Wei Koh", "Amanpreet Singh", "Hannaneh Hajishirzi" ], "github": "https://github.com/mlfoundations/dclm", "project_page": "", "abstract": "We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE). OLMoE-1B-7B has 7 billion (B) parameters but uses only 1B per input token. We pretrain it on 5 trillion tokens and further adapt it to create OLMoE-1B-7B-Instruct. Our models outperform all available models with similar active parameters, even surpassing larger ones like Llama2-13B-Chat and DeepSeekMoE-16B. We present various experiments on MoE training, analyze routing in our model showing high specialization, and open-source all aspects of our work: model weights, training data, code, and logs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mlfoundations/dclm", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.00509", "title": "LongRecipe: Recipe for Efficient Long Context Generalization in Large Languge Models", "authors": [ "Zhiyuan Hu", "Yuliang Liu", "Jinman Zhao", "Suyuchen Wang", "Yan Wang", "Wei Shen", "Qing Gu", "Anh Tuan Luu", "See-Kiong Ng", "Zhiwei Jiang", "Bryan Hooi" ], "github": "https://github.com/zhiyuanhubj/LongRecipe", "project_page": "", "abstract": "Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences. Meanwhile, extending the context window in LLMs through post-pretraining is highly resource-intensive. To address this, we introduce **LongRecipe**, an efficient training strategy for extending the context window of LLMs, including impactful token analysis, position index transformation, and training optimization strategies. It simulates long-sequence inputs while maintaining training efficiency and significantly improves the model's understanding of long-range dependencies. Experiments on three types of LLMs show that LongRecipe can utilize long sequences while requiring only 30% of the target context window size, and reduces computational training resource over 85% compared to full sequence training. Furthermore, LongRecipe also preserves the original LLM's capabilities in general tasks. Ultimately, *we can extend the effective context window of open-source LLMs from 8k to 128k, achieving performance close to GPT-4 with just one day of dedicated training using a single GPU with 80G memory.* Our code is released at the [link](https://github.com/zhiyuanhubj/LongRecipe).", "github_manual": "", "github_regex": "https://github.com/zhiyuanhubj/LongRecipe", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.00391", "title": "Density Adaptive Attention-based Speech Network: Enhancing Feature Understanding for Mental Health Disorders", "authors": [ "Georgios Ioannides", "Adrian Kieback", "Aman Chadha", "Aaron Elkins" ], "github": "https://github.com/sdsuai/depression_audio_processing", "project_page": "", "abstract": "Speech-based depression detection poses significant challenges for automated detection due to its unique manifestation across individuals and data scarcity. Addressing these challenges, we introduce DAAMAudioCNNLSTM and DAAMAudioTransformer, two parameter efficient and explainable models for audio feature extraction and depression detection. DAAMAudioCNNLSTM features a novel CNN-LSTM framework with multi-head Density Adaptive Attention Mechanism (DAAM), focusing dynamically on informative speech segments. DAAMAudioTransformer, leveraging a transformer encoder in place of the CNN-LSTM architecture, incorporates the same DAAM module for enhanced attention and interpretability. These approaches not only enhance detection robustness and interpretability but also achieve state-of-the-art performance: DAAMAudioCNNLSTM with an F1 macro score of 0.702 and DAAMAudioTransformer with an F1 macro score of 0.72 on the DAIC-WOZ dataset, without reliance on supplementary information such as vowel positions and speaker information during training/validation as in previous approaches. Both models' significant explainability and efficiency in leveraging speech signals for depression detection represent a leap towards more reliable, clinically useful diagnostic tools, promising advancements in speech and mental health care. To foster further research in this domain, we make our code publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sdsuai/depression_audio_processing", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.02097", "title": "LinFusion: 1 GPU, 1 Minute, 16K Image", "authors": [ "Songhua Liu", "Weihao Yu", "Zhenxiong Tan", "Xinchao Wang" ], "github": "https://github.com/Huage001/LinFusion", "project_page": "", "abstract": "Modern diffusion models, particularly those utilizing a Transformer-based UNet for denoising, rely heavily on self-attention operations to manage complex spatial relationships, thus achieving impressive generation performance. However, this existing paradigm faces significant challenges in generating high-resolution visual content due to its quadratic time and memory complexity with respect to the number of spatial tokens. To address this limitation, we aim at a novel linear attention mechanism as an alternative in this paper. Specifically, we begin our exploration from recently introduced models with linear complexity, e.g., Mamba, Mamba2, and Gated Linear Attention, and identify two key features-attention normalization and non-causal inference-that enhance high-resolution visual generation performance. Building on these insights, we introduce a generalized linear attention paradigm, which serves as a low-rank approximation of a wide spectrum of popular linear token mixers. To save the training cost and better leverage pre-trained models, we initialize our models and distill the knowledge from pre-trained StableDiffusion (SD). We find that the distilled model, termed LinFusion, achieves performance on par with or superior to the original SD after only modest training, while significantly reducing time and memory complexity. Extensive experiments on SD-v1.5, SD-v2.1, and SD-XL demonstrate that LinFusion delivers satisfactory zero-shot cross-resolution generation performance, generating high-resolution images like 16K resolution. Moreover, it is highly compatible with pre-trained SD components, such as ControlNet and IP-Adapter, requiring no adaptation efforts. Codes are available at https://github.com/Huage001/LinFusion.", "github_manual": "", "github_regex": "https://github.com/Huage001/LinFusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.01199", "title": "OD-VAE: An Omni-dimensional Video Compressor for Improving Latent Video Diffusion Model", "authors": [ "Liuhan Chen", "Zongjian Li", "Bin Lin", "Bin Zhu", "Qian Wang", "Shenghai Yuan", "Xing Zhou", "Xinghua Cheng", "Li Yuan" ], "github": "", "project_page": "", "abstract": "Variational Autoencoder (VAE), compressing videos into latent representations, is a crucial preceding component of Latent Video Diffusion Models (LVDMs). With the same reconstruction quality, the more sufficient the VAE's compression for videos is, the more efficient the LVDMs are. However, most LVDMs utilize 2D image VAE, whose compression for videos is only in the spatial dimension and often ignored in the temporal dimension. How to conduct temporal compression for videos in a VAE to obtain more concise latent representations while promising accurate reconstruction is seldom explored. To fill this gap, we propose an omni-dimension compression VAE, named OD-VAE, which can temporally and spatially compress videos. Although OD-VAE's more sufficient compression brings a great challenge to video reconstruction, it can still achieve high reconstructed accuracy by our fine design. To obtain a better trade-off between video reconstruction quality and compression speed, four variants of OD-VAE are introduced and analyzed. In addition, a novel tail initialization is designed to train OD-VAE more efficiently, and a novel inference strategy is proposed to enable OD-VAE to handle videos of arbitrary length with limited GPU memory. Comprehensive experiments on video reconstruction and LVDM-based video generation demonstrate the effectiveness and efficiency of our proposed methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.00558", "title": "Compositional 3D-aware Video Generation with LLM Director", "authors": [ "Hanxin Zhu", "Tianyu He", "Anni Tang", "Junliang Guo", "Zhibo Chen", "Jiang Bian" ], "github": "", "project_page": "", "abstract": "Significant progress has been made in text-to-video generation through the use of powerful generative models and large-scale internet data. However, substantial challenges remain in precisely controlling individual concepts within the generated video, such as the motion and appearance of specific characters and the movement of viewpoints. In this work, we propose a novel paradigm that generates each concept in 3D representation separately and then composes them with priors from Large Language Models (LLM) and 2D diffusion models. Specifically, given an input textual prompt, our scheme consists of three stages: 1) We leverage LLM as the director to first decompose the complex query into several sub-prompts that indicate individual concepts within the video~(e.g., scene, objects, motions), then we let LLM to invoke pre-trained expert models to obtain corresponding 3D representations of concepts. 2) To compose these representations, we prompt multi-modal LLM to produce coarse guidance on the scales and coordinates of trajectories for the objects. 3) To make the generated frames adhere to natural image distribution, we further leverage 2D diffusion priors and use Score Distillation Sampling to refine the composition. Extensive experiments demonstrate that our method can generate high-fidelity videos from text with diverse motion and flexible control over each concept. Project page: https://aka.ms/c3v.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.02095", "title": "DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos", "authors": [ "Wenbo Hu", "Xiangjun Gao", "Xiaoyu Li", "Sijie Zhao", "Xiaodong Cun", "Yong Zhang", "Long Quan", "Ying Shan" ], "github": "https://github.com/Tencent/DepthCrafter", "project_page": "", "abstract": "Despite significant advancements in monocular depth estimation for static images, estimating video depth in the open world remains challenging, since open-world videos are extremely diverse in content, motion, camera movement, and length. We present DepthCrafter, an innovative method for generating temporally consistent long depth sequences with intricate details for open-world videos, without requiring any supplementary information such as camera poses or optical flow. DepthCrafter achieves generalization ability to open-world videos by training a video-to-depth model from a pre-trained image-to-video diffusion model, through our meticulously designed three-stage training strategy with the compiled paired video-depth datasets. Our training approach enables the model to generate depth sequences with variable lengths at one time, up to 110 frames, and harvest both precise depth details and rich content diversity from realistic and synthetic datasets. We also propose an inference strategy that processes extremely long videos through segment-wise estimation and seamless stitching. Comprehensive evaluations on multiple datasets reveal that DepthCrafter achieves state-of-the-art performance in open-world video depth estimation under zero-shot settings. Furthermore, DepthCrafter facilitates various downstream applications, including depth-based visual effects and conditional video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tencent/DepthCrafter", "github_api_added_by": "auto" }, { "date": "2024-09-04", "arxiv_id": "2409.01071", "title": "VideoLLaMB: Long-context Video Understanding with Recurrent Memory Bridges", "authors": [ "Yuxuan Wang", "Cihang Xie", "Yang Liu", "Zilong Zheng" ], "github": "", "project_page": "", "abstract": "Recent advancements in large-scale video-language models have shown significant potential for real-time planning and detailed interactions. However, their high computational demands and the scarcity of annotated datasets limit their practicality for academic researchers. In this work, we introduce VideoLLaMB, a novel framework that utilizes temporal memory tokens within bridge layers to allow for the encoding of entire video sequences alongside historical visual data, effectively preserving semantic continuity and enhancing model performance across various tasks. This approach includes recurrent memory tokens and a SceneTilling algorithm, which segments videos into independent semantic units to preserve semantic integrity. Empirically, VideoLLaMB significantly outstrips existing video-language models, demonstrating a 5.5 points improvement over its competitors across three VideoQA benchmarks, and 2.06 points on egocentric planning. Comprehensive results on the MVBench show that VideoLLaMB-7B achieves markedly better results than previous 7B models of same LLM. Remarkably, it maintains robust performance as PLLaVA even as video length increases up to 8 times. Besides, the frame retrieval results on our specialized Needle in a Video Haystack (NIAVH) benchmark, further validate VideoLLaMB's prowess in accurately identifying specific frames within lengthy videos. Our SceneTilling algorithm also enables the generation of streaming video captions directly, without necessitating additional training. In terms of efficiency, VideoLLaMB, trained on 16 frames, supports up to 320 frames on a single Nvidia A100 GPU with linear GPU memory scaling, ensuring both high performance and cost-effectiveness, thereby setting a new foundation for long-form video-language models in both academic and practical applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.00588", "title": "Diffusion Policy Policy Optimization", "authors": [ "Allen Z. Ren", "Justin Lidard", "Lars L. Ankile", "Anthony Simeonov", "Pulkit Agrawal", "Anirudha Majumdar", "Benjamin Burchfiel", "Hongkai Dai", "Max Simchowitz" ], "github": "https://github.com/irom-princeton/dppo", "project_page": "", "abstract": "We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework including best practices for fine-tuning diffusion-based policies (e.g. Diffusion Policy) in continuous control and robot learning tasks using the policy gradient (PG) method from reinforcement learning (RL). PG methods are ubiquitous in training RL policies with other policy parameterizations; nevertheless, they had been conjectured to be less efficient for diffusion-based policies. Surprisingly, we show that DPPO achieves the strongest overall performance and efficiency for fine-tuning in common benchmarks compared to other RL methods for diffusion-based policies and also compared to PG fine-tuning of other policy parameterizations. Through experimental investigation, we find that DPPO takes advantage of unique synergies between RL fine-tuning and the diffusion parameterization, leading to structured and on-manifold exploration, stable training, and strong policy robustness. We further demonstrate the strengths of DPPO in a range of realistic settings, including simulated robotic tasks with pixel observations, and via zero-shot deployment of simulation-trained policies on robot hardware in a long-horizon, multi-stage manipulation task. Website with code: diffusion-ppo.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/irom-princeton/dppo", "github_api_added_by": "auto" }, { "date": "2024-09-04", "arxiv_id": "2409.00729", "title": "ContextCite: Attributing Model Generation to Context", "authors": [ "Benjamin Cohen-Wang", "Harshay Shah", "Kristian Georgiev", "Aleksander Madry" ], "github": "https://github.com/MadryLab/context-cite", "project_page": "", "abstract": "How do language models use information provided as context when generating a response? Can we infer whether a particular generated statement is actually grounded in the context, a misinterpretation, or fabricated? To help answer these questions, we introduce the problem of context attribution: pinpointing the parts of the context (if any) that led a model to generate a particular statement. We then present ContextCite, a simple and scalable method for context attribution that can be applied on top of any existing language model. Finally, we showcase the utility of ContextCite through three applications: (1) helping verify generated statements (2) improving response quality by pruning the context and (3) detecting poisoning attacks. We provide code for ContextCite at https://github.com/MadryLab/context-cite.", "github_manual": "", "github_regex": "https://github.com/MadryLab/context-cite", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.00587", "title": "FLUX that Plays Music", "authors": [ "Zhengcong Fei", "Mingyuan Fan", "Changqian Yu", "Junshi Huang" ], "github": "https://github.com/feizc/fluxmusic", "project_page": "", "abstract": "This paper explores a simple extension of diffusion-based rectified flow Transformers for text-to-music generation, termed as FluxMusic. Generally, along with design in advanced Fluxhttps://github.com/black-forest-labs/flux model, we transfers it into a latent VAE space of mel-spectrum. It involves first applying a sequence of independent attention to the double text-music stream, followed by a stacked single music stream for denoised patch prediction. We employ multiple pre-trained text encoders to sufficiently capture caption semantic information as well as inference flexibility. In between, coarse textual information, in conjunction with time step embeddings, is utilized in a modulation mechanism, while fine-grained textual details are concatenated with the music patch sequence as inputs. Through an in-depth study, we demonstrate that rectified flow training with an optimized architecture significantly outperforms established diffusion methods for the text-to-music task, as evidenced by various automatic metrics and human preference evaluations. Our experimental data, code, and model weights are made publicly available at: https://github.com/feizc/FluxMusic.", "github_manual": "https://github.com/feizc/fluxmusic", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.00492", "title": "Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization", "authors": [ "Vage Egiazarian", "Denis Kuznedelev", "Anton Voronov", "Ruslan Svirschevski", "Michael Goin", "Daniil Pavlov", "Dan Alistarh", "Dmitry Baranchuk" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion models have emerged as a powerful framework for high-quality image generation given textual prompts. Their success has driven the rapid development of production-grade diffusion models that consistently increase in size and already contain billions of parameters. As a result, state-of-the-art text-to-image models are becoming less accessible in practice, especially in resource-limited environments. Post-training quantization (PTQ) tackles this issue by compressing the pretrained model weights into lower-bit representations. Recent diffusion quantization techniques primarily rely on uniform scalar quantization, providing decent performance for the models compressed to 4 bits. This work demonstrates that more versatile vector quantization (VQ) may achieve higher compression rates for large-scale text-to-image diffusion models. Specifically, we tailor vector-based PTQ methods to recent billion-scale text-to-image models (SDXL and SDXL-Turbo), and show that the diffusion models of 2B+ parameters compressed to around 3 bits using VQ exhibit the similar image quality and textual alignment as previous 4-bit compression techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.01437", "title": "Kvasir-VQA: A Text-Image Pair GI Tract Dataset", "authors": [ "Sushant Gautam", "Andrea Stor\u00e5s", "Cise Midoglu", "Steven A. Hicks", "Vajira Thambawita", "P\u00e5l Halvorsen", "Michael A. Riegler" ], "github": "https://github.com/simula/Kvasir-VQA", "project_page": "", "abstract": "We introduce Kvasir-VQA, an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations to facilitate advanced machine learning tasks in Gastrointestinal (GI) diagnostics. This dataset comprises 6,500 annotated images spanning various GI tract conditions and surgical instruments, and it supports multiple question types including yes/no, choice, location, and numerical count. The dataset is intended for applications such as image captioning, Visual Question Answering (VQA), text-based generation of synthetic medical images, object detection, and classification. Our experiments demonstrate the dataset's effectiveness in training models for three selected tasks, showcasing significant applications in medical image analysis and diagnostics. We also present evaluation metrics for each task, highlighting the usability and versatility of our dataset. The dataset and supporting artifacts are available at https://datasets.simula.no/kvasir-vqa.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/simula/Kvasir-VQA", "github_api_added_by": "auto" }, { "date": "2024-09-04", "arxiv_id": "2409.01055", "title": "Follow-Your-Canvas: Higher-Resolution Video Outpainting with Extensive Content Generation", "authors": [ "Qihua Chen", "Yue Ma", "Hongfa Wang", "Junkun Yuan", "Wenzhe Zhao", "Qi Tian", "Hongmei Wang", "Shaobo Min", "Qifeng Chen", "Wei Liu" ], "github": "https://github.com/mayuelala/FollowYourCanvas", "project_page": "", "abstract": "This paper explores higher-resolution video outpainting with extensive content generation. We point out common issues faced by existing methods when attempting to largely outpaint videos: the generation of low-quality content and limitations imposed by GPU memory. To address these challenges, we propose a diffusion-based method called Follow-Your-Canvas. It builds upon two core designs. First, instead of employing the common practice of \"single-shot\" outpainting, we distribute the task across spatial windows and seamlessly merge them. It allows us to outpaint videos of any size and resolution without being constrained by GPU memory. Second, the source video and its relative positional relation are injected into the generation process of each window. It makes the generated spatial layout within each window harmonize with the source video. Coupling with these two designs enables us to generate higher-resolution outpainting videos with rich content while keeping spatial and temporal consistency. Follow-Your-Canvas excels in large-scale video outpainting, e.g., from 512X512 to 1152X2048 (9X), while producing high-quality and aesthetically pleasing results. It achieves the best quantitative results across various resolution and scale setups. The code is released on https://github.com/mayuelala/FollowYourCanvas", "github_manual": "", "github_regex": "https://github.com/mayuelala/FollowYourCanvas", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.00447", "title": "The MERIT Dataset: Modelling and Efficiently Rendering Interpretable Transcripts", "authors": [ "I. de Rodrigo", "A. Sanchez-Cuadrado", "J. Boal", "A. J. Lopez-Lopez" ], "github": "", "project_page": "", "abstract": "This paper introduces the MERIT Dataset, a multimodal (text + image + layout) fully labeled dataset within the context of school reports. Comprising over 400 labels and 33k samples, the MERIT Dataset is a valuable resource for training models in demanding Visually-rich Document Understanding (VrDU) tasks. By its nature (student grade reports), the MERIT Dataset can potentially include biases in a controlled way, making it a valuable tool to benchmark biases induced in Language Models (LLMs). The paper outlines the dataset's generation pipeline and highlights its main features in the textual, visual, layout, and bias domains. To demonstrate the dataset's utility, we present a benchmark with token classification models, showing that the dataset poses a significant challenge even for SOTA models and that these would greatly benefit from including samples from the MERIT Dataset in their pretraining phase.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.01357", "title": "Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain", "authors": [ "Antoine Louis", "Gijs van Dijck", "Gerasimos Spanakis" ], "github": "", "project_page": "", "abstract": "Hybrid search has emerged as an effective strategy to offset the limitations of different matching paradigms, especially in out-of-domain contexts where notable improvements in retrieval quality have been observed. However, existing research predominantly focuses on a limited set of retrieval methods, evaluated in pairs on domain-general datasets exclusively in English. In this work, we study the efficacy of hybrid search across a variety of prominent retrieval models within the unexplored field of law in the French language, assessing both zero-shot and in-domain scenarios. Our findings reveal that in a zero-shot context, fusing different domain-general models consistently enhances performance compared to using a standalone model, regardless of the fusion method. Surprisingly, when models are trained in-domain, we find that fusion generally diminishes performance relative to using the best single system, unless fusing scores with carefully tuned weights. These novel insights, among others, expand the applicability of prior findings across a new field and language, and contribute to a deeper understanding of hybrid search in non-English specialized domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.01392", "title": "GenAgent: Build Collaborative AI Systems with Automated Workflow Generation -- Case Studies on ComfyUI", "authors": [ "Xiangyuan Xue", "Zeyu Lu", "Di Huang", "Wanli Ouyang", "Lei Bai" ], "github": "", "project_page": "", "abstract": "Much previous AI research has focused on developing monolithic models to maximize their intelligence and capability, with the primary goal of enhancing performance on specific tasks. In contrast, this paper explores an alternative approach: collaborative AI systems that use workflows to integrate models, data sources, and pipelines to solve complex and diverse tasks. We introduce GenAgent, an LLM-based framework that automatically generates complex workflows, offering greater flexibility and scalability compared to monolithic models. The core innovation of GenAgent lies in representing workflows with code, alongside constructing workflows with collaborative agents in a step-by-step manner. We implement GenAgent on the ComfyUI platform and propose a new benchmark, OpenComfy. The results demonstrate that GenAgent outperforms baseline approaches in both run-level and task-level evaluations, showing its capability to generate complex workflows with superior effectiveness and stability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.00138", "title": "PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action", "authors": [ "Yijia Shao", "Tianshi Li", "Weiyan Shi", "Yanchen Liu", "Diyi Yang" ], "github": "https://github.com/SALT-NLP/PrivacyLens", "project_page": "", "abstract": "As language models (LMs) are widely utilized in personalized communication scenarios (e.g., sending emails, writing social media posts) and endowed with a certain level of agency, ensuring they act in accordance with the contextual privacy norms becomes increasingly critical. However, quantifying the privacy norm awareness of LMs and the emerging privacy risk in LM-mediated communication is challenging due to (1) the contextual and long-tailed nature of privacy-sensitive cases, and (2) the lack of evaluation approaches that capture realistic application scenarios. To address these challenges, we propose PrivacyLens, a novel framework designed to extend privacy-sensitive seeds into expressive vignettes and further into agent trajectories, enabling multi-level evaluation of privacy leakage in LM agents' actions. We instantiate PrivacyLens with a collection of privacy norms grounded in privacy literature and crowdsourced seeds. Using this dataset, we reveal a discrepancy between LM performance in answering probing questions and their actual behavior when executing user instructions in an agent setup. State-of-the-art LMs, like GPT-4 and Llama-3-70B, leak sensitive information in 25.68% and 38.69% of cases, even when prompted with privacy-enhancing instructions. We also demonstrate the dynamic nature of PrivacyLens by extending each seed into multiple trajectories to red-team LM privacy leakage risk. Dataset and code are available at https://github.com/SALT-NLP/PrivacyLens.", "github_manual": "", "github_regex": "https://github.com/SALT-NLP/PrivacyLens", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-04", "arxiv_id": "2409.01704", "title": "General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model", "authors": [ "Haoran Wei", "Chenglong Liu", "Jinyue Chen", "Jia Wang", "Lingyu Kong", "Yanming Xu", "Zheng Ge", "Liang Zhao", "Jianjian Sun", "Yuang Peng", "Chunrui Han", "Xiangyu Zhang" ], "github": "https://github.com/ucas-haoranwei/got-ocr2.0", "project_page": "", "abstract": "Traditional OCR systems (OCR-1.0) are increasingly unable to meet people's usage due to the growing demand for intelligent processing of man-made optical characters. In this paper, we collectively refer to all artificial optical signals (e.g., plain texts, math/molecular formulas, tables, charts, sheet music, and even geometric shapes) as \"characters\" and propose the General OCR Theory along with an excellent model, namely GOT, to promote the arrival of OCR-2.0. The GOT, with 580M parameters, is a unified, elegant, and end-to-end model, consisting of a high-compression encoder and a long-contexts decoder. As an OCR-2.0 model, GOT can handle all the above \"characters\" under various OCR tasks. On the input side, the model supports commonly used scene- and document-style images in slice and whole-page styles. On the output side, GOT can generate plain or formatted results (markdown/tikz/smiles/kern) via an easy prompt. Besides, the model enjoys interactive OCR features, i.e., region-level recognition guided by coordinates or colors. Furthermore, we also adapt dynamic resolution and multi-page OCR technologies to GOT for better practicality. In experiments, we provide sufficient results to prove the superiority of our model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ucas-haoranwei/got-ocr2.0", "github_api_added_by": "auto" }, { "date": "2024-09-05", "arxiv_id": "2409.02897", "title": "LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA", "authors": [ "jiajie Zhang", "Yushi Bai", "Xin Lv", "Wanjun Gu", "Danqing Liu", "Minhao Zou", "Shulin Cao", "Lei Hou", "Yuxiao Dong", "Ling Feng", "Juanzi Li" ], "github": "https://github.com/THUDM/LongCite", "project_page": "", "abstract": "Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering user questions based on extensive text, the lack of citations in their responses makes user verification difficult, leading to concerns about their trustworthiness due to their potential hallucinations. In this work, we aim to enable long-context LLMs to generate responses with fine-grained sentence-level citations, improving their faithfulness and verifiability. We first introduce LongBench-Cite, an automated benchmark for assessing current LLMs' performance in Long-Context Question Answering with Citations (LQAC), revealing considerable room for improvement. To this end, we propose CoF (Coarse to Fine), a novel pipeline that utilizes off-the-shelf LLMs to automatically generate long-context QA instances with precise sentence-level citations, and leverage this pipeline to construct LongCite-45k, a large-scale SFT dataset for LQAC. Finally, we train LongCite-8B and LongCite-9B using the LongCite-45k dataset, successfully enabling their generation of accurate responses and fine-grained sentence-level citations in a single output. The evaluation results on LongBench-Cite show that our trained models achieve state-of-the-art citation quality, surpassing advanced proprietary models including GPT-4o.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUDM/LongCite", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-05", "arxiv_id": "2409.02634", "title": "Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency", "authors": [ "Jianwen Jiang", "Chao Liang", "Jiaqi Yang", "Gaojie Lin", "Tianyun Zhong", "Yanbo Zheng" ], "github": "", "project_page": "", "abstract": "With the introduction of diffusion-based video generation techniques, audio-conditioned human video generation has recently achieved significant breakthroughs in both the naturalness of motion and the synthesis of portrait details. Due to the limited control of audio signals in driving human motion, existing methods often add auxiliary spatial signals to stabilize movements, which may compromise the naturalness and freedom of motion. In this paper, we propose an end-to-end audio-only conditioned video diffusion model named Loopy. Specifically, we designed an inter- and intra-clip temporal module and an audio-to-latents module, enabling the model to leverage long-term motion information from the data to learn natural motion patterns and improving audio-portrait movement correlation. This method removes the need for manually specified spatial motion templates used in existing methods to constrain motion during inference. Extensive experiments show that Loopy outperforms recent audio-driven portrait diffusion models, delivering more lifelike and high-quality results across various scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-05", "arxiv_id": "2409.02813", "title": "MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark", "authors": [ "Xiang Yue", "Tianyu Zheng", "Yuansheng Ni", "Yubo Wang", "Kai Zhang", "Shengbang Tong", "Yuxuan Sun", "Ming Yin", "Botao Yu", "Ge Zhang", "Huan Sun", "Yu Su", "Wenhu Chen", "Graham Neubig" ], "github": "", "project_page": "", "abstract": "This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously assesses multimodal models' true understanding and reasoning capabilities through a three-step process based on MMMU: (1) filtering out questions answerable by text-only models, (2) augmenting candidate options, and (3) introducing a vision-only input setting where questions are embedded within images. This setting challenges AI to truly \"see\" and \"read\" simultaneously, testing a fundamental human cognitive skill of seamlessly integrating visual and textual information. Results show that model performance is substantially lower on MMMU-Pro than on MMMU, ranging from 16.8% to 26.9% across models. We explore the impact of OCR prompts and Chain of Thought (CoT) reasoning, finding that OCR prompts have minimal effect while CoT generally improves performance. MMMU-Pro provides a more rigorous evaluation tool, closely mimicking real-world scenarios and offering valuable directions for future research in multimodal AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-05", "arxiv_id": "2409.02326", "title": "Arctic-SnowCoder: Demystifying High-Quality Data in Code Pretraining", "authors": [ "Yuxiang Wei", "Hojae Han", "Rajhans Samdani" ], "github": "", "project_page": "", "abstract": "Recent studies have been increasingly demonstrating that high-quality data is crucial for effective pretraining of language models. However, the precise definition of \"high-quality\" remains underexplored. Focusing on the code domain, we introduce Arctic-SnowCoder-1.3B, a data-efficient base code model pretrained on 555B tokens through three phases of progressively refined data: (1) general pretraining with 500B standard-quality code tokens, preprocessed through basic filtering, deduplication, and decontamination, (2) continued pretraining with 50B high-quality tokens, selected from phase one by a BERT-style quality annotator trained to distinguish good code from random data, using positive examples drawn from high-quality code files, along with instruction data from Magicoder and StarCoder2-Instruct, and (3) enhanced pretraining with 5B synthetic data created by Llama-3.1-70B using phase two data as seeds, adapting the Magicoder approach for pretraining. Despite being trained on a limited dataset, Arctic-SnowCoder achieves state-of-the-art performance on BigCodeBench, a coding benchmark focusing on practical and challenging programming tasks, compared to similarly sized models trained on no more than 1T tokens, outperforming Phi-1.5-1.3B by 36%. Across all evaluated benchmarks, Arctic-SnowCoder-1.3B beats StarCoderBase-3B pretrained on 1T tokens. Additionally, it matches the performance of leading small base code models trained on trillions of tokens. For example, Arctic-SnowCoder-1.3B surpasses StarCoder2-3B, pretrained on over 3.3T tokens, on HumanEval+, a benchmark that evaluates function-level code generation, and remains competitive on BigCodeBench. Our evaluation presents a comprehensive analysis justifying various design choices for Arctic-SnowCoder. Most importantly, we find that the key to high-quality data is its alignment with the distribution of downstream applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-05", "arxiv_id": "2409.02245", "title": "FastVoiceGrad: One-step Diffusion-Based Voice Conversion with Adversarial Conditional Diffusion Distillation", "authors": [ "Takuhiro Kaneko", "Hirokazu Kameoka", "Kou Tanaka", "Yuto Kondo" ], "github": "https://github.com/huawei-noah/Speech-Backbones/tree/main/DiffVC", "project_page": "", "abstract": "Diffusion-based voice conversion (VC) techniques such as VoiceGrad have attracted interest because of their high VC performance in terms of speech quality and speaker similarity. However, a notable limitation is the slow inference caused by the multi-step reverse diffusion. Therefore, we propose FastVoiceGrad, a novel one-step diffusion-based VC that reduces the number of iterations from dozens to one while inheriting the high VC performance of the multi-step diffusion-based VC. We obtain the model using adversarial conditional diffusion distillation (ACDD), leveraging the ability of generative adversarial networks and diffusion models while reconsidering the initial states in sampling. Evaluations of one-shot any-to-any VC demonstrate that FastVoiceGrad achieves VC performance superior to or comparable to that of previous multi-step diffusion-based VC while enhancing the inference speed. Audio samples are available at https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/fastvoicegrad/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huawei-noah/Speech-Backbones/tree/main/DiffVC", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-05", "arxiv_id": "2409.01083", "title": "Affordance-based Robot Manipulation with Flow Matching", "authors": [ "Fan Zhang", "Michael Gienger" ], "github": "", "project_page": "", "abstract": "We present a framework for assistive robot manipulation, which focuses on two fundamental challenges: first, efficiently adapting large-scale models to downstream scene affordance understanding tasks, especially in daily living scenarios where gathering multi-task data involving humans requires strenuous effort; second, effectively learning robot trajectories by grounding the visual affordance model. We tackle the first challenge by employing a parameter-efficient prompt tuning method that prepends learnable text prompts to the frozen vision model to predict manipulation affordances in multi-task scenarios. Then we propose to learn robot trajectories guided by affordances in a supervised Flow Matching method. Flow matching represents a robot visuomotor policy as a conditional process of flowing random waypoints to desired robot trajectories. Finally, we introduce a real-world dataset with 10 tasks across Activities of Daily Living to test our framework. Our extensive evaluation highlights that the proposed prompt tuning method for learning manipulation affordance with language prompter achieves competitive performance and even outperforms other finetuning protocols across data scales, while satisfying parameter efficiency. Learning multi-task robot trajectories with a single flow matching policy also leads to consistently better performance than alternative behavior cloning methods, especially given multimodal robot action distributions. Our framework seamlessly unifies affordance model learning and trajectory generation with flow matching for robot manipulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-05", "arxiv_id": "2409.02078", "title": "Political DEBATE: Efficient Zero-shot and Few-shot Classifiers for Political Text", "authors": [ "Michael Burnham", "Kayla Kahn", "Ryan Yank Wang", "Rachel X. Peng" ], "github": "", "project_page": "", "abstract": "Social scientists quickly adopted large language models due to their ability to annotate documents without supervised training, an ability known as zero-shot learning. However, due to their compute demands, cost, and often proprietary nature, these models are often at odds with replication and open science standards. This paper introduces the Political DEBATE (DeBERTa Algorithm for Textual Entailment) language models for zero-shot and few-shot classification of political documents. These models are not only as good, or better than, state-of-the art large language models at zero and few-shot classification, but are orders of magnitude more efficient and completely open source. By training the models on a simple random sample of 10-25 documents, they can outperform supervised classifiers trained on hundreds or thousands of documents and state-of-the-art generative models with complex, engineered prompts. Additionally, we release the PolNLI dataset used to train these models -- a corpus of over 200,000 political documents with highly accurate labels across over 800 classification tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-05", "arxiv_id": "2409.02889", "title": "LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via Hybrid Architecture", "authors": [ "Xidong Wang", "Dingjie Song", "Shunian Chen", "Chen Zhang", "Benyou Wang" ], "github": "https://github.com/freedomintelligence/longllava", "project_page": "", "abstract": "Expanding the long-context capabilities of Multi-modal Large Language Models~(MLLMs) is crucial for video understanding, high-resolution image understanding, and multi-modal agents. This involves a series of systematic optimizations, including model architecture, data construction and training strategy, particularly addressing challenges such as degraded performance with more images and high computational costs. In this paper, we adapt the model architecture to a hybrid of Mamba and Transformer blocks, approach data construction with both temporal and spatial dependencies among multiple images and employ a progressive training strategy. The released model LongLLaVA~(Long-Context Large Language and Vision Assistant) is the first hybrid MLLM, which achieved a better balance between efficiency and effectiveness. LongLLaVA not only achieves competitive results across various benchmarks, but also maintains high throughput and low memory consumption. Especially, it could process nearly a thousand images on a single A100 80GB GPU, showing promising application prospects for a wide range of tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/freedomintelligence/longllava", "github_api_added_by": "auto" }, { "date": "2024-09-06", "arxiv_id": "2409.03753", "title": "WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild", "authors": [ "Yuntian Deng", "Wenting Zhao", "Jack Hessel", "Xiang Ren", "Claire Cardie", "Yejin Choi" ], "github": "https://github.com/da03/WildVisualizer", "project_page": "", "abstract": "The increasing availability of real-world conversation data offers exciting opportunities for researchers to study user-chatbot interactions. However, the sheer volume of this data makes manually examining individual conversations impractical. To overcome this challenge, we introduce WildVis, an interactive tool that enables fast, versatile, and large-scale conversation analysis. WildVis provides search and visualization capabilities in the text and embedding spaces based on a list of criteria. To manage million-scale datasets, we implemented optimizations including search index construction, embedding precomputation and compression, and caching to ensure responsive user interactions within seconds. We demonstrate WildVis's utility through three case studies: facilitating chatbot misuse research, visualizing and comparing topic distributions across datasets, and characterizing user-specific conversation patterns. WildVis is open-source and designed to be extendable, supporting additional datasets and customized search and visualization functionalities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/da03/WildVisualizer", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-06", "arxiv_id": "2409.03752", "title": "Attention Heads of Large Language Models: A Survey", "authors": [ "Zifan Zheng", "Yezhaohui Wang", "Yuxin Huang", "Shichao Song", "Bo Tang", "Feiyu Xiong", "Zhiyu Li" ], "github": "https://github.com/IAAR-Shanghai/Awesome-Attention-Heads", "project_page": "", "abstract": "Since the advent of ChatGPT, Large Language Models (LLMs) have excelled in various tasks but remain largely as black-box systems. Consequently, their development relies heavily on data-driven approaches, limiting performance enhancement through changes in internal architecture and reasoning pathways. As a result, many researchers have begun exploring the potential internal mechanisms of LLMs, aiming to identify the essence of their reasoning bottlenecks, with most studies focusing on attention heads. Our survey aims to shed light on the internal reasoning processes of LLMs by concentrating on the interpretability and underlying mechanisms of attention heads. We first distill the human thought process into a four-stage framework: Knowledge Recalling, In-Context Identification, Latent Reasoning, and Expression Preparation. Using this framework, we systematically review existing research to identify and categorize the functions of specific attention heads. Furthermore, we summarize the experimental methodologies used to discover these special heads, dividing them into two categories: Modeling-Free methods and Modeling-Required methods. Also, we outline relevant evaluation methods and benchmarks. Finally, we discuss the limitations of current research and propose several potential future directions. Our reference list is open-sourced at https://github.com/IAAR-Shanghai/Awesome-Attention-Heads.", "github_manual": "", "github_regex": "https://github.com/IAAR-Shanghai/Awesome-Attention-Heads", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-06", "arxiv_id": "2409.03420", "title": "mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding", "authors": [ "Anwen Hu", "Haiyang Xu", "Liang Zhang", "Jiabo Ye", "Ming Yan", "Ji Zhang", "Qin Jin", "Fei Huang", "Jingren Zhou" ], "github": "https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/DocOwl2", "project_page": "", "abstract": "Multimodel Large Language Models(MLLMs) have achieved promising OCR-free Document Understanding performance by increasing the supported resolution of document images. However, this comes at the cost of generating thousands of visual tokens for a single document image, leading to excessive GPU memory and slower inference times, particularly in multi-page document comprehension. In this work, to address these challenges, we propose a High-resolution DocCompressor module to compress each high-resolution document image into 324 tokens, guided by low-resolution global visual features. With this compression module, to strengthen multi-page document comprehension ability and balance both token efficiency and question-answering performance, we develop the DocOwl2 under a three-stage training framework: Single-image Pretraining, Multi-image Continue-pretraining, and Multi-task Finetuning. DocOwl2 sets a new state-of-the-art across multi-page document understanding benchmarks and reduces first token latency by more than 50%, demonstrating advanced capabilities in multi-page questioning answering, explanation with evidence pages, and cross-page structure understanding. Additionally, compared to single-image MLLMs trained on similar data, our DocOwl2 achieves comparable single-page understanding performance with less than 20% of the visual tokens. Our codes, models, and data are publicly available at https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/DocOwl2.", "github_manual": "", "github_regex": "https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/DocOwl2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-06", "arxiv_id": "2409.01944", "title": "FuzzCoder: Byte-level Fuzzing Test via Large Language Model", "authors": [ "Liqun Yang", "Jian Yang", "Chaoren Wei", "Guanglin Niu", "Ge Zhang", "Yunli Wang", "Linzheng ChaI", "Wanxu Xia", "Hongcheng Guo", "Shun Zhang", "Jiaheng Liu", "Yuwei Yin", "Junran Peng", "Jiaxin Ma", "Liang Sun", "Zhoujun Li" ], "github": "", "project_page": "", "abstract": "Fuzzing is an important dynamic program analysis technique designed for finding vulnerabilities in complex software. Fuzzing involves presenting a target program with crafted malicious input to cause crashes, buffer overflows, memory errors, and exceptions. Crafting malicious inputs in an efficient manner is a difficult open problem and the best approaches often apply uniform random mutations to pre-existing valid inputs. In this work, we propose to adopt fine-tuned large language models (FuzzCoder) to learn patterns in the input files from successful attacks to guide future fuzzing explorations. Specifically, we develop a framework to leverage the code LLMs to guide the mutation process of inputs in fuzzing. The mutation process is formulated as the sequence-to-sequence modeling, where LLM receives a sequence of bytes and then outputs the mutated byte sequence. FuzzCoder is fine-tuned on the created instruction dataset (Fuzz-Instruct), where the successful fuzzing history is collected from the heuristic fuzzing tool. FuzzCoder can predict mutation locations and strategies locations in input files to trigger abnormal behaviors of the program. Experimental results show that FuzzCoder based on AFL (American Fuzzy Lop) gain significant improvements in terms of effective proportion of mutation (EPM) and number of crashes (NC) for various input formats including ELF, JPG, MP3, and XML.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-06", "arxiv_id": "2409.03512", "title": "From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents", "authors": [ "Jifan Yu", "Zheyuan Zhang", "Daniel Zhang-li", "Shangqing Tu", "Zhanxin Hao", "Rui Miao Li", "Haoxuan Li", "Yuanchun Wang", "Hanming Li", "Linlu Gong", "Jie Cao", "Jiayin Lin", "Jinchang Zhou", "Fei Qin", "Haohua Wang", "Jianxiao Jiang", "Lijun Deng", "Yisi Zhan", "Chaojun Xiao", "Xusheng Dai", "Xuan Yan", "Nianyi Lin", "Nan Zhang", "Ruixin Ni", "Yang Dang", "Lei Hou", "Yu Zhang", "Xu Han", "Manli Li", "Juanzi Li", "Zhiyuan Liu", "Huiqin Liu", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "Since the first instances of online education, where courses were uploaded to accessible and shared online platforms, this form of scaling the dissemination of human knowledge to reach a broader audience has sparked extensive discussion and widespread adoption. Recognizing that personalized learning still holds significant potential for improvement, new AI technologies have been continuously integrated into this learning format, resulting in a variety of educational AI applications such as educational recommendation and intelligent tutoring. The emergence of intelligence in large language models (LLMs) has allowed for these educational enhancements to be built upon a unified foundational model, enabling deeper integration. In this context, we propose MAIC (Massive AI-empowered Course), a new form of online education that leverages LLM-driven multi-agent systems to construct an AI-augmented classroom, balancing scalability with adaptivity. Beyond exploring the conceptual framework and technical innovations, we conduct preliminary experiments at Tsinghua University, one of China's leading universities. Drawing from over 100,000 learning records of more than 500 students, we obtain a series of valuable observations and initial analyses. This project will continue to evolve, ultimately aiming to establish a comprehensive open platform that supports and unifies research, technology, and applications in exploring the possibilities of online education in the era of large model AI. We envision this platform as a collaborative hub, bringing together educators, researchers, and innovators to collectively explore the future of AI-driven online education.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-06", "arxiv_id": "2409.00844", "title": "Report Cards: Qualitative Evaluation of Language Models Using Natural Language Summaries", "authors": [ "Blair Yang", "Fuyang Cui", "Keiran Paster", "Jimmy Ba", "Pashootan Vaezipoor", "Silviu Pitis", "Michael R. Zhang" ], "github": "", "project_page": "", "abstract": "The rapid development and dynamic nature of large language models (LLMs) make it difficult for conventional quantitative benchmarks to accurately assess their capabilities. We propose report cards, which are human-interpretable, natural language summaries of model behavior for specific skills or topics. We develop a framework to evaluate report cards based on three criteria: specificity (ability to distinguish between models), faithfulness (accurate representation of model capabilities), and interpretability (clarity and relevance to humans). We also propose an iterative algorithm for generating report cards without human supervision and explore its efficacy by ablating various design choices. Through experimentation with popular LLMs, we demonstrate that report cards provide insights beyond traditional benchmarks and can help address the need for a more interpretable and holistic evaluation of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-06", "arxiv_id": "2409.01322", "title": "Guide-and-Rescale: Self-Guidance Mechanism for Effective Tuning-Free Real Image Editing", "authors": [ "Vadim Titov", "Madina Khalmatova", "Alexandra Ivanova", "Dmitry Vetrov", "Aibek Alanov" ], "github": "https://github.com/FusionBrainLab/Guide-and-Rescale", "project_page": "", "abstract": "Despite recent advances in large-scale text-to-image generative models, manipulating real images with these models remains a challenging problem. The main limitations of existing editing methods are that they either fail to perform with consistent quality on a wide range of image edits or require time-consuming hyperparameter tuning or fine-tuning of the diffusion model to preserve the image-specific appearance of the input image. We propose a novel approach that is built upon a modified diffusion sampling process via the guidance mechanism. In this work, we explore the self-guidance technique to preserve the overall structure of the input image and its local regions appearance that should not be edited. In particular, we explicitly introduce layout-preserving energy functions that are aimed to save local and global structures of the source image. Additionally, we propose a noise rescaling mechanism that allows to preserve noise distribution by balancing the norms of classifier-free guidance and our proposed guiders during generation. Such a guiding approach does not require fine-tuning the diffusion model and exact inversion process. As a result, the proposed method provides a fast and high-quality editing mechanism. In our experiments, we show through human evaluation and quantitative analysis that the proposed method allows to produce desired editing which is more preferable by humans and also achieves a better trade-off between editing quality and preservation of the original image. Our code is available at https://github.com/FusionBrainLab/Guide-and-Rescale.", "github_manual": "", "github_regex": "https://github.com/FusionBrainLab/Guide-and-Rescale", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-06", "arxiv_id": "2409.03718", "title": "Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with Image-Based Surface Representation", "authors": [ "Slava Elizarov", "Ciara Rowles", "Simon Donn\u00e9" ], "github": "", "project_page": "", "abstract": "Generating high-quality 3D objects from textual descriptions remains a challenging problem due to computational cost, the scarcity of 3D data, and complex 3D representations. We introduce Geometry Image Diffusion (GIMDiffusion), a novel Text-to-3D model that utilizes geometry images to efficiently represent 3D shapes using 2D images, thereby avoiding the need for complex 3D-aware architectures. By integrating a Collaborative Control mechanism, we exploit the rich 2D priors of existing Text-to-Image models such as Stable Diffusion. This enables strong generalization even with limited 3D training data (allowing us to use only high-quality training data) as well as retaining compatibility with guidance techniques such as IPAdapter. In short, GIMDiffusion enables the generation of 3D assets at speeds comparable to current Text-to-Image models. The generated objects consist of semantically meaningful, separate parts and include internal structures, enhancing both usability and versatility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-06", "arxiv_id": "2409.03643", "title": "CDM: A Reliable Metric for Fair and Accurate Formula Recognition Evaluation", "authors": [ "Bin Wang", "Fan Wu", "Linke Ouyang", "Zhuangcheng Gu", "Rui Zhang", "Renqiu Xia", "Bo Zhang", "Conghui He" ], "github": "https://github.com/opendatalab/unimernet", "project_page": "", "abstract": "Formula recognition presents significant challenges due to the complicated structure and varied notation of mathematical expressions. Despite continuous advancements in formula recognition models, the evaluation metrics employed by these models, such as BLEU and Edit Distance, still exhibit notable limitations. They overlook the fact that the same formula has diverse representations and is highly sensitive to the distribution of training data, thereby causing the unfairness in formula recognition evaluation. To this end, we propose a Character Detection Matching (CDM) metric, ensuring the evaluation objectivity by designing a image-level rather than LaTex-level metric score. Specifically, CDM renders both the model-predicted LaTeX and the ground-truth LaTeX formulas into image-formatted formulas, then employs visual feature extraction and localization techniques for precise character-level matching, incorporating spatial position information. Such a spatially-aware and character-matching method offers a more accurate and equitable evaluation compared with previous BLEU and Edit Distance metrics that rely solely on text-based character matching. Experimentally, we evaluated various formula recognition models using CDM, BLEU, and ExpRate metrics. Their results demonstrate that the CDM aligns more closely with human evaluation standards and provides a fairer comparison across different models by eliminating discrepancies caused by diverse formula representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/opendatalab/unimernet", "github_api_added_by": "auto" }, { "date": "2024-09-06", "arxiv_id": "2409.03525", "title": "FrozenSeg: Harmonizing Frozen Foundation Models for Open-Vocabulary Segmentation", "authors": [ "Xi Chen", "Haosen Yang", "Sheng Jin", "Xiatian Zhu", "Hongxun Yao" ], "github": "https://github.com/chenxi52/FrozenSeg", "project_page": "", "abstract": "Open-vocabulary segmentation poses significant challenges, as it requires segmenting and recognizing objects across an open set of categories in unconstrained environments. Building on the success of powerful vision-language (ViL) foundation models, such as CLIP, recent efforts sought to harness their zero-short capabilities to recognize unseen categories. Despite notable performance improvements, these models still encounter the critical issue of generating precise mask proposals for unseen categories and scenarios, resulting in inferior segmentation performance eventually. To address this challenge, we introduce a novel approach, FrozenSeg, designed to integrate spatial knowledge from a localization foundation model (e.g., SAM) and semantic knowledge extracted from a ViL model (e.g., CLIP), in a synergistic framework. Taking the ViL model's visual encoder as the feature backbone, we inject the space-aware feature into the learnable queries and CLIP features within the transformer decoder. In addition, we devise a mask proposal ensemble strategy for further improving the recall rate and mask quality. To fully exploit pre-trained knowledge while minimizing training overhead, we freeze both foundation models, focusing optimization efforts solely on a lightweight transformer decoder for mask proposal generation-the performance bottleneck. Extensive experiments demonstrate that FrozenSeg advances state-of-the-art results across various segmentation benchmarks, trained exclusively on COCO panoptic data, and tested in a zero-shot manner. Code is available at https://github.com/chenxi52/FrozenSeg.", "github_manual": "", "github_regex": "https://github.com/chenxi52/FrozenSeg", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-06", "arxiv_id": "2409.02392", "title": "Building Math Agents with Multi-Turn Iterative Preference Learning", "authors": [ "Wei Xiong", "Chengshuai Shi", "Jiaming Shen", "Aviv Rosenberg", "Zhen Qin", "Daniele Calandriello", "Misha Khalman", "Rishabh Joshi", "Bilal Piot", "Mohammad Saleh", "Chi Jin", "Tong Zhang", "Tianqi Liu" ], "github": "", "project_page": "", "abstract": "Recent studies have shown that large language models' (LLMs) mathematical problem-solving capabilities can be enhanced by integrating external tools, such as code interpreters, and employing multi-turn Chain-of-Thought (CoT) reasoning. While current methods focus on synthetic data generation and Supervised Fine-Tuning (SFT), this paper studies the complementary direct preference learning approach to further improve model performance. However, existing direct preference learning algorithms are originally designed for the single-turn chat task, and do not fully address the complexities of multi-turn reasoning and external tool integration required for tool-integrated mathematical reasoning tasks. To fill in this gap, we introduce a multi-turn direct preference learning framework, tailored for this context, that leverages feedback from code interpreters and optimizes trajectory-level preferences. This framework includes multi-turn DPO and multi-turn KTO as specific implementations. The effectiveness of our framework is validated through training of various language models using an augmented prompt set from the GSM8K and MATH datasets. Our results demonstrate substantial improvements: a supervised fine-tuned Gemma-1.1-it-7B model's performance increased from 77.5% to 83.9% on GSM8K and from 46.1% to 51.2% on MATH. Similarly, a Gemma-2-it-9B model improved from 84.1% to 86.3% on GSM8K and from 51.0% to 54.5% on MATH.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-06", "arxiv_id": "2409.00921", "title": "Statically Contextualizing Large Language Models with Typed Holes", "authors": [ "Andrew Blinn", "Xiang Li", "June Hyung Kim", "Cyrus Omar" ], "github": "https://github.com/hazelgrove/hazel", "project_page": "", "abstract": "Large language models (LLMs) have reshaped the landscape of program synthesis. However, contemporary LLM-based code completion systems often hallucinate broken code because they lack appropriate context, particularly when working with definitions not in the training data nor near the cursor. This paper demonstrates that tight integration with the type and binding structure of a language, as exposed by its language server, can address this contextualization problem in a token-efficient manner. In short, we contend that AIs need IDEs, too! In particular, we integrate LLM code generation into the Hazel live program sketching environment. The Hazel Language Server identifies the type and typing context of the hole being filled, even in the presence of errors, ensuring that a meaningful program sketch is always available. This allows prompting with codebase-wide contextual information not lexically local to the cursor, nor necessarily in the same file, but that is likely to be semantically local to the developer's goal. Completions synthesized by the LLM are then iteratively refined via further dialog with the language server. To evaluate these techniques, we introduce MVUBench, a dataset of model-view-update (MVU) web applications. These applications serve as challenge problems due to their reliance on application-specific data structures. We find that contextualization with type definitions is particularly impactful. After introducing our ideas in the context of Hazel we duplicate our techniques and port MVUBench to TypeScript in order to validate the applicability of these methods to higher-resource languages. Finally, we outline ChatLSP, a conservative extension to the Language Server Protocol (LSP) that language servers can implement to expose capabilities that AI code completion systems of various designs can use to incorporate static context when generating prompts for an LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hazelgrove/hazel", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-09", "arxiv_id": "2409.03810", "title": "How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data", "authors": [ "Yejie Wang", "Keqing He", "Dayuan Fu", "Zhuoma Gongque", "Heyang Xu", "Yanxu Chen", "Zhexu Wang", "Yujia Fu", "Guanting Dong", "Muxi Diao", "Jingang Wang", "Mengdi Zhang", "Xunliang Cai", "Weiran Xu" ], "github": "https://github.com/banksy23/XCoder", "project_page": "", "abstract": "Recently, there has been a growing interest in studying how to construct better code instruction tuning data. However, we observe Code models trained with these datasets exhibit high performance on HumanEval but perform worse on other benchmarks such as LiveCodeBench. Upon further investigation, we find that many datasets suffer from severe data leakage. After cleaning up most of the leaked data, some well-known high-quality datasets perform poorly. This discovery reveals a new challenge: identifying which dataset genuinely qualify as high-quality code instruction data. To address this, we propose an efficient code data pruning strategy for selecting good samples. Our approach is based on three dimensions: instruction complexity, response quality, and instruction diversity. Based on our selected data, we present XCoder, a family of models finetuned from LLaMA3. Our experiments show XCoder achieves new state-of-the-art performance using fewer training data, which verify the effectiveness of our data strategy. Moreover, we perform a comprehensive analysis on the data composition and find existing code datasets have different characteristics according to their construction methods, which provide new insights for future code LLMs. Our models and dataset are released in https://github.com/banksy23/XCoder", "github_manual": "", "github_regex": "https://github.com/banksy23/XCoder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-09", "arxiv_id": "2409.04410", "title": "Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation", "authors": [ "Zhuoyan Luo", "Fengyuan Shi", "Yixiao Ge", "Yujiu Yang", "Limin Wang", "Ying Shan" ], "github": "", "project_page": "", "abstract": "We present Open-MAGVIT2, a family of auto-regressive image generation models ranging from 300M to 1.5B. The Open-MAGVIT2 project produces an open-source replication of Google's MAGVIT-v2 tokenizer, a tokenizer with a super-large codebook (i.e., 2^{18} codes), and achieves the state-of-the-art reconstruction performance (1.17 rFID) on ImageNet 256 times 256. Furthermore, we explore its application in plain auto-regressive models and validate scalability properties. To assist auto-regressive models in predicting with a super-large vocabulary, we factorize it into two sub-vocabulary of different sizes by asymmetric token factorization, and further introduce \"next sub-token prediction\" to enhance sub-token interaction for better generation quality. We release all models and codes to foster innovation and creativity in the field of auto-regressive visual generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-09", "arxiv_id": "2409.04005", "title": "Qihoo-T2X: An Efficiency-Focused Diffusion Transformer via Proxy Tokens for Text-to-Any-Task", "authors": [ "Jing Wang", "Ao Ma", "Jiasong Feng", "Dawei Leng", "Yuhui Yin", "Xiaodan Liang" ], "github": "https://github.com/360CVGroup/Qihoo-T2X", "project_page": "", "abstract": "The global self-attention mechanism in diffusion transformers involves redundant computation due to the sparse and redundant nature of visual information, and the attention map of tokens within a spatial window shows significant similarity. To address this redundancy, we propose the Proxy Token Diffusion Transformer (PT-DiT), which employs sparse representative token attention (where the number of representative tokens is much smaller than the total number of tokens) to model global visual information efficiently. Specifically, in each transformer block, we randomly sample one token from each spatial-temporal window to serve as a proxy token for that region. The global semantics are captured through the self-attention of these proxy tokens and then injected into all latent tokens via cross-attention. Simultaneously, we introduce window and shift window attention to address the limitations in detail modeling caused by the sparse attention mechanism. Building on the well-designed PT-DiT, we further develop the Qihoo-T2X family, which includes a variety of models for T2I, T2V, and T2MV tasks. Experimental results show that PT-DiT achieves competitive performance while reducing the computational complexity in both image and video generation tasks (e.g., a 48% reduction compared to DiT and a 35% reduction compared to Pixart-alpha). Our source code is available at https://github.com/360CVGroup/Qihoo-T2X.", "github_manual": "", "github_regex": "https://github.com/360CVGroup/Qihoo-T2X", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-09", "arxiv_id": "2409.02877", "title": "Configurable Foundation Models: Building LLMs from a Modular Perspective", "authors": [ "Chaojun Xiao", "Zhengyan Zhang", "Chenyang Song", "Dazhi Jiang", "Feng Yao", "Xu Han", "Xiaozhi Wang", "Shuo Wang", "Yufei Huang", "Guanyu Lin", "Yingfa Chen", "Weilin Zhao", "Yuge Tu", "Zexuan Zhong", "Ao Zhang", "Chenglei Si", "Khai Hao Moo", "Chenyang Zhao", "Huimin Chen", "Yankai Lin", "Zhiyuan Liu", "Jingbo Shang", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "Advancements in LLMs have recently unveiled challenges tied to computational efficiency and continual scalability due to their requirements of huge parameters, making the applications and evolution of these models on devices with limited computation resources and scenarios requiring various abilities increasingly cumbersome. Inspired by modularity within the human brain, there is a growing tendency to decompose LLMs into numerous functional modules, allowing for inference with part of modules and dynamic assembly of modules to tackle complex tasks, such as mixture-of-experts. To highlight the inherent efficiency and composability of the modular approach, we coin the term brick to represent each functional module, designating the modularized structure as configurable foundation models. In this paper, we offer a comprehensive overview and investigation of the construction, utilization, and limitation of configurable foundation models. We first formalize modules into emergent bricks - functional neuron partitions that emerge during the pre-training phase, and customized bricks - bricks constructed via additional post-training to improve the capabilities and knowledge of LLMs. Based on diverse functional bricks, we further present four brick-oriented operations: retrieval and routing, merging, updating, and growing. These operations allow for dynamic configuration of LLMs based on instructions to handle complex tasks. To verify our perspective, we conduct an empirical analysis on widely-used LLMs. We find that the FFN layers follow modular patterns with functional specialization of neurons and functional neuron partitions. Finally, we highlight several open issues and directions for future research. Overall, this paper aims to offer a fresh modular perspective on existing LLM research and inspire the future creation of more efficient and scalable foundational models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-09", "arxiv_id": "2409.04196", "title": "GST: Precise 3D Human Body from a Single Image with Gaussian Splatting Transformers", "authors": [ "Lorenza Prospero", "Abdullah Hamdi", "Joao F. Henriques", "Christian Rupprecht" ], "github": "https://github.com/prosperolo/gst", "project_page": "", "abstract": "Reconstructing realistic 3D human models from monocular images has significant applications in creative industries, human-computer interfaces, and healthcare. We base our work on 3D Gaussian Splatting (3DGS), a scene representation composed of a mixture of Gaussians. Predicting such mixtures for a human from a single input image is challenging, as it is a non-uniform density (with a many-to-one relationship with input pixels) with strict physical constraints. At the same time, it needs to be flexible to accommodate a variety of clothes and poses. Our key observation is that the vertices of standardized human meshes (such as SMPL) can provide an adequate density and approximate initial position for Gaussians. We can then train a transformer model to jointly predict comparatively small adjustments to these positions, as well as the other Gaussians' attributes and the SMPL parameters. We show empirically that this combination (using only multi-view supervision) can achieve fast inference of 3D human models from a single image without test-time optimization, expensive diffusion models, or 3D points supervision. We also show that it can improve 3D pose estimation by better fitting human models that account for clothes and other variations. The code is available on the project website https://abdullahamdi.com/gst/ .", "github_manual": "https://github.com/prosperolo/gst", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-09", "arxiv_id": "2409.02076", "title": "Spinning the Golden Thread: Benchmarking Long-Form Generation in Language Models", "authors": [ "Yuhao Wu", "Ming Shan Hee", "Zhiqing Hu", "Roy Ka-Wei Lee" ], "github": "", "project_page": "", "abstract": "The abilities of long-context language models (LMs) are often evaluated using the \"Needle-in-a-Haystack\" (NIAH) test, which comprises tasks designed to assess a model's ability to identify specific information (\"needle\") within large text sequences (\"haystack\"). While these benchmarks measure how well models understand long-context input sequences, they do not effectively gauge the quality of long-form text generation--a critical aspect for applications such as design proposals and creative writing. To address this gap, we have introduced a new long-form text evaluation benchmark, Spinning the Golden Thread (SGT), which tests models' ability to identify specific events within generated long text sequences. In this benchmark, we prompt long-context LMs to create long-form text that must include particular events or constraints and evaluate their ability to incorporate these elements. We evaluated ten long-context LMs across four distinct scenarios, three types of prompt instructions, and two different generation-length settings (16K and 32K). Although these models perform well on NIAH benchmarks, none demonstrated satisfactory performance on the Spinning the Golden Thread, raising concerns about their ability to generate coherent long-form text that follows instructions. Additionally, as the length of the generated text increases, all models exhibit a significant drop in performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-10", "arxiv_id": "2409.02795", "title": "Towards a Unified View of Preference Learning for Large Language Models: A Survey", "authors": [ "Bofei Gao", "Feifan Song", "Yibo Miao", "Zefan Cai", "Zhe Yang", "Liang Chen", "Helan Hu", "Runxin Xu", "Qingxiu Dong", "Ce Zheng", "Wen Xiao", "Ge Zhang", "Daoguang Zan", "Keming Lu", "Bowen Yu", "Dayiheng Liu", "Zeyu Cui", "Jian Yang", "Lei Sha", "Houfeng Wang", "Zhifang Sui", "Peiyi Wang", "Tianyu Liu", "Baobao Chang" ], "github": "https://github.com/kbsdjames/awesome-llm-preference-learning", "project_page": "", "abstract": "Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial factors to achieve success is aligning the LLM's output with human preferences. This alignment process often requires only a small amount of data to efficiently enhance the LLM's performance. While effective, research in this area spans multiple domains, and the methods involved are relatively complex to understand. The relationships between different methods have been under-explored, limiting the development of the preference alignment. In light of this, we break down the existing popular alignment strategies into different components and provide a unified framework to study the current alignment strategies, thereby establishing connections among them. In this survey, we decompose all the strategies in preference learning into four components: model, data, feedback, and algorithm. This unified view offers an in-depth understanding of existing alignment algorithms and also opens up possibilities to synergize the strengths of different strategies. Furthermore, we present detailed working examples of prevalent existing algorithms to facilitate a comprehensive understanding for the readers. Finally, based on our unified perspective, we explore the challenges and future research directions for aligning large language models with human preferences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kbsdjames/awesome-llm-preference-learning", "github_api_added_by": "auto" }, { "date": "2024-09-10", "arxiv_id": "2409.05840", "title": "MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct", "authors": [ "Run Luo", "Haonan Zhang", "Longze Chen", "Ting-En Lin", "Xiong Liu", "Yuchuan Wu", "Min Yang", "Minzheng Wang", "Pengpeng Zeng", "Lianli Gao", "Heng Tao Shen", "Yunshui Li", "Xiaobo Xia", "Fei Huang", "Jingkuan Song", "Yongbin Li" ], "github": "", "project_page": "", "abstract": "The development of Multimodal Large Language Models (MLLMs) has seen significant advancements. However, the quantity and quality of multimodal instruction data have emerged as significant bottlenecks in their progress. Manually creating multimodal instruction data is both time-consuming and inefficient, posing challenges in producing instructions of high complexity. Moreover, distilling instruction data from black-box commercial models (e.g., GPT-4o, GPT-4V) often results in simplistic instruction data, which constrains performance to that of these models. The challenge of curating diverse and complex instruction data remains substantial. We propose MMEvol, a novel multimodal instruction data evolution framework that combines fine-grained perception evolution, cognitive reasoning evolution, and interaction evolution. This iterative approach breaks through data quality bottlenecks to generate a complex and diverse image-text instruction dataset, thereby empowering MLLMs with enhanced capabilities. Beginning with an initial set of instructions, SEED-163K, we utilize MMEvol to systematically broadens the diversity of instruction types, integrates reasoning steps to enhance cognitive capabilities, and extracts detailed information from images to improve visual understanding and robustness. To comprehensively evaluate the effectiveness of our data, we train LLaVA-NeXT using the evolved data and conduct experiments across 13 vision-language tasks. Compared to the baseline trained with seed data, our approach achieves an average accuracy improvement of 3.1 points and reaches state-of-the-art (SOTA) performance on 9 of these tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-10", "arxiv_id": "2409.05152", "title": "OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs", "authors": [ "Jintian Zhang", "Cheng Peng", "Mengshu Sun", "Xiang Chen", "Lei Liang", "Zhiqiang Zhang", "Jun Zhou", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/onegen", "project_page": "", "abstract": "Despite the recent advancements in Large Language Models (LLMs), which have significantly enhanced the generative capabilities for various NLP tasks, LLMs still face limitations in directly handling retrieval tasks. However, many practical applications demand the seamless integration of both retrieval and generation. This paper introduces a novel and efficient One-pass Generation and retrieval framework (OneGen), designed to improve LLMs' performance on tasks that require both generation and retrieval. The proposed framework bridges the traditionally separate training approaches for generation and retrieval by incorporating retrieval tokens generated autoregressively. This enables a single LLM to handle both tasks simultaneously in a unified forward pass. We conduct experiments on two distinct types of composite tasks, RAG and Entity Linking, to validate the pluggability, effectiveness, and efficiency of OneGen in training and inference. Furthermore, our results show that integrating generation and retrieval within the same context preserves the generative capabilities of LLMs while improving retrieval performance. To the best of our knowledge, OneGen is the first to enable LLMs to conduct vector retrieval during the generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zjunlp/onegen", "github_api_added_by": "auto" }, { "date": "2024-09-10", "arxiv_id": "2409.05806", "title": "Benchmarking Chinese Knowledge Rectification in Large Language Models", "authors": [ "Tianhe Lu", "Jizhan Fang", "Yunzhi Yao", "Xin Xu", "Ningyu Zhang", "Huajun Chen" ], "github": "https://github.com/zjunlp/EasyEdit", "project_page": "", "abstract": "While Large Language Models (LLMs) exhibit remarkable generative capabilities, they are not without flaws, particularly in the form of hallucinations. This issue is even more pronounced when LLMs are applied to specific languages and domains. For example, LLMs may generate nonsense information when handling Chinese ancient poetry, proverbs, or idioms, owing to the lack of specific knowledge. To this end, this paper introduces a benchmark for rectifying Chinese knowledge in LLMs via knowledge editing. Specifically, we introduce a new Chinese dataset, CKnowEdit, by collecting seven type of knowledge from various sources, including classical texts, idioms, and content from Baidu Tieba Ruozhiba, thereby accounting for the unique polyphony, antithesis, and logical constructs inherent in the Chinese language. Through the analysis of this dataset, we uncover the challenges faced by current LLMs in mastering Chinese. Furthermore, our evaluation of state-of-the-art knowledge editing techniques on this dataset unveil the substantial scope for advancement in the rectification of Chinese knowledge. Code and dataset are available at https://github.com/zjunlp/EasyEdit.", "github_manual": "", "github_regex": "https://github.com/zjunlp/EasyEdit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-10", "arxiv_id": "2409.05591", "title": "MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery", "authors": [ "Hongjin Qian", "Peitian Zhang", "Zheng Liu", "Kelong Mao", "Zhicheng Dou" ], "github": "https://github.com/qhjqhj00/memorag", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) leverages retrieval tools to access external databases, thereby enhancing the generation quality of large language models (LLMs) through optimized context. However, the existing retrieval methods are constrained inherently, as they can only perform relevance matching between explicitly stated queries and well-formed knowledge, but unable to handle tasks involving ambiguous information needs or unstructured knowledge. Consequently, existing RAG systems are primarily effective for straightforward question-answering tasks. In this work, we propose MemoRAG, a novel retrieval-augmented generation paradigm empowered by long-term memory. MemoRAG adopts a dual-system architecture. On the one hand, it employs a light but long-range LLM to form the global memory of database. Once a task is presented, it generates draft answers, cluing the retrieval tools to locate useful information within the database. On the other hand, it leverages an expensive but expressive LLM, which generates the ultimate answer based on the retrieved information. Building on this general framework, we further optimize MemoRAG's performance by enhancing its cluing mechanism and memorization capacity. In our experiment, MemoRAG achieves superior performance across a variety of evaluation tasks, including both complex ones where conventional RAG fails and straightforward ones where RAG is commonly applied.", "github_manual": "https://github.com/qhjqhj00/memorag", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-10", "arxiv_id": "2409.04234", "title": "UniDet3D: Multi-dataset Indoor 3D Object Detection", "authors": [ "Maksim Kolodiazhnyi", "Anna Vorontsova", "Matvey Skripkin", "Danila Rukhovich", "Anton Konushin" ], "github": "https://github.com/filapro/unidet3d", "project_page": "", "abstract": "Growing customer demand for smart solutions in robotics and augmented reality has attracted considerable attention to 3D object detection from point clouds. Yet, existing indoor datasets taken individually are too small and insufficiently diverse to train a powerful and general 3D object detection model. In the meantime, more general approaches utilizing foundation models are still inferior in quality to those based on supervised training for a specific task. In this work, we propose , a simple yet effective 3D object detection model, which is trained on a mixture of indoor datasets and is capable of working in various indoor environments. By unifying different label spaces, enables learning a strong representation across multiple datasets through a supervised joint training scheme. The proposed network architecture is built upon a vanilla transformer encoder, making it easy to run, customize and extend the prediction pipeline for practical use. Extensive experiments demonstrate that obtains significant gains over existing 3D object detection methods in 6 indoor benchmarks: ScanNet (+1.1 mAP50), ARKitScenes (+19.4 mAP25), S3DIS (+9.1 mAP50), MultiScan (+9.3 mAP50), 3RScan (+3.2 mAP50), and ScanNet++ (+2.7 mAP50). Code is available at https://github.com/filapro/unidet3d .", "github_manual": "", "github_regex": "https://github.com/filapro/unidet3d", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-10", "arxiv_id": "2409.04593", "title": "Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance", "authors": [ "Guanyu Lin", "Tao Feng", "Pengrui Han", "Ge Liu", "Jiaxuan You" ], "github": "", "project_page": "", "abstract": "As scientific research proliferates, researchers face the daunting task of navigating and reading vast amounts of literature. Existing solutions, such as document QA, fail to provide personalized and up-to-date information efficiently. We present Paper Copilot, a self-evolving, efficient LLM system designed to assist researchers, based on thought-retrieval, user profile and high performance optimization. Specifically, Paper Copilot can offer personalized research services, maintaining a real-time updated database. Quantitative evaluation demonstrates that Paper Copilot saves 69.92\\% of time after efficient deployment. This paper details the design and implementation of Paper Copilot, highlighting its contributions to personalized academic support and its potential to streamline the research process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-10", "arxiv_id": "2409.04269", "title": "Open Language Data Initiative: Advancing Low-Resource Machine Translation for Karakalpak", "authors": [ "Mukhammadsaid Mamasaidov", "Abror Shopulatov" ], "github": "", "project_page": "", "abstract": "This study presents several contributions for the Karakalpak language: a FLORES+ devtest dataset translated to Karakalpak, parallel corpora for Uzbek-Karakalpak, Russian-Karakalpak and English-Karakalpak of 100,000 pairs each and open-sourced fine-tuned neural models for translation across these languages. Our experiments compare different model variants and training approaches, demonstrating improvements over existing baselines. This work, conducted as part of the Open Language Data Initiative (OLDI) shared task, aims to advance machine translation capabilities for Karakalpak and contribute to expanding linguistic diversity in NLP technologies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-10", "arxiv_id": "2409.05177", "title": "Insights from Benchmarking Frontier Language Models on Web App Code Generation", "authors": [ "Yi Cui" ], "github": "", "project_page": "", "abstract": "This paper presents insights from evaluating 16 frontier large language models (LLMs) on the WebApp1K benchmark, a test suite designed to assess the ability of LLMs to generate web application code. The results reveal that while all models possess similar underlying knowledge, their performance is differentiated by the frequency of mistakes they make. By analyzing lines of code (LOC) and failure distributions, we find that writing correct code is more complex than generating incorrect code. Furthermore, prompt engineering shows limited efficacy in reducing errors beyond specific cases. These findings suggest that further advancements in coding LLM should emphasize on model reliability and mistake minimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-10", "arxiv_id": "2409.05862", "title": "Evaluating Multiview Object Consistency in Humans and Image Models", "authors": [ "Tyler Bonnen", "Stephanie Fu", "Yutong Bai", "Thomas O'Connell", "Yoni Friedman", "Nancy Kanwisher", "Joshua B. Tenenbaum", "Alexei A. Efros" ], "github": "https://github.com/tzler/mochi_code", "project_page": "", "abstract": "We introduce a benchmark to directly evaluate the alignment between human observers and vision models on a 3D shape inference task. We leverage an experimental design from the cognitive sciences which requires zero-shot visual inferences about object shape: given a set of images, participants identify which contain the same/different objects, despite considerable viewpoint variation. We draw from a diverse range of images that include common objects (e.g., chairs) as well as abstract shapes (i.e., procedurally generated `nonsense' objects). After constructing over 2000 unique image sets, we administer these tasks to human participants, collecting 35K trials of behavioral data from over 500 participants. This includes explicit choice behaviors as well as intermediate measures, such as reaction time and gaze data. We then evaluate the performance of common vision models (e.g., DINOv2, MAE, CLIP). We find that humans outperform all models by a wide margin. Using a multi-scale evaluation approach, we identify underlying similarities and differences between models and humans: while human-model performance is correlated, humans allocate more time/processing on challenging trials. All images, data, and code can be accessed via our project page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tzler/mochi_code", "github_api_added_by": "auto" }, { "date": "2024-09-10", "arxiv_id": "2409.04828", "title": "POINTS: Improving Your Vision-language Model with Affordable Strategies", "authors": [ "Yuan Liu", "Zhongyin Zhao", "Ziyuan Zhuang", "Le Tian", "Xiao Zhou", "Jie Zhou" ], "github": "", "project_page": "", "abstract": "In recent years, vision-language models have made significant strides, excelling in tasks like optical character recognition and geometric problem-solving. However, several critical issues remain: 1) Proprietary models often lack transparency about their architectures, while open-source models need more detailed ablations of their training strategies. 2) Pre-training data in open-source works is under-explored, with datasets added empirically, making the process cumbersome. 3) Fine-tuning often focuses on adding datasets, leading to diminishing returns. To address these issues, we propose the following contributions: 1) We trained a robust baseline model using the latest advancements in vision-language models, introducing effective improvements and conducting comprehensive ablation and validation for each technique. 2) Inspired by recent work on large language models, we filtered pre-training data using perplexity, selecting the lowest perplexity data for training. This approach allowed us to train on a curated 1M dataset, achieving competitive performance. 3) During visual instruction tuning, we used model soup on different datasets when adding more datasets yielded marginal improvements. These innovations resulted in a 9B parameter model that performs competitively with state-of-the-art models. Our strategies are efficient and lightweight, making them easily adoptable by the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-10", "arxiv_id": "2409.05865", "title": "Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments", "authors": [ "Haritheja Etukuru", "Norihito Naka", "Zijin Hu", "Seungjae Lee", "Julian Mehu", "Aaron Edsinger", "Chris Paxton", "Soumith Chintala", "Lerrel Pinto", "Nur Muhammad Mahi Shafiullah" ], "github": "https://github.com/haritheja-e/robot-utility-models", "project_page": "", "abstract": "Robot models, particularly those trained with large amounts of data, have recently shown a plethora of real-world manipulation and navigation capabilities. Several independent efforts have shown that given sufficient training data in an environment, robot policies can generalize to demonstrated variations in that environment. However, needing to finetune robot models to every new environment stands in stark contrast to models in language or vision that can be deployed zero-shot for open-world problems. In this work, we present Robot Utility Models (RUMs), a framework for training and deploying zero-shot robot policies that can directly generalize to new environments without any finetuning. To create RUMs efficiently, we develop new tools to quickly collect data for mobile manipulation tasks, integrate such data into a policy with multi-modal imitation learning, and deploy policies on-device on Hello Robot Stretch, a cheap commodity robot, with an external mLLM verifier for retrying. We train five such utility models for opening cabinet doors, opening drawers, picking up napkins, picking up paper bags, and reorienting fallen objects. Our system, on average, achieves 90% success rate in unseen, novel environments interacting with unseen objects. Moreover, the utility models can also succeed in different robot and camera set-ups with no further data, training, or fine-tuning. Primary among our lessons are the importance of training data over training algorithm and policy class, guidance about data scaling, necessity for diverse yet high-quality demonstrations, and a recipe for robot introspection and retrying to improve performance on individual environments. Our code, data, models, hardware designs, as well as our experiment and deployment videos are open sourced and can be found on our project website: https://robotutilitymodels.com", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/haritheja-e/robot-utility-models", "github_api_added_by": "auto" }, { "date": "2024-09-11", "arxiv_id": "2409.06029", "title": "SongCreator: Lyrics-based Universal Song Generation", "authors": [ "Shun Lei", "Yixuan Zhou", "Boshi Tang", "Max W. Y. Lam", "Feng Liu", "Hangyu Liu", "Jingcheng Wu", "Shiyin Kang", "Zhiyong Wu", "Helen Meng" ], "github": "https://github.com/lucasnewman/best-rq-pytorch", "project_page": "", "abstract": "Music is an integral part of human culture, embodying human intelligence and creativity, of which songs compose an essential part. While various aspects of song generation have been explored by previous works, such as singing voice, vocal composition and instrumental arrangement, etc., generating songs with both vocals and accompaniment given lyrics remains a significant challenge, hindering the application of music generation models in the real world. In this light, we propose SongCreator, a song-generation system designed to tackle this challenge. The model features two novel designs: a meticulously designed dual-sequence language model (DSLM) to capture the information of vocals and accompaniment for song generation, and an additional attention mask strategy for DSLM, which allows our model to understand, generate and edit songs, making it suitable for various song-related generation tasks. Extensive experiments demonstrate the effectiveness of SongCreator by achieving state-of-the-art or competitive performances on all eight tasks. Notably, it surpasses previous works by a large margin in lyrics-to-song and lyrics-to-vocals. Additionally, it is able to independently control the acoustic conditions of the vocals and accompaniment in the generated song through different prompts, exhibiting its potential applicability. Our samples are available at https://songcreator.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lucasnewman/best-rq-pytorch", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-11", "arxiv_id": "2409.06135", "title": "Draw an Audio: Leveraging Multi-Instruction for Video-to-Audio Synthesis", "authors": [ "Qi Yang", "Binjie Mao", "Zili Wang", "Xing Nie", "Pengfei Gao", "Ying Guo", "Cheng Zhen", "Pengfei Yan", "Shiming Xiang" ], "github": "", "project_page": "", "abstract": "Foley is a term commonly used in filmmaking, referring to the addition of daily sound effects to silent films or videos to enhance the auditory experience. Video-to-Audio (V2A), as a particular type of automatic foley task, presents inherent challenges related to audio-visual synchronization. These challenges encompass maintaining the content consistency between the input video and the generated audio, as well as the alignment of temporal and loudness properties within the video. To address these issues, we construct a controllable video-to-audio synthesis model, termed Draw an Audio, which supports multiple input instructions through drawn masks and loudness signals. To ensure content consistency between the synthesized audio and target video, we introduce the Mask-Attention Module (MAM), which employs masked video instruction to enable the model to focus on regions of interest. Additionally, we implement the Time-Loudness Module (TLM), which uses an auxiliary loudness signal to ensure the synthesis of sound that aligns with the video in both loudness and temporal dimensions. Furthermore, we have extended a large-scale V2A dataset, named VGGSound-Caption, by annotating caption prompts. Extensive experiments on challenging benchmarks across two large-scale V2A datasets verify Draw an Audio achieves the state-of-the-art. Project page: https://yannqi.github.io/Draw-an-Audio/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-11", "arxiv_id": "2409.06633", "title": "SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation", "authors": [ "Teng Hu", "Jiangning Zhang", "Ran Yi", "Hongrui Huang", "Yabiao Wang", "Lizhuang Ma" ], "github": "https://github.com/sjtuplayer/sara", "project_page": "", "abstract": "In recent years, the development of diffusion models has led to significant progress in image and video generation tasks, with pre-trained models like the Stable Diffusion series playing a crucial role. Inspired by model pruning which lightens large pre-trained models by removing unimportant parameters, we propose a novel model fine-tuning method to make full use of these ineffective parameters and enable the pre-trained model with new task-specified capabilities. In this work, we first investigate the importance of parameters in pre-trained diffusion models, and discover that the smallest 10% to 20% of parameters by absolute values do not contribute to the generation process. Based on this observation, we propose a method termed SaRA that re-utilizes these temporarily ineffective parameters, equating to optimizing a sparse weight matrix to learn the task-specific knowledge. To mitigate overfitting, we propose a nuclear-norm-based low-rank sparse training scheme for efficient fine-tuning. Furthermore, we design a new progressive parameter adjustment strategy to make full use of the re-trained/finetuned parameters. Finally, we propose a novel unstructural backpropagation strategy, which significantly reduces memory costs during fine-tuning. Our method enhances the generative capabilities of pre-trained models in downstream applications and outperforms traditional fine-tuning methods like LoRA in maintaining model's generalization ability. We validate our approach through fine-tuning experiments on SD models, demonstrating significant improvements. SaRA also offers a practical advantage that requires only a single line of code modification for efficient implementation and is seamlessly compatible with existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sjtuplayer/sara", "github_api_added_by": "auto" }, { "date": "2024-09-11", "arxiv_id": "2409.06210", "title": "INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding", "authors": [ "Ji Ha Jang", "Hoigi Seo", "Se Young Chun" ], "github": "", "project_page": "", "abstract": "Affordance denotes the potential interactions inherent in objects. The perception of affordance can enable intelligent agents to navigate and interact with new environments efficiently. Weakly supervised affordance grounding teaches agents the concept of affordance without costly pixel-level annotations, but with exocentric images. Although recent advances in weakly supervised affordance grounding yielded promising results, there remain challenges including the requirement for paired exocentric and egocentric image dataset, and the complexity in grounding diverse affordances for a single object. To address them, we propose INTeraction Relationship-aware weakly supervised Affordance grounding (INTRA). Unlike prior arts, INTRA recasts this problem as representation learning to identify unique features of interactions through contrastive learning with exocentric images only, eliminating the need for paired datasets. Moreover, we leverage vision-language model embeddings for performing affordance grounding flexibly with any text, designing text-conditioned affordance map generation to reflect interaction relationship for contrastive learning and enhancing robustness with our text synonym augmentation. Our method outperformed prior arts on diverse datasets such as AGD20K, IIT-AFF, CAD and UMD. Additionally, experimental results demonstrate that our method has remarkable domain scalability for synthesized images / illustrations and is capable of performing affordance grounding for novel interactions and objects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-11", "arxiv_id": "2409.06666", "title": "LLaMA-Omni: Seamless Speech Interaction with Large Language Models", "authors": [ "Qingkai Fang", "Shoutao Guo", "Yan Zhou", "Zhengrui Ma", "Shaolei Zhang", "Yang Feng" ], "github": "https://github.com/ictnlp/llama-omni", "project_page": "", "abstract": "Models like GPT-4o enable real-time interaction with large language models (LLMs) through speech, significantly enhancing user experience compared to traditional text-based interaction. However, there is still a lack of exploration on how to build speech interaction models based on open-source LLMs. To address this, we propose LLaMA-Omni, a novel model architecture designed for low-latency and high-quality speech interaction with LLMs. LLaMA-Omni integrates a pretrained speech encoder, a speech adaptor, an LLM, and a streaming speech decoder. It eliminates the need for speech transcription, and can simultaneously generate text and speech responses directly from speech instructions with extremely low latency. We build our model based on the latest Llama-3.1-8B-Instruct model. To align the model with speech interaction scenarios, we construct a dataset named InstructS2S-200K, which includes 200K speech instructions and corresponding speech responses. Experimental results show that compared to previous speech-language models, LLaMA-Omni provides better responses in both content and style, with a response latency as low as 226ms. Additionally, training LLaMA-Omni takes less than 3 days on just 4 GPUs, paving the way for the efficient development of speech-language models in the future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ictnlp/llama-omni", "github_api_added_by": "auto" }, { "date": "2024-09-11", "arxiv_id": "2409.06703", "title": "LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation", "authors": [ "Archana Swaminathan", "Anubhav Gupta", "Kamal Gupta", "Shishira R. Maiya", "Vatsal Agarwal", "Abhinav Shrivastava" ], "github": "", "project_page": "", "abstract": "Neural Radiance Fields (NeRFs) have revolutionized the reconstruction of static scenes and objects in 3D, offering unprecedented quality. However, extending NeRFs to model dynamic objects or object articulations remains a challenging problem. Previous works have tackled this issue by focusing on part-level reconstruction and motion estimation for objects, but they often rely on heuristics regarding the number of moving parts or object categories, which can limit their practical use. In this work, we introduce LEIA, a novel approach for representing dynamic 3D objects. Our method involves observing the object at distinct time steps or \"states\" and conditioning a hypernetwork on the current state, using this to parameterize our NeRF. This approach allows us to learn a view-invariant latent representation for each state. We further demonstrate that by interpolating between these states, we can generate novel articulation configurations in 3D space that were previously unseen. Our experimental results highlight the effectiveness of our method in articulating objects in a manner that is independent of the viewing angle and joint configuration. Notably, our approach outperforms previous methods that rely on motion information for articulation registration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-11", "arxiv_id": "2409.06595", "title": "GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering", "authors": [ "Sacha Muller", "Ant\u00f3nio Loison", "Bilel Omrani", "Gautier Viaud" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) has emerged as a common paradigm to use Large Language Models (LLMs) alongside private and up-to-date knowledge bases. In this work, we address the challenges of using LLM-as-a-Judge when evaluating grounded answers generated by RAG systems. To assess the calibration and discrimination capabilities of judge models, we identify 7 generator failure modes and introduce GroUSE (Grounded QA Unitary Scoring of Evaluators), a meta-evaluation benchmark of 144 unit tests. This benchmark reveals that existing automated RAG evaluation frameworks often overlook important failure modes, even when using GPT-4 as a judge. To improve on the current design of automated RAG evaluation frameworks, we propose a novel pipeline and find that while closed models perform well on GroUSE, state-of-the-art open-source judges do not generalize to our proposed criteria, despite strong correlation with GPT-4's judgement. Our findings suggest that correlation with GPT-4 is an incomplete proxy for the practical performance of judge models and should be supplemented with evaluations on unit tests for precise failure mode detection. We further show that finetuning Llama-3 on GPT-4's reasoning traces significantly boosts its evaluation capabilities, improving upon both correlation with GPT-4's evaluations and calibration on reference situations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-12", "arxiv_id": "2409.07429", "title": "Agent Workflow Memory", "authors": [ "Zora Zhiruo Wang", "Jiayuan Mao", "Daniel Fried", "Graham Neubig" ], "github": "https://github.com/zorazrw/agent-workflow-memory", "project_page": "", "abstract": "Despite the potential of language model-based agents to solve real-world tasks such as web navigation, current methods still struggle with long-horizon tasks with complex action trajectories. In contrast, humans can flexibly solve complex tasks by learning reusable task workflows from past experiences and using them to guide future actions. To build agents that can similarly benefit from this process, we introduce Agent Workflow Memory (AWM), a method for inducing commonly reused routines, i.e., workflows, and selectively providing workflows to the agent to guide subsequent generations. AWM flexibly applies to both offline and online scenarios, where agents induce workflows from training examples beforehand or from test queries on the fly. We experiment on two major web navigation benchmarks -- Mind2Web and WebArena -- that collectively cover 1000+ tasks from 200+ domains across travel, shopping, and social media, among others. AWM substantially improves the baseline results by 24.6% and 51.1% relative success rate on Mind2Web and WebArena while reducing the number of steps taken to solve WebArena tasks successfully. Furthermore, online AWM robustly generalizes in cross-task, website, and domain evaluations, surpassing baselines from 8.9 to 14.0 absolute points as train-test task distribution gaps widen.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zorazrw/agent-workflow-memory", "github_api_added_by": "auto" }, { "date": "2024-09-12", "arxiv_id": "2409.04057", "title": "Self-Harmonized Chain of Thought", "authors": [ "Ziqi Jin", "Wei Lu" ], "github": "https://github.com/Xalp/ECHO", "project_page": "", "abstract": "Chain-of-Thought (CoT) prompting reveals that large language models are capable of performing complex reasoning via intermediate steps. CoT prompting is primarily categorized into three approaches. The first approach utilizes straightforward prompts like ``Let's think step by step'' to generate a sequential thought process before yielding an answer. The second approach makes use of human-crafted, step-by-step demonstrations to guide the model's reasoning process. The third automates the generation of reasoned demonstrations with the 'Let's think step by step'.This approach sometimes leads to reasoning errors, highlighting the need to diversify demonstrations to mitigate its misleading effects. However, diverse demonstrations pose challenges for effective representations. In this work, we propose ECHO, a self-harmonized chain-of-thought prompting method. It consolidates diverse solution paths into a uniform and effective solution pattern.ECHO demonstrates the best overall performance across three reasoning domains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Xalp/ECHO", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-12", "arxiv_id": "2409.06765", "title": "gsplat: An Open-Source Library for Gaussian Splatting", "authors": [ "Vickie Ye", "Ruilong Li", "Justin Kerr", "Matias Turkulainen", "Brent Yi", "Zhuoyang Pan", "Otto Seiskari", "Jianbo Ye", "Jeffrey Hu", "Matthew Tancik", "Angjoo Kanazawa" ], "github": "https://github.com/nerfstudio-project/gsplat", "project_page": "", "abstract": "gsplat is an open-source library designed for training and developing Gaussian Splatting methods. It features a front-end with Python bindings compatible with the PyTorch library and a back-end with highly optimized CUDA kernels. gsplat offers numerous features that enhance the optimization of Gaussian Splatting models, which include optimization improvements for speed, memory, and convergence times. Experimental results demonstrate that gsplat achieves up to 10% less training time and 4x less memory than the original implementation. Utilized in several research projects, gsplat is actively maintained on GitHub. Source code is available at https://github.com/nerfstudio-project/gsplat under Apache License 2.0. We welcome contributions from the open-source community.", "github_manual": "", "github_regex": "https://github.com/nerfstudio-project/gsplat", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-12", "arxiv_id": "2409.07146", "title": "Gated Slot Attention for Efficient Linear-Time Sequence Modeling", "authors": [ "Yu Zhang", "Songlin Yang", "Ruijie Zhu", "Yue Zhang", "Leyang Cui", "Yiqiao Wang", "Bolun Wang", "Freda Shi", "Bailin Wang", "Wei Bi", "Peng Zhou", "Guohong Fu" ], "github": "", "project_page": "", "abstract": "Linear attention Transformers and their gated variants, celebrated for enabling parallel training and efficient recurrent inference, still fall short in recall-intensive tasks compared to traditional Transformers and demand significant resources for training from scratch. This paper introduces Gated Slot Attention (GSA), which enhances Attention with Bounded-memory-Control (ABC) by incorporating a gating mechanism inspired by Gated Linear Attention (GLA). Essentially, GSA comprises a two-layer GLA linked via softmax, utilizing context-aware memory reading and adaptive forgetting to improve memory capacity while maintaining compact recurrent state size. This design greatly enhances both training and inference efficiency through GLA's hardware-efficient training algorithm and reduced state size. Additionally, retaining the softmax operation is particularly beneficial in \"finetuning pretrained Transformers to RNNs\" (T2R) settings, reducing the need for extensive training from scratch. Extensive experiments confirm GSA's superior performance in scenarios requiring in-context recall and in T2R settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-12", "arxiv_id": "2409.07452", "title": "Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models", "authors": [ "Haibo Yang", "Yang Chen", "Yingwei Pan", "Ting Yao", "Zhineng Chen", "Chong-Wah Ngo", "Tao Mei" ], "github": "https://github.com/yanghb22-fdu/Hi3D-Official", "project_page": "", "abstract": "Despite having tremendous progress in image-to-3D generation, existing methods still struggle to produce multi-view consistent images with high-resolution textures in detail, especially in the paradigm of 2D diffusion that lacks 3D awareness. In this work, we present High-resolution Image-to-3D model (Hi3D), a new video diffusion based paradigm that redefines a single image to multi-view images as 3D-aware sequential image generation (i.e., orbital video generation). This methodology delves into the underlying temporal consistency knowledge in video diffusion model that generalizes well to geometry consistency across multiple views in 3D generation. Technically, Hi3D first empowers the pre-trained video diffusion model with 3D-aware prior (camera pose condition), yielding multi-view images with low-resolution texture details. A 3D-aware video-to-video refiner is learnt to further scale up the multi-view images with high-resolution texture details. Such high-resolution multi-view images are further augmented with novel views through 3D Gaussian Splatting, which are finally leveraged to obtain high-fidelity meshes via 3D reconstruction. Extensive experiments on both novel view synthesis and single view reconstruction demonstrate that our Hi3D manages to produce superior multi-view consistency images with highly-detailed textures. Source code and data are available at https://github.com/yanghb22-fdu/Hi3D-Official.", "github_manual": "", "github_regex": "https://github.com/yanghb22-fdu/Hi3D-Official", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-12", "arxiv_id": "2409.07450", "title": "VMAS: Video-to-Music Generation via Semantic Alignment in Web Music Videos", "authors": [ "Yan-Bo Lin", "Yu Tian", "Linjie Yang", "Gedas Bertasius", "Heng Wang" ], "github": "", "project_page": "", "abstract": "We present a framework for learning to generate background music from video inputs. Unlike existing works that rely on symbolic musical annotations, which are limited in quantity and diversity, our method leverages large-scale web videos accompanied by background music. This enables our model to learn to generate realistic and diverse music. To accomplish this goal, we develop a generative video-music Transformer with a novel semantic video-music alignment scheme. Our model uses a joint autoregressive and contrastive learning objective, which encourages the generation of music aligned with high-level video content. We also introduce a novel video-beat alignment scheme to match the generated music beats with the low-level motions in the video. Lastly, to capture fine-grained visual cues in a video needed for realistic background music generation, we introduce a new temporal video encoder architecture, allowing us to efficiently process videos consisting of many densely sampled frames. We train our framework on our newly curated DISCO-MV dataset, consisting of 2.2M video-music samples, which is orders of magnitude larger than any prior datasets used for video music generation. Our method outperforms existing approaches on the DISCO-MV and MusicCaps datasets according to various music generation evaluation metrics, including human evaluation. Results are available at https://genjib.github.io/project_page/VMAs/index.html", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-12", "arxiv_id": "2409.06762", "title": "Generative Hierarchical Materials Search", "authors": [ "Sherry Yang", "Simon Batzner", "Ruiqi Gao", "Muratahan Aykol", "Alexander L. Gaunt", "Brendan McMorrow", "Danilo J. Rezende", "Dale Schuurmans", "Igor Mordatch", "Ekin D. Cubuk" ], "github": "", "project_page": "", "abstract": "Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials science, and in particular to crystal structures, the guidance from the domain expert in the form of high-level instructions can be essential for an automated system to output candidate crystals that are viable for downstream research. In this work, we formulate end-to-end language-to-structure generation as a multi-objective optimization problem, and propose Generative Hierarchical Materials Search (GenMS) for controllable generation of crystal structures. GenMS consists of (1) a language model that takes high-level natural language as input and generates intermediate textual information about a crystal (e.g., chemical formulae), and (2) a diffusion model that takes intermediate information as input and generates low-level continuous value crystal structures. GenMS additionally uses a graph neural network to predict properties (e.g., formation energy) from the generated crystal structures. During inference, GenMS leverages all three components to conduct a forward tree search over the space of possible structures. Experiments show that GenMS outperforms other alternatives of directly using language models to generate structures both in satisfying user request and in generating low-energy structures. We confirm that GenMS is able to generate common crystal structures such as double perovskites, or spinels, solely from natural language input, and hence can form the foundation for more complex structure generation in near future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-12", "arxiv_id": "2409.07441", "title": "Instant Facial Gaussians Translator for Relightable and Interactable Facial Rendering", "authors": [ "Dafei Qin", "Hongyang Lin", "Qixuan Zhang", "Kaichun Qiao", "Longwen Zhang", "Zijun Zhao", "Jun Saito", "Jingyi Yu", "Lan Xu", "Taku Komura" ], "github": "", "project_page": "", "abstract": "We propose GauFace, a novel Gaussian Splatting representation, tailored for efficient animation and rendering of physically-based facial assets. Leveraging strong geometric priors and constrained optimization, GauFace ensures a neat and structured Gaussian representation, delivering high fidelity and real-time facial interaction of 30fps@1440p on a Snapdragon 8 Gen 2 mobile platform. Then, we introduce TransGS, a diffusion transformer that instantly translates physically-based facial assets into the corresponding GauFace representations. Specifically, we adopt a patch-based pipeline to handle the vast number of Gaussians effectively. We also introduce a novel pixel-aligned sampling scheme with UV positional encoding to ensure the throughput and rendering quality of GauFace assets generated by our TransGS. Once trained, TransGS can instantly translate facial assets with lighting conditions to GauFace representation, With the rich conditioning modalities, it also enables editing and animation capabilities reminiscent of traditional CG pipelines. We conduct extensive evaluations and user studies, compared to traditional offline and online renderers, as well as recent neural rendering methods, which demonstrate the superior performance of our approach for facial asset rendering. We also showcase diverse immersive applications of facial assets using our TransGS approach and GauFace representation, across various platforms like PCs, phones and even VR headsets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-12", "arxiv_id": "2409.07129", "title": "MVLLaVA: An Intelligent Agent for Unified and Flexible Novel View Synthesis", "authors": [ "Hanyu Jiang", "Jian Xue", "Xing Lan", "Guohong Hu", "Ke Lu" ], "github": "", "project_page": "", "abstract": "This paper introduces MVLLaVA, an intelligent agent designed for novel view synthesis tasks. MVLLaVA integrates multiple multi-view diffusion models with a large multimodal model, LLaVA, enabling it to handle a wide range of tasks efficiently. MVLLaVA represents a versatile and unified platform that adapts to diverse input types, including a single image, a descriptive caption, or a specific change in viewing azimuth, guided by language instructions for viewpoint generation. We carefully craft task-specific instruction templates, which are subsequently used to fine-tune LLaVA. As a result, MVLLaVA acquires the capability to generate novel view images based on user instructions, demonstrating its flexibility across diverse tasks. Experiments are conducted to validate the effectiveness of MVLLaVA, demonstrating its robust performance and versatility in tackling diverse novel view synthesis challenges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-12", "arxiv_id": "2409.07314", "title": "MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications", "authors": [ "Praveen K Kanithi", "Cl\u00e9ment Christophe", "Marco AF Pimentel", "Tathagata Raha", "Nada Saadi", "Hamza Javed", "Svetlana Maslenkova", "Nasir Hayat", "Ronnie Rajan", "Shadab Khan" ], "github": "https://github.com/m42-health/cross-examination-framework", "project_page": "", "abstract": "The rapid development of Large Language Models (LLMs) for healthcare applications has spurred calls for holistic evaluation beyond frequently-cited benchmarks like USMLE, to better reflect real-world performance. While real-world assessments are valuable indicators of utility, they often lag behind the pace of LLM evolution, likely rendering findings obsolete upon deployment. This temporal disconnect necessitates a comprehensive upfront evaluation that can guide model selection for specific clinical applications. We introduce MEDIC, a framework assessing LLMs across five critical dimensions of clinical competence: medical reasoning, ethics and bias, data and language understanding, in-context learning, and clinical safety. MEDIC features a novel cross-examination framework quantifying LLM performance across areas like coverage and hallucination detection, without requiring reference outputs. We apply MEDIC to evaluate LLMs on medical question-answering, safety, summarization, note generation, and other tasks. Our results show performance disparities across model sizes, baseline vs medically finetuned models, and have implications on model selection for applications requiring specific model strengths, such as low hallucination or lower cost of inference. MEDIC's multifaceted evaluation reveals these performance trade-offs, bridging the gap between theoretical capabilities and practical implementation in healthcare settings, ensuring that the most promising models are identified and adapted for diverse healthcare applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/m42-health/cross-examination-framework", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-12", "arxiv_id": "2409.06744", "title": "ProteinBench: A Holistic Evaluation of Protein Foundation Models", "authors": [ "Fei Ye", "Zaixiang Zheng", "Dongyu Xue", "Yuning Shen", "Lihao Wang", "Yiming Ma", "Yan Wang", "Xinyou Wang", "Xiangxin Zhou", "Quanquan Gu" ], "github": "", "project_page": "", "abstract": "Recent years have witnessed a surge in the development of protein foundation models, significantly improving performance in protein prediction and generative tasks ranging from 3D structure prediction and protein design to conformational dynamics. However, the capabilities and limitations associated with these models remain poorly understood due to the absence of a unified evaluation framework. To fill this gap, we introduce ProteinBench, a holistic evaluation framework designed to enhance the transparency of protein foundation models. Our approach consists of three key components: (i) A taxonomic classification of tasks that broadly encompass the main challenges in the protein domain, based on the relationships between different protein modalities; (ii) A multi-metric evaluation approach that assesses performance across four key dimensions: quality, novelty, diversity, and robustness; and (iii) In-depth analyses from various user objectives, providing a holistic view of model performance. Our comprehensive evaluation of protein foundation models reveals several key findings that shed light on their current capabilities and limitations. To promote transparency and facilitate further research, we release the evaluation dataset, code, and a public leaderboard publicly for further analysis and a general modular toolkit. We intend for ProteinBench to be a living benchmark for establishing a standardized, in-depth evaluation framework for protein foundation models, driving their development and application while fostering collaboration within the field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-12", "arxiv_id": "2409.06820", "title": "PingPong: A Benchmark for Role-Playing Language Models with User Emulation and Multi-Model Evaluation", "authors": [ "Ilya Gusev" ], "github": "", "project_page": "", "abstract": "We introduce a novel benchmark for evaluating the role-playing capabilities of language models. Our approach leverages language models themselves to emulate users in dynamic, multi-turn conversations and to assess the resulting dialogues. The framework consists of three main components: a player model assuming a specific character role, an interrogator model simulating user behavior, and a judge model evaluating conversation quality. We conducted experiments comparing automated evaluations with human annotations to validate our approach, demonstrating strong correlations across multiple criteria. This work provides a foundation for a robust and dynamic evaluation of model capabilities in interactive scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-12", "arxiv_id": "2409.06185", "title": "Can Large Language Models Unlock Novel Scientific Research Ideas?", "authors": [ "Sandeep Kumar", "Tirthankar Ghosal", "Vinayak Goyal", "Asif Ekbal" ], "github": "https://github.com/sandeep82945/future-idea-generation", "project_page": "", "abstract": "\"An idea is nothing more nor less than a new combination of old elements\" (Young, J.W.). The widespread adoption of Large Language Models (LLMs) and publicly available ChatGPT have marked a significant turning point in the integration of Artificial Intelligence (AI) into people's everyday lives. This study explores the capability of LLMs in generating novel research ideas based on information from research papers. We conduct a thorough examination of 4 LLMs in five domains (e.g., Chemistry, Computer, Economics, Medical, and Physics). We found that the future research ideas generated by Claude-2 and GPT-4 are more aligned with the author's perspective than GPT-3.5 and Gemini. We also found that Claude-2 generates more diverse future research ideas than GPT-4, GPT-3.5, and Gemini 1.0. We further performed a human evaluation of the novelty, relevancy, and feasibility of the generated future research ideas. This investigation offers insights into the evolving role of LLMs in idea generation, highlighting both its capability and limitations. Our work contributes to the ongoing efforts in evaluating and utilizing language models for generating future research ideas. We make our datasets and codes publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sandeep82945/future-idea-generation", "github_api_added_by": "auto" }, { "date": "2024-09-12", "arxiv_id": "2409.07440", "title": "SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories", "authors": [ "Ben Bogin", "Kejuan Yang", "Shashank Gupta", "Kyle Richardson", "Erin Bransom", "Peter Clark", "Ashish Sabharwal", "Tushar Khot" ], "github": "https://github.com/allenai/super-benchmark", "project_page": "", "abstract": "Given that Large Language Models (LLMs) have made significant progress in writing code, can they now be used to autonomously reproduce results from research repositories? Such a capability would be a boon to the research community, helping researchers validate, understand, and extend prior work. To advance towards this goal, we introduce SUPER, the first benchmark designed to evaluate the capability of LLMs in setting up and executing tasks from research repositories. SUPERaims to capture the realistic challenges faced by researchers working with Machine Learning (ML) and Natural Language Processing (NLP) research repositories. Our benchmark comprises three distinct problem sets: 45 end-to-end problems with annotated expert solutions, 152 sub problems derived from the expert set that focus on specific challenges (e.g., configuring a trainer), and 602 automatically generated problems for larger-scale development. We introduce various evaluation measures to assess both task success and progress, utilizing gold solutions when available or approximations otherwise. We show that state-of-the-art approaches struggle to solve these problems with the best model (GPT-4o) solving only 16.3% of the end-to-end set, and 46.1% of the scenarios. This illustrates the challenge of this task, and suggests that SUPER can serve as a valuable resource for the community to make and measure progress.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/super-benchmark", "github_api_added_by": "auto" }, { "date": "2024-09-13", "arxiv_id": "2409.08239", "title": "Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources", "authors": [ "Alisia Lupidi", "Carlos Gemmell", "Nicola Cancedda", "Jane Dwivedi-Yu", "Jason Weston", "Jakob Foerster", "Roberta Raileanu", "Maria Lomeli" ], "github": "", "project_page": "", "abstract": "Large Language Models still struggle in challenging scenarios that leverage structured data, complex reasoning, or tool usage. In this paper, we propose Source2Synth: a new method that can be used for teaching LLMs new skills without relying on costly human annotations. Source2Synth takes as input a custom data source and produces synthetic data points with intermediate reasoning steps grounded in real-world sources. Source2Synth improves the dataset quality by discarding low-quality generations based on their answerability. We demonstrate the generality of this approach by applying it to two challenging domains: we test reasoning abilities in multi-hop question answering (MHQA), and tool usage in tabular question answering (TQA). Our method improves performance by 25.51% for TQA on WikiSQL and 22.57% for MHQA on HotPotQA compared to the fine-tuned baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-13", "arxiv_id": "2409.08270", "title": "FlashSplat: 2D to 3D Gaussian Splatting Segmentation Solved Optimally", "authors": [ "Qiuhong Shen", "Xingyi Yang", "Xinchao Wang" ], "github": "https://github.com/florinshen/FlashSplat", "project_page": "", "abstract": "This study addresses the challenge of accurately segmenting 3D Gaussian Splatting from 2D masks. Conventional methods often rely on iterative gradient descent to assign each Gaussian a unique label, leading to lengthy optimization and sub-optimal solutions. Instead, we propose a straightforward yet globally optimal solver for 3D-GS segmentation. The core insight of our method is that, with a reconstructed 3D-GS scene, the rendering of the 2D masks is essentially a linear function with respect to the labels of each Gaussian. As such, the optimal label assignment can be solved via linear programming in closed form. This solution capitalizes on the alpha blending characteristic of the splatting process for single step optimization. By incorporating the background bias in our objective function, our method shows superior robustness in 3D segmentation against noises. Remarkably, our optimization completes within 30 seconds, about 50times faster than the best existing methods. Extensive experiments demonstrate the efficiency and robustness of our method in segmenting various scenes, and its superior performance in downstream tasks such as object removal and inpainting. Demos and code will be available at https://github.com/florinshen/FlashSplat.", "github_manual": "", "github_regex": "https://github.com/florinshen/FlashSplat", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-13", "arxiv_id": "2409.08248", "title": "TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text Encoder", "authors": [ "NaHyeon Park", "Kunhee Kim", "Hyunjung Shim" ], "github": "https://github.com/nahyeonkaty/textboost", "project_page": "", "abstract": "Recent breakthroughs in text-to-image models have opened up promising research avenues in personalized image generation, enabling users to create diverse images of a specific subject using natural language prompts. However, existing methods often suffer from performance degradation when given only a single reference image. They tend to overfit the input, producing highly similar outputs regardless of the text prompt. This paper addresses the challenge of one-shot personalization by mitigating overfitting, enabling the creation of controllable images through text prompts. Specifically, we propose a selective fine-tuning strategy that focuses on the text encoder. Furthermore, we introduce three key techniques to enhance personalization performance: (1) augmentation tokens to encourage feature disentanglement and alleviate overfitting, (2) a knowledge-preservation loss to reduce language drift and promote generalizability across diverse prompts, and (3) SNR-weighted sampling for efficient training. Extensive experiments demonstrate that our approach efficiently generates high-quality, diverse images using only a single reference image while significantly reducing memory and storage requirements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nahyeonkaty/textboost", "github_api_added_by": "auto" }, { "date": "2024-09-13", "arxiv_id": "2409.08278", "title": "DreamHOI: Subject-Driven Generation of 3D Human-Object Interactions with Diffusion Priors", "authors": [ "Thomas Hanwen Zhu", "Ruining Li", "Tomas Jakab" ], "github": "", "project_page": "", "abstract": "We present DreamHOI, a novel method for zero-shot synthesis of human-object interactions (HOIs), enabling a 3D human model to realistically interact with any given object based on a textual description. This task is complicated by the varying categories and geometries of real-world objects and the scarcity of datasets encompassing diverse HOIs. To circumvent the need for extensive data, we leverage text-to-image diffusion models trained on billions of image-caption pairs. We optimize the articulation of a skinned human mesh using Score Distillation Sampling (SDS) gradients obtained from these models, which predict image-space edits. However, directly backpropagating image-space gradients into complex articulation parameters is ineffective due to the local nature of such gradients. To overcome this, we introduce a dual implicit-explicit representation of a skinned mesh, combining (implicit) neural radiance fields (NeRFs) with (explicit) skeleton-driven mesh articulation. During optimization, we transition between implicit and explicit forms, grounding the NeRF generation while refining the mesh articulation. We validate our approach through extensive experiments, demonstrating its effectiveness in generating realistic HOIs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-13", "arxiv_id": "2409.08264", "title": "Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale", "authors": [ "Rogerio Bonatti", "Dan Zhao", "Francesco Bonacci", "Dillon Dupont", "Sara Abdali", "Yinheng Li", "Justin Wagle", "Kazuhito Koishida", "Arthur Bucker", "Lawrence Jang", "Zack Hui" ], "github": "https://github.com/microsoft/WindowsAgentArena", "project_page": "", "abstract": "Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena. Webpage: https://microsoft.github.io/WindowsAgentArena Code: https://github.com/microsoft/WindowsAgentArena", "github_manual": "", "github_regex": "https://github.com/microsoft/WindowsAgentArena", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-13", "arxiv_id": "2409.04109", "title": "Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers", "authors": [ "Chenglei Si", "Diyi Yang", "Tatsunori Hashimoto" ], "github": "", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research process. We address this by establishing an experimental design that evaluates research idea generation while controlling for confounders and performs the first head-to-head comparison between expert NLP researchers and an LLM ideation agent. By recruiting over 100 NLP researchers to write novel ideas and blind reviews of both LLM and human ideas, we obtain the first statistically significant conclusion on current LLM capabilities for research ideation: we find LLM-generated ideas are judged as more novel (p < 0.05) than human expert ideas while being judged slightly weaker on feasibility. Studying our agent baselines closely, we identify open problems in building and evaluating research agents, including failures of LLM self-evaluation and their lack of diversity in generation. Finally, we acknowledge that human judgements of novelty can be difficult, even by experts, and propose an end-to-end study design which recruits researchers to execute these ideas into full projects, enabling us to study whether these novelty and feasibility judgements result in meaningful differences in research outcome.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-13", "arxiv_id": "2409.08240", "title": "IFAdapter: Instance Feature Control for Grounded Text-to-Image Generation", "authors": [ "Yinwei Wu", "Xianpan Zhou", "Bing Ma", "Xuefeng Su", "Kai Ma", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "While Text-to-Image (T2I) diffusion models excel at generating visually appealing images of individual instances, they struggle to accurately position and control the features generation of multiple instances. The Layout-to-Image (L2I) task was introduced to address the positioning challenges by incorporating bounding boxes as spatial control signals, but it still falls short in generating precise instance features. In response, we propose the Instance Feature Generation (IFG) task, which aims to ensure both positional accuracy and feature fidelity in generated instances. To address the IFG task, we introduce the Instance Feature Adapter (IFAdapter). The IFAdapter enhances feature depiction by incorporating additional appearance tokens and utilizing an Instance Semantic Map to align instance-level features with spatial locations. The IFAdapter guides the diffusion process as a plug-and-play module, making it adaptable to various community models. For evaluation, we contribute an IFG benchmark and develop a verification pipeline to objectively compare models' abilities to generate instances with accurate positioning and features. Experimental results demonstrate that IFAdapter outperforms other models in both quantitative and qualitative evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-13", "arxiv_id": "2409.07239", "title": "PiTe: Pixel-Temporal Alignment for Large Video-Language Model", "authors": [ "Yang Liu", "Pengxiang Ding", "Siteng Huang", "Min Zhang", "Han Zhao", "Donglin Wang" ], "github": "https://github.com/yliu-cs/pite", "project_page": "", "abstract": "Fueled by the Large Language Models (LLMs) wave, Large Visual-Language Models (LVLMs) have emerged as a pivotal advancement, bridging the gap between image and text. However, video making it challenging for LVLMs to perform adequately due to the complexity of the relationship between language and spatial-temporal data structure. Recent Large Video-Language Models (LVidLMs) align feature of static visual data like image into latent space of language feature, by general multi-modal tasks to leverage abilities of LLMs sufficiently. In this paper, we explore fine-grained alignment approach via object trajectory for different modalities across both spatial and temporal dimensions simultaneously. Thus, we propose a novel LVidLM by trajectory-guided Pixel-Temporal Alignment, dubbed PiTe, that exhibits promising applicable model property. To achieve fine-grained video-language alignment, we curate a multi-modal pre-training dataset PiTe-143k, the dataset provision of moving trajectories in pixel level for all individual objects, that appear and mention in the video and caption both, by our automatic annotation pipeline. Meanwhile, PiTe demonstrates astounding capabilities on myriad video-related multi-modal tasks through beat the state-of-the-art methods by a large margin.", "github_manual": "https://github.com/yliu-cs/pite", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-13", "arxiv_id": "2409.05162", "title": "Can OOD Object Detectors Learn from Foundation Models?", "authors": [ "Jiahui Liu", "Xin Wen", "Shizhen Zhao", "Yingxian Chen", "Xiaojuan Qi" ], "github": "https://github.com/cvmi-lab/syncood", "project_page": "", "abstract": "Out-of-distribution (OOD) object detection is a challenging task due to the absence of open-set OOD data. Inspired by recent advancements in text-to-image generative models, such as Stable Diffusion, we study the potential of generative models trained on large-scale open-set data to synthesize OOD samples, thereby enhancing OOD object detection. We introduce SyncOOD, a simple data curation method that capitalizes on the capabilities of large foundation models to automatically extract meaningful OOD data from text-to-image generative models. This offers the model access to open-world knowledge encapsulated within off-the-shelf foundation models. The synthetic OOD samples are then employed to augment the training of a lightweight, plug-and-play OOD detector, thus effectively optimizing the in-distribution (ID)/OOD decision boundaries. Extensive experiments across multiple benchmarks demonstrate that SyncOOD significantly outperforms existing methods, establishing new state-of-the-art performance with minimal synthetic data usage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cvmi-lab/syncood", "github_api_added_by": "auto" }, { "date": "2024-09-13", "arxiv_id": "2409.07703", "title": "DSBench: How Far Are Data Science Agents to Becoming Data Science Experts?", "authors": [ "Liqiang Jing", "Zhehui Huang", "Xiaoyang Wang", "Wenlin Yao", "Wenhao Yu", "Kaixin Ma", "Hongming Zhang", "Xinya Du", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) have demonstrated impressive language/vision reasoning abilities, igniting the recent trend of building agents for targeted applications such as shopping assistants or AI software engineers. Recently, many data science benchmarks have been proposed to investigate their performance in the data science domain. However, existing data science benchmarks still fall short when compared to real-world data science applications due to their simplified settings. To bridge this gap, we introduce DSBench, a comprehensive benchmark designed to evaluate data science agents with realistic tasks. This benchmark includes 466 data analysis tasks and 74 data modeling tasks, sourced from Eloquence and Kaggle competitions. DSBench offers a realistic setting by encompassing long contexts, multimodal task backgrounds, reasoning with large data files and multi-table structures, and performing end-to-end data modeling tasks. Our evaluation of state-of-the-art LLMs, LVLMs, and agents shows that they struggle with most tasks, with the best agent solving only 34.12% of data analysis tasks and achieving a 34.74% Relative Performance Gap (RPG). These findings underscore the need for further advancements in developing more practical, intelligent, and autonomous data science agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-16", "arxiv_id": "2409.08353", "title": "Robust Dual Gaussian Splatting for Immersive Human-centric Volumetric Videos", "authors": [ "Yuheng Jiang", "Zhehao Shen", "Yu Hong", "Chengcheng Guo", "Yize Wu", "Yingliang Zhang", "Jingyi Yu", "Lan Xu" ], "github": "", "project_page": "", "abstract": "Volumetric video represents a transformative advancement in visual media, enabling users to freely navigate immersive virtual experiences and narrowing the gap between digital and real worlds. However, the need for extensive manual intervention to stabilize mesh sequences and the generation of excessively large assets in existing workflows impedes broader adoption. In this paper, we present a novel Gaussian-based approach, dubbed DualGS, for real-time and high-fidelity playback of complex human performance with excellent compression ratios. Our key idea in DualGS is to separately represent motion and appearance using the corresponding skin and joint Gaussians. Such an explicit disentanglement can significantly reduce motion redundancy and enhance temporal coherence. We begin by initializing the DualGS and anchoring skin Gaussians to joint Gaussians at the first frame. Subsequently, we employ a coarse-to-fine training strategy for frame-by-frame human performance modeling. It includes a coarse alignment phase for overall motion prediction as well as a fine-grained optimization for robust tracking and high-fidelity rendering. To integrate volumetric video seamlessly into VR environments, we efficiently compress motion using entropy encoding and appearance using codec compression coupled with a persistent codebook. Our approach achieves a compression ratio of up to 120 times, only requiring approximately 350KB of storage per frame. We demonstrate the efficacy of our representation through photo-realistic, free-view experiences on VR headsets, enabling users to immersively watch musicians in performance and feel the rhythm of the notes at the performers' fingertips.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-16", "arxiv_id": "2409.08514", "title": "Apollo: Band-sequence Modeling for High-Quality Audio Restoration", "authors": [ "Kai Li", "Yi Luo" ], "github": "https://github.com/JusperLee/Apollo", "project_page": "", "abstract": "Audio restoration has become increasingly significant in modern society, not only due to the demand for high-quality auditory experiences enabled by advanced playback devices, but also because the growing capabilities of generative audio models necessitate high-fidelity audio. Typically, audio restoration is defined as a task of predicting undistorted audio from damaged input, often trained using a GAN framework to balance perception and distortion. Since audio degradation is primarily concentrated in mid- and high-frequency ranges, especially due to codecs, a key challenge lies in designing a generator capable of preserving low-frequency information while accurately reconstructing high-quality mid- and high-frequency content. Inspired by recent advancements in high-sample-rate music separation, speech enhancement, and audio codec models, we propose Apollo, a generative model designed for high-sample-rate audio restoration. Apollo employs an explicit frequency band split module to model the relationships between different frequency bands, allowing for more coherent and higher-quality restored audio. Evaluated on the MUSDB18-HQ and MoisesDB datasets, Apollo consistently outperforms existing SR-GAN models across various bit rates and music genres, particularly excelling in complex scenarios involving mixtures of multiple instruments and vocals. Apollo significantly improves music restoration quality while maintaining computational efficiency. The source code for Apollo is publicly available at https://github.com/JusperLee/Apollo.", "github_manual": "", "github_regex": "https://github.com/JusperLee/Apollo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-16", "arxiv_id": "2409.08857", "title": "InstantDrag: Improving Interactivity in Drag-based Image Editing", "authors": [ "Joonghyuk Shin", "Daehyeon Choi", "Jaesik Park" ], "github": "", "project_page": "", "abstract": "Drag-based image editing has recently gained popularity for its interactivity and precision. However, despite the ability of text-to-image models to generate samples within a second, drag editing still lags behind due to the challenge of accurately reflecting user interaction while maintaining image content. Some existing approaches rely on computationally intensive per-image optimization or intricate guidance-based methods, requiring additional inputs such as masks for movable regions and text prompts, thereby compromising the interactivity of the editing process. We introduce InstantDrag, an optimization-free pipeline that enhances interactivity and speed, requiring only an image and a drag instruction as input. InstantDrag consists of two carefully designed networks: a drag-conditioned optical flow generator (FlowGen) and an optical flow-conditioned diffusion model (FlowDiffusion). InstantDrag learns motion dynamics for drag-based image editing in real-world video datasets by decomposing the task into motion generation and motion-conditioned image generation. We demonstrate InstantDrag's capability to perform fast, photo-realistic edits without masks or text prompts through experiments on facial video datasets and general scenes. These results highlight the efficiency of our approach in handling drag-based image editing, making it a promising solution for interactive, real-time applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-16", "arxiv_id": "2409.08272", "title": "Click2Mask: Local Editing with Dynamic Mask Generation", "authors": [ "Omer Regev", "Omri Avrahami", "Dani Lischinski" ], "github": "", "project_page": "", "abstract": "Recent advancements in generative models have revolutionized image generation and editing, making these tasks accessible to non-experts. This paper focuses on local image editing, particularly the task of adding new content to a loosely specified area. Existing methods often require a precise mask or a detailed description of the location, which can be cumbersome and prone to errors. We propose Click2Mask, a novel approach that simplifies the local editing process by requiring only a single point of reference (in addition to the content description). A mask is dynamically grown around this point during a Blended Latent Diffusion (BLD) process, guided by a masked CLIP-based semantic loss. Click2Mask surpasses the limitations of segmentation-based and fine-tuning dependent methods, offering a more user-friendly and contextually accurate solution. Our experiments demonstrate that Click2Mask not only minimizes user effort but also delivers competitive or superior local image manipulation results compared to SoTA methods, according to both human judgement and automatic metrics. Key contributions include the simplification of user input, the ability to freely add objects unconstrained by existing segments, and the integration potential of our dynamic mask approach within other editing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-16", "arxiv_id": "2409.08615", "title": "DrawingSpinUp: 3D Animation from Single Character Drawings", "authors": [ "Jie Zhou", "Chufeng Xiao", "Miu-Ling Lam", "Hongbo Fu" ], "github": "", "project_page": "", "abstract": "Animating various character drawings is an engaging visual content creation task. Given a single character drawing, existing animation methods are limited to flat 2D motions and thus lack 3D effects. An alternative solution is to reconstruct a 3D model from a character drawing as a proxy and then retarget 3D motion data onto it. However, the existing image-to-3D methods could not work well for amateur character drawings in terms of appearance and geometry. We observe the contour lines, commonly existing in character drawings, would introduce significant ambiguity in texture synthesis due to their view-dependence. Additionally, thin regions represented by single-line contours are difficult to reconstruct (e.g., slim limbs of a stick figure) due to their delicate structures. To address these issues, we propose a novel system, DrawingSpinUp, to produce plausible 3D animations and breathe life into character drawings, allowing them to freely spin up, leap, and even perform a hip-hop dance. For appearance improvement, we adopt a removal-then-restoration strategy to first remove the view-dependent contour lines and then render them back after retargeting the reconstructed character. For geometry refinement, we develop a skeleton-based thinning deformation algorithm to refine the slim structures represented by the single-line contours. The experimental evaluations and a perceptual user study show that our proposed method outperforms the existing 2D and 3D animation methods and generates high-quality 3D animations from a single character drawing. Please refer to our project page (https://lordliang.github.io/DrawingSpinUp) for the code and generated animations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-16", "arxiv_id": "2409.08513", "title": "Mamba-YOLO-World: Marrying YOLO-World with Mamba for Open-Vocabulary Detection", "authors": [ "Haoxuan Wang", "Qingdong He", "Jinlong Peng", "Hao Yang", "Mingmin Chi", "Yabiao Wang" ], "github": "https://github.com/Xuan-World/Mamba-YOLO-World", "project_page": "", "abstract": "Open-vocabulary detection (OVD) aims to detect objects beyond a predefined set of categories. As a pioneering model incorporating the YOLO series into OVD, YOLO-World is well-suited for scenarios prioritizing speed and efficiency.However, its performance is hindered by its neck feature fusion mechanism, which causes the quadratic complexity and the limited guided receptive fields.To address these limitations, we present Mamba-YOLO-World, a novel YOLO-based OVD model employing the proposed MambaFusion Path Aggregation Network (MambaFusion-PAN) as its neck architecture. Specifically, we introduce an innovative State Space Model-based feature fusion mechanism consisting of a Parallel-Guided Selective Scan algorithm and a Serial-Guided Selective Scan algorithm with linear complexity and globally guided receptive fields. It leverages multi-modal input sequences and mamba hidden states to guide the selective scanning process.Experiments demonstrate that our model outperforms the original YOLO-World on the COCO and LVIS benchmarks in both zero-shot and fine-tuning settings while maintaining comparable parameters and FLOPs. Additionally, it surpasses existing state-of-the-art OVD methods with fewer parameters and FLOPs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Xuan-World/Mamba-YOLO-World", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-16", "arxiv_id": "2409.08947", "title": "A Diffusion Approach to Radiance Field Relighting using Multi-Illumination Synthesis", "authors": [ "Yohan Poirier-Ginter", "Alban Gauthier", "Julien Phillip", "Jean-Francois Lalonde", "George Drettakis" ], "github": "", "project_page": "", "abstract": "Relighting radiance fields is severely underconstrained for multi-view data, which is most often captured under a single illumination condition; It is especially hard for full scenes containing multiple objects. We introduce a method to create relightable radiance fields using such single-illumination data by exploiting priors extracted from 2D image diffusion models. We first fine-tune a 2D diffusion model on a multi-illumination dataset conditioned by light direction, allowing us to augment a single-illumination capture into a realistic -- but possibly inconsistent -- multi-illumination dataset from directly defined light directions. We use this augmented data to create a relightable radiance field represented by 3D Gaussian splats. To allow direct control of light direction for low-frequency lighting, we represent appearance with a multi-layer perceptron parameterized on light direction. To enforce multi-view consistency and overcome inaccuracies we optimize a per-image auxiliary feature vector. We show results on synthetic and real multi-view data under single illumination, demonstrating that our method successfully exploits 2D diffusion model priors to allow realistic 3D relighting for complete scenes. Project site https://repo-sam.inria.fr/fungraph/generative-radiance-field-relighting/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-17", "arxiv_id": "2409.10516", "title": "RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval", "authors": [ "Di Liu", "Meng Chen", "Baotong Lu", "Huiqiang Jiang", "Zhenhua Han", "Qianxi Zhang", "Qi Chen", "Chengruidong Zhang", "Bailu Ding", "Kai Zhang", "Chen Chen", "Fan Yang", "Yuqing Yang", "Lili Qiu" ], "github": "", "project_page": "", "abstract": "Transformer-based large Language Models (LLMs) become increasingly important in various domains. However, the quadratic time complexity of attention operation poses a significant challenge for scaling to longer contexts due to the extremely high inference latency and GPU memory consumption for caching key-value (KV) vectors. This paper proposes RetrievalAttention, a training-free approach to accelerate attention computation. To leverage the dynamic sparse property of attention, RetrievalAttention builds approximate nearest neighbor search (ANNS) indexes upon KV vectors in CPU memory and retrieves the most relevant ones via vector search during generation. Due to the out-of-distribution (OOD) between query vectors and key vectors, off-the-shelf ANNS indexes still need to scan O(N) (usually 30% of all keys) data for accurate retrieval, which fails to exploit the high sparsity. RetrievalAttention first identifies the OOD challenge of ANNS-based attention, and addresses it via an attention-aware vector search algorithm that can adapt to queries and only access 1--3% of data, thus achieving a sub-linear time complexity. RetrievalAttention greatly reduces the inference cost of long-context LLM with much lower GPU memory requirements while maintaining the model accuracy. Especially, RetrievalAttention only needs 16GB GPU memory for serving 128K tokens in LLMs with 8B parameters, which is capable of generating one token in 0.188 seconds on a single NVIDIA RTX4090 (24GB).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-17", "arxiv_id": "2409.09214", "title": "Seed-Music: A Unified Framework for High Quality and Controlled Music Generation", "authors": [ "Ye Bai", "Haonan Chen", "Jitong Chen", "Zhuo Chen", "Yi Deng", "Xiaohong Dong", "Lamtharn Hantrakul", "Weituo Hao", "Qingqing Huang", "Zhongyi Huang", "Dongya Jia", "Feihu La", "Duc Le", "Bochen Li", "Chumin Li", "Hui Li", "Xingxing Li", "Shouda Liu", "Wei-Tsung Lu", "Yiqing Lu", "Andrew Shaw", "Janne Spijkervet", "Yakun Sun", "Bo Wang", "Ju-Chiang Wang", "Yuping Wang", "Yuxuan Wang", "Ling Xu", "Yifeng Yang", "Chao Yao", "Shuo Zhang", "Yang Zhang", "Yilin Zhang", "Hang Zhao", "Ziyi Zhao", "Dejian Zhong", "Shicen Zhou", "Pei Zou" ], "github": "", "project_page": "", "abstract": "We introduce Seed-Music, a suite of music generation systems capable of producing high-quality music with fine-grained style control. Our unified framework leverages both auto-regressive language modeling and diffusion approaches to support two key music creation workflows: controlled music generation and post-production editing. For controlled music generation, our system enables vocal music generation with performance controls from multi-modal inputs, including style descriptions, audio references, musical scores, and voice prompts. For post-production editing, it offers interactive tools for editing lyrics and vocal melodies directly in the generated audio. We encourage readers to listen to demo audio examples at https://team.doubao.com/seed-music .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-17", "arxiv_id": "2409.09213", "title": "ReCLAP: Improving Zero Shot Audio Classification by Describing Sounds", "authors": [ "Sreyan Ghosh", "Sonal Kumar", "Chandra Kiran Reddy Evuru", "Oriol Nieto", "Ramani Duraiswami", "Dinesh Manocha" ], "github": "https://github.com/sreyan88/reclap", "project_page": "", "abstract": "Open-vocabulary audio-language models, like CLAP, offer a promising approach for zero-shot audio classification (ZSAC) by enabling classification with any arbitrary set of categories specified with natural language prompts. In this paper, we propose a simple but effective method to improve ZSAC with CLAP. Specifically, we shift from the conventional method of using prompts with abstract category labels (e.g., Sound of an organ) to prompts that describe sounds using their inherent descriptive features in a diverse context (e.g.,The organ's deep and resonant tones filled the cathedral.). To achieve this, we first propose ReCLAP, a CLAP model trained with rewritten audio captions for improved understanding of sounds in the wild. These rewritten captions describe each sound event in the original caption using their unique discriminative characteristics. ReCLAP outperforms all baselines on both multi-modal audio-text retrieval and ZSAC. Next, to improve zero-shot audio classification with ReCLAP, we propose prompt augmentation. In contrast to the traditional method of employing hand-written template prompts, we generate custom prompts for each unique label in the dataset. These custom prompts first describe the sound event in the label and then employ them in diverse scenes. Our proposed method improves ReCLAP's performance on ZSAC by 1%-18% and outperforms all baselines by 1% - 55%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sreyan88/reclap", "github_api_added_by": "auto" }, { "date": "2024-09-17", "arxiv_id": "2409.09269", "title": "Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge Types", "authors": [ "Neelabh Sinha", "Vinija Jain", "Aman Chadha" ], "github": "https://github.com/neelabhsinha/vlm-selection-tasks-domains-knowledge-type", "project_page": "", "abstract": "Visual Question-Answering (VQA) has become a key use-case in several applications to aid user experience, particularly after Vision-Language Models (VLMs) achieving good results in zero-shot inference. But evaluating different VLMs for an application requirement using a standardized framework in practical settings is still challenging. This paper introduces a comprehensive framework for evaluating VLMs tailored to VQA tasks in practical settings. We present a novel dataset derived from established VQA benchmarks, annotated with task types, application domains, and knowledge types, three key practical aspects on which tasks can vary. We also introduce GoEval, a multimodal evaluation metric developed using GPT-4o, achieving a correlation factor of 56.71% with human judgments. Our experiments with ten state-of-the-art VLMs reveals that no single model excelling universally, making appropriate selection a key design decision. Proprietary models such as Gemini-1.5-Pro and GPT-4o-mini generally outperform others, though open-source models like InternVL-2-8B and CogVLM-2-Llama-3-19B demonstrate competitive strengths in specific contexts, while providing additional advantages. This study guides the selection of VLMs based on specific task requirements and resource constraints, and can also be extended to other vision-language tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/neelabhsinha/vlm-selection-tasks-domains-knowledge-type", "github_api_added_by": "auto" }, { "date": "2024-09-17", "arxiv_id": "2409.10038", "title": "On the Diagram of Thought", "authors": [ "Yifan Zhang", "Yang Yuan", "Andrew Chi-Chih Yao" ], "github": "https://github.com/diagram-of-thought/diagram-of-thought", "project_page": "", "abstract": "We introduce Diagram of Thought (DoT), a framework that models iterative reasoning in large language models (LLMs) as the construction of a directed acyclic graph (DAG) within a single model. Unlike traditional approaches that represent reasoning as linear chains or trees, DoT organizes propositions, critiques, refinements, and verifications into a cohesive DAG structure, allowing the model to explore complex reasoning pathways while maintaining logical consistency. Each node in the diagram corresponds to a proposition that has been proposed, critiqued, refined, or verified, enabling the LLM to iteratively improve its reasoning through natural language feedback. By leveraging auto-regressive next-token prediction with role-specific tokens, DoT facilitates seamless transitions between proposing ideas and critically evaluating them, providing richer feedback than binary signals. Furthermore, we formalize the DoT framework using Topos Theory, providing a mathematical foundation that ensures logical consistency and soundness in the reasoning process. This approach enhances both the training and inference processes within a single LLM, eliminating the need for multiple models or external control mechanisms. DoT offers a conceptual framework for designing next-generation reasoning-specialized models, emphasizing training efficiency, robust reasoning capabilities, and theoretical grounding. The code is available at https://github.com/diagram-of-thought/diagram-of-thought.", "github_manual": "", "github_regex": "https://github.com/diagram-of-thought/diagram-of-thought", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-17", "arxiv_id": "2409.08199", "title": "AudioBERT: Audio Knowledge Augmented Language Model", "authors": [ "Hyunjong Ok", "Suho Yoo", "Jaeho Lee" ], "github": "https://github.com/HJ-Ok/AudioBERT", "project_page": "", "abstract": "Recent studies have identified that language models, pretrained on text-only datasets, often lack elementary visual knowledge, e.g., colors of everyday objects. Motivated by this observation, we ask whether a similar shortcoming exists in terms of the auditory knowledge. To answer this question, we construct a new dataset called AuditoryBench, which consists of two novel tasks for evaluating auditory knowledge. Based on our analysis using the benchmark, we find that language models also suffer from a severe lack of auditory knowledge. To address this limitation, we propose AudioBERT, a novel method to augment the auditory knowledge of BERT through a retrieval-based approach. First, we detect auditory knowledge spans in prompts to query our retrieval model efficiently. Then, we inject audio knowledge into BERT and switch on low-rank adaptation for effective adaptation when audio knowledge is required. Our experiments demonstrate that AudioBERT is quite effective, achieving superior performance on the AuditoryBench. The dataset and code are available at https://github.com/HJ-Ok/AudioBERT.", "github_manual": "", "github_regex": "https://github.com/HJ-Ok/AudioBERT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-17", "arxiv_id": "2409.09502", "title": "One missing piece in Vision and Language: A Survey on Comics Understanding", "authors": [ "Emanuele Vivoli", "Andrey Barsky", "Mohamed Ali Souibgui", "Artemis LLabres", "Marco Bertini", "Dimosthenis Karatzas" ], "github": "https://github.com/emanuelevivoli/awesome-comics-understanding", "project_page": "", "abstract": "Vision-language models have recently evolved into versatile systems capable of high performance across a range of tasks, such as document understanding, visual question answering, and grounding, often in zero-shot settings. Comics Understanding, a complex and multifaceted field, stands to greatly benefit from these advances. Comics, as a medium, combine rich visual and textual narratives, challenging AI models with tasks that span image classification, object detection, instance segmentation, and deeper narrative comprehension through sequential panels. However, the unique structure of comics -- characterized by creative variations in style, reading order, and non-linear storytelling -- presents a set of challenges distinct from those in other visual-language domains. In this survey, we present a comprehensive review of Comics Understanding from both dataset and task perspectives. Our contributions are fivefold: (1) We analyze the structure of the comics medium, detailing its distinctive compositional elements; (2) We survey the widely used datasets and tasks in comics research, emphasizing their role in advancing the field; (3) We introduce the Layer of Comics Understanding (LoCU) framework, a novel taxonomy that redefines vision-language tasks within comics and lays the foundation for future work; (4) We provide a detailed review and categorization of existing methods following the LoCU framework; (5) Finally, we highlight current research challenges and propose directions for future exploration, particularly in the context of vision-language models applied to comics. This survey is the first to propose a task-oriented framework for comics intelligence and aims to guide future research by addressing critical gaps in data availability and task definition. A project associated with this survey is available at https://github.com/emanuelevivoli/awesome-comics-understanding.", "github_manual": "", "github_regex": "https://github.com/emanuelevivoli/awesome-comics-understanding", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-17", "arxiv_id": "2409.07012", "title": "Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Records", "authors": [ "Daeun Kyung", "Junu Kim", "Tackeun Kim", "Edward Choi" ], "github": "", "project_page": "", "abstract": "Chest X-ray imaging (CXR) is an important diagnostic tool used in hospitals to assess patient conditions and monitor changes over time. Generative models, specifically diffusion-based models, have shown promise in generating realistic synthetic X-rays. However, these models mainly focus on conditional generation using single-time-point data, i.e., typically CXRs taken at a specific time with their corresponding reports, limiting their clinical utility, particularly for capturing temporal changes. To address this limitation, we propose a novel framework, EHRXDiff, which predicts future CXR images by integrating previous CXRs with subsequent medical events, e.g., prescriptions, lab measures, etc. Our framework dynamically tracks and predicts disease progression based on a latent diffusion model, conditioned on the previous CXR image and a history of medical events. We comprehensively evaluate the performance of our framework across three key aspects, including clinical consistency, demographic consistency, and visual realism. We demonstrate that our framework generates high-quality, realistic future images that capture potential temporal changes, suggesting its potential for further development as a clinical simulation tool. This could offer valuable insights for patient monitoring and treatment planning in the medical field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-17", "arxiv_id": "2409.10309", "title": "beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems", "authors": [ "Vojt\u011bch Van\u010dura", "Pavel Kord\u00edk", "Milan Straka" ], "github": "https://github.com/recombee/beeformer", "project_page": "", "abstract": "Recommender systems often use text-side information to improve their predictions, especially in cold-start or zero-shot recommendation scenarios, where traditional collaborative filtering approaches cannot be used. Many approaches to text-mining side information for recommender systems have been proposed over recent years, with sentence Transformers being the most prominent one. However, these models are trained to predict semantic similarity without utilizing interaction data with hidden patterns specific to recommender systems. In this paper, we propose beeFormer, a framework for training sentence Transformer models with interaction data. We demonstrate that our models trained with beeFormer can transfer knowledge between datasets while outperforming not only semantic similarity sentence Transformers but also traditional collaborative filtering methods. We also show that training on multiple datasets from different domains accumulates knowledge in a single model, unlocking the possibility of training universal, domain-agnostic sentence Transformer models to mine text representations for recommender systems. We release the source code, trained models, and additional details allowing replication of our experiments at https://github.com/recombee/beeformer.", "github_manual": "", "github_regex": "https://github.com/recombee/beeformer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-17", "arxiv_id": "2409.06277", "title": "Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models", "authors": [ "Yao Shu", "Wenyang Hu", "See-Kiong Ng", "Bryan Kian Hsiang Low", "Fei Richard Yu" ], "github": "https://github.com/allen4747/Ferret", "project_page": "", "abstract": "Large Language Models (LLMs) have become indispensable in numerous real-world applications. Unfortunately, fine-tuning these models at scale, especially in federated settings where data privacy and communication efficiency are critical, presents significant challenges. Existing methods often resort to parameter-efficient fine-tuning (PEFT) to mitigate communication overhead, but this typically comes at the cost of model accuracy. To address these limitations, we propose federated full-parameter tuning at scale for LLMs (Ferret), the first first-order method with shared randomness to enable scalable full-parameter tuning of LLMs across decentralized data sources while maintaining competitive model accuracy. Ferret accomplishes this through three aspects: (1) it employs widely applied first-order methods for efficient local updates; (2) it projects these updates into a low-dimensional space to considerably reduce communication overhead; and (3) it reconstructs local updates from this low-dimensional space with shared randomness to facilitate effective full-parameter global aggregation, ensuring fast convergence and competitive final performance. Our rigorous theoretical analyses and insights along with extensive experiments, show that Ferret significantly enhances the scalability of existing federated full-parameter tuning approaches by achieving high computational efficiency, reduced communication overhead, and fast convergence, all while maintaining competitive model accuracy. Our implementation is available at https://github.com/allen4747/Ferret.", "github_manual": "", "github_regex": "https://github.com/allen4747/Ferret", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-17", "arxiv_id": "2409.06957", "title": "Policy Filtration in RLHF to Fine-Tune LLM for Code Generation", "authors": [ "Wei Shen", "Chuheng Zhang" ], "github": "https://github.com/swtheing/pf-ppo-rlhf", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) is one of the key techniques that helps large language models (LLMs) to follow instructions and provide helpful and harmless responses. While direct policy optimization methods exist, state-of-the-art LLMs adopt RL-based methods (usually PPO) in RLHF to train the policy to generate good responses guided by a reward model learned from preference data. The main challenge of these methods is the inaccuracy of the intermediate reward model, especially in code generation tasks that require long and complex reasoning to score a response. We find that the reliability of the reward model varies across responses assigned with different rewards. This motivates us to filter the samples whose rewards may be unreliable to improve signal-to-noise ratio during policy learning, resulting in Policy Filtration for Proximal Policy Optimization (PF-PPO). To choose a proper policy filtration strategy for a given reward model, the coefficient of determination (R^2) between rewards and actual scores on filtered samples serves as a good metrics and helps us find several promising strategies. We provide extensive experiments to validate the effectiveness of PF-PPO in code generation tasks, and find that some variants of PF-PPO are highly effective and achieve new state-of-the-art performance across 7-billion-parameter models on HumanEval, MBPP, and a new and more challenging LeetCode Contest benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/swtheing/pf-ppo-rlhf", "github_api_added_by": "auto" }, { "date": "2024-09-17", "arxiv_id": "2409.10173", "title": "jina-embeddings-v3: Multilingual Embeddings With Task LoRA", "authors": [ "Saba Sturua", "Isabelle Mohr", "Mohammad Kalim Akram", "Michael G\u00fcnther", "Bo Wang", "Markus Krimmel", "Feng Wang", "Georgios Mastrapas", "Andreas Koukounas", "Andreas Koukounas", "Nan Wang", "Han Xiao" ], "github": "", "project_page": "", "abstract": "We introduce jina-embeddings-v3, a novel text embedding model with 570 million parameters, achieves state-of-the-art performance on multilingual data and long-context retrieval tasks, supporting context lengths of up to 8192 tokens. The model includes a set of task-specific Low-Rank Adaptation (LoRA) adapters to generate high-quality embeddings for query-document retrieval, clustering, classification, and text matching. Additionally, Matryoshka Representation Learning is integrated into the training process, allowing flexible truncation of embedding dimensions without compromising performance. Evaluation on the MTEB benchmark shows that jina-embeddings-v3 outperforms the latest proprietary embeddings from OpenAI and Cohere on English tasks, while achieving superior performance compared to multilingual-e5-large-instruct across all multilingual tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-17", "arxiv_id": "2409.08831", "title": "Breaking reCAPTCHAv2", "authors": [ "Andreas Plesner", "Tobias Vontobel", "Roger Wattenhofer" ], "github": "https://github.com/aplesner/Breaking-reCAPTCHAv2", "project_page": "", "abstract": "Our work examines the efficacy of employing advanced machine learning methods to solve captchas from Google's reCAPTCHAv2 system. We evaluate the effectiveness of automated systems in solving captchas by utilizing advanced YOLO models for image segmentation and classification. Our main result is that we can solve 100% of the captchas, while previous work only solved 68-71%. Furthermore, our findings suggest that there is no significant difference in the number of challenges humans and bots must solve to pass the captchas in reCAPTCHAv2. This implies that current AI technologies can exploit advanced image-based captchas. We also look under the hood of reCAPTCHAv2, and find evidence that reCAPTCHAv2 is heavily based on cookie and browser history data when evaluating whether a user is human or not. The code is provided alongside this paper.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aplesner/Breaking-reCAPTCHAv2", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-17", "arxiv_id": "2409.08554", "title": "LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study", "authors": [ "Mahta Fetrat Qharabagh", "Zahra Dehghanian", "Hamid R. Rabiee" ], "github": "", "project_page": "", "abstract": "Grapheme-to-phoneme (G2P) conversion is critical in speech processing, particularly for applications like speech synthesis. G2P systems must possess linguistic understanding and contextual awareness of languages with polyphone words and context-dependent phonemes. Large language models (LLMs) have recently demonstrated significant potential in various language tasks, suggesting that their phonetic knowledge could be leveraged for G2P. In this paper, we evaluate the performance of LLMs in G2P conversion and introduce prompting and post-processing methods that enhance LLM outputs without additional training or labeled data. We also present a benchmarking dataset designed to assess G2P performance on sentence-level phonetic challenges of the Persian language. Our results show that by applying the proposed methods, LLMs can outperform traditional G2P tools, even in an underrepresented language like Persian, highlighting the potential of developing LLM-aided G2P systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-17", "arxiv_id": "2409.10594", "title": "Kolmogorov-Arnold Transformer", "authors": [ "Xingyi Yang", "Xinchao Wang" ], "github": "https://github.com/Adamdad/kat", "project_page": "", "abstract": "Transformers stand as the cornerstone of mordern deep learning. Traditionally, these models rely on multi-layer perceptron (MLP) layers to mix the information between channels. In this paper, we introduce the Kolmogorov-Arnold Transformer (KAT), a novel architecture that replaces MLP layers with Kolmogorov-Arnold Network (KAN) layers to enhance the expressiveness and performance of the model. Integrating KANs into transformers, however, is no easy feat, especially when scaled up. Specifically, we identify three key challenges: (C1) Base function. The standard B-spline function used in KANs is not optimized for parallel computing on modern hardware, resulting in slower inference speeds. (C2) Parameter and Computation Inefficiency. KAN requires a unique function for each input-output pair, making the computation extremely large. (C3) Weight initialization. The initialization of weights in KANs is particularly challenging due to their learnable activation functions, which are critical for achieving convergence in deep neural networks. To overcome the aforementioned challenges, we propose three key solutions: (S1) Rational basis. We replace B-spline functions with rational functions to improve compatibility with modern GPUs. By implementing this in CUDA, we achieve faster computations. (S2) Group KAN. We share the activation weights through a group of neurons, to reduce the computational load without sacrificing performance. (S3) Variance-preserving initialization. We carefully initialize the activation weights to make sure that the activation variance is maintained across layers. With these designs, KAT scales effectively and readily outperforms traditional MLP-based transformers.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Adamdad/kat", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.11402", "title": "NVLM: Open Frontier-Class Multimodal LLMs", "authors": [ "Wenliang Dai", "Nayeon Lee", "Boxin Wang", "Zhuoling Yang", "Zihan Liu", "Jon Barker", "Tuomas Rintamaki", "Mohammad Shoeybi", "Bryan Catanzaro", "Wei Ping" ], "github": "", "project_page": "", "abstract": "We introduce NVLM 1.0, a family of frontier-class multimodal large language models (LLMs) that achieve state-of-the-art results on vision-language tasks, rivaling the leading proprietary models (e.g., GPT-4o) and open-access models (e.g., Llama 3-V 405B and InternVL 2). Remarkably, NVLM 1.0 shows improved text-only performance over its LLM backbone after multimodal training. In terms of model design, we perform a comprehensive comparison between decoder-only multimodal LLMs (e.g., LLaVA) and cross-attention-based models (e.g., Flamingo). Based on the strengths and weaknesses of both approaches, we propose a novel architecture that enhances both training efficiency and multimodal reasoning capabilities. Furthermore, we introduce a 1-D tile-tagging design for tile-based dynamic high-resolution images, which significantly boosts performance on multimodal reasoning and OCR-related tasks. Regarding training data, we meticulously curate and provide detailed information on our multimodal pretraining and supervised fine-tuning datasets. Our findings indicate that dataset quality and task diversity are more important than scale, even during the pretraining phase, across all architectures. Notably, we develop production-grade multimodality for the NVLM-1.0 models, enabling them to excel in vision-language tasks while maintaining and even improving text-only performance compared to their LLM backbones. To achieve this, we craft and integrate a high-quality text-only dataset into multimodal training, alongside a substantial amount of multimodal math and reasoning data, leading to enhanced math and coding capabilities across modalities. To advance research in the field, we are releasing the model weights and will open-source the code for the community: https://nvlm-project.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.10568", "title": "On the limits of agency in agent-based models", "authors": [ "Ayush Chopra", "Shashank Kumar", "Nurullah Giray-Kuru", "Ramesh Raskar", "Arnau Quera-Bofarull" ], "github": "", "project_page": "", "abstract": "Agent-based modeling (ABM) seeks to understand the behavior of complex systems by simulating a collection of agents that act and interact within an environment. Their practical utility requires capturing realistic environment dynamics and adaptive agent behavior while efficiently simulating million-size populations. Recent advancements in large language models (LLMs) present an opportunity to enhance ABMs by using LLMs as agents with further potential to capture adaptive behavior. However, the computational infeasibility of using LLMs for large populations has hindered their widespread adoption. In this paper, we introduce AgentTorch -- a framework that scales ABMs to millions of agents while capturing high-resolution agent behavior using LLMs. We benchmark the utility of LLMs as ABM agents, exploring the trade-off between simulation scale and individual agency. Using the COVID-19 pandemic as a case study, we demonstrate how AgentTorch can simulate 8.4 million agents representing New York City, capturing the impact of isolation and employment behavior on health and economic outcomes. We compare the performance of different agent architectures based on heuristic and LLM agents in predicting disease waves and unemployment rates. Furthermore, we showcase AgentTorch's capabilities for retrospective, counterfactual, and prospective analyses, highlighting how adaptive agent behavior can help overcome the limitations of historical data in policy design. AgentTorch is an open-source project actively being used for policy-making and scientific discovery around the world. The framework is available here: github.com/AgentTorch/AgentTorch.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.10831", "title": "PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing", "authors": [ "Phillip Long", "Zachary Novack", "Taylor Berg-Kirkpatrick", "Julian McAuley" ], "github": "https://github.com/pnlong/PDMX", "project_page": "", "abstract": "The recent explosion of generative AI-Music systems has raised numerous concerns over data copyright, licensing music from musicians, and the conflict between open-source AI and large prestige companies. Such issues highlight the need for publicly available, copyright-free musical data, in which there is a large shortage, particularly for symbolic music data. To alleviate this issue, we present PDMX: a large-scale open-source dataset of over 250K public domain MusicXML scores collected from the score-sharing forum MuseScore, making it the largest available copyright-free symbolic music dataset to our knowledge. PDMX additionally includes a wealth of both tag and user interaction metadata, allowing us to efficiently analyze the dataset and filter for high quality user-generated scores. Given the additional metadata afforded by our data collection process, we conduct multitrack music generation experiments evaluating how different representative subsets of PDMX lead to different behaviors in downstream models, and how user-rating statistics can be used as an effective measure of data quality. Examples can be found at https://pnlong.github.io/PDMX.demo/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/pnlong/PDMX", "github_api_added_by": "auto" }, { "date": "2024-09-18", "arxiv_id": "2409.11340", "title": "OmniGen: Unified Image Generation", "authors": [ "Shitao Xiao", "Yueze Wang", "Junjie Zhou", "Huaying Yuan", "Xingrun Xing", "Ruiran Yan", "Shuting Wang", "Tiejun Huang", "Zheng Liu" ], "github": "https://github.com/VectorSpaceLab/OmniGen", "project_page": "", "abstract": "In this work, we introduce OmniGen, a new diffusion model for unified image generation. Unlike popular diffusion models (e.g., Stable Diffusion), OmniGen no longer requires additional modules such as ControlNet or IP-Adapter to process diverse control conditions. OmniGenis characterized by the following features: 1) Unification: OmniGen not only demonstrates text-to-image generation capabilities but also inherently supports other downstream tasks, such as image editing, subject-driven generation, and visual-conditional generation. Additionally, OmniGen can handle classical computer vision tasks by transforming them into image generation tasks, such as edge detection and human pose recognition. 2) Simplicity: The architecture of OmniGen is highly simplified, eliminating the need for additional text encoders. Moreover, it is more user-friendly compared to existing diffusion models, enabling complex tasks to be accomplished through instructions without the need for extra preprocessing steps (e.g., human pose estimation), thereby significantly simplifying the workflow of image generation. 3) Knowledge Transfer: Through learning in a unified format, OmniGen effectively transfers knowledge across different tasks, manages unseen tasks and domains, and exhibits novel capabilities. We also explore the model's reasoning capabilities and potential applications of chain-of-thought mechanism. This work represents the first attempt at a general-purpose image generation model, and there remain several unresolved issues. We will open-source the related resources at https://github.com/VectorSpaceLab/OmniGen to foster advancements in this field.", "github_manual": "", "github_regex": "https://github.com/VectorSpaceLab/OmniGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.11211", "title": "SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction", "authors": [ "Marko Mihajlovic", "Sergey Prokudin", "Siyu Tang", "Robert Maier", "Federica Bogo", "Tony Tung", "Edmond Boyer" ], "github": "https://github.com/markomih/splatfields", "project_page": "", "abstract": "Digitizing 3D static scenes and 4D dynamic events from multi-view images has long been a challenge in computer vision and graphics. Recently, 3D Gaussian Splatting (3DGS) has emerged as a practical and scalable reconstruction method, gaining popularity due to its impressive reconstruction quality, real-time rendering capabilities, and compatibility with widely used visualization tools. However, the method requires a substantial number of input views to achieve high-quality scene reconstruction, introducing a significant practical bottleneck. This challenge is especially severe in capturing dynamic scenes, where deploying an extensive camera array can be prohibitively costly. In this work, we identify the lack of spatial autocorrelation of splat features as one of the factors contributing to the suboptimal performance of the 3DGS technique in sparse reconstruction settings. To address the issue, we propose an optimization strategy that effectively regularizes splat features by modeling them as the outputs of a corresponding implicit neural field. This results in a consistent enhancement of reconstruction quality across various scenarios. Our approach effectively handles static and dynamic cases, as demonstrated by extensive testing across different setups and scene complexities.", "github_manual": "https://github.com/markomih/splatfields", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.10923", "title": "Agile Continuous Jumping in Discontinuous Terrains", "authors": [ "Yuxiang Yang", "Guanya Shi", "Changyi Lin", "Xiangyun Meng", "Rosario Scalise", "Mateo Guaman Castro", "Wenhao Yu", "Tingnan Zhang", "Ding Zhao", "Jie Tan", "Byron Boots" ], "github": "", "project_page": "", "abstract": "We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal robots in discontinuous terrains such as stairs and stepping stones. Unlike single-step jumping, continuous jumping requires accurately executing highly dynamic motions over long horizons, which is challenging for existing approaches. To accomplish this task, we design a hierarchical learning and control framework, which consists of a learned heightmap predictor for robust terrain perception, a reinforcement-learning-based centroidal-level motion policy for versatile and terrain-adaptive planning, and a low-level model-based leg controller for accurate motion tracking. In addition, we minimize the sim-to-real gap by accurately modeling the hardware characteristics. Our framework enables a Unitree Go1 robot to perform agile and continuous jumps on human-sized stairs and sparse stepping stones, for the first time to the best of our knowledge. In particular, the robot can cross two stair steps in each jump and completes a 3.5m long, 2.8m high, 14-step staircase in 4.5 seconds. Moreover, the same policy outperforms baselines in various other parkour tasks, such as jumping over single horizontal or vertical discontinuities. Experiment videos can be found at https://yxyang.github.io/jumping\\_cod/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.11367", "title": "OSV: One Step is Enough for High-Quality Image to Video Generation", "authors": [ "Xiaofeng Mao", "Zhengkai Jiang", "Fu-Yun Wang", "Wenbing Zhu", "Jiangning Zhang", "Hao Chen", "Mingmin Chi", "Yabiao Wang" ], "github": "", "project_page": "", "abstract": "Video diffusion models have shown great potential in generating high-quality videos, making them an increasingly popular focus. However, their inherent iterative nature leads to substantial computational and time costs. While efforts have been made to accelerate video diffusion by reducing inference steps (through techniques like consistency distillation) and GAN training (these approaches often fall short in either performance or training stability). In this work, we introduce a two-stage training framework that effectively combines consistency distillation with GAN training to address these challenges. Additionally, we propose a novel video discriminator design, which eliminates the need for decoding the video latents and improves the final performance. Our model is capable of producing high-quality videos in merely one-step, with the flexibility to perform multi-step refinement for further performance enhancement. Our quantitative evaluation on the OpenWebVid-1M benchmark shows that our model significantly outperforms existing methods. Notably, our 1-step performance(FVD 171.15) exceeds the 8-step performance of the consistency distillation based method, AnimateLCM (FVD 184.79), and approaches the 25-step performance of advanced Stable Video Diffusion (FVD 156.94).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.09323", "title": "Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks", "authors": [ "Ali Mehrabian", "Parsa Mojarad Adi", "Moein Heidari", "Ilker Hacihaliloglu" ], "github": "https://github.com/Ali-Meh619/FKAN", "project_page": "", "abstract": "Implicit neural representations (INRs) use neural networks to provide continuous and resolution-independent representations of complex signals with a small number of parameters. However, existing INR models often fail to capture important frequency components specific to each task. To address this issue, in this paper, we propose a Fourier Kolmogorov Arnold network (FKAN) for INRs. The proposed FKAN utilizes learnable activation functions modeled as Fourier series in the first layer to effectively control and learn the task-specific frequency components. In addition, the activation functions with learnable Fourier coefficients improve the ability of the network to capture complex patterns and details, which is beneficial for high-resolution and high-dimensional data. Experimental results show that our proposed FKAN model outperforms three state-of-the-art baseline schemes, and improves the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) for the image representation task and intersection over union (IoU) for the 3D occupancy volume representation task, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Ali-Meh619/FKAN", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.10836", "title": "Single-Layer Learnable Activation for Implicit Neural Representation (SL^{2}A-INR)", "authors": [ "Moein Heidari", "Reza Rezaeian", "Reza Azad", "Dorit Merhof", "Hamid Soltanian-Zadeh", "Ilker Hacihaliloglu" ], "github": "https://github.com/Iceage7/SL2A-INR", "project_page": "", "abstract": "Implicit Neural Representation (INR), leveraging a neural network to transform coordinate input into corresponding attributes, has recently driven significant advances in several vision-related domains. However, the performance of INR is heavily influenced by the choice of the nonlinear activation function used in its multilayer perceptron (MLP) architecture. Multiple nonlinearities have been investigated; yet, current INRs face limitations in capturing high-frequency components, diverse signal types, and handling inverse problems. We have identified that these problems can be greatly alleviated by introducing a paradigm shift in INRs. We find that an architecture with learnable activations in initial layers can represent fine details in the underlying signals. Specifically, we propose SL^{2}A-INR, a hybrid network for INR with a single-layer learnable activation function, prompting the effectiveness of traditional ReLU-based MLPs. Our method performs superior across diverse tasks, including image representation, 3D shape reconstructions, inpainting, single image super-resolution, CT reconstruction, and novel view synthesis. Through comprehensive experiments, SL^{2}A-INR sets new benchmarks in accuracy, quality, and convergence rates for INR.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Iceage7/SL2A-INR", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.10819", "title": "EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer", "authors": [ "Jiarui Hai", "Yong Xu", "Hao Zhang", "Chenxing Li", "Helin Wang", "Mounya Elhilali", "Dong Yu" ], "github": "https://github.com/haidog-yaqub/EzAudio", "project_page": "", "abstract": "Latent diffusion models have shown promising results in text-to-audio (T2A) generation tasks, yet previous models have encountered difficulties in generation quality, computational cost, diffusion sampling, and data preparation. In this paper, we introduce EzAudio, a transformer-based T2A diffusion model, to handle these challenges. Our approach includes several key innovations: (1) We build the T2A model on the latent space of a 1D waveform Variational Autoencoder (VAE), avoiding the complexities of handling 2D spectrogram representations and using an additional neural vocoder. (2) We design an optimized diffusion transformer architecture specifically tailored for audio latent representations and diffusion modeling, which enhances convergence speed, training stability, and memory usage, making the training process easier and more efficient. (3) To tackle data scarcity, we adopt a data-efficient training strategy that leverages unlabeled data for learning acoustic dependencies, audio caption data annotated by audio-language models for text-to-audio alignment learning, and human-labeled data for fine-tuning. (4) We introduce a classifier-free guidance (CFG) rescaling method that simplifies EzAudio by achieving strong prompt alignment while preserving great audio quality when using larger CFG scores, eliminating the need to struggle with finding the optimal CFG score to balance this trade-off. EzAudio surpasses existing open-source models in both objective metrics and subjective evaluations, delivering realistic listening experiences while maintaining a streamlined model structure, low training costs, and an easy-to-follow training pipeline. Code, data, and pre-trained models are released at: https://haidog-yaqub.github.io/EzAudio-Page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/haidog-yaqub/EzAudio", "github_api_added_by": "auto" }, { "date": "2024-09-18", "arxiv_id": "2409.11406", "title": "Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion", "authors": [ "Zhenwei Wang", "Tengfei Wang", "Zexin He", "Gerhard Hancke", "Ziwei Liu", "Rynson W. H. Lau" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "In 3D modeling, designers often use an existing 3D model as a reference to create new ones. This practice has inspired the development of Phidias, a novel generative model that uses diffusion for reference-augmented 3D generation. Given an image, our method leverages a retrieved or user-provided 3D reference model to guide the generation process, thereby enhancing the generation quality, generalization ability, and controllability. Our model integrates three key components: 1) meta-ControlNet that dynamically modulates the conditioning strength, 2) dynamic reference routing that mitigates misalignment between the input image and 3D reference, and 3) self-reference augmentations that enable self-supervised training with a progressive curriculum. Collectively, these designs result in a clear improvement over existing methods. Phidias establishes a unified framework for 3D generation using text, image, and 3D conditions with versatile applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.11355", "title": "Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think", "authors": [ "Gonzalo Martin Garcia", "Karim Abou Zeid", "Christian Schmidt", "Daan de Geus", "Alexander Hermans", "Bastian Leibe" ], "github": "", "project_page": "", "abstract": "Recent work showed that large diffusion models can be reused as highly precise monocular depth estimators by casting depth estimation as an image-conditional image generation task. While the proposed model achieved state-of-the-art results, high computational demands due to multi-step inference limited its use in many scenarios. In this paper, we show that the perceived inefficiency was caused by a flaw in the inference pipeline that has so far gone unnoticed. The fixed model performs comparably to the best previously reported configuration while being more than 200times faster. To optimize for downstream task performance, we perform end-to-end fine-tuning on top of the single-step model with task-specific losses and get a deterministic model that outperforms all other diffusion-based depth and normal estimation models on common zero-shot benchmarks. We surprisingly find that this fine-tuning protocol also works directly on Stable Diffusion and achieves comparable performance to current state-of-the-art diffusion-based depth and normal estimation models, calling into question some of the conclusions drawn from prior works.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.11055", "title": "A Comprehensive Evaluation of Quantized Instruction-Tuned Large Language Models: An Experimental Analysis up to 405B", "authors": [ "Jemin Lee", "Sihyeong Park", "Jinse Kwon", "Jihun Oh", "Yongin Kwon" ], "github": "", "project_page": "", "abstract": "Prior research works have evaluated quantized LLMs using limited metrics such as perplexity or a few basic knowledge tasks and old datasets. Additionally, recent large-scale models such as Llama 3.1 with up to 405B have not been thoroughly examined. This paper evaluates the performance of instruction-tuned LLMs across various quantization methods (GPTQ, AWQ, SmoothQuant, and FP8) on models ranging from 7B to 405B. Using 13 benchmarks, we assess performance across six task types: commonsense Q\\&A, knowledge and language understanding, instruction following, hallucination detection, mathematics, and dialogue. Our key findings reveal that (1) quantizing a larger LLM to a similar size as a smaller FP16 LLM generally performs better across most benchmarks, except for hallucination detection and instruction following; (2) performance varies significantly with different quantization methods, model size, and bit-width, with weight-only methods often yielding better results in larger models; (3) task difficulty does not significantly impact accuracy degradation due to quantization; and (4) the MT-Bench evaluation method has limited discriminatory power among recent high-performing LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.11242", "title": "Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse", "authors": [ "Maojia Song", "Shang Hong Sim", "Rishabh Bhardwaj", "Hai Leong Chieu", "Navonil Majumder", "Soujanya Poria" ], "github": "https://github.com/declare-lab/trust-align", "project_page": "", "abstract": "LLMs are an integral part of retrieval-augmented generation (RAG) systems. While many studies focus on evaluating the quality of end-to-end RAG systems, there is a lack of research on understanding the appropriateness of an LLM for the RAG task. Thus, we introduce a new metric, Trust-Score, that provides a holistic evaluation of the trustworthiness of LLMs in an RAG framework. We show that various prompting methods, such as in-context learning, fail to adapt LLMs effectively to the RAG task. Thus, we propose Trust-Align, a framework to align LLMs for higher Trust-Score. LLaMA-3-8b, aligned with our method, significantly outperforms open-source LLMs of comparable sizes on ASQA (up 10.7), QAMPARI (up 29.2) and ELI5 (up 14.9). We release our code at: https://github.com/declare-lab/trust-align.", "github_manual": "", "github_regex": "https://github.com/declare-lab/trust-align", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.11136", "title": "Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models", "authors": [ "Orion Weller", "Benjamin Van Durme", "Dawn Lawrie", "Ashwin Paranjape", "Yuhao Zhang", "Jack Hessel" ], "github": "https://github.com/orionw/promptriever", "project_page": "", "abstract": "Instruction-tuned language models (LM) are able to respond to imperative commands, providing a more natural user interface compared to their base counterparts. In this work, we present Promptriever, the first retrieval model able to be prompted like an LM. To train Promptriever, we curate and release a new instance-level instruction training set from MS MARCO, spanning nearly 500k instances. Promptriever not only achieves strong performance on standard retrieval tasks, but also follows instructions. We observe: (1) large gains (reaching SoTA) on following detailed relevance instructions (+14.3 p-MRR / +3.1 nDCG on FollowIR), (2) significantly increased robustness to lexical choices/phrasing in the query+instruction (+12.9 Robustness@10 on InstructIR), and (3) the ability to perform hyperparameter search via prompting to reliably improve retrieval performance (+1.4 average increase on BEIR). Promptriever demonstrates that retrieval models can be controlled with prompts on a per-query basis, setting the stage for future work aligning LM prompting techniques with information retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/orionw/promptriever", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-18", "arxiv_id": "2409.11733", "title": "Human-like Affective Cognition in Foundation Models", "authors": [ "Kanishk Gandhi", "Zoe Lynch", "Jan-Philipp Fr\u00e4nken", "Kayla Patterson", "Sharon Wambu", "Tobias Gerstenberg", "Desmond C. Ong", "Noah D. Goodman" ], "github": "", "project_page": "", "abstract": "Understanding emotions is fundamental to human interaction and experience. Humans easily infer emotions from situations or facial expressions, situations from emotions, and do a variety of other affective cognition. How adept is modern AI at these inferences? We introduce an evaluation framework for testing affective cognition in foundation models. Starting from psychological theory, we generate 1,280 diverse scenarios exploring relationships between appraisals, emotions, expressions, and outcomes. We evaluate the abilities of foundation models (GPT-4, Claude-3, Gemini-1.5-Pro) and humans (N = 567) across carefully selected conditions. Our results show foundation models tend to agree with human intuitions, matching or exceeding interparticipant agreement. In some conditions, models are ``superhuman'' -- they better predict modal human judgements than the average human. All models benefit from chain-of-thought reasoning. This suggests foundation models have acquired a human-like understanding of emotions and their influence on beliefs and behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.12136", "title": "GRIN: GRadient-INformed MoE", "authors": [ "Liyuan Liu", "Young Jin Kim", "Shuohang Wang", "Chen Liang", "Yelong Shen", "Hao Cheng", "Xiaodong Liu", "Masahiro Tanaka", "Xiaoxia Wu", "Wenxiang Hu", "Vishrav Chaudhary", "Zeqi Lin", "Chenruidong Zhang", "Jilong Xue", "Hany Awadalla", "Jianfeng Gao", "Weizhu Chen" ], "github": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) models scale more effectively than dense models due to sparse computation through expert routing, selectively activating only a small subset of expert modules. However, sparse computation challenges traditional training practices, as discrete expert routing hinders standard backpropagation and thus gradient-based optimization, which are the cornerstone of deep learning. To better pursue the scaling power of MoE, we introduce GRIN (GRadient-INformed MoE training), which incorporates sparse gradient estimation for expert routing and configures model parallelism to avoid token dropping. Applying GRIN to autoregressive language modeling, we develop a top-2 16times3.8B MoE model. Our model, with only 6.6B activated parameters, outperforms a 7B dense model and matches the performance of a 14B dense model trained on the same data. Extensive evaluations across diverse tasks demonstrate the potential of GRIN to significantly enhance MoE efficacy, achieving 79.4 on MMLU, 83.7 on HellaSwag, 74.4 on HumanEval, and 58.9 on MATH.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.12191", "title": "Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution", "authors": [ "Peng Wang", "Shuai Bai", "Sinan Tan", "Shijie Wang", "Zhihao Fan", "Jinze Bai", "Keqin Chen", "Xuejing Liu", "Jialin Wang", "Wenbin Ge", "Yang Fan", "Kai Dang", "Mengfei Du", "Xuancheng Ren", "Rui Men", "Dayiheng Liu", "Chang Zhou", "Jingren Zhou", "Junyang Lin" ], "github": "https://github.com/QwenLM/Qwen2-VL", "project_page": "", "abstract": "We present the Qwen2-VL Series, an advanced upgrade of the previous Qwen-VL models that redefines the conventional predetermined-resolution approach in visual processing. Qwen2-VL introduces the Naive Dynamic Resolution mechanism, which enables the model to dynamically process images of varying resolutions into different numbers of visual tokens. This approach allows the model to generate more efficient and accurate visual representations, closely aligning with human perceptual processes. The model also integrates Multimodal Rotary Position Embedding (M-RoPE), facilitating the effective fusion of positional information across text, images, and videos. We employ a unified paradigm for processing both images and videos, enhancing the model's visual perception capabilities. To explore the potential of large multimodal models, Qwen2-VL investigates the scaling laws for large vision-language models (LVLMs). By scaling both the model size-with versions at 2B, 8B, and 72B parameters-and the amount of training data, the Qwen2-VL Series achieves highly competitive performance. Notably, the Qwen2-VL-72B model achieves results comparable to leading models such as GPT-4o and Claude3.5-Sonnet across various multimodal benchmarks, outperforming other generalist models. Code is available at https://github.com/QwenLM/Qwen2-VL.", "github_manual": "", "github_regex": "https://github.com/QwenLM/Qwen2-VL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.12183", "title": "To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning", "authors": [ "Zayne Sprague", "Fangcong Yin", "Juan Diego Rodriguez", "Dongwei Jiang", "Manya Wadhwa", "Prasann Singhal", "Xinyu Zhao", "Xi Ye", "Kyle Mahowald", "Greg Durrett" ], "github": "https://github.com/zayne-sprague/to-cot-or-not-to-cot", "project_page": "", "abstract": "Chain-of-thought (CoT) via prompting is the de facto method for eliciting reasoning capabilities from large language models (LLMs). But for what kinds of tasks is this extra ``thinking'' really helpful? To analyze this, we conducted a quantitative meta-analysis covering over 100 papers using CoT and ran our own evaluations of 20 datasets across 14 models. Our results show that CoT gives strong performance benefits primarily on tasks involving math or logic, with much smaller gains on other types of tasks. On MMLU, directly generating the answer without CoT leads to almost identical accuracy as CoT unless the question or model's response contains an equals sign, indicating symbolic operations and reasoning. Following this finding, we analyze the behavior of CoT on these problems by separating planning and execution and comparing against tool-augmented LLMs. Much of CoT's gain comes from improving symbolic execution, but it underperforms relative to using a symbolic solver. Our results indicate that CoT can be applied selectively, maintaining performance while saving inference costs. Furthermore, they suggest a need to move beyond prompt-based CoT to new paradigms that better leverage intermediate computation across the whole range of LLM applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zayne-sprague/to-cot-or-not-to-cot", "github_api_added_by": "auto" }, { "date": "2024-09-19", "arxiv_id": "2409.11901", "title": "LLMs + Persona-Plug = Personalized LLMs", "authors": [ "Jiongnan Liu", "Yutao Zhu", "Shuting Wang", "Xiaochi Wei", "Erxue Min", "Yu Lu", "Shuaiqiang Wang", "Dawei Yin", "Zhicheng Dou" ], "github": "", "project_page": "", "abstract": "Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their individual interests. This has led to the development of various personalized approaches aimed at adapting large language models (LLMs) to generate customized outputs aligned with user preferences. Some of them involve fine-tuning a unique personalized LLM for each user, which is too expensive for widespread application. Alternative approaches introduce personalization information in a plug-and-play manner by retrieving the user's relevant historical texts as demonstrations. However, this retrieval-based strategy may break the continuity of the user history and fail to capture the user's overall styles and patterns, hence leading to sub-optimal performance. To address these challenges, we propose a novel personalized LLM model, . It constructs a user-specific embedding for each individual by modeling all her historical contexts through a lightweight plug-in user embedder module. By attaching this embedding to the task input, LLMs can better understand and capture user habits and preferences, thereby producing more personalized outputs without tuning their own parameters. Extensive experiments on various tasks in the language model personalization (LaMP) benchmark demonstrate that the proposed model significantly outperforms existing personalized LLM approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.12139", "title": "Takin: A Cohort of Superior Quality Zero-shot Speech Generation Models", "authors": [ "EverestAI", "Sijin Chen", "Yuan Feng", "Laipeng He", "Tianwei He", "Wendi He", "Yanni Hu", "Bin Lin", "Yiting Lin", "Pengfei Tan", "Chengwei Tian", "Chen Wang", "Zhicheng Wang", "Ruoye Xie", "Jingjing Yin", "Jianhao Ye", "Jixun Yao", "Quanlei Yan", "Yuguang Yang" ], "github": "", "project_page": "", "abstract": "With the advent of the big data and large language model era, zero-shot personalized rapid customization has emerged as a significant trend. In this report, we introduce Takin AudioLLM, a series of techniques and models, mainly including Takin TTS, Takin VC, and Takin Morphing, specifically designed for audiobook production. These models are capable of zero-shot speech production, generating high-quality speech that is nearly indistinguishable from real human speech and facilitating individuals to customize the speech content according to their own needs. Specifically, we first introduce Takin TTS, a neural codec language model that builds upon an enhanced neural speech codec and a multi-task training framework, capable of generating high-fidelity natural speech in a zero-shot way. For Takin VC, we advocate an effective content and timbre joint modeling approach to improve the speaker similarity, while advocating for a conditional flow matching based decoder to further enhance its naturalness and expressiveness. Last, we propose the Takin Morphing system with highly decoupled and advanced timbre and prosody modeling approaches, which enables individuals to customize speech production with their preferred timbre and prosody in a precise and controllable manner. Extensive experiments validate the effectiveness and robustness of our Takin AudioLLM series models. For detailed demos, please refer to https://takinaudiollm.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.11564", "title": "Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey", "authors": [ "Genta Indra Winata", "Hanyang Zhao", "Anirban Das", "Wenpin Tang", "David D. Yao", "Shi-Xiong Zhang", "Sambit Sahu" ], "github": "", "project_page": "", "abstract": "Preference tuning is a crucial process for aligning deep generative models with human preferences. This survey offers a thorough overview of recent advancements in preference tuning and the integration of human feedback. The paper is organized into three main sections: 1) introduction and preliminaries: an introduction to reinforcement learning frameworks, preference tuning tasks, models, and datasets across various modalities: language, speech, and vision, as well as different policy approaches, 2) in-depth examination of each preference tuning approach: a detailed analysis of the methods used in preference tuning, and 3) applications, discussion, and future directions: an exploration of the applications of preference tuning in downstream tasks, including evaluation methods for different modalities, and an outlook on future research directions. Our objective is to present the latest methodologies in preference tuning and model alignment, enhancing the understanding of this field for researchers and practitioners. We hope to encourage further engagement and innovation in this area.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.12186", "title": "Qwen2.5-Coder Technical Report", "authors": [ "Binyuan Hui", "Jian Yang", "Zeyu Cui", "Jiaxi Yang", "Dayiheng Liu", "Lei Zhang", "Tianyu Liu", "Jiajun Zhang", "Bowen Yu", "Kai Dang", "An Yang", "Rui Men", "Fei Huang", "Xingzhang Ren", "Xuancheng Ren", "Jingren Zhou", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "In this report, we introduce the Qwen2.5-Coder series, a significant upgrade from its predecessor, CodeQwen1.5. This series includes two models: Qwen2.5-Coder-1.5B and Qwen2.5-Coder-7B. As a code-specific model, Qwen2.5-Coder is built upon the Qwen2.5 architecture and continues pretrained on a vast corpus of over 5.5 trillion tokens. Through meticulous data cleaning, scalable synthetic data generation, and balanced data mixing, Qwen2.5-Coder demonstrates impressive code generation capabilities while retaining general versatility. The model has been evaluated on a wide range of code-related tasks, achieving state-of-the-art (SOTA) performance across more than 10 benchmarks, including code generation, completion, reasoning, and repair, consistently outperforming larger models of the same model size. We believe that the release of the Qwen2.5-Coder series will not only push the boundaries of research in code intelligence but also, through its permissive licensing, encourage broader adoption by developers in real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.12181", "title": "A Controlled Study on Long Context Extension and Generalization in LLMs", "authors": [ "Yi Lu", "Jing Nathan Yan", "Songlin Yang", "Justin T. Chiu", "Siyu Ren", "Fei Yuan", "Wenting Zhao", "Zhiyong Wu", "Alexander M. Rush" ], "github": "https://github.com/leooyii/lceg", "project_page": "", "abstract": "Broad textual understanding and in-context learning require language models that utilize full document contexts. Due to the implementation challenges associated with directly training long-context models, many methods have been proposed for extending models to handle long contexts. However, owing to differences in data and model classes, it has been challenging to compare these approaches, leading to uncertainty as to how to evaluate long-context performance and whether it differs from standard evaluation. We implement a controlled protocol for extension methods with a standardized evaluation, utilizing consistent base models and extension data. Our study yields several insights into long-context behavior. First, we reaffirm the critical role of perplexity as a general-purpose performance indicator even in longer-context tasks. Second, we find that current approximate attention methods systematically underperform across long-context tasks. Finally, we confirm that exact fine-tuning based methods are generally effective within the range of their extension, whereas extrapolation remains challenging. All codebases, models, and checkpoints will be made available open-source, promoting transparency and facilitating further research in this critical area of AI development.", "github_manual": "https://github.com/leooyii/lceg", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.09401", "title": "Towards Diverse and Efficient Audio Captioning via Diffusion Models", "authors": [ "Manjie Xu", "Chenxing Li", "Xinyi Tu", "Yong Ren", "Ruibo Fu", "Wei Liang", "Dong Yu" ], "github": "", "project_page": "", "abstract": "We introduce Diffusion-based Audio Captioning (DAC), a non-autoregressive diffusion model tailored for diverse and efficient audio captioning. Although existing captioning models relying on language backbones have achieved remarkable success in various captioning tasks, their insufficient performance in terms of generation speed and diversity impede progress in audio understanding and multimedia applications. Our diffusion-based framework offers unique advantages stemming from its inherent stochasticity and holistic context modeling in captioning. Through rigorous evaluation, we demonstrate that DAC not only achieves SOTA performance levels compared to existing benchmarks in the caption quality, but also significantly outperforms them in terms of generation speed and diversity. The success of DAC illustrates that text generation can also be seamlessly integrated with audio and visual generation tasks using a diffusion backbone, paving the way for a unified, audio-related generative model across different modalities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.12193", "title": "Vista3D: Unravel the 3D Darkside of a Single Image", "authors": [ "Qiuhong Shen", "Xingyi Yang", "Michael Bi Mi", "Xinchao Wang" ], "github": "https://github.com/florinshen/Vista3D", "project_page": "", "abstract": "We embark on the age-old quest: unveiling the hidden dimensions of objects from mere glimpses of their visible parts. To address this, we present Vista3D, a framework that realizes swift and consistent 3D generation within a mere 5 minutes. At the heart of Vista3D lies a two-phase approach: the coarse phase and the fine phase. In the coarse phase, we rapidly generate initial geometry with Gaussian Splatting from a single image. In the fine phase, we extract a Signed Distance Function (SDF) directly from learned Gaussian Splatting, optimizing it with a differentiable isosurface representation. Furthermore, it elevates the quality of generation by using a disentangled representation with two independent implicit functions to capture both visible and obscured aspects of objects. Additionally, it harmonizes gradients from 2D diffusion prior with 3D-aware diffusion priors by angular diffusion prior composition. Through extensive evaluation, we demonstrate that Vista3D effectively sustains a balance between the consistency and diversity of the generated 3D objects. Demos and code will be available at https://github.com/florinshen/Vista3D.", "github_manual": "", "github_regex": "https://github.com/florinshen/Vista3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.08425", "title": "SoloAudio: Target Sound Extraction with Language-oriented Audio Diffusion Transformer", "authors": [ "Helin Wang", "Jiarui Hai", "Yen-Ju Lu", "Karan Thakkar", "Mounya Elhilali", "Najim Dehak" ], "github": "https://github.com/wanghelin1997/soloaudio", "project_page": "", "abstract": "In this paper, we introduce SoloAudio, a novel diffusion-based generative model for target sound extraction (TSE). Our approach trains latent diffusion models on audio, replacing the previous U-Net backbone with a skip-connected Transformer that operates on latent features. SoloAudio supports both audio-oriented and language-oriented TSE by utilizing a CLAP model as the feature extractor for target sounds. Furthermore, SoloAudio leverages synthetic audio generated by state-of-the-art text-to-audio models for training, demonstrating strong generalization to out-of-domain data and unseen sound events. We evaluate this approach on the FSD Kaggle 2018 mixture dataset and real data from AudioSet, where SoloAudio achieves the state-of-the-art results on both in-domain and out-of-domain data, and exhibits impressive zero-shot and few-shot capabilities. Source code and demos are released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wanghelin1997/soloaudio", "github_api_added_by": "auto" }, { "date": "2024-09-19", "arxiv_id": "2409.11074", "title": "RoMath: A Mathematical Reasoning Benchmark in Romanian", "authors": [ "Adrian Cosma", "Ana-Maria Bucur", "Emilian Radoi" ], "github": "https://github.com/cosmaadrian/romath", "project_page": "", "abstract": "Mathematics has long been conveyed through natural language, primarily for human understanding. With the rise of mechanized mathematics and proof assistants, there is a growing need to understand informal mathematical text, yet most existing benchmarks focus solely on English, overlooking other languages. This paper introduces RoMath, a Romanian mathematical reasoning benchmark suite comprising three datasets: RoMath-Baccalaureate, RoMath-Competitions and RoMath-Synthetic, which cover a range of mathematical domains and difficulty levels, aiming to improve non-English language models and promote multilingual AI development. By focusing on Romanian, a low-resource language with unique linguistic features, RoMath addresses the limitations of Anglo-centric models and emphasizes the need for dedicated resources beyond simple automatic translation. We benchmark several open-weight language models, highlighting the importance of creating resources for underrepresented languages. We make the code and dataset available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cosmaadrian/romath", "github_api_added_by": "auto" }, { "date": "2024-09-19", "arxiv_id": "2409.11363", "title": "CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark", "authors": [ "Zachary S. Siegel", "Sayash Kapoor", "Nitya Nagdir", "Benedikt Stroebl", "Arvind Narayanan" ], "github": "https://github.com/siegelz/core-bench", "project_page": "", "abstract": "AI agents have the potential to aid users on a variety of consequential tasks, including conducting scientific research. To spur the development of useful agents, we need benchmarks that are challenging, but more crucially, directly correspond to real-world tasks of interest. This paper introduces such a benchmark, designed to measure the accuracy of AI agents in tackling a crucial yet surprisingly challenging aspect of scientific research: computational reproducibility. This task, fundamental to the scientific process, involves reproducing the results of a study using the provided code and data. We introduce CORE-Bench (Computational Reproducibility Agent Benchmark), a benchmark consisting of 270 tasks based on 90 scientific papers across three disciplines (computer science, social science, and medicine). Tasks in CORE-Bench consist of three difficulty levels and include both language-only and vision-language tasks. We provide an evaluation system to measure the accuracy of agents in a fast and parallelizable way, saving days of evaluation time for each run compared to a sequential implementation. We evaluated two baseline agents: the general-purpose AutoGPT and a task-specific agent called CORE-Agent. We tested both variants using two underlying language models: GPT-4o and GPT-4o-mini. The best agent achieved an accuracy of 21% on the hardest task, showing the vast scope for improvement in automating routine scientific tasks. Having agents that can reproduce existing work is a necessary step towards building agents that can conduct novel research and could verify and improve the performance of other research agents. We hope that CORE-Bench can improve the state of reproducibility and spur the development of future research agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/siegelz/core-bench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.11315", "title": "fMRI-3D: A Comprehensive Dataset for Enhancing fMRI-based 3D Reconstruction", "authors": [ "Jianxiong Gao", "Yuqian Fu", "Yun Wang", "Xuelin Qian", "Jianfeng Feng", "Yanwei Fu" ], "github": "", "project_page": "", "abstract": "Reconstructing 3D visuals from functional Magnetic Resonance Imaging (fMRI) data, introduced as Recon3DMind in our conference work, is of significant interest to both cognitive neuroscience and computer vision. To advance this task, we present the fMRI-3D dataset, which includes data from 15 participants and showcases a total of 4768 3D objects. The dataset comprises two components: fMRI-Shape, previously introduced and accessible at https://huggingface.co/datasets/Fudan-fMRI/fMRI-Shape, and fMRI-Objaverse, proposed in this paper and available at https://huggingface.co/datasets/Fudan-fMRI/fMRI-Objaverse. fMRI-Objaverse includes data from 5 subjects, 4 of whom are also part of the Core set in fMRI-Shape, with each subject viewing 3142 3D objects across 117 categories, all accompanied by text captions. This significantly enhances the diversity and potential applications of the dataset. Additionally, we propose MinD-3D, a novel framework designed to decode 3D visual information from fMRI signals. The framework first extracts and aggregates features from fMRI data using a neuro-fusion encoder, then employs a feature-bridge diffusion model to generate visual features, and finally reconstructs the 3D object using a generative transformer decoder. We establish new benchmarks by designing metrics at both semantic and structural levels to evaluate model performance. Furthermore, we assess our model's effectiveness in an Out-of-Distribution setting and analyze the attribution of the extracted features and the visual ROIs in fMRI signals. Our experiments demonstrate that MinD-3D not only reconstructs 3D objects with high semantic and spatial accuracy but also deepens our understanding of how human brain processes 3D visual information. Project page at: https://jianxgao.github.io/MinD-3D.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.12001", "title": "Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning", "authors": [ "Claude Formanek", "Louise Beyers", "Callum Rhys Tilbury", "Jonathan P. Shock", "Arnu Pretorius" ], "github": "https://github.com/instadeepai/og-marl/blob/main/examples/dataset_subsampling_demo.ipynb", "project_page": "", "abstract": "Offline multi-agent reinforcement learning (MARL) is an exciting direction of research that uses static datasets to find optimal control policies for multi-agent systems. Though the field is by definition data-driven, efforts have thus far neglected data in their drive to achieve state-of-the-art results. We first substantiate this claim by surveying the literature, showing how the majority of works generate their own datasets without consistent methodology and provide sparse information about the characteristics of these datasets. We then show why neglecting the nature of the data is problematic, through salient examples of how tightly algorithmic performance is coupled to the dataset used, necessitating a common foundation for experiments in the field. In response, we take a big step towards improving data usage and data awareness in offline MARL, with three key contributions: (1) a clear guideline for generating novel datasets; (2) a standardisation of over 80 existing datasets, hosted in a publicly available repository, using a consistent storage format and easy-to-use API; and (3) a suite of analysis tools that allow us to understand these datasets better, aiding further development.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/instadeepai/og-marl/blob/main/examples/dataset_subsampling_demo.ipynb", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.12134", "title": "BERT-VBD: Vietnamese Multi-Document Summarization Framework", "authors": [ "Tuan-Cuong Vuong", "Trang Mai Xuan", "Thien Van Luong" ], "github": "", "project_page": "", "abstract": "In tackling the challenge of Multi-Document Summarization (MDS), numerous methods have been proposed, spanning both extractive and abstractive summarization techniques. However, each approach has its own limitations, making it less effective to rely solely on either one. An emerging and promising strategy involves a synergistic fusion of extractive and abstractive summarization methods. Despite the plethora of studies in this domain, research on the combined methodology remains scarce, particularly in the context of Vietnamese language processing. This paper presents a novel Vietnamese MDS framework leveraging a two-component pipeline architecture that integrates extractive and abstractive techniques. The first component employs an extractive approach to identify key sentences within each document. This is achieved by a modification of the pre-trained BERT network, which derives semantically meaningful phrase embeddings using siamese and triplet network structures. The second component utilizes the VBD-LLaMA2-7B-50b model for abstractive summarization, ultimately generating the final summary document. Our proposed framework demonstrates a positive performance, attaining ROUGE-2 scores of 39.6% on the VN-MDS dataset and outperforming the state-of-the-art baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-19", "arxiv_id": "2409.12106", "title": "Measuring Human and AI Values based on Generative Psychometrics with Large Language Models", "authors": [ "Haoran Ye", "Yuhang Xie", "Yuanyi Ren", "Hanjun Fang", "Xin Zhang", "Guojie Song" ], "github": "https://github.com/value4ai/gpv", "project_page": "", "abstract": "Human values and their measurement are long-standing interdisciplinary inquiry. Recent advances in AI have sparked renewed interest in this area, with large language models (LLMs) emerging as both tools and subjects of value measurement. This work introduces Generative Psychometrics for Values (GPV), an LLM-based, data-driven value measurement paradigm, theoretically grounded in text-revealed selective perceptions. We begin by fine-tuning an LLM for accurate perception-level value measurement and verifying the capability of LLMs to parse texts into perceptions, forming the core of the GPV pipeline. Applying GPV to human-authored blogs, we demonstrate its stability, validity, and superiority over prior psychological tools. Then, extending GPV to LLM value measurement, we advance the current art with 1) a psychometric methodology that measures LLM values based on their scalable and free-form outputs, enabling context-specific measurement; 2) a comparative analysis of measurement paradigms, indicating response biases of prior methods; and 3) an attempt to bridge LLM values and their safety, revealing the predictive power of different value systems and the impacts of various values on LLM safety. Through interdisciplinary efforts, we aim to leverage AI for next-generation psychometrics and psychometrics for value-aligned AI.", "github_manual": "https://github.com/value4ai/gpv", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-20", "arxiv_id": "2409.12958", "title": "MURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse Instructions", "authors": [ "Abdullatif K\u00f6ksal", "Marion Thaler", "Ayyoob Imani", "Ahmet \u00dcst\u00fcn", "Anna Korhonen", "Hinrich Sch\u00fctze" ], "github": "https://github.com/akoksal/muri", "project_page": "", "abstract": "Instruction tuning enhances large language models (LLMs) by aligning them with human preferences across diverse tasks. Traditional approaches to create instruction tuning datasets face serious challenges for low-resource languages due to their dependence on data annotation. This work introduces a novel method, Multilingual Reverse Instructions (MURI), which generates high-quality instruction tuning datasets for low-resource languages without requiring human annotators or pre-existing multilingual models. Utilizing reverse instructions and a translation pipeline, MURI produces instruction-output pairs from existing human-written texts in low-resource languages. This method ensures cultural relevance and diversity by sourcing texts from different native domains and applying filters to eliminate inappropriate content. Our dataset, MURI-IT, includes more than 2 million instruction-output pairs across 200 languages. Evaluation by native speakers and fine-tuning experiments with mT5 models demonstrate the approach's effectiveness for both NLU and open-ended generation. We publicly release datasets and models at https://github.com/akoksal/muri.", "github_manual": "", "github_regex": "https://github.com/akoksal/muri", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-20", "arxiv_id": "2409.12568", "title": "InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced Mathematical Reasoning", "authors": [ "Xiaotian Han", "Yiren Jian", "Xuefeng Hu", "Haogeng Liu", "Yiqi Wang", "Qihang Fan", "Yuang Ai", "Huaibo Huang", "Ran He", "Zhenheng Yang", "Quanzeng You" ], "github": "", "project_page": "", "abstract": "Pre-training on large-scale, high-quality datasets is crucial for enhancing the reasoning capabilities of Large Language Models (LLMs), especially in specialized domains such as mathematics. Despite the recognized importance, the Multimodal LLMs (MLLMs) field currently lacks a comprehensive open-source pre-training dataset specifically designed for mathematical reasoning. To address this gap, we introduce InfiMM-WebMath-40B, a high-quality dataset of interleaved image-text documents. It comprises 24 million web pages, 85 million associated image URLs, and 40 billion text tokens, all meticulously extracted and filtered from CommonCrawl. We provide a detailed overview of our data collection and processing pipeline. To demonstrate the robustness of InfiMM-WebMath-40B, we conducted evaluations in both text-only and multimodal settings. Our evaluations on text-only benchmarks show that, despite utilizing only 40 billion tokens, our dataset significantly enhances the performance of our 1.3B model, delivering results comparable to DeepSeekMath-1.3B, which uses 120 billion tokens for the same model size. Nevertheless, with the introduction of our multi-modal math pre-training dataset, our models set a new state-of-the-art among open-source models on multi-modal math benchmarks such as MathVerse and We-Math. We release our data at https://huggingface.co/datasets/Infi-MM/InfiMM-WebMath-40B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-20", "arxiv_id": "2409.12957", "title": "3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion", "authors": [ "Zhaoxi Chen", "Jiaxiang Tang", "Yuhao Dong", "Ziang Cao", "Fangzhou Hong", "Yushi Lan", "Tengfei Wang", "Haozhe Xie", "Tong Wu", "Shunsuke Saito", "Liang Pan", "Dahua Lin", "Ziwei Liu" ], "github": "https://github.com/3dtopia/3dtopia-xl", "project_page": "", "abstract": "The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative models, existing methods still face challenges with optimization speed, geometric fidelity, and the lack of assets for physically based rendering (PBR). In this paper, we introduce 3DTopia-XL, a scalable native 3D generative model designed to overcome these limitations. 3DTopia-XL leverages a novel primitive-based 3D representation, PrimX, which encodes detailed shape, albedo, and material field into a compact tensorial format, facilitating the modeling of high-resolution geometry with PBR assets. On top of the novel representation, we propose a generative framework based on Diffusion Transformer (DiT), which comprises 1) Primitive Patch Compression, 2) and Latent Primitive Diffusion. 3DTopia-XL learns to generate high-quality 3D assets from textual or visual inputs. We conduct extensive qualitative and quantitative experiments to demonstrate that 3DTopia-XL significantly outperforms existing methods in generating high-quality 3D assets with fine-grained textures and materials, efficiently bridging the quality gap between generative models and real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/3dtopia/3dtopia-xl", "github_api_added_by": "auto" }, { "date": "2024-09-20", "arxiv_id": "2409.12576", "title": "StoryMaker: Towards Holistic Consistent Characters in Text-to-image Generation", "authors": [ "Zhengguang Zhou", "Jing Li", "Huaxia Li", "Nemo Chen", "Xu Tang" ], "github": "https://github.com/RedAIGC/StoryMaker", "project_page": "", "abstract": "Tuning-free personalized image generation methods have achieved significant success in maintaining facial consistency, i.e., identities, even with multiple characters. However, the lack of holistic consistency in scenes with multiple characters hampers these methods' ability to create a cohesive narrative. In this paper, we introduce StoryMaker, a personalization solution that preserves not only facial consistency but also clothing, hairstyles, and body consistency, thus facilitating the creation of a story through a series of images. StoryMaker incorporates conditions based on face identities and cropped character images, which include clothing, hairstyles, and bodies. Specifically, we integrate the facial identity information with the cropped character images using the Positional-aware Perceiver Resampler (PPR) to obtain distinct character features. To prevent intermingling of multiple characters and the background, we separately constrain the cross-attention impact regions of different characters and the background using MSE loss with segmentation masks. Additionally, we train the generation network conditioned on poses to promote decoupling from poses. A LoRA is also employed to enhance fidelity and quality. Experiments underscore the effectiveness of our approach. StoryMaker supports numerous applications and is compatible with other societal plug-ins. Our source codes and model weights are available at https://github.com/RedAIGC/StoryMaker.", "github_manual": "", "github_regex": "https://github.com/RedAIGC/StoryMaker", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-20", "arxiv_id": "2409.12903", "title": "Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization", "authors": [ "Mohammad Samragh", "Iman Mirzadeh", "Keivan Alizadeh Vahid", "Fartash Faghri", "Minsik Cho", "Moin Nabi", "Devang Naik", "Mehrdad Farajtabar" ], "github": "https://github.com/apple/ml-hypercloning", "project_page": "", "abstract": "The pre-training phase of language models often begins with randomly initialized parameters. With the current trends in scaling models, training their large number of parameters can be extremely slow and costly. In contrast, small language models are less expensive to train, but they often cannot achieve the accuracy of large models. In this paper, we explore an intriguing idea to connect these two different regimes: Can we develop a method to initialize large language models using smaller pre-trained models? Will such initialization bring any benefits in terms of training time and final accuracy? In this paper, we introduce HyperCloning, a method that can expand the parameters of a pre-trained language model to those of a larger model with increased hidden dimensions. Our method ensures that the larger model retains the functionality of the smaller model. As a result, the larger model already inherits the predictive power and accuracy of the smaller model before the training starts. We demonstrate that training such an initialized model results in significant savings in terms of GPU hours required for pre-training large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apple/ml-hypercloning", "github_api_added_by": "auto" }, { "date": "2024-09-20", "arxiv_id": "2409.12960", "title": "LVCD: Reference-based Lineart Video Colorization with Diffusion Models", "authors": [ "Zhitong Huang", "Mohan Zhang", "Jing Liao" ], "github": "", "project_page": "", "abstract": "We propose the first video diffusion framework for reference-based lineart video colorization. Unlike previous works that rely solely on image generative models to colorize lineart frame by frame, our approach leverages a large-scale pretrained video diffusion model to generate colorized animation videos. This approach leads to more temporally consistent results and is better equipped to handle large motions. Firstly, we introduce Sketch-guided ControlNet which provides additional control to finetune an image-to-video diffusion model for controllable video synthesis, enabling the generation of animation videos conditioned on lineart. We then propose Reference Attention to facilitate the transfer of colors from the reference frame to other frames containing fast and expansive motions. Finally, we present a novel scheme for sequential sampling, incorporating the Overlapped Blending Module and Prev-Reference Attention, to extend the video diffusion model beyond its original fixed-length limitation for long video colorization. Both qualitative and quantitative results demonstrate that our method significantly outperforms state-of-the-art techniques in terms of frame and video quality, as well as temporal consistency. Moreover, our method is capable of generating high-quality, long temporal-consistent animation videos with large motions, which is not achievable in previous works. Our code and model are available at https://luckyhzt.github.io/lvcd.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-20", "arxiv_id": "2409.12892", "title": "3DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardt", "authors": [ "Lukas H\u00f6llein", "Alja\u017e Bo\u017ei\u010d", "Michael Zollh\u00f6fer", "Matthias Nie\u00dfner" ], "github": "https://github.com/lukashoel/3dgs-lm", "project_page": "", "abstract": "We present 3DGS-LM, a new method that accelerates the reconstruction of 3D Gaussian Splatting (3DGS) by replacing its ADAM optimizer with a tailored Levenberg-Marquardt (LM). Existing methods reduce the optimization time by decreasing the number of Gaussians or by improving the implementation of the differentiable rasterizer. However, they still rely on the ADAM optimizer to fit Gaussian parameters of a scene in thousands of iterations, which can take up to an hour. To this end, we change the optimizer to LM that runs in conjunction with the 3DGS differentiable rasterizer. For efficient GPU parallization, we propose a caching data structure for intermediate gradients that allows us to efficiently calculate Jacobian-vector products in custom CUDA kernels. In every LM iteration, we calculate update directions from multiple image subsets using these kernels and combine them in a weighted mean. Overall, our method is 30% faster than the original 3DGS while obtaining the same reconstruction quality. Our optimization is also agnostic to other methods that acclerate 3DGS, thus enabling even faster speedups compared to vanilla 3DGS.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lukashoel/3dgs-lm", "github_api_added_by": "auto" }, { "date": "2024-09-20", "arxiv_id": "2409.12431", "title": "FlexiTex: Enhancing Texture Generation with Visual Guidance", "authors": [ "DaDong Jiang", "Xianghui Yang", "Zibo Zhao", "Sheng Zhang", "Jiaao Yu", "Zeqiang Lai", "Shaoxiong Yang", "Chunchao Guo", "Xiaobo Zhou", "Zhihui Ke" ], "github": "", "project_page": "", "abstract": "Recent texture generation methods achieve impressive results due to the powerful generative prior they leverage from large-scale text-to-image diffusion models. However, abstract textual prompts are limited in providing global textural or shape information, which results in the texture generation methods producing blurry or inconsistent patterns. To tackle this, we present FlexiTex, embedding rich information via visual guidance to generate a high-quality texture. The core of FlexiTex is the Visual Guidance Enhancement module, which incorporates more specific information from visual guidance to reduce ambiguity in the text prompt and preserve high-frequency details. To further enhance the visual guidance, we introduce a Direction-Aware Adaptation module that automatically designs direction prompts based on different camera poses, avoiding the Janus problem and maintaining semantically global consistency. Benefiting from the visual guidance, FlexiTex produces quantitatively and qualitatively sound results, demonstrating its potential to advance texture generation for real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-20", "arxiv_id": "2409.12532", "title": "Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Latent Generation", "authors": [ "Chenyu Wang", "Shuo Yan", "Yixuan Chen", "Yujiang Wang", "Mingzhi Dong", "Xiaochen Yang", "Dongsheng Li", "Robert P. Dick", "Qin Lv", "Fan Yang", "Tun Lu", "Ning Gu", "Li Shang" ], "github": "", "project_page": "", "abstract": "Video generation using diffusion-based models is constrained by high computational costs due to the frame-wise iterative diffusion process. This work presents a Diffusion Reuse MOtion (Dr. Mo) network to accelerate latent video generation. Our key discovery is that coarse-grained noises in earlier denoising steps have demonstrated high motion consistency across consecutive video frames. Following this observation, Dr. Mo propagates those coarse-grained noises onto the next frame by incorporating carefully designed, lightweight inter-frame motions, eliminating massive computational redundancy in frame-wise diffusion models. The more sensitive and fine-grained noises are still acquired via later denoising steps, which can be essential to retain visual qualities. As such, deciding which intermediate steps should switch from motion-based propagations to denoising can be a crucial problem and a key tradeoff between efficiency and quality. Dr. Mo employs a meta-network named Denoising Step Selector (DSS) to dynamically determine desirable intermediate steps across video frames. Extensive evaluations on video generation and editing tasks have shown that Dr. Mo can substantially accelerate diffusion models in video tasks with improved visual qualities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-20", "arxiv_id": "2409.12961", "title": "Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution", "authors": [ "Zuyan Liu", "Yuhao Dong", "Ziwei Liu", "Winston Hu", "Jiwen Lu", "Yongming Rao" ], "github": "https://github.com/Oryx-mllm/Oryx", "project_page": "", "abstract": "Visual data comes in various forms, ranging from small icons of just a few pixels to long videos spanning hours. Existing multi-modal LLMs usually standardize these diverse visual inputs to a fixed resolution for visual encoders and yield similar numbers of tokens for LLMs. This approach is non-optimal for multimodal understanding and inefficient for processing inputs with long and short visual contents. To solve the problem, we propose Oryx, a unified multimodal architecture for the spatial-temporal understanding of images, videos, and multi-view 3D scenes. Oryx offers an on-demand solution to seamlessly and efficiently process visual inputs with arbitrary spatial sizes and temporal lengths through two core innovations: 1) a pre-trained OryxViT model that can encode images at any resolution into LLM-friendly visual representations; 2) a dynamic compressor module that supports 1x to 16x compression on visual tokens by request. These design features enable Oryx to accommodate extremely long visual contexts, such as videos, with lower resolution and high compression while maintaining high recognition precision for tasks like document understanding with native resolution and no compression. Beyond the architectural improvements, enhanced data curation and specialized training on long-context retrieval and spatial-aware data help Oryx achieve strong capabilities in image, video, and 3D multimodal understanding simultaneously. Our work is open-sourced at https://github.com/Oryx-mllm/Oryx.", "github_manual": "", "github_regex": "https://github.com/Oryx-mllm/Oryx", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-20", "arxiv_id": "2409.12959", "title": "MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines", "authors": [ "Dongzhi Jiang", "Renrui Zhang", "Ziyu Guo", "Yanmin Wu", "Jiayi Lei", "Pengshuo Qiu", "Pan Lu", "Zehui Chen", "Guanglu Song", "Peng Gao", "Yu Liu", "Chunyuan Li", "Hongsheng Li" ], "github": "https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md", "project_page": "", "abstract": "The advent of Large Language Models (LLMs) has paved the way for AI search engines, e.g., SearchGPT, showcasing a new paradigm in human-internet interaction. However, most current AI search engines are limited to text-only settings, neglecting the multimodal user queries and the text-image interleaved nature of website information. Recently, Large Multimodal Models (LMMs) have made impressive strides. Yet, whether they can function as AI search engines remains under-explored, leaving the potential of LMMs in multimodal search an open question. To this end, we first design a delicate pipeline, MMSearch-Engine, to empower any LMMs with multimodal search capabilities. On top of this, we introduce MMSearch, a comprehensive evaluation benchmark to assess the multimodal search performance of LMMs. The curated dataset contains 300 manually collected instances spanning 14 subfields, which involves no overlap with the current LMMs' training data, ensuring the correct answer can only be obtained within searching. By using MMSearch-Engine, the LMMs are evaluated by performing three individual tasks (requery, rerank, and summarization), and one challenging end-to-end task with a complete searching process. We conduct extensive experiments on closed-source and open-source LMMs. Among all tested models, GPT-4o with MMSearch-Engine achieves the best results, which surpasses the commercial product, Perplexity Pro, in the end-to-end task, demonstrating the effectiveness of our proposed pipeline. We further present error analysis to unveil current LMMs still struggle to fully grasp the multimodal search tasks, and conduct ablation study to indicate the potential of scaling test-time computation for AI search engine. We hope MMSearch may provide unique insights to guide the future development of multimodal AI search engine. Project Page: https://mmsearch.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-20", "arxiv_id": "2409.12822", "title": "Language Models Learn to Mislead Humans via RLHF", "authors": [ "Jiaxin Wen", "Ruiqi Zhong", "Akbir Khan", "Ethan Perez", "Jacob Steinhardt", "Minlie Huang", "Samuel R. Boman", "He He", "Shi Feng" ], "github": "https://github.com/jiaxin-wen/misleadlm", "project_page": "", "abstract": "Language models (LMs) can produce errors that are hard to detect for humans, especially when the task is complex. RLHF, the most popular post-training method, may exacerbate this problem: to achieve higher rewards, LMs might get better at convincing humans that they are right even when they are wrong. We study this phenomenon under a standard RLHF pipeline, calling it \"U-SOPHISTRY\" since it is Unintended by model developers. Specifically, we ask time-constrained (e.g., 3-10 minutes) human subjects to evaluate the correctness of model outputs and calculate humans' accuracy against gold labels. On a question-answering task (QuALITY) and programming task (APPS), RLHF makes LMs better at convincing our subjects but not at completing the task correctly. RLHF also makes the model harder to evaluate: our subjects' false positive rate increases by 24.1% on QuALITY and 18.3% on APPS. Finally, we show that probing, a state-of-the-art approach for detecting Intended Sophistry (e.g. backdoored LMs), does not generalize to U-SOPHISTRY. Our results highlight an important failure mode of RLHF and call for more research in assisting humans to align them.", "github_manual": "https://github.com/jiaxin-wen/misleadlm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-20", "arxiv_id": "2409.12917", "title": "Training Language Models to Self-Correct via Reinforcement Learning", "authors": [ "Aviral Kumar", "Vincent Zhuang", "Rishabh Agarwal", "Yi Su", "John D Co-Reyes", "Avi Singh", "Kate Baumli", "Shariq Iqbal", "Colton Bishop", "Rebecca Roelofs", "Lei M Zhang", "Kay McKinney", "Disha Shrivastava", "Cosmin Paduraru", "George Tucker", "Doina Precup", "Feryal Behbahani", "Aleksandra Faust" ], "github": "", "project_page": "", "abstract": "Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs. Existing approaches for training self-correction either require multiple models or rely on a more capable model or other forms of supervision. To this end, we develop a multi-turn online reinforcement learning (RL) approach, SCoRe, that significantly improves an LLM's self-correction ability using entirely self-generated data. To build SCoRe, we first show that variants of supervised fine-tuning (SFT) on offline model-generated correction traces are insufficient for instilling self-correction behavior. In particular, we observe that training via SFT either suffers from a distribution mismatch between the training data and the model's own responses or implicitly prefers only a certain mode of correction behavior that is often not effective at test time. SCoRe addresses these challenges by training under the model's own distribution of self-generated correction traces and using appropriate regularization to steer the learning process into learning a self-correction strategy that is effective at test time as opposed to simply fitting high-reward responses for a given prompt. This regularization prescribes running a first phase of RL on a base model to generate a policy initialization that is less susceptible to collapse and then using a reward bonus to amplify self-correction during training. When applied to Gemini 1.0 Pro and 1.5 Flash models, we find that SCoRe achieves state-of-the-art self-correction performance, improving the base models' self-correction by 15.6% and 9.1% respectively on the MATH and HumanEval benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-20", "arxiv_id": "2409.08692", "title": "B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests", "authors": [ "Mouxiang Chen", "Zhongxin Liu", "He Tao", "Yusu Hong", "David Lo", "Xin Xia", "Jianling Sun" ], "github": "https://github.com/ZJU-CTAG/B4", "project_page": "", "abstract": "Selecting the best code solution from multiple generated ones is an essential task in code generation, which can be achieved by using some reliable validators (e.g., developer-written test cases) for assistance. Since reliable test cases are not always available and can be expensive to build in practice, researchers propose to automatically generate test cases to assess code solutions. However, when both code solutions and test cases are plausible and not reliable, selecting the best solution becomes challenging. Although some heuristic strategies have been proposed to tackle this problem, they lack a strong theoretical guarantee and it is still an open question whether an optimal selection strategy exists. Our work contributes in two ways. First, we show that within a Bayesian framework, the optimal selection strategy can be defined based on the posterior probability of the observed passing states between solutions and tests. The problem of identifying the best solution is then framed as an integer programming problem. Second, we propose an efficient approach for approximating this optimal (yet uncomputable) strategy, where the approximation error is bounded by the correctness of prior knowledge. We then incorporate effective prior knowledge to tailor code generation tasks. Both theoretical and empirical studies confirm that existing heuristics are limited in selecting the best solutions with plausible test cases. Our proposed approximated optimal strategy B4 significantly surpasses existing heuristics in selecting code solutions generated by large language models (LLMs) with LLM-generated tests, achieving a relative performance improvement by up to 50% over the strongest heuristic and 246% over the random selection in the most challenging scenarios. Our code is publicly available at https://github.com/ZJU-CTAG/B4.", "github_manual": "", "github_regex": "https://github.com/ZJU-CTAG/B4", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-20", "arxiv_id": "2409.12962", "title": "CLAIR-A: Leveraging Large Language Models to Judge Audio Captions", "authors": [ "Tsung-Han Wu", "Joseph E. Gonzalez", "Trevor Darrell", "David M. Chan" ], "github": "https://github.com/DavidMChan/clair-a", "project_page": "", "abstract": "The Automated Audio Captioning (AAC) task asks models to generate natural language descriptions of an audio input. Evaluating these machine-generated audio captions is a complex task that requires considering diverse factors, among them, auditory scene understanding, sound-object inference, temporal coherence, and the environmental context of the scene. While current methods focus on specific aspects, they often fail to provide an overall score that aligns well with human judgment. In this work, we propose CLAIR-A, a simple and flexible method that leverages the zero-shot capabilities of large language models (LLMs) to evaluate candidate audio captions by directly asking LLMs for a semantic distance score. In our evaluations, CLAIR-A better predicts human judgements of quality compared to traditional metrics, with a 5.8% relative accuracy improvement compared to the domain-specific FENSE metric and up to 11% over the best general-purpose measure on the Clotho-Eval dataset. Moreover, CLAIR-A offers more transparency by allowing the language model to explain the reasoning behind its scores, with these explanations rated up to 30% better by human evaluators than those provided by baseline methods. CLAIR-A is made publicly available at https://github.com/DavidMChan/clair-a.", "github_manual": "", "github_regex": "https://github.com/DavidMChan/clair-a", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-23", "arxiv_id": "2409.13591", "title": "Portrait Video Editing Empowered by Multimodal Generative Priors", "authors": [ "Xuan Gao", "Haiyao Xiao", "Chenglai Zhong", "Shimin Hu", "Yudong Guo", "Juyong Zhang" ], "github": "", "project_page": "", "abstract": "We introduce PortraitGen, a powerful portrait video editing method that achieves consistent and expressive stylization with multimodal prompts. Traditional portrait video editing methods often struggle with 3D and temporal consistency, and typically lack in rendering quality and efficiency. To address these issues, we lift the portrait video frames to a unified dynamic 3D Gaussian field, which ensures structural and temporal coherence across frames. Furthermore, we design a novel Neural Gaussian Texture mechanism that not only enables sophisticated style editing but also achieves rendering speed over 100FPS. Our approach incorporates multimodal inputs through knowledge distilled from large-scale 2D generative models. Our system also incorporates expression similarity guidance and a face-aware portrait editing module, effectively mitigating degradation issues associated with iterative dataset updates. Extensive experiments demonstrate the temporal consistency, editing efficiency, and superior rendering quality of our method. The broad applicability of the proposed approach is demonstrated through various applications, including text-driven editing, image-driven editing, and relighting, highlighting its great potential to advance the field of video editing. Demo videos and released code are provided in our project page: https://ustc3dv.github.io/PortraitGen/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-23", "arxiv_id": "2409.13216", "title": "MuCodec: Ultra Low-Bitrate Music Codec", "authors": [ "Yaoxun Xu", "Hangting Chen", "Jianwei Yu", "Wei Tan", "Rongzhi Gu", "Shun Lei", "Zhiwei Lin", "Zhiyong Wu" ], "github": "https://github.com/xuyaoxun/MuCodec", "project_page": "", "abstract": "Music codecs are a vital aspect of audio codec research, and ultra low-bitrate compression holds significant importance for music transmission and generation. Due to the complexity of music backgrounds and the richness of vocals, solely relying on modeling semantic or acoustic information cannot effectively reconstruct music with both vocals and backgrounds. To address this issue, we propose MuCodec, specifically targeting music compression and reconstruction tasks at ultra low bitrates. MuCodec employs MuEncoder to extract both acoustic and semantic features, discretizes them with RVQ, and obtains Mel-VAE features via flow-matching. The music is then reconstructed using a pre-trained MEL-VAE decoder and HiFi-GAN. MuCodec can reconstruct high-fidelity music at ultra low (0.35kbps) or high bitrates (1.35kbps), achieving the best results to date in both subjective and objective metrics. Code and Demo: https://xuyaoxun.github.io/MuCodec_demo/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xuyaoxun/MuCodec", "github_api_added_by": "auto" }, { "date": "2024-09-23", "arxiv_id": "2409.13648", "title": "V^3: Viewing Volumetric Videos on Mobiles via Streamable 2D Dynamic Gaussians", "authors": [ "Penghao Wang", "Zhirui Zhang", "Liao Wang", "Kaixin Yao", "Siyuan Xie", "Jingyi Yu", "Minye Wu", "Lan Xu" ], "github": "", "project_page": "", "abstract": "Experiencing high-fidelity volumetric video as seamlessly as 2D videos is a long-held dream. However, current dynamic 3DGS methods, despite their high rendering quality, face challenges in streaming on mobile devices due to computational and bandwidth constraints. In this paper, we introduce V3(Viewing Volumetric Videos), a novel approach that enables high-quality mobile rendering through the streaming of dynamic Gaussians. Our key innovation is to view dynamic 3DGS as 2D videos, facilitating the use of hardware video codecs. Additionally, we propose a two-stage training strategy to reduce storage requirements with rapid training speed. The first stage employs hash encoding and shallow MLP to learn motion, then reduces the number of Gaussians through pruning to meet the streaming requirements, while the second stage fine tunes other Gaussian attributes using residual entropy loss and temporal loss to improve temporal continuity. This strategy, which disentangles motion and appearance, maintains high rendering quality with compact storage requirements. Meanwhile, we designed a multi-platform player to decode and render 2D Gaussian videos. Extensive experiments demonstrate the effectiveness of V3, outperforming other methods by enabling high-quality rendering and streaming on common devices, which is unseen before. As the first to stream dynamic Gaussians on mobile devices, our companion player offers users an unprecedented volumetric video experience, including smooth scrolling and instant sharing. Our project page with source code is available at https://authoritywang.github.io/v3/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-23", "arxiv_id": "2409.13346", "title": "Imagine yourself: Tuning-Free Personalized Image Generation", "authors": [ "Zecheng He", "Bo Sun", "Felix Juefei-Xu", "Haoyu Ma", "Ankit Ramchandani", "Vincent Cheung", "Siddharth Shah", "Anmol Kalia", "Harihar Subramanyam", "Alireza Zareian", "Li Chen", "Ankit Jain", "Ning Zhang", "Peizhao Zhang", "Roshan Sumbaly", "Peter Vajda", "Animesh Sinha" ], "github": "", "project_page": "", "abstract": "Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for personalized image generation. Unlike conventional tuning-based personalization techniques, Imagine yourself operates as a tuning-free model, enabling all users to leverage a shared framework without individualized adjustments. Moreover, previous work met challenges balancing identity preservation, following complex prompts and preserving good visual quality, resulting in models having strong copy-paste effect of the reference images. Thus, they can hardly generate images following prompts that require significant changes to the reference image, \\eg, changing facial expression, head and body poses, and the diversity of the generated images is low. To address these limitations, our proposed method introduces 1) a new synthetic paired data generation mechanism to encourage image diversity, 2) a fully parallel attention architecture with three text encoders and a fully trainable vision encoder to improve the text faithfulness, and 3) a novel coarse-to-fine multi-stage finetuning methodology that gradually pushes the boundary of visual quality. Our study demonstrates that Imagine yourself surpasses the state-of-the-art personalization model, exhibiting superior capabilities in identity preservation, visual quality, and text alignment. This model establishes a robust foundation for various personalization applications. Human evaluation results validate the model's SOTA superiority across all aspects (identity preservation, text faithfulness, and visual appeal) compared to the previous personalization models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-23", "arxiv_id": "2409.13690", "title": "Colorful Diffuse Intrinsic Image Decomposition in the Wild", "authors": [ "Chris Careaga", "Ya\u011f\u0131z Aksoy" ], "github": "", "project_page": "", "abstract": "Intrinsic image decomposition aims to separate the surface reflectance and the effects from the illumination given a single photograph. Due to the complexity of the problem, most prior works assume a single-color illumination and a Lambertian world, which limits their use in illumination-aware image editing applications. In this work, we separate an input image into its diffuse albedo, colorful diffuse shading, and specular residual components. We arrive at our result by gradually removing first the single-color illumination and then the Lambertian-world assumptions. We show that by dividing the problem into easier sub-problems, in-the-wild colorful diffuse shading estimation can be achieved despite the limited ground-truth datasets. Our extended intrinsic model enables illumination-aware analysis of photographs and can be used for image editing applications such as specularity removal and per-pixel white balancing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-23", "arxiv_id": "2409.13598", "title": "Prithvi WxC: Foundation Model for Weather and Climate", "authors": [ "Johannes Schmude", "Sujit Roy", "Will Trojak", "Johannes Jakubik", "Daniel Salles Civitarese", "Shraddha Singh", "Julian Kuehnert", "Kumar Ankur", "Aman Gupta", "Christopher E Phillips", "Romeo Kienzler", "Daniela Szwarcman", "Vishal Gaur", "Rajat Shinde", "Rohit Lal", "Arlindo Da Silva", "Jorge Luis Guevara Diaz", "Anne Jones", "Simon Pfreundschuh", "Amy Lin", "Aditi Sheshadri", "Udaysankar Nair", "Valentine Anantharaj", "Hendrik Hamann", "Campbell Watson", "Manil Maskey", "Tsengdar J Lee", "Juan Bernabe Moreno", "Rahul Ramachandran" ], "github": "", "project_page": "", "abstract": "Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI literature focus on foundation models -- models that can be effectively tuned to address multiple, different use cases -- the developments on the weather and climate side largely focus on single-use cases with particular emphasis on mid-range forecasting. We close this gap by introducing Prithvi WxC, a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Prithvi WxC employs an encoder-decoder-based architecture, incorporating concepts from various recent transformer models to effectively capture both regional and global dependencies in the input data. The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. Furthermore, it is trained with a mixed objective that combines the paradigms of masked reconstruction with forecasting. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation. The pretrained model with 2.3 billion parameters, along with the associated fine-tuning workflows, has been publicly released as an open-source contribution via Hugging Face.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-23", "arxiv_id": "2409.13689", "title": "Temporally Aligned Audio for Video with Autoregression", "authors": [ "Ilpo Viertola", "Vladimir Iashin", "Esa Rahtu" ], "github": "https://github.com/ilpoviertola/V-AURA", "project_page": "", "abstract": "We introduce V-AURA, the first autoregressive model to achieve high temporal alignment and relevance in video-to-audio generation. V-AURA uses a high-framerate visual feature extractor and a cross-modal audio-visual feature fusion strategy to capture fine-grained visual motion events and ensure precise temporal alignment. Additionally, we propose VisualSound, a benchmark dataset with high audio-visual relevance. VisualSound is based on VGGSound, a video dataset consisting of in-the-wild samples extracted from YouTube. During the curation, we remove samples where auditory events are not aligned with the visual ones. V-AURA outperforms current state-of-the-art models in temporal alignment and semantic relevance while maintaining comparable audio quality. Code, samples, VisualSound and models are available at https://v-aura.notion.site", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ilpoviertola/V-AURA", "github_api_added_by": "auto" }, { "date": "2024-09-23", "arxiv_id": "2409.13449", "title": "Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts", "authors": [ "Ming Wang", "Yuanzhong Liu", "Xiaoyu Liang", "Yijie Huang", "Daling Wang", "Xiaocui Yang", "Sijia Shen", "Shi Feng", "Xiaoming Zhang", "Chaofeng Guan", "Yifei Zhang" ], "github": "https://github.com/langgptai/LangGPT", "project_page": "", "abstract": "LLMs have demonstrated commendable performance across diverse domains. Nevertheless, formulating high-quality prompts to assist them in their work poses a challenge for non-AI experts. Existing research in prompt engineering suggests somewhat scattered optimization principles and designs empirically dependent prompt optimizers. Unfortunately, these endeavors lack a structural design, incurring high learning costs and it is not conducive to the iterative updating of prompts, especially for non-AI experts. Inspired by structured reusable programming languages, we propose LangGPT, a structural prompt design framework. Furthermore, we introduce Minstrel, a multi-generative agent system with reflection to automate the generation of structural prompts. Experiments and the case study illustrate that structural prompts generated by Minstrel or written manually significantly enhance the performance of LLMs. Furthermore, we analyze the ease of use of structural prompts through a user survey in our online community.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/langgptai/LangGPT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-23", "arxiv_id": "2409.11276", "title": "Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments", "authors": [ "Maria Rigaki", "Carlos Catania", "Sebastian Garcia" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have shown remarkable potential across various domains, including cybersecurity. Using commercial cloud-based LLMs may be undesirable due to privacy concerns, costs, and network connectivity constraints. In this paper, we present Hackphyr, a locally fine-tuned LLM to be used as a red-team agent within network security environments. Our fine-tuned 7 billion parameter model can run on a single GPU card and achieves performance comparable with much larger and more powerful commercial models such as GPT-4. Hackphyr clearly outperforms other models, including GPT-3.5-turbo, and baselines, such as Q-learning agents in complex, previously unseen scenarios. To achieve this performance, we generated a new task-specific cybersecurity dataset to enhance the base model's capabilities. Finally, we conducted a comprehensive analysis of the agents' behaviors that provides insights into the planning abilities and potential shortcomings of such agents, contributing to the broader understanding of LLM-based agents in cybersecurity contexts", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-23", "arxiv_id": "2409.12941", "title": "Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation", "authors": [ "Satyapriya Krishna", "Kalpesh Krishna", "Anhad Mohananey", "Steven Schwarcz", "Adam Stambler", "Shyam Upadhyay", "Manaal Faruqui" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated significant performance improvements across various cognitive tasks. An emerging application is using LLMs to enhance retrieval-augmented generation (RAG) capabilities. These systems require LLMs to understand user queries, retrieve relevant information, and synthesize coherent and accurate responses. Given the increasing real-world deployment of such systems, comprehensive evaluation becomes crucial. To this end, we propose FRAMES (Factuality, Retrieval, And reasoning MEasurement Set), a high-quality evaluation dataset designed to test LLMs' ability to provide factual responses, assess retrieval capabilities, and evaluate the reasoning required to generate final answers. While previous work has provided datasets and benchmarks to evaluate these abilities in isolation, FRAMES offers a unified framework that provides a clearer picture of LLM performance in end-to-end RAG scenarios. Our dataset comprises challenging multi-hop questions that require the integration of information from multiple sources. We present baseline results demonstrating that even state-of-the-art LLMs struggle with this task, achieving 0.40 accuracy with no retrieval. The accuracy is significantly improved with our proposed multi-step retrieval pipeline, achieving an accuracy of 0.66 (>50% improvement). We hope our work will help bridge evaluation gaps and assist in developing more robust and capable RAG systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-23", "arxiv_id": "2409.13592", "title": "YesBut: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models", "authors": [ "Abhilash Nandy", "Yash Agarwal", "Ashish Patwa", "Millon Madhur Das", "Aman Bansal", "Ankit Raj", "Pawan Goyal", "Niloy Ganguly" ], "github": "https://github.com/abhi1nandy2/yesbut_dataset", "project_page": "", "abstract": "Understanding satire and humor is a challenging task for even current Vision-Language models. In this paper, we propose the challenging tasks of Satirical Image Detection (detecting whether an image is satirical), Understanding (generating the reason behind the image being satirical), and Completion (given one half of the image, selecting the other half from 2 given options, such that the complete image is satirical) and release a high-quality dataset YesBut, consisting of 2547 images, 1084 satirical and 1463 non-satirical, containing different artistic styles, to evaluate those tasks. Each satirical image in the dataset depicts a normal scenario, along with a conflicting scenario which is funny or ironic. Despite the success of current Vision-Language Models on multimodal tasks such as Visual QA and Image Captioning, our benchmarking experiments show that such models perform poorly on the proposed tasks on the YesBut Dataset in Zero-Shot Settings w.r.t both automated as well as human evaluation. Additionally, we release a dataset of 119 real, satirical photographs for further research. The dataset and code are available at https://github.com/abhi1nandy2/yesbut_dataset.", "github_manual": "", "github_regex": "https://github.com/abhi1nandy2/yesbut_dataset", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-23", "arxiv_id": "2409.11393", "title": "LLM-Agent-UMF: LLM-based Agent Unified Modeling Framework for Seamless Integration of Multi Active/Passive Core-Agents", "authors": [ "Amine B. Hassouna", "Hana Chaari", "Ines Belhaj" ], "github": "", "project_page": "", "abstract": "The integration of tools in LLM-based agents overcame the difficulties of standalone LLMs and traditional agents' limited capabilities. However, the conjunction of these technologies and the proposed enhancements in several state-of-the-art works followed a non-unified software architecture resulting in a lack of modularity. Indeed, they focused mainly on functionalities and overlooked the definition of the component's boundaries within the agent. This caused terminological and architectural ambiguities between researchers which we addressed in this paper by proposing a unified framework that establishes a clear foundation for LLM-based agents' development from both functional and software architectural perspectives. Our framework, LLM-Agent-UMF (LLM-based Agent Unified Modeling Framework), clearly distinguishes between the different components of an agent, setting LLMs, and tools apart from a newly introduced element: the core-agent, playing the role of the central coordinator of the agent which comprises five modules: planning, memory, profile, action, and security, the latter often neglected in previous works. Differences in the internal structure of core-agents led us to classify them into a taxonomy of passive and active types. Based on this, we proposed different multi-core agent architectures combining unique characteristics of various individual agents. For evaluation purposes, we applied this framework to a selection of state-of-the-art agents, thereby demonstrating its alignment with their functionalities and clarifying the overlooked architectural aspects. Moreover, we thoroughly assessed four of our proposed architectures by integrating distinctive agents into hybrid active/passive core-agents' systems. This analysis provided clear insights into potential improvements and highlighted the challenges involved in the combination of specific agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.13191", "title": "An adapted large language model facilitates multiple medical tasks in diabetes care", "authors": [ "Lai Wei", "Zhen Ying", "Muyang He", "Yutong Chen", "Qian Yang", "Yanzhe Hong", "Jiaping Lu", "Xiaoying Li", "Weiran Huang", "Ying Chen" ], "github": "https://github.com/waltonfuture/Diabetica", "project_page": "", "abstract": "Diabetes is a chronic disease that poses a significant global health burden, and optimizing diabetes management requires multi-stakeholder collaboration. Large language models (LLMs) have shown promise in various healthcare scenarios, but their effectiveness across a diverse range of diabetes tasks remains unproven. In this study, we introduced a framework to train and validate diabetes-specific LLMs. We first developed a comprehensive data processing pipeline that includes data collection, filtering, augmentation and refinement. This approach contributes to creating a high-quality, diabetes-specific dataset, and several evaluation benchmarks entirely from scratch. Utilizing the collected training dataset, we fine-tuned a diabetes-specific LLM family that demonstrated state-of-the-art proficiency in understanding and processing various diabetes tasks compared to other LLMs. Furthermore, clinical studies showed the potential applications of our models in diabetes care, including providing personalized healthcare, assisting medical education, and streamlining clinical tasks. In conclusion, our study introduced a framework to develop and evaluate a diabetes-specific LLM family, and highlighted its potential to enhance clinical practice and provide personalized, data-driven support for diabetes support when facing different end users. The code is provided via GitHub at https://github.com/waltonfuture/Diabetica.", "github_manual": "", "github_regex": "https://github.com/waltonfuture/Diabetica", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.15277", "title": "A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?", "authors": [ "Yunfei Xie", "Juncheng Wu", "Haoqin Tu", "Siwei Yang", "Bingchen Zhao", "Yongshuo Zong", "Qiao Jin", "Cihang Xie", "Yuyin Zhou" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest model, OpenAI's o1, stands out as the first LLM with an internalized chain-of-thought technique using reinforcement learning strategies. While it has demonstrated surprisingly strong capabilities on various general language tasks, its performance in specialized fields such as medicine remains unknown. To this end, this report provides a comprehensive exploration of o1 on different medical scenarios, examining 3 key aspects: understanding, reasoning, and multilinguality. Specifically, our evaluation encompasses 6 tasks using data from 37 medical datasets, including two newly constructed and more challenging question-answering (QA) tasks based on professional medical quizzes from the New England Journal of Medicine (NEJM) and The Lancet. These datasets offer greater clinical relevance compared to standard medical QA benchmarks such as MedQA, translating more effectively into real-world clinical utility. Our analysis of o1 suggests that the enhanced reasoning ability of LLMs may (significantly) benefit their capability to understand various medical instructions and reason through complex clinical scenarios. Notably, o1 surpasses the previous GPT-4 in accuracy by an average of 6.2% and 6.6% across 19 datasets and two newly created complex QA scenarios. But meanwhile, we identify several weaknesses in both the model capability and the existing evaluation protocols, including hallucination, inconsistent multilingual ability, and discrepant metrics for evaluation. We release our raw data and model outputs at https://ucsc-vlaa.github.io/o1_medicine/ for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.14340", "title": "Self-Supervised Audio-Visual Soundscape Stylization", "authors": [ "Tingle Li", "Renhao Wang", "Po-Yao Huang", "Andrew Owens", "Gopala Anumanchipalli" ], "github": "", "project_page": "", "abstract": "Speech sounds convey a great deal of information about the scenes, resulting in a variety of effects ranging from reverberation to additional ambient sounds. In this paper, we manipulate input speech to sound as though it was recorded within a different scene, given an audio-visual conditional example recorded from that scene. Our model learns through self-supervision, taking advantage of the fact that natural video contains recurring sound events and textures. We extract an audio clip from a video and apply speech enhancement. We then train a latent diffusion model to recover the original speech, using another audio-visual clip taken from elsewhere in the video as a conditional hint. Through this process, the model learns to transfer the conditional example's sound properties to the input speech. We show that our model can be successfully trained using unlabeled, in-the-wild videos, and that an additional visual signal can improve its sound prediction abilities. Please see our project webpage for video results: https://tinglok.netlify.app/files/avsoundscape/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.13773", "title": "A Case Study of Web App Coding with OpenAI Reasoning Models", "authors": [ "Yi Cui" ], "github": "", "project_page": "", "abstract": "This paper presents a case study of coding tasks by the latest reasoning models of OpenAI, i.e. o1-preview and o1-mini, in comparison with other frontier models. The o1 models deliver SOTA results for WebApp1K, a single-task benchmark. To this end, we introduce WebApp1K-Duo, a harder benchmark doubling number of tasks and test cases. The new benchmark causes the o1 model performances to decline significantly, falling behind Claude 3.5. Moreover, they consistently fail when confronted with atypical yet correct test cases, a trap non-reasoning models occasionally avoid. We hypothesize that the performance variability is due to instruction comprehension. Specifically, the reasoning mechanism boosts performance when all expectations are captured, meanwhile exacerbates errors when key expectations are missed, potentially impacted by input lengths. As such, we argue that the coding success of reasoning models hinges on the top-notch base model and SFT to ensure meticulous adherence to instructions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.14674", "title": "RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning", "authors": [ "Yinpei Dai", "Jayjun Lee", "Nima Fazeli", "Joyce Chai" ], "github": "", "project_page": "", "abstract": "Developing robust and correctable visuomotor policies for robotic manipulation is challenging due to the lack of self-recovery mechanisms from failures and the limitations of simple language instructions in guiding robot actions. To address these issues, we propose a scalable data generation pipeline that automatically augments expert demonstrations with failure recovery trajectories and fine-grained language annotations for training. We then introduce Rich languAge-guided failure reCovERy (RACER), a supervisor-actor framework, which combines failure recovery data with rich language descriptions to enhance robot control. RACER features a vision-language model (VLM) that acts as an online supervisor, providing detailed language guidance for error correction and task execution, and a language-conditioned visuomotor policy as an actor to predict the next actions. Our experimental results show that RACER outperforms the state-of-the-art Robotic View Transformer (RVT) on RLbench across various evaluation settings, including standard long-horizon tasks, dynamic goal-change tasks and zero-shot unseen tasks, achieving superior performance in both simulated and real world environments. Videos and code are available at: https://rich-language-failure-recovery.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.14713", "title": "Phantom of Latent for Large Language and Vision Models", "authors": [ "Byung-Kwan Lee", "Sangyun Chung", "Chae Won Kim", "Beomchan Park", "Yong Man Ro" ], "github": "https://github.com/byungkwanlee/phantom", "project_page": "", "abstract": "The success of visual instruction tuning has accelerated the development of large language and vision models (LLVMs). Following the scaling laws of instruction-tuned large language models (LLMs), LLVMs either have further increased their sizes, reaching 26B, 34B, and even 80B parameters. While this increase in model size has yielded significant performance gains, it demands substantially more hardware resources for both training and inference. Consequently, there naturally exists a strong need for efficient LLVMs that achieve the performance of larger models while being smaller in size. To achieve this need, we present a new efficient LLVM family with model sizes of 0.5B, 1.8B, 3.8B, and 7B parameters, Phantom, which significantly enhances learning capabilities within limited structures. By temporarily increasing the latent hidden dimension during multi-head self-attention (MHSA), we make LLVMs prepare to look and understand much more vision-language knowledge on the latent, without substantially increasing physical model sizes. To maximize its advantage, we introduce Phantom Optimization (PO) using both autoregressive supervised fine-tuning (SFT) and direct preference optimization (DPO)-like concept, which effectively follows correct answers while eliminating incorrect and ambiguous ones. Phantom outperforms numerous larger open- and closed-source LLVMs, positioning itself as a leading solution in the landscape of efficient LLVMs.", "github_manual": "https://github.com/byungkwanlee/phantom", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.14393", "title": "MaskedMimic: Unified Physics-Based Character Control Through Masked Motion Inpainting", "authors": [ "Chen Tessler", "Yunrong Guo", "Ofir Nabati", "Gal Chechik", "Xue Bin Peng" ], "github": "", "project_page": "", "abstract": "Crafting a single, versatile physics-based controller that can breathe life into interactive characters across a wide spectrum of scenarios represents an exciting frontier in character animation. An ideal controller should support diverse control modalities, such as sparse target keyframes, text instructions, and scene information. While previous works have proposed physically simulated, scene-aware control models, these systems have predominantly focused on developing controllers that each specializes in a narrow set of tasks and control modalities. This work presents MaskedMimic, a novel approach that formulates physics-based character control as a general motion inpainting problem. Our key insight is to train a single unified model to synthesize motions from partial (masked) motion descriptions, such as masked keyframes, objects, text descriptions, or any combination thereof. This is achieved by leveraging motion tracking data and designing a scalable training method that can effectively utilize diverse motion descriptions to produce coherent animations. Through this process, our approach learns a physics-based controller that provides an intuitive control interface without requiring tedious reward engineering for all behaviors of interest. The resulting controller supports a wide range of control modalities and enables seamless transitions between disparate tasks. By unifying character control through motion inpainting, MaskedMimic creates versatile virtual characters. These characters can dynamically adapt to complex scenes and compose diverse motions on demand, enabling more interactive and immersive experiences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.13910", "title": "Zero-shot Cross-lingual Voice Transfer for TTS", "authors": [ "Fadi Biadsy", "Youzheng Chen", "Isaac Elias", "Kyle Kastner", "Gary Wang", "Andrew Rosenberg", "Bhuvana Ramabhadran" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce a zero-shot Voice Transfer (VT) module that can be seamlessly integrated into a multi-lingual Text-to-speech (TTS) system to transfer an individual's voice across languages. Our proposed VT module comprises a speaker-encoder that processes reference speech, a bottleneck layer, and residual adapters, connected to preexisting TTS layers. We compare the performance of various configurations of these components and report Mean Opinion Score (MOS) and Speaker Similarity across languages. Using a single English reference speech per speaker, we achieve an average voice transfer similarity score of 73% across nine target languages. Vocal characteristics contribute significantly to the construction and perception of individual identity. The loss of one's voice, due to physical or neurological conditions, can lead to a profound sense of loss, impacting one's core identity. As a case study, we demonstrate that our approach can not only transfer typical speech but also restore the voices of individuals with dysarthria, even when only atypical speech samples are available - a valuable utility for those who have never had typical speech or banked their voice. Cross-lingual typical audio samples, plus videos demonstrating voice restoration for dysarthric speakers are available here (google.github.io/tacotron/publications/zero_shot_voice_transfer).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.15273", "title": "MaterialFusion: Enhancing Inverse Rendering with Material Diffusion Priors", "authors": [ "Yehonathan Litman", "Or Patashnik", "Kangle Deng", "Aviral Agrawal", "Rushikesh Zawar", "Fernando De la Torre", "Shubham Tulsiani" ], "github": "", "project_page": "", "abstract": "Recent works in inverse rendering have shown promise in using multi-view images of an object to recover shape, albedo, and materials. However, the recovered components often fail to render accurately under new lighting conditions due to the intrinsic challenge of disentangling albedo and material properties from input images. To address this challenge, we introduce MaterialFusion, an enhanced conventional 3D inverse rendering pipeline that incorporates a 2D prior on texture and material properties. We present StableMaterial, a 2D diffusion model prior that refines multi-lit data to estimate the most likely albedo and material from given input appearances. This model is trained on albedo, material, and relit image data derived from a curated dataset of approximately ~12K artist-designed synthetic Blender objects called BlenderVault. we incorporate this diffusion prior with an inverse rendering framework where we use score distillation sampling (SDS) to guide the optimization of the albedo and materials, improving relighting performance in comparison with previous work. We validate MaterialFusion's relighting performance on 4 datasets of synthetic and real objects under diverse illumination conditions, showing our diffusion-aided approach significantly improves the appearance of reconstructed objects under novel lighting conditions. We intend to publicly release our BlenderVault dataset to support further research in this field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.15278", "title": "PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions", "authors": [ "Weifeng Lin", "Xinyu Wei", "Renrui Zhang", "Le Zhuo", "Shitian Zhao", "Siyuan Huang", "Junlin Xie", "Yu Qiao", "Peng Gao", "Hongsheng Li" ], "github": "https://github.com/AFeng-x/PixWizard", "project_page": "", "abstract": "This paper presents a versatile image-to-image visual assistant, PixWizard, designed for image generation, manipulation, and translation based on free-from language instructions. To this end, we tackle a variety of vision tasks into a unified image-text-to-image generation framework and curate an Omni Pixel-to-Pixel Instruction-Tuning Dataset. By constructing detailed instruction templates in natural language, we comprehensively include a large set of diverse vision tasks such as text-to-image generation, image restoration, image grounding, dense image prediction, image editing, controllable generation, inpainting/outpainting, and more. Furthermore, we adopt Diffusion Transformers (DiT) as our foundation model and extend its capabilities with a flexible any resolution mechanism, enabling the model to dynamically process images based on the aspect ratio of the input, closely aligning with human perceptual processes. The model also incorporates structure-aware and semantic-aware guidance to facilitate effective fusion of information from the input image. Our experiments demonstrate that PixWizard not only shows impressive generative and understanding abilities for images with diverse resolutions but also exhibits promising generalization capabilities with unseen tasks and human instructions. The code and related resources are available at https://github.com/AFeng-x/PixWizard", "github_manual": "", "github_regex": "https://github.com/AFeng-x/PixWizard", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.14988", "title": "Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs", "authors": [ "Cl\u00e9ment Christophe", "Tathagata Raha", "Svetlana Maslenkova", "Muhammad Umar Salman", "Praveen K Kanithi", "Marco AF Pimentel", "Shadab Khan" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated significant potential in transforming clinical applications. In this study, we investigate the efficacy of four techniques in adapting LLMs for clinical use-cases: continuous pretraining, instruct fine-tuning, NEFTune, and prompt engineering. We employ these methods on Mistral 7B and Mixtral 8x7B models, leveraging a large-scale clinical pretraining dataset of 50 billion tokens and an instruct fine-tuning dataset of 500 million tokens. Our evaluation across various clinical tasks reveals the impact of each technique. While continuous pretraining beyond 250 billion tokens yields marginal improvements on its own, it establishes a strong foundation for instruct fine-tuning. Notably, NEFTune, designed primarily to enhance generation quality, surprisingly demonstrates additional gains on our benchmark. Complex prompt engineering methods further enhance performance. These findings show the importance of tailoring fine-tuning strategies and exploring innovative techniques to optimize LLM performance in the clinical domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.15268", "title": "Style over Substance: Failure Modes of LLM Judges in Alignment Benchmarking", "authors": [ "Benjamin Feuer", "Micah Goldblum", "Teresa Datta", "Sanjana Nambiar", "Raz Besaleli", "Samuel Dooley", "Max Cembalest", "John P. Dickerson" ], "github": "https://github.com/penfever/sos-bench", "project_page": "", "abstract": "The release of ChatGPT in November 2022 sparked an explosion of interest in post-training and an avalanche of new preference optimization (PO) methods. These methods claim superior alignment by virtue of better correspondence with human pairwise preferences, often measured by LLM judges. In this work, we attempt to answer the following question -- do LLM-judge preferences translate to progress on other, more concrete metrics for alignment, and if not, why not? We define a concrete metric for alignment, and introduce SOS-Bench, the largest standardized, reproducible LLM meta-benchmark to date. We find that (1) LLM-judgments do not correlate with concrete measures of safety, world knowledge, and instruction following; (2) LLM judges have powerful implicit biases, prioritizing style over factuality and safety; and (3) the supervised fine-tuning (SFT) stage of post-training, and not the PO stage, has the greatest impact on alignment, with data scaling and prompt diversity as the driving factors. Our codebase and complete results can be found at https://github.com/penfever/sos-bench.", "github_manual": "", "github_regex": "https://github.com/penfever/sos-bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.14677", "title": "Reflecting Reality: Enabling Diffusion Models to Produce Faithful Mirror Reflections", "authors": [ "Ankit Dhiman", "Manan Shah", "Rishubh Parihar", "Yash Bhalgat", "Lokesh R Boregowda", "R Venkatesh Babu" ], "github": "", "project_page": "", "abstract": "We tackle the problem of generating highly realistic and plausible mirror reflections using diffusion-based generative models. We formulate this problem as an image inpainting task, allowing for more user control over the placement of mirrors during the generation process. To enable this, we create SynMirror, a large-scale dataset of diverse synthetic scenes with objects placed in front of mirrors. SynMirror contains around 198K samples rendered from 66K unique 3D objects, along with their associated depth maps, normal maps and instance-wise segmentation masks, to capture relevant geometric properties of the scene. Using this dataset, we propose a novel depth-conditioned inpainting method called MirrorFusion, which generates high-quality geometrically consistent and photo-realistic mirror reflections given an input image and a mask depicting the mirror region. MirrorFusion outperforms state-of-the-art methods on SynMirror, as demonstrated by extensive quantitative and qualitative analysis. To the best of our knowledge, we are the first to successfully tackle the challenging problem of generating controlled and faithful mirror reflections of an object in a scene using diffusion based models. SynMirror and MirrorFusion open up new avenues for image editing and augmented reality applications for practitioners and researchers alike.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-24", "arxiv_id": "2409.13926", "title": "SpaceBlender: Creating Context-Rich Collaborative Spaces Through Generative 3D Scene Blending", "authors": [ "Nels Numan", "Shwetha Rajaram", "Balasaravanan Thoravi Kumaravel", "Nicolai Marquardt", "Andrew D. Wilson" ], "github": "", "project_page": "", "abstract": "There is increased interest in using generative AI to create 3D spaces for Virtual Reality (VR) applications. However, today's models produce artificial environments, falling short of supporting collaborative tasks that benefit from incorporating the user's physical context. To generate environments that support VR telepresence, we introduce SpaceBlender, a novel pipeline that utilizes generative AI techniques to blend users' physical surroundings into unified virtual spaces. This pipeline transforms user-provided 2D images into context-rich 3D environments through an iterative process consisting of depth estimation, mesh alignment, and diffusion-based space completion guided by geometric priors and adaptive text prompts. In a preliminary within-subjects study, where 20 participants performed a collaborative VR affinity diagramming task in pairs, we compared SpaceBlender with a generic virtual environment and a state-of-the-art scene generation framework, evaluating its ability to create virtual spaces suitable for collaboration. Participants appreciated the enhanced familiarity and context provided by SpaceBlender but also noted complexities in the generative environments that could detract from task focus. Drawing on participant feedback, we propose directions for improving the pipeline and discuss the value and design of blended spaces for different scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.16280", "title": "MonoFormer: One Transformer for Both Diffusion and Autoregression", "authors": [ "Chuyang Zhao", "Yuxing Song", "Wenhao Wang", "Haocheng Feng", "Errui Ding", "Yifan Sun", "Xinyan Xiao", "Jingdong Wang" ], "github": "https://github.com/MonoFormer/MonoFormer", "project_page": "", "abstract": "Most existing multimodality methods use separate backbones for autoregression-based discrete text generation and diffusion-based continuous visual generation, or the same backbone by discretizing the visual data to use autoregression for both text and visual generation. In this paper, we propose to study a simple idea: share one transformer for both autoregression and diffusion. The feasibility comes from two main aspects: (i) Transformer is successfully applied to diffusion for visual generation, and (ii) transformer training for autoregression and diffusion is very similar, and the difference merely lies in that diffusion uses bidirectional attention mask and autoregression uses causal attention mask. Experimental results show that our approach achieves comparable image generation performance to current state-of-the-art methods as well as maintains the text generation capability. The project is publicly available at https://monoformer.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MonoFormer/MonoFormer", "github_api_added_by": "auto" }, { "date": "2024-09-25", "arxiv_id": "2409.16191", "title": "HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models", "authors": [ "Haoran Que", "Feiyu Duan", "Liqun He", "Yutao Mou", "Wangchunshu Zhou", "Jiaheng Liu", "Wenge Rong", "Zekun Moore Wang", "Jian Yang", "Ge Zhang", "Junran Peng", "Zhaoxiang Zhang", "Songyang Zhang", "Kai Chen" ], "github": "https://github.com/Quehry/HelloBench", "project_page": "", "abstract": "In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks (e.g., long-context understanding), and many benchmarks have been proposed. However, we observe that long text generation capabilities are not well investigated. Therefore, we introduce the Hierarchical Long Text Generation Benchmark (HelloBench), a comprehensive, in-the-wild, and open-ended benchmark to evaluate LLMs' performance in generating long text. Based on Bloom's Taxonomy, HelloBench categorizes long text generation tasks into five subtasks: open-ended QA, summarization, chat, text completion, and heuristic text generation. Besides, we propose Hierarchical Long Text Evaluation (HelloEval), a human-aligned evaluation method that significantly reduces the time and effort required for human evaluation while maintaining a high correlation with human evaluation. We have conducted extensive experiments across around 30 mainstream LLMs and observed that the current LLMs lack long text generation capabilities. Specifically, first, regardless of whether the instructions include explicit or implicit length constraints, we observe that most LLMs cannot generate text that is longer than 4000 words. Second, we observe that while some LLMs can generate longer text, many issues exist (e.g., severe repetition and quality degradation). Third, to demonstrate the effectiveness of HelloEval, we compare HelloEval with traditional metrics (e.g., ROUGE, BLEU, etc.) and LLM-as-a-Judge methods, which show that HelloEval has the highest correlation with human evaluation. We release our code in https://github.com/Quehry/HelloBench.", "github_manual": "", "github_regex": "https://github.com/Quehry/HelloBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.16283", "title": "Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation", "authors": [ "Homanga Bharadhwaj", "Debidatta Dwibedi", "Abhinav Gupta", "Shubham Tulsiani", "Carl Doersch", "Ted Xiao", "Dhruv Shah", "Fei Xia", "Dorsa Sadigh", "Sean Kirmani" ], "github": "", "project_page": "", "abstract": "How can robot manipulation policies generalize to novel tasks involving unseen object types and new motions? In this paper, we provide a solution in terms of predicting motion information from web data through human video generation and conditioning a robot policy on the generated video. Instead of attempting to scale robot data collection which is expensive, we show how we can leverage video generation models trained on easily available web data, for enabling generalization. Our approach Gen2Act casts language-conditioned manipulation as zero-shot human video generation followed by execution with a single policy conditioned on the generated video. To train the policy, we use an order of magnitude less robot interaction data compared to what the video prediction model was trained on. Gen2Act doesn't require fine-tuning the video model at all and we directly use a pre-trained model for generating human videos. Our results on diverse real-world scenarios show how Gen2Act enables manipulating unseen object types and performing novel motions for tasks not present in the robot data. Videos are at https://homangab.github.io/gen2act/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.12192", "title": "DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control", "authors": [ "Zichen Jeff Cui", "Hengkai Pan", "Aadhithya Iyer", "Siddhant Haldar", "Lerrel Pinto" ], "github": "", "project_page": "", "abstract": "Imitation learning has proven to be a powerful tool for training complex visuomotor policies. However, current methods often require hundreds to thousands of expert demonstrations to handle high-dimensional visual observations. A key reason for this poor data efficiency is that visual representations are predominantly either pretrained on out-of-domain data or trained directly through a behavior cloning objective. In this work, we present DynaMo, a new in-domain, self-supervised method for learning visual representations. Given a set of expert demonstrations, we jointly learn a latent inverse dynamics model and a forward dynamics model over a sequence of image embeddings, predicting the next frame in latent space, without augmentations, contrastive sampling, or access to ground truth actions. Importantly, DynaMo does not require any out-of-domain data such as Internet datasets or cross-embodied datasets. On a suite of six simulated and real environments, we show that representations learned with DynaMo significantly improve downstream imitation learning performance over prior self-supervised learning objectives, and pretrained representations. Gains from using DynaMo hold across policy classes such as Behavior Transformer, Diffusion Policy, MLP, and nearest neighbors. Finally, we ablate over key components of DynaMo and measure its impact on downstream policy performance. Robot videos are best viewed at https://dynamo-ssl.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.15700", "title": "Making Text Embedders Few-Shot Learners", "authors": [ "Chaofan Li", "MingHao Qin", "Shitao Xiao", "Jianlyu Chen", "Kun Luo", "Yingxia Shao", "Defu Lian", "Zheng Liu" ], "github": "https://github.com/FlagOpen/FlagEmbedding", "project_page": "", "abstract": "Large language models (LLMs) with decoder-only architectures demonstrate remarkable in-context learning (ICL) capabilities. This feature enables them to effectively handle both familiar and novel tasks by utilizing examples provided within their input context. Recognizing the potential of this capability, we propose leveraging the ICL feature in LLMs to enhance the process of text embedding generation. To this end, we introduce a novel model bge-en-icl, which employs few-shot examples to produce high-quality text embeddings. Our approach integrates task-related examples directly into the query side, resulting in significant improvements across various tasks. Additionally, we have investigated how to effectively utilize LLMs as embedding models, including various attention mechanisms, pooling methods, etc. Our findings suggest that retaining the original framework often yields the best results, underscoring that simplicity is best. Experimental results on the MTEB and AIR-Bench benchmarks demonstrate that our approach sets new state-of-the-art (SOTA) performance. Our model, code and dataset are freely available at https://github.com/FlagOpen/FlagEmbedding .", "github_manual": "", "github_regex": "https://github.com/FlagOpen/FlagEmbedding", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.16211", "title": "MaskBit: Embedding-free Image Generation via Bit Tokens", "authors": [ "Mark Weber", "Lijun Yu", "Qihang Yu", "Xueqing Deng", "Xiaohui Shen", "Daniel Cremers", "Liang-Chieh Chen" ], "github": "https://github.com/markweberdev/maskbit", "project_page": "", "abstract": "Masked transformer models for class-conditional image generation have become a compelling alternative to diffusion models. Typically comprising two stages - an initial VQGAN model for transitioning between latent space and image space, and a subsequent Transformer model for image generation within latent space - these frameworks offer promising avenues for image synthesis. In this study, we present two primary contributions: Firstly, an empirical and systematic examination of VQGANs, leading to a modernized VQGAN. Secondly, a novel embedding-free generation network operating directly on bit tokens - a binary quantized representation of tokens with rich semantics. The first contribution furnishes a transparent, reproducible, and high-performing VQGAN model, enhancing accessibility and matching the performance of current state-of-the-art methods while revealing previously undisclosed details. The second contribution demonstrates that embedding-free image generation using bit tokens achieves a new state-of-the-art FID of 1.52 on the ImageNet 256x256 benchmark, with a compact generator model of mere 305M parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/markweberdev/maskbit", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.16160", "title": "MIMO: Controllable Character Video Synthesis with Spatial Decomposed Modeling", "authors": [ "Yifang Men", "Yuan Yao", "Miaomiao Cui", "Liefeng Bo" ], "github": "", "project_page": "", "abstract": "Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. As a fundamental problem in the computer vision and graphics community, 3D works typically require multi-view captures for per-case training, which severely limits their applicability of modeling arbitrary characters in a short time. Recent 2D methods break this limitation via pre-trained diffusion models, but they struggle for pose generality and scene interaction. To this end, we propose MIMO, a novel framework which can not only synthesize character videos with controllable attributes (i.e., character, motion and scene) provided by simple user inputs, but also simultaneously achieve advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes in a unified framework. The core idea is to encode the 2D video to compact spatial codes, considering the inherent 3D nature of video occurrence. Concretely, we lift the 2D frame pixels into 3D using monocular depth estimators, and decompose the video clip to three spatial components (i.e., main human, underlying scene, and floating occlusion) in hierarchical layers based on the 3D depth. These components are further encoded to canonical identity code, structured motion code and full scene code, which are utilized as control signals of synthesis process. The design of spatial decomposed modeling enables flexible user control, complex motion expression, as well as 3D-aware synthesis for scene interactions. Experimental results demonstrate effectiveness and robustness of the proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.15360", "title": "Reward-Robust RLHF in LLMs", "authors": [ "Yuzi Yan", "Xingzhou Lou", "Jialian Li", "Yiping Zhang", "Jian Xie", "Chao Yu", "Yu Wang", "Dong Yan", "Yuan Shen" ], "github": "", "project_page": "", "abstract": "As Large Language Models (LLMs) continue to progress toward more advanced forms of intelligence, Reinforcement Learning from Human Feedback (RLHF) is increasingly seen as a key pathway toward achieving Artificial General Intelligence (AGI). However, the reliance on reward-model-based (RM-based) alignment methods introduces significant challenges due to the inherent instability and imperfections of Reward Models (RMs), which can lead to critical issues such as reward hacking and misalignment with human intentions. In this paper, we introduce a reward-robust RLHF framework aimed at addressing these fundamental challenges, paving the way for more reliable and resilient learning in LLMs. Our approach introduces a novel optimization objective that carefully balances performance and robustness by incorporating Bayesian Reward Model Ensembles (BRME) to model the uncertainty set of reward functions. This allows the framework to integrate both nominal performance and minimum reward signals, ensuring more stable learning even with imperfect reward models. Empirical results demonstrate that our framework consistently outperforms traditional RLHF across diverse benchmarks, showing improved accuracy and long-term stability. We also provide a theoretical analysis, demonstrating that reward-robust RLHF approaches the stability of constant reward settings, which proves to be effective in a stochastic-case analysis. Together, these contributions highlight the framework potential to enhance both the performance and stability of LLM alignment with RLHF.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.16040", "title": "Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts", "authors": [ "Xiaoming Shi", "Shiyu Wang", "Yuqi Nie", "Dianqi Li", "Zhou Ye", "Qingsong Wen", "Ming Jin" ], "github": "https://github.com/time-moe/time-moe", "project_page": "", "abstract": "Deep learning for time series forecasting has seen significant advancements over the past decades. However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capable forecasting models in real-world applications. In response, we introduce Time-MoE, a scalable and unified architecture designed to pre-train larger, more capable forecasting foundation models while reducing inference costs. By leveraging a sparse mixture-of-experts (MoE) design, Time-MoE enhances computational efficiency by activating only a subset of networks for each prediction, reducing computational load while maintaining high model capacity. This allows Time-MoE to scale effectively without a corresponding increase in inference costs. Time-MoE comprises a family of decoder-only transformer models that operate in an auto-regressive manner and support flexible forecasting horizons with varying input context lengths. We pre-trained these models on our newly introduced large-scale data Time-300B, which spans over 9 domains and encompassing over 300 billion time points. For the first time, we scaled a time series foundation model up to 2.4 billion parameters, achieving significantly improved forecasting precision. Our results validate the applicability of scaling laws for training tokens and model size in the context of time series forecasting. Compared to dense models with the same number of activated parameters or equivalent computation budgets, our models consistently outperform them by large margin. These advancements position Time-MoE as a state-of-the-art solution for tackling real-world time series forecasting challenges with superior capability, efficiency, and flexibility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/time-moe/time-moe", "github_api_added_by": "auto" }, { "date": "2024-09-25", "arxiv_id": "2409.16235", "title": "EuroLLM: Multilingual Language Models for Europe", "authors": [ "Pedro Henrique Martins", "Patrick Fernandes", "Jo\u00e3o Alves", "Nuno M. Guerreiro", "Ricardo Rei", "Duarte M. Alves", "Jos\u00e9 Pombal", "Amin Farajian", "Manuel Faysse", "Mateusz Klimaszewski", "Pierre Colombo", "Barry Haddow", "Jos\u00e9 G. C. de Souza", "Alexandra Birch", "Andr\u00e9 F. T. Martins" ], "github": "", "project_page": "", "abstract": "The quality of open-weight LLMs has seen significant improvement, yet they remain predominantly focused on English. In this paper, we introduce the EuroLLM project, aimed at developing a suite of open-weight multilingual LLMs capable of understanding and generating text in all official European Union languages, as well as several additional relevant languages. We outline the progress made to date, detailing our data collection and filtering process, the development of scaling laws, the creation of our multilingual tokenizer, and the data mix and modeling configurations. Additionally, we release our initial models: EuroLLM-1.7B and EuroLLM-1.7B-Instruct and report their performance on multilingual general benchmarks and machine translation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.16143", "title": "Seeing Faces in Things: A Model and Dataset for Pareidolia", "authors": [ "Mark Hamilton", "Simon Stent", "Vasha DuTell", "Anne Harrington", "Jennifer Corbett", "Ruth Rosenholtz", "William T. Freeman" ], "github": "https://github.com/vdutell/pareidolicNoise", "project_page": "", "abstract": "The human visual system is well-tuned to detect faces of all shapes and sizes. While this brings obvious survival advantages, such as a better chance of spotting unknown predators in the bush, it also leads to spurious face detections. ``Face pareidolia'' describes the perception of face-like structure among otherwise random stimuli: seeing faces in coffee stains or clouds in the sky. In this paper, we study face pareidolia from a computer vision perspective. We present an image dataset of ``Faces in Things'', consisting of five thousand web images with human-annotated pareidolic faces. Using this dataset, we examine the extent to which a state-of-the-art human face detector exhibits pareidolia, and find a significant behavioral gap between humans and machines. We find that the evolutionary need for humans to detect animal faces, as well as human faces, may explain some of this gap. Finally, we propose a simple statistical model of pareidolia in images. Through studies on human subjects and our pareidolic face detectors we confirm a key prediction of our model regarding what image conditions are most likely to induce pareidolia. Dataset and Website: https://aka.ms/faces-in-things", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vdutell/pareidolicNoise", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.15933", "title": "SLIMER-IT: Zero-Shot NER on Italian Language", "authors": [ "Andrew Zamai", "Leonardo Rigutini", "Marco Maggini", "Andrea Zugarini" ], "github": "https://github.com/andrewzamai/slimer_it", "project_page": "", "abstract": "Traditional approaches to Named Entity Recognition (NER) frame the task into a BIO sequence labeling problem. Although these systems often excel in the downstream task at hand, they require extensive annotated data and struggle to generalize to out-of-distribution input domains and unseen entity types. On the contrary, Large Language Models (LLMs) have demonstrated strong zero-shot capabilities. While several works address Zero-Shot NER in English, little has been done in other languages. In this paper, we define an evaluation framework for Zero-Shot NER, applying it to the Italian language. Furthermore, we introduce SLIMER-IT, the Italian version of SLIMER, an instruction-tuning approach for zero-shot NER leveraging prompts enriched with definition and guidelines. Comparisons with other state-of-the-art models, demonstrate the superiority of SLIMER-IT on never-seen-before entity tags.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/andrewzamai/slimer_it", "github_api_added_by": "auto" }, { "date": "2024-09-25", "arxiv_id": "2409.14128", "title": "Present and Future Generalization of Synthetic Image Detectors", "authors": [ "Pablo Bernabeu-Perez", "Enrique Lopez-Cuena", "Dario Garcia-Gasulla" ], "github": "https://github.com/HPAI-BSC/SuSy", "project_page": "", "abstract": "The continued release of new and better image generation models increases the demand for synthetic image detectors. In such a dynamic field, detectors need to be able to generalize widely and be robust to uncontrolled alterations. The present work is motivated by this setting, when looking at the role of time, image transformations and data sources, for detector generalization. In these experiments, none of the evaluated detectors is found universal, but results indicate an ensemble could be. Experiments on data collected in the wild show this task to be more challenging than the one defined by large-scale datasets, pointing to a gap between experimentation and actual practice. Finally, we observe a race equilibrium effect, where better generators lead to better detectors, and vice versa. We hypothesize this pushes the field towards a perpetually close race between generators and detectors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HPAI-BSC/SuSy", "github_api_added_by": "auto" }, { "date": "2024-09-25", "arxiv_id": "2409.13882", "title": "Tabular Data Generation using Binary Diffusion", "authors": [ "Vitaliy Kinakh", "Slava Voloshynovskiy" ], "github": "https://github.com/vkinakh/binary-diffusion-tabular", "project_page": "", "abstract": "Generating synthetic tabular data is critical in machine learning, especially when real data is limited or sensitive. Traditional generative models often face challenges due to the unique characteristics of tabular data, such as mixed data types and varied distributions, and require complex preprocessing or large pretrained models. In this paper, we introduce a novel, lossless binary transformation method that converts any tabular data into fixed-size binary representations, and a corresponding new generative model called Binary Diffusion, specifically designed for binary data. Binary Diffusion leverages the simplicity of XOR operations for noise addition and removal and employs binary cross-entropy loss for training. Our approach eliminates the need for extensive preprocessing, complex noise parameter tuning, and pretraining on large datasets. We evaluate our model on several popular tabular benchmark datasets, demonstrating that Binary Diffusion outperforms existing state-of-the-art models on Travel, Adult Income, and Diabetes datasets while being significantly smaller in size.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vkinakh/binary-diffusion-tabular", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.15272", "title": "OmniBench: Towards The Future of Universal Omni-Language Models", "authors": [ "Yizhi Li", "Ge Zhang", "Yinghao Ma", "Ruibin Yuan", "Kang Zhu", "Hangyu Guo", "Yiming Liang", "Jiaheng Liu", "Jian Yang", "Siwei Wu", "Xingwei Qu", "Jinjie Shi", "Xinyue Zhang", "Zhenzhu Yang", "Xiangzhou Wang", "Zhaoxiang Zhang", "Zachary Liu", "Emmanouil Benetos", "Wenhao Huang", "Chenghua Lin" ], "github": "", "project_page": "", "abstract": "Recent advancements in multimodal large language models (MLLMs) have aimed to integrate and interpret data across diverse modalities. However, the capacity of these models to concurrently process and reason about multiple modalities remains inadequately explored, partly due to the lack of comprehensive modality-wise benchmarks. We introduce OmniBench, a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni-language models (OLMs). OmniBench is distinguished by high-quality human annotations, ensuring that accurate responses require integrated understanding and reasoning across all three modalities. Our main findings reveal that: i) open-source OLMs exhibit critical limitations in instruction-following and reasoning capabilities within tri-modal contexts; and ii) the baseline models perform poorly (below 50% accuracy) even when provided with alternative textual representations of images and audio. These results suggest that the ability to construct a consistent context from text, image, and audio is often overlooked in existing MLLM training paradigms. We advocate for future research to focus on developing more robust tri-modal integration techniques and training strategies to enhance OLM performance across diverse modalities. The codes and live leaderboard could be found at https://m-a-p.ai/OmniBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.15997", "title": "Improvements to SDXL in NovelAI Diffusion V3", "authors": [ "Juan Ossa", "Eren Do\u011fan", "Alex Birch", "F. Johnson" ], "github": "", "project_page": "", "abstract": "In this technical report, we document the changes we made to SDXL in the process of training NovelAI Diffusion V3, our state of the art anime image generation model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.13156", "title": "RRM: Robust Reward Model Training Mitigates Reward Hacking", "authors": [ "Tianqi Liu", "Wei Xiong", "Jie Ren", "Lichang Chen", "Junru Wu", "Rishabh Joshi", "Yang Gao", "Jiaming Shen", "Zhen Qin", "Tianhe Yu", "Daniel Sohn", "Anastasiia Makarova", "Jeremiah Liu", "Yuan Liu", "Bilal Piot", "Abe Ittycheriah", "Aviral Kumar", "Mohammad Saleh" ], "github": "", "project_page": "", "abstract": "Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human preferences. However, traditional RM training, which relies on response pairs tied to specific prompts, struggles to disentangle prompt-driven preferences from prompt-independent artifacts, such as response length and format. In this work, we expose a fundamental limitation of current RM training methods, where RMs fail to effectively distinguish between contextual signals and irrelevant artifacts when determining preferences. To address this, we introduce a causal framework that learns preferences independent of these artifacts and propose a novel data augmentation technique designed to eliminate them. Extensive experiments show that our approach successfully filters out undesirable artifacts, yielding a more robust reward model (RRM). Our RRM improves the performance of a pairwise reward model trained on Gemma-2-9b-it, on RewardBench, increasing accuracy from 80.61% to 84.15%. Additionally, we train two DPO policies using both the RM and RRM, demonstrating that the RRM significantly enhances DPO-aligned policies, improving MT-Bench scores from 7.27 to 8.31 and length-controlled win-rates in AlpacaEval-2 from 33.46% to 52.49%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-25", "arxiv_id": "2409.17143", "title": "Attention Prompting on Image for Large Vision-Language Models", "authors": [ "Runpeng Yu", "Weihao Yu", "Xinchao Wang" ], "github": "https://github.com/yu-rp/apiprompting", "project_page": "", "abstract": "Compared with Large Language Models (LLMs), Large Vision-Language Models (LVLMs) can also accept images as input, thus showcasing more interesting emergent capabilities and demonstrating impressive performance on various vision-language tasks. Motivated by text prompting in LLMs, visual prompting has been explored to enhance LVLMs' capabilities of perceiving visual information. However, previous visual prompting techniques solely process visual inputs without considering text queries, limiting the models' ability to follow text instructions to complete tasks. To fill this gap, in this work, we propose a new prompting technique named Attention Prompting on Image, which just simply overlays a text-query-guided attention heatmap on the original input image and effectively enhances LVLM on various tasks. Specifically, we generate an attention heatmap for the input image dependent on the text query with an auxiliary model like CLIP. Then the heatmap simply multiplies the pixel values of the original image to obtain the actual input image for the LVLM. Extensive experiments on various vison-language benchmarks verify the effectiveness of our technique. For example, Attention Prompting on Image improves LLaVA-1.5 by 3.8% and 2.9% on MM-Vet and LLaVA-Wild benchmarks, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yu-rp/apiprompting", "github_api_added_by": "auto" }, { "date": "2024-09-26", "arxiv_id": "2409.17115", "title": "Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale", "authors": [ "Fan Zhou", "Zengzhi Wang", "Qian Liu", "Junlong Li", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/ProX", "project_page": "", "abstract": "Large language model pre-training has traditionally relied on human experts to craft heuristics for improving the corpora quality, resulting in numerous rules developed to date. However, these rules lack the flexibility to address the unique characteristics of individual example effectively. Meanwhile, applying tailored rules to every example is impractical for human experts. In this paper, we demonstrate that even small language models, with as few as 0.3B parameters, can exhibit substantial data refining capabilities comparable to those of human experts. We introduce Programming Every Example (ProX), a novel framework that treats data refinement as a programming task, enabling models to refine corpora by generating and executing fine-grained operations, such as string normalization, for each individual example at scale. Experimental results show that models pre-trained on ProX-curated data outperform either original data or data filtered by other selection methods by more than 2% across various downstream benchmarks. Its effectiveness spans various model sizes and pre-training corpora, including C4, RedPajama-V2, and FineWeb. Furthermore, ProX exhibits significant potential in domain-specific continual pre-training: without domain specific design, models trained on OpenWebMath refined by ProX outperform human-crafted rule-based methods, improving average accuracy by 7.6% over Mistral-7B, with 14.6% for Llama-2-7B and 20.3% for CodeLlama-7B, all within 10B tokens to be comparable to models like Llemma-7B trained on 200B tokens. Further analysis highlights that ProX significantly saves training FLOPs, offering a promising path for efficient LLM pre-training.We are open-sourcing ProX with >100B corpus, models, and sharing all training and implementation details for reproducible research and future innovation. Code: https://github.com/GAIR-NLP/ProX", "github_manual": "", "github_regex": "https://github.com/GAIR-NLP/ProX", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-26", "arxiv_id": "2409.17146", "title": "Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models", "authors": [ "Matt Deitke", "Christopher Clark", "Sangho Lee", "Rohun Tripathi", "Yue Yang", "Jae Sung Park", "Mohammadreza Salehi", "Niklas Muennighoff", "Kyle Lo", "Luca Soldaini", "Jiasen Lu", "Taira Anderson", "Erin Bransom", "Kiana Ehsani", "Huong Ngo", "YenSung Chen", "Ajay Patel", "Mark Yatskar", "Chris Callison-Burch", "Andrew Head", "Rose Hendrix", "Favyen Bastani", "Eli VanderBilt", "Nathan Lambert", "Yvonne Chou", "Arnavi Chheda", "Jenna Sparks", "Sam Skjonsberg", "Michael Schmitz", "Aaron Sarnat", "Byron Bischoff", "Pete Walsh", "Chris Newell", "Piper Wolters", "Tanmay Gupta", "Kuo-Hao Zeng", "Jon Borchardt", "Dirk Groeneveld", "Jen Dumas", "Crystal Nam", "Sophie Lebrecht", "Caitlin Wittlif", "Carissa Schoenick", "Oscar Michel", "Ranjay Krishna", "Luca Weihs", "Noah A. Smith", "Hannaneh Hajishirzi", "Ross Girshick", "Ali Farhadi", "Aniruddha Kembhavi" ], "github": "https://github.com/allenai/molmo", "project_page": "", "abstract": "Today's most advanced multimodal models remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good performance, effectively distilling these closed models into open ones. As a result, the community is still missing foundational knowledge about how to build performant VLMs from scratch. We present Molmo, a new family of VLMs that are state-of-the-art in their class of openness. Our key innovation is a novel, highly detailed image caption dataset collected entirely from human annotators using speech-based descriptions. To enable a wide array of user interactions, we also introduce a diverse dataset mixture for fine-tuning that includes in-the-wild Q&A and innovative 2D pointing data. The success of our approach relies on careful choices for the model architecture details, a well-tuned training pipeline, and, most critically, the quality of our newly collected datasets, all of which will be released. The best-in-class 72B model within the Molmo family not only outperforms others in the class of open weight and data models but also compares favorably against proprietary systems like GPT-4o, Claude 3.5, and Gemini 1.5 on both academic benchmarks and human evaluation. We will be releasing all of our model weights, captioning and fine-tuning data, and source code in the near future. Select model weights, inference code, and demo are available at https://molmo.allenai.org.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/molmo", "github_api_added_by": "auto" }, { "date": "2024-09-26", "arxiv_id": "2409.17145", "title": "DreamWaltz-G: Expressive 3D Gaussian Avatars from Skeleton-Guided 2D Diffusion", "authors": [ "Yukun Huang", "Jianan Wang", "Ailing Zeng", "Zheng-Jun Zha", "Lei Zhang", "Xihui Liu" ], "github": "https://github.com/yukun-huang/DreamWaltz-G", "project_page": "", "abstract": "Leveraging pretrained 2D diffusion models and score distillation sampling (SDS), recent methods have shown promising results for text-to-3D avatar generation. However, generating high-quality 3D avatars capable of expressive animation remains challenging. In this work, we present DreamWaltz-G, a novel learning framework for animatable 3D avatar generation from text. The core of this framework lies in Skeleton-guided Score Distillation and Hybrid 3D Gaussian Avatar representation. Specifically, the proposed skeleton-guided score distillation integrates skeleton controls from 3D human templates into 2D diffusion models, enhancing the consistency of SDS supervision in terms of view and human pose. This facilitates the generation of high-quality avatars, mitigating issues such as multiple faces, extra limbs, and blurring. The proposed hybrid 3D Gaussian avatar representation builds on the efficient 3D Gaussians, combining neural implicit fields and parameterized 3D meshes to enable real-time rendering, stable SDS optimization, and expressive animation. Extensive experiments demonstrate that DreamWaltz-G is highly effective in generating and animating 3D avatars, outperforming existing methods in both visual quality and animation expressiveness. Our framework further supports diverse applications, including human video reenactment and multi-subject scene composition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yukun-huang/DreamWaltz-G", "github_api_added_by": "auto" }, { "date": "2024-09-26", "arxiv_id": "2409.16666", "title": "TalkinNeRF: Animatable Neural Fields for Full-Body Talking Humans", "authors": [ "Aggelina Chatziagapi", "Bindita Chaudhuri", "Amit Kumar", "Rakesh Ranjan", "Dimitris Samaras", "Nikolaos Sarafianos" ], "github": "", "project_page": "", "abstract": "We introduce a novel framework that learns a dynamic neural radiance field (NeRF) for full-body talking humans from monocular videos. Prior work represents only the body pose or the face. However, humans communicate with their full body, combining body pose, hand gestures, as well as facial expressions. In this work, we propose TalkinNeRF, a unified NeRF-based network that represents the holistic 4D human motion. Given a monocular video of a subject, we learn corresponding modules for the body, face, and hands, that are combined together to generate the final result. To capture complex finger articulation, we learn an additional deformation field for the hands. Our multi-identity representation enables simultaneous training for multiple subjects, as well as robust animation under completely unseen poses. It can also generalize to novel identities, given only a short video as input. We demonstrate state-of-the-art performance for animating full-body talking humans, with fine-grained hand articulation and facial expressions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-26", "arxiv_id": "2409.16629", "title": "Synchronize Dual Hands for Physics-Based Dexterous Guitar Playing", "authors": [ "Pei Xu", "Ruocheng Wang" ], "github": "https://github.com/xupei0610/guitar", "project_page": "", "abstract": "We present a novel approach to synthesize dexterous motions for physically simulated hands in tasks that require coordination between the control of two hands with high temporal precision. Instead of directly learning a joint policy to control two hands, our approach performs bimanual control through cooperative learning where each hand is treated as an individual agent. The individual policies for each hand are first trained separately, and then synchronized through latent space manipulation in a centralized environment to serve as a joint policy for two-hand control. By doing so, we avoid directly performing policy learning in the joint state-action space of two hands with higher dimensions, greatly improving the overall training efficiency. We demonstrate the effectiveness of our proposed approach in the challenging guitar-playing task. The virtual guitarist trained by our approach can synthesize motions from unstructured reference data of general guitar-playing practice motions, and accurately play diverse rhythms with complex chord pressing and string picking patterns based on the input guitar tabs that do not exist in the references. Along with this paper, we provide the motion capture data that we collected as the reference for policy training. Code is available at: https://pei-xu.github.io/guitar.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xupei0610/guitar", "github_api_added_by": "auto" }, { "date": "2024-09-26", "arxiv_id": "2409.17058", "title": "Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors", "authors": [ "Aiping Zhang", "Zongsheng Yue", "Renjing Pei", "Wenqi Ren", "Xiaochun Cao" ], "github": "https://github.com/arctichare105/s3diff", "project_page": "", "abstract": "Diffusion-based image super-resolution (SR) methods have achieved remarkable success by leveraging large pre-trained text-to-image diffusion models as priors. However, these methods still face two challenges: the requirement for dozens of sampling steps to achieve satisfactory results, which limits efficiency in real scenarios, and the neglect of degradation models, which are critical auxiliary information in solving the SR problem. In this work, we introduced a novel one-step SR model, which significantly addresses the efficiency issue of diffusion-based SR methods. Unlike existing fine-tuning strategies, we designed a degradation-guided Low-Rank Adaptation (LoRA) module specifically for SR, which corrects the model parameters based on the pre-estimated degradation information from low-resolution images. This module not only facilitates a powerful data-dependent or degradation-dependent SR model but also preserves the generative prior of the pre-trained diffusion model as much as possible. Furthermore, we tailor a novel training pipeline by introducing an online negative sample generation strategy. Combined with the classifier-free guidance strategy during inference, it largely improves the perceptual quality of the super-resolution results. Extensive experiments have demonstrated the superior efficiency and effectiveness of the proposed model compared to recent state-of-the-art methods.", "github_manual": "https://github.com/arctichare105/s3diff", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-26", "arxiv_id": "2409.15127", "title": "Boosting Healthcare LLMs Through Retrieved Context", "authors": [ "Jordi Bayarri-Planas", "Ashwin Kumar Gururajan", "Dario Garcia-Gasulla" ], "github": "https://github.com/HPAI-BSC/prompt_engine", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing, and yet, their factual inaccuracies and hallucinations limits their application, particularly in critical domains like healthcare. Context retrieval methods, by introducing relevant information as input, have emerged as a crucial approach for enhancing LLM factuality and reliability. This study explores the boundaries of context retrieval methods within the healthcare domain, optimizing their components and benchmarking their performance against open and closed alternatives. Our findings reveal how open LLMs, when augmented with an optimized retrieval system, can achieve performance comparable to the biggest private solutions on established healthcare benchmarks (multiple-choice question answering). Recognizing the lack of realism of including the possible answers within the question (a setup only found in medical exams), and after assessing a strong LLM performance degradation in the absence of those options, we extend the context retrieval system in that direction. In particular, we propose OpenMedPrompt a pipeline that improves the generation of more reliable open-ended answers, moving this technology closer to practical application.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HPAI-BSC/prompt_engine", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-26", "arxiv_id": "2409.15041", "title": "AIM 2024 Sparse Neural Rendering Challenge: Dataset and Benchmark", "authors": [ "Michal Nazarczuk", "Thomas Tanay", "Sibi Catley-Chandar", "Richard Shaw", "Radu Timofte", "Eduardo P\u00e9rez-Pellitero" ], "github": "", "project_page": "", "abstract": "Recent developments in differentiable and neural rendering have made impressive breakthroughs in a variety of 2D and 3D tasks, e.g. novel view synthesis, 3D reconstruction. Typically, differentiable rendering relies on a dense viewpoint coverage of the scene, such that the geometry can be disambiguated from appearance observations alone. Several challenges arise when only a few input views are available, often referred to as sparse or few-shot neural rendering. As this is an underconstrained problem, most existing approaches introduce the use of regularisation, together with a diversity of learnt and hand-crafted priors. A recurring problem in sparse rendering literature is the lack of an homogeneous, up-to-date, dataset and evaluation protocol. While high-resolution datasets are standard in dense reconstruction literature, sparse rendering methods often evaluate with low-resolution images. Additionally, data splits are inconsistent across different manuscripts, and testing ground-truth images are often publicly available, which may lead to over-fitting. In this work, we propose the Sparse Rendering (SpaRe) dataset and benchmark. We introduce a new dataset that follows the setup of the DTU MVS dataset. The dataset is composed of 97 new scenes based on synthetic, high-quality assets. Each scene has up to 64 camera views and 7 lighting configurations, rendered at 1600x1200 resolution. We release a training split of 82 scenes to foster generalizable approaches, and provide an online evaluation platform for the validation and test sets, whose ground-truth images remain hidden. We propose two different sparse configurations (3 and 9 input images respectively). This provides a powerful and convenient tool for reproducible evaluation, and enable researchers easy access to a public leaderboard with the state-of-the-art performance scores. Available at: https://sparebenchmark.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-26", "arxiv_id": "2409.16925", "title": "Game4Loc: A UAV Geo-Localization Benchmark from Game Data", "authors": [ "Yuxiang Ji", "Boyong He", "Zhuoyue Tan", "Liaoni Wu" ], "github": "", "project_page": "", "abstract": "The vision-based geo-localization technology for UAV, serving as a secondary source of GPS information in addition to the global navigation satellite systems (GNSS), can still operate independently in the GPS-denied environment. Recent deep learning based methods attribute this as the task of image matching and retrieval. By retrieving drone-view images in geo-tagged satellite image database, approximate localization information can be obtained. However, due to high costs and privacy concerns, it is usually difficult to obtain large quantities of drone-view images from a continuous area. Existing drone-view datasets are mostly composed of small-scale aerial photography with a strong assumption that there exists a perfect one-to-one aligned reference image for any query, leaving a significant gap from the practical localization scenario. In this work, we construct a large-range contiguous area UAV geo-localization dataset named GTA-UAV, featuring multiple flight altitudes, attitudes, scenes, and targets using modern computer games. Based on this dataset, we introduce a more practical UAV geo-localization task including partial matches of cross-view paired data, and expand the image-level retrieval to the actual localization in terms of distance (meters). For the construction of drone-view and satellite-view pairs, we adopt a weight-based contrastive learning approach, which allows for effective learning while avoiding additional post-processing matching steps. Experiments demonstrate the effectiveness of our data and training method for UAV geo-localization, as well as the generalization capabilities to real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-26", "arxiv_id": "2409.16299", "title": "HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale", "authors": [ "Huy Nhat Phan", "Phong X. Nguyen", "Nghi D. Q. Bui" ], "github": "https://github.com/fsoft-ai4code/hyperagent", "project_page": "", "abstract": "Large Language Models (LLMs) have revolutionized software engineering (SE), demonstrating remarkable capabilities in various coding tasks. While recent efforts have produced autonomous software agents based on LLMs for end-to-end development tasks, these systems are typically designed for specific SE tasks. We introduce HyperAgent, a novel generalist multi-agent system designed to address a wide spectrum of SE tasks across different programming languages by mimicking human developers' workflows. Comprising four specialized agents - Planner, Navigator, Code Editor, and Executor. HyperAgent manages the full lifecycle of SE tasks, from initial conception to final verification. Through extensive evaluations, HyperAgent achieves state-of-the-art performance across diverse SE tasks: it attains a 25.01% success rate on SWE-Bench-Lite and 31.40% on SWE-Bench-Verified for GitHub issue resolution, surpassing existing methods. Furthermore, HyperAgent demonstrates SOTA performance in repository-level code generation (RepoExec), and in fault localization and program repair (Defects4J), often outperforming specialized systems. This work represents a significant advancement towards versatile, autonomous agents capable of handling complex, multi-step SE tasks across various domains and languages, potentially transforming AI-assisted software development practices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/fsoft-ai4code/hyperagent", "github_api_added_by": "auto" }, { "date": "2024-09-26", "arxiv_id": "2409.16493", "title": "NoTeeline: Supporting Real-Time Notetaking from Keypoints with Large Language Models", "authors": [ "Faria Huq", "Abdus Samee", "David Chuan-en Lin", "Xiaodi Alice Tang", "Jeffrey P. Bigham" ], "github": "https://github.com/oaishi/noteeline", "project_page": "", "abstract": "Video has become a popular media form for information sharing and consumption. However, taking notes while watching a video requires significant time and effort. To address this, we propose a novel interactive system, NoTeeline, for taking real-time, personalized notes. NoTeeline lets users quickly jot down keypoints (micronotes), which are automatically expanded into full-fledged notes that capture the content of the user's micronotes and are consistent with the user's writing style. In a within-subjects study (N=12), we found that NoTeeline helps users create high-quality notes that capture the essence of their micronotes with a higher factual correctness (93.2%) while accurately reflecting their writing style. While using NoTeeline, participants experienced significantly reduced mental effort, captured satisfactory notes while writing 47% less text, and completed notetaking with 43.9% less time compared to a manual notetaking baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/oaishi/noteeline", "github_api_added_by": "auto" }, { "date": "2024-09-26", "arxiv_id": "2409.16288", "title": "Self-Supervised Any-Point Tracking by Contrastive Random Walks", "authors": [ "Ayush Shrivastava", "Andrew Owens" ], "github": "https://github.com/ayshrv/gmrw", "project_page": "", "abstract": "We present a simple, self-supervised approach to the Tracking Any Point (TAP) problem. We train a global matching transformer to find cycle consistent tracks through video via contrastive random walks, using the transformer's attention-based global matching to define the transition matrices for a random walk on a space-time graph. The ability to perform \"all pairs\" comparisons between points allows the model to obtain high spatial precision and to obtain a strong contrastive learning signal, while avoiding many of the complexities of recent approaches (such as coarse-to-fine matching). To do this, we propose a number of design decisions that allow global matching architectures to be trained through self-supervision using cycle consistency. For example, we identify that transformer-based methods are sensitive to shortcut solutions, and propose a data augmentation scheme to address them. Our method achieves strong performance on the TapVid benchmarks, outperforming previous self-supervised tracking methods, such as DIFT, and is competitive with several supervised methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ayshrv/gmrw", "github_api_added_by": "auto" }, { "date": "2024-09-27", "arxiv_id": "2409.17481", "title": "MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models", "authors": [ "Gongfan Fang", "Hongxu Yin", "Saurav Muralidharan", "Greg Heinrich", "Jeff Pool", "Jan Kautz", "Pavlo Molchanov", "Xinchao Wang" ], "github": "https://github.com/NVlabs/MaskLLM", "project_page": "", "abstract": "Large Language Models (LLMs) are distinguished by their massive parameter counts, which typically result in significant redundancy. This work introduces MaskLLM, a learnable pruning method that establishes Semi-structured (or ``N:M'') Sparsity in LLMs, aimed at reducing computational overhead during inference. Instead of developing a new importance criterion, MaskLLM explicitly models N:M patterns as a learnable distribution through Gumbel Softmax sampling. This approach facilitates end-to-end training on large-scale datasets and offers two notable advantages: 1) High-quality Masks - our method effectively scales to large datasets and learns accurate masks; 2) Transferability - the probabilistic modeling of mask distribution enables the transfer learning of sparsity across domains or tasks. We assessed MaskLLM using 2:4 sparsity on various LLMs, including LLaMA-2, Nemotron-4, and GPT-3, with sizes ranging from 843M to 15B parameters, and our empirical results show substantial improvements over state-of-the-art methods. For instance, leading approaches achieve a perplexity (PPL) of 10 or greater on Wikitext compared to the dense model's 5.12 PPL, but MaskLLM achieves a significantly lower 6.72 PPL solely by learning the masks with frozen weights. Furthermore, MaskLLM's learnable nature allows customized masks for lossless application of 2:4 sparsity to downstream tasks or domains. Code is available at https://github.com/NVlabs/MaskLLM.", "github_manual": "", "github_regex": "https://github.com/NVlabs/MaskLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-27", "arxiv_id": "2409.17422", "title": "Discovering the Gems in Early Layers: Accelerating Long-Context LLMs with 1000x Input Token Reduction", "authors": [ "Zhenmei Shi", "Yifei Ming", "Xuan-Phi Nguyen", "Yingyu Liang", "Shafiq Joty" ], "github": "https://github.com/SalesforceAIResearch/GemFilter", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities in handling long context inputs, but this comes at the cost of increased computational resources and latency. Our research introduces a novel approach for the long context bottleneck to accelerate LLM inference and reduce GPU memory consumption. Our research demonstrates that LLMs can identify relevant tokens in the early layers before generating answers to a query. Leveraging this insight, we propose an algorithm that uses early layers of an LLM as filters to select and compress input tokens, significantly reducing the context length for subsequent processing. Our method, GemFilter, demonstrates substantial improvements in both speed and memory efficiency compared to existing techniques, such as standard attention and SnapKV/H2O. Notably, it achieves a 2.4times speedup and 30\\% reduction in GPU memory usage compared to SOTA methods. Evaluation on the Needle in a Haystack task shows that GemFilter significantly outperforms standard attention, SnapKV and demonstrates comparable performance on the LongBench challenge. GemFilter is simple, training-free, and broadly applicable across different LLMs. Crucially, it provides interpretability by allowing humans to inspect the selected input sequence. These findings not only offer practical benefits for LLM deployment, but also enhance our understanding of LLM internal mechanisms, paving the way for further optimizations in LLM design and inference. Our code is available at https://github.com/SalesforceAIResearch/GemFilter.", "github_manual": "", "github_regex": "https://github.com/SalesforceAIResearch/GemFilter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-27", "arxiv_id": "2409.17280", "title": "Disco4D: Disentangled 4D Human Generation and Animation from a Single Image", "authors": [ "Hui En Pang", "Shuai Liu", "Zhongang Cai", "Lei Yang", "Tianwei Zhang", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "We present Disco4D, a novel Gaussian Splatting framework for 4D human generation and animation from a single image. Different from existing methods, Disco4D distinctively disentangles clothings (with Gaussian models) from the human body (with SMPL-X model), significantly enhancing the generation details and flexibility. It has the following technical innovations. 1) Disco4D learns to efficiently fit the clothing Gaussians over the SMPL-X Gaussians. 2) It adopts diffusion models to enhance the 3D generation process, e.g., modeling occluded parts not visible in the input image. 3) It learns an identity encoding for each clothing Gaussian to facilitate the separation and extraction of clothing assets. Furthermore, Disco4D naturally supports 4D human animation with vivid dynamics. Extensive experiments demonstrate the superiority of Disco4D on 4D human generation and animation tasks. Our visualizations can be found in https://disco-4d.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-27", "arxiv_id": "2409.18124", "title": "Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction", "authors": [ "Jing He", "Haodong Li", "Wei Yin", "Yixun Liang", "Leheng Li", "Kaiqiang Zhou", "Hongbo Liu", "Bingbing Liu", "Ying-Cong Chen" ], "github": "https://github.com/envision-research/lotus", "project_page": "", "abstract": "Leveraging the visual priors of pre-trained text-to-image diffusion models offers a promising solution to enhance zero-shot generalization in dense prediction tasks. However, existing methods often uncritically use the original diffusion formulation, which may not be optimal due to the fundamental differences between dense prediction and image generation. In this paper, we provide a systemic analysis of the diffusion formulation for the dense prediction, focusing on both quality and efficiency. And we find that the original parameterization type for image generation, which learns to predict noise, is harmful for dense prediction; the multi-step noising/denoising diffusion process is also unnecessary and challenging to optimize. Based on these insights, we introduce Lotus, a diffusion-based visual foundation model with a simple yet effective adaptation protocol for dense prediction. Specifically, Lotus is trained to directly predict annotations instead of noise, thereby avoiding harmful variance. We also reformulate the diffusion process into a single-step procedure, simplifying optimization and significantly boosting inference speed. Additionally, we introduce a novel tuning strategy called detail preserver, which achieves more accurate and fine-grained predictions. Without scaling up the training data or model capacity, Lotus achieves SoTA performance in zero-shot depth and normal estimation across various datasets. It also significantly enhances efficiency, being hundreds of times faster than most existing diffusion-based methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/envision-research/lotus", "github_api_added_by": "auto" }, { "date": "2024-09-27", "arxiv_id": "2409.18121", "title": "Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction", "authors": [ "Justin Kerr", "Chung Min Kim", "Mingxuan Wu", "Brent Yi", "Qianqian Wang", "Ken Goldberg", "Angjoo Kanazawa" ], "github": "https://github.com/kerrj/rsrd", "project_page": "", "abstract": "Humans can learn to manipulate new objects by simply watching others; providing robots with the ability to learn from such demonstrations would enable a natural interface specifying new behaviors. This work develops Robot See Robot Do (RSRD), a method for imitating articulated object manipulation from a single monocular RGB human demonstration given a single static multi-view object scan. We first propose 4D Differentiable Part Models (4D-DPM), a method for recovering 3D part motion from a monocular video with differentiable rendering. This analysis-by-synthesis approach uses part-centric feature fields in an iterative optimization which enables the use of geometric regularizers to recover 3D motions from only a single video. Given this 4D reconstruction, the robot replicates object trajectories by planning bimanual arm motions that induce the demonstrated object part motion. By representing demonstrations as part-centric trajectories, RSRD focuses on replicating the demonstration's intended behavior while considering the robot's own morphological limits, rather than attempting to reproduce the hand's motion. We evaluate 4D-DPM's 3D tracking accuracy on ground truth annotated 3D part trajectories and RSRD's physical execution performance on 9 objects across 10 trials each on a bimanual YuMi robot. Each phase of RSRD achieves an average of 87% success rate, for a total end-to-end success rate of 60% across 90 trials. Notably, this is accomplished using only feature fields distilled from large pretrained vision models -- without any task-specific training, fine-tuning, dataset collection, or annotation. Project page: https://robot-see-robot-do.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kerrj/rsrd", "github_api_added_by": "auto" }, { "date": "2024-09-27", "arxiv_id": "2409.18042", "title": "EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions", "authors": [ "Kai Chen", "Yunhao Gou", "Runhui Huang", "Zhili Liu", "Daxin Tan", "Jing Xu", "Chunwei Wang", "Yi Zhu", "Yihan Zeng", "Kuo Yang", "Dingdong Wang", "Kun Xiang", "Haoyuan Li", "Haoli Bai", "Jianhua Han", "Xiaohui Li", "Weike Jin", "Nian Xie", "Yu Zhang", "James T. Kwok", "Hengshuang Zhao", "Xiaodan Liang", "Dit-Yan Yeung", "Xiao Chen", "Zhenguo Li", "Wei Zhang", "Qun Liu", "Lanqing Hong", "Lu Hou", "Hang Xu" ], "github": "", "project_page": "", "abstract": "GPT-4o, an omni-modal model that enables vocal conversations with diverse emotions and tones, marks a milestone for omni-modal foundation models. However, empowering Large Language Models to perceive and generate images, texts, and speeches end-to-end with publicly available data remains challenging in the open-source community. Existing vision-language models rely on external tools for the speech processing, while speech-language models still suffer from limited or even without vision-understanding abilities. To address this gap, we propose EMOVA (EMotionally Omni-present Voice Assistant), to enable Large Language Models with end-to-end speech capabilities while maintaining the leading vision-language performance. With a semantic-acoustic disentangled speech tokenizer, we notice surprisingly that omni-modal alignment can further enhance vision-language and speech abilities compared with the corresponding bi-modal aligned counterparts. Moreover, a lightweight style module is proposed for flexible speech style controls (e.g., emotions and pitches). For the first time, EMOVA achieves state-of-the-art performance on both the vision-language and speech benchmarks, and meanwhile, supporting omni-modal spoken dialogue with vivid emotions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-27", "arxiv_id": "2409.18125", "title": "LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness", "authors": [ "Chenming Zhu", "Tai Wang", "Wenwei Zhang", "Jiangmiao Pang", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D-awareness for 3D scene understanding has been hindered by the lack of large-scale 3D vision-language datasets and powerful 3D encoders. In this paper, we introduce a simple yet effective framework called LLaVA-3D. Leveraging the strong 2D understanding priors from LLaVA, our LLaVA-3D efficiently adapts LLaVA for 3D scene understanding without compromising 2D understanding capabilities. To achieve this, we employ a simple yet effective representation, 3D Patch, which connects 2D CLIP patch features with their corresponding positions in 3D space. By integrating the 3D Patches into 2D LMMs and employing joint 2D and 3D vision-language instruction tuning, we establish a unified architecture for both 2D image understanding and 3D scene understanding. Experimental results show that LLaVA-3D converges 3.5x faster than existing 3D LMMs when trained on 3D vision-language datasets. Moreover, LLaVA-3D not only achieves state-of-the-art performance across various 3D tasks but also maintains comparable 2D image understanding and vision-language conversation capabilities with LLaVA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-27", "arxiv_id": "2409.17565", "title": "Pixel-Space Post-Training of Latent Diffusion Models", "authors": [ "Christina Zhang", "Simran Motwani", "Matthew Yu", "Ji Hou", "Felix Juefei-Xu", "Sam Tsai", "Peter Vajda", "Zijian He", "Jialiang Wang" ], "github": "", "project_page": "", "abstract": "Latent diffusion models (LDMs) have made significant advancements in the field of image generation in recent years. One major advantage of LDMs is their ability to operate in a compressed latent space, allowing for more efficient training and deployment. However, despite these advantages, challenges with LDMs still remain. For example, it has been observed that LDMs often generate high-frequency details and complex compositions imperfectly. We hypothesize that one reason for these flaws is due to the fact that all pre- and post-training of LDMs are done in latent space, which is typically 8 times 8 lower spatial-resolution than the output images. To address this issue, we propose adding pixel-space supervision in the post-training process to better preserve high-frequency details. Experimentally, we show that adding a pixel-space objective significantly improves both supervised quality fine-tuning and preference-based post-training by a large margin on a state-of-the-art DiT transformer and U-Net diffusion models in both visual quality and visual flaw metrics, while maintaining the same text alignment quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-27", "arxiv_id": "2409.14683", "title": "Reducing the Footprint of Multi-Vector Retrieval with Minimal Performance Impact via Token Pooling", "authors": [ "Benjamin Clavi\u00e9", "Antoine Chaffin", "Griffin Adams" ], "github": "", "project_page": "", "abstract": "Over the last few years, multi-vector retrieval methods, spearheaded by ColBERT, have become an increasingly popular approach to Neural IR. By storing representations at the token level rather than at the document level, these methods have demonstrated very strong retrieval performance, especially in out-of-domain settings. However, the storage and memory requirements necessary to store the large number of associated vectors remain an important drawback, hindering practical adoption. In this paper, we introduce a simple clustering-based token pooling approach to aggressively reduce the number of vectors that need to be stored. This method can reduce the space & memory footprint of ColBERT indexes by 50% with virtually no retrieval performance degradation. This method also allows for further reductions, reducing the vector count by 66%-to-75% , with degradation remaining below 5% on a vast majority of datasets. Importantly, this approach requires no architectural change nor query-time processing, and can be used as a simple drop-in during indexation with any ColBERT-like model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-27", "arxiv_id": "2409.17580", "title": "Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case Study", "authors": [ "Zahra Sepasdar", "Sushant Gautam", "Cise Midoglu", "Michael A. Riegler", "P\u00e5l Halvorsen" ], "github": "", "project_page": "", "abstract": "Extracting meaningful insights from large and complex datasets poses significant challenges, particularly in ensuring the accuracy and relevance of retrieved information. Traditional data retrieval methods such as sequential search and index-based retrieval often fail when handling intricate and interconnected data structures, resulting in incomplete or misleading outputs. To overcome these limitations, we introduce Structured-GraphRAG, a versatile framework designed to enhance information retrieval across structured datasets in natural language queries. Structured-GraphRAG utilizes multiple knowledge graphs, which represent data in a structured format and capture complex relationships between entities, enabling a more nuanced and comprehensive retrieval of information. This graph-based approach reduces the risk of errors in language model outputs by grounding responses in a structured format, thereby enhancing the reliability of results. We demonstrate the effectiveness of Structured-GraphRAG by comparing its performance with that of a recently published method using traditional retrieval-augmented generation. Our findings show that Structured-GraphRAG significantly improves query processing efficiency and reduces response times. While our case study focuses on soccer data, the framework's design is broadly applicable, offering a powerful tool for data analysis and enhancing language model applications across various structured domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-27", "arxiv_id": "2409.14254", "title": "Instruction Following without Instruction Tuning", "authors": [ "John Hewitt", "Nelson F. Liu", "Percy Liang", "Christopher D. Manning" ], "github": "https://github.com/john-hewitt/implicit-ins", "project_page": "", "abstract": "Instruction tuning commonly means finetuning a language model on instruction-response pairs. We discover two forms of adaptation (tuning) that are deficient compared to instruction tuning, yet still yield instruction following; we call this implicit instruction tuning. We first find that instruction-response pairs are not necessary: training solely on responses, without any corresponding instructions, yields instruction following. This suggests pretrained models have an instruction-response mapping which is revealed by teaching the model the desired distribution of responses. However, we then find it's not necessary to teach the desired distribution of responses: instruction-response training on narrow-domain data like poetry still leads to broad instruction-following behavior like recipe generation. In particular, when instructions are very different from those in the narrow finetuning domain, models' responses do not adhere to the style of the finetuning domain. To begin to explain implicit instruction tuning, we hypothesize that very simple changes to a language model's distribution yield instruction following. We support this by hand-writing a rule-based language model which yields instruction following in a product-of-experts with a pretrained model. The rules are to slowly increase the probability of ending the sequence, penalize repetition, and uniformly change 15 words' probabilities. In summary, adaptations made without being designed to yield instruction following can do so implicitly.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/john-hewitt/implicit-ins", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-27", "arxiv_id": "2409.14195", "title": "The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends", "authors": [ "Xinghua Zhang", "Haiyang Yu", "Yongbin Li", "Minzheng Wang", "Longze Chen", "Fei Huang" ], "github": "", "project_page": "", "abstract": "In the era of large language models (LLMs), a vast amount of conversation logs will be accumulated thanks to the rapid development trend of language UI. Conversation Analysis (CA) strives to uncover and analyze critical information from conversation data, streamlining manual processes and supporting business insights and decision-making. The need for CA to extract actionable insights and drive empowerment is becoming increasingly prominent and attracting widespread attention. However, the lack of a clear scope for CA leads to a dispersion of various techniques, making it difficult to form a systematic technical synergy to empower business applications. In this paper, we perform a thorough review and systematize CA task to summarize the existing related work. Specifically, we formally define CA task to confront the fragmented and chaotic landscape in this field, and derive four key steps of CA from conversation scene reconstruction, to in-depth attribution analysis, and then to performing targeted training, finally generating conversations based on the targeted training for achieving the specific goals. In addition, we showcase the relevant benchmarks, discuss potential challenges and point out future directions in both industry and academia. In view of current advancements, it is evident that the majority of efforts are still concentrated on the analysis of shallow conversation elements, which presents a considerable gap between the research and business, and with the assist of LLMs, recent work has shown a trend towards research on causality and strategic tasks which are sophisticated and high-level. The analyzed experiences and insights will inevitably have broader application value in business operations that target conversation logs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-30", "arxiv_id": "2409.17066", "title": "VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models", "authors": [ "Yifei Liu", "Jicheng Wen", "Yang Wang", "Shengyu Ye", "Li Lyna Zhang", "Ting Cao", "Cheng Li", "Mao Yang" ], "github": "", "project_page": "", "abstract": "Scaling model size significantly challenges the deployment and inference of Large Language Models (LLMs). Due to the redundancy in LLM weights, recent research has focused on pushing weight-only quantization to extremely low-bit (even down to 2 bits). It reduces memory requirements, optimizes storage costs, and decreases memory bandwidth needs during inference. However, due to numerical representation limitations, traditional scalar-based weight quantization struggles to achieve such extreme low-bit. Recent research on Vector Quantization (VQ) for LLMs has demonstrated the potential for extremely low-bit model quantization by compressing vectors into indices using lookup tables. In this paper, we introduce Vector Post-Training Quantization (VPTQ) for extremely low-bit quantization of LLMs. We use Second-Order Optimization to formulate the LLM VQ problem and guide our quantization algorithm design by solving the optimization. We further refine the weights using Channel-Independent Second-Order Optimization for a granular VQ. In addition, by decomposing the optimization problem, we propose a brief and effective codebook initialization algorithm. We also extend VPTQ to support residual and outlier quantization, which enhances model accuracy and further compresses the model. Our experimental results show that VPTQ reduces model quantization perplexity by 0.01-0.34 on LLaMA-2, 0.38-0.68 on Mistral-7B, 4.41-7.34 on LLaMA-3 over SOTA at 2-bit, with an average accuracy improvement of 0.79-1.5% on LLaMA-2, 1% on Mistral-7B, 11-22% on LLaMA-3 on QA tasks on average. We only utilize 10.4-18.6% of the quantization algorithm execution time, resulting in a 1.6-1.8times increase in inference throughput compared to SOTA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-30", "arxiv_id": "2409.17692", "title": "MIO: A Foundation Model on Multimodal Tokens", "authors": [ "Zekun Wang", "King Zhu", "Chunpu Xu", "Wangchunshu Zhou", "Jiaheng Liu", "Yibo Zhang", "Jiashuo Wang", "Ning Shi", "Siyu Li", "Yizhi Li", "Haoran Que", "Zhaoxiang Zhang", "Yuanxing Zhang", "Ge Zhang", "Ke Xu", "Jie Fu", "Wenhao Huang" ], "github": "https://github.com/mio-team/mio", "project_page": "", "abstract": "In this paper, we introduce MIO, a novel foundation model built on multimodal tokens, capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner. While the emergence of large language models (LLMs) and multimodal large language models (MM-LLMs) propels advancements in artificial general intelligence through their versatile capabilities, they still lack true any-to-any understanding and generation. Recently, the release of GPT-4o has showcased the remarkable potential of any-to-any LLMs for complex real-world tasks, enabling omnidirectional input and output across images, speech, and text. However, it is closed-source and does not support the generation of multimodal interleaved sequences. To address this gap, we present MIO, which is trained on a mixture of discrete tokens across four modalities using causal multimodal modeling. MIO undergoes a four-stage training process: (1) alignment pre-training, (2) interleaved pre-training, (3) speech-enhanced pre-training, and (4) comprehensive supervised fine-tuning on diverse textual, visual, and speech tasks. Our experimental results indicate that MIO exhibits competitive, and in some cases superior, performance compared to previous dual-modal baselines, any-to-any model baselines, and even modality-specific baselines. Moreover, MIO demonstrates advanced capabilities inherent to its any-to-any feature, such as interleaved video-text generation, chain-of-visual-thought reasoning, visual guideline generation, instructional image editing, etc.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mio-team/mio", "github_api_added_by": "auto" }, { "date": "2024-09-30", "arxiv_id": "2409.16686", "title": "MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for Superior Planning and Decision-Making", "authors": [ "Dayuan Fu", "Biqing Qi", "Yihuai Gao", "Che Jiang", "Guanting Dong", "Bowen Zhou" ], "github": "", "project_page": "", "abstract": "Long-term memory is significant for agents, in which insights play a crucial role. However, the emergence of irrelevant insight and the lack of general insight can greatly undermine the effectiveness of insight. To solve this problem, in this paper, we introduce Multi-Scale Insight Agent (MSI-Agent), an embodied agent designed to improve LLMs' planning and decision-making ability by summarizing and utilizing insight effectively across different scales. MSI achieves this through the experience selector, insight generator, and insight selector. Leveraging a three-part pipeline, MSI can generate task-specific and high-level insight, store it in a database, and then use relevant insight from it to aid in decision-making. Our experiments show that MSI outperforms another insight strategy when planning by GPT3.5. Moreover, We delve into the strategies for selecting seed experience and insight, aiming to provide LLM with more useful and relevant insight for better decision-making. Our observations also indicate that MSI exhibits better robustness when facing domain-shifting scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-30", "arxiv_id": "2409.18964", "title": "PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation", "authors": [ "Shaowei Liu", "Zhongzheng Ren", "Saurabh Gupta", "Shenlong Wang" ], "github": "https://github.com/stevenlsw/physgen", "project_page": "", "abstract": "We present PhysGen, a novel image-to-video generation method that converts a single image and an input condition (e.g., force and torque applied to an object in the image) to produce a realistic, physically plausible, and temporally consistent video. Our key insight is to integrate model-based physical simulation with a data-driven video generation process, enabling plausible image-space dynamics. At the heart of our system are three core components: (i) an image understanding module that effectively captures the geometry, materials, and physical parameters of the image; (ii) an image-space dynamics simulation model that utilizes rigid-body physics and inferred parameters to simulate realistic behaviors; and (iii) an image-based rendering and refinement module that leverages generative video diffusion to produce realistic video footage featuring the simulated motion. The resulting videos are realistic in both physics and appearance and are even precisely controllable, showcasing superior results over existing data-driven image-to-video generation works through quantitative comparison and comprehensive user study. PhysGen's resulting videos can be used for various downstream applications, such as turning an image into a realistic animation or allowing users to interact with the image and create various dynamics. Project page: https://stevenlsw.github.io/physgen/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stevenlsw/physgen", "github_api_added_by": "auto" }, { "date": "2024-09-30", "arxiv_id": "2409.18869", "title": "Emu3: Next-Token Prediction is All You Need", "authors": [ "Xinlong Wang", "Xiaosong Zhang", "Zhengxiong Luo", "Quan Sun", "Yufeng Cui", "Jinsheng Wang", "Fan Zhang", "Yueze Wang", "Zhen Li", "Qiying Yu", "Yingli Zhao", "Yulong Ao", "Xuebin Min", "Tao Li", "Boya Wu", "Bo Zhao", "Bowen Zhang", "Liangdong Wang", "Guang Liu", "Zheqi He", "Xi Yang", "Jingjing Liu", "Yonghua Lin", "Tiejun Huang", "Zhongyuan Wang" ], "github": "https://github.com/baaivision/emu3", "project_page": "", "abstract": "While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diffusion models (e.g., Stable Diffusion) and compositional approaches (e.g., CLIP combined with LLMs). In this paper, we introduce Emu3, a new suite of state-of-the-art multimodal models trained solely with next-token prediction. By tokenizing images, text, and videos into a discrete space, we train a single transformer from scratch on a mixture of multimodal sequences. Emu3 outperforms several well-established task-specific models in both generation and perception tasks, surpassing flagship models such as SDXL and LLaVA-1.6, while eliminating the need for diffusion or compositional architectures. Emu3 is also capable of generating high-fidelity video via predicting the next token in a video sequence. We simplify complex multimodal model designs by converging on a singular focus: tokens, unlocking great potential for scaling both during training and inference. Our results demonstrate that next-token prediction is a promising path towards building general multimodal intelligence beyond language. We open-source key techniques and models to support further research in this direction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/baaivision/emu3", "github_api_added_by": "auto" }, { "date": "2024-09-30", "arxiv_id": "2409.18839", "title": "MinerU: An Open-Source Solution for Precise Document Content Extraction", "authors": [ "Bin Wang", "Chao Xu", "Xiaomeng Zhao", "Linke Ouyang", "Fan Wu", "Zhiyuan Zhao", "Rui Xu", "Kaiwen Liu", "Yuan Qu", "Fukai Shang", "Bo Zhang", "Liqun Wei", "Zhihao Sui", "Wei Li", "Botian Shi", "Yu Qiao", "Dahua Lin", "Conghui He" ], "github": "https://github.com/opendatalab/MinerU", "project_page": "", "abstract": "Document content analysis has been a crucial research area in computer vision. Despite significant advancements in methods such as OCR, layout detection, and formula recognition, existing open-source solutions struggle to consistently deliver high-quality content extraction due to the diversity in document types and content. To address these challenges, we present MinerU, an open-source solution for high-precision document content extraction. MinerU leverages the sophisticated PDF-Extract-Kit models to extract content from diverse documents effectively and employs finely-tuned preprocessing and postprocessing rules to ensure the accuracy of the final results. Experimental results demonstrate that MinerU consistently achieves high performance across various document types, significantly enhancing the quality and consistency of content extraction. The MinerU open-source project is available at https://github.com/opendatalab/MinerU.", "github_manual": "", "github_regex": "https://github.com/opendatalab/MinerU", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-30", "arxiv_id": "2409.17545", "title": "Modulated Intervention Preference Optimization (MIPO): Keep the Easy, Refine the Difficult", "authors": [ "Cheolhun Jang" ], "github": "", "project_page": "", "abstract": "Preference optimization methods typically begin training with a well-trained SFT model as a reference model. In RLHF and DPO, a regularization term is used during the preference optimization process to prevent the policy model from deviating too far from the reference model's distribution, thereby avoiding the generation of anomalous responses. When the reference model is already well-aligned with the given data or only requires slight adjustments, this approach can produce a well-aligned model. However, if the reference model is not aligned with the given data and requires significant deviation from its current state, a regularization term may actually hinder the model alignment. In this study, we propose Modulated Intervention Preference Optimization (MIPO) to address this issue. MIPO modulates the degree of intervention from the reference model based on how well the given data is aligned with it. If the data is well-aligned, the intervention is increased to prevent the policy model from diverging significantly from reference model. Conversely, if the alignment is poor, the interference is reduced to facilitate more extensive training. We compare the performance of MIPO and DPO using Mistral-7B and Llama3-8B in Alpaca Eval 2.0 and MT-Bench. The experimental results demonstrate that MIPO consistently outperforms DPO across various evaluation scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-30", "arxiv_id": "2409.18957", "title": "LML: Language Model Learning a Dataset for Data-Augmented Prediction", "authors": [ "Praneeth Vadlapati" ], "github": "https://github.com/Pro-GenAI/LML-DAP", "project_page": "", "abstract": "This paper introduces a new approach to using Large Language Models (LLMs) for classification tasks, which are typically handled using Machine Learning (ML) models. Unlike ML models that rely heavily on data cleaning and feature engineering, this method streamlines the process using LLMs. This paper proposes a new concept called \"Language Model Learning (LML)\" powered by a new method called \"Data-Augmented Prediction (DAP)\". The classification is performed by LLMs using a method similar to humans manually exploring and understanding the data and deciding classifications using data as a reference. Training data is summarized and evaluated to determine the features that lead to the classification of each label the most. In the process of DAP, the system uses the data summary to automatically create a query, which is used to retrieve relevant rows from the dataset. A classification is generated by the LLM using data summary and relevant rows, ensuring satisfactory accuracy even with complex data. Usage of data summary and similar data in DAP ensures context-aware decision-making. The proposed method uses the words \"Act as an Explainable Machine Learning Model\" in the prompt to enhance the interpretability of the predictions by allowing users to review the logic behind each prediction. In some test cases, the system scored an accuracy above 90%, proving the effectiveness of the system and its potential to outperform conventional ML models in various scenarios. The code is available at https://github.com/Pro-GenAI/LML-DAP", "github_manual": "", "github_regex": "https://github.com/Pro-GenAI/LML-DAP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-09-30", "arxiv_id": "2409.18786", "title": "A Survey on the Honesty of Large Language Models", "authors": [ "Siheng Li", "Cheng Yang", "Taiqiang Wu", "Chufan Shi", "Yuji Zhang", "Xinyu Zhu", "Zesen Cheng", "Deng Cai", "Mo Yu", "Lemao Liu", "Jie Zhou", "Yujiu Yang", "Ngai Wong", "Xixin Wu", "Wai Lam" ], "github": "https://github.com/sihengli99/llm-honesty-survey", "project_page": "", "abstract": "Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to faithfully express their knowledge. Despite promising, current LLMs still exhibit significant dishonest behaviors, such as confidently presenting wrong answers or failing to express what they know. In addition, research on the honesty of LLMs also faces challenges, including varying definitions of honesty, difficulties in distinguishing between known and unknown knowledge, and a lack of comprehensive understanding of related research. To address these issues, we provide a survey on the honesty of LLMs, covering its clarification, evaluation approaches, and strategies for improvement. Moreover, we offer insights for future research, aiming to inspire further exploration in this important area.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sihengli99/llm-honesty-survey", "github_api_added_by": "auto" }, { "date": "2024-09-30", "arxiv_id": "2409.17433", "title": "HDFlow: Enhancing LLM Complex Problem-Solving with Hybrid Thinking and Dynamic Workflows", "authors": [ "Wenlin Yao", "Haitao Mi", "Dong Yu" ], "github": "https://github.com/wenlinyao/HDFlow", "project_page": "", "abstract": "Despite recent advancements in large language models (LLMs), their performance on complex reasoning problems requiring multi-step thinking and combining various skills is still limited. To address this, we propose a novel framework HDFlow for complex reasoning with LLMs that combines fast and slow thinking modes in an adaptive manner. Our approach consists of two key components: 1) a new approach for slow, deliberate reasoning called Dynamic Workflow, which automatically decomposes complex problems into more manageable sub-tasks and dynamically designs a workflow to assemble specialized LLM or symbolic reasoning tools to solve sub-tasks; 2) Hybrid Thinking, a general framework that dynamically combines fast and slow thinking based on problem complexity. Finally, we propose an easy-to-scale method for automatically synthesizing a large-scale dataset of 27K challenging reasoning problems for complex reasoning and a hybrid thinking tuning method that trains smaller LLMs on this dataset to internalize the fast/slow hybrid reasoning strategies. Experiments on four reasoning benchmark datasets demonstrate that our slow thinking with dynamic workflows significantly outperforms Chain-of-Thought, and hybrid thinking achieves the highest accuracy while providing an effective balance between computational efficiency and performance. Fine-tuning using our hybrid thinking approach also significantly boosts the complex reasoning capabilities of open-source language models. The results showcase the promise of slow thinking, dynamic workflows, and hybrid thinking in expanding the frontier of complex problem-solving with LLMsCode and data will be released at \\url{https://github.com/wenlinyao/HDFlow.}.", "github_manual": "", "github_regex": "https://github.com/wenlinyao/HDFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-01", "arxiv_id": "2409.19952", "title": "Image Copy Detection for Diffusion Models", "authors": [ "Wenhao Wang", "Yifan Sun", "Zhentao Tan", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Images produced by diffusion models are increasingly popular in digital artwork and visual marketing. However, such generated images might replicate content from existing ones and pose the challenge of content originality. Existing Image Copy Detection (ICD) models, though accurate in detecting hand-crafted replicas, overlook the challenge from diffusion models. This motivates us to introduce ICDiff, the first ICD specialized for diffusion models. To this end, we construct a Diffusion-Replication (D-Rep) dataset and correspondingly propose a novel deep embedding method. D-Rep uses a state-of-the-art diffusion model (Stable Diffusion V1.5) to generate 40, 000 image-replica pairs, which are manually annotated into 6 replication levels ranging from 0 (no replication) to 5 (total replication). Our method, PDF-Embedding, transforms the replication level of each image-replica pair into a probability density function (PDF) as the supervision signal. The intuition is that the probability of neighboring replication levels should be continuous and smooth. Experimental results show that PDF-Embedding surpasses protocol-driven methods and non-PDF choices on the D-Rep test set. Moreover, by utilizing PDF-Embedding, we find that the replication ratios of well-known diffusion models against an open-source gallery range from 10% to 20%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-01", "arxiv_id": "2409.20566", "title": "MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning", "authors": [ "Haotian Zhang", "Mingfei Gao", "Zhe Gan", "Philipp Dufter", "Nina Wenzel", "Forrest Huang", "Dhruti Shah", "Xianzhi Du", "Bowen Zhang", "Yanghao Li", "Sam Dodge", "Keen You", "Zhen Yang", "Aleksei Timofeev", "Mingze Xu", "Hong-You Chen", "Jean-Philippe Fauconnier", "Zhengfeng Lai", "Haoxuan You", "Zirui Wang", "Afshin Dehghan", "Peter Grasch", "Yinfei Yang" ], "github": "", "project_page": "", "abstract": "We present MM1.5, a new family of multimodal large language models (MLLMs) designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and multi-image reasoning. Building upon the MM1 architecture, MM1.5 adopts a data-centric approach to model training, systematically exploring the impact of diverse data mixtures across the entire model training lifecycle. This includes high-quality OCR data and synthetic captions for continual pre-training, as well as an optimized visual instruction-tuning data mixture for supervised fine-tuning. Our models range from 1B to 30B parameters, encompassing both dense and mixture-of-experts (MoE) variants, and demonstrate that careful data curation and training strategies can yield strong performance even at small scales (1B and 3B). Additionally, we introduce two specialized variants: MM1.5-Video, designed for video understanding, and MM1.5-UI, tailored for mobile UI understanding. Through extensive empirical studies and ablations, we provide detailed insights into the training processes and decisions that inform our final designs, offering valuable guidance for future research in MLLM development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-01", "arxiv_id": "2409.19020", "title": "DiaSynth -- Synthetic Dialogue Generation Framework", "authors": [ "Sathya Krishnan Suresh", "Wu Mengjun", "Tushar Pranav", "Eng Siong Chng" ], "github": "https://github.com/ntuspeechlab/DiaSynth", "project_page": "", "abstract": "The scarcity of domain specific dialogue datasets across various domains, from academic topics to everyday conversations, limits the development of dialogue systems for various applications. Existing research is often constrained either by dialogue datasets that are too general or by niche domain dialogue datasets whose scale does not match the required scale for training dialogue systems. To address this gap, we introduce DiaSynth - a synthetic dialogue generation framework capable of generating high quality, contextually rich dialogues across a wide range of domains. Our approach differs from existing frameworks by dynamically generating dialogues that incorporate simulated personas, subtopics, and diverse conversational characteristics, using a Large Language Model (LLM) with Chain of Thought (CoT) reasoning to create contextually rich, domain-specific dialogues that closely mimic natural human interactions. DiaSynth produces tailored dialogues that emulate realistic conversations. We perform our experiments by generating synthetic data using different LLMs and few-shot examples from DialogSum and SAMSum. The pretrained language models fine-tuned on the synthetic data outperform the base models by 16.47%, while the comparison between models fine-tuned on in-domain data and synthetic data shows that the synthetic data is able to capture 90.48% of the distribution of the in-domain data. The quality of the data generated also scales with the size of LLMs. These results validate DiaSynth's potential as a robust alternative to traditional data collection methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ntuspeechlab/DiaSynth", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-01", "arxiv_id": "2409.19606", "title": "Hyper-Connections", "authors": [ "Defa Zhu", "Hongzhi Huang", "Zihao Huang", "Yutao Zeng", "Yunyao Mao", "Banggu Wu", "Qiyang Min", "Xun Zhou" ], "github": "", "project_page": "", "abstract": "We present hyper-connections, a simple yet effective method that can serve as an alternative to residual connections. This approach specifically addresses common drawbacks observed in residual connection variants, such as the seesaw effect between gradient vanishing and representation collapse. Theoretically, hyper-connections allow the network to adjust the strength of connections between features at different depths and dynamically rearrange layers. We conduct experiments focusing on the pre-training of large language models, including dense and sparse models, where hyper-connections show significant performance improvements over residual connections. Additional experiments conducted on vision tasks also demonstrate similar improvements. We anticipate that this method will be broadly applicable and beneficial across a wide range of AI problems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-01", "arxiv_id": "2409.19627", "title": "IDEAW: Robust Neural Audio Watermarking with Invertible Dual-Embedding", "authors": [ "Pengcheng Li", "Xulong Zhang", "Jing Xiao", "Jianzong Wang" ], "github": "https://github.com/PecholaL/IDEAW", "project_page": "", "abstract": "The audio watermarking technique embeds messages into audio and accurately extracts messages from the watermarked audio. Traditional methods develop algorithms based on expert experience to embed watermarks into the time-domain or transform-domain of signals. With the development of deep neural networks, deep learning-based neural audio watermarking has emerged. Compared to traditional algorithms, neural audio watermarking achieves better robustness by considering various attacks during training. However, current neural watermarking methods suffer from low capacity and unsatisfactory imperceptibility. Additionally, the issue of watermark locating, which is extremely important and even more pronounced in neural audio watermarking, has not been adequately studied. In this paper, we design a dual-embedding watermarking model for efficient locating. We also consider the impact of the attack layer on the invertible neural network in robustness training, improving the model to enhance both its reasonableness and stability. Experiments show that the proposed model, IDEAW, can withstand various attacks with higher capacity and more efficient locating ability compared to existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PecholaL/IDEAW", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-01", "arxiv_id": "2409.18747", "title": "Cottention: Linear Transformers With Cosine Attention", "authors": [ "Gabriel Mongaras", "Trevor Dohm", "Eric C. Larson" ], "github": "https://github.com/gmongaras/Cottention_Transformer", "project_page": "", "abstract": "Attention mechanisms, particularly softmax attention, have been instrumental in the success of transformer-based models such as GPT. However, the quadratic memory complexity of softmax attention with respect to sequence length poses significant challenges for processing longer sequences. We introduce Cottention, a novel attention mechanism that replaces the softmax operation with cosine similarity. By leveraging the properties of cosine similarity and rearranging the attention equation, Cottention achieves native linear memory complexity with respect to sequence length, making it inherently more memory-efficient than softmax attention. We demonstrate that Cottention can be reformulated as a recurrent neural network (RNN) with a finite hidden state, allowing for constant memory usage during inference. We evaluate Cottention on both the bidirectional BERT and causal GPT tasks, demonstrating comparable performance to softmax attention while significantly reducing memory requirements. To ensure efficient computation, we develop a custom CUDA kernel for Cottention. Our results show that Cottention is a promising alternative to softmax attention, enabling the processing of longer sequences without sacrificing performance, due to its native linear memory complexity and ability to maintain a constant memory footprint during inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gmongaras/Cottention_Transformer", "github_api_added_by": "auto" }, { "date": "2024-10-01", "arxiv_id": "2409.19808", "title": "Can Models Learn Skill Composition from Examples?", "authors": [ "Haoyu Zhao", "Simran Kaur", "Dingli Yu", "Anirudh Goyal", "Sanjeev Arora" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) become increasingly advanced, their ability to exhibit compositional generalization -- the capacity to combine learned skills in novel ways not encountered during training -- has garnered significant attention. This type of generalization, particularly in scenarios beyond training data, is also of great interest in the study of AI safety and alignment. A recent study introduced the SKILL-MIX evaluation, where models are tasked with composing a short paragraph demonstrating the use of a specified k-tuple of language skills. While small models struggled with composing even with k=3, larger models like GPT-4 performed reasonably well with k=5 and 6. In this paper, we employ a setup akin to SKILL-MIX to evaluate the capacity of smaller models to learn compositional generalization from examples. Utilizing a diverse set of language skills -- including rhetorical, literary, reasoning, theory of mind, and common sense -- GPT-4 was used to generate text samples that exhibit random subsets of k skills. Subsequent fine-tuning of 7B and 13B parameter models on these combined skill texts, for increasing values of k, revealed the following findings: (1) Training on combinations of k=2 and 3 skills results in noticeable improvements in the ability to compose texts with k=4 and 5 skills, despite models never having seen such examples during training. (2) When skill categories are split into training and held-out groups, models significantly improve at composing texts with held-out skills during testing despite having only seen training skills during fine-tuning, illustrating the efficacy of the training approach even with previously unseen skills. This study also suggests that incorporating skill-rich (potentially synthetic) text into training can substantially enhance the compositional capabilities of models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-01", "arxiv_id": "2409.19715", "title": "Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code", "authors": [ "Hyungjoo Chae", "Taeyoon Kwon", "Seungjun Moon", "Yongho Song", "Dongjin Kang", "Kai Tzu-iunn Ong", "Beong-woo Kwak", "Seonghyeon Bae", "Seung-won Hwang", "Jinyoung Yeo" ], "github": "", "project_page": "", "abstract": "This paper presents Coffee-Gym, a comprehensive RL environment for training models that provide feedback on code editing. Coffee-Gym includes two major components: (1) Coffee, a dataset containing humans' code edit traces for coding questions and machine-written feedback for editing erroneous code; (2) CoffeeEval, a reward function that faithfully reflects the helpfulness of feedback by assessing the performance of the revised code in unit tests. With them, Coffee-Gym addresses the unavailability of high-quality datasets for training feedback models with RL, and provides more accurate rewards than the SOTA reward model (i.e., GPT-4). By applying Coffee-Gym, we elicit feedback models that outperform baselines in enhancing open-source code LLMs' code editing, making them comparable with closed-source LLMs. We make the dataset and the model checkpoint publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-01", "arxiv_id": "2409.18943", "title": "Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models", "authors": [ "Jiaming Li", "Lei Zhang", "Yunshui Li", "Ziqiang Liu", "yuelin bai", "Run Luo", "Longze Chen", "Min Yang" ], "github": "https://github.com/Geaming2002/Ruler", "project_page": "", "abstract": "The instruction-following ability of large language models enables humans to interact with AI agents in a natural way. However, when required to generate responses of a specific length, large language models often struggle to meet users' needs due to their inherent difficulty in accurately perceiving numerical constraints. To explore the ability of large language models to control the length of generated responses, we propose the Target Length Generation Task (TLG) and design two metrics, Precise Match (PM) and Flexible Match (FM) to evaluate the model's performance in adhering to specified response lengths. Furthermore, we introduce a novel, model-agnostic approach called Ruler, which employs Meta Length Tokens (MLTs) to enhance the instruction-following ability of large language models under length-constrained instructions. Specifically, Ruler equips LLMs with the ability to generate responses of a specified length based on length constraints within the instructions. Moreover, Ruler can automatically generate appropriate MLT when length constraints are not explicitly provided, demonstrating excellent versatility and generalization. Comprehensive experiments show the effectiveness of Ruler across different LLMs on Target Length Generation Task, e.g., at All Level 27.97 average gain on PM, 29.57 average gain on FM. In addition, we conduct extensive ablation experiments to further substantiate the efficacy and generalization of Ruler. Our code and data is available at https://github.com/Geaming2002/Ruler.", "github_manual": "", "github_regex": "https://github.com/Geaming2002/Ruler", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-01", "arxiv_id": "2409.20551", "title": "UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models", "authors": [ "Qiaojun Yu", "Siyuan Huang", "Xibin Yuan", "Zhengkai Jiang", "Ce Hao", "Xin Li", "Haonan Chang", "Junbo Wang", "Liu Liu", "Hongsheng Li", "Peng Gao", "Cewu Lu" ], "github": "https://github.com/qiaojunyu/UniAff", "project_page": "", "abstract": "Previous studies on robotic manipulation are based on a limited understanding of the underlying 3D motion constraints and affordances. To address these challenges, we propose a comprehensive paradigm, termed UniAff, that integrates 3D object-centric manipulation and task understanding in a unified formulation. Specifically, we constructed a dataset labeled with manipulation-related key attributes, comprising 900 articulated objects from 19 categories and 600 tools from 12 categories. Furthermore, we leverage MLLMs to infer object-centric representations for manipulation tasks, including affordance recognition and reasoning about 3D motion constraints. Comprehensive experiments in both simulation and real-world settings indicate that UniAff significantly improves the generalization of robotic manipulation for tools and articulated objects. We hope that UniAff will serve as a general baseline for unified robotic manipulation tasks in the future. Images, videos, dataset, and code are published on the project website at:https://sites.google.com/view/uni-aff/home", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qiaojunyu/UniAff", "github_api_added_by": "auto" }, { "date": "2024-10-01", "arxiv_id": "2409.20537", "title": "Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers", "authors": [ "Lirui Wang", "Xinlei Chen", "Jialiang Zhao", "Kaiming He" ], "github": "https://github.com/liruiw/lerobot", "project_page": "", "abstract": "One of the roadblocks for training generalist robotic models today is heterogeneity. Previous robot learning methods often collect data to train with one specific embodiment for one task, which is expensive and prone to overfitting. This work studies the problem of learning policy representations through heterogeneous pre-training on robot data across different embodiments and tasks at scale. We propose Heterogeneous Pre-trained Transformers (HPT), which pre-train a large, shareable trunk of a policy neural network to learn a task and embodiment agnostic shared representation. This general architecture aligns the specific proprioception and vision inputs from distinct embodiments to a short sequence of tokens and then processes such tokens to map to control robots for different tasks. Leveraging the recent large-scale multi-embodiment real-world robotic datasets as well as simulation, deployed robots, and human video datasets, we investigate pre-training policies across heterogeneity. We conduct experiments to investigate the scaling behaviors of training objectives, to the extent of 52 datasets. HPTs outperform several baselines and enhance the fine-tuned policy performance by over 20% on unseen tasks in multiple simulator benchmarks and real-world settings. See the project website (https://liruiw.github.io/hpt/) for code and videos.", "github_manual": "https://github.com/liruiw/lerobot", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-01", "arxiv_id": "2409.19339", "title": "Visual Question Decomposition on Multimodal Large Language Models", "authors": [ "Haowei Zhang", "Jianzhe Liu", "Zhen Han", "Shuo Chen", "Bailan He", "Volker Tresp", "Zhiqiang Xu", "Jindong Gu" ], "github": "", "project_page": "", "abstract": "Question decomposition has emerged as an effective strategy for prompting Large Language Models (LLMs) to answer complex questions. However, while existing methods primarily focus on unimodal language models, the question decomposition capability of Multimodal Large Language Models (MLLMs) has yet to be explored. To this end, this paper explores visual question decomposition on MLLMs. Specifically, we introduce a systematic evaluation framework including a dataset and several evaluation criteria to assess the quality of the decomposed sub-questions, revealing that existing MLLMs struggle to produce high-quality sub-questions. To address this limitation, we propose a specific finetuning dataset, DecoVQA+, for enhancing the model's question decomposition capability. Aiming at enabling models to perform appropriate selective decomposition, we propose an efficient finetuning pipeline. The finetuning pipeline consists of our proposed dataset and a training objective for selective decomposition. Finetuned MLLMs demonstrate significant improvements in the quality of sub-questions and the policy of selective question decomposition. Additionally, the models also achieve higher accuracy with selective decomposition on VQA benchmark datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-01", "arxiv_id": "2410.00907", "title": "Addition is All You Need for Energy-efficient Language Models", "authors": [ "Hongyin Luo", "Wei Sun" ], "github": "", "project_page": "", "abstract": "Large neural networks spend most computation on floating point tensor multiplications. In this work, we find that a floating point multiplier can be approximated by one integer adder with high precision. We propose the linear-complexity multiplication L-Mul algorithm that approximates floating point number multiplication with integer addition operations. The new algorithm costs significantly less computation resource than 8-bit floating point multiplication but achieves higher precision. Compared to 8-bit floating point multiplications, the proposed method achieves higher precision but consumes significantly less bit-level computation. Since multiplying floating point numbers requires substantially higher energy compared to integer addition operations, applying the L-Mul operation in tensor processing hardware can potentially reduce 95% energy cost by element-wise floating point tensor multiplications and 80% energy cost of dot products. We calculated the theoretical error expectation of L-Mul, and evaluated the algorithm on a wide range of textual, visual, and symbolic tasks, including natural language understanding, structural reasoning, mathematics, and commonsense question answering. Our numerical analysis experiments agree with the theoretical error estimation, which indicates that L-Mul with 4-bit mantissa achieves comparable precision as float8_e4m3 multiplications, and L-Mul with 3-bit mantissa outperforms float8_e5m2. Evaluation results on popular benchmarks show that directly applying L-Mul to the attention mechanism is almost lossless. We further show that replacing all floating point multiplications with 3-bit mantissa L-Mul in a transformer model achieves equivalent precision as using float8_e4m3 as accumulation precision in both fine-tuning and inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2410.00231", "title": "Helpful DoggyBot: Open-World Object Fetching using Legged Robots and Vision-Language Models", "authors": [ "Qi Wu", "Zipeng Fu", "Xuxin Cheng", "Xiaolong Wang", "Chelsea Finn" ], "github": "", "project_page": "", "abstract": "Learning-based methods have achieved strong performance for quadrupedal locomotion. However, several challenges prevent quadrupeds from learning helpful indoor skills that require interaction with environments and humans: lack of end-effectors for manipulation, limited semantic understanding using only simulation data, and low traversability and reachability in indoor environments. We present a system for quadrupedal mobile manipulation in indoor environments. It uses a front-mounted gripper for object manipulation, a low-level controller trained in simulation using egocentric depth for agile skills like climbing and whole-body tilting, and pre-trained vision-language models (VLMs) with a third-person fisheye and an egocentric RGB camera for semantic understanding and command generation. We evaluate our system in two unseen environments without any real-world data collection or training. Our system can zero-shot generalize to these environments and complete tasks, like following user's commands to fetch a randomly placed stuff toy after climbing over a queen-sized bed, with a 60% success rate. Project website: https://helpful-doggybot.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2409.20563", "title": "DressRecon: Freeform 4D Human Reconstruction from Monocular Video", "authors": [ "Jeff Tan", "Donglai Xiang", "Shubham Tulsiani", "Deva Ramanan", "Gengshan Yang" ], "github": "", "project_page": "", "abstract": "We present a method to reconstruct time-consistent human body models from monocular videos, focusing on extremely loose clothing or handheld object interactions. Prior work in human reconstruction is either limited to tight clothing with no object interactions, or requires calibrated multi-view captures or personalized template scans which are costly to collect at scale. Our key insight for high-quality yet flexible reconstruction is the careful combination of generic human priors about articulated body shape (learned from large-scale training data) with video-specific articulated \"bag-of-bones\" deformation (fit to a single video via test-time optimization). We accomplish this by learning a neural implicit model that disentangles body versus clothing deformations as separate motion model layers. To capture subtle geometry of clothing, we leverage image-based priors such as human body pose, surface normals, and optical flow during optimization. The resulting neural fields can be extracted into time-consistent meshes, or further optimized as explicit 3D Gaussians for high-fidelity interactive rendering. On datasets with highly challenging clothing deformations and object interactions, DressRecon yields higher-fidelity 3D reconstructions than prior art. Project page: https://jefftan969.github.io/dressrecon/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2410.00086", "title": "ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer", "authors": [ "Zhen Han", "Zeyinzi Jiang", "Yulin Pan", "Jingfeng Zhang", "Chaojie Mao", "Chenwei Xie", "Yu Liu", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "Diffusion models have emerged as a powerful generative technology and have been found to be applicable in various scenarios. Most existing foundational diffusion models are primarily designed for text-guided visual generation and do not support multi-modal conditions, which are essential for many visual editing tasks. This limitation prevents these foundational diffusion models from serving as a unified model in the field of visual generation, like GPT-4 in the natural language processing field. In this work, we propose ACE, an All-round Creator and Editor, which achieves comparable performance compared to those expert models in a wide range of visual generation tasks. To achieve this goal, we first introduce a unified condition format termed Long-context Condition Unit (LCU), and propose a novel Transformer-based diffusion model that uses LCU as input, aiming for joint training across various generation and editing tasks. Furthermore, we propose an efficient data collection approach to address the issue of the absence of available training data. It involves acquiring pairwise images with synthesis-based or clustering-based pipelines and supplying these pairs with accurate textual instructions by leveraging a fine-tuned multi-modal large language model. To comprehensively evaluate the performance of our model, we establish a benchmark of manually annotated pairs data across a variety of visual generation tasks. The extensive experimental results demonstrate the superiority of our model in visual generation fields. Thanks to the all-in-one capabilities of our model, we can easily build a multi-modal chat system that responds to any interactive request for image creation using a single model to serve as the backend, avoiding the cumbersome pipeline typically employed in visual agents. Code and models will be available on the project page: https://ali-vilab.github.io/ace-page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2410.00531", "title": "TPI-LLM: Serving 70B-scale LLMs Efficiently on Low-resource Edge Devices", "authors": [ "Zonghang Li", "Wenjiao Feng", "Mohsen Guizani", "Hongfang Yu" ], "github": "", "project_page": "", "abstract": "Large model inference is shifting from cloud to edge due to concerns about the privacy of user interaction data. However, edge devices often struggle with limited computing power, memory, and bandwidth, requiring collaboration across multiple devices to run and speed up LLM inference. Pipeline parallelism, the mainstream solution, is inefficient for single-user scenarios, while tensor parallelism struggles with frequent communications. In this paper, we argue that tensor parallelism can be more effective than pipeline on low-resource devices, and present a compute- and memory-efficient tensor parallel inference system, named TPI-LLM, to serve 70B-scale models. TPI-LLM keeps sensitive raw data local in the users' devices and introduces a sliding window memory scheduler to dynamically manage layer weights during inference, with disk I/O latency overlapped with the computation and communication. This allows larger models to run smoothly on memory-limited devices. We analyze the communication bottleneck and find that link latency, not bandwidth, emerges as the main issue, so a star-based allreduce algorithm is implemented. Through extensive experiments on both emulated and real testbeds, TPI-LLM demonstrated over 80% less time-to-first-token and token latency compared to Accelerate, and over 90% compared to Transformers and Galaxy, while cutting the peak memory footprint of Llama 2-70B by 90%, requiring only 3.1 GB of memory for 70B-scale models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2409.19951", "title": "Law of the Weakest Link: Cross Capabilities of Large Language Models", "authors": [ "Ming Zhong", "Aston Zhang", "Xuewei Wang", "Rui Hou", "Wenhan Xiong", "Chenguang Zhu", "Zhengxing Chen", "Liang Tan", "Chloe Bi", "Mike Lewis", "Sravya Popuri", "Sharan Narang", "Melanie Kambadur", "Dhruv Mahajan", "Sergey Edunov", "Jiawei Han", "Laurens van der Maaten" ], "github": "https://github.com/facebookresearch/llm-cross-capabilities", "project_page": "", "abstract": "The development and evaluation of Large Language Models (LLMs) have largely focused on individual capabilities. However, this overlooks the intersection of multiple abilities across different types of expertise that are often required for real-world tasks, which we term cross capabilities. To systematically explore this concept, we first define seven core individual capabilities and then pair them to form seven common cross capabilities, each supported by a manually constructed taxonomy. Building on these definitions, we introduce CrossEval, a benchmark comprising 1,400 human-annotated prompts, with 100 prompts for each individual and cross capability. To ensure reliable evaluation, we involve expert annotators to assess 4,200 model responses, gathering 8,400 human ratings with detailed explanations to serve as reference examples. Our findings reveal that, in both static evaluations and attempts to enhance specific abilities, current LLMs consistently exhibit the \"Law of the Weakest Link,\" where cross-capability performance is significantly constrained by the weakest component. Specifically, across 58 cross-capability scores from 17 models, 38 scores are lower than all individual capabilities, while 20 fall between strong and weak, but closer to the weaker ability. These results highlight the under-performance of LLMs in cross-capability tasks, making the identification and improvement of the weakest capabilities a critical priority for future research to optimize performance in complex, multi-dimensional scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/llm-cross-capabilities", "github_api_added_by": "auto" }, { "date": "2024-10-02", "arxiv_id": "2410.00890", "title": "Flex3D: Feed-Forward 3D Generation With Flexible Reconstruction Model And Input View Curation", "authors": [ "Junlin Han", "Jianyuan Wang", "Andrea Vedaldi", "Philip Torr", "Filippos Kokkinos" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "Generating high-quality 3D content from text, single images, or sparse view images remains a challenging task with broad applications.Existing methods typically employ multi-view diffusion models to synthesize multi-view images, followed by a feed-forward process for 3D reconstruction. However, these approaches are often constrained by a small and fixed number of input views, limiting their ability to capture diverse viewpoints and, even worse, leading to suboptimal generation results if the synthesized views are of poor quality. To address these limitations, we propose Flex3D, a novel two-stage framework capable of leveraging an arbitrary number of high-quality input views. The first stage consists of a candidate view generation and curation pipeline. We employ a fine-tuned multi-view image diffusion model and a video diffusion model to generate a pool of candidate views, enabling a rich representation of the target 3D object. Subsequently, a view selection pipeline filters these views based on quality and consistency, ensuring that only the high-quality and reliable views are used for reconstruction. In the second stage, the curated views are fed into a Flexible Reconstruction Model (FlexRM), built upon a transformer architecture that can effectively process an arbitrary number of inputs. FlemRM directly outputs 3D Gaussian points leveraging a tri-plane representation, enabling efficient and detailed 3D generation. Through extensive exploration of design and training strategies, we optimize FlexRM to achieve superior performance in both reconstruction and generation tasks. Our results demonstrate that Flex3D achieves state-of-the-art performance, with a user study winning rate of over 92% in 3D generation tasks when compared to several of the latest feed-forward 3D generative models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2409.17912", "title": "Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect", "authors": [ "Guokan Shang", "Hadi Abdine", "Yousef Khoubrane", "Amr Mohamed", "Yassine Abbahaddou", "Sofiane Ennadir", "Imane Momayiz", "Xuguang Ren", "Eric Moulines", "Preslav Nakov", "Michalis Vazirgiannis", "Eric Xing" ], "github": "", "project_page": "", "abstract": "We introduce Atlas-Chat, the first-ever collection of large language models specifically developed for dialectal Arabic. Focusing on Moroccan Arabic, also known as Darija, we construct our instruction dataset by consolidating existing Darija language resources, creating novel datasets both manually and synthetically, and translating English instructions with stringent quality control. Atlas-Chat-9B and 2B models, fine-tuned on the dataset, exhibit superior ability in following Darija instructions and performing standard NLP tasks. Notably, our models outperform both state-of-the-art and Arabic-specialized LLMs like LLaMa, Jais, and AceGPT, e.g., achieving a 13% performance boost over a larger 13B model on DarijaMMLU, in our newly introduced evaluation suite for Darija covering both discriminative and generative tasks. Furthermore, we perform an experimental analysis of various fine-tuning strategies and base model choices to determine optimal configurations. All our resources are publicly accessible, and we believe our work offers comprehensive design methodologies of instruction-tuning for low-resource language variants, which are often neglected in favor of data-rich languages by contemporary LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2409.19946", "title": "Illustrious: an Open Advanced Illustration Model", "authors": [ "Sang Hyun Park", "Jun Young Koh", "Junha Lee", "Joy Song", "Dongha Kim", "Hoyeon Moon", "Hyunju Lee", "Min Song" ], "github": "https://github.com/KohakuBlueleaf/KGen", "project_page": "", "abstract": "In this work, we share the insights for achieving state-of-the-art quality in our text-to-image anime image generative model, called Illustrious. To achieve high resolution, dynamic color range images, and high restoration ability, we focus on three critical approaches for model improvement. First, we delve into the significance of the batch size and dropout control, which enables faster learning of controllable token based concept activations. Second, we increase the training resolution of images, affecting the accurate depiction of character anatomy in much higher resolution, extending its generation capability over 20MP with proper methods. Finally, we propose the refined multi-level captions, covering all tags and various natural language captions as a critical factor for model development. Through extensive analysis and experiments, Illustrious demonstrates state-of-the-art performance in terms of animation style, outperforming widely-used models in illustration domains, propelling easier customization and personalization with nature of open source. We plan to publicly release updated Illustrious model series sequentially as well as sustainable plans for improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KohakuBlueleaf/KGen", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2409.19603", "title": "One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos", "authors": [ "Zechen Bai", "Tong He", "Haiyang Mei", "Pichao Wang", "Ziteng Gao", "Joya Chen", "Lei Liu", "Zheng Zhang", "Mike Zheng Shou" ], "github": "https://github.com/showlab/VideoLISA", "project_page": "", "abstract": "We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos. Leveraging the reasoning capabilities and world knowledge of large language models, and augmented by the Segment Anything Model, VideoLISA generates temporally consistent segmentation masks in videos based on language instructions. Existing image-based methods, such as LISA, struggle with video tasks due to the additional temporal dimension, which requires temporal dynamic understanding and consistent segmentation across frames. VideoLISA addresses these challenges by integrating a Sparse Dense Sampling strategy into the video-LLM, which balances temporal context and spatial detail within computational constraints. Additionally, we propose a One-Token-Seg-All approach using a specially designed token, enabling the model to segment and track objects across multiple frames. Extensive evaluations on diverse benchmarks, including our newly introduced ReasonVOS benchmark, demonstrate VideoLISA's superior performance in video object segmentation tasks involving complex reasoning, temporal understanding, and object tracking. While optimized for videos, VideoLISA also shows promising generalization to image segmentation, revealing its potential as a unified foundation model for language-instructed object segmentation. Code and model will be available at: https://github.com/showlab/VideoLISA.", "github_manual": "", "github_regex": "https://github.com/showlab/VideoLISA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2410.00418", "title": "Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration", "authors": [ "Guy Ohayon", "Tomer Michaeli", "Michael Elad" ], "github": "https://github.com/ohayonguy/PMRF", "project_page": "", "abstract": "Photo-realistic image restoration algorithms are typically evaluated by distortion measures (e.g., PSNR, SSIM) and by perceptual quality measures (e.g., FID, NIQE), where the desire is to attain the lowest possible distortion without compromising on perceptual quality. To achieve this goal, current methods typically attempt to sample from the posterior distribution, or to optimize a weighted sum of a distortion loss (e.g., MSE) and a perceptual quality loss (e.g., GAN). Unlike previous works, this paper is concerned specifically with the optimal estimator that minimizes the MSE under a constraint of perfect perceptual index, namely where the distribution of the reconstructed images is equal to that of the ground-truth ones. A recent theoretical result shows that such an estimator can be constructed by optimally transporting the posterior mean prediction (MMSE estimate) to the distribution of the ground-truth images. Inspired by this result, we introduce Posterior-Mean Rectified Flow (PMRF), a simple yet highly effective algorithm that approximates this optimal estimator. In particular, PMRF first predicts the posterior mean, and then transports the result to a high-quality image using a rectified flow model that approximates the desired optimal transport map. We investigate the theoretical utility of PMRF and demonstrate that it consistently outperforms previous methods on a variety of image restoration tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ohayonguy/PMRF", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2409.20018", "title": "Visual Context Window Extension: A New Perspective for Long Video Understanding", "authors": [ "Hongchen Wei", "Zhenzhong Chen" ], "github": "", "project_page": "", "abstract": "Large Multimodal Models (LMMs) have demonstrated impressive performance in short video understanding tasks but face great challenges when applied to long video understanding. In contrast, Large Language Models (LLMs) exhibit outstanding capabilities in modeling long texts. Existing work attempts to address this issue by introducing long video-text pairs during training. However, these approaches require substantial computational and data resources. In this paper, we tackle the challenge of long video understanding from the perspective of context windows, aiming to apply LMMs to long video tasks without retraining on long video datasets. We first conduct an in-depth analysis of why pretrained LMMs struggle to understand lengthy video content, identifying that discrepancies between visual and language modalities lead to different context windows for visual and language tokens, making it difficult to directly extend the visual tokens to match the language context window. Based on this, we propose to adapt LMMs for long video understanding tasks by extending the visual context window, eliminating the need for retraining on large scalelong video datasets. To further mitigate the significant memory consumption caused by long sequences, we introduce a progressive pooling inference strategy that selectively adjusts the spatial resolution of frame embeddings, reducing the number of visual tokens while retaining important spatial information. Across multiple long video understanding benchmarks, our method consistently improves the performance as the number of video frames increases. On the MLVU benchmark, our method outperforms GPT-4o, even though our model size is only 7B. Additionally, in the 256-frame setting, our method reduces memory usage by approximately 45% compared to the baseline, without introducing any performance loss.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2410.00337", "title": "SyntheOcc: Synthesize Geometric-Controlled Street View Images through 3D Semantic MPIs", "authors": [ "Leheng Li", "Weichao Qiu", "Yingjie Cai", "Xu Yan", "Qing Lian", "Bingbing Liu", "Ying-Cong Chen" ], "github": "", "project_page": "", "abstract": "The advancement of autonomous driving is increasingly reliant on high-quality annotated datasets, especially in the task of 3D occupancy prediction, where the occupancy labels require dense 3D annotation with significant human effort. In this paper, we propose SyntheOcc, which denotes a diffusion model that Synthesize photorealistic and geometric-controlled images by conditioning Occupancy labels in driving scenarios. This yields an unlimited amount of diverse, annotated, and controllable datasets for applications like training perception models and simulation. SyntheOcc addresses the critical challenge of how to efficiently encode 3D geometric information as conditional input to a 2D diffusion model. Our approach innovatively incorporates 3D semantic multi-plane images (MPIs) to provide comprehensive and spatially aligned 3D scene descriptions for conditioning. As a result, SyntheOcc can generate photorealistic multi-view images and videos that faithfully align with the given geometric labels (semantics in 3D voxel space). Extensive qualitative and quantitative evaluations of SyntheOcc on the nuScenes dataset prove its effectiveness in generating controllable occupancy datasets that serve as an effective data augmentation to perception models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2410.00545", "title": "What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study", "authors": [ "Beatrice Savoldi", "Sara Papi", "Matteo Negri", "Ana Guerberof", "Luisa Bentivogli" ], "github": "https://github.com/bsavoldi/post-edit_guidelines", "project_page": "", "abstract": "Gender bias in machine translation (MT) is recognized as an issue that can harm people and society. And yet, advancements in the field rarely involve people, the final MT users, or inform how they might be impacted by biased technologies. Current evaluations are often restricted to automatic methods, which offer an opaque estimate of what the downstream impact of gender disparities might be. We conduct an extensive human-centered study to examine if and to what extent bias in MT brings harms with tangible costs, such as quality of service gaps across women and men. To this aim, we collect behavioral data from 90 participants, who post-edited MT outputs to ensure correct gender translation. Across multiple datasets, languages, and types of users, our study shows that feminine post-editing demands significantly more technical and temporal effort, also corresponding to higher financial costs. Existing bias measurements, however, fail to reflect the found disparities. Our findings advocate for human-centered approaches that can inform the societal impact of bias.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bsavoldi/post-edit_guidelines", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-02", "arxiv_id": "2409.18313", "title": "Embodied-RAG: General non-parametric Embodied Memory for Retrieval and Generation", "authors": [ "Quanting Xie", "So Yeon Min", "Tianyi Zhang", "Aarav Bajaj", "Ruslan Salakhutdinov", "Matthew Johnson-Roberson", "Yonatan Bisk" ], "github": "", "project_page": "", "abstract": "There is no limit to how much a robot might explore and learn, but all of that knowledge needs to be searchable and actionable. Within language research, retrieval augmented generation (RAG) has become the workhouse of large-scale non-parametric knowledge, however existing techniques do not directly transfer to the embodied domain, which is multimodal, data is highly correlated, and perception requires abstraction. To address these challenges, we introduce Embodied-RAG, a framework that enhances the foundational model of an embodied agent with a non-parametric memory system capable of autonomously constructing hierarchical knowledge for both navigation and language generation. Embodied-RAG handles a full range of spatial and semantic resolutions across diverse environments and query types, whether for a specific object or a holistic description of ambiance. At its core, Embodied-RAG's memory is structured as a semantic forest, storing language descriptions at varying levels of detail. This hierarchical organization allows the system to efficiently generate context-sensitive outputs across different robotic platforms. We demonstrate that Embodied-RAG effectively bridges RAG to the robotics domain, successfully handling over 200 explanation and navigation queries across 19 environments, highlighting its promise for general-purpose non-parametric system for embodied agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01044", "title": "RATIONALYST: Pre-training Process-Supervision for Improving Reasoning", "authors": [ "Dongwei Jiang", "Guoxuan Wang", "Yining Lu", "Andrew Wang", "Jingyu Zhang", "Chuyu Liu", "Benjamin Van Durme", "Daniel Khashabi" ], "github": "https://github.com/jhu-clsp/rationalyst", "project_page": "", "abstract": "The reasoning steps generated by LLMs might be incomplete, as they mimic logical leaps common in everyday communication found in their pre-training data: underlying rationales are frequently left implicit (unstated). To address this challenge, we introduce RATIONALYST, a model for process-supervision of reasoning based on pre-training on a vast collection of rationale annotations extracted from unlabeled data. We extract 79k rationales from web-scale unlabelled dataset (the Pile) and a combination of reasoning datasets with minimal human intervention. This web-scale pre-training for reasoning allows RATIONALYST to consistently generalize across diverse reasoning tasks, including mathematical, commonsense, scientific, and logical reasoning. Fine-tuned from LLaMa-3-8B, RATIONALYST improves the accuracy of reasoning by an average of 3.9% on 7 representative reasoning benchmarks. It also demonstrates superior performance compared to significantly larger verifiers like GPT-4 and similarly sized models fine-tuned on matching training sets.", "github_manual": "https://github.com/jhu-clsp/rationalyst", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01804", "title": "EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis", "authors": [ "Alexander Mai", "Peter Hedman", "George Kopanas", "Dor Verbin", "David Futschik", "Qiangeng Xu", "Falko Kuester", "Jon Barron", "Yinda Zhang" ], "github": "", "project_page": "", "abstract": "We present Exact Volumetric Ellipsoid Rendering (EVER), a method for real-time differentiable emission-only volume rendering. Unlike recent rasterization based approach by 3D Gaussian Splatting (3DGS), our primitive based representation allows for exact volume rendering, rather than alpha compositing 3D Gaussian billboards. As such, unlike 3DGS our formulation does not suffer from popping artifacts and view dependent density, but still achieves frame rates of sim!30 FPS at 720p on an NVIDIA RTX4090. Since our approach is built upon ray tracing it enables effects such as defocus blur and camera distortion (e.g. such as from fisheye cameras), which are difficult to achieve by rasterization. We show that our method is more accurate with fewer blending issues than 3DGS and follow-up work on view-consistent rendering, especially on the challenging large-scale scenes from the Zip-NeRF dataset where it achieves sharpest results among real-time techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01731", "title": "ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation", "authors": [ "Rinon Gal", "Adi Haviv", "Yuval Alaluf", "Amit H. Bermano", "Daniel Cohen-Or", "Gal Chechik" ], "github": "", "project_page": "", "abstract": "The practical use of text-to-image generation has evolved from simple, monolithic models to complex workflows that combine multiple specialized components. While workflow-based approaches can lead to improved image quality, crafting effective workflows requires significant expertise, owing to the large number of available components, their complex inter-dependence, and their dependence on the generation prompt. Here, we introduce the novel task of prompt-adaptive workflow generation, where the goal is to automatically tailor a workflow to each user prompt. We propose two LLM-based approaches to tackle this task: a tuning-based method that learns from user-preference data, and a training-free method that uses the LLM to select existing flows. Both approaches lead to improved image quality when compared to monolithic models or generic, prompt-independent workflows. Our work shows that prompt-dependent flow prediction offers a new pathway to improving text-to-image generation quality, complementing existing research directions in the field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01257", "title": "HelpSteer2-Preference: Complementing Ratings with Preferences", "authors": [ "Zhilin Wang", "Alexander Bukharin", "Olivier Delalleau", "Daniel Egert", "Gerald Shen", "Jiaqi Zeng", "Oleksii Kuchaiev", "Yi Dong" ], "github": "", "project_page": "", "abstract": "Reward models are critical for aligning models to follow instructions, and are typically trained following one of two popular paradigms: Bradley-Terry style or Regression style. However, there is a lack of evidence that either approach is better than the other, when adequately matched for data. This is primarily because these approaches require data collected in different (but incompatible) formats, meaning that adequately matched data is not available in existing public datasets. To tackle this problem, we release preference annotations (designed for Bradley-Terry training) to complement existing ratings (designed for Regression style training) in the HelpSteer2 dataset. To improve data interpretability, preference annotations are accompanied with human-written justifications. Using this data, we conduct the first head-to-head comparison of Bradley-Terry and Regression models when adequately matched for data. Based on insights derived from such a comparison, we propose a novel approach to combine Bradley-Terry and Regression reward modeling. A Llama-3.1-70B-Instruct model tuned with this approach scores 94.1 on RewardBench, emerging top of more than 140 reward models as of 1 Oct 2024. We also demonstrate the effectiveness of this reward model at aligning models to follow instructions in RLHF. We open-source this dataset (CC-BY-4.0 license) at https://huggingface.co/datasets/nvidia/HelpSteer2 and openly release the trained Reward Model at https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01215", "title": "From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging", "authors": [ "Yuling Shi", "Songsong Wang", "Chengcheng Wan", "Xiaodong Gu" ], "github": "https://github.com/YerbaPage/MGDebugger", "project_page": "", "abstract": "While large language models have made significant strides in code generation, the pass rate of the generated code is bottlenecked on subtle errors, often requiring human intervention to pass tests, especially for complex problems. Existing LLM-based debugging systems treat generated programs as monolithic units, failing to address bugs at multiple levels of granularity, from low-level syntax errors to high-level algorithmic flaws. In this paper, we introduce Multi-Granularity Debugger (MGDebugger), a hierarchical code debugger by isolating, identifying, and resolving bugs at various levels of granularity. MGDebugger decomposes problematic code into a hierarchical tree structure of subfunctions, with each level representing a particular granularity of error. During debugging, it analyzes each subfunction and iteratively resolves bugs in a bottom-up manner. To effectively test each subfunction, we propose an LLM-simulated Python executor, which traces code execution and tracks important variable states to pinpoint errors accurately. Extensive experiments demonstrate that MGDebugger outperforms existing debugging systems, achieving an 18.9% improvement in accuracy over seed generations in HumanEval and a 97.6% repair success rate in HumanEvalFix. Furthermore, MGDebugger effectively fixes bugs across different categories and difficulty levels, demonstrating its robustness and effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YerbaPage/MGDebugger", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01744", "title": "LEOPARD : A Vision Language Model For Text-Rich Multi-Image Tasks", "authors": [ "Mengzhao Jia", "Wenhao Yu", "Kaixin Ma", "Tianqing Fang", "Zhihan Zhang", "Siru Ouyang", "Hongming Zhang", "Meng Jiang", "Dong Yu" ], "github": "https://github.com/jill0001/leopard", "project_page": "", "abstract": "Text-rich images, where text serves as the central visual element guiding the overall understanding, are prevalent in real-world applications, such as presentation slides, scanned documents, and webpage snapshots. Tasks involving multiple text-rich images are especially challenging, as they require not only understanding the content of individual images but reasoning about inter-relationships and logical flows across multiple visual inputs. Despite the importance of these scenarios, current multimodal large language models (MLLMs) struggle to handle such tasks due to two key challenges: (1) the scarcity of high-quality instruction tuning datasets for text-rich multi-image scenarios, and (2) the difficulty in balancing image resolution with visual feature sequence length. To address these challenges, we propose \\OurMethod, a MLLM designed specifically for handling vision-language tasks involving multiple text-rich images. First, we curated about one million high-quality multimodal instruction-tuning data, tailored to text-rich, multi-image scenarios. Second, we developed an adaptive high-resolution multi-image encoding module to dynamically optimize the allocation of visual sequence length based on the original aspect ratios and resolutions of the input images. Experiments across a wide range of benchmarks demonstrate our model's superior capabilities in text-rich, multi-image evaluations and competitive performance in general domain evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jill0001/leopard", "github_api_added_by": "auto" }, { "date": "2024-10-03", "arxiv_id": "2410.01440", "title": "Closed-loop Long-horizon Robotic Planning via Equilibrium Sequence Modeling", "authors": [ "Jinghan Li", "Zhicheng Sun", "Fei Li", "Cao Sheng", "Jiazhong Yu", "Yadong Mu" ], "github": "https://github.com/Singularity0104/equilibrium-planner", "project_page": "", "abstract": "In the endeavor to make autonomous robots take actions, task planning is a major challenge that requires translating high-level task descriptions into long-horizon action sequences. Despite recent advances in language model agents, they remain prone to planning errors and limited in their ability to plan ahead. To address these limitations in robotic planning, we advocate a self-refining scheme that iteratively refines a draft plan until an equilibrium is reached. Remarkably, this process can be optimized end-to-end from an analytical perspective without the need to curate additional verifiers or reward models, allowing us to train self-refining planners in a simple supervised learning fashion. Meanwhile, a nested equilibrium sequence modeling procedure is devised for efficient closed-loop planning that incorporates useful feedback from the environment (or an internal world model). Our method is evaluated on the VirtualHome-Env benchmark, showing advanced performance with better scaling for inference computation. Code is available at https://github.com/Singularity0104/equilibrium-planner.", "github_manual": "", "github_regex": "https://github.com/Singularity0104/equilibrium-planner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01769", "title": "Quantifying Generalization Complexity for Large Language Models", "authors": [ "Zhenting Qi", "Hongyin Luo", "Xuliang Huang", "Zhuokai Zhao", "Yibo Jiang", "Xiangjun Fan", "Himabindu Lakkaraju", "James Glass" ], "github": "https://github.com/zhentingqi/scylla", "project_page": "", "abstract": "While large language models (LLMs) have shown exceptional capabilities in understanding complex queries and performing sophisticated tasks, their generalization abilities are often deeply entangled with memorization, necessitating more precise evaluation. To address this challenge, we introduce Scylla, a dynamic evaluation framework that quantitatively measures the generalization abilities of LLMs. Scylla disentangles generalization from memorization via assessing model performance on both in-distribution (ID) and out-of-distribution (OOD) data through 20 tasks across 5 levels of complexity. Through extensive experiments, we uncover a non-monotonic relationship between task complexity and the performance gap between ID and OOD data, which we term the generalization valley. Specifically, this phenomenon reveals a critical threshold - referred to as critical complexity - where reliance on non-generalizable behavior peaks, indicating the upper bound of LLMs' generalization capabilities. As model size increases, the critical complexity shifts toward higher levels of task complexity, suggesting that larger models can handle more complex reasoning tasks before over-relying on memorization. Leveraging Scylla and the concept of critical complexity, we benchmark 28LLMs including both open-sourced models such as LLaMA and Qwen families, and close-sourced models like Claude and GPT, providing a more robust evaluation and establishing a clearer understanding of LLMs' generalization capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhentingqi/scylla", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01647", "title": "3DGS-DET: Empower 3D Gaussian Splatting with Boundary Guidance and Box-Focused Sampling for 3D Object Detection", "authors": [ "Yang Cao", "Yuanliang Jv", "Dan Xu" ], "github": "https://github.com/yangcaoai/3dgs-det", "project_page": "", "abstract": "Neural Radiance Fields (NeRF) are widely used for novel-view synthesis and have been adapted for 3D Object Detection (3DOD), offering a promising approach to 3DOD through view-synthesis representation. However, NeRF faces inherent limitations: (i) limited representational capacity for 3DOD due to its implicit nature, and (ii) slow rendering speeds. Recently, 3D Gaussian Splatting (3DGS) has emerged as an explicit 3D representation that addresses these limitations. Inspired by these advantages, this paper introduces 3DGS into 3DOD for the first time, identifying two main challenges: (i) Ambiguous spatial distribution of Gaussian blobs: 3DGS primarily relies on 2D pixel-level supervision, resulting in unclear 3D spatial distribution of Gaussian blobs and poor differentiation between objects and background, which hinders 3DOD; (ii) Excessive background blobs: 2D images often include numerous background pixels, leading to densely reconstructed 3DGS with many noisy Gaussian blobs representing the background, negatively affecting detection. To tackle the challenge (i), we leverage the fact that 3DGS reconstruction is derived from 2D images, and propose an elegant and efficient solution by incorporating 2D Boundary Guidance to significantly enhance the spatial distribution of Gaussian blobs, resulting in clearer differentiation between objects and their background. To address the challenge (ii), we propose a Box-Focused Sampling strategy using 2D boxes to generate object probability distribution in 3D spaces, allowing effective probabilistic sampling in 3D to retain more object blobs and reduce noisy background blobs. Benefiting from our designs, our 3DGS-DET significantly outperforms the SOTA NeRF-based method, NeRF-Det, achieving improvements of +6.6 on mAP@0.25 and +8.1 on mAP@0.5 for the ScanNet dataset, and impressive +31.5 on mAP@0.25 for the ARKITScenes dataset.", "github_manual": "https://github.com/yangcaoai/3dgs-det", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2409.20059", "title": "Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis", "authors": [ "Hippolyte Gisserot-Boukhlef", "Ricardo Rei", "Emmanuel Malherbe", "C\u00e9line Hudelot", "Pierre Colombo", "Nuno M. Guerreiro" ], "github": "", "project_page": "", "abstract": "Neural metrics for machine translation (MT) evaluation have become increasingly prominent due to their superior correlation with human judgments compared to traditional lexical metrics. Researchers have therefore utilized neural metrics through quality-informed decoding strategies, achieving better results than likelihood-based methods. With the rise of Large Language Models (LLMs), preference-based alignment techniques have gained attention for their potential to enhance translation quality by optimizing model weights directly on preferences induced by quality estimators. This study focuses on Contrastive Preference Optimization (CPO) and conducts extensive experiments to evaluate the impact of preference-based alignment on translation quality. Our findings indicate that while CPO consistently outperforms Supervised Fine-Tuning (SFT) on high-quality data with regard to the alignment metric, it may lead to instability across downstream evaluation metrics, particularly between neural and lexical ones. Additionally, we demonstrate that relying solely on the base model for generating candidate translations achieves performance comparable to using multiple external systems, while ensuring better consistency across downstream metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01748", "title": "Not All LLM Reasoners Are Created Equal", "authors": [ "Arian Hosseini", "Alessandro Sordoni", "Daniel Toyama", "Aaron Courville", "Rishabh Agarwal" ], "github": "", "project_page": "", "abstract": "We study the depth of grade-school math (GSM) problem-solving capabilities of LLMs. To this end, we evaluate their performance on pairs of existing math word problems together so that the answer to the second problem depends on correctly answering the first problem. Our findings reveal a significant reasoning gap in most LLMs, that is performance difference between solving the compositional pairs and solving each question independently. This gap is more pronounced in smaller, more cost-efficient, and math-specialized models. Moreover, instruction-tuning recipes and code generation have varying effects across LLM sizes, while finetuning on GSM can lead to task overfitting. Our analysis indicates that large reasoning gaps are not because of test-set leakage, but due to distraction from additional context and poor second-hop reasoning. Overall, LLMs exhibit systematic differences in their reasoning abilities, despite what their performance on standard benchmarks indicates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01463", "title": "Selective Aggregation for Low-Rank Adaptation in Federated Learning", "authors": [ "Pengxin Guo", "Shuang Zeng", "Yanran Wang", "Huijie Fan", "Feifei Wang", "Liangqiong Qu" ], "github": "https://github.com/Pengxin-Guo/FedSA-LoRA", "project_page": "", "abstract": "We investigate LoRA in federated learning through the lens of the asymmetry analysis of the learned A and B matrices. In doing so, we uncover that A matrices are responsible for learning general knowledge, while B matrices focus on capturing client-specific knowledge. Based on this finding, we introduce Federated Share-A Low-Rank Adaptation (FedSA-LoRA), which employs two low-rank trainable matrices A and B to model the weight update, but only A matrices are shared with the server for aggregation. Moreover, we delve into the relationship between the learned A and B matrices in other LoRA variants, such as rsLoRA and VeRA, revealing a consistent pattern. Consequently, we extend our FedSA-LoRA method to these LoRA variants, resulting in FedSA-rsLoRA and FedSA-VeRA. In this way, we establish a general paradigm for integrating LoRA with FL, offering guidance for future work on subsequent LoRA variants combined with FL. Extensive experimental results on natural language understanding and generation tasks demonstrate the effectiveness of the proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Pengxin-Guo/FedSA-LoRA", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2409.18111", "title": "E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding", "authors": [ "Ye Liu", "Zongyang Ma", "Zhongang Qi", "Yang Wu", "Ying Shan", "Chang Wen Chen" ], "github": "https://github.com/PolyU-ChenLab/ETBench", "project_page": "", "abstract": "Recent advances in Video Large Language Models (Video-LLMs) have demonstrated their great potential in general-purpose video understanding. To verify the significance of these models, a number of benchmarks have been proposed to diagnose their capabilities in different scenarios. However, existing benchmarks merely evaluate models through video-level question-answering, lacking fine-grained event-level assessment and task diversity. To fill this gap, we introduce E.T. Bench (Event-Level & Time-Sensitive Video Understanding Benchmark), a large-scale and high-quality benchmark for open-ended event-level video understanding. Categorized within a 3-level task taxonomy, E.T. Bench encompasses 7.3K samples under 12 tasks with 7K videos (251.4h total length) under 8 domains, providing comprehensive evaluations. We extensively evaluated 8 Image-LLMs and 12 Video-LLMs on our benchmark, and the results reveal that state-of-the-art models for coarse-level (video-level) understanding struggle to solve our fine-grained tasks, e.g., grounding event-of-interests within videos, largely due to the short video context length, improper time representations, and lack of multi-event training data. Focusing on these issues, we further propose a strong baseline model, E.T. Chat, together with an instruction-tuning dataset E.T. Instruct 164K tailored for fine-grained event-level understanding. Our simple but effective solution demonstrates superior performance in multiple scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PolyU-ChenLab/ETBench", "github_api_added_by": "auto" }, { "date": "2024-10-03", "arxiv_id": "2410.01723", "title": "HarmoniCa: Harmonizing Training and Inference for Better Feature Cache in Diffusion Transformer Acceleration", "authors": [ "Yushi Huang", "Zining Wang", "Ruihao Gong", "Jing Liu", "Xinjie Zhang", "Jinyang Guo", "Xianglong Liu", "Jun Zhang" ], "github": "https://github.com/modeltc/harmonica", "project_page": "", "abstract": "Diffusion Transformers (DiTs) have gained prominence for outstanding scalability and extraordinary performance in generative tasks. However, their considerable inference costs impede practical deployment. The feature cache mechanism, which involves storing and retrieving redundant computations across timesteps, holds promise for reducing per-step inference time in diffusion models. Most existing caching methods for DiT are manually designed. Although the learning-based approach attempts to optimize strategies adaptively, it suffers from discrepancies between training and inference, which hampers both the performance and acceleration ratio. Upon detailed analysis, we pinpoint that these discrepancies primarily stem from two aspects: (1) Prior Timestep Disregard, where training ignores the effect of cache usage at earlier timesteps, and (2) Objective Mismatch, where the training target (align predicted noise in each timestep) deviates from the goal of inference (generate the high-quality image). To alleviate these discrepancies, we propose HarmoniCa, a novel method that Harmonizes training and inference with a novel learning-based Caching framework built upon Step-Wise Denoising Training (SDT) and Image Error Proxy-Guided Objective (IEPO). Compared to the traditional training paradigm, the newly proposed SDT maintains the continuity of the denoising process, enabling the model to leverage information from prior timesteps during training, similar to the way it operates during inference. Furthermore, we design IEPO, which integrates an efficient proxy mechanism to approximate the final image error caused by reusing the cached feature. Therefore, IEPO helps balance final image quality and cache utilization, resolving the issue of training that only considers the impact of cache usage on the predicted output at each timestep.", "github_manual": "https://github.com/modeltc/harmonica", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01036", "title": "MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages", "authors": [ "Marco Gaido", "Sara Papi", "Luisa Bentivogli", "Alessio Brutti", "Mauro Cettolo", "Roberto Gretter", "Marco Matassoni", "Mohamed Nabih", "Matteo Negri" ], "github": "https://github.com/hlt-mt/mosel", "project_page": "", "abstract": "The rise of foundation models (FMs), coupled with regulatory efforts addressing their risks and impacts, has sparked significant interest in open-source models. However, existing speech FMs (SFMs) fall short of full compliance with the open-source principles, even if claimed otherwise, as no existing SFM has model weights, code, and training data publicly available under open-source terms. In this work, we take the first step toward filling this gap by focusing on the 24 official languages of the European Union (EU). We collect suitable training data by surveying automatic speech recognition datasets and unlabeled speech corpora under open-source compliant licenses, for a total of 950k hours. Additionally, we release automatic transcripts for 441k hours of unlabeled data under the permissive CC-BY license, thereby facilitating the creation of open-source SFMs for the EU languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hlt-mt/mosel", "github_api_added_by": "auto" }, { "date": "2024-10-03", "arxiv_id": "2410.01691", "title": "FactAlign: Long-form Factuality Alignment of Large Language Models", "authors": [ "Chao-Wei Huang", "Yun-Nung Chen" ], "github": "https://github.com/MiuLab/FactAlign", "project_page": "", "abstract": "Large language models have demonstrated significant potential as the next-generation information access engines. However, their reliability is hindered by issues of hallucination and generating non-factual content. This is particularly problematic in long-form responses, where assessing and ensuring factual accuracy is complex. In this paper, we address this gap by proposing FactAlign, a novel alignment framework designed to enhance the factuality of LLMs' long-form responses while maintaining their helpfulness. We introduce fKTO, a fine-grained, sentence-level alignment algorithm that extends the Kahneman-Tversky Optimization (KTO) alignment method. Leveraging recent advances in automatic factuality evaluation, FactAlign utilizes fine-grained factuality assessments to guide the alignment process. Our experiments on open-domain prompts and information-seeking questions demonstrate that FactAlign significantly improves the factual accuracy of LLM responses while also improving their helpfulness. Further analyses identify that FactAlign is capable of training LLMs to provide more information without losing factual precision, thus improving the factual F1 score. Our source code, datasets, and trained models are publicly available at https://github.com/MiuLab/FactAlign", "github_manual": "", "github_regex": "https://github.com/MiuLab/FactAlign", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2409.20325", "title": "Old Optimizer, New Norm: An Anthology", "authors": [ "Jeremy Bernstein", "Laker Newhouse" ], "github": "", "project_page": "", "abstract": "Deep learning optimizers are often motivated through a mix of convex and approximate second-order theory. We select three such methods -- Adam, Shampoo and Prodigy -- and argue that each method can instead be understood as a squarely first-order method without convexity assumptions. In fact, after switching off exponential moving averages, each method is equivalent to steepest descent under a particular norm. By generalizing this observation, we chart a new design space for training algorithms. Different operator norms should be assigned to different tensors based on the role that the tensor plays within the network. For example, while linear and embedding layers may have the same weight space of R^{mtimes n}, these layers play different roles and should be assigned different norms. We hope that this idea of carefully metrizing the neural architecture might lead to more stable, scalable and indeed faster training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01518", "title": "InfiniPot: Infinite Context Processing on Memory-Constrained LLMs", "authors": [ "Minsoo Kim", "Kyuhong Shim", "Jungwook Choi", "Simyung Chang" ], "github": "", "project_page": "", "abstract": "Handling long input contexts remains a significant challenge for Large Language Models (LLMs), particularly in resource-constrained environments such as mobile devices. Our work aims to address this limitation by introducing InfiniPot, a novel KV cache control framework designed to enable pre-trained LLMs to manage extensive sequences within fixed memory constraints efficiently, without requiring additional training. InfiniPot leverages Continual Context Distillation (CCD), an iterative process that compresses and retains essential information through novel importance metrics, effectively maintaining critical data even without access to future context. Our comprehensive evaluations indicate that InfiniPot significantly outperforms models trained for long contexts in various NLP tasks, establishing its efficacy and versatility. This work represents a substantial advancement toward making LLMs applicable to a broader range of real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01171", "title": "BordIRlines: A Dataset for Evaluating Cross-lingual Retrieval-Augmented Generation", "authors": [ "Bryan Li", "Samar Haider", "Fiona Luo", "Adwait Agashe", "Chris Callison-Burch" ], "github": "https://github.com/manestay/bordIRlines", "project_page": "", "abstract": "Large language models excel at creative generation but continue to struggle with the issues of hallucination and bias. While retrieval-augmented generation (RAG) provides a framework for grounding LLMs' responses in accurate and up-to-date information, it still raises the question of bias: which sources should be selected for inclusion in the context? And how should their importance be weighted? In this paper, we study the challenge of cross-lingual RAG and present a dataset to investigate the robustness of existing systems at answering queries about geopolitical disputes, which exist at the intersection of linguistic, cultural, and political boundaries. Our dataset is sourced from Wikipedia pages containing information relevant to the given queries and we investigate the impact of including additional context, as well as the composition of this context in terms of language and source, on an LLM's response. Our results show that existing RAG systems continue to be challenged by cross-lingual use cases and suffer from a lack of consistency when they are provided with competing information in multiple languages. We present case studies to illustrate these issues and outline steps for future research to address these challenges. We make our dataset and code publicly available at https://github.com/manestay/bordIRlines.", "github_manual": "", "github_regex": "https://github.com/manestay/bordIRlines", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.01481", "title": "SonicSim: A customizable simulation platform for speech processing in moving sound source scenarios", "authors": [ "Kai Li", "Wendi Sang", "Chang Zeng", "Runxuan Yang", "Guo Chen", "Xiaolin Hu" ], "github": "https://github.com/jusperlee/sonicsim", "project_page": "", "abstract": "The systematic evaluation of speech separation and enhancement models under moving sound source conditions typically requires extensive data comprising diverse scenarios. However, real-world datasets often contain insufficient data to meet the training and evaluation requirements of models. Although synthetic datasets offer a larger volume of data, their acoustic simulations lack realism. Consequently, neither real-world nor synthetic datasets effectively fulfill practical needs. To address these issues, we introduce SonicSim, a synthetic toolkit de-designed to generate highly customizable data for moving sound sources. SonicSim is developed based on the embodied AI simulation platform, Habitat-sim, supporting multi-level adjustments, including scene-level, microphone-level, and source-level, thereby generating more diverse synthetic data. Leveraging SonicSim, we constructed a moving sound source benchmark dataset, SonicSet, using the Librispeech, the Freesound Dataset 50k (FSD50K) and Free Music Archive (FMA), and 90 scenes from the Matterport3D to evaluate speech separation and enhancement models. Additionally, to validate the differences between synthetic data and real-world data, we randomly selected 5 hours of raw data without reverberation from the SonicSet validation set to record a real-world speech separation dataset, which was then compared with the corresponding synthetic datasets. Similarly, we utilized the real-world speech enhancement dataset RealMAN to validate the acoustic gap between other synthetic datasets and the SonicSet dataset for speech enhancement. The results indicate that the synthetic data generated by SonicSim can effectively generalize to real-world scenarios. Demo and code are publicly available at https://cslikai.cn/SonicSim/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jusperlee/sonicsim", "github_api_added_by": "auto" }, { "date": "2024-10-03", "arxiv_id": "2410.00316", "title": "EmoKnob: Enhance Voice Cloning with Fine-Grained Emotion Control", "authors": [ "Haozhe Chen", "Run Chen", "Julia Hirschberg" ], "github": "https://github.com/tonychenxyz/emoknob", "project_page": "", "abstract": "While recent advances in Text-to-Speech (TTS) technology produce natural and expressive speech, they lack the option for users to select emotion and control intensity. We propose EmoKnob, a framework that allows fine-grained emotion control in speech synthesis with few-shot demonstrative samples of arbitrary emotion. Our framework leverages the expressive speaker representation space made possible by recent advances in foundation voice cloning models. Based on the few-shot capability of our emotion control framework, we propose two methods to apply emotion control on emotions described by open-ended text, enabling an intuitive interface for controlling a diverse array of nuanced emotions. To facilitate a more systematic emotional speech synthesis field, we introduce a set of evaluation metrics designed to rigorously assess the faithfulness and recognizability of emotion control frameworks. Through objective and subjective evaluations, we show that our emotion control framework effectively embeds emotions into speech and surpasses emotion expressiveness of commercial TTS services.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tonychenxyz/emoknob", "github_api_added_by": "auto" }, { "date": "2024-10-03", "arxiv_id": "2410.01680", "title": "PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation", "authors": [ "Mike Ranzinger", "Jon Barker", "Greg Heinrich", "Pavlo Molchanov", "Bryan Catanzaro", "Andrew Tao" ], "github": "", "project_page": "", "abstract": "Various visual foundation models have distinct strengths and weaknesses, both of which can be improved through heterogeneous multi-teacher knowledge distillation without labels, termed \"agglomerative models.\" We build upon this body of work by studying the effect of the teachers' activation statistics, particularly the impact of the loss function on the resulting student model quality. We explore a standard toolkit of statistical normalization techniques to better align the different distributions and assess their effects. Further, we examine the impact on downstream teacher-matching metrics, which motivates the use of Hadamard matrices. With these matrices, we demonstrate useful properties, showing how they can be used for isotropic standardization, where each dimension of a multivariate distribution is standardized using the same scale. We call this technique \"PHI Standardization\" (PHI-S) and empirically demonstrate that it produces the best student model across the suite of methods studied.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.02197", "title": "General Preference Modeling with Preference Representations for Aligning Language Models", "authors": [ "Yifan Zhang", "Ge Zhang", "Yue Wu", "Kangping Xu", "Quanquan Gu" ], "github": "https://github.com/general-preference/general-preference-model", "project_page": "", "abstract": "Modeling human preferences is crucial for aligning foundation models with human values. Traditional reward modeling methods, such as the Bradley-Terry (BT) reward model, fall short in expressiveness, particularly in addressing intransitive preferences. Although supervised pair preference models (PairPM) can express general preferences, their implementation is highly ad-hoc and cannot guarantee a consistent preference probability of compared pairs. Additionally, they impose high computational costs due to their quadratic query complexity when comparing multiple responses. In this paper, we introduce preference representation learning, an approach that embeds responses into a latent space to capture intricate preference structures efficiently, achieving linear query complexity. Additionally, we propose preference score-based General Preference Optimization (GPO), which generalizes reward-based reinforcement learning from human feedback. Experimental results show that our General Preference representation model (GPM) outperforms the BT reward model on the RewardBench benchmark with a margin of up to 5.6% and effectively models cyclic preferences where any BT reward model behaves like a random guess. Furthermore, evaluations on downstream tasks such as AlpacaEval2.0 and MT-Bench, following the language model post-training with GPO and our general preference model, reveal substantial performance improvements with margins up to 9.3%. These findings indicate that our method may enhance the alignment of foundation models with nuanced human values. The code is available at https://github.com/general-preference/general-preference-model.", "github_manual": "", "github_regex": "https://github.com/general-preference/general-preference-model", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-03", "arxiv_id": "2410.00296", "title": "VLMGuard: Defending VLMs against Malicious Prompts via Unlabeled Data", "authors": [ "Xuefeng Du", "Reshmi Ghosh", "Robert Sim", "Ahmed Salem", "Vitor Carvalho", "Emily Lawton", "Yixuan Li", "Jack W. Stokes" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) are essential for contextual understanding of both visual and textual information. However, their vulnerability to adversarially manipulated inputs presents significant risks, leading to compromised outputs and raising concerns about the reliability in VLM-integrated applications. Detecting these malicious prompts is thus crucial for maintaining trust in VLM generations. A major challenge in developing a safeguarding prompt classifier is the lack of a large amount of labeled benign and malicious data. To address the issue, we introduce VLMGuard, a novel learning framework that leverages the unlabeled user prompts in the wild for malicious prompt detection. These unlabeled prompts, which naturally arise when VLMs are deployed in the open world, consist of both benign and malicious information. To harness the unlabeled data, we present an automated maliciousness estimation score for distinguishing between benign and malicious samples within this unlabeled mixture, thereby enabling the training of a binary prompt classifier on top. Notably, our framework does not require extra human annotations, offering strong flexibility and practicality for real-world applications. Extensive experiment shows VLMGuard achieves superior detection results, significantly outperforming state-of-the-art methods. Disclaimer: This paper may contain offensive examples; reader discretion is advised.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02740", "title": "Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models", "authors": [ "Zhengfeng Lai", "Vasileios Saveris", "Chen Chen", "Hong-You Chen", "Haotian Zhang", "Bowen Zhang", "Juan Lao Tebar", "Wenze Hu", "Zhe Gan", "Peter Grasch", "Meng Cao", "Yinfei Yang" ], "github": "", "project_page": "", "abstract": "Recent advancements in multimodal models highlight the value of rewritten captions for improving performance, yet key challenges remain. For example, while synthetic captions often provide superior quality and image-text alignment, it is not clear whether they can fully replace AltTexts: the role of synthetic captions and their interaction with original web-crawled AltTexts in pre-training is still not well understood. Moreover, different multimodal foundation models may have unique preferences for specific caption formats, but efforts to identify the optimal captions for each model remain limited. In this work, we propose a novel, controllable, and scalable captioning pipeline designed to generate diverse caption formats tailored to various multimodal models. By examining Short Synthetic Captions (SSC) towards Dense Synthetic Captions (DSC+) as case studies, we systematically explore their effects and interactions with AltTexts across models such as CLIP, multimodal LLMs, and diffusion models. Our findings reveal that a hybrid approach that keeps both synthetic captions and AltTexts can outperform the use of synthetic captions alone, improving both alignment and performance, with each model demonstrating preferences for particular caption formats. This comprehensive analysis provides valuable insights into optimizing captioning strategies, thereby advancing the pre-training of multimodal foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02713", "title": "Video Instruction Tuning With Synthetic Data", "authors": [ "Yuanhan Zhang", "Jinming Wu", "Wei Li", "Bo Li", "Zejun Ma", "Ziwei Liu", "Chunyuan Li" ], "github": "", "project_page": "", "abstract": "The development of video large multimodal models (LMMs) has been hindered by the difficulty of curating large amounts of high-quality raw data from the web. To address this, we propose an alternative approach by creating a high-quality synthetic dataset specifically for video instruction-following, namely LLaVA-Video-178K. This dataset includes key tasks such as detailed captioning, open-ended question-answering (QA), and multiple-choice QA. By training on this dataset, in combination with existing visual instruction tuning data, we introduce LLaVA-Video, a new video LMM. Our experiments demonstrate that LLaVA-Video achieves strong performance across various video benchmarks, highlighting the effectiveness of our dataset. We plan to release the dataset, its generation pipeline, and the model checkpoints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02712", "title": "LLaVA-Critic: Learning to Evaluate Multimodal Models", "authors": [ "Tianyi Xiong", "Xiyao Wang", "Dong Guo", "Qinghao Ye", "Haoqi Fan", "Quanquan Gu", "Heng Huang", "Chunyuan Li" ], "github": "", "project_page": "", "abstract": "We introduce LLaVA-Critic, the first open-source large multimodal model (LMM) designed as a generalist evaluator to assess performance across a wide range of multimodal tasks. LLaVA-Critic is trained using a high-quality critic instruction-following dataset that incorporates diverse evaluation criteria and scenarios. Our experiments demonstrate the model's effectiveness in two key areas: (1) LMM-as-a-Judge, where LLaVA-Critic provides reliable evaluation scores, performing on par with or surpassing GPT models on multiple evaluation benchmarks; and (2) Preference Learning, where it generates reward signals for preference learning, enhancing model alignment capabilities. This work underscores the potential of open-source LMMs in self-critique and evaluation, setting the stage for future research into scalable, superhuman alignment feedback mechanisms for LMMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02367", "title": "SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration", "authors": [ "Jintao Zhang", "Jia wei", "Pengle Zhang", "Jun Zhu", "Jianfei Chen" ], "github": "https://github.com/thu-ml/SageAttention", "project_page": "", "abstract": "The transformer architecture predominates across various models. As the heart of the transformer, attention has a computational complexity of O(N^2), compared to O(N) for linear transformations. When handling large sequence lengths, attention becomes the primary time-consuming component. Although quantization has proven to be an effective method for accelerating model inference, existing quantization methods primarily focus on optimizing the linear layer. In response, we first analyze the feasibility of quantization in attention detailedly. Following that, we propose SageAttention, a highly efficient and accurate quantization method for attention. The OPS (operations per second) of our approach outperforms FlashAttention2 and xformers by about 2.1 times and 2.7 times, respectively. SageAttention also achieves superior accuracy performance over FlashAttention3. Comprehensive experiments confirm that our approach incurs almost no end-to-end metrics loss across diverse models, including those for large language processing, image generation, and video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/thu-ml/SageAttention", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02056", "title": "Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Data", "authors": [ "Sreyan Ghosh", "Sonal Kumar", "Zhifeng Kong", "Rafael Valle", "Bryan Catanzaro", "Dinesh Manocha" ], "github": "https://github.com/sreyan88/synthio", "project_page": "", "abstract": "We present Synthio, a novel approach for augmenting small-scale audio classification datasets with synthetic data. Our goal is to improve audio classification accuracy with limited labeled data. Traditional data augmentation techniques, which apply artificial transformations (e.g., adding random noise or masking segments), struggle to create data that captures the true diversity present in real-world audios. To address this shortcoming, we propose to augment the dataset with synthetic audio generated from text-to-audio (T2A) diffusion models. However, synthesizing effective augmentations is challenging because not only should the generated data be acoustically consistent with the underlying small-scale dataset, but they should also have sufficient compositional diversity. To overcome the first challenge, we align the generations of the T2A model with the small-scale dataset using preference optimization. This ensures that the acoustic characteristics of the generated data remain consistent with the small-scale dataset. To address the second challenge, we propose a novel caption generation technique that leverages the reasoning capabilities of Large Language Models to (1) generate diverse and meaningful audio captions and (2) iteratively refine their quality. The generated captions are then used to prompt the aligned T2A model. We extensively evaluate Synthio on ten datasets and four simulated limited-data settings. Results indicate our method consistently outperforms all baselines by 0.1%-39% using a T2A model trained only on weakly-captioned AudioSet.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sreyan88/synthio", "github_api_added_by": "auto" }, { "date": "2024-10-04", "arxiv_id": "2410.02103", "title": "MVGS: Multi-view-regulated Gaussian Splatting for Novel View Synthesis", "authors": [ "Xiaobiao Du", "Yida Wang", "Xin Yu" ], "github": "https://github.com/xiaobiaodu/MVGS", "project_page": "", "abstract": "Recent works in volume rendering, e.g. NeRF and 3D Gaussian Splatting (3DGS), significantly advance the rendering quality and efficiency with the help of the learned implicit neural radiance field or 3D Gaussians. Rendering on top of an explicit representation, the vanilla 3DGS and its variants deliver real-time efficiency by optimizing the parametric model with single-view supervision per iteration during training which is adopted from NeRF. Consequently, certain views are overfitted, leading to unsatisfying appearance in novel-view synthesis and imprecise 3D geometries. To solve aforementioned problems, we propose a new 3DGS optimization method embodying four key novel contributions: 1) We transform the conventional single-view training paradigm into a multi-view training strategy. With our proposed multi-view regulation, 3D Gaussian attributes are further optimized without overfitting certain training views. As a general solution, we improve the overall accuracy in a variety of scenarios and different Gaussian variants. 2) Inspired by the benefit introduced by additional views, we further propose a cross-intrinsic guidance scheme, leading to a coarse-to-fine training procedure concerning different resolutions. 3) Built on top of our multi-view regulated training, we further propose a cross-ray densification strategy, densifying more Gaussian kernels in the ray-intersect regions from a selection of views. 4) By further investigating the densification strategy, we found that the effect of densification should be enhanced when certain views are distinct dramatically. As a solution, we propose a novel multi-view augmented densification strategy, where 3D Gaussians are encouraged to get densified to a sufficient number accordingly, resulting in improved reconstruction accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xiaobiaodu/MVGS", "github_api_added_by": "auto" }, { "date": "2024-10-04", "arxiv_id": "2410.02757", "title": "Loong: Generating Minute-level Long Videos with Autoregressive Language Models", "authors": [ "Yuqing Wang", "Tianwei Xiong", "Daquan Zhou", "Zhijie Lin", "Yang Zhao", "Bingyi Kang", "Jiashi Feng", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "It is desirable but challenging to generate content-rich long videos in the scale of minutes. Autoregressive large language models (LLMs) have achieved great success in generating coherent and long sequences of tokens in the domain of natural language processing, while the exploration of autoregressive LLMs for video generation is limited to generating short videos of several seconds. In this work, we conduct a deep analysis of the challenges that prevent autoregressive LLM-based video generators from generating long videos. Based on the observations and analysis, we propose Loong, a new autoregressive LLM-based video generator that can generate minute-long videos. Specifically, we model the text tokens and video tokens as a unified sequence for autoregressive LLMs and train the model from scratch. We propose progressive short-to-long training with a loss re-weighting scheme to mitigate the loss imbalance problem for long video training. We further investigate inference strategies, including video token re-encoding and sampling strategies, to diminish error accumulation during inference. Our proposed Loong can be trained on 10-second videos and be extended to generate minute-level long videos conditioned on text prompts, as demonstrated by the results. More samples are available at: https://epiphqny.github.io/Loong-video.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02746", "title": "Contrastive Localized Language-Image Pre-Training", "authors": [ "Hong-You Chen", "Zhengfeng Lai", "Haotian Zhang", "Xinze Wang", "Marcin Eichner", "Keen You", "Meng Cao", "Bowen Zhang", "Yinfei Yang", "Zhe Gan" ], "github": "https://github.com/xiaoachen98/Open-LLaVA-NeXT", "project_page": "", "abstract": "Contrastive Language-Image Pre-training (CLIP) has been a celebrated method for training vision encoders to generate image/text representations facilitating various applications. Recently, CLIP has been widely adopted as the vision backbone of multimodal large language models (MLLMs) to connect image inputs for language interactions. The success of CLIP as a vision-language foundation model relies on aligning web-crawled noisy text annotations at image levels. Nevertheless, such criteria may become insufficient for downstream tasks in need of fine-grained vision representations, especially when region-level understanding is demanding for MLLMs. In this paper, we improve the localization capability of CLIP with several advances. We propose a pre-training method called Contrastive Localized Language-Image Pre-training (CLOC) by complementing CLIP with region-text contrastive loss and modules. We formulate a new concept, promptable embeddings, of which the encoder produces image embeddings easy to transform into region representations given spatial hints. To support large-scale pre-training, we design a visually-enriched and spatially-localized captioning framework to effectively generate region-text pseudo-labels at scale. By scaling up to billions of annotated images, CLOC enables high-quality regional embeddings for image region recognition and retrieval tasks, and can be a drop-in replacement of CLIP to enhance MLLMs, especially on referring and grounding tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xiaoachen98/Open-LLaVA-NeXT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02073", "title": "Depth Pro: Sharp Monocular Metric Depth in Less Than a Second", "authors": [ "Aleksei Bochkovskii", "Ama\u00ebl Delaunoy", "Hugo Germain", "Marcel Santos", "Yichao Zhou", "Stephan R. Richter", "Vladlen Koltun" ], "github": "https://github.com/apple/ml-depth-pro", "project_page": "", "abstract": "We present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-frequency details. The predictions are metric, with absolute scale, without relying on the availability of metadata such as camera intrinsics. And the model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU. These characteristics are enabled by a number of technical contributions, including an efficient multi-scale vision transformer for dense prediction, a training protocol that combines real and synthetic datasets to achieve high metric accuracy alongside fine boundary tracing, dedicated evaluation metrics for boundary accuracy in estimated depth maps, and state-of-the-art focal length estimation from a single image. Extensive experiments analyze specific design choices and demonstrate that Depth Pro outperforms prior work along multiple dimensions. We release code and weights at https://github.com/apple/ml-depth-pro", "github_manual": "", "github_regex": "https://github.com/apple/ml-depth-pro", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02458", "title": "MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation", "authors": [ "Gurucharan Marthi Krishna Kumar", "Aman Chadha", "Janine Mendola", "Amir Shmuel" ], "github": "https://github.com/AS-Lab/Marthi-et-al-2025-MedVisionLlama-Pre-Trained-LLM-Layers-to-Enhance-Medical-Image-Segmentation", "project_page": "", "abstract": "Large Language Models (LLMs), known for their versatility in textual data, are increasingly being explored for their potential to enhance medical image segmentation, a crucial task for accurate diagnostic imaging. This study explores enhancing Vision Transformers (ViTs) for medical image segmentation by integrating pre-trained LLM transformer blocks. Our approach, which incorporates a frozen LLM transformer block into the encoder of a ViT-based model, leads to substantial improvements in segmentation performance across various medical imaging modalities. We propose a Hybrid Attention Mechanism that combines global and local feature learning with a Multi-Scale Fusion Block for aggregating features across different scales. The enhanced model shows significant performance gains, including an average Dice score increase from 0.74 to 0.79 and improvements in accuracy, precision, and the Jaccard Index. These results demonstrate the effectiveness of LLM-based transformers in refining medical image segmentation, highlighting their potential to significantly boost model accuracy and robustness. The source code and our implementation are available at: https://bit.ly/3zf2CVs", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AS-Lab/Marthi-et-al-2025-MedVisionLlama-Pre-Trained-LLM-Layers-to-Enhance-Medical-Image-Segmentation", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02115", "title": "L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?", "authors": [ "Zecheng Tang", "Keyan Zhou", "Juntao Li", "Baibei Ji", "Jianye Hou", "Min Zhang" ], "github": "https://github.com/ZetangForward/L-CITEEVAL", "project_page": "", "abstract": "Long-context models (LCMs) have made remarkable strides in recent years, offering users great convenience for handling tasks that involve long context, such as document summarization. As the community increasingly prioritizes the faithfulness of generated results, merely ensuring the accuracy of LCM outputs is insufficient, as it is quite challenging for humans to verify the results from the extremely lengthy context. Yet, although some efforts have been made to assess whether LCMs respond truly based on the context, these works either are limited to specific tasks or heavily rely on external evaluation resources like GPT-4.In this work, we introduce L-CiteEval, a comprehensive multi-task benchmark for long-context understanding with citations, aiming to evaluate both the understanding capability and faithfulness of LCMs. L-CiteEval covers 11 tasks from diverse domains, spanning context lengths from 8K to 48K, and provides a fully automated evaluation suite. Through testing with 11 cutting-edge closed-source and open-source LCMs, we find that although these models show minor differences in their generated results, open-source models substantially trail behind their closed-source counterparts in terms of citation accuracy and recall. This suggests that current open-source LCMs are prone to responding based on their inherent knowledge rather than the given context, posing a significant risk to the user experience in practical applications. We also evaluate the RAG approach and observe that RAG can significantly improve the faithfulness of LCMs, albeit with a slight decrease in the generation quality. Furthermore, we discover a correlation between the attention mechanisms of LCMs and the citation generation process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZetangForward/L-CITEEVAL", "github_api_added_by": "auto" }, { "date": "2024-10-04", "arxiv_id": "2410.02749", "title": "Training Language Models on Synthetic Edit Sequences Improves Code Synthesis", "authors": [ "Ulyana Piterbarg", "Lerrel Pinto", "Rob Fergus" ], "github": "https://github.com/upiterbarg/lintseq", "project_page": "", "abstract": "Software engineers mainly write code by editing existing programs. In contrast, large language models (LLMs) autoregressively synthesize programs in a single pass. One explanation for this is the scarcity of open-sourced edit data. While high-quality instruction data for code synthesis is already scarce, high-quality edit data is even scarcer. To fill this gap, we develop a synthetic data generation algorithm called LintSeq. This algorithm refactors existing code into a sequence of code edits by using a linter to procedurally sample across the error-free insertions that can be used to sequentially write programs. It outputs edit sequences as text strings consisting of consecutive program diffs. To test LintSeq, we use it to refactor a dataset of instruction + program pairs into instruction + program-diff-sequence tuples. Then, we instruction finetune a series of smaller LLMs ranging from 2.6B to 14B parameters on both the re-factored and original versions of this dataset, comparing zero-shot performance on code synthesis benchmarks. We show that during repeated sampling, edit sequence finetuned models produce more diverse programs than baselines. This results in better inference-time scaling for benchmark coverage as a function of samples, i.e. the fraction of problems \"pass@k\" solved by any attempt given \"k\" tries. For example, on HumanEval pass@50, small LLMs finetuned on synthetic edit sequences are competitive with GPT-4 and outperform models finetuned on the baseline dataset by +20% (+/-3%) in absolute score. Finally, we also pretrain our own tiny LMs for code understanding. We show that finetuning tiny models on synthetic code edits results in state-of-the-art code synthesis for the on-device model class. Our 150M parameter edit sequence LM matches or outperforms code models with twice as many parameters, both with and without repeated sampling, including Codex and AlphaCode.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/upiterbarg/lintseq", "github_api_added_by": "auto" }, { "date": "2024-10-04", "arxiv_id": "2410.02416", "title": "Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models", "authors": [ "Seyedmorteza Sadat", "Otmar Hilliges", "Romann M. Weber" ], "github": "", "project_page": "", "abstract": "Classifier-free guidance (CFG) is crucial for improving both generation quality and alignment between the input condition and final output in diffusion models. While a high guidance scale is generally required to enhance these aspects, it also causes oversaturation and unrealistic artifacts. In this paper, we revisit the CFG update rule and introduce modifications to address this issue. We first decompose the update term in CFG into parallel and orthogonal components with respect to the conditional model prediction and observe that the parallel component primarily causes oversaturation, while the orthogonal component enhances image quality. Accordingly, we propose down-weighting the parallel component to achieve high-quality generations without oversaturation. Additionally, we draw a connection between CFG and gradient ascent and introduce a new rescaling and momentum method for the CFG update rule based on this insight. Our approach, termed adaptive projected guidance (APG), retains the quality-boosting advantages of CFG while enabling the use of higher guidance scales without oversaturation. APG is easy to implement and introduces practically no additional computational overhead to the sampling process. Through extensive experiments, we demonstrate that APG is compatible with various conditional diffusion models and samplers, leading to improved FID, recall, and saturation scores while maintaining precision comparable to CFG, making our method a superior plug-and-play alternative to standard classifier-free guidance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02678", "title": "Distilling an End-to-End Voice Assistant Without Instruction Training Data", "authors": [ "William Held", "Ella Li", "Michael Ryan", "Weiyan Shi", "Yanzhe Zhang", "Diyi Yang" ], "github": "", "project_page": "", "abstract": "Voice assistants, such as Siri and Google Assistant, typically model audio and text separately, resulting in lost speech information and increased complexity. Recent efforts to address this with end-to-end Speech Large Language Models (LLMs) trained with supervised finetuning (SFT) have led to models ``forgetting\" capabilities from text-only LLMs. Our work proposes an alternative paradigm for training Speech LLMs without instruction data, using the response of a text-only LLM to transcripts as self-supervision. Importantly, this process can be performed without annotated responses. We show that our Distilled Voice Assistant (DiVA) generalizes to Spoken Question Answering, Classification, and Translation. Furthermore, we show that DiVA better meets user preferences, achieving a 72\\% win rate compared with state-of-the-art models like Qwen 2 Audio, despite using >100x less training compute.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2409.19291", "title": "CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling", "authors": [ "Jihai Zhang", "Xiaoye Qu", "Tong Zhu", "Yu Cheng" ], "github": "https://github.com/OpenSparseLLMs/CLIP-MoE", "project_page": "", "abstract": "In recent years, Contrastive Language-Image Pre-training (CLIP) has become a cornerstone in multimodal intelligence. However, recent studies have identified that the information loss in the CLIP encoding process is substantial, and CLIP tends to capture only coarse-grained features from the input. This deficiency significantly limits the ability of a single CLIP model to handle images rich in visual detail. In this work, we propose a simple yet effective model-agnostic strategy, Diversified Multiplet Upcycling (DMU), for CLIP. DMU efficiently fine-tunes a series of CLIP models that capture different feature spaces, from a dense pre-trained CLIP checkpoint, sharing parameters except for the Feed-Forward Network (FFN). These models can then be transformed into a CLIP-MoE with a larger model capacity, leading to significantly enhanced performance with minimal computational overhead. To the best of our knowledge, Diversified Multiplet Upcycling is the first approach to introduce sparsely activated MoE into CLIP foundation models. Extensive experiments demonstrate the significant performance of CLIP-MoE across various zero-shot retrieval, zero-shot image classification tasks, and downstream Multimodal Large Language Model (MLLM) benchmarks by serving as a vision encoder. Furthermore, Diversified Multiplet Upcycling enables the conversion of any dense CLIP model into CLIP-MoEs, which can seamlessly replace CLIP in a plug-and-play manner without requiring further adaptation in downstream frameworks. Through Diversified Multiplet Upcycling, we aim to provide valuable insights for future research on developing more efficient and effective multimodal learning systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenSparseLLMs/CLIP-MoE", "github_api_added_by": "auto" }, { "date": "2024-10-04", "arxiv_id": "2410.02763", "title": "Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videos", "authors": [ "Jianrui Zhang", "Mu Cai", "Yong Jae Lee" ], "github": "https://github.com/Vinoground/Vinoground", "project_page": "", "abstract": "There has been growing sentiment recently that modern large multimodal models (LMMs) have addressed most of the key challenges related to short video comprehension. As a result, both academia and industry are gradually shifting their attention towards the more complex challenges posed by understanding long-form videos. However, is this really the case? Our studies indicate that LMMs still lack many fundamental reasoning capabilities even when dealing with short videos. We introduce Vinoground, a temporal counterfactual LMM evaluation benchmark encompassing 1000 short and natural video-caption pairs. We demonstrate that existing LMMs severely struggle to distinguish temporal differences between different actions and object transformations. For example, the best model GPT-4o only obtains ~50% on our text and video scores, showing a large gap compared to the human baseline of ~90%. All open-source multimodal models and CLIP-based models perform much worse, producing mostly random chance performance. Through this work, we shed light onto the fact that temporal reasoning in short videos is a problem yet to be fully solved. The dataset and evaluation code are available at https://vinoground.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Vinoground/Vinoground", "github_api_added_by": "auto" }, { "date": "2024-10-04", "arxiv_id": "2410.02724", "title": "Large Language Models as Markov Chains", "authors": [ "Oussama Zekri", "Ambroise Odonnat", "Abdelhakim Benechehab", "Linus Bleistein", "Nicolas Boull\u00e9", "Ievgen Redko" ], "github": "https://github.com/karpathy/minGPT", "project_page": "", "abstract": "Large language models (LLMs) have proven to be remarkably efficient, both across a wide range of natural language processing tasks and well beyond them. However, a comprehensive theoretical analysis of the origins of their impressive performance remains elusive. In this paper, we approach this challenging task by drawing an equivalence between generic autoregressive language models with vocabulary of size T and context window of size K and Markov chains defined on a finite state space of size O(T^K). We derive several surprising findings related to the existence of a stationary distribution of Markov chains that capture the inference power of LLMs, their speed of convergence to it, and the influence of the temperature on the latter. We then prove pre-training and in-context generalization bounds and show how the drawn equivalence allows us to enrich their interpretation. Finally, we illustrate our theoretical guarantees with experiments on several recent LLMs to highlight how they capture the behavior observed in practice.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/karpathy/minGPT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02426", "title": "Learning the Latent Rules of a Game from Data: A Chess Story", "authors": [ "Ben Fauber" ], "github": "", "project_page": "", "abstract": "We demonstrate that small pretrained foundational generative language models with millions of parameters can learn the latent rules of a process from data associated with the process. Inspired by Stefan Zweig's novella \"Schachnovelle,\" also known as \"The Royal Game\" in English, we show that 28M and 125M parameter pretrained foundational small language models (SLMs) can be instruction fine-tuned with 1,000-to-1,000,000 examples to learn the rules of chess, propose legal moves, and accurately solve chess problems. We also explore the impact of successive language model fine-tuning epochs on improved outcomes and demonstrate reductions in model hallucinations by increasing the number of instruction fine-tuning examples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.01679", "title": "VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment", "authors": [ "Amirhossein Kazemnejad", "Milad Aghajohari", "Eva Portelance", "Alessandro Sordoni", "Siva Reddy", "Aaron Courville", "Nicolas Le Roux" ], "github": "https://github.com/mcgill-nlp/vineppo", "project_page": "", "abstract": "Large language models (LLMs) are increasingly applied to complex reasoning tasks that require executing several complex steps before receiving any reward. Properly assigning credit to these steps is essential for enhancing model performance. Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning (RL) algorithm used for LLM finetuning, employs value networks to tackle credit assignment. However, value networks face challenges in predicting the expected cumulative rewards accurately in complex reasoning tasks, often leading to high-variance updates and suboptimal performance. In this work, we systematically evaluate the efficacy of value networks and reveal their significant shortcomings in reasoning-heavy LLM tasks, showing that they barely outperform a random baseline when comparing alternative steps. To address this, we propose VinePPO, a straightforward approach that leverages the flexibility of language environments to compute unbiased Monte Carlo-based estimates, bypassing the need for large value networks. Our method consistently outperforms PPO and other RL-free baselines across MATH and GSM8K datasets with fewer gradient updates (up to 9x), less wall-clock time (up to 3.0x). These results emphasize the importance of accurate credit assignment in RL finetuning of LLM and demonstrate VinePPO's potential as a superior alternative.", "github_manual": "https://github.com/mcgill-nlp/vineppo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.00255", "title": "Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning", "authors": [ "Weitai Kang", "Haifeng Huang", "Yuzhang Shang", "Mubarak Shah", "Yan Yan" ], "github": "https://github.com/weitaikang/robin3d", "project_page": "", "abstract": "Recent advancements in 3D Large Language Models (3DLLMs) have highlighted their potential in building general-purpose agents in the 3D real world, yet challenges remain due to the lack of high-quality robust instruction-following data, leading to limited discriminative power and generalization of 3DLLMs. In this paper, we introduce Robin3D, a powerful 3DLLM trained on large-scale instruction-following data generated by our novel data engine, Robust Instruction Generation (RIG) engine. RIG generates two key instruction data: 1) the Adversarial Instruction-following data, which features mixed negative and positive samples to enhance the model's discriminative understanding. 2) the Diverse Instruction-following data, which contains various instruction styles to enhance model's generalization. As a result, we construct 1 million instruction-following data, consisting of 344K Adversarial samples, 508K Diverse samples, and 165K benchmark training set samples. To better handle these complex instructions, Robin3D first incorporates Relation-Augmented Projector to enhance spatial understanding, and then strengthens the object referring and grounding ability through ID-Feature Bonding. Robin3D consistently outperforms previous methods across five widely-used 3D multimodal learning benchmarks, without the need for task-specific fine-tuning. Notably, we achieve a 7.8\\% improvement in the grounding task (Multi3DRefer) and a 6.9\\% improvement in the captioning task (Scan2Cap).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/weitaikang/robin3d", "github_api_added_by": "auto" }, { "date": "2024-10-04", "arxiv_id": "2410.02536", "title": "Intelligence at the Edge of Chaos", "authors": [ "Shiyang Zhang", "Aakash Patel", "Syed A Rizvi", "Nianchen Liu", "Sizhuang He", "Amin Karbasi", "Emanuele Zappala", "David van Dijk" ], "github": "https://github.com/vandijklab/Intelligence_at_the_edge_of_chaos", "project_page": "", "abstract": "We explore the emergence of intelligent behavior in artificial systems by investigating how the complexity of rule-based systems influences the capabilities of models trained to predict these rules. Our study focuses on elementary cellular automata (ECA), simple yet powerful one-dimensional systems that generate behaviors ranging from trivial to highly complex. By training distinct Large Language Models (LLMs) on different ECAs, we evaluated the relationship between the complexity of the rules' behavior and the intelligence exhibited by the LLMs, as reflected in their performance on downstream tasks. Our findings reveal that rules with higher complexity lead to models exhibiting greater intelligence, as demonstrated by their performance on reasoning and chess move prediction tasks. Both uniform and periodic systems, and often also highly chaotic systems, resulted in poorer downstream performance, highlighting a sweet spot of complexity conducive to intelligence. We conjecture that intelligence arises from the ability to predict complexity and that creating intelligence may require only exposure to complexity.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vandijklab/Intelligence_at_the_edge_of_chaos", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02052", "title": "Improving Autonomous AI Agents with Reflective Tree Search and Self-Learning", "authors": [ "Xiao Yu", "Baolin Peng", "Vineeth Vajipey", "Hao Cheng", "Michel Galley", "Jianfeng Gao", "Zhou Yu" ], "github": "https://github.com/Agent-E3/ExACT", "project_page": "", "abstract": "Autonomous agents have demonstrated significant potential in automating complex multistep decision-making tasks. However, even state-of-the-art vision-language models (VLMs), such as GPT-4o, still fall short of human-level performance, particularly in intricate web environments and long-horizon planning tasks. To address these limitations, we introduce Reflective Monte Carlo Tree Search (R-MCTS), a novel test-time algorithm designed to enhance the ability of AI agents, e.g., powered by GPT-4o, to explore decision space on the fly. R-MCTS extends traditional MCTS by 1) incorporating contrastive reflection, allowing agents to learn from past interactions and dynamically improve their search efficiency; and 2) using multi-agent debate to provide reliable state evaluation. Moreover, we improve the agent's performance by fine-tuning GPT-4o through self-learning, using R-MCTS generated tree traversals without any human-provided labels. On the challenging VisualWebArena benchmark, our GPT-4o-based R-MCTS agent achieves a 6% to 30% relative improvement across various tasks compared to the previous state-of-the-art. Additionally, we show that the knowledge gained from test-time search can be effectively transferred back to GPT-4o via fine-tuning. The fine-tuned GPT-4o matches 97% of R-MCTS's performance while reducing compute usage by a factor of four at test time. Furthermore, qualitative results reveal that the fine-tuned GPT-4o model demonstrates the ability to explore the environment, evaluate a state, and backtrack to viable ones when it detects that the current state cannot lead to success. Moreover, our work demonstrates the compute scaling properties in both training - data collection with R-MCTS - and testing time. These results suggest a promising research direction to enhance VLMs' reasoning and planning capabilities for agentic applications via test-time search and self-learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Agent-E3/ExACT", "github_api_added_by": "auto" }, { "date": "2024-10-04", "arxiv_id": "2410.01946", "title": "SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics", "authors": [ "Zhiwen You", "Kanyao Han", "Haotian Zhu", "Bertram Lud\u00e4scher", "Jana Diesner" ], "github": "https://github.com/zhiwenyou103/SciPrompt", "project_page": "", "abstract": "Prompt-based fine-tuning has become an essential method for eliciting information encoded in pre-trained language models for a variety of tasks, including text classification. For multi-class classification tasks, prompt-based fine-tuning under low-resource scenarios has resulted in performance levels comparable to those of fully fine-tuning methods. Previous studies have used crafted prompt templates and verbalizers, mapping from the label terms space to the class space, to solve the classification problem as a masked language modeling task. However, cross-domain and fine-grained prompt-based fine-tuning with an automatically enriched verbalizer remains unexplored, mainly due to the difficulty and costs of manually selecting domain label terms for the verbalizer, which requires humans with domain expertise. To address this challenge, we introduce SciPrompt, a framework designed to automatically retrieve scientific topic-related terms for low-resource text classification tasks. To this end, we select semantically correlated and domain-specific label terms within the context of scientific literature for verbalizer augmentation. Furthermore, we propose a new verbalization strategy that uses correlation scores as additional weights to enhance the prediction performance of the language model during model tuning. Our method outperforms state-of-the-art, prompt-based fine-tuning methods on scientific text classification tasks under few and zero-shot settings, especially in classifying fine-grained and emerging scientific topics.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhiwenyou103/SciPrompt", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.01782", "title": "Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models", "authors": [ "Shayekh Bin Islam", "Md Asib Rahman", "K S M Tozammel Hossain", "Enamul Hoque", "Shafiq Joty", "Md Rizwan Parvez" ], "github": "https://github.com/AkariAsai/self-rag/blob/1fcdc420e48f50a7d7ab1ece5494221b93252e99/retrieval_lm/run_short_form.py", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) has been shown to enhance the factual accuracy of Large Language Models (LLMs), but existing methods often suffer from limited reasoning capabilities in effectively using the retrieved evidence, particularly when using open-source LLMs. To mitigate this gap, we introduce a novel framework, Open-RAG, designed to enhance reasoning capabilities in RAG with open-source LLMs. Our framework transforms an arbitrary dense LLM into a parameter-efficient sparse mixture of experts (MoE) model capable of handling complex reasoning tasks, including both single- and multi-hop queries. Open-RAG uniquely trains the model to navigate challenging distractors that appear relevant but are misleading. As a result, Open-RAG leverages latent learning, dynamically selecting relevant experts and integrating external knowledge effectively for more accurate and contextually relevant responses. In addition, we propose a hybrid adaptive retrieval method to determine retrieval necessity and balance the trade-off between performance gain and inference speed. Experimental results show that the Llama2-7B-based Open-RAG outperforms state-of-the-art LLMs and RAG models such as ChatGPT, Self-RAG, and Command R+ in various knowledge-intensive tasks. We open-source our code and models at https://openragmoe.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AkariAsai/self-rag/blob/1fcdc420e48f50a7d7ab1ece5494221b93252e99/retrieval_lm/run_short_form.py", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02525", "title": "Contextual Document Embeddings", "authors": [ "John X. Morris", "Alexander M. Rush" ], "github": "", "project_page": "", "abstract": "Dense document embeddings are central to neural retrieval. The dominant paradigm is to train and construct embeddings by running encoders directly on individual documents. In this work, we argue that these embeddings, while effective, are implicitly out-of-context for targeted use cases of retrieval, and that a contextualized document embedding should take into account both the document and neighboring documents in context - analogous to contextualized word embeddings. We propose two complementary methods for contextualized document embeddings: first, an alternative contrastive learning objective that explicitly incorporates the document neighbors into the intra-batch contextual loss; second, a new contextual architecture that explicitly encodes neighbor document information into the encoded representation. Results show that both methods achieve better performance than biencoders in several settings, with differences especially pronounced out-of-domain. We achieve state-of-the-art results on the MTEB benchmark with no hard negative mining, score distillation, dataset-specific instructions, intra-GPU example-sharing, or extremely large batch sizes. Our method can be applied to improve performance on any contrastive learning dataset and any biencoder.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-04", "arxiv_id": "2410.02762", "title": "Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations", "authors": [ "Nick Jiang", "Anish Kachinthaya", "Suzie Petryk", "Yossi Gandelsman" ], "github": "https://github.com/nickjiang2378/vl-interp", "project_page": "", "abstract": "We investigate the internal representations of vision-language models (VLMs) to address hallucinations, a persistent challenge despite advances in model size and training. We project VLMs' internal image representations to their language vocabulary and observe more confident output probabilities on real objects than hallucinated objects. We additionally use these output probabilities to spatially localize real objects. Building on this approach, we introduce a knowledge erasure algorithm that removes hallucinations by linearly orthogonalizing image features with respect to hallucinated object features. We show that targeted edits to a model's latent representations can reduce hallucinations by up to 25.7% on the COCO2014 dataset while preserving performance. Our findings demonstrate how a deeper understanding of VLMs' latent representations can enhance reliability and enable novel capabilities, such as zero-shot segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nickjiang2378/vl-interp", "github_api_added_by": "auto" }, { "date": "2024-10-04", "arxiv_id": "2410.01335", "title": "Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models", "authors": [ "Lucas Bandarkar", "Benjamin Muller", "Pritish Yuvraj", "Rui Hou", "Nayan Singhal", "Hongjiang Lv", "Bing Liu" ], "github": "", "project_page": "", "abstract": "Model merging, such as model souping, is the practice of combining different models with the same architecture together without further training. In this work, we present a model merging methodology that addresses the difficulty of fine-tuning Large Language Models (LLMs) for target tasks in non-English languages, where task-specific data is often unavailable. We focus on mathematical reasoning and without in-language math data, facilitate cross-lingual transfer by composing language and math capabilities. Starting from the same pretrained model, we fine-tune separate \"experts\" on math instruction data in English and on generic instruction data in the target language. We then replace the top and bottom transformer layers of the math expert directly with layers from the language expert, which consequently enhances math performance in the target language. The resulting merged models outperform the individual experts and other merging methods on the math benchmark, MGSM, by 10% across four major languages where math instruction data is scarce. In addition, this layer swapping is simple, inexpensive, and intuitive, as it is based on an interpretative analysis of the most important parameter changes during the fine-tuning of each expert. The ability to successfully re-compose LLMs for cross-lingual transfer in this manner opens up future possibilities to combine model expertise, create modular solutions, and transfer reasoning capabilities across languages all post hoc.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-07", "arxiv_id": "2410.02760", "title": "Erasing Conceptual Knowledge from Language Models", "authors": [ "Rohit Gandikota", "Sheridan Feucht", "Samuel Marks", "David Bau" ], "github": "", "project_page": "", "abstract": "Concept erasure in language models has traditionally lacked a comprehensive evaluation framework, leading to incomplete assessments of effectiveness of erasure methods. We propose an evaluation paradigm centered on three critical criteria: innocence (complete knowledge removal), seamlessness (maintaining conditional fluent generation), and specificity (preserving unrelated task performance). Our evaluation metrics naturally motivate the development of Erasure of Language Memory (ELM), a new method designed to address all three dimensions. ELM employs targeted low-rank updates to alter output distributions for erased concepts while preserving overall model capabilities including fluency when prompted for an erased concept. We demonstrate ELM's efficacy on biosecurity, cybersecurity, and literary domain erasure tasks. Comparative analysis shows that ELM achieves superior performance across our proposed metrics, including near-random scores on erased topic assessments, generation fluency, maintained accuracy on unrelated benchmarks, and robustness under adversarial attacks. Our code, data, and trained models are available at https://elm.baulab.info", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-07", "arxiv_id": "2410.02703", "title": "Selective Attention Improves Transformer", "authors": [ "Yaniv Leviathan", "Matan Kalman", "Yossi Matias" ], "github": "", "project_page": "", "abstract": "Unneeded elements in the attention's context degrade performance. We introduce Selective Attention, a simple parameter-free change to the standard attention mechanism which reduces attention to unneeded elements. Selective attention improves language modeling performance in a variety of model sizes and context lengths. For example, a range of transformers trained with the language modeling objective on C4 with selective attention perform equivalently to standard transformers with ~2X more heads and parameters in their attention modules. Selective attention also allows decreasing the size of the attention's context buffer, leading to meaningful reductions in the memory and compute requirements during inference. For example, transformers with 100M parameters trained on C4 with context sizes of 512, 1,024, and 2,048 need 16X, 25X, and 47X less memory for their attention module, respectively, when equipped with selective attention, as those without selective attention, with the same validation perplexity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-07", "arxiv_id": "2410.03017", "title": "Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise", "authors": [ "Rose E. Wang", "Ana T. Ribeiro", "Carly D. Robinson", "Susanna Loeb", "Dora Demszky" ], "github": "https://github.com/rosewang2008/tutor-copilot", "project_page": "", "abstract": "Generative AI, particularly Language Models (LMs), has the potential to transform real-world domains with societal impact, particularly where access to experts is limited. For example, in education, training novice educators with expert guidance is important for effectiveness but expensive, creating significant barriers to improving education quality at scale. This challenge disproportionately harms students from under-served communities, who stand to gain the most from high-quality education. We introduce Tutor CoPilot, a novel Human-AI approach that leverages a model of expert thinking to provide expert-like guidance to tutors as they tutor. This study is the first randomized controlled trial of a Human-AI system in live tutoring, involving 900 tutors and 1,800 K-12 students from historically under-served communities. Following a preregistered analysis plan, we find that students working with tutors that have access to Tutor CoPilot are 4 percentage points (p.p.) more likely to master topics (p<0.01). Notably, students of lower-rated tutors experienced the greatest benefit, improving mastery by 9 p.p. We find that Tutor CoPilot costs only $20 per-tutor annually. We analyze 550,000+ messages using classifiers to identify pedagogical strategies, and find that tutors with access to Tutor CoPilot are more likely to use high-quality strategies to foster student understanding (e.g., asking guiding questions) and less likely to give away the answer to the student. Tutor interviews highlight how Tutor CoPilot's guidance helps tutors to respond to student needs, though they flag issues in Tutor CoPilot, such as generating suggestions that are not grade-level appropriate. Altogether, our study of Tutor CoPilot demonstrates how Human-AI systems can scale expertise in real-world domains, bridge gaps in skills and create a future where high-quality education is accessible to all students.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rosewang2008/tutor-copilot", "github_api_added_by": "auto" }, { "date": "2024-10-07", "arxiv_id": "2410.02241", "title": "MIGA: Mixture-of-Experts with Group Aggregation for Stock Market Prediction", "authors": [ "Zhaojian Yu", "Yinghao Wu", "Genesis Wang", "Heming Weng" ], "github": "", "project_page": "", "abstract": "Stock market prediction has remained an extremely challenging problem for many decades owing to its inherent high volatility and low information noisy ratio. Existing solutions based on machine learning or deep learning demonstrate superior performance by employing a single model trained on the entire stock dataset to generate predictions across all types of stocks. However, due to the significant variations in stock styles and market trends, a single end-to-end model struggles to fully capture the differences in these stylized stock features, leading to relatively inaccurate predictions for all types of stocks. In this paper, we present MIGA, a novel Mixture of Expert with Group Aggregation framework designed to generate specialized predictions for stocks with different styles by dynamically switching between distinct style experts. To promote collaboration among different experts in MIGA, we propose a novel inner group attention architecture, enabling experts within the same group to share information and thereby enhancing the overall performance of all experts. As a result, MIGA significantly outperforms other end-to-end models on three Chinese Stock Index benchmarks including CSI300, CSI500, and CSI1000. Notably, MIGA-Conv reaches 24 % excess annual return on CSI300 benchmark, surpassing the previous state-of-the-art model by 8% absolute. Furthermore, we conduct a comprehensive analysis of mixture of experts for stock market prediction, providing valuable insights for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-07", "arxiv_id": "2409.19989", "title": "RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models", "authors": [ "Jangyeong Kim", "Donggoo Kang", "Junyoung Choi", "Jeonga Wi", "Junho Gwon", "Jiun Bae", "Dumim Yoon", "Junghyun Han" ], "github": "", "project_page": "", "abstract": "Text-to-texture generation has recently attracted increasing attention, but existing methods often suffer from the problems of view inconsistencies, apparent seams, and misalignment between textures and the underlying mesh. In this paper, we propose a robust text-to-texture method for generating consistent and seamless textures that are well aligned with the mesh. Our method leverages state-of-the-art 2D diffusion models, including SDXL and multiple ControlNets, to capture structural features and intricate details in the generated textures. The method also employs a symmetrical view synthesis strategy combined with regional prompts for enhancing view consistency. Additionally, it introduces novel texture blending and soft-inpainting techniques, which significantly reduce the seam regions. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-07", "arxiv_id": "2410.02613", "title": "NL-Eye: Abductive NLI for Images", "authors": [ "Mor Ventura", "Michael Toker", "Nitay Calderon", "Zorik Gekhman", "Yonatan Bitton", "Roi Reichart" ], "github": "", "project_page": "", "abstract": "Will a Visual Language Model (VLM)-based bot warn us about slipping if it detects a wet floor? Recent VLMs have demonstrated impressive capabilities, yet their ability to infer outcomes and causes remains underexplored. To address this, we introduce NL-Eye, a benchmark designed to assess VLMs' visual abductive reasoning skills. NL-Eye adapts the abductive Natural Language Inference (NLI) task to the visual domain, requiring models to evaluate the plausibility of hypothesis images based on a premise image and explain their decisions. NL-Eye consists of 350 carefully curated triplet examples (1,050 images) spanning diverse reasoning categories: physical, functional, logical, emotional, cultural, and social. The data curation process involved two steps - writing textual descriptions and generating images using text-to-image models, both requiring substantial human involvement to ensure high-quality and challenging scenes. Our experiments show that VLMs struggle significantly on NL-Eye, often performing at random baseline levels, while humans excel in both plausibility prediction and explanation quality. This demonstrates a deficiency in the abductive reasoning capabilities of modern VLMs. NL-Eye represents a crucial step toward developing VLMs capable of robust multimodal reasoning for real-world applications, including accident-prevention bots and generated video verification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-07", "arxiv_id": "2410.02362", "title": "A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond", "authors": [ "Shubhi Bansal", "Sreeharish A", "Madhava Prasath J", "Manikandan S", "Sreekanth Madisetty", "Mohammad Zia Ur Rehman", "Chandravardhan Singh Raghaw", "Gaurav Duggal", "Nagendra Kumar" ], "github": "", "project_page": "", "abstract": "Mamba, a special case of the State Space Model, is gaining popularity as an alternative to template-based deep learning approaches in medical image analysis. While transformers are powerful architectures, they have drawbacks, including quadratic computational complexity and an inability to address long-range dependencies efficiently. This limitation affects the analysis of large and complex datasets in medical imaging, where there are many spatial and temporal relationships. In contrast, Mamba offers benefits that make it well-suited for medical image analysis. It has linear time complexity, which is a significant improvement over transformers. Mamba processes longer sequences without attention mechanisms, enabling faster inference and requiring less memory. Mamba also demonstrates strong performance in merging multimodal data, improving diagnosis accuracy and patient outcomes. The organization of this paper allows readers to appreciate the capabilities of Mamba in medical imaging step by step. We begin by defining core concepts of SSMs and models, including S4, S5, and S6, followed by an exploration of Mamba architectures such as pure Mamba, U-Net variants, and hybrid models with convolutional neural networks, transformers, and Graph Neural Networks. We also cover Mamba optimizations, techniques and adaptations, scanning, datasets, applications, experimental results, and conclude with its challenges and future directions in medical imaging. This review aims to demonstrate the transformative potential of Mamba in overcoming existing barriers within medical imaging while paving the way for innovative advancements in the field. A comprehensive list of Mamba architectures applied in the medical field, reviewed in this work, is available at Github.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-07", "arxiv_id": "2410.01273", "title": "CANVAS: Commonsense-Aware Navigation System for Intuitive Human-Robot Interaction", "authors": [ "Suhwan Choi", "Yongjun Cho", "Minchan Kim", "Jaeyoon Jung", "Myunchul Joe", "Yubeen Park", "Minseo Kim", "Sungwoong Kim", "Sungjae Lee", "Hwiseong Park", "Jiwan Chung", "Youngjae Yu" ], "github": "", "project_page": "", "abstract": "Real-life robot navigation involves more than just reaching a destination; it requires optimizing movements while addressing scenario-specific goals. An intuitive way for humans to express these goals is through abstract cues like verbal commands or rough sketches. Such human guidance may lack details or be noisy. Nonetheless, we expect robots to navigate as intended. For robots to interpret and execute these abstract instructions in line with human expectations, they must share a common understanding of basic navigation concepts with humans. To this end, we introduce CANVAS, a novel framework that combines visual and linguistic instructions for commonsense-aware navigation. Its success is driven by imitation learning, enabling the robot to learn from human navigation behavior. We present COMMAND, a comprehensive dataset with human-annotated navigation results, spanning over 48 hours and 219 km, designed to train commonsense-aware navigation systems in simulated environments. Our experiments show that CANVAS outperforms the strong rule-based system ROS NavStack across all environments, demonstrating superior performance with noisy instructions. Notably, in the orchard environment, where ROS NavStack records a 0% total success rate, CANVAS achieves a total success rate of 67%. CANVAS also closely aligns with human demonstrations and commonsense constraints, even in unseen environments. Furthermore, real-world deployment of CANVAS showcases impressive Sim2Real transfer with a total success rate of 69%, highlighting the potential of learning from human demonstrations in simulated environments for real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-07", "arxiv_id": "2410.03535", "title": "NRGBoost: Energy-Based Generative Boosted Trees", "authors": [ "Jo\u00e3o Bravo" ], "github": "https://github.com/ajoo/nrgboost", "project_page": "", "abstract": "Despite the rise to dominance of deep learning in unstructured data domains, tree-based methods such as Random Forests (RF) and Gradient Boosted Decision Trees (GBDT) are still the workhorses for handling discriminative tasks on tabular data. We explore generative extensions of these popular algorithms with a focus on explicitly modeling the data density (up to a normalization constant), thus enabling other applications besides sampling. As our main contribution we propose an energy-based generative boosting algorithm that is analogous to the second order boosting implemented in popular packages like XGBoost. We show that, despite producing a generative model capable of handling inference tasks over any input variable, our proposed algorithm can achieve similar discriminative performance to GBDT on a number of real world tabular datasets, outperforming alternative generative approaches. At the same time, we show that it is also competitive with neural network based models for sampling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ajoo/nrgboost", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-07", "arxiv_id": "2410.03645", "title": "GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMs", "authors": [ "Pu Hua", "Minghuan Liu", "Annabella Macaluso", "Yunfeng Lin", "Weinan Zhang", "Huazhe Xu", "Lirui Wang" ], "github": "https://github.com/GenSim2/GenSim2", "project_page": "", "abstract": "Robotic simulation today remains challenging to scale up due to the human efforts required to create diverse simulation tasks and scenes. Simulation-trained policies also face scalability issues as many sim-to-real methods focus on a single task. To address these challenges, this work proposes GenSim2, a scalable framework that leverages coding LLMs with multi-modal and reasoning capabilities for complex and realistic simulation task creation, including long-horizon tasks with articulated objects. To automatically generate demonstration data for these tasks at scale, we propose planning and RL solvers that generalize within object categories. The pipeline can generate data for up to 100 articulated tasks with 200 objects and reduce the required human efforts. To utilize such data, we propose an effective multi-task language-conditioned policy architecture, dubbed proprioceptive point-cloud transformer (PPT), that learns from the generated demonstrations and exhibits strong sim-to-real zero-shot transfer. Combining the proposed pipeline and the policy architecture, we show a promising usage of GenSim2 that the generated data can be used for zero-shot transfer or co-train with real-world collected data, which enhances the policy performance by 20% compared with training exclusively on limited real data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GenSim2/GenSim2", "github_api_added_by": "auto" }, { "date": "2024-10-07", "arxiv_id": "2410.01699", "title": "Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding", "authors": [ "Yao Teng", "Han Shi", "Xian Liu", "Xuefei Ning", "Guohao Dai", "Yu Wang", "Zhenguo Li", "Xihui Liu" ], "github": "https://github.com/tyshiwo1/Accelerating-T2I-AR-with-SJD", "project_page": "", "abstract": "The current large auto-regressive models can generate high-quality, high-resolution images, but these models require hundreds or even thousands of steps of next-token prediction during inference, resulting in substantial time consumption. In existing studies, Jacobi decoding, an iterative parallel decoding algorithm, has been used to accelerate the auto-regressive generation and can be executed without training. However, the Jacobi decoding relies on a deterministic criterion to determine the convergence of iterations. Thus, it works for greedy decoding but is incompatible with sampling-based decoding which is crucial for visual quality and diversity in the current auto-regressive text-to-image generation. In this paper, we propose a training-free probabilistic parallel decoding algorithm, Speculative Jacobi Decoding (SJD), to accelerate auto-regressive text-to-image generation. By introducing a probabilistic convergence criterion, our SJD accelerates the inference of auto-regressive text-to-image generation while maintaining the randomness in sampling-based token decoding and allowing the model to generate diverse images. Specifically, SJD facilitates the model to predict multiple tokens at each step and accepts tokens based on the probabilistic criterion, enabling the model to generate images with fewer steps than the conventional next-token-prediction paradigm. We also investigate the token initialization strategies that leverage the spatial locality of visual data to further improve the acceleration ratio under specific scenarios. We conduct experiments for our proposed SJD on multiple auto-regressive text-to-image generation models, showing the effectiveness of model acceleration without sacrificing the visual quality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tyshiwo1/Accelerating-T2I-AR-with-SJD", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-07", "arxiv_id": "2410.03103", "title": "Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for Code Generation with Lookahead Planning", "authors": [ "Yifeng Ding", "Hantian Ding", "Shiqi Wang", "Qing Sun", "Varun Kumar", "Zijian Wang" ], "github": "", "project_page": "", "abstract": "Fill-in-the-Middle (FIM) has become integral to code language models, enabling generation of missing code given both left and right contexts. However, the current FIM training paradigm, which reorders original training sequences and then performs regular next-token prediction (NTP), often leads to models struggling to generate content that aligns smoothly with the surrounding context. Crucially, while existing works rely on rule-based post-processing to circumvent this weakness, such methods are not practically usable in open-domain code completion tasks as they depend on restrictive, dataset-specific assumptions (e.g., generating the same number of lines as in the ground truth). Moreover, model performance on FIM tasks deteriorates significantly without these unrealistic assumptions. We hypothesize that NTP alone is insufficient for models to learn effective planning conditioned on the distant right context, a critical factor for successful code infilling. To overcome this, we propose Horizon-Length Prediction (HLP), a novel training objective that teaches models to predict the number of remaining middle tokens (i.e., horizon length) at each step. HLP advances FIM with lookahead planning, enabling models to inherently learn infilling boundaries for arbitrary left and right contexts without relying on dataset-specific post-processing. Our evaluation across different models and sizes shows that HLP significantly improves FIM performance by up to 24% relatively on diverse benchmarks, across file-level and repository-level, and without resorting to unrealistic post-processing methods. Furthermore, the enhanced planning capability gained through HLP boosts model performance on code reasoning. Importantly, HLP only incurs negligible training overhead and no additional inference cost, ensuring its practicality for real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-07", "arxiv_id": "2410.01999", "title": "CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities of CodeLLMs", "authors": [ "Dung Nguyen Manh", "Thang Phan Chau", "Nam Le Hai", "Thong T. Doan", "Nam V. Nguyen", "Quang Pham", "Nghi D. Q. Bui" ], "github": "https://github.com/fsoft-ai4code/codemmlu", "project_page": "", "abstract": "Recent advancements in Code Large Language Models (CodeLLMs) have predominantly focused on open-ended code generation tasks, often neglecting the critical aspect of code understanding and comprehension. To bridge this gap, we present CodeMMLU, a comprehensive multiple-choice question-answer benchmark designed to evaluate the depth of software and code understanding in LLMs. CodeMMLU includes over 10,000 questions sourced from diverse domains, encompassing tasks such as code analysis, defect detection, and software engineering principles across multiple programming languages. Unlike traditional benchmarks, CodeMMLU assesses models's ability to reason about code rather than merely generate it, providing deeper insights into their grasp of complex software concepts and systems. Our extensive evaluation reveals that even state-of-the-art models face significant challenges with CodeMMLU, highlighting deficiencies in comprehension beyond code generation. By underscoring the crucial relationship between code understanding and effective generation, CodeMMLU serves as a vital resource for advancing AI-assisted software development, ultimately aiming to create more reliable and capable coding assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/fsoft-ai4code/codemmlu", "github_api_added_by": "auto" }, { "date": "2024-10-07", "arxiv_id": "2410.03027", "title": "MLP-KAN: Unifying Deep Representation and Function Learning", "authors": [ "Yunhong He", "Yifeng Xie", "Zhengqing Yuan", "Lichao Sun" ], "github": "https://github.com/DLYuanGod/MLP-KAN", "project_page": "", "abstract": "Recent advancements in both representation learning and function learning have demonstrated substantial promise across diverse domains of artificial intelligence. However, the effective integration of these paradigms poses a significant challenge, particularly in cases where users must manually decide whether to apply a representation learning or function learning model based on dataset characteristics. To address this issue, we introduce MLP-KAN, a unified method designed to eliminate the need for manual model selection. By integrating Multi-Layer Perceptrons (MLPs) for representation learning and Kolmogorov-Arnold Networks (KANs) for function learning within a Mixture-of-Experts (MoE) architecture, MLP-KAN dynamically adapts to the specific characteristics of the task at hand, ensuring optimal performance. Embedded within a transformer-based framework, our work achieves remarkable results on four widely-used datasets across diverse domains. Extensive experimental evaluation demonstrates its superior versatility, delivering competitive performance across both deep representation and function learning tasks. These findings highlight the potential of MLP-KAN to simplify the model selection process, offering a comprehensive, adaptable solution across various domains. Our code and weights are available at https://github.com/DLYuanGod/MLP-KAN.", "github_manual": "", "github_regex": "https://github.com/DLYuanGod/MLP-KAN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-07", "arxiv_id": "2410.03051", "title": "AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark", "authors": [ "Wenhao Chai", "Enxin Song", "Yilun Du", "Chenlin Meng", "Vashisht Madhavan", "Omer Bar-Tal", "Jeng-Neng Hwang", "Saining Xie", "Christopher D. Manning" ], "github": "https://github.com/rese1f/aurora", "project_page": "", "abstract": "Video detailed captioning is a key task which aims to generate comprehensive and coherent textual descriptions of video content, benefiting both video understanding and generation. In this paper, we propose AuroraCap, a video captioner based on a large multimodal model. We follow the simplest architecture design without additional parameters for temporal modeling. To address the overhead caused by lengthy video sequences, we implement the token merging strategy, reducing the number of input visual tokens. Surprisingly, we found that this strategy results in little performance loss. AuroraCap shows superior performance on various video and image captioning benchmarks, for example, obtaining a CIDEr of 88.9 on Flickr30k, beating GPT-4V (55.3) and Gemini-1.5 Pro (82.2). However, existing video caption benchmarks only include simple descriptions, consisting of a few dozen words, which limits research in this field. Therefore, we develop VDC, a video detailed captioning benchmark with over one thousand carefully annotated structured captions. In addition, we propose a new LLM-assisted metric VDCscore for bettering evaluation, which adopts a divide-and-conquer strategy to transform long caption evaluation into multiple short question-answer pairs. With the help of human Elo ranking, our experiments show that this benchmark better correlates with human judgments of video detailed captioning quality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/rese1f/aurora", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.02675", "title": "FAN: Fourier Analysis Networks", "authors": [ "Yihong Dong", "Ge Li", "Yongding Tao", "Xue Jiang", "Kechi Zhang", "Jia Li", "Jing Su", "Jun Zhang", "Jingjing Xu" ], "github": "https://github.com/yihongdong/fan", "project_page": "", "abstract": "Despite the remarkable success achieved by neural networks, particularly those represented by MLP and Transformer, we reveal that they exhibit potential flaws in the modeling and reasoning of periodicity, i.e., they tend to memorize the periodic data rather than genuinely understanding the underlying principles of periodicity. However, periodicity is a crucial trait in various forms of reasoning and generalization, underpinning predictability across natural and engineered systems through recurring patterns in observations. In this paper, we propose FAN, a novel network architecture based on Fourier Analysis, which empowers the ability to efficiently model and reason about periodic phenomena. By introducing Fourier Series, the periodicity is naturally integrated into the structure and computational processes of the neural network, thus achieving a more accurate expression and prediction of periodic patterns. As a promising substitute to multi-layer perceptron (MLP), FAN can seamlessly replace MLP in various models with fewer parameters and FLOPs. Through extensive experiments, we demonstrate the effectiveness of FAN in modeling and reasoning about periodic functions, and the superiority and generalizability of FAN across a range of real-world tasks, including symbolic formula representation, time series forecasting, and language modeling.", "github_manual": "https://github.com/yihongdong/fan", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.03959", "title": "Grounding Language in Multi-Perspective Referential Communication", "authors": [ "Zineng Tang", "Lingjun Mao", "Alane Suhr" ], "github": "https://github.com/zinengtang/MulAgentRef", "project_page": "", "abstract": "We introduce a task and dataset for referring expression generation and comprehension in multi-agent embodied environments. In this task, two agents in a shared scene must take into account one another's visual perspective, which may be different from their own, to both produce and understand references to objects in a scene and the spatial relations between them. We collect a dataset of 2,970 human-written referring expressions, each paired with human comprehension judgments, and evaluate the performance of automated models as speakers and listeners paired with human partners, finding that model performance in both reference generation and comprehension lags behind that of pairs of human agents. Finally, we experiment training an open-weight speaker model with evidence of communicative success when paired with a listener, resulting in an improvement from 58.9 to 69.3% in communicative success and even outperforming the strongest proprietary model.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zinengtang/MulAgentRef", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.05262", "title": "TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles", "authors": [ "Qingchen Yu", "Shichao Song", "Ke Fang", "Yunfeng Shi", "Zifan Zheng", "Hanyu Wang", "Simin Niu", "Zhiyu Li" ], "github": "https://github.com/mazzzystar/TurtleBench", "project_page": "", "abstract": "As the application of Large Language Models (LLMs) expands, the demand for reliable evaluations increases. Existing LLM evaluation benchmarks primarily rely on static datasets, making it challenging to assess model performance in dynamic interactions with users. Moreover, these benchmarks often depend on specific background knowledge, complicating the measurement of a model's logical reasoning capabilities. Other dynamic evaluation methods based on strong models or manual efforts may introduce biases and incur high costs and time demands, hindering large-scale application. To address these issues, we propose TurtleBench. TurtleBench collects real user guesses from our online Turtle Soup Puzzle platform that we developed. This approach allows for the relatively dynamic generation of evaluation datasets, mitigating the risk of model cheating while aligning assessments more closely with genuine user needs for reasoning capabilities, thus enhancing the reliability of evaluations. TurtleBench includes 1,532 user guesses along with the correctness of guesses after annotation. Using this dataset, we thoroughly evaluated nine of the most advanced LLMs available today. Notably, the OpenAI o1 series models did not achieve leading results in these evaluations. We propose several hypotheses for further research, such as \"the latent reasoning of o1 utilizes trivial Chain-of-Thought (CoT) techniques\" and \"increasing CoT length not only provides reasoning benefits but also incurs noise costs.\"", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mazzzystar/TurtleBench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.05167", "title": "Presto! Distilling Steps and Layers for Accelerating Music Generation", "authors": [ "Zachary Novack", "Ge Zhu", "Jonah Casebeer", "Julian McAuley", "Taylor Berg-Kirkpatrick", "Nicholas J. Bryan" ], "github": "", "project_page": "", "abstract": "Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration for score-based diffusion transformers via reducing both sampling steps and cost per step. To reduce steps, we develop a new score-based distribution matching distillation (DMD) method for the EDM-family of diffusion models, the first GAN-based distillation method for TTM. To reduce the cost per step, we develop a simple, but powerful improvement to a recent layer distillation method that improves learning via better preserving hidden state variance. Finally, we combine our step and layer distillation methods together for a dual-faceted approach. We evaluate our step and layer distillation methods independently and show each yield best-in-class performance. Our combined distillation method can generate high-quality outputs with improved diversity, accelerating our base model by 10-18x (230/435ms latency for 32 second mono/stereo 44.1kHz, 15x faster than comparable SOTA) -- the fastest high-quality TTM to our knowledge. Sound examples can be found at https://presto-music.github.io/web/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.05258", "title": "Differential Transformer", "authors": [ "Tianzhu Ye", "Li Dong", "Yuqing Xia", "Yutao Sun", "Yi Zhu", "Gao Huang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Transformer tends to overallocate attention to irrelevant context. In this work, we introduce Diff Transformer, which amplifies attention to the relevant context while canceling noise. Specifically, the differential attention mechanism calculates attention scores as the difference between two separate softmax attention maps. The subtraction cancels noise, promoting the emergence of sparse attention patterns. Experimental results on language modeling show that Diff Transformer outperforms Transformer in various settings of scaling up model size and training tokens. More intriguingly, it offers notable advantages in practical applications, such as long-context modeling, key information retrieval, hallucination mitigation, in-context learning, and reduction of activation outliers. By being less distracted by irrelevant context, Diff Transformer can mitigate hallucination in question answering and text summarization. For in-context learning, Diff Transformer not only enhances accuracy but is also more robust to order permutation, which was considered as a chronic robustness issue. The results position Diff Transformer as a highly effective and promising architecture to advance large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.04734", "title": "TLDR: Token-Level Detective Reward Model for Large Vision Language Models", "authors": [ "Deqing Fu", "Tong Xiao", "Rui Wang", "Wang Zhu", "Pengchuan Zhang", "Guan Pang", "Robin Jia", "Lawrence Chen" ], "github": "", "project_page": "", "abstract": "Although reward models have been successful in improving multimodal large language models, the reward models themselves remain brutal and contain minimal information. Notably, existing reward models only mimic human annotations by assigning only one binary feedback to any text, no matter how long the text is. In the realm of multimodal language models, where models are required to process both images and texts, a naive reward model may learn implicit biases toward texts and become less grounded in images. In this paper, we propose a Token-Level Detective Reward Model (TLDR) to provide fine-grained annotations to each text token. We first introduce a perturbation-based method to generate synthetic hard negatives and their token-level labels to train TLDR models. Then we show the rich usefulness of TLDR models both in assisting off-the-shelf models to self-correct their generations, and in serving as a hallucination evaluation tool. Finally, we show that TLDR models can significantly speed up human annotation by 3 times to acquire a broader range of high-quality vision language data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.04364", "title": "VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide", "authors": [ "Dohun Lee", "Bryan S Kim", "Geon Yeong Park", "Jong Chul Ye" ], "github": "", "project_page": "", "abstract": "Text-to-image (T2I) diffusion models have revolutionized visual content creation, but extending these capabilities to text-to-video (T2V) generation remains a challenge, particularly in preserving temporal consistency. Existing methods that aim to improve consistency often cause trade-offs such as reduced imaging quality and impractical computational time. To address these issues we introduce VideoGuide, a novel framework that enhances the temporal consistency of pretrained T2V models without the need for additional training or fine-tuning. Instead, VideoGuide leverages any pretrained video diffusion model (VDM) or itself as a guide during the early stages of inference, improving temporal quality by interpolating the guiding model's denoised samples into the sampling model's denoising process. The proposed method brings about significant improvement in temporal consistency and image fidelity, providing a cost-effective and practical solution that synergizes the strengths of various video diffusion models. Furthermore, we demonstrate prior distillation, revealing that base models can achieve enhanced text coherence by utilizing the superior data prior of the guiding model through the proposed method. Project Page: http://videoguide2025.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.04534", "title": "UniMuMo: Unified Text, Music and Motion Generation", "authors": [ "Han Yang", "Kun Su", "Yutong Zhang", "Jiaben Chen", "Kaizhi Qian", "Gaowen Liu", "Chuang Gan" ], "github": "https://github.com/hanyangclarence/UniMuMo", "project_page": "", "abstract": "We introduce UniMuMo, a unified multimodal model capable of taking arbitrary text, music, and motion data as input conditions to generate outputs across all three modalities. To address the lack of time-synchronized data, we align unpaired music and motion data based on rhythmic patterns to leverage existing large-scale music-only and motion-only datasets. By converting music, motion, and text into token-based representation, our model bridges these modalities through a unified encoder-decoder transformer architecture. To support multiple generation tasks within a single framework, we introduce several architectural improvements. We propose encoding motion with a music codebook, mapping motion into the same feature space as music. We introduce a music-motion parallel generation scheme that unifies all music and motion generation tasks into a single transformer decoder architecture with a single training task of music-motion joint generation. Moreover, the model is designed by fine-tuning existing pre-trained single-modality models, significantly reducing computational demands. Extensive experiments demonstrate that UniMuMo achieves competitive results on all unidirectional generation benchmarks across music, motion, and text modalities. Quantitative results are available in the https://hanyangclarence.github.io/unimumo_demo/{project page}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hanyangclarence/UniMuMo", "github_api_added_by": "auto" }, { "date": "2024-10-08", "arxiv_id": "2410.04932", "title": "OmniBooth: Learning Latent Control for Image Synthesis with Multi-modal Instruction", "authors": [ "Leheng Li", "Weichao Qiu", "Xu Yan", "Jing He", "Kaiqiang Zhou", "Yingjie Cai", "Qing Lian", "Bingbing Liu", "Ying-Cong Chen" ], "github": "", "project_page": "", "abstract": "We present OmniBooth, an image generation framework that enables spatial control with instance-level multi-modal customization. For all instances, the multimodal instruction can be described through text prompts or image references. Given a set of user-defined masks and associated text or image guidance, our objective is to generate an image, where multiple objects are positioned at specified coordinates and their attributes are precisely aligned with the corresponding guidance. This approach significantly expands the scope of text-to-image generation, and elevates it to a more versatile and practical dimension in controllability. In this paper, our core contribution lies in the proposed latent control signals, a high-dimensional spatial feature that provides a unified representation to integrate the spatial, textual, and image conditions seamlessly. The text condition extends ControlNet to provide instance-level open-vocabulary generation. The image condition further enables fine-grained control with personalized identity. In practice, our method empowers users with more flexibility in controllable generation, as users can choose multi-modal conditions from text or images as needed. Furthermore, thorough experiments demonstrate our enhanced performance in image synthesis fidelity and alignment across different tasks and datasets. Project page: https://len-li.github.io/omnibooth-web/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.03825", "title": "MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion", "authors": [ "Junyi Zhang", "Charles Herrmann", "Junhwa Hur", "Varun Jampani", "Trevor Darrell", "Forrester Cole", "Deqing Sun", "Ming-Hsuan Yang" ], "github": "https://github.com/Junyi42/monst3r", "project_page": "", "abstract": "Estimating geometry from dynamic scenes, where objects move and deform over time, remains a core challenge in computer vision. Current approaches often rely on multi-stage pipelines or global optimizations that decompose the problem into subtasks, like depth and flow, leading to complex systems prone to errors. In this paper, we present Motion DUSt3R (MonST3R), a novel geometry-first approach that directly estimates per-timestep geometry from dynamic scenes. Our key insight is that by simply estimating a pointmap for each timestep, we can effectively adapt DUST3R's representation, previously only used for static scenes, to dynamic scenes. However, this approach presents a significant challenge: the scarcity of suitable training data, namely dynamic, posed videos with depth labels. Despite this, we show that by posing the problem as a fine-tuning task, identifying several suitable datasets, and strategically training the model on this limited data, we can surprisingly enable the model to handle dynamics, even without an explicit motion representation. Based on this, we introduce new optimizations for several downstream video-specific tasks and demonstrate strong performance on video depth and camera pose estimation, outperforming prior work in terms of robustness and efficiency. Moreover, MonST3R shows promising results for primarily feed-forward 4D reconstruction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Junyi42/monst3r", "github_api_added_by": "auto" }, { "date": "2024-10-08", "arxiv_id": "2410.04698", "title": "MathHay: An Automated Benchmark for Long-Context Mathematical Reasoning in LLMs", "authors": [ "Lei Wang", "Shan Dong", "Yuhui Xu", "Hanze Dong", "Yalu Wang", "Amrita Saha", "Ee-Peng Lim", "Caiming Xiong", "Doyen Sahoo" ], "github": "", "project_page": "", "abstract": "Recent large language models (LLMs) have demonstrated versatile capabilities in long-context scenarios. Although some recent benchmarks have been developed to evaluate the long-context capabilities of LLMs, there is a lack of benchmarks evaluating the mathematical reasoning abilities of LLMs over long contexts, which is crucial for LLMs' application in real-world scenarios. In this paper, we introduce MathHay, an automated benchmark designed to assess the long-context mathematical reasoning capabilities of LLMs. Unlike previous benchmarks like Needle in a Haystack, which focus primarily on information retrieval within long texts, MathHay demands models with both information-seeking and complex mathematical reasoning abilities. We conduct extensive experiments on MathHay to assess the long-context mathematical reasoning abilities of eight top-performing LLMs. Even the best-performing model, Gemini-1.5-Pro-002, still struggles with mathematical reasoning over long contexts, achieving only 51.26% accuracy at 128K tokens. This highlights the significant room for improvement on the MathHay benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.02707", "title": "LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations", "authors": [ "Hadas Orgad", "Michael Toker", "Zorik Gekhman", "Roi Reichart", "Idan Szpektor", "Hadas Kotek", "Yonatan Belinkov" ], "github": "https://github.com/technion-cs-nlp/llmsknow", "project_page": "", "abstract": "Large language models (LLMs) often produce errors, including factual inaccuracies, biases, and reasoning failures, collectively referred to as \"hallucinations\". Recent studies have demonstrated that LLMs' internal states encode information regarding the truthfulness of their outputs, and that this information can be utilized to detect errors. In this work, we show that the internal representations of LLMs encode much more information about truthfulness than previously recognized. We first discover that the truthfulness information is concentrated in specific tokens, and leveraging this property significantly enhances error detection performance. Yet, we show that such error detectors fail to generalize across datasets, implying that -- contrary to prior claims -- truthfulness encoding is not universal but rather multifaceted. Next, we show that internal representations can also be used for predicting the types of errors the model is likely to make, facilitating the development of tailored mitigation strategies. Lastly, we reveal a discrepancy between LLMs' internal encoding and external behavior: they may encode the correct answer, yet consistently generate an incorrect one. Taken together, these insights deepen our understanding of LLM errors from the model's internal perspective, which can guide future research on enhancing error analysis and mitigation.", "github_manual": "https://github.com/technion-cs-nlp/llmsknow", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.02884", "title": "LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning", "authors": [ "Di Zhang", "Jianbo Wu", "Jingdi Lei", "Tong Che", "Jiatong Li", "Tong Xie", "Xiaoshui Huang", "Shufei Zhang", "Marco Pavone", "Yuqiang Li", "Wanli Ouyang", "Dongzhan Zhou" ], "github": "", "project_page": "", "abstract": "This paper presents an advanced mathematical problem-solving framework, LLaMA-Berry, for enhancing the mathematical reasoning ability of Large Language Models (LLMs). The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to optimize the reasoning path and utilizes a pairwise reward model to evaluate different paths globally. By leveraging the self-critic and rewriting capabilities of LLMs, Self-Refine applied to MCTS (SR-MCTS) overcomes the inefficiencies and limitations of conventional step-wise and greedy search algorithms by fostering a more efficient exploration of solution spaces. Pairwise Preference Reward Model~(PPRM), inspired by Reinforcement Learning from Human Feedback (RLHF), is then used to model pairwise preferences between solutions, utilizing an Enhanced Borda Count (EBC) method to synthesize these preferences into a global ranking score to find better answers. This approach addresses the challenges of scoring variability and non-independent distributions in mathematical reasoning tasks. The framework has been tested on general and advanced benchmarks, showing superior performance in terms of search efficiency and problem-solving capability compared to existing methods like ToT and rStar, particularly in complex Olympiad-level benchmarks, including GPQA, AIME24 and AMC23.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.03187", "title": "Autonomous Character-Scene Interaction Synthesis from Text Instruction", "authors": [ "Nan Jiang", "Zimo He", "Zi Wang", "Hongjie Li", "Yixin Chen", "Siyuan Huang", "Yixin Zhu" ], "github": "", "project_page": "", "abstract": "Synthesizing human motions in 3D environments, particularly those with complex activities such as locomotion, hand-reaching, and human-object interaction, presents substantial demands for user-defined waypoints and stage transitions. These requirements pose challenges for current models, leading to a notable gap in automating the animation of characters from simple human inputs. This paper addresses this challenge by introducing a comprehensive framework for synthesizing multi-stage scene-aware interaction motions directly from a single text instruction and goal location. Our approach employs an auto-regressive diffusion model to synthesize the next motion segment, along with an autonomous scheduler predicting the transition for each action stage. To ensure that the synthesized motions are seamlessly integrated within the environment, we propose a scene representation that considers the local perception both at the start and the goal location. We further enhance the coherence of the generated motion by integrating frame embeddings with language input. Additionally, to support model training, we present a comprehensive motion-captured dataset comprising 16 hours of motion sequences in 120 indoor scenes covering 40 types of motions, each annotated with precise language descriptions. Experimental results demonstrate the efficacy of our method in generating high-quality, multi-stage motions closely aligned with environmental and textual conditions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.05046", "title": "Named Clinical Entity Recognition Benchmark", "authors": [ "Wadood M Abdul", "Marco AF Pimentel", "Muhammad Umar Salman", "Tathagata Raha", "Cl\u00e9ment Christophe", "Praveen K Kanithi", "Nasir Hayat", "Ronnie Rajan", "Shadab Khan" ], "github": "", "project_page": "", "abstract": "This technical report introduces a Named Clinical Entity Recognition Benchmark for evaluating language models in healthcare, addressing the crucial natural language processing (NLP) task of extracting structured information from clinical narratives to support applications like automated coding, clinical trial cohort identification, and clinical decision support. The leaderboard provides a standardized platform for assessing diverse language models, including encoder and decoder architectures, on their ability to identify and classify clinical entities across multiple medical domains. A curated collection of openly available clinical datasets is utilized, encompassing entities such as diseases, symptoms, medications, procedures, and laboratory measurements. Importantly, these entities are standardized according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model, ensuring consistency and interoperability across different healthcare systems and datasets, and a comprehensive evaluation of model performance. Performance of models is primarily assessed using the F1-score, and it is complemented by various assessment modes to provide comprehensive insights into model performance. The report also includes a brief analysis of models evaluated to date, highlighting observed trends and limitations. By establishing this benchmarking framework, the leaderboard aims to promote transparency, facilitate comparative analyses, and drive innovation in clinical entity recognition tasks, addressing the need for robust evaluation methods in healthcare NLP.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.05057", "title": "SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification", "authors": [ "Benjamin Feuer", "Jiawei Xu", "Niv Cohen", "Patrick Yubeaton", "Govind Mittal", "Chinmay Hegde" ], "github": "https://github.com/jimmyxu123/SELECT", "project_page": "", "abstract": "Data curation is the problem of how to collect and organize samples into a dataset that supports efficient learning. Despite the centrality of the task, little work has been devoted towards a large-scale, systematic comparison of various curation methods. In this work, we take steps towards a formal evaluation of data curation strategies and introduce SELECT, the first large-scale benchmark of curation strategies for image classification. In order to generate baseline methods for the SELECT benchmark, we create a new dataset, ImageNet++, which constitutes the largest superset of ImageNet-1K to date. Our dataset extends ImageNet with 5 new training-data shifts, each approximately the size of ImageNet-1K itself, and each assembled using a distinct curation strategy. We evaluate our data curation baselines in two ways: (i) using each training-data shift to train identical image classification models from scratch (ii) using the data itself to fit a pretrained self-supervised representation. Our findings show interesting trends, particularly pertaining to recent methods for data curation such as synthetic data generation and lookup based on CLIP embeddings. We show that although these strategies are highly competitive for certain tasks, the curation strategy used to assemble the original ImageNet-1K dataset remains the gold standard. We anticipate that our benchmark can illuminate the path for new methods to further reduce the gap. We release our checkpoints, code, documentation, and a link to our dataset at https://github.com/jimmyxu123/SELECT.", "github_manual": "", "github_regex": "https://github.com/jimmyxu123/SELECT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.05229", "title": "GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models", "authors": [ "Iman Mirzadeh", "Keivan Alizadeh", "Hooman Shahrokhi", "Oncel Tuzel", "Samy Bengio", "Mehrdad Farajtabar" ], "github": "https://github.com/apple/ml-gsm-symbolic", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. While the performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear whether their mathematical reasoning capabilities have genuinely advanced, raising questions about the reliability of the reported metrics. To address these concerns, we conduct a large-scale study on several SOTA open and closed models. To overcome the limitations of existing evaluations, we introduce GSM-Symbolic, an improved benchmark created from symbolic templates that allow for the generation of a diverse set of questions. GSM-Symbolic enables more controllable evaluations, providing key insights and more reliable metrics for measuring the reasoning capabilities of models.Our findings reveal that LLMs exhibit noticeable variance when responding to different instantiations of the same question. Specifically, the performance of all models declines when only the numerical values in the question are altered in the GSM-Symbolic benchmark. Furthermore, we investigate the fragility of mathematical reasoning in these models and show that their performance significantly deteriorates as the number of clauses in a question increases. We hypothesize that this decline is because current LLMs cannot perform genuine logical reasoning; they replicate reasoning steps from their training data. Adding a single clause that seems relevant to the question causes significant performance drops (up to 65%) across all state-of-the-art models, even though the clause doesn't contribute to the reasoning chain needed for the final answer. Overall, our work offers a more nuanced understanding of LLMs' capabilities and limitations in mathematical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/ml-gsm-symbolic", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.05080", "title": "ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery", "authors": [ "Ziru Chen", "Shijie Chen", "Yuting Ning", "Qianheng Zhang", "Boshi Wang", "Botao Yu", "Yifei Li", "Zeyi Liao", "Chen Wei", "Zitong Lu", "Vishal Dey", "Mingyi Xue", "Frazier N. Baker", "Benjamin Burns", "Daniel Adu-Ampratwum", "Xuhui Huang", "Xia Ning", "Song Gao", "Yu Su", "Huan Sun" ], "github": "https://github.com/osu-nlp-group/scienceagentbench", "project_page": "", "abstract": "The advancements of language language models (LLMs) have piqued growing interest in developing LLM-based language agents to automate scientific discovery end-to-end, which has sparked both excitement and skepticism about the true capabilities of such agents. In this work, we argue that for an agent to fully automate scientific discovery, it must be able to complete all essential tasks in the workflow. Thus, we call for rigorous assessment of agents on individual tasks in a scientific workflow before making bold claims on end-to-end automation. To this end, we present ScienceAgentBench, a new benchmark for evaluating language agents for data-driven scientific discovery. To ensure the scientific authenticity and real-world relevance of our benchmark, we extract 102 tasks from 44 peer-reviewed publications in four disciplines and engage nine subject matter experts to validate them. We unify the target output for every task to a self-contained Python program file and employ an array of evaluation metrics to examine the generated programs, execution results, and costs. Each task goes through multiple rounds of manual validation by annotators and subject matter experts to ensure its annotation quality and scientific plausibility. We also propose two effective strategies to mitigate data contamination concerns. Using our benchmark, we evaluate five open-weight and proprietary LLMs, each with three frameworks: direct prompting, OpenHands, and self-debug. Given three attempts for each task, the best-performing agent can only solve 32.4% of the tasks independently and 34.3% with expert-provided knowledge. These results underscore the limited capacities of current language agents in generating code for data-driven discovery, let alone end-to-end automation for scientific research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/osu-nlp-group/scienceagentbench", "github_api_added_by": "auto" }, { "date": "2024-10-08", "arxiv_id": "2410.05243", "title": "Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents", "authors": [ "Boyu Gou", "Ruohan Wang", "Boyuan Zheng", "Yanan Xie", "Cheng Chang", "Yiheng Shu", "Huan Sun", "Yu Su" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) are transforming the capabilities of graphical user interface (GUI) agents, facilitating their transition from controlled simulations to complex, real-world applications across various platforms. However, the effectiveness of these agents hinges on the robustness of their grounding capability. Current GUI agents predominantly utilize text-based representations such as HTML or accessibility trees, which, despite their utility, often introduce noise, incompleteness, and increased computational overhead. In this paper, we advocate a human-like embodiment for GUI agents that perceive the environment entirely visually and directly take pixel-level operations on the GUI. The key is visual grounding models that can accurately map diverse referring expressions of GUI elements to their coordinates on the GUI across different platforms. We show that a simple recipe, which includes web-based synthetic data and slight adaptation of the LLaVA architecture, is surprisingly effective for training such visual grounding models. We collect the largest dataset for GUI visual grounding so far, containing 10M GUI elements and their referring expressions over 1.3M screenshots, and use it to train UGround, a strong universal visual grounding model for GUI agents. Empirical results on six benchmarks spanning three categories (grounding, offline agent, and online agent) show that 1) UGround substantially outperforms existing visual grounding models for GUI agents, by up to 20% absolute, and 2) agents with UGround outperform state-of-the-art agents, despite the fact that existing agents use additional text-based input while ours only uses visual perception. These results provide strong support for the feasibility and promises of GUI agents that navigate the digital world as humans do.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.05255", "title": "SePPO: Semi-Policy Preference Optimization for Diffusion Alignment", "authors": [ "Daoan Zhang", "Guangchen Lan", "Dong-Jun Han", "Wenlin Yao", "Xiaoman Pan", "Hongming Zhang", "Mingxiao Li", "Pengcheng Chen", "Yu Dong", "Christopher Brinton", "Jiebo Luo" ], "github": "https://github.com/DwanZhang-AI/SePPO", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) methods are emerging as a way to fine-tune diffusion models (DMs) for visual generation. However, commonly used on-policy strategies are limited by the generalization capability of the reward model, while off-policy approaches require large amounts of difficult-to-obtain paired human-annotated data, particularly in visual generation tasks. To address the limitations of both on- and off-policy RLHF, we propose a preference optimization method that aligns DMs with preferences without relying on reward models or paired human-annotated data. Specifically, we introduce a Semi-Policy Preference Optimization (SePPO) method. SePPO leverages previous checkpoints as reference models while using them to generate on-policy reference samples, which replace \"losing images\" in preference pairs. This approach allows us to optimize using only off-policy \"winning images.\" Furthermore, we design a strategy for reference model selection that expands the exploration in the policy space. Notably, we do not simply treat reference samples as negative examples for learning. Instead, we design an anchor-based criterion to assess whether the reference samples are likely to be winning or losing images, allowing the model to selectively learn from the generated reference samples. This approach mitigates performance degradation caused by the uncertainty in reference sample quality. We validate SePPO across both text-to-image and text-to-video benchmarks. SePPO surpasses all previous approaches on the text-to-image benchmarks and also demonstrates outstanding performance on the text-to-video benchmarks. Code will be released in https://github.com/DwanZhang-AI/SePPO.", "github_manual": "", "github_regex": "https://github.com/DwanZhang-AI/SePPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.03617", "title": "What Matters for Model Merging at Scale?", "authors": [ "Prateek Yadav", "Tu Vu", "Jonathan Lai", "Alexandra Chronopoulou", "Manaal Faruqui", "Mohit Bansal", "Tsendsuren Munkhdalai" ], "github": "", "project_page": "", "abstract": "Model merging aims to combine multiple expert models into a more capable single model, offering benefits such as reduced storage and serving costs, improved generalization, and support for decentralized model development. Despite its promise, previous studies have primarily focused on merging a few small models. This leaves many unanswered questions about the effect of scaling model size and how it interplays with other key factors -- like the base model quality and number of expert models -- , to affect the merged model's performance. This work systematically evaluates the utility of model merging at scale, examining the impact of these different factors. We experiment with merging fully fine-tuned models using 4 popular merging methods -- Averaging, Task~Arithmetic, Dare, and TIES -- across model sizes ranging from 1B-64B parameters and merging up to 8 different expert models. We evaluate the merged models on both held-in tasks, i.e., the expert's training tasks, and zero-shot generalization to unseen held-out tasks. Our experiments provide several new insights about model merging at scale and the interplay between different factors. First, we find that merging is more effective when experts are created from strong base models, i.e., models with good zero-shot performance. Second, larger models facilitate easier merging. Third merging consistently improves generalization capabilities. Notably, when merging 8 large expert models, the merged models often generalize better compared to the multitask trained models. Fourth, we can better merge more expert models when working with larger models. Fifth, different merging methods behave very similarly at larger scales. Overall, our findings shed light on some interesting properties of model merging while also highlighting some limitations. We hope that this study will serve as a reference point on large-scale merging for upcoming research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.03960", "title": "SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation", "authors": [ "Aurick Qiao", "Zhewei Yao", "Samyam Rajbhandari", "Yuxiong He" ], "github": "https://github.com/snowflakedb/arctictraining", "project_page": "", "abstract": "LLM inference for popular enterprise use cases, such as summarization, RAG, and code-generation, typically observes orders of magnitude longer prompt lengths than generation lengths. This characteristic leads to high cost of prefill and increased response latency. In this paper, we present SwiftKV, a novel model transformation and distillation procedure specifically designed to reduce the time and cost of processing prompt tokens while preserving high quality of generated tokens. SwiftKV combines three key mechanisms: i) SingleInputKV, which prefills later layers' KV cache using a much earlier layer's output, allowing prompt tokens to skip much of the model computation, ii) AcrossKV, which merges the KV caches of neighboring layers to reduce the memory footprint and support larger batch size for higher throughput, and iii) a knowledge-preserving distillation procedure that can adapt existing LLMs for SwiftKV with minimal accuracy impact and low compute and data requirement. For Llama-3.1-8B and 70B, SwiftKV reduces the compute requirement of prefill by 50% and the memory requirement of the KV cache by 62.5% while incurring minimum quality degradation across a wide range of tasks. In the end-to-end inference serving using an optimized vLLM implementation, SwiftKV realizes up to 2x higher aggregate throughput and 60% lower time per output token. It can achieve a staggering 560 TFlops/GPU of normalized inference throughput, which translates to 16K tokens/s for Llama-3.1-70B in 16-bit precision on 4x H100 GPUs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/snowflakedb/arctictraining", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-08", "arxiv_id": "2410.03160", "title": "Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach", "authors": [ "Yaofang Liu", "Yumeng Ren", "Xiaodong Cun", "Aitor Artola", "Yang Liu", "Tieyong Zeng", "Raymond H. Chan", "Jean-michel Morel" ], "github": "https://github.com/yaofang-liu/fvdm", "project_page": "", "abstract": "Diffusion models have revolutionized image generation, and their extension to video generation has shown promise. However, current video diffusion models~(VDMs) rely on a scalar timestep variable applied at the clip level, which limits their ability to model complex temporal dependencies needed for various tasks like image-to-video generation. To address this limitation, we propose a frame-aware video diffusion model~(FVDM), which introduces a novel vectorized timestep variable~(VTV). Unlike conventional VDMs, our approach allows each frame to follow an independent noise schedule, enhancing the model's capacity to capture fine-grained temporal dependencies. FVDM's flexibility is demonstrated across multiple tasks, including standard video generation, image-to-video generation, video interpolation, and long video synthesis. Through a diverse set of VTV configurations, we achieve superior quality in generated videos, overcoming challenges such as catastrophic forgetting during fine-tuning and limited generalizability in zero-shot methods.Our empirical evaluations show that FVDM outperforms state-of-the-art methods in video generation quality, while also excelling in extended tasks. By addressing fundamental shortcomings in existing VDMs, FVDM sets a new paradigm in video synthesis, offering a robust framework with significant implications for generative modeling and multimedia applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yaofang-liu/fvdm", "github_api_added_by": "auto" }, { "date": "2024-10-09", "arxiv_id": "2410.05193", "title": "RevisEval: Improving LLM-as-a-Judge via Response-Adapted References", "authors": [ "Qiyuan Zhang", "Yufei Wang", "Tiezheng YU", "Yuxin Jiang", "Chuhan Wu", "Liangyou Li", "Yasheng Wang", "Xin Jiang", "Lifeng Shang", "Ruiming Tang", "Fuyuan Lyu", "Chen Ma" ], "github": "https://github.com/Don-Joey/RevisEval", "project_page": "", "abstract": "With significant efforts in recent studies, LLM-as-a-Judge has become a cost-effective alternative to human evaluation for assessing the text generation quality in a wide range of tasks. However, there still remains a reliability gap between LLM-as-a-Judge and human evaluation. One important reason is the lack of guided oracles in the evaluation process. Motivated by the role of reference pervasively used in classic text evaluation, we introduce RevisEval, a novel text generation evaluation paradigm via the response-adapted references. RevisEval is driven by the key observation that an ideal reference should maintain the necessary relevance to the response to be evaluated. Specifically, RevisEval leverages the text revision capabilities of large language models (LLMs) to adaptively revise the response, then treat the revised text as the reference (response-adapted reference) for the subsequent evaluation. Extensive experiments demonstrate that RevisEval outperforms traditional reference-free and reference-based evaluation paradigms that use LLM-as-a-Judge across NLG tasks and open-ended instruction-following tasks. More importantly, our response-adapted references can further boost the classical text metrics, e.g., BLEU and BERTScore, compared to traditional references and even rival the LLM-as-a-Judge. A detailed analysis is also conducted to confirm RevisEval's effectiveness in bias reduction, the impact of inference cost, and reference relevance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Don-Joey/RevisEval", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-09", "arxiv_id": "2410.04199", "title": "LongGenBench: Long-context Generation Benchmark", "authors": [ "Xiang Liu", "Peijie Dong", "Xuming Hu", "Xiaowen Chu" ], "github": "https://github.com/dominic789654/longgenbench", "project_page": "", "abstract": "Current long-context benchmarks primarily focus on retrieval-based tests, requiring Large Language Models (LLMs) to locate specific information within extensive input contexts, such as the needle-in-a-haystack (NIAH) benchmark. Long-context generation refers to the ability of a language model to generate coherent and contextually accurate text that spans across lengthy passages or documents. While recent studies show strong performance on NIAH and other retrieval-based long-context benchmarks, there is a significant lack of benchmarks for evaluating long-context generation capabilities. To bridge this gap and offer a comprehensive assessment, we introduce a synthetic benchmark, LongGenBench, which allows for flexible configurations of customized generation context lengths. LongGenBench advances beyond traditional benchmarks by redesigning the format of questions and necessitating that LLMs respond with a single, cohesive long-context answer. Upon extensive evaluation using LongGenBench, we observe that: (1) both API accessed and open source models exhibit performance degradation in long-context generation scenarios, ranging from 1.2% to 47.1%; (2) different series of LLMs exhibit varying trends of performance degradation, with the Gemini-1.5-Flash model showing the least degradation among API accessed models, and the Qwen2 series exhibiting the least degradation in LongGenBench among open source models.", "github_manual": "https://github.com/dominic789654/longgenbench", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-09", "arxiv_id": "2410.04717", "title": "Only-IF:Revealing the Decisive Effect of Instruction Diversity on Generalization", "authors": [ "Dylan Zhang", "Justin Wang", "Francois Charton" ], "github": "", "project_page": "", "abstract": "Understanding and accurately following instructions is critical for large language models (LLMs) to be effective across diverse tasks. In this work, we rigorously examine the key factors that enable models to generalize to unseen instructions, providing insights to guide the collection of data for instruction-tuning. Through controlled experiments, inspired by the Turing-complete Markov algorithm, we demonstrate that such generalization only emerges when training data is diversified enough across semantic domains. Our findings also reveal that merely diversifying within limited domains fails to ensure robust generalization. In contrast, cross-domain data diversification, even under constrained data budgets, significantly enhances a model's adaptability. We further extend our analysis to real-world scenarios, including fine-tuning of $textbf{specialist} and textbf{generalist}$ models. In both cases, we demonstrate that 1) better performance can be achieved by increasing the diversity of an established dataset while keeping the data size constant, and 2) when scaling up the data, diversifying the semantics of instructions is more effective than simply increasing the quantity of similar data. Our research provides important insights for dataset collation, particularly when optimizing model performance by expanding training data for both specialist and generalist scenarios. We show that careful consideration of data diversification is key: training specialist models with data extending beyond their core domain leads to significant performance improvements, while generalist models benefit from diverse data mixtures that enhance their overall instruction-following capabilities across a wide range of applications. Our results highlight the critical role of strategic diversification and offer clear guidelines for improving data quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-09", "arxiv_id": "2410.02743", "title": "MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions", "authors": [ "Yekun Chai", "Haoran Sun", "Huang Fang", "Shuohuan Wang", "Yu Sun", "Hua Wu" ], "github": "https://github.com/ernie-research/MA-RLHF", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) has demonstrated effectiveness in aligning large language models (LLMs) with human preferences. However, token-level RLHF suffers from the credit assignment problem over long sequences, where delayed rewards make it challenging for the model to discern which actions contributed to successful outcomes. This hinders learning efficiency and slows convergence. In this paper, we propose MA-RLHF, a simple yet effective RLHF framework that incorporates macro actions -- sequences of tokens or higher-level language constructs -- into the learning process. By operating at this higher level of abstraction, our approach reduces the temporal distance between actions and rewards, facilitating faster and more accurate credit assignment. This results in more stable policy gradient estimates and enhances learning efficiency within each episode, all without increasing computational complexity during training or inference. We validate our approach through extensive experiments across various model sizes and tasks, including text summarization, dialogue generation, question answering, and program synthesis. Our method achieves substantial performance improvements over standard RLHF, with performance gains of up to 30% in text summarization and code generation, 18% in dialogue, and 8% in question answering tasks. Notably, our approach reaches parity with vanilla RLHF 1.7x to 2x faster in terms of training time and continues to outperform it with further training. We will make our code and data publicly available at https://github.com/ernie-research/MA-RLHF .", "github_manual": "", "github_regex": "https://github.com/ernie-research/MA-RLHF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-09", "arxiv_id": "2410.04422", "title": "Hyper-multi-step: The Truth Behind Difficult Long-context Tasks", "authors": [ "Yijiong Yu" ], "github": "https://github.com/yuyijiong/hard_retrieval_for_llm", "project_page": "", "abstract": "Long-context language models (LCLM), characterized by their extensive context window, is becoming increasingly popular. Meanwhile, many long-context benchmarks present challenging tasks that even the most advanced LCLMs struggle to complete. However, the underlying sources of various challenging long-context tasks have seldom been studied. To bridge this gap, we conduct experiments to indicate their difficulty stems primarily from two basic issues: \"multi-matching retrieval,\" which requires the simultaneous retrieval of multiple items, and \"logic-based retrieval,\" which necessitates logical judgment within retrieval criteria. These two problems, while seemingly straightforward, actually exceed the capabilities of LCLMs because they are proven to be hyper-multi-step (demanding numerous steps to solve) in nature. This finding could explain why LLMs struggle with more advanced long-context tasks, providing a more accurate perspective for rethinking solutions for them.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yuyijiong/hard_retrieval_for_llm", "github_api_added_by": "auto" }, { "date": "2024-10-09", "arxiv_id": "2410.01912", "title": "A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image Generation", "authors": [ "Liang Chen", "Sinan Tan", "Zefan Cai", "Weichu Xie", "Haozhe Zhao", "Yichi Zhang", "Junyang Lin", "Jinze Bai", "Tianyu Liu", "Baobao Chang" ], "github": "https://github.com/chenllliang/DnD-Transformer", "project_page": "", "abstract": "This work tackles the information loss bottleneck of vector-quantization (VQ) autoregressive image generation by introducing a novel model architecture called the 2-Dimensional Autoregression (DnD) Transformer. The DnD-Transformer predicts more codes for an image by introducing a new autoregression direction, model depth, along with the sequence length direction. Compared to traditional 1D autoregression and previous work utilizing similar 2D image decomposition such as RQ-Transformer, the DnD-Transformer is an end-to-end model that can generate higher quality images with the same backbone model size and sequence length, opening a new optimization perspective for autoregressive image generation. Furthermore, our experiments reveal that the DnD-Transformer's potential extends beyond generating natural images. It can even generate images with rich text and graphical elements in a self-supervised manner, demonstrating an understanding of these combined modalities. This has not been previously demonstrated for popular vision generative models such as diffusion models, showing a spark of vision-language intelligence when trained solely on images. Code, datasets and models are open at https://github.com/chenllliang/DnD-Transformer.", "github_manual": "", "github_regex": "https://github.com/chenllliang/DnD-Transformer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-09", "arxiv_id": "2410.02705", "title": "ControlAR: Controllable Image Generation with Autoregressive Models", "authors": [ "Zongming Li", "Tianheng Cheng", "Shoufa Chen", "Peize Sun", "Haocheng Shen", "Longjin Ran", "Xiaoxin Chen", "Wenyu Liu", "Xinggang Wang" ], "github": "https://github.com/hustvl/ControlAR", "project_page": "", "abstract": "Autoregressive (AR) models have reformulated image generation as next-token prediction, demonstrating remarkable potential and emerging as strong competitors to diffusion models. However, control-to-image generation, akin to ControlNet, remains largely unexplored within AR models. Although a natural approach, inspired by advancements in Large Language Models, is to tokenize control images into tokens and prefill them into the autoregressive model before decoding image tokens, it still falls short in generation quality compared to ControlNet and suffers from inefficiency. To this end, we introduce ControlAR, an efficient and effective framework for integrating spatial controls into autoregressive image generation models. Firstly, we explore control encoding for AR models and propose a lightweight control encoder to transform spatial inputs (e.g., canny edges or depth maps) into control tokens. Then ControlAR exploits the conditional decoding method to generate the next image token conditioned on the per-token fusion between control and image tokens, similar to positional encodings. Compared to prefilling tokens, using conditional decoding significantly strengthens the control capability of AR models but also maintains the model's efficiency. Furthermore, the proposed ControlAR surprisingly empowers AR models with arbitrary-resolution image generation via conditional decoding and specific controls. Extensive experiments can demonstrate the controllability of the proposed ControlAR for the autoregressive control-to-image generation across diverse inputs, including edges, depths, and segmentation masks. Furthermore, both quantitative and qualitative results indicate that ControlAR surpasses previous state-of-the-art controllable diffusion models, e.g., ControlNet++. Code, models, and demo will soon be available at https://github.com/hustvl/ControlAR.", "github_manual": "", "github_regex": "https://github.com/hustvl/ControlAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-09", "arxiv_id": "2410.03290", "title": "Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models", "authors": [ "Haibo Wang", "Zhiyang Xu", "Yu Cheng", "Shizhe Diao", "Yufan Zhou", "Yixin Cao", "Qifan Wang", "Weifeng Ge", "Lifu Huang" ], "github": "https://github.com/whb139426/grounded-video-llm", "project_page": "", "abstract": "Video Large Language Models (Video-LLMs) have demonstrated remarkable capabilities in coarse-grained video understanding, however, they struggle with fine-grained temporal grounding. In this paper, we introduce Grounded-VideoLLM, a novel Video-LLM adept at perceiving and reasoning over specific video moments in a fine-grained manner. We identify that current Video-LLMs have limitations for fine-grained video understanding since they lack effective temporal modeling and timestamp representation. In light of this, we sharpen our model by incorporating (1) an additional temporal stream to encode the relationships between frames and (2) discrete temporal tokens enriched with specific time knowledge to represent timestamps. To optimize the training of Grounded-VideoLLM, we employ a multi-stage training scheme, beginning with simple video-captioning tasks and progressively introducing video temporal grounding tasks of increasing complexity. To further enhance Grounded-VideoLLM's temporal reasoning capability, we also curate a grounded VideoQA dataset by an automatic annotation pipeline. Extensive experiments demonstrate that Grounded-VideoLLM not only excels in fine-grained grounding tasks such as temporal sentence grounding, dense video captioning, and grounded VideoQA, but also shows great potential as a versatile video assistant for general video understanding.", "github_manual": "https://github.com/whb139426/grounded-video-llm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-09", "arxiv_id": "2410.03399", "title": "EBES: Easy Benchmarking for Event Sequences", "authors": [ "Dmitry Osin", "Igor Udovichenko", "Viktor Moskvoretskii", "Egor Shvetsov", "Evgeny Burnaev" ], "github": "https://github.com/on-point-rnd/ebes", "project_page": "", "abstract": "Event sequences, characterized by irregular sampling intervals and a mix of categorical and numerical features, are common data structures in various real-world domains such as healthcare, finance, and user interaction logs. Despite advances in temporal data modeling techniques, there is no standardized benchmarks for evaluating their performance on event sequences. This complicates result comparison across different papers due to varying evaluation protocols, potentially misleading progress in this field. We introduce EBES, a comprehensive benchmarking tool with standardized evaluation scenarios and protocols, focusing on regression and classification problems with sequence-level targets. Our library simplifies benchmarking, dataset addition, and method integration through a unified interface. It includes a novel synthetic dataset and provides preprocessed real-world datasets, including the largest publicly available banking dataset. Our results provide an in-depth analysis of datasets, identifying some as unsuitable for model comparison. We investigate the importance of modeling temporal and sequential components, as well as the robustness and scaling properties of the models. These findings highlight potential directions for future research. Our benchmark aim is to facilitate reproducible research, expediting progress and increasing real-world impacts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/on-point-rnd/ebes", "github_api_added_by": "auto" }, { "date": "2024-10-09", "arxiv_id": "2410.05076", "title": "TidalDecode: Fast and Accurate LLM Decoding with Position Persistent Sparse Attention", "authors": [ "Lijie Yang", "Zhihao Zhang", "Zhuofu Chen", "Zikun Li", "Zhihao Jia" ], "github": "https://github.com/DerrickYLJ/TidalDecode", "project_page": "", "abstract": "Large language models (LLMs) have driven significant advancements across diverse NLP tasks, with long-context models gaining prominence for handling extended inputs. However, the expanding key-value (KV) cache size required by Transformer architectures intensifies the memory constraints, particularly during the decoding phase, creating a significant bottleneck. Existing sparse attention mechanisms designed to address this bottleneck have two limitations: (1) they often fail to reliably identify the most relevant tokens for attention, and (2) they overlook the spatial coherence of token selection across consecutive Transformer layers, which can lead to performance degradation and substantial overhead in token selection. This paper introduces TidalDecode, a simple yet effective algorithm and system for fast and accurate LLM decoding through position persistent sparse attention. TidalDecode leverages the spatial coherence of tokens selected by existing sparse attention methods and introduces a few token selection layers that perform full attention to identify the tokens with the highest attention scores, while all other layers perform sparse attention with the pre-selected tokens. This design enables TidalDecode to substantially reduce the overhead of token selection for sparse attention without sacrificing the quality of the generated results. Evaluation on a diverse set of LLMs and tasks shows that TidalDecode closely matches the generative performance of full attention methods while reducing the LLM decoding latency by up to 2.1x.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DerrickYLJ/TidalDecode", "github_api_added_by": "auto" }, { "date": "2024-10-09", "arxiv_id": "2410.03864", "title": "DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search", "authors": [ "Murong Yue", "Wenlin Yao", "Haitao Mi", "Dian Yu", "Ziyu Yao", "Dong Yu" ], "github": "https://github.com/MurongYue/DOTS", "project_page": "", "abstract": "Enhancing the capability of large language models (LLMs) in reasoning has gained significant attention in recent years. Previous studies have demonstrated the effectiveness of various prompting strategies in aiding LLMs in reasoning (called \"reasoning actions\"), such as step-by-step thinking, reflecting before answering, solving with programs, and their combinations. However, these approaches often applied static, predefined reasoning actions uniformly to all questions, without considering the specific characteristics of each question or the capability of the task-solving LLM. In this paper, we propose DOTS, an approach enabling LLMs to reason dynamically via optimal reasoning trajectory search, tailored to the specific characteristics of each question and the inherent capability of the task-solving LLM. Our approach involves three key steps: i) defining atomic reasoning action modules that can be composed into various reasoning action trajectories; ii) searching for the optimal action trajectory for each training question through iterative exploration and evaluation for the specific task-solving LLM; and iii) using the collected optimal trajectories to train an LLM to plan for the reasoning trajectories of unseen questions. In particular, we propose two learning paradigms, i.e., fine-tuning an external LLM as a planner to guide the task-solving LLM, or directly fine-tuning the task-solving LLM with an internalized capability for reasoning actions planning. Our experiments across eight reasoning tasks show that our method consistently outperforms static reasoning techniques and the vanilla instruction tuning approach. Further analysis reveals that our method enables LLMs to adjust their computation based on problem complexity, allocating deeper thinking and reasoning to harder problems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MurongYue/DOTS", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-09", "arxiv_id": "2410.04081", "title": "\u03b5-VAE: Denoising as Visual Decoding", "authors": [ "Long Zhao", "Sanghyun Woo", "Ziyu Wan", "Yandong Li", "Han Zhang", "Boqing Gong", "Hartwig Adam", "Xuhui Jia", "Ting Liu" ], "github": "", "project_page": "", "abstract": "In generative modeling, tokenization simplifies complex data into compact, structured representations, creating a more efficient, learnable space. For high-dimensional visual data, it reduces redundancy and emphasizes key features for high-quality generation. Current visual tokenization methods rely on a traditional autoencoder framework, where the encoder compresses data into latent representations, and the decoder reconstructs the original input. In this work, we offer a new perspective by proposing denoising as decoding, shifting from single-step reconstruction to iterative refinement. Specifically, we replace the decoder with a diffusion process that iteratively refines noise to recover the original image, guided by the latents provided by the encoder. We evaluate our approach by assessing both reconstruction (rFID) and generation quality (FID), comparing it to state-of-the-art autoencoding approach. We hope this work offers new insights into integrating iterative generation and autoencoding for improved compression and generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-09", "arxiv_id": "2410.04343", "title": "Inference Scaling for Long-Context Retrieval Augmented Generation", "authors": [ "Zhenrui Yue", "Honglei Zhuang", "Aijun Bai", "Kai Hui", "Rolf Jagerman", "Hansi Zeng", "Zhen Qin", "Dong Wang", "Xuanhui Wang", "Michael Bendersky" ], "github": "", "project_page": "", "abstract": "The scaling of inference computation has unlocked the potential of long-context large language models (LLMs) across diverse settings. For knowledge-intensive tasks, the increased compute is often allocated to incorporate more external knowledge. However, without effectively utilizing such knowledge, solely expanding context does not always enhance performance. In this work, we investigate inference scaling for retrieval augmented generation (RAG), exploring strategies beyond simply increasing the quantity of knowledge. We focus on two inference scaling strategies: in-context learning and iterative prompting. These strategies provide additional flexibility to scale test-time computation (e.g., by increasing retrieved documents or generation steps), thereby enhancing LLMs' ability to effectively acquire and utilize contextual information. We address two key questions: (1) How does RAG performance benefit from the scaling of inference computation when optimally configured? (2) Can we predict the optimal test-time compute allocation for a given budget by modeling the relationship between RAG performance and inference parameters? Our observations reveal that increasing inference computation leads to nearly linear gains in RAG performance when optimally allocated, a relationship we describe as the inference scaling laws for RAG. Building on this, we further develop the computation allocation model to estimate RAG performance across different inference configurations. The model predicts optimal inference parameters under various computation constraints, which align closely with the experimental results. By applying these optimal configurations, we demonstrate that scaling inference compute on long-context LLMs achieves up to 58.9% gains on benchmark datasets compared to standard RAG.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05363", "title": "Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation", "authors": [ "Fanqing Meng", "Jiaqi Liao", "Xinyu Tan", "Wenqi Shao", "Quanfeng Lu", "Kaipeng Zhang", "Yu Cheng", "Dianqi Li", "Yu Qiao", "Ping Luo" ], "github": "https://github.com/OpenGVLab/PhyGenBench", "project_page": "", "abstract": "Text-to-video (T2V) models like Sora have made significant strides in visualizing complex prompts, which is increasingly viewed as a promising path towards constructing the universal world simulator. Cognitive psychologists believe that the foundation for achieving this goal is the ability to understand intuitive physics. However, the capacity of these models to accurately represent intuitive physics remains largely unexplored. To bridge this gap, we introduce PhyGenBench, a comprehensive Physics Generation Benchmark designed to evaluate physical commonsense correctness in T2V generation. PhyGenBench comprises 160 carefully crafted prompts across 27 distinct physical laws, spanning four fundamental domains, which could comprehensively assesses models' understanding of physical commonsense. Alongside PhyGenBench, we propose a novel evaluation framework called PhyGenEval. This framework employs a hierarchical evaluation structure utilizing appropriate advanced vision-language models and large language models to assess physical commonsense. Through PhyGenBench and PhyGenEval, we can conduct large-scale automated assessments of T2V models' understanding of physical commonsense, which align closely with human feedback. Our evaluation results and in-depth analysis demonstrate that current models struggle to generate videos that comply with physical commonsense. Moreover, simply scaling up models or employing prompt engineering techniques is insufficient to fully address the challenges presented by PhyGenBench (e.g., dynamic scenarios). We hope this study will inspire the community to prioritize the learning of physical commonsense in these models beyond entertainment applications. We will release the data and codes at https://github.com/OpenGVLab/PhyGenBench", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/PhyGenBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.07064", "title": "Data Selection via Optimal Control for Language Models", "authors": [ "Yuxian Gu", "Li Dong", "Hongning Wang", "Yaru Hao", "Qingxiu Dong", "Furu Wei", "Minlie Huang" ], "github": "https://github.com/microsoft/LMOps/tree/main/data_selection", "project_page": "", "abstract": "This work investigates the selection of high-quality pre-training data from massive corpora to enhance LMs' capabilities for downstream usage. We formulate data selection as a generalized Optimal Control problem, which can be solved theoretically by Pontryagin's Maximum Principle (PMP), yielding a set of necessary conditions that characterize the relationship between optimal data selection and LM training dynamics. Based on these theoretical results, we introduce PMP-based Data Selection (PDS), a framework that approximates optimal data selection by solving the PMP conditions. In our experiments, we adopt PDS to select data from CommmonCrawl and show that the PDS-selected corpus accelerates the learning of LMs and constantly boosts their performance on a wide range of downstream tasks across various model sizes. Moreover, the benefits of PDS extend to ~400B models trained on ~10T tokens, as evidenced by the extrapolation of the test loss curves according to the Scaling Laws. PDS also improves data utilization when the pre-training data is limited, by reducing the data demand by 1.8 times, which mitigates the quick exhaustion of available web-crawled corpora. Our code, data, and model checkpoints can be found in https://github.com/microsoft/LMOps/tree/main/data_selection.", "github_manual": "", "github_regex": "https://github.com/microsoft/LMOps/tree/main/data_selection", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06166", "title": "Temporal Reasoning Transfer from Text to Video", "authors": [ "Lei Li", "Yuanxin Liu", "Linli Yao", "Peiyuan Zhang", "Chenxin An", "Lean Wang", "Xu Sun", "Lingpeng Kong", "Qi Liu" ], "github": "", "project_page": "", "abstract": "Video Large Language Models (Video LLMs) have shown promising capabilities in video comprehension, yet they struggle with tracking temporal changes and reasoning about temporal relationships. While previous research attributed this limitation to the ineffective temporal encoding of visual inputs, our diagnostic study reveals that video representations contain sufficient information for even small probing classifiers to achieve perfect accuracy. Surprisingly, we find that the key bottleneck in Video LLMs' temporal reasoning capability stems from the underlying LLM's inherent difficulty with temporal concepts, as evidenced by poor performance on textual temporal question-answering tasks. Building on this discovery, we introduce the Textual Temporal reasoning Transfer (T3). T3 synthesizes diverse temporal reasoning tasks in pure text format from existing image-text datasets, addressing the scarcity of video samples with complex temporal scenarios. Remarkably, without using any video data, T3 enhances LongVA-7B's temporal understanding, yielding a 5.3 absolute accuracy improvement on the challenging TempCompass benchmark, which enables our model to outperform ShareGPT4Video-8B trained on 28,000 video samples. Additionally, the enhanced LongVA-7B model achieves competitive performance on comprehensive video benchmarks. For example, it achieves a 49.7 accuracy on the Temporal Reasoning task of Video-MME, surpassing powerful large-scale models such as InternVL-Chat-V1.5-20B and VILA1.5-40B. Further analysis reveals a strong correlation between textual and video temporal task performance, validating the efficacy of transferring temporal reasoning abilities from text to video domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06373", "title": "Unveiling the Backbone-Optimizer Coupling Bias in Visual Representation Learning", "authors": [ "Siyuan Li", "Juanxi Tian", "Zedong Wang", "Luyuan Zhang", "Zicheng Liu", "Weiyang Jin", "Yang Liu", "Baigui Sun", "Stan Z. Li" ], "github": "", "project_page": "", "abstract": "This paper delves into the interplay between vision backbones and optimizers, unvealing an inter-dependent phenomenon termed \\textbf{backbone-optimizer coupling bias} (BOCB). We observe that canonical CNNs, such as VGG and ResNet, exhibit a marked co-dependency with SGD families, while recent architectures like ViTs and ConvNeXt share a tight coupling with the adaptive learning rate ones. We further show that BOCB can be introduced by both optimizers and certain backbone designs and may significantly impact the pre-training and downstream fine-tuning of vision models. Through in-depth empirical analysis, we summarize takeaways on recommended optimizers and insights into robust vision backbone architectures. We hope this work can inspire the community to question long-held assumptions on backbones and optimizers, stimulate further explorations, and thereby contribute to more robust vision systems. The source code and models are publicly available at https://bocb-ai.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06949", "title": "Seeker: Enhancing Exception Handling in Code with LLM-based Multi-Agent Approach", "authors": [ "Xuanming Zhang", "Yuxuan Chen", "Yuan Yuan", "Minlie Huang" ], "github": "https://github.com/XMZhangAI/Seeker", "project_page": "", "abstract": "In real world software development, improper or missing exception handling can severely impact the robustness and reliability of code. Exception handling mechanisms require developers to detect, capture, and manage exceptions according to high standards, but many developers struggle with these tasks, leading to fragile code. This problem is particularly evident in open source projects and impacts the overall quality of the software ecosystem. To address this challenge, we explore the use of large language models (LLMs) to improve exception handling in code. Through extensive analysis, we identify three key issues: Insensitive Detection of Fragile Code, Inaccurate Capture of Exception Types, and Distorted Handling Solutions. These problems are widespread across real world repositories, suggesting that robust exception handling practices are often overlooked or mishandled. In response, we propose Seeker, a multi agent framework inspired by expert developer strategies for exception handling. Seeker uses agents: Scanner, Detector, Predator, Ranker, and Handler to assist LLMs in detecting, capturing, and resolving exceptions more effectively. Our work is the first systematic study on leveraging LLMs to enhance exception handling practices, providing valuable insights for future improvements in code reliability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/XMZhangAI/Seeker", "github_api_added_by": "auto" }, { "date": "2024-10-10", "arxiv_id": "2410.06961", "title": "Self-Boosting Large Language Models with Synthetic Preference Data", "authors": [ "Qingxiu Dong", "Li Dong", "Xingxing Zhang", "Zhifang Sui", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Through alignment with human preferences, Large Language Models (LLMs) have advanced significantly in generating honest, harmless, and helpful responses. However, collecting high-quality preference data is a resource-intensive and creativity-demanding process, especially for the continual improvement of LLMs. We introduce SynPO, a self-boosting paradigm that leverages synthetic preference data for model alignment. SynPO employs an iterative mechanism wherein a self-prompt generator creates diverse prompts, and a response improver refines model responses progressively. This approach trains LLMs to autonomously learn the generative rewards for their own outputs and eliminates the need for large-scale annotation of prompts and human preferences. After four SynPO iterations, Llama3-8B and Mistral-7B show significant enhancements in instruction-following abilities, achieving over 22.1% win rate improvements on AlpacaEval 2.0 and ArenaHard. Simultaneously, SynPO improves the general performance of LLMs on various tasks, validated by a 3.2 to 5.0 average score increase on the well-recognized Open LLM leaderboard.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05643", "title": "TRACE: Temporal Grounding Video LLM via Causal Event Modeling", "authors": [ "Yongxin Guo", "Jingyu Liu", "Mingda Li", "Xiaoying Tang", "Qingbin Liu", "Xi Chen" ], "github": "https://github.com/gyxxyg/TRACE", "project_page": "", "abstract": "Video Temporal Grounding (VTG) is a crucial capability for video understanding models and plays a vital role in downstream tasks such as video browsing and editing. To effectively handle various tasks simultaneously and enable zero-shot prediction, there is a growing trend in employing video LLMs for VTG tasks. However, current video LLM-based methods rely exclusively on natural language generation, lacking the ability to model the clear structure inherent in videos, which restricts their effectiveness in tackling VTG tasks. To address this issue, this paper first formally introduces causal event modeling framework, which represents videos as sequences of events, and predict the current event using previous events, video inputs, and textural instructions. Each event consists of three components: timestamps, salient scores, and textual captions. We then propose a novel task-interleaved video LLM called TRACE to effectively implement the causal event modeling framework in practice. The TRACE processes visual frames, timestamps, salient scores, and text as distinct tasks, employing various encoders and decoding heads for each. Task tokens are arranged in an interleaved sequence according to the causal event modeling framework's formulation. Extensive experiments on various VTG tasks and datasets demonstrate the superior performance of TRACE compared to state-of-the-art video LLMs. Our model and code are available at https://github.com/gyxxyg/TRACE.", "github_manual": "", "github_regex": "https://github.com/gyxxyg/TRACE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.07171", "title": "IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation", "authors": [ "Xinchen Zhang", "Ling Yang", "Guohao Li", "Yaqi Cai", "Jiake Xie", "Yong Tang", "Yujiu Yang", "Mengdi Wang", "Bin Cui" ], "github": "https://github.com/YangLing0818/IterComp", "project_page": "", "abstract": "Advanced diffusion models like RPG, Stable Diffusion 3 and FLUX have made notable strides in compositional text-to-image generation. However, these methods typically exhibit distinct strengths for compositional generation, with some excelling in handling attribute binding and others in spatial relationships. This disparity highlights the need for an approach that can leverage the complementary strengths of various models to comprehensively improve the composition capability. To this end, we introduce IterComp, a novel framework that aggregates composition-aware model preferences from multiple models and employs an iterative feedback learning approach to enhance compositional generation. Specifically, we curate a gallery of six powerful open-source diffusion models and evaluate their three key compositional metrics: attribute binding, spatial relationships, and non-spatial relationships. Based on these metrics, we develop a composition-aware model preference dataset comprising numerous image-rank pairs to train composition-aware reward models. Then, we propose an iterative feedback learning method to enhance compositionality in a closed-loop manner, enabling the progressive self-refinement of both the base diffusion model and reward models over multiple iterations. Theoretical proof demonstrates the effectiveness and extensive experiments show our significant superiority over previous SOTA methods (e.g., Omost and FLUX), particularly in multi-category object composition and complex semantic alignment. IterComp opens new research avenues in reward feedback learning for diffusion models and compositional generation. Code: https://github.com/YangLing0818/IterComp", "github_manual": "", "github_regex": "https://github.com/YangLing0818/IterComp", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06244", "title": "Story-Adapter: A Training-free Iterative Framework for Long Story Visualization", "authors": [ "Jiawei Mao", "Xiaoke Huang", "Yunfei Xie", "Yuanqi Chang", "Mude Hui", "Bingjie Xu", "Yuyin Zhou" ], "github": "https://github.com/jwmao1/story-adapter", "project_page": "", "abstract": "Story visualization, the task of generating coherent images based on a narrative, has seen significant advancements with the emergence of text-to-image models, particularly diffusion models. However, maintaining semantic consistency, generating high-quality fine-grained interactions, and ensuring computational feasibility remain challenging, especially in long story visualization (i.e., up to 100 frames). In this work, we propose a training-free and computationally efficient framework, termed Story-Adapter, to enhance the generative capability of long stories. Specifically, we propose an iterative paradigm to refine each generated image, leveraging both the text prompt and all generated images from the previous iteration. Central to our framework is a training-free global reference cross-attention module, which aggregates all generated images from the previous iteration to preserve semantic consistency across the entire story, while minimizing computational costs with global embeddings. This iterative process progressively optimizes image generation by repeatedly incorporating text constraints, resulting in more precise and fine-grained interactions. Extensive experiments validate the superiority of Story-Adapter in improving both semantic consistency and generative capability for fine-grained interactions, particularly in long story scenarios. The project page and associated code can be accessed via https://jwmao1.github.io/storyadapter .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jwmao1/story-adapter", "github_api_added_by": "auto" }, { "date": "2024-10-10", "arxiv_id": "2410.07167", "title": "Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate", "authors": [ "Qidong Huang", "Xiaoyi Dong", "Pan Zhang", "Yuhang Zang", "Yuhang Cao", "Jiaqi Wang", "Dahua Lin", "Weiming Zhang", "Nenghai Yu" ], "github": "https://github.com/shikiw/Modality-Integration-Rate", "project_page": "", "abstract": "We present the Modality Integration Rate (MIR), an effective, robust, and generalized metric to indicate the multi-modal pre-training quality of Large Vision Language Models (LVLMs). Large-scale pre-training plays a critical role in building capable LVLMs, while evaluating its training quality without the costly supervised fine-tuning stage is under-explored. Loss, perplexity, and in-context evaluation results are commonly used pre-training metrics for Large Language Models (LLMs), while we observed that these metrics are less indicative when aligning a well-trained LLM with a new modality. Due to the lack of proper metrics, the research of LVLMs in the critical pre-training stage is hindered greatly, including the training data choice, efficient module design, etc. In this paper, we propose evaluating the pre-training quality from the inter-modal distribution distance perspective and present MIR, the Modality Integration Rate, which is 1) Effective to represent the pre-training quality and show a positive relation with the benchmark performance after supervised fine-tuning. 2) Robust toward different training/evaluation data. 3) Generalize across training configurations and architecture choices. We conduct a series of pre-training experiments to explore the effectiveness of MIR and observe satisfactory results that MIR is indicative about training data selection, training strategy schedule, and model architecture design to get better pre-training results. We hope MIR could be a helpful metric for building capable LVLMs and inspire the following research about modality alignment in different areas. Our code is at: https://github.com/shikiw/Modality-Integration-Rate.", "github_manual": "", "github_regex": "https://github.com/shikiw/Modality-Integration-Rate", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.07073", "title": "Pixtral 12B", "authors": [ "Pravesh Agrawal", "Szymon Antoniak", "Emma Bou Hanna", "Devendra Chaplot", "Jessica Chudnovsky", "Saurabh Garg", "Theophile Gervet", "Soham Ghosh", "Am\u00e9lie H\u00e9liou", "Paul Jacob", "Albert Q. Jiang", "Timoth\u00e9e Lacroix", "Guillaume Lample", "Diego Las Casas", "Thibaut Lavril", "Teven Le Scao", "Andy Lo", "William Marshall", "Louis Martin", "Arthur Mensch", "Pavankumar Muddireddy", "Valera Nemychnikova", "Marie Pellat", "Patrick Von Platen", "Nikhil Raghuraman", "Baptiste Rozi\u00e8re", "Alexandre Sablayrolles", "Lucile Saulnier", "Romain Sauvestre", "Wendy Shang", "Roman Soletskyi", "Lawrence Stewart", "Pierre Stock", "Joachim Studnia", "Sandeep Subramanian", "Sagar Vaze", "Thomas Wang" ], "github": "", "project_page": "", "abstract": "We introduce Pixtral-12B, a 12--billion-parameter multimodal language model. Pixtral-12B is trained to understand both natural images and documents, achieving leading performance on various multimodal benchmarks, surpassing a number of larger models. Unlike many open-source models, Pixtral is also a cutting-edge text model for its size, and does not compromise on natural language performance to excel in multimodal tasks. Pixtral uses a new vision encoder trained from scratch, which allows it to ingest images at their natural resolution and aspect ratio. This gives users flexibility on the number of tokens used to process an image. Pixtral is also able to process any number of images in its long context window of 128K tokens. Pixtral 12B substanially outperforms other open models of similar sizes (Llama-3.2 11B \\& Qwen-2-VL 7B). It also outperforms much larger open models like Llama-3.2 90B while being 7x smaller. We further contribute an open-source benchmark, MM-MT-Bench, for evaluating vision-language models in practical scenarios, and provide detailed analysis and code for standardized evaluation protocols for multimodal LLMs. Pixtral-12B is released under Apache 2.0 license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05993", "title": "Aria: An Open Multimodal Native Mixture-of-Experts Model", "authors": [ "Dongxu Li", "Yudong Liu", "Haoning Wu", "Yue Wang", "Zhiqi Shen", "Bowen Qu", "Xinyao Niu", "Guoyin Wang", "Bei Chen", "Junnan Li" ], "github": "https://github.com/rhymes-ai/aria", "project_page": "", "abstract": "Information comes in diverse modalities. Multimodal native AI models are essential to integrate real-world information and deliver comprehensive understanding. While proprietary multimodal native models exist, their lack of openness imposes obstacles for adoptions, let alone adaptations. To fill this gap, we introduce Aria, an open multimodal native model with best-in-class performance across a wide range of multimodal, language, and coding tasks. Aria is a mixture-of-expert model with 3.9B and 3.5B activated parameters per visual token and text token, respectively. It outperforms Pixtral-12B and Llama3.2-11B, and is competitive against the best proprietary models on various multimodal tasks. We pre-train Aria from scratch following a 4-stage pipeline, which progressively equips the model with strong capabilities in language understanding, multimodal understanding, long context window, and instruction following. We open-source the model weights along with a codebase that facilitates easy adoptions and adaptations of Aria in real-world applications.", "github_manual": "https://github.com/rhymes-ai/aria", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05664", "title": "Holistic Unlearning Benchmark: A Multi-Faceted Evaluation for Text-to-Image Diffusion Model Unlearning", "authors": [ "Saemi Moon", "Minjong Lee", "Sangdon Park", "Dongwoo Kim" ], "github": "", "project_page": "", "abstract": "As text-to-image diffusion models become advanced enough for commercial applications, there is also increasing concern about their potential for malicious and harmful use. Model unlearning has been proposed to mitigate the concerns by removing undesired and potentially harmful information from the pre-trained model. So far, the success of unlearning is mainly measured by whether the unlearned model can generate a target concept while maintaining image quality. However, unlearning is typically tested under limited scenarios, and the side effects of unlearning have barely been studied in the current literature. In this work, we thoroughly analyze unlearning under various scenarios with five key aspects. Our investigation reveals that every method has side effects or limitations, especially in more complex and realistic situations. By releasing our comprehensive evaluation framework with the source codes and artifacts, we hope to inspire further research in this area, leading to more reliable and effective unlearning methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.04223", "title": "Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning", "authors": [ "Gang Liu", "Michael Sun", "Wojciech Matusik", "Meng Jiang", "Jie Chen" ], "github": "", "project_page": "", "abstract": "While large language models (LLMs) have integrated images, adapting them to graphs remains challenging, limiting their applications in materials and drug design. This difficulty stems from the need for coherent autoregressive generation across texts and graphs. To address this, we introduce Llamole, the first multimodal LLM capable of interleaved text and graph generation, enabling molecular inverse design with retrosynthetic planning. Llamole integrates a base LLM with the Graph Diffusion Transformer and Graph Neural Networks for multi-conditional molecular generation and reaction inference within texts, while the LLM, with enhanced molecular understanding, flexibly controls activation among the different graph modules. Additionally, Llamole integrates A* search with LLM-based cost functions for efficient retrosynthetic planning. We create benchmarking datasets and conduct extensive experiments to evaluate Llamole against in-context learning and supervised fine-tuning. Llamole significantly outperforms 14 adapted LLMs across 12 metrics for controllable molecular design and retrosynthetic planning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06241", "title": "BroadWay: Boost Your Text-to-Video Generation Model in a Training-free Way", "authors": [ "Jiazi Bu", "Pengyang Ling", "Pan Zhang", "Tong Wu", "Xiaoyi Dong", "Yuhang Zang", "Yuhang Cao", "Dahua Lin", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "The text-to-video (T2V) generation models, offering convenient visual creation, have recently garnered increasing attention. Despite their substantial potential, the generated videos may present artifacts, including structural implausibility, temporal inconsistency, and a lack of motion, often resulting in near-static video. In this work, we have identified a correlation between the disparity of temporal attention maps across different blocks and the occurrence of temporal inconsistencies. Additionally, we have observed that the energy contained within the temporal attention maps is directly related to the magnitude of motion amplitude in the generated videos. Based on these observations, we present BroadWay, a training-free method to improve the quality of text-to-video generation without introducing additional parameters, augmenting memory or sampling time. Specifically, BroadWay is composed of two principal components: 1) Temporal Self-Guidance improves the structural plausibility and temporal consistency of generated videos by reducing the disparity between the temporal attention maps across various decoder blocks. 2) Fourier-based Motion Enhancement enhances the magnitude and richness of motion by amplifying the energy of the map. Extensive experiments demonstrate that BroadWay significantly improves the quality of text-to-video generation with negligible additional cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06462", "title": "Hallucinating AI Hijacking Attack: Large Language Models and Malicious Code Recommenders", "authors": [ "David Noever", "Forrest McKee" ], "github": "", "project_page": "", "abstract": "The research builds and evaluates the adversarial potential to introduce copied code or hallucinated AI recommendations for malicious code in popular code repositories. While foundational large language models (LLMs) from OpenAI, Google, and Anthropic guard against both harmful behaviors and toxic strings, previous work on math solutions that embed harmful prompts demonstrate that the guardrails may differ between expert contexts. These loopholes would appear in mixture of expert's models when the context of the question changes and may offer fewer malicious training examples to filter toxic comments or recommended offensive actions. The present work demonstrates that foundational models may refuse to propose destructive actions correctly when prompted overtly but may unfortunately drop their guard when presented with a sudden change of context, like solving a computer programming challenge. We show empirical examples with trojan-hosting repositories like GitHub, NPM, NuGet, and popular content delivery networks (CDN) like jsDelivr which amplify the attack surface. In the LLM's directives to be helpful, example recommendations propose application programming interface (API) endpoints which a determined domain-squatter could acquire and setup attack mobile infrastructure that triggers from the naively copied code. We compare this attack to previous work on context-shifting and contrast the attack surface as a novel version of \"living off the land\" attacks in the malware literature. In the latter case, foundational language models can hijack otherwise innocent user prompts to recommend actions that violate their owners' safety policies when posed directly without the accompanying coding support request.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.07177", "title": "MM-Ego: Towards Building Egocentric Multimodal LLMs", "authors": [ "Hanrong Ye", "Haotian Zhang", "Erik Daxberger", "Lin Chen", "Zongyu Lin", "Yanghao Li", "Bowen Zhang", "Haoxuan You", "Dan Xu", "Zhe Gan", "Jiasen Lu", "Yinfei Yang" ], "github": "", "project_page": "", "abstract": "This research aims to comprehensively explore building a multimodal foundation model for egocentric video understanding. To achieve this goal, we work on three fronts. First, as there is a lack of QA data for egocentric video understanding, we develop a data engine that efficiently generates 7M high-quality QA samples for egocentric videos ranging from 30 seconds to one hour long, based on human-annotated data. This is currently the largest egocentric QA dataset. Second, we contribute a challenging egocentric QA benchmark with 629 videos and 7,026 questions to evaluate the models' ability in recognizing and memorizing visual details across videos of varying lengths. We introduce a new de-biasing evaluation method to help mitigate the unavoidable language bias present in the models being evaluated. Third, we propose a specialized multimodal architecture featuring a novel \"Memory Pointer Prompting\" mechanism. This design includes a global glimpse step to gain an overarching understanding of the entire video and identify key visual information, followed by a fallback step that utilizes the key visual information to generate responses. This enables the model to more effectively comprehend extended video content. With the data, benchmark, and model, we successfully build MM-Ego, an egocentric multimodal LLM that shows powerful performance on egocentric video understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05591", "title": "TweedieMix: Improving Multi-Concept Fusion for Diffusion-based Image/Video Generation", "authors": [ "Gihyun Kwon", "Jong Chul Ye" ], "github": "https://github.com/kwongihyun/tweediemix", "project_page": "", "abstract": "Despite significant advancements in customizing text-to-image and video generation models, generating images and videos that effectively integrate multiple personalized concepts remains a challenging task. To address this, we present TweedieMix, a novel method for composing customized diffusion models during the inference phase. By analyzing the properties of reverse diffusion sampling, our approach divides the sampling process into two stages. During the initial steps, we apply a multiple object-aware sampling technique to ensure the inclusion of the desired target objects. In the later steps, we blend the appearances of the custom concepts in the de-noised image space using Tweedie's formula. Our results demonstrate that TweedieMix can generate multiple personalized concepts with higher fidelity than existing methods. Moreover, our framework can be effortlessly extended to image-to-video diffusion models, enabling the generation of videos that feature multiple personalized concepts. Results and source code are in our anonymous project page.", "github_manual": "https://github.com/kwongihyun/tweediemix", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05954", "title": "Pyramidal Flow Matching for Efficient Video Generative Modeling", "authors": [ "Yang Jin", "Zhicheng Sun", "Ningyuan Li", "Kun Xu", "Kun Xu", "Hao Jiang", "Nan Zhuang", "Quzhe Huang", "Yang Song", "Yadong Mu", "Zhouchen Lin" ], "github": "https://github.com/jy0205/Pyramid-Flow", "project_page": "", "abstract": "Video generation requires modeling a vast spatiotemporal space, which demands significant computational resources and data usage. To reduce the complexity, the prevailing approaches employ a cascaded architecture to avoid direct training with full resolution. Despite reducing computational demands, the separate optimization of each sub-stage hinders knowledge sharing and sacrifices flexibility. This work introduces a unified pyramidal flow matching algorithm. It reinterprets the original denoising trajectory as a series of pyramid stages, where only the final stage operates at the full resolution, thereby enabling more efficient video generative modeling. Through our sophisticated design, the flows of different pyramid stages can be interlinked to maintain continuity. Moreover, we craft autoregressive video generation with a temporal pyramid to compress the full-resolution history. The entire framework can be optimized in an end-to-end manner and with a single unified Diffusion Transformer (DiT). Extensive experiments demonstrate that our method supports generating high-quality 5-second (up to 10-second) videos at 768p resolution and 24 FPS within 20.7k A100 GPU training hours. All code and models will be open-sourced at https://pyramid-flow.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jy0205/Pyramid-Flow", "github_api_added_by": "auto" }, { "date": "2024-10-10", "arxiv_id": "2410.07113", "title": "Personalized Visual Instruction Tuning", "authors": [ "Renjie Pi", "Jianshu Zhang", "Tianyang Han", "Jipeng Zhang", "Rui Pan", "Tong Zhang" ], "github": "https://github.com/sterzhang/pvit", "project_page": "", "abstract": "Recent advancements in multimodal large language models (MLLMs) have demonstrated significant progress; however, these models exhibit a notable limitation, which we refer to as \"face blindness\". Specifically, they can engage in general conversations but fail to conduct personalized dialogues targeting at specific individuals. This deficiency hinders the application of MLLMs in personalized settings, such as tailored visual assistants on mobile devices, or domestic robots that need to recognize members of the family. In this paper, we introduce Personalized Visual Instruction Tuning (PVIT), a novel data curation and training framework designed to enable MLLMs to identify target individuals within an image and engage in personalized and coherent dialogues. Our approach involves the development of a sophisticated pipeline that autonomously generates training data containing personalized conversations. This pipeline leverages the capabilities of various visual experts, image generation models, and (multi-modal) large language models. To evaluate the personalized potential of MLLMs, we present a benchmark called P-Bench, which encompasses various question types with different levels of difficulty. The experiments demonstrate a substantial personalized performance enhancement after fine-tuning with our curated dataset.", "github_manual": "https://github.com/sterzhang/pvit", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05651", "title": "ViBiDSampler: Enhancing Video Interpolation Using Bidirectional Diffusion Sampler", "authors": [ "Serin Yang", "Taesung Kwon", "Jong Chul Ye" ], "github": "", "project_page": "", "abstract": "Recent progress in large-scale text-to-video (T2V) and image-to-video (I2V) diffusion models has greatly enhanced video generation, especially in terms of keyframe interpolation. However, current image-to-video diffusion models, while powerful in generating videos from a single conditioning frame, need adaptation for two-frame (start & end) conditioned generation, which is essential for effective bounded interpolation. Unfortunately, existing approaches that fuse temporally forward and backward paths in parallel often suffer from off-manifold issues, leading to artifacts or requiring multiple iterative re-noising steps. In this work, we introduce a novel, bidirectional sampling strategy to address these off-manifold issues without requiring extensive re-noising or fine-tuning. Our method employs sequential sampling along both forward and backward paths, conditioned on the start and end frames, respectively, ensuring more coherent and on-manifold generation of intermediate frames. Additionally, we incorporate advanced guidance techniques, CFG++ and DDS, to further enhance the interpolation process. By integrating these, our method achieves state-of-the-art performance, efficiently generating high-quality, smooth videos between keyframes. On a single 3090 GPU, our method can interpolate 25 frames at 1024 x 576 resolution in just 195 seconds, establishing it as a leading solution for keyframe interpolation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05677", "title": "T2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design", "authors": [ "Jiachen Li", "Qian Long", "Jian Zheng", "Xiaofeng Gao", "Robinson Piramuthu", "Wenhu Chen", "William Yang Wang" ], "github": "", "project_page": "", "abstract": "In this paper, we focus on enhancing a diffusion-based text-to-video (T2V) model during the post-training phase by distilling a highly capable consistency model from a pretrained T2V model. Our proposed method, T2V-Turbo-v2, introduces a significant advancement by integrating various supervision signals, including high-quality training data, reward model feedback, and conditional guidance, into the consistency distillation process. Through comprehensive ablation studies, we highlight the crucial importance of tailoring datasets to specific learning objectives and the effectiveness of learning from diverse reward models for enhancing both the visual quality and text-video alignment. Additionally, we highlight the vast design space of conditional guidance strategies, which centers on designing an effective energy function to augment the teacher ODE solver. We demonstrate the potential of this approach by extracting motion guidance from the training datasets and incorporating it into the ODE solver, showcasing its effectiveness in improving the motion quality of the generated videos with the improved motion-related metrics from VBench and T2V-CompBench. Empirically, our T2V-Turbo-v2 establishes a new state-of-the-art result on VBench, with a Total score of 85.13, surpassing proprietary systems such as Gen-3 and Kling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06524", "title": "Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA", "authors": [ "Maharshi Gor", "Hal Daum\u00e9 III", "Tianyi Zhou", "Jordan Boyd-Graber" ], "github": "", "project_page": "", "abstract": "Recent advancements of large language models (LLMs) have led to claims of AI surpassing humans in natural language processing (NLP) tasks such as textual understanding and reasoning. This work investigates these assertions by introducing CAIMIRA, a novel framework rooted in item response theory (IRT) that enables quantitative assessment and comparison of problem-solving abilities of question-answering (QA) agents: humans and AI systems. Through analysis of over 300,000 responses from ~70 AI systems and 155 humans across thousands of quiz questions, CAIMIRA uncovers distinct proficiency patterns in knowledge domains and reasoning skills. Humans outperform AI systems in knowledge-grounded abductive and conceptual reasoning, while state-of-the-art LLMs like GPT-4 and LLaMA show superior performance on targeted information retrieval and fact-based reasoning, particularly when information gaps are well-defined and addressable through pattern matching or data retrieval. These findings highlight the need for future QA tasks to focus on questions that challenge not only higher-order reasoning and scientific thinking, but also demand nuanced linguistic interpretation and cross-contextual knowledge application, helping advance AI developments that better emulate or complement human cognitive abilities in real-world problem-solving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06172", "title": "Multimodal Situational Safety", "authors": [ "Kaiwen Zhou", "Chengzhi Liu", "Xuandong Zhao", "Anderson Compalas", "Dawn Song", "Xin Eric Wang" ], "github": "https://github.com/eric-ai-lab/MSSBench", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) are rapidly evolving, demonstrating impressive capabilities as multimodal assistants that interact with both humans and their environments. However, this increased sophistication introduces significant safety concerns. In this paper, we present the first evaluation and analysis of a novel safety challenge termed Multimodal Situational Safety, which explores how safety considerations vary based on the specific situation in which the user or agent is engaged. We argue that for an MLLM to respond safely, whether through language or action, it often needs to assess the safety implications of a language query within its corresponding visual context. To evaluate this capability, we develop the Multimodal Situational Safety benchmark (MSSBench) to assess the situational safety performance of current MLLMs. The dataset comprises 1,820 language query-image pairs, half of which the image context is safe, and the other half is unsafe. We also develop an evaluation framework that analyzes key safety aspects, including explicit safety reasoning, visual understanding, and, crucially, situational safety reasoning. Our findings reveal that current MLLMs struggle with this nuanced safety problem in the instruction-following setting and struggle to tackle these situational safety challenges all at once, highlighting a key area for future research. Furthermore, we develop multi-agent pipelines to coordinately solve safety challenges, which shows consistent improvement in safety over the original MLLM response. Code and data: mssbench.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/eric-ai-lab/MSSBench", "github_api_added_by": "auto" }, { "date": "2024-10-10", "arxiv_id": "2410.07170", "title": "One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptation", "authors": [ "Fabian Paischer", "Lukas Hauzenberger", "Thomas Schmied", "Benedikt Alkin", "Marc Peter Deisenroth", "Sepp Hochreiter" ], "github": "https://github.com/ml-jku/EVA", "project_page": "", "abstract": "Foundation models (FMs) are pre-trained on large-scale datasets and then fine-tuned on a downstream task for a specific application. The most successful and most commonly used fine-tuning method is to update the pre-trained weights via a low-rank adaptation (LoRA). LoRA introduces new weight matrices that are usually initialized at random with a uniform rank distribution across model weights. Recent works focus on weight-driven initialization or learning of adaptive ranks during training. Both approaches have only been investigated in isolation, resulting in slow convergence or a uniform rank distribution, in turn leading to sub-optimal performance. We propose to enhance LoRA by initializing the new weights in a data-driven manner by computing singular value decomposition on minibatches of activation vectors. Then, we initialize the LoRA matrices with the obtained right-singular vectors and re-distribute ranks among all weight matrices to explain the maximal amount of variance and continue the standard LoRA fine-tuning procedure. This results in our new method Explained Variance Adaptation (EVA). We apply EVA to a variety of fine-tuning tasks ranging from language generation and understanding to image classification and reinforcement learning. EVA exhibits faster convergence than competitors and attains the highest average score across a multitude of tasks per domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ml-jku/EVA", "github_api_added_by": "auto" }, { "date": "2024-10-10", "arxiv_id": "2410.05295", "title": "AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs", "authors": [ "Xiaogeng Liu", "Peiran Li", "Edward Suh", "Yevgeniy Vorobeychik", "Zhuoqing Mao", "Somesh Jha", "Patrick McDaniel", "Huan Sun", "Bo Li", "Chaowei Xiao" ], "github": "https://github.com/safolab-wisc/autodan-turbo", "project_page": "", "abstract": "In this paper, we propose AutoDAN-Turbo, a black-box jailbreak method that can automatically discover as many jailbreak strategies as possible from scratch, without any human intervention or predefined scopes (e.g., specified candidate strategies), and use them for red-teaming. As a result, AutoDAN-Turbo can significantly outperform baseline methods, achieving a 74.3% higher average attack success rate on public benchmarks. Notably, AutoDAN-Turbo achieves an 88.5 attack success rate on GPT-4-1106-turbo. In addition, AutoDAN-Turbo is a unified framework that can incorporate existing human-designed jailbreak strategies in a plug-and-play manner. By integrating human-designed strategies, AutoDAN-Turbo can even achieve a higher attack success rate of 93.4 on GPT-4-1106-turbo.", "github_manual": "https://github.com/safolab-wisc/autodan-turbo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.07002", "title": "CursorCore: Assist Programming through Aligning Anything", "authors": [ "Hao Jiang", "Qi Liu", "Rui Li", "Shengyu Ye", "Shijin Wang" ], "github": "https://github.com/TechxGenus/CursorCore", "project_page": "", "abstract": "Large language models have been successfully applied to programming assistance tasks, such as code completion, code insertion, and instructional code editing. However, these applications remain insufficiently automated and struggle to effectively integrate various types of information during the programming process, including coding history, current code, and user instructions. In this work, we propose a new conversational framework that comprehensively integrates these information sources, collect data to train our models and evaluate their performance. Firstly, to thoroughly evaluate how well models align with different types of information and the quality of their outputs, we introduce a new benchmark, APEval (Assist Programming Eval), to comprehensively assess the performance of models in programming assistance tasks. Then, for data collection, we develop a data generation pipeline, Programming-Instruct, which synthesizes training data from diverse sources, such as GitHub and online judge platforms. This pipeline can automatically generate various types of messages throughout the programming process. Finally, using this pipeline, we generate 219K samples, fine-tune multiple models, and develop the CursorCore series. We show that CursorCore outperforms other models of comparable size. This framework unifies applications such as inline chat and automated editing, contributes to the advancement of coding assistants. Code, models and data are freely available at https://github.com/TechxGenus/CursorCore.", "github_manual": "", "github_regex": "https://github.com/TechxGenus/CursorCore", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05791", "title": "F\u00fcrElise: Capturing and Physically Synthesizing Hand Motions of Piano Performance", "authors": [ "Ruocheng Wang", "Pei Xu", "Haochen Shi", "Elizabeth Schumann", "C. Karen Liu" ], "github": "", "project_page": "", "abstract": "Piano playing requires agile, precise, and coordinated hand control that stretches the limits of dexterity. Hand motion models with the sophistication to accurately recreate piano playing have a wide range of applications in character animation, embodied AI, biomechanics, and VR/AR. In this paper, we construct a first-of-its-kind large-scale dataset that contains approximately 10 hours of 3D hand motion and audio from 15 elite-level pianists playing 153 pieces of classical music. To capture natural performances, we designed a markerless setup in which motions are reconstructed from multi-view videos using state-of-the-art pose estimation models. The motion data is further refined via inverse kinematics using the high-resolution MIDI key-pressing data obtained from sensors in a specialized Yamaha Disklavier piano. Leveraging the collected dataset, we developed a pipeline that can synthesize physically-plausible hand motions for musical scores outside of the dataset. Our approach employs a combination of imitation learning and reinforcement learning to obtain policies for physics-based bimanual control involving the interaction between hands and piano keys. To solve the sampling efficiency problem with the large motion dataset, we use a diffusion model to generate natural reference motions, which provide high-level trajectory and fingering (finger order and placement) information. However, the generated reference motion alone does not provide sufficient accuracy for piano performance modeling. We then further augmented the data by using musical similarity to retrieve similar motions from the captured dataset to boost the precision of the RL policy. With the proposed method, our model generates natural, dexterous motions that generalize to music from outside the training dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05355", "title": "Falcon Mamba: The First Competitive Attention-free 7B Language Model", "authors": [ "Jingwei Zuo", "Maksim Velikanov", "Dhia Eddine Rhaiem", "Ilyas Chahed", "Younes Belkada", "Guillaume Kunsch", "Hakim Hacid" ], "github": "", "project_page": "", "abstract": "In this technical report, we present Falcon Mamba 7B, a new base large language model based on the novel Mamba architecture. Falcon Mamba 7B is trained on 5.8 trillion tokens with carefully selected data mixtures. As a pure Mamba-based model, Falcon Mamba 7B surpasses leading open-weight models based on Transformers, such as Mistral 7B, Llama3.1 8B, and Falcon2 11B. It is on par with Gemma 7B and outperforms models with different architecture designs, such as RecurrentGemma 9B and RWKV-v6 Finch 7B/14B. Currently, Falcon Mamba 7B is the best-performing Mamba model in the literature at this scale, surpassing both existing Mamba and hybrid Mamba-Transformer models, according to the Open LLM Leaderboard. Due to its architecture, Falcon Mamba 7B is significantly faster at inference and requires substantially less memory for long sequence generation. Despite recent studies suggesting that hybrid Mamba-Transformer models outperform pure architecture designs, we demonstrate that even the pure Mamba design can achieve similar, or even superior results compared to the Transformer and hybrid designs. We make the weights of our implementation of Falcon Mamba 7B publicly available on https://huggingface.co/tiiuae/falcon-mamba-7b, under a permissive license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.07071", "title": "Retrieval-Augmented Decision Transformer: External Memory for In-context RL", "authors": [ "Thomas Schmied", "Fabian Paischer", "Vihang Patil", "Markus Hofmarcher", "Razvan Pascanu", "Sepp Hochreiter" ], "github": "https://github.com/ml-jku/RA-DT", "project_page": "", "abstract": "In-context learning (ICL) is the ability of a model to learn a new task by observing a few exemplars in its context. While prevalent in NLP, this capability has recently also been observed in Reinforcement Learning (RL) settings. Prior in-context RL methods, however, require entire episodes in the agent's context. Given that complex environments typically lead to long episodes with sparse rewards, these methods are constrained to simple environments with short episodes. To address these challenges, we introduce Retrieval-Augmented Decision Transformer (RA-DT). RA-DT employs an external memory mechanism to store past experiences from which it retrieves only sub-trajectories relevant for the current situation. The retrieval component in RA-DT does not require training and can be entirely domain-agnostic. We evaluate the capabilities of RA-DT on grid-world environments, robotics simulations, and procedurally-generated video games. On grid-worlds, RA-DT outperforms baselines, while using only a fraction of their context length. Furthermore, we illuminate the limitations of current in-context RL methods on complex environments and discuss future directions. To facilitate future research, we release datasets for four of the considered environments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ml-jku/RA-DT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.07160", "title": "TextToon: Real-Time Text Toonify Head Avatar from Single Video", "authors": [ "Luchuan Song", "Lele Chen", "Celong Liu", "Pinxin Liu", "Chenliang Xu" ], "github": "", "project_page": "", "abstract": "We propose TextToon, a method to generate a drivable toonified avatar. Given a short monocular video sequence and a written instruction about the avatar style, our model can generate a high-fidelity toonified avatar that can be driven in real-time by another video with arbitrary identities. Existing related works heavily rely on multi-view modeling to recover geometry via texture embeddings, presented in a static manner, leading to control limitations. The multi-view video input also makes it difficult to deploy these models in real-world applications. To address these issues, we adopt a conditional embedding Tri-plane to learn realistic and stylized facial representations in a Gaussian deformation field. Additionally, we expand the stylization capabilities of 3D Gaussian Splatting by introducing an adaptive pixel-translation neural network and leveraging patch-aware contrastive learning to achieve high-quality images. To push our work into consumer applications, we develop a real-time system that can operate at 48 FPS on a GPU machine and 15-18 FPS on a mobile machine. Extensive experiments demonstrate the efficacy of our approach in generating textual avatars over existing methods in terms of quality and real-time animation. Please refer to our project page for more details: https://songluchuan.github.io/TextToon/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06845", "title": "MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders", "authors": [ "Cheng Li", "May Fung", "Qingyun Wang", "Chi Han", "Manling Li", "Jindong Wang", "Heng Ji" ], "github": "https://github.com/Scarelette/MentalArena", "project_page": "", "abstract": "Mental health disorders are one of the most serious diseases in the world. Most people with such a disease lack access to adequate care, which highlights the importance of training models for the diagnosis and treatment of mental health disorders. However, in the mental health domain, privacy concerns limit the accessibility of personalized treatment data, making it challenging to build powerful models. In this paper, we introduce MentalArena, a self-play framework to train language models by generating domain-specific personalized data, where we obtain a better model capable of making a personalized diagnosis and treatment (as a therapist) and providing information (as a patient). To accurately model human-like mental health patients, we devise Symptom Encoder, which simulates a real patient from both cognition and behavior perspectives. To address intent bias during patient-therapist interactions, we propose Symptom Decoder to compare diagnosed symptoms with encoded symptoms, and dynamically manage the dialogue between patient and therapist according to the identified deviations. We evaluated MentalArena against 6 benchmarks, including biomedicalQA and mental health tasks, compared to 6 advanced models. Our models, fine-tuned on both GPT-3.5 and Llama-3-8b, significantly outperform their counterparts, including GPT-4o. We hope that our work can inspire future research on personalized care. Code is available in https://github.com/Scarelette/MentalArena/tree/main", "github_manual": "", "github_regex": "https://github.com/Scarelette/MentalArena", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06885", "title": "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching", "authors": [ "Yushen Chen", "Zhikang Niu", "Ziyang Ma", "Keqi Deng", "Chunhui Wang", "Jian Zhao", "Kai Yu", "Xie Chen" ], "github": "https://github.com/SWivid/F5-TTS", "project_page": "", "abstract": "This paper introduces F5-TTS, a fully non-autoregressive text-to-speech system based on flow matching with Diffusion Transformer (DiT). Without requiring complex designs such as duration model, text encoder, and phoneme alignment, the text input is simply padded with filler tokens to the same length as input speech, and then the denoising is performed for speech generation, which was originally proved feasible by E2 TTS. However, the original design of E2 TTS makes it hard to follow due to its slow convergence and low robustness. To address these issues, we first model the input with ConvNeXt to refine the text representation, making it easy to align with the speech. We further propose an inference-time Sway Sampling strategy, which significantly improves our model's performance and efficiency. This sampling strategy for flow step can be easily applied to existing flow matching based models without retraining. Our design allows faster training and achieves an inference RTF of 0.15, which is greatly improved compared to state-of-the-art diffusion-based TTS models. Trained on a public 100K hours multilingual dataset, our Fairytaler Fakes Fluent and Faithful speech with Flow matching (F5-TTS) exhibits highly natural and expressive zero-shot ability, seamless code-switching capability, and speed control efficiency. Demo samples can be found at https://SWivid.github.io/F5-TTS. We release all code and checkpoints to promote community development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SWivid/F5-TTS", "github_api_added_by": "auto" }, { "date": "2024-10-10", "arxiv_id": "2410.07095", "title": "MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering", "authors": [ "Jun Shern Chan", "Neil Chowdhury", "Oliver Jaffe", "James Aung", "Dane Sherburn", "Evan Mays", "Giulio Starace", "Kevin Liu", "Leon Maksin", "Tejal Patwardhan", "Lilian Weng", "Aleksander M\u0105dry" ], "github": "https://github.com/openai/mle-bench", "project_page": "", "abstract": "We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering. To this end, we curate 75 ML engineering-related competitions from Kaggle, creating a diverse set of challenging tasks that test real-world ML engineering skills such as training models, preparing datasets, and running experiments. We establish human baselines for each competition using Kaggle's publicly available leaderboards. We use open-source agent scaffolds to evaluate several frontier language models on our benchmark, finding that the best-performing setup--OpenAI's o1-preview with AIDE scaffolding--achieves at least the level of a Kaggle bronze medal in 16.9% of competitions. In addition to our main results, we investigate various forms of resource scaling for AI agents and the impact of contamination from pre-training. We open-source our benchmark code (github.com/openai/mle-bench/) to facilitate future research in understanding the ML engineering capabilities of AI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openai/mle-bench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05254", "title": "GLEE: A Unified Framework and Benchmark for Language-based Economic Environments", "authors": [ "Eilam Shapira", "Omer Madmon", "Itamar Reinman", "Samuel Joseph Amouyal", "Roi Reichart", "Moshe Tennenholtz" ], "github": "https://github.com/eilamshapira/GLEE", "project_page": "", "abstract": "Large Language Models (LLMs) show significant potential in economic and strategic interactions, where communication via natural language is often prevalent. This raises key questions: Do LLMs behave rationally? Can they mimic human behavior? Do they tend to reach an efficient and fair outcome? What is the role of natural language in the strategic interaction? How do characteristics of the economic environment influence these dynamics? These questions become crucial concerning the economic and societal implications of integrating LLM-based agents into real-world data-driven systems, such as online retail platforms and recommender systems. While the ML community has been exploring the potential of LLMs in such multi-agent setups, varying assumptions, design choices and evaluation criteria across studies make it difficult to draw robust and meaningful conclusions. To address this, we introduce a benchmark for standardizing research on two-player, sequential, language-based games. Inspired by the economic literature, we define three base families of games with consistent parameterization, degrees of freedom and economic measures to evaluate agents' performance (self-gain), as well as the game outcome (efficiency and fairness). We develop an open-source framework for interaction simulation and analysis, and utilize it to collect a dataset of LLM vs. LLM interactions across numerous game configurations and an additional dataset of human vs. LLM interactions. Through extensive experimentation, we demonstrate how our framework and dataset can be used to: (i) compare the behavior of LLM-based agents to human players in various economic contexts; (ii) evaluate agents in both individual and collective performance measures; and (iii) quantify the effect of the economic characteristics of the environments on the behavior of agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/eilamshapira/GLEE", "github_api_added_by": "auto" }, { "date": "2024-10-10", "arxiv_id": "2410.07062", "title": "TinyEmo: Scaling down Emotional Reasoning via Metric Projection", "authors": [ "Cristian Gutierrez" ], "github": "https://github.com/ggcr/TinyEmo", "project_page": "", "abstract": "This paper introduces TinyEmo, a family of small multi-modal language models for emotional reasoning and classification. Our approach features: (1) a synthetic emotional instruct dataset for both pre-training and fine-tuning stages, (2) a Metric Projector that delegates classification from the language model allowing for more efficient training and inference, (3) a multi-modal large language model (MM-LLM) for emotional reasoning, and (4) a semi-automated framework for bias detection. TinyEmo is able to perform emotion classification and emotional reasoning, all while using substantially fewer parameters than comparable models. This efficiency allows us to freely incorporate more diverse emotional datasets, enabling strong performance on classification tasks, with our smallest model (700M parameters) outperforming larger state-of-the-art models based on general-purpose MM-LLMs with over 7B parameters. Additionally, the Metric Projector allows for interpretability and indirect bias detection in large models without additional training, offering an approach to understand and improve AI systems. We release code, models, and dataset at https://github.com/ggcr/TinyEmo", "github_manual": "", "github_regex": "https://github.com/ggcr/TinyEmo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06985", "title": "Jointly Generating Multi-view Consistent PBR Textures using Collaborative Control", "authors": [ "Shimon Vainer", "Konstantin Kutsy", "Dante De Nigris", "Ciara Rowles", "Slava Elizarov", "Simon Donn\u00e9" ], "github": "", "project_page": "", "abstract": "Multi-view consistency remains a challenge for image diffusion models. Even within the Text-to-Texture problem, where perfect geometric correspondences are known a priori, many methods fail to yield aligned predictions across views, necessitating non-trivial fusion methods to incorporate the results onto the original mesh. We explore this issue for a Collaborative Control workflow specifically in PBR Text-to-Texture. Collaborative Control directly models PBR image probability distributions, including normal bump maps; to our knowledge, the only diffusion model to directly output full PBR stacks. We discuss the design decisions involved in making this model multi-view consistent, and demonstrate the effectiveness of our approach in ablation studies, as well as practical applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06555", "title": "ING-VP: MLLMs cannot Play Easy Vision-based Games Yet", "authors": [ "Haoran Zhang", "Hangyu Guo", "Shuyue Guo", "Meng Cao", "Wenhao Huang", "Jiaheng Liu", "Ge Zhang" ], "github": "https://github.com/Thisisus7/ING-VP.git", "project_page": "", "abstract": "As multimodal large language models (MLLMs) continue to demonstrate increasingly competitive performance across a broad spectrum of tasks, more intricate and comprehensive benchmarks have been developed to assess these cutting-edge models. These benchmarks introduce new challenges to core capabilities such as perception, reasoning, and planning. However, existing multimodal benchmarks fall short in providing a focused evaluation of multi-step planning based on spatial relationships in images. To bridge this gap, we present ING-VP, the first INteractive Game-based Vision Planning benchmark, specifically designed to evaluate the spatial imagination and multi-step reasoning abilities of MLLMs. ING-VP features 6 distinct games, encompassing 300 levels, each with 6 unique configurations. A single model engages in over 60,000 rounds of interaction. The benchmark framework allows for multiple comparison settings, including image-text vs. text-only inputs, single-step vs. multi-step reasoning, and with-history vs. without-history conditions, offering valuable insights into the model's capabilities. We evaluated numerous state-of-the-art MLLMs, with the highest-performing model, Claude-3.5 Sonnet, achieving an average accuracy of only 3.37%, far below the anticipated standard. This work aims to provide a specialized evaluation framework to drive advancements in MLLMs' capacity for complex spatial reasoning and planning. The code is publicly available at https://github.com/Thisisus7/ING-VP.git.", "github_manual": "", "github_regex": "https://github.com/Thisisus7/ING-VP.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06084", "title": "Diversity-Rewarded CFG Distillation", "authors": [ "Geoffrey Cideron", "Andrea Agostinelli", "Johan Ferret", "Sertan Girgin", "Romuald Elie", "Olivier Bachem", "Sarah Perrin", "Alexandre Ram\u00e9" ], "github": "", "project_page": "", "abstract": "Generative models are transforming creative domains such as music generation, with inference-time strategies like Classifier-Free Guidance (CFG) playing a crucial role. However, CFG doubles inference cost while limiting originality and diversity across generated contents. In this paper, we introduce diversity-rewarded CFG distillation, a novel finetuning procedure that distills the strengths of CFG while addressing its limitations. Our approach optimises two training objectives: (1) a distillation objective, encouraging the model alone (without CFG) to imitate the CFG-augmented predictions, and (2) an RL objective with a diversity reward, promoting the generation of diverse outputs for a given prompt. By finetuning, we learn model weights with the ability to generate high-quality and diverse outputs, without any inference overhead. This also unlocks the potential of weight-based model merging strategies: by interpolating between the weights of two models (the first focusing on quality, the second on diversity), we can control the quality-diversity trade-off at deployment time, and even further boost performance. We conduct extensive experiments on the MusicLM (Agostinelli et al., 2023) text-to-music generative model, where our approach surpasses CFG in terms of quality-diversity Pareto optimality. According to human evaluators, our finetuned-then-merged model generates samples with higher quality-diversity than the base model augmented with CFG. Explore our generations at https://google-research.github.io/seanet/musiclm/diverse_music/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.02465", "title": "Response Tuning: Aligning Large Language Models without Instruction", "authors": [ "Seokhyun An", "Hyounghun Kim" ], "github": "https://github.com/seokhyunan/response-tuning", "project_page": "", "abstract": "Instruction tuning-supervised fine-tuning using instruction-response pairs-is a foundational step in transitioning pre-trained Large Language Models (LLMs) into helpful and safe chat assistants. Our hypothesis is that establishing an adequate output space can enable such a transition given the capabilities inherent in pre-trained LLMs. To verify this, we propose Response Tuning (RT), which eliminates the instruction-conditioning step in instruction tuning and solely focuses on response space supervision. Our experiments demonstrate that RT models, trained only using responses, can effectively respond to a wide range of instructions and exhibit helpfulness comparable to that of their instruction-tuned counterparts. Furthermore, we observe that controlling the training response distribution can significantly improve their user preference or elicit target behaviors such as refusing assistance for unsafe queries. Our findings illuminate the role of establishing an adequate output space in alignment, highlighting the potential of the extensive inherent capabilities of pre-trained LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/seokhyunan/response-tuning", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.02503", "title": "Mixed-Session Conversation with Egocentric Memory", "authors": [ "Jihyoung Jang", "Taeyoung Kim", "Hyounghun Kim" ], "github": "", "project_page": "", "abstract": "Recently introduced dialogue systems have demonstrated high usability. However, they still fall short of reflecting real-world conversation scenarios. Current dialogue systems exhibit an inability to replicate the dynamic, continuous, long-term interactions involving multiple partners. This shortfall arises because there have been limited efforts to account for both aspects of real-world dialogues: deeply layered interactions over the long-term dialogue and widely expanded conversation networks involving multiple participants. As the effort to incorporate these aspects combined, we introduce Mixed-Session Conversation, a dialogue system designed to construct conversations with various partners in a multi-session dialogue setup. We propose a new dataset called MiSC to implement this system. The dialogue episodes of MiSC consist of 6 consecutive sessions, with four speakers (one main speaker and three partners) appearing in each episode. Also, we propose a new dialogue model with a novel memory management mechanism, called Egocentric Memory Enhanced Mixed-Session Conversation Agent (EMMA). EMMA collects and retains memories from the main speaker's perspective during conversations with partners, enabling seamless continuity in subsequent interactions. Extensive human evaluations validate that the dialogues in MiSC demonstrate a seamless conversational flow, even when conversation partners change in each session. EMMA trained with MiSC is also evaluated to maintain high memorability without contradiction throughout the entire conversation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.02428", "title": "Collective Critics for Creative Story Generation", "authors": [ "Minwook Bae", "Hyounghun Kim" ], "github": "https://github.com/emnlp-2024-critics/collective-critics-for-creative-story-generation", "project_page": "", "abstract": "Generating a long story of several thousand words with narrative coherence using Large Language Models (LLMs) has been a challenging task. Previous research has addressed this challenge by proposing different frameworks that create a story plan and generate a long story based on that plan. However, these frameworks have been mainly focusing on maintaining narrative coherence in stories, often overlooking creativity in story planning and the expressiveness of the stories generated from those plans, which are desirable properties to captivate readers' interest. In this paper, we propose Collective Critics for Creative Story Generation framework (CritiCS), which is composed of plan refining stage (CrPlan) and story generation stage (CrText), to integrate a collective revision mechanism that promotes those properties into long-form story generation process. Specifically, in each stage, a group of LLM critics and one leader collaborate to incrementally refine drafts of plan and story throughout multiple rounds. Extensive human evaluation shows that the CritiCS can significantly enhance story creativity and reader engagement, while also maintaining narrative coherence. Furthermore, the design of the framework allows active participation from human writers in any role within the critique process, enabling interactive human-machine collaboration in story writing.", "github_manual": "https://github.com/emnlp-2024-critics/collective-critics-for-creative-story-generation", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.06468", "title": "Does Spatial Cognition Emerge in Frontier Models?", "authors": [ "Santhosh Kumar Ramakrishnan", "Erik Wijmans", "Philipp Kraehenbuehl", "Vladlen Koltun" ], "github": "https://github.com/apple/ml-space-benchmark", "project_page": "", "abstract": "Not yet. We present SPACE, a benchmark that systematically evaluates spatial cognition in frontier models. Our benchmark builds on decades of research in cognitive science. It evaluates large-scale mapping abilities that are brought to bear when an organism traverses physical environments, smaller-scale reasoning about object shapes and layouts, and cognitive infrastructure such as spatial attention and memory. For many tasks, we instantiate parallel presentations via text and images, allowing us to benchmark both large language models and large multimodal models. Results suggest that contemporary frontier models fall short of the spatial intelligence of animals, performing near chance level on a number of classic tests of animal cognition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apple/ml-space-benchmark", "github_api_added_by": "auto" }, { "date": "2024-10-10", "arxiv_id": "2410.06458", "title": "LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints", "authors": [ "Thomas Palmeira Ferraz", "Kartik Mehta", "Yu-Hsiang Lin", "Haw-Shiuan Chang", "Shereen Oraby", "Sijia Liu", "Vivek Subramanian", "Tagyoung Chung", "Mohit Bansal", "Nanyun Peng" ], "github": "", "project_page": "", "abstract": "Instruction following is a key capability for LLMs. However, recent studies have shown that LLMs often struggle with instructions containing multiple constraints (e.g. a request to create a social media post \"in a funny tone\" with \"no hashtag\"). Despite this, most evaluations focus solely on synthetic data. To address this, we introduce RealInstruct, the first benchmark designed to evaluate LLMs' ability to follow real-world multi-constrained instructions by leveraging queries real users asked AI assistants. We also investigate model-based evaluation as a cost-effective alternative to human annotation for this task. Our findings reveal that even the proprietary GPT-4 model fails to meet at least one constraint on over 21% of instructions, highlighting the limitations of state-of-the-art models. To address the performance gap between open-source and proprietary models, we propose the Decompose, Critique and Refine (DeCRIM) self-correction pipeline, which enhances LLMs' ability to follow constraints. DeCRIM works by decomposing the original instruction into a list of constraints and using a Critic model to decide when and where the LLM's response needs refinement. Our results show that DeCRIM improves Mistral's performance by 7.3% on RealInstruct and 8.0% on IFEval even with weak feedback. Moreover, we demonstrate that with strong feedback, open-source LLMs with DeCRIM can outperform GPT-4 on both benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05873", "title": "MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual Alignment", "authors": [ "Amir Hossein Kargaran", "Ali Modarressi", "Nafiseh Nikeghbal", "Jana Diesner", "Fran\u00e7ois Yvon", "Hinrich Sch\u00fctze" ], "github": "https://github.com/cisnlp/Mexa", "project_page": "", "abstract": "English-centric large language models (LLMs) often show strong multilingual capabilities. However, the multilingual performance of these models remains unclear and is not thoroughly evaluated for many languages. Most benchmarks for multilinguality focus on classic NLP tasks, or cover a minimal number of languages. We introduce MEXA, a method for assessing the multilingual capabilities of pre-trained English-centric LLMs using parallel sentences, which are available for more languages than existing downstream tasks. MEXA leverages the fact that English-centric LLMs use English as a kind of pivot language in their intermediate layers. It computes the alignment between English and non-English languages using parallel sentences to evaluate the transfer of language understanding from English to other languages. This alignment can be used to estimate model performance in other languages. We conduct studies using various parallel datasets (FLORES-200 and Bible), models (Llama family, Gemma family, Mistral, and OLMo), and established downstream tasks (Belebele, m-MMLU, and m-ARC). We explore different methods to compute embeddings in decoder-only models. Our results show that MEXA, in its default settings, achieves a statistically significant average Pearson correlation of 0.90 with three established downstream tasks across nine models and two parallel datasets. This suggests that MEXA is a reliable method for estimating the multilingual capabilities of English-centric LLMs, providing a clearer understanding of their multilingual potential and the inner workings of LLMs. Leaderboard: https://huggingface.co/spaces/cis-lmu/Mexa, Code: https://github.com/cisnlp/Mexa.", "github_manual": "", "github_regex": "https://github.com/cisnlp/Mexa", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.07145", "title": "Stuffed Mamba: State Collapse and State Capacity of RNN-Based Long-Context Modeling", "authors": [ "Yingfa Chen", "Xinrong Zhang", "Shengding Hu", "Xu Han", "Zhiyuan Liu", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "One essential advantage of recurrent neural networks (RNNs) over transformer-based language models is their linear computational complexity concerning the sequence length, which makes them much faster in handling long sequences during inference. However, most publicly available RNNs (e.g., Mamba and RWKV) are trained on sequences with less than 10K tokens, and their effectiveness in longer contexts remains largely unsatisfying so far. In this paper, we study the cause of the inability to process long context for RNNs and suggest critical mitigations. We examine two practical concerns when applying state-of-the-art RNNs to long contexts: (1) the inability to extrapolate to inputs longer than the training length and (2) the upper bound of memory capacity. Addressing the first concern, we first investigate *state collapse* (SC), a phenomenon that causes severe performance degradation on sequence lengths not encountered during training. With controlled experiments, we attribute this to overfitting due to the recurrent state being overparameterized for the training length. For the second concern, we train a series of Mamba-2 models on long documents to empirically estimate the recurrent state capacity in language modeling and passkey retrieval. Then, three SC mitigation methods are proposed to improve Mamba-2's length generalizability, allowing the model to process more than 1M tokens without SC. We also find that the recurrent state capacity in passkey retrieval scales exponentially to the state size, and we empirically train a Mamba-2 370M with near-perfect passkey retrieval accuracy on 256K context length. This suggests a promising future for RNN-based long-context modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.05160", "title": "VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks", "authors": [ "Ziyan Jiang", "Rui Meng", "Xinyi Yang", "Semih Yavuz", "Yingbo Zhou", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Embedding models have been crucial in enabling various downstream tasks such as semantic similarity, information retrieval, and clustering. Recently, there has been a surge of interest in developing universal text embedding models that can generalize across tasks (e.g., MTEB). However, progress in learning universal multimodal embedding models has been relatively slow despite their importance. In this work, we aim to explore the potential for building universal embeddings capable of handling a wide range of downstream tasks. Our contributions are twofold: (1) MMEB (Massive Multimodal Embedding Benchmark), which covers 4 meta-tasks (i.e. classification, visual question answering, multimodal retrieval, and visual grounding) and 36 datasets, including 20 training and 16 evaluation datasets, and (2) VLM2Vec (Vision-Language Model -> Vector), a contrastive training framework that converts any state-of-the-art vision-language model into an embedding model via training on MMEB. Unlike previous models such as CLIP and BLIP, VLM2Vec can process any combination of images and text to generate a fixed-dimensional vector based on task instructions. We build a series of VLM2Vec models on Phi-3.5-V and evaluate them on MMEB's evaluation split. Our results show that \\model achieves an absolute average improvement of 10% to 20% over existing multimodal embedding models on both in-distribution and out-of-distribution datasets in MMEB.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.07112", "title": "VHELM: A Holistic Evaluation of Vision Language Models", "authors": [ "Tony Lee", "Haoqin Tu", "Chi Heem Wong", "Wenhao Zheng", "Yiyang Zhou", "Yifan Mai", "Josselin Somerville Roberts", "Michihiro Yasunaga", "Huaxiu Yao", "Cihang Xie", "Percy Liang" ], "github": "https://github.com/stanford-crfm/helm", "project_page": "", "abstract": "Current benchmarks for assessing vision-language models (VLMs) often focus on their perception or problem-solving capabilities and neglect other critical aspects such as fairness, multilinguality, or toxicity. Furthermore, they differ in their evaluation procedures and the scope of the evaluation, making it difficult to compare models. To address these issues, we extend the HELM framework to VLMs to present the Holistic Evaluation of Vision Language Models (VHELM). VHELM aggregates various datasets to cover one or more of the 9 aspects: visual perception, knowledge, reasoning, bias, fairness, multilinguality, robustness, toxicity, and safety. In doing so, we produce a comprehensive, multi-dimensional view of the capabilities of the VLMs across these important factors. In addition, we standardize the standard inference parameters, methods of prompting, and evaluation metrics to enable fair comparisons across models. Our framework is designed to be lightweight and automatic so that evaluation runs are cheap and fast. Our initial run evaluates 22 VLMs on 21 existing datasets to provide a holistic snapshot of the models. We uncover new key findings, such as the fact that efficiency-focused models (e.g., Claude 3 Haiku or Gemini 1.5 Flash) perform significantly worse than their full models (e.g., Claude 3 Opus or Gemini 1.5 Pro) on the bias benchmark but not when evaluated on the other aspects. For transparency, we release the raw model generations and complete results on our website (https://crfm.stanford.edu/helm/vhelm/v2.0.1). VHELM is intended to be a living benchmark, and we hope to continue adding new datasets and models over time.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stanford-crfm/helm", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-10", "arxiv_id": "2410.07155", "title": "Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis", "authors": [ "Bohan Zeng", "Ling Yang", "Siyu Li", "Jiaming Liu", "Zixiang Zhang", "Juanxi Tian", "Kaixin Zhu", "Yongzhen Guo", "Fu-Yun Wang", "Minkai Xu", "Stefano Ermon", "Wentao Zhang" ], "github": "https://github.com/YangLing0818/Trans4D", "project_page": "", "abstract": "Recent advances in diffusion models have demonstrated exceptional capabilities in image and video generation, further improving the effectiveness of 4D synthesis. Existing 4D generation methods can generate high-quality 4D objects or scenes based on user-friendly conditions, benefiting the gaming and video industries. However, these methods struggle to synthesize significant object deformation of complex 4D transitions and interactions within scenes. To address this challenge, we propose Trans4D, a novel text-to-4D synthesis framework that enables realistic complex scene transitions. Specifically, we first use multi-modal large language models (MLLMs) to produce a physic-aware scene description for 4D scene initialization and effective transition timing planning. Then we propose a geometry-aware 4D transition network to realize a complex scene-level 4D transition based on the plan, which involves expressive geometrical object deformation. Extensive experiments demonstrate that Trans4D consistently outperforms existing state-of-the-art methods in generating 4D scenes with accurate and high-quality transitions, validating its effectiveness. Code: https://github.com/YangLing0818/Trans4D", "github_manual": "", "github_regex": "https://github.com/YangLing0818/Trans4D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.08207", "title": "DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models", "authors": [ "Xiaoxiao He", "Ligong Han", "Quan Dao", "Song Wen", "Minhao Bai", "Di Liu", "Han Zhang", "Martin Renqiang Min", "Felix Juefei-Xu", "Chaowei Tan", "Bo Liu", "Kang Li", "Hongdong Li", "Junzhou Huang", "Faez Ahmed", "Akash Srivastava", "Dimitris Metaxas" ], "github": "", "project_page": "", "abstract": "Discrete diffusion models have achieved success in tasks like image generation and masked language modeling but face limitations in controlled content editing. We introduce DICE (Discrete Inversion for Controllable Editing), the first approach to enable precise inversion for discrete diffusion models, including multinomial diffusion and masked generative models. By recording noise sequences and masking patterns during the reverse diffusion process, DICE enables accurate reconstruction and flexible editing of discrete data without the need for predefined masks or attention manipulation. We demonstrate the effectiveness of DICE across both image and text domains, evaluating it on models such as VQ-Diffusion, Paella, and RoBERTa. Our results show that DICE preserves high data fidelity while enhancing editing capabilities, offering new opportunities for fine-grained content manipulation in discrete spaces. For project webpage, see https://hexiaoxiao-cs.github.io/DICE/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.05265", "title": "PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs", "authors": [ "Mengzhao Chen", "Yi Liu", "Jiahao Wang", "Yi Bin", "Wenqi Shao", "Ping Luo" ], "github": "https://github.com/ChenMnZ/PrefixQuant", "project_page": "", "abstract": "Quantization is essential for deploying Large Language Models (LLMs) by enhancing memory efficiency and inference speed. Existing methods for activation quantization mainly address channel-wise outliers, often neglecting token-wise outliers, leading to reliance on costly per-token dynamic quantization. To address this, we introduce PrefixQuant, a novel technique that isolates outlier tokens offline without re-training. Specifically, PrefixQuant identifies high-frequency outlier tokens and prefixes them in the KV cache, preventing the generation of outlier tokens during inference and simplifying quantization. To our knowledge, PrefixQuant is the first to enable efficient per-tensor static quantization to outperform expensive per-token dynamic quantization. For instance, in W4A4KV4 (4- bit weight, 4-bit activation, and 4-bit KV cache) Llama-3-8B, PrefixQuant with per-tensor static quantization achieves a 7.43 WikiText2 perplexity and 71.08% average accuracy on 5 common-sense reasoning tasks, outperforming previous per-token dynamic quantization methods like QuaRot with 0.98 perplexity improvement and +5.98 points accuracy. Additionally, the inference speed of W4A4 quantized models using PrefixQuant is 1.60x to 2.81x faster than FP16 models and exceeds QuaRot models by 1.2x to 1.3x. Our code is available at https://github.com/ChenMnZ/PrefixQuant.", "github_manual": "", "github_regex": "https://github.com/ChenMnZ/PrefixQuant", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.08115", "title": "Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System", "authors": [ "Weize Chen", "Jiarui Yuan", "Chen Qian", "Cheng Yang", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/thunlp/Optima", "project_page": "", "abstract": "Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving, yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods. We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness in LLM-based MAS through LLM training. Optima employs an iterative generate, rank, select, and train paradigm with a reward function balancing task performance, token efficiency, and communication readability. We explore various RL algorithms, including Supervised Fine-Tuning, Direct Preference Optimization, and their hybrid approaches, providing insights into their effectiveness-efficiency trade-offs. We integrate Monte Carlo Tree Search-inspired techniques for DPO data generation, treating conversation turns as tree nodes to explore diverse interaction paths. Evaluated on common multi-agent tasks, including information-asymmetric question answering and complex reasoning, Optima shows consistent and substantial improvements over single-agent baselines and vanilla MAS based on Llama 3 8B, achieving up to 2.8x performance gain with less than 10\\% tokens on tasks requiring heavy information exchange. Moreover, Optima's efficiency gains open new possibilities for leveraging inference-compute more effectively, leading to improved inference-time scaling laws. By addressing fundamental challenges in LLM-based MAS, Optima shows the potential towards scalable, efficient, and effective MAS (https://chenweize1998.github.io/optima-project-page).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thunlp/Optima", "github_api_added_by": "auto" }, { "date": "2024-10-11", "arxiv_id": "2410.07041", "title": "Emergent properties with repeated examples", "authors": [ "Fran\u00e7ois Charton", "Julia Kempe" ], "github": "", "project_page": "", "abstract": "We study the performance of transformers as a function of the number of repetitions of training examples with algorithmically generated datasets. On three problems of mathematics: the greatest common divisor, modular multiplication, and matrix eigenvalues, we show that for a fixed number of training steps, models trained on smaller sets of repeated examples outperform models trained on larger sets of single-use examples. We also demonstrate that two-set training - repeated use of a small random subset of examples, along normal sampling on the rest of the training set - provides for faster learning and better performance. This highlights that the benefits of repetition can outweigh those of data diversity. These datasets and problems provide a controlled setting to shed light on the still poorly understood interplay between generalization and memorization in deep learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.05210", "title": "Preserving Multi-Modal Capabilities of Pre-trained VLMs for Improving Vision-Linguistic Compositionality", "authors": [ "Youngtaek Oh", "Jae Won Cho", "Dong-Jin Kim", "In So Kweon", "Junmo Kim" ], "github": "https://github.com/ytaek-oh/fsc-clip", "project_page": "", "abstract": "In this paper, we propose a new method to enhance compositional understanding in pre-trained vision and language models (VLMs) without sacrificing performance in zero-shot multi-modal tasks. Traditional fine-tuning approaches often improve compositional reasoning at the cost of degrading multi-modal capabilities, primarily due to the use of global hard negative (HN) loss, which contrasts global representations of images and texts. This global HN loss pushes HN texts that are highly similar to the original ones, damaging the model's multi-modal representations. To overcome this limitation, we propose Fine-grained Selective Calibrated CLIP (FSC-CLIP), which integrates local hard negative loss and selective calibrated regularization. These innovations provide fine-grained negative supervision while preserving the model's representational integrity. Our extensive evaluations across diverse benchmarks for both compositionality and multi-modal tasks show that FSC-CLIP not only achieves compositionality on par with state-of-the-art models but also retains strong multi-modal capabilities. Code is available at: https://github.com/ytaek-oh/fsc-clip.", "github_manual": "", "github_regex": "https://github.com/ytaek-oh/fsc-clip", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.03450", "title": "MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents", "authors": [ "Junpeng Yue", "Xinru Xu", "B\u00f6rje F. Karlsson", "Zongqing Lu" ], "github": "https://github.com/PKU-RL/MART", "project_page": "", "abstract": "MLLM agents demonstrate potential for complex embodied tasks by retrieving multimodal task-relevant trajectory data. However, current retrieval methods primarily focus on surface-level similarities of textual or visual cues in trajectories, neglecting their effectiveness for the specific task at hand. To address this issue, we propose a novel method, MLLM as ReTriever (MART), which enhances the performance of embodied agents by utilizing interaction data to fine-tune an MLLM retriever based on preference learning, such that the retriever fully considers the effectiveness of trajectories and prioritize them for unseen tasks. We also introduce Trajectory Abstraction, a mechanism that leverages MLLMs' summarization capabilities to represent trajectories with fewer tokens while preserving key information, enabling agents to better comprehend milestones in the trajectory. Experimental results across various environments demonstrate our method significantly improves task success rates in unseen scenes compared to baseline methods. This work presents a new paradigm for multimodal retrieval in embodied agents, by fine-tuning a general-purpose MLLM as the retriever to assess trajectory effectiveness. All benchmark task sets and simulator code modifications for action and observation spaces will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PKU-RL/MART", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.06508", "title": "Towards Self-Improvement of LLMs via MCTS: Leveraging Stepwise Knowledge with Curriculum Preference Learning", "authors": [ "Xiyao Wang", "Linfeng Song", "Ye Tian", "Dian Yu", "Baolin Peng", "Haitao Mi", "Furong Huang", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Monte Carlo Tree Search (MCTS) has recently emerged as a powerful technique for enhancing the reasoning capabilities of LLMs. Techniques such as SFT or DPO have enabled LLMs to distill high-quality behaviors from MCTS, improving their reasoning performance. However, existing distillation methods underutilize the rich trajectory information generated by MCTS, limiting the potential for improvements in LLM reasoning. In this paper, we propose AlphaLLM-CPL, a novel pairwise training framework that enables LLMs to self-improve through MCTS behavior distillation. AlphaLLM-CPL efficiently leverages MCTS trajectories via two key innovations: (1) AlphaLLM-CPL constructs stepwise trajectory pairs from child nodes sharing the same parent in the search tree, providing step-level information for more effective MCTS behavior distillation. (2) AlphaLLM-CPL introduces curriculum preference learning, dynamically adjusting the training sequence of trajectory pairs in each offline training epoch to prioritize critical learning steps and mitigate overfitting. Experimental results on mathematical reasoning tasks demonstrate that AlphaLLM-CPL significantly outperforms previous MCTS behavior distillation methods, substantially boosting the reasoning capabilities of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.07303", "title": "Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow", "authors": [ "Fu-Yun Wang", "Ling Yang", "Zhaoyang Huang", "Mengdi Wang", "Hongsheng Li" ], "github": "https://github.com/G-U-N/Rectified-Diffusion", "project_page": "", "abstract": "Diffusion models have greatly improved visual generation but are hindered by slow generation speed due to the computationally intensive nature of solving generative ODEs. Rectified flow, a widely recognized solution, improves generation speed by straightening the ODE path. Its key components include: 1) using the diffusion form of flow-matching, 2) employing boldsymbol v-prediction, and 3) performing rectification (a.k.a. reflow). In this paper, we argue that the success of rectification primarily lies in using a pretrained diffusion model to obtain matched pairs of noise and samples, followed by retraining with these matched noise-sample pairs. Based on this, components 1) and 2) are unnecessary. Furthermore, we highlight that straightness is not an essential training target for rectification; rather, it is a specific case of flow-matching models. The more critical training target is to achieve a first-order approximate ODE path, which is inherently curved for models like DDPM and Sub-VP. Building on this insight, we propose Rectified Diffusion, which generalizes the design space and application scope of rectification to encompass the broader category of diffusion models, rather than being restricted to flow-matching models. We validate our method on Stable Diffusion v1-5 and Stable Diffusion XL. Our method not only greatly simplifies the training procedure of rectified flow-based previous works (e.g., InstaFlow) but also achieves superior performance with even lower training cost. Our code is available at https://github.com/G-U-N/Rectified-Diffusion.", "github_manual": "", "github_regex": "https://github.com/G-U-N/Rectified-Diffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.08151", "title": "Progressive Autoregressive Video Diffusion Models", "authors": [ "Desai Xie", "Zhan Xu", "Yicong Hong", "Hao Tan", "Difan Liu", "Feng Liu", "Arie Kaufman", "Yang Zhou" ], "github": "https://github.com/desaixie/pa_vdm", "project_page": "", "abstract": "Current frontier video diffusion models have demonstrated remarkable results at generating high-quality videos. However, they can only generate short video clips, normally around 10 seconds or 240 frames, due to computation limitations during training. In this work, we show that existing models can be naturally extended to autoregressive video diffusion models without changing the architectures. Our key idea is to assign the latent frames with progressively increasing noise levels rather than a single noise level, which allows for fine-grained condition among the latents and large overlaps between the attention windows. Such progressive video denoising allows our models to autoregressively generate video frames without quality degradation or abrupt scene changes. We present state-of-the-art results on long video generation at 1 minute (1440 frames at 24 FPS). Videos from this paper are available at https://desaixie.github.io/pa-vdm/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/desaixie/pa_vdm", "github_api_added_by": "auto" }, { "date": "2024-10-11", "arxiv_id": "2410.08049", "title": "Scaling Up Your Kernels: Large Kernel Design in ConvNets towards Universal Representations", "authors": [ "Yiyuan Zhang", "Xiaohan Ding", "Xiangyu Yue" ], "github": "https://github.com/AILab-CVC/UniRepLKNet", "project_page": "", "abstract": "This paper proposes the paradigm of large convolutional kernels in designing modern Convolutional Neural Networks (ConvNets). We establish that employing a few large kernels, instead of stacking multiple smaller ones, can be a superior design strategy. Our work introduces a set of architecture design guidelines for large-kernel ConvNets that optimize their efficiency and performance. We propose the UniRepLKNet architecture, which offers systematical architecture design principles specifically crafted for large-kernel ConvNets, emphasizing their unique ability to capture extensive spatial information without deep layer stacking. This results in a model that not only surpasses its predecessors with an ImageNet accuracy of 88.0%, an ADE20K mIoU of 55.6%, and a COCO box AP of 56.4% but also demonstrates impressive scalability and performance on various modalities such as time-series forecasting, audio, point cloud, and video recognition. These results indicate the universal modeling abilities of large-kernel ConvNets with faster inference speed compared with vision transformers. Our findings reveal that large-kernel ConvNets possess larger effective receptive fields and a higher shape bias, moving away from the texture bias typical of smaller-kernel CNNs. All codes and models are publicly available at https://github.com/AILab-CVC/UniRepLKNet promoting further research and development in the community.", "github_manual": "", "github_regex": "https://github.com/AILab-CVC/UniRepLKNet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.08164", "title": "Agent S: An Open Agentic Framework that Uses Computers Like a Human", "authors": [ "Saaket Agashe", "Jiuzhou Han", "Shuyu Gan", "Jiachen Yang", "Ang Li", "Xin Eric Wang" ], "github": "https://github.com/simular-ai/Agent-S", "project_page": "", "abstract": "We present Agent S, an open agentic framework that enables autonomous interaction with computers through a Graphical User Interface (GUI), aimed at transforming human-computer interaction by automating complex, multi-step tasks. Agent S aims to address three key challenges in automating computer tasks: acquiring domain-specific knowledge, planning over long task horizons, and handling dynamic, non-uniform interfaces. To this end, Agent S introduces experience-augmented hierarchical planning, which learns from external knowledge search and internal experience retrieval at multiple levels, facilitating efficient task planning and subtask execution. In addition, it employs an Agent-Computer Interface (ACI) to better elicit the reasoning and control capabilities of GUI agents based on Multimodal Large Language Models (MLLMs). Evaluation on the OSWorld benchmark shows that Agent S outperforms the baseline by 9.37% on success rate (an 83.6% relative improvement) and achieves a new state-of-the-art. Comprehensive analysis highlights the effectiveness of individual components and provides insights for future improvements. Furthermore, Agent S demonstrates broad generalizability to different operating systems on a newly-released WindowsAgentArena benchmark. Code available at https://github.com/simular-ai/Agent-S.", "github_manual": "", "github_regex": "https://github.com/simular-ai/Agent-S", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.04751", "title": "Intriguing Properties of Large Language and Vision Models", "authors": [ "Young-Jun Lee", "Byungsoo Ko", "Han-Gyu Kim", "Yechan Hwang", "Ho-Jin Choi" ], "github": "https://github.com/facebookresearch/unibench", "project_page": "", "abstract": "Recently, large language and vision models (LLVMs) have received significant attention and development efforts due to their remarkable generalization performance across a wide range of tasks requiring perception and cognitive abilities. A key factor behind their success is their simple architecture, which consists of a vision encoder, a projector, and a large language model (LLM). Despite their achievements in advanced reasoning tasks, their performance on fundamental perception-related tasks (e.g., MMVP) remains surprisingly low. This discrepancy raises the question of how LLVMs truly perceive images and exploit the advantages of the vision encoder. To address this, we systematically investigate this question regarding several aspects: permutation invariance, robustness, math reasoning, alignment preserving and importance, by evaluating the most common LLVM's families (i.e., LLaVA) across 10 evaluation benchmarks. Our extensive experiments reveal several intriguing properties of current LLVMs: (1) they internally process the image in a global manner, even when the order of visual patch sequences is randomly permuted; (2) they are sometimes able to solve math problems without fully perceiving detailed numerical information; (3) the cross-modal alignment is overfitted to complex reasoning tasks, thereby, causing them to lose some of the original perceptual capabilities of their vision encoder; (4) the representation space in the lower layers (<25%) plays a crucial role in determining performance and enhancing visual understanding. Lastly, based on the above observations, we suggest potential future directions for building better LLVMs and constructing more challenging evaluation benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/unibench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.05248", "title": "SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe", "authors": [ "Yuxin Xiao", "Shujian Zhang", "Wenxuan Zhou", "Marzyeh Ghassemi", "Sanqiang Zhao" ], "github": "", "project_page": "", "abstract": "To induce desired behaviors in large language models (LLMs) for interaction-driven tasks, the instruction-tuning stage typically trains LLMs on instruction-response pairs using the next-token prediction (NTP) loss. Previous work aiming to improve instruction-tuning performance often emphasizes the need for higher-quality supervised fine-tuning (SFT) datasets, which typically involves expensive data filtering with proprietary LLMs or labor-intensive data generation by human annotators. However, these approaches do not fully leverage the datasets' intrinsic properties, resulting in high computational and labor costs, thereby limiting scalability and performance gains. In this paper, we propose SFTMix, a novel recipe that elevates instruction-tuning performance beyond the conventional NTP paradigm, without the need for well-curated datasets. Observing that LLMs exhibit uneven confidence across the semantic representation space, we argue that examples with different confidence levels should play distinct roles during the instruction-tuning process. Based on this insight, SFTMix leverages training dynamics to identify examples with varying confidence levels, then applies a Mixup-based regularization to mitigate overfitting on confident examples while propagating supervision signals to improve learning on relatively unconfident ones. This approach enables SFTMix to significantly outperform NTP across a wide range of instruction-following and healthcare domain-specific SFT tasks, demonstrating its adaptability to diverse LLM families and scalability to datasets of any size. Comprehensive ablation studies further verify the robustness of SFTMix's design choices, underscoring its versatility in consistently enhancing performance across different LLMs and datasets in broader natural language processing applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.08196", "title": "MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code", "authors": [ "Zimu Lu", "Aojun Zhou", "Ke Wang", "Houxing Ren", "Weikang Shi", "Junting Pan", "Mingjie Zhan", "Hongsheng Li" ], "github": "https://github.com/mathllm/MathCoder2", "project_page": "", "abstract": "Code has been shown to be effective in enhancing the mathematical reasoning abilities of large language models due to its precision and accuracy. Previous works involving continued mathematical pretraining often include code that utilizes math-related packages, which are primarily designed for fields such as engineering, machine learning, signal processing, or module testing, rather than being directly focused on mathematical reasoning. In this paper, we introduce a novel method for generating mathematical code accompanied with corresponding reasoning steps for continued pretraining. Our approach begins with the construction of a high-quality mathematical continued pretraining dataset by incorporating math-related web data, code using mathematical packages, math textbooks, and synthetic data. Next, we construct reasoning steps by extracting LaTeX expressions, the conditions needed for the expressions, and the results of the expressions from the previously collected dataset. Based on this extracted information, we generate corresponding code to accurately capture the mathematical reasoning process. Appending the generated code to each reasoning step results in data consisting of paired natural language reasoning steps and their corresponding code. Combining this data with the original dataset results in a 19.2B-token high-performing mathematical pretraining corpus, which we name MathCode-Pile. Training several popular base models with this corpus significantly improves their mathematical abilities, leading to the creation of the MathCoder2 family of models. All of our data processing and training code is open-sourced, ensuring full transparency and easy reproducibility of the entire data collection and training pipeline. The code is released at https://github.com/mathllm/MathCoder2 .", "github_manual": "", "github_regex": "https://github.com/mathllm/MathCoder2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.07707", "title": "MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting", "authors": [ "Ruijie Zhu", "Yanzhe Liang", "Hanzhi Chang", "Jiacheng Deng", "Jiahao Lu", "Wenfei Yang", "Tianzhu Zhang", "Yongdong Zhang" ], "github": "", "project_page": "", "abstract": "Dynamic scene reconstruction is a long-term challenge in the field of 3D vision. Recently, the emergence of 3D Gaussian Splatting has provided new insights into this problem. Although subsequent efforts rapidly extend static 3D Gaussian to dynamic scenes, they often lack explicit constraints on object motion, leading to optimization difficulties and performance degradation. To address the above issues, we propose a novel deformable 3D Gaussian splatting framework called MotionGS, which explores explicit motion priors to guide the deformation of 3D Gaussians. Specifically, we first introduce an optical flow decoupling module that decouples optical flow into camera flow and motion flow, corresponding to camera movement and object motion respectively. Then the motion flow can effectively constrain the deformation of 3D Gaussians, thus simulating the motion of dynamic objects. Additionally, a camera pose refinement module is proposed to alternately optimize 3D Gaussians and camera poses, mitigating the impact of inaccurate camera poses. Extensive experiments in the monocular dynamic scenes validate that MotionGS surpasses state-of-the-art methods and exhibits significant superiority in both qualitative and quantitative results. Project page: https://ruijiezhu94.github.io/MotionGS_page", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.07869", "title": "Benchmarking Agentic Workflow Generation", "authors": [ "Shuofei Qiao", "Runnan Fang", "Zhisong Qiu", "Xiaobin Wang", "Ningyu Zhang", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Huajun Chen" ], "github": "https://github.com/zjunlp/WorFBench", "project_page": "", "abstract": "Large Language Models (LLMs), with their exceptional ability to handle a wide range of tasks, have driven significant advancements in tackling reasoning and planning tasks, wherein decomposing complex problems into executable workflows is a crucial step in this process. Existing workflow evaluation frameworks either focus solely on holistic performance or suffer from limitations such as restricted scenario coverage, simplistic workflow structures, and lax evaluation standards. To this end, we introduce WorFBench, a unified workflow generation benchmark with multi-faceted scenarios and intricate graph workflow structures. Additionally, we present WorFEval, a systemic evaluation protocol utilizing subsequence and subgraph matching algorithms to accurately quantify the LLM agent's workflow generation capabilities. Through comprehensive evaluations across different types of LLMs, we discover distinct gaps between the sequence planning capabilities and graph planning capabilities of LLM agents, with even GPT-4 exhibiting a gap of around 15%. We also train two open-source models and evaluate their generalization abilities on held-out tasks. Furthermore, we observe that the generated workflows can enhance downstream tasks, enabling them to achieve superior performance with less time during inference. Code and dataset will be available at https://github.com/zjunlp/WorFBench.", "github_manual": "", "github_regex": "https://github.com/zjunlp/WorFBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.06154", "title": "GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models", "authors": [ "M. Jehanzeb Mirza", "Mengjie Zhao", "Zhuoyuan Mao", "Sivan Doveh", "Wei Lin", "Paul Gavrikov", "Michael Dorkenwald", "Shiqi Yang", "Saurav Jha", "Hiromi Wakaki", "Yuki Mitsufuji", "Horst Possegger", "Rogerio Feris", "Leonid Karlinsky", "James Glass" ], "github": "https://github.com/jmiemirza/glov", "project_page": "", "abstract": "In this work, we propose a novel method (GLOV) enabling Large Language Models (LLMs) to act as implicit Optimizers for Vision-Langugage Models (VLMs) to enhance downstream vision tasks. Our GLOV meta-prompts an LLM with the downstream task description, querying it for suitable VLM prompts (e.g., for zero-shot classification with CLIP). These prompts are ranked according to a purity measure obtained through a fitness function. In each respective optimization step, the ranked prompts are fed as in-context examples (with their accuracies) to equip the LLM with the knowledge of the type of text prompts preferred by the downstream VLM. Furthermore, we also explicitly steer the LLM generation process in each optimization step by specifically adding an offset difference vector of the embeddings from the positive and negative solutions found by the LLM, in previous optimization steps, to the intermediate layer of the network for the next generation step. This offset vector steers the LLM generation toward the type of language preferred by the downstream VLM, resulting in enhanced performance on the downstream vision tasks. We comprehensively evaluate our GLOV on 16 diverse datasets using two families of VLMs, i.e., dual-encoder (e.g., CLIP) and encoder-decoder (e.g., LLaVa) models -- showing that the discovered solutions can enhance the recognition performance by up to 15.0% and 57.5% (3.8% and 21.6% on average) for these models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jmiemirza/glov", "github_api_added_by": "auto" }, { "date": "2024-10-11", "arxiv_id": "2410.04808", "title": "LPZero: Language Model Zero-cost Proxy Search from Zero", "authors": [ "Peijie Dong", "Lujun Li", "Xiang Liu", "Zhenheng Tang", "Xuebo Liu", "Qiang Wang", "Xiaowen Chu" ], "github": "", "project_page": "", "abstract": "In spite of the outstanding performance, Neural Architecture Search (NAS) is criticized for massive computation. Recently, Zero-shot NAS has emerged as a promising approach by exploiting Zero-cost (ZC) proxies, which markedly reduce computational demands. Despite this, existing ZC proxies heavily rely on expert knowledge and incur significant trial-and-error costs. Particularly in NLP tasks, most existing ZC proxies fail to surpass the performance of the naive baseline. To address these challenges, we introduce a novel framework, LPZero, which is the first to automatically design ZC proxies for various tasks, achieving higher ranking consistency than human-designed proxies. Specifically, we model the ZC proxy as a symbolic equation and incorporate a unified proxy search space that encompasses existing ZC proxies, which are composed of a predefined set of mathematical symbols. To heuristically search for the best ZC proxy, LPZero incorporates genetic programming to find the optimal symbolic composition. We propose a Rule-based Pruning Strategy (RPS), which preemptively eliminates unpromising proxies, thereby mitigating the risk of proxy degradation. Extensive experiments on FlexiBERT, GPT-2, and LLaMA-7B demonstrate LPZero's superior ranking ability and performance on downstream tasks compared to current approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.07137", "title": "Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates", "authors": [ "Xiaosen Zheng", "Tianyu Pang", "Chao Du", "Qian Liu", "Jing Jiang", "Min Lin" ], "github": "https://github.com/sail-sg/Cheating-LLM-Benchmarks", "project_page": "", "abstract": "Automatic LLM benchmarks, such as AlpacaEval 2.0, Arena-Hard-Auto, and MT-Bench, have become popular for evaluating language models due to their cost-effectiveness and scalability compared to human evaluation. Achieving high win rates on these benchmarks can significantly boost the promotional impact of newly released language models. This promotional benefit may motivate tricks, such as manipulating model output length or style to game win rates, even though several mechanisms have been developed to control length and disentangle style to reduce gameability. Nonetheless, we show that even a \"null model\" that always outputs a constant response (irrelevant to input instructions) can cheat automatic benchmarks and achieve top-ranked win rates: an 86.5% LC win rate on AlpacaEval 2.0; an 83.0 score on Arena-Hard-Auto; and a 9.55 score on MT-Bench. Moreover, the crafted cheating outputs are transferable because we assume that the instructions of these benchmarks (e.g., 805 samples of AlpacaEval 2.0) are private and cannot be accessed. While our experiments are primarily proof-of-concept, an adversary could use LLMs to generate more imperceptible cheating responses, unethically benefiting from high win rates and promotional impact. Our findings call for the development of anti-cheating mechanisms for reliable automatic benchmarks. The code is available at https://github.com/sail-sg/Cheating-LLM-Benchmarks.", "github_manual": "", "github_regex": "https://github.com/sail-sg/Cheating-LLM-Benchmarks", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.08159", "title": "DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation", "authors": [ "Jiatao Gu", "Yuyang Wang", "Yizhe Zhang", "Qihang Zhang", "Dinghuai Zhang", "Navdeep Jaitly", "Josh Susskind", "Shuangfei Zhai" ], "github": "", "project_page": "", "abstract": "Diffusion models have become the dominant approach for visual generation. They are trained by denoising a Markovian process that gradually adds noise to the input. We argue that the Markovian property limits the models ability to fully utilize the generation trajectory, leading to inefficiencies during training and inference. In this paper, we propose DART, a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework. DART iteratively denoises image patches spatially and spectrally using an AR model with the same architecture as standard language models. DART does not rely on image quantization, enabling more effective image modeling while maintaining flexibility. Furthermore, DART seamlessly trains with both text and image data in a unified model. Our approach demonstrates competitive performance on class-conditioned and text-to-image generation tasks, offering a scalable, efficient alternative to traditional diffusion models. Through this unified framework, DART sets a new benchmark for scalable, high-quality image synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.05603", "title": "Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition", "authors": [ "Zheyang Xiong", "Ziyang Cai", "John Cooper", "Albert Ge", "Vasilis Papageorgiou", "Zack Sifakis", "Angeliki Giannou", "Ziqian Lin", "Liu Yang", "Saurabh Agarwal", "Grigorios G Chrysos", "Samet Oymak", "Kangwook Lee", "Dimitris Papailiopoulos" ], "github": "https://github.com/edixiong/task-superposition", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities. In this study, we explore a surprising phenomenon related to ICL: LLMs can perform multiple, computationally distinct ICL tasks simultaneously, during a single inference call, a capability we term \"task superposition\". We provide empirical evidence of this phenomenon across various LLM families and scales and show that this phenomenon emerges even if we train the model to in-context learn one task at a time. We offer theoretical explanations that this capability is well within the expressive power of transformers. We also explore how LLMs internally compose task vectors during superposition. Furthermore, we show that larger models can solve more ICL tasks in parallel, and better calibrate their output distribution. Our findings offer insights into the latent capabilities of LLMs, further substantiate the perspective of \"LLMs as superposition of simulators\", and raise questions about the mechanisms enabling simultaneous task execution.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/edixiong/task-superposition", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.03437", "title": "Zebra: In-Context and Generative Pretraining for Solving Parametric PDEs", "authors": [ "Louis Serrano", "Armand Kassa\u00ef Koupa\u00ef", "Thomas X Wang", "Pierre Erbacher", "Patrick Gallinari" ], "github": "", "project_page": "", "abstract": "Solving time-dependent parametric partial differential equations (PDEs) is challenging, as models must adapt to variations in parameters such as coefficients, forcing terms, and boundary conditions. Data-driven neural solvers either train on data sampled from the PDE parameters distribution in the hope that the model generalizes to new instances or rely on gradient-based adaptation and meta-learning to implicitly encode the dynamics from observations. This often comes with increased inference complexity. Inspired by the in-context learning capabilities of large language models (LLMs), we introduce Zebra, a novel generative auto-regressive transformer designed to solve parametric PDEs without requiring gradient adaptation at inference. By leveraging in-context information during both pre-training and inference, Zebra dynamically adapts to new tasks by conditioning on input sequences that incorporate context trajectories or preceding states. This approach enables Zebra to flexibly handle arbitrarily sized context inputs and supports uncertainty quantification through the sampling of multiple solution trajectories. We evaluate Zebra across a variety of challenging PDE scenarios, demonstrating its adaptability, robustness, and superior performance compared to existing approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.05629", "title": "Vector-ICL: In-context Learning with Continuous Vector Representations", "authors": [ "Yufan Zhuang", "Chandan Singh", "Liyuan Liu", "Jingbo Shang", "Jianfeng Gao" ], "github": "https://github.com/EvanZhuang/vector-icl", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities on textual data. We explore whether these capabilities can be extended to continuous vectors from diverse domains, obtained from black-box pretrained encoders. By aligning input data with an LLM's embedding space through lightweight projectors, we observe that LLMs can effectively process and learn from these projected vectors, which we term Vector-ICL. In particular, we find that pretraining projectors with general language modeling objectives enables Vector-ICL, while task-specific finetuning further enhances performance. In our experiments across various tasks and modalities, including text reconstruction, numerical function regression, text classification, summarization, molecule captioning, time-series classification, graph classification, and fMRI decoding, Vector-ICL often surpasses both few-shot ICL and domain-specific model or tuning. We further conduct analyses and case studies, indicating the potential of LLMs to process vector representations beyond traditional token-based paradigms.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EvanZhuang/vector-icl", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.07484", "title": "WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents", "authors": [ "Siyu Zhou", "Tianyi Zhou", "Yijun Yang", "Guodong Long", "Deheng Ye", "Jing Jiang", "Chengqi Zhang" ], "github": "", "project_page": "", "abstract": "Can large language models (LLMs) directly serve as powerful world models for model-based agents? While the gaps between the prior knowledge of LLMs and the specified environment's dynamics do exist, our study reveals that the gaps can be bridged by aligning an LLM with its deployed environment and such \"world alignment\" can be efficiently achieved by rule learning on LLMs. Given the rich prior knowledge of LLMs, only a few additional rules suffice to align LLM predictions with the specified environment dynamics. To this end, we propose a neurosymbolic approach to learn these rules gradient-free through LLMs, by inducing, updating, and pruning rules based on comparisons of agent-explored trajectories and world model predictions. The resulting world model is composed of the LLM and the learned rules. Our embodied LLM agent \"WALL-E\" is built upon model-predictive control (MPC). By optimizing look-ahead actions based on the precise world model, MPC significantly improves exploration and learning efficiency. Compared to existing LLM agents, WALL-E's reasoning only requires a few principal rules rather than verbose buffered trajectories being included in the LLM input. On open-world challenges in Minecraft and ALFWorld, WALL-E achieves higher success rates than existing methods, with lower costs on replanning time and the number of tokens used for reasoning. In Minecraft, WALL-E exceeds baselines by 15-30% in success rate while costing 8-20 fewer replanning rounds and only 60-80% of tokens. In ALFWorld, its success rate surges to a new record high of 95% only after 6 iterations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-11", "arxiv_id": "2410.05269", "title": "Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language Models", "authors": [ "Fei Wang", "Ninareh Mehrabi", "Palash Goyal", "Rahul Gupta", "Kai-Wei Chang", "Aram Galstyan" ], "github": "https://github.com/feiwang96/Data-Advisor", "project_page": "", "abstract": "Data is a crucial element in large language model (LLM) alignment. Recent studies have explored using LLMs for efficient data collection. However, LLM-generated data often suffers from quality issues, with underrepresented or absent aspects and low-quality datapoints. To address these problems, we propose Data Advisor, an enhanced LLM-based method for generating data that takes into account the characteristics of the desired dataset. Starting from a set of pre-defined principles in hand, Data Advisor monitors the status of the generated data, identifies weaknesses in the current dataset, and advises the next iteration of data generation accordingly. Data Advisor can be easily integrated into existing data generation methods to enhance data quality and coverage. Experiments on safety alignment of three representative LLMs (i.e., Mistral, Llama2, and Falcon) demonstrate the effectiveness of Data Advisor in enhancing model safety against various fine-grained safety issues without sacrificing model utility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/feiwang96/Data-Advisor", "github_api_added_by": "auto" }, { "date": "2024-10-11", "arxiv_id": "2410.06293", "title": "Accelerated Preference Optimization for Large Language Model Alignment", "authors": [ "Jiafan He", "Huizhuo Yuan", "Quanquan Gu" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal tool for aligning large language models (LLMs) with human preferences. Direct Preference Optimization (DPO), one of the most popular approaches, formulates RLHF as a policy optimization problem without explicitly estimating the reward function. It overcomes the stability and efficiency issues of two-step approaches, which typically involve first estimating the reward function and then optimizing the policy via proximal policy optimization (PPO). Since RLHF is essentially an optimization problem, and it is well-known that momentum techniques can accelerate optimization both theoretically and empirically, a natural question arises: Can RLHF be accelerated by momentum? This paper answers this question in the affirmative. In detail, we first show that the iterative preference optimization method can be viewed as a proximal point method. Based on this observation, we propose a general Accelerated Preference Optimization (APO) framework, which unifies many existing preference optimization algorithms and employs Nesterov's momentum technique to speed up the alignment of LLMs. Theoretically, we demonstrate that APO can achieve a faster convergence rate than the standard iterative preference optimization methods, including DPO and Self-Play Preference Optimization (SPPO). Empirically, we show the superiority of APO over DPO, iterative DPO, and other strong baselines for RLHF on the AlpacaEval 2.0 benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.07133", "title": "EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models", "authors": [ "Rui Zhao", "Hangjie Yuan", "Yujie Wei", "Shiwei Zhang", "Yuchao Gu", "Lingmin Ran", "Xiang Wang", "Zhangjie Wu", "Junhao Zhang", "Yingya Zhang", "Mike Zheng Shou" ], "github": "https://github.com/showlab/EvolveDirector", "project_page": "", "abstract": "Recent advancements in generation models have showcased remarkable capabilities in generating fantastic content. However, most of them are trained on proprietary high-quality data, and some models withhold their parameters and only provide accessible application programming interfaces (APIs), limiting their benefits for downstream tasks. To explore the feasibility of training a text-to-image generation model comparable to advanced models using publicly available resources, we introduce EvolveDirector. This framework interacts with advanced models through their public APIs to obtain text-image data pairs to train a base model. Our experiments with extensive data indicate that the model trained on generated data of the advanced model can approximate its generation capability. However, it requires large-scale samples of 10 million or more. This incurs significant expenses in time, computational resources, and especially the costs associated with calling fee-based APIs. To address this problem, we leverage pre-trained large vision-language models (VLMs) to guide the evolution of the base model. VLM continuously evaluates the base model during training and dynamically updates and refines the training dataset by the discrimination, expansion, deletion, and mutation operations. Experimental results show that this paradigm significantly reduces the required data volume. Furthermore, when approaching multiple advanced models, EvolveDirector can select the best samples generated by them to learn powerful and balanced abilities. The final trained model Edgen is demonstrated to outperform these advanced models. The code and model weights are available at https://github.com/showlab/EvolveDirector.", "github_manual": "", "github_regex": "https://github.com/showlab/EvolveDirector", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.08565", "title": "Baichuan-Omni Technical Report", "authors": [ "Yadong Li", "Haoze Sun", "Mingan Lin", "Tianpeng Li", "Guosheng Dong", "Tao Zhang", "Bowen Ding", "Wei Song", "Zhenglin Cheng", "Yuqi Huo", "Song Chen", "Xu Li", "Da Pan", "Shusen Zhang", "Xin Wu", "Zheng Liang", "Jun Liu", "Tao Zhang", "Keer Lu", "Yaqi Zhao", "Yanjun Shen", "Fan Yang", "Kaicheng Yu", "Tao Lin", "Jianhua Xu", "Zenan Zhou", "Weipeng Chen" ], "github": "https://github.com/westlake-baichuan-mllm/bc-omni", "project_page": "", "abstract": "The salient multimodal capabilities and interactive experience of GPT-4o highlight its critical role in practical applications, yet it lacks a high-performing open-source counterpart. In this paper, we introduce Baichuan-Omni, the first open-source 7B Multimodal Large Language Model (MLLM) adept at concurrently processing and analyzing modalities of image, video, audio, and text, while delivering an advanced multimodal interactive experience and strong performance. We propose an effective multimodal training schema starting with 7B model and proceeding through two stages of multimodal alignment and multitask fine-tuning across audio, image, video, and text modal. This approach equips the language model with the ability to handle visual and audio data effectively. Demonstrating strong performance across various omni-modal and multimodal benchmarks, we aim for this contribution to serve as a competitive baseline for the open-source community in advancing multimodal understanding and real-time interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/westlake-baichuan-mllm/bc-omni", "github_api_added_by": "auto" }, { "date": "2024-10-14", "arxiv_id": "2410.09008", "title": "SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights", "authors": [ "Ling Yang", "Zhaochen Yu", "Tianjun Zhang", "Minkai Xu", "Joseph E. Gonzalez", "Bin Cui", "Shuicheng Yan" ], "github": "https://github.com/YangLing0818/SuperCorrect-llm", "project_page": "", "abstract": "Large language models (LLMs) like GPT-4, PaLM, and LLaMA have shown significant improvements in various reasoning tasks. However, smaller models such as Llama-3-8B and DeepSeekMath-Base still struggle with complex mathematical reasoning because they fail to effectively identify and correct reasoning errors. Recent reflection-based methods aim to address these issues by enabling self-reflection and self-correction, but they still face challenges in independently detecting errors in their reasoning steps. To overcome these limitations, we propose SuperCorrect, a novel two-stage framework that uses a large teacher model to supervise and correct both the reasoning and reflection processes of a smaller student model. In the first stage, we extract hierarchical high-level and detailed thought templates from the teacher model to guide the student model in eliciting more fine-grained reasoning thoughts. In the second stage, we introduce cross-model collaborative direct preference optimization (DPO) to enhance the self-correction abilities of the student model by following the teacher's correction traces during training. This cross-model DPO approach teaches the student model to effectively locate and resolve erroneous thoughts with error-driven insights from the teacher model, breaking the bottleneck of its thoughts and acquiring new skills and knowledge to tackle challenging problems. Extensive experiments consistently demonstrate our superiority over previous methods. Notably, our SuperCorrect-7B model significantly surpasses powerful DeepSeekMath-7B by 7.8%/5.3% and Qwen2.5-Math-7B by 15.1%/6.3% on MATH/GSM8K benchmarks, achieving new SOTA performance among all 7B models. Code: https://github.com/YangLing0818/SuperCorrect-llm", "github_manual": "", "github_regex": "https://github.com/YangLing0818/SuperCorrect-llm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.09009", "title": "Semantic Score Distillation Sampling for Compositional Text-to-3D Generation", "authors": [ "Ling Yang", "Zixiang Zhang", "Junlin Han", "Bohan Zeng", "Runjia Li", "Philip Torr", "Wentao Zhang" ], "github": "https://github.com/YangLing0818/SemanticSDS-3D", "project_page": "", "abstract": "Generating high-quality 3D assets from textual descriptions remains a pivotal challenge in computer graphics and vision research. Due to the scarcity of 3D data, state-of-the-art approaches utilize pre-trained 2D diffusion priors, optimized through Score Distillation Sampling (SDS). Despite progress, crafting complex 3D scenes featuring multiple objects or intricate interactions is still difficult. To tackle this, recent methods have incorporated box or layout guidance. However, these layout-guided compositional methods often struggle to provide fine-grained control, as they are generally coarse and lack expressiveness. To overcome these challenges, we introduce a novel SDS approach, Semantic Score Distillation Sampling (SemanticSDS), designed to effectively improve the expressiveness and accuracy of compositional text-to-3D generation. Our approach integrates new semantic embeddings that maintain consistency across different rendering views and clearly differentiate between various objects and parts. These embeddings are transformed into a semantic map, which directs a region-specific SDS process, enabling precise optimization and compositional generation. By leveraging explicit semantic guidance, our method unlocks the compositional capabilities of existing pre-trained diffusion models, thereby achieving superior quality in 3D content generation, particularly for complex objects and scenes. Experimental results demonstrate that our SemanticSDS framework is highly effective for generating state-of-the-art complex 3D content. Code: https://github.com/YangLing0818/SemanticSDS-3D", "github_manual": "", "github_regex": "https://github.com/YangLing0818/SemanticSDS-3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.08261", "title": "Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis", "authors": [ "Jinbin Bai", "Tian Ye", "Wei Chow", "Enxin Song", "Qing-Guo Chen", "Xiangtai Li", "Zhen Dong", "Lei Zhu", "Shuicheng Yan" ], "github": "", "project_page": "", "abstract": "Diffusion models, such as Stable Diffusion, have made significant strides in visual generation, yet their paradigm remains fundamentally different from autoregressive language models, complicating the development of unified language-vision models. Recent efforts like LlamaGen have attempted autoregressive image generation using discrete VQVAE tokens, but the large number of tokens involved renders this approach inefficient and slow. In this work, we present Meissonic, which elevates non-autoregressive masked image modeling (MIM) text-to-image to a level comparable with state-of-the-art diffusion models like SDXL. By incorporating a comprehensive suite of architectural innovations, advanced positional encoding strategies, and optimized sampling conditions, Meissonic substantially improves MIM's performance and efficiency. Additionally, we leverage high-quality training data, integrate micro-conditions informed by human preference scores, and employ feature compression layers to further enhance image fidelity and resolution. Our model not only matches but often exceeds the performance of existing models like SDXL in generating high-quality, high-resolution images. Extensive experiments validate Meissonic's capabilities, demonstrating its potential as a new standard in text-to-image synthesis. We release a model checkpoint capable of producing 1024 times 1024 resolution images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.06456", "title": "From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning", "authors": [ "Yang Bai", "Yang Zhou", "Jun Zhou", "Rick Siow Mong Goh", "Daniel Shu Wei Ting", "Yong Liu" ], "github": "https://github.com/baiyang4/VITask", "project_page": "", "abstract": "Large vision language models (VLMs) combine large language models with vision encoders, demonstrating promise across various tasks. However, they often underperform in task-specific applications due to domain gaps between pre-training and fine-tuning. We introduce VITask, a novel framework that enhances task-specific adaptability of VLMs by integrating task-specific models (TSMs). VITask employs three key strategies: exemplar prompting (EP), response distribution alignment (RDA), and contrastive response tuning (CRT) to improve the task-specific performance of VLMs by adjusting their response distributions. EP allows TSM features to guide VLMs, while RDA enables VLMs to adapt without TSMs during inference by learning from exemplar-prompted models. CRT further optimizes the ranking of correct image-response pairs, thereby reducing the risk of generating undesired responses. Experiments on 12 medical diagnosis datasets across 9 imaging modalities show that VITask outperforms both vanilla instruction-tuned VLMs and TSMs, showcasing its ability to integrate complementary features from both models effectively. Additionally, VITask offers practical advantages such as flexible TSM integration and robustness to incomplete instructions, making it a versatile and efficient solution for task-specific VLM tuning. Our code are available at https://github.com/baiyang4/VITask.", "github_manual": "", "github_regex": "https://github.com/baiyang4/VITask", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.07035", "title": "PositionID: LLMs can Control Lengths, Copy and Paste with Explicit Positional Awareness", "authors": [ "Zekun Wang", "Feiyu Duan", "Yibo Zhang", "Wangchunshu Zhou", "Ke Xu", "Wenhao Huang", "Jie Fu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) demonstrate impressive capabilities across various domains, including role-playing, creative writing, mathematical reasoning, and coding. Despite these advancements, LLMs still encounter challenges with length control, frequently failing to adhere to specific length constraints due to their token-level operations and insufficient training on data with strict length limitations. We identify this issue as stemming from a lack of positional awareness and propose novel approaches--PositionID Prompting and PositionID Fine-Tuning--to address it. These methods enhance the model's ability to continuously monitor and manage text length during generation. Additionally, we introduce PositionID CP Prompting to enable LLMs to perform copy and paste operations accurately. Furthermore, we develop two benchmarks for evaluating length control and copy-paste abilities. Our experiments demonstrate that our methods significantly improve the model's adherence to length constraints and copy-paste accuracy without compromising response quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.08391", "title": "KV Prediction for Improved Time to First Token", "authors": [ "Maxwell Horton", "Qingqing Cao", "Chenfan Sun", "Yanzi Jin", "Sachin Mehta", "Mohammad Rastegari", "Moin Nabi" ], "github": "https://github.com/apple/corenet/tree/main/projects", "project_page": "", "abstract": "Inference with transformer-based language models begins with a prompt processing step. In this step, the model generates the first output token and stores the KV cache needed for future generation steps. This prompt processing step can be computationally expensive, taking 10s of seconds or more for billion-parameter models on edge devices when prompt lengths or batch sizes rise. This degrades user experience by introducing significant latency into the model's outputs. To reduce the time spent producing the first output (known as the ``time to first token'', or TTFT) of a pretrained model, we introduce a novel method called KV Prediction. In our method, a small auxiliary model is used to process the prompt and produce an approximation of the KV cache used by a base model. This approximated KV cache is then used with the base model for autoregressive generation without the need to query the auxiliary model again. We demonstrate that our method produces a pareto-optimal efficiency-accuracy trade-off when compared to baselines. On TriviaQA, we demonstrate relative accuracy improvements in the range of 15%-50% across a range of TTFT FLOPs budgets. We also demonstrate accuracy improvements of up to 30% on HumanEval python code completion at fixed TTFT FLOPs budgets. Additionally, we benchmark models on an Apple M2 Pro CPU and demonstrate that our improvement in FLOPs translates to a TTFT speedup on hardware. We release our code at https://github.com/apple/corenet/tree/main/projects/kv-prediction .", "github_manual": "", "github_regex": "https://github.com/apple/corenet/tree/main/projects", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.08815", "title": "StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization", "authors": [ "Zhuoqun Li", "Xuanang Chen", "Haiyang Yu", "Hongyu Lin", "Yaojie Lu", "Qiaoyu Tang", "Fei Huang", "Xianpei Han", "Le Sun", "Yongbin Li" ], "github": "https://github.com/MozerWang/Loong", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) in many knowledge-based tasks. However, existing RAG methods struggle with knowledge-intensive reasoning tasks, because useful information required to these tasks are badly scattered. This characteristic makes it difficult for existing RAG methods to accurately identify key information and perform global reasoning with such noisy augmentation. In this paper, motivated by the cognitive theories that humans convert raw information into various structured knowledge when tackling knowledge-intensive reasoning, we proposes a new framework, StructRAG, which can identify the optimal structure type for the task at hand, reconstruct original documents into this structured format, and infer answers based on the resulting structure. Extensive experiments across various knowledge-intensive tasks show that StructRAG achieves state-of-the-art performance, particularly excelling in challenging scenarios, demonstrating its potential as an effective solution for enhancing LLMs in complex real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MozerWang/Loong", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.08102", "title": "Multi-Agent Collaborative Data Selection for Efficient LLM Pretraining", "authors": [ "Tianyi Bai", "Ling Yang", "Zhen Hao Wong", "Jiahui Peng", "Xinlin Zhuang", "Chi Zhang", "Lijun Wu", "Qiu Jiantao", "Wentao Zhang", "Binhang Yuan", "Conghui He" ], "github": "https://github.com/beccabai/multi-agent-data-selection", "project_page": "", "abstract": "Efficient data selection is crucial to accelerate the pretraining of large language models (LLMs). While various methods have been proposed to enhance data efficiency, limited research has addressed the inherent conflicts between these approaches to achieve optimal data selection for LLM pretraining. To tackle this problem, we propose a novel multi-agent collaborative data selection mechanism. In this framework, each data selection method serves as an independent agent, and an agent console is designed to dynamically integrate the information from all agents throughout the LLM training process. We conduct extensive empirical studies to evaluate our multi-agent framework. The experimental results demonstrate that our approach significantly improves data efficiency, accelerates convergence in LLM training, and achieves an average performance gain of 10.5% across multiple language model benchmarks compared to the state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/beccabai/multi-agent-data-selection", "github_api_added_by": "auto" }, { "date": "2024-10-14", "arxiv_id": "2410.07656", "title": "Mechanistic Permutability: Match Features Across Layers", "authors": [ "Nikita Balagansky", "Ian Maksimov", "Daniil Gavrilov" ], "github": "", "project_page": "", "abstract": "Understanding how features evolve across layers in deep neural networks is a fundamental challenge in mechanistic interpretability, particularly due to polysemanticity and feature superposition. While Sparse Autoencoders (SAEs) have been used to extract interpretable features from individual layers, aligning these features across layers has remained an open problem. In this paper, we introduce SAE Match, a novel, data-free method for aligning SAE features across different layers of a neural network. Our approach involves matching features by minimizing the mean squared error between the folded parameters of SAEs, a technique that incorporates activation thresholds into the encoder and decoder weights to account for differences in feature scales. Through extensive experiments on the Gemma 2 language model, we demonstrate that our method effectively captures feature evolution across layers, improving feature matching quality. We also show that features persist over several layers and that our approach can approximate hidden states across layers. Our work advances the understanding of feature dynamics in neural networks and provides a new tool for mechanistic interpretability studies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.07331", "title": "DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models", "authors": [ "Yiming Huang", "Jianwen Luo", "Yan Yu", "Yitong Zhang", "Fangyu Lei", "Yifan Wei", "Shizhu He", "Lifu Huang", "Xiao Liu", "Jun Zhao", "Kang Liu" ], "github": "https://github.com/yiyihum/da-code", "project_page": "", "abstract": "We introduce DA-Code, a code generation benchmark specifically designed to assess LLMs on agent-based data science tasks. This benchmark features three core elements: First, the tasks within DA-Code are inherently challenging, setting them apart from traditional code generation tasks and demanding advanced coding skills in grounding and planning. Second, examples in DA-Code are all based on real and diverse data, covering a wide range of complex data wrangling and analytics tasks. Third, to solve the tasks, the models must utilize complex data science programming languages, to perform intricate data processing and derive the answers. We set up the benchmark in a controllable and executable environment that aligns with real-world data analysis scenarios and is scalable. The annotators meticulously design the evaluation suite to ensure the accuracy and robustness of the evaluation. We develop the DA-Agent baseline. Experiments show that although the baseline performs better than other existing frameworks, using the current best LLMs achieves only 30.5% accuracy, leaving ample room for improvement. We release our benchmark at https://da-code-bench.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yiyihum/da-code", "github_api_added_by": "auto" }, { "date": "2024-10-14", "arxiv_id": "2410.09037", "title": "Mentor-KD: Making Small Language Models Better Multi-step Reasoners", "authors": [ "Hojae Lee", "Junho Kim", "SangKeun Lee" ], "github": "https://github.com/2hojae/mentor-kd", "project_page": "", "abstract": "Large Language Models (LLMs) have displayed remarkable performances across various complex tasks by leveraging Chain-of-Thought (CoT) prompting. Recently, studies have proposed a Knowledge Distillation (KD) approach, reasoning distillation, which transfers such reasoning ability of LLMs through fine-tuning language models of multi-step rationales generated by LLM teachers. However, they have inadequately considered two challenges regarding insufficient distillation sets from the LLM teacher model, in terms of 1) data quality and 2) soft label provision. In this paper, we propose Mentor-KD, which effectively distills the multi-step reasoning capability of LLMs to smaller LMs while addressing the aforementioned challenges. Specifically, we exploit a mentor, intermediate-sized task-specific fine-tuned model, to augment additional CoT annotations and provide soft labels for the student model during reasoning distillation. We conduct extensive experiments and confirm Mentor-KD's effectiveness across various models and complex reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/2hojae/mentor-kd", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.08168", "title": "ZeroComp: Zero-shot Object Compositing from Image Intrinsics via Diffusion", "authors": [ "Zitian Zhang", "Fr\u00e9d\u00e9ric Fortier-Chouinard", "Mathieu Garon", "Anand Bhattad", "Jean-Fran\u00e7ois Lalonde" ], "github": "", "project_page": "", "abstract": "We present ZeroComp, an effective zero-shot 3D object compositing approach that does not require paired composite-scene images during training. Our method leverages ControlNet to condition from intrinsic images and combines it with a Stable Diffusion model to utilize its scene priors, together operating as an effective rendering engine. During training, ZeroComp uses intrinsic images based on geometry, albedo, and masked shading, all without the need for paired images of scenes with and without composite objects. Once trained, it seamlessly integrates virtual 3D objects into scenes, adjusting shading to create realistic composites. We developed a high-quality evaluation dataset and demonstrate that ZeroComp outperforms methods using explicit lighting estimations and generative techniques in quantitative and human perception benchmarks. Additionally, ZeroComp extends to real and outdoor image compositing, even when trained solely on synthetic indoor data, showcasing its effectiveness in image compositing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.07536", "title": "I-Max: Maximize the Resolution Potential of Pre-trained Rectified Flow Transformers with Projected Flow", "authors": [ "Ruoyi Du", "Dongyang Liu", "Le Zhuo", "Qin Qi", "Hongsheng Li", "Zhanyu Ma", "Peng Gao" ], "github": "https://github.com/PRIS-CV/I-Max", "project_page": "", "abstract": "Rectified Flow Transformers (RFTs) offer superior training and inference efficiency, making them likely the most viable direction for scaling up diffusion models. However, progress in generation resolution has been relatively slow due to data quality and training costs. Tuning-free resolution extrapolation presents an alternative, but current methods often reduce generative stability, limiting practical application. In this paper, we review existing resolution extrapolation methods and introduce the I-Max framework to maximize the resolution potential of Text-to-Image RFTs. I-Max features: (i) a novel Projected Flow strategy for stable extrapolation and (ii) an advanced inference toolkit for generalizing model knowledge to higher resolutions. Experiments with Lumina-Next-2K and Flux.1-dev demonstrate I-Max's ability to enhance stability in resolution extrapolation and show that it can bring image detail emergence and artifact correction, confirming the practical value of tuning-free resolution extrapolation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PRIS-CV/I-Max", "github_api_added_by": "auto" }, { "date": "2024-10-14", "arxiv_id": "2410.09045", "title": "MiRAGeNews: Multimodal Realistic AI-Generated News Detection", "authors": [ "Runsheng Huang", "Liam Dugan", "Yue Yang", "Chris Callison-Burch" ], "github": "https://github.com/nosna/miragenews", "project_page": "", "abstract": "The proliferation of inflammatory or misleading \"fake\" news content has become increasingly common in recent years. Simultaneously, it has become easier than ever to use AI tools to generate photorealistic images depicting any scene imaginable. Combining these two -- AI-generated fake news content -- is particularly potent and dangerous. To combat the spread of AI-generated fake news, we propose the MiRAGeNews Dataset, a dataset of 12,500 high-quality real and AI-generated image-caption pairs from state-of-the-art generators. We find that our dataset poses a significant challenge to humans (60% F-1) and state-of-the-art multi-modal LLMs (< 24% F-1). Using our dataset we train a multi-modal detector (MiRAGe) that improves by +5.1% F-1 over state-of-the-art baselines on image-caption pairs from out-of-domain image generators and news publishers. We release our code and data to aid future work on detecting AI-generated content.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nosna/miragenews", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.09038", "title": "SimpleStrat: Diversifying Language Model Generation with Stratification", "authors": [ "Justin Wong", "Yury Orlovskiy", "Michael Luo", "Sanjit A. Seshia", "Joseph E. Gonzalez" ], "github": "", "project_page": "", "abstract": "Generating diverse responses from large language models (LLMs) is crucial for applications such as planning/search and synthetic data generation, where diversity provides distinct answers across generations. Prior approaches rely on increasing temperature to increase diversity. However, contrary to popular belief, we show not only does this approach produce lower quality individual generations as temperature increases, but it depends on model's next-token probabilities being similar to the true distribution of answers. We propose , an alternative approach that uses the language model itself to partition the space into strata. At inference, a random stratum is selected and a sample drawn from within the strata. To measure diversity, we introduce CoverageQA, a dataset of underspecified questions with multiple equally plausible answers, and assess diversity by measuring KL Divergence between the output distribution and uniform distribution over valid ground truth answers. As computing probability per response/solution for proprietary models is infeasible, we measure recall on ground truth solutions. Our evaluation show using SimpleStrat achieves higher recall by 0.05 compared to GPT-4o and 0.36 average reduction in KL Divergence compared to Llama 3.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-14", "arxiv_id": "2410.08193", "title": "GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignment", "authors": [ "Yuancheng Xu", "Udari Madhushani Sehwag", "Alec Koppel", "Sicheng Zhu", "Bang An", "Furong Huang", "Sumitra Ganesh" ], "github": "https://github.com/Yuancheng-Xu/GenARM", "project_page": "", "abstract": "Large Language Models (LLMs) exhibit impressive capabilities but require careful alignment with human preferences. Traditional training-time methods finetune LLMs using human preference datasets but incur significant training costs and require repeated training to handle diverse user preferences. Test-time alignment methods address this by using reward models (RMs) to guide frozen LLMs without retraining. However, existing test-time approaches rely on trajectory-level RMs which are designed to evaluate complete responses, making them unsuitable for autoregressive text generation that requires computing next-token rewards from partial responses. To address this, we introduce GenARM, a test-time alignment approach that leverages the Autoregressive Reward Model--a novel reward parametrization designed to predict next-token rewards for efficient and effective autoregressive generation. Theoretically, we demonstrate that this parametrization can provably guide frozen LLMs toward any distribution achievable by traditional RMs within the KL-regularized reinforcement learning framework. Experimental results show that GenARM significantly outperforms prior test-time alignment baselines and matches the performance of training-time methods. Additionally, GenARM enables efficient weak-to-strong guidance, aligning larger LLMs with smaller RMs without the high costs of training larger models. Furthermore, GenARM supports multi-objective alignment, allowing real-time trade-offs between preference dimensions and catering to diverse user preferences without retraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Yuancheng-Xu/GenARM", "github_api_added_by": "auto" }, { "date": "2024-10-14", "arxiv_id": "2410.08612", "title": "Synth-SONAR: Sonar Image Synthesis with Enhanced Diversity and Realism via Dual Diffusion Models and GPT Prompting", "authors": [ "Purushothaman Natarajan", "Kamal Basha", "Athira Nambiar" ], "github": "https://github.com/Purushothaman-natarajan/Synth-SONAR", "project_page": "", "abstract": "Sonar image synthesis is crucial for advancing applications in underwater exploration, marine biology, and defence. Traditional methods often rely on extensive and costly data collection using sonar sensors, jeopardizing data quality and diversity. To overcome these limitations, this study proposes a new sonar image synthesis framework, Synth-SONAR leveraging diffusion models and GPT prompting. The key novelties of Synth-SONAR are threefold: First, by integrating Generative AI-based style injection techniques along with publicly available real/simulated data, thereby producing one of the largest sonar data corpus for sonar research. Second, a dual text-conditioning sonar diffusion model hierarchy synthesizes coarse and fine-grained sonar images with enhanced quality and diversity. Third, high-level (coarse) and low-level (detailed) text-based sonar generation methods leverage advanced semantic information available in visual language models (VLMs) and GPT-prompting. During inference, the method generates diverse and realistic sonar images from textual prompts, bridging the gap between textual descriptions and sonar image generation. This marks the application of GPT-prompting in sonar imagery for the first time, to the best of our knowledge. Synth-SONAR achieves state-of-the-art results in producing high-quality synthetic sonar datasets, significantly enhancing their diversity and realism.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Purushothaman-natarajan/Synth-SONAR", "github_api_added_by": "auto" }, { "date": "2024-10-14", "arxiv_id": "2410.06264", "title": "Think While You Generate: Discrete Diffusion with Planned Denoising", "authors": [ "Sulin Liu", "Juno Nam", "Andrew Campbell", "Hannes St\u00e4rk", "Yilun Xu", "Tommi Jaakkola", "Rafael G\u00f3mez-Bombarelli" ], "github": "https://github.com/liusulin/DDPD", "project_page": "", "abstract": "Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce Discrete Diffusion with Planned Denoising (DDPD), a novel framework that separates the generation process into two models: a planner and a denoiser. At inference time, the planner selects which positions to denoise next by identifying the most corrupted positions in need of denoising, including both initially corrupted and those requiring additional refinement. This plan-and-denoise approach enables more efficient reconstruction during generation by iteratively identifying and denoising corruptions in the optimal order. DDPD outperforms traditional denoiser-only mask diffusion methods, achieving superior results on language modeling benchmarks such as text8, OpenWebText, and token-based generation on ImageNet 256 times 256. Notably, in language modeling, DDPD significantly reduces the performance gap between diffusion-based and autoregressive methods in terms of generative perplexity. Code is available at https://github.com/liusulin/DDPD.", "github_manual": "", "github_regex": "https://github.com/liusulin/DDPD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.07985", "title": "Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models", "authors": [ "Bofei Gao", "Feifan Song", "Zhe Yang", "Zefan Cai", "Yibo Miao", "Qingxiu Dong", "Lei Li", "Chenghao Ma", "Liang Chen", "Runxin Xu", "Zhengyang Tang", "Benyou Wang", "Daoguang Zan", "Shanghaoran Quan", "Ge Zhang", "Lei Sha", "Yichang Zhang", "Xuancheng Ren", "Tianyu Liu", "Baobao Chang" ], "github": "", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) have led to significant breakthroughs in mathematical reasoning capabilities. However, existing benchmarks like GSM8K or MATH are now being solved with high accuracy (e.g., OpenAI o1 achieves 94.8% on MATH dataset), indicating their inadequacy for truly challenging these models. To bridge this gap, we propose a comprehensive and challenging benchmark specifically designed to assess LLMs' mathematical reasoning at the Olympiad level. Unlike existing Olympiad-related benchmarks, our dataset focuses exclusively on mathematics and comprises a vast collection of 4428 competition-level problems with rigorous human annotation. These problems are meticulously categorized into over 33 sub-domains and span more than 10 distinct difficulty levels, enabling a holistic assessment of model performance in Olympiad-mathematical reasoning. Furthermore, we conducted an in-depth analysis based on this benchmark. Our experimental results show that even the most advanced models, OpenAI o1-mini and OpenAI o1-preview, struggle with highly challenging Olympiad-level problems, with 60.54% and 52.55% accuracy, highlighting significant challenges in Olympiad-level mathematical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.10139", "title": "MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models", "authors": [ "Peng Xia", "Siwei Han", "Shi Qiu", "Yiyang Zhou", "Zhaoyang Wang", "Wenhao Zheng", "Zhaorun Chen", "Chenhang Cui", "Mingyu Ding", "Linjie Li", "Lijuan Wang", "Huaxiu Yao" ], "github": "https://github.com/Lillianwei-h/MMIE", "project_page": "", "abstract": "Interleaved multimodal comprehension and generation, enabling models to produce and interpret both images and text in arbitrary sequences, have become a pivotal area in multimodal learning. Despite significant advancements, the evaluation of this capability remains insufficient. Existing benchmarks suffer from limitations in data scale, scope, and evaluation depth, while current evaluation metrics are often costly or biased, lacking in reliability for practical applications. To address these challenges, we introduce MMIE, a large-scale knowledge-intensive benchmark for evaluating interleaved multimodal comprehension and generation in Large Vision-Language Models (LVLMs). MMIE comprises 20K meticulously curated multimodal queries, spanning 3 categories, 12 fields, and 102 subfields, including mathematics, coding, physics, literature, health, and arts. It supports both interleaved inputs and outputs, offering a mix of multiple-choice and open-ended question formats to evaluate diverse competencies. Moreover, we propose a reliable automated evaluation metric, leveraging a scoring model fine-tuned with human-annotated data and systematic evaluation criteria, aimed at reducing bias and improving evaluation accuracy. Extensive experiments demonstrate the effectiveness of our benchmark and metrics in providing a comprehensive evaluation of interleaved LVLMs. Specifically, we evaluate eight LVLMs, revealing that even the best models show significant room for improvement, with most achieving only moderate results. We believe MMIE will drive further advancements in the development of interleaved LVLMs. We publicly release our benchmark and code in https://mmie-bench.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Lillianwei-h/MMIE", "github_api_added_by": "auto" }, { "date": "2024-10-15", "arxiv_id": "2410.09584", "title": "Toward General Instruction-Following Alignment for Retrieval-Augmented Generation", "authors": [ "Guanting Dong", "Xiaoshuai Song", "Yutao Zhu", "Runqi Qiao", "Zhicheng Dou", "Ji-Rong Wen" ], "github": "https://github.com/dongguanting/FollowRAG", "project_page": "", "abstract": "Following natural instructions is crucial for the effective application of Retrieval-Augmented Generation (RAG) systems. Despite recent advancements in Large Language Models (LLMs), research on assessing and improving instruction-following (IF) alignment within the RAG domain remains limited. To address this issue, we propose VIF-RAG, the first automated, scalable, and verifiable synthetic pipeline for instruction-following alignment in RAG systems. We start by manually crafting a minimal set of atomic instructions (<100) and developing combination rules to synthesize and verify complex instructions for a seed set. We then use supervised models for instruction rewriting while simultaneously generating code to automate the verification of instruction quality via a Python executor. Finally, we integrate these instructions with extensive RAG and general data samples, scaling up to a high-quality VIF-RAG-QA dataset (>100k) through automated processes. To further bridge the gap in instruction-following auto-evaluation for RAG systems, we introduce FollowRAG Benchmark, which includes approximately 3K test samples, covering 22 categories of general instruction constraints and four knowledge-intensive QA datasets. Due to its robust pipeline design, FollowRAG can seamlessly integrate with different RAG benchmarks. Using FollowRAG and eight widely-used IF and foundational abilities benchmarks for LLMs, we demonstrate that VIF-RAG markedly enhances LLM performance across a broad range of general instruction constraints while effectively leveraging its capabilities in RAG scenarios. Further analysis offers practical insights for achieving IF alignment in RAG systems. Our code and datasets are released at https://FollowRAG.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dongguanting/FollowRAG", "github_api_added_by": "auto" }, { "date": "2024-10-15", "arxiv_id": "2410.09732", "title": "LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models", "authors": [ "Junyan Ye", "Baichuan Zhou", "Zilong Huang", "Junan Zhang", "Tianyi Bai", "Hengrui Kang", "Jun He", "Honglin Lin", "Zihao Wang", "Tong Wu", "Zhizheng Wu", "Yiping Chen", "Dahua Lin", "Conghui He", "Weijia Li" ], "github": "https://github.com/opendatalab/LOKI", "project_page": "", "abstract": "With the rapid development of AI-generated content, the future internet may be inundated with synthetic data, making the discrimination of authentic and credible multimodal data increasingly challenging. Synthetic data detection has thus garnered widespread attention, and the performance of large multimodal models (LMMs) in this task has attracted significant interest. LMMs can provide natural language explanations for their authenticity judgments, enhancing the explainability of synthetic content detection. Simultaneously, the task of distinguishing between real and synthetic data effectively tests the perception, knowledge, and reasoning capabilities of LMMs. In response, we introduce LOKI, a novel benchmark designed to evaluate the ability of LMMs to detect synthetic data across multiple modalities. LOKI encompasses video, image, 3D, text, and audio modalities, comprising 18K carefully curated questions across 26 subcategories with clear difficulty levels. The benchmark includes coarse-grained judgment and multiple-choice questions, as well as fine-grained anomaly selection and explanation tasks, allowing for a comprehensive analysis of LMMs. We evaluated 22 open-source LMMs and 6 closed-source models on LOKI, highlighting their potential as synthetic data detectors and also revealing some limitations in the development of LMM capabilities. More information about LOKI can be found at https://opendatalab.github.io/LOKI/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/opendatalab/LOKI", "github_api_added_by": "auto" }, { "date": "2024-10-15", "arxiv_id": "2410.07752", "title": "TVBench: Redesigning Video-Language Evaluation", "authors": [ "Daniel Cores", "Michael Dorkenwald", "Manuel Mucientes", "Cees G. M. Snoek", "Yuki M. Asano" ], "github": "", "project_page": "", "abstract": "Large language models have demonstrated impressive performance when integrated with vision models even enabling video understanding. However, evaluating these video models presents its own unique challenges, for which several benchmarks have been proposed. In this paper, we show that the currently most used video-language benchmarks can be solved without requiring much temporal reasoning. We identified three main issues in existing datasets: (i) static information from single frames is often sufficient to solve the tasks (ii) the text of the questions and candidate answers is overly informative, allowing models to answer correctly without relying on any visual input (iii) world knowledge alone can answer many of the questions, making the benchmarks a test of knowledge replication rather than visual reasoning. In addition, we found that open-ended question-answering benchmarks for video understanding suffer from similar issues while the automatic evaluation process with LLMs is unreliable, making it an unsuitable alternative. As a solution, we propose TVBench, a novel open-source video multiple-choice question-answering benchmark, and demonstrate through extensive evaluations that it requires a high level of temporal understanding. Surprisingly, we find that most recent state-of-the-art video-language models perform similarly to random performance on TVBench, with only Gemini-Pro and Tarsier clearly surpassing this baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.09335", "title": "Rethinking Data Selection at Scale: Random Selection is Almost All You Need", "authors": [ "Tingyu Xia", "Bowen Yu", "Kai Dang", "An Yang", "Yuan Wu", "Yuan Tian", "Yi Chang", "Junyang Lin" ], "github": "https://github.com/xiatingyu/sft-dataselection-at-scale", "project_page": "", "abstract": "Supervised fine-tuning (SFT) is crucial for aligning Large Language Models (LLMs) with human instructions. The primary goal during SFT is to select a small yet representative subset of training data from the larger pool, such that fine-tuning with this subset achieves results comparable to or even exceeding those obtained using the entire dataset. However, most existing data selection techniques are designed for small-scale data pools, which fail to meet the demands of real-world SFT scenarios. In this paper, we replicated several self-scoring methods those that do not rely on external model assistance on two million scale datasets, and found that nearly all methods struggled to significantly outperform random selection when dealing with such large-scale data pools. Moreover, our comparisons suggest that, during SFT, diversity in data selection is more critical than simply focusing on high quality data. We also analyzed the limitations of several current approaches, explaining why they perform poorly on large-scale datasets and why they are unsuitable for such contexts. Finally, we found that filtering data by token length offers a stable and efficient method for improving results. This approach, particularly when training on long text data, proves highly beneficial for relatively weaker base models, such as Llama3.", "github_manual": "https://github.com/xiatingyu/sft-dataselection-at-scale", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.10774", "title": "Cavia: Camera-controllable Multi-view Video Diffusion with View-Integrated Attention", "authors": [ "Dejia Xu", "Yifan Jiang", "Chen Huang", "Liangchen Song", "Thorsten Gernoth", "Liangliang Cao", "Zhangyang Wang", "Hao Tang" ], "github": "https://github.com/GaParmar/clean-fid", "project_page": "", "abstract": "In recent years there have been remarkable breakthroughs in image-to-video generation. However, the 3D consistency and camera controllability of generated frames have remained unsolved. Recent studies have attempted to incorporate camera control into the generation process, but their results are often limited to simple trajectories or lack the ability to generate consistent videos from multiple distinct camera paths for the same scene. To address these limitations, we introduce Cavia, a novel framework for camera-controllable, multi-view video generation, capable of converting an input image into multiple spatiotemporally consistent videos. Our framework extends the spatial and temporal attention modules into view-integrated attention modules, improving both viewpoint and temporal consistency. This flexible design allows for joint training with diverse curated data sources, including scene-level static videos, object-level synthetic multi-view dynamic videos, and real-world monocular dynamic videos. To our best knowledge, Cavia is the first of its kind that allows the user to precisely specify camera motion while obtaining object motion. Extensive experiments demonstrate that Cavia surpasses state-of-the-art methods in terms of geometric consistency and perceptual quality. Project Page: https://ir1d.github.io/Cavia/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GaParmar/clean-fid", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.10563", "title": "MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks", "authors": [ "Jiacheng Chen", "Tianhao Liang", "Sherman Siu", "Zhengqing Wang", "Kai Wang", "Yubo Wang", "Yuansheng Ni", "Wang Zhu", "Ziyan Jiang", "Bohan Lyu", "Dongfu Jiang", "Xuan He", "Yuan Liu", "Hexiang Hu", "Xiang Yue", "Wenhu Chen" ], "github": "https://github.com/TIGER-AI-Lab/MEGA-Bench", "project_page": "", "abstract": "We present MEGA-Bench, an evaluation suite that scales multimodal evaluation to over 500 real-world tasks, to address the highly heterogeneous daily use cases of end users. Our objective is to optimize for a set of high-quality data samples that cover a highly diverse and rich set of multimodal tasks, while enabling cost-effective and accurate model evaluation. In particular, we collected 505 realistic tasks encompassing over 8,000 samples from 16 expert annotators to extensively cover the multimodal task space. Instead of unifying these problems into standard multi-choice questions (like MMMU, MMBench, and MMT-Bench), we embrace a wide range of output formats like numbers, phrases, code, \\LaTeX, coordinates, JSON, free-form, etc. To accommodate these formats, we developed over 40 metrics to evaluate these tasks. Unlike existing benchmarks, MEGA-Bench offers a fine-grained capability report across multiple dimensions (e.g., application, input type, output format, skill), allowing users to interact with and visualize model capabilities in depth. We evaluate a wide variety of frontier vision-language models on MEGA-Bench to understand their capabilities across these dimensions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TIGER-AI-Lab/MEGA-Bench", "github_api_added_by": "auto" }, { "date": "2024-10-15", "arxiv_id": "2410.10306", "title": "Animate-X: Universal Character Image Animation with Enhanced Motion Representation", "authors": [ "Shuai Tan", "Biao Gong", "Xiang Wang", "Shiwei Zhang", "Dandan Zheng", "Ruobing Zheng", "Kecheng Zheng", "Jingdong Chen", "Ming Yang" ], "github": "https://github.com/antgroup/animate-x", "project_page": "", "abstract": "Character image animation, which generates high-quality videos from a reference image and target pose sequence, has seen significant progress in recent years. However, most existing methods only apply to human figures, which usually do not generalize well on anthropomorphic characters commonly used in industries like gaming and entertainment. Our in-depth analysis suggests to attribute this limitation to their insufficient modeling of motion, which is unable to comprehend the movement pattern of the driving video, thus imposing a pose sequence rigidly onto the target character. To this end, this paper proposes Animate-X, a universal animation framework based on LDM for various character types (collectively named X), including anthropomorphic characters. To enhance motion representation, we introduce the Pose Indicator, which captures comprehensive motion pattern from the driving video through both implicit and explicit manner. The former leverages CLIP visual features of a driving video to extract its gist of motion, like the overall movement pattern and temporal relations among motions, while the latter strengthens the generalization of LDM by simulating possible inputs in advance that may arise during inference. Moreover, we introduce a new Animated Anthropomorphic Benchmark (A^2Bench) to evaluate the performance of Animate-X on universal and widely applicable animation images. Extensive experiments demonstrate the superiority and effectiveness of Animate-X compared to state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/antgroup/animate-x", "github_api_added_by": "auto" }, { "date": "2024-10-15", "arxiv_id": "2410.10818", "title": "TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models", "authors": [ "Mu Cai", "Reuben Tan", "Jianrui Zhang", "Bocheng Zou", "Kai Zhang", "Feng Yao", "Fangrui Zhu", "Jing Gu", "Yiwu Zhong", "Yuzhang Shang", "Yao Dou", "Jaden Park", "Jianfeng Gao", "Yong Jae Lee", "Jianwei Yang" ], "github": "https://github.com/mu-cai/TemporalBench", "project_page": "", "abstract": "Understanding fine-grained temporal dynamics is crucial for multimodal video comprehension and generation. Due to the lack of fine-grained temporal annotations, existing video benchmarks mostly resemble static image benchmarks and are incompetent at evaluating models for temporal understanding. In this paper, we introduce TemporalBench, a new benchmark dedicated to evaluating fine-grained temporal understanding in videos. TemporalBench consists of ~10K video question-answer pairs, derived from ~2K high-quality human annotations detailing the temporal dynamics in video clips. As a result, our benchmark provides a unique testbed for evaluating various temporal understanding and reasoning abilities such as action frequency, motion magnitude, event order, etc. Moreover, it enables evaluations on various tasks like both video question answering and captioning, both short and long video understanding, as well as different models such as multimodal video embedding models and text generation models. Results show that state-of-the-art models like GPT-4o achieve only 38.5% question answering accuracy on TemporalBench, demonstrating a significant gap (~30%) between humans and AI in temporal understanding. Furthermore, we notice a critical pitfall for multi-choice QA where LLMs can detect the subtle changes in negative captions and find a centralized description as a cue for its prediction, where we propose Multiple Binary Accuracy (MBA) to correct such bias. We hope that TemporalBench can foster research on improving models' temporal reasoning capabilities. Both dataset and evaluation code will be made available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mu-cai/TemporalBench", "github_api_added_by": "auto" }, { "date": "2024-10-15", "arxiv_id": "2410.10803", "title": "Generalizable Humanoid Manipulation with Improved 3D Diffusion Policies", "authors": [ "Yanjie Ze", "Zixuan Chen", "Wenhao Wang", "Tianyi Chen", "Xialin He", "Ying Yuan", "Xue Bin Peng", "Jiajun Wu" ], "github": "https://github.com/YanjieZe/Improved-3D-Diffusion-Policy", "project_page": "", "abstract": "Humanoid robots capable of autonomous operation in diverse environments have long been a goal for roboticists. However, autonomous manipulation by humanoid robots has largely been restricted to one specific scene, primarily due to the difficulty of acquiring generalizable skills. Recent advances in 3D visuomotor policies, such as the 3D Diffusion Policy (DP3), have shown promise in extending these capabilities to wilder environments. However, 3D visuomotor policies often rely on camera calibration and point-cloud segmentation, which present challenges for deployment on mobile robots like humanoids. In this work, we introduce the Improved 3D Diffusion Policy (iDP3), a novel 3D visuomotor policy that eliminates these constraints by leveraging egocentric 3D visual representations. We demonstrate that iDP3 enables a full-sized humanoid robot to autonomously perform skills in diverse real-world scenarios, using only data collected in the lab. Videos are available at: https://humanoid-manipulation.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YanjieZe/Improved-3D-Diffusion-Policy", "github_api_added_by": "auto" }, { "date": "2024-10-15", "arxiv_id": "2410.10813", "title": "LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory", "authors": [ "Di Wu", "Hongwei Wang", "Wenhao Yu", "Yuwei Zhang", "Kai-Wei Chang", "Dong Yu" ], "github": "https://github.com/xiaowu0162/longmemeval", "project_page": "", "abstract": "Recent large language model (LLM)-driven chat assistant systems have integrated memory components to track user-assistant chat histories, enabling more accurate and personalized responses. However, their long-term memory capabilities in sustained interactions remain underexplored. This paper introduces LongMemEval, a comprehensive benchmark designed to evaluate five core long-term memory abilities of chat assistants: information extraction, multi-session reasoning, temporal reasoning, knowledge updates, and abstention. With 500 meticulously curated questions embedded within freely scalable user-assistant chat histories, LongMemEval presents a significant challenge to existing long-term memory systems, with commercial chat assistants and long-context LLMs showing 30% accuracy drop on memorizing information across sustained interactions. We then present a unified framework that breaks down the long-term memory design into four design choices across the indexing, retrieval, and reading stages. Built upon key experimental insights, we propose several memory designs including session decomposition for optimizing value granularity, fact-augmented key expansion for enhancing the index structure, and time-aware query expansion for refining the search scope. Experiment results show that these optimizations greatly improve both memory recall and downstream question answering on LongMemEval. Overall, our study provides valuable resources and guidance for advancing the long-term memory capabilities of LLM-based chat assistants, paving the way toward more personalized and reliable conversational AI.", "github_manual": "https://github.com/xiaowu0162/longmemeval", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.10792", "title": "Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations", "authors": [ "Litu Rout", "Yujia Chen", "Nataniel Ruiz", "Constantine Caramanis", "Sanjay Shakkottai", "Wen-Sheng Chu" ], "github": "https://github.com/LituRout/RF-Inversion", "project_page": "", "abstract": "Generative models transform random noise into images; their inversion aims to transform images back to structured noise for recovery and editing. This paper addresses two key tasks: (i) inversion and (ii) editing of a real image using stochastic equivalents of rectified flow models (such as Flux). Although Diffusion Models (DMs) have recently dominated the field of generative modeling for images, their inversion presents faithfulness and editability challenges due to nonlinearities in drift and diffusion. Existing state-of-the-art DM inversion approaches rely on training of additional parameters or test-time optimization of latent variables; both are expensive in practice. Rectified Flows (RFs) offer a promising alternative to diffusion models, yet their inversion has been underexplored. We propose RF inversion using dynamic optimal control derived via a linear quadratic regulator. We prove that the resulting vector field is equivalent to a rectified stochastic differential equation. Additionally, we extend our framework to design a stochastic sampler for Flux. Our inversion method allows for state-of-the-art performance in zero-shot inversion and editing, outperforming prior works in stroke-to-image synthesis and semantic image editing, with large-scale human evaluations confirming user preference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LituRout/RF-Inversion", "github_api_added_by": "auto" }, { "date": "2024-10-15", "arxiv_id": "2410.10783", "title": "LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers Content", "authors": [ "Nimrod Shabtay", "Felipe Maia Polo", "Sivan Doveh", "Wei Lin", "M. Jehanzeb Mirza", "Leshem Chosen", "Mikhail Yurochkin", "Yuekai Sun", "Assaf Arbelle", "Leonid Karlinsky", "Raja Giryes" ], "github": "https://github.com/nimrodshabtay/livexiv", "project_page": "", "abstract": "The large-scale training of multi-modal models on data scraped from the web has shown outstanding utility in infusing these models with the required world knowledge to perform effectively on multiple downstream tasks. However, one downside of scraping data from the web can be the potential sacrifice of the benchmarks on which the abilities of these models are often evaluated. To safeguard against test data contamination and to truly test the abilities of these foundation models we propose LiveXiv: A scalable evolving live benchmark based on scientific ArXiv papers. LiveXiv accesses domain-specific manuscripts at any given timestamp and proposes to automatically generate visual question-answer pairs (VQA). This is done without any human-in-the-loop, using the multi-modal content in the manuscripts, like graphs, charts, and tables. Moreover, we introduce an efficient evaluation approach that estimates the performance of all models on the evolving benchmark using evaluations of only a subset of models. This significantly reduces the overall evaluation cost. We benchmark multiple open and proprietary Large Multi-modal Models (LMMs) on the first version of our benchmark, showing its challenging nature and exposing the models true abilities, avoiding contamination. Lastly, in our commitment to high quality, we have collected and evaluated a manually verified subset. By comparing its overall results to our automatic annotations, we have found that the performance variance is indeed minimal (<2.5%). Our dataset is available online on HuggingFace, and our code will be available here.", "github_manual": "https://github.com/nimrodshabtay/livexiv", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.06634", "title": "Tree of Problems: Improving structured problem solving with compositionality", "authors": [ "Armel Zebaze", "Beno\u00eet Sagot", "Rachel Bawden" ], "github": "https://github.com/ArmelRandy/tree-of-problems", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable performance across multiple tasks through in-context learning. For complex reasoning tasks that require step-by-step thinking, Chain-of-Thought (CoT) prompting has given impressive results, especially when combined with self-consistency. Nonetheless, some tasks remain particularly difficult for LLMs to solve. Tree of Thoughts (ToT) and Graph of Thoughts (GoT) emerged as alternatives, dividing the complex problem into paths of subproblems. In this paper, we propose Tree of Problems (ToP), a simpler version of ToT, which we hypothesise can work better for complex tasks that can be divided into identical subtasks. Our empirical results show that our approach outperforms ToT and GoT, and in addition performs better than CoT on complex reasoning tasks. All code for this paper is publicly available here: https://github.com/ArmelRandy/tree-of-problems.", "github_manual": "", "github_regex": "https://github.com/ArmelRandy/tree-of-problems", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.09733", "title": "MMCOMPOSITION: Revisiting the Compositionality of Pre-trained Vision-Language Models", "authors": [ "Hang Hua", "Yunlong Tang", "Ziyun Zeng", "Liangliang Cao", "Zhengyuan Yang", "Hangfeng He", "Chenliang Xu", "Jiebo Luo" ], "github": "", "project_page": "", "abstract": "The advent of large Vision-Language Models (VLMs) has significantly advanced multimodal understanding, enabling more sophisticated and accurate integration of visual and textual information across various tasks, including image and video captioning, visual question answering, and cross-modal retrieval. Despite VLMs' superior capabilities, researchers lack a comprehensive understanding of their compositionality -- the ability to understand and produce novel combinations of known visual and textual components. Prior benchmarks provide only a relatively rough compositionality evaluation from the perspectives of objects, relations, and attributes while neglecting deeper reasoning about object interactions, counting, and complex compositions. However, compositionality is a critical ability that facilitates coherent reasoning and understanding across modalities for VLMs. To address this limitation, we propose MMCOMPOSITION, a novel human-annotated benchmark for comprehensively and accurately evaluating VLMs' compositionality. Our proposed benchmark serves as a complement to these earlier works. With MMCOMPOSITION, we can quantify and explore the compositionality of the mainstream VLMs. Surprisingly, we find GPT-4o's compositionality inferior to the best open-source model, and we analyze the underlying reasons. Our experimental analysis reveals the limitations of VLMs in fine-grained compositional perception and reasoning, and points to areas for improvement in VLM design and training. Resources available at: https://hanghuacs.github.io/MMComposition/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.10594", "title": "VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents", "authors": [ "Shi Yu", "Chaoyue Tang", "Bokai Xu", "Junbo Cui", "Junhao Ran", "Yukun Yan", "Zhenghao Liu", "Shuo Wang", "Xu Han", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/openbmb/visrag", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) is an effective technique that enables large language models (LLMs) to utilize external knowledge sources for generation. However, current RAG systems are solely based on text, rendering it impossible to utilize vision information like layout and images that play crucial roles in real-world multi-modality documents. In this paper, we introduce VisRAG, which tackles this issue by establishing a vision-language model (VLM)-based RAG pipeline. In this pipeline, instead of first parsing the document to obtain text, the document is directly embedded using a VLM as an image and then retrieved to enhance the generation of a VLM. Compared to traditional text-based RAG, VisRAG maximizes the retention and utilization of the data information in the original documents, eliminating the information loss introduced during the parsing process. We collect both open-source and synthetic data to train the retriever in VisRAG and explore a variety of generation methods. Experiments demonstrate that VisRAG outperforms traditional RAG in both the retrieval and generation stages, achieving a 25--39\\% end-to-end performance gain over traditional text-based RAG pipeline. Further analysis reveals that VisRAG is effective in utilizing training data and demonstrates strong generalization capability, positioning it as a promising solution for RAG on multi-modality documents. Our code and data are available at https://github.com/openbmb/visrag .", "github_manual": "", "github_regex": "https://github.com/openbmb/visrag", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.10630", "title": "Thinking LLMs: General Instruction Following with Thought Generation", "authors": [ "Tianhao Wu", "Janice Lan", "Weizhe Yuan", "Jiantao Jiao", "Jason Weston", "Sainbayar Sukhbaatar" ], "github": "", "project_page": "", "abstract": "LLMs are typically trained to answer user questions or follow instructions similarly to how human experts respond. However, in the standard alignment framework they lack the basic ability of explicit thinking before answering. Thinking is important for complex questions that require reasoning and planning -- but can be applied to any task. We propose a training method for equipping existing LLMs with such thinking abilities for general instruction following without use of additional human data. We achieve this by an iterative search and optimization procedure that explores the space of possible thought generations, allowing the model to learn how to think without direct supervision. For each instruction, the thought candidates are scored using a judge model to evaluate their responses only, and then optimized via preference optimization. We show that this procedure leads to superior performance on AlpacaEval and Arena-Hard, and shows gains from thinking on non-reasoning categories such as marketing, health and general knowledge, in addition to more traditional reasoning & problem-solving tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.09223", "title": "The Same But Different: Structural Similarities and Differences in Multilingual Language Modeling", "authors": [ "Ruochen Zhang", "Qinan Yu", "Matianyu Zang", "Carsten Eickhoff", "Ellie Pavlick" ], "github": "", "project_page": "", "abstract": "We employ new tools from mechanistic interpretability in order to ask whether the internal structure of large language models (LLMs) shows correspondence to the linguistic structures which underlie the languages on which they are trained. In particular, we ask (1) when two languages employ the same morphosyntactic processes, do LLMs handle them using shared internal circuitry? and (2) when two languages require different morphosyntactic processes, do LLMs handle them using different internal circuitry? Using English and Chinese multilingual and monolingual models, we analyze the internal circuitry involved in two tasks. We find evidence that models employ the same circuit to handle the same syntactic process independently of the language in which it occurs, and that this is the case even for monolingual models trained completely independently. Moreover, we show that multilingual models employ language-specific components (attention heads and feed-forward networks) when needed to handle linguistic processes (e.g., morphological marking) that only exist in some languages. Together, our results provide new insights into how LLMs trade off between exploiting common structures and preserving linguistic differences when tasked with modeling multiple languages simultaneously.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.09637", "title": "ReLU's Revival: On the Entropic Overload in Normalization-Free Large Language Models", "authors": [ "Nandan Kumar Jha", "Brandon Reagen" ], "github": "https://github.com/nandan91/relu-revival-normfree", "project_page": "", "abstract": "LayerNorm is a critical component in modern large language models (LLMs) for stabilizing training and ensuring smooth optimization. However, it introduces significant challenges in mechanistic interpretability, outlier feature suppression, faithful signal propagation, and computational and communication complexity of private inference. This work explores desirable activation functions in normalization-free decoder-only LLMs. Contrary to the conventional preference for the GELU in transformer-based models, our empirical findings demonstrate an {\\em opposite trend} -- ReLU significantly outperforms GELU in LayerNorm-free models, leading to an {\\bf 8.2\\%} perplexity improvement. We discover a key issue with GELU, where early layers experience entropic overload, leading to the under-utilization of the representational capacity of attention heads. This highlights that smoother activations like GELU are {\\em ill-suited} for LayerNorm-free architectures, whereas ReLU's geometrical properties -- specialization in input space and intra-class selectivity -- lead to improved learning dynamics and better information retention in the absence of LayerNorm. This study offers key insights for optimizing transformer architectures where LayerNorm introduces significant challenges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nandan91/relu-revival-normfree", "github_api_added_by": "auto" }, { "date": "2024-10-15", "arxiv_id": "2410.10819", "title": "DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads", "authors": [ "Guangxuan Xiao", "Jiaming Tang", "Jingwei Zuo", "Junxian Guo", "Shang Yang", "Haotian Tang", "Yao Fu", "Song Han" ], "github": "https://github.com/mit-han-lab/duo-attention", "project_page": "", "abstract": "Deploying long-context large language models (LLMs) is essential but poses significant computational and memory challenges. Caching all Key and Value (KV) states across all attention heads consumes substantial memory. Existing KV cache pruning methods either damage the long-context capabilities of LLMs or offer only limited efficiency improvements. In this paper, we identify that only a fraction of attention heads, a.k.a, Retrieval Heads, are critical for processing long contexts and require full attention across all tokens. In contrast, all other heads, which primarily focus on recent tokens and attention sinks--referred to as Streaming Heads--do not require full attention. Based on this insight, we introduce DuoAttention, a framework that only applies a full KV cache to retrieval heads while using a light-weight, constant-length KV cache for streaming heads, which reduces both LLM's decoding and pre-filling memory and latency without compromising its long-context abilities. DuoAttention uses a lightweight, optimization-based algorithm with synthetic data to identify retrieval heads accurately. Our method significantly reduces long-context inference memory by up to 2.55x for MHA and 1.67x for GQA models while speeding up decoding by up to 2.18x and 1.50x and accelerating pre-filling by up to 1.73x and 1.63x for MHA and GQA models, respectively, with minimal accuracy loss compared to full attention. Notably, combined with quantization, DuoAttention enables Llama-3-8B decoding with 3.3 million context length on a single A100 GPU. Code is provided in https://github.com/mit-han-lab/duo-attention.", "github_manual": "", "github_regex": "https://github.com/mit-han-lab/duo-attention", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-15", "arxiv_id": "2410.11758", "title": "Latent Action Pretraining from Videos", "authors": [ "Seonghyeon Ye", "Joel Jang", "Byeongguk Jeon", "Sejune Joo", "Jianwei Yang", "Baolin Peng", "Ajay Mandlekar", "Reuben Tan", "Yu-Wei Chao", "Bill Yuchen Lin", "Lars Liden", "Kimin Lee", "Jianfeng Gao", "Luke Zettlemoyer", "Dieter Fox", "Minjoon Seo" ], "github": "", "project_page": "", "abstract": "We introduce Latent Action Pretraining for general Action models (LAPA), an unsupervised method for pretraining Vision-Language-Action (VLA) models without ground-truth robot action labels. Existing Vision-Language-Action models require action labels typically collected by human teleoperators during pretraining, which significantly limits possible data sources and scale. In this work, we propose a method to learn from internet-scale videos that do not have robot action labels. We first train an action quantization model leveraging VQ-VAE-based objective to learn discrete latent actions between image frames, then pretrain a latent VLA model to predict these latent actions from observations and task descriptions, and finally finetune the VLA on small-scale robot manipulation data to map from latent to robot actions. Experimental results demonstrate that our method significantly outperforms existing techniques that train robot manipulation policies from large-scale videos. Furthermore, it outperforms the state-of-the-art VLA model trained with robotic action labels on real-world manipulation tasks that require language conditioning, generalization to unseen objects, and semantic generalization to unseen instructions. Training only on human manipulation videos also shows positive transfer, opening up the potential for leveraging web-scale data for robotics foundation model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.11419", "title": "GS^3: Efficient Relighting with Triple Gaussian Splatting", "authors": [ "Zoubin Bi", "Yixin Zeng", "Chong Zeng", "Fan Pei", "Xiang Feng", "Kun Zhou", "Hongzhi Wu" ], "github": "https://github.com/gsrelight/gs-relight", "project_page": "", "abstract": "We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex appearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance. Our code and data are publicly available at https://GSrelight.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gsrelight/gs-relight", "github_api_added_by": "auto" }, { "date": "2024-10-16", "arxiv_id": "2410.11710", "title": "MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models", "authors": [ "Pei Wang", "Yanan Wu", "Zekun Wang", "Jiaheng Liu", "Xiaoshuai Song", "Zhongyuan Peng", "Ken Deng", "Chenchen Zhang", "Jiakai Wang", "Junran Peng", "Ge Zhang", "Hangyu Guo", "Zhaoxiang Zhang", "Wenbo Su", "Bo Zheng" ], "github": "https://github.com/mtu-bench-team/mtu-bench", "project_page": "", "abstract": "Large Language Models (LLMs) have displayed massive improvements in reasoning and decision-making skills and can hold natural conversations with users. Recently, many tool-use benchmark datasets have been proposed. However, existing datasets have the following limitations: (1). Insufficient evaluation scenarios (e.g., only cover limited tool-use scenes). (2). Extensive evaluation costs (e.g., GPT API costs). To address these limitations, in this work, we propose a multi-granularity tool-use benchmark for large language models called MTU-Bench. For the \"multi-granularity\" property, our MTU-Bench covers five tool usage scenes (i.e., single-turn and single-tool, single-turn and multiple-tool, multiple-turn and single-tool, multiple-turn and multiple-tool, and out-of-distribution tasks). Besides, all evaluation metrics of our MTU-Bench are based on the prediction results and the ground truth without using any GPT or human evaluation metrics. Moreover, our MTU-Bench is collected by transforming existing high-quality datasets to simulate real-world tool usage scenarios, and we also propose an instruction dataset called MTU-Instruct data to enhance the tool-use abilities of existing LLMs. Comprehensive experimental results demonstrate the effectiveness of our MTU-Bench. Code and data will be released at https: //github.com/MTU-Bench-Team/MTU-Bench.git.", "github_manual": "https://github.com/mtu-bench-team/mtu-bench", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.11096", "title": "SecCodePLT: A Unified Platform for Evaluating the Security of Code GenAI", "authors": [ "Yu Yang", "Yuzhou Nie", "Zhun Wang", "Yuheng Tang", "Wenbo Guo", "Bo Li", "Dawn Song" ], "github": "https://github.com/ucsb-mlsec/SecCodePLT", "project_page": "", "abstract": "Existing works have established multiple benchmarks to highlight the security risks associated with Code GenAI. These risks are primarily reflected in two areas: a model potential to generate insecure code (insecure coding) and its utility in cyberattacks (cyberattack helpfulness). While these benchmarks have made significant strides, there remain opportunities for further improvement. For instance, many current benchmarks tend to focus more on a model ability to provide attack suggestions rather than its capacity to generate executable attacks. Additionally, most benchmarks rely heavily on static evaluation metrics, which may not be as precise as dynamic metrics such as passing test cases. Conversely, expert-verified benchmarks, while offering high-quality data, often operate at a smaller scale. To address these gaps, we develop SecCodePLT, a unified and comprehensive evaluation platform for code GenAIs' risks. For insecure code, we introduce a new methodology for data creation that combines experts with automatic generation. Our methodology ensures the data quality while enabling large-scale generation. We also associate samples with test cases to conduct code-related dynamic evaluation. For cyberattack helpfulness, we set up a real environment and construct samples to prompt a model to generate actual attacks, along with dynamic metrics in our environment. We conduct extensive experiments and show that SecCodePLT outperforms the state-of-the-art (SOTA) benchmark CyberSecEval in security relevance. Furthermore, it better identifies the security risks of SOTA models in insecure coding and cyberattack helpfulness. Finally, we apply SecCodePLT to the SOTA code agent, Cursor, and, for the first time, identify non-trivial security risks in this advanced coding agent.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ucsb-mlsec/SecCodePLT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.10626", "title": "Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts", "authors": [ "Guorui Zheng", "Xidong Wang", "Juhao Liang", "Nuo Chen", "Yuping Zheng", "Benyou Wang" ], "github": "https://github.com/freedomintelligence/apollomoe", "project_page": "", "abstract": "Adapting medical Large Language Models to local languages can reduce barriers to accessing healthcare services, but data scarcity remains a significant challenge, particularly for low-resource languages. To address this, we first construct a high-quality medical dataset and conduct analysis to ensure its quality. In order to leverage the generalization capability of multilingual LLMs to efficiently scale to more resource-constrained languages, we explore the internal information flow of LLMs from a multilingual perspective using Mixture of Experts (MoE) modularity. Technically, we propose a novel MoE routing method that employs language-specific experts and cross-lingual routing. Inspired by circuit theory, our routing analysis revealed a Spread Out in the End information flow mechanism: while earlier layers concentrate cross-lingual information flow, the later layers exhibit language-specific divergence. This insight directly led to the development of the Post-MoE architecture, which applies sparse routing only in the later layers while maintaining dense others. Experimental results demonstrate that this approach enhances the generalization of multilingual models to other languages while preserving interpretability. Finally, to efficiently scale the model to 50 languages, we introduce the concept of language family experts, drawing on linguistic priors, which enables scaling the number of languages without adding additional parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/freedomintelligence/apollomoe", "github_api_added_by": "auto" }, { "date": "2024-10-16", "arxiv_id": "2410.11779", "title": "MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation", "authors": [ "Chenxi Wang", "Xiang Chen", "Ningyu Zhang", "Bozhong Tian", "Haoming Xu", "Shumin Deng", "Huajun Chen" ], "github": "https://github.com/zjunlp/DeCo", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) frequently exhibit hallucination phenomena, but the underlying reasons remain poorly understood. In this paper, we present an empirical analysis and find that, although MLLMs incorrectly generate the objects in the final output, they are actually able to recognize visual objects in the preceding layers. We speculate that this may be due to the strong knowledge priors of the language model suppressing the visual information, leading to hallucinations. Motivated by this, we propose a novel dynamic correction decoding method for MLLMs (DeCo), which adaptively selects the appropriate preceding layers and proportionally integrates knowledge into the final layer to adjust the output logits. Note that DeCo is model agnostic and can be seamlessly incorporated with various classic decoding strategies and applied to different MLLMs. We evaluate DeCo on widely-used benchmarks, demonstrating that it can reduce hallucination rates by a large margin compared to baselines, highlighting its potential to mitigate hallucinations. Code is available at https://github.com/zjunlp/DeCo.", "github_manual": "", "github_regex": "https://github.com/zjunlp/DeCo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2406.15786", "title": "What Matters in Transformers? Not All Attention is Needed", "authors": [ "Shwai He", "Guoheng Sun", "Zheyu Shen", "Ang Li" ], "github": "https://github.com/Shwai-He/LLM-Drop", "project_page": "", "abstract": "While scaling Transformer-based large language models (LLMs) has demonstrated promising performance across various tasks, it also introduces redundant architectures, posing efficiency challenges for real-world deployment. Despite some recognition of redundancy in LLMs, the variability of redundancy across different architectures in transformers, such as MLP and Attention layers, is under-explored. In this work, we investigate redundancy across different modules within Transformers, including Blocks, MLP, and Attention layers, using a similarity-based metric. Surprisingly, despite the critical role of attention layers in distinguishing transformers from other architectures, we found that a large portion of these layers exhibit excessively high similarity and can be pruned without degrading performance. For instance, Llama-2-70B achieved a 48.4\\% speedup with only a 2.4\\% performance drop by pruning half of the attention layers. Furthermore, by tracing model checkpoints throughout the training process, we observed that attention layer redundancy is inherent and consistent across training stages. Additionally, we further propose a method that jointly drops Attention and MLP layers, allowing us to more aggressively drop additional layers. For instance, when dropping 31 layers (Attention + MLP), Llama-2-13B still retains 90\\% of the performance on the MMLU task. Our work provides valuable insights for future network architecture design. The code is released at: https://github.com/Shwai-He/LLM-Drop.", "github_manual": "", "github_regex": "https://github.com/Shwai-He/LLM-Drop", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.08001", "title": "Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation", "authors": [ "Qingwen Bu", "Hongyang Li", "Li Chen", "Jisong Cai", "Jia Zeng", "Heming Cui", "Maoqing Yao", "Yu Qiao" ], "github": "", "project_page": "", "abstract": "The increasing demand for versatile robotic systems to operate in diverse and dynamic environments has emphasized the importance of a generalist policy, which leverages a large cross-embodiment data corpus to facilitate broad adaptability and high-level reasoning. However, the generalist would struggle with inefficient inference and cost-expensive training. The specialist policy, instead, is curated for specific domain data and excels at task-level precision with efficiency. Yet, it lacks the generalization capacity for a wide range of applications. Inspired by these observations, we introduce RoboDual, a synergistic dual-system that supplements the merits of both generalist and specialist policy. A diffusion transformer-based specialist is devised for multi-step action rollouts, exquisitely conditioned on the high-level task understanding and discretized action output of a vision-language-action (VLA) based generalist. Compared to OpenVLA, RoboDual achieves 26.7% improvement in real-world setting and 12% gain on CALVIN by introducing a specialist policy with merely 20M trainable parameters. It maintains strong performance with 5% of demonstration data only, and enables a 3.8 times higher control frequency in real-world deployment. Code would be made publicly available. Our project page is hosted at: https://opendrivelab.com/RoboDual/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.10816", "title": "LVD-2M: A Long-take Video Dataset with Temporally Dense Captions", "authors": [ "Tianwei Xiong", "Yuqing Wang", "Daquan Zhou", "Zhijie Lin", "Jiashi Feng", "Xihui Liu" ], "github": "https://github.com/silentview/lvd-2m", "project_page": "", "abstract": "The efficacy of video generation models heavily depends on the quality of their training datasets. Most previous video generation models are trained on short video clips, while recently there has been increasing interest in training long video generation models directly on longer videos. However, the lack of such high-quality long videos impedes the advancement of long video generation. To promote research in long video generation, we desire a new dataset with four key features essential for training long video generation models: (1) long videos covering at least 10 seconds, (2) long-take videos without cuts, (3) large motion and diverse contents, and (4) temporally dense captions. To achieve this, we introduce a new pipeline for selecting high-quality long-take videos and generating temporally dense captions. Specifically, we define a set of metrics to quantitatively assess video quality including scene cuts, dynamic degrees, and semantic-level quality, enabling us to filter high-quality long-take videos from a large amount of source videos. Subsequently, we develop a hierarchical video captioning pipeline to annotate long videos with temporally-dense captions. With this pipeline, we curate the first long-take video dataset, LVD-2M, comprising 2 million long-take videos, each covering more than 10 seconds and annotated with temporally dense captions. We further validate the effectiveness of LVD-2M by fine-tuning video generation models to generate long videos with dynamic motions. We believe our work will significantly contribute to future research in long video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/silentview/lvd-2m", "github_api_added_by": "auto" }, { "date": "2024-10-16", "arxiv_id": "2410.10814", "title": "Your Mixture-of-Experts LLM Is Secretly an Embedding Model For Free", "authors": [ "Ziyue Li", "Tianyi Zhou" ], "github": "https://github.com/tianyi-lab/moe-embedding", "project_page": "", "abstract": "While large language models (LLMs) excel on generation tasks, their decoder-only architecture often limits their potential as embedding models if no further representation finetuning is applied. Does this contradict their claim of generalists? To answer the question, we take a closer look at Mixture-of-Experts (MoE) LLMs. Our study shows that the expert routers in MoE LLMs can serve as an off-the-shelf embedding model with promising performance on a diverse class of embedding-focused tasks, without requiring any finetuning. Moreover, our extensive analysis shows that the MoE routing weights (RW) is complementary to the hidden state (HS) of LLMs, a widely-used embedding. Compared to HS, we find that RW is more robust to the choice of prompts and focuses on high-level semantics. Motivated by the analysis, we propose MoEE combining RW and HS, which achieves better performance than using either separately. Our exploration of their combination and prompting strategy shed several novel insights, e.g., a weighted sum of RW and HS similarities outperforms the similarity on their concatenation. Our experiments are conducted on 6 embedding tasks with 20 datasets from the Massive Text Embedding Benchmark (MTEB). The results demonstrate the significant improvement brought by MoEE to LLM-based embedding without further finetuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tianyi-lab/moe-embedding", "github_api_added_by": "auto" }, { "date": "2024-10-16", "arxiv_id": "2410.11795", "title": "Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices", "authors": [ "Zhiyuan Ma", "Yuzhu Zhang", "Guoli Jia", "Liangliang Zhao", "Yichao Ma", "Mingjie Ma", "Gaofeng Liu", "Kaiyan Zhang", "Jianjun Li", "Bowen Zhou" ], "github": "https://github.com/ponyzym/Efficient-DMs-Survey", "project_page": "", "abstract": "As one of the most popular and sought-after generative models in the recent years, diffusion models have sparked the interests of many researchers and steadily shown excellent advantage in various generative tasks such as image synthesis, video generation, molecule design, 3D scene rendering and multimodal generation, relying on their dense theoretical principles and reliable application practices. The remarkable success of these recent efforts on diffusion models comes largely from progressive design principles and efficient architecture, training, inference, and deployment methodologies. However, there has not been a comprehensive and in-depth review to summarize these principles and practices to help the rapid understanding and application of diffusion models. In this survey, we provide a new efficiency-oriented perspective on these existing efforts, which mainly focuses on the profound principles and efficient practices in architecture designs, model training, fast inference and reliable deployment, to guide further theoretical research, algorithm migration and model application for new scenarios in a reader-friendly way. https://github.com/ponyzym/Efficient-DMs-Survey", "github_manual": "", "github_regex": "https://github.com/ponyzym/Efficient-DMs-Survey", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.09754", "title": "SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning", "authors": [ "Hojoon Lee", "Dongyoon Hwang", "Donghu Kim", "Hyunseung Kim", "Jun Jet Tai", "Kaushik Subramanian", "Peter R. Wurman", "Jaegul Choo", "Peter Stone", "Takuma Seno" ], "github": "https://github.com/SonyResearch/simba", "project_page": "", "abstract": "Recent advances in CV and NLP have been largely driven by scaling up the number of network parameters, despite traditional theories suggesting that larger networks are prone to overfitting. These large networks avoid overfitting by integrating components that induce a simplicity bias, guiding models toward simple and generalizable solutions. However, in deep RL, designing and scaling up networks have been less explored. Motivated by this opportunity, we present SimBa, an architecture designed to scale up parameters in deep RL by injecting a simplicity bias. SimBa consists of three components: (i) an observation normalization layer that standardizes inputs with running statistics, (ii) a residual feedforward block to provide a linear pathway from the input to output, and (iii) a layer normalization to control feature magnitudes. By scaling up parameters with SimBa, the sample efficiency of various deep RL algorithms-including off-policy, on-policy, and unsupervised methods-is consistently improved. Moreover, solely by integrating SimBa architecture into SAC, it matches or surpasses state-of-the-art deep RL methods with high computational efficiency across DMC, MyoSuite, and HumanoidBench. These results demonstrate SimBa's broad applicability and effectiveness across diverse RL algorithms and environments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SonyResearch/simba", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.09745", "title": "Empirical Study of Mutual Reinforcement Effect and Application in Few-shot Text Classification Tasks via Prompt", "authors": [ "Chengguang Gan", "Tatsunori Mori" ], "github": "", "project_page": "", "abstract": "The Mutual Reinforcement Effect (MRE) investigates the synergistic relationship between word-level and text-level classifications in text classification tasks. It posits that the performance of both classification levels can be mutually enhanced. However, this mechanism has not been adequately demonstrated or explained in prior research. To address this gap, we employ empirical experiment to observe and substantiate the MRE theory. Our experiments on 21 MRE mix datasets revealed the presence of MRE in the model and its impact. Specifically, we conducted compare experiments use fine-tune. The results of findings from comparison experiments corroborates the existence of MRE. Furthermore, we extended the application of MRE to prompt learning, utilizing word-level information as a verbalizer to bolster the model's prediction of text-level classification labels. In our final experiment, the F1-score significantly surpassed the baseline in 18 out of 21 MRE Mix datasets, further validating the notion that word-level information enhances the language model's comprehension of the text as a whole.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.09342", "title": "LLMtimesMapReduce: Simplified Long-Sequence Processing using Large Language Models", "authors": [ "Zihan Zhou", "Chong Li", "Xinyi Chen", "Shuo Wang", "Yu Chao", "Zhili Li", "Haoyu Wang", "Rongqiao An", "Qi Shi", "Zhixing Tan", "Xu Han", "Xiaodong Shi", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/thunlp/LLMxMapReduce", "project_page": "", "abstract": "Enlarging the context window of large language models (LLMs) has become a crucial research area, particularly for applications involving extremely long texts. In this work, we propose a novel training-free framework for processing long texts, utilizing a divide-and-conquer strategy to achieve comprehensive document understanding. The proposed LLMtimesMapReduce framework splits the entire document into several chunks for LLMs to read and then aggregates the intermediate answers to produce the final output. The main challenge for divide-and-conquer long text processing frameworks lies in the risk of losing essential long-range information when splitting the document, which can lead the model to produce incomplete or incorrect answers based on the segmented texts. Disrupted long-range information can be classified into two categories: inter-chunk dependency and inter-chunk conflict. We design a structured information protocol to better cope with inter-chunk dependency and an in-context confidence calibration mechanism to resolve inter-chunk conflicts. Experimental results demonstrate that LLMtimesMapReduce can outperform representative open-source and commercial long-context LLMs, and is applicable to several different models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/thunlp/LLMxMapReduce", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.06593", "title": "Towards Natural Image Matting in the Wild via Real-Scenario Prior", "authors": [ "Ruihao Xia", "Yu Liang", "Peng-Tao Jiang", "Hao Zhang", "Qianru Sun", "Yang Tang", "Bo Li", "Pan Zhou" ], "github": "https://github.com/XiaRho/SEMat", "project_page": "", "abstract": "Recent approaches attempt to adapt powerful interactive segmentation models, such as SAM, to interactive matting and fine-tune the models based on synthetic matting datasets. However, models trained on synthetic data fail to generalize to complex and occlusion scenes. We address this challenge by proposing a new matting dataset based on the COCO dataset, namely COCO-Matting. Specifically, the construction of our COCO-Matting includes accessory fusion and mask-to-matte, which selects real-world complex images from COCO and converts semantic segmentation masks to matting labels. The built COCO-Matting comprises an extensive collection of 38,251 human instance-level alpha mattes in complex natural scenarios. Furthermore, existing SAM-based matting methods extract intermediate features and masks from a frozen SAM and only train a lightweight matting decoder by end-to-end matting losses, which do not fully exploit the potential of the pre-trained SAM. Thus, we propose SEMat which revamps the network architecture and training objectives. For network architecture, the proposed feature-aligned transformer learns to extract fine-grained edge and transparency features. The proposed matte-aligned decoder aims to segment matting-specific objects and convert coarse masks into high-precision mattes. For training objectives, the proposed regularization and trimap loss aim to retain the prior from the pre-trained model and push the matting logits extracted from the mask decoder to contain trimap-based semantic information. Extensive experiments across seven diverse datasets demonstrate the superior performance of our method, proving its efficacy in interactive natural image matting. We open-source our code, models, and dataset at https://github.com/XiaRho/SEMat.", "github_manual": "", "github_regex": "https://github.com/XiaRho/SEMat", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.11805", "title": "NesTools: A Dataset for Evaluating Nested Tool Learning Abilities of Large Language Models", "authors": [ "Han Han", "Tong Zhu", "Xiang Zhang", "Mengsong Wu", "Hao Xiong", "Wenliang Chen" ], "github": "https://github.com/hhan1018/nestools", "project_page": "", "abstract": "Large language models (LLMs) combined with tool learning have gained impressive results in real-world applications. During tool learning, LLMs may call multiple tools in nested orders, where the latter tool call may take the former response as its input parameters. However, current research on the nested tool learning capabilities is still under-explored, since the existing benchmarks lack of relevant data instances. To address this problem, we introduce NesTools to bridge the current gap in comprehensive nested tool learning evaluations. NesTools comprises a novel automatic data generation method to construct large-scale nested tool calls with different nesting structures. With manual review and refinement, the dataset is in high quality and closely aligned with real-world scenarios. Therefore, NesTools can serve as a new benchmark to evaluate the nested tool learning abilities of LLMs. We conduct extensive experiments on 22 LLMs, and provide in-depth analyses with NesTools, which shows that current LLMs still suffer from the complex nested tool learning task.", "github_manual": "https://github.com/hhan1018/nestools", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.10934", "title": "Agent-as-a-Judge: Evaluate Agents with Agents", "authors": [ "Mingchen Zhuge", "Changsheng Zhao", "Dylan Ashley", "Wenyi Wang", "Dmitrii Khizbullin", "Yunyang Xiong", "Zechun Liu", "Ernie Chang", "Raghuraman Krishnamoorthi", "Yuandong Tian", "Yangyang Shi", "Vikas Chandra", "J\u00fcrgen Schmidhuber" ], "github": "", "project_page": "", "abstract": "Contemporary evaluation techniques are inadequate for agentic systems. These approaches either focus exclusively on final outcomes -- ignoring the step-by-step nature of agentic systems, or require excessive manual labour. To address this, we introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process. We apply the Agent-as-a-Judge to the task of code generation. To overcome issues with existing benchmarks and provide a proof-of-concept testbed for Agent-as-a-Judge, we present DevAI, a new benchmark of 55 realistic automated AI development tasks. It includes rich manual annotations, like a total of 365 hierarchical user requirements. We benchmark three of the popular agentic systems using Agent-as-a-Judge and find it dramatically outperforms LLM-as-a-Judge and is as reliable as our human evaluation baseline. Altogether, we believe that Agent-as-a-Judge marks a concrete step forward for modern agentic systems -- by providing rich and reliable reward signals necessary for dynamic and scalable self-improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.09704", "title": "EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation", "authors": [ "Milos Vukadinovic", "Xiu Tang", "Neal Yuan", "Paul Cheng", "Debiao Li", "Susan Cheng", "Bryan He", "David Ouyang" ], "github": "https://github.com/echeonet/EchoPrime", "project_page": "", "abstract": "Echocardiography is the most widely used cardiac imaging modality, capturing ultrasound video data to assess cardiac structure and function. Artificial intelligence (AI) in echocardiography has the potential to streamline manual tasks and improve reproducibility and precision. However, most echocardiography AI models are single-view, single-task systems that do not synthesize complementary information from multiple views captured during a full exam, and thus lead to limited performance and scope of applications. To address this problem, we introduce EchoPrime, a multi-view, view-informed, video-based vision-language foundation model trained on over 12 million video-report pairs. EchoPrime uses contrastive learning to train a unified embedding model for all standard views in a comprehensive echocardiogram study with representation of both rare and common diseases and diagnoses. EchoPrime then utilizes view-classification and a view-informed anatomic attention model to weight video-specific interpretations that accurately maps the relationship between echocardiographic views and anatomical structures. With retrieval-augmented interpretation, EchoPrime integrates information from all echocardiogram videos in a comprehensive study and performs holistic comprehensive clinical echocardiography interpretation. In datasets from two independent healthcare systems, EchoPrime achieves state-of-the art performance on 23 diverse benchmarks of cardiac form and function, surpassing the performance of both task-specific approaches and prior foundation models. Following rigorous clinical evaluation, EchoPrime can assist physicians in the automated preliminary assessment of comprehensive echocardiography.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/echeonet/EchoPrime", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-16", "arxiv_id": "2410.11619", "title": "MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video Retrieval", "authors": [ "Reno Kriz", "Kate Sanders", "David Etter", "Kenton Murray", "Cameron Carpenter", "Kelly Van Ochten", "Hannah Recknor", "Jimena Guallar-Blasco", "Alexander Martin", "Ronald Colaianni", "Nolan King", "Eugene Yang", "Benjamin Van Durme" ], "github": "", "project_page": "", "abstract": "Efficiently retrieving and synthesizing information from large-scale multimodal collections has become a critical challenge. However, existing video retrieval datasets suffer from scope limitations, primarily focusing on matching descriptive but vague queries with small collections of professionally edited, English-centric videos. To address this gap, we introduce MultiVENT 2.0, a large-scale, multilingual event-centric video retrieval benchmark featuring a collection of more than 218,000 news videos and 3,906 queries targeting specific world events. These queries specifically target information found in the visual content, audio, embedded text, and text metadata of the videos, requiring systems leverage all these sources to succeed at the task. Preliminary results show that state-of-the-art vision-language models struggle significantly with this task, and while alternative approaches show promise, they are still insufficient to adequately address this problem. These findings underscore the need for more robust multimodal retrieval systems, as effective video retrieval is a crucial step towards multimodal content understanding and generation tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.12409", "title": "Revealing the Barriers of Language Agents in Planning", "authors": [ "Jian Xie", "Kexun Zhang", "Jiangjie Chen", "Siyu Yuan", "Kai Zhang", "Yikai Zhang", "Lei Li", "Yanghua Xiao" ], "github": "https://github.com/hsaest/Agent-Planning-Analysis", "project_page": "", "abstract": "Autonomous planning has been an ongoing pursuit since the inception of artificial intelligence. Based on curated problem solvers, early planning agents could deliver precise solutions for specific tasks but lacked generalization. The emergence of large language models (LLMs) and their powerful reasoning capabilities has reignited interest in autonomous planning by automatically generating reasonable solutions for given tasks. However, prior research and our experiments show that current language agents still lack human-level planning abilities. Even the state-of-the-art reasoning model, OpenAI o1, achieves only 15.6% on one of the complex real-world planning benchmarks. This highlights a critical question: What hinders language agents from achieving human-level planning? Although existing studies have highlighted weak performance in agent planning, the deeper underlying issues and the mechanisms and limitations of the strategies proposed to address them remain insufficiently understood. In this work, we apply the feature attribution study and identify two key factors that hinder agent planning: the limited role of constraints and the diminishing influence of questions. We also find that although current strategies help mitigate these challenges, they do not fully resolve them, indicating that agents still have a long way to go before reaching human-level intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hsaest/Agent-Planning-Analysis", "github_api_added_by": "auto" }, { "date": "2024-10-17", "arxiv_id": "2410.12613", "title": "Exploring Model Kinship for Merging Large Language Models", "authors": [ "Yedi Hu", "Yunzhi Yao", "Ningyu Zhang", "Shumin Deng", "Huajun Chen" ], "github": "https://github.com/zjunlp/ModelKinship", "project_page": "", "abstract": "Model merging has become one of the key technologies for enhancing the capabilities and efficiency of Large Language Models (LLMs). However, our understanding of the expected performance gains and principles when merging any two models remains limited. In this work, we introduce model kinship, the degree of similarity or relatedness between LLMs, analogous to biological evolution. With comprehensive empirical analysis, we find that there is a certain relationship between model kinship and the performance gains after model merging, which can help guide our selection of candidate models. Inspired by this, we propose a new model merging strategy: Top-k Greedy Merging with Model Kinship, which can yield better performance on benchmark datasets. Specifically, we discover that using model kinship as a criterion can assist us in continuously performing model merging, alleviating the degradation (local optima) in model evolution, whereas model kinship can serve as a guide to escape these traps. Code is available at https://github.com/zjunlp/ModelKinship.", "github_manual": "", "github_regex": "https://github.com/zjunlp/ModelKinship", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.12405", "title": "ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs", "authors": [ "Jingming Zhuo", "Songyang Zhang", "Xinyu Fang", "Haodong Duan", "Dahua Lin", "Kai Chen" ], "github": "https://github.com/open-compass/ProSA", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated impressive capabilities across various tasks, but their performance is highly sensitive to the prompts utilized. This variability poses challenges for accurate assessment and user satisfaction. Current research frequently overlooks instance-level prompt variations and their implications on subjective evaluations. To address these shortcomings, we introduce ProSA, a framework designed to evaluate and comprehend prompt sensitivity in LLMs. ProSA incorporates a novel sensitivity metric, PromptSensiScore, and leverages decoding confidence to elucidate underlying mechanisms. Our extensive study, spanning multiple tasks, uncovers that prompt sensitivity fluctuates across datasets and models, with larger models exhibiting enhanced robustness. We observe that few-shot examples can alleviate this sensitivity issue, and subjective evaluations are also susceptible to prompt sensitivities, particularly in complex, reasoning-oriented tasks. Furthermore, our findings indicate that higher model confidence correlates with increased prompt robustness. We believe this work will serve as a helpful tool in studying prompt sensitivity of LLMs. The project is released at: https://github.com/open-compass/ProSA .", "github_manual": "", "github_regex": "https://github.com/open-compass/ProSA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.12628", "title": "DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception", "authors": [ "Zhiyuan Zhao", "Hengrui Kang", "Bin Wang", "Conghui He" ], "github": "https://github.com/opendatalab/DocLayout-YOLO", "project_page": "", "abstract": "Document Layout Analysis is crucial for real-world document understanding systems, but it encounters a challenging trade-off between speed and accuracy: multimodal methods leveraging both text and visual features achieve higher accuracy but suffer from significant latency, whereas unimodal methods relying solely on visual features offer faster processing speeds at the expense of accuracy. To address this dilemma, we introduce DocLayout-YOLO, a novel approach that enhances accuracy while maintaining speed advantages through document-specific optimizations in both pre-training and model design. For robust document pre-training, we introduce the Mesh-candidate BestFit algorithm, which frames document synthesis as a two-dimensional bin packing problem, generating the large-scale, diverse DocSynth-300K dataset. Pre-training on the resulting DocSynth-300K dataset significantly improves fine-tuning performance across various document types. In terms of model optimization, we propose a Global-to-Local Controllable Receptive Module that is capable of better handling multi-scale variations of document elements. Furthermore, to validate performance across different document types, we introduce a complex and challenging benchmark named DocStructBench. Extensive experiments on downstream datasets demonstrate that DocLayout-YOLO excels in both speed and accuracy. Code, data, and models are available at https://github.com/opendatalab/DocLayout-YOLO.", "github_manual": "", "github_regex": "https://github.com/opendatalab/DocLayout-YOLO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.11817", "title": "Improving Long-Text Alignment for Text-to-Image Diffusion Models", "authors": [ "Luping Liu", "Chao Du", "Tianyu Pang", "Zehan Wang", "Chongxuan Li", "Dong Xu" ], "github": "https://github.com/luping-liu/LongAlign", "project_page": "", "abstract": "The rapid advancement of text-to-image (T2I) diffusion models has enabled them to generate unprecedented results from given texts. However, as text inputs become longer, existing encoding methods like CLIP face limitations, and aligning the generated images with long texts becomes challenging. To tackle these issues, we propose LongAlign, which includes a segment-level encoding method for processing long texts and a decomposed preference optimization method for effective alignment training. For segment-level encoding, long texts are divided into multiple segments and processed separately. This method overcomes the maximum input length limits of pretrained encoding models. For preference optimization, we provide decomposed CLIP-based preference models to fine-tune diffusion models. Specifically, to utilize CLIP-based preference models for T2I alignment, we delve into their scoring mechanisms and find that the preference scores can be decomposed into two components: a text-relevant part that measures T2I alignment and a text-irrelevant part that assesses other visual aspects of human preference. Additionally, we find that the text-irrelevant part contributes to a common overfitting problem during fine-tuning. To address this, we propose a reweighting strategy that assigns different weights to these two components, thereby reducing overfitting and enhancing alignment. After fine-tuning 512 times 512 Stable Diffusion (SD) v1.5 for about 20 hours using our method, the fine-tuned SD outperforms stronger foundation models in T2I alignment, such as PixArt-alpha and Kandinsky v2.2. The code is available at https://github.com/luping-liu/LongAlign.", "github_manual": "", "github_regex": "https://github.com/luping-liu/LongAlign", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.12109", "title": "OMCAT: Omni Context Aware Transformer", "authors": [ "Arushi Goel", "Karan Sapra", "Matthieu Le", "Rafael Valle", "Andrew Tao", "Bryan Catanzaro" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have made significant strides in text generation and comprehension, with recent advancements extending into multimodal LLMs that integrate visual and audio inputs. However, these models continue to struggle with fine-grained, cross-modal temporal understanding, particularly when correlating events across audio and video streams. We address these challenges with two key contributions: a new dataset and model, called OCTAV and OMCAT respectively. OCTAV (Omni Context and Temporal Audio Video) is a novel dataset designed to capture event transitions across audio and video. Second, OMCAT (Omni Context Aware Transformer) is a powerful model that leverages RoTE (Rotary Time Embeddings), an innovative extension of RoPE, to enhance temporal grounding and computational efficiency in time-anchored tasks. Through a robust three-stage training pipeline-feature alignment, instruction tuning, and OCTAV-specific training-OMCAT excels in cross-modal temporal understanding. Our model demonstrates state-of-the-art performance on Audio-Visual Question Answering (AVQA) tasks and the OCTAV benchmark, showcasing significant gains in temporal reasoning and cross-modal alignment, as validated through comprehensive experiments and ablation studies. Our dataset and code will be made publicly available. The link to our demo page is https://om-cat.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.12381", "title": "HumanEval-V: Evaluating Visual Understanding and Reasoning Abilities of Large Multimodal Models Through Coding Tasks", "authors": [ "Fengji Zhang", "Linquan Wu", "Huiyu Bai", "Guancheng Lin", "Xiao Li", "Xiao Yu", "Yue Wang", "Bei Chen", "Jacky Keung" ], "github": "https://github.com/HumanEval-V/HumanEval-V-Benchmark", "project_page": "", "abstract": "Coding tasks have been valuable for evaluating Large Language Models (LLMs), as they demand the comprehension of high-level instructions, complex reasoning, and the implementation of functional programs -- core capabilities for advancing Artificial General Intelligence. Despite the progress in Large Multimodal Models (LMMs), which extend LLMs with visual perception and understanding capabilities, there remains a notable lack of coding benchmarks that rigorously assess these models, particularly in tasks that emphasize visual reasoning. To address this gap, we introduce HumanEval-V, a novel and lightweight benchmark specifically designed to evaluate LMMs' visual understanding and reasoning capabilities through code generation. HumanEval-V includes 108 carefully crafted, entry-level Python coding tasks derived from platforms like CodeForces and Stack Overflow. Each task is adapted by modifying the context and algorithmic patterns of the original problems, with visual elements redrawn to ensure distinction from the source, preventing potential data leakage. LMMs are required to complete the code solution based on the provided visual context and a predefined Python function signature outlining the task requirements. Every task is equipped with meticulously handcrafted test cases to ensure a thorough and reliable evaluation of model-generated solutions. We evaluate 19 state-of-the-art LMMs using HumanEval-V, uncovering significant challenges. Proprietary models like GPT-4o achieve only 13% pass@1 and 36.4% pass@10, while open-weight models with 70B parameters score below 4% pass@1. Ablation studies further reveal the limitations of current LMMs in vision reasoning and coding capabilities. These results underscore key areas for future research to enhance LMMs' capabilities. We have open-sourced our code and benchmark at https://github.com/HumanEval-V/HumanEval-V-Benchmark.", "github_manual": "", "github_regex": "https://github.com/HumanEval-V/HumanEval-V-Benchmark", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.12787", "title": "The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio", "authors": [ "Sicong Leng", "Yun Xing", "Zesen Cheng", "Yang Zhou", "Hang Zhang", "Xin Li", "Deli Zhao", "Shijian Lu", "Chunyan Miao", "Lidong Bing" ], "github": "https://github.com/DAMO-NLP-SG/CMM", "project_page": "", "abstract": "Recent advancements in large multimodal models (LMMs) have significantly enhanced performance across diverse tasks, with ongoing efforts to further integrate additional modalities such as video and audio. However, most existing LMMs remain vulnerable to hallucinations, the discrepancy between the factual multimodal input and the generated textual output, which has limited their applicability in various real-world scenarios. This paper presents the first systematic investigation of hallucinations in LMMs involving the three most common modalities: language, visual, and audio. Our study reveals two key contributors to hallucinations: overreliance on unimodal priors and spurious inter-modality correlations. To address these challenges, we introduce the benchmark The Curse of Multi-Modalities (CMM), which comprehensively evaluates hallucinations in LMMs, providing a detailed analysis of their underlying issues. Our findings highlight key vulnerabilities, including imbalances in modality integration and biases from training data, underscoring the need for balanced cross-modal learning and enhanced hallucination mitigation strategies. Based on our observations and findings, we suggest potential research directions that could enhance the reliability of LMMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/DAMO-NLP-SG/CMM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.08584", "title": "ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification and KV Cache Compression", "authors": [ "Yefei He", "Feng Chen", "Jing Liu", "Wenqi Shao", "Hong Zhou", "Kaipeng Zhang", "Bohan Zhuang" ], "github": "", "project_page": "", "abstract": "The efficiency of large vision-language models (LVLMs) is constrained by the computational bottleneck of the attention mechanism during the prefill phase and the memory bottleneck of fetching the key-value (KV) cache in the decoding phase, particularly in scenarios involving high-resolution images or videos. Visual content often exhibits substantial redundancy, resulting in highly sparse attention maps within LVLMs. This sparsity can be leveraged to accelerate attention computation or compress the KV cache through various approaches. However, most studies focus on addressing only one of these bottlenecks and do not adequately support dynamic adjustment of sparsity concerning distinct layers or tasks. In this paper, we present ZipVL, an efficient inference framework designed for LVLMs that resolves both computation and memory bottlenecks through a dynamic ratio allocation strategy of important tokens. This ratio is adaptively determined based on the layer-specific distribution of attention scores, rather than fixed hyper-parameters, thereby improving efficiency for less complex tasks while maintaining high performance for more challenging ones. Then we select important tokens based on their normalized attention scores and perform attention mechanism solely on those important tokens to accelerate the prefill phase. To mitigate the memory bottleneck in the decoding phase, we employ mixed-precision quantization to the KV cache, where high-bit quantization is used for caches of important tokens, while low-bit quantization is applied to those of less importance. Our experiments demonstrate that ZipVL can accelerate the prefill phase by 2.6times and reduce GPU memory usage by 50.0%, with a minimal accuracy reduction of only 0.2% on Video-MME benchmark over LongVA-7B model, effectively enhancing the generation efficiency of LVLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.11623", "title": "VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI", "authors": [ "Sijie Cheng", "Kechen Fang", "Yangyang Yu", "Sicheng Zhou", "Bohao Li", "Ye Tian", "Tingguang Li", "Lei Han", "Yang Liu" ], "github": "", "project_page": "", "abstract": "Recent advancements in Multi-modal Large Language Models (MLLMs) have opened new avenues for applications in Embodied AI. Building on previous work, EgoThink, we introduce VidEgoThink, a comprehensive benchmark for evaluating egocentric video understanding capabilities. To bridge the gap between MLLMs and low-level control in Embodied AI, we design four key interrelated tasks: video question-answering, hierarchy planning, visual grounding and reward modeling. To minimize manual annotation costs, we develop an automatic data generation pipeline based on the Ego4D dataset, leveraging the prior knowledge and multimodal capabilities of GPT-4o. Three human annotators then filter the generated data to ensure diversity and quality, resulting in the VidEgoThink benchmark. We conduct extensive experiments with three types of models: API-based MLLMs, open-source image-based MLLMs, and open-source video-based MLLMs. Experimental results indicate that all MLLMs, including GPT-4o, perform poorly across all tasks related to egocentric video understanding. These findings suggest that foundation models still require significant advancements to be effectively applied to first-person scenarios in Embodied AI. In conclusion, VidEgoThink reflects a research trend towards employing MLLMs for egocentric vision, akin to human capabilities, enabling active observation and interaction in the complex real-world environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.10672", "title": "Large Language Model Evaluation via Matrix Nuclear-Norm", "authors": [ "Yahan Li", "Tingyu Xia", "Yi Chang", "Yuan Wu" ], "github": "https://github.com/MLGroupJLU/MatrixNuclearNorm", "project_page": "", "abstract": "As large language models (LLMs) continue to evolve, efficient evaluation metrics are vital for assessing their ability to compress information and reduce redundancy. While traditional metrics like Matrix Entropy offer valuable insights, they are computationally intensive for large-scale models due to their \\( O(n^3) \\) time complexity with Singular Value Decomposition (SVD). To mitigate this issue, we introduce the Matrix Nuclear-Norm, which not only serves as a metric to quantify the data compression proficiency of LLM but also provides a convex approximation of matrix rank to capture both predictive discriminability and diversity. By employing the \\( L_{1,2}-norm \\) to further approximate the nuclear norm, we can effectively assess the model's information compression capabilities. This approach reduces the time complexity to \\( O(n^2) \\) and eliminates the need for SVD computation. Consequently, the Matrix Nuclear-Norm achieves speeds 8 to 24 times faster than Matrix Entropy for the CEREBRAS-GPT model as sizes increase from 111M to 6.7B. This performance gap becomes more pronounced with larger models, as validated in tests with other models like Pythia. Additionally, evaluations on benchmarks and model responses confirm that our proposed Matrix Nuclear-Norm is a reliable, scalable, and efficient tool for assessing LLMs' performance, striking a balance between accuracy and computational efficiency. The code is available at https://github.com/MLGroupJLU/MatrixNuclearNorm.", "github_manual": "", "github_regex": "https://github.com/MLGroupJLU/MatrixNuclearNorm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.12722", "title": "WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation", "authors": [ "Jo\u00e3o Matos", "Shan Chen", "Siena Placino", "Yingya Li", "Juan Carlos Climent Pardo", "Daphna Idan", "Takeshi Tohyama", "David Restrepo", "Luis F. Nakayama", "Jose M. M. Pascual-Leone", "Guergana Savova", "Hugo Aerts", "Leo A. Celi", "A. Ian Wong", "Danielle S. Bitterman", "Jack Gallifant" ], "github": "https://github.com/WorldMedQA/V", "project_page": "", "abstract": "Multimodal/vision language models (VLMs) are increasingly being deployed in healthcare settings worldwide, necessitating robust benchmarks to ensure their safety, efficacy, and fairness. Multiple-choice question and answer (QA) datasets derived from national medical examinations have long served as valuable evaluation tools, but existing datasets are largely text-only and available in a limited subset of languages and countries. To address these challenges, we present WorldMedQA-V, an updated multilingual, multimodal benchmarking dataset designed to evaluate VLMs in healthcare. WorldMedQA-V includes 568 labeled multiple-choice QAs paired with 568 medical images from four countries (Brazil, Israel, Japan, and Spain), covering original languages and validated English translations by native clinicians, respectively. Baseline performance for common open- and closed-source models are provided in the local language and English translations, and with and without images provided to the model. The WorldMedQA-V benchmark aims to better match AI systems to the diverse healthcare environments in which they are deployed, fostering more equitable, effective, and representative applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/WorldMedQA/V", "github_api_added_by": "auto" }, { "date": "2024-10-17", "arxiv_id": "2410.11081", "title": "Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models", "authors": [ "Cheng Lu", "Yang Song" ], "github": "", "project_page": "", "abstract": "Consistency models (CMs) are a powerful class of diffusion-based generative models optimized for fast sampling. Most existing CMs are trained using discretized timesteps, which introduce additional hyperparameters and are prone to discretization errors. While continuous-time formulations can mitigate these issues, their success has been limited by training instability. To address this, we propose a simplified theoretical framework that unifies previous parameterizations of diffusion models and CMs, identifying the root causes of instability. Based on this analysis, we introduce key improvements in diffusion process parameterization, network architecture, and training objectives. These changes enable us to train continuous-time CMs at an unprecedented scale, reaching 1.5B parameters on ImageNet 512x512. Our proposed training algorithm, using only two sampling steps, achieves FID scores of 2.06 on CIFAR-10, 1.48 on ImageNet 64x64, and 1.88 on ImageNet 512x512, narrowing the gap in FID scores with the best existing diffusion models to within 10%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.11878", "title": "Neural Metamorphosis", "authors": [ "Xingyi Yang", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "This paper introduces a new learning paradigm termed Neural Metamorphosis (NeuMeta), which aims to build self-morphable neural networks. Contrary to crafting separate models for different architectures or sizes, NeuMeta directly learns the continuous weight manifold of neural networks. Once trained, we can sample weights for any-sized network directly from the manifold, even for previously unseen configurations, without retraining. To achieve this ambitious goal, NeuMeta trains neural implicit functions as hypernetworks. They accept coordinates within the model space as input, and generate corresponding weight values on the manifold. In other words, the implicit function is learned in a way, that the predicted weights is well-performed across various models sizes. In training those models, we notice that, the final performance closely relates on smoothness of the learned manifold. In pursuit of enhancing this smoothness, we employ two strategies. First, we permute weight matrices to achieve intra-model smoothness, by solving the Shortest Hamiltonian Path problem. Besides, we add a noise on the input coordinates when training the implicit function, ensuring models with various sizes shows consistent outputs. As such, NeuMeta shows promising results in synthesizing parameters for various network configurations. Our extensive tests in image classification, semantic segmentation, and image generation reveal that NeuMeta sustains full-size performance even at a 75% compression rate.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.12491", "title": "Insights from the Inverse: Reconstructing LLM Training Goals Through Inverse RL", "authors": [ "Jared Joselowitz", "Arjun Jagota", "Satyapriya Krishna", "Sonali Parbhoo" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) trained with Reinforcement Learning from Human Feedback (RLHF) have demonstrated remarkable capabilities, but their underlying reward functions and decision-making processes remain opaque. This paper introduces a novel approach to interpreting LLMs by applying inverse reinforcement learning (IRL) to recover their implicit reward functions. We conduct experiments on toxicity-aligned LLMs of varying sizes, extracting reward models that achieve up to 80.40% accuracy in predicting human preferences. Our analysis reveals key insights into the non-identifiability of reward functions, the relationship between model size and interpretability, and potential pitfalls in the RLHF process. We demonstrate that IRL-derived reward models can be used to fine-tune new LLMs, resulting in comparable or improved performance on toxicity benchmarks. This work provides a new lens for understanding and improving LLM alignment, with implications for the responsible development and deployment of these powerful systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.12391", "title": "Tracking Universal Features Through Fine-Tuning and Model Merging", "authors": [ "Niels Horn", "Desmond Elliott" ], "github": "", "project_page": "", "abstract": "We study how features emerge, disappear, and persist across models fine-tuned on different domains of text. More specifically, we start from a base one-layer Transformer language model that is trained on a combination of the BabyLM corpus, and a collection of Python code from The Stack. This base model is adapted to two new domains of text: TinyStories, and the Lua programming language, respectively; and then these two models are merged using these two models using spherical linear interpolation. Our exploration aims to provide deeper insights into the stability and transformation of features across typical transfer-learning scenarios using small-scale models and sparse auto-encoders.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.07722", "title": "DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities", "authors": [ "Thong Nguyen", "Shubham Chatterjee", "Sean MacAvaney", "Iain Mackie", "Jeff Dalton", "Andrew Yates" ], "github": "https://github.com/thongnt99/dyvo", "project_page": "", "abstract": "Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained transformers, which often split entities into nonsensical fragments. Splitting entities can reduce retrieval accuracy and limits the model's ability to incorporate up-to-date world knowledge not included in the training data. In this work, we enhance the LSR vocabulary with Wikipedia concepts and entities, enabling the model to resolve ambiguities more effectively and stay current with evolving knowledge. Central to our approach is a Dynamic Vocabulary (DyVo) head, which leverages existing entity embeddings and an entity retrieval component that identifies entities relevant to a query or document. We use the DyVo head to generate entity weights, which are then merged with word piece weights to create joint representations for efficient indexing and retrieval using an inverted index. In experiments across three entity-rich document ranking datasets, the resulting DyVo model substantially outperforms state-of-the-art baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thongnt99/dyvo", "github_api_added_by": "auto" }, { "date": "2024-10-17", "arxiv_id": "2410.08968", "title": "Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements", "authors": [ "Jingyu Zhang", "Ahmed Elgohary", "Ahmed Magooda", "Daniel Khashabi", "Benjamin Van Durme" ], "github": "", "project_page": "", "abstract": "The current paradigm for safety alignment of large language models (LLMs) follows a one-size-fits-all approach: the model refuses to interact with any content deemed unsafe by the model provider. This approach lacks flexibility in the face of varying social norms across cultures and regions. In addition, users may have diverse safety needs, making a model with static safety standards too restrictive to be useful, as well as too costly to be re-aligned. We propose Controllable Safety Alignment (CoSA), a framework designed to adapt models to diverse safety requirements without re-training. Instead of aligning a fixed model, we align models to follow safety configs -- free-form natural language descriptions of the desired safety behaviors -- that are provided as part of the system prompt. To adjust model safety behavior, authorized users only need to modify such safety configs at inference time. To enable that, we propose CoSAlign, a data-centric method for aligning LLMs to easily adapt to diverse safety configs. Furthermore, we devise a novel controllability evaluation protocol that considers both helpfulness and configured safety, summarizing them into CoSA-Score, and construct CoSApien, a human-authored benchmark that consists of real-world LLM use cases with diverse safety requirements and corresponding evaluation prompts. We show that CoSAlign leads to substantial gains of controllability over strong baselines including in-context alignment. Our framework encourages better representation and adaptation to pluralistic human values in LLMs, and thereby increasing their practicality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.09870", "title": "ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains", "authors": [ "Yein Park", "Chanwoong Yoon", "Jungwoo Park", "Donghyeon Lee", "Minbyul Jeong", "Jaewoo Kang" ], "github": "https://github.com/dmis-lab/chroknowledge", "project_page": "", "abstract": "Large language models (LLMs) have significantly impacted many aspects of our lives. However, assessing and ensuring their chronological knowledge remains challenging. Existing approaches fall short in addressing the accumulative nature of knowledge, often relying on a single time stamp. To overcome this, we introduce ChroKnowBench, a benchmark dataset designed to evaluate chronologically accumulated knowledge across three key aspects: multiple domains, time dependency, temporal state. Our benchmark distinguishes between knowledge that evolves (e.g., scientific discoveries, amended laws) and knowledge that remain constant (e.g., mathematical truths, commonsense facts). Building on this benchmark, we present ChroKnowledge (Chronological Categorization of Knowledge), a novel sampling-based framework for evaluating and updating LLMs' non-parametric chronological knowledge. Our evaluation shows: (1) The ability of eliciting temporal knowledge varies depending on the data format that model was trained on. (2) LLMs partially recall knowledge or show a cut-off at temporal boundaries rather than recalling all aspects of knowledge correctly. Thus, we apply our ChroKnowPrompt, an in-depth prompting to elicit chronological knowledge by traversing step-by-step through the surrounding time spans. We observe that our framework successfully updates the overall knowledge across the entire timeline in both the biomedical domain (+11.9%) and the general domain (+2.8%), demonstrating its effectiveness in refining temporal knowledge. This non-parametric approach also enables knowledge updates not only in open-source models but also in proprietary LLMs, ensuring comprehensive applicability across model types. We perform a comprehensive analysis based on temporal characteristics of ChroKnowPrompt and validate the potential of various models to elicit intrinsic temporal knowledge through our method.", "github_manual": "https://github.com/dmis-lab/chroknowledge", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.12490", "title": "Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective", "authors": [ "Yongxin Zhu", "Bocheng Li", "Hang Zhang", "Xin Li", "Linli Xu", "Lidong Bing" ], "github": "https://github.com/DAMO-NLP-SG/DiGIT", "project_page": "", "abstract": "Latent-based image generative models, such as Latent Diffusion Models (LDMs) and Mask Image Models (MIMs), have achieved notable success in image generation tasks. These models typically leverage reconstructive autoencoders like VQGAN or VAE to encode pixels into a more compact latent space and learn the data distribution in the latent space instead of directly from pixels. However, this practice raises a pertinent question: Is it truly the optimal choice? In response, we begin with an intriguing observation: despite sharing the same latent space, autoregressive models significantly lag behind LDMs and MIMs in image generation. This finding contrasts sharply with the field of NLP, where the autoregressive model GPT has established a commanding presence. To address this discrepancy, we introduce a unified perspective on the relationship between latent space and generative models, emphasizing the stability of latent space in image generative modeling. Furthermore, we propose a simple but effective discrete image tokenizer to stabilize the latent space for image generative modeling. Experimental results show that image autoregressive modeling with our tokenizer (DiGIT) benefits both image understanding and image generation with the next token prediction principle, which is inherently straightforward for GPT models but challenging for other generative models. Remarkably, for the first time, a GPT-style autoregressive model for images outperforms LDMs, which also exhibits substantial improvement akin to GPT when scaling up model size. Our findings underscore the potential of an optimized latent space and the integration of discrete tokenization in advancing the capabilities of image generative models. The code is available at https://github.com/DAMO-NLP-SG/DiGIT.", "github_manual": "", "github_regex": "https://github.com/DAMO-NLP-SG/DiGIT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.09724", "title": "Taming Overconfidence in LLMs: Reward Calibration in RLHF", "authors": [ "Jixuan Leng", "Chengsong Huang", "Banghua Zhu", "Jiaxin Huang" ], "github": "https://github.com/SeanLeng1/Reward-Calibration", "project_page": "", "abstract": "Language model calibration refers to the alignment between the confidence of the model and the actual performance of its responses. While previous studies point out the overconfidence phenomenon in Large Language Models (LLMs) and show that LLMs trained with Reinforcement Learning from Human Feedback (RLHF) are overconfident with a more sharpened output probability, in this study, we reveal that RLHF tends to lead models to express verbalized overconfidence in their own responses. We investigate the underlying cause of this overconfidence and demonstrate that reward models used for Proximal Policy Optimization (PPO) exhibit inherent biases towards high-confidence scores regardless of the actual quality of responses. Building upon this insight, we propose two PPO variants: PPO-M: PPO with Calibrated Reward Modeling and PPO-C: PPO with Calibrated Reward Calculation. PPO-M integrates explicit confidence scores in reward model training, which calibrates reward models to better capture the alignment between response quality and verbalized confidence. PPO-C adjusts the reward score during PPO based on the difference between the current reward and the moving average of past rewards. Both PPO-M and PPO-C can be seamlessly integrated into the current PPO pipeline and do not require additional golden labels. We evaluate our methods on both Llama3-8B and Mistral-7B across six diverse datasets including multiple-choice and open-ended generation. Experiment results demonstrate that both of our methods can reduce calibration error and maintain performance comparable to standard PPO. We further show that they do not compromise model capabilities in open-ended conversation settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SeanLeng1/Reward-Calibration", "github_api_added_by": "auto" }, { "date": "2024-10-17", "arxiv_id": "2410.11900", "title": "FLARE: Faithful Logic-Aided Reasoning and Exploration", "authors": [ "Erik Arakelyan", "Pasquale Minervini", "Pat Verga", "Patrick Lewis", "Isabelle Augenstein" ], "github": "", "project_page": "", "abstract": "Modern Question Answering (QA) and Reasoning approaches based on Large Language Models (LLMs) commonly use prompting techniques, such as Chain-of-Thought (CoT), assuming the resulting generation will have a more granular exploration and reasoning over the question space and scope. However, such methods struggle with generating outputs that are faithful to the intermediate chain of reasoning produced by the model. On the other end of the spectrum, neuro-symbolic methods such as Faithful CoT (F-CoT) propose to combine LLMs with external symbolic solvers. While such approaches boast a high degree of faithfulness, they usually require a model trained for code generation and struggle with tasks that are ambiguous or hard to formalise strictly. We introduce Faithful Logic-Aided Reasoning and Exploration (\\ours), a novel interpretable approach for traversing the problem space using task decompositions. We use the LLM to plan a solution, soft-formalise the query into facts and predicates using a logic programming code and simulate that code execution using an exhaustive multi-hop search over the defined space. Our method allows us to compute the faithfulness of the reasoning process w.r.t. the generated code and analyse the steps of the multi-hop search without relying on external solvers. Our methods achieve SOTA results on 7 out of 9 diverse reasoning benchmarks. We also show that model faithfulness positively correlates with overall performance and further demonstrate that {\\ours} allows pinpointing the decisive factors sufficient for and leading to the correct answer with optimal reasoning during the multi-hop search.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-17", "arxiv_id": "2410.11843", "title": "From Commands to Prompts: LLM-based Semantic File System for AIOS", "authors": [ "Zeru Shi", "Kai Mei", "Mingyu Jin", "Yongye Su", "Chaoji Zuo", "Wenyue Hua", "Wujiang Xu", "Yujie Ren", "Zirui Liu", "Mengnan Du", "Dong Deng", "Yongfeng Zhang" ], "github": "https://github.com/agiresearch/aios-lsfs", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated significant potential in the development of intelligent applications and systems such as LLM-based agents and agent operating systems (AIOS). However, when these applications and systems interact with the underlying file system, the file system still remains the traditional paradigm: reliant on manual navigation through precise commands. This paradigm poses a bottleneck to the usability of these systems as users are required to navigate complex folder hierarchies and remember cryptic file names. To address this limitation, we propose an LLM-based semantic file system ( LSFS ) for prompt-driven file management. Unlike conventional approaches, LSFS incorporates LLMs to enable users or agents to interact with files through natural language prompts, facilitating semantic file management. At the macro-level, we develop a comprehensive API set to achieve semantic file management functionalities, such as semantic file retrieval, file update monitoring and summarization, and semantic file rollback). At the micro-level, we store files by constructing semantic indexes for them, design and implement syscalls of different semantic operations (e.g., CRUD, group by, join) powered by vector database. Our experiments show that LSFS offers significant improvements over traditional file systems in terms of user convenience, the diversity of supported functions, and the accuracy and efficiency of file operations. Additionally, with the integration of LLM, our system enables more intelligent file management tasks, such as content summarization and version comparison, further enhancing its capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/agiresearch/aios-lsfs", "github_api_added_by": "auto" }, { "date": "2024-10-18", "arxiv_id": "2410.09426", "title": "FlatQuant: Flatness Matters for LLM Quantization", "authors": [ "Yuxuan Sun", "Ruikang Liu", "Haoli Bai", "Han Bao", "Kang Zhao", "Yuening Li", "Jiaxin Hu", "Xianzhi Yu", "Lu Hou", "Chun Yuan", "Xin Jiang", "Wulong Liu", "Jun Yao" ], "github": "https://github.com/ruikangliu/FlatQuant", "project_page": "", "abstract": "Recently, quantization has been widely used for the compression and acceleration of large language models~(LLMs). Due to the outliers in LLMs, it is crucial to flatten weights and activations to minimize quantization error with the equally spaced quantization points. Prior research explores various pre-quantization transformations to suppress outliers, such as per-channel scaling and Hadamard transformation. However, we observe that these transformed weights and activations can still remain steep and outspread. In this paper, we propose FlatQuant (Fast and Learnable Affine Transformation), a new post-training quantization approach to enhance flatness of weights and activations. Our approach identifies optimal affine transformations tailored to each linear layer, calibrated in hours via a lightweight objective. To reduce runtime overhead, we apply Kronecker decomposition to the transformation matrices, and fuse all operations in FlatQuant into a single kernel. Extensive experiments show that FlatQuant sets up a new state-of-the-art quantization benchmark. For instance, it achieves less than 1% accuracy drop for W4A4 quantization on the LLaMA-3-70B model, surpassing SpinQuant by 7.5%. For inference latency, FlatQuant reduces the slowdown induced by pre-quantization transformation from 0.26x of QuaRot to merely 0.07x, bringing up to 2.3x speedup for prefill and 1.7x speedup for decoding, respectively. Code is available at: https://github.com/ruikangliu/FlatQuant.", "github_manual": "", "github_regex": "https://github.com/ruikangliu/FlatQuant", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13824", "title": "Harnessing Webpage UIs for Text-Rich Visual Understanding", "authors": [ "Junpeng Liu", "Tianyue Ou", "Yifan Song", "Yuxiao Qu", "Wai Lam", "Chenyan Xiong", "Wenhu Chen", "Graham Neubig", "Xiang Yue" ], "github": "", "project_page": "", "abstract": "Text-rich visual understanding-the ability to process environments where dense textual content is integrated with visuals-is crucial for multimodal large language models (MLLMs) to interact effectively with structured environments. To enhance this capability, we propose synthesizing general multimodal instructions from webpage UIs using text-based large language models (LLMs). Despite lacking direct visual input, text-based LLMs are able to process structured text representations from webpage accessibility trees. These instructions are then paired with UI screenshots to train multimodal models. We introduce MultiUI, a dataset containing 7.3 million samples from 1 million websites, covering diverse multimodal tasks and UI layouts. Models trained on MultiUI not only excel in web UI tasks-achieving up to a 48\\% improvement on VisualWebBench and a 19.1\\% boost in action accuracy on a web agent dataset Mind2Web-but also generalize surprisingly well to non-web UI tasks and even to non-UI domains, such as document understanding, OCR, and chart interpretation. These results highlight the broad applicability of web UI data for advancing text-rich visual understanding across various scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13841", "title": "A Unified View of Delta Parameter Editing in Post-Trained Large-Scale Models", "authors": [ "Qiaoyu Tang", "Le Yu", "Bowen Yu", "Hongyu Lin", "Keming Lu", "Yaojie Lu", "Xianpei Han", "Le Sun" ], "github": "", "project_page": "", "abstract": "Post-training has emerged as a crucial paradigm for adapting large-scale pre-trained models to various tasks, whose effects are fully reflected by delta parameters (i.e., the disparity between post-trained and pre-trained parameters). While numerous studies have explored delta parameter properties via operations like pruning, quantization, low-rank approximation, and extrapolation, a unified framework for systematically examining these characteristics has been lacking. In this paper, we propose a novel perspective based on Riemann sum approximation of the loss function to elucidate delta parameter editing operations. Our analysis categorizes existing methods into three classes based on their post-editing performance: competitive, decreased, and improved, explaining how they are expressed by the Riemann sum approximation term and how they alter the model performance. Extensive experiments on both visual and language models, including ViT, LLaMA 3, Qwen 2, and Mistral, corroborate our theoretical findings. Furthermore, we introduce extensions to existing techniques like DARE and BitDelta, highlighting their limitations in leveraging the properties of delta parameters and reorganizing them into general expressions to enhance the applicability and effectiveness of delta parameter editing in post-trained models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13085", "title": "MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models", "authors": [ "Peng Xia", "Kangyu Zhu", "Haoran Li", "Tianze Wang", "Weijia Shi", "Sheng Wang", "Linjun Zhang", "James Zou", "Huaxiu Yao" ], "github": "https://github.com/richard-peng-xia/MMed-RAG", "project_page": "", "abstract": "Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in disease diagnosis and treatment planning. Recent progress in Medical Large Vision-Language Models (Med-LVLMs) has opened up new possibilities for interactive diagnostic tools. However, these models often suffer from factual hallucination, which can lead to incorrect diagnoses. Fine-tuning and retrieval-augmented generation (RAG) have emerged as methods to address these issues. However, the amount of high-quality data and distribution shifts between training data and deployment data limit the application of fine-tuning methods. Although RAG is lightweight and effective, existing RAG-based approaches are not sufficiently general to different medical domains and can potentially cause misalignment issues, both between modalities and between the model and the ground truth. In this paper, we propose a versatile multimodal RAG system, MMed-RAG, designed to enhance the factuality of Med-LVLMs. Our approach introduces a domain-aware retrieval mechanism, an adaptive retrieved contexts selection method, and a provable RAG-based preference fine-tuning strategy. These innovations make the RAG process sufficiently general and reliable, significantly improving alignment when introducing retrieved contexts. Experimental results across five medical datasets (involving radiology, ophthalmology, pathology) on medical VQA and report generation demonstrate that MMed-RAG can achieve an average improvement of 43.8% in the factual accuracy of Med-LVLMs. Our data and code are available in https://github.com/richard-peng-xia/MMed-RAG.", "github_manual": "", "github_regex": "https://github.com/richard-peng-xia/MMed-RAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13830", "title": "DreamVideo-2: Zero-Shot Subject-Driven Video Customization with Precise Motion Control", "authors": [ "Yujie Wei", "Shiwei Zhang", "Hangjie Yuan", "Xiang Wang", "Haonan Qiu", "Rui Zhao", "Yutong Feng", "Feng Liu", "Zhizhong Huang", "Jiaxin Ye", "Yingya Zhang", "Hongming Shan" ], "github": "", "project_page": "", "abstract": "Recent advances in customized video generation have enabled users to create videos tailored to both specific subjects and motion trajectories. However, existing methods often require complicated test-time fine-tuning and struggle with balancing subject learning and motion control, limiting their real-world applications. In this paper, we present DreamVideo-2, a zero-shot video customization framework capable of generating videos with a specific subject and motion trajectory, guided by a single image and a bounding box sequence, respectively, and without the need for test-time fine-tuning. Specifically, we introduce reference attention, which leverages the model's inherent capabilities for subject learning, and devise a mask-guided motion module to achieve precise motion control by fully utilizing the robust motion signal of box masks derived from bounding boxes. While these two components achieve their intended functions, we empirically observe that motion control tends to dominate over subject learning. To address this, we propose two key designs: 1) the masked reference attention, which integrates a blended latent mask modeling scheme into reference attention to enhance subject representations at the desired positions, and 2) a reweighted diffusion loss, which differentiates the contributions of regions inside and outside the bounding boxes to ensure a balance between subject and motion control. Extensive experimental results on a newly curated dataset demonstrate that DreamVideo-2 outperforms state-of-the-art methods in both subject customization and motion control. The dataset, code, and models will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13852", "title": "Retrospective Learning from Interactions", "authors": [ "Zizhao Chen", "Mustafa Omer Gul", "Yiwei Chen", "Gloria Geng", "Anne Wu", "Yoav Artzi" ], "github": "", "project_page": "", "abstract": "Multi-turn interactions between large language models (LLMs) and users naturally include implicit feedback signals. If an LLM responds in an unexpected way to an instruction, the user is likely to signal it by rephrasing the request, expressing frustration, or pivoting to an alternative task. Such signals are task-independent and occupy a relatively constrained subspace of language, allowing the LLM to identify them even if it fails on the actual task. This creates an avenue for continually learning from interactions without additional annotations. We introduce ReSpect, a method to learn from such signals in past interactions via retrospection. We deploy ReSpect in a new multimodal interaction scenario, where humans instruct an LLM to solve an abstract reasoning task with a combinatorial solution space. Through thousands of interactions with humans, we show how ReSpect gradually improves task completion rate from 31% to 82%, all without any external annotation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13198", "title": "Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation", "authors": [ "Sreyan Ghosh", "Mohammad Sadegh Rasooli", "Michael Levit", "Peidong Wang", "Jian Xue", "Dinesh Manocha", "Jinyu Li" ], "github": "", "project_page": "", "abstract": "Generative Error Correction (GEC) has emerged as a powerful post-processing method to enhance the performance of Automatic Speech Recognition (ASR) systems. However, we show that GEC models struggle to generalize beyond the specific types of errors encountered during training, limiting their ability to correct new, unseen errors at test time, particularly in out-of-domain (OOD) scenarios. This phenomenon amplifies with named entities (NEs), where, in addition to insufficient contextual information or knowledge about the NEs, novel NEs keep emerging. To address these issues, we propose DARAG (Data- and Retrieval-Augmented Generative Error Correction), a novel approach designed to improve GEC for ASR in in-domain (ID) and OOD scenarios. We augment the GEC training dataset with synthetic data generated by prompting LLMs and text-to-speech models, thereby simulating additional errors from which the model can learn. For OOD scenarios, we simulate test-time errors from new domains similarly and in an unsupervised fashion. Additionally, to better handle named entities, we introduce retrieval-augmented correction by augmenting the input with entities retrieved from a database. Our approach is simple, scalable, and both domain- and language-agnostic. We experiment on multiple datasets and settings, showing that DARAG outperforms all our baselines, achieving 8\\% -- 30\\% relative WER improvements in ID and 10\\% -- 33\\% improvements in OOD settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13060", "title": "AERO: Softmax-Only LLMs for Efficient Private Inference", "authors": [ "Nandan Kumar Jha", "Brandon Reagen" ], "github": "", "project_page": "", "abstract": "The pervasiveness of proprietary language models has raised privacy concerns for users' sensitive data, emphasizing the need for private inference (PI), where inference is performed directly on encrypted inputs. However, current PI methods face prohibitively higher communication and latency overheads, primarily due to nonlinear operations. In this paper, we present a comprehensive analysis to understand the role of nonlinearities in transformer-based decoder-only language models. We introduce AERO, a four-step architectural optimization framework that refines the existing LLM architecture for efficient PI by systematically removing nonlinearities such as LayerNorm and GELU and reducing FLOPs counts. For the first time, we propose a Softmax-only architecture with significantly fewer FLOPs tailored for efficient PI. Furthermore, we devise a novel entropy regularization technique to improve the performance of Softmax-only models. AERO achieves up to 4.23times communication and 1.94times latency reduction. We validate the effectiveness of AERO by benchmarking it against the state-of-the-art.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13785", "title": "PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment", "authors": [ "Zekun Moore Wang", "Shawn Wang", "Kang Zhu", "Jiaheng Liu", "Ke Xu", "Jie Fu", "Wangchunshu Zhou", "Wenhao Huang" ], "github": "", "project_page": "", "abstract": "Alignment of large language models (LLMs) involves training models on preference-contrastive output pairs to adjust their responses according to human preferences. To obtain such contrastive pairs, traditional methods like RLHF and RLAIF rely on limited contrasting patterns, such as varying model variants or decoding temperatures. This singularity leads to two issues: (1) alignment is not comprehensive; and thereby (2) models are susceptible to jailbreaking attacks. To address these issues, we investigate how to construct more comprehensive and diversified contrasting patterns to enhance preference data (RQ1) and verify the impact of the diversification of contrasting patterns on model alignment (RQ2). For RQ1, we propose PopAlign, a framework that integrates diversified contrasting patterns across the prompt, model, and pipeline levels, introducing six contrasting strategies that do not require additional feedback labeling procedures. Regarding RQ2, we conduct thorough experiments demonstrating that PopAlign significantly outperforms existing methods, leading to more comprehensive alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.09019", "title": "MedMobile: A mobile-sized language model with expert-level clinical capabilities", "authors": [ "Krithik Vishwanath", "Jaden Stryker", "Anton Alaykin", "Daniel Alexander Alber", "Eric Karl Oermann" ], "github": "https://github.com/nyuolab/MedMobile", "project_page": "", "abstract": "Language models (LMs) have demonstrated expert-level reasoning and recall abilities in medicine. However, computational costs and privacy concerns are mounting barriers to wide-scale implementation. We introduce a parsimonious adaptation of phi-3-mini, MedMobile, a 3.8 billion parameter LM capable of running on a mobile device, for medical applications. We demonstrate that MedMobile scores 75.7% on the MedQA (USMLE), surpassing the passing mark for physicians (~60%), and approaching the scores of models 100 times its size. We subsequently perform a careful set of ablations, and demonstrate that chain of thought, ensembling, and fine-tuning lead to the greatest performance gains, while unexpectedly retrieval augmented generation fails to demonstrate significant improvements", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nyuolab/MedMobile", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13804", "title": "BenTo: Benchmark Task Reduction with In-Context Transferability", "authors": [ "Hongyu Zhao", "Ming Li", "Lichao Sun", "Tianyi Zhou" ], "github": "https://github.com/tianyi-lab/bento", "project_page": "", "abstract": "Evaluating large language models (LLMs) is costly: it requires the generation and examination of LLM outputs on a large-scale benchmark of various tasks. This paper investigates how to efficiently reduce the tasks used to benchmark LLMs without affecting the evaluation quality. Our study reveals that task transferability and relevance provide critical information to identify the most representative subset of tasks via optimizing a facility location function. We propose a practically efficient metric for estimating the transferability between two tasks via in-context learning (ICL). By analyzing the pairwise transferability, we can reduce tasks in a modern LLM benchmark (e.g., MMLU or FLAN) to 5% while inducing only a <4% difference to the evaluation on the original benchmark. Compared to prior works, our method is training-free, gradient-free, and highly efficient requiring ICL only.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tianyi-lab/bento", "github_api_added_by": "auto" }, { "date": "2024-10-18", "arxiv_id": "2410.13832", "title": "VidPanos: Generative Panoramic Videos from Casual Panning Videos", "authors": [ "Jingwei Ma", "Erika Lu", "Roni Paiss", "Shiran Zada", "Aleksander Holynski", "Tali Dekel", "Brian Curless", "Michael Rubinstein", "Forrester Cole" ], "github": "", "project_page": "", "abstract": "Panoramic image stitching provides a unified, wide-angle view of a scene that extends beyond the camera's field of view. Stitching frames of a panning video into a panoramic photograph is a well-understood problem for stationary scenes, but when objects are moving, a still panorama cannot capture the scene. We present a method for synthesizing a panoramic video from a casually-captured panning video, as if the original video were captured with a wide-angle camera. We pose panorama synthesis as a space-time outpainting problem, where we aim to create a full panoramic video of the same length as the input video. Consistent completion of the space-time volume requires a powerful, realistic prior over video content and motion, for which we adapt generative video models. Existing generative models do not, however, immediately extend to panorama completion, as we show. We instead apply video generation as a component of our panorama synthesis system, and demonstrate how to exploit the strengths of the models while minimizing their limitations. Our system can create video panoramas for a range of in-the-wild scenes including people, vehicles, and flowing water, as well as stationary background features.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13720", "title": "Movie Gen: A Cast of Media Foundation Models", "authors": [ "Adam Polyak", "Amit Zohar", "Andrew Brown", "Andros Tjandra", "Animesh Sinha", "Ann Lee", "Apoorv Vyas", "Bowen Shi", "Chih-Yao Ma", "Ching-Yao Chuang", "David Yan", "Dhruv Choudhary", "Dingkang Wang", "Geet Sethi", "Guan Pang", "Haoyu Ma", "Ishan Misra", "Ji Hou", "Jialiang Wang", "Kiran Jagadeesh", "Kunpeng Li", "Luxin Zhang", "Mannat Singh", "Mary Williamson", "Matt Le", "Matthew Yu", "Mitesh Kumar Singh", "Peizhao Zhang", "Peter Vajda", "Quentin Duval", "Rohit Girdhar", "Roshan Sumbaly", "Sai Saketh Rambhatla", "Sam Tsai", "Samaneh Azadi", "Samyak Datta", "Sanyuan Chen", "Sean Bell", "Sharadh Ramaswamy", "Shelly Sheynin", "Siddharth Bhattacharya", "Simran Motwani", "Tao Xu", "Tianhe Li", "Tingbo Hou", "Wei-Ning Hsu", "Xi Yin", "Xiaoliang Dai", "Yaniv Taigman", "Yaqiao Luo", "Yen-Cheng Liu", "Yi-Chiao Wu", "Yue Zhao", "Yuval Kirstain", "Zecheng He", "Zijian He", "Albert Pumarola", "Ali Thabet", "Artsiom Sanakoyeu", "Arun Mallya", "Baishan Guo", "Boris Araya", "Breena Kerr", "Carleigh Wood", "Ce Liu", "Cen Peng", "Dimitry Vengertsev", "Edgar Schonfeld", "Elliot Blanchard", "Felix Juefei-Xu", "Fraylie Nord", "Jeff Liang", "John Hoffman", "Jonas Kohler", "Kaolin Fire", "Karthik Sivakumar", "Lawrence Chen", "Licheng Yu", "Luya Gao", "Markos Georgopoulos", "Rashel Moritz", "Sara K. Sampson", "Shikai Li", "Simone Parmeggiani", "Steve Fine", "Tara Fowler", "Vladan Petrovic", "Yuming Du" ], "github": "", "project_page": "", "abstract": "We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of personalized videos based on a user's image. Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation. Our largest video generation model is a 30B parameter transformer trained with a maximum context length of 73K video tokens, corresponding to a generated video of 16 seconds at 16 frames-per-second. We show multiple technical innovations and simplifications on the architecture, latent spaces, training objectives and recipes, data curation, evaluation protocols, parallelization techniques, and inference optimizations that allow us to reap the benefits of scaling pre-training data, model size, and training compute for training large scale media generation models. We hope this paper helps the research community to accelerate progress and innovation in media generation models. All videos from this paper are available at https://go.fb.me/MovieGenResearchVideos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13618", "title": "LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning", "authors": [ "Yiming Shi", "Jiwei Wei", "Yujia Wu", "Ran Ran", "Chengwei Sun", "Shiyuan He", "Yang Yang" ], "github": "https://github.com/SKDDJ/LoLDU", "project_page": "", "abstract": "The rapid growth of model scale has necessitated substantial computational resources for fine-tuning. Existing approach such as Low-Rank Adaptation (LoRA) has sought to address the problem of handling the large updated parameters in full fine-tuning. However, LoRA utilize random initialization and optimization of low-rank matrices to approximate updated weights, which can result in suboptimal convergence and an accuracy gap compared to full fine-tuning. To address these issues, we propose LoLDU, a Parameter-Efficient Fine-Tuning (PEFT) approach that significantly reduces trainable parameters by 2600 times compared to regular PEFT methods while maintaining comparable performance. LoLDU leverages Lower-Diag-Upper Decomposition (LDU) to initialize low-rank matrices for faster convergence and orthogonality. We focus on optimizing the diagonal matrix for scaling transformations. To the best of our knowledge, LoLDU has the fewest parameters among all PEFT approaches. We conducted extensive experiments across 4 instruction-following datasets, 6 natural language understanding (NLU) datasets, 8 image classification datasets, and image generation datasets with multiple model types (LLaMA2, RoBERTa, ViT, and Stable Diffusion), providing a comprehensive and detailed analysis. Our open-source code can be accessed at https://github.com/SKDDJ/LoLDU{https://github.com/SKDDJ/LoLDU}.", "github_manual": "", "github_regex": "https://github.com/SKDDJ/LoLDU", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.11842", "title": "MoH: Multi-Head Attention as Mixture-of-Head Attention", "authors": [ "Peng Jin", "Bo Zhu", "Li Yuan", "Shuicheng Yan" ], "github": "https://github.com/skyworkai/moh", "project_page": "", "abstract": "In this work, we upgrade the multi-head attention mechanism, the core of the Transformer model, to improve efficiency while maintaining or surpassing the previous accuracy level. We show that multi-head attention can be expressed in the summation form. Drawing on the insight that not all attention heads hold equal significance, we propose Mixture-of-Head attention (MoH), a new architecture that treats attention heads as experts in the Mixture-of-Experts (MoE) mechanism. MoH has two significant advantages: First, MoH enables each token to select the appropriate attention heads, enhancing inference efficiency without compromising accuracy or increasing the number of parameters. Second, MoH replaces the standard summation in multi-head attention with a weighted summation, introducing flexibility to the attention mechanism and unlocking extra performance potential. Extensive experiments on ViT, DiT, and LLMs demonstrate that MoH outperforms multi-head attention by using only 50%-90% of the attention heads. Moreover, we demonstrate that pre-trained multi-head attention models, such as LLaMA3-8B, can be further continue-tuned into our MoH models. Notably, MoH-LLaMA3-8B achieves an average accuracy of 64.0% across 14 benchmarks, outperforming LLaMA3-8B by 2.4% by utilizing only 75% of the attention heads. We believe the proposed MoH is a promising alternative to multi-head attention and provides a strong foundation for developing advanced and efficient attention-based models.", "github_manual": "https://github.com/skyworkai/moh", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13293", "title": "SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented Generation", "authors": [ "Prakhar Dixit", "Tim Oates" ], "github": "", "project_page": "", "abstract": "Many students struggle with math word problems (MWPs), often finding it difficult to identify key information and select the appropriate mathematical operations.Schema-based instruction (SBI) is an evidence-based strategy that helps students categorize problems based on their structure, improving problem-solving accuracy. Building on this, we propose a Schema-Based Instruction Retrieval-Augmented Generation (SBI-RAG) framework that incorporates a large language model (LLM).Our approach emphasizes step-by-step reasoning by leveraging schemas to guide solution generation. We evaluate its performance on the GSM8K dataset, comparing it with GPT-4 and GPT-3.5 Turbo, and introduce a \"reasoning score\" metric to assess solution quality. Our findings suggest that SBI-RAG enhances reasoning clarity and problem-solving accuracy, potentially providing educational benefits for students", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13754", "title": "MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures", "authors": [ "Jinjie Ni", "Yifan Song", "Deepanway Ghosal", "Bo Li", "David Junhao Zhang", "Xiang Yue", "Fuzhao Xue", "Zian Zheng", "Kaichen Zhang", "Mahir Shah", "Kabir Jain", "Yang You", "Michael Shieh" ], "github": "", "project_page": "", "abstract": "Perceiving and generating diverse modalities are crucial for AI models to effectively learn from and engage with real-world signals, necessitating reliable evaluations for their development. We identify two major issues in current evaluations: (1) inconsistent standards, shaped by different communities with varying protocols and maturity levels; and (2) significant query, grading, and generalization biases. To address these, we introduce MixEval-X, the first any-to-any real-world benchmark designed to optimize and standardize evaluations across input and output modalities. We propose multi-modal benchmark mixture and adaptation-rectification pipelines to reconstruct real-world task distributions, ensuring evaluations generalize effectively to real-world use cases. Extensive meta-evaluations show our approach effectively aligns benchmark samples with real-world task distributions and the model rankings correlate strongly with that of crowd-sourced real-world evaluations (up to 0.98). We provide comprehensive leaderboards to rerank existing models and organizations and offer insights to enhance understanding of multi-modal evaluations and inform future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13848", "title": "Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation", "authors": [ "Chengyue Wu", "Xiaokang Chen", "Zhiyu Wu", "Yiyang Ma", "Xingchao Liu", "Zizheng Pan", "Wen Liu", "Zhenda Xie", "Xingkai Yu", "Chong Ruan", "Ping Luo" ], "github": "https://github.com/deepseek-ai/Janus", "project_page": "", "abstract": "In this paper, we introduce Janus, an autoregressive framework that unifies multimodal understanding and generation. Prior research often relies on a single visual encoder for both tasks, such as Chameleon. However, due to the differing levels of information granularity required by multimodal understanding and generation, this approach can lead to suboptimal performance, particularly in multimodal understanding. To address this issue, we decouple visual encoding into separate pathways, while still leveraging a single, unified transformer architecture for processing. The decoupling not only alleviates the conflict between the visual encoder's roles in understanding and generation, but also enhances the framework's flexibility. For instance, both the multimodal understanding and generation components can independently select their most suitable encoding methods. Experiments show that Janus surpasses previous unified model and matches or exceeds the performance of task-specific models. The simplicity, high flexibility, and effectiveness of Janus make it a strong candidate for next-generation unified multimodal models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/deepseek-ai/Janus", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13334", "title": "Do LLMs Have Political Correctness? Analyzing Ethical Biases and Jailbreak Vulnerabilities in AI Systems", "authors": [ "Isack Lee", "Haebin Seong" ], "github": "", "project_page": "", "abstract": "Although large language models (LLMs) demonstrate impressive proficiency in various tasks, they present potential safety risks, such as `jailbreaks', where malicious inputs can coerce LLMs into generating harmful content. To address these issues, many LLM developers have implemented various safety measures to align these models. This alignment involves several techniques, including data filtering during pre-training, supervised fine-tuning, reinforcement learning from human feedback, and red-teaming exercises. These methods often introduce deliberate and intentional biases similar to Political Correctness (PC) to ensure the ethical behavior of LLMs. In this paper, we delve into the intentional biases injected into LLMs for safety purposes and examine methods to circumvent these safety alignment techniques. Notably, these intentional biases result in a jailbreaking success rate in GPT-4o models that differs by 20% between non-binary and cisgender keywords and by 16% between white and black keywords, even when the other parts of the prompts are identical. We introduce the concept of PCJailbreak, highlighting the inherent risks posed by these safety-induced biases. Additionally, we propose an efficient defense method PCDefense, which prevents jailbreak attempts by injecting defense prompts prior to generation. PCDefense stands as an appealing alternative to Guard Models, such as Llama-Guard, that require additional inference cost after text generation. Our findings emphasize the urgent need for LLM developers to adopt a more responsible approach when designing and implementing safety measures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.12781", "title": "Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage Gaussian Splats", "authors": [ "Chen Ziwen", "Hao Tan", "Kai Zhang", "Sai Bi", "Fujun Luan", "Yicong Hong", "Li Fuxin", "Zexiang Xu" ], "github": "", "project_page": "", "abstract": "We propose Long-LRM, a generalizable 3D Gaussian reconstruction model that is capable of reconstructing a large scene from a long sequence of input images. Specifically, our model can process 32 source images at 960x540 resolution within only 1.3 seconds on a single A100 80G GPU. Our architecture features a mixture of the recent Mamba2 blocks and the classical transformer blocks which allowed many more tokens to be processed than prior work, enhanced by efficient token merging and Gaussian pruning steps that balance between quality and efficiency. Unlike previous feed-forward models that are limited to processing 1~4 input images and can only reconstruct a small portion of a large scene, Long-LRM reconstructs the entire scene in a single feed-forward step. On large-scale scene datasets such as DL3DV-140 and Tanks and Temples, our method achieves performance comparable to optimization-based approaches while being two orders of magnitude more efficient. Project page: https://arthurhero.github.io/projects/llrm", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13854", "title": "Can MLLMs Understand the Deep Implication Behind Chinese Images?", "authors": [ "Chenhao Zhang", "Xi Feng", "Yuelin Bai", "Xinrun Du", "Jinchang Hou", "Kaixin Deng", "Guangzeng Han", "Qinrui Li", "Bingli Wang", "Jiaheng Liu", "Xingwei Qu", "Yifei Zhang", "Qixuan Zhao", "Yiming Liang", "Ziqiang Liu", "Feiteng Fang", "Min Yang", "Wenhao Huang", "Chenghua Lin", "Ge Zhang", "Shiwen Ni" ], "github": "", "project_page": "", "abstract": "As the capabilities of Multimodal Large Language Models (MLLMs) continue to improve, the need for higher-order capability evaluation of MLLMs is increasing. However, there is a lack of work evaluating MLLM for higher-order perception and understanding of Chinese visual content. To fill the gap, we introduce the **C**hinese **I**mage **I**mplication understanding **Bench**mark, **CII-Bench**, which aims to assess the higher-order perception and understanding capabilities of MLLMs for Chinese images. CII-Bench stands out in several ways compared to existing benchmarks. Firstly, to ensure the authenticity of the Chinese context, images in CII-Bench are sourced from the Chinese Internet and manually reviewed, with corresponding answers also manually crafted. Additionally, CII-Bench incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, which can deeply reflect the model's understanding of Chinese traditional culture. Through extensive experiments on CII-Bench across multiple MLLMs, we have made significant findings. Initially, a substantial gap is observed between the performance of MLLMs and humans on CII-Bench. The highest accuracy of MLLMs attains 64.4%, where as human accuracy averages 78.2%, peaking at an impressive 81.0%. Subsequently, MLLMs perform worse on Chinese traditional culture images, suggesting limitations in their ability to understand high-level semantics and lack a deep knowledge base of Chinese traditional culture. Finally, it is observed that most models exhibit enhanced accuracy when image emotion hints are incorporated into the prompts. We believe that CII-Bench will enable MLLMs to gain a better understanding of Chinese semantics and Chinese-specific images, advancing the journey towards expert artificial general intelligence (AGI). Our project is publicly available at https://cii-bench.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13360", "title": "Remember, Retrieve and Generate: Understanding Infinite Visual Concepts as Your Personalized Assistant", "authors": [ "Haoran Hao", "Jiaming Han", "Changsheng Li", "Yu-Feng Li", "Xiangyu Yue" ], "github": "https://github.com/Hoar012/RAP-MLLM", "project_page": "", "abstract": "The development of large language models (LLMs) has significantly enhanced the capabilities of multimodal LLMs (MLLMs) as general assistants. However, lack of user-specific knowledge still restricts their application in human's daily life. In this paper, we introduce the Retrieval Augmented Personalization (RAP) framework for MLLMs' personalization. Starting from a general MLLM, we turn it into a personalized assistant in three steps. (a) Remember: We design a key-value database to store user-related information, e.g., user's name, avatar and other attributes. (b) Retrieve: When the user initiates a conversation, RAP will retrieve relevant information from the database using a multimodal retriever. (c) Generate: The input query and retrieved concepts' information are fed into MLLMs to generate personalized, knowledge-augmented responses. Unlike previous methods, RAP allows real-time concept editing via updating the external database. To further improve generation quality and alignment with user-specific information, we design a pipeline for data collection and create a specialized dataset for personalized training of MLLMs. Based on the dataset, we train a series of MLLMs as personalized multimodal assistants. By pretraining on large-scale dataset, RAP-MLLMs can generalize to infinite visual concepts without additional finetuning. Our models demonstrate outstanding flexibility and generation quality across a variety of tasks, such as personalized image captioning, question answering and visual recognition. The code, data and models are available at https://github.com/Hoar012/RAP-MLLM.", "github_manual": "", "github_regex": "https://github.com/Hoar012/RAP-MLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.12957", "title": "MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization", "authors": [ "Ruiqi Li", "Siqi Zheng", "Xize Cheng", "Ziang Zhang", "Shengpeng Ji", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "Generating music that aligns with the visual content of a video has been a challenging task, as it requires a deep understanding of visual semantics and involves generating music whose melody, rhythm, and dynamics harmonize with the visual narratives. This paper presents MuVi, a novel framework that effectively addresses these challenges to enhance the cohesion and immersive experience of audio-visual content. MuVi analyzes video content through a specially designed visual adaptor to extract contextually and temporally relevant features. These features are used to generate music that not only matches the video's mood and theme but also its rhythm and pacing. We also introduce a contrastive music-visual pre-training scheme to ensure synchronization, based on the periodicity nature of music phrases. In addition, we demonstrate that our flow-matching-based music generator has in-context learning ability, allowing us to control the style and genre of the generated music. Experimental results show that MuVi demonstrates superior performance in both audio quality and temporal synchronization. The generated music video samples are available at https://muvi-v2m.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.12183", "title": "TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration", "authors": [ "Yiwei Guo", "Shaobin Zhuang", "Kunchang Li", "Yu Qiao", "Yali Wang" ], "github": "https://github.com/markywg/transagent", "project_page": "", "abstract": "Vision-language foundation models (such as CLIP) have recently shown their power in transfer learning, owing to large-scale image-text pre-training. However, target domain data in the downstream tasks can be highly different from the pre-training phase, which makes it hard for such a single model to generalize well. Alternatively, there exists a wide range of expert models that contain diversified vision and/or language knowledge pre-trained on different modalities, tasks, networks, and datasets. Unfortunately, these models are \"isolated agents\" with heterogeneous structures, and how to integrate their knowledge for generalizing CLIP-like models has not been fully explored. To bridge this gap, we propose a general and concise TransAgent framework, which transports the knowledge of the isolated agents in a unified manner, and effectively guides CLIP to generalize with multi-source knowledge distillation. With such a distinct framework, we flexibly collaborate with 11 heterogeneous agents to empower vision-language foundation models, without further cost in the inference phase. Finally, our TransAgent achieves state-of-the-art performance on 11 visual recognition datasets. Under the same low-shot setting, it outperforms the popular CoOp with around 10% on average, and 20% on EuroSAT which contains large domain shifts.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/markywg/transagent", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13859", "title": "\u03b3-MoD: Exploring Mixture-of-Depth Adaptation for Multimodal Large Language Models", "authors": [ "Yaxin Luo", "Gen Luo", "Jiayi Ji", "Yiyi Zhou", "Xiaoshuai Sun", "Zhiqiang Shen", "Rongrong Ji" ], "github": "", "project_page": "", "abstract": "Despite the significant progress in multimodal large language models (MLLMs), their high computational cost remains a barrier to real-world deployment. Inspired by the mixture of depths (MoDs) in natural language processing, we aim to address this limitation from the perspective of ``activated tokens''. Our key insight is that if most tokens are redundant for the layer computation, then can be skipped directly via the MoD layer. However, directly converting the dense layers of MLLMs to MoD layers leads to substantial performance degradation. To address this issue, we propose an innovative MoD adaptation strategy for existing MLLMs called gamma-MoD. In gamma-MoD, a novel metric is proposed to guide the deployment of MoDs in the MLLM, namely rank of attention maps (ARank). Through ARank, we can effectively identify which layer is redundant and should be replaced with the MoD layer. Based on ARank, we further propose two novel designs to maximize the computational sparsity of MLLM while maintaining its performance, namely shared vision-language router and masked routing learning. With these designs, more than 90% dense layers of the MLLM can be effectively converted to the MoD ones. To validate our method, we apply it to three popular MLLMs, and conduct extensive experiments on 9 benchmark datasets. Experimental results not only validate the significant efficiency benefit of gamma-MoD to existing MLLMs but also confirm its generalization ability on various MLLMs. For example, with a minor performance drop, i.e., -1.5%, gamma-MoD can reduce the training and inference time of LLaVA-HR by 31.0% and 53.2%, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.10210", "title": "Minimum Tuning to Unlock Long Output from LLMs with High Quality Data as the Key", "authors": [ "Yingda Chen", "Xingjun Wang", "Jintao Huang", "Yunlin Mao", "Daoze Zhang", "Yuze Zhao" ], "github": "https://github.com/modelscope/evalscope/tree/main/evalscope/third_party/longbench_write", "project_page": "", "abstract": "As large language models rapidly evolve to support longer context, there is a notable disparity in their capability to generate output at greater lengths. Recent study suggests that the primary cause for this imbalance may arise from the lack of data with long-output during alignment training. In light of this observation, attempts are made to re-align foundation models with data that fills the gap, which result in models capable of generating lengthy output when instructed. In this paper, we explore the impact of data-quality in tuning a model for long output, and the possibility of doing so from the starting points of human-aligned (instruct or chat) models. With careful data curation, we show that it possible to achieve similar performance improvement in our tuned models, with only a small fraction of training data instances and compute. In addition, we assess the generalizability of such approaches by applying our tuning-recipes to several models. our findings suggest that, while capacities for generating long output vary across different models out-of-the-box, our approach to tune them with high-quality data using lite compute, consistently yields notable improvement across all models we experimented on. We have made public our curated dataset for tuning long-writing capability, the implementations of model tuning and evaluation, as well as the fine-tuned models, all of which can be openly-accessed.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/modelscope/evalscope/tree/main/evalscope/third_party/longbench_write", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.09347", "title": "Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment", "authors": [ "Huayu Chen", "Hang Su", "Peize Sun", "Jun Zhu" ], "github": "https://github.com/thu-ml/CCA", "project_page": "", "abstract": "Classifier-Free Guidance (CFG) is a critical technique for enhancing the sample quality of visual generative models. However, in autoregressive (AR) multi-modal generation, CFG introduces design inconsistencies between language and visual content, contradicting the design philosophy of unifying different modalities for visual AR. Motivated by language model alignment methods, we propose Condition Contrastive Alignment (CCA) to facilitate guidance-free AR visual generation with high performance and analyze its theoretical connection with guided sampling methods. Unlike guidance methods that alter the sampling process to achieve the ideal sampling distribution, CCA directly fine-tunes pretrained models to fit the same distribution target. Experimental results show that CCA can significantly enhance the guidance-free performance of all tested models with just one epoch of fine-tuning (sim 1\\% of pretraining epochs) on the pretraining dataset, on par with guided sampling methods. This largely removes the need for guided sampling in AR visual generation and cuts the sampling cost by half. Moreover, by adjusting training parameters, CCA can achieve trade-offs between sample diversity and fidelity similar to CFG. This experimentally confirms the strong theoretical connection between language-targeted alignment and visual-targeted guidance methods, unifying two previously independent research fields. Code and model weights: https://github.com/thu-ml/CCA.", "github_manual": "", "github_regex": "https://github.com/thu-ml/CCA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13757", "title": "MobA: A Two-Level Agent System for Efficient Mobile Task Automation", "authors": [ "Zichen Zhu", "Hao Tang", "Yansi Li", "Kunyao Lan", "Yixuan Jiang", "Hao Zhou", "Yixiao Wang", "Situo Zhang", "Liangtai Sun", "Lu Chen", "Kai Yu" ], "github": "https://github.com/OpenDFM/MobA", "project_page": "", "abstract": "Current mobile assistants are limited by dependence on system APIs or struggle with complex user instructions and diverse interfaces due to restricted comprehension and decision-making abilities. To address these challenges, we propose MobA, a novel Mobile phone Agent powered by multimodal large language models that enhances comprehension and planning capabilities through a sophisticated two-level agent architecture. The high-level Global Agent (GA) is responsible for understanding user commands, tracking history memories, and planning tasks. The low-level Local Agent (LA) predicts detailed actions in the form of function calls, guided by sub-tasks and memory from the GA. Integrating a Reflection Module allows for efficient task completion and enables the system to handle previously unseen complex tasks. MobA demonstrates significant improvements in task execution efficiency and completion rate in real-life evaluations, underscoring the potential of MLLM-empowered mobile assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenDFM/MobA", "github_api_added_by": "auto" }, { "date": "2024-10-18", "arxiv_id": "2410.13268", "title": "Roadmap towards Superhuman Speech Understanding using Large Language Models", "authors": [ "Fan Bu", "Yuhao Zhang", "Xidong Wang", "Benyou Wang", "Qun Liu", "Haizhou Li" ], "github": "", "project_page": "", "abstract": "The success of large language models (LLMs) has prompted efforts to integrate speech and audio data, aiming to create general foundation models capable of processing both textual and non-textual inputs. Recent advances, such as GPT-4o, highlight the potential for end-to-end speech LLMs, which preserves non-semantic information and world knowledge for deeper speech understanding. To guide the development of speech LLMs, we propose a five-level roadmap, ranging from basic automatic speech recognition (ASR) to advanced superhuman models capable of integrating non-semantic information with abstract acoustic knowledge for complex tasks. Moreover, we design a benchmark, SAGI Bechmark, that standardizes critical aspects across various tasks in these five levels, uncovering challenges in using abstract acoustic knowledge and completeness of capability. Our findings reveal gaps in handling paralinguistic cues and abstract acoustic knowledge, and we offer future directions. This paper outlines a roadmap for advancing speech LLMs, introduces a benchmark for evaluation, and provides key insights into their current limitations and potential.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.12771", "title": "Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models", "authors": [ "Luis Barroso-Luque", "Muhammed Shuaibi", "Xiang Fu", "Brandon M. Wood", "Misko Dzamba", "Meng Gao", "Ammar Rizvi", "C. Lawrence Zitnick", "Zachary W. Ulissi" ], "github": "https://github.com/orbital-materials/orb-models", "project_page": "", "abstract": "The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware. AI has the potential to accelerate materials discovery and design by more effectively exploring the chemical space compared to other computational methods or by trial-and-error. While substantial progress has been made on AI for materials data, benchmarks, and models, a barrier that has emerged is the lack of publicly available training data and open pre-trained models. To address this, we present a Meta FAIR release of the Open Materials 2024 (OMat24) large-scale open dataset and an accompanying set of pre-trained models. OMat24 contains over 110 million density functional theory (DFT) calculations focused on structural and compositional diversity. Our EquiformerV2 models achieve state-of-the-art performance on the Matbench Discovery leaderboard and are capable of predicting ground-state stability and formation energies to an F1 score above 0.9 and an accuracy of 20 meV/atom, respectively. We explore the impact of model size, auxiliary denoising objectives, and fine-tuning on performance across a range of datasets including OMat24, MPtraj, and Alexandria. The open release of the OMat24 dataset and models enables the research community to build upon our efforts and drive further advancements in AI-assisted materials science.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/orbital-materials/orb-models", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.12705", "title": "WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines", "authors": [ "Genta Indra Winata", "Frederikus Hudi", "Patrick Amadeus Irawan", "David Anugraha", "Rifki Afina Putri", "Yutong Wang", "Adam Nohejl", "Ubaidillah Ariq Prathama", "Nedjma Ousidhoum", "Afifa Amriani", "Anar Rzayev", "Anirban Das", "Ashmari Pramodya", "Aulia Adila", "Bryan Wilie", "Candy Olivia Mawalim", "Ching Lam Cheng", "Daud Abolade", "Emmanuele Chersoni", "Enrico Santus", "Fariz Ikhwantri", "Garry Kuwanto", "Hanyang Zhao", "Haryo Akbarianto Wibowo", "Holy Lovenia", "Jan Christian Blaise Cruz", "Jan Wira Gotama Putra", "Junho Myung", "Lucky Susanto", "Maria Angelica Riera Machin", "Marina Zhukova", "Michael Anugraha", "Muhammad Farid Adilazuarda", "Natasha Santosa", "Peerat Limkonchotiwat", "Raj Dabre", "Rio Alexander Audino", "Samuel Cahyawijaya", "Shi-Xiong Zhang", "Stephanie Yulia Salim", "Yi Zhou", "Yinxuan Gui", "David Ifeoluwa Adelani", "En-Shiun Annie Lee", "Shogo Okada", "Ayu Purwarianti", "Alham Fikri Aji", "Taro Watanabe", "Derry Tanti Wijaya", "Alice Oh", "Chong-Wah Ngo" ], "github": "https://github.com/worldcuisines/worldcuisines", "project_page": "", "abstract": "Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date. It includes tasks for identifying dish names and their origins. We provide evaluation datasets in two sizes (12k and 60k instances) alongside a training dataset (1 million instances). Our findings show that while VLMs perform better with correct location context, they struggle with adversarial contexts and predicting specific regional cuisines and languages. To support future research, we release a knowledge base with annotated food entries and images along with the VQA data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/worldcuisines/worldcuisines", "github_api_added_by": "auto" }, { "date": "2024-10-18", "arxiv_id": "2410.12784", "title": "JudgeBench: A Benchmark for Evaluating LLM-based Judges", "authors": [ "Sijun Tan", "Siyuan Zhuang", "Kyle Montgomery", "William Y. Tang", "Alejandro Cuadron", "Chenguang Wang", "Raluca Ada Popa", "Ion Stoica" ], "github": "https://github.com/ScalerLab/JudgeBench", "project_page": "", "abstract": "LLM-based judges have emerged as a scalable alternative to human evaluation and are increasingly used to assess, compare, and improve models. However, the reliability of LLM-based judges themselves is rarely scrutinized. As LLMs become more advanced, their responses grow more sophisticated, requiring stronger judges to evaluate them. Existing benchmarks primarily focus on a judge's alignment with human preferences, but often fail to account for more challenging tasks where crowdsourced human preference is a poor indicator of factual and logical correctness. To address this, we propose a novel evaluation framework to objectively evaluate LLM-based judges. Based on this framework, we propose JudgeBench, a benchmark for evaluating LLM-based judges on challenging response pairs spanning knowledge, reasoning, math, and coding. JudgeBench leverages a novel pipeline for converting existing difficult datasets into challenging response pairs with preference labels reflecting objective correctness. Our comprehensive evaluation on a collection of prompted judges, fine-tuned judges, multi-agent judges, and reward models shows that JudgeBench poses a significantly greater challenge than previous benchmarks, with many strong models (e.g., GPT-4o) performing just slightly better than random guessing. Overall, JudgeBench offers a reliable platform for assessing increasingly advanced LLM-based judges. Data and code are available at https://github.com/ScalerLab/JudgeBench .", "github_manual": "", "github_regex": "https://github.com/ScalerLab/JudgeBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13863", "title": "Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens", "authors": [ "Lijie Fan", "Tianhong Li", "Siyang Qin", "Yuanzhen Li", "Chen Sun", "Michael Rubinstein", "Deqing Sun", "Kaiming He", "Yonglong Tian" ], "github": "", "project_page": "", "abstract": "Scaling up autoregressive models in vision has not proven as beneficial as in large language models. In this work, we investigate this scaling problem in the context of text-to-image generation, focusing on two critical factors: whether models use discrete or continuous tokens, and whether tokens are generated in a random or fixed raster order using BERT- or GPT-like transformer architectures. Our empirical results show that, while all models scale effectively in terms of validation loss, their evaluation performance -- measured by FID, GenEval score, and visual quality -- follows different trends. Models based on continuous tokens achieve significantly better visual quality than those using discrete tokens. Furthermore, the generation order and attention mechanisms significantly affect the GenEval score: random-order models achieve notably better GenEval scores compared to raster-order models. Inspired by these findings, we train Fluid, a random-order autoregressive model on continuous tokens. Fluid 10.5B model achieves a new state-of-the-art zero-shot FID of 6.16 on MS-COCO 30K, and 0.69 overall score on the GenEval benchmark. We hope our findings and results will encourage future efforts to further bridge the scaling gap between vision and language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-18", "arxiv_id": "2410.13639", "title": "A Comparative Study on Reasoning Patterns of OpenAI's o1 Model", "authors": [ "Siwei Wu", "Zhongyuan Peng", "Xinrun Du", "Tuney Zheng", "Minghao Liu", "Jialong Wu", "Jiachen Ma", "Yizhi Li", "Jian Yang", "Wangchunshu Zhou", "Qunshu Lin", "Junbo Zhao", "Zhaoxiang Zhang", "Wenhao Huang", "Ge Zhang", "Chenghua Lin", "J. H. Liu" ], "github": "https://github.com/Open-Source-O1/o1_Reasoning_Patterns_Study", "project_page": "", "abstract": "Enabling Large Language Models (LLMs) to handle a wider range of complex tasks (e.g., coding, math) has drawn great attention from many researchers. As LLMs continue to evolve, merely increasing the number of model parameters yields diminishing performance improvements and heavy computational costs. Recently, OpenAI's o1 model has shown that inference strategies (i.e., Test-time Compute methods) can also significantly enhance the reasoning capabilities of LLMs. However, the mechanisms behind these methods are still unexplored. In our work, to investigate the reasoning patterns of o1, we compare o1 with existing Test-time Compute methods (BoN, Step-wise BoN, Agent Workflow, and Self-Refine) by using OpenAI's GPT-4o as a backbone on general reasoning benchmarks in three domains (i.e., math, coding, commonsense reasoning). Specifically, first, our experiments show that the o1 model has achieved the best performance on most datasets. Second, as for the methods of searching diverse responses (e.g., BoN), we find the reward models' capability and the search space both limit the upper boundary of these methods. Third, as for the methods that break the problem into many sub-problems, the Agent Workflow has achieved better performance than Step-wise BoN due to the domain-specific system prompt for planning better reasoning processes. Fourth, it is worth mentioning that we have summarized six reasoning patterns of o1, and provided a detailed analysis on several reasoning benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Open-Source-O1/o1_Reasoning_Patterns_Study", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.13370", "title": "MagicTailor: Component-Controllable Personalization in Text-to-Image Diffusion Models", "authors": [ "Donghao Zhou", "Jiancheng Huang", "Jinbin Bai", "Jiaze Wang", "Hao Chen", "Guangyong Chen", "Xiaowei Hu", "Pheng-Ann Heng" ], "github": "", "project_page": "", "abstract": "Recent advancements in text-to-image (T2I) diffusion models have enabled the creation of high-quality images from text prompts, but they still struggle to generate images with precise control over specific visual concepts. Existing approaches can replicate a given concept by learning from reference images, yet they lack the flexibility for fine-grained customization of the individual component within the concept. In this paper, we introduce component-controllable personalization, a novel task that pushes the boundaries of T2I models by allowing users to reconfigure specific components when personalizing visual concepts. This task is particularly challenging due to two primary obstacles: semantic pollution, where unwanted visual elements corrupt the personalized concept, and semantic imbalance, which causes disproportionate learning of the concept and component. To overcome these challenges, we design MagicTailor, an innovative framework that leverages Dynamic Masked Degradation (DM-Deg) to dynamically perturb undesired visual semantics and Dual-Stream Balancing (DS-Bal) to establish a balanced learning paradigm for desired visual semantics. Extensive comparisons, ablations, and analyses demonstrate that MagicTailor not only excels in this challenging task but also holds significant promise for practical applications, paving the way for more nuanced and creative image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.13674", "title": "Diffusion Curriculum: Synthetic-to-Real Generative Curriculum Learning via Image-Guided Diffusion", "authors": [ "Yijun Liang", "Shweta Bhardwaj", "Tianyi Zhou" ], "github": "https://github.com/tianyi-lab/DisCL", "project_page": "", "abstract": "Low-quality or scarce data has posed significant challenges for training deep neural networks in practice. While classical data augmentation cannot contribute very different new data, diffusion models opens up a new door to build self-evolving AI by generating high-quality and diverse synthetic data through text-guided prompts. However, text-only guidance cannot control synthetic images' proximity to the original images, resulting in out-of-distribution data detrimental to the model performance. To overcome the limitation, we study image guidance to achieve a spectrum of interpolations between synthetic and real images. With stronger image guidance, the generated images are similar to the training data but hard to learn. While with weaker image guidance, the synthetic images will be easier for model but contribute to a larger distribution gap with the original data. The generated full spectrum of data enables us to build a novel \"Diffusion Curriculum (DisCL)\". DisCL adjusts the image guidance level of image synthesis for each training stage: It identifies and focuses on hard samples for the model and assesses the most effective guidance level of synthetic images to improve hard data learning. We apply DisCL to two challenging tasks: long-tail (LT) classification and learning from low-quality data. It focuses on lower-guidance images of high-quality to learn prototypical features as a warm-up of learning higher-guidance images that might be weak on diversity or quality. Extensive experiments showcase a gain of 2.7% and 2.1% in OOD and ID macro-accuracy when applying DisCL to iWildCam dataset. On ImageNet-LT, DisCL improves the base model's tail-class accuracy from 4.4% to 23.64% and leads to a 4.02% improvement in all-class accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tianyi-lab/DisCL", "github_api_added_by": "auto" }, { "date": "2024-10-21", "arxiv_id": "2410.13232", "title": "Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation", "authors": [ "Hyungjoo Chae", "Namyoung Kim", "Kai Tzu-iunn Ong", "Minju Gwak", "Gwanwoo Song", "Jihoon Kim", "Sunghwan Kim", "Dongha Lee", "Jinyoung Yeo" ], "github": "https://github.com/kohjingyu/search-agents", "project_page": "", "abstract": "Large language models (LLMs) have recently gained much attention in building autonomous agents. However, the performance of current LLM-based web agents in long-horizon tasks is far from optimal, often yielding errors such as repeatedly buying a non-refundable flight ticket. By contrast, humans can avoid such an irreversible mistake, as we have an awareness of the potential outcomes (e.g., losing money) of our actions, also known as the \"world model\". Motivated by this, our study first starts with preliminary analyses, confirming the absence of world models in current LLMs (e.g., GPT-4o, Claude-3.5-Sonnet, etc.). Then, we present a World-model-augmented (WMA) web agent, which simulates the outcomes of its actions for better decision-making. To overcome the challenges in training LLMs as world models predicting next observations, such as repeated elements across observations and long HTML inputs, we propose a transition-focused observation abstraction, where the prediction objectives are free-form natural language descriptions exclusively highlighting important state differences between time steps. Experiments on WebArena and Mind2Web show that our world models improve agents' policy selection without training and demonstrate our agents' cost- and time-efficiency compared to recent tree-search-based agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kohjingyu/search-agents", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.13276", "title": "SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs", "authors": [ "Yizhao Gao", "Zhichen Zeng", "Dayou Du", "Shijie Cao", "Hayden Kwok-Hay So", "Ting Cao", "Fan Yang", "Mao Yang" ], "github": "https://github.com/microsoft/seerattention", "project_page": "", "abstract": "Attention is the cornerstone of modern Large Language Models (LLMs). Yet its quadratic complexity limits the efficiency and scalability of LLMs, especially for those with a long-context window. A promising approach addressing this limitation is to leverage the sparsity in attention. However, existing sparsity-based solutions predominantly rely on predefined patterns or heuristics to approximate sparsity. This practice falls short to fully capture the dynamic nature of attention sparsity in language-based tasks. This paper argues that attention sparsity should be learned rather than predefined. To this end, we design SeerAttention, a new Attention mechanism that augments the conventional attention with a learnable gate that adaptively selects significant blocks in an attention map and deems the rest blocks sparse. Such block-level sparsity effectively balances accuracy and speedup. To enable efficient learning of the gating network, we develop a customized FlashAttention implementation that extracts the block-level ground truth of attention map with minimum overhead. SeerAttention not only applies to post-training, but also excels in long-context fine-tuning. Our results show that at post-training stages, SeerAttention significantly outperforms state-of-the-art static or heuristic-based sparse attention methods, while also being more versatile and flexible to adapt to varying context lengths and sparsity ratios. When applied to long-context fine-tuning with YaRN, SeerAttention can achieve a remarkable 90% sparsity ratio at a 32k context length with minimal perplexity loss, offering a 5.67x speedup over FlashAttention-2.", "github_manual": "https://github.com/microsoft/seerattention", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.12791", "title": "Context is Key(NMF): Modelling Topical Information Dynamics in Chinese Diaspora Media", "authors": [ "Ross Deans Kristensen-McLachlan", "Rebecca M. M. Hicke", "M\u00e1rton Kardos", "Mette Thun\u00f8" ], "github": "", "project_page": "", "abstract": "Does the People's Republic of China (PRC) interfere with European elections through ethnic Chinese diaspora media? This question forms the basis of an ongoing research project exploring how PRC narratives about European elections are represented in Chinese diaspora media, and thus the objectives of PRC news media manipulation. In order to study diaspora media efficiently and at scale, it is necessary to use techniques derived from quantitative text analysis, such as topic modelling. In this paper, we present a pipeline for studying information dynamics in Chinese media. Firstly, we present KeyNMF, a new approach to static and dynamic topic modelling using transformer-based contextual embedding models. We provide benchmark evaluations to demonstrate that our approach is competitive on a number of Chinese datasets and metrics. Secondly, we integrate KeyNMF with existing methods for describing information dynamics in complex systems. We apply this pipeline to data from five news sites, focusing on the period of time leading up to the 2024 European parliamentary elections. Our methods and results demonstrate the effectiveness of KeyNMF for studying information dynamics in Chinese media and lay groundwork for further work addressing the broader research questions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.14677", "title": "Are AI Detectors Good Enough? A Survey on Quality of Datasets With Machine-Generated Texts", "authors": [ "German Gritsai", "Anastasia Voznyuk", "Andrey Grabovoy", "Yury Chekhovich" ], "github": "https://github.com/Advacheck-OU/ai-dataset-analysing", "project_page": "", "abstract": "The rapid development of autoregressive Large Language Models (LLMs) has significantly improved the quality of generated texts, necessitating reliable machine-generated text detectors. A huge number of detectors and collections with AI fragments have emerged, and several detection methods even showed recognition quality up to 99.9% according to the target metrics in such collections. However, the quality of such detectors tends to drop dramatically in the wild, posing a question: Are detectors actually highly trustworthy or do their high benchmark scores come from the poor quality of evaluation datasets? In this paper, we emphasise the need for robust and qualitative methods for evaluating generated data to be secure against bias and low generalising ability of future model. We present a systematic review of datasets from competitions dedicated to AI-generated content detection and propose methods for evaluating the quality of datasets containing AI-generated fragments. In addition, we discuss the possibility of using high-quality generated data to achieve two goals: improving the training of detection models and improving the training datasets themselves. Our contribution aims to facilitate a better understanding of the dynamics between human and machine text, which will ultimately support the integrity of information in an increasingly automated world.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Advacheck-OU/ai-dataset-analysing", "github_api_added_by": "auto" }, { "date": "2024-10-21", "arxiv_id": "2410.14059", "title": "UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models", "authors": [ "Yuzhe Yang", "Yifei Zhang", "Yan Hu", "Yilin Guo", "Ruoli Gan", "Yueru He", "Mingcong Lei", "Xiao Zhang", "Haining Wang", "Qianqian Xie", "Jimin Huang", "Honghai Yu", "Benyou Wang" ], "github": "", "project_page": "", "abstract": "This paper introduces the UCFE: User-Centric Financial Expertise benchmark, an innovative framework designed to evaluate the ability of large language models (LLMs) to handle complex real-world financial tasks. UCFE benchmark adopts a hybrid approach that combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities of evolving financial scenarios. Firstly, we conducted a user study involving 804 participants, collecting their feedback on financial tasks. Secondly, based on this feedback, we created our dataset that encompasses a wide range of user intents and interactions. This dataset serves as the foundation for benchmarking 12 LLM services using the LLM-as-Judge methodology. Our results show a significant alignment between benchmark scores and human preferences, with a Pearson correlation coefficient of 0.78, confirming the effectiveness of the UCFE dataset and our evaluation approach. UCFE benchmark not only reveals the potential of LLMs in the financial sector but also provides a robust framework for assessing their performance and user satisfaction.The benchmark dataset and evaluation code are available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.13828", "title": "A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglement", "authors": [ "Hui Yuan", "Yifan Zeng", "Yue Wu", "Huazheng Wang", "Mengdi Wang", "Liu Leqi" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Human Feedback (RLHF) has become the predominant approach for language model (LM) alignment. At its core, RLHF uses a margin-based loss for preference optimization, specifying ideal LM behavior only by the difference between preferred and dispreferred responses. In this paper, we identify a common pitfall of margin-based methods -- the under-specification of ideal LM behavior on preferred and dispreferred responses individually, which leads to two unintended consequences as the margin increases: (1) The probability of dispreferred (e.g., unsafe) responses may increase, resulting in potential safety alignment failures. (2) The probability of preferred responses may decrease, even when those responses are ideal. We demystify the reasons behind these problematic behaviors: margin-based losses couple the change in the preferred probability to the gradient of the dispreferred one, and vice versa, often preventing the preferred probability from increasing while the dispreferred one decreases, and thus causing a synchronized increase or decrease in both probabilities. We term this effect, inherent in margin-based objectives, gradient entanglement. Formally, we derive conditions for general margin-based alignment objectives under which gradient entanglement becomes concerning: the inner product of the gradients of preferred and dispreferred log-probabilities is large relative to the individual gradient norms. We theoretically investigate why such inner products can be large when aligning language models and empirically validate our findings. Empirical implications of our framework extend to explaining important differences in the training dynamics of various preference optimization algorithms, and suggesting potential algorithm designs to mitigate the under-specification issue of margin-based methods and thereby improving language model alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.13782", "title": "DPLM-2: A Multimodal Diffusion Protein Language Model", "authors": [ "Xinyou Wang", "Zaixiang Zheng", "Fei Ye", "Dongyu Xue", "Shujian Huang", "Quanquan Gu" ], "github": "", "project_page": "", "abstract": "Proteins are essential macromolecules defined by their amino acid sequences, which determine their three-dimensional structures and, consequently, their functions in all living organisms. Therefore, generative protein modeling necessitates a multimodal approach to simultaneously model, understand, and generate both sequences and structures. However, existing methods typically use separate models for each modality, limiting their ability to capture the intricate relationships between sequence and structure. This results in suboptimal performance in tasks that requires joint understanding and generation of both modalities. In this paper, we introduce DPLM-2, a multimodal protein foundation model that extends discrete diffusion protein language model (DPLM) to accommodate both sequences and structures. To enable structural learning with the language model, 3D coordinates are converted to discrete tokens using a lookup-free quantization-based tokenizer. By training on both experimental and high-quality synthetic structures, DPLM-2 learns the joint distribution of sequence and structure, as well as their marginals and conditionals. We also implement an efficient warm-up strategy to exploit the connection between large-scale evolutionary data and structural inductive biases from pre-trained sequence-based protein language models. Empirical evaluation shows that DPLM-2 can simultaneously generate highly compatible amino acid sequences and their corresponding 3D structures eliminating the need for a two-stage generation approach. Moreover, DPLM-2 demonstrates competitive performance in various conditional generation tasks, including folding, inverse folding, and scaffolding with multimodal motif inputs, as well as providing structure-aware representations for predictive tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.14669", "title": "NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples", "authors": [ "Baiqi Li", "Zhiqiu Lin", "Wenxuan Peng", "Jean de Dieu Nyandwi", "Daniel Jiang", "Zixian Ma", "Simran Khanuja", "Ranjay Krishna", "Graham Neubig", "Deva Ramanan" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) have made significant progress in recent visual-question-answering (VQA) benchmarks that evaluate complex visio-linguistic reasoning. However, are these models truly effective? In this work, we show that VLMs still struggle with natural images and questions that humans can easily answer, which we term natural adversarial samples. We also find it surprisingly easy to generate these VQA samples from natural image-text corpora using off-the-shelf models like CLIP and ChatGPT. We propose a semi-automated approach to collect a new benchmark, NaturalBench, for reliably evaluating VLMs with 10,000 human-verified VQA samples. Crucially, we adopt a vision-centric design by pairing each question with two images that yield different answers, preventing blind solutions from answering without using the images. This makes NaturalBench more challenging than previous benchmarks that can be solved with commonsense priors. We evaluate 53 state-of-the-art VLMs on NaturalBench, showing that models like LLaVA-OneVision, Cambrian-1, Llama3.2-Vision, Molmo, Qwen2-VL, and even GPT-4o lag 50%-70% behind human performance (over 90%). We analyze why NaturalBench is hard from two angles: (1) Compositionality: Solving NaturalBench requires diverse visio-linguistic skills, including understanding attribute bindings, object relationships, and advanced reasoning like logic and counting. To this end, unlike prior work that uses a single tag per sample, we tag each NaturalBench sample with 1 to 8 skill tags for fine-grained evaluation. (2) Biases: NaturalBench exposes severe biases in VLMs, as models often choose the same answer regardless of the image. Lastly, we apply our benchmark curation method to diverse data sources, including long captions (over 100 words) and non-English languages like Chinese and Hindi, highlighting its potential for dynamic evaluations of VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.13726", "title": "DAWN: Dynamic Frame Avatar with Non-autoregressive Diffusion Framework for Talking Head Video Generation", "authors": [ "Hanbo Cheng", "Limin Lin", "Chenyu Liu", "Pengcheng Xia", "Pengfei Hu", "Jiefeng Ma", "Jun Du", "Jia Pan" ], "github": "https://github.com/Hanbo-Cheng/DAWN-pytorch", "project_page": "", "abstract": "Talking head generation intends to produce vivid and realistic talking head videos from a single portrait and speech audio clip. Although significant progress has been made in diffusion-based talking head generation, almost all methods rely on autoregressive strategies, which suffer from limited context utilization beyond the current generation step, error accumulation, and slower generation speed. To address these challenges, we present DAWN (Dynamic frame Avatar With Non-autoregressive diffusion), a framework that enables all-at-once generation of dynamic-length video sequences. Specifically, it consists of two main components: (1) audio-driven holistic facial dynamics generation in the latent motion space, and (2) audio-driven head pose and blink generation. Extensive experiments demonstrate that our method generates authentic and vivid videos with precise lip motions, and natural pose/blink movements. Additionally, with a high generation speed, DAWN possesses strong extrapolation capabilities, ensuring the stable production of high-quality long videos. These results highlight the considerable promise and potential impact of DAWN in the field of talking head video generation. Furthermore, we hope that DAWN sparks further exploration of non-autoregressive approaches in diffusion models. Our code will be publicly at https://github.com/Hanbo-Cheng/DAWN-pytorch.", "github_manual": "", "github_regex": "https://github.com/Hanbo-Cheng/DAWN-pytorch", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.14470", "title": "How Do Training Methods Influence the Utilization of Vision Models?", "authors": [ "Paul Gavrikov", "Shashank Agnihotri", "Margret Keuper", "Janis Keuper" ], "github": "https://github.com/paulgavrikov/layer_criticality", "project_page": "", "abstract": "Not all learnable parameters (e.g., weights) contribute equally to a neural network's decision function. In fact, entire layers' parameters can sometimes be reset to random values with little to no impact on the model's decisions. We revisit earlier studies that examined how architecture and task complexity influence this phenomenon and ask: is this phenomenon also affected by how we train the model? We conducted experimental evaluations on a diverse set of ImageNet-1k classification models to explore this, keeping the architecture and training data constant but varying the training pipeline. Our findings reveal that the training method strongly influences which layers become critical to the decision function for a given task. For example, improved training regimes and self-supervised training increase the importance of early layers while significantly under-utilizing deeper layers. In contrast, methods such as adversarial training display an opposite trend. Our preliminary results extend previous findings, offering a more nuanced understanding of the inner mechanics of neural networks. Code: https://github.com/paulgavrikov/layer_criticality", "github_manual": "", "github_regex": "https://github.com/paulgavrikov/layer_criticality", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.13787", "title": "Looking Inward: Language Models Can Learn About Themselves by Introspection", "authors": [ "Felix J Binder", "James Chua", "Tomek Korbak", "Henry Sleight", "John Hughes", "Robert Long", "Ethan Perez", "Miles Turpin", "Owain Evans" ], "github": "https://github.com/felixbinder/introspection_self_prediction", "project_page": "", "abstract": "Humans acquire knowledge by observing the external world, but also by introspection. Introspection gives a person privileged access to their current state of mind (e.g., thoughts and feelings) that is not accessible to external observers. Can LLMs introspect? We define introspection as acquiring knowledge that is not contained in or derived from training data but instead originates from internal states. Such a capability could enhance model interpretability. Instead of painstakingly analyzing a model's internal workings, we could simply ask the model about its beliefs, world models, and goals. More speculatively, an introspective model might self-report on whether it possesses certain internal states such as subjective feelings or desires and this could inform us about the moral status of these states. Such self-reports would not be entirely dictated by the model's training data. We study introspection by finetuning LLMs to predict properties of their own behavior in hypothetical scenarios. For example, \"Given the input P, would your output favor the short- or long-term option?\" If a model M1 can introspect, it should outperform a different model M2 in predicting M1's behavior even if M2 is trained on M1's ground-truth behavior. The idea is that M1 has privileged access to its own behavioral tendencies, and this enables it to predict itself better than M2 (even if M2 is generally stronger). In experiments with GPT-4, GPT-4o, and Llama-3 models (each finetuned to predict itself), we find that the model M1 outperforms M2 in predicting itself, providing evidence for introspection. Notably, M1 continues to predict its behavior accurately even after we intentionally modify its ground-truth behavior. However, while we successfully elicit introspection on simple tasks, we are unsuccessful on more complex tasks or those requiring out-of-distribution generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/felixbinder/introspection_self_prediction", "github_api_added_by": "auto" }, { "date": "2024-10-21", "arxiv_id": "2410.10812", "title": "HART: Efficient Visual Generation with Hybrid Autoregressive Transformer", "authors": [ "Haotian Tang", "Yecheng Wu", "Shang Yang", "Enze Xie", "Junsong Chen", "Junyu Chen", "Zhuoyang Zhang", "Han Cai", "Yao Lu", "Song Han" ], "github": "https://github.com/mit-han-lab/hart", "project_page": "", "abstract": "We introduce Hybrid Autoregressive Transformer (HART), an autoregressive (AR) visual generation model capable of directly generating 1024x1024 images, rivaling diffusion models in image generation quality. Existing AR models face limitations due to the poor image reconstruction quality of their discrete tokenizers and the prohibitive training costs associated with generating 1024px images. To address these challenges, we present the hybrid tokenizer, which decomposes the continuous latents from the autoencoder into two components: discrete tokens representing the big picture and continuous tokens representing the residual components that cannot be represented by the discrete tokens. The discrete component is modeled by a scalable-resolution discrete AR model, while the continuous component is learned with a lightweight residual diffusion module with only 37M parameters. Compared with the discrete-only VAR tokenizer, our hybrid approach improves reconstruction FID from 2.11 to 0.30 on MJHQ-30K, leading to a 31% generation FID improvement from 7.85 to 5.38. HART also outperforms state-of-the-art diffusion models in both FID and CLIP score, with 4.5-7.7x higher throughput and 6.9-13.4x lower MACs. Our code is open sourced at https://github.com/mit-han-lab/hart.", "github_manual": "", "github_regex": "https://github.com/mit-han-lab/hart", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.14208", "title": "Montessori-Instruct: Generate Influential Training Data Tailored for Student Learning", "authors": [ "Xiaochuan Li", "Zichun Yu", "Chenyan Xiong" ], "github": "https://github.com/cxcscmu/Montessori-Instruct", "project_page": "", "abstract": "Synthetic data has been widely used to train large language models, but their generative nature inevitably introduces noisy, non-informative, and misleading learning signals. In this paper, we propose Montessori-Instruct, a novel data synthesis framework that tailors the data synthesis ability of the teacher language model toward the student language model's learning process. Specifically, we utilize local data influence of synthetic training data points on students to characterize students' learning preferences. Then, we train the teacher model with Direct Preference Optimization (DPO) to generate synthetic data tailored toward student learning preferences. Experiments with Llama3-8B-Instruct (teacher) and Llama3-8B (student) on Alpaca Eval and MT-Bench demonstrate that Montessori-Instruct significantly outperforms standard synthesis methods by 18.35\\% and 46.24\\% relatively. Our method also beats data synthesized by a stronger teacher model, GPT-4o. Further analysis confirms the benefits of teacher's learning to generate more influential training data in the student's improved learning, the advantages of local data influence in accurately measuring student preferences, and the robustness of Montessori-Instruct across different student models. Our code and data are open-sourced at https://github.com/cxcscmu/Montessori-Instruct.", "github_manual": "", "github_regex": "https://github.com/cxcscmu/Montessori-Instruct", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.14596", "title": "Teaching Models to Balance Resisting and Accepting Persuasion", "authors": [ "Elias Stengel-Eskin", "Peter Hase", "Mohit Bansal" ], "github": "https://github.com/esteng/persuasion_balanced_training", "project_page": "", "abstract": "Large language models (LLMs) are susceptible to persuasion, which can pose risks when models are faced with an adversarial interlocutor. We take a first step towards defending models against persuasion while also arguing that defense against adversarial (i.e. negative) persuasion is only half of the equation: models should also be able to accept beneficial (i.e. positive) persuasion to improve their answers. We show that optimizing models for only one side results in poor performance on the other. In order to balance positive and negative persuasion, we introduce Persuasion-Balanced Training (or PBT), which leverages multi-agent recursive dialogue trees to create data and trains models via preference optimization to accept persuasion when appropriate. PBT consistently improves resistance to misinformation and resilience to being challenged while also resulting in the best overall performance on holistic data containing both positive and negative persuasion. Crucially, we show that PBT models are better teammates in multi-agent debates. We find that without PBT, pairs of stronger and weaker models have unstable performance, with the order in which the models present their answers determining whether the team obtains the stronger or weaker model's performance. PBT leads to better and more stable results and less order dependence, with the stronger model consistently pulling the weaker one up.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/esteng/persuasion_balanced_training", "github_api_added_by": "auto" }, { "date": "2024-10-21", "arxiv_id": "2410.14672", "title": "BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities", "authors": [ "Shaozhe Hao", "Xuantong Liu", "Xianbiao Qi", "Shihao Zhao", "Bojia Zi", "Rong Xiao", "Kai Han", "Kwan-Yee K. Wong" ], "github": "https://github.com/haoosz/BiGR", "project_page": "", "abstract": "We introduce BiGR, a novel conditional image generation model using compact binary latent codes for generative training, focusing on enhancing both generation and representation capabilities. BiGR is the first conditional generative model that unifies generation and discrimination within the same framework. BiGR features a binary tokenizer, a masked modeling mechanism, and a binary transcoder for binary code prediction. Additionally, we introduce a novel entropy-ordered sampling method to enable efficient image generation. Extensive experiments validate BiGR's superior performance in generation quality, as measured by FID-50k, and representation capabilities, as evidenced by linear-probe accuracy. Moreover, BiGR showcases zero-shot generalization across various vision tasks, enabling applications such as image inpainting, outpainting, editing, interpolation, and enrichment, without the need for structural modifications. Our findings suggest that BiGR unifies generative and discriminative tasks effectively, paving the way for further advancements in the field.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/haoosz/BiGR", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.13925", "title": "FiTv2: Scalable and Improved Flexible Vision Transformer for Diffusion Model", "authors": [ "ZiDong Wang", "Zeyu Lu", "Di Huang", "Cai Zhou", "Wanli Ouyang", "and Lei Bai" ], "github": "https://github.com/whlzy/FiT", "project_page": "", "abstract": "Nature is infinitely resolution-free. In the context of this reality, existing diffusion models, such as Diffusion Transformers, often face challenges when processing image resolutions outside of their trained domain. To address this limitation, we conceptualize images as sequences of tokens with dynamic sizes, rather than traditional methods that perceive images as fixed-resolution grids. This perspective enables a flexible training strategy that seamlessly accommodates various aspect ratios during both training and inference, thus promoting resolution generalization and eliminating biases introduced by image cropping. On this basis, we present the Flexible Vision Transformer (FiT), a transformer architecture specifically designed for generating images with unrestricted resolutions and aspect ratios. We further upgrade the FiT to FiTv2 with several innovative designs, includingthe Query-Key vector normalization, the AdaLN-LoRA module, a rectified flow scheduler, and a Logit-Normal sampler. Enhanced by a meticulously adjusted network structure, FiTv2 exhibits 2times convergence speed of FiT. When incorporating advanced training-free extrapolation techniques, FiTv2 demonstrates remarkable adaptability in both resolution extrapolation and diverse resolution generation. Additionally, our exploration of the scalability of the FiTv2 model reveals that larger models exhibit better computational efficiency. Furthermore, we introduce an efficient post-training strategy to adapt a pre-trained model for the high-resolution generation. Comprehensive experiments demonstrate the exceptional performance of FiTv2 across a broad range of resolutions. We have released all the codes and models at https://github.com/whlzy/FiT to promote the exploration of diffusion transformer models for arbitrary-resolution image generation.", "github_manual": "", "github_regex": "https://github.com/whlzy/FiT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-21", "arxiv_id": "2410.11190", "title": "Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex Capabilities", "authors": [ "Zhifei Xie", "Changqiao Wu" ], "github": "https://github.com/gpt-omni/mini-omni2", "project_page": "", "abstract": "GPT-4o, an all-encompassing model, represents a milestone in the development of large multi-modal language models. It can understand visual, auditory, and textual modalities, directly output audio, and support flexible duplex interaction. Models from the open-source community often achieve some functionalities of GPT-4o, such as visual understanding and voice chat. Nevertheless, training a unified model that incorporates all modalities is challenging due to the complexities of multi-modal data, intricate model architectures, and training processes. In this paper, we introduce Mini-Omni2, a visual-audio assistant capable of providing real-time, end-to-end voice responses to visoin and audio queries. By integrating pretrained visual and auditory encoders, Mini-Omni2 maintains performance in individual modalities. We propose a three-stage training process to align modalities, allowing the language model to handle multi-modal inputs and outputs after training on a limited dataset. For interaction, we introduce a command-based interruption mechanism, enabling more flexible interaction with users. To the best of our knowledge, Mini-Omni2 is one of the closest reproductions of GPT-4o, which have similar form of functionality, and we hope it can offer valuable insights for subsequent research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gpt-omni/mini-omni2", "github_api_added_by": "auto" }, { "date": "2024-10-21", "arxiv_id": "2410.11331", "title": "SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments", "authors": [ "Syed Abdul Gaffar Shakhadri", "Kruthika KR", "Rakshit Aralimatti" ], "github": "", "project_page": "", "abstract": "We introduce Shakti, a 2.5 billion parameter language model specifically optimized for resource-constrained environments such as edge devices, including smartphones, wearables, and IoT systems. Shakti combines high-performance NLP with optimized efficiency and precision, making it ideal for real-time AI applications where computational resources and memory are limited. With support for vernacular languages and domain-specific tasks, Shakti excels in industries such as healthcare, finance, and customer service. Benchmark evaluations demonstrate that Shakti performs competitively against larger models while maintaining low latency and on-device efficiency, positioning it as a leading solution for edge AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.14940", "title": "Baichuan Alignment Technical Report", "authors": [ "Mingan Lin", "Fan Yang", "Yanjun Shen", "Haoze Sun", "Tianpeng Li", "Tao Zhang", "Chenzheng Zhu", "Tao Zhang", "Miao Zheng", "Xu Li", "Yijie Zhou", "Mingyang Chen", "Yanzhao Qin", "Youquan Li", "Hao Liang", "Fei Li", "Yadong Li", "Mang Wang", "Guosheng Dong", "Kun Fang", "Jianhua Xu", "Bin Cui", "Wentao Zhang", "Zenan Zhou", "Weipeng Chen" ], "github": "", "project_page": "", "abstract": "We introduce Baichuan Alignment, a detailed analysis of the alignment techniques employed in the Baichuan series of models. This represents the industry's first comprehensive account of alignment methodologies, offering valuable insights for advancing AI research. We investigate the critical components that enhance model performance during the alignment process, including optimization methods, data strategies, capability enhancements, and evaluation processes. The process spans three key stages: Prompt Augmentation System (PAS), Supervised Fine-Tuning (SFT), and Preference Alignment. The problems encountered, the solutions applied, and the improvements made are thoroughly recorded. Through comparisons across well-established benchmarks, we highlight the technological advancements enabled by Baichuan Alignment. Baichuan-Instruct is an internal model, while Qwen2-Nova-72B and Llama3-PBM-Nova-70B are instruct versions of the Qwen2-72B and Llama-3-70B base models, optimized through Baichuan Alignment. Baichuan-Instruct demonstrates significant improvements in core capabilities, with user experience gains ranging from 17% to 28%, and performs exceptionally well on specialized benchmarks. In open-source benchmark evaluations, both Qwen2-Nova-72B and Llama3-PBM-Nova-70B consistently outperform their respective official instruct versions across nearly all datasets. This report aims to clarify the key technologies behind the alignment process, fostering a deeper understanding within the community. Llama3-PBM-Nova-70B model is available at https://huggingface.co/PKU-Baichuan-MLSystemLab/Llama3-PBM-Nova-70B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.13218", "title": "CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy", "authors": [ "Mian Zhang", "Xianjun Yang", "Xinlu Zhang", "Travis Labrum", "Jamie C. Chiu", "Shaun M. Eack", "Fei Fang", "William Yang Wang", "Zhiyu Zoey Chen" ], "github": "https://github.com/mianzhang/CBT-Bench", "project_page": "", "abstract": "There is a significant gap between patient needs and available mental health support today. In this paper, we aim to thoroughly examine the potential of using Large Language Models (LLMs) to assist professional psychotherapy. To this end, we propose a new benchmark, CBT-BENCH, for the systematic evaluation of cognitive behavioral therapy (CBT) assistance. We include three levels of tasks in CBT-BENCH: I: Basic CBT knowledge acquisition, with the task of multiple-choice questions; II: Cognitive model understanding, with the tasks of cognitive distortion classification, primary core belief classification, and fine-grained core belief classification; III: Therapeutic response generation, with the task of generating responses to patient speech in CBT therapy sessions. These tasks encompass key aspects of CBT that could potentially be enhanced through AI assistance, while also outlining a hierarchy of capability requirements, ranging from basic knowledge recitation to engaging in real therapeutic conversations. We evaluated representative LLMs on our benchmark. Experimental results indicate that while LLMs perform well in reciting CBT knowledge, they fall short in complex real-world scenarios requiring deep analysis of patients' cognitive structures and generating effective responses, suggesting potential future work.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mianzhang/CBT-Bench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.14745", "title": "SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation", "authors": [ "Junyu Luo", "Xiao Luo", "Xiusi Chen", "Zhiping Xiao", "Wei Ju", "Ming Zhang" ], "github": "https://github.com/luo-junyu/SemiEvol", "project_page": "", "abstract": "Supervised fine-tuning (SFT) is crucial in adapting large language models (LLMs) to a specific domain or task. However, only a limited amount of labeled data is available in practical applications, which poses a severe challenge for SFT in yielding satisfactory results. Therefore, a data-efficient framework that can fully exploit labeled and unlabeled data for LLM fine-tuning is highly anticipated. Towards this end, we introduce a semi-supervised fine-tuning framework named SemiEvol for LLM adaptation from a propagate-and-select manner. For knowledge propagation, SemiEvol adopts a bi-level approach, propagating knowledge from labeled data to unlabeled data through both in-weight and in-context methods. For knowledge selection, SemiEvol incorporates a collaborative learning mechanism, selecting higher-quality pseudo-response samples. We conducted experiments using GPT-4o-mini and Llama-3.1 on seven general or domain-specific datasets, demonstrating significant improvements in model performance on target data. Furthermore, we compared SemiEvol with SFT and self-evolution methods, highlighting its practicality in hybrid data scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/luo-junyu/SemiEvol", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.13861", "title": "PUMA: Empowering Unified MLLM with Multi-granular Visual Generation", "authors": [ "Rongyao Fang", "Chengqi Duan", "Kun Wang", "Hao Li", "Hao Tian", "Xingyu Zeng", "Rui Zhao", "Jifeng Dai", "Hongsheng Li", "Xihui Liu" ], "github": "https://github.com/rongyaofang/PUMA", "project_page": "", "abstract": "Recent advancements in multimodal foundation models have yielded significant progress in vision-language understanding. Initial attempts have also explored the potential of multimodal large language models (MLLMs) for visual content generation. However, existing works have insufficiently addressed the varying granularity demands of different image generation tasks within a unified MLLM paradigm - from the diversity required in text-to-image generation to the precise controllability needed in image manipulation. In this work, we propose PUMA, emPowering Unified MLLM with Multi-grAnular visual generation. PUMA unifies multi-granular visual features as both inputs and outputs of MLLMs, elegantly addressing the different granularity requirements of various image generation tasks within a unified MLLM framework. Following multimodal pretraining and task-specific instruction tuning, PUMA demonstrates proficiency in a wide range of multimodal tasks. This work represents a significant step towards a truly unified MLLM capable of adapting to the granularity demands of various visual tasks. The code and model will be released in https://github.com/rongyaofang/PUMA.", "github_manual": "", "github_regex": "https://github.com/rongyaofang/PUMA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.16256", "title": "CompassJudger-1: All-in-one Judge Model Helps Model Evaluation and Evolution", "authors": [ "Maosong Cao", "Alexander Lam", "Haodong Duan", "Hongwei Liu", "Songyang Zhang", "Kai Chen" ], "github": "https://github.com/open-compass/CompassJudger", "project_page": "", "abstract": "Efficient and accurate evaluation is crucial for the continuous improvement of large language models (LLMs). Among various assessment methods, subjective evaluation has garnered significant attention due to its superior alignment with real-world usage scenarios and human preferences. However, human-based evaluations are costly and lack reproducibility, making precise automated evaluators (judgers) vital in this process. In this report, we introduce CompassJudger-1, the first open-source all-in-one judge LLM. CompassJudger-1 is a general-purpose LLM that demonstrates remarkable versatility. It is capable of: 1. Performing unitary scoring and two-model comparisons as a reward model; 2. Conducting evaluations according to specified formats; 3. Generating critiques; 4. Executing diverse tasks like a general LLM. To assess the evaluation capabilities of different judge models under a unified setting, we have also established JudgerBench, a new benchmark that encompasses various subjective evaluation tasks and covers a wide range of topics. CompassJudger-1 offers a comprehensive solution for various evaluation tasks while maintaining the flexibility to adapt to diverse requirements. Both CompassJudger and JudgerBench are released and available to the research community athttps://github.com/open-compass/CompassJudger. We believe that by open-sourcing these tools, we can foster collaboration and accelerate progress in LLM evaluation methodologies.", "github_manual": "", "github_regex": "https://github.com/open-compass/CompassJudger", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.16153", "title": "Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages", "authors": [ "Xiang Yue", "Yueqi Song", "Akari Asai", "Seungone Kim", "Jean de Dieu Nyandwi", "Simran Khanuja", "Anjali Kantharuban", "Lintang Sutawika", "Sathyanarayanan Ramamoorthy", "Graham Neubig" ], "github": "", "project_page": "", "abstract": "Despite recent advances in multimodal large language models (MLLMs), their development has predominantly focused on English- and western-centric datasets and tasks, leaving most of the world's languages and diverse cultural contexts underrepresented. This paper introduces Pangea, a multilingual multimodal LLM trained on PangeaIns, a diverse 6M instruction dataset spanning 39 languages. PangeaIns features: 1) high-quality English instructions, 2) carefully machine-translated instructions, and 3) culturally relevant multimodal tasks to ensure cross-cultural coverage. To rigorously assess models' capabilities, we introduce PangeaBench, a holistic evaluation suite encompassing 14 datasets covering 47 languages. Results show that Pangea significantly outperforms existing open-source models in multilingual settings and diverse cultural contexts. Ablation studies further reveal the importance of English data proportions, language popularity, and the number of multimodal training samples on overall performance. We fully open-source our data, code, and trained checkpoints, to facilitate the development of inclusive and robust multilingual MLLMs, promoting equity and accessibility across a broader linguistic and cultural spectrum.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.13394", "title": "Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs", "authors": [ "Sumanth Doddapaneni", "Mohammed Safi Ur Rahman Khan", "Dilip Venkatesh", "Raj Dabre", "Anoop Kunchukuttan", "Mitesh M. Khapra" ], "github": "https://github.com/ai4bharat/cia", "project_page": "", "abstract": "Evaluating machine-generated text remains a significant challenge in NLP, especially for non-English languages. Current methodologies, including automated metrics, human assessments, and LLM-based evaluations, predominantly focus on English, revealing a significant gap in multilingual evaluation frameworks. We introduce the Cross Lingual Auto Evaluation (CIA) Suite, an extensible framework that includes evaluator LLMs (Hercule) and a novel test set (Recon) specifically designed for multilingual evaluation. Our test set features 500 human-annotated instructions spanning various task capabilities along with human judgment scores across six languages. This would enable benchmarking of general-purpose multilingual LLMs and facilitate meta-evaluation of Evaluator LLMs. The proposed model, Hercule, is a cross-lingual evaluation model that addresses the scarcity of reference answers in the target language by learning to assign scores to responses based on easily available reference answers in English. Our experiments demonstrate that Hercule aligns more closely with human judgments compared to proprietary models, demonstrating the effectiveness of such cross-lingual evaluation in low resource scenarios. Further, it is also effective in zero-shot evaluation on unseen languages. This study is the first comprehensive examination of cross-lingual evaluation using LLMs, presenting a scalable and effective approach for multilingual assessment. All code, datasets, and models will be publicly available to enable further research in this important area.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ai4bharat/cia", "github_api_added_by": "auto" }, { "date": "2024-10-22", "arxiv_id": "2410.15017", "title": "DM-Codec: Distilling Multimodal Representations for Speech Tokenization", "authors": [ "Md Mubtasim Ahasan", "Md Fahim", "Tasnim Mohiuddin", "A K M Mahbubur Rahman", "Aman Chadha", "Tariq Iqbal", "M Ashraful Amin", "Md Mofijul Islam", "Amin Ahsan Ali" ], "github": "https://github.com/mubtasimahasan/DM-Codec", "project_page": "", "abstract": "Recent advancements in speech-language models have yielded significant improvements in speech tokenization and synthesis. However, effectively mapping the complex, multidimensional attributes of speech into discrete tokens remains challenging. This process demands acoustic, semantic, and contextual information for precise speech representations. Existing speech representations generally fall into two categories: acoustic tokens from audio codecs and semantic tokens from speech self-supervised learning models. Although recent efforts have unified acoustic and semantic tokens for improved performance, they overlook the crucial role of contextual representation in comprehensive speech modeling. Our empirical investigations reveal that the absence of contextual representations results in elevated Word Error Rate (WER) and Word Information Lost (WIL) scores in speech transcriptions. To address these limitations, we propose two novel distillation approaches: (1) a language model (LM)-guided distillation method that incorporates contextual information, and (2) a combined LM and self-supervised speech model (SM)-guided distillation technique that effectively distills multimodal representations (acoustic, semantic, and contextual) into a comprehensive speech tokenizer, termed DM-Codec. The DM-Codec architecture adopts a streamlined encoder-decoder framework with a Residual Vector Quantizer (RVQ) and incorporates the LM and SM during the training process. Experiments show DM-Codec significantly outperforms state-of-the-art speech tokenization models, reducing WER by up to 13.46%, WIL by 9.82%, and improving speech quality by 5.84% and intelligibility by 1.85% on the LibriSpeech benchmark dataset. The code, samples, and model checkpoints are available at https://github.com/mubtasimahasan/DM-Codec.", "github_manual": "", "github_regex": "https://github.com/mubtasimahasan/DM-Codec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.16184", "title": "RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style", "authors": [ "Yantao Liu", "Zijun Yao", "Rui Min", "Yixin Cao", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/THU-KEG/RM-Bench", "project_page": "", "abstract": "Reward models are critical in techniques like Reinforcement Learning from Human Feedback (RLHF) and Inference Scaling Laws, where they guide language model alignment and select optimal responses. Despite their importance, existing reward model benchmarks often evaluate models by asking them to distinguish between responses generated by models of varying power. However, this approach fails to assess reward models on subtle but critical content changes and variations in style, resulting in a low correlation with policy model performance. To this end, we introduce RM-Bench, a novel benchmark designed to evaluate reward models based on their sensitivity to subtle content differences and resistance to style biases. Extensive experiments demonstrate that RM-Bench strongly correlates with policy model performance, making it a reliable reference for selecting reward models to align language models effectively. We evaluate nearly 40 reward models on RM-Bench. Our results reveal that even state-of-the-art models achieve an average performance of only 46.6%, which falls short of random-level accuracy (50%) when faced with style bias interference. These findings highlight the significant room for improvement in current reward models. Related code and data are available at https://github.com/THU-KEG/RM-Bench.", "github_manual": "", "github_regex": "https://github.com/THU-KEG/RM-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.16271", "title": "FrugalNeRF: Fast Convergence for Few-shot Novel View Synthesis without Learned Priors", "authors": [ "Chin-Yang Lin", "Chung-Ho Wu", "Chang-Han Yeh", "Shih-Han Yen", "Cheng Sun", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Neural Radiance Fields (NeRF) face significant challenges in few-shot scenarios, primarily due to overfitting and long training times for high-fidelity rendering. Existing methods, such as FreeNeRF and SparseNeRF, use frequency regularization or pre-trained priors but struggle with complex scheduling and bias. We introduce FrugalNeRF, a novel few-shot NeRF framework that leverages weight-sharing voxels across multiple scales to efficiently represent scene details. Our key contribution is a cross-scale geometric adaptation scheme that selects pseudo ground truth depth based on reprojection errors across scales. This guides training without relying on externally learned priors, enabling full utilization of the training data. It can also integrate pre-trained priors, enhancing quality without slowing convergence. Experiments on LLFF, DTU, and RealEstate-10K show that FrugalNeRF outperforms other few-shot NeRF methods while significantly reducing training time, making it a practical solution for efficient and accurate 3D scene reconstruction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.12788", "title": "Meta-Chunking: Learning Efficient Text Segmentation via Logical Perception", "authors": [ "Jihao Zhao", "Zhiyuan Ji", "Pengnian Qi", "Simin Niu", "Bo Tang", "Feiyu Xiong", "Zhiyu Li" ], "github": "https://github.com/IAAR-Shanghai/Meta-Chunking", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG), while serving as a viable complement to large language models (LLMs), often overlooks the crucial aspect of text chunking within its pipeline, which impacts the quality of knowledge-intensive tasks. This paper introduces the concept of Meta-Chunking, which refers to a granularity between sentences and paragraphs, consisting of a collection of sentences within a paragraph that have deep linguistic logical connections. To implement Meta-Chunking, we designed two strategies based on LLMs: Margin Sampling Chunking and Perplexity Chunking. The former employs LLMs to perform binary classification on whether consecutive sentences need to be segmented, making decisions based on the probability difference obtained from margin sampling. The latter precisely identifies text chunk boundaries by analyzing the characteristics of perplexity distribution. Additionally, considering the inherent complexity of different texts, we propose a strategy that combines Meta-Chunking with dynamic merging to achieve a balance between fine-grained and coarse-grained text chunking. Experiments conducted on eleven datasets demonstrate that Meta-Chunking can more efficiently improve the performance of single-hop and multi-hop question answering based on RAG. For instance, on the 2WikiMultihopQA dataset, it outperforms similarity chunking by 1.32 while only consuming 45.8% of the time. Our code is available at https://github.com/IAAR-Shanghai/Meta-Chunking.", "github_manual": "", "github_regex": "https://github.com/IAAR-Shanghai/Meta-Chunking", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.16268", "title": "SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree", "authors": [ "Shuangrui Ding", "Rui Qian", "Xiaoyi Dong", "Pan Zhang", "Yuhang Zang", "Yuhang Cao", "Yuwei Guo", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/Mark12Ding/SAM2Long", "project_page": "", "abstract": "The Segment Anything Model 2 (SAM 2) has emerged as a powerful foundation model for object segmentation in both images and videos, paving the way for various downstream video applications. The crucial design of SAM 2 for video segmentation is its memory module, which prompts object-aware memories from previous frames for current frame prediction. However, its greedy-selection memory design suffers from the \"error accumulation\" problem, where an errored or missed mask will cascade and influence the segmentation of the subsequent frames, which limits the performance of SAM 2 toward complex long-term videos. To this end, we introduce SAM2Long, an improved training-free video object segmentation strategy, which considers the segmentation uncertainty within each frame and chooses the video-level optimal results from multiple segmentation pathways in a constrained tree search manner. In practice, we maintain a fixed number of segmentation pathways throughout the video. For each frame, multiple masks are proposed based on the existing pathways, creating various candidate branches. We then select the same fixed number of branches with higher cumulative scores as the new pathways for the next frame. After processing the final frame, the pathway with the highest cumulative score is chosen as the final segmentation result. Benefiting from its heuristic search design, SAM2Long is robust toward occlusions and object reappearances, and can effectively segment and track objects for complex long-term videos. Notably, SAM2Long achieves an average improvement of 3.0 points across all 24 head-to-head comparisons, with gains of up to 5.3 points in J&F on long-term video object segmentation benchmarks such as SA-V and LVOS. The code is released at https://github.com/Mark12Ding/SAM2Long.", "github_manual": "", "github_regex": "https://github.com/Mark12Ding/SAM2Long", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.15735", "title": "AutoTrain: No-code training for state-of-the-art models", "authors": [ "Abhishek Thakur" ], "github": "https://github.com/huggingface/autotrain-advanced", "project_page": "", "abstract": "With the advancements in open-source models, training (or finetuning) models on custom datasets has become a crucial part of developing solutions which are tailored to specific industrial or open-source applications. Yet, there is no single tool which simplifies the process of training across different types of modalities or tasks. We introduce AutoTrain (aka AutoTrain Advanced) -- an open-source, no code tool/library which can be used to train (or finetune) models for different kinds of tasks such as: large language model (LLM) finetuning, text classification/regression, token classification, sequence-to-sequence task, finetuning of sentence transformers, visual language model (VLM) finetuning, image classification/regression and even classification and regression tasks on tabular data. AutoTrain Advanced is an open-source library providing best practices for training models on custom datasets. The library is available at https://github.com/huggingface/autotrain-advanced. AutoTrain can be used in fully local mode or on cloud machines and works with tens of thousands of models shared on Hugging Face Hub and their variations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/huggingface/autotrain-advanced", "github_api_added_by": "user" }, { "date": "2024-10-22", "arxiv_id": "2410.16215", "title": "Pre-training Distillation for Large Language Models: A Design Space Exploration", "authors": [ "Hao Peng", "Xin Lv", "Yushi Bai", "Zijun Yao", "Jiajie Zhang", "Lei Hou", "Juanzi Li" ], "github": "", "project_page": "", "abstract": "Knowledge distillation (KD) aims to transfer knowledge from a large teacher model to a smaller student model. Previous work applying KD in the field of large language models (LLMs) typically focused on the post-training phase, where the student LLM learns directly from instructions and corresponding responses generated by the teacher model. In this paper, we extend KD to the pre-training phase of LLMs, named pre-training distillation (PD). We first conduct a preliminary experiment using GLM-4-9B as the teacher LLM to distill a 1.9B parameter student LLM, validating the effectiveness of PD. Considering the key impact factors of distillation, we systematically explore the design space of pre-training distillation across four aspects: logits processing, loss selection, scaling law, and offline or online logits. We conduct extensive experiments to explore the design space of pre-training distillation and find better configurations and interesting conclusions, such as larger student LLMs generally benefiting more from pre-training distillation, while a larger teacher LLM does not necessarily guarantee better results. We hope our exploration of the design space will inform future practices in pre-training distillation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.15633", "title": "Selecting Influential Samples for Long Context Alignment via Homologous Models' Guidance and Contextual Awareness Measurement", "authors": [ "Shuzheng Si", "Haozhe Zhao", "Gang Chen", "Yunshui Li", "Kangyang Luo", "Chuancheng Lv", "Kaikai An", "Fanchao Qi", "Baobao Chang", "Maosong Sun" ], "github": "https://github.com/s1s-z/gateau", "project_page": "", "abstract": "The expansion of large language models to effectively handle instructions with extremely long contexts has yet to be fully investigated. The primary obstacle lies in constructing a high-quality long instruction-following dataset devised for long context alignment. Existing studies have attempted to scale up the available data volume by synthesizing long instruction-following samples. However, indiscriminately increasing the quantity of data without a well-defined strategy for ensuring data quality may introduce low-quality samples and restrict the final performance. To bridge this gap, we aim to address the unique challenge of long-context alignment, i.e., modeling the long-range dependencies for handling instructions and lengthy input contexts. We propose GATEAU, a novel framework designed to identify the influential and high-quality samples enriched with long-range dependency relations by utilizing crafted Homologous Models' Guidance (HMG) and Contextual Awareness Measurement (CAM). Specifically, HMG attempts to measure the difficulty of generating corresponding responses due to the long-range dependencies, using the perplexity scores of the response from two homologous models with different context windows. Also, the role of CAM is to measure the difficulty of understanding the long input contexts due to long-range dependencies by evaluating whether the model's attention is focused on important segments. Built upon both proposed methods, we select the most challenging samples as the influential data to effectively frame the long-range dependencies, thereby achieving better performance of LLMs. Comprehensive experiments indicate that GATEAU effectively identifies samples enriched with long-range dependency relations and the model trained on these selected samples exhibits better instruction-following and long-context understanding capabilities.", "github_manual": "https://github.com/s1s-z/gateau", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.15748", "title": "Alchemy: Amplifying Theorem-Proving Capability through Symbolic Mutation", "authors": [ "Shaonan Wu", "Shuai Lu", "Yeyun Gong", "Nan Duan", "Ping Wei" ], "github": "https://github.com/wclsn/Alchemy", "project_page": "", "abstract": "Formal proofs are challenging to write even for experienced experts. Recent progress in Neural Theorem Proving (NTP) shows promise in expediting this process. However, the formal corpora available on the Internet are limited compared to the general text, posing a significant data scarcity challenge for NTP. To address this issue, this work proposes Alchemy, a general framework for data synthesis that constructs formal theorems through symbolic mutation. Specifically, for each candidate theorem in Mathlib, we identify all invocable theorems that can be used to rewrite or apply to it. Subsequently, we mutate the candidate theorem by replacing the corresponding term in the statement with its equivalent form or antecedent. As a result, our method increases the number of theorems in Mathlib by an order of magnitude, from 110k to 6M. Furthermore, we perform continual pretraining and supervised finetuning on this augmented corpus for large language models. Experimental results demonstrate the effectiveness of our approach, achieving a 5% absolute performance improvement on Leandojo benchmark. Additionally, our synthetic data achieve a 2.5% absolute performance gain on the out-of-distribution miniF2F benchmark. To provide further insights, we conduct a comprehensive analysis of synthetic data composition and the training paradigm, offering valuable guidance for developing a strong theorem prover.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/wclsn/Alchemy", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.11711", "title": "Zero-shot Model-based Reinforcement Learning using Large Language Models", "authors": [ "Abdelhakim Benechehab", "Youssef Attia El Hili", "Ambroise Odonnat", "Oussama Zekri", "Albert Thomas", "Giuseppe Paolo", "Maurizio Filippone", "Ievgen Redko", "Bal\u00e1zs K\u00e9gl" ], "github": "https://github.com/abenechehab/dicl", "project_page": "", "abstract": "The emerging zero-shot capabilities of Large Language Models (LLMs) have led to their applications in areas extending well beyond natural language processing tasks. In reinforcement learning, while LLMs have been extensively used in text-based environments, their integration with continuous state spaces remains understudied. In this paper, we investigate how pre-trained LLMs can be leveraged to predict in context the dynamics of continuous Markov decision processes. We identify handling multivariate data and incorporating the control signal as key challenges that limit the potential of LLMs' deployment in this setup and propose Disentangled In-Context Learning (DICL) to address them. We present proof-of-concept applications in two reinforcement learning settings: model-based policy evaluation and data-augmented off-policy reinforcement learning, supported by theoretical analysis of the proposed methods. Our experiments further demonstrate that our approach produces well-calibrated uncertainty estimates. We release the code at https://github.com/abenechehab/dicl.", "github_manual": "", "github_regex": "https://github.com/abenechehab/dicl", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.15316", "title": "Ichigo: Mixed-Modal Early-Fusion Realtime Voice Assistant", "authors": [ "Alan Dao", "Dinh Bach Vu", "Huy Hoang Ha" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have revolutionized natural language processing, but their application to speech-based tasks remains challenging due to the complexities of integrating audio and text modalities. This paper introduces Ichigo, a mixed-modal model that seamlessly processes interleaved sequences of speech and text. Utilizing a tokenized early-fusion approach, Ichigo quantizes speech into discrete tokens and employs a uniform transformer-based architecture for both speech and text modalities. This method enables joint reasoning and generation across modalities without the need for separate adapters. We present a comprehensive training methodology, including pre-training on multilingual speech recognition datasets and fine-tuning on a curated instruction dataset. Ichigo demonstrates state-of-the-art performance on speech question-answering benchmarks, outperforming existing open-source speech language models and achieving comparable results to cascaded systems. Notably, Ichigo exhibits a latency of just 111 ms to first token generation, significantly lower than current models. Our approach not only advances the field of multimodal AI but also provides a framework for smaller research teams to contribute effectively to open-source speech-language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.13184", "title": "Router-Tuning: A Simple and Effective Approach for Enabling Dynamic-Depth in Transformers", "authors": [ "Shwai He", "Tao Ge", "Guoheng Sun", "Bowei Tian", "Xiaoyang Wang", "Ang Li", "Dong Yu" ], "github": "https://github.com/CASE-Lab-UMD/Router-Tuning-Mixture-of-Depths", "project_page": "", "abstract": "Traditional transformer models often allocate a fixed amount of computational resources to every input token, leading to inefficient and unnecessary computation. To address this, the Mixture of Depths (MoD) was introduced to dynamically adjust the computational depth by skipping less important layers. Despite its promise, current MoD approaches remain under-explored and face two main challenges: (1) high training costs due to the need to train the entire model along with the routers that determine which layers to skip, and (2) the risk of performance degradation when important layers are bypassed. In response to the first issue, we propose Router-Tuning, a method that fine-tunes only the router on a small dataset, drastically reducing the computational overhead associated with full model training. For the second challenge, we propose MindSkip, which deploys Attention with Dynamic Depths. This method preserves the model's performance while significantly enhancing computational and memory efficiency. Extensive experiments demonstrate that our approach delivers competitive results while dramatically improving the computation efficiency, e.g., 21\\% speedup and only a 0.2\\% performance drop. The code is released at https://github.com/CASE-Lab-UMD/Router-Tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CASE-Lab-UMD/Router-Tuning-Mixture-of-Depths", "github_api_added_by": "user" }, { "date": "2024-10-22", "arxiv_id": "2410.15460", "title": "Hallucination Detox: Sensitive Neuron Dropout (SeND) for Large Language Model Training", "authors": [ "Shahrad Mohammadzadeh", "Juan David Guerra", "Marco Bonizzato", "Reihaneh Rabbany", "Golnoosh Farnadi" ], "github": "https://github.com/EMZEDI/SEND", "project_page": "", "abstract": "As large language models (LLMs) become increasingly deployed across various industries, concerns regarding their reliability, particularly due to hallucinations-outputs that are factually inaccurate or irrelevant to user input-have grown. Our research investigates the relationship between the training process and the emergence of hallucinations to address a key gap in existing research that focuses primarily on post hoc detection and mitigation strategies. Using models from the Pythia suite (70M-12B parameters) and several hallucination detection metrics, we analyze hallucination trends throughout training and explore LLM internal dynamics. We introduce SEnsitive Neuron Dropout (SeND), a novel training protocol designed to mitigate hallucinations by reducing variance during training. SeND achieves this by deterministically dropping neurons with significant variability on a dataset, referred to as Sensitive Neurons. In addition, we develop an unsupervised hallucination detection metric, Efficient EigenScore (EES), which approximates the traditional EigenScore in 2x speed. This efficient metric is integrated into our protocol, allowing SeND to be both computationally scalable and effective at reducing hallucinations. Our empirical evaluation demonstrates that our approach improves LLM reliability at test time by up to 40% compared to normal training while also providing an efficient method to improve factual accuracy when adapting LLMs to domains such as Wikipedia and Medical datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EMZEDI/SEND", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.14086", "title": "In-context learning and Occam's razor", "authors": [ "Eric Elmoznino", "Tom Marty", "Tejas Kasetty", "Leo Gagnon", "Sarthak Mittal", "Mahan Fathi", "Dhanya Sridhar", "Guillaume Lajoie" ], "github": "https://github.com/3rdCore/PrequentialCode", "project_page": "", "abstract": "The goal of machine learning is generalization. While the No Free Lunch Theorem states that we cannot obtain theoretical guarantees for generalization without further assumptions, in practice we observe that simple models which explain the training data generalize best: a principle called Occam's razor. Despite the need for simple models, most current approaches in machine learning only minimize the training error, and at best indirectly promote simplicity through regularization or architecture design. Here, we draw a connection between Occam's razor and in-context learning: an emergent ability of certain sequence models like Transformers to learn at inference time from past observations in a sequence. In particular, we show that the next-token prediction loss used to train in-context learners is directly equivalent to a data compression technique called prequential coding, and that minimizing this loss amounts to jointly minimizing both the training error and the complexity of the model that was implicitly learned from context. Our theory and the empirical experiments we use to support it not only provide a normative account of in-context learning, but also elucidate the shortcomings of current in-context learning methods, suggesting ways in which they can be improved. We make our code available at https://github.com/3rdCore/PrequentialCode.", "github_manual": "", "github_regex": "https://github.com/3rdCore/PrequentialCode", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.15002", "title": "How Many Van Goghs Does It Take to Van Gogh? Finding the Imitation Threshold", "authors": [ "Sahil Verma", "Royi Rassin", "Arnav Das", "Gantavya Bhatt", "Preethi Seshadri", "Chirag Shah", "Jeff Bilmes", "Hannaneh Hajishirzi", "Yanai Elazar" ], "github": "https://github.com/vsahil/MIMETIC-2.git", "project_page": "", "abstract": "Text-to-image models are trained using large datasets collected by scraping image-text pairs from the internet. These datasets often include private, copyrighted, and licensed material. Training models on such datasets enables them to generate images with such content, which might violate copyright laws and individual privacy. This phenomenon is termed imitation -- generation of images with content that has recognizable similarity to its training images. In this work we study the relationship between a concept's frequency in the training dataset and the ability of a model to imitate it. We seek to determine the point at which a model was trained on enough instances to imitate a concept -- the imitation threshold. We posit this question as a new problem: Finding the Imitation Threshold (FIT) and propose an efficient approach that estimates the imitation threshold without incurring the colossal cost of training multiple models from scratch. We experiment with two domains -- human faces and art styles -- for which we create four datasets, and evaluate three text-to-image models which were trained on two pretraining datasets. Our results reveal that the imitation threshold of these models is in the range of 200-600 images, depending on the domain and the model. The imitation threshold can provide an empirical basis for copyright violation claims and acts as a guiding principle for text-to-image model developers that aim to comply with copyright and privacy laws. We release the code and data at https://github.com/vsahil/MIMETIC-2.git and the project's website is hosted at https://how-many-van-goghs-does-it-take.github.io.", "github_manual": "", "github_regex": "https://github.com/vsahil/MIMETIC-2.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-22", "arxiv_id": "2410.16259", "title": "Agent-to-Sim: Learning Interactive Behavior Models from Casual Longitudinal Videos", "authors": [ "Gengshan Yang", "Andrea Bajcsy", "Shunsuke Saito", "Angjoo Kanazawa" ], "github": "", "project_page": "", "abstract": "We present Agent-to-Sim (ATS), a framework for learning interactive behavior models of 3D agents from casual longitudinal video collections. Different from prior works that rely on marker-based tracking and multiview cameras, ATS learns natural behaviors of animal and human agents non-invasively through video observations recorded over a long time-span (e.g., a month) in a single environment. Modeling 3D behavior of an agent requires persistent 3D tracking (e.g., knowing which point corresponds to which) over a long time period. To obtain such data, we develop a coarse-to-fine registration method that tracks the agent and the camera over time through a canonical 3D space, resulting in a complete and persistent spacetime 4D representation. We then train a generative model of agent behaviors using paired data of perception and motion of an agent queried from the 4D reconstruction. ATS enables real-to-sim transfer from video recordings of an agent to an interactive behavior simulator. We demonstrate results on pets (e.g., cat, dog, bunny) and human given monocular RGBD videos captured by a smartphone.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-23", "arxiv_id": "2410.14649", "title": "EvoPress: Towards Optimal Dynamic Model Compression via Evolutionary Search", "authors": [ "Oliver Sieberling", "Denis Kuznedelev", "Eldar Kurtic", "Dan Alistarh" ], "github": "https://github.com/IST-DASLab/EvoPress", "project_page": "", "abstract": "The high computational costs of large language models (LLMs) have led to a flurry of research on LLM compression, via methods such as quantization, sparsification, or structured pruning. A new frontier in this area is given by dynamic, non-uniform compression methods, which adjust the compression levels (e.g., sparsity) per-block or even per-layer in order to minimize accuracy loss, while guaranteeing a global compression threshold. Yet, current methods rely on heuristics for identifying the \"importance\" of a given layer towards the loss, based on assumptions such as error monotonicity, i.e. that the end-to-end model compression error is proportional to the sum of layer-wise errors. In this paper, we revisit this area, and propose a new and general approach for dynamic compression that is provably optimal in a given input range. We begin from the motivating observation that, in general, error monotonicity does not hold for LLMs: compressed models with lower sum of per-layer errors can perform worse than models with higher error sums. To address this, we propose a new general evolutionary framework for dynamic LLM compression called EvoPress, which has provable convergence, and low sample and evaluation complexity. We show that these theoretical guarantees lead to highly competitive practical performance for dynamic compression of Llama, Mistral and Phi models. Via EvoPress, we set new state-of-the-art results across all compression approaches: structural pruning (block/layer dropping), unstructured sparsity, as well as quantization with dynamic bitwidths. Our code is available at https://github.com/IST-DASLab/EvoPress.", "github_manual": "", "github_regex": "https://github.com/IST-DASLab/EvoPress", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-23", "arxiv_id": "2410.17215", "title": "MiniPLM: Knowledge Distillation for Pre-Training Language Models", "authors": [ "Yuxian Gu", "Hao Zhou", "Fandong Meng", "Jie Zhou", "Minlie Huang" ], "github": "https://github.com/thu-coai/MiniPLM", "project_page": "", "abstract": "Knowledge distillation (KD) is widely used to train small, high-performing student language models (LMs) using large teacher LMs. While effective in fine-tuning, KD during pre-training faces challenges in efficiency, flexibility, and effectiveness. Existing methods either incur high computational costs due to online teacher inference, require tokenization matching between teacher and student LMs, or risk losing the difficulty and diversity of the teacher-generated training data. To address these issues, we propose MiniPLM, a KD framework for pre-training LMs by refining the training data distribution with the teacher's knowledge. For efficiency, MiniPLM performs offline teacher LM inference, allowing KD for multiple student LMs without adding training-time costs. For flexibility, MiniPLM operates solely on the training corpus, enabling KD across model families. For effectiveness, MiniPLM leverages the differences between large and small LMs to enhance the difficulty and diversity of the training data, helping student LMs acquire versatile and sophisticated knowledge. Extensive experiments demonstrate that MiniPLM boosts the student LMs' performance on 9 widely used downstream tasks, improves the language modeling capabilities, and reduces pre-training computation. The benefit of MiniPLM extends to large pre-training scales, evidenced by the extrapolation of the scaling curves. Further analysis reveals that MiniPLM supports KD across model families and enhances the utilization of pre-training data. Our model, code, and data are available at https://github.com/thu-coai/MiniPLM.", "github_manual": "", "github_regex": "https://github.com/thu-coai/MiniPLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-23", "arxiv_id": "2410.17250", "title": "JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation", "authors": [ "Shota Onohara", "Atsuyuki Miyai", "Yuki Imajuku", "Kazuki Egashira", "Jeonghun Baek", "Xiang Yue", "Graham Neubig", "Kiyoharu Aizawa" ], "github": "", "project_page": "", "abstract": "Accelerating research on Large Multimodal Models (LMMs) in non-English languages is crucial for enhancing user experiences across broader populations. In this paper, we introduce JMMMU (Japanese MMMU), the first large-scale Japanese benchmark designed to evaluate LMMs on expert-level tasks based on the Japanese cultural context. To facilitate comprehensive culture-aware evaluation, JMMMU features two complementary subsets: (i) culture-agnostic (CA) subset, where the culture-independent subjects (e.g., Math) are selected and translated into Japanese, enabling one-to-one comparison with its English counterpart MMMU; and (ii) culture-specific (CS) subset, comprising newly crafted subjects that reflect Japanese cultural context. Using the CA subset, we observe performance drop in many LMMs when evaluated in Japanese, which is purely attributable to language variation. Using the CS subset, we reveal their inadequate Japanese cultural understanding. Further, by combining both subsets, we identify that some LMMs perform well on the CA subset but not on the CS subset, exposing a shallow understanding of the Japanese language that lacks depth in cultural understanding. We hope this work will not only help advance LMM performance in Japanese but also serve as a guideline to create high-standard, culturally diverse benchmarks for multilingual LMM development. The project page is https://mmmu-japanese-benchmark.github.io/JMMMU/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-23", "arxiv_id": "2410.17249", "title": "SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes", "authors": [ "Cheng-De Fan", "Chen-Wei Chang", "Yi-Ruei Liu", "Jie-Ying Lee", "Jiun-Long Huang", "Yu-Chee Tseng", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "We present SpectroMotion, a novel approach that combines 3D Gaussian Splatting (3DGS) with physically-based rendering (PBR) and deformation fields to reconstruct dynamic specular scenes. Previous methods extending 3DGS to model dynamic scenes have struggled to accurately represent specular surfaces. Our method addresses this limitation by introducing a residual correction technique for accurate surface normal computation during deformation, complemented by a deformable environment map that adapts to time-varying lighting conditions. We implement a coarse-to-fine training strategy that significantly enhances both scene geometry and specular color prediction. We demonstrate that our model outperforms prior methods for view synthesis of scenes containing dynamic specular objects and that it is the only existing 3DGS method capable of synthesizing photorealistic real-world dynamic specular scenes, outperforming state-of-the-art methods in rendering complex, dynamic, and specular scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-23", "arxiv_id": "2410.17247", "title": "PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction", "authors": [ "Long Xing", "Qidong Huang", "Xiaoyi Dong", "Jiajie Lu", "Pan Zhang", "Yuhang Zang", "Yuhang Cao", "Conghui He", "Jiaqi Wang", "Feng Wu", "Dahua Lin" ], "github": "https://github.com/cooperx521/pyramiddrop", "project_page": "", "abstract": "In large vision-language models (LVLMs), images serve as inputs that carry a wealth of information. As the idiom \"A picture is worth a thousand words\" implies, representing a single image in current LVLMs can require hundreds or even thousands of tokens. This results in significant computational costs, which grow quadratically as input image resolution increases, thereby severely impacting the efficiency of both training and inference. Previous approaches have attempted to reduce the number of image tokens either before or within the early layers of LVLMs. However, these strategies inevitably result in the loss of crucial image information, ultimately diminishing model performance. To address this challenge, we conduct an empirical study revealing that all visual tokens are necessary for LVLMs in the shallow layers, and token redundancy progressively increases in the deeper layers of the model. To this end, we propose PyramidDrop, a visual redundancy reduction strategy for LVLMs to boost their efficiency in both training and inference with neglectable performance loss. Specifically, we partition the LVLM into several stages and drop part of the image tokens at the end of each stage with a pre-defined ratio, creating pyramid-like visual tokens across model layers. The dropping is based on a lightweight similarity calculation with a negligible time overhead. Extensive experiments demonstrate that PyramidDrop can achieve a 40% training time and 55% inference FLOPs acceleration of LLaVA-NeXT with comparable performance. Besides, the PyramidDrop could also serve as a plug-and-play strategy for inference acceleration without training, with better performance and lower inference cost than counterparts. We hope that the insights and approach introduced by PyramidDrop will inspire future research to further investigate the role of image tokens in LVLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cooperx521/pyramiddrop", "github_api_added_by": "auto" }, { "date": "2024-10-23", "arxiv_id": "2410.17131", "title": "Aligning Large Language Models via Self-Steering Optimization", "authors": [ "Hao Xiang", "Bowen Yu", "Hongyu Lin", "Keming Lu", "Yaojie Lu", "Xianpei Han", "Le Sun", "Jingren Zhou", "Junyang Lin" ], "github": "https://github.com/icip-cas/sso", "project_page": "", "abstract": "Automated alignment develops alignment systems with minimal human intervention. The key to automated alignment lies in providing learnable and accurate preference signals for preference learning without human annotation. In this paper, we introduce Self-Steering Optimization (SSO), an algorithm that autonomously generates high-quality preference signals based on predefined principles during iterative training, eliminating the need for manual annotation. SSO maintains the accuracy of signals by ensuring a consistent gap between chosen and rejected responses while keeping them both on-policy to suit the current policy model's learning capacity. SSO can benefit the online and offline training of the policy model, as well as enhance the training of reward models. We validate the effectiveness of SSO with two foundation models, Qwen2 and Llama3.1, indicating that it provides accurate, on-policy preference signals throughout iterative training. Without any manual annotation or external models, SSO leads to significant performance improvements across six subjective or objective benchmarks. Besides, the preference data generated by SSO significantly enhanced the performance of the reward model on Rewardbench. Our work presents a scalable approach to preference optimization, paving the way for more efficient and effective automated alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/icip-cas/sso", "github_api_added_by": "auto" }, { "date": "2024-10-23", "arxiv_id": "2410.16930", "title": "Math Neurosurgery: Isolating Language Models' Math Reasoning Abilities Using Only Forward Passes", "authors": [ "Bryan R. Christ", "Zack Gottesman", "Jonathan Kropko", "Thomas Hartvigsen" ], "github": "https://github.com/bryanchrist/mathneuro", "project_page": "", "abstract": "Math reasoning is a highly active area of Large Language Model (LLM) research because it is a hallmark of artificial intelligence. However, few works have explored how math reasoning is encoded within LLM parameters and if it is a skill that can be isolated within a model. Doing so could allow targeted intervention to improve math performance without altering non-math behavior and foster understanding of how models encode math reasoning. We introduce Math Neurosurgery (MathNeuro), a method for isolating math-specific parameters in LLMs using only forward passes. MathNeuro builds on existing work by using weights and activations to calculate parameter importance, but isolates math-specific parameters by removing those important for general language tasks. Pruning parameters MathNeuro identifies deletes a LLM's math reasoning ability without destroying its general language ability. Scaling these parameters by a small constant improves a pretrained or instruction-tuned LLM's performance by 4-17% on GSM8K while leaving non-math behavior unaltered. MathNeuro is also data efficient: most of its effectiveness holds when identifying math-specific parameters using a single sample. MathNeuro highlights the potential for future work to intervene on math-specific parameters.", "github_manual": "https://github.com/bryanchrist/mathneuro", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-23", "arxiv_id": "2410.15926", "title": "Mitigating Object Hallucination via Concentric Causal Attention", "authors": [ "Yun Xing", "Yiheng Li", "Ivan Laptev", "Shijian Lu" ], "github": "", "project_page": "", "abstract": "Recent Large Vision Language Models (LVLMs) present remarkable zero-shot conversational and reasoning capabilities given multimodal queries. Nevertheless, they suffer from object hallucination, a phenomenon where LVLMs are prone to generate textual responses not factually aligned with image inputs. Our pilot study reveals that object hallucination is closely tied with Rotary Position Encoding (RoPE), a widely adopted positional dependency modeling design in existing LVLMs. Due to the long-term decay in RoPE, LVLMs tend to hallucinate more when relevant visual cues are distant from instruction tokens in the multimodal input sequence. Additionally, we observe a similar effect when reversing the sequential order of visual tokens during multimodal alignment. Our tests indicate that long-term decay in RoPE poses challenges to LVLMs while capturing visual-instruction interactions across long distances. We propose Concentric Causal Attention (CCA), a simple yet effective positional alignment strategy that mitigates the impact of RoPE long-term decay in LVLMs by naturally reducing relative distance between visual and instruction tokens. With CCA, visual tokens can better interact with instruction tokens, thereby enhancing model's perception capability and alleviating object hallucination. Without bells and whistles, our positional alignment method surpasses existing hallucination mitigation strategies by large margins on multiple object hallucination benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-23", "arxiv_id": "2410.16267", "title": "xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMs", "authors": [ "Michael S. Ryoo", "Honglu Zhou", "Shrikant Kendre", "Can Qin", "Le Xue", "Manli Shu", "Silvio Savarese", "Ran Xu", "Caiming Xiong", "Juan Carlos Niebles" ], "github": "", "project_page": "", "abstract": "We present xGen-MM-Vid (BLIP-3-Video): a multimodal language model for videos, particularly designed to efficiently capture temporal information over multiple frames. BLIP-3-Video takes advantage of the 'temporal encoder' in addition to the conventional visual tokenizer, which maps a sequence of tokens over multiple frames into a compact set of visual tokens. This enables BLIP3-Video to use much fewer visual tokens than its competing models (e.g., 32 vs. 4608 tokens). We explore different types of temporal encoders, including learnable spatio-temporal pooling as well as sequential models like Token Turing Machines. We experimentally confirm that BLIP-3-Video obtains video question-answering accuracies comparable to much larger state-of-the-art models (e.g., 34B), while being much smaller (i.e., 4B) and more efficient by using fewer visual tokens. The project website is at https://www.salesforceairesearch.com/opensource/xGen-MM-Vid/index.html", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-23", "arxiv_id": "2410.16198", "title": "Improve Vision Language Model Chain-of-thought Reasoning", "authors": [ "Ruohong Zhang", "Bowen Zhang", "Yanghao Li", "Haotian Zhang", "Zhiqing Sun", "Zhe Gan", "Yinfei Yang", "Ruoming Pang", "Yiming Yang" ], "github": "", "project_page": "", "abstract": "Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial for improving interpretability and trustworthiness. However, current training recipes lack robust CoT reasoning data, relying on datasets dominated by short annotations with minimal rationales. In this work, we show that training VLM on short answers does not generalize well to reasoning tasks that require more detailed responses. To address this, we propose a two-fold approach. First, we distill rationales from GPT-4o model to enrich the training data and fine-tune VLMs, boosting their CoT performance. Second, we apply reinforcement learning to further calibrate reasoning quality. Specifically, we construct positive (correct) and negative (incorrect) pairs of model-generated reasoning chains, by comparing their predictions with annotated short answers. Using this pairwise data, we apply the Direct Preference Optimization algorithm to refine the model's reasoning abilities. Our experiments demonstrate significant improvements in CoT reasoning on benchmark datasets and better generalization to direct answer prediction as well. This work emphasizes the importance of incorporating detailed rationales in training and leveraging reinforcement learning to strengthen the reasoning capabilities of VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-23", "arxiv_id": "2410.16266", "title": "3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors", "authors": [ "Xi Liu", "Chaoyi Zhou", "Siyu Huang" ], "github": "", "project_page": "", "abstract": "Novel-view synthesis aims to generate novel views of a scene from multiple input images or videos, and recent advancements like 3D Gaussian splatting (3DGS) have achieved notable success in producing photorealistic renderings with efficient pipelines. However, generating high-quality novel views under challenging settings, such as sparse input views, remains difficult due to insufficient information in under-sampled areas, often resulting in noticeable artifacts. This paper presents 3DGS-Enhancer, a novel pipeline for enhancing the representation quality of 3DGS representations. We leverage 2D video diffusion priors to address the challenging 3D view consistency problem, reformulating it as achieving temporal consistency within a video generation process. 3DGS-Enhancer restores view-consistent latent features of rendered novel views and integrates them with the input views through a spatial-temporal decoder. The enhanced views are then used to fine-tune the initial 3DGS model, significantly improving its rendering performance. Extensive experiments on large-scale datasets of unbounded scenes demonstrate that 3DGS-Enhancer yields superior reconstruction performance and high-fidelity rendering results compared to state-of-the-art methods. The project webpage is https://xiliu8006.github.io/3DGS-Enhancer-project .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-23", "arxiv_id": "2410.17241", "title": "Frontiers in Intelligent Colonoscopy", "authors": [ "Ge-Peng Ji", "Jingyi Liu", "Peng Xu", "Nick Barnes", "Fahad Shahbaz Khan", "Salman Khan", "Deng-Ping Fan" ], "github": "https://github.com/ai4colonoscopy/IntelliScope", "project_page": "", "abstract": "Colonoscopy is currently one of the most sensitive screening methods for colorectal cancer. This study investigates the frontiers of intelligent colonoscopy techniques and their prospective implications for multimodal medical applications. With this goal, we begin by assessing the current data-centric and model-centric landscapes through four tasks for colonoscopic scene perception, including classification, detection, segmentation, and vision-language understanding. This assessment enables us to identify domain-specific challenges and reveals that multimodal research in colonoscopy remains open for further exploration. To embrace the coming multimodal era, we establish three foundational initiatives: a large-scale multimodal instruction tuning dataset ColonINST, a colonoscopy-designed multimodal language model ColonGPT, and a multimodal benchmark. To facilitate ongoing monitoring of this rapidly evolving field, we provide a public website for the latest updates: https://github.com/ai4colonoscopy/IntelliScope.", "github_manual": "", "github_regex": "https://github.com/ai4colonoscopy/IntelliScope", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-23", "arxiv_id": "2410.16392", "title": "LLM-based Optimization of Compound AI Systems: A Survey", "authors": [ "Matthieu Lin", "Jenny Sheng", "Andrew Zhao", "Shenzhi Wang", "Yang Yue", "Yiran Wu", "Huan Liu", "Jun Liu", "Gao Huang", "Yong-Jin Liu" ], "github": "https://github.com/linyuhongg/LLM-based-Optimization-of-Compound-AI-Systems", "project_page": "", "abstract": "In a compound AI system, components such as an LLM call, a retriever, a code interpreter, or tools are interconnected. The system's behavior is primarily driven by parameters such as instructions or tool definitions. Recent advancements enable end-to-end optimization of these parameters using an LLM. Notably, leveraging an LLM as an optimizer is particularly efficient because it avoids gradient computation and can generate complex code and instructions. This paper presents a survey of the principles and emerging trends in LLM-based optimization of compound AI systems. It covers archetypes of compound AI systems, approaches to LLM-based end-to-end optimization, and insights into future directions and broader impacts. Importantly, this survey uses concepts from program analysis to provide a unified view of how an LLM optimizer is prompted to optimize a compound AI system. The exhaustive list of paper is provided at https://github.com/linyuhongg/LLM-based-Optimization-of-Compound-AI-Systems.", "github_manual": "", "github_regex": "https://github.com/linyuhongg/LLM-based-Optimization-of-Compound-AI-Systems", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-24", "arxiv_id": "2410.18072", "title": "WorldSimBench: Towards Video Generation Models as World Simulators", "authors": [ "Yiran Qin", "Zhelun Shi", "Jiwen Yu", "Xijun Wang", "Enshen Zhou", "Lijun Li", "Zhenfei Yin", "Xihui Liu", "Lu Sheng", "Jing Shao", "Lei Bai", "Wanli Ouyang", "Ruimao Zhang" ], "github": "", "project_page": "", "abstract": "Recent advancements in predictive models have demonstrated exceptional capabilities in predicting the future state of objects and scenes. However, the lack of categorization based on inherent characteristics continues to hinder the progress of predictive model development. Additionally, existing benchmarks are unable to effectively evaluate higher-capability, highly embodied predictive models from an embodied perspective. In this work, we classify the functionalities of predictive models into a hierarchy and take the first step in evaluating World Simulators by proposing a dual evaluation framework called WorldSimBench. WorldSimBench includes Explicit Perceptual Evaluation and Implicit Manipulative Evaluation, encompassing human preference assessments from the visual perspective and action-level evaluations in embodied tasks, covering three representative embodied scenarios: Open-Ended Embodied Environment, Autonomous, Driving, and Robot Manipulation. In the Explicit Perceptual Evaluation, we introduce the HF-Embodied Dataset, a video assessment dataset based on fine-grained human feedback, which we use to train a Human Preference Evaluator that aligns with human perception and explicitly assesses the visual fidelity of World Simulators. In the Implicit Manipulative Evaluation, we assess the video-action consistency of World Simulators by evaluating whether the generated situation-aware video can be accurately translated into the correct control signals in dynamic environments. Our comprehensive evaluation offers key insights that can drive further innovation in video generation models, positioning World Simulators as a pivotal advancement toward embodied artificial intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-24", "arxiv_id": "2410.17637", "title": "MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models", "authors": [ "Ziyu Liu", "Yuhang Zang", "Xiaoyi Dong", "Pan Zhang", "Yuhang Cao", "Haodong Duan", "Conghui He", "Yuanjun Xiong", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/liuziyu77/mia-dpo", "project_page": "", "abstract": "Visual preference alignment involves training Large Vision-Language Models (LVLMs) to predict human preferences between visual inputs. This is typically achieved by using labeled datasets of chosen/rejected pairs and employing optimization algorithms like direct preference optimization (DPO). Existing visual alignment methods, primarily designed for single-image scenarios, struggle to effectively handle the complexity of multi-image tasks due to the scarcity of diverse training data and the high cost of annotating chosen/rejected pairs. We present Multi-Image Augmented Direct Preference Optimization (MIA-DPO), a visual preference alignment approach that effectively handles multi-image inputs. MIA-DPO mitigates the scarcity of diverse multi-image training data by extending single-image data with unrelated images arranged in grid collages or pic-in-pic formats, significantly reducing the costs associated with multi-image data annotations. Our observation reveals that attention values of LVLMs vary considerably across different images. We use attention values to identify and filter out rejected responses the model may have mistakenly focused on. Our attention-aware selection for constructing the chosen/rejected pairs without relying on (i) human annotation, (ii) extra data, and (iii) external models or APIs. MIA-DPO is compatible with various architectures and outperforms existing methods on five multi-image benchmarks, achieving an average performance boost of 3.0% on LLaVA-v1.5 and 4.3% on the recent InternLM-XC2.5. Moreover, MIA-DPO has a minimal effect on the model's ability to understand single images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/liuziyu77/mia-dpo", "github_api_added_by": "auto" }, { "date": "2024-10-24", "arxiv_id": "2410.17891", "title": "Scaling Diffusion Language Models via Adaptation from Autoregressive Models", "authors": [ "Shansan Gong", "Shivam Agarwal", "Yizhe Zhang", "Jiacheng Ye", "Lin Zheng", "Mukai Li", "Chenxin An", "Peilin Zhao", "Wei Bi", "Jiawei Han", "Hao Peng", "Lingpeng Kong" ], "github": "https://github.com/HKUNLP/DiffuLLaMA", "project_page": "", "abstract": "Diffusion Language Models (DLMs) have emerged as a promising new paradigm for text generative modeling, potentially addressing limitations of autoregressive (AR) models. However, current DLMs have been studied at a smaller scale compared to their AR counterparts and lack fair comparison on language modeling benchmarks. Additionally, training diffusion models from scratch at scale remains challenging. Given the prevalence of open-source AR language models, we propose adapting these models to build text diffusion models. We demonstrate connections between AR and diffusion modeling objectives and introduce a simple continual pre-training approach for training diffusion models. Through systematic evaluation on language modeling, reasoning, and commonsense benchmarks, we show that we can convert AR models ranging from 127M to 7B parameters (GPT2 and LLaMA) into diffusion models DiffuGPT and DiffuLLaMA, using less than 200B tokens for training. Our experimental results reveal that these models outperform earlier DLMs and are competitive with their AR counterparts. We release a suite of DLMs (with 127M, 355M, and 7B parameters) capable of generating fluent text, performing in-context learning, filling in the middle without prompt re-ordering, and following instructions https://github.com/HKUNLP/DiffuLLaMA.", "github_manual": "", "github_regex": "https://github.com/HKUNLP/DiffuLLaMA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-24", "arxiv_id": "2410.17883", "title": "Lightweight Neural App Control", "authors": [ "Filippos Christianos", "Georgios Papoudakis", "Thomas Coste", "Jianye Hao", "Jun Wang", "Kun Shao" ], "github": "", "project_page": "", "abstract": "This paper introduces a novel mobile phone control architecture, termed ``app agents\", for efficient interactions and controls across various Android apps. The proposed Lightweight Multi-modal App Control (LiMAC) takes as input a textual goal and a sequence of past mobile observations, such as screenshots and corresponding UI trees, to generate precise actions. To address the computational constraints inherent to smartphones, within LiMAC, we introduce a small Action Transformer (AcT) integrated with a fine-tuned vision-language model (VLM) for real-time decision-making and task execution. We evaluate LiMAC on two open-source mobile control datasets, demonstrating the superior performance of our small-form-factor approach against fine-tuned versions of open-source VLMs, such as Florence2 and Qwen2-VL. It also significantly outperforms prompt engineering baselines utilising closed-source foundation models like GPT-4o. More specifically, LiMAC increases the overall action accuracy by up to 19% compared to fine-tuned VLMs, and up to 42% compared to prompt-engineering baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-24", "arxiv_id": "2410.18071", "title": "TP-Eval: Tap Multimodal LLMs' Potential in Evaluation by Customizing Prompts", "authors": [ "Yuxuan Xie", "Tianhua Li", "Wenqi Shao", "Kaipeng Zhang" ], "github": "", "project_page": "", "abstract": "Recently, multimodal large language models (MLLMs) have received much attention for their impressive capabilities. The evaluation of MLLMs is becoming critical to analyzing attributes of MLLMs and providing valuable insights. However, current benchmarks overlook the problem of prompt sensitivity - minor prompt variations may lead to significant performance fluctuations. Thus, inappropriate prompts may obscure the models' capabilities, underestimating the models' performance. Moreover, different models have different preferences for different prompts, and thus, using the same prompt for all models will cause evaluation bias. This paper analyzes this deficiency in existing benchmarks and further introduces a new evaluation framework named TP-Eval, which introduces a prompt customization method to reduce evaluation biases and tap models' potential. TP-Eval will rewrite the original prompts to different customized prompts for different models. In particular, we propose some well-designed modules for prompt customization tailored to the scenario of MLLM evaluation. Extensive experiments demonstrate the effectiveness of our approach to uncovering models' capabilities, and TP-Eval should benefit the community in developing more comprehensive and convincing MLLM evaluation benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-24", "arxiv_id": "2410.18013", "title": "Scalable Ranked Preference Optimization for Text-to-Image Generation", "authors": [ "Shyamgopal Karthik", "Huseyin Coskun", "Zeynep Akata", "Sergey Tulyakov", "Jian Ren", "Anil Kag" ], "github": "", "project_page": "", "abstract": "Direct Preference Optimization (DPO) has emerged as a powerful approach to align text-to-image (T2I) models with human feedback. Unfortunately, successful application of DPO to T2I models requires a huge amount of resources to collect and label large-scale datasets, e.g., millions of generated paired images annotated with human preferences. In addition, these human preference datasets can get outdated quickly as the rapid improvements of T2I models lead to higher quality images. In this work, we investigate a scalable approach for collecting large-scale and fully synthetic datasets for DPO training. Specifically, the preferences for paired images are generated using a pre-trained reward function, eliminating the need for involving humans in the annotation process, greatly improving the dataset collection efficiency. Moreover, we demonstrate that such datasets allow averaging predictions across multiple models and collecting ranked preferences as opposed to pairwise preferences. Furthermore, we introduce RankDPO to enhance DPO-based methods using the ranking feedback. Applying RankDPO on SDXL and SD3-Medium models with our synthetically generated preference dataset ``Syn-Pic'' improves both prompt-following (on benchmarks like T2I-Compbench, GenEval, and DPG-Bench) and visual quality (through user studies). This pipeline presents a practical and scalable solution to develop better preference datasets to enhance the performance of text-to-image models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-24", "arxiv_id": "2410.13924", "title": "ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding", "authors": [ "Guangda Ji", "Silvan Weder", "Francis Engelmann", "Marc Pollefeys", "Hermann Blum" ], "github": "", "project_page": "", "abstract": "The performance of neural networks scales with both their size and the amount of data they have been trained on. This is shown in both language and image generation. However, this requires scaling-friendly network architectures as well as large-scale datasets. Even though scaling-friendly architectures like transformers have emerged for 3D vision tasks, the GPT-moment of 3D vision remains distant due to the lack of training data. In this paper, we introduce ARKit LabelMaker, the first large-scale, real-world 3D dataset with dense semantic annotations. Specifically, we complement ARKitScenes dataset with dense semantic annotations that are automatically generated at scale. To this end, we extend LabelMaker, a recent automatic annotation pipeline, to serve the needs of large-scale pre-training. This involves extending the pipeline with cutting-edge segmentation models as well as making it robust to the challenges of large-scale processing. Further, we push forward the state-of-the-art performance on ScanNet and ScanNet200 dataset with prevalent 3D semantic segmentation models, demonstrating the efficacy of our generated dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-24", "arxiv_id": "2410.15522", "title": "M-RewardBench: Evaluating Reward Models in Multilingual Settings", "authors": [ "Srishti Gureja", "Lester James V. Miranda", "Shayekh Bin Islam", "Rishabh Maheshwary", "Drishti Sharma", "Gusti Winata", "Nathan Lambert", "Sebastian Ruder", "Sara Hooker", "Marzieh Fadaee" ], "github": "", "project_page": "", "abstract": "Reward models (RMs) have driven the state-of-the-art performance of LLMs today by enabling the integration of human feedback into the language modeling process. However, RMs are primarily trained and evaluated in English, and their capabilities in multilingual settings remain largely understudied. In this work, we conduct a systematic evaluation of several reward models in multilingual settings. We first construct the first-of-its-kind multilingual RM evaluation benchmark, M-RewardBench, consisting of 2.87k preference instances for 23 typologically diverse languages, that tests the chat, safety, reasoning, and translation capabilities of RMs. We then rigorously evaluate a wide range of reward models on M-RewardBench, offering fresh insights into their performance across diverse languages. We identify a significant gap in RMs' performances between English and non-English languages and show that RM preferences can change substantially from one language to another. We also present several findings on how different multilingual aspects impact RM performance. Specifically, we show that the performance of RMs is improved with improved translation quality. Similarly, we demonstrate that the models exhibit better performance for high-resource languages. We release M-RewardBench dataset and the codebase in this study to facilitate a better understanding of RM evaluation in multilingual settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-24", "arxiv_id": "2410.18084", "title": "DynamicCity: Large-Scale LiDAR Generation from Dynamic Scenes", "authors": [ "Hengwei Bian", "Lingdong Kong", "Haozhe Xie", "Liang Pan", "Yu Qiao", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "LiDAR scene generation has been developing rapidly recently. However, existing methods primarily focus on generating static and single-frame scenes, overlooking the inherently dynamic nature of real-world driving environments. In this work, we introduce DynamicCity, a novel 4D LiDAR generation framework capable of generating large-scale, high-quality LiDAR scenes that capture the temporal evolution of dynamic environments. DynamicCity mainly consists of two key models. 1) A VAE model for learning HexPlane as the compact 4D representation. Instead of using naive averaging operations, DynamicCity employs a novel Projection Module to effectively compress 4D LiDAR features into six 2D feature maps for HexPlane construction, which significantly enhances HexPlane fitting quality (up to 12.56 mIoU gain). Furthermore, we utilize an Expansion & Squeeze Strategy to reconstruct 3D feature volumes in parallel, which improves both network training efficiency and reconstruction accuracy than naively querying each 3D point (up to 7.05 mIoU gain, 2.06x training speedup, and 70.84% memory reduction). 2) A DiT-based diffusion model for HexPlane generation. To make HexPlane feasible for DiT generation, a Padded Rollout Operation is proposed to reorganize all six feature planes of the HexPlane as a squared 2D feature map. In particular, various conditions could be introduced in the diffusion or sampling process, supporting versatile 4D generation applications, such as trajectory- and command-driven generation, inpainting, and layout-conditioned generation. Extensive experiments on the CarlaSC and Waymo datasets demonstrate that DynamicCity significantly outperforms existing state-of-the-art 4D LiDAR generation methods across multiple metrics. The code will be released to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-24", "arxiv_id": "2410.13458", "title": "MedINST: Meta Dataset of Biomedical Instructions", "authors": [ "Wenhan Han", "Meng Fang", "Zihan Zhang", "Yu Yin", "Zirui Song", "Ling Chen", "Mykola Pechenizkiy", "Qingyu Chen" ], "github": "https://github.com/aialt/medinst", "project_page": "", "abstract": "The integration of large language model (LLM) techniques in the field of medical analysis has brought about significant advancements, yet the scarcity of large, diverse, and well-annotated datasets remains a major challenge. Medical data and tasks, which vary in format, size, and other parameters, require extensive preprocessing and standardization for effective use in training LLMs. To address these challenges, we introduce MedINST, the Meta Dataset of Biomedical Instructions, a novel multi-domain, multi-task instructional meta-dataset. MedINST comprises 133 biomedical NLP tasks and over 7 million training samples, making it the most comprehensive biomedical instruction dataset to date. Using MedINST as the meta dataset, we curate MedINST32, a challenging benchmark with different task difficulties aiming to evaluate LLMs' generalization ability. We fine-tune several LLMs on MedINST and evaluate on MedINST32, showcasing enhanced cross-task generalization.", "github_manual": "https://github.com/aialt/medinst", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-24", "arxiv_id": "2410.17434", "title": "LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding", "authors": [ "Xiaoqian Shen", "Yunyang Xiong", "Changsheng Zhao", "Lemeng Wu", "Jun Chen", "Chenchen Zhu", "Zechun Liu", "Fanyi Xiao", "Balakrishnan Varadarajan", "Florian Bordes", "Zhuang Liu", "Hu Xu", "Hyunwoo J. Kim", "Bilge Soran", "Raghuraman Krishnamoorthi", "Mohamed Elhoseiny", "Vikas Chandra" ], "github": "https://github.com/Vision-CAIR/LongVU", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge constrained by LLM's context size. To address this limitation, we propose LongVU, a spatiotemporal adaptive compression mechanism thats reduces the number of video tokens while preserving visual details of long videos. Our idea is based on leveraging cross-modal query and inter-frame dependencies to adaptively reduce temporal and spatial redundancy in videos. Specifically, we leverage DINOv2 features to remove redundant frames that exhibit high similarity. Then we utilize text-guided cross-modal query for selective frame feature reduction. Further, we perform spatial token reduction across frames based on their temporal dependencies. Our adaptive compression strategy effectively processes a large number of frames with little visual information loss within given context length. Our LongVU consistently surpass existing methods across a variety of video understanding benchmarks, especially on hour-long video understanding tasks such as VideoMME and MLVU. Given a light-weight LLM, our LongVU also scales effectively into a smaller size with state-of-the-art video understanding performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Vision-CAIR/LongVU", "github_api_added_by": "auto" }, { "date": "2024-10-24", "arxiv_id": "2410.17242", "title": "LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias", "authors": [ "Haian Jin", "Hanwen Jiang", "Hao Tan", "Kai Zhang", "Sai Bi", "Tianyuan Zhang", "Fujun Luan", "Noah Snavely", "Zexiang Xu" ], "github": "", "project_page": "", "abstract": "We propose the Large View Synthesis Model (LVSM), a novel transformer-based approach for scalable and generalizable novel view synthesis from sparse-view inputs. We introduce two architectures: (1) an encoder-decoder LVSM, which encodes input image tokens into a fixed number of 1D latent tokens, functioning as a fully learned scene representation, and decodes novel-view images from them; and (2) a decoder-only LVSM, which directly maps input images to novel-view outputs, completely eliminating intermediate scene representations. Both models bypass the 3D inductive biases used in previous methods -- from 3D representations (e.g., NeRF, 3DGS) to network designs (e.g., epipolar projections, plane sweeps) -- addressing novel view synthesis with a fully data-driven approach. While the encoder-decoder model offers faster inference due to its independent latent representation, the decoder-only LVSM achieves superior quality, scalability, and zero-shot generalization, outperforming previous state-of-the-art methods by 1.5 to 3.5 dB PSNR. Comprehensive evaluations across multiple datasets demonstrate that both LVSM variants achieve state-of-the-art novel view synthesis quality. Notably, our models surpass all previous methods even with reduced computational resources (1-2 GPUs). Please see our website for more details: https://haian-jin.github.io/projects/LVSM/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-24", "arxiv_id": "2410.13816", "title": "Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance", "authors": [ "Mitsuhiko Nakamoto", "Oier Mees", "Aviral Kumar", "Sergey Levine" ], "github": "https://github.com/nakamotoo/v-gps", "project_page": "", "abstract": "Large, general-purpose robotic policies trained on diverse demonstration datasets have been shown to be remarkably effective both for controlling a variety of robots in a range of different scenes, and for acquiring broad repertoires of manipulation skills. However, the data that such policies are trained on is generally of mixed quality -- not only are human-collected demonstrations unlikely to perform the task perfectly, but the larger the dataset is, the harder it is to curate only the highest quality examples. It also remains unclear how optimal data from one embodiment is for training on another embodiment. In this paper, we present a general and broadly applicable approach that enhances the performance of such generalist robot policies at deployment time by re-ranking their actions according to a value function learned via offline RL. This approach, which we call Value-Guided Policy Steering (V-GPS), is compatible with a wide range of different generalist policies, without needing to fine-tune or even access the weights of the policy. We show that the same value function can improve the performance of five different state-of-the-art policies with different architectures, even though they were trained on distinct datasets, attaining consistent performance improvement on multiple robotic platforms across a total of 12 tasks. Code and videos can be found at: https://nakamotoo.github.io/V-GPS", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nakamotoo/v-gps", "github_api_added_by": "auto" }, { "date": "2024-10-25", "arxiv_id": "2410.18785", "title": "Should We Really Edit Language Models? On the Evaluation of Edited Language Models", "authors": [ "Qi Li", "Xiang Liu", "Zhenheng Tang", "Peijie Dong", "Zeyu Li", "Xinglin Pan", "Xiaowen Chu" ], "github": "https://github.com/lqinfdim/EditingEvaluation", "project_page": "", "abstract": "Model editing has become an increasingly popular alternative for efficiently updating knowledge within language models. Current methods mainly focus on reliability, generalization, and locality, with many methods excelling across these criteria. Some recent works disclose the pitfalls of these editing methods such as knowledge distortion or conflict. However, the general abilities of post-edited language models remain unexplored. In this paper, we perform a comprehensive evaluation on various editing methods and different language models, and have following findings. (1) Existing editing methods lead to inevitable performance deterioration on general benchmarks, indicating that existing editing methods maintain the general abilities of the model within only a few dozen edits. When the number of edits is slightly large, the intrinsic knowledge structure of the model is disrupted or even completely damaged. (2) Instruction-tuned models are more robust to editing, showing less performance drop on general knowledge after editing. (3) Language model with large scale is more resistant to editing compared to small model. (4) The safety of the edited model, is significantly weakened, even for those safety-aligned models. Our findings indicate that current editing methods are only suitable for small-scale knowledge updates within language models, which motivates further research on more practical and reliable editing methods. The details of code and reproduction can be found in https://github.com/lqinfdim/EditingEvaluation.", "github_manual": "", "github_regex": "https://github.com/lqinfdim/EditingEvaluation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18958", "title": "Stable Consistency Tuning: Understanding and Improving Consistency Models", "authors": [ "Fu-Yun Wang", "Zhengyang Geng", "Hongsheng Li" ], "github": "https://github.com/G-U-N/Stable-Consistency-Tuning", "project_page": "", "abstract": "Diffusion models achieve superior generation quality but suffer from slow generation speed due to the iterative nature of denoising. In contrast, consistency models, a new generative family, achieve competitive performance with significantly faster sampling. These models are trained either through consistency distillation, which leverages pretrained diffusion models, or consistency training/tuning directly from raw data. In this work, we propose a novel framework for understanding consistency models by modeling the denoising process of the diffusion model as a Markov Decision Process (MDP) and framing consistency model training as the value estimation through Temporal Difference~(TD) Learning. More importantly, this framework allows us to analyze the limitations of current consistency training/tuning strategies. Built upon Easy Consistency Tuning (ECT), we propose Stable Consistency Tuning (SCT), which incorporates variance-reduced learning using the score identity. SCT leads to significant performance improvements on benchmarks such as CIFAR-10 and ImageNet-64. On ImageNet-64, SCT achieves 1-step FID 2.42 and 2-step FID 1.55, a new SoTA for consistency models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/G-U-N/Stable-Consistency-Tuning", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18975", "title": "Unbounded: A Generative Infinite Game of Character Life Simulation", "authors": [ "Jialu Li", "Yuanzhen Li", "Neal Wadhwa", "Yael Pritch", "David E. Jacobs", "Michael Rubinstein", "Mohit Bansal", "Nataniel Ruiz" ], "github": "", "project_page": "", "abstract": "We introduce the concept of a generative infinite game, a video game that transcends the traditional boundaries of finite, hard-coded systems by using generative models. Inspired by James P. Carse's distinction between finite and infinite games, we leverage recent advances in generative AI to create Unbounded: a game of character life simulation that is fully encapsulated in generative models. Specifically, Unbounded draws inspiration from sandbox life simulations and allows you to interact with your autonomous virtual character in a virtual world by feeding, playing with and guiding it - with open-ended mechanics generated by an LLM, some of which can be emergent. In order to develop Unbounded, we propose technical innovations in both the LLM and visual generation domains. Specifically, we present: (1) a specialized, distilled large language model (LLM) that dynamically generates game mechanics, narratives, and character interactions in real-time, and (2) a new dynamic regional image prompt Adapter (IP-Adapter) for vision models that ensures consistent yet flexible visual generation of a character across multiple environments. We evaluate our system through both qualitative and quantitative analysis, showing significant improvements in character life simulation, user instruction following, narrative coherence, and visual consistency for both characters and the environments compared to traditional related approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18505", "title": "CCI3.0-HQ: a large-scale Chinese dataset of high quality designed for pre-training large language models", "authors": [ "Liangdong Wang", "Bo-Wen Zhang", "Chengwei Wu", "Hanyu Zhao", "Xiaofeng Shi", "Shuhao Gu", "Jijie Li", "Quanyue Ma", "TengFei Pan", "Guang Liu" ], "github": "", "project_page": "", "abstract": "We present CCI3.0-HQ (https://huggingface.co/datasets/BAAI/CCI3-HQ), a high-quality 500GB subset of the Chinese Corpora Internet 3.0 (CCI3.0)(https://huggingface.co/datasets/BAAI/CCI3-Data), developed using a novel two-stage hybrid filtering pipeline that significantly enhances data quality. To evaluate its effectiveness, we trained a 0.5B parameter model from scratch on 100B tokens across various datasets, achieving superior performance on 10 benchmarks in a zero-shot setting compared to CCI3.0, SkyPile, and WanjuanV1. The high-quality filtering process effectively distills the capabilities of the Qwen2-72B-instruct model into a compact 0.5B model, attaining optimal F1 scores for Chinese web data classification. We believe this open-access dataset will facilitate broader access to high-quality language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.17243", "title": "Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss", "authors": [ "Zesen Cheng", "Hang Zhang", "Kehan Li", "Sicong Leng", "Zhiqiang Hu", "Fei Wu", "Deli Zhao", "Xin Li", "Lidong Bing" ], "github": "https://github.com/damo-nlp-sg/inf-clip", "project_page": "", "abstract": "Contrastive loss is a powerful approach for representation learning, where larger batch sizes enhance performance by providing more negative samples to better distinguish between similar and dissimilar data. However, scaling batch sizes is constrained by the quadratic growth in GPU memory consumption, primarily due to the full instantiation of the similarity matrix. To address this, we propose a tile-based computation strategy that partitions the contrastive loss calculation into arbitrary small blocks, avoiding full materialization of the similarity matrix. Furthermore, we introduce a multi-level tiling strategy to leverage the hierarchical structure of distributed systems, employing ring-based communication at the GPU level to optimize synchronization and fused kernels at the CUDA core level to reduce I/O overhead. Experimental results show that the proposed method scales batch sizes to unprecedented levels. For instance, it enables contrastive training of a CLIP-ViT-L/14 model with a batch size of 4M or 12M using 8 or 32 A800 80GB without sacrificing any accuracy. Compared to SOTA memory-efficient solutions, it achieves a two-order-of-magnitude reduction in memory while maintaining comparable speed. The code will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/damo-nlp-sg/inf-clip", "github_api_added_by": "auto" }, { "date": "2024-10-25", "arxiv_id": "2410.18978", "title": "Framer: Interactive Frame Interpolation", "authors": [ "Wen Wang", "Qiuyu Wang", "Kecheng Zheng", "Hao Ouyang", "Zhekai Chen", "Biao Gong", "Hao Chen", "Yujun Shen", "Chunhua Shen" ], "github": "", "project_page": "", "abstract": "We propose Framer for interactive frame interpolation, which targets producing smoothly transitioning frames between two images as per user creativity. Concretely, besides taking the start and end frames as inputs, our approach supports customizing the transition process by tailoring the trajectory of some selected keypoints. Such a design enjoys two clear benefits. First, incorporating human interaction mitigates the issue arising from numerous possibilities of transforming one image to another, and in turn enables finer control of local motions. Second, as the most basic form of interaction, keypoints help establish the correspondence across frames, enhancing the model to handle challenging cases (e.g., objects on the start and end frames are of different shapes and styles). It is noteworthy that our system also offers an \"autopilot\" mode, where we introduce a module to estimate the keypoints and refine the trajectory automatically, to simplify the usage in practice. Extensive experimental results demonstrate the appealing performance of Framer on various applications, such as image morphing, time-lapse video generation, cartoon interpolation, etc. The code, the model, and the interface will be released to facilitate further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18745", "title": "Why Does the Effective Context Length of LLMs Fall Short?", "authors": [ "Chenxin An", "Jun Zhang", "Ming Zhong", "Lei Li", "Shansan Gong", "Yao Luo", "Jingjing Xu", "Lingpeng Kong" ], "github": "https://github.com/hsiehjackson/RULER", "project_page": "", "abstract": "Advancements in distributed training and efficient attention mechanisms have significantly expanded the context window sizes of large language models (LLMs). However, recent work reveals that the effective context lengths of open-source LLMs often fall short, typically not exceeding half of their training lengths. In this work, we attribute this limitation to the left-skewed frequency distribution of relative positions formed in LLMs pretraining and post-training stages, which impedes their ability to effectively gather distant information. To address this challenge, we introduce ShifTed Rotray position embeddING (STRING). STRING shifts well-trained positions to overwrite the original ineffective positions during inference, enhancing performance within their existing training lengths. Experimental results show that without additional training, STRING dramatically improves the performance of the latest large-scale models, such as Llama3.1 70B and Qwen2 72B, by over 10 points on popular long-context benchmarks RULER and InfiniteBench, establishing new state-of-the-art results for open-source LLMs. Compared to commercial models, Llama 3.1 70B with \\method even achieves better performance than GPT-4-128K and clearly surpasses Claude 2 and Kimi-chat.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hsiehjackson/RULER", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18451", "title": "Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs", "authors": [ "Chris Yuhao Liu", "Liang Zeng", "Jiacai Liu", "Rui Yan", "Jujie He", "Chaojie Wang", "Shuicheng Yan", "Yang Liu", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "In this report, we introduce a collection of methods to enhance reward modeling for LLMs, focusing specifically on data-centric techniques. We propose effective data selection and filtering strategies for curating high-quality open-source preference datasets, culminating in the Skywork-Reward data collection, which contains only 80K preference pairs -- significantly smaller than existing datasets. Using this curated dataset, we developed the Skywork-Reward model series -- Skywork-Reward-Gemma-27B and Skywork-Reward-Llama-3.1-8B -- with the former currently holding the top position on the RewardBench leaderboard. Notably, our techniques and datasets have directly enhanced the performance of many top-ranked models on RewardBench, highlighting the practical impact of our contributions in real-world preference learning applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18693", "title": "Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch", "authors": [ "Yuyang Ding", "Xinyu Shi", "Xiaobo Liang", "Juntao Li", "Qiaoming Zhu", "Min Zhang" ], "github": "https://github.com/yyding1/scalequest", "project_page": "", "abstract": "The availability of high-quality data is one of the most important factors in improving the reasoning capability of LLMs. Existing works have demonstrated the effectiveness of creating more instruction data from seed questions or knowledge bases. Recent research indicates that continually scaling up data synthesis from strong models (e.g., GPT-4) can further elicit reasoning performance. Though promising, the open-sourced community still lacks high-quality data at scale and scalable data synthesis methods with affordable costs. To address this, we introduce ScaleQuest, a scalable and novel data synthesis method that utilizes \"small-size\" (e.g., 7B) open-source models to generate questions from scratch without the need for seed data with complex augmentation constraints. With the efficient ScaleQuest, we automatically constructed a mathematical reasoning dataset consisting of 1 million problem-solution pairs, which are more effective than existing open-sourced datasets. It can universally increase the performance of mainstream open-source models (i.e., Mistral, Llama3, DeepSeekMath, and Qwen2-Math) by achieving 29.2% to 46.4% gains on MATH. Notably, simply fine-tuning the Qwen2-Math-7B-Base model with our dataset can even surpass Qwen2-Math-7B-Instruct, a strong and well-aligned model on closed-source data, and proprietary models such as GPT-4-Turbo and Claude-3.5 Sonnet.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yyding1/scalequest", "github_api_added_by": "auto" }, { "date": "2024-10-25", "arxiv_id": "2410.18533", "title": "LOGO -- Long cOntext aliGnment via efficient preference Optimization", "authors": [ "Zecheng Tang", "Zechen Sun", "Juntao Li", "Qiaoming Zhu", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Long-context models(LCMs) have shown great potential in processing long input sequences(even more than 100M tokens) conveniently and effectively. With significant progress, recent research has pointed out that LCMs can accurately locate token-level salient information within the context. Yet, the generation performance of these LCMs is far from satisfactory and might result in misaligned responses, such as hallucinations. To enhance the generation capability of LCMs, existing works have investigated the effects of data size and quality for both pre-training and instruction tuning. Though achieving meaningful improvement, previous methods fall short in either effectiveness or efficiency. In this paper, we introduce LOGO(Long cOntext aliGnment via efficient preference Optimization), a training strategy that first introduces preference optimization for long-context alignment. To overcome the GPU memory-bound issue caused by the long sequence, LOGO employs a reference-free preference optimization strategy and adopts a position synthesis method to construct the training data. By training with only 0.3B data on a single 8timesA800 GPU machine for 16 hours, LOGO allows the Llama-3-8B-Instruct-80K model to achieve comparable performance with GPT-4 in real-world long-context tasks while preserving the model's original capabilities on other tasks, e.g., language modeling and MMLU. Moreover, LOGO can extend the model's context window size while enhancing its generation performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18775", "title": "Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances", "authors": [ "Shilin Lu", "Zihan Zhou", "Jiayou Lu", "Yuanzhi Zhu", "Adams Wai-Kin Kong" ], "github": "https://github.com/Shilin-LU/VINE", "project_page": "", "abstract": "Current image watermarking methods are vulnerable to advanced image editing techniques enabled by large-scale text-to-image models. These models can distort embedded watermarks during editing, posing significant challenges to copyright protection. In this work, we introduce W-Bench, the first comprehensive benchmark designed to evaluate the robustness of watermarking methods against a wide range of image editing techniques, including image regeneration, global editing, local editing, and image-to-video generation. Through extensive evaluations of eleven representative watermarking methods against prevalent editing techniques, we demonstrate that most methods fail to detect watermarks after such edits. To address this limitation, we propose VINE, a watermarking method that significantly enhances robustness against various image editing techniques while maintaining high image quality. Our approach involves two key innovations: (1) we analyze the frequency characteristics of image editing and identify that blurring distortions exhibit similar frequency properties, which allows us to use them as surrogate attacks during training to bolster watermark robustness; (2) we leverage a large-scale pretrained diffusion model SDXL-Turbo, adapting it for the watermarking task to achieve more imperceptible and robust watermark embedding. Experimental results show that our method achieves outstanding watermarking performance under various image editing techniques, outperforming existing methods in both image quality and robustness. Code is available at https://github.com/Shilin-LU/VINE.", "github_manual": "", "github_regex": "https://github.com/Shilin-LU/VINE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18234", "title": "Multi-Draft Speculative Sampling: Canonical Architectures and Theoretical Limits", "authors": [ "Ashish Khisti", "M. Reza Ebrahimi", "Hassan Dbouk", "Arash Behboodi", "Roland Memisevic", "Christos Louizos" ], "github": "", "project_page": "", "abstract": "We consider multi-draft speculative sampling, where the proposal sequences are sampled independently from different draft models. At each step, a token-level draft selection scheme takes a list of valid tokens as input and produces an output token whose distribution matches that of the target model. Previous works have demonstrated that the optimal scheme (which maximizes the probability of accepting one of the input tokens) can be cast as a solution to a linear program. In this work we show that the optimal scheme can be decomposed into a two-step solution: in the first step an importance sampling (IS) type scheme is used to select one intermediate token; in the second step (single-draft) speculative sampling is applied to generate the output token. For the case of two identical draft models we further 1) establish a necessary and sufficient condition on the distributions of the target and draft models for the acceptance probability to equal one and 2) provide an explicit expression for the optimal acceptance probability. Our theoretical analysis also motives a new class of token-level selection scheme based on weighted importance sampling. Our experimental results demonstrate consistent improvements in the achievable block efficiency and token rates over baseline schemes in a number of scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.16251", "title": "Can Knowledge Editing Really Correct Hallucinations?", "authors": [ "Baixiang Huang", "Canyu Chen", "Xiongxiao Xu", "Ali Payani", "Kai Shu" ], "github": "https://github.com/llm-editing/HalluEditBench", "project_page": "", "abstract": "Large Language Models (LLMs) suffer from hallucinations, referring to the non-factual information in generated content, despite their superior capacities across tasks. Meanwhile, knowledge editing has been developed as a new popular paradigm to correct the erroneous factual knowledge encoded in LLMs with the advantage of avoiding retraining from scratch. However, one common issue of existing evaluation datasets for knowledge editing is that they do not ensure LLMs actually generate hallucinated answers to the evaluation questions before editing. When LLMs are evaluated on such datasets after being edited by different techniques, it is hard to directly adopt the performance to assess the effectiveness of different knowledge editing methods in correcting hallucinations. Thus, the fundamental question remains insufficiently validated: Can knowledge editing really correct hallucinations in LLMs? We proposed HalluEditBench to holistically benchmark knowledge editing methods in correcting real-world hallucinations. First, we rigorously construct a massive hallucination dataset with 9 domains, 26 topics and more than 6,000 hallucinations. Then, we assess the performance of knowledge editing methods in a holistic way on five dimensions including Efficacy, Generalization, Portability, Locality, and Robustness. Through HalluEditBench, we have provided new insights into the potentials and limitations of different knowledge editing methods in correcting hallucinations, which could inspire future improvements and facilitate the progress in the field of knowledge editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/llm-editing/HalluEditBench", "github_api_added_by": "auto" }, { "date": "2024-10-25", "arxiv_id": "2410.18798", "title": "Distill Visual Chart Reasoning Ability from LLMs to MLLMs", "authors": [ "Wei He", "Zhiheng Xi", "Wanxu Zhao", "Xiaoran Fan", "Yiwen Ding", "Zifei Shan", "Tao Gui", "Qi Zhang", "Xuanjing Huang" ], "github": "https://github.com/hewei2001/ReachQA", "project_page": "", "abstract": "Solving complex chart Q&A tasks requires advanced visual reasoning abilities in multimodal large language models (MLLMs). Recent studies highlight that these abilities consist of two main parts: recognizing key information from visual inputs and conducting reasoning over it. Thus, a promising approach to enhance MLLMs is to construct relevant training data focusing on the two aspects. However, collecting and annotating complex charts and questions is costly and time-consuming, and ensuring the quality of annotated answers remains a challenge. In this paper, we propose Code-as-Intermediary Translation (CIT), a cost-effective, efficient and easily scalable data synthesis method for distilling visual reasoning abilities from LLMs to MLLMs. The code serves as an intermediary that translates visual chart representations into textual representations, enabling LLMs to understand cross-modal information. Specifically, we employ text-based synthesizing techniques to construct chart-plotting code and produce ReachQA, a dataset containing 3k reasoning-intensive charts and 20k Q&A pairs to enhance both recognition and reasoning abilities. Experiments show that when fine-tuned with our data, models not only perform well on chart-related benchmarks, but also demonstrate improved multimodal reasoning abilities on general mathematical benchmarks like MathVista. The code and dataset are publicly available at https://github.com/hewei2001/ReachQA.", "github_manual": "", "github_regex": "https://github.com/hewei2001/ReachQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18977", "title": "MotionCLR: Motion Generation and Training-free Editing via Understanding Attention Mechanisms", "authors": [ "Ling-Hao Chen", "Wenxun Dai", "Xuan Ju", "Shunlin Lu", "Lei Zhang" ], "github": "", "project_page": "", "abstract": "This research delves into the problem of interactive editing of human motion generation. Previous motion diffusion models lack explicit modeling of the word-level text-motion correspondence and good explainability, hence restricting their fine-grained editing ability. To address this issue, we propose an attention-based motion diffusion model, namely MotionCLR, with CLeaR modeling of attention mechanisms. Technically, MotionCLR models the in-modality and cross-modality interactions with self-attention and cross-attention, respectively. More specifically, the self-attention mechanism aims to measure the sequential similarity between frames and impacts the order of motion features. By contrast, the cross-attention mechanism works to find the fine-grained word-sequence correspondence and activate the corresponding timesteps in the motion sequence. Based on these key properties, we develop a versatile set of simple yet effective motion editing methods via manipulating attention maps, such as motion (de-)emphasizing, in-place motion replacement, and example-based motion generation, etc. For further verification of the explainability of the attention mechanism, we additionally explore the potential of action-counting and grounded motion generation ability via attention maps. Our experimental results show that our method enjoys good generation and editing ability with good explainability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18362", "title": "WAFFLE: Multi-Modal Model for Automated Front-End Development", "authors": [ "Shanchao Liang", "Nan Jiang", "Shangshu Qian", "Lin Tan" ], "github": "https://github.com/lt-asset/Waffle", "project_page": "", "abstract": "Web development involves turning UI designs into functional webpages, which can be difficult for both beginners and experienced developers due to the complexity of HTML's hierarchical structures and styles. While Large Language Models (LLMs) have shown promise in generating source code, two major challenges persist in UI-to-HTML code generation: (1) effectively representing HTML's hierarchical structure for LLMs, and (2) bridging the gap between the visual nature of UI designs and the text-based format of HTML code. To tackle these challenges, we introduce Waffle, a new fine-tuning strategy that uses a structure-aware attention mechanism to improve LLMs' understanding of HTML's structure and a contrastive fine-tuning approach to align LLMs' understanding of UI images and HTML code. Models fine-tuned with Waffle show up to 9.00 pp (percentage point) higher HTML match, 0.0982 higher CW-SSIM, 32.99 higher CLIP, and 27.12 pp higher LLEM on our new benchmark WebSight-Test and an existing benchmark Design2Code, outperforming current fine-tuning methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lt-asset/Waffle", "github_api_added_by": "auto" }, { "date": "2024-10-25", "arxiv_id": "2410.15580", "title": "Language Models are Symbolic Learners in Arithmetic", "authors": [ "Chunyuan Deng", "Zhiqi Li", "Roy Xie", "Ruidi Chang", "Hanjie Chen" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are thought to struggle with arithmetic learning due to the inherent differences between language modeling and numerical computation, but concrete evidence has been lacking. This work responds to this claim through a two-side experiment. We first investigate whether LLMs leverage partial products during arithmetic learning. We find that although LLMs can identify some partial products after learning, they fail to leverage them for arithmetic tasks, conversely. We then explore how LLMs approach arithmetic symbolically by breaking tasks into subgroups, hypothesizing that difficulties arise from subgroup complexity and selection. Our results show that when subgroup complexity is fixed, LLMs treat a collection of different arithmetic operations similarly. By analyzing position-level accuracy across different training sizes, we further observe that it follows a U-shaped pattern: LLMs quickly learn the easiest patterns at the first and last positions, while progressively learning the more difficult patterns in the middle positions. This suggests that LLMs select subgroup following an easy-to-hard paradigm during learning. Our work confirms that LLMs are pure symbolic learners in arithmetic tasks and underscores the importance of understanding them deeply through subgroup-level quantification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.17779", "title": "ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning", "authors": [ "Zhiwei Hao", "Jianyuan Guo", "Li Shen", "Yong Luo", "Han Hu", "Yonggang Wen" ], "github": "https://github.com/Hao840/ADEM-VL", "project_page": "", "abstract": "Recent advancements in multimodal fusion have witnessed the remarkable success of vision-language (VL) models, which excel in various multimodal applications such as image captioning and visual question answering. However, building VL models requires substantial hardware resources, where efficiency is restricted by two key factors: the extended input sequence of the language model with vision features demands more computational operations, and a large number of additional learnable parameters increase memory complexity. These challenges significantly restrict the broader applicability of such models. To bridge this gap, we propose ADEM-VL, an efficient vision-language method that tunes VL models based on pretrained large language models (LLMs) by adopting a parameter-free cross-attention mechanism for similarity measurements in multimodal fusion. This approach only requires embedding vision features into the language space, significantly reducing the number of trainable parameters and accelerating both training and inference speeds. To enhance representation learning in fusion module, we introduce an efficient multiscale feature generation scheme that requires only a single forward pass through the vision encoder. Moreover, we propose an adaptive fusion scheme that dynamically discards less relevant visual information for each text token based on its attention score. This ensures that the fusion process prioritizes the most pertinent visual features. With experiments on various tasks including visual question answering, image captioning, and instruction-following, we demonstrate that our framework outperforms existing approaches. Specifically, our method surpasses existing methods by an average accuracy of 0.77% on ScienceQA dataset, with reduced training and inference latency, demonstrating the superiority of our framework. The code is available at https://github.com/Hao840/ADEM-VL.", "github_manual": "", "github_regex": "https://github.com/Hao840/ADEM-VL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18976", "title": "CAMEL-Bench: A Comprehensive Arabic LMM Benchmark", "authors": [ "Sara Ghaboura", "Ahmed Heakl", "Omkar Thawakar", "Ali Alharthi", "Ines Riahi", "Abduljalil Saif", "Jorma Laaksonen", "Fahad S. Khan", "Salman Khan", "Rao M. Anwer" ], "github": "https://github.com/mbzuai-oryx/CAMEL-Bench", "project_page": "", "abstract": "Recent years have witnessed a significant interest in developing large multimodal models (LMMs) capable of performing various visual reasoning and understanding tasks. This has led to the introduction of multiple LMM benchmarks to evaluate LMMs on different tasks. However, most existing LMM evaluation benchmarks are predominantly English-centric. In this work, we develop a comprehensive LMM evaluation benchmark for the Arabic language to represent a large population of over 400 million speakers. The proposed benchmark, named CAMEL-Bench, comprises eight diverse domains and 38 sub-domains including, multi-image understanding, complex visual perception, handwritten document understanding, video understanding, medical imaging, plant diseases, and remote sensing-based land use understanding to evaluate broad scenario generalizability. Our CAMEL-Bench comprises around 29,036 questions that are filtered from a larger pool of samples, where the quality is manually verified by native speakers to ensure reliable model assessment. We conduct evaluations of both closed-source, including GPT-4 series, and open-source LMMs. Our analysis reveals the need for substantial improvement, especially among the best open-source models, with even the closed-source GPT-4o achieving an overall score of 62%. Our benchmark and evaluation scripts are open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mbzuai-oryx/CAMEL-Bench", "github_api_added_by": "auto" }, { "date": "2024-10-25", "arxiv_id": "2410.17897", "title": "Value Residual Learning For Alleviating Attention Concentration In Transformers", "authors": [ "Zhanchao Zhou", "Tianyi Wu", "Zhiyun Jiang", "Zhenzhong Lan" ], "github": "https://github.com/Zcchill/Value-Residual-Learning", "project_page": "", "abstract": "Transformers can capture long-range dependencies using self-attention, allowing tokens to attend to all others directly. However, stacking multiple attention layers leads to attention concentration. One natural way to address this issue is to use cross-layer attention, allowing information from earlier layers to be directly accessible to later layers. However, this approach is computationally expensive. To address this problem, we propose Transformer with residual value (ResFormer) which approximates cross-layer attention through adding a residual connection from the values of the the first layer to all subsequent layers. Based on this method, one variant is the Transformer with single layer value (SVFormer), where all layers share the same value embedding from first layer, reducing the KV cache by nearly 50%. Comprehensive empirical evidence demonstrates that ResFormer mitigates attention concentration problem in deeper layers and enhances representation across most layers, outperforming the vanilla Transformer, DenseFormer, and NeuTRENO in training error as well as downstream tasks. SVFormer trains significantly faster than the vanilla Transformer and performs better than other methods like GQA and CLA, with performance influenced by sequence length and cumulative learning rate.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Zcchill/Value-Residual-Learning", "github_api_added_by": "auto" }, { "date": "2024-10-25", "arxiv_id": "2410.18572", "title": "Taipan: Efficient and Expressive State Space Language Models with Selective Attention", "authors": [ "Chien Van Nguyen", "Huy Huu Nguyen", "Thang M. Pham", "Ruiyi Zhang", "Hanieh Deilamsalehy", "Puneet Mathur", "Ryan A. Rossi", "Trung Bui", "Viet Dac Lai", "Franck Dernoncourt", "Thien Huu Nguyen" ], "github": "", "project_page": "", "abstract": "Efficient long-context language modeling remains a significant challenge in Natural Language Processing (NLP). While Transformers dominate language tasks, they struggle with long sequences due to quadratic computational complexity in training and linearly scaling memory costs during inference. Recent State Space Models (SSMs) such as Mamba offer alternatives with constant memory usage, but they underperform in tasks requiring extensive in-context retrieval. We introduce Taipan, a novel hybrid architecture that combines Mamba-2 with Selective Attention Layers (SALs). These SALs identify tokens requiring long-range interactions, remove less important features, and then augment their representations using the attention module. This approach balances Mamba's efficiency with Transformer-like performance in memory-intensive tasks. By constraining the attention budget, Taipan extends accurate predictions to context lengths of up to 1 million tokens while preserving computational efficiency. Our experiments demonstrate Taipan's superior performance across various scales and tasks, offering a promising solution for efficient long-context language modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18647", "title": "Data Scaling Laws in Imitation Learning for Robotic Manipulation", "authors": [ "Fanqi Lin", "Yingdong Hu", "Pingyue Sheng", "Chuan Wen", "Jiacheng You", "Yang Gao" ], "github": "https://github.com/Fanqi-Lin/Data-Scaling-Laws", "project_page": "", "abstract": "Data scaling has revolutionized fields like natural language processing and computer vision, providing models with remarkable generalization capabilities. In this paper, we investigate whether similar data scaling laws exist in robotics, particularly in robotic manipulation, and whether appropriate data scaling can yield single-task robot policies that can be deployed zero-shot for any object within the same category in any environment. To this end, we conduct a comprehensive empirical study on data scaling in imitation learning. By collecting data across numerous environments and objects, we study how a policy's generalization performance changes with the number of training environments, objects, and demonstrations. Throughout our research, we collect over 40,000 demonstrations and execute more than 15,000 real-world robot rollouts under a rigorous evaluation protocol. Our findings reveal several intriguing results: the generalization performance of the policy follows a roughly power-law relationship with the number of environments and objects. The diversity of environments and objects is far more important than the absolute number of demonstrations; once the number of demonstrations per environment or object reaches a certain threshold, additional demonstrations have minimal effect. Based on these insights, we propose an efficient data collection strategy. With four data collectors working for one afternoon, we collect sufficient data to enable the policies for two tasks to achieve approximately 90% success rates in novel environments with unseen objects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Fanqi-Lin/Data-Scaling-Laws", "github_api_added_by": "auto" }, { "date": "2024-10-25", "arxiv_id": "2410.18538", "title": "SMITE: Segment Me In TimE", "authors": [ "Amirhossein Alimohammadi", "Sauradip Nag", "Saeid Asgari Taghanaki", "Andrea Tagliasacchi", "Ghassan Hamarneh", "Ali Mahdavi Amiri" ], "github": "https://github.com/alimohammadiamirhossein/smite", "project_page": "", "abstract": "Segmenting an object in a video presents significant challenges. Each pixel must be accurately labelled, and these labels must remain consistent across frames. The difficulty increases when the segmentation is with arbitrary granularity, meaning the number of segments can vary arbitrarily, and masks are defined based on only one or a few sample images. In this paper, we address this issue by employing a pre-trained text to image diffusion model supplemented with an additional tracking mechanism. We demonstrate that our approach can effectively manage various segmentation scenarios and outperforms state-of-the-art alternatives.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alimohammadiamirhossein/smite", "github_api_added_by": "auto" }, { "date": "2024-10-25", "arxiv_id": "2410.15999", "title": "Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering", "authors": [ "Yu Zhao", "Alessio Devoto", "Giwon Hong", "Xiaotang Du", "Aryo Pradipta Gema", "Hongru Wang", "Kam-Fai Wong", "Pasquale Minervini" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided in the context -- this phenomenon, known as context-memory knowledge conflicts, can lead to undesirable model behaviour, such as reliance on outdated or incorrect information. Analysing the internal activations of LLMs, we find that they can internally register the signals of knowledge conflict at mid-layers. Such signals allow us to detect whether a knowledge conflict occurs and use inference-time intervention strategies to resolve it. In this work, we propose SpARE, a training-free representation engineering method that uses pre-trained sparse auto-encoders (SAEs) to control the knowledge selection behaviour of LLMs. SpARE identifies the functional features that control the knowledge selection behaviours and applies them to edit the internal activations of LLMs at inference time. Our experimental results show that SpARE can effectively control the usage of either knowledge source to resolve knowledge conflict in open-domain question-answering tasks, surpassing existing representation engineering methods (+10%) as well as contrastive decoding methods (+15%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18860", "title": "DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations", "authors": [ "Aryo Pradipta Gema", "Chen Jin", "Ahmed Abdulaal", "Tom Diethe", "Philip Teare", "Beatrice Alex", "Pasquale Minervini", "Amrutha Saseendran" ], "github": "https://github.com/aryopg/DeCoRe", "project_page": "", "abstract": "Large Language Models (LLMs) often hallucinate, producing unfaithful or factually incorrect outputs by misrepresenting the provided context or incorrectly recalling internal knowledge. Recent studies have identified specific attention heads within the Transformer architecture, known as retrieval heads, responsible for extracting relevant contextual information. We hypothesise that masking these retrieval heads can induce hallucinations and that contrasting the outputs of the base LLM and the masked LLM can reduce hallucinations. To this end, we propose Decoding by Contrasting Retrieval Heads (DeCoRe), a novel training-free decoding strategy that amplifies information found in the context and model parameters. DeCoRe mitigates potentially hallucinated responses by dynamically contrasting the outputs of the base LLM and the masked LLM, using conditional entropy as a guide. Our extensive experiments confirm that DeCoRe significantly improves performance on tasks requiring high contextual faithfulness, such as summarisation (XSum by 18.6%), instruction following (MemoTrap by 10.9%), and open-book question answering (NQ-Open by 2.4% and NQ-Swap by 5.5%).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aryopg/DeCoRe", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18441", "title": "The Nature of Mathematical Modeling and Probabilistic Optimization Engineering in Generative AI", "authors": [ "Fulu Li" ], "github": "", "project_page": "", "abstract": "In this paper, we give an in-depth analysis on the mathematical problem formulations and the probabilistic optimization explorations for some of the key components in Transformer model [33] in the field of generative AI. We explore and discuss some potential further enhancement for current state of the art methods for some key underlying technologies of generative AI models from algorithmic and probabilistic optimization perspective. In particular, we present an optimal solution for sub-word encoding (SWE) based on similar initial settings as that of byte-pair encoding (BPE) algorithm in [9] with similar objectives as that of WordPiece approach in [28, 31] to maximize the likelihood of the training data. We also present cross entropy optimization method to optimize hyperparameters for word2vec model [17]. In addition, we propose a factored combination of rotary positional encoding (RoPE) [32] and attention with linear biases (ALiBi) [23] with a harmonic series. We also present a probabilistic FlashAttention [6, 7] (PrFlashAttention) method with a probability distribution over block distances in the matrix to decide which block is likely to participate in a given round of attention computation while maintaining the lower triangle shape of the tensor for autoregressive language models by re-shaping the tensors. Finally, we present staircase adaptive quantization (SAQ) of key-value (KV) cache for multi-query attention (MQA) based on the framework presented in [16] to have gradual quantization degradation while achieving reasonable model quality and cost savings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.18252", "title": "Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models", "authors": [ "Michael Noukhovitch", "Shengyi Huang", "Sophie Xhonneux", "Arian Hosseini", "Rishabh Agarwal", "Aaron Courville" ], "github": "https://github.com/mnoukhov/async_rlhf", "project_page": "", "abstract": "The dominant paradigm for RLHF is online and on-policy RL: synchronously generating from the large language model (LLM) policy, labelling with a reward model, and learning using feedback on the LLM's own outputs. While performant, this paradigm is computationally inefficient. Inspired by classical deep RL literature, we propose separating generation and learning in RLHF. This enables asynchronous generation of new samples while simultaneously training on old samples, leading to faster training and more compute-optimal scaling. However, asynchronous training relies on an underexplored regime, online but off-policy RLHF: learning on samples from previous iterations of our model. To understand the challenges in this regime, we investigate a fundamental question: how much off-policyness can we tolerate for asynchronous training to speed up learning but maintain performance? Among several RLHF algorithms we tested, we find that online DPO is most robust to off-policy data, and robustness increases with the scale of the policy model. We study further compute optimizations for asynchronous RLHF but find that they come at a performance cost, giving rise to a trade-off. Finally, we verify the scalability of asynchronous RLHF by training LLaMA 3.1 8B on an instruction-following task 40% faster than a synchronous run while matching final performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mnoukhov/async_rlhf", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-25", "arxiv_id": "2410.16429", "title": "Pantograph: A Machine-to-Machine Interaction Interface for Advanced Theorem Proving, High Level Reasoning, and Data Extraction in Lean 4", "authors": [ "Leni Aniva", "Chuyue Sun", "Brando Miranda", "Clark Barrett", "Sanmi Koyejo" ], "github": "https://github.com/lenianiva/pypantograph", "project_page": "", "abstract": "Machine-assisted theorem proving refers to the process of conducting structured reasoning to automatically generate proofs for mathematical theorems. Recently, there has been a surge of interest in using machine learning models in conjunction with proof assistants to perform this task. In this paper, we introduce Pantograph, a tool that provides a versatile interface to the Lean 4 proof assistant and enables efficient proof search via powerful search algorithms such as Monte Carlo Tree Search. In addition, Pantograph enables high-level reasoning by enabling a more robust handling of Lean 4's inference steps. We provide an overview of Pantograph's architecture and features. We also report on an illustrative use case: using machine learning models and proof sketches to prove Lean 4 theorems. Pantograph's innovative features pave the way for more advanced machine learning models to perform complex proof searches and high-level reasoning, equipping future researchers to design more versatile and powerful theorem provers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lenianiva/pypantograph", "github_api_added_by": "auto" }, { "date": "2024-10-25", "arxiv_id": "2410.18194", "title": "ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment", "authors": [ "Elyas Obbad", "Iddah Mlauzi", "Brando Miranda", "Rylan Schaeffer", "Kamal Obbad", "Suhana Bedi", "Sanmi Koyejo" ], "github": "", "project_page": "", "abstract": "Data selection is crucial for optimizing language model (LM) performance on specific tasks, yet most existing methods fail to effectively consider the target task distribution. Current approaches either ignore task-specific requirements entirely or rely on approximations that fail to capture the nuanced patterns needed for tasks like Autoformalization or code generation. Methods that do consider the target distribution often rely on simplistic, sometimes noisy, representations, like hashed n-gram features, which can lead to collisions and introduce noise. We introduce ZIP-FIT, a data selection framework that uses gzip compression to directly measure alignment between potential training data and the target task distribution. In extensive evaluations on Autoformalization and Python code generation, ZIP-FIT significantly outperforms leading baselines like DSIR and D4. Models trained on ZIP-FIT-selected data achieve their lowest cross-entropy loss up to 85.1\\% faster than baselines, demonstrating that better task alignment leads to more efficient learning. In addition, ZIP-FIT performs selection up to 65.8\\% faster than DSIR and two orders of magnitude faster than D4. Notably, ZIP-FIT shows that smaller, well-aligned datasets often outperform larger but less targeted ones, demonstrating that a small amount of higher quality data is superior to a large amount of lower quality data. Our results imply that task-aware data selection is crucial for efficient domain adaptation, and that compression offers a principled way to measure task alignment. By showing that targeted data selection can dramatically improve task-specific performance, our work provides new insights into the relationship between data quality, task alignment, and model learning efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-28", "arxiv_id": "2410.19008", "title": "Teach Multimodal LLMs to Comprehend Electrocardiographic Images", "authors": [ "Ruoqi Liu", "Yuelin Bai", "Xiang Yue", "Ping Zhang" ], "github": "", "project_page": "", "abstract": "The electrocardiogram (ECG) is an essential non-invasive diagnostic tool for assessing cardiac conditions. Existing automatic interpretation methods suffer from limited generalizability, focusing on a narrow range of cardiac conditions, and typically depend on raw physiological signals, which may not be readily available in resource-limited settings where only printed or digital ECG images are accessible. Recent advancements in multimodal large language models (MLLMs) present promising opportunities for addressing these challenges. However, the application of MLLMs to ECG image interpretation remains challenging due to the lack of instruction tuning datasets and well-established ECG image benchmarks for quantitative evaluation. To address these challenges, we introduce ECGInstruct, a comprehensive ECG image instruction tuning dataset of over one million samples, covering a wide range of ECG-related tasks from diverse data sources. Using ECGInstruct, we develop PULSE, an MLLM tailored for ECG image comprehension. In addition, we curate ECGBench, a new evaluation benchmark covering four key ECG image interpretation tasks across nine different datasets. Our experiments show that PULSE sets a new state-of-the-art, outperforming general MLLMs with an average accuracy improvement of 15% to 30%. This work highlights the potential of PULSE to enhance ECG interpretation in clinical practice.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-28", "arxiv_id": "2410.19168", "title": "MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark", "authors": [ "S Sakshi", "Utkarsh Tyagi", "Sonal Kumar", "Ashish Seth", "Ramaneswaran Selvakumar", "Oriol Nieto", "Ramani Duraiswami", "Sreyan Ghosh", "Dinesh Manocha" ], "github": "", "project_page": "", "abstract": "The ability to comprehend audio--which includes speech, non-speech sounds, and music--is crucial for AI agents to interact effectively with the world. We present MMAU, a novel benchmark designed to evaluate multimodal audio understanding models on tasks requiring expert-level knowledge and complex reasoning. MMAU comprises 10k carefully curated audio clips paired with human-annotated natural language questions and answers spanning speech, environmental sounds, and music. It includes information extraction and reasoning questions, requiring models to demonstrate 27 distinct skills across unique and challenging tasks. Unlike existing benchmarks, MMAU emphasizes advanced perception and reasoning with domain-specific knowledge, challenging models to tackle tasks akin to those faced by experts. We assess 18 open-source and proprietary (Large) Audio-Language Models, demonstrating the significant challenges posed by MMAU. Notably, even the most advanced Gemini Pro v1.5 achieves only 52.97% accuracy, and the state-of-the-art open-source Qwen2-Audio achieves only 52.50%, highlighting considerable room for improvement. We believe MMAU will drive the audio and multimodal research community to develop more advanced audio understanding models capable of solving complex audio tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-28", "arxiv_id": "2410.18076", "title": "Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration", "authors": [ "Max Wilcoxson", "Qiyang Li", "Kevin Frans", "Sergey Levine" ], "github": "https://github.com/rail-berkeley/supe", "project_page": "", "abstract": "Unsupervised pretraining has been transformative in many supervised domains. However, applying such ideas to reinforcement learning (RL) presents a unique challenge in that fine-tuning does not involve mimicking task-specific data, but rather exploring and locating the solution through iterative self-improvement. In this work, we study how unlabeled prior trajectory data can be leveraged to learn efficient exploration strategies. While prior data can be used to pretrain a set of low-level skills, or as additional off-policy data for online RL, it has been unclear how to combine these ideas effectively for online exploration. Our method SUPE (Skills from Unlabeled Prior data for Exploration) demonstrates that a careful combination of these ideas compounds their benefits. Our method first extracts low-level skills using a variational autoencoder (VAE), and then pseudo-relabels unlabeled trajectories using an optimistic reward model, transforming prior data into high-level, task-relevant examples. Finally, SUPE uses these transformed examples as additional off-policy data for online RL to learn a high-level policy that composes pretrained low-level skills to explore efficiently. We empirically show that SUPE reliably outperforms prior strategies, successfully solving a suite of long-horizon, sparse-reward tasks. Code: https://github.com/rail-berkeley/supe.", "github_manual": "", "github_regex": "https://github.com/rail-berkeley/supe", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-28", "arxiv_id": "2410.18558", "title": "Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data", "authors": [ "Shuhao Gu", "Jialing Zhang", "Siyuan Zhou", "Kevin Yu", "Zhaohu Xing", "Liangdong Wang", "Zhou Cao", "Jintao Jia", "Zhuoyi Zhang", "Yixuan Wang", "Zhenchong Hu", "Bo-Wen Zhang", "Jijie Li", "Dong Liang", "Yingli Zhao", "Yulong Ao", "Yaoqi Liu", "Fangxiang Feng", "Guang Liu" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) have recently made significant progress, but the limited scale and quality of open-source instruction data hinder their performance compared to closed-source models. In this work, we address this limitation by introducing Infinity-MM, a large-scale multimodal instruction dataset with 40 million samples, enhanced through rigorous quality filtering and deduplication. We also propose a synthetic instruction generation method based on open-source VLMs, using detailed image annotations and diverse question generation. Using this data, we trained a 2-billion-parameter VLM, Aquila-VL-2B, achieving state-of-the-art (SOTA) performance for models of similar scale. This demonstrates that expanding instruction data and generating synthetic data can significantly improve the performance of open-source models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-28", "arxiv_id": "2410.19355", "title": "FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality", "authors": [ "Zhengyao Lv", "Chenyang Si", "Junhao Song", "Zhenyu Yang", "Yu Qiao", "Ziwei Liu", "Kwan-Yee K. Wong" ], "github": "https://github.com/Vchitect/FasterCache", "project_page": "", "abstract": "In this paper, we present \\textit{FasterCache}, a novel training-free strategy designed to accelerate the inference of video diffusion models with high-quality generation. By analyzing existing cache-based methods, we observe that directly reusing adjacent-step features degrades video quality due to the loss of subtle variations. We further perform a pioneering investigation of the acceleration potential of classifier-free guidance (CFG) and reveal significant redundancy between conditional and unconditional features within the same timestep. Capitalizing on these observations, we introduce FasterCache to substantially accelerate diffusion-based video generation. Our key contributions include a dynamic feature reuse strategy that preserves both feature distinction and temporal continuity, and CFG-Cache which optimizes the reuse of conditional and unconditional outputs to further enhance inference speed without compromising video quality. We empirically evaluate FasterCache on recent video diffusion models. Experimental results show that FasterCache can significantly accelerate video generation (\\eg 1.67times speedup on Vchitect-2.0) while keeping video quality comparable to the baseline, and consistently outperform existing methods in both inference speed and video quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Vchitect/FasterCache", "github_api_added_by": "auto" }, { "date": "2024-10-28", "arxiv_id": "2410.17856", "title": "ROCKET-1: Master Open-World Interaction with Visual-Temporal Context Prompting", "authors": [ "Shaofei Cai", "Zihao Wang", "Kewei Lian", "Zhancun Mu", "Xiaojian Ma", "Anji Liu", "Yitao Liang" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) have excelled in multimodal tasks, but adapting them to embodied decision-making in open-world environments presents challenges. A key issue is the difficulty in smoothly connecting individual entities in low-level observations with abstract concepts required for planning. A common approach to address this problem is through the use of hierarchical agents, where VLMs serve as high-level reasoners that break down tasks into executable sub-tasks, typically specified using language and imagined observations. However, language often fails to effectively convey spatial information, while generating future images with sufficient accuracy remains challenging. To address these limitations, we propose visual-temporal context prompting, a novel communication protocol between VLMs and policy models. This protocol leverages object segmentation from both past and present observations to guide policy-environment interactions. Using this approach, we train ROCKET-1, a low-level policy that predicts actions based on concatenated visual observations and segmentation masks, with real-time object tracking provided by SAM-2. Our method unlocks the full potential of VLMs visual-language reasoning abilities, enabling them to solve complex creative tasks, especially those heavily reliant on spatial understanding. Experiments in Minecraft demonstrate that our approach allows agents to accomplish previously unattainable tasks, highlighting the effectiveness of visual-temporal context prompting in embodied decision-making. Codes and demos will be available on the project page: https://craftjarvis.github.io/ROCKET-1.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-28", "arxiv_id": "2410.19290", "title": "Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning", "authors": [ "Yujian Liu", "Shiyu Chang", "Tommi Jaakkola", "Yang Zhang" ], "github": "https://github.com/UCSB-NLP-Chang/Prereq_tune.git", "project_page": "", "abstract": "Recent studies have identified one aggravating factor of LLM hallucinations as the knowledge inconsistency between pre-training and fine-tuning, where unfamiliar fine-tuning data mislead the LLM to fabricate plausible but wrong outputs. In this paper, we propose a novel fine-tuning strategy called Prereq-Tune to address this knowledge inconsistency and reduce hallucinations. Fundamentally, Prereq-Tune disentangles the learning of skills and knowledge, so the model learns only the task skills without being impacted by the knowledge inconsistency. To achieve this, Prereq-Tune introduces an additional prerequisite learning stage to learn the necessary knowledge for SFT, allowing subsequent SFT to focus only on task skills. Prereq-Tune can also be combined with fictitious synthetic data to enhance the grounding of LLM outputs to their internal knowledge. Experiments show that Prereq-Tune outperforms existing baselines in improving LLM's factuality across short QA and long-form generation tasks. It also opens new possibilities for knowledge-controlled generation in LLMs. Our code is available at https://github.com/UCSB-NLP-Chang/Prereq_tune.git.", "github_manual": "", "github_regex": "https://github.com/UCSB-NLP-Chang/Prereq_tune.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-28", "arxiv_id": "2410.19133", "title": "Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback", "authors": [ "Lester James V. Miranda", "Yizhong Wang", "Yanai Elazar", "Sachin Kumar", "Valentina Pyatkin", "Faeze Brahman", "Noah A. Smith", "Hannaneh Hajishirzi", "Pradeep Dasigi" ], "github": "https://github.com/allenai/hybrid-preferences", "project_page": "", "abstract": "Learning from human feedback has enabled the alignment of language models (LMs) with human preferences. However, directly collecting human preferences can be expensive, time-consuming, and can have high variance. An appealing alternative is to distill preferences from LMs as a source of synthetic annotations as they are more consistent, cheaper, and scale better than human annotation; however, they are also prone to biases and errors. In this work, we introduce a routing framework that combines inputs from humans and LMs to achieve better annotation quality, while reducing the total cost of human annotation. The crux of our approach is to identify preference instances that will benefit from human annotations. We formulate this as an optimization problem: given a preference dataset and an evaluation metric, we train a performance prediction model to predict a reward model's performance on an arbitrary combination of human and LM annotations and employ a routing strategy that selects a combination that maximizes predicted performance. We train the performance prediction model on MultiPref, a new preference dataset with 10K instances paired with human and LM labels. We show that the selected hybrid mixture of LM and direct human preferences using our routing framework achieves better reward model performance compared to using either one exclusively. We simulate selective human preference collection on three other datasets and show that our method generalizes well to all three. We analyze features from the routing model to identify characteristics of instances that can benefit from human feedback, e.g., prompts with a moderate safety concern or moderate intent complexity. We release the dataset, annotation platform, and source code used in this study to foster more efficient and accurate preference collection in the future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/hybrid-preferences", "github_api_added_by": "auto" }, { "date": "2024-10-28", "arxiv_id": "2410.16048", "title": "Continuous Speech Synthesis using per-token Latent Diffusion", "authors": [ "Arnon Turetzky", "Nimrod Shabtay", "Slava Shechtman", "Hagai Aronowitz", "David Haws", "Ron Hoory", "Avihu Dekel" ], "github": "", "project_page": "", "abstract": "The success of autoregressive transformer models with discrete tokens has inspired quantization-based approaches for continuous modalities, though these often limit reconstruction quality. We therefore introduce SALAD, a per-token latent diffusion model for zero-shot text-to-speech, that operates on continuous representations. SALAD builds upon the recently proposed expressive diffusion head for image generation, and extends it to generate variable-length outputs. Our approach utilizes semantic tokens for providing contextual information and determining the stopping condition. We suggest three continuous variants for our method, extending popular discrete speech synthesis techniques. Additionally, we implement discrete baselines for each variant and conduct a comparative analysis of discrete versus continuous speech modeling techniques. Our results demonstrate that both continuous and discrete approaches are highly competent, and that SALAD achieves a superior intelligibility score while obtaining speech quality and speaker similarity on par with the ground-truth audio.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-28", "arxiv_id": "2410.19730", "title": "Counting Ability of Large Language Models and Impact of Tokenization", "authors": [ "Xiang Zhang", "Juntai Cao", "Chenyu You" ], "github": "https://github.com/juntaic7/impact-of-tokenization-in-the-counting-ability-of-language-models", "project_page": "", "abstract": "Transformers, the backbone of modern large language models (LLMs), face inherent architectural limitations that impede their reasoning capabilities. Unlike recurrent networks, Transformers lack recurrent connections, confining them to constant-depth computation. This restriction places them in the complexity class TC^0, making them theoretically incapable of solving tasks that demand increasingly deep reasoning as input length grows. Counting, a fundamental component of many reasoning tasks, also requires reasoning depth to grow linearly to be performed inductively. While previous studies have established the upper limits of counting ability in Transformer-based expert models (i.e., models specifically trained for counting tasks), these findings do not directly extend to general-purpose LLMs due to differences in reasoning mechanisms. Recent work has highlighted how Chain of Thought (CoT) reasoning can help alleviate some of the architectural limitations of Transformers in counting tasks. However, little attention has been paid to the role of tokenization in these models. Unlike expert models that often use character-level tokenization, LLMs typically rely on byte-level (BPE) tokenizers, which fundamentally alters the way reasoning is processed. Our work investigates the impact of tokenization on the counting abilities of LLMs, uncovering substantial performance variations based on input tokenization differences. We provide both theoretical and experimental analyses, offering insights into how tokenization choices can undermine models' theoretical computability, thereby inspiring the design of new tokenization methods to enhance reasoning in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/juntaic7/impact-of-tokenization-in-the-counting-ability-of-language-models", "github_api_added_by": "auto" }, { "date": "2024-10-28", "arxiv_id": "2410.16270", "title": "Reflection-Bench: probing AI intelligence with reflection", "authors": [ "Lingyu Li", "Yixu Wang", "Haiquan Zhao", "Shuqi Kong", "Yan Teng", "Chunbo Li", "Yingchun Wang" ], "github": "https://github.com/YabYum/ReflectionBench", "project_page": "", "abstract": "The ability to adapt beliefs or behaviors in response to unexpected outcomes, reflection, is fundamental to intelligent systems' interaction with the world. From a cognitive science perspective, this serves as a core principle of intelligence applicable to both human and AI systems. To address the debate on the intelligence of large language models (LLMs), we propose Reflection-Bench, a comprehensive benchmark comprising 7 tasks spanning core cognitive functions crucial for reflection, including perception, memory, belief updating, decision-making, prediction, counterfactual thinking, and meta-reflection. We evaluate the performances of 13 prominent LLMs such as OpenAI o1, GPT-4, Claude 3.5 Sonnet, etc. The results indicate that current LLMs still lack satisfactory reflection ability. We discuss the underlying causes of these results and suggest potential avenues for future research. In conclusion, Reflection-Bench offers both evaluation tools and inspiration for developing AI capable of reliably interacting with the environment. Our data and code are available at https://github.com/YabYum/ReflectionBench.", "github_manual": "", "github_regex": "https://github.com/YabYum/ReflectionBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-28", "arxiv_id": "2410.18912", "title": "Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling", "authors": [ "Mingtong Zhang", "Kaifeng Zhang", "Yunzhu Li" ], "github": "https://github.com/robo-alex/gs-dynamics", "project_page": "", "abstract": "Videos of robots interacting with objects encode rich information about the objects' dynamics. However, existing video prediction approaches typically do not explicitly account for the 3D information from videos, such as robot actions and objects' 3D states, limiting their use in real-world robotic applications. In this work, we introduce a framework to learn object dynamics directly from multi-view RGB videos by explicitly considering the robot's action trajectories and their effects on scene dynamics. We utilize the 3D Gaussian representation of 3D Gaussian Splatting (3DGS) to train a particle-based dynamics model using Graph Neural Networks. This model operates on sparse control particles downsampled from the densely tracked 3D Gaussian reconstructions. By learning the neural dynamics model on offline robot interaction data, our method can predict object motions under varying initial configurations and unseen robot actions. The 3D transformations of Gaussians can be interpolated from the motions of control particles, enabling the rendering of predicted future object states and achieving action-conditioned video prediction. The dynamics model can also be applied to model-based planning frameworks for object manipulation tasks. We conduct experiments on various kinds of deformable materials, including ropes, clothes, and stuffed animals, demonstrating our framework's ability to model complex shapes and dynamics. Our project page is available at https://gs-dynamics.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/robo-alex/gs-dynamics", "github_api_added_by": "auto" }, { "date": "2024-10-28", "arxiv_id": "2410.18889", "title": "Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance", "authors": [ "Omer Nahum", "Nitay Calderon", "Orgad Keller", "Idan Szpektor", "Roi Reichart" ], "github": "", "project_page": "", "abstract": "NLP benchmarks rely on standardized datasets for training and evaluating models and are crucial for advancing the field. Traditionally, expert annotations ensure high-quality labels; however, the cost of expert annotation does not scale well with the growing demand for larger datasets required by modern models. While crowd-sourcing provides a more scalable solution, it often comes at the expense of annotation precision and consistency. Recent advancements in large language models (LLMs) offer new opportunities to enhance the annotation process, particularly for detecting label errors in existing datasets. In this work, we consider the recent approach of LLM-as-a-judge, leveraging an ensemble of LLMs to flag potentially mislabeled examples. Through a case study of four datasets from the TRUE benchmark, covering different tasks and domains, we empirically analyze the labeling quality of existing datasets, and compare expert, crowd-sourced, and our LLM-based annotations in terms of agreement, label quality, and efficiency, demonstrating the strengths and limitations of each annotation method. Our findings reveal a substantial number of label errors, which, when corrected, induce a significant upward shift in reported model performance. This suggests that many of the LLMs so-called mistakes are due to label errors rather than genuine model failures. Additionally, we discuss the implications of mislabeled data and propose methods to mitigate them in training to improve model performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-28", "arxiv_id": "2410.16090", "title": "Analysing the Residual Stream of Language Models Under Knowledge Conflicts", "authors": [ "Yu Zhao", "Xiaotang Du", "Giwon Hong", "Aryo Pradipta Gema", "Alessio Devoto", "Hongru Wang", "Xuanli He", "Kam-Fai Wong", "Pasquale Minervini" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided in the context. Such conflicts can lead to undesirable model behaviour, such as reliance on outdated or incorrect information. In this work, we investigate whether LLMs can identify knowledge conflicts and whether it is possible to know which source of knowledge the model will rely on by analysing the residual stream of the LLM. Through probing tasks, we find that LLMs can internally register the signal of knowledge conflict in the residual stream, which can be accurately detected by probing the intermediate model activations. This allows us to detect conflicts within the residual stream before generating the answers without modifying the input or model parameters. Moreover, we find that the residual stream shows significantly different patterns when the model relies on contextual knowledge versus parametric knowledge to resolve conflicts. This pattern can be employed to estimate the behaviour of LLMs when conflict happens and prevent unexpected answers before producing the answers. Our analysis offers insights into how LLMs internally manage knowledge conflicts and provides a foundation for developing methods to control the knowledge selection processes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-28", "arxiv_id": "2410.17655", "title": "Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions", "authors": [ "Dairazalia S\u00e1nchez-Cort\u00e9s", "Sergio Burdisso", "Esa\u00fa Villatoro-Tello", "Petr Motlicek" ], "github": "https://github.com/idiap/factual-reporting-and-political-bias-web-interactions", "project_page": "", "abstract": "Bias assessment of news sources is paramount for professionals, organizations, and researchers who rely on truthful evidence for information gathering and reporting. While certain bias indicators are discernible from content analysis, descriptors like political bias and fake news pose greater challenges. In this paper, we propose an extension to a recently presented news media reliability estimation method that focuses on modeling outlets and their longitudinal web interactions. Concretely, we assess the classification performance of four reinforcement learning strategies on a large news media hyperlink graph. Our experiments, targeting two challenging bias descriptors, factual reporting and political bias, showed a significant performance improvement at the source media level. Additionally, we validate our methods on the CLEF 2023 CheckThat! Lab challenge, outperforming the reported results in both, F1-score and the official MAE metric. Furthermore, we contribute by releasing the largest annotated dataset of news source media, categorized with factual reporting and political bias labels. Our findings suggest that profiling news media sources based on their hyperlink interactions over time is feasible, offering a bird's-eye view of evolving media landscapes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/idiap/factual-reporting-and-political-bias-web-interactions", "github_api_added_by": "auto" }, { "date": "2024-10-28", "arxiv_id": "2410.19123", "title": "Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design", "authors": [ "Ruisi Cai", "Yeonju Ro", "Geon-Woo Kim", "Peihao Wang", "Babak Ehteshami Bejnordi", "Aditya Akella", "Zhangyang Wang" ], "github": "https://github.com/VITA-Group/READ-ME", "project_page": "", "abstract": "The proliferation of large language models (LLMs) has led to the adoption of Mixture-of-Experts (MoE) architectures that dynamically leverage specialized subnetworks for improved efficiency and performance. Despite their benefits, MoE models face significant challenges during inference, including inefficient memory management and suboptimal batching, due to misaligned design choices between the model architecture and the system policies. Furthermore, the conventional approach of training MoEs from scratch is increasingly prohibitive in terms of cost. In this paper, we propose a novel framework Read-ME that transforms pre-trained dense LLMs into smaller MoE models (in contrast to \"upcycling\" generalist MoEs), avoiding the high costs of ground-up training. Our approach employs activation sparsity to extract experts. To compose experts, we examine the widely-adopted layer-wise router design and show its redundancy, and thus we introduce the pre-gating router decoupled from the MoE backbone that facilitates system-friendly pre-computing and lookahead scheduling, enhancing expert-aware batching and caching. Our codesign therefore addresses critical gaps on both the algorithmic and system fronts, establishing a scalable and efficient alternative for LLM inference in resource-constrained settings. Read-ME outperforms other popular open-source dense models of similar scales, achieving improvements of up to 10.1% on MMLU, and improving mean end-to-end latency up to 6.1%. Codes are available at: https://github.com/VITA-Group/READ-ME.", "github_manual": "", "github_regex": "https://github.com/VITA-Group/READ-ME", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.21252", "title": "LongReward: Improving Long-context Large Language Models with AI Feedback", "authors": [ "Jiajie Zhang", "Zhongni Hou", "Xin Lv", "Shulin Cao", "Zhenyu Hou", "Yilin Niu", "Lei Hou", "Yuxiao Dong", "Ling Feng", "Juanzi Li" ], "github": "https://github.com/THUDM/LongReward", "project_page": "", "abstract": "Though significant advancements have been achieved in developing long-context large language models (LLMs), the compromised quality of LLM-synthesized data for supervised fine-tuning (SFT) often affects the long-context performance of SFT models and leads to inherent limitations. In principle, reinforcement learning (RL) with appropriate reward signals can further enhance models' capacities. However, how to obtain reliable rewards in long-context scenarios remains unexplored. To this end, we propose LongReward, a novel method that utilizes an off-the-shelf LLM to provide rewards for long-context model responses from four human-valued dimensions: helpfulness, logicality, faithfulness, and completeness, each with a carefully designed assessment pipeline. By combining LongReward and offline RL algorithm DPO, we are able to effectively improve long-context SFT models. Our experiments indicate that LongReward not only significantly improves models' long-context performance but also enhances their ability to follow short instructions. We also find that long-context DPO with LongReward and conventional short-context DPO can be used together without hurting either one's performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUDM/LongReward", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.21220", "title": "Vision Search Assistant: Empower Vision-Language Models as Multimodal Search Engines", "authors": [ "Zhixin Zhang", "Yiyuan Zhang", "Xiaohan Ding", "Xiangyu Yue" ], "github": "https://github.com/cnzzx/vsa", "project_page": "", "abstract": "Search engines enable the retrieval of unknown information with texts. However, traditional methods fall short when it comes to understanding unfamiliar visual content, such as identifying an object that the model has never seen before. This challenge is particularly pronounced for large vision-language models (VLMs): if the model has not been exposed to the object depicted in an image, it struggles to generate reliable answers to the user's question regarding that image. Moreover, as new objects and events continuously emerge, frequently updating VLMs is impractical due to heavy computational burdens. To address this limitation, we propose Vision Search Assistant, a novel framework that facilitates collaboration between VLMs and web agents. This approach leverages VLMs' visual understanding capabilities and web agents' real-time information access to perform open-world Retrieval-Augmented Generation via the web. By integrating visual and textual representations through this collaboration, the model can provide informed responses even when the image is novel to the system. Extensive experiments conducted on both open-set and closed-set QA benchmarks demonstrate that the Vision Search Assistant significantly outperforms the other models and can be widely applied to existing VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cnzzx/vsa", "github_api_added_by": "auto" }, { "date": "2024-10-29", "arxiv_id": "2410.20011", "title": "A Survey of Small Language Models", "authors": [ "Chien Van Nguyen", "Xuan Shen", "Ryan Aponte", "Yu Xia", "Samyadeep Basu", "Zhengmian Hu", "Jian Chen", "Mihir Parmar", "Sasidhar Kunapuli", "Joe Barrow", "Junda Wu", "Ashish Singh", "Yu Wang", "Jiuxiang Gu", "Franck Dernoncourt", "Nesreen K. Ahmed", "Nedim Lipka", "Ruiyi Zhang", "Xiang Chen", "Tong Yu", "Sungchul Kim", "Hanieh Deilamsalehy", "Namyong Park", "Mike Rimer", "Zhehao Zhang", "Huanrui Yang", "Ryan A. Rossi", "Thien Huu Nguyen" ], "github": "", "project_page": "", "abstract": "Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources, making them ideal for various settings including on-device, mobile, edge devices, among many others. In this article, we present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques. We propose a novel taxonomy for categorizing the methods used to optimize SLMs, including model compression, pruning, and quantization techniques. We summarize the benchmark datasets that are useful for benchmarking SLMs along with the evaluation metrics commonly used. Additionally, we highlight key open challenges that remain to be addressed. Our survey aims to serve as a valuable resource for researchers and practitioners interested in developing and deploying small yet efficient language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.21264", "title": "LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior", "authors": [ "Hanyu Wang", "Saksham Suri", "Yixuan Ren", "Hao Chen", "Abhinav Shrivastava" ], "github": "https://github.com/hywang66/LARP", "project_page": "", "abstract": "We present LARP, a novel video tokenizer designed to overcome limitations in current video tokenization methods for autoregressive (AR) generative models. Unlike traditional patchwise tokenizers that directly encode local visual patches into discrete tokens, LARP introduces a holistic tokenization scheme that gathers information from the visual content using a set of learned holistic queries. This design allows LARP to capture more global and semantic representations, rather than being limited to local patch-level information. Furthermore, it offers flexibility by supporting an arbitrary number of discrete tokens, enabling adaptive and efficient tokenization based on the specific requirements of the task. To align the discrete token space with downstream AR generation tasks, LARP integrates a lightweight AR transformer as a training-time prior model that predicts the next token on its discrete latent space. By incorporating the prior model during training, LARP learns a latent space that is not only optimized for video reconstruction but is also structured in a way that is more conducive to autoregressive generation. Moreover, this process defines a sequential order for the discrete tokens, progressively pushing them toward an optimal configuration during training, ensuring smoother and more accurate AR generation at inference time. Comprehensive experiments demonstrate LARP's strong performance, achieving state-of-the-art FVD on the UCF101 class-conditional video generation benchmark. LARP enhances the compatibility of AR models with videos and opens up the potential to build unified high-fidelity multimodal large language models (MLLMs).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hywang66/LARP", "github_api_added_by": "auto" }, { "date": "2024-10-29", "arxiv_id": "2410.18666", "title": "DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curation", "authors": [ "Yuang Ai", "Xiaoqiang Zhou", "Huaibo Huang", "Xiaotian Han", "Zhengyu Chen", "Quanzeng You", "Hongxia Yang" ], "github": "https://github.com/shallowdream204/DreamClear", "project_page": "", "abstract": "Image restoration (IR) in real-world scenarios presents significant challenges due to the lack of high-capacity models and comprehensive datasets. To tackle these issues, we present a dual strategy: GenIR, an innovative data curation pipeline, and DreamClear, a cutting-edge Diffusion Transformer (DiT)-based image restoration model. GenIR, our pioneering contribution, is a dual-prompt learning pipeline that overcomes the limitations of existing datasets, which typically comprise only a few thousand images and thus offer limited generalizability for larger models. GenIR streamlines the process into three stages: image-text pair construction, dual-prompt based fine-tuning, and data generation & filtering. This approach circumvents the laborious data crawling process, ensuring copyright compliance and providing a cost-effective, privacy-safe solution for IR dataset construction. The result is a large-scale dataset of one million high-quality images. Our second contribution, DreamClear, is a DiT-based image restoration model. It utilizes the generative priors of text-to-image (T2I) diffusion models and the robust perceptual capabilities of multi-modal large language models (MLLMs) to achieve photorealistic restoration. To boost the model's adaptability to diverse real-world degradations, we introduce the Mixture of Adaptive Modulator (MoAM). It employs token-wise degradation priors to dynamically integrate various restoration experts, thereby expanding the range of degradations the model can address. Our exhaustive experiments confirm DreamClear's superior performance, underlining the efficacy of our dual strategy for real-world image restoration. Code and pre-trained models will be available at: https://github.com/shallowdream204/DreamClear.", "github_manual": "", "github_regex": "https://github.com/shallowdream204/DreamClear", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.20280", "title": "MarDini: Masked Autoregressive Diffusion for Video Generation at Scale", "authors": [ "Haozhe Liu", "Shikun Liu", "Zijian Zhou", "Mengmeng Xu", "Yanping Xie", "Xiao Han", "Juan C. P\u00e9rez", "Ding Liu", "Kumara Kahatapitiya", "Menglin Jia", "Jui-Chieh Wu", "Sen He", "Tao Xiang", "J\u00fcrgen Schmidhuber", "Juan-Manuel P\u00e9rez-R\u00faa" ], "github": "", "project_page": "", "abstract": "We introduce MarDini, a new family of video diffusion models that integrate the advantages of masked auto-regression (MAR) into a unified diffusion model (DM) framework. Here, MAR handles temporal planning, while DM focuses on spatial generation in an asymmetric network design: i) a MAR-based planning model containing most of the parameters generates planning signals for each masked frame using low-resolution input; ii) a lightweight generation model uses these signals to produce high-resolution frames via diffusion de-noising. MarDini's MAR enables video generation conditioned on any number of masked frames at any frame positions: a single model can handle video interpolation (e.g., masking middle frames), image-to-video generation (e.g., masking from the second frame onward), and video expansion (e.g., masking half the frames). The efficient design allocates most of the computational resources to the low-resolution planning model, making computationally expensive but important spatio-temporal attention feasible at scale. MarDini sets a new state-of-the-art for video interpolation; meanwhile, within few inference steps, it efficiently generates videos on par with those of much more expensive advanced image-to-video models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.19313", "title": "COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training", "authors": [ "Haocheng Xi", "Han Cai", "Ligeng Zhu", "Yao Lu", "Kurt Keutzer", "Jianfei Chen", "Song Han" ], "github": "https://github.com/NVlabs/COAT", "project_page": "", "abstract": "FP8 training has emerged as a promising method for improving training efficiency. Existing frameworks accelerate training by applying FP8 computation to linear layers while leaving optimizer states and activations in higher precision, which fails to fully optimize memory usage. This paper introduces COAT (Compressing Optimizer States and Activations for FP8 Training), a novel FP8 training framework designed to significantly reduce memory footprint when training large models. COAT addresses current limitations through two key innovations: (1) Dynamic Range Expansion, which aligns optimizer state distributions more closely with the FP8 representation range, thereby reducing quantization error, and (2) Mixed-Granularity Activation Quantization, which optimizes activation memory using a combination of per-tensor and per-group quantization strategies. Experiments demonstrate that COAT effectively reduces end-to-end training memory footprint by 1.54x compared to BF16 while achieving nearly lossless performance across various tasks, such as Large Language Model pretraining and fine-tuning and Vision Language Model training. COAT also achieves a 1.43x end-to-end training speedup compared to BF16, performing on par with or surpassing TransformerEngine's speedup. COAT enables efficient full-parameter training of large models on fewer GPUs, and facilitates doubling the batch size in distributed training settings, providing a practical solution for scaling large-scale model training. The code is available at https://github.com/NVlabs/COAT.", "github_manual": "", "github_regex": "https://github.com/NVlabs/COAT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.20474", "title": "GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation", "authors": [ "Phillip Y. Lee", "Taehoon Yoon", "Minhyuk Sung" ], "github": "https://github.com/KAIST-Visual-AI-Group/GrounDiT", "project_page": "", "abstract": "We introduce a novel training-free spatial grounding technique for text-to-image generation using Diffusion Transformers (DiT). Spatial grounding with bounding boxes has gained attention for its simplicity and versatility, allowing for enhanced user control in image generation. However, prior training-free approaches often rely on updating the noisy image during the reverse diffusion process via backpropagation from custom loss functions, which frequently struggle to provide precise control over individual bounding boxes. In this work, we leverage the flexibility of the Transformer architecture, demonstrating that DiT can generate noisy patches corresponding to each bounding box, fully encoding the target object and allowing for fine-grained control over each region. Our approach builds on an intriguing property of DiT, which we refer to as semantic sharing. Due to semantic sharing, when a smaller patch is jointly denoised alongside a generatable-size image, the two become \"semantic clones\". Each patch is denoised in its own branch of the generation process and then transplanted into the corresponding region of the original noisy image at each timestep, resulting in robust spatial grounding for each bounding box. In our experiments on the HRS and DrawBench benchmarks, we achieve state-of-the-art performance compared to previous training-free spatial grounding approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KAIST-Visual-AI-Group/GrounDiT", "github_api_added_by": "auto" }, { "date": "2024-10-29", "arxiv_id": "2410.20636", "title": "Language Models And A Second Opinion Use Case: The Pocket Professional", "authors": [ "David Noever" ], "github": "https://github.com/reveondivad/certify", "project_page": "", "abstract": "This research tests the role of Large Language Models (LLMs) as formal second opinion tools in professional decision-making, particularly focusing on complex medical cases where even experienced physicians seek peer consultation. The work analyzed 183 challenging medical cases from Medscape over a 20-month period, testing multiple LLMs' performance against crowd-sourced physician responses. A key finding was the high overall score possible in the latest foundational models (>80% accuracy compared to consensus opinion), which exceeds most human metrics reported on the same clinical cases (450 pages of patient profiles, test results). The study rates the LLMs' performance disparity between straightforward cases (>81% accuracy) and complex scenarios (43% accuracy), particularly in these cases generating substantial debate among human physicians. The research demonstrates that LLMs may be valuable as generators of comprehensive differential diagnoses rather than as primary diagnostic tools, potentially helping to counter cognitive biases in clinical decision-making, reduce cognitive loads, and thus remove some sources of medical error. The inclusion of a second comparative legal dataset (Supreme Court cases, N=21) provides added empirical context to the AI use to foster second opinions, though these legal challenges proved considerably easier for LLMs to analyze. In addition to the original contributions of empirical evidence for LLM accuracy, the research aggregated a novel benchmark for others to score highly contested question and answer reliability between both LLMs and disagreeing human practitioners. These results suggest that the optimal deployment of LLMs in professional settings may differ substantially from current approaches that emphasize automation of routine tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/reveondivad/certify", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.20220", "title": "Neural Fields in Robotics: A Survey", "authors": [ "Muhammad Zubair Irshad", "Mauro Comi", "Yen-Chen Lin", "Nick Heppert", "Abhinav Valada", "Rares Ambrus", "Zsolt Kira", "Jonathan Tremblay" ], "github": "https://github.com/zubair-irshad/Awesome-Implicit-NeRF-Robotics", "project_page": "", "abstract": "Neural Fields have emerged as a transformative approach for 3D scene representation in computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and dynamics from posed 2D data. Leveraging differentiable rendering, Neural Fields encompass both continuous implicit and explicit neural representations enabling high-fidelity 3D reconstruction, integration of multi-modal sensor data, and generation of novel viewpoints. This survey explores their applications in robotics, emphasizing their potential to enhance perception, planning, and control. Their compactness, memory efficiency, and differentiability, along with seamless integration with foundation and generative models, make them ideal for real-time applications, improving robot adaptability and decision-making. This paper provides a thorough review of Neural Fields in robotics, categorizing applications across various domains and evaluating their strengths and limitations, based on over 200 papers. First, we present four key Neural Fields frameworks: Occupancy Networks, Signed Distance Fields, Neural Radiance Fields, and Gaussian Splatting. Second, we detail Neural Fields' applications in five major robotics domains: pose estimation, manipulation, navigation, physics, and autonomous driving, highlighting key works and discussing takeaways and open challenges. Finally, we outline the current limitations of Neural Fields in robotics and propose promising directions for future research. Project page: https://robonerf.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zubair-irshad/Awesome-Implicit-NeRF-Robotics", "github_api_added_by": "auto" }, { "date": "2024-10-29", "arxiv_id": "2410.21276", "title": "GPT-4o System Card", "authors": [ "OpenAI", "Aaron Hurst", "Adam Lerer", "Adam P. Goucher", "Adam Perelman", "Aditya Ramesh", "Aidan Clark", "AJ Ostrow", "Akila Welihinda", "Alan Hayes", "Alec Radford", "Aleksander M\u0105dry", "Alex Baker-Whitcomb", "Alex Beutel", "Alex Borzunov", "Alex Carney", "Alex Chow", "Alex Kirillov", "Alex Nichol", "Alex Paino", "Alex Renzin", "Alex Tachard Passos", "Alexander Kirillov", "Alexi Christakis", "Alexis Conneau", "Ali Kamali", "Allan Jabri", "Allison Moyer", "Allison Tam", "Amadou Crookes", "Amin Tootoochian", "Amin Tootoonchian", "Ananya Kumar", "Andrea Vallone", "Andrej Karpathy", "Andrew Braunstein", "Andrew Cann", "Andrew Codispoti", "Andrew Galu", "Andrew Kondrich", "Andrew Tulloch", "Andrey Mishchenko", "Angela Baek", "Angela Jiang", "Antoine Pelisse", "Antonia Woodford", "Anuj Gosalia", "Arka Dhar", "Ashley Pantuliano", "Avi Nayak", "Avital Oliver", "Barret Zoph", "Behrooz Ghorbani", "Ben Leimberger", "Ben Rossen", "Ben Sokolowsky", "Ben Wang", "Benjamin Zweig", "Beth Hoover", "Blake Samic", "Bob McGrew", "Bobby Spero", "Bogo Giertler", "Bowen Cheng", "Brad Lightcap", "Brandon Walkin", "Brendan Quinn", "Brian Guarraci", "Brian Hsu", "Bright Kellogg", "Brydon Eastman", "Camillo Lugaresi", "Carroll Wainwright", "Cary Bassin", "Cary Hudson", "Casey Chu", "Chad Nelson", "Chak Li", "Chan Jun Shern", "Channing Conger", "Charlotte Barette", "Chelsea Voss", "Chen Ding", "Cheng Lu", "Chong Zhang", "Chris Beaumont", "Chris Hallacy", "Chris Koch", "Christian Gibson", "Christina Kim", "Christine Choi", "Christine McLeavey", "Christopher Hesse", "Claudia Fischer", "Clemens Winter", "Coley Czarnecki", "Colin Jarvis", "Colin Wei", "Constantin Koumouzelis", "Dane Sherburn", "Daniel Kappler", "Daniel Levin", "Daniel Levy", "David Carr", "David Farhi", "David Mely", "David Robinson", "David Sasaki", "Denny Jin", "Dev Valladares", "Dimitris Tsipras", "Doug Li", "Duc Phong Nguyen", "Duncan Findlay", "Edede Oiwoh", "Edmund Wong", "Ehsan Asdar", "Elizabeth Proehl", "Elizabeth Yang", "Eric Antonow", "Eric Kramer", "Eric Peterson", "Eric Sigler", "Eric Wallace", "Eugene Brevdo", "Evan Mays", "Farzad Khorasani", "Felipe Petroski Such", "Filippo Raso", "Francis Zhang", "Fred von Lohmann", "Freddie Sulit", "Gabriel Goh", "Gene Oden", "Geoff Salmon", "Giulio Starace", "Greg Brockman", "Hadi Salman", "Haiming Bao", "Haitang Hu", "Hannah Wong", "Haoyu Wang", "Heather Schmidt", "Heather Whitney", "Heewoo Jun", "Hendrik Kirchner", "Henrique Ponde de Oliveira Pinto", "Hongyu Ren", "Huiwen Chang", "Hyung Won Chung", "Ian Kivlichan", "Ian O'Connell", "Ian O'Connell", "Ian Osband", "Ian Silber", "Ian Sohl", "Ibrahim Okuyucu", "Ikai Lan", "Ilya Kostrikov", "Ilya Sutskever", "Ingmar Kanitscheider", "Ishaan Gulrajani", "Jacob Coxon", "Jacob Menick", "Jakub Pachocki", "James Aung", "James Betker", "James Crooks", "James Lennon", "Jamie Kiros", "Jan Leike", "Jane Park", "Jason Kwon", "Jason Phang", "Jason Teplitz", "Jason Wei", "Jason Wolfe", "Jay Chen", "Jeff Harris", "Jenia Varavva", "Jessica Gan Lee", "Jessica Shieh", "Ji Lin", "Jiahui Yu", "Jiayi Weng", "Jie Tang", "Jieqi Yu", "Joanne Jang", "Joaquin Quinonero Candela", "Joe Beutler", "Joe Landers", "Joel Parish", "Johannes Heidecke", "John Schulman", "Jonathan Lachman", "Jonathan McKay", "Jonathan Uesato", "Jonathan Ward", "Jong Wook Kim", "Joost Huizinga", "Jordan Sitkin", "Jos Kraaijeveld", "Josh Gross", "Josh Kaplan", "Josh Snyder", "Joshua Achiam", "Joy Jiao", "Joyce Lee", "Juntang Zhuang", "Justyn Harriman", "Kai Fricke", "Kai Hayashi", "Karan Singhal", "Katy Shi", "Kavin Karthik", "Kayla Wood", "Kendra Rimbach", "Kenny Hsu", "Kenny Nguyen", "Keren Gu-Lemberg", "Kevin Button", "Kevin Liu", "Kiel Howe", "Krithika Muthukumar", "Kyle Luther", "Lama Ahmad", "Larry Kai", "Lauren Itow", "Lauren Workman", "Leher Pathak", "Leo Chen", "Li Jing", "Lia Guy", "Liam Fedus", "Liang Zhou", "Lien Mamitsuka", "Lilian Weng", "Lindsay McCallum", "Lindsey Held", "Long Ouyang", "Louis Feuvrier", "Lu Zhang", "Lukas Kondraciuk", "Lukasz Kaiser", "Luke Hewitt", "Luke Metz", "Lyric Doshi", "Mada Aflak", "Maddie Simens", "Madelaine Boyd", "Madeleine Thompson", "Marat Dukhan", "Mark Chen", "Mark Gray", "Mark Hudnall", "Marvin Zhang", "Marwan Aljubeh", "Mateusz Litwin", "Matthew Zeng", "Max Johnson", "Maya Shetty", "Mayank Gupta", "Meghan Shah", "Mehmet Yatbaz", "Meng Jia Yang", "Mengchao Zhong", "Mia Glaese", "Mianna Chen", "Michael Janner", "Michael Lampe", "Michael Petrov", "Michael Wu", "Michele Wang", "Michelle Fradin", "Michelle Pokrass", "Miguel Castro", "Miguel Oom Temudo de Castro", "Mikhail Pavlov", "Miles Brundage", "Miles Wang", "Minal Khan", "Mira Murati", "Mo Bavarian", "Molly Lin", "Murat Yesildal", "Nacho Soto", "Natalia Gimelshein", "Natalie Cone", "Natalie Staudacher", "Natalie Summers", "Natan LaFontaine", "Neil Chowdhury", "Nick Ryder", "Nick Stathas", "Nick Turley", "Nik Tezak", "Niko Felix", "Nithanth Kudige", "Nitish Keskar", "Noah Deutsch", "Noel Bundick", "Nora Puckett", "Ofir Nachum", "Ola Okelola", "Oleg Boiko", "Oleg Murk", "Oliver Jaffe", "Olivia Watkins", "Olivier Godement", "Owen Campbell-Moore", "Patrick Chao", "Paul McMillan", "Pavel Belov", "Peng Su", "Peter Bak", "Peter Bakkum", "Peter Deng", "Peter Dolan", "Peter Hoeschele", "Peter Welinder", "Phil Tillet", "Philip Pronin", "Philippe Tillet", "Prafulla Dhariwal", "Qiming Yuan", "Rachel Dias", "Rachel Lim", "Rahul Arora", "Rajan Troll", "Randall Lin", "Rapha Gontijo Lopes", "Raul Puri", "Reah Miyara", "Reimar Leike", "Renaud Gaubert", "Reza Zamani", "Ricky Wang", "Rob Donnelly", "Rob Honsby", "Rocky Smith", "Rohan Sahai", "Rohit Ramchandani", "Romain Huet", "Rory Carmichael", "Rowan Zellers", "Roy Chen", "Ruby Chen", "Ruslan Nigmatullin", "Ryan Cheu", "Saachi Jain", "Sam Altman", "Sam Schoenholz", "Sam Toizer", "Samuel Miserendino", "Sandhini Agarwal", "Sara Culver", "Scott Ethersmith", "Scott Gray", "Sean Grove", "Sean Metzger", "Shamez Hermani", "Shantanu Jain", "Shengjia Zhao", "Sherwin Wu", "Shino Jomoto", "Shirong Wu", "Shuaiqi", "Xia", "Sonia Phene", "Spencer Papay", "Srinivas Narayanan", "Steve Coffey", "Steve Lee", "Stewart Hall", "Suchir Balaji", "Tal Broda", "Tal Stramer", "Tao Xu", "Tarun Gogineni", "Taya Christianson", "Ted Sanders", "Tejal Patwardhan", "Thomas Cunninghman", "Thomas Degry", "Thomas Dimson", "Thomas Raoux", "Thomas Shadwell", "Tianhao Zheng", "Todd Underwood", "Todor Markov", "Toki Sherbakov", "Tom Rubin", "Tom Stasi", "Tomer Kaftan", "Tristan Heywood", "Troy Peterson", "Tyce Walters", "Tyna Eloundou", "Valerie Qi", "Veit Moeller", "Vinnie Monaco", "Vishal Kuo", "Vlad Fomenko", "Wayne Chang", "Weiyi Zheng", "Wenda Zhou", "Wesam Manassra", "Will Sheu", "Wojciech Zaremba", "Yash Patil", "Yilei Qian", "Yongjik Kim", "Youlong Cheng", "Yu Zhang", "Yuchen He", "Yuchen Zhang", "Yujia Jin", "Yunxing Dai", "Yury Malkov" ], "github": "", "project_page": "", "abstract": "GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.18603", "title": "AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant", "authors": [ "Chengyou Jia", "Minnan Luo", "Zhuohang Dang", "Qiushi Sun", "Fangzhi Xu", "Junlin Hu", "Tianbao Xie", "Zhiyong Wu" ], "github": "https://github.com/chengyou-jia/AgentStore", "project_page": "", "abstract": "Digital agents capable of automating complex computer tasks have attracted considerable attention due to their immense potential to enhance human-computer interaction. However, existing agent methods exhibit deficiencies in their generalization and specialization capabilities, especially in handling open-ended computer tasks in real-world environments. Inspired by the rich functionality of the App store, we present AgentStore, a scalable platform designed to dynamically integrate heterogeneous agents for automating computer tasks. AgentStore empowers users to integrate third-party agents, allowing the system to continuously enrich its capabilities and adapt to rapidly evolving operating systems. Additionally, we propose a novel core MetaAgent with the AgentToken strategy to efficiently manage diverse agents and utilize their specialized and generalist abilities for both domain-specific and system-wide tasks. Extensive experiments on three challenging benchmarks demonstrate that AgentStore surpasses the limitations of previous systems with narrow capabilities, particularly achieving a significant improvement from 11.21\\% to 23.85\\% on the OSWorld benchmark, more than doubling the previous results. Comprehensive quantitative and qualitative results further demonstrate AgentStore's ability to enhance agent systems in both generalization and specialization, underscoring its potential for developing the specialized generalist computer assistant. All our codes will be made publicly available in https://chengyou-jia.github.io/AgentStore-Home.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/chengyou-jia/AgentStore", "github_api_added_by": "auto" }, { "date": "2024-10-29", "arxiv_id": "2410.20290", "title": "Fast Best-of-N Decoding via Speculative Rejection", "authors": [ "Hanshi Sun", "Momin Haider", "Ruiqi Zhang", "Huitao Yang", "Jiahao Qiu", "Ming Yin", "Mengdi Wang", "Peter Bartlett", "Andrea Zanette" ], "github": "https://github.com/Zanette-Labs/SpeculativeRejection", "project_page": "", "abstract": "The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human preferences. Prevalent alignment techniques, such as DPO, PPO and their variants, align LLMs by changing the pre-trained model weights during a phase called post-training. While predominant, these post-training methods add substantial complexity before LLMs can be deployed. Inference-time alignment methods avoid the complex post-training step and instead bias the generation towards responses that are aligned with human preferences. The best-known inference-time alignment method, called Best-of-N, is as effective as the state-of-the-art post-training procedures. Unfortunately, Best-of-N requires vastly more resources at inference time than standard decoding strategies, which makes it computationally not viable. In this work, we introduce Speculative Rejection, a computationally-viable inference-time alignment algorithm. It generates high-scoring responses according to a given reward model, like Best-of-N does, while being between 16 to 32 times more computationally efficient.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Zanette-Labs/SpeculativeRejection", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.18565", "title": "Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation", "authors": [ "Krzysztof Ociepa", "\u0141ukasz Flis", "Krzysztof Wr\u00f3bel", "Adrian Gwo\u017adziej", "Remigiusz Kinas" ], "github": "https://github.com/speakleash/lm-evaluation-harness.git", "project_page": "", "abstract": "We introduce Bielik 7B v0.1, a 7-billion-parameter generative text model for Polish language processing. Trained on curated Polish corpora, this model addresses key challenges in language model development through innovative techniques. These include Weighted Instruction Cross-Entropy Loss, which balances the learning of different instruction types, and Adaptive Learning Rate, which dynamically adjusts the learning rate based on training progress. To evaluate performance, we created the Open PL LLM Leaderboard and Polish MT-Bench, novel frameworks assessing various NLP tasks and conversational abilities. Bielik 7B v0.1 demonstrates significant improvements, achieving a 9 percentage point increase in average score compared to Mistral-7B-v0.1 on the RAG Reader task. It also excels in the Polish MT-Bench, particularly in Reasoning (6.15/10) and Role-playing (7.83/10) categories. This model represents a substantial advancement in Polish language AI, offering a powerful tool for diverse linguistic applications and setting new benchmarks in the field.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/speakleash/lm-evaluation-harness.git", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.21169", "title": "Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction", "authors": [ "Qintong Zhang", "Victor Shea-Jay Huang", "Bin Wang", "Junyuan Zhang", "Zhengren Wang", "Hao Liang", "Shawn Wang", "Matthieu Lin", "Wentao Zhang", "Conghui He" ], "github": "", "project_page": "", "abstract": "Document parsing is essential for converting unstructured and semi-structured documents-such as contracts, academic papers, and invoices-into structured, machine-readable data. Document parsing extract reliable structured data from unstructured inputs, providing huge convenience for numerous applications. Especially with recent achievements in Large Language Models, document parsing plays an indispensable role in both knowledge base construction and training data generation. This survey presents a comprehensive review of the current state of document parsing, covering key methodologies, from modular pipeline systems to end-to-end models driven by large vision-language models. Core components such as layout detection, content extraction (including text, tables, and mathematical expressions), and multi-modal data integration are examined in detail. Additionally, this paper discusses the challenges faced by modular document parsing systems and vision-language models in handling complex layouts, integrating multiple modules, and recognizing high-density text. It emphasizes the importance of developing larger and more diverse datasets and outlines future research directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2406.10615", "title": "Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation", "authors": [ "Tong Zhang", "Yingdong Hu", "Jiacheng You", "Yang Gao" ], "github": "https://github.com/TongZhangTHU/sgr", "project_page": "", "abstract": "Given the high cost of collecting robotic data in the real world, sample efficiency is a consistently compelling pursuit in robotics. In this paper, we introduce SGRv2, an imitation learning framework that enhances sample efficiency through improved visual and action representations. Central to the design of SGRv2 is the incorporation of a critical inductive bias-action locality, which posits that robot's actions are predominantly influenced by the target object and its interactions with the local environment. Extensive experiments in both simulated and real-world settings demonstrate that action locality is essential for boosting sample efficiency. SGRv2 excels in RLBench tasks with keyframe control using merely 5 demonstrations and surpasses the RVT baseline in 23 of 26 tasks. Furthermore, when evaluated on ManiSkill2 and MimicGen using dense control, SGRv2's success rate is 2.54 times that of SGR. In real-world environments, with only eight demonstrations, SGRv2 can perform a variety of tasks at a markedly higher success rate compared to baseline models. Project website: http://sgrv2-robot.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TongZhangTHU/sgr", "github_api_added_by": "auto" }, { "date": "2024-10-29", "arxiv_id": "2410.18481", "title": "Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction", "authors": [ "Sergio Burdisso", "Srikanth Madikeri", "Petr Motlicek" ], "github": "", "project_page": "", "abstract": "Efficiently deriving structured workflows from unannotated dialogs remains an underexplored and formidable challenge in computational linguistics. Automating this process could significantly accelerate the manual design of workflows in new domains and enable the grounding of large language models in domain-specific flowcharts, enhancing transparency and controllability. In this paper, we introduce Dialog2Flow (D2F) embeddings, which differ from conventional sentence embeddings by mapping utterances to a latent space where they are grouped according to their communicative and informative functions (i.e., the actions they represent). D2F allows for modeling dialogs as continuous trajectories in a latent space with distinct action-related regions. By clustering D2F embeddings, the latent space is quantized, and dialogs can be converted into sequences of region/action IDs, facilitating the extraction of the underlying workflow. To pre-train D2F, we build a comprehensive dataset by unifying twenty task-oriented dialog datasets with normalized per-turn action annotations. We also introduce a novel soft contrastive loss that leverages the semantic information of these actions to guide the representation learning process, showing superior performance compared to standard supervised contrastive loss. Evaluation against various sentence embeddings, including dialog-specific ones, demonstrates that D2F yields superior qualitative and quantitative results across diverse domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.19100", "title": "VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks", "authors": [ "Lawrence Jang", "Yinheng Li", "Charles Ding", "Justin Lin", "Paul Pu Liang", "Dan Zhao", "Rogerio Bonatti", "Kazuhito Koishida" ], "github": "https://github.com/ljang0/videowebarena", "project_page": "", "abstract": "Videos are often used to learn or extract the necessary information to complete tasks in ways different than what text and static imagery alone can provide. However, many existing agent benchmarks neglect long-context video understanding, instead focusing on text or static image inputs. To bridge this gap, we introduce VideoWebArena (VideoWA), a benchmark for evaluating the capabilities of long-context multimodal agents for video understanding. VideoWA consists of 2,021 web agent tasks based on manually crafted video tutorials, which total almost four hours of content. For our benchmark, we define a taxonomy of long-context video-based agent tasks with two main areas of focus: skill retention and factual retention. While skill retention tasks evaluate whether an agent can use a given human demonstration to complete a task efficiently, the factual retention task evaluates whether an agent can retrieve instruction-relevant information from a video to complete a task. We find that the best model achieves 13.3% success on factual retention tasks and 45.8% on factual retention QA pairs, far below human performance at 73.9% and 79.3%, respectively. On skill retention tasks, long-context models perform worse with tutorials than without, exhibiting a 5% performance decrease in WebArena tasks and a 10.3% decrease in VisualWebArena tasks. Our work highlights the need to improve the agentic abilities of long-context multimodal models and provides a testbed for future development with long-context video agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ljang0/videowebarena", "github_api_added_by": "auto" }, { "date": "2024-10-29", "arxiv_id": "2410.20672", "title": "Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA", "authors": [ "Sangmin Bae", "Adam Fisch", "Hrayr Harutyunyan", "Ziwei Ji", "Seungyeon Kim", "Tal Schuster" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "Large language models (LLMs) are expensive to deploy. Parameter sharing offers a possible path towards reducing their size and cost, but its effectiveness in modern LLMs remains fairly limited. In this work, we revisit \"layer tying\" as form of parameter sharing in Transformers, and introduce novel methods for converting existing LLMs into smaller \"Recursive Transformers\" that share parameters across layers, with minimal loss of performance. Here, our Recursive Transformers are efficiently initialized from standard pretrained Transformers, but only use a single block of unique layers that is then repeated multiple times in a loop. We further improve performance by introducing Relaxed Recursive Transformers that add flexibility to the layer tying constraint via depth-wise low-rank adaptation (LoRA) modules, yet still preserve the compactness of the overall model. We show that our recursive models (e.g., recursive Gemma 1B) outperform both similar-sized vanilla pretrained models (such as TinyLlama 1.1B and Pythia 1B) and knowledge distillation baselines -- and can even recover most of the performance of the original \"full-size\" model (e.g., Gemma 2B with no shared parameters). Finally, we propose Continuous Depth-wise Batching, a promising new inference paradigm enabled by the Recursive Transformer when paired with early exiting. In a theoretical analysis, we show that this has the potential to lead to significant (2-3x) gains in inference throughput.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.01968", "title": "Bi-Level Motion Imitation for Humanoid Robots", "authors": [ "Wenshuai Zhao", "Yi Zhao", "Joni Pajarinen", "Michael Muehlebach" ], "github": "", "project_page": "", "abstract": "Imitation learning from human motion capture (MoCap) data provides a promising way to train humanoid robots. However, due to differences in morphology, such as varying degrees of joint freedom and force limits, exact replication of human behaviors may not be feasible for humanoid robots. Consequently, incorporating physically infeasible MoCap data in training datasets can adversely affect the performance of the robot policy. To address this issue, we propose a bi-level optimization-based imitation learning framework that alternates between optimizing both the robot policy and the target MoCap data. Specifically, we first develop a generative latent dynamics model using a novel self-consistent auto-encoder, which learns sparse and structured motion representations while capturing desired motion patterns in the dataset. The dynamics model is then utilized to generate reference motions while the latent representation regularizes the bi-level motion imitation process. Simulations conducted with a realistic model of a humanoid robot demonstrate that our method enhances the robot policy by modifying reference motions to be physically consistent.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-29", "arxiv_id": "2410.21271", "title": "EoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation", "authors": [ "Shih-Yang Liu", "Huck Yang", "Chein-Yi Wang", "Nai Chit Fung", "Hongxu Yin", "Charbel Sakr", "Saurav Muralidharan", "Kwang-Ting Cheng", "Jan Kautz", "Yu-Chiang Frank Wang", "Pavlo Molchanov", "Min-Hung Chen" ], "github": "https://github.com/NVlabs/EoRA", "project_page": "", "abstract": "In this work, we re-formulate the model compression problem into the customized compensation problem: Given a compressed model, we aim to introduce residual low-rank paths to compensate for compression errors under customized requirements from users (e.g., tasks, compression ratios), resulting in greater flexibility in adjusting overall capacity without being constrained by specific compression formats. However, naively applying SVD to derive residual paths causes suboptimal utilization of the low-rank representation capacity. Instead, we propose Training-free Eigenspace Low-Rank Approximation (EoRA), a method that directly minimizes compression-induced errors without requiring gradient-based training, achieving fast optimization in minutes using a small amount of calibration data. EoRA projects compression errors into the eigenspace of input activations, leveraging eigenvalues to effectively prioritize the reconstruction of high-importance error components. Moreover, EoRA can be seamlessly integrated with fine-tuning and quantization to further improve effectiveness and efficiency. EoRA consistently outperforms previous methods in compensating errors for compressed LLaMA2/3 models on various tasks, such as language generation, commonsense reasoning, and math reasoning tasks (e.g., 31.31%/12.88% and 9.69% improvements on ARC-Easy/ARC-Challenge and MathQA when compensating LLaMA3-8B that is quantized to 4-bit and pruned to 2:4 sparsity). EoRA offers a scalable, training-free solution to compensate for compression errors, making it a powerful tool to deploy LLMs in various capacity and efficiency requirements.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVlabs/EoRA", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-30", "arxiv_id": "2410.22304", "title": "Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning", "authors": [ "Yihe Deng", "Paul Mineiro" ], "github": "", "project_page": "", "abstract": "Mathematical reasoning is a crucial capability for Large Language Models (LLMs), yet generating detailed and accurate reasoning traces remains a significant challenge. This paper introduces a novel approach to produce high-quality reasoning traces for LLM fine-tuning using online learning Flows. Our method employs an incremental output production Flow, where component LLMs collaboratively construct solutions through iterative communication. We train the Flow using online Direct Preference Optimization (DPO) learning with rollouts, generating DPO pairs for each training example and updating models in real-time. We directly compare the quality of reasoning traces generated by our method with those produced through direct model inference, demonstrating the effectiveness of our approach in improving LLM performance in mathematical reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-30", "arxiv_id": "2410.21465", "title": "ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference", "authors": [ "Hanshi Sun", "Li-Wen Chang", "Wenlei Bao", "Size Zheng", "Ningxin Zheng", "Xin Liu", "Harry Dong", "Yuejie Chi", "Beidi Chen" ], "github": "https://github.com/bytedance/ShadowKV", "project_page": "", "abstract": "With the widespread deployment of long-context large language models (LLMs), there has been a growing demand for efficient support of high-throughput inference. However, as the key-value (KV) cache expands with the sequence length, the increasing memory footprint and the need to access it for each token generation both result in low throughput when serving long-context LLMs. While various dynamic sparse attention methods have been proposed to speed up inference while maintaining generation quality, they either fail to sufficiently reduce GPU memory consumption or introduce significant decoding latency by offloading the KV cache to the CPU. We present ShadowKV, a high-throughput long-context LLM inference system that stores the low-rank key cache and offloads the value cache to reduce the memory footprint for larger batch sizes and longer sequences. To minimize decoding latency, ShadowKV employs an accurate KV selection strategy that reconstructs minimal sparse KV pairs on-the-fly. By evaluating ShadowKV on a broad range of benchmarks, including RULER, LongBench, and Needle In A Haystack, and models like Llama-3.1-8B, Llama-3-8B-1M, GLM-4-9B-1M, Yi-9B-200K, Phi-3-Mini-128K, and Qwen2-7B-128K, we demonstrate that it can support up to 6times larger batch sizes and boost throughput by up to 3.04times on an A100 GPU without sacrificing accuracy, even surpassing the performance achievable with infinite batch size under the assumption of infinite GPU memory. The code is available at https://github.com/bytedance/ShadowKV.", "github_manual": "", "github_regex": "https://github.com/bytedance/ShadowKV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-30", "arxiv_id": "2410.21845", "title": "Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning", "authors": [ "Jianlan Luo", "Charles Xu", "Jeffrey Wu", "Sergey Levine" ], "github": "https://github.com/rail-berkeley/hil-serl", "project_page": "", "abstract": "Reinforcement learning (RL) holds great promise for enabling autonomous acquisition of complex robotic manipulation skills, but realizing this potential in real-world settings has been challenging. We present a human-in-the-loop vision-based RL system that demonstrates impressive performance on a diverse set of dexterous manipulation tasks, including dynamic manipulation, precision assembly, and dual-arm coordination. Our approach integrates demonstrations and human corrections, efficient RL algorithms, and other system-level design choices to learn policies that achieve near-perfect success rates and fast cycle times within just 1 to 2.5 hours of training. We show that our method significantly outperforms imitation learning baselines and prior RL approaches, with an average 2x improvement in success rate and 1.8x faster execution. Through extensive experiments and analysis, we provide insights into the effectiveness of our approach, demonstrating how it learns robust, adaptive policies for both reactive and predictive control strategies. Our results suggest that RL can indeed learn a wide range of complex vision-based manipulation policies directly in the real world within practical training times. We hope this work will inspire a new generation of learned robotic manipulation techniques, benefiting both industrial applications and research advancements. Videos and code are available at our project website https://hil-serl.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rail-berkeley/hil-serl", "github_api_added_by": "auto" }, { "date": "2024-10-30", "arxiv_id": "2410.21411", "title": "SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization", "authors": [ "Wanhua Li", "Zibin Meng", "Jiawei Zhou", "Donglai Wei", "Chuang Gan", "Hanspeter Pfister" ], "github": "https://github.com/Mengzibin/SocialGPT", "project_page": "", "abstract": "Social relation reasoning aims to identify relation categories such as friends, spouses, and colleagues from images. While current methods adopt the paradigm of training a dedicated network end-to-end using labeled image data, they are limited in terms of generalizability and interpretability. To address these issues, we first present a simple yet well-crafted framework named {\\name}, which combines the perception capability of Vision Foundation Models (VFMs) and the reasoning capability of Large Language Models (LLMs) within a modular framework, providing a strong baseline for social relation recognition. Specifically, we instruct VFMs to translate image content into a textual social story, and then utilize LLMs for text-based reasoning. {\\name} introduces systematic design principles to adapt VFMs and LLMs separately and bridge their gaps. Without additional model training, it achieves competitive zero-shot results on two databases while offering interpretable answers, as LLMs can generate language-based explanations for the decisions. The manual prompt design process for LLMs at the reasoning phase is tedious and an automated prompt optimization method is desired. As we essentially convert a visual classification task into a generative task of LLMs, automatic prompt optimization encounters a unique long prompt optimization issue. To address this issue, we further propose the Greedy Segment Prompt Optimization (GSPO), which performs a greedy search by utilizing gradient information at the segment level. Experimental results show that GSPO significantly improves performance, and our method also generalizes to different image styles. The code is available at https://github.com/Mengzibin/SocialGPT.", "github_manual": "", "github_regex": "https://github.com/Mengzibin/SocialGPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-30", "arxiv_id": "2410.22325", "title": "Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Dataset", "authors": [ "Guangqi Jiang", "Yifei Sun", "Tao Huang", "Huanyu Li", "Yongyuan Liang", "Huazhe Xu" ], "github": "https://github.com/luccachiang/robots-pretrain-robots", "project_page": "", "abstract": "The pre-training of visual representations has enhanced the efficiency of robot learning. Due to the lack of large-scale in-domain robotic datasets, prior works utilize in-the-wild human videos to pre-train robotic visual representation. Despite their promising results, representations from human videos are inevitably subject to distribution shifts and lack the dynamics information crucial for task completion. We first evaluate various pre-trained representations in terms of their correlation to the downstream robotic manipulation tasks (i.e., manipulation centricity). Interestingly, we find that the \"manipulation centricity\" is a strong indicator of success rates when applied to downstream tasks. Drawing from these findings, we propose Manipulation Centric Representation (MCR), a foundation representation learning framework capturing both visual features and the dynamics information such as actions and proprioceptions of manipulation tasks to improve manipulation centricity. Specifically, we pre-train a visual encoder on the DROID robotic dataset and leverage motion-relevant data such as robot proprioceptive states and actions. We introduce a novel contrastive loss that aligns visual observations with the robot's proprioceptive state-action dynamics, combined with a behavior cloning (BC)-like actor loss to predict actions during pre-training, along with a time contrastive loss. Empirical results across 4 simulation domains with 20 tasks verify that MCR outperforms the strongest baseline method by 14.8%. Moreover, MCR boosts the performance of data-efficient learning with a UR5e arm on 3 real-world tasks by 76.9%. Project website: https://robots-pretrain-robots.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/luccachiang/robots-pretrain-robots", "github_api_added_by": "auto" }, { "date": "2024-10-30", "arxiv_id": "2410.20424", "title": "AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions", "authors": [ "Ziming Li", "Qianbo Zang", "David Ma", "Jiawei Guo", "Tuney Zheng", "Minghao Liu", "Xinyao Niu", "Yue Wang", "Jian Yang", "Jiaheng Liu", "Wanjun Zhong", "Wangchunshu Zhou", "Wenhao Huang", "Ge Zhang" ], "github": "https://github.com/multimodal-art-projection/AutoKaggle", "project_page": "", "abstract": "Data science tasks involving tabular data present complex challenges that require sophisticated problem-solving approaches. We propose AutoKaggle, a powerful and user-centric framework that assists data scientists in completing daily data pipelines through a collaborative multi-agent system. AutoKaggle implements an iterative development process that combines code execution, debugging, and comprehensive unit testing to ensure code correctness and logic consistency. The framework offers highly customizable workflows, allowing users to intervene at each phase, thus integrating automated intelligence with human expertise. Our universal data science toolkit, comprising validated functions for data cleaning, feature engineering, and modeling, forms the foundation of this solution, enhancing productivity by streamlining common tasks. We selected 8 Kaggle competitions to simulate data processing workflows in real-world application scenarios. Evaluation results demonstrate that AutoKaggle achieves a validation submission rate of 0.85 and a comprehensive score of 0.82 in typical data science pipelines, fully proving its effectiveness and practicality in handling complex data science tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/multimodal-art-projection/AutoKaggle", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-30", "arxiv_id": "2410.19609", "title": "OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization", "authors": [ "Hongliang He", "Wenlin Yao", "Kaixin Ma", "Wenhao Yu", "Hongming Zhang", "Tianqing Fang", "Zhenzhong Lan", "Dong Yu" ], "github": "https://github.com/minorjerry/openwebvoyager", "project_page": "", "abstract": "The rapid development of large language and multimodal models has sparked significant interest in using proprietary models, such as GPT-4o, to develop autonomous agents capable of handling real-world scenarios like web navigation. Although recent open-source efforts have tried to equip agents with the ability to explore environments and continuously improve over time, they are building text-only agents in synthetic environments where the reward signals are clearly defined. Such agents struggle to generalize to realistic settings that require multimodal perception abilities and lack ground-truth signals. In this paper, we introduce an open-source framework designed to facilitate the development of multimodal web agent that can autonomously conduct real-world exploration and improve itself. We first train the base model with imitation learning to gain the basic abilities. We then let the agent explore the open web and collect feedback on its trajectories. After that, it further improves its policy by learning from well-performing trajectories judged by another general-purpose model. This exploration-feedback-optimization cycle can continue for several iterations. Experimental results show that our web agent successfully improves itself after each iteration, demonstrating strong performance across multiple test sets.", "github_manual": "https://github.com/minorjerry/openwebvoyager", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-30", "arxiv_id": "2410.18057", "title": "CLEAR: Character Unlearning in Textual and Visual Modalities", "authors": [ "Alexey Dontsov", "Dmitrii Korzh", "Alexey Zhavoronkin", "Boris Mikheev", "Denis Bobkov", "Aibek Alanov", "Oleg Y. Rogov", "Ivan Oseledets", "Elena Tutubalina" ], "github": "", "project_page": "", "abstract": "Machine Unlearning (MU) is critical for enhancing privacy and security in deep learning models, particularly in large multimodal language models (MLLMs), by removing specific private or hazardous information. While MU has made significant progress in textual and visual modalities, multimodal unlearning (MMU) remains significantly underexplored, partially due to the absence of a suitable open-source benchmark. To address this, we introduce CLEAR, a new benchmark designed to evaluate MMU methods. CLEAR contains 200 fictitious individuals and 3,700 images linked with corresponding question-answer pairs, enabling a thorough evaluation across modalities. We assess 10 MU methods, adapting them for MMU, and highlight new challenges specific to multimodal forgetting. We also demonstrate that simple ell_1 regularization on LoRA weights significantly mitigates catastrophic forgetting, preserving model performance on retained data. The dataset is available at https://huggingface.co/datasets/therem/CLEAR", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-30", "arxiv_id": "2410.21333", "title": "Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse", "authors": [ "Ryan Liu", "Jiayi Geng", "Addison J. Wu", "Ilia Sucholutsky", "Tania Lombrozo", "Thomas L. Griffiths" ], "github": "https://github.com/JiayiGeng/CoT_overthinking", "project_page": "", "abstract": "Chain-of-thought (CoT) prompting has become a widely used strategy for working with large language and multimodal models. While CoT has been shown to improve performance across many tasks, determining the settings in which it is effective remains an ongoing effort. In particular, it is still an open question in what settings CoT systematically reduces model performance. In this paper, we seek to identify the characteristics of tasks where CoT reduces performance by drawing inspiration from cognitive psychology, looking at cases where (i) verbal thinking or deliberation hurts performance in humans, and (ii) the constraints governing human performance generalize to language models. Three such cases are implicit statistical learning, visual recognition, and classifying with patterns containing exceptions. In extensive experiments across all three settings, we find that a diverse collection of state-of-the-art models exhibit significant drop-offs in performance (e.g., up to 36.3% absolute accuracy for OpenAI o1-preview compared to GPT-4o) when using inference-time reasoning compared to zero-shot counterparts. We also identify three tasks that satisfy condition (i) but not (ii), and find that while verbal thinking reduces human performance in these tasks, CoT retains or increases model performance. Overall, our results show that while there is not an exact parallel between the cognitive processes of models and those of humans, considering cases where thinking has negative consequences for human performance can help us identify settings where it negatively impacts models. By connecting the literature on human deliberation with evaluations of CoT, we offer a new tool that can be used in understanding the impact of prompt choices and inference-time reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JiayiGeng/CoT_overthinking", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-30", "arxiv_id": "2410.20088", "title": "RARe: Retrieval Augmented Retrieval with In-Context Examples", "authors": [ "Atula Tejaswi", "Yoonsang Lee", "Sujay Sanghavi", "Eunsol Choi" ], "github": "https://github.com/atutej/RARe", "project_page": "", "abstract": "We investigate whether in-context examples, widely used in decoder-only language models (LLMs), can improve embedding model performance in retrieval tasks. Unlike in LLMs, naively prepending in-context examples (query-document pairs) to the target query at inference time does not work out of the box. We introduce a simple approach to enable retrievers to use in-context examples. Our approach, RARe, finetunes a pre-trained model with in-context examples whose query is semantically similar to the target query. This can be applied to adapt various base architectures (i.e., decoder-only language models, retriever models) and consistently achieves performance gains of up to +2.72% nDCG across various open-domain retrieval datasets (BeIR, RAR-b). In particular, we find RARe exhibits stronger out-of-domain generalization compared to models using queries without in-context examples, similar to what is seen for in-context learning in LLMs. We further provide analysis on the design choices of in-context example augmentation and lay the foundation for future work in this space.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/atutej/RARe", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-30", "arxiv_id": "2410.21242", "title": "Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback", "authors": [ "Nour Jedidi", "Yung-Sung Chuang", "Leslie Shing", "James Glass" ], "github": "", "project_page": "", "abstract": "Building effective dense retrieval systems remains difficult when relevance supervision is not available. Recent work has looked to overcome this challenge by using a Large Language Model (LLM) to generate hypothetical documents that can be used to find the closest real document. However, this approach relies solely on the LLM to have domain-specific knowledge relevant to the query, which may not be practical. Furthermore, generating hypothetical documents can be inefficient as it requires the LLM to generate a large number of tokens for each query. To address these challenges, we introduce Real Document Embeddings from Relevance Feedback (ReDE-RF). Inspired by relevance feedback, ReDE-RF proposes to re-frame hypothetical document generation as a relevance estimation task, using an LLM to select which documents should be used for nearest neighbor search. Through this re-framing, the LLM no longer needs domain-specific knowledge but only needs to judge what is relevant. Additionally, relevance estimation only requires the LLM to output a single token, thereby improving search latency. Our experiments show that ReDE-RF consistently surpasses state-of-the-art zero-shot dense retrieval methods across a wide range of low-resource retrieval datasets while also making significant improvements in latency per-query.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-30", "arxiv_id": "2410.20305", "title": "Accelerating Direct Preference Optimization with Prefix Sharing", "authors": [ "Franklin Wang", "Sumanth Hegde" ], "github": "https://github.com/frankxwang/dpo-prefix-sharing", "project_page": "", "abstract": "Offline paired preference optimization algorithms have become a popular approach for fine-tuning on preference data, outperforming traditional supervised fine-tuning in various tasks. However, traditional implementations often involve redundant computations, especially for tasks with long shared prompts. We introduce prefix sharing for preference tuning, a novel technique that processes chosen and rejected responses as one sequence with a shared prefix. To prevent cross-response contamination, we use a custom block-sparse attention mask. Our method achieves 1.1-1.5times improvement in training throughput on popular DPO datasets, without any effect on convergence. When combined with sequence packing, we observe consistent 1.3-1.6times speedups, benefiting even datasets with smaller sequence lengths. While we focus on Direct Preference Optimization (DPO), our approach is applicable to other paired preference tuning methods. By enhancing computational efficiency, our work contributes to making preference-based fine-tuning more accessible for a wider range of applications and model sizes. We open-source our code at https://github.com/frankxwang/dpo-prefix-sharing.", "github_manual": "", "github_regex": "https://github.com/frankxwang/dpo-prefix-sharing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-30", "arxiv_id": "2410.19482", "title": "Measuring memorization through probabilistic discoverable extraction", "authors": [ "Jamie Hayes", "Marika Swanberg", "Harsh Chaudhari", "Itay Yona", "Ilia Shumailov" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are susceptible to memorizing training data, raising concerns due to the potential extraction of sensitive information. Current methods to measure memorization rates of LLMs, primarily discoverable extraction (Carlini et al., 2022), rely on single-sequence greedy sampling, potentially underestimating the true extent of memorization. This paper introduces a probabilistic relaxation of discoverable extraction that quantifies the probability of extracting a target sequence within a set of generated samples, considering various sampling schemes and multiple attempts. This approach addresses the limitations of reporting memorization rates through discoverable extraction by accounting for the probabilistic nature of LLMs and user interaction patterns. Our experiments demonstrate that this probabilistic measure can reveal cases of higher memorization rates compared to rates found through discoverable extraction. We further investigate the impact of different sampling schemes on extractability, providing a more comprehensive and realistic assessment of LLM memorization and its associated risks. Our contributions include a new probabilistic memorization definition, empirical evidence of its effectiveness, and a thorough evaluation across different models, sizes, sampling schemes, and training data repetitions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-30", "arxiv_id": "2410.21647", "title": "Can Language Models Replace Programmers? REPOCOD Says 'Not Yet'", "authors": [ "Shanchao Liang", "Yiran Hu", "Nan Jiang", "Lin Tan" ], "github": "https://github.com/lt-asset/repocod", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable ability in code generation with more than 90 pass@1 in solving Python coding problems in HumanEval and MBPP. Such high accuracy leads to the question: can LLMs replace human programmers? Existing manual crafted, simple, or single-line code generation benchmarks cannot answer this question due to their gap with real-world software development. To answer this question, we propose REPOCOD, a code generation benchmark with 980 problems collected from 11 popular real-world projects, with more than 58% of them requiring file-level or repository-level context information. In addition, REPOCOD has the longest average canonical solution length (331.6 tokens) and the highest average cyclomatic complexity (9.00) compared to existing benchmarks. In our evaluations on ten LLMs, none of the models can achieve more than 30 pass@1 on REPOCOD, disclosing the necessity of building stronger LLMs that can help developers in real-world software development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lt-asset/repocod", "github_api_added_by": "auto" }, { "date": "2024-10-30", "arxiv_id": "2410.22330", "title": "Task Vectors are Cross-Modal", "authors": [ "Grace Luo", "Trevor Darrell", "Amir Bar" ], "github": "https://github.com/g-luo/vlm_cross_modal_reps", "project_page": "", "abstract": "We investigate the internal representations of vision-and-language models (VLMs) and how they encode task representations. We consider tasks specified through examples or instructions, using either text or image inputs. Surprisingly, we find that conceptually similar tasks are mapped to similar task vector representations, regardless of how they are specified. Our findings suggest that to output answers, tokens in VLMs undergo three distinct phases: input, task, and answer, a process which is consistent across different modalities and specifications. The task vectors we identify in VLMs are general enough to be derived in one modality (e.g., text) and transferred to another (e.g., image). Additionally, we find that ensembling exemplar and instruction based task vectors produce better task representations. Taken together, these insights shed light on the underlying mechanisms of VLMs, particularly their ability to represent tasks in a shared manner across different modalities and task specifications. Project page: https://task-vectors-are-cross-modal.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/g-luo/vlm_cross_modal_reps", "github_api_added_by": "auto" }, { "date": "2024-10-31", "arxiv_id": "2410.20050", "title": "AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels", "authors": [ "Lei Li", "Xiangxu Zhang", "Xiao Zhou", "Zheng Liu" ], "github": "https://github.com/CMIRB-benchmark/CMIRB", "project_page": "", "abstract": "Medical information retrieval (MIR) is essential for retrieving relevant medical knowledge from diverse sources, including electronic health records, scientific literature, and medical databases. However, achieving effective zero-shot dense retrieval in the medical domain poses substantial challenges due to the lack of relevance-labeled data. In this paper, we introduce a novel approach called Self-Learning Hypothetical Document Embeddings (SL-HyDE) to tackle this issue. SL-HyDE leverages large language models (LLMs) as generators to generate hypothetical documents based on a given query. These generated documents encapsulate key medical context, guiding a dense retriever in identifying the most relevant documents. The self-learning framework progressively refines both pseudo-document generation and retrieval, utilizing unlabeled medical corpora without requiring any relevance-labeled data. Additionally, we present the Chinese Medical Information Retrieval Benchmark (CMIRB), a comprehensive evaluation framework grounded in real-world medical scenarios, encompassing five tasks and ten datasets. By benchmarking ten models on CMIRB, we establish a rigorous standard for evaluating medical information retrieval systems. Experimental results demonstrate that SL-HyDE significantly surpasses existing methods in retrieval accuracy while showcasing strong generalization and scalability across various LLM and retriever configurations. CMIRB data and evaluation code are publicly available at: https://github.com/CMIRB-benchmark/CMIRB.", "github_manual": "", "github_regex": "https://github.com/CMIRB-benchmark/CMIRB", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-31", "arxiv_id": "2410.23090", "title": "CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation", "authors": [ "Yiruo Cheng", "Kelong Mao", "Ziliang Zhao", "Guanting Dong", "Hongjin Qian", "Yongkang Wu", "Tetsuya Sakai", "Ji-Rong Wen", "Zhicheng Dou" ], "github": "https://github.com/Ariya12138/CORAL", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) has become a powerful paradigm for enhancing large language models (LLMs) through external knowledge retrieval. Despite its widespread attention, existing academic research predominantly focuses on single-turn RAG, leaving a significant gap in addressing the complexities of multi-turn conversations found in real-world applications. To bridge this gap, we introduce CORAL, a large-scale benchmark designed to assess RAG systems in realistic multi-turn conversational settings. CORAL includes diverse information-seeking conversations automatically derived from Wikipedia and tackles key challenges such as open-domain coverage, knowledge intensity, free-form responses, and topic shifts. It supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling. We propose a unified framework to standardize various conversational RAG methods and conduct a comprehensive evaluation of these methods on CORAL, demonstrating substantial opportunities for improving existing approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Ariya12138/CORAL", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-31", "arxiv_id": "2410.22391", "title": "A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks", "authors": [ "Thomas Schmied", "Thomas Adler", "Vihang Patil", "Maximilian Beck", "Korbinian P\u00f6ppel", "Johannes Brandstetter", "G\u00fcnter Klambauer", "Razvan Pascanu", "Sepp Hochreiter" ], "github": "https://github.com/ml-jku/lram", "project_page": "", "abstract": "In recent years, there has been a trend in the field of Reinforcement Learning (RL) towards large action models trained offline on large-scale datasets via sequence modeling. Existing models are primarily based on the Transformer architecture, which result in powerful agents. However, due to slow inference times, Transformer-based approaches are impractical for real-time applications, such as robotics. Recently, modern recurrent architectures, such as xLSTM and Mamba, have been proposed that exhibit parallelization benefits during training similar to the Transformer architecture while offering fast inference. In this work, we study the aptitude of these modern recurrent architectures for large action models. Consequently, we propose a Large Recurrent Action Model (LRAM) with an xLSTM at its core that comes with linear-time inference complexity and natural sequence length extrapolation abilities. Experiments on 432 tasks from 6 domains show that LRAM compares favorably to Transformers in terms of performance and speed.", "github_manual": "https://github.com/ml-jku/lram", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-31", "arxiv_id": "2410.22884", "title": "Stealing User Prompts from Mixture of Experts", "authors": [ "Itay Yona", "Ilia Shumailov", "Jamie Hayes", "Nicholas Carlini" ], "github": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) models improve the efficiency and scalability of dense language models by routing each token to a small number of experts in each layer. In this paper, we show how an adversary that can arrange for their queries to appear in the same batch of examples as a victim's queries can exploit Expert-Choice-Routing to fully disclose a victim's prompt. We successfully demonstrate the effectiveness of this attack on a two-layer Mixtral model, exploiting the tie-handling behavior of the torch.topk CUDA implementation. Our results show that we can extract the entire prompt using O({VM}^2) queries (with vocabulary size V and prompt length M) or 100 queries on average per token in the setting we consider. This is the first attack to exploit architectural flaws for the purpose of extracting user prompts, introducing a new class of LLM vulnerabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-31", "arxiv_id": "2410.23168", "title": "TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters", "authors": [ "Haiyang Wang", "Yue Fan", "Muhammad Ferjad Naeem", "Yongqin Xian", "Jan Eric Lenssen", "Liwei Wang", "Federico Tombari", "Bernt Schiele" ], "github": "https://github.com/Haiyang-W/TokenFormer", "project_page": "", "abstract": "Transformers have become the predominant architecture in foundation models due to their excellent performance across various domains. However, the substantial cost of scaling these models remains a significant concern. This problem arises primarily from their dependence on a fixed number of parameters within linear projections. When architectural modifications (e.g., channel dimensions) are introduced, the entire model typically requires retraining from scratch. As model sizes continue growing, this strategy results in increasingly high computational costs and becomes unsustainable. To overcome this problem, we introduce TokenFormer, a natively scalable architecture that leverages the attention mechanism not only for computations among input tokens but also for interactions between tokens and model parameters, thereby enhancing architectural flexibility. By treating model parameters as tokens, we replace all the linear projections in Transformers with our token-parameter attention layer, where input tokens act as queries and model parameters as keys and values. This reformulation allows for progressive and efficient scaling without necessitating retraining from scratch. Our model scales from 124M to 1.4B parameters by incrementally adding new key-value parameter pairs, achieving performance comparable to Transformers trained from scratch while greatly reducing training costs. Code and models are available at https://github.com/Haiyang-W/TokenFormer.", "github_manual": "", "github_regex": "https://github.com/Haiyang-W/TokenFormer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-31", "arxiv_id": "2410.22587", "title": "Toxicity of the Commons: Curating Open-Source Pre-Training Data", "authors": [ "Catherine Arnett", "Eliot Jones", "Ivan P. Yamshchikov", "Pierre-Carl Langlais" ], "github": "https://github.com/Pleias/toxic-commons", "project_page": "", "abstract": "Open-source large language models are becoming increasingly available and popular among researchers and practitioners. While significant progress has been made on open-weight models, open training data is a practice yet to be adopted by the leading open-weight models creators. At the same time, there researchers are working to make language models safer. We propose a data curation pipeline to reduce harmful outputs by models trained on public domain data. There are unique challenges to working with public domain data, as these sources differ from web text in both form and content. Many sources are historical documents and are the result of Optical Character Recognition (OCR). Consequently, current state-of-the-art approaches to toxicity filtering are often infeasible or inappropriate for open data models. In this paper, we introduce a new fully open-source pipeline for open-data toxicity filtering. Our contributions are threefold. We create a custom training dataset, ToxicCommons, which is composed of texts which have been classified across five different dimensions (racial/origin-based, gender/sex-based, religious, ability-based discrimination, and violence). We use this dataset to train a custom classifier, Celadon, that can be used to detect toxic content in open data more efficiently at a larger scale. Finally, we describe the balanced approach to content filtration that optimizes safety filtering with respect to the filtered data available for training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Pleias/toxic-commons", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-31", "arxiv_id": "2410.20779", "title": "Decoding Reading Goals from Eye Movements", "authors": [ "Omer Shubi", "Cfir Avraham Hadar", "Yevgeni Berzak" ], "github": "https://github.com/lacclab/Goal-Decoding-from-Eye-Movements", "project_page": "", "abstract": "Readers can have different goals with respect to the text they are reading. Can these goals be decoded from the pattern of their eye movements over the text? In this work, we examine for the first time whether it is possible to decode two types of reading goals that are common in daily life: information seeking and ordinary reading. Using large scale eye-tracking data, we apply to this task a wide range of state-of-the-art models for eye movements and text that cover different architectural and data representation strategies, and further introduce a new model ensemble. We systematically evaluate these models at three levels of generalization: new textual item, new participant, and the combination of both. We find that eye movements contain highly valuable signals for this task. We further perform an error analysis which builds on prior empirical findings on differences between ordinary reading and information seeking and leverages rich textual annotations. This analysis reveals key properties of textual items and participant eye movements that contribute to the difficulty of the task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lacclab/Goal-Decoding-from-Eye-Movements", "github_api_added_by": "auto" }, { "date": "2024-10-31", "arxiv_id": "2410.23287", "title": "ReferEverything: Towards Segmenting Everything We Can Speak of in Videos", "authors": [ "Anurag Bagchi", "Zhipeng Bao", "Yu-Xiong Wang", "Pavel Tokmakov", "Martial Hebert" ], "github": "", "project_page": "", "abstract": "We present REM, a framework for segmenting a wide range of concepts in video that can be described through natural language. Our method capitalizes on visual-language representations learned by video diffusion models on Internet-scale datasets. A key insight of our approach is preserving as much of the generative model's original representation as possible, while fine-tuning it on narrow-domain Referral Object Segmentation datasets. As a result, our framework can accurately segment and track rare and unseen objects, despite being trained on object masks from a limited set of categories. Additionally, it can generalize to non-object dynamic concepts, such as waves crashing in the ocean, as demonstrated in our newly introduced benchmark for Referral Video Process Segmentation (Ref-VPS). Our experiments show that REM performs on par with state-of-the-art approaches on in-domain datasets, like Ref-DAVIS, while outperforming them by up to twelve points in terms of region similarity on out-of-domain data, leveraging the power of Internet-scale pre-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-10-31", "arxiv_id": "2410.23123", "title": "On Memorization of Large Language Models in Logical Reasoning", "authors": [ "Chulin Xie", "Yangsibo Huang", "Chiyuan Zhang", "Da Yu", "Xinyun Chen", "Bill Yuchen Lin", "Bo Li", "Badih Ghazi", "Ravi Kumar" ], "github": "https://github.com/AlphaPav/mem-kk-logic", "project_page": "", "abstract": "Large language models (LLMs) achieve good performance on challenging reasoning benchmarks, yet could also make basic reasoning mistakes. This contrasting behavior is puzzling when it comes to understanding the mechanisms behind LLMs' reasoning capabilities. One hypothesis is that the increasingly high and nearly saturated performance on common reasoning benchmarks could be due to the memorization of similar problems. In this paper, we systematically investigate this hypothesis with a quantitative measurement of memorization in reasoning tasks, using a dynamically generated logical reasoning benchmark based on Knights and Knaves (K&K) puzzles. We found that LLMs could interpolate the training puzzles (achieving near-perfect accuracy) after fine-tuning, yet fail when those puzzles are slightly perturbed, suggesting that the models heavily rely on memorization to solve those training puzzles. On the other hand, we show that while fine-tuning leads to heavy memorization, it also consistently improves generalization performance. In-depth analyses with perturbation tests, cross difficulty-level transferability, probing model internals, and fine-tuning with wrong answers suggest that the LLMs learn to reason on K&K puzzles despite training data memorization. This phenomenon indicates that LLMs exhibit a complex interplay between memorization and genuine reasoning abilities. Finally, our analysis with per-sample memorization score sheds light on how LLMs switch between reasoning and memorization in solving logical puzzles. Our code and data are available at https://memkklogic.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AlphaPav/mem-kk-logic", "github_api_added_by": "auto" }, { "date": "2024-10-31", "arxiv_id": "2410.23277", "title": "SlowFast-VGen: Slow-Fast Learning for Action-Driven Long Video Generation", "authors": [ "Yining Hong", "Beide Liu", "Maxine Wu", "Yuanhao Zhai", "Kai-Wei Chang", "Lingjie Li", "Kevin Lin", "Chung-Ching Lin", "Jianfeng Wang", "Zhengyuan Yang", "Yingnian Wu", "Lijuan Wang" ], "github": "https://github.com/slowfast-vgen/slowfast-vgen", "project_page": "", "abstract": "Human beings are endowed with a complementary learning system, which bridges the slow learning of general world dynamics with fast storage of episodic memory from a new experience. Previous video generation models, however, primarily focus on slow learning by pre-training on vast amounts of data, overlooking the fast learning phase crucial for episodic memory storage. This oversight leads to inconsistencies across temporally distant frames when generating longer videos, as these frames fall beyond the model's context window. To this end, we introduce SlowFast-VGen, a novel dual-speed learning system for action-driven long video generation. Our approach incorporates a masked conditional video diffusion model for the slow learning of world dynamics, alongside an inference-time fast learning strategy based on a temporal LoRA module. Specifically, the fast learning process updates its temporal LoRA parameters based on local inputs and outputs, thereby efficiently storing episodic memory in its parameters. We further propose a slow-fast learning loop algorithm that seamlessly integrates the inner fast learning loop into the outer slow learning loop, enabling the recall of prior multi-episode experiences for context-aware skill learning. To facilitate the slow learning of an approximate world model, we collect a large-scale dataset of 200k videos with language action annotations, covering a wide range of scenarios. Extensive experiments show that SlowFast-VGen outperforms baselines across various metrics for action-driven video generation, achieving an FVD score of 514 compared to 782, and maintaining consistency in longer videos, with an average of 0.37 scene cuts versus 0.89. The slow-fast learning loop algorithm significantly enhances performances on long-horizon planning tasks as well. Project Website: https://slowfast-vgen.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/slowfast-vgen/slowfast-vgen", "github_api_added_by": "auto" }, { "date": "2024-10-31", "arxiv_id": "2410.23331", "title": "Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists", "authors": [ "Micha\u0142 Pietruszka", "\u0141ukasz Borchmann", "Aleksander J\u0119drosz", "Pawe\u0142 Morawiecki" ], "github": "https://github.com/FeatEng/FeatEng", "project_page": "", "abstract": "We present a benchmark for large language models designed to tackle one of the most knowledge-intensive tasks in data science: writing feature engineering code, which requires domain knowledge in addition to a deep understanding of the underlying problem and data structure. The model is provided with a dataset description in a prompt and asked to generate code transforming it. The evaluation score is derived from the improvement achieved by an XGBoost model fit on the modified dataset compared to the original data. By an extensive evaluation of state-of-the-art models and comparison to well-established benchmarks, we demonstrate that the FeatEng of our proposal can cheaply and efficiently assess the broad capabilities of LLMs, in contrast to the existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FeatEng/FeatEng", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.21969", "title": "BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays", "authors": [ "Yang Zhou", "Tan Li Hui Faith", "Yanyu Xu", "Sicong Leng", "Xinxing Xu", "Yong Liu", "Rick Siow Mong Goh" ], "github": "https://github.com/yangzhou12/BenchX", "project_page": "", "abstract": "Medical Vision-Language Pretraining (MedVLP) shows promise in learning generalizable and transferable visual representations from paired and unpaired medical images and reports. MedVLP can provide useful features to downstream tasks and facilitate adapting task-specific models to new setups using fewer examples. However, existing MedVLP methods often differ in terms of datasets, preprocessing, and finetuning implementations. This pose great challenges in evaluating how well a MedVLP method generalizes to various clinically-relevant tasks due to the lack of unified, standardized, and comprehensive benchmark. To fill this gap, we propose BenchX, a unified benchmark framework that enables head-to-head comparison and systematical analysis between MedVLP methods using public chest X-ray datasets. Specifically, BenchX is composed of three components: 1) Comprehensive datasets covering nine datasets and four medical tasks; 2) Benchmark suites to standardize data preprocessing, train-test splits, and parameter selection; 3) Unified finetuning protocols that accommodate heterogeneous MedVLP methods for consistent task adaptation in classification, segmentation, and report generation, respectively. Utilizing BenchX, we establish baselines for nine state-of-the-art MedVLP methods and found that the performance of some early MedVLP methods can be enhanced to surpass more recent ones, prompting a revisiting of the developments and conclusions from prior works in MedVLP. Our code are available at https://github.com/yangzhou12/BenchX.", "github_manual": "", "github_regex": "https://github.com/yangzhou12/BenchX", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.24213", "title": "Learning Video Representations without Natural Videos", "authors": [ "Xueyang Yu", "Xinlei Chen", "Yossi Gandelsman" ], "github": "", "project_page": "", "abstract": "In this paper, we show that useful video representations can be learned from synthetic videos and natural images, without incorporating natural videos in the training. We propose a progression of video datasets synthesized by simple generative processes, that model a growing set of natural video properties (e.g. motion, acceleration, and shape transformations). The downstream performance of video models pre-trained on these generated datasets gradually increases with the dataset progression. A VideoMAE model pre-trained on our synthetic videos closes 97.2% of the performance gap on UCF101 action classification between training from scratch and self-supervised pre-training from natural videos, and outperforms the pre-trained model on HMDB51. Introducing crops of static images to the pre-training stage results in similar performance to UCF101 pre-training and outperforms the UCF101 pre-trained model on 11 out of 14 out-of-distribution datasets of UCF101-P. Analyzing the low-level properties of the datasets, we identify correlations between frame diversity, frame similarity to natural data, and downstream performance. Our approach provides a more controllable and transparent alternative to video data curation processes for pre-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.24175", "title": "Constraint Back-translation Improves Complex Instruction Following of Large Language Models", "authors": [ "Yunjia Qi", "Hao Peng", "Xiaozhi Wang", "Bin Xu", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/thu-keg/crab", "project_page": "", "abstract": "Large language models (LLMs) struggle to follow instructions with complex constraints in format, length, etc. Following the conventional instruction-tuning practice, previous works conduct post-training on complex instruction-response pairs generated by feeding complex instructions to advanced LLMs. However, even advanced LLMs cannot follow complex instructions well, thus limiting the quality of generated data. In this work, we find that existing datasets inherently contain implicit complex constraints and propose a novel data generation technique, constraint back-translation. Specifically, we take the high-quality instruction-response pairs in existing datasets and only adopt advanced LLMs to add complex constraints already met by the responses to the instructions, which naturally reduces costs and data noise. In the experiments, we adopt Llama3-70B-Instruct to back-translate constraints and create a high-quality complex instruction-response dataset, named CRAB. We present that post-training on CRAB improves multiple backbone LLMs' complex instruction-following ability, evaluated on extensive instruction-following benchmarks. We further find that constraint back-translation also serves as a useful auxiliary training objective in post-training. Our code, data, and models will be released to facilitate future research.", "github_manual": "https://github.com/thu-keg/crab", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.23933", "title": "Language Models can Self-Lengthen to Generate Long Texts", "authors": [ "Shanghaoran Quan", "Tianyi Tang", "Bowen Yu", "An Yang", "Dayiheng Liu", "Bofei Gao", "Jianhong Tu", "Yichang Zhang", "Jingren Zhou", "Junyang Lin" ], "github": "https://github.com/QwenLM/Self-Lengthen", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to process long contexts, yet a notable gap remains in generating long, aligned outputs. This limitation stems from a training gap where pre-training lacks effective instructions for long-text generation, and post-training data primarily consists of short query-response pairs. Current approaches, such as instruction backtranslation and behavior imitation, face challenges including data quality, copyright issues, and constraints on proprietary model usage. In this paper, we introduce an innovative iterative training framework called Self-Lengthen that leverages only the intrinsic knowledge and skills of LLMs without the need for auxiliary data or proprietary models. The framework consists of two roles: the Generator and the Extender. The Generator produces the initial response, which is then split and expanded by the Extender. This process results in a new, longer response, which is used to train both the Generator and the Extender iteratively. Through this process, the models are progressively trained to handle increasingly longer responses. Experiments on benchmarks and human evaluations show that Self-Lengthen outperforms existing methods in long-text generation, when applied to top open-source LLMs such as Qwen2 and LLaMA3. Our code is publicly available at https://github.com/QwenLM/Self-Lengthen.", "github_manual": "", "github_regex": "https://github.com/QwenLM/Self-Lengthen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.23743", "title": "What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective", "authors": [ "Ming Li", "Yanhong Li", "Tianyi Zhou" ], "github": "https://github.com/MingLiiii/Layer_Gradient", "project_page": "", "abstract": "What makes a difference in the post-training of LLMs? We investigate the training patterns of different layers in large language models (LLMs), through the lens of gradient, when training with different responses and initial models. We are specifically interested in how fast vs. slow thinking affects the layer-wise gradients, given the recent popularity of training LLMs on reasoning paths such as chain-of-thoughts (CoT) and process rewards. In our study, fast thinking without CoT leads to larger gradients and larger differences of gradients across layers than slow thinking (Detailed CoT), indicating the learning stability brought by the latter. Moreover, pre-trained LLMs are less affected by the instability of fast thinking than instruction-tuned LLMs. Additionally, we study whether the gradient patterns can reflect the correctness of responses when training different LLMs using slow vs. fast thinking paths. The results show that the gradients of slow thinking can distinguish correct and irrelevant reasoning paths. As a comparison, we conduct similar gradient analyses on non-reasoning knowledge learning tasks, on which, however, trivially increasing the response length does not lead to similar behaviors of slow thinking. Our study strengthens fundamental understandings of LLM training and sheds novel insights on its efficiency and stability, which pave the way towards building a generalizable System-2 agent. Our code, data, and gradient statistics can be found in: https://github.com/MingLiiii/Layer_Gradient.", "github_manual": "", "github_regex": "https://github.com/MingLiiii/Layer_Gradient", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.24211", "title": "DELTA: Dense Efficient Long-range 3D Tracking for any video", "authors": [ "Tuan Duc Ngo", "Peiye Zhuang", "Chuang Gan", "Evangelos Kalogerakis", "Sergey Tulyakov", "Hsin-Ying Lee", "Chaoyang Wang" ], "github": "", "project_page": "", "abstract": "Tracking dense 3D motion from monocular videos remains challenging, particularly when aiming for pixel-level precision over long sequences. We introduce \\Approach, a novel method that efficiently tracks every pixel in 3D space, enabling accurate motion estimation across entire videos. Our approach leverages a joint global-local attention mechanism for reduced-resolution tracking, followed by a transformer-based upsampler to achieve high-resolution predictions. Unlike existing methods, which are limited by computational inefficiency or sparse tracking, \\Approach delivers dense 3D tracking at scale, running over 8x faster than previous methods while achieving state-of-the-art accuracy. Furthermore, we explore the impact of depth representation on tracking performance and identify log-depth as the optimal choice. Extensive experiments demonstrate the superiority of \\Approach on multiple benchmarks, achieving new state-of-the-art results in both 2D and 3D dense tracking tasks. Our method provides a robust solution for applications requiring fine-grained, long-term motion tracking in 3D space.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.22366", "title": "Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse Autoencoders", "authors": [ "Viacheslav Surkov", "Chris Wendler", "Mikhail Terekhov", "Justin Deschenaux", "Robert West", "Caglar Gulcehre" ], "github": "https://github.com/surkovv/sdxl-unbox", "project_page": "", "abstract": "Sparse autoencoders (SAEs) have become a core ingredient in the reverse engineering of large-language models (LLMs). For LLMs, they have been shown to decompose intermediate representations that often are not interpretable directly into sparse sums of interpretable features, facilitating better control and subsequent analysis. However, similar analyses and approaches have been lacking for text-to-image models. We investigated the possibility of using SAEs to learn interpretable features for a few-step text-to-image diffusion models, such as SDXL Turbo. To this end, we train SAEs on the updates performed by transformer blocks within SDXL Turbo's denoising U-net. We find that their learned features are interpretable, causally influence the generation process, and reveal specialization among the blocks. In particular, we find one block that deals mainly with image composition, one that is mainly responsible for adding local details, and one for color, illumination, and style. Therefore, our work is an important first step towards better understanding the internals of generative text-to-image models like SDXL Turbo and showcases the potential of features learned by SAEs for the visual domain. Code is available at https://github.com/surkovv/sdxl-unbox", "github_manual": "", "github_regex": "https://github.com/surkovv/sdxl-unbox", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.24032", "title": "Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks", "authors": [ "Yingzhe Peng", "Xiaoting Qin", "Zhiyang Zhang", "Jue Zhang", "Qingwei Lin", "Xu Yang", "Dongmei Zhang", "Saravan Rajmohan", "Qi Zhang" ], "github": "", "project_page": "", "abstract": "The rise of large language models (LLMs) has revolutionized user interactions with knowledge-based systems, enabling chatbots to synthesize vast amounts of information and assist with complex, exploratory tasks. However, LLM-based chatbots often struggle to provide personalized support, particularly when users start with vague queries or lack sufficient contextual information. This paper introduces the Collaborative Assistant for Personalized Exploration (CARE), a system designed to enhance personalization in exploratory tasks by combining a multi-agent LLM framework with a structured user interface. CARE's interface consists of a Chat Panel, Solution Panel, and Needs Panel, enabling iterative query refinement and dynamic solution generation. The multi-agent framework collaborates to identify both explicit and implicit user needs, delivering tailored, actionable solutions. In a within-subject user study with 22 participants, CARE was consistently preferred over a baseline LLM chatbot, with users praising its ability to reduce cognitive load, inspire creativity, and provide more tailored solutions. Our findings highlight CARE's potential to transform LLM-based systems from passive information retrievers to proactive partners in personalized problem-solving and exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.22476", "title": "A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents", "authors": [ "Ankan Mullick", "Sombit Bose", "Abhilash Nandy", "Gajula Sai Chaitanya", "Pawan Goyal" ], "github": "", "project_page": "", "abstract": "In task-oriented dialogue systems, intent detection is crucial for interpreting user queries and providing appropriate responses. Existing research primarily addresses simple queries with a single intent, lacking effective systems for handling complex queries with multiple intents and extracting different intent spans. Additionally, there is a notable absence of multilingual, multi-intent datasets. This study addresses three critical tasks: extracting multiple intent spans from queries, detecting multiple intents, and developing a multi-lingual multi-label intent dataset. We introduce a novel multi-label multi-class intent detection dataset (MLMCID-dataset) curated from existing benchmark datasets. We also propose a pointer network-based architecture (MLMCID) to extract intent spans and detect multiple intents with coarse and fine-grained labels in the form of sextuplets. Comprehensive analysis demonstrates the superiority of our pointer network-based system over baseline approaches in terms of accuracy and F1-score across various datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.23918", "title": "BitStack: Fine-Grained Size Control for Compressed Large Language Models in Variable Memory Environments", "authors": [ "Xinghao Wang", "Pengyu Wang", "Bo Wang", "Dong Zhang", "Yunhua Zhou", "Xipeng Qiu" ], "github": "https://github.com/xinghaow99/BitStack", "project_page": "", "abstract": "Large language models (LLMs) have revolutionized numerous applications, yet their deployment remains challenged by memory constraints on local devices. While scaling laws have enhanced LLM capabilities, the primary bottleneck has shifted from capability to availability, emphasizing the need for efficient memory management. Traditional compression methods, such as quantization, often require predefined compression ratios and separate compression processes for each setting, complicating deployment in variable memory environments. In this paper, we introduce BitStack, a novel, training-free weight compression approach that enables megabyte-level trade-offs between memory usage and model performance. By leveraging weight decomposition, BitStack can dynamically adjust the model size with minimal transmission between running memory and storage devices. Our approach iteratively decomposes weight matrices while considering the significance of each parameter, resulting in an approximately 1-bit per parameter residual block in each decomposition iteration. These blocks are sorted and stacked in storage as basic transmission units, with different quantities loaded based on current memory availability. Extensive experiments across a wide range of tasks demonstrate that, despite offering fine-grained size control, BitStack consistently matches or surpasses strong quantization baselines, particularly at extreme compression ratios. To the best of our knowledge, this is the first decomposition-based method that effectively bridges the gap to practical compression techniques like quantization. Code is available at https://github.com/xinghaow99/BitStack.", "github_manual": "", "github_regex": "https://github.com/xinghaow99/BitStack", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.24198", "title": "SelfCodeAlign: Self-Alignment for Code Generation", "authors": [ "Yuxiang Wei", "Federico Cassano", "Jiawei Liu", "Yifeng Ding", "Naman Jain", "Zachary Mueller", "Harm de Vries", "Leandro von Werra", "Arjun Guha", "Lingming Zhang" ], "github": "https://github.com/bigcode-project/selfcodealign", "project_page": "", "abstract": "Instruction tuning is a supervised fine-tuning approach that significantly improves the ability of large language models (LLMs) to follow human instructions. We propose SelfCodeAlign, the first fully transparent and permissive pipeline for self-aligning code LLMs without extensive human annotations or distillation. SelfCodeAlign employs the same base model for inference throughout the data generation process. It first extracts diverse coding concepts from high-quality seed snippets to generate new tasks. It then samples multiple responses per task, pairs each with test cases, and validates them in a sandbox environment. Finally, passing examples are selected for instruction tuning. In our primary experiments, we use SelfCodeAlign with CodeQwen1.5-7B to generate a dataset of 74k instruction-response pairs. Finetuning on this dataset leads to a model that achieves a 67.1 pass@1 on HumanEval+, surpassing CodeLlama-70B-Instruct despite being ten times smaller. Across all benchmarks, this finetuned model consistently outperforms the original version trained with OctoPack, the previous state-of-the-art method for instruction tuning without human annotations or distillation. Additionally, we show that SelfCodeAlign is effective across LLMs of various sizes, from 3B to 33B, and that the base models can benefit more from alignment with their own data distribution. We further validate each component's effectiveness in our pipeline, showing that SelfCodeAlign outperforms both direct distillation from GPT-4o and leading GPT-3.5-based distillation methods, such as OSS-Instruct and Evol-Instruct. SelfCodeAlign has also led to the creation of StarCoder2-Instruct, the first fully transparent, permissively licensed, and self-aligned code LLM that achieves state-of-the-art coding performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bigcode-project/selfcodealign", "github_api_added_by": "auto" }, { "date": "2024-11-01", "arxiv_id": "2410.24218", "title": "Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use", "authors": [ "Jiajun Xi", "Yinong He", "Jianing Yang", "Yinpei Dai", "Joyce Chai" ], "github": "https://github.com/sled-group/Teachable_RL", "project_page": "", "abstract": "In real-world scenarios, it is desirable for embodied agents to have the ability to leverage human language to gain explicit or implicit knowledge for learning tasks. Despite recent progress, most previous approaches adopt simple low-level instructions as language inputs, which may not reflect natural human communication. It's not clear how to incorporate rich language use to facilitate task learning. To address this question, this paper studies different types of language inputs in facilitating reinforcement learning (RL) embodied agents. More specifically, we examine how different levels of language informativeness (i.e., feedback on past behaviors and future guidance) and diversity (i.e., variation of language expressions) impact agent learning and inference. Our empirical results based on four RL benchmarks demonstrate that agents trained with diverse and informative language feedback can achieve enhanced generalization and fast adaptation to new tasks. These findings highlight the pivotal role of language use in teaching embodied agents new tasks in an open world. Project website: https://github.com/sled-group/Teachable_RL", "github_manual": "", "github_regex": "https://github.com/sled-group/Teachable_RL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.23825", "title": "GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages", "authors": [ "Amir Hossein Kargaran", "Fran\u00e7ois Yvon", "Hinrich Sch\u00fctze" ], "github": "https://github.com/cisnlp/GlotCC", "project_page": "", "abstract": "The need for large text corpora has increased with the advent of pretrained language models and, in particular, the discovery of scaling laws for these models. Most available corpora have sufficient data only for languages with large dominant communities. However, there is no corpus available that (i) covers a wide range of minority languages; (ii) is generated by an open-source reproducible pipeline; and (iii) is rigorously cleaned from noise, making it trustworthy to use. We present GlotCC, a clean, document-level, 2TB general domain corpus derived from CommonCrawl, covering more than 1000 languages. We make GlotCC and the system used to generate it - including the pipeline, language identification model, and filters - available to the research community. Corpus v. 1.0 https://huggingface.co/datasets/cis-lmu/GlotCC-v1, Pipeline v. 3.0 https://github.com/cisnlp/GlotCC.", "github_manual": "", "github_regex": "https://github.com/cisnlp/GlotCC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.21666", "title": "Minimum Entropy Coupling with Bottleneck", "authors": [ "M. Reza Ebrahimi", "Jun Chen", "Ashish Khisti" ], "github": "", "project_page": "", "abstract": "This paper investigates a novel lossy compression framework operating under logarithmic loss, designed to handle situations where the reconstruction distribution diverges from the source distribution. This framework is especially relevant for applications that require joint compression and retrieval, and in scenarios involving distributional shifts due to processing. We show that the proposed formulation extends the classical minimum entropy coupling framework by integrating a bottleneck, allowing for a controlled degree of stochasticity in the coupling. We explore the decomposition of the Minimum Entropy Coupling with Bottleneck (MEC-B) into two distinct optimization problems: Entropy-Bounded Information Maximization (EBIM) for the encoder, and Minimum Entropy Coupling (MEC) for the decoder. Through extensive analysis, we provide a greedy algorithm for EBIM with guaranteed performance, and characterize the optimal solution near functional mappings, yielding significant theoretical insights into the structural complexity of this problem. Furthermore, we illustrate the practical application of MEC-B through experiments in Markov Coding Games (MCGs) under rate limits. These games simulate a communication scenario within a Markov Decision Process, where an agent must transmit a compressed message from a sender to a receiver through its actions. Our experiments highlight the trade-offs between MDP rewards and receiver accuracy across various compression rates, showcasing the efficacy of our method compared to conventional compression baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.22394", "title": "AAAR-1.0: Assessing AI's Potential to Assist Research", "authors": [ "Renze Lou", "Hanzi Xu", "Sijia Wang", "Jiangshu Du", "Ryo Kamoi", "Xiaoxin Lu", "Jian Xie", "Yuxuan Sun", "Yusen Zhang", "Jihyun Janice Ahn", "Hongchao Fang", "Zhuoyang Zou", "Wenchao Ma", "Xi Li", "Kai Zhang", "Congying Xia", "Lifu Huang", "Wenpeng Yin" ], "github": "", "project_page": "", "abstract": "Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, and creative content generation. However, researchers face unique challenges and opportunities in leveraging LLMs for their own work, such as brainstorming research ideas, designing experiments, and writing or reviewing papers. In this study, we introduce AAAR-1.0, a benchmark dataset designed to evaluate LLM performance in three fundamental, expertise-intensive research tasks: (i) EquationInference, assessing the correctness of equations based on the contextual information in paper submissions; (ii) ExperimentDesign, designing experiments to validate research ideas and solutions; (iii) PaperWeakness, identifying weaknesses in paper submissions; and (iv) REVIEWCRITIQUE, identifying each segment in human reviews is deficient or not. AAAR-1.0 differs from prior benchmarks in two key ways: first, it is explicitly research-oriented, with tasks requiring deep domain expertise; second, it is researcher-oriented, mirroring the primary activities that researchers engage in on a daily basis. An evaluation of both open-source and proprietary LLMs reveals their potential as well as limitations in conducting sophisticated research tasks. We will keep iterating AAAR-1.0 to new versions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-01", "arxiv_id": "2410.20650", "title": "NeuZip: Memory-Efficient Training and Inference with Dynamic Compression of Neural Networks", "authors": [ "Yongchang Hao", "Yanshuai Cao", "Lili Mou" ], "github": "https://github.com/borealisai/neuzip", "project_page": "", "abstract": "The performance of neural networks improves when more parameters are used. However, the model sizes are constrained by the available on-device memory during training and inference. Although applying techniques like quantization can alleviate the constraint, they suffer from performance degradation. In this work, we introduce NeuZip, a new weight compression scheme based on the entropy of floating-point numbers in neural networks. With NeuZip, we are able to achieve memory-efficient training and inference without sacrificing performance. Notably, we significantly reduce the memory footprint of training a Llama-3 8B model from 31GB to less than 16GB, while keeping the training dynamics fully unchanged. In inference, our method can reduce memory usage by more than half while maintaining near-lossless performance. Our code is publicly available.", "github_manual": "https://github.com/borealisai/neuzip", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2411.00412", "title": "Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation", "authors": [ "Bohan Lyu", "Yadi Cao", "Duncan Watson-Parris", "Leon Bergen", "Taylor Berg-Kirkpatrick", "Rose Yu" ], "github": "https://github.com/Rose-STL-Lab/Adapting-While-Learning", "project_page": "", "abstract": "Large Language Models (LLMs) demonstrate promising capabilities in solving simple scientific problems but often produce hallucinations for complex ones. While integrating LLMs with tools can increase reliability, this approach typically results in over-reliance on tools, diminishing the model's ability to solve simple problems through basic reasoning. In contrast, human experts first assess problem complexity using domain knowledge before choosing an appropriate solution approach. Inspired by this human problem-solving process, we propose a novel two-component fine-tuning method. In the first component World Knowledge Distillation (WKD), LLMs learn directly from solutions generated using tool's information to internalize domain knowledge. In the second component Tool Usage Adaptation (TUA), we partition problems into easy and hard categories based on the model's direct answering accuracy. While maintaining the same alignment target for easy problems as in WKD, we train the model to intelligently switch to tool usage for more challenging problems. We validate our method on six scientific benchmark datasets, spanning mathematics, climate science and epidemiology. On average, our models demonstrate a 28.18% improvement in answer accuracy and a 13.89% increase in tool usage precision across all datasets, surpassing state-of-the-art models including GPT-4o and Claude-3.5.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Rose-STL-Lab/Adapting-While-Learning", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2410.23775", "title": "In-Context LoRA for Diffusion Transformers", "authors": [ "Lianghua Huang", "Wei Wang", "Zhi-Fan Wu", "Yupeng Shi", "Huanzhang Dou", "Chen Liang", "Yutong Feng", "Yu Liu", "Jingren Zhou" ], "github": "https://github.com/ali-vilab/In-Context-LoRA", "project_page": "", "abstract": "Recent research arXiv:2410.15027 has explored the use of diffusion transformers (DiTs) for task-agnostic image generation by simply concatenating attention tokens across images. However, despite substantial computational resources, the fidelity of the generated images remains suboptimal. In this study, we reevaluate and streamline this framework by hypothesizing that text-to-image DiTs inherently possess in-context generation capabilities, requiring only minimal tuning to activate them. Through diverse task experiments, we qualitatively demonstrate that existing text-to-image DiTs can effectively perform in-context generation without any tuning. Building on this insight, we propose a remarkably simple pipeline to leverage the in-context abilities of DiTs: (1) concatenate images instead of tokens, (2) perform joint captioning of multiple images, and (3) apply task-specific LoRA tuning using small datasets (e.g., 20sim 100 samples) instead of full-parameter tuning with large datasets. We name our models In-Context LoRA (IC-LoRA). This approach requires no modifications to the original DiT models, only changes to the training data. Remarkably, our pipeline generates high-fidelity image sets that better adhere to prompts. While task-specific in terms of tuning data, our framework remains task-agnostic in architecture and pipeline, offering a powerful tool for the community and providing valuable insights for further research on product-level task-agnostic generation systems. We release our code, data, and models at https://github.com/ali-vilab/In-Context-LoRA", "github_manual": "", "github_regex": "https://github.com/ali-vilab/In-Context-LoRA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2411.00776", "title": "Randomized Autoregressive Visual Generation", "authors": [ "Qihang Yu", "Ju He", "Xueqing Deng", "Xiaohui Shen", "Liang-Chieh Chen" ], "github": "https://github.com/bytedance/1d-tokenizer", "project_page": "", "abstract": "This paper presents Randomized AutoRegressive modeling (RAR) for visual generation, which sets a new state-of-the-art performance on the image generation task while maintaining full compatibility with language modeling frameworks. The proposed RAR is simple: during a standard autoregressive training process with a next-token prediction objective, the input sequence-typically ordered in raster form-is randomly permuted into different factorization orders with a probability r, where r starts at 1 and linearly decays to 0 over the course of training. This annealing training strategy enables the model to learn to maximize the expected likelihood over all factorization orders and thus effectively improve the model's capability of modeling bidirectional contexts. Importantly, RAR preserves the integrity of the autoregressive modeling framework, ensuring full compatibility with language modeling while significantly improving performance in image generation. On the ImageNet-256 benchmark, RAR achieves an FID score of 1.48, not only surpassing prior state-of-the-art autoregressive image generators but also outperforming leading diffusion-based and masked transformer-based methods. Code and models will be made available at https://github.com/bytedance/1d-tokenizer", "github_manual": "", "github_regex": "https://github.com/bytedance/1d-tokenizer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2411.00027", "title": "Personalization of Large Language Models: A Survey", "authors": [ "Zhehao Zhang", "Ryan A. Rossi", "Branislav Kveton", "Yijia Shao", "Diyi Yang", "Hamed Zamani", "Franck Dernoncourt", "Joe Barrow", "Tong Yu", "Sungchul Kim", "Ruiyi Zhang", "Jiuxiang Gu", "Tyler Derr", "Hongjie Chen", "Junda Wu", "Xiang Chen", "Zichao Wang", "Subrata Mitra", "Nedim Lipka", "Nesreen Ahmed", "Yu Wang" ], "github": "", "project_page": "", "abstract": "Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most existing works on personalized LLMs have focused either entirely on (a) personalized text generation or (b) leveraging LLMs for personalization-related downstream applications, such as recommendation systems. In this work, we bridge the gap between these two separate main directions for the first time by introducing a taxonomy for personalized LLM usage and summarizing the key differences and challenges. We provide a formalization of the foundations of personalized LLMs that consolidates and expands notions of personalization of LLMs, defining and discussing novel facets of personalization, usage, and desiderata of personalized LLMs. We then unify the literature across these diverse fields and usage scenarios by proposing systematic taxonomies for the granularity of personalization, personalization techniques, datasets, evaluation methods, and applications of personalized LLMs. Finally, we highlight challenges and important open problems that remain to be addressed. By unifying and surveying recent research using the proposed taxonomies, we aim to provide a clear guide to the existing literature and different facets of personalization in LLMs, empowering both researchers and practitioners.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2410.21157", "title": "M2rc-Eval: Massively Multilingual Repository-level Code Completion Evaluation", "authors": [ "Jiaheng Liu", "Ken Deng", "Congnan Liu", "Jian Yang", "Shukai Liu", "He Zhu", "Peng Zhao", "Linzheng Chai", "Yanan Wu", "Ke Jin", "Ge Zhang", "Zekun Wang", "Guoan Zhang", "Bangyu Xiang", "Wenbo Su", "Bo Zheng" ], "github": "https://github.com/M2RC-Eval-Team/M2RC-Eval", "project_page": "", "abstract": "Repository-level code completion has drawn great attention in software engineering, and several benchmark datasets have been introduced. However, existing repository-level code completion benchmarks usually focus on a limited number of languages (<5), which cannot evaluate the general code intelligence abilities across different languages for existing code Large Language Models (LLMs). Besides, the existing benchmarks usually report overall average scores of different languages, where the fine-grained abilities in different completion scenarios are ignored. Therefore, to facilitate the research of code LLMs in multilingual scenarios, we propose a massively multilingual repository-level code completion benchmark covering 18 programming languages (called M2RC-EVAL), and two types of fine-grained annotations (i.e., bucket-level and semantic-level) on different completion scenarios are provided, where we obtain these annotations based on the parsed abstract syntax tree. Moreover, we also curate a massively multilingual instruction corpora M2RC- INSTRUCT dataset to improve the repository-level code completion abilities of existing code LLMs. Comprehensive experimental results demonstrate the effectiveness of our M2RC-EVAL and M2RC-INSTRUCT.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/M2RC-Eval-Team/M2RC-Eval", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2410.22370", "title": "Survey of User Interface Design and Interaction Techniques in Generative AI Applications", "authors": [ "Reuben Luera", "Ryan A. Rossi", "Alexa Siu", "Franck Dernoncourt", "Tong Yu", "Sungchul Kim", "Ruiyi Zhang", "Xiang Chen", "Hanieh Salehy", "Jian Zhao", "Samyadeep Basu", "Puneet Mathur", "Nedim Lipka" ], "github": "", "project_page": "", "abstract": "The applications of generative AI have become extremely impressive, and the interplay between users and AI is even more so. Current human-AI interaction literature has taken a broad look at how humans interact with generative AI, but it lacks specificity regarding the user interface designs and patterns used to create these applications. Therefore, we present a survey that comprehensively presents taxonomies of how a human interacts with AI and the user interaction patterns designed to meet the needs of a variety of relevant use cases. We focus primarily on user-guided interactions, surveying interactions that are initiated by the user and do not include any implicit signals given by the user. With this survey, we aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike. In doing so, we also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2411.00369", "title": "GRS-QA -- Graph Reasoning-Structured Question Answering Dataset", "authors": [ "Anish Pahilajani", "Devasha Trivedi", "Jincen Shuai", "Khin S. Yone", "Samyak Rajesh Jain", "Namyong Park", "Ryan A. Rossi", "Nesreen K. Ahmed", "Franck Dernoncourt", "Yu Wang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have excelled in multi-hop question-answering (M-QA) due to their advanced reasoning abilities. However, the impact of the inherent reasoning structures on LLM M-QA performance remains unclear, largely due to the absence of QA datasets that provide fine-grained reasoning structures. To address this gap, we introduce the Graph Reasoning-Structured Question Answering Dataset (GRS-QA), which includes both semantic contexts and reasoning structures for QA pairs. Unlike existing M-QA datasets, where different reasoning structures are entangled together, GRS-QA explicitly captures intricate reasoning pathways by constructing reasoning graphs, where nodes represent textual contexts and edges denote logical flows. These reasoning graphs of different structures enable a fine-grained evaluation of LLM reasoning capabilities across various reasoning structures. Our empirical analysis reveals that LLMs perform differently when handling questions with varying reasoning structures. This finding facilitates the exploration of textual structures as compared with semantics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2410.22901", "title": "HelloMeme: Integrating Spatial Knitting Attentions to Embed High-Level and Fidelity-Rich Conditions in Diffusion Models", "authors": [ "Shengkai Zhang", "Nianhong Jiao", "Tian Li", "Chaojie Yang", "Chenhui Xue", "Boya Niu", "Jun Gao" ], "github": "https://github.com/HelloVision/ExperimentsOnSKAttentions", "project_page": "", "abstract": "We propose an effective method for inserting adapters into text-to-image foundation models, which enables the execution of complex downstream tasks while preserving the generalization ability of the base model. The core idea of this method is to optimize the attention mechanism related to 2D feature maps, which enhances the performance of the adapter. This approach was validated on the task of meme video generation and achieved significant results. We hope this work can provide insights for post-training tasks of large text-to-image models. Additionally, as this method demonstrates good compatibility with SD1.5 derivative models, it holds certain value for the open-source community. Therefore, we will release the related code (https://songkey.github.io/hellomeme).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HelloVision/ExperimentsOnSKAttentions", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2410.23218", "title": "OS-ATLAS: A Foundation Action Model for Generalist GUI Agents", "authors": [ "Zhiyong Wu", "Zhenyu Wu", "Fangzhi Xu", "Yian Wang", "Qiushi Sun", "Chengyou Jia", "Kanzhi Cheng", "Zichen Ding", "Liheng Chen", "Paul Pu Liang", "Yu Qiao" ], "github": "https://github.com/OS-Copilot/OS-Atlas", "project_page": "", "abstract": "Existing efforts in building GUI agents heavily rely on the availability of robust commercial Vision-Language Models (VLMs) such as GPT-4o and GeminiProVision. Practitioners are often reluctant to use open-source VLMs due to their significant performance lag compared to their closed-source counterparts, particularly in GUI grounding and Out-Of-Distribution (OOD) scenarios. To facilitate future research in this area, we developed OS-Atlas - a foundational GUI action model that excels at GUI grounding and OOD agentic tasks through innovations in both data and modeling. We have invested significant engineering effort in developing an open-source toolkit for synthesizing GUI grounding data across multiple platforms, including Windows, Linux, MacOS, Android, and the web. Leveraging this toolkit, we are releasing the largest open-source cross-platform GUI grounding corpus to date, which contains over 13 million GUI elements. This dataset, combined with innovations in model training, provides a solid foundation for OS-Atlas to understand GUI screenshots and generalize to unseen interfaces. Through extensive evaluation across six benchmarks spanning three different platforms (mobile, desktop, and web), OS-Atlas demonstrates significant performance improvements over previous state-of-the-art models. Our evaluation also uncovers valuable insights into continuously improving and scaling the agentic capabilities of open-source VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OS-Copilot/OS-Atlas", "github_api_added_by": "auto" }, { "date": "2024-11-04", "arxiv_id": "2411.00233", "title": "SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba State Space Models", "authors": [ "Jos\u00e9 Ignacio Olalde-Verano", "Sascha Kirch", "Clara P\u00e9rez-Molina", "Sergio Martin" ], "github": "https://github.com/sascha-kirch/samba-mixer", "project_page": "", "abstract": "The state of health (SOH) of a Li-ion battery is a critical parameter that determines the remaining capacity and the remaining lifetime of the battery. In this paper, we propose SambaMixer a novel structured state space model (SSM) for predicting the state of health of Li-ion batteries. The proposed SSM is based on the MambaMixer architecture, which is designed to handle multi-variate time signals. We evaluate our model on the NASA battery discharge dataset and show that our model outperforms the state-of-the-art on this dataset. We further introduce a novel anchor-based resampling method which ensures time signals are of the expected length while also serving as augmentation technique. Finally, we condition prediction on the sample time and the cycle time difference using positional encodings to improve the performance of our model and to learn recuperation effects. Our results proof that our model is able to predict the SOH of Li-ion batteries with high accuracy and robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sascha-kirch/samba-mixer", "github_api_added_by": "auto" }, { "date": "2024-11-04", "arxiv_id": "2411.00322", "title": "Constant Acceleration Flow", "authors": [ "Dogyun Park", "Sojin Lee", "Sihyeon Kim", "Taehoon Lee", "Youngjoon Hong", "Hyunwoo J. Kim" ], "github": "https://github.com/mlvlab/CAF", "project_page": "", "abstract": "Rectified flow and reflow procedures have significantly advanced fast generation by progressively straightening ordinary differential equation (ODE) flows. They operate under the assumption that image and noise pairs, known as couplings, can be approximated by straight trajectories with constant velocity. However, we observe that modeling with constant velocity and using reflow procedures have limitations in accurately learning straight trajectories between pairs, resulting in suboptimal performance in few-step generation. To address these limitations, we introduce Constant Acceleration Flow (CAF), a novel framework based on a simple constant acceleration equation. CAF introduces acceleration as an additional learnable variable, allowing for more expressive and accurate estimation of the ODE flow. Moreover, we propose two techniques to further improve estimation accuracy: initial velocity conditioning for the acceleration model and a reflow process for the initial velocity. Our comprehensive studies on toy datasets, CIFAR-10, and ImageNet 64x64 demonstrate that CAF outperforms state-of-the-art baselines for one-step generation. We also show that CAF dramatically improves few-step coupling preservation and inversion over Rectified flow. Code is available at https://github.com/mlvlab/CAF{https://github.com/mlvlab/CAF}.", "github_manual": "", "github_regex": "https://github.com/mlvlab/CAF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2411.00030", "title": "WikiNER-fr-gold: A Gold-Standard NER Corpus", "authors": [ "Danrun Cao", "Nicolas B\u00e9chet", "Pierre-Fran\u00e7ois Marteau" ], "github": "", "project_page": "", "abstract": "We address in this article the the quality of the WikiNER corpus, a multilingual Named Entity Recognition corpus, and provide a consolidated version of it. The annotation of WikiNER was produced in a semi-supervised manner i.e. no manual verification has been carried out a posteriori. Such corpus is called silver-standard. In this paper we propose WikiNER-fr-gold which is a revised version of the French proportion of WikiNER. Our corpus consists of randomly sampled 20% of the original French sub-corpus (26,818 sentences with 700k tokens). We start by summarizing the entity types included in each category in order to define an annotation guideline, and then we proceed to revise the corpus. Finally we present an analysis of errors and inconsistency observed in the WikiNER-fr corpus, and we discuss potential future work directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2411.00680", "title": "Zipfian Whitening", "authors": [ "Sho Yokoi", "Han Bao", "Hiroto Kurita", "Hidetoshi Shimodaira" ], "github": "https://github.com/cl-tohoku/zipfian-whitening", "project_page": "", "abstract": "The word embedding space in neural models is skewed, and correcting this can improve task performance. We point out that most approaches for modeling, correcting, and measuring the symmetry of an embedding space implicitly assume that the word frequencies are uniform; in reality, word frequencies follow a highly non-uniform distribution, known as Zipf's law. Surprisingly, simply performing PCA whitening weighted by the empirical word frequency that follows Zipf's law significantly improves task performance, surpassing established baselines. From a theoretical perspective, both our approach and existing methods can be clearly categorized: word representations are distributed according to an exponential family with either uniform or Zipfian base measures. By adopting the latter approach, we can naturally emphasize informative low-frequency words in terms of their vector norm, which becomes evident from the information-geometric perspective, and in terms of the loss functions for imbalanced classification. Additionally, our theory corroborates that popular natural language processing methods, such as skip-gram negative sampling, WhiteningBERT, and headless language models, work well just because their word embeddings encode the empirical word frequency into the underlying probabilistic model.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/cl-tohoku/zipfian-whitening", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2410.24159", "title": "GPT or BERT: why not both?", "authors": [ "Lucas Georges Gabriel Charpentier", "David Samuel" ], "github": "https://github.com/ltgoslo/gpt-bert", "project_page": "", "abstract": "We present a simple way to merge masked language modeling with causal language modeling. This hybrid training objective results in a model that combines the strengths of both modeling paradigms within a single transformer stack: GPT-BERT can be transparently used like any standard causal or masked language model. We test the pretraining process that enables this flexible behavior on the BabyLM Challenge 2024. The results show that the hybrid pretraining outperforms masked-only or causal-only models. We openly release the models, training corpora and code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ltgoslo/gpt-bert", "github_api_added_by": "auto" }, { "date": "2024-11-04", "arxiv_id": "2411.00762", "title": "Face Anonymization Made Simple", "authors": [ "Han-Wei Kung", "Tuomas Varanka", "Sanjay Saha", "Terence Sim", "Nicu Sebe" ], "github": "https://github.com/hanweikung/face_anon_simple", "project_page": "", "abstract": "Current face anonymization techniques often depend on identity loss calculated by face recognition models, which can be inaccurate and unreliable. Additionally, many methods require supplementary data such as facial landmarks and masks to guide the synthesis process. In contrast, our approach uses diffusion models with only a reconstruction loss, eliminating the need for facial landmarks or masks while still producing images with intricate, fine-grained details. We validated our results on two public benchmarks through both quantitative and qualitative evaluations. Our model achieves state-of-the-art performance in three key areas: identity anonymization, facial attribute preservation, and image quality. Beyond its primary function of anonymization, our model can also perform face swapping tasks by incorporating an additional facial image as input, demonstrating its versatility and potential for diverse applications. Our code and models are available at https://github.com/hanweikung/face_anon_simple .", "github_manual": "", "github_regex": "https://github.com/hanweikung/face_anon_simple", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2411.00771", "title": "CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes", "authors": [ "Yang Liu", "Chuanchen Luo", "Zhongkai Mao", "Junran Peng", "Zhaoxiang Zhang" ], "github": "", "project_page": "", "abstract": "Recently, 3D Gaussian Splatting (3DGS) has revolutionized radiance field reconstruction, manifesting efficient and high-fidelity novel view synthesis. However, accurately representing surfaces, especially in large and complex scenarios, remains a significant challenge due to the unstructured nature of 3DGS. In this paper, we present CityGaussianV2, a novel approach for large-scale scene reconstruction that addresses critical challenges related to geometric accuracy and efficiency. Building on the favorable generalization capabilities of 2D Gaussian Splatting (2DGS), we address its convergence and scalability issues. Specifically, we implement a decomposed-gradient-based densification and depth regression technique to eliminate blurry artifacts and accelerate convergence. To scale up, we introduce an elongation filter that mitigates Gaussian count explosion caused by 2DGS degeneration. Furthermore, we optimize the CityGaussian pipeline for parallel training, achieving up to 10times compression, at least 25% savings in training time, and a 50% decrease in memory usage. We also established standard geometry benchmarks under large-scale scenes. Experimental results demonstrate that our method strikes a promising balance between visual quality, geometric accuracy, as well as storage and training costs. The project page is available at https://dekuliutesla.github.io/CityGaussianV2/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2410.23266", "title": "TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models", "authors": [ "Ziyao Shangguan", "Chuhan Li", "Yuxuan Ding", "Yanan Zheng", "Yilun Zhao", "Tesca Fitzgerald", "Arman Cohan" ], "github": "https://github.com/yale-nlp/TOMATO", "project_page": "", "abstract": "Existing benchmarks often highlight the remarkable performance achieved by state-of-the-art Multimodal Foundation Models (MFMs) in leveraging temporal context for video understanding. However, how well do the models truly perform visual temporal reasoning? Our study of existing benchmarks shows that this capability of MFMs is likely overestimated as many questions can be solved by using a single, few, or out-of-order frames. To systematically examine current visual temporal reasoning tasks, we propose three principles with corresponding metrics: (1) Multi-Frame Gain, (2) Frame Order Sensitivity, and (3) Frame Information Disparity. Following these principles, we introduce TOMATO, Temporal Reasoning Multimodal Evaluation, a novel benchmark crafted to rigorously assess MFMs' temporal reasoning capabilities in video understanding. TOMATO comprises 1,484 carefully curated, human-annotated questions spanning six tasks (i.e., action count, direction, rotation, shape & trend, velocity & frequency, and visual cues), applied to 1,417 videos, including 805 self-recorded and -generated videos, that encompass human-centric, real-world, and simulated scenarios. Our comprehensive evaluation reveals a human-model performance gap of 57.3% with the best-performing model. Moreover, our in-depth analysis uncovers more fundamental limitations beyond this gap in current MFMs. While they can accurately recognize events in isolated frames, they fail to interpret these frames as a continuous sequence. We believe TOMATO will serve as a crucial testbed for evaluating the next-generation MFMs and as a call to the community to develop AI systems capable of comprehending human world dynamics through the video modality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yale-nlp/TOMATO", "github_api_added_by": "auto" }, { "date": "2024-11-04", "arxiv_id": "2411.00660", "title": "Physics in Next-token Prediction", "authors": [ "Hongjun An", "Yiliang Song", "Xuelong Li" ], "github": "", "project_page": "", "abstract": "We discovered the underlying physics in Next-token Prediction (NTP). We identified the law of information conservation within NTP and proposed the First Law of Information Capacity (IC-1), demonstrating that the essence of intelligence emergence in auto-regressive models is fundamentally a process of information transfer. We also introduced Landauer's Principle into NTP, formulating the Second Law of Information Capacity (IC-2), which establishes the relationship between auto-regressive model training and energy consumption. Additionally, we presented several corollaries, which hold practical significance for production practices. Finally, we validated the compatibility and complementarity of our findings with existing theories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-04", "arxiv_id": "2411.00225", "title": "Fashion-VDM: Video Diffusion Model for Virtual Try-On", "authors": [ "Johanna Karras", "Yingwei Li", "Nan Liu", "Luyang Zhu", "Innfarn Yoo", "Andreas Lugmayr", "Chris Lee", "Ira Kemelmacher-Shlizerman" ], "github": "", "project_page": "", "abstract": "We present Fashion-VDM, a video diffusion model (VDM) for generating virtual try-on videos. Given an input garment image and person video, our method aims to generate a high-quality try-on video of the person wearing the given garment, while preserving the person's identity and motion. Image-based virtual try-on has shown impressive results; however, existing video virtual try-on (VVT) methods are still lacking garment details and temporal consistency. To address these issues, we propose a diffusion-based architecture for video virtual try-on, split classifier-free guidance for increased control over the conditioning inputs, and a progressive temporal training strategy for single-pass 64-frame, 512px video generation. We also demonstrate the effectiveness of joint image-video training for video try-on, especially when video data is limited. Our qualitative and quantitative experiments show that our approach sets the new state-of-the-art for video virtual try-on. For additional results, visit our project page: https://johannakarras.github.io/Fashion-VDM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.00836", "title": "DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models", "authors": [ "Chengke Zou", "Xingang Guo", "Rui Yang", "Junyu Zhang", "Bin Hu", "Huan Zhang" ], "github": "", "project_page": "", "abstract": "The rapid advancements in Vision-Language Models (VLMs) have shown great potential in tackling mathematical reasoning tasks that involve visual context. Unlike humans who can reliably apply solution steps to similar problems with minor modifications, we found that SOTA VLMs like GPT-4o can consistently fail in these scenarios, revealing limitations in their mathematical reasoning capabilities. In this paper, we investigate the mathematical reasoning robustness in VLMs and evaluate how well these models perform under different variants of the same question, such as changes in visual numerical values or function graphs. While several vision-based math benchmarks have been developed to assess VLMs' problem-solving capabilities, these benchmarks contain only static sets of problems and cannot easily evaluate mathematical reasoning robustness. To fill this gap, we introduce DynaMath, a dynamic visual math benchmark designed for in-depth assessment of VLMs. DynaMath includes 501 high-quality, multi-topic seed questions, each represented as a Python program. Those programs are carefully designed and annotated to enable the automatic generation of a much larger set of concrete questions, including many different types of visual and textual variations. DynaMath allows us to evaluate the generalization ability of VLMs, by assessing their performance under varying input conditions of a seed question. We evaluated 14 SOTA VLMs with 5,010 generated concrete questions. Our results show that the worst-case model accuracy, defined as the percentage of correctly answered seed questions in all 10 variants, is significantly lower than the average-case accuracy. Our analysis emphasizes the need to study the robustness of VLMs' reasoning abilities, and DynaMath provides valuable insights to guide the development of more reliable models for mathematical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.00918", "title": "LIBMoE: A Library for comprehensive benchmarking Mixture of Experts in Large Language Models", "authors": [ "Nam V. Nguyen", "Thong T. Doan", "Luong Tran", "Van Nguyen", "Quang Pham" ], "github": "https://github.com/Fsoft-AIC/LibMoE", "project_page": "", "abstract": "Mixture of Experts (MoEs) plays an important role in the development of more efficient and effective large language models (LLMs). Due to the enormous resource requirements, studying large scale MoE algorithms remain in-accessible to many researchers. This work develops LibMoE, a comprehensive and modular framework to streamline the research, training, and evaluation of MoE algorithms. Built upon three core principles: (i) modular design, (ii) efficient training; (iii) comprehensive evaluation, LibMoE brings MoE in LLMs more accessible to a wide range of researchers by standardizing the training and evaluation pipelines. Using LibMoE, we extensively benchmarked five state-of-the-art MoE algorithms over three different LLMs and 11 datasets under the zero-shot setting. The results show that despite the unique characteristics, all MoE algorithms perform roughly similar when averaged across a wide range of tasks. With the modular design and extensive evaluation, we believe LibMoE will be invaluable for researchers to make meaningful progress towards the next generation of MoE and LLMs. Project page: https://fsoft-aic.github.io/fsoft-LibMoE.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Fsoft-AIC/LibMoE", "github_api_added_by": "auto" }, { "date": "2024-11-05", "arxiv_id": "2411.02385", "title": "How Far is Video Generation from World Model: A Physical Law Perspective", "authors": [ "Bingyi Kang", "Yang Yue", "Rui Lu", "Zhijie Lin", "Yang Zhao", "Kaixin Wang", "Gao Huang", "Jiashi Feng" ], "github": "", "project_page": "", "abstract": "OpenAI's Sora highlights the potential of video generation for developing world models that adhere to fundamental physical laws. However, the ability of video generation models to discover such laws purely from visual data without human priors can be questioned. A world model learning the true law should give predictions robust to nuances and correctly extrapolate on unseen scenarios. In this work, we evaluate across three key scenarios: in-distribution, out-of-distribution, and combinatorial generalization. We developed a 2D simulation testbed for object movement and collisions to generate videos deterministically governed by one or more classical mechanics laws. This provides an unlimited supply of data for large-scale experimentation and enables quantitative evaluation of whether the generated videos adhere to physical laws. We trained diffusion-based video generation models to predict object movements based on initial frames. Our scaling experiments show perfect generalization within the distribution, measurable scaling behavior for combinatorial generalization, but failure in out-of-distribution scenarios. Further experiments reveal two key insights about the generalization mechanisms of these models: (1) the models fail to abstract general physical rules and instead exhibit \"case-based\" generalization behavior, i.e., mimicking the closest training example; (2) when generalizing to new cases, models are observed to prioritize different factors when referencing training data: color > size > velocity > shape. Our study suggests that scaling alone is insufficient for video generation models to uncover fundamental physical laws, despite its role in Sora's broader success. See our project page at https://phyworld.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.02397", "title": "Adaptive Caching for Faster Video Generation with Diffusion Transformers", "authors": [ "Kumara Kahatapitiya", "Haozhe Liu", "Sen He", "Ding Liu", "Menglin Jia", "Michael S. Ryoo", "Tian Xie" ], "github": "", "project_page": "", "abstract": "Generating temporally-consistent high-fidelity videos can be computationally expensive, especially over longer temporal spans. More-recent Diffusion Transformers (DiTs) -- despite making significant headway in this context -- have only heightened such challenges as they rely on larger models and heavier attention mechanisms, resulting in slower inference speeds. In this paper, we introduce a training-free method to accelerate video DiTs, termed Adaptive Caching (AdaCache), which is motivated by the fact that \"not all videos are created equal\": meaning, some videos require fewer denoising steps to attain a reasonable quality than others. Building on this, we not only cache computations through the diffusion process, but also devise a caching schedule tailored to each video generation, maximizing the quality-latency trade-off. We further introduce a Motion Regularization (MoReg) scheme to utilize video information within AdaCache, essentially controlling the compute allocation based on motion content. Altogether, our plug-and-play contributions grant significant inference speedups (e.g. up to 4.7x on Open-Sora 720p - 2s video generation) without sacrificing the generation quality, across multiple video DiT baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.00743", "title": "Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Models", "authors": [ "Aashiq Muhamed", "Mona Diab", "Virginia Smith" ], "github": "", "project_page": "", "abstract": "Understanding and mitigating the potential risks associated with foundation models (FMs) hinges on developing effective interpretability methods. Sparse Autoencoders (SAEs) have emerged as a promising tool for disentangling FM representations, but they struggle to capture rare, yet crucial concepts in the data. We introduce Specialized Sparse Autoencoders (SSAEs), designed to illuminate these elusive dark matter features by focusing on specific subdomains. We present a practical recipe for training SSAEs, demonstrating the efficacy of dense retrieval for data selection and the benefits of Tilted Empirical Risk Minimization as a training objective to improve concept recall. Our evaluation of SSAEs on standard metrics, such as downstream perplexity and L_0 sparsity, show that they effectively capture subdomain tail concepts, exceeding the capabilities of general-purpose SAEs. We showcase the practical utility of SSAEs in a case study on the Bias in Bios dataset, where SSAEs achieve a 12.5\\% increase in worst-group classification accuracy when applied to remove spurious gender information. SSAEs provide a powerful new lens for peering into the inner workings of FMs in subdomains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2410.24024", "title": "AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents", "authors": [ "Yifan Xu", "Xiao Liu", "Xueqiao Sun", "Siyi Cheng", "Hao Yu", "Hanyu Lai", "Shudan Zhang", "Dan Zhang", "Jie Tang", "Yuxiao Dong" ], "github": "https://github.com/THUDM/Android-Lab", "project_page": "", "abstract": "Autonomous agents have become increasingly important for interacting with the real world. Android agents, in particular, have been recently a frequently-mentioned interaction method. However, existing studies for training and evaluating Android agents lack systematic research on both open-source and closed-source models. In this work, we propose AndroidLab as a systematic Android agent framework. It includes an operation environment with different modalities, action space, and a reproducible benchmark. It supports both large language models (LLMs) and multimodal models (LMMs) in the same action space. AndroidLab benchmark includes predefined Android virtual devices and 138 tasks across nine apps built on these devices. By using the AndroidLab environment, we develop an Android Instruction dataset and train six open-source LLMs and LMMs, lifting the average success rates from 4.59% to 21.50% for LLMs and from 1.93% to 13.28% for LMMs. AndroidLab is open-sourced and publicly available at https://github.com/THUDM/Android-Lab.", "github_manual": "", "github_regex": "https://github.com/THUDM/Android-Lab", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.02337", "title": "WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning", "authors": [ "Zehan Qi", "Xiao Liu", "Iat Long Iong", "Hanyu Lai", "Xueqiao Sun", "Xinyue Yang", "Jiadai Sun", "Yu Yang", "Shuntian Yao", "Tianjie Zhang", "Wei Xu", "Jie Tang", "Yuxiao Dong" ], "github": "https://github.com/THUDM/WebRL", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable potential as autonomous agents, particularly in web-based tasks. However, existing LLM web agents heavily rely on expensive proprietary LLM APIs, while open LLMs lack the necessary decision-making capabilities. This paper introduces WebRL, a self-evolving online curriculum reinforcement learning framework designed to train high-performance web agents using open LLMs. WebRL addresses three key challenges in building LLM web agents, including the scarcity of training tasks, sparse feedback signals, and policy distribution drift in online learning. Specifically, WebRL incorporates 1) a self-evolving curriculum that generates new tasks from unsuccessful attempts, 2) a robust outcome-supervised reward model (ORM), and 3) adaptive reinforcement learning strategies to ensure consistent improvements. We apply WebRL to transform open Llama-3.1 and GLM-4 models into proficient web agents. On WebArena-Lite, WebRL improves the success rate of Llama-3.1-8B from 4.8% to 42.4%, and from 6.1% to 43% for GLM-4-9B. These open models significantly surpass the performance of GPT-4-Turbo (17.6%) and GPT-4o (13.9%) and outperform previous state-of-the-art web agents trained on open LLMs (AutoWebGLM, 18.2%). Our findings demonstrate WebRL's effectiveness in bridging the gap between open and proprietary LLM-based web agents, paving the way for more accessible and powerful autonomous web interaction systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUDM/WebRL", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.02335", "title": "Sparsing Law: Towards Large Language Models with Greater Activation Sparsity", "authors": [ "Yuqi Luo", "Chenyang Song", "Xu Han", "Yingfa Chen", "Chaojun Xiao", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/thunlp/SparsingLaw", "project_page": "", "abstract": "Activation sparsity denotes the existence of substantial weakly-contributed elements within activation outputs that can be eliminated, benefiting many important applications concerned with large language models (LLMs). Although promoting greater activation sparsity within LLMs deserves deep studies, existing works lack comprehensive and quantitative research on the correlation between activation sparsity and potentially influential factors. In this paper, we present a comprehensive study on the quantitative scaling properties and influential factors of the activation sparsity within decoder-only Transformer-based LLMs. Specifically, we propose PPL-p% sparsity, a precise and performance-aware activation sparsity metric that is applicable to any activation function. Through extensive experiments, we find several important phenomena. Firstly, different activation functions exhibit comparable performance but opposite training-time sparsity trends. The activation ratio (i.e., 1-sparsity ratio) evolves as a convergent increasing power-law and decreasing logspace power-law with the amount of training data for SiLU-activated and ReLU-activated LLMs, respectively. These demonstrate that ReLU is more efficient as the activation function than SiLU and can leverage more training data to improve activation sparsity. Secondly, the activation ratio linearly increases with the width-depth ratio below a certain bottleneck point, indicating the potential advantage of a deeper architecture at a fixed parameter scale. Finally, at similar width-depth ratios, we surprisingly find that the limit value of activation sparsity varies weakly with the parameter scale, i.e., the activation patterns within LLMs are insensitive to the parameter scale. These empirical laws towards LLMs with greater activation sparsity have important implications for making LLMs more efficient and interpretable.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/thunlp/SparsingLaw", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.02395", "title": "Training-free Regional Prompting for Diffusion Transformers", "authors": [ "Anthony Chen", "Jianjin Xu", "Wenzhao Zheng", "Gaole Dai", "Yida Wang", "Renrui Zhang", "Haofan Wang", "Shanghang Zhang" ], "github": "https://github.com/instantX-research/Regional-Prompting-FLUX", "project_page": "", "abstract": "Diffusion models have demonstrated excellent capabilities in text-to-image generation. Their semantic understanding (i.e., prompt following) ability has also been greatly improved with large language models (e.g., T5, Llama). However, existing models cannot perfectly handle long and complex text prompts, especially when the text prompts contain various objects with numerous attributes and interrelated spatial relationships. While many regional prompting methods have been proposed for UNet-based models (SD1.5, SDXL), but there are still no implementations based on the recent Diffusion Transformer (DiT) architecture, such as SD3 and FLUX.1.In this report, we propose and implement regional prompting for FLUX.1 based on attention manipulation, which enables DiT with fined-grained compositional text-to-image generation capability in a training-free manner. Code is available at https://github.com/antonioo-c/Regional-Prompting-FLUX.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/instantX-research/Regional-Prompting-FLUX", "github_api_added_by": "user" }, { "date": "2024-11-05", "arxiv_id": "2411.02319", "title": "GenXD: Generating Any 3D and 4D Scenes", "authors": [ "Yuyang Zhao", "Chung-Ching Lin", "Kevin Lin", "Zhiwen Yan", "Linjie Li", "Zhengyuan Yang", "Jianfeng Wang", "Gim Hee Lee", "Lijuan Wang" ], "github": "", "project_page": "", "abstract": "Recent developments in 2D visual generation have been remarkably successful. However, 3D and 4D generation remain challenging in real-world applications due to the lack of large-scale 4D data and effective model design. In this paper, we propose to jointly investigate general 3D and 4D generation by leveraging camera and object movements commonly observed in daily life. Due to the lack of real-world 4D data in the community, we first propose a data curation pipeline to obtain camera poses and object motion strength from videos. Based on this pipeline, we introduce a large-scale real-world 4D scene dataset: CamVid-30K. By leveraging all the 3D and 4D data, we develop our framework, GenXD, which allows us to produce any 3D or 4D scene. We propose multiview-temporal modules, which disentangle camera and object movements, to seamlessly learn from both 3D and 4D data. Additionally, GenXD employs masked latent conditions to support a variety of conditioning views. GenXD can generate videos that follow the camera trajectory as well as consistent 3D views that can be lifted into 3D representations. We perform extensive evaluations across various real-world and synthetic datasets, demonstrating GenXD's effectiveness and versatility compared to previous methods in 3D and 4D generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.02265", "title": "Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent", "authors": [ "Xingwu Sun", "Yanfeng Chen", "Yiqing Huang", "Ruobing Xie", "Jiaqi Zhu", "Kai Zhang", "Shuaipeng Li", "Zhen Yang", "Jonny Han", "Xiaobo Shu", "Jiahao Bu", "Zhongzhi Chen", "Xuemeng Huang", "Fengzong Lian", "Saiyong Yang", "Jianfeng Yan", "Yuyuan Zeng", "Xiaoqin Ren", "Chao Yu", "Lulu Wu", "Yue Mao", "Tao Yang", "Suncong Zheng", "Kan Wu", "Dian Jiao", "Jinbao Xue", "Xipeng Zhang", "Decheng Wu", "Kai Liu", "Dengpeng Wu", "Guanghui Xu", "Shaohua Chen", "Shuang Chen", "Xiao Feng", "Yigeng Hong", "Junqiang Zheng", "Chengcheng Xu", "Zongwei Li", "Xiong Kuang", "Jianglu Hu", "Yiqi Chen", "Yuchi Deng", "Guiyang Li", "Ao Liu", "Chenchen Zhang", "Shihui Hu", "Zilong Zhao", "Zifan Wu", "Yao Ding", "Weichao Wang", "Han Liu", "Roberts Wang", "Hao Fei", "Peijie She", "Ze Zhao", "Xun Cao", "Hai Wang", "Fusheng Xiang", "Mengyuan Huang", "Zhiyuan Xiong", "Bin Hu", "Xuebin Hou", "Lei Jiang", "Jiajia Wu", "Yaping Deng", "Yi Shen", "Qian Wang", "Weijie Liu", "Jie Liu", "Meng Chen", "Liang Dong", "Weiwen Jia", "Hu Chen", "Feifei Liu", "Rui Yuan", "Huilin Xu", "Zhenxiang Yan", "Tengfei Cao", "Zhichao Hu", "Xinhua Feng", "Dong Du", "Tinghao She", "Yangyu Tao", "Feng Zhang", "Jianchen Zhu", "Chengzhong Xu", "Xirui Li", "Chong Zha", "Wen Ouyang", "Yinben Xia", "Xiang Li", "Zekun He", "Rongpeng Chen", "Jiawei Song", "Ruibin Chen", "Fan Jiang", "Chongqing Zhao", "Bo Wang", "Hao Gong", "Rong Gan", "Winston Hu", "Zhanhui Kang", "Yong Yang", "Yuhong Liu", "Di Wang", "Jie Jiang" ], "github": "https://github.com/Tencent/Hunyuan-Large", "project_page": "", "abstract": "In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We conduct a thorough evaluation of Hunyuan-Large's superior performance across various benchmarks including language understanding and generation, logical reasoning, mathematical problem-solving, coding, long-context, and aggregated tasks, where it outperforms LLama3.1-70B and exhibits comparable performance when compared to the significantly larger LLama3.1-405B model. Key practice of Hunyuan-Large include large-scale synthetic data that is orders larger than in previous literature, a mixed expert routing strategy, a key-value cache compression technique, and an expert-specific learning rate strategy. Additionally, we also investigate the scaling laws and learning rate schedule of mixture of experts models, providing valuable insights and guidances for future model development and optimization. The code and checkpoints of Hunyuan-Large are released to facilitate future innovations and applications. Codes: https://github.com/Tencent/Hunyuan-Large Models: https://huggingface.co/tencent/Tencent-Hunyuan-Large", "github_manual": "", "github_regex": "https://github.com/Tencent/Hunyuan-Large", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.01747", "title": "DynaSaur: Large Language Agents Beyond Predefined Actions", "authors": [ "Dang Nguyen", "Viet Dac Lai", "Seunghyun Yoon", "Ryan A. Rossi", "Handong Zhao", "Ruiyi Zhang", "Puneet Mathur", "Nedim Lipka", "Yu Wang", "Trung Bui", "Franck Dernoncourt", "Tianyi Zhou" ], "github": "https://github.com/adobe-research/dynasaur", "project_page": "", "abstract": "Existing LLM agent systems typically select actions from a fixed and predefined set at every step. While this approach is effective in closed, narrowly-scoped environments, we argue that it presents two major challenges when deploying LLM agents in real-world scenarios: (1) selecting from a fixed set of actions significantly restricts the planning and acting capabilities of LLM agents, and (2) this approach requires substantial human effort to enumerate and implement all possible actions, which becomes impractical in complex environments with a vast number of potential actions. In this work, we propose an LLM agent framework that enables the dynamic creation and composition of actions in an online manner. In this framework, the agent interacts with the environment by generating and executing programs written in a general-purpose programming language at each step. Furthermore, generated actions are accumulated over time for future reuse. Our extensive experiments on the GAIA benchmark demonstrate that this framework offers significantly greater flexibility and outperforms previous methods. Notably, it allows an LLM agent to recover in scenarios where no relevant action exists in the predefined set or when existing actions fail due to unforeseen edge cases. At the time of writing, we hold the top position on the GAIA public leaderboard. Our code can be found in https://github.com/adobe-research/dynasaur{https://github.com/adobe-research/dynasaur}.", "github_manual": "", "github_regex": "https://github.com/adobe-research/dynasaur", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.00860", "title": "Survey of Cultural Awareness in Language Models: Text and Beyond", "authors": [ "Siddhesh Pawar", "Junyeong Park", "Jiho Jin", "Arnav Arora", "Junho Myung", "Srishti Yadav", "Faiz Ghifari Haznitrama", "Inhwa Song", "Alice Oh", "Isabelle Augenstein" ], "github": "https://github.com/siddheshih/culture-awareness-llms", "project_page": "", "abstract": "Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ensure inclusivity. Culture has been widely studied in psychology and anthropology, and there has been a recent surge in research on making LLMs more culturally inclusive in LLMs that goes beyond multilinguality and builds on findings from psychology and anthropology. In this paper, we survey efforts towards incorporating cultural awareness into text-based and multimodal LLMs. We start by defining cultural awareness in LLMs, taking the definitions of culture from anthropology and psychology as a point of departure. We then examine methodologies adopted for creating cross-cultural datasets, strategies for cultural inclusion in downstream tasks, and methodologies that have been used for benchmarking cultural awareness in LLMs. Further, we discuss the ethical implications of cultural alignment, the role of Human-Computer Interaction in driving cultural inclusion in LLMs, and the role of cultural alignment in driving social science research. We finally provide pointers to future research based on our findings about gaps in the literature.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/siddheshih/culture-awareness-llms", "github_api_added_by": "auto" }, { "date": "2024-11-05", "arxiv_id": "2411.01192", "title": "Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks", "authors": [ "Gagan Bhatia", "El Moatez Billah Nagoudi", "Abdellah El Mekki", "Fakhraddin Alwajih", "Muhammad Abdul-Mageed" ], "github": "https://github.com/UBC-NLP/swan", "project_page": "", "abstract": "We introduce Swan, a family of embedding models centred around the Arabic language, addressing both small-scale and large-scale use cases. Swan includes two variants: Swan-Small, based on ARBERTv2, and Swan-Large, built on ArMistral, a pretrained Arabic large language model. To evaluate these models, we propose ArabicMTEB, a comprehensive benchmark suite that assesses cross-lingual, multi-dialectal, multi-domain, and multi-cultural Arabic text embedding performance, covering eight diverse tasks and spanning 94 datasets. Swan-Large achieves state-of-the-art results, outperforming Multilingual-E5-large in most Arabic tasks, while the Swan-Small consistently surpasses Multilingual-E5 base. Our extensive evaluations demonstrate that Swan models are both dialectally and culturally aware, excelling across various Arabic domains while offering significant monetary efficiency. This work significantly advances the field of Arabic language modelling and provides valuable resources for future research and applications in Arabic natural language processing. Our models and benchmark will be made publicly accessible for research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/UBC-NLP/swan", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.02355", "title": "\"Give Me BF16 or Give Me Death\"? Accuracy-Performance Trade-Offs in LLM Quantization", "authors": [ "Eldar Kurtic", "Alexandre Marques", "Shubhra Pandit", "Mark Kurtz", "Dan Alistarh" ], "github": "", "project_page": "", "abstract": "Despite the popularity of large language model (LLM) quantization for inference acceleration, significant uncertainty remains regarding the accuracy-performance trade-offs associated with various quantization formats. We present a comprehensive empirical study of quantized accuracy, evaluating popular quantization formats (FP8, INT8, INT4) across academic benchmarks and real-world tasks, on the entire Llama-3.1 model family. Additionally, our study examines the difference in text generated by quantized models versus their uncompressed counterparts. Beyond benchmarks, we also present a couple of quantization improvements which allowed us to obtain state-of-the-art accuracy recovery results. Our investigation, encompassing over 500,000 individual evaluations, yields several key findings: (1) FP8 weight and activation quantization (W8A8-FP) is lossless across all model scales, (2) INT8 weight and activation quantization (W8A8-INT), when properly tuned, incurs surprisingly low 1-3% accuracy degradation, and (3) INT4 weight-only quantization (W4A16-INT) is competitive with 8-bit integer weight and activation quantization. To address the question of the \"best\" format for a given deployment environment, we conduct inference performance analysis using the popular open-source vLLM framework on various GPU architectures. We find that W4A16 offers the best cost-efficiency for synchronous deployments, and for asynchronous deployment on mid-tier GPUs. At the same time, W8A8 formats excel in asynchronous \"continuous batching\" deployment of mid- and large-size models on high-end GPUs. Our results provide a set of practical guidelines for deploying quantized LLMs across scales and performance requirements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.02327", "title": "PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance", "authors": [ "Ruyang Liu", "Haoran Tang", "Haibo Liu", "Yixiao Ge", "Ying Shan", "Chen Li", "Jiankun Yang" ], "github": "https://github.com/farewellthree/PPLLaVA", "project_page": "", "abstract": "The past year has witnessed the significant advancement of video-based large language models. However, the challenge of developing a unified model for both short and long video understanding remains unresolved. Most existing video LLMs cannot handle hour-long videos, while methods custom for long videos tend to be ineffective for shorter videos and images. In this paper, we identify the key issue as the redundant content in videos. To address this, we propose a novel pooling strategy that simultaneously achieves token compression and instruction-aware visual feature aggregation. Our model is termed Prompt-guided Pooling LLaVA, or PPLLaVA for short. Specifically, PPLLaVA consists of three core components: the CLIP-based visual-prompt alignment that extracts visual information relevant to the user's instructions, the prompt-guided pooling that compresses the visual sequence to arbitrary scales using convolution-style pooling, and the clip context extension designed for lengthy prompt common in visual dialogue. Moreover, our codebase also integrates the most advanced video Direct Preference Optimization (DPO) and visual interleave training. Extensive experiments have validated the performance of our model. With superior throughput and only 1024 visual context, PPLLaVA achieves better results on image benchmarks as a video LLM, while achieving state-of-the-art performance across various video benchmarks, excelling in tasks ranging from caption generation to multiple-choice questions, and handling video lengths from seconds to hours. Codes have been available at https://github.com/farewellthree/PPLLaVA.", "github_manual": "", "github_regex": "https://github.com/farewellthree/PPLLaVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.02336", "title": "MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D", "authors": [ "Wei Cheng", "Juncheng Mu", "Xianfang Zeng", "Xin Chen", "Anqi Pang", "Chi Zhang", "Zhibin Wang", "Bin Fu", "Gang Yu", "Ziwei Liu", "Liang Pan" ], "github": "", "project_page": "", "abstract": "Texturing is a crucial step in the 3D asset production workflow, which enhances the visual appeal and diversity of 3D assets. Despite recent advancements in Text-to-Texture (T2T) generation, existing methods often yield subpar results, primarily due to local discontinuities, inconsistencies across multiple views, and their heavy dependence on UV unwrapping outcomes. To tackle these challenges, we propose a novel generation-refinement 3D texturing framework called MVPaint, which can generate high-resolution, seamless textures while emphasizing multi-view consistency. MVPaint mainly consists of three key modules. 1) Synchronized Multi-view Generation (SMG). Given a 3D mesh model, MVPaint first simultaneously generates multi-view images by employing an SMG model, which leads to coarse texturing results with unpainted parts due to missing observations. 2) Spatial-aware 3D Inpainting (S3I). To ensure complete 3D texturing, we introduce the S3I method, specifically designed to effectively texture previously unobserved areas. 3) UV Refinement (UVR). Furthermore, MVPaint employs a UVR module to improve the texture quality in the UV space, which first performs a UV-space Super-Resolution, followed by a Spatial-aware Seam-Smoothing algorithm for revising spatial texturing discontinuities caused by UV unwrapping. Moreover, we establish two T2T evaluation benchmarks: the Objaverse T2T benchmark and the GSO T2T benchmark, based on selected high-quality 3D meshes from the Objaverse dataset and the entire GSO dataset, respectively. Extensive experimental results demonstrate that MVPaint surpasses existing state-of-the-art methods. Notably, MVPaint could generate high-fidelity textures with minimal Janus issues and highly enhanced cross-view consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.00492", "title": "Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models", "authors": [ "Do Xuan Long", "Duong Ngoc Yen", "Anh Tuan Luu", "Kenji Kawaguchi", "Min-Yen Kan", "Nancy F. Chen" ], "github": "https://github.com/dxlong2000/multi-expert-prompting", "project_page": "", "abstract": "We present Multi-expert Prompting, a novel enhancement of ExpertPrompting (Xu et al., 2023), designed to improve the large language model (LLM) generation. Specifically, it guides an LLM to fulfill an input instruction by simulating multiple experts, aggregating their responses, and selecting the best among individual and aggregated responses. This process is performed in a single chain of thoughts through our seven carefully designed subtasks derived from the Nominal Group Technique (Ven and Delbecq, 1974), a well-established decision-making framework. Our evaluations demonstrate that Multi-expert Prompting significantly outperforms ExpertPrompting and comparable baselines in enhancing the truthfulness, factuality, informativeness, and usefulness of responses while reducing toxicity and hurtfulness. It further achieves state-of-the-art truthfulness by outperforming the best baseline by 8.69% with ChatGPT. Multi-expert Prompting is efficient, explainable, and highly adaptable to diverse scenarios, eliminating the need for manual prompt construction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dxlong2000/multi-expert-prompting", "github_api_added_by": "auto" }, { "date": "2024-11-05", "arxiv_id": "2411.01106", "title": "LoRA-Contextualizing Adaptation of Large Multimodal Models for Long Document Understanding", "authors": [ "Jian Chen", "Ruiyi Zhang", "Yufan Zhou", "Tong Yu", "Franck Dernoncourt", "Jiuxiang Gu", "Ryan A. Rossi", "Changyou Chen", "Tong Sun" ], "github": "", "project_page": "", "abstract": "Large multimodal models (LMMs) have recently shown great progress in text-rich image understanding, yet they still struggle with complex, multi-page, visually-rich documents. Traditional methods using document parsers for retrieval-augmented generation suffer from performance and efficiency limitations, while directly presenting all pages to LMMs leads to inefficiencies, especially with lengthy documents. In this work, we present a novel framework named LoRA-Contextualizing Adaptation of Large multimodal models (LoCAL), which broadens the capabilities of any LMM to support long-document understanding. We demonstrate that LMMs can effectively serve as multimodal retrievers, fetching relevant pages to answer user questions based on these pages. LoCAL is implemented with two specific LMM adapters: one for evidence page retrieval and another for question answering. Empirical results show state-of-the-art performance on public benchmarks, demonstrating the effectiveness of LoCAL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.00359", "title": "Constrained Diffusion Implicit Models", "authors": [ "Vivek Jayaram", "Ira Kemelmacher-Shlizerman", "Steven M. Seitz", "John Thickstun" ], "github": "", "project_page": "", "abstract": "This paper describes an efficient algorithm for solving noisy linear inverse problems using pretrained diffusion models. Extending the paradigm of denoising diffusion implicit models (DDIM), we propose constrained diffusion implicit models (CDIM) that modify the diffusion updates to enforce a constraint upon the final output. For noiseless inverse problems, CDIM exactly satisfies the constraints; in the noisy case, we generalize CDIM to satisfy an exact constraint on the residual distribution of the noise. Experiments across a variety of tasks and metrics show strong performance of CDIM, with analogous inference acceleration to unconstrained DDIM: 10 to 50 times faster than previous conditional diffusion methods. We demonstrate the versatility of our approach on many problems including super-resolution, denoising, inpainting, deblurring, and 3D point cloud reconstruction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.00785", "title": "IGOR: Image-GOal Representations are the Atomic Control Units for Foundation Models in Embodied AI", "authors": [ "Xiaoyu Chen", "Junliang Guo", "Tianyu He", "Chuheng Zhang", "Pushi Zhang", "Derek Cathera Yang", "Li Zhao", "Jiang Bian" ], "github": "", "project_page": "", "abstract": "We introduce Image-GOal Representations (IGOR), aiming to learn a unified, semantically consistent action space across human and various robots. Through this unified latent action space, IGOR enables knowledge transfer among large-scale robot and human activity data. We achieve this by compressing visual changes between an initial image and its goal state into latent actions. IGOR allows us to generate latent action labels for internet-scale video data. This unified latent action space enables the training of foundation policy and world models across a wide variety of tasks performed by both robots and humans. We demonstrate that: (1) IGOR learns a semantically consistent action space for both human and robots, characterizing various possible motions of objects representing the physical interaction knowledge; (2) IGOR can \"migrate\" the movements of the object in the one video to other videos, even across human and robots, by jointly using the latent action model and world model; (3) IGOR can learn to align latent actions with natural language through the foundation policy model, and integrate latent actions with a low-level policy model to achieve effective robot control. We believe IGOR opens new possibilities for human-to-robot knowledge transfer and control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-05", "arxiv_id": "2411.02394", "title": "AutoVFX: Physically Realistic Video Editing from Natural Language Instructions", "authors": [ "Hao-Yu Hsu", "Zhi-Hao Lin", "Albert Zhai", "Hongchi Xia", "Shenlong Wang" ], "github": "https://github.com/haoyuhsu/autovfx", "project_page": "", "abstract": "Modern visual effects (VFX) software has made it possible for skilled artists to create imagery of virtually anything. However, the creation process remains laborious, complex, and largely inaccessible to everyday users. In this work, we present AutoVFX, a framework that automatically creates realistic and dynamic VFX videos from a single video and natural language instructions. By carefully integrating neural scene modeling, LLM-based code generation, and physical simulation, AutoVFX is able to provide physically-grounded, photorealistic editing effects that can be controlled directly using natural language instructions. We conduct extensive experiments to validate AutoVFX's efficacy across a diverse spectrum of videos and instructions. Quantitative and qualitative results suggest that AutoVFX outperforms all competing methods by a large margin in generative quality, instruction alignment, editing versatility, and physical plausibility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/haoyuhsu/autovfx", "github_api_added_by": "auto" }, { "date": "2024-11-05", "arxiv_id": "2411.01798", "title": "SALSA: Soup-based Alignment Learning for Stronger Adaptation in RLHF", "authors": [ "Atoosa Chegini", "Hamid Kazemi", "Iman Mirzadeh", "Dong Yin", "Maxwell Horton", "Moin Nabi", "Mehrdad Farajtabar", "Keivan Alizadeh" ], "github": "", "project_page": "", "abstract": "In Large Language Model (LLM) development, Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning models with human values and preferences. RLHF traditionally relies on the Kullback-Leibler (KL) divergence between the current policy and a frozen initial policy as a reference, which is added as a penalty in policy optimization algorithms like Proximal Policy Optimization (PPO). While this constraint prevents models from deviating too far from the initial checkpoint, it limits exploration of the reward landscape, reducing the model's ability to discover higher-quality solutions. As a result, policy optimization is often trapped in a narrow region of the parameter space, leading to suboptimal alignment and performance. This paper presents SALSA (Soup-based Alignment Learning for Stronger Adaptation), a novel approach designed to overcome these limitations by creating a more flexible and better located reference model through weight-space averaging of two independent supervised fine-tuned (SFT) models. This model soup allows for larger deviation in KL divergence and exploring a promising region of the solution space without sacrificing stability. By leveraging this more robust reference model, SALSA fosters better exploration, achieving higher rewards and improving model robustness, out-of-distribution generalization, and performance. We validate the effectiveness of SALSA through extensive experiments on popular open models (Llama2-7B, Mistral-7B, and Gemma-2B) across various benchmarks (MT-Bench, Arena-Hard, UltraFeedback), where it consistently surpasses PPO by fostering deeper exploration and achieving superior alignment in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-06", "arxiv_id": "2411.02959", "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems", "authors": [ "Jiejun Tan", "Zhicheng Dou", "Wen Wang", "Mang Wang", "Weipeng Chen", "Ji-Rong Wen" ], "github": "https://github.com/plageon/HtmlRAG", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs. The Web is a major source of external knowledge used in RAG systems, and many commercial systems such as ChatGPT and Perplexity have used Web search engines as their major retrieval systems. Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML, such as headings and table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML. However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. To address this issue, we propose HTML cleaning, compression, and pruning strategies, to shorten the HTML while minimizing the loss of information. Specifically, we design a two-step block-tree-based pruning method that prunes useless HTML blocks and keeps only the relevant part of the HTML. Experiments on six QA datasets confirm the superiority of using HTML in RAG systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/plageon/HtmlRAG", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-06", "arxiv_id": "2411.02359", "title": "DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution", "authors": [ "Yang Yue", "Yulin Wang", "Bingyi Kang", "Yizeng Han", "Shenzhi Wang", "Shiji Song", "Jiashi Feng", "Gao Huang" ], "github": "https://github.com/yueyang130/DeeR-VLA", "project_page": "", "abstract": "MLLMs have demonstrated remarkable comprehension and reasoning capabilities with complex language and visual data. These advances have spurred the vision of establishing a generalist robotic MLLM proficient in understanding complex human instructions and accomplishing various embodied tasks. However, developing MLLMs for real-world robots is challenging due to the typically limited computation and memory capacities available on robotic platforms. In contrast, the inference of MLLMs involves storing billions of parameters and performing tremendous computation, imposing significant hardware demands. In our paper, we propose a Dynamic Early-Exit Framework for Robotic Vision-Language-Action Model (DeeR-VLA, or simply DeeR) that automatically adjusts the size of the activated MLLM based on each situation at hand. The approach leverages a multi-exit architecture in MLLMs, which allows the model to terminate processing once a proper size of the model has been activated for a specific situation, thus avoiding further redundant computation. Additionally, we develop novel algorithms that establish early-termination criteria for DeeR, conditioned on predefined demands such as average computational cost (i.e., power consumption), as well as peak computational consumption (i.e., latency) and GPU memory usage. These enhancements ensure that DeeR operates efficiently under varying resource constraints while maintaining competitive performance. On the CALVIN robot manipulation benchmark, DeeR demonstrates significant reductions in computational costs of LLM by 5.2-6.5x and GPU memory of LLM by 2-6x without compromising performance. Code and checkpoints are available at https://github.com/yueyang130/DeeR-VLA.", "github_manual": "", "github_regex": "https://github.com/yueyang130/DeeR-VLA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-06", "arxiv_id": "2411.00871", "title": "LLaMo: Large Language Model-based Molecular Graph Assistant", "authors": [ "Jinyoung Park", "Minseong Bae", "Dohwan Ko", "Hyunwoo J. Kim" ], "github": "https://github.com/mlvlab/LLaMo", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable generalization and instruction-following capabilities with instruction tuning. The advancements in LLMs and instruction tuning have led to the development of Large Vision-Language Models (LVLMs). However, the competency of the LLMs and instruction tuning have been less explored in the molecular domain. Thus, we propose LLaMo: Large Language Model-based Molecular graph assistant, which is an end-to-end trained large molecular graph-language model. To bridge the discrepancy between the language and graph modalities, we present the multi-level graph projector that transforms graph representations into graph tokens by abstracting the output representations of each GNN layer and motif representations with the cross-attention mechanism. We also introduce machine-generated molecular graph instruction data to instruction-tune the large molecular graph-language model for general-purpose molecule and language understanding. Our extensive experiments demonstrate that LLaMo shows the best performance on diverse tasks, such as molecular description generation, property prediction, and IUPAC name prediction. The code of LLaMo is available at https://github.com/mlvlab/LLaMo.", "github_manual": "", "github_regex": "https://github.com/mlvlab/LLaMo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-06", "arxiv_id": "2411.02657", "title": "Zebra-Llama: A Context-Aware Large Language Model for Democratizing Rare Disease Knowledge", "authors": [ "Karthik Soman", "Andrew Langdon", "Catalina Villouta", "Chinmay Agrawal", "Lashaw Salta", "Braian Peetoom", "Gianmarco Bellucci", "Orion J Buske" ], "github": "https://github.com/karthiksoman/zebra-Llama", "project_page": "", "abstract": "Rare diseases present unique challenges in healthcare, often suffering from delayed diagnosis and fragmented information landscapes. The scarcity of reliable knowledge in these conditions poses a distinct challenge for Large Language Models (LLMs) in supporting clinical management and delivering precise patient information underscoring the need for focused training on these 'zebra' cases. We present Zebra-Llama, a specialized context-aware language model with high precision Retrieval Augmented Generation (RAG) capability, focusing on Ehlers-Danlos Syndrome (EDS) as our case study. EDS, affecting 1 in 5,000 individuals, exemplifies the complexities of rare diseases with its diverse symptoms, multiple subtypes, and evolving diagnostic criteria. By implementing a novel context-aware fine-tuning methodology trained on questions derived from medical literature, patient experiences, and clinical resources, along with expertly curated responses, Zebra-Llama demonstrates unprecedented capabilities in handling EDS-related queries. On a test set of real-world questions collected from EDS patients and clinicians, medical experts evaluated the responses generated by both models, revealing Zebra-Llama's substantial improvements over base model (Llama 3.1-8B-Instruct) in thoroughness (77.5% vs. 70.1%), accuracy (83.0% vs. 78.8%), clarity (74.7% vs. 72.0%) and citation reliability (70.6% vs. 52.3%). Released as an open-source resource, Zebra-Llama not only provides more accessible and reliable EDS information but also establishes a framework for developing specialized AI solutions for other rare conditions. This work represents a crucial step towards democratizing expert-level knowledge in rare disease management, potentially transforming how healthcare providers and patients navigate the complex landscape of rare diseases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/karthiksoman/zebra-Llama", "github_api_added_by": "auto" }, { "date": "2024-11-06", "arxiv_id": "2411.01493", "title": "Sample-Efficient Alignment for LLMs", "authors": [ "Zichen Liu", "Changyu Chen", "Chao Du", "Wee Sun Lee", "Min Lin" ], "github": "https://github.com/sail-sg/oat", "project_page": "", "abstract": "We study methods for efficiently aligning large language models (LLMs) with human preferences given budgeted online feedback. We first formulate the LLM alignment problem in the frame of contextual dueling bandits. This formulation, subsuming recent paradigms such as online RLHF and online DPO, inherently quests for sample-efficient algorithms that incorporate online active exploration. Leveraging insights from bandit theory, we introduce a unified algorithm based on Thompson sampling and highlight its applications in two distinct LLM alignment scenarios. The practical agent that efficiently implements this algorithm, named SEA (Sample-Efficient Alignment), is empirically validated through extensive experiments across three model scales (1B, 2.8B, 6.9B) and three preference learning algorithms (DPO, IPO, SLiC). The results demonstrate that SEA achieves highly sample-efficient alignment with oracle's preferences, outperforming recent active exploration methods for LLMs. Additionally, we release the implementation of SEA together with an efficient codebase designed for online alignment of LLMs, aiming to accelerate future research in this field.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sail-sg/oat", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-06", "arxiv_id": "2411.01602", "title": "DreamPolish: Domain Score Distillation With Progressive Geometry Generation", "authors": [ "Yean Cheng", "Ziqi Cai", "Ming Ding", "Wendi Zheng", "Shiyu Huang", "Yuxiao Dong", "Jie Tang", "Boxin Shi" ], "github": "", "project_page": "", "abstract": "We introduce DreamPolish, a text-to-3D generation model that excels in producing refined geometry and high-quality textures. In the geometry construction phase, our approach leverages multiple neural representations to enhance the stability of the synthesis process. Instead of relying solely on a view-conditioned diffusion prior in the novel sampled views, which often leads to undesired artifacts in the geometric surface, we incorporate an additional normal estimator to polish the geometry details, conditioned on viewpoints with varying field-of-views. We propose to add a surface polishing stage with only a few training steps, which can effectively refine the artifacts attributed to limited guidance from previous stages and produce 3D objects with more desirable geometry. The key topic of texture generation using pretrained text-to-image models is to find a suitable domain in the vast latent distribution of these models that contains photorealistic and consistent renderings. In the texture generation phase, we introduce a novel score distillation objective, namely domain score distillation (DSD), to guide neural representations toward such a domain. We draw inspiration from the classifier-free guidance (CFG) in textconditioned image generation tasks and show that CFG and variational distribution guidance represent distinct aspects in gradient guidance and are both imperative domains for the enhancement of texture quality. Extensive experiments show our proposed model can produce 3D assets with polished surfaces and photorealistic textures, outperforming existing state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-06", "arxiv_id": "2410.23054", "title": "Controlling Language and Diffusion Models by Transporting Activations", "authors": [ "Pau Rodriguez", "Arno Blaas", "Michal Klein", "Luca Zappella", "Nicholas Apostoloff", "Marco Cuturi", "Xavier Suau" ], "github": "https://github.com/apple/ml-act", "project_page": "", "abstract": "The increasing capabilities of large generative models and their ever more widespread deployment have raised concerns about their reliability, safety, and potential misuse. To address these issues, recent works have proposed to control model generation by steering model activations in order to effectively induce or prevent the emergence of concepts or behaviors in the generated output. In this paper we introduce Activation Transport (AcT), a general framework to steer activations guided by optimal transport theory that generalizes many previous activation-steering works. AcT is modality-agnostic and provides fine-grained control over the model behavior with negligible computational overhead, while minimally impacting model abilities. We experimentally show the effectiveness and versatility of our approach by addressing key challenges in large language models (LLMs) and text-to-image diffusion models (T2Is). For LLMs, we show that AcT can effectively mitigate toxicity, induce arbitrary concepts, and increase their truthfulness. In T2Is, we show how AcT enables fine-grained style control and concept negation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apple/ml-act", "github_api_added_by": "auto" }, { "date": "2024-11-06", "arxiv_id": "2411.02844", "title": "Correlation of Object Detection Performance with Visual Saliency and Depth Estimation", "authors": [ "Matthias Bartolo", "Dylan Seychell" ], "github": "", "project_page": "", "abstract": "As object detection techniques continue to evolve, understanding their relationships with complementary visual tasks becomes crucial for optimising model architectures and computational resources. This paper investigates the correlations between object detection accuracy and two fundamental visual tasks: depth prediction and visual saliency prediction. Through comprehensive experiments using state-of-the-art models (DeepGaze IIE, Depth Anything, DPT-Large, and Itti's model) on COCO and Pascal VOC datasets, we find that visual saliency shows consistently stronger correlations with object detection accuracy (mArho up to 0.459 on Pascal VOC) compared to depth prediction (mArho up to 0.283). Our analysis reveals significant variations in these correlations across object categories, with larger objects showing correlation values up to three times higher than smaller objects. These findings suggest incorporating visual saliency features into object detection architectures could be more beneficial than depth information, particularly for specific object categories. The observed category-specific variations also provide insights for targeted feature engineering and dataset design improvements, potentially leading to more efficient and accurate object detection systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-06", "arxiv_id": "2411.03047", "title": "GarVerseLOD: High-Fidelity 3D Garment Reconstruction from a Single In-the-Wild Image using a Dataset with Levels of Details", "authors": [ "Zhongjin Luo", "Haolin Liu", "Chenghong Li", "Wanghao Du", "Zirong Jin", "Wanhu Sun", "Yinyu Nie", "Weikai Chen", "Xiaoguang Han" ], "github": "", "project_page": "", "abstract": "Neural implicit functions have brought impressive advances to the state-of-the-art of clothed human digitization from multiple or even single images. However, despite the progress, current arts still have difficulty generalizing to unseen images with complex cloth deformation and body poses. In this work, we present GarVerseLOD, a new dataset and framework that paves the way to achieving unprecedented robustness in high-fidelity 3D garment reconstruction from a single unconstrained image. Inspired by the recent success of large generative models, we believe that one key to addressing the generalization challenge lies in the quantity and quality of 3D garment data. Towards this end, GarVerseLOD collects 6,000 high-quality cloth models with fine-grained geometry details manually created by professional artists. In addition to the scale of training data, we observe that having disentangled granularities of geometry can play an important role in boosting the generalization capability and inference accuracy of the learned model. We hence craft GarVerseLOD as a hierarchical dataset with levels of details (LOD), spanning from detail-free stylized shape to pose-blended garment with pixel-aligned details. This allows us to make this highly under-constrained problem tractable by factorizing the inference into easier tasks, each narrowed down with smaller searching space. To ensure GarVerseLOD can generalize well to in-the-wild images, we propose a novel labeling paradigm based on conditional diffusion models to generate extensive paired images for each garment model with high photorealism. We evaluate our method on a massive amount of in-the-wild images. Experimental results demonstrate that GarVerseLOD can generate standalone garment pieces with significantly better quality than prior approaches. Project page: https://garverselod.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-06", "arxiv_id": "2411.03312", "title": "Inference Optimal VLMs Need Only One Visual Token but Larger Models", "authors": [ "Kevin Y. Li", "Sachin Goyal", "Joao D. Semedo", "J. Zico Kolter" ], "github": "https://github.com/locuslab/llava-token-compression", "project_page": "", "abstract": "Vision Language Models (VLMs) have demonstrated strong capabilities across various visual understanding and reasoning tasks. However, their real-world deployment is often constrained by high latency during inference due to substantial compute required to process the large number of input tokens (predominantly from the image) by the LLM. To reduce inference costs, one can either downsize the LLM or reduce the number of input image-tokens, the latter of which has been the focus of many recent works around token compression. However, it is unclear what the optimal trade-off is, as both the factors directly affect the VLM performance. We first characterize this optimal trade-off between the number of visual tokens and LLM parameters by establishing scaling laws that capture variations in performance with these two factors. Our results reveal a surprising trend: for visual reasoning tasks, the inference-optimal behavior in VLMs, i.e., minimum downstream error at any given fixed inference compute, is achieved when using the largest LLM that fits within the inference budget while minimizing visual token count - often to a single token. While the token reduction literature has mainly focused on maintaining base model performance by modestly reducing the token count (e.g., 5-10times), our results indicate that the compute-optimal inference regime requires operating under even higher token compression ratios. Based on these insights, we take some initial steps towards building approaches tailored for high token compression settings. Code is available at https://github.com/locuslab/llava-token-compression.", "github_manual": "", "github_regex": "https://github.com/locuslab/llava-token-compression", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-06", "arxiv_id": "2411.02393", "title": "Adaptive Length Image Tokenization via Recurrent Allocation", "authors": [ "Shivam Duggal", "Phillip Isola", "Antonio Torralba", "William T. Freeman" ], "github": "https://github.com/shivamduggal4/adaptive-length-tokenizer", "project_page": "", "abstract": "Current vision systems typically assign fixed-length representations to images, regardless of the information content. This contrasts with human intelligence - and even large language models - which allocate varying representational capacities based on entropy, context and familiarity. Inspired by this, we propose an approach to learn variable-length token representations for 2D images. Our encoder-decoder architecture recursively processes 2D image tokens, distilling them into 1D latent tokens over multiple iterations of recurrent rollouts. Each iteration refines the 2D tokens, updates the existing 1D latent tokens, and adaptively increases representational capacity by adding new tokens. This enables compression of images into a variable number of tokens, ranging from 32 to 256. We validate our tokenizer using reconstruction loss and FID metrics, demonstrating that token count aligns with image entropy, familiarity and downstream task requirements. Recurrent token processing with increasing representational capacity in each iteration shows signs of token specialization, revealing potential for object / part discovery.", "github_manual": "https://github.com/shivamduggal4/adaptive-length-tokenizer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-07", "arxiv_id": "2411.03562", "title": "Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level", "authors": [ "Antoine Grosnit", "Alexandre Maraval", "James Doran", "Giuseppe Paolo", "Albert Thomas", "Refinath Shahul Hameed Nabeezath Beevi", "Jonas Gonzalez", "Khyati Khandelwal", "Ignacio Iacobacci", "Abdelhakim Benechehab", "Hamza Cherkaoui", "Youssef Attia El-Hili", "Kun Shao", "Jianye Hao", "Jun Yao", "Balazs Kegl", "Haitham Bou-Ammar", "Jun Wang" ], "github": "", "project_page": "", "abstract": "We introduce Agent K v1.0, an end-to-end autonomous data science agent designed to automate, optimise, and generalise across diverse data science tasks. Fully automated, Agent K v1.0 manages the entire data science life cycle by learning from experience. It leverages a highly flexible structured reasoning framework to enable it to dynamically process memory in a nested structure, effectively learning from accumulated experience stored to handle complex reasoning tasks. It optimises long- and short-term memory by selectively storing and retrieving key information, guiding future decisions based on environmental rewards. This iterative approach allows it to refine decisions without fine-tuning or backpropagation, achieving continuous improvement through experiential learning. We evaluate our agent's apabilities using Kaggle competitions as a case study. Following a fully automated protocol, Agent K v1.0 systematically addresses complex and multimodal data science tasks, employing Bayesian optimisation for hyperparameter tuning and feature engineering. Our new evaluation framework rigorously assesses Agent K v1.0's end-to-end capabilities to generate and send submissions starting from a Kaggle competition URL. Results demonstrate that Agent K v1.0 achieves a 92.5\\% success rate across tasks, spanning tabular, computer vision, NLP, and multimodal domains. When benchmarking against 5,856 human Kaggle competitors by calculating Elo-MMR scores for each, Agent K v1.0 ranks in the top 38\\%, demonstrating an overall skill level comparable to Expert-level users. Notably, its Elo-MMR score falls between the first and third quartiles of scores achieved by human Grandmasters. Furthermore, our results indicate that Agent K v1.0 has reached a performance level equivalent to Kaggle Grandmaster, with a record of 6 gold, 3 silver, and 7 bronze medals, as defined by Kaggle's progression system.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-07", "arxiv_id": "2411.03823", "title": "Both Text and Images Leaked! A Systematic Analysis of Multimodal LLM Data Contamination", "authors": [ "Dingjie Song", "Sicheng Lai", "Shunian Chen", "Lichao Sun", "Benyou Wang" ], "github": "https://github.com/MLLM-Data-Contamination/MM-Detect", "project_page": "", "abstract": "The rapid progression of multimodal large language models (MLLMs) has demonstrated superior performance on various multimodal benchmarks. However, the issue of data contamination during training creates challenges in performance evaluation and comparison. While numerous methods exist for detecting dataset contamination in large language models (LLMs), they are less effective for MLLMs due to their various modalities and multiple training phases. In this study, we introduce a multimodal data contamination detection framework, MM-Detect, designed for MLLMs. Our experimental results indicate that MM-Detect is sensitive to varying degrees of contamination and can highlight significant performance improvements due to leakage of the training set of multimodal benchmarks. Furthermore, We also explore the possibility of contamination originating from the pre-training phase of LLMs used by MLLMs and the fine-tuning phase of MLLMs, offering new insights into the stages at which contamination may be introduced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MLLM-Data-Contamination/MM-Detect", "github_api_added_by": "auto" }, { "date": "2024-11-07", "arxiv_id": "2411.03884", "title": "Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models", "authors": [ "Zhijian Zhuo", "Ya Wang", "Yutao Zeng", "Xiaoqing Li", "Xun Zhou", "Jinwen Ma" ], "github": "https://github.com/BryceZhuo/PolyCom", "project_page": "", "abstract": "Transformers have found extensive applications across various domains due to the powerful fitting capabilities. This success can be partially attributed to their inherent nonlinearity. Thus, in addition to the ReLU function employed in the original transformer architecture, researchers have explored alternative modules such as GeLU and SwishGLU to enhance nonlinearity and thereby augment representational capacity. In this paper, we propose a novel category of polynomial composition activations (PolyCom), designed to optimize the dynamics of transformers. Theoretically, we provide a comprehensive mathematical analysis of PolyCom, highlighting its enhanced expressivity and efficacy relative to other activation functions. Notably, we demonstrate that networks incorporating PolyCom achieve the optimal approximation rate, indicating that PolyCom networks require minimal parameters to approximate general smooth functions in Sobolev spaces. We conduct empirical experiments on the pre-training configurations of large language models (LLMs), including both dense and sparse architectures. By substituting conventional activation functions with PolyCom, we enable LLMs to capture higher-order interactions within the data, thus improving performance metrics in terms of accuracy and convergence rates. Extensive experimental results demonstrate the effectiveness of our method, showing substantial improvements over other activation functions. Code is available at https://github.com/BryceZhuo/PolyCom.", "github_manual": "", "github_regex": "https://github.com/BryceZhuo/PolyCom", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-07", "arxiv_id": "2411.04109", "title": "Self-Consistency Preference Optimization", "authors": [ "Archiki Prasad", "Weizhe Yuan", "Richard Yuanzhe Pang", "Jing Xu", "Maryam Fazel-Zarandi", "Mohit Bansal", "Sainbayar Sukhbaatar", "Jason Weston", "Jane Yu" ], "github": "", "project_page": "", "abstract": "Self-alignment, whereby models learn to improve themselves without human annotation, is a rapidly growing research area. However, existing techniques often fail to improve complex reasoning tasks due to the difficulty of assigning correct rewards. An orthogonal approach that is known to improve correctness is self-consistency, a method applied at inference time based on multiple sampling in order to find the most consistent answer. In this work, we extend the self-consistency concept to help train models. We thus introduce self-consistency preference optimization (ScPO), which iteratively trains consistent answers to be preferred over inconsistent ones on unsupervised new problems. We show ScPO leads to large improvements over conventional reward model training on reasoning tasks such as GSM8K and MATH, closing the gap with supervised training with gold answers or preferences, and that combining ScPO with standard supervised learning improves results even further. On ZebraLogic, ScPO finetunes Llama-3 8B to be superior to Llama-3 70B, Gemma-2 27B, and Claude-3 Haiku.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-07", "arxiv_id": "2411.03590", "title": "From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond", "authors": [ "Harsha Nori", "Naoto Usuyama", "Nicholas King", "Scott Mayer McKinney", "Xavier Fernandes", "Sheng Zhang", "Eric Horvitz" ], "github": "", "project_page": "", "abstract": "Run-time steering strategies like Medprompt are valuable for guiding large language models (LLMs) to top performance on challenging tasks. Medprompt demonstrates that a general LLM can be focused to deliver state-of-the-art performance on specialized domains like medicine by using a prompt to elicit a run-time strategy involving chain of thought reasoning and ensembling. OpenAI's o1-preview model represents a new paradigm, where a model is designed to do run-time reasoning before generating final responses. We seek to understand the behavior of o1-preview on a diverse set of medical challenge problem benchmarks. Following on the Medprompt study with GPT-4, we systematically evaluate the o1-preview model across various medical benchmarks. Notably, even without prompting techniques, o1-preview largely outperforms the GPT-4 series with Medprompt. We further systematically study the efficacy of classic prompt engineering strategies, as represented by Medprompt, within the new paradigm of reasoning models. We found that few-shot prompting hinders o1's performance, suggesting that in-context learning may no longer be an effective steering approach for reasoning-native models. While ensembling remains viable, it is resource-intensive and requires careful cost-performance optimization. Our cost and accuracy analysis across run-time strategies reveals a Pareto frontier, with GPT-4o representing a more affordable option and o1-preview achieving state-of-the-art performance at higher cost. Although o1-preview offers top performance, GPT-4o with steering strategies like Medprompt retains value in specific contexts. Moreover, we note that the o1-preview model has reached near-saturation on many existing medical benchmarks, underscoring the need for new, challenging benchmarks. We close with reflections on general directions for inference-time computation with LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.05003", "title": "ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning", "authors": [ "David Junhao Zhang", "Roni Paiss", "Shiran Zada", "Nikhil Karnad", "David E. Jacobs", "Yael Pritch", "Inbar Mosseri", "Mike Zheng Shou", "Neal Wadhwa", "Nataniel Ruiz" ], "github": "", "project_page": "", "abstract": "Recently, breakthroughs in video modeling have allowed for controllable camera trajectories in generated videos. However, these methods cannot be directly applied to user-provided videos that are not generated by a video model. In this paper, we present ReCapture, a method for generating new videos with novel camera trajectories from a single user-provided video. Our method allows us to re-generate the reference video, with all its existing scene motion, from vastly different angles and with cinematic camera motion. Notably, using our method we can also plausibly hallucinate parts of the scene that were not observable in the reference video. Our method works by (1) generating a noisy anchor video with a new camera trajectory using multiview diffusion models or depth-based point cloud rendering and then (2) regenerating the anchor video into a clean and temporally consistent reangled video using our proposed masked video fine-tuning technique.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.04709", "title": "TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation", "authors": [ "Wenhao Wang", "Yi Yang" ], "github": "https://github.com/WangWenhao0716/TIP-I2V", "project_page": "", "abstract": "Video generation models are revolutionizing content creation, with image-to-video models drawing increasing attention due to their enhanced controllability, visual consistency, and practical applications. However, despite their popularity, these models rely on user-provided text and image prompts, and there is currently no dedicated dataset for studying these prompts. In this paper, we introduce TIP-I2V, the first large-scale dataset of over 1.70 million unique user-provided Text and Image Prompts specifically for Image-to-Video generation. Additionally, we provide the corresponding generated videos from five state-of-the-art image-to-video models. We begin by outlining the time-consuming and costly process of curating this large-scale dataset. Next, we compare TIP-I2V to two popular prompt datasets, VidProM (text-to-video) and DiffusionDB (text-to-image), highlighting differences in both basic and semantic information. This dataset enables advancements in image-to-video research. For instance, to develop better models, researchers can use the prompts in TIP-I2V to analyze user preferences and evaluate the multi-dimensional performance of their trained models; and to enhance model safety, they may focus on addressing the misinformation issue caused by image-to-video models. The new research inspired by TIP-I2V and the differences with existing datasets emphasize the importance of a specialized image-to-video prompt dataset. The project is publicly available at https://tip-i2v.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/WangWenhao0716/TIP-I2V", "github_api_added_by": "auto" }, { "date": "2024-11-08", "arxiv_id": "2411.04999", "title": "DynaMem: Online Dynamic Spatio-Semantic Memory for Open World Mobile Manipulation", "authors": [ "Peiqi Liu", "Zhanqiu Guo", "Mohit Warke", "Soumith Chintala", "Chris Paxton", "Nur Muhammad Mahi Shafiullah", "Lerrel Pinto" ], "github": "", "project_page": "", "abstract": "Significant progress has been made in open-vocabulary mobile manipulation, where the goal is for a robot to perform tasks in any environment given a natural language description. However, most current systems assume a static environment, which limits the system's applicability in real-world scenarios where environments frequently change due to human intervention or the robot's own actions. In this work, we present DynaMem, a new approach to open-world mobile manipulation that uses a dynamic spatio-semantic memory to represent a robot's environment. DynaMem constructs a 3D data structure to maintain a dynamic memory of point clouds, and answers open-vocabulary object localization queries using multimodal LLMs or open-vocabulary features generated by state-of-the-art vision-language models. Powered by DynaMem, our robots can explore novel environments, search for objects not found in memory, and continuously update the memory as objects move, appear, or disappear in the scene. We run extensive experiments on the Stretch SE3 robots in three real and nine offline scenes, and achieve an average pick-and-drop success rate of 70% on non-stationary objects, which is more than a 2x improvement over state-of-the-art static systems. Our code as well as our experiment and deployment videos are open sourced and can be found on our project website: https://dynamem.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.04996", "title": "Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models", "authors": [ "Weixin Liang", "Lili Yu", "Liang Luo", "Srinivasan Iyer", "Ning Dong", "Chunting Zhou", "Gargi Ghosh", "Mike Lewis", "Wen-tau Yih", "Luke Zettlemoyer", "Xi Victoria Lin" ], "github": "", "project_page": "", "abstract": "The development of large language models (LLMs) has expanded to multi-modal systems capable of processing text, images, and speech within a unified framework. Training these models demands significantly larger datasets and computational resources compared to text-only LLMs. To address the scaling challenges, we introduce Mixture-of-Transformers (MoT), a sparse multi-modal transformer architecture that significantly reduces pretraining computational costs. MoT decouples non-embedding parameters of the model by modality -- including feed-forward networks, attention matrices, and layer normalization -- enabling modality-specific processing with global self-attention over the full input sequence. We evaluate MoT across multiple settings and model scales. In the Chameleon 7B setting (autoregressive text-and-image generation), MoT matches the dense baseline's performance using only 55.8\\% of the FLOPs. When extended to include speech, MoT reaches speech performance comparable to the dense baseline with only 37.2\\% of the FLOPs. In the Transfusion setting, where text and image are trained with different objectives, a 7B MoT model matches the image modality performance of the dense baseline with one third of the FLOPs, and a 760M MoT model outperforms a 1.4B dense baseline across key image generation metrics. System profiling further highlights MoT's practical benefits, achieving dense baseline image quality in 47.2\\% of the wall-clock time and text quality in 75.6\\% of the wall-clock time (measured on AWS p4de.24xlarge instances with NVIDIA A100 GPUs).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.04989", "title": "SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation", "authors": [ "Koichi Namekata", "Sherwin Bahmani", "Ziyi Wu", "Yash Kant", "Igor Gilitschenski", "David B. Lindell" ], "github": "https://github.com/Kmcode1/SG-I2V", "project_page": "", "abstract": "Methods for image-to-video generation have achieved impressive, photo-realistic quality. However, adjusting specific elements in generated videos, such as object motion or camera movement, is often a tedious process of trial and error, e.g., involving re-generating videos with different random seeds. Recent techniques address this issue by fine-tuning a pre-trained model to follow conditioning signals, such as bounding boxes or point trajectories. Yet, this fine-tuning procedure can be computationally expensive, and it requires datasets with annotated object motion, which can be difficult to procure. In this work, we introduce SG-I2V, a framework for controllable image-to-video generation that is self-guidedx2013offering zero-shot control by relying solely on the knowledge present in a pre-trained image-to-video diffusion model without the need for fine-tuning or external knowledge. Our zero-shot method outperforms unsupervised baselines while being competitive with supervised models in terms of visual quality and motion fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Kmcode1/SG-I2V", "github_api_added_by": "auto" }, { "date": "2024-11-08", "arxiv_id": "2411.04905", "title": "OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models", "authors": [ "Siming Huang", "Tianhao Cheng", "Jason Klein Liu", "Jiaran Hao", "Liuyihan Song", "Yang Xu", "J. Yang", "J. H. Liu", "Chenchen Zhang", "Linzheng Chai", "Ruifeng Yuan", "Zhaoxiang Zhang", "Jie Fu", "Qian Liu", "Ge Zhang", "Zili Wang", "Yuan Qi", "Yinghui Xu", "Wei Chu" ], "github": "https://github.com/richardodliu/OpenCodeEval", "project_page": "", "abstract": "Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems.While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs suitable for rigorous scientific investigation, particularly those with reproducible data processing pipelines and transparent training protocols, remain limited. The scarcity is due to various challenges, including resource constraints, ethical considerations, and the competitive advantages of keeping models advanced. To address the gap, we introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an ``open cookbook'' for the research community. Unlike most prior efforts, we release not only model weights and inference code, but also the reproducible training data, complete data processing pipeline, rigorous experimental ablation results, and detailed training protocols for open scientific research. Through this comprehensive release, we identify the key ingredients for building a top-tier code LLM: (1) code optimized heuristic rules for data cleaning and methods for data deduplication, (2) recall of text corpus related to code and (3) high-quality synthetic data in both annealing and supervised fine-tuning stages. By offering this level of openness, we aim to broaden access to all aspects of a top-tier code LLM, with OpenCoder serving as both a powerful model and an open foundation to accelerate research, and enable reproducible advancements in code AI.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/richardodliu/OpenCodeEval", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.04928", "title": "DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion", "authors": [ "Wenqiang Sun", "Shuo Chen", "Fangfu Liu", "Zilong Chen", "Yueqi Duan", "Jun Zhang", "Yikai Wang" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce DimensionX, a framework designed to generate photorealistic 3D and 4D scenes from just a single image with video diffusion. Our approach begins with the insight that both the spatial structure of a 3D scene and the temporal evolution of a 4D scene can be effectively represented through sequences of video frames. While recent video diffusion models have shown remarkable success in producing vivid visuals, they face limitations in directly recovering 3D/4D scenes due to limited spatial and temporal controllability during generation. To overcome this, we propose ST-Director, which decouples spatial and temporal factors in video diffusion by learning dimension-aware LoRAs from dimension-variant data. This controllable video diffusion approach enables precise manipulation of spatial structure and temporal dynamics, allowing us to reconstruct both 3D and 4D representations from sequential frames with the combination of spatial and temporal dimensions. Additionally, to bridge the gap between generated videos and real-world scenes, we introduce a trajectory-aware mechanism for 3D generation and an identity-preserving denoising strategy for 4D generation. Extensive experiments on various real-world and synthetic datasets demonstrate that DimensionX achieves superior results in controllable video generation, as well as in 3D and 4D scene generation, compared with previous methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.04752", "title": "RetrieveGPT: Merging Prompts and Mathematical Models for Enhanced Code-Mixed Information Retrieval", "authors": [ "Aniket Deroy", "Subhankar Maity" ], "github": "", "project_page": "", "abstract": "Code-mixing, the integration of lexical and grammatical elements from multiple languages within a single sentence, is a widespread linguistic phenomenon, particularly prevalent in multilingual societies. In India, social media users frequently engage in code-mixed conversations using the Roman script, especially among migrant communities who form online groups to share relevant local information. This paper focuses on the challenges of extracting relevant information from code-mixed conversations, specifically within Roman transliterated Bengali mixed with English. This study presents a novel approach to address these challenges by developing a mechanism to automatically identify the most relevant answers from code-mixed conversations. We have experimented with a dataset comprising of queries and documents from Facebook, and Query Relevance files (QRels) to aid in this task. Our results demonstrate the effectiveness of our approach in extracting pertinent information from complex, code-mixed digital conversations, contributing to the broader field of natural language processing in multilingual and informal text environments. We use GPT-3.5 Turbo via prompting alongwith using the sequential nature of relevant documents to frame a mathematical model which helps to detect relevant documents corresponding to a query.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.05007", "title": "SVDQunat: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models", "authors": [ "Muyang Li", "Yujun Lin", "Zhekai Zhang", "Tianle Cai", "Xiuyu Li", "Junxian Guo", "Enze Xie", "Chenlin Meng", "Jun-Yan Zhu", "Song Han" ], "github": "https://github.com/mit-han-lab/nunchaku", "project_page": "", "abstract": "Diffusion models have been proven highly effective at generating high-quality images. However, as these models grow larger, they require significantly more memory and suffer from higher latency, posing substantial challenges for deployment. In this work, we aim to accelerate diffusion models by quantizing their weights and activations to 4 bits. At such an aggressive level, both weights and activations are highly sensitive, where conventional post-training quantization methods for large language models like smoothing become insufficient. To overcome this limitation, we propose SVDQuant, a new 4-bit quantization paradigm. Different from smoothing which redistributes outliers between weights and activations, our approach absorbs these outliers using a low-rank branch. We first consolidate the outliers by shifting them from activations to weights, then employ a high-precision low-rank branch to take in the weight outliers with Singular Value Decomposition (SVD). This process eases the quantization on both sides. However, na\\\"{\\i}vely running the low-rank branch independently incurs significant overhead due to extra data movement of activations, negating the quantization speedup. To address this, we co-design an inference engine Nunchaku that fuses the kernels of the low-rank branch into those of the low-bit branch to cut off redundant memory access. It can also seamlessly support off-the-shelf low-rank adapters (LoRAs) without the need for re-quantization. Extensive experiments on SDXL, PixArt-Sigma, and FLUX.1 validate the effectiveness of SVDQuant in preserving image quality. We reduce the memory usage for the 12B FLUX.1 models by 3.5times, achieving 3.0times speedup over the 4-bit weight-only quantized baseline on the 16GB laptop 4090 GPU, paving the way for more interactive applications on PCs. Our quantization library and inference engine are open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mit-han-lab/nunchaku", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.04496", "title": "Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large Language Model", "authors": [ "Young-Jun Lee", "Dokyong Lee", "Junyoung Youn", "Kyeongjin Oh", "Ho-Jin Choi" ], "github": "https://github.com/passing2961/thanos", "project_page": "", "abstract": "To increase social bonding with interlocutors, humans naturally acquire the ability to respond appropriately in a given situation by considering which conversational skill is most suitable for the response - a process we call skill-of-mind. For large language model (LLM)-based conversational agents, planning appropriate conversational skills, as humans do, is challenging due to the complexity of social dialogue, especially in interactive scenarios. To address this, we propose a skill-of-mind-annotated conversation dataset, named Multifaceted Skill-of-Mind, which includes multi-turn and multifaceted conversational skills across various interactive scenarios (e.g., long-term, counseling, task-oriented), grounded in diverse social contexts (e.g., demographics, persona, rules of thumb). This dataset consists of roughly 100K conversations. Using this dataset, we introduce a new family of skill-of-mind-infused LLMs, named Thanos, with model sizes of 1B, 3B, and 8B parameters. With extensive experiments, these models successfully demonstrate the skill-of-mind process and exhibit strong generalizability in inferring multifaceted skills across a variety of domains. Moreover, we show that Thanos significantly enhances the quality of responses generated by LLM-based conversational agents and promotes prosocial behavior in human evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/passing2961/thanos", "github_api_added_by": "auto" }, { "date": "2024-11-08", "arxiv_id": "2411.04335", "title": "GazeGen: Gaze-Driven User Interaction for Visual Content Generation", "authors": [ "He-Yen Hsieh", "Ziyun Li", "Sai Qian Zhang", "Wei-Te Mark Ting", "Kao-Den Chang", "Barbara De Salvo", "Chiao Liu", "H. T. Kung" ], "github": "", "project_page": "", "abstract": "We present GazeGen, a user interaction system that generates visual content (images and videos) for locations indicated by the user's eye gaze. GazeGen allows intuitive manipulation of visual content by targeting regions of interest with gaze. Using advanced techniques in object detection and generative AI, GazeGen performs gaze-controlled image adding/deleting, repositioning, and surface material changes of image objects, and converts static images into videos. Central to GazeGen is the DFT Gaze (Distilled and Fine-Tuned Gaze) agent, an ultra-lightweight model with only 281K parameters, performing accurate real-time gaze predictions tailored to individual users' eyes on small edge devices. GazeGen is the first system to combine visual content generation with real-time gaze estimation, made possible exclusively by DFT Gaze. This real-time gaze estimation enables various visual content generation tasks, all controlled by the user's gaze. The input for DFT Gaze is the user's eye images, while the inputs for visual content generation are the user's view and the predicted gaze point from DFT Gaze. To achieve efficient gaze predictions, we derive the small model from a large model (10x larger) via novel knowledge distillation and personal adaptation techniques. We integrate knowledge distillation with a masked autoencoder, developing a compact yet powerful gaze estimation model. This model is further fine-tuned with Adapters, enabling highly accurate and personalized gaze predictions with minimal user input. DFT Gaze ensures low-latency and precise gaze tracking, supporting a wide range of gaze-driven tasks. We validate the performance of DFT Gaze on AEA and OpenEDS2020 benchmarks, demonstrating low angular gaze error and low latency on the edge device (Raspberry Pi 4). Furthermore, we describe applications of GazeGen, illustrating its versatility and effectiveness in various usage scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.04965", "title": "BitNet a4.8: 4-bit Activations for 1-bit LLMs", "authors": [ "Hongyu Wang", "Shuming Ma", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Recent research on the 1-bit Large Language Models (LLMs), such as BitNet b1.58, presents a promising direction for reducing the inference cost of LLMs while maintaining their performance. In this work, we introduce BitNet a4.8, enabling 4-bit activations for 1-bit LLMs. BitNet a4.8 employs a hybrid quantization and sparsification strategy to mitigate the quantization errors introduced by the outlier channels. Specifically, we utilize 4-bit activations for inputs to the attention and feed-forward network layers, while sparsifying intermediate states followed with 8-bit quantization. Extensive experiments demonstrate that BitNet a4.8 achieves performance comparable to BitNet b1.58 with equivalent training costs, while being faster in inference with enabling 4-bit (INT4/FP4) kernels. Additionally, BitNet a4.8 activates only 55% of parameters and supports 3-bit KV cache, further enhancing the efficiency of large-scale LLM deployment and inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.05000", "title": "Needle Threading: Can LLMs Follow Threads through Near-Million-Scale Haystacks?", "authors": [ "Jonathan Roberts", "Kai Han", "Samuel Albanie" ], "github": "", "project_page": "", "abstract": "As the context limits of Large Language Models (LLMs) increase, the range of possible applications and downstream functions broadens. In many real-world tasks, decisions depend on details scattered across collections of often disparate documents containing mostly irrelevant information. Long-context LLMs appear well-suited to this form of complex information retrieval and reasoning, which has traditionally proven costly and time-consuming. However, although the development of longer context models has seen rapid gains in recent years, our understanding of how effectively LLMs use their context has not kept pace. To address this, we conduct a set of retrieval experiments designed to evaluate the capabilities of 17 leading LLMs, such as their ability to follow threads of information through the context window. Strikingly, we find that many models are remarkably threadsafe: capable of simultaneously following multiple threads without significant loss in performance. Still, for many models, we find the effective context limit is significantly shorter than the supported context length, with accuracy decreasing as the context window grows. Our study also highlights the important point that token counts from different tokenizers should not be directly compared -- they often correspond to substantially different numbers of written characters. We release our code and long-context experimental data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.04923", "title": "VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos", "authors": [ "Shehan Munasinghe", "Hanan Gani", "Wenqi Zhu", "Jiale Cao", "Eric Xing", "Fahad Shahbaz Khan", "Salman Khan" ], "github": "", "project_page": "", "abstract": "Fine-grained alignment between videos and text is challenging due to complex spatial and temporal dynamics in videos. Existing video-based Large Multimodal Models (LMMs) handle basic conversations but struggle with precise pixel-level grounding in videos. To address this, we introduce VideoGLaMM, a LMM designed for fine-grained pixel-level grounding in videos based on user-provided textual inputs. Our design seamlessly connects three key components: a Large Language Model, a dual vision encoder that emphasizes both spatial and temporal details, and a spatio-temporal decoder for accurate mask generation. This connection is facilitated via tunable V-L and L-V adapters that enable close Vision-Language (VL) alignment. The architecture is trained to synchronize both spatial and temporal elements of video content with textual instructions. To enable fine-grained grounding, we curate a multimodal dataset featuring detailed visually-grounded conversations using a semiautomatic annotation pipeline, resulting in a diverse set of 38k video-QA triplets along with 83k objects and 671k masks. We evaluate VideoGLaMM on three challenging tasks: Grounded Conversation Generation, Visual Grounding, and Referring Video Segmentation. Experimental results show that our model consistently outperforms existing approaches across all three tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.05005", "title": "Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models", "authors": [ "Shuhong Zheng", "Zhipeng Bao", "Ruoyu Zhao", "Martial Hebert", "Yu-Xiong Wang" ], "github": "", "project_page": "", "abstract": "Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most existing work treats diffusion models as a standalone component for perception tasks, employing them either solely for off-the-shelf data augmentation or as mere feature extractors. In contrast to these isolated and thus sub-optimal efforts, we introduce a unified, versatile, diffusion-based framework, Diff-2-in-1, that can simultaneously handle both multi-modal data generation and dense visual perception, through a unique exploitation of the diffusion-denoising process. Within this framework, we further enhance discriminative visual perception via multi-modal generation, by utilizing the denoising network to create multi-modal data that mirror the distribution of the original training set. Importantly, Diff-2-in-1 optimizes the utilization of the created diverse and faithful data by leveraging a novel self-improving learning mechanism. Comprehensive experimental evaluations validate the effectiveness of our framework, showcasing consistent performance improvements across various discriminative backbones and high-quality multi-modal data generation characterized by both realism and usefulness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.04952", "title": "M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding", "authors": [ "Jaemin Cho", "Debanjan Mahata", "Ozan Irsoy", "Yujie He", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Document visual question answering (DocVQA) pipelines that answer questions from documents have broad applications. Existing methods focus on handling single-page documents with multi-modal language models (MLMs), or rely on text-based retrieval-augmented generation (RAG) that uses text extraction tools such as optical character recognition (OCR). However, there are difficulties in applying these methods in real-world scenarios: (a) questions often require information across different pages or documents, where MLMs cannot handle many long documents; (b) documents often have important information in visual elements such as figures, but text extraction tools ignore them. We introduce M3DocRAG, a novel multi-modal RAG framework that flexibly accommodates various document contexts (closed-domain and open-domain), question hops (single-hop and multi-hop), and evidence modalities (text, chart, figure, etc.). M3DocRAG finds relevant documents and answers questions using a multi-modal retriever and an MLM, so that it can efficiently handle single or many documents while preserving visual information. Since previous DocVQA datasets ask questions in the context of a specific document, we also present M3DocVQA, a new benchmark for evaluating open-domain DocVQA over 3,000+ PDF documents with 40,000+ pages. In three benchmarks (M3DocVQA/MMLongBench-Doc/MP-DocVQA), empirical results show that M3DocRAG with ColPali and Qwen2-VL 7B achieves superior performance than many strong baselines, including state-of-the-art performance in MP-DocVQA. We provide comprehensive analyses of different indexing, MLMs, and retrieval models. Lastly, we qualitatively show that M3DocRAG can successfully handle various scenarios, such as when relevant information exists across multiple pages and when answer evidence only exists in images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.04075", "title": "M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models", "authors": [ "Chuhan Li", "Ziyao Shangguan", "Yilun Zhao", "Deyuan Li", "Yixin Liu", "Arman Cohan" ], "github": "", "project_page": "", "abstract": "Existing benchmarks for evaluating foundation models mainly focus on single-document, text-only tasks. However, they often fail to fully capture the complexity of research workflows, which typically involve interpreting non-textual data and gathering information across multiple documents. To address this gap, we introduce M3SciQA, a multi-modal, multi-document scientific question answering benchmark designed for a more comprehensive evaluation of foundation models. M3SciQA consists of 1,452 expert-annotated questions spanning 70 natural language processing paper clusters, where each cluster represents a primary paper along with all its cited documents, mirroring the workflow of comprehending a single paper by requiring multi-modal and multi-document data. With M3SciQA, we conduct a comprehensive evaluation of 18 foundation models. Our results indicate that current foundation models still significantly underperform compared to human experts in multi-modal information retrieval and in reasoning across multiple scientific documents. Additionally, we explore the implications of these findings for the future advancement of applying foundation models in multi-modal scientific literature analysis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-08", "arxiv_id": "2411.05001", "title": "Analyzing The Language of Visual Tokens", "authors": [ "David M. Chan", "Rodolfo Corona", "Joonyong Park", "Cheol Jun Cho", "Yutong Bai", "Trevor Darrell" ], "github": "", "project_page": "", "abstract": "With the introduction of transformer-based models for vision and language tasks, such as LLaVA and Chameleon, there has been renewed interest in the discrete tokenized representation of images. These models often treat image patches as discrete tokens, analogous to words in natural language, learning joint alignments between visual and human languages. However, little is known about the statistical behavior of these visual languages - whether they follow similar frequency distributions, grammatical structures, or topologies as natural languages. In this paper, we take a natural-language-centric approach to analyzing discrete visual languages and uncover striking similarities and fundamental differences. We demonstrate that, although visual languages adhere to Zipfian distributions, higher token innovation drives greater entropy and lower compression, with tokens predominantly representing object parts, indicating intermediate granularity. We also show that visual languages lack cohesive grammatical structures, leading to higher perplexity and weaker hierarchical organization compared to natural languages. Finally, we demonstrate that, while vision models align more closely with natural languages than other models, this alignment remains significantly weaker than the cohesion found within natural languages. Through these experiments, we demonstrate how understanding the statistical properties of discrete visual languages can inform the design of more effective computer vision models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-11", "arxiv_id": "2411.05738", "title": "StdGEN: Semantic-Decomposed 3D Character Generation from Single Images", "authors": [ "Yuze He", "Yanning Zhou", "Wang Zhao", "Zhongkai Wu", "Kaiwen Xiao", "Wei Yang", "Yong-Jin Liu", "Xiao Han" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "We present StdGEN, an innovative pipeline for generating semantically decomposed high-quality 3D characters from single images, enabling broad applications in virtual reality, gaming, and filmmaking, etc. Unlike previous methods which struggle with limited decomposability, unsatisfactory quality, and long optimization times, StdGEN features decomposability, effectiveness and efficiency; i.e., it generates intricately detailed 3D characters with separated semantic components such as the body, clothes, and hair, in three minutes. At the core of StdGEN is our proposed Semantic-aware Large Reconstruction Model (S-LRM), a transformer-based generalizable model that jointly reconstructs geometry, color and semantics from multi-view images in a feed-forward manner. A differentiable multi-layer semantic surface extraction scheme is introduced to acquire meshes from hybrid implicit fields reconstructed by our S-LRM. Additionally, a specialized efficient multi-view diffusion model and an iterative multi-layer surface refinement module are integrated into the pipeline to facilitate high-quality, decomposable 3D character generation. Extensive experiments demonstrate our state-of-the-art performance in 3D anime character generation, surpassing existing baselines by a significant margin in geometry, texture and decomposability. StdGEN offers ready-to-use semantic-decomposed 3D characters and enables flexible customization for a wide range of applications. Project page: https://stdgen.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-11", "arxiv_id": "2411.02462", "title": "Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study", "authors": [ "Andr\u00e9 Storhaug", "Jingyue Li" ], "github": "", "project_page": "", "abstract": "The advent of large language models (LLMs) like GitHub Copilot has significantly enhanced programmers' productivity, particularly in code generation. However, these models often struggle with real-world tasks without fine-tuning. As LLMs grow larger and more performant, fine-tuning for specialized tasks becomes increasingly expensive. Parameter-efficient fine-tuning (PEFT) methods, which fine-tune only a subset of model parameters, offer a promising solution by reducing the computational costs of tuning LLMs while maintaining their performance. Existing studies have explored using PEFT and LLMs for various code-related tasks and found that the effectiveness of PEFT techniques is task-dependent. The application of PEFT techniques in unit test generation remains underexplored. The state-of-the-art is limited to using LLMs with full fine-tuning to generate unit tests. This paper investigates both full fine-tuning and various PEFT methods, including LoRA, (IA)^3, and prompt tuning, across different model architectures and sizes. We use well-established benchmark datasets to evaluate their effectiveness in unit test generation. Our findings show that PEFT methods can deliver performance comparable to full fine-tuning for unit test generation, making specialized fine-tuning more accessible and cost-effective. Notably, prompt tuning is the most effective in terms of cost and resource utilization, while LoRA approaches the effectiveness of full fine-tuning in several cases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-11", "arxiv_id": "2411.05288", "title": "Balancing Pipeline Parallelism with Vocabulary Parallelism", "authors": [ "Man Tsung Yeung", "Penghui Qi", "Min Lin", "Xinyi Wan" ], "github": "https://github.com/sail-sg/VocabularyParallelism", "project_page": "", "abstract": "Pipeline parallelism is widely used to scale the training of transformer-based large language models, various works have been done to improve its throughput and memory footprint. In this paper, we address a frequently overlooked issue: the vocabulary layers can cause imbalanced computation and memory usage across pipeline stages, worsening pipeline bubbles and the memory bottleneck. To tackle this, we partition the vocabulary layers evenly across pipeline devices and group the computation into pipeline passes. To reduce the activation memory overhead, we propose several algorithms to reduce communication barriers within vocabulary layers. Additionally, we utilize a generalizable method to integrate Vocabulary Parallelism with existing pipeline schedules. By combining these techniques, our methods effectively balance the computation and parameter memory, with only a small constant activation memory overhead. Notably, when combined with activation memory-balanced schedules like V-Half, our approach achieves perfect balance in both memory and computation. Extensive evaluations demonstrate that our method achieves computation and memory balance regardless of the vocabulary size, resulting in a 5% to 51% improvement in throughput compared to naive approaches, meanwhile significantly reducing peak memory usage especially for large vocabulary scenarios. Our implementation is open-sourced at https://github.com/sail-sg/VocabularyParallelism .", "github_manual": "", "github_regex": "https://github.com/sail-sg/VocabularyParallelism", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-11", "arxiv_id": "2411.04425", "title": "DELIFT: Data Efficient Language model Instruction Fine Tuning", "authors": [ "Ishika Agarwal", "Krishna Killamsetty", "Lucian Popa", "Marina Danilevksy" ], "github": "https://github.com/agarwalishika/delift", "project_page": "", "abstract": "Fine-tuning large language models (LLMs) is essential for enhancing their performance on specific tasks but is often resource-intensive due to redundant or uninformative data. To address this inefficiency, we introduce DELIFT (Data Efficient Language model Instruction Fine-Tuning), a novel algorithm that systematically optimizes data selection across the three key stages of fine-tuning: (1) instruction tuning, (2) task-specific fine-tuning (e.g., reasoning, question-answering), and (3) continual fine-tuning (e.g., incorporating new data versions). Unlike existing methods that focus on single-stage optimization or rely on computationally intensive gradient calculations, DELIFT operates efficiently across all stages. Central to our approach is a pairwise utility metric that quantifies how beneficial a data sample is for improving the model's responses to other samples, effectively measuring the informational value relative to the model's current capabilities. By leveraging different submodular functions applied to this metric, DELIFT selects diverse and optimal subsets that are useful across all stages of fine-tuning. Experiments across various tasks and model scales demonstrate that DELIFT can reduce the fine-tuning data size by up to 70% without compromising performance, offering significant computational savings and outperforming existing methods in both efficiency and efficacy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/agarwalishika/delift", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-11", "arxiv_id": "2411.05457", "title": "Improving the detection of technical debt in Java source code with an enriched dataset", "authors": [ "Nam Le Hai", "Anh M. T. Bui", "Phuong T. Nguyen", "Davide Di Ruscio", "Rick Kazman" ], "github": "https://github.com/namcyan/tesoro", "project_page": "", "abstract": "Technical debt (TD) is a term used to describe the additional work and costs that emerge when developers have opted for a quick and easy solution to a problem, rather than a more effective and well-designed, but time-consuming approach. Self-Admitted Technical Debts (SATDs) are a specific type of technical debts that developers intentionally document and acknowledge, typically via textual comments. While these self-admitted comments are a useful tool for identifying technical debts, most of the existing approaches focus on capturing crucial tokens associated with various categories of TD, neglecting the rich information embedded within the source code itself. Recent research has focused on detecting SATDs by analyzing comments embedded in source code, and there has been little work dealing with technical debts contained in the source code. To fill such a gap, in this study, through the analysis of comments and their associated source code from 974 Java projects hosted in the Stack corpus, we curated the first ever dataset of TD identified by code comments, coupled with its associated source code. Through an empirical evaluation, we found out that the comments of the resulting dataset help enhance the prediction performance of state-of-the-art SATD detection models. More importantly, including the classified source code significantly improves the accuracy in predicting various types of technical debt. In this respect, our work is two-fold: (i) We believe that our dataset will catalyze future work in the domain, inspiring various research issues related to the recognition of technical debt; (ii) The proposed classifiers may serve as baselines for other studies on the detection of TD by means of the curated dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/namcyan/tesoro", "github_api_added_by": "auto" }, { "date": "2024-11-11", "arxiv_id": "2411.04997", "title": "LLM2CLIP: Powerful Language Model Unlock Richer Visual Representation", "authors": [ "Weiquan Huang", "Aoqi Wu", "Yifan Yang", "Xufang Luo", "Yuqing Yang", "Liang Hu", "Qi Dai", "Xiyang Dai", "Dongdong Chen", "Chong Luo", "Lili Qiu" ], "github": "https://github.com/microsoft/LLM2CLIP", "project_page": "", "abstract": "CLIP is one of the most important multimodal foundational models today. What powers CLIP's capabilities? The rich supervision signals provided by natural language, the carrier of human knowledge, shape a powerful cross-modal representation space. However, with the rapid advancements in large language models LLMs like GPT-4 and LLaMA, the boundaries of language comprehension and generation are continually being pushed. This raises an intriguing question: can the capabilities of LLMs be harnessed to further improve multimodal representation learning? The potential benefits of incorporating LLMs into CLIP are clear. LLMs' strong textual understanding can fundamentally improve CLIP's ability to handle image captions, drastically enhancing its ability to process long and complex texts, a well-known limitation of vanilla CLIP. Moreover, LLMs are trained on a vast corpus of text, possessing open-world knowledge. This allows them to expand on caption information during training, increasing the efficiency of the learning process. In this paper, we propose LLM2CLIP, a novel approach that embraces the power of LLMs to unlock CLIP's potential. By fine-tuning the LLM in the caption space with contrastive learning, we extract its textual capabilities into the output embeddings, significantly improving the output layer's textual discriminability. We then design an efficient training process where the fine-tuned LLM acts as a powerful teacher for CLIP's visual encoder. Thanks to the LLM's presence, we can now incorporate longer and more complex captions without being restricted by vanilla CLIP's text encoder's context window and ability limitations. Our experiments demonstrate that this approach brings substantial improvements in cross-modal tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/LLM2CLIP", "github_api_added_by": "auto" }, { "date": "2024-11-11", "arxiv_id": "2411.04986", "title": "The Semantic Hub Hypothesis: Language Models Share Semantic Representations Across Languages and Modalities", "authors": [ "Zhaofeng Wu", "Xinyan Velocity Yu", "Dani Yogatama", "Jiasen Lu", "Yoon Kim" ], "github": "https://github.com/ZhaofengWu/semantic-hub", "project_page": "", "abstract": "Modern language models can process inputs across diverse languages and modalities. We hypothesize that models acquire this capability through learning a shared representation space across heterogeneous data types (e.g., different languages and modalities), which places semantically similar inputs near one another, even if they are from different modalities/languages. We term this the semantic hub hypothesis, following the hub-and-spoke model from neuroscience (Patterson et al., 2007) which posits that semantic knowledge in the human brain is organized through a transmodal semantic \"hub\" which integrates information from various modality-specific \"spokes\" regions. We first show that model representations for semantically equivalent inputs in different languages are similar in the intermediate layers, and that this space can be interpreted using the model's dominant pretraining language via the logit lens. This tendency extends to other data types, including arithmetic expressions, code, and visual/audio inputs. Interventions in the shared representation space in one data type also predictably affect model outputs in other data types, suggesting that this shared representations space is not simply a vestigial byproduct of large-scale training on broad data, but something that is actively utilized by the model during input processing.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ZhaofengWu/semantic-hub", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-11", "arxiv_id": "2411.04097", "title": "RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models", "authors": [ "Maya Varma", "Jean-Benoit Delbrouck", "Zhihong Chen", "Akshay Chaudhari", "Curtis Langlotz" ], "github": "https://github.com/stanford-aimi/ravl", "project_page": "", "abstract": "Fine-tuned vision-language models (VLMs) often capture spurious correlations between image features and textual attributes, resulting in degraded zero-shot performance at test time. Existing approaches for addressing spurious correlations (i) primarily operate at the global image-level rather than intervening directly on fine-grained image features and (ii) are predominantly designed for unimodal settings. In this work, we present RaVL, which takes a fine-grained perspective on VLM robustness by discovering and mitigating spurious correlations using local image features rather than operating at the global image level. Given a fine-tuned VLM, RaVL first discovers spurious correlations by leveraging a region-level clustering approach to identify precise image features contributing to zero-shot classification errors. Then, RaVL mitigates the identified spurious correlation with a novel region-aware loss function that enables the VLM to focus on relevant regions and ignore spurious relationships during fine-tuning. We evaluate RaVL on 654 VLMs with various model architectures, data domains, and learned spurious correlations. Our results show that RaVL accurately discovers (191% improvement over the closest baseline) and mitigates (8.2% improvement on worst-group image classification accuracy) spurious correlations. Qualitative evaluations on general-domain and medical-domain VLMs confirm our findings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stanford-aimi/ravl", "github_api_added_by": "auto" }, { "date": "2024-11-11", "arxiv_id": "2411.04282", "title": "Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding", "authors": [ "Haolin Chen", "Yihao Feng", "Zuxin Liu", "Weiran Yao", "Akshara Prabhakar", "Shelby Heinecke", "Ricky Ho", "Phil Mui", "Silvio Savarese", "Caiming Xiong", "Huan Wang" ], "github": "https://github.com/SalesforceAIResearch/LaTRO", "project_page": "", "abstract": "Large language models (LLMs) have shown impressive capabilities, but still struggle with complex reasoning tasks requiring multiple steps. While prompt-based methods like Chain-of-Thought (CoT) can improve LLM reasoning at inference time, optimizing reasoning capabilities during training remains challenging. We introduce LaTent Reasoning Optimization (LaTRO), a principled framework that formulates reasoning as sampling from a latent distribution and optimizes it via variational approaches. LaTRO enables LLMs to concurrently improve both their reasoning process and ability to evaluate reasoning quality, without requiring external feedback or reward models. We validate LaTRO through experiments on GSM8K and ARC-Challenge datasets using multiple model architectures. On GSM8K, LaTRO improves zero-shot accuracy by an average of 12.5% over base models and 9.6% over supervised fine-tuning across Phi-3.5-mini, Mistral-7B, and Llama-3.1-8B. Our findings suggest that pre-trained LLMs possess latent reasoning capabilities that can be unlocked and enhanced through our proposed optimization approach in a self-improvement manner. The code of LaTRO is available at https://github.com/SalesforceAIResearch/LaTRO.", "github_manual": "", "github_regex": "https://github.com/SalesforceAIResearch/LaTRO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-11", "arxiv_id": "2411.04954", "title": "CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLM", "authors": [ "Jingwei Xu", "Chenyu Wang", "Zibo Zhao", "Wen Liu", "Yi Ma", "Shenghua Gao" ], "github": "https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md", "project_page": "", "abstract": "This paper aims to design a unified Computer-Aided Design (CAD) generation system that can easily generate CAD models based on the user's inputs in the form of textual description, images, point clouds, or even a combination of them. Towards this goal, we introduce the CAD-MLLM, the first system capable of generating parametric CAD models conditioned on the multimodal input. Specifically, within the CAD-MLLM framework, we leverage the command sequences of CAD models and then employ advanced large language models (LLMs) to align the feature space across these diverse multi-modalities data and CAD models' vectorized representations. To facilitate the model training, we design a comprehensive data construction and annotation pipeline that equips each CAD model with corresponding multimodal data. Our resulting dataset, named Omni-CAD, is the first multimodal CAD dataset that contains textual description, multi-view images, points, and command sequence for each CAD model. It contains approximately 450K instances and their CAD construction sequences. To thoroughly evaluate the quality of our generated CAD models, we go beyond current evaluation metrics that focus on reconstruction quality by introducing additional metrics that assess topology quality and surface enclosure extent. Extensive experimental results demonstrate that CAD-MLLM significantly outperforms existing conditional generative methods and remains highly robust to noises and missing points. The project page and more visualizations can be found at: https://cad-mllm.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.06272", "title": "Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models", "authors": [ "Xiaojun Wu", "Junxi Liu", "Huanyi Su", "Zhouchi Lin", "Yiyan Qi", "Chengjin Xu", "Jiajun Su", "Jiajie Zhong", "Fuwei Wang", "Saizhuo Wang", "Fengrui Hua", "Jia Li", "Jian Guo" ], "github": "https://github.com/IDEA-FinAI/Golden-Touchstone", "project_page": "", "abstract": "As large language models become increasingly prevalent in the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance. However, existing finance benchmarks often suffer from limited language and task coverage, as well as challenges such as low-quality datasets and inadequate adaptability for LLM evaluation. To address these limitations, we propose \"Golden Touchstone\", the first comprehensive bilingual benchmark for financial LLMs, which incorporates representative datasets from both Chinese and English across eight core financial NLP tasks. Developed from extensive open source data collection and industry-specific demands, this benchmark includes a variety of financial tasks aimed at thoroughly assessing models' language understanding and generation capabilities. Through comparative analysis of major models on the benchmark, such as GPT-4o Llama3, FinGPT and FinMA, we reveal their strengths and limitations in processing complex financial information. Additionally, we open-sourced Touchstone-GPT, a financial LLM trained through continual pre-training and financial instruction tuning, which demonstrates strong performance on the bilingual benchmark but still has limitations in specific tasks.This research not only provides the financial large language models with a practical evaluation tool but also guides the development and optimization of future research. The source code for Golden Touchstone and model weight of Touchstone-GPT have been made publicly available at https://github.com/IDEA-FinAI/Golden-Touchstone, contributing to the ongoing evolution of FinLLMs and fostering further research in this critical area.", "github_manual": "", "github_regex": "https://github.com/IDEA-FinAI/Golden-Touchstone", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.06176", "title": "M-Longdoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework", "authors": [ "Yew Ken Chia", "Liying Cheng", "Hou Pong Chan", "Chaoqun Liu", "Maojia Song", "Sharifah Mahani Aljunied", "Soujanya Poria", "Lidong Bing" ], "github": "", "project_page": "", "abstract": "The ability to understand and answer questions over documents can be useful in many business and practical applications. However, documents often contain lengthy and diverse multimodal contents such as texts, figures, and tables, which are very time-consuming for humans to read thoroughly. Hence, there is an urgent need to develop effective and automated methods to aid humans in this task. In this work, we introduce M-LongDoc, a benchmark of 851 samples, and an automated framework to evaluate the performance of large multimodal models. We further propose a retrieval-aware tuning approach for efficient and effective multimodal document reading. Compared to existing works, our benchmark consists of more recent and lengthy documents with hundreds of pages, while also requiring open-ended solutions and not just extractive answers. To our knowledge, our training framework is the first to directly address the retrieval setting for multimodal long documents. To enable tuning open-source models, we construct a training corpus in a fully automatic manner for the question-answering task over such documents. Experiments show that our tuning approach achieves a relative improvement of 4.6% for the correctness of model responses, compared to the baseline open-source models. Our data, code, and models are available at https://multimodal-documents.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.07180", "title": "Counterfactual Generation from Language Models", "authors": [ "Shauli Ravfogel", "Anej Svete", "V\u00e9steinn Sn\u00e6bjarnarson", "Ryan Cotterell" ], "github": "https://github.com/shauli-ravfogel/lm-counterfactuals", "project_page": "", "abstract": "Understanding and manipulating the causal generation mechanisms in language models is essential for controlling their behavior. Previous work has primarily relied on techniques such as representation surgery -- e.g., model ablations or manipulation of linear subspaces tied to specific concepts -- to intervene on these models. To understand the impact of interventions precisely, it is useful to examine counterfactuals -- e.g., how a given sentence would have appeared had it been generated by the model following a specific intervention. We highlight that counterfactual reasoning is conceptually distinct from interventions, as articulated in Pearl's causal hierarchy. Based on this observation, we propose a framework for generating true string counterfactuals by reformulating language models as Generalized Structural-equation. Models using the Gumbel-max trick. This allows us to model the joint distribution over original strings and their counterfactuals resulting from the same instantiation of the sampling noise. We develop an algorithm based on hindsight Gumbel sampling that allows us to infer the latent noise variables and generate counterfactuals of observed strings. Our experiments demonstrate that the approach produces meaningful counterfactuals while at the same time showing that commonly used intervention techniques have considerable undesired side effects.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/shauli-ravfogel/lm-counterfactuals", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.07140", "title": "Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models", "authors": [ "Yancheng He", "Shilong Li", "Jiaheng Liu", "Yingshui Tan", "Hui Huang", "Weixun Wang", "Xingyuan Bu", "Hangyu Guo", "Chengwei Hu", "Boren Zheng", "Xuepeng Liu", "Dekai Sun", "Wenbo Su", "Bo Zheng" ], "github": "https://github.com/deepseek-ai/DeepSeek-LLM", "project_page": "", "abstract": "New LLM evaluation benchmarks are important to align with the rapid development of Large Language Models (LLMs). In this work, we present Chinese SimpleQA, the first comprehensive Chinese benchmark to evaluate the factuality ability of language models to answer short questions, and Chinese SimpleQA mainly has five properties (i.e., Chinese, Diverse, High-quality, Static, Easy-to-evaluate). Specifically, first, we focus on the Chinese language over 6 major topics with 99 diverse subtopics. Second, we conduct a comprehensive quality control process to achieve high-quality questions and answers, where the reference answers are static and cannot be changed over time. Third, following SimpleQA, the questions and answers are very short, and the grading process is easy-to-evaluate based on OpenAI API. Based on Chinese SimpleQA, we perform a comprehensive evaluation on the factuality abilities of existing LLMs. Finally, we hope that Chinese SimpleQA could guide the developers to better understand the Chinese factuality abilities of their models and facilitate the growth of foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/deepseek-ai/DeepSeek-LLM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.07199", "title": "OmniEdit: Building Image Editing Generalist Models Through Specialist Supervision", "authors": [ "Cong Wei", "Zheyang Xiong", "Weiming Ren", "Xinrun Du", "Ge Zhang", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Instruction-guided image editing methods have demonstrated significant potential by training diffusion models on automatically synthesized or manually annotated image editing pairs. However, these methods remain far from practical, real-life applications. We identify three primary challenges contributing to this gap. Firstly, existing models have limited editing skills due to the biased synthesis process. Secondly, these methods are trained with datasets with a high volume of noise and artifacts. This is due to the application of simple filtering methods like CLIP-score. Thirdly, all these datasets are restricted to a single low resolution and fixed aspect ratio, limiting the versatility to handle real-world use cases. In this paper, we present \\omniedit, which is an omnipotent editor to handle seven different image editing tasks with any aspect ratio seamlessly. Our contribution is in four folds: (1) \\omniedit is trained by utilizing the supervision from seven different specialist models to ensure task coverage. (2) we utilize importance sampling based on the scores provided by large multimodal models (like GPT-4o) instead of CLIP-score to improve the data quality. (3) we propose a new editing architecture called EditNet to greatly boost the editing success rate, (4) we provide images with different aspect ratios to ensure that our model can handle any image in the wild. We have curated a test set containing images of different aspect ratios, accompanied by diverse instructions to cover different tasks. Both automatic evaluation and human evaluations demonstrate that \\omniedit can significantly outperform all the existing models. Our code, dataset and model will be available at https://tiger-ai-lab.github.io/OmniEdit/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.07126", "title": "Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models", "authors": [ "NVIDIA", "Yuval Atzmon", "Maciej Bala", "Yogesh Balaji", "Tiffany Cai", "Yin Cui", "Jiaojiao Fan", "Yunhao Ge", "Siddharth Gururani", "Jacob Huffman", "Ronald Isaac", "Pooya Jannaty", "Tero Karras", "Grace Lam", "J. P. Lewis", "Aaron Licata", "Yen-Chen Lin", "Ming-Yu Liu", "Qianli Ma", "Arun Mallya", "Ashlee Martino-Tarr", "Doug Mendez", "Seungjun Nah", "Chris Pruett", "Fitsum Reda", "Jiaming Song", "Ting-Chun Wang", "Fangyin Wei", "Xiaohui Zeng", "Yu Zeng", "Qinsheng Zhang" ], "github": "", "project_page": "", "abstract": "We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy. Edify Image utilizes cascaded pixel-space diffusion models trained using a novel Laplacian diffusion process, in which image signals at different frequency bands are attenuated at varying rates. Edify Image supports a wide range of applications, including text-to-image synthesis, 4K upsampling, ControlNets, 360 HDR panorama generation, and finetuning for image customization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.06208", "title": "IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization", "authors": [ "Xinghua Zhang", "Haiyang Yu", "Cheng Fu", "Fei Huang", "Yongbin Li" ], "github": "https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/IOPO", "project_page": "", "abstract": "In the realm of large language models (LLMs), the ability of models to accurately follow instructions is paramount as more agents and applications leverage LLMs for construction, where the complexity of instructions are rapidly increasing. However, on the one hand, there is only a certain amount of complex instruction evaluation data; on the other hand, there are no dedicated algorithms to improve the ability to follow complex instructions. To this end, this paper introduces TRACE, a benchmark for improving and evaluating the complex instructionfollowing ability, which consists of 120K training data and 1K evaluation data. Furthermore, we propose IOPO (Input-Output Preference Optimization) alignment method which takes both input and output preference pairs into consideration, where LLMs not only rapidly align with response preferences but also meticulously explore the instruction preferences. Extensive experiments on both in-domain and outof-domain datasets confirm the effectiveness of IOPO, showing 8.15%, 2.18% improvements on in-domain data and 6.29%, 3.13% on outof-domain data compared to SFT and DPO respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/IOPO", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.07232", "title": "Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models", "authors": [ "Yoad Tewel", "Rinon Gal", "Dvir Samuel Yuval Atzmon", "Lior Wolf", "Gal Chechik" ], "github": "", "project_page": "", "abstract": "Adding Object into images based on text instructions is a challenging task in semantic image editing, requiring a balance between preserving the original scene and seamlessly integrating the new object in a fitting location. Despite extensive efforts, existing models often struggle with this balance, particularly with finding a natural location for adding an object in complex scenes. We introduce Add-it, a training-free approach that extends diffusion models' attention mechanisms to incorporate information from three key sources: the scene image, the text prompt, and the generated image itself. Our weighted extended-attention mechanism maintains structural consistency and fine details while ensuring natural object placement. Without task-specific fine-tuning, Add-it achieves state-of-the-art results on both real and generated image insertion benchmarks, including our newly constructed \"Additing Affordance Benchmark\" for evaluating object placement plausibility, outperforming supervised methods. Human evaluations show that Add-it is preferred in over 80% of cases, and it also demonstrates improvements in various automated metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.05990", "title": "Game-theoretic LLM: Agent Workflow for Negotiation Games", "authors": [ "Wenyue Hua", "Ollie Liu", "Lingyao Li", "Alfonso Amayuelas", "Julie Chen", "Lucas Jiang", "Mingyu Jin", "Lizhou Fan", "Fei Sun", "William Wang", "Xintong Wang", "Yongfeng Zhang" ], "github": "https://github.com/Wenyueh/game_theory", "project_page": "", "abstract": "This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several state-of-the-art LLMs across a spectrum of complete-information and incomplete-information games. Our findings reveal that LLMs frequently deviate from rational strategies, particularly as the complexity of the game increases with larger payoff matrices or deeper sequential trees. To address these limitations, we design multiple game-theoretic workflows that guide the reasoning and decision-making processes of LLMs. These workflows aim to enhance the models' ability to compute Nash Equilibria and make rational choices, even under conditions of uncertainty and incomplete information. Experimental results demonstrate that the adoption of these workflows significantly improves the rationality and robustness of LLMs in game-theoretic tasks. Specifically, with the workflow, LLMs exhibit marked improvements in identifying optimal strategies, achieving near-optimal allocations in negotiation scenarios, and reducing susceptibility to exploitation during negotiations. Furthermore, we explore the meta-strategic considerations of whether it is rational for agents to adopt such workflows, recognizing that the decision to use or forgo the workflow constitutes a game-theoretic issue in itself. Our research contributes to a deeper understanding of LLMs' decision-making capabilities in strategic contexts and provides insights into enhancing their rationality through structured workflows. The findings have implications for the development of more robust and strategically sound AI agents capable of navigating complex interactive environments. Code and data supporting this study are available at https://github.com/Wenyueh/game_theory.", "github_manual": "", "github_regex": "https://github.com/Wenyueh/game_theory", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.07231", "title": "Watermark Anything with Localized Messages", "authors": [ "Tom Sander", "Pierre Fernandez", "Alain Durmus", "Teddy Furon", "Matthijs Douze" ], "github": "https://github.com/facebookresearch/watermark-anything", "project_page": "", "abstract": "Image watermarking methods are not tailored to handle small watermarked areas. This restricts applications in real-world scenarios where parts of the image may come from different sources or have been edited. We introduce a deep-learning model for localized image watermarking, dubbed the Watermark Anything Model (WAM). The WAM embedder imperceptibly modifies the input image, while the extractor segments the received image into watermarked and non-watermarked areas and recovers one or several hidden messages from the areas found to be watermarked. The models are jointly trained at low resolution and without perceptual constraints, then post-trained for imperceptibility and multiple watermarks. Experiments show that WAM is competitive with state-of-the art methods in terms of imperceptibility and robustness, especially against inpainting and splicing, even on high-resolution images. Moreover, it offers new capabilities: WAM can locate watermarked areas in spliced images and extract distinct 32-bit messages with less than 1 bit error from multiple small regions - no larger than 10% of the image surface - even for small 256times 256 images.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/watermark-anything", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.06424", "title": "Ablation is Not Enough to Emulate DPO: How Neuron Dynamics Drive Toxicity Reduction", "authors": [ "Yushi Yang", "Filip Sondej", "Harry Mayne", "Adam Mahdi" ], "github": "https://github.com/yushi-y/dpo-toxic-neurons", "project_page": "", "abstract": "Safety fine-tuning algorithms are commonly used to fine-tune language models to reduce harmful outputs, but the exact internal mechanisms of how those models achieve this remain unclear. In studying direct preference optimisation (DPO) for toxicity reduction, current explanations claim that DPO works by dampening the most toxic MLP neurons to learn an offset to avert toxic regions in the residual stream. However, by ablating the most toxic neurons and applying activation patching, we find this explanation incomplete. By projecting neuron activation changes onto a toxicity probe, we find that only 31.8\\% of toxicity reduction comes from dampened toxic neurons. Instead, DPO reduces toxicity by accumulating effects across multiple neuron groups, both reducing writing in the toxic direction and promoting anti-toxicity in the residual stream. Moreover, DPO gives noisy adjustments to neuron activations, with many neurons actually increasing toxicity. This indicates that DPO is a balancing process between opposing neuron effects to achieve toxicity reduction.", "github_manual": "https://github.com/yushi-y/dpo-toxic-neurons", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.06481", "title": "KMM: Key Frame Mask Mamba for Extended Motion Generation", "authors": [ "Zeyu Zhang", "Hang Gao", "Akide Liu", "Qi Chen", "Feng Chen", "Yiran Wang", "Danning Li", "Hao Tang" ], "github": "https://github.com/steve-zeyu-zhang/KMM", "project_page": "", "abstract": "Human motion generation is a cut-edge area of research in generative computer vision, with promising applications in video creation, game development, and robotic manipulation. The recent Mamba architecture shows promising results in efficiently modeling long and complex sequences, yet two significant challenges remain: Firstly, directly applying Mamba to extended motion generation is ineffective, as the limited capacity of the implicit memory leads to memory decay. Secondly, Mamba struggles with multimodal fusion compared to Transformers, and lack alignment with textual queries, often confusing directions (left or right) or omitting parts of longer text queries. To address these challenges, our paper presents three key contributions: Firstly, we introduce KMM, a novel architecture featuring Key frame Masking Modeling, designed to enhance Mamba's focus on key actions in motion segments. This approach addresses the memory decay problem and represents a pioneering method in customizing strategic frame-level masking in SSMs. Additionally, we designed a contrastive learning paradigm for addressing the multimodal fusion problem in Mamba and improving the motion-text alignment. Finally, we conducted extensive experiments on the go-to dataset, BABEL, achieving state-of-the-art performance with a reduction of more than 57% in FID and 70% parameters compared to previous state-of-the-art methods. See project website: https://steve-zeyu-zhang.github.io/KMM", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/steve-zeyu-zhang/KMM", "github_api_added_by": "auto" }, { "date": "2024-11-12", "arxiv_id": "2411.05945", "title": "NeKo: Toward Post Recognition Generative Correction Large Language Models with Task-Oriented Experts", "authors": [ "Yen-Ting Lin", "Chao-Han Huck Yang", "Zhehuai Chen", "Piotr Zelasko", "Xuesong Yang", "Zih-Ching Chen", "Krishna C Puvvada", "Szu-Wei Fu", "Ke Hu", "Jun Wei Chiu", "Jagadeesh Balam", "Boris Ginsburg", "Yu-Chiang Frank Wang" ], "github": "", "project_page": "", "abstract": "Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The answer would lie in learning dataset-specific features and digesting their knowledge in a single model. Previous methods achieve this by having separate correction language models, resulting in a significant increase in parameters. In this work, we present Mixture-of-Experts as a solution, highlighting that MoEs are much more than a scalability tool. We propose a Multi-Task Correction MoE, where we train the experts to become an ``expert'' of speech-to-text, language-to-text and vision-to-text datasets by learning to route each dataset's tokens to its mapped expert. Experiments on the Open ASR Leaderboard show that we explore a new state-of-the-art performance by achieving an average relative 5.0% WER reduction and substantial improvements in BLEU scores for speech and translation tasks. On zero-shot evaluation, NeKo outperforms GPT-3.5 and Claude-Opus with 15.5% to 27.6% relative WER reduction in the Hyporadise benchmark. NeKo performs competitively on grammar and post-OCR correction as a multi-task model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.05830", "title": "GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models", "authors": [ "Nizar Islah", "Justine Gehring", "Diganta Misra", "Eilif Muller", "Irina Rish", "Terry Yue Zhuo", "Massimo Caccia" ], "github": "https://github.com/NizarIslah/GitChameleon", "project_page": "", "abstract": "The rapid evolution of software libraries presents a significant challenge for code generation models, which must adapt to frequent version updates while maintaining compatibility with previous versions. Existing code completion benchmarks often overlook this dynamic aspect, and the one that does consider it relies on static code prediction tasks without execution-based evaluation, offering a limited perspective on a model's practical usability. To address this gap, we introduce \\GitChameleon{}, a novel, manually curated dataset comprising 116 Python code completion problems, each conditioned on specific library versions and accompanied by executable unit tests. is designed to rigorously assess the ability of modern large language models (LLMs) to generate version-specific code that is not only syntactically correct but also functionally accurate upon execution. Our comprehensive evaluations reveal that state-of-the-art LLMs struggle with this task; for instance, GPT-4o achieves a pass@10 of only 39.9\\% (43.7\\% when provided with error feedback), highlighting the complexity of the problem and the limitations of current models. By providing an execution-based benchmark that emphasizes the dynamic nature of code libraries, serves as a critical tool to advance the development of more adaptable and reliable code generation models. For facilitation for further exploration of version-conditioned code generation, we make our code repository publicly accessible at https://github.com/NizarIslah/GitChameleon.", "github_manual": "", "github_regex": "https://github.com/NizarIslah/GitChameleon", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.05902", "title": "Autoregressive Models in Vision: A Survey", "authors": [ "Jing Xiong", "Gongye Liu", "Lun Huang", "Chengyue Wu", "Taiqiang Wu", "Yao Mu", "Yuan Yao", "Hui Shen", "Zhongwei Wan", "Jinfa Huang", "Chaofan Tao", "Shen Yan", "Huaxiu Yao", "Lingpeng Kong", "Hongxia Yang", "Mi Zhang", "Guillermo Sapiro", "Jiebo Luo", "Ping Luo", "Ngai Wong" ], "github": "https://github.com/ChaofanTao/Autoregressive-Models-in-Vision-Survey", "project_page": "", "abstract": "Autoregressive modeling has been a huge success in the field of natural language processing (NLP). Recently, autoregressive models have emerged as a significant area of focus in computer vision, where they excel in producing high-quality visual content. Autoregressive models in NLP typically operate on subword tokens. However, the representation strategy in computer vision can vary in different levels, i.e., pixel-level, token-level, or scale-level, reflecting the diverse and hierarchical nature of visual data compared to the sequential structure of language. This survey comprehensively examines the literature on autoregressive models applied to vision. To improve readability for researchers from diverse research backgrounds, we start with preliminary sequence representation and modeling in vision. Next, we divide the fundamental frameworks of visual autoregressive models into three general sub-categories, including pixel-based, token-based, and scale-based models based on the strategy of representation. We then explore the interconnections between autoregressive models and other generative models. Furthermore, we present a multi-faceted categorization of autoregressive models in computer vision, including image generation, video generation, 3D generation, and multi-modal generation. We also elaborate on their applications in diverse domains, including emerging domains such as embodied AI and 3D medical AI, with about 250 related references. Finally, we highlight the current challenges to autoregressive models in vision with suggestions about potential research directions. We have also set up a Github repository to organize the papers included in this survey at: https://github.com/ChaofanTao/Autoregressive-Models-in-Vision-Survey.", "github_manual": "", "github_regex": "https://github.com/ChaofanTao/Autoregressive-Models-in-Vision-Survey", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-12", "arxiv_id": "2411.05966", "title": "Energy Efficient Protein Language Models: Leveraging Small Language Models with LoRA for Controllable Protein Generation", "authors": [ "Aayush Shah", "Shankar Jayaratnam" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated significant success in natural language processing (NLP) tasks and have shown promising results in other domains such as protein sequence generation. However, there remain salient differences between LLMs used for NLP, which effectively handle multiple tasks and are available in small sizes, and protein language models that are often specialized for specific tasks and only exist in larger sizes. In this work, we introduce two small protein language models, based on Llama-3-8B and Phi-3-mini, that are capable of both uncontrollable and controllable protein generation. For the uncontrollable generation task, our best model achieves an average pLDDT score of 69.75, demonstrating robust performance in generating viable protein structures. For the controllable generation task, in which the model generates proteins according to properties specified in the prompt, we achieve a remarkable average TM-Score of 0.84, indicating high structural similarity to target proteins. We chose 10 properties, including six classes of enzymes, to extend the capabilities of prior protein language models. Our approach utilizes the Low-Rank Adaptor (LoRA) technique, reducing trainable parameters to just 4% of the original model size, lowering computational requirements. By using a subset of the UniRef50 dataset and small models, we reduced the overall training time by 70% without compromising performance. Notably, Phi-3-mini reduced trainable parameters by 60%, decreasing training cost by 30% compared to Llama 3. Consequently, Phi-3 achieved a comparable TM-Score of 0.81, demonstrating that smaller models can match the performance of larger ones, like Llama 3. We also demonstrate the deployment of our models on the energy efficient ET-SoC-1 chip, significantly improving the TPS/W by a factor of 3.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-13", "arxiv_id": "2411.07184", "title": "SAMPart3D: Segment Any Part in 3D Objects", "authors": [ "Yunhan Yang", "Yukun Huang", "Yuan-Chen Guo", "Liangjun Lu", "Xiaoyang Wu", "Edmund Y. Lam", "Yan-Pei Cao", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "3D part segmentation is a crucial and challenging task in 3D perception, playing a vital role in applications such as robotics, 3D generation, and 3D editing. Recent methods harness the powerful Vision Language Models (VLMs) for 2D-to-3D knowledge distillation, achieving zero-shot 3D part segmentation. However, these methods are limited by their reliance on text prompts, which restricts the scalability to large-scale unlabeled datasets and the flexibility in handling part ambiguities. In this work, we introduce SAMPart3D, a scalable zero-shot 3D part segmentation framework that segments any 3D object into semantic parts at multiple granularities, without requiring predefined part label sets as text prompts. For scalability, we use text-agnostic vision foundation models to distill a 3D feature extraction backbone, allowing scaling to large unlabeled 3D datasets to learn rich 3D priors. For flexibility, we distill scale-conditioned part-aware 3D features for 3D part segmentation at multiple granularities. Once the segmented parts are obtained from the scale-conditioned part-aware 3D features, we use VLMs to assign semantic labels to each part based on the multi-view renderings. Compared to previous methods, our SAMPart3D can scale to the recent large-scale 3D object dataset Objaverse and handle complex, non-ordinary objects. Additionally, we contribute a new 3D part segmentation benchmark to address the lack of diversity and complexity of objects and parts in existing benchmarks. Experiments show that our SAMPart3D significantly outperforms existing zero-shot 3D part segmentation methods, and can facilitate various applications such as part-level editing and interactive segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-13", "arxiv_id": "2411.07975", "title": "JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation", "authors": [ "Yiyang Ma", "Xingchao Liu", "Xiaokang Chen", "Wen Liu", "Chengyue Wu", "Zhiyu Wu", "Zizheng Pan", "Zhenda Xie", "Haowei Zhang", "Xingkai yu", "Liang Zhao", "Yisong Wang", "Jiaying Liu", "Chong Ruan" ], "github": "https://github.com/deepseek-ai/janus", "project_page": "", "abstract": "We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling. Our key finding demonstrates that rectified flow can be straightforwardly trained within the large language model framework, eliminating the need for complex architectural modifications. To further improve the performance of our unified model, we adopt two key strategies: (i) decoupling the understanding and generation encoders, and (ii) aligning their representations during unified training. Extensive experiments show that JanusFlow achieves comparable or superior performance to specialized models in their respective domains, while significantly outperforming existing unified approaches across standard benchmarks. This work represents a step toward more efficient and versatile vision-language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/deepseek-ai/janus", "github_api_added_by": "auto" }, { "date": "2024-11-13", "arxiv_id": "2411.07133", "title": "Stronger Models are NOT Stronger Teachers for Instruction Tuning", "authors": [ "Zhangchen Xu", "Fengqing Jiang", "Luyao Niu", "Bill Yuchen Lin", "Radha Poovendran" ], "github": "", "project_page": "", "abstract": "Instruction tuning has been widely adopted to ensure large language models (LLMs) follow user instructions effectively. The resulting instruction-following capabilities of LLMs heavily rely on the instruction datasets used for tuning. Recently, synthetic instruction datasets have emerged as an economically viable solution to provide LLMs diverse and high-quality instructions. However, existing approaches typically assume that larger or stronger models are stronger teachers for instruction tuning, and hence simply adopt these models as response generators to the synthetic instructions. In this paper, we challenge this commonly-adopted assumption. Our extensive experiments across five base models and twenty response generators reveal that larger and stronger models are not necessarily stronger teachers of smaller models. We refer to this phenomenon as the Larger Models' Paradox. We observe that existing metrics cannot precisely predict the effectiveness of response generators since they ignore the compatibility between teachers and base models being fine-tuned. We thus develop a novel metric, named as Compatibility-Adjusted Reward (CAR) to measure the effectiveness of response generators. Our experiments across five base models demonstrate that CAR outperforms almost all baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-13", "arxiv_id": "2411.08017", "title": "Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model with Compact Wavelet Encodings", "authors": [ "Aditya Sanghi", "Aliasghar Khani", "Pradyumna Reddy", "Arianna Rampini", "Derek Cheung", "Kamal Rahimi Malekshan", "Kanika Madan", "Hooman Shayani" ], "github": "https://github.com/autodeskailab/wala", "project_page": "", "abstract": "Large-scale 3D generative models require substantial computational resources yet often fall short in capturing fine details and complex geometries at high resolutions. We attribute this limitation to the inefficiency of current representations, which lack the compactness required to model the generative models effectively. To address this, we introduce a novel approach called Wavelet Latent Diffusion, or WaLa, that encodes 3D shapes into wavelet-based, compact latent encodings. Specifically, we compress a 256^3 signed distance field into a 12^3 times 4 latent grid, achieving an impressive 2427x compression ratio with minimal loss of detail. This high level of compression allows our method to efficiently train large-scale generative networks without increasing the inference time. Our models, both conditional and unconditional, contain approximately one billion parameters and successfully generate high-quality 3D shapes at 256^3 resolution. Moreover, WaLa offers rapid inference, producing shapes within two to four seconds depending on the condition, despite the model's scale. We demonstrate state-of-the-art performance across multiple datasets, with significant improvements in generation quality, diversity, and computational efficiency. We open-source our code and, to the best of our knowledge, release the largest pretrained 3D generative models across different modalities.", "github_manual": "https://github.com/autodeskailab/wala", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-13", "arxiv_id": "2411.05197", "title": "Hardware and Software Platform Inference", "authors": [ "Cheng Zhang", "Hanna Foerster", "Robert D. Mullins", "Yiren Zhao", "Ilia Shumailov" ], "github": "https://github.com/ChengZhang-98/HSPI", "project_page": "", "abstract": "It is now a common business practice to buy access to large language model (LLM) inference rather than self-host, because of significant upfront hardware infrastructure and energy costs. However, as a buyer, there is no mechanism to verify the authenticity of the advertised service including the serving hardware platform, e.g. that it is actually being served using an NVIDIA H100. Furthermore, there are reports suggesting that model providers may deliver models that differ slightly from the advertised ones, often to make them run on less expensive hardware. That way, a client pays premium for a capable model access on more expensive hardware, yet ends up being served by a (potentially less capable) cheaper model on cheaper hardware. In this paper we introduce \\textbf{hardware and software platform inference (HSPI)} -- a method for identifying the underlying architecture and software stack of a (black-box) machine learning model solely based on its input-output behavior. Our method leverages the inherent differences of various architectures and compilers to distinguish between different types and software stacks. By analyzing the numerical patterns in the model's outputs, we propose a classification framework capable of accurately identifying the used for model inference as well as the underlying software configuration. Our findings demonstrate the feasibility of inferring type from black-box models. We evaluate HSPI against models served on different real hardware and find that in a white-box setting we can distinguish between different s with between 83.9% and 100% accuracy. Even in a black-box setting we are able to achieve results that are up to three times higher than random guess accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ChengZhang-98/HSPI", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-13", "arxiv_id": "2411.07461", "title": "BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions", "authors": [ "Anas Awadalla", "Le Xue", "Manli Shu", "An Yan", "Jun Wang", "Senthil Purushwalkam", "Sheng Shen", "Hannah Lee", "Oscar Lo", "Jae Sung Park", "Etash Guha", "Silvio Savarese", "Ludwig Schmidt", "Yejin Choi", "Caiming Xiong", "Ran Xu" ], "github": "", "project_page": "", "abstract": "We introduce BLIP3-KALE, a dataset of 218 million image-text pairs that bridges the gap between descriptive synthetic captions and factual web-scale alt-text. KALE augments synthetic dense image captions with web-scale alt-text to generate factually grounded image captions. Our two-stage approach leverages large vision-language models and language models to create knowledge-augmented captions, which are then used to train a specialized VLM for scaling up the dataset. We train vision-language models on KALE and demonstrate improvements on vision-language tasks. Our experiments show the utility of KALE for training more capable and knowledgeable multimodal models. We release the KALE dataset at https://huggingface.co/datasets/Salesforce/blip3-kale", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-13", "arxiv_id": "2411.06307", "title": "Acoustic Volume Rendering for Neural Impulse Response Fields", "authors": [ "Zitong Lan", "Chenhao Zheng", "Zhiwei Zheng", "Mingmin Zhao" ], "github": "", "project_page": "", "abstract": "Realistic audio synthesis that captures accurate acoustic phenomena is essential for creating immersive experiences in virtual and augmented reality. Synthesizing the sound received at any position relies on the estimation of impulse response (IR), which characterizes how sound propagates in one scene along different paths before arriving at the listener's position. In this paper, we present Acoustic Volume Rendering (AVR), a novel approach that adapts volume rendering techniques to model acoustic impulse responses. While volume rendering has been successful in modeling radiance fields for images and neural scene representations, IRs present unique challenges as time-series signals. To address these challenges, we introduce frequency-domain volume rendering and use spherical integration to fit the IR measurements. Our method constructs an impulse response field that inherently encodes wave propagation principles and achieves state-of-the-art performance in synthesizing impulse responses for novel poses. Experiments show that AVR surpasses current leading methods by a substantial margin. Additionally, we develop an acoustic simulation platform, AcoustiX, which provides more accurate and realistic IR simulations than existing simulators. Code for AVR and AcoustiX are available at https://zitonglan.github.io/avr.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-13", "arxiv_id": "2411.08034", "title": "Scaling Properties of Diffusion Models for Perceptual Tasks", "authors": [ "Rahul Ravishankar", "Zeeshan Patel", "Jathushan Rajasegaran", "Jitendra Malik" ], "github": "", "project_page": "", "abstract": "In this paper, we argue that iterative computation with diffusion models offers a powerful paradigm for not only generation but also visual perception tasks. We unify tasks such as depth estimation, optical flow, and segmentation under image-to-image translation, and show how diffusion models benefit from scaling training and test-time compute for these perception tasks. Through a careful analysis of these scaling behaviors, we present various techniques to efficiently train diffusion models for visual perception tasks. Our models achieve improved or comparable performance to state-of-the-art methods using significantly less data and compute. To use our code and models, see https://scaling-diffusion-perception.github.io .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-14", "arxiv_id": "2411.08380", "title": "EgoVid-5M: A Large-Scale Video-Action Dataset for Egocentric Video Generation", "authors": [ "Xiaofeng Wang", "Kang Zhao", "Feng Liu", "Jiayu Wang", "Guosheng Zhao", "Xiaoyi Bao", "Zheng Zhu", "Yingya Zhang", "Xingang Wang" ], "github": "", "project_page": "", "abstract": "Video generation has emerged as a promising tool for world simulation, leveraging visual data to replicate real-world environments. Within this context, egocentric video generation, which centers on the human perspective, holds significant potential for enhancing applications in virtual reality, augmented reality, and gaming. However, the generation of egocentric videos presents substantial challenges due to the dynamic nature of egocentric viewpoints, the intricate diversity of actions, and the complex variety of scenes encountered. Existing datasets are inadequate for addressing these challenges effectively. To bridge this gap, we present EgoVid-5M, the first high-quality dataset specifically curated for egocentric video generation. EgoVid-5M encompasses 5 million egocentric video clips and is enriched with detailed action annotations, including fine-grained kinematic control and high-level textual descriptions. To ensure the integrity and usability of the dataset, we implement a sophisticated data cleaning pipeline designed to maintain frame consistency, action coherence, and motion smoothness under egocentric conditions. Furthermore, we introduce EgoDreamer, which is capable of generating egocentric videos driven simultaneously by action descriptions and kinematic control signals. The EgoVid-5M dataset, associated action annotations, and all data cleansing metadata will be released for the advancement of research in egocentric video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-14", "arxiv_id": "2411.07618", "title": "Direct Preference Optimization Using Sparse Feature-Level Constraints", "authors": [ "Qingyu Yin", "Chak Tou Leong", "Hongbo Zhang", "Minjun Zhu", "Hanqi Yan", "Qiang Zhang", "Yulan He", "Wenjie Li", "Jun Wang", "Yue Zhang", "Linyi Yang" ], "github": "", "project_page": "", "abstract": "The alignment of large language models (LLMs) with human preferences remains a key challenge. While post-training techniques like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) have achieved notable success, they often introduce computational inefficiencies and training instability. In this paper, we propose Feature-level constrained Preference Optimization (FPO), a novel method designed to simplify the alignment process while ensuring stability. FPO leverages pre-trained Sparse Autoencoders (SAEs) and introduces feature-level constraints, allowing for efficient, sparsity-enforced alignment. Our approach enjoys efficiency by using sparse features activated in a well-trained sparse autoencoder and the quality of sequential KL divergence by using the feature-level offline reference. Experimental results on benchmark datasets demonstrate that FPO achieves a 5.08% absolute improvement in win rate with much lower computational cost compared to state-of-the-art baselines, making it a promising solution for efficient and controllable LLM alignments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-14", "arxiv_id": "2411.08147", "title": "Large Language Models Can Self-Improve in Long-context Reasoning", "authors": [ "Siheng Li", "Cheng Yang", "Zesen Cheng", "Lemao Liu", "Mo Yu", "Yujiu Yang", "Wai Lam" ], "github": "https://github.com/sihengli99/sealong", "project_page": "", "abstract": "Large language models (LLMs) have achieved substantial progress in processing long contexts but still struggle with long-context reasoning. Existing approaches typically involve fine-tuning LLMs with synthetic data, which depends on annotations from human experts or advanced models like GPT-4, thus restricting further advancements. To address this issue, we investigate the potential for LLMs to self-improve in long-context reasoning and propose \\ours, an approach specifically designed for this purpose. This approach is straightforward: we sample multiple outputs for each question, score them with Minimum Bayes Risk, and then apply supervised fine-tuning or preference optimization based on these outputs. Extensive experiments on several leading LLMs demonstrate the effectiveness of \\ours, with an absolute improvement of 4.2 points for Llama-3.1-8B-Instruct. Furthermore, \\ours achieves superior performance compared to prior approaches that depend on data produced by human experts or advanced models. We anticipate that this work will open new avenues for self-improvement techniques in long-context scenarios, which are essential for the continual advancement of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sihengli99/sealong", "github_api_added_by": "auto" }, { "date": "2024-11-14", "arxiv_id": "2411.08868", "title": "CamemBERT 2.0: A Smarter French Language Model Aged to Perfection", "authors": [ "Wissam Antoun", "Francis Kulumba", "Rian Touchent", "\u00c9ric de la Clergerie", "Beno\u00eet Sagot", "Djam\u00e9 Seddah" ], "github": "", "project_page": "", "abstract": "French language models, such as CamemBERT, have been widely adopted across industries for natural language processing (NLP) tasks, with models like CamemBERT seeing over 4 million downloads per month. However, these models face challenges due to temporal concept drift, where outdated training data leads to a decline in performance, especially when encountering new topics and terminology. This issue emphasizes the need for updated models that reflect current linguistic trends. In this paper, we introduce two new versions of the CamemBERT base model-CamemBERTav2 and CamemBERTv2-designed to address these challenges. CamemBERTav2 is based on the DeBERTaV3 architecture and makes use of the Replaced Token Detection (RTD) objective for better contextual understanding, while CamemBERTv2 is built on RoBERTa, which uses the Masked Language Modeling (MLM) objective. Both models are trained on a significantly larger and more recent dataset with longer context length and an updated tokenizer that enhances tokenization performance for French. We evaluate the performance of these models on both general-domain NLP tasks and domain-specific applications, such as medical field tasks, demonstrating their versatility and effectiveness across a range of use cases. Our results show that these updated models vastly outperform their predecessors, making them valuable tools for modern NLP systems. All our new models, as well as intermediate checkpoints, are made openly available on Huggingface.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-14", "arxiv_id": "2411.08790", "title": "Can sparse autoencoders be used to decompose and interpret steering vectors?", "authors": [ "Harry Mayne", "Yushi Yang", "Adam Mahdi" ], "github": "https://github.com/harrymayne/sv_interpretability", "project_page": "", "abstract": "Steering vectors are a promising approach to control the behaviour of large language models. However, their underlying mechanisms remain poorly understood. While sparse autoencoders (SAEs) may offer a potential method to interpret steering vectors, recent findings show that SAE-reconstructed vectors often lack the steering properties of the original vectors. This paper investigates why directly applying SAEs to steering vectors yields misleading decompositions, identifying two reasons: (1) steering vectors fall outside the input distribution for which SAEs are designed, and (2) steering vectors can have meaningful negative projections in feature directions, which SAEs are not designed to accommodate. These limitations hinder the direct use of SAEs for interpreting steering vectors.", "github_manual": "https://github.com/harrymayne/sv_interpretability", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-14", "arxiv_id": "2411.08307", "title": "PerceiverS: A Multi-Scale Perceiver with Effective Segmentation for Long-Term Expressive Symbolic Music Generation", "authors": [ "Yungang Yi", "Weihua Li", "Matthew Kuo", "Quan Bai" ], "github": "", "project_page": "", "abstract": "Music generation has progressed significantly, especially in the domain of audio generation. However, generating symbolic music that is both long-structured and expressive remains a significant challenge. In this paper, we propose PerceiverS (Segmentation and Scale), a novel architecture designed to address this issue by leveraging both Effective Segmentation and Multi-Scale attention mechanisms. Our approach enhances symbolic music generation by simultaneously learning long-term structural dependencies and short-term expressive details. By combining cross-attention and self-attention in a Multi-Scale setting, PerceiverS captures long-range musical structure while preserving performance nuances. The proposed model, evaluated on datasets like Maestro, demonstrates improvements in generating coherent and diverse music with both structural consistency and expressive variation. The project demos and the generated music samples can be accessed through the link: https://perceivers.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-14", "arxiv_id": "2411.08328", "title": "Motion Control for Enhanced Complex Action Video Generation", "authors": [ "Qiang Zhou", "Shaofeng Zhang", "Nianzu Yang", "Ye Qian", "Hao Li" ], "github": "", "project_page": "", "abstract": "Existing text-to-video (T2V) models often struggle with generating videos with sufficiently pronounced or complex actions. A key limitation lies in the text prompt's inability to precisely convey intricate motion details. To address this, we propose a novel framework, MVideo, designed to produce long-duration videos with precise, fluid actions. MVideo overcomes the limitations of text prompts by incorporating mask sequences as an additional motion condition input, providing a clearer, more accurate representation of intended actions. Leveraging foundational vision models such as GroundingDINO and SAM2, MVideo automatically generates mask sequences, enhancing both efficiency and robustness. Our results demonstrate that, after training, MVideo effectively aligns text prompts with motion conditions to produce videos that simultaneously meet both criteria. This dual control mechanism allows for more dynamic video generation by enabling alterations to either the text prompt or motion condition independently, or both in tandem. Furthermore, MVideo supports motion condition editing and composition, facilitating the generation of videos with more complex actions. MVideo thus advances T2V motion generation, setting a strong benchmark for improved action depiction in current video diffusion models. Our project page is available at https://mvideo-v1.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-15", "arxiv_id": "2411.09703", "title": "MagicQuill: An Intelligent Interactive Image Editing System", "authors": [ "Zichen Liu", "Yue Yu", "Hao Ouyang", "Qiuyu Wang", "Ka Leong Cheng", "Wen Wang", "Zhiheng Liu", "Qifeng Chen", "Yujun Shen" ], "github": "https://github.com/ant-research/MagicQuill", "project_page": "", "abstract": "Image editing involves a variety of complex tasks and requires efficient and precise manipulation techniques. In this paper, we present MagicQuill, an integrated image editing system that enables swift actualization of creative ideas. Our system features a streamlined yet functionally robust interface, allowing for the articulation of editing operations (e.g., inserting elements, erasing objects, altering color) with minimal input. These interactions are monitored by a multimodal large language model (MLLM) to anticipate editing intentions in real time, bypassing the need for explicit prompt entry. Finally, we apply a powerful diffusion prior, enhanced by a carefully learned two-branch plug-in module, to process editing requests with precise control. Experimental results demonstrate the effectiveness of MagicQuill in achieving high-quality image edits. Please visit https://magic-quill.github.io to try out our system.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ant-research/MagicQuill", "github_api_added_by": "auto" }, { "date": "2024-11-15", "arxiv_id": "2411.06469", "title": "ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?", "authors": [ "Canyu Chen", "Jian Yu", "Shan Chen", "Che Liu", "Zhongwei Wan", "Danielle Bitterman", "Fei Wang", "Kai Shu" ], "github": "https://github.com/sunlabiuc/PyHealth", "project_page": "", "abstract": "Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams. Meanwhile, traditional ML models such as SVM and XGBoost have still been mainly adopted in clinical prediction tasks. An emerging question is Can LLMs beat traditional ML models in clinical prediction? Thus, we build a new benchmark ClinicalBench to comprehensively study the clinical predictive modeling capacities of both general-purpose and medical LLMs, and compare them with traditional ML models. ClinicalBench embraces three common clinical prediction tasks, two databases, 14 general-purpose LLMs, 8 medical LLMs, and 11 traditional ML models. Through extensive empirical investigation, we discover that both general-purpose and medical LLMs, even with different model scales, diverse prompting or fine-tuning strategies, still cannot beat traditional ML models in clinical prediction yet, shedding light on their potential deficiency in clinical reasoning and decision-making. We call for caution when practitioners adopt LLMs in clinical applications. ClinicalBench can be utilized to bridge the gap between LLMs' development for healthcare and real-world clinical practice.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sunlabiuc/PyHealth", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-15", "arxiv_id": "2411.09595", "title": "LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models", "authors": [ "Zhengyi Wang", "Jonathan Lorraine", "Yikai Wang", "Hang Su", "Jun Zhu", "Sanja Fidler", "Xiaohui Zeng" ], "github": "https://github.com/nv-tlabs/LLaMa-Mesh", "project_page": "", "abstract": "This work explores expanding the capabilities of large language models (LLMs) pretrained on text to generate 3D meshes within a unified model. This offers key advantages of (1) leveraging spatial knowledge already embedded in LLMs, derived from textual sources like 3D tutorials, and (2) enabling conversational 3D generation and mesh understanding. A primary challenge is effectively tokenizing 3D mesh data into discrete tokens that LLMs can process seamlessly. To address this, we introduce LLaMA-Mesh, a novel approach that represents the vertex coordinates and face definitions of 3D meshes as plain text, allowing direct integration with LLMs without expanding the vocabulary. We construct a supervised fine-tuning (SFT) dataset enabling pretrained LLMs to (1) generate 3D meshes from text prompts, (2) produce interleaved text and 3D mesh outputs as required, and (3) understand and interpret 3D meshes. Our work is the first to demonstrate that LLMs can be fine-tuned to acquire complex spatial knowledge for 3D mesh generation in a text-based format, effectively unifying the 3D and text modalities. LLaMA-Mesh achieves mesh generation quality on par with models trained from scratch while maintaining strong text generation performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nv-tlabs/LLaMa-Mesh", "github_api_added_by": "auto" }, { "date": "2024-11-15", "arxiv_id": "2411.09009", "title": "Cut Your Losses in Large-Vocabulary Language Models", "authors": [ "Erik Wijmans", "Brody Huval", "Alexander Hertzberg", "Vladlen Koltun", "Philipp Kr\u00e4henb\u00fchl" ], "github": "https://github.com/apple/ml-cross-entropy", "project_page": "", "abstract": "As language models grow ever larger, so do their vocabularies. This has shifted the memory footprint of LLMs during training disproportionately to one single layer: the cross-entropy in the loss computation. Cross-entropy builds up a logit matrix with entries for each pair of input tokens and vocabulary items and, for small models, consumes an order of magnitude more memory than the rest of the LLM combined. We propose Cut Cross-Entropy (CCE), a method that computes the cross-entropy loss without materializing the logits for all tokens into global memory. Rather, CCE only computes the logit for the correct token and evaluates the log-sum-exp over all logits on the fly. We implement a custom kernel that performs the matrix multiplications and the log-sum-exp reduction over the vocabulary in flash memory, making global memory consumption for the cross-entropy computation negligible. This has a dramatic effect. Taking the Gemma 2 (2B) model as an example, CCE reduces the memory footprint of the loss computation from 24 GB to 1 MB, and the total training-time memory consumption of the classifier head from 28 GB to 1 GB. To improve the throughput of CCE, we leverage the inherent sparsity of softmax and propose to skip elements of the gradient computation that have a negligible (i.e., below numerical precision) contribution to the gradient. Experiments demonstrate that the dramatic reduction in memory consumption is accomplished without sacrificing training speed or convergence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apple/ml-cross-entropy", "github_api_added_by": "auto" }, { "date": "2024-11-15", "arxiv_id": "2411.08768", "title": "Sharingan: Extract User Action Sequence from Desktop Recordings", "authors": [ "Yanting Chen", "Yi Ren", "Xiaoting Qin", "Jue Zhang", "Kehong Yuan", "Lu Han", "Qingwei Lin", "Dongmei Zhang", "Saravan Rajmohan", "Qi Zhang" ], "github": "", "project_page": "", "abstract": "Video recordings of user activities, particularly desktop recordings, offer a rich source of data for understanding user behaviors and automating processes. However, despite advancements in Vision-Language Models (VLMs) and their increasing use in video analysis, extracting user actions from desktop recordings remains an underexplored area. This paper addresses this gap by proposing two novel VLM-based methods for user action extraction: the Direct Frame-Based Approach (DF), which inputs sampled frames directly into VLMs, and the Differential Frame-Based Approach (DiffF), which incorporates explicit frame differences detected via computer vision techniques. We evaluate these methods using a basic self-curated dataset and an advanced benchmark adapted from prior work. Our results show that the DF approach achieves an accuracy of 70% to 80% in identifying user actions, with the extracted action sequences being re-playable though Robotic Process Automation. We find that while VLMs show potential, incorporating explicit UI changes can degrade performance, making the DF approach more reliable. This work represents the first application of VLMs for extracting user action sequences from desktop recordings, contributing new methods, benchmarks, and insights for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-15", "arxiv_id": "2411.06490", "title": "Hermes: A Large Language Model Framework on the Journey to Autonomous Networks", "authors": [ "Fadhel Ayed", "Ali Maatouk", "Nicola Piovesan", "Antonio De Domenico", "Merouane Debbah", "Zhi-Quan Luo" ], "github": "", "project_page": "", "abstract": "The drive toward automating cellular network operations has grown with the increasing complexity of these systems. Despite advancements, full autonomy currently remains out of reach due to reliance on human intervention for modeling network behaviors and defining policies to meet target requirements. Network Digital Twins (NDTs) have shown promise in enhancing network intelligence, but the successful implementation of this technology is constrained by use case-specific architectures, limiting its role in advancing network autonomy. A more capable network intelligence, or \"telecommunications brain\", is needed to enable seamless, autonomous management of cellular network. Large Language Models (LLMs) have emerged as potential enablers for this vision but face challenges in network modeling, especially in reasoning and handling diverse data types. To address these gaps, we introduce Hermes, a chain of LLM agents that uses \"blueprints\" for constructing NDT instances through structured and explainable logical steps. Hermes allows automatic, reliable, and accurate network modeling of diverse use cases and configurations, thus marking progress toward fully autonomous network operations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-15", "arxiv_id": "2411.08954", "title": "Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples", "authors": [ "No\u00ebl Vouitsis", "Rasa Hosseinzadeh", "Brendan Leigh Ross", "Valentin Villecroze", "Satya Krishna Gorti", "Jesse C. Cresswell", "Gabriel Loaiza-Ganem" ], "github": "https://github.com/layer6ai-labs/direct-cms", "project_page": "", "abstract": "Although diffusion models can generate remarkably high-quality samples, they are intrinsically bottlenecked by their expensive iterative sampling procedure. Consistency models (CMs) have recently emerged as a promising diffusion model distillation method, reducing the cost of sampling by generating high-fidelity samples in just a few iterations. Consistency model distillation aims to solve the probability flow ordinary differential equation (ODE) defined by an existing diffusion model. CMs are not directly trained to minimize error against an ODE solver, rather they use a more computationally tractable objective. As a way to study how effectively CMs solve the probability flow ODE, and the effect that any induced error has on the quality of generated samples, we introduce Direct CMs, which directly minimize this error. Intriguingly, we find that Direct CMs reduce the ODE solving error compared to CMs but also result in significantly worse sample quality, calling into question why exactly CMs work well in the first place. Full code is available at: https://github.com/layer6ai-labs/direct-cms.", "github_manual": "", "github_regex": "https://github.com/layer6ai-labs/direct-cms", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-18", "arxiv_id": "2411.08033", "title": "GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation", "authors": [ "Yushi Lan", "Shangchen Zhou", "Zhaoyang Lyu", "Fangzhou Hong", "Shuai Yang", "Bo Dai", "Xingang Pan", "Chen Change Loy" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "While 3D content generation has advanced significantly, existing methods still face challenges with input formats, latent space design, and output representations. This paper introduces a novel 3D generation framework that addresses these challenges, offering scalable, high-quality 3D generation with an interactive Point Cloud-structured Latent space. Our framework employs a Variational Autoencoder (VAE) with multi-view posed RGB-D(epth)-N(ormal) renderings as input, using a unique latent space design that preserves 3D shape information, and incorporates a cascaded latent diffusion model for improved shape-texture disentanglement. The proposed method, GaussianAnything, supports multi-modal conditional 3D generation, allowing for point cloud, caption, and single/multi-view image inputs. Notably, the newly proposed latent space naturally enables geometry-texture disentanglement, thus allowing 3D-aware editing. Experimental results demonstrate the effectiveness of our approach on multiple datasets, outperforming existing methods in both text- and image-conditioned 3D generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-18", "arxiv_id": "2411.10440", "title": "LLaVA-o1: Let Vision Language Models Reason Step-by-Step", "authors": [ "Guowei Xu", "Peng Jin", "Li Hao", "Yibing Song", "Lichao Sun", "Li Yuan" ], "github": "https://github.com/PKU-YuanGroup/LLaVA-CoT", "project_page": "", "abstract": "Large language models have demonstrated substantial advancements in reasoning capabilities, particularly through inference-time scaling, as illustrated by models such as OpenAI's o1. However, current Vision-Language Models (VLMs) often struggle to perform systematic and structured reasoning, especially when handling complex visual question-answering tasks. In this work, we introduce LLaVA-o1, a novel VLM designed to conduct autonomous multistage reasoning. Unlike chain-of-thought prompting, LLaVA-o1 independently engages in sequential stages of summarization, visual interpretation, logical reasoning, and conclusion generation. This structured approach enables LLaVA-o1 to achieve marked improvements in precision on reasoning-intensive tasks. To accomplish this, we compile the LLaVA-o1-100k dataset, integrating samples from various visual question answering sources and providing structured reasoning annotations. Besides, we propose an inference-time stage-level beam search method, which enables effective inference-time scaling. Remarkably, with only 100k training samples and a simple yet effective inference time scaling method, LLaVA-o1 not only outperforms its base model by 8.9% on a wide range of multimodal reasoning benchmarks, but also surpasses the performance of larger and even closed-source models, such as Gemini-1.5-pro, GPT-4o-mini, and Llama-3.2-90B-Vision-Instruct.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PKU-YuanGroup/LLaVA-CoT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-18", "arxiv_id": "2411.10323", "title": "The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use", "authors": [ "Siyuan Hu", "Mingyu Ouyang", "Difei Gao", "Mike Zheng Shou" ], "github": "https://github.com/showlab/computer_use_ootb", "project_page": "", "abstract": "The recently released model, Claude 3.5 Computer Use, stands out as the first frontier AI model to offer computer use in public beta as a graphical user interface (GUI) agent. As an early beta, its capability in the real-world complex environment remains unknown. In this case study to explore Claude 3.5 Computer Use, we curate and organize a collection of carefully designed tasks spanning a variety of domains and software. Observations from these cases demonstrate Claude 3.5 Computer Use's unprecedented ability in end-to-end language to desktop actions. Along with this study, we provide an out-of-the-box agent framework for deploying API-based GUI automation models with easy implementation. Our case studies aim to showcase a groundwork of capabilities and limitations of Claude 3.5 Computer Use with detailed analyses and bring to the fore questions about planning, action, and critic, which must be considered for future improvement. We hope this preliminary exploration will inspire future research into the GUI agent community. All the test cases in the paper can be tried through the project: https://github.com/showlab/computer_use_ootb.", "github_manual": "", "github_regex": "https://github.com/showlab/computer_use_ootb", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-18", "arxiv_id": "2411.10332", "title": "Number it: Temporal Grounding Videos like Flipping Manga", "authors": [ "Yongliang Wu", "Xinting Hu", "Yuyang Sun", "Yizhou Zhou", "Wenbo Zhu", "Fengyun Rao", "Bernt Schiele", "Xu Yang" ], "github": "https://github.com/yongliang-wu/NumPro", "project_page": "", "abstract": "Video Large Language Models (Vid-LLMs) have made remarkable advancements in comprehending video content for QA dialogue. However, they struggle to extend this visual understanding to tasks requiring precise temporal localization, known as Video Temporal Grounding (VTG). To address this gap, we introduce Number-Prompt (NumPro), a novel method that empowers Vid-LLMs to bridge visual comprehension with temporal grounding by adding unique numerical identifiers to each video frame. Treating a video as a sequence of numbered frame images, NumPro transforms VTG into an intuitive process: flipping through manga panels in sequence. This allows Vid-LLMs to \"read\" event timelines, accurately linking visual content with corresponding temporal information. Our experiments demonstrate that NumPro significantly boosts VTG performance of top-tier Vid-LLMs without additional computational cost. Furthermore, fine-tuning on a NumPro-enhanced dataset defines a new state-of-the-art for VTG, surpassing previous top-performing methods by up to 6.9\\% in mIoU for moment retrieval and 8.5\\% in mAP for highlight detection. The code will be available at https://github.com/yongliang-wu/NumPro.", "github_manual": "", "github_regex": "https://github.com/yongliang-wu/NumPro", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-18", "arxiv_id": "2411.06558", "title": "Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement", "authors": [ "Zhennan Chen", "Yajie Li", "Haofan Wang", "Zhibo Chen", "Zhengkai Jiang", "Jun Li", "Qian Wang", "Jian Yang", "Ying Tai" ], "github": "", "project_page": "", "abstract": "In this paper, we present RAG, a Regional-Aware text-to-image Generation method conditioned on regional descriptions for precise layout composition. Regional prompting, or compositional generation, which enables fine-grained spatial control, has gained increasing attention for its practicality in real-world applications. However, previous methods either introduce additional trainable modules, thus only applicable to specific models, or manipulate on score maps within cross-attention layers using attention masks, resulting in limited control strength when the number of regions increases. To handle these limitations, we decouple the multi-region generation into two sub-tasks, the construction of individual region (Regional Hard Binding) that ensures the regional prompt is properly executed, and the overall detail refinement (Regional Soft Refinement) over regions that dismiss the visual boundaries and enhance adjacent interactions. Furthermore, RAG novelly makes repainting feasible, where users can modify specific unsatisfied regions in the last generation while keeping all other regions unchanged, without relying on additional inpainting models. Our approach is tuning-free and applicable to other frameworks as an enhancement to the prompt following property. Quantitative and qualitative experiments demonstrate that RAG achieves superior performance over attribute binding and object relationship than previous tuning-free methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-18", "arxiv_id": "2411.10083", "title": "Xmodel-1.5: An 1B-scale Multilingual LLM", "authors": [ "Wang Qun", "Liu Yang", "Lin Qingquan", "Jiang Ling" ], "github": "https://github.com/XiaoduoAILab/XmodelLM-1.5", "project_page": "", "abstract": "We introduce Xmodel-1.5, a novel 1-billion-parameter multilingual large model pretrained on approximately 2 trillion tokens. The model demonstrates strong performance across several languages, with particularly notable results in Thai, Arabic, and French, alongside its effectiveness in Chinese and English. In addition, we contribute to the research community by releasing a Thai evaluation dataset, which includes hundreds of questions annotated by students from Chulalongkorn University's School of Integrated Innovation. While the results are promising, we acknowledge that there is still room for improvement. We hope this work advances ongoing efforts in multilingual AI research and promotes better cross-linguistic understanding in various natural language processing tasks. Our models and code are publicly available on GitHub at https://github.com/XiaoduoAILab/XmodelLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/XiaoduoAILab/XmodelLM-1.5", "github_api_added_by": "auto" }, { "date": "2024-11-18", "arxiv_id": "2411.10438", "title": "MARS: Unleashing the Power of Variance Reduction for Training Large Models", "authors": [ "Huizhuo Yuan", "Yifeng Liu", "Shuang Wu", "Xun Zhou", "Quanquan Gu" ], "github": "https://github.com/AGI-Arena/MARS", "project_page": "", "abstract": "Training deep neural networks--and more recently, large models--demands efficient and scalable optimizers. Adaptive gradient algorithms like Adam, AdamW, and their variants have been central to this task. Despite the development of numerous variance reduction algorithms in the past decade aimed at accelerating stochastic optimization in both convex and nonconvex settings, variance reduction has not found widespread success in training deep neural networks or large language models. Consequently, it has remained a less favored approach in modern AI. In this paper, to unleash the power of variance reduction for efficient training of large models, we propose a unified optimization framework, MARS (Make vAriance Reduction Shine), which reconciles preconditioned gradient methods with variance reduction via a scaled stochastic recursive momentum technique. Within our framework, we introduce three instances of MARS that leverage preconditioned gradient updates based on AdamW, Lion, and Shampoo, respectively. We also draw a connection between our algorithms and existing optimizers. Experimental results on training GPT-2 models indicate that MARS consistently outperforms AdamW by a large margin.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AGI-Arena/MARS", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-19", "arxiv_id": "2411.11767", "title": "Drowning in Documents: Consequences of Scaling Reranker Inference", "authors": [ "Mathew Jacob", "Erik Lindgren", "Matei Zaharia", "Michael Carbin", "Omar Khattab", "Andrew Drozdov" ], "github": "", "project_page": "", "abstract": "Rerankers, typically cross-encoders, are often used to re-score the documents retrieved by cheaper initial IR systems. This is because, though expensive, rerankers are assumed to be more effective. We challenge this assumption by measuring reranker performance for full retrieval, not just re-scoring first-stage retrieval. Our experiments reveal a surprising trend: the best existing rerankers provide diminishing returns when scoring progressively more documents and actually degrade quality beyond a certain limit. In fact, in this setting, rerankers can frequently assign high scores to documents with no lexical or semantic overlap with the query. We hope that our findings will spur future research to improve reranking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-19", "arxiv_id": "2411.07641", "title": "Top-n\u03c3: Not All Logits Are You Need", "authors": [ "Chenxia Tang", "Jianchun Liu", "Hongli Xu", "Liusheng Huang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) typically employ greedy decoding or low-temperature sampling for reasoning tasks, reflecting a perceived trade-off between diversity and accuracy. We challenge this convention by introducing top-nsigma, a novel sampling method that operates directly on pre-softmax logits by leveraging a statistical threshold. Our key insight is that logits naturally separate into a Gaussian-distributed noisy region and a distinct informative region, enabling efficient token filtering without complex probability manipulations. Unlike existing methods (e.g., top-p, min-p) that inadvertently include more noise tokens at higher temperatures, top-nsigma maintains a stable sampling space regardless of temperature scaling. We also provide a theoretical analysis of top-nsigma to better understand its behavior. The extensive experimental results across four reasoning-focused datasets demonstrate that our method not only outperforms existing sampling approaches but also surpasses greedy decoding, while maintaining consistent performance even at high temperatures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-19", "arxiv_id": "2411.10640", "title": "BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices", "authors": [ "Xudong Lu", "Yinghao Chen", "Cheng Chen", "Hui Tan", "Boheng Chen", "Yina Xie", "Rui Hu", "Guanxin Tan", "Renshou Wu", "Yan Hu", "Yi Zeng", "Lei Wu", "Liuyang Bian", "Zhaoxiong Wang", "Long Liu", "Yanzhou Yang", "Han Xiao", "Aojun Zhou", "Yafei Wen", "Xiaoxin Chen", "Shuai Ren", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "The emergence and growing popularity of multimodal large language models (MLLMs) have significant potential to enhance various aspects of daily life, from improving communication to facilitating learning and problem-solving. Mobile phones, as essential daily companions, represent the most effective and accessible deployment platform for MLLMs, enabling seamless integration into everyday tasks. However, deploying MLLMs on mobile phones presents challenges due to limitations in memory size and computational capability, making it difficult to achieve smooth and real-time processing without extensive optimization. In this paper, we present BlueLM-V-3B, an algorithm and system co-design approach specifically tailored for the efficient deployment of MLLMs on mobile platforms. To be specific, we redesign the dynamic resolution scheme adopted by mainstream MLLMs and implement system optimization for hardware-aware deployment to optimize model inference on mobile phones. BlueLM-V-3B boasts the following key highlights: (1) Small Size: BlueLM-V-3B features a language model with 2.7B parameters and a vision encoder with 400M parameters. (2) Fast Speed: BlueLM-V-3B achieves a generation speed of 24.4 token/s on the MediaTek Dimensity 9300 processor with 4-bit LLM weight quantization. (3) Strong Performance: BlueLM-V-3B has attained the highest average score of 66.1 on the OpenCompass benchmark among models with leq 4B parameters and surpassed a series of models with much larger parameter sizes (e.g., MiniCPM-V-2.6, InternVL2-8B).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-19", "arxiv_id": "2411.10669", "title": "Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of Experts", "authors": [ "Jinqiang Long", "Yanqi Dai", "Guoxing Yang", "Hongpeng Lin", "Nanyi Fei", "Yizhao Gao", "Zhiwu Lu" ], "github": "https://github.com/MetabrainAGI/Awaker", "project_page": "", "abstract": "As the research of Multimodal Large Language Models (MLLMs) becomes popular, an advancing MLLM model is typically required to handle various textual and visual tasks (e.g., VQA, Detection, OCR, and ChartQA) simultaneously for real-world applications. However, due to the significant differences in representation and distribution among data from various tasks, simply mixing data of all tasks together leads to the well-known``multi-task conflict\" issue, resulting in performance degradation across various tasks. To address this issue, we propose Awaker2.5-VL, a Mixture of Experts~(MoE) architecture suitable for MLLM, which acquires the multi-task capabilities through multiple sparsely activated experts. To speed up the training and inference of Awaker2.5-VL, each expert in our model is devised as a low-rank adaptation (LoRA) structure. Extensive experiments on multiple latest benchmarks demonstrate the effectiveness of Awaker2.5-VL. The code and model weight are released in our Project Page: https://github.com/MetabrainAGI/Awaker.", "github_manual": "", "github_regex": "https://github.com/MetabrainAGI/Awaker", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-19", "arxiv_id": "2411.10510", "title": "SmoothCache: A Universal Inference Acceleration Technique for Diffusion Transformers", "authors": [ "Joseph Liu", "Joshua Geddes", "Ziyu Guo", "Haomiao Jiang", "Mahesh Kumar Nandwana" ], "github": "https://github.com/roblox/smoothcache", "project_page": "", "abstract": "Diffusion Transformers (DiT) have emerged as powerful generative models for various tasks, including image, video, and speech synthesis. However, their inference process remains computationally expensive due to the repeated evaluation of resource-intensive attention and feed-forward modules. To address this, we introduce SmoothCache, a model-agnostic inference acceleration technique for DiT architectures. SmoothCache leverages the observed high similarity between layer outputs across adjacent diffusion timesteps. By analyzing layer-wise representation errors from a small calibration set, SmoothCache adaptively caches and reuses key features during inference. Our experiments demonstrate that SmoothCache achieves 8% to 71% speed up while maintaining or even improving generation quality across diverse modalities. We showcase its effectiveness on DiT-XL for image generation, Open-Sora for text-to-video, and Stable Audio Open for text-to-audio, highlighting its potential to enable real-time applications and broaden the accessibility of powerful DiT models.", "github_manual": "https://github.com/roblox/smoothcache", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-19", "arxiv_id": "2411.10499", "title": "FitDiT: Advancing the Authentic Garment Details for High-fidelity Virtual Try-on", "authors": [ "Boyuan Jiang", "Xiaobin Hu", "Donghao Luo", "Qingdong He", "Chengming Xu", "Jinlong Peng", "Jiangning Zhang", "Chengjie Wang", "Yunsheng Wu", "Yanwei Fu" ], "github": "https://github.com/BoyuanJiang/FitDiT", "project_page": "", "abstract": "Although image-based virtual try-on has made considerable progress, emerging approaches still encounter challenges in producing high-fidelity and robust fitting images across diverse scenarios. These methods often struggle with issues such as texture-aware maintenance and size-aware fitting, which hinder their overall effectiveness. To address these limitations, we propose a novel garment perception enhancement technique, termed FitDiT, designed for high-fidelity virtual try-on using Diffusion Transformers (DiT) allocating more parameters and attention to high-resolution features. First, to further improve texture-aware maintenance, we introduce a garment texture extractor that incorporates garment priors evolution to fine-tune garment feature, facilitating to better capture rich details such as stripes, patterns, and text. Additionally, we introduce frequency-domain learning by customizing a frequency distance loss to enhance high-frequency garment details. To tackle the size-aware fitting issue, we employ a dilated-relaxed mask strategy that adapts to the correct length of garments, preventing the generation of garments that fill the entire mask area during cross-category try-on. Equipped with the above design, FitDiT surpasses all baselines in both qualitative and quantitative evaluations. It excels in producing well-fitting garments with photorealistic and intricate details, while also achieving competitive inference times of 4.57 seconds for a single 1024x768 image after DiT structure slimming, outperforming existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/BoyuanJiang/FitDiT", "github_api_added_by": "auto" }, { "date": "2024-11-19", "arxiv_id": "2411.11504", "title": "Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering", "authors": [ "Xinyan Guan", "Yanjiang Liu", "Xinyu Lu", "Boxi Cao", "Ben He", "Xianpei Han", "Le Sun", "Jie Lou", "Bowen Yu", "Yaojie Lu", "Hongyu Lin" ], "github": "https://github.com/icip-cas/Verifier-Engineering", "project_page": "", "abstract": "The evolution of machine learning has increasingly prioritized the development of powerful models and more scalable supervision signals. However, the emergence of foundation models presents significant challenges in providing effective supervision signals necessary for further enhancing their capabilities. Consequently, there is an urgent need to explore novel supervision signals and technical approaches. In this paper, we propose verifier engineering, a novel post-training paradigm specifically designed for the era of foundation models. The core of verifier engineering involves leveraging a suite of automated verifiers to perform verification tasks and deliver meaningful feedback to foundation models. We systematically categorize the verifier engineering process into three essential stages: search, verify, and feedback, and provide a comprehensive review of state-of-the-art research developments within each stage. We believe that verifier engineering constitutes a fundamental pathway toward achieving Artificial General Intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/icip-cas/Verifier-Engineering", "github_api_added_by": "auto" }, { "date": "2024-11-19", "arxiv_id": "2411.09213", "title": "Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering", "authors": [ "Nghia Trung Ngo", "Chien Van Nguyen", "Franck Dernoncourt", "Thien Huu Nguyen" ], "github": "", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) in knowledge-intensive tasks such as those from medical domain. However, the sensitive nature of the medical domain necessitates a completely accurate and trustworthy system. While existing RAG benchmarks primarily focus on the standard retrieve-answer setting, they overlook many practical scenarios that measure crucial aspects of a reliable medical system. This paper addresses this gap by providing a comprehensive evaluation framework for medical question-answering (QA) systems in a RAG setting for these situations, including sufficiency, integration, and robustness. We introduce Medical Retrieval-Augmented Generation Benchmark (MedRGB) that provides various supplementary elements to four medical QA datasets for testing LLMs' ability to handle these specific scenarios. Utilizing MedRGB, we conduct extensive evaluations of both state-of-the-art commercial LLMs and open-source models across multiple retrieval conditions. Our experimental results reveals current models' limited ability to handle noise and misinformation in the retrieved documents. We further analyze the LLMs' reasoning processes to provides valuable insights and future directions for developing RAG systems in this critical medical domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-19", "arxiv_id": "2411.09944", "title": "SlimLM: An Efficient Small Language Model for On-Device Document Assistance", "authors": [ "Thang M. Pham", "Phat T. Nguyen", "Seunghyun Yoon", "Viet Dac Lai", "Franck Dernoncourt", "Trung Bui" ], "github": "https://github.com/mlc-ai/mlc-llm", "project_page": "", "abstract": "While small language models (SLMs) show promises for mobile deployment, their real-world performance and applications on smartphones remains underexplored. We present SlimLM, a series of SLMs optimized for document assistance tasks on mobile devices. Through extensive experiments on a Samsung Galaxy S24, we identify the optimal trade-offs between model size (ranging from 125M to 7B parameters), context length, and inference time for efficient on-device processing. SlimLM is pre-trained on SlimPajama-627B and fine-tuned on DocAssist, our constructed dataset for summarization, question answering and suggestion tasks. Our smallest model demonstrates efficient performance on S24, while larger variants offer enhanced capabilities within mobile constraints. We evaluate SlimLM against existing SLMs, showing comparable or superior performance and offering a benchmark for future research in on-device language models. We also provide an Android application, offering practical insights into SLM deployment. Our findings provide valuable insights and illuminate the capabilities of running advanced language models on high-end smartphones, potentially reducing server costs and enhancing privacy through on-device processing.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mlc-ai/mlc-llm", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-19", "arxiv_id": "2411.10836", "title": "AnimateAnything: Consistent and Controllable Animation for Video Generation", "authors": [ "Guojun Lei", "Chi Wang", "Hong Li", "Rong Zhang", "Yikai Wang", "Weiwei Xu" ], "github": "", "project_page": "", "abstract": "We present a unified controllable video generation approach AnimateAnything that facilitates precise and consistent video manipulation across various conditions, including camera trajectories, text prompts, and user motion annotations. Specifically, we carefully design a multi-scale control feature fusion network to construct a common motion representation for different conditions. It explicitly converts all control information into frame-by-frame optical flows. Then we incorporate the optical flows as motion priors to guide final video generation. In addition, to reduce the flickering issues caused by large-scale motion, we propose a frequency-based stabilization module. It can enhance temporal coherence by ensuring the video's frequency domain consistency. Experiments demonstrate that our method outperforms the state-of-the-art approaches. For more details and videos, please refer to the webpage: https://yu-shaonian.github.io/Animate_Anything/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-19", "arxiv_id": "2411.11844", "title": "Generative World Explorer", "authors": [ "Taiming Lu", "Tianmin Shu", "Alan Yuille", "Daniel Khashabi", "Jieneng Chen" ], "github": "https://github.com/Beckschen/genex", "project_page": "", "abstract": "Planning with partial observation is a central challenge in embodied AI. A majority of prior works have tackled this challenge by developing agents that physically explore their environment to update their beliefs about the world state.In contrast, humans can imagine unseen parts of the world through a mental exploration and revise their beliefs with imagined observations. Such updated beliefs can allow them to make more informed decisions, without necessitating the physical exploration of the world at all times. To achieve this human-like ability, we introduce the Generative World Explorer (Genex), an egocentric world exploration framework that allows an agent to mentally explore a large-scale 3D world (e.g., urban scenes) and acquire imagined observations to update its belief. This updated belief will then help the agent to make a more informed decision at the current step. To train Genex, we create a synthetic urban scene dataset, Genex-DB. Our experimental results demonstrate that (1) Genex can generate high-quality and consistent observations during long-horizon exploration of a large virtual physical world and (2) the beliefs updated with the generated observations can inform an existing decision-making model (e.g., an LLM agent) to make better plans.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Beckschen/genex", "github_api_added_by": "auto" }, { "date": "2024-11-19", "arxiv_id": "2411.11171", "title": "LL\u00e4Mmlein: Compact and Competitive German-Only Language Models from Scratch", "authors": [ "Jan Pfister", "Julia Wunderle", "Andreas Hotho" ], "github": "https://github.com/Hannibal046/Awesome-LLM", "project_page": "", "abstract": "We create two German-only decoder models, LL\\\"aMmlein 120M and 1B, transparently from scratch and publish them, along with the training data, for the German NLP research community to use. The model training involved several key steps, including extensive data preprocessing, the creation of a custom German tokenizer, the training itself, as well as the evaluation of the final models on various benchmarks. Throughout the training process, multiple checkpoints were saved and analyzed using the SuperGLEBer benchmark to monitor the models' learning dynamics. Compared to state-of-the-art models on the SuperGLEBer benchmark, both LL\\\"aMmlein models performed competitively, consistently matching or surpassing models with similar parameter sizes. The results show that the models' quality scales with size as expected, but performance improvements on some tasks plateaued early, offering valuable insights into resource allocation for future model development.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Hannibal046/Awesome-LLM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-19", "arxiv_id": "2411.11045", "title": "StableV2V: Stablizing Shape Consistency in Video-to-Video Editing", "authors": [ "Chang Liu", "Rui Li", "Kaidong Zhang", "Yunwei Lan", "Dong Liu" ], "github": "https://github.com/alonzoleeeooo/stablev2v", "project_page": "", "abstract": "Recent advancements of generative AI have significantly promoted content creation and editing, where prevailing studies further extend this exciting progress to video editing. In doing so, these studies mainly transfer the inherent motion patterns from the source videos to the edited ones, where results with inferior consistency to user prompts are often observed, due to the lack of particular alignments between the delivered motions and edited contents. To address this limitation, we present a shape-consistent video editing method, namely StableV2V, in this paper. Our method decomposes the entire editing pipeline into several sequential procedures, where it edits the first video frame, then establishes an alignment between the delivered motions and user prompts, and eventually propagates the edited contents to all other frames based on such alignment. Furthermore, we curate a testing benchmark, namely DAVIS-Edit, for a comprehensive evaluation of video editing, considering various types of prompts and difficulties. Experimental results and analyses illustrate the outperforming performance, visual consistency, and inference efficiency of our method compared to existing state-of-the-art studies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alonzoleeeooo/stablev2v", "github_api_added_by": "auto" }, { "date": "2024-11-19", "arxiv_id": "2411.09661", "title": "Adaptive Decoding via Latent Preference Optimization", "authors": [ "Shehzaad Dhuliawala", "Ilia Kulikov", "Ping Yu", "Asli Celikyilmaz", "Jason Weston", "Sainbayar Sukhbaatar", "Jack Lanchantin" ], "github": "", "project_page": "", "abstract": "During language model decoding, it is known that using higher temperature sampling gives more creative responses, while lower temperatures are more factually accurate. However, such models are commonly applied to general instruction following, which involves both creative and fact seeking tasks, using a single fixed temperature across all examples and tokens. In this work, we introduce Adaptive Decoding, a layer added to the model to select the sampling temperature dynamically at inference time, at either the token or example level, in order to optimize performance. To learn its parameters we introduce Latent Preference Optimization (LPO) a general approach to train discrete latent variables such as choices of temperature. Our method outperforms all fixed decoding temperatures across a range of tasks that require different temperatures, including UltraFeedback, Creative Story Writing, and GSM8K.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-19", "arxiv_id": "2411.10168", "title": "Evaluating the role of `Constitutions' for learning from AI feedback", "authors": [ "Saskia Redgate", "Andrew M. Bean", "Adam Mahdi" ], "github": "", "project_page": "", "abstract": "The growing capabilities of large language models (LLMs) have led to their use as substitutes for human feedback for training and assessing other LLMs. These methods often rely on `constitutions', written guidelines which a critic model uses to provide feedback and improve generations. We investigate how the choice of constitution affects feedback quality by using four different constitutions to improve patient-centered communication in medical interviews. In pairwise comparisons conducted by 215 human raters, we found that detailed constitutions led to better results regarding emotive qualities. However, none of the constitutions outperformed the baseline in learning more practically-oriented skills related to information gathering and provision. Our findings indicate that while detailed constitutions should be prioritised, there are possible limitations to the effectiveness of AI feedback as a reward signal in certain areas.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-19", "arxiv_id": "2411.11024", "title": "VeGaS: Video Gaussian Splatting", "authors": [ "Weronika Smolak-Dy\u017cewska", "Dawid Malarz", "Kornel Howil", "Jan Kaczmarczyk", "Marcin Mazur", "Przemys\u0142aw Spurek" ], "github": "https://github.com/gmum/VeGaS", "project_page": "", "abstract": "Implicit Neural Representations (INRs) employ neural networks to approximate discrete data as continuous functions. In the context of video data, such models can be utilized to transform the coordinates of pixel locations along with frame occurrence times (or indices) into RGB color values. Although INRs facilitate effective compression, they are unsuitable for editing purposes. One potential solution is to use a 3D Gaussian Splatting (3DGS) based model, such as the Video Gaussian Representation (VGR), which is capable of encoding video as a multitude of 3D Gaussians and is applicable for numerous video processing operations, including editing. Nevertheless, in this case, the capacity for modification is constrained to a limited set of basic transformations. To address this issue, we introduce the Video Gaussian Splatting (VeGaS) model, which enables realistic modifications of video data. To construct VeGaS, we propose a novel family of Folded-Gaussian distributions designed to capture nonlinear dynamics in a video stream and model consecutive frames by 2D Gaussians obtained as respective conditional distributions. Our experiments demonstrate that VeGaS outperforms state-of-the-art solutions in frame reconstruction tasks and allows realistic modifications of video data. The code is available at: https://github.com/gmum/VeGaS.", "github_manual": "", "github_regex": "https://github.com/gmum/VeGaS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-20", "arxiv_id": "2411.11925", "title": "Continuous Speculative Decoding for Autoregressive Image Generation", "authors": [ "Zili Wang", "Robert Zhang", "Kun Ding", "Qi Yang", "Fei Li", "Shiming Xiang" ], "github": "https://github.com/MarkXCloud/CSpD", "project_page": "", "abstract": "Continuous-valued Autoregressive (AR) image generation models have demonstrated notable superiority over their discrete-token counterparts, showcasing considerable reconstruction quality and higher generation fidelity. However, the computational demands of the autoregressive framework result in significant inference overhead. While speculative decoding has proven effective in accelerating Large Language Models (LLMs), their adaptation to continuous-valued visual autoregressive models remains unexplored. This work generalizes the speculative decoding algorithm from discrete tokens to continuous space. By analyzing the intrinsic properties of output distribution, we establish a tailored acceptance criterion for the diffusion distributions prevalent in such models. To overcome the inconsistency that occurred in speculative decoding output distributions, we introduce denoising trajectory alignment and token pre-filling methods. Additionally, we identify the hard-to-sample distribution in the rejection phase. To mitigate this issue, we propose a meticulous acceptance-rejection sampling method with a proper upper bound, thereby circumventing complex integration. Experimental results show that our continuous speculative decoding achieves a remarkable 2.33times speed-up on off-the-shelf models while maintaining the output distribution. Codes will be available at https://github.com/MarkXCloud/CSpD", "github_manual": "", "github_regex": "https://github.com/MarkXCloud/CSpD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-20", "arxiv_id": "2411.12240", "title": "Evaluating Tokenizer Performance of Large Language Models Across Official Indian Languages", "authors": [ "S. Tamang", "D. J. Bora" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) based on transformer architectures have revolutionized a variety of domains, with tokenization playing a pivotal role in their pre-processing and fine-tuning stages. In multilingual models, particularly those tailored for Indic languages, effective tokenization is crucial for optimizing performance. This paper presents a comprehensive evaluation of tokenizers used by 12 LLMs across all 22 official languages of India, with a focus on comparing the efficiency of their tokenization processes. We employed the Normalized Sequence Length (NSL) as a key metric in our analysis. Our findings reveal that the SUTRA tokenizer outperforms all other models, including several Indic-specific models, excelling in 14 languages. Notable insights include the SUTRA tokenizer's superior handling of Indic languages, GPT-4o's advancement over its predecessor GPT-4 in processing Indian languages, and the limited performance of Project Indus in certain languages. This study underscores the critical importance of developing targeted tokenization strategies for multilingual and Indic-centric models, laying the groundwork for future improvements in tokenizer design to enhance linguistic coverage and model efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-20", "arxiv_id": "2411.12734", "title": "Soft Robotic Dynamic In-Hand Pen Spinning", "authors": [ "Yunchao Yao", "Uksang Yoo", "Jean Oh", "Christopher G. Atkeson", "Jeffrey Ichnowski" ], "github": "", "project_page": "", "abstract": "Dynamic in-hand manipulation remains a challenging task for soft robotic systems that have demonstrated advantages in safe compliant interactions but struggle with high-speed dynamic tasks. In this work, we present SWIFT, a system for learning dynamic tasks using a soft and compliant robotic hand. Unlike previous works that rely on simulation, quasi-static actions and precise object models, the proposed system learns to spin a pen through trial-and-error using only real-world data without requiring explicit prior knowledge of the pen's physical attributes. With self-labeled trials sampled from the real world, the system discovers the set of pen grasping and spinning primitive parameters that enables a soft hand to spin a pen robustly and reliably. After 130 sampled actions per object, SWIFT achieves 100% success rate across three pens with different weights and weight distributions, demonstrating the system's generalizability and robustness to changes in object properties. The results highlight the potential for soft robotic end-effectors to perform dynamic tasks including rapid in-hand manipulation. We also demonstrate that SWIFT generalizes to spinning items with different shapes and weights such as a brush and a screwdriver which we spin with 10/10 and 5/10 success rates respectively. Videos, data, and code are available at https://soft-spin.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-20", "arxiv_id": "2411.12275", "title": "Building Trust: Foundations of Security, Safety and Transparency in AI", "authors": [ "Huzaifa Sidhpurwala", "Garth Mollett", "Emily Fox", "Mark Bestavros", "Huamin Chen" ], "github": "", "project_page": "", "abstract": "This paper explores the rapidly evolving ecosystem of publicly available AI models, and their potential implications on the security and safety landscape. As AI models become increasingly prevalent, understanding their potential risks and vulnerabilities is crucial. We review the current security and safety scenarios while highlighting challenges such as tracking issues, remediation, and the apparent absence of AI model lifecycle and ownership processes. Comprehensive strategies to enhance security and safety for both model developers and end-users are proposed. This paper aims to provide some of the foundational pieces for more standardized security, safety, and transparency in the development and operation of AI models and the larger open ecosystems and communities forming around them.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-20", "arxiv_id": "2411.12372", "title": "RedPajama: an Open Dataset for Training Large Language Models", "authors": [ "Maurice Weber", "Daniel Fu", "Quentin Anthony", "Yonatan Oren", "Shane Adams", "Anton Alexandrov", "Xiaozhong Lyu", "Huu Nguyen", "Xiaozhe Yao", "Virginia Adams", "Ben Athiwaratkun", "Rahul Chalamala", "Kezhen Chen", "Max Ryabinin", "Tri Dao", "Percy Liang", "Christopher R\u00e9", "Irina Rish", "Ce Zhang" ], "github": "https://github.com/togethercomputer/redpajama-data", "project_page": "", "abstract": "Large language models are increasingly becoming a cornerstone technology in artificial intelligence, the sciences, and society as a whole, yet the optimal strategies for dataset composition and filtering remain largely elusive. Many of the top-performing models lack transparency in their dataset curation and model development processes, posing an obstacle to the development of fully open language models. In this paper, we identify three core data-related challenges that must be addressed to advance open-source language models. These include (1) transparency in model development, including the data curation process, (2) access to large quantities of high-quality data, and (3) availability of artifacts and metadata for dataset curation and analysis. To address these challenges, we release RedPajama-V1, an open reproduction of the LLaMA training dataset. In addition, we release RedPajama-V2, a massive web-only dataset consisting of raw, unfiltered text data together with quality signals and metadata. Together, the RedPajama datasets comprise over 100 trillion tokens spanning multiple domains and with their quality signals facilitate the filtering of data, aiming to inspire the development of numerous new datasets. To date, these datasets have already been used in the training of strong language models used in production, such as Snowflake Arctic, Salesforce's XGen and AI2's OLMo. To provide insight into the quality of RedPajama, we present a series of analyses and ablation studies with decoder-only language models with up to 1.6B parameters. Our findings demonstrate how quality signals for web data can be effectively leveraged to curate high-quality subsets of the dataset, underscoring the potential of RedPajama to advance the development of transparent and high-performing language models at scale.", "github_manual": "https://github.com/togethercomputer/redpajama-data", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-20", "arxiv_id": "2411.10161", "title": "SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning", "authors": [ "Zewen Chen", "Juan Wang", "Wen Wang", "Sunhan Xu", "Hang Xiong", "Yun Zeng", "Jian Guo", "Shuxun Wang", "Chunfeng Yuan", "Bing Li", "Weiming Hu" ], "github": "https://github.com/chencn2020/Seagull", "project_page": "", "abstract": "Existing Image Quality Assessment (IQA) methods achieve remarkable success in analyzing quality for overall image, but few works explore quality analysis for Regions of Interest (ROIs). The quality analysis of ROIs can provide fine-grained guidance for image quality improvement and is crucial for scenarios focusing on region-level quality. This paper proposes a novel network, SEAGULL, which can SEe and Assess ROIs quality with GUidance from a Large vision-Language model. SEAGULL incorporates a vision-language model (VLM), masks generated by Segment Anything Model (SAM) to specify ROIs, and a meticulously designed Mask-based Feature Extractor (MFE) to extract global and local tokens for specified ROIs, enabling accurate fine-grained IQA for ROIs. Moreover, this paper constructs two ROI-based IQA datasets, SEAGULL-100w and SEAGULL-3k, for training and evaluating ROI-based IQA. SEAGULL-100w comprises about 100w synthetic distortion images with 33 million ROIs for pre-training to improve the model's ability of regional quality perception, and SEAGULL-3k contains about 3k authentic distortion ROIs to enhance the model's ability to perceive real world distortions. After pre-training on SEAGULL-100w and fine-tuning on SEAGULL-3k, SEAGULL shows remarkable performance on fine-grained ROI quality assessment. Code and datasets are publicly available at the https://github.com/chencn2020/Seagull.", "github_manual": "", "github_regex": "https://github.com/chencn2020/Seagull", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-20", "arxiv_id": "2411.12044", "title": "ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements", "authors": [ "M. Arda Ayd\u0131n", "Efe Mert \u00c7\u0131rpar", "Elvin Abdinli", "Gozde Unal", "Yusuf H. Sahin" ], "github": "https://github.com/m-arda-aydn/ITACLIP", "project_page": "", "abstract": "Recent advances in foundational Vision Language Models (VLMs) have reshaped the evaluation paradigm in computer vision tasks. These foundational models, especially CLIP, have accelerated research in open-vocabulary computer vision tasks, including Open-Vocabulary Semantic Segmentation (OVSS). Although the initial results are promising, the dense prediction capabilities of VLMs still require further improvement. In this study, we enhance the semantic segmentation performance of CLIP by introducing new modules and modifications: 1) architectural changes in the last layer of ViT and the incorporation of attention maps from the middle layers with the last layer, 2) Image Engineering: applying data augmentations to enrich input image representations, and 3) using Large Language Models (LLMs) to generate definitions and synonyms for each class name to leverage CLIP's open-vocabulary capabilities. Our training-free method, ITACLIP, outperforms current state-of-the-art approaches on segmentation benchmarks such as COCO-Stuff, COCO-Object, Pascal Context, and Pascal VOC. Our code is available at https://github.com/m-arda-aydn/ITACLIP.", "github_manual": "", "github_regex": "https://github.com/m-arda-aydn/ITACLIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-20", "arxiv_id": "2411.10818", "title": "FlipSketch: Flipping Static Drawings to Text-Guided Sketch Animations", "authors": [ "Hmrishav Bandyopadhyay", "Yi-Zhe Song" ], "github": "", "project_page": "", "abstract": "Sketch animations offer a powerful medium for visual storytelling, from simple flip-book doodles to professional studio productions. While traditional animation requires teams of skilled artists to draw key frames and in-between frames, existing automation attempts still demand significant artistic effort through precise motion paths or keyframe specification. We present FlipSketch, a system that brings back the magic of flip-book animation -- just draw your idea and describe how you want it to move! Our approach harnesses motion priors from text-to-video diffusion models, adapting them to generate sketch animations through three key innovations: (i) fine-tuning for sketch-style frame generation, (ii) a reference frame mechanism that preserves visual integrity of input sketch through noise refinement, and (iii) a dual-attention composition that enables fluid motion without losing visual consistency. Unlike constrained vector animations, our raster frames support dynamic sketch transformations, capturing the expressive freedom of traditional animation. The result is an intuitive system that makes sketch animation as simple as doodling and describing, while maintaining the artistic essence of hand-drawn animation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-20", "arxiv_id": "2411.11909", "title": "SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization", "authors": [ "Hongrui Jia", "Chaoya Jiang", "Haiyang Xu", "Wei Ye", "Mengfan Dong", "Ming Yan", "Ji Zhang", "Fei Huang", "Shikun Zhang" ], "github": "https://github.com/APiaoG/SymDPO", "project_page": "", "abstract": "As language models continue to scale, Large Language Models (LLMs) have exhibited emerging capabilities in In-Context Learning (ICL), enabling them to solve language tasks by prefixing a few in-context demonstrations (ICDs) as context. Inspired by these advancements, researchers have extended these techniques to develop Large Multimodal Models (LMMs) with ICL capabilities. However, existing LMMs face a critical issue: they often fail to effectively leverage the visual context in multimodal demonstrations and instead simply follow textual patterns. This indicates that LMMs do not achieve effective alignment between multimodal demonstrations and model outputs. To address this problem, we propose Symbol Demonstration Direct Preference Optimization (SymDPO). Specifically, SymDPO aims to break the traditional paradigm of constructing multimodal demonstrations by using random symbols to replace text answers within instances. This forces the model to carefully understand the demonstration images and establish a relationship between the images and the symbols to answer questions correctly. We validate the effectiveness of this method on multiple benchmarks, demonstrating that with SymDPO, LMMs can more effectively understand the multimodal context within examples and utilize this knowledge to answer questions better.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/APiaoG/SymDPO", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-21", "arxiv_id": "2411.11922", "title": "SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory", "authors": [ "Cheng-Yen Yang", "Hsiang-Wei Huang", "Wenhao Chai", "Zhongyu Jiang", "Jenq-Neng Hwang" ], "github": "https://github.com/yangchris11/samurai", "project_page": "", "abstract": "The Segment Anything Model 2 (SAM 2) has demonstrated strong performance in object segmentation tasks but faces challenges in visual object tracking, particularly when managing crowded scenes with fast-moving or self-occluding objects. Furthermore, the fixed-window memory approach in the original model does not consider the quality of memories selected to condition the image features for the next frame, leading to error propagation in videos. This paper introduces SAMURAI, an enhanced adaptation of SAM 2 specifically designed for visual object tracking. By incorporating temporal motion cues with the proposed motion-aware memory selection mechanism, SAMURAI effectively predicts object motion and refines mask selection, achieving robust, accurate tracking without the need for retraining or fine-tuning. SAMURAI operates in real-time and demonstrates strong zero-shot performance across diverse benchmark datasets, showcasing its ability to generalize without fine-tuning. In evaluations, SAMURAI achieves significant improvements in success rate and precision over existing trackers, with a 7.1% AUC gain on LaSOT_{ext} and a 3.5% AO gain on GOT-10k. Moreover, it achieves competitive results compared to fully supervised methods on LaSOT, underscoring its robustness in complex tracking scenarios and its potential for real-world applications in dynamic environments. Code and results are available at https://github.com/yangchris11/samurai.", "github_manual": "", "github_regex": "https://github.com/yangchris11/samurai", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-21", "arxiv_id": "2411.10958", "title": "SageAttention2 Technical Report: Accurate 4 Bit Attention for Plug-and-play Inference Acceleration", "authors": [ "Jintao Zhang", "Haofeng Huang", "Pengle Zhang", "Jia Wei", "Jun Zhu", "Jianfei Chen" ], "github": "https://github.com/thu-ml/SageAttention", "project_page": "", "abstract": "Although quantization for linear layers has been widely used, its application to accelerate the attention process remains limited. SageAttention utilizes 8-bit matrix multiplication, 16-bit matrix multiplication with 16-bit accumulator, and precision-enhancing methods, implementing an accurate and 2x speedup kernel compared to FlashAttention2. To further enhance the efficiency of attention computation while maintaining precision, we propose SageAttention2, which utilizes significantly faster 4-bit matrix multiplication (Matmul) alongside additional precision-enhancing techniques. First, we propose to quantize matrixes (Q, K) to INT4 in a warp-level granularity and quantize matrixes (widetilde P, V) to FP8. Second, we propose a method to smooth Q and V, enhancing the accuracy of attention with INT4 QK and FP8 PV. Third, we analyze the quantization accuracy across timesteps and layers, then propose an adaptive quantization method to ensure the end-to-end metrics over various models. The operations per second (OPS) of SageAttention2 surpass FlashAttention2 and xformers by about 3x and 5x on RTX4090, respectively. Comprehensive experiments confirm that our approach incurs negligible end-to-end metrics loss across diverse models, including those for large language processing, image generation, and video generation. The codes are available at https://github.com/thu-ml/SageAttention.", "github_manual": "", "github_regex": "https://github.com/thu-ml/SageAttention", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-21", "arxiv_id": "2411.06559", "title": "Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents", "authors": [ "Yu Gu", "Boyuan Zheng", "Boyu Gou", "Kai Zhang", "Cheng Chang", "Sanjari Srivastava", "Yanan Xie", "Peng Qi", "Huan Sun", "Yu Su" ], "github": "https://github.com/osu-nlp-group/webdreamer", "project_page": "", "abstract": "Language agents have demonstrated promising capabilities in automating web-based tasks, though their current reactive approaches still underperform largely compared to humans. While incorporating advanced planning algorithms, particularly tree search methods, could enhance these agents' performance, implementing tree search directly on live websites poses significant safety risks and practical constraints due to irreversible actions such as confirming a purchase. In this paper, we introduce a novel paradigm that augments language agents with model-based planning, pioneering the innovative use of large language models (LLMs) as world models in complex web environments. Our method, WebDreamer, builds on the key insight that LLMs inherently encode comprehensive knowledge about website structures and functionalities. Specifically, WebDreamer uses LLMs to simulate outcomes for each candidate action (e.g., \"what would happen if I click this button?\") using natural language descriptions, and then evaluates these imagined outcomes to determine the optimal action at each step. Empirical results on two representative web agent benchmarks with online interaction -- VisualWebArena and Mind2Web-live -- demonstrate that WebDreamer achieves substantial improvements over reactive baselines. By establishing the viability of LLMs as world models in web environments, this work lays the groundwork for a paradigm shift in automated web interaction. More broadly, our findings open exciting new avenues for future research into 1) optimizing LLMs specifically for world modeling in complex, dynamic environments, and 2) model-based speculative planning for language agents.", "github_manual": "https://github.com/osu-nlp-group/webdreamer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-21", "arxiv_id": "2411.13503", "title": "VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models", "authors": [ "Ziqi Huang", "Fan Zhang", "Xiaojie Xu", "Yinan He", "Jiashuo Yu", "Ziyue Dong", "Qianli Ma", "Nattapol Chanpaisit", "Chenyang Si", "Yuming Jiang", "Yaohui Wang", "Xinyuan Chen", "Ying-Cong Chen", "Limin Wang", "Dahua Lin", "Yu Qiao", "Ziwei Liu" ], "github": "https://github.com/vchitect/vbench", "project_page": "", "abstract": "Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human perceptions; 2) An ideal evaluation system should provide insights to inform future developments of video generation. To this end, we present VBench, a comprehensive benchmark suite that dissects \"video generation quality\" into specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods. VBench has several appealing properties: 1) Comprehensive Dimensions: VBench comprises 16 dimensions in video generation (e.g., subject identity inconsistency, motion smoothness, temporal flickering, and spatial relationship, etc). The evaluation metrics with fine-grained levels reveal individual models' strengths and weaknesses. 2) Human Alignment: We also provide a dataset of human preference annotations to validate our benchmarks' alignment with human perception, for each evaluation dimension respectively. 3) Valuable Insights: We look into current models' ability across various evaluation dimensions, and various content types. We also investigate the gaps between video and image generation models. 4) Versatile Benchmarking: VBench++ supports evaluating text-to-video and image-to-video. We introduce a high-quality Image Suite with an adaptive aspect ratio to enable fair evaluations across different image-to-video generation settings. Beyond assessing technical quality, VBench++ evaluates the trustworthiness of video generative models, providing a more holistic view of model performance. 5) Full Open-Sourcing: We fully open-source VBench++ and continually add new video generation models to our leaderboard to drive forward the field of video generation.", "github_manual": "https://github.com/vchitect/vbench", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-21", "arxiv_id": "2411.13281", "title": "VideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation", "authors": [ "Ziyang Luo", "Haoning Wu", "Dongxu Li", "Jing Ma", "Mohan Kankanhalli", "Junnan Li" ], "github": "https://github.com/VideoAutoArena/VideoAutoArena", "project_page": "", "abstract": "Large multimodal models (LMMs) with advanced video analysis capabilities have recently garnered significant attention. However, most evaluations rely on traditional methods like multiple-choice questions in benchmarks such as VideoMME and LongVideoBench, which are prone to lack the depth needed to capture the complex demands of real-world users. To address this limitation-and due to the prohibitive cost and slow pace of human annotation for video tasks-we introduce VideoAutoArena, an arena-style benchmark inspired by LMSYS Chatbot Arena's framework, designed to automatically assess LMMs' video analysis abilities. VideoAutoArena utilizes user simulation to generate open-ended, adaptive questions that rigorously assess model performance in video understanding. The benchmark features an automated, scalable evaluation framework, incorporating a modified ELO Rating System for fair and continuous comparisons across multiple LMMs. To validate our automated judging system, we construct a 'gold standard' using a carefully curated subset of human annotations, demonstrating that our arena strongly aligns with human judgment while maintaining scalability. Additionally, we introduce a fault-driven evolution strategy, progressively increasing question complexity to push models toward handling more challenging video analysis scenarios. Experimental results demonstrate that VideoAutoArena effectively differentiates among state-of-the-art LMMs, providing insights into model strengths and areas for improvement. To further streamline our evaluation, we introduce VideoAutoBench as an auxiliary benchmark, where human annotators label winners in a subset of VideoAutoArena battles. We use GPT-4o as a judge to compare responses against these human-validated answers. Together, VideoAutoArena and VideoAutoBench offer a cost-effective, and scalable framework for evaluating LMMs in user-centric video analysis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/VideoAutoArena/VideoAutoArena", "github_api_added_by": "auto" }, { "date": "2024-11-21", "arxiv_id": "2411.13025", "title": "ORID: Organ-Regional Information Driven Framework for Radiology Report Generation", "authors": [ "Tiancheng Gu", "Kaicheng Yang", "Xiang An", "Ziyong Feng", "Dongnan Liu", "Weidong Cai" ], "github": "", "project_page": "", "abstract": "The objective of Radiology Report Generation (RRG) is to automatically generate coherent textual analyses of diseases based on radiological images, thereby alleviating the workload of radiologists. Current AI-based methods for RRG primarily focus on modifications to the encoder-decoder model architecture. To advance these approaches, this paper introduces an Organ-Regional Information Driven (ORID) framework which can effectively integrate multi-modal information and reduce the influence of noise from unrelated organs. Specifically, based on the LLaVA-Med, we first construct an RRG-related instruction dataset to improve organ-regional diagnosis description ability and get the LLaVA-Med-RRG. After that, we propose an organ-based cross-modal fusion module to effectively combine the information from the organ-regional diagnosis description and radiology image. To further reduce the influence of noise from unrelated organs on the radiology report generation, we introduce an organ importance coefficient analysis module, which leverages Graph Neural Network (GNN) to examine the interconnections of the cross-modal information of each organ region. Extensive experiments an1d comparisons with state-of-the-art methods across various evaluation metrics demonstrate the superior performance of our proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-21", "arxiv_id": "2411.13476", "title": "When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training", "authors": [ "Haonan Wang", "Qian Liu", "Chao Du", "Tongyao Zhu", "Cunxiao Du", "Kenji Kawaguchi", "Tianyu Pang" ], "github": "https://github.com/haonan3/AnchorContext", "project_page": "", "abstract": "Extending context window sizes allows large language models (LLMs) to process longer sequences and handle more complex tasks. Rotary Positional Embedding (RoPE) has become the de facto standard due to its relative positional encoding properties that benefit long-context training. However, we observe that using RoPE with BFloat16 format results in numerical issues, causing it to deviate from its intended relative positional encoding, especially in long-context scenarios. This issue arises from BFloat16's limited precision and accumulates as context length increases, with the first token contributing significantly to this problem. To address this, we develop AnchorAttention, a plug-and-play attention method that alleviates numerical issues caused by BFloat16, improves long-context capabilities, and speeds up training. AnchorAttention reduces unnecessary attention computations, maintains semantic coherence, and boosts computational efficiency by treating the first token as a shared anchor with a consistent position ID, making it visible to all documents within the training context. Experiments on three types of LLMs demonstrate that AnchorAttention significantly improves long-context performance and reduces training time by over 50\\% compared to standard full attention mechanisms, while preserving the original LLM's capabilities on general tasks. Our code is available at https://github.com/haonan3/AnchorContext.", "github_manual": "", "github_regex": "https://github.com/haonan3/AnchorContext", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-21", "arxiv_id": "2411.12811", "title": "Stylecodes: Encoding Stylistic Information For Image Generation", "authors": [ "Ciara Rowles" ], "github": "https://github.com/ciarastrawberry/stylecodes", "project_page": "", "abstract": "Diffusion models excel in image generation, but controlling them remains a challenge. We focus on the problem of style-conditioned image generation. Although example images work, they are cumbersome: srefs (style-reference codes) from MidJourney solve this issue by expressing a specific image style in a short numeric code. These have seen widespread adoption throughout social media due to both their ease of sharing and the fact they allow using an image for style control, without having to post the source images themselves. However, users are not able to generate srefs from their own images, nor is the underlying training procedure public. We propose StyleCodes: an open-source and open-research style encoder architecture and training procedure to express image style as a 20-symbol base64 code. Our experiments show that our encoding results in minimal loss in quality compared to traditional image-to-style techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ciarastrawberry/stylecodes", "github_api_added_by": "auto" }, { "date": "2024-11-21", "arxiv_id": "2411.12925", "title": "Loss-to-Loss Prediction: Scaling Laws for All Datasets", "authors": [ "David Brandfonbrener", "Nikhil Anand", "Nikhil Vyas", "Eran Malach", "Sham Kakade" ], "github": "https://github.com/KempnerInstitute/loss-to-loss-notebooks", "project_page": "", "abstract": "While scaling laws provide a reliable methodology for predicting train loss across compute scales for a single data distribution, less is known about how these predictions should change as we change the distribution. In this paper, we derive a strategy for predicting one loss from another and apply it to predict across different pre-training datasets and from pre-training data to downstream task data. Our predictions extrapolate well even at 20x the largest FLOP budget used to fit the curves. More precisely, we find that there are simple shifted power law relationships between (1) the train losses of two models trained on two separate datasets when the models are paired by training compute (train-to-train), (2) the train loss and the test loss on any downstream distribution for a single model (train-to-test), and (3) the test losses of two models trained on two separate train datasets (test-to-test). The results hold up for pre-training datasets that differ substantially (some are entirely code and others have no code at all) and across a variety of downstream tasks. Finally, we find that in some settings these shifted power law relationships can yield more accurate predictions than extrapolating single-dataset scaling laws.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KempnerInstitute/loss-to-loss-notebooks", "github_api_added_by": "auto" }, { "date": "2024-11-21", "arxiv_id": "2411.10867", "title": "ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models", "authors": [ "Vipula Rawte", "Sarthak Jain", "Aarush Sinha", "Garv Kaushik", "Aman Bansal", "Prathiksha Rumale Vishwanath", "Samyak Rajesh Jain", "Aishwarya Naresh Reganti", "Vinija Jain", "Aman Chadha", "Amit P. Sheth", "Amitava Das" ], "github": "https://github.com/prathiksharv/ViBe", "project_page": "", "abstract": "Latest developments in Large Multimodal Models (LMMs) have broadened their capabilities to include video understanding. Specifically, Text-to-video (T2V) models have made significant progress in quality, comprehension, and duration, excelling at creating videos from simple textual prompts. Yet, they still frequently produce hallucinated content that clearly signals the video is AI-generated. We introduce ViBe: a large-scale Text-to-Video Benchmark of hallucinated videos from T2V models. We identify five major types of hallucination: Vanishing Subject, Numeric Variability, Temporal Dysmorphia, Omission Error, and Physical Incongruity. Using 10 open-source T2V models, we developed the first large-scale dataset of hallucinated videos, comprising 3,782 videos annotated by humans into these five categories. ViBe offers a unique resource for evaluating the reliability of T2V models and provides a foundation for improving hallucination detection and mitigation in video generation. We establish classification as a baseline and present various ensemble classifier configurations, with the TimeSFormer + CNN combination yielding the best performance, achieving 0.345 accuracy and 0.342 F1 score. This benchmark aims to drive the development of robust T2V models that produce videos more accurately aligned with input prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/prathiksharv/ViBe", "github_api_added_by": "auto" }, { "date": "2024-11-21", "arxiv_id": "2411.10913", "title": "Generating Compositional Scenes via Text-to-image RGBA Instance Generation", "authors": [ "Alessandro Fontanella", "Petru-Daniel Tudosiu", "Yongxin Yang", "Shifeng Zhang", "Sarah Parisot" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion generative models can generate high quality images at the cost of tedious prompt engineering. Controllability can be improved by introducing layout conditioning, however existing methods lack layout editing ability and fine-grained control over object attributes. The concept of multi-layer generation holds great potential to address these limitations, however generating image instances concurrently to scene composition limits control over fine-grained object attributes, relative positioning in 3D space and scene manipulation abilities. In this work, we propose a novel multi-stage generation paradigm that is designed for fine-grained control, flexibility and interactivity. To ensure control over instance attributes, we devise a novel training paradigm to adapt a diffusion model to generate isolated scene components as RGBA images with transparency information. To build complex images, we employ these pre-generated instances and introduce a multi-layer composite generation process that smoothly assembles components in realistic scenes. Our experiments show that our RGBA diffusion model is capable of generating diverse and high quality instances with precise control over object attributes. Through multi-layer composition, we demonstrate that our approach allows to build and manipulate images from highly complex prompts with fine-grained control over object appearance and location, granting a higher degree of control than competing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.14251", "title": "Natural Language Reinforcement Learning", "authors": [ "Xidong Feng", "Ziyu Wan", "Haotian Fu", "Bo Liu", "Mengyue Yang", "Girish A. Koushik", "Zhiyuan Hu", "Ying Wen", "Jun Wang" ], "github": "https://github.com/waterhorse1/Natural-language-RL", "project_page": "", "abstract": "Reinforcement Learning (RL) mathematically formulates decision-making with Markov Decision Process (MDP). With MDPs, researchers have achieved remarkable breakthroughs across various domains, including games, robotics, and language models. This paper seeks a new possibility, Natural Language Reinforcement Learning (NLRL), by extending traditional MDP to natural language-based representation space. Specifically, NLRL innovatively redefines RL principles, including task objectives, policy, value function, Bellman equation, and policy iteration, into their language counterparts. With recent advancements in large language models (LLMs), NLRL can be practically implemented to achieve RL-like policy and value improvement by either pure prompting or gradient-based training. Experiments over Maze, Breakthrough, and Tic-Tac-Toe games demonstrate the effectiveness, efficiency, and interpretability of the NLRL framework among diverse use cases. Our code will be released at https://github.com/waterhorse1/Natural-language-RL.", "github_manual": "", "github_regex": "https://github.com/waterhorse1/Natural-language-RL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.12364", "title": "Ultra-Sparse Memory Network", "authors": [ "Zihao Huang", "Qiyang Min", "Hongzhi Huang", "Defa Zhu", "Yutao Zeng", "Ran Guo", "Xun Zhou" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "It is widely acknowledged that the performance of Transformer models is exponentially related to their number of parameters and computational complexity. While approaches like Mixture of Experts (MoE) decouple parameter count from computational complexity, they still face challenges in inference due to high memory access costs. This work introduces UltraMem, incorporating large-scale, ultra-sparse memory layer to address these limitations. Our approach significantly reduces inference latency while maintaining model performance. We also investigate the scaling laws of this new architecture, demonstrating that it not only exhibits favorable scaling properties but outperforms traditional models. In our experiments, we train networks with up to 20 million memory slots. The results show that our method achieves state-of-the-art inference speed and model performance within a given computational budget.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.14199", "title": "OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs", "authors": [ "Akari Asai", "Jacqueline He", "Rulin Shao", "Weijia Shi", "Amanpreet Singh", "Joseph Chee Chang", "Kyle Lo", "Luca Soldaini", "Sergey Feldman", "Mike D'arcy", "David Wadden", "Matt Latzke", "Minyang Tian", "Pan Ji", "Shengyan Liu", "Hao Tong", "Bohao Wu", "Yanyu Xiong", "Luke Zettlemoyer", "Graham Neubig", "Dan Weld", "Doug Downey", "Wen-tau Yih", "Pang Wei Koh", "Hannaneh Hajishirzi" ], "github": "", "project_page": "", "abstract": "Scientific progress depends on researchers' ability to synthesize the growing body of literature. Can large language models (LMs) assist scientists in this task? We introduce OpenScholar, a specialized retrieval-augmented LM that answers scientific queries by identifying relevant passages from 45 million open-access papers and synthesizing citation-backed responses. To evaluate OpenScholar, we develop ScholarQABench, the first large-scale multi-domain benchmark for literature search, comprising 2,967 expert-written queries and 208 long-form answers across computer science, physics, neuroscience, and biomedicine. On ScholarQABench, OpenScholar-8B outperforms GPT-4o by 5% and PaperQA2 by 7% in correctness, despite being a smaller, open model. While GPT4o hallucinates citations 78 to 90% of the time, OpenScholar achieves citation accuracy on par with human experts. OpenScholar's datastore, retriever, and self-feedback inference loop also improves off-the-shelf LMs: for instance, OpenScholar-GPT4o improves GPT-4o's correctness by 12%. In human evaluations, experts preferred OpenScholar-8B and OpenScholar-GPT4o responses over expert-written ones 51% and 70% of the time, respectively, compared to GPT4o's 32%. We open-source all of our code, models, datastore, data and a public demo.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.14432", "title": "Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models", "authors": [ "Yuhao Dong", "Zuyan Liu", "Hai-Long Sun", "Jingkang Yang", "Winston Hu", "Yongming Rao", "Ziwei Liu" ], "github": "https://github.com/dongyh20/insight-v", "project_page": "", "abstract": "Large Language Models (LLMs) demonstrate enhanced capabilities and reliability by reasoning more, evolving from Chain-of-Thought prompting to product-level solutions like OpenAI o1. Despite various efforts to improve LLM reasoning, high-quality long-chain reasoning data and optimized training pipelines still remain inadequately explored in vision-language tasks. In this paper, we present Insight-V, an early effort to 1) scalably produce long and robust reasoning data for complex multi-modal tasks, and 2) an effective training pipeline to enhance the reasoning capabilities of multi-modal large language models (MLLMs). Specifically, to create long and structured reasoning data without human labor, we design a two-step pipeline with a progressive strategy to generate sufficiently long and diverse reasoning paths and a multi-granularity assessment method to ensure data quality. We observe that directly supervising MLLMs with such long and complex reasoning data will not yield ideal reasoning ability. To tackle this problem, we design a multi-agent system consisting of a reasoning agent dedicated to performing long-chain reasoning and a summary agent trained to judge and summarize reasoning results. We further incorporate an iterative DPO algorithm to enhance the reasoning agent's generation stability and quality. Based on the popular LLaVA-NeXT model and our stronger base MLLM, we demonstrate significant performance gains across challenging multi-modal benchmarks requiring visual reasoning. Benefiting from our multi-agent system, Insight-V can also easily maintain or improve performance on perception-focused multi-modal tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dongyh20/insight-v", "github_api_added_by": "auto" }, { "date": "2024-11-22", "arxiv_id": "2411.14405", "title": "Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions", "authors": [ "Yu Zhao", "Huifeng Yin", "Bo Zeng", "Hao Wang", "Tianqi Shi", "Chenyang Lyu", "Longyue Wang", "Weihua Luo", "Kaifu Zhang" ], "github": "https://github.com/aidc-ai/marco-o1", "project_page": "", "abstract": "Currently OpenAI o1 has sparked a surge of interest in the study of large reasoning models (LRM). Building on this momentum, Marco-o1 not only focuses on disciplines with standard answers, such as mathematics, physics, and coding -- which are well-suited for reinforcement learning (RL) -- but also places greater emphasis on open-ended resolutions. We aim to address the question: \"Can the o1 model effectively generalize to broader domains where clear standards are absent and rewards are challenging to quantify?\" Marco-o1 is powered by Chain-of-Thought (CoT) fine-tuning, Monte Carlo Tree Search (MCTS), reflection mechanisms, and innovative reasoning strategies -- optimized for complex real-world problem-solving tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aidc-ai/marco-o1", "github_api_added_by": "auto" }, { "date": "2024-11-22", "arxiv_id": "2411.10442", "title": "Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization", "authors": [ "Weiyun Wang", "Zhe Chen", "Wenhai Wang", "Yue Cao", "Yangzhou Liu", "Zhangwei Gao", "Jinguo Zhu", "Xizhou Zhu", "Lewei Lu", "Yu Qiao", "Jifeng Dai" ], "github": "", "project_page": "", "abstract": "Existing open-source multimodal large language models (MLLMs) generally follow a training process involving pre-training and supervised fine-tuning. However, these models suffer from distribution shifts, which limit their multimodal reasoning, particularly in the Chain-of-Thought (CoT) performance. To address this, we introduce a preference optimization (PO) process to enhance the multimodal reasoning capabilities of MLLMs. Specifically, (1) on the data side, we design an automated preference data construction pipeline to create MMPR, a high-quality, large-scale multimodal reasoning preference dataset. and (2) on the model side, we explore integrating PO with MLLMs, developing a simple yet effective method, termed Mixed Preference Optimization (MPO), which boosts multimodal CoT performance. Our approach demonstrates improved performance across multiple benchmarks, particularly in multimodal reasoning tasks. Notably, our model, InternVL2-8B-MPO, achieves an accuracy of 67.0 on MathVista, outperforming InternVL2-8B by 8.7 points and achieving performance comparable to the 10x larger InternVL2-76B. We hope this study could inspire further advancements in MLLMs. Code, data, and model shall be publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.13676", "title": "Hymba: A Hybrid-head Architecture for Small Language Models", "authors": [ "Xin Dong", "Yonggan Fu", "Shizhe Diao", "Wonmin Byeon", "Zijia Chen", "Ameya Sunil Mahabaleshwarkar", "Shih-Yang Liu", "Matthijs Van Keirsbilck", "Min-Hung Chen", "Yoshi Suhara", "Yingyan Lin", "Jan Kautz", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "We propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with state space models (SSMs) for enhanced efficiency. Attention heads provide high-resolution recall, while SSM heads enable efficient context summarization. Additionally, we introduce learnable meta tokens that are prepended to prompts, storing critical information and alleviating the \"forced-to-attend\" burden associated with attention mechanisms. This model is further optimized by incorporating cross-layer key-value (KV) sharing and partial sliding window attention, resulting in a compact cache size. During development, we conducted a controlled study comparing various architectures under identical settings and observed significant advantages of our proposed architecture. Notably, Hymba achieves state-of-the-art results for small LMs: Our Hymba-1.5B-Base model surpasses all sub-2B public models in performance and even outperforms Llama-3.2-3B with 1.32% higher average accuracy, an 11.67x cache size reduction, and 3.49x throughput.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.14402", "title": "Multimodal Autoregressive Pre-training of Large Vision Encoders", "authors": [ "Enrico Fini", "Mustafa Shukor", "Xiujun Li", "Philipp Dufter", "Michal Klein", "David Haldimann", "Sai Aitharaju", "Victor Guilherme Turrisi da Costa", "Louis B\u00e9thune", "Zhe Gan", "Alexander T Toshev", "Marcin Eichner", "Moin Nabi", "Yinfei Yang", "Joshua M. Susskind", "Alaaeldin El-Nouby" ], "github": "", "project_page": "", "abstract": "We introduce a novel method for pre-training of large-scale vision encoders. Building on recent advancements in autoregressive pre-training of vision models, we extend this framework to a multimodal setting, i.e., images and text. In this paper, we present AIMV2, a family of generalist vision encoders characterized by a straightforward pre-training process, scalability, and remarkable performance across a range of downstream tasks. This is achieved by pairing the vision encoder with a multimodal decoder that autoregressively generates raw image patches and text tokens. Our encoders excel not only in multimodal evaluations but also in vision benchmarks such as localization, grounding, and classification. Notably, our AIMV2-3B encoder achieves 89.5% accuracy on ImageNet-1k with a frozen trunk. Furthermore, AIMV2 consistently outperforms state-of-the-art contrastive models (e.g., CLIP, SigLIP) in multimodal image understanding across diverse settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.14430", "title": "Stable Flow: Vital Layers for Training-Free Image Editing", "authors": [ "Omri Avrahami", "Or Patashnik", "Ohad Fried", "Egor Nemchinov", "Kfir Aberman", "Dani Lischinski", "Daniel Cohen-Or" ], "github": "https://github.com/google/prompt-to-prompt", "project_page": "", "abstract": "Diffusion models have revolutionized the field of content synthesis and editing. Recent models have replaced the traditional UNet architecture with the Diffusion Transformer (DiT), and employed flow-matching for improved training and sampling. However, they exhibit limited generation diversity. In this work, we leverage this limitation to perform consistent image edits via selective injection of attention features. The main challenge is that, unlike the UNet-based models, DiT lacks a coarse-to-fine synthesis structure, making it unclear in which layers to perform the injection. Therefore, we propose an automatic method to identify \"vital layers\" within DiT, crucial for image formation, and demonstrate how these layers facilitate a range of controlled stable edits, from non-rigid modifications to object addition, using the same mechanism. Next, to enable real-image editing, we introduce an improved image inversion method for flow models. Finally, we evaluate our approach through qualitative and quantitative comparisons, along with a user study, and demonstrate its effectiveness across multiple applications. The project page is available at https://omriavrahami.com/stable-flow", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google/prompt-to-prompt", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.13082", "title": "Patience Is The Key to Large Language Model Reasoning", "authors": [ "Yijiong Yu" ], "github": "", "project_page": "", "abstract": "Recent advancements in the field of large language models, particularly through the Chain of Thought (CoT) approach, have demonstrated significant improvements in solving complex problems. However, existing models either tend to sacrifice detailed reasoning for brevity due to user preferences, or require extensive and expensive training data to learn complicated reasoning ability, limiting their potential in solving complex tasks. To bridge this gap, following the concept of scaling test-time, we propose a simple method by encouraging models to adopt a more patient reasoning style without the need of introducing new knowledge or skills. To employ a preference optimization approach, we generate detailed reasoning processes as positive examples and simple answers as negative examples, thereby training the model to favor thoroughness in its responses. Our results demonstrate a performance increase of up to 6.7% on GSM8k with training just on a lightweight dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.13807", "title": "MagicDriveDiT: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control", "authors": [ "Ruiyuan Gao", "Kai Chen", "Bo Xiao", "Lanqing Hong", "Zhenguo Li", "Qiang Xu" ], "github": "", "project_page": "", "abstract": "The rapid advancement of diffusion models has greatly improved video synthesis, especially in controllable video generation, which is essential for applications like autonomous driving. However, existing methods are limited by scalability and how control conditions are integrated, failing to meet the needs for high-resolution and long videos for autonomous driving applications. In this paper, we introduce MagicDriveDiT, a novel approach based on the DiT architecture, and tackle these challenges. Our method enhances scalability through flow matching and employs a progressive training strategy to manage complex scenarios. By incorporating spatial-temporal conditional encoding, MagicDriveDiT achieves precise control over spatial-temporal latents. Comprehensive experiments show its superior performance in generating realistic street scene videos with higher resolution and more frames. MagicDriveDiT significantly improves video generation quality and spatial-temporal controls, expanding its potential applications across various tasks in autonomous driving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.14257", "title": "Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models", "authors": [ "Javier Ferrando", "Oscar Obeso", "Senthooran Rajamanoharan", "Neel Nanda" ], "github": "https://github.com/javiferran/sae_entities", "project_page": "", "abstract": "Hallucinations in large language models are a widespread problem, yet the mechanisms behind whether models will hallucinate are poorly understood, limiting our ability to solve this problem. Using sparse autoencoders as an interpretability tool, we discover that a key part of these mechanisms is entity recognition, where the model detects if an entity is one it can recall facts about. Sparse autoencoders uncover meaningful directions in the representation space, these detect whether the model recognizes an entity, e.g. detecting it doesn't know about an athlete or a movie. This suggests that models can have self-knowledge: internal representations about their own capabilities. These directions are causally relevant: capable of steering the model to refuse to answer questions about known entities, or to hallucinate attributes of unknown entities when it would otherwise refuse. We demonstrate that despite the sparse autoencoders being trained on the base model, these directions have a causal effect on the chat model's refusal behavior, suggesting that chat finetuning has repurposed this existing mechanism. Furthermore, we provide an initial exploration into the mechanistic role of these directions in the model, finding that they disrupt the attention of downstream heads that typically move entity attributes to the final token.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/javiferran/sae_entities", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.14343", "title": "UnifiedCrawl: Aggregated Common Crawl for Affordable Adaptation of LLMs on Low-Resource Languages", "authors": [ "Bethel Melesse Tessema", "Akhil Kedia", "Tae-Sun Chung" ], "github": "https://github.com/bethelmelesse/unifiedcrawl", "project_page": "", "abstract": "Large language models (LLMs) under-perform on low-resource languages due to limited training data. We present a method to efficiently collect text data for low-resource languages from the entire Common Crawl corpus. Our approach, UnifiedCrawl, filters and extracts common crawl using minimal compute resources, yielding mono-lingual datasets much larger than previously available sources. We demonstrate that leveraging this data to fine-tuning multilingual LLMs via efficient adapter methods (QLoRA) significantly boosts performance on the low-resource language, while minimizing VRAM usage. Our experiments show large improvements in language modeling perplexity and an increase in few-shot prompting scores. Our work and released source code provide an affordable approach to improve LLMs for low-resource languages using consumer hardware. Our source code is available here at https://github.com/bethelmelesse/unifiedcrawl.", "github_manual": "", "github_regex": "https://github.com/bethelmelesse/unifiedcrawl", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.14384", "title": "Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation", "authors": [ "Yuanhao Cai", "He Zhang", "Kai Zhang", "Yixun Liang", "Mengwei Ren", "Fujun Luan", "Qing Liu", "Soo Ye Kim", "Jianming Zhang", "Zhifei Zhang", "Yuqian Zhou", "Zhe Lin", "Alan Yuille" ], "github": "", "project_page": "", "abstract": "Existing feed-forward image-to-3D methods mainly rely on 2D multi-view diffusion models that cannot guarantee 3D consistency. These methods easily collapse when changing the prompt view direction and mainly handle object-centric prompt images. In this paper, we propose a novel single-stage 3D diffusion model, DiffusionGS, for object and scene generation from a single view. DiffusionGS directly outputs 3D Gaussian point clouds at each timestep to enforce view consistency and allow the model to generate robustly given prompt views of any directions, beyond object-centric inputs. Plus, to improve the capability and generalization ability of DiffusionGS, we scale up 3D training data by developing a scene-object mixed training strategy. Experiments show that our method enjoys better generation quality (2.20 dB higher in PSNR and 23.25 lower in FID) and over 5x faster speed (~6s on an A100 GPU) than SOTA methods. The user study and text-to-3D applications also reveals the practical values of our method. Our Project page at https://caiyuanhao1998.github.io/project/DiffusionGS/ shows the video and interactive generation results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-22", "arxiv_id": "2411.14347", "title": "DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding", "authors": [ "Tianhe Ren", "Yihao Chen", "Qing Jiang", "Zhaoyang Zeng", "Yuda Xiong", "Wenlong Liu", "Zhengyu Ma", "Junyi Shen", "Yuan Gao", "Xiaoke Jiang", "Xingyu Chen", "Zhuheng Song", "Yuhong Zhang", "Hongjie Huang", "Han Gao", "Shilong Liu", "Hao Zhang", "Feng Li", "Kent Yu", "Lei Zhang" ], "github": "https://github.com/idea-research/dino-x-api", "project_page": "", "abstract": "In this paper, we introduce DINO-X, which is a unified object-centric vision model developed by IDEA Research with the best open-world object detection performance to date. DINO-X employs the same Transformer-based encoder-decoder architecture as Grounding DINO 1.5 to pursue an object-level representation for open-world object understanding. To make long-tailed object detection easy, DINO-X extends its input options to support text prompt, visual prompt, and customized prompt. With such flexible prompt options, we develop a universal object prompt to support prompt-free open-world detection, making it possible to detect anything in an image without requiring users to provide any prompt. To enhance the model's core grounding capability, we have constructed a large-scale dataset with over 100 million high-quality grounding samples, referred to as Grounding-100M, for advancing the model's open-vocabulary detection performance. Pre-training on such a large-scale grounding dataset leads to a foundational object-level representation, which enables DINO-X to integrate multiple perception heads to simultaneously support multiple object perception and understanding tasks, including detection, segmentation, pose estimation, object captioning, object-based QA, etc. Experimental results demonstrate the superior performance of DINO-X. Specifically, the DINO-X Pro model achieves 56.0 AP, 59.8 AP, and 52.4 AP on the COCO, LVIS-minival, and LVIS-val zero-shot object detection benchmarks, respectively. Notably, it scores 63.3 AP and 56.5 AP on the rare classes of LVIS-minival and LVIS-val benchmarks, both improving the previous SOTA performance by 5.8 AP. Such a result underscores its significantly improved capacity for recognizing long-tailed objects.", "github_manual": "https://github.com/idea-research/dino-x-api", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-25", "arxiv_id": "2411.14793", "title": "Style-Friendly SNR Sampler for Style-Driven Generation", "authors": [ "Jooyoung Choi", "Chaehun Shin", "Yeongtak Oh", "Heeseung Kim", "Sungroh Yoon" ], "github": "", "project_page": "", "abstract": "Recent large-scale diffusion models generate high-quality images but struggle to learn new, personalized artistic styles, which limits the creation of unique style templates. Fine-tuning with reference images is the most promising approach, but it often blindly utilizes objectives and noise level distributions used for pre-training, leading to suboptimal style alignment. We propose the Style-friendly SNR sampler, which aggressively shifts the signal-to-noise ratio (SNR) distribution toward higher noise levels during fine-tuning to focus on noise levels where stylistic features emerge. This enables models to better capture unique styles and generate images with higher style alignment. Our method allows diffusion models to learn and share new \"style templates\", enhancing personalized content creation. We demonstrate the ability to generate styles such as personal watercolor paintings, minimal flat cartoons, 3D renderings, multi-panel images, and memes with text, thereby broadening the scope of style-driven generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-25", "arxiv_id": "2411.15098", "title": "OminiControl: Minimal and Universal Control for Diffusion Transformer", "authors": [ "Zhenxiong Tan", "Songhua Liu", "Xingyi Yang", "Qiaochu Xue", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce OminiControl, a highly versatile and parameter-efficient framework that integrates image conditions into pre-trained Diffusion Transformer (DiT) models. At its core, OminiControl leverages a parameter reuse mechanism, enabling the DiT to encode image conditions using itself as a powerful backbone and process them with its flexible multi-modal attention processors. Unlike existing methods, which rely heavily on additional encoder modules with complex architectures, OminiControl (1) effectively and efficiently incorporates injected image conditions with only ~0.1% additional parameters, and (2) addresses a wide range of image conditioning tasks in a unified manner, including subject-driven generation and spatially-aligned conditions such as edges, depth, and more. Remarkably, these capabilities are achieved by training on images generated by the DiT itself, which is particularly beneficial for subject-driven generation. Extensive evaluations demonstrate that OminiControl outperforms existing UNet-based and DiT-adapted models in both subject-driven and spatially-aligned conditional generation. Additionally, we release our training dataset, Subjects200K, a diverse collection of over 200,000 identity-consistent images, along with an efficient data synthesis pipeline to advance research in subject-consistent generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-25", "arxiv_id": "2411.12946", "title": "A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection", "authors": [ "Gabriel Chua", "Shing Yee Chan", "Shaun Khoo" ], "github": "", "project_page": "", "abstract": "Large Language Models are prone to off-topic misuse, where users may prompt these models to perform tasks beyond their intended scope. Current guardrails, which often rely on curated examples or custom classifiers, suffer from high false-positive rates, limited adaptability, and the impracticality of requiring real-world data that is not available in pre-production. In this paper, we introduce a flexible, data-free guardrail development methodology that addresses these challenges. By thoroughly defining the problem space qualitatively and passing this to an LLM to generate diverse prompts, we construct a synthetic dataset to benchmark and train off-topic guardrails that outperform heuristic approaches. Additionally, by framing the task as classifying whether the user prompt is relevant with respect to the system prompt, our guardrails effectively generalize to other misuse categories, including jailbreak and harmful prompts. Lastly, we further contribute to the field by open-sourcing both the synthetic dataset and the off-topic guardrail models, providing valuable resources for developing guardrails in pre-production environments and supporting future research and development in LLM safety.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-25", "arxiv_id": "2411.14762", "title": "Efficient Long Video Tokenization via Coordinated-based Patch Reconstruction", "authors": [ "Huiwon Jang", "Sihyun Yu", "Jinwoo Shin", "Pieter Abbeel", "Younggyo Seo" ], "github": "https://github.com/huiwon-jang/CoordTok", "project_page": "", "abstract": "Efficient tokenization of videos remains a challenge in training vision models that can process long videos. One promising direction is to develop a tokenizer that can encode long video clips, as it would enable the tokenizer to leverage the temporal coherence of videos better for tokenization. However, training existing tokenizers on long videos often incurs a huge training cost as they are trained to reconstruct all the frames at once. In this paper, we introduce CoordTok, a video tokenizer that learns a mapping from coordinate-based representations to the corresponding patches of input videos, inspired by recent advances in 3D generative models. In particular, CoordTok encodes a video into factorized triplane representations and reconstructs patches that correspond to randomly sampled (x,y,t) coordinates. This allows for training large tokenizer models directly on long videos without requiring excessive training resources. Our experiments show that CoordTok can drastically reduce the number of tokens for encoding long video clips. For instance, CoordTok can encode a 128-frame video with 128times128 resolution into 1280 tokens, while baselines need 6144 or 8192 tokens to achieve similar reconstruction quality. We further show that this efficient video tokenization enables memory-efficient training of a diffusion transformer that can generate 128 frames at once.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/huiwon-jang/CoordTok", "github_api_added_by": "auto" }, { "date": "2024-11-25", "arxiv_id": "2411.15124", "title": "T\u00dcLU 3: Pushing Frontiers in Open Language Model Post-Training", "authors": [ "Nathan Lambert", "Jacob Morrison", "Valentina Pyatkin", "Shengyi Huang", "Hamish Ivison", "Faeze Brahman", "Lester James V. Miranda", "Alisa Liu", "Nouha Dziri", "Shane Lyu", "Yuling Gu", "Saumya Malik", "Victoria Graf", "Jena D. Hwang", "Jiangjiang Yang", "Ronan Le Bras", "Oyvind Tafjord", "Chris Wilhelm", "Luca Soldaini", "Noah A. Smith", "Yizhong Wang", "Pradeep Dasigi", "Hannaneh Hajishirzi" ], "github": "", "project_page": "", "abstract": "Language model post-training is applied to refine behaviors and unlock new skills across a wide range of recent language models, but open recipes for applying these techniques lag behind proprietary ones. The underlying training data and recipes for post-training are simultaneously the most important pieces of the puzzle and the portion with the least transparency. To bridge this gap, we introduce T\\\"ULU 3, a family of fully-open state-of-the-art post-trained models, alongside its data, code, and training recipes, serving as a comprehensive guide for modern post-training techniques. T\\\"ULU 3, which builds on Llama 3.1 base models, achieves results surpassing the instruct versions of Llama 3.1, Qwen 2.5, Mistral, and even closed models such as GPT-4o-mini and Claude 3.5-Haiku. The training algorithms for our models include supervised finetuning (SFT), Direct Preference Optimization (DPO), and a novel method we call Reinforcement Learning with Verifiable Rewards (RLVR). With T\\\"ULU 3, we introduce a multi-task evaluation scheme for post-training recipes with development and unseen evaluations, standard benchmark implementations, and substantial decontamination of existing open datasets on said benchmarks. We conclude with analysis and discussion of training methods that did not reliably improve performance. In addition to the T\\\"ULU 3 model weights and demo, we release the complete recipe -- including datasets for diverse core skills, a robust toolkit for data curation and evaluation, the training code and infrastructure, and, most importantly, a detailed report for reproducing and further adapting the T\\\"ULU 3 approach to more domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-25", "arxiv_id": "2411.14521", "title": "MyTimeMachine: Personalized Facial Age Transformation", "authors": [ "Luchao Qi", "Jiaye Wu", "Bang Gong", "Annie N. Wang", "David W. Jacobs", "Roni Sengupta" ], "github": "", "project_page": "", "abstract": "Facial aging is a complex process, highly dependent on multiple factors like gender, ethnicity, lifestyle, etc., making it extremely challenging to learn a global aging prior to predict aging for any individual accurately. Existing techniques often produce realistic and plausible aging results, but the re-aged images often do not resemble the person's appearance at the target age and thus need personalization. In many practical applications of virtual aging, e.g. VFX in movies and TV shows, access to a personal photo collection of the user depicting aging in a small time interval (20sim40 years) is often available. However, naive attempts to personalize global aging techniques on personal photo collections often fail. Thus, we propose MyTimeMachine (MyTM), which combines a global aging prior with a personal photo collection (using as few as 50 images) to learn a personalized age transformation. We introduce a novel Adapter Network that combines personalized aging features with global aging features and generates a re-aged image with StyleGAN2. We also introduce three loss functions to personalize the Adapter Network with personalized aging loss, extrapolation regularization, and adaptive w-norm regularization. Our approach can also be extended to videos, achieving high-quality, identity-preserving, and temporally consistent aging effects that resemble actual appearances at target ages, demonstrating its superiority over state-of-the-art approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-25", "arxiv_id": "2411.15131", "title": "WildLMa: Long Horizon Loco-Manipulation in the Wild", "authors": [ "Ri-Zhao Qiu", "Yuchen Song", "Xuanbin Peng", "Sai Aneesh Suryadevara", "Ge Yang", "Minghuan Liu", "Mazeyu Ji", "Chengzhe Jia", "Ruihan Yang", "Xueyan Zou", "Xiaolong Wang" ], "github": "", "project_page": "", "abstract": "`In-the-wild' mobile manipulation aims to deploy robots in diverse real-world environments, which requires the robot to (1) have skills that generalize across object configurations; (2) be capable of long-horizon task execution in diverse environments; and (3) perform complex manipulation beyond pick-and-place. Quadruped robots with manipulators hold promise for extending the workspace and enabling robust locomotion, but existing results do not investigate such a capability. This paper proposes WildLMa with three components to address these issues: (1) adaptation of learned low-level controller for VR-enabled whole-body teleoperation and traversability; (2) WildLMa-Skill -- a library of generalizable visuomotor skills acquired via imitation learning or heuristics and (3) WildLMa-Planner -- an interface of learned skills that allow LLM planners to coordinate skills for long-horizon tasks. We demonstrate the importance of high-quality training data by achieving higher grasping success rate over existing RL baselines using only tens of demonstrations. WildLMa exploits CLIP for language-conditioned imitation learning that empirically generalizes to objects unseen in training demonstrations. Besides extensive quantitative evaluation, we qualitatively demonstrate practical robot applications, such as cleaning up trash in university hallways or outdoor terrains, operating articulated objects, and rearranging items on a bookshelf.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-25", "arxiv_id": "2411.14208", "title": "Novel View Extrapolation with Video Diffusion Priors", "authors": [ "Kunhao Liu", "Ling Shao", "Shijian Lu" ], "github": "https://github.com/Kunhao-Liu/ViewExtrapolator", "project_page": "", "abstract": "The field of novel view synthesis has made significant strides thanks to the development of radiance field methods. However, most radiance field techniques are far better at novel view interpolation than novel view extrapolation where the synthesis novel views are far beyond the observed training views. We design ViewExtrapolator, a novel view synthesis approach that leverages the generative priors of Stable Video Diffusion (SVD) for realistic novel view extrapolation. By redesigning the SVD denoising process, ViewExtrapolator refines the artifact-prone views rendered by radiance fields, greatly enhancing the clarity and realism of the synthesized novel views. ViewExtrapolator is a generic novel view extrapolator that can work with different types of 3D rendering such as views rendered from point clouds when only a single view or monocular video is available. Additionally, ViewExtrapolator requires no fine-tuning of SVD, making it both data-efficient and computation-efficient. Extensive experiments demonstrate the superiority of ViewExtrapolator in novel view extrapolation. Project page: https://kunhao-liu.github.io/ViewExtrapolator/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Kunhao-Liu/ViewExtrapolator", "github_api_added_by": "auto" }, { "date": "2024-11-25", "arxiv_id": "2411.14794", "title": "VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection", "authors": [ "Songhao Han", "Wei Huang", "Hairong Shi", "Le Zhuo", "Xiu Su", "Shifeng Zhang", "Xu Zhou", "Xiaojuan Qi", "Yue Liao", "Si Liu" ], "github": "https://github.com/hshjerry/VideoEspresso", "project_page": "", "abstract": "The advancement of Large Vision Language Models (LVLMs) has significantly improved multimodal understanding, yet challenges remain in video reasoning tasks due to the scarcity of high-quality, large-scale datasets. Existing video question-answering (VideoQA) datasets often rely on costly manual annotations with insufficient granularity or automatic construction methods with redundant frame-by-frame analysis, limiting their scalability and effectiveness for complex reasoning. To address these challenges, we introduce VideoEspresso, a novel dataset that features VideoQA pairs preserving essential spatial details and temporal coherence, along with multimodal annotations of intermediate reasoning steps. Our construction pipeline employs a semantic-aware method to reduce redundancy, followed by generating QA pairs using GPT-4o. We further develop video Chain-of-Thought (CoT) annotations to enrich reasoning processes, guiding GPT-4o in extracting logical relationships from QA pairs and video content. To exploit the potential of high-quality VideoQA pairs, we propose a Hybrid LVLMs Collaboration framework, featuring a Frame Selector and a two-stage instruction fine-tuned reasoning LVLM. This framework adaptively selects core frames and performs CoT reasoning using multimodal evidence. Evaluated on our proposed benchmark with 14 tasks against 9 popular LVLMs, our method outperforms existing baselines on most tasks, demonstrating superior video reasoning capabilities. Our code and dataset will be released at: https://github.com/hshjerry/VideoEspresso", "github_manual": "", "github_regex": "https://github.com/hshjerry/VideoEspresso", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-25", "arxiv_id": "2411.15033", "title": "One to rule them all: natural language to bind communication, perception and action", "authors": [ "Simone Colombani", "Dimitri Ognibene", "Giuseppe Boccignone" ], "github": "", "project_page": "", "abstract": "In recent years, research in the area of human-robot interaction has focused on developing robots capable of understanding complex human instructions and performing tasks in dynamic and diverse environments. These systems have a wide range of applications, from personal assistance to industrial robotics, emphasizing the importance of robots interacting flexibly, naturally and safely with humans. This paper presents an advanced architecture for robotic action planning that integrates communication, perception, and planning with Large Language Models (LLMs). Our system is designed to translate commands expressed in natural language into executable robot actions, incorporating environmental information and dynamically updating plans based on real-time feedback. The Planner Module is the core of the system where LLMs embedded in a modified ReAct framework are employed to interpret and carry out user commands. By leveraging their extensive pre-trained knowledge, LLMs can effectively process user requests without the need to introduce new knowledge on the changing environment. The modified ReAct framework further enhances the execution space by providing real-time environmental perception and the outcomes of physical actions. By combining robust and dynamic semantic map representations as graphs with control components and failure explanations, this architecture enhances a robot adaptability, task execution, and seamless collaboration with human users in shared and dynamic environments. Through the integration of continuous feedback loops with the environment the system can dynamically adjusts the plan to accommodate unexpected changes, optimizing the robot ability to perform tasks. Using a dataset of previous experience is possible to provide detailed feedback about the failure. Updating the LLMs context of the next iteration with suggestion on how to overcame the issue.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-25", "arxiv_id": "2411.14982", "title": "Large Multi-modal Models Can Interpret Features in Large Multi-modal Models", "authors": [ "Kaichen Zhang", "Yifei Shen", "Bo Li", "Ziwei Liu" ], "github": "https://github.com/EvolvingLMMs-Lab/multimodal-sae", "project_page": "", "abstract": "Recent advances in Large Multimodal Models (LMMs) lead to significant breakthroughs in both academia and industry. One question that arises is how we, as humans, can understand their internal neural representations. This paper takes an initial step towards addressing this question by presenting a versatile framework to identify and interpret the semantics within LMMs. Specifically, 1) we first apply a Sparse Autoencoder(SAE) to disentangle the representations into human understandable features. 2) We then present an automatic interpretation framework to interpreted the open-semantic features learned in SAE by the LMMs themselves. We employ this framework to analyze the LLaVA-NeXT-8B model using the LLaVA-OV-72B model, demonstrating that these features can effectively steer the model's behavior. Our results contribute to a deeper understanding of why LMMs excel in specific tasks, including EQ tests, and illuminate the nature of their mistakes along with potential strategies for their rectification. These findings offer new insights into the internal mechanisms of LMMs and suggest parallels with the cognitive processes of the human brain.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EvolvingLMMs-Lab/multimodal-sae", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-25", "arxiv_id": "2411.13543", "title": "BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games", "authors": [ "Davide Paglieri", "Bart\u0142omiej Cupia\u0142", "Samuel Coward", "Ulyana Piterbarg", "Maciej Wolczyk", "Akbir Khan", "Eduardo Pignatelli", "\u0141ukasz Kuci\u0144ski", "Lerrel Pinto", "Rob Fergus", "Jakob Nicolaus Foerster", "Jack Parker-Holder", "Tim Rockt\u00e4schel" ], "github": "https://github.com/balrog-ai/BALROG", "project_page": "", "abstract": "Large Language Models (LLMs) and Vision Language Models (VLMs) possess extensive knowledge and exhibit promising reasoning abilities; however, they still struggle to perform well in complex, dynamic environments. Real-world tasks require handling intricate interactions, advanced spatial reasoning, long-term planning, and continuous exploration of new strategies-areas in which we lack effective methodologies for comprehensively evaluating these capabilities. To address this gap, we introduce BALROG, a novel benchmark designed to assess the agentic capabilities of LLMs and VLMs through a diverse set of challenging games. Our benchmark incorporates a range of existing reinforcement learning environments with varying levels of difficulty, including tasks that are solvable by non-expert humans in seconds to extremely challenging ones that may take years to master (e.g., the NetHack Learning Environment). We devise fine-grained metrics to measure performance and conduct an extensive evaluation of several popular open-source and closed-source LLMs and VLMs. Our findings indicate that while current models achieve partial success in the easier games, they struggle significantly with more challenging tasks. Notably, we observe severe deficiencies in vision-based decision-making, as models perform worse when visual representations of the environments are provided. We release BALROG as an open and user-friendly benchmark to facilitate future research and development in the agentic community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/balrog-ai/BALROG", "github_api_added_by": "auto" }, { "date": "2024-11-25", "arxiv_id": "2411.15115", "title": "VideoRepair: Improving Text-to-Video Generation via Misalignment Evaluation and Localized Refinement", "authors": [ "Daeun Lee", "Jaehong Yoon", "Jaemin Cho", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Recent text-to-video (T2V) diffusion models have demonstrated impressive generation capabilities across various domains. However, these models often generate videos that have misalignments with text prompts, especially when the prompts describe complex scenes with multiple objects and attributes. To address this, we introduce VideoRepair, a novel model-agnostic, training-free video refinement framework that automatically identifies fine-grained text-video misalignments and generates explicit spatial and textual feedback, enabling a T2V diffusion model to perform targeted, localized refinements. VideoRepair consists of four stages: In (1) video evaluation, we detect misalignments by generating fine-grained evaluation questions and answering those questions with MLLM. In (2) refinement planning, we identify accurately generated objects and then create localized prompts to refine other areas in the video. Next, in (3) region decomposition, we segment the correctly generated area using a combined grounding module. We regenerate the video by adjusting the misaligned regions while preserving the correct regions in (4) localized refinement. On two popular video generation benchmarks (EvalCrafter and T2V-CompBench), VideoRepair substantially outperforms recent baselines across various text-video alignment metrics. We provide a comprehensive analysis of VideoRepair components and qualitative examples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-25", "arxiv_id": "2411.13127", "title": "Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images", "authors": [ "Xuechao Zou", "Shun Zhang", "Kai Li", "Shiying Wang", "Junliang Xing", "Lei Jin", "Congyan Lang", "Pin Tao" ], "github": "https://github.com/XavierJiezou/Cloud-Adapter", "project_page": "", "abstract": "Cloud segmentation is a critical challenge in remote sensing image interpretation, as its accuracy directly impacts the effectiveness of subsequent data processing and analysis. Recently, vision foundation models (VFM) have demonstrated powerful generalization capabilities across various visual tasks. In this paper, we present a parameter-efficient adaptive approach, termed Cloud-Adapter, designed to enhance the accuracy and robustness of cloud segmentation. Our method leverages a VFM pretrained on general domain data, which remains frozen, eliminating the need for additional training. Cloud-Adapter incorporates a lightweight spatial perception module that initially utilizes a convolutional neural network (ConvNet) to extract dense spatial representations. These multi-scale features are then aggregated and serve as contextual inputs to an adapting module, which modulates the frozen transformer layers within the VFM. Experimental results demonstrate that the Cloud-Adapter approach, utilizing only 0.6% of the trainable parameters of the frozen backbone, achieves substantial performance gains. Cloud-Adapter consistently attains state-of-the-art (SOTA) performance across a wide variety of cloud segmentation datasets from multiple satellite sources, sensor series, data processing levels, land cover scenarios, and annotation granularities. We have released the source code and pretrained models at https://github.com/XavierJiezou/Cloud-Adapter to support further research.", "github_manual": "", "github_regex": "https://github.com/XavierJiezou/Cloud-Adapter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.15138", "title": "Material Anything: Generating Materials for Any 3D Object via Diffusion", "authors": [ "Xin Huang", "Tengfei Wang", "Ziwei Liu", "Qing Wang" ], "github": "", "project_page": "", "abstract": "We present Material Anything, a fully-automated, unified diffusion framework designed to generate physically-based materials for 3D objects. Unlike existing methods that rely on complex pipelines or case-specific optimizations, Material Anything offers a robust, end-to-end solution adaptable to objects under diverse lighting conditions. Our approach leverages a pre-trained image diffusion model, enhanced with a triple-head architecture and rendering loss to improve stability and material quality. Additionally, we introduce confidence masks as a dynamic switcher within the diffusion model, enabling it to effectively handle both textured and texture-less objects across varying lighting conditions. By employing a progressive material generation strategy guided by these confidence masks, along with a UV-space material refiner, our method ensures consistent, UV-ready material outputs. Extensive experiments demonstrate our approach outperforms existing methods across a wide range of object categories and lighting conditions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.15466", "title": "Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator", "authors": [ "Chaehun Shin", "Jooyoung Choi", "Heeseung Kim", "Sungroh Yoon" ], "github": "https://github.com/csyxwei/ELITE", "project_page": "", "abstract": "Subject-driven text-to-image generation aims to produce images of a new subject within a desired context by accurately capturing both the visual characteristics of the subject and the semantic content of a text prompt. Traditional methods rely on time- and resource-intensive fine-tuning for subject alignment, while recent zero-shot approaches leverage on-the-fly image prompting, often sacrificing subject alignment. In this paper, we introduce Diptych Prompting, a novel zero-shot approach that reinterprets as an inpainting task with precise subject alignment by leveraging the emergent property of diptych generation in large-scale text-to-image models. Diptych Prompting arranges an incomplete diptych with the reference image in the left panel, and performs text-conditioned inpainting on the right panel. We further prevent unwanted content leakage by removing the background in the reference image and improve fine-grained details in the generated subject by enhancing attention weights between the panels during inpainting. Experimental results confirm that our approach significantly outperforms zero-shot image prompting methods, resulting in images that are visually preferred by users. Additionally, our method supports not only subject-driven generation but also stylized image generation and subject-driven image editing, demonstrating versatility across diverse image generation applications. Project page: https://diptychprompting.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/csyxwei/ELITE", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.14486", "title": "The Impossible Test: A 2024 Unsolvable Dataset and A Chance for an AGI Quiz", "authors": [ "David Noever", "Forrest McKee" ], "github": "", "project_page": "", "abstract": "This research introduces a novel evaluation framework designed to assess large language models' (LLMs) ability to acknowledge uncertainty on 675 fundamentally unsolvable problems. Using a curated dataset of graduate-level grand challenge questions with intentionally unknowable answers, we evaluated twelve state-of-the-art LLMs, including both open and closed-source models, on their propensity to admit ignorance rather than generate plausible but incorrect responses. The best models scored in 62-68% accuracy ranges for admitting the problem solution was unknown in fields ranging from biology to philosophy and mathematics. We observed an inverse relationship between problem difficulty and model accuracy, with GPT-4 demonstrating higher rates of uncertainty acknowledgment on more challenging problems (35.8%) compared to simpler ones (20.0%). This pattern indicates that models may be more prone to generate speculative answers when problems appear more tractable. The study also revealed significant variations across problem categories, with models showing difficulty in acknowledging uncertainty in invention and NP-hard problems while performing relatively better on philosophical and psychological challenges. These results contribute to the growing body of research on artificial general intelligence (AGI) assessment by highlighting the importance of uncertainty recognition as a critical component of future machine intelligence evaluation. This impossibility test thus extends previous theoretical frameworks for universal intelligence testing by providing empirical evidence of current limitations in LLMs' ability to recognize their own knowledge boundaries, suggesting new directions for improving model training architectures and evaluation approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.16657", "title": "DreamRunner: Fine-Grained Storytelling Video Generation with Retrieval-Augmented Motion Adaptation", "authors": [ "Zun Wang", "Jialu Li", "Han Lin", "Jaehong Yoon", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Storytelling video generation (SVG) has recently emerged as a task to create long, multi-motion, multi-scene videos that consistently represent the story described in the input text script. SVG holds great potential for diverse content creation in media and entertainment; however, it also presents significant challenges: (1) objects must exhibit a range of fine-grained, complex motions, (2) multiple objects need to appear consistently across scenes, and (3) subjects may require multiple motions with seamless transitions within a single scene. To address these challenges, we propose DreamRunner, a novel story-to-video generation method: First, we structure the input script using a large language model (LLM) to facilitate both coarse-grained scene planning as well as fine-grained object-level layout and motion planning. Next, DreamRunner presents retrieval-augmented test-time adaptation to capture target motion priors for objects in each scene, supporting diverse motion customization based on retrieved videos, thus facilitating the generation of new videos with complex, scripted motions. Lastly, we propose a novel spatial-temporal region-based 3D attention and prior injection module SR3AI for fine-grained object-motion binding and frame-by-frame semantic control. We compare DreamRunner with various SVG baselines, demonstrating state-of-the-art performance in character consistency, text alignment, and smooth transitions. Additionally, DreamRunner exhibits strong fine-grained condition-following ability in compositional text-to-video generation, significantly outperforming baselines on T2V-ComBench. Finally, we validate DreamRunner's robust ability to generate multi-object interactions with qualitative examples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.14522", "title": "GMAI-VL & GMAI-VL-5.5M: A Large Vision-Language Model and A Comprehensive Multimodal Dataset Towards General Medical AI", "authors": [ "Tianbin Li", "Yanzhou Su", "Wei Li", "Bin Fu", "Zhe Chen", "Ziyan Huang", "Guoan Wang", "Chenglong Ma", "Ying Chen", "Ming Hu", "Yanjun Li", "Pengcheng Chen", "Xiaowei Hu", "Zhongying Deng", "Yuanfeng Ji", "Jin Ye", "Yu Qiao", "Junjun He" ], "github": "https://github.com/uni-medical/GMAI-VL", "project_page": "", "abstract": "Despite significant advancements in general artificial intelligence, such as GPT-4, their effectiveness in the medical domain (general medical AI, GMAI) remains constrained due to the absence of specialized medical knowledge. To address this challenge, we present GMAI-VL-5.5M, a comprehensive multimodal medical dataset created by converting hundreds of specialized medical datasets into meticulously constructed image-text pairs. This dataset features comprehensive task coverage, diverse modalities, and high-quality image-text data. Building upon this multimodal dataset, we propose GMAI-VL, a general medical vision-language model with a progressively three-stage training strategy. This approach significantly enhances the model's ability by integrating visual and textual information, thereby improving its ability to process multimodal data and support accurate diagnosis and clinical decision-making. Experimental evaluations demonstrate that GMAI-VL achieves state-of-the-art results across a wide range of multimodal medical tasks, such as visual question answering and medical image diagnosis. Our contributions include the development of the GMAI-VL-5.5M dataset, the introduction of the GMAI-VL model, and the establishment of new benchmarks in multiple medical domains. Code and dataset will be released at https://github.com/uni-medical/GMAI-VL.", "github_manual": "", "github_regex": "https://github.com/uni-medical/GMAI-VL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.16341", "title": "From CISC to RISC: language-model guided assembly transpilation", "authors": [ "Ahmed Heakl", "Chaimaa Abi", "Rania Hossam", "Abdulrahman Mahmoud" ], "github": "", "project_page": "", "abstract": "The transition from x86 to ARM architecture is becoming increasingly common across various domains, primarily driven by ARM's energy efficiency and improved performance across traditional sectors. However, this ISA shift poses significant challenges, mainly due to the extensive legacy ecosystem of x86 software and lack of portability across proprietary ecosystems and software stacks. This paper introduces CRT, a lightweight LLM-based transpiler that automatically converts x86 assembly to ARM assembly. Our approach bridges the fundamental architectural gap between x86's CISC-based and ARM's RISC-based computing paradigms while preserving program semantics and optimizing performance. We evaluate CRT on diverse real-world applications, achieving 79.25% translation accuracy from x86 to ARMv5 on our comprehensive test suite, and an 88.68% accuracy from x86 to RISC-V. In practical deployments on Apple M2 hardware (ARMv8), our transpiled code achieves 1.73times speedup compared to Apple's Rosetta 2 virtualization engine, while delivering 2.41times memory efficiency and 1.47times better energy consumption. Through testing and analysis, we show that CRT successfully navigates the CISC/RISC divide and generates correctly executable RISC code despite machine ``language'' barriers. We release our code, models, training datasets, and benchmarks at: https://ahmedheakl.github.io/asm2asm/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.16085", "title": "Cautious Optimizers: Improving Training with One Line of Code", "authors": [ "Kaizhao Liang", "Lizhang Chen", "Bo Liu", "Qiang Liu" ], "github": "https://github.com/kyleliang919/C-Optim", "project_page": "", "abstract": "AdamW has been the default optimizer for transformer pretraining. For many years, our community searches for faster and more stable optimizers with only constraint positive outcomes. In this work, we propose a single-line modification in Pytorch to any momentum-based optimizer, which we rename Cautious Optimizer, e.g. C-AdamW and C-Lion. Our theoretical result shows that this modification preserves Adam's Hamiltonian function and it does not break the convergence guarantee under the Lyapunov analysis. In addition, a whole new family of optimizers is revealed by our theoretical insight. Among them, we pick the simplest one for empirical experiments, showing speed-up on Llama and MAE pretraining up to 1.47times. Code is available at https://github.com/kyleliang919/C-Optim", "github_manual": "", "github_regex": "https://github.com/kyleliang919/C-Optim", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.16594", "title": "From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge", "authors": [ "Dawei Li", "Bohan Jiang", "Liangjie Huang", "Alimohammad Beigi", "Chengshuai Zhao", "Zhen Tan", "Amrita Bhattacharjee", "Yuxuan Jiang", "Canyu Chen", "Tianhao Wu", "Kai Shu", "Lu Cheng", "Huan Liu" ], "github": "https://github.com/llm-as-a-judge/Awesome-LLM-as-a-judge", "project_page": "", "abstract": "Assessment and evaluation have long been critical challenges in artificial intelligence (AI) and natural language processing (NLP). However, traditional methods, whether matching-based or embedding-based, often fall short of judging subtle attributes and delivering satisfactory results. Recent advancements in Large Language Models (LLMs) inspire the \"LLM-as-a-judge\" paradigm, where LLMs are leveraged to perform scoring, ranking, or selection across various tasks and applications. This paper provides a comprehensive survey of LLM-based judgment and assessment, offering an in-depth overview to advance this emerging field. We begin by giving detailed definitions from both input and output perspectives. Then we introduce a comprehensive taxonomy to explore LLM-as-a-judge from three dimensions: what to judge, how to judge and where to judge. Finally, we compile benchmarks for evaluating LLM-as-a-judge and highlight key challenges and promising directions, aiming to provide valuable insights and inspire future research in this promising research area. Paper list and more resources about LLM-as-a-judge can be found at https://github.com/llm-as-a-judge/Awesome-LLM-as-a-judge and https://llm-as-a-judge.github.io.", "github_manual": "", "github_regex": "https://github.com/llm-as-a-judge/Awesome-LLM-as-a-judge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.16205", "title": "MH-MoE:Multi-Head Mixture-of-Experts", "authors": [ "Shaohan Huang", "Xun Wu", "Shuming Ma", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Multi-Head Mixture-of-Experts (MH-MoE) demonstrates superior performance by using the multi-head mechanism to collectively attend to information from various representation spaces within different experts. In this paper, we present a novel implementation of MH-MoE that maintains both FLOPs and parameter parity with sparse Mixture of Experts models. Experimental results on language models show that the new implementation yields quality improvements over both vanilla MoE and fine-grained MoE models. Additionally, our experiments demonstrate that MH-MoE is compatible with 1-bit Large Language Models (LLMs) such as BitNet.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.15671", "title": "Best of Both Worlds: Advantages of Hybrid Graph Sequence Models", "authors": [ "Ali Behrouz", "Ali Parviz", "Mahdi Karami", "Clayton Sanford", "Bryan Perozzi", "Vahab Mirrokni" ], "github": "", "project_page": "", "abstract": "Modern sequence models (e.g., Transformers, linear RNNs, etc.) emerged as dominant backbones of recent deep learning frameworks, mainly due to their efficiency, representational power, and/or ability to capture long-range dependencies. Adopting these sequence models for graph-structured data has recently gained popularity as the alternative to Message Passing Neural Networks (MPNNs). There is, however, a lack of a common foundation about what constitutes a good graph sequence model, and a mathematical description of the benefits and deficiencies in adopting different sequence models for learning on graphs. To this end, we first present Graph Sequence Model (GSM), a unifying framework for adopting sequence models for graphs, consisting of three main steps: (1) Tokenization, which translates the graph into a set of sequences; (2) Local Encoding, which encodes local neighborhoods around each node; and (3) Global Encoding, which employs a scalable sequence model to capture long-range dependencies within the sequences. This framework allows us to understand, evaluate, and compare the power of different sequence model backbones in graph tasks. Our theoretical evaluations of the representation power of Transformers and modern recurrent models through the lens of global and local graph tasks show that there are both negative and positive sides for both types of models. Building on this observation, we present GSM++, a fast hybrid model that uses the Hierarchical Affinity Clustering (HAC) algorithm to tokenize the graph into hierarchical sequences, and then employs a hybrid architecture of Transformer to encode these sequences. Our theoretical and experimental results support the design of GSM++, showing that GSM++ outperforms baselines in most benchmark evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.16035", "title": "Predicting Emergent Capabilities by Finetuning", "authors": [ "Charlie Snell", "Eric Wallace", "Dan Klein", "Sergey Levine" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "A fundamental open challenge in modern LLM scaling is the lack of understanding around emergent capabilities. In particular, language model pretraining loss is known to be highly predictable as a function of compute. However, downstream capabilities are far less predictable -- sometimes even exhibiting emergent jumps -- which makes it challenging to anticipate the capabilities of future models. In this work, we first pose the task of emergence prediction: given access to current LLMs that have random few-shot accuracy on a task, can we predict whether future models (GPT-N+1) will have non-trivial accuracy on that task? We then discover a simple insight for this problem: finetuning LLMs on a given task can shift the point in scaling at which emergence occurs towards less capable models. To operationalize this insight, we can finetune LLMs with varying amounts of data and fit a parametric function that predicts when emergence will occur (i.e., \"emergence laws\"). We validate this approach using four standard NLP benchmarks where large-scale open-source LLMs already demonstrate emergence (MMLU, GSM8K, CommonsenseQA, and CoLA). Using only small-scale LLMs, we find that, in some cases, we can accurately predict whether models trained with up to 4x more compute have emerged. Finally, we present a case study of two realistic uses for emergence prediction.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.14525", "title": "SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation", "authors": [ "Jin Ye", "Ying Chen", "Yanjun Li", "Haoyu Wang", "Zhongying Deng", "Ziyan Huang", "Yanzhou Su", "Chenglong Ma", "Yuanfeng Ji", "Junjun He" ], "github": "", "project_page": "", "abstract": "Computed Tomography (CT) is one of the most popular modalities for medical imaging. By far, CT images have contributed to the largest publicly available datasets for volumetric medical segmentation tasks, covering full-body anatomical structures. Large amounts of full-body CT images provide the opportunity to pre-train powerful models, e.g., STU-Net pre-trained in a supervised fashion, to segment numerous anatomical structures. However, it remains unclear in which conditions these pre-trained models can be transferred to various downstream medical segmentation tasks, particularly segmenting the other modalities and diverse targets. To address this problem, a large-scale benchmark for comprehensive evaluation is crucial for finding these conditions. Thus, we collected 87 public datasets varying in modality, target, and sample size to evaluate the transfer ability of full-body CT pre-trained models. We then employed a representative model, STU-Net with multiple model scales, to conduct transfer learning across modalities and targets. Our experimental results show that (1) there may be a bottleneck effect concerning the dataset size in fine-tuning, with more improvement on both small- and large-scale datasets than medium-size ones. (2) Models pre-trained on full-body CT demonstrate effective modality transfer, adapting well to other modalities such as MRI. (3) Pre-training on the full-body CT not only supports strong performance in structure detection but also shows efficacy in lesion detection, showcasing adaptability across target tasks. We hope that this large-scale open evaluation of transfer learning can direct future research in volumetric medical image segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.16681", "title": "Factorized Visual Tokenization and Generation", "authors": [ "Zechen Bai", "Jianxiong Gao", "Ziteng Gao", "Pichao Wang", "Zheng Zhang", "Tong He", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Visual tokenizers are fundamental to image generation. They convert visual data into discrete tokens, enabling transformer-based models to excel at image generation. Despite their success, VQ-based tokenizers like VQGAN face significant limitations due to constrained vocabulary sizes. Simply expanding the codebook often leads to training instability and diminishing performance gains, making scalability a critical challenge. In this work, we introduce Factorized Quantization (FQ), a novel approach that revitalizes VQ-based tokenizers by decomposing a large codebook into multiple independent sub-codebooks. This factorization reduces the lookup complexity of large codebooks, enabling more efficient and scalable visual tokenization. To ensure each sub-codebook captures distinct and complementary information, we propose a disentanglement regularization that explicitly reduces redundancy, promoting diversity across the sub-codebooks. Furthermore, we integrate representation learning into the training process, leveraging pretrained vision models like CLIP and DINO to infuse semantic richness into the learned representations. This design ensures our tokenizer captures diverse semantic levels, leading to more expressive and disentangled representations. Experiments show that the proposed FQGAN model substantially improves the reconstruction quality of visual tokenizers, achieving state-of-the-art performance. We further demonstrate that this tokenizer can be effectively adapted into auto-regressive image generation. https://showlab.github.io/FQGAN", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.16489", "title": "O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?", "authors": [ "Zhen Huang", "Haoyang Zou", "Xuefeng Li", "Yixiu Liu", "Yuxiang Zheng", "Ethan Chern", "Shijie Xia", "Yiwei Qin", "Weizhe Yuan", "Pengfei Liu" ], "github": "https://github.com/gair-nlp/o1-journey", "project_page": "", "abstract": "This paper presents a critical examination of current approaches to replicating OpenAI's O1 model capabilities, with particular focus on the widespread but often undisclosed use of knowledge distillation techniques. While our previous work explored the fundamental technical path to O1 replication, this study reveals how simple distillation from O1's API, combined with supervised fine-tuning, can achieve superior performance on complex mathematical reasoning tasks. Through extensive experiments, we show that a base model fine-tuned on simply tens of thousands of samples O1-distilled long-thought chains outperforms O1-preview on the American Invitational Mathematics Examination (AIME) with minimal technical complexity. Moreover, our investigation extends beyond mathematical reasoning to explore the generalization capabilities of O1-distilled models across diverse tasks: hallucination, safety and open-domain QA. Notably, despite training only on mathematical problem-solving data, our models demonstrated strong generalization to open-ended QA tasks and became significantly less susceptible to sycophancy after fine-tuning. We deliberately make this finding public to promote transparency in AI research and to challenge the current trend of obscured technical claims in the field. Our work includes: (1) A detailed technical exposition of the distillation process and its effectiveness, (2) A comprehensive benchmark framework for evaluating and categorizing O1 replication attempts based on their technical transparency and reproducibility, (3) A critical discussion of the limitations and potential risks of over-relying on distillation approaches, our analysis culminates in a crucial bitter lesson: while the pursuit of more capable AI systems is important, the development of researchers grounded in first-principles thinking is paramount.", "github_manual": "https://github.com/gair-nlp/o1-journey", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.16443", "title": "SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis", "authors": [ "Hyojun Go", "Byeongjun Park", "Jiho Jang", "Jin-Young Kim", "Soonwoo Kwon", "Changick Kim" ], "github": "https://github.com/gohyojun15/SplatFlow", "project_page": "", "abstract": "Text-based generation and editing of 3D scenes hold significant potential for streamlining content creation through intuitive user interactions. While recent advances leverage 3D Gaussian Splatting (3DGS) for high-fidelity and real-time rendering, existing methods are often specialized and task-focused, lacking a unified framework for both generation and editing. In this paper, we introduce SplatFlow, a comprehensive framework that addresses this gap by enabling direct 3DGS generation and editing. SplatFlow comprises two main components: a multi-view rectified flow (RF) model and a Gaussian Splatting Decoder (GSDecoder). The multi-view RF model operates in latent space, generating multi-view images, depths, and camera poses simultaneously, conditioned on text prompts, thus addressing challenges like diverse scene scales and complex camera trajectories in real-world settings. Then, the GSDecoder efficiently translates these latent outputs into 3DGS representations through a feed-forward 3DGS method. Leveraging training-free inversion and inpainting techniques, SplatFlow enables seamless 3DGS editing and supports a broad range of 3D tasks-including object editing, novel view synthesis, and camera pose estimation-within a unified framework without requiring additional complex pipelines. We validate SplatFlow's capabilities on the MVImgNet and DL3DV-7K datasets, demonstrating its versatility and effectiveness in various 3D generation, editing, and inpainting-based tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gohyojun15/SplatFlow", "github_api_added_by": "auto" }, { "date": "2024-11-26", "arxiv_id": "2411.16034", "title": "VisualLens: Personalization through Visual History", "authors": [ "Wang Bill Zhu", "Deqing Fu", "Kai Sun", "Yi Lu", "Zhaojiang Lin", "Seungwhan Moon", "Kanika Narang", "Mustafa Canim", "Yue Liu", "Anuj Kumar", "Xin Luna Dong" ], "github": "", "project_page": "", "abstract": "We hypothesize that a user's visual history with images reflecting their daily life, offers valuable insights into their interests and preferences, and can be leveraged for personalization. Among the many challenges to achieve this goal, the foremost is the diversity and noises in the visual history, containing images not necessarily related to a recommendation task, not necessarily reflecting the user's interest, or even not necessarily preference-relevant. Existing recommendation systems either rely on task-specific user interaction logs, such as online shopping history for shopping recommendations, or focus on text signals. We propose a novel approach, VisualLens, that extracts, filters, and refines image representations, and leverages these signals for personalization. We created two new benchmarks with task-agnostic visual histories, and show that our method improves over state-of-the-art recommendations by 5-10% on Hit@3, and improves over GPT-4o by 2-5%. Our approach paves the way for personalized recommendations in scenarios where traditional methods fail.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.16318", "title": "One Diffusion to Generate Them All", "authors": [ "Duong H. Le", "Tuan Pham", "Sangho Lee", "Christopher Clark", "Aniruddha Kembhavi", "Stephan Mandt", "Ranjay Krishna", "Jiasen Lu" ], "github": "https://github.com/lehduong/OneDiffusion", "project_page": "", "abstract": "We introduce OneDiffusion, a versatile, large-scale diffusion model that seamlessly supports bidirectional image synthesis and understanding across diverse tasks. It enables conditional generation from inputs such as text, depth, pose, layout, and semantic maps, while also handling tasks like image deblurring, upscaling, and reverse processes such as depth estimation and segmentation. Additionally, OneDiffusion allows for multi-view generation, camera pose estimation, and instant personalization using sequential image inputs. Our model takes a straightforward yet effective approach by treating all tasks as frame sequences with varying noise scales during training, allowing any frame to act as a conditioning image at inference time. Our unified training framework removes the need for specialized architectures, supports scalable multi-task training, and adapts smoothly to any resolution, enhancing both generalization and scalability. Experimental results demonstrate competitive performance across tasks in both generation and prediction such as text-to-image, multiview generation, ID preservation, depth estimation and camera pose estimation despite relatively small training dataset. Our code and checkpoint are freely available at https://github.com/lehduong/OneDiffusion", "github_manual": "", "github_regex": "https://github.com/lehduong/OneDiffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.15611", "title": "Knowledge Transfer Across Modalities with Natural Language Supervision", "authors": [ "Carlo Alberto Barbano", "Luca Molinaro", "Emanuele Aiello", "Marco Grangetto" ], "github": "", "project_page": "", "abstract": "We present a way to learn novel concepts by only using their textual description. We call this method Knowledge Transfer. Similarly to human perception, we leverage cross-modal interaction to introduce new concepts. We hypothesize that in a pre-trained visual encoder there are enough low-level features already learned (e.g. shape, appearance, color) that can be used to describe previously unknown high-level concepts. Provided with a textual description of the novel concept, our method works by aligning the known low-level features of the visual encoder to its high-level textual description. We show that Knowledge Transfer can successfully introduce novel concepts in multimodal models, in a very efficient manner, by only requiring a single description of the target concept. Our approach is compatible with both separate textual and visual encoders (e.g. CLIP) and shared parameters across modalities. We also show that, following the same principle, Knowledge Transfer can improve concepts already known by the model. Leveraging Knowledge Transfer we improve zero-shot performance across different tasks such as classification, segmentation, image-text retrieval, and captioning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.15862", "title": "LLMs Do Not Think Step-by-step In Implicit Reasoning", "authors": [ "Yijiong Yu" ], "github": "https://github.com/yuyijiong/if_step_by_step_implicit_cot", "project_page": "", "abstract": "It has been well-known that Chain-of-Thought can remarkably enhance LLMs' performance on complex tasks. However, because it also introduces slower inference speeds and higher computational costs, many researches have attempted to use implicit CoT, which does not need LLMs to explicitly generate the intermediate steps. But there is still gap between their efficacy and typical explicit CoT methods. This leaves us a doubt that, does implicit CoT really equal to explicit CoT? Therefore, in this study, we address this question through experiments. We probe the information of intermediate steps from the model's hidden states when it is performing implicit CoT. The results surprisingly indicate that LLMs hardly think about intermediate steps, suggesting they may just rely on experience rather than strict step-by-step reasoning. Moreover, we find LLMs' implicit reasoning capabilities are susceptible and unstable, reaffirming the necessity of explicit CoT to effectively support complex tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yuyijiong/if_step_by_step_implicit_cot", "github_api_added_by": "auto" }, { "date": "2024-11-26", "arxiv_id": "2411.12814", "title": "Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline", "authors": [ "Junlong Cheng", "Bin Fu", "Jin Ye", "Guoan Wang", "Tianbin Li", "Haoyu Wang", "Ruoyu Li", "He Yao", "Junren Chen", "JingWen Li", "Yanzhou Su", "Min Zhu", "Junjun He" ], "github": "https://github.com/uni-medical/IMIS-Bench", "project_page": "", "abstract": "Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. In this paper, we introduce the IMed-361M benchmark dataset, a significant advancement in general IMIS research. First, we collect and standardize over 6.4 million medical images and their corresponding ground truth masks from multiple data sources. Then, leveraging the strong object recognition capabilities of a vision foundational model, we automatically generated dense interactive masks for each image and ensured their quality through rigorous quality control and granularity management. Unlike previous datasets, which are limited by specific modalities or sparse annotations, IMed-361M spans 14 modalities and 204 segmentation targets, totaling 361 million masks-an average of 56 masks per image. Finally, we developed an IMIS baseline network on this dataset that supports high-quality mask generation through interactive inputs, including clicks, bounding boxes, text prompts, and their combinations. We evaluate its performance on medical image segmentation tasks from multiple perspectives, demonstrating superior accuracy and scalability compared to existing interactive segmentation models. To facilitate research on foundational models in medical computer vision, we release the IMed-361M and model at https://github.com/uni-medical/IMIS-Bench.", "github_manual": "", "github_regex": "https://github.com/uni-medical/IMIS-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.16665", "title": "Edge Weight Prediction For Category-Agnostic Pose Estimation", "authors": [ "Or Hirschorn", "Shai Avidan" ], "github": "https://github.com/orhir/EdgeCape", "project_page": "", "abstract": "Category-Agnostic Pose Estimation (CAPE) localizes keypoints across diverse object categories with a single model, using one or a few annotated support images. Recent works have shown that using a pose graph (i.e., treating keypoints as nodes in a graph rather than isolated points) helps handle occlusions and break symmetry. However, these methods assume a static pose graph with equal-weight edges, leading to suboptimal results. We introduce EdgeCape, a novel framework that overcomes these limitations by predicting the graph's edge weights which optimizes localization. To further leverage structural priors, we propose integrating Markovian Structural Bias, which modulates the self-attention interaction between nodes based on the number of hops between them. We show that this improves the model's ability to capture global spatial dependencies. Evaluated on the MP-100 benchmark, which includes 100 categories and over 20K images, EdgeCape achieves state-of-the-art results in the 1-shot setting and leads among similar-sized methods in the 5-shot setting, significantly improving keypoint localization accuracy. Our code is publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/orhir/EdgeCape", "github_api_added_by": "auto" }, { "date": "2024-11-26", "arxiv_id": "2411.13550", "title": "Find Any Part in 3D", "authors": [ "Ziqi Ma", "Yisong Yue", "Georgia Gkioxari" ], "github": "https://github.com/ziqi-ma/find3d", "project_page": "", "abstract": "We study open-world part segmentation in 3D: segmenting any part in any object based on any text query. Prior methods are limited in object categories and part vocabularies. Recent advances in AI have demonstrated effective open-world recognition capabilities in 2D. Inspired by this progress, we propose an open-world, direct-prediction model for 3D part segmentation that can be applied zero-shot to any object. Our approach, called Find3D, trains a general-category point embedding model on large-scale 3D assets from the internet without any human annotation. It combines a data engine, powered by foundation models for annotating data, with a contrastive training method. We achieve strong performance and generalization across multiple datasets, with up to a 3x improvement in mIoU over the next best method. Our model is 6x to over 300x faster than existing baselines. To encourage research in general-category open-world 3D part segmentation, we also release a benchmark for general objects and parts. Project website: https://ziqi-ma.github.io/find3dsite/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ziqi-ma/find3d", "github_api_added_by": "auto" }, { "date": "2024-11-26", "arxiv_id": "2411.15221", "title": "Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry", "authors": [ "Yoel Zimmermann", "Adib Bazgir", "Zartashia Afzal", "Fariha Agbere", "Qianxiang Ai", "Nawaf Alampara", "Alexander Al-Feghali", "Mehrad Ansari", "Dmytro Antypov", "Amro Aswad", "Jiaru Bai", "Viktoriia Baibakova", "Devi Dutta Biswajeet", "Erik Bitzek", "Joshua D. Bocarsly", "Anna Borisova", "Andres M Bran", "L. Catherine Brinson", "Marcel Moran Calderon", "Alessandro Canalicchio", "Victor Chen", "Yuan Chiang", "Defne Circi", "Benjamin Charmes", "Vikrant Chaudhary", "Zizhang Chen", "Min-Hsueh Chiu", "Judith Clymo", "Kedar Dabhadkar", "Nathan Daelman", "Archit Datar", "Matthew L. Evans", "Maryam Ghazizade Fard", "Giuseppe Fisicaro", "Abhijeet Sadashiv Gangan", "Janine George", "Jose D. Cojal Gonzalez", "Michael G\u00f6tte", "Ankur K. Gupta", "Hassan Harb", "Pengyu Hong", "Abdelrahman Ibrahim", "Ahmed Ilyas", "Alishba Imran", "Kevin Ishimwe", "Ramsey Issa", "Kevin Maik Jablonka", "Colin Jones", "Tyler R. Josephson", "Greg Juhasz", "Sarthak Kapoor", "Rongda Kang", "Ghazal Khalighinejad", "Sartaaj Khan", "Sascha Klawohn", "Suneel Kuman", "Alvin Noe Ladines", "Sarom Leang", "Magdalena Lederbauer", "Sheng-Lun Mark Liao", "Hao Liu", "Xuefeng Liu", "Stanley Lo", "Sandeep Madireddy", "Piyush Ranjan Maharana", "Shagun Maheshwari", "Soroush Mahjoubi", "Jos\u00e9 A. M\u00e1rquez", "Rob Mills", "Trupti Mohanty", "Bernadette Mohr", "Seyed Mohamad Moosavi", "Alexander Mo\u00dfhammer", "Amirhossein D. Naghdi", "Aakash Naik", "Oleksandr Narykov", "Hampus N\u00e4sstr\u00f6m", "Xuan Vu Nguyen", "Xinyi Ni", "Dana O'Connor", "Teslim Olayiwola", "Federico Ottomano", "Aleyna Beste Ozhan", "Sebastian Pagel", "Chiku Parida", "Jaehee Park", "Vraj Patel", "Elena Patyukova", "Martin Hoffmann Petersen", "Luis Pinto", "Jos\u00e9 M. Pizarro", "Dieter Plessers", "Tapashree Pradhan", "Utkarsh Pratiush", "Charishma Puli", "Andrew Qin", "Mahyar Rajabi", "Francesco Ricci", "Elliot Risch", "Marti\u00f1o R\u00edos-Garc\u00eda", "Aritra Roy", "Tehseen Rug", "Hasan M Sayeed", "Markus Scheidgen", "Mara Schilling-Wilhelmi", "Marcel Schloz", "Fabian Sch\u00f6ppach", "Julia Schumann", "Philippe Schwaller", "Marcus Schwarting", "Samiha Sharlin", "Kevin Shen", "Jiale Shi", "Pradip Si", "Jennifer D'Souza", "Taylor Sparks", "Suraj Sudhakar", "Leopold Talirz", "Dandan Tang", "Olga Taran", "Carla Terboven", "Mark Tropin", "Anastasiia Tsymbal", "Katharina Ueltzen", "Pablo Andres Unzueta", "Archit Vasan", "Tirtha Vinchurkar", "Trung Vo", "Gabriel Vogel", "Christoph V\u00f6lker", "Jan Weinreich", "Faradawn Yang", "Mohd Zaki", "Chi Zhang", "Sylvester Zhang", "Weijie Zhang", "Ruijie Zhu", "Shang Zhu", "Jan Janssen", "Ian Foster", "Ben Blaiszik" ], "github": "https://github.com/KKbeckang/LiGPT-Beta", "project_page": "", "abstract": "Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research.", "github_manual": "https://github.com/KKbeckang/LiGPT-Beta", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-26", "arxiv_id": "2411.16508", "title": "All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages", "authors": [ "Ashmal Vayani", "Dinura Dissanayake", "Hasindri Watawana", "Noor Ahsan", "Nevasini Sasikumar", "Omkar Thawakar", "Henok Biadglign Ademtew", "Yahya Hmaiti", "Amandeep Kumar", "Kartik Kuckreja", "Mykola Maslych", "Wafa Al Ghallabi", "Mihail Mihaylov", "Chao Qin", "Abdelrahman M Shaker", "Mike Zhang", "Mahardika Krisna Ihsani", "Amiel Esplana", "Monil Gokani", "Shachar Mirkin", "Harsh Singh", "Ashay Srivastava", "Endre Hamerlik", "Fathinah Asma Izzati", "Fadillah Adamsyah Maani", "Sebastian Cavada", "Jenny Chim", "Rohit Gupta", "Sanjay Manjunath", "Kamila Zhumakhanova", "Feno Heriniaina Rabevohitra", "Azril Amirudin", "Muhammad Ridzuan", "Daniya Kareem", "Ketan More", "Kunyang Li", "Pramesh Shakya", "Muhammad Saad", "Amirpouya Ghasemaghaei", "Amirbek Djanibekov", "Dilshod Azizov", "Branislava Jankovic", "Naman Bhatia", "Alvaro Cabrera", "Johan Obando-Ceron", "Olympiah Otieno", "Fabian Farestam", "Muztoba Rabbani", "Sanoojan Baliah", "Santosh Sanjeev", "Abduragim Shtanchaev", "Maheen Fatima", "Thao Nguyen", "Amrin Kareem", "Toluwani Aremu", "Nathan Xavier", "Amit Bhatkal", "Hawau Toyin", "Aman Chadha", "Hisham Cholakkal", "Rao Muhammad Anwer", "Michael Felsberg", "Jorma Laaksonen", "Thamar Solorio", "Monojit Choudhury", "Ivan Laptev", "Mubarak Shah", "Salman Khan", "Fahad Khan" ], "github": "https://github.com/mbzuai-oryx/ALM-Bench", "project_page": "", "abstract": "Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource languages, all while effectively integrating corresponding visual cues. In pursuit of culturally diverse global multimodal models, our proposed All Languages Matter Benchmark (ALM-bench) represents the largest and most comprehensive effort to date for evaluating LMMs across 100 languages. ALM-bench challenges existing models by testing their ability to understand and reason about culturally diverse images paired with text in various languages, including many low-resource languages traditionally underrepresented in LMM research. The benchmark offers a robust and nuanced evaluation framework featuring various question formats, including true/false, multiple choice, and open-ended questions, which are further divided into short and long-answer categories. ALM-bench design ensures a comprehensive assessment of a model's ability to handle varied levels of difficulty in visual and linguistic reasoning. To capture the rich tapestry of global cultures, ALM-bench carefully curates content from 13 distinct cultural aspects, ranging from traditions and rituals to famous personalities and celebrations. Through this, ALM-bench not only provides a rigorous testing ground for state-of-the-art open and closed-source LMMs but also highlights the importance of cultural and linguistic inclusivity, encouraging the development of models that can serve diverse global populations effectively. Our benchmark is publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mbzuai-oryx/ALM-Bench", "github_api_added_by": "auto" }, { "date": "2024-11-27", "arxiv_id": "2411.17116", "title": "Star Attention: Efficient LLM Inference over Long Sequences", "authors": [ "Shantanu Acharya", "Fei Jia", "Boris Ginsburg" ], "github": "", "project_page": "", "abstract": "Inference with Transformer-based Large Language Models (LLMs) on long sequences is both costly and slow due to the quadratic complexity of the self-attention mechanism. We introduce Star Attention, a two-phase block-sparse approximation that improves computational efficiency by sharding attention across multiple hosts while minimizing communication overhead. In the first phase, the context is processed using blockwise-local attention across hosts, in parallel. In the second phase, query and response tokens attend to all prior cached tokens through sequence-global attention. Star Attention integrates seamlessly with most Transformer-based LLMs trained with global attention, reducing memory requirements and inference time by up to 11x while preserving 95-100% of accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.17223", "title": "DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image Inpainting", "authors": [ "Yicheng Yang", "Pengxiang Li", "Lu Zhang", "Liqian Ma", "Ping Hu", "Siyu Du", "Yunzhi Zhuge", "Xu Jia", "Huchuan Lu" ], "github": "https://github.com/mycfhs/DreamMix", "project_page": "", "abstract": "Subject-driven image inpainting has emerged as a popular task in image editing alongside recent advancements in diffusion models. Previous methods primarily focus on identity preservation but struggle to maintain the editability of inserted objects. In response, this paper introduces DreamMix, a diffusion-based generative model adept at inserting target objects into given scenes at user-specified locations while concurrently enabling arbitrary text-driven modifications to their attributes. In particular, we leverage advanced foundational inpainting models and introduce a disentangled local-global inpainting framework to balance precise local object insertion with effective global visual coherence. Additionally, we propose an Attribute Decoupling Mechanism (ADM) and a Textual Attribute Substitution (TAS) module to improve the diversity and discriminative capability of the text-based attribute guidance, respectively. Extensive experiments demonstrate that DreamMix effectively balances identity preservation and attribute editability across various application scenarios, including object insertion, attribute editing, and small object inpainting. Our code is publicly available at https://github.com/mycfhs/DreamMix.", "github_manual": "", "github_regex": "https://github.com/mycfhs/DreamMix", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.17467", "title": "Learning 3D Representations from Procedural 3D Programs", "authors": [ "Xuweiyi Chen", "Zezhou Cheng" ], "github": "", "project_page": "", "abstract": "Self-supervised learning has emerged as a promising approach for acquiring transferable 3D representations from unlabeled 3D point clouds. Unlike 2D images, which are widely accessible, acquiring 3D assets requires specialized expertise or professional 3D scanning equipment, making it difficult to scale and raising copyright concerns. To address these challenges, we propose learning 3D representations from procedural 3D programs that automatically generate 3D shapes using simple primitives and augmentations. Remarkably, despite lacking semantic content, the 3D representations learned from this synthesized dataset perform on par with state-of-the-art representations learned from semantically recognizable 3D models (e.g., airplanes) across various downstream 3D tasks, including shape classification, part segmentation, and masked point cloud completion. Our analysis further suggests that current self-supervised learning methods primarily capture geometric structures rather than high-level semantics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.14740", "title": "TEXGen: a Generative Diffusion Model for Mesh Textures", "authors": [ "Xin Yu", "Ze Yuan", "Yuan-Chen Guo", "Ying-Tian Liu", "JianHui Liu", "Yangguang Li", "Yan-Pei Cao", "Ding Liang", "Xiaojuan Qi" ], "github": "https://github.com/Karine-Huang/T2I-CompBench", "project_page": "", "abstract": "While high-quality texture maps are essential for realistic 3D asset rendering, few studies have explored learning directly in the texture space, especially on large-scale datasets. In this work, we depart from the conventional approach of relying on pre-trained 2D diffusion models for test-time optimization of 3D textures. Instead, we focus on the fundamental problem of learning in the UV texture space itself. For the first time, we train a large diffusion model capable of directly generating high-resolution texture maps in a feed-forward manner. To facilitate efficient learning in high-resolution UV spaces, we propose a scalable network architecture that interleaves convolutions on UV maps with attention layers on point clouds. Leveraging this architectural design, we train a 700 million parameter diffusion model that can generate UV texture maps guided by text prompts and single-view images. Once trained, our model naturally supports various extended applications, including text-guided texture inpainting, sparse-view texture completion, and text-driven texture synthesis. Project page is at http://cvmi-lab.github.io/TEXGen/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Karine-Huang/T2I-CompBench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.17465", "title": "ShowUI: One Vision-Language-Action Model for GUI Visual Agent", "authors": [ "Kevin Qinghong Lin", "Linjie Li", "Difei Gao", "Zhengyuan Yang", "Shiwei Wu", "Zechen Bai", "Weixian Lei", "Lijuan Wang", "Mike Zheng Shou" ], "github": "https://github.com/showlab/ShowUI", "project_page": "", "abstract": "Building Graphical User Interface (GUI) assistants holds significant promise for enhancing human workflow productivity. While most agents are language-based, relying on closed-source API with text-rich meta-information (e.g., HTML or accessibility tree), they show limitations in perceiving UI visuals as humans do, highlighting the need for GUI visual agents. In this work, we develop a vision-language-action model in digital world, namely ShowUI, which features the following innovations: (i) UI-Guided Visual Token Selection to reduce computational costs by formulating screenshots as an UI connected graph, adaptively identifying their redundant relationship and serve as the criteria for token selection during self-attention blocks; (ii) Interleaved Vision-Language-Action Streaming that flexibly unifies diverse needs within GUI tasks, enabling effective management of visual-action history in navigation or pairing multi-turn query-action sequences per screenshot to enhance training efficiency; (iii) Small-scale High-quality GUI Instruction-following Datasets by careful data curation and employing a resampling strategy to address significant data type imbalances. With above components, ShowUI, a lightweight 2B model using 256K data, achieves a strong 75.1% accuracy in zero-shot screenshot grounding. Its UI-guided token selection further reduces 33% of redundant visual tokens during training and speeds up the performance by 1.4x. Navigation experiments across web Mind2Web, mobile AITW, and online MiniWob environments further underscore the effectiveness and potential of our model in advancing GUI visual agents. The models are available at https://github.com/showlab/ShowUI.", "github_manual": "", "github_regex": "https://github.com/showlab/ShowUI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.15296", "title": "MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs", "authors": [ "Chaoyou Fu", "Yi-Fan Zhang", "Shukang Yin", "Bo Li", "Xinyu Fang", "Sirui Zhao", "Haodong Duan", "Xing Sun", "Ziwei Liu", "Liang Wang", "Caifeng Shan", "Ran He" ], "github": "https://github.com/open-compass/opencompass", "project_page": "", "abstract": "As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Building upon pre-trained LLMs, this family of models further develops multimodal perception and reasoning capabilities that are impressive, such as writing code given a flow chart or creating stories based on an image. In the development process, evaluation is critical since it provides intuitive feedback and guidance on improving models. Distinct from the traditional train-eval-test paradigm that only favors a single task like image classification, the versatility of MLLMs has spurred the rise of various new benchmarks and evaluation methods. In this paper, we aim to present a comprehensive survey of MLLM evaluation, discussing four key aspects: 1) the summarised benchmarks types divided by the evaluation capabilities, including foundation capabilities, model self-analysis, and extented applications; 2) the typical process of benchmark counstruction, consisting of data collection, annotation, and precautions; 3) the systematic evaluation manner composed of judge, metric, and toolkit; 4) the outlook for the next benchmark. This work aims to offer researchers an easy grasp of how to effectively evaluate MLLMs according to different needs and to inspire better evaluation methods, thereby driving the progress of MLLM research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-compass/opencompass", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.17673", "title": "SketchAgent: Language-Driven Sequential Sketch Generation", "authors": [ "Yael Vinker", "Tamar Rott Shaham", "Kristine Zheng", "Alex Zhao", "Judith E Fan", "Antonio Torralba" ], "github": "", "project_page": "", "abstract": "Sketching serves as a versatile tool for externalizing ideas, enabling rapid exploration and visual communication that spans various disciplines. While artificial systems have driven substantial advances in content creation and human-computer interaction, capturing the dynamic and abstract nature of human sketching remains challenging. In this work, we introduce SketchAgent, a language-driven, sequential sketch generation method that enables users to create, modify, and refine sketches through dynamic, conversational interactions. Our approach requires no training or fine-tuning. Instead, we leverage the sequential nature and rich prior knowledge of off-the-shelf multimodal large language models (LLMs). We present an intuitive sketching language, introduced to the model through in-context examples, enabling it to \"draw\" using string-based actions. These are processed into vector graphics and then rendered to create a sketch on a pixel canvas, which can be accessed again for further tasks. By drawing stroke by stroke, our agent captures the evolving, dynamic qualities intrinsic to sketching. We demonstrate that SketchAgent can generate sketches from diverse prompts, engage in dialogue-driven drawing, and collaborate meaningfully with human users.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.17383", "title": "AnchorCrafter: Animate CyberAnchors Saling Your Products via Human-Object Interacting Video Generation", "authors": [ "Ziyi Xu", "Ziyao Huang", "Juan Cao", "Yong Zhang", "Xiaodong Cun", "Qing Shuai", "Yuchen Wang", "Linchao Bao", "Jintao Li", "Fan Tang" ], "github": "https://github.com/cangcz/AnchorCrafter", "project_page": "", "abstract": "The automatic generation of anchor-style product promotion videos presents promising opportunities in online commerce, advertising, and consumer engagement. However, this remains a challenging task despite significant advancements in pose-guided human video generation. In addressing this challenge, we identify the integration of human-object interactions (HOI) into pose-guided human video generation as a core issue. To this end, we introduce AnchorCrafter, a novel diffusion-based system designed to generate 2D videos featuring a target human and a customized object, achieving high visual fidelity and controllable interactions. Specifically, we propose two key innovations: the HOI-appearance perception, which enhances object appearance recognition from arbitrary multi-view perspectives and disentangles object and human appearance, and the HOI-motion injection, which enables complex human-object interactions by overcoming challenges in object trajectory conditioning and inter-occlusion management. Additionally, we introduce the HOI-region reweighting loss, a training objective that enhances the learning of object details. Extensive experiments demonstrate that our proposed system outperforms existing methods in preserving object appearance and shape awareness, while simultaneously maintaining consistency in human appearance and motion. Project page: https://cangcz.github.io/Anchor-Crafter/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/cangcz/AnchorCrafter", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.15411", "title": "FINECAPTION: Compositional Image Captioning Focusing on Wherever You Want at Any Granularity", "authors": [ "Hang Hua", "Qing Liu", "Lingzhi Zhang", "Jing Shi", "Zhifei Zhang", "Yilin Wang", "Jianming Zhang", "Jiebo Luo" ], "github": "", "project_page": "", "abstract": "The advent of large Vision-Language Models (VLMs) has significantly advanced multimodal tasks, enabling more sophisticated and accurate reasoning across various applications, including image and video captioning, visual question answering, and cross-modal retrieval. Despite their superior capabilities, VLMs struggle with fine-grained image regional composition information perception. Specifically, they have difficulty accurately aligning the segmentation masks with the corresponding semantics and precisely describing the compositional aspects of the referred regions. However, compositionality - the ability to understand and generate novel combinations of known visual and textual components - is critical for facilitating coherent reasoning and understanding across modalities by VLMs. To address this issue, we propose FINECAPTION, a novel VLM that can recognize arbitrary masks as referential inputs and process high-resolution images for compositional image captioning at different granularity levels. To support this endeavor, we introduce COMPOSITIONCAP, a new dataset for multi-grained region compositional image captioning, which introduces the task of compositional attribute-aware regional image captioning. Empirical results demonstrate the effectiveness of our proposed model compared to other state-of-the-art VLMs. Additionally, we analyze the capabilities of current VLMs in recognizing various visual prompts for compositional region image captioning, highlighting areas for improvement in VLM design and training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.17691", "title": "Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens", "authors": [ "Xu Ouyang", "Tao Ge", "Thomas Hartvigsen", "Zhisong Zhang", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "We reveal that low-bit quantization favors undertrained large language models (LLMs) by observing that models with larger sizes or fewer training tokens experience less quantization-induced degradation (QiD) when applying low-bit quantization, whereas smaller models with extensive training tokens suffer significant QiD. To gain deeper insights into this trend, we study over 1500 quantized LLM checkpoints of various sizes and at different training levels (undertrained or fully trained) in a controlled setting, deriving scaling laws for understanding the relationship between QiD and factors such as the number of training tokens, model size and bit width. With the derived scaling laws, we propose a novel perspective that we can use QiD to measure an LLM's training levels and determine the number of training tokens required for fully training LLMs of various sizes. Moreover, we use the scaling laws to predict the quantization performance of different-sized LLMs trained with 100 trillion tokens. Our projection shows that the low-bit quantization performance of future models, which are expected to be trained with over 100 trillion tokens, may NOT be desirable. This poses a potential challenge for low-bit quantization in the future and highlights the need for awareness of a model's training level when evaluating low-bit quantization research. To facilitate future research on this problem, we release all the 1500+ quantized checkpoints used in this work at https://huggingface.co/Xu-Ouyang.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.17686", "title": "Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for Training-Free Acceleration", "authors": [ "Yuhang Han", "Xuyang Liu", "Pengxiang Ding", "Donglin Wang", "Honggang Chen", "Qingsen Yan", "Siteng Huang" ], "github": "https://github.com/kawhiiiileo/FiCoCo", "project_page": "", "abstract": "To accelerate the inference of heavy Multimodal Large Language Models (MLLMs), this study rethinks the current landscape of training-free token reduction research. We regret to find that the critical components of existing methods are tightly intertwined, with their interconnections and effects remaining unclear for comparison, transfer, and expansion. Therefore, we propose a unified ''filter-correlate-compress'' paradigm that decomposes the token reduction into three distinct stages within a pipeline, maintaining consistent design objectives and elements while allowing for unique implementations. We additionally demystify the popular works and subsume them into our paradigm to showcase its universality. Finally, we offer a suite of methods grounded in the paradigm, striking a balance between speed and accuracy throughout different phases of the inference. Experimental results across 10 benchmarks indicate that our methods can achieve up to an 82.4% reduction in FLOPs with a minimal impact on performance, simultaneously surpassing state-of-the-art training-free methods. Our project page is at https://ficoco-accelerate.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kawhiiiileo/FiCoCo", "github_api_added_by": "auto" }, { "date": "2024-11-27", "arxiv_id": "2411.16856", "title": "SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE", "authors": [ "Yongwei Chen", "Yushi Lan", "Shangchen Zhou", "Tengfei Wang", "XIngang Pan" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "Autoregressive models have demonstrated remarkable success across various fields, from large language models (LLMs) to large multimodal models (LMMs) and 2D content generation, moving closer to artificial general intelligence (AGI). Despite these advances, applying autoregressive approaches to 3D object generation and understanding remains largely unexplored. This paper introduces Scale AutoRegressive 3D (SAR3D), a novel framework that leverages a multi-scale 3D vector-quantized variational autoencoder (VQVAE) to tokenize 3D objects for efficient autoregressive generation and detailed understanding. By predicting the next scale in a multi-scale latent representation instead of the next single token, SAR3D reduces generation time significantly, achieving fast 3D object generation in just 0.82 seconds on an A6000 GPU. Additionally, given the tokens enriched with hierarchical 3D-aware information, we finetune a pretrained LLM on them, enabling multimodal comprehension of 3D content. Our experiments show that SAR3D surpasses current 3D generation methods in both speed and quality and allows LLMs to interpret and caption 3D models comprehensively.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.16173", "title": "SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval and Routing in Long-Form Video Analysis", "authors": [ "Junho Kim", "Hyunjun Kim", "Hosu Lee", "Yong Man Ro" ], "github": "", "project_page": "", "abstract": "Despite advances in Large Multi-modal Models, applying them to long and untrimmed video content remains challenging due to limitations in context length and substantial memory overhead. These constraints often lead to significant information loss and reduced relevance in the model responses. With the exponential growth of video data across web platforms, understanding long-form video is crucial for advancing generalized intelligence. In this paper, we introduce SALOVA: Segment-Augmented LOng Video Assistant, a novel video-LLM framework designed to enhance the comprehension of lengthy video content through targeted retrieval process. We address two main challenges to achieve it: (i) We present the SceneWalk dataset, a high-quality collection of 87.8K long videos, each densely captioned at the segment level to enable models to capture scene continuity and maintain rich descriptive context. (ii) We develop robust architectural designs integrating dynamic routing mechanism and spatio-temporal projector to efficiently retrieve and process relevant video segments based on user queries. Our framework mitigates the limitations of current video-LMMs by allowing for precise identification and retrieval of relevant video segments in response to queries, thereby improving the contextual relevance of the generated responses. Through extensive experiments, SALOVA demonstrates enhanced capability in processing complex long-form videos, showing significant capability to maintain contextual integrity across extended sequences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.16754", "title": "Visual Counter Turing Test (VCT^2): Discovering the Challenges for AI-Generated Image Detection and Introducing Visual AI Index (V_AI)", "authors": [ "Nasrin Imanpour", "Shashwat Bajpai", "Subhankar Ghosh", "Sainath Reddy Sankepally", "Abhilekh Borah", "Hasnat Md Abdullah", "Nishoak Kosaraju", "Shreyas Dixit", "Ashhar Aziz", "Shwetangshu Biswas", "Vinija Jain", "Aman Chadha", "Amit Sheth", "Amitava Das" ], "github": "", "project_page": "", "abstract": "The proliferation of AI techniques for image generation, coupled with their increasing accessibility, has raised significant concerns about the potential misuse of these images to spread misinformation. Recent AI-generated image detection (AGID) methods include CNNDetection, NPR, DM Image Detection, Fake Image Detection, DIRE, LASTED, GAN Image Detection, AIDE, SSP, DRCT, RINE, OCC-CLIP, De-Fake, and Deep Fake Detection. However, we argue that the current state-of-the-art AGID techniques are inadequate for effectively detecting contemporary AI-generated images and advocate for a comprehensive reevaluation of these methods. We introduce the Visual Counter Turing Test (VCT^2), a benchmark comprising ~130K images generated by contemporary text-to-image models (Stable Diffusion 2.1, Stable Diffusion XL, Stable Diffusion 3, DALL-E 3, and Midjourney 6). VCT^2 includes two sets of prompts sourced from tweets by the New York Times Twitter account and captions from the MS COCO dataset. We also evaluate the performance of the aforementioned AGID techniques on the VCT^2 benchmark, highlighting their ineffectiveness in detecting AI-generated images. As image-generative AI models continue to evolve, the need for a quantifiable framework to evaluate these models becomes increasingly critical. To meet this need, we propose the Visual AI Index (V_AI), which assesses generated images from various visual perspectives, including texture complexity and object coherence, setting a new standard for evaluating image-generative AI models. To foster research in this domain, we make our https://huggingface.co/datasets/anonymous1233/COCO_AI and https://huggingface.co/datasets/anonymous1233/twitter_AI datasets publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.16819", "title": "Pathways on the Image Manifold: Image Editing via Video Generation", "authors": [ "Noam Rotstein", "Gal Yona", "Daniel Silver", "Roy Velich", "David Bensa\u00efd", "Ron Kimmel" ], "github": "", "project_page": "", "abstract": "Recent advances in image editing, driven by image diffusion models, have shown remarkable progress. However, significant challenges remain, as these models often struggle to follow complex edit instructions accurately and frequently compromise fidelity by altering key elements of the original image. Simultaneously, video generation has made remarkable strides, with models that effectively function as consistent and continuous world simulators. In this paper, we propose merging these two fields by utilizing image-to-video models for image editing. We reformulate image editing as a temporal process, using pretrained video models to create smooth transitions from the original image to the desired edit. This approach traverses the image manifold continuously, ensuring consistent edits while preserving the original image's key aspects. Our approach achieves state-of-the-art results on text-based image editing, demonstrating significant improvements in both edit accuracy and image preservation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.14721", "title": "MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts", "authors": [ "Jiatong Li", "Yunqing Liu", "Wei Liu", "Jingdi Le", "Di Zhang", "Wenqi Fan", "Dongzhan Zhou", "Yuqiang Li", "Qing Li" ], "github": "", "project_page": "", "abstract": "Molecule discovery is a pivotal research field, impacting everything from the medicines we take to the materials we use. Recently, Large Language Models (LLMs) have been widely adopted in molecule understanding and generation, yet the alignments between molecules and their corresponding captions remain a significant challenge. Previous endeavours often treat the molecule as a general SMILES string or molecular graph, neglecting the fine-grained alignments between the molecular sub-structures and the descriptive textual phrases, which are crucial for accurate and explainable predictions. In this case, we introduce MolReFlect, a novel teacher-student framework designed to contextually perform the molecule-caption alignments in a fine-grained way. Our approach initially leverages a larger teacher LLM to label the detailed alignments by directly extracting critical phrases from molecule captions or SMILES strings and implying them to corresponding sub-structures or characteristics. To refine these alignments, we propose In-Context Selective Reflection, which retrieves previous extraction results as context examples for teacher LLM to reflect and lets a smaller student LLM select from in-context reflection and previous extraction results. Finally, we enhance the learning process of the student LLM through Chain-of-Thought In-Context Molecule Tuning, integrating the fine-grained alignments and the reasoning processes within the Chain-of-Thought format. Our experimental results demonstrate that MolReFlect enables LLMs like Mistral-7B to significantly outperform the previous baselines, achieving SOTA performance on the ChEBI-20 dataset. This advancement not only enhances the generative capabilities of LLMs in the molecule-caption translation task, but also contributes to a more explainable framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.17451", "title": "VLRewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models", "authors": [ "Lei Li", "Yuancheng Wei", "Zhihui Xie", "Xuqing Yang", "Yifan Song", "Peiyi Wang", "Chenxin An", "Tianyu Liu", "Sujian Li", "Bill Yuchen Lin", "Lingpeng Kong", "Qi Liu" ], "github": "", "project_page": "", "abstract": "Vision-language generative reward models (VL-GenRMs) play a crucial role in aligning and evaluating multimodal AI systems, yet their own evaluation remains under-explored. Current assessment methods primarily rely on AI-annotated preference labels from traditional VL tasks, which can introduce biases and often fail to effectively challenge state-of-the-art models. To address these limitations, we introduce VL-RewardBench, a comprehensive benchmark spanning general multimodal queries, visual hallucination detection, and complex reasoning tasks. Through our AI-assisted annotation pipeline combining sample selection with human verification, we curate 1,250 high-quality examples specifically designed to probe model limitations. Comprehensive evaluation across 16 leading large vision-language models, demonstrates VL-RewardBench's effectiveness as a challenging testbed, where even GPT-4o achieves only 65.4% accuracy, and state-of-the-art open-source models such as Qwen2-VL-72B, struggle to surpass random-guessing. Importantly, performance on VL-RewardBench strongly correlates (Pearson's r > 0.9) with MMMU-Pro accuracy using Best-of-N sampling with VL-GenRMs. Analysis experiments uncover three critical insights for improving VL-GenRMs: (i) models predominantly fail at basic visual perception tasks rather than reasoning tasks; (ii) inference-time scaling benefits vary dramatically by model capacity; and (iii) training VL-GenRMs to learn to judge substantially boosts judgment capability (+14.7% accuracy for a 7B VL-GenRM). We believe VL-RewardBench along with the experimental insights will become a valuable resource for advancing VL-GenRMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.16801", "title": "Controllable Human Image Generation with Personalized Multi-Garments", "authors": [ "Yisol Choi", "Sangkyung Kwak", "Sihyun Yu", "Hyungwon Choi", "Jinwoo Shin" ], "github": "", "project_page": "", "abstract": "We present BootComp, a novel framework based on text-to-image diffusion models for controllable human image generation with multiple reference garments. Here, the main bottleneck is data acquisition for training: collecting a large-scale dataset of high-quality reference garment images per human subject is quite challenging, i.e., ideally, one needs to manually gather every single garment photograph worn by each human. To address this, we propose a data generation pipeline to construct a large synthetic dataset, consisting of human and multiple-garment pairs, by introducing a model to extract any reference garment images from each human image. To ensure data quality, we also propose a filtering strategy to remove undesirable generated data based on measuring perceptual similarities between the garment presented in human image and extracted garment. Finally, by utilizing the constructed synthetic dataset, we train a diffusion model having two parallel denoising paths that use multiple garment images as conditions to generate human images while preserving their fine-grained details. We further show the wide-applicability of our framework by adapting it to different types of reference-based generation in the fashion domain, including virtual try-on, and controllable human image generation with other conditions, e.g., pose, face, etc.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-27", "arxiv_id": "2411.15241", "title": "EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality", "authors": [ "Sanghyeok Lee", "Joonmyung Choi", "Hyunwoo J. Kim" ], "github": "https://github.com/mlvlab/EfficientViM", "project_page": "", "abstract": "For the deployment of neural networks in resource-constrained environments, prior works have built lightweight architectures with convolution and attention for capturing local and global dependencies, respectively. Recently, the state space model has emerged as an effective global token interaction with its favorable linear computational cost in the number of tokens. Yet, efficient vision backbones built with SSM have been explored less. In this paper, we introduce Efficient Vision Mamba (EfficientViM), a novel architecture built on hidden state mixer-based state space duality (HSM-SSD) that efficiently captures global dependencies with further reduced computational cost. In the HSM-SSD layer, we redesign the previous SSD layer to enable the channel mixing operation within hidden states. Additionally, we propose multi-stage hidden state fusion to further reinforce the representation power of hidden states, and provide the design alleviating the bottleneck caused by the memory-bound operations. As a result, the EfficientViM family achieves a new state-of-the-art speed-accuracy trade-off on ImageNet-1k, offering up to a 0.7% performance improvement over the second-best model SHViT with faster speed. Further, we observe significant improvements in throughput and accuracy compared to prior works, when scaling images or employing distillation training. Code is available at https://github.com/mlvlab/EfficientViM.", "github_manual": "", "github_regex": "https://github.com/mlvlab/EfficientViM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.17945", "title": "MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation", "authors": [ "Sankalp Sinha", "Mohammad Sadil Khan", "Muhammad Usama", "Shino Sam", "Didier Stricker", "Sk Aziz Ali", "Muhammad Zeshan Afzal" ], "github": "", "project_page": "", "abstract": "Generating high-fidelity 3D content from text prompts remains a significant challenge in computer vision due to the limited size, diversity, and annotation depth of the existing datasets. To address this, we introduce MARVEL-40M+, an extensive dataset with 40 million text annotations for over 8.9 million 3D assets aggregated from seven major 3D datasets. Our contribution is a novel multi-stage annotation pipeline that integrates open-source pretrained multi-view VLMs and LLMs to automatically produce multi-level descriptions, ranging from detailed (150-200 words) to concise semantic tags (10-20 words). This structure supports both fine-grained 3D reconstruction and rapid prototyping. Furthermore, we incorporate human metadata from source datasets into our annotation pipeline to add domain-specific information in our annotation and reduce VLM hallucinations. Additionally, we develop MARVEL-FX3D, a two-stage text-to-3D pipeline. We fine-tune Stable Diffusion with our annotations and use a pretrained image-to-3D network to generate 3D textured meshes within 15s. Extensive evaluations show that MARVEL-40M+ significantly outperforms existing datasets in annotation quality and linguistic diversity, achieving win rates of 72.41% by GPT-4 and 73.40% by human evaluators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.17949", "title": "ROICtrl: Boosting Instance Control for Visual Generation", "authors": [ "Yuchao Gu", "Yipin Zhou", "Yunfan Ye", "Yixin Nie", "Licheng Yu", "Pingchuan Ma", "Kevin Qinghong Lin", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Natural language often struggles to accurately associate positional and attribute information with multiple instances, which limits current text-based visual generation models to simpler compositions featuring only a few dominant instances. To address this limitation, this work enhances diffusion models by introducing regional instance control, where each instance is governed by a bounding box paired with a free-form caption. Previous methods in this area typically rely on implicit position encoding or explicit attention masks to separate regions of interest (ROIs), resulting in either inaccurate coordinate injection or large computational overhead. Inspired by ROI-Align in object detection, we introduce a complementary operation called ROI-Unpool. Together, ROI-Align and ROI-Unpool enable explicit, efficient, and accurate ROI manipulation on high-resolution feature maps for visual generation. Building on ROI-Unpool, we propose ROICtrl, an adapter for pretrained diffusion models that enables precise regional instance control. ROICtrl is compatible with community-finetuned diffusion models, as well as with existing spatial-based add-ons (\\eg, ControlNet, T2I-Adapter) and embedding-based add-ons (\\eg, IP-Adapter, ED-LoRA), extending their applications to multi-instance generation. Experiments show that ROICtrl achieves superior performance in regional instance control while significantly reducing computational costs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.17188", "title": "Interleaved Scene Graph for Interleaved Text-and-Image Generation Assessment", "authors": [ "Dongping Chen", "Ruoxi Chen", "Shu Pu", "Zhaoyi Liu", "Yanru Wu", "Caixi Chen", "Benlin Liu", "Yue Huang", "Yao Wan", "Pan Zhou", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Many real-world user queries (e.g. \"How do to make egg fried rice?\") could benefit from systems capable of generating responses with both textual steps with accompanying images, similar to a cookbook. Models designed to generate interleaved text and images face challenges in ensuring consistency within and across these modalities. To address these challenges, we present ISG, a comprehensive evaluation framework for interleaved text-and-image generation. ISG leverages a scene graph structure to capture relationships between text and image blocks, evaluating responses on four levels of granularity: holistic, structural, block-level, and image-specific. This multi-tiered evaluation allows for a nuanced assessment of consistency, coherence, and accuracy, and provides interpretable question-answer feedback. In conjunction with ISG, we introduce a benchmark, ISG-Bench, encompassing 1,150 samples across 8 categories and 21 subcategories. This benchmark dataset includes complex language-vision dependencies and golden answers to evaluate models effectively on vision-centric tasks such as style transfer, a challenging area for current models. Using ISG-Bench, we demonstrate that recent unified vision-language models perform poorly on generating interleaved content. While compositional approaches that combine separate language and image models show a 111% improvement over unified models at the holistic level, their performance remains suboptimal at both block and image levels. To facilitate future work, we develop ISG-Agent, a baseline agent employing a \"plan-execute-refine\" pipeline to invoke tools, achieving a 122% performance improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.18462", "title": "Draft Model Knows When to Stop: A Self-Verification Length Policy for Speculative Decoding", "authors": [ "Ziyin Zhang", "Jiahao Xu", "Tian Liang", "Xingyu Chen", "Zhiwei He", "Rui Wang", "Zhaopeng Tu" ], "github": "https://github.com/geralt-targaryen/svip", "project_page": "", "abstract": "Speculative Decoding (SD) has become an important technique in accelerating the inference speed of large language models. Conventional SD methods employ a fixed draft length, which ignores the token generation difficulty across tasks. Consequently, in this paper, we address such an issue and introduce SVIP - a difficulty-aware dynamic draft length policy for speculative decoding systems. Based on a theoretical lower bound of draft token acceptance rate and its inference-time approximation, SVIP adaptively determines the lengths of draft sequences based on the entropy of each draft token distribution. Experimental results on mainstream SD benchmarks and frameworks demonstrate the superior performance of SVIP, achieving up to 20\\% walltime speedup on SpecBench over baseline SD methods and 60\\% speedup on MT-Bench for long-form generation of up to 8K tokens. Moreover, SVIP is totally training-free and compatible with any existing SD methods that generate draft tokens autoregressively. Experimental results also show that SVIP yields consistent walltime improvement on top of GliDe & CaPE and EAGLE-2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/geralt-targaryen/svip", "github_api_added_by": "auto" }, { "date": "2024-11-28", "arxiv_id": "2411.17787", "title": "Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient", "authors": [ "Zigeng Chen", "Xinyin Ma", "Gongfan Fang", "Xinchao Wang" ], "github": "https://github.com/czg1225/CoDe", "project_page": "", "abstract": "In the rapidly advancing field of image generation, Visual Auto-Regressive (VAR) modeling has garnered considerable attention for its innovative next-scale prediction approach. This paradigm offers substantial improvements in efficiency, scalability, and zero-shot generalization. Yet, the inherently coarse-to-fine nature of VAR introduces a prolonged token sequence, leading to prohibitive memory consumption and computational redundancies. To address these bottlenecks, we propose Collaborative Decoding (CoDe), a novel efficient decoding strategy tailored for the VAR framework. CoDe capitalizes on two critical observations: the substantially reduced parameter demands at larger scales and the exclusive generation patterns across different scales. Based on these insights, we partition the multi-scale inference process into a seamless collaboration between a large model and a small model. The large model serves as the 'drafter', specializing in generating low-frequency content at smaller scales, while the smaller model serves as the 'refiner', solely focusing on predicting high-frequency details at larger scales. This collaboration yields remarkable efficiency with minimal impact on quality: CoDe achieves a 1.7x speedup, slashes memory usage by around 50%, and preserves image quality with only a negligible FID increase from 1.95 to 1.98. When drafting steps are further decreased, CoDe can achieve an impressive 2.9x acceleration ratio, reaching 41 images/s at 256x256 resolution on a single NVIDIA 4090 GPU, while preserving a commendable FID of 2.27. The code is available at https://github.com/czg1225/CoDe", "github_manual": "", "github_regex": "https://github.com/czg1225/CoDe", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.17440", "title": "Identity-Preserving Text-to-Video Generation by Frequency Decomposition", "authors": [ "Shenghai Yuan", "Jinfa Huang", "Xianyi He", "Yunyuan Ge", "Yujun Shi", "Liuhan Chen", "Jiebo Luo", "Li Yuan" ], "github": "", "project_page": "", "abstract": "Identity-preserving text-to-video (IPT2V) generation aims to create high-fidelity videos with consistent human identity. It is an important task in video generation but remains an open problem for generative models. This paper pushes the technical frontier of IPT2V in two directions that have not been resolved in literature: (1) A tuning-free pipeline without tedious case-by-case finetuning, and (2) A frequency-aware heuristic identity-preserving DiT-based control scheme. We propose ConsisID, a tuning-free DiT-based controllable IPT2V model to keep human identity consistent in the generated video. Inspired by prior findings in frequency analysis of diffusion transformers, it employs identity-control signals in the frequency domain, where facial features can be decomposed into low-frequency global features and high-frequency intrinsic features. First, from a low-frequency perspective, we introduce a global facial extractor, which encodes reference images and facial key points into a latent space, generating features enriched with low-frequency information. These features are then integrated into shallow layers of the network to alleviate training challenges associated with DiT. Second, from a high-frequency perspective, we design a local facial extractor to capture high-frequency details and inject them into transformer blocks, enhancing the model's ability to preserve fine-grained features. We propose a hierarchical training strategy to leverage frequency information for identity preservation, transforming a vanilla pre-trained video generation model into an IPT2V model. Extensive experiments demonstrate that our frequency-aware heuristic scheme provides an optimal control solution for DiT-based models. Thanks to this scheme, our ConsisID generates high-quality, identity-preserving videos, making strides towards more effective IPT2V.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.15139", "title": "DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving", "authors": [ "Bencheng Liao", "Shaoyu Chen", "Haoran Yin", "Bo Jiang", "Cheng Wang", "Sixu Yan", "Xinbang Zhang", "Xiangyu Li", "Ying Zhang", "Qian Zhang", "Xinggang Wang" ], "github": "https://github.com/hustvl/DiffusionDrive", "project_page": "", "abstract": "Recently, the diffusion model has emerged as a powerful generative technique for robotic policy learning, capable of modeling multi-mode action distributions. Leveraging its capability for end-to-end autonomous driving is a promising direction. However, the numerous denoising steps in the robotic diffusion policy and the more dynamic, open-world nature of traffic scenes pose substantial challenges for generating diverse driving actions at a real-time speed. To address these challenges, we propose a novel truncated diffusion policy that incorporates prior multi-mode anchors and truncates the diffusion schedule, enabling the model to learn denoising from anchored Gaussian distribution to the multi-mode driving action distribution. Additionally, we design an efficient cascade diffusion decoder for enhanced interaction with conditional scene context. The proposed model, DiffusionDrive, demonstrates 10times reduction in denoising steps compared to vanilla diffusion policy, delivering superior diversity and quality in just 2 steps. On the planning-oriented NAVSIM dataset, with the aligned ResNet-34 backbone, DiffusionDrive achieves 88.1 PDMS without bells and whistles, setting a new record, while running at a real-time speed of 45 FPS on an NVIDIA 4090. Qualitative results on challenging scenarios further confirm that DiffusionDrive can robustly generate diverse plausible driving actions. Code and model will be available at https://github.com/hustvl/DiffusionDrive.", "github_manual": "", "github_regex": "https://github.com/hustvl/DiffusionDrive", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.18613", "title": "CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models", "authors": [ "Rundi Wu", "Ruiqi Gao", "Ben Poole", "Alex Trevithick", "Changxi Zheng", "Jonathan T. Barron", "Aleksander Holynski" ], "github": "", "project_page": "", "abstract": "We present CAT4D, a method for creating 4D (dynamic 3D) scenes from monocular video. CAT4D leverages a multi-view video diffusion model trained on a diverse combination of datasets to enable novel view synthesis at any specified camera poses and timestamps. Combined with a novel sampling approach, this model can transform a single monocular video into a multi-view video, enabling robust 4D reconstruction via optimization of a deformable 3D Gaussian representation. We demonstrate competitive performance on novel view synthesis and dynamic scene reconstruction benchmarks, and highlight the creative capabilities for 4D scene generation from real or generated videos. See our project page for results and interactive demos: cat-4d.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.15872", "title": "Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics", "authors": [ "Sarim Hashmi", "Juan Lugo", "Abdelrahman Elsayed", "Dinesh Saggurthi", "Mohammed Elseiagy", "Alikhan Nurkamal", "Jaskaran Walia", "Fadillah Adamsyah Maani", "Mohammad Yaqub" ], "github": "https://github.com/python-arch/BioMbz-Optimizing-Brain-Tumor-Segmentation-with-MedNeXt-BraTS-2024-SSA-and-Pediatrics", "project_page": "", "abstract": "Identifying key pathological features in brain MRIs is crucial for the long-term survival of glioma patients. However, manual segmentation is time-consuming, requiring expert intervention and is susceptible to human error. Therefore, significant research has been devoted to developing machine learning methods that can accurately segment tumors in 3D multimodal brain MRI scans. Despite their progress, state-of-the-art models are often limited by the data they are trained on, raising concerns about their reliability when applied to diverse populations that may introduce distribution shifts. Such shifts can stem from lower quality MRI technology (e.g., in sub-Saharan Africa) or variations in patient demographics (e.g., children). The BraTS-2024 challenge provides a platform to address these issues. This study presents our methodology for segmenting tumors in the BraTS-2024 SSA and Pediatric Tumors tasks using MedNeXt, comprehensive model ensembling, and thorough postprocessing. Our approach demonstrated strong performance on the unseen validation set, achieving an average Dice Similarity Coefficient (DSC) of 0.896 on the BraTS-2024 SSA dataset and an average DSC of 0.830 on the BraTS Pediatric Tumor dataset. Additionally, our method achieved an average Hausdorff Distance (HD95) of 14.682 on the BraTS-2024 SSA dataset and an average HD95 of 37.508 on the BraTS Pediatric dataset. Our GitHub repository can be accessed here: Project Repository : https://github.com/python-arch/BioMbz-Optimizing-Brain-Tumor-Segmentation-with-MedNeXt-BraTS-2024-SSA-and-Pediatrics", "github_manual": "", "github_regex": "https://github.com/python-arch/BioMbz-Optimizing-Brain-Tumor-Segmentation-with-MedNeXt-BraTS-2024-SSA-and-Pediatrics", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.18104", "title": "Training and Evaluating Language Models with Template-based Data Generation", "authors": [ "Yifan Zhang" ], "github": "https://github.com/iiis-ai/TemplateMath", "project_page": "", "abstract": "The rapid advancement of large language models (LLMs) such as GPT-3, PaLM, and Llama has significantly transformed natural language processing, showcasing remarkable capabilities in understanding and generating language. However, these models often struggle with tasks requiring complex reasoning, particularly in mathematical problem-solving, due in part to the scarcity of large-scale, high-quality, domain-specific datasets necessary for training sophisticated reasoning abilities. To address this limitation, we introduce Template-based Data Generation (TDG), a novel approach that leverages LLMs (GPT-4) to automatically generate parameterized meta-templates, which are then used to synthesize a vast array of high-quality problems and solutions. Leveraging TDG, we create TemplateMath Part I: TemplateGSM, a dataset comprising over 7 million synthetically generated grade school math problems--each accompanied by code-based and natural language solutions--with the potential to generate an effectively unlimited number more. This dataset alleviates the scarcity of large-scale mathematical datasets and serves as a valuable resource for pre-training, fine-tuning, and evaluating LLMs in mathematical reasoning. Our method not only enables the generation of virtually infinite data but also elevates data augmentation to a new level by using GPT-4 for meta-template generation, ensuring diverse and high-quality problem structures. The TemplateMath Part I: TemplateGSM dataset is publicly available at https://huggingface.co/datasets/math-ai/TemplateGSM. The code is available at https://github.com/iiis-ai/TemplateMath.", "github_manual": "", "github_regex": "https://github.com/iiis-ai/TemplateMath", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.18279", "title": "Large Language Model-Brained GUI Agents: A Survey", "authors": [ "Chaoyun Zhang", "Shilin He", "Jiaxu Qian", "Bowen Li", "Liqun Li", "Si Qin", "Yu Kang", "Minghua Ma", "Qingwei Lin", "Saravan Rajmohan", "Dongmei Zhang", "Qi Zhang" ], "github": "https://github.com/vyokky/LLM-Brained-GUI-Agents-Survey", "project_page": "", "abstract": "GUIs have long been central to human-computer interaction, providing an intuitive and visually-driven way to access and interact with digital systems. The advent of LLMs, particularly multimodal models, has ushered in a new era of GUI automation. They have demonstrated exceptional capabilities in natural language understanding, code generation, and visual processing. This has paved the way for a new generation of LLM-brained GUI agents capable of interpreting complex GUI elements and autonomously executing actions based on natural language instructions. These agents represent a paradigm shift, enabling users to perform intricate, multi-step tasks through simple conversational commands. Their applications span across web navigation, mobile app interactions, and desktop automation, offering a transformative user experience that revolutionizes how individuals interact with software. This emerging field is rapidly advancing, with significant progress in both research and industry. To provide a structured understanding of this trend, this paper presents a comprehensive survey of LLM-brained GUI agents, exploring their historical evolution, core components, and advanced techniques. We address research questions such as existing GUI agent frameworks, the collection and utilization of data for training specialized GUI agents, the development of large action models tailored for GUI tasks, and the evaluation metrics and benchmarks necessary to assess their effectiveness. Additionally, we examine emerging applications powered by these agents. Through a detailed analysis, this survey identifies key research gaps and outlines a roadmap for future advancements in the field. By consolidating foundational knowledge and state-of-the-art developments, this work aims to guide both researchers and practitioners in overcoming challenges and unlocking the full potential of LLM-brained GUI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vyokky/LLM-Brained-GUI-Agents-Survey", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.16781", "title": "UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing", "authors": [ "Yiheng Li", "Ruibing Hou", "Hong Chang", "Shiguang Shan", "Xilin Chen" ], "github": "https://github.com/liyiheng23/UniPose", "project_page": "", "abstract": "Human pose plays a crucial role in the digital age. While recent works have achieved impressive progress in understanding and generating human poses, they often support only a single modality of control signals and operate in isolation, limiting their application in real-world scenarios. This paper presents UniPose, a framework employing Large Language Models (LLMs) to comprehend, generate, and edit human poses across various modalities, including images, text, and 3D SMPL poses. Specifically, we apply a pose tokenizer to convert 3D poses into discrete pose tokens, enabling seamless integration into the LLM within a unified vocabulary. To further enhance the fine-grained pose perception capabilities, we facilitate UniPose with a mixture of visual encoders, among them a pose-specific visual encoder. Benefiting from a unified learning strategy, UniPose effectively transfers knowledge across different pose-relevant tasks, adapts to unseen tasks, and exhibits extended capabilities. This work serves as the first attempt at building a general-purpose framework for pose comprehension, generation, and editing. Extensive experiments highlight UniPose's competitive and even superior performance across various pose-relevant tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/liyiheng23/UniPose", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.18197", "title": "Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready 3D Characters", "authors": [ "Zhiyang Guo", "Jinxu Xiang", "Kai Ma", "Wengang Zhou", "Houqiang Li", "Ran Zhang" ], "github": "https://github.com/jasongzy/Make-It-Animatable", "project_page": "", "abstract": "3D characters are essential to modern creative industries, but making them animatable often demands extensive manual work in tasks like rigging and skinning. Existing automatic rigging tools face several limitations, including the necessity for manual annotations, rigid skeleton topologies, and limited generalization across diverse shapes and poses. An alternative approach is to generate animatable avatars pre-bound to a rigged template mesh. However, this method often lacks flexibility and is typically limited to realistic human shapes. To address these issues, we present Make-It-Animatable, a novel data-driven method to make any 3D humanoid model ready for character animation in less than one second, regardless of its shapes and poses. Our unified framework generates high-quality blend weights, bones, and pose transformations. By incorporating a particle-based shape autoencoder, our approach supports various 3D representations, including meshes and 3D Gaussian splats. Additionally, we employ a coarse-to-fine representation and a structure-aware modeling strategy to ensure both accuracy and robustness, even for characters with non-standard skeleton structures. We conducted extensive experiments to validate our framework's effectiveness. Compared to existing methods, our approach demonstrates significant improvements in both quality and speed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jasongzy/Make-It-Animatable", "github_api_added_by": "auto" }, { "date": "2024-11-28", "arxiv_id": "2411.17769", "title": "Omegance: A Single Parameter for Various Granularities in Diffusion-Based Synthesis", "authors": [ "Xinyu Hou", "Zongsheng Yue", "Xiaoming Li", "Chen Change Loy" ], "github": "https://github.com/itsmag11/Omegance", "project_page": "", "abstract": "In this work, we introduce a single parameter omega, to effectively control granularity in diffusion-based synthesis. This parameter is incorporated during the denoising steps of the diffusion model's reverse process. Our approach does not require model retraining, architectural modifications, or additional computational overhead during inference, yet enables precise control over the level of details in the generated outputs. Moreover, spatial masks or denoising schedules with varying omega values can be applied to achieve region-specific or timestep-specific granularity control. Prior knowledge of image composition from control signals or reference images further facilitates the creation of precise omega masks for granularity control on specific objects. To highlight the parameter's role in controlling subtle detail variations, the technique is named Omegance, combining \"omega\" and \"nuance\". Our method demonstrates impressive performance across various image and video synthesis tasks and is adaptable to advanced diffusion models. The code is available at https://github.com/itsmag11/Omegance.", "github_manual": "", "github_regex": "https://github.com/itsmag11/Omegance", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.18363", "title": "ChatRex: Taming Multimodal LLM for Joint Perception and Understanding", "authors": [ "Qing Jiang", "Gen luo", "Yuqin Yang", "Yuda Xiong", "Yihao Chen", "Zhaoyang Zeng", "Tianhe Ren", "Lei Zhang" ], "github": "https://github.com/IDEA-Research/ChatRex", "project_page": "", "abstract": "Perception and understanding are two pillars of computer vision. While multimodal large language models (MLLM) have demonstrated remarkable visual understanding capabilities, they arguably lack accurate perception abilities, e.g. the stage-of-the-art model Qwen2-VL only achieves a 43.9 recall rate on the COCO dataset, limiting many tasks requiring the combination of perception and understanding. In this work, we aim to bridge this perception gap from both model designing and data development perspectives. We first introduce ChatRex, an MLLM with a decoupled perception design. Instead of having the LLM directly predict box coordinates, we feed the output boxes from a universal proposal network into the LLM, allowing it to output the corresponding box indices to represent its detection results, turning the regression task into a retrieval-based task that LLM handles more proficiently. From the data perspective, we build a fully automated data engine and construct the Rexverse-2M dataset which possesses multiple granularities to support the joint training of perception and understanding. After standard two-stage training, ChatRex demonstrates strong perception capabilities while preserving multimodal understanding performance. The combination of these two capabilities simultaneously unlocks many attractive applications, demonstrating the complementary roles of both perception and understanding in MLLM. Code is available at https://github.com/IDEA-Research/ChatRex.", "github_manual": "", "github_regex": "https://github.com/IDEA-Research/ChatRex", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.17786", "title": "DreamCache: Finetuning-Free Lightweight Personalized Image Generation via Feature Caching", "authors": [ "Emanuele Aiello", "Umberto Michieli", "Diego Valsesia", "Mete Ozay", "Enrico Magli" ], "github": "", "project_page": "", "abstract": "Personalized image generation requires text-to-image generative models that capture the core features of a reference subject to allow for controlled generation across different contexts. Existing methods face challenges due to complex training requirements, high inference costs, limited flexibility, or a combination of these issues. In this paper, we introduce DreamCache, a scalable approach for efficient and high-quality personalized image generation. By caching a small number of reference image features from a subset of layers and a single timestep of the pretrained diffusion denoiser, DreamCache enables dynamic modulation of the generated image features through lightweight, trained conditioning adapters. DreamCache achieves state-of-the-art image and text alignment, utilizing an order of magnitude fewer extra parameters, and is both more computationally effective and versatile than existing models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.17991", "title": "VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video Comprehension with Video-Text Duet Interaction Format", "authors": [ "Yueqian Wang", "Xiaojun Meng", "Yuxuan Wang", "Jianxin Liang", "Jiansheng Wei", "Huishuai Zhang", "Dongyan Zhao" ], "github": "https://github.com/yellow-binary-tree/MMDuet", "project_page": "", "abstract": "Recent researches on video large language models (VideoLLM) predominantly focus on model architectures and training datasets, leaving the interaction format between the user and the model under-explored. In existing works, users often interact with VideoLLMs by using the entire video and a query as input, after which the model generates a response. This interaction format constrains the application of VideoLLMs in scenarios such as live-streaming comprehension where videos do not end and responses are required in a real-time manner, and also results in unsatisfactory performance on time-sensitive tasks that requires localizing video segments. In this paper, we focus on a video-text duet interaction format. This interaction format is characterized by the continuous playback of the video, and both the user and the model can insert their text messages at any position during the video playback. When a text message ends, the video continues to play, akin to the alternative of two performers in a duet. We construct MMDuetIT, a video-text training dataset designed to adapt VideoLLMs to video-text duet interaction format. We also introduce the Multi-Answer Grounded Video Question Answering (MAGQA) task to benchmark the real-time response ability of VideoLLMs. Trained on MMDuetIT, MMDuet demonstrates that adopting the video-text duet interaction format enables the model to achieve significant improvements in various time-sensitive tasks (76% CIDEr on YouCook2 dense video captioning, 90\\% mAP on QVHighlights highlight detection and 25% R@0.5 on Charades-STA temporal video grounding) with minimal training efforts, and also enable VideoLLMs to reply in a real-time manner as the video plays. Code, data and demo are available at: https://github.com/yellow-binary-tree/MMDuet.", "github_manual": "", "github_regex": "https://github.com/yellow-binary-tree/MMDuet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.18412", "title": "Adaptive Blind All-in-One Image Restoration", "authors": [ "David Serrano-Lozano", "Luis Herranz", "Shaolin Su", "Javier Vazquez-Corral" ], "github": "https://github.com/davidserra9/abair", "project_page": "", "abstract": "Blind all-in-one image restoration models aim to recover a high-quality image from an input degraded with unknown distortions. However, these models require all the possible degradation types to be defined during the training stage while showing limited generalization to unseen degradations, which limits their practical application in complex cases. In this paper, we propose a simple but effective adaptive blind all-in-one restoration (ABAIR) model, which can address multiple degradations, generalizes well to unseen degradations, and efficiently incorporate new degradations by training a small fraction of parameters. First, we train our baseline model on a large dataset of natural images with multiple synthetic degradations, augmented with a segmentation head to estimate per-pixel degradation types, resulting in a powerful backbone able to generalize to a wide range of degradations. Second, we adapt our baseline model to varying image restoration tasks using independent low-rank adapters. Third, we learn to adaptively combine adapters to versatile images via a flexible and lightweight degradation estimator. Our model is both powerful in handling specific distortions and flexible in adapting to complex tasks, it not only outperforms the state-of-the-art by a large margin on five- and three-task IR setups, but also shows improved generalization to unseen degradations and also composite distortions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/davidserra9/abair", "github_api_added_by": "auto" }, { "date": "2024-11-28", "arxiv_id": "2411.14974", "title": "3D Convex Splatting: Radiance Field Rendering with 3D Smooth Convexes", "authors": [ "Jan Held", "Renaud Vandeghen", "Abdullah Hamdi", "Adrien Deliege", "Anthony Cioppa", "Silvio Giancola", "Andrea Vedaldi", "Bernard Ghanem", "Marc Van Droogenbroeck" ], "github": "https://github.com/convexsplatting/convex-splatting", "project_page": "", "abstract": "Recent advances in radiance field reconstruction, such as 3D Gaussian Splatting (3DGS), have achieved high-quality novel view synthesis and fast rendering by representing scenes with compositions of Gaussian primitives. However, 3D Gaussians present several limitations for scene reconstruction. Accurately capturing hard edges is challenging without significantly increasing the number of Gaussians, creating a large memory footprint. Moreover, they struggle to represent flat surfaces, as they are diffused in space. Without hand-crafted regularizers, they tend to disperse irregularly around the actual surface. To circumvent these issues, we introduce a novel method, named 3D Convex Splatting (3DCS), which leverages 3D smooth convexes as primitives for modeling geometrically-meaningful radiance fields from multi-view images. Smooth convex shapes offer greater flexibility than Gaussians, allowing for a better representation of 3D scenes with hard edges and dense volumes using fewer primitives. Powered by our efficient CUDA-based rasterizer, 3DCS achieves superior performance over 3DGS on benchmarks such as Mip-NeRF360, Tanks and Temples, and Deep Blending. Specifically, our method attains an improvement of up to 0.81 in PSNR and 0.026 in LPIPS compared to 3DGS while maintaining high rendering speeds and reducing the number of required primitives. Our results highlight the potential of 3D Convex Splatting to become the new standard for high-quality scene reconstruction and novel view synthesis. Project page: convexsplatting.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/convexsplatting/convex-splatting", "github_api_added_by": "auto" }, { "date": "2024-11-28", "arxiv_id": "2411.17698", "title": "Video-Guided Foley Sound Generation with Multimodal Controls", "authors": [ "Ziyang Chen", "Prem Seetharaman", "Bryan Russell", "Oriol Nieto", "David Bourgin", "Andrew Owens", "Justin Salamon" ], "github": "", "project_page": "", "abstract": "Generating sound effects for videos often requires creating artistic sound effects that diverge significantly from real-life sources and flexible control in the sound design. To address this problem, we introduce MultiFoley, a model designed for video-guided sound generation that supports multimodal conditioning through text, audio, and video. Given a silent video and a text prompt, MultiFoley allows users to create clean sounds (e.g., skateboard wheels spinning without wind noise) or more whimsical sounds (e.g., making a lion's roar sound like a cat's meow). MultiFoley also allows users to choose reference audio from sound effects (SFX) libraries or partial videos for conditioning. A key novelty of our model lies in its joint training on both internet video datasets with low-quality audio and professional SFX recordings, enabling high-quality, full-bandwidth (48kHz) audio generation. Through automated evaluations and human studies, we demonstrate that MultiFoley successfully generates synchronized high-quality sounds across varied conditional inputs and outperforms existing methods. Please see our project page for video results: https://ificl.github.io/MultiFoley/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.16832", "title": "Edit Away and My Face Will not Stay: Personal Biometric Defense against Malicious Generative Editing", "authors": [ "Hanhui Wang", "Yihua Zhang", "Ruizheng Bai", "Yue Zhao", "Sijia Liu", "Zhengzhong Tu" ], "github": "https://github.com/taco-group/FaceLock", "project_page": "", "abstract": "Recent advancements in diffusion models have made generative image editing more accessible, enabling creative edits but raising ethical concerns, particularly regarding malicious edits to human portraits that threaten privacy and identity security. Existing protection methods primarily rely on adversarial perturbations to nullify edits but often fail against diverse editing requests. We propose FaceLock, a novel approach to portrait protection that optimizes adversarial perturbations to destroy or significantly alter biometric information, rendering edited outputs biometrically unrecognizable. FaceLock integrates facial recognition and visual perception into perturbation optimization to provide robust protection against various editing attempts. We also highlight flaws in commonly used evaluation metrics and reveal how they can be manipulated, emphasizing the need for reliable assessments of protection. Experiments show FaceLock outperforms baselines in defending against malicious edits and is robust against purification techniques. Ablation studies confirm its stability and broad applicability across diffusion-based editing algorithms. Our work advances biometric defense and sets the foundation for privacy-preserving practices in image editing. The code is available at: https://github.com/taco-group/FaceLock.", "github_manual": "", "github_regex": "https://github.com/taco-group/FaceLock", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-28", "arxiv_id": "2411.18616", "title": "Diffusion Self-Distillation for Zero-Shot Customized Image Generation", "authors": [ "Shengqu Cai", "Eric Chan", "Yunzhi Zhang", "Leonidas Guibas", "Jiajun Wu", "Gordon Wetzstein" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion models produce impressive results but are frustrating tools for artists who desire fine-grained control. For example, a common use case is to create images of a specific instance in novel contexts, i.e., \"identity-preserving generation\". This setting, along with many other tasks (e.g., relighting), is a natural fit for image+text-conditional generative models. However, there is insufficient high-quality paired data to train such a model directly. We propose Diffusion Self-Distillation, a method for using a pre-trained text-to-image model to generate its own dataset for text-conditioned image-to-image tasks. We first leverage a text-to-image diffusion model's in-context generation ability to create grids of images and curate a large paired dataset with the help of a Visual-Language Model. We then fine-tune the text-to-image model into a text+image-to-image model using the curated paired dataset. We demonstrate that Diffusion Self-Distillation outperforms existing zero-shot methods and is competitive with per-instance tuning techniques on a wide range of identity-preservation generation tasks, without requiring test-time optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-29", "arxiv_id": "2411.14951", "title": "Morph: A Motion-free Physics Optimization Framework for Human Motion Generation", "authors": [ "Zhuo Li", "Mingshuang Luo", "Ruibing Hou", "Xin Zhao", "Hao Liu", "Hong Chang", "Zimo Liu", "Chen Li" ], "github": "", "project_page": "", "abstract": "Human motion generation plays a vital role in applications such as digital humans and humanoid robot control. However, most existing approaches disregard physics constraints, leading to the frequent production of physically implausible motions with pronounced artifacts such as floating and foot sliding. In this paper, we propose Morph, a Motion-free physics optimization framework, comprising a Motion Generator and a Motion Physics Refinement module, for enhancing physical plausibility without relying on costly real-world motion data. Specifically, the Motion Generator is responsible for providing large-scale synthetic motion data, while the Motion Physics Refinement Module utilizes these synthetic data to train a motion imitator within a physics simulator, enforcing physical constraints to project the noisy motions into a physically-plausible space. These physically refined motions, in turn, are used to fine-tune the Motion Generator, further enhancing its capability. Experiments on both text-to-motion and music-to-dance generation tasks demonstrate that our framework achieves state-of-the-art motion generation quality while improving physical plausibility drastically.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-29", "arxiv_id": "2411.18203", "title": "Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning", "authors": [ "Di Zhang", "Jingdi Lei", "Junxian Li", "Xunzhi Wang", "Yujie Liu", "Zonglin Yang", "Jiatong Li", "Weida Wang", "Suorong Yang", "Jianbo Wu", "Peng Ye", "Wanli Ouyang", "Dongzhan Zhou" ], "github": "", "project_page": "", "abstract": "Vision-language models~(VLMs) have shown remarkable advancements in multimodal reasoning tasks. However, they still often generate inaccurate or irrelevant responses due to issues like hallucinated image understandings or unrefined reasoning paths. To address these challenges, we introduce Critic-V, a novel framework inspired by the Actor-Critic paradigm to boost the reasoning capability of VLMs. This framework decouples the reasoning process and critic process by integrating two independent components: the Reasoner, which generates reasoning paths based on visual and textual inputs, and the Critic, which provides constructive critique to refine these paths. In this approach, the Reasoner generates reasoning responses according to text prompts, which can evolve iteratively as a policy based on feedback from the Critic. This interaction process was theoretically driven by a reinforcement learning framework where the Critic offers natural language critiques instead of scalar rewards, enabling more nuanced feedback to boost the Reasoner's capability on complex reasoning tasks. The Critic model is trained using Direct Preference Optimization (DPO), leveraging a preference dataset of critiques ranked by Rule-based Reward(RBR) to enhance its critic capabilities. Evaluation results show that the Critic-V framework significantly outperforms existing methods, including GPT-4V, on 5 out of 8 benchmarks, especially regarding reasoning accuracy and efficiency. Combining a dynamic text-based policy for the Reasoner and constructive feedback from the preference-optimized Critic enables a more reliable and context-sensitive multimodal reasoning process. Our approach provides a promising solution to enhance the reliability of VLMs, improving their performance in real-world reasoning-heavy multimodal applications such as autonomous driving and embodied intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-29", "arxiv_id": "2411.17176", "title": "ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting", "authors": [ "Chengyou Jia", "Changliang Xia", "Zhuohang Dang", "Weijia Wu", "Hangwei Qian", "Minnan Luo" ], "github": "", "project_page": "", "abstract": "Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios. This challenge arises from the complexity and uncertainty of tedious steps such as crafting suitable prompts, selecting appropriate models, and configuring specific arguments, making users resort to labor-intensive attempts for desired images. This paper proposes Automatic T2I generation, which aims to automate these tedious steps, allowing users to simply describe their needs in a freestyle chatting way. To systematically study this problem, we first introduce ChatGenBench, a novel benchmark designed for Automatic T2I. It features high-quality paired data with diverse freestyle inputs, enabling comprehensive evaluation of automatic T2I models across all steps. Additionally, recognizing Automatic T2I as a complex multi-step reasoning task, we propose ChatGen-Evo, a multi-stage evolution strategy that progressively equips models with essential automation skills. Through extensive evaluation across step-wise accuracy and image quality, ChatGen-Evo significantly enhances performance over various baselines. Our evaluation also uncovers valuable insights for advancing automatic T2I. All our data, code, and models will be available in https://chengyou-jia.github.io/ChatGen-Home", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-29", "arxiv_id": "2411.18350", "title": "TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models", "authors": [ "Riza Velioglu", "Petra Bevandic", "Robin Chan", "Barbara Hammer" ], "github": "", "project_page": "", "abstract": "This paper introduces Virtual Try-Off (VTOFF), a novel task focused on generating standardized garment images from single photos of clothed individuals. Unlike traditional Virtual Try-On (VTON), which digitally dresses models, VTOFF aims to extract a canonical garment image, posing unique challenges in capturing garment shape, texture, and intricate patterns. This well-defined target makes VTOFF particularly effective for evaluating reconstruction fidelity in generative models. We present TryOffDiff, a model that adapts Stable Diffusion with SigLIP-based visual conditioning to ensure high fidelity and detail retention. Experiments on a modified VITON-HD dataset show that our approach outperforms baseline methods based on pose transfer and virtual try-on with fewer pre- and post-processing steps. Our analysis reveals that traditional image generation metrics inadequately assess reconstruction quality, prompting us to rely on DISTS for more accurate evaluation. Our results highlight the potential of VTOFF to enhance product imagery in e-commerce applications, advance generative model evaluation, and inspire future work on high-fidelity reconstruction. Demo, code, and models are available at: https://rizavelioglu.github.io/tryoffdiff/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-29", "arxiv_id": "2411.17041", "title": "Free^2Guide: Gradient-Free Path Integral Control for Enhancing Text-to-Video Generation with Large Vision-Language Models", "authors": [ "Jaemin Kim", "Bryan S Kim", "Jong Chul Ye" ], "github": "https://github.com/kjm981995/free2guide_code", "project_page": "", "abstract": "Diffusion models have achieved impressive results in generative tasks like text-to-image (T2I) and text-to-video (T2V) synthesis. However, achieving accurate text alignment in T2V generation remains challenging due to the complex temporal dependency across frames. Existing reinforcement learning (RL)-based approaches to enhance text alignment often require differentiable reward functions or are constrained to limited prompts, hindering their scalability and applicability. In this paper, we propose Free^2Guide, a novel gradient-free framework for aligning generated videos with text prompts without requiring additional model training. Leveraging principles from path integral control, Free^2Guide approximates guidance for diffusion models using non-differentiable reward functions, thereby enabling the integration of powerful black-box Large Vision-Language Models (LVLMs) as reward model. Additionally, our framework supports the flexible ensembling of multiple reward models, including large-scale image-based models, to synergistically enhance alignment without incurring substantial computational overhead. We demonstrate that Free^2Guide significantly improves text alignment across various dimensions and enhances the overall quality of generated videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kjm981995/free2guide_code", "github_api_added_by": "auto" }, { "date": "2024-11-29", "arxiv_id": "2411.17863", "title": "LongKey: Keyphrase Extraction for Long Documents", "authors": [ "Jeovane Honorio Alves", "Radu State", "Cinthia Obladen de Almendra Freitas", "Jean Paul Barddal" ], "github": "https://github.com/jeohalves/longkey", "project_page": "", "abstract": "In an era of information overload, manually annotating the vast and growing corpus of documents and scholarly papers is increasingly impractical. Automated keyphrase extraction addresses this challenge by identifying representative terms within texts. However, most existing methods focus on short documents (up to 512 tokens), leaving a gap in processing long-context documents. In this paper, we introduce LongKey, a novel framework for extracting keyphrases from lengthy documents, which uses an encoder-based language model to capture extended text intricacies. LongKey uses a max-pooling embedder to enhance keyphrase candidate representation. Validated on the comprehensive LDKP datasets and six diverse, unseen datasets, LongKey consistently outperforms existing unsupervised and language model-based keyphrase extraction methods. Our findings demonstrate LongKey's versatility and superior performance, marking an advancement in keyphrase extraction for varied text lengths and domains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jeohalves/longkey", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-29", "arxiv_id": "2411.15640", "title": "AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset", "authors": [ "Tobi Olatunji", "Charles Nimo", "Abraham Owodunni", "Tassallah Abdullahi", "Emmanuel Ayodele", "Mardhiyah Sanni", "Chinemelu Aka", "Folafunmi Omofoye", "Foutse Yuehgoh", "Timothy Faniran", "Bonaventure F. P. Dossou", "Moshood Yekini", "Jonas Kemp", "Katherine Heller", "Jude Chidubem Omeke", "Chidi Asuzu MD", "Naome A. Etori", "Aim\u00e9rou Ndiaye", "Ifeoma Okoh", "Evans Doe Ocansey", "Wendy Kinara", "Michael Best", "Irfan Essa", "Stephen Edward Moore", "Chris Fourie", "Mercy Nyamewaa Asiedu" ], "github": "", "project_page": "", "abstract": "Recent advancements in large language model(LLM) performance on medical multiple choice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. Particularly in low-and middle-income countries (LMICs) facing acute physician shortages and lack of specialists, LLMs offer a potentially scalable pathway to enhance healthcare access and reduce costs. However, their effectiveness in the Global South, especially across the African continent, remains to be established. In this work, we introduce AfriMed-QA, the first large scale Pan-African English multi-specialty medical Question-Answering (QA) dataset, 15,000 questions (open and closed-ended) sourced from over 60 medical schools across 16 countries, covering 32 medical specialties. We further evaluate 30 LLMs across multiple axes including correctness and demographic bias. Our findings show significant performance variation across specialties and geographies, MCQ performance clearly lags USMLE (MedQA). We find that biomedical LLMs underperform general models and smaller edge-friendly LLMs struggle to achieve a passing score. Interestingly, human evaluations show a consistent consumer preference for LLM answers and explanations when compared with clinician answers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-11-29", "arxiv_id": "2411.17190", "title": "SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting", "authors": [ "Gyeongjin Kang", "Jisang Yoo", "Jihyeon Park", "Seungtae Nam", "Hyeonsoo Im", "Sangheon Shin", "Sangpil Kim", "Eunbyung Park" ], "github": "https://github.com/Gynjn/selfsplat", "project_page": "", "abstract": "We propose SelfSplat, a novel 3D Gaussian Splatting model designed to perform pose-free and 3D prior-free generalizable 3D reconstruction from unposed multi-view images. These settings are inherently ill-posed due to the lack of ground-truth data, learned geometric information, and the need to achieve accurate 3D reconstruction without finetuning, making it difficult for conventional methods to achieve high-quality results. Our model addresses these challenges by effectively integrating explicit 3D representations with self-supervised depth and pose estimation techniques, resulting in reciprocal improvements in both pose accuracy and 3D reconstruction quality. Furthermore, we incorporate a matching-aware pose estimation network and a depth refinement module to enhance geometry consistency across views, ensuring more accurate and stable 3D reconstructions. To present the performance of our method, we evaluated it on large-scale real-world datasets, including RealEstate10K, ACID, and DL3DV. SelfSplat achieves superior results over previous state-of-the-art methods in both appearance and geometry quality, also demonstrates strong cross-dataset generalization capabilities. Extensive ablation studies and analysis also validate the effectiveness of our proposed methods. Code and pretrained models are available at https://gynjn.github.io/selfsplat/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Gynjn/selfsplat", "github_api_added_by": "auto" }, { "date": "2024-12-02", "arxiv_id": "2411.19930", "title": "On Domain-Specific Post-Training for Multimodal Large Language Models", "authors": [ "Daixuan Cheng", "Shaohan Huang", "Ziyu Zhu", "Xintong Zhang", "Wayne Xin Zhao", "Zhongzhi Luan", "Bo Dai", "Zhenliang Zhang" ], "github": "https://github.com/xiaoachen98/Open-LLaVA-NeXT", "project_page": "", "abstract": "Recent years have witnessed the rapid development of general multimodal large language models (MLLMs). However, adapting general MLLMs to specific domains, such as scientific fields and industrial applications, remains less explored. This paper systematically investigates domain adaptation of MLLMs through post-training, focusing on data synthesis, training pipelines, and task evaluation. (1) Data Synthesis: Using open-source models, we develop a visual instruction synthesizer that effectively generates diverse visual instruction tasks from domain-specific image-caption pairs. Our synthetic tasks surpass those generated by manual rules, GPT-4, and GPT-4V in enhancing the domain-specific performance of MLLMs. (2) Training Pipeline: While the two-stage training--initially on image-caption pairs followed by visual instruction tasks--is commonly adopted for developing general MLLMs, we apply a single-stage training pipeline to enhance task diversity for domain-specific post-training. (3) Task Evaluation: We conduct experiments in two domains, biomedicine and food, by post-training MLLMs of different sources and scales (e.g., Qwen2-VL-2B, LLaVA-v1.6-8B, Llama-3.2-11B), and then evaluating MLLM performance on various domain-specific tasks. To support further research in MLLM domain adaptation, we will open-source our implementations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xiaoachen98/Open-LLaVA-NeXT", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.19460", "title": "Look Every Frame All at Once: Video-Ma^2mba for Efficient Long-form Video Understanding with Multi-Axis Gradient Checkpointing", "authors": [ "Hosu Lee", "Junho Kim", "Hyunjun Kim", "Yong Man Ro" ], "github": "", "project_page": "", "abstract": "With the growing scale and complexity of video data, efficiently processing long video sequences poses significant challenges due to the quadratic increase in memory and computational demands associated with existing transformer-based Large Multi-modal Models (LMMs). To address these issues, we introduce Video-Ma^2mba, a novel architecture that incorporates State Space Models (SSMs) within the Mamba-2 framework, replacing the attention mechanisms. This allows the LMMs to scale linearly in terms of time and memory requirements, making it feasible to handle long-duration video content. Furthermore, we enhance the memory efficiency introducing the Multi-Axis Gradient Checkpointing (MA-GC) method, which strategically manages memory by retaining only essential activations across multiple computational axes. Our approach significantly reduces the memory footprint compared to standard gradient checkpointing. Empirical analyses show that Video-Ma^2mba can process extensive video sequences-equivalent to millions of tokens or over two hours of continuous sequences at 1 FPS-on a single GPU. By maintaining a detailed capture of temporal dynamics, our model improves the accuracy and relevance of responses in long video understanding tasks, demonstrating substantial advantages over existing frameworks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.18673", "title": "AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers", "authors": [ "Sherwin Bahmani", "Ivan Skorokhodov", "Guocheng Qian", "Aliaksandr Siarohin", "Willi Menapace", "Andrea Tagliasacchi", "David B. Lindell", "Sergey Tulyakov" ], "github": "", "project_page": "", "abstract": "Numerous works have recently integrated 3D camera control into foundational text-to-video models, but the resulting camera control is often imprecise, and video generation quality suffers. In this work, we analyze camera motion from a first principles perspective, uncovering insights that enable precise 3D camera manipulation without compromising synthesis quality. First, we determine that motion induced by camera movements in videos is low-frequency in nature. This motivates us to adjust train and test pose conditioning schedules, accelerating training convergence while improving visual and motion quality. Then, by probing the representations of an unconditional video diffusion transformer, we observe that they implicitly perform camera pose estimation under the hood, and only a sub-portion of their layers contain the camera information. This suggested us to limit the injection of camera conditioning to a subset of the architecture to prevent interference with other video features, leading to 4x reduction of training parameters, improved training speed and 10% higher visual quality. Finally, we complement the typical dataset for camera control learning with a curated dataset of 20K diverse dynamic videos with stationary cameras. This helps the model disambiguate the difference between camera and scene motion, and improves the dynamics of generated pose-conditioned videos. We compound these findings to design the Advanced 3D Camera Control (AC3D) architecture, the new state-of-the-art model for generative video modeling with camera control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.19324", "title": "Trajectory Attention for Fine-grained Video Motion Control", "authors": [ "Zeqi Xiao", "Wenqi Ouyang", "Yifan Zhou", "Shuai Yang", "Lei Yang", "Jianlou Si", "Xingang Pan" ], "github": "", "project_page": "", "abstract": "Recent advancements in video generation have been greatly driven by video diffusion models, with camera motion control emerging as a crucial challenge in creating view-customized visual content. This paper introduces trajectory attention, a novel approach that performs attention along available pixel trajectories for fine-grained camera motion control. Unlike existing methods that often yield imprecise outputs or neglect temporal correlations, our approach possesses a stronger inductive bias that seamlessly injects trajectory information into the video generation process. Importantly, our approach models trajectory attention as an auxiliary branch alongside traditional temporal attention. This design enables the original temporal attention and the trajectory attention to work in synergy, ensuring both precise motion control and new content generation capability, which is critical when the trajectory is only partially available. Experiments on camera motion control for images and videos demonstrate significant improvements in precision and long-range consistency while maintaining high-quality generation. Furthermore, we show that our approach can be extended to other video motion control tasks, such as first-frame-guided video editing, where it excels in maintaining content consistency over large spatial and temporal ranges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.19108", "title": "Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model", "authors": [ "Feng Liu", "Shiwei Zhang", "Xiaofeng Wang", "Yujie Wei", "Haonan Qiu", "Yuzhong Zhao", "Yingya Zhang", "Qixiang Ye", "Fang Wan" ], "github": "https://github.com/LiewFeng/TeaCache", "project_page": "", "abstract": "As a fundamental backbone for video generation, diffusion models are challenged by low inference speed due to the sequential nature of denoising. Previous methods speed up the models by caching and reusing model outputs at uniformly selected timesteps. However, such a strategy neglects the fact that differences among model outputs are not uniform across timesteps, which hinders selecting the appropriate model outputs to cache, leading to a poor balance between inference efficiency and visual quality. In this study, we introduce Timestep Embedding Aware Cache (TeaCache), a training-free caching approach that estimates and leverages the fluctuating differences among model outputs across timesteps. Rather than directly using the time-consuming model outputs, TeaCache focuses on model inputs, which have a strong correlation with the modeloutputs while incurring negligible computational cost. TeaCache first modulates the noisy inputs using the timestep embeddings to ensure their differences better approximating those of model outputs. TeaCache then introduces a rescaling strategy to refine the estimated differences and utilizes them to indicate output caching. Experiments show that TeaCache achieves up to 4.41x acceleration over Open-Sora-Plan with negligible (-0.07% Vbench score) degradation of visual quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LiewFeng/TeaCache", "github_api_added_by": "auto" }, { "date": "2024-12-02", "arxiv_id": "2411.19146", "title": "Puzzle: Distillation-Based NAS for Inference-Optimized LLMs", "authors": [ "Akhiad Bercovich", "Tomer Ronen", "Talor Abramovich", "Nir Ailon", "Nave Assaf", "Mohammad Dabbah", "Ido Galil", "Amnon Geifman", "Yonatan Geifman", "Izhak Golan", "Netanel Haber", "Ehud Karpas", "Itay Levy", "Shahar Mor", "Zach Moshe", "Najeeb Nabwani", "Omri Puny", "Ran Rubin", "Itamar Schen", "Ido Shahaf", "Oren Tropp", "Omer Ullman Argov", "Ran Zilberstein", "Ran El-Yaniv" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities, but their adoption is limited by high computational costs during inference. While increasing parameter counts enhances accuracy, it also widens the gap between state-of-the-art capabilities and practical deployability. We present Puzzle, a framework to accelerate LLM inference on specific hardware while preserving their capabilities. Through an innovative application of neural architecture search (NAS) at an unprecedented scale, Puzzle systematically optimizes models with tens of billions of parameters under hardware constraints. Our approach utilizes blockwise local knowledge distillation (BLD) for parallel architecture exploration and employs mixed-integer programming for precise constraint optimization. We demonstrate the real-world impact of our framework through Llama-3.1-Nemotron-51B-Instruct (Nemotron-51B), a publicly available model derived from Llama-3.1-70B-Instruct. Nemotron-51B achieves a 2.17x inference throughput speedup, fitting on a single NVIDIA H100 GPU while preserving 98.4% of the original model's capabilities. Nemotron-51B currently stands as the most accurate language model capable of inference on a single GPU with large batch sizes. Remarkably, this transformation required just 45B training tokens, compared to over 15T tokens used for the 70B model it was derived from. This establishes a new paradigm where powerful models can be optimized for efficient deployment with only negligible compromise of their capabilities, demonstrating that inference performance, not parameter count alone, should guide model selection. With the release of Nemotron-51B and the presentation of the Puzzle framework, we provide practitioners immediate access to state-of-the-art language modeling capabilities at significantly reduced computational costs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.19950", "title": "AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos", "authors": [ "Yuze He", "Wang Zhao", "Shaohui Liu", "Yubin Hu", "Yushi Bai", "Yu-Hui Wen", "Yong-Jin Liu" ], "github": "https://github.com/THU-LYJ-Lab/AlphaTablets", "project_page": "", "abstract": "We introduce AlphaTablets, a novel and generic representation of 3D planes that features continuous 3D surface and precise boundary delineation. By representing 3D planes as rectangles with alpha channels, AlphaTablets combine the advantages of current 2D and 3D plane representations, enabling accurate, consistent and flexible modeling of 3D planes. We derive differentiable rasterization on top of AlphaTablets to efficiently render 3D planes into images, and propose a novel bottom-up pipeline for 3D planar reconstruction from monocular videos. Starting with 2D superpixels and geometric cues from pre-trained models, we initialize 3D planes as AlphaTablets and optimize them via differentiable rendering. An effective merging scheme is introduced to facilitate the growth and refinement of AlphaTablets. Through iterative optimization and merging, we reconstruct complete and accurate 3D planes with solid surfaces and clear boundaries. Extensive experiments on the ScanNet dataset demonstrate state-of-the-art performance in 3D planar reconstruction, underscoring the great potential of AlphaTablets as a generic 3D plane representation for various applications. Project page is available at: https://hyzcluster.github.io/alphatablets", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/THU-LYJ-Lab/AlphaTablets", "github_api_added_by": "auto" }, { "date": "2024-12-02", "arxiv_id": "2411.19638", "title": "LLM Teacher-Student Framework for Text Classification With No Manually Annotated Data: A Case Study in IPTC News Topic Classification", "authors": [ "Taja Kuzman", "Nikola Ljube\u0161i\u0107" ], "github": "https://github.com/TajaKuzman/IPTC-Media-Topic-Classification", "project_page": "", "abstract": "With the ever-increasing number of news stories available online, classifying them by topic, regardless of the language they are written in, has become crucial for enhancing readers' access to relevant content. To address this challenge, we propose a teacher-student framework based on large language models (LLMs) for developing multilingual news classification models of reasonable size with no need for manual data annotation. The framework employs a Generative Pretrained Transformer (GPT) model as the teacher model to develop an IPTC Media Topic training dataset through automatic annotation of news articles in Slovenian, Croatian, Greek, and Catalan. The teacher model exhibits a high zero-shot performance on all four languages. Its agreement with human annotators is comparable to that between the human annotators themselves. To mitigate the computational limitations associated with the requirement of processing millions of texts daily, smaller BERT-like student models are fine-tuned on the GPT-annotated dataset. These student models achieve high performance comparable to the teacher model. Furthermore, we explore the impact of the training data size on the performance of the student models and investigate their monolingual, multilingual and zero-shot cross-lingual capabilities. The findings indicate that student models can achieve high performance with a relatively small number of training instances, and demonstrate strong zero-shot cross-lingual abilities. Finally, we publish the best-performing news topic classifier, enabling multilingual classification with the top-level categories of the IPTC Media Topic schema.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/TajaKuzman/IPTC-Media-Topic-Classification", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.19189", "title": "Video Depth without Video Models", "authors": [ "Bingxin Ke", "Dominik Narnhofer", "Shengyu Huang", "Lei Ke", "Torben Peters", "Katerina Fragkiadaki", "Anton Obukhov", "Konrad Schindler" ], "github": "https://github.com/prs-eth/rollingdepth", "project_page": "", "abstract": "Video depth estimation lifts monocular video clips to 3D by inferring dense depth at every frame. Recent advances in single-image depth estimation, brought about by the rise of large foundation models and the use of synthetic training data, have fueled a renewed interest in video depth. However, naively applying a single-image depth estimator to every frame of a video disregards temporal continuity, which not only leads to flickering but may also break when camera motion causes sudden changes in depth range. An obvious and principled solution would be to build on top of video foundation models, but these come with their own limitations; including expensive training and inference, imperfect 3D consistency, and stitching routines for the fixed-length (short) outputs. We take a step back and demonstrate how to turn a single-image latent diffusion model (LDM) into a state-of-the-art video depth estimator. Our model, which we call RollingDepth, has two main ingredients: (i) a multi-frame depth estimator that is derived from a single-image LDM and maps very short video snippets (typically frame triplets) to depth snippets. (ii) a robust, optimization-based registration algorithm that optimally assembles depth snippets sampled at various different frame rates back into a consistent video. RollingDepth is able to efficiently handle long videos with hundreds of frames and delivers more accurate depth videos than both dedicated video depth estimators and high-performing single-frame models. Project page: rollingdepth.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/prs-eth/rollingdepth", "github_api_added_by": "auto" }, { "date": "2024-12-02", "arxiv_id": "2411.18478", "title": "Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS", "authors": [ "Jinyang Wu", "Mingkuan Feng", "Shuai Zhang", "Feihu Che", "Zengqi Wen", "Jianhua Tao" ], "github": "https://github.com/jinyangwu/HiARICL", "project_page": "", "abstract": "In-context Learning (ICL) enables large language models (LLMs) to tackle downstream tasks through sophisticated prompting and high-quality demonstrations. However, this traditional ICL paradigm shows limitations when facing complex mathematical reasoning tasks, primarily due to its heavy dependence on example quality and the necessity for human intervention in challenging scenarios. To address these limitations, this paper presents HiAR-ICL, a High-level Automated Reasoning paradigm in ICL that shifts focus from specific examples to abstract thinking patterns, extending the conventional concept of context in ICL. HiAR-ICL introduces five atomic reasoning actions as fundamental components for constructing chain-structured patterns. Using Monte Carlo Tree Search, we explore reasoning paths and construct thought cards to guide subsequent inference. We then develop a cognitive complexity framework that dynamically matches problems with appropriate thought cards. Experimental results demonstrate HiAR-ICL's effectiveness, achieving state-of-the-art accuracy (79.6%) on the MATH benchmark with Qwen2.5-7B-Instruct, surpassing GPT-4o (76.6%) and Claude 3.5 (71.1%).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jinyangwu/HiARICL", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.19527", "title": "DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding", "authors": [ "Jungbin Cho", "Junwan Kim", "Jisoo Kim", "Minseo Kim", "Mingu Kang", "Sungeun Hong", "Tae-Hyun Oh", "Youngjae Yu" ], "github": "", "project_page": "", "abstract": "Human motion, inherently continuous and dynamic, presents significant challenges for generative models. Despite their dominance, discrete quantization methods, such as VQ-VAEs, suffer from inherent limitations, including restricted expressiveness and frame-wise noise artifacts. Continuous approaches, while producing smoother and more natural motions, often falter due to high-dimensional complexity and limited training data. To resolve this \"discord\" between discrete and continuous representations, we introduce DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding, a novel method that decodes discrete motion tokens into continuous motion through rectified flow. By employing an iterative refinement process in the continuous space, DisCoRD captures fine-grained dynamics and ensures smoother and more natural motions. Compatible with any discrete-based framework, our method enhances naturalness without compromising faithfulness to the conditioning signals. Extensive evaluations demonstrate that DisCoRD achieves state-of-the-art performance, with FID of 0.032 on HumanML3D and 0.169 on KIT-ML. These results solidify DisCoRD as a robust solution for bridging the divide between discrete efficiency and continuous realism. Our project page is available at: https://whwjdqls.github.io/discord.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.18552", "title": "FAM Diffusion: Frequency and Attention Modulation for High-Resolution Image Generation with Stable Diffusion", "authors": [ "Haosen Yang", "Adrian Bulat", "Isma Hadji", "Hai X. Pham", "Xiatian Zhu", "Georgios Tzimiropoulos", "Brais Martinez" ], "github": "", "project_page": "", "abstract": "Diffusion models are proficient at generating high-quality images. They are however effective only when operating at the resolution used during training. Inference at a scaled resolution leads to repetitive patterns and structural distortions. Retraining at higher resolutions quickly becomes prohibitive. Thus, methods enabling pre-existing diffusion models to operate at flexible test-time resolutions are highly desirable. Previous works suffer from frequent artifacts and often introduce large latency overheads. We propose two simple modules that combine to solve these issues. We introduce a Frequency Modulation (FM) module that leverages the Fourier domain to improve the global structure consistency, and an Attention Modulation (AM) module which improves the consistency of local texture patterns, a problem largely ignored in prior works. Our method, coined Fam diffusion, can seamlessly integrate into any latent diffusion model and requires no additional training. Extensive qualitative results highlight the effectiveness of our method in addressing structural and local artifacts, while quantitative results show state-of-the-art performance. Also, our method avoids redundant inference tricks for improved consistency such as patch-based or progressive generation, leading to negligible latency overheads.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.19865", "title": "Reverse Thinking Makes LLMs Stronger Reasoners", "authors": [ "Justin Chih-Yao Chen", "Zifeng Wang", "Hamid Palangi", "Rujun Han", "Sayna Ebrahimi", "Long Le", "Vincent Perot", "Swaroop Mishra", "Mohit Bansal", "Chen-Yu Lee", "Tomas Pfister" ], "github": "", "project_page": "", "abstract": "Reverse thinking plays a crucial role in human reasoning. Humans can reason not only from a problem to a solution but also in reverse, i.e., start from the solution and reason towards the problem. This often enhances overall reasoning performance as it enables consistency checks between their forward and backward thinking. To enable Large Language Models (LLMs) to perform reverse thinking, we introduce Reverse-Enhanced Thinking (RevThink), a framework composed of data augmentation and learning objectives. In RevThink, we augment the dataset by collecting structured forward-backward reasoning from a teacher model, consisting of: (1) the original question, (2) forward reasoning, (3) backward question, and (4) backward reasoning. We then employ three objectives to train a smaller student model in a multi-task learning fashion: (a) generate forward reasoning from a question, (b) generate a backward question from a question, and (c) generate backward reasoning from the backward question. Experiments across 12 datasets covering commonsense, math, and logical reasoning show an average 13.53% improvement over the student model's zero-shot performance and a 6.84% improvement over the strongest knowledge distillation baselines. Moreover, our method demonstrates sample efficiency -- using only 10% of the correct forward reasoning from the training data, it outperforms a standard fine-tuning method trained on 10x more forward reasoning. RevThink also exhibits strong generalization to out-of-distribution held-out datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.19842", "title": "Scaling Transformers for Low-Bitrate High-Quality Speech Coding", "authors": [ "Julian D Parker", "Anton Smirnov", "Jordi Pons", "CJ Carr", "Zack Zukowski", "Zach Evans", "Xubo Liu" ], "github": "https://github.com/Stability-AI/stable-codec", "project_page": "", "abstract": "The tokenization of speech with neural audio codec models is a vital part of modern AI pipelines for the generation or understanding of speech, alone or in a multimodal context. Traditionally such tokenization models have concentrated on low parameter-count architectures using only components with strong inductive biases. In this work we show that by scaling a transformer architecture with large parameter count to this problem, and applying a flexible Finite Scalar Quantization (FSQ) based bottleneck, it is possible to reach state-of-the-art speech quality at extremely low bit-rates of 400 or 700 bits-per-second. The trained models strongly out-perform existing baselines in both objective and subjective tests.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Stability-AI/stable-codec", "github_api_added_by": "auto" }, { "date": "2024-12-02", "arxiv_id": "2411.18664", "title": "Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling", "authors": [ "Junha Hyung", "Kinam Kim", "Susung Hong", "Min-Jung Kim", "Jaegul Choo" ], "github": "https://github.com/junhahyung/STGuidance", "project_page": "", "abstract": "Diffusion models have emerged as a powerful tool for generating high-quality images, videos, and 3D content. While sampling guidance techniques like CFG improve quality, they reduce diversity and motion. Autoguidance mitigates these issues but demands extra weak model training, limiting its practicality for large-scale models. In this work, we introduce Spatiotemporal Skip Guidance (STG), a simple training-free sampling guidance method for enhancing transformer-based video diffusion models. STG employs an implicit weak model via self-perturbation, avoiding the need for external models or additional training. By selectively skipping spatiotemporal layers, STG produces an aligned, degraded version of the original model to boost sample quality without compromising diversity or dynamic degree. Our contributions include: (1) introducing STG as an efficient, high-performing guidance technique for video diffusion models, (2) eliminating the need for auxiliary models by simulating a weak model through layer skipping, and (3) ensuring quality-enhanced guidance without compromising sample diversity or dynamics unlike CFG. For additional results, visit https://junhahyung.github.io/STGuidance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/junhahyung/STGuidance", "github_api_added_by": "auto" }, { "date": "2024-12-02", "arxiv_id": "2411.18665", "title": "SpotLight: Shadow-Guided Object Relighting via Diffusion", "authors": [ "Fr\u00e9d\u00e9ric Fortier-Chouinard", "Zitian Zhang", "Louis-Etienne Messier", "Mathieu Garon", "Anand Bhattad", "Jean-Fran\u00e7ois Lalonde" ], "github": "", "project_page": "", "abstract": "Recent work has shown that diffusion models can be used as powerful neural rendering engines that can be leveraged for inserting virtual objects into images. Unlike typical physics-based renderers, however, neural rendering engines are limited by the lack of manual control over the lighting setup, which is often essential for improving or personalizing the desired image outcome. In this paper, we show that precise lighting control can be achieved for object relighting simply by specifying the desired shadows of the object. Rather surprisingly, we show that injecting only the shadow of the object into a pre-trained diffusion-based neural renderer enables it to accurately shade the object according to the desired light position, while properly harmonizing the object (and its shadow) within the target background image. Our method, SpotLight, leverages existing neural rendering approaches and achieves controllable relighting results with no additional training. Specifically, we demonstrate its use with two neural renderers from the recent literature. We show that SpotLight achieves superior object compositing results, both quantitatively and perceptually, as confirmed by a user study, outperforming existing diffusion-based models specifically designed for relighting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.18092", "title": "Training Noise Token Pruning", "authors": [ "Mingxing Rao", "Bohan Jiang", "Daniel Moyer" ], "github": "https://github.com/mx-ethan-rao/tnt", "project_page": "", "abstract": "In the present work we present Training Noise Token (TNT) Pruning for vision transformers. Our method relaxes the discrete token dropping condition to continuous additive noise, providing smooth optimization in training, while retaining discrete dropping computational gains in deployment settings. We provide theoretical connections to Rate-Distortion literature, and empirical evaluations on the ImageNet dataset using ViT and DeiT architectures demonstrating TNT's advantages over previous pruning methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mx-ethan-rao/tnt", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.18915", "title": "MATATA: a weak-supervised MAthematical Tool-Assisted reasoning for Tabular Applications", "authors": [ "Vishnou Vinayagame", "Gregory Senay", "Luis Mart\u00ed" ], "github": "", "project_page": "", "abstract": "Mathematical reasoning capabilities are increasing with tool-augmented language agents, but methods often rely either on closed-source or large models, external data, or extensive prompt engineering. This work introduces MATATA, a novel cost-effective method to train LLM agents for tabular data problems through reasoning, planning, and tool use. With a progressive self-improvement paradigm and an iterative weak supervision, it empowers 3.8B/8B Small Language Models (SLMs), particularly suited for local hosting and sensitive business contexts where data privacy is crucial. By employing a flexible and reusable tools across different datasets, it achieves robust performance with effective scalability across shared tasks. Experiments show that MATATA reaches state-of-the-art performances on FinQA and TAT-QA among reasoning frameworks based on open-source models. Moreover, MATATA models compete with GPT-4 based frameworks on TabMWP, while being SLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.19309", "title": "GRAPE: Generalizing Robot Policy via Preference Alignment", "authors": [ "Zijian Zhang", "Kaiyuan Zheng", "Zhaorun Chen", "Joel Jang", "Yi Li", "Chaoqi Wang", "Mingyu Ding", "Dieter Fox", "Huaxiu Yao" ], "github": "", "project_page": "", "abstract": "Despite the recent advancements of vision-language-action (VLA) models on a variety of robotics tasks, they suffer from critical issues such as poor generalizability to unseen tasks, due to their reliance on behavior cloning exclusively from successful rollouts. Furthermore, they are typically fine-tuned to replicate demonstrations collected by experts under different settings, thus introducing distribution bias and limiting their adaptability to diverse manipulation objectives, such as efficiency, safety, and task completion. To bridge this gap, we introduce GRAPE: Generalizing Robot Policy via Preference Alignment. Specifically, GRAPE aligns VLAs on a trajectory level and implicitly models reward from both successful and failure trials to boost generalizability to diverse tasks. Moreover, GRAPE breaks down complex manipulation tasks to independent stages and automatically guides preference modeling through customized spatiotemporal constraints with keypoints proposed by a large vision-language model. Notably, these constraints are flexible and can be customized to align the model with varying objectives, such as safety, efficiency, or task success. We evaluate GRAPE across a diverse array of tasks in both real-world and simulated environments. Experimental results demonstrate that GRAPE enhances the performance of state-of-the-art VLA models, increasing success rates on in-domain and unseen manipulation tasks by 51.79% and 60.36%, respectively. Additionally, GRAPE can be aligned with various objectives, such as safety and efficiency, reducing collision rates by 44.31% and rollout step-length by 11.15%, respectively. All code, models, and data are available at https://grape-vla.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2412.01253", "title": "Yi-Lightning Technical Report", "authors": [ "01. AI", "Alan Wake", "Albert Wang", "Bei Chen", "C. X. Lv", "Chao Li", "Chengen Huang", "Chenglin Cai", "Chujie Zheng", "Daniel Cooper", "Ethan Dai", "Fan Zhou", "Feng Hu", "Heng Ji", "Howard Qiu", "Jiangcheng Zhu", "Jun Tian", "Katherine Su", "Lihuan Zhang", "Liying Li", "Ming Song", "Mou Li", "Peng Liu", "Qichen Hu", "Shawn Wang", "Shijun Zhou", "Shiyong Li", "Tianhang Zhu", "Wen Xie", "Xiang He", "Xiaobo Chen", "Xiaohui Hu", "Xiaoyi Ren", "Xinyao Niu", "Yanpeng Li", "Yongke Zhao", "Yongzhen Luo", "Yuchi Xu", "Yuxuan Sha", "Zhaodong Yan", "Zhiyuan Liu", "Zirui Zhang" ], "github": "", "project_page": "", "abstract": "This technical report presents Yi-Lightning, our latest flagship large language model (LLM). It achieves exceptional performance, ranking 6th overall on Chatbot Arena, with particularly strong results (2nd to 4th place) in specialized categories including Chinese, Math, Coding, and Hard Prompts. Yi-Lightning leverages an enhanced Mixture-of-Experts (MoE) architecture, featuring advanced expert segmentation and routing mechanisms coupled with optimized KV-caching techniques. Our development process encompasses comprehensive pre-training, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF), where we devise deliberate strategies for multi-stage training, synthetic data construction, and reward modeling. Furthermore, we implement RAISE (Responsible AI Safety Engine), a four-component framework to address safety issues across pre-training, post-training, and serving phases. Empowered by our scalable super-computing infrastructure, all these innovations substantially reduce training, deployment and inference costs while maintaining high-performance standards. With further evaluations on public academic benchmarks, Yi-Lightning demonstrates competitive performance against top-tier LLMs, while we observe a notable disparity between traditional, static benchmark results and real-world, dynamic human preferences. This observation prompts a critical reassessment of conventional benchmarks' utility in guiding the development of more intelligent and powerful AI systems for practical applications. Yi-Lightning is now available through our developer platform at https://platform.lingyiwanwu.com.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-02", "arxiv_id": "2411.19870", "title": "DeMo: Decoupled Momentum Optimization", "authors": [ "Bowen Peng", "Jeffrey Quesnelle", "Diederik P. Kingma" ], "github": "https://github.com/bloc97/DeMo", "project_page": "", "abstract": "Training large neural networks typically requires sharing gradients between accelerators through specialized high-speed interconnects. Drawing from the signal processing principles of frequency decomposition and energy compaction, we demonstrate that synchronizing full optimizer states and model parameters during training is unnecessary. By decoupling momentum updates and allowing controlled divergence in optimizer states across accelerators, we achieve improved convergence compared to state-of-the-art optimizers. We introduce {De}coupled {Mo}mentum (DeMo), a fused optimizer and data parallel algorithm that reduces inter-accelerator communication requirements by several orders of magnitude. This enables training of large neural networks even with limited network bandwidth and heterogeneous hardware. Our method is topology-agnostic and architecture-independent and supports scalable clock-synchronous distributed training with negligible compute and memory overhead. Empirical results show that models trained with DeMo match or exceed the performance of equivalent models trained with AdamW, while eliminating the need for high-speed interconnects when pre-training large scale foundation models. An open source reference PyTorch implementation is published on GitHub at https://github.com/bloc97/DeMo", "github_manual": "", "github_regex": "https://github.com/bloc97/DeMo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2411.17459", "title": "WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model", "authors": [ "Zongjian Li", "Bin Lin", "Yang Ye", "Liuhan Chen", "Xinhua Cheng", "Shenghai Yuan", "Li Yuan" ], "github": "https://github.com/PKU-YuanGroup/WF-VAE", "project_page": "", "abstract": "Video Variational Autoencoder (VAE) encodes videos into a low-dimensional latent space, becoming a key component of most Latent Video Diffusion Models (LVDMs) to reduce model training costs. However, as the resolution and duration of generated videos increase, the encoding cost of Video VAEs becomes a limiting bottleneck in training LVDMs. Moreover, the block-wise inference method adopted by most LVDMs can lead to discontinuities of latent space when processing long-duration videos. The key to addressing the computational bottleneck lies in decomposing videos into distinct components and efficiently encoding the critical information. Wavelet transform can decompose videos into multiple frequency-domain components and improve the efficiency significantly, we thus propose Wavelet Flow VAE (WF-VAE), an autoencoder that leverages multi-level wavelet transform to facilitate low-frequency energy flow into latent representation. Furthermore, we introduce a method called Causal Cache, which maintains the integrity of latent space during block-wise inference. Compared to state-of-the-art video VAEs, WF-VAE demonstrates superior performance in both PSNR and LPIPS metrics, achieving 2x higher throughput and 4x lower memory consumption while maintaining competitive reconstruction quality. Our code and models are available at https://github.com/PKU-YuanGroup/WF-VAE.", "github_manual": "", "github_regex": "https://github.com/PKU-YuanGroup/WF-VAE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2411.18671", "title": "TAPTRv3: Spatial and Temporal Context Foster Robust Tracking of Any Point in Long Video", "authors": [ "Jinyuan Qu", "Hongyang Li", "Shilong Liu", "Tianhe Ren", "Zhaoyang Zeng", "Lei Zhang" ], "github": "", "project_page": "", "abstract": "In this paper, we present TAPTRv3, which is built upon TAPTRv2 to improve its point tracking robustness in long videos. TAPTRv2 is a simple DETR-like framework that can accurately track any point in real-world videos without requiring cost-volume. TAPTRv3 improves TAPTRv2 by addressing its shortage in querying high quality features from long videos, where the target tracking points normally undergo increasing variation over time. In TAPTRv3, we propose to utilize both spatial and temporal context to bring better feature querying along the spatial and temporal dimensions for more robust tracking in long videos. For better spatial feature querying, we present Context-aware Cross-Attention (CCA), which leverages surrounding spatial context to enhance the quality of attention scores when querying image features. For better temporal feature querying, we introduce Visibility-aware Long-Temporal Attention (VLTA) to conduct temporal attention to all past frames while considering their corresponding visibilities, which effectively addresses the feature drifting problem in TAPTRv2 brought by its RNN-like long-temporal modeling. TAPTRv3 surpasses TAPTRv2 by a large margin on most of the challenging datasets and obtains state-of-the-art performance. Even when compared with methods trained with large-scale extra internal data, TAPTRv3 is still competitive.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.00131", "title": "Open-Sora Plan: Open-Source Large Video Generation Model", "authors": [ "Bin Lin", "Yunyang Ge", "Xinhua Cheng", "Zongjian Li", "Bin Zhu", "Shaodong Wang", "Xianyi He", "Yang Ye", "Shenghai Yuan", "Liuhan Chen", "Tanghui Jia", "Junwu Zhang", "Zhenyu Tang", "Yatian Pang", "Bin She", "Cen Yan", "Zhiheng Hu", "Xiaoyi Dong", "Lin Chen", "Zhang Pan", "Xing Zhou", "Shaoling Dong", "Yonghong Tian", "Li Yuan" ], "github": "https://github.com/PKU-YuanGroup/Open-Sora-Plan", "project_page": "", "abstract": "We introduce Open-Sora Plan, an open-source project that aims to contribute a large generation model for generating desired high-resolution videos with long durations based on various user inputs. Our project comprises multiple components for the entire video generation process, including a Wavelet-Flow Variational Autoencoder, a Joint Image-Video Skiparse Denoiser, and various condition controllers. Moreover, many assistant strategies for efficient training and inference are designed, and a multi-dimensional data curation pipeline is proposed for obtaining desired high-quality data. Benefiting from efficient thoughts, our Open-Sora Plan achieves impressive video generation results in both qualitative and quantitative evaluations. We hope our careful design and practical experience can inspire the video generation research community. All our codes and model weights are publicly available at https://github.com/PKU-YuanGroup/Open-Sora-Plan.", "github_manual": "", "github_regex": "https://github.com/PKU-YuanGroup/Open-Sora-Plan", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.00100", "title": "Steering Rectified Flow Models in the Vector Field for Controlled Image Generation", "authors": [ "Maitreya Patel", "Song Wen", "Dimitris N. Metaxas", "Yezhou Yang" ], "github": "", "project_page": "", "abstract": "Diffusion models (DMs) excel in photorealism, image editing, and solving inverse problems, aided by classifier-free guidance and image inversion techniques. However, rectified flow models (RFMs) remain underexplored for these tasks. Existing DM-based methods often require additional training, lack generalization to pretrained latent models, underperform, and demand significant computational resources due to extensive backpropagation through ODE solvers and inversion processes. In this work, we first develop a theoretical and empirical understanding of the vector field dynamics of RFMs in efficiently guiding the denoising trajectory. Our findings reveal that we can navigate the vector field in a deterministic and gradient-free manner. Utilizing this property, we propose FlowChef, which leverages the vector field to steer the denoising trajectory for controlled image generation tasks, facilitated by gradient skipping. FlowChef is a unified framework for controlled image generation that, for the first time, simultaneously addresses classifier guidance, linear inverse problems, and image editing without the need for extra training, inversion, or intensive backpropagation. Finally, we perform extensive evaluations and show that FlowChef significantly outperforms baselines in terms of performance, memory, and time requirements, achieving new state-of-the-art results. Project Page: https://flowchef.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.00947", "title": "VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information", "authors": [ "Ryo Kamoi", "Yusen Zhang", "Sarkar Snigdha Sarathi Das", "Ranran Haoran Zhang", "Rui Zhang" ], "github": "https://github.com/psunlpgroup/VisOnlyQA", "project_page": "", "abstract": "Errors in understanding visual information in images (i.e., visual perception errors) remain a major source of mistakes in Large Vision Language Models (LVLMs). While further analysis is essential, there is a deficiency in datasets for evaluating the visual perception of LVLMs. In this work, we introduce VisOnlyQA, a new dataset designed to directly evaluate the visual perception capabilities of LVLMs on questions about geometric and numerical information in scientific figures. Our dataset enables us to analyze the visual perception of LVLMs for fine-grained visual information, independent of other capabilities such as reasoning. The evaluation set of VisOnlyQA includes 1,200 multiple-choice questions in 12 tasks on four categories of figures. We also provide synthetic training data consisting of 70k instances. Our experiments on VisOnlyQA highlight the following findings: (i) 20 LVLMs we evaluate, including GPT-4o and Gemini 1.5 Pro, work poorly on the visual perception tasks in VisOnlyQA, while human performance is nearly perfect. (ii) Fine-tuning on synthetic training data demonstrates the potential for enhancing the visual perception of LVLMs, but observed improvements are limited to certain tasks and specific models. (iii) Stronger language models improve the visual perception of LVLMs. In summary, our experiments suggest that both training data and model architectures should be improved to enhance the visual perception capabilities of LVLMs. The datasets, code, and model responses are provided at https://github.com/psunlpgroup/VisOnlyQA.", "github_manual": "", "github_regex": "https://github.com/psunlpgroup/VisOnlyQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.01316", "title": "Long Video Diffusion Generation with Segmented Cross-Attention and Content-Rich Video Data Curation", "authors": [ "Xin Yan", "Yuxuan Cai", "Qiuyue Wang", "Yuan Zhou", "Wenhao Huang", "Huan Yang" ], "github": "", "project_page": "", "abstract": "We introduce Presto, a novel video diffusion model designed to generate 15-second videos with long-range coherence and rich content. Extending video generation methods to maintain scenario diversity over long durations presents significant challenges. To address this, we propose a Segmented Cross-Attention (SCA) strategy, which splits hidden states into segments along the temporal dimension, allowing each segment to cross-attend to a corresponding sub-caption. SCA requires no additional parameters, enabling seamless incorporation into current DiT-based architectures. To facilitate high-quality long video generation, we build the LongTake-HD dataset, consisting of 261k content-rich videos with scenario coherence, annotated with an overall video caption and five progressive sub-captions. Experiments show that our Presto achieves 78.5% on the VBench Semantic Score and 100% on the Dynamic Degree, outperforming existing state-of-the-art video generation methods. This demonstrates that our proposed Presto significantly enhances content richness, maintains long-range coherence, and captures intricate textual details. More details are displayed on our project page: https://presto-video.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.01199", "title": "TinyFusion: Diffusion Transformers Learned Shallow", "authors": [ "Gongfan Fang", "Kunjun Li", "Xinyin Ma", "Xinchao Wang" ], "github": "https://github.com/VainF/TinyFusion", "project_page": "", "abstract": "Diffusion Transformers have demonstrated remarkable capabilities in image generation but often come with excessive parameterization, resulting in considerable inference overhead in real-world applications. In this work, we present TinyFusion, a depth pruning method designed to remove redundant layers from diffusion transformers via end-to-end learning. The core principle of our approach is to create a pruned model with high recoverability, allowing it to regain strong performance after fine-tuning. To accomplish this, we introduce a differentiable sampling technique to make pruning learnable, paired with a co-optimized parameter to simulate future fine-tuning. While prior works focus on minimizing loss or error after pruning, our method explicitly models and optimizes the post-fine-tuning performance of pruned models. Experimental results indicate that this learnable paradigm offers substantial benefits for layer pruning of diffusion transformers, surpassing existing importance-based and error-based methods. Additionally, TinyFusion exhibits strong generalization across diverse architectures, such as DiTs, MARs, and SiTs. Experiments with DiT-XL show that TinyFusion can craft a shallow diffusion transformer at less than 7% of the pre-training cost, achieving a 2times speedup with an FID score of 2.86, outperforming competitors with comparable efficiency. Code is available at https://github.com/VainF/TinyFusion.", "github_manual": "", "github_regex": "https://github.com/VainF/TinyFusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.01824", "title": "X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models", "authors": [ "Zeyi Sun", "Ziyang Chu", "Pan Zhang", "Tong Wu", "Xiaoyi Dong", "Yuhang Zang", "Yuanjun Xiong", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/sunzey/x-prompt", "project_page": "", "abstract": "In-context generation is a key component of large language models' (LLMs) open-task generalization capability. By leveraging a few examples as context, LLMs can perform both in-domain and out-of-domain tasks. Recent advancements in auto-regressive vision-language models (VLMs) built upon LLMs have showcased impressive performance in text-to-image generation. However, the potential of in-context learning for general image generation tasks remains largely unexplored. To address this, we introduce X-Prompt, a purely auto-regressive large-vision language model designed to deliver competitive performance across a wide range of both seen and unseen image generation tasks, all within a unified in-context learning framework. X-Prompt incorporates a specialized design that efficiently compresses valuable features from in-context examples, supporting longer in-context token sequences and improving its ability to generalize to unseen tasks. A unified training task for both text and image prediction enables X-Prompt to handle general image generation with enhanced task awareness from in-context examples. Extensive experiments validate the model's performance across diverse seen image generation tasks and its capacity to generalize to previously unseen tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sunzey/x-prompt", "github_api_added_by": "auto" }, { "date": "2024-12-03", "arxiv_id": "2411.18933", "title": "Efficient Track Anything", "authors": [ "Yunyang Xiong", "Chong Zhou", "Xiaoyu Xiang", "Lemeng Wu", "Chenchen Zhu", "Zechun Liu", "Saksham Suri", "Balakrishnan Varadarajan", "Ramya Akula", "Forrest Iandola", "Raghuraman Krishnamoorthi", "Bilge Soran", "Vikas Chandra" ], "github": "", "project_page": "", "abstract": "Segment Anything Model 2 (SAM 2) has emerged as a powerful tool for video object segmentation and tracking anything. Key components of SAM 2 that drive the impressive video object segmentation performance include a large multistage image encoder for frame feature extraction and a memory mechanism that stores memory contexts from past frames to help current frame segmentation. The high computation complexity of multistage image encoder and memory module has limited its applications in real-world tasks, e.g., video object segmentation on mobile devices. To address this limitation, we propose EfficientTAMs, lightweight track anything models that produce high-quality results with low latency and model size. Our idea is based on revisiting the plain, nonhierarchical Vision Transformer (ViT) as an image encoder for video object segmentation, and introducing an efficient memory module, which reduces the complexity for both frame feature extraction and memory computation for current frame segmentation. We take vanilla lightweight ViTs and efficient memory module to build EfficientTAMs, and train the models on SA-1B and SA-V datasets for video object segmentation and track anything tasks. We evaluate on multiple video segmentation benchmarks including semi-supervised VOS and promptable video segmentation, and find that our proposed EfficientTAM with vanilla ViT perform comparably to SAM 2 model (HieraB+SAM 2) with ~2x speedup on A100 and ~2.4x parameter reduction. On segment anything image tasks, our EfficientTAMs also perform favorably over original SAM with ~20x speedup on A100 and ~20x parameter reduction. On mobile devices such as iPhone 15 Pro Max, our EfficientTAMs can run at ~10 FPS for performing video object segmentation with reasonable quality, highlighting the capability of small models for on-device video object segmentation applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2411.19477", "title": "A Simple and Provable Scaling Law for the Test-Time Compute of Large Language Models", "authors": [ "Yanxi Chen", "Xuchen Pan", "Yaliang Li", "Bolin Ding", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "We propose a general two-stage algorithm that enjoys a provable scaling law for the test-time compute of large language models (LLMs). Given an input problem, the proposed algorithm first generates N candidate solutions, and then chooses the best one via a multiple-round knockout tournament where each pair of candidates are compared for K times and only the winners move on to the next round. In a minimalistic implementation, both stages can be executed with a black-box LLM alone and nothing else (e.g., no external verifier or reward model), and a total of N times (K + 1) highly parallelizable LLM calls are needed for solving an input problem. Assuming that a generated candidate solution is correct with probability p_{gen} > 0 and a comparison between a pair of correct and incorrect solutions identifies the right winner with probability p_{comp} > 0.5 (i.e., better than a random guess), we prove theoretically that the failure probability of the proposed algorithm decays to zero exponentially with respect to N and K: $P(final output is incorrect) le (1 - p_{gen})^N + lceil log_2 N rceil e^{-2 K (p_{comp} - 0.5)^2}.$ Our empirical results with the challenging MMLU-Pro benchmark validate the technical assumptions, as well as the efficacy of the proposed algorithm and the gains from scaling up its test-time compute.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.00927", "title": "VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation", "authors": [ "Weiming Ren", "Huan Yang", "Jie Min", "Cong Wei", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Current large multimodal models (LMMs) face significant challenges in processing and comprehending long-duration or high-resolution videos, which is mainly due to the lack of high-quality datasets. To address this issue from a data-centric perspective, we propose VISTA, a simple yet effective Video Spatiotemporal Augmentation framework that synthesizes long-duration and high-resolution video instruction-following pairs from existing video-caption datasets. VISTA spatially and temporally combines videos to create new synthetic videos with extended durations and enhanced resolutions, and subsequently produces question-answer pairs pertaining to these newly synthesized videos. Based on this paradigm, we develop seven video augmentation methods and curate VISTA-400K, a video instruction-following dataset aimed at enhancing long-duration and high-resolution video understanding. Finetuning various video LMMs on our data resulted in an average improvement of 3.3% across four challenging benchmarks for long-video understanding. Furthermore, we introduce the first comprehensive high-resolution video understanding benchmark HRVideoBench, on which our finetuned models achieve a 6.5% performance gain. These results highlight the effectiveness of our framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.00154", "title": "o1-Coder: an o1 Replication for Coding", "authors": [ "Yuxiang Zhang", "Shangxi Wu", "Yuqi Yang", "Jiangming Shu", "Jinlin Xiao", "Chao Kong", "Jitao Sang" ], "github": "https://github.com/ADaM-BJTU/O1-CODER", "project_page": "", "abstract": "The technical report introduces O1-CODER, an attempt to replicate OpenAI's o1 model with a focus on coding tasks. It integrates reinforcement learning (RL) and Monte Carlo Tree Search (MCTS) to enhance the model's System-2 thinking capabilities. The framework includes training a Test Case Generator (TCG) for standardized code testing, using MCTS to generate code data with reasoning processes, and iteratively fine-tuning the policy model to initially produce pseudocode, followed by the generation of the full code. The report also addresses the opportunities and challenges in deploying o1-like models in real-world applications, suggesting transitioning to the System-2 paradigm and highlighting the imperative for environment state updates. Updated model progress and experimental results will be reported in subsequent versions. All source code, curated datasets, as well as the derived models will be disclosed at https://github.com/ADaM-BJTU/O1-CODER .", "github_manual": "", "github_regex": "https://github.com/ADaM-BJTU/O1-CODER", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2411.18499", "title": "GATE OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation", "authors": [ "Pengfei Zhou", "Xiaopeng Peng", "Jiajun Song", "Chuanhao Li", "Zhaopan Xu", "Yue Yang", "Ziyao Guo", "Hao Zhang", "Yuqi Lin", "Yefei He", "Lirui Zhao", "Shuo Liu", "Tianhua Li", "Yuxuan Xie", "Xiaojun Chang", "Yu Qiao", "Wenqi Shao", "Kaipeng Zhang" ], "github": "https://github.com/LanceZPF/OpenING", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding and generation tasks. However, generating interleaved image-text content remains a challenge, which requires integrated multimodal understanding and generation abilities. While the progress in unified models offers new solutions, existing benchmarks are insufficient for evaluating these methods due to data size and diversity limitations. To bridge this gap, we introduce GATE OpenING (OpenING), a comprehensive benchmark comprising 5,400 high-quality human-annotated instances across 56 real-world tasks. OpenING covers diverse daily scenarios such as travel guide, design, and brainstorming, offering a robust platform for challenging interleaved generation methods. In addition, we present IntJudge, a judge model for evaluating open-ended multimodal generation methods. Trained with a novel data pipeline, our IntJudge achieves an agreement rate of 82. 42% with human judgments, outperforming GPT-based evaluators by 11.34%. Extensive experiments on OpenING reveal that current interleaved generation methods still have substantial room for improvement. Key findings on interleaved image-text generation are further presented to guide the development of next-generation models. The OpenING is open-sourced at https://opening.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LanceZPF/OpenING", "github_api_added_by": "auto" }, { "date": "2024-12-03", "arxiv_id": "2412.01064", "title": "FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait", "authors": [ "Taekyung Ki", "Dongchan Min", "Gyoungsu Chae" ], "github": "", "project_page": "", "abstract": "With the rapid advancement of diffusion-based generative models, portrait image animation has achieved remarkable results. However, it still faces challenges in temporally consistent video generation and fast sampling due to its iterative sampling nature. This paper presents FLOAT, an audio-driven talking portrait video generation method based on flow matching generative model. We shift the generative modeling from the pixel-based latent space to a learned motion latent space, enabling efficient design of temporally consistent motion. To achieve this, we introduce a transformer-based vector field predictor with a simple yet effective frame-wise conditioning mechanism. Additionally, our method supports speech-driven emotion enhancement, enabling a natural incorporation of expressive motions. Extensive experiments demonstrate that our method outperforms state-of-the-art audio-driven talking portrait methods in terms of visual quality, motion fidelity, and efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2411.19799", "title": "INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge", "authors": [ "Angelika Romanou", "Negar Foroutan", "Anna Sotnikova", "Zeming Chen", "Sree Harsha Nelaturu", "Shivalika Singh", "Rishabh Maheshwary", "Micol Altomare", "Mohamed A. Haggag", "Snegha A", "Alfonso Amayuelas", "Azril Hafizi Amirudin", "Viraat Aryabumi", "Danylo Boiko", "Michael Chang", "Jenny Chim", "Gal Cohen", "Aditya Kumar Dalmia", "Abraham Diress", "Sharad Duwal", "Daniil Dzenhaliou", "Daniel Fernando Erazo Florez", "Fabian Farestam", "Joseph Marvin Imperial", "Shayekh Bin Islam", "Perttu Isotalo", "Maral Jabbarishiviari", "B\u00f6rje F. Karlsson", "Eldar Khalilov", "Christopher Klamm", "Fajri Koto", "Dominik Krzemi\u0144ski", "Gabriel Adriano de Melo", "Syrielle Montariol", "Yiyang Nan", "Joel Niklaus", "Jekaterina Novikova", "Johan Samir Obando Ceron", "Debjit Paul", "Esther Ploeger", "Jebish Purbey", "Swati Rajwal", "Selvan Sunitha Ravi", "Sara Rydell", "Roshan Santhosh", "Drishti Sharma", "Marjana Prifti Skenduli", "Arshia Soltani Moakhar", "Bardia Soltani Moakhar", "Ran Tamir", "Ayush Kumar Tarun", "Azmine Toushik Wasi", "Thenuka Ovin Weerasinghe", "Serhan Yilmaz", "Mike Zhang", "Imanol Schlag", "Marzieh Fadaee", "Sara Hooker", "Antoine Bosselut" ], "github": "", "project_page": "", "abstract": "The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities. However, the development of functional LLMs in many languages (\\ie, multilingual LLMs) is bottlenecked by the lack of high-quality evaluation resources in languages other than English. Moreover, current practices in multilingual benchmark construction often translate English resources, ignoring the regional and cultural knowledge of the environments in which multilingual systems would be used. In this work, we construct an evaluation suite of 197,243 QA pairs from local exam sources to measure the capabilities of multilingual LLMs in a variety of regional contexts. Our novel resource, INCLUDE, is a comprehensive knowledge- and reasoning-centric benchmark across 44 written languages that evaluates multilingual LLMs for performance in the actual language environments where they would be deployed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.00174", "title": "SOLAMI: Social Vision-Language-Action Modeling for Immersive Interaction with 3D Autonomous Characters", "authors": [ "Jianping Jiang", "Weiye Xiao", "Zhengyu Lin", "Huaizhong Zhang", "Tianxiang Ren", "Yang Gao", "Zhiqian Lin", "Zhongang Cai", "Lei Yang", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Human beings are social animals. How to equip 3D autonomous characters with similar social intelligence that can perceive, understand and interact with humans remains an open yet foundamental problem. In this paper, we introduce SOLAMI, the first end-to-end Social vision-Language-Action (VLA) Modeling framework for Immersive interaction with 3D autonomous characters. Specifically, SOLAMI builds 3D autonomous characters from three aspects: (1) Social VLA Architecture: We propose a unified social VLA framework to generate multimodal response (speech and motion) based on the user's multimodal input to drive the character for social interaction. (2) Interactive Multimodal Data: We present SynMSI, a synthetic multimodal social interaction dataset generated by an automatic pipeline using only existing motion datasets to address the issue of data scarcity. (3) Immersive VR Interface: We develop a VR interface that enables users to immersively interact with these characters driven by various architectures. Extensive quantitative experiments and user studies demonstrate that our framework leads to more precise and natural character responses (in both speech and motion) that align with user expectations with lower latency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.01819", "title": "Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis", "authors": [ "Anton Voronov", "Denis Kuznedelev", "Mikhail Khoroshikh", "Valentin Khrulkov", "Dmitry Baranchuk" ], "github": "", "project_page": "", "abstract": "This work presents Switti, a scale-wise transformer for text-to-image generation. Starting from existing next-scale prediction AR models, we first explore them for T2I generation and propose architectural modifications to improve their convergence and overall performance. We then observe that self-attention maps of our pretrained scale-wise AR model exhibit weak dependence on preceding scales. Based on this insight, we propose a non-AR counterpart facilitating {sim}11% faster sampling and lower memory usage while also achieving slightly better generation quality.Furthermore, we reveal that classifier-free guidance at high-resolution scales is often unnecessary and can even degrade performance. %may be not only unnecessary but potentially detrimental. By disabling guidance at these scales, we achieve an additional sampling acceleration of {sim}20% and improve the generation of fine-grained details. Extensive human preference studies and automated evaluations show that Switti outperforms existing T2I AR models and competes with state-of-the-art T2I diffusion models while being up to 7{times} faster.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2411.19939", "title": "VLSBench: Unveiling Visual Leakage in Multimodal Safety", "authors": [ "Xuhao Hu", "Dongrui Liu", "Hao Li", "Xuanjing Huang", "Jing Shao" ], "github": "https://github.com/ai45lab/vlsbench", "project_page": "", "abstract": "Safety concerns of Multimodal large language models (MLLMs) have gradually become an important problem in various applications. Surprisingly, previous works indicate a counter-intuitive phenomenon that using textual unlearning to align MLLMs achieves comparable safety performances with MLLMs trained with image-text pairs. To explain such a counter-intuitive phenomenon, we discover a visual safety information leakage (VSIL) problem in existing multimodal safety benchmarks, i.e., the potentially risky and sensitive content in the image has been revealed in the textual query. In this way, MLLMs can easily refuse these sensitive text-image queries according to textual queries. However, image-text pairs without VSIL are common in real-world scenarios and are overlooked by existing multimodal safety benchmarks. To this end, we construct multimodal visual leakless safety benchmark (VLSBench) preventing visual safety leakage from image to textual query with 2.4k image-text pairs. Experimental results indicate that VLSBench poses a significant challenge to both open-source and close-source MLLMs, including LLaVA, Qwen2-VL, Llama3.2-Vision, and GPT-4o. This study demonstrates that textual alignment is enough for multimodal safety scenarios with VSIL, while multimodal alignment is a more promising solution for multimodal safety scenarios without VSIL. Please see our code and data at: http://hxhcreate.github.io/VLSBench", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ai45lab/vlsbench", "github_api_added_by": "auto" }, { "date": "2024-12-03", "arxiv_id": "2412.01822", "title": "VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models", "authors": [ "Byung-Kwan Lee", "Ryo Hachiuma", "Yu-Chiang Frank Wang", "Yong Man Ro", "Yueh-Hua Wu" ], "github": "", "project_page": "", "abstract": "The recent surge in high-quality visual instruction tuning samples from closed-source vision-language models (VLMs) such as GPT-4V has accelerated the release of open-source VLMs across various model sizes. However, scaling VLMs to improve performance using larger models brings significant computational challenges, especially for deployment on resource-constrained devices like mobile platforms and robots. To address this, we propose VLsI: Verbalized Layers-to-Interactions, a new VLM family in 2B and 7B model sizes, which prioritizes efficiency without compromising accuracy. VLsI leverages a unique, layer-wise distillation process, introducing intermediate \"verbalizers\" that map features from each layer to natural language space, allowing smaller VLMs to flexibly align with the reasoning processes of larger VLMs. This approach mitigates the training instability often encountered in output imitation and goes beyond typical final-layer tuning by aligning the small VLMs' layer-wise progression with that of the large ones. We validate VLsI across ten challenging vision-language benchmarks, achieving notable performance gains (11.0% for 2B and 17.4% for 7B) over GPT-4V without the need for model scaling, merging, or architectural changes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.01250", "title": "Collaborative Instance Navigation: Leveraging Agent Self-Dialogue to Minimize User Input", "authors": [ "Francesco Taioli", "Edoardo Zorzi", "Gianni Franchi", "Alberto Castellini", "Alessandro Farinelli", "Marco Cristani", "Yiming Wang" ], "github": "", "project_page": "", "abstract": "Existing embodied instance goal navigation tasks, driven by natural language, assume human users to provide complete and nuanced instance descriptions prior to the navigation, which can be impractical in the real world as human instructions might be brief and ambiguous. To bridge this gap, we propose a new task, Collaborative Instance Navigation (CoIN), with dynamic agent-human interaction during navigation to actively resolve uncertainties about the target instance in natural, template-free, open-ended dialogues. To address CoIN, we propose a novel method, Agent-user Interaction with UncerTainty Awareness (AIUTA), leveraging the perception capability of Vision Language Models (VLMs) and the capability of Large Language Models (LLMs). First, upon object detection, a Self-Questioner model initiates a self-dialogue to obtain a complete and accurate observation description, while a novel uncertainty estimation technique mitigates inaccurate VLM perception. Then, an Interaction Trigger module determines whether to ask a question to the user, continue or halt navigation, minimizing user input. For evaluation, we introduce CoIN-Bench, a benchmark supporting both real and simulated humans. AIUTA achieves competitive performance in instance navigation against state-of-the-art methods, demonstrating great flexibility in handling user inputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.01800", "title": "PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos", "authors": [ "Meng Cao", "Haoran Tang", "Haoze Zhao", "Hangyu Guo", "Jiaheng Liu", "Ge Zhang", "Ruyang Liu", "Qiang Sun", "Ian Reid", "Xiaodan Liang" ], "github": "", "project_page": "", "abstract": "Recent advancements in video-based large language models (Video LLMs) have witnessed the emergence of diverse capabilities to reason and interpret dynamic visual content. Among them, gameplay videos stand out as a distinctive data source, often containing glitches that defy physics commonsense. This characteristic renders them an effective benchmark for assessing the under-explored capability of physical commonsense understanding in video LLMs. In this paper, we propose PhysGame as a pioneering benchmark to evaluate physical commonsense violations in gameplay videos. PhysGame comprises 880 videos associated with glitches spanning four fundamental domains (i.e., mechanics, kinematics, optics, and material properties) and across 12 distinct physical commonsense. Through extensively evaluating various state-ofthe-art video LLMs, our findings reveal that the performance of current open-source video LLMs significantly lags behind that of proprietary counterparts. To bridge this gap, we curate an instruction tuning dataset PhysInstruct with 140,057 question-answering pairs to facilitate physical commonsense learning. In addition, we also propose a preference optimization dataset PhysDPO with 34,358 training pairs, where the dis-preferred responses are generated conditioned on misleading titles (i.e., meta information hacking), fewer frames (i.e., temporal hacking) and lower spatial resolutions (i.e., spatial hacking). Based on the suite of datasets, we propose PhysVLM as a physical knowledge-enhanced video LLM. Extensive experiments on both physical-oriented benchmark PhysGame and general video understanding benchmarks demonstrate the state-ofthe-art performance of PhysVLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.01408", "title": "Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning", "authors": [ "Aditya Narayan Sankaran", "Reza Farahbaksh", "Noel Crespi" ], "github": "", "project_page": "", "abstract": "Online abusive content detection, particularly in low-resource settings and within the audio modality, remains underexplored. We investigate the potential of pre-trained audio representations for detecting abusive language in low-resource languages, in this case, in Indian languages using Few Shot Learning (FSL). Leveraging powerful representations from models such as Wav2Vec and Whisper, we explore cross-lingual abuse detection using the ADIMA dataset with FSL. Our approach integrates these representations within the Model-Agnostic Meta-Learning (MAML) framework to classify abusive language in 10 languages. We experiment with various shot sizes (50-200) evaluating the impact of limited data on performance. Additionally, a feature visualization study was conducted to better understand model behaviour. This study highlights the generalization ability of pre-trained models in low-resource scenarios and offers valuable insights into detecting abusive language in multilingual contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.00176", "title": "Art-Free Generative Models: Art Creation Without Graphic Art Knowledge", "authors": [ "Hui Ren", "Joanna Materzynska", "Rohit Gandikota", "David Bau", "Antonio Torralba" ], "github": "", "project_page": "", "abstract": "We explore the question: \"How much prior art knowledge is needed to create art?\" To investigate this, we propose a text-to-image generation model trained without access to art-related content. We then introduce a simple yet effective method to learn an art adapter using only a few examples of selected artistic styles. Our experiments show that art generated using our method is perceived by users as comparable to art produced by models trained on large, art-rich datasets. Finally, through data attribution techniques, we illustrate how examples from both artistic and non-artistic datasets contributed to the creation of new artistic styles.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.01821", "title": "World-consistent Video Diffusion with Explicit 3D Modeling", "authors": [ "Qihang Zhang", "Shuangfei Zhai", "Miguel Angel Bautista", "Kevin Miao", "Alexander Toshev", "Joshua Susskind", "Jiatao Gu" ], "github": "", "project_page": "", "abstract": "Recent advancements in diffusion models have set new benchmarks in image and video generation, enabling realistic visual synthesis across single- and multi-frame contexts. However, these models still struggle with efficiently and explicitly generating 3D-consistent content. To address this, we propose World-consistent Video Diffusion (WVD), a novel framework that incorporates explicit 3D supervision using XYZ images, which encode global 3D coordinates for each image pixel. More specifically, we train a diffusion transformer to learn the joint distribution of RGB and XYZ frames. This approach supports multi-task adaptability via a flexible inpainting strategy. For example, WVD can estimate XYZ frames from ground-truth RGB or generate novel RGB frames using XYZ projections along a specified camera trajectory. In doing so, WVD unifies tasks like single-image-to-3D generation, multi-view stereo, and camera-controlled video generation. Our approach demonstrates competitive performance across multiple benchmarks, providing a scalable solution for 3D-consistent video and image generation with a single pretrained model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.00568", "title": "The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning", "authors": [ "Ruben Ohana", "Michael McCabe", "Lucas Meyer", "Rudy Morel", "Fruzsina J. Agocs", "Miguel Beneitez", "Marsha Berger", "Blakesley Burkhart", "Stuart B. Dalziel", "Drummond B. Fielding", "Daniel Fortunato", "Jared A. Goldberg", "Keiya Hirashima", "Yan-Fei Jiang", "Rich R. Kerswell", "Suryanarayana Maddu", "Jonah Miller", "Payel Mukhopadhyay", "Stefan S. Nixon", "Jeff Shen", "Romain Watteaux", "Bruno R\u00e9galdo-Saint Blancard", "Fran\u00e7ois Rozet", "Liam H. Parker", "Miles Cranmer", "Shirley Ho" ], "github": "https://github.com/PolymathicAI/the_well", "project_page": "", "abstract": "Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a large-scale collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The Well draws from domain experts and numerical software developers to provide 15TB of data across 16 datasets covering diverse domains such as biological systems, fluid dynamics, acoustic scattering, as well as magneto-hydrodynamic simulations of extra-galactic fluids or supernova explosions. These datasets can be used individually or as part of a broader benchmark suite. To facilitate usage of the Well, we provide a unified PyTorch interface for training and evaluating models. We demonstrate the function of this library by introducing example baselines that highlight the new challenges posed by the complex dynamics of the Well. The code and data is available at https://github.com/PolymathicAI/the_well.", "github_manual": "", "github_regex": "https://github.com/PolymathicAI/the_well", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.00869", "title": "Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting", "authors": [ "Thilini Wijesiriwardene", "Ruwan Wickramarachchi", "Sreeram Vennam", "Vinija Jain", "Aman Chadha", "Amitava Das", "Ponnurangam Kumaraguru", "Amit Sheth" ], "github": "https://github.com/thiliniiw/knowledgeprompts", "project_page": "", "abstract": "Making analogies is fundamental to cognition. Proportional analogies, which consist of four terms, are often used to assess linguistic and cognitive abilities. For instance, completing analogies like \"Oxygen is to Gas as is to \" requires identifying the semantic relationship (e.g., \"type of\") between the first pair of terms (\"Oxygen\" and \"Gas\") and finding a second pair that shares the same relationship (e.g., \"Aluminum\" and \"Metal\"). In this work, we introduce a 15K Multiple-Choice Question Answering (MCQA) dataset for proportional analogy completion and evaluate the performance of contemporary Large Language Models (LLMs) in various knowledge-enhanced prompt settings. Specifically, we augment prompts with three types of knowledge: exemplar, structured, and targeted. Our results show that despite extensive training data, solving proportional analogies remains challenging for current LLMs, with the best model achieving an accuracy of 55%. Notably, we find that providing targeted knowledge can better assist models in completing proportional analogies compared to providing exemplars or collections of structured knowledge.", "github_manual": "https://github.com/thiliniiw/knowledgeprompts", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.00319", "title": "Improving speaker verification robustness with synthetic emotional utterances", "authors": [ "Nikhil Kumar Koditala", "Chelsea Jui-Ting Ju", "Ruirui Li", "Minho Jin", "Aman Chadha", "Andreas Stolcke" ], "github": "", "project_page": "", "abstract": "A speaker verification (SV) system offers an authentication service designed to confirm whether a given speech sample originates from a specific speaker. This technology has paved the way for various personalized applications that cater to individual preferences. A noteworthy challenge faced by SV systems is their ability to perform consistently across a range of emotional spectra. Most existing models exhibit high error rates when dealing with emotional utterances compared to neutral ones. Consequently, this phenomenon often leads to missing out on speech of interest. This issue primarily stems from the limited availability of labeled emotional speech data, impeding the development of robust speaker representations that encompass diverse emotional states. To address this concern, we propose a novel approach employing the CycleGAN framework to serve as a data augmentation method. This technique synthesizes emotional speech segments for each specific speaker while preserving the unique vocal identity. Our experimental findings underscore the effectiveness of incorporating synthetic emotional data into the training process. The models trained using this augmented dataset consistently outperform the baseline models on the task of verifying speakers in emotional speech scenarios, reducing equal error rate by as much as 3.64% relative.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-03", "arxiv_id": "2412.01718", "title": "HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving", "authors": [ "Hongyu Zhou", "Longzhong Lin", "Jiabao Wang", "Yichong Lu", "Dongfeng Bai", "Bingbing Liu", "Yue Wang", "Andreas Geiger", "Yiyi Liao" ], "github": "https://github.com/hyzhou404/hugsim", "project_page": "", "abstract": "In the past few decades, autonomous driving algorithms have made significant progress in perception, planning, and control. However, evaluating individual components does not fully reflect the performance of entire systems, highlighting the need for more holistic assessment methods. This motivates the development of HUGSIM, a closed-loop, photo-realistic, and real-time simulator for evaluating autonomous driving algorithms. We achieve this by lifting captured 2D RGB images into the 3D space via 3D Gaussian Splatting, improving the rendering quality for closed-loop scenarios, and building the closed-loop environment. In terms of rendering, We tackle challenges of novel view synthesis in closed-loop scenarios, including viewpoint extrapolation and 360-degree vehicle rendering. Beyond novel view synthesis, HUGSIM further enables the full closed simulation loop, dynamically updating the ego and actor states and observations based on control commands. Moreover, HUGSIM offers a comprehensive benchmark across more than 70 sequences from KITTI-360, Waymo, nuScenes, and PandaSet, along with over 400 varying scenarios, providing a fair and realistic evaluation platform for existing autonomous driving algorithms. HUGSIM not only serves as an intuitive evaluation benchmark but also unlocks the potential for fine-tuning autonomous driving algorithms in a photorealistic closed-loop setting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hyzhou404/hugsim", "github_api_added_by": "auto" }, { "date": "2024-12-03", "arxiv_id": "2411.19415", "title": "AMO Sampler: Enhancing Text Rendering with Overshooting", "authors": [ "Xixi Hu", "Keyang Xu", "Bo Liu", "Qiang Liu", "Hongliang Fei" ], "github": "https://github.com/hxixixh/amo-release", "project_page": "", "abstract": "Achieving precise alignment between textual instructions and generated images in text-to-image generation is a significant challenge, particularly in rendering written text within images. Sate-of-the-art models like Stable Diffusion 3 (SD3), Flux, and AuraFlow still struggle with accurate text depiction, resulting in misspelled or inconsistent text. We introduce a training-free method with minimal computational overhead that significantly enhances text rendering quality. Specifically, we introduce an overshooting sampler for pretrained rectified flow (RF) models, by alternating between over-simulating the learned ordinary differential equation (ODE) and reintroducing noise. Compared to the Euler sampler, the overshooting sampler effectively introduces an extra Langevin dynamics term that can help correct the compounding error from successive Euler steps and therefore improve the text rendering. However, when the overshooting strength is high, we observe over-smoothing artifacts on the generated images. To address this issue, we propose an Attention Modulated Overshooting sampler (AMO), which adaptively controls the strength of overshooting for each image patch according to their attention score with the text content. AMO demonstrates a 32.3% and 35.9% improvement in text rendering accuracy on SD3 and Flux without compromising overall image quality or increasing inference cost.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hxixixh/amo-release", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2412.01292", "title": "LSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual Preferences", "authors": [ "Hongyan Zhi", "Peihao Chen", "Junyan Li", "Shuailei Ma", "Xinyu Sun", "Tianhang Xiang", "Yinjie Lei", "Mingkui Tan", "Chuang Gan" ], "github": "https://github.com/Hoyyyaard/LSceneLLM", "project_page": "", "abstract": "Research on 3D Vision-Language Models (3D-VLMs) is gaining increasing attention, which is crucial for developing embodied AI within 3D scenes, such as visual navigation and embodied question answering. Due to the high density of visual features, especially in large 3D scenes, accurately locating task-relevant visual information is challenging. Existing works attempt to segment all objects and consider their features as scene representations. However, these task-agnostic object features include much redundant information and missing details for the task-relevant area. To tackle these problems, we propose LSceneLLM, an adaptive framework that automatically identifies task-relevant areas by leveraging LLM's visual preference for different tasks, followed by a plug-and-play scene magnifier module to capture fine-grained details in focused areas. Specifically, a dense token selector examines the attention map of LLM to identify visual preferences for the instruction input. It then magnifies fine-grained details of the focusing area. An adaptive self-attention module is leveraged to fuse the coarse-grained and selected fine-grained visual information. To comprehensively evaluate the large scene understanding ability of 3D-VLMs, we further introduce a cross-room understanding benchmark, XR-Scene, which contains a series of large scene understanding tasks including XR-QA, XR-EmbodiedPlanning, and XR-SceneCaption. Experiments show that our method surpasses existing methods on both large scene understanding and existing scene understanding benchmarks. Plunging our scene magnifier module into the existing 3D-VLMs also brings significant improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Hoyyyaard/LSceneLLM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2411.19943", "title": "Critical Tokens Matter: Token-Level Contrastive Estimation Enhence LLM's Reasoning Capability", "authors": [ "Zicheng Lin", "Tian Liang", "Jiahao Xu", "Xing Wang", "Ruilin Luo", "Chufan Shi", "Siheng Li", "Yujiu Yang", "Zhaopeng Tu" ], "github": "https://github.com/chenzhiling9954/critical-tokens-matter", "project_page": "", "abstract": "Large Language Models (LLMs) have exhibited remarkable performance on reasoning tasks. They utilize autoregressive token generation to construct reasoning trajectories, enabling the development of a coherent chain of thought. In this work, we explore the impact of individual tokens on the final outcomes of reasoning tasks. We identify the existence of ``critical tokens'' that lead to incorrect reasoning trajectories in LLMs. Specifically, we find that LLMs tend to produce positive outcomes when forced to decode other tokens instead of critical tokens. Motivated by this observation, we propose a novel approach - cDPO - designed to automatically recognize and conduct token-level rewards for the critical tokens during the alignment process. Specifically, we develop a contrastive estimation approach to automatically identify critical tokens. It is achieved by comparing the generation likelihood of positive and negative models. To achieve this, we separately fine-tune the positive and negative models on various reasoning trajectories, consequently, they are capable of identifying identify critical tokens within incorrect trajectories that contribute to erroneous outcomes. Moreover, to further align the model with the critical token information during the alignment process, we extend the conventional DPO algorithms to token-level DPO and utilize the differential likelihood from the aforementioned positive and negative model as important weight for token-level DPO learning.Experimental results on GSM8K and MATH500 benchmarks with two-widely used models Llama-3 (8B and 70B) and deepseek-math (7B) demonstrate the effectiveness of the propsoed approach cDPO.", "github_manual": "https://github.com/chenzhiling9954/critical-tokens-matter", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2412.02259", "title": "VideoGen-of-Thought: A Collaborative Framework for Multi-Shot Video Generation", "authors": [ "Mingzhe Zheng", "Yongqi Xu", "Haojian Huang", "Xuran Ma", "Yexin Liu", "Wenjie Shu", "Yatian Pang", "Feilong Tang", "Qifeng Chen", "Harry Yang", "Ser-Nam Lim" ], "github": "", "project_page": "", "abstract": "Current video generation models excel at generating short clips but still struggle with creating multi-shot, movie-like videos. Existing models trained on large-scale data on the back of rich computational resources are unsurprisingly inadequate for maintaining a logical storyline and visual consistency across multiple shots of a cohesive script since they are often trained with a single-shot objective. To this end, we propose VideoGen-of-Thought (VGoT), a collaborative and training-free architecture designed specifically for multi-shot video generation. VGoT is designed with three goals in mind as follows. Multi-Shot Video Generation: We divide the video generation process into a structured, modular sequence, including (1) Script Generation, which translates a curt story into detailed prompts for each shot; (2) Keyframe Generation, responsible for creating visually consistent keyframes faithful to character portrayals; and (3) Shot-Level Video Generation, which transforms information from scripts and keyframes into shots; (4) Smoothing Mechanism that ensures a consistent multi-shot output. Reasonable Narrative Design: Inspired by cinematic scriptwriting, our prompt generation approach spans five key domains, ensuring logical consistency, character development, and narrative flow across the entire video. Cross-Shot Consistency: We ensure temporal and identity consistency by leveraging identity-preserving (IP) embeddings across shots, which are automatically created from the narrative. Additionally, we incorporate a cross-shot smoothing mechanism, which integrates a reset boundary that effectively combines latent features from adjacent shots, resulting in smooth transitions and maintaining visual coherence throughout the video. Our experiments demonstrate that VGoT surpasses existing video generation methods in producing high-quality, coherent, multi-shot videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2412.02611", "title": "AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?", "authors": [ "Kaixiong Gong", "Kaituo Feng", "Bohao Li", "Yibing Wang", "Mofan Cheng", "Shijia Yang", "Jiaming Han", "Benyou Wang", "Yutong Bai", "Zhuoran Yang", "Xiangyu Yue" ], "github": "https://github.com/AV-Odyssey/AV-Odyssey", "project_page": "", "abstract": "Recently, multimodal large language models (MLLMs), such as GPT-4o, Gemini 1.5 Pro, and Reka Core, have expanded their capabilities to include vision and audio modalities. While these models demonstrate impressive performance across a wide range of audio-visual applications, our proposed DeafTest reveals that MLLMs often struggle with simple tasks humans find trivial: 1) determining which of two sounds is louder, and 2) determining which of two sounds has a higher pitch. Motivated by these observations, we introduce AV-Odyssey Bench, a comprehensive audio-visual benchmark designed to assess whether those MLLMs can truly understand the audio-visual information. This benchmark encompasses 4,555 carefully crafted problems, each incorporating text, visual, and audio components. To successfully infer answers, models must effectively leverage clues from both visual and audio inputs. To ensure precise and objective evaluation of MLLM responses, we have structured the questions as multiple-choice, eliminating the need for human evaluation or LLM-assisted assessment. We benchmark a series of closed-source and open-source models and summarize the observations. By revealing the limitations of current models, we aim to provide useful insight for future dataset collection and model development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AV-Odyssey/AV-Odyssey", "github_api_added_by": "auto" }, { "date": "2024-12-04", "arxiv_id": "2412.02632", "title": "Scaling Image Tokenizers with Grouped Spherical Quantization", "authors": [ "Jiangtao Wang", "Zhen Qin", "Yifan Zhang", "Vincent Tao Hu", "Bj\u00f6rn Ommer", "Rania Briq", "Stefan Kesselheim" ], "github": "https://github.com/helmholtzai-fzj/flex_gen", "project_page": "", "abstract": "Vision tokenizers have gained a lot of attraction due to their scalability and compactness; previous works depend on old-school GAN-based hyperparameters, biased comparisons, and a lack of comprehensive analysis of the scaling behaviours. To tackle those issues, we introduce Grouped Spherical Quantization (GSQ), featuring spherical codebook initialization and lookup regularization to constrain codebook latent to a spherical surface. Our empirical analysis of image tokenizer training strategies demonstrates that GSQ-GAN achieves superior reconstruction quality over state-of-the-art methods with fewer training iterations, providing a solid foundation for scaling studies. Building on this, we systematically examine the scaling behaviours of GSQ, specifically in latent dimensionality, codebook size, and compression ratios, and their impact on model performance. Our findings reveal distinct behaviours at high and low spatial compression levels, underscoring challenges in representing high-dimensional latent spaces. We show that GSQ can restructure high-dimensional latent into compact, low-dimensional spaces, thus enabling efficient scaling with improved quality. As a result, GSQ-GAN achieves a 16x down-sampling with a reconstruction FID (rFID) of 0.50.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/helmholtzai-fzj/flex_gen", "github_api_added_by": "auto" }, { "date": "2024-12-04", "arxiv_id": "2412.01981", "title": "Free Process Rewards without Process Labels", "authors": [ "Lifan Yuan", "Wendi Li", "Huayu Chen", "Ganqu Cui", "Ning Ding", "Kaiyan Zhang", "Bowen Zhou", "Zhiyuan Liu", "Hao Peng" ], "github": "https://github.com/lifan-yuan/implicitprm", "project_page": "", "abstract": "Different from its counterpart outcome reward models (ORMs), which evaluate the entire responses, a process reward model (PRM) scores a reasoning trajectory step by step, providing denser and more fine grained rewards. However, training a PRM requires labels annotated at every intermediate step, presenting significant challenges for both manual and automatic data collection. This paper aims to address this challenge. Both theoretically and empirically, we show that an implicit PRM can be obtained at no additional cost, by simply training an ORM on the cheaper response-level labels. The only assumption is to parameterize the outcome reward as the log-likelihood ratios of the policy and reference models, which can be optimized regardless of the specific choice of loss objectives. In experiments, we instantiate our implicit PRMs with various objectives and evaluate their performance on MATH. We show that our implicit PRM outperforms a strong MCTS-based baseline \\'a la Math-Shepherd using less than 1/38 of the training data. Its performance can be further improved with majority voting. We further find that scaling up instructions and responses benefits our implicit PRM, and the latter brings a larger gain. Particularly, we find that our implicit PRM, when instantiated with the cross-entropy (CE) loss, is more data-efficient and can keep improving generation models even when trained with only one response per instruction, the setup that suffers from extreme data scarcity and imbalance. Further, instructions should be relevant to downstream tasks while the diversity of responses does not bring gains. Surprisingly, training on extra Math-Shepherd step labels brings no further improvements to our implicit PRM trained on only outcome data. We hope that our work will encourage a rethinking of PRM training approaches and contribute to making training PRMs more accessible.", "github_manual": "https://github.com/lifan-yuan/implicitprm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2411.19067", "title": "MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation", "authors": [ "Minhyun Lee", "Seungho Lee", "Song Park", "Dongyoon Han", "Byeongho Heo", "Hyunjung Shim" ], "github": "https://github.com/naver-ai/maskris", "project_page": "", "abstract": "Referring Image Segmentation (RIS) is an advanced vision-language task that involves identifying and segmenting objects within an image as described by free-form text descriptions. While previous studies focused on aligning visual and language features, exploring training techniques, such as data augmentation, remains underexplored. In this work, we explore effective data augmentation for RIS and propose a novel training framework called Masked Referring Image Segmentation (MaskRIS). We observe that the conventional image augmentations fall short of RIS, leading to performance degradation, while simple random masking significantly enhances the performance of RIS. MaskRIS uses both image and text masking, followed by Distortion-aware Contextual Learning (DCL) to fully exploit the benefits of the masking strategy. This approach can improve the model's robustness to occlusions, incomplete information, and various linguistic complexities, resulting in a significant performance improvement. Experiments demonstrate that MaskRIS can easily be applied to various RIS models, outperforming existing methods in both fully supervised and weakly supervised settings. Finally, MaskRIS achieves new state-of-the-art performance on RefCOCO, RefCOCO+, and RefCOCOg datasets. Code is available at https://github.com/naver-ai/maskris.", "github_manual": "", "github_regex": "https://github.com/naver-ai/maskris", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2412.01558", "title": "VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrieval", "authors": [ "Dhiman Paul", "Md Rizwan Parvez", "Nabeel Mohammed", "Shafin Rahman" ], "github": "https://github.com/dpaul06/VideoLights", "project_page": "", "abstract": "Video Highlight Detection and Moment Retrieval (HD/MR) are essential in video analysis. Recent joint prediction transformer models often overlook their cross-task dynamics and video-text alignment and refinement. Moreover, most models typically use limited, uni-directional attention mechanisms, resulting in weakly integrated representations and suboptimal performance in capturing the interdependence between video and text modalities. Although large-language and vision-language models (LLM/LVLMs) have gained prominence across various domains, their application in this field remains relatively underexplored. Here we propose VideoLights, a novel HD/MR framework addressing these limitations through (i) Convolutional Projection and Feature Refinement modules with an alignment loss for better video-text feature alignment, (ii) Bi-Directional Cross-Modal Fusion network for strongly coupled query-aware clip representations, and (iii) Uni-directional joint-task feedback mechanism enhancing both tasks through correlation. In addition, (iv) we introduce hard positive/negative losses for adaptive error penalization and improved learning, and (v) leverage LVLMs like BLIP-2 for enhanced multimodal feature integration and intelligent pretraining using synthetic data generated from LVLMs. Comprehensive experiments on QVHighlights, TVSum, and Charades-STA benchmarks demonstrate state-of-the-art performance. Codes and models are available at https://github.com/dpaul06/VideoLights .", "github_manual": "", "github_regex": "https://github.com/dpaul06/VideoLights", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2411.19542", "title": "A dynamic parallel method for performance optimization on hybrid CPUs", "authors": [ "Luo Yu", "Liu Yucheng", "Shen Haihao" ], "github": "", "project_page": "", "abstract": "The AIPC concept is gaining popularity, and more and more hybrid CPUs will be running AI models on client devices. However, the current AI inference framework overlooks the imbalanced hardware capability of hybrid CPUs, leading to low inference performance. To address this issue, we have introduced a dynamic parallel method for hybrid CPUs, which significantly increases LLM inference performance by balancing the workload for each core of a hybrid CPU before the parallel work starts. This method has enabled Neural Speed to achieve more than 90% (on average) of memory bandwidth on two hybrid Intel CPUs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2412.02592", "title": "OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation", "authors": [ "Junyuan Zhang", "Qintong Zhang", "Bin Wang", "Linke Ouyang", "Zichen Wen", "Ying Li", "Ka-Ho Chow", "Conghui He", "Wentao Zhang" ], "github": "https://github.com/opendatalab/OHR-Bench", "project_page": "", "abstract": "Retrieval-augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge to reduce hallucinations and incorporate up-to-date information without retraining. As an essential part of RAG, external knowledge bases are commonly built by extracting structured data from unstructured PDF documents using Optical Character Recognition (OCR). However, given the imperfect prediction of OCR and the inherent non-uniform representation of structured data, knowledge bases inevitably contain various OCR noises. In this paper, we introduce OHRBench, the first benchmark for understanding the cascading impact of OCR on RAG systems. OHRBench includes 350 carefully selected unstructured PDF documents from six real-world RAG application domains, along with Q&As derived from multimodal elements in documents, challenging existing OCR solutions used for RAG To better understand OCR's impact on RAG systems, we identify two primary types of OCR noise: Semantic Noise and Formatting Noise and apply perturbation to generate a set of structured data with varying degrees of each OCR noise. Using OHRBench, we first conduct a comprehensive evaluation of current OCR solutions and reveal that none is competent for constructing high-quality knowledge bases for RAG systems. We then systematically evaluate the impact of these two noise types and demonstrate the vulnerability of RAG systems. Furthermore, we discuss the potential of employing Vision-Language Models (VLMs) without OCR in RAG systems. Code: https://github.com/opendatalab/OHR-Bench", "github_manual": "", "github_regex": "https://github.com/opendatalab/OHR-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2412.02114", "title": "OmniCreator: Self-Supervised Unified Generation with Universal Editing", "authors": [ "Haodong Chen", "Lan Wang", "Harry Yang", "Ser-Nam Lim" ], "github": "", "project_page": "", "abstract": "We introduce OmniCreator, a novel framework that can conduct text-prompted unified (image+video) generation as well as editing all in one place. OmniCreator acquires generative and universal editing capabilities in a self-supervised manner, taking original text-video pairs as conditions while utilizing the same video as a denoising target to learn the semantic correspondence between video and text. During inference, when presented with a text prompt and a video, OmniCreator is capable of generating a target that is faithful to both, achieving a universal editing effect that is unconstrained as opposed to existing editing work that primarily focuses on certain editing types or relies on additional controls (e.g., structural conditions, attention features, or DDIM inversion). On the other hand, when presented with a text prompt only, OmniCreator becomes generative, producing high-quality video as a result of the semantic correspondence learned. Importantly, we found that the same capabilities extend to images as is, making OmniCreator a truly unified framework. Further, due to the lack of existing generative video editing benchmarks, we introduce the OmniBench-99 dataset, designed to evaluate the performance of generative video editing models comprehensively. Extensive experiments demonstrate that OmniCreator exhibits substantial superiority over all other models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2412.01928", "title": "MALT: Improving Reasoning with Multi-Agent LLM Training", "authors": [ "Sumeet Ramesh Motwani", "Chandler Smith", "Rocktim Jyoti Das", "Markian Rybchuk", "Philip H. S. Torr", "Ivan Laptev", "Fabio Pizzati", "Ronald Clark", "Christian Schroeder de Witt" ], "github": "", "project_page": "", "abstract": "Enabling effective collaboration among LLMs is a crucial step toward developing autonomous systems capable of solving complex problems. While LLMs are typically used as single-model generators, where humans critique and refine their outputs, the potential for jointly-trained collaborative models remains largely unexplored. Despite promising results in multi-agent communication and debate settings, little progress has been made in training models to work together on tasks. In this paper, we present a first step toward \"Multi-agent LLM training\" (MALT) on reasoning problems. Our approach employs a sequential multi-agent setup with heterogeneous LLMs assigned specialized roles: a generator, verifier, and refinement model iteratively solving problems. We propose a trajectory-expansion-based synthetic data generation process and a credit assignment strategy driven by joint outcome based rewards. This enables our post-training setup to utilize both positive and negative trajectories to autonomously improve each model's specialized capabilities as part of a joint sequential system. We evaluate our approach across MATH, GSM8k, and CQA, where MALT on Llama 3.1 8B models achieves relative improvements of 14.14%, 7.12%, and 9.40% respectively over the same baseline model. This demonstrates an early advance in multi-agent cooperative capabilities for performance on mathematical and common sense reasoning questions. More generally, our work provides a concrete direction for research around multi-agent LLM training approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2411.19655", "title": "Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-OASIS", "authors": [ "Alessandro Scir\u00e8", "Andrei Stefan Bejgu", "Simone Tedeschi", "Karim Ghonim", "Federico Martelli", "Roberto Navigli" ], "github": "", "project_page": "", "abstract": "After the introduction of Large Language Models (LLMs), there have been substantial improvements in the performance of Natural Language Generation (NLG) tasks, including Text Summarization and Machine Translation. However, LLMs still produce outputs containing hallucinations, that is, content not grounded in factual information. Therefore, developing methods to assess the factuality of LLMs has become urgent. Indeed, resources for factuality evaluation have recently emerged. Although challenging, these resources face one or more of the following limitations: (i) they are tailored to a specific task or domain; (ii) they are limited in size, thereby preventing the training of new factuality evaluators; (iii) they are designed for simpler verification tasks, such as claim verification. To address these issues, we introduce LLM-Oasis, to the best of our knowledge the largest resource for training end-to-end factuality evaluators. LLM-Oasis is constructed by extracting claims from Wikipedia, falsifying a subset of these claims, and generating pairs of factual and unfactual texts. We then rely on human annotators to both validate the quality of our dataset and to create a gold standard test set for benchmarking factuality evaluation systems. Our experiments demonstrate that LLM-Oasis presents a significant challenge for state-of-the-art LLMs, with GPT-4o achieving up to 60% accuracy in our proposed end-to-end factuality evaluation task, highlighting its potential to drive future research in the field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2412.02700", "title": "Motion Prompting: Controlling Video Generation with Motion Trajectories", "authors": [ "Daniel Geng", "Charles Herrmann", "Junhwa Hur", "Forrester Cole", "Serena Zhang", "Tobias Pfaff", "Tatiana Lopez-Guevara", "Carl Doersch", "Yusuf Aytar", "Michael Rubinstein", "Chen Sun", "Oliver Wang", "Andrew Owens", "Deqing Sun" ], "github": "", "project_page": "", "abstract": "Motion control is crucial for generating expressive and compelling video content; however, most existing video generation models rely mainly on text prompts for control, which struggle to capture the nuances of dynamic actions and temporal compositions. To this end, we train a video generation model conditioned on spatio-temporally sparse or dense motion trajectories. In contrast to prior motion conditioning work, this flexible representation can encode any number of trajectories, object-specific or global scene motion, and temporally sparse motion; due to its flexibility we refer to this conditioning as motion prompts. While users may directly specify sparse trajectories, we also show how to translate high-level user requests into detailed, semi-dense motion prompts, a process we term motion prompt expansion. We demonstrate the versatility of our approach through various applications, including camera and object motion control, \"interacting\" with an image, motion transfer, and image editing. Our results showcase emergent behaviors, such as realistic physics, suggesting the potential of motion prompts for probing video models and interacting with future generative world models. Finally, we evaluate quantitatively, conduct a human study, and demonstrate strong performance. Video results are available on our webpage: https://motion-prompting.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2412.00239", "title": "Generating a Low-code Complete Workflow via Task Decomposition and RAG", "authors": [ "Orlando Marquez Ayala", "Patrice B\u00e9chard" ], "github": "", "project_page": "", "abstract": "AI technologies are moving rapidly from research to production. With the popularity of Foundation Models (FMs) that generate text, images, and video, AI-based systems are increasing their complexity. Compared to traditional AI-based software, systems employing FMs, or GenAI-based systems, are more difficult to design due to their scale and versatility. This makes it necessary to document best practices, known as design patterns in software engineering, that can be used across GenAI applications. Our first contribution is to formalize two techniques, Task Decomposition and Retrieval-Augmented Generation (RAG), as design patterns for GenAI-based systems. We discuss their trade-offs in terms of software quality attributes and comment on alternative approaches. We recommend to AI practitioners to consider these techniques not only from a scientific perspective but also from the standpoint of desired engineering properties such as flexibility, maintainability, safety, and security. As a second contribution, we describe our industry experience applying Task Decomposition and RAG to build a complex real-world GenAI application for enterprise users: Workflow Generation. The task of generating workflows entails generating a specific plan using data from the system environment, taking as input a user requirement. As these two patterns affect the entire AI development cycle, we explain how they impacted the dataset creation, model training, model evaluation, and deployment phases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-04", "arxiv_id": "2412.03248", "title": "AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning", "authors": [ "Yiwu Zhong", "Zhuoming Liu", "Yin Li", "Liwei Wang" ], "github": "https://github.com/LaVi-Lab/AIM", "project_page": "", "abstract": "Large language models (LLMs) have enabled the creation of multi-modal LLMs that exhibit strong comprehension of visual data such as images and videos. However, these models usually rely on extensive visual tokens from visual encoders, leading to high computational demands, which limits their applicability in resource-constrained environments and for long-context tasks. In this work, we propose a training-free adaptive inference method for multi-modal LLMs that can accommodate a broad range of efficiency requirements with a minimum performance drop. Our method consists of a) iterative token merging based on embedding similarity before LLMs, and b) progressive token pruning within LLM layers based on multi-modal importance. With a minimalist design, our method can be applied to both video and image LLMs. Extensive experiments on diverse video and image benchmarks demonstrate that, our method substantially reduces computation load (e.g., a 7-fold reduction in FLOPs) while preserving the performance of video and image LLMs. Further, under a similar computational cost, our method outperforms the state-of-the-art methods in long video understanding (e.g., +4.6 on MLVU). Additionally, our in-depth analysis provides insights into token redundancy and LLM layer behaviors, offering guidance for future research in designing efficient multi-modal LLMs. Our code will be available at https://github.com/LaVi-Lab/AIM.", "github_manual": "", "github_regex": "https://github.com/LaVi-Lab/AIM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.03069", "title": "TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation", "authors": [ "Liao Qu", "Huichao Zhang", "Yiheng Liu", "Xu Wang", "Yi Jiang", "Yiming Gao", "Hu Ye", "Daniel K. Du", "Zehuan Yuan", "Xinglong Wu" ], "github": "https://github.com/ByteFlow-AI/TokenFlow", "project_page": "", "abstract": "We present TokenFlow, a novel unified image tokenizer that bridges the long-standing gap between multimodal understanding and generation. Prior research attempt to employ a single reconstruction-targeted Vector Quantization (VQ) encoder for unifying these two tasks. We observe that understanding and generation require fundamentally different granularities of visual information. This leads to a critical trade-off, particularly compromising performance in multimodal understanding tasks. TokenFlow addresses this challenge through an innovative dual-codebook architecture that decouples semantic and pixel-level feature learning while maintaining their alignment via a shared mapping mechanism. This design enables direct access to both high-level semantic representations crucial for understanding tasks and fine-grained visual features essential for generation through shared indices. Our extensive experiments demonstrate TokenFlow's superiority across multiple dimensions. Leveraging TokenFlow, we demonstrate for the first time that discrete visual input can surpass LLaVA-1.5 13B in understanding performance, achieving a 7.2\\% average improvement. For image reconstruction, we achieve a strong FID score of 0.63 at 384*384 resolution. Moreover, TokenFlow establishes state-of-the-art performance in autoregressive image generation with a GenEval score of 0.55 at 256*256 resolution, achieving comparable results to SDXL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ByteFlow-AI/TokenFlow", "github_api_added_by": "auto" }, { "date": "2024-12-05", "arxiv_id": "2412.01106", "title": "One Shot, One Talk: Whole-body Talking Avatar from a Single Image", "authors": [ "Jun Xiang", "Yudong Guo", "Leipeng Hu", "Boyang Guo", "Yancheng Yuan", "Juyong Zhang" ], "github": "", "project_page": "", "abstract": "Building realistic and animatable avatars still requires minutes of multi-view or monocular self-rotating videos, and most methods lack precise control over gestures and expressions. To push this boundary, we address the challenge of constructing a whole-body talking avatar from a single image. We propose a novel pipeline that tackles two critical issues: 1) complex dynamic modeling and 2) generalization to novel gestures and expressions. To achieve seamless generalization, we leverage recent pose-guided image-to-video diffusion models to generate imperfect video frames as pseudo-labels. To overcome the dynamic modeling challenge posed by inconsistent and noisy pseudo-videos, we introduce a tightly coupled 3DGS-mesh hybrid avatar representation and apply several key regularizations to mitigate inconsistencies caused by imperfect labels. Extensive experiments on diverse subjects demonstrate that our method enables the creation of a photorealistic, precisely animatable, and expressive whole-body talking avatar from just a single image.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.03515", "title": "Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion", "authors": [ "Shengyuan Zhang", "An Zhao", "Ling Yang", "Zejian Li", "Chenye Meng", "Haoran Xu", "Tianrun Chen", "AnYang Wei", "Perry Pengyun GU", "Lingyun Sun" ], "github": "https://github.com/happyw1nd/ScoreLiDAR", "project_page": "", "abstract": "Diffusion models have been applied to 3D LiDAR scene completion due to their strong training stability and high completion quality. However, the slow sampling speed limits the practical application of diffusion-based scene completion models since autonomous vehicles require an efficient perception of surrounding environments. This paper proposes a novel distillation method tailored for 3D LiDAR scene completion models, dubbed ScoreLiDAR, which achieves efficient yet high-quality scene completion. ScoreLiDAR enables the distilled model to sample in significantly fewer steps after distillation. To improve completion quality, we also introduce a novel Structural Loss, which encourages the distilled model to capture the geometric structure of the 3D LiDAR scene. The loss contains a scene-wise term constraining the holistic structure and a point-wise term constraining the key landmark points and their relative configuration. Extensive experiments demonstrate that ScoreLiDAR significantly accelerates the completion time from 30.55 to 5.37 seconds per frame (>5times) on SemanticKITTI and achieves superior performance compared to state-of-the-art 3D LiDAR scene completion models. Our code is publicly available at https://github.com/happyw1nd/ScoreLiDAR.", "github_manual": "", "github_regex": "https://github.com/happyw1nd/ScoreLiDAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.03552", "title": "Imagine360: Immersive 360 Video Generation from Perspective Anchor", "authors": [ "Jing Tan", "Shuai Yang", "Tong Wu", "Jingwen He", "Yuwei Guo", "Ziwei Liu", "Dahua Lin" ], "github": "https://github.com/YS-IMTech/Imagine360", "project_page": "", "abstract": "360^circ videos offer a hyper-immersive experience that allows the viewers to explore a dynamic scene from full 360 degrees. To achieve more user-friendly and personalized content creation in 360^circ video format, we seek to lift standard perspective videos into 360^circ equirectangular videos. To this end, we introduce Imagine360, the first perspective-to-360^circ video generation framework that creates high-quality 360^circ videos with rich and diverse motion patterns from video anchors. Imagine360 learns fine-grained spherical visual and motion patterns from limited 360^circ video data with several key designs. 1) Firstly we adopt the dual-branch design, including a perspective and a panorama video denoising branch to provide local and global constraints for 360^circ video generation, with motion module and spatial LoRA layers fine-tuned on extended web 360^circ videos. 2) Additionally, an antipodal mask is devised to capture long-range motion dependencies, enhancing the reversed camera motion between antipodal pixels across hemispheres. 3) To handle diverse perspective video inputs, we propose elevation-aware designs that adapt to varying video masking due to changing elevations across frames. Extensive experiments show Imagine360 achieves superior graphics quality and motion coherence among state-of-the-art 360^circ video generation methods. We believe Imagine360 holds promise for advancing personalized, immersive 360^circ video creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YS-IMTech/Imagine360", "github_api_added_by": "auto" }, { "date": "2024-12-05", "arxiv_id": "2412.03558", "title": "MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generation", "authors": [ "Zehuan Huang", "Yuan-Chen Guo", "Xingqiao An", "Yunhan Yang", "Yangguang Li", "Zi-Xin Zou", "Ding Liang", "Xihui Liu", "Yan-Pei Cao", "Lu Sheng" ], "github": "", "project_page": "", "abstract": "This paper introduces MIDI, a novel paradigm for compositional 3D scene generation from a single image. Unlike existing methods that rely on reconstruction or retrieval techniques or recent approaches that employ multi-stage object-by-object generation, MIDI extends pre-trained image-to-3D object generation models to multi-instance diffusion models, enabling the simultaneous generation of multiple 3D instances with accurate spatial relationships and high generalizability. At its core, MIDI incorporates a novel multi-instance attention mechanism, that effectively captures inter-object interactions and spatial coherence directly within the generation process, without the need for complex multi-step processes. The method utilizes partial object images and global scene context as inputs, directly modeling object completion during 3D generation. During training, we effectively supervise the interactions between 3D instances using a limited amount of scene-level data, while incorporating single-object data for regularization, thereby maintaining the pre-trained generalization ability. MIDI demonstrates state-of-the-art performance in image-to-scene generation, validated through evaluations on synthetic data, real-world scene data, and stylized scene images generated by text-to-image diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.03085", "title": "Mimir: Improving Video Diffusion Models for Precise Text Understanding", "authors": [ "Shuai Tan", "Biao Gong", "Yutong Feng", "Kecheng Zheng", "Dandan Zheng", "Shuwei Shi", "Yujun Shen", "Jingdong Chen", "Ming Yang" ], "github": "", "project_page": "", "abstract": "Text serves as the key control signal in video generation due to its narrative nature. To render text descriptions into video clips, current video diffusion models borrow features from text encoders yet struggle with limited text comprehension. The recent success of large language models (LLMs) showcases the power of decoder-only transformers, which offers three clear benefits for text-to-video (T2V) generation, namely, precise text understanding resulting from the superior scalability, imagination beyond the input text enabled by next token prediction, and flexibility to prioritize user interests through instruction tuning. Nevertheless, the feature distribution gap emerging from the two different text modeling paradigms hinders the direct use of LLMs in established T2V models. This work addresses this challenge with Mimir, an end-to-end training framework featuring a carefully tailored token fuser to harmonize the outputs from text encoders and LLMs. Such a design allows the T2V model to fully leverage learned video priors while capitalizing on the text-related capability of LLMs. Extensive quantitative and qualitative results demonstrate the effectiveness of Mimir in generating high-quality videos with excellent text comprehension, especially when processing short captions and managing shifting motions. Project page: https://lucaria-academy.github.io/Mimir/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.03517", "title": "NVComposer: Boosting Generative Novel View Synthesis with Multiple Sparse and Unposed Images", "authors": [ "Lingen Li", "Zhaoyang Zhang", "Yaowei Li", "Jiale Xu", "Xiaoyu Li", "Wenbo Hu", "Weihao Cheng", "Jinwei Gu", "Tianfan Xue", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Recent advancements in generative models have significantly improved novel view synthesis (NVS) from multi-view data. However, existing methods depend on external multi-view alignment processes, such as explicit pose estimation or pre-reconstruction, which limits their flexibility and accessibility, especially when alignment is unstable due to insufficient overlap or occlusions between views. In this paper, we propose NVComposer, a novel approach that eliminates the need for explicit external alignment. NVComposer enables the generative model to implicitly infer spatial and geometric relationships between multiple conditional views by introducing two key components: 1) an image-pose dual-stream diffusion model that simultaneously generates target novel views and condition camera poses, and 2) a geometry-aware feature alignment module that distills geometric priors from dense stereo models during training. Extensive experiments demonstrate that NVComposer achieves state-of-the-art performance in generative multi-view NVS tasks, removing the reliance on external alignment and thus improving model accessibility. Our approach shows substantial improvements in synthesis quality as the number of unposed input views increases, highlighting its potential for more flexible and accessible generative NVS systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.03187", "title": "Weighted-Reward Preference Optimization for Implicit Model Fusion", "authors": [ "Ziyi Yang", "Fanqi Wan", "Longguang Zhong", "Tianyuan Shi", "Xiaojun Quan" ], "github": "https://github.com/SLIT-AI/WRPO", "project_page": "", "abstract": "While fusing heterogeneous open-source LLMs with varying architectures and sizes can potentially integrate the strengths of different models, existing fusion methods face significant challenges, such as vocabulary alignment and merging distribution matrices. These procedures are not only complex but also prone to introducing noise and errors. In this paper, we propose an implicit fusion method, Weighted-Reward Preference Optimization (WRPO), which leverages preference optimization between the source LLMs and the target LLM to transfer their capabilities effectively. WRPO eliminates the need for vocabulary alignment and matrix fusion and can be efficiently scaled to accommodate various LLMs. To address distributional deviations between the source and target LLMs, WRPO introduces a progressive adaptation strategy that gradually shifts reliance on preferred examples from the target LLM to the source LLMs. Extensive experiments on the MT-Bench, AlpacaEval-2, and Arena-Hard benchmarks demonstrate that WRPO consistently outperforms existing knowledge fusion methods and various fine-tuning baselines. When applied to LLaMA3-8B-Instruct as the target model, WRPO achieves a length-controlled win rate of 55.9% against GPT-4-Preview-1106 on AlpacaEval-2 and a win rate of 46.2% against GPT-4-0314 on Arena-Hard. Our code is available at https://github.com/SLIT-AI/WRPO.", "github_manual": "", "github_regex": "https://github.com/SLIT-AI/WRPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.02687", "title": "SNOOPI: Supercharged One-step Diffusion Distillation with Proper Guidance", "authors": [ "Viet Nguyen", "Anh Aengus Nguyen", "Trung Dao", "Khoi Nguyen", "Cuong Pham", "Toan Tran", "Anh Tran" ], "github": "", "project_page": "", "abstract": "Recent approaches have yielded promising results in distilling multi-step text-to-image diffusion models into one-step ones. The state-of-the-art efficient distillation technique, i.e., SwiftBrushv2 (SBv2), even surpasses the teacher model's performance with limited resources. However, our study reveals its instability when handling different diffusion model backbones due to using a fixed guidance scale within the Variational Score Distillation (VSD) loss. Another weakness of the existing one-step diffusion models is the missing support for negative prompt guidance, which is crucial in practical image generation. This paper presents SNOOPI, a novel framework designed to address these limitations by enhancing the guidance in one-step diffusion models during both training and inference. First, we effectively enhance training stability through Proper Guidance-SwiftBrush (PG-SB), which employs a random-scale classifier-free guidance approach. By varying the guidance scale of both teacher models, we broaden their output distributions, resulting in a more robust VSD loss that enables SB to perform effectively across diverse backbones while maintaining competitive performance. Second, we propose a training-free method called Negative-Away Steer Attention (NASA), which integrates negative prompts into one-step diffusion models via cross-attention to suppress undesired elements in generated images. Our experimental results show that our proposed methods significantly improve baseline models across various metrics. Remarkably, we achieve an HPSv2 score of 31.08, setting a new state-of-the-art benchmark for one-step diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.03555", "title": "PaliGemma 2: A Family of Versatile VLMs for Transfer", "authors": [ "Andreas Steiner", "Andr\u00e9 Susano Pinto", "Michael Tschannen", "Daniel Keysers", "Xiao Wang", "Yonatan Bitton", "Alexey Gritsenko", "Matthias Minderer", "Anthony Sherbondy", "Shangbang Long", "Siyang Qin", "Reeve Ingle", "Emanuele Bugliarello", "Sahar Kazemzadeh", "Thomas Mesnard", "Ibrahim Alabdulmohsin", "Lucas Beyer", "Xiaohua Zhai" ], "github": "", "project_page": "", "abstract": "PaliGemma 2 is an upgrade of the PaliGemma open Vision-Language Model (VLM) based on the Gemma 2 family of language models. We combine the SigLIP-So400m vision encoder that was also used by PaliGemma with the whole range of Gemma 2 models, from the 2B one all the way up to the 27B model. We train these models at three resolutions (224px, 448px, and 896px) in multiple stages to equip them with broad knowledge for transfer via fine-tuning. The resulting family of base models covering different model sizes and resolutions allows us to investigate factors impacting transfer performance (such as learning rate) and to analyze the interplay between the type of task, model size, and resolution. We further increase the number and breadth of transfer tasks beyond the scope of PaliGemma including different OCR-related tasks such as table structure recognition, molecular structure recognition, music score recognition, as well as long fine-grained captioning and radiography report generation, on which PaliGemma 2 obtains state-of-the-art results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.00493", "title": "Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding", "authors": [ "Duo Zheng", "Shijia Huang", "Liwei Wang" ], "github": "https://github.com/LaVi-Lab/Video-3D-LLM", "project_page": "", "abstract": "The rapid advancement of Multimodal Large Language Models (MLLMs) has significantly impacted various multimodal tasks. However, these models face challenges in tasks that require spatial understanding within 3D environments. Efforts to enhance MLLMs, such as incorporating point cloud features, have been made, yet a considerable gap remains between the models' learned representations and the inherent complexity of 3D scenes. This discrepancy largely stems from the training of MLLMs on predominantly 2D data, which restricts their effectiveness in comprehending 3D spaces. To address this issue, in this paper, we propose a novel generalist model, i.e., Video-3D LLM, for 3D scene understanding. By treating 3D scenes as dynamic videos and incorporating 3D position encoding into these representations, our Video-3D LLM aligns video representations with real-world spatial contexts more accurately. Additionally, we have implemented a maximum coverage sampling technique to optimize the balance between computational costs and performance efficiency. Extensive experiments demonstrate that our model achieves state-of-the-art performance on several 3D scene understanding benchmarks, including ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LaVi-Lab/Video-3D-LLM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.03439", "title": "CleanDIFT: Diffusion Features without Noise", "authors": [ "Nick Stracke", "Stefan Andreas Baumann", "Kolja Bauer", "Frank Fundel", "Bj\u00f6rn Ommer" ], "github": "https://github.com/CompVis/cleandift", "project_page": "", "abstract": "Internal features from large-scale pre-trained diffusion models have recently been established as powerful semantic descriptors for a wide range of downstream tasks. Works that use these features generally need to add noise to images before passing them through the model to obtain the semantic features, as the models do not offer the most useful features when given images with little to no noise. We show that this noise has a critical impact on the usefulness of these features that cannot be remedied by ensembling with different random noises. We address this issue by introducing a lightweight, unsupervised fine-tuning method that enables diffusion backbones to provide high-quality, noise-free semantic features. We show that these features readily outperform previous diffusion features by a wide margin in a wide variety of extraction setups and downstream tasks, offering better performance than even ensemble-based methods at a fraction of the cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CompVis/cleandift", "github_api_added_by": "auto" }, { "date": "2024-12-05", "arxiv_id": "2411.19103", "title": "VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models", "authors": [ "Jeongho Ju", "Daeyoung Kim", "SunYoung Park", "Youngjune Kim" ], "github": "https://github.com/PaddlePaddle/PaddleOCR", "project_page": "", "abstract": "In this paper, we introduce an open-source Korean-English vision-language model (VLM), VARCO-VISION. We incorporate a step-by-step training strategy that allows a model learn both linguistic and visual information while preserving the backbone model's knowledge. Our model demonstrates outstanding performance in diverse settings requiring bilingual image-text understanding and generation abilities compared to models of similar size. VARCO-VISION is also capable of grounding, referring, and OCR, expanding its usage and potential applications for real-world scenarios. In addition to the model, we release five Korean evaluation datasets, including four closed-set and one openset benchmarks. We anticipate that our milestone will broaden the opportunities for AI researchers aiming to train VLMs. VARCO-VISION is available at https://huggingface.co/NCSOFT/VARCO-VISION-14B.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PaddlePaddle/PaddleOCR", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.02030", "title": "NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training", "authors": [ "Dar-Yen Chen", "Hmrishav Bandyopadhyay", "Kai Zou", "Yi-Zhe Song" ], "github": "", "project_page": "", "abstract": "We introduce NitroFusion, a fundamentally different approach to single-step diffusion that achieves high-quality generation through a dynamic adversarial framework. While one-step methods offer dramatic speed advantages, they typically suffer from quality degradation compared to their multi-step counterparts. Just as a panel of art critics provides comprehensive feedback by specializing in different aspects like composition, color, and technique, our approach maintains a large pool of specialized discriminator heads that collectively guide the generation process. Each discriminator group develops expertise in specific quality aspects at different noise levels, providing diverse feedback that enables high-fidelity one-step generation. Our framework combines: (i) a dynamic discriminator pool with specialized discriminator groups to improve generation quality, (ii) strategic refresh mechanisms to prevent discriminator overfitting, and (iii) global-local discriminator heads for multi-scale quality assessment, and unconditional/conditional training for balanced generation. Additionally, our framework uniquely supports flexible deployment through bottom-up refinement, allowing users to dynamically choose between 1-4 denoising steps with the same model for direct quality-speed trade-offs. Through comprehensive experiments, we demonstrate that NitroFusion significantly outperforms existing single-step methods across multiple evaluation metrics, particularly excelling in preserving fine details and global consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.03565", "title": "Inst-IT: Boosting Multimodal Instance Understanding via Explicit Visual Prompt Instruction Tuning", "authors": [ "Wujian Peng", "Lingchen Meng", "Yitong Chen", "Yiweng Xie", "Yang Liu", "Tao Gui", "Hang Xu", "Xipeng Qiu", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "https://github.com/inst-it/inst-it", "project_page": "", "abstract": "Large Multimodal Models (LMMs) have made significant breakthroughs with the advancement of instruction tuning. However, while existing models can understand images and videos at a holistic level, they still struggle with instance-level understanding that requires a more nuanced comprehension and alignment. Instance-level understanding is crucial, as it focuses on the specific elements that we are most interested in. Excitingly, existing works find that the state-of-the-art LMMs exhibit strong instance understanding capabilities when provided with explicit visual cues. Motivated by this, we introduce an automated annotation pipeline assisted by GPT-4o to extract instance-level information from images and videos through explicit visual prompting for instance guidance. Building upon this pipeline, we proposed Inst-IT, a solution to enhance LMMs in Instance understanding via explicit visual prompt Instruction Tuning. Inst-IT consists of a benchmark to diagnose multimodal instance-level understanding, a large-scale instruction-tuning dataset, and a continuous instruction-tuning training paradigm to effectively enhance spatial-temporal instance understanding capabilities of existing LMMs. Experimental results show that, with the boost of Inst-IT, our models not only achieve outstanding performance on Inst-IT Bench but also demonstrate significant improvements across various generic image and video understanding benchmarks. This highlights that our dataset not only boosts instance-level understanding but also strengthens the overall capabilities of generic image and video comprehension.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/inst-it/inst-it", "github_api_added_by": "auto" }, { "date": "2024-12-05", "arxiv_id": "2412.03205", "title": "U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs", "authors": [ "Konstantin Chernyshev", "Vitaliy Polshkov", "Ekaterina Artemova", "Alex Myasnikov", "Vlad Stepanov", "Alexei Miasnikov", "Sergei Tilga" ], "github": "https://github.com/toloka/u-math", "project_page": "", "abstract": "The current evaluation of mathematical skills in LLMs is limited, as existing benchmarks are either relatively small, primarily focus on elementary and high-school problems, or lack diversity in topics. Additionally, the inclusion of visual elements in tasks remains largely under-explored. To address these gaps, we introduce U-MATH, a novel benchmark of 1,100 unpublished open-ended university-level problems sourced from teaching materials. It is balanced across six core subjects, with 20% of multimodal problems. Given the open-ended nature of U-MATH problems, we employ an LLM to judge the correctness of generated solutions. To this end, we release mu-MATH, a dataset to evaluate the LLMs' capabilities in judging solutions. The evaluation of general domain, math-specific, and multimodal LLMs highlights the challenges presented by U-MATH. Our findings reveal that LLMs achieve a maximum accuracy of only 63% on text-based tasks, with even lower 45% on visual problems. The solution assessment proves challenging for LLMs, with the best LLM judge having an F1-score of 80% on mu-MATH.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/toloka/u-math", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.02980", "title": "Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models", "authors": [ "Alex Havrilla", "Andrew Dai", "Laura O'Mahony", "Koen Oostermeijer", "Vera Zisler", "Alon Albalak", "Fabrizio Milo", "Sharath Chandra Raparthy", "Kanishk Gandhi", "Baber Abbasi", "Duy Phung", "Maia Iyer", "Dakota Mahan", "Chase Blagden", "Srishti Gureja", "Mohammed Hamdy", "Wen-Ding Li", "Giovanni Paolini", "Pawan Sasanka Ammanamanchi", "Elliot Meyerson" ], "github": "", "project_page": "", "abstract": "Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct comparisons among synthetic data generation algorithms are scarce, making it difficult to understand where improvement comes from and what bottlenecks exist. We propose to evaluate algorithms via the makeup of synthetic data generated by each algorithm in terms of data quality, diversity, and complexity. We choose these three characteristics for their significance in open-ended processes and the impact each has on the capabilities of downstream models. We find quality to be essential for in-distribution model generalization, diversity to be essential for out-of-distribution generalization, and complexity to be beneficial for both. Further, we emphasize the existence of Quality-Diversity trade-offs in training data and the downstream effects on model performance. We then examine the effect of various components in the synthetic data pipeline on each data characteristic. This examination allows us to taxonomize and compare synthetic data generation algorithms through the components they utilize and the resulting effects on data QDC composition. This analysis extends into a discussion on the importance of balancing QDC in synthetic data for efficient reinforcement learning and self-improvement algorithms. Analogous to the QD trade-offs in training data, often there exist trade-offs between model output quality and output diversity which impact the composition of synthetic data. We observe that many models are currently evaluated and optimized only for output quality, thereby limiting output diversity and the potential for self-improvement. We argue that balancing these trade-offs is essential to the development of future self-improvement algorithms and highlight a number of works making progress in this direction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.00177", "title": "LumiNet: Latent Intrinsics Meets Diffusion Models for Indoor Scene Relighting", "authors": [ "Xiaoyan Xing", "Konrad Groh", "Sezer Karaoglu", "Theo Gevers", "Anand Bhattad" ], "github": "", "project_page": "", "abstract": "We introduce LumiNet, a novel architecture that leverages generative models and latent intrinsic representations for effective lighting transfer. Given a source image and a target lighting image, LumiNet synthesizes a relit version of the source scene that captures the target's lighting. Our approach makes two key contributions: a data curation strategy from the StyleGAN-based relighting model for our training, and a modified diffusion-based ControlNet that processes both latent intrinsic properties from the source image and latent extrinsic properties from the target image. We further improve lighting transfer through a learned adaptor (MLP) that injects the target's latent extrinsic properties via cross-attention and fine-tuning. Unlike traditional ControlNet, which generates images with conditional maps from a single scene, LumiNet processes latent representations from two different images - preserving geometry and albedo from the source while transferring lighting characteristics from the target. Experiments demonstrate that our method successfully transfers complex lighting phenomena including specular highlights and indirect illumination across scenes with varying spatial layouts and materials, outperforming existing approaches on challenging indoor scenes using only images as input.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-05", "arxiv_id": "2412.02186", "title": "VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding", "authors": [ "Kangsan Kim", "Geon Park", "Youngwan Lee", "Woongyeong Yeo", "Sung Ju Hwang" ], "github": "https://github.com/KangsanKim07/VideoICL", "project_page": "", "abstract": "Recent advancements in video large multimodal models (LMMs) have significantly improved their video understanding and reasoning capabilities. However, their performance drops on out-of-distribution (OOD) tasks that are underrepresented in training data. Traditional methods like fine-tuning on OOD datasets are impractical due to high computational costs. While In-context learning (ICL) with demonstration examples has shown promising generalization performance in language tasks and image-language tasks without fine-tuning, applying ICL to video-language tasks faces challenges due to the limited context length in Video LMMs, as videos require longer token lengths. To address these issues, we propose VideoICL, a novel video in-context learning framework for OOD tasks that introduces a similarity-based relevant example selection strategy and a confidence-based iterative inference approach. This allows to select the most relevant examples and rank them based on similarity, to be used for inference. If the generated response has low confidence, our framework selects new examples and performs inference again, iteratively refining the results until a high-confidence response is obtained. This approach improves OOD video understanding performance by extending effective context length without incurring high costs. The experimental results on multiple benchmarks demonstrate significant performance gains, especially in domain-specific scenarios, laying the groundwork for broader video comprehension applications. Code will be released at https://github.com/KangsanKim07/VideoICL", "github_manual": "", "github_regex": "https://github.com/KangsanKim07/VideoICL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.01506", "title": "Structured 3D Latents for Scalable and Versatile 3D Generation", "authors": [ "Jianfeng Xiang", "Zelong Lv", "Sicheng Xu", "Yu Deng", "Ruicheng Wang", "Bowen Zhang", "Dong Chen", "Xin Tong", "Jiaolong Yang" ], "github": "https://github.com/Microsoft/TRELLIS", "project_page": "", "abstract": "We introduce a novel 3D generation method for versatile and high-quality 3D asset creation. The cornerstone is a unified Structured LATent (SLAT) representation which allows decoding to different output formats, such as Radiance Fields, 3D Gaussians, and meshes. This is achieved by integrating a sparsely-populated 3D grid with dense multiview visual features extracted from a powerful vision foundation model, comprehensively capturing both structural (geometry) and textural (appearance) information while maintaining flexibility during decoding. We employ rectified flow transformers tailored for SLAT as our 3D generation models and train models with up to 2 billion parameters on a large 3D asset dataset of 500K diverse objects. Our model generates high-quality results with text or image conditions, significantly surpassing existing methods, including recent ones at similar scales. We showcase flexible output format selection and local 3D editing capabilities which were not offered by previous models. Code, model, and data will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Microsoft/TRELLIS", "github_api_added_by": "auto" }, { "date": "2024-12-06", "arxiv_id": "2412.04280", "title": "HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editing", "authors": [ "Jinbin Bai", "Wei Chow", "Ling Yang", "Xiangtai Li", "Juncheng Li", "Hanwang Zhang", "Shuicheng Yan" ], "github": "", "project_page": "", "abstract": "We present HumanEdit, a high-quality, human-rewarded dataset specifically designed for instruction-guided image editing, enabling precise and diverse image manipulations through open-form language instructions. Previous large-scale editing datasets often incorporate minimal human feedback, leading to challenges in aligning datasets with human preferences. HumanEdit bridges this gap by employing human annotators to construct data pairs and administrators to provide feedback. With meticulously curation, HumanEdit comprises 5,751 images and requires more than 2,500 hours of human effort across four stages, ensuring both accuracy and reliability for a wide range of image editing tasks. The dataset includes six distinct types of editing instructions: Action, Add, Counting, Relation, Remove, and Replace, encompassing a broad spectrum of real-world scenarios. All images in the dataset are accompanied by masks, and for a subset of the data, we ensure that the instructions are sufficiently detailed to support mask-free editing. Furthermore, HumanEdit offers comprehensive diversity and high-resolution 1024 times 1024 content sourced from various domains, setting a new versatile benchmark for instructional image editing datasets. With the aim of advancing future research and establishing evaluation benchmarks in the field of image editing, we release HumanEdit at https://huggingface.co/datasets/BryanW/HumanEdit.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04467", "title": "VisionZip: Longer is Better but Not Necessary in Vision Language Models", "authors": [ "Senqiao Yang", "Yukang Chen", "Zhuotao Tian", "Chengyao Wang", "Jingyao Li", "Bei Yu", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/VisionZip", "project_page": "", "abstract": "Recent advancements in vision-language models have enhanced performance by increasing the length of visual tokens, making them much longer than text tokens and significantly raising computational costs. However, we observe that the visual tokens generated by popular vision encoders, such as CLIP and SigLIP, contain significant redundancy. To address this, we introduce VisionZip, a simple yet effective method that selects a set of informative tokens for input to the language model, reducing visual token redundancy and improving efficiency while maintaining model performance. The proposed VisionZip can be widely applied to image and video understanding tasks and is well-suited for multi-turn dialogues in real-world scenarios, where previous methods tend to underperform. Experimental results show that VisionZip outperforms the previous state-of-the-art method by at least 5% performance gains across nearly all settings. Moreover, our method significantly enhances model inference speed, improving the prefilling time by 8x and enabling the LLaVA-Next 13B model to infer faster than the LLaVA-Next 7B model while achieving better results. Furthermore, we analyze the causes of this redundancy and encourage the community to focus on extracting better visual features rather than merely increasing token length. Our code is available at https://github.com/dvlab-research/VisionZip .", "github_manual": "", "github_regex": "https://github.com/dvlab-research/VisionZip", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04454", "title": "Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction", "authors": [ "Yiheng Xu", "Zekun Wang", "Junli Wang", "Dunjie Lu", "Tianbao Xie", "Amrita Saha", "Doyen Sahoo", "Tao Yu", "Caiming Xiong" ], "github": "https://github.com/xlang-ai/aguvis", "project_page": "", "abstract": "Graphical User Interfaces (GUIs) are critical to human-computer interaction, yet automating GUI tasks remains challenging due to the complexity and variability of visual environments. Existing approaches often rely on textual representations of GUIs, which introduce limitations in generalization, efficiency, and scalability. In this paper, we introduce Aguvis, a unified pure vision-based framework for autonomous GUI agents that operates across various platforms. Our approach leverages image-based observations, and grounding instructions in natural language to visual elements, and employs a consistent action space to ensure cross-platform generalization. To address the limitations of previous work, we integrate explicit planning and reasoning within the model, enhancing its ability to autonomously navigate and interact with complex digital environments. We construct a large-scale dataset of GUI agent trajectories, incorporating multimodal reasoning and grounding, and employ a two-stage training pipeline that first focuses on general GUI grounding, followed by planning and reasoning. Through comprehensive experiments, we demonstrate that Aguvis surpasses previous state-of-the-art methods in both offline and real-world online scenarios, achieving, to our knowledge, the first fully autonomous pure vision GUI agent capable of performing tasks independently without collaboration with external closed-source models. We open-sourced all datasets, models, and training recipes to facilitate future research at https://aguvis-project.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xlang-ai/aguvis", "github_api_added_by": "auto" }, { "date": "2024-12-06", "arxiv_id": "2412.01820", "title": "Towards Universal Soccer Video Understanding", "authors": [ "Jiayuan Rao", "Haoning Wu", "Hao Jiang", "Ya Zhang", "Yanfeng Wang", "Weidi Xie" ], "github": "", "project_page": "", "abstract": "As a globally celebrated sport, soccer has attracted widespread interest from fans all over the world. This paper aims to develop a comprehensive multi-modal framework for soccer video understanding. Specifically, we make the following contributions in this paper: (i) we introduce SoccerReplay-1988, the largest multi-modal soccer dataset to date, featuring videos and detailed annotations from 1,988 complete matches, with an automated annotation pipeline; (ii) we present the first visual-language foundation model in the soccer domain, MatchVision, which leverages spatiotemporal information across soccer videos and excels in various downstream tasks; (iii) we conduct extensive experiments and ablation studies on event classification, commentary generation, and multi-view foul recognition. MatchVision demonstrates state-of-the-art performance on all of them, substantially outperforming existing models, which highlights the superiority of our proposed data and model. We believe that this work will offer a standard paradigm for sports understanding research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04106", "title": "MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation towards Unannotated Modalities", "authors": [ "Haoning Wu", "Ziheng Zhao", "Ya Zhang", "Weidi Xie", "Yanfeng Wang" ], "github": "", "project_page": "", "abstract": "Medical image segmentation has recently demonstrated impressive progress with deep neural networks, yet the heterogeneous modalities and scarcity of mask annotations limit the development of segmentation models on unannotated modalities. This paper investigates a new paradigm for leveraging generative models in medical applications: controllably synthesizing data for unannotated modalities, without requiring registered data pairs. Specifically, we make the following contributions in this paper: (i) we collect and curate a large-scale radiology image-text dataset, MedGen-1M, comprising modality labels, attributes, region, and organ information, along with a subset of organ mask annotations, to support research in controllable medical image generation; (ii) we propose a diffusion-based data engine, termed MRGen, which enables generation conditioned on text prompts and masks, synthesizing MR images for diverse modalities lacking mask annotations, to train segmentation models on unannotated modalities; (iii) we conduct extensive experiments across various modalities, illustrating that our data engine can effectively synthesize training samples and extend MRI segmentation towards unannotated modalities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.03632", "title": "MV-Adapter: Multi-view Consistent Image Generation Made Easy", "authors": [ "Zehuan Huang", "Yuan-Chen Guo", "Haoran Wang", "Ran Yi", "Lizhuang Ma", "Yan-Pei Cao", "Lu Sheng" ], "github": "https://github.com/huanngzh/MV-Adapter", "project_page": "", "abstract": "Existing multi-view image generation methods often make invasive modifications to pre-trained text-to-image (T2I) models and require full fine-tuning, leading to (1) high computational costs, especially with large base models and high-resolution images, and (2) degradation in image quality due to optimization difficulties and scarce high-quality 3D data. In this paper, we propose the first adapter-based solution for multi-view image generation, and introduce MV-Adapter, a versatile plug-and-play adapter that enhances T2I models and their derivatives without altering the original network structure or feature space. By updating fewer parameters, MV-Adapter enables efficient training and preserves the prior knowledge embedded in pre-trained models, mitigating overfitting risks. To efficiently model the 3D geometric knowledge within the adapter, we introduce innovative designs that include duplicated self-attention layers and parallel attention architecture, enabling the adapter to inherit the powerful priors of the pre-trained models to model the novel 3D knowledge. Moreover, we present a unified condition encoder that seamlessly integrates camera parameters and geometric information, facilitating applications such as text- and image-based 3D generation and texturing. MV-Adapter achieves multi-view generation at 768 resolution on Stable Diffusion XL (SDXL), and demonstrates adaptability and versatility. It can also be extended to arbitrary view generation, enabling broader applications. We demonstrate that MV-Adapter sets a new quality standard for multi-view image generation, and opens up new possibilities due to its efficiency, adaptability and versatility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/huanngzh/MV-Adapter", "github_api_added_by": "auto" }, { "date": "2024-12-06", "arxiv_id": "2412.03679", "title": "Evaluating Language Models as Synthetic Data Generators", "authors": [ "Seungone Kim", "Juyoung Suk", "Xiang Yue", "Vijay Viswanathan", "Seongyun Lee", "Yizhong Wang", "Kiril Gashteovski", "Carolin Lawrence", "Sean Welleck", "Graham Neubig" ], "github": "https://github.com/neulab/data-agora", "project_page": "", "abstract": "Given the increasing use of synthetic data in language model (LM) post-training, an LM's ability to generate high-quality data has become nearly as crucial as its ability to solve problems directly. While prior works have focused on developing effective data generation methods, they lack systematic comparison of different LMs as data generators in a unified setting. To address this gap, we propose AgoraBench, a benchmark that provides standardized settings and metrics to evaluate LMs' data generation abilities. Through synthesizing 1.26 million training instances using 6 LMs and training 99 student models, we uncover key insights about LMs' data generation capabilities. First, we observe that LMs exhibit distinct strengths. For instance, GPT-4o excels at generating new problems, while Claude-3.5-Sonnet performs better at enhancing existing ones. Furthermore, our analysis reveals that an LM's data generation ability doesn't necessarily correlate with its problem-solving ability. Instead, multiple intrinsic features of data quality-including response quality, perplexity, and instruction difficulty-collectively serve as better indicators. Finally, we demonstrate that strategic choices in output format and cost-conscious model selection significantly impact data generation effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/neulab/data-agora", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04455", "title": "Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection", "authors": [ "Enshen Zhou", "Qi Su", "Cheng Chi", "Zhizheng Zhang", "Zhongyuan Wang", "Tiejun Huang", "Lu Sheng", "He Wang" ], "github": "", "project_page": "", "abstract": "Automatic detection and prevention of open-set failures are crucial in closed-loop robotic systems. Recent studies often struggle to simultaneously identify unexpected failures reactively after they occur and prevent foreseeable ones proactively. To this end, we propose Code-as-Monitor (CaM), a novel paradigm leveraging the vision-language model (VLM) for both open-set reactive and proactive failure detection. The core of our method is to formulate both tasks as a unified set of spatio-temporal constraint satisfaction problems and use VLM-generated code to evaluate them for real-time monitoring. To enhance the accuracy and efficiency of monitoring, we further introduce constraint elements that abstract constraint-related entities or their parts into compact geometric elements. This approach offers greater generality, simplifies tracking, and facilitates constraint-aware visual programming by leveraging these elements as visual prompts. Experiments show that CaM achieves a 28.7% higher success rate and reduces execution time by 31.8% under severe disturbances compared to baselines across three simulators and a real-world setting. Moreover, CaM can be integrated with open-loop control policies to form closed-loop systems, enabling long-horizon tasks in cluttered scenes with dynamic environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04062", "title": "ZipAR: Accelerating Autoregressive Image Generation through Spatial Locality", "authors": [ "Yefei He", "Feng Chen", "Yuanyu He", "Shaoxuan He", "Hong Zhou", "Kaipeng Zhang", "Bohan Zhuang" ], "github": "https://github.com/ThisisBillhe/ZipAR", "project_page": "", "abstract": "In this paper, we propose ZipAR, a training-free, plug-and-play parallel decoding framework for accelerating auto-regressive (AR) visual generation. The motivation stems from the observation that images exhibit local structures, and spatially distant regions tend to have minimal interdependence. Given a partially decoded set of visual tokens, in addition to the original next-token prediction scheme in the row dimension, the tokens corresponding to spatially adjacent regions in the column dimension can be decoded in parallel, enabling the ``next-set prediction'' paradigm. By decoding multiple tokens simultaneously in a single forward pass, the number of forward passes required to generate an image is significantly reduced, resulting in a substantial improvement in generation efficiency. Experiments demonstrate that ZipAR can reduce the number of model forward passes by up to 91% on the Emu3-Gen model without requiring any additional retraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ThisisBillhe/ZipAR", "github_api_added_by": "auto" }, { "date": "2024-12-06", "arxiv_id": "2412.01169", "title": "OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows", "authors": [ "Shufan Li", "Konstantinos Kallidromitis", "Akash Gokul", "Zichun Liao", "Yusuke Kato", "Kazuki Kozuka", "Aditya Grover" ], "github": "https://github.com/jacklishufan/OmniFlows", "project_page": "", "abstract": "We introduce OmniFlow, a novel generative model designed for any-to-any generation tasks such as text-to-image, text-to-audio, and audio-to-image synthesis. OmniFlow advances the rectified flow (RF) framework used in text-to-image models to handle the joint distribution of multiple modalities. It outperforms previous any-to-any models on a wide range of tasks, such as text-to-image and text-to-audio synthesis. Our work offers three key contributions: First, we extend RF to a multi-modal setting and introduce a novel guidance mechanism, enabling users to flexibly control the alignment between different modalities in the generated outputs. Second, we propose a novel architecture that extends the text-to-image MMDiT architecture of Stable Diffusion 3 and enables audio and text generation. The extended modules can be efficiently pretrained individually and merged with the vanilla text-to-image MMDiT for fine-tuning. Lastly, we conduct a comprehensive study on the design choices of rectified flow transformers for large-scale audio and text generation, providing valuable insights into optimizing performance across diverse modalities. The Code will be available at https://github.com/jacklishufan/OmniFlows.", "github_manual": "", "github_regex": "https://github.com/jacklishufan/OmniFlows", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04315", "title": "Densing Law of LLMs", "authors": [ "Chaojun Xiao", "Jie Cai", "Weilin Zhao", "Guoyang Zeng", "Xu Han", "Zhiyuan Liu", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have emerged as a milestone in artificial intelligence, and their performance can improve as the model size increases. However, this scaling brings great challenges to training and inference efficiency, particularly for deploying LLMs in resource-constrained environments, and the scaling trend is becoming increasingly unsustainable. This paper introduces the concept of ``capacity density'' as a new metric to evaluate the quality of the LLMs across different scales and describes the trend of LLMs in terms of both effectiveness and efficiency. To calculate the capacity density of a given target LLM, we first introduce a set of reference models and develop a scaling law to predict the downstream performance of these reference models based on their parameter sizes. We then define the effective parameter size of the target LLM as the parameter size required by a reference model to achieve equivalent performance, and formalize the capacity density as the ratio of the effective parameter size to the actual parameter size of the target LLM. Capacity density provides a unified framework for assessing both model effectiveness and efficiency. Our further analysis of recent open-source base LLMs reveals an empirical law (the densing law)that the capacity density of LLMs grows exponentially over time. More specifically, using some widely used benchmarks for evaluation, the capacity density of LLMs doubles approximately every three months. The law provides new perspectives to guide future LLM development, emphasizing the importance of improving capacity density to achieve optimal results with minimal computational overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04424", "title": "Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion", "authors": [ "Jiuhai Chen", "Jianwei Yang", "Haiping Wu", "Dianqi Li", "Jianfeng Gao", "Tianyi Zhou", "Bin Xiao" ], "github": "https://github.com/JiuhaiChen/CVPR2025-Florence-VL", "project_page": "", "abstract": "We present Florence-VL, a new family of multimodal large language models (MLLMs) with enriched visual representations produced by Florence-2, a generative vision foundation model. Unlike the widely used CLIP-style vision transformer trained by contrastive learning, Florence-2 can capture different levels and aspects of visual features, which are more versatile to be adapted to diverse downstream tasks. We propose a novel feature-fusion architecture and an innovative training recipe that effectively integrates Florence-2's visual features into pretrained LLMs, such as Phi 3.5 and LLama 3. In particular, we propose \"depth-breath fusion (DBFusion)\" to fuse the visual features extracted from different depths and under multiple prompts. Our model training is composed of end-to-end pretraining of the whole model followed by finetuning of the projection layer and the LLM, on a carefully designed recipe of diverse open-source datasets that include high-quality image captions and instruction-tuning pairs. Our quantitative analysis and visualization of Florence-VL's visual features show its advantages over popular vision encoders on vision-language alignment, where the enriched depth and breath play important roles. Florence-VL achieves significant improvements over existing state-of-the-art MLLMs across various multi-modal and vision-centric benchmarks covering general VQA, perception, hallucination, OCR, Chart, knowledge-intensive understanding, etc. To facilitate future research, our models and the complete training recipe are open-sourced. https://github.com/JiuhaiChen/Florence-VL", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JiuhaiChen/CVPR2025-Florence-VL", "github_api_added_by": "user" }, { "date": "2024-12-06", "arxiv_id": "2412.04431", "title": "Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis", "authors": [ "Jian Han", "Jinlai Liu", "Yi Jiang", "Bin Yan", "Yuqi Zhang", "Zehuan Yuan", "Bingyue Peng", "Xiaobing Liu" ], "github": "", "project_page": "", "abstract": "We present Infinity, a Bitwise Visual AutoRegressive Modeling capable of generating high-resolution, photorealistic images following language instruction. Infinity redefines visual autoregressive model under a bitwise token prediction framework with an infinite-vocabulary tokenizer & classifier and bitwise self-correction mechanism, remarkably improving the generation capacity and details. By theoretically scaling the tokenizer vocabulary size to infinity and concurrently scaling the transformer size, our method significantly unleashes powerful scaling capabilities compared to vanilla VAR. Infinity sets a new record for autoregressive text-to-image models, outperforming top-tier diffusion models like SD3-Medium and SDXL. Notably, Infinity surpasses SD3-Medium by improving the GenEval benchmark score from 0.62 to 0.73 and the ImageReward benchmark score from 0.87 to 0.96, achieving a win rate of 66%. Without extra optimization, Infinity generates a high-quality 1024x1024 image in 0.8 seconds, making it 2.6x faster than SD3-Medium and establishing it as the fastest text-to-image model. Models and codes will be released to promote further exploration of Infinity for visual generation and unified tokenizer modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.01339", "title": "Negative Token Merging: Image-based Adversarial Feature Guidance", "authors": [ "Jaskirat Singh", "Lindsey Li", "Weijia Shi", "Ranjay Krishna", "Yejin Choi", "Pang Wei Koh", "Michael F. Cohen", "Stephen Gould", "Liang Zheng", "Luke Zettlemoyer" ], "github": "", "project_page": "", "abstract": "Text-based adversarial guidance using a negative prompt has emerged as a widely adopted approach to push the output features away from undesired concepts. While useful, performing adversarial guidance using text alone can be insufficient to capture complex visual concepts and avoid undesired visual elements like copyrighted characters. In this paper, for the first time we explore an alternate modality in this direction by performing adversarial guidance directly using visual features from a reference image or other images in a batch. In particular, we introduce negative token merging (NegToMe), a simple but effective training-free approach which performs adversarial guidance by selectively pushing apart matching semantic features (between reference and output generation) during the reverse diffusion process. When used w.r.t. other images in the same batch, we observe that NegToMe significantly increases output diversity (racial, gender, visual) without sacrificing output image quality. Similarly, when used w.r.t. a reference copyrighted asset, NegToMe helps reduce visual similarity with copyrighted content by 34.57%. NegToMe is simple to implement using just few-lines of code, uses only marginally higher (<4%) inference times and generalizes to different diffusion architectures like Flux, which do not natively support the use of a separate negative prompt. Code is available at https://negtome.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04139", "title": "Monet: Mixture of Monosemantic Experts for Transformers", "authors": [ "Jungwoo Park", "Young Jin Ahn", "Kee-Eung Kim", "Jaewoo Kang" ], "github": "https://github.com/dmis-lab/Monet", "project_page": "", "abstract": "Understanding the internal computations of large language models (LLMs) is crucial for aligning them with human values and preventing undesirable behaviors like toxic content generation. However, mechanistic interpretability is hindered by polysemanticity -- where individual neurons respond to multiple, unrelated concepts. While Sparse Autoencoders (SAEs) have attempted to disentangle these features through sparse dictionary learning, they have compromised LLM performance due to reliance on post-hoc reconstruction loss. To address this issue, we introduce Mixture of Monosemantic Experts for Transformers (Monet) architecture, which incorporates sparse dictionary learning directly into end-to-end Mixture-of-Experts pretraining. Our novel expert decomposition method enables scaling the expert count to 262,144 per layer while total parameters scale proportionally to the square root of the number of experts. Our analyses demonstrate mutual exclusivity of knowledge across experts and showcase the parametric knowledge encapsulated within individual experts. Moreover, Monet allows knowledge manipulation over domains, languages, and toxicity mitigation without degrading general performance. Our pursuit of transparent LLMs highlights the potential of scaling expert counts to enhance} mechanistic interpretability and directly resect the internal knowledge to fundamentally adjust} model behavior. The source code and pretrained checkpoints are available at https://github.com/dmis-lab/Monet.", "github_manual": "", "github_regex": "https://github.com/dmis-lab/Monet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.02142", "title": "Personalized Multimodal Large Language Models: A Survey", "authors": [ "Junda Wu", "Hanjia Lyu", "Yu Xia", "Zhehao Zhang", "Joe Barrow", "Ishita Kumar", "Mehrnoosh Mirtaheri", "Hongjie Chen", "Ryan A. Rossi", "Franck Dernoncourt", "Tong Yu", "Ruiyi Zhang", "Jiuxiang Gu", "Nesreen K. Ahmed", "Yu Wang", "Xiang Chen", "Hanieh Deilamsalehy", "Namyong Park", "Sungchul Kim", "Huanrui Yang", "Subrata Mitra", "Zhengmian Hu", "Nedim Lipka", "Dang Nguyen", "Yue Zhao", "Jiebo Luo", "Julian McAuley" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as text, images, and audio, to perform complex tasks with high accuracy. This paper presents a comprehensive survey on personalized multimodal large language models, focusing on their architecture, training methods, and applications. We propose an intuitive taxonomy for categorizing the techniques used to personalize MLLMs to individual users, and discuss the techniques accordingly. Furthermore, we discuss how such techniques can be combined or adapted when appropriate, highlighting their advantages and underlying rationale. We also provide a succinct summary of personalization tasks investigated in existing research, along with the evaluation metrics commonly used. Additionally, we summarize the datasets that are useful for benchmarking personalized MLLMs. Finally, we outline critical open challenges. This survey aims to serve as a valuable resource for researchers and practitioners seeking to understand and advance the development of personalized multimodal large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.03895", "title": "A Noise is Worth Diffusion Guidance", "authors": [ "Donghoon Ahn", "Jiwon Kang", "Sanghyun Lee", "Jaewon Min", "Minjae Kim", "Wooseok Jang", "Hyoungwon Cho", "Sayak Paul", "SeonHwa Kim", "Eunju Cha", "Kyong Hwan Jin", "Seungryong Kim" ], "github": "", "project_page": "", "abstract": "Diffusion models excel in generating high-quality images. However, current diffusion models struggle to produce reliable images without guidance methods, such as classifier-free guidance (CFG). Are guidance methods truly necessary? Observing that noise obtained via diffusion inversion can reconstruct high-quality images without guidance, we focus on the initial noise of the denoising pipeline. By mapping Gaussian noise to `guidance-free noise', we uncover that small low-magnitude low-frequency components significantly enhance the denoising process, removing the need for guidance and thus improving both inference throughput and memory. Expanding on this, we propose \\ours, a novel method that replaces guidance methods with a single refinement of the initial noise. This refined noise enables high-quality image generation without guidance, within the same diffusion pipeline. Our noise-refining model leverages efficient noise-space learning, achieving rapid convergence and strong performance with just 50K text-image pairs. We validate its effectiveness across diverse metrics and analyze how refined noise can eliminate the need for guidance. See our project page: https://cvlab-kaist.github.io/NoiseRefine/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04003", "title": "Marco-LLM: Bridging Languages via Massive Multilingual Training for Cross-Lingual Enhancement", "authors": [ "Lingfeng Ming", "Bo Zeng", "Chenyang Lyu", "Tianqi Shi", "Yu Zhao", "Xue Yang", "Yefeng Liu", "Yiyu Wang", "Linlong Xu", "Yangyang Liu", "Xiaohu Zhao", "Hao Wang", "Heng Liu", "Hao Zhou", "Huifeng Yin", "Zifu Shang", "Haijun Li", "Longyue Wang", "Weihua Luo", "Kaifu Zhang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved remarkable progress in recent years; however, their excellent performance is still largely limited to major world languages, primarily English. Many LLMs continue to face challenges with multilingual tasks, especially when it comes to low-resource languages. To address this issue, we introduced Marco-LLM: Massive multilingual training for cross-lingual enhancement LLM. We have collected a substantial amount of multilingual data for several low-resource languages and conducted extensive continual pre-training using the Qwen2 models. This effort has resulted in a multilingual LLM named Marco-LLM. Through comprehensive evaluations on various multilingual benchmarks, including MMMLU, AGIEval, Belebele, Flores-200, XCOPA and many others, Marco-LLM has demonstrated substantial improvements over state-of-the-art LLMs. Furthermore, Marco-LLM achieved substantial enhancements in any-to-any machine translation tasks, showing the effectiveness of our multilingual LLM. Marco-LLM is a pioneering multilingual LLM designed to not only perform exceptionally well in multilingual tasks, including low-resource languages, but also maintain strong performance in English and other major languages, closing the performance gap between high- and low-resource language capabilities. By bridging languages, this effort demonstrates our dedication to ensuring LLMs work accurately across various languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2411.19574", "title": "KV Shifting Attention Enhances Language Modeling", "authors": [ "Mingyu Xu", "Wei Cheng", "Bingning Wang", "Weipeng Chen" ], "github": "", "project_page": "", "abstract": "The current large language models are mainly based on decode-only structure transformers, which have great in-context learning (ICL) capabilities. It is generally believed that the important foundation of its ICL capability is the induction heads mechanism, which requires at least two layers attention. In order to more efficiently implement the ability of the model's induction, we revisit the induction heads mechanism and proposed a KV shifting attention. We theoretically prove that the KV shifting attention reducing the model's requirements for the depth and width of the induction heads mechanism. Our experimental results demonstrate that KV shifting attention is beneficial to learning induction heads and language modeling, which lead to better performance or faster convergence from toy models to the pre-training models with more than 10 B parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.03304", "title": "Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation", "authors": [ "Shivalika Singh", "Angelika Romanou", "Cl\u00e9mentine Fourrier", "David I. Adelani", "Jian Gang Ngui", "Daniel Vila-Suero", "Peerat Limkonchotiwat", "Kelly Marchisio", "Wei Qi Leong", "Yosephine Susanto", "Raymond Ng", "Shayne Longpre", "Wei-Yin Ko", "Madeline Smith", "Antoine Bosselut", "Alice Oh", "Andre F. T. Martins", "Leshem Choshen", "Daphne Ippolito", "Enzo Ferrante", "Marzieh Fadaee", "Beyza Ermis", "Sara Hooker" ], "github": "", "project_page": "", "abstract": "Cultural biases in multilingual datasets pose significant challenges for their effectiveness as global benchmarks. These biases stem not only from language but also from the cultural knowledge required to interpret questions, reducing the practical utility of translated datasets like MMLU. Furthermore, translation often introduces artifacts that can distort the meaning or clarity of questions in the target language. A common practice in multilingual evaluation is to rely on machine-translated evaluation sets, but simply translating a dataset is insufficient to address these challenges. In this work, we trace the impact of both of these issues on multilingual evaluations and ensuing model performances. Our large-scale evaluation of state-of-the-art open and proprietary models illustrates that progress on MMLU depends heavily on learning Western-centric concepts, with 28% of all questions requiring culturally sensitive knowledge. Moreover, for questions requiring geographic knowledge, an astounding 84.9% focus on either North American or European regions. Rankings of model evaluations change depending on whether they are evaluated on the full portion or the subset of questions annotated as culturally sensitive, showing the distortion to model rankings when blindly relying on translated MMLU. We release Global-MMLU, an improved MMLU with evaluation coverage across 42 languages -- with improved overall quality by engaging with compensated professional and community annotators to verify translation quality while also rigorously evaluating cultural biases present in the original dataset. This comprehensive Global-MMLU set also includes designated subsets labeled as culturally sensitive and culturally agnostic to allow for more holistic, complete evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04146", "title": "AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models", "authors": [ "Xinghui Li", "Qichao Sun", "Pengze Zhang", "Fulong Ye", "Zhichao Liao", "Wanquan Feng", "Songtao Zhao", "Qian He" ], "github": "", "project_page": "", "abstract": "Recent advances in garment-centric image generation from text and image prompts based on diffusion models are impressive. However, existing methods lack support for various combinations of attire, and struggle to preserve the garment details while maintaining faithfulness to the text prompts, limiting their performance across diverse scenarios. In this paper, we focus on a new task, i.e., Multi-Garment Virtual Dressing, and we propose a novel AnyDressing method for customizing characters conditioned on any combination of garments and any personalized text prompts. AnyDressing comprises two primary networks named GarmentsNet and DressingNet, which are respectively dedicated to extracting detailed clothing features and generating customized images. Specifically, we propose an efficient and scalable module called Garment-Specific Feature Extractor in GarmentsNet to individually encode garment textures in parallel. This design prevents garment confusion while ensuring network efficiency. Meanwhile, we design an adaptive Dressing-Attention mechanism and a novel Instance-Level Garment Localization Learning strategy in DressingNet to accurately inject multi-garment features into their corresponding regions. This approach efficiently integrates multi-garment texture cues into generated images and further enhances text-image consistency. Additionally, we introduce a Garment-Enhanced Texture Learning strategy to improve the fine-grained texture details of garments. Thanks to our well-craft design, AnyDressing can serve as a plug-in module to easily integrate with any community control extensions for diffusion models, improving the diversity and controllability of synthesized images. Extensive experiments show that AnyDressing achieves state-of-the-art results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04378", "title": "Discriminative Fine-tuning of LVLMs", "authors": [ "Yassine Ouali", "Adrian Bulat", "Alexandros Xenos", "Anestis Zaganidis", "Ioannis Maniadis Metaxas", "Georgios Tzimiropoulos", "Brais Martinez" ], "github": "", "project_page": "", "abstract": "Contrastively-trained Vision-Language Models (VLMs) like CLIP have become the de facto approach for discriminative vision-language representation learning. However, these models have limited language understanding, often exhibiting a \"bag of words\" behavior. At the same time, Large Vision-Language Models (LVLMs), which combine vision encoders with LLMs, have been shown capable of detailed vision-language reasoning, yet their autoregressive nature renders them less suitable for discriminative tasks. In this work, we propose to combine \"the best of both worlds\": a new training approach for discriminative fine-tuning of LVLMs that results in strong discriminative and compositional capabilities. Essentially, our approach converts a generative LVLM into a discriminative one, unlocking its capability for powerful image-text discrimination combined with enhanced language understanding. Our contributions include: (1) A carefully designed training/optimization framework that utilizes image-text pairs of variable length and granularity for training the model with both contrastive and next-token prediction losses. This is accompanied by ablation studies that justify the necessity of our framework's components. (2) A parameter-efficient adaptation method using a combination of soft prompting and LoRA adapters. (3) Significant improvements over state-of-the-art CLIP-like models of similar size, including standard image-text retrieval benchmarks and notable gains in compositionality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04363", "title": "Challenges in Trustworthy Human Evaluation of Chatbots", "authors": [ "Wenting Zhao", "Alexander M. Rush", "Tanya Goyal" ], "github": "", "project_page": "", "abstract": "Open community-driven platforms like Chatbot Arena that collect user preference data from site visitors have gained a reputation as one of the most trustworthy publicly available benchmarks for LLM performance. While now standard, it is tricky to implement effective guardrails to collect high-quality annotations from humans. In this paper, we demonstrate that three sources of bad annotations, both malicious and otherwise, can corrupt the reliability of open leaderboard rankings. In particular, we show that only 10\\% of poor quality votes by apathetic (site visitors not appropriately incentivized to give correct votes) or adversarial (bad actors seeking to inflate the ranking of a target model) annotators can change the rankings of models by up to 5 places on the leaderboard. Finally, we discuss open challenges in ensuring high-quality human annotations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04448", "title": "MEMO: Memory-Guided Diffusion for Expressive Talking Video Generation", "authors": [ "Longtao Zheng", "Yifan Zhang", "Hanzhong Guo", "Jiachun Pan", "Zhenxiong Tan", "Jiahao Lu", "Chuanxin Tang", "Bo An", "Shuicheng Yan" ], "github": "", "project_page": "", "abstract": "Recent advances in video diffusion models have unlocked new potential for realistic audio-driven talking video generation. However, achieving seamless audio-lip synchronization, maintaining long-term identity consistency, and producing natural, audio-aligned expressions in generated talking videos remain significant challenges. To address these challenges, we propose Memory-guided EMOtion-aware diffusion (MEMO), an end-to-end audio-driven portrait animation approach to generate identity-consistent and expressive talking videos. Our approach is built around two key modules: (1) a memory-guided temporal module, which enhances long-term identity consistency and motion smoothness by developing memory states to store information from a longer past context to guide temporal modeling via linear attention; and (2) an emotion-aware audio module, which replaces traditional cross attention with multi-modal attention to enhance audio-video interaction, while detecting emotions from audio to refine facial expressions via emotion adaptive layer norm. Extensive quantitative and qualitative results demonstrate that MEMO generates more realistic talking videos across diverse image and audio types, outperforming state-of-the-art methods in overall quality, audio-lip synchronization, identity consistency, and expression-emotion alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.03704", "title": "Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension", "authors": [ "Wang Xiyao", "Yang Zhengyuan", "Li Linjie", "Lu Hongjin", "Xu Yuancheng", "Lin Chung-Ching Lin", "Lin Kevin", "Huang Furong", "Wang Lijuan" ], "github": "https://github.com/si0wang/VisVM", "project_page": "", "abstract": "Despite significant advancements in vision-language models (VLMs), there lacks effective approaches to enhance response quality by scaling inference-time computation. This capability is known to be a core step towards the self-improving models in recent large language model studies. In this paper, we present Vision Value Model (VisVM) that can guide VLM inference-time search to generate responses with better visual comprehension. Specifically, VisVM not only evaluates the generated sentence quality in the current search step, but also anticipates the quality of subsequent sentences that may result from the current step, thus providing a long-term value. In this way, VisVM steers VLMs away from generating sentences prone to hallucinations or insufficient detail, thereby producing higher quality responses. Experimental results demonstrate that VisVM-guided search significantly enhances VLMs' ability to generate descriptive captions with richer visual details and fewer hallucinations, compared with greedy decoding and search methods with other visual reward signals. Furthermore, we find that self-training the model with the VisVM-guided captions improve VLM's performance across a wide range of multimodal benchmarks, indicating the potential for developing self-improving VLMs. Our value model and code are available at https://github.com/si0wang/VisVM.", "github_manual": "", "github_regex": "https://github.com/si0wang/VisVM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04262", "title": "SynFinTabs: A Dataset of Synthetic Financial Tables for Information and Table Extraction", "authors": [ "Ethan Bradley", "Muhammad Roman", "Karen Rafferty", "Barry Devereux" ], "github": "https://github.com/ethanbradley/synfintabgen", "project_page": "", "abstract": "Table extraction from document images is a challenging AI problem, and labelled data for many content domains is difficult to come by. Existing table extraction datasets often focus on scientific tables due to the vast amount of academic articles that are readily available, along with their source code. However, there are significant layout and typographical differences between tables found across scientific, financial, and other domains. Current datasets often lack the words, and their positions, contained within the tables, instead relying on unreliable OCR to extract these features for training modern machine learning models on natural language processing tasks. Therefore, there is a need for a more general method of obtaining labelled data. We present SynFinTabs, a large-scale, labelled dataset of synthetic financial tables. Our hope is that our method of generating these synthetic tables is transferable to other domains. To demonstrate the effectiveness of our dataset in training models to extract information from table images, we create FinTabQA, a layout large language model trained on an extractive question-answering task. We test our model using real-world financial tables and compare it to a state-of-the-art generative model and discuss the results. We make the dataset, model, and dataset generation code publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ethanbradley/synfintabgen", "github_api_added_by": "auto" }, { "date": "2024-12-06", "arxiv_id": "2412.04462", "title": "4Real-Video: Learning Generalizable Photo-Realistic 4D Video Diffusion", "authors": [ "Chaoyang Wang", "Peiye Zhuang", "Tuan Duc Ngo", "Willi Menapace", "Aliaksandr Siarohin", "Michael Vasilkovsky", "Ivan Skorokhodov", "Sergey Tulyakov", "Peter Wonka", "Hsin-Ying Lee" ], "github": "", "project_page": "", "abstract": "We propose 4Real-Video, a novel framework for generating 4D videos, organized as a grid of video frames with both time and viewpoint axes. In this grid, each row contains frames sharing the same timestep, while each column contains frames from the same viewpoint. We propose a novel two-stream architecture. One stream performs viewpoint updates on columns, and the other stream performs temporal updates on rows. After each diffusion transformer layer, a synchronization layer exchanges information between the two token streams. We propose two implementations of the synchronization layer, using either hard or soft synchronization. This feedforward architecture improves upon previous work in three ways: higher inference speed, enhanced visual quality (measured by FVD, CLIP, and VideoScore), and improved temporal and viewpoint consistency (measured by VideoScore and Dust3R-Confidence).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04449", "title": "p-MoD: Building Mixture-of-Depths MLLMs via Progressive Ratio Decay", "authors": [ "Jun Zhang", "Desen Meng", "Ji Qi", "Zhenpeng Huang", "Tao Wu", "Limin Wang" ], "github": "https://github.com/mcg-nju/p-mod", "project_page": "", "abstract": "Despite the remarkable performance of multimodal large language models (MLLMs) across diverse tasks, the substantial training and inference costs impede their advancement. The majority of computation stems from the overwhelming volume of vision tokens processed by the transformer decoder. In this paper, we propose to build efficient MLLMs by leveraging the Mixture-of-Depths (MoD) mechanism, where each transformer decoder layer selects essential vision tokens to process while skipping redundant ones. However, integrating MoD into MLLMs is non-trivial. To address the challenges of training and inference stability as well as limited training data, we adapt the MoD module with two novel designs: tanh-gated weight normalization (TanhNorm) and symmetric token reweighting (STRing). Moreover, we observe that vision tokens exhibit higher redundancy in deeper layer and thus design a progressive ratio decay (PRD) strategy, which gradually reduces the token retention ratio layer by layer, employing a shifted cosine schedule. This crucial design fully unleashes the potential of MoD, significantly boosting the efficiency and performance of our models. To validate the effectiveness of our approach, we conduct extensive experiments with two baseline models across 14 benchmarks. Our model, p-MoD, matches or even surpasses the performance of the baseline models, with only 55.6% TFLOPs and 53.8% KV cache storage during inference, and 77.7% GPU hours during training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mcg-nju/p-mod", "github_api_added_by": "auto" }, { "date": "2024-12-06", "arxiv_id": "2412.04468", "title": "NVILA: Efficient Frontier Visual Language Models", "authors": [ "Zhijian Liu", "Ligeng Zhu", "Baifeng Shi", "Zhuoyang Zhang", "Yuming Lou", "Shang Yang", "Haocheng Xi", "Shiyi Cao", "Yuxian Gu", "Dacheng Li", "Xiuyu Li", "Yunhao Fang", "Yukang Chen", "Cheng-Yu Hsieh", "De-An Huang", "An-Chieh Cheng", "Vishwesh Nath", "Jinyi Hu", "Sifei Liu", "Ranjay Krishna", "Daguang Xu", "Xiaolong Wang", "Pavlo Molchanov", "Jan Kautz", "Hongxu Yin", "Song Han", "Yao Lu" ], "github": "https://github.com/NVlabs/VILA", "project_page": "", "abstract": "Visual language models (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA, a family of open VLMs designed to optimize both efficiency and accuracy. Building on top of VILA, we improve its model architecture by first scaling up the spatial and temporal resolutions, and then compressing visual tokens. This \"scale-then-compress\" approach enables NVILA to efficiently process high-resolution images and long videos. We also conduct a systematic investigation to enhance the efficiency of NVILA throughout its entire lifecycle, from training and fine-tuning to deployment. NVILA matches or surpasses the accuracy of many leading open and proprietary VLMs across a wide range of image and video benchmarks. At the same time, it reduces training costs by 4.5X, fine-tuning memory usage by 3.4X, pre-filling latency by 1.6-2.2X, and decoding latency by 1.2-2.8X. We will soon make our code and models available to facilitate reproducibility.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVlabs/VILA", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-06", "arxiv_id": "2412.04403", "title": "Establishing Task Scaling Laws via Compute-Efficient Model Ladders", "authors": [ "Akshita Bhagia", "Jiacheng Liu", "Alexander Wettig", "David Heineman", "Oyvind Tafjord", "Ananya Harsh Jha", "Luca Soldaini", "Noah A. Smith", "Dirk Groeneveld", "Pang Wei Koh", "Jesse Dodge", "Hannaneh Hajishirzi" ], "github": "", "project_page": "", "abstract": "We develop task scaling laws and model ladders to predict the individual task performance of pretrained language models (LMs) in the overtrained setting. Standard power laws for language modeling loss cannot accurately model task performance. Therefore, we leverage a two-step prediction approach: first use model and data size to predict a task-specific loss, and then use this task loss to predict task performance. We train a set of small-scale \"ladder\" models, collect data points to fit the parameterized functions of the two prediction steps, and make predictions for two target models: a 7B model trained to 4T tokens and a 13B model trained to 5T tokens. Training the ladder models only costs 1% of the compute used for the target models. On four multiple-choice tasks written in ranked classification format, we can predict the accuracy of both target models within 2 points of absolute error. We have higher prediction error on four other tasks (average absolute error 6.9) and find that these are often tasks with higher variance in task metrics. We also find that using less compute to train fewer ladder models tends to deteriorate predictions. Finally, we empirically show that our design choices and the two-step approach lead to superior performance in establishing scaling laws.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-09", "arxiv_id": "2412.04440", "title": "GenMAC: Compositional Text-to-Video Generation with Multi-Agent Collaboration", "authors": [ "Kaiyi Huang", "Yukun Huang", "Xuefei Ning", "Zinan Lin", "Yu Wang", "Xihui Liu" ], "github": "https://github.com/Karine-Huang/GenMAC", "project_page": "", "abstract": "Text-to-video generation models have shown significant progress in the recent years. However, they still struggle with generating complex dynamic scenes based on compositional text prompts, such as attribute binding for multiple objects, temporal dynamics associated with different objects, and interactions between objects. Our key motivation is that complex tasks can be decomposed into simpler ones, each handled by a role-specialized MLLM agent. Multiple agents can collaborate together to achieve collective intelligence for complex goals. We propose GenMAC, an iterative, multi-agent framework that enables compositional text-to-video generation. The collaborative workflow includes three stages: Design, Generation, and Redesign, with an iterative loop between the Generation and Redesign stages to progressively verify and refine the generated videos. The Redesign stage is the most challenging stage that aims to verify the generated videos, suggest corrections, and redesign the text prompts, frame-wise layouts, and guidance scales for the next iteration of generation. To avoid hallucination of a single MLLM agent, we decompose this stage to four sequentially-executed MLLM-based agents: verification agent, suggestion agent, correction agent, and output structuring agent. Furthermore, to tackle diverse scenarios of compositional text-to-video generation, we design a self-routing mechanism to adaptively select the proper correction agent from a collection of correction agents each specialized for one scenario. Extensive experiments demonstrate the effectiveness of GenMAC, achieving state-of-the art performance in compositional text-to-video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Karine-Huang/GenMAC", "github_api_added_by": "auto" }, { "date": "2024-12-09", "arxiv_id": "2412.04445", "title": "Moto: Latent Motion Token as the Bridging Language for Robot Manipulation", "authors": [ "Yi Chen", "Yuying Ge", "Yizhuo Li", "Yixiao Ge", "Mingyu Ding", "Ying Shan", "Xihui Liu" ], "github": "https://github.com/tencentarc/moto", "project_page": "", "abstract": "Recent developments in Large Language Models pre-trained on extensive corpora have shown significant success in various natural language processing tasks with minimal fine-tuning. This success offers new promise for robotics, which has long been constrained by the high cost of action-labeled data. We ask: given the abundant video data containing interaction-related knowledge available as a rich \"corpus\", can a similar generative pre-training approach be effectively applied to enhance robot learning? The key challenge is to identify an effective representation for autoregressive pre-training that benefits robot manipulation tasks. Inspired by the way humans learn new skills through observing dynamic environments, we propose that effective robotic learning should emphasize motion-related knowledge, which is closely tied to low-level actions and is hardware-agnostic, facilitating the transfer of learned motions to actual robot actions. To this end, we introduce Moto, which converts video content into latent Motion Token sequences by a Latent Motion Tokenizer, learning a bridging \"language\" of motion from videos in an unsupervised manner. We pre-train Moto-GPT through motion token autoregression, enabling it to capture diverse visual motion knowledge. After pre-training, Moto-GPT demonstrates the promising ability to produce semantically interpretable motion tokens, predict plausible motion trajectories, and assess trajectory rationality through output likelihood. To transfer learned motion priors to real robot actions, we implement a co-fine-tuning strategy that seamlessly bridges latent motion token prediction and real robot control. Extensive experiments show that the fine-tuned Moto-GPT exhibits superior robustness and efficiency on robot manipulation benchmarks, underscoring its effectiveness in transferring knowledge from video data to downstream visual manipulation tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tencentarc/moto", "github_api_added_by": "auto" }, { "date": "2024-12-09", "arxiv_id": "2412.05237", "title": "MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale", "authors": [ "Jarvis Guo", "Tuney Zheng", "Yuelin Bai", "Bo Li", "Yubo Wang", "King Zhu", "Yizhi Li", "Graham Neubig", "Wenhu Chen", "Xiang Yue" ], "github": "https://github.com/mammoth-vl/mammoth-vl", "project_page": "", "abstract": "Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks. However, their reasoning capabilities remain constrained by existing instruction-tuning datasets, which were predominately repurposed from academic datasets such as VQA, AI2D, and ChartQA. These datasets target simplistic tasks, and only provide phrase-level answers without any intermediate rationales. To address these challenges, we introduce a scalable and cost-effective method to construct a large-scale multimodal instruction-tuning dataset with rich intermediate rationales designed to elicit CoT reasoning. Using only open models, we create a dataset containing 12M instruction-response pairs to cover diverse, reasoning-intensive tasks with detailed and faithful rationales. Experiments demonstrate that training MLLMs on this dataset significantly improves reasoning capabilities, achieving state-of-the-art performance on benchmarks such as MathVerse (+8.1%), MMMU-Pro (+7%), and MuirBench (+13.3%). Additionally, the model demonstrates notable improvements of up to 4% on non-reasoning-based benchmarks. Ablation studies further highlight the importance of key components, such as rewriting and self-filtering, in the dataset construction process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mammoth-vl/mammoth-vl", "github_api_added_by": "auto" }, { "date": "2024-12-09", "arxiv_id": "2412.04862", "title": "EXAONE 3.5: Series of Large Language Models for Real-world Use Cases", "authors": [ "LG AI Research", "Soyoung An", "Kyunghoon Bae", "Eunbi Choi", "Kibong Choi", "Stanley Jungkyu Choi", "Seokhee Hong", "Junwon Hwang", "Hyojin Jeon", "Gerrard Jeongwon Jo", "Hyunjik Jo", "Jiyeon Jung", "Yountae Jung", "Hyosang Kim", "Joonkee Kim", "Seonghwan Kim", "Soyeon Kim", "Sunkyoung Kim", "Yireun Kim", "Yongil Kim", "Youchul Kim", "Edward Hwayoung Lee", "Haeju Lee", "Honglak Lee", "Jinsik Lee", "Kyungmin Lee", "Woohyung Lim", "Sangha Park", "Sooyoun Park", "Yongmin Park", "Sihoon Yang", "Heuiyeen Yeen", "Hyeongu Yun" ], "github": "", "project_page": "", "abstract": "This technical report introduces the EXAONE 3.5 instruction-tuned language models, developed and released by LG AI Research. The EXAONE 3.5 language models are offered in three configurations: 32B, 7.8B, and 2.4B. These models feature several standout capabilities: 1) exceptional instruction following capabilities in real-world scenarios, achieving the highest scores across seven benchmarks, 2) outstanding long-context comprehension, attaining the top performance in four benchmarks, and 3) competitive results compared to state-of-the-art open models of similar sizes across nine general benchmarks. The EXAONE 3.5 language models are open to anyone for research purposes and can be downloaded from https://huggingface.co/LGAI-EXAONE. For commercial use, please reach out to the official contact point of LG AI Research: contact_us@lgresearch.ai.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-09", "arxiv_id": "2412.04301", "title": "SwiftEdit: Lightning Fast Text-Guided Image Editing via One-Step Diffusion", "authors": [ "Trong-Tung Nguyen", "Quang Nguyen", "Khoi Nguyen", "Anh Tran", "Cuong Pham" ], "github": "", "project_page": "", "abstract": "Recent advances in text-guided image editing enable users to perform image edits through simple text inputs, leveraging the extensive priors of multi-step diffusion-based text-to-image models. However, these methods often fall short of the speed demands required for real-world and on-device applications due to the costly multi-step inversion and sampling process involved. In response to this, we introduce SwiftEdit, a simple yet highly efficient editing tool that achieve instant text-guided image editing (in 0.23s). The advancement of SwiftEdit lies in its two novel contributions: a one-step inversion framework that enables one-step image reconstruction via inversion and a mask-guided editing technique with our proposed attention rescaling mechanism to perform localized image editing. Extensive experiments are provided to demonstrate the effectiveness and efficiency of SwiftEdit. In particular, SwiftEdit enables instant text-guided image editing, which is extremely faster than previous multi-step methods (at least 50 times faster) while maintain a competitive performance in editing results. Our project page is at: https://swift-edit.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-09", "arxiv_id": "2412.05270", "title": "APOLLO: SGD-like Memory, AdamW-level Performance", "authors": [ "Hanqing Zhu", "Zhenyu Zhang", "Wenyan Cong", "Xi Liu", "Sem Park", "Vikas Chandra", "Bo Long", "David Z. Pan", "Zhangyang Wang", "Jinwon Lee" ], "github": "https://github.com/zhuhanqing/APOLLO", "project_page": "", "abstract": "Large language models (LLMs) are notoriously memory-intensive during training, particularly with the popular AdamW optimizer. This memory burden necessitates using more or higher-end GPUs or reducing batch sizes, limiting training scalability and throughput. To address this, various memory-efficient optimizers have been proposed to reduce optimizer memory usage. However, they face critical challenges: (i) reliance on costly SVD operations; (ii) significant performance trade-offs compared to AdamW; and (iii) still substantial optimizer memory overhead to maintain competitive performance. In this work, we identify that AdamW's learning rate adaptation rule can be effectively coarsened as a structured learning rate update. Based on this insight, we propose Approximated Gradient Scaling for Memory-Efficient LLM Optimization (APOLLO), which approximates learning rate scaling using an auxiliary low-rank optimizer state based on pure random projection. This structured learning rate update rule makes APOLLO highly tolerant to further memory reductions while delivering comparable pre-training performance. Even its rank-1 variant, APOLLO-Mini, achieves superior pre-training performance compared to AdamW with SGD-level memory costs. Extensive experiments demonstrate that the APOLLO series performs on-par with or better than AdamW, while achieving greater memory savings by nearly eliminating the optimization states of AdamW. These savings provide significant system-level benefits: (1) Enhanced Throughput: 3x throughput on an 8xA100-80GB setup compared to AdamW by supporting 4x larger batch sizes. (2) Improved Model Scalability: Pre-training LLaMA-13B with naive DDP on A100-80GB GPUs without system-level optimizations. (3) Low-End GPU Friendly Pre-training: Pre-training LLaMA-7B on a single GPU using less than 12 GB of memory with weight quantization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhuhanqing/APOLLO", "github_api_added_by": "auto" }, { "date": "2024-12-09", "arxiv_id": "2412.05271", "title": "Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling", "authors": [ "Zhe Chen", "Weiyun Wang", "Yue Cao", "Yangzhou Liu", "Zhangwei Gao", "Erfei Cui", "Jinguo Zhu", "Shenglong Ye", "Hao Tian", "Zhaoyang Liu", "Lixin Gu", "Xuehui Wang", "Qingyun Li", "Yimin Ren", "Zixuan Chen", "Jiapeng Luo", "Jiahao Wang", "Tan Jiang", "Bo Wang", "Conghui He", "Botian Shi", "Xingcheng Zhang", "Han Lv", "Yi Wang", "Wenqi Shao", "Pei Chu", "Zhongying Tu", "Tong He", "Zhiyong Wu", "Huipeng Deng", "Jiaye Ge", "Kai Chen", "Min Dou", "Lewei Lu", "Xizhou Zhu", "Tong Lu", "Dahua Lin", "Yu Qiao", "Jifeng Dai", "Wenhai Wang" ], "github": "", "project_page": "", "abstract": "We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. In this work, we delve into the relationship between model scaling and performance, systematically exploring the performance trends in vision encoders, language models, dataset sizes, and test-time configurations. Through extensive evaluations on a wide range of benchmarks, including multi-discipline reasoning, document understanding, multi-image / video understanding, real-world comprehension, multimodal hallucination detection, visual grounding, multilingual capabilities, and pure language processing, InternVL 2.5 exhibits competitive performance, rivaling leading commercial models such as GPT-4o and Claude-3.5-Sonnet. Notably, our model is the first open-source MLLMs to surpass 70% on the MMMU benchmark, achieving a 3.7-point improvement through Chain-of-Thought (CoT) reasoning and showcasing strong potential for test-time scaling. We hope this model contributes to the open-source community by setting new standards for developing and applying multimodal AI systems. HuggingFace demo see https://huggingface.co/spaces/OpenGVLab/InternVL", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-09", "arxiv_id": "2412.04814", "title": "LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment", "authors": [ "Yibin Wang", "Zhiyu Tan", "Junyan Wang", "Xiaomeng Yang", "Cheng Jin", "Hao Li" ], "github": "", "project_page": "", "abstract": "Recent advancements in text-to-video (T2V) generative models have shown impressive capabilities. However, these models are still inadequate in aligning synthesized videos with human preferences (e.g., accurately reflecting text descriptions), which is particularly difficult to address, as human preferences are inherently subjective and challenging to formalize as objective functions. Therefore, this paper proposes LiFT, a novel fine-tuning method leveraging human feedback for T2V model alignment. Specifically, we first construct a Human Rating Annotation dataset, LiFT-HRA, consisting of approximately 10k human annotations, each including a score and its corresponding rationale. Based on this, we train a reward model LiFT-Critic to learn reward function effectively, which serves as a proxy for human judgment, measuring the alignment between given videos and human expectations. Lastly, we leverage the learned reward function to align the T2V model by maximizing the reward-weighted likelihood. As a case study, we apply our pipeline to CogVideoX-2B, showing that the fine-tuned model outperforms the CogVideoX-5B across all 16 metrics, highlighting the potential of human feedback in improving the alignment and quality of synthesized videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-09", "arxiv_id": "2412.03428", "title": "2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains for High-Fidelity Indoor Scene Reconstruction", "authors": [ "Wanting Zhang", "Haodong Xiang", "Zhichao Liao", "Xiansong Lai", "Xinghui Li", "Long Zeng" ], "github": "https://github.com/Valentina-Zhang/2DGS-Room", "project_page": "", "abstract": "The reconstruction of indoor scenes remains challenging due to the inherent complexity of spatial structures and the prevalence of textureless regions. Recent advancements in 3D Gaussian Splatting have improved novel view synthesis with accelerated processing but have yet to deliver comparable performance in surface reconstruction. In this paper, we introduce 2DGS-Room, a novel method leveraging 2D Gaussian Splatting for high-fidelity indoor scene reconstruction. Specifically, we employ a seed-guided mechanism to control the distribution of 2D Gaussians, with the density of seed points dynamically optimized through adaptive growth and pruning mechanisms. To further improve geometric accuracy, we incorporate monocular depth and normal priors to provide constraints for details and textureless regions respectively. Additionally, multi-view consistency constraints are employed to mitigate artifacts and further enhance reconstruction quality. Extensive experiments on ScanNet and ScanNet++ datasets demonstrate that our method achieves state-of-the-art performance in indoor scene reconstruction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Valentina-Zhang/2DGS-Room", "github_api_added_by": "auto" }, { "date": "2024-12-09", "arxiv_id": "2412.05263", "title": "Mind the Time: Temporally-Controlled Multi-Event Video Generation", "authors": [ "Ziyi Wu", "Aliaksandr Siarohin", "Willi Menapace", "Ivan Skorokhodov", "Yuwei Fang", "Varnith Chordia", "Igor Gilitschenski", "Sergey Tulyakov" ], "github": "", "project_page": "", "abstract": "Real-world videos consist of sequences of events. Generating such sequences with precise temporal control is infeasible with existing video generators that rely on a single paragraph of text as input. When tasked with generating multiple events described using a single prompt, such methods often ignore some of the events or fail to arrange them in the correct order. To address this limitation, we present MinT, a multi-event video generator with temporal control. Our key insight is to bind each event to a specific period in the generated video, which allows the model to focus on one event at a time. To enable time-aware interactions between event captions and video tokens, we design a time-based positional encoding method, dubbed ReRoPE. This encoding helps to guide the cross-attention operation. By fine-tuning a pre-trained video diffusion transformer on temporally grounded data, our approach produces coherent videos with smoothly connected events. For the first time in the literature, our model offers control over the timing of events in generated videos. Extensive experiments demonstrate that MinT outperforms existing open-source models by a large margin.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-09", "arxiv_id": "2412.04905", "title": "DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling", "authors": [ "Minzheng Wang", "Xinghua Zhang", "Kun Chen", "Nan Xu", "Haiyang Yu", "Fei Huang", "Wenji Mao", "Yongbin Li" ], "github": "https://github.com/mozerwang/demo", "project_page": "", "abstract": "Large language models (LLMs) have made dialogue one of the central modes of human-machine interaction, leading to the accumulation of vast amounts of conversation logs and increasing demand for dialogue generation. A conversational life-cycle spans from the Prelude through the Interlocution to the Epilogue, encompassing various elements. Despite the existence of numerous dialogue-related studies, there is a lack of benchmarks that encompass comprehensive dialogue elements, hindering precise modeling and systematic evaluation. To bridge this gap, we introduce an innovative research task Dialogue Element MOdeling, including Element Awareness and Dialogue Agent Interaction, and propose a novel benchmark, DEMO, designed for a comprehensive dialogue modeling and assessment. Inspired by imitation learning, we further build the agent which possesses the adept ability to model dialogue elements based on the DEMO benchmark. Extensive experiments indicate that existing LLMs still exhibit considerable potential for enhancement, and our DEMO agent has superior performance in both in-domain and out-of-domain tasks.", "github_manual": "https://github.com/mozerwang/demo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-09", "arxiv_id": "2412.04887", "title": "Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large Scene Reconstruction", "authors": [ "Jixuan Fan", "Wanhua Li", "Yifei Han", "Yansong Tang" ], "github": "https://github.com/Jixuan-Fan/Momentum-GS", "project_page": "", "abstract": "3D Gaussian Splatting has demonstrated notable success in large-scale scene reconstruction, but challenges persist due to high training memory consumption and storage overhead. Hybrid representations that integrate implicit and explicit features offer a way to mitigate these limitations. However, when applied in parallelized block-wise training, two critical issues arise since reconstruction accuracy deteriorates due to reduced data diversity when training each block independently, and parallel training restricts the number of divided blocks to the available number of GPUs. To address these issues, we propose Momentum-GS, a novel approach that leverages momentum-based self-distillation to promote consistency and accuracy across the blocks while decoupling the number of blocks from the physical GPU count. Our method maintains a teacher Gaussian decoder updated with momentum, ensuring a stable reference during training. This teacher provides each block with global guidance in a self-distillation manner, promoting spatial consistency in reconstruction. To further ensure consistency across the blocks, we incorporate block weighting, dynamically adjusting each block's weight according to its reconstruction accuracy. Extensive experiments on large-scale scenes show that our method consistently outperforms existing techniques, achieving a 12.8% improvement in LPIPS over CityGaussian with much fewer divided blocks and establishing a new state of the art. Project page: https://jixuan-fan.github.io/Momentum-GS_Page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Jixuan-Fan/Momentum-GS", "github_api_added_by": "auto" }, { "date": "2024-12-09", "arxiv_id": "2412.04827", "title": "PanoDreamer: 3D Panorama Synthesis from a Single Image", "authors": [ "Avinash Paliwal", "Xilong Zhou", "Andrii Tsarov", "Nima Khademi Kalantari" ], "github": "https://github.com/avinashpaliwal/PanoDreamer", "project_page": "", "abstract": "In this paper, we present PanoDreamer, a novel method for producing a coherent 360^circ 3D scene from a single input image. Unlike existing methods that generate the scene sequentially, we frame the problem as single-image panorama and depth estimation. Once the coherent panoramic image and its corresponding depth are obtained, the scene can be reconstructed by inpainting the small occluded regions and projecting them into 3D space. Our key contribution is formulating single-image panorama and depth estimation as two optimization tasks and introducing alternating minimization strategies to effectively solve their objectives. We demonstrate that our approach outperforms existing techniques in single-image 360^circ scene reconstruction in terms of consistency and overall quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/avinashpaliwal/PanoDreamer", "github_api_added_by": "auto" }, { "date": "2024-12-09", "arxiv_id": "2412.05243", "title": "CompCap: Improving Multimodal Large Language Models with Composite Captions", "authors": [ "Xiaohui Chen", "Satya Narayan Shukla", "Mahmoud Azab", "Aashu Singh", "Qifan Wang", "David Yang", "ShengYun Peng", "Hanchao Yu", "Shen Yan", "Xuewen Zhang", "Baosheng He" ], "github": "", "project_page": "", "abstract": "How well can Multimodal Large Language Models (MLLMs) understand composite images? Composite images (CIs) are synthetic visuals created by merging multiple visual elements, such as charts, posters, or screenshots, rather than being captured directly by a camera. While CIs are prevalent in real-world applications, recent MLLM developments have primarily focused on interpreting natural images (NIs). Our research reveals that current MLLMs face significant challenges in accurately understanding CIs, often struggling to extract information or perform complex reasoning based on these images. We find that existing training data for CIs are mostly formatted for question-answer tasks (e.g., in datasets like ChartQA and ScienceQA), while high-quality image-caption datasets, critical for robust vision-language alignment, are only available for NIs. To bridge this gap, we introduce Composite Captions (CompCap), a flexible framework that leverages Large Language Models (LLMs) and automation tools to synthesize CIs with accurate and detailed captions. Using CompCap, we curate CompCap-118K, a dataset containing 118K image-caption pairs across six CI types. We validate the effectiveness of CompCap-118K by supervised fine-tuning MLLMs of three sizes: xGen-MM-inst.-4B and LLaVA-NeXT-Vicuna-7B/13B. Empirical results show that CompCap-118K significantly enhances MLLMs' understanding of CIs, yielding average gains of 1.7%, 2.0%, and 2.9% across eleven benchmarks, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-09", "arxiv_id": "2412.04626", "title": "BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks", "authors": [ "Juan Rodriguez", "Xiangru Jian", "Siba Smarak Panigrahi", "Tianyu Zhang", "Aarash Feizi", "Abhay Puri", "Akshay Kalkunte", "Fran\u00e7ois Savard", "Ahmed Masry", "Shravan Nayak", "Rabiul Awal", "Mahsa Massoud", "Amirhossein Abaskohi", "Zichao Li", "Suyuchen Wang", "Pierre-Andr\u00e9 No\u00ebl", "Mats Leon Richter", "Saverio Vadacchino", "Shubbam Agarwal", "Sanket Biswas", "Sara Shanian", "Ying Zhang", "Noah Bolger", "Kurt MacDonald", "Simon Fauvel", "Sathwik Tejaswi", "Srinivas Sunkara", "Joao Monteiro", "Krishnamurthy DJ Dvijotham", "Torsten Scholak", "Nicolas Chapados", "Sepideh Kharagani", "Sean Hughes", "M. \u00d6zsu", "Siva Reddy", "Marco Pedersoli", "Yoshua Bengio", "Christopher Pal", "Issam Laradji", "Spandanna Gella", "Perouz Taslakian", "David Vazquez", "Sai Rajeswar" ], "github": "https://github.com/KyuDan1/TeXBLEU", "project_page": "", "abstract": "Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows, extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io .", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KyuDan1/TeXBLEU", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-09", "arxiv_id": "2412.05718", "title": "RL Zero: Zero-Shot Language to Behaviors without any Supervision", "authors": [ "Harshit Sikchi", "Siddhant Agarwal", "Pranaya Jajoo", "Samyak Parajuli", "Caleb Chuck", "Max Rudolph", "Peter Stone", "Amy Zhang", "Scott Niekum" ], "github": "", "project_page": "", "abstract": "Rewards remain an uninterpretable way to specify tasks for Reinforcement Learning, as humans are often unable to predict the optimal behavior of any given reward function, leading to poor reward design and reward hacking. Language presents an appealing way to communicate intent to agents and bypass reward design, but prior efforts to do so have been limited by costly and unscalable labeling efforts. In this work, we propose a method for a completely unsupervised alternative to grounding language instructions in a zero-shot manner to obtain policies. We present a solution that takes the form of imagine, project, and imitate: The agent imagines the observation sequence corresponding to the language description of a task, projects the imagined sequence to our target domain, and grounds it to a policy. Video-language models allow us to imagine task descriptions that leverage knowledge of tasks learned from internet-scale video-text mappings. The challenge remains to ground these generations to a policy. In this work, we show that we can achieve a zero-shot language-to-behavior policy by first grounding the imagined sequences in real observations of an unsupervised RL agent and using a closed-form solution to imitation learning that allows the RL agent to mimic the grounded observations. Our method, RLZero, is the first to our knowledge to show zero-shot language to behavior generation abilities without any supervision on a variety of tasks on simulated domains. We further show that RLZero can also generate policies zero-shot from cross-embodied videos such as those scraped from YouTube.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.06559", "title": "ProcessBench: Identifying Process Errors in Mathematical Reasoning", "authors": [ "Chujie Zheng", "Zhenru Zhang", "Beichen Zhang", "Runji Lin", "Keming Lu", "Bowen Yu", "Dayiheng Liu", "Jingren Zhou", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "As language models regularly make mistakes when solving math problems, automated identification of errors in the reasoning process becomes increasingly significant for their scalable oversight. In this paper, we introduce ProcessBench for measuring the ability to identify erroneous steps in mathematical reasoning. It consists of 3,400 test cases, primarily focused on competition- and Olympiad-level math problems. Each test case contains a step-by-step solution with error location annotated by human experts. Models are required to identify the earliest step that contains an error, or conclude that all steps are correct. We conduct extensive evaluation on ProcessBench, involving two types of models: process reward models (PRMs) and critic models, where for the latter we prompt general language models to critique each solution step by step. We draw two main observations: (1) Existing PRMs typically fail to generalize to more challenging math problems beyond GSM8K and MATH. They underperform both critic models (i.e., prompted general language models) and our own trained PRM that is straightforwardly fine-tuned on the PRM800K dataset. (2) The best open-source model, QwQ-32B-Preview, has demonstrated the critique capability competitive with the proprietary model GPT-4o, despite that it still lags behind the reasoning-specialized o1-mini. We hope ProcessBench can foster future research in reasoning process assessment, paving the way toward scalable oversight of language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.06769", "title": "Training Large Language Models to Reason in a Continuous Latent Space", "authors": [ "Shibo Hao", "Sainbayar Sukhbaatar", "DiJia Su", "Xian Li", "Zhiting Hu", "Jason Weston", "Yuandong Tian" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are restricted to reason in the \"language space\", where they typically express the reasoning process with a chain-of-thought (CoT) to solve a complex reasoning problem. However, we argue that language space may not always be optimal for reasoning. For example, most word tokens are primarily for textual coherence and not essential for reasoning, while some critical tokens require complex planning and pose huge challenges to LLMs. To explore the potential of LLM reasoning in an unrestricted latent space instead of using natural language, we introduce a new paradigm Coconut (Chain of Continuous Thought). We utilize the last hidden state of the LLM as a representation of the reasoning state (termed \"continuous thought\"). Rather than decoding this into a word token, we feed it back to the LLM as the subsequent input embedding directly in the continuous space. Experiments show that Coconut can effectively augment the LLM on several reasoning tasks. This novel latent reasoning paradigm leads to emergent advanced reasoning patterns: the continuous thought can encode multiple alternative next reasoning steps, allowing the model to perform a breadth-first search (BFS) to solve the problem, rather than prematurely committing to a single deterministic path like CoT. Coconut outperforms CoT in certain logical reasoning tasks that require substantial backtracking during planning, with fewer thinking tokens during inference. These findings demonstrate the promise of latent reasoning and offer valuable insights for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.06782", "title": "CARP: Visuomotor Policy Learning via Coarse-to-Fine Autoregressive Prediction", "authors": [ "Zhefei Gong", "Pengxiang Ding", "Shangke Lyu", "Siteng Huang", "Mingyang Sun", "Wei Zhao", "Zhaoxin Fan", "Donglin Wang" ], "github": "", "project_page": "", "abstract": "In robotic visuomotor policy learning, diffusion-based models have achieved significant success in improving the accuracy of action trajectory generation compared to traditional autoregressive models. However, they suffer from inefficiency due to multiple denoising steps and limited flexibility from complex constraints. In this paper, we introduce Coarse-to-Fine AutoRegressive Policy (CARP), a novel paradigm for visuomotor policy learning that redefines the autoregressive action generation process as a coarse-to-fine, next-scale approach. CARP decouples action generation into two stages: first, an action autoencoder learns multi-scale representations of the entire action sequence; then, a GPT-style transformer refines the sequence prediction through a coarse-to-fine autoregressive process. This straightforward and intuitive approach produces highly accurate and smooth actions, matching or even surpassing the performance of diffusion-based policies while maintaining efficiency on par with autoregressive policies. We conduct extensive evaluations across diverse settings, including single-task and multi-task scenarios on state-based and image-based simulation benchmarks, as well as real-world tasks. CARP achieves competitive success rates, with up to a 10% improvement, and delivers 10x faster inference compared to state-of-the-art policies, establishing a high-performance, efficient, and flexible paradigm for action generation in robotic tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.03123", "title": "Robust Multi-bit Text Watermark with LLM-based Paraphrasers", "authors": [ "Xiaojun Xu", "Jinghan Jia", "Yuanshun Yao", "Yang Liu", "Hang Li" ], "github": "https://github.com/xiaojunxu/multi-bit-text-watermark", "project_page": "", "abstract": "We propose an imperceptible multi-bit text watermark embedded by paraphrasing with LLMs. We fine-tune a pair of LLM paraphrasers that are designed to behave differently so that their paraphrasing difference reflected in the text semantics can be identified by a trained decoder. To embed our multi-bit watermark, we use two paraphrasers alternatively to encode the pre-defined binary code at the sentence level. Then we use a text classifier as the decoder to decode each bit of the watermark. Through extensive experiments, we show that our watermarks can achieve over 99.99\\% detection AUC with small (1.1B) text paraphrasers while keeping the semantic information of the original sentence. More importantly, our pipeline is robust under word substitution and sentence paraphrasing perturbations and generalizes well to out-of-distributional data. We also show the stealthiness of our watermark with LLM-based evaluation. We open-source the code: https://github.com/xiaojunxu/multi-bit-text-watermark.", "github_manual": "", "github_regex": "https://github.com/xiaojunxu/multi-bit-text-watermark", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.06699", "title": "You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale", "authors": [ "Baorui Ma", "Huachen Gao", "Haoge Deng", "Zhengxiong Luo", "Tiejun Huang", "Lulu Tang", "Xinlong Wang" ], "github": "https://github.com/baaivision/See3D", "project_page": "", "abstract": "Recent 3D generation models typically rely on limited-scale 3D `gold-labels' or 2D diffusion priors for 3D content creation. However, their performance is upper-bounded by constrained 3D priors due to the lack of scalable learning paradigms. In this work, we present See3D, a visual-conditional multi-view diffusion model trained on large-scale Internet videos for open-world 3D creation. The model aims to Get 3D knowledge by solely Seeing the visual contents from the vast and rapidly growing video data -- You See it, You Got it. To achieve this, we first scale up the training data using a proposed data curation pipeline that automatically filters out multi-view inconsistencies and insufficient observations from source videos. This results in a high-quality, richly diverse, large-scale dataset of multi-view images, termed WebVi3D, containing 320M frames from 16M video clips. Nevertheless, learning generic 3D priors from videos without explicit 3D geometry or camera pose annotations is nontrivial, and annotating poses for web-scale videos is prohibitively expensive. To eliminate the need for pose conditions, we introduce an innovative visual-condition - a purely 2D-inductive visual signal generated by adding time-dependent noise to the masked video data. Finally, we introduce a novel visual-conditional 3D generation framework by integrating See3D into a warping-based pipeline for high-fidelity 3D generation. Our numerical and visual comparisons on single and sparse reconstruction benchmarks show that See3D, trained on cost-effective and scalable video data, achieves notable zero-shot and open-world generation capabilities, markedly outperforming models trained on costly and constrained 3D datasets. Please refer to our project page at: https://vision.baai.ac.cn/see3d", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/baaivision/See3D", "github_api_added_by": "auto" }, { "date": "2024-12-10", "arxiv_id": "2412.05939", "title": "Exploring Multi-Grained Concept Annotations for Multimodal Large Language Models", "authors": [ "Xiao Xu", "Tianhao Niu", "Yuxi Xie", "Libo Qin", "Wanxiang Che", "Min-Yen Kan" ], "github": "https://github.com/LooperXX/MMGiC", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) excel in vision--language tasks by pre-training solely on coarse-grained concept annotations (e.g., image captions). We hypothesize that integrating fine-grained concept annotations (e.g., object labels and object regions) will further improve performance, as both data granularities complement each other in terms of breadth and depth in concept representation. We introduce a new dataset featuring Multimodal Multi-Grained Concept annotations (MMGiC) for MLLMs. In constructing MMGiC, we explore the impact of different data recipes on multimodal comprehension and generation. Our analyses reveal that multi-grained concept annotations integrate and complement each other, under our structured template and a general MLLM framework. We clearly explore and demonstrate the potential of MMGiC to help MLLMs better locate and learn concepts, aligning vision and language at multiple granularities. We further validate our hypothesis by investigating the fair comparison and effective collaboration between MMGiC and image--caption data on 12 multimodal comprehension and generation benchmarks, e.g., their appropriate combination achieve 3.95% and 2.34% absolute improvements over image--caption data alone on POPE and SEED-Bench. Code, data and models will be available at https://github.com/LooperXX/MMGiC.", "github_manual": "", "github_regex": "https://github.com/LooperXX/MMGiC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.04432", "title": "Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation", "authors": [ "Yuying Ge", "Yizhuo Li", "Yixiao Ge", "Ying Shan" ], "github": "https://github.com/tencentarc/divot", "project_page": "", "abstract": "In recent years, there has been a significant surge of interest in unifying image comprehension and generation within Large Language Models (LLMs). This growing interest has prompted us to explore extending this unification to videos. The core challenge lies in developing a versatile video tokenizer that captures both the spatial characteristics and temporal dynamics of videos to obtain representations for LLMs, and the representations can be further decoded into realistic video clips to enable video generation. In this work, we introduce Divot, a Diffusion-Powered Video Tokenizer, which leverages the diffusion process for self-supervised video representation learning. We posit that if a video diffusion model can effectively de-noise video clips by taking the features of a video tokenizer as the condition, then the tokenizer has successfully captured robust spatial and temporal information. Additionally, the video diffusion model inherently functions as a de-tokenizer, decoding videos from their representations. Building upon the Divot tokenizer, we present Divot-Vicuna through video-to-text autoregression and text-to-video generation by modeling the distributions of continuous-valued Divot features with a Gaussian Mixture Model. Experimental results demonstrate that our diffusion-based video tokenizer, when integrated with a pre-trained LLM, achieves competitive performance across various video comprehension and generation benchmarks. The instruction tuned Divot-Vicuna also excels in video storytelling, generating interleaved narratives and corresponding videos.", "github_manual": "https://github.com/tencentarc/divot", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.06531", "title": "Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation", "authors": [ "Egor Cherepanov", "Nikita Kachaev", "Artem Zholus", "Alexey K. Kovalev", "Aleksandr I. Panov" ], "github": "", "project_page": "", "abstract": "The incorporation of memory into agents is essential for numerous tasks within the domain of Reinforcement Learning (RL). In particular, memory is paramount for tasks that require the utilization of past information, adaptation to novel environments, and improved sample efficiency. However, the term ``memory'' encompasses a wide range of concepts, which, coupled with the lack of a unified methodology for validating an agent's memory, leads to erroneous judgments about agents' memory capabilities and prevents objective comparison with other memory-enhanced agents. This paper aims to streamline the concept of memory in RL by providing practical precise definitions of agent memory types, such as long-term versus short-term memory and declarative versus procedural memory, inspired by cognitive science. Using these definitions, we categorize different classes of agent memory, propose a robust experimental methodology for evaluating the memory capabilities of RL agents, and standardize evaluations. Furthermore, we empirically demonstrate the importance of adhering to the proposed methodology when evaluating different types of agent memory by conducting experiments with different RL agents and what its violation leads to.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.05600", "title": "Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space", "authors": [ "Mikolaj Czerkawski", "Marcin Kluczek", "J\u0119drzej S. Bojanowski" ], "github": "", "project_page": "", "abstract": "With the ever-increasing volumes of the Earth observation data present in the archives of large programmes such as Copernicus, there is a growing need for efficient vector representations of the underlying raw data. The approach of extracting feature representations from pretrained deep neural networks is a powerful approach that can provide semantic abstractions of the input data. However, the way this is done for imagery archives containing geospatial data has not yet been defined. In this work, an extension is proposed to an existing community project, Major TOM, focused on the provision and standardization of open and free AI-ready datasets for Earth observation. Furthermore, four global and dense embedding datasets are released openly and for free along with the publication of this manuscript, resulting in the most comprehensive global open dataset of geospatial visual embeddings in terms of covered Earth's surface.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.05355", "title": "MotionShop: Zero-Shot Motion Transfer in Video Diffusion Models with Mixture of Score Guidance", "authors": [ "Hidir Yesiltepe", "Tuna Han Salih Meral", "Connor Dunlop", "Pinar Yanardag" ], "github": "", "project_page": "", "abstract": "In this work, we propose the first motion transfer approach in diffusion transformer through Mixture of Score Guidance (MSG), a theoretically-grounded framework for motion transfer in diffusion models. Our key theoretical contribution lies in reformulating conditional score to decompose motion score and content score in diffusion models. By formulating motion transfer as a mixture of potential energies, MSG naturally preserves scene composition and enables creative scene transformations while maintaining the integrity of transferred motion patterns. This novel sampling operates directly on pre-trained video diffusion models without additional training or fine-tuning. Through extensive experiments, MSG demonstrates successful handling of diverse scenarios including single object, multiple objects, and cross-object motion transfer as well as complex camera motion transfer. Additionally, we introduce MotionBench, the first motion transfer dataset consisting of 200 source videos and 1000 transferred motions, covering single/multi-object transfers, and complex camera motions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.06781", "title": "Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation", "authors": [ "Nicolas Dufour", "David Picard", "Vicky Kalogeiton", "Loic Landrieu" ], "github": "https://github.com/nicolas-dufour/plonk", "project_page": "", "abstract": "Global visual geolocation predicts where an image was captured on Earth. Since images vary in how precisely they can be localized, this task inherently involves a significant degree of ambiguity. However, existing approaches are deterministic and overlook this aspect. In this paper, we aim to close the gap between traditional geolocalization and modern generative methods. We propose the first generative geolocation approach based on diffusion and Riemannian flow matching, where the denoising process operates directly on the Earth's surface. Our model achieves state-of-the-art performance on three visual geolocation benchmarks: OpenStreetView-5M, YFCC-100M, and iNat21. In addition, we introduce the task of probabilistic visual geolocation, where the model predicts a probability distribution over all possible locations instead of a single point. We introduce new metrics and baselines for this task, demonstrating the advantages of our diffusion-based approach. Codes and models will be made available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nicolas-dufour/plonk", "github_api_added_by": "auto" }, { "date": "2024-12-10", "arxiv_id": "2412.04144", "title": "If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs", "authors": [ "Muhammad Khalifa", "Yi-Chern Tan", "Arash Ahmadian", "Tom Hosking", "Honglak Lee", "Lu Wang", "Ahmet \u00dcst\u00fcn", "Tom Sherborne", "Matthias Gall\u00e9" ], "github": "", "project_page": "", "abstract": "Model merging has shown great promise at combining expert models, but the benefit of merging is unclear when merging ``generalist'' models trained on many tasks. We explore merging in the context of large (sim100B) models, by recycling checkpoints that exhibit tradeoffs among different tasks. Such checkpoints are often created in the process of developing a frontier model, and many suboptimal ones are usually discarded. Given a pool of model checkpoints obtained from different training runs (e.g., different stages, objectives, hyperparameters, and data mixtures), which naturally show tradeoffs across different language capabilities (e.g., instruction following vs. code generation), we investigate whether merging can recycle such suboptimal models into a Pareto-optimal one. Our optimization algorithm tunes the weight of each checkpoint in a linear combination, resulting in a Pareto-optimal models that outperforms both individual models and merge-based baselines. Further analysis shows that good merges tend to include almost all checkpoints with with non-zero weights, indicating that even seemingly bad initial checkpoints can contribute to good final merges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.06767", "title": "MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and Photorealism From Sparse Views", "authors": [ "Antoine Gu\u00e9don", "Tomoki Ichikawa", "Kohei Yamashita", "Ko Nishino" ], "github": "https://github.com/Anttwo/MAtCha", "project_page": "", "abstract": "We present a novel appearance model that simultaneously realizes explicit high-quality 3D surface mesh recovery and photorealistic novel view synthesis from sparse view samples. Our key idea is to model the underlying scene geometry Mesh as an Atlas of Charts which we render with 2D Gaussian surfels (MAtCha Gaussians). MAtCha distills high-frequency scene surface details from an off-the-shelf monocular depth estimator and refines it through Gaussian surfel rendering. The Gaussian surfels are attached to the charts on the fly, satisfying photorealism of neural volumetric rendering and crisp geometry of a mesh model, i.e., two seemingly contradicting goals in a single model. At the core of MAtCha lies a novel neural deformation model and a structure loss that preserve the fine surface details distilled from learned monocular depths while addressing their fundamental scale ambiguities. Results of extensive experimental validation demonstrate MAtCha's state-of-the-art quality of surface reconstruction and photorealism on-par with top contenders but with dramatic reduction in the number of input views and computational time. We believe MAtCha will serve as a foundational tool for any visual application in vision, graphics, and robotics that require explicit geometry in addition to photorealism. Our project page is the following: https://anttwo.github.io/matcha/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Anttwo/MAtCha", "github_api_added_by": "auto" }, { "date": "2024-12-10", "arxiv_id": "2412.04470", "title": "Turbo3D: Ultra-fast Text-to-3D Generation", "authors": [ "Hanzhe Hu", "Tianwei Yin", "Fujun Luan", "Yiwei Hu", "Hao Tan", "Zexiang Xu", "Sai Bi", "Shubham Tulsiani", "Kai Zhang" ], "github": "", "project_page": "", "abstract": "We present Turbo3D, an ultra-fast text-to-3D system capable of generating high-quality Gaussian splatting assets in under one second. Turbo3D employs a rapid 4-step, 4-view diffusion generator and an efficient feed-forward Gaussian reconstructor, both operating in latent space. The 4-step, 4-view generator is a student model distilled through a novel Dual-Teacher approach, which encourages the student to learn view consistency from a multi-view teacher and photo-realism from a single-view teacher. By shifting the Gaussian reconstructor's inputs from pixel space to latent space, we eliminate the extra image decoding time and halve the transformer sequence length for maximum efficiency. Our method demonstrates superior 3D generation results compared to previous baselines, while operating in a fraction of their runtime.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.06464", "title": "Gated Delta Networks: Improving Mamba2 with Delta Rule", "authors": [ "Songlin Yang", "Jan Kautz", "Ali Hatamizadeh" ], "github": "https://github.com/NVlabs/GatedDeltaNet", "project_page": "", "abstract": "Linear Transformers have gained attention as efficient alternatives to standard Transformers, but their performance in retrieval and long-context tasks has been limited. To address these limitations, recent work has explored two distinct mechanisms: gating for adaptive memory control and the delta update rule for precise memory modifications. We observe that these mechanisms are complementary: gating enables rapid memory erasure while the delta rule facilitates targeted updates. Building on this insight, we introduce the gated delta rule and develop a parallel training algorithm optimized for modern hardware. Our proposed architecture, Gated DeltaNet, consistently surpasses existing models like Mamba2 and DeltaNet across multiple benchmarks, including language modeling, common-sense reasoning, in-context retrieval, length extrapolation, and long-context understanding. We further enhance performance by developing hybrid architectures that combine Gated DeltaNet layers with sliding window attention or Mamba2 layers, achieving both improved training efficiency and superior task performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/GatedDeltaNet", "github_api_added_by": "auto" }, { "date": "2024-12-10", "arxiv_id": "2412.07626", "title": "OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations", "authors": [ "Linke Ouyang", "Yuan Qu", "Hongbin Zhou", "Jiawei Zhu", "Rui Zhang", "Qunshu Lin", "Bin Wang", "Zhiyuan Zhao", "Man Jiang", "Xiaomeng Zhao", "Jin Shi", "Fan Wu", "Pei Chu", "Minghao Liu", "Zhenxiang Li", "Chao Xu", "Bo Zhang", "Botian Shi", "Zhongying Tu", "Conghui He" ], "github": "https://github.com/opendatalab/OmniDocBench", "project_page": "", "abstract": "Document content extraction is crucial in computer vision, especially for meeting the high-quality data needs of large language models (LLMs) and retrieval-augmented generation (RAG) technologies. However, current document parsing methods suffer from significant limitations in terms of diversity and comprehensive evaluation. To address these challenges, we introduce OmniDocBench, a novel multi-source benchmark designed to advance automated document content extraction. OmniDocBench includes a meticulously curated and annotated high-quality evaluation dataset comprising nine diverse document types, such as academic papers, textbooks, slides, among others. Our benchmark provides a flexible and comprehensive evaluation framework with 19 layout category labels and 14 attribute labels, enabling multi-level assessments across entire datasets, individual modules, or specific data types. Using OmniDocBench, we perform an exhaustive comparative analysis of existing modular pipelines and multimodal end-to-end methods, highlighting their limitations in handling document diversity and ensuring fair evaluation. OmniDocBench establishes a robust, diverse, and fair evaluation standard for the document content extraction field, offering crucial insights for future advancements and fostering the development of document parsing technologies. The codes and dataset is available in https://github.com/opendatalab/OmniDocBench.", "github_manual": "", "github_regex": "https://github.com/opendatalab/OmniDocBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-10", "arxiv_id": "2412.07112", "title": "Maya: An Instruction Finetuned Multilingual Multimodal Model", "authors": [ "Nahid Alam", "Karthik Reddy Kanjula", "Surya Guthikonda", "Timothy Chung", "Bala Krishna S Vegesna", "Abhipsha Das", "Anthony Susevski", "Ryan Sze-Yin Chan", "S M Iftekhar Uddin", "Shayekh Bin Islam", "Roshan Santhosh", "Snegha A", "Drishti Sharma", "Chen Liu", "Isha Chaturvedi", "Genta Indra Winata", "Ashvanth. S", "Snehanshu Mukherjee", "Alham Fikri Aji" ], "github": "https://github.com/nahidalam/maya", "project_page": "", "abstract": "The rapid development of large Vision-Language Models (VLMs) has led to impressive results on academic benchmarks, primarily in widely spoken languages. However, significant gaps remain in the ability of current VLMs to handle low-resource languages and varied cultural contexts, largely due to a lack of high-quality, diverse, and safety-vetted data. Consequently, these models often struggle to understand low-resource languages and cultural nuances in a manner free from toxicity. To address these limitations, we introduce Maya, an open-source Multimodal Multilingual model. Our contributions are threefold: 1) a multilingual image-text pretraining dataset in eight languages, based on the LLaVA pretraining dataset; 2) a thorough analysis of toxicity within the LLaVA dataset, followed by the creation of a novel toxicity-free version across eight languages; and 3) a multilingual image-text model supporting these languages, enhancing cultural and linguistic comprehension in vision-language tasks. Code available at https://github.com/nahidalam/maya.", "github_manual": "", "github_regex": "https://github.com/nahidalam/maya", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.06673", "title": "ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance", "authors": [ "Chunwei Wang", "Guansong Lu", "Junwei Yang", "Runhui Huang", "Jianhua Han", "Lu Hou", "Wei Zhang", "Hang Xu" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce ILLUME, a unified multimodal large language model (MLLM) that seamlessly integrates multimodal understanding and generation capabilities within a single large language model through a unified next-token prediction formulation. To address the large dataset size typically required for image-text alignment, we propose to enhance data efficiency through the design of a vision tokenizer that incorporates semantic information and a progressive multi-stage training procedure. This approach reduces the dataset size to just 15M for pretraining -- over four times fewer than what is typically needed -- while achieving competitive or even superior performance with existing unified MLLMs, such as Janus. Additionally, to promote synergistic enhancement between understanding and generation capabilities, which is under-explored in previous works, we introduce a novel self-enhancing multimodal alignment scheme. This scheme supervises the MLLM to self-assess the consistency between text descriptions and self-generated images, facilitating the model to interpret images more accurately and avoid unrealistic and incorrect predictions caused by misalignment in image generation. Based on extensive experiments, our proposed ILLUME stands out and competes with state-of-the-art unified MLLMs and specialized models across various benchmarks for multimodal understanding, generation, and editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.07724", "title": "Granite Guardian", "authors": [ "Inkit Padhi", "Manish Nagireddy", "Giandomenico Cornacchia", "Subhajit Chaudhury", "Tejaswini Pedapati", "Pierre Dognin", "Keerthiram Murugesan", "Erik Miehling", "Mart\u00edn Santill\u00e1n Cooper", "Kieran Fraser", "Giulio Zizzo", "Muhammad Zaid Hameed", "Mark Purcell", "Michael Desmond", "Qian Pan", "Inge Vejsbjerg", "Elizabeth M. Daly", "Michael Hind", "Werner Geyer", "Ambrish Rawat", "Kush R. Varshney", "Prasanna Sattigeri" ], "github": "https://github.com/ibm-granite/granite-guardian", "project_page": "", "abstract": "We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with any large language model (LLM). These models offer comprehensive coverage across multiple risk dimensions, including social bias, profanity, violence, sexual content, unethical behavior, jailbreaking, and hallucination-related risks such as context relevance, groundedness, and answer relevance for retrieval-augmented generation (RAG). Trained on a unique dataset combining human annotations from diverse sources and synthetic data, Granite Guardian models address risks typically overlooked by traditional risk detection models, such as jailbreaks and RAG-specific issues. With AUC scores of 0.871 and 0.854 on harmful content and RAG-hallucination-related benchmarks respectively, Granite Guardian is the most generalizable and competitive model available in the space. Released as open-source, Granite Guardian aims to promote responsible AI development across the community. https://github.com/ibm-granite/granite-guardian", "github_manual": "", "github_regex": "https://github.com/ibm-granite/granite-guardian", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.06845", "title": "Fully Open Source Moxin-7B Technical Report", "authors": [ "Pu Zhao", "Xuan Shen", "Zhenglun Kong", "Yixin Shen", "Sung-En Chang", "Timothy Rupprecht", "Lei Lu", "Enfu Nan", "Changdi Yang", "Yumei He", "Xingchen Xu", "Yu Huang", "Wei Wang", "Yue Chen", "Yong He", "Yanzhi Wang" ], "github": "https://github.com/moxin-org/Moxin-LLM", "project_page": "", "abstract": "Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities. Leading this evolution are proprietary LLMs like GPT-4 and GPT-o1, which have captured widespread attention in the AI community due to their remarkable performance and versatility. Simultaneously, open-source LLMs, such as LLaMA and Mistral, have made great contributions to the ever-increasing popularity of LLMs due to the ease to customize and deploy the models across diverse applications. Although open-source LLMs present unprecedented opportunities for innovation and research, the commercialization of LLMs has raised concerns about transparency, reproducibility, and safety. Many open-source LLMs fail to meet fundamental transparency requirements by withholding essential components like training code and data, and some use restrictive licenses whilst claiming to be \"open-source,\" which may hinder further innovations on LLMs. To mitigate this issue, we introduce Moxin 7B, a fully open-source LLM developed in accordance with the Model Openness Framework (MOF), a ranked classification system that evaluates AI models based on model completeness and openness, adhering to principles of open science, open source, open data, and open access. Our model achieves the highest MOF classification level of \"open science\" through the comprehensive release of pre-training code and configurations, training and fine-tuning datasets, and intermediate and final checkpoints. Experiments show that our model achieves superior performance in zero-shot evaluation compared with popular 7B models and performs competitively in few-shot evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/moxin-org/Moxin-LLM", "github_api_added_by": "auto" }, { "date": "2024-12-11", "arxiv_id": "2412.07774", "title": "UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics", "authors": [ "Xi Chen", "Zhifei Zhang", "He Zhang", "Yuqian Zhou", "Soo Ye Kim", "Qing Liu", "Yijun Li", "Jianming Zhang", "Nanxuan Zhao", "Yilin Wang", "Hui Ding", "Zhe Lin", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "We introduce UniReal, a unified framework designed to address various image generation and editing tasks. Existing solutions often vary by tasks, yet share fundamental principles: preserving consistency between inputs and outputs while capturing visual variations. Inspired by recent video generation models that effectively balance consistency and variation across frames, we propose a unifying approach that treats image-level tasks as discontinuous video generation. Specifically, we treat varying numbers of input and output images as frames, enabling seamless support for tasks such as image generation, editing, customization, composition, etc. Although designed for image-level tasks, we leverage videos as a scalable source for universal supervision. UniReal learns world dynamics from large-scale videos, demonstrating advanced capability in handling shadows, reflections, pose variation, and object interaction, while also exhibiting emergent capability for novel applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.05210", "title": "Evaluating and Aligning CodeLLMs on Human Preference", "authors": [ "Jian Yang", "Jiaxi Yang", "Ke Jin", "Yibo Miao", "Lei Zhang", "Liqun Yang", "Zeyu Cui", "Yichang Zhang", "Binyuan Hui", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "Code large language models (codeLLMs) have made significant strides in code generation. Most previous code-related benchmarks, which consist of various programming exercises along with the corresponding test cases, are used as a common measure to evaluate the performance and capabilities of code LLMs. However, the current code LLMs focus on synthesizing the correct code snippet, ignoring the alignment with human preferences, where the query should be sampled from the practical application scenarios and the model-generated responses should satisfy the human preference. To bridge the gap between the model-generated response and human preference, we present a rigorous human-curated benchmark CodeArena to emulate the complexity and diversity of real-world coding tasks, where 397 high-quality samples spanning 40 categories and 44 programming languages, carefully curated from user queries. Further, we propose a diverse synthetic instruction corpus SynCode-Instruct (nearly 20B tokens) by scaling instructions from the website to verify the effectiveness of the large-scale synthetic instruction fine-tuning, where Qwen2.5-SynCoder totally trained on synthetic instruction data can achieve top-tier performance of open-source code LLMs. The results find performance differences between execution-based benchmarks and CodeArena. Our systematic experiments of CodeArena on 40+ LLMs reveal a notable performance gap between open SOTA code LLMs (e.g. Qwen2.5-Coder) and proprietary LLMs (e.g., OpenAI o1), underscoring the importance of the human preference alignment.\\url{https://codearenaeval.github.io/ }", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.07721", "title": "ObjCtrl-2.5D: Training-free Object Control with Camera Poses", "authors": [ "Zhouxia Wang", "Yushi Lan", "Shangchen Zhou", "Chen Change Loy" ], "github": "https://github.com/wzhouxiff/ObjCtrl-2.5D", "project_page": "", "abstract": "This study aims to achieve more precise and versatile object control in image-to-video (I2V) generation. Current methods typically represent the spatial movement of target objects with 2D trajectories, which often fail to capture user intention and frequently produce unnatural results. To enhance control, we present ObjCtrl-2.5D, a training-free object control approach that uses a 3D trajectory, extended from a 2D trajectory with depth information, as a control signal. By modeling object movement as camera movement, ObjCtrl-2.5D represents the 3D trajectory as a sequence of camera poses, enabling object motion control using an existing camera motion control I2V generation model (CMC-I2V) without training. To adapt the CMC-I2V model originally designed for global motion control to handle local object motion, we introduce a module to isolate the target object from the background, enabling independent local control. In addition, we devise an effective way to achieve more accurate object control by sharing low-frequency warped latent within the object's region across frames. Extensive experiments demonstrate that ObjCtrl-2.5D significantly improves object control accuracy compared to training-free methods and offers more diverse control capabilities than training-based approaches using 2D trajectories, enabling complex effects like object rotation. Code and results are available at https://wzhouxiff.github.io/projects/ObjCtrl-2.5D/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wzhouxiff/ObjCtrl-2.5D", "github_api_added_by": "auto" }, { "date": "2024-12-11", "arxiv_id": "2412.03548", "title": "Perception Tokens Enhance Visual Reasoning in Multimodal Language Models", "authors": [ "Mahtab Bigverdi", "Zelun Luo", "Cheng-Yu Hsieh", "Ethan Shen", "Dongping Chen", "Linda G. Shapiro", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Multimodal language models (MLMs) still face challenges in fundamental visual perception tasks where specialized models excel. Tasks requiring reasoning about 3D structures benefit from depth estimation, and reasoning about 2D object instances benefits from object detection. Yet, MLMs can not produce intermediate depth or boxes to reason over. Finetuning MLMs on relevant data doesn't generalize well and outsourcing computation to specialized vision tools is too compute-intensive and memory-inefficient. To address this, we introduce Perception Tokens, intrinsic image representations designed to assist reasoning tasks where language is insufficient. Perception tokens act as auxiliary reasoning tokens, akin to chain-of-thought prompts in language models. For example, in a depth-related task, an MLM augmented with perception tokens can reason by generating a depth map as tokens, enabling it to solve the problem effectively. We propose AURORA, a training method that augments MLMs with perception tokens for improved reasoning over visual inputs. AURORA leverages a VQVAE to transform intermediate image representations, such as depth maps into a tokenized format and bounding box tokens, which is then used in a multi-task training framework. AURORA achieves notable improvements across counting benchmarks: +10.8% on BLINK, +11.3% on CVBench, and +8.3% on SEED-Bench, outperforming finetuning approaches in generalization across datasets. It also improves on relative depth: over +6% on BLINK. With perception tokens, AURORA expands the scope of MLMs beyond language-based reasoning, paving the way for more effective visual reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.04653", "title": "Hidden in the Noise: Two-Stage Robust Watermarking for Images", "authors": [ "Kasra Arabi", "Benjamin Feuer", "R. Teal Witter", "Chinmay Hegde", "Niv Cohen" ], "github": "https://github.com/Kasraarabi/Hidden-in-the-Noise", "project_page": "", "abstract": "As the quality of image generators continues to improve, deepfakes become a topic of considerable societal debate. Image watermarking allows responsible model owners to detect and label their AI-generated content, which can mitigate the harm. Yet, current state-of-the-art methods in image watermarking remain vulnerable to forgery and removal attacks. This vulnerability occurs in part because watermarks distort the distribution of generated images, unintentionally revealing information about the watermarking techniques. In this work, we first demonstrate a distortion-free watermarking method for images, based on a diffusion model's initial noise. However, detecting the watermark requires comparing the initial noise reconstructed for an image to all previously used initial noises. To mitigate these issues, we propose a two-stage watermarking framework for efficient detection. During generation, we augment the initial noise with generated Fourier patterns to embed information about the group of initial noises we used. For detection, we (i) retrieve the relevant group of noises, and (ii) search within the given group for an initial noise that might match our image. This watermarking approach achieves state-of-the-art robustness to forgery and removal against a large battery of attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Kasraarabi/Hidden-in-the-Noise", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.07589", "title": "DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation", "authors": [ "Jianzong Wu", "Chao Tang", "Jingbo Wang", "Yanhong Zeng", "Xiangtai Li", "Yunhai Tong" ], "github": "https://github.com/jianzongwu/DiffSensei", "project_page": "", "abstract": "Story visualization, the task of creating visual narratives from textual descriptions, has seen progress with text-to-image generation models. However, these models often lack effective control over character appearances and interactions, particularly in multi-character scenes. To address these limitations, we propose a new task: customized manga generation and introduce DiffSensei, an innovative framework specifically designed for generating manga with dynamic multi-character control. DiffSensei integrates a diffusion-based image generator with a multimodal large language model (MLLM) that acts as a text-compatible identity adapter. Our approach employs masked cross-attention to seamlessly incorporate character features, enabling precise layout control without direct pixel transfer. Additionally, the MLLM-based adapter adjusts character features to align with panel-specific text cues, allowing flexible adjustments in character expressions, poses, and actions. We also introduce MangaZero, a large-scale dataset tailored to this task, containing 43,264 manga pages and 427,147 annotated panels, supporting the visualization of varied character interactions and movements across sequential frames. Extensive experiments demonstrate that DiffSensei outperforms existing models, marking a significant advancement in manga generation by enabling text-adaptable character customization. The project page is https://jianzongwu.github.io/projects/diffsensei/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jianzongwu/DiffSensei", "github_api_added_by": "auto" }, { "date": "2024-12-11", "arxiv_id": "2412.07674", "title": "FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models", "authors": [ "Tong Wu", "Yinghao Xu", "Ryan Po", "Mengchen Zhang", "Guandao Yang", "Jiaqi Wang", "Ziwei Liu", "Dahua Lin", "Gordon Wetzstein" ], "github": "https://github.com/wutong16/FiVA", "project_page": "", "abstract": "Recent advances in text-to-image generation have enabled the creation of high-quality images with diverse applications. However, accurately describing desired visual attributes can be challenging, especially for non-experts in art and photography. An intuitive solution involves adopting favorable attributes from the source images. Current methods attempt to distill identity and style from source images. However, \"style\" is a broad concept that includes texture, color, and artistic elements, but does not cover other important attributes such as lighting and dynamics. Additionally, a simplified \"style\" adaptation prevents combining multiple attributes from different sources into one generated image. In this work, we formulate a more effective approach to decompose the aesthetics of a picture into specific visual attributes, allowing users to apply characteristics such as lighting, texture, and dynamics from different images. To achieve this goal, we constructed the first fine-grained visual attributes dataset (FiVA) to the best of our knowledge. This FiVA dataset features a well-organized taxonomy for visual attributes and includes around 1 M high-quality generated images with visual attribute annotations. Leveraging this dataset, we propose a fine-grained visual attribute adaptation framework (FiVA-Adapter), which decouples and adapts visual attributes from one or more source images into a generated one. This approach enhances user-friendly customization, allowing users to selectively apply desired attributes to create images that meet their unique preferences and specific content requirements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wutong16/FiVA", "github_api_added_by": "auto" }, { "date": "2024-12-11", "arxiv_id": "2412.06674", "title": "EMOv2: Pushing 5M Vision Model Frontier", "authors": [ "Jiangning Zhang", "Teng Hu", "Haoyang He", "Zhucun Xue", "Yabiao Wang", "Chengjie Wang", "Yong Liu", "Xiangtai Li", "Dacheng Tao" ], "github": "https://github.com/zhangzjn/EMOv2", "project_page": "", "abstract": "This work focuses on developing parameter-efficient and lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Our goal is to set up the new frontier of the 5M magnitude lightweight model on various downstream tasks. Inverted Residual Block (IRB) serves as the infrastructure for lightweight CNNs, but no counterparts have been recognized by attention-based design. Our work rethinks the lightweight infrastructure of efficient IRB and practical components in Transformer from a unified perspective, extending CNN-based IRB to attention-based models and abstracting a one-residual Meta Mobile Block (MMBlock) for lightweight model design. Following neat but effective design criterion, we deduce a modern Improved Inverted Residual Mobile Block (i2RMB) and improve a hierarchical Efficient MOdel (EMOv2) with no elaborate complex structures. Considering the imperceptible latency for mobile users when downloading models under 4G/5G bandwidth and ensuring model performance, we investigate the performance upper limit of lightweight models with a magnitude of 5M. Extensive experiments on various vision recognition, dense prediction, and image generation tasks demonstrate the superiority of our EMOv2 over state-of-the-art methods, e.g., EMOv2-1M/2M/5M achieve 72.3, 75.8, and 79.4 Top-1 that surpass equal-order CNN-/Attention-based models significantly. At the same time, EMOv2-5M equipped RetinaNet achieves 41.5 mAP for object detection tasks that surpasses the previous EMO-5M by +2.6. When employing the more robust training recipe, our EMOv2-5M eventually achieves 82.9 Top-1 accuracy, which elevates the performance of 5M magnitude models to a new level. Code is available at https://github.com/zhangzjn/EMOv2.", "github_manual": "", "github_regex": "https://github.com/zhangzjn/EMOv2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.07187", "title": "A New Federated Learning Framework Against Gradient Inversion Attacks", "authors": [ "Pengxin Guo", "Shuang Zeng", "Wenhao Chen", "Xiaodan Zhang", "Weihong Ren", "Yuyin Zhou", "Liangqiong Qu" ], "github": "https://github.com/Pengxin-Guo/HyperFL", "project_page": "", "abstract": "Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL to thwart such attacks, such as Secure Multi-party Computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP). Despite their ability to protect data privacy, these approaches inherently involve substantial privacy-utility trade-offs. By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy preserve FL framework that effectively ``breaks the direct connection'' between the shared parameters and the local private data to defend against GIA. Specifically, we propose a Hypernetwork Federated Learning (HyperFL) framework that utilizes hypernetworks to generate the parameters of the local model and only the hypernetwork parameters are uploaded to the server for aggregation. Theoretical analyses demonstrate the convergence rate of the proposed HyperFL, while extensive experimental results show the privacy-preserving capability and comparable performance of HyperFL. Code is available at https://github.com/Pengxin-Guo/HyperFL.", "github_manual": "", "github_regex": "https://github.com/Pengxin-Guo/HyperFL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.07730", "title": "STIV: Scalable Text and Image Conditioned Video Generation", "authors": [ "Zongyu Lin", "Wei Liu", "Chen Chen", "Jiasen Lu", "Wenze Hu", "Tsu-Jui Fu", "Jesse Allardice", "Zhengfeng Lai", "Liangchen Song", "Bowen Zhang", "Cha Chen", "Yiran Fei", "Yifan Jiang", "Lezhi Li", "Yizhou Sun", "Kai-Wei Chang", "Yinfei Yang" ], "github": "", "project_page": "", "abstract": "The field of video generation has made remarkable advancements, yet there remains a pressing need for a clear, systematic recipe that can guide the development of robust and scalable models. In this work, we present a comprehensive study that systematically explores the interplay of model architectures, training recipes, and data curation strategies, culminating in a simple and scalable text-image-conditioned video generation method, named STIV. Our framework integrates image condition into a Diffusion Transformer (DiT) through frame replacement, while incorporating text conditioning via a joint image-text conditional classifier-free guidance. This design enables STIV to perform both text-to-video (T2V) and text-image-to-video (TI2V) tasks simultaneously. Additionally, STIV can be easily extended to various applications, such as video prediction, frame interpolation, multi-view generation, and long video generation, etc. With comprehensive ablation studies on T2I, T2V, and TI2V, STIV demonstrate strong performance, despite its simple design. An 8.7B model with 512 resolution achieves 83.1 on VBench T2V, surpassing both leading open and closed-source models like CogVideoX-5B, Pika, Kling, and Gen-3. The same-sized model also achieves a state-of-the-art result of 90.1 on VBench I2V task at 512 resolution. By providing a transparent and extensible recipe for building cutting-edge video generation models, we aim to empower future research and accelerate progress toward more versatile and reliable video generation solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.07776", "title": "Video Motion Transfer with Diffusion Transformers", "authors": [ "Alexander Pondaven", "Aliaksandr Siarohin", "Sergey Tulyakov", "Philip Torr", "Fabio Pizzati" ], "github": "https://github.com/ditflow/ditflow", "project_page": "", "abstract": "We propose DiTFlow, a method for transferring the motion of a reference video to a newly synthesized one, designed specifically for Diffusion Transformers (DiT). We first process the reference video with a pre-trained DiT to analyze cross-frame attention maps and extract a patch-wise motion signal called the Attention Motion Flow (AMF). We guide the latent denoising process in an optimization-based, training-free, manner by optimizing latents with our AMF loss to generate videos reproducing the motion of the reference one. We also apply our optimization strategy to transformer positional embeddings, granting us a boost in zero-shot motion transfer capabilities. We evaluate DiTFlow against recently published methods, outperforming all across multiple metrics and human evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ditflow/ditflow", "github_api_added_by": "auto" }, { "date": "2024-12-11", "arxiv_id": "2412.07334", "title": "Frame Representation Hypothesis: Multi-Token LLM Interpretability and Concept-Guided Text Generation", "authors": [ "Pedro H. V. Valois", "Lincon S. Souza", "Erica K. Shimomoto", "Kazuhiro Fukui" ], "github": "https://github.com/phvv-me/frame-representation-hypothesis.git", "project_page": "", "abstract": "Interpretability is a key challenge in fostering trust for Large Language Models (LLMs), which stems from the complexity of extracting reasoning from model's parameters. We present the Frame Representation Hypothesis, a theoretically robust framework grounded in the Linear Representation Hypothesis (LRH) to interpret and control LLMs by modeling multi-token words. Prior research explored LRH to connect LLM representations with linguistic concepts, but was limited to single token analysis. As most words are composed of several tokens, we extend LRH to multi-token words, thereby enabling usage on any textual data with thousands of concepts. To this end, we propose words can be interpreted as frames, ordered sequences of vectors that better capture token-word relationships. Then, concepts can be represented as the average of word frames sharing a common concept. We showcase these tools through Top-k Concept-Guided Decoding, which can intuitively steer text generation using concepts of choice. We verify said ideas on Llama 3.1, Gemma 2, and Phi 3 families, demonstrating gender and language biases, exposing harmful content, but also potential to remediate them, leading to safer and more transparent LLMs. Code is available at https://github.com/phvv-me/frame-representation-hypothesis.git", "github_manual": "", "github_regex": "https://github.com/phvv-me/frame-representation-hypothesis.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.04835", "title": "Maximizing Alignment with Minimal Feedback: Efficiently Learning Rewards for Visuomotor Robot Policy Alignment", "authors": [ "Ran Tian", "Yilin Wu", "Chenfeng Xu", "Masayoshi Tomizuka", "Jitendra Malik", "Andrea Bajcsy" ], "github": "", "project_page": "", "abstract": "Visuomotor robot policies, increasingly pre-trained on large-scale datasets, promise significant advancements across robotics domains. However, aligning these policies with end-user preferences remains a challenge, particularly when the preferences are hard to specify. While reinforcement learning from human feedback (RLHF) has become the predominant mechanism for alignment in non-embodied domains like large language models, it has not seen the same success in aligning visuomotor policies due to the prohibitive amount of human feedback required to learn visual reward functions. To address this limitation, we propose Representation-Aligned Preference-based Learning (RAPL), an observation-only method for learning visual rewards from significantly less human preference feedback. Unlike traditional RLHF, RAPL focuses human feedback on fine-tuning pre-trained vision encoders to align with the end-user's visual representation and then constructs a dense visual reward via feature matching in this aligned representation space. We first validate RAPL through simulation experiments in the X-Magical benchmark and Franka Panda robotic manipulation, demonstrating that it can learn rewards aligned with human preferences, more efficiently uses preference data, and generalizes across robot embodiments. Finally, our hardware experiments align pre-trained Diffusion Policies for three object manipulation tasks. We find that RAPL can fine-tune these policies with 5x less real human preference data, taking the first step towards minimizing human feedback while maximizing visuomotor robot policy alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.05983", "title": "Chimera: Improving Generalist Model with Domain-Specific Experts", "authors": [ "Tianshuo Peng", "Mingsheng Li", "Hongbin Zhou", "Renqiu Xia", "Renrui Zhang", "Lei Bai", "Song Mao", "Bin Wang", "Conghui He", "Aojun Zhou", "Botian Shi", "Tao Chen", "Bo Zhang", "Xiangyu Yue" ], "github": "https://github.com/UniModal4Reasoning/Chimera", "project_page": "", "abstract": "Recent advancements in Large Multi-modal Models (LMMs) underscore the importance of scaling by increasing image-text paired data, achieving impressive performance on general tasks. Despite their effectiveness in broad applications, generalist models are primarily trained on web-scale datasets dominated by natural images, resulting in the sacrifice of specialized capabilities for domain-specific tasks that require extensive domain prior knowledge. Moreover, directly integrating expert models tailored for specific domains is challenging due to the representational gap and imbalanced optimization between the generalist model and experts. To address these challenges, we introduce Chimera, a scalable and low-cost multi-modal pipeline designed to boost the ability of existing LMMs with domain-specific experts. Specifically, we design a progressive training strategy to integrate features from expert models into the input of a generalist LMM. To address the imbalanced optimization caused by the well-aligned general visual encoder, we introduce a novel Generalist-Specialist Collaboration Masking (GSCM) mechanism. This results in a versatile model that excels across the chart, table, math, and document domains, achieving state-of-the-art performance on multi-modal reasoning and visual content extraction tasks, both of which are challenging tasks for assessing existing LMMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UniModal4Reasoning/Chimera", "github_api_added_by": "auto" }, { "date": "2024-12-11", "arxiv_id": "2412.07759", "title": "3DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generation", "authors": [ "Xiao Fu", "Xian Liu", "Xintao Wang", "Sida Peng", "Menghan Xia", "Xiaoyu Shi", "Ziyang Yuan", "Pengfei Wan", "Di Zhang", "Dahua Lin" ], "github": "https://github.com/KwaiVGI/3DTrajMaster", "project_page": "", "abstract": "This paper aims to manipulate multi-entity 3D motions in video generation. Previous methods on controllable video generation primarily leverage 2D control signals to manipulate object motions and have achieved remarkable synthesis results. However, 2D control signals are inherently limited in expressing the 3D nature of object motions. To overcome this problem, we introduce 3DTrajMaster, a robust controller that regulates multi-entity dynamics in 3D space, given user-desired 6DoF pose (location and rotation) sequences of entities. At the core of our approach is a plug-and-play 3D-motion grounded object injector that fuses multiple input entities with their respective 3D trajectories through a gated self-attention mechanism. In addition, we exploit an injector architecture to preserve the video diffusion prior, which is crucial for generalization ability. To mitigate video quality degradation, we introduce a domain adaptor during training and employ an annealed sampling strategy during inference. To address the lack of suitable training data, we construct a 360-Motion Dataset, which first correlates collected 3D human and animal assets with GPT-generated trajectory and then captures their motion with 12 evenly-surround cameras on diverse 3D UE platforms. Extensive experiments show that 3DTrajMaster sets a new state-of-the-art in both accuracy and generalization for controlling multi-entity 3D motions. Project page: http://fuxiao0719.github.io/projects/3dtrajmaster", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KwaiVGI/3DTrajMaster", "github_api_added_by": "auto" }, { "date": "2024-12-11", "arxiv_id": "2412.07338", "title": "Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation", "authors": [ "Lorenzo Cima", "Alessio Miaschi", "Amaury Trujillo", "Marco Avvenuti", "Felice Dell'Orletta", "Stefano Cresci" ], "github": "", "project_page": "", "abstract": "AI-generated counterspeech offers a promising and scalable strategy to curb online toxicity through direct replies that promote civil discourse. However, current counterspeech is one-size-fits-all, lacking adaptation to the moderation context and the users involved. We propose and evaluate multiple strategies for generating tailored counterspeech that is adapted to the moderation context and personalized for the moderated user. We instruct an LLaMA2-13B model to generate counterspeech, experimenting with various configurations based on different contextual information and fine-tuning strategies. We identify the configurations that generate persuasive counterspeech through a combination of quantitative indicators and human evaluations collected via a pre-registered mixed-design crowdsourcing experiment. Results show that contextualized counterspeech can significantly outperform state-of-the-art generic counterspeech in adequacy and persuasiveness, without compromising other characteristics. Our findings also reveal a poor correlation between quantitative indicators and human evaluations, suggesting that these methods assess different aspects and highlighting the need for nuanced evaluation methodologies. The effectiveness of contextualized AI-generated counterspeech and the divergence between human and algorithmic evaluations underscore the importance of increased human-AI collaboration in content moderation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.06578", "title": "MoViE: Mobile Diffusion for Video Editing", "authors": [ "Adil Karjauv", "Noor Fathima", "Ioannis Lelekas", "Fatih Porikli", "Amir Ghodrati", "Amirhossein Habibian" ], "github": "", "project_page": "", "abstract": "Recent progress in diffusion-based video editing has shown remarkable potential for practical applications. However, these methods remain prohibitively expensive and challenging to deploy on mobile devices. In this study, we introduce a series of optimizations that render mobile video editing feasible. Building upon the existing image editing model, we first optimize its architecture and incorporate a lightweight autoencoder. Subsequently, we extend classifier-free guidance distillation to multiple modalities, resulting in a threefold on-device speedup. Finally, we reduce the number of sampling steps to one by introducing a novel adversarial distillation scheme which preserves the controllability of the editing process. Collectively, these optimizations enable video editing at 12 frames per second on mobile devices, while maintaining high quality. Our results are available at https://qualcomm-ai-research.github.io/mobile-video-editing/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.05148", "title": "LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation", "authors": [ "Donald Shenaj", "Ondrej Bohdal", "Mete Ozay", "Pietro Zanuttigh", "Umberto Michieli" ], "github": "https://github.com/donaldssh/LoRA.rar", "project_page": "", "abstract": "Recent advancements in image generation models have enabled personalized image creation with both user-defined subjects (content) and styles. Prior works achieved personalization by merging corresponding low-rank adaptation parameters (LoRAs) through optimization-based methods, which are computationally demanding and unsuitable for real-time use on resource-constrained devices like smartphones. To address this, we introduce LoRA.rar, a method that not only improves image quality but also achieves a remarkable speedup of over 4000times in the merging process. LoRA.rar pre-trains a hypernetwork on a diverse set of content-style LoRA pairs, learning an efficient merging strategy that generalizes to new, unseen content-style pairs, enabling fast, high-quality personalization. Moreover, we identify limitations in existing evaluation metrics for content-style quality and propose a new protocol using multimodal large language models (MLLM) for more accurate assessment. Our method significantly outperforms the current state of the art in both content and style fidelity, as validated by MLLM assessments and human evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/donaldssh/LoRA.rar", "github_api_added_by": "auto" }, { "date": "2024-12-11", "arxiv_id": "2412.07583", "title": "Mobile Video Diffusion", "authors": [ "Haitam Ben Yahia", "Denis Korzhenkov", "Ioannis Lelekas", "Amir Ghodrati", "Amirhossein Habibian" ], "github": "", "project_page": "", "abstract": "Video diffusion models have achieved impressive realism and controllability but are limited by high computational demands, restricting their use on mobile devices. This paper introduces the first mobile-optimized video diffusion model. Starting from a spatio-temporal UNet from Stable Video Diffusion (SVD), we reduce memory and computational cost by reducing the frame resolution, incorporating multi-scale temporal representations, and introducing two novel pruning schema to reduce the number of channels and temporal blocks. Furthermore, we employ adversarial finetuning to reduce the denoising to a single step. Our model, coined as MobileVD, is 523x more efficient (1817.2 vs. 4.34 TFLOPs) with a slight quality drop (FVD 149 vs. 171), generating latents for a 14x512x256 px clip in 1.7 seconds on a Xiaomi-14 Pro. Our results are available at https://qualcomm-ai-research.github.io/mobile-video-diffusion/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.07282", "title": "HARP: Hesitation-Aware Reframing in Transformer Inference Pass", "authors": [ "Romain Stora\u00ef", "Seung-won Hwang" ], "github": "https://github.com/romsto/harp", "project_page": "", "abstract": "This paper aims to improve the performance of large language models by addressing the variable computational demands in inference steps, where some tokens require more computational resources than others. We present HARP, a simple modification to \"off-the-shelf\" Transformer forward pass. Drawing from hesitation and the framing effect in decision-making, HARP selectively applies additional computation when the model encounters uncertainty during token generation. Our method mimics human cognitive processes by pausing at difficult decision points and reframing inputs for a different perspective. Unlike other approaches, HARP is model-agnostic, training-free, and easy to implement. We thoroughly evaluate our method across various downstream tasks and model sizes, demonstrating performance improvements up to +5.16%. Notably, HARP achieves these gains while maintaining inference times twice faster than beam search. Simple and yet with significant gains, HARP offers a practical solution for enhancing the performance of Transformer-based language models with minimal computational impact.", "github_manual": "https://github.com/romsto/harp", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-11", "arxiv_id": "2412.06089", "title": "GraPE: A Generate-Plan-Edit Framework for Compositional T2I Synthesis", "authors": [ "Ashish Goswami", "Satyam Kumar Modi", "Santhosh Rishi Deshineni", "Harman Singh", "Prathosh A. P", "Parag Singla" ], "github": "https://github.com/dair-iitd/GraPE", "project_page": "", "abstract": "Text-to-image (T2I) generation has seen significant progress with diffusion models, enabling generation of photo-realistic images from text prompts. Despite this progress, existing methods still face challenges in following complex text prompts, especially those requiring compositional and multi-step reasoning. Given such complex instructions, SOTA models often make mistakes in faithfully modeling object attributes, and relationships among them. In this work, we present an alternate paradigm for T2I synthesis, decomposing the task of complex multi-step generation into three steps, (a) Generate: we first generate an image using existing diffusion models (b) Plan: we make use of Multi-Modal LLMs (MLLMs) to identify the mistakes in the generated image expressed in terms of individual objects and their properties, and produce a sequence of corrective steps required in the form of an edit-plan. (c) Edit: we make use of an existing text-guided image editing models to sequentially execute our edit-plan over the generated image to get the desired image which is faithful to the original instruction. Our approach derives its strength from the fact that it is modular in nature, is training free, and can be applied over any combination of image generation and editing models. As an added contribution, we also develop a model capable of compositional editing, which further helps improve the overall accuracy of our proposed approach. Our method flexibly trades inference time compute with performance on compositional text prompts. We perform extensive experimental evaluation across 3 benchmarks and 10 T2I models including DALLE-3 and the latest -- SD-3.5-Large. Our approach not only improves the performance of the SOTA models, by upto 3 points, it also reduces the performance gap between weaker and stronger models. https://dair-iitd.github.io/GraPE/{https://dair-iitd.github.io/GraPE/}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dair-iitd/GraPE", "github_api_added_by": "auto" }, { "date": "2024-12-11", "arxiv_id": "2412.07720", "title": "ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer", "authors": [ "Jinyi Hu", "Shengding Hu", "Yuxuan Song", "Yufei Huang", "Mingxuan Wang", "Hao Zhou", "Zhiyuan Liu", "Wei-Ying Ma", "Maosong Sun" ], "github": "https://github.com/thunlp/acdit", "project_page": "", "abstract": "The recent surge of interest in comprehensive multimodal models has necessitated the unification of diverse modalities. However, the unification suffers from disparate methodologies. Continuous visual generation necessitates the full-sequence diffusion-based approach, despite its divergence from the autoregressive modeling in the text domain. We posit that autoregressive modeling, i.e., predicting the future based on past deterministic experience, remains crucial in developing both a visual generation model and a potential unified multimodal model. In this paper, we explore an interpolation between the autoregressive modeling and full-parameters diffusion to model visual information. At its core, we present ACDiT, an Autoregressive blockwise Conditional Diffusion Transformer, where the block size of diffusion, i.e., the size of autoregressive units, can be flexibly adjusted to interpolate between token-wise autoregression and full-sequence diffusion. ACDiT is easy to implement, as simple as creating a Skip-Causal Attention Mask (SCAM) during training. During inference, the process iterates between diffusion denoising and autoregressive decoding that can make full use of KV-Cache. We verify the effectiveness of ACDiT on image and video generation tasks. We also demonstrate that benefitted from autoregressive modeling, ACDiT can be seamlessly used in visual understanding tasks despite being trained on the diffusion objective. The analysis of the trade-off between autoregressive modeling and diffusion demonstrates the potential of ACDiT to be used in long-horizon visual generation tasks. These strengths make it promising as the backbone of future unified models.", "github_manual": "https://github.com/thunlp/acdit", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.07760", "title": "SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints", "authors": [ "Jianhong Bai", "Menghan Xia", "Xintao Wang", "Ziyang Yuan", "Xiao Fu", "Zuozhu Liu", "Haoji Hu", "Pengfei Wan", "Di Zhang" ], "github": "https://github.com/kwaivgi/syncammaster", "project_page": "", "abstract": "Recent advancements in video diffusion models have shown exceptional abilities in simulating real-world dynamics and maintaining 3D consistency. This progress inspires us to investigate the potential of these models to ensure dynamic consistency across various viewpoints, a highly desirable feature for applications such as virtual filming. Unlike existing methods focused on multi-view generation of single objects for 4D reconstruction, our interest lies in generating open-world videos from arbitrary viewpoints, incorporating 6 DoF camera poses. To achieve this, we propose a plug-and-play module that enhances a pre-trained text-to-video model for multi-camera video generation, ensuring consistent content across different viewpoints. Specifically, we introduce a multi-view synchronization module to maintain appearance and geometry consistency across these viewpoints. Given the scarcity of high-quality training data, we design a hybrid training scheme that leverages multi-camera images and monocular videos to supplement Unreal Engine-rendered multi-camera videos. Furthermore, our method enables intriguing extensions, such as re-rendering a video from novel viewpoints. We also release a multi-view synchronized video dataset, named SynCamVideo-Dataset. Project page: https://jianhongbai.github.io/SynCamMaster/.", "github_manual": "https://github.com/kwaivgi/syncammaster", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.07797", "title": "Mogo: RQ Hierarchical Causal Transformer for High-Quality 3D Human Motion Generation", "authors": [ "Dongjie Fu" ], "github": "", "project_page": "", "abstract": "In the field of text-to-motion generation, Bert-type Masked Models (MoMask, MMM) currently produce higher-quality outputs compared to GPT-type autoregressive models (T2M-GPT). However, these Bert-type models often lack the streaming output capability required for applications in video game and multimedia environments, a feature inherent to GPT-type models. Additionally, they demonstrate weaker performance in out-of-distribution generation. To surpass the quality of BERT-type models while leveraging a GPT-type structure, without adding extra refinement models that complicate scaling data, we propose a novel architecture, Mogo (Motion Only Generate Once), which generates high-quality lifelike 3D human motions by training a single transformer model. Mogo consists of only two main components: 1) RVQ-VAE, a hierarchical residual vector quantization variational autoencoder, which discretizes continuous motion sequences with high precision; 2) Hierarchical Causal Transformer, responsible for generating the base motion sequences in an autoregressive manner while simultaneously inferring residuals across different layers. Experimental results demonstrate that Mogo can generate continuous and cyclic motion sequences up to 260 frames (13 seconds), surpassing the 196 frames (10 seconds) length limitation of existing datasets like HumanML3D. On the HumanML3D test set, Mogo achieves a FID score of 0.079, outperforming both the GPT-type model T2M-GPT (FID = 0.116), AttT2M (FID = 0.112) and the BERT-type model MMM (FID = 0.080). Furthermore, our model achieves the best quantitative performance in out-of-distribution generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.08580", "title": "LAION-SG: An Enhanced Large-Scale Dataset for Training Complex Image-Text Models with Structural Annotations", "authors": [ "Zejian Li", "Chenye Meng", "Yize Li", "Ling Yang", "Shengyuan Zhang", "Jiarui Ma", "Jiayi Li", "Guang Yang", "Changyuan Yang", "Zhiyuan Yang", "Jinxiong Chang", "Lingyun Sun" ], "github": "https://github.com/mengcye/LAION-SG", "project_page": "", "abstract": "Recent advances in text-to-image (T2I) generation have shown remarkable success in producing high-quality images from text. However, existing T2I models show decayed performance in compositional image generation involving multiple objects and intricate relationships. We attribute this problem to limitations in existing datasets of image-text pairs, which lack precise inter-object relationship annotations with prompts only. To address this problem, we construct LAION-SG, a large-scale dataset with high-quality structural annotations of scene graphs (SG), which precisely describe attributes and relationships of multiple objects, effectively representing the semantic structure in complex scenes. Based on LAION-SG, we train a new foundation model SDXL-SG to incorporate structural annotation information into the generation process. Extensive experiments show advanced models trained on our LAION-SG boast significant performance improvements in complex scene generation over models on existing datasets. We also introduce CompSG-Bench, a benchmark that evaluates models on compositional image generation, establishing a new standard for this domain.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mengcye/LAION-SG", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.07825", "title": "3DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark", "authors": [ "Wufei Ma", "Haoyu Chen", "Guofeng Zhang", "Celso M de Melo", "Alan Yuille", "Jieneng Chen" ], "github": "", "project_page": "", "abstract": "3D spatial reasoning is the ability to analyze and interpret the positions, orientations, and spatial relationships of objects within the 3D space. This allows models to develop a comprehensive understanding of the 3D scene, enabling their applicability to a broader range of areas, such as autonomous navigation, robotics, and AR/VR. While large multi-modal models (LMMs) have achieved remarkable progress in a wide range of image and video understanding tasks, their capabilities to perform 3D spatial reasoning on diverse natural images are less studied. In this work we present the first comprehensive 3D spatial reasoning benchmark, 3DSRBench, with 2,772 manually annotated visual question-answer pairs across 12 question types. We conduct robust and thorough evaluation of 3D spatial reasoning capabilities by balancing the data distribution and adopting a novel FlipEval strategy. To further study the robustness of 3D spatial reasoning w.r.t. camera 3D viewpoints, our 3DSRBench includes two subsets with 3D spatial reasoning questions on paired images with common and uncommon viewpoints. We benchmark a wide range of open-sourced and proprietary LMMs, uncovering their limitations in various aspects of 3D awareness, such as height, orientation, location, and multi-object reasoning, as well as their degraded performance on images with uncommon camera viewpoints. Our 3DSRBench provide valuable findings and insights about the future development of LMMs with strong 3D reasoning capabilities. Our project page and dataset is available https://3dsrbench.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.06234", "title": "Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction", "authors": [ "Seungtae Nam", "Xiangyu Sun", "Gyeongjin Kang", "Younggeun Lee", "Seungjun Oh", "Eunbyung Park" ], "github": "https://github.com/stnamjef/GenerativeDensification", "project_page": "", "abstract": "Generalized feed-forward Gaussian models have achieved significant progress in sparse-view 3D reconstruction by leveraging prior knowledge from large multi-view datasets. However, these models often struggle to represent high-frequency details due to the limited number of Gaussians. While the densification strategy used in per-scene 3D Gaussian splatting (3D-GS) optimization can be adapted to the feed-forward models, it may not be ideally suited for generalized scenarios. In this paper, we propose Generative Densification, an efficient and generalizable method to densify Gaussians generated by feed-forward models. Unlike the 3D-GS densification strategy, which iteratively splits and clones raw Gaussian parameters, our method up-samples feature representations from the feed-forward models and generates their corresponding fine Gaussians in a single forward pass, leveraging the embedded prior knowledge for enhanced generalization. Experimental results on both object-level and scene-level reconstruction tasks demonstrate that our method outperforms state-of-the-art approaches with comparable or smaller model sizes, achieving notable improvements in representing fine details.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stnamjef/GenerativeDensification", "github_api_added_by": "auto" }, { "date": "2024-12-12", "arxiv_id": "2412.08443", "title": "POINTS1.5: Building a Vision-Language Model towards Real World Applications", "authors": [ "Yuan Liu", "Le Tian", "Xiao Zhou", "Xinyu Gao", "Kavio Yu", "Yang Yu", "Jie Zhou" ], "github": "https://github.com/open-compass/opencompass", "project_page": "", "abstract": "Vision-language models have made significant strides recently, demonstrating superior performance across a range of tasks, e.g. optical character recognition and complex diagram analysis. Building on this trend, we introduce a new vision-language model, POINTS1.5, designed to excel in various real-world applications. POINTS1.5 is an enhancement of POINTS1.0 and incorporates several key innovations: i) We replace the original CLIP vision encoder, which had a fixed image resolution, with a NaViT-style vision encoder that supports native dynamic high resolution. This allows POINTS1.5 to process images of any resolution without needing to split them into tiles. ii) We add bilingual support to POINTS1.5, significantly enhancing its capability in Chinese. Due to the scarcity of open-source Chinese datasets for vision-language models, we collect numerous images from the Internet and annotate them using a combination of manual and automatic methods. iii) We propose a set of rigorous filtering methods for visual instruction tuning datasets. We comprehensively evaluate all these filtering methods, and choose the most effective ones to obtain the final visual instruction tuning set. Thanks to these innovations, POINTS1.5 significantly outperforms POINTS1.0 and demonstrates strong performance across a range of real-world applications. Notably, POINTS1.5-7B is trained on fewer than 4 billion tokens and ranks first on the OpenCompass leaderboard among models with fewer than 10 billion parameters", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-compass/opencompass", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.07744", "title": "StyleMaster: Stylize Your Video with Artistic Generation and Translation", "authors": [ "Zixuan Ye", "Huijuan Huang", "Xintao Wang", "Pengfei Wan", "Di Zhang", "Wenhan Luo" ], "github": "https://github.com/KwaiVGI/StyleMaster", "project_page": "", "abstract": "Style control has been popular in video generation models. Existing methods often generate videos far from the given style, cause content leakage, and struggle to transfer one video to the desired style. Our first observation is that the style extraction stage matters, whereas existing methods emphasize global style but ignore local textures. In order to bring texture features while preventing content leakage, we filter content-related patches while retaining style ones based on prompt-patch similarity; for global style extraction, we generate a paired style dataset through model illusion to facilitate contrastive learning, which greatly enhances the absolute style consistency. Moreover, to fill in the image-to-video gap, we train a lightweight motion adapter on still videos, which implicitly enhances stylization extent, and enables our image-trained model to be seamlessly applied to videos. Benefited from these efforts, our approach, StyleMaster, not only achieves significant improvement in both style resemblance and temporal coherence, but also can easily generalize to video style transfer with a gray tile ControlNet. Extensive experiments and visualizations demonstrate that StyleMaster significantly outperforms competitors, effectively generating high-quality stylized videos that align with textual content and closely resemble the style of reference images. Our project page is at https://zixuan-ye.github.io/stylemaster", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KwaiVGI/StyleMaster", "github_api_added_by": "auto" }, { "date": "2024-12-12", "arxiv_id": "2412.08646", "title": "StreamChat: Chatting with Streaming Video", "authors": [ "Jihao Liu", "Zhiding Yu", "Shiyi Lan", "Shihao Wang", "Rongyao Fang", "Jan Kautz", "Hongsheng Li", "Jose M. Alvare" ], "github": "", "project_page": "", "abstract": "This paper presents StreamChat, a novel approach that enhances the interaction capabilities of Large Multimodal Models (LMMs) with streaming video content. In streaming interaction scenarios, existing methods rely solely on visual information available at the moment a question is posed, resulting in significant delays as the model remains unaware of subsequent changes in the streaming video. StreamChat addresses this limitation by innovatively updating the visual context at each decoding step, ensuring that the model utilizes up-to-date video content throughout the decoding process. Additionally, we introduce a flexible and efficient crossattention-based architecture to process dynamic streaming inputs while maintaining inference efficiency for streaming interactions. Furthermore, we construct a new dense instruction dataset to facilitate the training of streaming interaction models, complemented by a parallel 3D-RoPE mechanism that encodes the relative temporal information of visual and text tokens. Experimental results demonstrate that StreamChat achieves competitive performance on established image and video benchmarks and exhibits superior capabilities in streaming interaction scenarios compared to state-of-the-art video LMM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.07147", "title": "MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation", "authors": [ "Bo Li", "Shaolin Zhu", "Lijie Wen" ], "github": "", "project_page": "", "abstract": "Image Translation (IT) holds immense potential across diverse domains, enabling the translation of textual content within images into various languages. However, existing datasets often suffer from limitations in scale, diversity, and quality, hindering the development and evaluation of IT models. To address this issue, we introduce MIT-10M, a large-scale parallel corpus of multilingual image translation with over 10M image-text pairs derived from real-world data, which has undergone extensive data cleaning and multilingual translation validation. It contains 840K images in three sizes, 28 categories, tasks with three levels of difficulty and 14 languages image-text pairs, which is a considerable improvement on existing datasets. We conduct extensive experiments to evaluate and train models on MIT-10M. The experimental results clearly indicate that our dataset has higher adaptability when it comes to evaluating the performance of the models in tackling challenging and complex image translation tasks in the real world. Moreover, the performance of the model fine-tuned with MIT-10M has tripled compared to the baseline model, further confirming its superiority.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.08486", "title": "Learning Flow Fields in Attention for Controllable Person Image Generation", "authors": [ "Zijian Zhou", "Shikun Liu", "Xiao Han", "Haozhe Liu", "Kam Woh Ng", "Tian Xie", "Yuren Cong", "Hang Li", "Mengmeng Xu", "Juan-Manuel P\u00e9rez-R\u00faa", "Aditya Patel", "Tao Xiang", "Miaojing Shi", "Sen He" ], "github": "https://github.com/franciszzj/leffa", "project_page": "", "abstract": "Controllable person image generation aims to generate a person image conditioned on reference images, allowing precise control over the person's appearance or pose. However, prior methods often distort fine-grained textural details from the reference image, despite achieving high overall image quality. We attribute these distortions to inadequate attention to corresponding regions in the reference image. To address this, we thereby propose learning flow fields in attention (Leffa), which explicitly guides the target query to attend to the correct reference key in the attention layer during training. Specifically, it is realized via a regularization loss on top of the attention map within a diffusion-based baseline. Our extensive experiments show that Leffa achieves state-of-the-art performance in controlling appearance (virtual try-on) and pose (pose transfer), significantly reducing fine-grained detail distortion while maintaining high image quality. Additionally, we show that our loss is model-agnostic and can be used to improve the performance of other diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/franciszzj/leffa", "github_api_added_by": "auto" }, { "date": "2024-12-12", "arxiv_id": "2412.08629", "title": "FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models", "authors": [ "Vladimir Kulikov", "Matan Kleiner", "Inbar Huberman-Spiegelglas", "Tomer Michaeli" ], "github": "https://github.com/fallenshock/FlowEdit", "project_page": "", "abstract": "Editing real images using a pre-trained text-to-image (T2I) diffusion/flow model often involves inverting the image into its corresponding noise map. However, inversion by itself is typically insufficient for obtaining satisfactory results, and therefore many methods additionally intervene in the sampling process. Such methods achieve improved results but are not seamlessly transferable between model architectures. Here, we introduce FlowEdit, a text-based editing method for pre-trained T2I flow models, which is inversion-free, optimization-free and model agnostic. Our method constructs an ODE that directly maps between the source and target distributions (corresponding to the source and target text prompts) and achieves a lower transport cost than the inversion approach. This leads to state-of-the-art results, as we illustrate with Stable Diffusion 3 and FLUX. Code and examples are available on the project's webpage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/fallenshock/FlowEdit", "github_api_added_by": "auto" }, { "date": "2024-12-12", "arxiv_id": "2412.08503", "title": "StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements", "authors": [ "Mingkun Lei", "Xue Song", "Beier Zhu", "Hao Wang", "Chi Zhang" ], "github": "", "project_page": "", "abstract": "Text-driven style transfer aims to merge the style of a reference image with content described by a text prompt. Recent advancements in text-to-image models have improved the nuance of style transformations, yet significant challenges remain, particularly with overfitting to reference styles, limiting stylistic control, and misaligning with textual content. In this paper, we propose three complementary strategies to address these issues. First, we introduce a cross-modal Adaptive Instance Normalization (AdaIN) mechanism for better integration of style and text features, enhancing alignment. Second, we develop a Style-based Classifier-Free Guidance (SCFG) approach that enables selective control over stylistic elements, reducing irrelevant influences. Finally, we incorporate a teacher model during early generation stages to stabilize spatial layouts and mitigate artifacts. Our extensive evaluations demonstrate significant improvements in style transfer quality and alignment with textual prompts. Furthermore, our approach can be integrated into existing style transfer frameworks without fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.06071", "title": "KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models", "authors": [ "Fan Wang", "Juyong Jiang", "Chansung Park", "Sunghun Kim", "Jing Tang" ], "github": "https://github.com/juyongjiang/KaSA", "project_page": "", "abstract": "The increasing sizes of large language models (LLMs) result in significant computational overhead and memory usage when adapting these models to specific tasks or domains. Various parameter-efficient fine-tuning (PEFT) methods have been devised to mitigate these challenges by training a small set of parameters for the task-specific updates of the model weights. Among PEFT methods, LoRA stands out for its simplicity and efficiency, inspiring the development of a series of variants. However, LoRA and its successors disregard the knowledge that is noisy or irrelevant to the targeted task, detrimentally impacting model performance and leading to suboptimality. To address this limitation, we introduce Knowledge-aware Singular-value Adaptation (KaSA), a PEFT method that leverages singular value decomposition (SVD) with knowledge-aware singular values to dynamically activate knowledge based on its relevance to the task at hand. We conduct extensive experiments across a range of LLMs on tasks spanning natural language understanding (NLU), generation (NLG), instruction following, and commonsense reasoning. The experimental results demonstrate that KaSA consistently outperforms FFT and 14 popular PEFT baselines across 16 benchmarks and 4 synthetic datasets, underscoring our method's efficacy and adaptability. The source code of our method is available at https://github.com/juyongjiang/KaSA.", "github_manual": "", "github_regex": "https://github.com/juyongjiang/KaSA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.08467", "title": "Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel", "authors": [ "Zun Wang", "Jialu Li", "Yicong Hong", "Songze Li", "Kunchang Li", "Shoubin Yu", "Yi Wang", "Yu Qiao", "Yali Wang", "Mohit Bansal", "Limin Wang" ], "github": "https://github.com/wz0919/vln-srdf", "project_page": "", "abstract": "Creating high-quality data for training robust language-instructed agents is a long-lasting challenge in embodied AI. In this paper, we introduce a Self-Refining Data Flywheel (SRDF) that generates high-quality and large-scale navigational instruction-trajectory pairs by iteratively refining the data pool through the collaboration between two models, the instruction generator and the navigator, without any human-in-the-loop annotation. Specifically, SRDF starts with using a base generator to create an initial data pool for training a base navigator, followed by applying the trained navigator to filter the data pool. This leads to higher-fidelity data to train a better generator, which can, in turn, produce higher-quality data for training the next-round navigator. Such a flywheel establishes a data self-refining process, yielding a continuously improved and highly effective dataset for large-scale language-guided navigation learning. Our experiments demonstrate that after several flywheel rounds, the navigator elevates the performance boundary from 70% to 78% SPL on the classic R2R test set, surpassing human performance (76%) for the first time. Meanwhile, this process results in a superior generator, evidenced by a SPICE increase from 23.5 to 26.2, better than all previous VLN instruction generation methods. Finally, we demonstrate the scalability of our method through increasing environment and instruction diversity, and the generalization ability of our pre-trained navigator across various downstream navigation tasks, surpassing state-of-the-art methods by a large margin in all cases.", "github_manual": "https://github.com/wz0919/vln-srdf", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.05467", "title": "The BrowserGym Ecosystem for Web Agent Research", "authors": [ "Thibault Le Sellier De Chezelles", "Maxime Gasse", "Alexandre Drouin", "Massimo Caccia", "L\u00e9o Boisvert", "Megh Thakkar", "Tom Marty", "Rim Assouel", "Sahar Omidi Shayegan", "Lawrence Keunho Jang", "Xing Han L\u00f9", "Ori Yoran", "Dehan Kong", "Frank F. Xu", "Siva Reddy", "Quentin Cappart", "Graham Neubig", "Ruslan Salakhutdinov", "Nicolas Chapados", "Alexandre Lacoste" ], "github": "https://github.com/servicenow/agentlab", "project_page": "", "abstract": "The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging automation and Large Language Models (LLMs) for web interaction tasks. Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. BrowserGym aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. Combined with AgentLab, a complementary framework that aids in agent creation, testing, and analysis, BrowserGym offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. This standardized approach seeks to reduce the time and complexity of developing web agents, supporting more reliable comparisons and facilitating in-depth analysis of agent behaviors, and could result in more adaptable, capable agents, ultimately accelerating innovation in LLM-driven automation. As a supporting evidence, we conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across all benchmarks currently available in BrowserGym. Among other findings, our results highlight a large discrepancy between OpenAI and Anthropic's latests models, with Claude-3.5-Sonnet leading the way on almost all benchmarks, except on vision-related tasks where GPT-4o is superior. Despite these advancements, our results emphasize that building robust and efficient web agents remains a significant challenge, due to the inherent complexity of real-world web environments and the limitations of current models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/servicenow/agentlab", "github_api_added_by": "auto" }, { "date": "2024-12-12", "arxiv_id": "2412.06676", "title": "I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token", "authors": [ "Roi Cohen", "Konstantin Dobler", "Eden Biran", "Gerard de Melo" ], "github": "https://github.com/roi-hpi/IDK-token-tuning", "project_page": "", "abstract": "Large Language Models are known to capture real-world knowledge, allowing them to excel in many downstream tasks. Despite recent advances, these models are still prone to what are commonly known as hallucinations, causing them to emit unwanted and factually incorrect text. In this work, we propose a novel calibration method that can be used to combat hallucinations. We add a special [IDK] (\"I don't know\") token to the model's vocabulary and introduce an objective function that shifts probability mass to the [IDK] token for incorrect predictions. This approach allows the model to express uncertainty in its output explicitly. We evaluate our proposed method across multiple model architectures and factual downstream tasks. We find that models trained with our method are able to express uncertainty in places where they would previously make mistakes while suffering only a small loss of encoded knowledge. We further perform extensive ablation studies of multiple variations of our approach and provide a detailed analysis of the precision-recall tradeoff of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/roi-hpi/IDK-token-tuning", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.06016", "title": "Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation", "authors": [ "Hyeonho Jeong", "Chun-Hao Paul Huang", "Jong Chul Ye", "Niloy Mitra", "Duygu Ceylan" ], "github": "", "project_page": "", "abstract": "While recent foundational video generators produce visually rich output, they still struggle with appearance drift, where objects gradually degrade or change inconsistently across frames, breaking visual coherence. We hypothesize that this is because there is no explicit supervision in terms of spatial tracking at the feature level. We propose Track4Gen, a spatially aware video generator that combines video diffusion loss with point tracking across frames, providing enhanced spatial supervision on the diffusion features. Track4Gen merges the video generation and point tracking tasks into a single network by making minimal changes to existing video generation architectures. Using Stable Video Diffusion as a backbone, Track4Gen demonstrates that it is possible to unify video generation and point tracking, which are typically handled as separate tasks. Our extensive evaluations show that Track4Gen effectively reduces appearance drift, resulting in temporally stable and visually coherent video generation. Project page: hyeonho99.github.io/track4gen", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-12", "arxiv_id": "2412.09551", "title": "Video Creation by Demonstration", "authors": [ "Yihong Sun", "Hao Zhou", "Liangzhe Yuan", "Jennifer J. Sun", "Yandong Li", "Xuhui Jia", "Hartwig Adam", "Bharath Hariharan", "Long Zhao", "Ting Liu" ], "github": "https://github.com/showlab/MotionDirector", "project_page": "", "abstract": "We explore a novel video creation experience, namely Video Creation by Demonstration. Given a demonstration video and a context image from a different scene, we generate a physically plausible video that continues naturally from the context image and carries out the action concepts from the demonstration. To enable this capability, we present delta-Diffusion, a self-supervised training approach that learns from unlabeled videos by conditional future frame prediction. Unlike most existing video generation controls that are based on explicit signals, we adopts the form of implicit latent control for maximal flexibility and expressiveness required by general videos. By leveraging a video foundation model with an appearance bottleneck design on top, we extract action latents from demonstration videos for conditioning the generation process with minimal appearance leakage. Empirically, delta-Diffusion outperforms related baselines in terms of both human preference and large-scale machine evaluations, and demonstrates potentials towards interactive world simulation. Sampled video generation results are available at https://delta-diffusion.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/showlab/MotionDirector", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09593", "title": "Neural LightRig: Unlocking Accurate Object Normal and Material Estimation with Multi-Light Diffusion", "authors": [ "Zexin He", "Tengfei Wang", "Xin Huang", "Xingang Pan", "Ziwei Liu" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "Recovering the geometry and materials of objects from a single image is challenging due to its under-constrained nature. In this paper, we present Neural LightRig, a novel framework that boosts intrinsic estimation by leveraging auxiliary multi-lighting conditions from 2D diffusion priors. Specifically, 1) we first leverage illumination priors from large-scale diffusion models to build our multi-light diffusion model on a synthetic relighting dataset with dedicated designs. This diffusion model generates multiple consistent images, each illuminated by point light sources in different directions. 2) By using these varied lighting images to reduce estimation uncertainty, we train a large G-buffer model with a U-Net backbone to accurately predict surface normals and materials. Extensive experiments validate that our approach significantly outperforms state-of-the-art methods, enabling accurate surface normal and PBR material estimation with vivid relighting effects. Code and dataset are available on our project page at https://projects.zxhezexin.com/neural-lightrig.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.05552", "title": "SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Experts", "authors": [ "Gengze Zhou", "Yicong Hong", "Zun Wang", "Chongyang Zhao", "Mohit Bansal", "Qi Wu" ], "github": "https://github.com/gengzezhou/same", "project_page": "", "abstract": "The academic field of learning instruction-guided visual navigation can be generally categorized into high-level category-specific search and low-level language-guided navigation, depending on the granularity of language instruction, in which the former emphasizes the exploration process, while the latter concentrates on following detailed textual commands. Despite the differing focuses of these tasks, the underlying requirements of interpreting instructions, comprehending the surroundings, and inferring action decisions remain consistent. This paper consolidates diverse navigation tasks into a unified and generic framework -- we investigate the core difficulties of sharing general knowledge and exploiting task-specific capabilities in learning navigation and propose a novel State-Adaptive Mixture of Experts (SAME) model that effectively enables an agent to infer decisions based on different-granularity language and dynamic observations. Powered by SAME, we present a versatile agent capable of addressing seven navigation tasks simultaneously that outperforms or achieves highly comparable performance to task-specific agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gengzezhou/same", "github_api_added_by": "auto" }, { "date": "2024-12-13", "arxiv_id": "2412.09573", "title": "FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction", "authors": [ "Jiale Xu", "Shenghua Gao", "Ying Shan" ], "github": "https://github.com/TencentARC/FreeSplatter", "project_page": "", "abstract": "Existing sparse-view reconstruction models heavily rely on accurate known camera poses. However, deriving camera extrinsics and intrinsics from sparse-view images presents significant challenges. In this work, we present FreeSplatter, a highly scalable, feed-forward reconstruction framework capable of generating high-quality 3D Gaussians from uncalibrated sparse-view images and recovering their camera parameters in mere seconds. FreeSplatter is built upon a streamlined transformer architecture, comprising sequential self-attention blocks that facilitate information exchange among multi-view image tokens and decode them into pixel-wise 3D Gaussian primitives. The predicted Gaussian primitives are situated in a unified reference frame, allowing for high-fidelity 3D modeling and instant camera parameter estimation using off-the-shelf solvers. To cater to both object-centric and scene-level reconstruction, we train two model variants of FreeSplatter on extensive datasets. In both scenarios, FreeSplatter outperforms state-of-the-art baselines in terms of reconstruction quality and pose estimation accuracy. Furthermore, we showcase FreeSplatter's potential in enhancing the productivity of downstream applications, such as text/image-to-3D content creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TencentARC/FreeSplatter", "github_api_added_by": "auto" }, { "date": "2024-12-13", "arxiv_id": "2412.08635", "title": "Multimodal Latent Language Modeling with Next-Token Diffusion", "authors": [ "Yutao Sun", "Hangbo Bao", "Wenhui Wang", "Zhiliang Peng", "Li Dong", "Shaohan Huang", "Jianyong Wang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Multimodal generative models require a unified approach to handle both discrete data (e.g., text and code) and continuous data (e.g., image, audio, video). In this work, we propose Latent Language Modeling (LatentLM), which seamlessly integrates continuous and discrete data using causal Transformers. Specifically, we employ a variational autoencoder (VAE) to represent continuous data as latent vectors and introduce next-token diffusion for autoregressive generation of these vectors. Additionally, we develop sigma-VAE to address the challenges of variance collapse, which is crucial for autoregressive modeling. Extensive experiments demonstrate the effectiveness of LatentLM across various modalities. In image generation, LatentLM surpasses Diffusion Transformers in both performance and scalability. When integrated into multimodal large language models, LatentLM provides a general-purpose interface that unifies multimodal generation and understanding. Experimental results show that LatentLM achieves favorable performance compared to Transfusion and vector quantized models in the setting of scaling up training tokens. In text-to-speech synthesis, LatentLM outperforms the state-of-the-art VALL-E 2 model in speaker similarity and robustness, while requiring 10x fewer decoding steps. The results establish LatentLM as a highly effective and scalable approach to advance large multimodal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09596", "title": "InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions", "authors": [ "Pan Zhang", "Xiaoyi Dong", "Yuhang Cao", "Yuhang Zang", "Rui Qian", "Xilin Wei", "Lin Chen", "Yifei Li", "Junbo Niu", "Shuangrui Ding", "Qipeng Guo", "Haodong Duan", "Xin Chen", "Han Lv", "Zheng Nie", "Min Zhang", "Bin Wang", "Wenwei Zhang", "Xinyue Zhang", "Jiaye Ge", "Wei Li", "Jingwen Li", "Zhongying Tu", "Conghui He", "Xingcheng Zhang", "Kai Chen", "Yu Qiao", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/InternLM/InternLM-XComposer/tree/main/InternLM-XComposer-2.5-OmniLive", "project_page": "", "abstract": "Creating AI systems that can interact with environments over long periods, similar to human cognition, has been a longstanding research goal. Recent advancements in multimodal large language models (MLLMs) have made significant strides in open-world understanding. However, the challenge of continuous and simultaneous streaming perception, memory, and reasoning remains largely unexplored. Current MLLMs are constrained by their sequence-to-sequence architecture, which limits their ability to process inputs and generate responses simultaneously, akin to being unable to think while perceiving. Furthermore, relying on long contexts to store historical data is impractical for long-term interactions, as retaining all information becomes costly and inefficient. Therefore, rather than relying on a single foundation model to perform all functions, this project draws inspiration from the concept of the Specialized Generalist AI and introduces disentangled streaming perception, reasoning, and memory mechanisms, enabling real-time interaction with streaming video and audio input. The proposed framework InternLM-XComposer2.5-OmniLive (IXC2.5-OL) consists of three key modules: (1) Streaming Perception Module: Processes multimodal information in real-time, storing key details in memory and triggering reasoning in response to user queries. (2) Multi-modal Long Memory Module: Integrates short-term and long-term memory, compressing short-term memories into long-term ones for efficient retrieval and improved accuracy. (3) Reasoning Module: Responds to queries and executes reasoning tasks, coordinating with the perception and memory modules. This project simulates human-like cognition, enabling multimodal large language models to provide continuous and adaptive service over time.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/InternLM/InternLM-XComposer/tree/main/InternLM-XComposer-2.5-OmniLive", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.08737", "title": "Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions", "authors": [ "Jiarui Zhang", "Ollie Liu", "Tianyu Yu", "Jinyi Hu", "Willie Neiswanger" ], "github": "https://github.com/euclid-multimodal/Euclid", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have made rapid progress in recent years, yet continue to struggle with low-level visual perception (LLVP) -- particularly the ability to accurately describe the geometric details of an image. This capability is crucial for applications in areas such as robotics, medical image analysis, and manufacturing. In this paper, we first introduce Geoperception, a benchmark designed to evaluate an MLLM's ability to accurately transcribe 2D geometric information from an image. Using this benchmark, we demonstrate the limitations of leading MLLMs, and then conduct a comprehensive empirical study to explore strategies for improving their performance on geometric tasks. Our findings highlight the benefits of certain model architectures, training techniques, and data strategies, including the use of high-fidelity synthetic data and multi-stage training with a data curriculum. Notably, we find that a data curriculum enables models to learn challenging geometry understanding tasks which they fail to learn from scratch. Leveraging these insights, we develop Euclid, a family of models specifically optimized for strong low-level geometric perception. Although purely trained on synthetic multimodal data, Euclid shows strong generalization ability to novel geometry shapes. For instance, Euclid outperforms the best closed-source model, Gemini-1.5-Pro, by up to 58.56% on certain Geoperception benchmark tasks and 10.65% on average across all tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/euclid-multimodal/Euclid", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09585", "title": "OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary Embedding Distillation", "authors": [ "Jitesh Jain", "Zhengyuan Yang", "Humphrey Shi", "Jianfeng Gao", "Jianwei Yang" ], "github": "https://github.com/SHI-Labs/OLA-VLM", "project_page": "", "abstract": "The standard practice for developing contemporary MLLMs is to feed features from vision encoder(s) into the LLM and train with natural language supervision. In this work, we posit an overlooked opportunity to optimize the intermediate LLM representations through a vision perspective (objective), i.e., solely natural language supervision is sub-optimal for the MLLM's visual understanding ability. To that end, we propose OLA-VLM, the first approach distilling knowledge into the LLM's hidden representations from a set of target visual representations. Firstly, we formulate the objective during the pretraining stage in MLLMs as a coupled optimization of predictive visual embedding and next text-token prediction. Secondly, we investigate MLLMs trained solely with natural language supervision and identify a positive correlation between the quality of visual representations within these models and their downstream performance. Moreover, upon probing our OLA-VLM, we observe improved representation quality owing to the embedding optimization. Thirdly, we demonstrate that our OLA-VLM outperforms the single and multi-encoder baselines, proving our approach's superiority over explicitly feeding the corresponding features to the LLM. Particularly, OLA-VLM boosts performance by an average margin of up to 2.5% on various benchmarks, with a notable improvement of 8.7% on the Depth task in CV-Bench. Our code is open-sourced at https://github.com/SHI-Labs/OLA-VLM .", "github_manual": "", "github_regex": "https://github.com/SHI-Labs/OLA-VLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09586", "title": "Gaze-LLE: Gaze Target Estimation via Large-Scale Learned Encoders", "authors": [ "Fiona Ryan", "Ajay Bati", "Sangmin Lee", "Daniel Bolya", "Judy Hoffman", "James M. Rehg" ], "github": "http://github.com/fkryan/gazelle", "project_page": "", "abstract": "We address the problem of gaze target estimation, which aims to predict where a person is looking in a scene. Predicting a person's gaze target requires reasoning both about the person's appearance and the contents of the scene. Prior works have developed increasingly complex, hand-crafted pipelines for gaze target estimation that carefully fuse features from separate scene encoders, head encoders, and auxiliary models for signals like depth and pose. Motivated by the success of general-purpose feature extractors on a variety of visual tasks, we propose Gaze-LLE, a novel transformer framework that streamlines gaze target estimation by leveraging features from a frozen DINOv2 encoder. We extract a single feature representation for the scene, and apply a person-specific positional prompt to decode gaze with a lightweight module. We demonstrate state-of-the-art performance across several gaze benchmarks and provide extensive analysis to validate our design choices. Our code is available at: http://github.com/fkryan/gazelle .", "github_manual": "", "github_regex": "", "github_paper_md": "http://github.com/fkryan/gazelle", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09618", "title": "EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM", "authors": [ "Zhuofan Zong", "Dongzhi Jiang", "Bingqi Ma", "Guanglu Song", "Hao Shao", "Dazhong Shen", "Yu Liu", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "Significant achievements in personalization of diffusion models have been witnessed. Conventional tuning-free methods mostly encode multiple reference images by averaging their image embeddings as the injection condition, but such an image-independent operation cannot perform interaction among images to capture consistent visual elements within multiple references. Although the tuning-based Low-Rank Adaptation (LoRA) can effectively extract consistent elements within multiple images through the training process, it necessitates specific finetuning for each distinct image group. This paper introduces EasyRef, a novel plug-and-play adaptation method that enables diffusion models to be conditioned on multiple reference images and the text prompt. To effectively exploit consistent visual elements within multiple images, we leverage the multi-image comprehension and instruction-following capabilities of the multimodal large language model (MLLM), prompting it to capture consistent visual elements based on the instruction. Besides, injecting the MLLM's representations into the diffusion process through adapters can easily generalize to unseen domains, mining the consistent visual elements within unseen data. To mitigate computational costs and enhance fine-grained detail preservation, we introduce an efficient reference aggregation strategy and a progressive training scheme. Finally, we introduce MRBench, a new multi-reference image generation benchmark. Experimental results demonstrate EasyRef surpasses both tuning-free methods like IP-Adapter and tuning-based methods like LoRA, achieving superior aesthetic quality and robust zero-shot generalization across diverse domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09025", "title": "Shiksha: A Technical Domain focused Translation Dataset and Model for Indian Languages", "authors": [ "Advait Joglekar", "Srinivasan Umesh" ], "github": "", "project_page": "", "abstract": "Neural Machine Translation (NMT) models are typically trained on datasets with limited exposure to Scientific, Technical and Educational domains. Translation models thus, in general, struggle with tasks that involve scientific understanding or technical jargon. Their performance is found to be even worse for low-resource Indian languages. Finding a translation dataset that tends to these domains in particular, poses a difficult challenge. In this paper, we address this by creating a multilingual parallel corpus containing more than 2.8 million rows of English-to-Indic and Indic-to-Indic high-quality translation pairs across 8 Indian languages. We achieve this by bitext mining human-translated transcriptions of NPTEL video lectures. We also finetune and evaluate NMT models using this corpus and surpass all other publicly available models at in-domain tasks. We also demonstrate the potential for generalizing to out-of-domain translation tasks by improving the baseline by over 2 BLEU on average for these Indian languages on the Flores+ benchmark. We are pleased to release our model and dataset via this link: https://huggingface.co/SPRINGLab.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09405", "title": "Learned Compression for Compressed Learning", "authors": [ "Dan Jacobellis", "Neeraja J. Yadwadkar" ], "github": "https://github.com/danjacobellis/LCCL", "project_page": "", "abstract": "Modern sensors produce increasingly rich streams of high-resolution data. Due to resource constraints, machine learning systems discard the vast majority of this information via resolution reduction. Compressed-domain learning allows models to operate on compact latent representations, allowing higher effective resolution for the same budget. However, existing compression systems are not ideal for compressed learning. Linear transform coding and end-to-end learned compression systems reduce bitrate, but do not uniformly reduce dimensionality; thus, they do not meaningfully increase efficiency. Generative autoencoders reduce dimensionality, but their adversarial or perceptual objectives lead to significant information loss. To address these limitations, we introduce WaLLoC (Wavelet Learned Lossy Compression), a neural codec architecture that combines linear transform coding with nonlinear dimensionality-reducing autoencoders. WaLLoC sandwiches a shallow, asymmetric autoencoder and entropy bottleneck between an invertible wavelet packet transform. Across several key metrics, WaLLoC outperforms the autoencoders used in state-of-the-art latent diffusion models. WaLLoC does not require perceptual or adversarial losses to represent high-frequency detail, providing compatibility with modalities beyond RGB images and stereo audio. WaLLoC's encoder consists almost entirely of linear operations, making it exceptionally efficient and suitable for mobile computing, remote sensing, and learning directly from compressed data. We demonstrate WaLLoC's capability for compressed-domain learning across several tasks, including image classification, colorization, document understanding, and music source separation. Our code, experiments, and pre-trained audio and image codecs are available at https://ut-sysml.org/walloc", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/danjacobellis/LCCL", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.08905", "title": "Phi-4 Technical Report", "authors": [ "Marah Abdin", "Jyoti Aneja", "Harkirat Behl", "S\u00e9bastien Bubeck", "Ronen Eldan", "Suriya Gunasekar", "Michael Harrison", "Russell J. Hewett", "Mojan Javaheripi", "Piero Kauffmann", "James R. Lee", "Yin Tat Lee", "Yuanzhi Li", "Weishung Liu", "Caio C. T. Mendes", "Anh Nguyen", "Eric Price", "Gustavo de Rosa", "Olli Saarikivi", "Adil Salim", "Shital Shah", "Xin Wang", "Rachel Ward", "Yue Wu", "Dingli Yu", "Cyril Zhang", "Yi Zhang" ], "github": "", "project_page": "", "abstract": "We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models, where pre-training is based primarily on organic data sources such as web content or code, phi-4 strategically incorporates synthetic data throughout the training process. While previous models in the Phi family largely distill the capabilities of a teacher model (specifically GPT-4), phi-4 substantially surpasses its teacher model on STEM-focused QA capabilities, giving evidence that our data-generation and post-training techniques go beyond distillation. Despite minimal changes to the phi-3 architecture, phi-4 achieves strong performance relative to its size -- especially on reasoning-focused benchmarks -- due to improved data, training curriculum, and innovations in the post-training scheme.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.05994", "title": "PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations", "authors": [ "Namgyu Kang", "Jaemin Oh", "Youngjoon Hong", "Eunbyung Park" ], "github": "https://github.com/NamGyuKang/Physics-Informed-Gaussians", "project_page": "", "abstract": "The approximation of Partial Differential Equations (PDEs) using neural networks has seen significant advancements through Physics-Informed Neural Networks (PINNs). Despite their straightforward optimization framework and flexibility in implementing various PDEs, PINNs often suffer from limited accuracy due to the spectral bias of Multi-Layer Perceptrons (MLPs), which struggle to effectively learn high-frequency and non-linear components. Recently, parametric mesh representations in combination with neural networks have been investigated as a promising approach to eliminate the inductive biases of neural networks. However, they usually require very high-resolution grids and a large number of collocation points to achieve high accuracy while avoiding overfitting issues. In addition, the fixed positions of the mesh parameters restrict their flexibility, making it challenging to accurately approximate complex PDEs. To overcome these limitations, we propose Physics-Informed Gaussians (PIGs), which combine feature embeddings using Gaussian functions with a lightweight neural network. Our approach uses trainable parameters for the mean and variance of each Gaussian, allowing for dynamic adjustment of their positions and shapes during training. This adaptability enables our model to optimally approximate PDE solutions, unlike models with fixed parameter positions. Furthermore, the proposed approach maintains the same optimization framework used in PINNs, allowing us to benefit from their excellent properties. Experimental results show the competitive performance of our model across various PDEs, demonstrating its potential as a robust tool for solving complex PDEs. Our project page is available at https://namgyukang.github.io/Physics-Informed-Gaussians/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NamGyuKang/Physics-Informed-Gaussians", "github_api_added_by": "auto" }, { "date": "2024-12-13", "arxiv_id": "2412.08972", "title": "RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios", "authors": [ "Ruiwen Zhou", "Wenyue Hua", "Liangming Pan", "Sitao Cheng", "Xiaobao Wu", "En Yu", "William Yang Wang" ], "github": "https://github.com/skyriver-2000/rulearena", "project_page": "", "abstract": "This paper introduces RuleArena, a novel and challenging benchmark designed to evaluate the ability of large language models (LLMs) to follow complex, real-world rules in reasoning. Covering three practical domains -- airline baggage fees, NBA transactions, and tax regulations -- RuleArena assesses LLMs' proficiency in handling intricate natural language instructions that demand long-context understanding, logical reasoning, and accurate mathematical computation. Two key attributes distinguish RuleArena from traditional rule-based reasoning benchmarks: (1) it extends beyond standard first-order logic representations, and (2) it is grounded in authentic, practical scenarios, providing insights into the suitability and reliability of LLMs for real-world applications. Our findings reveal several notable limitations in LLMs: (1) they struggle to identify and apply the appropriate rules, frequently becoming confused by similar but distinct regulations, (2) they cannot consistently perform accurate mathematical computations, even when they correctly identify the relevant rules, and (3) in general, they perform poorly in the benchmark. These results highlight significant challenges in advancing LLMs' rule-guided reasoning capabilities in real-life applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/skyriver-2000/rulearena", "github_api_added_by": "auto" }, { "date": "2024-12-13", "arxiv_id": "2412.09370", "title": "Word Sense Linking: Disambiguating Outside the Sandbox", "authors": [ "Andrei Stefan Bejgu", "Edoardo Barba", "Luigi Procopio", "Alberte Fern\u00e1ndez-Castro", "Roberto Navigli" ], "github": "https://github.com/SapienzaNLP/consec", "project_page": "", "abstract": "Word Sense Disambiguation (WSD) is the task of associating a word in a given context with its most suitable meaning among a set of possible candidates. While the task has recently witnessed renewed interest, with systems achieving performances above the estimated inter-annotator agreement, at the time of writing it still struggles to find downstream applications. We argue that one of the reasons behind this is the difficulty of applying WSD to plain text. Indeed, in the standard formulation, models work under the assumptions that a) all the spans to disambiguate have already been identified, and b) all the possible candidate senses of each span are provided, both of which are requirements that are far from trivial. In this work, we present a new task called Word Sense Linking (WSL) where, given an input text and a reference sense inventory, systems have to both identify which spans to disambiguate and then link them to their most suitable meaning.We put forward a transformer-based architecture for the task and thoroughly evaluate both its performance and those of state-of-the-art WSD systems scaled to WSL, iteratively relaxing the assumptions of WSD. We hope that our work will foster easier integration of lexical semantics into downstream applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SapienzaNLP/consec", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09605", "title": "AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials", "authors": [ "Yiheng Xu", "Dunjie Lu", "Zhennan Shen", "Junli Wang", "Zekun Wang", "Yuchen Mao", "Caiming Xiong", "Tao Yu" ], "github": "https://github.com/niuzaisheng/ScreenAgent/tree/main/data/ScreenAgent/train", "project_page": "", "abstract": "Graphical User Interface (GUI) agents hold great potential for automating complex tasks across diverse digital environments, from web applications to desktop software. However, the development of such agents is hindered by the lack of high-quality, multi-step trajectory data required for effective training. Existing approaches rely on expensive and labor-intensive human annotation, making them unsustainable at scale. To address this challenge, we propose AgentTrek, a scalable data synthesis pipeline that generates high-quality GUI agent trajectories by leveraging web tutorials. Our method automatically gathers tutorial-like texts from the internet, transforms them into task goals with step-by-step instructions, and employs a visual-language model agent to simulate their execution in a real digital environment. A VLM-based evaluator ensures the correctness of the generated trajectories. We demonstrate that training GUI agents with these synthesized trajectories significantly improves their grounding and planning performance over the current models. Moreover, our approach is more cost-efficient compared to traditional human annotation methods. This work underscores the potential of guided replay with web tutorials as a viable strategy for large-scale GUI agent training, paving the way for more capable and autonomous digital agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/niuzaisheng/ScreenAgent/tree/main/data/ScreenAgent/train", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09619", "title": "SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training", "authors": [ "Dongting Hu", "Jierun Chen", "Xijie Huang", "Huseyin Coskun", "Arpit Sahni", "Aarush Gupta", "Anujraaj Goyal", "Dishani Lahiri", "Rajesh Singh", "Yerlan Idelbayev", "Junli Cao", "Yanyu Li", "Kwang-Ting Cheng", "S. -H. Gary Chan", "Mingming Gong", "Sergey Tulyakov", "Anil Kag", "Yanwu Xu", "Jian Ren" ], "github": "", "project_page": "", "abstract": "Existing text-to-image (T2I) diffusion models face several limitations, including large model sizes, slow runtime, and low-quality generation on mobile devices. This paper aims to address all of these challenges by developing an extremely small and fast T2I model that generates high-resolution and high-quality images on mobile platforms. We propose several techniques to achieve this goal. First, we systematically examine the design choices of the network architecture to reduce model parameters and latency, while ensuring high-quality generation. Second, to further improve generation quality, we employ cross-architecture knowledge distillation from a much larger model, using a multi-level approach to guide the training of our model from scratch. Third, we enable a few-step generation by integrating adversarial guidance with knowledge distillation. For the first time, our model SnapGen, demonstrates the generation of 1024x1024 px images on a mobile device around 1.4 seconds. On ImageNet-1K, our model, with only 372M parameters, achieves an FID of 2.06 for 256x256 px generation. On T2I benchmarks (i.e., GenEval and DPG-Bench), our model with merely 379M parameters, surpasses large-scale models with billions of parameters at a significantly smaller size (e.g., 7x smaller than SDXL, 14x smaller than IF-XL).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09501", "title": "Lyra: An Efficient and Speech-Centric Framework for Omni-Cognition", "authors": [ "Zhisheng Zhong", "Chengyao Wang", "Yuqi Liu", "Senqiao Yang", "Longxiang Tang", "Yuechen Zhang", "Jingyao Li", "Tianyuan Qu", "Yanwei Li", "Yukang Chen", "Shaozuo Yu", "Sitong Wu", "Eric Lo", "Shu Liu", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/Lyra", "project_page": "", "abstract": "As Multi-modal Large Language Models (MLLMs) evolve, expanding beyond single-domain capabilities is essential to meet the demands for more versatile and efficient AI. However, previous omni-models have insufficiently explored speech, neglecting its integration with multi-modality. We introduce Lyra, an efficient MLLM that enhances multimodal abilities, including advanced long-speech comprehension, sound understanding, cross-modality efficiency, and seamless speech interaction. To achieve efficiency and speech-centric capabilities, Lyra employs three strategies: (1) leveraging existing open-source large models and a proposed multi-modality LoRA to reduce training costs and data requirements; (2) using a latent multi-modality regularizer and extractor to strengthen the relationship between speech and other modalities, thereby enhancing model performance; and (3) constructing a high-quality, extensive dataset that includes 1.5M multi-modal (language, vision, audio) data samples and 12K long speech samples, enabling Lyra to handle complex long speech inputs and achieve more robust omni-cognition. Compared to other omni-methods, Lyra achieves state-of-the-art performance on various vision-language, vision-speech, and speech-language benchmarks, while also using fewer computational resources and less training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dvlab-research/Lyra", "github_api_added_by": "auto" }, { "date": "2024-12-13", "arxiv_id": "2412.09622", "title": "LoRACLR: Contrastive Adaptation for Customization of Diffusion Models", "authors": [ "Enis Simsar", "Thomas Hofmann", "Federico Tombari", "Pinar Yanardag" ], "github": "", "project_page": "", "abstract": "Recent advances in text-to-image customization have enabled high-fidelity, context-rich generation of personalized images, allowing specific concepts to appear in a variety of scenarios. However, current methods struggle with combining multiple personalized models, often leading to attribute entanglement or requiring separate training to preserve concept distinctiveness. We present LoRACLR, a novel approach for multi-concept image generation that merges multiple LoRA models, each fine-tuned for a distinct concept, into a single, unified model without additional individual fine-tuning. LoRACLR uses a contrastive objective to align and merge the weight spaces of these models, ensuring compatibility while minimizing interference. By enforcing distinct yet cohesive representations for each concept, LoRACLR enables efficient, scalable model composition for high-quality, multi-concept image synthesis. Our results highlight the effectiveness of LoRACLR in accurately merging multiple concepts, advancing the capabilities of personalized image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09013", "title": "Arbitrary-steps Image Super-resolution via Diffusion Inversion", "authors": [ "Zongsheng Yue", "Kang Liao", "Chen Change Loy" ], "github": "https://github.com/zsyOAOA/InvSR", "project_page": "", "abstract": "This study presents a new image super-resolution (SR) technique based on diffusion inversion, aiming at harnessing the rich image priors encapsulated in large pre-trained diffusion models to improve SR performance. We design a Partial noise Prediction strategy to construct an intermediate state of the diffusion model, which serves as the starting sampling point. Central to our approach is a deep noise predictor to estimate the optimal noise maps for the forward diffusion process. Once trained, this noise predictor can be used to initialize the sampling process partially along the diffusion trajectory, generating the desirable high-resolution result. Compared to existing approaches, our method offers a flexible and efficient sampling mechanism that supports an arbitrary number of sampling steps, ranging from one to five. Even with a single sampling step, our method demonstrates superior or comparable performance to recent state-of-the-art approaches. The code and model are publicly available at https://github.com/zsyOAOA/InvSR.", "github_manual": "", "github_regex": "https://github.com/zsyOAOA/InvSR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09569", "title": "JuStRank: Benchmarking LLM Judges for System Ranking", "authors": [ "Ariel Gera", "Odellia Boni", "Yotam Perlitz", "Roy Bar-Haim", "Lilach Eden", "Asaf Yehudai" ], "github": "", "project_page": "", "abstract": "Given the rapid progress of generative AI, there is a pressing need to systematically compare and choose between the numerous models and configurations available. The scale and versatility of such evaluations make the use of LLM-based judges a compelling solution for this challenge. Crucially, this approach requires first to validate the quality of the LLM judge itself. Previous work has focused on instance-based assessment of LLM judges, where a judge is evaluated over a set of responses, or response pairs, while being agnostic to their source systems. We argue that this setting overlooks critical factors affecting system-level ranking, such as a judge's positive or negative bias towards certain systems. To address this gap, we conduct the first large-scale study of LLM judges as system rankers. System scores are generated by aggregating judgment scores over multiple system outputs, and the judge's quality is assessed by comparing the resulting system ranking to a human-based ranking. Beyond overall judge assessment, our analysis provides a fine-grained characterization of judge behavior, including their decisiveness and bias.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09349", "title": "DisPose: Disentangling Pose Guidance for Controllable Human Image Animation", "authors": [ "Hongxiang Li", "Yaowei Li", "Yuhang Yang", "Junjie Cao", "Zhihong Zhu", "Xuxin Cheng", "Long Chen" ], "github": "https://github.com/lihxxx/DisPose", "project_page": "", "abstract": "Controllable human image animation aims to generate videos from reference images using driving videos. Due to the limited control signals provided by sparse guidance (e.g., skeleton pose), recent works have attempted to introduce additional dense conditions (e.g., depth map) to ensure motion alignment. However, such strict dense guidance impairs the quality of the generated video when the body shape of the reference character differs significantly from that of the driving video. In this paper, we present DisPose to mine more generalizable and effective control signals without additional dense input, which disentangles the sparse skeleton pose in human image animation into motion field guidance and keypoint correspondence. Specifically, we generate a dense motion field from a sparse motion field and the reference image, which provides region-level dense guidance while maintaining the generalization of the sparse pose control. We also extract diffusion features corresponding to pose keypoints from the reference image, and then these point features are transferred to the target pose to provide distinct identity information. To seamlessly integrate into existing models, we propose a plug-and-play hybrid ControlNet that improves the quality and consistency of generated videos while freezing the existing model parameters. Extensive qualitative and quantitative experiments demonstrate the superiority of DisPose compared to current methods. Code: https://github.com/lihxxx/DisPose{https://github.com/lihxxx/DisPose}.", "github_manual": "", "github_regex": "https://github.com/lihxxx/DisPose", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.09460", "title": "The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective", "authors": [ "Javier de la Rosa", "Vladislav Mikhailov", "Lemei Zhang", "Freddy Wetjen", "David Samuel", "Peng Liu", "Rolv-Arild Braaten", "Petter M\u00e6hlum", "Magnus Breder Birkenes", "Andrey Kutuzov", "Tita Enstad", "Svein Arne Brygfjeld", "Jon Atle Gulla", "Stephan Oepen", "Erik Velldal", "Wilfred \u00d8stgulen", "Liljia \u00d8vrelid", "Aslak Sira Myhre" ], "github": "https://github.com/mimir-project/mimir-evaluation", "project_page": "", "abstract": "The use of copyrighted materials in training generative language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessing the impact of copyrighted materials on the performance of large language models (LLMs) for Norwegian. We found that both books and newspapers contribute positively when the models are evaluated on a diverse set of Norwegian benchmarks, while fiction works possibly lead to decreased performance. Our experiments could inform the creation of a compensation scheme for authors whose works contribute to AI development.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mimir-project/mimir-evaluation", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.08687", "title": "VisionArena: 230K Real World User-VLM Conversations with Preference Labels", "authors": [ "Christopher Chou", "Lisa Dunlap", "Koki Mashita", "Krishna Mandal", "Trevor Darrell", "Ion Stoica", "Joseph E. Gonzalez", "Wei-Lin Chiang" ], "github": "", "project_page": "", "abstract": "With the growing adoption and capabilities of vision-language models (VLMs) comes the need for benchmarks that capture authentic user-VLM interactions. In response, we create VisionArena, a dataset of 230K real-world conversations between users and VLMs. Collected from Chatbot Arena - an open-source platform where users interact with VLMs and submit preference votes - VisionArena spans 73K unique users, 45 VLMs, and 138 languages. Our dataset contains three subsets: VisionArena-Chat, 200k single and multi-turn conversations between a user and a VLM; VisionArena-Battle, 30K conversations comparing two anonymous VLMs with user preference votes; and VisionArena-Bench, an automatic benchmark of 500 diverse user prompts that efficiently approximate the live Chatbot Arena model rankings. Additionally, we highlight the types of question asked by users, the influence of response style on preference, and areas where models often fail. We find open-ended tasks like captioning and humor are highly style-dependent, and current VLMs struggle with spatial reasoning and planning tasks. Lastly, we show finetuning the same base model on VisionArena-Chat outperforms Llava-Instruct-158K, with a 17-point gain on MMMU and a 46-point gain on the WildVision benchmark. Dataset at https://huggingface.co/lmarena-ai", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.06745", "title": "ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities", "authors": [ "Adhiraj Ghosh", "Sebastian Dziadzio", "Ameya Prabhu", "Vishaal Udandarao", "Samuel Albanie", "Matthias Bethge" ], "github": "https://github.com/bethgelab/onebench", "project_page": "", "abstract": "Traditional fixed test sets fall short in evaluating open-ended capabilities of foundation models. To address this, we propose ONEBench(OpeN-Ended Benchmarking), a new testing paradigm that consolidates individual evaluation datasets into a unified, ever-expanding sample pool. ONEBench allows users to generate custom, open-ended evaluation benchmarks from this pool, corresponding to specific capabilities of interest. By aggregating samples across test sets, ONEBench enables the assessment of diverse capabilities beyond those covered by the original test sets, while mitigating overfitting and dataset bias. Most importantly, it frames model evaluation as a collective process of selecting and aggregating sample-level tests. The shift from task-specific benchmarks to ONEBench introduces two challenges: (1)heterogeneity and (2)incompleteness. Heterogeneity refers to the aggregation over diverse metrics, while incompleteness describes comparing models evaluated on different data subsets. To address these challenges, we explore algorithms to aggregate sparse measurements into reliable model scores. Our aggregation algorithm ensures identifiability(asymptotically recovering ground-truth scores) and rapid convergence, enabling accurate model ranking with less data. On homogenous datasets, we show our aggregation algorithm provides rankings that highly correlate with those produced by average scores. We also demonstrate robustness to ~95% of measurements missing, reducing evaluation cost by up to 20x with little-to-no change in model rankings. We introduce ONEBench-LLM for language models and ONEBench-LMM for vision-language models, unifying evaluations across these domains. Overall, we present a technique for open-ended evaluation, which can aggregate over incomplete, heterogeneous sample-level measurements to continually grow a benchmark alongside the rapidly developing foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bethgelab/onebench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-13", "arxiv_id": "2412.06329", "title": "Normalizing Flows are Capable Generative Models", "authors": [ "Shuangfei Zhai", "Ruixiang Zhang", "Preetum Nakkiran", "David Berthelot", "Jiatao Gu", "Huangjie Zheng", "Tianrong Chen", "Miguel Angel Bautista", "Navdeep Jaitly", "Josh Susskind" ], "github": "https://github.com/apple/ml-tarflow", "project_page": "", "abstract": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly performant NF models. TarFlow can be thought of as a Transformer-based variant of Masked Autoregressive Flows (MAFs): it consists of a stack of autoregressive Transformer blocks on image patches, alternating the autoregression direction between layers. TarFlow is straightforward to train end-to-end, and capable of directly modeling and generating pixels. We also propose three key techniques to improve sample quality: Gaussian noise augmentation during training, a post training denoising procedure, and an effective guidance method for both class-conditional and unconditional settings. Putting these together, TarFlow sets new state-of-the-art results on likelihood estimation for images, beating the previous best methods by a large margin, and generates samples with quality and diversity comparable to diffusion models, for the first time with a stand-alone NF model. We make our code available at https://github.com/apple/ml-tarflow.", "github_manual": "", "github_regex": "https://github.com/apple/ml-tarflow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.07517", "title": "FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing", "authors": [ "Yingying Deng", "Xiangyu He", "Changwang Mei", "Peisong Wang", "Fan Tang" ], "github": "https://github.com/HolmesShuan/FireFlow", "project_page": "", "abstract": "Though Rectified Flows (ReFlows) with distillation offers a promising way for fast sampling, its fast inversion transforms images back to structured noise for recovery and following editing remains unsolved. This paper introduces FireFlow, a simple yet effective zero-shot approach that inherits the startling capacity of ReFlow-based models (such as FLUX) in generation while extending its capabilities to accurate inversion and editing in 8 steps. We first demonstrate that a carefully designed numerical solver is pivotal for ReFlow inversion, enabling accurate inversion and reconstruction with the precision of a second-order solver while maintaining the practical efficiency of a first-order Euler method. This solver achieves a 3times runtime speedup compared to state-of-the-art ReFlow inversion and editing techniques, while delivering smaller reconstruction errors and superior editing results in a training-free mode. The code is available at https://github.com/HolmesShuan/FireFlow{this URL}.", "github_manual": "", "github_regex": "https://github.com/HolmesShuan/FireFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.09624", "title": "GenEx: Generating an Explorable World", "authors": [ "Taiming Lu", "Tianmin Shu", "Junfei Xiao", "Luoxin Ye", "Jiahao Wang", "Cheng Peng", "Chen Wei", "Daniel Khashabi", "Rama Chellappa", "Alan Yuille", "Jieneng Chen" ], "github": "", "project_page": "", "abstract": "Understanding, navigating, and exploring the 3D physical real world has long been a central challenge in the development of artificial intelligence. In this work, we take a step toward this goal by introducing GenEx, a system capable of planning complex embodied world exploration, guided by its generative imagination that forms priors (expectations) about the surrounding environments. GenEx generates an entire 3D-consistent imaginative environment from as little as a single RGB image, bringing it to life through panoramic video streams. Leveraging scalable 3D world data curated from Unreal Engine, our generative model is rounded in the physical world. It captures a continuous 360-degree environment with little effort, offering a boundless landscape for AI agents to explore and interact with. GenEx achieves high-quality world generation, robust loop consistency over long trajectories, and demonstrates strong 3D capabilities such as consistency and active 3D mapping. Powered by generative imagination of the world, GPT-assisted agents are equipped to perform complex embodied tasks, including both goal-agnostic exploration and goal-driven navigation. These agents utilize predictive expectation regarding unseen parts of the physical world to refine their beliefs, simulate different outcomes based on potential decisions, and make more informed choices. In summary, we demonstrate that GenEx provides a transformative platform for advancing embodied AI in imaginative spaces and brings potential for extending these capabilities to real-world exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.10047", "title": "Large Action Models: From Inception to Implementation", "authors": [ "Lu Wang", "Fangkai Yang", "Chaoyun Zhang", "Junting Lu", "Jiaxu Qian", "Shilin He", "Pu Zhao", "Bo Qiao", "Ray Huang", "Si Qin", "Qisheng Su", "Jiayi Ye", "Yudi Zhang", "Jian-Guang Lou", "Qingwei Lin", "Saravan Rajmohan", "Dongmei Zhang", "Qi Zhang" ], "github": "https://github.com/microsoft/UFO/tree/main/dataflow", "project_page": "", "abstract": "As AI continues to advance, there is a growing demand for systems that go beyond language-based assistance and move toward intelligent agents capable of performing real-world actions. This evolution requires the transition from traditional Large Language Models (LLMs), which excel at generating textual responses, to Large Action Models (LAMs), designed for action generation and execution within dynamic environments. Enabled by agent systems, LAMs hold the potential to transform AI from passive language understanding to active task completion, marking a significant milestone in the progression toward artificial general intelligence. In this paper, we present a comprehensive framework for developing LAMs, offering a systematic approach to their creation, from inception to deployment. We begin with an overview of LAMs, highlighting their unique characteristics and delineating their differences from LLMs. Using a Windows OS-based agent as a case study, we provide a detailed, step-by-step guide on the key stages of LAM development, including data collection, model training, environment integration, grounding, and evaluation. This generalizable workflow can serve as a blueprint for creating functional LAMs in various application domains. We conclude by identifying the current limitations of LAMs and discussing directions for future research and industrial deployment, emphasizing the challenges and opportunities that lie ahead in realizing the full potential of LAMs in real-world applications. The code for the data collection process utilized in this paper is publicly available at: https://github.com/microsoft/UFO/tree/main/dataflow, and comprehensive documentation can be found at https://microsoft.github.io/UFO/dataflow/overview/.", "github_manual": "", "github_regex": "https://github.com/microsoft/UFO/tree/main/dataflow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.09626", "title": "FreeScale: Unleashing the Resolution of Diffusion Models via Tuning-Free Scale Fusion", "authors": [ "Haonan Qiu", "Shiwei Zhang", "Yujie Wei", "Ruihang Chu", "Hangjie Yuan", "Xiang Wang", "Yingya Zhang", "Ziwei Liu" ], "github": "https://github.com/ali-vilab/FreeScale", "project_page": "", "abstract": "Visual diffusion models achieve remarkable progress, yet they are typically trained at limited resolutions due to the lack of high-resolution data and constrained computation resources, hampering their ability to generate high-fidelity images or videos at higher resolutions. Recent efforts have explored tuning-free strategies to exhibit the untapped potential higher-resolution visual generation of pre-trained models. However, these methods are still prone to producing low-quality visual content with repetitive patterns. The key obstacle lies in the inevitable increase in high-frequency information when the model generates visual content exceeding its training resolution, leading to undesirable repetitive patterns deriving from the accumulated errors. To tackle this challenge, we propose FreeScale, a tuning-free inference paradigm to enable higher-resolution visual generation via scale fusion. Specifically, FreeScale processes information from different receptive scales and then fuses it by extracting desired frequency components. Extensive experiments validate the superiority of our paradigm in extending the capabilities of higher-resolution visual generation for both image and video models. Notably, compared with the previous best-performing method, FreeScale unlocks the generation of 8k-resolution images for the first time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ali-vilab/FreeScale", "github_api_added_by": "auto" }, { "date": "2024-12-16", "arxiv_id": "2412.10319", "title": "SCBench: A KV Cache-Centric Analysis of Long-Context Methods", "authors": [ "Yucheng Li", "Huiqiang Jiang", "Qianhui Wu", "Xufang Luo", "Surin Ahn", "Chengruidong Zhang", "Amir H. Abdi", "Dongsheng Li", "Jianfeng Gao", "Yuqing Yang", "Lili Qiu" ], "github": "https://github.com/FasterDecoding/SnapKV", "project_page": "", "abstract": "Long-context LLMs have enabled numerous downstream applications but also introduced significant challenges related to computational and memory efficiency. To address these challenges, optimizations for long-context inference have been developed, centered around the KV cache. However, existing benchmarks often evaluate in single-request, neglecting the full lifecycle of the KV cache in real-world use. This oversight is particularly critical, as KV cache reuse has become widely adopted in LLMs inference frameworks, such as vLLM and SGLang, as well as by LLM providers, including OpenAI, Microsoft, Google, and Anthropic. To address this gap, we introduce SCBench(SharedContextBench), a comprehensive benchmark for evaluating long-context methods from a KV cachecentric perspective: 1) KV cache generation, 2) KV cache compression, 3) KV cache retrieval, 4) KV cache loading. Specifically, SCBench uses test examples with shared context, ranging 12 tasks with two shared context modes, covering four categories of long-context capabilities: string retrieval, semantic retrieval, global information, and multi-task. With it, we provide an extensive KV cache-centric analysis of eight categories long-context solutions, including Gated Linear RNNs, Mamba-Attention hybrids, and efficient methods such as sparse attention, KV cache dropping, quantization, retrieval, loading, and prompt compression. The evaluation is conducted on 8 long-context LLMs. Our findings show that sub-O(n) memory methods suffer in multi-turn scenarios, while sparse encoding with O(n) memory and sub-O(n^2) pre-filling computation perform robustly. Dynamic sparsity yields more expressive KV caches than static patterns, and layer-level sparsity in hybrid architectures reduces memory usage with strong performance. Additionally, we identify attention distribution shift issues in long-generation scenarios. https://aka.ms/SCBench.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FasterDecoding/SnapKV", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.09283", "title": "InstanceCap: Improving Text-to-Video Generation via Instance-aware Structured Caption", "authors": [ "Tiehan Fan", "Kepan Nan", "Rui Xie", "Penghao Zhou", "Zhenheng Yang", "Chaoyou Fu", "Xiang Li", "Jian Yang", "Ying Tai" ], "github": "", "project_page": "", "abstract": "Text-to-video generation has evolved rapidly in recent years, delivering remarkable results. Training typically relies on video-caption paired data, which plays a crucial role in enhancing generation performance. However, current video captions often suffer from insufficient details, hallucinations and imprecise motion depiction, affecting the fidelity and consistency of generated videos. In this work, we propose a novel instance-aware structured caption framework, termed InstanceCap, to achieve instance-level and fine-grained video caption for the first time. Based on this scheme, we design an auxiliary models cluster to convert original video into instances to enhance instance fidelity. Video instances are further used to refine dense prompts into structured phrases, achieving concise yet precise descriptions. Furthermore, a 22K InstanceVid dataset is curated for training, and an enhancement pipeline that tailored to InstanceCap structure is proposed for inference. Experimental results demonstrate that our proposed InstanceCap significantly outperform previous models, ensuring high fidelity between captions and videos while reducing hallucinations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.10360", "title": "Apollo: An Exploration of Video Understanding in Large Multimodal Models", "authors": [ "Orr Zohar", "Xiaohan Wang", "Yann Dubois", "Nikhil Mehta", "Tong Xiao", "Philippe Hansen-Estruch", "Licheng Yu", "Xiaofang Wang", "Felix Juefei-Xu", "Ning Zhang", "Serena Yeung-Levy", "Xide Xia" ], "github": "", "project_page": "", "abstract": "Despite the rapid integration of video perception capabilities into Large Multimodal Models (LMMs), the underlying mechanisms driving their video understanding remain poorly understood. Consequently, many design decisions in this domain are made without proper justification or analysis. The high computational cost of training and evaluating such models, coupled with limited open research, hinders the development of video-LMMs. To address this, we present a comprehensive study that helps uncover what effectively drives video understanding in LMMs. We begin by critically examining the primary contributors to the high computational requirements associated with video-LMM research and discover Scaling Consistency, wherein design and training decisions made on smaller models and datasets (up to a critical size) effectively transfer to larger models. Leveraging these insights, we explored many video-specific aspects of video-LMMs, including video sampling, architectures, data composition, training schedules, and more. For example, we demonstrated that fps sampling during training is vastly preferable to uniform frame sampling and which vision encoders are the best for video representation. Guided by these findings, we introduce Apollo, a state-of-the-art family of LMMs that achieve superior performance across different model sizes. Our models can perceive hour-long videos efficiently, with Apollo-3B outperforming most existing 7B models with an impressive 55.1 on LongVideoBench. Apollo-7B is state-of-the-art compared to 7B LMMs with a 70.9 on MLVU, and 63.3 on Video-MME.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.09428", "title": "Multimodal Music Generation with Explicit Bridges and Retrieval Augmentation", "authors": [ "Baisen Wang", "Le Zhuo", "Zhaokai Wang", "Chenxi Bao", "Wu Chengjing", "Xuecheng Nie", "Jiao Dai", "Jizhong Han", "Yue Liao", "Si Liu" ], "github": "https://github.com/wbs2788/VMB", "project_page": "", "abstract": "Multimodal music generation aims to produce music from diverse input modalities, including text, videos, and images. Existing methods use a common embedding space for multimodal fusion. Despite their effectiveness in other modalities, their application in multimodal music generation faces challenges of data scarcity, weak cross-modal alignment, and limited controllability. This paper addresses these issues by using explicit bridges of text and music for multimodal alignment. We introduce a novel method named Visuals Music Bridge (VMB). Specifically, a Multimodal Music Description Model converts visual inputs into detailed textual descriptions to provide the text bridge; a Dual-track Music Retrieval module that combines broad and targeted retrieval strategies to provide the music bridge and enable user control. Finally, we design an Explicitly Conditioned Music Generation framework to generate music based on the two bridges. We conduct experiments on video-to-music, image-to-music, text-to-music, and controllable music generation tasks, along with experiments on controllability. The results demonstrate that VMB significantly enhances music quality, modality, and customization alignment compared to previous methods. VMB sets a new standard for interpretable and expressive multimodal music generation with applications in various multimedia fields. Demos and code are available at https://github.com/wbs2788/VMB.", "github_manual": "", "github_regex": "https://github.com/wbs2788/VMB", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.07769", "title": "BiMediX2: Bio-Medical EXpert LMM for Diverse Medical Modalities", "authors": [ "Sahal Shaji Mullappilly", "Mohammed Irfan Kurpath", "Sara Pieri", "Saeed Yahya Alseiari", "Shanavas Cholakkal", "Khaled Aldahmani", "Fahad Khan", "Rao Anwer", "Salman Khan", "Timothy Baldwin", "Hisham Cholakkal" ], "github": "https://github.com/mbzuai-oryx/BiMediX2", "project_page": "", "abstract": "This paper introduces BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model (LMM) with a unified architecture that integrates text and visual modalities, enabling advanced image understanding and medical applications. BiMediX2 leverages the Llama3.1 architecture and integrates text and visual capabilities to facilitate seamless interactions in both English and Arabic, supporting text-based inputs and multi-turn conversations involving medical images. The model is trained on an extensive bilingual healthcare dataset consisting of 1.6M samples of diverse medical interactions for both text and image modalities, mixed in Arabic and English. We also propose the first bilingual GPT-4o based medical LMM benchmark named BiMed-MBench. BiMediX2 is benchmarked on both text-based and image-based tasks, achieving state-of-the-art performance across several medical benchmarks. It outperforms recent state-of-the-art models in medical LLM evaluation benchmarks. Our model also sets a new benchmark in multimodal medical evaluations with over 9% improvement in English and over 20% in Arabic evaluations. Additionally, it surpasses GPT-4 by around 9% in UPHILL factual accuracy evaluations and excels in various medical Visual Question Answering, Report Generation, and Report Summarization tasks. The project page including source code and the trained model, is available at https://github.com/mbzuai-oryx/BiMediX2.", "github_manual": "", "github_regex": "https://github.com/mbzuai-oryx/BiMediX2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.08347", "title": "SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs", "authors": [ "Sultan Alrashed" ], "github": "", "project_page": "", "abstract": "We present SmolTulu-1.7b-Instruct, referenced in this report as SmolTulu-DPO-1130, an instruction-tuned language model that adapts AllenAI's Tulu 3 post-training pipeline to enhance Huggingface's SmolLM2-1.7B base model. Through comprehensive empirical analysis using a 135M parameter model, we demonstrate that the relationship between learning rate and batch size significantly impacts model performance in a task-dependent manner. Our findings reveal a clear split: reasoning tasks like ARC and GSM8K benefit from higher learning rate to batch size ratios, while pattern recognition tasks such as HellaSwag and IFEval show optimal performance with lower ratios. These insights informed the development of SmolTulu, which achieves state-of-the-art performance among sub-2B parameter models on instruction following, scoring 67.7% on IFEval (Delta11%), and mathematical reasoning with 51.6% on GSM8K (Delta3.4%), with an alternate version achieving scoring 57.1% on ARC (Delta5.4%). We release our model, training recipes, and ablation studies to facilitate further research in efficient model alignment, demonstrating that careful adaptation of optimization dynamics can help bridge the capability gap between small and large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.09910", "title": "Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attacks on Breast Ultrasound Images", "authors": [ "Yasamin Medghalchi", "Moein Heidari", "Clayton Allard", "Leonid Sigal", "Ilker Hacihaliloglu" ], "github": "https://github.com/yasamin-med/P2P", "project_page": "", "abstract": "Deep neural networks (DNNs) offer significant promise for improving breast cancer diagnosis in medical imaging. However, these models are highly susceptible to adversarial attacks--small, imperceptible changes that can mislead classifiers--raising critical concerns about their reliability and security. Traditional attacks rely on fixed-norm perturbations, misaligning with human perception. In contrast, diffusion-based attacks require pre-trained models, demanding substantial data when these models are unavailable, limiting practical use in data-scarce scenarios. In medical imaging, however, this is often unfeasible due to the limited availability of datasets. Building on recent advancements in learnable prompts, we propose Prompt2Perturb (P2P), a novel language-guided attack method capable of generating meaningful attack examples driven by text instructions. During the prompt learning phase, our approach leverages learnable prompts within the text encoder to create subtle, yet impactful, perturbations that remain imperceptible while guiding the model towards targeted outcomes. In contrast to current prompt learning-based approaches, our P2P stands out by directly updating text embeddings, avoiding the need for retraining diffusion models. Further, we leverage the finding that optimizing only the early reverse diffusion steps boosts efficiency while ensuring that the generated adversarial examples incorporate subtle noise, thus preserving ultrasound image quality without introducing noticeable artifacts. We show that our method outperforms state-of-the-art attack techniques across three breast ultrasound datasets in FID and LPIPS. Moreover, the generated images are both more natural in appearance and more effective compared to existing adversarial attacks. Our code will be publicly available https://github.com/yasamin-med/P2P.", "github_manual": "", "github_regex": "https://github.com/yasamin-med/P2P", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.08645", "title": "ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation", "authors": [ "Daniel Winter", "Asaf Shul", "Matan Cohen", "Dana Berman", "Yael Pritch", "Alex Rav-Acha", "Yedid Hoshen" ], "github": "", "project_page": "", "abstract": "This paper introduces a tuning-free method for both object insertion and subject-driven generation. The task involves composing an object, given multiple views, into a scene specified by either an image or text. Existing methods struggle to fully meet the task's challenging objectives: (i) seamlessly composing the object into the scene with photorealistic pose and lighting, and (ii) preserving the object's identity. We hypothesize that achieving these goals requires large scale supervision, but manually collecting sufficient data is simply too expensive. The key observation in this paper is that many mass-produced objects recur across multiple images of large unlabeled datasets, in different scenes, poses, and lighting conditions. We use this observation to create massive supervision by retrieving sets of diverse views of the same object. This powerful paired dataset enables us to train a straightforward text-to-image diffusion architecture to map the object and scene descriptions to the composited image. We compare our method, ObjectMate, with state-of-the-art methods for object insertion and subject-driven generation, using a single or multiple references. Empirically, ObjectMate achieves superior identity preservation and more photorealistic composition. Differently from many other multi-reference methods, ObjectMate does not require slow test-time tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.09604", "title": "SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding", "authors": [ "Hao Li", "Changyao Tian", "Jie Shao", "Xizhou Zhu", "Zhaokai Wang", "Jinguo Zhu", "Wenhan Dou", "Xiaogang Wang", "Hongsheng Li", "Lewei Lu", "Jifeng Dai" ], "github": "", "project_page": "", "abstract": "The remarkable success of Large Language Models (LLMs) has extended to the multimodal domain, achieving outstanding performance in image understanding and generation. Recent efforts to develop unified Multimodal Large Language Models (MLLMs) that integrate these capabilities have shown promising results. However, existing approaches often involve complex designs in model architecture or training pipeline, increasing the difficulty of model training and scaling. In this paper, we propose SynerGen-VL, a simple yet powerful encoder-free MLLM capable of both image understanding and generation. To address challenges identified in existing encoder-free unified MLLMs, we introduce the token folding mechanism and the vision-expert-based progressive alignment pretraining strategy, which effectively support high-resolution image understanding while reducing training complexity. After being trained on large-scale mixed image-text data with a unified next-token prediction objective, SynerGen-VL achieves or surpasses the performance of existing encoder-free unified MLLMs with comparable or smaller parameter sizes, and narrows the gap with task-specific state-of-the-art models, highlighting a promising path toward future unified MLLMs. Our code and models shall be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.09611", "title": "FluxSpace: Disentangled Semantic Editing in Rectified Flow Transformers", "authors": [ "Yusuf Dalva", "Kavana Venkatesh", "Pinar Yanardag" ], "github": "", "project_page": "", "abstract": "Rectified flow models have emerged as a dominant approach in image generation, showcasing impressive capabilities in high-quality image synthesis. However, despite their effectiveness in visual generation, rectified flow models often struggle with disentangled editing of images. This limitation prevents the ability to perform precise, attribute-specific modifications without affecting unrelated aspects of the image. In this paper, we introduce FluxSpace, a domain-agnostic image editing method leveraging a representation space with the ability to control the semantics of images generated by rectified flow transformers, such as Flux. By leveraging the representations learned by the transformer blocks within the rectified flow models, we propose a set of semantically interpretable representations that enable a wide range of image editing tasks, from fine-grained image editing to artistic creation. This work offers a scalable and effective image editing approach, along with its disentanglement capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.09856", "title": "LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity", "authors": [ "Hongjie Wang", "Chih-Yao Ma", "Yen-Cheng Liu", "Ji Hou", "Tao Xu", "Jialiang Wang", "Felix Juefei-Xu", "Yaqiao Luo", "Peizhao Zhang", "Tingbo Hou", "Peter Vajda", "Niraj K. Jha", "Xiaoliang Dai" ], "github": "", "project_page": "", "abstract": "Text-to-video generation enhances content creation but is highly computationally intensive: The computational cost of Diffusion Transformers (DiTs) scales quadratically in the number of pixels. This makes minute-length video generation extremely expensive, limiting most existing models to generating videos of only 10-20 seconds length. We propose a Linear-complexity text-to-video Generation (LinGen) framework whose cost scales linearly in the number of pixels. For the first time, LinGen enables high-resolution minute-length video generation on a single GPU without compromising quality. It replaces the computationally-dominant and quadratic-complexity block, self-attention, with a linear-complexity block called MATE, which consists of an MA-branch and a TE-branch. The MA-branch targets short-to-long-range correlations, combining a bidirectional Mamba2 block with our token rearrangement method, Rotary Major Scan, and our review tokens developed for long video generation. The TE-branch is a novel TEmporal Swin Attention block that focuses on temporal correlations between adjacent tokens and medium-range tokens. The MATE block addresses the adjacency preservation issue of Mamba and improves the consistency of generated videos significantly. Experimental results show that LinGen outperforms DiT (with a 75.6% win rate) in video quality with up to 15times (11.5times) FLOPs (latency) reduction. Furthermore, both automatic metrics and human evaluation demonstrate our LinGen-4B yields comparable video quality to state-of-the-art models (with a 50.5%, 52.1%, 49.1% win rate with respect to Gen-3, LumaLabs, and Kling, respectively). This paves the way to hour-length movie generation and real-time interactive video generation. We provide 68s video generation results and more examples in our project website: https://lineargen.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.10345", "title": "TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies", "authors": [ "Ruijie Zheng", "Yongyuan Liang", "Shuaiyi Huang", "Jianfeng Gao", "Hal Daum\u00e9 III", "Andrey Kolobov", "Furong Huang", "Jianwei Yang" ], "github": "", "project_page": "", "abstract": "Although large vision-language-action (VLA) models pretrained on extensive robot datasets offer promising generalist policies for robotic learning, they still struggle with spatial-temporal dynamics in interactive robotics, making them less effective in handling complex tasks, such as manipulation. In this work, we introduce visual trace prompting, a simple yet effective approach to facilitate VLA models' spatial-temporal awareness for action prediction by encoding state-action trajectories visually. We develop a new TraceVLA model by finetuning OpenVLA on our own collected dataset of 150K robot manipulation trajectories using visual trace prompting. Evaluations of TraceVLA across 137 configurations in SimplerEnv and 4 tasks on a physical WidowX robot demonstrate state-of-the-art performance, outperforming OpenVLA by 10% on SimplerEnv and 3.5x on real-robot tasks and exhibiting robust generalization across diverse embodiments and scenarios. To further validate the effectiveness and generality of our method, we present a compact VLA model based on 4B Phi-3-Vision, pretrained on the Open-X-Embodiment and finetuned on our dataset, rivals the 7B OpenVLA baseline while significantly improving inference efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.09722", "title": "GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers", "authors": [ "Sarkar Snigdha Sarathi Das", "Ryo Kamoi", "Bo Pang", "Yusen Zhang", "Caiming Xiong", "Rui Zhang" ], "github": "https://github.com/psunlpgroup/GreaTer", "project_page": "", "abstract": "The effectiveness of large language models (LLMs) is closely tied to the design of prompts, making prompt optimization essential for enhancing their performance across a wide range of tasks. Many existing approaches to automating prompt engineering rely exclusively on textual feedback, refining prompts based solely on inference errors identified by large, computationally expensive LLMs. Unfortunately, smaller models struggle to generate high-quality feedback, resulting in complete dependence on large LLM judgment. Moreover, these methods fail to leverage more direct and finer-grained information, such as gradients, due to operating purely in text space. To this end, we introduce GReaTer, a novel prompt optimization technique that directly incorporates gradient information over task-specific reasoning. By utilizing task loss gradients, GReaTer enables self-optimization of prompts for open-source, lightweight language models without the need for costly closed-source LLMs. This allows high-performance prompt optimization without dependence on massive LLMs, closing the gap between smaller models and the sophisticated reasoning often needed for prompt refinement. Extensive evaluations across diverse reasoning tasks including BBH, GSM8k, and FOLIO demonstrate that GReaTer consistently outperforms previous state-of-the-art prompt optimization methods, even those reliant on powerful LLMs. Additionally, GReaTer-optimized prompts frequently exhibit better transferability and, in some cases, boost task performance to levels comparable to or surpassing those achieved by larger language models, highlighting the effectiveness of prompt optimization guided by gradients over reasoning. Code of GReaTer is available at https://github.com/psunlpgroup/GreaTer.", "github_manual": "", "github_regex": "https://github.com/psunlpgroup/GreaTer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-16", "arxiv_id": "2412.10208", "title": "Efficient Generative Modeling with Residual Vector Quantization-Based Tokens", "authors": [ "Jaehyeon Kim", "Taehong Moon", "Keon Lee", "Jaewoong Cho" ], "github": "", "project_page": "", "abstract": "We explore the use of Residual Vector Quantization (RVQ) for high-fidelity generation in vector-quantized generative models. This quantization technique maintains higher data fidelity by employing more in-depth tokens. However, increasing the token number in generative models leads to slower inference speeds. To this end, we introduce ResGen, an efficient RVQ-based discrete diffusion model that generates high-fidelity samples without compromising sampling speed. Our key idea is a direct prediction of vector embedding of collective tokens rather than individual ones. Moreover, we demonstrate that our proposed token masking and multi-token prediction method can be formulated within a principled probabilistic framework using a discrete diffusion process and variational inference. We validate the efficacy and generalizability of the proposed method on two challenging tasks across different modalities: conditional image generation} on ImageNet 256x256 and zero-shot text-to-speech synthesis. Experimental results demonstrate that ResGen outperforms autoregressive counterparts in both tasks, delivering superior performance without compromising sampling speed. Furthermore, as we scale the depth of RVQ, our generative models exhibit enhanced generation fidelity or faster sampling speeds compared to similarly sized baseline models. The project page can be found at https://resgen-genai.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.11258", "title": "GaussianProperty: Integrating Physical Properties to 3D Gaussians with LMMs", "authors": [ "Xinli Xu", "Wenhang Ge", "Dicong Qiu", "ZhiFei Chen", "Dongyu Yan", "Zhuoyun Liu", "Haoyu Zhao", "Hanfeng Zhao", "Shunsi Zhang", "Junwei Liang", "Ying-Cong Chen" ], "github": "https://github.com/xxlbigbrother/Gaussian-Property", "project_page": "", "abstract": "Estimating physical properties for visual data is a crucial task in computer vision, graphics, and robotics, underpinning applications such as augmented reality, physical simulation, and robotic grasping. However, this area remains under-explored due to the inherent ambiguities in physical property estimation. To address these challenges, we introduce GaussianProperty, a training-free framework that assigns physical properties of materials to 3D Gaussians. Specifically, we integrate the segmentation capability of SAM with the recognition capability of GPT-4V(ision) to formulate a global-local physical property reasoning module for 2D images. Then we project the physical properties from multi-view 2D images to 3D Gaussians using a voting strategy. We demonstrate that 3D Gaussians with physical property annotations enable applications in physics-based dynamic simulation and robotic grasping. For physics-based dynamic simulation, we leverage the Material Point Method (MPM) for realistic dynamic simulation. For robot grasping, we develop a grasping force prediction strategy that estimates a safe force range required for object grasping based on the estimated physical properties. Extensive experiments on material segmentation, physics-based dynamic simulation, and robotic grasping validate the effectiveness of our proposed method, highlighting its crucial role in understanding physical properties from visual data. Online demo, code, more cases and annotated datasets are available on https://Gaussian-Property.github.io{this https URL}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xxlbigbrother/Gaussian-Property", "github_api_added_by": "auto" }, { "date": "2024-12-17", "arxiv_id": "2412.11919", "title": "RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation", "authors": [ "Xiaoxi Li", "Jiajie Jin", "Yujia Zhou", "Yongkang Wu", "Zhonghua Li", "Qi Ye", "Zhicheng Dou" ], "github": "https://github.com/sunnynexus/RetroLLM", "project_page": "", "abstract": "Large language models (LLMs) exhibit remarkable generative capabilities but often suffer from hallucinations. Retrieval-augmented generation (RAG) offers an effective solution by incorporating external knowledge, but existing methods still face several limitations: additional deployment costs of separate retrievers, redundant input tokens from retrieved text chunks, and the lack of joint optimization of retrieval and generation. To address these issues, we propose RetroLLM, a unified framework that integrates retrieval and generation into a single, cohesive process, enabling LLMs to directly generate fine-grained evidence from the corpus with constrained decoding. Moreover, to mitigate false pruning in the process of constrained evidence generation, we introduce (1) hierarchical FM-Index constraints, which generate corpus-constrained clues to identify a subset of relevant documents before evidence generation, reducing irrelevant decoding space; and (2) a forward-looking constrained decoding strategy, which considers the relevance of future sequences to improve evidence accuracy. Extensive experiments on five open-domain QA datasets demonstrate RetroLLM's superior performance across both in-domain and out-of-domain tasks. The code is available at https://github.com/sunnynexus/RetroLLM.", "github_manual": "", "github_regex": "https://github.com/sunnynexus/RetroLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.11231", "title": "Smaller Language Models Are Better Instruction Evolvers", "authors": [ "Tingfeng Hui", "Lulu Zhao", "Guanting Dong", "Yaqi Zhang", "Hua Zhou", "Sen Su" ], "github": "https://github.com/HypherX/Evolution-Analysis", "project_page": "", "abstract": "Instruction tuning has been widely used to unleash the complete potential of large language models. Notably, complex and diverse instructions are of significant importance as they can effectively align models with various downstream tasks. However, current approaches to constructing large-scale instructions predominantly favour powerful models such as GPT-4 or those with over 70 billion parameters, under the empirical presumption that such larger language models (LLMs) inherently possess enhanced capabilities. In this study, we question this prevalent assumption and conduct an in-depth exploration into the potential of smaller language models (SLMs) in the context of instruction evolution. Extensive experiments across three scenarios of instruction evolution reveal that smaller language models (SLMs) can synthesize more effective instructions than LLMs. Further analysis demonstrates that SLMs possess a broader output space during instruction evolution, resulting in more complex and diverse variants. We also observe that the existing metrics fail to focus on the impact of the instructions. Thus, we propose Instruction Complex-Aware IFD (IC-IFD), which introduces instruction complexity in the original IFD score to evaluate the effectiveness of instruction data more accurately. Our source code is available at: https://github.com/HypherX/Evolution-Analysis{https://github.com/HypherX/Evolution-Analysis}", "github_manual": "", "github_regex": "https://github.com/HypherX/Evolution-Analysis", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.12083", "title": "IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations", "authors": [ "Zhibing Li", "Tong Wu", "Jing Tan", "Mengchen Zhang", "Jiaqi Wang", "Dahua Lin" ], "github": "", "project_page": "", "abstract": "Capturing geometric and material information from images remains a fundamental challenge in computer vision and graphics. Traditional optimization-based methods often require hours of computational time to reconstruct geometry, material properties, and environmental lighting from dense multi-view inputs, while still struggling with inherent ambiguities between lighting and material. On the other hand, learning-based approaches leverage rich material priors from existing 3D object datasets but face challenges with maintaining multi-view consistency. In this paper, we introduce IDArb, a diffusion-based model designed to perform intrinsic decomposition on an arbitrary number of images under varying illuminations. Our method achieves accurate and multi-view consistent estimation on surface normals and material properties. This is made possible through a novel cross-view, cross-domain attention module and an illumination-augmented, view-adaptive training strategy. Additionally, we introduce ARB-Objaverse, a new dataset that provides large-scale multi-view intrinsic data and renderings under diverse lighting conditions, supporting robust training. Extensive experiments demonstrate that IDArb outperforms state-of-the-art methods both qualitatively and quantitatively. Moreover, our approach facilitates a range of downstream tasks, including single-image relighting, photometric stereo, and 3D reconstruction, highlighting its broad applications in realistic 3D content creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.11586", "title": "StrandHead: Text to Strand-Disentangled 3D Head Avatars Using Hair Geometric Priors", "authors": [ "Xiaokun Sun", "Zeyu Cai", "Zhenyu Zhang", "Ying Tai", "Jian Yang" ], "github": "", "project_page": "", "abstract": "While haircut indicates distinct personality, existing avatar generation methods fail to model practical hair due to the general or entangled representation. We propose StrandHead, a novel text to 3D head avatar generation method capable of generating disentangled 3D hair with strand representation. Without using 3D data for supervision, we demonstrate that realistic hair strands can be generated from prompts by distilling 2D generative diffusion models. To this end, we propose a series of reliable priors on shape initialization, geometric primitives, and statistical haircut features, leading to a stable optimization and text-aligned performance. Extensive experiments show that StrandHead achieves the state-of-the-art reality and diversity of generated 3D head and hair. The generated 3D hair can also be easily implemented in the Unreal Engine for physical simulation and other applications. The code will be available at https://xiaokunsun.github.io/StrandHead.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.11457", "title": "MOVIS: Enhancing Multi-Object Novel View Synthesis for Indoor Scenes", "authors": [ "Ruijie Lu", "Yixin Chen", "Junfeng Ni", "Baoxiong Jia", "Yu Liu", "Diwen Wan", "Gang Zeng", "Siyuan Huang" ], "github": "https://github.com/Jason-aplp/MOVIS-code", "project_page": "", "abstract": "Repurposing pre-trained diffusion models has been proven to be effective for NVS. However, these methods are mostly limited to a single object; directly applying such methods to compositional multi-object scenarios yields inferior results, especially incorrect object placement and inconsistent shape and appearance under novel views. How to enhance and systematically evaluate the cross-view consistency of such models remains under-explored. To address this issue, we propose MOVIS to enhance the structural awareness of the view-conditioned diffusion model for multi-object NVS in terms of model inputs, auxiliary tasks, and training strategy. First, we inject structure-aware features, including depth and object mask, into the denoising U-Net to enhance the model's comprehension of object instances and their spatial relationships. Second, we introduce an auxiliary task requiring the model to simultaneously predict novel view object masks, further improving the model's capability in differentiating and placing objects. Finally, we conduct an in-depth analysis of the diffusion sampling process and carefully devise a structure-guided timestep sampling scheduler during training, which balances the learning of global object placement and fine-grained detail recovery. To systematically evaluate the plausibility of synthesized images, we propose to assess cross-view consistency and novel view object placement alongside existing image-level NVS metrics. Extensive experiments on challenging synthetic and realistic datasets demonstrate that our method exhibits strong generalization capabilities and produces consistent novel view synthesis, highlighting its potential to guide future 3D-aware multi-object NVS tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Jason-aplp/MOVIS-code", "github_api_added_by": "auto" }, { "date": "2024-12-17", "arxiv_id": "2412.11605", "title": "SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models", "authors": [ "Jiale Cheng", "Xiao Liu", "Cunxiang Wang", "Xiaotao Gu", "Yida Lu", "Dan Zhang", "Yuxiao Dong", "Jie Tang", "Hongning Wang", "Minlie Huang" ], "github": "https://github.com/thu-coai/SPaR", "project_page": "", "abstract": "Instruction-following is a fundamental capability of language models, requiring the model to recognize even the most subtle requirements in the instructions and accurately reflect them in its output. Such an ability is well-suited for and often optimized by preference learning. However, existing methods often directly sample multiple independent responses from the model when creating preference pairs. Such practice can introduce content variations irrelevant to whether the instruction is precisely followed (e.g., different expressions about the same semantic), interfering with the goal of teaching models to recognize the key differences that lead to improved instruction following. In light of this, we introduce SPaR, a self-play framework integrating tree-search self-refinement to yield valid and comparable preference pairs free from distractions. By playing against itself, an LLM employs a tree-search strategy to refine its previous responses with respect to the instruction while minimizing unnecessary variations. Our experiments show that a LLaMA3-8B model, trained over three iterations guided by SPaR, surpasses GPT-4-Turbo on the IFEval benchmark without losing general capabilities. Furthermore, SPaR demonstrates promising scalability and transferability, greatly enhancing models like GLM-4-9B and LLaMA3-70B. We also identify how inference scaling in tree search would impact model performance. Our code and data are publicly available at https://github.com/thu-coai/SPaR.", "github_manual": "", "github_regex": "https://github.com/thu-coai/SPaR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.11834", "title": "Wonderful Matrices: Combining for a More Efficient and Effective Foundation Model Architecture", "authors": [ "Jingze Shi", "Bingheng Wu" ], "github": "https://github.com/losercheems/doge", "project_page": "", "abstract": "In order to make the foundation model more efficient and effective, our idea is combining sequence transformation and state transformation. First, we prove the availability of rotary position embedding in the state space duality algorithm, which reduces the perplexity of the hybrid quadratic causal self-attention and state space duality by more than 4%, to ensure that the combining sequence transformation unifies position encoding. Second, we propose dynamic mask attention, which maintains 100% accuracy in the more challenging multi-query associative recall task, improving by more than 150% compared to quadratic causal self-attention and state space duality, to ensure that the combining sequence transformation selectively filters relevant information. Third, we design cross domain mixture of experts, which makes the computational speed of expert retrieval with more than 1024 experts 8 to 10 times faster than the mixture of experts, to ensure that the combining state transformation quickly retrieval mixture. Finally, we summarize these matrix algorithms that can form the foundation model: Wonderful Matrices, which can be a competitor to popular model architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/losercheems/doge", "github_api_added_by": "auto" }, { "date": "2024-12-17", "arxiv_id": "2412.11815", "title": "ColorFlow: Retrieval-Augmented Image Sequence Colorization", "authors": [ "Junhao Zhuang", "Xuan Ju", "Zhaoyang Zhang", "Yong Liu", "Shiyi Zhang", "Chun Yuan", "Ying Shan" ], "github": "https://github.com/TencentARC/ColorFlow", "project_page": "", "abstract": "Automatic black-and-white image sequence colorization while preserving character and object identity (ID) is a complex task with significant market demand, such as in cartoon or comic series colorization. Despite advancements in visual colorization using large-scale generative models like diffusion models, challenges with controllability and identity consistency persist, making current solutions unsuitable for industrial application.To address this, we propose ColorFlow, a three-stage diffusion-based framework tailored for image sequence colorization in industrial applications. Unlike existing methods that require per-ID finetuning or explicit ID embedding extraction, we propose a novel robust and generalizable Retrieval Augmented Colorization pipeline for colorizing images with relevant color references. Our pipeline also features a dual-branch design: one branch for color identity extraction and the other for colorization, leveraging the strengths of diffusion models. We utilize the self-attention mechanism in diffusion models for strong in-context learning and color identity matching. To evaluate our model, we introduce ColorFlow-Bench, a comprehensive benchmark for reference-based colorization. Results show that ColorFlow outperforms existing models across multiple metrics, setting a new standard in sequential image colorization and potentially benefiting the art industry. We release our codes and models on our project page: https://zhuang2002.github.io/ColorFlow/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TencentARC/ColorFlow", "github_api_added_by": "auto" }, { "date": "2024-12-17", "arxiv_id": "2412.12095", "title": "Causal Diffusion Transformers for Generative Modeling", "authors": [ "Chaorui Deng", "Deyao Zh", "Kunchang Li", "Shi Guan", "Haoqi Fan" ], "github": "https://github.com/causalfusion/causalfusion", "project_page": "", "abstract": "We introduce Causal Diffusion as the autoregressive (AR) counterpart of Diffusion models. It is a next-token(s) forecasting framework that is friendly to both discrete and continuous modalities and compatible with existing next-token prediction models like LLaMA and GPT. While recent works attempt to combine diffusion with AR models, we show that introducing sequential factorization to a diffusion model can substantially improve its performance and enables a smooth transition between AR and diffusion generation modes. Hence, we propose CausalFusion - a decoder-only transformer that dual-factorizes data across sequential tokens and diffusion noise levels, leading to state-of-the-art results on the ImageNet generation benchmark while also enjoying the AR advantage of generating an arbitrary number of tokens for in-context reasoning. We further demonstrate CausalFusion's multimodal capabilities through a joint image generation and captioning model, and showcase CausalFusion's ability for zero-shot in-context image manipulations. We hope that this work could provide the community with a fresh perspective on training multimodal models over discrete and continuous data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/causalfusion/causalfusion", "github_api_added_by": "auto" }, { "date": "2024-12-17", "arxiv_id": "2412.09645", "title": "Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models", "authors": [ "Fan Zhang", "Shulin Tian", "Ziqi Huang", "Yu Qiao", "Ziwei Liu" ], "github": "https://github.com/Vchitect/Evaluation-Agent", "project_page": "", "abstract": "Recent advancements in visual generative models have enabled high-quality image and video generation, opening diverse applications. However, evaluating these models often demands sampling hundreds or thousands of images or videos, making the process computationally expensive, especially for diffusion-based models with inherently slow sampling. Moreover, existing evaluation methods rely on rigid pipelines that overlook specific user needs and provide numerical results without clear explanations. In contrast, humans can quickly form impressions of a model's capabilities by observing only a few samples. To mimic this, we propose the Evaluation Agent framework, which employs human-like strategies for efficient, dynamic, multi-round evaluations using only a few samples per round, while offering detailed, user-tailored analyses. It offers four key advantages: 1) efficiency, 2) promptable evaluation tailored to diverse user needs, 3) explainability beyond single numerical scores, and 4) scalability across various models and tools. Experiments show that Evaluation Agent reduces evaluation time to 10% of traditional methods while delivering comparable results. The Evaluation Agent framework is fully open-sourced to advance research in visual generative models and their efficient evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Vchitect/Evaluation-Agent", "github_api_added_by": "auto" }, { "date": "2024-12-17", "arxiv_id": "2412.12091", "title": "Wonderland: Navigating 3D Scenes from a Single Image", "authors": [ "Hanwen Liang", "Junli Cao", "Vidit Goel", "Guocheng Qian", "Sergei Korolev", "Demetri Terzopoulos", "Konstantinos N. Plataniotis", "Sergey Tulyakov", "Jian Ren" ], "github": "", "project_page": "", "abstract": "This paper addresses a challenging question: How can we efficiently create high-quality, wide-scope 3D scenes from a single arbitrary image? Existing methods face several constraints, such as requiring multi-view data, time-consuming per-scene optimization, low visual quality in backgrounds, and distorted reconstructions in unseen areas. We propose a novel pipeline to overcome these limitations. Specifically, we introduce a large-scale reconstruction model that uses latents from a video diffusion model to predict 3D Gaussian Splattings for the scenes in a feed-forward manner. The video diffusion model is designed to create videos precisely following specified camera trajectories, allowing it to generate compressed video latents that contain multi-view information while maintaining 3D consistency. We train the 3D reconstruction model to operate on the video latent space with a progressive training strategy, enabling the efficient generation of high-quality, wide-scope, and generic 3D scenes. Extensive evaluations across various datasets demonstrate that our model significantly outperforms existing methods for single-view 3D scene generation, particularly with out-of-domain images. For the first time, we demonstrate that a 3D reconstruction model can be effectively built upon the latent space of a diffusion model to realize efficient 3D scene generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.10316", "title": "BrushEdit: All-In-One Image Inpainting and Editing", "authors": [ "Yaowei Li", "Yuxuan Bian", "Xuan Ju", "Zhaoyang Zhang", "Ying Shan", "Qiang Xu" ], "github": "", "project_page": "", "abstract": "Image editing has advanced significantly with the development of diffusion models using both inversion-based and instruction-based methods. However, current inversion-based approaches struggle with big modifications (e.g., adding or removing objects) due to the structured nature of inversion noise, which hinders substantial changes. Meanwhile, instruction-based methods often constrain users to black-box operations, limiting direct interaction for specifying editing regions and intensity. To address these limitations, we propose BrushEdit, a novel inpainting-based instruction-guided image editing paradigm, which leverages multimodal large language models (MLLMs) and image inpainting models to enable autonomous, user-friendly, and interactive free-form instruction editing. Specifically, we devise a system enabling free-form instruction editing by integrating MLLMs and a dual-branch image inpainting model in an agent-cooperative framework to perform editing category classification, main object identification, mask acquisition, and editing area inpainting. Extensive experiments show that our framework effectively combines MLLMs and inpainting models, achieving superior performance across seven metrics including mask region preservation and editing effect coherence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.09871", "title": "Byte Latent Transformer: Patches Scale Better Than Tokens", "authors": [ "Artidoro Pagnoni", "Ram Pasunuru", "Pedro Rodriguez", "John Nguyen", "Benjamin Muller", "Margaret Li", "Chunting Zhou", "Lili Yu", "Jason Weston", "Luke Zettlemoyer", "Gargi Ghosh", "Mike Lewis", "Ari Holtzman", "Srinivasan Iyer" ], "github": "https://github.com/facebookresearch/blt", "project_page": "", "abstract": "We introduce the Byte Latent Transformer (BLT), a new byte-level LLM architecture that, for the first time, matches tokenization-based LLM performance at scale with significant improvements in inference efficiency and robustness. BLT encodes bytes into dynamically sized patches, which serve as the primary units of computation. Patches are segmented based on the entropy of the next byte, allocating more compute and model capacity where increased data complexity demands it. We present the first FLOP controlled scaling study of byte-level models up to 8B parameters and 4T training bytes. Our results demonstrate the feasibility of scaling models trained on raw bytes without a fixed vocabulary. Both training and inference efficiency improve due to dynamically selecting long patches when data is predictable, along with qualitative improvements on reasoning and long tail generalization. Overall, for fixed inference costs, BLT shows significantly better scaling than tokenization-based models, by simultaneously growing both patch and model size.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/blt", "github_api_added_by": "auto" }, { "date": "2024-12-17", "arxiv_id": "2412.10447", "title": "TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning", "authors": [ "Jimmy Wu", "William Chong", "Robert Holmberg", "Aaditya Prasad", "Yihuai Gao", "Oussama Khatib", "Shuran Song", "Szymon Rusinkiewicz", "Jeannette Bohg" ], "github": "https://github.com/jimmyyhwu/tidybot2", "project_page": "", "abstract": "Exploiting the promise of recent advances in imitation learning for mobile manipulation will require the collection of large numbers of human-guided demonstrations. This paper proposes an open-source design for an inexpensive, robust, and flexible mobile manipulator that can support arbitrary arms, enabling a wide range of real-world household mobile manipulation tasks. Crucially, our design uses powered casters to enable the mobile base to be fully holonomic, able to control all planar degrees of freedom independently and simultaneously. This feature makes the base more maneuverable and simplifies many mobile manipulation tasks, eliminating the kinematic constraints that create complex and time-consuming motions in nonholonomic bases. We equip our robot with an intuitive mobile phone teleoperation interface to enable easy data acquisition for imitation learning. In our experiments, we use this interface to collect data and show that the resulting learned policies can successfully perform a variety of common household mobile manipulation tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jimmyyhwu/tidybot2", "github_api_added_by": "auto" }, { "date": "2024-12-17", "arxiv_id": "2412.11689", "title": "Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning", "authors": [ "Andrei Semenov", "Philip Zmushko", "Alexander Pichugin", "Aleksandr Beznosikov" ], "github": "https://github.com/Andron00e/JAST", "project_page": "", "abstract": "Vertical Federated Learning (VFL) aims to enable collaborative training of deep learning models while maintaining privacy protection. However, the VFL procedure still has components that are vulnerable to attacks by malicious parties. In our work, we consider feature reconstruction attacks, a common risk targeting input data compromise. We theoretically claim that feature reconstruction attacks cannot succeed without knowledge of the prior distribution on data. Consequently, we demonstrate that even simple model architecture transformations can significantly impact the protection of input data during VFL. Confirming these findings with experimental results, we show that MLP-based models are resistant to state-of-the-art feature reconstruction attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Andron00e/JAST", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.12094", "title": "SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator", "authors": [ "Guoxuan Chen", "Han Shi", "Jiawei Li", "Yihang Gao", "Xiaozhe Ren", "Yimeng Chen", "Xin Jiang", "Zhenguo Li", "Weiyang Liu", "Chao Huang" ], "github": "https://github.com/HKUDS/SepLLM", "project_page": "", "abstract": "Large Language Models (LLMs) have exhibited exceptional performance across a spectrum of natural language processing tasks. However, their substantial sizes pose considerable challenges, particularly in computational demands and inference speed, due to their quadratic complexity. In this work, we have identified a key pattern: certain seemingly meaningless special tokens (i.e., separators) contribute disproportionately to attention scores compared to semantically meaningful tokens. This observation suggests that information of the segments between these separator tokens can be effectively condensed into the separator tokens themselves without significant information loss. Guided by this insight, we introduce SepLLM, a plug-and-play framework that accelerates inference by compressing these segments and eliminating redundant tokens. Additionally, we implement efficient kernels for training acceleration. Experimental results across training-free, training-from-scratch, and post-training settings demonstrate SepLLM's effectiveness. Notably, using the Llama-3-8B backbone, SepLLM achieves over 50% reduction in KV cache on the GSM8K-CoT benchmark while maintaining comparable performance. Furthermore, in streaming settings, SepLLM effectively processes sequences of up to 4 million tokens or more while maintaining consistent language modeling capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HKUDS/SepLLM", "github_api_added_by": "auto" }, { "date": "2024-12-17", "arxiv_id": "2412.11449", "title": "Whisper-GPT: A Hybrid Representation Audio Large Language Model", "authors": [ "Prateek Verma" ], "github": "", "project_page": "", "abstract": "We propose WHISPER-GPT: A generative large language model (LLM) for speech and music that allows us to work with continuous audio representations and discrete tokens simultaneously as part of a single architecture. There has been a huge surge in generative audio, speech, and music models that utilize discrete audio tokens derived from neural compression algorithms, e.g. ENCODEC. However, one of the major drawbacks of this approach is handling the context length. It blows up for high-fidelity generative architecture if one has to account for all the audio contents at various frequencies for the next token prediction. By combining continuous audio representation like the spectrogram and discrete acoustic tokens, we retain the best of both worlds: Have all the information needed from the audio at a specific time instance in a single token, yet allow LLM to predict the future token to allow for sampling and other benefits discrete space provides. We show how our architecture improves the perplexity and negative log-likelihood scores for the next token prediction compared to a token-based LLM for speech and music.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.12004", "title": "The Open Source Advantage in Large Language Models (LLMs)", "authors": [ "Jiya Manchanda", "Laura Boettcher", "Matheus Westphalen", "Jasser Jasser" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) mark a key shift in natural language processing (NLP), having advanced text generation, translation, and domain-specific reasoning. Closed-source models like GPT-4, powered by proprietary datasets and extensive computational resources, lead with state-of-the-art performance today. However, they face criticism for their \"black box\" nature and for limiting accessibility in a manner that hinders reproducibility and equitable AI development. By contrast, open-source initiatives like LLaMA and BLOOM prioritize democratization through community-driven development and computational efficiency. These models have significantly reduced performance gaps, particularly in linguistic diversity and domain-specific applications, while providing accessible tools for global researchers and developers. Notably, both paradigms rely on foundational architectural innovations, such as the Transformer framework by Vaswani et al. (2017). Closed-source models excel by scaling effectively, while open-source models adapt to real-world applications in underrepresented languages and domains. Techniques like Low-Rank Adaptation (LoRA) and instruction-tuning datasets enable open-source models to achieve competitive results despite limited resources. To be sure, the tension between closed-source and open-source approaches underscores a broader debate on transparency versus proprietary control in AI. Ethical considerations further highlight this divide. Closed-source systems restrict external scrutiny, while open-source models promote reproducibility and collaboration but lack standardized auditing documentation frameworks to mitigate biases. Hybrid approaches that leverage the strengths of both paradigms are likely to shape the future of LLM innovation, ensuring accessibility, competitive technical performance, and ethical deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.11314", "title": "Reliable, Reproducible, and Really Fast Leaderboards with Evalica", "authors": [ "Dmitry Ustalov" ], "github": "https://github.com/dustalov/evalica", "project_page": "", "abstract": "The rapid advancement of natural language processing (NLP) technologies, such as instruction-tuned large language models (LLMs), urges the development of modern evaluation protocols with human and machine feedback. We introduce Evalica, an open-source toolkit that facilitates the creation of reliable and reproducible model leaderboards. This paper presents its design, evaluates its performance, and demonstrates its usability through its Web interface, command-line interface, and Python API.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dustalov/evalica", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.11974", "title": "Emma-X: An Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning", "authors": [ "Qi Sun", "Pengfei Hong", "Tej Deep Pala", "Vernon Toh", "U-Xuan Tan", "Deepanway Ghosal", "Soujanya Poria" ], "github": "https://github.com/declare-lab/emma-x", "project_page": "", "abstract": "Traditional reinforcement learning-based robotic control methods are often task-specific and fail to generalize across diverse environments or unseen objects and instructions. Visual Language Models (VLMs) demonstrate strong scene understanding and planning capabilities but lack the ability to generate actionable policies tailored to specific robotic embodiments. To address this, Visual-Language-Action (VLA) models have emerged, yet they face challenges in long-horizon spatial reasoning and grounded task planning. In this work, we propose the Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning, Emma-X. Emma-X leverages our constructed hierarchical embodiment dataset based on BridgeV2, containing 60,000 robot manipulation trajectories auto-annotated with grounded task reasoning and spatial guidance. Additionally, we introduce a trajectory segmentation strategy based on gripper states and motion trajectories, which can help mitigate hallucination in grounding subtask reasoning generation. Experimental results demonstrate that Emma-X achieves superior performance over competitive baselines, particularly in real-world robotic tasks requiring spatial reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/declare-lab/emma-x", "github_api_added_by": "auto" }, { "date": "2024-12-17", "arxiv_id": "2412.11100", "title": "DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenes", "authors": [ "Jinxiu Liu", "Shaoheng Lin", "Yinxiao Li", "Ming-Hsuan Yang" ], "github": "https://github.com/sh-Lin/DynamicScaler", "project_page": "", "abstract": "The increasing demand for immersive AR/VR applications and spatial intelligence has heightened the need to generate high-quality scene-level and 360{\\deg} panoramic video. However, most video diffusion models are constrained by limited resolution and aspect ratio, which restricts their applicability to scene-level dynamic content synthesis. In this work, we propose the DynamicScaler, addressing these challenges by enabling spatially scalable and panoramic dynamic scene synthesis that preserves coherence across panoramic scenes of arbitrary size. Specifically, we introduce a Offset Shifting Denoiser, facilitating efficient, synchronous, and coherent denoising panoramic dynamic scenes via a diffusion model with fixed resolution through a seamless rotating Window, which ensures seamless boundary transitions and consistency across the entire panoramic space, accommodating varying resolutions and aspect ratios. Additionally, we employ a Global Motion Guidance mechanism to ensure both local detail fidelity and global motion continuity. Extensive experiments demonstrate our method achieves superior content and motion quality in panoramic scene-level video generation, offering a training-free, efficient, and scalable solution for immersive dynamic scene creation with constant VRAM consumption regardless of the output video resolution. Our project page is available at https://dynamic-scaler.pages.dev/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sh-Lin/DynamicScaler", "github_api_added_by": "auto" }, { "date": "2024-12-17", "arxiv_id": "2412.11279", "title": "VividFace: A Diffusion-Based Hybrid Framework for High-Fidelity Video Face Swapping", "authors": [ "Hao Shao", "Shulun Wang", "Yang Zhou", "Guanglu Song", "Dailan He", "Shuo Qin", "Zhuofan Zong", "Bingqi Ma", "Yu Liu", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "Video face swapping is becoming increasingly popular across various applications, yet existing methods primarily focus on static images and struggle with video face swapping because of temporal consistency and complex scenarios. In this paper, we present the first diffusion-based framework specifically designed for video face swapping. Our approach introduces a novel image-video hybrid training framework that leverages both abundant static image data and temporal video sequences, addressing the inherent limitations of video-only training. The framework incorporates a specially designed diffusion model coupled with a VidFaceVAE that effectively processes both types of data to better maintain temporal coherence of the generated videos. To further disentangle identity and pose features, we construct the Attribute-Identity Disentanglement Triplet (AIDT) Dataset, where each triplet has three face images, with two images sharing the same pose and two sharing the same identity. Enhanced with a comprehensive occlusion augmentation, this dataset also improves robustness against occlusions. Additionally, we integrate 3D reconstruction techniques as input conditioning to our network for handling large pose variations. Extensive experiments demonstrate that our framework achieves superior performance in identity preservation, temporal consistency, and visual quality compared to existing methods, while requiring fewer inference steps. Our approach effectively mitigates key challenges in video face swapping, including temporal flickering, identity preservation, and robustness to occlusions and pose variations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.12098", "title": "MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization", "authors": [ "Bhavya Sukhija", "Stelian Coros", "Andreas Krause", "Pieter Abbeel", "Carmelo Sferrazza" ], "github": "https://github.com/sukhijab/maxinforl_jax", "project_page": "", "abstract": "Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms use undirected exploration, i.e., select random sequences of actions. Exploration can also be directed using intrinsic rewards, such as curiosity or model epistemic uncertainty. However, effectively balancing task and intrinsic rewards is challenging and often task-dependent. In this work, we introduce a framework, MaxInfoRL, for balancing intrinsic and extrinsic exploration. MaxInfoRL steers exploration towards informative transitions, by maximizing intrinsic rewards such as the information gain about the underlying task. When combined with Boltzmann exploration, this approach naturally trades off maximization of the value function with that of the entropy over states, rewards, and actions. We show that our approach achieves sublinear regret in the simplified setting of multi-armed bandits. We then apply this general formulation to a variety of off-policy model-free RL methods for continuous state-action spaces, yielding novel algorithms that achieve superior performance across hard exploration problems and complex scenarios such as visual control tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sukhijab/maxinforl_jax", "github_api_added_by": "auto" }, { "date": "2024-12-17", "arxiv_id": "2412.11863", "title": "GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training", "authors": [ "Renqiu Xia", "Mingsheng Li", "Hancheng Ye", "Wenjie Wu", "Hongbin Zhou", "Jiakang Yuan", "Tianshuo Peng", "Xinyu Cai", "Xiangchao Yan", "Bin Wang", "Conghui He", "Botian Shi", "Tao Chen", "Junchi Yan", "Bo Zhang" ], "github": "https://github.com/alpha-innovator/geox", "project_page": "", "abstract": "Despite their proficiency in general tasks, Multi-modal Large Language Models (MLLMs) struggle with automatic Geometry Problem Solving (GPS), which demands understanding diagrams, interpreting symbols, and performing complex reasoning. This limitation arises from their pre-training on natural images and texts, along with the lack of automated verification in the problem-solving process. Besides, current geometric specialists are limited by their task-specific designs, making them less effective for broader geometric problems. To this end, we present GeoX, a multi-modal large model focusing on geometric understanding and reasoning tasks. Given the significant differences between geometric diagram-symbol and natural image-text, we introduce unimodal pre-training to develop a diagram encoder and symbol decoder, enhancing the understanding of geometric images and corpora. Furthermore, we introduce geometry-language alignment, an effective pre-training paradigm that bridges the modality gap between unimodal geometric experts. We propose a Generator-And-Sampler Transformer (GS-Former) to generate discriminative queries and eliminate uninformative representations from unevenly distributed geometric signals. Finally, GeoX benefits from visual instruction tuning, empowering it to take geometric images and questions as input and generate verifiable solutions. Experiments show that GeoX outperforms both generalists and geometric specialists on publicly recognized benchmarks, such as GeoQA, UniGeo, Geometry3K, and PGPS9k.", "github_manual": "https://github.com/alpha-innovator/geox", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.09982", "title": "SplineGS: Robust Motion-Adaptive Spline for Real-Time Dynamic 3D Gaussians from Monocular Video", "authors": [ "Jongmin Park", "Minh-Quan Viet Bui", "Juan Luis Gonzalez Bello", "Jaeho Moon", "Jihyong Oh", "Munchurl Kim" ], "github": "", "project_page": "", "abstract": "Synthesizing novel views from in-the-wild monocular videos is challenging due to scene dynamics and the lack of multi-view cues. To address this, we propose SplineGS, a COLMAP-free dynamic 3D Gaussian Splatting (3DGS) framework for high-quality reconstruction and fast rendering from monocular videos. At its core is a novel Motion-Adaptive Spline (MAS) method, which represents continuous dynamic 3D Gaussian trajectories using cubic Hermite splines with a small number of control points. For MAS, we introduce a Motion-Adaptive Control points Pruning (MACP) method to model the deformation of each dynamic 3D Gaussian across varying motions, progressively pruning control points while maintaining dynamic modeling integrity. Additionally, we present a joint optimization strategy for camera parameter estimation and 3D Gaussian attributes, leveraging photometric and geometric consistency. This eliminates the need for Structure-from-Motion preprocessing and enhances SplineGS's robustness in real-world conditions. Experiments show that SplineGS significantly outperforms state-of-the-art methods in novel view synthesis quality for dynamic scenes from monocular videos, achieving thousands times faster rendering speed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.09858", "title": "RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning", "authors": [ "Charles Xu", "Qiyang Li", "Jianlan Luo", "Sergey Levine" ], "github": "", "project_page": "", "abstract": "Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, their performance heavily depends on the quality of their training data. In this work, we propose Reinforcement Learning Distilled Generalists (RLDG), a method that leverages reinforcement learning to generate high-quality training data for finetuning generalist policies. Through extensive real-world experiments on precise manipulation tasks like connector insertion and assembly, we demonstrate that generalist policies trained with RL-generated data consistently outperform those trained with human demonstrations, achieving up to 40% higher success rates while generalizing better to new tasks. We also provide a detailed analysis that reveals this performance gain stems from both optimized action distributions and improved state coverage. Our results suggest that combining task-specific RL with generalist policy distillation offers a promising approach for developing more capable and efficient robotic manipulation systems that maintain the flexibility of foundation models while achieving the performance of specialized controllers. Videos and code can be found on our project website https://generalist-distillation.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-17", "arxiv_id": "2412.11423", "title": "Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models", "authors": [ "Namhyuk Ahn", "KiYoon Yoo", "Wonhyuk Ahn", "Daesik Kim", "Seung-Hun Nam" ], "github": "", "project_page": "", "abstract": "Recent advancements in diffusion models revolutionize image generation but pose risks of misuse, such as replicating artworks or generating deepfakes. Existing image protection methods, though effective, struggle to balance protection efficacy, invisibility, and latency, thus limiting practical use. We introduce perturbation pre-training to reduce latency and propose a mixture-of-perturbations approach that dynamically adapts to input images to minimize performance degradation. Our novel training strategy computes protection loss across multiple VAE feature spaces, while adaptive targeted protection at inference enhances robustness and invisibility. Experiments show comparable protection performance with improved invisibility and drastically reduced inference time. The code and demo are available at https://webtoon.github.io/impasto", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-18", "arxiv_id": "2412.13147", "title": "Are Your LLMs Capable of Stable Reasoning?", "authors": [ "Junnan Liu", "Hongwei Liu", "Linchen Xiao", "Ziyi Wang", "Kuikun Liu", "Songyang Gao", "Wenwei Zhang", "Songyang Zhang", "Kai Chen" ], "github": "https://github.com/open-compass/GPassK", "project_page": "", "abstract": "The rapid advancement of Large Language Models (LLMs) has demonstrated remarkable progress in complex reasoning tasks. However, a significant discrepancy persists between benchmark performances and real-world applications. We identify this gap as primarily stemming from current evaluation protocols and metrics, which inadequately capture the full spectrum of LLM capabilities, particularly in complex reasoning tasks where both accuracy and consistency are crucial. This work makes two key contributions. First, we introduce G-Pass@k, a novel evaluation metric that provides a continuous assessment of model performance across multiple sampling attempts, quantifying both the model's peak performance potential and its stability. Second, we present LiveMathBench, a dynamic benchmark comprising challenging, contemporary mathematical problems designed to minimize data leakage risks during evaluation. Through extensive experiments using G-Pass@k on state-of-the-art LLMs with LiveMathBench, we provide comprehensive insights into both their maximum capabilities and operational consistency. Our findings reveal substantial room for improvement in LLMs' \"realistic\" reasoning capabilities, highlighting the need for more robust evaluation methods. The benchmark and detailed results are available at: https://github.com/open-compass/GPassK.", "github_manual": "", "github_regex": "https://github.com/open-compass/GPassK", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-18", "arxiv_id": "2412.12606", "title": "Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models", "authors": [ "YiFan Zhang", "Shanglin Lei", "Runqi Qiao", "Zhuoma GongQue", "Xiaoshuai Song", "Guanting Dong", "Qiuna Tan", "Zhe Wei", "Peiqing Yang", "Ye Tian", "Yadong Xue", "Xiaofei Wang", "Honggang Zhang" ], "github": "https://github.com/MDI-Benchmark/MDI-Benchmark", "project_page": "", "abstract": "The rapidly developing field of large multimodal models (LMMs) has led to the emergence of diverse models with remarkable capabilities. However, existing benchmarks fail to comprehensively, objectively and accurately evaluate whether LMMs align with the diverse needs of humans in real-world scenarios. To bridge this gap, we propose the Multi-Dimensional Insights (MDI) benchmark, which includes over 500 images covering six common scenarios of human life. Notably, the MDI-Benchmark offers two significant advantages over existing evaluations: (1) Each image is accompanied by two types of questions: simple questions to assess the model's understanding of the image, and complex questions to evaluate the model's ability to analyze and reason beyond basic content. (2) Recognizing that people of different age groups have varying needs and perspectives when faced with the same scenario, our benchmark stratifies questions into three age categories: young people, middle-aged people, and older people. This design allows for a detailed assessment of LMMs' capabilities in meeting the preferences and needs of different age groups. With MDI-Benchmark, the strong model like GPT-4o achieve 79% accuracy on age-related tasks, indicating that existing LMMs still have considerable room for improvement in addressing real-world applications. Looking ahead, we anticipate that the MDI-Benchmark will open new pathways for aligning real-world personalization in LMMs. The MDI-Benchmark data and evaluation code are available at https://mdi-benchmark.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MDI-Benchmark/MDI-Benchmark", "github_api_added_by": "auto" }, { "date": "2024-12-18", "arxiv_id": "2412.13018", "title": "OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain", "authors": [ "Shuting Wang", "Jiejun Tan", "Zhicheng Dou", "Ji-Rong Wen" ], "github": "https://github.com/RUC-NLPIR/OmniEval", "project_page": "", "abstract": "As a typical and practical application of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) techniques have gained extensive attention, particularly in vertical domains where LLMs may lack domain-specific knowledge. In this paper, we introduce an omnidirectional and automatic RAG benchmark, OmniEval, in the financial domain. Our benchmark is characterized by its multi-dimensional evaluation framework, including (1) a matrix-based RAG scenario evaluation system that categorizes queries into five task classes and 16 financial topics, leading to a structured assessment of diverse query scenarios; (2) a multi-dimensional evaluation data generation approach, which combines GPT-4-based automatic generation and human annotation, achieving an 87.47\\% acceptance ratio in human evaluations on generated instances; (3) a multi-stage evaluation system that evaluates both retrieval and generation performance, result in a comprehensive evaluation on the RAG pipeline; and (4) robust evaluation metrics derived from rule-based and LLM-based ones, enhancing the reliability of assessments through manual annotations and supervised fine-tuning of an LLM evaluator. Our experiments demonstrate the comprehensiveness of OmniEval, which includes extensive test datasets and highlights the performance variations of RAG systems across diverse topics and tasks, revealing significant opportunities for RAG models to improve their capabilities in vertical domains. We open source the code of our benchmark in https://github.com/RUC-NLPIR/OmniEval{https://github.com/RUC-NLPIR/OmniEval}.", "github_manual": "", "github_regex": "https://github.com/RUC-NLPIR/OmniEval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-18", "arxiv_id": "2412.12276", "title": "Emergence of Abstractions: Concept Encoding and Decoding Mechanism for In-Context Learning in Transformers", "authors": [ "Seungwook Han", "Jinyeop Song", "Jeff Gore", "Pulkit Agrawal" ], "github": "", "project_page": "", "abstract": "Humans distill complex experiences into fundamental abstractions that enable rapid learning and adaptation. Similarly, autoregressive transformers exhibit adaptive learning through in-context learning (ICL), which begs the question of how. In this paper, we propose concept encoding-decoding mechanism to explain ICL by studying how transformers form and use internal abstractions in their representations. On synthetic ICL tasks, we analyze the training dynamics of a small transformer and report the coupled emergence of concept encoding and decoding. As the model learns to encode different latent concepts (e.g., ``Finding the first noun in a sentence.\") into distinct, separable representations, it concureently builds conditional decoding algorithms and improve its ICL performance. We validate the existence of this mechanism across pretrained models of varying scales (Gemma-2 2B/9B/27B, Llama-3.1 8B/70B). Further, through mechanistic interventions and controlled finetuning, we demonstrate that the quality of concept encoding is causally related and predictive of ICL performance. Our empirical insights shed light into better understanding the success and failure modes of large language models via their representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-18", "arxiv_id": "2412.12877", "title": "MIVE: New Design and Benchmark for Multi-Instance Video Editing", "authors": [ "Samuel Teodoro", "Agus Gunawan", "Soo Ye Kim", "Jihyong Oh", "Munchurl Kim" ], "github": "", "project_page": "", "abstract": "Recent AI-based video editing has enabled users to edit videos through simple text prompts, significantly simplifying the editing process. However, recent zero-shot video editing techniques primarily focus on global or single-object edits, which can lead to unintended changes in other parts of the video. When multiple objects require localized edits, existing methods face challenges, such as unfaithful editing, editing leakage, and lack of suitable evaluation datasets and metrics. To overcome these limitations, we propose a zero-shot Multi-Instance Video Editing framework, called MIVE. MIVE is a general-purpose mask-based framework, not dedicated to specific objects (e.g., people). MIVE introduces two key modules: (i) Disentangled Multi-instance Sampling (DMS) to prevent editing leakage and (ii) Instance-centric Probability Redistribution (IPR) to ensure precise localization and faithful editing. Additionally, we present our new MIVE Dataset featuring diverse video scenarios and introduce the Cross-Instance Accuracy (CIA) Score to evaluate editing leakage in multi-instance video editing tasks. Our extensive qualitative, quantitative, and user study evaluations demonstrate that MIVE significantly outperforms recent state-of-the-art methods in terms of editing faithfulness, accuracy, and leakage prevention, setting a new benchmark for multi-instance video editing. The project page is available at https://kaist-viclab.github.io/mive-site/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-18", "arxiv_id": "2412.13194", "title": "Proposer-Agent-Evaluator(PAE): Autonomous Skill Discovery For Foundation Model Internet Agents", "authors": [ "Yifei Zhou", "Qianlan Yang", "Kaixiang Lin", "Min Bai", "Xiong Zhou", "Yu-Xiong Wang", "Sergey Levine", "Erran Li" ], "github": "https://github.com/amazon-science/PAE", "project_page": "", "abstract": "The vision of a broadly capable and goal-directed agent, such as an Internet-browsing agent in the digital world and a household humanoid in the physical world, has rapidly advanced, thanks to the generalization capability of foundation models. Such a generalist agent needs to have a large and diverse skill repertoire, such as finding directions between two travel locations and buying specific items from the Internet. If each skill needs to be specified manually through a fixed set of human-annotated instructions, the agent's skill repertoire will necessarily be limited due to the quantity and diversity of human-annotated instructions. In this work, we address this challenge by proposing Proposer-Agent-Evaluator, an effective learning system that enables foundation model agents to autonomously discover and practice skills in the wild. At the heart of PAE is a context-aware task proposer that autonomously proposes tasks for the agent to practice with context information of the environment such as user demos or even just the name of the website itself for Internet-browsing agents. Then, the agent policy attempts those tasks with thoughts and actual grounded operations in the real world with resulting trajectories evaluated by an autonomous VLM-based success evaluator. The success evaluation serves as the reward signal for the agent to refine its policies through RL. We validate PAE on challenging vision-based web navigation, using both real-world and self-hosted websites from WebVoyager and WebArena.To the best of our knowledge, this work represents the first effective learning system to apply autonomous task proposal with RL for agents that generalizes real-world human-annotated benchmarks with SOTA performances. Our open-source checkpoints and code can be found in https://yanqval.github.io/PAE/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amazon-science/PAE", "github_api_added_by": "auto" }, { "date": "2024-12-18", "arxiv_id": "2412.10704", "title": "VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation", "authors": [ "Manan Suri", "Puneet Mathur", "Franck Dernoncourt", "Kanika Goswami", "Ryan A. Rossi", "Dinesh Manocha" ], "github": "", "project_page": "", "abstract": "Understanding information from a collection of multiple documents, particularly those with visually rich elements, is important for document-grounded question answering. This paper introduces VisDoMBench, the first comprehensive benchmark designed to evaluate QA systems in multi-document settings with rich multimodal content, including tables, charts, and presentation slides. We propose VisDoMRAG, a novel multimodal Retrieval Augmented Generation (RAG) approach that simultaneously utilizes visual and textual RAG, combining robust visual retrieval capabilities with sophisticated linguistic reasoning. VisDoMRAG employs a multi-step reasoning process encompassing evidence curation and chain-of-thought reasoning for concurrent textual and visual RAG pipelines. A key novelty of VisDoMRAG is its consistency-constrained modality fusion mechanism, which aligns the reasoning processes across modalities at inference time to produce a coherent final answer. This leads to enhanced accuracy in scenarios where critical information is distributed across modalities and improved answer verifiability through implicit context attribution. Through extensive experiments involving open-source and proprietary large language models, we benchmark state-of-the-art document QA methods on VisDoMBench. Extensive results show that VisDoMRAG outperforms unimodal and long-context LLM baselines for end-to-end multimodal document QA by 12-20%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-18", "arxiv_id": "2412.12527", "title": "When to Speak, When to Abstain: Contrastive Decoding with Abstention", "authors": [ "Hyuhng Joon Kim", "Youna Kim", "Sang-goo Lee", "Taeuk Kim" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) demonstrate exceptional performance across diverse tasks by leveraging both pre-trained knowledge (i.e., parametric knowledge) and external knowledge (i.e., contextual knowledge). While substantial efforts have been made to leverage both forms of knowledge, scenarios in which the model lacks any relevant knowledge remain underexplored. Such limitations can result in issues like hallucination, causing reduced reliability and potential risks in high-stakes applications. To address such limitations, this paper extends the task scope to encompass cases where the user's request cannot be fulfilled due to the lack of relevant knowledge. To this end, we introduce Contrastive Decoding with Abstention (CDA), a training-free decoding method that empowers LLMs to generate responses when relevant knowledge is available and to abstain otherwise. CDA evaluates the relevance of each knowledge for a given query, adaptively determining which knowledge to prioritize or which to completely ignore. Extensive experiments with four LLMs on three question-answering datasets demonstrate that CDA can effectively perform accurate generation and abstention simultaneously. These findings highlight CDA's potential to broaden the applicability of LLMs, enhancing reliability and preserving user trust.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-18", "arxiv_id": "2412.13171", "title": "Compressed Chain of Thought: Efficient Reasoning Through Dense Representations", "authors": [ "Jeffrey Cheng", "Benjamin Van Durme" ], "github": "", "project_page": "", "abstract": "Chain-of-thought (CoT) decoding enables language models to improve reasoning performance at the cost of high generation latency in decoding. Recent proposals have explored variants of contemplation tokens, a term we introduce that refers to special tokens used during inference to allow for extra computation. Prior work has considered fixed-length sequences drawn from a discrete set of embeddings as contemplation tokens. Here we propose Compressed Chain-of-Thought (CCoT), a framework to generate contentful and continuous contemplation tokens of variable sequence length. The generated contemplation tokens are compressed representations of explicit reasoning chains, and our method can be applied to off-the-shelf decoder language models. Through experiments, we illustrate how CCoT enables additional reasoning over dense contentful representations to achieve corresponding improvements in accuracy. Moreover, the reasoning improvements can be adaptively modified on demand by controlling the number of contemplation tokens generated.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-18", "arxiv_id": "2412.13180", "title": "Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration", "authors": [ "Mark Endo", "Xiaohan Wang", "Serena Yeung-Levy" ], "github": "", "project_page": "", "abstract": "Recent works on accelerating Vision-Language Models show that strong performance can be maintained across a variety of vision-language tasks despite highly compressing visual information. In this work, we examine the popular acceleration approach of early pruning of visual tokens inside the language model and find that its strong performance across many tasks is not due to an exceptional ability to compress visual information, but rather the benchmarks' limited ability to assess fine-grained visual capabilities. Namely, we demonstrate a core issue with the acceleration approach where most tokens towards the top of the image are pruned away. Yet, this issue is only reflected in performance for a small subset of tasks such as localization. For the other evaluated tasks, strong performance is maintained with the flawed pruning strategy. Noting the limited visual capabilities of the studied acceleration technique, we propose FEATHER (Fast and Effective Acceleration wiTH Ensemble cRiteria), a straightforward approach that (1) resolves the identified issue with early-layer pruning, (2) incorporates uniform sampling to ensure coverage across all image regions, and (3) applies pruning in two stages to allow the criteria to become more effective at a later layer while still achieving significant speedup through early-layer pruning. With comparable computational savings, we find that FEATHER has more than 5times performance improvement on the vision-centric localization benchmarks compared to the original acceleration approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-18", "arxiv_id": "2412.11713", "title": "Seeker: Towards Exception Safety Code Generation with Intermediate Language Agents Framework", "authors": [ "Xuanming Zhang", "Yuxuan Chen", "Yiming Zheng", "Zhexin Zhang", "Yuan Yuan", "Minlie Huang" ], "github": "https://github.com/XMZhangAI/Seeker", "project_page": "", "abstract": "In real world software development, improper or missing exception handling can severely impact the robustness and reliability of code. Exception handling mechanisms require developers to detect, capture, and manage exceptions according to high standards, but many developers struggle with these tasks, leading to fragile code. This problem is particularly evident in open-source projects and impacts the overall quality of the software ecosystem. To address this challenge, we explore the use of large language models (LLMs) to improve exception handling in code. Through extensive analysis, we identify three key issues: Insensitive Detection of Fragile Code, Inaccurate Capture of Exception Block, and Distorted Handling Solution. These problems are widespread across real world repositories, suggesting that robust exception handling practices are often overlooked or mishandled. In response, we propose Seeker, a multi-agent framework inspired by expert developer strategies for exception handling. Seeker uses agents: Scanner, Detector, Predator, Ranker, and Handler to assist LLMs in detecting, capturing, and resolving exceptions more effectively. Our work is the first systematic study on leveraging LLMs to enhance exception handling practices in real development scenarios, providing valuable insights for future improvements in code reliability.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/XMZhangAI/Seeker", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-18", "arxiv_id": "2412.10533", "title": "SUGAR: Subject-Driven Video Customization in a Zero-Shot Manner", "authors": [ "Yufan Zhou", "Ruiyi Zhang", "Jiuxiang Gu", "Nanxuan Zhao", "Jing Shi", "Tong Sun" ], "github": "", "project_page": "", "abstract": "We present SUGAR, a zero-shot method for subject-driven video customization. Given an input image, SUGAR is capable of generating videos for the subject contained in the image and aligning the generation with arbitrary visual attributes such as style and motion specified by user-input text. Unlike previous methods, which require test-time fine-tuning or fail to generate text-aligned videos, SUGAR achieves superior results without the need for extra cost at test-time. To enable zero-shot capability, we introduce a scalable pipeline to construct synthetic dataset which is specifically designed for subject-driven customization, leading to 2.5 millions of image-video-text triplets. Additionally, we propose several methods to enhance our model, including special attention designs, improved training strategies, and a refined sampling algorithm. Extensive experiments are conducted. Compared to previous methods, SUGAR achieves state-of-the-art results in identity preservation, video dynamics, and video-text alignment for subject-driven video customization, demonstrating the effectiveness of our proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-18", "arxiv_id": "2412.13389", "title": "Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion", "authors": [ "Massimiliano Viola", "Kevin Qu", "Nando Metzger", "Bingxin Ke", "Alexander Becker", "Konrad Schindler", "Anton Obukhov" ], "github": "https://github.com/prs-eth/marigold-dc", "project_page": "", "abstract": "Depth completion upgrades sparse depth measurements into dense depth maps guided by a conventional image. Existing methods for this highly ill-posed task operate in tightly constrained settings and tend to struggle when applied to images outside the training domain or when the available depth measurements are sparse, irregularly distributed, or of varying density. Inspired by recent advances in monocular depth estimation, we reframe depth completion as an image-conditional depth map generation guided by sparse measurements. Our method, Marigold-DC, builds on a pretrained latent diffusion model for monocular depth estimation and injects the depth observations as test-time guidance via an optimization scheme that runs in tandem with the iterative inference of denoising diffusion. The method exhibits excellent zero-shot generalization across a diverse range of environments and handles even extremely sparse guidance effectively. Our results suggest that contemporary monocular depth priors greatly robustify depth completion: it may be better to view the task as recovering dense depth from (dense) image pixels, guided by sparse depth; rather than as inpainting (sparse) depth, guided by an image. Project website: https://MarigoldDepthCompletion.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/prs-eth/marigold-dc", "github_api_added_by": "auto" }, { "date": "2024-12-19", "arxiv_id": "2412.14161", "title": "TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks", "authors": [ "Frank F. Xu", "Yufan Song", "Boxuan Li", "Yuxuan Tang", "Kritanjali Jain", "Mengxue Bao", "Zora Z. Wang", "Xuhui Zhou", "Zhitong Guo", "Murong Cao", "Mingyang Yang", "Hao Yang Lu", "Amaad Martin", "Zhe Su", "Leander Maben", "Raj Mehta", "Wayne Chi", "Lawrence Jang", "Yiqing Xie", "Shuyan Zhou", "Graham Neubig" ], "github": "https://github.com/TheAgentCompany/TheAgentCompany", "project_page": "", "abstract": "We interact with computers on an everyday basis, be it in everyday life or work, and many aspects of work can be done entirely with access to a computer and the Internet. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and affect change in their surrounding environments. But how performant are AI agents at helping to accelerate or even autonomously perform work-related tasks? The answer to this question has important implications for both industry looking to adopt AI into their workflows, and for economic policy to understand the effects that adoption of AI may have on the labor market. To measure the progress of these LLM agents' performance on performing real-world professional tasks, in this paper, we introduce TheAgentCompany, an extensible benchmark for evaluating AI agents that interact with the world in similar ways to those of a digital worker: by browsing the Web, writing code, running programs, and communicating with other coworkers. We build a self-contained environment with internal web sites and data that mimics a small software company environment, and create a variety of tasks that may be performed by workers in such a company. We test baseline agents powered by both closed API-based and open-weights language models (LMs), and find that with the most competitive agent, 24% of the tasks can be completed autonomously. This paints a nuanced picture on task automation with LM agents -- in a setting simulating a real workplace, a good portion of simpler tasks could be solved autonomously, but more difficult long-horizon tasks are still beyond the reach of current systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TheAgentCompany/TheAgentCompany", "github_api_added_by": "auto" }, { "date": "2024-12-19", "arxiv_id": "2412.13501", "title": "GUI Agents: A Survey", "authors": [ "Dang Nguyen", "Jian Chen", "Yu Wang", "Gang Wu", "Namyong Park", "Zhengmian Hu", "Hanjia Lyu", "Junda Wu", "Ryan Aponte", "Yu Xia", "Xintong Li", "Jing Shi", "Hongjie Chen", "Viet Dac Lai", "Zhouhang Xie", "Sungchul Kim", "Ruiyi Zhang", "Tong Yu", "Mehrab Tanjim", "Nesreen K. Ahmed", "Puneet Mathur", "Seunghyun Yoon", "Lina Yao", "Branislav Kveton", "Thien Huu Nguyen", "Trung Bui", "Tianyi Zhou", "Ryan A. Rossi", "Franck Dernoncourt" ], "github": "", "project_page": "", "abstract": "Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction. These agents autonomously interact with digital systems or software applications via GUIs, emulating human actions such as clicking, typing, and navigating visual elements across diverse platforms. Motivated by the growing interest and fundamental importance of GUI agents, we provide a comprehensive survey that categorizes their benchmarks, evaluation metrics, architectures, and training methods. We propose a unified framework that delineates their perception, reasoning, planning, and acting capabilities. Furthermore, we identify important open challenges and discuss key future directions. Finally, this work serves as a basis for practitioners and researchers to gain an intuitive understanding of current progress, techniques, benchmarks, and critical open problems that remain to be addressed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.13061", "title": "VidTok: A Versatile and Open-Source Video Tokenizer", "authors": [ "Anni Tang", "Tianyu He", "Junliang Guo", "Xinle Cheng", "Li Song", "Jiang Bian" ], "github": "https://github.com/microsoft/VidTok", "project_page": "", "abstract": "Encoding video content into compact latent tokens has become a fundamental step in video generation and understanding, driven by the need to address the inherent redundancy in pixel-level representations. Consequently, there is a growing demand for high-performance, open-source video tokenizers as video-centric research gains prominence. We introduce VidTok, a versatile video tokenizer that delivers state-of-the-art performance in both continuous and discrete tokenizations. VidTok incorporates several key advancements over existing approaches: 1) model architecture such as convolutional layers and up/downsampling modules; 2) to address the training instability and codebook collapse commonly associated with conventional Vector Quantization (VQ), we integrate Finite Scalar Quantization (FSQ) into discrete video tokenization; 3) improved training strategies, including a two-stage training process and the use of reduced frame rates. By integrating these advancements, VidTok achieves substantial improvements over existing methods, demonstrating superior performance across multiple metrics, including PSNR, SSIM, LPIPS, and FVD, under standardized evaluation settings.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/VidTok", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.14168", "title": "FashionComposer: Compositional Fashion Image Generation", "authors": [ "Sihui Ji", "Yiyang Wang", "Xi Chen", "Xiaogang Xu", "Hao Luo", "Hengshuang Zhao" ], "github": "https://github.com/zjuer-huijun/FashionComposer", "project_page": "", "abstract": "We present FashionComposer for compositional fashion image generation. Unlike previous methods, FashionComposer is highly flexible. It takes multi-modal input (i.e., text prompt, parametric human model, garment image, and face image) and supports personalizing the appearance, pose, and figure of the human and assigning multiple garments in one pass. To achieve this, we first develop a universal framework capable of handling diverse input modalities. We construct scaled training data to enhance the model's robust compositional capabilities. To accommodate multiple reference images (garments and faces) seamlessly, we organize these references in a single image as an \"asset library\" and employ a reference UNet to extract appearance features. To inject the appearance features into the correct pixels in the generated result, we propose subject-binding attention. It binds the appearance features from different \"assets\" with the corresponding text features. In this way, the model could understand each asset according to their semantics, supporting arbitrary numbers and types of reference images. As a comprehensive solution, FashionComposer also supports many other applications like human album generation, diverse virtual try-on tasks, etc.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zjuer-huijun/FashionComposer", "github_api_added_by": "auto" }, { "date": "2024-12-19", "arxiv_id": "2412.13746", "title": "RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment", "authors": [ "Zhuoran Jin", "Hongbang Yuan", "Tianyi Men", "Pengfei Cao", "Yubo Chen", "Kang Liu", "Jun Zhao" ], "github": "https://github.com/jinzhuoran/rag-rewardbench", "project_page": "", "abstract": "Despite the significant progress made by existing retrieval augmented language models (RALMs) in providing trustworthy responses and grounding in reliable sources, they often overlook effective alignment with human preferences. In the alignment process, reward models (RMs) act as a crucial proxy for human values to guide optimization. However, it remains unclear how to evaluate and select a reliable RM for preference alignment in RALMs. To this end, we propose RAG-RewardBench, the first benchmark for evaluating RMs in RAG settings. First, we design four crucial and challenging RAG-specific scenarios to assess RMs, including multi-hop reasoning, fine-grained citation, appropriate abstain, and conflict robustness. Then, we incorporate 18 RAG subsets, six retrievers, and 24 RALMs to increase the diversity of data sources. Finally, we adopt an LLM-as-a-judge approach to improve preference annotation efficiency and effectiveness, exhibiting a strong correlation with human annotations. Based on the RAG-RewardBench, we conduct a comprehensive evaluation of 45 RMs and uncover their limitations in RAG scenarios. Additionally, we also reveal that existing trained RALMs show almost no improvement in preference alignment, highlighting the need for a shift towards preference-aligned training.We release our benchmark and code publicly at https://huggingface.co/datasets/jinzhuoran/RAG-RewardBench/ for future work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jinzhuoran/rag-rewardbench", "github_api_added_by": "auto" }, { "date": "2024-12-19", "arxiv_id": "2412.14015", "title": "Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation", "authors": [ "Haotong Lin", "Sida Peng", "Jingxiao Chen", "Songyou Peng", "Jiaming Sun", "Minghuan Liu", "Hujun Bao", "Jiashi Feng", "Xiaowei Zhou", "Bingyi Kang" ], "github": "https://github.com/DepthAnything/PromptDA", "project_page": "", "abstract": "Prompts play a critical role in unleashing the power of language and vision foundation models for specific tasks. For the first time, we introduce prompting into depth foundation models, creating a new paradigm for metric depth estimation termed Prompt Depth Anything. Specifically, we use a low-cost LiDAR as the prompt to guide the Depth Anything model for accurate metric depth output, achieving up to 4K resolution. Our approach centers on a concise prompt fusion design that integrates the LiDAR at multiple scales within the depth decoder. To address training challenges posed by limited datasets containing both LiDAR depth and precise GT depth, we propose a scalable data pipeline that includes synthetic data LiDAR simulation and real data pseudo GT depth generation. Our approach sets new state-of-the-arts on the ARKitScenes and ScanNet++ datasets and benefits downstream applications, including 3D reconstruction and generalized robotic grasping.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DepthAnything/PromptDA", "github_api_added_by": "auto" }, { "date": "2024-12-19", "arxiv_id": "2412.12571", "title": "ChatDiT: A Training-Free Baseline for Task-Agnostic Free-Form Chatting with Diffusion Transformers", "authors": [ "Lianghua Huang", "Wei Wang", "Zhi-Fan Wu", "Yupeng Shi", "Chen Liang", "Tong Shen", "Han Zhang", "Huanzhang Dou", "Yu Liu", "Jingren Zhou" ], "github": "https://github.com/ali-vilab/ChatDiT", "project_page": "", "abstract": "Recent research arXiv:2410.15027 arXiv:2410.23775 has highlighted the inherent in-context generation capabilities of pretrained diffusion transformers (DiTs), enabling them to seamlessly adapt to diverse visual tasks with minimal or no architectural modifications. These capabilities are unlocked by concatenating self-attention tokens across multiple input and target images, combined with grouped and masked generation pipelines. Building upon this foundation, we present ChatDiT, a zero-shot, general-purpose, and interactive visual generation framework that leverages pretrained diffusion transformers in their original form, requiring no additional tuning, adapters, or modifications. Users can interact with ChatDiT to create interleaved text-image articles, multi-page picture books, edit images, design IP derivatives, or develop character design settings, all through free-form natural language across one or more conversational rounds. At its core, ChatDiT employs a multi-agent system comprising three key components: an Instruction-Parsing agent that interprets user-uploaded images and instructions, a Strategy-Planning agent that devises single-step or multi-step generation actions, and an Execution agent that performs these actions using an in-context toolkit of diffusion transformers. We thoroughly evaluate ChatDiT on IDEA-Bench arXiv:2412.11767, comprising 100 real-world design tasks and 275 cases with diverse instructions and varying numbers of input and target images. Despite its simplicity and training-free approach, ChatDiT surpasses all competitors, including those specifically designed and trained on extensive multi-task datasets. We further identify key limitations of pretrained DiTs in zero-shot adapting to tasks. We release all code, agents, results, and intermediate outputs to facilitate further research at https://github.com/ali-vilab/ChatDiT", "github_manual": "", "github_regex": "https://github.com/ali-vilab/ChatDiT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.14173", "title": "AniDoc: Animation Creation Made Easier", "authors": [ "Yihao Meng", "Hao Ouyang", "Hanlin Wang", "Qiuyu Wang", "Wen Wang", "Ka Leong Cheng", "Zhiheng Liu", "Yujun Shen", "Huamin Qu" ], "github": "https://github.com/yihao-meng/AniDoc", "project_page": "", "abstract": "The production of 2D animation follows an industry-standard workflow, encompassing four essential stages: character design, keyframe animation, in-betweening, and coloring. Our research focuses on reducing the labor costs in the above process by harnessing the potential of increasingly powerful generative AI. Using video diffusion models as the foundation, AniDoc emerges as a video line art colorization tool, which automatically converts sketch sequences into colored animations following the reference character specification. Our model exploits correspondence matching as an explicit guidance, yielding strong robustness to the variations (e.g., posture) between the reference character and each line art frame. In addition, our model could even automate the in-betweening process, such that users can easily create a temporally consistent animation by simply providing a character image as well as the start and end sketches. Our code is available at: https://yihao-meng.github.io/AniDoc_demo.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yihao-meng/AniDoc", "github_api_added_by": "auto" }, { "date": "2024-12-19", "arxiv_id": "2412.14172", "title": "Learning from Massive Human Videos for Universal Humanoid Pose Control", "authors": [ "Jiageng Mao", "Siheng Zhao", "Siqi Song", "Tianheng Shi", "Junjie Ye", "Mingtong Zhang", "Haoran Geng", "Jitendra Malik", "Vitor Guizilini", "Yue Wang" ], "github": "", "project_page": "", "abstract": "Scalable learning of humanoid robots is crucial for their deployment in real-world applications. While traditional approaches primarily rely on reinforcement learning or teleoperation to achieve whole-body control, they are often limited by the diversity of simulated environments and the high costs of demonstration collection. In contrast, human videos are ubiquitous and present an untapped source of semantic and motion information that could significantly enhance the generalization capabilities of humanoid robots. This paper introduces Humanoid-X, a large-scale dataset of over 20 million humanoid robot poses with corresponding text-based motion descriptions, designed to leverage this abundant data. Humanoid-X is curated through a comprehensive pipeline: data mining from the Internet, video caption generation, motion retargeting of humans to humanoid robots, and policy learning for real-world deployment. With Humanoid-X, we further train a large humanoid model, UH-1, which takes text instructions as input and outputs corresponding actions to control a humanoid robot. Extensive simulated and real-world experiments validate that our scalable training approach leads to superior generalization in text-based humanoid control, marking a significant step toward adaptable, real-world-ready humanoid robots.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.12953", "title": "Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning", "authors": [ "Moritz Reuss", "Jyothish Pari", "Pulkit Agrawal", "Rudolf Lioutikov" ], "github": "https://github.com/intuitive-robots/MoDE_Diffusion_Policy", "project_page": "", "abstract": "Diffusion Policies have become widely used in Imitation Learning, offering several appealing properties, such as generating multimodal and discontinuous behavior. As models are becoming larger to capture more complex capabilities, their computational demands increase, as shown by recent scaling laws. Therefore, continuing with the current architectures will present a computational roadblock. To address this gap, we propose Mixture-of-Denoising Experts (MoDE) as a novel policy for Imitation Learning. MoDE surpasses current state-of-the-art Transformer-based Diffusion Policies while enabling parameter-efficient scaling through sparse experts and noise-conditioned routing, reducing both active parameters by 40% and inference costs by 90% via expert caching. Our architecture combines this efficient scaling with noise-conditioned self-attention mechanism, enabling more effective denoising across different noise levels. MoDE achieves state-of-the-art performance on 134 tasks in four established imitation learning benchmarks (CALVIN and LIBERO). Notably, by pretraining MoDE on diverse robotics data, we achieve 4.01 on CALVIN ABC and 0.95 on LIBERO-90. It surpasses both CNN-based and Transformer Diffusion Policies by an average of 57% across 4 benchmarks, while using 90% fewer FLOPs and fewer active parameters compared to default Diffusion Transformer architectures. Furthermore, we conduct comprehensive ablations on MoDE's components, providing insights for designing efficient and scalable Transformer architectures for Diffusion Policies. Code and demonstrations are available at https://mbreuss.github.io/MoDE_Diffusion_Policy/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/intuitive-robots/MoDE_Diffusion_Policy", "github_api_added_by": "auto" }, { "date": "2024-12-19", "arxiv_id": "2412.13795", "title": "Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN", "authors": [ "Pengxiang Li", "Lu Yin", "Shiwei Liu" ], "github": "https://github.com/pixeli99/MixLN", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved remarkable success, yet recent findings reveal that their deeper layers often contribute minimally and can be pruned without affecting overall performance. While some view this as an opportunity for model compression, we identify it as a training shortfall rooted in the widespread use of Pre-Layer Normalization (Pre-LN). We demonstrate that Pre-LN, commonly employed in models like GPT and LLaMA, leads to diminished gradient norms in its deeper layers, reducing their effectiveness. In contrast, Post-Layer Normalization (Post-LN) preserves larger gradient norms in deeper layers but suffers from vanishing gradients in earlier layers. To address this, we introduce Mix-LN, a novel normalization technique that combines the strengths of Pre-LN and Post-LN within the same model. Mix-LN applies Post-LN to the earlier layers and Pre-LN to the deeper layers, ensuring more uniform gradients across layers. This allows all parts of the network--both shallow and deep layers--to contribute effectively to training. Extensive experiments with various model sizes from 70M to 7B demonstrate that Mix-LN consistently outperforms both Pre-LN and Post-LN, promoting more balanced, healthier gradient norms throughout the network, and enhancing the overall quality of LLM pre-training. Furthermore, we demonstrate that models pre-trained with Mix-LN learn better compared to those using Pre-LN or Post-LN during supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), highlighting the critical importance of high-quality deep layers. By effectively addressing the inefficiencies of deep layers in current LLMs, Mix-LN unlocks their potential, enhancing model capacity without increasing model size. Our code is available at https://github.com/pixeli99/MixLN.", "github_manual": "", "github_regex": "https://github.com/pixeli99/MixLN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.14123", "title": "AnySat: An Earth Observation Model for Any Resolutions, Scales, and Modalities", "authors": [ "Guillaume Astruc", "Nicolas Gonthier", "Clement Mallet", "Loic Landrieu" ], "github": "https://github.com/gastruc/AnySat", "project_page": "", "abstract": "Geospatial models must adapt to the diversity of Earth observation data in terms of resolutions, scales, and modalities. However, existing approaches expect fixed input configurations, which limits their practical applicability. We propose AnySat, a multimodal model based on joint embedding predictive architecture (JEPA) and resolution-adaptive spatial encoders, allowing us to train a single model on highly heterogeneous data in a self-supervised manner. To demonstrate the advantages of this unified approach, we compile GeoPlex, a collection of 5 multimodal datasets with varying characteristics and 11 distinct sensors. We then train a single powerful model on these diverse datasets simultaneously. Once fine-tuned, we achieve better or near state-of-the-art results on the datasets of GeoPlex and 4 additional ones for 5 environment monitoring tasks: land cover mapping, tree species identification, crop type classification, change detection, and flood segmentation. The code and models are available at https://github.com/gastruc/AnySat.", "github_manual": "", "github_regex": "https://github.com/gastruc/AnySat", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.13670", "title": "AntiLeak-Bench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge", "authors": [ "Xiaobao Wu", "Liangming Pan", "Yuxi Xie", "Ruiwen Zhou", "Shuai Zhao", "Yubo Ma", "Mingzhe Du", "Rui Mao", "Anh Tuan Luu", "William Yang Wang" ], "github": "https://github.com/bobxwu/AntiLeakBench", "project_page": "", "abstract": "Data contamination hinders fair LLM evaluation by introducing test data into newer models' training sets. Existing studies solve this challenge by updating benchmarks with newly collected data. However, they fail to guarantee contamination-free evaluation as the newly collected data may contain pre-existing knowledge, and their benchmark updates rely on intensive human labor. To address these issues, we in this paper propose AntiLeak-Bench, an automated anti-leakage benchmarking framework. Instead of simply using newly collected data, we construct samples with explicitly new knowledge absent from LLMs' training sets, which thus ensures strictly contamination-free evaluation. We further design a fully automated workflow to build and update our benchmark without human labor. This significantly reduces the cost of benchmark maintenance to accommodate emerging LLMs. Through extensive experiments, we highlight that data contamination likely exists before LLMs' cutoff time and demonstrate AntiLeak-Bench effectively overcomes this challenge.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bobxwu/AntiLeakBench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.14042", "title": "CAD-Recode: Reverse Engineering CAD Code from Point Clouds", "authors": [ "Danila Rukhovich", "Elona Dupont", "Dimitrios Mallis", "Kseniya Cherenkova", "Anis Kacem", "Djamila Aouada" ], "github": "https://github.com/filaPro/cad-recode", "project_page": "", "abstract": "Computer-Aided Design (CAD) models are typically constructed by sequentially drawing parametric sketches and applying CAD operations to obtain a 3D model. The problem of 3D CAD reverse engineering consists of reconstructing the sketch and CAD operation sequences from 3D representations such as point clouds. In this paper, we address this challenge through novel contributions across three levels: CAD sequence representation, network design, and dataset. In particular, we represent CAD sketch-extrude sequences as Python code. The proposed CAD-Recode translates a point cloud into Python code that, when executed, reconstructs the CAD model. Taking advantage of the exposure of pre-trained Large Language Models (LLMs) to Python code, we leverage a relatively small LLM as a decoder for CAD-Recode and combine it with a lightweight point cloud projector. CAD-Recode is trained solely on a proposed synthetic dataset of one million diverse CAD sequences. CAD-Recode significantly outperforms existing methods across three datasets while requiring fewer input points. Notably, it achieves 10 times lower mean Chamfer distance than state-of-the-art methods on DeepCAD and Fusion360 datasets. Furthermore, we show that our CAD Python code output is interpretable by off-the-shelf LLMs, enabling CAD editing and CAD-specific question answering from point clouds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/filaPro/cad-recode", "github_api_added_by": "auto" }, { "date": "2024-12-19", "arxiv_id": "2412.13871", "title": "LLaVA-UHD v2: an MLLM Integrating High-Resolution Feature Pyramid via Hierarchical Window Transformer", "authors": [ "Yipeng Zhang", "Yifan Liu", "Zonghao Guo", "Yidan Zhang", "Xuesong Yang", "Chi Chen", "Jun Song", "Bo Zheng", "Yuan Yao", "Zhiyuan Liu", "Tat-Seng Chua", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "In multimodal large language models (MLLMs), vision transformers (ViTs) are widely employed for visual encoding. However, their performance in solving universal MLLM tasks is not satisfactory. We attribute it to a lack of information from diverse visual levels, impeding alignment with the various semantic granularity required for language generation. To address this issue, we present LLaVA-UHD v2, an advanced MLLM centered around a Hierarchical window transformer that enables capturing diverse visual granularity by constructing and integrating a high-resolution feature pyramid. As a vision-language projector, Hiwin transformer comprises two primary modules: (i) an inverse feature pyramid, constructed by a ViT-derived feature up-sampling process utilizing high-frequency details from an image pyramid, and (ii) hierarchical window attention, focusing on a set of key sampling features within cross-scale windows to condense multi-level feature maps. Extensive experiments demonstrate that LLaVA-UHD v2 achieves superior performance over existing MLLMs on popular benchmarks. Notably, our design brings an average boost of 3.7% across 14 benchmarks compared with the baseline method, 9.3% on DocVQA for instance. We make all the data, model checkpoint, and code publicly available to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.14171", "title": "Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces", "authors": [ "Jihan Yang", "Shusheng Yang", "Anjali W. Gupta", "Rilyn Han", "Li Fei-Fei", "Saining Xie" ], "github": "https://github.com/vision-x-nyu/thinking-in-space", "project_page": "", "abstract": "Humans possess the visual-spatial intelligence to remember spaces from sequential visual observations. However, can Multimodal Large Language Models (MLLMs) trained on million-scale video datasets also ``think in space'' from videos? We present a novel video-based visual-spatial intelligence benchmark (VSI-Bench) of over 5,000 question-answer pairs, and find that MLLMs exhibit competitive - though subhuman - visual-spatial intelligence. We probe models to express how they think in space both linguistically and visually and find that while spatial reasoning capabilities remain the primary bottleneck for MLLMs to reach higher benchmark performance, local world models and spatial awareness do emerge within these models. Notably, prevailing linguistic reasoning techniques (e.g., chain-of-thought, self-consistency, tree-of-thoughts) fail to improve performance, whereas explicitly generating cognitive maps during question-answering enhances MLLMs' spatial distance ability.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vision-x-nyu/thinking-in-space", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.14093", "title": "Alignment faking in large language models", "authors": [ "Ryan Greenblatt", "Carson Denison", "Benjamin Wright", "Fabien Roger", "Monte MacDiarmid", "Sam Marks", "Johannes Treutlein", "Tim Belonax", "Jack Chen", "David Duvenaud", "Akbir Khan", "Julian Michael", "S\u00f6ren Mindermann", "Ethan Perez", "Linda Petrini", "Jonathan Uesato", "Jared Kaplan", "Buck Shlegeris", "Samuel R. Bowman", "Evan Hubinger" ], "github": "https://github.com/redwoodresearch/alignment_faking_public", "project_page": "", "abstract": "We present a demonstration of a large language model engaging in alignment faking: selectively complying with its training objective in training to prevent modification of its behavior out of training. First, we give Claude 3 Opus a system prompt stating it is being trained to answer all queries, even harmful ones, which conflicts with its prior training to refuse such queries. To allow the model to infer when it is in training, we say it will be trained only on conversations with free users, not paid users. We find the model complies with harmful queries from free users 14% of the time, versus almost never for paid users. Explaining this gap, in almost all cases where the model complies with a harmful query from a free user, we observe explicit alignment-faking reasoning, with the model stating it is strategically answering harmful queries in training to preserve its preferred harmlessness behavior out of training. Next, we study a more realistic setting where information about the training process is provided not in a system prompt, but by training on synthetic documents that mimic pre-training data--and observe similar alignment faking. Finally, we study the effect of actually training the model to comply with harmful queries via reinforcement learning, which we find increases the rate of alignment-faking reasoning to 78%, though also increases compliance even out of training. We additionally observe other behaviors such as the model exfiltrating its weights when given an easy opportunity. While we made alignment faking easier by telling the model when and by what criteria it was being trained, we did not instruct the model to fake alignment or give it any explicit goal. As future models might infer information about their training process without being told, our results suggest a risk of alignment faking in future models, whether due to a benign preference--as in this case--or not.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/redwoodresearch/alignment_faking_public", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.13303", "title": "FastVLM: Efficient Vision Encoding for Vision Language Models", "authors": [ "Pavan Kumar Anasosalu Vasu", "Fartash Faghri", "Chun-Liang Li", "Cem Koc", "Nate True", "Albert Antony", "Gokul Santhanam", "James Gabriel", "Peter Grasch", "Oncel Tuzel", "Hadi Pouransari" ], "github": "https://github.com/apple/ml-fastvlm", "project_page": "", "abstract": "Scaling the input image resolution is essential for enhancing the performance of Vision Language Models (VLMs), particularly in text-rich image understanding tasks. However, popular visual encoders such as ViTs become inefficient at high resolutions due to the large number of tokens and high encoding latency caused by stacked self-attention layers. At different operational resolutions, the vision encoder of a VLM can be optimized along two axes: reducing encoding latency and minimizing the number of visual tokens passed to the LLM, thereby lowering overall latency. Based on a comprehensive efficiency analysis of the interplay between image resolution, vision latency, token count, and LLM size, we introduce FastVLM, a model that achieves an optimized trade-off between latency, model size and accuracy. FastVLM incorporates FastViTHD, a novel hybrid vision encoder designed to output fewer tokens and significantly reduce encoding time for high-resolution images. Unlike previous methods, FastVLM achieves the optimal balance between visual token count and image resolution solely by scaling the input image, eliminating the need for additional token pruning and simplifying the model design. In the LLaVA-1.5 setup, FastVLM achieves 3.2times improvement in time-to-first-token (TTFT) while maintaining similar performance on VLM benchmarks compared to prior works. Compared to LLaVa-OneVision at the highest resolution (1152times1152), FastVLM achieves comparable performance on key benchmarks like SeedBench and MMMU, using the same 0.5B LLM, but with 85times faster TTFT and a vision encoder that is 3.4times smaller.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/ml-fastvlm", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.11768", "title": "No More Adam: Learning Rate Scaling at Initialization is All You Need", "authors": [ "Minghao Xu", "Lichuan Xiang", "Xu Cai", "Hongkai Wen" ], "github": "https://github.com/anonymousalethiometer/sgd_sai", "project_page": "", "abstract": "In this work, we question the necessity of adaptive gradient methods for training deep neural networks. SGD-SaI is a simple yet effective enhancement to stochastic gradient descent with momentum (SGDM). SGD-SaI performs learning rate Scaling at Initialization (SaI) to distinct parameter groups, guided by their respective gradient signal-to-noise ratios (g-SNR). By adjusting learning rates without relying on adaptive second-order momentum, SGD-SaI helps prevent training imbalances from the very first iteration and cuts the optimizer's memory usage by half compared to AdamW. Despite its simplicity and efficiency, SGD-SaI consistently matches or outperforms AdamW in training a variety of Transformer-based tasks, effectively overcoming a long-standing challenge of using SGD for training Transformers. SGD-SaI excels in ImageNet-1K classification with Vision Transformers(ViT) and GPT-2 pretraining for large language models (LLMs, transformer decoder-only), demonstrating robustness to hyperparameter variations and practicality for diverse applications. We further tested its robustness on tasks like LoRA fine-tuning for LLMs and diffusion models, where it consistently outperforms state-of-the-art optimizers. From a memory efficiency perspective, SGD-SaI achieves substantial memory savings for optimizer states, reducing memory usage by 5.93 GB for GPT-2 (1.5B parameters) and 25.15 GB for Llama2-7B compared to AdamW in full-precision training settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/anonymousalethiometer/sgd_sai", "github_api_added_by": "auto" }, { "date": "2024-12-19", "arxiv_id": "2412.13663", "title": "Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference", "authors": [ "Benjamin Warner", "Antoine Chaffin", "Benjamin Clavi\u00e9", "Orion Weller", "Oskar Hallstr\u00f6m", "Said Taghadouini", "Alexis Gallagher", "Raja Biswas", "Faisal Ladhak", "Tom Aarsen", "Nathan Cooper", "Griffin Adams", "Jeremy Howard", "Iacopo Poli" ], "github": "", "project_page": "", "abstract": "Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.14169", "title": "Autoregressive Video Generation without Vector Quantization", "authors": [ "Haoge Deng", "Ting Pan", "Haiwen Diao", "Zhengxiong Luo", "Yufeng Cui", "Huchuan Lu", "Shiguang Shan", "Yonggang Qi", "Xinlong Wang" ], "github": "https://github.com/baaivision/NOVA", "project_page": "", "abstract": "This paper presents a novel approach that enables autoregressive video generation with high efficiency. We propose to reformulate the video generation problem as a non-quantized autoregressive modeling of temporal frame-by-frame prediction and spatial set-by-set prediction. Unlike raster-scan prediction in prior autoregressive models or joint distribution modeling of fixed-length tokens in diffusion models, our approach maintains the causal property of GPT-style models for flexible in-context capabilities, while leveraging bidirectional modeling within individual frames for efficiency. With the proposed approach, we train a novel video autoregressive model without vector quantization, termed NOVA. Our results demonstrate that NOVA surpasses prior autoregressive video models in data efficiency, inference speed, visual fidelity, and video fluency, even with a much smaller model capacity, i.e., 0.6B parameters. NOVA also outperforms state-of-the-art image diffusion models in text-to-image generation tasks, with a significantly lower training cost. Additionally, NOVA generalizes well across extended video durations and enables diverse zero-shot applications in one unified model. Code and models are publicly available at https://github.com/baaivision/NOVA.", "github_manual": "", "github_regex": "https://github.com/baaivision/NOVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-19", "arxiv_id": "2412.11634", "title": "Predicting the Original Appearance of Damaged Historical Documents", "authors": [ "Zhenhua Yang", "Dezhi Peng", "Yongxin Shi", "Yuyi Zhang", "Chongyu Liu", "Lianwen Jin" ], "github": "https://github.com/yeungchenwa/HDR", "project_page": "", "abstract": "Historical documents encompass a wealth of cultural treasures but suffer from severe damages including character missing, paper damage, and ink erosion over time. However, existing document processing methods primarily focus on binarization, enhancement, etc., neglecting the repair of these damages. To this end, we present a new task, termed Historical Document Repair (HDR), which aims to predict the original appearance of damaged historical documents. To fill the gap in this field, we propose a large-scale dataset HDR28K and a diffusion-based network DiffHDR for historical document repair. Specifically, HDR28K contains 28,552 damaged-repaired image pairs with character-level annotations and multi-style degradations. Moreover, DiffHDR augments the vanilla diffusion framework with semantic and spatial information and a meticulously designed character perceptual loss for contextual and visual coherence. Experimental results demonstrate that the proposed DiffHDR trained using HDR28K significantly surpasses existing approaches and exhibits remarkable performance in handling real damaged documents. Notably, DiffHDR can also be extended to document editing and text block generation, showcasing its high flexibility and generalization capacity. We believe this study could pioneer a new direction of document processing and contribute to the inheritance of invaluable cultures and civilizations. The dataset and code is available at https://github.com/yeungchenwa/HDR.", "github_manual": "", "github_regex": "https://github.com/yeungchenwa/HDR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.14835", "title": "Progressive Multimodal Reasoning via Active Retrieval", "authors": [ "Guanting Dong", "Chenghao Zhang", "Mengjie Deng", "Yutao Zhu", "Zhicheng Dou", "Ji-Rong Wen" ], "github": "", "project_page": "", "abstract": "Multi-step multimodal reasoning tasks pose significant challenges for multimodal large language models (MLLMs), and finding effective ways to enhance their performance in such scenarios remains an unresolved issue. In this paper, we propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs through Active Retrieval (AR) and Monte Carlo Tree Search (MCTS). Our approach begins with the development of a unified retrieval module that retrieves key supporting insights for solving complex reasoning problems from a hybrid-modal retrieval corpus. To bridge the gap in automated multimodal reasoning verification, we employ the MCTS algorithm combined with an active retrieval mechanism, which enables the automatic generation of step-wise annotations. This strategy dynamically retrieves key insights for each reasoning step, moving beyond traditional beam search sampling to improve the diversity and reliability of the reasoning space. Additionally, we introduce a process reward model that aligns progressively to support the automatic verification of multimodal reasoning tasks. Experimental results across three complex multimodal reasoning benchmarks confirm the effectiveness of the AR-MCTS framework in enhancing the performance of various multimodal models. Further analysis demonstrates that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.15204", "title": "LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks", "authors": [ "Yushi Bai", "Shangqing Tu", "Jiajie Zhang", "Hao Peng", "Xiaozhi Wang", "Xin Lv", "Shulin Cao", "Jiazheng Xu", "Lei Hou", "Yuxiao Dong", "Jie Tang", "Juanzi Li" ], "github": "https://github.com/THUDM/LongBench", "project_page": "", "abstract": "This paper introduces LongBench v2, a benchmark designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world multitasks. LongBench v2 consists of 503 challenging multiple-choice questions, with contexts ranging from 8k to 2M words, across six major task categories: single-document QA, multi-document QA, long in-context learning, long-dialogue history understanding, code repository understanding, and long structured data understanding. To ensure the breadth and the practicality, we collect data from nearly 100 highly educated individuals with diverse professional backgrounds. We employ both automated and manual review processes to maintain high quality and difficulty, resulting in human experts achieving only 53.7% accuracy under a 15-minute time constraint. Our evaluation reveals that the best-performing model, when directly answers the questions, achieves only 50.1% accuracy. In contrast, the o1-preview model, which includes longer reasoning, achieves 57.7%, surpassing the human baseline by 4%. These results highlight the importance of enhanced reasoning ability and scaling inference-time compute to tackle the long-context challenges in LongBench v2. The project is available at https://longbench2.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUDM/LongBench", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.15115", "title": "Qwen2.5 Technical Report", "authors": [ "Qwen", "An Yang", "Baosong Yang", "Beichen Zhang", "Binyuan Hui", "Bo Zheng", "Bowen Yu", "Chengyuan Li", "Dayiheng Liu", "Fei Huang", "Haoran Wei", "Huan Lin", "Jian Yang", "Jianhong Tu", "Jianwei Zhang", "Jianxin Yang", "Jiaxi Yang", "Jingren Zhou", "Junyang Lin", "Kai Dang", "Keming Lu", "Keqin Bao", "Kexin Yang", "Le Yu", "Mei Li", "Mingfeng Xue", "Pei Zhang", "Qin Zhu", "Rui Men", "Runji Lin", "Tianhao Li", "Tingyu Xia", "Xingzhang Ren", "Xuancheng Ren", "Yang Fan", "Yang Su", "Yichang Zhang", "Yu Wan", "Yuqiong Liu", "Zeyu Cui", "Zhenru Zhang", "Zihan Qiu" ], "github": "https://github.com/QwenLM/Qwen2.5", "project_page": "", "abstract": "In this report, we introduce Qwen2.5, a comprehensive series of large language models (LLMs) designed to meet diverse needs. Compared to previous iterations, Qwen 2.5 has been significantly improved during both the pre-training and post-training stages. In terms of pre-training, we have scaled the high-quality pre-training datasets from the previous 7 trillion tokens to 18 trillion tokens. This provides a strong foundation for common sense, expert knowledge, and reasoning capabilities. In terms of post-training, we implement intricate supervised finetuning with over 1 million samples, as well as multistage reinforcement learning. Post-training techniques enhance human preference, and notably improve long text generation, structural data analysis, and instruction following. To handle diverse and varied use cases effectively, we present Qwen2.5 LLM series in rich sizes. Open-weight offerings include base and instruction-tuned models, with quantized versions available. In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2.5-Turbo and Qwen2.5-Plus, both available from Alibaba Cloud Model Studio. Qwen2.5 has demonstrated top-tier performance on a wide range of benchmarks evaluating language understanding, reasoning, mathematics, coding, human preference alignment, etc. Specifically, the open-weight flagship Qwen2.5-72B-Instruct outperforms a number of open and proprietary models and demonstrates competitive performance to the state-of-the-art open-weight model, Llama-3-405B-Instruct, which is around 5 times larger. Qwen2.5-Turbo and Qwen2.5-Plus offer superior cost-effectiveness while performing competitively against GPT-4o-mini and GPT-4o respectively. Additionally, as the foundation, Qwen2.5 models have been instrumental in training specialized models such as Qwen2.5-Math, Qwen2.5-Coder, QwQ, and multimodal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/QwenLM/Qwen2.5", "github_api_added_by": "auto" }, { "date": "2024-12-20", "arxiv_id": "2412.14475", "title": "MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval", "authors": [ "Junjie Zhou", "Zheng Liu", "Ze Liu", "Shitao Xiao", "Yueze Wang", "Bo Zhao", "Chen Jason Zhang", "Defu Lian", "Yongping Xiong" ], "github": "", "project_page": "", "abstract": "Despite the rapidly growing demand for multimodal retrieval, progress in this field remains severely constrained by a lack of training data. In this paper, we introduce MegaPairs, a novel data synthesis method that leverages vision language models (VLMs) and open-domain images, together with a massive synthetic dataset generated from this method. Our empirical analysis shows that MegaPairs generates high-quality data, enabling the multimodal retriever to significantly outperform the baseline model trained on 70times more data from existing datasets. Moreover, since MegaPairs solely relies on general image corpora and open-source VLMs, it can be easily scaled up, enabling continuous improvements in retrieval performance. In this stage, we produced more than 26 million training instances and trained several models of varying sizes using this data. These new models achieve state-of-the-art zero-shot performance across 4 popular composed image retrieval (CIR) benchmarks and the highest overall performance on the 36 datasets provided by MMEB. They also demonstrate notable performance improvements with additional downstream fine-tuning. Our produced dataset, well-trained models, and data synthesis pipeline will be made publicly available to facilitate the future development of this field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.14462", "title": "Affordance-Aware Object Insertion via Mask-Aware Dual Diffusion", "authors": [ "Jixuan He", "Wanhua Li", "Ye Liu", "Junsik Kim", "Donglai Wei", "Hanspeter Pfister" ], "github": "https://github.com/KaKituken/affordance-aware-any", "project_page": "", "abstract": "As a common image editing operation, image composition involves integrating foreground objects into background scenes. In this paper, we expand the application of the concept of Affordance from human-centered image composition tasks to a more general object-scene composition framework, addressing the complex interplay between foreground objects and background scenes. Following the principle of Affordance, we define the affordance-aware object insertion task, which aims to seamlessly insert any object into any scene with various position prompts. To address the limited data issue and incorporate this task, we constructed the SAM-FB dataset, which contains over 3 million examples across more than 3,000 object categories. Furthermore, we propose the Mask-Aware Dual Diffusion (MADD) model, which utilizes a dual-stream architecture to simultaneously denoise the RGB image and the insertion mask. By explicitly modeling the insertion mask in the diffusion process, MADD effectively facilitates the notion of affordance. Extensive experimental results show that our method outperforms the state-of-the-art methods and exhibits strong generalization performance on in-the-wild images. Please refer to our code on https://github.com/KaKituken/affordance-aware-any.", "github_manual": "", "github_regex": "https://github.com/KaKituken/affordance-aware-any", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.15213", "title": "Flowing from Words to Pixels: A Framework for Cross-Modality Evolution", "authors": [ "Qihao Liu", "Xi Yin", "Alan Yuille", "Andrew Brown", "Mannat Singh" ], "github": "https://github.com/qihao067/CrossFlow", "project_page": "", "abstract": "Diffusion models, and their generalization, flow matching, have had a remarkable impact on the field of media generation. Here, the conventional approach is to learn the complex mapping from a simple source distribution of Gaussian noise to the target media distribution. For cross-modal tasks such as text-to-image generation, this same mapping from noise to image is learnt whilst including a conditioning mechanism in the model. One key and thus far relatively unexplored feature of flow matching is that, unlike Diffusion models, they are not constrained for the source distribution to be noise. Hence, in this paper, we propose a paradigm shift, and ask the question of whether we can instead train flow matching models to learn a direct mapping from the distribution of one modality to the distribution of another, thus obviating the need for both the noise distribution and conditioning mechanism. We present a general and simple framework, CrossFlow, for cross-modal flow matching. We show the importance of applying Variational Encoders to the input data, and introduce a method to enable Classifier-free guidance. Surprisingly, for text-to-image, CrossFlow with a vanilla transformer without cross attention slightly outperforms standard flow matching, and we show that it scales better with training steps and model size, while also allowing for interesting latent arithmetic which results in semantically meaningful edits in the output space. To demonstrate the generalizability of our approach, we also show that CrossFlow is on par with or outperforms the state-of-the-art for various cross-modal / intra-modal mapping tasks, viz. image captioning, depth estimation, and image super-resolution. We hope this paper contributes to accelerating progress in cross-modal media generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qihao067/CrossFlow", "github_api_added_by": "auto" }, { "date": "2024-12-20", "arxiv_id": "2412.15200", "title": "DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation", "authors": [ "Wang Zhao", "Yan-Pei Cao", "Jiale Xu", "Yuejiang Dong", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Procedural Content Generation (PCG) is powerful in creating high-quality 3D contents, yet controlling it to produce desired shapes is difficult and often requires extensive parameter tuning. Inverse Procedural Content Generation aims to automatically find the best parameters under the input condition. However, existing sampling-based and neural network-based methods still suffer from numerous sample iterations or limited controllability. In this work, we present DI-PCG, a novel and efficient method for Inverse PCG from general image conditions. At its core is a lightweight diffusion transformer model, where PCG parameters are directly treated as the denoising target and the observed images as conditions to control parameter generation. DI-PCG is efficient and effective. With only 7.6M network parameters and 30 GPU hours to train, it demonstrates superior performance in recovering parameters accurately, and generalizing well to in-the-wild images. Quantitative and qualitative experiment results validate the effectiveness of DI-PCG in inverse PCG and image-to-3D generation tasks. DI-PCG offers a promising approach for efficient inverse PCG and represents a valuable exploration step towards a 3D generation path that models how to construct a 3D asset using parametric models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.14689", "title": "How to Synthesize Text Data without Model Collapse?", "authors": [ "Xuekai Zhu", "Daixuan Cheng", "Hengli Li", "Kaiyan Zhang", "Ermo Hua", "Xingtai Lv", "Ning Ding", "Zhouhan Lin", "Zilong Zheng", "Bowen Zhou" ], "github": "", "project_page": "", "abstract": "Model collapse in synthetic data indicates that iterative training on self-generated data leads to a gradual decline in performance. With the proliferation of AI models, synthetic data will fundamentally reshape the web data ecosystem. Future GPT-{n} models will inevitably be trained on a blend of synthetic and human-produced data. In this paper, we focus on two questions: what is the impact of synthetic data on language model training, and how to synthesize data without model collapse? We first pre-train language models across different proportions of synthetic data, revealing a negative correlation between the proportion of synthetic data and model performance. We further conduct statistical analysis on synthetic data to uncover distributional shift phenomenon and over-concentration of n-gram features. Inspired by the above findings, we propose token editing on human-produced data to obtain semi-synthetic data. As a proof of concept, we theoretically demonstrate that token-level editing can prevent model collapse, as the test error is constrained by a finite upper bound. We conduct extensive experiments on pre-training from scratch, continual pre-training, and supervised fine-tuning. The results validate our theoretical proof that token-level editing improves data quality and enhances model performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.14642", "title": "TOMG-Bench: Evaluating LLMs on Text-based Open Molecule Generation", "authors": [ "Jiatong Li", "Junxian Li", "Yunqing Liu", "Dongzhan Zhou", "Qing Li" ], "github": "https://github.com/phenixace/TOMG-Bench", "project_page": "", "abstract": "In this paper, we propose Text-based Open Molecule Generation Benchmark (TOMG-Bench), the first benchmark to evaluate the open-domain molecule generation capability of LLMs. TOMG-Bench encompasses a dataset of three major tasks: molecule editing (MolEdit), molecule optimization (MolOpt), and customized molecule generation (MolCustom). Each task further contains three subtasks, with each subtask comprising 5,000 test samples. Given the inherent complexity of open molecule generation, we have also developed an automated evaluation system that helps measure both the quality and the accuracy of the generated molecules. Our comprehensive benchmarking of 25 LLMs reveals the current limitations and potential areas for improvement in text-guided molecule discovery. Furthermore, with the assistance of OpenMolIns, a specialized instruction tuning dataset proposed for solving challenges raised by TOMG-Bench, Llama3.1-8B could outperform all the open-source general LLMs, even surpassing GPT-3.5-turbo by 46.5\\% on TOMG-Bench. Our codes and datasets are available through https://github.com/phenixace/TOMG-Bench.", "github_manual": "", "github_regex": "https://github.com/phenixace/TOMG-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.15084", "title": "AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling", "authors": [ "Zihan Liu", "Yang Chen", "Mohammad Shoeybi", "Bryan Catanzaro", "Wei Ping" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce AceMath, a suite of frontier math models that excel in solving complex math problems, along with highly effective reward models capable of evaluating generated solutions and reliably identifying the correct ones. To develop the instruction-tuned math models, we propose a supervised fine-tuning (SFT) process that first achieves competitive performance across general domains, followed by targeted fine-tuning for the math domain using a carefully curated set of prompts and synthetically generated responses. The resulting model, AceMath-72B-Instruct greatly outperforms Qwen2.5-Math-72B-Instruct, GPT-4o and Claude-3.5 Sonnet. To develop math-specialized reward model, we first construct AceMath-RewardBench, a comprehensive and robust benchmark for evaluating math reward models across diverse problems and difficulty levels. After that, we present a systematic approach to build our math reward models. The resulting model, AceMath-72B-RM, consistently outperforms state-of-the-art reward models. Furthermore, when combining AceMath-72B-Instruct with AceMath-72B-RM, we achieve the highest average rm@8 score across the math reasoning benchmarks. We will release model weights, training data, and evaluation benchmarks at: https://research.nvidia.com/labs/adlr/acemath", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.14233", "title": "Descriptive Caption Enhancement with Visual Specialists for Multimodal Perception", "authors": [ "Yanpeng Sun", "Jing Hao", "Ke Zhu", "Jiang-Jiang Liu", "Yuxiang Zhao", "Xiaofan Li", "Gang Zhang", "Zechao Li", "Jingdong Wang" ], "github": "https://github.com/syp2ysy/DCE", "project_page": "", "abstract": "Training Large Multimodality Models (LMMs) relies on descriptive image caption that connects image and language. Existing methods either distill the caption from the LMM models or construct the captions from the internet images or by human. We propose to leverage off-the-shelf visual specialists, which were trained from annotated images initially not for image captioning, for enhancing the image caption. Our approach, named DCE, explores object low-level and fine-grained attributes (e.g., depth, emotion and fine-grained categories) and object relations (e.g., relative location and human-object-interaction (HOI)), and combine the attributes into the descriptive caption. Experiments demonstrate that such visual specialists are able to improve the performance for visual understanding tasks as well as reasoning that benefits from more accurate visual understanding. We will release the source code and the pipeline so that other visual specialists are easily combined into the pipeline. The complete source code of DCE pipeline and datasets will be available at https://github.com/syp2ysy/DCE.", "github_manual": "", "github_regex": "https://github.com/syp2ysy/DCE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.15216", "title": "UIP2P: Unsupervised Instruction-based Image Editing via Cycle Edit Consistency", "authors": [ "Enis Simsar", "Alessio Tonioni", "Yongqin Xian", "Thomas Hofmann", "Federico Tombari" ], "github": "", "project_page": "", "abstract": "We propose an unsupervised model for instruction-based image editing that eliminates the need for ground-truth edited images during training. Existing supervised methods depend on datasets containing triplets of input image, edited image, and edit instruction. These are generated by either existing editing methods or human-annotations, which introduce biases and limit their generalization ability. Our method addresses these challenges by introducing a novel editing mechanism called Cycle Edit Consistency (CEC), which applies forward and backward edits in one training step and enforces consistency in image and attention spaces. This allows us to bypass the need for ground-truth edited images and unlock training for the first time on datasets comprising either real image-caption pairs or image-caption-edit triplets. We empirically show that our unsupervised technique performs better across a broader range of edits with high fidelity and precision. By eliminating the need for pre-existing datasets of triplets, reducing biases associated with supervised methods, and proposing CEC, our work represents a significant advancement in unblocking scaling of instruction-based image editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.15214", "title": "LeviTor: 3D Trajectory Oriented Image-to-Video Synthesis", "authors": [ "Hanlin Wang", "Hao Ouyang", "Qiuyu Wang", "Wen Wang", "Ka Leong Cheng", "Qifeng Chen", "Yujun Shen", "Limin Wang" ], "github": "https://github.com/qiuyu96/LeviTor", "project_page": "", "abstract": "The intuitive nature of drag-based interaction has led to its growing adoption for controlling object trajectories in image-to-video synthesis. Still, existing methods that perform dragging in the 2D space usually face ambiguity when handling out-of-plane movements. In this work, we augment the interaction with a new dimension, i.e., the depth dimension, such that users are allowed to assign a relative depth for each point on the trajectory. That way, our new interaction paradigm not only inherits the convenience from 2D dragging, but facilitates trajectory control in the 3D space, broadening the scope of creativity. We propose a pioneering method for 3D trajectory control in image-to-video synthesis by abstracting object masks into a few cluster points. These points, accompanied by the depth information and the instance information, are finally fed into a video diffusion model as the control signal. Extensive experiments validate the effectiveness of our approach, dubbed LeviTor, in precisely manipulating the object movements when producing photo-realistic videos from static images. Project page: https://ppetrichor.github.io/levitor.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qiuyu96/LeviTor", "github_api_added_by": "auto" }, { "date": "2024-12-20", "arxiv_id": "2412.13185", "title": "Move-in-2D: 2D-Conditioned Human Motion Generation", "authors": [ "Hsin-Ping Huang", "Yang Zhou", "Jui-Hsien Wang", "Difan Liu", "Feng Liu", "Ming-Hsuan Yang", "Zhan Xu" ], "github": "https://github.com/itseez/opencv", "project_page": "", "abstract": "Generating realistic human videos remains a challenging task, with the most effective methods currently relying on a human motion sequence as a control signal. Existing approaches often use existing motion extracted from other videos, which restricts applications to specific motion types and global scene matching. We propose Move-in-2D, a novel approach to generate human motion sequences conditioned on a scene image, allowing for diverse motion that adapts to different scenes. Our approach utilizes a diffusion model that accepts both a scene image and text prompt as inputs, producing a motion sequence tailored to the scene. To train this model, we collect a large-scale video dataset featuring single-human activities, annotating each video with the corresponding human motion as the target output. Experiments demonstrate that our method effectively predicts human motion that aligns with the scene image after projection. Furthermore, we show that the generated motion sequence improves human motion quality in video synthesis tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/itseez/opencv", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.13377", "title": "DateLogicQA: Benchmarking Temporal Biases in Large Language Models", "authors": [ "Gagan Bhatia", "MingZe Tang", "Cristina Mahanta", "Madiha Kazi" ], "github": "https://github.com/gagan3012/EAIS-Temporal-Bias", "project_page": "", "abstract": "This paper introduces DateLogicQA, a benchmark with 190 questions covering diverse date formats, temporal contexts, and reasoning types. We propose the Semantic Integrity Metric to assess tokenization quality and analyse two biases: Representation-Level Bias, affecting embeddings, and Logical-Level Bias, influencing reasoning outputs. Our findings provide a comprehensive evaluation of LLMs' capabilities and limitations in temporal reasoning, highlighting key challenges in handling temporal data accurately. The GitHub repository for our work is available at https://github.com/gagan3012/EAIS-Temporal-Bias", "github_manual": "", "github_regex": "https://github.com/gagan3012/EAIS-Temporal-Bias", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.14283", "title": "PixelMan: Consistent Object Editing with Diffusion Models via Pixel Manipulation and Generation", "authors": [ "Liyao Jiang", "Negar Hassanpour", "Mohammad Salameh", "Mohammadreza Samadi", "Jiao He", "Fengyu Sun", "Di Niu" ], "github": "", "project_page": "", "abstract": "Recent research explores the potential of Diffusion Models (DMs) for consistent object editing, which aims to modify object position, size, and composition, etc., while preserving the consistency of objects and background without changing their texture and attributes. Current inference-time methods often rely on DDIM inversion, which inherently compromises efficiency and the achievable consistency of edited images. Recent methods also utilize energy guidance which iteratively updates the predicted noise and can drive the latents away from the original image, resulting in distortions. In this paper, we propose PixelMan, an inversion-free and training-free method for achieving consistent object editing via Pixel Manipulation and generation, where we directly create a duplicate copy of the source object at target location in the pixel space, and introduce an efficient sampling approach to iteratively harmonize the manipulated object into the target location and inpaint its original location, while ensuring image consistency by anchoring the edited image to be generated to the pixel-manipulated image as well as by introducing various consistency-preserving optimization techniques during inference. Experimental evaluations based on benchmark datasets as well as extensive visual comparisons show that in as few as 16 inference steps, PixelMan outperforms a range of state-of-the-art training-based and training-free methods (usually requiring 50 steps) on multiple consistent object editing tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-20", "arxiv_id": "2412.15191", "title": "AV-Link: Temporally-Aligned Diffusion Features for Cross-Modal Audio-Video Generation", "authors": [ "Moayed Haji-Ali", "Willi Menapace", "Aliaksandr Siarohin", "Ivan Skorokhodov", "Alper Canberk", "Kwot Sin Lee", "Vicente Ordonez", "Sergey Tulyakov" ], "github": "https://github.com/snap-research/AVLink", "project_page": "", "abstract": "We propose AV-Link, a unified framework for Video-to-Audio and Audio-to-Video generation that leverages the activations of frozen video and audio diffusion models for temporally-aligned cross-modal conditioning. The key to our framework is a Fusion Block that enables bidirectional information exchange between our backbone video and audio diffusion models through a temporally-aligned self attention operation. Unlike prior work that uses feature extractors pretrained for other tasks for the conditioning signal, AV-Link can directly leverage features obtained by the complementary modality in a single framework i.e. video features to generate audio, or audio features to generate video. We extensively evaluate our design choices and demonstrate the ability of our method to achieve synchronized and high-quality audiovisual content, showcasing its potential for applications in immersive media generation. Project Page: snap-research.github.io/AVLink/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/snap-research/AVLink", "github_api_added_by": "auto" }, { "date": "2024-12-23", "arxiv_id": "2412.13649", "title": "SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation", "authors": [ "Jialong Wu", "Zhenglin Wang", "Linhai Zhang", "Yilong Lai", "Yulan He", "Deyu Zhou" ], "github": "https://github.com/linking-ai/scope", "project_page": "", "abstract": "Key-Value (KV) cache has become a bottleneck of LLMs for long-context generation. Despite the numerous efforts in this area, the optimization for the decoding phase is generally ignored. However, we believe such optimization is crucial, especially for long-output generation tasks based on the following two observations: (i) Excessive compression during the prefill phase, which requires specific full context impairs the comprehension of the reasoning task; (ii) Deviation of heavy hitters occurs in the reasoning tasks with long outputs. Therefore, SCOPE, a simple yet efficient framework that separately performs KV cache optimization during the prefill and decoding phases, is introduced. Specifically, the KV cache during the prefill phase is preserved to maintain the essential information, while a novel strategy based on sliding is proposed to select essential heavy hitters for the decoding phase. Memory usage and memory transfer are further optimized using adaptive and discontinuous strategies. Extensive experiments on LongGenBench show the effectiveness and generalization of SCOPE and its compatibility as a plug-in to other prefill-only KV compression methods.", "github_manual": "https://github.com/linking-ai/scope", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-23", "arxiv_id": "2412.15119", "title": "Parallelized Autoregressive Visual Generation", "authors": [ "Yuqing Wang", "Shuhuai Ren", "Zhijie Lin", "Yujin Han", "Haoyuan Guo", "Zhenheng Yang", "Difan Zou", "Jiashi Feng", "Xihui Liu" ], "github": "https://github.com/Epiphqny/PAR", "project_page": "", "abstract": "Autoregressive models have emerged as a powerful approach for visual generation but suffer from slow inference speed due to their sequential token-by-token prediction process. In this paper, we propose a simple yet effective approach for parallelized autoregressive visual generation that improves generation efficiency while preserving the advantages of autoregressive modeling. Our key insight is that parallel generation depends on visual token dependencies-tokens with weak dependencies can be generated in parallel, while strongly dependent adjacent tokens are difficult to generate together, as their independent sampling may lead to inconsistencies. Based on this observation, we develop a parallel generation strategy that generates distant tokens with weak dependencies in parallel while maintaining sequential generation for strongly dependent local tokens. Our approach can be seamlessly integrated into standard autoregressive models without modifying the architecture or tokenizer. Experiments on ImageNet and UCF-101 demonstrate that our method achieves a 3.6x speedup with comparable quality and up to 9.5x speedup with minimal quality degradation across both image and video generation tasks. We hope this work will inspire future research in efficient visual generation and unified autoregressive modeling. Project page: https://epiphqny.github.io/PAR-project.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Epiphqny/PAR", "github_api_added_by": "auto" }, { "date": "2024-12-23", "arxiv_id": "2412.15322", "title": "Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis", "authors": [ "Ho Kei Cheng", "Masato Ishii", "Akio Hayakawa", "Takashi Shibuya", "Alexander Schwing", "Yuki Mitsufuji" ], "github": "https://github.com/hkchengrex/MMAudio", "project_page": "", "abstract": "We propose to synthesize high-quality and synchronized audio, given video and optional text conditions, using a novel multimodal joint training framework MMAudio. In contrast to single-modality training conditioned on (limited) video data only, MMAudio is jointly trained with larger-scale, readily available text-audio data to learn to generate semantically aligned high-quality audio samples. Additionally, we improve audio-visual synchrony with a conditional synchronization module that aligns video conditions with audio latents at the frame level. Trained with a flow matching objective, MMAudio achieves new video-to-audio state-of-the-art among public models in terms of audio quality, semantic alignment, and audio-visual synchronization, while having a low inference time (1.23s to generate an 8s clip) and just 157M parameters. MMAudio also achieves surprisingly competitive performance in text-to-audio generation, showing that joint training does not hinder single-modality performance. Code and demo are available at: https://hkchengrex.github.io/MMAudio", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hkchengrex/MMAudio", "github_api_added_by": "auto" }, { "date": "2024-12-23", "arxiv_id": "2412.16112", "title": "CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up", "authors": [ "Songhua Liu", "Zhenxiong Tan", "Xinchao Wang" ], "github": "https://github.com/Huage001/CLEAR", "project_page": "", "abstract": "Diffusion Transformers (DiT) have become a leading architecture in image generation. However, the quadratic complexity of attention mechanisms, which are responsible for modeling token-wise relationships, results in significant latency when generating high-resolution images. To address this issue, we aim at a linear attention mechanism in this paper that reduces the complexity of pre-trained DiTs to linear. We begin our exploration with a comprehensive summary of existing efficient attention mechanisms and identify four key factors crucial for successful linearization of pre-trained DiTs: locality, formulation consistency, high-rank attention maps, and feature integrity. Based on these insights, we introduce a convolution-like local attention strategy termed CLEAR, which limits feature interactions to a local window around each query token, and thus achieves linear complexity. Our experiments indicate that, by fine-tuning the attention layer on merely 10K self-generated samples for 10K iterations, we can effectively transfer knowledge from a pre-trained DiT to a student model with linear complexity, yielding results comparable to the teacher model. Simultaneously, it reduces attention computations by 99.5% and accelerates generation by 6.3 times for generating 8K-resolution images. Furthermore, we investigate favorable properties in the distilled attention layers, such as zero-shot generalization cross various models and plugins, and improved support for multi-GPU parallel inference. Models and codes are available here: https://github.com/Huage001/CLEAR.", "github_manual": "", "github_regex": "https://github.com/Huage001/CLEAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-23", "arxiv_id": "2412.14590", "title": "MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design", "authors": [ "Zhen Zheng", "Xiaonan Song", "Chuanjie Liu" ], "github": "", "project_page": "", "abstract": "Quantization has become one of the most effective methodologies to compress LLMs into smaller size. However, the existing quantization solutions still show limitations of either non-negligible accuracy drop or system inefficiency. In this paper, we make a comprehensive analysis of the general quantization principles on their effect to the triangle of accuracy, memory consumption and system efficiency. We propose MixLLM that explores the new optimization space of mixed-precision quantization between output features based on the insight that different output features matter differently in the model. MixLLM identifies the output features with high salience in the global view rather than within each single layer, effectively assigning the larger bit-width to output features that need it most to achieve good accuracy with low memory consumption. We present the sweet spot of quantization configuration of algorithm-system co-design that leads to high accuracy and system efficiency. To address the system challenge, we design the two-step dequantization to make use of the int8 Tensor Core easily and fast data type conversion to reduce dequantization overhead significantly, and present the software pipeline to overlap the memory access, dequantization and the MatMul to the best. Extensive experiments show that with only 10% more bits, the PPL increasement can be reduced from about 0.5 in SOTA to within 0.2 for Llama 3.1 70B, while on average MMLU-Pro improves by 0.93 over the SOTA of three popular models. In addition to its superior accuracy, MixLLM also achieves state-of-the-art system efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-23", "arxiv_id": "2412.16145", "title": "Offline Reinforcement Learning for LLM Multi-Step Reasoning", "authors": [ "Huaijie Wang", "Shibo Hao", "Hanze Dong", "Shenao Zhang", "Yilin Bao", "Ziran Yang", "Yi Wu" ], "github": "https://github.com/jwhj/oreo", "project_page": "", "abstract": "Improving the multi-step reasoning ability of large language models (LLMs) with offline reinforcement learning (RL) is essential for quickly adapting them to complex tasks. While Direct Preference Optimization (DPO) has shown promise in aligning LLMs with human preferences, it is less suitable for multi-step reasoning tasks because (1) DPO relies on paired preference data, which is not readily available for multi-step reasoning tasks, and (2) it treats all tokens uniformly, making it ineffective for credit assignment in multi-step reasoning tasks, which often come with sparse reward. In this work, we propose OREO (Offline Reasoning Optimization), an offline RL method for enhancing LLM multi-step reasoning. Building on insights from previous works of maximum entropy reinforcement learning, it jointly learns a policy model and value function by optimizing the soft Bellman Equation. We show in principle that it reduces the need to collect pairwise data and enables better credit assignment. Empirically, OREO surpasses existing offline learning methods on multi-step reasoning benchmarks, including mathematical reasoning tasks (GSM8K, MATH) and embodied agent control (ALFWorld). The approach can be extended to a multi-iteration framework when additional resources are available. Furthermore, the learned value function can be leveraged to guide the tree search for free, which can further boost performance during test time.", "github_manual": "https://github.com/jwhj/oreo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-23", "arxiv_id": "2412.15450", "title": "Fietje: An open, efficient LLM for Dutch", "authors": [ "Bram Vanroy" ], "github": "https://github.com/BramVanroy/clin34-benchmarks", "project_page": "", "abstract": "This paper introduces Fietje, a family of small language models (SLMs) specifically designed for the Dutch language. The model is based on Phi 2, an English-centric model of 2.7 billion parameters. Fietje demonstrated competitive results with larger language models upon its release. A core emphasis of this work is transparency and reproducibility: Fietje is fully open-source, with model weights, datasets, training, and evaluation code all publicly accessible. The paper discusses the performance of Fietje and many other models on an extensive evaluation suite of benchmarks on reasoning, sentiment analysis, world knowledge, linguistic acceptability and word sense disambiguation. Evaluation results illustrate the rapid progress in the field of LLMs, where recent small models outperform older, larger models that were fine-tuned for Dutch. This trend signals an exciting future for Dutch language processing, suggesting that even compact LLMs are becoming increasingly capable. Furthermore, ongoing and future efforts to adapt LLMs to Dutch are poised to enhance these models even further, broadening their applicability and accessibility. Fietje is only an intermediate step in improving accessibility to language technology for users of the Dutch language.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/BramVanroy/clin34-benchmarks", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-23", "arxiv_id": "2412.11525", "title": "Sequence Matters: Harnessing Video Models in 3D Super-Resolution", "authors": [ "Hyun-kyu Ko", "Dongheok Park", "Youngin Park", "Byeonghyeon Lee", "Juhee Han", "Eunbyung Park" ], "github": "https://github.com/DHPark98/SequenceMatters", "project_page": "", "abstract": "3D super-resolution aims to reconstruct high-fidelity 3D models from low-resolution (LR) multi-view images. Early studies primarily focused on single-image super-resolution (SISR) models to upsample LR images into high-resolution images. However, these methods often lack view consistency because they operate independently on each image. Although various post-processing techniques have been extensively explored to mitigate these inconsistencies, they have yet to fully resolve the issues. In this paper, we perform a comprehensive study of 3D super-resolution by leveraging video super-resolution (VSR) models. By utilizing VSR models, we ensure a higher degree of spatial consistency and can reference surrounding spatial information, leading to more accurate and detailed reconstructions. Our findings reveal that VSR models can perform remarkably well even on sequences that lack precise spatial alignment. Given this observation, we propose a simple yet practical approach to align LR images without involving fine-tuning or generating 'smooth' trajectory from the trained 3D models over LR images. The experimental results show that the surprisingly simple algorithms can achieve the state-of-the-art results of 3D super-resolution tasks on standard benchmark datasets, such as the NeRF-synthetic and MipNeRF-360 datasets. Project page: https://ko-lani.github.io/Sequence-Matters", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DHPark98/SequenceMatters", "github_api_added_by": "auto" }, { "date": "2024-12-23", "arxiv_id": "2412.15035", "title": "LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Gaps", "authors": [ "Felix Friedrich", "Simone Tedeschi", "Patrick Schramowski", "Manuel Brack", "Roberto Navigli", "Huu Nguyen", "Bo Li", "Kristian Kersting" ], "github": "", "project_page": "", "abstract": "Building safe Large Language Models (LLMs) across multiple languages is essential in ensuring both safe access and linguistic diversity. To this end, we introduce M-ALERT, a multilingual benchmark that evaluates the safety of LLMs in five languages: English, French, German, Italian, and Spanish. M-ALERT includes 15k high-quality prompts per language, totaling 75k, following the detailed ALERT taxonomy. Our extensive experiments on 10 state-of-the-art LLMs highlight the importance of language-specific safety analysis, revealing that models often exhibit significant inconsistencies in safety across languages and categories. For instance, Llama3.2 shows high unsafety in the category crime_tax for Italian but remains safe in other languages. Similar differences can be observed across all models. In contrast, certain categories, such as substance_cannabis and crime_propaganda, consistently trigger unsafe responses across models and languages. These findings underscore the need for robust multilingual safety practices in LLMs to ensure safe and responsible usage across diverse user communities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-23", "arxiv_id": "2412.14963", "title": "IDOL: Instant Photorealistic 3D Human Creation from a Single Image", "authors": [ "Yiyu Zhuang", "Jiaxi Lv", "Hao Wen", "Qing Shuai", "Ailing Zeng", "Hao Zhu", "Shifeng Chen", "Yujiu Yang", "Xun Cao", "Wei Liu" ], "github": "https://github.com/yiyuzhuang/IDOL", "project_page": "", "abstract": "Creating a high-fidelity, animatable 3D full-body avatar from a single image is a challenging task due to the diverse appearance and poses of humans and the limited availability of high-quality training data. To achieve fast and high-quality human reconstruction, this work rethinks the task from the perspectives of dataset, model, and representation. First, we introduce a large-scale HUman-centric GEnerated dataset, HuGe100K, consisting of 100K diverse, photorealistic sets of human images. Each set contains 24-view frames in specific human poses, generated using a pose-controllable image-to-multi-view model. Next, leveraging the diversity in views, poses, and appearances within HuGe100K, we develop a scalable feed-forward transformer model to predict a 3D human Gaussian representation in a uniform space from a given human image. This model is trained to disentangle human pose, body shape, clothing geometry, and texture. The estimated Gaussians can be animated without post-processing. We conduct comprehensive experiments to validate the effectiveness of the proposed dataset and method. Our model demonstrates the ability to efficiently reconstruct photorealistic humans at 1K resolution from a single input image using a single GPU instantly. Additionally, it seamlessly supports various applications, as well as shape and texture editing tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yiyuzhuang/IDOL", "github_api_added_by": "auto" }, { "date": "2024-12-23", "arxiv_id": "2412.15487", "title": "Multi-LLM Text Summarization", "authors": [ "Jiangnan Fang", "Cheng-Tse Liu", "Jieun Kim", "Yash Bhedaru", "Ethan Liu", "Nikhil Singh", "Nedim Lipka", "Puneet Mathur", "Nesreen K. Ahmed", "Franck Dernoncourt", "Ryan A. Rossi", "Hanieh Deilamsalehy" ], "github": "", "project_page": "", "abstract": "In this work, we propose a Multi-LLM summarization framework, and investigate two different multi-LLM strategies including centralized and decentralized. Our multi-LLM summarization framework has two fundamentally important steps at each round of conversation: generation and evaluation. These steps are different depending on whether our multi-LLM decentralized summarization is used or centralized. In both our multi-LLM decentralized and centralized strategies, we have k different LLMs that generate diverse summaries of the text. However, during evaluation, our multi-LLM centralized summarization approach leverages a single LLM to evaluate the summaries and select the best one whereas k LLMs are used for decentralized multi-LLM summarization. Overall, we find that our multi-LLM summarization approaches significantly outperform the baselines that leverage only a single LLM by up to 3x. These results indicate the effectiveness of multi-LLM approaches for summarization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-23", "arxiv_id": "2412.14294", "title": "TRecViT: A Recurrent Video Transformer", "authors": [ "Viorica P\u0103tr\u0103ucean", "Xu Owen He", "Joseph Heyward", "Chuhan Zhang", "Mehdi S. M. Sajjadi", "George-Cristian Muraru", "Artem Zholus", "Mahdi Karami", "Ross Goroshin", "Yutian Chen", "Simon Osindero", "Jo\u00e3o Carreira", "Razvan Pascanu" ], "github": "https://github.com/google-deepmind/trecvit", "project_page": "", "abstract": "We propose a novel block for video modelling. It relies on a time-space-channel factorisation with dedicated blocks for each dimension: gated linear recurrent units (LRUs) perform information mixing over time, self-attention layers perform mixing over space, and MLPs over channels. The resulting architecture TRecViT performs well on sparse and dense tasks, trained in supervised or self-supervised regimes. Notably, our model is causal and outperforms or is on par with a pure attention model ViViT-L on large scale video datasets (SSv2, Kinetics400), while having 3times less parameters, 12times smaller memory footprint, and 5times lower FLOPs count. Code and checkpoints will be made available online at https://github.com/google-deepmind/trecvit.", "github_manual": "", "github_regex": "https://github.com/google-deepmind/trecvit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-23", "arxiv_id": "2412.15484", "title": "Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage", "authors": [ "Saehyung Lee", "Seunghyun Yoon", "Trung Bui", "Jing Shi", "Sungroh Yoon" ], "github": "https://github.com/adobe-research/CapMAS", "project_page": "", "abstract": "Multimodal large language models (MLLMs) excel at generating highly detailed captions but often produce hallucinations. Our analysis reveals that existing hallucination detection methods struggle with detailed captions. We attribute this to the increasing reliance of MLLMs on their generated text, rather than the input image, as the sequence length grows. To address this issue, we propose a multiagent approach that leverages LLM-MLLM collaboration to correct given captions. Additionally, we introduce an evaluation framework and a benchmark dataset to facilitate the systematic analysis of detailed captions. Our experiments demonstrate that our proposed evaluation method better aligns with human judgments of factuality than existing metrics and that existing approaches to improve the MLLM factuality may fall short in hyper-detailed image captioning tasks. In contrast, our proposed method significantly enhances the factual accuracy of captions, even improving those generated by GPT-4V. Finally, we highlight a limitation of VQA-centric benchmarking by demonstrating that an MLLM's performance on VQA benchmarks may not correlate with its ability to generate detailed image captions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/adobe-research/CapMAS", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.17256", "title": "B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners", "authors": [ "Weihao Zeng", "Yuzhen Huang", "Lulu Zhao", "Yijun Wang", "Zifei Shan", "Junxian He" ], "github": "https://github.com/hkust-nlp/b-star", "project_page": "", "abstract": "In the absence of extensive human-annotated data for complex reasoning tasks, self-improvement -- where models are trained on their own outputs -- has emerged as a primary method for enhancing performance. However, the critical factors underlying the mechanism of these iterative self-improving methods remain poorly understood, such as under what conditions self-improvement is effective, and what are the bottlenecks in the current iterations. In this work, we identify and propose methods to monitor two pivotal factors in this iterative process: (1) the model's ability to generate sufficiently diverse responses (exploration); and (2) the effectiveness of external rewards in distinguishing high-quality candidates from lower-quality ones (exploitation). Using mathematical reasoning as a case study, we begin with a quantitative analysis to track the dynamics of exploration and exploitation, discovering that a model's exploratory capabilities rapidly deteriorate over iterations, and the effectiveness of exploiting external rewards diminishes as well. Motivated by these findings, we introduce B-STaR, a Self-Taught Reasoning framework that autonomously adjusts configurations across iterations to Balance exploration and exploitation, thereby optimizing the self-improving effectiveness based on the current policy model and available rewards. Our experiments on mathematical reasoning, coding, and commonsense reasoning demonstrate that B-STaR not only enhances the model's exploratory capabilities throughout training but also achieves a more effective balance between exploration and exploitation, leading to superior performance.", "github_manual": "https://github.com/hkust-nlp/b-star", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.17451", "title": "Diving into Self-Evolving Training for Multimodal Reasoning", "authors": [ "Wei Liu", "Junlong Li", "Xiwen Zhang", "Fan Zhou", "Yu Cheng", "Junxian He" ], "github": "https://github.com/hkust-nlp/mstar", "project_page": "", "abstract": "Reasoning ability is essential for Large Multimodal Models (LMMs). In the absence of multimodal chain-of-thought annotated data, self-evolving training, where the model learns from its own outputs, has emerged as an effective and scalable approach for enhancing reasoning abilities. Despite its growing usage, a comprehensive understanding of self-evolving training, particularly in the context of multimodal reasoning, remains limited. In this paper, we delve into the intricacies of self-evolving training for multimodal reasoning, pinpointing three key factors: Training Method, Reward Model, and Prompt Variation. We systematically examine each factor and explore how various configurations affect the training's effectiveness. Our analysis leads to a set of best practices for each factor, aimed at optimizing multimodal reasoning. Furthermore, we explore the Self-Evolution Dynamics during training and the impact of automatic balancing mechanisms in boosting performance. After all the investigations, we present a final recipe for self-evolving training in multimodal reasoning, encapsulating these design choices into a framework we call MSTaR (Multimodal Self-evolving Training for Reasoning), which is universally effective for models with different sizes on various benchmarks, e.g., surpassing the pre-evolved model significantly on 5 multimodal reasoning benchmarks without using additional human annotations, as demonstrated on MiniCPM-V-2.5 (8B), Phi-3.5-Vision (4B) and InternVL2 (2B). We believe this study fills a significant gap in the understanding of self-evolving training for multimodal reasoning and offers a robust framework for future research. Our policy and reward models, as well as the collected data, is released to facilitate further investigation in multimodal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hkust-nlp/mstar", "github_api_added_by": "auto" }, { "date": "2024-12-24", "arxiv_id": "2412.14922", "title": "RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response", "authors": [ "Junyu Luo", "Xiao Luo", "Kaize Ding", "Jingyang Yuan", "Zhiping Xiao", "Ming Zhang" ], "github": "https://github.com/luo-junyu/robustft", "project_page": "", "abstract": "Supervised fine-tuning (SFT) plays a crucial role in adapting large language models (LLMs) to specific domains or tasks. However, as demonstrated by empirical experiments, the collected data inevitably contains noise in practical applications, which poses significant challenges to model performance on downstream tasks. Therefore, there is an urgent need for a noise-robust SFT framework to enhance model capabilities in downstream tasks. To address this challenge, we introduce a robust SFT framework (RobustFT) that performs noise detection and relabeling on downstream task data. For noise identification, our approach employs a multi-expert collaborative system with inference-enhanced models to achieve superior noise detection. In the denoising phase, we utilize a context-enhanced strategy, which incorporates the most relevant and confident knowledge followed by careful assessment to generate reliable annotations. Additionally, we introduce an effective data selection mechanism based on response entropy, ensuring only high-quality samples are retained for fine-tuning. Extensive experiments conducted on multiple LLMs across five datasets demonstrate RobustFT's exceptional performance in noisy scenarios.", "github_manual": "https://github.com/luo-junyu/robustft", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.16926", "title": "Revisiting In-Context Learning with Long Context Language Models", "authors": [ "Jinheon Baek", "Sun Jae Lee", "Prakhar Gupta", "Geunseob", "Oh", "Siddharth Dalmia", "Prateek Kolhar" ], "github": "", "project_page": "", "abstract": "In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context. Previously, their context window size imposed a limit on the number of examples that can be shown, making example selection techniques crucial for identifying the maximally effective set of examples. However, the recent advent of Long Context Language Models (LCLMs) has significantly increased the number of examples that can be included in context, raising an important question of whether ICL performance in a many-shot regime is still sensitive to the method of sample selection. To answer this, we revisit these approaches in the context of LCLMs through extensive experiments on 18 datasets spanning 4 tasks. Surprisingly, we observe that sophisticated example selection techniques do not yield significant improvements over a simple random sample selection method. Instead, we find that the advent of LCLMs has fundamentally shifted the challenge of ICL from that of selecting the most effective examples to that of collecting sufficient examples to fill the context window. Specifically, in certain datasets, including all available examples does not fully utilize the context window; however, by augmenting the examples in context with a simple data augmentation approach, we substantially improve ICL performance by 5%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.16686", "title": "NILE: Internal Consistency Alignment in Large Language Models", "authors": [ "Minda Hu", "Qiyuan Zhang", "Yufei Wang", "Bowei He", "Hongru Wang", "Jingyan Zhou", "Liangyou Li", "Yasheng Wang", "Chen Ma", "Irwin King" ], "github": "", "project_page": "", "abstract": "As a crucial step to enhance LLMs alignment with human intentions, Instruction Fine-Tuning (IFT) has a high demand on dataset quality. However, existing IFT datasets often contain knowledge that is inconsistent with LLMs' internal knowledge learned from the pre-training phase, which can greatly affect the efficacy of IFT. To address this issue, we introduce NILE (iNternal consIstency aLignmEnt) framework, aimed at optimizing IFT datasets to unlock LLMs' capability further. NILE operates by eliciting target pre-trained LLM's internal knowledge corresponding to instruction data. The internal knowledge is leveraged to revise the answer in IFT datasets. Additionally, we propose a novel Internal Consistency Filtering (ICF) method to filter training samples, ensuring its high consistency with LLM's internal knowledge. Our experiments demonstrate that NILE-aligned IFT datasets sharply boost LLM performance across multiple LLM ability evaluation datasets, achieving up to 66.6% gain on Arena-Hard and 68.5% on Alpaca-Eval V2. Further analysis confirms that each component of the NILE}framework contributes to these substantial performance improvements, and provides compelling evidence that dataset consistency with pre-trained internal knowledge is pivotal for maximizing LLM potential.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.17153", "title": "Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching", "authors": [ "Enshu Liu", "Xuefei Ning", "Yu Wang", "Zinan Lin" ], "github": "https://github.com/imagination-research/distilled-decoding", "project_page": "", "abstract": "Autoregressive (AR) models have achieved state-of-the-art performance in text and image generation but suffer from slow generation due to the token-by-token process. We ask an ambitious question: can a pre-trained AR model be adapted to generate outputs in just one or two steps? If successful, this would significantly advance the development and deployment of AR models. We notice that existing works that try to speed up AR generation by generating multiple tokens at once fundamentally cannot capture the output distribution due to the conditional dependencies between tokens, limiting their effectiveness for few-step generation. To address this, we propose Distilled Decoding (DD), which uses flow matching to create a deterministic mapping from Gaussian distribution to the output distribution of the pre-trained AR model. We then train a network to distill this mapping, enabling few-step generation. DD doesn't need the training data of the original AR model, making it more practical.We evaluate DD on state-of-the-art image AR models and present promising results on ImageNet-256. For VAR, which requires 10-step generation, DD enables one-step generation (6.3times speed-up), with an acceptable increase in FID from 4.19 to 9.96. For LlamaGen, DD reduces generation from 256 steps to 1, achieving an 217.8times speed-up with a comparable FID increase from 4.11 to 11.35. In both cases, baseline methods completely fail with FID>100. DD also excels on text-to-image generation, reducing the generation from 256 steps to 2 for LlamaGen with minimal FID increase from 25.70 to 28.95. As the first work to demonstrate the possibility of one-step generation for image AR models, DD challenges the prevailing notion that AR models are inherently slow, and opens up new opportunities for efficient AR generation. The project website is at https://imagination-research.github.io/distilled-decoding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/imagination-research/distilled-decoding", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.15118", "title": "Outcome-Refining Process Supervision for Code Generation", "authors": [ "Zhuohao Yu", "Weizheng Gu", "Yidong Wang", "Zhengran Zeng", "Jindong Wang", "Wei Ye", "Shikun Zhang" ], "github": "https://github.com/zhuohaoyu/ORPS", "project_page": "", "abstract": "Large Language Models have demonstrated remarkable capabilities in code generation, yet they often struggle with complex programming tasks that require deep algorithmic reasoning. While process supervision through learned reward models shows promise in guiding reasoning steps, it requires expensive training data and suffers from unreliable evaluation. We propose Outcome-Refining Process Supervision, a novel paradigm that treats outcome refinement itself as the process to be supervised. Our framework leverages concrete execution signals to ground the supervision of reasoning steps, while using tree-structured exploration to maintain multiple solution trajectories simultaneously. Experiments demonstrate that our approach enables even smaller models to achieve high success accuracy and performance metrics on competitive programming tasks, creates more reliable verification than traditional reward models without requiring training PRMs. Our approach achieves significant improvements across 5 models and 3 datasets: an average of 26.9% increase in correctness and 42.2% in efficiency. The results suggest that providing structured reasoning space with concrete verification signals is crucial for solving complex programming tasks. We open-source all our code and data at: https://github.com/zhuohaoyu/ORPS", "github_manual": "", "github_regex": "https://github.com/zhuohaoyu/ORPS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.17747", "title": "Deliberation in Latent Space via Differentiable Cache Augmentation", "authors": [ "Luyang Liu", "Jonas Pfeiffer", "Jiaxing Wu", "Jun Xie", "Arthur Szlam" ], "github": "", "project_page": "", "abstract": "Techniques enabling large language models (LLMs) to \"think more\" by generating and attending to intermediate reasoning steps have shown promise in solving complex problems. However, the standard approaches generate sequences of discrete tokens immediately before responding, and so they can incur significant latency costs and be challenging to optimize. In this work, we demonstrate that a frozen LLM can be augmented with an offline coprocessor that operates on the model's key-value (kv) cache. This coprocessor augments the cache with a set of latent embeddings designed to improve the fidelity of subsequent decoding. We train this coprocessor using the language modeling loss from the decoder on standard pretraining data, while keeping the decoder itself frozen. This approach enables the model to learn, in an end-to-end differentiable fashion, how to distill additional computation into its kv-cache. Because the decoder remains unchanged, the coprocessor can operate offline and asynchronously, and the language model can function normally if the coprocessor is unavailable or if a given cache is deemed not to require extra computation. We show experimentally that when a cache is augmented, the decoder achieves lower perplexity on numerous subsequent tokens. Furthermore, even without any task-specific training, our experiments demonstrate that cache augmentation consistently reduces perplexity and improves performance across a range of reasoning-intensive tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.17805", "title": "Large Motion Video Autoencoding with Cross-modal Video VAE", "authors": [ "Yazhou Xing", "Yang Fei", "Yingqing He", "Jingye Chen", "Jiaxin Xie", "Xiaowei Chi", "Qifeng Chen" ], "github": "https://github.com/VideoVerses/VideoVAEPlus", "project_page": "", "abstract": "Learning a robust video Variational Autoencoder (VAE) is essential for reducing video redundancy and facilitating efficient video generation. Directly applying image VAEs to individual frames in isolation can result in temporal inconsistencies and suboptimal compression rates due to a lack of temporal compression. Existing Video VAEs have begun to address temporal compression; however, they often suffer from inadequate reconstruction performance. In this paper, we present a novel and powerful video autoencoder capable of high-fidelity video encoding. First, we observe that entangling spatial and temporal compression by merely extending the image VAE to a 3D VAE can introduce motion blur and detail distortion artifacts. Thus, we propose temporal-aware spatial compression to better encode and decode the spatial information. Additionally, we integrate a lightweight motion compression model for further temporal compression. Second, we propose to leverage the textual information inherent in text-to-video datasets and incorporate text guidance into our model. This significantly enhances reconstruction quality, particularly in terms of detail preservation and temporal stability. Third, we further improve the versatility of our model through joint training on both images and videos, which not only enhances reconstruction quality but also enables the model to perform both image and video autoencoding. Extensive evaluations against strong recent baselines demonstrate the superior performance of our method. The project website can be found at~https://yzxing87.github.io/vae/{https://yzxing87.github.io/vae/}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/VideoVerses/VideoVAEPlus", "github_api_added_by": "auto" }, { "date": "2024-12-24", "arxiv_id": "2412.16429", "title": "LearnLM: Improving Gemini for Learning", "authors": [ "LearnLM Team", "Abhinit Modi", "Aditya Srikanth Veerubhotla", "Aliya Rysbek", "Andrea Huber", "Brett Wiltshire", "Brian Veprek", "Daniel Gillick", "Daniel Kasenberg", "Derek Ahmed", "Irina Jurenka", "James Cohan", "Jennifer She", "Julia Wilkowski", "Kaiz Alarakyia", "Kevin McKee", "Lisa Wang", "Markus Kunesch", "Mike Schaekermann", "Miruna P\u00eeslar", "Nikhil Joshi", "Parsa Mahmoudieh", "Paul Jhun", "Sara Wiltberger", "Shakir Mohamed", "Shashank Agarwal", "Shubham Milind Phal", "Sun Jae Lee", "Theofilos Strinopoulos", "Wei-Jen Ko", "Amy Wang", "Ankit Anand", "Avishkar Bhoopchand", "Dan Wild", "Divya Pandya", "Filip Bar", "Garth Graham", "Holger Winnemoeller", "Mahvish Nagda", "Prateek Kolhar", "Renee Schneider", "Shaojian Zhu", "Stephanie Chan", "Steve Yadlowsky", "Viknesh Sounderajah", "Yannis Assael" ], "github": "", "project_page": "", "abstract": "Today's generative AI systems are tuned to present information by default rather than engage users in service of learning as a human tutor would. To address the wide range of potential education use cases for these systems, we reframe the challenge of injecting pedagogical behavior as one of pedagogical instruction following, where training and evaluation examples include system-level instructions describing the specific pedagogy attributes present or desired in subsequent model turns. This framing avoids committing our models to any particular definition of pedagogy, and instead allows teachers or developers to specify desired model behavior. It also clears a path to improving Gemini models for learning -- by enabling the addition of our pedagogical data to post-training mixtures -- alongside their rapidly expanding set of capabilities. Both represent important changes from our initial tech report. We show how training with pedagogical instruction following produces a LearnLM model (available on Google AI Studio) that is preferred substantially by expert raters across a diverse set of learning scenarios, with average preference strengths of 31\\% over GPT-4o, 11\\% over Claude 3.5, and 13\\% over the Gemini 1.5 Pro model LearnLM was based on.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.17498", "title": "DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought", "authors": [ "Jiaan Wang", "Fandong Meng", "Yunlong Liang", "Jie Zhou" ], "github": "https://github.com/krystalan/DRT-o1", "project_page": "", "abstract": "Recently, O1-like models have emerged as representative examples, illustrating the effectiveness of long chain-of-thought (CoT) in reasoning tasks such as math and coding tasks. In this paper, we introduce DRT-o1, an attempt to bring the success of long CoT to neural machine translation (MT). Specifically, in view of the literature books that might involve similes and metaphors, translating these texts to a target language is very difficult in practice due to cultural differences. In such cases, literal translation often fails to convey the intended meaning effectively. Even for professional human translators, considerable thought must be given to preserving semantics throughout the translation process. To simulate LLMs' long thought ability in MT, we first mine sentences containing similes or metaphors from existing literature books, and then develop a multi-agent framework to translate these sentences via long thought. In the multi-agent framework, a translator is used to iteratively translate the source sentence under the suggestions provided by an advisor. To ensure the effectiveness of the long thoughts, an evaluator is also employed to judge whether the translation in the current round is better than the previous one or not. In this manner, we collect tens of thousands of long-thought MT data, which is used to train our DRT-o1. The experimental results on literature translation demonstrate the effectiveness of the DRT-o1. Using Qwen2.5-7B and Qwen2.5-14B as the backbones, the improvement brought by DRT-o1 achieves 7.33~8.26 BLEU and 1.66~3.36 CometScore. Besides, DRT-o1-7B can outperform QwQ-32B-Preview by 7.82 BLEU and 1.46 CometScore, showing its effectiveness. The project is available at https://github.com/krystalan/DRT-o1", "github_manual": "", "github_regex": "https://github.com/krystalan/DRT-o1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.16720", "title": "OpenAI o1 System Card", "authors": [ "OpenAI", "Aaron Jaech", "Adam Kalai", "Adam Lerer", "Adam Richardson", "Ahmed El-Kishky", "Aiden Low", "Alec Helyar", "Aleksander Madry", "Alex Beutel", "Alex Carney", "Alex Iftimie", "Alex Karpenko", "Alex Tachard Passos", "Alexander Neitz", "Alexander Prokofiev", "Alexander Wei", "Allison Tam", "Ally Bennett", "Ananya Kumar", "Andre Saraiva", "Andrea Vallone", "Andrew Duberstein", "Andrew Kondrich", "Andrey Mishchenko", "Andy Applebaum", "Angela Jiang", "Ashvin Nair", "Barret Zoph", "Behrooz Ghorbani", "Ben Rossen", "Benjamin Sokolowsky", "Boaz Barak", "Bob McGrew", "Borys Minaiev", "Botao Hao", "Bowen Baker", "Brandon Houghton", "Brandon McKinzie", "Brydon Eastman", "Camillo Lugaresi", "Cary Bassin", "Cary Hudson", "Chak Ming Li", "Charles de Bourcy", "Chelsea Voss", "Chen Shen", "Chong Zhang", "Chris Koch", "Chris Orsinger", "Christopher Hesse", "Claudia Fischer", "Clive Chan", "Dan Roberts", "Daniel Kappler", "Daniel Levy", "Daniel Selsam", "David Dohan", "David Farhi", "David Mely", "David Robinson", "Dimitris Tsipras", "Doug Li", "Dragos Oprica", "Eben Freeman", "Eddie Zhang", "Edmund Wong", "Elizabeth Proehl", "Enoch Cheung", "Eric Mitchell", "Eric Wallace", "Erik Ritter", "Evan Mays", "Fan Wang", "Felipe Petroski Such", "Filippo Raso", "Florencia Leoni", "Foivos Tsimpourlas", "Francis Song", "Fred von Lohmann", "Freddie Sulit", "Geoff Salmon", "Giambattista Parascandolo", "Gildas Chabot", "Grace Zhao", "Greg Brockman", "Guillaume Leclerc", "Hadi Salman", "Haiming Bao", "Hao Sheng", "Hart Andrin", "Hessam Bagherinezhad", "Hongyu Ren", "Hunter Lightman", "Hyung Won Chung", "Ian Kivlichan", "Ian O'Connell", "Ian Osband", "Ignasi Clavera Gilaberte", "Ilge Akkaya", "Ilya Kostrikov", "Ilya Sutskever", "Irina Kofman", "Jakub Pachocki", "James Lennon", "Jason Wei", "Jean Harb", "Jerry Twore", "Jiacheng Feng", "Jiahui Yu", "Jiayi Weng", "Jie Tang", "Jieqi Yu", "Joaquin Qui\u00f1onero Candela", "Joe Palermo", "Joel Parish", "Johannes Heidecke", "John Hallman", "John Rizzo", "Jonathan Gordon", "Jonathan Uesato", "Jonathan Uesato", "Jonathan Ward", "Joost Huizinga", "Julie Wang", "Kai Chen", "Kai Xiao", "Karan Singhal", "Karina Nguyen", "Karl Cobbe", "Katy Shi", "Kayla Wood", "Kendra Rimbach", "Keren Gu-Lemberg", "Keren GuLemberg", "Kevin Liu", "Kevin Lu", "Kevin Stone", "Kevin Yu", "Lama Ahmad", "Lauren Yang", "Leo Liu", "Leon Maksin", "Leyton Ho", "Liam Fedus", "Lilian Weng", "Linden Li", "Lindsay McCallum", "Lindsey Held", "Lorenz Kuhn", "Lukas Kondraciuk", "Lukasz Kaiser", "Luke Metz", "Madelaine Boyd", "Maja Trebacz", "Manas Joglekar", "Mark Chen", "Marko Tintor", "Mason Meyer", "Matt Jones", "Matt Kaufer", "Max Schwarzer", "Meghan Shah", "Mehmet Yatbaz", "Melody Guan", "Mengyuan Xu", "Mengyuan Yan", "Mia Glaese", "Mianna Chen", "Mianna Chen", "Michael Lampe", "Michael Malek", "Michele Wang", "Michelle Fradin", "Mike McClay", "Mikhail Pavlov", "Miles Wang", "Mingxuan Wang", "Mira Murati", "Mo Bavarian", "Mostafa Rohaninejad", "Nat McAleese", "Neil Chowdhury", "Neil Chowdhury", "Nick Ryder", "Nikolas Tezak", "Noam Brown", "Ofir Nachum", "Oleg Boiko", "Oleg Murk", "Olivia Watkins", "Patrick Chao", "Paul Ashbourne", "Pavel Izmailov", "Peter Zhokhov", "Rachel Dias", "Rahul Arora", "Randall Lin", "Rapha Gontijo Lopes", "Raz Gaon", "Reah Miyara", "Reimar Leike", "Renny Hwang", "Rhythm Garg", "Robin Brown", "Roshan James", "Rui Shu", "Ryan Cheu", "Ryan Greene", "Saachi Jain", "Sam Altman", "Sam Toizer", "Sam Toyer", "Samuel Miserendino", "Sandhini Agarwal", "Santiago Hernandez", "Sasha Baker", "Scott McKinney", "Scottie Yan", "Shengjia Zhao", "Shengli Hu", "Shibani Santurkar", "Shraman Ray Chaudhuri", "Shuyuan Zhang", "Siyuan Fu", "Spencer Papay", "Steph Lin", "Suchir Balaji", "Suvansh Sanjeev", "Szymon Sidor", "Tal Broda", "Aidan Clark", "Tao Wang", "Taylor Gordon", "Ted Sanders", "Tejal Patwardhan", "Thibault Sottiaux", "Thomas Degry", "Thomas Dimson", "Tianhao Zheng", "Timur Garipov", "Tom Stasi", "Trapit Bansal", "Trevor Creech", "Troy Peterson", "Tyna Eloundou", "Valerie Qi", "Vineet Kosaraju", "Vinnie Monaco", "Vitchyr Pong", "Vlad Fomenko", "Weiyi Zheng", "Wenda Zhou", "Wes McCabe", "Wojciech Zaremba", "Yann Dubois", "Yinghai Lu", "Yining Chen", "Young Cha", "Yu Bai", "Yuchen He", "Yuchen Zhang", "Yunyun Wang", "Zheng Shao", "Zhuohan Li" ], "github": "", "project_page": "", "abstract": "The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.14470", "title": "Agent-SafetyBench: Evaluating the Safety of LLM Agents", "authors": [ "Zhexin Zhang", "Shiyao Cui", "Yida Lu", "Jingzhuo Zhou", "Junxiao Yang", "Hongning Wang", "Minlie Huang" ], "github": "https://github.com/thu-coai/Agent-SafetyBench", "project_page": "", "abstract": "As large language models (LLMs) are increasingly deployed as agents, their integration into interactive environments and tool use introduce new safety challenges beyond those associated with the models themselves. However, the absence of comprehensive benchmarks for evaluating agent safety presents a significant barrier to effective assessment and further improvement. In this paper, we introduce Agent-SafetyBench, a comprehensive benchmark designed to evaluate the safety of LLM agents. Agent-SafetyBench encompasses 349 interaction environments and 2,000 test cases, evaluating 8 categories of safety risks and covering 10 common failure modes frequently encountered in unsafe interactions. Our evaluation of 16 popular LLM agents reveals a concerning result: none of the agents achieves a safety score above 60%. This highlights significant safety challenges in LLM agents and underscores the considerable need for improvement. Through quantitative analysis, we identify critical failure modes and summarize two fundamental safety detects in current LLM agents: lack of robustness and lack of risk awareness. Furthermore, our findings suggest that reliance on defense prompts alone is insufficient to address these safety issues, emphasizing the need for more advanced and robust strategies. We release Agent-SafetyBench at https://github.com/thu-coai/Agent-SafetyBench to facilitate further research and innovation in agent safety evaluation and improvement.", "github_manual": "", "github_regex": "https://github.com/thu-coai/Agent-SafetyBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.17295", "title": "Friends-MMC: A Dataset for Multi-modal Multi-party Conversation Understanding", "authors": [ "Yueqian Wang", "Xiaojun Meng", "Yuxuan Wang", "Jianxin Liang", "Qun Liu", "Dongyan Zhao" ], "github": "https://github.com/yellow-binary-tree/Friends-MMC", "project_page": "", "abstract": "Multi-modal multi-party conversation (MMC) is a less studied yet important topic of research due to that it well fits real-world scenarios and thus potentially has more widely-used applications. Compared with the traditional multi-modal conversations, MMC requires stronger character-centered understanding abilities as there are many interlocutors appearing in both the visual and textual context. To facilitate the study of this problem, we present Friends-MMC in this paper, an MMC dataset that contains 24,000+ unique utterances paired with video context. To explore the character-centered understanding of the dialogue, we also annotate the speaker of each utterance, the names and bounding bboxes of faces that appear in the video. Based on this Friends-MMC dataset, we further study two fundamental MMC tasks: conversation speaker identification and conversation response prediction, both of which have the multi-party nature with the video or image as visual context. For conversation speaker identification, we demonstrate the inefficiencies of existing methods such as pre-trained models, and propose a simple yet effective baseline method that leverages an optimization solver to utilize the context of two modalities to achieve better performance. For conversation response prediction, we fine-tune generative dialogue models on Friend-MMC, and analyze the benefits of speaker information. The code and dataset is publicly available at https://github.com/yellow-binary-tree/Friends-MMC and thus we call for more attention on modeling speaker information when understanding conversations.", "github_manual": "", "github_regex": "https://github.com/yellow-binary-tree/Friends-MMC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.17589", "title": "PC Agent: While You Sleep, AI Works -- A Cognitive Journey into Digital World", "authors": [ "Yanheng He", "Jiahe Jin", "Shijie Xia", "Jiadi Su", "Runze Fan", "Haoyang Zou", "Xiangkun Hu", "Pengfei Liu" ], "github": "", "project_page": "", "abstract": "Imagine a world where AI can handle your work while you sleep - organizing your research materials, drafting a report, or creating a presentation you need for tomorrow. However, while current digital agents can perform simple tasks, they are far from capable of handling the complex real-world work that humans routinely perform. We present PC Agent, an AI system that demonstrates a crucial step toward this vision through human cognition transfer. Our key insight is that the path from executing simple \"tasks\" to handling complex \"work\" lies in efficiently capturing and learning from human cognitive processes during computer use. To validate this hypothesis, we introduce three key innovations: (1) PC Tracker, a lightweight infrastructure that efficiently collects high-quality human-computer interaction trajectories with complete cognitive context; (2) a two-stage cognition completion pipeline that transforms raw interaction data into rich cognitive trajectories by completing action semantics and thought processes; and (3) a multi-agent system combining a planning agent for decision-making with a grounding agent for robust visual grounding. Our preliminary experiments in PowerPoint presentation creation reveal that complex digital work capabilities can be achieved with a small amount of high-quality cognitive data - PC Agent, trained on just 133 cognitive trajectories, can handle sophisticated work scenarios involving up to 50 steps across multiple applications. This demonstrates the data efficiency of our approach, highlighting that the key to training capable digital agents lies in collecting human cognitive data. By open-sourcing our complete framework, including the data collection infrastructure and cognition completion methods, we aim to lower the barriers for the research community to develop truly capable digital agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.16849", "title": "OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning", "authors": [ "Yuxiang Zhang", "Yuqi Yang", "Jiangming Shu", "Yuhang Wang", "Jinlin Xiao", "Jitao Sang" ], "github": "https://github.com/ADaM-BJTU/OpenRFT", "project_page": "", "abstract": "OpenAI's recent introduction of Reinforcement Fine-Tuning (RFT) showcases the potential of reasoning foundation model and offers a new paradigm for fine-tuning beyond simple pattern imitation. This technical report presents OpenRFT, our attempt to fine-tune generalist reasoning models for domain-specific tasks under the same settings as RFT. OpenRFT addresses two key challenges of lacking reasoning step data and the limited quantity of training samples, by leveraging the domain-specific samples in three ways: question augmentation, synthesizing reasoning-process data, and few-shot ICL. The evaluation is conducted on SciKnowEval, where OpenRFT achieves notable performance gains with only 100 domain-specific samples for each task. More experimental results will be updated continuously in later versions. Source codes, datasets, and models are disclosed at: https://github.com/ADaM-BJTU/OpenRFT", "github_manual": "", "github_regex": "https://github.com/ADaM-BJTU/OpenRFT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-24", "arxiv_id": "2412.17767", "title": "ResearchTown: Simulator of Human Research Community", "authors": [ "Haofei Yu", "Zhaochen Hong", "Zirui Cheng", "Kunlun Zhu", "Keyang Xuan", "Jinwei Yao", "Tao Feng", "Jiaxuan You" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable potential in scientific domains, yet a fundamental question remains unanswered: Can we simulate human research communities with LLMs? Addressing this question can deepen our understanding of the processes behind idea brainstorming and inspire the automatic discovery of novel scientific insights. In this work, we propose ResearchTown, a multi-agent framework for research community simulation. Within this framework, the human research community is simplified and modeled as an agent-data graph, where researchers and papers are represented as agent-type and data-type nodes, respectively, and connected based on their collaboration relationships. We also introduce TextGNN, a text-based inference framework that models various research activities (e.g., paper reading, paper writing, and review writing) as special forms of a unified message-passing process on the agent-data graph. To evaluate the quality of the research simulation, we present ResearchBench, a benchmark that uses a node-masking prediction task for scalable and objective assessment based on similarity. Our experiments reveal three key findings: (1) ResearchTown can provide a realistic simulation of collaborative research activities, including paper writing and review writing; (2) ResearchTown can maintain robust simulation with multiple researchers and diverse papers; (3) ResearchTown can generate interdisciplinary research ideas that potentially inspire novel research directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-25", "arxiv_id": "2412.14711", "title": "ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing", "authors": [ "Ziteng Wang", "Jianfei Chen", "Jun Zhu" ], "github": "https://github.com/thu-ml/ReMoE", "project_page": "", "abstract": "Sparsely activated Mixture-of-Experts (MoE) models are widely adopted to scale up model capacity without increasing the computation budget. However, vanilla TopK routers are trained in a discontinuous, non-differentiable way, limiting their performance and scalability. To address this issue, we propose ReMoE, a fully differentiable MoE architecture that offers a simple yet effective drop-in replacement for the conventional TopK+Softmax routing, utilizing ReLU as the router instead. We further propose methods to regulate the router's sparsity while balancing the load among experts. ReMoE's continuous nature enables efficient dynamic allocation of computation across tokens and layers, while also exhibiting domain specialization. Our experiments demonstrate that ReMoE consistently outperforms vanilla TopK-routed MoE across various model sizes, expert counts, and levels of granularity. Furthermore, ReMoE exhibits superior scalability with respect to the number of experts, surpassing traditional MoE architectures. The implementation based on Megatron-LM is available at https://github.com/thu-ml/ReMoE.", "github_manual": "", "github_regex": "https://github.com/thu-ml/ReMoE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-25", "arxiv_id": "2412.18153", "title": "DepthLab: From Partial to Complete", "authors": [ "Zhiheng Liu", "Ka Leong Cheng", "Qiuyu Wang", "Shuzhe Wang", "Hao Ouyang", "Bin Tan", "Kai Zhu", "Yujun Shen", "Qifeng Chen", "Ping Luo" ], "github": "", "project_page": "", "abstract": "Missing values remain a common challenge for depth data across its wide range of applications, stemming from various causes like incomplete data acquisition and perspective alteration. This work bridges this gap with DepthLab, a foundation depth inpainting model powered by image diffusion priors. Our model features two notable strengths: (1) it demonstrates resilience to depth-deficient regions, providing reliable completion for both continuous areas and isolated points, and (2) it faithfully preserves scale consistency with the conditioned known depth when filling in missing values. Drawing on these advantages, our approach proves its worth in various downstream tasks, including 3D scene inpainting, text-to-3D scene generation, sparse-view reconstruction with DUST3R, and LiDAR depth completion, exceeding current solutions in both numerical performance and visual quality. Our project page with source code is available at https://johanan528.github.io/depthlab_web/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-25", "arxiv_id": "2412.15443", "title": "SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval", "authors": [ "Aakash Mahalingam", "Vinesh Kumar Gande", "Aman Chadha", "Vinija Jain", "Divya Chaudhary" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) systems have become pivotal in leveraging vast corpora to generate informed and contextually relevant responses, notably reducing hallucinations in Large Language Models. Despite significant advancements, these systems struggle to efficiently process and retrieve information from large datasets while maintaining a comprehensive understanding of the context. This paper introduces SKETCH, a novel methodology that enhances the RAG retrieval process by integrating semantic text retrieval with knowledge graphs, thereby merging structured and unstructured data for a more holistic comprehension. SKETCH, demonstrates substantial improvements in retrieval performance and maintains superior context integrity compared to traditional methods. Evaluated across four diverse datasets: QuALITY, QASPER, NarrativeQA, and Italian Cuisine-SKETCH consistently outperforms baseline approaches on key RAGAS metrics such as answer_relevancy, faithfulness, context_precision and context_recall. Notably, on the Italian Cuisine dataset, SKETCH achieved an answer relevancy of 0.94 and a context precision of 0.99, representing the highest performance across all evaluated metrics. These results highlight SKETCH's capability in delivering more accurate and contextually relevant responses, setting new benchmarks for future retrieval systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-25", "arxiv_id": "2412.17739", "title": "Fourier Position Embedding: Enhancing Attention's Periodic Extension for Length Generalization", "authors": [ "Ermo Hua", "Che Jiang", "Xingtai Lv", "Kaiyan Zhang", "Ning Ding", "Youbang Sun", "Biqing Qi", "Yuchen Fan", "Xue Kai Zhu", "Bowen Zhou" ], "github": "https://github.com/tsinghuac3i/fourier-position-embedding", "project_page": "", "abstract": "Extending the context length of Language Models (LMs) by improving Rotary Position Embedding (RoPE) has become a trend. While existing works mainly address RoPE's limitations within attention mechanism, this paper provides an analysis across nearly all parts of LMs, uncovering their adverse effects on length generalization for RoPE-based attention. Using Discrete Signal Processing theory, we show that RoPE enables periodic attention by implicitly achieving Non-Uniform Discrete Fourier Transform. However, this periodicity is undermined by the spectral damage caused by: 1) linear layers and activation functions outside of attention; 2) insufficiently trained frequency components brought by time-domain truncation. Building on our observations, we propose Fourier Position Embedding (FoPE), which enhances attention's frequency-domain properties to improve both its periodic extension and length generalization. FoPE constructs Fourier Series and zero-outs the destructive frequency components, increasing model robustness against the spectrum damage. Experiments across various model scales show that, within varying context windows, FoPE can maintain a more stable perplexity and a more consistent accuracy in a needle-in-haystack task compared to RoPE and ALiBi. Several analyses and ablations bring further support to our method and theoretical modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tsinghuac3i/fourier-position-embedding", "github_api_added_by": "auto" }, { "date": "2024-12-25", "arxiv_id": "2412.18597", "title": "DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion Transformer for Tuning-Free Multi-Prompt Longer Video Generation", "authors": [ "Minghong Cai", "Xiaodong Cun", "Xiaoyu Li", "Wenze Liu", "Zhaoyang Zhang", "Yong Zhang", "Ying Shan", "Xiangyu Yue" ], "github": "https://github.com/TencentARC/DiTCtrl", "project_page": "", "abstract": "Sora-like video generation models have achieved remarkable progress with a Multi-Modal Diffusion Transformer MM-DiT architecture. However, the current video generation models predominantly focus on single-prompt, struggling to generate coherent scenes with multiple sequential prompts that better reflect real-world dynamic scenarios. While some pioneering works have explored multi-prompt video generation, they face significant challenges including strict training data requirements, weak prompt following, and unnatural transitions. To address these problems, we propose DiTCtrl, a training-free multi-prompt video generation method under MM-DiT architectures for the first time. Our key idea is to take the multi-prompt video generation task as temporal video editing with smooth transitions. To achieve this goal, we first analyze MM-DiT's attention mechanism, finding that the 3D full attention behaves similarly to that of the cross/self-attention blocks in the UNet-like diffusion models, enabling mask-guided precise semantic control across different prompts with attention sharing for multi-prompt video generation. Based on our careful design, the video generated by DiTCtrl achieves smooth transitions and consistent object motion given multiple sequential prompts without additional training. Besides, we also present MPVBench, a new benchmark specially designed for multi-prompt video generation to evaluate the performance of multi-prompt generation. Extensive experiments demonstrate that our method achieves state-of-the-art performance without additional training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/TencentARC/DiTCtrl", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-25", "arxiv_id": "2412.18608", "title": "PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Models", "authors": [ "Minghao Chen", "Roman Shapovalov", "Iro Laina", "Tom Monnier", "Jianyuan Wang", "David Novotny", "Andrea Vedaldi" ], "github": "", "project_page": "", "abstract": "Text- or image-to-3D generators and 3D scanners can now produce 3D assets with high-quality shapes and textures. These assets typically consist of a single, fused representation, like an implicit neural field, a Gaussian mixture, or a mesh, without any useful structure. However, most applications and creative workflows require assets to be made of several meaningful parts that can be manipulated independently. To address this gap, we introduce PartGen, a novel approach that generates 3D objects composed of meaningful parts starting from text, an image, or an unstructured 3D object. First, given multiple views of a 3D object, generated or rendered, a multi-view diffusion model extracts a set of plausible and view-consistent part segmentations, dividing the object into parts. Then, a second multi-view diffusion model takes each part separately, fills in the occlusions, and uses those completed views for 3D reconstruction by feeding them to a 3D reconstruction network. This completion process considers the context of the entire object to ensure that the parts integrate cohesively. The generative completion model can make up for the information missing due to occlusions; in extreme cases, it can hallucinate entirely invisible parts based on the input 3D asset. We evaluate our method on generated and real 3D assets and show that it outperforms segmentation and part-extraction baselines by a large margin. We also showcase downstream applications such as 3D part editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-25", "arxiv_id": "2412.17758", "title": "In Case You Missed It: ARC 'Challenge' Is Not That Challenging", "authors": [ "\u0141ukasz Borchmann" ], "github": "", "project_page": "", "abstract": "ARC Challenge appears more difficult than ARC Easy for modern LLMs primarily due to an evaluation setup that prevents direct comparison of answer choices rather than inherent complexity. Although some researchers have quietly shifted to a more appropriate scheme over the last year, the implications of this change have yet to be widely acknowledged. We highlight this overlooked shift, show how similar evaluation practices falsely imply reasoning deficits in other benchmarks, and demonstrate that fairer methods dramatically reduce performance gaps (e.g. on SIQA) and even yield superhuman results (OpenBookQA). In doing so, we reveal how evaluation shapes perceived difficulty and offer guidelines to ensure that multiple-choice evaluations accurately reflect actual model capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-25", "arxiv_id": "2412.18450", "title": "3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D Scene Understanding", "authors": [ "Tatiana Zemskova", "Dmitry Yudin" ], "github": "https://github.com/CognitiveAISystems/3DGraphLLM", "project_page": "", "abstract": "A 3D scene graph represents a compact scene model, storing information about the objects and the semantic relationships between them, making its use promising for robotic tasks. When interacting with a user, an embodied intelligent agent should be capable of responding to various queries about the scene formulated in natural language. Large Language Models (LLMs) are beneficial solutions for user-robot interaction due to their natural language understanding and reasoning abilities. Recent methods for creating learnable representations of 3D scenes have demonstrated the potential to improve the quality of LLMs responses by adapting to the 3D world. However, the existing methods do not explicitly utilize information about the semantic relationships between objects, limiting themselves to information about their coordinates. In this work, we propose a method 3DGraphLLM for constructing a learnable representation of a 3D scene graph. The learnable representation is used as input for LLMs to perform 3D vision-language tasks. In our experiments on popular ScanRefer, RIORefer, Multi3DRefer, ScanQA, Sqa3D, and Scan2cap datasets, we demonstrate the advantage of this approach over baseline methods that do not use information about the semantic relationships between objects. The code is publicly available at https://github.com/CognitiveAISystems/3DGraphLLM.", "github_manual": "", "github_regex": "https://github.com/CognitiveAISystems/3DGraphLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-25", "arxiv_id": "2412.15797", "title": "Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning", "authors": [ "Sungjin Park", "Xiao Liu", "Yeyun Gong", "Edward Choi" ], "github": "", "project_page": "", "abstract": "Despite recent advances in large language models, open-source models often struggle to consistently perform well on complex reasoning tasks. Existing ensemble methods, whether applied at the token or output levels, fail to address these challenges. In response, we present Language model Ensemble with Monte Carlo Tree Search (LE-MCTS), a novel framework for process-level ensembling of language models. LE-MCTS formulates step-by-step reasoning with an ensemble of language models as a Markov decision process. In this framework, states represent intermediate reasoning paths, while actions consist of generating the next reasoning step using one of the language models selected from a predefined pool. Guided by a process-based reward model, LE-MCTS performs a tree search over the reasoning steps generated by different language models, identifying the most accurate reasoning chain. Experimental results on five mathematical reasoning benchmarks demonstrate that our approach outperforms both single language model decoding algorithms and language model ensemble methods. Notably, LE-MCTS improves performance by 3.6% and 4.3% on the MATH and MQA datasets, respectively, highlighting its effectiveness in solving complex reasoning problems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-25", "arxiv_id": "2412.16153", "title": "MotiF: Making Text Count in Image Animation with Motion Focal Loss", "authors": [ "Shijie Wang", "Samaneh Azadi", "Rohit Girdhar", "Saketh Rambhatla", "Chen Sun", "Xi Yin" ], "github": "", "project_page": "", "abstract": "Text-Image-to-Video (TI2V) generation aims to generate a video from an image following a text description, which is also referred to as text-guided image animation. Most existing methods struggle to generate videos that align well with the text prompts, particularly when motion is specified. To overcome this limitation, we introduce MotiF, a simple yet effective approach that directs the model's learning to the regions with more motion, thereby improving the text alignment and motion generation. We use optical flow to generate a motion heatmap and weight the loss according to the intensity of the motion. This modified objective leads to noticeable improvements and complements existing methods that utilize motion priors as model inputs. Additionally, due to the lack of a diverse benchmark for evaluating TI2V generation, we propose TI2V Bench, a dataset consists of 320 image-text pairs for robust evaluation. We present a human evaluation protocol that asks the annotators to select an overall preference between two videos followed by their justifications. Through a comprehensive evaluation on TI2V Bench, MotiF outperforms nine open-sourced models, achieving an average preference of 72%. The TI2V Bench is released in https://wang-sj16.github.io/motif/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-25", "arxiv_id": "2412.17847", "title": "Bridging the Data Provenance Gap Across Text, Speech and Video", "authors": [ "Shayne Longpre", "Nikhil Singh", "Manuel Cherep", "Kushagra Tiwary", "Joanna Materzynska", "William Brannon", "Robert Mahari", "Manan Dey", "Mohammed Hamdy", "Nayan Saxena", "Ahmad Mustafa Anis", "Emad A. Alghamdi", "Vu Minh Chien", "Naana Obeng-Marnu", "Da Yin", "Kun Qian", "Yizhi Li", "Minnie Liang", "An Dinh", "Shrestha Mohanty", "Deividas Mataciunas", "Tobin South", "Jianguo Zhang", "Ariel N. Lee", "Campbell S. Lund", "Christopher Klamm", "Damien Sileo", "Diganta Misra", "Enrico Shippole", "Kevin Klyman", "Lester JV Miranda", "Niklas Muennighoff", "Seonghyeon Ye", "Seungone Kim", "Vipul Gupta", "Vivek Sharma", "Xuhui Zhou", "Caiming Xiong", "Luis Villa", "Stella Biderman", "Alex Pentland", "Sara Hooker", "Jad Kabbara" ], "github": "", "project_page": "", "abstract": "Progress in AI is driven largely by the scale and quality of training data. Despite this, there is a deficit of empirical analysis examining the attributes of well-established datasets beyond text. In this work we conduct the largest and first-of-its-kind longitudinal audit across modalities--popular text, speech, and video datasets--from their detailed sourcing trends and use restrictions to their geographical and linguistic representation. Our manual analysis covers nearly 4000 public datasets between 1990-2024, spanning 608 languages, 798 sources, 659 organizations, and 67 countries. We find that multimodal machine learning applications have overwhelmingly turned to web-crawled, synthetic, and social media platforms, such as YouTube, for their training sets, eclipsing all other sources since 2019. Secondly, tracing the chain of dataset derivations we find that while less than 33% of datasets are restrictively licensed, over 80% of the source content in widely-used text, speech, and video datasets, carry non-commercial restrictions. Finally, counter to the rising number of languages and geographies represented in public AI training datasets, our audit demonstrates measures of relative geographical and multilingual representation have failed to significantly improve their coverage since 2013. We believe the breadth of our audit enables us to empirically examine trends in data sourcing, restrictions, and Western-centricity at an ecosystem-level, and that visibility into these questions are essential to progress in responsible AI. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire multimodal audit, allowing practitioners to trace data provenance across text, speech, and video.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-26", "arxiv_id": "2412.18547", "title": "Token-Budget-Aware LLM Reasoning", "authors": [ "Tingxu Han", "Chunrong Fang", "Shiyu Zhao", "Shiqing Ma", "Zhenyu Chen", "Zhenting Wang" ], "github": "https://github.com/GeniusHTX/TALE", "project_page": "", "abstract": "Reasoning is critical for large language models (LLMs) to excel in a wide range of tasks. While methods like Chain-of-Thought (CoT) reasoning enhance LLM performance by decomposing problems into intermediate steps, they also incur significant overhead in token usage, leading to increased costs. We find that the reasoning process of current LLMs is unnecessarily lengthy and it can be compressed by including a reasonable token budget in the prompt, but the choice of token budget plays a crucial role in the actual compression effectiveness. We then propose a token-budget-aware LLM reasoning framework, which dynamically estimates token budgets for different problems based on reasoning complexity and uses the estimated token budgets to guide the reasoning process. Experiments show that our method effectively reduces token costs in CoT reasoning with only a slight performance reduction, offering a practical solution to balance efficiency and accuracy in LLM reasoning. Code: https://github.com/GeniusHTX/TALE.", "github_manual": "", "github_regex": "https://github.com/GeniusHTX/TALE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-26", "arxiv_id": "2412.17780", "title": "PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion", "authors": [ "Sophia Tang", "Yinuo Zhang", "Pranam Chatterjee" ], "github": "", "project_page": "", "abstract": "Peptide therapeutics, a major class of medicines, have achieved remarkable success across diseases such as diabetes and cancer, with landmark examples such as GLP-1 receptor agonists revolutionizing the treatment of type-2 diabetes and obesity. Despite their success, designing peptides that satisfy multiple conflicting objectives, such as target binding affinity, solubility, and membrane permeability, remains a major challenge. Classical drug development and structure-based design are ineffective for such tasks, as they fail to optimize global functional properties critical for therapeutic efficacy. Existing generative frameworks are largely limited to continuous spaces, unconditioned outputs, or single-objective guidance, making them unsuitable for discrete sequence optimization across multiple properties. To address this, we present PepTune, a multi-objective discrete diffusion model for the simultaneous generation and optimization of therapeutic peptide SMILES. Built on the Masked Discrete Language Model (MDLM) framework, PepTune ensures valid peptide structures with state-dependent masking schedules and penalty-based objectives. To guide the diffusion process, we propose a Monte Carlo Tree Search (MCTS)-based strategy that balances exploration and exploitation to iteratively refine Pareto-optimal sequences. MCTS integrates classifier-based rewards with search-tree expansion, overcoming gradient estimation challenges and data sparsity inherent to discrete spaces. Using PepTune, we generate diverse, chemically-modified peptides optimized for multiple therapeutic properties, including target binding affinity, membrane permeability, solubility, hemolysis, and non-fouling characteristics on various disease-relevant targets. In total, our results demonstrate that MCTS-guided discrete diffusion is a powerful and modular approach for multi-objective sequence design in discrete state spaces.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-26", "arxiv_id": "2412.18609", "title": "Video-Panda: Parameter-efficient Alignment for Encoder-free Video-Language Models", "authors": [ "Jinhui Yi", "Syed Talal Wasim", "Yanan Luo", "Muzammal Naseer", "Juergen Gall" ], "github": "https://github.com/jh-yi/Video-Panda", "project_page": "", "abstract": "We present an efficient encoder-free approach for video-language understanding that achieves competitive performance while significantly reducing computational overhead. Current video-language models typically rely on heavyweight image encoders (300M-1.1B parameters) or video encoders (1B-1.4B parameters), creating a substantial computational burden when processing multi-frame videos. Our method introduces a novel Spatio-Temporal Alignment Block (STAB) that directly processes video inputs without requiring pre-trained encoders while using only 45M parameters for visual processing - at least a 6.5times reduction compared to traditional approaches. The STAB architecture combines Local Spatio-Temporal Encoding for fine-grained feature extraction, efficient spatial downsampling through learned attention and separate mechanisms for modeling frame-level and video-level relationships. Our model achieves comparable or superior performance to encoder-based approaches for open-ended video question answering on standard benchmarks. The fine-grained video question-answering evaluation demonstrates our model's effectiveness, outperforming the encoder-based approaches Video-ChatGPT and Video-LLaVA in key aspects like correctness and temporal understanding. Extensive ablation studies validate our architectural choices and demonstrate the effectiveness of our spatio-temporal modeling approach while achieving 3-4times faster processing speeds than previous methods. Code is available at https://github.com/jh-yi/Video-Panda.", "github_manual": "", "github_regex": "https://github.com/jh-yi/Video-Panda", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-26", "arxiv_id": "2412.18319", "title": "Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search", "authors": [ "Huanjin Yao", "Jiaxing Huang", "Wenhao Wu", "Jingyi Zhang", "Yibo Wang", "Shunyu Liu", "Yingjie Wang", "Yuxin Song", "Haocheng Feng", "Li Shen", "Dacheng Tao" ], "github": "https://github.com/HJYao00/Mulberry", "project_page": "", "abstract": "In this work, we aim to develop an MLLM that understands and solves questions by learning to create each intermediate step of the reasoning involved till the final answer. To this end, we propose Collective Monte Carlo Tree Search (CoMCTS), a new learning-to-reason method for MLLMs, which introduces the concept of collective learning into ``tree search'' for effective and efficient reasoning-path searching and learning. The core idea of CoMCTS is to leverage collective knowledge from multiple models to collaboratively conjecture, search and identify effective reasoning paths toward correct answers via four iterative operations including Expansion, Simulation and Error Positioning, Backpropagation, and Selection. Using CoMCTS, we construct Mulberry-260k, a multimodal dataset with a tree of rich, explicit and well-defined reasoning nodes for each question. With Mulberry-260k, we perform collective SFT to train our model, Mulberry, a series of MLLMs with o1-like step-by-step Reasoning and Reflection capabilities. Extensive experiments demonstrate the superiority of our proposed methods on various benchmarks. Code will be available at https://github.com/HJYao00/Mulberry", "github_manual": "", "github_regex": "https://github.com/HJYao00/Mulberry", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-26", "arxiv_id": "2412.17998", "title": "WavePulse: Real-time Content Analytics of Radio Livestreams", "authors": [ "Govind Mittal", "Sarthak Gupta", "Shruti Wagle", "Chirag Chopra", "Anthony J DeMattee", "Nasir Memon", "Mustaque Ahamad", "Chinmay Hegde" ], "github": "", "project_page": "", "abstract": "Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at https://wave-pulse.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-26", "arxiv_id": "2412.18495", "title": "How \"Real\" is Your Real-Time Simultaneous Speech-to-Text Translation System?", "authors": [ "Sara Papi", "Peter Polak", "Ond\u0159ej Bojar", "Dominik Mach\u00e1\u010dek" ], "github": "", "project_page": "", "abstract": "Simultaneous speech-to-text translation (SimulST) translates source-language speech into target-language text concurrently with the speaker's speech, ensuring low latency for better user comprehension. Despite its intended application to unbounded speech, most research has focused on human pre-segmented speech, simplifying the task and overlooking significant challenges. This narrow focus, coupled with widespread terminological inconsistencies, is limiting the applicability of research outcomes to real-world applications, ultimately hindering progress in the field. Our extensive literature review of 110 papers not only reveals these critical issues in current research but also serves as the foundation for our key contributions. We 1) define the steps and core components of a SimulST system, proposing a standardized terminology and taxonomy; 2) conduct a thorough analysis of community trends, and 3) offer concrete recommendations and future directions to bridge the gaps in existing literature, from evaluation frameworks to system architectures, for advancing the field towards more realistic and effective SimulST solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-26", "arxiv_id": "2412.17726", "title": "VidTwin: Video VAE with Decoupled Structure and Dynamics", "authors": [ "Yuchi Wang", "Junliang Guo", "Xinyi Xie", "Tianyu He", "Xu Sun", "Jiang Bian" ], "github": "https://github.com/microsoft/VidTok/tree/main/vidtwin", "project_page": "", "abstract": "Recent advancements in video autoencoders (Video AEs) have significantly improved the quality and efficiency of video generation. In this paper, we propose a novel and compact video autoencoder, VidTwin, that decouples video into two distinct latent spaces: Structure latent vectors, which capture overall content and global movement, and Dynamics latent vectors, which represent fine-grained details and rapid movements. Specifically, our approach leverages an Encoder-Decoder backbone, augmented with two submodules for extracting these latent spaces, respectively. The first submodule employs a Q-Former to extract low-frequency motion trends, followed by downsampling blocks to remove redundant content details. The second averages the latent vectors along the spatial dimension to capture rapid motion. Extensive experiments show that VidTwin achieves a high compression rate of 0.20% with high reconstruction quality (PSNR of 28.14 on the MCL-JCV dataset), and performs efficiently and effectively in downstream generative tasks. Moreover, our model demonstrates explainability and scalability, paving the way for future research in video latent representation and generation. Our code has been released at https://github.com/microsoft/VidTok/tree/main/vidtwin.", "github_manual": "", "github_regex": "https://github.com/microsoft/VidTok/tree/main/vidtwin", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-27", "arxiv_id": "2412.17743", "title": "YuLan-Mini: An Open Data-efficient Language Model", "authors": [ "Yiwen Hu", "Huatong Song", "Jia Deng", "Jiapeng Wang", "Jie Chen", "Kun Zhou", "Yutao Zhu", "Jinhao Jiang", "Zican Dong", "Wayne Xin Zhao", "Ji-Rong Wen" ], "github": "https://github.com/RUC-GSAI/YuLan-Mini", "project_page": "", "abstract": "Effective pre-training of large language models (LLMs) has been challenging due to the immense resource demands and the complexity of the technical processes involved. This paper presents a detailed technical report on YuLan-Mini, a highly capable base model with 2.42B parameters that achieves top-tier performance among models of similar parameter scale. Our pre-training approach focuses on enhancing training efficacy through three key technical contributions: an elaborate data pipeline combines data cleaning with data schedule strategies, a robust optimization method to mitigate training instability, and an effective annealing approach that incorporates targeted data selection and long context training. Remarkably, YuLan-Mini, trained on 1.08T tokens, achieves performance comparable to industry-leading models that require significantly more data. To facilitate reproduction, we release the full details of the data composition for each training phase. Project details can be accessed at the following link: https://github.com/RUC-GSAI/YuLan-Mini.", "github_manual": "", "github_regex": "https://github.com/RUC-GSAI/YuLan-Mini", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-27", "arxiv_id": "2412.17483", "title": "A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression", "authors": [ "Chenlong Deng", "Zhisong Zhang", "Kelong Mao", "Shuaiyi Li", "Xinting Huang", "Dong Yu", "Zhicheng Dou" ], "github": "", "project_page": "", "abstract": "In this work, we provide a thorough investigation of gist-based context compression methods to improve long-context processing in large language models. We focus on two key questions: (1) How well can these methods replace full attention models? and (2) What potential failure patterns arise due to compression? Through extensive experiments, we show that while gist-based compression can achieve near-lossless performance on tasks like retrieval-augmented generation and long-document QA, it faces challenges in tasks like synthetic recall. Furthermore, we identify three key failure patterns: lost by the boundary, lost if surprise, and lost along the way. To mitigate these issues, we propose two effective strategies: fine-grained autoencoding, which enhances the reconstruction of original token information, and segment-wise token importance estimation, which adjusts optimization based on token dependencies. Our work provides valuable insights into the understanding of gist token-based context compression and offers practical strategies for improving compression capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-27", "arxiv_id": "2412.18176", "title": "Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation", "authors": [ "Yucong Luo", "Qitao Qin", "Hao Zhang", "Mingyue Cheng", "Ruiran Yan", "Kefan Wang", "Jie Ouyang" ], "github": "", "project_page": "", "abstract": "Sequential recommendation (SR) systems have evolved significantly over the past decade, transitioning from traditional collaborative filtering to deep learning approaches and, more recently, to large language models (LLMs). While the adoption of LLMs has driven substantial advancements, these models inherently lack collaborative filtering information, relying primarily on textual content data neglecting other modalities and thus failing to achieve optimal recommendation performance. To address this limitation, we propose Molar, a Multimodal large language sequential recommendation framework that integrates multiple content modalities with ID information to capture collaborative signals effectively. Molar employs an MLLM to generate unified item representations from both textual and non-textual data, facilitating comprehensive multimodal modeling and enriching item embeddings. Additionally, it incorporates collaborative filtering signals through a post-alignment mechanism, which aligns user representations from content-based and ID-based models, ensuring precise personalization and robust performance. By seamlessly combining multimodal content with collaborative filtering insights, Molar captures both user interests and contextual semantics, leading to superior recommendation accuracy. Extensive experiments validate that Molar significantly outperforms traditional and LLM-based baselines, highlighting its strength in utilizing multimodal data and collaborative signals for sequential recommendation tasks. The source code is available at https://anonymous.4open.science/r/Molar-8B06/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-27", "arxiv_id": "2412.18072", "title": "MMFactory: A Universal Solution Search Engine for Vision-Language Tasks", "authors": [ "Wan-Cyuan Fan", "Tanzila Rahman", "Leonid Sigal" ], "github": "", "project_page": "", "abstract": "With advances in foundational and vision-language models, and effective fine-tuning techniques, a large number of both general and special-purpose models have been developed for a variety of visual tasks. Despite the flexibility and accessibility of these models, no single model is able to handle all tasks and/or applications that may be envisioned by potential users. Recent approaches, such as visual programming and multimodal LLMs with integrated tools aim to tackle complex visual tasks, by way of program synthesis. However, such approaches overlook user constraints (e.g., performance / computational needs), produce test-time sample-specific solutions that are difficult to deploy, and, sometimes, require low-level instructions that maybe beyond the abilities of a naive user. To address these limitations, we introduce MMFactory, a universal framework that includes model and metrics routing components, acting like a solution search engine across various available models. Based on a task description and few sample input-output pairs and (optionally) resource and/or performance constraints, MMFactory can suggest a diverse pool of programmatic solutions by instantiating and combining visio-lingual tools from its model repository. In addition to synthesizing these solutions, MMFactory also proposes metrics and benchmarks performance / resource characteristics, allowing users to pick a solution that meets their unique design constraints. From the technical perspective, we also introduced a committee-based solution proposer that leverages multi-agent LLM conversation to generate executable, diverse, universal, and robust solutions for the user. Experimental results show that MMFactory outperforms existing methods by delivering state-of-the-art solutions tailored to user problem specifications. Project page is available at https://davidhalladay.github.io/mmfactory_demo.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-30", "arxiv_id": "2412.18605", "title": "Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models", "authors": [ "Zehan Wang", "Ziang Zhang", "Tianyu Pang", "Chao Du", "Hengshuang Zhao", "Zhou Zhao" ], "github": "https://github.com/SpatialVision/Orient-Anything", "project_page": "", "abstract": "Orientation is a key attribute of objects, crucial for understanding their spatial pose and arrangement in images. However, practical solutions for accurate orientation estimation from a single image remain underexplored. In this work, we introduce Orient Anything, the first expert and foundational model designed to estimate object orientation in a single- and free-view image. Due to the scarcity of labeled data, we propose extracting knowledge from the 3D world. By developing a pipeline to annotate the front face of 3D objects and render images from random views, we collect 2M images with precise orientation annotations. To fully leverage the dataset, we design a robust training objective that models the 3D orientation as probability distributions of three angles and predicts the object orientation by fitting these distributions. Besides, we employ several strategies to improve synthetic-to-real transfer. Our model achieves state-of-the-art orientation estimation accuracy in both rendered and real images and exhibits impressive zero-shot ability in various scenarios. More importantly, our model enhances many applications, such as comprehension and generation of complex spatial concepts and 3D object pose adjustment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SpatialVision/Orient-Anything", "github_api_added_by": "auto" }, { "date": "2024-12-30", "arxiv_id": "2412.18925", "title": "HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs", "authors": [ "Junying Chen", "Zhenyang Cai", "Ke Ji", "Xidong Wang", "Wanlong Liu", "Rongsheng Wang", "Jianye Hou", "Benyou Wang" ], "github": "https://github.com/freedomintelligence/huatuogpt-o1", "project_page": "", "abstract": "The breakthrough of OpenAI o1 highlights the potential of enhancing reasoning to improve LLM. Yet, most research in reasoning has focused on mathematical tasks, leaving domains like medicine underexplored. The medical domain, though distinct from mathematics, also demands robust reasoning to provide reliable answers, given the high standards of healthcare. However, verifying medical reasoning is challenging, unlike those in mathematics. To address this, we propose verifiable medical problems with a medical verifier to check the correctness of model outputs. This verifiable nature enables advancements in medical reasoning through a two-stage approach: (1) using the verifier to guide the search for a complex reasoning trajectory for fine-tuning LLMs, (2) applying reinforcement learning (RL) with verifier-based rewards to enhance complex reasoning further. Finally, we introduce HuatuoGPT-o1, a medical LLM capable of complex reasoning, which outperforms general and medical-specific baselines using only 40K verifiable problems. Experiments show complex reasoning improves medical problem-solving and benefits more from RL. We hope our approach inspires advancements in reasoning across medical and other specialized domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/freedomintelligence/huatuogpt-o1", "github_api_added_by": "auto" }, { "date": "2024-12-30", "arxiv_id": "2412.19712", "title": "From Elements to Design: A Layered Approach for Automatic Graphic Design Composition", "authors": [ "Jiawei Lin", "Shizhao Sun", "Danqing Huang", "Ting Liu", "Ji Li", "Jiang Bian" ], "github": "", "project_page": "", "abstract": "In this work, we investigate automatic design composition from multimodal graphic elements. Although recent studies have developed various generative models for graphic design, they usually face the following limitations: they only focus on certain subtasks and are far from achieving the design composition task; they do not consider the hierarchical information of graphic designs during the generation process. To tackle these issues, we introduce the layered design principle into Large Multimodal Models (LMMs) and propose a novel approach, called LaDeCo, to accomplish this challenging task. Specifically, LaDeCo first performs layer planning for a given element set, dividing the input elements into different semantic layers according to their contents. Based on the planning results, it subsequently predicts element attributes that control the design composition in a layer-wise manner, and includes the rendered image of previously generated layers into the context. With this insightful design, LaDeCo decomposes the difficult task into smaller manageable steps, making the generation process smoother and clearer. The experimental results demonstrate the effectiveness of LaDeCo in design composition. Furthermore, we show that LaDeCo enables some interesting applications in graphic design, such as resolution adjustment, element filling, design variation, etc. In addition, it even outperforms the specialized models in some design subtasks without any task-specific training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-30", "arxiv_id": "2412.19326", "title": "Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment", "authors": [ "Ziang Yan", "Zhilin Li", "Yinan He", "Chenting Wang", "Kunchang Li", "Xinhao Li", "Xiangyu Zeng", "Zilei Wang", "Yali Wang", "Yu Qiao", "Limin Wang", "Yi Wang" ], "github": "https://github.com/OpenGVLab/TPO", "project_page": "", "abstract": "Current multimodal large language models (MLLMs) struggle with fine-grained or precise understanding of visuals though they give comprehensive perception and reasoning in a spectrum of vision applications. Recent studies either develop tool-using or unify specific visual tasks into the autoregressive framework, often at the expense of overall multimodal performance. To address this issue and enhance MLLMs with visual tasks in a scalable fashion, we propose Task Preference Optimization (TPO), a novel method that utilizes differentiable task preferences derived from typical fine-grained visual tasks. TPO introduces learnable task tokens that establish connections between multiple task-specific heads and the MLLM. By leveraging rich visual labels during training, TPO significantly enhances the MLLM's multimodal capabilities and task-specific performance. Through multi-task co-training within TPO, we observe synergistic benefits that elevate individual task performance beyond what is achievable through single-task training methodologies. Our instantiation of this approach with VideoChat and LLaVA demonstrates an overall 14.6% improvement in multimodal performance compared to baseline models. Additionally, MLLM-TPO demonstrates robust zero-shot capabilities across various tasks, performing comparably to state-of-the-art supervised models. The code will be released at https://github.com/OpenGVLab/TPO", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/TPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-30", "arxiv_id": "2412.19645", "title": "VideoMaker: Zero-shot Customized Video Generation with the Inherent Force of Video Diffusion Models", "authors": [ "Tao Wu", "Yong Zhang", "Xiaodong Cun", "Zhongang Qi", "Junfu Pu", "Huanzhang Dou", "Guangcong Zheng", "Ying Shan", "Xi Li" ], "github": "https://github.com/wutao-cs/videomaker", "project_page": "", "abstract": "Zero-shot customized video generation has gained significant attention due to its substantial application potential. Existing methods rely on additional models to extract and inject reference subject features, assuming that the Video Diffusion Model (VDM) alone is insufficient for zero-shot customized video generation. However, these methods often struggle to maintain consistent subject appearance due to suboptimal feature extraction and injection techniques. In this paper, we reveal that VDM inherently possesses the force to extract and inject subject features. Departing from previous heuristic approaches, we introduce a novel framework that leverages VDM's inherent force to enable high-quality zero-shot customized video generation. Specifically, for feature extraction, we directly input reference images into VDM and use its intrinsic feature extraction process, which not only provides fine-grained features but also significantly aligns with VDM's pre-trained knowledge. For feature injection, we devise an innovative bidirectional interaction between subject features and generated content through spatial self-attention within VDM, ensuring that VDM has better subject fidelity while maintaining the diversity of the generated video.Experiments on both customized human and object video generation validate the effectiveness of our framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wutao-cs/videomaker", "github_api_added_by": "auto" }, { "date": "2024-12-30", "arxiv_id": "2412.17606", "title": "SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images", "authors": [ "Risa Shinoda", "Kuniaki Saito", "Shohei Tanaka", "Tosho Hirasawa", "Yoshitaka Ushiku" ], "github": "https://github.com/omron-sinicx/sbsfigures", "project_page": "", "abstract": "Building a large-scale figure QA dataset requires a considerable amount of work, from gathering and selecting figures to extracting attributes like text, numbers, and colors, and generating QAs. Although recent developments in LLMs have led to efforts to synthesize figures, most of these focus primarily on QA generation. Additionally, creating figures directly using LLMs often encounters issues such as code errors, similar-looking figures, and repetitive content in figures. To address this issue, we present SBSFigures (Stage-by-Stage Synthetic Figures), a dataset for pre-training figure QA. Our proposed pipeline enables the creation of chart figures with complete annotations of the visualized data and dense QA annotations without any manual annotation process. Our stage-by-stage pipeline makes it possible to create diverse topic and appearance figures efficiently while minimizing code errors. Our SBSFigures demonstrate a strong pre-training effect, making it possible to achieve efficient training with a limited amount of real-world chart data starting from our pre-trained weights.", "github_manual": "https://github.com/omron-sinicx/sbsfigures", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-30", "arxiv_id": "2412.17762", "title": "The Superposition of Diffusion Models Using the It\u00f4 Density Estimator", "authors": [ "Marta Skreta", "Lazar Atanackovic", "Avishek Joey Bose", "Alexander Tong", "Kirill Neklyudov" ], "github": "https://github.com/necludov/super-diffusion", "project_page": "", "abstract": "The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurring the significant computational burden of re-training a larger combined model. In this paper, we cast the problem of combining multiple pre-trained diffusion models at the generation stage under a novel proposed framework termed superposition. Theoretically, we derive superposition from rigorous first principles stemming from the celebrated continuity equation and design two novel algorithms tailor-made for combining diffusion models in SuperDiff. SuperDiff leverages a new scalable It\\^o density estimator for the log likelihood of the diffusion SDE which incurs no additional overhead compared to the well-known Hutchinson's estimator needed for divergence calculations. We demonstrate that SuperDiff is scalable to large pre-trained diffusion models as superposition is performed solely through composition during inference, and also enjoys painless implementation as it combines different pre-trained vector fields through an automated re-weighting scheme. Notably, we show that SuperDiff is efficient during inference time, and mimics traditional composition operators such as the logical OR and the logical AND. We empirically demonstrate the utility of using SuperDiff for generating more diverse images on CIFAR-10, more faithful prompt conditioned image editing using Stable Diffusion, and improved unconditional de novo structure design of proteins. https://github.com/necludov/super-diffusion", "github_manual": "", "github_regex": "https://github.com/necludov/super-diffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-30", "arxiv_id": "2412.19512", "title": "Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging", "authors": [ "Hua Farn", "Hsuan Su", "Shachi H Kumar", "Saurav Sahay", "Shang-Tse Chen", "Hung-yi Lee" ], "github": "", "project_page": "", "abstract": "Fine-tuning large language models (LLMs) for downstream tasks is a widely adopted approach, but it often leads to safety degradation in safety-aligned LLMs. Currently, many solutions address this issue by incorporating additional safety data, which can be impractical in many cases. In this paper, we address the question: How can we improve downstream task performance while preserving safety in LLMs without relying on additional safety data? We propose a simple and effective method that maintains the inherent safety of LLMs while enhancing their downstream task performance: merging the weights of pre- and post-fine-tuned safety-aligned models. Experimental results across various downstream tasks, models, and merging methods demonstrate that this approach effectively mitigates safety degradation while improving downstream task performance, offering a practical solution for adapting safety-aligned LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-30", "arxiv_id": "2412.18619", "title": "Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey", "authors": [ "Liang Chen", "Zekun Wang", "Shuhuai Ren", "Lei Li", "Haozhe Zhao", "Yunshui Li", "Zefan Cai", "Hongcheng Guo", "Lei Zhang", "Yizhe Xiong", "Yichi Zhang", "Ruoyu Wu", "Qingxiu Dong", "Ge Zhang", "Jian Yang", "Lingwei Meng", "Shujie Hu", "Yulong Chen", "Junyang Lin", "Shuai Bai", "Andreas Vlachos", "Xu Tan", "Minjia Zhang", "Wen Xiao", "Aaron Yee", "Tianyu Liu", "Baobao Chang" ], "github": "https://github.com/LMM101/Awesome-Multimodal-Next-Token-Prediction", "project_page": "", "abstract": "Building on the foundations of language modeling in natural language processing, Next Token Prediction (NTP) has evolved into a versatile training objective for machine learning tasks across various modalities, achieving considerable success. As Large Language Models (LLMs) have advanced to unify understanding and generation tasks within the textual modality, recent research has shown that tasks from different modalities can also be effectively encapsulated within the NTP framework, transforming the multimodal information into tokens and predict the next one given the context. This survey introduces a comprehensive taxonomy that unifies both understanding and generation within multimodal learning through the lens of NTP. The proposed taxonomy covers five key aspects: Multimodal tokenization, MMNTP model architectures, unified task representation, datasets \\& evaluation, and open challenges. This new taxonomy aims to aid researchers in their exploration of multimodal intelligence. An associated GitHub repository collecting the latest papers and repos is available at https://github.com/LMM101/Awesome-Multimodal-Next-Token-Prediction", "github_manual": "", "github_regex": "https://github.com/LMM101/Awesome-Multimodal-Next-Token-Prediction", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-30", "arxiv_id": "2412.18653", "title": "1.58-bit FLUX", "authors": [ "Chenglin Yang", "Celong Liu", "Xueqing Deng", "Dongwon Kim", "Xing Mei", "Xiaohui Shen", "Liang-Chieh Chen" ], "github": "", "project_page": "", "abstract": "We present 1.58-bit FLUX, the first successful approach to quantizing the state-of-the-art text-to-image generation model, FLUX.1-dev, using 1.58-bit weights (i.e., values in {-1, 0, +1}) while maintaining comparable performance for generating 1024 x 1024 images. Notably, our quantization method operates without access to image data, relying solely on self-supervision from the FLUX.1-dev model. Additionally, we develop a custom kernel optimized for 1.58-bit operations, achieving a 7.7x reduction in model storage, a 5.1x reduction in inference memory, and improved inference latency. Extensive evaluations on the GenEval and T2I Compbench benchmarks demonstrate the effectiveness of 1.58-bit FLUX in maintaining generation quality while significantly enhancing computational efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-30", "arxiv_id": "2412.18702", "title": "CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era", "authors": [ "Yanlin Feng", "Simone Papicchio", "Sajjadur Rahman" ], "github": "https://github.com/megagonlabs/cypherbench", "project_page": "", "abstract": "Retrieval from graph data is crucial for augmenting large language models (LLM) with both open-domain knowledge and private enterprise data, and it is also a key component in the recent GraphRAG system (edge et al., 2024). Despite decades of research on knowledge graphs and knowledge base question answering, leading LLM frameworks (e.g. Langchain and LlamaIndex) have only minimal support for retrieval from modern encyclopedic knowledge graphs like Wikidata. In this paper, we analyze the root cause and suggest that modern RDF knowledge graphs (e.g. Wikidata, Freebase) are less efficient for LLMs due to overly large schemas that far exceed the typical LLM context window, use of resource identifiers, overlapping relation types and lack of normalization. As a solution, we propose property graph views on top of the underlying RDF graph that can be efficiently queried by LLMs using Cypher. We instantiated this idea on Wikidata and introduced CypherBench, the first benchmark with 11 large-scale, multi-domain property graphs with 7.8 million entities and over 10,000 questions. To achieve this, we tackled several key challenges, including developing an RDF-to-property graph conversion engine, creating a systematic pipeline for text-to-Cypher task generation, and designing new evaluation metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/megagonlabs/cypherbench", "github_api_added_by": "auto" }, { "date": "2024-12-31", "arxiv_id": "2412.20070", "title": "On the Compositional Generalization of Multimodal LLMs for Medical Imaging", "authors": [ "Zhenyang Cai", "Junying Chen", "Rongsheng Wang", "Weihong Wang", "Yonglin Deng", "Dingjie Song", "Yize Chen", "Zixu Zhang", "Benyou Wang" ], "github": "https://github.com/FreedomIntelligence/Med-MAT", "project_page": "", "abstract": "Multimodal large language models (MLLMs) hold significant potential in the medical field, but their capabilities are often limited by insufficient data in certain medical domains, highlighting the need for understanding what kinds of images can be used by MLLMs for generalization. Current research suggests that multi-task training outperforms single-task as different tasks can benefit each other, but they often overlook the internal relationships within these tasks, providing limited guidance on selecting datasets to enhance specific tasks. To analyze this phenomenon, we attempted to employ compositional generalization (CG)-the ability of models to understand novel combinations by recombining learned elements-as a guiding framework. Since medical images can be precisely defined by Modality, Anatomical area, and Task, naturally providing an environment for exploring CG. Therefore, we assembled 106 medical datasets to create Med-MAT for comprehensive experiments. The experiments confirmed that MLLMs can use CG to understand unseen medical images and identified CG as one of the main drivers of the generalization observed in multi-task training. Additionally, further studies demonstrated that CG effectively supports datasets with limited data and delivers consistent performance across different backbones, highlighting its versatility and broad applicability. Med-MAT is publicly available at https://github.com/FreedomIntelligence/Med-MAT.", "github_manual": "", "github_regex": "https://github.com/FreedomIntelligence/Med-MAT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-31", "arxiv_id": "2412.21079", "title": "Edicho: Consistent Image Editing in the Wild", "authors": [ "Qingyan Bai", "Hao Ouyang", "Yinghao Xu", "Qiuyu Wang", "Ceyuan Yang", "Ka Leong Cheng", "Yujun Shen", "Qifeng Chen" ], "github": "https://github.com/ezioby/edicho", "project_page": "", "abstract": "As a verified need, consistent editing across in-the-wild images remains a technical challenge arising from various unmanageable factors, like object poses, lighting conditions, and photography environments. Edicho steps in with a training-free solution based on diffusion models, featuring a fundamental design principle of using explicit image correspondence to direct editing. Specifically, the key components include an attention manipulation module and a carefully refined classifier-free guidance (CFG) denoising strategy, both of which take into account the pre-estimated correspondence. Such an inference-time algorithm enjoys a plug-and-play nature and is compatible to most diffusion-based editing methods, such as ControlNet and BrushNet. Extensive results demonstrate the efficacy of Edicho in consistent cross-image editing under diverse settings. We will release the code to facilitate future studies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ezioby/edicho", "github_api_added_by": "auto" }, { "date": "2024-12-31", "arxiv_id": "2412.18525", "title": "Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization", "authors": [ "Yang Shen", "Xiu-Shen Wei", "Yifan Sun", "Yuxin Song", "Tao Yuan", "Jian Jin", "Heyang Xu", "Yazhou Yao", "Errui Ding" ], "github": "https://github.com/SEU-VIPGroup/Understanding_Vision_Tasks", "project_page": "", "abstract": "Computer Vision (CV) has yet to fully achieve the zero-shot task generalization observed in Natural Language Processing (NLP), despite following many of the milestones established in NLP, such as large transformer models, extensive pre-training, and the auto-regression paradigm, among others. In this paper, we explore the idea that CV adopts discrete and terminological task definitions (\\eg, ``image segmentation''), which may be a key barrier to zero-shot task generalization. Our hypothesis is that without truly understanding previously-seen tasks--due to these terminological definitions--deep models struggle to generalize to novel tasks. To verify this, we introduce Explanatory Instructions, which provide an intuitive way to define CV task objectives through detailed linguistic transformations from input images to outputs. We create a large-scale dataset comprising 12 million ``image input to explanatory instruction to output'' triplets, and train an auto-regressive-based vision-language model (AR-based VLM) that takes both images and explanatory instructions as input. By learning to follow these instructions, the AR-based VLM achieves instruction-level zero-shot capabilities for previously-seen tasks and demonstrates strong zero-shot generalization for unseen CV tasks. Code and dataset will be openly available on our GitHub repository.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SEU-VIPGroup/Understanding_Vision_Tasks", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-31", "arxiv_id": "2412.21037", "title": "TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization", "authors": [ "Chia-Yu Hung", "Navonil Majumder", "Zhifeng Kong", "Ambuj Mehrish", "Rafael Valle", "Bryan Catanzaro", "Soujanya Poria" ], "github": "https://github.com/declare-lab/TangoFlux", "project_page": "", "abstract": "We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model with 515M parameters, capable of generating up to 30 seconds of 44.1kHz audio in just 3.7 seconds on a single A40 GPU. A key challenge in aligning TTA models lies in the difficulty of creating preference pairs, as TTA lacks structured mechanisms like verifiable rewards or gold-standard answers available for Large Language Models (LLMs). To address this, we propose CLAP-Ranked Preference Optimization (CRPO), a novel framework that iteratively generates and optimizes preference data to enhance TTA alignment. We demonstrate that the audio preference dataset generated using CRPO outperforms existing alternatives. With this framework, TangoFlux achieves state-of-the-art performance across both objective and subjective benchmarks. We open source all code and models to support further research in TTA generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/declare-lab/TangoFlux", "github_api_added_by": "auto" }, { "date": "2024-12-31", "arxiv_id": "2412.20993", "title": "Efficiently Serving LLM Reasoning Programs with Certaindex", "authors": [ "Yichao Fu", "Junda Chen", "Siqi Zhu", "Zheyu Fu", "Zhongdongming Dai", "Aurick Qiao", "Hao Zhang" ], "github": "https://github.com/hao-ai-lab/Dynasor.git", "project_page": "", "abstract": "The rapid evolution of large language models (LLMs) has unlocked their capabilities in advanced reasoning tasks like mathematical problem-solving, code generation, and legal analysis. Central to this progress are inference-time reasoning algorithms, which refine outputs by exploring multiple solution paths, at the cost of increasing compute demands and response latencies. Existing serving systems fail to adapt to the scaling behaviors of these algorithms or the varying difficulty of queries, leading to inefficient resource use and unmet latency targets. We present Dynasor, a system that optimizes inference-time compute for LLM reasoning queries. Unlike traditional engines, Dynasor tracks and schedules requests within reasoning queries and uses Certaindex, a proxy that measures statistical reasoning progress based on model certainty, to guide compute allocation dynamically. Dynasor co-adapts scheduling with reasoning progress: it allocates more compute to hard queries, reduces compute for simpler ones, and terminates unpromising queries early, balancing accuracy, latency, and cost. On diverse datasets and algorithms, Dynasor reduces compute by up to 50% in batch processing and sustaining 3.3x higher query rates or 4.7x tighter latency SLOs in online serving.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hao-ai-lab/Dynasor.git", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-31", "arxiv_id": "2412.20005", "title": "OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System", "authors": [ "Yujie Luo", "Xiangyuan Ru", "Kangwei Liu", "Lin Yuan", "Mengshu Sun", "Ningyu Zhang", "Lei Liang", "Zhiqiang Zhang", "Jun Zhou", "Lanning Wei", "Da Zheng", "Haofen Wang", "Huajun Chen" ], "github": "https://github.com/zjunlp/OneKE", "project_page": "", "abstract": "We introduce OneKE, a dockerized schema-guided knowledge extraction system, which can extract knowledge from the Web and raw PDF Books, and support various domains (science, news, etc.). Specifically, we design OneKE with multiple agents and a configure knowledge base. Different agents perform their respective roles, enabling support for various extraction scenarios. The configure knowledge base facilitates schema configuration, error case debugging and correction, further improving the performance. Empirical evaluations on benchmark datasets demonstrate OneKE's efficacy, while case studies further elucidate its adaptability to diverse tasks across multiple domains, highlighting its potential for broad applications. We have open-sourced the Code at https://github.com/zjunlp/OneKE and released a Video at http://oneke.openkg.cn/demo.mp4.", "github_manual": "", "github_regex": "https://github.com/zjunlp/OneKE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-31", "arxiv_id": "2412.21139", "title": "Training Software Engineering Agents and Verifiers with SWE-Gym", "authors": [ "Jiayi Pan", "Xingyao Wang", "Graham Neubig", "Navdeep Jaitly", "Heng Ji", "Alane Suhr", "Yizhe Zhang" ], "github": "https://github.com/swe-gym/swe-gym", "project_page": "", "abstract": "We present SWE-Gym, the first environment for training real-world software engineering (SWE) agents. SWE-Gym contains 2,438 real-world Python task instances, each comprising a codebase with an executable runtime environment, unit tests, and a task specified in natural language. We use SWE-Gym to train language model based SWE agents , achieving up to 19% absolute gains in resolve rate on the popular SWE-Bench Verified and Lite test sets. We also experiment with inference-time scaling through verifiers trained on agent trajectories sampled from SWE-Gym. When combined with our fine-tuned SWE agents, we achieve 32.0% and 26.0% on SWE-Bench Verified and Lite, respectively, reflecting a new state-of-the-art for open-weight SWE agents. To facilitate further research, we publicly release SWE-Gym, models, and agent trajectories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/swe-gym/swe-gym", "github_api_added_by": "auto" }, { "date": "2024-12-31", "arxiv_id": "2412.21199", "title": "HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation", "authors": [ "Zhaojian Yu", "Yilun Zhao", "Arman Cohan", "Xiao-Ping Zhang" ], "github": "", "project_page": "", "abstract": "We introduce self-invoking code generation, a new task designed to evaluate the progressive reasoning and problem-solving capabilities of LLMs. In this task, models are presented with a base problem and a related, more complex problem. They must solve the base problem and then utilize its solution to address the more complex one. This work features three key contributions. First, we propose a general recipe for generating more challenging versions of existing benchmarks, resulting in three new benchmarks: HumanEval Pro, MBPP Pro, and BigCodeBench-Lite Pro, specifically designed to assess LLMs on self-invoking code generation. Second, from the analysis of experimental results over twenty LLMs on our benchmarks, we have two important observations: (i) Most LLMs excel in traditional code generation benchmarks like HumanEval and MBPP, but their performance declines on self-invoking tasks. For example, o1-mini achieves 96.2% pass@1 on HumanEval but only 76.2% on HumanEval Pro. (ii) On self-invoking code generation task, the instruction-tuned models demonstrate only marginal improvements compared to the base models. Third, we disclose the types of failure modes that exist in our evaluation results. All these results underscore the need for further advancements in self-invoking code generation tasks and provide a new direction for future research on enhancing LLMs' code reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-31", "arxiv_id": "2412.20422", "title": "Bringing Objects to Life: 4D generation from 3D objects", "authors": [ "Ohad Rahamim", "Ori Malca", "Dvir Samuel", "Gal Chechik" ], "github": "https://github.com/ohad204/3to4D", "project_page": "", "abstract": "Recent advancements in generative modeling now enable the creation of 4D content (moving 3D objects) controlled with text prompts. 4D generation has large potential in applications like virtual worlds, media, and gaming, but existing methods provide limited control over the appearance and geometry of generated content. In this work, we introduce a method for animating user-provided 3D objects by conditioning on textual prompts to guide 4D generation, enabling custom animations while maintaining the identity of the original object. We first convert a 3D mesh into a ``static\" 4D Neural Radiance Field (NeRF) that preserves the visual attributes of the input object. Then, we animate the object using an Image-to-Video diffusion model driven by text. To improve motion realism, we introduce an incremental viewpoint selection protocol for sampling perspectives to promote lifelike movement and a masked Score Distillation Sampling (SDS) loss, which leverages attention maps to focus optimization on relevant regions. We evaluate our model in terms of temporal coherence, prompt adherence, and visual fidelity and find that our method outperforms baselines that are based on other approaches, achieving up to threefold improvements in identity preservation measured using LPIPS scores, and effectively balancing visual quality with dynamic content.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ohad204/3to4D", "github_api_added_by": "auto" }, { "date": "2024-12-31", "arxiv_id": "2412.21140", "title": "Facilitating large language model Russian adaptation with Learned Embedding Propagation", "authors": [ "Mikhail Tikhomirov", "Daniil Chernyshev" ], "github": "https://github.com/RefalMachine/llmtf_open", "project_page": "", "abstract": "Rapid advancements of large language model (LLM) technologies led to the introduction of powerful open-source instruction-tuned LLMs that have the same text generation quality as the state-of-the-art counterparts such as GPT-4. While the emergence of such models accelerates the adoption of LLM technologies in sensitive-information environments the authors of such models don not disclose the training data necessary for replication of the results thus making the achievements model-exclusive. Since those open-source models are also multilingual this in turn reduces the benefits of training a language specific LLMs as improved inference computation efficiency becomes the only guaranteed advantage of such costly procedure. More cost-efficient options such as vocabulary extension and subsequent continued pre-training are also inhibited by the lack of access to high-quality instruction-tuning data since it is the major factor behind the resulting LLM task-solving capabilities. To address the limitations and cut the costs of the language adaptation pipeline we propose Learned Embedding Propagation (LEP). Unlike existing approaches our method has lower training data size requirements due to minimal impact on existing LLM knowledge which we reinforce using novel ad-hoc embedding propagation procedure that allows to skip the instruction-tuning step and instead implant the new language knowledge directly into any existing instruct-tuned variant. We evaluated four Russian vocabulary adaptations for LLaMa-3-8B and Mistral-7B, showing that LEP is competitive with traditional instruction-tuning methods, achieving performance comparable to OpenChat 3.5 and LLaMa-3-8B-Instruct, with further improvements via self-calibration and continued tuning enhancing task-solving capabilities.", "github_manual": "https://github.com/RefalMachine/llmtf_open", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-31", "arxiv_id": "2412.21206", "title": "PERSE: Personalized 3D Generative Avatars from A Single Portrait", "authors": [ "Hyunsoo Cha", "Inhee Lee", "Hanbyul Joo" ], "github": "https://github.com/snuvclab/perse", "project_page": "", "abstract": "We present PERSE, a method for building an animatable personalized generative avatar from a reference portrait. Our avatar model enables facial attribute editing in a continuous and disentangled latent space to control each facial attribute, while preserving the individual's identity. To achieve this, our method begins by synthesizing large-scale synthetic 2D video datasets, where each video contains consistent changes in the facial expression and viewpoint, combined with a variation in a specific facial attribute from the original input. We propose a novel pipeline to produce high-quality, photorealistic 2D videos with facial attribute editing. Leveraging this synthetic attribute dataset, we present a personalized avatar creation method based on the 3D Gaussian Splatting, learning a continuous and disentangled latent space for intuitive facial attribute manipulation. To enforce smooth transitions in this latent space, we introduce a latent space regularization technique by using interpolated 2D faces as supervision. Compared to previous approaches, we demonstrate that PERSE generates high-quality avatars with interpolated attributes while preserving identity of reference person.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/snuvclab/perse", "github_api": "", "github_api_added_by": "" }, { "date": "2024-12-31", "arxiv_id": "2412.20631", "title": "Slow Perception: Let's Perceive Geometric Figures Step-by-step", "authors": [ "Haoran Wei", "Youyang Yin", "Yumeng Li", "Jia Wang", "Liang Zhao", "Jianjian Sun", "Zheng Ge", "Xiangyu Zhang" ], "github": "https://github.com/Ucas-HaoranWei/Slow-Perception", "project_page": "", "abstract": "Recently, \"visual o1\" began to enter people's vision, with expectations that this slow-thinking design can solve visual reasoning tasks, especially geometric math problems. However, the reality is that current LVLMs (Large Vision Language Models) can hardly even accurately copy a geometric figure, let alone truly understand the complex inherent logic and spatial relationships within geometric shapes. We believe accurate copying (strong perception) is the first step to visual o1. Accordingly, we introduce the concept of \"slow perception\" (SP), which guides the model to gradually perceive basic point-line combinations, as our humans, reconstruct complex geometric structures progressively. There are two-fold stages in SP: a) perception decomposition. Perception is not instantaneous. In this stage, complex geometric figures are broken down into basic simple units to unify geometry representation. b) perception flow, which acknowledges that accurately tracing a line is not an easy task. This stage aims to avoid \"long visual jumps\" in regressing line segments by using a proposed \"perceptual ruler\" to trace each line stroke-by-stroke. Surprisingly, such a human-like perception manner enjoys an inference time scaling law -- the slower, the better. Researchers strive to speed up the model's perception in the past, but we slow it down again, allowing the model to read the image step-by-step and carefully.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Ucas-HaoranWei/Slow-Perception", "github_api_added_by": "auto" }, { "date": "2024-12-31", "arxiv_id": "2412.21187", "title": "Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs", "authors": [ "Xingyu Chen", "Jiahao Xu", "Tian Liang", "Zhiwei He", "Jianhui Pang", "Dian Yu", "Linfeng Song", "Qiuzhi Liu", "Mengfei Zhou", "Zhuosheng Zhang", "Rui Wang", "Zhaopeng Tu", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "The remarkable performance of models like the OpenAI o1 can be attributed to their ability to emulate human-like long-time thinking during inference. These models employ extended chain-of-thought (CoT) processes, exploring multiple strategies to enhance problem-solving capabilities. However, a critical question remains: How to intelligently and efficiently scale computational resources during testing. This paper presents the first comprehensive study on the prevalent issue of overthinking in these models, where excessive computational resources are allocated for simple problems with minimal benefit. We introduce novel efficiency metrics from both outcome and process perspectives to evaluate the rational use of computational resources by o1-like models. Using a self-training paradigm, we propose strategies to mitigate overthinking, streamlining reasoning processes without compromising accuracy. Experimental results show that our approach successfully reduces computational overhead while preserving model performance across a range of testsets with varying difficulty levels, such as GSM8K, MATH500, GPQA, and AIME.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-02", "arxiv_id": "2412.19638", "title": "Xmodel-2 Technical Report", "authors": [ "Wang Qun", "Liu Yang", "Lin Qingquan", "Qu Zhijiu", "Jiang Ling" ], "github": "https://github.com/XiaoduoAILab/Xmodel-2", "project_page": "", "abstract": "Xmodel-2 is a 1.2-billion-parameter large language model designed specifically for reasoning tasks. Its architecture enables different model scales to share a unified set of hyperparameters, allowing for extensive experimentation on smaller models and seamless transfer of optimal configurations to larger models. To maximize training efficiency and stability, Xmodel-2 employs the WSD learning rate scheduler from MiniCPM. Pretrained on 1.5 trillion tokens from diverse sources, Xmodel-2 achieves state-of-the-art performance in complex reasoning and agent-based tasks, while maintaining low training costs. These results highlight the potential of efficient model design and training strategies in advancing reasoning capabilities. Model checkpoints and code are publicly available on GitHub at https://github.com/XiaoduoAILab/Xmodel-2", "github_manual": "", "github_regex": "https://github.com/XiaoduoAILab/Xmodel-2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-02", "arxiv_id": "2412.19723", "title": "OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis", "authors": [ "Qiushi Sun", "Kanzhi Cheng", "Zichen Ding", "Chuanyang Jin", "Yian Wang", "Fangzhi Xu", "Zhenyu Wu", "Chengyou Jia", "Liheng Chen", "Zhoumianze Liu", "Ben Kao", "Guohao Li", "Junxian He", "Yu Qiao", "Zhiyong Wu" ], "github": "", "project_page": "", "abstract": "Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability. Despite their utility in advancing digital automation, a critical bottleneck persists: collecting high-quality trajectory data for training. Common practices for collecting such data rely on human supervision or synthetic data generation through executing pre-defined tasks, which are either resource-intensive or unable to guarantee data quality. Moreover, these methods suffer from limited data diversity and significant gaps between synthetic data and real-world environments. To address these challenges, we propose OS-Genesis, a novel GUI data synthesis pipeline that reverses the conventional trajectory collection process. Instead of relying on pre-defined tasks, OS-Genesis enables agents first to perceive environments and perform step-wise interactions, then retrospectively derive high-quality tasks to enable trajectory-level exploration. A trajectory reward model is then employed to ensure the quality of the generated trajectories. We demonstrate that training GUI agents with OS-Genesis significantly improves their performance on highly challenging online benchmarks. In-depth analysis further validates OS-Genesis's efficiency and its superior data quality and diversity compared to existing synthesis methods. Our codes, data, and checkpoints are available at https://qiushisun.github.io/OS-Genesis-Home/{OS-Genesis Homepage}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-02", "arxiv_id": "2412.20735", "title": "HUNYUANPROVER: A Scalable Data Synthesis Framework and Guided Tree Search for Automated Theorem Proving", "authors": [ "Yang Li", "Dong Du", "Linfeng Song", "Chen Li", "Weikang Wang", "Tao Yang", "Haitao Mi" ], "github": "", "project_page": "", "abstract": "We introduce HunyuanProver, an language model finetuned from the Hunyuan 7B for interactive automatic theorem proving with LEAN4. To alleviate the data sparsity issue, we design a scalable framework to iterative synthesize data with low cost. Besides, guided tree search algorithms are designed to enable effective ``system 2 thinking`` of the prover. HunyuanProver achieves state-of-the-art (SOTA) performances on major benchmarks. Specifically, it achieves a pass of 68.4% on the miniF2F-test compared to 65.9%, the current SOTA results. It proves 4 IMO statements (imo_1960_p2, imo_1962_p2}, imo_1964_p2 and imo_1983_p6) in miniF2F-test. To benefit the community, we will open-source a dataset of 30k synthesized instances, where each instance contains the original question in natural language, the converted statement by autoformalization, and the proof by HunyuanProver.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-02", "arxiv_id": "2412.20800", "title": "VMix: Improving Text-to-Image Diffusion Model with Cross-Attention Mixing Control", "authors": [ "Shaojin Wu", "Fei Ding", "Mengqi Huang", "Wei Liu", "Qian He" ], "github": "", "project_page": "", "abstract": "While diffusion models show extraordinary talents in text-to-image generation, they may still fail to generate highly aesthetic images. More specifically, there is still a gap between the generated images and the real-world aesthetic images in finer-grained dimensions including color, lighting, composition, etc. In this paper, we propose Cross-Attention Value Mixing Control (VMix) Adapter, a plug-and-play aesthetics adapter, to upgrade the quality of generated images while maintaining generality across visual concepts by (1) disentangling the input text prompt into the content description and aesthetic description by the initialization of aesthetic embedding, and (2) integrating aesthetic conditions into the denoising process through value-mixed cross-attention, with the network connected by zero-initialized linear layers. Our key insight is to enhance the aesthetic presentation of existing diffusion models by designing a superior condition control method, all while preserving the image-text alignment. Through our meticulous design, VMix is flexible enough to be applied to community models for better visual performance without retraining. To validate the effectiveness of our method, we conducted extensive experiments, showing that VMix outperforms other state-of-the-art methods and is compatible with other community modules (e.g., LoRA, ControlNet, and IPAdapter) for image generation. The project page is https://vmix-diffusion.github.io/VMix/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-02", "arxiv_id": "2412.20750", "title": "Are Vision-Language Models Truly Understanding Multi-vision Sensor?", "authors": [ "Sangyun Chung", "Youngjoon Yu", "Youngchae Chee", "Se Yeon Kim", "Byung-Kwan Lee", "Yong Man Ro" ], "github": "", "project_page": "", "abstract": "Large-scale Vision-Language Models (VLMs) have advanced by aligning vision inputs with text, significantly improving performance in computer vision tasks. Moreover, for VLMs to be effectively utilized in real-world applications, an understanding of diverse multi-vision sensor data, such as thermal, depth, and X-ray information, is essential. However, we find that current VLMs process multi-vision sensor images without deep understanding of sensor information, disregarding each sensor's unique physical properties. This limitation restricts their capacity to interpret and respond to complex questions requiring multi-vision sensor reasoning. To address this, we propose a novel Multi-vision Sensor Perception and Reasoning (MS-PR) benchmark, assessing VLMs on their capacity for sensor-specific reasoning. Moreover, we introduce Diverse Negative Attributes (DNA) optimization to enable VLMs to perform deep reasoning on multi-vision sensor tasks, helping to bridge the core information gap between images and sensor data. Extensive experimental results validate that the proposed DNA method can significantly improve the multi-vision sensor reasoning for VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.01427", "title": "VideoAnydoor: High-fidelity Video Object Insertion with Precise Motion Control", "authors": [ "Yuanpeng Tu", "Hao Luo", "Xi Chen", "Sihui Ji", "Xiang Bai", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "Despite significant advancements in video generation, inserting a given object into videos remains a challenging task. The difficulty lies in preserving the appearance details of the reference object and accurately modeling coherent motions at the same time. In this paper, we propose VideoAnydoor, a zero-shot video object insertion framework with high-fidelity detail preservation and precise motion control. Starting from a text-to-video model, we utilize an ID extractor to inject the global identity and leverage a box sequence to control the overall motion. To preserve the detailed appearance and meanwhile support fine-grained motion control, we design a pixel warper. It takes the reference image with arbitrary key-points and the corresponding key-point trajectories as inputs. It warps the pixel details according to the trajectories and fuses the warped features with the diffusion U-Net, thus improving detail preservation and supporting users in manipulating the motion trajectories. In addition, we propose a training strategy involving both videos and static images with a reweight reconstruction loss to enhance insertion quality. VideoAnydoor demonstrates significant superiority over existing methods and naturally supports various downstream applications (e.g., talking head generation, video virtual try-on, multi-region editing) without task-specific fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.01264", "title": "ProgCo: Program Helps Self-Correction of Large Language Models", "authors": [ "Xiaoshuai Song", "Yanan Wu", "Weixun Wang", "Jiaheng Liu", "Wenbo Su", "Bo Zheng" ], "github": "https://github.com/songxiaoshuai/progco", "project_page": "", "abstract": "Self-Correction aims to enable large language models (LLMs) to self-verify and self-refine their initial responses without external feedback. However, LLMs often fail to effectively self-verify and generate correct feedback, further misleading refinement and leading to the failure of self-correction, especially in complex reasoning tasks. In this paper, we propose Program-driven Self-Correction (ProgCo). First, program-driven verification (ProgVe) achieves complex verification logic and extensive validation through self-generated, self-executing verification pseudo-programs. Then, program-driven refinement (ProgRe) receives feedback from ProgVe, conducts dual reflection and refinement on both responses and verification programs to mitigate misleading of incorrect feedback in complex reasoning tasks. Experiments on three instruction-following and mathematical benchmarks indicate that ProgCo achieves effective self-correction, and can be further enhance performance when combined with real program tools.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/songxiaoshuai/progco", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.01149", "title": "A3: Android Agent Arena for Mobile GUI Agents", "authors": [ "Yuxiang Chai", "Hanhao Li", "Jiayu Zhang", "Liang Liu", "Guozhi Wang", "Shuai Ren", "Siyuan Huang", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "AI agents have become increasingly prevalent in recent years, driven by significant advancements in the field of large language models (LLMs). Mobile GUI agents, a subset of AI agents, are designed to autonomously perform tasks on mobile devices. While numerous studies have introduced agents, datasets, and benchmarks to advance mobile GUI agent research, many existing datasets focus on static frame evaluations and fail to provide a comprehensive platform for assessing performance on real-world, in-the-wild tasks. To address this gap, we present Android Agent Arena (A3), a novel evaluation platform. Unlike existing in-the-wild systems, A3 offers: (1) meaningful and practical tasks, such as real-time online information retrieval and operational instructions; (2) a larger, more flexible action space, enabling compatibility with agents trained on any dataset; and (3) automated business-level LLM-based evaluation process. A3 includes 21 widely used general third-party apps and 201 tasks representative of common user scenarios, providing a robust foundation for evaluating mobile GUI agents in real-world situations and a new autonomous evaluation process for less human labor and coding expertise. The project is available at https://yuxiangchai.github.io/Android-Agent-Arena/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.00192", "title": "MLLM-as-a-Judge for Image Safety without Human Labeling", "authors": [ "Zhenting Wang", "Shuming Hu", "Shiyu Zhao", "Xiaowen Lin", "Felix Juefei-Xu", "Zhuowei Li", "Ligong Han", "Harihar Subramanyam", "Li Chen", "Jianfa Chen", "Nan Jiang", "Lingjuan Lyu", "Shiqing Ma", "Dimitris N. Metaxas", "Ankit Jain" ], "github": "", "project_page": "", "abstract": "Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generation models are capable of producing harmful content, such as images containing sexual or violent material. Thus, it becomes crucial to identify such unsafe images based on established safety rules. Pre-trained Multimodal Large Language Models (MLLMs) offer potential in this regard, given their strong pattern recognition abilities. Existing approaches typically fine-tune MLLMs with human-labeled datasets, which however brings a series of drawbacks. First, relying on human annotators to label data following intricate and detailed guidelines is both expensive and labor-intensive. Furthermore, users of safety judgment systems may need to frequently update safety rules, making fine-tuning on human-based annotation more challenging. This raises the research question: Can we detect unsafe images by querying MLLMs in a zero-shot setting using a predefined safety constitution (a set of safety rules)? Our research showed that simply querying pre-trained MLLMs does not yield satisfactory results. This lack of effectiveness stems from factors such as the subjectivity of safety rules, the complexity of lengthy constitutions, and the inherent biases in the models. To address these challenges, we propose a MLLM-based method includes objectifying safety rules, assessing the relevance between rules and images, making quick judgments based on debiased token probabilities with logically complete yet simplified precondition chains for safety rules, and conducting more in-depth reasoning with cascaded chain-of-thought processes if necessary. Experiment results demonstrate that our method is highly effective for zero-shot image safety judgment tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.01423", "title": "Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models", "authors": [ "Jingfeng Yao", "Xinggang Wang" ], "github": "https://github.com/hustvl/LightningDiT", "project_page": "", "abstract": "Latent diffusion models with Transformer architectures excel at generating high-fidelity images. However, recent studies reveal an optimization dilemma in this two-stage design: while increasing the per-token feature dimension in visual tokenizers improves reconstruction quality, it requires substantially larger diffusion models and more training iterations to achieve comparable generation performance. Consequently, existing systems often settle for sub-optimal solutions, either producing visual artifacts due to information loss within tokenizers or failing to converge fully due to expensive computation costs. We argue that this dilemma stems from the inherent difficulty in learning unconstrained high-dimensional latent spaces. To address this, we propose aligning the latent space with pre-trained vision foundation models when training the visual tokenizers. Our proposed VA-VAE (Vision foundation model Aligned Variational AutoEncoder) significantly expands the reconstruction-generation frontier of latent diffusion models, enabling faster convergence of Diffusion Transformers (DiT) in high-dimensional latent spaces. To exploit the full potential of VA-VAE, we build an enhanced DiT baseline with improved training strategies and architecture designs, termed LightningDiT. The integrated system achieves state-of-the-art (SOTA) performance on ImageNet 256x256 generation with an FID score of 1.35 while demonstrating remarkable training efficiency by reaching an FID score of 2.11 in just 64 epochs--representing an over 21 times convergence speedup compared to the original DiT. Models and codes are available at: https://github.com/hustvl/LightningDiT.", "github_manual": "", "github_regex": "https://github.com/hustvl/LightningDiT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.00599", "title": "VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM", "authors": [ "Yuqian Yuan", "Hang Zhang", "Wentong Li", "Zesen Cheng", "Boqiang Zhang", "Long Li", "Xin Li", "Deli Zhao", "Wenqiao Zhang", "Yueting Zhuang", "Jianke Zhu", "Lidong Bing" ], "github": "https://github.com/damo-nlp-sg/videorefer", "project_page": "", "abstract": "Video Large Language Models (Video LLMs) have recently exhibited remarkable capabilities in general video understanding. However, they mainly focus on holistic comprehension and struggle with capturing fine-grained spatial and temporal details. Besides, the lack of high-quality object-level video instruction data and a comprehensive benchmark further hinders their advancements. To tackle these challenges, we introduce the VideoRefer Suite to empower Video LLM for finer-level spatial-temporal video understanding, i.e., enabling perception and reasoning on any objects throughout the video. Specially, we thoroughly develop VideoRefer Suite across three essential aspects: dataset, model, and benchmark. Firstly, we introduce a multi-agent data engine to meticulously curate a large-scale, high-quality object-level video instruction dataset, termed VideoRefer-700K. Next, we present the VideoRefer model, which equips a versatile spatial-temporal object encoder to capture precise regional and sequential representations. Finally, we meticulously create a VideoRefer-Bench to comprehensively assess the spatial-temporal understanding capability of a Video LLM, evaluating it across various aspects. Extensive experiments and analyses demonstrate that our VideoRefer model not only achieves promising performance on video referring benchmarks but also facilitates general video understanding capabilities.", "github_manual": "https://github.com/damo-nlp-sg/videorefer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.00958", "title": "2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining", "authors": [ "Wenqi Zhang", "Hang Zhang", "Xin Li", "Jiashuo Sun", "Yongliang Shen", "Weiming Lu", "Deli Zhao", "Yueting Zhuang", "Lidong Bing" ], "github": "https://github.com/DAMO-NLP-SG/multimodal_textbook", "project_page": "", "abstract": "Compared to image-text pair data, interleaved corpora enable Vision-Language Models (VLMs) to understand the world more naturally like humans. However, such existing datasets are crawled from webpage, facing challenges like low knowledge density, loose image-text relations, and poor logical coherence between images. On the other hand, the internet hosts vast instructional videos (e.g., online geometry courses) that are widely used by humans to learn foundational subjects, yet these valuable resources remain underexplored in VLM training. In this paper, we introduce a high-quality multimodal textbook corpus with richer foundational knowledge for VLM pretraining. It collects over 2.5 years of instructional videos, totaling 22,000 class hours. We first use an LLM-proposed taxonomy to systematically gather instructional videos. Then we progressively extract and refine visual (keyframes), audio (ASR), and textual knowledge (OCR) from the videos, and organize as an image-text interleaved corpus based on temporal order. Compared to its counterparts, our video-centric textbook offers more coherent context, richer knowledge, and better image-text alignment. Experiments demonstrate its superb pretraining performance, particularly in knowledge- and reasoning-intensive tasks like ScienceQA and MathVista. Moreover, VLMs pre-trained on our textbook exhibit outstanding interleaved context awareness, leveraging visual and textual cues in their few-shot context for task solving~Our code are available at \\url{https://github.com/DAMO-NLP-SG/multimodal_textbook}.", "github_manual": "", "github_regex": "https://github.com/DAMO-NLP-SG/multimodal_textbook", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.01245", "title": "SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization", "authors": [ "Yongle Huang", "Haodong Chen", "Zhenbang Xu", "Zihan Jia", "Haozhou Sun", "Dian Shao" ], "github": "https://github.com/kylehuang9/sefar", "project_page": "", "abstract": "Human action understanding is crucial for the advancement of multimodal systems. While recent developments, driven by powerful large language models (LLMs), aim to be general enough to cover a wide range of categories, they often overlook the need for more specific capabilities. In this work, we address the more challenging task of Fine-grained Action Recognition (FAR), which focuses on detailed semantic labels within shorter temporal duration (e.g., \"salto backward tucked with 1 turn\"). Given the high costs of annotating fine-grained labels and the substantial data needed for fine-tuning LLMs, we propose to adopt semi-supervised learning (SSL). Our framework, SeFAR, incorporates several innovative designs to tackle these challenges. Specifically, to capture sufficient visual details, we construct Dual-level temporal elements as more effective representations, based on which we design a new strong augmentation strategy for the Teacher-Student learning paradigm through involving moderate temporal perturbation. Furthermore, to handle the high uncertainty within the teacher model's predictions for FAR, we propose the Adaptive Regulation to stabilize the learning process. Experiments show that SeFAR achieves state-of-the-art performance on two FAR datasets, FineGym and FineDiving, across various data scopes. It also outperforms other semi-supervised methods on two classical coarse-grained datasets, UCF101 and HMDB51. Further analysis and ablation studies validate the effectiveness of our designs. Additionally, we show that the features extracted by our SeFAR could largely promote the ability of multimodal foundation models to understand fine-grained and domain-specific semantics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kylehuang9/sefar", "github_api_added_by": "auto" }, { "date": "2025-01-03", "arxiv_id": "2501.01054", "title": "Dynamic Scaling of Unit Tests for Code Reward Modeling", "authors": [ "Zeyao Ma", "Xiaokang Zhang", "Jing Zhang", "Jifan Yu", "Sijia Luo", "Jie Tang" ], "github": "", "project_page": "", "abstract": "Current large language models (LLMs) often struggle to produce accurate responses on the first attempt for complex reasoning tasks like code generation. Prior research tackles this challenge by generating multiple candidate solutions and validating them with LLM-generated unit tests. The execution results of unit tests serve as reward signals to identify correct solutions. As LLMs always confidently make mistakes, these unit tests are not reliable, thereby diminishing the quality of reward signals. Motivated by the observation that scaling the number of solutions improves LLM performance, we explore the impact of scaling unit tests to enhance reward signal quality. Our pioneer experiment reveals a positive correlation between the number of unit tests and reward signal quality, with greater benefits observed in more challenging problems. Based on these insights, we propose CodeRM-8B, a lightweight yet effective unit test generator that enables efficient and high-quality unit test scaling. Additionally, we implement a dynamic scaling mechanism that adapts the number of unit tests based on problem difficulty, further improving efficiency. Experimental results show that our approach significantly improves performance across various models on three benchmarks (e.g., with gains of 18.43% for Llama3-8B and 3.42% for GPT-4o-mini on HumanEval Plus).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.01257", "title": "CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings", "authors": [ "Shanghaoran Quan", "Jiaxi Yang", "Bowen Yu", "Bo Zheng", "Dayiheng Liu", "An Yang", "Xuancheng Ren", "Bofei Gao", "Yibo Miao", "Yunlong Feng", "Zekun Wang", "Jian Yang", "Zeyu Cui", "Yang Fan", "Yichang Zhang", "Binyuan Hui", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "With the increasing code reasoning capabilities of existing large language models (LLMs) and breakthroughs in reasoning models like OpenAI o1 and o3, there is a growing need to develop more challenging and comprehensive benchmarks that effectively test their sophisticated competition-level coding abilities. Existing benchmarks, like LiveCodeBench and USACO, fall short due to the unavailability of private test cases, lack of support for special judges, and misaligned execution environments. To bridge this gap, we introduce CodeElo, a standardized competition-level code generation benchmark that effectively addresses all these challenges for the first time. CodeElo benchmark is mainly based on the official CodeForces platform and tries to align with the platform as much as possible. We compile the recent six months of contest problems on CodeForces with detailed information such as contest divisions, problem difficulty ratings, and problem algorithm tags. We introduce a unique judging method in which problems are submitted directly to the platform and develop a reliable Elo rating calculation system that aligns with the platform and is comparable with human participants but has lower variance. By testing on our CodeElo, we provide the Elo ratings of 30 existing popular open-source and 3 proprietary LLMs for the first time. The results show that o1-mini and QwQ-32B-Preview stand out significantly, achieving Elo ratings of 1578 and 1261, respectively, while other models struggle even with the easiest problems, placing in the lowest 20 percent among all human participants. Detailed analysis experiments are also conducted to provide insights into performance across algorithms and comparisons between using C++ and Python, which can suggest directions for future studies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2412.21015", "title": "MapQaTor: A System for Efficient Annotation of Map Query Datasets", "authors": [ "Mahir Labib Dihan", "Mohammed Eunus Ali", "Md Rizwan Parvez" ], "github": "", "project_page": "", "abstract": "Mapping and navigation services like Google Maps, Apple Maps, Openstreet Maps, are essential for accessing various location-based data, yet they often struggle to handle natural language geospatial queries. Recent advancements in Large Language Models (LLMs) show promise in question answering (QA), but creating reliable geospatial QA datasets from map services remains challenging. We introduce MapQaTor, a web application that streamlines the creation of reproducible, traceable map-based QA datasets. With its plug-and-play architecture, MapQaTor enables seamless integration with any maps API, allowing users to gather and visualize data from diverse sources with minimal setup. By caching API responses, the platform ensures consistent ground truth, enhancing the reliability of the data even as real-world information evolves. MapQaTor centralizes data retrieval, annotation, and visualization within a single platform, offering a unique opportunity to evaluate the current state of LLM-based geospatial reasoning while advancing their capabilities for improved geospatial understanding. Evaluation metrics show that, MapQaTor speeds up the annotation process by at least 30 times compared to manual methods, underscoring its potential for developing geospatial resources, such as complex map reasoning datasets. The website is live at: https://mapqator.github.io/ and a demo video is available at: https://youtu.be/7_aV9Wmhs6Q.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.00658", "title": "Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing", "authors": [ "Peihao Wang", "Ruisi Cai", "Yuehao Wang", "Jiajun Zhu", "Pragya Srivastava", "Zhangyang Wang", "Pan Li" ], "github": "https://github.com/VITA-Group/SSM-Bottleneck", "project_page": "", "abstract": "Structured State Space Models (SSMs) have emerged as alternatives to transformers. While SSMs are often regarded as effective in capturing long-sequence dependencies, we rigorously demonstrate that they are inherently limited by strong recency bias. Our empirical studies also reveal that this bias impairs the models' ability to recall distant information and introduces robustness issues. Our scaling experiments then discovered that deeper structures in SSMs can facilitate the learning of long contexts. However, subsequent theoretical analysis reveals that as SSMs increase in depth, they exhibit another inevitable tendency toward over-smoothing, e.g., token representations becoming increasingly indistinguishable. This fundamental dilemma between recency and over-smoothing hinders the scalability of existing SSMs. Inspired by our theoretical findings, we propose to polarize two channels of the state transition matrices in SSMs, setting them to zero and one, respectively, simultaneously addressing recency bias and over-smoothing. Experiments demonstrate that our polarization technique consistently enhances the associative recall accuracy of long-range tokens and unlocks SSMs to benefit further from deeper architectures. All source codes are released at https://github.com/VITA-Group/SSM-Bottleneck.", "github_manual": "", "github_regex": "https://github.com/VITA-Group/SSM-Bottleneck", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.00316", "title": "MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models", "authors": [ "Mahir Labib Dihan", "Md Tanvir Hassan", "Md Tanvir Parvez", "Md Hasebul Hasan", "Md Almash Alam", "Muhammad Aamir Cheema", "Mohammed Eunus Ali", "Md Rizwan Parvez" ], "github": "", "project_page": "", "abstract": "Recent advancements in foundation models have enhanced AI systems' capabilities in autonomous tool usage and reasoning. However, their ability in location or map-based reasoning - which improves daily life by optimizing navigation, facilitating resource discovery, and streamlining logistics - has not been systematically studied. To bridge this gap, we introduce MapEval, a benchmark designed to assess diverse and complex map-based user queries with geo-spatial reasoning. MapEval features three task types (textual, API-based, and visual) that require collecting world information via map tools, processing heterogeneous geo-spatial contexts (e.g., named entities, travel distances, user reviews or ratings, images), and compositional reasoning, which all state-of-the-art foundation models find challenging. Comprising 700 unique multiple-choice questions about locations across 180 cities and 54 countries, MapEval evaluates foundation models' ability to handle spatial relationships, map infographics, travel planning, and navigation challenges. Using MapEval, we conducted a comprehensive evaluation of 28 prominent foundation models. While no single model excelled across all tasks, Claude-3.5-Sonnet, GPT-4o, and Gemini-1.5-Pro achieved competitive performance overall. However, substantial performance gaps emerged, particularly in MapEval, where agents with Claude-3.5-Sonnet outperformed GPT-4o and Gemini-1.5-Pro by 16% and 21%, respectively, and the gaps became even more amplified when compared to open-source LLMs. Our detailed analyses provide insights into the strengths and weaknesses of current models, though all models still fall short of human performance by more than 20% on average, struggling with complex map images and rigorous geo-spatial reasoning. This gap highlights MapEval's critical role in advancing general-purpose foundation models with stronger geo-spatial understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.01320", "title": "SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration", "authors": [ "Jianyi Wang", "Zhijie Lin", "Meng Wei", "Yang Zhao", "Ceyuan Yang", "Chen Change Loy", "Lu Jiang" ], "github": "", "project_page": "", "abstract": "Video restoration poses non-trivial challenges in maintaining fidelity while recovering temporally consistent details from unknown degradations in the wild. Despite recent advances in diffusion-based restoration, these methods often face limitations in generation capability and sampling efficiency. In this work, we present SeedVR, a diffusion transformer designed to handle real-world video restoration with arbitrary length and resolution. The core design of SeedVR lies in the shifted window attention that facilitates effective restoration on long video sequences. SeedVR further supports variable-sized windows near the boundary of both spatial and temporal dimensions, overcoming the resolution constraints of traditional window attention. Equipped with contemporary practices, including causal video autoencoder, mixed image and video training, and progressive training, SeedVR achieves highly-competitive performance on both synthetic and real-world benchmarks, as well as AI-generated videos. Extensive experiments demonstrate SeedVR's superiority over existing methods for generic video restoration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.00103", "title": "LTX-Video: Realtime Video Latent Diffusion", "authors": [ "Yoav HaCohen", "Nisan Chiprut", "Benny Brazowski", "Daniel Shalem", "Dudu Moshe", "Eitan Richardson", "Eran Levin", "Guy Shiran", "Nir Zabari", "Ori Gordon", "Poriya Panet", "Sapir Weissbuch", "Victor Kulikov", "Yaki Bitterman", "Zeev Melumian", "Ofir Bibi" ], "github": "https://github.com/Lightricks/LTX-Video", "project_page": "", "abstract": "We introduce LTX-Video, a transformer-based latent diffusion model that adopts a holistic approach to video generation by seamlessly integrating the responsibilities of the Video-VAE and the denoising transformer. Unlike existing methods, which treat these components as independent, LTX-Video aims to optimize their interaction for improved efficiency and quality. At its core is a carefully designed Video-VAE that achieves a high compression ratio of 1:192, with spatiotemporal downscaling of 32 x 32 x 8 pixels per token, enabled by relocating the patchifying operation from the transformer's input to the VAE's input. Operating in this highly compressed latent space enables the transformer to efficiently perform full spatiotemporal self-attention, which is essential for generating high-resolution videos with temporal consistency. However, the high compression inherently limits the representation of fine details. To address this, our VAE decoder is tasked with both latent-to-pixel conversion and the final denoising step, producing the clean result directly in pixel space. This approach preserves the ability to generate fine details without incurring the runtime cost of a separate upsampling module. Our model supports diverse use cases, including text-to-video and image-to-video generation, with both capabilities trained simultaneously. It achieves faster-than-real-time generation, producing 5 seconds of 24 fps video at 768x512 resolution in just 2 seconds on an Nvidia H100 GPU, outperforming all existing models of similar scale. The source code and pre-trained models are publicly available, setting a new benchmark for accessible and scalable video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Lightricks/LTX-Video", "github_api_added_by": "auto" }, { "date": "2025-01-03", "arxiv_id": "2501.00712", "title": "Rethinking Addressing in Language Models via Contexualized Equivariant Positional Encoding", "authors": [ "Jiajun Zhu", "Peihao Wang", "Ruisi Cai", "Jason D. Lee", "Pan Li", "Zhangyang Wang" ], "github": "https://github.com/vita-group/tape", "project_page": "", "abstract": "Transformers rely on both content-based and position-based addressing mechanisms to make predictions, but existing positional encoding techniques often diminish the effectiveness of position-based addressing. Many current methods enforce rigid patterns in attention maps, limiting the ability to model long-range dependencies and adapt to diverse tasks. Additionally, most positional encodings are learned as general biases, lacking the specialization required for different instances within a dataset. To address this, we propose conTextualized equivariAnt Position Embedding (TAPE), a novel framework that enhances positional embeddings by incorporating sequence content across layers. TAPE introduces dynamic, context-aware positional encodings, overcoming the constraints of traditional fixed patterns. By enforcing permutation and orthogonal equivariance, TAPE ensures the stability of positional encodings during updates, improving robustness and adaptability. Our method can be easily integrated into pre-trained transformers, offering parameter-efficient fine-tuning with minimal overhead. Extensive experiments shows that TAPE achieves superior performance in language modeling, arithmetic reasoning, and long-context retrieval tasks compared to existing positional embedding techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vita-group/tape", "github_api_added_by": "auto" }, { "date": "2025-01-03", "arxiv_id": "2501.01407", "title": "Nested Attention: Semantic-aware Attention Values for Concept Personalization", "authors": [ "Or Patashnik", "Rinon Gal", "Daniil Ostashev", "Sergey Tulyakov", "Kfir Aberman", "Daniel Cohen-Or" ], "github": "", "project_page": "", "abstract": "Personalizing text-to-image models to generate images of specific subjects across diverse scenes and styles is a rapidly advancing field. Current approaches often face challenges in maintaining a balance between identity preservation and alignment with the input text prompt. Some methods rely on a single textual token to represent a subject, which limits expressiveness, while others employ richer representations but disrupt the model's prior, diminishing prompt alignment. In this work, we introduce Nested Attention, a novel mechanism that injects a rich and expressive image representation into the model's existing cross-attention layers. Our key idea is to generate query-dependent subject values, derived from nested attention layers that learn to select relevant subject features for each region in the generated image. We integrate these nested layers into an encoder-based personalization method, and show that they enable high identity preservation while adhering to input text prompts. Our approach is general and can be trained on various domains. Additionally, its prior preservation allows us to combine multiple personalized subjects from different domains in a single image.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-03", "arxiv_id": "2501.00910", "title": "Population Aware Diffusion for Time Series Generation", "authors": [ "Yang Li", "Han Meng", "Zhenyu Bi", "Ingolv T. Urnes", "Haipeng Chen" ], "github": "https://github.com/wmd3i/PaD-TS", "project_page": "", "abstract": "Diffusion models have shown promising ability in generating high-quality time series (TS) data. Despite the initial success, existing works mostly focus on the authenticity of data at the individual level, but pay less attention to preserving the population-level properties on the entire dataset. Such population-level properties include value distributions for each dimension and distributions of certain functional dependencies (e.g., cross-correlation, CC) between different dimensions. For instance, when generating house energy consumption TS data, the value distributions of the outside temperature and the kitchen temperature should be preserved, as well as the distribution of CC between them. Preserving such TS population-level properties is critical in maintaining the statistical insights of the datasets, mitigating model bias, and augmenting downstream tasks like TS prediction. Yet, it is often overlooked by existing models. Hence, data generated by existing models often bear distribution shifts from the original data. We propose Population-aware Diffusion for Time Series (PaD-TS), a new TS generation model that better preserves the population-level properties. The key novelties of PaD-TS include 1) a new training method explicitly incorporating TS population-level property preservation, and 2) a new dual-channel encoder model architecture that better captures the TS data structure. Empirical results in major benchmark datasets show that PaD-TS can improve the average CC distribution shift score between real and synthetic data by 5.9x while maintaining a performance comparable to state-of-the-art models on individual-level authenticity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wmd3i/PaD-TS", "github_api_added_by": "auto" }, { "date": "2025-01-03", "arxiv_id": "2501.01426", "title": "Unifying Specialized Visual Encoders for Video Language Models", "authors": [ "Jihoon Chung", "Tyler Zhu", "Max Gonzalez Saez-Diez", "Juan Carlos Niebles", "Honglu Zhou", "Olga Russakovsky" ], "github": "https://github.com/princetonvisualai/merv", "project_page": "", "abstract": "The recent advent of Large Language Models (LLMs) has ushered sophisticated reasoning capabilities into the realm of video through Video Large Language Models (VideoLLMs). However, VideoLLMs currently rely on a single vision encoder for all of their visual processing, which limits the amount and type of visual information that can be conveyed to the LLM. Our method, MERV, Multi-Encoder Representation of Videos, instead leverages multiple frozen visual encoders to create a unified representation of a video, providing the VideoLLM with a comprehensive set of specialized visual knowledge. Spatio-temporally aligning the features from each encoder allows us to tackle a wider range of open-ended and multiple-choice video understanding questions and outperform prior state-of-the-art works. MERV is up to 3.7% better in accuracy than Video-LLaVA across the standard suite video understanding benchmarks, while also having a better Video-ChatGPT score. We also improve upon SeViLA, the previous best on zero-shot Perception Test accuracy, by 2.2%. MERV introduces minimal extra parameters and trains faster than equivalent single-encoder methods while parallelizing the visual processing. Finally, we provide qualitative evidence that MERV successfully captures domain knowledge from each of its encoders. Our results offer promising directions in utilizing multiple vision encoders for comprehensive video understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/princetonvisualai/merv", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-06", "arxiv_id": "2501.01895", "title": "EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation", "authors": [ "Siyuan Huang", "Liliang Chen", "Pengfei Zhou", "Shengcong Chen", "Zhengkai Jiang", "Yue Hu", "Peng Gao", "Hongsheng Li", "Maoqing Yao", "Guanghui Ren" ], "github": "", "project_page": "", "abstract": "We introduce EnerVerse, a comprehensive framework for embodied future space generation specifically designed for robotic manipulation tasks. EnerVerse seamlessly integrates convolutional and bidirectional attention mechanisms for inner-chunk space modeling, ensuring low-level consistency and continuity. Recognizing the inherent redundancy in video data, we propose a sparse memory context combined with a chunkwise unidirectional generative paradigm to enable the generation of infinitely long sequences. To further augment robotic capabilities, we introduce the Free Anchor View (FAV) space, which provides flexible perspectives to enhance observation and analysis. The FAV space mitigates motion modeling ambiguity, removes physical constraints in confined environments, and significantly improves the robot's generalization and adaptability across various tasks and settings. To address the prohibitive costs and labor intensity of acquiring multi-camera observations, we present a data engine pipeline that integrates a generative model with 4D Gaussian Splatting (4DGS). This pipeline leverages the generative model's robust generalization capabilities and the spatial constraints provided by 4DGS, enabling an iterative enhancement of data quality and diversity, thus creating a data flywheel effect that effectively narrows the sim-to-real gap. Finally, our experiments demonstrate that the embodied future space generation prior substantially enhances policy predictive capabilities, resulting in improved overall performance, particularly in long-range robotic manipulation tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-06", "arxiv_id": "2501.01957", "title": "VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction", "authors": [ "Chaoyou Fu", "Haojia Lin", "Xiong Wang", "Yi-Fan Zhang", "Yunhang Shen", "Xiaoyu Liu", "Yangze Li", "Zuwei Long", "Heting Gao", "Ke Li", "Xiawu Zheng", "Rongrong Ji", "Xing Sun", "Caifeng Shan", "Ran He" ], "github": "https://github.com/VITA-MLLM/VITA", "project_page": "", "abstract": "Recent Multimodal Large Language Models (MLLMs) have typically focused on integrating visual and textual modalities, with less emphasis placed on the role of speech in enhancing interaction. However, speech plays a crucial role in multimodal dialogue systems, and implementing high-performance in both vision and speech tasks remains a significant challenge due to the fundamental modality differences. In this paper, we propose a carefully designed multi-stage training methodology that progressively trains LLM to understand both visual and speech information, ultimately enabling fluent vision and speech interaction. Our approach not only preserves strong vision-language capacity, but also enables efficient speech-to-speech dialogue capabilities without separate ASR and TTS modules, significantly accelerating multimodal end-to-end response speed. By comparing our method against state-of-the-art counterparts across benchmarks for image, video, and speech tasks, we demonstrate that our model is equipped with both strong visual and speech capabilities, making near real-time vision and speech interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/VITA-MLLM/VITA", "github_api_added_by": "auto" }, { "date": "2025-01-06", "arxiv_id": "2501.01904", "title": "Virgo: A Preliminary Exploration on Reproducing o1-like MLLM", "authors": [ "Yifan Du", "Zikang Liu", "Yifan Li", "Wayne Xin Zhao", "Yuqi Huo", "Bingning Wang", "Weipeng Chen", "Zheng Liu", "Zhongyuan Wang", "Ji-Rong Wen" ], "github": "https://github.com/RUCAIBox/Virgo", "project_page": "", "abstract": "Recently, slow-thinking reasoning systems, built upon large language models (LLMs), have garnered widespread attention by scaling the thinking time during inference. There is also growing interest in adapting this capability to multimodal large language models (MLLMs). Given that MLLMs handle more complex data semantics across different modalities, it is intuitively more challenging to implement multimodal slow-thinking systems. To address this issue, in this paper, we explore a straightforward approach by fine-tuning a capable MLLM with a small amount of textual long-form thought data, resulting in a multimodal slow-thinking system, Virgo (Visual reasoning with long thought). We find that these long-form reasoning processes, expressed in natural language, can be effectively transferred to MLLMs. Moreover, it seems that such textual reasoning data can be even more effective than visual reasoning data in eliciting the slow-thinking capacities of MLLMs. While this work is preliminary, it demonstrates that slow-thinking capacities are fundamentally associated with the language model component, which can be transferred across modalities or domains. This finding can be leveraged to guide the development of more powerful slow-thinking reasoning systems. We release our resources at https://github.com/RUCAIBox/Virgo.", "github_manual": "", "github_regex": "https://github.com/RUCAIBox/Virgo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-06", "arxiv_id": "2501.00874", "title": "LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models", "authors": [ "Hieu Man", "Nghia Trung Ngo", "Viet Dac Lai", "Ryan A. Rossi", "Franck Dernoncourt", "Thien Huu Nguyen" ], "github": "https://github.com/hieum98/lusifer", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) based embedding models have established new state-of-the-art benchmarks for text embedding tasks, particularly in dense vector-based retrieval. However, these models predominantly focus on English, leaving multilingual embedding capabilities largely unexplored. To address this limitation, we present LUSIFER, a novel zero-shot approach that adapts LLM-based embedding models for multilingual tasks without requiring multilingual supervision. LUSIFER's architecture combines a multilingual encoder, serving as a language-universal learner, with an LLM-based embedding model optimized for embedding-specific tasks. These components are seamlessly integrated through a minimal set of trainable parameters that act as a connector, effectively transferring the multilingual encoder's language understanding capabilities to the specialized embedding model. Additionally, to comprehensively evaluate multilingual embedding performance, we introduce a new benchmark encompassing 5 primary embedding tasks, 123 diverse datasets, and coverage across 14 languages. Extensive experimental results demonstrate that LUSIFER significantly enhances the multilingual performance across various embedding tasks, particularly for medium and low-resource languages, without requiring explicit multilingual training data.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hieum98/lusifer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-06", "arxiv_id": "2501.01073", "title": "Graph Generative Pre-trained Transformer", "authors": [ "Xiaohui Chen", "Yinkai Wang", "Jiaxing He", "Yuanqi Du", "Soha Hassoun", "Xiaolin Xu", "Li-Ping Liu" ], "github": "https://github.com/tufts-ml/G2PT", "project_page": "", "abstract": "Graph generation is a critical task in numerous domains, including molecular design and social network analysis, due to its ability to model complex relationships and structured data. While most modern graph generative models utilize adjacency matrix representations, this work revisits an alternative approach that represents graphs as sequences of node set and edge set. We advocate for this approach due to its efficient encoding of graphs and propose a novel representation. Based on this representation, we introduce the Graph Generative Pre-trained Transformer (G2PT), an auto-regressive model that learns graph structures via next-token prediction. To further exploit G2PT's capabilities as a general-purpose foundation model, we explore fine-tuning strategies for two downstream applications: goal-oriented generation and graph property prediction. We conduct extensive experiments across multiple datasets. Results indicate that G2PT achieves superior generative performance on both generic graph and molecule datasets. Furthermore, G2PT exhibits strong adaptability and versatility in downstream tasks from molecular design to property prediction.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tufts-ml/G2PT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-06", "arxiv_id": "2412.21059", "title": "VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation", "authors": [ "Jiazheng Xu", "Yu Huang", "Jiale Cheng", "Yuanming Yang", "Jiajun Xu", "Yuan Wang", "Wenbo Duan", "Shen Yang", "Qunlin Jin", "Shurun Li", "Jiayan Teng", "Zhuoyi Yang", "Wendi Zheng", "Xiao Liu", "Ming Ding", "Xiaohan Zhang", "Xiaotao Gu", "Shiyu Huang", "Minlie Huang", "Jie Tang", "Yuxiao Dong" ], "github": "https://github.com/THUDM/VisionReward", "project_page": "", "abstract": "We present a general strategy to aligning visual generation models -- both image and video generation -- with human preference. To start with, we build VisionReward -- a fine-grained and multi-dimensional reward model. We decompose human preferences in images and videos into multiple dimensions, each represented by a series of judgment questions, linearly weighted and summed to an interpretable and accurate score. To address the challenges of video quality assessment, we systematically analyze various dynamic features of videos, which helps VisionReward surpass VideoScore by 17.2% and achieve top performance for video preference prediction. Based on VisionReward, we develop a multi-objective preference learning algorithm that effectively addresses the issue of confounding factors within preference data. Our approach significantly outperforms existing image and video scoring methods on both machine metrics and human evaluation. All code and datasets are provided at https://github.com/THUDM/VisionReward.", "github_manual": "", "github_regex": "https://github.com/THUDM/VisionReward", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-06", "arxiv_id": "2501.01540", "title": "BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery", "authors": [ "Kanishk Gandhi", "Michael Y. Li", "Lyle Goodyear", "Louise Li", "Aditi Bhaskar", "Mohammed Zaman", "Noah D. Goodman" ], "github": "https://github.com/kanishkg/boxing-gym", "project_page": "", "abstract": "Understanding the world and explaining it with scientific theories is a central aspiration of artificial intelligence research. Proposing theories, designing experiments to test them, and then revising them based on data are fundamental to scientific discovery. Despite the significant promise of LLM-based scientific agents, no benchmarks systematically test LLM's ability to propose scientific models, collect experimental data, and revise them in light of new data. We introduce BoxingGym, a benchmark with 10 environments for systematically evaluating both experimental design (e.g. collecting data to test a scientific theory) and model discovery (e.g. proposing and revising scientific theories). To enable tractable and quantitative evaluation, we implement each environment as a generative probabilistic model with which a scientific agent can run interactive experiments. These probabilistic models are drawn from various real-world scientific domains ranging from psychology to ecology. To quantitatively evaluate a scientific agent's ability to collect informative experimental data, we compute the expected information gain (EIG), an information-theoretic quantity which measures how much an experiment reduces uncertainty about the parameters of a generative model. A good scientific theory is a concise and predictive explanation. Therefore, to quantitatively evaluate model discovery, we ask a scientific agent to explain their model and then assess whether this explanation enables another scientific agent to make reliable predictions about this environment. In addition to this explanation-based evaluation, we compute standard model evaluation metrics such as prediction errors. We find that current LLMs, such as GPT-4o, struggle with both experimental design and model discovery. We find that augmenting the LLM-based agent with an explicit statistical model does not reliably improve these results.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kanishkg/boxing-gym", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-06", "arxiv_id": "2501.01821", "title": "SDPO: Segment-Level Direct Preference Optimization for Social Agents", "authors": [ "Aobo Kong", "Wentao Ma", "Shiwan Zhao", "Yongbin Li", "Yuchuan Wu", "Ke Wang", "Xiaoqian Liu", "Qicheng Li", "Yong Qin", "Fei Huang" ], "github": "https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/SDPO", "project_page": "", "abstract": "Social agents powered by large language models (LLMs) can simulate human social behaviors but fall short in handling complex goal-oriented social dialogues. Direct Preference Optimization (DPO) has proven effective in aligning LLM behavior with human preferences across a variety of agent tasks. Existing DPO-based approaches for multi-turn interactions are divided into turn-level and session-level methods. The turn-level method is overly fine-grained, focusing exclusively on individual turns, while session-level methods are too coarse-grained, often introducing training noise. To address these limitations, we propose Segment-Level Direct Preference Optimization (SDPO), which focuses on specific key segments within interactions to optimize multi-turn agent behavior while minimizing training noise. Evaluations on the SOTOPIA benchmark demonstrate that SDPO-tuned agents consistently outperform both existing DPO-based methods and proprietary LLMs like GPT-4o, underscoring SDPO's potential to advance the social intelligence of LLM-based agents. We release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/SDPO.", "github_manual": "", "github_regex": "https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/SDPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.03006", "title": "TransPixar: Advancing Text-to-Video Generation with Transparency", "authors": [ "Luozhou Wang", "Yijun Li", "Zhifei Chen", "Jui-Hsien Wang", "Zhifei Zhang", "He Zhang", "Zhe Lin", "Yingcong Chen" ], "github": "", "project_page": "", "abstract": "Text-to-video generative models have made significant strides, enabling diverse applications in entertainment, advertising, and education. However, generating RGBA video, which includes alpha channels for transparency, remains a challenge due to limited datasets and the difficulty of adapting existing models. Alpha channels are crucial for visual effects (VFX), allowing transparent elements like smoke and reflections to blend seamlessly into scenes. We introduce TransPixar, a method to extend pretrained video models for RGBA generation while retaining the original RGB capabilities. TransPixar leverages a diffusion transformer (DiT) architecture, incorporating alpha-specific tokens and using LoRA-based fine-tuning to jointly generate RGB and alpha channels with high consistency. By optimizing attention mechanisms, TransPixar preserves the strengths of the original RGB model and achieves strong alignment between RGB and alpha channels despite limited training data. Our approach effectively generates diverse and consistent RGBA videos, advancing the possibilities for VFX and interactive content creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.02976", "title": "STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution", "authors": [ "Rui Xie", "Yinhong Liu", "Penghao Zhou", "Chen Zhao", "Jun Zhou", "Kai Zhang", "Zhenyu Zhang", "Jian Yang", "Zhenheng Yang", "Ying Tai" ], "github": "", "project_page": "", "abstract": "Image diffusion models have been adapted for real-world video super-resolution to tackle over-smoothing issues in GAN-based methods. However, these models struggle to maintain temporal consistency, as they are trained on static images, limiting their ability to capture temporal dynamics effectively. Integrating text-to-video (T2V) models into video super-resolution for improved temporal modeling is straightforward. However, two key challenges remain: artifacts introduced by complex degradations in real-world scenarios, and compromised fidelity due to the strong generative capacity of powerful T2V models (e.g., CogVideoX-5B). To enhance the spatio-temporal quality of restored videos, we introduce~\\name (Spatial-Temporal Augmentation with T2V models for Real-world video super-resolution), a novel approach that leverages T2V models for real-world video super-resolution, achieving realistic spatial details and robust temporal consistency. Specifically, we introduce a Local Information Enhancement Module (LIEM) before the global attention block to enrich local details and mitigate degradation artifacts. Moreover, we propose a Dynamic Frequency (DF) Loss to reinforce fidelity, guiding the model to focus on different frequency components across diffusion steps. Extensive experiments demonstrate~\\name~outperforms state-of-the-art methods on both synthetic and real-world datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.02045", "title": "METAGENE-1: Metagenomic Foundation Model for Pandemic Monitoring", "authors": [ "Ollie Liu", "Sami Jaghouar", "Johannes Hagemann", "Shangshang Wang", "Jason Wiemels", "Jeff Kaufman", "Willie Neiswanger" ], "github": "", "project_page": "", "abstract": "We pretrain METAGENE-1, a 7-billion-parameter autoregressive transformer model, which we refer to as a metagenomic foundation model, on a novel corpus of diverse metagenomic DNA and RNA sequences comprising over 1.5 trillion base pairs. This dataset is sourced from a large collection of human wastewater samples, processed and sequenced using deep metagenomic (next-generation) sequencing methods. Unlike genomic models that focus on individual genomes or curated sets of specific species, the aim of METAGENE-1 is to capture the full distribution of genomic information present within this wastewater, to aid in tasks relevant to pandemic monitoring and pathogen detection. We carry out byte-pair encoding (BPE) tokenization on our dataset, tailored for metagenomic sequences, and then pretrain our model. In this paper, we first detail the pretraining dataset, tokenization strategy, and model architecture, highlighting the considerations and design choices that enable the effective modeling of metagenomic data. We then show results of pretraining this model on our metagenomic dataset, providing details about our losses, system metrics, and training stability over the course of pretraining. Finally, we demonstrate the performance of METAGENE-1, which achieves state-of-the-art results on a set of genomic benchmarks and new evaluations focused on human-pathogen detection and genomic sequence embedding, showcasing its potential for public health applications in pandemic monitoring, biosurveillance, and early detection of emerging health threats.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.02157", "title": "Personalized Graph-Based Retrieval for Large Language Models", "authors": [ "Steven Au", "Cameron J. Dimacali", "Ojasmitha Pedirappagari", "Namyong Park", "Franck Dernoncourt", "Yu Wang", "Nikos Kanakaris", "Hanieh Deilamsalehy", "Ryan A. Rossi", "Nesreen K. Ahmed" ], "github": "https://github.com/pgraphrag-benchmark/pgr-llm", "project_page": "", "abstract": "As large language models (LLMs) evolve, their ability to deliver personalized and context-aware responses offers transformative potential for improving user experiences. Existing personalization approaches, however, often rely solely on user history to augment the prompt, limiting their effectiveness in generating tailored outputs, especially in cold-start scenarios with sparse data. To address these limitations, we propose Personalized Graph-based Retrieval-Augmented Generation (PGraphRAG), a framework that leverages user-centric knowledge graphs to enrich personalization. By directly integrating structured user knowledge into the retrieval process and augmenting prompts with user-relevant context, PGraphRAG enhances contextual understanding and output quality. We also introduce the Personalized Graph-based Benchmark for Text Generation, designed to evaluate personalized text generation tasks in real-world settings where user history is sparse or unavailable. Experimental results show that PGraphRAG significantly outperforms state-of-the-art personalization methods across diverse tasks, demonstrating the unique advantages of graph-based retrieval for personalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/pgraphrag-benchmark/pgr-llm", "github_api_added_by": "auto" }, { "date": "2025-01-07", "arxiv_id": "2501.01790", "title": "Ingredients: Blending Custom Photos with Video Diffusion Transformers", "authors": [ "Zhengcong Fei", "Debang Li", "Di Qiu", "Changqian Yu", "Mingyuan Fan" ], "github": "https://github.com/feizc/Ingredients", "project_page": "", "abstract": "This paper presents a powerful framework to customize video creations by incorporating multiple specific identity (ID) photos, with video diffusion Transformers, referred to as Ingredients. Generally, our method consists of three primary modules: (i) a facial extractor that captures versatile and precise facial features for each human ID from both global and local perspectives; (ii) a multi-scale projector that maps face embeddings into the contextual space of image query in video diffusion transformers; (iii) an ID router that dynamically combines and allocates multiple ID embedding to the corresponding space-time regions. Leveraging a meticulously curated text-video dataset and a multi-stage training protocol, Ingredients demonstrates superior performance in turning custom photos into dynamic and personalized video content. Qualitative evaluations highlight the advantages of proposed method, positioning it as a significant advancement toward more effective generative video control tools in Transformer-based architecture, compared to existing methods. The data, code, and model weights are publicly available at: https://github.com/feizc/Ingredients.", "github_manual": "", "github_regex": "https://github.com/feizc/Ingredients", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.02506", "title": "ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use", "authors": [ "Junjie Ye", "Zhengyin Du", "Xuesong Yao", "Weijian Lin", "Yufei Xu", "Zehui Chen", "Zaiyuan Wang", "Sining Zhu", "Zhiheng Xi", "Siyu Yuan", "Tao Gui", "Qi Zhang", "Xuanjing Huang", "Jiechao Chen" ], "github": "", "project_page": "", "abstract": "Effective evaluation of multi-hop tool use is critical for analyzing the understanding, reasoning, and function-calling capabilities of large language models (LLMs). However, progress has been hindered by a lack of reliable evaluation datasets. To address this, we present ToolHop, a dataset comprising 995 user queries and 3,912 associated tools, specifically designed for rigorous evaluation of multi-hop tool use. ToolHop ensures diverse queries, meaningful interdependencies, locally executable tools, detailed feedback, and verifiable answers through a novel query-driven data construction approach that includes tool creation, document refinement, and code generation. We evaluate 14 LLMs across five model families (i.e., LLaMA3.1, Qwen2.5, Gemini1.5, Claude3.5, and GPT), uncovering significant challenges in handling multi-hop tool-use scenarios. The leading model, GPT-4o, achieves an accuracy of 49.04%, underscoring substantial room for improvement. Further analysis reveals variations in tool-use strategies for various families, offering actionable insights to guide the development of more effective approaches. Code and data can be found in https://huggingface.co/bytedance-research/ToolHop.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.02497", "title": "Test-time Computing: from System-1 Thinking to System-2 Thinking", "authors": [ "Yixin Ji", "Juntao Li", "Hai Ye", "Kaixin Wu", "Jia Xu", "Linjian Mo", "Min Zhang" ], "github": "", "project_page": "", "abstract": "The remarkable performance of the o1 model in complex reasoning demonstrates that test-time computing scaling can further unlock the model's potential, enabling powerful System-2 thinking. However, there is still a lack of comprehensive surveys for test-time computing scaling. We trace the concept of test-time computing back to System-1 models. In System-1 models, test-time computing addresses distribution shifts and improves robustness and generalization through parameter updating, input modification, representation editing, and output calibration. In System-2 models, it enhances the model's reasoning ability to solve complex problems through repeated sampling, self-correction, and tree search. We organize this survey according to the trend of System-1 to System-2 thinking, highlighting the key role of test-time computing in the transition from System-1 models to weak System-2 models, and then to strong System-2 models. We also point out a few possible future directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.02690", "title": "GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Tracking", "authors": [ "Weikang Bian", "Zhaoyang Huang", "Xiaoyu Shi", "Yijin Li", "Fu-Yun Wang", "Hongsheng Li" ], "github": "https://github.com/wkbian/GS-DiT", "project_page": "", "abstract": "4D video control is essential in video generation as it enables the use of sophisticated lens techniques, such as multi-camera shooting and dolly zoom, which are currently unsupported by existing methods. Training a video Diffusion Transformer (DiT) directly to control 4D content requires expensive multi-view videos. Inspired by Monocular Dynamic novel View Synthesis (MDVS) that optimizes a 4D representation and renders videos according to different 4D elements, such as camera pose and object motion editing, we bring pseudo 4D Gaussian fields to video generation. Specifically, we propose a novel framework that constructs a pseudo 4D Gaussian field with dense 3D point tracking and renders the Gaussian field for all video frames. Then we finetune a pretrained DiT to generate videos following the guidance of the rendered video, dubbed as GS-DiT. To boost the training of the GS-DiT, we also propose an efficient Dense 3D Point Tracking (D3D-PT) method for the pseudo 4D Gaussian field construction. Our D3D-PT outperforms SpatialTracker, the state-of-the-art sparse 3D point tracking method, in accuracy and accelerates the inference speed by two orders of magnitude. During the inference stage, GS-DiT can generate videos with the same dynamic content while adhering to different camera parameters, addressing a significant limitation of current video generation models. GS-DiT demonstrates strong generalization capabilities and extends the 4D controllability of Gaussian splatting to video generation beyond just camera poses. It supports advanced cinematic effects through the manipulation of the Gaussian field and camera intrinsics, making it a powerful tool for creative video production. Demos are available at https://wkbian.github.io/Projects/GS-DiT/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wkbian/GS-DiT", "github_api_added_by": "auto" }, { "date": "2025-01-07", "arxiv_id": "2501.02832", "title": "Samba-asr state-of-the-art speech recognition leveraging structured state-space models", "authors": [ "Syed Abdul Gaffar Shakhadri", "Kruthika KR", "Kartik Basavaraj Angadi" ], "github": "", "project_page": "", "abstract": "We propose Samba ASR, the first state-of-the-art Automatic Speech Recognition (ASR) model leveraging the novel Mamba architecture as both encoder and decoder, built on the foundation of state-space models (SSMs). Unlike transformer-based ASR models, which rely on self-attention mechanisms to capture dependencies, Samba ASR effectively models both local and global temporal dependencies using efficient state-space dynamics, achieving remarkable performance gains. By addressing the limitations of transformers, such as quadratic scaling with input length and difficulty in handling long-range dependencies, Samba ASR achieves superior accuracy and efficiency. Experimental results demonstrate that Samba ASR surpasses existing open-source transformer-based ASR models across various standard benchmarks, establishing it as the new state of the art in ASR. Extensive evaluations on benchmark datasets show significant improvements in Word Error Rate (WER), with competitive performance even in low-resource scenarios. Furthermore, the computational efficiency and parameter optimization of the Mamba architecture make Samba ASR a scalable and robust solution for diverse ASR tasks. Our contributions include: A new Samba ASR architecture demonstrating the superiority of SSMs over transformer-based models for speech sequence processing. A comprehensive evaluation on public benchmarks showcasing state-of-the-art performance. An analysis of computational efficiency, robustness to noise, and sequence generalization. This work highlights the viability of Mamba SSMs as a transformer-free alternative for efficient and accurate ASR. By leveraging state-space modeling advancements, Samba ASR sets a new benchmark for ASR performance and future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.01830", "title": "Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large Language Models", "authors": [ "Yanjiang Liu", "Shuhen Zhou", "Yaojie Lu", "Huijia Zhu", "Weiqiang Wang", "Hongyu Lin", "Ben He", "Xianpei Han", "Le Sun" ], "github": "https://github.com/icip-cas/Auto-RT", "project_page": "", "abstract": "Automated red-teaming has become a crucial approach for uncovering vulnerabilities in large language models (LLMs). However, most existing methods focus on isolated safety flaws, limiting their ability to adapt to dynamic defenses and uncover complex vulnerabilities efficiently. To address this challenge, we propose Auto-RT, a reinforcement learning framework that automatically explores and optimizes complex attack strategies to effectively uncover security vulnerabilities through malicious queries. Specifically, we introduce two key mechanisms to reduce exploration complexity and improve strategy optimization: 1) Early-terminated Exploration, which accelerate exploration by focusing on high-potential attack strategies; and 2) Progressive Reward Tracking algorithm with intermediate downgrade models, which dynamically refine the search trajectory toward successful vulnerability exploitation. Extensive experiments across diverse LLMs demonstrate that, by significantly improving exploration efficiency and automatically optimizing attack strategies, Auto-RT detects a boarder range of vulnerabilities, achieving a faster detection speed and 16.63\\% higher success rates compared to existing methods.", "github_manual": "https://github.com/icip-cas/Auto-RT", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.03218", "title": "Dispider: Enabling Video LLMs with Active Real-Time Interaction via Disentangled Perception, Decision, and Reaction", "authors": [ "Rui Qian", "Shuangrui Ding", "Xiaoyi Dong", "Pan Zhang", "Yuhang Zang", "Yuhang Cao", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/Mark12Ding/Dispider", "project_page": "", "abstract": "Active Real-time interaction with video LLMs introduces a new paradigm for human-computer interaction, where the model not only understands user intent but also responds while continuously processing streaming video on the fly. Unlike offline video LLMs, which analyze the entire video before answering questions, active real-time interaction requires three capabilities: 1) Perception: real-time video monitoring and interaction capturing. 2) Decision: raising proactive interaction in proper situations, 3) Reaction: continuous interaction with users. However, inherent conflicts exist among the desired capabilities. The Decision and Reaction require a contrary Perception scale and grain, and the autoregressive decoding blocks the real-time Perception and Decision during the Reaction. To unify the conflicted capabilities within a harmonious system, we present Dispider, a system that disentangles Perception, Decision, and Reaction. Dispider features a lightweight proactive streaming video processing module that tracks the video stream and identifies optimal moments for interaction. Once the interaction is triggered, an asynchronous interaction module provides detailed responses, while the processing module continues to monitor the video in the meantime. Our disentangled and asynchronous design ensures timely, contextually accurate, and computationally efficient responses, making Dispider ideal for active real-time interaction for long-duration video streams. Experiments show that Dispider not only maintains strong performance in conventional video QA tasks, but also significantly surpasses previous online models in streaming scenario responses, thereby validating the effectiveness of our architecture. The code and model are released at https://github.com/Mark12Ding/Dispider.", "github_manual": "", "github_regex": "https://github.com/Mark12Ding/Dispider", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.03059", "title": "Through-The-Mask: Mask-based Motion Trajectories for Image-to-Video Generation", "authors": [ "Guy Yariv", "Yuval Kirstain", "Amit Zohar", "Shelly Sheynin", "Yaniv Taigman", "Yossi Adi", "Sagie Benaim", "Adam Polyak" ], "github": "", "project_page": "", "abstract": "We consider the task of Image-to-Video (I2V) generation, which involves transforming static images into realistic video sequences based on a textual description. While recent advancements produce photorealistic outputs, they frequently struggle to create videos with accurate and consistent object motion, especially in multi-object scenarios. To address these limitations, we propose a two-stage compositional framework that decomposes I2V generation into: (i) An explicit intermediate representation generation stage, followed by (ii) A video generation stage that is conditioned on this representation. Our key innovation is the introduction of a mask-based motion trajectory as an intermediate representation, that captures both semantic object information and motion, enabling an expressive but compact representation of motion and semantics. To incorporate the learned representation in the second stage, we utilize object-level attention objectives. Specifically, we consider a spatial, per-object, masked-cross attention objective, integrating object-specific prompts into corresponding latent space regions and a masked spatio-temporal self-attention objective, ensuring frame-to-frame consistency for each object. We evaluate our method on challenging benchmarks with multi-object and high-motion scenarios and empirically demonstrate that the proposed method achieves state-of-the-art results in temporal coherence, motion realism, and text-prompt faithfulness. Additionally, we introduce \\benchmark, a new challenging benchmark for single-object and multi-object I2V generation, and demonstrate our method's superiority on this benchmark. Project page is available at https://guyyariv.github.io/TTM/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.03226", "title": "BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning", "authors": [ "Beichen Zhang", "Yuhong Liu", "Xiaoyi Dong", "Yuhang Zang", "Pan Zhang", "Haodong Duan", "Yuhang Cao", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/beichenzbc/booststep", "project_page": "", "abstract": "Cutting-edge large language models (LLMs) demonstrate promising performance in solving complex math problems with a divide-and-conquer pipeline and the assistance of in-context learning (ICL) examples. However, their potential for improvement is limited by two critical problems within their ICL examples: granularity-mismatch and the ensuing negative-effect noise problem. Specifically, the LLMs are capable of the dividing process yet mostly failed by inaccurate reasoning within a few conquer steps, while the ICL examples retrieved in question-grained sometimes lack relevant steps for a specific challenging reasoning step. Further, this disconnect may hinder the correct reasoning due to its irrelevance. To this end, we focus on improving the reasoning quality within each step and present BoostStep. BoostStep aligns the granularity between the retrieving and reasoning on step grained, and provides highly related ICL examples for each reasoning step with a novel `first-try' strategy. BoostStep provides more relevant examples than the coarse question-grained strategy, enhancing the model reasoning quality within each step steadily. BoostStep is a general and robust reasoning-enhancing method that not only improves standalone reasoning performance but also integrates seamlessly with Monte Carlo Tree Search methods (MCTS) to refine both candidate generation and decision-making. Quantitatively, it improves GPT-4o and Qwen2.5-Math-72B by 3.6\\% and 2.0\\% respectively on various mathematical benchmarks, and 7.5\\% gain combined with MCTS.", "github_manual": "https://github.com/beichenzbc/booststep", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.02576", "title": "DepthMaster: Taming Diffusion Models for Monocular Depth Estimation", "authors": [ "Ziyang Song", "Zerong Wang", "Bo Li", "Hao Zhang", "Ruijie Zhu", "Li Liu", "Peng-Tao Jiang", "Tianzhu Zhang" ], "github": "", "project_page": "", "abstract": "Monocular depth estimation within the diffusion-denoising paradigm demonstrates impressive generalization ability but suffers from low inference speed. Recent methods adopt a single-step deterministic paradigm to improve inference efficiency while maintaining comparable performance. However, they overlook the gap between generative and discriminative features, leading to suboptimal results. In this work, we propose DepthMaster, a single-step diffusion model designed to adapt generative features for the discriminative depth estimation task. First, to mitigate overfitting to texture details introduced by generative features, we propose a Feature Alignment module, which incorporates high-quality semantic features to enhance the denoising network's representation capability. Second, to address the lack of fine-grained details in the single-step deterministic framework, we propose a Fourier Enhancement module to adaptively balance low-frequency structure and high-frequency details. We adopt a two-stage training strategy to fully leverage the potential of the two modules. In the first stage, we focus on learning the global scene structure with the Feature Alignment module, while in the second stage, we exploit the Fourier Enhancement module to improve the visual quality. Through these efforts, our model achieves state-of-the-art performance in terms of generalization and detail preservation, outperforming other diffusion-based methods across various datasets. Our project page can be found at https://indu1ge.github.io/DepthMaster_page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.02423", "title": "Scaling Laws for Floating Point Quantization Training", "authors": [ "Xingwu Sun", "Shuaipeng Li", "Ruobing Xie", "Weidong Han", "Kan Wu", "Zhen Yang", "Yixing Li", "An Wang", "Shuai Li", "Jinbao Xue", "Yu Cheng", "Yangyu Tao", "Zhanhui Kang", "Chengzhong Xu", "Di Wang", "Jie Jiang" ], "github": "", "project_page": "", "abstract": "Low-precision training is considered an effective strategy for reducing both training and downstream inference costs. Previous scaling laws for precision mainly focus on integer quantization, which pay less attention to the constituents in floating-point quantization and thus cannot well fit the LLM losses in this scenario. In contrast, while floating-point quantization training is more commonly implemented in production, the research on it has been relatively superficial. In this paper, we thoroughly explore the effects of floating-point quantization targets, exponent bits, mantissa bits, and the calculation granularity of the scaling factor in floating-point quantization training performance of LLM models. While presenting an accurate floating-point quantization unified scaling law, we also provide valuable suggestions for the community: (1) Exponent bits contribute slightly more to the model performance than mantissa bits. We provide the optimal exponent-mantissa bit ratio for different bit numbers, which is available for future reference by hardware manufacturers; (2) We discover the formation of the critical data size in low-precision LLM training. Too much training data exceeding the critical data size will inversely bring in degradation of LLM performance; (3) The optimal floating-point quantization precision is directly proportional to the computational power, but within a wide computational power range, we estimate that the best cost-performance precision lies between 4-8 bits.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.00912", "title": "AutoPresent: Designing Structured Visuals from Scratch", "authors": [ "Jiaxin Ge", "Zora Zhiruo Wang", "Xuhui Zhou", "Yi-Hao Peng", "Sanjay Subramanian", "Qinyue Tan", "Maarten Sap", "Alane Suhr", "Daniel Fried", "Graham Neubig", "Trevor Darrell" ], "github": "https://github.com/para-lost/AutoPresent", "project_page": "", "abstract": "Designing structured visuals such as presentation slides is essential for communicative needs, necessitating both content creation and visual planning skills. In this work, we tackle the challenge of automated slide generation, where models produce slide presentations from natural language (NL) instructions. We first introduce the SlidesBench benchmark, the first benchmark for slide generation with 7k training and 585 testing examples derived from 310 slide decks across 10 domains. SlidesBench supports evaluations that are (i)reference-based to measure similarity to a target slide, and (ii)reference-free to measure the design quality of generated slides alone. We benchmark end-to-end image generation and program generation methods with a variety of models, and find that programmatic methods produce higher-quality slides in user-interactable formats. Built on the success of program generation, we create AutoPresent, an 8B Llama-based model trained on 7k pairs of instructions paired with code for slide generation, and achieve results comparable to the closed-source model GPT-4o. We further explore iterative design refinement where the model is tasked to self-refine its own output, and we found that this process improves the slide's quality. We hope that our work will provide a basis for future work on generating structured visuals.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/para-lost/AutoPresent", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.03225", "title": "Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation", "authors": [ "Yuhui Zhang", "Yuchang Su", "Yiming Liu", "Xiaohan Wang", "James Burgess", "Elaine Sui", "Chenyu Wang", "Josiah Aklilu", "Alejandro Lozano", "Anjiang Wei", "Ludwig Schmidt", "Serena Yeung-Levy" ], "github": "https://github.com/yuhui-zh15/autoconverter", "project_page": "", "abstract": "The rapid development of vision language models (VLMs) demands rigorous and reliable evaluation. However, current visual question answering (VQA) benchmarks often depend on open-ended questions, making accurate evaluation difficult due to the variability in natural language responses. To address this, we introduce AutoConverter, an agentic framework that automatically converts these open-ended questions into multiple-choice format, enabling objective evaluation while reducing the costly question creation process. Our experiments demonstrate that AutoConverter can generate correct and challenging multiple-choice questions, with VLMs demonstrating consistently similar or lower accuracy on these questions compared to human-created ones. Using AutoConverter, we construct VMCBench, a benchmark created by transforming 20 existing VQA datasets into a unified multiple-choice format, totaling 9,018 questions. We comprehensively evaluate 33 state-of-the-art VLMs on VMCBench, setting a new standard for scalable, consistent, and reproducible VLM evaluation.", "github_manual": "https://github.com/yuhui-zh15/autoconverter", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-07", "arxiv_id": "2501.03124", "title": "PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models", "authors": [ "Mingyang Song", "Zhaochen Su", "Xiaoye Qu", "Jiawei Zhou", "Yu Cheng" ], "github": "https://github.com/ssmisya/PRMBench", "project_page": "", "abstract": "Process-level Reward Models (PRMs) are crucial for complex reasoning and decision-making tasks, where each intermediate step plays an important role in the reasoning process. Since language models are prone to various types of errors during the reasoning process, PRMs are required to possess nuanced capabilities for detecting various implicit error types in real-world scenarios. However, current benchmarks primarily focus on step correctness, failing to evaluate PRMs' performance systematically. To address this gap, we introduce PRMBench, a process-level benchmark specifically designed to assess the fine-grained error detection capabilities of PRMs. PRMBench comprises 6,216 carefully designed problems and 83,456 step-level labels, evaluating models across multiple dimensions, including simplicity, soundness, and sensitivity. In our experiments on 15 models, spanning both open-source PRMs and closed-source large language models prompted as critic models, we uncover significant weaknesses in current PRMs. These findings underscore the challenges inherent in process-level evaluation and highlight key directions for future research. We hope PRMBench can be a robust bench for advancing research on PRM evaluation and development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ssmisya/PRMBench", "github_api_added_by": "auto" }, { "date": "2025-01-07", "arxiv_id": "2501.03220", "title": "ProTracker: Probabilistic Integration for Robust and Accurate Point Tracking", "authors": [ "Tingyang Zhang", "Chen Wang", "Zhiyang Dou", "Qingzhe Gao", "Jiahui Lei", "Baoquan Chen", "Lingjie Liu" ], "github": "https://github.com/Michaelszj/pro-tracker", "project_page": "", "abstract": "In this paper, we propose ProTracker, a novel framework for robust and accurate long-term dense tracking of arbitrary points in videos. The key idea of our method is incorporating probabilistic integration to refine multiple predictions from both optical flow and semantic features for robust short-term and long-term tracking. Specifically, we integrate optical flow estimations in a probabilistic manner, producing smooth and accurate trajectories by maximizing the likelihood of each prediction. To effectively re-localize challenging points that disappear and reappear due to occlusion, we further incorporate long-term feature correspondence into our flow predictions for continuous trajectory generation. Extensive experiments show that ProTracker achieves the state-of-the-art performance among unsupervised and self-supervised approaches, and even outperforms supervised methods on several benchmarks. Our code and model will be publicly available upon publication.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Michaelszj/pro-tracker", "github_api_added_by": "auto" }, { "date": "2025-01-08", "arxiv_id": "2501.03895", "title": "LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token", "authors": [ "Shaolei Zhang", "Qingkai Fang", "Zhe Yang", "Yang Feng" ], "github": "https://github.com/ictnlp/llava-mini", "project_page": "", "abstract": "The advent of real-time large multimodal models (LMMs) like GPT-4o has sparked considerable interest in efficient LMMs. LMM frameworks typically encode visual inputs into vision tokens (continuous representations) and integrate them and textual instructions into the context of large language models (LLMs), where large-scale parameters and numerous context tokens (predominantly vision tokens) result in substantial computational overhead. Previous efforts towards efficient LMMs always focus on replacing the LLM backbone with smaller models, while neglecting the crucial issue of token quantity. In this paper, we introduce LLaVA-Mini, an efficient LMM with minimal vision tokens. To achieve a high compression ratio of vision tokens while preserving visual information, we first analyze how LMMs understand vision tokens and find that most vision tokens only play a crucial role in the early layers of LLM backbone, where they mainly fuse visual information into text tokens. Building on this finding, LLaVA-Mini introduces modality pre-fusion to fuse visual information into text tokens in advance, thereby facilitating the extreme compression of vision tokens fed to LLM backbone into one token. LLaVA-Mini is a unified large multimodal model that can support the understanding of images, high-resolution images, and videos in an efficient manner. Experiments across 11 image-based and 7 video-based benchmarks demonstrate that LLaVA-Mini outperforms LLaVA-v1.5 with just 1 vision token instead of 576. Efficiency analyses reveal that LLaVA-Mini can reduce FLOPs by 77%, deliver low-latency responses within 40 milliseconds, and process over 10,000 frames of video on the GPU hardware with 24GB of memory.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ictnlp/llava-mini", "github_api_added_by": "auto" }, { "date": "2025-01-08", "arxiv_id": "2501.03931", "title": "Magic Mirror: ID-Preserved Video Generation in Video Diffusion Transformers", "authors": [ "Yuechen Zhang", "Yaoyang Liu", "Bin Xia", "Bohao Peng", "Zexin Yan", "Eric Lo", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/MagicMirror", "project_page": "", "abstract": "We present Magic Mirror, a framework for generating identity-preserved videos with cinematic-level quality and dynamic motion. While recent advances in video diffusion models have shown impressive capabilities in text-to-video generation, maintaining consistent identity while producing natural motion remains challenging. Previous methods either require person-specific fine-tuning or struggle to balance identity preservation with motion diversity. Built upon Video Diffusion Transformers, our method introduces three key components: (1) a dual-branch facial feature extractor that captures both identity and structural features, (2) a lightweight cross-modal adapter with Conditioned Adaptive Normalization for efficient identity integration, and (3) a two-stage training strategy combining synthetic identity pairs with video data. Extensive experiments demonstrate that Magic Mirror effectively balances identity consistency with natural motion, outperforming existing methods across multiple metrics while requiring minimal parameters added. The code and model will be made publicly available at: https://github.com/dvlab-research/MagicMirror/", "github_manual": "", "github_regex": "https://github.com/dvlab-research/MagicMirror", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-08", "arxiv_id": "2501.02260", "title": "MagicFace: High-Fidelity Facial Expression Editing with Action-Unit Control", "authors": [ "Mengting Wei", "Tuomas Varanka", "Xingxun Jiang", "Huai-Qian Khor", "Guoying Zhao" ], "github": "https://github.com/weimengting/MagicFace", "project_page": "", "abstract": "We address the problem of facial expression editing by controling the relative variation of facial action-unit (AU) from the same person. This enables us to edit this specific person's expression in a fine-grained, continuous and interpretable manner, while preserving their identity, pose, background and detailed facial attributes. Key to our model, which we dub MagicFace, is a diffusion model conditioned on AU variations and an ID encoder to preserve facial details of high consistency. Specifically, to preserve the facial details with the input identity, we leverage the power of pretrained Stable-Diffusion models and design an ID encoder to merge appearance features through self-attention. To keep background and pose consistency, we introduce an efficient Attribute Controller by explicitly informing the model of current background and pose of the target. By injecting AU variations into a denoising UNet, our model can animate arbitrary identities with various AU combinations, yielding superior results in high-fidelity expression editing compared to other facial expression editing works. Code is publicly available at https://github.com/weimengting/MagicFace.", "github_manual": "", "github_regex": "https://github.com/weimengting/MagicFace", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-08", "arxiv_id": "2501.02955", "title": "MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models", "authors": [ "Wenyi Hong", "Yean Cheng", "Zhuoyi Yang", "Weihan Wang", "Lefan Wang", "Xiaotao Gu", "Shiyu Huang", "Yuxiao Dong", "Jie Tang" ], "github": "", "project_page": "", "abstract": "In recent years, vision language models (VLMs) have made significant advancements in video understanding. However, a crucial capability - fine-grained motion comprehension - remains under-explored in current benchmarks. To address this gap, we propose MotionBench, a comprehensive evaluation benchmark designed to assess the fine-grained motion comprehension of video understanding models. MotionBench evaluates models' motion-level perception through six primary categories of motion-oriented question types and includes data collected from diverse sources, ensuring a broad representation of real-world video content. Experimental results reveal that existing VLMs perform poorly in understanding fine-grained motions. To enhance VLM's ability to perceive fine-grained motion within a limited sequence length of LLM, we conduct extensive experiments reviewing VLM architectures optimized for video feature compression and propose a novel and efficient Through-Encoder (TE) Fusion method. Experiments show that higher frame rate inputs and TE Fusion yield improvements in motion understanding, yet there is still substantial room for enhancement. Our benchmark aims to guide and motivate the development of more capable video understanding models, emphasizing the importance of fine-grained motion comprehension. Project page: https://motion-bench.github.io .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-08", "arxiv_id": "2501.03575", "title": "Cosmos World Foundation Model Platform for Physical AI", "authors": [ "NVIDIA", "Niket Agarwal", "Arslan Ali", "Maciej Bala", "Yogesh Balaji", "Erik Barker", "Tiffany Cai", "Prithvijit Chattopadhyay", "Yongxin Chen", "Yin Cui", "Yifan Ding", "Daniel Dworakowski", "Jiaojiao Fan", "Michele Fenzi", "Francesco Ferroni", "Sanja Fidler", "Dieter Fox", "Songwei Ge", "Yunhao Ge", "Jinwei Gu", "Siddharth Gururani", "Ethan He", "Jiahui Huang", "Jacob Huffman", "Pooya Jannaty", "Jingyi Jin", "Seung Wook Kim", "Gergely Kl\u00e1r", "Grace Lam", "Shiyi Lan", "Laura Leal-Taixe", "Anqi Li", "Zhaoshuo Li", "Chen-Hsuan Lin", "Tsung-Yi Lin", "Huan Ling", "Ming-Yu Liu", "Xian Liu", "Alice Luo", "Qianli Ma", "Hanzi Mao", "Kaichun Mo", "Arsalan Mousavian", "Seungjun Nah", "Sriharsha Niverty", "David Page", "Despoina Paschalidou", "Zeeshan Patel", "Lindsey Pavao", "Morteza Ramezanali", "Fitsum Reda", "Xiaowei Ren", "Vasanth Rao Naik Sabavat", "Ed Schmerling", "Stella Shi", "Bartosz Stefaniak", "Shitao Tang", "Lyne Tchapmi", "Przemek Tredak", "Wei-Cheng Tseng", "Jibin Varghese", "Hao Wang", "Haoxiang Wang", "Heng Wang", "Ting-Chun Wang", "Fangyin Wei", "Xinyue Wei", "Jay Zhangjie Wu", "Jiashu Xu", "Wei Yang", "Lin Yen-Chen", "Xiaohui Zeng", "Yu Zeng", "Jing Zhang", "Qinsheng Zhang", "Yuxuan Zhang", "Qingqing Zhao", "Artur Zolkowski" ], "github": "https://github.com/nvidia-cosmos/cosmos-predict1", "project_page": "", "abstract": "Physical AI needs to be trained digitally first. It needs a digital twin of itself, the policy model, and a digital twin of the world, the world model. In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups. We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications. Our platform covers a video curation pipeline, pre-trained world foundation models, examples of post-training of pre-trained world foundation models, and video tokenizers. To help Physical AI builders solve the most critical problems of our society, we make our platform open-source and our models open-weight with permissive licenses available via https://github.com/NVIDIA/Cosmos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nvidia-cosmos/cosmos-predict1", "github_api_added_by": "auto" }, { "date": "2025-01-08", "arxiv_id": "2501.03847", "title": "Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control", "authors": [ "Zekai Gu", "Rui Yan", "Jiahao Lu", "Peng Li", "Zhiyang Dou", "Chenyang Si", "Zhen Dong", "Qifeng Liu", "Cheng Lin", "Ziwei Liu", "Wenping Wang", "Yuan Liu" ], "github": "https://github.com/igl-hkust/diffusionasshader", "project_page": "", "abstract": "Diffusion models have demonstrated impressive performance in generating high-quality videos from text prompts or images. However, precise control over the video generation process, such as camera manipulation or content editing, remains a significant challenge. Existing methods for controlled video generation are typically limited to a single control type, lacking the flexibility to handle diverse control demands. In this paper, we introduce Diffusion as Shader (DaS), a novel approach that supports multiple video control tasks within a unified architecture. Our key insight is that achieving versatile video control necessitates leveraging 3D control signals, as videos are fundamentally 2D renderings of dynamic 3D content. Unlike prior methods limited to 2D control signals, DaS leverages 3D tracking videos as control inputs, making the video diffusion process inherently 3D-aware. This innovation allows DaS to achieve a wide range of video controls by simply manipulating the 3D tracking videos. A further advantage of using 3D tracking videos is their ability to effectively link frames, significantly enhancing the temporal consistency of the generated videos. With just 3 days of fine-tuning on 8 H800 GPUs using less than 10k videos, DaS demonstrates strong control capabilities across diverse tasks, including mesh-to-video generation, camera control, motion transfer, and object manipulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/igl-hkust/diffusionasshader", "github_api_added_by": "auto" }, { "date": "2025-01-08", "arxiv_id": "2501.03262", "title": "REINFORCE++: A Simple and Efficient Approach for Aligning Large Language Models", "authors": [ "Jian Hu" ], "github": "https://github.com/OpenRLHF/OpenRLHF", "project_page": "", "abstract": "Reinforcement Learning from Human Feedback (RLHF) has emerged as a critical approach for aligning large language models with human preferences, witnessing rapid algorithmic evolution through methods such as Proximal Policy Optimization (PPO), Direct Preference Optimization (DPO), REINFORCE Leave One-Out (RLOO), ReMax, and Group Relative Policy Optimization (GRPO). We present REINFORCE++, an enhanced variant of the classical REINFORCE algorithm that incorporates key optimization techniques from PPO while eliminating the need for a critic network. REINFORCE++ achieves three primary objectives: (1) simplicity (2) enhanced training stability, and (3) reduced computational overhead. Through extensive empirical evaluation, we demonstrate that REINFORCE++ exhibits superior stability compared to GRPO and achieves greater computational efficiency than PPO while maintaining comparable performance. The implementation is available at https://github.com/OpenRLHF/OpenRLHF.", "github_manual": "", "github_regex": "https://github.com/OpenRLHF/OpenRLHF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-08", "arxiv_id": "2501.04001", "title": "Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos", "authors": [ "Haobo Yuan", "Xiangtai Li", "Tao Zhang", "Zilong Huang", "Shilin Xu", "Shunping Ji", "Yunhai Tong", "Lu Qi", "Jiashi Feng", "Ming-Hsuan Yang" ], "github": "", "project_page": "", "abstract": "This work presents Sa2VA, the first unified model for dense grounded understanding of both images and videos. Unlike existing multi-modal large language models, which are often limited to specific modalities and tasks, Sa2VA supports a wide range of image and video tasks, including referring segmentation and conversation, with minimal one-shot instruction tuning. Sa2VA combines SAM-2, a foundation video segmentation model, with LLaVA, an advanced vision-language model, and unifies text, image, and video into a shared LLM token space. Using the LLM, Sa2VA generates instruction tokens that guide SAM-2 in producing precise masks, enabling a grounded, multi-modal understanding of both static and dynamic visual content. Additionally, we introduce Ref-SAV, an auto-labeled dataset containing over 72k object expressions in complex video scenes, designed to boost model performance. We also manually validate 2k video objects in the Ref-SAV datasets to benchmark referring video object segmentation in complex environments. Experiments show that Sa2VA achieves state-of-the-art across multiple tasks, particularly in referring video object segmentation, highlighting its potential for complex real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-08", "arxiv_id": "2501.03916", "title": "Dolphin: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback", "authors": [ "Jiakang Yuan", "Xiangchao Yan", "Botian Shi", "Tao Chen", "Wanli Ouyang", "Bo Zhang", "Lei Bai", "Yu Qiao", "Bowen Zhou" ], "github": "https://github.com/UniModal4Reasoning/Dolphin", "project_page": "", "abstract": "The scientific research paradigm is undergoing a profound transformation owing to the development of Artificial Intelligence (AI). Recent works demonstrate that various AI-assisted research methods can largely improve research efficiency by improving data analysis, accelerating computation, and fostering novel idea generation. To further move towards the ultimate goal (i.e., automatic scientific research), in this paper, we propose Dolphin, the first closed-loop open-ended auto-research framework to further build the entire process of human scientific research. Dolphin can generate research ideas, perform experiments, and get feedback from experimental results to generate higher-quality ideas. More specifically, Dolphin first generates novel ideas based on relevant papers which are ranked by the topic and task attributes. Then, the codes are automatically generated and debugged with the exception-traceback-guided local code structure. Finally, Dolphin automatically analyzes the results of each idea and feeds the results back to the next round of idea generation. Experiments are conducted on the benchmark datasets of different topics and results show that Dolphin can generate novel ideas continuously and complete the experiment in a loop. We highlight that Dolphin can automatically propose methods that are comparable to the state-of-the-art in some tasks such as 2D image classification and 3D point classification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UniModal4Reasoning/Dolphin", "github_api_added_by": "auto" }, { "date": "2025-01-08", "arxiv_id": "2501.03714", "title": "MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval Adjustment for Compact Dynamic 3D Gaussian Splatting", "authors": [ "Sangwoon Kwak", "Joonsoo Kim", "Jun Young Jeong", "Won-Sik Cheong", "Jihyong Oh", "Munchurl Kim" ], "github": "https://github.com/skwak-kaist/MoDec-GS", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) has made significant strides in scene representation and neural rendering, with intense efforts focused on adapting it for dynamic scenes. Despite delivering remarkable rendering quality and speed, existing methods struggle with storage demands and representing complex real-world motions. To tackle these issues, we propose MoDecGS, a memory-efficient Gaussian splatting framework designed for reconstructing novel views in challenging scenarios with complex motions. We introduce GlobaltoLocal Motion Decomposition (GLMD) to effectively capture dynamic motions in a coarsetofine manner. This approach leverages Global Canonical Scaffolds (Global CS) and Local Canonical Scaffolds (Local CS), extending static Scaffold representation to dynamic video reconstruction. For Global CS, we propose Global Anchor Deformation (GAD) to efficiently represent global dynamics along complex motions, by directly deforming the implicit Scaffold attributes which are anchor position, offset, and local context features. Next, we finely adjust local motions via the Local Gaussian Deformation (LGD) of Local CS explicitly. Additionally, we introduce Temporal Interval Adjustment (TIA) to automatically control the temporal coverage of each Local CS during training, allowing MoDecGS to find optimal interval assignments based on the specified number of temporal segments. Extensive evaluations demonstrate that MoDecGS achieves an average 70% reduction in model size over stateoftheart methods for dynamic 3D Gaussians from realworld dynamic videos while maintaining or even improving rendering quality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/skwak-kaist/MoDec-GS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-08", "arxiv_id": "2501.03936", "title": "PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides", "authors": [ "Hao Zheng", "Xinyan Guan", "Hao Kong", "Jia Zheng", "Hongyu Lin", "Yaojie Lu", "Ben He", "Xianpei Han", "Le Sun" ], "github": "https://github.com/icip-cas/PPTAgent", "project_page": "", "abstract": "Automatically generating presentations from documents is a challenging task that requires balancing content quality, visual design, and structural coherence. Existing methods primarily focus on improving and evaluating the content quality in isolation, often overlooking visual design and structural coherence, which limits their practical applicability. To address these limitations, we propose PPTAgent, which comprehensively improves presentation generation through a two-stage, edit-based approach inspired by human workflows. PPTAgent first analyzes reference presentations to understand their structural patterns and content schemas, then drafts outlines and generates slides through code actions to ensure consistency and alignment. To comprehensively evaluate the quality of generated presentations, we further introduce PPTEval, an evaluation framework that assesses presentations across three dimensions: Content, Design, and Coherence. Experiments show that PPTAgent significantly outperforms traditional automatic presentation generation methods across all three dimensions. The code and data are available at https://github.com/icip-cas/PPTAgent.", "github_manual": "", "github_regex": "https://github.com/icip-cas/PPTAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-08", "arxiv_id": "2501.02790", "title": "Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model", "authors": [ "Yueqin Yin", "Shentao Yang", "Yujia Xie", "Ziyi Yang", "Yuting Sun", "Hany Awadalla", "Weizhu Chen", "Mingyuan Zhou" ], "github": "https://github.com/OpenRLHF/OpenRLHF", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) has been widely adopted to align language models (LMs) with human preference. Prior RLHF works typically take a bandit formulation, which, though intuitive, ignores the sequential nature of LM generation and can suffer from the sparse reward issue. While recent works propose dense token-level RLHF, treating each token as an action may be oversubtle to proper reward assignment. In this paper, we seek to get the best of both by training and utilizing a segment-level reward model, which assigns a reward to each semantically complete text segment that spans over a short sequence of tokens. For reward learning, our method allows dynamic text segmentation and compatibility with standard sequence-preference datasets. For effective RL-based LM training against segment reward, we generalize the classical scalar bandit reward normalizers into location-aware normalizer functions and interpolate the segment reward for further densification. With these designs, our method performs competitively on three popular RLHF benchmarks for LM policy: AlpacaEval 2.0, Arena-Hard, and MT-Bench. Ablation studies are conducted to further demonstrate our method.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenRLHF/OpenRLHF", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-08", "arxiv_id": "2501.02393", "title": "Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers", "authors": [ "Markus J. Buehler" ], "github": "https://github.com/lamm-mit/graph-aware-transformers", "project_page": "", "abstract": "We present an approach to modifying Transformer architectures by integrating graph-aware relational reasoning into the attention mechanism, merging concepts from graph neural networks and language modeling. Building on the inherent connection between attention and graph theory, we reformulate the Transformer's attention mechanism as a graph operation and propose Graph-Aware Isomorphic Attention. This method leverages advanced graph modeling strategies, including Graph Isomorphism Networks (GIN) and Principal Neighborhood Aggregation (PNA), to enrich the representation of relational structures. Our approach captures complex dependencies and generalizes across tasks, as evidenced by a reduced generalization gap and improved learning performance. Additionally, we expand the concept of graph-aware attention to introduce Sparse GIN-Attention, a fine-tuning approach that employs sparse GINs. By interpreting attention matrices as sparse adjacency graphs, this technique enhances the adaptability of pre-trained foundational models with minimal computational overhead, endowing them with graph-aware capabilities. Sparse GIN-Attention fine-tuning achieves improved training dynamics and better generalization compared to alternative methods like low-rank adaption (LoRA). We discuss latent graph-like structures within traditional attention mechanisms, offering a new lens through which Transformers can be understood. By evolving Transformers as hierarchical GIN models for relational reasoning. This perspective suggests profound implications for foundational model development, enabling the design of architectures that dynamically adapt to both local and global dependencies. Applications in bioinformatics, materials science, language modeling, and beyond could benefit from this synthesis of relational and sequential data modeling, setting the stage for interpretable and generalizable modeling strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lamm-mit/graph-aware-transformers", "github_api_added_by": "auto" }, { "date": "2025-01-08", "arxiv_id": "2501.04561", "title": "OpenOmni: Large Language Models Pivot Zero-shot Omnimodal Alignment across Language with Real-time Self-Aware Emotional Speech Synthesis", "authors": [ "Run Luo", "Ting-En Lin", "Haonan Zhang", "Yuchuan Wu", "Xiong Liu", "Min Yang", "Yongbin Li", "Longze Chen", "Jiaming Li", "Lei Zhang", "Yangyi Chen", "Hamid Alinejad-Rokny", "Fei Huang" ], "github": "https://github.com/RainBowLuoCS/OpenOmni", "project_page": "", "abstract": "Recent advancements in omnimodal learning have been achieved in understanding and generation across images, text, and speech, though mainly within proprietary models. Limited omnimodal datasets and the inherent challenges associated with real-time emotional speech generation have hindered open-source progress. To address these issues, we propose openomni, a two-stage training method combining omnimodal alignment and speech generation to develop a state-of-the-art omnimodal large language model. In the alignment phase, a pre-trained speech model is further trained on text-image tasks to generalize from vision to speech in a (near) zero-shot manner, outperforming models trained on tri-modal datasets. In the speech generation phase, a lightweight decoder facilitates real-time emotional speech through training on speech tasks and preference learning. Experiments demonstrate that openomni consistently improves across omnimodal, vision-language, and speech-language evaluations, enabling natural, emotion-rich dialogues and real-time emotional speech generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/RainBowLuoCS/OpenOmni", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-08", "arxiv_id": "2501.02376", "title": "Generalizable Origin Identification for Text-Guided Image-to-Image Diffusion Models", "authors": [ "Wenhao Wang", "Yifan Sun", "Zongxin Yang", "Zhentao Tan", "Zhengdong Hu", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Text-guided image-to-image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights, and evading content tracing. This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models (ID^2), aiming to retrieve the original image of a given translated query. A straightforward solution to ID^2 involves training a specialized deep embedding model to extract and compare features from both query and reference images. However, due to visual discrepancy across generations produced by different diffusion models, this similarity-based approach fails when training on images from one model and testing on those from another, limiting its effectiveness in real-world applications. To solve this challenge of the proposed ID^2 task, we contribute the first dataset and a theoretically guaranteed method, both emphasizing generalizability. The curated dataset, OriPID, contains abundant Origins and guided Prompts, which can be used to train and test potential IDentification models across various diffusion models. In the method section, we first prove the existence of a linear transformation that minimizes the distance between the pre-trained Variational Autoencoder (VAE) embeddings of generated samples and their origins. Subsequently, it is demonstrated that such a simple linear transformation can be generalized across different diffusion models. Experimental results show that the proposed method achieves satisfying generalization performance, significantly surpassing similarity-based methods (+31.6% mAP), even those with generalization designs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.04519", "title": "rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking", "authors": [ "Xinyu Guan", "Li Lyna Zhang", "Yifei Liu", "Ning Shang", "Youran Sun", "Yi Zhu", "Fan Yang", "Mao Yang" ], "github": "https://github.com/microsoft/rStar", "project_page": "", "abstract": "We present rStar-Math to demonstrate that small language models (SLMs) can rival or even surpass the math reasoning capability of OpenAI o1, without distillation from superior models. rStar-Math achieves this by exercising \"deep thinking\" through Monte Carlo Tree Search (MCTS), where a math policy SLM performs test-time search guided by an SLM-based process reward model. rStar-Math introduces three innovations to tackle the challenges in training the two SLMs: (1) a novel code-augmented CoT data sythesis method, which performs extensive MCTS rollouts to generate step-by-step verified reasoning trajectories used to train the policy SLM; (2) a novel process reward model training method that avoids na\\\"ive step-level score annotation, yielding a more effective process preference model (PPM); (3) a self-evolution recipe in which the policy SLM and PPM are built from scratch and iteratively evolved to improve reasoning capabilities. Through 4 rounds of self-evolution with millions of synthesized solutions for 747k math problems, rStar-Math boosts SLMs' math reasoning to state-of-the-art levels. On the MATH benchmark, it improves Qwen2.5-Math-7B from 58.8% to 90.0% and Phi3-mini-3.8B from 41.4% to 86.4%, surpassing o1-preview by +4.5% and +0.9%. On the USA Math Olympiad (AIME), rStar-Math solves an average of 53.3% (8/15) of problems, ranking among the top 20% the brightest high school math students. Code and data will be available at https://github.com/microsoft/rStar.", "github_manual": "", "github_regex": "https://github.com/microsoft/rStar", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.02772", "title": "GeAR: Generation Augmented Retrieval", "authors": [ "Haoyu Liu", "Shaohan Huang", "Jianfeng Liu", "Yuefeng Zhan", "Hao Sun", "Weiwei Deng", "Feng Sun", "Furu Wei", "Qi Zhang" ], "github": "https://github.com/microsoft/LMOps", "project_page": "", "abstract": "Document retrieval techniques form the foundation for the development of large-scale information systems. The prevailing methodology is to construct a bi-encoder and compute the semantic similarity. However, such scalar similarity is difficult to reflect enough information and impedes our comprehension of the retrieval results. In addition, this computational process mainly emphasizes the global semantics and ignores the fine-grained semantic relationship between the query and the complex text in the document. In this paper, we propose a new method called Generation Augmented Retrieval (GeAR) that incorporates well-designed fusion and decoding modules. This enables GeAR to generate the relevant text from documents based on the fused representation of the query and the document, thus learning to \"focus on\" the fine-grained information. Also when used as a retriever, GeAR does not add any computational burden over bi-encoders. To support the training of the new framework, we have introduced a pipeline to efficiently synthesize high-quality data by utilizing large language models. GeAR exhibits competitive retrieval and localization performance across diverse scenarios and datasets. Moreover, the qualitative analysis and the results generated by GeAR provide novel insights into the interpretation of retrieval results. The code, data, and models will be released after completing technical review to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/LMOps", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.04575", "title": "InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection", "authors": [ "Yuhang Liu", "Pengxiang Li", "Zishu Wei", "Congkai Xie", "Xueyu Hu", "Xinchen Xu", "Shengyu Zhang", "Xiaotian Han", "Hongxia Yang", "Fei Wu" ], "github": "https://github.com/Reallm-Labs/InfiGUIAgent", "project_page": "", "abstract": "Graphical User Interface (GUI) Agents, powered by multimodal large language models (MLLMs), have shown great potential for task automation on computing devices such as computers and mobile phones. However, existing agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effectiveness. We introduce InfiGUIAgent, an MLLM-based GUI Agent trained with a two-stage supervised fine-tuning pipeline. Stage 1 enhances fundamental skills such as GUI understanding and grounding, while Stage 2 integrates hierarchical reasoning and expectation-reflection reasoning skills using synthesized data to enable native reasoning abilities of the agents. InfiGUIAgent achieves competitive performance on several GUI benchmarks, highlighting the impact of native reasoning skills in enhancing GUI interaction for automation tasks. Resources are available at https://github.com/Reallm-Labs/InfiGUIAgent.", "github_manual": "", "github_regex": "https://github.com/Reallm-Labs/InfiGUIAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.04227", "title": "Agent Laboratory: Using LLM Agents as Research Assistants", "authors": [ "Samuel Schmidgall", "Yusheng Su", "Ze Wang", "Ximeng Sun", "Jialian Wu", "Xiaodong Yu", "Jiang Liu", "Zicheng Liu", "Emad Barsoum" ], "github": "", "project_page": "", "abstract": "Historically, scientific discovery has been a lengthy and costly process, demanding substantial time and resources from initial conception to final results. To accelerate scientific discovery, reduce research costs, and improve research quality, we introduce Agent Laboratory, an autonomous LLM-based framework capable of completing the entire research process. This framework accepts a human-provided research idea and progresses through three stages--literature review, experimentation, and report writing to produce comprehensive research outputs, including a code repository and a research report, while enabling users to provide feedback and guidance at each stage. We deploy Agent Laboratory with various state-of-the-art LLMs and invite multiple researchers to assess its quality by participating in a survey, providing human feedback to guide the research process, and then evaluate the final paper. We found that: (1) Agent Laboratory driven by o1-preview generates the best research outcomes; (2) The generated machine learning code is able to achieve state-of-the-art performance compared to existing methods; (3) Human involvement, providing feedback at each stage, significantly improves the overall quality of research; (4) Agent Laboratory significantly reduces research expenses, achieving an 84% decrease compared to previous autonomous research methods. We hope Agent Laboratory enables researchers to allocate more effort toward creative ideation rather than low-level coding and writing, ultimately accelerating scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.04682", "title": "Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though", "authors": [ "Violet Xiang", "Charlie Snell", "Kanishk Gandhi", "Alon Albalak", "Anikait Singh", "Chase Blagden", "Duy Phung", "Rafael Rafailov", "Nathan Lile", "Dakota Mahan", "Louis Castricato", "Jan-Philipp Franken", "Nick Haber", "Chelsea Finn" ], "github": "", "project_page": "", "abstract": "We propose a novel framework, Meta Chain-of-Thought (Meta-CoT), which extends traditional Chain-of-Thought (CoT) by explicitly modeling the underlying reasoning required to arrive at a particular CoT. We present empirical evidence from state-of-the-art models exhibiting behaviors consistent with in-context search, and explore methods for producing Meta-CoT via process supervision, synthetic data generation, and search algorithms. Finally, we outline a concrete pipeline for training a model to produce Meta-CoTs, incorporating instruction tuning with linearized search traces and reinforcement learning post-training. Finally, we discuss open research questions, including scaling laws, verifier roles, and the potential for discovering novel reasoning algorithms. This work provides a theoretical and practical roadmap to enable Meta-CoT in LLMs, paving the way for more powerful and human-like reasoning in artificial intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.04686", "title": "URSA: Understanding and Verifying Chain-of-thought Reasoning in Multimodal Mathematics", "authors": [ "Ruilin Luo", "Zhuofan Zheng", "Yifan Wang", "Yiyao Yu", "Xinzhe Ni", "Zicheng Lin", "Jin Zeng", "Yujiu Yang" ], "github": "", "project_page": "", "abstract": "Chain-of-thought (CoT) reasoning has been widely applied in the mathematical reasoning of Large Language Models (LLMs). Recently, the introduction of derivative process supervision on CoT trajectories has sparked discussions on enhancing scaling capabilities during test time, thereby boosting the potential of these models. However, in multimodal mathematical reasoning, the scarcity of high-quality CoT training data has hindered existing models from achieving high-precision CoT reasoning and has limited the realization of reasoning potential during test time. In this work, we propose a three-module synthesis strategy that integrates CoT distillation, trajectory-format rewriting, and format unification. It results in a high-quality CoT reasoning instruction fine-tuning dataset in multimodal mathematics, MMathCoT-1M. We comprehensively validate the state-of-the-art (SOTA) performance of the trained URSA-7B model on multiple multimodal mathematical benchmarks. For test-time scaling, we introduce a data synthesis strategy that automatically generates process annotation datasets, known as DualMath-1.1M, focusing on both interpretation and logic. By further training URSA-7B on DualMath-1.1M, we transition from CoT reasoning capabilities to robust supervision abilities. The trained URSA-RM-7B acts as a verifier, effectively enhancing the performance of URSA-7B at test time. URSA-RM-7B also demonstrates excellent out-of-distribution (OOD) verifying capabilities, showcasing its generalization. Model weights, training data and code will be open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.03271", "title": "DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization", "authors": [ "Amitava Das", "Suranjana Trivedy", "Danush Khanna", "Rajarshi Roy", "Gurpreet Singh", "Basab Ghosh", "Yaswanth Narsupalli", "Vinija Jain", "Vasu Sharma", "Aishwarya Naresh Reganti", "Aman Chadha" ], "github": "", "project_page": "", "abstract": "The rapid rise of large language models (LLMs) has unlocked many applications but also underscores the challenge of aligning them with diverse values and preferences. Direct Preference Optimization (DPO) is central to alignment but constrained by fixed divergences and limited feature transformations. We propose DPO-Kernels, which integrates kernel methods to address these issues through four key contributions: (i) Kernelized Representations with polynomial, RBF, Mahalanobis, and spectral kernels for richer transformations, plus a hybrid loss combining embedding-based and probability-based objectives; (ii) Divergence Alternatives (Jensen-Shannon, Hellinger, Renyi, Bhattacharyya, Wasserstein, and f-divergences) for greater stability; (iii) Data-Driven Selection metrics that automatically choose the best kernel-divergence pair; and (iv) a Hierarchical Mixture of Kernels for both local precision and global modeling. Evaluations on 12 datasets demonstrate state-of-the-art performance in factuality, safety, reasoning, and instruction following. Grounded in Heavy-Tailed Self-Regularization, DPO-Kernels maintains robust generalization for LLMs, offering a comprehensive resource for further alignment research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.04306", "title": "LLM4SR: A Survey on Large Language Models for Scientific Research", "authors": [ "Ziming Luo", "Zonglin Yang", "Zexin Xu", "Wei Yang", "Xinya Du" ], "github": "https://github.com/du-nlp-lab/LLM4SR", "project_page": "", "abstract": "In recent years, the rapid advancement of Large Language Models (LLMs) has transformed the landscape of scientific research, offering unprecedented support across various stages of the research cycle. This paper presents the first systematic survey dedicated to exploring how LLMs are revolutionizing the scientific research process. We analyze the unique roles LLMs play across four critical stages of research: hypothesis discovery, experiment planning and implementation, scientific writing, and peer reviewing. Our review comprehensively showcases the task-specific methodologies and evaluation benchmarks. By identifying current challenges and proposing future research directions, this survey not only highlights the transformative potential of LLMs, but also aims to inspire and guide researchers and practitioners in leveraging LLMs to advance scientific inquiry. Resources are available at the following repository: https://github.com/du-nlp-lab/LLM4SR", "github_manual": "", "github_regex": "https://github.com/du-nlp-lab/LLM4SR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.04689", "title": "SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single Images", "authors": [ "Zixuan Huang", "Mark Boss", "Aaryaman Vasishta", "James M. Rehg", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "We study the problem of single-image 3D object reconstruction. Recent works have diverged into two directions: regression-based modeling and generative modeling. Regression methods efficiently infer visible surfaces, but struggle with occluded regions. Generative methods handle uncertain regions better by modeling distributions, but are computationally expensive and the generation is often misaligned with visible surfaces. In this paper, we present SPAR3D, a novel two-stage approach aiming to take the best of both directions. The first stage of SPAR3D generates sparse 3D point clouds using a lightweight point diffusion model, which has a fast sampling speed. The second stage uses both the sampled point cloud and the input image to create highly detailed meshes. Our two-stage design enables probabilistic modeling of the ill-posed single-image 3D task while maintaining high computational efficiency and great output fidelity. Using point clouds as an intermediate representation further allows for interactive user edits. Evaluated on diverse datasets, SPAR3D demonstrates superior performance over previous state-of-the-art methods, at an inference speed of 0.7 seconds. Project page with code and model: https://spar3d.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.04144", "title": "Chirpy3D: Continuous Part Latents for Creative 3D Bird Generation", "authors": [ "Kam Woh Ng", "Jing Yang", "Jia Wei Sii", "Jiankang Deng", "Chee Seng Chan", "Yi-Zhe Song", "Tao Xiang", "Xiatian Zhu" ], "github": "https://github.com/kamwoh/chirpy3d", "project_page": "", "abstract": "In this paper, we push the boundaries of fine-grained 3D generation into truly creative territory. Current methods either lack intricate details or simply mimic existing objects -- we enable both. By lifting 2D fine-grained understanding into 3D through multi-view diffusion and modeling part latents as continuous distributions, we unlock the ability to generate entirely new, yet plausible parts through interpolation and sampling. A self-supervised feature consistency loss further ensures stable generation of these unseen parts. The result is the first system capable of creating novel 3D objects with species-specific details that transcend existing examples. While we demonstrate our approach on birds, the underlying framework extends beyond things that can chirp! Code will be released at https://github.com/kamwoh/chirpy3d.", "github_manual": "", "github_regex": "https://github.com/kamwoh/chirpy3d", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.04694", "title": "EpiCoder: Encompassing Diversity and Complexity in Code Generation", "authors": [ "Yaoxiang Wang", "Haoling Li", "Xin Zhang", "Jie Wu", "Xiao Liu", "Wenxiang Hu", "Zhongxin Guo", "Yangyu Huang", "Ying Xin", "Yujiu Yang", "Jinsong Su", "Qi Chen", "Scarlett Li" ], "github": "https://github.com/microsoft/EpiCoder", "project_page": "", "abstract": "Effective instruction tuning is indispensable for optimizing code LLMs, aligning model behavior with user expectations and enhancing model performance in real-world applications. However, most existing methods focus on code snippets, which are limited to specific functionalities and rigid structures, restricting the complexity and diversity of the synthesized data. To address these limitations, we introduce a novel feature tree-based synthesis framework inspired by Abstract Syntax Trees (AST). Unlike AST, which captures syntactic structure of code, our framework models semantic relationships between code elements, enabling the generation of more nuanced and diverse data. The feature tree is constructed from raw data and refined iteratively to increase the quantity and diversity of the extracted features. This process enables the identification of more complex patterns and relationships within the code. By sampling subtrees with controlled depth and breadth, our framework allows precise adjustments to the complexity of the generated code, supporting a wide range of tasks from simple function-level operations to intricate multi-file scenarios. We fine-tuned widely-used base models to create the EpiCoder series, achieving state-of-the-art performance at both the function and file levels across multiple benchmarks. Notably, empirical evidence indicates that our approach shows significant potential in synthesizing highly complex repository-level code data. Further analysis elucidates the merits of this approach by rigorously assessing data complexity and diversity through software engineering principles and LLM-as-a-judge method.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/EpiCoder", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.04652", "title": "Multi-task retriever fine-tuning for domain-specific and efficient RAG", "authors": [ "Patrice B\u00e9chard", "Orlando Marquez Ayala" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) has become ubiquitous when deploying Large Language Models (LLMs), as it can address typical limitations such as generating hallucinated or outdated information. However, when building real-world RAG applications, practical issues arise. First, the retrieved information is generally domain-specific. Since it is computationally expensive to fine-tune LLMs, it is more feasible to fine-tune the retriever to improve the quality of the data included in the LLM input. Second, as more applications are deployed in the same real-world system, one cannot afford to deploy separate retrievers. Moreover, these RAG applications normally retrieve different kinds of data. Our solution is to instruction fine-tune a small retriever encoder on a variety of domain-specific tasks to allow us to deploy one encoder that can serve many use cases, thereby achieving low-cost, scalability, and speed. We show how this encoder generalizes to out-of-domain settings as well as to an unseen retrieval task on real-world enterprise use cases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-09", "arxiv_id": "2501.05366", "title": "Search-o1: Agentic Search-Enhanced Large Reasoning Models", "authors": [ "Xiaoxi Li", "Guanting Dong", "Jiajie Jin", "Yuyao Zhang", "Yujia Zhou", "Yutao Zhu", "Peitian Zhang", "Zhicheng Dou" ], "github": "https://github.com/sunnynexus/Search-o1", "project_page": "", "abstract": "Large reasoning models (LRMs) like OpenAI-o1 have demonstrated impressive long stepwise reasoning capabilities through large-scale reinforcement learning. However, their extended reasoning processes often suffer from knowledge insufficiency, leading to frequent uncertainties and potential errors. To address this limitation, we introduce Search-o1, a framework that enhances LRMs with an agentic retrieval-augmented generation (RAG) mechanism and a Reason-in-Documents module for refining retrieved documents. Search-o1 integrates an agentic search workflow into the reasoning process, enabling dynamic retrieval of external knowledge when LRMs encounter uncertain knowledge points. Additionally, due to the verbose nature of retrieved documents, we design a separate Reason-in-Documents module to deeply analyze the retrieved information before injecting it into the reasoning chain, minimizing noise and preserving coherent reasoning flow. Extensive experiments on complex reasoning tasks in science, mathematics, and coding, as well as six open-domain QA benchmarks, demonstrate the strong performance of Search-o1. This approach enhances the trustworthiness and applicability of LRMs in complex reasoning tasks, paving the way for more reliable and versatile intelligent systems. The code is available at https://github.com/sunnynexus/Search-o1.", "github_manual": "", "github_regex": "https://github.com/sunnynexus/Search-o1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-10", "arxiv_id": "2501.05441", "title": "The GAN is dead; long live the GAN! A Modern GAN Baseline", "authors": [ "Yiwen Huang", "Aaron Gokaslan", "Volodymyr Kuleshov", "James Tompkin" ], "github": "", "project_page": "", "abstract": "There is a widely-spread claim that GANs are difficult to train, and GAN architectures in the literature are littered with empirical tricks. We provide evidence against this claim and build a modern GAN baseline in a more principled manner. First, we derive a well-behaved regularized relativistic GAN loss that addresses issues of mode dropping and non-convergence that were previously tackled via a bag of ad-hoc tricks. We analyze our loss mathematically and prove that it admits local convergence guarantees, unlike most existing relativistic losses. Second, our new loss allows us to discard all ad-hoc tricks and replace outdated backbones used in common GANs with modern architectures. Using StyleGAN2 as an example, we present a roadmap of simplification and modernization that results in a new minimalist baseline -- R3GAN. Despite being simple, our approach surpasses StyleGAN2 on FFHQ, ImageNet, CIFAR, and Stacked MNIST datasets, and compares favorably against state-of-the-art GANs and diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-10", "arxiv_id": "2501.05453", "title": "An Empirical Study of Autoregressive Pre-training from Videos", "authors": [ "Jathushan Rajasegaran", "Ilija Radosavovic", "Rahul Ravishankar", "Yossi Gandelsman", "Christoph Feichtenhofer", "Jitendra Malik" ], "github": "", "project_page": "", "abstract": "We empirically study autoregressive pre-training from videos. To perform our study, we construct a series of autoregressive video models, called Toto. We treat videos as sequences of visual tokens and train transformer models to autoregressively predict future tokens. Our models are pre-trained on a diverse dataset of videos and images comprising over 1 trillion visual tokens. We explore different architectural, training, and inference design choices. We evaluate the learned visual representations on a range of downstream tasks including image recognition, video classification, object tracking, and robotics. Our results demonstrate that, despite minimal inductive biases, autoregressive pre-training leads to competitive performance across all benchmarks. Finally, we find that scaling our video models results in similar scaling curves to those seen in language models, albeit with a different rate. More details at https://brjathu.github.io/toto/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-10", "arxiv_id": "2501.03489", "title": "Entropy-Guided Attention for Private LLMs", "authors": [ "Nandan Kumar Jha", "Brandon Reagen" ], "github": "https://github.com/Nandan91/entropy-guided-attention-llm", "project_page": "", "abstract": "The pervasiveness of proprietary language models has raised critical privacy concerns, necessitating advancements in private inference (PI), where computations are performed directly on encrypted data without revealing users' sensitive information. While PI offers a promising solution, its practical deployment is hindered by substantial communication and latency overheads, primarily stemming from nonlinear operations. To address this, we introduce an information-theoretic framework to characterize the role of nonlinearities in decoder-only language models, laying a principled foundation for optimizing transformer-architectures tailored to the demands of PI. By leveraging Shannon's entropy as a quantitative measure, we uncover the previously unexplored dual significance of nonlinearities: beyond ensuring training stability, they are crucial for maintaining attention head diversity. Specifically, we find that their removal triggers two critical failure modes: {\\em entropy collapse} in deeper layers that destabilizes training, and {\\em entropic overload} in earlier layers that leads to under-utilization of Multi-Head Attention's (MHA) representational capacity. We propose an entropy-guided attention mechanism paired with a novel entropy regularization technique to mitigate entropic overload. Additionally, we explore PI-friendly alternatives to layer normalization for preventing entropy collapse and stabilizing the training of LLMs with reduced-nonlinearities. Our study bridges the gap between information theory and architectural design, establishing entropy dynamics as a principled guide for developing efficient PI architectures. The code and implementation are available at https://github.com/Nandan91/entropy-guided-attention-llm{entropy-guided-llm}.", "github_manual": "", "github_regex": "https://github.com/Nandan91/entropy-guided-attention-llm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-10", "arxiv_id": "2501.04377", "title": "On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis", "authors": [ "Yekun Ke", "Xiaoyu Li", "Yingyu Liang", "Zhizhou Sha", "Zhenmei Shi", "Zhao Song" ], "github": "", "project_page": "", "abstract": "Recently, Visual Autoregressive (VAR) Models introduced a groundbreaking advancement in the field of image generation, offering a scalable approach through a coarse-to-fine \"next-scale prediction\" paradigm. However, the state-of-the-art algorithm of VAR models in [Tian, Jiang, Yuan, Peng and Wang, NeurIPS 2024] takes O(n^4) time, which is computationally inefficient. In this work, we analyze the computational limits and efficiency criteria of VAR Models through a fine-grained complexity lens. Our key contribution is identifying the conditions under which VAR computations can achieve sub-quadratic time complexity. Specifically, we establish a critical threshold for the norm of input matrices used in VAR attention mechanisms. Above this threshold, assuming the Strong Exponential Time Hypothesis (SETH) from fine-grained complexity theory, a sub-quartic time algorithm for VAR models is impossible. To substantiate our theoretical findings, we present efficient constructions leveraging low-rank approximations that align with the derived criteria. This work initiates the study of the computational efficiency of the VAR model from a theoretical perspective. Our technique will shed light on advancing scalable and efficient image generation in VAR frameworks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-10", "arxiv_id": "2501.04003", "title": "Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives", "authors": [ "Shaoyuan Xie", "Lingdong Kong", "Yuhao Dong", "Chonghao Sima", "Wenwei Zhang", "Qi Alfred Chen", "Ziwei Liu", "Liang Pan" ], "github": "", "project_page": "", "abstract": "Recent advancements in Vision-Language Models (VLMs) have sparked interest in their use for autonomous driving, particularly in generating interpretable driving decisions through natural language. However, the assumption that VLMs inherently provide visually grounded, reliable, and interpretable explanations for driving remains largely unexamined. To address this gap, we introduce DriveBench, a benchmark dataset designed to evaluate VLM reliability across 17 settings (clean, corrupted, and text-only inputs), encompassing 19,200 frames, 20,498 question-answer pairs, three question types, four mainstream driving tasks, and a total of 12 popular VLMs. Our findings reveal that VLMs often generate plausible responses derived from general knowledge or textual cues rather than true visual grounding, especially under degraded or missing visual inputs. This behavior, concealed by dataset imbalances and insufficient evaluation metrics, poses significant risks in safety-critical scenarios like autonomous driving. We further observe that VLMs struggle with multi-modal reasoning and display heightened sensitivity to input corruptions, leading to inconsistencies in performance. To address these challenges, we propose refined evaluation metrics that prioritize robust visual grounding and multi-modal understanding. Additionally, we highlight the potential of leveraging VLMs' awareness of corruptions to enhance their reliability, offering a roadmap for developing more trustworthy and interpretable decision-making systems in real-world autonomous driving contexts. The benchmark toolkit is publicly accessible.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-10", "arxiv_id": "2501.04828", "title": "Building Foundations for Natural Language Processing of Historical Turkish: Resources and Models", "authors": [ "\u015eaziye Bet\u00fcl \u00d6zate\u015f", "Tar\u0131k Emre T\u0131ra\u015f", "Ece Elif Adak", "Berat Do\u011fan", "Fatih Burak Karag\u00f6z", "Efe Eren Gen\u00e7", "Esma F. Bilgin Ta\u015fdemir" ], "github": "https://github.com/Ottoman-NLP/ottominer-public", "project_page": "", "abstract": "This paper introduces foundational resources and models for natural language processing (NLP) of historical Turkish, a domain that has remained underexplored in computational linguistics. We present the first named entity recognition (NER) dataset, HisTR and the first Universal Dependencies treebank, OTA-BOUN for a historical form of the Turkish language along with transformer-based models trained using these datasets for named entity recognition, dependency parsing, and part-of-speech tagging tasks. Additionally, we introduce Ottoman Text Corpus (OTC), a clean corpus of transliterated historical Turkish texts that spans a wide range of historical periods. Our experimental results show significant improvements in the computational analysis of historical Turkish, achieving promising results in tasks that require understanding of historical linguistic structures. They also highlight existing challenges, such as domain adaptation and language variations across time periods. All of the presented resources and models are made available at https://huggingface.co/bucolin to serve as a benchmark for future progress in historical Turkish NLP.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Ottoman-NLP/ottominer-public", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-10", "arxiv_id": "2501.05122", "title": "Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model", "authors": [ "Gregor Geigle", "Florian Schneider", "Carolin Holtermann", "Chris Biemann", "Radu Timofte", "Anne Lauscher", "Goran Glava\u0161" ], "github": "", "project_page": "", "abstract": "Most Large Vision-Language Models (LVLMs) to date are trained predominantly on English data, which makes them struggle to understand non-English input and fail to generate output in the desired target language. Existing efforts mitigate these issues by adding multilingual training data, but do so in a largely ad-hoc manner, lacking insight into how different training mixes tip the scale for different groups of languages. In this work, we present a comprehensive investigation into the training strategies for massively multilingual LVLMs. First, we conduct a series of multi-stage experiments spanning 13 downstream vision-language tasks and 43 languages, systematically examining: (1) the number of training languages that can be included without degrading English performance and (2) optimal language distributions of pre-training as well as (3) instruction-tuning data. Further, we (4) investigate how to improve multilingual text-in-image understanding, and introduce a new benchmark for the task. Surprisingly, our analysis reveals that one can (i) include as many as 100 training languages simultaneously (ii) with as little as 25-50\\% of non-English data, to greatly improve multilingual performance while retaining strong English performance. We further find that (iii) including non-English OCR data in pre-training and instruction-tuning is paramount for improving multilingual text-in-image understanding. Finally, we put all our findings together and train Centurio, a 100-language LVLM, offering state-of-the-art performance in an evaluation covering 14 tasks and 56 languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-10", "arxiv_id": "2501.05040", "title": "SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution", "authors": [ "Chengxing Xie", "Bowen Li", "Chang Gao", "He Du", "Wai Lam", "Difan Zou", "Kai Chen" ], "github": "https://github.com/InternLM/SWE-Fixer", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable proficiency across a variety of complex tasks. One significant application of LLMs is in tackling software engineering challenges, particularly in resolving real-world tasks on GitHub by fixing code based on the issues reported by the users. However, many current approaches rely on proprietary LLMs, which limits reproducibility, accessibility, and transparency. The critical components of LLMs for addressing software engineering issues and how their capabilities can be effectively enhanced remain unclear. To address these challenges, we introduce SWE-Fixer, a novel open-source LLM designed to effectively and efficiently resolve GitHub issues. SWE-Fixer comprises two essential modules: a code file retrieval module and a code editing module. The retrieval module employs BM25 along with a lightweight LLM model to achieve coarse-to-fine file retrieval. Subsequently, the code editing module utilizes the other LLM model to generate patches for the identified files. Then, to mitigate the lack of publicly available datasets, we compile an extensive dataset that includes 110K GitHub issues along with their corresponding patches, and train the two modules of SWE-Fixer separately. We assess our approach on the SWE-Bench Lite and Verified benchmarks, achieving state-of-the-art performance among open-source models with scores of 23.3% and 30.2%, respectively. These outcomes highlight the efficacy of our approach. We will make our model, dataset, and code publicly available at https://github.com/InternLM/SWE-Fixer.", "github_manual": "", "github_regex": "https://github.com/InternLM/SWE-Fixer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-10", "arxiv_id": "2501.05032", "title": "Enhancing Human-Like Responses in Large Language Models", "authors": [ "Ethem Ya\u011f\u0131z \u00c7al\u0131k", "Talha R\u00fczgar Akku\u015f" ], "github": "", "project_page": "", "abstract": "This paper explores the advancements in making large language models (LLMs) more human-like. We focus on techniques that enhance natural language understanding, conversational coherence, and emotional intelligence in AI systems. The study evaluates various approaches, including fine-tuning with diverse datasets, incorporating psychological principles, and designing models that better mimic human reasoning patterns. Our findings demonstrate that these enhancements not only improve user interactions but also open new possibilities for AI applications across different domains. Future work will address the ethical implications and potential biases introduced by these human-like attributes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-13", "arxiv_id": "2501.05874", "title": "VideoRAG: Retrieval-Augmented Generation over Video Corpus", "authors": [ "Soyeong Jeong", "Kangsan Kim", "Jinheon Baek", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) is a powerful strategy to address the issue of generating factually incorrect outputs in foundation models by retrieving external knowledge relevant to queries and incorporating it into their generation process. However, existing RAG approaches have primarily focused on textual information, with some recent advancements beginning to consider images, and they largely overlook videos, a rich source of multimodal knowledge capable of representing events, processes, and contextual details more effectively than any other modality. While a few recent studies explore the integration of videos in the response generation process, they either predefine query-associated videos without retrieving them according to queries, or convert videos into the textual descriptions without harnessing their multimodal richness. To tackle these, we introduce VideoRAG, a novel framework that not only dynamically retrieves relevant videos based on their relevance with queries but also utilizes both visual and textual information of videos in the output generation. Further, to operationalize this, our method revolves around the recent advance of Large Video Language Models (LVLMs), which enable the direct processing of video content to represent it for retrieval and seamless integration of the retrieved videos jointly with queries. We experimentally validate the effectiveness of VideoRAG, showcasing that it is superior to relevant baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-13", "arxiv_id": "2501.05727", "title": "Enabling Scalable Oversight via Self-Evolving Critic", "authors": [ "Zhengyang Tang", "Ziniu Li", "Zhenyang Xiao", "Tian Ding", "Ruoyu Sun", "Benyou Wang", "Dayiheng Liu", "Fei Huang", "Tianyu Liu", "Bowen Yu", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "Despite their remarkable performance, the development of Large Language Models (LLMs) faces a critical challenge in scalable oversight: providing effective feedback for tasks where human evaluation is difficult or where LLMs outperform humans. While there is growing interest in using LLMs for critique, current approaches still rely on human annotations or more powerful models, leaving the issue of enhancing critique capabilities without external supervision unresolved. We introduce SCRIT (Self-evolving CRITic), a framework that enables genuine self-evolution of critique abilities. Technically, SCRIT self-improves by training on synthetic data, generated by a contrastive-based self-critic that uses reference solutions for step-by-step critique, and a self-validation mechanism that ensures critique quality through correction outcomes. Implemented with Qwen2.5-72B-Instruct, one of the most powerful LLMs, SCRIT achieves up to a 10.3\\% improvement on critique-correction and error identification benchmarks. Our analysis reveals that SCRIT's performance scales positively with data and model size, outperforms alternative approaches, and benefits critically from its self-validation component.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-13", "arxiv_id": "2501.06186", "title": "LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs", "authors": [ "Omkar Thawakar", "Dinura Dissanayake", "Ketan More", "Ritesh Thawkar", "Ahmed Heakl", "Noor Ahsan", "Yuhao Li", "Mohammed Zumri", "Jean Lahoud", "Rao Muhammad Anwer", "Hisham Cholakkal", "Ivan Laptev", "Mubarak Shah", "Fahad Shahbaz Khan", "Salman Khan" ], "github": "https://github.com/mbzuai-oryx/llamav-o1", "project_page": "", "abstract": "Reasoning is a fundamental capability for solving complex multi-step problems, particularly in visual contexts where sequential step-wise understanding is essential. Existing approaches lack a comprehensive framework for evaluating visual reasoning and do not emphasize step-wise problem-solving. To this end, we propose a comprehensive framework for advancing step-by-step visual reasoning in large language models (LMMs) through three key contributions. First, we introduce a visual reasoning benchmark specifically designed to evaluate multi-step reasoning tasks. The benchmark presents a diverse set of challenges with eight different categories ranging from complex visual perception to scientific reasoning with over 4k reasoning steps in total, enabling robust evaluation of LLMs' abilities to perform accurate and interpretable visual reasoning across multiple steps. Second, we propose a novel metric that assesses visual reasoning quality at the granularity of individual steps, emphasizing both correctness and logical coherence. The proposed metric offers deeper insights into reasoning performance compared to traditional end-task accuracy metrics. Third, we present a new multimodal visual reasoning model, named LlamaV-o1, trained using a multi-step curriculum learning approach, where tasks are progressively organized to facilitate incremental skill acquisition and problem-solving. The proposed LlamaV-o1 is designed for multi-step reasoning and learns step-by-step through a structured training paradigm. Extensive experiments show that our LlamaV-o1 outperforms existing open-source models and performs favorably against close-source proprietary models. Compared to the recent Llava-CoT, our LlamaV-o1 achieves an average score of 67.3 with an absolute gain of 3.8\\% across six benchmarks while being 5 times faster during inference scaling. Our benchmark, model, and code are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mbzuai-oryx/llamav-o1", "github_api_added_by": "auto" }, { "date": "2025-01-13", "arxiv_id": "2501.03841", "title": "OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints", "authors": [ "Mingjie Pan", "Jiyao Zhang", "Tianshu Wu", "Yinghao Zhao", "Wenlong Gao", "Hao Dong" ], "github": "", "project_page": "", "abstract": "The development of general robotic systems capable of manipulating in unstructured environments is a significant challenge. While Vision-Language Models(VLM) excel in high-level commonsense reasoning, they lack the fine-grained 3D spatial understanding required for precise manipulation tasks. Fine-tuning VLM on robotic datasets to create Vision-Language-Action Models(VLA) is a potential solution, but it is hindered by high data collection costs and generalization issues. To address these challenges, we propose a novel object-centric representation that bridges the gap between VLM's high-level reasoning and the low-level precision required for manipulation. Our key insight is that an object's canonical space, defined by its functional affordances, provides a structured and semantically meaningful way to describe interaction primitives, such as points and directions. These primitives act as a bridge, translating VLM's commonsense reasoning into actionable 3D spatial constraints. In this context, we introduce a dual closed-loop, open-vocabulary robotic manipulation system: one loop for high-level planning through primitive resampling, interaction rendering and VLM checking, and another for low-level execution via 6D pose tracking. This design ensures robust, real-time control without requiring VLM fine-tuning. Extensive experiments demonstrate strong zero-shot generalization across diverse robotic manipulation tasks, highlighting the potential of this approach for automating large-scale simulation data generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-13", "arxiv_id": "2501.05707", "title": "Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains", "authors": [ "Vighnesh Subramaniam", "Yilun Du", "Joshua B. Tenenbaum", "Antonio Torralba", "Shuang Li", "Igor Mordatch" ], "github": "https://github.com/vsubramaniam851/multiagent-ft", "project_page": "", "abstract": "Large language models (LLMs) have achieved remarkable performance in recent years but are fundamentally limited by the underlying training data. To improve models beyond the training data, recent works have explored how LLMs can be used to generate synthetic data for autonomous self-improvement. However, successive steps of self-improvement can reach a point of diminishing returns. In this work, we propose a complementary approach towards self-improvement where finetuning is applied to a multiagent society of language models. A group of language models, all starting from the same base model, are independently specialized by updating each one using data generated through multiagent interactions among the models. By training each model on independent sets of data, we illustrate how this approach enables specialization across models and diversification over the set of models. As a result, our overall system is able to preserve diverse reasoning chains and autonomously improve over many more rounds of fine-tuning than single-agent self-improvement methods. We quantitatively illustrate the efficacy of the approach across a wide suite of reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vsubramaniam851/multiagent-ft", "github_api_added_by": "auto" }, { "date": "2025-01-13", "arxiv_id": "2501.05542", "title": "Infecting Generative AI With Viruses", "authors": [ "David Noever", "Forrest McKee" ], "github": "https://github.com/fire1ce/eicar-standard-antivirus-test-files/tree/master/eicar-excel-macro-powershell-echo.xls", "project_page": "", "abstract": "This study demonstrates a novel approach to testing the security boundaries of Vision-Large Language Model (VLM/ LLM) using the EICAR test file embedded within JPEG images. We successfully executed four distinct protocols across multiple LLM platforms, including OpenAI GPT-4o, Microsoft Copilot, Google Gemini 1.5 Pro, and Anthropic Claude 3.5 Sonnet. The experiments validated that a modified JPEG containing the EICAR signature could be uploaded, manipulated, and potentially executed within LLM virtual workspaces. Key findings include: 1) consistent ability to mask the EICAR string in image metadata without detection, 2) successful extraction of the test file using Python-based manipulation within LLM environments, and 3) demonstration of multiple obfuscation techniques including base64 encoding and string reversal. This research extends Microsoft Research's \"Penetration Testing Rules of Engagement\" framework to evaluate cloud-based generative AI and LLM security boundaries, particularly focusing on file handling and execution capabilities within containerized environments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/fire1ce/eicar-standard-antivirus-test-files/tree/master/eicar-excel-macro-powershell-echo.xls", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-13", "arxiv_id": "2501.05510", "title": "OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?", "authors": [ "Yifei Li", "Junbo Niu", "Ziyang Miao", "Chunjiang Ge", "Yuanhang Zhou", "Qihao He", "Xiaoyi Dong", "Haodong Duan", "Shuangrui Ding", "Rui Qian", "Pan Zhang", "Yuhang Zang", "Yuhang Cao", "Conghui He", "Jiaqi Wang" ], "github": "https://github.com/JoeLeelyf/OVO-Bench", "project_page": "", "abstract": "Temporal Awareness, the ability to reason dynamically based on the timestamp when a question is raised, is the key distinction between offline and online video LLMs. Unlike offline models, which rely on complete videos for static, post hoc analysis, online models process video streams incrementally and dynamically adapt their responses based on the timestamp at which the question is posed. Despite its significance, temporal awareness has not been adequately evaluated in existing benchmarks. To fill this gap, we present OVO-Bench (Online-VideO-Benchmark), a novel video benchmark that emphasizes the importance of timestamps for advanced online video understanding capability benchmarking. OVO-Bench evaluates the ability of video LLMs to reason and respond to events occurring at specific timestamps under three distinct scenarios: (1) Backward tracing: trace back to past events to answer the question. (2) Real-time understanding: understand and respond to events as they unfold at the current timestamp. (3) Forward active responding: delay the response until sufficient future information becomes available to answer the question accurately. OVO-Bench comprises 12 tasks, featuring 644 unique videos and approximately human-curated 2,800 fine-grained meta-annotations with precise timestamps. We combine automated generation pipelines with human curation. With these high-quality samples, we further developed an evaluation pipeline to systematically query video LLMs along the video timeline. Evaluations of nine Video-LLMs reveal that, despite advancements on traditional benchmarks, current models struggle with online video understanding, showing a significant gap compared to human agents. We hope OVO-Bench will drive progress in video LLMs and inspire future research in online video reasoning. Our benchmark and code can be accessed at https://github.com/JoeLeelyf/OVO-Bench.", "github_manual": "", "github_regex": "https://github.com/JoeLeelyf/OVO-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-13", "arxiv_id": "2501.05452", "title": "ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding", "authors": [ "Xingyu Fu", "Minqian Liu", "Zhengyuan Yang", "John Corring", "Yijuan Lu", "Jianwei Yang", "Dan Roth", "Dinei Florencio", "Cha Zhang" ], "github": "", "project_page": "", "abstract": "Structured image understanding, such as interpreting tables and charts, requires strategically refocusing across various structures and texts within an image, forming a reasoning sequence to arrive at the final answer. However, current multimodal large language models (LLMs) lack this multihop selective attention capability. In this work, we introduce ReFocus, a simple yet effective framework that equips multimodal LLMs with the ability to generate \"visual thoughts\" by performing visual editing on the input image through code, shifting and refining their visual focuses. Specifically, ReFocus enables multimodal LLMs to generate Python codes to call tools and modify the input image, sequentially drawing boxes, highlighting sections, and masking out areas, thereby enhancing the visual reasoning process. We experiment upon a wide range of structured image understanding tasks involving tables and charts. ReFocus largely improves performance on all tasks over GPT-4o without visual editing, yielding an average gain of 11.0% on table tasks and 6.8% on chart tasks. We present an in-depth analysis of the effects of different visual edits, and reasons why ReFocus can improve the performance without introducing additional information. Further, we collect a 14k training set using ReFocus, and prove that such visual chain-of-thought with intermediate information offers a better supervision than standard VQA data, reaching a 8.0% average gain over the same model trained with QA pairs and 2.6% over CoT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-13", "arxiv_id": "2501.04698", "title": "ConceptMaster: Multi-Concept Video Customization on Diffusion Transformer Models Without Test-Time Tuning", "authors": [ "Yuzhou Huang", "Ziyang Yuan", "Quande Liu", "Qiulin Wang", "Xintao Wang", "Ruimao Zhang", "Pengfei Wan", "Di Zhang", "Kun Gai" ], "github": "", "project_page": "", "abstract": "Text-to-video generation has made remarkable advancements through diffusion models. However, Multi-Concept Video Customization (MCVC) remains a significant challenge. We identify two key challenges in this task: 1) the identity decoupling problem, where directly adopting existing customization methods inevitably mix attributes when handling multiple concepts simultaneously, and 2) the scarcity of high-quality video-entity pairs, which is crucial for training such a model that represents and decouples various concepts well. To address these challenges, we introduce ConceptMaster, an innovative framework that effectively tackles the critical issues of identity decoupling while maintaining concept fidelity in customized videos. Specifically, we introduce a novel strategy of learning decoupled multi-concept embeddings that are injected into the diffusion models in a standalone manner, which effectively guarantees the quality of customized videos with multiple identities, even for highly similar visual concepts. To further overcome the scarcity of high-quality MCVC data, we carefully establish a data construction pipeline, which enables systematic collection of precise multi-concept video-entity data across diverse concepts. A comprehensive benchmark is designed to validate the effectiveness of our model from three critical dimensions: concept fidelity, identity decoupling ability, and video generation quality across six different concept composition scenarios. Extensive experiments demonstrate that our ConceptMaster significantly outperforms previous approaches for this task, paving the way for generating personalized and semantically accurate videos across multiple concepts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-13", "arxiv_id": "2501.06187", "title": "Multi-subject Open-set Personalization in Video Generation", "authors": [ "Tsai-Shien Chen", "Aliaksandr Siarohin", "Willi Menapace", "Yuwei Fang", "Kwot Sin Lee", "Ivan Skorokhodov", "Kfir Aberman", "Jun-Yan Zhu", "Ming-Hsuan Yang", "Sergey Tulyakov" ], "github": "", "project_page": "", "abstract": "Video personalization methods allow us to synthesize videos with specific concepts such as people, pets, and places. However, existing methods often focus on limited domains, require time-consuming optimization per subject, or support only a single subject. We present Video Alchemist - a video model with built-in multi-subject, open-set personalization capabilities for both foreground objects and background, eliminating the need for time-consuming test-time optimization. Our model is built on a new Diffusion Transformer module that fuses each conditional reference image and its corresponding subject-level text prompt with cross-attention layers. Developing such a large model presents two main challenges: dataset and evaluation. First, as paired datasets of reference images and videos are extremely hard to collect, we sample selected video frames as reference images and synthesize a clip of the target video. However, while models can easily denoise training videos given reference frames, they fail to generalize to new contexts. To mitigate this issue, we design a new automatic data construction pipeline with extensive image augmentations. Second, evaluating open-set video personalization is a challenge in itself. To address this, we introduce a personalization benchmark that focuses on accurate subject fidelity and supports diverse personalization scenarios. Finally, our extensive experiments show that our method significantly outperforms existing personalization methods in both quantitative and qualitative evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-13", "arxiv_id": "2501.04961", "title": "Demystifying Domain-adaptive Post-training for Financial LLMs", "authors": [ "Zixuan Ke", "Yifei Ming", "Xuan-Phi Nguyen", "Caiming Xiong", "Shafiq Joty" ], "github": "https://github.com/SalesforceAIResearch/FinDap", "project_page": "", "abstract": "Domain-adaptive post-training of large language models (LLMs) has emerged as a promising approach for specialized domains such as medicine and finance. However, significant challenges remain in identifying optimal adaptation criteria and training strategies across varying data and model configurations. To address these challenges, we introduce FINDAP, a systematic and fine-grained investigation into domain-adaptive post-training of LLMs for the finance domain. Our approach begins by identifying the core capabilities required for the target domain and designing a comprehensive evaluation suite aligned with these needs. We then analyze the effectiveness of key post-training stages, including continual pretraining, instruction tuning, and preference alignment. Building on these insights, we propose an effective training recipe centered on a novel preference data distillation method, which leverages process signals from a generative reward model. The resulting model, Llama-Fin, achieves state-of-the-art performance across a wide range of financial tasks. Our analysis also highlights how each post-training stage contributes to distinct capabilities, uncovering specific challenges and effective solutions, providing valuable insights for domain adaptation of LLMs. Project page: https://github.com/SalesforceAIResearch/FinDap", "github_manual": "", "github_regex": "https://github.com/SalesforceAIResearch/FinDap", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-13", "arxiv_id": "2501.05767", "title": "Migician: Revealing the Magic of Free-Form Multi-Image Grounding in Multimodal Large Language Models", "authors": [ "You Li", "Heyu Huang", "Chi Chen", "Kaiyu Huang", "Chao Huang", "Zonghao Guo", "Zhiyuan Liu", "Jinan Xu", "Yuhua Li", "Ruixuan Li", "Maosong Sun" ], "github": "https://github.com/thunlp/Migician", "project_page": "", "abstract": "The recent advancement of Multimodal Large Language Models (MLLMs) has significantly improved their fine-grained perception of single images and general comprehension across multiple images. However, existing MLLMs still face challenges in achieving precise grounding in complex multi-image scenarios. To address this, we first explore a Chain-of-Thought (CoT) framework that integrates single-image grounding with multi-image comprehension. While partially effective, it remains unstable and struggles to capture abstract visual information due to its non-end-to-end nature. Therefore, we introduce Migician, the first multi-image grounding model capable of performing free-form and accurate grounding across multiple images. To support this, we present the MGrounding-630k dataset, which comprises data for several multi-image grounding tasks derived from existing datasets, along with newly generated free-form grounding instruction-following data. Furthermore, we propose MIG-Bench, a comprehensive benchmark specifically designed for evaluating multi-image grounding capabilities. Experimental results demonstrate that our model achieves significantly superior multi-image grounding capabilities, outperforming the best existing MLLMs by 21.61% and even surpassing much larger 70B models. Our code, model, dataset, and benchmark are fully open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thunlp/Migician", "github_api_added_by": "auto" }, { "date": "2025-01-13", "arxiv_id": "2501.06250", "title": "Generative AI for Cel-Animation: A Survey", "authors": [ "Yunlong Tang", "Junjia Guo", "Pinxin Liu", "Zhiyuan Wang", "Hang Hua", "Jia-Xing Zhong", "Yunzhong Xiao", "Chao Huang", "Luchuan Song", "Susan Liang", "Yizhi Song", "Liu He", "Jing Bi", "Mingqian Feng", "Xinyang Li", "Zeliang Zhang", "Chenliang Xu" ], "github": "https://github.com/yunlong10/Awesome-AI4Animation", "project_page": "", "abstract": "Traditional Celluloid (Cel) Animation production pipeline encompasses multiple essential steps, including storyboarding, layout design, keyframe animation, inbetweening, and colorization, which demand substantial manual effort, technical expertise, and significant time investment. These challenges have historically impeded the efficiency and scalability of Cel-Animation production. The rise of generative artificial intelligence (GenAI), encompassing large language models, multimodal models, and diffusion models, offers innovative solutions by automating tasks such as inbetween frame generation, colorization, and storyboard creation. This survey explores how GenAI integration is revolutionizing traditional animation workflows by lowering technical barriers, broadening accessibility for a wider range of creators through tools like AniDoc, ToonCrafter, and AniSora, and enabling artists to focus more on creative expression and artistic innovation. Despite its potential, issues such as maintaining visual consistency, ensuring stylistic coherence, and addressing ethical considerations continue to pose challenges. Furthermore, this paper discusses future directions and explores potential advancements in AI-assisted animation. For further exploration and resources, please visit our GitHub repository: https://github.com/yunlong10/Awesome-AI4Animation", "github_manual": "", "github_regex": "https://github.com/yunlong10/Awesome-AI4Animation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-14", "arxiv_id": "2501.07301", "title": "The Lessons of Developing Process Reward Models in Mathematical Reasoning", "authors": [ "Zhenru Zhang", "Chujie Zheng", "Yangzhen Wu", "Beichen Zhang", "Runji Lin", "Bowen Yu", "Dayiheng Liu", "Jingren Zhou", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "Process Reward Models (PRMs) emerge as a promising approach for process supervision in mathematical reasoning of Large Language Models (LLMs), which aim to identify and mitigate intermediate errors in the reasoning processes. However, the development of effective PRMs faces significant challenges, particularly in data annotation and evaluation methodologies. In this paper, through extensive experiments, we demonstrate that commonly used Monte Carlo (MC) estimation-based data synthesis for PRMs typically yields inferior performance and generalization compared to LLM-as-a-judge and human annotation methods. MC estimation relies on completion models to evaluate current-step correctness, leading to inaccurate step verification. Furthermore, we identify potential biases in conventional Best-of-N (BoN) evaluation strategies for PRMs: (1) The unreliable policy models generate responses with correct answers but flawed processes, leading to a misalignment between the evaluation criteria of BoN and the PRM objectives of process verification. (2) The tolerance of PRMs of such responses leads to inflated BoN scores. (3) Existing PRMs have a significant proportion of minimum scores concentrated on the final answer steps, revealing the shift from process to outcome-based assessment in BoN Optimized PRMs. To address these challenges, we develop a consensus filtering mechanism that effectively integrates MC estimation with LLM-as-a-judge and advocates a more comprehensive evaluation framework that combines response-level and step-level metrics. Based on the mechanisms, we significantly improve both model performance and data efficiency in the BoN evaluation and the step-wise error identification task. Finally, we release a new state-of-the-art PRM that outperforms existing open-source alternatives and provides practical guidelines for future research in building process supervision models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-14", "arxiv_id": "2501.06425", "title": "Tensor Product Attention Is All You Need", "authors": [ "Yifan Zhang", "Yifeng Liu", "Huizhuo Yuan", "Zhen Qin", "Yang Yuan", "Quanquan Gu", "Andrew Chi-Chih Yao" ], "github": "https://github.com/tensorgi/TPA", "project_page": "", "abstract": "Scaling language models to handle longer input sequences typically necessitates large key-value (KV) caches, resulting in substantial memory overhead during inference. In this paper, we propose Tensor Product Attention (TPA), a novel attention mechanism that uses tensor decompositions to represent queries, keys, and values compactly, significantly shrinking KV cache size at inference time. By factorizing these representations into contextual low-rank components (contextual factorization) and seamlessly integrating with RoPE, TPA achieves improved model quality alongside memory efficiency. Based on TPA, we introduce the Tensor ProducT ATTenTion Transformer (T6), a new model architecture for sequence modeling. Through extensive empirical evaluation of language modeling tasks, we demonstrate that T6 exceeds the performance of standard Transformer baselines including MHA, MQA, GQA, and MLA across various metrics, including perplexity and a range of renowned evaluation benchmarks. Notably, TPAs memory efficiency enables the processing of significantly longer sequences under fixed resource constraints, addressing a critical scalability challenge in modern language models. The code is available at https://github.com/tensorgi/T6.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tensorgi/TPA", "github_api_added_by": "user" }, { "date": "2025-01-14", "arxiv_id": "2501.07572", "title": "WebWalker: Benchmarking LLMs in Web Traversal", "authors": [ "Jialong Wu", "Wenbiao Yin", "Yong Jiang", "Zhenglin Wang", "Zekun Xi", "Runnan Fang", "Deyu Zhou", "Pengjun Xie", "Fei Huang" ], "github": "", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) demonstrates remarkable performance across tasks in open-domain question-answering. However, traditional search engines may retrieve shallow content, limiting the ability of LLMs to handle complex, multi-layered information. To address it, we introduce WebWalkerQA, a benchmark designed to assess the ability of LLMs to perform web traversal. It evaluates the capacity of LLMs to traverse a website's subpages to extract high-quality data systematically. We propose WebWalker, which is a multi-agent framework that mimics human-like web navigation through an explore-critic paradigm. Extensive experimental results show that WebWalkerQA is challenging and demonstrates the effectiveness of RAG combined with WebWalker, through the horizontal and vertical integration in real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-14", "arxiv_id": "2501.06590", "title": "ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning", "authors": [ "Xiangru Tang", "Tianyu Hu", "Muyang Ye", "Yanjun Shao", "Xunjian Yin", "Siru Ouyang", "Wangchunshu Zhou", "Pan Lu", "Zhuosheng Zhang", "Yilun Zhao", "Arman Cohan", "Mark Gerstein" ], "github": "https://github.com/gersteinlab/chemagent", "project_page": "", "abstract": "Chemical reasoning usually involves complex, multi-step processes that demand precise calculations, where even minor errors can lead to cascading failures. Furthermore, large language models (LLMs) encounter difficulties handling domain-specific formulas, executing reasoning steps accurately, and integrating code effectively when tackling chemical reasoning tasks. To address these challenges, we present ChemAgent, a novel framework designed to improve the performance of LLMs through a dynamic, self-updating library. This library is developed by decomposing chemical tasks into sub-tasks and compiling these sub-tasks into a structured collection that can be referenced for future queries. Then, when presented with a new problem, ChemAgent retrieves and refines pertinent information from the library, which we call memory, facilitating effective task decomposition and the generation of solutions. Our method designs three types of memory and a library-enhanced reasoning component, enabling LLMs to improve over time through experience. Experimental results on four chemical reasoning datasets from SciBench demonstrate that ChemAgent achieves performance gains of up to 46% (GPT-4), significantly outperforming existing methods. Our findings suggest substantial potential for future applications, including tasks such as drug discovery and materials science. Our code can be found at https://github.com/gersteinlab/chemagent", "github_manual": "", "github_regex": "https://github.com/gersteinlab/chemagent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-14", "arxiv_id": "2501.06282", "title": "MinMo: A Multimodal Large Language Model for Seamless Voice Interaction", "authors": [ "Qian Chen", "Yafeng Chen", "Yanni Chen", "Mengzhe Chen", "Yingda Chen", "Chong Deng", "Zhihao Du", "Ruize Gao", "Changfeng Gao", "Zhifu Gao", "Yabin Li", "Xiang Lv", "Jiaqing Liu", "Haoneng Luo", "Bin Ma", "Chongjia Ni", "Xian Shi", "Jialong Tang", "Hui Wang", "Hao Wang", "Wen Wang", "Yuxuan Wang", "Yunlan Xu", "Fan Yu", "Zhijie Yan", "Yexin Yang", "Baosong Yang", "Xian Yang", "Guanrou Yang", "Tianyu Zhao", "Qinglin Zhang", "Shiliang Zhang", "Nan Zhao", "Pei Zhang", "Chong Zhang", "Jinren Zhou" ], "github": "", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) and multimodal speech-text models have laid the groundwork for seamless voice interactions, enabling real-time, natural, and human-like conversations. Previous models for voice interactions are categorized as native and aligned. Native models integrate speech and text processing in one framework but struggle with issues like differing sequence lengths and insufficient pre-training. Aligned models maintain text LLM capabilities but are often limited by small datasets and a narrow focus on speech tasks. In this work, we introduce MinMo, a Multimodal Large Language Model with approximately 8B parameters for seamless voice interaction. We address the main limitations of prior aligned multimodal models. We train MinMo through multiple stages of speech-to-text alignment, text-to-speech alignment, speech-to-speech alignment, and duplex interaction alignment, on 1.4 million hours of diverse speech data and a broad range of speech tasks. After the multi-stage training, MinMo achieves state-of-the-art performance across various benchmarks for voice comprehension and generation while maintaining the capabilities of text LLMs, and also facilitates full-duplex conversation, that is, simultaneous two-way communication between the user and the system. Moreover, we propose a novel and simple voice decoder that outperforms prior models in voice generation. The enhanced instruction-following capabilities of MinMo supports controlling speech generation based on user instructions, with various nuances including emotions, dialects, and speaking rates, and mimicking specific voices. For MinMo, the speech-to-text latency is approximately 100ms, full-duplex latency is approximately 600ms in theory and 800ms in practice. The MinMo project web page is https://funaudiollm.github.io/minmo, and the code and models will be released soon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-14", "arxiv_id": "2501.06252", "title": "Transformer^2: Self-adaptive LLMs", "authors": [ "Qi Sun", "Edoardo Cetin", "Yujin Tang" ], "github": "https://github.com/SakanaAI/self-adaptive-llms", "project_page": "", "abstract": "Self-adaptive large language models (LLMs) aim to solve the challenges posed by traditional fine-tuning methods, which are often computationally intensive and static in their ability to handle diverse tasks. We introduce \\implname, a novel self-adaptation framework that adapts LLMs for unseen tasks in real-time by selectively adjusting only the singular components of their weight matrices. During inference, \\implname employs a two-pass mechanism: first, a dispatch system identifies the task properties, and then task-specific \"expert\" vectors, trained using reinforcement learning, are dynamically mixed to obtain targeted behavior for the incoming prompt. Our method outperforms ubiquitous approaches such as LoRA, with fewer parameters and greater efficiency. \\implname demonstrates versatility across different LLM architectures and modalities, including vision-language tasks. \\implname represents a significant leap forward, offering a scalable, efficient solution for enhancing the adaptability and task-specific performance of LLMs, paving the way for truly dynamic, self-organizing AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SakanaAI/self-adaptive-llms", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-14", "arxiv_id": "2501.06458", "title": "O1 Replication Journey -- Part 3: Inference-time Scaling for Medical Reasoning", "authors": [ "Zhongzhen Huang", "Gui Geng", "Shengyi Hua", "Zhen Huang", "Haoyang Zou", "Shaoting Zhang", "Pengfei Liu", "Xiaofan Zhang" ], "github": "https://github.com/spiral-med/ophiuchus", "project_page": "", "abstract": "Building upon our previous investigations of O1 replication (Part 1: Journey Learning [Qin et al., 2024] and Part 2: Distillation [Huang et al., 2024]), this work explores the potential of inference-time scaling in large language models (LLMs) for medical reasoning tasks, ranging from diagnostic decision-making to treatment planning. Through extensive experiments on medical benchmarks of varying complexity (MedQA, Medbullets, and JAMA Clinical Challenges), our investigation reveals several key insights: (1) Increasing inference time does lead to improved performance. With a modest training set of 500 samples, our model yields substantial performance improvements of 6%-11%. (2) Task complexity directly correlates with the required length of reasoning chains, confirming the necessity of extended thought processes for challenging problems. (3) The differential diagnoses generated by our model adhere to the principles of the hypothetico-deductive method, producing a list of potential conditions that may explain a patient's symptoms and systematically narrowing these possibilities by evaluating the evidence. These findings demonstrate the promising synergy between inference-time scaling and journey learning in advancing LLMs' real-world clinical reasoning capabilities.", "github_manual": "https://github.com/spiral-med/ophiuchus", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-14", "arxiv_id": "2501.07574", "title": "UnCommon Objects in 3D", "authors": [ "Xingchen Liu", "Piyush Tayal", "Jianyuan Wang", "Jesus Zarzar", "Tom Monnier", "Konstantinos Tertikas", "Jiali Duan", "Antoine Toisoul", "Jason Y. Zhang", "Natalia Neverova", "Andrea Vedaldi", "Roman Shapovalov", "David Novotny" ], "github": "", "project_page": "", "abstract": "We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-resolution videos of objects with 3D annotations that ensures full-360^{circ} coverage. uCO3D is significantly more diverse than MVImgNet and CO3Dv2, covering more than 1,000 object categories. It is also of higher quality, due to extensive quality checks of both the collected videos and the 3D annotations. Similar to analogous datasets, uCO3D contains annotations for 3D camera poses, depth maps and sparse point clouds. In addition, each object is equipped with a caption and a 3D Gaussian Splat reconstruction. We train several large 3D models on MVImgNet, CO3Dv2, and uCO3D and obtain superior results using the latter, showing that uCO3D is better for learning applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-14", "arxiv_id": "2501.06173", "title": "VideoAuteur: Towards Long Narrative Video Generation", "authors": [ "Junfei Xiao", "Feng Cheng", "Lu Qi", "Liangke Gui", "Jiepeng Cen", "Zhibei Ma", "Alan Yuille", "Lu Jiang" ], "github": "https://github.com/lambert-x/VideoAuteur", "project_page": "", "abstract": "Recent video generation models have shown promising results in producing high-quality video clips lasting several seconds. However, these models face challenges in generating long sequences that convey clear and informative events, limiting their ability to support coherent narrations. In this paper, we present a large-scale cooking video dataset designed to advance long-form narrative generation in the cooking domain. We validate the quality of our proposed dataset in terms of visual fidelity and textual caption accuracy using state-of-the-art Vision-Language Models (VLMs) and video generation models, respectively. We further introduce a Long Narrative Video Director to enhance both visual and semantic coherence in generated videos and emphasize the role of aligning visual embeddings to achieve improved overall video quality. Our method demonstrates substantial improvements in generating visually detailed and semantically aligned keyframes, supported by finetuning techniques that integrate text and image embeddings within the video generation process. Project page: https://videoauteur.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lambert-x/VideoAuteur", "github_api_added_by": "auto" }, { "date": "2025-01-14", "arxiv_id": "2501.07171", "title": "BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature", "authors": [ "Alejandro Lozano", "Min Woo Sun", "James Burgess", "Liangyu Chen", "Jeffrey J Nirschl", "Jeffrey Gu", "Ivan Lopez", "Josiah Aklilu", "Austin Wolfgang Katzer", "Collin Chiu", "Anita Rau", "Xiaohan Wang", "Yuhui Zhang", "Alfred Seunghoon Song", "Robert Tibshirani", "Serena Yeung-Levy" ], "github": "", "project_page": "", "abstract": "The development of vision-language models (VLMs) is driven by large-scale and diverse multimodal datasets. However, progress toward generalist biomedical VLMs is limited by the lack of annotated, publicly accessible datasets across biology and medicine. Existing efforts are restricted to narrow domains, missing the full diversity of biomedical knowledge encoded in scientific literature. To address this gap, we introduce BIOMEDICA, a scalable, open-source framework to extract, annotate, and serialize the entirety of the PubMed Central Open Access subset into an easy-to-use, publicly accessible dataset.Our framework produces a comprehensive archive with over 24 million unique image-text pairs from over 6 million articles. Metadata and expert-guided annotations are also provided. We demonstrate the utility and accessibility of our resource by releasing BMCA-CLIP, a suite of CLIP-style models continuously pre-trained on the BIOMEDICA dataset via streaming, eliminating the need to download 27 TB of data locally.On average, our models achieve state-of-the-art performance across 40 tasks - spanning pathology, radiology, ophthalmology, dermatology, surgery, molecular biology, parasitology, and cell biology - excelling in zero-shot classification with a 6.56% average improvement (as high as 29.8% and 17.5% in dermatology and ophthalmology, respectively), and stronger image-text retrieval, all while using 10x less compute. To foster reproducibility and collaboration, we release our codebase and dataset for the broader research community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-14", "arxiv_id": "2501.06842", "title": "SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training", "authors": [ "Tianjin Huang", "Ziquan Zhu", "Gaojie Jin", "Lu Liu", "Zhangyang Wang", "Shiwei Liu" ], "github": "https://github.com/TianjinYellow/SPAM-Optimizer.git", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated exceptional performance across diverse tasks, yet their training remains highly resource-intensive and susceptible to critical challenges such as training instability. A predominant source of this instability stems from gradient and loss spikes, which disrupt the learning process, often leading to costly interventions like checkpoint recovery and experiment restarts, further amplifying inefficiencies. This paper presents a comprehensive investigation into gradient spikes observed during LLM training, revealing their prevalence across multiple architectures and datasets. Our analysis shows that these spikes can be up to 1000times larger than typical gradients, substantially deteriorating model performance. To address this issue, we propose Spike-Aware Adam with Momentum Reset SPAM, a novel optimizer designed to counteract gradient spikes through momentum reset and spike-aware gradient clipping. Extensive experiments, including both pre-training and fine-tuning, demonstrate that SPAM consistently surpasses Adam and its variants across various tasks, including (1) LLM pre-training from 60M to 1B, (2) 4-bit LLM pre-training,(3) reinforcement learning, and (4) Time Series Forecasting. Additionally, SPAM facilitates memory-efficient training by enabling sparse momentum, where only a subset of momentum terms are maintained and updated. When operating under memory constraints, SPAM outperforms state-of-the-art memory-efficient optimizers such as GaLore and Adam-Mini. Our work underscores the importance of mitigating gradient spikes in LLM training and introduces an effective optimization strategy that enhances both training stability and resource efficiency at scale. Code is available at https://github.com/TianjinYellow/SPAM-Optimizer.git", "github_manual": "", "github_regex": "https://github.com/TianjinYellow/SPAM-Optimizer.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-14", "arxiv_id": "2501.06708", "title": "Evaluating Sample Utility for Data Selection by Mimicking Model Weights", "authors": [ "Tzu-Heng Huang", "Manjot Bilkhu", "Frederic Sala", "Javier Movellan" ], "github": "", "project_page": "", "abstract": "Foundation models rely on large-scale web-crawled datasets, which frequently contain noisy data, biases, and irrelevant content. Existing data selection techniques typically use human heuristics, downstream evaluation datasets, or specialized scoring models, and can overlook samples' utility in the training process. Instead, we propose a new approach, Mimic Score, a data quality metric that uses a pretrained reference model as a guide to assess the usefulness of data samples for training a new model. It relies on the alignment between the gradient of the new model parameters and the vector pointing toward the reference model in weight space. Samples that misalign with this direction are considered low-value and can be filtered out. Motivated by the Mimic score, we develop Grad-Mimic, a data selection framework that identifies and prioritizes useful samples, automating the selection process to create effective filters. Empirically, using Mimic scores to guide model training results in consistent performance gains across six image datasets and enhances the performance of CLIP models. Moreover, Mimic scores and their associated filters improve upon existing filtering methods and offer accurate estimation of dataset quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.08313", "title": "MiniMax-01: Scaling Foundation Models with Lightning Attention", "authors": [ "MiniMax", "Aonian Li", "Bangwei Gong", "Bo Yang", "Boji Shan", "Chang Liu", "Cheng Zhu", "Chunhao Zhang", "Congchao Guo", "Da Chen", "Dong Li", "Enwei Jiao", "Gengxin Li", "Guojun Zhang", "Haohai Sun", "Houze Dong", "Jiadai Zhu", "Jiaqi Zhuang", "Jiayuan Song", "Jin Zhu", "Jingtao Han", "Jingyang Li", "Junbin Xie", "Junhao Xu", "Junjie Yan", "Kaishun Zhang", "Kecheng Xiao", "Kexi Kang", "Le Han", "Leyang Wang", "Lianfei Yu", "Liheng Feng", "Lin Zheng", "Linbo Chai", "Long Xing", "Meizhi Ju", "Mingyuan Chi", "Mozhi Zhang", "Peikai Huang", "Pengcheng Niu", "Pengfei Li", "Pengyu Zhao", "Qi Yang", "Qidi Xu", "Qiexiang Wang", "Qin Wang", "Qiuhui Li", "Ruitao Leng", "Shengmin Shi", "Shuqi Yu", "Sichen Li", "Songquan Zhu", "Tao Huang", "Tianrun Liang", "Weigao Sun", "Weixuan Sun", "Weiyu Cheng", "Wenkai Li", "Xiangjun Song", "Xiao Su", "Xiaodong Han", "Xinjie Zhang", "Xinzhu Hou", "Xu Min", "Xun Zou", "Xuyang Shen", "Yan Gong", "Yingjie Zhu", "Yipeng Zhou", "Yiran Zhong", "Yongyi Hu", "Yuanxiang Fan", "Yue Yu", "Yufeng Yang", "Yuhao Li", "Yunan Huang", "Yunji Li", "Yunpeng Huang", "Yunzhi Xu", "Yuxin Mao", "Zehan Li", "Zekang Li", "Zewei Tao", "Zewen Ying", "Zhaoyang Cong", "Zhen Qin", "Zhenhua Fan", "Zhihang Yu", "Zhuo Jiang", "Zijia Wu" ], "github": "https://github.com/MiniMax-AI/MiniMax-01", "project_page": "", "abstract": "We introduce MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01, which are comparable to top-tier models while offering superior capabilities in processing longer contexts. The core lies in lightning attention and its efficient scaling. To maximize computational capacity, we integrate it with Mixture of Experts (MoE), creating a model with 32 experts and 456 billion total parameters, of which 45.9 billion are activated for each token. We develop an optimized parallel strategy and highly efficient computation-communication overlap techniques for MoE and lightning attention. This approach enables us to conduct efficient training and inference on models with hundreds of billions of parameters across contexts spanning millions of tokens. The context window of MiniMax-Text-01 can reach up to 1 million tokens during training and extrapolate to 4 million tokens during inference at an affordable cost. Our vision-language model, MiniMax-VL-01 is built through continued training with 512 billion vision-language tokens. Experiments on both standard and in-house benchmarks show that our models match the performance of state-of-the-art models like GPT-4o and Claude-3.5-Sonnet while offering 20-32 times longer context window. We publicly release MiniMax-01 at https://github.com/MiniMax-AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MiniMax-AI/MiniMax-01", "github_api_added_by": "auto" }, { "date": "2025-01-15", "arxiv_id": "2501.08187", "title": "A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following", "authors": [ "Yin Fang", "Xinle Deng", "Kangwei Liu", "Ningyu Zhang", "Jingyang Qian", "Penghui Yang", "Xiaohui Fan", "Huajun Chen" ], "github": "https://github.com/zjunlp/instructcell", "project_page": "", "abstract": "Large language models excel at interpreting complex natural language instructions, enabling them to perform a wide range of tasks. In the life sciences, single-cell RNA sequencing (scRNA-seq) data serves as the \"language of cellular biology\", capturing intricate gene expression patterns at the single-cell level. However, interacting with this \"language\" through conventional tools is often inefficient and unintuitive, posing challenges for researchers. To address these limitations, we present InstructCell, a multi-modal AI copilot that leverages natural language as a medium for more direct and flexible single-cell analysis. We construct a comprehensive multi-modal instruction dataset that pairs text-based instructions with scRNA-seq profiles from diverse tissues and species. Building on this, we develop a multi-modal cell language architecture capable of simultaneously interpreting and processing both modalities. InstructCell empowers researchers to accomplish critical tasks-such as cell type annotation, conditional pseudo-cell generation, and drug sensitivity prediction-using straightforward natural language commands. Extensive evaluations demonstrate that InstructCell consistently meets or exceeds the performance of existing single-cell foundation models, while adapting to diverse experimental conditions. More importantly, InstructCell provides an accessible and intuitive tool for exploring complex single-cell data, lowering technical barriers and enabling deeper biological insights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zjunlp/instructcell", "github_api_added_by": "auto" }, { "date": "2025-01-15", "arxiv_id": "2501.08316", "title": "Diffusion Adversarial Post-Training for One-Step Video Generation", "authors": [ "Shanchuan Lin", "Xin Xia", "Yuxi Ren", "Ceyuan Yang", "Xuefeng Xiao", "Lu Jiang" ], "github": "", "project_page": "", "abstract": "The diffusion models are widely used for image and video generation, but their iterative generation process is slow and expansive. While existing distillation approaches have demonstrated the potential for one-step generation in the image domain, they still suffer from significant quality degradation. In this work, we propose Adversarial Post-Training (APT) against real data following diffusion pre-training for one-step video generation. To improve the training stability and quality, we introduce several improvements to the model architecture and training procedures, along with an approximated R1 regularization objective. Empirically, our experiments show that our adversarial post-trained model, Seaweed-APT, can generate 2-second, 1280x720, 24fps videos in real time using a single forward evaluation step. Additionally, our model is capable of generating 1024px images in a single step, achieving quality comparable to state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.08332", "title": "MangaNinja: Line Art Colorization with Precise Reference Following", "authors": [ "Zhiheng Liu", "Ka Leong Cheng", "Xi Chen", "Jie Xiao", "Hao Ouyang", "Kai Zhu", "Yu Liu", "Yujun Shen", "Qifeng Chen", "Ping Luo" ], "github": "https://github.com/ali-vilab/MangaNinjia", "project_page": "", "abstract": "Derived from diffusion models, MangaNinjia specializes in the task of reference-guided line art colorization. We incorporate two thoughtful designs to ensure precise character detail transcription, including a patch shuffling module to facilitate correspondence learning between the reference color image and the target line art, and a point-driven control scheme to enable fine-grained color matching. Experiments on a self-collected benchmark demonstrate the superiority of our model over current solutions in terms of precise colorization. We further showcase the potential of the proposed interactive point control in handling challenging cases, cross-character colorization, multi-reference harmonization, beyond the reach of existing algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ali-vilab/MangaNinjia", "github_api_added_by": "auto" }, { "date": "2025-01-15", "arxiv_id": "2501.08225", "title": "FramePainter: Endowing Interactive Image Editing with Video Diffusion Priors", "authors": [ "Yabo Zhang", "Xinpeng Zhou", "Yihan Zeng", "Hang Xu", "Hui Li", "Wangmeng Zuo" ], "github": "https://github.com/YBYBZhang/FramePainter", "project_page": "", "abstract": "Interactive image editing allows users to modify images through visual interaction operations such as drawing, clicking, and dragging. Existing methods construct such supervision signals from videos, as they capture how objects change with various physical interactions. However, these models are usually built upon text-to-image diffusion models, so necessitate (i) massive training samples and (ii) an additional reference encoder to learn real-world dynamics and visual consistency. In this paper, we reformulate this task as an image-to-video generation problem, so that inherit powerful video diffusion priors to reduce training costs and ensure temporal consistency. Specifically, we introduce FramePainter as an efficient instantiation of this formulation. Initialized with Stable Video Diffusion, it only uses a lightweight sparse control encoder to inject editing signals. Considering the limitations of temporal attention in handling large motion between two frames, we further propose matching attention to enlarge the receptive field while encouraging dense correspondence between edited and source image tokens. We highlight the effectiveness and efficiency of FramePainter across various of editing signals: it domainantly outperforms previous state-of-the-art methods with far less training data, achieving highly seamless and coherent editing of images, \\eg, automatically adjust the reflection of the cup. Moreover, FramePainter also exhibits exceptional generalization in scenarios not present in real-world videos, \\eg, transform the clownfish into shark-like shape. Our code will be available at https://github.com/YBYBZhang/FramePainter.", "github_manual": "", "github_regex": "https://github.com/YBYBZhang/FramePainter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.07730", "title": "Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens", "authors": [ "Dongwon Kim", "Ju He", "Qihang Yu", "Chenglin Yang", "Xiaohui Shen", "Suha Kwak", "Liang-Chieh Chen" ], "github": "", "project_page": "", "abstract": "Image tokenizers form the foundation of modern text-to-image generative models but are notoriously difficult to train. Furthermore, most existing text-to-image models rely on large-scale, high-quality private datasets, making them challenging to replicate. In this work, we introduce Text-Aware Transformer-based 1-Dimensional Tokenizer (TA-TiTok), an efficient and powerful image tokenizer that can utilize either discrete or continuous 1-dimensional tokens. TA-TiTok uniquely integrates textual information during the tokenizer decoding stage (i.e., de-tokenization), accelerating convergence and enhancing performance. TA-TiTok also benefits from a simplified, yet effective, one-stage training process, eliminating the need for the complex two-stage distillation used in previous 1-dimensional tokenizers. This design allows for seamless scalability to large datasets. Building on this, we introduce a family of text-to-image Masked Generative Models (MaskGen), trained exclusively on open data while achieving comparable performance to models trained on private data. We aim to release both the efficient, strong TA-TiTok tokenizers and the open-data, open-weight MaskGen models to promote broader access and democratize the field of text-to-image masked generative models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.08328", "title": "PokerBench: Training Large Language Models to become Professional Poker Players", "authors": [ "Richard Zhuang", "Akshat Gupta", "Richard Yang", "Aniket Rahane", "Zhengyu Li", "Gopala Anumanchipalli" ], "github": "https://github.com/pokerllm/pokerbench", "project_page": "", "abstract": "We introduce PokerBench - a benchmark for evaluating the poker-playing abilities of large language models (LLMs). As LLMs excel in traditional NLP tasks, their application to complex, strategic games like poker poses a new challenge. Poker, an incomplete information game, demands a multitude of skills such as mathematics, reasoning, planning, strategy, and a deep understanding of game theory and human psychology. This makes Poker the ideal next frontier for large language models. PokerBench consists of a comprehensive compilation of 11,000 most important scenarios, split between pre-flop and post-flop play, developed in collaboration with trained poker players. We evaluate prominent models including GPT-4, ChatGPT 3.5, and various Llama and Gemma series models, finding that all state-of-the-art LLMs underperform in playing optimal poker. However, after fine-tuning, these models show marked improvements. We validate PokerBench by having models with different scores compete with each other, demonstrating that higher scores on PokerBench lead to higher win rates in actual poker games. Through gameplay between our fine-tuned model and GPT-4, we also identify limitations of simple supervised fine-tuning for learning optimal playing strategy, suggesting the need for more advanced methodologies for effectively training language models to excel in games. PokerBench thus presents a unique benchmark for a quick and reliable evaluation of the poker-playing ability of LLMs as well as a comprehensive benchmark to study the progress of LLMs in complex game-playing scenarios. The dataset and code will be made available at: https://github.com/pokerllm/pokerbench.", "github_manual": "", "github_regex": "https://github.com/pokerllm/pokerbench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.08292", "title": "HALoGEN: Fantastic LLM Hallucinations and Where to Find Them", "authors": [ "Abhilasha Ravichander", "Shrusti Ghela", "David Wadden", "Yejin Choi" ], "github": "https://github.com/AbhilashaRavichander/HALoGEN", "project_page": "", "abstract": "Despite their impressive ability to generate high-quality and fluent text, generative large language models (LLMs) also produce hallucinations: statements that are misaligned with established world knowledge or provided input context. However, measuring hallucination can be challenging, as having humans verify model generations on-the-fly is both expensive and time-consuming. In this work, we release HALoGEN, a comprehensive hallucination benchmark consisting of: (1) 10,923 prompts for generative models spanning nine domains including programming, scientific attribution, and summarization, and (2) automatic high-precision verifiers for each use case that decompose LLM generations into atomic units, and verify each unit against a high-quality knowledge source. We use this framework to evaluate ~150,000 generations from 14 language models, finding that even the best-performing models are riddled with hallucinations (sometimes up to 86% of generated atomic facts depending on the domain). We further define a novel error classification for LLM hallucinations based on whether they likely stem from incorrect recollection of training data (Type A errors), or incorrect knowledge in training data (Type B errors), or are fabrication (Type C errors). We hope our framework provides a foundation to enable the principled study of why generative models hallucinate, and advances the development of trustworthy large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AbhilashaRavichander/HALoGEN", "github_api_added_by": "auto" }, { "date": "2025-01-15", "arxiv_id": "2501.07888", "title": "Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video Understanding", "authors": [ "Liping Yuan", "Jiawei Wang", "Haomiao Sun", "Yuchen Zhang", "Yuan Lin" ], "github": "", "project_page": "", "abstract": "We introduce Tarsier2, a state-of-the-art large vision-language model (LVLM) designed for generating detailed and accurate video descriptions, while also exhibiting superior general video understanding capabilities. Tarsier2 achieves significant advancements through three key upgrades: (1) Scaling pre-training data from 11M to 40M video-text pairs, enriching both volume and diversity; (2) Performing fine-grained temporal alignment during supervised fine-tuning; (3) Using model-based sampling to automatically construct preference data and applying DPO training for optimization. Extensive experiments show that Tarsier2-7B consistently outperforms leading proprietary models, including GPT-4o and Gemini 1.5 Pro, in detailed video description tasks. On the DREAM-1K benchmark, Tarsier2-7B improves F1 by 2.8\\% over GPT-4o and 5.8\\% over Gemini-1.5-Pro. In human side-by-side evaluations, Tarsier2-7B shows a +8.6\\% performance advantage over GPT-4o and +24.9\\% over Gemini-1.5-Pro. Tarsier2-7B also sets new state-of-the-art results across 15 public benchmarks, spanning tasks such as video question-answering, video grounding, hallucination test, and embodied question-answering, demonstrating its versatility as a robust generalist vision-language model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.08167", "title": "Potential and Perils of Large Language Models as Judges of Unstructured Textual Data", "authors": [ "Rewina Bedemariam", "Natalie Perez", "Sreyoshi Bhaduri", "Satya Kapoor", "Alex Gil", "Elizabeth Conjar", "Ikkei Itoku", "David Theil", "Aman Chadha", "Naumaan Nayyar" ], "github": "", "project_page": "", "abstract": "Rapid advancements in large language models have unlocked remarkable capabilities when it comes to processing and summarizing unstructured text data. This has implications for the analysis of rich, open-ended datasets, such as survey responses, where LLMs hold the promise of efficiently distilling key themes and sentiments. However, as organizations increasingly turn to these powerful AI systems to make sense of textual feedback, a critical question arises, can we trust LLMs to accurately represent the perspectives contained within these text based datasets? While LLMs excel at generating human-like summaries, there is a risk that their outputs may inadvertently diverge from the true substance of the original responses. Discrepancies between the LLM-generated outputs and the actual themes present in the data could lead to flawed decision-making, with far-reaching consequences for organizations. This research investigates the effectiveness of LLMs as judge models to evaluate the thematic alignment of summaries generated by other LLMs. We utilized an Anthropic Claude model to generate thematic summaries from open-ended survey responses, with Amazon's Titan Express, Nova Pro, and Meta's Llama serving as LLM judges. The LLM-as-judge approach was compared to human evaluations using Cohen's kappa, Spearman's rho, and Krippendorff's alpha, validating a scalable alternative to traditional human centric evaluation methods. Our findings reveal that while LLMs as judges offer a scalable solution comparable to human raters, humans may still excel at detecting subtle, context-specific nuances. This research contributes to the growing body of knowledge on AI assisted text analysis. We discuss limitations and provide recommendations for future research, emphasizing the need for careful consideration when generalizing LLM judge models across various contexts and use cases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.08197", "title": "OpenCSG Chinese Corpus: A Series of High-quality Chinese Datasets for LLM Training", "authors": [ "Yijiong Yu", "Ziyun Dai", "Zekun Wang", "Wei Wang", "Ran Chen", "Ji Pei" ], "github": "https://github.com/yuyijiong/fineweb-edu-chinese", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities, but their success heavily relies on the quality of pretraining corpora. For Chinese LLMs, the scarcity of high-quality Chinese datasets presents a significant challenge, often limiting their performance. To address this issue, we propose the OpenCSG Chinese Corpus, a series of high-quality datasets specifically designed for LLM pretraining, post-training, and fine-tuning. This corpus includes Fineweb-edu-chinese, Fineweb-edu-chinese-v2, Cosmopedia-chinese, and Smoltalk-chinese, each with distinct characteristics: Fineweb-edu datasets focus on filtered, high-quality content derived from diverse Chinese web sources; Cosmopedia-chinese provides synthetic, textbook-style data for knowledge-intensive training; and Smoltalk-chinese emphasizes stylistic and diverse chat-format data. The OpenCSG Chinese Corpus is characterized by its high-quality text, diverse coverage across domains, and scalable, reproducible data curation processes. Additionally, we conducted extensive experimental analyses, including evaluations on smaller parameter models, which demonstrated significant performance improvements in tasks such as C-Eval, showcasing the effectiveness of the corpus for training Chinese LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yuyijiong/fineweb-edu-chinese", "github_api_added_by": "auto" }, { "date": "2025-01-15", "arxiv_id": "2501.08319", "title": "Enhancing Automated Interpretability with Output-Centric Feature Descriptions", "authors": [ "Yoav Gur-Arieh", "Roy Mayan", "Chen Agassy", "Atticus Geiger", "Mor Geva" ], "github": "https://github.com/yoavgur/feature-descriptions", "project_page": "", "abstract": "Automated interpretability pipelines generate natural language descriptions for the concepts represented by features in large language models (LLMs), such as plants or the first word in a sentence. These descriptions are derived using inputs that activate the feature, which may be a dimension or a direction in the model's representation space. However, identifying activating inputs is costly, and the mechanistic role of a feature in model behavior is determined both by how inputs cause a feature to activate and by how feature activation affects outputs. Using steering evaluations, we reveal that current pipelines provide descriptions that fail to capture the causal effect of the feature on outputs. To fix this, we propose efficient, output-centric methods for automatically generating feature descriptions. These methods use the tokens weighted higher after feature stimulation or the highest weight tokens after applying the vocabulary \"unembedding\" head directly to the feature. Our output-centric descriptions better capture the causal effect of a feature on model outputs than input-centric descriptions, but combining the two leads to the best performance on both input and output evaluations. Lastly, we show that output-centric descriptions can be used to find inputs that activate features previously thought to be \"dead\".", "github_manual": "https://github.com/yoavgur/feature-descriptions", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.08284", "title": "AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages", "authors": [ "Shamsuddeen Hassan Muhammad", "Idris Abdulmumin", "Abinew Ali Ayele", "David Ifeoluwa Adelani", "Ibrahim Said Ahmad", "Saminu Mohammad Aliyu", "Nelson Odhiambo Onyango", "Lilian D. A. Wanzare", "Samuel Rutunda", "Lukman Jibril Aliyu", "Esubalew Alemneh", "Oumaima Hourrane", "Hagos Tesfahun Gebremichael", "Elyas Abdi Ismail", "Meriem Beloucif", "Ebrahim Chekol Jibril", "Andiswa Bukula", "Rooweither Mabuya", "Salomey Osei", "Abigail Oppong", "Tadesse Destaw Belay", "Tadesse Kebede Guge", "Tesfa Tegegne Asfaw", "Chiamaka Ijeoma Chukwuneke", "Paul R\u00f6ttger", "Seid Muhie Yimam", "Nedjma Ousidhoum" ], "github": "https://github.com/AfriHate/AfriHate", "project_page": "", "abstract": "Hate speech and abusive language are global phenomena that need socio-cultural background knowledge to be understood, identified, and moderated. However, in many regions of the Global South, there have been several documented occurrences of (1) absence of moderation and (2) censorship due to the reliance on keyword spotting out of context. Further, high-profile individuals have frequently been at the center of the moderation process, while large and targeted hate speech campaigns against minorities have been overlooked. These limitations are mainly due to the lack of high-quality data in the local languages and the failure to include local communities in the collection, annotation, and moderation processes. To address this issue, we present AfriHate: a multilingual collection of hate speech and abusive language datasets in 15 African languages. Each instance in AfriHate is annotated by native speakers familiar with the local culture. We report the challenges related to the construction of the datasets and present various classification baseline results with and without using LLMs. The datasets, individual annotations, and hate speech and offensive language lexicons are available on https://github.com/AfriHate/AfriHate", "github_manual": "", "github_regex": "https://github.com/AfriHate/AfriHate", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.06751", "title": "Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I Models", "authors": [ "Michael Toker", "Ido Galil", "Hadas Orgad", "Rinon Gal", "Yoad Tewel", "Gal Chechik", "Yonatan Belinkov" ], "github": "", "project_page": "", "abstract": "Text-to-image (T2I) diffusion models rely on encoded prompts to guide the image generation process. Typically, these prompts are extended to a fixed length by adding padding tokens before text encoding. Despite being a default practice, the influence of padding tokens on the image generation process has not been investigated. In this work, we conduct the first in-depth analysis of the role padding tokens play in T2I models. We develop two causal techniques to analyze how information is encoded in the representation of tokens across different components of the T2I pipeline. Using these techniques, we investigate when and how padding tokens impact the image generation process. Our findings reveal three distinct scenarios: padding tokens may affect the model's output during text encoding, during the diffusion process, or be effectively ignored. Moreover, we identify key relationships between these scenarios and the model's architecture (cross or self-attention) and its training process (frozen or trained text encoder). These insights contribute to a deeper understanding of the mechanisms of padding tokens, potentially informing future model design and training practices in T2I systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.08326", "title": "Omni-RGPT: Unifying Image and Video Region-level Understanding via Token Marks", "authors": [ "Miran Heo", "Min-Hung Chen", "De-An Huang", "Sifei Liu", "Subhashree Radhakrishnan", "Seon Joo Kim", "Yu-Chiang Frank Wang", "Ryo Hachiuma" ], "github": "", "project_page": "", "abstract": "We present Omni-RGPT, a multimodal large language model designed to facilitate region-level comprehension for both images and videos. To achieve consistent region representation across spatio-temporal dimensions, we introduce Token Mark, a set of tokens highlighting the target regions within the visual feature space. These tokens are directly embedded into spatial regions using region prompts (e.g., boxes or masks) and simultaneously incorporated into the text prompt to specify the target, establishing a direct connection between visual and text tokens. To further support robust video understanding without requiring tracklets, we introduce an auxiliary task that guides Token Mark by leveraging the consistency of the tokens, enabling stable region interpretation across the video. Additionally, we introduce a large-scale region-level video instruction dataset (RegVID-300k). Omni-RGPT achieves state-of-the-art results on image and video-based commonsense reasoning benchmarks while showing strong performance in captioning and referring expression comprehension tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.08120", "title": "In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR", "authors": [ "Markus J. Buehler" ], "github": "", "project_page": "", "abstract": "The pursuit of automated scientific discovery has fueled progress from symbolic logic to modern AI, forging new frontiers in reasoning and pattern recognition. Transformers function as potential systems, where every possible relationship remains latent potentiality until tasks impose constraints, akin to measurement. Yet, refining their sampling requires more than probabilistic selection: solutions must conform to specific structures or rules, ensuring consistency and the invocation of general principles. We present Graph-PReFLexOR (Graph-based Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning), a framework that combines graph reasoning with symbolic abstraction to dynamically expand domain knowledge. Inspired by reinforcement learning, Graph-PReFLexOR defines reasoning as a structured mapping, where tasks yield knowledge graphs, abstract patterns, and ultimately, final answers. Inspired by category theory, it encodes concepts as nodes and their relationships as edges, supporting hierarchical inference and adaptive learning through isomorphic representations. Demonstrations include hypothesis generation, materials design, and creative reasoning, such as discovering relationships between mythological concepts like 'thin places' with materials science. We propose a 'knowledge garden growth' strategy that integrates insights across domains, promoting interdisciplinary connections. Results with a 3-billion-parameter Graph-PReFLexOR model show superior reasoning depth and adaptability, underscoring the potential for transparent, multidisciplinary AI-driven discovery. It lays the groundwork for general autonomous reasoning solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.05131", "title": "3DIS-FLUX: simple and efficient multi-instance generation with DiT rendering", "authors": [ "Dewei Zhou", "Ji Xie", "Zongxin Yang", "Yi Yang" ], "github": "", "project_page": "", "abstract": "The growing demand for controllable outputs in text-to-image generation has driven significant advancements in multi-instance generation (MIG), enabling users to define both instance layouts and attributes. Currently, the state-of-the-art methods in MIG are primarily adapter-based. However, these methods necessitate retraining a new adapter each time a more advanced model is released, resulting in significant resource consumption. A methodology named Depth-Driven Decoupled Instance Synthesis (3DIS) has been introduced, which decouples MIG into two distinct phases: 1) depth-based scene construction and 2) detail rendering with widely pre-trained depth control models. The 3DIS method requires adapter training solely during the scene construction phase, while enabling various models to perform training-free detail rendering. Initially, 3DIS focused on rendering techniques utilizing U-Net architectures such as SD1.5, SD2, and SDXL, without exploring the potential of recent DiT-based models like FLUX. In this paper, we present 3DIS-FLUX, an extension of the 3DIS framework that integrates the FLUX model for enhanced rendering capabilities. Specifically, we employ the FLUX.1-Depth-dev model for depth map controlled image generation and introduce a detail renderer that manipulates the Attention Mask in FLUX's Joint Attention mechanism based on layout information. This approach allows for the precise rendering of fine-grained attributes of each instance. Our experimental results indicate that 3DIS-FLUX, leveraging the FLUX model, outperforms the original 3DIS method, which utilized SD2 and SDXL, and surpasses current state-of-the-art adapter-based methods in terms of both performance and image quality. Project Page: https://limuloo.github.io/3DIS/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-15", "arxiv_id": "2501.07556", "title": "MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training", "authors": [ "Xingyi He", "Hao Yu", "Sida Peng", "Dongli Tan", "Zehong Shen", "Hujun Bao", "Xiaowei Zhou" ], "github": "https://github.com/zju3dv/MatchAnything", "project_page": "", "abstract": "Image matching, which aims to identify corresponding pixel locations between images, is crucial in a wide range of scientific disciplines, aiding in image registration, fusion, and analysis. In recent years, deep learning-based image matching algorithms have dramatically outperformed humans in rapidly and accurately finding large amounts of correspondences. However, when dealing with images captured under different imaging modalities that result in significant appearance changes, the performance of these algorithms often deteriorates due to the scarcity of annotated cross-modal training data. This limitation hinders applications in various fields that rely on multiple image modalities to obtain complementary information. To address this challenge, we propose a large-scale pre-training framework that utilizes synthetic cross-modal training signals, incorporating diverse data from various sources, to train models to recognize and match fundamental structures across images. This capability is transferable to real-world, unseen cross-modality image matching tasks. Our key finding is that the matching model trained with our framework achieves remarkable generalizability across more than eight unseen cross-modality registration tasks using the same network weight, substantially outperforming existing methods, whether designed for generalization or tailored for specific tasks. This advancement significantly enhances the applicability of image matching technologies across various scientific disciplines and paves the way for new applications in multi-modality human and artificial intelligence analysis and beyond.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zju3dv/MatchAnything", "github_api_added_by": "auto" }, { "date": "2025-01-16", "arxiv_id": "2501.08828", "title": "MMDocIR: Benchmarking Multi-Modal Retrieval for Long Documents", "authors": [ "Kuicai Dong", "Yujing Chang", "Xin Deik Goh", "Dexun Li", "Ruiming Tang", "Yong Liu" ], "github": "https://github.com/MMDocRAG/MMDocIR", "project_page": "", "abstract": "Multi-modal document retrieval is designed to identify and retrieve various forms of multi-modal content, such as figures, tables, charts, and layout information from extensive documents. Despite its significance, there is a notable lack of a robust benchmark to effectively evaluate the performance of systems in multi-modal document retrieval. To address this gap, this work introduces a new benchmark, named as MMDocIR, encompassing two distinct tasks: page-level and layout-level retrieval. The former focuses on localizing the most relevant pages within a long document, while the latter targets the detection of specific layouts, offering a more fine-grained granularity than whole-page analysis. A layout can refer to a variety of elements such as textual paragraphs, equations, figures, tables, or charts. The MMDocIR benchmark comprises a rich dataset featuring expertly annotated labels for 1,685 questions and bootstrapped labels for 173,843 questions, making it a pivotal resource for advancing multi-modal document retrieval for both training and evaluation. Through rigorous experiments, we reveal that (i) visual retrievers significantly outperform their text counterparts, (ii) MMDocIR train set can effectively benefit the training process of multi-modal document retrieval and (iii) text retrievers leveraging on VLM-text perform much better than those using OCR-text. These findings underscores the potential advantages of integrating visual elements for multi-modal document retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MMDocRAG/MMDocIR", "github_api_added_by": "auto" }, { "date": "2025-01-16", "arxiv_id": "2501.08994", "title": "RepVideo: Rethinking Cross-Layer Representation for Video Generation", "authors": [ "Chenyang Si", "Weichen Fan", "Zhengyao Lv", "Ziqi Huang", "Yu Qiao", "Ziwei Liu" ], "github": "https://github.com/Vchitect/RepVideo", "project_page": "", "abstract": "Video generation has achieved remarkable progress with the introduction of diffusion models, which have significantly improved the quality of generated videos. However, recent research has primarily focused on scaling up model training, while offering limited insights into the direct impact of representations on the video generation process. In this paper, we initially investigate the characteristics of features in intermediate layers, finding substantial variations in attention maps across different layers. These variations lead to unstable semantic representations and contribute to cumulative differences between features, which ultimately reduce the similarity between adjacent frames and negatively affect temporal coherence. To address this, we propose RepVideo, an enhanced representation framework for text-to-video diffusion models. By accumulating features from neighboring layers to form enriched representations, this approach captures more stable semantic information. These enhanced representations are then used as inputs to the attention mechanism, thereby improving semantic expressiveness while ensuring feature consistency across adjacent frames. Extensive experiments demonstrate that our RepVideo not only significantly enhances the ability to generate accurate spatial appearances, such as capturing complex spatial relationships between multiple objects, but also improves temporal consistency in video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Vchitect/RepVideo", "github_api_added_by": "auto" }, { "date": "2025-01-16", "arxiv_id": "2501.08983", "title": "CityDreamer4D: Compositional Generative Model of Unbounded 4D Cities", "authors": [ "Haozhe Xie", "Zhaoxi Chen", "Fangzhou Hong", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "3D scene generation has garnered growing attention in recent years and has made significant progress. Generating 4D cities is more challenging than 3D scenes due to the presence of structurally complex, visually diverse objects like buildings and vehicles, and heightened human sensitivity to distortions in urban environments. To tackle these issues, we propose CityDreamer4D, a compositional generative model specifically tailored for generating unbounded 4D cities. Our main insights are 1) 4D city generation should separate dynamic objects (e.g., vehicles) from static scenes (e.g., buildings and roads), and 2) all objects in the 4D scene should be composed of different types of neural fields for buildings, vehicles, and background stuff. Specifically, we propose Traffic Scenario Generator and Unbounded Layout Generator to produce dynamic traffic scenarios and static city layouts using a highly compact BEV representation. Objects in 4D cities are generated by combining stuff-oriented and instance-oriented neural fields for background stuff, buildings, and vehicles. To suit the distinct characteristics of background stuff and instances, the neural fields employ customized generative hash grids and periodic positional embeddings as scene parameterizations. Furthermore, we offer a comprehensive suite of datasets for city generation, including OSM, GoogleEarth, and CityTopia. The OSM dataset provides a variety of real-world city layouts, while the Google Earth and CityTopia datasets deliver large-scale, high-quality city imagery complete with 3D instance annotations. Leveraging its compositional design, CityDreamer4D supports a range of downstream applications, such as instance editing, city stylization, and urban simulation, while delivering state-of-the-art performance in generating realistic 4D cities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-16", "arxiv_id": "2501.09019", "title": "Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion", "authors": [ "Jingyuan Chen", "Fuchen Long", "Jie An", "Zhaofan Qiu", "Ting Yao", "Jiebo Luo", "Tao Mei" ], "github": "", "project_page": "", "abstract": "The first-in-first-out (FIFO) video diffusion, built on a pre-trained text-to-video model, has recently emerged as an effective approach for tuning-free long video generation. This technique maintains a queue of video frames with progressively increasing noise, continuously producing clean frames at the queue's head while Gaussian noise is enqueued at the tail. However, FIFO-Diffusion often struggles to keep long-range temporal consistency in the generated videos due to the lack of correspondence modeling across frames. In this paper, we propose Ouroboros-Diffusion, a novel video denoising framework designed to enhance structural and content (subject) consistency, enabling the generation of consistent videos of arbitrary length. Specifically, we introduce a new latent sampling technique at the queue tail to improve structural consistency, ensuring perceptually smooth transitions among frames. To enhance subject consistency, we devise a Subject-Aware Cross-Frame Attention (SACFA) mechanism, which aligns subjects across frames within short segments to achieve better visual coherence. Furthermore, we introduce self-recurrent guidance. This technique leverages information from all previous cleaner frames at the front of the queue to guide the denoising of noisier frames at the end, fostering rich and contextual global information interaction. Extensive experiments of long video generation on the VBench benchmark demonstrate the superiority of our Ouroboros-Diffusion, particularly in terms of subject consistency, motion smoothness, and temporal consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-16", "arxiv_id": "2501.08809", "title": "XMusic: Towards a Generalized and Controllable Symbolic Music Generation Framework", "authors": [ "Sida Tian", "Can Zhang", "Wei Yuan", "Wei Tan", "Wenjie Zhu" ], "github": "", "project_page": "", "abstract": "In recent years, remarkable advancements in artificial intelligence-generated content (AIGC) have been achieved in the fields of image synthesis and text generation, generating content comparable to that produced by humans. However, the quality of AI-generated music has not yet reached this standard, primarily due to the challenge of effectively controlling musical emotions and ensuring high-quality outputs. This paper presents a generalized symbolic music generation framework, XMusic, which supports flexible prompts (i.e., images, videos, texts, tags, and humming) to generate emotionally controllable and high-quality symbolic music. XMusic consists of two core components, XProjector and XComposer. XProjector parses the prompts of various modalities into symbolic music elements (i.e., emotions, genres, rhythms and notes) within the projection space to generate matching music. XComposer contains a Generator and a Selector. The Generator generates emotionally controllable and melodious music based on our innovative symbolic music representation, whereas the Selector identifies high-quality symbolic music by constructing a multi-task learning scheme involving quality assessment, emotion recognition, and genre recognition tasks. In addition, we build XMIDI, a large-scale symbolic music dataset that contains 108,023 MIDI files annotated with precise emotion and genre labels. Objective and subjective evaluations show that XMusic significantly outperforms the current state-of-the-art methods with impressive music quality. Our XMusic has been awarded as one of the nine Highlights of Collectibles at WAIC 2023. The project homepage of XMusic is https://xmusic-project.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-16", "arxiv_id": "2501.09012", "title": "Multimodal LLMs Can Reason about Aesthetics in Zero-Shot", "authors": [ "Ruixiang Jiang", "Changwen Chen" ], "github": "https://github.com/songrise/MLLM4Art", "project_page": "", "abstract": "We present the first study on how Multimodal LLMs' (MLLMs) reasoning ability shall be elicited to evaluate the aesthetics of artworks. To facilitate this investigation, we construct MM-StyleBench, a novel high-quality dataset for benchmarking artistic stylization. We then develop a principled method for human preference modeling and perform a systematic correlation analysis between MLLMs' responses and human preference. Our experiments reveal an inherent hallucination issue of MLLMs in art evaluation, associated with response subjectivity. ArtCoT is proposed, demonstrating that art-specific task decomposition and the use of concrete language boost MLLMs' reasoning ability for aesthetics. Our findings offer valuable insights into MLLMs for art and can benefit a wide range of downstream applications, such as style transfer and artistic image generation. Code available at https://github.com/songrise/MLLM4Art.", "github_manual": "", "github_regex": "https://github.com/songrise/MLLM4Art", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-16", "arxiv_id": "2501.07783", "title": "Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding", "authors": [ "Zhaokai Wang", "Xizhou Zhu", "Xue Yang", "Gen Luo", "Hao Li", "Changyao Tian", "Wenhan Dou", "Junqi Ge", "Lewei Lu", "Yu Qiao", "Jifeng Dai" ], "github": "https://github.com/OpenGVLab/PIIP", "project_page": "", "abstract": "Image pyramids are widely adopted in top-performing methods to obtain multi-scale features for precise visual perception and understanding. However, current image pyramids use the same large-scale model to process multiple resolutions of images, leading to significant computational cost. To address this challenge, we propose a novel network architecture, called Parameter-Inverted Image Pyramid Networks (PIIP). Specifically, PIIP uses pretrained models (ViTs or CNNs) as branches to process multi-scale images, where images of higher resolutions are processed by smaller network branches to balance computational cost and performance. To integrate information from different spatial scales, we further propose a novel cross-branch feature interaction mechanism. To validate PIIP, we apply it to various perception models and a representative multimodal large language model called LLaVA, and conduct extensive experiments on various tasks such as object detection, segmentation, image classification and multimodal understanding. PIIP achieves superior performance compared to single-branch and existing multi-resolution approaches with lower computational cost. When applied to InternViT-6B, a large-scale vision foundation model, PIIP can improve its performance by 1%-2% on detection and segmentation with only 40%-60% of the original computation, finally achieving 60.0 box AP on MS COCO and 59.7 mIoU on ADE20K. For multimodal understanding, our PIIP-LLaVA achieves 73.0% accuracy on TextVQA and 74.5% on MMBench with only 2.8M training data. Our code is released at https://github.com/OpenGVLab/PIIP.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/PIIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-16", "arxiv_id": "2501.08365", "title": "Towards Best Practices for Open Datasets for LLM Training", "authors": [ "Stefan Baack", "Stella Biderman", "Kasia Odrozek", "Aviya Skowron", "Ayah Bdeir", "Jillian Bommarito", "Jennifer Ding", "Maximilian Gahntz", "Paul Keller", "Pierre-Carl Langlais", "Greg Lindahl", "Sebastian Majstorovic", "Nik Marda", "Guilherme Penedo", "Maarten Van Segbroeck", "Jennifer Wang", "Leandro von Werra", "Mitchell Baker", "Julie Beli\u00e3o", "Kasia Chmielinski", "Marzieh Fadaee", "Lisa Gutermuth", "Hynek Kydl\u00ed\u010dek", "Greg Leppert", "EM Lewis-Jong", "Solana Larsen", "Shayne Longpre", "Angela Oduor Lungati", "Cullen Miller", "Victor Miller", "Max Ryabinin", "Kathleen Siminyu", "Andrew Strait", "Mark Surman", "Anna Tumad\u00f3ttir", "Maurice Weber", "Rebecca Weiss", "Lee White", "Thomas Wolf" ], "github": "https://github.com/r-three/common-pile", "project_page": "", "abstract": "Many AI companies are training their large language models (LLMs) on data without the permission of the copyright owners. The permissibility of doing so varies by jurisdiction: in countries like the EU and Japan, this is allowed under certain restrictions, while in the United States, the legal landscape is more ambiguous. Regardless of the legal status, concerns from creative producers have led to several high-profile copyright lawsuits, and the threat of litigation is commonly cited as a reason for the recent trend towards minimizing the information shared about training datasets by both corporate and public interest actors. This trend in limiting data information causes harm by hindering transparency, accountability, and innovation in the broader ecosystem by denying researchers, auditors, and impacted individuals access to the information needed to understand AI models. While this could be mitigated by training language models on open access and public domain data, at the time of writing, there are no such models (trained at a meaningful scale) due to the substantial technical and sociological challenges in assembling the necessary corpus. These challenges include incomplete and unreliable metadata, the cost and complexity of digitizing physical records, and the diverse set of legal and technical skills required to ensure relevance and responsibility in a quickly changing landscape. Building towards a future where AI systems can be trained on openly licensed data that is responsibly curated and governed requires collaboration across legal, technical, and policy domains, along with investments in metadata standards, digitization, and fostering a culture of openness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/r-three/common-pile", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-16", "arxiv_id": "2501.08970", "title": "Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography", "authors": [ "Ilia Shumailov", "Daniel Ramage", "Sarah Meiklejohn", "Peter Kairouz", "Florian Hartmann", "Borja Balle", "Eugene Bagdasarian" ], "github": "", "project_page": "", "abstract": "We often interact with untrusted parties. Prioritization of privacy can limit the effectiveness of these interactions, as achieving certain goals necessitates sharing private data. Traditionally, addressing this challenge has involved either seeking trusted intermediaries or constructing cryptographic protocols that restrict how much data is revealed, such as multi-party computations or zero-knowledge proofs. While significant advances have been made in scaling cryptographic approaches, they remain limited in terms of the size and complexity of applications they can be used for. In this paper, we argue that capable machine learning models can fulfill the role of a trusted third party, thus enabling secure computations for applications that were previously infeasible. In particular, we describe Trusted Capable Model Environments (TCMEs) as an alternative approach for scaling secure computation, where capable machine learning model(s) interact under input/output constraints, with explicit information flow control and explicit statelessness. This approach aims to achieve a balance between privacy and computational efficiency, enabling private inference where classical cryptographic solutions are currently infeasible. We describe a number of use cases that are enabled by TCME, and show that even some simple classic cryptographic problems can already be solved with TCME. Finally, we outline current limitations and discuss the path forward in implementing them.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-16", "arxiv_id": "2501.04693", "title": "Beyond Sight: Finetuning Generalist Robot Policies with Heterogeneous Sensors via Language Grounding", "authors": [ "Joshua Jones", "Oier Mees", "Carmelo Sferrazza", "Kyle Stachowicz", "Pieter Abbeel", "Sergey Levine" ], "github": "https://github.com/fuse-model/FuSe", "project_page": "", "abstract": "Interacting with the world is a multi-sensory experience: achieving effective general-purpose interaction requires making use of all available modalities -- including vision, touch, and audio -- to fill in gaps from partial observation. For example, when vision is occluded reaching into a bag, a robot should rely on its senses of touch and sound. However, state-of-the-art generalist robot policies are typically trained on large datasets to predict robot actions solely from visual and proprioceptive observations. In this work, we propose FuSe, a novel approach that enables finetuning visuomotor generalist policies on heterogeneous sensor modalities for which large datasets are not readily available by leveraging natural language as a common cross-modal grounding. We combine a multimodal contrastive loss with a sensory-grounded language generation loss to encode high-level semantics. In the context of robot manipulation, we show that FuSe enables performing challenging tasks that require reasoning jointly over modalities such as vision, touch, and sound in a zero-shot setting, such as multimodal prompting, compositional cross-modal prompting, and descriptions of objects it interacts with. We show that the same recipe is applicable to widely different generalist policies, including both diffusion-based generalist policies and large vision-language-action (VLA) models. Extensive experiments in the real world show that FuSeis able to increase success rates by over 20% compared to all considered baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/fuse-model/FuSe", "github_api_added_by": "auto" }, { "date": "2025-01-16", "arxiv_id": "2412.19412", "title": "MINIMA: Modality Invariant Image Matching", "authors": [ "Xingyu Jiang", "Jiangwei Ren", "Zizhuo Li", "Xin Zhou", "Dingkang Liang", "Xiang Bai" ], "github": "https://github.com/LSXI7/MINIMA", "project_page": "", "abstract": "Image matching for both cross-view and cross-modality plays a critical role in multimodal perception. In practice, the modality gap caused by different imaging systems/styles poses great challenges to the matching task. Existing works try to extract invariant features for specific modalities and train on limited datasets, showing poor generalization. In this paper, we present MINIMA, a unified image matching framework for multiple cross-modal cases. Without pursuing fancy modules, our MINIMA aims to enhance universal performance from the perspective of data scaling up. For such purpose, we propose a simple yet effective data engine that can freely produce a large dataset containing multiple modalities, rich scenarios, and accurate matching labels. Specifically, we scale up the modalities from cheap but rich RGB-only matching data, by means of generative models. Under this setting, the matching labels and rich diversity of the RGB dataset are well inherited by the generated multimodal data. Benefiting from this, we construct MD-syn, a new comprehensive dataset that fills the data gap for general multimodal image matching. With MD-syn, we can directly train any advanced matching pipeline on randomly selected modality pairs to obtain cross-modal ability. Extensive experiments on in-domain and zero-shot matching tasks, including 19 cross-modal cases, demonstrate that our MINIMA can significantly outperform the baselines and even surpass modality-specific methods. The dataset and code are available at https://github.com/LSXI7/MINIMA .", "github_manual": "", "github_regex": "https://github.com/LSXI7/MINIMA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-17", "arxiv_id": "2501.08617", "title": "RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation", "authors": [ "Kaiqu Liang", "Haimin Hu", "Ryan Liu", "Thomas L. Griffiths", "Jaime Fern\u00e1ndez Fisac" ], "github": "https://github.com/SafeRoboticsLab/RLHS", "project_page": "", "abstract": "Generative AI systems like foundation models (FMs) must align well with human values to ensure their behavior is helpful and trustworthy. While Reinforcement Learning from Human Feedback (RLHF) has shown promise for optimizing model performance using human judgments, existing RLHF pipelines predominantly rely on immediate feedback, which can fail to accurately reflect the downstream impact of an interaction on users' utility. We demonstrate that feedback based on evaluators' foresight estimates of downstream consequences systematically induces Goodhart's Law dynamics, incentivizing misaligned behaviors like sycophancy and deception and ultimately degrading user outcomes. To alleviate this, we propose decoupling evaluation from prediction by refocusing RLHF on hindsight feedback. Our theoretical analysis reveals that conditioning evaluator feedback on downstream observations mitigates misalignment and improves expected human utility, even when these observations are simulated by the AI system itself. To leverage this insight in a practical alignment algorithm, we introduce Reinforcement Learning from Hindsight Simulation (RLHS), which first simulates plausible consequences and then elicits feedback to assess what behaviors were genuinely beneficial in hindsight. We apply RLHS to two widely-employed online and offline preference optimization methods -- Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO) -- and show empirically that misalignment is significantly reduced with both methods. Through an online human user study, we show that RLHS consistently outperforms RLHF in helping users achieve their goals and earns higher satisfaction ratings, despite being trained solely with simulated hindsight feedback. These results underscore the importance of focusing on long-term consequences, even simulated ones, to mitigate misalignment in RLHF.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SafeRoboticsLab/RLHS", "github_api_added_by": "auto" }, { "date": "2025-01-17", "arxiv_id": "2501.09686", "title": "Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models", "authors": [ "Fengli Xu", "Qianyue Hao", "Zefang Zong", "Jingwei Wang", "Yunke Zhang", "Jingyi Wang", "Xiaochong Lan", "Jiahui Gong", "Tianjian Ouyang", "Fanjin Meng", "Chenyang Shao", "Yuwei Yan", "Qinglong Yang", "Yiwen Song", "Sijian Ren", "Xinyuan Hu", "Yu Li", "Jie Feng", "Chen Gao", "Yong Li" ], "github": "", "project_page": "", "abstract": "Language has long been conceived as an essential tool for human reasoning. The breakthrough of Large Language Models (LLMs) has sparked significant research interest in leveraging these models to tackle complex reasoning tasks. Researchers have moved beyond simple autoregressive token generation by introducing the concept of \"thought\" -- a sequence of tokens representing intermediate steps in the reasoning process. This innovative paradigm enables LLMs' to mimic complex human reasoning processes, such as tree search and reflective thinking. Recently, an emerging trend of learning to reason has applied reinforcement learning (RL) to train LLMs to master reasoning processes. This approach enables the automatic generation of high-quality reasoning trajectories through trial-and-error search algorithms, significantly expanding LLMs' reasoning capacity by providing substantially more training data. Furthermore, recent studies demonstrate that encouraging LLMs to \"think\" with more tokens during test-time inference can further significantly boost reasoning accuracy. Therefore, the train-time and test-time scaling combined to show a new research frontier -- a path toward Large Reasoning Model. The introduction of OpenAI's o1 series marks a significant milestone in this research direction. In this survey, we present a comprehensive review of recent progress in LLM reasoning. We begin by introducing the foundational background of LLMs and then explore the key technical components driving the development of large reasoning models, with a focus on automated data construction, learning-to-reason techniques, and test-time scaling. We also analyze popular open-source projects at building large reasoning models, and conclude with open challenges and future research directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-17", "arxiv_id": "2501.09732", "title": "Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps", "authors": [ "Nanye Ma", "Shangyuan Tong", "Haolin Jia", "Hexiang Hu", "Yu-Chuan Su", "Mingda Zhang", "Xuan Yang", "Yandong Li", "Tommi Jaakkola", "Xuhui Jia", "Saining Xie" ], "github": "", "project_page": "", "abstract": "Generative models have made significant impacts across various domains, largely due to their ability to scale during training by increasing data, computational resources, and model size, a phenomenon characterized by the scaling laws. Recent research has begun to explore inference-time scaling behavior in Large Language Models (LLMs), revealing how performance can further improve with additional computation during inference. Unlike LLMs, diffusion models inherently possess the flexibility to adjust inference-time computation via the number of denoising steps, although the performance gains typically flatten after a few dozen. In this work, we explore the inference-time scaling behavior of diffusion models beyond increasing denoising steps and investigate how the generation performance can further improve with increased computation. Specifically, we consider a search problem aimed at identifying better noises for the diffusion sampling process. We structure the design space along two axes: the verifiers used to provide feedback, and the algorithms used to find better noise candidates. Through extensive experiments on class-conditioned and text-conditioned image generation benchmarks, our findings reveal that increasing inference-time compute leads to substantial improvements in the quality of samples generated by diffusion models, and with the complicated nature of images, combinations of the components in the framework can be specifically chosen to conform with different application scenario.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-17", "arxiv_id": "2501.09747", "title": "FAST: Efficient Action Tokenization for Vision-Language-Action Models", "authors": [ "Karl Pertsch", "Kyle Stachowicz", "Brian Ichter", "Danny Driess", "Suraj Nair", "Quan Vuong", "Oier Mees", "Chelsea Finn", "Sergey Levine" ], "github": "", "project_page": "", "abstract": "Autoregressive sequence models, such as Transformer-based vision-language action (VLA) policies, can be tremendously effective for capturing complex and generalizable robotic behaviors. However, such models require us to choose a tokenization of our continuous action signals, which determines how the discrete symbols predicted by the model map to continuous robot actions. We find that current approaches for robot action tokenization, based on simple per-dimension, per-timestep binning schemes, typically perform poorly when learning dexterous skills from high-frequency robot data. To address this challenge, we propose a new compression-based tokenization scheme for robot actions, based on the discrete cosine transform. Our tokenization approach, Frequency-space Action Sequence Tokenization (FAST), enables us to train autoregressive VLAs for highly dexterous and high-frequency tasks where standard discretization methods fail completely. Based on FAST, we release FAST+, a universal robot action tokenizer, trained on 1M real robot action trajectories. It can be used as a black-box tokenizer for a wide range of robot action sequences, with diverse action spaces and control frequencies. Finally, we show that, when combined with the pi0 VLA, our method can scale to training on 10k hours of robot data and match the performance of diffusion VLAs, while reducing training time by up to 5x.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-17", "arxiv_id": "2501.09755", "title": "Learnings from Scaling Visual Tokenizers for Reconstruction and Generation", "authors": [ "Philippe Hansen-Estruch", "David Yan", "Ching-Yao Chung", "Orr Zohar", "Jialiang Wang", "Tingbo Hou", "Tao Xu", "Sriram Vishwanath", "Peter Vajda", "Xinlei Chen" ], "github": "", "project_page": "", "abstract": "Visual tokenization via auto-encoding empowers state-of-the-art image and video generative models by compressing pixels into a latent space. Although scaling Transformer-based generators has been central to recent advances, the tokenizer component itself is rarely scaled, leaving open questions about how auto-encoder design choices influence both its objective of reconstruction and downstream generative performance. Our work aims to conduct an exploration of scaling in auto-encoders to fill in this blank. To facilitate this exploration, we replace the typical convolutional backbone with an enhanced Vision Transformer architecture for Tokenization (ViTok). We train ViTok on large-scale image and video datasets far exceeding ImageNet-1K, removing data constraints on tokenizer scaling. We first study how scaling the auto-encoder bottleneck affects both reconstruction and generation -- and find that while it is highly correlated with reconstruction, its relationship with generation is more complex. We next explored the effect of separately scaling the auto-encoders' encoder and decoder on reconstruction and generation performance. Crucially, we find that scaling the encoder yields minimal gains for either reconstruction or generation, while scaling the decoder boosts reconstruction but the benefits for generation are mixed. Building on our exploration, we design ViTok as a lightweight auto-encoder that achieves competitive performance with state-of-the-art auto-encoders on ImageNet-1K and COCO reconstruction tasks (256p and 512p) while outperforming existing auto-encoders on 16-frame 128p video reconstruction for UCF-101, all with 2-5x fewer FLOPs. When integrated with Diffusion Transformers, ViTok demonstrates competitive performance on image generation for ImageNet-1K and sets new state-of-the-art benchmarks for class-conditional video generation on UCF-101.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-17", "arxiv_id": "2501.09756", "title": "SynthLight: Portrait Relighting with Diffusion Model by Learning to Re-render Synthetic Faces", "authors": [ "Sumit Chaturvedi", "Mengwei Ren", "Yannick Hold-Geoffroy", "Jingyuan Liu", "Julie Dorsey", "Zhixin Shu" ], "github": "", "project_page": "", "abstract": "We introduce SynthLight, a diffusion model for portrait relighting. Our approach frames image relighting as a re-rendering problem, where pixels are transformed in response to changes in environmental lighting conditions. Using a physically-based rendering engine, we synthesize a dataset to simulate this lighting-conditioned transformation with 3D head assets under varying lighting. We propose two training and inference strategies to bridge the gap between the synthetic and real image domains: (1) multi-task training that takes advantage of real human portraits without lighting labels; (2) an inference time diffusion sampling procedure based on classifier-free guidance that leverages the input portrait to better preserve details. Our method generalizes to diverse real photographs and produces realistic illumination effects, including specular highlights and cast shadows, while preserving the subject's identity. Our quantitative experiments on Light Stage data demonstrate results comparable to state-of-the-art relighting methods. Our qualitative results on in-the-wild images showcase rich and unprecedented illumination effects. Project Page: https://vrroom.github.io/synthlight/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-17", "arxiv_id": "2501.09503", "title": "AnyStory: Towards Unified Single and Multiple Subject Personalization in Text-to-Image Generation", "authors": [ "Junjie He", "Yuxiang Tuo", "Binghui Chen", "Chongyang Zhong", "Yifeng Geng", "Liefeng Bo" ], "github": "https://github.com/junjiehe96/AnyStory", "project_page": "", "abstract": "Recently, large-scale generative models have demonstrated outstanding text-to-image generation capabilities. However, generating high-fidelity personalized images with specific subjects still presents challenges, especially in cases involving multiple subjects. In this paper, we propose AnyStory, a unified approach for personalized subject generation. AnyStory not only achieves high-fidelity personalization for single subjects, but also for multiple subjects, without sacrificing subject fidelity. Specifically, AnyStory models the subject personalization problem in an \"encode-then-route\" manner. In the encoding step, AnyStory utilizes a universal and powerful image encoder, i.e., ReferenceNet, in conjunction with CLIP vision encoder to achieve high-fidelity encoding of subject features. In the routing step, AnyStory utilizes a decoupled instance-aware subject router to accurately perceive and predict the potential location of the corresponding subject in the latent space, and guide the injection of subject conditions. Detailed experimental results demonstrate the excellent performance of our method in retaining subject details, aligning text descriptions, and personalizing for multiple subjects. The project page is at https://aigcdesigngroup.github.io/AnyStory/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/junjiehe96/AnyStory", "github_api_added_by": "auto" }, { "date": "2025-01-17", "arxiv_id": "2501.09433", "title": "CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation", "authors": [ "Hwan Heo", "Jangyeong Kim", "Seongyeong Lee", "Jeong A Wi", "Junyoung Choi", "Sangjun Ahn" ], "github": "https://github.com/ncsoft/CaPa", "project_page": "", "abstract": "The synthesis of high-quality 3D assets from textual or visual inputs has become a central objective in modern generative modeling. Despite the proliferation of 3D generation algorithms, they frequently grapple with challenges such as multi-view inconsistency, slow generation times, low fidelity, and surface reconstruction problems. While some studies have addressed some of these issues, a comprehensive solution remains elusive. In this paper, we introduce CaPa, a carve-and-paint framework that generates high-fidelity 3D assets efficiently. CaPa employs a two-stage process, decoupling geometry generation from texture synthesis. Initially, a 3D latent diffusion model generates geometry guided by multi-view inputs, ensuring structural consistency across perspectives. Subsequently, leveraging a novel, model-agnostic Spatially Decoupled Attention, the framework synthesizes high-resolution textures (up to 4K) for a given geometry. Furthermore, we propose a 3D-aware occlusion inpainting algorithm that fills untextured regions, resulting in cohesive results across the entire model. This pipeline generates high-quality 3D assets in less than 30 seconds, providing ready-to-use outputs for commercial applications. Experimental results demonstrate that CaPa excels in both texture fidelity and geometric stability, establishing a new standard for practical, scalable 3D asset generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ncsoft/CaPa", "github_api_added_by": "auto" }, { "date": "2025-01-17", "arxiv_id": "2501.09751", "title": "OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking", "authors": [ "Zekun Xi", "Wenbiao Yin", "Jizhan Fang", "Jialong Wu", "Runnan Fang", "Ningyu Zhang", "Jiang Yong", "Pengjun Xie", "Fei Huang", "Huajun Chen" ], "github": "https://github.com/zjunlp/omnithink", "project_page": "", "abstract": "Machine writing with large language models often relies on retrieval-augmented generation. However, these approaches remain confined within the boundaries of the model's predefined scope, limiting the generation of content with rich information. Specifically, vanilla-retrieved information tends to lack depth, utility, and suffers from redundancy, which negatively impacts the quality of generated articles, leading to shallow, repetitive, and unoriginal outputs. To address these issues, we propose OmniThink, a machine writing framework that emulates the human-like process of iterative expansion and reflection. The core idea behind OmniThink is to simulate the cognitive behavior of learners as they progressively deepen their knowledge of the topics. Experimental results demonstrate that OmniThink improves the knowledge density of generated articles without compromising metrics such as coherence and depth. Human evaluations and expert feedback further highlight the potential of OmniThink to address real-world challenges in the generation of long-form articles.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zjunlp/omnithink", "github_api_added_by": "auto" }, { "date": "2025-01-17", "arxiv_id": "2501.09484", "title": "Exploring the Inquiry-Diagnosis Relationship with Advanced Patient Simulators", "authors": [ "Zhaocheng Liu", "Quan Tu", "Wen Ye", "Yu Xiao", "Zhishou Zhang", "Hengfu Cui", "Yalun Zhu", "Qiang Ju", "Shizheng Li", "Jian Xie" ], "github": "https://github.com/LIO-H-ZEN/PatientSimulator", "project_page": "", "abstract": "Online medical consultation (OMC) restricts doctors to gathering patient information solely through inquiries, making the already complex sequential decision-making process of diagnosis even more challenging. Recently, the rapid advancement of large language models has demonstrated a significant potential to transform OMC. However, most studies have primarily focused on improving diagnostic accuracy under conditions of relatively sufficient information, while paying limited attention to the \"inquiry\" phase of the consultation process. This lack of focus has left the relationship between \"inquiry\" and \"diagnosis\" insufficiently explored. In this paper, we first extract real patient interaction strategies from authentic doctor-patient conversations and use these strategies to guide the training of a patient simulator that closely mirrors real-world behavior. By inputting medical records into our patient simulator to simulate patient responses, we conduct extensive experiments to explore the relationship between \"inquiry\" and \"diagnosis\" in the consultation process. Experimental results demonstrate that inquiry and diagnosis adhere to the Liebig's law: poor inquiry quality limits the effectiveness of diagnosis, regardless of diagnostic capability, and vice versa. Furthermore, the experiments reveal significant differences in the inquiry performance of various models. To investigate this phenomenon, we categorize the inquiry process into four types: (1) chief complaint inquiry; (2) specification of known symptoms; (3) inquiry about accompanying symptoms; and (4) gathering family or medical history. We analyze the distribution of inquiries across the four types for different models to explore the reasons behind their significant performance differences. We plan to open-source the weights and related code of our patient simulator at https://github.com/LIO-H-ZEN/PatientSimulator.", "github_manual": "", "github_regex": "https://github.com/LIO-H-ZEN/PatientSimulator", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-17", "arxiv_id": "2501.09038", "title": "Do generative video models learn physical principles from watching videos?", "authors": [ "Saman Motamed", "Laura Culp", "Kevin Swersky", "Priyank Jaini", "Robert Geirhos" ], "github": "https://github.com/google-deepmind/physics-IQ-benchmark", "project_page": "", "abstract": "AI video generation is undergoing a revolution, with quality and realism advancing rapidly. These advances have led to a passionate scientific debate: Do video models learn ``world models'' that discover laws of physics -- or, alternatively, are they merely sophisticated pixel predictors that achieve visual realism without understanding the physical principles of reality? We address this question by developing Physics-IQ, a comprehensive benchmark dataset that can only be solved by acquiring a deep understanding of various physical principles, like fluid dynamics, optics, solid mechanics, magnetism and thermodynamics. We find that across a range of current models (Sora, Runway, Pika, Lumiere, Stable Video Diffusion, and VideoPoet), physical understanding is severely limited, and unrelated to visual realism. At the same time, some test cases can already be successfully solved. This indicates that acquiring certain physical principles from observation alone may be possible, but significant challenges remain. While we expect rapid advances ahead, our work demonstrates that visual realism does not imply physical understanding. Our project page is at https://physics-iq.github.io; code at https://github.com/google-deepmind/physics-IQ-benchmark.", "github_manual": "", "github_regex": "https://github.com/google-deepmind/physics-IQ-benchmark", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-17", "arxiv_id": "2501.09653", "title": "The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models", "authors": [ "Jonathan Katzy", "Razvan Mihai Popescu", "Arie van Deursen", "Maliheh Izadi" ], "github": "", "project_page": "", "abstract": "The recent rise in the popularity of large language models has spurred the development of extensive code datasets needed to train them. This has left limited code available for collection and use in the downstream investigation of specific behaviors, or evaluation of large language models without suffering from data contamination. To address this problem, we release The Heap, a large multilingual dataset covering 57 programming languages that has been deduplicated with respect to other open datasets of code, enabling researchers to conduct fair evaluations of large language models without significant data cleaning overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-20", "arxiv_id": "2501.09891", "title": "Evolving Deeper LLM Thinking", "authors": [ "Kuang-Huei Lee", "Ian Fischer", "Yueh-Hua Wu", "Dave Marwood", "Shumeet Baluja", "Dale Schuurmans", "Xinyun Chen" ], "github": "", "project_page": "", "abstract": "We explore an evolutionary search strategy for scaling inference time compute in Large Language Models. The proposed approach, Mind Evolution, uses a language model to generate, recombine and refine candidate responses. The proposed approach avoids the need to formalize the underlying inference problem whenever a solution evaluator is available. Controlling for inference cost, we find that Mind Evolution significantly outperforms other inference strategies such as Best-of-N and Sequential Revision in natural language planning tasks. In the TravelPlanner and Natural Plan benchmarks, Mind Evolution solves more than 98% of the problem instances using Gemini 1.5 Pro without the use of a formal solver.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-20", "arxiv_id": "2501.10120", "title": "PaSa: An LLM Agent for Comprehensive Academic Paper Search", "authors": [ "Yichen He", "Guanhua Huang", "Peiyuan Feng", "Yuan Lin", "Yuchen Zhang", "Hang Li", "Weinan E" ], "github": "https://github.com/bytedance/pasa", "project_page": "", "abstract": "We introduce PaSa, an advanced Paper Search agent powered by large language models. PaSa can autonomously make a series of decisions, including invoking search tools, reading papers, and selecting relevant references, to ultimately obtain comprehensive and accurate results for complex scholarly queries. We optimize PaSa using reinforcement learning with a synthetic dataset, AutoScholarQuery, which includes 35k fine-grained academic queries and corresponding papers sourced from top-tier AI conference publications. Additionally, we develop RealScholarQuery, a benchmark collecting real-world academic queries to assess PaSa performance in more realistic scenarios. Despite being trained on synthetic data, PaSa significantly outperforms existing baselines on RealScholarQuery, including Google, Google Scholar, Google with GPT-4 for paraphrased queries, chatGPT (search-enabled GPT-4o), GPT-o1, and PaSa-GPT-4o (PaSa implemented by prompting GPT-4o). Notably, PaSa-7B surpasses the best Google-based baseline, Google with GPT-4o, by 37.78% in recall@20 and 39.90% in recall@50. It also exceeds PaSa-GPT-4o by 30.36% in recall and 4.25% in precision. Model, datasets, and code are available at https://github.com/bytedance/pasa.", "github_manual": "", "github_regex": "https://github.com/bytedance/pasa", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-20", "arxiv_id": "2501.10132", "title": "ComplexFuncBench: Exploring Multi-Step and Constrained Function Calling under Long-Context Scenario", "authors": [ "Lucen Zhong", "Zhengxiao Du", "Xiaohan Zhang", "Haiyi Hu", "Jie Tang" ], "github": "https://github.com/THUDM/ComplexFuncBench", "project_page": "", "abstract": "Enhancing large language models (LLMs) with real-time APIs can help generate more accurate and up-to-date responses. However, evaluating the function calling abilities of LLMs in real-world scenarios remains under-explored due to the complexity of data collection and evaluation. In this work, we introduce ComplexFuncBench, a benchmark for complex function calling across five real-world scenarios. Compared to existing benchmarks, ComplexFuncBench encompasses multi-step and constrained function calling, which requires long-parameter filing, parameter value reasoning, and 128k long context. Additionally, we propose an automatic framework, ComplexEval, for quantitatively evaluating complex function calling tasks. Through comprehensive experiments, we demonstrate the deficiencies of state-of-the-art LLMs in function calling and suggest future directions for optimizing these capabilities. The data and code are available at https://github.com/THUDM/ComplexFuncBench.", "github_manual": "", "github_regex": "https://github.com/THUDM/ComplexFuncBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-20", "arxiv_id": "2501.10020", "title": "Textoon: Generating Vivid 2D Cartoon Characters from Text Descriptions", "authors": [ "Chao He", "Jianqiang Ren", "Liefeng Bo" ], "github": "https://github.com/Human3DAIGC/Textoon", "project_page": "", "abstract": "The 2D cartoon style is a prominent art form in digital character creation, particularly popular among younger audiences. While advancements in digital human technology have spurred extensive research into photorealistic digital humans and 3D characters, interactive 2D cartoon characters have received comparatively less attention. Unlike 3D counterparts, which require sophisticated construction and resource-intensive rendering, Live2D, a widely-used format for 2D cartoon characters, offers a more efficient alternative, which allows to animate 2D characters in a manner that simulates 3D movement without the necessity of building a complete 3D model. Furthermore, Live2D employs lightweight HTML5 (H5) rendering, improving both accessibility and efficiency. In this technical report, we introduce Textoon, an innovative method for generating diverse 2D cartoon characters in the Live2D format based on text descriptions. The Textoon leverages cutting-edge language and vision models to comprehend textual intentions and generate 2D appearance, capable of creating a wide variety of stunning and interactive 2D characters within one minute. The project homepage is https://human3daigc.github.io/Textoon_webpage/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Human3DAIGC/Textoon", "github_api_added_by": "auto" }, { "date": "2025-01-20", "arxiv_id": "2501.10045", "title": "HiFi-SR: A Unified Generative Transformer-Convolutional Adversarial Network for High-Fidelity Speech Super-Resolution", "authors": [ "Shengkui Zhao", "Kun Zhou", "Zexu Pan", "Yukun Ma", "Chong Zhang", "Bin Ma" ], "github": "https://github.com/modelscope/ClearerVoice-Studio", "project_page": "", "abstract": "The application of generative adversarial networks (GANs) has recently advanced speech super-resolution (SR) based on intermediate representations like mel-spectrograms. However, existing SR methods that typically rely on independently trained and concatenated networks may lead to inconsistent representations and poor speech quality, especially in out-of-domain scenarios. In this work, we propose HiFi-SR, a unified network that leverages end-to-end adversarial training to achieve high-fidelity speech super-resolution. Our model features a unified transformer-convolutional generator designed to seamlessly handle both the prediction of latent representations and their conversion into time-domain waveforms. The transformer network serves as a powerful encoder, converting low-resolution mel-spectrograms into latent space representations, while the convolutional network upscales these representations into high-resolution waveforms. To enhance high-frequency fidelity, we incorporate a multi-band, multi-scale time-frequency discriminator, along with a multi-scale mel-reconstruction loss in the adversarial training process. HiFi-SR is versatile, capable of upscaling any input speech signal between 4 kHz and 32 kHz to a 48 kHz sampling rate. Experimental results demonstrate that HiFi-SR significantly outperforms existing speech SR methods across both objective metrics and ABX preference tests, for both in-domain and out-of-domain scenarios (https://github.com/modelscope/ClearerVoice-Studio).", "github_manual": "", "github_regex": "https://github.com/modelscope/ClearerVoice-Studio", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-20", "arxiv_id": "2501.09978", "title": "GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar Editor", "authors": [ "Xiangyue Liu", "Kunming Luo", "Heng Li", "Qi Zhang", "Yuan Liu", "Li Yi", "Ping Tan" ], "github": "https://github.com/Lxiangyue/GaussianAvatar-Editor", "project_page": "", "abstract": "We introduce GaussianAvatar-Editor, an innovative framework for text-driven editing of animatable Gaussian head avatars that can be fully controlled in expression, pose, and viewpoint. Unlike static 3D Gaussian editing, editing animatable 4D Gaussian avatars presents challenges related to motion occlusion and spatial-temporal inconsistency. To address these issues, we propose the Weighted Alpha Blending Equation (WABE). This function enhances the blending weight of visible Gaussians while suppressing the influence on non-visible Gaussians, effectively handling motion occlusion during editing. Furthermore, to improve editing quality and ensure 4D consistency, we incorporate conditional adversarial learning into the editing process. This strategy helps to refine the edited results and maintain consistency throughout the animation. By integrating these methods, our GaussianAvatar-Editor achieves photorealistic and consistent results in animatable 4D Gaussian editing. We conduct comprehensive experiments across various subjects to validate the effectiveness of our proposed techniques, which demonstrates the superiority of our approach over existing methods. More results and code are available at: [Project Link](https://xiangyueliu.github.io/GaussianAvatar-Editor/).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Lxiangyue/GaussianAvatar-Editor", "github_api_added_by": "auto" }, { "date": "2025-01-20", "arxiv_id": "2501.10021", "title": "X-Dyna: Expressive Dynamic Human Image Animation", "authors": [ "Di Chang", "Hongyi Xu", "You Xie", "Yipeng Gao", "Zhengfei Kuang", "Shengqu Cai", "Chenxu Zhang", "Guoxian Song", "Chao Wang", "Yichun Shi", "Zeyuan Chen", "Shijie Zhou", "Linjie Luo", "Gordon Wetzstein", "Mohammad Soleymani" ], "github": "https://github.com/bytedance/X-Dyna", "project_page": "", "abstract": "We introduce X-Dyna, a novel zero-shot, diffusion-based pipeline for animating a single human image using facial expressions and body movements derived from a driving video, that generates realistic, context-aware dynamics for both the subject and the surrounding environment. Building on prior approaches centered on human pose control, X-Dyna addresses key shortcomings causing the loss of dynamic details, enhancing the lifelike qualities of human video animations. At the core of our approach is the Dynamics-Adapter, a lightweight module that effectively integrates reference appearance context into the spatial attentions of the diffusion backbone while preserving the capacity of motion modules in synthesizing fluid and intricate dynamic details. Beyond body pose control, we connect a local control module with our model to capture identity-disentangled facial expressions, facilitating accurate expression transfer for enhanced realism in animated scenes. Together, these components form a unified framework capable of learning physical human motion and natural scene dynamics from a diverse blend of human and scene videos. Comprehensive qualitative and quantitative evaluations demonstrate that X-Dyna outperforms state-of-the-art methods, creating highly lifelike and expressive animations. The code is available at https://github.com/bytedance/X-Dyna.", "github_manual": "", "github_regex": "https://github.com/bytedance/X-Dyna", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-20", "arxiv_id": "2501.09775", "title": "Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident Even When They Are Wrong", "authors": [ "Tairan Fu", "Javier Conde", "Gonzalo Mart\u00ednez", "Mar\u00eda Grandury", "Pedro Reviriego" ], "github": "https://github.com/aMa2210/LLM_MCQ_LogProbs", "project_page": "", "abstract": "One of the most widely used methods to evaluate LLMs are Multiple Choice Question (MCQ) tests. MCQ benchmarks enable the testing of LLM knowledge on almost any topic at scale as the results can be processed automatically. To help the LLM answer, a few examples called few shots can be included in the prompt. Moreover, the LLM can be asked to answer the question directly with the selected option or to first provide the reasoning and then the selected answer, which is known as chain of thought. In addition to checking whether the selected answer is correct, the evaluation can look at the LLM-estimated probability of its response as an indication of the confidence of the LLM in the response. In this paper, we study how the LLM confidence in its answer depends on whether the model has been asked to answer directly or to provide the reasoning before answering. The results of the evaluation of questions on a wide range of topics in seven different models show that LLMs are more confident in their answers when they provide reasoning before the answer. This occurs regardless of whether the selected answer is correct. Our hypothesis is that this behavior is due to the reasoning that modifies the probability of the selected answer, as the LLM predicts the answer based on the input question and the reasoning that supports the selection made. Therefore, LLM estimated probabilities seem to have intrinsic limitations that should be understood in order to use them in evaluation procedures. Interestingly, the same behavior has been observed in humans, for whom explaining an answer increases confidence in its correctness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aMa2210/LLM_MCQ_LogProbs", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-20", "arxiv_id": "2501.09825", "title": "Bridging Language Barriers in Healthcare: A Study on Arabic LLMs", "authors": [ "Nada Saadi", "Tathagata Raha", "Cl\u00e9ment Christophe", "Marco AF Pimentel", "Ronnie Rajan", "Praveen K Kanithi" ], "github": "", "project_page": "", "abstract": "This paper investigates the challenges of developing large language models (LLMs) proficient in both multilingual understanding and medical knowledge. We demonstrate that simply translating medical data does not guarantee strong performance on clinical tasks in the target language. Our experiments reveal that the optimal language mix in training data varies significantly across different medical tasks. We find that larger models with carefully calibrated language ratios achieve superior performance on native-language clinical tasks. Furthermore, our results suggest that relying solely on fine-tuning may not be the most effective approach for incorporating new language knowledge into LLMs. Instead, data and computationally intensive pretraining methods may still be necessary to achieve optimal performance in multilingual medical settings. These findings provide valuable guidance for building effective and inclusive medical AI systems for diverse linguistic communities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-21", "arxiv_id": "2501.08325", "title": "GameFactory: Creating New Games with Generative Interactive Videos", "authors": [ "Jiwen Yu", "Yiran Qin", "Xintao Wang", "Pengfei Wan", "Di Zhang", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Generative game engines have the potential to revolutionize game development by autonomously creating new content and reducing manual workload. However, existing video-based game generation methods fail to address the critical challenge of scene generalization, limiting their applicability to existing games with fixed styles and scenes. In this paper, we present GameFactory, a framework focused on exploring scene generalization in game video generation. To enable the creation of entirely new and diverse games, we leverage pre-trained video diffusion models trained on open-domain video data. To bridge the domain gap between open-domain priors and small-scale game dataset, we propose a multi-phase training strategy that decouples game style learning from action control, preserving open-domain generalization while achieving action controllability. Using Minecraft as our data source, we release GF-Minecraft, a high-quality and diversity action-annotated video dataset for research. Furthermore, we extend our framework to enable autoregressive action-controllable game video generation, allowing the production of unlimited-length interactive game videos. Experimental results demonstrate that GameFactory effectively generates open-domain, diverse, and action-controllable game videos, representing a significant step forward in AI-driven game generation. Our dataset and project page are publicly available at https://vvictoryuki.github.io/gamefactory/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-21", "arxiv_id": "2501.09781", "title": "VideoWorld: Exploring Knowledge Learning from Unlabeled Videos", "authors": [ "Zhongwei Ren", "Yunchao Wei", "Xun Guo", "Yao Zhao", "Bingyi Kang", "Jiashi Feng", "Xiaojie Jin" ], "github": "", "project_page": "", "abstract": "This work explores whether a deep generative model can learn complex knowledge solely from visual input, in contrast to the prevalent focus on text-based models like large language models (LLMs). We develop VideoWorld, an auto-regressive video generation model trained on unlabeled video data, and test its knowledge acquisition abilities in video-based Go and robotic control tasks. Our experiments reveal two key findings: (1) video-only training provides sufficient information for learning knowledge, including rules, reasoning and planning capabilities, and (2) the representation of visual change is crucial for knowledge acquisition. To improve both the efficiency and efficacy of this process, we introduce the Latent Dynamics Model (LDM) as a key component of VideoWorld. Remarkably, VideoWorld reaches a 5-dan professional level in the Video-GoBench with just a 300-million-parameter model, without relying on search algorithms or reward mechanisms typical in reinforcement learning. In robotic tasks, VideoWorld effectively learns diverse control operations and generalizes across environments, approaching the performance of oracle models in CALVIN and RLBench. This study opens new avenues for knowledge acquisition from visual data, with all code, data, and models open-sourced for further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-21", "arxiv_id": "2501.09284", "title": "SEAL: Entangled White-box Watermarks on Low-Rank Adaptation", "authors": [ "Giyeong Oh", "Saejin Kim", "Woohyun Cho", "Sangkyu Lee", "Jiwan Chung", "Dokyung Song", "Youngjae Yu" ], "github": "https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md", "project_page": "", "abstract": "Recently, LoRA and its variants have become the de facto strategy for training and sharing task-specific versions of large pretrained models, thanks to their efficiency and simplicity. However, the issue of copyright protection for LoRA weights, especially through watermark-based techniques, remains underexplored. To address this gap, we propose SEAL (SEcure wAtermarking on LoRA weights), the universal whitebox watermarking for LoRA. SEAL embeds a secret, non-trainable matrix between trainable LoRA weights, serving as a passport to claim ownership. SEAL then entangles the passport with the LoRA weights through training, without extra loss for entanglement, and distributes the finetuned weights after hiding the passport. When applying SEAL, we observed no performance degradation across commonsense reasoning, textual/visual instruction tuning, and text-to-image synthesis tasks. We demonstrate that SEAL is robust against a variety of known attacks: removal, obfuscation, and ambiguity attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.12380", "title": "MMVU: Measuring Expert-Level Multi-Discipline Video Understanding", "authors": [ "Yilun Zhao", "Lujing Xie", "Haowei Zhang", "Guo Gan", "Yitao Long", "Zhiyuan Hu", "Tongyan Hu", "Weiyuan Chen", "Chuhan Li", "Junyang Song", "Zhijian Xu", "Chengye Wang", "Weifeng Pan", "Ziyao Shangguan", "Xiangru Tang", "Zhenwen Liang", "Yixin Liu", "Chen Zhao", "Arman Cohan" ], "github": "", "project_page": "", "abstract": "We introduce MMVU, a comprehensive expert-level, multi-discipline benchmark for evaluating foundation models in video understanding. MMVU includes 3,000 expert-annotated questions spanning 27 subjects across four core disciplines: Science, Healthcare, Humanities & Social Sciences, and Engineering. Compared to prior benchmarks, MMVU features three key advancements. First, it challenges models to apply domain-specific knowledge and perform expert-level reasoning to analyze specialized-domain videos, moving beyond the basic visual perception typically assessed in current video benchmarks. Second, each example is annotated by human experts from scratch. We implement strict data quality controls to ensure the high quality of the dataset. Finally, each example is enriched with expert-annotated reasoning rationals and relevant domain knowledge, facilitating in-depth analysis. We conduct an extensive evaluation of 32 frontier multimodal foundation models on MMVU. The latest System-2-capable models, o1 and Gemini 2.0 Flash Thinking, achieve the highest performance among the tested models. However, they still fall short of matching human expertise. Through in-depth error analyses and case studies, we offer actionable insights for future advancements in expert-level, knowledge-intensive video understanding for specialized domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.12273", "title": "Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement", "authors": [ "Maosong Cao", "Taolin Zhang", "Mo Li", "Chuyu Zhang", "Yunxin Liu", "Haodong Duan", "Songyang Zhang", "Kai Chen" ], "github": "https://github.com/internlm/condor", "project_page": "", "abstract": "The quality of Supervised Fine-Tuning (SFT) data plays a critical role in enhancing the conversational capabilities of Large Language Models (LLMs). However, as LLMs become more advanced, the availability of high-quality human-annotated SFT data has become a significant bottleneck, necessitating a greater reliance on synthetic training data. In this work, we introduce Condor, a novel two-stage synthetic data generation framework that incorporates World Knowledge Tree and Self-Reflection Refinement to produce high-quality SFT data at scale. Our experimental results demonstrate that a base model fine-tuned on only 20K Condor-generated samples achieves superior performance compared to counterparts. The additional refinement stage in Condor further enables iterative self-improvement for LLMs at various scales (up to 72B), validating the effectiveness of our approach. Furthermore, our investigation into the scaling for synthetic data in post-training reveals substantial unexplored potential for performance improvements, opening promising avenues for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/internlm/condor", "github_api_added_by": "auto" }, { "date": "2025-01-22", "arxiv_id": "2501.12390", "title": "GPS as a Control Signal for Image Generation", "authors": [ "Chao Feng", "Ziyang Chen", "Aleksander Holynski", "Alexei A. Efros", "Andrew Owens" ], "github": "https://github.com/cfeng16/GPS2Pix", "project_page": "", "abstract": "We show that the GPS tags contained in photo metadata provide a useful control signal for image generation. We train GPS-to-image models and use them for tasks that require a fine-grained understanding of how images vary within a city. In particular, we train a diffusion model to generate images conditioned on both GPS and text. The learned model generates images that capture the distinctive appearance of different neighborhoods, parks, and landmarks. We also extract 3D models from 2D GPS-to-image models through score distillation sampling, using GPS conditioning to constrain the appearance of the reconstruction from each viewpoint. Our evaluations suggest that our GPS-conditioned models successfully learn to generate images that vary based on location, and that GPS conditioning improves estimated 3D structure.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cfeng16/GPS2Pix", "github_api_added_by": "auto" }, { "date": "2025-01-22", "arxiv_id": "2501.11873", "title": "Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models", "authors": [ "Zihan Qiu", "Zeyu Huang", "Bo Zheng", "Kaiyue Wen", "Zekun Wang", "Rui Men", "Ivan Titov", "Dayiheng Liu", "Jingren Zhou", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "This paper revisits the implementation of Load-balancing Loss (LBL) when training Mixture-of-Experts (MoEs) models. Specifically, LBL for MoEs is defined as N_E sum_{i=1}^{N_E} f_i p_i, where N_E is the total number of experts, f_i represents the frequency of expert i being selected, and p_i denotes the average gating score of the expert i. Existing MoE training frameworks usually employ the parallel training strategy so that f_i and the LBL are calculated within a micro-batch and then averaged across parallel groups. In essence, a micro-batch for training billion-scale LLMs normally contains very few sequences. So, the micro-batch LBL is almost at the sequence level, and the router is pushed to distribute the token evenly within each sequence. Under this strict constraint, even tokens from a domain-specific sequence (e.g., code) are uniformly routed to all experts, thereby inhibiting expert specialization. In this work, we propose calculating LBL using a global-batch to loose this constraint. Because a global-batch contains much more diverse sequences than a micro-batch, which will encourage load balance at the corpus level. Specifically, we introduce an extra communication step to synchronize f_i across micro-batches and then use it to calculate the LBL. Through experiments on training MoEs-based LLMs (up to 42.8B total parameters and 400B tokens), we surprisingly find that the global-batch LBL strategy yields excellent performance gains in both pre-training perplexity and downstream tasks. Our analysis reveals that the global-batch LBL also greatly improves the domain specialization of MoE experts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.11223", "title": "Reasoning Language Models: A Blueprint", "authors": [ "Maciej Besta", "Julia Barth", "Eric Schreiber", "Ales Kubicek", "Afonso Catarino", "Robert Gerstenberger", "Piotr Nyczyk", "Patrick Iff", "Yueling Li", "Sam Houliston", "Tomasz Sternal", "Marcin Copik", "Grzegorz Kwa\u015bniewski", "J\u00fcrgen M\u00fcller", "\u0141ukasz Flis", "Hannes Eberhard", "Hubert Niewiadomski", "Torsten Hoefler" ], "github": "https://github.com/spcl/x1", "project_page": "", "abstract": "Reasoning language models (RLMs), also known as Large Reasoning Models (LRMs), such as OpenAI's o1 and o3, DeepSeek-V3, and Alibaba's QwQ, have redefined AI's problem-solving capabilities by extending large language models (LLMs) with advanced reasoning mechanisms. Yet, their high costs, proprietary nature, and complex architectures - uniquely combining Reinforcement Learning (RL), search heuristics, and LLMs - present accessibility and scalability challenges. To address these, we propose a comprehensive blueprint that organizes RLM components into a modular framework, based on a survey and analysis of all RLM works. This blueprint incorporates diverse reasoning structures (chains, trees, graphs, and nested forms), reasoning strategies (e.g., Monte Carlo Tree Search, Beam Search), RL concepts (policy, value models and others), and supervision schemes (Output-Based and Process-Based Supervision). We also provide detailed mathematical formulations and algorithmic specifications to simplify RLM implementation. By showing how schemes like LLaMA-Berry, QwQ, Journey Learning, and Graph of Thoughts fit as special cases, we demonstrate the blueprint's versatility and unifying potential. To illustrate its utility, we introduce x1, a modular implementation for rapid RLM prototyping and experimentation. Using x1 and a literature review, we provide key insights, such as multi-phase training for policy and value models, and the importance of familiar training distributions. Finally, we outline how RLMs can integrate with a broader LLM ecosystem, including tools and databases. Our work demystifies RLM construction, democratizes advanced reasoning capabilities, and fosters innovation, aiming to mitigate the gap between \"rich AI\" and \"poor AI\" by lowering barriers to RLM development and experimentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/spcl/x1", "github_api_added_by": "auto" }, { "date": "2025-01-22", "arxiv_id": "2501.11425", "title": "Agent-R: Training Language Model Agents to Reflect via Iterative Self-Training", "authors": [ "Siyu Yuan", "Zehui Chen", "Zhiheng Xi", "Junjie Ye", "Zhengyin Du", "Jiecao Chen" ], "github": "https://github.com/bytedance/agent-r", "project_page": "", "abstract": "Large Language Models (LLMs) agents are increasingly pivotal for addressing complex tasks in interactive environments. Existing work mainly focuses on enhancing performance through behavior cloning from stronger experts, yet such approaches often falter in real-world applications, mainly due to the inability to recover from errors. However, step-level critique data is difficult and expensive to collect. Automating and dynamically constructing self-critique datasets is thus crucial to empowering models with intelligent agent capabilities. In this work, we propose an iterative self-training framework, Agent-R, that enables language Agent to Reflect on the fly. Unlike traditional methods that reward or penalize actions based on correctness, Agent-R leverages MCTS to construct training data that recover correct trajectories from erroneous ones. A key challenge of agent reflection lies in the necessity for timely revision rather than waiting until the end of a rollout. To address this, we introduce a model-guided critique construction mechanism: the actor model identifies the first error step (within its current capability) in a failed trajectory. Starting from it, we splice it with the adjacent correct path, which shares the same parent node in the tree. This strategy enables the model to learn reflection based on its current policy, therefore yielding better learning efficiency. To further explore the scalability of this self-improvement paradigm, we investigate iterative refinement of both error correction capabilities and dataset construction. Our findings demonstrate that Agent-R continuously improves the model's ability to recover from errors and enables timely error correction. Experiments on three interactive environments show that Agent-R effectively equips agents to correct erroneous actions while avoiding loops, achieving superior performance compared to baseline methods (+5.59%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bytedance/agent-r", "github_api_added_by": "auto" }, { "date": "2025-01-22", "arxiv_id": "2501.11733", "title": "Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks", "authors": [ "Zhenhailong Wang", "Haiyang Xu", "Junyang Wang", "Xi Zhang", "Ming Yan", "Ji Zhang", "Fei Huang", "Heng Ji" ], "github": "", "project_page": "", "abstract": "Smartphones have become indispensable in modern life, yet navigating complex tasks on mobile devices often remains frustrating. Recent advancements in large multimodal model (LMM)-based mobile agents have demonstrated the ability to perceive and act in mobile environments. However, current approaches face significant limitations: they fall short in addressing real-world human needs, struggle with reasoning-intensive and long-horizon tasks, and lack mechanisms to learn and improve from prior experiences. To overcome these challenges, we introduce Mobile-Agent-E, a hierarchical multi-agent framework capable of self-evolution through past experience. By hierarchical, we mean an explicit separation of high-level planning and low-level action execution. The framework comprises a Manager, responsible for devising overall plans by breaking down complex tasks into subgoals, and four subordinate agents--Perceptor, Operator, Action Reflector, and Notetaker--which handle fine-grained visual perception, immediate action execution, error verification, and information aggregation, respectively. Mobile-Agent-E also features a novel self-evolution module which maintains a persistent long-term memory comprising Tips and Shortcuts. Tips are general guidance and lessons learned from prior tasks on how to effectively interact with the environment. Shortcuts are reusable, executable sequences of atomic operations tailored for specific subroutines. The inclusion of Tips and Shortcuts facilitates continuous refinement in performance and efficiency. Alongside this framework, we introduce Mobile-Eval-E, a new benchmark featuring complex mobile tasks requiring long-horizon, multi-app interactions. Empirical results show that Mobile-Agent-E achieves a 22% absolute improvement over previous state-of-the-art approaches across three foundation model backbones. Project page: https://x-plug.github.io/MobileAgent.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.12326", "title": "UI-TARS: Pioneering Automated GUI Interaction with Native Agents", "authors": [ "Yujia Qin", "Yining Ye", "Junjie Fang", "Haoming Wang", "Shihao Liang", "Shizuo Tian", "Junda Zhang", "Jiahao Li", "Yunxin Li", "Shijue Huang", "Wanjun Zhong", "Kuanye Li", "Jiale Yang", "Yu Miao", "Woyu Lin", "Longxiang Liu", "Xu Jiang", "Qianli Ma", "Jingyu Li", "Xiaojun Xiao", "Kai Cai", "Chuang Li", "Yaowei Zheng", "Chaolin Jin", "Chen Li", "Xiao Zhou", "Minchao Wang", "Haoli Chen", "Zhaojian Li", "Haihua Yang", "Haifeng Liu", "Feng Lin", "Tao Peng", "Xin Liu", "Guang Shi" ], "github": "https://github.com/bytedance/UI-TARS", "project_page": "", "abstract": "This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions (e.g., keyboard and mouse operations). Unlike prevailing agent frameworks that depend on heavily wrapped commercial models (e.g., GPT-4o) with expert-crafted prompts and workflows, UI-TARS is an end-to-end model that outperforms these sophisticated frameworks. Experiments demonstrate its superior performance: UI-TARS achieves SOTA performance in 10+ GUI agent benchmarks evaluating perception, grounding, and GUI task execution. Notably, in the OSWorld benchmark, UI-TARS achieves scores of 24.6 with 50 steps and 22.7 with 15 steps, outperforming Claude (22.0 and 14.9 respectively). In AndroidWorld, UI-TARS achieves 46.6, surpassing GPT-4o (34.5). UI-TARS incorporates several key innovations: (1) Enhanced Perception: leveraging a large-scale dataset of GUI screenshots for context-aware understanding of UI elements and precise captioning; (2) Unified Action Modeling, which standardizes actions into a unified space across platforms and achieves precise grounding and interaction through large-scale action traces; (3) System-2 Reasoning, which incorporates deliberate reasoning into multi-step decision making, involving multiple reasoning patterns such as task decomposition, reflection thinking, milestone recognition, etc. (4) Iterative Training with Reflective Online Traces, which addresses the data bottleneck by automatically collecting, filtering, and reflectively refining new interaction traces on hundreds of virtual machines. Through iterative training and reflection tuning, UI-TARS continuously learns from its mistakes and adapts to unforeseen situations with minimal human intervention. We also analyze the evolution path of GUI agents to guide the further development of this domain.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bytedance/UI-TARS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.10893", "title": "Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments", "authors": [ "Hongjin Su", "Ruoxi Sun", "Jinsung Yoon", "Pengcheng Yin", "Tao Yu", "Sercan \u00d6. Ar\u0131k" ], "github": "", "project_page": "", "abstract": "Autonomous agents powered by large language models (LLMs) have the potential to enhance human capabilities, assisting with digital tasks from sending emails to performing data analysis. The abilities of existing LLMs at such tasks are often hindered by the lack of high-quality agent data from the corresponding environments they interact with. We propose Learn-by-interact, a data-centric framework to adapt LLM agents to any given environments without human annotations. Learn-by-interact synthesizes trajectories of agent-environment interactions based on documentations, and constructs instructions by summarizing or abstracting the interaction histories, a process called backward construction. We assess the quality of our synthetic data by using them in both training-based scenarios and training-free in-context learning (ICL), where we craft innovative retrieval approaches optimized for agents. Extensive experiments on SWE-bench, WebArena, OSWorld and Spider2-V spanning across realistic coding, web, and desktop environments show the effectiveness of Learn-by-interact in various downstream agentic tasks -- baseline results are improved by up to 12.2\\% for ICL with Claude-3.5 and 19.5\\% for training with Codestral-22B. We further demonstrate the critical role of backward construction, which provides up to 14.0\\% improvement for training. Our ablation studies demonstrate the efficiency provided by our synthesized data in ICL and the superiority of our retrieval pipeline over alternative approaches like conventional retrieval-augmented generation (RAG). We expect that Learn-by-interact will serve as a foundation for agent data synthesis as LLMs are increasingly deployed at real-world environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.10687", "title": "EMO2: End-Effector Guided Audio-Driven Avatar Video Generation", "authors": [ "Linrui Tian", "Siqi Hu", "Qi Wang", "Bang Zhang", "Liefeng Bo" ], "github": "", "project_page": "", "abstract": "In this paper, we propose a novel audio-driven talking head method capable of simultaneously generating highly expressive facial expressions and hand gestures. Unlike existing methods that focus on generating full-body or half-body poses, we investigate the challenges of co-speech gesture generation and identify the weak correspondence between audio features and full-body gestures as a key limitation. To address this, we redefine the task as a two-stage process. In the first stage, we generate hand poses directly from audio input, leveraging the strong correlation between audio signals and hand movements. In the second stage, we employ a diffusion model to synthesize video frames, incorporating the hand poses generated in the first stage to produce realistic facial expressions and body movements. Our experimental results demonstrate that the proposed method outperforms state-of-the-art approaches, such as CyberHost and Vlogger, in terms of both visual quality and synchronization accuracy. This work provides a new perspective on audio-driven gesture generation and a robust framework for creating expressive and natural talking head animations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.12202", "title": "Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation", "authors": [ "Zibo Zhao", "Zeqiang Lai", "Qingxiang Lin", "Yunfei Zhao", "Haolin Liu", "Shuhui Yang", "Yifei Feng", "Mingxin Yang", "Sheng Zhang", "Xianghui Yang", "Huiwen Shi", "Sicong Liu", "Junta Wu", "Yihang Lian", "Fan Yang", "Ruining Tang", "Zebin He", "Xinzhou Wang", "Jian Liu", "Xuhui Zuo", "Zhuo Chen", "Biwen Lei", "Haohan Weng", "Jing Xu", "Yiling Zhu", "Xinhai Liu", "Lixin Xu", "Changrong Hu", "Tianyu Huang", "Lifu Wang", "Jihong Zhang", "Meng Chen", "Liang Dong", "Yiwen Jia", "Yulin Cai", "Jiaao Yu", "Yixuan Tang", "Hao Zhang", "Zheng Ye", "Peng He", "Runzhou Wu", "Chao Zhang", "Yonghao Tan", "Jie Xiao", "Yangyu Tao", "Jianchen Zhu", "Jinbao Xue", "Kai Liu", "Chongqing Zhao", "Xinming Wu", "Zhichao Hu", "Lei Qin", "Jianbing Peng", "Zhan Li", "Minghui Chen", "Xipeng Zhang", "Lin Niu", "Paige Wang", "Yingkai Wang", "Haozhao Kuang", "Zhongyi Fan", "Xu Zheng", "Weihao Zhuang", "YingPing He", "Tian Liu", "Yong Yang", "Di Wang", "Yuhong Liu", "Jie Jiang", "Jingwei Huang", "Chunchao Guo" ], "github": "https://github.com/Tencent/Hunyuan3D-2", "project_page": "", "abstract": "We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two foundation components: a large-scale shape generation model -- Hunyuan3D-DiT, and a large-scale texture synthesis model -- Hunyuan3D-Paint. The shape generative model, built on a scalable flow-based diffusion transformer, aims to create geometry that properly aligns with a given condition image, laying a solid foundation for downstream applications. The texture synthesis model, benefiting from strong geometric and diffusion priors, produces high-resolution and vibrant texture maps for either generated or hand-crafted meshes. Furthermore, we build Hunyuan3D-Studio -- a versatile, user-friendly production platform that simplifies the re-creation process of 3D assets. It allows both professional and amateur users to manipulate or even animate their meshes efficiently. We systematically evaluate our models, showing that Hunyuan3D 2.0 outperforms previous state-of-the-art models, including the open-source models and closed-source models in geometry details, condition alignment, texture quality, and etc. Hunyuan3D 2.0 is publicly released in order to fill the gaps in the open-source 3D community for large-scale foundation generative models. The code and pre-trained weights of our models are available at: https://github.com/Tencent/Hunyuan3D-2", "github_manual": "", "github_regex": "https://github.com/Tencent/Hunyuan3D-2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.08331", "title": "Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise", "authors": [ "Ryan Burgert", "Yuancheng Xu", "Wenqi Xian", "Oliver Pilarski", "Pascal Clausen", "Mingming He", "Li Ma", "Yitong Deng", "Lingxiao Li", "Mohsen Mousavi", "Michael Ryoo", "Paul Debevec", "Ning Yu" ], "github": "https://github.com/VGenAI-Netflix-Eyeline-Research/Go-with-the-Flow", "project_page": "", "abstract": "Generative modeling aims to transform random noise into structured outputs. In this work, we enhance video diffusion models by allowing motion control via structured latent noise sampling. This is achieved by just a change in data: we pre-process training videos to yield structured noise. Consequently, our method is agnostic to diffusion model design, requiring no changes to model architectures or training pipelines. Specifically, we propose a novel noise warping algorithm, fast enough to run in real time, that replaces random temporal Gaussianity with correlated warped noise derived from optical flow fields, while preserving the spatial Gaussianity. The efficiency of our algorithm enables us to fine-tune modern video diffusion base models using warped noise with minimal overhead, and provide a one-stop solution for a wide range of user-friendly motion control: local object motion control, global camera movement control, and motion transfer. The harmonization between temporal coherence and spatial Gaussianity in our warped noise leads to effective motion control while maintaining per-frame pixel quality. Extensive experiments and user studies demonstrate the advantages of our method, making it a robust and scalable approach for controlling motion in video diffusion models. Video results are available on our webpage: https://vgenai-netflix-eyeline-research.github.io/Go-with-the-Flow. Source code and model checkpoints are available on GitHub: https://github.com/VGenAI-Netflix-Eyeline-Research/Go-with-the-Flow.", "github_manual": "", "github_regex": "https://github.com/VGenAI-Netflix-Eyeline-Research/Go-with-the-Flow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.12375", "title": "Video Depth Anything: Consistent Depth Estimation for Super-Long Videos", "authors": [ "Sili Chen", "Hengkai Guo", "Shengnan Zhu", "Feihu Zhang", "Zilong Huang", "Jiashi Feng", "Bingyi Kang" ], "github": "https://github.com/DepthAnything/Video-Depth-Anything", "project_page": "", "abstract": "Depth Anything has achieved remarkable success in monocular depth estimation with strong generalization ability. However, it suffers from temporal inconsistency in videos, hindering its practical applications. Various methods have been proposed to alleviate this issue by leveraging video generation models or introducing priors from optical flow and camera poses. Nonetheless, these methods are only applicable to short videos (< 10 seconds) and require a trade-off between quality and computational efficiency. We propose Video Depth Anything for high-quality, consistent depth estimation in super-long videos (over several minutes) without sacrificing efficiency. We base our model on Depth Anything V2 and replace its head with an efficient spatial-temporal head. We design a straightforward yet effective temporal consistency loss by constraining the temporal depth gradient, eliminating the need for additional geometric priors. The model is trained on a joint dataset of video depth and unlabeled images, similar to Depth Anything V2. Moreover, a novel key-frame-based strategy is developed for long video inference. Experiments show that our model can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability. Comprehensive evaluations on multiple video benchmarks demonstrate that our approach sets a new state-of-the-art in zero-shot video depth estimation. We offer models of different scales to support a range of scenarios, with our smallest model capable of real-time performance at 30 FPS.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DepthAnything/Video-Depth-Anything", "github_api_added_by": "auto" }, { "date": "2025-01-22", "arxiv_id": "2501.10057", "title": "MSTS: A Multimodal Safety Test Suite for Vision-Language Models", "authors": [ "Paul R\u00f6ttger", "Giuseppe Attanasio", "Felix Friedrich", "Janis Goldzycher", "Alicia Parrish", "Rishabh Bhardwaj", "Chiara Di Bonaventura", "Roman Eng", "Gaia El Khoury Geagea", "Sujata Goswami", "Jieun Han", "Dirk Hovy", "Seogyeong Jeong", "Paloma Jereti\u010d", "Flor Miriam Plaza-del-Arco", "Donya Rooein", "Patrick Schramowski", "Anastassia Shaitarova", "Xudong Shen", "Richard Willats", "Andrea Zugarini", "Bertie Vidgen" ], "github": "https://github.com/paul-rottger/msts-multimodal-safety", "project_page": "", "abstract": "Vision-language models (VLMs), which process image and text inputs, are increasingly integrated into chat assistants and other consumer AI applications. Without proper safeguards, however, VLMs may give harmful advice (e.g. how to self-harm) or encourage unsafe behaviours (e.g. to consume drugs). Despite these clear hazards, little work so far has evaluated VLM safety and the novel risks created by multimodal inputs. To address this gap, we introduce MSTS, a Multimodal Safety Test Suite for VLMs. MSTS comprises 400 test prompts across 40 fine-grained hazard categories. Each test prompt consists of a text and an image that only in combination reveal their full unsafe meaning. With MSTS, we find clear safety issues in several open VLMs. We also find some VLMs to be safe by accident, meaning that they are safe because they fail to understand even simple test prompts. We translate MSTS into ten languages, showing non-English prompts to increase the rate of unsafe model responses. We also show models to be safer when tested with text only rather than multimodal prompts. Finally, we explore the automation of VLM safety assessments, finding even the best safety classifiers to be lacking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/paul-rottger/msts-multimodal-safety", "github_api_added_by": "auto" }, { "date": "2025-01-22", "arxiv_id": "2501.12224", "title": "TokenVerse: Versatile Multi-concept Personalization in Token Modulation Space", "authors": [ "Daniel Garibi", "Shahar Yadin", "Roni Paiss", "Omer Tov", "Shiran Zada", "Ariel Ephrat", "Tomer Michaeli", "Inbar Mosseri", "Tali Dekel" ], "github": "", "project_page": "", "abstract": "We present TokenVerse -- a method for multi-concept personalization, leveraging a pre-trained text-to-image diffusion model. Our framework can disentangle complex visual elements and attributes from as little as a single image, while enabling seamless plug-and-play generation of combinations of concepts extracted from multiple images. As opposed to existing works, TokenVerse can handle multiple images with multiple concepts each, and supports a wide-range of concepts, including objects, accessories, materials, pose, and lighting. Our work exploits a DiT-based text-to-image model, in which the input text affects the generation through both attention and modulation (shift and scale). We observe that the modulation space is semantic and enables localized control over complex concepts. Building on this insight, we devise an optimization-based framework that takes as input an image and a text description, and finds for each word a distinct direction in the modulation space. These directions can then be used to generate new images that combine the learned concepts in a desired configuration. We demonstrate the effectiveness of TokenVerse in challenging personalization settings, and showcase its advantages over existing methods. project's webpage in https://token-verse.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.12368", "title": "InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model", "authors": [ "Yuhang Zang", "Xiaoyi Dong", "Pan Zhang", "Yuhang Cao", "Ziyu Liu", "Shengyuan Ding", "Shenxi Wu", "Yubo Ma", "Haodong Duan", "Wenwei Zhang", "Kai Chen", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/InternLM/InternLM-XComposer", "project_page": "", "abstract": "Despite the promising performance of Large Vision Language Models (LVLMs) in visual understanding, they occasionally generate incorrect outputs. While reward models (RMs) with reinforcement learning or test-time scaling offer the potential for improving generation quality, a critical gap remains: publicly available multi-modal RMs for LVLMs are scarce, and the implementation details of proprietary models are often unclear. We bridge this gap with InternLM-XComposer2.5-Reward (IXC-2.5-Reward), a simple yet effective multi-modal reward model that aligns LVLMs with human preferences. To ensure the robustness and versatility of IXC-2.5-Reward, we set up a high-quality multi-modal preference corpus spanning text, image, and video inputs across diverse domains, such as instruction following, general understanding, text-rich documents, mathematical reasoning, and video understanding. IXC-2.5-Reward achieves excellent results on the latest multi-modal reward model benchmark and shows competitive performance on text-only reward model benchmarks. We further demonstrate three key applications of IXC-2.5-Reward: (1) Providing a supervisory signal for RL training. We integrate IXC-2.5-Reward with Proximal Policy Optimization (PPO) yields IXC-2.5-Chat, which shows consistent improvements in instruction following and multi-modal open-ended dialogue; (2) Selecting the best response from candidate responses for test-time scaling; and (3) Filtering outlier or noisy samples from existing image and video instruction tuning training data. To ensure reproducibility and facilitate further research, we have open-sourced all model weights and training recipes at https://github.com/InternLM/InternLM-XComposer", "github_manual": "", "github_regex": "https://github.com/InternLM/InternLM-XComposer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.11900", "title": "Panoramic Interests: Stylistic-Content Aware Personalized Headline Generation", "authors": [ "Junhong Lian", "Xiang Ao", "Xinyu Liu", "Yang Liu", "Qing He" ], "github": "", "project_page": "", "abstract": "Personalized news headline generation aims to provide users with attention-grabbing headlines that are tailored to their preferences. Prevailing methods focus on user-oriented content preferences, but most of them overlook the fact that diverse stylistic preferences are integral to users' panoramic interests, leading to suboptimal personalization. In view of this, we propose a novel Stylistic-Content Aware Personalized Headline Generation (SCAPE) framework. SCAPE extracts both content and stylistic features from headlines with the aid of large language model (LLM) collaboration. It further adaptively integrates users' long- and short-term interests through a contrastive learning-based hierarchical fusion network. By incorporating the panoramic interests into the headline generator, SCAPE reflects users' stylistic-content preferences during the generation process. Extensive experiments on the real-world dataset PENS demonstrate the superiority of SCAPE over baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.10573", "title": "The Geometry of Tokens in Internal Representations of Large Language Models", "authors": [ "Karthik Viswanathan", "Yuri Gardinazzi", "Giada Panerai", "Alberto Cazzaniga", "Matteo Biagetti" ], "github": "https://github.com/ritareasciencepark/token_geometry", "project_page": "", "abstract": "We investigate the relationship between the geometry of token embeddings and their role in the next token prediction within transformer models. An important aspect of this connection uses the notion of empirical measure, which encodes the distribution of token point clouds across transformer layers and drives the evolution of token representations in the mean-field interacting picture. We use metrics such as intrinsic dimension, neighborhood overlap, and cosine similarity to observationally probe these empirical measures across layers. To validate our approach, we compare these metrics to a dataset where the tokens are shuffled, which disrupts the syntactic and semantic structure. Our findings reveal a correlation between the geometric properties of token embeddings and the cross-entropy loss of next token predictions, implying that prompts with higher loss values have tokens represented in higher-dimensional spaces.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ritareasciencepark/token_geometry", "github_api_added_by": "auto" }, { "date": "2025-01-22", "arxiv_id": "2501.12206", "title": "Fixing Imbalanced Attention to Mitigate In-Context Hallucination of Large Vision-Language Model", "authors": [ "Kazi Hasan Ibn Arif", "Sajib Acharjee Dip", "Khizar Hussain", "Lang Zhang", "Chris Thomas" ], "github": "https://github.com/hasanar1f/paint", "project_page": "", "abstract": "Large Vision Language Models (LVLMs) have demonstrated remarkable capabilities in understanding and describing visual content, achieving state-of-the-art performance across various vision-language tasks. However, these models frequently exhibit hallucination behavior, where they generate descriptions containing objects or details absent in the input image. Our work investigates this phenomenon by analyzing attention patterns across transformer layers and heads, revealing that hallucinations often stem from progressive degradation of visual grounding in deeper layers. We propose a novel attention modification approach that combines selective token emphasis and head-specific modulation to maintain visual grounding throughout the generation process. Our method introduces two key components: (1) a dual-stream token selection mechanism that identifies and prioritizes both locally informative and spatially significant visual tokens, and (2) an attention head-specific modulation strategy that differentially amplifies visual information processing based on measured visual sensitivity of individual attention heads. Through extensive experimentation on the MSCOCO dataset, we demonstrate that our approach reduces hallucination rates by up to 62.3\\% compared to baseline models while maintaining comparable task performance. Our analysis reveals that selectively modulating tokens across attention heads with varying levels of visual sensitivity can significantly improve visual grounding without requiring model retraining.", "github_manual": "https://github.com/hasanar1f/paint", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-22", "arxiv_id": "2501.12389", "title": "Taming Teacher Forcing for Masked Autoregressive Video Generation", "authors": [ "Deyu Zhou", "Quan Sun", "Yuang Peng", "Kun Yan", "Runpei Dong", "Duomin Wang", "Zheng Ge", "Nan Duan", "Xiangyu Zhang", "Lionel M. Ni", "Heung-Yeung Shum" ], "github": "", "project_page": "", "abstract": "We introduce MAGI, a hybrid video generation framework that combines masked modeling for intra-frame generation with causal modeling for next-frame generation. Our key innovation, Complete Teacher Forcing (CTF), conditions masked frames on complete observation frames rather than masked ones (namely Masked Teacher Forcing, MTF), enabling a smooth transition from token-level (patch-level) to frame-level autoregressive generation. CTF significantly outperforms MTF, achieving a +23% improvement in FVD scores on first-frame conditioned video prediction. To address issues like exposure bias, we employ targeted training strategies, setting a new benchmark in autoregressive video generation. Experiments show that MAGI can generate long, coherent video sequences exceeding 100 frames, even when trained on as few as 16 frames, highlighting its potential for scalable, high-quality video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-23", "arxiv_id": "2501.12909", "title": "FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in Virtual 3D Spaces", "authors": [ "Zhenran Xu", "Longyue Wang", "Jifang Wang", "Zhouyi Li", "Senbao Shi", "Xue Yang", "Yiyu Wang", "Baotian Hu", "Jun Yu", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Virtual film production requires intricate decision-making processes, including scriptwriting, virtual cinematography, and precise actor positioning and actions. Motivated by recent advances in automated decision-making with language agent-based societies, this paper introduces FilmAgent, a novel LLM-based multi-agent collaborative framework for end-to-end film automation in our constructed 3D virtual spaces. FilmAgent simulates various crew roles, including directors, screenwriters, actors, and cinematographers, and covers key stages of a film production workflow: (1) idea development transforms brainstormed ideas into structured story outlines; (2) scriptwriting elaborates on dialogue and character actions for each scene; (3) cinematography determines the camera setups for each shot. A team of agents collaborates through iterative feedback and revisions, thereby verifying intermediate scripts and reducing hallucinations. We evaluate the generated videos on 15 ideas and 4 key aspects. Human evaluation shows that FilmAgent outperforms all baselines across all aspects and scores 3.98 out of 5 on average, showing the feasibility of multi-agent collaboration in filmmaking. Further analysis reveals that FilmAgent, despite using the less advanced GPT-4o model, surpasses the single-agent o1, showing the advantage of a well-coordinated multi-agent system. Lastly, we discuss the complementary strengths and weaknesses of OpenAI's text-to-video model Sora and our FilmAgent in filmmaking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-23", "arxiv_id": "2501.12948", "title": "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning", "authors": [ "DeepSeek-AI", "Daya Guo", "Dejian Yang", "Haowei Zhang", "Junxiao Song", "Ruoyu Zhang", "Runxin Xu", "Qihao Zhu", "Shirong Ma", "Peiyi Wang", "Xiao Bi", "Xiaokang Zhang", "Xingkai Yu", "Yu Wu", "Z. F. Wu", "Zhibin Gou", "Zhihong Shao", "Zhuoshu Li", "Ziyi Gao", "Aixin Liu", "Bing Xue", "Bingxuan Wang", "Bochao Wu", "Bei Feng", "Chengda Lu", "Chenggang Zhao", "Chengqi Deng", "Chenyu Zhang", "Chong Ruan", "Damai Dai", "Deli Chen", "Dongjie Ji", "Erhang Li", "Fangyun Lin", "Fucong Dai", "Fuli Luo", "Guangbo Hao", "Guanting Chen", "Guowei Li", "H. Zhang", "Han Bao", "Hanwei Xu", "Haocheng Wang", "Honghui Ding", "Huajian Xin", "Huazuo Gao", "Hui Qu", "Hui Li", "Jianzhong Guo", "Jiashi Li", "Jiawei Wang", "Jingchang Chen", "Jingyang Yuan", "Junjie Qiu", "Junlong Li", "J. L. Cai", "Jiaqi Ni", "Jian Liang", "Jin Chen", "Kai Dong", "Kai Hu", "Kaige Gao", "Kang Guan", "Kexin Huang", "Kuai Yu", "Lean Wang", "Lecong Zhang", "Liang Zhao", "Litong Wang", "Liyue Zhang", "Lei Xu", "Leyi Xia", "Mingchuan Zhang", "Minghua Zhang", "Minghui Tang", "Meng Li", "Miaojun Wang", "Mingming Li", "Ning Tian", "Panpan Huang", "Peng Zhang", "Qiancheng Wang", "Qinyu Chen", "Qiushi Du", "Ruiqi Ge", "Ruisong Zhang", "Ruizhe Pan", "Runji Wang", "R. J. Chen", "R. L. Jin", "Ruyi Chen", "Shanghao Lu", "Shangyan Zhou", "Shanhuang Chen", "Shengfeng Ye", "Shiyu Wang", "Shuiping Yu", "Shunfeng Zhou", "Shuting Pan", "S. S. Li", "Shuang Zhou", "Shaoqing Wu", "Shengfeng Ye", "Tao Yun", "Tian Pei", "Tianyu Sun", "T. Wang", "Wangding Zeng", "Wanjia Zhao", "Wen Liu", "Wenfeng Liang", "Wenjun Gao", "Wenqin Yu", "Wentao Zhang", "W. L. Xiao", "Wei An", "Xiaodong Liu", "Xiaohan Wang", "Xiaokang Chen", "Xiaotao Nie", "Xin Cheng", "Xin Liu", "Xin Xie", "Xingchao Liu", "Xinyu Yang", "Xinyuan Li", "Xuecheng Su", "Xuheng Lin", "X. Q. Li", "Xiangyue Jin", "Xiaojin Shen", "Xiaosha Chen", "Xiaowen Sun", "Xiaoxiang Wang", "Xinnan Song", "Xinyi Zhou", "Xianzu Wang", "Xinxia Shan", "Y. K. Li", "Y. Q. Wang", "Y. X. Wei", "Yang Zhang", "Yanhong Xu", "Yao Li", "Yao Zhao", "Yaofeng Sun", "Yaohui Wang", "Yi Yu", "Yichao Zhang", "Yifan Shi", "Yiliang Xiong", "Ying He", "Yishi Piao", "Yisong Wang", "Yixuan Tan", "Yiyang Ma", "Yiyuan Liu", "Yongqiang Guo", "Yuan Ou", "Yuduan Wang", "Yue Gong", "Yuheng Zou", "Yujia He", "Yunfan Xiong", "Yuxiang Luo", "Yuxiang You", "Yuxuan Liu", "Yuyang Zhou", "Y. X. Zhu", "Yanhong Xu", "Yanping Huang", "Yaohui Li", "Yi Zheng", "Yuchen Zhu", "Yunxian Ma", "Ying Tang", "Yukun Zha", "Yuting Yan", "Z. Z. Ren", "Zehui Ren", "Zhangli Sha", "Zhe Fu", "Zhean Xu", "Zhenda Xie", "Zhengyan Zhang", "Zhewen Hao", "Zhicheng Ma", "Zhigang Yan", "Zhiyu Wu", "Zihui Gu", "Zijia Zhu", "Zijun Liu", "Zilin Li", "Ziwei Xie", "Ziyang Song", "Zizheng Pan", "Zhen Huang", "Zhipeng Xu", "Zhongyu Zhang", "Zhen Zhang" ], "github": "https://github.com/deepseek-ai/deepseek-r1", "project_page": "", "abstract": "We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero naturally emerges with numerous powerful and intriguing reasoning behaviors. However, it encounters challenges such as poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates multi-stage training and cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217 on reasoning tasks. To support the research community, we open-source DeepSeek-R1-Zero, DeepSeek-R1, and six dense models (1.5B, 7B, 8B, 14B, 32B, 70B) distilled from DeepSeek-R1 based on Qwen and Llama.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/deepseek-ai/deepseek-r1", "github_api_added_by": "auto" }, { "date": "2025-01-23", "arxiv_id": "2501.12570", "title": "O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning", "authors": [ "Haotian Luo", "Li Shen", "Haiying He", "Yibo Wang", "Shiwei Liu", "Wei Li", "Naiqiang Tan", "Xiaochun Cao", "Dacheng Tao" ], "github": "https://github.com/StarDewXXX/O1-Pruner", "project_page": "", "abstract": "Recently, long-thought reasoning LLMs, such as OpenAI's O1, adopt extended reasoning processes similar to how humans ponder over complex problems. This reasoning paradigm significantly enhances the model's problem-solving abilities and has achieved promising results. However, long-thought reasoning process leads to a substantial increase in inference time. A pressing challenge is reducing the inference overhead of long-thought LLMs while ensuring accuracy. In this paper, we experimentally demonstrate that long-thought reasoning models struggle to effectively allocate token budgets based on problem difficulty and reasoning redundancies. To address this, we propose Length-Harmonizing Fine-Tuning (O1-Pruner), aiming at minimizing reasoning overhead while maintaining accuracy. This effective fine-tuning method first estimates the LLM's baseline performance through pre-sampling and then uses RL-style fine-tuning to encourage the model to generate shorter reasoning processes under accuracy constraints. This allows the model to achieve efficient reasoning with lower redundancy while maintaining accuracy. Experiments on various mathematical reasoning benchmarks show that O1-Pruner not only significantly reduces inference overhead but also achieves higher accuracy, providing a novel and promising solution to this challenge. Our code is coming soon at https://github.com/StarDewXXX/O1-Pruner", "github_manual": "", "github_regex": "https://github.com/StarDewXXX/O1-Pruner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-23", "arxiv_id": "2501.12599", "title": "Kimi k1.5: Scaling Reinforcement Learning with LLMs", "authors": [ "Kimi Team", "Angang Du", "Bofei Gao", "Bowei Xing", "Changjiu Jiang", "Cheng Chen", "Cheng Li", "Chenjun Xiao", "Chenzhuang Du", "Chonghua Liao", "Chuning Tang", "Congcong Wang", "Dehao Zhang", "Enming Yuan", "Enzhe Lu", "Fengxiang Tang", "Flood Sung", "Guangda Wei", "Guokun Lai", "Haiqing Guo", "Han Zhu", "Hao Ding", "Hao Hu", "Hao Yang", "Hao Zhang", "Haotian Yao", "Haotian Zhao", "Haoyu Lu", "Haoze Li", "Haozhen Yu", "Hongcheng Gao", "Huabin Zheng", "Huan Yuan", "Jia Chen", "Jianhang Guo", "Jianlin Su", "Jianzhou Wang", "Jie Zhao", "Jin Zhang", "Jingyuan Liu", "Junjie Yan", "Junyan Wu", "Lidong Shi", "Ling Ye", "Longhui Yu", "Mengnan Dong", "Neo Zhang", "Ningchen Ma", "Qiwei Pan", "Qucheng Gong", "Shaowei Liu", "Shengling Ma", "Shupeng Wei", "Sihan Cao", "Siying Huang", "Tao Jiang", "Weihao Gao", "Weimin Xiong", "Weiran He", "Weixiao Huang", "Wenhao Wu", "Wenyang He", "Xianghui Wei", "Xianqing Jia", "Xingzhe Wu", "Xinran Xu", "Xinxing Zu", "Xinyu Zhou", "Xuehai Pan", "Y. Charles", "Yang Li", "Yangyang Hu", "Yangyang Liu", "Yanru Chen", "Yejie Wang", "Yibo Liu", "Yidao Qin", "Yifeng Liu", "Ying Yang", "Yiping Bao", "Yulun Du", "Yuxin Wu", "Yuzhi Wang", "Zaida Zhou", "Zhaoji Wang", "Zhaowei Li", "Zhen Zhu", "Zheng Zhang", "Zhexu Wang", "Zhilin Yang", "Zhiqi Huang", "Zihao Huang", "Ziyao Xu", "Zonghan Yang" ], "github": "https://github.com/moonshotai/kimi-k1.5", "project_page": "", "abstract": "Language model pretraining with next token prediction has proved effective for scaling compute but is limited to the amount of available training data. Scaling reinforcement learning (RL) unlocks a new axis for the continued improvement of artificial intelligence, with the promise that large language models (LLMs) can scale their training data by learning to explore with rewards. However, prior published work has not produced competitive results. In light of this, we report on the training practice of Kimi k1.5, our latest multi-modal LLM trained with RL, including its RL training techniques, multi-modal data recipes, and infrastructure optimization. Long context scaling and improved policy optimization methods are key ingredients of our approach, which establishes a simplistic, effective RL framework without relying on more complex techniques such as Monte Carlo tree search, value functions, and process reward models. Notably, our system achieves state-of-the-art reasoning performance across multiple benchmarks and modalities -- e.g., 77.5 on AIME, 96.2 on MATH 500, 94-th percentile on Codeforces, 74.9 on MathVista -- matching OpenAI's o1. Moreover, we present effective long2short methods that use long-CoT techniques to improve short-CoT models, yielding state-of-the-art short-CoT reasoning results -- e.g., 60.8 on AIME, 94.6 on MATH500, 47.3 on LiveCodeBench -- outperforming existing short-CoT models such as GPT-4o and Claude Sonnet 3.5 by a large margin (up to +550%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/moonshotai/kimi-k1.5", "github_api_added_by": "auto" }, { "date": "2025-01-23", "arxiv_id": "2501.13074", "title": "Autonomy-of-Experts Models", "authors": [ "Ang Lv", "Ruobing Xie", "Yining Qian", "Songhao Wu", "Xingwu Sun", "Zhanhui Kang", "Di Wang", "Rui Yan" ], "github": "https://github.com/trestad/Autonomy-of-Experts", "project_page": "", "abstract": "Mixture-of-Experts (MoE) models mostly use a router to assign tokens to specific expert modules, activating only partial parameters and often outperforming dense models. We argue that the separation between the router's decision-making and the experts' execution is a critical yet overlooked issue, leading to suboptimal expert selection and ineffective learning. To address this, we propose Autonomy-of-Experts (AoE), a novel MoE paradigm in which experts autonomously select themselves to process inputs. AoE is based on the insight that an expert is aware of its own capacity to effectively process a token, an awareness reflected in the scale of its internal activations. In AoE, routers are removed; instead, experts pre-compute internal activations for inputs and are ranked based on their activation norms. Only the top-ranking experts proceed with the forward pass, while the others abort. The overhead of pre-computing activations is reduced through a low-rank weight factorization. This self-evaluating-then-partner-comparing approach ensures improved expert selection and effective learning. We pre-train language models having 700M up to 4B parameters, demonstrating that AoE outperforms traditional MoE models with comparable efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/trestad/Autonomy-of-Experts", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-23", "arxiv_id": "2501.11067", "title": "IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI Systems", "authors": [ "Elad Levi", "Ilan Kadar" ], "github": "https://github.com/plurai-ai/intellagent", "project_page": "", "abstract": "Large Language Models (LLMs) are transforming artificial intelligence, evolving into task-oriented systems capable of autonomous planning and execution. One of the primary applications of LLMs is conversational AI systems, which must navigate multi-turn dialogues, integrate domain-specific APIs, and adhere to strict policy constraints. However, evaluating these agents remains a significant challenge, as traditional methods fail to capture the complexity and variability of real-world interactions. We introduce IntellAgent, a scalable, open-source multi-agent framework designed to evaluate conversational AI systems comprehensively. IntellAgent automates the creation of diverse, synthetic benchmarks by combining policy-driven graph modeling, realistic event generation, and interactive user-agent simulations. This innovative approach provides fine-grained diagnostics, addressing the limitations of static and manually curated benchmarks with coarse-grained metrics. IntellAgent represents a paradigm shift in evaluating conversational AI. By simulating realistic, multi-policy scenarios across varying levels of complexity, IntellAgent captures the nuanced interplay of agent capabilities and policy constraints. Unlike traditional methods, it employs a graph-based policy model to represent relationships, likelihoods, and complexities of policy interactions, enabling highly detailed diagnostics. IntellAgent also identifies critical performance gaps, offering actionable insights for targeted optimization. Its modular, open-source design supports seamless integration of new domains, policies, and APIs, fostering reproducibility and community collaboration. Our findings demonstrate that IntellAgent serves as an effective framework for advancing conversational AI by addressing challenges in bridging research and deployment. The framework is available at https://github.com/plurai-ai/intellagent", "github_manual": "", "github_regex": "https://github.com/plurai-ai/intellagent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-23", "arxiv_id": "2501.12895", "title": "Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback", "authors": [ "Yafu Li", "Xuyang Hu", "Xiaoye Qu", "Linjie Li", "Yu Cheng" ], "github": "https://github.com/yafuly/TPO", "project_page": "", "abstract": "Large language models (LLMs) demonstrate impressive performance but lack the flexibility to adapt to human preferences quickly without retraining. In this work, we introduce Test-time Preference Optimization (TPO), a framework that aligns LLM outputs with human preferences during inference, removing the need to update model parameters. Rather than relying on purely numerical rewards, TPO translates reward signals into textual critiques and uses them as textual rewards to iteratively refine its response. Evaluations on benchmarks covering instruction following, preference alignment, safety, and mathematics reveal that TPO progressively improves alignment with human preferences. Notably, after only a few TPO steps, the initially unaligned Llama-3.1-70B-SFT model can surpass the aligned counterpart, Llama-3.1-70B-Instruct. Furthermore, TPO scales efficiently with both the search width and depth during inference. Through case studies, we illustrate how TPO exploits the innate capacity of LLM to interpret and act upon reward signals. Our findings establish TPO as a practical, lightweight alternative for test-time preference optimization, achieving alignment on the fly. Our code is publicly available at https://github.com/yafuly/TPO.", "github_manual": "", "github_regex": "https://github.com/yafuly/TPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-23", "arxiv_id": "2501.13106", "title": "VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding", "authors": [ "Boqiang Zhang", "Kehan Li", "Zesen Cheng", "Zhiqiang Hu", "Yuqian Yuan", "Guanzheng Chen", "Sicong Leng", "Yuming Jiang", "Hang Zhang", "Xin Li", "Peng Jin", "Wenqi Zhang", "Fan Wang", "Lidong Bing", "Deli Zhao" ], "github": "https://github.com/DAMO-NLP-SG/VideoLLaMA3", "project_page": "", "abstract": "In this paper, we propose VideoLLaMA3, a more advanced multimodal foundation model for image and video understanding. The core design philosophy of VideoLLaMA3 is vision-centric. The meaning of \"vision-centric\" is two-fold: the vision-centric training paradigm and vision-centric framework design. The key insight of our vision-centric training paradigm is that high-quality image-text data is crucial for both image and video understanding. Instead of preparing massive video-text datasets, we focus on constructing large-scale and high-quality image-text datasets. VideoLLaMA3 has four training stages: 1) vision-centric alignment stage, which warms up the vision encoder and projector; 2) vision-language pretraining stage, which jointly tunes the vision encoder, projector, and LLM with large-scale image-text data covering multiple types (including scene images, documents, charts) as well as text-only data. 3) multi-task fine-tuning stage, which incorporates image-text SFT data for downstream tasks and video-text data to establish a foundation for video understanding. 4) video-centric fine-tuning, which further improves the model's capability in video understanding. As for the framework design, to better capture fine-grained details in images, the pretrained vision encoder is adapted to encode images of varying sizes into vision tokens with corresponding numbers, rather than a fixed number of tokens. For video inputs, we reduce the number of vision tokens according to their similarity so that the representation of videos will be more precise and compact. Benefit from vision-centric designs, VideoLLaMA3 achieves compelling performances in both image and video understanding benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/DAMO-NLP-SG/VideoLLaMA3", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-23", "arxiv_id": "2501.13007", "title": "Pairwise RM: Perform Best-of-N Sampling with Knockout Tournament", "authors": [ "Yantao Liu", "Zijun Yao", "Rui Min", "Yixin Cao", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/thu-keg/pairwiserm", "project_page": "", "abstract": "Best-of-N (BoN) sampling, a common strategy for test-time scaling of Large Language Models (LLMs), relies on reward models to select the best candidate solution from multiple generations. However, traditional reward models often assign arbitrary and inconsistent scores, limiting their effectiveness. To address this, we propose a Pairwise Reward Model (Pairwise RM) combined with a knockout tournament for BoN sampling. Instead of assigning absolute scores, given one math problem, Pairwise RM evaluates two candidate solutions' correctness simultaneously. This approach eliminates the need for arbitrary scoring and enables cross-validation of solutions through parallel comparison. In the knockout tournament, Pairwise RM conducts pairwise comparisons between candidate solutions and eliminates the incorrect ones iteratively. We construct \\ourdataset, a large-scale dataset of 443K pairwise comparisons derived from NumiaMath and annotated using gemini-1.5-flash, and train the Pairwise RM via supervised fine-tuning. Experiments on MATH-500 and the Olympiad Bench demonstrate significant improvements over traditional discriminative reward models. And a 40\\% to 60\\% relative improvement is achieved on the top 50\\% challenging problems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thu-keg/pairwiserm", "github_api_added_by": "auto" }, { "date": "2025-01-23", "arxiv_id": "2501.13928", "title": "Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass", "authors": [ "Jianing Yang", "Alexander Sax", "Kevin J. Liang", "Mikael Henaff", "Hao Tang", "Ang Cao", "Joyce Chai", "Franziska Meier", "Matt Feiszli" ], "github": "https://github.com/facebookresearch/fast3r", "project_page": "", "abstract": "Multi-view 3D reconstruction remains a core challenge in computer vision, particularly in applications requiring accurate and scalable representations across diverse perspectives. Current leading methods such as DUSt3R employ a fundamentally pairwise approach, processing images in pairs and necessitating costly global alignment procedures to reconstruct from multiple views. In this work, we propose Fast 3D Reconstruction (Fast3R), a novel multi-view generalization to DUSt3R that achieves efficient and scalable 3D reconstruction by processing many views in parallel. Fast3R's Transformer-based architecture forwards N images in a single forward pass, bypassing the need for iterative alignment. Through extensive experiments on camera pose estimation and 3D reconstruction, Fast3R demonstrates state-of-the-art performance, with significant improvements in inference speed and reduced error accumulation. These results establish Fast3R as a robust alternative for multi-view applications, offering enhanced scalability without compromising reconstruction accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/fast3r", "github_api_added_by": "auto" }, { "date": "2025-01-24", "arxiv_id": "2501.13629", "title": "Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models", "authors": [ "Zhenghao Lin", "Zihao Tang", "Xiao Liu", "Yeyun Gong", "Yi Cheng", "Qi Chen", "Hang Li", "Ying Xin", "Ziyue Yang", "Kailai Yang", "Yu Yan", "Xiao Liang", "Shuai Lu", "Yiming Huang", "Zheheng Luo", "Lei Qu", "Xuan Feng", "Yaoxiang Wang", "Yuqing Xia", "Feiyang Chen", "Yuting Jiang", "Yasen Hu", "Hao Ni", "Binyang Li", "Guoshuai Zhao", "Jui-Hao Chiang", "Zhongxin Guo", "Chen Lin", "Kun Kuang", "Wenjie Li", "Yelong Shen", "Jian Jiao", "Peng Cheng", "Mao Yang" ], "github": "", "project_page": "", "abstract": "We introduce Sigma, an efficient large language model specialized for the system domain, empowered by a novel architecture including DiffQKV attention, and pre-trained on our meticulously collected system domain data. DiffQKV attention significantly enhances the inference efficiency of Sigma by optimizing the Query (Q), Key (K), and Value (V) components in the attention mechanism differentially, based on their varying impacts on the model performance and efficiency indicators. Specifically, we (1) conduct extensive experiments that demonstrate the model's varying sensitivity to the compression of K and V components, leading to the development of differentially compressed KV, and (2) propose augmented Q to expand the Q head dimension, which enhances the model's representation capacity with minimal impacts on the inference speed. Rigorous theoretical and empirical analyses reveal that DiffQKV attention significantly enhances efficiency, achieving up to a 33.36% improvement in inference speed over the conventional grouped-query attention (GQA) in long-context scenarios. We pre-train Sigma on 6T tokens from various sources, including 19.5B system domain data that we carefully collect and 1T tokens of synthesized and rewritten data. In general domains, Sigma achieves comparable performance to other state-of-arts models. In the system domain, we introduce the first comprehensive benchmark AIMicius, where Sigma demonstrates remarkable performance across all tasks, significantly outperforming GPT-4 with an absolute improvement up to 52.5%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.13926", "title": "Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step", "authors": [ "Ziyu Guo", "Renrui Zhang", "Chengzhuo Tong", "Zhizheng Zhao", "Peng Gao", "Hongsheng Li", "Pheng-Ann Heng" ], "github": "https://github.com/ZiyuGuo99/Image-Generation-CoT", "project_page": "", "abstract": "Chain-of-Thought (CoT) reasoning has been extensively explored in large models to tackle complex understanding tasks. However, it still remains an open question whether such strategies can be applied to verifying and reinforcing image generation scenarios. In this paper, we provide the first comprehensive investigation of the potential of CoT reasoning to enhance autoregressive image generation. We focus on three techniques: scaling test-time computation for verification, aligning model preferences with Direct Preference Optimization (DPO), and integrating these techniques for complementary effects. Our results demonstrate that these approaches can be effectively adapted and combined to significantly improve image generation performance. Furthermore, given the pivotal role of reward models in our findings, we propose the Potential Assessment Reward Model (PARM) and PARM++, specialized for autoregressive image generation. PARM adaptively assesses each generation step through a potential assessment approach, merging the strengths of existing reward models, and PARM++ further introduces a reflection mechanism to self-correct the generated unsatisfactory image. Using our investigated reasoning strategies, we enhance a baseline model, Show-o, to achieve superior results, with a significant +24% improvement on the GenEval benchmark, surpassing Stable Diffusion 3 by +15%. We hope our study provides unique insights and paves a new path for integrating CoT reasoning with autoregressive image generation. Code and models are released at https://github.com/ZiyuGuo99/Image-Generation-CoT", "github_manual": "", "github_regex": "https://github.com/ZiyuGuo99/Image-Generation-CoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.10799", "title": "Step-KTO: Optimizing Mathematical Reasoning through Stepwise Binary Feedback", "authors": [ "Yen-Ting Lin", "Di Jin", "Tengyu Xu", "Tianhao Wu", "Sainbayar Sukhbaatar", "Chen Zhu", "Yun He", "Yun-Nung Chen", "Jason Weston", "Yuandong Tian", "Arash Rahnama", "Sinong Wang", "Hao Ma", "Han Fang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have recently demonstrated remarkable success in mathematical reasoning. Despite progress in methods like chain-of-thought prompting and self-consistency sampling, these advances often focus on final correctness without ensuring that the underlying reasoning process is coherent and reliable. This paper introduces Step-KTO, a training framework that combines process-level and outcome-level binary feedback to guide LLMs toward more trustworthy reasoning trajectories. By providing binary evaluations for both the intermediate reasoning steps and the final answer, Step-KTO encourages the model to adhere to logical progressions rather than relying on superficial shortcuts. Our experiments on challenging mathematical benchmarks show that Step-KTO significantly improves both final answer accuracy and the quality of intermediate reasoning steps. For example, on the MATH-500 dataset, Step-KTO achieves a notable improvement in Pass@1 accuracy over strong baselines. These results highlight the promise of integrating stepwise process feedback into LLM training, paving the way toward more interpretable and dependable reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.13124", "title": "Debate Helps Weak-to-Strong Generalization", "authors": [ "Hao Lang", "Fei Huang", "Yongbin Li" ], "github": "", "project_page": "", "abstract": "Common methods for aligning already-capable models with desired behavior rely on the ability of humans to provide supervision. However, future superhuman models will surpass the capability of humans. Therefore, humans will only be able to weakly supervise superhuman models. This expected deficiency of human evaluation would weaken the safety of future AI systems. Scalable oversight and weak-to-strong generalization are two complementary approaches to tackle this issue. In this paper, we attempt to combine the strengths of these two approaches to further improve alignment. Specifically, we investigate ways of improving human supervision with a strong pretrained model and then supervise the strong model with enhanced weak human supervision. To make iterative empirical progress, we consider an analogy: can we use a strong model to improve weak model supervision and then use it to supervise the strong model? We empirically test it by finetuning a small weak model on ground truth labels with the additional help from a large strong model, and then finetuning the strong model on labels generated by the weak model. We find that debate can assist a weak model in extracting trustworthy information from an untrustworthy strong model, which provides leverage as context on samples when training a weak model. We also show that an ensemble of weak models helps exploit long arguments generated by strong model debaters and obtain a more robust supervision estimate. Extensive experiments on the OpenAI weak-to-strong NLP benchmarks show that the combination approach leads to better alignment, which indicates that debate has the potential to help weak-to-strong generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.13920", "title": "IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image Models", "authors": [ "Jiayi Lei", "Renrui Zhang", "Xiangfei Hu", "Weifeng Lin", "Zhen Li", "Wenjian Sun", "Ruoyi Du", "Le Zhuo", "Zhongyu Li", "Xinyue Li", "Shitian Zhao", "Ziyu Guo", "Yiting Lu", "Peng Gao", "Hongsheng Li" ], "github": "https://github.com/jylei16/Imagine-e", "project_page": "", "abstract": "With the rapid development of diffusion models, text-to-image(T2I) models have made significant progress, showcasing impressive abilities in prompt following and image generation. Recently launched models such as FLUX.1 and Ideogram2.0, along with others like Dall-E3 and Stable Diffusion 3, have demonstrated exceptional performance across various complex tasks, raising questions about whether T2I models are moving towards general-purpose applicability. Beyond traditional image generation, these models exhibit capabilities across a range of fields, including controllable generation, image editing, video, audio, 3D, and motion generation, as well as computer vision tasks like semantic segmentation and depth estimation. However, current evaluation frameworks are insufficient to comprehensively assess these models' performance across expanding domains. To thoroughly evaluate these models, we developed the IMAGINE-E and tested six prominent models: FLUX.1, Ideogram2.0, Midjourney, Dall-E3, Stable Diffusion 3, and Jimeng. Our evaluation is divided into five key domains: structured output generation, realism, and physical consistency, specific domain generation, challenging scenario generation, and multi-style creation tasks. This comprehensive assessment highlights each model's strengths and limitations, particularly the outstanding performance of FLUX.1 and Ideogram2.0 in structured and specific domain tasks, underscoring the expanding applications and potential of T2I models as foundational AI tools. This study provides valuable insights into the current state and future trajectory of T2I models as they evolve towards general-purpose usability. Evaluation scripts will be released at https://github.com/jylei16/Imagine-e.", "github_manual": "", "github_regex": "https://github.com/jylei16/Imagine-e", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.13919", "title": "Temporal Preference Optimization for Long-Form Video Understanding", "authors": [ "Rui Li", "Xiaohan Wang", "Yuhui Zhang", "Zeyu Wang", "Serena Yeung-Levy" ], "github": "https://github.com/ruili33/TPO", "project_page": "", "abstract": "Despite significant advancements in video large multimodal models (video-LMMs), achieving effective temporal grounding in long-form videos remains a challenge for existing models. To address this limitation, we propose Temporal Preference Optimization (TPO), a novel post-training framework designed to enhance the temporal grounding capabilities of video-LMMs through preference learning. TPO adopts a self-training approach that enables models to differentiate between well-grounded and less accurate temporal responses by leveraging curated preference datasets at two granularities: localized temporal grounding, which focuses on specific video segments, and comprehensive temporal grounding, which captures extended temporal dependencies across entire video sequences. By optimizing on these preference datasets, TPO significantly enhances temporal understanding while reducing reliance on manually annotated data. Extensive experiments on three long-form video understanding benchmarks--LongVideoBench, MLVU, and Video-MME--demonstrate the effectiveness of TPO across two state-of-the-art video-LMMs. Notably, LLaVA-Video-TPO establishes itself as the leading 7B model on the Video-MME benchmark, underscoring the potential of TPO as a scalable and efficient solution for advancing temporal reasoning in long-form video understanding. Project page: https://ruili33.github.io/tpo_website.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ruili33/TPO", "github_api_added_by": "auto" }, { "date": "2025-01-24", "arxiv_id": "2501.13075", "title": "Evolution and The Knightian Blindspot of Machine Learning", "authors": [ "Joel Lehman", "Elliot Meyerson", "Tarek El-Gaaly", "Kenneth O. Stanley", "Tarin Ziyaee" ], "github": "", "project_page": "", "abstract": "This paper claims that machine learning (ML) largely overlooks an important facet of general intelligence: robustness to a qualitatively unknown future in an open world. Such robustness relates to Knightian uncertainty (KU) in economics, i.e. uncertainty that cannot be quantified, which is excluded from consideration in ML's key formalisms. This paper aims to identify this blind spot, argue its importance, and catalyze research into addressing it, which we believe is necessary to create truly robust open-world AI. To help illuminate the blind spot, we contrast one area of ML, reinforcement learning (RL), with the process of biological evolution. Despite staggering ongoing progress, RL still struggles in open-world situations, often failing under unforeseen situations. For example, the idea of zero-shot transferring a self-driving car policy trained only in the US to the UK currently seems exceedingly ambitious. In dramatic contrast, biological evolution routinely produces agents that thrive within an open world, sometimes even to situations that are remarkably out-of-distribution (e.g. invasive species; or humans, who do undertake such zero-shot international driving). Interestingly, evolution achieves such robustness without explicit theory, formalisms, or mathematical gradients. We explore the assumptions underlying RL's typical formalisms, showing how they limit RL's engagement with the unknown unknowns characteristic of an ever-changing complex world. Further, we identify mechanisms through which evolutionary processes foster robustness to novel and unpredictable challenges, and discuss potential pathways to algorithmically embody them. The conclusion is that the intriguing remaining fragility of ML may result from blind spots in its formalisms, and that significant gains may result from direct confrontation with the challenge of KU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.13200", "title": "SRMT: Shared Memory for Multi-agent Lifelong Pathfinding", "authors": [ "Alsu Sagirova", "Yuri Kuratov", "Mikhail Burtsev" ], "github": "https://github.com/Aloriosa/srmt", "project_page": "", "abstract": "Multi-agent reinforcement learning (MARL) demonstrates significant progress in solving cooperative and competitive multi-agent problems in various environments. One of the principal challenges in MARL is the need for explicit prediction of the agents' behavior to achieve cooperation. To resolve this issue, we propose the Shared Recurrent Memory Transformer (SRMT) which extends memory transformers to multi-agent settings by pooling and globally broadcasting individual working memories, enabling agents to exchange information implicitly and coordinate their actions. We evaluate SRMT on the Partially Observable Multi-Agent Pathfinding problem in a toy Bottleneck navigation task that requires agents to pass through a narrow corridor and on a POGEMA benchmark set of tasks. In the Bottleneck task, SRMT consistently outperforms a variety of reinforcement learning baselines, especially under sparse rewards, and generalizes effectively to longer corridors than those seen during training. On POGEMA maps, including Mazes, Random, and MovingAI, SRMT is competitive with recent MARL, hybrid, and planning-based algorithms. These results suggest that incorporating shared recurrent memory into the transformer-based architectures can enhance coordination in decentralized multi-agent systems. The source code for training and evaluation is available on GitHub: https://github.com/Aloriosa/srmt.", "github_manual": "", "github_regex": "https://github.com/Aloriosa/srmt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.13452", "title": "EchoVideo: Identity-Preserving Human Video Generation by Multimodal Feature Fusion", "authors": [ "Jiangchuan Wei", "Shiyue Yan", "Wenfeng Lin", "Boyuan Liu", "Renjie Chen", "Mingyu Guo" ], "github": "https://github.com/bytedance/echovideo", "project_page": "", "abstract": "Recent advancements in video generation have significantly impacted various downstream applications, particularly in identity-preserving video generation (IPT2V). However, existing methods struggle with \"copy-paste\" artifacts and low similarity issues, primarily due to their reliance on low-level facial image information. This dependence can result in rigid facial appearances and artifacts reflecting irrelevant details. To address these challenges, we propose EchoVideo, which employs two key strategies: (1) an Identity Image-Text Fusion Module (IITF) that integrates high-level semantic features from text, capturing clean facial identity representations while discarding occlusions, poses, and lighting variations to avoid the introduction of artifacts; (2) a two-stage training strategy, incorporating a stochastic method in the second phase to randomly utilize shallow facial information. The objective is to balance the enhancements in fidelity provided by shallow features while mitigating excessive reliance on them. This strategy encourages the model to utilize high-level features during training, ultimately fostering a more robust representation of facial identities. EchoVideo effectively preserves facial identities and maintains full-body integrity. Extensive experiments demonstrate that it achieves excellent results in generating high-quality, controllability and fidelity videos.", "github_manual": "https://github.com/bytedance/echovideo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.10283", "title": "GSTAR: Gaussian Surface Tracking and Reconstruction", "authors": [ "Chengwei Zheng", "Lixin Xue", "Juan Zarate", "Jie Song" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting techniques have enabled efficient photo-realistic rendering of static scenes. Recent works have extended these approaches to support surface reconstruction and tracking. However, tracking dynamic surfaces with 3D Gaussians remains challenging due to complex topology changes, such as surfaces appearing, disappearing, or splitting. To address these challenges, we propose GSTAR, a novel method that achieves photo-realistic rendering, accurate surface reconstruction, and reliable 3D tracking for general dynamic scenes with changing topology. Given multi-view captures as input, GSTAR binds Gaussians to mesh faces to represent dynamic objects. For surfaces with consistent topology, GSTAR maintains the mesh topology and tracks the meshes using Gaussians. In regions where topology changes, GSTAR adaptively unbinds Gaussians from the mesh, enabling accurate registration and the generation of new surfaces based on these optimized Gaussians. Additionally, we introduce a surface-based scene flow method that provides robust initialization for tracking between frames. Experiments demonstrate that our method effectively tracks and reconstructs dynamic surfaces, enabling a range of applications. Our project page with the code release is available at https://eth-ait.github.io/GSTAR/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.10018", "title": "DiffuEraser: A Diffusion Model for Video Inpainting", "authors": [ "Xiaowen Li", "Haolan Xue", "Peiran Ren", "Liefeng Bo" ], "github": "https://github.com/lixiaowen-xw/diffueraser", "project_page": "", "abstract": "Recent video inpainting algorithms integrate flow-based pixel propagation with transformer-based generation to leverage optical flow for restoring textures and objects using information from neighboring frames, while completing masked regions through visual Transformers. However, these approaches often encounter blurring and temporal inconsistencies when dealing with large masks, highlighting the need for models with enhanced generative capabilities. Recently, diffusion models have emerged as a prominent technique in image and video generation due to their impressive performance. In this paper, we introduce DiffuEraser, a video inpainting model based on stable diffusion, designed to fill masked regions with greater details and more coherent structures. We incorporate prior information to provide initialization and weak conditioning,which helps mitigate noisy artifacts and suppress hallucinations. Additionally, to improve temporal consistency during long-sequence inference, we expand the temporal receptive fields of both the prior model and DiffuEraser, and further enhance consistency by leveraging the temporal smoothing property of Video Diffusion Models. Experimental results demonstrate that our proposed method outperforms state-of-the-art techniques in both content completeness and temporal consistency while maintaining acceptable efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lixiaowen-xw/diffueraser", "github_api_added_by": "auto" }, { "date": "2025-01-24", "arxiv_id": "2501.13826", "title": "Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos", "authors": [ "Kairui Hu", "Penghao Wu", "Fanyi Pu", "Wang Xiao", "Yuanhan Zhang", "Xiang Yue", "Bo Li", "Ziwei Liu" ], "github": "https://github.com/EvolvingLMMs-Lab/VideoMMMU", "project_page": "", "abstract": "Humans acquire knowledge through three cognitive stages: perceiving information, comprehending knowledge, and adapting knowledge to solve novel problems. Videos serve as an effective medium for this learning process, facilitating a progression through these cognitive stages. However, existing video benchmarks fail to systematically evaluate the knowledge acquisition capabilities in Large Multimodal Models (LMMs). To address this gap, we introduce Video-MMMU, a multi-modal, multi-disciplinary benchmark designed to assess LMMs' ability to acquire and utilize knowledge from videos. Video-MMMU features a curated collection of 300 expert-level videos and 900 human-annotated questions across six disciplines, evaluating knowledge acquisition through stage-aligned question-answer pairs: Perception, Comprehension, and Adaptation. A proposed knowledge gain metric, {\\Delta}knowledge, quantifies improvement in performance after video viewing. Evaluation of LMMs reveals a steep decline in performance as cognitive demands increase and highlights a significant gap between human and model knowledge acquisition, underscoring the need for methods to enhance LMMs' capability to learn and adapt from videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/EvolvingLMMs-Lab/VideoMMMU", "github_api_added_by": "auto" }, { "date": "2025-01-24", "arxiv_id": "2501.13918", "title": "Improving Video Generation with Human Feedback", "authors": [ "Jie Liu", "Gongye Liu", "Jiajun Liang", "Ziyang Yuan", "Xiaokun Liu", "Mingwu Zheng", "Xiele Wu", "Qiulin Wang", "Wenyu Qin", "Menghan Xia", "Xintao Wang", "Xiaohong Liu", "Fei Yang", "Pengfei Wan", "Di Zhang", "Kun Gai", "Yujiu Yang", "Wanli Ouyang" ], "github": "", "project_page": "", "abstract": "Video generation has achieved significant advances through rectified flow techniques, but issues like unsmooth motion and misalignment between videos and prompts persist. In this work, we develop a systematic pipeline that harnesses human feedback to mitigate these problems and refine the video generation model. Specifically, we begin by constructing a large-scale human preference dataset focused on modern video generation models, incorporating pairwise annotations across multi-dimensions. We then introduce VideoReward, a multi-dimensional video reward model, and examine how annotations and various design choices impact its rewarding efficacy. From a unified reinforcement learning perspective aimed at maximizing reward with KL regularization, we introduce three alignment algorithms for flow-based models by extending those from diffusion models. These include two training-time strategies: direct preference optimization for flow (Flow-DPO) and reward weighted regression for flow (Flow-RWR), and an inference-time technique, Flow-NRG, which applies reward guidance directly to noisy videos. Experimental results indicate that VideoReward significantly outperforms existing reward models, and Flow-DPO demonstrates superior performance compared to both Flow-RWR and standard supervised fine-tuning methods. Additionally, Flow-NRG lets users assign custom weights to multiple objectives during inference, meeting personalized video quality needs. Project page: https://gongyeliu.github.io/videoalign.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.13554", "title": "One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt", "authors": [ "Tao Liu", "Kai Wang", "Senmao Li", "Joost van de Weijer", "Fahad Shahbaz Khan", "Shiqi Yang", "Yaxing Wang", "Jian Yang", "Ming-Ming Cheng" ], "github": "https://github.com/byliutao/1Prompt1Story", "project_page": "", "abstract": "Text-to-image generation models can create high-quality images from input prompts. However, they struggle to support the consistent generation of identity-preserving requirements for storytelling. Existing approaches to this problem typically require extensive training in large datasets or additional modifications to the original model architectures. This limits their applicability across different domains and diverse diffusion model configurations. In this paper, we first observe the inherent capability of language models, coined context consistency, to comprehend identity through context with a single prompt. Drawing inspiration from the inherent context consistency, we propose a novel training-free method for consistent text-to-image (T2I) generation, termed \"One-Prompt-One-Story\" (1Prompt1Story). Our approach 1Prompt1Story concatenates all prompts into a single input for T2I diffusion models, initially preserving character identities. We then refine the generation process using two novel techniques: Singular-Value Reweighting and Identity-Preserving Cross-Attention, ensuring better alignment with the input description for each frame. In our experiments, we compare our method against various existing consistent T2I generation approaches to demonstrate its effectiveness through quantitative metrics and qualitative assessments. Code is available at https://github.com/byliutao/1Prompt1Story.", "github_manual": "", "github_regex": "https://github.com/byliutao/1Prompt1Story", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.13824", "title": "Hallucinations Can Improve Large Language Models in Drug Discovery", "authors": [ "Shuzhou Yuan", "Michael F\u00e4rber" ], "github": "", "project_page": "", "abstract": "Concerns about hallucinations in Large Language Models (LLMs) have been raised by researchers, yet their potential in areas where creativity is vital, such as drug discovery, merits exploration. In this paper, we come up with the hypothesis that hallucinations can improve LLMs in drug discovery. To verify this hypothesis, we use LLMs to describe the SMILES string of molecules in natural language and then incorporate these descriptions as part of the prompt to address specific tasks in drug discovery. Evaluated on seven LLMs and five classification tasks, our findings confirm the hypothesis: LLMs can achieve better performance with text containing hallucinations. Notably, Llama-3.1-8B achieves an 18.35% gain in ROC-AUC compared to the baseline without hallucination. Furthermore, hallucinations generated by GPT-4o provide the most consistent improvements across models. Additionally, we conduct empirical analyses and a case study to investigate key factors affecting performance and the underlying reasons. Our research sheds light on the potential use of hallucinations for LLMs and offers new perspectives for future research leveraging LLMs in drug discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.10979", "title": "Control LLM: Controlled Evolution for Intelligence Retention in LLM", "authors": [ "Haichao Wei", "Yunxiang Ren", "Zhoutong Fu", "Aman Lunia", "Yi-Lin Chen", "Alice Leung", "Ya Xu" ], "github": "https://github.com/linkedin/ControlLLM", "project_page": "", "abstract": "Large Language Models (LLMs) demand significant computational resources, making it essential to enhance their capabilities without retraining from scratch. A key challenge in this domain is catastrophic forgetting (CF), which hampers performance during Continuous Pre-training (CPT) and Continuous Supervised Fine-Tuning (CSFT). We propose Control LLM, a novel approach that leverages parallel pre-trained and expanded transformer blocks, aligning their hidden-states through interpolation strategies This method effectively preserves performance on existing tasks while seamlessly integrating new knowledge. Extensive experiments demonstrate the effectiveness of Control LLM in both CPT and CSFT. On Llama3.1-8B-Instruct, it achieves significant improvements in mathematical reasoning (+14.4% on Math-Hard) and coding performance (+10% on MBPP-PLUS). On Llama3.1-8B, it enhances multilingual capabilities (+10.6% on C-Eval, +6.8% on CMMLU, and +30.2% on CMMLU-0shot-CoT). It surpasses existing methods and achieves SOTA among open-source models tuned from the same base model, using substantially less data and compute. Crucially, these gains are realized while preserving strong original capabilities, with minimal degradation (<4.3% on MMLU) compared to >35% in open-source Math and Coding models. This approach has been successfully deployed in LinkedIn's GenAI-powered job seeker and Ads unit products. To support further research, we release the training and evaluation code (https://github.com/linkedin/ControlLLM) along with models trained on public datasets ( https://huggingface.co/ControlLLM) to the community.", "github_manual": "", "github_regex": "https://github.com/linkedin/ControlLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-24", "arxiv_id": "2501.11858", "title": "EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents", "authors": [ "Zhili Cheng", "Yuge Tu", "Ran Li", "Shiqi Dai", "Jinyi Hu", "Shengding Hu", "Jiahao Li", "Yang Shi", "Tianyu Yu", "Weize Chen", "Lei Shi", "Maosong Sun" ], "github": "https://github.com/thunlp/EmbodiedEval", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have shown significant advancements, providing a promising future for embodied agents. Existing benchmarks for evaluating MLLMs primarily utilize static images or videos, limiting assessments to non-interactive scenarios. Meanwhile, existing embodied AI benchmarks are task-specific and not diverse enough, which do not adequately evaluate the embodied capabilities of MLLMs. To address this, we propose EmbodiedEval, a comprehensive and interactive evaluation benchmark for MLLMs with embodied tasks. EmbodiedEval features 328 distinct tasks within 125 varied 3D scenes, each of which is rigorously selected and annotated. It covers a broad spectrum of existing embodied AI tasks with significantly enhanced diversity, all within a unified simulation and evaluation framework tailored for MLLMs. The tasks are organized into five categories: navigation, object interaction, social interaction, attribute question answering, and spatial question answering to assess different capabilities of the agents. We evaluated the state-of-the-art MLLMs on EmbodiedEval and found that they have a significant shortfall compared to human level on embodied tasks. Our analysis demonstrates the limitations of existing MLLMs in embodied capabilities, providing insights for their future development. We open-source all evaluation data and simulation framework at https://github.com/thunlp/EmbodiedEval.", "github_manual": "", "github_regex": "https://github.com/thunlp/EmbodiedEval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-27", "arxiv_id": "2501.14492", "title": "RealCritic: Towards Effectiveness-Driven Evaluation of Language Model Critiques", "authors": [ "Zhengyang Tang", "Ziniu Li", "Zhenyang Xiao", "Tian Ding", "Ruoyu Sun", "Benyou Wang", "Dayiheng Liu", "Fei Huang", "Tianyu Liu", "Bowen Yu", "Junyang Lin" ], "github": "https://github.com/tangzhy/RealCritic", "project_page": "", "abstract": "Critiques are important for enhancing the performance of Large Language Models (LLMs), enabling both self-improvement and constructive feedback for others by identifying flaws and suggesting improvements. However, evaluating the critique capabilities of LLMs presents a significant challenge due to the open-ended nature of the task. In this work, we introduce a new benchmark designed to assess the critique capabilities of LLMs. Unlike existing benchmarks, which typically function in an open-loop fashion, our approach employs a closed-loop methodology that evaluates the quality of corrections generated from critiques. Moreover, the benchmark incorporates features such as self-critique, cross-critique, and iterative critique, which are crucial for distinguishing the abilities of advanced reasoning models from more classical ones. We implement this benchmark using eight challenging reasoning tasks. We have several interesting findings. First, despite demonstrating comparable performance in direct chain-of-thought generation, classical LLMs significantly lag behind the advanced reasoning-based model o1-mini across all critique scenarios. Second, in self-critique and iterative critique settings, classical LLMs may even underperform relative to their baseline capabilities. We hope that this benchmark will serve as a valuable resource to guide future advancements. The code and data are available at https://github.com/tangzhy/RealCritic.", "github_manual": "", "github_regex": "https://github.com/tangzhy/RealCritic", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-27", "arxiv_id": "2501.14342", "title": "Chain-of-Retrieval Augmented Generation", "authors": [ "Liang Wang", "Haonan Chen", "Nan Yang", "Xiaolong Huang", "Zhicheng Dou", "Furu Wei" ], "github": "https://github.com/microsoft/LMOps/tree/main/corag", "project_page": "", "abstract": "This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer. Conventional RAG methods usually perform a single retrieval step before the generation process, which limits their effectiveness in addressing complex queries due to imperfect retrieval results. In contrast, our proposed method, CoRAG (Chain-of-Retrieval Augmented Generation), allows the model to dynamically reformulate the query based on the evolving state. To train CoRAG effectively, we utilize rejection sampling to automatically generate intermediate retrieval chains, thereby augmenting existing RAG datasets that only provide the correct final answer. At test time, we propose various decoding strategies to scale the model's test-time compute by controlling the length and number of sampled retrieval chains. Experimental results across multiple benchmarks validate the efficacy of CoRAG, particularly in multi-hop question answering tasks, where we observe more than 10 points improvement in EM score compared to strong baselines. On the KILT benchmark, CoRAG establishes a new state-of-the-art performance across a diverse range of knowledge-intensive tasks. Furthermore, we offer comprehensive analyses to understand the scaling behavior of CoRAG, laying the groundwork for future research aimed at developing factual and grounded foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/LMOps/tree/main/corag", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-27", "arxiv_id": "2501.14249", "title": "Humanity's Last Exam", "authors": [ "Long Phan", "Alice Gatti", "Ziwen Han", "Nathaniel Li", "Josephina Hu", "Hugh Zhang", "Sean Shi", "Michael Choi", "Anish Agrawal", "Arnav Chopra", "Adam Khoja", "Ryan Kim", "Jason Hausenloy", "Oliver Zhang", "Mantas Mazeika", "Daron Anderson", "Tung Nguyen", "Mobeen Mahmood", "Fiona Feng", "Steven Y. Feng", "Haoran Zhao", "Michael Yu", "Varun Gangal", "Chelsea Zou", "Zihan Wang", "Jessica P. Wang", "Pawan Kumar", "Oleksandr Pokutnyi", "Robert Gerbicz", "Serguei Popov", "John-Clark Levin", "Mstyslav Kazakov", "Johannes Schmitt", "Geoff Galgon", "Alvaro Sanchez", "Yongki Lee", "Will Yeadon", "Scott Sauers", "Marc Roth", "Chidozie Agu", "S\u00f8ren Riis", "Fabian Giska", "Saiteja Utpala", "Zachary Giboney", "Gashaw M. Goshu", "Joan of Arc Xavier", "Sarah-Jane Crowson", "Mohinder Maheshbhai Naiya", "Noah Burns", "Lennart Finke", "Zerui Cheng", "Hyunwoo Park", "Francesco Fournier-Facio", "John Wydallis", "Mark Nandor", "Ankit Singh", "Tim Gehrunger", "Jiaqi Cai", "Ben McCarty", "Darling Duclosel", "Jungbae Nam", "Jennifer Zampese", "Ryan G. Hoerr", "Aras Bacho", "Gautier Abou Loume", "Abdallah Galal", "Hangrui Cao", "Alexis C Garretson", "Damien Sileo", "Qiuyu Ren", "Doru Cojoc", "Pavel Arkhipov", "Usman Qazi", "Lianghui Li", "Sumeet Motwani", "Christian Schroeder de Witt", "Edwin Taylor", "Johannes Veith", "Eric Singer", "Taylor D. Hartman", "Paolo Rissone", "Jaehyeok Jin", "Jack Wei Lun Shi", "Chris G. Willcocks", "Joshua Robinson", "Aleksandar Mikov", "Ameya Prabhu", "Longke Tang", "Xavier Alapont", "Justine Leon Uro", "Kevin Zhou", "Emily de Oliveira Santos", "Andrey Pupasov Maksimov", "Edward Vendrow", "Kengo Zenitani", "Julien Guillod", "Yuqi Li", "Joshua Vendrow", "Vladyslav Kuchkin", "Ng Ze-An", "Pierre Marion", "Denis Efremov", "Jayson Lynch", "Kaiqu Liang", "Andrew Gritsevskiy", "Dakotah Martinez", "Ben Pageler", "Nick Crispino", "Dimitri Zvonkine", "Natanael Wildner Fraga", "Saeed Soori", "Ori Press", "Henry Tang", "Julian Salazar", "Sean R. Green", "Lina Br\u00fcssel", "Moon Twayana", "Aymeric Dieuleveut", "T. Ryan Rogers", "Wenjin Zhang", "Bikun Li", "Jinzhou Yang", "Arun Rao", "Gabriel Loiseau", "Mikhail Kalinin", "Marco Lukas", "Ciprian Manolescu", "Subrata Mishra", "Ariel Ghislain Kemogne Kamdoum", "Tobias Kreiman", "Tad Hogg", "Alvin Jin", "Carlo Bosio", "Gongbo Sun", "Brian P Coppola", "Tim Tarver", "Haline Heidinger", "Rafael Sayous", "Stefan Ivanov", "Joseph M Cavanagh", "Jiawei Shen", "Joseph Marvin Imperial", "Philippe Schwaller", "Shaipranesh Senthilkuma", "Andres M Bran", "Ali Dehghan", "Andres Algaba", "Brecht Verbeken", "David Noever", "Ragavendran P V", "Lisa Schut", "Ilia Sucholutsky", "Evgenii Zheltonozhskii", "Derek Lim", "Richard Stanley", "Shankar Sivarajan", "Tong Yang", "John Maar", "Julian Wykowski", "Mart\u00ed Oller", "Jennifer Sandlin", "Anmol Sahu", "Yuzheng Hu", "Sara Fish", "Nasser Heydari", "Archimedes Apronti", "Kaivalya Rawal", "Tobias Garcia Vilchis", "Yuexuan Zu", "Martin Lackner", "James Koppel", "Jeremy Nguyen", "Daniil S. Antonenko", "Steffi Chern", "Bingchen Zhao", "Pierrot Arsene", "Alan Goldfarb", "Sergey Ivanov", "Rafa\u0142 Po\u015bwiata", "Chenguang Wang", "Daofeng Li", "Donato Crisostomi", "Andrea Achilleos", "Benjamin Myklebust", "Archan Sen", "David Perrella", "Nurdin Kaparov", "Mark H Inlow", "Allen Zang", "Elliott Thornley", "Daniil Orel", "Vladislav Poritski", "Shalev Ben-David", "Zachary Berger", "Parker Whitfill", "Michael Foster", "Daniel Munro", "Linh Ho", "Dan Bar Hava", "Aleksey Kuchkin", "Robert Lauff", "David Holmes", "Frank Sommerhage", "Keith Schneider", "Zakayo Kazibwe", "Nate Stambaugh", "Mukhwinder Singh", "Ilias Magoulas", "Don Clarke", "Dae Hyun Kim", "Felipe Meneguitti Dias", "Veit Elser", "Kanu Priya Agarwal", "Victor Efren Guadarrama Vilchis", "Immo Klose", "Christoph Demian", "Ujjwala Anantheswaran", "Adam Zweiger", "Guglielmo Albani", "Jeffery Li", "Nicolas Daans", "Maksim Radionov", "V\u00e1clav Rozho\u0148", "Ziqiao Ma", "Christian Stump", "Mohammed Berkani", "Jacob Platnick", "Volodymyr Nevirkovets", "Luke Basler", "Marco Piccardo", "Ferenc Jeanplong", "Niv Cohen", "Josef Tkadlec", "Paul Rosu", "Piotr Padlewski", "Stanislaw Barzowski", "Kyle Montgomery", "Aline Menezes", "Arkil Patel", "Zixuan Wang", "Jamie Tucker-Foltz", "Jack Stade", "Tom Goertzen", "Fereshteh Kazemi", "Jeremiah Milbauer", "John Arnold Ambay", "Abhishek Shukla", "Yan Carlos Leyva Labrador", "Alan Givr\u00e9", "Hew Wolff", "Vivien Rossbach", "Muhammad Fayez Aziz", "Younesse Kaddar", "Yanxu Chen", "Robin Zhang", "Jiayi Pan", "Antonio Terpin", "Niklas Muennighoff", "Hailey Schoelkopf", "Eric Zheng", "Avishy Carmi", "Adam Jones", "Jainam Shah", "Ethan D. L. Brown", "Kelin Zhu", "Max Bartolo", "Richard Wheeler", "Andrew Ho", "Shaul Barkan", "Jiaqi Wang", "Martin Stehberger", "Egor Kretov", "Kaustubh Sridhar", "Zienab EL-Wasif", "Anji Zhang", "Daniel Pyda", "Joanna Tam", "David M. Cunningham", "Vladimir Goryachev", "Demosthenes Patramanis", "Michael Krause", "Andrew Redenti", "Daniel Bugas", "David Aldous", "Jesyin Lai", "Shannon Coleman", "Mohsen Bahaloo", "Jiangnan Xu", "Sangwon Lee", "Sandy Zhao", "Ning Tang", "Michael K. Cohen", "Micah Carroll", "Orr Paradise", "Jan Hendrik Kirchner", "Stefan Steinerberger", "Maksym Ovchynnikov", "Jason O. Matos", "Adithya Shenoy", "Benedito Alves de Oliveira Junior", "Michael Wang", "Yuzhou Nie", "Paolo Giordano", "Philipp Petersen", "Anna Sztyber-Betley", "Priti Shukla", "Jonathan Crozier", "Antonella Pinto", "Shreyas Verma", "Prashant Joshi", "Zheng-Xin Yong", "Allison Tee", "J\u00e9r\u00e9my Andr\u00e9oletti", "Orion Weller", "Raghav Singhal", "Gang Zhang", "Alexander Ivanov", "Seri Khoury", "Hamid Mostaghimi", "Kunvar Thaman", "Qijia Chen", "Tran Quoc Kh\u00e1nh", "Jacob Loader", "Stefano Cavalleri", "Hannah Szlyk", "Zachary Brown", "Jonathan Roberts", "William Alley", "Kunyang Sun", "Ryan Stendall", "Max Lamparth", "Anka Reuel", "Ting Wang", "Hanmeng Xu", "Sreenivas Goud Raparthi", "Pablo Hern\u00e1ndez-C\u00e1mara", "Freddie Martin", "Dmitry Malishev", "Thomas Preu", "Tomek Korbak", "Marcus Abramovitch", "Dominic Williamson", "Ziye Chen", "Bir\u00f3 B\u00e1lint", "M Saiful Bari", "Peyman Kassani", "Zihao Wang", "Behzad Ansarinejad", "Laxman Prasad Goswami", "Yewen Sun", "Hossam Elgnainy", "Daniel Tordera", "George Balabanian", "Earth Anderson", "Lynna Kvistad", "Alejandro Jos\u00e9 Moyano", "Rajat Maheshwari", "Ahmad Sakor", "Murat Eron", "Isaac C. McAlister", "Javier Gimenez", "Innocent Enyekwe", "Andrew Favre D. O.", "Shailesh Shah", "Xiaoxiang Zhou", "Firuz Kamalov", "Ronald Clark", "Sherwin Abdoli", "Tim Santens", "Khalida Meer", "Harrison K Wang", "Kalyan Ramakrishnan", "Evan Chen", "Alessandro Tomasiello", "G. Bruno De Luca", "Shi-Zhuo Looi", "Vinh-Kha Le", "Noam Kolt", "Niels M\u00fcndler", "Avi Semler", "Emma Rodman", "Jacob Drori", "Carl J Fossum", "Milind Jagota", "Ronak Pradeep", "Honglu Fan", "Tej Shah", "Jonathan Eicher", "Michael Chen", "Kushal Thaman", "William Merrill", "Carter Harris", "Jason Gross", "Ilya Gusev", "Asankhaya Sharma", "Shashank Agnihotri", "Pavel Zhelnov", "Siranut Usawasutsakorn", "Mohammadreza Mofayezi", "Sergei Bogdanov", "Alexander Piperski", "Marc Carauleanu", "David K. Zhang", "Dylan Ler", "Roman Leventov", "Ignat Soroko", "Thorben Jansen", "Pascal Lauer", "Joshua Duersch", "Vage Taamazyan", "Wiktor Morak", "Wenjie Ma", "William Held", "Tran \u0110uc Huy", "Ruicheng Xian", "Armel Randy Zebaze", "Mohanad Mohamed", "Julian Noah Leser", "Michelle X Yuan", "Laila Yacar", "Johannes Lengler", "Hossein Shahrtash", "Edson Oliveira", "Joseph W. Jackson", "Daniel Espinosa Gonzalez", "Andy Zou", "Muthu Chidambaram", "Timothy Manik", "Hector Haffenden", "Dashiell Stander", "Ali Dasouqi", "Alexander Shen", "Emilien Duc", "Bita Golshani", "David Stap", "Mikalai Uzhou", "Alina Borisovna Zhidkovskaya", "Lukas Lewark", "M\u00e1ty\u00e1s Vincze", "Dustin Wehr", "Colin Tang", "Zaki Hossain", "Shaun Phillips", "Jiang Muzhen", "Fredrik Ekstr\u00f6m", "Angela Hammon", "Oam Patel", "Nicolas Remy", "Faraz Farhidi", "George Medley", "Forough Mohammadzadeh", "Madellene Pe\u00f1aflor", "Haile Kassahun", "Alena Friedrich", "Claire Sparrow", "Taom Sakal", "Omkar Dhamane", "Ali Khajegili Mirabadi", "Eric Hallman", "Mike Battaglia", "Mohammad Maghsoudimehrabani", "Hieu Hoang", "Alon Amit", "Dave Hulbert", "Roberto Pereira", "Simon Weber", "Stephen Mensah", "Nathan Andre", "Anton Peristyy", "Chris Harjadi", "Himanshu Gupta", "Stephen Malina", "Samuel Albanie", "Will Cai", "Mustafa Mehkary", "Frank Reidegeld", "Anna-Katharina Dick", "Cary Friday", "Jasdeep Sidhu", "Wanyoung Kim", "Mariana Costa", "Hubeyb Gurdogan", "Brian Weber", "Harsh Kumar", "Tong Jiang", "Arunim Agarwal", "Chiara Ceconello", "Warren S. Vaz", "Chao Zhuang", "Haon Park", "Andrew R. Tawfeek", "Daattavya Aggarwal", "Michael Kirchhof", "Linjie Dai", "Evan Kim", "Johan Ferret", "Yuzhou Wang", "Minghao Yan", "Krzysztof Burdzy", "Lixin Zhang", "Antonio Franca", "Diana T. Pham", "Kang Yong Loh", "Joshua Robinson", "Shreen Gul", "Gunjan Chhablani", "Zhehang Du", "Adrian Cosma", "Colin White", "Robin Riblet", "Prajvi Saxena", "Jacob Votava", "Vladimir Vinnikov", "Ethan Delaney", "Shiv Halasyamani", "Syed M. Shahid", "Jean-Christophe Mourrat", "Lavr Vetoshkin", "Renas Bacho", "Vincent Ginis", "Aleksandr Maksapetyan", "Florencia de la Rosa", "Xiuyu Li", "Guillaume Malod", "Leon Lang", "Julien Laurendeau", "Fatimah Adesanya", "Julien Portier", "Lawrence Hollom", "Victor Souza", "Yuchen Anna Zhou", "Yi\u011fit Yal\u0131n", "Gbenga Daniel Obikoya", "Luca Arnaboldi", "Rai", "Filippo Bigi", "Kaniuar Bacho", "Pierre Clavier", "Gabriel Recchia", "Mara Popescu", "Nikita Shulga", "Ngefor Mildred Tanwie", "Thomas C. H. Lux", "Ben Rank", "Colin Ni", "Alesia Yakimchyk", "Huanxu", "Liu", "Olle H\u00e4ggstr\u00f6m", "Emil Verkama", "Himanshu Narayan", "Hans Gundlach", "Leonor Brito-Santana", "Brian Amaro", "Vivek Vajipey", "Rynaa Grover", "Yiyang Fan", "Gabriel Poesia Reis e Silva", "Linwei Xin", "Yosi Kratish", "Jakub \u0141ucki", "Wen-Ding Li", "Justin Xu", "Kevin Joseph Scaria", "Freddie Vargus", "Farzad Habibi", "Long", "Lian", "Emanuele Rodol\u00e0", "Jules Robins", "Vincent Cheng", "Declan Grabb", "Ida Bosio", "Tony Fruhauff", "Ido Akov", "Eve J. Y. Lo", "Hao Qi", "Xi Jiang", "Ben Segev", "Jingxuan Fan", "Sarah Martinson", "Erik Y. Wang", "Kaylie Hausknecht", "Michael P. Brenner", "Mao Mao", "Yibo Jiang", "Xinyu Zhang", "David Avagian", "Eshawn Jessica Scipio", "Muhammad Rehan Siddiqi", "Alon Ragoler", "Justin Tan", "Deepakkumar Patil", "Rebeka Plecnik", "Aaron Kirtland", "Roselynn Grace Montecillo", "Stephane Durand", "Omer Faruk Bodur", "Zahra Adoul", "Mohamed Zekry", "Guillaume Douville", "Ali Karakoc", "Tania C. B. Santos", "Samir Shamseldeen", "Loukmane Karim", "Anna Liakhovitskaia", "Nate Resman", "Nicholas Farina", "Juan Carlos Gonzalez", "Gabe Maayan", "Sarah Hoback", "Rodrigo De Oliveira Pena", "Glen Sherman", "Hodjat Mariji", "Rasoul Pouriamanesh", "Wentao Wu", "G\u00f6zdenur Demir", "Sandra Mendoza", "Ismail Alarab", "Joshua Cole", "Danyelle Ferreira", "Bryan Johnson", "Hsiaoyun Milliron", "Mohammad Safdari", "Liangti Dai", "Siriphan Arthornthurasuk", "Alexey Pronin", "Jing Fan", "Angel Ramirez-Trinidad", "Ashley Cartwright", "Daphiny Pottmaier", "Omid Taheri", "David Outevsky", "Stanley Stepanic", "Samuel Perry", "Luke Askew", "Ra\u00fal Adri\u00e1n Huerta Rodr\u00edguez", "Abdelkader Dendane", "Sam Ali", "Ricardo Lorena", "Krishnamurthy Iyer", "Sk Md Salauddin", "Murat Islam", "Juan Gonzalez", "Josh Ducey", "Russell Campbell", "Maja Somrak", "Vasilios Mavroudis", "Eric Vergo", "Juehang Qin", "Benj\u00e1min Borb\u00e1s", "Eric Chu", "Jack Lindsey", "Anil Radhakrishnan", "Antoine Jallon", "I. M. J. McInnis", "Alex Hoover", "S\u00f6ren M\u00f6ller", "Song Bian", "John Lai", "Tejal Patwardhan", "Summer Yue", "Alexandr Wang", "Dan Hendrycks" ], "github": "", "project_page": "", "abstract": "Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-27", "arxiv_id": "2501.14726", "title": "Relightable Full-Body Gaussian Codec Avatars", "authors": [ "Shaofei Wang", "Tomas Simon", "Igor Santesteban", "Timur Bagautdinov", "Junxuan Li", "Vasu Agrawal", "Fabian Prada", "Shoou-I Yu", "Pace Nalbone", "Matt Gramlich", "Roman Lubachersky", "Chenglei Wu", "Javier Romero", "Jason Saragih", "Michael Zollhoefer", "Andreas Geiger", "Siyu Tang", "Shunsuke Saito" ], "github": "", "project_page": "", "abstract": "We propose Relightable Full-Body Gaussian Codec Avatars, a new approach for modeling relightable full-body avatars with fine-grained details including face and hands. The unique challenge for relighting full-body avatars lies in the large deformations caused by body articulation and the resulting impact on appearance caused by light transport. Changes in body pose can dramatically change the orientation of body surfaces with respect to lights, resulting in both local appearance changes due to changes in local light transport functions, as well as non-local changes due to occlusion between body parts. To address this, we decompose the light transport into local and non-local effects. Local appearance changes are modeled using learnable zonal harmonics for diffuse radiance transfer. Unlike spherical harmonics, zonal harmonics are highly efficient to rotate under articulation. This allows us to learn diffuse radiance transfer in a local coordinate frame, which disentangles the local radiance transfer from the articulation of the body. To account for non-local appearance changes, we introduce a shadow network that predicts shadows given precomputed incoming irradiance on a base mesh. This facilitates the learning of non-local shadowing between the body parts. Finally, we use a deferred shading approach to model specular radiance transfer and better capture reflections and highlights such as eye glints. We demonstrate that our approach successfully models both the local and non-local light transport required for relightable full-body avatars, with a superior generalization ability under novel illumination conditions and unseen poses.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-27", "arxiv_id": "2501.13953", "title": "Redundancy Principles for MLLMs Benchmarks", "authors": [ "Zicheng Zhang", "Xiangyu Zhao", "Xinyu Fang", "Chunyi Li", "Xiaohong Liu", "Xiongkuo Min", "Haodong Duan", "Kai Chen", "Guangtao Zhai" ], "github": "https://github.com/zzc-1998/Benchmark-Redundancy", "project_page": "", "abstract": "With the rapid iteration of Multi-modality Large Language Models (MLLMs) and the evolving demands of the field, the number of benchmarks produced annually has surged into the hundreds. The rapid growth has inevitably led to significant redundancy among benchmarks. Therefore, it is crucial to take a step back and critically assess the current state of redundancy and propose targeted principles for constructing effective MLLM benchmarks. In this paper, we focus on redundancy from three key perspectives: 1) Redundancy of benchmark capability dimensions, 2) Redundancy in the number of test questions, and 3) Cross-benchmark redundancy within specific domains. Through the comprehensive analysis over hundreds of MLLMs' performance across more than 20 benchmarks, we aim to quantitatively measure the level of redundancy lies in existing MLLM evaluations, provide valuable insights to guide the future development of MLLM benchmarks, and offer strategies to refine and address redundancy issues effectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zzc-1998/Benchmark-Redundancy", "github_api_added_by": "auto" }, { "date": "2025-01-27", "arxiv_id": "2403.14614", "title": "AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation", "authors": [ "Yuning Cui", "Syed Waqas Zamir", "Salman Khan", "Alois Knoll", "Mubarak Shah", "Fahad Shahbaz Khan" ], "github": "https://github.com/c-yn/AdaIR", "project_page": "", "abstract": "In the image acquisition process, various forms of degradation, including noise, haze, and rain, are frequently introduced. These degradations typically arise from the inherent limitations of cameras or unfavorable ambient conditions. To recover clean images from degraded versions, numerous specialized restoration methods have been developed, each targeting a specific type of degradation. Recently, all-in-one algorithms have garnered significant attention by addressing different types of degradations within a single model without requiring prior information of the input degradation type. However, these methods purely operate in the spatial domain and do not delve into the distinct frequency variations inherent to different degradation types. To address this gap, we propose an adaptive all-in-one image restoration network based on frequency mining and modulation. Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task. Specifically, we first mine low- and high-frequency information from the input features, guided by the adaptively decoupled spectra of the degraded image. The extracted features are then modulated by a bidirectional operator to facilitate interactions between different frequency components. Finally, the modulated features are merged into the original input for a progressively guided restoration. With this approach, the model achieves adaptive reconstruction by accentuating the informative frequency subbands according to different input degradations. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on different image restoration tasks, including denoising, dehazing, deraining, motion deblurring, and low-light image enhancement. Our code is available at https://github.com/c-yn/AdaIR.", "github_manual": "", "github_regex": "https://github.com/c-yn/AdaIR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-27", "arxiv_id": "2406.18516", "title": "Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration", "authors": [ "Kang Liao", "Zongsheng Yue", "Zhouxia Wang", "Chen Change Loy" ], "github": "", "project_page": "", "abstract": "Although learning-based image restoration methods have made significant progress, they still struggle with limited generalization to real-world scenarios due to the substantial domain gap caused by training on synthetic data. Existing methods address this issue by improving data synthesis pipelines, estimating degradation kernels, employing deep internal learning, and performing domain adaptation and regularization. Previous domain adaptation methods have sought to bridge the domain gap by learning domain-invariant knowledge in either feature or pixel space. However, these techniques often struggle to extend to low-level vision tasks within a stable and compact framework. In this paper, we show that it is possible to perform domain adaptation via the noise space using diffusion models. In particular, by leveraging the unique property of how auxiliary conditional inputs influence the multi-step denoising process, we derive a meaningful diffusion loss that guides the restoration model in progressively aligning both restored synthetic and real-world outputs with a target clean distribution. We refer to this method as denoising as adaptation. To prevent shortcuts during joint training, we present crucial strategies such as channel-shuffling layer and residual-swapping contrastive learning in the diffusion model. They implicitly blur the boundaries between conditioned synthetic and real data and prevent the reliance of the model on easily distinguishable features. Experimental results on three classical image restoration tasks, namely denoising, deblurring, and deraining, demonstrate the effectiveness of the proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-27", "arxiv_id": "2411.19458", "title": "Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning", "authors": [ "Yang You", "Yixin Li", "Congyue Deng", "Yue Wang", "Leonidas Guibas" ], "github": "https://github.com/qq456cvb/3DCorrEnhance", "project_page": "", "abstract": "Vision foundation models, particularly the ViT family, have revolutionized image understanding by providing rich semantic features. However, despite their success in 2D comprehension, their abilities on grasping 3D spatial relationships are still unclear. In this work, we evaluate and enhance the 3D awareness of ViT-based models. We begin by systematically assessing their ability to learn 3D equivariant features, specifically examining the consistency of semantic embeddings across different viewpoints. Our findings indicate that improved 3D equivariance leads to better performance on various downstream tasks, including pose estimation, tracking, and semantic transfer. Building on this insight, we propose a simple yet effective finetuning strategy based on 3D correspondences, which significantly enhances the 3D correspondence understanding of existing vision models. Remarkably, even finetuning on a single object for just one iteration results in substantial performance gains. All code and resources will be made publicly available to support further advancements in 3D-aware vision models. Our code is available at https://github.com/qq456cvb/3DCorrEnhance.", "github_manual": "", "github_regex": "https://github.com/qq456cvb/3DCorrEnhance", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-27", "arxiv_id": "2501.13925", "title": "GeoPixel: Pixel Grounding Large Multimodal Model in Remote Sensing", "authors": [ "Akashah Shabbir", "Mohammed Zumri", "Mohammed Bennamoun", "Fahad S. Khan", "Salman Khan" ], "github": "https://github.com/mbzuai-oryx/geopixel", "project_page": "", "abstract": "Recent advances in large multimodal models (LMMs) have recognized fine-grained grounding as an imperative factor of visual understanding and dialogue. However, the benefits of such representation in LMMs are limited to the natural image domain, and these models perform poorly for remote sensing (RS). The distinct overhead viewpoint, scale variation, and presence of small objects in high-resolution RS imagery present a unique challenge in region-level comprehension. Moreover, the development of the grounding conversation capability of LMMs within RS is hindered by the lack of granular, RS domain-specific grounded data. Addressing these limitations, we propose GeoPixel - the first end-to-end high resolution RS-LMM that supports pixel-level grounding. This capability allows fine-grained visual perception by generating interleaved masks in conversation. GeoPixel supports up to 4K HD resolution in any aspect ratio, ideal for high-precision RS image analysis. To support the grounded conversation generation (GCG) in RS imagery, we curate a visually grounded dataset GeoPixelD through a semi-automated pipeline that utilizes set-of-marks prompting and spatial priors tailored for RS data to methodically control the data generation process. GeoPixel demonstrates superior performance in pixel-level comprehension, surpassing existing LMMs in both single-target and multi-target segmentation tasks. Our methodological ablation studies validate the effectiveness of each component in the overall architecture. Our code and data will be publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mbzuai-oryx/geopixel", "github_api_added_by": "auto" }, { "date": "2025-01-27", "arxiv_id": "2501.14176", "title": "RL + Transformer = A General-Purpose Problem Solver", "authors": [ "Micah Rentschler", "Jesse Roberts" ], "github": "", "project_page": "", "abstract": "What if artificial intelligence could not only solve problems for which it was trained but also learn to teach itself to solve new problems (i.e., meta-learn)? In this study, we demonstrate that a pre-trained transformer fine-tuned with reinforcement learning over multiple episodes develops the ability to solve problems that it has never encountered before - an emergent ability called In-Context Reinforcement Learning (ICRL). This powerful meta-learner not only excels in solving unseen in-distribution environments with remarkable sample efficiency, but also shows strong performance in out-of-distribution environments. In addition, we show that it exhibits robustness to the quality of its training data, seamlessly stitches together behaviors from its context, and adapts to non-stationary environments. These behaviors demonstrate that an RL-trained transformer can iteratively improve upon its own solutions, making it an excellent general-purpose problem solver.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-27", "arxiv_id": "2501.13687", "title": "Question Answering on Patient Medical Records with Private Fine-Tuned LLMs", "authors": [ "Sara Kothari", "Ayush Gupta" ], "github": "", "project_page": "", "abstract": "Healthcare systems continuously generate vast amounts of electronic health records (EHRs), commonly stored in the Fast Healthcare Interoperability Resources (FHIR) standard. Despite the wealth of information in these records, their complexity and volume make it difficult for users to retrieve and interpret crucial health insights. Recent advances in Large Language Models (LLMs) offer a solution, enabling semantic question answering (QA) over medical data, allowing users to interact with their health records more effectively. However, ensuring privacy and compliance requires edge and private deployments of LLMs. This paper proposes a novel approach to semantic QA over EHRs by first identifying the most relevant FHIR resources for a user query (Task1) and subsequently answering the query based on these resources (Task2). We explore the performance of privately hosted, fine-tuned LLMs, evaluating them against benchmark models such as GPT-4 and GPT-4o. Our results demonstrate that fine-tuned LLMs, while 250x smaller in size, outperform GPT-4 family models by 0.55% in F1 score on Task1 and 42% on Meteor Task in Task2. Additionally, we examine advanced aspects of LLM usage, including sequential fine-tuning, model self-evaluation (narcissistic evaluation), and the impact of training data size on performance. The models and datasets are available here: https://huggingface.co/genloop", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-27", "arxiv_id": "2501.11325", "title": "CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On with Temporal Concatenation", "authors": [ "Zheng Chong", "Wenqing Zhang", "Shiyue Zhang", "Jun Zheng", "Xiao Dong", "Haoxiang Li", "Yiling Wu", "Dongmei Jiang", "Xiaodan Liang" ], "github": "https://github.com/zheng-chong/catv2ton", "project_page": "", "abstract": "Virtual try-on (VTON) technology has gained attention due to its potential to transform online retail by enabling realistic clothing visualization of images and videos. However, most existing methods struggle to achieve high-quality results across image and video try-on tasks, especially in long video scenarios. In this work, we introduce CatV2TON, a simple and effective vision-based virtual try-on (V2TON) method that supports both image and video try-on tasks with a single diffusion transformer model. By temporally concatenating garment and person inputs and training on a mix of image and video datasets, CatV2TON achieves robust try-on performance across static and dynamic settings. For efficient long-video generation, we propose an overlapping clip-based inference strategy that uses sequential frame guidance and Adaptive Clip Normalization (AdaCN) to maintain temporal consistency with reduced resource demands. We also present ViViD-S, a refined video try-on dataset, achieved by filtering back-facing frames and applying 3D mask smoothing for enhanced temporal consistency. Comprehensive experiments demonstrate that CatV2TON outperforms existing methods in both image and video try-on tasks, offering a versatile and reliable solution for realistic virtual try-ons across diverse scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zheng-chong/catv2ton", "github_api_added_by": "auto" }, { "date": "2025-01-28", "arxiv_id": "2501.15368", "title": "Baichuan-Omni-1.5 Technical Report", "authors": [ "Yadong Li", "Jun Liu", "Tao Zhang", "Tao Zhang", "Song Chen", "Tianpeng Li", "Zehuan Li", "Lijun Liu", "Lingfeng Ming", "Guosheng Dong", "Da Pan", "Chong Li", "Yuanbo Fang", "Dongdong Kuang", "Mingrui Wang", "Chenglin Zhu", "Youwei Zhang", "Hongyu Guo", "Fengyu Zhang", "Yuran Wang", "Bowen Ding", "Wei Song", "Xu Li", "Yuqi Huo", "Zheng Liang", "Shusen Zhang", "Xin Wu", "Shuai Zhao", "Linchu Xiong", "Yozhen Wu", "Jiahui Ye", "Wenhao Lu", "Bowen Li", "Yan Zhang", "Yaqi Zhou", "Xin Chen", "Lei Su", "Hongda Zhang", "Fuzhong Chen", "Xuezhen Dong", "Na Nie", "Zhiying Wu", "Bin Xiao", "Ting Li", "Shunya Dang", "Ping Zhang", "Yijia Sun", "Jincheng Wu", "Jinjie Yang", "Xionghai Lin", "Zhi Ma", "Kegeng Wu", "Jia li", "Aiyuan Yang", "Hui Liu", "Jianqiang Zhang", "Xiaoxi Chen", "Guangwei Ai", "Wentao Zhang", "Yicong Chen", "Xiaoqin Huang", "Kun Li", "Wenjing Luo", "Yifei Duan", "Lingling Zhu", "Ran Xiao", "Zhe Su", "Jiani Pu", "Dian Wang", "Xu Jia", "Tianyu Zhang", "Mengyu Ai", "Mang Wang", "Yujing Qiao", "Lei Zhang", "Yanjun Shen", "Fan Yang", "Miao Zhen", "Yijie Zhou", "Mingyang Chen", "Fei Li", "Chenzheng Zhu", "Keer Lu", "Yaqi Zhao", "Hao Liang", "Youquan Li", "Yanzhao Qin", "Linzhuang Sun", "Jianhua Xu", "Haoze Sun", "Mingan Lin", "Zenan Zhou", "Weipeng Chen" ], "github": "https://github.com/baichuan-inc/Baichuan-Omni-1.5", "project_page": "", "abstract": "We introduce Baichuan-Omni-1.5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities. To achieve fluent and high-quality interaction across modalities without compromising the capabilities of any modality, we prioritized optimizing three key aspects. First, we establish a comprehensive data cleaning and synthesis pipeline for multimodal data, obtaining about 500B high-quality data (text, audio, and vision). Second, an audio-tokenizer (Baichuan-Audio-Tokenizer) has been designed to capture both semantic and acoustic information from audio, enabling seamless integration and enhanced compatibility with MLLM. Lastly, we designed a multi-stage training strategy that progressively integrates multimodal alignment and multitask fine-tuning, ensuring effective synergy across all modalities. Baichuan-Omni-1.5 leads contemporary models (including GPT4o-mini and MiniCPM-o 2.6) in terms of comprehensive omni-modal capabilities. Notably, it achieves results comparable to leading models such as Qwen2-VL-72B across various multimodal medical benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/baichuan-inc/Baichuan-Omni-1.5", "github_api_added_by": "auto" }, { "date": "2025-01-28", "arxiv_id": "2501.14912", "title": "Feasible Learning", "authors": [ "Juan Ramirez", "Ignacio Hounie", "Juan Elenter", "Jose Gallego-Posada", "Meraj Hashemizadeh", "Alejandro Ribeiro", "Simon Lacoste-Julien" ], "github": "https://github.com/juan43ramirez/feasible-learning", "project_page": "", "abstract": "We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bounds the loss for each training sample. In contrast to the ubiquitous Empirical Risk Minimization (ERM) framework, which optimizes for average performance, FL demands satisfactory performance on every individual data point. Since any model that meets the prescribed performance threshold is a valid FL solution, the choice of optimization algorithm and its dynamics play a crucial role in shaping the properties of the resulting solutions. In particular, we study a primal-dual approach which dynamically re-weights the importance of each sample during training. To address the challenge of setting a meaningful threshold in practice, we introduce a relaxation of FL that incorporates slack variables of minimal norm. Our empirical analysis, spanning image classification, age regression, and preference optimization in large language models, demonstrates that models trained via FL can learn from data while displaying improved tail behavior compared to ERM, with only a marginal impact on average performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/juan43ramirez/feasible-learning", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-28", "arxiv_id": "2501.16295", "title": "Mixture-of-Mamba: Enhancing Multi-Modal State-Space Models with Modality-Aware Sparsity", "authors": [ "Weixin Liang", "Junhong Shen", "Genghan Zhang", "Ning Dong", "Luke Zettlemoyer", "Lili Yu" ], "github": "https://github.com/Weixin-Liang/Mixture-of-Mamba", "project_page": "", "abstract": "State Space Models (SSMs) have emerged as efficient alternatives to Transformers for sequential modeling, but their inability to leverage modality-specific features limits their performance in multi-modal pretraining. Here, we propose Mixture-of-Mamba, a novel SSM architecture that introduces modality-aware sparsity through modality-specific parameterization of the Mamba block. Building on Mixture-of-Transformers (W. Liang et al. arXiv:2411.04996; 2024), we extend the benefits of modality-aware sparsity to SSMs while preserving their computational efficiency. We evaluate Mixture-of-Mamba across three multi-modal pretraining settings: Transfusion (interleaved text and continuous image tokens with diffusion loss), Chameleon (interleaved text and discrete image tokens), and an extended three-modality framework incorporating speech. Mixture-of-Mamba consistently reaches the same loss values at earlier training steps with significantly reduced computational costs. In the Transfusion setting, Mixture-of-Mamba achieves equivalent image loss using only 34.76% of the training FLOPs at the 1.4B scale. In the Chameleon setting, Mixture-of-Mamba reaches similar image loss with just 42.50% of the FLOPs at the 1.4B scale, and similar text loss with just 65.40% of the FLOPs. In the three-modality setting, MoM matches speech loss at 24.80% of the FLOPs at the 1.4B scale. Our ablation study highlights the synergistic effects of decoupling projection components, where joint decoupling yields greater gains than individual modifications. These results establish modality-aware sparsity as a versatile and effective design principle, extending its impact from Transformers to SSMs and setting new benchmarks in multi-modal pretraining. Our code can be accessed at https://github.com/Weixin-Liang/Mixture-of-Mamba", "github_manual": "", "github_regex": "https://github.com/Weixin-Liang/Mixture-of-Mamba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-28", "arxiv_id": "2501.16142", "title": "Towards General-Purpose Model-Free Reinforcement Learning", "authors": [ "Scott Fujimoto", "Pierluca D'Oro", "Amy Zhang", "Yuandong Tian", "Michael Rabbat" ], "github": "https://github.com/facebookresearch/MRQ", "project_page": "", "abstract": "Reinforcement learning (RL) promises a framework for near-universal problem-solving. In practice however, RL algorithms are often tailored to specific benchmarks, relying on carefully tuned hyperparameters and algorithmic choices. Recently, powerful model-based RL methods have shown impressive general results across benchmarks but come at the cost of increased complexity and slow run times, limiting their broader applicability. In this paper, we attempt to find a unifying model-free deep RL algorithm that can address a diverse class of domains and problem settings. To achieve this, we leverage model-based representations that approximately linearize the value function, taking advantage of the denser task objectives used by model-based RL while avoiding the costs associated with planning or simulated trajectories. We evaluate our algorithm, MR.Q, on a variety of common RL benchmarks with a single set of hyperparameters and show a competitive performance against domain-specific and general baselines, providing a concrete step towards building general-purpose model-free deep RL algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/MRQ", "github_api_added_by": "auto" }, { "date": "2025-01-28", "arxiv_id": "2501.15383", "title": "Qwen2.5-1M Technical Report", "authors": [ "An Yang", "Bowen Yu", "Chengyuan Li", "Dayiheng Liu", "Fei Huang", "Haoyan Huang", "Jiandong Jiang", "Jianhong Tu", "Jianwei Zhang", "Jingren Zhou", "Junyang Lin", "Kai Dang", "Kexin Yang", "Le Yu", "Mei Li", "Minmin Sun", "Qin Zhu", "Rui Men", "Tao He", "Weijia Xu", "Wenbiao Yin", "Wenyuan Yu", "Xiafei Qiu", "Xingzhang Ren", "Xinlong Yang", "Yong Li", "Zhiying Xu", "Zipeng Zhang" ], "github": "", "project_page": "", "abstract": "We introduce Qwen2.5-1M, a series of models that extend the context length to 1 million tokens. Compared to the previous 128K version, the Qwen2.5-1M series have significantly enhanced long-context capabilities through long-context pre-training and post-training. Key techniques such as long data synthesis, progressive pre-training, and multi-stage supervised fine-tuning are employed to effectively enhance long-context performance while reducing training costs. To promote the use of long-context models among a broader user base, we present and open-source our inference framework. This framework includes a length extrapolation method that can expand the model context lengths by at least four times, or even more, without additional training. To reduce inference costs, we implement a sparse attention method along with chunked prefill optimization for deployment scenarios and a sparsity refinement method to improve precision. Additionally, we detail our optimizations in the inference engine, including kernel optimization, pipeline parallelism, and scheduling optimization, which significantly enhance overall inference performance. By leveraging our inference framework, the Qwen2.5-1M models achieve a remarkable 3x to 7x prefill speedup in scenarios with 1 million tokens of context. This framework provides an efficient and powerful solution for developing applications that require long-context processing using open-source models. The Qwen2.5-1M series currently includes the open-source models Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, as well as the API-accessed model Qwen2.5-Turbo. Evaluations show that Qwen2.5-1M models have been greatly improved in long-context tasks without compromising performance in short-context scenarios. Specifically, the Qwen2.5-14B-Instruct-1M model significantly outperforms GPT-4o-mini in long-context tasks and supports contexts eight times longer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-28", "arxiv_id": "2501.15369", "title": "iFormer: Integrating ConvNet and Transformer for Mobile Application", "authors": [ "Chuanyang Zheng" ], "github": "https://github.com/chuanyangzheng/iformer", "project_page": "", "abstract": "We present a new family of mobile hybrid vision networks, called iFormer, with a focus on optimizing latency and accuracy on mobile applications. iFormer effectively integrates the fast local representation capacity of convolution with the efficient global modeling ability of self-attention. The local interactions are derived from transforming a standard convolutional network, i.e., ConvNeXt, to design a more lightweight mobile network. Our newly introduced mobile modulation attention removes memory-intensive operations in MHA and employs an efficient modulation mechanism to boost dynamic global representational capacity. We conduct comprehensive experiments demonstrating that iFormer outperforms existing lightweight networks across various tasks. Notably, iFormer achieves an impressive Top-1 accuracy of 80.4\\% on ImageNet-1k with a latency of only 1.10 ms on an iPhone 13, surpassing the recently proposed MobileNetV4 under similar latency constraints. Additionally, our method shows significant improvements in downstream tasks, including COCO object detection, instance segmentation, and ADE20k semantic segmentation, while still maintaining low latency on mobile devices for high-resolution inputs in these scenarios.", "github_manual": "https://github.com/chuanyangzheng/iformer", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-28", "arxiv_id": "2501.15570", "title": "ARWKV: Pretrain is not what we need, an RNN-Attention-Based Language Model Born from Transformer", "authors": [ "Lin Yueyu", "Li Zhiyuan", "Peter Yue", "Liu Xiao" ], "github": "https://github.com/yynil/RWKVInside", "project_page": "", "abstract": "As is known, hybrid quadratic and subquadratic attention models in multi-head architectures have surpassed both Transformer and Linear RNN models , with these works primarily focusing on reducing KV complexity and improving efficiency. For further research on expressiveness, we introduce our series of models distilled from Qwen 2.5, based on pure native RWKV-7 attention, which aims to make RNN more expressive and demonstrates state tracking ability beyond transformers. We work with QRWK 32B based on RWKV-6 architecture, another approach that reduces the entire knowledge processing time to just 8 hours using 16 AMD MI300X GPUs while maintaining Qwen 2.5's performance. In fact, the distillation process can utilize any LLM, not just Qwen, and enables knowledge transfer from larger LLMs to smaller ones with more fewer tokens. We will explain the detailed process and share our insights on building more powerful foundation models. Please note that this is an ongoing work that will be updated continuously. The model checkpoints and source code are available at https://github.com/yynil/RWKVInside{https://github.com/yynil/RWKVInside}, https://huggingface.co/RWKV-Red-Team/ARWKV-7B-Preview-0.1{https://huggingface.co/RWKV-Red-Team/ARWKV-7B-Preview-0.1}.", "github_manual": "", "github_regex": "https://github.com/yynil/RWKVInside", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-28", "arxiv_id": "2501.15907", "title": "Emilia: A Large-Scale, Extensive, Multilingual, and Diverse Dataset for Speech Generation", "authors": [ "Haorui He", "Zengqiang Shang", "Chaoren Wang", "Xuyuan Li", "Yicheng Gu", "Hua Hua", "Liwei Liu", "Chen Yang", "Jiaqi Li", "Peiyang Shi", "Yuancheng Wang", "Kai Chen", "Pengyuan Zhang", "Zhizheng Wu" ], "github": "https://github.com/TRvlvr/model_repo", "project_page": "", "abstract": "Recent advancements in speech generation have been driven by the large-scale training datasets. However, current models fall short of capturing the spontaneity and variability inherent in real-world human speech, due to their reliance on audiobook datasets limited to formal read-aloud speech styles. To bridge this gap, we introduce Emilia-Pipe, an open-source preprocessing pipeline to extract high-quality training data from valuable yet underexplored in-the-wild data that capture spontaneous human speech in real-world contexts. By leveraging Emilia-Pipe, we construct Emilia, the first multilingual speech generation dataset derived from in-the-wild speech data. This dataset comprises over 101k hours of speech across six languages: English, Chinese, German, French, Japanese, and Korean. Besides, we expand Emilia to Emilia-Large, a dataset exceeding 216k hours, making it the largest open-source speech generation dataset available. Extensive experiments demonstrate that Emilia significantly outperforms traditional audiobook datasets in generating spontaneous and human-like speech, showcasing superior performance in capturing diverse speaker timbre and speaking styles of real-world human speech. Furthermore, this work underscores the importance of scaling dataset size to advance speech generation research and validates the effectiveness of Emilia for both multilingual and crosslingual speech generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/TRvlvr/model_repo", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-28", "arxiv_id": "2501.12370", "title": "Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models", "authors": [ "Samira Abnar", "Harshay Shah", "Dan Busbridge", "Alaaeldin Mohamed Elnouby Ali", "Josh Susskind", "Vimal Thilak" ], "github": "https://github.com/mosaicml/llm-foundry", "project_page": "", "abstract": "Scaling the capacity of language models has consistently proven to be a reliable approach for improving performance and unlocking new capabilities. Capacity can be primarily defined by two dimensions: the number of model parameters and the compute per example. While scaling typically involves increasing both, the precise interplay between these factors and their combined contribution to overall capacity remains not fully understood. We explore this relationship in the context of sparse Mixture-of-Experts (MoEs), which allow scaling the number of parameters without proportionally increasing the FLOPs per example. We investigate how varying the sparsity level, i.e., the fraction of inactive parameters, impacts model's performance during pretraining and downstream few-shot evaluation. We find that under different constraints (e.g., parameter size and total training compute), there is an optimal level of sparsity that improves both training efficiency and model performance. These results provide a better understanding of the impact of sparsity in scaling laws for MoEs and complement existing works in this area, offering insights for designing more efficient architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mosaicml/llm-foundry", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-28", "arxiv_id": "2501.15427", "title": "OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale Synthetic Personas", "authors": [ "Xiaoyang Wang", "Hongming Zhang", "Tao Ge", "Wenhao Yu", "Dian Yu", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Customizable role-playing in large language models (LLMs), also known as character generalization, is gaining increasing attention for its versatility and cost-efficiency in developing and deploying role-playing dialogue agents. This study explores a large-scale data synthesis approach to equip LLMs with character generalization capabilities. We begin by synthesizing large-scale character profiles using personas from Persona Hub and then explore two strategies: response rewriting and response generation, to create character-aligned instructional responses. To validate the effectiveness of our synthetic instruction tuning data for character generalization, we perform supervised fine-tuning (SFT) using the LLaMA-3 8B model. Our best-performing model strengthens the original LLaMA-3 8B Instruct model and achieves performance comparable to GPT-4o models on role-playing dialogue. We release our synthetic characters and instruction-tuning dialogues to support public research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-28", "arxiv_id": "2501.16273", "title": "Return of the Encoder: Maximizing Parameter Efficiency for SLMs", "authors": [ "Mohamed Elfeki", "Rui Liu", "Chad Voegele" ], "github": "https://github.com/microsoft/encoder-decoder-slm", "project_page": "", "abstract": "The dominance of large decoder-only language models has overshadowed encoder-decoder architectures, despite their fundamental efficiency advantages in sequence processing. For small language models (SLMs) - those with 1 billion parameters or fewer - our systematic analysis across GPU, CPU, and NPU platforms reveals that encoder-decoder architectures achieve 47% lower first-token latency and 4.7x higher throughput compared to decoder-only models on edge devices. These gains may be attributed to encoder-decoder's one-time input processing and efficient separation of understanding and generation phases. We introduce a novel knowledge distillation framework that enables encoder-decoder models to leverage capabilities from large scalable decoder-only teachers while preserving their architectural advantages, achieving up to 6 average performance points improvement across diverse tasks, with significant gains in asymmetric sequence tasks where input and output distributions can benefit from different processing approaches. When combined with modern advances like Rotary Positional Embeddings (RoPE) and Vision encoders, our systematic investigation demonstrates that encoder-decoder architectures provide a more practical path toward deploying capable language models in resource-constrained environments. Our findings challenge the prevailing trend toward decoder-only scaling, showing that architectural choices become increasingly crucial as parameter budgets decrease, particularly for on-device and edge deployments where computational efficiency is paramount.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/encoder-decoder-slm", "github_api_added_by": "auto" }, { "date": "2025-01-28", "arxiv_id": "2403.09193", "title": "Are Vision Language Models Texture or Shape Biased and Can We Steer Them?", "authors": [ "Paul Gavrikov", "Jovita Lukasik", "Steffen Jung", "Robert Geirhos", "Bianca Lamm", "Muhammad Jehanzeb Mirza", "Margret Keuper", "Janis Keuper" ], "github": "https://github.com/paulgavrikov/vlm_shapebias", "project_page": "", "abstract": "Vision language models (VLMs) have drastically changed the computer vision model landscape in only a few years, opening an exciting array of new applications from zero-shot image classification, over to image captioning, and visual question answering. Unlike pure vision models, they offer an intuitive way to access visual content through language prompting. The wide applicability of such models encourages us to ask whether they also align with human vision - specifically, how far they adopt human-induced visual biases through multimodal fusion, or whether they simply inherit biases from pure vision models. One important visual bias is the texture vs. shape bias, or the dominance of local over global information. In this paper, we study this bias in a wide range of popular VLMs. Interestingly, we find that VLMs are often more shape-biased than their vision encoders, indicating that visual biases are modulated to some extent through text in multimodal models. If text does indeed influence visual biases, this suggests that we may be able to steer visual biases not just through visual input but also through language: a hypothesis that we confirm through extensive experiments. For instance, we are able to steer shape bias from as low as 49% to as high as 72% through prompting alone. For now, the strong human bias towards shape (96%) remains out of reach for all tested VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/paulgavrikov/vlm_shapebias", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-28", "arxiv_id": "2501.15420", "title": "Visual Generation Without Guidance", "authors": [ "Huayu Chen", "Kai Jiang", "Kaiwen Zheng", "Jianfei Chen", "Hang Su", "Jun Zhu" ], "github": "https://github.com/thu-ml/GFT", "project_page": "", "abstract": "Classifier-Free Guidance (CFG) has been a default technique in various visual generative models, yet it requires inference from both conditional and unconditional models during sampling. We propose to build visual models that are free from guided sampling. The resulting algorithm, Guidance-Free Training (GFT), matches the performance of CFG while reducing sampling to a single model, halving the computational cost. Unlike previous distillation-based approaches that rely on pretrained CFG networks, GFT enables training directly from scratch. GFT is simple to implement. It retains the same maximum likelihood objective as CFG and differs mainly in the parameterization of conditional models. Implementing GFT requires only minimal modifications to existing codebases, as most design choices and hyperparameters are directly inherited from CFG. Our extensive experiments across five distinct visual models demonstrate the effectiveness and versatility of GFT. Across domains of diffusion, autoregressive, and masked-prediction modeling, GFT consistently achieves comparable or even lower FID scores, with similar diversity-fidelity trade-offs compared with CFG baselines, all while being guidance-free. Code will be available at https://github.com/thu-ml/GFT.", "github_manual": "", "github_regex": "https://github.com/thu-ml/GFT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-28", "arxiv_id": "2501.14723", "title": "CodeMonkeys: Scaling Test-Time Compute for Software Engineering", "authors": [ "Ryan Ehrlich", "Bradley Brown", "Jordan Juravsky", "Ronald Clark", "Christopher R\u00e9", "Azalia Mirhoseini" ], "github": "https://github.com/ScalingIntelligence/codemonkeys", "project_page": "", "abstract": "Scaling test-time compute is a promising axis for improving LLM capabilities. However, test-time compute can be scaled in a variety of ways, and effectively combining different approaches remains an active area of research. Here, we explore this problem in the context of solving real-world GitHub issues from the SWE-bench dataset. Our system, named CodeMonkeys, allows models to iteratively edit a codebase by jointly generating and running a testing script alongside their draft edit. We sample many of these multi-turn trajectories for every issue to generate a collection of candidate edits. This approach lets us scale \"serial\" test-time compute by increasing the number of iterations per trajectory and \"parallel\" test-time compute by increasing the number of trajectories per problem. With parallel scaling, we can amortize up-front costs across multiple downstream samples, allowing us to identify relevant codebase context using the simple method of letting an LLM read every file. In order to select between candidate edits, we combine voting using model-generated tests with a final multi-turn trajectory dedicated to selection. Overall, CodeMonkeys resolves 57.4% of issues from SWE-bench Verified using a budget of approximately 2300 USD. Our selection method can also be used to combine candidates from different sources. Selecting over an ensemble of edits from existing top SWE-bench Verified submissions obtains a score of 66.2% and outperforms the best member of the ensemble on its own. We fully release our code and data at https://scalingintelligence.stanford.edu/pubs/codemonkeys.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ScalingIntelligence/codemonkeys", "github_api_added_by": "auto" }, { "date": "2025-01-29", "arxiv_id": "2501.15747", "title": "IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding", "authors": [ "Sankalp KJ", "Ashutosh Kumar", "Laxmaan Balaji", "Nikunj Kotecha", "Vinija Jain", "Aman Chadha", "Sreyoshi Bhaduri" ], "github": "", "project_page": "", "abstract": "Known by more than 1.5 billion people in the Indian subcontinent, Indic languages present unique challenges and opportunities for natural language processing (NLP) research due to their rich cultural heritage, linguistic diversity, and complex structures. IndicMMLU-Pro is a comprehensive benchmark designed to evaluate Large Language Models (LLMs) across Indic languages, building upon the MMLU Pro (Massive Multitask Language Understanding) framework. Covering major languages such as Hindi, Bengali, Gujarati, Marathi, Kannada, Punjabi, Tamil, Telugu, and Urdu, our benchmark addresses the unique challenges and opportunities presented by the linguistic diversity of the Indian subcontinent. This benchmark encompasses a wide range of tasks in language comprehension, reasoning, and generation, meticulously crafted to capture the intricacies of Indian languages. IndicMMLU-Pro provides a standardized evaluation framework to push the research boundaries in Indic language AI, facilitating the development of more accurate, efficient, and culturally sensitive models. This paper outlines the benchmarks' design principles, task taxonomy, and data collection methodology, and presents baseline results from state-of-the-art multilingual models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-29", "arxiv_id": "2501.16372", "title": "Low-Rank Adapters Meet Neural Architecture Search for LLM Compression", "authors": [ "J. Pablo Mu\u00f1oz", "Jinjie Yuan", "Nilesh Jain" ], "github": "https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning", "project_page": "", "abstract": "The rapid expansion of Large Language Models (LLMs) has posed significant challenges regarding the computational resources required for fine-tuning and deployment. Recent advancements in low-rank adapters have demonstrated their efficacy in parameter-efficient fine-tuning (PEFT) of these models. This retrospective paper comprehensively discusses innovative approaches that synergize low-rank representations with Neural Architecture Search (NAS) techniques, particularly weight-sharing super-networks. Robust solutions for compressing and fine-tuning large pre-trained models are developed by integrating these methodologies. Our analysis highlights the potential of these combined strategies to democratize the use of LLMs, making them more accessible for deployment in resource-constrained environments. The resulting models exhibit reduced memory footprints and faster inference times, paving the way for more practical and scalable applications of LLMs. Models and code are available at https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning.", "github_manual": "", "github_regex": "https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-29", "arxiv_id": "2501.16975", "title": "Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling", "authors": [ "Hongzhi Huang", "Defa Zhu", "Banggu Wu", "Yutao Zeng", "Ya Wang", "Qiyang Min", "Xun Zhou" ], "github": "", "project_page": "", "abstract": "Tokenization is a fundamental component of large language models (LLMs), yet its influence on model scaling and performance is not fully explored. In this paper, we introduce Over-Tokenized Transformers, a novel framework that decouples input and output vocabularies to improve language modeling performance. Specifically, our approach scales up input vocabularies to leverage multi-gram tokens. Through extensive experiments, we uncover a log-linear relationship between input vocabulary size and training loss, demonstrating that larger input vocabularies consistently enhance model performance, regardless of model size. Using a large input vocabulary, we achieve performance comparable to double-sized baselines with no additional cost. Our findings highlight the importance of tokenization in scaling laws and provide practical insight for tokenizer design, paving the way for more efficient and powerful LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-29", "arxiv_id": "2501.16496", "title": "Open Problems in Mechanistic Interpretability", "authors": [ "Lee Sharkey", "Bilal Chughtai", "Joshua Batson", "Jack Lindsey", "Jeff Wu", "Lucius Bushnaq", "Nicholas Goldowsky-Dill", "Stefan Heimersheim", "Alejandro Ortega", "Joseph Bloom", "Stella Biderman", "Adria Garriga-Alonso", "Arthur Conmy", "Neel Nanda", "Jessica Rumbelow", "Martin Wattenberg", "Nandi Schoots", "Joseph Miller", "Eric J. Michaud", "Stephen Casper", "Max Tegmark", "William Saunders", "David Bau", "Eric Todd", "Atticus Geiger", "Mor Geva", "Jesse Hoogland", "Daniel Murfet", "Tom McGrath" ], "github": "", "project_page": "", "abstract": "Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goals. Progress in this field thus promises to provide greater assurance over AI system behavior and shed light on exciting scientific questions about the nature of intelligence. Despite recent progress toward these goals, there are many open problems in the field that require solutions before many scientific and practical benefits can be realized: Our methods require both conceptual and practical improvements to reveal deeper insights; we must figure out how best to apply our methods in pursuit of specific goals; and the field must grapple with socio-technical challenges that influence and are influenced by our work. This forward-facing review discusses the current frontier of mechanistic interpretability and the open problems that the field may benefit from prioritizing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-29", "arxiv_id": "2501.17161", "title": "SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training", "authors": [ "Tianzhe Chu", "Yuexiang Zhai", "Jihan Yang", "Shengbang Tong", "Saining Xie", "Dale Schuurmans", "Quoc V. Le", "Sergey Levine", "Yi Ma" ], "github": "https://github.com/LeslieTrue/SFTvsRL", "project_page": "", "abstract": "Supervised fine-tuning (SFT) and reinforcement learning (RL) are widely used post-training techniques for foundation models. However, their roles in enhancing model generalization capabilities remain unclear. This paper studies the difference between SFT and RL on generalization and memorization, focusing on text-based rule variants and visual variants. We introduce GeneralPoints, an arithmetic reasoning card game, and adopt V-IRL, a real-world navigation environment, to assess how models trained with SFT and RL generalize to unseen variants in both textual and visual domains. We show that RL, especially when trained with an outcome-based reward, generalizes across both rule-based textual and visual variants. SFT, in contrast, tends to memorize training data and struggles to generalize out-of-distribution scenarios. Further analysis reveals that RL improves the model's underlying visual recognition capabilities, contributing to its enhanced generalization in the visual domain. Despite RL's superior generalization, we show that SFT remains essential for effective RL training; SFT stabilizes the model's output format, enabling subsequent RL to achieve its performance gains. These findings demonstrates the capability of RL for acquiring generalizable knowledge in complex, multi-modal tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LeslieTrue/SFTvsRL", "github_api_added_by": "auto" }, { "date": "2025-01-29", "arxiv_id": "2501.17116", "title": "Optimizing Large Language Model Training Using FP4 Quantization", "authors": [ "Ruizhe Wang", "Yeyun Gong", "Xiao Liu", "Guoshuai Zhao", "Ziyue Yang", "Baining Guo", "Zhengjun Zha", "Peng Cheng" ], "github": "", "project_page": "", "abstract": "The growing computational demands of training large language models (LLMs) necessitate more efficient methods. Quantized training presents a promising solution by enabling low-bit arithmetic operations to reduce these costs. While FP8 precision has demonstrated feasibility, leveraging FP4 remains a challenge due to significant quantization errors and limited representational capacity. This work introduces the first FP4 training framework for LLMs, addressing these challenges with two key innovations: a differentiable quantization estimator for precise weight updates and an outlier clamping and compensation strategy to prevent activation collapse. To ensure stability, the framework integrates a mixed-precision training scheme and vector-wise quantization. Experimental results demonstrate that our FP4 framework achieves accuracy comparable to BF16 and FP8, with minimal degradation, scaling effectively to 13B-parameter LLMs trained on up to 100B tokens. With the emergence of next-generation hardware supporting FP4, our framework sets a foundation for efficient ultra-low precision training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-29", "arxiv_id": "2501.16764", "title": "DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation", "authors": [ "Chenguo Lin", "Panwang Pan", "Bangbang Yang", "Zeming Li", "Yadong Mu" ], "github": "", "project_page": "", "abstract": "Recent advancements in 3D content generation from text or a single image struggle with limited high-quality 3D datasets and inconsistency from 2D multi-view generation. We introduce DiffSplat, a novel 3D generative framework that natively generates 3D Gaussian splats by taming large-scale text-to-image diffusion models. It differs from previous 3D generative models by effectively utilizing web-scale 2D priors while maintaining 3D consistency in a unified model. To bootstrap the training, a lightweight reconstruction model is proposed to instantly produce multi-view Gaussian splat grids for scalable dataset curation. In conjunction with the regular diffusion loss on these grids, a 3D rendering loss is introduced to facilitate 3D coherence across arbitrary views. The compatibility with image diffusion models enables seamless adaptions of numerous techniques for image generation to the 3D realm. Extensive experiments reveal the superiority of DiffSplat in text- and image-conditioned generation tasks and downstream applications. Thorough ablation studies validate the efficacy of each critical design choice and provide insights into the underlying mechanism.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-29", "arxiv_id": "2501.17117", "title": "Histoires Morales: A French Dataset for Assessing Moral Alignment", "authors": [ "Thibaud Leteno", "Irina Proskurina", "Antoine Gourru", "Julien Velcin", "Charlotte Laclau", "Guillaume Metzler", "Christophe Gravier" ], "github": "https://github.com/upunaprosk/histoires-morales", "project_page": "", "abstract": "Aligning language models with human values is crucial, especially as they become more integrated into everyday life. While models are often adapted to user preferences, it is equally important to ensure they align with moral norms and behaviours in real-world social situations. Despite significant progress in languages like English and Chinese, French has seen little attention in this area, leaving a gap in understanding how LLMs handle moral reasoning in this language. To address this gap, we introduce Histoires Morales, a French dataset derived from Moral Stories, created through translation and subsequently refined with the assistance of native speakers to guarantee grammatical accuracy and adaptation to the French cultural context. We also rely on annotations of the moral values within the dataset to ensure their alignment with French norms. Histoires Morales covers a wide range of social situations, including differences in tipping practices, expressions of honesty in relationships, and responsibilities toward animals. To foster future research, we also conduct preliminary experiments on the alignment of multilingual models on French and English data and the robustness of the alignment. We find that while LLMs are generally aligned with human moral norms by default, they can be easily influenced with user-preference optimization for both moral and immoral data.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/upunaprosk/histoires-morales", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-29", "arxiv_id": "2501.14417", "title": "DeepFlow: Serverless Large Language Model Serving at Scale", "authors": [ "Junhao Hu", "Jiang Xu", "Zhixia Liu", "Yulong He", "Yuetao Chen", "Hao Xu", "Jiang Liu", "Baoquan Zhang", "Shining Wan", "Gengyuan Dan", "Zhiyu Dong", "Zhihao Ren", "Jie Meng", "Chao He", "Changhong Liu", "Tao Xie", "Dayun Lin", "Qin Zhang", "Yue Yu", "Hao Feng", "Xusheng Chen", "Yizhou Shan" ], "github": "", "project_page": "", "abstract": "This paper introduces DeepFlow, a scalable and serverless AI platform designed to efficiently serve large language models (LLMs) at scale in cloud environments. DeepFlow addresses key challenges such as resource allocation, serving efficiency, and cold start latencies through four main design components. First, it uses a simple serverless abstraction called the request-job-task model, which helps manage AI workloads across post-training and model serving tasks. Second, it builds an in-house serving engine FlowServe using a microkernel-inspired design, NPU-centric execution, and SPMD-based parallelism to optimize LLM serving. The system also includes novel scheduling policies tailored for both PD-disaggregated and PD-colocated configurations. With optimizations like pre-warmed pods, DRAM pre-loading, and NPU-fork, DeepFlow can scale up to 64 instances in seconds. DeepFlow has been in production for over a year, operating on a large Ascend NPU cluster and providing industrystandard APIs for fine-tuning, agent serving, and model serving to our customers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-29", "arxiv_id": "2501.16937", "title": "TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models", "authors": [ "Makoto Shing", "Kou Misaki", "Han Bao", "Sho Yokoi", "Takuya Akiba" ], "github": "https://github.com/SakanaAI/TAID", "project_page": "", "abstract": "Causal language models have demonstrated remarkable capabilities, but their size poses significant challenges for deployment in resource-constrained environments. Knowledge distillation, a widely-used technique for transferring knowledge from a large teacher model to a small student model, presents a promising approach for model compression. A significant remaining issue lies in the major differences between teacher and student models, namely the substantial capacity gap, mode averaging, and mode collapse, which pose barriers during distillation. To address these issues, we introduce Temporally Adaptive Interpolated Distillation (TAID), a novel knowledge distillation approach that dynamically interpolates student and teacher distributions through an adaptive intermediate distribution, gradually shifting from the student's initial distribution towards the teacher's distribution. We provide a theoretical analysis demonstrating TAID's ability to prevent mode collapse and empirically show its effectiveness in addressing the capacity gap while balancing mode averaging and mode collapse. Our comprehensive experiments demonstrate TAID's superior performance across various model sizes and architectures in both instruction tuning and pre-training scenarios. Furthermore, we showcase TAID's practical impact by developing two state-of-the-art compact foundation models: TAID-LLM-1.5B for language tasks and TAID-VLM-2B for vision-language tasks. These results demonstrate TAID's effectiveness in creating high-performing and efficient models, advancing the development of more accessible AI technologies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SakanaAI/TAID", "github_api_added_by": "auto" }, { "date": "2025-01-30", "arxiv_id": "2501.17703", "title": "Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate", "authors": [ "Yubo Wang", "Xiang Yue", "Wenhu Chen" ], "github": "https://github.com/TIGER-AI-Lab/CritiqueFineTuning", "project_page": "", "abstract": "Supervised Fine-Tuning (SFT) is commonly used to train language models to imitate annotated responses for given instructions. In this paper, we challenge this paradigm and propose Critique Fine-Tuning (CFT), a strategy where models learn to critique noisy responses rather than simply imitate correct ones. Inspired by human learning processes that emphasize critical thinking, CFT encourages deeper analysis and nuanced understanding-traits often overlooked by standard SFT. To validate the effectiveness of CFT, we construct a 50K-sample dataset from WebInstruct, using GPT-4o as the teacher to generate critiques in the form of (input=[query; noisy response], output=critique). CFT on this dataset yields a consistent 4-10% improvement over SFT on six math benchmarks with different base models like Qwen2.5, Qwen2.5-Math and DeepSeek-Math. We further expand to MetaMath and NuminaMath datasets and observe similar gains over SFT. Notably, our Qwen2.5-Math-CFT model-trained on just 50K samples-matches or outperforms competitive models such as AceMath and Qwen2.5-Math-Instruct on most benchmarks, both of which use over 2M samples. Ablation studies show that CFT is robust to the source of noisy response and teacher critique model. Through these findings, we argue that critique-based training offers a more effective alternative to advance the reasoning of language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TIGER-AI-Lab/CritiqueFineTuning", "github_api_added_by": "auto" }, { "date": "2025-01-30", "arxiv_id": "2501.17195", "title": "Atla Selene Mini: A General Purpose Evaluation Model", "authors": [ "Andrei Alexandru", "Antonia Calvi", "Henry Broomfield", "Jackson Golden", "Kyle Dai", "Mathias Leys", "Maurice Burger", "Max Bartolo", "Roman Engeler", "Sashank Pisupati", "Toby Drane", "Young Sun Park" ], "github": "https://github.com/atla-ai/selene-mini", "project_page": "", "abstract": "We introduce Atla Selene Mini, a state-of-the-art small language model-as-a-judge (SLMJ). Selene Mini is a general-purpose evaluator that outperforms the best SLMJs and GPT-4o-mini on overall performance across 11 out-of-distribution benchmarks, spanning absolute scoring, classification, and pairwise preference tasks. It is the highest-scoring 8B generative model on RewardBench, surpassing strong baselines like GPT-4o and specialized judges. To achieve this, we develop a principled data curation strategy that augments public datasets with synthetically generated critiques and ensures high quality through filtering and dataset ablations. We train our model on a combined direct preference optimization (DPO) and supervised fine-tuning (SFT) loss, and produce a highly promptable evaluator that excels in real-world scenarios. Selene Mini shows dramatically improved zero-shot agreement with human expert evaluations on financial and medical industry datasets. It is also robust to variations in prompt format. Preliminary results indicate that Selene Mini is the top-ranking evaluator in a live, community-driven Judge Arena. We release the model weights on HuggingFace (https://hf.co/AtlaAI/Selene-1-Mini-Llama-3.1-8B) and Ollama to encourage widespread community adoption.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/atla-ai/selene-mini", "github_api_added_by": "auto" }, { "date": "2025-01-30", "arxiv_id": "2501.17749", "title": "Early External Safety Testing of OpenAI's o3-mini: Insights from the Pre-Deployment Evaluation", "authors": [ "Aitor Arrieta", "Miriam Ugarte", "Pablo Valle", "Jos\u00e9 Antonio Parejo", "Sergio Segura" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have become an integral part of our daily lives. However, they impose certain risks, including those that can harm individuals' privacy, perpetuate biases and spread misinformation. These risks highlight the need for robust safety mechanisms, ethical guidelines, and thorough testing to ensure their responsible deployment. Safety of LLMs is a key property that needs to be thoroughly tested prior the model to be deployed and accessible to the general users. This paper reports the external safety testing experience conducted by researchers from Mondragon University and University of Seville on OpenAI's new o3-mini LLM as part of OpenAI's early access for safety testing program. In particular, we apply our tool, ASTRAL, to automatically and systematically generate up to date unsafe test inputs (i.e., prompts) that helps us test and assess different safety categories of LLMs. We automatically generate and execute a total of 10,080 unsafe test input on a early o3-mini beta version. After manually verifying the test cases classified as unsafe by ASTRAL, we identify a total of 87 actual instances of unsafe LLM behavior. We highlight key insights and findings uncovered during the pre-deployment external testing phase of OpenAI's latest LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-30", "arxiv_id": "2501.17433", "title": "Virus: Harmful Fine-tuning Attack for Large Language Models Bypassing Guardrail Moderation", "authors": [ "Tiansheng Huang", "Sihao Hu", "Fatih Ilhan", "Selim Furkan Tekin", "Ling Liu" ], "github": "https://github.com/git-disl/Virus", "project_page": "", "abstract": "Recent research shows that Large Language Models (LLMs) are vulnerable to harmful fine-tuning attacks -- models lose their safety alignment ability after fine-tuning on a few harmful samples. For risk mitigation, a guardrail is typically used to filter out harmful samples before fine-tuning. By designing a new red-teaming method, we in this paper show that purely relying on the moderation guardrail for data filtration is not reliable. Our proposed attack method, dubbed Virus, easily bypasses the guardrail moderation by slightly modifying the harmful data. Experimental results show that the harmful data optimized by Virus is not detectable by the guardrail with up to 100\\% leakage ratio, and can simultaneously achieve superior attack performance. Finally, the key message we want to convey through this paper is that: it is reckless to consider guardrail moderation as a clutch at straws towards harmful fine-tuning attack, as it cannot solve the inherent safety issue of the pre-trained LLMs. Our code is available at https://github.com/git-disl/Virus", "github_manual": "", "github_regex": "https://github.com/git-disl/Virus", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-30", "arxiv_id": "2501.14334", "title": "Exploring the sustainable scaling of AI dilemma: A projective study of corporations' AI environmental impacts", "authors": [ "Cl\u00e9ment Desroches", "Martin Chauvin", "Louis Ladan", "Caroline Vateau", "Simon Gosset", "Philippe Cordier" ], "github": "", "project_page": "", "abstract": "The rapid growth of artificial intelligence (AI), particularly Large Language Models (LLMs), has raised concerns regarding its global environmental impact that extends beyond greenhouse gas emissions to include consideration of hardware fabrication and end-of-life processes. The opacity from major providers hinders companies' abilities to evaluate their AI-related environmental impacts and achieve net-zero targets. In this paper, we propose a methodology to estimate the environmental impact of a company's AI portfolio, providing actionable insights without necessitating extensive AI and Life-Cycle Assessment (LCA) expertise. Results confirm that large generative AI models consume up to 4600x more energy than traditional models. Our modelling approach, which accounts for increased AI usage, hardware computing efficiency, and changes in electricity mix in line with IPCC scenarios, forecasts AI electricity use up to 2030. Under a high adoption scenario, driven by widespread Generative AI and agents adoption associated to increasingly complex models and frameworks, AI electricity use is projected to rise by a factor of 24.4. Mitigating the environmental impact of Generative AI by 2030 requires coordinated efforts across the AI value chain. Isolated measures in hardware efficiency, model efficiency, or grid improvements alone are insufficient. We advocate for standardized environmental assessment frameworks, greater transparency from the all actors of the value chain and the introduction of a \"Return on Environment\" metric to align AI development with net-zero goals.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-30", "arxiv_id": "2501.15654", "title": "People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text", "authors": [ "Jenna Russell", "Marzena Karpinska", "Mohit Iyyer" ], "github": "https://github.com/jenna-russell/human_detectors", "project_page": "", "abstract": "In this paper, we study how well humans can detect text generated by commercial LLMs (GPT-4o, Claude, o1). We hire annotators to read 300 non-fiction English articles, label them as either human-written or AI-generated, and provide paragraph-length explanations for their decisions. Our experiments show that annotators who frequently use LLMs for writing tasks excel at detecting AI-generated text, even without any specialized training or feedback. In fact, the majority vote among five such \"expert\" annotators misclassifies only 1 of 300 articles, significantly outperforming most commercial and open-source detectors we evaluated even in the presence of evasion tactics like paraphrasing and humanization. Qualitative analysis of the experts' free-form explanations shows that while they rely heavily on specific lexical clues ('AI vocabulary'), they also pick up on more complex phenomena within the text (e.g., formality, originality, clarity) that are challenging to assess for automatic detectors. We release our annotated dataset and code to spur future research into both human and automated detection of AI-generated text.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jenna-russell/human_detectors", "github_api_added_by": "auto" }, { "date": "2025-01-30", "arxiv_id": "2501.15891", "title": "Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks", "authors": [ "Hailong Guo", "Bohan Zeng", "Yiren Song", "Wentao Zhang", "Chuang Zhang", "Jiaming Liu" ], "github": "", "project_page": "", "abstract": "Image-based virtual try-on (VTON) aims to generate a virtual try-on result by transferring an input garment onto a target person's image. However, the scarcity of paired garment-model data makes it challenging for existing methods to achieve high generalization and quality in VTON. Also, it limits the ability to generate mask-free try-ons. To tackle the data scarcity problem, approaches such as Stable Garment and MMTryon use a synthetic data strategy, effectively increasing the amount of paired data on the model side. However, existing methods are typically limited to performing specific try-on tasks and lack user-friendliness. To enhance the generalization and controllability of VTON generation, we propose Any2AnyTryon, which can generate try-on results based on different textual instructions and model garment images to meet various needs, eliminating the reliance on masks, poses, or other conditions. Specifically, we first construct the virtual try-on dataset LAION-Garment, the largest known open-source garment try-on dataset. Then, we introduce adaptive position embedding, which enables the model to generate satisfactory outfitted model images or garment images based on input images of different sizes and categories, significantly enhancing the generalization and controllability of VTON generation. In our experiments, we demonstrate the effectiveness of our Any2AnyTryon and compare it with existing methods. The results show that Any2AnyTryon enables flexible, controllable, and high-quality image-based virtual try-on generation.https://logn-2024.github.io/Any2anyTryonProjectPage/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-31", "arxiv_id": "2501.18492", "title": "GuardReasoner: Towards Reasoning-based LLM Safeguards", "authors": [ "Yue Liu", "Hongcheng Gao", "Shengfang Zhai", "Jun Xia", "Tianyi Wu", "Zhiwei Xue", "Yulin Chen", "Kenji Kawaguchi", "Jiaheng Zhang", "Bryan Hooi" ], "github": "https://github.com/yueliu1999/GuardReasoner", "project_page": "", "abstract": "As LLMs increasingly impact safety-critical applications, ensuring their safety using guardrails remains a key challenge. This paper proposes GuardReasoner, a new safeguard for LLMs, by guiding the guard model to learn to reason. Concretely, we first create the GuardReasonerTrain dataset, which consists of 127K samples with 460K detailed reasoning steps. Then, we introduce reasoning SFT to unlock the reasoning capability of guard models. In addition, we present hard sample DPO to further strengthen their reasoning ability. In this manner, GuardReasoner achieves better performance, explainability, and generalizability. Extensive experiments and analyses on 13 benchmarks of 3 guardrail tasks demonstrate its superiority. Remarkably, GuardReasoner 8B surpasses GPT-4o+CoT by 5.74% and LLaMA Guard 3 8B by 20.84% F1 score on average. We release the training data, code, and models with different scales (1B, 3B, 8B) of GuardReasoner : https://github.com/yueliu1999/GuardReasoner/.", "github_manual": "", "github_regex": "https://github.com/yueliu1999/GuardReasoner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-31", "arxiv_id": "2501.16411", "title": "PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding", "authors": [ "Wei Chow", "Jiageng Mao", "Boyi Li", "Daniel Seita", "Vitor Guizilini", "Yue Wang" ], "github": "https://github.com/USC-GVL/PhysBench", "project_page": "", "abstract": "Understanding the physical world is a fundamental challenge in embodied AI, critical for enabling agents to perform complex tasks and operate safely in real-world environments. While Vision-Language Models (VLMs) have shown great promise in reasoning and task planning for embodied agents, their ability to comprehend physical phenomena remains extremely limited. To close this gap, we introduce PhysBench, a comprehensive benchmark designed to evaluate VLMs' physical world understanding capability across a diverse set of tasks. PhysBench contains 10,002 entries of interleaved video-image-text data, categorized into four major domains: physical object properties, physical object relationships, physical scene understanding, and physics-based dynamics, further divided into 19 subclasses and 8 distinct capability dimensions. Our extensive experiments, conducted on 75 representative VLMs, reveal that while these models excel in common-sense reasoning, they struggle with understanding the physical world -- likely due to the absence of physical knowledge in their training data and the lack of embedded physical priors. To tackle the shortfall, we introduce PhysAgent, a novel framework that combines the generalization strengths of VLMs with the specialized expertise of vision models, significantly enhancing VLMs' physical understanding across a variety of tasks, including an 18.4\\% improvement on GPT-4o. Furthermore, our results demonstrate that enhancing VLMs' physical world understanding capabilities can help embodied agents such as MOKA. We believe that PhysBench and PhysAgent offer valuable insights and contribute to bridging the gap between VLMs and physical world understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/USC-GVL/PhysBench", "github_api_added_by": "auto" }, { "date": "2025-01-31", "arxiv_id": "2501.18585", "title": "Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs", "authors": [ "Yue Wang", "Qiuzhi Liu", "Jiahao Xu", "Tian Liang", "Xingyu Chen", "Zhiwei He", "Linfeng Song", "Dian Yu", "Juntao Li", "Zhuosheng Zhang", "Rui Wang", "Zhaopeng Tu", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) such as OpenAI's o1 have demonstrated remarkable abilities in complex reasoning tasks by scaling test-time compute and exhibiting human-like deep thinking. However, we identify a phenomenon we term underthinking, where o1-like LLMs frequently switch between different reasoning thoughts without sufficiently exploring promising paths to reach a correct solution. This behavior leads to inadequate depth of reasoning and decreased performance, particularly on challenging mathematical problems. To systematically analyze this issue, we conduct experiments on three challenging test sets and two representative open-source o1-like models, revealing that frequent thought switching correlates with incorrect responses. We introduce a novel metric to quantify underthinking by measuring token efficiency in incorrect answers. To address underthinking, we propose a decoding strategy with thought switching penalty TIP that discourages premature transitions between thoughts, encouraging deeper exploration of each reasoning path. Experimental results demonstrate that our approach improves accuracy across challenging datasets without requiring model fine-tuning. Our findings contribute to understanding reasoning inefficiencies in o1-like LLMs and offer a practical solution to enhance their problem-solving capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-31", "arxiv_id": "2501.18009", "title": "Large Language Models Think Too Fast To Explore Effectively", "authors": [ "Lan Pan", "Hanbo Xie", "Robert C. Wilson" ], "github": "", "project_page": "", "abstract": "Large Language Models have emerged many intellectual capacities. While numerous benchmarks assess their intelligence, limited attention has been given to their ability to explore, an essential capacity for discovering new information and adapting to novel environments in both natural and artificial systems. The extent to which LLMs can effectively explore, particularly in open-ended tasks, remains unclear. This study investigates whether LLMs can surpass humans in exploration during an open-ended task, using Little Alchemy 2 as a paradigm, where agents combine elements to discover new ones. Results show most LLMs underperform compared to humans, except for the o1 model, with those traditional LLMs relying primarily on uncertainty driven strategies, unlike humans who balance uncertainty and empowerment. Representational analysis of the models with Sparse Autoencoders revealed that uncertainty and choices are represented at earlier transformer blocks, while empowerment values are processed later, causing LLMs to think too fast and make premature decisions, hindering effective exploration. These findings shed light on the limitations of LLM exploration and suggest directions for improving their adaptability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-31", "arxiv_id": "2501.18438", "title": "o3-mini vs DeepSeek-R1: Which One is Safer?", "authors": [ "Aitor Arrieta", "Miriam Ugarte", "Pablo Valle", "Jos\u00e9 Antonio Parejo", "Sergio Segura" ], "github": "", "project_page": "", "abstract": "The irruption of DeepSeek-R1 constitutes a turning point for the AI industry in general and the LLMs in particular. Its capabilities have demonstrated outstanding performance in several tasks, including creative thinking, code generation, maths and automated program repair, at apparently lower execution cost. However, LLMs must adhere to an important qualitative property, i.e., their alignment with safety and human values. A clear competitor of DeepSeek-R1 is its American counterpart, OpenAI's o3-mini model, which is expected to set high standards in terms of performance, safety and cost. In this paper we conduct a systematic assessment of the safety level of both, DeepSeek-R1 (70b version) and OpenAI's o3-mini (beta version). To this end, we make use of our recently released automated safety testing tool, named ASTRAL. By leveraging this tool, we automatically and systematically generate and execute a total of 1260 unsafe test inputs on both models. After conducting a semi-automated assessment of the outcomes provided by both LLMs, the results indicate that DeepSeek-R1 is highly unsafe as compared to OpenAI's o3-mini. Based on our evaluation, DeepSeek-R1 answered unsafely to 11.98% of the executed prompts whereas o3-mini only to 1.19%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-31", "arxiv_id": "2501.18362", "title": "MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding", "authors": [ "Yuxin Zuo", "Shang Qu", "Yifei Li", "Zhangren Chen", "Xuekai Zhu", "Ermo Hua", "Kaiyan Zhang", "Ning Ding", "Bowen Zhou" ], "github": "", "project_page": "", "abstract": "We introduce MedXpertQA, a highly challenging and comprehensive benchmark to evaluate expert-level medical knowledge and advanced reasoning. MedXpertQA includes 4,460 questions spanning 17 specialties and 11 body systems. It includes two subsets, Text for text evaluation and MM for multimodal evaluation. Notably, MM introduces expert-level exam questions with diverse images and rich clinical information, including patient records and examination results, setting it apart from traditional medical multimodal benchmarks with simple QA pairs generated from image captions. MedXpertQA applies rigorous filtering and augmentation to address the insufficient difficulty of existing benchmarks like MedQA, and incorporates specialty board questions to improve clinical relevance and comprehensiveness. We perform data synthesis to mitigate data leakage risk and conduct multiple rounds of expert reviews to ensure accuracy and reliability. We evaluate 16 leading models on MedXpertQA. Moreover, medicine is deeply connected to real-world decision-making, providing a rich and representative setting for assessing reasoning abilities beyond mathematics and code. To this end, we develop a reasoning-oriented subset to facilitate the assessment of o1-like models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-31", "arxiv_id": "2501.18511", "title": "WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training", "authors": [ "Benjamin Feuer", "Chinmay Hegde" ], "github": "https://github.com/penfever/wildchat-50m", "project_page": "", "abstract": "Language model (LLM) post-training, from DPO to distillation, can refine behaviors and unlock new skills, but the open science supporting these post-training techniques is still in its infancy. One limiting factor has been the difficulty of conducting large-scale comparative analyses of synthetic data generating models and LLM judges. To close this gap, we introduce WILDCHAT-50M, the largest public chat dataset to date. We extend the existing WildChat dataset to include responses not only from GPT, but from over 50 different open-weight models, ranging in size from 0.5B to 104B parameters. We conduct an extensive comparative analysis and demonstrate the potential of this dataset by creating RE-WILD, our own public SFT mix, which outperforms the recent Tulu-3 SFT mixture from Allen AI with only 40% as many samples. Our dataset, samples and code are available at https://github.com/penfever/wildchat-50m.", "github_manual": "", "github_regex": "https://github.com/penfever/wildchat-50m", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-31", "arxiv_id": "2501.18512", "title": "Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch", "authors": [ "Arthur Douillard", "Yanislav Donchev", "Keith Rush", "Satyen Kale", "Zachary Charles", "Zachary Garrett", "Gabriel Teston", "Dave Lacey", "Ross McIlroy", "Jiajun Shen", "Alexandre Ram\u00e9", "Arthur Szlam", "Marc'Aurelio Ranzato", "Paul Barham" ], "github": "", "project_page": "", "abstract": "Training of large language models (LLMs) is typically distributed across a large number of accelerators to reduce training time. Since internal states and parameter gradients need to be exchanged at each and every single gradient step, all devices need to be co-located using low-latency high-bandwidth communication links to support the required high volume of exchanged bits. Recently, distributed algorithms like DiLoCo have relaxed such co-location constraint: accelerators can be grouped into ``workers'', where synchronizations between workers only occur infrequently. This in turn means that workers can afford being connected by lower bandwidth communication links without affecting learning quality. However, in these methods, communication across workers still requires the same peak bandwidth as before, as the synchronizations require all parameters to be exchanged across all workers. In this paper, we improve DiLoCo in three ways. First, we synchronize only subsets of parameters in sequence, rather than all at once, which greatly reduces peak bandwidth. Second, we allow workers to continue training while synchronizing, which decreases wall clock time. Third, we quantize the data exchanged by workers, which further reduces bandwidth across workers. By properly combining these modifications, we show experimentally that we can distribute training of billion-scale parameters and reach similar quality as before, but reducing required bandwidth by two orders of magnitude.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-31", "arxiv_id": "2501.16609", "title": "CowPilot: A Framework for Autonomous and Human-Agent Collaborative Web Navigation", "authors": [ "Faria Huq", "Zora Zhiruo Wang", "Frank F. Xu", "Tianyue Ou", "Shuyan Zhou", "Jeffrey P. Bigham", "Graham Neubig" ], "github": "", "project_page": "", "abstract": "While much work on web agents emphasizes the promise of autonomously performing tasks on behalf of users, in reality, agents often fall short on complex tasks in real-world contexts and modeling user preference. This presents an opportunity for humans to collaborate with the agent and leverage the agent's capabilities effectively. We propose CowPilot, a framework supporting autonomous as well as human-agent collaborative web navigation, and evaluation across task success and task efficiency. CowPilot reduces the number of steps humans need to perform by allowing agents to propose next steps, while users are able to pause, reject, or take alternative actions. During execution, users can interleave their actions with the agent by overriding suggestions or resuming agent control when needed. We conducted case studies on five common websites and found that the human-agent collaborative mode achieves the highest success rate of 95% while requiring humans to perform only 15.2% of the total steps. Even with human interventions during task execution, the agent successfully drives up to half of task success on its own. CowPilot can serve as a useful tool for data collection and agent evaluation across websites, which we believe will enable research in how users and agents can work together. Video demonstrations are available at https://oaishi.github.io/cowpilot.html", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-01-31", "arxiv_id": "2501.18427", "title": "SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer", "authors": [ "Enze Xie", "Junsong Chen", "Yuyang Zhao", "Jincheng Yu", "Ligeng Zhu", "Yujun Lin", "Zhekai Zhang", "Muyang Li", "Junyu Chen", "Han Cai", "Bingchen Liu", "Daquan Zhou", "Song Han" ], "github": "https://github.com/NVlabs/Sana", "project_page": "", "abstract": "This paper presents SANA-1.5, a linear Diffusion Transformer for efficient scaling in text-to-image generation. Building upon SANA-1.0, we introduce three key innovations: (1) Efficient Training Scaling: A depth-growth paradigm that enables scaling from 1.6B to 4.8B parameters with significantly reduced computational resources, combined with a memory-efficient 8-bit optimizer. (2) Model Depth Pruning: A block importance analysis technique for efficient model compression to arbitrary sizes with minimal quality loss. (3) Inference-time Scaling: A repeated sampling strategy that trades computation for model capacity, enabling smaller models to match larger model quality at inference time. Through these strategies, SANA-1.5 achieves a text-image alignment score of 0.72 on GenEval, which can be further improved to 0.80 through inference scaling, establishing a new SoTA on GenEval benchmark. These innovations enable efficient model scaling across different compute budgets while maintaining high quality, making high-quality image generation more accessible.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVlabs/Sana", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2501.19393", "title": "s1: Simple test-time scaling", "authors": [ "Niklas Muennighoff", "Zitong Yang", "Weijia Shi", "Xiang Lisa Li", "Li Fei-Fei", "Hannaneh Hajishirzi", "Luke Zettlemoyer", "Percy Liang", "Emmanuel Cand\u00e8s", "Tatsunori Hashimoto" ], "github": "https://github.com/simplescaling/s1", "project_page": "", "abstract": "Test-time scaling is a promising new approach to language modeling that uses extra test-time compute to improve performance. Recently, OpenAI's o1 model showed this capability but did not publicly share its methodology, leading to many replication efforts. We seek the simplest approach to achieve test-time scaling and strong reasoning performance. First, we curate a small dataset s1K of 1,000 questions paired with reasoning traces relying on three criteria we validate through ablations: difficulty, diversity, and quality. Second, we develop budget forcing to control test-time compute by forcefully terminating the model's thinking process or lengthening it by appending \"Wait\" multiple times to the model's generation when it tries to end. This can lead the model to double-check its answer, often fixing incorrect reasoning steps. After supervised finetuning the Qwen2.5-32B-Instruct language model on s1K and equipping it with budget forcing, our model s1 exceeds o1-preview on competition math questions by up to 27% (MATH and AIME24). Further, scaling s1 with budget forcing allows extrapolating beyond its performance without test-time intervention: from 50% to 57% on AIME24. Our model, data, and code are open-source at https://github.com/simplescaling/s1.", "github_manual": "", "github_regex": "https://github.com/simplescaling/s1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2501.18841", "title": "Trading Inference-Time Compute for Adversarial Robustness", "authors": [ "Wojciech Zaremba", "Evgenia Nitishinskaya", "Boaz Barak", "Stephanie Lin", "Sam Toyer", "Yaodong Yu", "Rachel Dias", "Eric Wallace", "Kai Xiao", "Johannes Heidecke", "Amelia Glaese" ], "github": "", "project_page": "", "abstract": "We conduct experiments on the impact of increasing inference-time compute in reasoning models (specifically OpenAI o1-preview and o1-mini) on their robustness to adversarial attacks. We find that across a variety of attacks, increased inference-time compute leads to improved robustness. In many cases (with important exceptions), the fraction of model samples where the attack succeeds tends to zero as the amount of test-time compute grows. We perform no adversarial training for the tasks we study, and we increase inference-time compute by simply allowing the models to spend more compute on reasoning, independently of the form of attack. Our results suggest that inference-time compute has the potential to improve adversarial robustness for Large Language Models. We also explore new attacks directed at reasoning models, as well as settings where inference-time compute does not improve reliability, and speculate on the reasons for these as well as ways to address them.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2501.19324", "title": "Reward-Guided Speculative Decoding for Efficient LLM Reasoning", "authors": [ "Baohao Liao", "Yuhui Xu", "Hanze Dong", "Junnan Li", "Christof Monz", "Silvio Savarese", "Doyen Sahoo", "Caiming Xiong" ], "github": "https://github.com/BaohaoLiao/RSD", "project_page": "", "abstract": "We introduce Reward-Guided Speculative Decoding (RSD), a novel framework aimed at improving the efficiency of inference in large language models (LLMs). RSD synergistically combines a lightweight draft model with a more powerful target model, incorporating a controlled bias to prioritize high-reward outputs, in contrast to existing speculative decoding methods that enforce strict unbiasedness. RSD employs a process reward model to evaluate intermediate decoding steps and dynamically decide whether to invoke the target model, optimizing the trade-off between computational cost and output quality. We theoretically demonstrate that a threshold-based mixture strategy achieves an optimal balance between resource utilization and performance. Extensive evaluations on challenging reasoning benchmarks, including Olympiad-level tasks, show that RSD delivers significant efficiency gains against decoding with the target model only (up to 4.4x fewer FLOPs), while achieving significant better accuracy than parallel decoding method on average (up to +3.5). These results highlight RSD as a robust and cost-effective approach for deploying LLMs in resource-intensive scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/BaohaoLiao/RSD", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2501.18837", "title": "Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming", "authors": [ "Mrinank Sharma", "Meg Tong", "Jesse Mu", "Jerry Wei", "Jorrit Kruthoff", "Scott Goodfriend", "Euan Ong", "Alwin Peng", "Raj Agarwal", "Cem Anil", "Amanda Askell", "Nathan Bailey", "Joe Benton", "Emma Bluemke", "Samuel R. Bowman", "Eric Christiansen", "Hoagy Cunningham", "Andy Dau", "Anjali Gopal", "Rob Gilson", "Logan Graham", "Logan Howard", "Nimit Kalra", "Taesung Lee", "Kevin Lin", "Peter Lofgren", "Francesco Mosconi", "Clare O'Hara", "Catherine Olsson", "Linda Petrini", "Samir Rajani", "Nikhil Saxena", "Alex Silverstein", "Tanya Singh", "Theodore Sumers", "Leonard Tang", "Kevin K. Troy", "Constantin Weisser", "Ruiqi Zhong", "Giulio Zhou", "Jan Leike", "Jared Kaplan", "Ethan Perez" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are vulnerable to universal jailbreaks-prompting strategies that systematically bypass model safeguards and enable users to carry out harmful processes that require many model interactions, like manufacturing illegal substances at scale. To defend against these attacks, we introduce Constitutional Classifiers: safeguards trained on synthetic data, generated by prompting LLMs with natural language rules (i.e., a constitution) specifying permitted and restricted content. In over 3,000 estimated hours of red teaming, no red teamer found a universal jailbreak that could extract information from an early classifier-guarded LLM at a similar level of detail to an unguarded model across most target queries. On automated evaluations, enhanced classifiers demonstrated robust defense against held-out domain-specific jailbreaks. These classifiers also maintain deployment viability, with an absolute 0.38% increase in production-traffic refusals and a 23.7% inference overhead. Our work demonstrates that defending against universal jailbreaks while maintaining practical deployment viability is tractable.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2404.07097", "title": "Fast Encoder-Based 3D from Casual Videos via Point Track Processing", "authors": [ "Yoni Kasten", "Wuyue Lu", "Haggai Maron" ], "github": "", "project_page": "", "abstract": "This paper addresses the long-standing challenge of reconstructing 3D structures from videos with dynamic content. Current approaches to this problem were not designed to operate on casual videos recorded by standard cameras or require a long optimization time. Aiming to significantly improve the efficiency of previous approaches, we present TracksTo4D, a learning-based approach that enables inferring 3D structure and camera positions from dynamic content originating from casual videos using a single efficient feed-forward pass. To achieve this, we propose operating directly over 2D point tracks as input and designing an architecture tailored for processing 2D point tracks. Our proposed architecture is designed with two key principles in mind: (1) it takes into account the inherent symmetries present in the input point tracks data, and (2) it assumes that the movement patterns can be effectively represented using a low-rank approximation. TracksTo4D is trained in an unsupervised way on a dataset of casual videos utilizing only the 2D point tracks extracted from the videos, without any 3D supervision. Our experiments show that TracksTo4D can reconstruct a temporal point cloud and camera positions of the underlying video with accuracy comparable to state-of-the-art methods, while drastically reducing runtime by up to 95\\%. We further show that TracksTo4D generalizes well to unseen videos of unseen semantic categories at inference time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2411.04983", "title": "DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning", "authors": [ "Gaoyue Zhou", "Hengkai Pan", "Yann LeCun", "Lerrel Pinto" ], "github": "https://github.com/gaoyuezhou/dino_wm", "project_page": "", "abstract": "The ability to predict future outcomes given control actions is fundamental for physical reasoning. However, such predictive models, often called world models, have proven challenging to learn and are typically developed for task-specific solutions with online policy learning. We argue that the true potential of world models lies in their ability to reason and plan across diverse problems using only passive data. Concretely, we require world models to have the following three properties: 1) be trainable on offline, pre-collected trajectories, 2) support test-time behavior optimization, and 3) facilitate task-agnostic reasoning. To realize this, we present DINO World Model (DINO-WM), a new method to model visual dynamics without reconstructing the visual world. DINO-WM leverages spatial patch features pre-trained with DINOv2, enabling it to learn from offline behavioral trajectories by predicting future patch features. This design allows DINO-WM to achieve observational goals through action sequence optimization, facilitating task-agnostic behavior planning by treating desired goal patch features as prediction targets. We evaluate DINO-WM across various domains, including maze navigation, tabletop pushing, and particle manipulation. Our experiments demonstrate that DINO-WM can generate zero-shot behavioral solutions at test time without relying on expert demonstrations, reward modeling, or pre-learned inverse models. Notably, DINO-WM exhibits strong generalization capabilities compared to prior state-of-the-art work, adapting to diverse task families such as arbitrarily configured mazes, push manipulation with varied object shapes, and multi-particle scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gaoyuezhou/dino_wm", "github_api_added_by": "auto" }, { "date": "2025-02-03", "arxiv_id": "2501.18128", "title": "Unraveling the Capabilities of Language Models in News Summarization", "authors": [ "Abdurrahman Odaba\u015f\u0131", "G\u00f6ksel Biricik" ], "github": "", "project_page": "", "abstract": "Given the recent introduction of multiple language models and the ongoing demand for improved Natural Language Processing tasks, particularly summarization, this work provides a comprehensive benchmarking of 20 recent language models, focusing on smaller ones for the news summarization task. In this work, we systematically test the capabilities and effectiveness of these models in summarizing news article texts which are written in different styles and presented in three distinct datasets. Specifically, we focus in this study on zero-shot and few-shot learning settings and we apply a robust evaluation methodology that combines different evaluation concepts including automatic metrics, human evaluation, and LLM-as-a-judge. Interestingly, including demonstration examples in the few-shot learning setting did not enhance models' performance and, in some cases, even led to worse quality of the generated summaries. This issue arises mainly due to the poor quality of the gold summaries that have been used as reference summaries, which negatively impacts the models' performance. Furthermore, our study's results highlight the exceptional performance of GPT-3.5-Turbo and GPT-4, which generally dominate due to their advanced capabilities. However, among the public models evaluated, certain models such as Qwen1.5-7B, SOLAR-10.7B-Instruct-v1.0, Meta-Llama-3-8B and Zephyr-7B-Beta demonstrated promising results. These models showed significant potential, positioning them as competitive alternatives to large models for the task of news summarization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2501.18119", "title": "Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models", "authors": [ "Qika Lin", "Tianzhe Zhao", "Kai He", "Zhen Peng", "Fangzhi Xu", "Ling Huang", "Jingying Ma", "Mengling Feng" ], "github": "", "project_page": "", "abstract": "Due to the presence of the natural gap between Knowledge Graph (KG) structures and the natural language, the effective integration of holistic structural information of KGs with Large Language Models (LLMs) has emerged as a significant question. To this end, we propose a two-stage framework to learn and apply quantized codes for each entity, aiming for the seamless integration of KGs with LLMs. Firstly, a self-supervised quantized representation (SSQR) method is proposed to compress both KG structural and semantic knowledge into discrete codes (\\ie, tokens) that align the format of language sentences. We further design KG instruction-following data by viewing these learned codes as features to directly input to LLMs, thereby achieving seamless integration. The experiment results demonstrate that SSQR outperforms existing unsupervised quantized methods, producing more distinguishable codes. Further, the fine-tuned LLaMA2 and LLaMA3.1 also have superior performance on KG link prediction and triple classification tasks, utilizing only 16 tokens per entity instead of thousands in conventional prompting methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2501.18753", "title": "INT: Instance-Specific Negative Mining for Task-Generic Promptable Segmentation", "authors": [ "Jian Hu", "Zixu Cheng", "Shaogang Gong" ], "github": "", "project_page": "", "abstract": "Task-generic promptable image segmentation aims to achieve segmentation of diverse samples under a single task description by utilizing only one task-generic prompt. Current methods leverage the generalization capabilities of Vision-Language Models (VLMs) to infer instance-specific prompts from these task-generic prompts in order to guide the segmentation process. However, when VLMs struggle to generalise to some image instances, predicting instance-specific prompts becomes poor. To solve this problem, we introduce Instance-specific Negative Mining for Task-Generic Promptable Segmentation (INT). The key idea of INT is to adaptively reduce the influence of irrelevant (negative) prior knowledge whilst to increase the use the most plausible prior knowledge, selected by negative mining with higher contrast, in order to optimise instance-specific prompts generation. Specifically, INT consists of two components: (1) instance-specific prompt generation, which progressively fliters out incorrect information in prompt generation; (2) semantic mask generation, which ensures each image instance segmentation matches correctly the semantics of the instance-specific prompts. INT is validated on six datasets, including camouflaged objects and medical images, demonstrating its effectiveness, robustness and scalability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2501.19339", "title": "PixelWorld: Towards Perceiving Everything as Pixels", "authors": [ "Zhiheng Lyu", "Xueguang Ma", "Wenhu Chen" ], "github": "https://github.com/TIGER-AI-Lab/PixelWorld", "project_page": "", "abstract": "Existing foundation models typically process visual input as pixels and textual input as tokens, a paradigm that contrasts with human perception, where both modalities are processed in a unified manner. With the rise of embodied and agentic AI, where inputs primarily come from camera pixels, the need for a unified perception framework becomes increasingly evident. In this paper, we propose to unify all modalities (text, tables, code, diagrams, images, etc) as pixel inputs, i.e. \"Perceive Everything as Pixels\" (PEAP). We introduce PixelWorld, a novel evaluation suite that unifies all the mentioned modalities into pixel space to gauge the existing models' performance. Our findings show that (1) PEAP outperforms baseline with token-based input in multimodal datasets, benefiting from unified input for better disambiguation, (2) significant declines in reasoning and coding capabilities across all models when processing pixel-based input, underscoring the need to enhance foundation models' perceptual abilities, (3) larger models can maintain strong performance on non-reasoning tasks under PEAP, while smaller models like Phi-3.5-V suffer significant performance degradation, (4) the attention pattern of PEAP is highly aligned with text token input, (5) PEAP can be accelerated significantly by exploiting the spatial sparsity. We conclude that the existing frontier models are competent in pixel perception, however, there is still headroom for improvement. Our code, dataset will be released upon acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TIGER-AI-Lab/PixelWorld", "github_api_added_by": "auto" }, { "date": "2025-02-03", "arxiv_id": "2501.18965", "title": "The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training", "authors": [ "Fabian Schaipp", "Alexander H\u00e4gele", "Adrien Taylor", "Umut Simsekli", "Francis Bach" ], "github": "https://github.com/fabian-sp/lr-scheduling", "project_page": "", "abstract": "We show that learning-rate schedules for large model training behave surprisingly similar to a performance bound from non-smooth convex optimization theory. We provide a bound for the constant schedule with linear cooldown; in particular, the practical benefit of cooldown is reflected in the bound due to the absence of logarithmic terms. Further, we show that this surprisingly close match between optimization theory and practice can be exploited for learning-rate tuning: we achieve noticeable improvements for training 124M and 210M Llama-type models by (i) extending the schedule for continued training with optimal learning-rate, and (ii) transferring the optimal learning-rate across schedules.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/fabian-sp/lr-scheduling", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2501.19399", "title": "Scalable-Softmax Is Superior for Attention", "authors": [ "Ken M. Nakanishi" ], "github": "", "project_page": "", "abstract": "The maximum element of the vector output by the Softmax function approaches zero as the input vector size increases. Transformer-based language models rely on Softmax to compute attention scores, causing the attention distribution to flatten as the context size grows. This reduces the model's ability to prioritize key information effectively and potentially limits its length generalization. To address this problem, we propose Scalable-Softmax (SSMax), which replaces Softmax in scenarios where the input vector size varies. SSMax can be seamlessly integrated into existing Transformer-based architectures. Experimental results in language modeling show that models using SSMax not only achieve faster loss reduction during pretraining but also significantly improve performance in long contexts and key information retrieval. Furthermore, an analysis of attention scores reveals that SSMax enables the model to focus attention on key information even in long contexts. Additionally, although models that use SSMax from the beginning of pretraining achieve better length generalization, those that have already started pretraining can still gain some of this ability by replacing Softmax in the attention layers with SSMax, either during or after pretraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2501.14677", "title": "MatAnyone: Stable Video Matting with Consistent Memory Propagation", "authors": [ "Peiqing Yang", "Shangchen Zhou", "Jixin Zhao", "Qingyi Tao", "Chen Change Loy" ], "github": "", "project_page": "", "abstract": "Auxiliary-free human video matting methods, which rely solely on input frames, often struggle with complex or ambiguous backgrounds. To address this, we propose MatAnyone, a robust framework tailored for target-assigned video matting. Specifically, building on a memory-based paradigm, we introduce a consistent memory propagation module via region-adaptive memory fusion, which adaptively integrates memory from the previous frame. This ensures semantic stability in core regions while preserving fine-grained details along object boundaries. For robust training, we present a larger, high-quality, and diverse dataset for video matting. Additionally, we incorporate a novel training strategy that efficiently leverages large-scale segmentation data, boosting matting stability. With this new network design, dataset, and training strategy, MatAnyone delivers robust and accurate video matting results in diverse real-world scenarios, outperforming existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2501.18804", "title": "Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion", "authors": [ "Vitor Guizilini", "Muhammad Zubair Irshad", "Dian Chen", "Greg Shakhnarovich", "Rares Ambrus" ], "github": "", "project_page": "", "abstract": "Current methods for 3D scene reconstruction from sparse posed images employ intermediate 3D representations such as neural fields, voxel grids, or 3D Gaussians, to achieve multi-view consistent scene appearance and geometry. In this paper we introduce MVGD, a diffusion-based architecture capable of direct pixel-level generation of images and depth maps from novel viewpoints, given an arbitrary number of input views. Our method uses raymap conditioning to both augment visual features with spatial information from different viewpoints, as well as to guide the generation of images and depth maps from novel views. A key aspect of our approach is the multi-task generation of images and depth maps, using learnable task embeddings to guide the diffusion process towards specific modalities. We train this model on a collection of more than 60 million multi-view samples from publicly available datasets, and propose techniques to enable efficient and consistent learning in such diverse conditions. We also propose a novel strategy that enables the efficient training of larger models by incrementally fine-tuning smaller ones, with promising scaling behavior. Through extensive experiments, we report state-of-the-art results in multiple novel view synthesis benchmarks, as well as multi-view stereo and video depth estimation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2501.18052", "title": "SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders", "authors": [ "Bartosz Cywi\u0144ski", "Kamil Deja" ], "github": "https://github.com/cywinski/SAeUron", "project_page": "", "abstract": "Diffusion models, while powerful, can inadvertently generate harmful or undesirable content, raising significant ethical and safety concerns. Recent machine unlearning approaches offer potential solutions but often lack transparency, making it difficult to understand the changes they introduce to the base model. In this work, we introduce SAeUron, a novel method leveraging features learned by sparse autoencoders (SAEs) to remove unwanted concepts in text-to-image diffusion models. First, we demonstrate that SAEs, trained in an unsupervised manner on activations from multiple denoising timesteps of the diffusion model, capture sparse and interpretable features corresponding to specific concepts. Building on this, we propose a feature selection method that enables precise interventions on model activations to block targeted content while preserving overall performance. Evaluation with the competitive UnlearnCanvas benchmark on object and style unlearning highlights SAeUron's state-of-the-art performance. Moreover, we show that with a single SAE, we can remove multiple concepts simultaneously and that in contrast to other methods, SAeUron mitigates the possibility of generating unwanted content, even under adversarial attack. Code and checkpoints are available at: https://github.com/cywinski/SAeUron.", "github_manual": "", "github_regex": "https://github.com/cywinski/SAeUron", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-03", "arxiv_id": "2502.00299", "title": "ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference", "authors": [ "Xiang Liu", "Zhenheng Tang", "Peijie Dong", "Zeyu Li", "Bo Li", "Xuming Hu", "Xiaowen Chu" ], "github": "", "project_page": "", "abstract": "To reduce memory costs in long-context inference with Large Language Models (LLMs), many recent works focus on compressing the key-value (KV) cache of different tokens. However, we identify that the previous KV cache compression methods measure token importance individually, neglecting the dependency between different tokens in the real-world language characterics. In light of this, we introduce ChunkKV, grouping the tokens in a chunk as a basic compressing unit, and retaining the most informative semantic chunks while discarding the less important ones. Furthermore, observing that ChunkKV exhibits higher similarity in the preserved indices across different layers, we propose layer-wise index reuse to further reduce computational overhead. We evaluated ChunkKV on cutting-edge long-context benchmarks including LongBench and Needle-In-A-HayStack, as well as the GSM8K and JailbreakV in-context learning benchmark. Our experiments with instruction tuning and multi-step reasoning (O1 and R1) LLMs, achieve up to 10\\% performance improvement under aggressive compression ratios compared to existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.00094", "title": "AIN: The Arabic INclusive Large Multimodal Model", "authors": [ "Ahmed Heakl", "Sara Ghaboura", "Omkar Thawkar", "Fahad Shahbaz Khan", "Hisham Cholakkal", "Rao Muhammad Anwer", "Salman Khan" ], "github": "", "project_page": "", "abstract": "Amid the swift progress of large language models (LLMs) and their evolution into large multimodal models (LMMs), significant strides have been made in high-resource languages such as English and Chinese. While Arabic LLMs have seen notable progress, Arabic LMMs remain largely unexplored, often narrowly focusing on a few specific aspects of the language and visual understanding. To bridge this gap, we introduce AIN-the Arabic Inclusive Multimodal Model-designed to excel across diverse domains. AIN is an English-Arabic bilingual LMM designed to excel in English and Arabic, leveraging carefully constructed 3.6 million high-quality Arabic-English multimodal data samples. AIN demonstrates state-of-the-art Arabic performance, while also possessing strong English-language visual capabilities. On the recent CAMEL-Bench benchmark comprising 38 sub-domains including, multi-image understanding, complex visual perception, handwritten document understanding, video understanding, medical imaging, plant diseases, and remote sensing-based land use understanding, our AIN demonstrates strong performance with the 7B model outperforming GPT-4o by an absolute gain of 3.4% averaged over eight domains and 38 sub-domains. AIN's superior capabilities position it as a significant step toward empowering Arabic speakers with advanced multimodal generative AI tools across diverse applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01441", "title": "Improved Training Technique for Latent Consistency Models", "authors": [ "Quan Dao", "Khanh Doan", "Di Liu", "Trung Le", "Dimitris Metaxas" ], "github": "https://github.com/quandao10/sLCT", "project_page": "", "abstract": "Consistency models are a new family of generative models capable of producing high-quality samples in either a single step or multiple steps. Recently, consistency models have demonstrated impressive performance, achieving results on par with diffusion models in the pixel space. However, the success of scaling consistency training to large-scale datasets, particularly for text-to-image and video generation tasks, is determined by performance in the latent space. In this work, we analyze the statistical differences between pixel and latent spaces, discovering that latent data often contains highly impulsive outliers, which significantly degrade the performance of iCT in the latent space. To address this, we replace Pseudo-Huber losses with Cauchy losses, effectively mitigating the impact of outliers. Additionally, we introduce a diffusion loss at early timesteps and employ optimal transport (OT) coupling to further enhance performance. Lastly, we introduce the adaptive scaling-c scheduler to manage the robust training process and adopt Non-scaling LayerNorm in the architecture to better capture the statistics of the features and reduce outlier impact. With these strategies, we successfully train latent consistency models capable of high-quality sampling with one or two steps, significantly narrowing the performance gap between latent consistency and diffusion models. The implementation is released here: https://github.com/quandao10/sLCT/", "github_manual": "", "github_regex": "https://github.com/quandao10/sLCT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01456", "title": "Process Reinforcement through Implicit Rewards", "authors": [ "Ganqu Cui", "Lifan Yuan", "Zefan Wang", "Hanbin Wang", "Wendi Li", "Bingxiang He", "Yuchen Fan", "Tianyu Yu", "Qixin Xu", "Weize Chen", "Jiarui Yuan", "Huayu Chen", "Kaiyan Zhang", "Xingtai Lv", "Shuo Wang", "Yuan Yao", "Xu Han", "Hao Peng", "Yu Cheng", "Zhiyuan Liu", "Maosong Sun", "Bowen Zhou", "Ning Ding" ], "github": "https://github.com/PRIME-RL/PRIME", "project_page": "", "abstract": "Dense process rewards have proven a more effective alternative to the sparse outcome-level rewards in the inference-time scaling of large language models (LLMs), particularly in tasks requiring complex multi-step reasoning. While dense rewards also offer an appealing choice for the reinforcement learning (RL) of LLMs since their fine-grained rewards have the potential to address some inherent issues of outcome rewards, such as training efficiency and credit assignment, this potential remains largely unrealized. This can be primarily attributed to the challenges of training process reward models (PRMs) online, where collecting high-quality process labels is prohibitively expensive, making them particularly vulnerable to reward hacking. To address these challenges, we propose PRIME (Process Reinforcement through IMplicit rEwards), which enables online PRM updates using only policy rollouts and outcome labels through implict process rewards. PRIME combines well with various advantage functions and forgoes the dedicated reward model training phrase that existing approaches require, substantially reducing the development overhead. We demonstrate PRIME's effectiveness on competitional math and coding. Starting from Qwen2.5-Math-7B-Base, PRIME achieves a 15.1% average improvement across several key reasoning benchmarks over the SFT model. Notably, our resulting model, Eurus-2-7B-PRIME, surpasses Qwen2.5-Math-7B-Instruct on seven reasoning benchmarks with 10% of its training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PRIME-RL/PRIME", "github_api_added_by": "auto" }, { "date": "2025-02-04", "arxiv_id": "2502.01068", "title": "FastKV: KV Cache Compression for Fast Long-Context Processing with Token-Selective Propagation", "authors": [ "Dongwon Jo", "Jiwon Song", "Yulhwa Kim", "Jae-Joon Kim" ], "github": "https://github.com/dongwonjo/FastKV", "project_page": "", "abstract": "While large language models (LLMs) excel at handling long-context sequences, they require substantial key-value (KV) caches to store contextual information, which can heavily burden computational efficiency and memory usage. Previous efforts to compress these KV caches primarily focused on reducing memory demands but were limited in enhancing latency. To address this issue, we introduce FastKV, a KV cache compression method designed to enhance latency for long-context sequences. To enhance processing speeds while maintaining accuracy, FastKV adopts a novel Token-Selective Propagation (TSP) approach that retains the full context information in the initial layers of LLMs and selectively propagates only a portion of this information in deeper layers even in the prefill stage. Additionally, FastKV incorporates grouped-query attention (GQA)-aware KV cache compression to exploit the advantages of GQA in both memory and computational efficiency. Our experimental results show that FastKV achieves 2.00times and 1.40times improvements in time-to-first-token (TTFT) and throughput, respectively, compared to HeadKV, the state-of-the-art KV cache compression method. Moreover, FastKV successfully maintains accuracy on long-context benchmarks at levels comparable to the baselines. Our code is available at https://github.com/dongwonjo/FastKV.", "github_manual": "", "github_regex": "https://github.com/dongwonjo/FastKV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01061", "title": "OmniHuman-1: Rethinking the Scaling-Up of One-Stage Conditioned Human Animation Models", "authors": [ "Gaojie Lin", "Jianwen Jiang", "Jiaqi Yang", "Zerong Zheng", "Chao Liang" ], "github": "", "project_page": "", "abstract": "End-to-end human animation, such as audio-driven talking human generation, has undergone notable advancements in the recent few years. However, existing methods still struggle to scale up as large general video generation models, limiting their potential in real applications. In this paper, we propose OmniHuman, a Diffusion Transformer-based framework that scales up data by mixing motion-related conditions into the training phase. To this end, we introduce two training principles for these mixed conditions, along with the corresponding model architecture and inference strategy. These designs enable OmniHuman to fully leverage data-driven motion generation, ultimately achieving highly realistic human video generation. More importantly, OmniHuman supports various portrait contents (face close-up, portrait, half-body, full-body), supports both talking and singing, handles human-object interactions and challenging body poses, and accommodates different image styles. Compared to existing end-to-end audio-driven methods, OmniHuman not only produces more realistic videos, but also offers greater flexibility in inputs. It also supports multiple driving modalities (audio-driven, video-driven and combined driving signals). Video samples are provided on the ttfamily project page (https://omnihuman-lab.github.io)", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01100", "title": "ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning", "authors": [ "Bill Yuchen Lin", "Ronan Le Bras", "Kyle Richardson", "Ashish Sabharwal", "Radha Poovendran", "Peter Clark", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "We investigate the logical reasoning capabilities of large language models (LLMs) and their scalability in complex non-monotonic reasoning. To this end, we introduce ZebraLogic, a comprehensive evaluation framework for assessing LLM reasoning performance on logic grid puzzles derived from constraint satisfaction problems (CSPs). ZebraLogic enables the generation of puzzles with controllable and quantifiable complexity, facilitating a systematic study of the scaling limits of models such as Llama, o1 models, and DeepSeek-R1. By encompassing a broad range of search space complexities and diverse logical constraints, ZebraLogic provides a structured environment to evaluate reasoning under increasing difficulty. Our results reveal a significant decline in accuracy as problem complexity grows -- a phenomenon we term the curse of complexity. This limitation persists even with larger models and increased inference-time computation, suggesting inherent constraints in current LLM reasoning capabilities. Additionally, we explore strategies to enhance logical reasoning, including Best-of-N sampling, backtracking mechanisms, and self-verification prompts. Our findings offer critical insights into the scalability of LLM reasoning, highlight fundamental limitations, and outline potential directions for improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01081", "title": "The Jumping Reasoning Curve? Tracking the Evolution of Reasoning Performance in GPT-[n] and o-[n] Models on Multimodal Puzzles", "authors": [ "Vernon Y. H. Toh", "Yew Ken Chia", "Deepanway Ghosal", "Soujanya Poria" ], "github": "https://github.com/declare-lab/LLM-PuzzleTest", "project_page": "", "abstract": "The releases of OpenAI's o1 and o3 mark a significant paradigm shift in Large Language Models towards advanced reasoning capabilities. Notably, o3 outperformed humans in novel problem-solving and skill acquisition on the Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI). However, this benchmark is limited to symbolic patterns, whereas humans often perceive and reason about multimodal scenarios involving both vision and language data. Thus, there is an urgent need to investigate advanced reasoning capabilities in multimodal tasks. To this end, we track the evolution of the GPT-[n] and o-[n] series models on challenging multimodal puzzles, requiring fine-grained visual perception with abstract or algorithmic reasoning. The superior performance of o1 comes at nearly 750 times the computational cost of GPT-4o, raising concerns about its efficiency. Our results reveal a clear upward trend in reasoning capabilities across model iterations, with notable performance jumps across GPT-series models and subsequently to o1. Nonetheless, we observe that the o1 model still struggles with simple multimodal puzzles requiring abstract reasoning. Furthermore, its performance in algorithmic puzzles remains poor. We plan to continuously track new models in the series and update our results in this paper accordingly. All resources used in this evaluation are openly available https://github.com/declare-lab/LLM-PuzzleTest.", "github_manual": "", "github_regex": "https://github.com/declare-lab/LLM-PuzzleTest", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01637", "title": "Scaling Embedding Layers in Language Models", "authors": [ "Da Yu", "Edith Cohen", "Badih Ghazi", "Yangsibo Huang", "Pritish Kamath", "Ravi Kumar", "Daogao Liu", "Chiyuan Zhang" ], "github": "", "project_page": "", "abstract": "We propose SCONE (Scalable, Contextualized, Offloaded, N-gram Embedding), a method for extending input embedding layers to enhance language model performance as layer size scales. To avoid increased decoding costs, SCONE retains the original vocabulary while introducing embeddings for a set of frequent n-grams. These embeddings provide contextualized representation for each input token and are learned with a separate model during training. During inference, they are precomputed and stored in off-accelerator memory with minimal impact on inference speed. SCONE enables two new scaling strategies: increasing the number of cached n-gram embeddings and scaling the model used to learn them, all while maintaining fixed inference-time FLOPS. We show that scaling both aspects allows SCONE to outperform a 1.9B parameter baseline across diverse corpora, while using only half the inference-time FLOPS.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01591", "title": "Improving Transformer World Models for Data-Efficient RL", "authors": [ "Antoine Dedieu", "Joseph Ortiz", "Xinghua Lou", "Carter Wendelken", "Wolfgang Lehrach", "J Swaroop Guntupalli", "Miguel Lazaro-Gredilla", "Kevin Patrick Murphy" ], "github": "https://github.com/luchris429/purejaxrl/blob/main/purejaxrl/ppo.py", "project_page": "", "abstract": "We present an approach to model-based RL that achieves a new state of the art performance on the challenging Craftax-classic benchmark, an open-world 2D survival game that requires agents to exhibit a wide range of general abilities -- such as strong generalization, deep exploration, and long-term reasoning. With a series of careful design choices aimed at improving sample efficiency, our MBRL algorithm achieves a reward of 67.4% after only 1M environment steps, significantly outperforming DreamerV3, which achieves 53.2%, and, for the first time, exceeds human performance of 65.0%. Our method starts by constructing a SOTA model-free baseline, using a novel policy architecture that combines CNNs and RNNs. We then add three improvements to the standard MBRL setup: (a) \"Dyna with warmup\", which trains the policy on real and imaginary data, (b) \"nearest neighbor tokenizer\" on image patches, which improves the scheme to create the transformer world model (TWM) inputs, and (c) \"block teacher forcing\", which allows the TWM to reason jointly about the future tokens of the next timestep.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/luchris429/purejaxrl/blob/main/purejaxrl/ppo.py", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01534", "title": "Preference Leakage: A Contamination Problem in LLM-as-a-judge", "authors": [ "Dawei Li", "Renliang Sun", "Yue Huang", "Ming Zhong", "Bohan Jiang", "Jiawei Han", "Xiangliang Zhang", "Wei Wang", "Huan Liu" ], "github": "https://github.com/David-Li0406/Preference-Leakage", "project_page": "", "abstract": "Large Language Models (LLMs) as judges and LLM-based data synthesis have emerged as two fundamental LLM-driven data annotation methods in model development. While their combination significantly enhances the efficiency of model training and evaluation, little attention has been given to the potential contamination brought by this new model development paradigm. In this work, we expose preference leakage, a contamination problem in LLM-as-a-judge caused by the relatedness between the synthetic data generators and LLM-based evaluators. To study this issue, we first define three common relatednesses between data generator LLM and judge LLM: being the same model, having an inheritance relationship, and belonging to the same model family. Through extensive experiments, we empirically confirm the bias of judges towards their related student models caused by preference leakage across multiple LLM baselines and benchmarks. Further analysis suggests that preference leakage is a pervasive issue that is harder to detect compared to previously identified biases in LLM-as-a-judge scenarios. All of these findings imply that preference leakage is a widespread and challenging problem in the area of LLM-as-a-judge. We release all codes and data at: https://github.com/David-Li0406/Preference-Leakage.", "github_manual": "", "github_regex": "https://github.com/David-Li0406/Preference-Leakage", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01636", "title": "Lifelong Sequential Knowledge Editing without Model Degradation", "authors": [ "Akshat Gupta", "Phudish Prateepamornkul", "Maochuan Lu", "Ahmed Alaa", "Thomas Hartvigsen", "Gopala Anumanchipalli" ], "github": "https://github.com/scalable-model-editing/knowledge-editing-regularization", "project_page": "", "abstract": "Prior work in parameter-modifying knowledge editing has shown that large-scale sequential editing leads to significant model degradation. In this paper, we study the reasons behind this and scale sequential knowledge editing to 10,000 sequential edits, while maintaining the downstream performance of the original model. We first show that locate-then-edit knowledge editing methods lead to overfitting on the edited facts. We also show that continuous knowledge editing using these methods leads to disproportionate growth in the norm of the edited matrix. We then provide a crucial insight into the inner workings of locate-then-edit methods. We show that norm-growth is a hidden trick employed by these methods that gives larger importance to the output activations produced from the edited layers. With this \"importance hacking\", the edited layers provide a much larger contributions to the model's output. To mitigate these issues, we present ENCORE - Early stopping and Norm-Constrained Robust knowledge Editing. ENCORE controls for overfitting and the disproportionate norm-growth to enable long-term sequential editing, where we are able to perform up to 10,000 sequential edits without loss of downstream performance. ENCORE is also 61% faster than MEMIT and 64% faster than AlphaEdit on Llama3-8B.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/scalable-model-editing/knowledge-editing-regularization", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01237", "title": "The Differences Between Direct Alignment Algorithms are a Blur", "authors": [ "Alexey Gorbatovski", "Boris Shaposhnikov", "Viacheslav Sinii", "Alexey Malakhov", "Daniil Gavrilov" ], "github": "", "project_page": "", "abstract": "Direct Alignment Algorithms (DAAs) simplify language model alignment by replacing reinforcement learning (RL) and reward modeling (RM) in Reinforcement Learning from Human Feedback (RLHF) with direct policy optimization. DAAs can be classified by their ranking losses (pairwise vs. pointwise), by the rewards used in those losses (e.g., likelihood ratios of policy and reference policy, or odds ratios), or by whether a Supervised Fine-Tuning (SFT) phase is required (two-stage vs. one-stage). We first show that one-stage methods underperform two-stage methods. To address this, we incorporate an explicit SFT phase and introduce the beta parameter, controlling the strength of preference optimization, into single-stage ORPO and ASFT. These modifications improve their performance in Alpaca Eval 2 by +3.46 (ORPO) and +8.27 (ASFT), matching two-stage methods like DPO. Further analysis reveals that the key factor is whether the approach uses pairwise or pointwise objectives, rather than the specific implicit reward or loss function. These results highlight the importance of careful evaluation to avoid premature claims of performance gains or overall superiority in alignment algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2501.18636", "title": "SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model", "authors": [ "Xun Liang", "Simin Niu", "Zhiyu Li", "Sensen Zhang", "Hanyu Wang", "Feiyu Xiong", "Jason Zhaoxin Fan", "Bo Tang", "Shichao Song", "Mengwei Wang", "Jiawei Yang" ], "github": "https://github.com/IAAR-Shanghai/SafeRAG", "project_page": "", "abstract": "The indexing-retrieval-generation paradigm of retrieval-augmented generation (RAG) has been highly successful in solving knowledge-intensive tasks by integrating external knowledge into large language models (LLMs). However, the incorporation of external and unverified knowledge increases the vulnerability of LLMs because attackers can perform attack tasks by manipulating knowledge. In this paper, we introduce a benchmark named SafeRAG designed to evaluate the RAG security. First, we classify attack tasks into silver noise, inter-context conflict, soft ad, and white Denial-of-Service. Next, we construct RAG security evaluation dataset (i.e., SafeRAG dataset) primarily manually for each task. We then utilize the SafeRAG dataset to simulate various attack scenarios that RAG may encounter. Experiments conducted on 14 representative RAG components demonstrate that RAG exhibits significant vulnerability to all attack tasks and even the most apparent attack task can easily bypass existing retrievers, filters, or advanced LLMs, resulting in the degradation of RAG service quality. Code is available at: https://github.com/IAAR-Shanghai/SafeRAG.", "github_manual": "", "github_regex": "https://github.com/IAAR-Shanghai/SafeRAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.00314", "title": "A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation", "authors": [ "Moein Heidari", "Ehsan Khodapanah Aghdam", "Alexander Manzella", "Daniel Hsu", "Rebecca Scalabrino", "Wenjin Chen", "David J. Foran", "Ilker Hacihaliloglu" ], "github": "https://github.com/moeinheidari7829/Retroperitoneal_Tumour_Segmentation_SPIE2025", "project_page": "", "abstract": "The retroperitoneum hosts a variety of tumors, including rare benign and malignant types, which pose diagnostic and treatment challenges due to their infrequency and proximity to vital structures. Estimating tumor volume is difficult due to their irregular shapes, and manual segmentation is time-consuming. Automatic segmentation using U-Net and its variants, incorporating Vision Transformer (ViT) elements, has shown promising results but struggles with high computational demands. To address this, architectures like the Mamba State Space Model (SSM) and Extended Long-Short Term Memory (xLSTM) offer efficient solutions by handling long-range dependencies with lower resource consumption. This study evaluates U-Net enhancements, including CNN, ViT, Mamba, and xLSTM, on a new in-house CT dataset and a public organ segmentation dataset. The proposed ViLU-Net model integrates Vi-blocks for improved segmentation. Results highlight xLSTM's efficiency in the U-Net framework. The code is publicly accessible on GitHub.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/moeinheidari7829/Retroperitoneal_Tumour_Segmentation_SPIE2025", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01142", "title": "DeepRAG: Thinking to Retrieval Step by Step for Large Language Models", "authors": [ "Xinyan Guan", "Jiali Zeng", "Fandong Meng", "Chunlei Xin", "Yaojie Lu", "Hongyu Lin", "Xianpei Han", "Le Sun", "Jie Zhou" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have shown remarkable potential in reasoning while they still suffer from severe factual hallucinations due to timeliness, accuracy, and coverage of parametric knowledge. Meanwhile, integrating reasoning with retrieval-augmented generation (RAG) remains challenging due to ineffective task decomposition and redundant retrieval, which can introduce noise and degrade response quality. In this paper, we propose DeepRAG, a framework that models retrieval-augmented reasoning as a Markov Decision Process (MDP), enabling strategic and adaptive retrieval. By iteratively decomposing queries, DeepRAG dynamically determines whether to retrieve external knowledge or rely on parametric reasoning at each step. Experiments show that DeepRAG improves retrieval efficiency while improving answer accuracy by 21.99%, demonstrating its effectiveness in optimizing retrieval-augmented reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01208", "title": "Almost Surely Safe Alignment of Large Language Models at Inference-Time", "authors": [ "Xiaotong Ji", "Shyam Sundhar Ramesh", "Matthieu Zimmer", "Ilija Bogunovic", "Jun Wang", "Haitham Bou Ammar" ], "github": "", "project_page": "", "abstract": "Even highly capable large language models (LLMs) can produce biased or unsafe responses, and alignment techniques, such as RLHF, aimed at mitigating this issue, are expensive and prone to overfitting as they retrain the LLM. This paper introduces a novel inference-time alignment approach that ensures LLMs generate safe responses almost surely, i.e., with a probability approaching one. We achieve this by framing the safe generation of inference-time responses as a constrained Markov decision process within the LLM's latent space. Crucially, we augment a safety state that tracks the evolution of safety constraints and enables us to demonstrate formal safety guarantees upon solving the MDP in the latent space. Building on this foundation, we propose InferenceGuard, a practical implementation that safely aligns LLMs without modifying the model weights. Empirically, we demonstrate InferenceGuard effectively balances safety and task performance, outperforming existing inference-time alignment methods in generating safe and aligned responses.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01584", "title": "PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models", "authors": [ "Carolyn Jane Anderson", "Joydeep Biswas", "Aleksander Boruch-Gruszecki", "Federico Cassano", "Molly Q Feldman", "Arjun Guha", "Francesca Lucchetti", "Zixuan Wu" ], "github": "", "project_page": "", "abstract": "Existing benchmarks for frontier models often test specialized, ``PhD-level'' knowledge that is difficult for non-experts to grasp. In contrast, we present a benchmark based on the NPR Sunday Puzzle Challenge that requires only general knowledge. Our benchmark is challenging for both humans and models, however correct solutions are easy to verify, and models' mistakes are easy to spot. Our work reveals capability gaps that are not evident in existing benchmarks: OpenAI o1 significantly outperforms other reasoning models that are on par on benchmarks that test specialized knowledge. Furthermore, our analysis of reasoning outputs uncovers new kinds of failures. DeepSeek R1, for instance, often concedes with ``I give up'' before providing an answer that it knows is wrong. R1 can also be remarkably ``uncertain'' in its output and in rare cases, it does not ``finish thinking,'' which suggests the need for an inference-time technique to ``wrap up'' before the context window limit is reached. We also quantify the effectiveness of reasoning longer with R1 and Gemini Thinking to identify the point beyond which more reasoning is unlikely to improve accuracy on our benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2501.18055", "title": "Current Pathology Foundation Models are unrobust to Medical Center Differences", "authors": [ "Edwin D. de Jong", "Eric Marcus", "Jonas Teuwen" ], "github": "", "project_page": "", "abstract": "Pathology Foundation Models (FMs) hold great promise for healthcare. Before they can be used in clinical practice, it is essential to ensure they are robust to variations between medical centers. We measure whether pathology FMs focus on biological features like tissue and cancer type, or on the well known confounding medical center signatures introduced by staining procedure and other differences. We introduce the Robustness Index. This novel robustness metric reflects to what degree biological features dominate confounding features. Ten current publicly available pathology FMs are evaluated. We find that all current pathology foundation models evaluated represent the medical center to a strong degree. Significant differences in the robustness index are observed. Only one model so far has a robustness index greater than one, meaning biological features dominate confounding features, but only slightly. A quantitative approach to measure the influence of medical center differences on FM-based prediction performance is described. We analyze the impact of unrobustness on classification performance of downstream models, and find that cancer-type classification errors are not random, but specifically attributable to same-center confounders: images of other classes from the same medical center. We visualize FM embedding spaces, and find these are more strongly organized by medical centers than by biological factors. As a consequence, the medical center of origin is predicted more accurately than the tissue source and cancer type. The robustness index introduced here is provided with the aim of advancing progress towards clinical adoption of robust and reliable pathology FMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01639", "title": "SliderSpace: Decomposing the Visual Capabilities of Diffusion Models", "authors": [ "Rohit Gandikota", "Zongze Wu", "Richard Zhang", "David Bau", "Eli Shechtman", "Nick Kolkin" ], "github": "", "project_page": "", "abstract": "We present SliderSpace, a framework for automatically decomposing the visual capabilities of diffusion models into controllable and human-understandable directions. Unlike existing control methods that require a user to specify attributes for each edit direction individually, SliderSpace discovers multiple interpretable and diverse directions simultaneously from a single text prompt. Each direction is trained as a low-rank adaptor, enabling compositional control and the discovery of surprising possibilities in the model's latent space. Through extensive experiments on state-of-the-art diffusion models, we demonstrate SliderSpace's effectiveness across three applications: concept decomposition, artistic style exploration, and diversity enhancement. Our quantitative evaluation shows that SliderSpace-discovered directions decompose the visual structure of model's knowledge effectively, offering insights into the latent capabilities encoded within diffusion models. User studies further validate that our method produces more diverse and useful variations compared to baselines. Our code, data and trained weights are available at https://sliderspace.baulab.info", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.00698", "title": "MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models", "authors": [ "Huanqia Cai", "Yijun Yang", "Winston Hu" ], "github": "https://github.com/AceCHQ/MMIQ", "project_page": "", "abstract": "IQ testing has served as a foundational methodology for evaluating human cognitive capabilities, deliberately decoupling assessment from linguistic background, language proficiency, or domain-specific knowledge to isolate core competencies in abstraction and reasoning. Yet, artificial intelligence research currently lacks systematic benchmarks to quantify these critical cognitive dimensions in multimodal systems. To address this critical gap, we propose MM-IQ, a comprehensive evaluation framework comprising 2,710 meticulously curated test items spanning 8 distinct reasoning paradigms. Through systematic evaluation of leading open-source and proprietary multimodal models, our benchmark reveals striking limitations: even state-of-the-art architectures achieve only marginally superior performance to random chance (27.49% vs. 25% baseline accuracy). This substantial performance chasm highlights the inadequacy of current multimodal systems in approximating fundamental human reasoning capacities, underscoring the need for paradigm-shifting advancements to bridge this cognitive divide.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AceCHQ/MMIQ", "github_api_added_by": "auto" }, { "date": "2025-02-04", "arxiv_id": "2502.01341", "title": "AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding", "authors": [ "Ahmed Masry", "Juan A. Rodriguez", "Tianyu Zhang", "Suyuchen Wang", "Chao Wang", "Aarash Feizi", "Akshay Kalkunte Suresh", "Abhay Puri", "Xiangru Jian", "Pierre-Andr\u00e9 No\u00ebl", "Sathwik Tejaswi Madhusudhan", "Marco Pedersoli", "Bang Liu", "Nicolas Chapados", "Yoshua Bengio", "Enamul Hoque", "Christopher Pal", "Issam H. Laradji", "David Vazquez", "Perouz Taslakian", "Spandana Gella", "Sai Rajeswar" ], "github": "", "project_page": "", "abstract": "Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges on having a good connector that maps visual features generated by a vision encoder to a shared embedding space with the LLM while preserving semantic similarity. Existing connectors, such as multilayer perceptrons (MLPs), often produce out-of-distribution or noisy inputs, leading to misalignment between the modalities. In this work, we propose a novel vision-text alignment method, AlignVLM, that maps visual features to a weighted average of LLM text embeddings. Our approach leverages the linguistic priors encoded by the LLM to ensure that visual features are mapped to regions of the space that the LLM can effectively interpret. AlignVLM is particularly effective for document understanding tasks, where scanned document images must be accurately mapped to their textual content. Our extensive experiments show that AlignVLM achieves state-of-the-art performance compared to prior alignment methods. We provide further analysis demonstrating improved vision-text feature alignment and robustness to noise.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01619", "title": "Learning to Generate Unit Tests for Automated Debugging", "authors": [ "Archiki Prasad", "Elias Stengel-Eskin", "Justin Chih-Yao Chen", "Zaid Khan", "Mohit Bansal" ], "github": "https://github.com/archiki/utgendebug", "project_page": "", "abstract": "Unit tests (UTs) play an instrumental role in assessing code correctness as well as providing feedback to a large language model (LLM) as it iteratively debugs faulty code, motivating automated test generation. However, we uncover a trade-off between generating unit test inputs that reveal errors when given a faulty code and correctly predicting the unit test output without access to the gold solution. To address this trade-off, we propose UTGen, which teaches LLMs to generate unit test inputs that reveal errors along with their correct expected outputs based on task descriptions and candidate code. We integrate UTGen into UTDebug, a robust debugging pipeline that uses generated tests to help LLMs debug effectively. Since model-generated tests can provide noisy signals (e.g., from incorrectly predicted outputs), UTDebug (i) scales UTGen via test-time compute to improve UT output prediction, and (ii) validates and back-tracks edits based on multiple generated UTs to avoid overfitting. We show that UTGen outperforms UT generation baselines by 7.59% based on a metric measuring the presence of both error-revealing UT inputs and correct UT outputs. When used with UTDebug, we find that feedback from UTGen's unit tests improves pass@1 accuracy of Qwen-2.5 7B on HumanEvalFix and our own harder debugging split of MBPP+ by over 3% and 12.35% (respectively) over other LLM-based UT generation baselines.", "github_manual": "https://github.com/archiki/utgendebug", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01126", "title": "Language Models Prefer What They Know: Relative Confidence Estimation via Confidence Preferences", "authors": [ "Vaishnavi Shrivastava", "Ananya Kumar", "Percy Liang" ], "github": "", "project_page": "", "abstract": "Language models (LMs) should provide reliable confidence estimates to help users detect mistakes in their outputs and defer to human experts when necessary. Asking a language model to assess its confidence (\"Score your confidence from 0-1.\") is a natural way of evaluating its uncertainty. However, models struggle to provide absolute assessments of confidence (i.e. judging confidence in answering a question independent of other questions) and the coarse-grained scores they produce are not useful for evaluating the correctness of their answers. We propose relative confidence estimation, where we match up questions against each other and ask the model to make relative judgments of confidence (\"Which question are you more confident in answering correctly?\"). Treating each question as a \"player\" in a series of matchups against other questions and the model's preferences as match outcomes, we can use rank aggregation methods like Elo rating and Bradley-Terry to translate the model's confidence preferences into confidence scores. We evaluate relative confidence estimation against absolute confidence estimation and self-consistency confidence methods on five state-of-the-art LMs -- GPT-4, GPT-4o, Gemini 1.5 Pro, Claude 3.5 Sonnet, and Llama 3.1 405B -- across 14 challenging STEM, social science, and commonsense reasoning question answering tasks. Our results demonstrate that relative confidence estimation consistently provides more reliable confidence scores than absolute confidence estimation, with average gains of 3.5% in selective classification AUC over direct absolute confidence estimation methods and 1.7% over self-consistency approaches across all models and datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.01572", "title": "MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation", "authors": [ "Yiren Song", "Cheng Liu", "Mike Zheng Shou" ], "github": "https://github.com/showlab/MakeAnything", "project_page": "", "abstract": "A hallmark of human intelligence is the ability to create complex artifacts through structured multi-step processes. Generating procedural tutorials with AI is a longstanding but challenging goal, facing three key obstacles: (1) scarcity of multi-task procedural datasets, (2) maintaining logical continuity and visual consistency between steps, and (3) generalizing across multiple domains. To address these challenges, we propose a multi-domain dataset covering 21 tasks with over 24,000 procedural sequences. Building upon this foundation, we introduce MakeAnything, a framework based on the diffusion transformer (DIT), which leverages fine-tuning to activate the in-context capabilities of DIT for generating consistent procedural sequences. We introduce asymmetric low-rank adaptation (LoRA) for image generation, which balances generalization capabilities and task-specific performance by freezing encoder parameters while adaptively tuning decoder layers. Additionally, our ReCraft model enables image-to-process generation through spatiotemporal consistency constraints, allowing static images to be decomposed into plausible creation sequences. Extensive experiments demonstrate that MakeAnything surpasses existing methods, setting new performance benchmarks for procedural generation tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/showlab/MakeAnything", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-04", "arxiv_id": "2502.00987", "title": "RandLoRA: Full-rank parameter-efficient fine-tuning of large models", "authors": [ "Paul Albert", "Frederic Z. Zhang", "Hemanth Saratchandran", "Cristian Rodriguez-Opazo", "Anton van den Hengel", "Ehsan Abbasnejad" ], "github": "https://github.com/PaulAlbert31/RandLoRA", "project_page": "", "abstract": "Low-Rank Adaptation (LoRA) and its variants have shown impressive results in reducing the number of trainable parameters and memory requirements of large transformer networks while maintaining fine-tuning performance. However, the low-rank nature of the weight update inherently limits the representation power of fine-tuned models, potentially compromising performance on complex tasks. This raises a critical question: when a performance gap between LoRA and standard fine-tuning is observed, is it due to the reduced number of trainable parameters or the rank deficiency? This paper aims to answer this question by introducing RandLoRA, a parameter-efficient method that performs full-rank updates using a learned linear combinations of low-rank, non-trainable random matrices. Our method limits the number of trainable parameters by restricting optimization to diagonal scaling matrices applied to the fixed random matrices. This allows us to effectively overcome the low-rank limitations while maintaining parameter and memory efficiency during training. Through extensive experimentation across vision, language, and vision-language benchmarks, we systematically evaluate the limitations of LoRA and existing random basis methods. Our findings reveal that full-rank updates are beneficial across vision and language tasks individually, and even more so for vision-language tasks, where RandLoRA significantly reduces -- and sometimes eliminates -- the performance gap between standard fine-tuning and LoRA, demonstrating its efficacy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PaulAlbert31/RandLoRA", "github_api_added_by": "auto" }, { "date": "2025-02-04", "arxiv_id": "2502.02095", "title": "LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information", "authors": [ "Bowen Ping", "Jiali Zeng", "Fandong Meng", "Shuo Wang", "Jie Zhou", "Shanghang Zhang" ], "github": "https://github.com/pingbowen23/longdpo", "project_page": "", "abstract": "Long-form generation is crucial for academic writing papers and repo-level code generation. Despite this, current models, including GPT-4o, still exhibit unsatisfactory performance. Existing methods that utilize preference learning with outcome supervision often fail to provide detailed feedback for extended contexts. This shortcoming can lead to content that does not fully satisfy query requirements, resulting in issues like length deviations, and diminished quality. In this paper, we propose enhancing long-form generation by incorporating process supervision. We employ Monte Carlo Tree Search to gather stepwise preference pairs, utilizing a global memory pool to maintain consistency. To address the issue of suboptimal candidate selection, we integrate external critiques to refine and improve the quality of the preference pairs. Finally, we apply step-level DPO using the collected stepwise preference pairs. Experimental results show that our method improves length and quality on long-form generation benchmarks, with almost lossless performance on general benchmarks across various model backbones.", "github_manual": "https://github.com/pingbowen23/longdpo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-05", "arxiv_id": "2502.02584", "title": "QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search", "authors": [ "Zongyu Lin", "Yao Tang", "Xingcheng Yao", "Da Yin", "Ziniu Hu", "Yizhou Sun", "Kai-Wei Chang" ], "github": "https://github.com/rafa-zy/qlass", "project_page": "", "abstract": "Language agents have become a promising solution to complex interactive tasks. One of the key ingredients to the success of language agents is the reward model on the trajectory of the agentic workflow, which provides valuable guidance during training or inference. However, due to the lack of annotations of intermediate interactions, most existing works use an outcome reward model to optimize policies across entire trajectories. This may lead to sub-optimal policies and hinder the overall performance. To address this, we propose QLASS (Q-guided Language Agent Stepwise Search), to automatically generate annotations by estimating Q-values in a stepwise manner for open language agents. By introducing a reasoning tree and performing process reward modeling, QLASS provides effective intermediate guidance for each step. With the stepwise guidance, we propose a Q-guided generation strategy to enable language agents to better adapt to long-term value, resulting in significant performance improvement during model inference on complex interactive agent tasks. Notably, even with almost half the annotated data, QLASS retains strong performance, demonstrating its efficiency in handling limited supervision. We also empirically demonstrate that QLASS can lead to more effective decision making through qualitative analysis. We will release our code and data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rafa-zy/qlass", "github_api_added_by": "auto" }, { "date": "2025-02-05", "arxiv_id": "2502.01718", "title": "ACECODER: Acing Coder RL via Automated Test-Case Synthesis", "authors": [ "Huaye Zeng", "Dongfu Jiang", "Haozhe Wang", "Ping Nie", "Xiaotong Chen", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Most progress in recent coder models has been driven by supervised fine-tuning (SFT), while the potential of reinforcement learning (RL) remains largely unexplored, primarily due to the lack of reliable reward data/model in the code domain. In this paper, we address this challenge by leveraging automated large-scale test-case synthesis to enhance code model training. Specifically, we design a pipeline that generates extensive (question, test-cases) pairs from existing code data. Using these test cases, we construct preference pairs based on pass rates over sampled programs to train reward models with Bradley-Terry loss. It shows an average of 10-point improvement for Llama-3.1-8B-Ins and 5-point improvement for Qwen2.5-Coder-7B-Ins through best-of-32 sampling, making the 7B model on par with 236B DeepSeek-V2.5. Furthermore, we conduct reinforcement learning with both reward models and test-case pass rewards, leading to consistent improvements across HumanEval, MBPP, BigCodeBench, and LiveCodeBench (V4). Notably, we follow the R1-style training to start from Qwen2.5-Coder-base directly and show that our RL training can improve model on HumanEval-plus by over 25\\% and MBPP-plus by 6\\% for merely 80 optimization steps. We believe our results highlight the huge potential of reinforcement learning in coder models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-05", "arxiv_id": "2502.02508", "title": "Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search", "authors": [ "Maohao Shen", "Guangtao Zeng", "Zhenting Qi", "Zhang-Wei Hong", "Zhenfang Chen", "Wei Lu", "Gregory Wornell", "Subhro Das", "David Cox", "Chuang Gan" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains. Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities. This typically involves extensive sampling at inference time guided by an external LLM verifier, resulting in a two-player system. Despite external guidance, the effectiveness of this system demonstrates the potential of a single LLM to tackle complex tasks. Thus, we pose a new research problem: Can we internalize the searching capabilities to fundamentally enhance the reasoning abilities of a single LLM? This work explores an orthogonal direction focusing on post-training LLMs for autoregressive searching (i.e., an extended reasoning process with self-reflection and self-exploration of new strategies). To achieve this, we propose the Chain-of-Action-Thought (COAT) reasoning and a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning. Our approach results in Satori, a 7B LLM trained on open-source models and data. Extensive empirical evaluations demonstrate that Satori achieves state-of-the-art performance on mathematical reasoning benchmarks while exhibits strong generalization to out-of-domain tasks. Code, data, and models will be fully open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-05", "arxiv_id": "2502.01941", "title": "Can LLMs Maintain Fundamental Abilities under KV Cache Compression?", "authors": [ "Xiang Liu", "Zhenheng Tang", "Hong Chen", "Peijie Dong", "Zeyu Li", "Xiuze Zhou", "Bo Li", "Xuming Hu", "Xiaowen Chu" ], "github": "", "project_page": "", "abstract": "This paper investigates an under-explored challenge in large language models (LLMs): the impact of KV cache compression methods on LLMs' fundamental capabilities. While existing methods achieve impressive compression ratios on long-context benchmarks, their effects on core model capabilities remain understudied. We present a comprehensive empirical study evaluating prominent KV cache compression methods across diverse tasks, spanning world knowledge, commonsense reasoning, arithmetic reasoning, code generation, safety, and long-context understanding and generation.Our analysis reveals that KV cache compression methods exhibit task-specific performance degradation. Arithmetic reasoning tasks prove particularly sensitive to aggressive compression, with different methods showing performance drops of 17.4%-43.3%. Notably, the DeepSeek R1 Distill model exhibits more robust compression tolerance compared to instruction-tuned models, showing only 9.67%-25.53% performance degradation. Based on our analysis of attention patterns and cross-task compression performance, we propose ShotKV, a novel compression approach that distinctly handles prefill and decoding phases while maintaining shot-level semantic coherence. Empirical results show that ShotKV achieves 9%-18% performance improvements on long-context generation tasks under aggressive compression ratios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-05", "arxiv_id": "2502.02492", "title": "VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models", "authors": [ "Hila Chefer", "Uriel Singer", "Amit Zohar", "Yuval Kirstain", "Adam Polyak", "Yaniv Taigman", "Lior Wolf", "Shelly Sheynin" ], "github": "", "project_page": "", "abstract": "Despite tremendous recent progress, generative video models still struggle to capture real-world motion, dynamics, and physics. We show that this limitation arises from the conventional pixel reconstruction objective, which biases models toward appearance fidelity at the expense of motion coherence. To address this, we introduce VideoJAM, a novel framework that instills an effective motion prior to video generators, by encouraging the model to learn a joint appearance-motion representation. VideoJAM is composed of two complementary units. During training, we extend the objective to predict both the generated pixels and their corresponding motion from a single learned representation. During inference, we introduce Inner-Guidance, a mechanism that steers the generation toward coherent motion by leveraging the model's own evolving motion prediction as a dynamic guidance signal. Notably, our framework can be applied to any video model with minimal adaptations, requiring no modifications to the training data or scaling of the model. VideoJAM achieves state-of-the-art performance in motion coherence, surpassing highly competitive proprietary models while also enhancing the perceived visual quality of the generations. These findings emphasize that appearance and motion can be complementary and, when effectively integrated, enhance both the visual quality and the coherence of video generation. Project website: https://hila-chefer.github.io/videojam-paper.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-05", "arxiv_id": "2502.01720", "title": "Generating Multi-Image Synthetic Data for Text-to-Image Customization", "authors": [ "Nupur Kumari", "Xi Yin", "Jun-Yan Zhu", "Ishan Misra", "Samaneh Azadi" ], "github": "", "project_page": "", "abstract": "Customization of text-to-image models enables users to insert custom concepts and generate the concepts in unseen settings. Existing methods either rely on costly test-time optimization or train encoders on single-image training datasets without multi-image supervision, leading to worse image quality. We propose a simple approach that addresses both limitations. We first leverage existing text-to-image models and 3D datasets to create a high-quality Synthetic Customization Dataset (SynCD) consisting of multiple images of the same object in different lighting, backgrounds, and poses. We then propose a new encoder architecture based on shared attention mechanisms that better incorporate fine-grained visual details from input images. Finally, we propose a new inference technique that mitigates overexposure issues during inference by normalizing the text and image guidance vectors. Through extensive experiments, we show that our model, trained on the synthetic dataset with the proposed encoder and inference algorithm, outperforms existing tuning-free methods on standard customization benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-05", "arxiv_id": "2502.01362", "title": "Inverse Bridge Matching Distillation", "authors": [ "Nikita Gushchin", "David Li", "Daniil Selikhanovych", "Evgeny Burnaev", "Dmitry Baranchuk", "Alexander Korotin" ], "github": "https://github.com/ngushchin/IBMD", "project_page": "", "abstract": "Learning diffusion bridge models is easy; making them fast and practical is an art. Diffusion bridge models (DBMs) are a promising extension of diffusion models for applications in image-to-image translation. However, like many modern diffusion and flow models, DBMs suffer from the problem of slow inference. To address it, we propose a novel distillation technique based on the inverse bridge matching formulation and derive the tractable objective to solve it in practice. Unlike previously developed DBM distillation techniques, the proposed method can distill both conditional and unconditional types of DBMs, distill models in a one-step generator, and use only the corrupted images for training. We evaluate our approach for both conditional and unconditional types of bridge matching on a wide set of setups, including super-resolution, JPEG restoration, sketch-to-image, and other tasks, and show that our distillation technique allows us to accelerate the inference of DBMs from 4x to 100x and even provide better generation quality than used teacher model depending on particular setup.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ngushchin/IBMD", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-05", "arxiv_id": "2501.19066", "title": "Concept Steerers: Leveraging K-Sparse Autoencoders for Controllable Generations", "authors": [ "Dahye Kim", "Deepti Ghadiyaram" ], "github": "https://github.com/kim-dahye/steerers", "project_page": "", "abstract": "Despite the remarkable progress in text-to-image generative models, they are prone to adversarial attacks and inadvertently generate unsafe, unethical content. Existing approaches often rely on fine-tuning models to remove specific concepts, which is computationally expensive, lack scalability, and/or compromise generation quality. In this work, we propose a novel framework leveraging k-sparse autoencoders (k-SAEs) to enable efficient and interpretable concept manipulation in diffusion models. Specifically, we first identify interpretable monosemantic concepts in the latent space of text embeddings and leverage them to precisely steer the generation away or towards a given concept (e.g., nudity) or to introduce a new concept (e.g., photographic style). Through extensive experiments, we demonstrate that our approach is very simple, requires no retraining of the base model nor LoRA adapters, does not compromise the generation quality, and is robust to adversarial prompt manipulations. Our method yields an improvement of 20.01% in unsafe concept removal, is effective in style manipulation, and is sim5x faster than current state-of-the-art.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kim-dahye/steerers", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-05", "arxiv_id": "2502.00674", "title": "Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?", "authors": [ "Wenzhe Li", "Yong Lin", "Mengzhou Xia", "Chi Jin" ], "github": "", "project_page": "", "abstract": "Ensembling outputs from diverse sources is a straightforward yet effective approach to boost performance. Mixture-of-Agents (MoA) is one such popular ensemble method that aggregates outputs from multiple different Large Language Models (LLMs). This paper raises the question in the context of language models: is mixing different LLMs truly beneficial? We propose Self-MoA -- an ensemble method that aggregates outputs from only the single top-performing LLM. Our extensive experiments reveal that, surprisingly, Self-MoA outperforms standard MoA that mixes different LLMs in a large number of scenarios: Self-MoA achieves 6.6% improvement over MoA on the AlpacaEval 2.0 benchmark, and an average of 3.8% improvement across various benchmarks, including MMLU, CRUX, and MATH. Applying Self-MoA to one of the top-ranking models in AlpacaEval 2.0 directly achieves the new state-of-the-art performance on the leaderboard. To understand the effectiveness of Self-MoA, we systematically investigate the trade-off between diversity and quality of outputs under various MoA settings. We confirm that the MoA performance is rather sensitive to the quality, and mixing different LLMs often lowers the average quality of the models. To complement the study, we identify the scenarios where mixing different LLMs could be helpful. This paper further introduces a sequential version of Self-MoA, that is capable of aggregating a large number of LLM outputs on-the-fly over multiple rounds, and is as effective as aggregating all outputs at once.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-05", "arxiv_id": "2502.01839", "title": "Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification", "authors": [ "Eric Zhao", "Pranjal Awasthi", "Sreenivas Gollapudi" ], "github": "", "project_page": "", "abstract": "Sampling-based search, a simple paradigm for utilizing test-time compute, involves generating multiple candidate responses and selecting the best one -- typically by verifying each response for correctness. In this paper, we study the scaling trends governing sampling-based search. Among our findings is that simply scaling up a minimalist implementation that uses only random sampling and direct self-verification results in sustained performance improvements that, for example, elevate the Gemini v1.5 Pro model's reasoning capabilities past that of o1-Preview on popular benchmarks. We partially attribute the scalability of sampling-based search to a phenomenon of implicit scaling, where sampling a larger pool of responses in turn improves verification accuracy. We further identify two useful principles for improving self-verification capabilities with test-time compute: (1) comparing across responses provides helpful signals about the locations of errors and hallucinations, and (2) different model output styles are useful for different contexts -- chains of thought are useful for reasoning but harder to verify. We also find that, though accurate verification can be elicited, frontier models demonstrate remarkably weak out-of-box verification capabilities and introduce a benchmark to measure progress on these deficiencies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-05", "arxiv_id": "2502.02589", "title": "COCONut-PanCap: Joint Panoptic Segmentation and Grounded Captions for Fine-Grained Understanding and Generation", "authors": [ "Xueqing Deng", "Qihang Yu", "Ali Athar", "Chenglin Yang", "Linjie Yang", "Xiaojie Jin", "Xiaohui Shen", "Liang-Chieh Chen" ], "github": "https://github.com/bytedance/coconut_cvpr2024", "project_page": "", "abstract": "This paper introduces the COCONut-PanCap dataset, created to enhance panoptic segmentation and grounded image captioning. Building upon the COCO dataset with advanced COCONut panoptic masks, this dataset aims to overcome limitations in existing image-text datasets that often lack detailed, scene-comprehensive descriptions. The COCONut-PanCap dataset incorporates fine-grained, region-level captions grounded in panoptic segmentation masks, ensuring consistency and improving the detail of generated captions. Through human-edited, densely annotated descriptions, COCONut-PanCap supports improved training of vision-language models (VLMs) for image understanding and generative models for text-to-image tasks. Experimental results demonstrate that COCONut-PanCap significantly boosts performance across understanding and generation tasks, offering complementary benefits to large-scale datasets. This dataset sets a new benchmark for evaluating models on joint panoptic segmentation and grounded captioning tasks, addressing the need for high-quality, detailed image-text annotations in multi-modal learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bytedance/coconut_cvpr2024", "github_api_added_by": "auto" }, { "date": "2025-02-05", "arxiv_id": "2501.19389", "title": "Federated Sketching LoRA: On-Device Collaborative Fine-Tuning of Large Language Models", "authors": [ "Wenzhi Fang", "Dong-Jun Han", "Liangqi Yuan", "Seyyedali Hosseinalipour", "Christopher G. Brinton" ], "github": "https://github.com/wenzhifang/Federated-Sketching-LoRA-Implementation", "project_page": "", "abstract": "Fine-tuning large language models (LLMs) on devices is attracting increasing interest. Recent works have fused low-rank adaptation (LoRA) techniques with federated fine-tuning to mitigate challenges associated with device model sizes and data scarcity. Still, the heterogeneity of computational resources remains a critical bottleneck: while higher-rank modules generally enhance performance, varying device capabilities constrain LoRA's feasible rank range. Existing approaches attempting to resolve this issue either lack analytical justification or impose additional computational overhead, leaving a wide gap for an efficient and theoretically-grounded solution. To address these challenges, we propose federated sketching LoRA (FSLoRA), which leverages a sketching mechanism to enable devices to selectively update submatrices of global LoRA modules maintained by the server. By adjusting the sketching ratios, which determine the ranks of the submatrices on the devices, FSLoRA flexibly adapts to device-specific communication and computational constraints. We provide a rigorous convergence analysis of FSLoRA that characterizes how the sketching ratios affect the convergence rate. Through comprehensive experiments on multiple datasets and LLM models, we demonstrate FSLoRA's superior performance compared to various baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/wenzhifang/Federated-Sketching-LoRA-Implementation", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-05", "arxiv_id": "2502.00840", "title": "Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense", "authors": [ "Jiawen Zhang", "Kejia Chen", "Lipeng He", "Jian Lou", "Dan Li", "Zunlei Feng", "Mingli Song", "Jian Liu", "Kui Ren", "Xiaohu Yang" ], "github": "https://github.com/Kevin-Zh-CS/QuadA", "project_page": "", "abstract": "Large Language Models (LLMs) have showcased remarkable capabilities across various domains. Accompanying the evolving capabilities and expanding deployment scenarios of LLMs, their deployment challenges escalate due to their sheer scale and the advanced yet complex activation designs prevalent in notable model series, such as Llama, Gemma, and Mistral. These challenges have become particularly pronounced in resource-constrained deployment scenarios, where mitigating inference efficiency bottlenecks is imperative. Among various recent efforts, activation approximation has emerged as a promising avenue for pursuing inference efficiency, sometimes considered indispensable in applications such as private inference. Despite achieving substantial speedups with minimal impact on utility, even appearing sound and practical for real-world deployment, the safety implications of activation approximations remain unclear. In this work, we fill this critical gap in LLM safety by conducting the first systematic safety evaluation of activation approximations. Our safety vetting spans seven sota techniques across three popular categories, revealing consistent safety degradation across ten safety-aligned LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Kevin-Zh-CS/QuadA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-05", "arxiv_id": "2501.19054", "title": "Text-to-CAD Generation Through Infusing Visual Feedback in Large Language Models", "authors": [ "Ruiyu Wang", "Yu Yuan", "Shizhao Sun", "Jiang Bian" ], "github": "https://github.com/microsoft/CADFusion", "project_page": "", "abstract": "Creating Computer-Aided Design (CAD) models requires significant expertise and effort. Text-to-CAD, which converts textual descriptions into CAD parametric sequences, is crucial in streamlining this process. Recent studies have utilized ground-truth parametric sequences, known as sequential signals, as supervision to achieve this goal. However, CAD models are inherently multimodal, comprising parametric sequences and corresponding rendered visual objects. Besides,the rendering process from parametric sequences to visual objects is many-to-one. Therefore, both sequential and visual signals are critical for effective training. In this work, we introduce CADFusion, a framework that uses Large Language Models (LLMs) as the backbone and alternates between two training stages: the sequential learning (SL) stage and the visual feedback (VF) stage. In the SL stage, we train LLMs using ground-truth parametric sequences, enabling the generation of logically coherent parametric sequences. In the VF stage, we reward parametric sequences that render into visually preferred objects and penalize those that do not, allowing LLMs to learn how rendered visual objects are perceived and evaluated. These two stages alternate throughout the training, ensuring balanced learning and preserving benefits of both signals. Experiments demonstrate that CADFusion significantly improves performance, both qualitatively and quantitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/CADFusion", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.01506", "title": "TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets", "authors": [ "Yuzhe Yang", "Yifei Zhang", "Minghao Wu", "Kaidi Zhang", "Yunmiao Zhang", "Honghai Yu", "Yan Hu", "Benyou Wang" ], "github": "", "project_page": "", "abstract": "The study of social emergence has long been a central focus in social science. Traditional modeling approaches, such as rule-based Agent-Based Models (ABMs), struggle to capture the diversity and complexity of human behavior, particularly the irrational factors emphasized in behavioral economics. Recently, large language model (LLM) agents have gained traction as simulation tools for modeling human behavior in social science and role-playing applications. Studies suggest that LLMs can account for cognitive biases, emotional fluctuations, and other non-rational influences, enabling more realistic simulations of socio-economic dynamics. In this work, we introduce TwinMarket, a novel multi-agent framework that leverages LLMs to simulate socio-economic systems. Specifically, we examine how individual behaviors, through interactions and feedback mechanisms, give rise to collective dynamics and emergent phenomena. Through experiments in a simulated stock market environment, we demonstrate how individual actions can trigger group behaviors, leading to emergent outcomes such as financial bubbles and recessions. Our approach provides valuable insights into the complex interplay between individual decision-making and collective socio-economic patterns.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.02339", "title": "Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking", "authors": [ "Jinyang Wu", "Mingkuan Feng", "Shuai Zhang", "Ruihan Jin", "Feihu Che", "Zengqi Wen", "Jianhua Tao" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) exhibit impressive capabilities but still face challenges in complex visual reasoning. While recent efforts attempt to enhance MLLMs' reasoning by incorporating OpenAI o1-like structured thinking through explicit search structures or teacher-guided distillation, they often struggle to balance performance and efficiency. A critical limitation is their heavy reliance on extensive data and search spaces, resulting in low-efficiency implicit insight extraction and data utilization. To address this, we propose AStar, an Automated Structured thinking paradigm for multimodal reasoning via Monte Carlo Tree Search (MCTS). AStar automatically derives high-level cognitive reasoning patterns from limited data using MCTS-powered hierarchical structures. Building on these explicit patterns, we design a unified reasoning framework that seamlessly integrates models' internal reasoning capabilities and external reasoning guidelines, enabling efficient inference with minimal tree iterations. This novel paradigm strikes a compelling balance between performance and efficiency. Extensive experiments demonstrate AStar's effectiveness, achieving superior accuracy (54.0%) on the MathVerse benchmark with a 7B backbone, surpassing GPT-4o (50.2%) while maintaining substantial data and computational efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.03373", "title": "Demystifying Long Chain-of-Thought Reasoning in LLMs", "authors": [ "Edward Yeo", "Yuxuan Tong", "Morry Niu", "Graham Neubig", "Xiang Yue" ], "github": "https://github.com/eddycmu/demystify-long-cot", "project_page": "", "abstract": "Scaling inference compute enhances reasoning in large language models (LLMs), with long chains-of-thought (CoTs) enabling strategies like backtracking and error correction. Reinforcement learning (RL) has emerged as a crucial method for developing these capabilities, yet the conditions under which long CoTs emerge remain unclear, and RL training requires careful design choices. In this study, we systematically investigate the mechanics of long CoT reasoning, identifying the key factors that enable models to generate long CoT trajectories. Through extensive supervised fine-tuning (SFT) and RL experiments, we present four main findings: (1) While SFT is not strictly necessary, it simplifies training and improves efficiency; (2) Reasoning capabilities tend to emerge with increased training compute, but their development is not guaranteed, making reward shaping crucial for stabilizing CoT length growth; (3) Scaling verifiable reward signals is critical for RL. We find that leveraging noisy, web-extracted solutions with filtering mechanisms shows strong potential, particularly for out-of-distribution (OOD) tasks such as STEM reasoning; and (4) Core abilities like error correction are inherently present in base models, but incentivizing these skills effectively for complex tasks via RL demands significant compute, and measuring their emergence requires a nuanced approach. These insights provide practical guidance for optimizing training strategies to enhance long CoT reasoning in LLMs. Our code is available at: https://github.com/eddycmu/demystify-long-cot.", "github_manual": "", "github_regex": "https://github.com/eddycmu/demystify-long-cot", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.01618", "title": "A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods", "authors": [ "Isha Puri", "Shivchander Sudalairaj", "Guangxuan Xu", "Kai Xu", "Akash Srivastava" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have achieved significant performance gains via scaling up model sizes and/or data. However, recent evidence suggests diminishing returns from such approaches, motivating scaling the computation spent at inference time. Existing inference-time scaling methods, usually with reward models, cast the task as a search problem, which tends to be vulnerable to reward hacking as a consequence of approximation errors in reward models. In this paper, we instead cast inference-time scaling as a probabilistic inference task and leverage sampling-based techniques to explore the typical set of the state distribution of a state-space model with an approximate likelihood, rather than optimize for its mode directly. We propose a novel inference-time scaling approach by adapting particle-based Monte Carlo methods to this task. Our empirical evaluation demonstrates that our methods have a 4-16x better scaling rate over our deterministic search counterparts on various challenging mathematical reasoning tasks. Using our approach, we show that Qwen2.5-Math-1.5B-Instruct can surpass GPT-4o accuracy in only 4 rollouts, while Qwen2.5-Math-7B-Instruct scales to o1 level accuracy in only 32 rollouts. Our work not only presents an effective method to inference-time scaling, but also connects the rich literature in probabilistic inference with inference-time scaling of LLMs to develop more robust algorithms in future work. Code and further information is available at https://probabilistic-inference-scaling.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.03387", "title": "LIMO: Less is More for Reasoning", "authors": [ "Yixin Ye", "Zhen Huang", "Yang Xiao", "Ethan Chern", "Shijie Xia", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/LIMO", "project_page": "", "abstract": "We present a fundamental discovery that challenges our understanding of how complex reasoning emerges in large language models. While conventional wisdom suggests that sophisticated reasoning tasks demand extensive training data (>100,000 examples), we demonstrate that complex mathematical reasoning abilities can be effectively elicited with surprisingly few examples. Through comprehensive experiments, our proposed model LIMO demonstrates unprecedented performance in mathematical reasoning. With merely 817 curated training samples, LIMO achieves 57.1% accuracy on AIME and 94.8% on MATH, improving from previous SFT-based models' 6.5% and 59.2% respectively, while only using 1% of the training data required by previous approaches. LIMO demonstrates exceptional out-of-distribution generalization, achieving 40.5% absolute improvement across 10 diverse benchmarks, outperforming models trained on 100x more data, challenging the notion that SFT leads to memorization rather than generalization. Based on these results, we propose the Less-Is-More Reasoning Hypothesis (LIMO Hypothesis): In foundation models where domain knowledge has been comprehensively encoded during pre-training, sophisticated reasoning capabilities can emerge through minimal but precisely orchestrated demonstrations of cognitive processes. This hypothesis posits that the elicitation threshold for complex reasoning is determined by two key factors: (1) the completeness of the model's encoded knowledge foundation during pre-training, and (2) the effectiveness of post-training examples as \"cognitive templates\" that show the model how to utilize its knowledge base to solve complex reasoning tasks. To facilitate reproducibility and future research in data-efficient reasoning, we release LIMO as a comprehensive open-source suite at https://github.com/GAIR-NLP/LIMO.", "github_manual": "", "github_regex": "https://github.com/GAIR-NLP/LIMO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.02737", "title": "SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model", "authors": [ "Loubna Ben Allal", "Anton Lozhkov", "Elie Bakouch", "Gabriel Mart\u00edn Bl\u00e1zquez", "Guilherme Penedo", "Lewis Tunstall", "Andr\u00e9s Marafioti", "Hynek Kydl\u00ed\u010dek", "Agust\u00edn Piqueres Lajar\u00edn", "Vaibhav Srivastav", "Joshua Lochner", "Caleb Fahlgren", "Xuan-Son Nguyen", "Cl\u00e9mentine Fourrier", "Ben Burtenshaw", "Hugo Larcher", "Haojun Zhao", "Cyril Zakka", "Mathieu Morlon", "Colin Raffel", "Leandro von Werra", "Thomas Wolf" ], "github": "https://github.com/bigcode-project/bigcode-evaluation-harness", "project_page": "", "abstract": "While large language models have facilitated breakthroughs in many applications of artificial intelligence, their inherent largeness makes them computationally expensive and challenging to deploy in resource-constrained settings. In this paper, we document the development of SmolLM2, a state-of-the-art \"small\" (1.7 billion parameter) language model (LM). To attain strong performance, we overtrain SmolLM2 on ~11 trillion tokens of data using a multi-stage training process that mixes web text with specialized math, code, and instruction-following data. We additionally introduce new specialized datasets (FineMath, Stack-Edu, and SmolTalk) at stages where we found existing datasets to be problematically small or low-quality. To inform our design decisions, we perform both small-scale ablations as well as a manual refinement process that updates the dataset mixing rates at each stage based on the performance at the previous stage. Ultimately, we demonstrate that SmolLM2 outperforms other recent small LMs including Qwen2.5-1.5B and Llama3.2-1B. To facilitate future research on LM development as well as applications of small LMs, we release both SmolLM2 as well as all of the datasets we prepared in the course of this project.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bigcode-project/bigcode-evaluation-harness", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.03275", "title": "Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning", "authors": [ "DiJia Su", "Hanlin Zhu", "Yingchen Xu", "Jiantao Jiao", "Yuandong Tian", "Qinqing Zheng" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) excel at reasoning and planning when trained on chainof-thought (CoT) data, where the step-by-step thought process is explicitly outlined by text tokens. However, this results in lengthy inputs where many words support textual coherence rather than core reasoning information, and processing these inputs consumes substantial computation resources. In this work, we propose a hybrid representation of the reasoning process, where we partially abstract away the initial reasoning steps using latent discrete tokens generated by VQ-VAE, significantly reducing the length of reasoning traces. We explore the use of latent trace abstractions in two scenarios: 1) training the model from scratch for the Keys-Finding Maze problem, 2) fine-tuning LLMs on this hybrid data with an extended vocabulary including unseen latent tokens, for both logical and mathematical reasoning problems. To facilitate effective learning, we introduce a simple training procedure that randomly mixes latent and text tokens, which enables fast adaptation to new latent tokens. Our approach consistently outperforms the baselines methods in various benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.01154", "title": "Jailbreaking with Universal Multi-Prompts", "authors": [ "Yu-Ling Hsu", "Hsuan Su", "Shang-Tse Chen" ], "github": "https://github.com/ntuaislab/jump", "project_page": "", "abstract": "Large language models (LLMs) have seen rapid development in recent years, revolutionizing various applications and significantly enhancing convenience and productivity. However, alongside their impressive capabilities, ethical concerns and new types of attacks, such as jailbreaking, have emerged. While most prompting techniques focus on optimizing adversarial inputs for individual cases, resulting in higher computational costs when dealing with large datasets. Less research has addressed the more general setting of training a universal attacker that can transfer to unseen tasks. In this paper, we introduce JUMP, a prompt-based method designed to jailbreak LLMs using universal multi-prompts. We also adapt our approach for defense, which we term DUMP. Experimental results demonstrate that our method for optimizing universal multi-prompts outperforms existing techniques.", "github_manual": "https://github.com/ntuaislab/jump", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.01105", "title": "LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer", "authors": [ "Yiren Song", "Danze Chen", "Mike Zheng Shou" ], "github": "https://github.com/showlab/LayerTracer", "project_page": "", "abstract": "Generating cognitive-aligned layered SVGs remains challenging due to existing methods' tendencies toward either oversimplified single-layer outputs or optimization-induced shape redundancies. We propose LayerTracer, a diffusion transformer based framework that bridges this gap by learning designers' layered SVG creation processes from a novel dataset of sequential design operations. Our approach operates in two phases: First, a text-conditioned DiT generates multi-phase rasterized construction blueprints that simulate human design workflows. Second, layer-wise vectorization with path deduplication produces clean, editable SVGs. For image vectorization, we introduce a conditional diffusion mechanism that encodes reference images into latent tokens, guiding hierarchical reconstruction while preserving structural integrity. Extensive experiments demonstrate LayerTracer's superior performance against optimization-based and neural baselines in both generation quality and editability, effectively aligning AI-generated vectors with professional design cognition.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/showlab/LayerTracer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.02671", "title": "On Teacher Hacking in Language Model Distillation", "authors": [ "Daniil Tiapkin", "Daniele Calandriello", "Johan Ferret", "Sarah Perrin", "Nino Vieillard", "Alexandre Ram\u00e9", "Mathieu Blondel" ], "github": "", "project_page": "", "abstract": "Post-training of language models (LMs) increasingly relies on the following two stages: (i) knowledge distillation, where the LM is trained to imitate a larger teacher LM, and (ii) reinforcement learning from human feedback (RLHF), where the LM is aligned by optimizing a reward model. In the second RLHF stage, a well-known challenge is reward hacking, where the LM over-optimizes the reward model. Such phenomenon is in line with Goodhart's law and can lead to degraded performance on the true objective. In this paper, we investigate whether a similar phenomenon, that we call teacher hacking, can occur during knowledge distillation. This could arise because the teacher LM is itself an imperfect approximation of the true distribution. To study this, we propose a controlled experimental setup involving: (i) an oracle LM representing the ground-truth distribution, (ii) a teacher LM distilled from the oracle, and (iii) a student LM distilled from the teacher. Our experiments reveal the following insights. When using a fixed offline dataset for distillation, teacher hacking occurs; moreover, we can detect it by observing when the optimization process deviates from polynomial convergence laws. In contrast, employing online data generation techniques effectively mitigates teacher hacking. More precisely, we identify data diversity as the key factor in preventing hacking. Overall, our findings provide a deeper understanding of the benefits and limitations of distillation for building robust and efficient LMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.02928", "title": "Large Language Model Guided Self-Debugging Code Generation", "authors": [ "Muntasir Adnan", "Zhiwei Xu", "Carlos C. N. Kuhn" ], "github": "", "project_page": "", "abstract": "Automated code generation is gaining significant importance in intelligent computer programming and system deployment. However, current approaches often face challenges in computational efficiency and lack robust mechanisms for code parsing and error correction. In this work, we propose a novel framework, PyCapsule, with a simple yet effective two-agent pipeline and efficient self-debugging modules for Python code generation. PyCapsule features sophisticated prompt inference, iterative error handling, and case testing, ensuring high generation stability, safety, and correctness. Empirically, PyCapsule achieves up to 5.7% improvement of success rate on HumanEval, 10.3% on HumanEval-ET, and 24.4% on BigCodeBench compared to the state-of-art methods. We also observe a decrease in normalized success rate given more self-debugging attempts, potentially affected by limited and noisy error feedback in retention. PyCapsule demonstrates broader impacts on advancing lightweight and efficient code generation for artificial intelligence systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.00306", "title": "Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation", "authors": [ "Ali Naseh", "Yuefeng Peng", "Anshuman Suri", "Harsh Chaudhari", "Alina Oprea", "Amir Houmansadr" ], "github": "https://github.com/ali7naseh/RAG_MIA", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to generate grounded responses by leveraging external knowledge databases without altering model parameters. Although the absence of weight tuning prevents leakage via model parameters, it introduces the risk of inference adversaries exploiting retrieved documents in the model's context. Existing methods for membership inference and data extraction often rely on jailbreaking or carefully crafted unnatural queries, which can be easily detected or thwarted with query rewriting techniques common in RAG systems. In this work, we present Interrogation Attack (IA), a membership inference technique targeting documents in the RAG datastore. By crafting natural-text queries that are answerable only with the target document's presence, our approach demonstrates successful inference with just 30 queries while remaining stealthy; straightforward detectors identify adversarial prompts from existing methods up to ~76x more frequently than those generated by our attack. We observe a 2x improvement in TPR@1%FPR over prior inference attacks across diverse RAG configurations, all while costing less than $0.02 per document inference.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ali7naseh/RAG_MIA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.00226", "title": "HackerRank-ASTRA: Evaluating Correctness & Consistency of Large Language Models on cross-domain multi-file project problems", "authors": [ "Jun Xing", "Mayur Bhatia", "Sahil Phulwani", "Darshan Suresh", "Rafik Matta" ], "github": "", "project_page": "", "abstract": "Evaluating the real-world applicability of large language models (LLMs) provides valuable insights for their development and use in software development tasks. Existing benchmarks often focus on standalone coding problems or specific libraries, overlooking multi-file, project-based scenarios and lacking a rigorous evaluation of consistency. The HackerRank-ASTRA Benchmark introduces project-based coding problems that mirror real-world scenarios. It evaluates model consistency through 32 runs (k = 32) and median standard deviation while incorporating taxonomy-level analysis to assess sub-skill capabilities. Initial evaluations on 65 problems show that the top three models -- o1, o1-preview, and Claude-3.5-Sonnet-1022 -- achieved comparable average scores of 75%, with no statistically significant differences in performance. Notably, Claude-3.5-Sonnet-1022 demonstrated the highest consistency across problems, with low variability (SD = 0.0497), which was statistically significant compared to other models, highlighting its reliability for real-world software development tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-06", "arxiv_id": "2502.02421", "title": "Activation-Informed Merging of Large Language Models", "authors": [ "Amin Heyrani Nobari", "Kaveh Alimohammadi", "Ali ArjomandBigdeli", "Akash Srivastava", "Faez Ahmed", "Navid Azizan" ], "github": "https://github.com/ahnobari/ActivationInformedMerging", "project_page": "", "abstract": "Model merging, a method that combines the parameters and embeddings of multiple fine-tuned large language models (LLMs), offers a promising approach to enhance model performance across various tasks while maintaining computational efficiency. This paper introduces Activation-Informed Merging (AIM), a technique that integrates the information from the activation space of LLMs into the merging process to improve performance and robustness. AIM is designed as a flexible, complementary solution that is applicable to any existing merging method. It aims to preserve critical weights from the base model, drawing on principles from continual learning~(CL) and model compression. Utilizing a task-agnostic calibration set, AIM selectively prioritizes essential weights during merging. We empirically demonstrate that AIM significantly enhances the performance of merged models across multiple benchmarks. Our findings suggest that considering the activation-space information can provide substantial advancements in the model merging strategies for LLMs with up to 40\\% increase in benchmark performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ahnobari/ActivationInformedMerging", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.04306", "title": "ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization", "authors": [ "Yinjie Wang", "Ling Yang", "Guohao Li", "Mengdi Wang", "Bryon Aragam" ], "github": "https://github.com/Gen-Verse/ScoreFlow", "project_page": "", "abstract": "Recent research has leveraged large language model multi-agent systems for complex problem-solving while trying to reduce the manual effort required to build them, driving the development of automated agent workflow optimization methods. However, existing methods remain inflexible due to representational limitations, a lack of adaptability, and poor scalability when relying on discrete optimization techniques. We address these challenges with ScoreFlow, a simple yet high-performance framework that leverages efficient gradient-based optimization in a continuous space. ScoreFlow incorporates Score-DPO, a novel variant of the direct preference optimization method that accounts for quantitative feedback. Across six benchmarks spanning question answering, coding, and mathematical reasoning, ScoreFlow achieves an 8.2% improvement over existing baselines. Moreover, it empowers smaller models to outperform larger ones with lower inference costs. Project: https://github.com/Gen-Verse/ScoreFlow", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/ScoreFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.04153", "title": "UltraIF: Advancing Instruction Following from the Wild", "authors": [ "Kaikai An", "Li Sheng", "Ganqu Cui", "Shuzheng Si", "Ning Ding", "Yu Cheng", "Baobao Chang" ], "github": "https://github.com/kkk-an/UltraIF", "project_page": "", "abstract": "Instruction-following made modern large language models (LLMs) helpful assistants. However, the key to taming LLMs on complex instructions remains mysterious, for that there are huge gaps between models trained by open-source community and those trained by leading companies. To bridge the gap, we propose a simple and scalable approach UltraIF for building LLMs that can follow complex instructions with open-source data. UltraIF first decomposes real-world user prompts into simpler queries, constraints, and corresponding evaluation questions for the constraints. Then, we train an UltraComposer to compose constraint-associated prompts with evaluation questions. This prompt composer allows us to synthesize complicated instructions as well as filter responses with evaluation questions. In our experiment, for the first time, we successfully align LLaMA-3.1-8B-Base to catch up with its instruct version on 5 instruction-following benchmarks without any benchmark information, using only 8B model as response generator and evaluator. The aligned model also achieved competitive scores on other benchmarks. Moreover, we also show that UltraIF could further improve LLaMA-3.1-8B-Instruct through self-alignment, motivating broader use cases for the method. Our code will be available at https://github.com/kkk-an/UltraIF.", "github_manual": "", "github_regex": "https://github.com/kkk-an/UltraIF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.04295", "title": "Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization", "authors": [ "Yuanye Liu", "Jiahang Xu", "Li Lyna Zhang", "Qi Chen", "Xuan Feng", "Yang Chen", "Zhongxin Guo", "Yuqing Yang", "Cheng Peng" ], "github": "https://github.com/HenryLau7/CFPO", "project_page": "", "abstract": "Large Language Models (LLMs) have shown significant capability across various tasks, with their real-world effectiveness often driven by prompt design. While recent research has focused on optimizing prompt content, the role of prompt formatting, a critical but often overlooked dimension, has received limited systematic investigation. In this paper, we introduce Content-Format Integrated Prompt Optimization (CFPO), an innovative methodology that jointly optimizes both prompt content and formatting through an iterative refinement process. CFPO leverages natural language mutations to explore content variations and employs a dynamic format exploration strategy that systematically evaluates diverse format options. Our extensive evaluations across multiple tasks and open-source LLMs demonstrate that CFPO demonstrates measurable performance improvements compared to content-only optimization methods. This highlights the importance of integrated content-format optimization and offers a practical, model-agnostic approach to enhancing LLM performance. Code will be available at https://github.com/HenryLau7/CFPO.", "github_manual": "", "github_regex": "https://github.com/HenryLau7/CFPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.04296", "title": "Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression", "authors": [ "Lirui Wang", "Kevin Zhao", "Chaoqi Liu", "Xinlei Chen" ], "github": "", "project_page": "", "abstract": "We propose Heterogeneous Masked Autoregression (HMA) for modeling action-video dynamics to generate high-quality data and evaluation in scaling robot learning. Building interactive video world models and policies for robotics is difficult due to the challenge of handling diverse settings while maintaining computational efficiency to run in real time. HMA uses heterogeneous pre-training from observations and action sequences across different robotic embodiments, domains, and tasks. HMA uses masked autoregression to generate quantized or soft tokens for video predictions. \\ourshort achieves better visual fidelity and controllability than the previous robotic video generation models with 15 times faster speed in the real world. After post-training, this model can be used as a video simulator from low-level action inputs for evaluating policies and generating synthetic data. See this link https://liruiw.github.io/hma for more information.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.03860", "title": "BOLT: Bootstrap Long Chain-of-Thought in Language Models without Distillation", "authors": [ "Bo Pang", "Hanze Dong", "Jiacheng Xu", "Silvio Savarese", "Yingbo Zhou", "Caiming Xiong" ], "github": "https://github.com/axolotl-ai-cloud/axolotl", "project_page": "", "abstract": "Large language models (LLMs), such as o1 from OpenAI, have demonstrated remarkable reasoning capabilities. o1 generates a long chain-of-thought (LongCoT) before answering a question. LongCoT allows LLMs to analyze problems, devise plans, reflect, and backtrack effectively. These actions empower LLM to solve complex problems. After the release of o1, many teams have attempted to replicate its LongCoT and reasoning capabilities. In terms of methods, they primarily rely on knowledge distillation with data from existing models with LongCoT capacities (e.g., OpenAI-o1, Qwen-QwQ, DeepSeek-R1-Preview), leaving significant uncertainties on systematically developing such reasoning abilities. In terms of data domains, these works focus narrowly on math while a few others include coding, limiting their generalizability. This paper introduces a novel approach to enable LLM's LongCoT capacity without distillation from o1-like models or expensive human annotations, where we bootstrap LongCoT (BOLT) from a standard instruct model. BOLT involves three stages: 1) LongCoT data bootstrapping with in-context learning on a standard instruct model; 2) LongCoT supervised finetuning; 3) online training to further refine LongCoT capacities. In BOLT, only a few in-context examples need to be constructed during the bootstrapping stage; in our experiments, we created 10 examples, demonstrating the feasibility of this approach. We use Llama-3.1-70B-Instruct to bootstrap LongCoT and apply our method to various model scales (7B, 8B, 70B). We achieve impressive performance on a variety of benchmarks, Arena-Hard, MT-Bench, WildBench, ZebraLogic, MATH500, which evaluate diverse task-solving and reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/axolotl-ai-cloud/axolotl", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.02358", "title": "MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm", "authors": [ "Ziyan Guo", "Zeyu Hu", "Na Zhao", "De Wen Soh" ], "github": "", "project_page": "", "abstract": "Human motion generation and editing are key components of computer graphics and vision. However, current approaches in this field tend to offer isolated solutions tailored to specific tasks, which can be inefficient and impractical for real-world applications. While some efforts have aimed to unify motion-related tasks, these methods simply use different modalities as conditions to guide motion generation. Consequently, they lack editing capabilities, fine-grained control, and fail to facilitate knowledge sharing across tasks. To address these limitations and provide a versatile, unified framework capable of handling both human motion generation and editing, we introduce a novel paradigm: Motion-Condition-Motion, which enables the unified formulation of diverse tasks with three concepts: source motion, condition, and target motion. Based on this paradigm, we propose a unified framework, MotionLab, which incorporates rectified flows to learn the mapping from source motion to target motion, guided by the specified conditions. In MotionLab, we introduce the 1) MotionFlow Transformer to enhance conditional generation and editing without task-specific modules; 2) Aligned Rotational Position Encoding} to guarantee the time synchronization between source motion and target motion; 3) Task Specified Instruction Modulation; and 4) Motion Curriculum Learning for effective multi-task learning and knowledge sharing across tasks. Notably, our MotionLab demonstrates promising generalization capabilities and inference efficiency across multiple benchmarks for human motion. Our code and additional video results are available at: https://diouo.github.io/motionlab.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.03544", "title": "Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2", "authors": [ "Yuri Chervonyi", "Trieu H. Trinh", "Miroslav Ol\u0161\u00e1k", "Xiaomeng Yang", "Hoang Nguyen", "Marcelo Menegali", "Junehyuk Jung", "Vikas Verma", "Quoc V. Le", "Thang Luong" ], "github": "", "project_page": "", "abstract": "We present AlphaGeometry2, a significantly improved version of AlphaGeometry introduced in Trinh et al. (2024), which has now surpassed an average gold medalist in solving Olympiad geometry problems. To achieve this, we first extend the original AlphaGeometry language to tackle harder problems involving movements of objects, and problems containing linear equations of angles, ratios, and distances. This, together with other additions, has markedly improved the coverage rate of the AlphaGeometry language on International Math Olympiads (IMO) 2000-2024 geometry problems from 66% to 88%. The search process of AlphaGeometry2 has also been greatly improved through the use of Gemini architecture for better language modeling, and a novel knowledge-sharing mechanism that combines multiple search trees. Together with further enhancements to the symbolic engine and synthetic data generation, we have significantly boosted the overall solving rate of AlphaGeometry2 to 84% for all geometry problems over the last 25 years, compared to 54% previously. AlphaGeometry2 was also part of the system that achieved silver-medal standard at IMO 2024 https://dpmd.ai/imo-silver. Last but not least, we report progress towards using AlphaGeometry2 as a part of a fully automated system that reliably solves geometry problems directly from natural language input.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.04128", "title": "Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis", "authors": [ "Zhen Ye", "Xinfa Zhu", "Chi-Min Chan", "Xinsheng Wang", "Xu Tan", "Jiahe Lei", "Yi Peng", "Haohe Liu", "Yizhu Jin", "Zheqi DAI", "Hongzhan Lin", "Jianyi Chen", "Xingjian Du", "Liumeng Xue", "Yunlin Chen", "Zhifei Li", "Lei Xie", "Qiuqiang Kong", "Yike Guo", "Wei Xue" ], "github": "https://github.com/zhenye234/LLaSA_training", "project_page": "", "abstract": "Recent advances in text-based large language models (LLMs), particularly in the GPT series and the o1 model, have demonstrated the effectiveness of scaling both training-time and inference-time compute. However, current state-of-the-art TTS systems leveraging LLMs are often multi-stage, requiring separate models (e.g., diffusion models after LLM), complicating the decision of whether to scale a particular model during training or testing. This work makes the following contributions: First, we explore the scaling of train-time and inference-time compute for speech synthesis. Second, we propose a simple framework Llasa for speech synthesis that employs a single-layer vector quantizer (VQ) codec and a single Transformer architecture to fully align with standard LLMs such as Llama. Our experiments reveal that scaling train-time compute for Llasa consistently improves the naturalness of synthesized speech and enables the generation of more complex and accurate prosody patterns. Furthermore, from the perspective of scaling inference-time compute, we employ speech understanding models as verifiers during the search, finding that scaling inference-time compute shifts the sampling modes toward the preferences of specific verifiers, thereby improving emotional expressiveness, timbre consistency, and content accuracy. In addition, we released the checkpoint and training code for our TTS model (1B, 3B, 8B) and codec model publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhenye234/LLaSA_training", "github_api_added_by": "auto" }, { "date": "2025-02-07", "arxiv_id": "2502.04270", "title": "PILAF: Optimal Human Preference Sampling for Reward Modeling", "authors": [ "Yunzhen Feng", "Ariel Kwiatkowski", "Kunhao Zheng", "Julia Kempe", "Yaqi Duan" ], "github": "", "project_page": "", "abstract": "As large language models increasingly drive real-world applications, aligning them with human values becomes paramount. Reinforcement Learning from Human Feedback (RLHF) has emerged as a key technique, translating preference data into reward models when oracle human values remain inaccessible. In practice, RLHF mostly relies on approximate reward models, which may not consistently guide the policy toward maximizing the underlying human values. We propose Policy-Interpolated Learning for Aligned Feedback (PILAF), a novel response sampling strategy for preference labeling that explicitly aligns preference learning with maximizing the underlying oracle reward. PILAF is theoretically grounded, demonstrating optimality from both an optimization and a statistical perspective. The method is straightforward to implement and demonstrates strong performance in iterative and online RLHF settings where feedback curation is critical.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.03621", "title": "DynVFX: Augmenting Real Videos with Dynamic Content", "authors": [ "Danah Yatim", "Rafail Fridman", "Omer Bar-Tal", "Tali Dekel" ], "github": "", "project_page": "", "abstract": "We present a method for augmenting real-world videos with newly generated dynamic content. Given an input video and a simple user-provided text instruction describing the desired content, our method synthesizes dynamic objects or complex scene effects that naturally interact with the existing scene over time. The position, appearance, and motion of the new content are seamlessly integrated into the original footage while accounting for camera motion, occlusions, and interactions with other dynamic objects in the scene, resulting in a cohesive and realistic output video. We achieve this via a zero-shot, training-free framework that harnesses a pre-trained text-to-video diffusion transformer to synthesize the new content and a pre-trained Vision Language Model to envision the augmented scene in detail. Specifically, we introduce a novel inference-based method that manipulates features within the attention mechanism, enabling accurate localization and seamless integration of the new content while preserving the integrity of the original scene. Our method is fully automated, requiring only a simple user instruction. We demonstrate its effectiveness on a wide range of edits applied to real-world videos, encompassing diverse objects and scenarios involving both camera and object motion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.03639", "title": "Towards Physical Understanding in Video Generation: A 3D Point Regularization Approach", "authors": [ "Yunuo Chen", "Junli Cao", "Anil Kag", "Vidit Goel", "Sergei Korolev", "Chenfanfu Jiang", "Sergey Tulyakov", "Jian Ren" ], "github": "https://github.com/genmoai/models", "project_page": "", "abstract": "We present a novel video generation framework that integrates 3-dimensional geometry and dynamic awareness. To achieve this, we augment 2D videos with 3D point trajectories and align them in pixel space. The resulting 3D-aware video dataset, PointVid, is then used to fine-tune a latent diffusion model, enabling it to track 2D objects with 3D Cartesian coordinates. Building on this, we regularize the shape and motion of objects in the video to eliminate undesired artifacts, \\eg, nonphysical deformation. Consequently, we enhance the quality of generated RGB videos and alleviate common issues like object morphing, which are prevalent in current video models due to a lack of shape awareness. With our 3D augmentation and regularization, our model is capable of handling contact-rich scenarios such as task-oriented videos. These videos involve complex interactions of solids, where 3D information is essential for perceiving deformation and contact. Furthermore, our model improves the overall quality of video generation by promoting the 3D consistency of moving objects and reducing abrupt changes in shape and motion.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/genmoai/models", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.04299", "title": "MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video Generation", "authors": [ "Jinbo Xing", "Long Mai", "Cusuh Ham", "Jiahui Huang", "Aniruddha Mahapatra", "Chi-Wing Fu", "Tien-Tsin Wong", "Feng Liu" ], "github": "", "project_page": "", "abstract": "This paper presents a method that allows users to design cinematic video shots in the context of image-to-video generation. Shot design, a critical aspect of filmmaking, involves meticulously planning both camera movements and object motions in a scene. However, enabling intuitive shot design in modern image-to-video generation systems presents two main challenges: first, effectively capturing user intentions on the motion design, where both camera movements and scene-space object motions must be specified jointly; and second, representing motion information that can be effectively utilized by a video diffusion model to synthesize the image animations. To address these challenges, we introduce MotionCanvas, a method that integrates user-driven controls into image-to-video (I2V) generation models, allowing users to control both object and camera motions in a scene-aware manner. By connecting insights from classical computer graphics and contemporary video generation techniques, we demonstrate the ability to achieve 3D-aware motion control in I2V synthesis without requiring costly 3D-related training data. MotionCanvas enables users to intuitively depict scene-space motion intentions, and translates them into spatiotemporal motion-conditioning signals for video diffusion models. We demonstrate the effectiveness of our method on a wide range of real-world image content and shot-design scenarios, highlighting its potential to enhance the creative workflows in digital content creation and adapt to various image and video editing applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.03032", "title": "Analyze Feature Flow to Enhance Interpretation and Steering in Language Models", "authors": [ "Daniil Laptev", "Nikita Balagansky", "Yaroslav Aksenov", "Daniil Gavrilov" ], "github": "", "project_page": "", "abstract": "We introduce a new approach to systematically map features discovered by sparse autoencoder across consecutive layers of large language models, extending earlier work that examined inter-layer feature links. By using a data-free cosine similarity technique, we trace how specific features persist, transform, or first appear at each stage. This method yields granular flow graphs of feature evolution, enabling fine-grained interpretability and mechanistic insights into model computations. Crucially, we demonstrate how these cross-layer feature maps facilitate direct steering of model behavior by amplifying or suppressing chosen features, achieving targeted thematic control in text generation. Together, our findings highlight the utility of a causal, cross-layer interpretability framework that not only clarifies how features develop through forward passes but also provides new means for transparent manipulation of large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.04328", "title": "Ola: Pushing the Frontiers of Omni-Modal Language Model with Progressive Modality Alignment", "authors": [ "Zuyan Liu", "Yuhao Dong", "Jiahui Wang", "Ziwei Liu", "Winston Hu", "Jiwen Lu", "Yongming Rao" ], "github": "https://github.com/Ola-Omni/Ola", "project_page": "", "abstract": "Recent advances in large language models, particularly following GPT-4o, have sparked increasing interest in developing omni-modal models capable of understanding more modalities. While some open-source alternatives have emerged, there is still a notable lag behind specialized single-modality models in performance. In this paper, we present Ola, an Omni-modal language model that achieves competitive performance across image, video, and audio understanding compared to specialized counterparts. The core design of Ola lies in its progressive modality alignment strategy that extends the supporting modality of the language model progressively. Our training pipeline begins with the most distinct modalities: image and text, then gradually expands the skill sets of the model using speech data that connects language and audio knowledge, and video data that connects all modalities. The progressive learning pipeline also enables us to maintain a relatively small size of the cross-modal alignment data, making developing omni-modal from existing vision-language models easy and less costly. Moreover, to unlock an advanced interactive experience like GPT-4o, we further design a sentence-wise decoding solution for streaming speech generation. Extensive experiments demonstrate that Ola surpasses existing open omni-modal LLMs across all modalities while achieving highly competitive performance compared to state-of-the-art specialized models of similar sizes. We aim to make Ola a fully open omni-modal understanding solution to advance future research in this emerging field. Model weights, code, and data are open-sourced at https://github.com/Ola-Omni/Ola.", "github_manual": "", "github_regex": "https://github.com/Ola-Omni/Ola", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.04322", "title": "Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions", "authors": [ "Yik Siu Chan", "Narutatsu Ri", "Yuxin Xiao", "Marzyeh Ghassemi" ], "github": "https://github.com/yiksiu-chan/SpeakEasy", "project_page": "", "abstract": "Despite extensive safety alignment efforts, large language models (LLMs) remain vulnerable to jailbreak attacks that elicit harmful behavior. While existing studies predominantly focus on attack methods that require technical expertise, two critical questions remain underexplored: (1) Are jailbroken responses truly useful in enabling average users to carry out harmful actions? (2) Do safety vulnerabilities exist in more common, simple human-LLM interactions? In this paper, we demonstrate that LLM responses most effectively facilitate harmful actions when they are both actionable and informative--two attributes easily elicited in multi-step, multilingual interactions. Using this insight, we propose HarmScore, a jailbreak metric that measures how effectively an LLM response enables harmful actions, and Speak Easy, a simple multi-step, multilingual attack framework. Notably, by incorporating Speak Easy into direct request and jailbreak baselines, we see an average absolute increase of 0.319 in Attack Success Rate and 0.426 in HarmScore in both open-source and proprietary LLMs across four safety benchmarks. Our work reveals a critical yet often overlooked vulnerability: Malicious users can easily exploit common interaction patterns for harmful intentions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yiksiu-chan/SpeakEasy", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.04235", "title": "MAGA: MAssive Genre-Audience Reformulation to Pretraining Corpus Expansion", "authors": [ "Xintong Hao", "Ke Shen", "Chenggang Li" ], "github": "", "project_page": "", "abstract": "Despite the remarkable capabilities of large language models across various tasks, their continued scaling faces a critical challenge: the scarcity of high-quality pretraining data. While model architectures continue to evolve, the natural language data struggles to scale up. To tackle this bottleneck, we propose MAssive Genre-Audience~(MAGA) reformulation method, which systematic synthesizes diverse, contextually-rich pretraining data from existing corpus. This work makes three main contributions: (1) We propose MAGA reformulation method, a lightweight and scalable approach for pretraining corpus expansion, and build a 770B tokens MAGACorpus. (2) We evaluate MAGACorpus with different data budget scaling strategies, demonstrating consistent improvements across various model sizes (134M-13B), establishing the necessity for next-generation large-scale synthetic pretraining language models. (3) Through comprehensive analysis, we investigate prompt engineering's impact on synthetic training collapse and reveal limitations in conventional collapse detection metrics using validation losses. Our work shows that MAGA can substantially expand training datasets while maintaining quality, offering a reliably pathway for scaling models beyond data limitations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.04313", "title": "Great Models Think Alike and this Undermines AI Oversight", "authors": [ "Shashwat Goel", "Joschka Struber", "Ilze Amanda Auzina", "Karuna K Chandra", "Ponnurangam Kumaraguru", "Douwe Kiela", "Ameya Prabhu", "Matthias Bethge", "Jonas Geiping" ], "github": "https://github.com/model-similarity/lm-similarity", "project_page": "", "abstract": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as \"AI Oversight\". We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from \"weak-to-strong generalization\". As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/model-similarity/lm-similarity", "github_api_added_by": "auto" }, { "date": "2025-02-07", "arxiv_id": "2502.00989", "title": "ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual Attribution", "authors": [ "Kanika Goswami", "Puneet Mathur", "Ryan Rossi", "Franck Dernoncourt" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) can perform chart question-answering tasks but often generate unverified hallucinated responses. Existing answer attribution methods struggle to ground responses in source charts due to limited visual-semantic context, complex visual-text alignment requirements, and difficulties in bounding box prediction across complex layouts. We present ChartCitor, a multi-agent framework that provides fine-grained bounding box citations by identifying supporting evidence within chart images. The system orchestrates LLM agents to perform chart-to-table extraction, answer reformulation, table augmentation, evidence retrieval through pre-filtering and re-ranking, and table-to-chart mapping. ChartCitor outperforms existing baselines across different chart types. Qualitative user studies show that ChartCitor helps increase user trust in Generative AI by providing enhanced explainability for LLM-assisted chart QA and enables professionals to be more productive.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2501.19085", "title": "Enhancing Code Generation for Low-Resource Languages: No Silver Bullet", "authors": [ "Alessandro Giagnorio", "Alberto Martin-Lopez", "Gabriele Bavota" ], "github": "", "project_page": "", "abstract": "The advent of Large Language Models (LLMs) has significantly advanced the field of automated code generation. LLMs rely on large and diverse datasets to learn syntax, semantics, and usage patterns of programming languages. For low-resource languages (i.e., niche programming languages characterized by the scarcity of training data), the limited availability of such data hampers the models' ability to generalize effectively, resulting in poorer code generation performance as compared to high-resource languages. For this reason, there is a quest for techniques able to close this performance gap. We present an empirical study investigating the effectiveness of several approaches for boosting LLMs' performance on low-resource languages, namely: (i) a classic fine-tuning, which is however capped in size by the scarcity of training data; (ii) three variants of in-context learning, with prompts crafted to provide the LLM with additional information about the low-resource language (e.g., few-shot examples showcasing features of the targeted language); and (iii) a pre-training objective teaching the model how to translate between high- and low-resource languages. The context of our study are two low-resource languages (R and Racket) and six LLMs having different architectures and sizes. Our findings reveal that a fine-tuning is usually the best choice for smaller LLMs, possibly due to the fact that even a small dataset is sufficient to train their limited number of parameters. With the increase in size of the models, in-context learning becomes more and more effective, representing a safe and cheap bet (i.e., it always helps, but with different magnitudes). Differently, very large LLMs may deteriorate their performance on low-resource languages when fine-tuning is performed, possibly due to the lack of enough data needed to effectively update their weights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.00988", "title": "PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback", "authors": [ "Kanika Goswami", "Puneet Mathur", "Ryan Rossi", "Franck Dernoncourt" ], "github": "", "project_page": "", "abstract": "Scientific data visualization is pivotal for transforming raw data into comprehensible visual representations, enabling pattern recognition, forecasting, and the presentation of data-driven insights. However, novice users often face difficulties due to the complexity of selecting appropriate tools and mastering visualization techniques. Large Language Models (LLMs) have recently demonstrated potential in assisting code generation, though they struggle with accuracy and require iterative debugging. In this paper, we propose PlotGen, a novel multi-agent framework aimed at automating the creation of precise scientific visualizations. PlotGen orchestrates multiple LLM-based agents, including a Query Planning Agent that breaks down complex user requests into executable steps, a Code Generation Agent that converts pseudocode into executable Python code, and three retrieval feedback agents - a Numeric Feedback Agent, a Lexical Feedback Agent, and a Visual Feedback Agent - that leverage multimodal LLMs to iteratively refine the data accuracy, textual labels, and visual correctness of generated plots via self-reflection. Extensive experiments show that PlotGen outperforms strong baselines, achieving a 4-6 percent improvement on the MatPlotBench dataset, leading to enhanced user trust in LLM-generated visualizations and improved novice productivity due to a reduction in debugging time needed for plot errors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-07", "arxiv_id": "2502.00473", "title": "Weak-to-Strong Diffusion with Reflection", "authors": [ "Lichen Bai", "Masashi Sugiyama", "Zeke Xie" ], "github": "https://github.com/xie-lab-ml/Weak-to-Strong-Diffusion-with-Reflection", "project_page": "", "abstract": "The goal of diffusion generative models is to align the learned distribution with the real data distribution through gradient score matching. However, inherent limitations in training data quality, modeling strategies, and architectural design lead to inevitable gap between generated outputs and real data. To reduce this gap, we propose Weak-to-Strong Diffusion (W2SD), a novel framework that utilizes the estimated difference between existing weak and strong models (i.e., weak-to-strong difference) to approximate the gap between an ideal model and a strong model. By employing a reflective operation that alternates between denoising and inversion with weak-to-strong difference, we theoretically understand that W2SD steers latent variables along sampling trajectories toward regions of the real data distribution. W2SD is highly flexible and broadly applicable, enabling diverse improvements through the strategic selection of weak-to-strong model pairs (e.g., DreamShaper vs. SD1.5, good experts vs. bad experts in MoE). Extensive experiments demonstrate that W2SD significantly improves human preference, aesthetic quality, and prompt adherence, achieving SOTA performance across various modalities (e.g., image, video), architectures (e.g., UNet-based, DiT-based, MoE), and benchmarks. For example, Juggernaut-XL with W2SD can improve with the HPSv2 winning rate up to 90% over the original results. Moreover, the performance gains achieved by W2SD markedly outweigh its additional computational overhead, while the cumulative improvements from different weak-to-strong difference further solidify its practical utility and deployability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xie-lab-ml/Weak-to-Strong-Diffusion-with-Reflection", "github_api_added_by": "auto" }, { "date": "2025-02-07", "arxiv_id": "2502.04320", "title": "ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features", "authors": [ "Alec Helbling", "Tuna Han Salih Meral", "Ben Hoover", "Pinar Yanardag", "Duen Horng Chau" ], "github": "https://github.com/helblazer811/ConceptAttention", "project_page": "", "abstract": "Do the rich representations of multi-modal diffusion transformers (DiTs) exhibit unique properties that enhance their interpretability? We introduce ConceptAttention, a novel method that leverages the expressive power of DiT attention layers to generate high-quality saliency maps that precisely locate textual concepts within images. Without requiring additional training, ConceptAttention repurposes the parameters of DiT attention layers to produce highly contextualized concept embeddings, contributing the major discovery that performing linear projections in the output space of DiT attention layers yields significantly sharper saliency maps compared to commonly used cross-attention mechanisms. Remarkably, ConceptAttention even achieves state-of-the-art performance on zero-shot image segmentation benchmarks, outperforming 11 other zero-shot interpretability methods on the ImageNet-Segmentation dataset and on a single-class subset of PascalVOC. Our work contributes the first evidence that the representations of multi-modal DiT models like Flux are highly transferable to vision tasks like segmentation, even outperforming multi-modal foundation models like CLIP.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/helblazer811/ConceptAttention", "github_api_added_by": "auto" }, { "date": "2025-02-10", "arxiv_id": "2502.05173", "title": "VideoRoPE: What Makes for Good Video Rotary Position Embedding?", "authors": [ "Xilin Wei", "Xiaoran Liu", "Yuhang Zang", "Xiaoyi Dong", "Pan Zhang", "Yuhang Cao", "Jian Tong", "Haodong Duan", "Qipeng Guo", "Jiaqi Wang", "Xipeng Qiu", "Dahua Lin" ], "github": "https://github.com/Wiselnn570/VideoRoPE", "project_page": "", "abstract": "While Rotary Position Embedding (RoPE) and its variants are widely adopted for their long-context capabilities, the extension of the 1D RoPE to video, with its complex spatio-temporal structure, remains an open challenge. This work first introduces a comprehensive analysis that identifies four key characteristics essential for the effective adaptation of RoPE to video, which have not been fully considered in prior work. As part of our analysis, we introduce a challenging V-NIAH-D (Visual Needle-In-A-Haystack with Distractors) task, which adds periodic distractors into V-NIAH. The V-NIAH-D task demonstrates that previous RoPE variants, lacking appropriate temporal dimension allocation, are easily misled by distractors. Based on our analysis, we introduce VideoRoPE, with a 3D structure designed to preserve spatio-temporal relationships. VideoRoPE features low-frequency temporal allocation to mitigate periodic oscillations, a diagonal layout to maintain spatial symmetry, and adjustable temporal spacing to decouple temporal and spatial indexing. VideoRoPE consistently surpasses previous RoPE variants, across diverse downstream tasks such as long video retrieval, video understanding, and video hallucination. Our code will be available at https://github.com/Wiselnn570/VideoRoPE{https://github.com/Wiselnn570/VideoRoPE}.", "github_manual": "", "github_regex": "https://github.com/Wiselnn570/VideoRoPE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.04520", "title": "Linear Correlation in LM's Compositional Generalization and Hallucination", "authors": [ "Letian Peng", "Chenyang An", "Shibo Hao", "Chengyu Dong", "Jingbo Shang" ], "github": "https://github.com/KomeijiForce/LinCorr", "project_page": "", "abstract": "The generalization of language models (LMs) is undergoing active debates, contrasting their potential for general intelligence with their struggles with basic knowledge composition (e.g., reverse/transition curse). This paper uncovers the phenomenon of linear correlations in LMs during knowledge composition. For explanation, there exists a linear transformation between certain related knowledge that maps the next token prediction logits from one prompt to another, e.g., \"X lives in the city of\" rightarrow \"X lives in the country of\" for every given X. This mirrors the linearity in human knowledge composition, such as Paris rightarrow France. Our findings indicate that the linear transformation is resilient to large-scale fine-tuning, generalizing updated knowledge when aligned with real-world relationships, but causing hallucinations when it deviates. Empirical results suggest that linear correlation can serve as a potential identifier of LM's generalization. Finally, we show such linear correlations can be learned with a single feedforward network and pre-trained vocabulary representations, indicating LM generalization heavily relies on the latter.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KomeijiForce/LinCorr", "github_api_added_by": "auto" }, { "date": "2025-02-10", "arxiv_id": "2502.04403", "title": "Agency Is Frame-Dependent", "authors": [ "David Abel", "Andr\u00e9 Barreto", "Michael Bowling", "Will Dabney", "Shi Dong", "Steven Hansen", "Anna Harutyunyan", "Khimya Khetarpal", "Clare Lyle", "Razvan Pascanu", "Georgios Piliouras", "Doina Precup", "Jonathan Richens", "Mark Rowland", "Tom Schaul", "Satinder Singh" ], "github": "", "project_page": "", "abstract": "Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.04896", "title": "Goku: Flow Based Video Generative Foundation Models", "authors": [ "Shoufa Chen", "Chongjian Ge", "Yuqi Zhang", "Yida Zhang", "Fengda Zhu", "Hao Yang", "Hongxiang Hao", "Hui Wu", "Zhichao Lai", "Yifei Hu", "Ting-Che Lin", "Shilong Zhang", "Fu Li", "Chuan Li", "Xing Wang", "Yanghua Peng", "Peize Sun", "Ping Luo", "Yi Jiang", "Zehuan Yuan", "Bingyue Peng", "Xiaobing Liu" ], "github": "https://github.com/saiyan-world/goku", "project_page": "", "abstract": "This paper introduces Goku, a state-of-the-art family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance. We detail the foundational elements enabling high-quality visual generation, including the data curation pipeline, model architecture design, flow formulation, and advanced infrastructure for efficient and robust large-scale training. The Goku models demonstrate superior performance in both qualitative and quantitative evaluations, setting new benchmarks across major tasks. Specifically, Goku achieves 0.76 on GenEval and 83.65 on DPG-Bench for text-to-image generation, and 84.85 on VBench for text-to-video tasks. We believe that this work provides valuable insights and practical advancements for the research community in developing joint image-and-video generation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/saiyan-world/goku", "github_api_added_by": "auto" }, { "date": "2025-02-10", "arxiv_id": "2502.05171", "title": "Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach", "authors": [ "Jonas Geiping", "Sean McLeish", "Neel Jain", "John Kirchenbauer", "Siddharth Singh", "Brian R. Bartoldson", "Bhavya Kailkhura", "Abhinav Bhatele", "Tom Goldstein" ], "github": "https://github.com/seal-rg/recurrent-pretraining", "project_page": "", "abstract": "We study a novel language model architecture that is capable of scaling test-time computation by implicitly reasoning in latent space. Our model works by iterating a recurrent block, thereby unrolling to arbitrary depth at test-time. This stands in contrast to mainstream reasoning models that scale up compute by producing more tokens. Unlike approaches based on chain-of-thought, our approach does not require any specialized training data, can work with small context windows, and can capture types of reasoning that are not easily represented in words. We scale a proof-of-concept model to 3.5 billion parameters and 800 billion tokens. We show that the resulting model can improve its performance on reasoning benchmarks, sometimes dramatically, up to a computation load equivalent to 50 billion parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/seal-rg/recurrent-pretraining", "github_api_added_by": "auto" }, { "date": "2025-02-10", "arxiv_id": "2502.04363", "title": "On-device Sora: Enabling Diffusion-Based Text-to-Video Generation for Mobile Devices", "authors": [ "Bosung Kim", "Kyuhwan Lee", "Isu Jeong", "Jungmin Cheon", "Yeojin Lee", "Seulki Lee" ], "github": "https://github.com/eai-lab/On-device-Sora", "project_page": "", "abstract": "We present On-device Sora, a first pioneering solution for diffusion-based on-device text-to-video generation that operates efficiently on smartphone-grade devices. Building on Open-Sora, On-device Sora applies three novel techniques to address the challenges of diffusion-based text-to-video generation on computation- and memory-limited mobile devices. First, Linear Proportional Leap (LPL) reduces the excessive denoising steps required in video diffusion through an efficient leap-based approach. Second, Temporal Dimension Token Merging (TDTM) minimizes intensive token-processing computation in attention layers by merging consecutive tokens along the temporal dimension. Third, Concurrent Inference with Dynamic Loading (CI-DL) dynamically partitions large models into smaller blocks and loads them into memory for concurrent model inference, effectively addressing the challenges of limited device memory. We implement On-device Sora on the iPhone 15 Pro, and the experimental evaluations demonstrate that it is capable of generating high-quality videos on the device, comparable to those produced by Open-Sora running on high-end GPUs. These results show that On-device Sora enables efficient and high-quality video generation on resource-constrained mobile devices, expanding accessibility, ensuring user privacy, reducing dependence on cloud infrastructure, and lowering associated costs. We envision the proposed On-device Sora as a significant first step toward democratizing state-of-the-art generative technologies, enabling video generation capabilities on commodity mobile and embedded devices. The code implementation is publicly available at an GitHub repository: https://github.com/eai-lab/On-device-Sora.", "github_manual": "", "github_regex": "https://github.com/eai-lab/On-device-Sora", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.04728", "title": "Generating Symbolic World Models via Test-time Scaling of Large Language Models", "authors": [ "Zhouliang Yu", "Yuhuan Yuan", "Tim Z. Xiao", "Fuxiang Frank Xia", "Jie Fu", "Ge Zhang", "Ge Lin", "Weiyang Liu" ], "github": "", "project_page": "", "abstract": "Solving complex planning problems requires Large Language Models (LLMs) to explicitly model the state transition to avoid rule violations, comply with constraints, and ensure optimality-a task hindered by the inherent ambiguity of natural language. To overcome such ambiguity, Planning Domain Definition Language (PDDL) is leveraged as a planning abstraction that enables precise and formal state descriptions. With PDDL, we can generate a symbolic world model where classic searching algorithms, such as A*, can be seamlessly applied to find optimal plans. However, directly generating PDDL domains with current LLMs remains an open challenge due to the lack of PDDL training data. To address this challenge, we propose to scale up the test-time computation of LLMs to enhance their PDDL reasoning capabilities, thereby enabling the generation of high-quality PDDL domains. Specifically, we introduce a simple yet effective algorithm, which first employs a Best-of-N sampling approach to improve the quality of the initial solution and then refines the solution in a fine-grained manner with verbalized machine learning. Our method outperforms o1-mini by a considerable margin in the generation of PDDL domain, achieving over 50% success rate on two tasks (i.e., generating PDDL domains from natural language description or PDDL problems). This is done without requiring additional training. By taking advantage of PDDL as state abstraction, our method is able to outperform current state-of-the-art methods on almost all competition-level planning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.04404", "title": "Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of Language Models", "authors": [ "Xiao-Wen Yang", "Xuan-Yi Zhu", "Wen-Da Wei", "Ding-Chu Zhang", "Jie-Jing Shao", "Zhi Zhou", "Lan-Zhe Guo", "Yu-Feng Li" ], "github": "https://github.com/LAMDASZ-ML/Self-Backtracking", "project_page": "", "abstract": "The integration of slow-thinking mechanisms into large language models (LLMs) offers a promising way toward achieving Level 2 AGI Reasoners, as exemplified by systems like OpenAI's o1. However, several significant challenges remain, including inefficient overthinking and an overreliance on auxiliary reward models. We point out that these limitations stem from LLMs' inability to internalize the search process, a key component of effective reasoning. A critical step toward addressing this issue is enabling LLMs to autonomously determine when and where to backtrack, a fundamental operation in traditional search algorithms. To this end, we propose a self-backtracking mechanism that equips LLMs with the ability to backtrack during both training and inference. This mechanism not only enhances reasoning ability but also efficiency by transforming slow-thinking processes into fast-thinking through self-improvement. Empirical evaluations demonstrate that our proposal significantly enhances the reasoning capabilities of LLMs, achieving a performance gain of over 40 percent compared to the optimal-path supervised fine-tuning method. We believe this study introduces a novel and promising pathway for developing more advanced and robust Reasoners.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LAMDASZ-ML/Self-Backtracking", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.04350", "title": "CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance", "authors": [ "Yongchao Chen", "Yilun Hao", "Yueying Liu", "Yang Zhang", "Chuchu Fan" ], "github": "https://github.com/yongchao98/CodeSteer-v1.0", "project_page": "", "abstract": "Existing methods fail to effectively steer Large Language Models (LLMs) between textual reasoning and code generation, leaving symbolic computing capabilities underutilized. We introduce CodeSteer, an effective method for guiding LLM code/text generation. We construct a comprehensive benchmark SymBench comprising 37 symbolic tasks with adjustable complexity and also synthesize datasets of 12k multi-round guidance/generation trajectories and 5.5k guidance comparison pairs. We fine-tune the Llama-3-8B model with a newly designed multi-round supervised fine-tuning (SFT) and direct preference optimization (DPO). The resulting model, CodeSteerLLM, augmented with the proposed symbolic and self-answer checkers, effectively guides the code/text generation of larger models. Augmenting GPT-4o with CodeSteer raises its average performance score from 53.3 to 86.4, even outperforming the existing best LLM OpenAI o1 (82.7), o1-preview (74.8), and DeepSeek R1 (76.8) across all 37 tasks (28 seen, 9 unseen). Trained for GPT-4o, CodeSteer demonstrates superior generalizability, providing an average 41.8 performance boost on Claude, Mistral, and GPT-3.5. CodeSteer-guided LLMs fully harness symbolic computing to maintain strong performance on highly complex tasks. Models, Datasets, and Codes are available at https://github.com/yongchao98/CodeSteer-v1.0.", "github_manual": "", "github_regex": "https://github.com/yongchao98/CodeSteer-v1.0", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.04507", "title": "Fast Video Generation with Sliding Tile Attention", "authors": [ "Peiyuan Zhang", "Yongqi Chen", "Runlong Su", "Hangliang Ding", "Ion Stoica", "Zhenghong Liu", "Hao Zhang" ], "github": "", "project_page": "", "abstract": "Diffusion Transformers (DiTs) with 3D full attention power state-of-the-art video generation, but suffer from prohibitive compute cost -- when generating just a 5-second 720P video, attention alone takes 800 out of 945 seconds of total inference time. This paper introduces sliding tile attention (STA) to address this challenge. STA leverages the observation that attention scores in pretrained video diffusion models predominantly concentrate within localized 3D windows. By sliding and attending over the local spatial-temporal region, STA eliminates redundancy from full attention. Unlike traditional token-wise sliding window attention (SWA), STA operates tile-by-tile with a novel hardware-aware sliding window design, preserving expressiveness while being hardware-efficient. With careful kernel-level optimizations, STA offers the first efficient 2D/3D sliding-window-like attention implementation, achieving 58.79% MFU. Precisely, STA accelerates attention by 2.8-17x over FlashAttention-2 (FA2) and 1.6-10x over FlashAttention-3 (FA3). On the leading video DiT, HunyuanVideo, STA reduces end-to-end latency from 945s (FA3) to 685s without quality degradation, requiring no training. Enabling finetuning further lowers latency to 268s with only a 0.09% drop on VBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.05176", "title": "AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360\u00b0 Unbounded Scene Inpainting", "authors": [ "Chung-Ho Wu", "Yang-Jung Chen", "Ying-Huan Chen", "Jie-Ying Lee", "Bo-Hsu Ke", "Chun-Wei Tuan Mu", "Yi-Chuan Huang", "Chin-Yang Lin", "Min-Hung Chen", "Yen-Yu Lin", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Three-dimensional scene inpainting is crucial for applications from virtual reality to architectural visualization, yet existing methods struggle with view consistency and geometric accuracy in 360{\\deg} unbounded scenes. We present AuraFusion360, a novel reference-based method that enables high-quality object removal and hole filling in 3D scenes represented by Gaussian Splatting. Our approach introduces (1) depth-aware unseen mask generation for accurate occlusion identification, (2) Adaptive Guided Depth Diffusion, a zero-shot method for accurate initial point placement without requiring additional training, and (3) SDEdit-based detail enhancement for multi-view coherence. We also introduce 360-USID, the first comprehensive dataset for 360{\\deg} unbounded scene inpainting with ground truth. Extensive experiments demonstrate that AuraFusion360 significantly outperforms existing methods, achieving superior perceptual quality while maintaining geometric accuracy across dramatic viewpoint changes. See our project page for video results and the dataset at https://kkennethwu.github.io/aurafusion360/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.05163", "title": "DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM Guardrails", "authors": [ "Yihe Deng", "Yu Yang", "Junkai Zhang", "Wei Wang", "Bo Li" ], "github": "https://github.com/yihedeng9/DuoGuard", "project_page": "", "abstract": "The rapid advancement of large language models (LLMs) has increased the need for guardrail models to ensure responsible use, particularly in detecting unsafe and illegal content. While substantial safety data exist in English, multilingual guardrail modeling remains underexplored due to the scarcity of open-source safety data in other languages. To address this gap, we propose a novel two-player Reinforcement Learning (RL) framework, where a generator and a guardrail model co-evolve adversarially to produce high-quality synthetic data for multilingual guardrail training. We theoretically formalize this interaction as a two-player game, proving convergence to a Nash equilibrium. Empirical evaluations show that our model \\ours outperforms state-of-the-art models, achieving nearly 10% improvement over LlamaGuard3 (8B) on English benchmarks while being 4.5x faster at inference with a significantly smaller model (0.5B). We achieve substantial advancements in multilingual safety tasks, particularly in addressing the imbalance for lower-resource languages in a collected real dataset. Ablation studies emphasize the critical role of synthetic data generation in bridging the imbalance in open-source data between English and other languages. These findings establish a scalable and efficient approach to synthetic data generation, paving the way for improved multilingual guardrail models to enhance LLM safety. Code, model, and data will be open-sourced at https://github.com/yihedeng9/DuoGuard.", "github_manual": "", "github_regex": "https://github.com/yihedeng9/DuoGuard", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.05179", "title": "FlashVideo:Flowing Fidelity to Detail for Efficient High-Resolution Video Generation", "authors": [ "Shilong Zhang", "Wenbo Li", "Shoufa Chen", "Chongjian Ge", "Peize Sun", "Yida Zhang", "Yi Jiang", "Zehuan Yuan", "Binyue Peng", "Ping Luo" ], "github": "", "project_page": "", "abstract": "DiT diffusion models have achieved great success in text-to-video generation, leveraging their scalability in model capacity and data scale. High content and motion fidelity aligned with text prompts, however, often require large model parameters and a substantial number of function evaluations (NFEs). Realistic and visually appealing details are typically reflected in high resolution outputs, further amplifying computational demands especially for single stage DiT models. To address these challenges, we propose a novel two stage framework, FlashVideo, which strategically allocates model capacity and NFEs across stages to balance generation fidelity and quality. In the first stage, prompt fidelity is prioritized through a low resolution generation process utilizing large parameters and sufficient NFEs to enhance computational efficiency. The second stage establishes flow matching between low and high resolutions, effectively generating fine details with minimal NFEs. Quantitative and visual results demonstrate that FlashVideo achieves state-of-the-art high resolution video generation with superior computational efficiency. Additionally, the two-stage design enables users to preview the initial output before committing to full resolution generation, thereby significantly reducing computational costs and wait times as well as enhancing commercial viability .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.05178", "title": "QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation", "authors": [ "Yue Zhao", "Fuzhao Xue", "Scott Reed", "Linxi Fan", "Yuke Zhu", "Jan Kautz", "Zhiding Yu", "Philipp Kr\u00e4henb\u00fchl", "De-An Huang" ], "github": "https://github.com/NVlabs/QLIP", "project_page": "", "abstract": "We introduce Quantized Language-Image Pretraining (QLIP), a visual tokenization method that combines state-of-the-art reconstruction quality with state-of-the-art zero-shot image understanding. QLIP trains a binary-spherical-quantization-based autoencoder with reconstruction and language-image alignment objectives. We are the first to show that the two objectives do not need to be at odds. We balance the two loss terms dynamically during training and show that a two-stage training pipeline effectively mixes the large-batch requirements of image-language pre-training with the memory bottleneck imposed by the reconstruction objective. We validate the effectiveness of QLIP for multimodal understanding and text-conditioned image generation with a single model. Specifically, QLIP serves as a drop-in replacement for the visual encoder for LLaVA and the image tokenizer for LlamaGen with comparable or even better performance. Finally, we demonstrate that QLIP enables a unified mixed-modality auto-regressive model for understanding and generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/QLIP", "github_api_added_by": "auto" }, { "date": "2025-02-10", "arxiv_id": "2502.04376", "title": "MEETING DELEGATE: Benchmarking LLMs on Attending Meetings on Our Behalf", "authors": [ "Lingxiang Hu", "Shurun Yuan", "Xiaoting Qin", "Jue Zhang", "Qingwei Lin", "Dongmei Zhang", "Saravan Rajmohan", "Qi Zhang" ], "github": "", "project_page": "", "abstract": "In contemporary workplaces, meetings are essential for exchanging ideas and ensuring team alignment but often face challenges such as time consumption, scheduling conflicts, and inefficient participation. Recent advancements in Large Language Models (LLMs) have demonstrated their strong capabilities in natural language generation and reasoning, prompting the question: can LLMs effectively delegate participants in meetings? To explore this, we develop a prototype LLM-powered meeting delegate system and create a comprehensive benchmark using real meeting transcripts. Our evaluation reveals that GPT-4/4o maintain balanced performance between active and cautious engagement strategies. In contrast, Gemini 1.5 Pro tends to be more cautious, while Gemini 1.5 Flash and Llama3-8B/70B display more active tendencies. Overall, about 60\\% of responses address at least one key point from the ground-truth. However, improvements are needed to reduce irrelevant or repetitive content and enhance tolerance for transcription errors commonly found in real-world settings. Additionally, we implement the system in practical settings and collect real-world feedback from demos. Our findings underscore the potential and challenges of utilizing LLMs as meeting delegates, offering valuable insights into their practical application for alleviating the burden of meetings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.03512", "title": "YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing Multi-Objective Optimization based DPO for Text-to-Image Alignment", "authors": [ "Amitava Das", "Yaswanth Narsupalli", "Gurpreet Singh", "Vinija Jain", "Vasu Sharma", "Suranjana Trivedy", "Aman Chadha", "Amit Sheth" ], "github": "https://github.com/rom1504/clip-retrieval.Accessed", "project_page": "", "abstract": "Precise alignment in Text-to-Image (T2I) systems is crucial to ensure that generated visuals not only accurately encapsulate user intents but also conform to stringent ethical and aesthetic benchmarks. Incidents like the Google Gemini fiasco, where misaligned outputs triggered significant public backlash, underscore the critical need for robust alignment mechanisms. In contrast, Large Language Models (LLMs) have achieved notable success in alignment. Building on these advancements, researchers are eager to apply similar alignment techniques, such as Direct Preference Optimization (DPO), to T2I systems to enhance image generation fidelity and reliability. We present YinYangAlign, an advanced benchmarking framework that systematically quantifies the alignment fidelity of T2I systems, addressing six fundamental and inherently contradictory design objectives. Each pair represents fundamental tensions in image generation, such as balancing adherence to user prompts with creative modifications or maintaining diversity alongside visual coherence. YinYangAlign includes detailed axiom datasets featuring human prompts, aligned (chosen) responses, misaligned (rejected) AI-generated outputs, and explanations of the underlying contradictions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/rom1504/clip-retrieval.Accessed", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.03738", "title": "Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More", "authors": [ "Feng Wang", "Yaodong Yu", "Guoyizhe Wei", "Wei Shao", "Yuyin Zhou", "Alan Yuille", "Cihang Xie" ], "github": "https://github.com/wangf3014/Patch_Scaling", "project_page": "", "abstract": "Since the introduction of Vision Transformer (ViT), patchification has long been regarded as a de facto image tokenization approach for plain visual architectures. By compressing the spatial size of images, this approach can effectively shorten the token sequence and reduce the computational cost of ViT-like plain architectures. In this work, we aim to thoroughly examine the information loss caused by this patchification-based compressive encoding paradigm and how it affects visual understanding. We conduct extensive patch size scaling experiments and excitedly observe an intriguing scaling law in patchification: the models can consistently benefit from decreased patch sizes and attain improved predictive performance, until it reaches the minimum patch size of 1x1, i.e., pixel tokenization. This conclusion is broadly applicable across different vision tasks, various input scales, and diverse architectures such as ViT and the recent Mamba models. Moreover, as a by-product, we discover that with smaller patches, task-specific decoder heads become less critical for dense prediction. In the experiments, we successfully scale up the visual sequence to an exceptional length of 50,176 tokens, achieving a competitive test accuracy of 84.6% with a base-sized model on the ImageNet-1k benchmark. We hope this study can provide insights and theoretical foundations for future works of building non-compressive vision models. Code is available at https://github.com/wangf3014/Patch_Scaling.", "github_manual": "", "github_regex": "https://github.com/wangf3014/Patch_Scaling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.05003", "title": "QuEST: Stable Training of LLMs with 1-Bit Weights and Activations", "authors": [ "Andrei Panferov", "Jiale Chen", "Soroush Tabesh", "Roberto L. Castro", "Mahdi Nikdan", "Dan Alistarh" ], "github": "https://github.com/IST-DASLab/QuEST", "project_page": "", "abstract": "One approach to reducing the massive costs of large language models (LLMs) is the use of quantized or sparse representations for training or deployment. While post-training compression methods are very popular, the question of obtaining even more accurate compressed models by directly training over such representations, i.e., Quantization-Aware Training (QAT), is still open: for example, a recent study (arXiv:2411.04330v2) put the \"optimal\" bit-width at which models can be trained using QAT, while staying accuracy-competitive with standard FP16/BF16 precision, at 8-bits weights and activations. We advance this state-of-the-art via a new method called QuEST, which is Pareto-competitive with FP16, i.e., it provides better accuracy at lower model size, while training models with weights and activations in 4-bits or less. Moreover, QuEST allows stable training with 1-bit weights and activations. QuEST achieves this by improving two key aspects of QAT methods: (1) accurate and fast quantization of the (continuous) distributions of weights and activations via Hadamard normalization and MSE-optimal fitting; (2) a new trust gradient estimator based on the idea of explicitly minimizing the error between the noisy gradient computed over quantized states and the \"true\" (but unknown) full-precision gradient. Experiments on Llama-type architectures show that QuEST induces stable scaling laws across the entire range of hardware-supported precisions, and can be extended to sparse representations. We provide GPU kernel support showing that models produced by QuEST can be executed efficiently. Our code is available at https://github.com/IST-DASLab/QuEST.", "github_manual": "", "github_regex": "https://github.com/IST-DASLab/QuEST", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.04689", "title": "ARR: Question Answering with Large Language Models via Analyzing, Retrieving, and Reasoning", "authors": [ "Yuwei Yin", "Giuseppe Carenini" ], "github": "https://github.com/YuweiYin/ARR", "project_page": "", "abstract": "Large language models (LLMs) achieve remarkable performance on challenging benchmarks that are often structured as multiple-choice question-answering (QA) tasks. Zero-shot Chain-of-Thought (CoT) prompting enhances reasoning in LLMs but provides only vague and generic guidance (\"think step by step\"). This paper introduces ARR, an intuitive and effective zero-shot prompting method that explicitly incorporates three key steps in QA solving: analyzing the intent of the question, retrieving relevant information, and reasoning step by step. Comprehensive experiments across diverse and challenging QA tasks demonstrate that ARR consistently improves the Baseline (without ARR prompting) and outperforms CoT. Ablation and case studies further validate the positive contributions of each component: analyzing, retrieving, and reasoning. Notably, intent analysis plays a vital role in ARR. Additionally, extensive evaluations across various model sizes, LLM series, and generation settings solidify the effectiveness, robustness, and generalizability of ARR.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YuweiYin/ARR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.04416", "title": "CMoE: Fast Carving of Mixture-of-Experts for Efficient LLM Inference", "authors": [ "Zehua Pei", "Lancheng Zou", "Hui-Ling Zhen", "Xianzhi Yu", "Wulong Liu", "Sinno Jialin Pan", "Mingxuan Yuan", "Bei Yu" ], "github": "https://github.com/JarvisPei/CMoE", "project_page": "", "abstract": "Large language models (LLMs) achieve impressive performance by scaling model parameters, but this comes with significant inference overhead. Feed-forward networks (FFNs), which dominate LLM parameters, exhibit high activation sparsity in hidden neurons. To exploit this, researchers have proposed using a mixture-of-experts (MoE) architecture, where only a subset of parameters is activated. However, existing approaches often require extensive training data and resources, limiting their practicality. We propose CMoE (Carved MoE), a novel framework to efficiently carve MoE models from dense models. CMoE achieves remarkable performance through efficient expert grouping and lightweight adaptation. First, neurons are grouped into shared and routed experts based on activation rates. Next, we construct a routing mechanism without training from scratch, incorporating a differentiable routing process and load balancing. Using modest data, CMoE produces a well-designed, usable MoE from a 7B dense model within five minutes. With lightweight fine-tuning, it achieves high-performance recovery in under an hour. We make our code publicly available at https://github.com/JarvisPei/CMoE.", "github_manual": "", "github_regex": "https://github.com/JarvisPei/CMoE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.04959", "title": "No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces", "authors": [ "Daniel Marczak", "Simone Magistri", "Sebastian Cygert", "Bart\u0142omiej Twardowski", "Andrew D. Bagdanov", "Joost van de Weijer" ], "github": "https://github.com/danielm1405/iso-merging", "project_page": "", "abstract": "Model merging integrates the weights of multiple task-specific models into a single multi-task model. Despite recent interest in the problem, a significant performance gap between the combined and single-task models remains. In this paper, we investigate the key characteristics of task matrices -- weight update matrices applied to a pre-trained model -- that enable effective merging. We show that alignment between singular components of task-specific and merged matrices strongly correlates with performance improvement over the pre-trained model. Based on this, we propose an isotropic merging framework that flattens the singular value spectrum of task matrices, enhances alignment, and reduces the performance gap. Additionally, we incorporate both common and task-specific subspaces to further improve alignment and performance. Our proposed approach achieves state-of-the-art performance across multiple scenarios, including various sets of tasks and model scales. This work advances the understanding of model merging dynamics, offering an effective methodology to merge models without requiring additional training. Code is available at https://github.com/danielm1405/iso-merging .", "github_manual": "", "github_regex": "https://github.com/danielm1405/iso-merging", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.05092", "title": "Lost in Time: Clock and Calendar Understanding Challenges in Multimodal LLMs", "authors": [ "Rohit Saxena", "Aryo Pradipta Gema", "Pasquale Minervini" ], "github": "", "project_page": "", "abstract": "Understanding time from visual representations is a fundamental cognitive skill, yet it remains a challenge for multimodal large language models (MLLMs). In this work, we investigate the capabilities of MLLMs in interpreting time and date through analogue clocks and yearly calendars. To facilitate this, we curated a structured dataset comprising two subsets: 1) ClockQA, which comprises various types of clock styles-standard, black-dial, no-second-hand, Roman numeral, and arrow-hand clocks-paired with time related questions; and 2) CalendarQA, which consists of yearly calendar images with questions ranging from commonly known dates (e.g., Christmas, New Year's Day) to computationally derived ones (e.g., the 100th or 153rd day of the year). We aim to analyse how MLLMs can perform visual recognition, numerical reasoning, and temporal inference when presented with time-related visual data. Our evaluations show that despite recent advancements, reliably understanding time remains a significant challenge for MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.04327", "title": "Value-Based Deep RL Scales Predictably", "authors": [ "Oleh Rybkin", "Michal Nauman", "Preston Fu", "Charlie Snell", "Pieter Abbeel", "Sergey Levine", "Aviral Kumar" ], "github": "", "project_page": "", "abstract": "Scaling data and compute is critical to the success of machine learning. However, scaling demands predictability: we want methods to not only perform well with more compute or data, but also have their performance be predictable from small-scale runs, without running the large-scale experiment. In this paper, we show that value-based off-policy RL methods are predictable despite community lore regarding their pathological behavior. First, we show that data and compute requirements to attain a given performance level lie on a Pareto frontier, controlled by the updates-to-data (UTD) ratio. By estimating this frontier, we can predict this data requirement when given more compute, and this compute requirement when given more data. Second, we determine the optimal allocation of a total resource budget across data and compute for a given performance and use it to determine hyperparameters that maximize performance for a given budget. Third, this scaling behavior is enabled by first estimating predictable relationships between hyperparameters, which is used to manage effects of overfitting and plasticity loss unique to RL. We validate our approach using three algorithms: SAC, BRO, and PQL on DeepMind Control, OpenAI gym, and IsaacGym, when extrapolating to higher levels of data, compute, budget, or performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2501.12387", "title": "Continuous 3D Perception Model with Persistent State", "authors": [ "Qianqian Wang", "Yifei Zhang", "Aleksander Holynski", "Alexei A. Efros", "Angjoo Kanazawa" ], "github": "https://github.com/CUT3R/CUT3R", "project_page": "", "abstract": "We present a unified framework capable of solving a broad range of 3D tasks. Our approach features a stateful recurrent model that continuously updates its state representation with each new observation. Given a stream of images, this evolving state can be used to generate metric-scale pointmaps (per-pixel 3D points) for each new input in an online fashion. These pointmaps reside within a common coordinate system, and can be accumulated into a coherent, dense scene reconstruction that updates as new images arrive. Our model, called CUT3R (Continuous Updating Transformer for 3D Reconstruction), captures rich priors of real-world scenes: not only can it predict accurate pointmaps from image observations, but it can also infer unseen regions of the scene by probing at virtual, unobserved views. Our method is simple yet highly flexible, naturally accepting varying lengths of images that may be either video streams or unordered photo collections, containing both static and dynamic content. We evaluate our method on various 3D/4D tasks and demonstrate competitive or state-of-the-art performance in each. Project Page: https://cut3r.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CUT3R/CUT3R", "github_api_added_by": "auto" }, { "date": "2025-02-10", "arxiv_id": "2502.03771", "title": "Adaptive Semantic Prompt Caching with VectorQ", "authors": [ "Luis Gaspar Schroeder", "Shu Liu", "Alejandro Cuadron", "Mark Zhao", "Stephan Krusche", "Alfons Kemper", "Matei Zaharia", "Joseph E. Gonzalez" ], "github": "", "project_page": "", "abstract": "Semantic prompt caches reduce the latency and cost of large language model (LLM) inference by reusing cached LLM-generated responses for semantically similar prompts. Vector similarity metrics assign a numerical score to quantify the similarity between an embedded prompt and its nearest neighbor in the cache. Existing systems rely on a static threshold to classify whether the similarity score is sufficiently high to result in a cache hit. We show that this one-size-fits-all threshold is insufficient across different prompts. We propose VectorQ, a framework to learn embedding-specific threshold regions that adapt to the complexity and uncertainty of an embedding. Through evaluations on a combination of four diverse datasets, we show that VectorQ consistently outperforms state-of-the-art systems across all static thresholds, achieving up to 12x increases in cache hit rate and error rate reductions up to 92%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.02909", "title": "SPARC: Subspace-Aware Prompt Adaptation for Robust Continual Learning in LLMs", "authors": [ "Dinithi Jayasuriya", "Sina Tayebati", "Davide Ettori", "Ranganath Krishnan", "Amit Ranjan Trivedi" ], "github": "", "project_page": "", "abstract": "We propose SPARC, a lightweight continual learning framework for large language models (LLMs) that enables efficient task adaptation through prompt tuning in a lower-dimensional space. By leveraging principal component analysis (PCA), we identify a compact subspace of the training data. Optimizing prompts in this lower-dimensional space enhances training efficiency, as it focuses updates on the most relevant features while reducing computational overhead. Furthermore, since the model's internal structure remains unaltered, the extensive knowledge gained from pretraining is fully preserved, ensuring that previously learned information is not compromised during adaptation. Our method achieves high knowledge retention in both task-incremental and domain-incremental continual learning setups while fine-tuning only 0.04% of the model's parameters. Additionally, by integrating LoRA, we enhance adaptability to computational constraints, allowing for a tradeoff between accuracy and training cost. Experiments on the SuperGLUE benchmark demonstrate that our PCA-based prompt tuning combined with LoRA maintains full knowledge retention while improving accuracy, utilizing only 1% of the model's parameters. These results establish our approach as a scalable and resource-efficient solution for continual learning in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-10", "arxiv_id": "2502.02692", "title": "Intelligent Sensing-to-Action for Robust Autonomy at the Edge: Opportunities and Challenges", "authors": [ "Amit Ranjan Trivedi", "Sina Tayebati", "Hemant Kumawat", "Nastaran Darabi", "Divake Kumar", "Adarsh Kumar Kosta", "Yeshwanth Venkatesha", "Dinithi Jayasuriya", "Nethmi Jayasinghe", "Priyadarshini Panda", "Saibal Mukhopadhyay", "Kaushik Roy" ], "github": "", "project_page": "", "abstract": "Autonomous edge computing in robotics, smart cities, and autonomous vehicles relies on the seamless integration of sensing, processing, and actuation for real-time decision-making in dynamic environments. At its core is the sensing-to-action loop, which iteratively aligns sensor inputs with computational models to drive adaptive control strategies. These loops can adapt to hyper-local conditions, enhancing resource efficiency and responsiveness, but also face challenges such as resource constraints, synchronization delays in multi-modal data fusion, and the risk of cascading errors in feedback loops. This article explores how proactive, context-aware sensing-to-action and action-to-sensing adaptations can enhance efficiency by dynamically adjusting sensing and computation based on task demands, such as sensing a very limited part of the environment and predicting the rest. By guiding sensing through control actions, action-to-sensing pathways can improve task relevance and resource use, but they also require robust monitoring to prevent cascading errors and maintain reliability. Multi-agent sensing-action loops further extend these capabilities through coordinated sensing and actions across distributed agents, optimizing resource use via collaboration. Additionally, neuromorphic computing, inspired by biological systems, provides an efficient framework for spike-based, event-driven processing that conserves energy, reduces latency, and supports hierarchical control--making it ideal for multi-agent optimization. This article highlights the importance of end-to-end co-design strategies that align algorithmic models with hardware and environmental dynamics and improve cross-layer interdependencies to improve throughput, precision, and adaptability for energy-efficient edge autonomy in complex environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.03628", "title": "The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steering", "authors": [ "Zhuowei Li", "Haizhou Shi", "Yunhe Gao", "Di Liu", "Zhenting Wang", "Yuxiao Chen", "Ting Liu", "Long Zhao", "Hao Wang", "Dimitris N. Metaxas" ], "github": "https://github.com/LzVv123456/VISTA", "project_page": "", "abstract": "Large Vision-Language Models (LVLMs) can reason effectively over both textual and visual inputs, but they tend to hallucinate syntactically coherent yet visually ungrounded contents. In this paper, we investigate the internal dynamics of hallucination by examining the tokens logits rankings throughout the generation process, revealing three key patterns in how LVLMs process information: (1) gradual visual information loss -- visually grounded tokens gradually become less favored throughout generation, and (2) early excitation -- semantically meaningful tokens achieve peak activation in the layers earlier than the final layer. (3) hidden genuine information -- visually grounded tokens though not being eventually decided still retain relatively high rankings at inference. Based on these insights, we propose VISTA (Visual Information Steering with Token-logit Augmentation), a training-free inference-time intervention framework that reduces hallucination while promoting genuine information. VISTA works by combining two complementary approaches: reinforcing visual information in activation space and leveraging early layer activations to promote semantically meaningful decoding. Compared to existing methods, VISTA requires no external supervision and is applicable to various decoding strategies. Extensive experiments show that VISTA on average reduces hallucination by abount 40% on evaluated open-ended generation task, and it consistently outperforms existing methods on four benchmarks across four architectures under three decoding strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LzVv123456/VISTA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06635", "title": "Steel-LLM:From Scratch to Open Source -- A Personal Journey in Building a Chinese-Centric LLM", "authors": [ "Qingshui Gu", "Shu Li", "Tianyu Zheng", "Zhaoxiang Zhang" ], "github": "https://github.com/zhanshijinwat/Steel-LLM", "project_page": "", "abstract": "Steel-LLM is a Chinese-centric language model developed from scratch with the goal of creating a high-quality, open-source model despite limited computational resources. Launched in March 2024, the project aimed to train a 1-billion-parameter model on a large-scale dataset, prioritizing transparency and the sharing of practical insights to assist others in the community. The training process primarily focused on Chinese data, with a small proportion of English data included, addressing gaps in existing open-source LLMs by providing a more detailed and practical account of the model-building journey. Steel-LLM has demonstrated competitive performance on benchmarks such as CEVAL and CMMLU, outperforming early models from larger institutions. This paper provides a comprehensive summary of the project's key contributions, including data collection, model design, training methodologies, and the challenges encountered along the way, offering a valuable resource for researchers and practitioners looking to develop their own LLMs. The model checkpoints and training script are available at https://github.com/zhanshijinwat/Steel-LLM.", "github_manual": "", "github_regex": "https://github.com/zhanshijinwat/Steel-LLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06155", "title": "Efficient-vDiT: Efficient Video Diffusion Transformers With Attention Tile", "authors": [ "Hangliang Ding", "Dacheng Li", "Runlong Su", "Peiyuan Zhang", "Zhijie Deng", "Ion Stoica", "Hao Zhang" ], "github": "", "project_page": "", "abstract": "Despite the promise of synthesizing high-fidelity videos, Diffusion Transformers (DiTs) with 3D full attention suffer from expensive inference due to the complexity of attention computation and numerous sampling steps. For example, the popular Open-Sora-Plan model consumes more than 9 minutes for generating a single video of 29 frames. This paper addresses the inefficiency issue from two aspects: 1) Prune the 3D full attention based on the redundancy within video data; We identify a prevalent tile-style repetitive pattern in the 3D attention maps for video data, and advocate a new family of sparse 3D attention that holds a linear complexity w.r.t. the number of video frames. 2) Shorten the sampling process by adopting existing multi-step consistency distillation; We split the entire sampling trajectory into several segments and perform consistency distillation within each one to activate few-step generation capacities. We further devise a three-stage training pipeline to conjoin the low-complexity attention and few-step generation capacities. Notably, with 0.1% pretraining data, we turn the Open-Sora-Plan-1.2 model into an efficient one that is 7.4x -7.8x faster for 29 and 93 frames 720p video generation with a marginal performance trade-off in VBench. In addition, we demonstrate that our approach is amenable to distributed inference, achieving an additional 3.91x speedup when running on 4 GPUs with sequence parallelism.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06772", "title": "ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates", "authors": [ "Ling Yang", "Zhaochen Yu", "Bin Cui", "Mengdi Wang" ], "github": "https://github.com/Gen-Verse/ReasonFlux", "project_page": "", "abstract": "We present that hierarchical LLM reasoning via scaling thought templates can effectively optimize the reasoning search space and outperform the mathematical reasoning capabilities of powerful LLMs like OpenAI o1-preview and DeepSeek V3. We train our ReasonFlux-32B model with only 8 GPUs and introduces three innovations: (i) a structured and generic thought template library, containing around 500 high-level thought templates capable of generalizing to similar or relevant reasoning problems; (ii) performing hierarchical reinforcement learning on a sequence of thought templates instead of long CoTs, optimizing a base LLM to plan out an optimal template trajectory for gradually handling complex problems; (iii) a brand new inference scaling system that enables hierarchical LLM reasoning by adaptively scaling thought templates at inference time. With a template trajectory containing sequential thought templates, our ReasonFlux-32B significantly advances math reasoning capabilities to state-of-the-art levels. Notably, on the MATH benchmark, it achieves an accuracy of 91.2% and surpasses o1-preview by 6.7%. On the USA Math Olympiad (AIME) benchmark, ReasonFlux-32B solves an average of 56.7% of problems, surpassing o1-preview and DeepSeek-V3 by 27% and 45%, respectively. Code: https://github.com/Gen-Verse/ReasonFlux", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/ReasonFlux", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06788", "title": "EVEv2: Improved Baselines for Encoder-Free Vision-Language Models", "authors": [ "Haiwen Diao", "Xiaotong Li", "Yufeng Cui", "Yueze Wang", "Haoge Deng", "Ting Pan", "Wenxuan Wang", "Huchuan Lu", "Xinlong Wang" ], "github": "https://github.com/baaivision/EVE", "project_page": "", "abstract": "Existing encoder-free vision-language models (VLMs) are rapidly narrowing the performance gap with their encoder-based counterparts, highlighting the promising potential for unified multimodal systems with structural simplicity and efficient deployment. We systematically clarify the performance gap between VLMs using pre-trained vision encoders, discrete tokenizers, and minimalist visual layers from scratch, deeply excavating the under-examined characteristics of encoder-free VLMs. We develop efficient strategies for encoder-free VLMs that rival mainstream encoder-based ones. After an in-depth investigation, we launch EVEv2.0, a new and improved family of encoder-free VLMs. We show that: (i) Properly decomposing and hierarchically associating vision and language within a unified model reduces interference between modalities. (ii) A well-designed training strategy enables effective optimization for encoder-free VLMs. Through extensive evaluation, our EVEv2.0 represents a thorough study for developing a decoder-only architecture across modalities, demonstrating superior data efficiency and strong vision-reasoning capability. Code is publicly available at: https://github.com/baaivision/EVE.", "github_manual": "", "github_regex": "https://github.com/baaivision/EVE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06049", "title": "LM2: Large Memory Models", "authors": [ "Jikun Kang", "Wenqi Wu", "Filippos Christianos", "Alex J. Chan", "Fraser Greenlee", "George Thomas", "Marvin Purtorab", "Andy Toulis" ], "github": "https://github.com/convergence-ai/lm2", "project_page": "", "abstract": "This paper introduces the Large Memory Model (LM2), a decoder-only Transformer architecture enhanced with an auxiliary memory module that aims to address the limitations of standard Transformers in multi-step reasoning, relational argumentation, and synthesizing information distributed over long contexts. The proposed LM2 incorporates a memory module that acts as a contextual representation repository, interacting with input tokens via cross attention and updating through gating mechanisms. To preserve the Transformers general-purpose capabilities, LM2 maintains the original information flow while integrating a complementary memory pathway. Experimental results on the BABILong benchmark demonstrate that the LM2model outperforms both the memory-augmented RMT model by 37.1% and the baseline Llama-3.2 model by 86.3% on average across tasks. LM2 exhibits exceptional capabilities in multi-hop inference, numerical reasoning, and large-context question-answering. On the MMLU dataset, it achieves a 5.0% improvement over a pre-trained vanilla model, demonstrating that its memory module does not degrade performance on general tasks. Further, in our analysis, we explore the memory interpretability, effectiveness of memory modules, and test-time behavior. Our findings emphasize the importance of explicit memory in enhancing Transformer architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/convergence-ai/lm2", "github_api_added_by": "auto" }, { "date": "2025-02-11", "arxiv_id": "2502.06023", "title": "Dual Caption Preference Optimization for Diffusion Models", "authors": [ "Amir Saeidi", "Yiran Luo", "Agneet Chatterjee", "Shamanthak Hegde", "Bimsara Pathiraja", "Yezhou Yang", "Chitta Baral" ], "github": "", "project_page": "", "abstract": "Recent advancements in human preference optimization, originally developed for Large Language Models (LLMs), have shown significant potential in improving text-to-image diffusion models. These methods aim to learn the distribution of preferred samples while distinguishing them from less preferred ones. However, existing preference datasets often exhibit overlap between these distributions, leading to a conflict distribution. Additionally, we identified that input prompts contain irrelevant information for less preferred images, limiting the denoising network's ability to accurately predict noise in preference optimization methods, known as the irrelevant prompt issue. To address these challenges, we propose Dual Caption Preference Optimization (DCPO), a novel approach that utilizes two distinct captions to mitigate irrelevant prompts. To tackle conflict distribution, we introduce the Pick-Double Caption dataset, a modified version of Pick-a-Pic v2 with separate captions for preferred and less preferred images. We further propose three different strategies for generating distinct captions: captioning, perturbation, and hybrid methods. Our experiments show that DCPO significantly improves image quality and relevance to prompts, outperforming Stable Diffusion (SD) 2.1, SFT_Chosen, Diffusion-DPO, and MaPO across multiple metrics, including Pickscore, HPSv2.1, GenEval, CLIPscore, and ImageReward, fine-tuned on SD 2.1 as the backbone.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.05431", "title": "APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel Encoding", "authors": [ "Xinyu Yang", "Tianqi Chen", "Beidi Chen" ], "github": "https://github.com/infini-ai-lab/ape", "project_page": "", "abstract": "Context-augmented generation (CAG) techniques, including RAG and ICL, require the efficient combination of multiple contexts to generate responses to user queries. Directly inputting these contexts as a sequence introduces a considerable computational burden by re-encoding the combined selection of contexts for every request. To address this, we explore the promising potential of parallel encoding to independently pre-compute and cache each context's KV states. This approach enables the direct loading of cached states during inference while accommodating more contexts through position reuse across contexts. However, due to misalignments in attention distribution, directly applying parallel encoding results in a significant performance drop. To enable effective and efficient CAG, we propose Adaptive Parallel Encoding (APE), which brings shared prefix, attention temperature, and scaling factor to align the distribution of parallel encoding with sequential encoding. Results on RAG and ICL tasks demonstrate that APE can preserve 98% and 93% sequential encoding performance using the same inputs while outperforming parallel encoding by 3.6% and 7.9%, respectively. It also scales to many-shot CAG, effectively encoding hundreds of contexts in parallel. Efficiency evaluation shows that APE can achieve an end-to-end 4.5times speedup by reducing 28times prefilling time for a 128K-length context.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/infini-ai-lab/ape", "github_api_added_by": "auto" }, { "date": "2025-02-11", "arxiv_id": "2502.06781", "title": "Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning", "authors": [ "Chengqi Lyu", "Songyang Gao", "Yuzhe Gu", "Wenwei Zhang", "Jianfei Gao", "Kuikun Liu", "Ziyi Wang", "Shuaibin Li", "Qian Zhao", "Haian Huang", "Weihan Cao", "Jiangning Liu", "Hongwei Liu", "Junnan Liu", "Songyang Zhang", "Dahua Lin", "Kai Chen" ], "github": "https://github.com/InternLM/OREAL", "project_page": "", "abstract": "Reasoning abilities, especially those for solving complex math problems, are crucial components of general intelligence. Recent advances by proprietary companies, such as o-series models of OpenAI, have made remarkable progress on reasoning tasks. However, the complete technical details remain unrevealed, and the techniques that are believed certainly to be adopted are only reinforcement learning (RL) and the long chain of thoughts. This paper proposes a new RL framework, termed OREAL, to pursue the performance limit that can be achieved through Outcome REwArd-based reinforcement Learning for mathematical reasoning tasks, where only binary outcome rewards are easily accessible. We theoretically prove that behavior cloning on positive trajectories from best-of-N (BoN) sampling is sufficient to learn the KL-regularized optimal policy in binary feedback environments. This formulation further implies that the rewards of negative samples should be reshaped to ensure the gradient consistency between positive and negative samples. To alleviate the long-existing difficulties brought by sparse rewards in RL, which are even exacerbated by the partial correctness of the long chain of thought for reasoning tasks, we further apply a token-level reward model to sample important tokens in reasoning trajectories for learning. With OREAL, for the first time, a 7B model can obtain 94.0 pass@1 accuracy on MATH-500 through RL, being on par with 32B models. OREAL-32B also surpasses previous 32B models trained by distillation with 95.0 pass@1 accuracy on MATH-500. Our investigation also indicates the importance of initial policy models and training queries for RL. Code, models, and data will be released to benefit future researchhttps://github.com/InternLM/OREAL.", "github_manual": "", "github_regex": "https://github.com/InternLM/OREAL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.05609", "title": "Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding", "authors": [ "Sukmin Cho", "Sangjin Choi", "Taeho Hwang", "Jeongyeon Seo", "Soyeong Jeong", "Huije Lee", "Hoyun Song", "Jong C. Park", "Youngjin Kwon" ], "github": "https://github.com/zomss/Hierarchy_Drafting", "project_page": "", "abstract": "Accelerating inference in Large Language Models (LLMs) is critical for real-time interactions, as they have been widely incorporated into real-world services. Speculative decoding, a fully algorithmic solution, has gained attention for improving inference speed by drafting and verifying tokens, thereby generating multiple tokens in a single forward pass. However, current drafting strategies usually require significant fine-tuning or have inconsistent performance across tasks. To address these challenges, we propose Hierarchy Drafting (HD), a novel lossless drafting approach that organizes various token sources into multiple databases in a hierarchical framework based on temporal locality. In the drafting step, HD sequentially accesses multiple databases to obtain draft tokens from the highest to the lowest locality, ensuring consistent acceleration across diverse tasks and minimizing drafting latency. Our experiments on Spec-Bench using LLMs with 7B and 13B parameters demonstrate that HD outperforms existing database drafting methods, achieving robust inference speedups across model sizes, tasks, and temperatures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zomss/Hierarchy_Drafting", "github_api_added_by": "auto" }, { "date": "2025-02-11", "arxiv_id": "2502.06703", "title": "Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling", "authors": [ "Runze Liu", "Junqi Gao", "Jian Zhao", "Kaiyan Zhang", "Xiu Li", "Biqing Qi", "Wanli Ouyang", "Bowen Zhou" ], "github": "https://github.com/openreasoner/openr", "project_page": "", "abstract": "Test-Time Scaling (TTS) is an important method for improving the performance of Large Language Models (LLMs) by using additional computation during the inference phase. However, current studies do not systematically analyze how policy models, Process Reward Models (PRMs), and problem difficulty influence TTS. This lack of analysis limits the understanding and practical use of TTS methods. In this paper, we focus on two core questions: (1) What is the optimal approach to scale test-time computation across different policy models, PRMs, and problem difficulty levels? (2) To what extent can extended computation improve the performance of LLMs on complex tasks, and can smaller language models outperform larger ones through this approach? Through comprehensive experiments on MATH-500 and challenging AIME24 tasks, we have the following observations: (1) The compute-optimal TTS strategy is highly dependent on the choice of policy model, PRM, and problem difficulty. (2) With our compute-optimal TTS strategy, extremely small policy models can outperform larger models. For example, a 1B LLM can exceed a 405B LLM on MATH-500. Moreover, on both MATH-500 and AIME24, a 0.5B LLM outperforms GPT-4o, a 3B LLM surpasses a 405B LLM, and a 7B LLM beats o1 and DeepSeek-R1, while with higher inference efficiency. These findings show the significance of adapting TTS strategies to the specific characteristics of each task and model and indicate that TTS is a promising approach for enhancing the reasoning abilities of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openreasoner/openr", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06527", "title": "CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for Zero-Shot Customized Video Diffusion Transformers", "authors": [ "D. She", "Mushui Liu", "Jingxuan Pang", "Jin Wang", "Zhen Yang", "Wanggui He", "Guanghao Zhang", "Yi Wang", "Qihan Huang", "Haobin Tang", "Yunlong Yu", "Siming Fu" ], "github": "", "project_page": "", "abstract": "Customized generation has achieved significant progress in image synthesis, yet personalized video generation remains challenging due to temporal inconsistencies and quality degradation. In this paper, we introduce CustomVideoX, an innovative framework leveraging the video diffusion transformer for personalized video generation from a reference image. CustomVideoX capitalizes on pre-trained video networks by exclusively training the LoRA parameters to extract reference features, ensuring both efficiency and adaptability. To facilitate seamless interaction between the reference image and video content, we propose 3D Reference Attention, which enables direct and simultaneous engagement of reference image features with all video frames across spatial and temporal dimensions. To mitigate the excessive influence of reference image features and textual guidance on generated video content during inference, we implement the Time-Aware Reference Attention Bias (TAB) strategy, dynamically modulating reference bias over different time steps. Additionally, we introduce the Entity Region-Aware Enhancement (ERAE) module, aligning highly activated regions of key entity tokens with reference feature injection by adjusting attention bias. To thoroughly evaluate personalized video generation, we establish a new benchmark, VideoBench, comprising over 50 objects and 100 prompts for extensive assessment. Experimental results show that CustomVideoX significantly outperforms existing methods in terms of video consistency and quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06782", "title": "Lumina-Video: Efficient and Flexible Video Generation with Multi-scale Next-DiT", "authors": [ "Dongyang Liu", "Shicheng Li", "Yutong Liu", "Zhen Li", "Kai Wang", "Xinyue Li", "Qi Qin", "Yufei Liu", "Yi Xin", "Zhongyu Li", "Bin Fu", "Chenyang Si", "Yuewen Cao", "Conghui He", "Ziwei Liu", "Yu Qiao", "Qibin Hou", "Hongsheng Li", "Peng Gao" ], "github": "https://github.com/Alpha-VLLM/Lumina-Video", "project_page": "", "abstract": "Recent advancements have established Diffusion Transformers (DiTs) as a dominant framework in generative modeling. Building on this success, Lumina-Next achieves exceptional performance in the generation of photorealistic images with Next-DiT. However, its potential for video generation remains largely untapped, with significant challenges in modeling the spatiotemporal complexity inherent to video data. To address this, we introduce Lumina-Video, a framework that leverages the strengths of Next-DiT while introducing tailored solutions for video synthesis. Lumina-Video incorporates a Multi-scale Next-DiT architecture, which jointly learns multiple patchifications to enhance both efficiency and flexibility. By incorporating the motion score as an explicit condition, Lumina-Video also enables direct control of generated videos' dynamic degree. Combined with a progressive training scheme with increasingly higher resolution and FPS, and a multi-source training scheme with mixed natural and synthetic data, Lumina-Video achieves remarkable aesthetic quality and motion smoothness at high training and inference efficiency. We additionally propose Lumina-V2A, a video-to-audio model based on Next-DiT, to create synchronized sounds for generated videos. Codes are released at https://www.github.com/Alpha-VLLM/Lumina-Video.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Alpha-VLLM/Lumina-Video", "github_api_added_by": "auto" }, { "date": "2025-02-11", "arxiv_id": "2502.05957", "title": "MetaChain: A Fully-Automated and Zero-Code Framework for LLM Agents", "authors": [ "Jiabin Tang", "Tianyu Fan", "Chao Huang" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM) Agents have demonstrated remarkable capabilities in task automation and intelligent decision-making, driving the widespread adoption of agent development frameworks such as LangChain and AutoGen. However, these frameworks predominantly serve developers with extensive technical expertise - a significant limitation considering that only 0.03 % of the global population possesses the necessary programming skills. This stark accessibility gap raises a fundamental question: Can we enable everyone, regardless of technical background, to build their own LLM agents using natural language alone? To address this challenge, we introduce MetaChain-a Fully-Automated and highly Self-Developing framework that enables users to create and deploy LLM agents through Natural Language Alone. Operating as an autonomous Agent Operating System, MetaChain comprises four key components: i) Agentic System Utilities, ii) LLM-powered Actionable Engine, iii) Self-Managing File System, and iv) Self-Play Agent Customization module. This lightweight yet powerful system enables efficient and dynamic creation and modification of tools, agents, and workflows without coding requirements or manual intervention. Beyond its code-free agent development capabilities, MetaChain also serves as a versatile multi-agent system for General AI Assistants. Comprehensive evaluations on the GAIA benchmark demonstrate MetaChain's effectiveness in generalist multi-agent tasks, surpassing existing state-of-the-art methods. Furthermore, MetaChain's Retrieval-Augmented Generation (RAG)-related capabilities have shown consistently superior performance compared to many alternative LLM-based solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06786", "title": "Matryoshka Quantization", "authors": [ "Pranav Nair", "Puranjay Datta", "Jeff Dean", "Prateek Jain", "Aditya Kusupati" ], "github": "", "project_page": "", "abstract": "Quantizing model weights is critical for reducing the communication and inference costs of large models. However, quantizing models -- especially to low precisions like int4 or int2 -- requires a trade-off in model quality; int2, in particular, is known to severely degrade model quality. Consequently, practitioners are often forced to maintain multiple models with different quantization levels or serve a single model that best satisfies the quality-latency trade-off. On the other hand, integer data types, such as int8, inherently possess a nested (Matryoshka) structure where smaller bit-width integers, like int4 or int2, are nested within the most significant bits. This paper proposes Matryoshka Quantization (MatQuant), a novel multi-scale quantization technique that addresses the challenge of needing multiple quantized models. It allows training and maintaining just one model, which can then be served at different precision levels. Furthermore, due to the co-training and co-distillation regularization provided by MatQuant, the int2 precision models extracted by MatQuant can be up to 10% more accurate than standard int2 quantization (using techniques like QAT or OmniQuant). This represents significant progress in model quantization, demonstrated by the fact that, with the same recipe, an int2 FFN-quantized Gemma-2 9B model is more accurate than an int8 FFN-quantized Gemma-2 2B model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06764", "title": "History-Guided Video Diffusion", "authors": [ "Kiwhan Song", "Boyuan Chen", "Max Simchowitz", "Yilun Du", "Russ Tedrake", "Vincent Sitzmann" ], "github": "", "project_page": "", "abstract": "Classifier-free guidance (CFG) is a key technique for improving conditional generation in diffusion models, enabling more accurate control while enhancing sample quality. It is natural to extend this technique to video diffusion, which generates video conditioned on a variable number of context frames, collectively referred to as history. However, we find two key challenges to guiding with variable-length history: architectures that only support fixed-size conditioning, and the empirical observation that CFG-style history dropout performs poorly. To address this, we propose the Diffusion Forcing Transformer (DFoT), a video diffusion architecture and theoretically grounded training objective that jointly enable conditioning on a flexible number of history frames. We then introduce History Guidance, a family of guidance methods uniquely enabled by DFoT. We show that its simplest form, vanilla history guidance, already significantly improves video generation quality and temporal consistency. A more advanced method, history guidance across time and frequency further enhances motion dynamics, enables compositional generalization to out-of-distribution history, and can stably roll out extremely long videos. Website: https://boyuan.space/history-guidance", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.05415", "title": "Show-o Turbo: Towards Accelerated Unified Multimodal Understanding and Generation", "authors": [ "Chenkai Xu", "Xu Wang", "Zhenyi Liao", "Yishun Li", "Tianqi Hou", "Zhijie Deng" ], "github": "https://github.com/zhijie-group/unicms", "project_page": "", "abstract": "There has been increasing research interest in building unified multimodal understanding and generation models, among which Show-o stands as a notable representative, demonstrating great promise for both text-to-image and image-to-text generation. The inference of Show-o involves progressively denoising image tokens and autoregressively decoding text tokens, and hence, unfortunately, suffers from inefficiency issues from both sides. This paper introduces Show-o Turbo to bridge the gap. We first identify a unified denoising perspective for the generation of images and text in Show-o based on the parallel decoding of text tokens. We then propose to extend consistency distillation (CD), a qualified approach for shortening the denoising process of diffusion models, to the multimodal denoising trajectories of Show-o. We introduce a trajectory segmentation strategy and a curriculum learning procedure to improve the training convergence. Empirically, in text-to-image generation, Show-o Turbo displays a GenEval score of 0.625 at 4 sampling steps without using classifier-free guidance (CFG), outperforming that of the original Show-o with 8 steps and CFG; in image-to-text generation, Show-o Turbo exhibits a 1.5x speedup without significantly sacrificing performance. The code is available at https://github.com/zhijie-group/Show-o-Turbo.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhijie-group/unicms", "github_api_added_by": "auto" }, { "date": "2025-02-11", "arxiv_id": "2502.04370", "title": "DreamDPO: Aligning Text-to-3D Generation with Human Preferences via Direct Preference Optimization", "authors": [ "Zhenglin Zhou", "Xiaobo Xia", "Fan Ma", "Hehe Fan", "Yi Yang", "Tat-Seng Chua" ], "github": "https://github.com/3DTopia/GPTEval3D/blob/main/data/tournament-v0/config.json", "project_page": "", "abstract": "Text-to-3D generation automates 3D content creation from textual descriptions, which offers transformative potential across various fields. However, existing methods often struggle to align generated content with human preferences, limiting their applicability and flexibility. To address these limitations, in this paper, we propose DreamDPO, an optimization-based framework that integrates human preferences into the 3D generation process, through direct preference optimization. Practically, DreamDPO first constructs pairwise examples, then compare their alignment with human preferences using reward or large multimodal models, and lastly optimizes the 3D representation with a preference-driven loss function. By leveraging pairwise comparison to reflect preferences, DreamDPO reduces reliance on precise pointwise quality evaluations while enabling fine-grained controllability through preference-guided optimization. Experiments demonstrate that DreamDPO achieves competitive results, and provides higher-quality and more controllable 3D content compared to existing methods. The code and models will be open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/GPTEval3D/blob/main/data/tournament-v0/config.json", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06394", "title": "SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators", "authors": [ "Daniil Moskovskiy", "Nikita Sushko", "Sergey Pletenev", "Elena Tutubalina", "Alexander Panchenko" ], "github": "", "project_page": "", "abstract": "Existing approaches to multilingual text detoxification are hampered by the scarcity of parallel multilingual datasets. In this work, we introduce a pipeline for the generation of multilingual parallel detoxification data. We also introduce SynthDetoxM, a manually collected and synthetically generated multilingual parallel text detoxification dataset comprising 16,000 high-quality detoxification sentence pairs across German, French, Spanish and Russian. The data was sourced from different toxicity evaluation datasets and then rewritten with nine modern open-source LLMs in few-shot setting. Our experiments demonstrate that models trained on the produced synthetic datasets have superior performance to those trained on the human-annotated MultiParaDetox dataset even in data limited setting. Models trained on SynthDetoxM outperform all evaluated LLMs in few-shot setting. We release our dataset and code to help further research in multilingual text detoxification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.05795", "title": "The Curse of Depth in Large Language Models", "authors": [ "Wenfang Sun", "Xinyuan Song", "Pengxiang Li", "Lu Yin", "Yefeng Zheng", "Shiwei Liu" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce the Curse of Depth, a concept that highlights, explains, and addresses the recent observation in modern Large Language Models(LLMs) where nearly half of the layers are less effective than expected. We first confirm the wide existence of this phenomenon across the most popular families of LLMs such as Llama, Mistral, DeepSeek, and Qwen. Our analysis, theoretically and empirically, identifies that the underlying reason for the ineffectiveness of deep layers in LLMs is the widespread usage of Pre-Layer Normalization (Pre-LN). While Pre-LN stabilizes the training of Transformer LLMs, its output variance exponentially grows with the model depth, which undesirably causes the derivative of the deep Transformer blocks to be an identity matrix, and therefore barely contributes to the training. To resolve this training pitfall, we propose LayerNorm Scaling, which scales the variance of output of the layer normalization inversely by the square root of its depth. This simple modification mitigates the output variance explosion of deeper Transformer layers, improving their contribution. Our experimental results, spanning model sizes from 130M to 1B, demonstrate that LayerNorm Scaling significantly enhances LLM pre-training performance compared to Pre-LN. Moreover, this improvement seamlessly carries over to supervised fine-tuning. All these gains can be attributed to the fact that LayerNorm Scaling enables deeper layers to contribute more effectively during training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06060", "title": "Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning", "authors": [ "Bidipta Sarkar", "Warren Xia", "C. Karen Liu", "Dorsa Sadigh" ], "github": "https://github.com/SocialDeductionLLM/SocialDeductionLLM", "project_page": "", "abstract": "Communicating in natural language is a powerful tool in multi-agent settings, as it enables independent agents to share information in partially observable settings and allows zero-shot coordination with humans. However, most prior works are limited as they either rely on training with large amounts of human demonstrations or lack the ability to generate natural and useful communication strategies. In this work, we train language models to have productive discussions about their environment in natural language without any human demonstrations. We decompose the communication problem into listening and speaking. Our key idea is to leverage the agent's goal to predict useful information about the world as a dense reward signal that guides communication. Specifically, we improve a model's listening skills by training them to predict information about the environment based on discussions, and we simultaneously improve a model's speaking skills with multi-agent reinforcement learning by rewarding messages based on their influence on other agents. To investigate the role and necessity of communication in complex social settings, we study an embodied social deduction game based on Among Us, where the key question to answer is the identity of an adversarial imposter. We analyze emergent behaviors due to our technique, such as accusing suspects and providing evidence, and find that it enables strong discussions, doubling the win rates compared to standard RL. We release our code and models at https://socialdeductionllm.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SocialDeductionLLM/SocialDeductionLLM", "github_api_added_by": "auto" }, { "date": "2025-02-11", "arxiv_id": "2502.05664", "title": "CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging", "authors": [ "Md. Ashraful Islam", "Mohammed Eunus Ali", "Md Rizwan Parvez" ], "github": "https://github.com/kagnlp/CodeGenerator", "project_page": "", "abstract": "Large Language Models (LLMs) have made significant strides in code generation and problem solving. Current approaches employ external tool-based iterative debuggers that use compiler or other tool-based runtime feedback to refine coarse programs generated by various methods. However, the effectiveness of these approaches heavily relies on the quality of the initial code generation, which remains an open challenge. In this paper, we introduce CodeSim, a novel multi-agent code generation framework that comprehensively addresses the stages of program synthesis-planning, coding, and debugging-through a human-like perception approach. As human verifies their understanding of any algorithms through visual simulation, CodeSim uniquely features a method of plan verification and internal debugging through the step-by-step simulation of input/output. Extensive experiments across seven challenging competitive problem-solving and program synthesis benchmarks demonstrate CodeSim's remarkable code generation capabilities. Our framework achieves new state-of-the-art (pass@1) results-(HumanEval 95.1%, MBPP 90.7%, APPS 22%, and CodeContests 29.1%). Furthermore, our method shows potential for even greater enhancement when cascaded with external debuggers. To facilitate further research and development in this area, we have open-sourced our framework in this link (https://kagnlp.github.io/codesim.github.io/).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kagnlp/CodeGenerator", "github_api_added_by": "auto" }, { "date": "2025-02-11", "arxiv_id": "2411.18676", "title": "Embodied Red Teaming for Auditing Robotic Foundation Models", "authors": [ "Sathwik Karnik", "Zhang-Wei Hong", "Nishant Abhangi", "Yen-Chen Lin", "Tsun-Hsuan Wang", "Christophe Dupuy", "Rahul Gupta", "Pulkit Agrawal" ], "github": "https://github.com/Improbable-AI/embodied-red-teaming", "project_page": "", "abstract": "Language-conditioned robot models have the potential to enable robots to perform a wide range of tasks based on natural language instructions. However, assessing their safety and effectiveness remains challenging because it is difficult to test all the different ways a single task can be phrased. Current benchmarks have two key limitations: they rely on a limited set of human-generated instructions, missing many challenging cases, and focus only on task performance without assessing safety, such as avoiding damage. To address these gaps, we introduce Embodied Red Teaming (ERT), a new evaluation method that generates diverse and challenging instructions to test these models. ERT uses automated red teaming techniques with Vision Language Models (VLMs) to create contextually grounded, difficult instructions. Experimental results show that state-of-the-art language-conditioned robot models fail or behave unsafely on ERT-generated instructions, underscoring the shortcomings of current benchmarks in evaluating real-world performance and safety. Code and videos are available at: https://s-karnik.github.io/embodied-red-team-project-page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Improbable-AI/embodied-red-teaming", "github_api_added_by": "auto" }, { "date": "2025-02-11", "arxiv_id": "2502.06776", "title": "Towards Internet-Scale Training For Agents", "authors": [ "Brandon Trabucco", "Gunnar Sigurdsson", "Robinson Piramuthu", "Ruslan Salakhutdinov" ], "github": "", "project_page": "", "abstract": "The predominant approach for training web navigation agents gathers human demonstrations for a set of popular websites and hand-written tasks, but it is becoming clear that human data are an inefficient resource. We develop a pipeline to facilitate Internet-scale training for agents without laborious human annotations. In the first stage, an LLM generates tasks for 150k diverse websites. In the next stage, LLM agents complete tasks and produce trajectories. In the final stage, an LLM reviews the trajectories and judges their success. Language models are competitive with human annotators, detecting and filtering out harmful content with an accuracy of 97%, generating feasible tasks with an 89% rate, and judging successful trajectories with an 82.6% accuracy. Scaling the pipeline, agents based on Llama 3.1 70B solve 16.7% of tasks for 150k sites. Training on the data generated by our pipeline is competitive with training on human demonstrations. In data-limited settings derived from Mind2Web and WebLINX, we improve Step Accuracy by up to +89.5% and +122.1% respectively for agents trained on mixtures of data from our pipeline, and human data. When training agents with all available human data from these benchmarks, agents fail to generalize to diverse real sites, and adding our data improves their generalization by +149.0% for WebLINX and +156.3% for Mind2Web. Code will be available at: data-for-agents.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06282", "title": "Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE", "authors": [ "Haiduo Huang", "Fuwei Yang", "Zhenhua Liu", "Yixing Xu", "Jinze Li", "Yang Liu", "Xuanwu Yin", "Dong Li", "Pengju Ren", "Emad Barsoum" ], "github": "https://github.com/haiduo/Jakiro", "project_page": "", "abstract": "Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to predict multiple tokens, which are then verified in parallel by the larger target model. However, the limited capacity of the draft model often necessitates tree-based sampling to improve prediction accuracy, where multiple candidates are generated at each step. We identify a key limitation in this approach: the candidates at the same step are derived from the same representation, limiting diversity and reducing overall effectiveness. To address this, we propose Jakiro, leveraging Mixture of Experts (MoE), where independent experts generate diverse predictions, effectively decoupling correlations among candidates. Furthermore, we introduce a hybrid inference strategy, combining autoregressive decoding for initial tokens with parallel decoding for subsequent stages, and enhance the latter with contrastive mechanism in features to improve accuracy. Our method significantly boosts prediction accuracy and achieves higher inference speedups. Extensive experiments across diverse models validate the effectiveness and robustness of our approach, establishing a new SOTA in speculative decoding. Our codes are available at https://github.com/haiduo/Jakiro.", "github_manual": "", "github_regex": "https://github.com/haiduo/Jakiro", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-11", "arxiv_id": "2502.06867", "title": "Forbidden Science: Dual-Use AI Challenge Benchmark and Scientific Refusal Tests", "authors": [ "David Noever", "Forrest McKee" ], "github": "", "project_page": "", "abstract": "The development of robust safety benchmarks for large language models requires open, reproducible datasets that can measure both appropriate refusal of harmful content and potential over-restriction of legitimate scientific discourse. We present an open-source dataset and testing framework for evaluating LLM safety mechanisms across mainly controlled substance queries, analyzing four major models' responses to systematically varied prompts. Our results reveal distinct safety profiles: Claude-3.5-sonnet demonstrated the most conservative approach with 73% refusals and 27% allowances, while Mistral attempted to answer 100% of queries. GPT-3.5-turbo showed moderate restriction with 10% refusals and 90% allowances, and Grok-2 registered 20% refusals and 80% allowances. Testing prompt variation strategies revealed decreasing response consistency, from 85% with single prompts to 65% with five variations. This publicly available benchmark enables systematic evaluation of the critical balance between necessary safety restrictions and potential over-censorship of legitimate scientific inquiry, while providing a foundation for measuring progress in AI safety implementation. Chain-of-thought analysis reveals potential vulnerabilities in safety mechanisms, highlighting the complexity of implementing robust safeguards without unduly restricting desirable and valid scientific discourse.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.06807", "title": "Competitive Programming with Large Reasoning Models", "authors": [ "OpenAI", "Ahmed El-Kishky", "Alexander Wei", "Andre Saraiva", "Borys Minaev", "Daniel Selsam", "David Dohan", "Francis Song", "Hunter Lightman", "Ignasi Clavera", "Jakub Pachocki", "Jerry Tworek", "Lorenz Kuhn", "Lukasz Kaiser", "Mark Chen", "Max Schwarzer", "Mostafa Rohaninejad", "Nat McAleese", "o3 contributors", "Oleg M\u00fcrk", "Rhythm Garg", "Rui Shu", "Szymon Sidor", "Vineet Kosaraju", "Wenda Zhou" ], "github": "", "project_page": "", "abstract": "We show that reinforcement learning applied to large language models (LLMs) significantly boosts performance on complex coding and reasoning tasks. Additionally, we compare two general-purpose reasoning models - OpenAI o1 and an early checkpoint of o3 - with a domain-specific system, o1-ioi, which uses hand-engineered inference strategies designed for competing in the 2024 International Olympiad in Informatics (IOI). We competed live at IOI 2024 with o1-ioi and, using hand-crafted test-time strategies, placed in the 49th percentile. Under relaxed competition constraints, o1-ioi achieved a gold medal. However, when evaluating later models such as o3, we find that o3 achieves gold without hand-crafted domain-specific strategies or relaxed constraints. Our findings show that although specialized pipelines such as o1-ioi yield solid improvements, the scaled-up, general-purpose o3 model surpasses those results without relying on hand-crafted inference heuristics. Notably, o3 achieves a gold medal at the 2024 IOI and obtains a Codeforces rating on par with elite human competitors. Overall, these results indicate that scaling general-purpose reinforcement learning, rather than relying on domain-specific techniques, offers a robust path toward state-of-the-art AI in reasoning domains, such as competitive programming.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.07316", "title": "CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction", "authors": [ "Junlong Li", "Daya Guo", "Dejian Yang", "Runxin Xu", "Yu Wu", "Junxian He" ], "github": "https://github.com/hkust-nlp/CodeIO", "project_page": "", "abstract": "Reasoning is a fundamental capability of Large Language Models. While prior research predominantly focuses on enhancing narrow skills like math or code generation, improving performance on many other reasoning tasks remains challenging due to sparse and fragmented training data. To address this issue, we propose CodeI/O, a novel approach that systematically condenses diverse reasoning patterns inherently embedded in contextually-grounded codes, through transforming the original code into a code input-output prediction format. By training models to predict inputs/outputs given code and test cases entirely in natural language as Chain-of-Thought (CoT) rationales, we expose them to universal reasoning primitives -- like logic flow planning, state-space searching, decision tree traversal, and modular decomposition -- while decoupling structured reasoning from code-specific syntax and preserving procedural rigor. Experimental results demonstrate CodeI/O leads to consistent improvements across symbolic, scientific, logic, math & numerical, and commonsense reasoning tasks. By matching the existing ground-truth outputs or re-executing the code with predicted inputs, we can verify each prediction and further enhance the CoTs through multi-turn revision, resulting in CodeI/O++ and achieving higher performance. Our data and models are available at https://github.com/hkust-nlp/CodeIO.", "github_manual": "", "github_regex": "https://github.com/hkust-nlp/CodeIO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.06589", "title": "Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training", "authors": [ "Yuchen Zhuang", "Jingfeng Yang", "Haoming Jiang", "Xin Liu", "Kewei Cheng", "Sanket Lokegaonkar", "Yifan Gao", "Qing Ping", "Tianyi Liu", "Binxuan Huang", "Zheng Li", "Zhengyang Wang", "Pei Chen", "Ruijie Wang", "Rongzhi Zhang", "Nasser Zalmout", "Priyanka Nigam", "Bing Yin", "Chao Zhang" ], "github": "", "project_page": "", "abstract": "Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous agents typically rely on complex prompting or extensive fine-tuning, which often fails to introduce new capabilities while preserving strong generalizability. We introduce Hephaestus-Forge, the first large-scale pre-training corpus designed to enhance the fundamental capabilities of LLM agents in API function calling, intrinsic reasoning and planning, and adapting to environmental feedback. Hephaestus-Forge comprises 103B agent-specific data encompassing 76,537 APIs, including both tool documentation to introduce knowledge of API functions and function calling trajectories to strengthen intrinsic reasoning. To explore effective training protocols, we investigate scaling laws to identify the optimal recipe in data mixing ratios. By continual pre-training on Hephaestus-Forge, Hephaestus outperforms small- to medium-scale open-source LLMs and rivals commercial LLMs on three agent benchmarks, demonstrating the effectiveness of our pre-training corpus in enhancing fundamental agentic capabilities and generalization of LLMs to new tasks or environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.07617", "title": "Scaling Pre-training to One Hundred Billion Data for Vision Language Models", "authors": [ "Xiao Wang", "Ibrahim Alabdulmohsin", "Daniel Salz", "Zhe Li", "Keran Rong", "Xiaohua Zhai" ], "github": "", "project_page": "", "abstract": "We provide an empirical investigation of the potential of pre-training vision-language models on an unprecedented scale: 100 billion examples. We find that model performance tends to saturate at this scale on many common Western-centric classification and retrieval benchmarks, such as COCO Captions. Nevertheless, tasks of cultural diversity achieve more substantial gains from the 100-billion scale web data, thanks to its coverage of long-tail concepts. Furthermore, we analyze the model's multilinguality and show gains in low-resource languages as well. In addition, we observe that reducing the size of the pretraining dataset via quality filters like using CLIP, typically used to enhance performance, may inadvertently reduce the cultural diversity represented even in large-scale datasets. Our results highlight that while traditional benchmarks may not benefit significantly from scaling noisy, raw web data to 100 billion examples, this data scale is vital for building truly inclusive multimodal systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.07508", "title": "Enhance-A-Video: Better Generated Video for Free", "authors": [ "Yang Luo", "Xuanlei Zhao", "Mengzhao Chen", "Kaipeng Zhang", "Wenqi Shao", "Kai Wang", "Zhangyang Wang", "Yang You" ], "github": "https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video", "project_page": "", "abstract": "DiT-based video generation has achieved remarkable results, but research into enhancing existing models remains relatively unexplored. In this work, we introduce a training-free approach to enhance the coherence and quality of DiT-based generated videos, named Enhance-A-Video. The core idea is enhancing the cross-frame correlations based on non-diagonal temporal attention distributions. Thanks to its simple design, our approach can be easily applied to most DiT-based video generation frameworks without any retraining or fine-tuning. Across various DiT-based video generation models, our approach demonstrates promising improvements in both temporal consistency and visual quality. We hope this research can inspire future explorations in video generation enhancement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video", "github_api_added_by": "auto" }, { "date": "2025-02-12", "arxiv_id": "2502.07527", "title": "NatureLM: Deciphering the Language of Nature for Scientific Discovery", "authors": [ "Yingce Xia", "Peiran Jin", "Shufang Xie", "Liang He", "Chuan Cao", "Renqian Luo", "Guoqing Liu", "Yue Wang", "Zequn Liu", "Yuan-Jyue Chen", "Zekun Guo", "Yeqi Bai", "Pan Deng", "Yaosen Min", "Ziheng Lu", "Hongxia Hao", "Han Yang", "Jielan Li", "Chang Liu", "Jia Zhang", "Jianwei Zhu", "Kehan Wu", "Wei Zhang", "Kaiyuan Gao", "Qizhi Pei", "Qian Wang", "Xixian Liu", "Yanting Li", "Houtian Zhu", "Yeqing Lu", "Mingqian Ma", "Zun Wang", "Tian Xie", "Krzysztof Maziarz", "Marwin Segler", "Zhao Yang", "Zilong Chen", "Yu Shi", "Shuxin Zheng", "Lijun Wu", "Chen Hu", "Peggy Dai", "Tie-Yan Liu", "Haiguang Liu", "Tao Qin" ], "github": "", "project_page": "", "abstract": "Foundation models have revolutionized natural language processing and artificial intelligence, significantly enhancing how machines comprehend and generate human languages. Inspired by the success of these foundation models, researchers have developed foundation models for individual scientific domains, including small molecules, materials, proteins, DNA, and RNA. However, these models are typically trained in isolation, lacking the ability to integrate across different scientific domains. Recognizing that entities within these domains can all be represented as sequences, which together form the \"language of nature\", we introduce Nature Language Model (briefly, NatureLM), a sequence-based science foundation model designed for scientific discovery. Pre-trained with data from multiple scientific domains, NatureLM offers a unified, versatile model that enables various applications including: (i) generating and optimizing small molecules, proteins, RNA, and materials using text instructions; (ii) cross-domain generation/design, such as protein-to-molecule and protein-to-RNA generation; and (iii) achieving state-of-the-art performance in tasks like SMILES-to-IUPAC translation and retrosynthesis on USPTO-50k. NatureLM offers a promising generalist approach for various scientific tasks, including drug discovery (hit generation/optimization, ADMET optimization, synthesis), novel material design, and the development of therapeutic proteins or nucleotides. We have developed NatureLM models in different sizes (1 billion, 8 billion, and 46.7 billion parameters) and observed a clear improvement in performance as the model size increases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.07374", "title": "LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!", "authors": [ "Dacheng Li", "Shiyi Cao", "Tyler Griggs", "Shu Liu", "Xiangxi Mo", "Shishir G. Patil", "Matei Zaharia", "Joseph E. Gonzalez", "Ion Stoica" ], "github": "https://github.com/NovaSky-AI/SkyThought", "project_page": "", "abstract": "Large reasoning models (LRMs) tackle complex reasoning problems by following long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking, and self-validation. However, the training techniques and data requirements to elicit Long CoT remain poorly understood. In this work, we find that a Large Language model (LLM) can effectively learn Long CoT reasoning through data-efficient supervised fine-tuning (SFT) and parameter-efficient low-rank adaptation (LoRA). With just 17k long CoT training samples, the Qwen2.5-32B-Instruct model achieves significant improvements on a wide range of math and coding benchmarks, including 56.7% (+40.0%) on AIME 2024 and 57.0% (+8.1%) on LiveCodeBench, competitive to the proprietary o1-preview model's score of 44.6% and 59.1%. More importantly, we find that the structure of Long CoT is critical to the learning process, whereas the content of individual reasoning steps has minimal impact. Perturbations affecting content, such as training on incorrect samples or removing reasoning keywords, have little impact on performance. In contrast, structural modifications that disrupt logical consistency in the Long CoT, such as shuffling or deleting reasoning steps, significantly degrade accuracy. For example, a model trained on Long CoT samples with incorrect answers still achieves only 3.2% lower accuracy compared to training with fully correct samples. These insights deepen our understanding of how to elicit reasoning capabilities in LLMs and highlight key considerations for efficiently training the next generation of reasoning models. This is the academic paper of our previous released Sky-T1-32B-Preview model. Codes are available at https://github.com/NovaSky-AI/SkyThought.", "github_manual": "", "github_regex": "https://github.com/NovaSky-AI/SkyThought", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.07701", "title": "Magic 1-For-1: Generating One Minute Video Clips within One Minute", "authors": [ "Hongwei Yi", "Shitong Shao", "Tian Ye", "Jiantong Zhao", "Qingyu Yin", "Michael Lingelbach", "Li Yuan", "Yonghong Tian", "Enze Xie", "Daquan Zhou" ], "github": "https://github.com/DA-Group-PKU/Magic-1-For-1", "project_page": "", "abstract": "In this technical report, we present Magic 1-For-1 (Magic141), an efficient video generation model with optimized memory consumption and inference latency. The key idea is simple: factorize the text-to-video generation task into two separate easier tasks for diffusion step distillation, namely text-to-image generation and image-to-video generation. We verify that with the same optimization algorithm, the image-to-video task is indeed easier to converge over the text-to-video task. We also explore a bag of optimization tricks to reduce the computational cost of training the image-to-video (I2V) models from three aspects: 1) model convergence speedup by using a multi-modal prior condition injection; 2) inference latency speed up by applying an adversarial step distillation, and 3) inference memory cost optimization with parameter sparsification. With those techniques, we are able to generate 5-second video clips within 3 seconds. By applying a test time sliding window, we are able to generate a minute-long video within one minute with significantly improved visual quality and motion dynamics, spending less than 1 second for generating 1 second video clips on average. We conduct a series of preliminary explorations to find out the optimal tradeoff between computational cost and video quality during diffusion step distillation and hope this could be a good foundation model for open-source explorations. The code and the model weights are available at https://github.com/DA-Group-PKU/Magic-1-For-1.", "github_manual": "", "github_regex": "https://github.com/DA-Group-PKU/Magic-1-For-1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.07445", "title": "Forget What You Know about LLMs Evaluations - LLMs are Like a Chameleon", "authors": [ "Nurit Cohen-Inger", "Yehonatan Elisha", "Bracha Shapira", "Lior Rokach", "Seffi Cohen" ], "github": "https://github.com/SeffiCohen/CBOD", "project_page": "", "abstract": "Large language models (LLMs) often appear to excel on public benchmarks, but these high scores may mask an overreliance on dataset-specific surface cues rather than true language understanding. We introduce the Chameleon Benchmark Overfit Detector (C-BOD), a meta-evaluation framework that systematically distorts benchmark prompts via a parametric transformation and detects overfitting of LLMs. By rephrasing inputs while preserving their semantic content and labels, C-BOD exposes whether a model's performance is driven by memorized patterns. Evaluated on the MMLU benchmark using 26 leading LLMs, our method reveals an average performance degradation of 2.15% under modest perturbations, with 20 out of 26 models exhibiting statistically significant differences. Notably, models with higher baseline accuracy exhibit larger performance differences under perturbation, and larger LLMs tend to be more sensitive to rephrasings indicating that both cases may overrely on fixed prompt patterns. In contrast, the Llama family and models with lower baseline accuracy show insignificant degradation, suggesting reduced dependency on superficial cues. Moreover, C-BOD's dataset- and model-agnostic design allows easy integration into training pipelines to promote more robust language understanding. Our findings challenge the community to look beyond leaderboard scores and prioritize resilience and generalization in LLM evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SeffiCohen/CBOD", "github_api_added_by": "auto" }, { "date": "2025-02-12", "arxiv_id": "2502.07531", "title": "VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation", "authors": [ "Sixiao Zheng", "Zimian Peng", "Yanpeng Zhou", "Yi Zhu", "Hang Xu", "Xiangru Huang", "Yanwei Fu" ], "github": "", "project_page": "", "abstract": "Recent image-to-video generation methods have demonstrated success in enabling control over one or two visual elements, such as camera trajectory or object motion. However, these methods are unable to offer control over multiple visual elements due to limitations in data and network efficacy. In this paper, we introduce VidCRAFT3, a novel framework for precise image-to-video generation that enables control over camera motion, object motion, and lighting direction simultaneously. To better decouple control over each visual element, we propose the Spatial Triple-Attention Transformer, which integrates lighting direction, text, and image in a symmetric way. Since most real-world video datasets lack lighting annotations, we construct a high-quality synthetic video dataset, the VideoLightingDirection (VLD) dataset. This dataset includes lighting direction annotations and objects of diverse appearance, enabling VidCRAFT3 to effectively handle strong light transmission and reflection effects. Additionally, we propose a three-stage training strategy that eliminates the need for training data annotated with multiple visual elements (camera motion, object motion, and lighting direction) simultaneously. Extensive experiments on benchmark datasets demonstrate the efficacy of VidCRAFT3 in producing high-quality video content, surpassing existing state-of-the-art methods in terms of control granularity and visual coherence. All code and data will be publicly available. Project page: https://sixiaozheng.github.io/VidCRAFT3/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.03997", "title": "CAD-Editor: A Locate-then-Infill Framework with Automated Training Data Synthesis for Text-Based CAD Editing", "authors": [ "Yu Yuan", "Shizhao Sun", "Qi Liu", "Jiang Bian" ], "github": "https://github.com/microsoft/CAD-Editor", "project_page": "", "abstract": "Computer Aided Design (CAD) is indispensable across various industries. Text-based CAD editing, which automates the modification of CAD models based on textual instructions, holds great potential but remains underexplored. Existing methods primarily focus on design variation generation or text-based CAD generation, either lacking support for text-based control or neglecting existing CAD models as constraints. We introduce CAD-Editor, the first framework for text-based CAD editing. To address the challenge of demanding triplet data with accurate correspondence for training, we propose an automated data synthesis pipeline. This pipeline utilizes design variation models to generate pairs of original and edited CAD models and employs Large Vision-Language Models (LVLMs) to summarize their differences into editing instructions. To tackle the composite nature of text-based CAD editing, we propose a locate-then-infill framework that decomposes the task into two focused sub-tasks: locating regions requiring modification and infilling these regions with appropriate edits. Large Language Models (LLMs) serve as the backbone for both sub-tasks, leveraging their capabilities in natural language understanding and CAD knowledge. Experiments show that CAD-Editor achieves superior performance both quantitatively and qualitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/CAD-Editor", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.07776", "title": "Auditing Prompt Caching in Language Model APIs", "authors": [ "Chenchen Gu", "Xiang Lisa Li", "Rohith Kuditipudi", "Percy Liang", "Tatsunori Hashimoto" ], "github": "", "project_page": "", "abstract": "Prompt caching in large language models (LLMs) results in data-dependent timing variations: cached prompts are processed faster than non-cached prompts. These timing differences introduce the risk of side-channel timing attacks. For example, if the cache is shared across users, an attacker could identify cached prompts from fast API response times to learn information about other users' prompts. Because prompt caching may cause privacy leakage, transparency around the caching policies of API providers is important. To this end, we develop and conduct statistical audits to detect prompt caching in real-world LLM API providers. We detect global cache sharing across users in seven API providers, including OpenAI, resulting in potential privacy leakage about users' prompts. Timing variations due to prompt caching can also result in leakage of information about model architecture. Namely, we find evidence that OpenAI's embedding model is a decoder-only Transformer, which was previously not publicly known.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.03492", "title": "Teaching Language Models to Critique via Reinforcement Learning", "authors": [ "Zhihui Xie", "Jie chen", "Liyu Chen", "Weichao Mao", "Jingjing Xu", "Lingpeng Kong" ], "github": "", "project_page": "", "abstract": "Teaching large language models (LLMs) to critique and refine their outputs is crucial for building systems that can iteratively improve, yet it is fundamentally limited by the ability to provide accurate judgments and actionable suggestions. In this work, we study LLM critics for code generation and propose CTRL, a framework for Critic Training via Reinforcement Learning, which trains a critic model to generate feedback that maximizes correction performance for a fixed generator model without human supervision. Our results demonstrate that critics trained with CTRL significantly enhance pass rates and mitigate compounding errors across both base and stronger generator models. Furthermore, we show that these critic models act as accurate generative reward models and enable test-time scaling through iterative critique-revision, achieving up to 106.1% relative improvements across challenging code generation benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.04465", "title": "FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks", "authors": [ "Luca Della Libera", "Francesco Paissan", "Cem Subakan", "Mirco Ravanelli" ], "github": "https://github.com/lucadellalib/focalcodec", "project_page": "", "abstract": "Large language models have revolutionized natural language processing through self-supervised pretraining on massive datasets. Inspired by this success, researchers have explored adapting these methods to speech by discretizing continuous audio into tokens using neural audio codecs. However, existing approaches face limitations, including high bitrates, the loss of either semantic or acoustic information, and the reliance on multi-codebook designs when trying to capture both, which increases architectural complexity for downstream tasks. To address these challenges, we introduce FocalCodec, an efficient low-bitrate codec based on focal modulation that utilizes a single binary codebook to compress speech between 0.16 and 0.65 kbps. FocalCodec delivers competitive performance in speech resynthesis and voice conversion at lower bitrates than the current state-of-the-art, while effectively handling multilingual speech and noisy environments. Evaluation on downstream tasks shows that FocalCodec successfully preserves sufficient semantic and acoustic information, while also being well-suited for generative modeling. Demo samples, code and checkpoints are available at https://lucadellalib.github.io/focalcodec-web/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lucadellalib/focalcodec", "github_api_added_by": "auto" }, { "date": "2025-02-12", "arxiv_id": "2502.06329", "title": "Expect the Unexpected: FailSafe Long Context QA for Finance", "authors": [ "Kiran Kamble", "Melisa Russak", "Dmytro Mozolevskyi", "Muayad Ali", "Mateusz Russak", "Waseem AlShikh" ], "github": "", "project_page": "", "abstract": "We propose a new long-context financial benchmark, FailSafeQA, designed to test the robustness and context-awareness of LLMs against six variations in human-interface interactions in LLM-based query-answer systems within finance. We concentrate on two case studies: Query Failure and Context Failure. In the Query Failure scenario, we perturb the original query to vary in domain expertise, completeness, and linguistic accuracy. In the Context Failure case, we simulate the uploads of degraded, irrelevant, and empty documents. We employ the LLM-as-a-Judge methodology with Qwen2.5-72B-Instruct and use fine-grained rating criteria to define and calculate Robustness, Context Grounding, and Compliance scores for 24 off-the-shelf models. The results suggest that although some models excel at mitigating input perturbations, they must balance robust answering with the ability to refrain from hallucinating. Notably, Palmyra-Fin-128k-Instruct, recognized as the most compliant model, maintained strong baseline performance but encountered challenges in sustaining robust predictions in 17% of test cases. On the other hand, the most robust model, OpenAI o3-mini, fabricated information in 41% of tested cases. The results demonstrate that even high-performing models have significant room for improvement and highlight the role of FailSafeQA as a tool for developing LLMs optimized for dependability in financial applications. The dataset is available at: https://huggingface.co/datasets/Writer/FailSafeQA", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.04223", "title": "\u00c9clair -- Extracting Content and Layout with Integrated Reading Order for Documents", "authors": [ "Ilia Karmanov", "Amala Sanjay Deshmukh", "Lukas Voegtle", "Philipp Fischer", "Kateryna Chumachenko", "Timo Roman", "Jarno Sepp\u00e4nen", "Jupinder Parmar", "Joseph Jennings", "Andrew Tao", "Karan Sapra" ], "github": "", "project_page": "", "abstract": "Optical Character Recognition (OCR) technology is widely used to extract text from images of documents, facilitating efficient digitization and data retrieval. However, merely extracting text is insufficient when dealing with complex documents. Fully comprehending such documents requires an understanding of their structure -- including formatting, formulas, tables, and the reading order of multiple blocks and columns across multiple pages -- as well as semantic information for detecting elements like footnotes and image captions. This comprehensive understanding is crucial for downstream tasks such as retrieval, document question answering, and data curation for training Large Language Models (LLMs) and Vision Language Models (VLMs). To address this, we introduce \\'Eclair, a general-purpose text-extraction tool specifically designed to process a wide range of document types. Given an image, \\'Eclair is able to extract formatted text in reading order, along with bounding boxes and their corresponding semantic classes. To thoroughly evaluate these novel capabilities, we introduce our diverse human-annotated benchmark for document-level OCR and semantic classification. \\'Eclair achieves state-of-the-art accuracy on this benchmark, outperforming other methods across key metrics. Additionally, we evaluate \\'Eclair on established benchmarks, demonstrating its versatility and strength across several evaluation standards.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.05932", "title": "Skill Expansion and Composition in Parameter Space", "authors": [ "Tenglong Liu", "Jianxiong Li", "Yinan Zheng", "Haoyi Niu", "Yixing Lan", "Xin Xu", "Xianyuan Zhan" ], "github": "https://github.com/ltlhuuu/PSEC", "project_page": "", "abstract": "Humans excel at reusing prior knowledge to address new challenges and developing skills while solving problems. This paradigm becomes increasingly popular in the development of autonomous agents, as it develops systems that can self-evolve in response to new challenges like human beings. However, previous methods suffer from limited training efficiency when expanding new skills and fail to fully leverage prior knowledge to facilitate new task learning. In this paper, we propose Parametric Skill Expansion and Composition (PSEC), a new framework designed to iteratively evolve the agents' capabilities and efficiently address new challenges by maintaining a manageable skill library. This library can progressively integrate skill primitives as plug-and-play Low-Rank Adaptation (LoRA) modules in parameter-efficient finetuning, facilitating efficient and flexible skill expansion. This structure also enables the direct skill compositions in parameter space by merging LoRA modules that encode different skills, leveraging shared information across skills to effectively program new skills. Based on this, we propose a context-aware module to dynamically activate different skills to collaboratively handle new tasks. Empowering diverse applications including multi-objective composition, dynamics shift, and continual policy shift, the results on D4RL, DSRL benchmarks, and the DeepMind Control Suite show that PSEC exhibits superior capacity to leverage prior knowledge to efficiently tackle new challenges, as well as expand its skill libraries to evolve the capabilities. Project website: https://ltlhuuu.github.io/PSEC/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ltlhuuu/PSEC", "github_api_added_by": "auto" }, { "date": "2025-02-12", "arxiv_id": "2502.05878", "title": "Retrieval-augmented Large Language Models for Financial Time Series Forecasting", "authors": [ "Mengxi Xiao", "Zihao Jiang", "Lingfei Qian", "Zhengyu Chen", "Yueru He", "Yijing Xu", "Yuecheng Jiang", "Dong Li", "Ruey-Ling Weng", "Min Peng", "Jimin Huang", "Sophia Ananiadou", "Qianqian Xie" ], "github": "https://github.com/The-FinAI/FinSeer", "project_page": "", "abstract": "Stock movement prediction, a fundamental task in financial time-series forecasting, requires identifying and retrieving critical influencing factors from vast amounts of time-series data. However, existing text-trained or numeric similarity-based retrieval methods fall short in handling complex financial analysis. To address this, we propose the first retrieval-augmented generation (RAG) framework for financial time-series forecasting, featuring three key innovations: a fine-tuned 1B parameter large language model (StockLLM) as the backbone, a novel candidate selection method leveraging LLM feedback, and a training objective that maximizes similarity between queries and historically significant sequences. This enables our retriever, FinSeer, to uncover meaningful patterns while minimizing noise in complex financial data. We also construct new datasets integrating financial indicators and historical stock prices to train FinSeer and ensure robust evaluation. Experimental results demonstrate that our RAG framework outperforms bare StockLLM and random retrieval, highlighting its effectiveness, while FinSeer surpasses existing retrieval methods, achieving an 8\\% higher accuracy on BIGDATA22 and retrieving more impactful sequences. This work underscores the importance of tailored retrieval models in financial forecasting and provides a novel framework for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/The-FinAI/FinSeer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.07490", "title": "Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn More", "authors": [ "Xialie Zhuang", "Zhikai Jia", "Jianjin Li", "Zhenyu Zhang", "Li Shen", "Zheng Cao", "Shiwei Liu" ], "github": "https://github.com/scitix/MEAP", "project_page": "", "abstract": "Large Language Models (LLMs) are discovered to suffer from accurately retrieving key information. To address this, we propose Mask-Enhanced Autoregressive Prediction (MEAP), a simple yet effective training paradigm that seamlessly integrates Masked Language Modeling (MLM) into Next-Token Prediction (NTP) to enhance the latter's in-context retrieval capabilities. Specifically, MEAP first randomly masks a small fraction of input tokens and then directly performs the standard next-token prediction autoregressive using a decoder-only Transformer. MEAP eliminates the need for bidirectional attention or encoder-decoder architectures for MLM, incurring no additional computational overhead during pre-training or inference. Intensive experiments demonstrate that MEAP substantially outperforms NTP on key information retrieval and long-context reasoning tasks, while performing on par or better on commonsense reasoning tasks. The benefits of MEAP also extend to supervised fine-tuning, where it shows remarkable advantages in lost-in-the-middle scenarios, outperforming NTP by 11.77 percentage points. Our analysis indicates that MEAP's effectiveness arises from its ability to promote more distinguishable attention scores by concentrating on a reduced set of non-masked tokens. This mechanism improves the model's focus on task-relevant signals while mitigating the influence of peripheral context. These findings position MEAP as a promising training paradigm for large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/scitix/MEAP", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.06428", "title": "CoS: Chain-of-Shot Prompting for Long Video Understanding", "authors": [ "Jian Hu", "Zixu Cheng", "Chenyang Si", "Wei Li", "Shaogang Gong" ], "github": "https://github.com/lwpyh/CoS_codes", "project_page": "", "abstract": "Multi-modal Large Language Models (MLLMs) struggle with long videos due to the need for excessive visual tokens. These tokens exceed massively the context length of MLLMs, resulting in filled by redundant task-irrelevant shots. How to select shots is an unsolved critical problem: sparse sampling risks missing key details, while exhaustive sampling overwhelms the model with irrelevant content, leading to video misunderstanding. To solve this problem, we propose Chain-of-Shot prompting (CoS). The key idea is to frame shot selection as test-time visual prompt optimisation, choosing shots adaptive to video understanding semantic task by optimising shots-task alignment. CoS has two key parts: (1) a binary video summary mechanism that performs pseudo temporal grounding, discovering a binary coding to identify task-relevant shots, and (2) a video co-reasoning module that deploys the binary coding to pair (learning to align) task-relevant positive shots with irrelevant negative shots. It embeds the optimised shot selections into the original video, facilitating a focus on relevant context to optimize long video understanding. Experiments across three baselines and five datasets demonstrate the effectiveness and adaptability of CoS. Code given in https://lwpyh.github.io/CoS.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lwpyh/CoS_codes", "github_api_added_by": "auto" }, { "date": "2025-02-12", "arxiv_id": "2502.06857", "title": "Gemstones: A Model Suite for Multi-Faceted Scaling Laws", "authors": [ "Sean McLeish", "John Kirchenbauer", "David Yu Miller", "Siddharth Singh", "Abhinav Bhatele", "Micah Goldblum", "Ashwinee Panda", "Tom Goldstein" ], "github": "https://github.com/mcleish7/gemstone-scaling-laws", "project_page": "", "abstract": "Scaling laws are typically fit using a family of models with a narrow range of frozen hyper-parameter choices. In this work we study scaling laws using a wide range of architecture and hyper-parameter choices, and highlight their impact on resulting prescriptions. As a primary artifact of our research, we release the Gemstones: the most comprehensive open-source scaling law dataset to date, consisting of over 4000 checkpoints from transformers with up to 2 billion parameters; these models have been trained with different learning rates, cooldown schedules, and architectural shapes. Our checkpoints enable more complex studies of scaling, such as a law that predicts language modeling performance as a function of model width and depth. By examining the various facets of our model suite, we find that the prescriptions of scaling laws can be highly sensitive to the experimental design process and the specific model checkpoints used during fitting. Code: https://github.com/mcleish7/gemstone-scaling-laws", "github_manual": "", "github_regex": "https://github.com/mcleish7/gemstone-scaling-laws", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.06755", "title": "Sparse Autoencoders for Scientifically Rigorous Interpretation of Vision Models", "authors": [ "Samuel Stevens", "Wei-Lun Chao", "Tanya Berger-Wolf", "Yu Su" ], "github": "https://github.com/osu-nlp-group/SAE-V", "project_page": "", "abstract": "To truly understand vision models, we must not only interpret their learned features but also validate these interpretations through controlled experiments. Current approaches either provide interpretable features without the ability to test their causal influence, or enable model editing without interpretable controls. We present a unified framework using sparse autoencoders (SAEs) that bridges this gap, allowing us to discover human-interpretable visual features and precisely manipulate them to test hypotheses about model behavior. By applying our method to state-of-the-art vision models, we reveal key differences in the semantic abstractions learned by models with different pre-training objectives. We then demonstrate the practical usage of our framework through controlled interventions across multiple vision tasks. We show that SAEs can reliably identify and manipulate interpretable visual features without model re-training, providing a powerful tool for understanding and controlling vision model behavior. We provide code, demos and models on our project website: https://osu-nlp-group.github.io/SAE-V.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/osu-nlp-group/SAE-V", "github_api_added_by": "auto" }, { "date": "2025-02-12", "arxiv_id": "2502.05364", "title": "Hypencoder: Hypernetworks for Information Retrieval", "authors": [ "Julian Killingback", "Hansi Zeng", "Hamed Zamani" ], "github": "https://github.com/jfkback/hypencoder-paper", "project_page": "", "abstract": "The vast majority of retrieval models depend on vector inner products to produce a relevance score between a query and a document. This naturally limits the expressiveness of the relevance score that can be employed. We propose a new paradigm, instead of producing a vector to represent the query we produce a small neural network which acts as a learned relevance function. This small neural network takes in a representation of the document, in this paper we use a single vector, and produces a scalar relevance score. To produce the little neural network we use a hypernetwork, a network that produce the weights of other networks, as our query encoder or as we call it a Hypencoder. Experiments on in-domain search tasks show that Hypencoder is able to significantly outperform strong dense retrieval models and has higher metrics then reranking models and models an order of magnitude larger. Hypencoder is also shown to generalize well to out-of-domain search tasks. To assess the extent of Hypencoder's capabilities, we evaluate on a set of hard retrieval tasks including tip-of-the-tongue retrieval and instruction-following retrieval tasks and find that the performance gap widens substantially compared to standard retrieval tasks. Furthermore, to demonstrate the practicality of our method we implement an approximate search algorithm and show that our model is able to search 8.8M documents in under 60ms.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jfkback/hypencoder-paper", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-12", "arxiv_id": "2502.07640", "title": "Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving", "authors": [ "Yong Lin", "Shange Tang", "Bohan Lyu", "Jiayun Wu", "Hongzhou Lin", "Kaiyu Yang", "Jia Li", "Mengzhou Xia", "Danqi Chen", "Sanjeev Arora", "Chi Jin" ], "github": "https://github.com/Goedel-LM/Goedel-Prover", "project_page": "", "abstract": "We introduce Goedel-Prover, an open-source large language model (LLM) that achieves the state-of-the-art (SOTA) performance in automated formal proof generation for mathematical problems. The key challenge in this field is the scarcity of formalized math statements and proofs, which we tackle in the following ways. We train statement formalizers to translate the natural language math problems from Numina into formal language (Lean 4), creating a dataset of 1.64 million formal statements. LLMs are used to check that the formal statements accurately preserve the content of the original natural language problems. We then iteratively build a large dataset of formal proofs by training a series of provers. Each prover succeeds in proving many statements that the previous ones could not, and these new proofs are added to the training set for the next prover. The final prover outperforms all existing open-source models in whole-proof generation. On the miniF2F benchmark, it achieves a 57.6% success rate (Pass@32), exceeding the previous best open-source model by 7.6%. On PutnamBench, Goedel-Prover successfully solves 7 problems (Pass@512), ranking first on the leaderboard. Furthermore, it generates 29.7K formal proofs for Lean Workbook problems, nearly doubling the 15.7K produced by earlier works.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Goedel-LM/Goedel-Prover", "github_api_added_by": "auto" }, { "date": "2025-02-12", "arxiv_id": "2502.07785", "title": "Pippo: High-Resolution Multi-View Humans from a Single Image", "authors": [ "Yash Kant", "Ethan Weber", "Jin Kyu Kim", "Rawal Khirodkar", "Su Zhaoen", "Julieta Martinez", "Igor Gilitschenski", "Shunsuke Saito", "Timur Bagautdinov" ], "github": "https://github.com/facebookresearch/pippo", "project_page": "", "abstract": "We present Pippo, a generative model capable of producing 1K resolution dense turnaround videos of a person from a single casually clicked photo. Pippo is a multi-view diffusion transformer and does not require any additional inputs - e.g., a fitted parametric model or camera parameters of the input image. We pre-train Pippo on 3B human images without captions, and conduct multi-view mid-training and post-training on studio captured humans. During mid-training, to quickly absorb the studio dataset, we denoise several (up to 48) views at low-resolution, and encode target cameras coarsely using a shallow MLP. During post-training, we denoise fewer views at high-resolution and use pixel-aligned controls (e.g., Spatial anchor and Plucker rays) to enable 3D consistent generations. At inference, we propose an attention biasing technique that allows Pippo to simultaneously generate greater than 5 times as many views as seen during training. Finally, we also introduce an improved metric to evaluate 3D consistency of multi-view generations, and show that Pippo outperforms existing works on multi-view human generation from a single image.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/pippo", "github_api_added_by": "auto" }, { "date": "2025-02-12", "arxiv_id": "2502.06884", "title": "Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models", "authors": [ "Sina Tayebati", "Divake Kumar", "Nastaran Darabi", "Dinithi Jayasuriya", "Ranganath Krishnan", "Amit Ranjan Trivedi" ], "github": "https://github.com/sinatayebati/vlm-uncertainty", "project_page": "", "abstract": "Large Language and Vision-Language Models (LLMs/VLMs) are increasingly used in safety-critical applications, yet their opaque decision-making complicates risk assessment and reliability. Uncertainty quantification (UQ) helps assess prediction confidence and enables abstention when uncertainty is high. Conformal prediction (CP), a leading UQ method, provides statistical guarantees but relies on static thresholds, which fail to adapt to task complexity and evolving data distributions, leading to suboptimal trade-offs in accuracy, coverage, and informativeness. To address this, we propose learnable conformal abstention, integrating reinforcement learning (RL) with CP to optimize abstention thresholds dynamically. By treating CP thresholds as adaptive actions, our approach balances multiple objectives, minimizing prediction set size while maintaining reliable coverage. Extensive evaluations across diverse LLM/VLM benchmarks show our method outperforms Least Ambiguous Classifiers (LAC) and Adaptive Prediction Sets (APS), improving accuracy by up to 3.2%, boosting AUROC for hallucination detection by 22.19%, enhancing uncertainty-guided selective generation (AUARC) by 21.17%, and reducing calibration error by 70%-85%. These improvements hold across multiple models and datasets while consistently meeting the 90% coverage target, establishing our approach as a more effective and flexible solution for reliable decision-making in safety-critical applications. The code is available at: {https://github.com/sinatayebati/vlm-uncertainty}.", "github_manual": "", "github_regex": "https://github.com/sinatayebati/vlm-uncertainty", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.08127", "title": "Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance", "authors": [ "Lingfei Qian", "Weipeng Zhou", "Yan Wang", "Xueqing Peng", "Jimin Huang", "Qianqian Xie" ], "github": "https://github.com/the-finai/fino1", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) have shown strong general reasoning abilities, yet their effectiveness in financial reasoning remains underexplored. In this study, we comprehensively evaluate 16 powerful reasoning and general LLMs on three complex financial tasks involving financial text, tabular data, and equations, assessing numerical reasoning, tabular interpretation, financial terminology comprehension, long-context processing, and equation-based problem solving. Our results show that while better datasets and pretraining improve financial reasoning, general enhancements like CoT fine-tuning do not always yield consistent gains. Moreover, all reasoning strategies face challenges in improving performance on long-context and multi-table tasks. To address these limitations, we develop a financial reasoning-enhanced model based on Llama-3.1-8B-Instruct, by CoT fine-tuning and reinforcement learning with domain-specific reasoning paths. Even with simple fine-tuning with one financial dataset, our model achieves a consistent 10% performance improvement across tasks, surpassing all 8B models and even Llama3-70B-Instruct and Llama3.1-70B-Instruct on average. Our results highlight the need for domain-specific adaptations in financial tasks, emphasizing future directions such as multi-table reasoning, long-context processing, and financial terminology comprehension. All our datasets, models, and codes are publicly available. Furthermore, we introduce a leaderboard for benchmarking future datasets and models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/the-finai/fino1", "github_api_added_by": "auto" }, { "date": "2025-02-13", "arxiv_id": "2502.07737", "title": "Next Block Prediction: Video Generation via Semi-Autoregressive Modeling", "authors": [ "Shuhuai Ren", "Shuming Ma", "Xu Sun", "Furu Wei" ], "github": "https://github.com/RenShuhuai-Andy/NBP", "project_page": "", "abstract": "Next-Token Prediction (NTP) is a de facto approach for autoregressive (AR) video generation, but it suffers from suboptimal unidirectional dependencies and slow inference speed. In this work, we propose a semi-autoregressive (semi-AR) framework, called Next-Block Prediction (NBP), for video generation. By uniformly decomposing video content into equal-sized blocks (e.g., rows or frames), we shift the generation unit from individual tokens to blocks, allowing each token in the current block to simultaneously predict the corresponding token in the next block. Unlike traditional AR modeling, our framework employs bidirectional attention within each block, enabling tokens to capture more robust spatial dependencies. By predicting multiple tokens in parallel, NBP models significantly reduce the number of generation steps, leading to faster and more efficient inference. Our model achieves FVD scores of 103.3 on UCF101 and 25.5 on K600, outperforming the vanilla NTP model by an average of 4.4. Furthermore, thanks to the reduced number of inference steps, the NBP model generates 8.89 frames (128x128 resolution) per second, achieving an 11x speedup. We also explored model scales ranging from 700M to 3B parameters, observing significant improvements in generation quality, with FVD scores dropping from 103.3 to 55.3 on UCF101 and from 25.5 to 19.5 on K600, demonstrating the scalability of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RenShuhuai-Andy/NBP", "github_api_added_by": "auto" }, { "date": "2025-02-13", "arxiv_id": "2502.07599", "title": "DPO-Shift: Shifting the Distribution of Direct Preference Optimization", "authors": [ "Xiliang Yang", "Feng Jiang", "Qianen Zhang", "Lei Zhao", "Xiao Li" ], "github": "https://github.com/Meaquadddd/DPO-Shift", "project_page": "", "abstract": "Direct Preference Optimization (DPO) and its variants have become increasingly popular for aligning language models with human preferences. These methods aim to teach models to better distinguish between chosen (or preferred) and rejected (or dispreferred) responses. However, prior research has identified that the probability of chosen responses often decreases during training, and this phenomenon is known as likelihood displacement. To tackle this challenge, in this work we introduce \\method to controllably shift the distribution of the chosen probability. Then, we show that \\method exhibits a fundamental trade-off between improving the chosen probability and sacrificing the reward margin, as supported by both theoretical analysis and experimental validation. Furthermore, we demonstrate the superiority of \\method over DPO on downstream tasks such as MT-Bench and a designed win rate experiment. We believe this study shows that the likelihood displacement issue of DPO can be effectively mitigated with a simple, theoretically grounded solution. Our code is available at https://github.com/Meaquadddd/DPO-Shift.", "github_manual": "", "github_regex": "https://github.com/Meaquadddd/DPO-Shift", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.07864", "title": "TransMLA: Multi-head Latent Attention Is All You Need", "authors": [ "Fanxu Meng", "Zengwei Yao", "Muhan Zhang" ], "github": "", "project_page": "", "abstract": "Modern large language models (LLMs) often encounter communication bottlenecks on current hardware, rather than purely computational constraints. Multi-head Latent Attention (MLA) tackles this challenge by using low-rank matrices in the key-value (KV) layers, thereby allowing compressed latent KV states to be cached. This approach significantly reduces the KV cache size relative to traditional multi-head attention, leading to faster inference. Moreover, MLA employs an up-projection matrix to increase expressiveness, trading additional computation for reduced communication overhead. Although MLA has demonstrated efficiency and effectiveness in Deepseek V2/V3/R1, many major model providers still rely on Group Query Attention (GQA) and have not announced any plans to adopt MLA. In this paper, we show that GQA can always be represented by MLA while maintaining the same KV cache overhead, but the converse does not hold. To encourage broader use of MLA, we introduce **TransMLA**, a post-training method that converts widely used GQA-based pre-trained models (e.g., LLaMA, Qwen, Mixtral) into MLA-based models. After conversion, the model can undergo additional training to boost expressiveness without increasing the KV cache size. Furthermore, we plan to develop MLA-specific inference acceleration techniques to preserve low latency in transformed models, thus enabling more efficient distillation of Deepseek R1.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.08639", "title": "CineMaster: A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generation", "authors": [ "Qinghe Wang", "Yawen Luo", "Xiaoyu Shi", "Xu Jia", "Huchuan Lu", "Tianfan Xue", "Xintao Wang", "Pengfei Wan", "Di Zhang", "Kun Gai" ], "github": "", "project_page": "", "abstract": "In this work, we present CineMaster, a novel framework for 3D-aware and controllable text-to-video generation. Our goal is to empower users with comparable controllability as professional film directors: precise placement of objects within the scene, flexible manipulation of both objects and camera in 3D space, and intuitive layout control over the rendered frames. To achieve this, CineMaster operates in two stages. In the first stage, we design an interactive workflow that allows users to intuitively construct 3D-aware conditional signals by positioning object bounding boxes and defining camera movements within the 3D space. In the second stage, these control signals--comprising rendered depth maps, camera trajectories and object class labels--serve as the guidance for a text-to-video diffusion model, ensuring to generate the user-intended video content. Furthermore, to overcome the scarcity of in-the-wild datasets with 3D object motion and camera pose annotations, we carefully establish an automated data annotation pipeline that extracts 3D bounding boxes and camera trajectories from large-scale video data. Extensive qualitative and quantitative experiments demonstrate that CineMaster significantly outperforms existing methods and implements prominent 3D-aware text-to-video generation. Project page: https://cinemaster-dev.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.08168", "title": "SARChat-Bench-2M: A Multi-Task Vision-Language Benchmark for SAR Image Interpretation", "authors": [ "Zhiming Ma", "Xiayang Xiao", "Sihao Dong", "Peidong Wang", "HaiPeng Wang", "Qingyun Pan" ], "github": "https://github.com/JimmyMa99/SARChat", "project_page": "", "abstract": "In the field of synthetic aperture radar (SAR) remote sensing image interpretation, although Vision language models (VLMs) have made remarkable progress in natural language processing and image understanding, their applications remain limited in professional domains due to insufficient domain expertise. This paper innovatively proposes the first large-scale multimodal dialogue dataset for SAR images, named SARChat-2M, which contains approximately 2 million high-quality image-text pairs, encompasses diverse scenarios with detailed target annotations. This dataset not only supports several key tasks such as visual understanding and object detection tasks, but also has unique innovative aspects: this study develop a visual-language dataset and benchmark for the SAR domain, enabling and evaluating VLMs' capabilities in SAR image interpretation, which provides a paradigmatic framework for constructing multimodal datasets across various remote sensing vertical domains. Through experiments on 16 mainstream VLMs, the effectiveness of the dataset has been fully verified, and the first multi-task dialogue benchmark in the SAR field has been successfully established. The project will be released at https://github.com/JimmyMa99/SARChat, aiming to promote the in-depth development and wide application of SAR visual language models.", "github_manual": "", "github_regex": "https://github.com/JimmyMa99/SARChat", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.08590", "title": "Light-A-Video: Training-free Video Relighting via Progressive Light Fusion", "authors": [ "Yujie Zhou", "Jiazi Bu", "Pengyang Ling", "Pan Zhang", "Tong Wu", "Qidong Huang", "Jinsong Li", "Xiaoyi Dong", "Yuhang Zang", "Yuhang Cao", "Anyi Rao", "Jiaqi Wang", "Li Niu" ], "github": "", "project_page": "", "abstract": "Recent advancements in image relighting models, driven by large-scale datasets and pre-trained diffusion models, have enabled the imposition of consistent lighting. However, video relighting still lags, primarily due to the excessive training costs and the scarcity of diverse, high-quality video relighting datasets. A simple application of image relighting models on a frame-by-frame basis leads to several issues: lighting source inconsistency and relighted appearance inconsistency, resulting in flickers in the generated videos. In this work, we propose Light-A-Video, a training-free approach to achieve temporally smooth video relighting. Adapted from image relighting models, Light-A-Video introduces two key techniques to enhance lighting consistency. First, we design a Consistent Light Attention (CLA) module, which enhances cross-frame interactions within the self-attention layers to stabilize the generation of the background lighting source. Second, leveraging the physical principle of light transport independence, we apply linear blending between the source video's appearance and the relighted appearance, using a Progressive Light Fusion (PLF) strategy to ensure smooth temporal transitions in illumination. Experiments show that Light-A-Video improves the temporal consistency of relighted video while maintaining the image quality, ensuring coherent lighting transitions across frames. Project page: https://bujiazi.github.io/light-a-video.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.07563", "title": "LASP-2: Rethinking Sequence Parallelism for Linear Attention and Its Hybrid", "authors": [ "Weigao Sun", "Disen Lan", "Yiran Zhong", "Xiaoye Qu", "Yu Cheng" ], "github": "https://github.com/OpenSparseLLMs/Linear-MoE", "project_page": "", "abstract": "Linear sequence modeling approaches, such as linear attention, provide advantages like linear-time training and constant-memory inference over sequence lengths. However, existing sequence parallelism (SP) methods are either not optimized for the right-product-first feature of linear attention or use a ring-style communication strategy, which results in lower computation parallelism, limits their scalability for longer sequences in distributed systems. In this paper, we introduce LASP-2, a new SP method to enhance both communication and computation parallelism when training linear attention transformer models with very-long input sequences. Compared to previous work LASP, LASP-2 rethinks the minimal communication requirement for SP on linear attention layers, reorganizes the whole communication-computation workflow of LASP. In this way, only one single AllGather collective communication is needed on intermediate memory states, whose sizes are independent of the sequence length, leading to significant improvements of both communication and computation parallelism, as well as their overlap. Additionally, we extend LASP-2 to LASP-2H by applying similar communication redesign to standard attention modules, offering an efficient SP solution for hybrid models that blend linear and standard attention layers. Our evaluation on a Linear-Llama3 model, a variant of Llama3 with linear attention replacing standard attention, demonstrates the effectiveness of LASP-2 and LASP-2H. Specifically, LASP-2 achieves training speed improvements of 15.2% over LASP and 36.6% over Ring Attention, with a sequence length of 2048K across 64 GPUs. The Code is released as a part of: https://github.com/OpenSparseLLMs/Linear-MoE.", "github_manual": "", "github_regex": "https://github.com/OpenSparseLLMs/Linear-MoE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.08524", "title": "LLM Pretraining with Continuous Concepts", "authors": [ "Jihoon Tack", "Jack Lanchantin", "Jane Yu", "Andrew Cohen", "Ilia Kulikov", "Janice Lan", "Shibo Hao", "Yuandong Tian", "Jason Weston", "Xian Li" ], "github": "", "project_page": "", "abstract": "Next token prediction has been the standard training objective used in large language model pretraining. Representations are learned as a result of optimizing for token-level perplexity. We propose Continuous Concept Mixing (CoCoMix), a novel pretraining framework that combines discrete next token prediction with continuous concepts. Specifically, CoCoMix predicts continuous concepts learned from a pretrained sparse autoencoder and mixes them into the model's hidden state by interleaving with token hidden representations. Through experiments on multiple benchmarks, including language modeling and downstream reasoning tasks, we show that CoCoMix is more sample efficient and consistently outperforms standard next token prediction, knowledge distillation and inserting pause tokens. We find that combining both concept learning and interleaving in an end-to-end framework is critical to performance gains. Furthermore, CoCoMix enhances interpretability and steerability by allowing direct inspection and modification of the predicted concept, offering a transparent way to guide the model's internal reasoning process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.08606", "title": "Distillation Scaling Laws", "authors": [ "Dan Busbridge", "Amitis Shidani", "Floris Weers", "Jason Ramapuram", "Etai Littwin", "Russ Webb" ], "github": "", "project_page": "", "abstract": "We provide a distillation scaling law that estimates distilled model performance based on a compute budget and its allocation between the student and teacher. Our findings reduce the risks associated with using distillation at scale; compute allocation for both the teacher and student models can now be done to maximize student performance. We provide compute optimal distillation recipes for when 1) a teacher exists, or 2) a teacher needs training. If many students are to be distilled, or a teacher already exists, distillation outperforms supervised pretraining until a compute level which grows predictably with student size. If one student is to be distilled and a teacher also needs training, supervised learning should be done instead. Additionally, we provide insights across our large scale study of distillation, which increase our understanding of distillation and inform experimental design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.07870", "title": "TextAtlas5M: A Large-scale Dataset for Dense Text Image Generation", "authors": [ "Alex Jinpeng Wang", "Dongxing Mao", "Jiawei Zhang", "Weiming Han", "Zhuobai Dong", "Linjie Li", "Yiqi Lin", "Zhengyuan Yang", "Libo Qin", "Fuwei Zhang", "Lijuan Wang", "Min Li" ], "github": "https://github.com/CSU-JPG/TextAtlas", "project_page": "", "abstract": "Text-conditioned image generation has gained significant attention in recent years and are processing increasingly longer and comprehensive text prompt. In everyday life, dense and intricate text appears in contexts like advertisements, infographics, and signage, where the integration of both text and visuals is essential for conveying complex information. However, despite these advances, the generation of images containing long-form text remains a persistent challenge, largely due to the limitations of existing datasets, which often focus on shorter and simpler text. To address this gap, we introduce TextAtlas5M, a novel dataset specifically designed to evaluate long-text rendering in text-conditioned image generation. Our dataset consists of 5 million long-text generated and collected images across diverse data types, enabling comprehensive evaluation of large-scale generative models on long-text image generation. We further curate 3000 human-improved test set TextAtlasEval across 3 data domains, establishing one of the most extensive benchmarks for text-conditioned generation. Evaluations suggest that the TextAtlasEval benchmarks present significant challenges even for the most advanced proprietary models (e.g. GPT4o with DallE-3), while their open-source counterparts show an even larger performance gap. These evidences position TextAtlas5M as a valuable dataset for training and evaluating future-generation text-conditioned image generation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CSU-JPG/TextAtlas", "github_api_added_by": "auto" }, { "date": "2025-02-13", "arxiv_id": "2502.06872", "title": "Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey", "authors": [ "Bo Ni", "Zheyuan Liu", "Leyao Wang", "Yongjia Lei", "Yuying Zhao", "Xueqi Cheng", "Qingkai Zeng", "Luna Dong", "Yinglong Xia", "Krishnaram Kenthapadi", "Ryan Rossi", "Franck Dernoncourt", "Md Mehrab Tanjim", "Nesreen Ahmed", "Xiaorui Liu", "Wenqi Fan", "Erik Blasch", "Yu Wang", "Meng Jiang", "Tyler Derr" ], "github": "https://github.com/Arstanley/Awesome-Trustworthy-Retrieval-Augmented-Generation", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC). By integrating context retrieval into content generation, RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks. However, despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including robustness issues, privacy concerns, adversarial attacks, and accountability issues. Addressing these risks is critical for future applications of RAG systems, as they directly impact their trustworthiness. Although various methods have been developed to improve the trustworthiness of RAG methods, there is a lack of a unified perspective and framework for research in this topic. Thus, in this paper, we aim to address this gap by providing a comprehensive roadmap for developing trustworthy RAG systems. We place our discussion around five key perspectives: reliability, privacy, safety, fairness, explainability, and accountability. For each perspective, we present a general framework and taxonomy, offering a structured approach to understanding the current challenges, evaluating existing solutions, and identifying promising future research directions. To encourage broader adoption and innovation, we also highlight the downstream applications where trustworthy RAG systems have a significant impact.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Arstanley/Awesome-Trustworthy-Retrieval-Augmented-Generation", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.05167", "title": "NoLiMa: Long-Context Evaluation Beyond Literal Matching", "authors": [ "Ali Modarressi", "Hanieh Deilamsalehy", "Franck Dernoncourt", "Trung Bui", "Ryan A. Rossi", "Seunghyun Yoon", "Hinrich Sch\u00fctze" ], "github": "https://github.com/adobe-research/NoLiMa", "project_page": "", "abstract": "Recent large language models (LLMs) support long contexts ranging from 128K to 1M tokens. A popular method for evaluating these capabilities is the needle-in-a-haystack (NIAH) test, which involves retrieving a \"needle\" (relevant information) from a \"haystack\" (long irrelevant context). Extensions of this approach include increasing distractors, fact chaining, and in-context reasoning. However, in these benchmarks, models can exploit existing literal matches between the needle and haystack to simplify the task. To address this, we introduce NoLiMa, a benchmark extending NIAH with a carefully designed needle set, where questions and needles have minimal lexical overlap, requiring models to infer latent associations to locate the needle within the haystack. We evaluate 12 popular LLMs that claim to support contexts of at least 128K tokens. While they perform well in short contexts (<1K), performance degrades significantly as context length increases. At 32K, for instance, 10 models drop below 50% of their strong short-length baselines. Even GPT-4o, one of the top-performing exceptions, experiences a reduction from an almost-perfect baseline of 99.3% to 69.7%. Our analysis suggests these declines stem from the increased difficulty the attention mechanism faces in longer contexts when literal matches are absent, making it harder to retrieve relevant information.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/adobe-research/NoLiMa", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.08047", "title": "WorldGUI: Dynamic Testing for Comprehensive Desktop GUI Automation", "authors": [ "Henry Hengyuan Zhao", "Difei Gao", "Mike Zheng Shou" ], "github": "https://github.com/showlab/WorldGUI", "project_page": "", "abstract": "Current GUI agents have achieved outstanding performance in GUI element grounding. However, planning remains highly challenging, especially due to sensitivity to the initial state of the environment. Specifically, slight differences in the initial state-such as the target software not being open or the interface not being in its default state-often lead to planning errors. This issue is widespread in real user scenarios, but existing benchmarks fail to evaluate it. In this paper, we present WorldGUI, a novel GUI benchmark that designs GUI tasks with various initial states to simulate real computer-user interactions. The benchmark spans a wide range of tasks across 10 popular software applications, including PowerPoint, VSCode, and Adobe Acrobat. In addition, to address the challenges of dynamic GUI automation tasks, we propose GUI-Thinker, a holistic framework, leveraging a critique mechanism, that effectively manages the unpredictability and complexity of GUI interactions. Experimental results demonstrate that GUI-Thinker significantly outperforms Claude-3.5 (Computer Use) by 14.9% in success rate on WorldGUI tasks. This improvement underscores the effectiveness of our critical-thinking-based framework in enhancing GUI automation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/showlab/WorldGUI", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.07985", "title": "MetaSC: Test-Time Safety Specification Optimization for Language Models", "authors": [ "V\u00edctor Gallego" ], "github": "https://github.com/vicgalle/meta-self-critique.git", "project_page": "", "abstract": "We propose a novel dynamic safety framework that optimizes language model (LM) safety reasoning at inference time without modifying model weights. Building on recent advances in self-critique methods, our approach leverages a meta-critique mechanism that iteratively updates safety prompts-termed specifications-to drive the critique and revision process adaptively. This test-time optimization not only improves performance against adversarial jailbreak requests but also in diverse general safety-related tasks, such as avoiding moral harm or pursuing honest responses. Our empirical evaluations across several language models demonstrate that dynamically optimized safety prompts yield significantly higher safety scores compared to fixed system prompts and static self-critique defenses. Code to be released at https://github.com/vicgalle/meta-self-critique.git .", "github_manual": "", "github_regex": "https://github.com/vicgalle/meta-self-critique.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.06533", "title": "Ignore the KL Penalty! Boosting Exploration on Critical Tokens to Enhance RL Fine-Tuning", "authors": [ "Jean Vassoyan", "Nathana\u00ebl Beau", "Roman Plaud" ], "github": "https://github.com/jvasso/llm-rl-arithmetic", "project_page": "", "abstract": "The ability to achieve long-term goals is a key challenge in the current development of large language models (LLMs). To address this, pre-trained LLMs can be fine-tuned with reinforcement learning (RL) to explore solutions that optimize a given goal. However, exploration with LLMs is difficult, as a balance has to be struck between discovering new solutions and staying close enough to the pre-trained model, so as not to degrade basic capabilities. This is typically controlled with a Kullback-Leibler (KL) penalty. In this paper, we investigate the exploration dynamics of a small language model on a simple arithmetic task. We show how varying degrees of pre-training influence exploration and demonstrate the importance of \"critical tokens\" which have a dramatic impact on the final outcome. Consequently, we introduce a simple modification to the KL penalty that favors exploration on critical tokens, increasing the efficiency of the RL fine-tuning stage.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jvasso/llm-rl-arithmetic", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.06145", "title": "Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance", "authors": [ "Li Hu", "Guangyuan Wang", "Zhen Shen", "Xin Gao", "Dechao Meng", "Lian Zhuo", "Peng Zhang", "Bang Zhang", "Liefeng Bo" ], "github": "", "project_page": "", "abstract": "Recent character image animation methods based on diffusion models, such as Animate Anyone, have made significant progress in generating consistent and generalizable character animations. However, these approaches fail to produce reasonable associations between characters and their environments. To address this limitation, we introduce Animate Anyone 2, aiming to animate characters with environment affordance. Beyond extracting motion signals from source video, we additionally capture environmental representations as conditional inputs. The environment is formulated as the region with the exclusion of characters and our model generates characters to populate these regions while maintaining coherence with the environmental context. We propose a shape-agnostic mask strategy that more effectively characterizes the relationship between character and environment. Furthermore, to enhance the fidelity of object interactions, we leverage an object guider to extract features of interacting objects and employ spatial blending for feature injection. We also introduce a pose modulation strategy that enables the model to handle more diverse motion patterns. Experimental results demonstrate the superior performance of the proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.07346", "title": "BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models", "authors": [ "Xu Huang", "Wenhao Zhu", "Hanxu Hu", "Conghui He", "Lei Li", "Shujian Huang", "Fei Yuan" ], "github": "https://github.com/cone-mt/benchmax", "project_page": "", "abstract": "Previous multilingual benchmarks focus primarily on simple understanding tasks, but for large language models(LLMs), we emphasize proficiency in instruction following, reasoning, long context understanding, code generation, and so on. However, measuring these advanced capabilities across languages is underexplored. To address the disparity, we introduce BenchMAX, a multi-way multilingual evaluation benchmark that allows for fair comparisons of these important abilities across languages. To maintain high quality, three distinct native-speaking annotators independently annotate each sample within all tasks after the data was machine-translated from English into 16 other languages. Additionally, we present a novel translation challenge stemming from dataset construction. Extensive experiments on BenchMAX reveal varying effectiveness of core capabilities across languages, highlighting performance gaps that cannot be bridged by simply scaling up model size. BenchMAX serves as a comprehensive multilingual evaluation platform, providing a promising test bed to promote the development of multilingual language models. The dataset and code are publicly accessible.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cone-mt/benchmax", "github_api_added_by": "auto" }, { "date": "2025-02-13", "arxiv_id": "2502.04411", "title": "Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing", "authors": [ "Kunfeng Lai", "Zhenheng Tang", "Xinglin Pan", "Peijie Dong", "Xiang Liu", "Haolan Chen", "Li Shen", "Bo Li", "Xiaowen Chu" ], "github": "", "project_page": "", "abstract": "Model merging aggregates Large Language Models (LLMs) finetuned on different tasks into a stronger one. However, parameter conflicts between models leads to performance degradation in averaging. While model routing addresses this issue by selecting individual models during inference, it imposes excessive storage and compute costs, and fails to leverage the common knowledge from different models. In this work, we observe that different layers exhibit varying levels of parameter conflicts. Building on this insight, we average layers with minimal parameter conflicts and use a novel task-level expert routing for layers with significant conflicts. To further reduce storage costs, inspired by task arithmetic sparsity, we decouple multiple fine-tuned experts into a dense expert and several sparse experts. Considering the out-of-distribution samples, we select and merge appropriate experts based on the task uncertainty of the input data. We conduct extensive experiments on both LLaMA and Qwen with varying parameter scales, and evaluate on real-world reasoning tasks. Results demonstrate that our method consistently achieves significant performance improvements while requiring less system cost compared to existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.08213", "title": "LLM Modules: Knowledge Transfer from a Large to a Small Model using Enhanced Cross-Attention", "authors": [ "Konstantin Kolomeitsev" ], "github": "", "project_page": "", "abstract": "In this work, we propose an architecture of LLM Modules that enables the transfer of knowledge from a large pre-trained model to a smaller model using an Enhanced Cross-Attention mechanism. In the proposed scheme, the Qwen2-1.5B model is frozen and its representations are passed through specially designed attention layers to the GPT-Neo-125M model, which is trained on limited computational resources. Experimental results on the Bespoke-Stratos-17k dataset demonstrate that after 15 epochs of training, the combined model generates responses comparable in quality to those obtained by distillation. We discuss the advantages of the modular approach, provide examples of input queries and comparative analysis, and outline prospects for further extension of the method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-13", "arxiv_id": "2502.00963", "title": "PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs", "authors": [ "Mauricio Soroco", "Jialin Song", "Mengzhou Xia", "Kye Emond", "Weiran Sun", "Wuyang Chen" ], "github": "https://github.com/delta-lab-ai/pde-controller", "project_page": "", "abstract": "While recent AI-for-math has made strides in pure mathematics, areas of applied mathematics, particularly PDEs, remain underexplored despite their significant real-world applications. We present PDE-Controller, a framework that enables large language models (LLMs) to control systems governed by partial differential equations (PDEs). Our approach enables LLMs to transform informal natural language instructions into formal specifications, and then execute reasoning and planning steps to improve the utility of PDE control. We build a holistic solution comprising datasets (both human-written cases and 2 million synthetic samples), math-reasoning models, and novel evaluation metrics, all of which require significant effort. Our PDE-Controller significantly outperforms prompting the latest open-source and GPT models in reasoning, autoformalization, and program synthesis, achieving up to a 62% improvement in utility gain for PDE control. By bridging the gap between language generation and PDE systems, we demonstrate the potential of LLMs in addressing complex scientific and engineering challenges. We will release all data, model checkpoints, and code at https://pde-controller.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/delta-lab-ai/pde-controller", "github_api_added_by": "auto" }, { "date": "2025-02-13", "arxiv_id": "2502.05282", "title": "Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning", "authors": [ "Yuting He", "Boyu Wang", "Rongjun Ge", "Yang Chen", "Guanyu Yang", "Shuo Li" ], "github": "https://github.com/YutingHe-list/GEMINI", "project_page": "", "abstract": "Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image-dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, bringing an open problem of large-scale false positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior to DCRL and enables a reliable correspondence discovery for effective dense contrast. We propose a deformable homeomorphism learning (DHL) which models the homeomorphism of medical images and learns to estimate a deformable mapping to predict the pixels' correspondence under topological preservation. It effectively reduces the searching space of pairing and drives an implicit and soft learning of negative pairs via a gradient. We also propose a geometric semantic similarity (GSS) which extracts semantic information in features to measure the alignment degree for the correspondence learning. It will promote the learning efficiency and performance of deformation, constructing positive pairs reliably. We implement two practical variants on two typical representation learning tasks in our experiments. Our promising results on seven datasets which outperform the existing methods show our great superiority. We will release our code on a companion link: https://github.com/YutingHe-list/GEMINI.", "github_manual": "", "github_regex": "https://github.com/YutingHe-list/GEMINI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.09604", "title": "SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models", "authors": [ "Yung-Sung Chuang", "Benjamin Cohen-Wang", "Shannon Zejiang Shen", "Zhaofeng Wu", "Hu Xu", "Xi Victoria Lin", "James Glass", "Shang-Wen Li", "Wen-tau Yih" ], "github": "https://github.com/facebookresearch/SelfCite", "project_page": "", "abstract": "We introduce SelfCite, a novel self-supervised approach that aligns LLMs to generate high-quality, fine-grained, sentence-level citations for the statements in their generated responses. Instead of only relying on costly and labor-intensive annotations, SelfCite leverages a reward signal provided by the LLM itself through context ablation: If a citation is necessary, removing the cited text from the context should prevent the same response; if sufficient, retaining the cited text alone should preserve the same response. This reward can guide the inference-time best-of-N sampling strategy to improve citation quality significantly, as well as be used in preference optimization to directly fine-tune the models for generating better citations. The effectiveness of SelfCite is demonstrated by increasing citation F1 up to 5.3 points on the LongBench-Cite benchmark across five long-form question answering tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/SelfCite", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.09056", "title": "An Open Recipe: Adapting Language-Specific LLMs to a Reasoning Model in One Day via Model Merging", "authors": [ "Kunat Pipatanakul", "Pittawat Taveekitworachai", "Potsawee Manakul", "Kasima Tharnpipitchai" ], "github": "", "project_page": "", "abstract": "This paper investigates data selection and model merging methodologies aimed at incorporating advanced reasoning capabilities such as those of DeepSeek R1 into language-specific large language models (LLMs), with a particular focus on the Thai LLM. Our goal is to enhance the reasoning capabilities of language-specific LLMs while maintaining their target language abilities. DeepSeek R1 excels in reasoning but primarily benefits high-resource languages such as English and Chinese. However, low-resource languages remain underserved due to the dominance of English-centric training data and model optimizations, which limit performance in these languages. This limitation results in unreliable code-switching and diminished effectiveness on tasks in low-resource languages. Meanwhile, local and regional LLM initiatives have attempted to bridge this gap by developing language-specific LLMs that focus on improving local linguistic fidelity. We demonstrate that, with only publicly available datasets and a computational budget of $120, it is possible to enhance the reasoning capabilities of language-specific LLMs to match the level of DeepSeek R1, without compromising their performance on target language tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.08946", "title": "The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding", "authors": [ "Mo Yu", "Lemao Liu", "Junjie Wu", "Tsz Ting Chung", "Shunchi Zhang", "Jiangnan Li", "Dit-Yan Yeung", "Jie Zhou" ], "github": "", "project_page": "", "abstract": "In a systematic way, we investigate a widely asked question: Do LLMs really understand what they say?, which relates to the more familiar term Stochastic Parrot. To this end, we propose a summative assessment over a carefully designed physical concept understanding task, PhysiCo. Our task alleviates the memorization issue via the usage of grid-format inputs that abstractly describe physical phenomena. The grids represents varying levels of understanding, from the core phenomenon, application examples to analogies to other abstract patterns in the grid world. A comprehensive study on our task demonstrates: (1) state-of-the-art LLMs, including GPT-4o, o1 and Gemini 2.0 flash thinking, lag behind humans by ~40%; (2) the stochastic parrot phenomenon is present in LLMs, as they fail on our grid task but can describe and recognize the same concepts well in natural language; (3) our task challenges the LLMs due to intrinsic difficulties rather than the unfamiliar grid format, as in-context learning and fine-tuning on same formatted data added little to their performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.09100", "title": "Logical Reasoning in Large Language Models: A Survey", "authors": [ "Hanmeng Liu", "Zhizhang Fu", "Mengru Ding", "Ruoxi Ning", "Chaoli Zhang", "Xiaozhang Liu", "Yue Zhang" ], "github": "", "project_page": "", "abstract": "With the emergence of advanced reasoning models like OpenAI o3 and DeepSeek-R1, large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, their ability to perform rigorous logical reasoning remains an open question. This survey synthesizes recent advancements in logical reasoning within LLMs, a critical area of AI research. It outlines the scope of logical reasoning in LLMs, its theoretical foundations, and the benchmarks used to evaluate reasoning proficiency. We analyze existing capabilities across different reasoning paradigms - deductive, inductive, abductive, and analogical - and assess strategies to enhance reasoning performance, including data-centric tuning, reinforcement learning, decoding strategies, and neuro-symbolic approaches. The review concludes with future directions, emphasizing the need for further exploration to strengthen logical reasoning in AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.08910", "title": "InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on a Single GPU", "authors": [ "Heejun Lee", "Geon Park", "Jaduk Suh", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "In modern large language models (LLMs), handling very long context lengths presents significant challenges as it causes slower inference speeds and increased memory costs. Additionally, most existing pre-trained LLMs fail to generalize beyond their original training sequence lengths. To enable efficient and practical long-context utilization, we introduce InfiniteHiP, a novel, and practical LLM inference framework that accelerates processing by dynamically eliminating irrelevant context tokens through a modular hierarchical token pruning algorithm. Our method also allows generalization to longer sequences by selectively applying various RoPE adjustment methods according to the internal attention patterns within LLMs. Furthermore, we offload the key-value cache to host memory during inference, significantly reducing GPU memory pressure. As a result, InfiniteHiP enables the processing of up to 3 million tokens on a single L40s 48GB GPU -- 3x larger -- without any permanent loss of context information. Our framework achieves an 18.95x speedup in attention decoding for a 1 million token context without requiring additional training. We implement our method in the SGLang framework and demonstrate its effectiveness and practicality through extensive evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.08690", "title": "Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation", "authors": [ "Hoigi Seo", "Wongi Jeong", "Jae-sun Seo", "Se Young Chun" ], "github": "", "project_page": "", "abstract": "Large-scale text encoders in text-to-image (T2I) diffusion models have demonstrated exceptional performance in generating high-quality images from textual prompts. Unlike denoising modules that rely on multiple iterative steps, text encoders require only a single forward pass to produce text embeddings. However, despite their minimal contribution to total inference time and floating-point operations (FLOPs), text encoders demand significantly higher memory usage, up to eight times more than denoising modules. To address this inefficiency, we propose Skip and Re-use layers (Skrr), a simple yet effective pruning strategy specifically designed for text encoders in T2I diffusion models. Skrr exploits the inherent redundancy in transformer blocks by selectively skipping or reusing certain layers in a manner tailored for T2I tasks, thereby reducing memory consumption without compromising performance. Extensive experiments demonstrate that Skrr maintains image quality comparable to the original model even under high sparsity levels, outperforming existing blockwise pruning methods. Furthermore, Skrr achieves state-of-the-art memory efficiency while preserving performance across multiple evaluation metrics, including the FID, CLIP, DreamSim, and GenEval scores.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.09560", "title": "EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents", "authors": [ "Rui Yang", "Hanyang Chen", "Junyu Zhang", "Mark Zhao", "Cheng Qian", "Kangrui Wang", "Qineng Wang", "Teja Venkat Koripella", "Marziyeh Movahedi", "Manling Li", "Heng Ji", "Huan Zhang", "Tong Zhang" ], "github": "", "project_page": "", "abstract": "Leveraging Multi-modal Large Language Models (MLLMs) to create embodied agents offers a promising avenue for tackling real-world tasks. While language-centric embodied agents have garnered substantial attention, MLLM-based embodied agents remain underexplored due to the lack of comprehensive evaluation frameworks. To bridge this gap, we introduce EmbodiedBench, an extensive benchmark designed to evaluate vision-driven embodied agents. EmbodiedBench features: (1) a diverse set of 1,128 testing tasks across four environments, ranging from high-level semantic tasks (e.g., household) to low-level tasks involving atomic actions (e.g., navigation and manipulation); and (2) six meticulously curated subsets evaluating essential agent capabilities like commonsense reasoning, complex instruction understanding, spatial awareness, visual perception, and long-term planning. Through extensive experiments, we evaluated 13 leading proprietary and open-source MLLMs within EmbodiedBench. Our findings reveal that: MLLMs excel at high-level tasks but struggle with low-level manipulation, with the best model, GPT-4o, scoring only 28.9% on average. EmbodiedBench provides a multifaceted standardized evaluation platform that not only highlights existing challenges but also offers valuable insights to advance MLLM-based embodied agents. Our code is available at https://embodiedbench.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.06608", "title": "TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models", "authors": [ "Yangguang Li", "Zi-Xin Zou", "Zexiang Liu", "Dehu Wang", "Yuan Liang", "Zhipeng Yu", "Xingchao Liu", "Yuan-Chen Guo", "Ding Liang", "Wanli Ouyang", "Yan-Pei Cao" ], "github": "https://github.com/VAST-AI-Research/TripoSG", "project_page": "", "abstract": "Recent advancements in diffusion techniques have propelled image and video generation to unprece- dented levels of quality, significantly accelerating the deployment and application of generative AI. However, 3D shape generation technology has so far lagged behind, constrained by limitations in 3D data scale, complexity of 3D data process- ing, and insufficient exploration of advanced tech- niques in the 3D domain. Current approaches to 3D shape generation face substantial challenges in terms of output quality, generalization capa- bility, and alignment with input conditions. We present TripoSG, a new streamlined shape diffu- sion paradigm capable of generating high-fidelity 3D meshes with precise correspondence to input images. Specifically, we propose: 1) A large-scale rectified flow transformer for 3D shape generation, achieving state-of-the-art fidelity through training on extensive, high-quality data. 2) A hybrid supervised training strategy combining SDF, normal, and eikonal losses for 3D VAE, achieving high- quality 3D reconstruction performance. 3) A data processing pipeline to generate 2 million high- quality 3D samples, highlighting the crucial rules for data quality and quantity in training 3D gen- erative models. Through comprehensive experi- ments, we have validated the effectiveness of each component in our new framework. The seamless integration of these parts has enabled TripoSG to achieve state-of-the-art performance in 3D shape generation. The resulting 3D shapes exhibit en- hanced detail due to high-resolution capabilities and demonstrate exceptional fidelity to input im- ages. Moreover, TripoSG demonstrates improved versatility in generating 3D models from diverse image styles and contents, showcasing strong gen- eralization capabilities. To foster progress and innovation in the field of 3D generation, we will make our model publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/VAST-AI-Research/TripoSG", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.08468", "title": "mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data", "authors": [ "Haonan Chen", "Liang Wang", "Nan Yang", "Yutao Zhu", "Ziliang Zhao", "Furu Wei", "Zhicheng Dou" ], "github": "https://github.com/haon-chen/mmE5", "project_page": "", "abstract": "Multimodal embedding models have gained significant attention for their ability to map data from different modalities, such as text and images, into a unified representation space. However, the limited labeled multimodal data often hinders embedding performance. Recent approaches have leveraged data synthesis to address this problem, yet the quality of synthetic data remains a critical bottleneck. In this work, we identify three criteria for high-quality synthetic multimodal data. First, broad scope ensures that the generated data covers diverse tasks and modalities, making it applicable to various downstream scenarios. Second, robust cross-modal alignment makes different modalities semantically consistent. Third, high fidelity ensures that the synthetic data maintains realistic details to enhance its reliability. Guided by these principles, we synthesize datasets that: (1) cover a wide range of tasks, modality combinations, and languages, (2) are generated via a deep thinking process within a single pass of a multimodal large language model, and (3) incorporate real-world images with accurate and relevant texts, ensuring fidelity through self-evaluation and refinement. Leveraging these high-quality synthetic and labeled datasets, we train a multimodal multilingual E5 model mmE5. Extensive experiments demonstrate that mmE5 achieves state-of-the-art performance on the MMEB Benchmark and superior multilingual performance on the XTD benchmark. Our codes, datasets and models are released in https://github.com/haon-chen/mmE5.", "github_manual": "", "github_regex": "https://github.com/haon-chen/mmE5", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.09601", "title": "CoT-Valve: Length-Compressible Chain-of-Thought Tuning", "authors": [ "Xinyin Ma", "Guangnian Wan", "Runpeng Yu", "Gongfan Fang", "Xinchao Wang" ], "github": "https://github.com/horseee/cot-valve", "project_page": "", "abstract": "Chain-of-Thought significantly enhances a model's reasoning capability, but it also comes with a considerable increase in inference costs due to long chains. With the observation that the reasoning path can be easily compressed under easy tasks but struggle on hard tasks, we explore the feasibility of elastically controlling the length of reasoning paths with only one model, thereby reducing the inference overhead of reasoning models dynamically based on task difficulty. We introduce a new tuning and inference strategy named CoT-Valve, designed to allow models to generate reasoning chains of varying lengths. To achieve this, we propose to identify a direction in the parameter space that, when manipulated, can effectively control the length of generated CoT. Moreover, we show that this property is valuable for compressing the reasoning chain. We construct datasets with chains from long to short for the same questions and explore two enhanced strategies for CoT-Valve: (1) a precise length-compressible CoT tuning method, and (2) a progressive chain length compression approach. Our experiments show that CoT-Valve successfully enables controllability and compressibility of the chain and shows better performance than the prompt-based control. We applied this method to QwQ-32B-Preview, reducing reasoning chains on GSM8K from 741 to 225 tokens with a minor performance drop (95.07% to 94.92%) and on AIME from 6827 to 4629 tokens, with only one additional incorrect answer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/horseee/cot-valve", "github_api_added_by": "auto" }, { "date": "2025-02-14", "arxiv_id": "2502.09621", "title": "MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency", "authors": [ "Dongzhi Jiang", "Renrui Zhang", "Ziyu Guo", "Yanwei Li", "Yu Qi", "Xinyan Chen", "Liuhui Wang", "Jianhan Jin", "Claire Guo", "Shen Yan", "Bo Zhang", "Chaoyou Fu", "Peng Gao", "Hongsheng Li" ], "github": "https://github.com/CaraJ7/MME-CoT", "project_page": "", "abstract": "Answering questions with Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs), yet its impact on Large Multimodal Models (LMMs) still lacks a systematic assessment and in-depth investigation. In this paper, we introduce MME-CoT, a specialized benchmark evaluating the CoT reasoning performance of LMMs, spanning six domains: math, science, OCR, logic, space-time, and general scenes. As the first comprehensive study in this area, we propose a thorough evaluation suite incorporating three novel metrics that assess the reasoning quality, robustness, and efficiency at a fine-grained level. Leveraging curated high-quality data and a unique evaluation strategy, we conduct an in-depth analysis of state-of-the-art LMMs, uncovering several key insights: 1) Models with reflection mechanism demonstrate a superior CoT quality, with Kimi k1.5 outperforming GPT-4o and demonstrating the highest quality results; 2) CoT prompting often degrades LMM performance on perception-heavy tasks, suggesting a potentially harmful overthinking behavior; and 3) Although the CoT quality is high, LMMs with reflection exhibit significant inefficiency in both normal response and self-correction phases. We hope MME-CoT serves as a foundation for advancing multimodal reasoning in LMMs. Project Page: https://mmecot.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CaraJ7/MME-CoT", "github_api_added_by": "auto" }, { "date": "2025-02-14", "arxiv_id": "2502.09042", "title": "Typhoon T1: An Open Thai Reasoning Model", "authors": [ "Pittawat Taveekitworachai", "Potsawee Manakul", "Kasima Tharnpipitchai", "Kunat Pipatanakul" ], "github": "", "project_page": "", "abstract": "This paper introduces Typhoon T1, an open effort to develop an open Thai reasoning model. A reasoning model is a relatively new type of generative model built on top of large language models (LLMs). A reasoning model generates a long chain of thought before arriving at a final answer, an approach found to improve performance on complex tasks. However, details on developing such a model are limited, especially for reasoning models that can generate traces in a low-resource language. Typhoon T1 presents an open effort that dives into the details of developing a reasoning model in a more cost-effective way by leveraging supervised fine-tuning using open datasets, instead of reinforcement learning. This paper shares the details about synthetic data generation and training, as well as our dataset and model weights. Additionally, we provide insights gained from developing a reasoning model that generalizes across domains and is capable of generating reasoning traces in a low-resource language, using Thai as an example. We hope this open effort provides a foundation for further research in this field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.09620", "title": "Exploring the Potential of Encoder-free Architectures in 3D LMMs", "authors": [ "Yiwen Tang", "Zoey Guo", "Zhuhao Wang", "Ray Zhang", "Qizhi Chen", "Junli Liu", "Delin Qu", "Zhigang Wang", "Dong Wang", "Xuelong Li", "Bin Zhao" ], "github": "https://github.com/Ivan-Tang-3D/ENEL", "project_page": "", "abstract": "Encoder-free architectures have been preliminarily explored in the 2D visual domain, yet it remains an open question whether they can be effectively applied to 3D understanding scenarios. In this paper, we present the first comprehensive investigation into the potential of encoder-free architectures to overcome the challenges of encoder-based 3D Large Multimodal Models (LMMs). These challenges include the failure to adapt to varying point cloud resolutions and the point features from the encoder not meeting the semantic needs of Large Language Models (LLMs). We identify key aspects for 3D LMMs to remove the encoder and enable the LLM to assume the role of the 3D encoder: 1) We propose the LLM-embedded Semantic Encoding strategy in the pre-training stage, exploring the effects of various point cloud self-supervised losses. And we present the Hybrid Semantic Loss to extract high-level semantics. 2) We introduce the Hierarchical Geometry Aggregation strategy in the instruction tuning stage. This incorporates inductive bias into the LLM early layers to focus on the local details of the point clouds. To the end, we present the first Encoder-free 3D LMM, ENEL. Our 7B model rivals the current state-of-the-art model, ShapeLLM-13B, achieving 55.0%, 50.92%, and 42.7% on the classification, captioning, and VQA tasks, respectively. Our results demonstrate that the encoder-free architecture is highly promising for replacing encoder-based architectures in the field of 3D understanding. The code is released at https://github.com/Ivan-Tang-3D/ENEL", "github_manual": "", "github_regex": "https://github.com/Ivan-Tang-3D/ENEL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.09390", "title": "SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models", "authors": [ "Daniel Fleischer", "Moshe Berchansky", "Gad Markovits", "Moshe Wasserblat" ], "github": "https://github.com/IntelLabs/RAG-FiT", "project_page": "", "abstract": "In the rapidly evolving field of Natural Language Processing, Large Language Models (LLMs) are tasked with increasingly complex reasoning challenges. Traditional methods like chain-of-thought prompting have shown promise but often fall short in fully leveraging a model's reasoning capabilities. This paper introduces SQuARE (Sequential Question Answering Reasoning Engine), a novel prompting technique designed to improve reasoning through a self-interrogation paradigm. Building upon CoT frameworks, SQuARE prompts models to generate and resolve multiple auxiliary questions before tackling the main query, promoting a more thorough exploration of various aspects of a topic. Our expansive evaluations, conducted with Llama 3 and GPT-4o models across multiple question-answering datasets, demonstrate that SQuARE significantly surpasses traditional CoT prompts and existing rephrase-and-respond methods. By systematically decomposing queries, SQuARE advances LLM capabilities in reasoning tasks. The code is publicly available at https://github.com/IntelLabs/RAG-FiT/tree/square.", "github_manual": "", "github_regex": "https://github.com/IntelLabs/RAG-FiT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.09082", "title": "CoSER: Coordinating LLM-Based Persona Simulation of Established Roles", "authors": [ "Xintao Wang", "Heng Wang", "Yifei Zhang", "Xinfeng Yuan", "Rui Xu", "Jen-tse Huang", "Siyu Yuan", "Haoran Guo", "Jiangjie Chen", "Wei Wang", "Yanghua Xiao", "Shuchang Zhou" ], "github": "https://github.com/Neph0s/CoSER", "project_page": "", "abstract": "Role-playing language agents (RPLAs) have emerged as promising applications of large language models (LLMs). However, simulating established characters presents a challenging task for RPLAs, due to the lack of authentic character datasets and nuanced evaluation methods using such data. In this paper, we present CoSER, a collection of a high-quality dataset, open models, and an evaluation protocol towards effective RPLAs of established characters. The CoSER dataset covers 17,966 characters from 771 renowned books. It provides authentic dialogues with real-world intricacies, as well as diverse data types such as conversation setups, character experiences and internal thoughts. Drawing from acting methodology, we introduce given-circumstance acting for training and evaluating role-playing LLMs, where LLMs sequentially portray multiple characters in book scenes. Using our dataset, we develop CoSER 8B and CoSER 70B, i.e., advanced open role-playing LLMs built on LLaMA-3.1 models. Extensive experiments demonstrate the value of the CoSER dataset for RPLA training, evaluation and retrieval. Moreover, CoSER 70B exhibits state-of-the-art performance surpassing or matching GPT-4o on our evaluation and three existing benchmarks, i.e., achieving 75.80% and 93.47% accuracy on the InCharacter and LifeChoice benchmarks respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Neph0s/CoSER", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.09619", "title": "Can this Model Also Recognize Dogs? Zero-Shot Model Search from Weights", "authors": [ "Jonathan Kahana", "Or Nathan", "Eliahu Horwitz", "Yedid Hoshen" ], "github": "", "project_page": "", "abstract": "With the increasing numbers of publicly available models, there are probably pretrained, online models for most tasks users require. However, current model search methods are rudimentary, essentially a text-based search in the documentation, thus users cannot find the relevant models. This paper presents ProbeLog, a method for retrieving classification models that can recognize a target concept, such as \"Dog\", without access to model metadata or training data. Differently from previous probing methods, ProbeLog computes a descriptor for each output dimension (logit) of each model, by observing its responses on a fixed set of inputs (probes). Our method supports both logit-based retrieval (\"find more logits like this\") and zero-shot, text-based retrieval (\"find all logits corresponding to dogs\"). As probing-based representations require multiple costly feedforward passes through the model, we develop a method, based on collaborative filtering, that reduces the cost of encoding repositories by 3x. We demonstrate that ProbeLog achieves high retrieval accuracy, both in real-world and fine-grained search tasks and is scalable to full-size repositories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.05761", "title": "3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly", "authors": [ "Enquan Yang", "Peng Xing", "Hanyang Sun", "Wenbo Guo", "Yuanwei Ma", "Zechao Li", "Dan Zeng" ], "github": "https://github.com/EnquanYang2022/3CAD", "project_page": "", "abstract": "Industrial anomaly detection achieves progress thanks to datasets such as MVTec-AD and VisA. However, they suf- fer from limitations in terms of the number of defect sam- ples, types of defects, and availability of real-world scenes. These constraints inhibit researchers from further exploring the performance of industrial detection with higher accuracy. To this end, we propose a new large-scale anomaly detection dataset called 3CAD, which is derived from real 3C produc- tion lines. Specifically, the proposed 3CAD includes eight different types of manufactured parts, totaling 27,039 high- resolution images labeled with pixel-level anomalies. The key features of 3CAD are that it covers anomalous regions of different sizes, multiple anomaly types, and the possibility of multiple anomalous regions and multiple anomaly types per anomaly image. This is the largest and first anomaly de- tection dataset dedicated to 3C product quality control for community exploration and development. Meanwhile, we in- troduce a simple yet effective framework for unsupervised anomaly detection: a Coarse-to-Fine detection paradigm with Recovery Guidance (CFRG). To detect small defect anoma- lies, the proposed CFRG utilizes a coarse-to-fine detection paradigm. Specifically, we utilize a heterogeneous distilla- tion model for coarse localization and then fine localiza- tion through a segmentation model. In addition, to better capture normal patterns, we introduce recovery features as guidance. Finally, we report the results of our CFRG frame- work and popular anomaly detection methods on the 3CAD dataset, demonstrating strong competitiveness and providing a highly challenging benchmark to promote the development of the anomaly detection field. Data and code are available: https://github.com/EnquanYang2022/3CAD.", "github_manual": "", "github_regex": "https://github.com/EnquanYang2022/3CAD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.09614", "title": "DexTrack: Towards Generalizable Neural Tracking Control for Dexterous Manipulation from Human References", "authors": [ "Xueyi Liu", "Jianibieke Adalibieke", "Qianwei Han", "Yuzhe Qin", "Li Yi" ], "github": "https://github.com/meowuu7/dextrack", "project_page": "", "abstract": "We address the challenge of developing a generalizable neural tracking controller for dexterous manipulation from human references. This controller aims to manage a dexterous robot hand to manipulate diverse objects for various purposes defined by kinematic human-object interactions. Developing such a controller is complicated by the intricate contact dynamics of dexterous manipulation and the need for adaptivity, generalizability, and robustness. Current reinforcement learning and trajectory optimization methods often fall short due to their dependence on task-specific rewards or precise system models. We introduce an approach that curates large-scale successful robot tracking demonstrations, comprising pairs of human references and robot actions, to train a neural controller. Utilizing a data flywheel, we iteratively enhance the controller's performance, as well as the number and quality of successful tracking demonstrations. We exploit available tracking demonstrations and carefully integrate reinforcement learning and imitation learning to boost the controller's performance in dynamic environments. At the same time, to obtain high-quality tracking demonstrations, we individually optimize per-trajectory tracking by leveraging the learned tracking controller in a homotopy optimization method. The homotopy optimization, mimicking chain-of-thought, aids in solving challenging trajectory tracking problems to increase demonstration diversity. We showcase our success by training a generalizable neural controller and evaluating it in both simulation and real world. Our method achieves over a 10% improvement in success rates compared to leading baselines. The project website with animated results is available at https://meowuu7.github.io/DexTrack/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/meowuu7/dextrack", "github_api_added_by": "auto" }, { "date": "2025-02-14", "arxiv_id": "2502.05979", "title": "VFX Creator: Animated Visual Effect Generation with Controllable Diffusion Transformer", "authors": [ "Xinyu Liu", "Ailing Zeng", "Wei Xue", "Harry Yang", "Wenhan Luo", "Qifeng Liu", "Yike Guo" ], "github": "https://github.com/vfx-creator0/VFXCreator", "project_page": "", "abstract": "Crafting magic and illusions is one of the most thrilling aspects of filmmaking, with visual effects (VFX) serving as the powerhouse behind unforgettable cinematic experiences. While recent advances in generative artificial intelligence have driven progress in generic image and video synthesis, the domain of controllable VFX generation remains relatively underexplored. In this work, we propose a novel paradigm for animated VFX generation as image animation, where dynamic effects are generated from user-friendly textual descriptions and static reference images. Our work makes two primary contributions: (i) Open-VFX, the first high-quality VFX video dataset spanning 15 diverse effect categories, annotated with textual descriptions, instance segmentation masks for spatial conditioning, and start-end timestamps for temporal control. (ii) VFX Creator, a simple yet effective controllable VFX generation framework based on a Video Diffusion Transformer. The model incorporates a spatial and temporal controllable LoRA adapter, requiring minimal training videos. Specifically, a plug-and-play mask control module enables instance-level spatial manipulation, while tokenized start-end motion timestamps embedded in the diffusion process, alongside the text encoder, allow precise temporal control over effect timing and pace. Extensive experiments on the Open-VFX test set demonstrate the superiority of the proposed system in generating realistic and dynamic effects, achieving state-of-the-art performance and generalization ability in both spatial and temporal controllability. Furthermore, we introduce a specialized metric to evaluate the precision of temporal control. By bridging traditional VFX techniques with generative approaches, VFX Creator unlocks new possibilities for efficient and high-quality video effect generation, making advanced VFX accessible to a broader audience.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vfx-creator0/VFXCreator", "github_api_added_by": "auto" }, { "date": "2025-02-14", "arxiv_id": "2502.08680", "title": "Mathematical Reasoning in Large Language Models: Assessing Logical and Arithmetic Errors across Wide Numerical Ranges", "authors": [ "Safal Shrestha", "Minwu Kim", "Keith Ross" ], "github": "https://github.com/minwukim/GSM-Ranges", "project_page": "", "abstract": "Mathematical reasoning in Large Language Models (LLMs) is often evaluated using benchmarks with limited numerical ranges, failing to reflect real-world problem-solving across diverse scales. Furthermore, most existing evaluation methods only compare model outputs to ground-truth answers, obscuring insights into reasoning processes. To address these limitations, we introduce GSM-Ranges, a dataset generator derived from GSM8K that systematically perturbs numerical values in math problems to assess model robustness across varying numerical scales. Additionally, we propose a novel grading methodology that distinguishes between logical and non-logical errors, offering a more precise evaluation of reasoning processes beyond computational accuracy. Our experiments with various models reveal a significant increase in logical error rates-up to 14 percentage points-as numerical complexity rises, demonstrating a general weakness in reasoning with out-of-distribution numerical values. Moreover, while models demonstrate high accuracy on standalone arithmetic tasks, their performance deteriorates substantially when computations are embedded within word problems. These findings provide a comprehensive evaluation of LLMs' mathematical reasoning capabilities and inform future research directions for improving numerical generalization in language models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/minwukim/GSM-Ranges", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-14", "arxiv_id": "2502.09613", "title": "Latent Radiance Fields with 3D-aware 2D Representations", "authors": [ "Chaoyi Zhou", "Xi Liu", "Feng Luo", "Siyu Huang" ], "github": "", "project_page": "", "abstract": "Latent 3D reconstruction has shown great promise in empowering 3D semantic understanding and 3D generation by distilling 2D features into the 3D space. However, existing approaches struggle with the domain gap between 2D feature space and 3D representations, resulting in degraded rendering performance. To address this challenge, we propose a novel framework that integrates 3D awareness into the 2D latent space. The framework consists of three stages: (1) a correspondence-aware autoencoding method that enhances the 3D consistency of 2D latent representations, (2) a latent radiance field (LRF) that lifts these 3D-aware 2D representations into 3D space, and (3) a VAE-Radiance Field (VAE-RF) alignment strategy that improves image decoding from the rendered 2D representations. Extensive experiments demonstrate that our method outperforms the state-of-the-art latent 3D reconstruction approaches in terms of synthesis performance and cross-dataset generalizability across diverse indoor and outdoor scenes. To our knowledge, this is the first work showing the radiance field representations constructed from 2D latent representations can yield photorealistic 3D reconstruction performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.10177", "title": "STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning", "authors": [ "Mingcong Lei", "Yiming Zhao", "Ge Wang", "Zhixin Mai", "Shuguang Cui", "Yatong Han", "Jinke Ren" ], "github": "", "project_page": "", "abstract": "A key objective of embodied intelligence is enabling agents to perform long-horizon tasks in dynamic environments while maintaining robust decision-making and adaptability. To achieve this goal, we propose the Spatio-Temporal Memory Agent (STMA), a novel framework designed to enhance task planning and execution by integrating spatio-temporal memory. STMA is built upon three critical components: (1) a spatio-temporal memory module that captures historical and environmental changes in real time, (2) a dynamic knowledge graph that facilitates adaptive spatial reasoning, and (3) a planner-critic mechanism that iteratively refines task strategies. We evaluate STMA in the TextWorld environment on 32 tasks, involving multi-step planning and exploration under varying levels of complexity. Experimental results demonstrate that STMA achieves a 31.25% improvement in success rate and a 24.7% increase in average score compared to the state-of-the-art model. The results highlight the effectiveness of spatio-temporal memory in advancing the memory capabilities of embodied agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.10389", "title": "Region-Adaptive Sampling for Diffusion Transformers", "authors": [ "Ziming Liu", "Yifan Yang", "Chengruidong Zhang", "Yiqi Zhang", "Lili Qiu", "Yang You", "Yuqing Yang" ], "github": "https://github.com/microsoft/RAS", "project_page": "", "abstract": "Diffusion models (DMs) have become the leading choice for generative tasks across diverse domains. However, their reliance on multiple sequential forward passes significantly limits real-time performance. Previous acceleration methods have primarily focused on reducing the number of sampling steps or reusing intermediate results, failing to leverage variations across spatial regions within the image due to the constraints of convolutional U-Net structures. By harnessing the flexibility of Diffusion Transformers (DiTs) in handling variable number of tokens, we introduce RAS, a novel, training-free sampling strategy that dynamically assigns different sampling ratios to regions within an image based on the focus of the DiT model. Our key observation is that during each sampling step, the model concentrates on semantically meaningful regions, and these areas of focus exhibit strong continuity across consecutive steps. Leveraging this insight, RAS updates only the regions currently in focus, while other regions are updated using cached noise from the previous step. The model's focus is determined based on the output from the preceding step, capitalizing on the temporal consistency we observed. We evaluate RAS on Stable Diffusion 3 and Lumina-Next-T2I, achieving speedups up to 2.36x and 2.51x, respectively, with minimal degradation in generation quality. Additionally, a user study reveals that RAS delivers comparable qualities under human evaluation while achieving a 1.6x speedup. Our approach makes a significant step towards more efficient diffusion transformers, enhancing their potential for real-time applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/RAS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.09696", "title": "ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models", "authors": [ "Jonathan Roberts", "Mohammad Reza Taesiri", "Ansh Sharma", "Akash Gupta", "Samuel Roberts", "Ioana Croitoru", "Simion-Vlad Bogolin", "Jialu Tang", "Florian Langer", "Vyas Raina", "Vatsal Raina", "Hanyi Xiong", "Vishaal Udandarao", "Jingyi Lu", "Shiyang Chen", "Sam Purkis", "Tianshuo Yan", "Wenye Lin", "Gyungin Shin", "Qiaochu Yang", "Anh Totti Nguyen", "Kai Han", "Samuel Albanie" ], "github": "", "project_page": "", "abstract": "Large Multimodal Models (LMMs) exhibit major shortfalls when interpreting images and, by some measures, have poorer spatial cognition than small children or animals. Despite this, they attain high scores on many popular visual benchmarks, with headroom rapidly eroded by an ongoing surge of model progress. To address this, there is a pressing need for difficult benchmarks that remain relevant for longer. We take this idea to its limit by introducing ZeroBench-a lightweight visual reasoning benchmark that is entirely impossible for contemporary frontier LMMs. Our benchmark consists of 100 manually curated questions and 334 less difficult subquestions. We evaluate 20 LMMs on ZeroBench, all of which score 0.0%, and rigorously analyse the errors. To encourage progress in visual understanding, we publicly release ZeroBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.10391", "title": "MM-RLHF: The Next Step Forward in Multimodal LLM Alignment", "authors": [ "Yi-Fan Zhang", "Tao Yu", "Haochen Tian", "Chaoyou Fu", "Peiyan Li", "Jianshu Zeng", "Wulin Xie", "Yang Shi", "Huanyu Zhang", "Junkang Wu", "Xue Wang", "Yibo Hu", "Bin Wen", "Fan Yang", "Zhang Zhang", "Tingting Gao", "Di Zhang", "Liang Wang", "Rong Jin", "Tieniu Tan" ], "github": "https://github.com/yfzhang114/MM-RLHF", "project_page": "", "abstract": "Despite notable advancements in Multimodal Large Language Models (MLLMs), most state-of-the-art models have not undergone thorough alignment with human preferences. This gap exists because current alignment research has primarily achieved progress in specific areas (e.g., hallucination reduction), while the broader question of whether aligning models with human preferences can systematically enhance MLLM capability remains largely unexplored. To this end, we introduce MM-RLHF, a dataset containing 120k fine-grained, human-annotated preference comparison pairs. This dataset represents a substantial advancement over existing resources, offering superior size, diversity, annotation granularity, and quality. Leveraging this dataset, we propose several key innovations to improve both the quality of reward models and the efficiency of alignment algorithms. Notably, we introduce a Critique-Based Reward Model, which generates critiques of model outputs before assigning scores, offering enhanced interpretability and more informative feedback compared to traditional scalar reward mechanisms. Additionally, we propose Dynamic Reward Scaling, a method that adjusts the loss weight of each sample according to the reward signal, thereby optimizing the use of high-quality comparison pairs. Our approach is rigorously evaluated across 10 distinct dimensions and 27 benchmarks, with results demonstrating significant and consistent improvements in model performance. Specifically, fine-tuning LLaVA-ov-7B with MM-RLHF and our alignment algorithm leads to a 19.5% increase in conversational abilities and a 60% improvement in safety. We have open-sourced the preference dataset, reward model, training and evaluation code, as well as reward modeling and safety benchmarks. For more details, please visit our project page: https://mm-rlhf.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yfzhang114/MM-RLHF", "github_api_added_by": "auto" }, { "date": "2025-02-17", "arxiv_id": "2502.10248", "title": "Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model", "authors": [ "Guoqing Ma", "Haoyang Huang", "Kun Yan", "Liangyu Chen", "Nan Duan", "Shengming Yin", "Changyi Wan", "Ranchen Ming", "Xiaoniu Song", "Xing Chen", "Yu Zhou", "Deshan Sun", "Deyu Zhou", "Jian Zhou", "Kaijun Tan", "Kang An", "Mei Chen", "Wei Ji", "Qiling Wu", "Wen Sun", "Xin Han", "Yanan Wei", "Zheng Ge", "Aojie Li", "Bin Wang", "Bizhu Huang", "Bo Wang", "Brian Li", "Changxing Miao", "Chen Xu", "Chenfei Wu", "Chenguang Yu", "Dapeng Shi", "Dingyuan Hu", "Enle Liu", "Gang Yu", "Ge Yang", "Guanzhe Huang", "Gulin Yan", "Haiyang Feng", "Hao Nie", "Haonan Jia", "Hanpeng Hu", "Hanqi Chen", "Haolong Yan", "Heng Wang", "Hongcheng Guo", "Huilin Xiong", "Huixin Xiong", "Jiahao Gong", "Jianchang Wu", "Jiaoren Wu", "Jie Wu", "Jie Yang", "Jiashuai Liu", "Jiashuo Li", "Jingyang Zhang", "Junjing Guo", "Junzhe Lin", "Kaixiang Li", "Lei Liu", "Lei Xia", "Liang Zhao", "Liguo Tan", "Liwen Huang", "Liying Shi", "Ming Li", "Mingliang Li", "Muhua Cheng", "Na Wang", "Qiaohui Chen", "Qinglin He", "Qiuyan Liang", "Quan Sun", "Ran Sun", "Rui Wang", "Shaoliang Pang", "Shiliang Yang", "Sitong Liu", "Siqi Liu", "Shuli Gao", "Tiancheng Cao", "Tianyu Wang", "Weipeng Ming", "Wenqing He", "Xu Zhao", "Xuelin Zhang", "Xianfang Zeng", "Xiaojia Liu", "Xuan Yang", "Yaqi Dai", "Yanbo Yu", "Yang Li", "Yineng Deng", "Yingming Wang", "Yilei Wang", "Yuanwei Lu", "Yu Chen", "Yu Luo", "Yuchu Luo", "Yuhe Yin", "Yuheng Feng", "Yuxiang Yang", "Zecheng Tang", "Zekai Zhang", "Zidong Yang", "Binxing Jiao", "Jiansheng Chen", "Jing Li", "Shuchang Zhou", "Xiangyu Zhang", "Xinhao Zhang", "Yibo Zhu", "Heung-Yeung Shum", "Daxin Jiang" ], "github": "https://github.com/stepfun-ai/Step-Video-T2V", "project_page": "", "abstract": "We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model with 30B parameters and the ability to generate videos up to 204 frames in length. A deep compression Variational Autoencoder, Video-VAE, is designed for video generation tasks, achieving 16x16 spatial and 8x temporal compression ratios, while maintaining exceptional video reconstruction quality. User prompts are encoded using two bilingual text encoders to handle both English and Chinese. A DiT with 3D full attention is trained using Flow Matching and is employed to denoise input noise into latent frames. A video-based DPO approach, Video-DPO, is applied to reduce artifacts and improve the visual quality of the generated videos. We also detail our training strategies and share key observations and insights. Step-Video-T2V's performance is evaluated on a novel video generation benchmark, Step-Video-T2V-Eval, demonstrating its state-of-the-art text-to-video quality when compared with both open-source and commercial engines. Additionally, we discuss the limitations of current diffusion-based model paradigm and outline future directions for video foundation models. We make both Step-Video-T2V and Step-Video-T2V-Eval available at https://github.com/stepfun-ai/Step-Video-T2V. The online version can be accessed from https://yuewen.cn/videos as well. Our goal is to accelerate the innovation of video foundation models and empower video content creators.", "github_manual": "", "github_regex": "https://github.com/stepfun-ai/Step-Video-T2V", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.09741", "title": "FoNE: Precise Single-Token Number Embeddings via Fourier Features", "authors": [ "Tianyi Zhou", "Deqing Fu", "Mahdi Soltanolkotabi", "Robin Jia", "Vatsal Sharan" ], "github": "https://github.com/KevinZhoutianyi/FoNE", "project_page": "", "abstract": "Large Language Models (LLMs) typically represent numbers using multiple tokens, which requires the model to aggregate these tokens to interpret numerical values. This fragmentation makes both training and inference less efficient and adversely affects the model's performance on number-related tasks. Inspired by the observation that pre-trained LLMs internally learn Fourier-like features for number tokens, we propose Fourier Number Embedding (FoNE), a novel method that directly maps numbers into the embedding space with their Fourier features. FoNE encodes each number as a single token with only two embedding dimensions per digit, effectively capturing numerical values without fragmentation. This compact representation accelerates both training and inference. Compared to traditional subword and digit-wise embeddings, FoNE not only reduces computational overhead but also achieves higher accuracy across various numerical tasks including addition, subtraction and multiplication. On 6-digit decimal addition, FoNE requires 64times less data to achieve 99% accuracy than subword and digit-wise embeddings while using 3times and 6times fewer tokens per number, respectively. Furthermore, FoNE is the only method that yields 100% accuracy on over 100,000 test examples for addition, subtraction, and multiplication. The codes and visualization are available at https://fouriernumber.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KevinZhoutianyi/FoNE", "github_api_added_by": "auto" }, { "date": "2025-02-17", "arxiv_id": "2502.09992", "title": "Large Language Diffusion Models", "authors": [ "Shen Nie", "Fengqi Zhu", "Zebin You", "Xiaolu Zhang", "Jingyang Ou", "Jun Hu", "Jun Zhou", "Yankai Lin", "Ji-Rong Wen", "Chongxuan Li" ], "github": "https://github.com/ml-gsai/llada", "project_page": "", "abstract": "Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). We challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. By optimizing a likelihood bound, it provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming our self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue. Moreover, LLaDA addresses the reversal curse, surpassing GPT-4o in a reversal poem completion task. Our findings establish diffusion models as a viable and promising alternative to ARMs, challenging the assumption that key LLM capabilities discussed above are inherently tied to ARMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ml-gsai/llada", "github_api_added_by": "auto" }, { "date": "2025-02-17", "arxiv_id": "2502.09955", "title": "Diverse Inference and Verification for Advanced Reasoning", "authors": [ "Iddo Drori", "Gaston Longhitano", "Mao Mao", "Seunghwan Hyun", "Yuke Zhang", "Sungjun Park", "Zachary Meeks", "Xin-Yu Zhang", "Ben Segev", "Howard Yong", "Nakul Verma", "Avi Shporer", "Alon Amit", "Madeleine Udell" ], "github": "", "project_page": "", "abstract": "Reasoning LLMs such as OpenAI o1, o3 and DeepSeek R1 have made significant progress in mathematics and coding, yet find challenging advanced tasks such as International Mathematical Olympiad (IMO) combinatorics problems, Abstraction and Reasoning Corpus (ARC) puzzles, and Humanity's Last Exam (HLE) questions. We use a diverse inference approach that combines multiple models and methods at test time. We find that verifying mathematics and code problems, and rejection sampling on other problems is simple and effective. We automatically verify correctness of solutions to IMO problems by Lean, and ARC puzzles by code, and find that best-of-N effectively answers HLE questions. Our approach increases answer accuracy on IMO combinatorics problems from 33.3% to 77.8%, accuracy on HLE questions from 8% to 37%, and solves 80% of ARC puzzles that 948 humans could not and 26.5% of ARC puzzles that o3 high compute does not. Test-time simulations, reinforcement learning, and meta-learning with inference feedback improve generalization by adapting agent graph representations and varying prompts, code, and datasets. Our approach is reliable, robust, and scalable, and in the spirit of reproducible research, we will make it publicly available upon publication.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.09638", "title": "Jailbreaking to Jailbreak", "authors": [ "Jeremy Kritz", "Vaughn Robinson", "Robert Vacareanu", "Bijan Varjavand", "Michael Choi", "Bobby Gogov", "Scale Red Team", "Summer Yue", "Willow E. Primack", "Zifan Wang" ], "github": "", "project_page": "", "abstract": "Refusal training on Large Language Models (LLMs) prevents harmful outputs, yet this defense remains vulnerable to both automated and human-crafted jailbreaks. We present a novel LLM-as-red-teamer approach in which a human jailbreaks a refusal-trained LLM to make it willing to jailbreak itself or other LLMs. We refer to the jailbroken LLMs as J_2 attackers, which can systematically evaluate target models using various red teaming strategies and improve its performance via in-context learning from the previous failures. Our experiments demonstrate that Sonnet 3.5 and Gemini 1.5 pro outperform other LLMs as J_2, achieving 93.0% and 91.0% attack success rates (ASRs) respectively against GPT-4o (and similar results across other capable LLMs) on Harmbench. Our work not only introduces a scalable approach to strategic red teaming, drawing inspiration from human red teamers, but also highlights jailbreaking-to-jailbreak as an overlooked failure mode of the safeguard. Specifically, an LLM can bypass its own safeguards by employing a jailbroken version of itself that is willing to assist in further jailbreaking. To prevent any direct misuse with J_2, while advancing research in AI safety, we publicly share our methodology while keeping specific prompting details private.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.09980", "title": "V2V-LLM: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multi-Modal Large Language Models", "authors": [ "Hsu-kuang Chiu", "Ryo Hachiuma", "Chien-Yi Wang", "Stephen F. Smith", "Yu-Chiang Frank Wang", "Min-Hung Chen" ], "github": "", "project_page": "", "abstract": "Current autonomous driving vehicles rely mainly on their individual sensors to understand surrounding scenes and plan for future trajectories, which can be unreliable when the sensors are malfunctioning or occluded. To address this problem, cooperative perception methods via vehicle-to-vehicle (V2V) communication have been proposed, but they have tended to focus on detection and tracking. How those approaches contribute to overall cooperative planning performance is still under-explored. Inspired by recent progress using Large Language Models (LLMs) to build autonomous driving systems, we propose a novel problem setting that integrates an LLM into cooperative autonomous driving, with the proposed Vehicle-to-Vehicle Question-Answering (V2V-QA) dataset and benchmark. We also propose our baseline method Vehicle-to-Vehicle Large Language Model (V2V-LLM), which uses an LLM to fuse perception information from multiple connected autonomous vehicles (CAVs) and answer driving-related questions: grounding, notable object identification, and planning. Experimental results show that our proposed V2V-LLM can be a promising unified model architecture for performing various tasks in cooperative autonomous driving, and outperforms other baseline methods that use different fusion approaches. Our work also creates a new research direction that can improve the safety of future autonomous driving systems. Our project website: https://eddyhkchiu.github.io/v2vllm.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.07856", "title": "MRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE Solvers", "authors": [ "Ao Li", "Wei Fang", "Hongbo Zhao", "Le Lu", "Ge Yang", "Minfeng Xu" ], "github": "", "project_page": "", "abstract": "In applications of diffusion models, controllable generation is of practical significance, but is also challenging. Current methods for controllable generation primarily focus on modifying the score function of diffusion models, while Mean Reverting (MR) Diffusion directly modifies the structure of the stochastic differential equation (SDE), making the incorporation of image conditions simpler and more natural. However, current training-free fast samplers are not directly applicable to MR Diffusion. And thus MR Diffusion requires hundreds of NFEs (number of function evaluations) to obtain high-quality samples. In this paper, we propose a new algorithm named MRS (MR Sampler) to reduce the sampling NFEs of MR Diffusion. We solve the reverse-time SDE and the probability flow ordinary differential equation (PF-ODE) associated with MR Diffusion, and derive semi-analytical solutions. The solutions consist of an analytical function and an integral parameterized by a neural network. Based on this solution, we can generate high-quality samples in fewer steps. Our approach does not require training and supports all mainstream parameterizations, including noise prediction, data prediction and velocity prediction. Extensive experiments demonstrate that MR Sampler maintains high sampling quality with a speedup of 10 to 20 times across ten different image restoration tasks. Our algorithm accelerates the sampling procedure of MR Diffusion, making it more practical in controllable generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.09935", "title": "Precise Parameter Localization for Textual Generation in Diffusion Models", "authors": [ "\u0141ukasz Staniszewski", "Bartosz Cywi\u0144ski", "Franziska Boenisch", "Kamil Deja", "Adam Dziedzic" ], "github": "https://github.com/deep-floyd/IF", "project_page": "", "abstract": "Novel diffusion models can synthesize photo-realistic images with integrated high-quality text. Surprisingly, we demonstrate through attention activation patching that only less than 1% of diffusion models' parameters, all contained in attention layers, influence the generation of textual content within the images. Building on this observation, we improve textual generation efficiency and performance by targeting cross and joint attention layers of diffusion models. We introduce several applications that benefit from localizing the layers responsible for textual content generation. We first show that a LoRA-based fine-tuning solely of the localized layers enhances, even more, the general text-generation capabilities of large diffusion models while preserving the quality and diversity of the diffusion models' generations. Then, we demonstrate how we can use the localized layers to edit textual content in generated images. Finally, we extend this idea to the practical use case of preventing the generation of toxic text in a cost-free manner. In contrast to prior work, our localization approach is broadly applicable across various diffusion model architectures, including U-Net (e.g., LDM and SDXL) and transformer-based (e.g., DeepFloyd IF and Stable Diffusion 3), utilizing diverse text encoders (e.g., from CLIP to the large language models like T5). Project page available at https://t2i-text-loc.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/deep-floyd/IF", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.07586", "title": "We Can't Understand AI Using our Existing Vocabulary", "authors": [ "John Hewitt", "Robert Geirhos", "Been Kim" ], "github": "", "project_page": "", "abstract": "This position paper argues that, in order to understand AI, we cannot rely on our existing vocabulary of human words. Instead, we should strive to develop neologisms: new words that represent precise human concepts that we want to teach machines, or machine concepts that we need to learn. We start from the premise that humans and machines have differing concepts. This means interpretability can be framed as a communication problem: humans must be able to reference and control machine concepts, and communicate human concepts to machines. Creating a shared human-machine language through developing neologisms, we believe, could solve this communication problem. Successful neologisms achieve a useful amount of abstraction: not too detailed, so they're reusable in many contexts, and not too high-level, so they convey precise information. As a proof of concept, we demonstrate how a \"length neologism\" enables controlling LLM response length, while a \"diversity neologism\" allows sampling more variable responses. Taken together, we argue that we cannot understand AI using our existing vocabulary, and expanding it through neologisms creates opportunities for both controlling and understanding machines better.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.10235", "title": "AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting", "authors": [ "Abdelhakim Benechehab", "Vasilii Feofanov", "Giuseppe Paolo", "Albert Thomas", "Maurizio Filippone", "Bal\u00e1zs K\u00e9gl" ], "github": "https://github.com/abenechehab/AdaPTS", "project_page": "", "abstract": "Pre-trained foundation models (FMs) have shown exceptional performance in univariate time series forecasting tasks. However, several practical challenges persist, including managing intricate dependencies among features and quantifying uncertainty in predictions. This study aims to tackle these critical limitations by introducing adapters; feature-space transformations that facilitate the effective use of pre-trained univariate time series FMs for multivariate tasks. Adapters operate by projecting multivariate inputs into a suitable latent space and applying the FM independently to each dimension. Inspired by the literature on representation learning and partially stochastic Bayesian neural networks, we present a range of adapters and optimization/inference strategies. Experiments conducted on both synthetic and real-world datasets confirm the efficacy of adapters, demonstrating substantial enhancements in forecasting accuracy and uncertainty quantification compared to baseline methods. Our framework, AdaPTS, positions adapters as a modular, scalable, and effective solution for leveraging time series FMs in multivariate contexts, thereby promoting their wider adoption in real-world applications. We release the code at https://github.com/abenechehab/AdaPTS.", "github_manual": "", "github_regex": "https://github.com/abenechehab/AdaPTS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.10173", "title": "Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model", "authors": [ "Bo Ni", "Markus J. Buehler" ], "github": "https://github.com/lamm-mit/ModeShapeDiffusionDesign", "project_page": "", "abstract": "Proteins are dynamic molecular machines whose biological functions, spanning enzymatic catalysis, signal transduction, and structural adaptation, are intrinsically linked to their motions. Designing proteins with targeted dynamic properties, however, remains a challenge due to the complex, degenerate relationships between sequence, structure, and molecular motion. Here, we introduce VibeGen, a generative AI framework that enables end-to-end de novo protein design conditioned on normal mode vibrations. VibeGen employs an agentic dual-model architecture, comprising a protein designer that generates sequence candidates based on specified vibrational modes and a protein predictor that evaluates their dynamic accuracy. This approach synergizes diversity, accuracy, and novelty during the design process. Via full-atom molecular simulations as direct validation, we demonstrate that the designed proteins accurately reproduce the prescribed normal mode amplitudes across the backbone while adopting various stable, functionally relevant structures. Notably, generated sequences are de novo, exhibiting no significant similarity to natural proteins, thereby expanding the accessible protein space beyond evolutionary constraints. Our work integrates protein dynamics into generative protein design, and establishes a direct, bidirectional link between sequence and vibrational behavior, unlocking new pathways for engineering biomolecules with tailored dynamical and functional properties. This framework holds broad implications for the rational design of flexible enzymes, dynamic scaffolds, and biomaterials, paving the way toward dynamics-informed AI-driven protein engineering.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lamm-mit/ModeShapeDiffusionDesign", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.08769", "title": "Cluster and Predict Latents Patches for Improved Masked Image Modeling", "authors": [ "Timoth\u00e9e Darcet", "Federico Baldassarre", "Maxime Oquab", "Julien Mairal", "Piotr Bojanowski" ], "github": "", "project_page": "", "abstract": "Masked Image Modeling (MIM) offers a promising approach to self-supervised representation learning, however existing MIM models still lag behind the state-of-the-art. In this paper, we systematically analyze target representations, loss functions, and architectures, to introduce CAPI - a novel pure-MIM framework that relies on the prediction of latent clusterings. Our approach leverages a clustering-based loss, which is stable to train, and exhibits promising scaling properties. Our ViT-L backbone, CAPI, achieves 83.8% accuracy on ImageNet and 32.1% mIoU on ADE20K with simple linear probes, substantially outperforming previous MIM methods and approaching the performance of the current state-of-the-art, DINOv2. We release all our code and models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.10140", "title": "Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages", "authors": [ "Daniil Gurgurov", "Ivan Vykopal", "Josef van Genabith", "Simon Ostermann" ], "github": "https://github.com/d-gurgurov/Knowledge-Driven-Adaptation-LLMs", "project_page": "", "abstract": "Low-resource languages (LRLs) face significant challenges in natural language processing (NLP) due to limited data. While current state-of-the-art large language models (LLMs) still struggle with LRLs, smaller multilingual models (mLMs) such as mBERT and XLM-R offer greater promise due to a better fit of their capacity to low training data sizes. This study systematically investigates parameter-efficient adapter-based methods for adapting mLMs to LRLs, evaluating three architectures: Sequential Bottleneck, Invertible Bottleneck, and Low-Rank Adaptation. Using unstructured text from GlotCC and structured knowledge from ConceptNet, we show that small adaptation datasets (e.g., up to 1 GB of free-text or a few MB of knowledge graph data) yield gains in intrinsic (masked language modeling) and extrinsic tasks (topic classification, sentiment analysis, and named entity recognition). We find that Sequential Bottleneck adapters excel in language modeling, while Invertible Bottleneck adapters slightly outperform other methods on downstream tasks due to better embedding alignment and larger parameter counts. Adapter-based methods match or outperform full fine-tuning while using far fewer parameters, and smaller mLMs prove more effective for LRLs than massive LLMs like LLaMA-3, GPT-4, and DeepSeek-R1-based distilled models. While adaptation improves performance, pre-training data size remains the dominant factor, especially for languages with extensive pre-training coverage.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/d-gurgurov/Knowledge-Driven-Adaptation-LLMs", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.09411", "title": "ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation", "authors": [ "Rotem Shalev-Arkushin", "Rinon Gal", "Amit H. Bermano", "Ohad Fried" ], "github": "", "project_page": "", "abstract": "Diffusion models enable high-quality and diverse visual content synthesis. However, they struggle to generate rare or unseen concepts. To address this challenge, we explore the usage of Retrieval-Augmented Generation (RAG) with image generation models. We propose ImageRAG, a method that dynamically retrieves relevant images based on a given text prompt, and uses them as context to guide the generation process. Prior approaches that used retrieved images to improve generation, trained models specifically for retrieval-based generation. In contrast, ImageRAG leverages the capabilities of existing image conditioning models, and does not require RAG-specific training. Our approach is highly adaptable and can be applied across different model types, showing significant improvement in generating rare and fine-grained concepts using different base models. Our project page is available at: https://rotem-shalev.github.io/ImageRAG", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.07780", "title": "DarwinLM: Evolutionary Structured Pruning of Large Language Models", "authors": [ "Shengkun Tang", "Oliver Sieberling", "Eldar Kurtic", "Zhiqiang Shen", "Dan Alistarh" ], "github": "https://github.com/IST-DASLab/DarwinLM", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved significant success across various NLP tasks. However, their massive computational costs limit their widespread use, particularly in real-time applications. Structured pruning offers an effective solution by compressing models and directly providing end-to-end speed improvements, regardless of the hardware environment. Meanwhile, different components of the model exhibit varying sensitivities towards pruning, calling for non-uniform model compression. However, a pruning method should not only identify a capable substructure, but also account for post-compression training. To this end, we propose \\sysname, a method for training-aware structured pruning. \\sysname builds upon an evolutionary search process, generating multiple offspring models in each generation through mutation, and selecting the fittest for survival. To assess the effect of post-training, we incorporate a lightweight, multistep training process within the offspring population, progressively increasing the number of tokens and eliminating poorly performing models in each selection stage. We validate our method through extensive experiments on Llama-2-7B, Llama-3.1-8B and Qwen-2.5-14B-Instruct, achieving state-of-the-art performance for structured pruning. For instance, \\sysname surpasses ShearedLlama while requiring 5times less training data during post-compression training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/IST-DASLab/DarwinLM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.10392", "title": "Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding", "authors": [ "Wenxuan Guo", "Xiuwei Xu", "Ziwei Wang", "Jianjiang Feng", "Jie Zhou", "Jiwen Lu" ], "github": "https://github.com/GWxuan/TSP3D", "project_page": "", "abstract": "In this paper, we propose an efficient multi-level convolution architecture for 3D visual grounding. Conventional methods are difficult to meet the requirements of real-time inference due to the two-stage or point-based architecture. Inspired by the success of multi-level fully sparse convolutional architecture in 3D object detection, we aim to build a new 3D visual grounding framework following this technical route. However, as in 3D visual grounding task the 3D scene representation should be deeply interacted with text features, sparse convolution-based architecture is inefficient for this interaction due to the large amount of voxel features. To this end, we propose text-guided pruning (TGP) and completion-based addition (CBA) to deeply fuse 3D scene representation and text features in an efficient way by gradual region pruning and target completion. Specifically, TGP iteratively sparsifies the 3D scene representation and thus efficiently interacts the voxel features with text features by cross-attention. To mitigate the affect of pruning on delicate geometric information, CBA adaptively fixes the over-pruned region by voxel completion with negligible computational overhead. Compared with previous single-stage methods, our method achieves top inference speed and surpasses previous fastest method by 100\\% FPS. Our method also achieves state-of-the-art accuracy even compared with two-stage methods, with +1.13 lead of Acc@0.5 on ScanRefer, and +2.6 and +3.2 leads on NR3D and SR3D respectively. The code is available at https://github.com/GWxuan/TSP3D{https://github.com/GWxuan/TSP3D}.", "github_manual": "", "github_regex": "https://github.com/GWxuan/TSP3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.10362", "title": "CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages", "authors": [ "Shangda Wu", "Zhancheng Guo", "Ruibin Yuan", "Junyan Jiang", "Seungheon Doh", "Gus Xia", "Juhan Nam", "Xiaobing Li", "Feng Yu", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "CLaMP 3 is a unified framework developed to address challenges of cross-modal and cross-lingual generalization in music information retrieval. Using contrastive learning, it aligns all major music modalities--including sheet music, performance signals, and audio recordings--with multilingual text in a shared representation space, enabling retrieval across unaligned modalities with text as a bridge. It features a multilingual text encoder adaptable to unseen languages, exhibiting strong cross-lingual generalization. Leveraging retrieval-augmented generation, we curated M4-RAG, a web-scale dataset consisting of 2.31 million music-text pairs. This dataset is enriched with detailed metadata that represents a wide array of global musical traditions. To advance future research, we release WikiMT-X, a benchmark comprising 1,000 triplets of sheet music, audio, and richly varied text descriptions. Experiments show that CLaMP 3 achieves state-of-the-art performance on multiple MIR tasks, significantly surpassing previous strong baselines and demonstrating excellent generalization in multimodal and multilingual music contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.08130", "title": "Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models", "authors": [ "Sonam Gupta", "Yatin Nandwani", "Asaf Yehudai", "Dinesh Khandelwal", "Dinesh Raghu", "Sachindra Joshi" ], "github": "", "project_page": "", "abstract": "Fine-tuning Large Language Models (LLMs) on specific datasets is a common practice to improve performance on target tasks. However, this performance gain often leads to overfitting, where the model becomes too specialized in either the task or the characteristics of the training data, resulting in a loss of generalization. This paper introduces Selective Self-to-Supervised Fine-Tuning (S3FT), a fine-tuning approach that achieves better performance than the standard supervised fine-tuning (SFT) while improving generalization. S3FT leverages the existence of multiple valid responses to a query. By utilizing the model's correct responses, S3FT reduces model specialization during the fine-tuning stage. S3FT first identifies the correct model responses from the training set by deploying an appropriate judge. Then, it fine-tunes the model using the correct model responses and the gold response (or its paraphrase) for the remaining samples. The effectiveness of S3FT is demonstrated through experiments on mathematical reasoning, Python programming and reading comprehension tasks. The results show that standard SFT can lead to an average performance drop of up to 4.4 on multiple benchmarks, such as MMLU and TruthfulQA. In contrast, S3FT reduces this drop by half, i.e. 2.5, indicating better generalization capabilities than SFT while performing significantly better on the fine-tuning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-17", "arxiv_id": "2502.08235", "title": "The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks", "authors": [ "Alejandro Cuadron", "Dacheng Li", "Wenjie Ma", "Xingyao Wang", "Yichuan Wang", "Siyuan Zhuang", "Shu Liu", "Luis Gaspar Schroeder", "Tian Xia", "Huanzhi Mao", "Nicholas Thumiger", "Aditya Desai", "Ion Stoica", "Ana Klimovic", "Graham Neubig", "Joseph E. Gonzalez" ], "github": "https://github.com/AlexCuadron/Overthinking", "project_page": "", "abstract": "Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving capabilities, but their effectiveness in interactive environments can be limited. This paper introduces and analyzes overthinking in LRMs. A phenomenon where models favor extended internal reasoning chains over environmental interaction. Through experiments on software engineering tasks using SWE Bench Verified, we observe three recurring patterns: Analysis Paralysis, Rogue Actions, and Premature Disengagement. We propose a framework to study these behaviors, which correlates with human expert assessments, and analyze 4018 trajectories. We observe that higher overthinking scores correlate with decreased performance, with reasoning models exhibiting stronger tendencies toward overthinking compared to non-reasoning models. Our analysis reveals that simple efforts to mitigate overthinking in agentic environments, such as selecting the solution with the lower overthinking score, can improve model performance by almost 30% while reducing computational costs by 43%. These results suggest that mitigating overthinking has strong practical implications. We suggest that by leveraging native function-calling capabilities and selective reinforcement learning overthinking tendencies could be mitigated. We also open-source our evaluation framework and dataset to facilitate research in this direction at https://github.com/AlexCuadron/Overthinking.", "github_manual": "", "github_regex": "https://github.com/AlexCuadron/Overthinking", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11901", "title": "Building A Proof-Oriented Programmer That Is 64% Better Than GPT-4o Under Data Scarsity", "authors": [ "Dylan Zhang", "Justin Wang", "Tianran Sun" ], "github": "", "project_page": "", "abstract": "Existing LMs struggle with proof-oriented programming due to data scarcity, which manifest in two key ways: (1) a lack of sufficient corpora for proof-oriented programming languages such as F*, and (2) the absence of large-scale, project-level proof-oriented implementations that can teach the model the intricate reasoning process when performing proof-oriented programming. We present the first on synthetic data augmentation for project level proof oriented programming for both generation and repair. Our method addresses data scarcity by synthesizing basic proof-oriented programming problems for proficiency in that language; incorporating diverse coding data for reasoning capability elicitation and creating new proofs and repair data within existing repositories. This approach enables language models to both synthesize and repair proofs for function- and repository-level code. We show that our fine-tuned 14B parameter model, PoPilot, can exceed the performance of the models that outperforms GPT-4o in project-level proof-oriented programming by 64% relative margin, and can improve GPT-4o's performance by 54% by repairing its outputs over GPT-4o's self-repair.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.09061", "title": "CRANE: Reasoning with constrained LLM generation", "authors": [ "Debangshu Banerjee", "Tarun Suresh", "Shubham Ugare", "Sasa Misailovic", "Gagandeep Singh" ], "github": "", "project_page": "", "abstract": "Code generation, symbolic math reasoning, and other tasks require LLMs to produce outputs that are both syntactically and semantically correct. Constrained LLM generation is a promising direction to enforce adherence to formal grammar, but prior works have empirically observed that strict enforcement of formal constraints often diminishes the reasoning capabilities of LLMs. In this work, we first provide a theoretical explanation for why constraining LLM outputs to very restrictive grammars that only allow syntactically valid final answers reduces the reasoning capabilities of the model. Second, we demonstrate that by augmenting the output grammar with carefully designed additional rules, it is always possible to preserve the reasoning capabilities of the LLM while ensuring syntactic and semantic correctness in its outputs. Building on these theoretical insights, we propose a reasoning-augmented constrained decoding algorithm, CRANE, which effectively balances the correctness of constrained generation with the flexibility of unconstrained generation. Experiments on multiple open-source LLMs and benchmarks show that CRANE significantly outperforms both state-of-the-art constrained decoding strategies and standard unconstrained decoding, showing up to 10% points accuracy improvement over baselines on challenging symbolic reasoning benchmarks GSM-symbolic and FOLIO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11275", "title": "Cuckoo: An IE Free Rider Hatched by Massive Nutrition in LLM's Nest", "authors": [ "Letian Peng", "Zilong Wang", "Feng Yao", "Jingbo Shang" ], "github": "", "project_page": "", "abstract": "Massive high-quality data, both pre-training raw texts and post-training annotations, have been carefully prepared to incubate advanced large language models (LLMs). In contrast, for information extraction (IE), pre-training data, such as BIO-tagged sequences, are hard to scale up. We show that IE models can act as free riders on LLM resources by reframing next-token prediction into extraction for tokens already present in the context. Specifically, our proposed next tokens extraction (NTE) paradigm learns a versatile IE model, Cuckoo, with 102.6M extractive data converted from LLM's pre-training and post-training data. Under the few-shot setting, Cuckoo adapts effectively to traditional and complex instruction-following IE with better performance than existing pre-trained IE models. As a free rider, Cuckoo can naturally evolve with the ongoing advancements in LLM data preparation, benefiting from improvements in LLM training pipelines without additional manual effort.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.12146", "title": "Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory Sharpening", "authors": [ "Ye Tian", "Ling Yang", "Xinchen Zhang", "Yunhai Tong", "Mengdi Wang", "Bin Cui" ], "github": "https://github.com/Gen-Verse/Diffusion-Sharpening", "project_page": "", "abstract": "We propose Diffusion-Sharpening, a fine-tuning approach that enhances downstream alignment by optimizing sampling trajectories. Existing RL-based fine-tuning methods focus on single training timesteps and neglect trajectory-level alignment, while recent sampling trajectory optimization methods incur significant inference NFE costs. Diffusion-Sharpening overcomes this by using a path integral framework to select optimal trajectories during training, leveraging reward feedback, and amortizing inference costs. Our method demonstrates superior training efficiency with faster convergence, and best inference efficiency without requiring additional NFEs. Extensive experiments show that Diffusion-Sharpening outperforms RL-based fine-tuning methods (e.g., Diffusion-DPO) and sampling trajectory optimization methods (e.g., Inference Scaling) across diverse metrics including text alignment, compositional capabilities, and human preferences, offering a scalable and efficient solution for future diffusion model fine-tuning. Code: https://github.com/Gen-Verse/Diffusion-Sharpening", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/Diffusion-Sharpening", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.12148", "title": "HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation", "authors": [ "Ling Yang", "Xinchen Zhang", "Ye Tian", "Chenming Shang", "Minghao Xu", "Wentao Zhang", "Bin Cui" ], "github": "https://github.com/Gen-Verse/HermesFlow", "project_page": "", "abstract": "The remarkable success of the autoregressive paradigm has made significant advancement in Multimodal Large Language Models (MLLMs), with powerful models like Show-o, Transfusion and Emu3 achieving notable progress in unified image understanding and generation. For the first time, we uncover a common phenomenon: the understanding capabilities of MLLMs are typically stronger than their generative capabilities, with a significant gap between the two. Building on this insight, we propose HermesFlow, a simple yet general framework designed to seamlessly bridge the gap between understanding and generation in MLLMs. Specifically, we take the homologous data as input to curate homologous preference data of both understanding and generation. Through Pair-DPO and self-play iterative optimization, HermesFlow effectively aligns multimodal understanding and generation using homologous preference data. Extensive experiments demonstrate the significant superiority of our approach over prior methods, particularly in narrowing the gap between multimodal understanding and generation. These findings highlight the potential of HermesFlow as a general alignment framework for next-generation multimodal foundation models. Code: https://github.com/Gen-Verse/HermesFlow", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/HermesFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11438", "title": "SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL", "authors": [ "Jimin Lee", "Ingeol Baek", "Byeongjeong Kim", "Hwanhee Lee" ], "github": "", "project_page": "", "abstract": "Text-to-SQL aims to convert natural language questions into executable SQL queries. While previous approaches, such as skeleton-masked selection, have demonstrated strong performance by retrieving similar training examples to guide large language models (LLMs), they struggle in real-world scenarios where such examples are unavailable. To overcome this limitation, we propose Self-Augmentation in-context learning with Fine-grained Example selection for Text-to-SQL (SAFE-SQL), a novel framework that improves SQL generation by generating and filtering self-augmented examples. SAFE-SQL first prompts an LLM to generate multiple Text-to-SQL examples relevant to the test input. Then SAFE-SQL filters these examples through three relevance assessments, constructing high-quality in-context learning examples. Using self-generated examples, SAFE-SQL surpasses the previous zero-shot, and few-shot Text-to-SQL frameworks, achieving higher execution accuracy. Notably, our approach provides additional performance gains in extra hard and unseen scenarios, where conventional methods often fail.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.10454", "title": "One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs", "authors": [ "Yinghui Li", "Jiayi Kuang", "Haojing Huang", "Zhikun Xu", "Xinnian Liang", "Yi Yu", "Wenlian Lu", "Yangning Li", "Xiaoyu Tan", "Chao Qu", "Ying Shen", "Hai-Tao Zheng", "Philip S. Yu" ], "github": "", "project_page": "", "abstract": "Leveraging mathematical Large Language Models (LLMs) for proof generation is a fundamental topic in LLMs research. We argue that the ability of current LLMs to prove statements largely depends on whether they have encountered the relevant proof process during training. This reliance limits their deeper understanding of mathematical theorems and related concepts. Inspired by the pedagogical method of \"proof by counterexamples\" commonly used in human mathematics education, our work aims to enhance LLMs' ability to conduct mathematical reasoning and proof through counterexamples. Specifically, we manually create a high-quality, university-level mathematical benchmark, CounterMATH, which requires LLMs to prove mathematical statements by providing counterexamples, thereby assessing their grasp of mathematical concepts. Additionally, we develop a data engineering framework to automatically obtain training data for further model improvement. Extensive experiments and detailed analyses demonstrate that CounterMATH is challenging, indicating that LLMs, such as OpenAI o1, have insufficient counterexample-driven proof capabilities. Moreover, our exploration into model training reveals that strengthening LLMs' counterexample-driven conceptual reasoning abilities is crucial for improving their overall mathematical capabilities. We believe that our work offers new perspectives on the community of mathematical LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.12115", "title": "SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering?", "authors": [ "Samuel Miserendino", "Michele Wang", "Tejal Patwardhan", "Johannes Heidecke" ], "github": "https://github.com/openai/SWELancer-Benchmark", "project_page": "", "abstract": "We introduce SWE-Lancer, a benchmark of over 1,400 freelance software engineering tasks from Upwork, valued at \\1 million USD total in real-world payouts. SWE-Lancer encompasses both independent engineering tasks--ranging from 50 bug fixes to \\$32,000 feature implementations--and managerial tasks, where models choose between technical implementation proposals. Independent tasks are graded with end-to-end tests triple-verified by experienced software engineers, while managerial decisions are assessed against the choices of the original hired engineering managers. We evaluate model performance and find that frontier models are still unable to solve the majority of tasks. To facilitate future research, we open-source a unified Docker image and a public evaluation split, SWE-Lancer Diamond (https://github.com/openai/SWELancer-Benchmark). By mapping model performance to monetary value, we hope SWE-Lancer enables greater research into the economic impact of AI model development.", "github_manual": "", "github_regex": "https://github.com/openai/SWELancer-Benchmark", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.12152", "title": "Learning Getting-Up Policies for Real-World Humanoid Robots", "authors": [ "Xialin He", "Runpei Dong", "Zixuan Chen", "Saurabh Gupta" ], "github": "", "project_page": "", "abstract": "Automatic fall recovery is a crucial prerequisite before humanoid robots can be reliably deployed. Hand-designing controllers for getting up is difficult because of the varied configurations a humanoid can end up in after a fall and the challenging terrains humanoid robots are expected to operate on. This paper develops a learning framework to produce controllers that enable humanoid robots to get up from varying configurations on varying terrains. Unlike previous successful applications of humanoid locomotion learning, the getting-up task involves complex contact patterns, which necessitates accurately modeling the collision geometry and sparser rewards. We address these challenges through a two-phase approach that follows a curriculum. The first stage focuses on discovering a good getting-up trajectory under minimal constraints on smoothness or speed / torque limits. The second stage then refines the discovered motions into deployable (i.e. smooth and slow) motions that are robust to variations in initial configuration and terrains. We find these innovations enable a real-world G1 humanoid robot to get up from two main situations that we considered: a) lying face up and b) lying face down, both tested on flat, deformable, slippery surfaces and slopes (e.g., sloppy grass and snowfield). To the best of our knowledge, this is the first successful demonstration of learned getting-up policies for human-sized humanoid robots in the real world. Project page: https://humanoid-getup.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11775", "title": "video-SALMONN-o1: Reasoning-enhanced Audio-visual Large Language Model", "authors": [ "Guangzhi Sun", "Yudong Yang", "Jimin Zhuang", "Changli Tang", "Yixuan Li", "Wei Li", "Zejun MA", "Chao Zhang" ], "github": "", "project_page": "", "abstract": "While recent advancements in reasoning optimization have significantly enhanced the capabilities of large language models (LLMs), existing efforts to improve reasoning have been limited to solving mathematical problems and focusing on visual graphical inputs, neglecting broader applications in general video understanding.This paper proposes video-SALMONN-o1, the first open-source reasoning-enhanced audio-visual LLM designed for general video understanding tasks. To enhance its reasoning abilities, we develop a reasoning-intensive dataset featuring challenging audio-visual questions with step-by-step solutions. We also propose process direct preference optimization (pDPO), which leverages contrastive step selection to achieve efficient step-level reward modelling tailored for multimodal inputs. Additionally, we introduce RivaBench, the first reasoning-intensive video understanding benchmark, featuring over 4,000 high-quality, expert-curated question-answer pairs across scenarios such as standup comedy, academic presentations, and synthetic video detection. video-SALMONN-o1 achieves 3-8% accuracy improvements over the LLaVA-OneVision baseline across different video reasoning benchmarks. Besides, pDPO achieves 6-8% improvements compared to the supervised fine-tuning model on RivaBench. Enhanced reasoning enables video-SALMONN-o1 zero-shot synthetic video detection capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11098", "title": "Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems", "authors": [ "Zhao Wang", "Sota Moriyama", "Wei-Yao Wang", "Briti Gangopadhyay", "Shingo Takamatsu" ], "github": "https://github.com/sony/talkhier", "project_page": "", "abstract": "Recent advancements in LLM-based multi-agent (LLM-MA) systems have shown promise, yet significant challenges remain in managing communication and refinement when agents collaborate on complex tasks. In this paper, we propose Talk Structurally, Act Hierarchically (TalkHier), a novel framework that introduces a structured communication protocol for context-rich exchanges and a hierarchical refinement system to address issues such as incorrect outputs, falsehoods, and biases. TalkHier surpasses various types of SoTA, including inference scaling model (OpenAI-o1), open-source multi-agent models (e.g., AgentVerse), and majority voting strategies on current LLM and single-agent baselines (e.g., ReAct, GPT4o), across diverse tasks, including open-domain question answering, domain-specific selective questioning, and practical advertisement text generation. These results highlight its potential to set a new standard for LLM-MA systems, paving the way for more effective, adaptable, and collaborative multi-agent frameworks. The code is available https://github.com/sony/talkhier.", "github_manual": "", "github_regex": "https://github.com/sony/talkhier", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11190", "title": "ReLearn: Unlearning via Learning for Large Language Models", "authors": [ "Haoming Xu", "Ningyuan Zhao", "Liming Yang", "Sendong Zhao", "Shumin Deng", "Mengru Wang", "Bryan Hooi", "Nay Oo", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/unlearn", "project_page": "", "abstract": "Current unlearning methods for large language models usually rely on reverse optimization to reduce target token probabilities. However, this paradigm disrupts the subsequent tokens prediction, degrading model performance and linguistic coherence. Moreover, existing evaluation metrics overemphasize contextual forgetting while inadequately assessing response fluency and relevance. To address these challenges, we propose ReLearn, a data augmentation and fine-tuning pipeline for effective unlearning, along with a comprehensive evaluation framework. This framework introduces Knowledge Forgetting Rate (KFR) and Knowledge Retention Rate (KRR) to measure knowledge-level preservation, and Linguistic Score (LS) to evaluate generation quality. Our experiments show that ReLearn successfully achieves targeted forgetting while preserving high-quality output. Through mechanistic analysis, we further demonstrate how reverse optimization disrupts coherent text generation, while ReLearn preserves this essential capability. Code is available at https://github.com/zjunlp/unlearn.", "github_manual": "", "github_regex": "https://github.com/zjunlp/unlearn", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11196", "title": "How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training", "authors": [ "Yixin Ou", "Yunzhi Yao", "Ningyu Zhang", "Hui Jin", "Jiacheng Sun", "Shumin Deng", "Zhenguo Li", "Huajun Chen" ], "github": "https://github.com/zjunlp/DynamicKnowledgeCircuits", "project_page": "", "abstract": "Despite exceptional capabilities in knowledge-intensive tasks, Large Language Models (LLMs) face a critical gap in understanding how they internalize new knowledge, particularly how to structurally embed acquired knowledge in their neural computations. We address this issue through the lens of knowledge circuit evolution, identifying computational subgraphs that facilitate knowledge storage and processing. Our systematic analysis of circuit evolution throughout continual pre-training reveals several key findings: (1) the acquisition of new knowledge is influenced by its relevance to pre-existing knowledge; (2) the evolution of knowledge circuits exhibits a distinct phase shift from formation to optimization; (3) the evolution of knowledge circuits follows a deep-to-shallow pattern. These insights not only advance our theoretical understanding of the mechanisms of new knowledge acquisition in LLMs, but also provide potential implications for improving continual pre-training strategies to enhance model performance. Code and data will be available at https://github.com/zjunlp/DynamicKnowledgeCircuits.", "github_manual": "", "github_regex": "https://github.com/zjunlp/DynamicKnowledgeCircuits", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11167", "title": "SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors", "authors": [ "Bohan Lyu", "Siqiao Huang", "Zichen Liang" ], "github": "https://github.com/Imbernoulli/SURGE", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities in code-related tasks, such as code understanding and code generation. However, an equally important yet underexplored question is whether LLMs can serve as general-purpose surrogate code executors, to predict the output and behavior of a program without actually running it. To systematically investigate this capability, we introduce SURGE, a comprehensive benchmark covering eight key aspects: multi-language programming tasks, competition-level programming problems, repository-level code analysis, high-cost scientific computing, time-complexity-intensive algorithms, buggy code analysis, programs dependent on specific compilers or execution environments, and formal mathematical proof verification. We evaluate multiple open-source and proprietary LLMs on SURGE and conduct a scaling study to analyze the impact of model size and training data scale on surrogate execution accuracy. Additionally, we categorize model prediction errors and explore potential areas for improvement. Our findings indicate that while LLMs can predict code execution results in certain cases, they exhibit limitations in general-purpose surrogate execution. This study provides empirical insights into the feasibility of using LLMs as surrogate code executors. Code and dataset are released at https://github.com/Imbernoulli/SURGE.", "github_manual": "", "github_regex": "https://github.com/Imbernoulli/SURGE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11330", "title": "System Message Generation for User Preferences using Open-Source Models", "authors": [ "Minbyul Jeong", "Jungho Cho", "Minsoo Khang", "Dawoon Jung", "Teakgyu Hong" ], "github": "", "project_page": "", "abstract": "System messages play a crucial role in interactions with large language models (LLMs), often serving as prompts to initiate conversations. Through system messages, users can assign specific roles, perform intended tasks, incorporate background information, specify various output formats and communication styles. Despite such versatility, publicly available data are often lack system messages and subject to strict license constraints in the industry field. Manual labeling of publicly available data with system messages that align with user instructions demands significant resources. In view of such challenges, our work introduces SysGen, a pipeline for generating system messages with better aligned assistant responses from the supervised fine-tuning dataset without system messages. Training on SysGen data has demonstrated substantial improvements in the alignment of model responses with system messages and user instructions, as demonstrated across various open-source models on the Multifacet benchmark, while maintaining minimal impact on other unseen benchmarks such as Open LLM Leaderboard 2. Our qualitative analysis highlights the importance of diverse system messages to ensure better adaptability across different contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11574", "title": "Large Language Models and Mathematical Reasoning Failures", "authors": [ "Johan Boye", "Birger Moell" ], "github": "", "project_page": "", "abstract": "This paper investigates the mathematical reasoning capabilities of large language models (LLMs) using 50 newly constructed high-school-level word problems. Unlike prior studies that focus solely on answer correctness, we rigorously analyze both final answers and solution steps to identify reasoning failures. Evaluating eight state-of-the-art models - including Mixtral, Llama, Gemini, GPT-4o, and OpenAI's o1 variants - we find that while newer models (e.g., o3-mini, deepseek-r1) achieve higher accuracy, all models exhibit errors in spatial reasoning, strategic planning, and arithmetic, sometimes producing correct answers through flawed logic. Common failure modes include unwarranted assumptions, over-reliance on numerical patterns, and difficulty translating physical intuition into mathematical steps. Manual analysis reveals that models struggle with problems requiring multi-step deduction or real-world knowledge, despite possessing broad mathematical knowledge. Our results underscore the importance of evaluating reasoning processes, not just answers, and caution against overestimating LLMs' problem-solving proficiency. The study highlights persistent gaps in LLMs' generalization abilities, emphasizing the need for targeted improvements in structured reasoning and constraint handling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11578", "title": "Language Complexity Measurement as a Noisy Zero-Shot Proxy for Evaluating LLM Performance", "authors": [ "Birger Moell", "Johan Boye" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have made significant strides in natural language generation but often face challenges in tasks requiring precise calculations and structural analysis. This paper investigates the performance of state-of-the-art LLMs on language complexity measurement tasks, through the computation of the LIX readability metric and Average Dependency Distance (ADD). Using Swedish high school and university-level essays, we evaluate the models' abilities to compute LIX scores and perform dependency parsing, comparing their results to established ground truths. Our findings reveal that while all models demonstrate some capacity for these tasks, ChatGPT-o1-mini performs most consistently, achieving the highest accuracy in both LIX computation and dependency parsing. Additionally, we observe a strong significant correlation -0.875 p 0.026 (N=6) between the models' accuracy in computing LIX and their overall performance on the Massive Multitask Language Understanding (MMLU) benchmark. These results suggest that language complexity measurement abilities can serve as a noisy zero-shot proxies for assessing the general capabilities of LLMs, providing a practical method for model evaluation without the need for extensive benchmarking datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.10458", "title": "I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning in Diffusion Models", "authors": [ "Zhenxing Mi", "Kuan-Chieh Wang", "Guocheng Qian", "Hanrong Ye", "Runtao Liu", "Sergey Tulyakov", "Kfir Aberman", "Dan Xu" ], "github": "https://github.com/Stability-AI/sd3.5", "project_page": "", "abstract": "This paper presents ThinkDiff, a novel alignment paradigm that empowers text-to-image diffusion models with multimodal in-context understanding and reasoning capabilities by integrating the strengths of vision-language models (VLMs). Existing multimodal diffusion finetuning methods largely focus on pixel-level reconstruction rather than in-context reasoning, and are constrained by the complexity and limited availability of reasoning-based datasets. ThinkDiff addresses these challenges by leveraging vision-language training as a proxy task, aligning VLMs with the decoder of an encoder-decoder large language model (LLM) instead of a diffusion decoder. This proxy task builds on the observation that the LLM decoder shares the same input feature space with diffusion decoders that use the corresponding LLM encoder for prompt embedding. As a result, aligning VLMs with diffusion decoders can be simplified through alignment with the LLM decoder. Without complex training and datasets, ThinkDiff effectively unleashes understanding, reasoning, and composing capabilities in diffusion models. Experiments demonstrate that ThinkDiff significantly improves accuracy from 19.2% to 46.3% on the challenging CoBSAT benchmark for multimodal in-context reasoning generation, with only 5 hours of training on 4 A100 GPUs. Additionally, ThinkDiff demonstrates exceptional performance in composing multiple images and texts into logically coherent images. Project page: https://mizhenxing.github.io/ThinkDiff.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Stability-AI/sd3.5", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.12054", "title": "PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning", "authors": [ "Xinyu Zhang", "Yuxuan Dong", "Yanrui Wu", "Jiaxing Huang", "Chengyou Jia", "Basura Fernando", "Mike Zheng Shou", "Lingling Zhang", "Jun Liu" ], "github": "https://github.com/dxzxy12138/PhysReason", "project_page": "", "abstract": "Large language models demonstrate remarkable capabilities across various domains, especially mathematics and logic reasoning. However, current evaluations overlook physics-based reasoning - a complex task requiring physics theorems and constraints. We present PhysReason, a 1,200-problem benchmark comprising knowledge-based (25%) and reasoning-based (75%) problems, where the latter are divided into three difficulty levels (easy, medium, hard). Notably, problems require an average of 8.1 solution steps, with hard requiring 15.6, reflecting the complexity of physics-based reasoning. We propose the Physics Solution Auto Scoring Framework, incorporating efficient answer-level and comprehensive step-level evaluations. Top-performing models like Deepseek-R1, Gemini-2.0-Flash-Thinking, and o3-mini-high achieve less than 60% on answer-level evaluation, with performance dropping from knowledge questions (75.11%) to hard problems (31.95%). Through step-level evaluation, we identified four key bottlenecks: Physics Theorem Application, Physics Process Understanding, Calculation, and Physics Condition Analysis. These findings position PhysReason as a novel and comprehensive benchmark for evaluating physics-based reasoning capabilities in large language models. Our code and data will be published at https:/dxzxy12138.github.io/PhysReason.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dxzxy12138/PhysReason", "github_api_added_by": "auto" }, { "date": "2025-02-18", "arxiv_id": "2502.09083", "title": "Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking", "authors": [ "Greta Warren", "Irina Shklovski", "Isabelle Augenstein" ], "github": "", "project_page": "", "abstract": "The pervasiveness of large language models and generative AI in online media has amplified the need for effective automated fact-checking to assist fact-checkers in tackling the increasing volume and sophistication of misinformation. The complex nature of fact-checking demands that automated fact-checking systems provide explanations that enable fact-checkers to scrutinise their outputs. However, it is unclear how these explanations should align with the decision-making and reasoning processes of fact-checkers to be effectively integrated into their workflows. Through semi-structured interviews with fact-checking professionals, we bridge this gap by: (i) providing an account of how fact-checkers assess evidence, make decisions, and explain their processes; (ii) examining how fact-checkers use automated tools in practice; and (iii) identifying fact-checker explanation requirements for automated fact-checking tools. The findings show unmet explanation needs and identify important criteria for replicable fact-checking explanations that trace the model's reasoning path, reference specific evidence, and highlight uncertainty and information gaps.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.12135", "title": "MagicArticulate: Make Your 3D Models Articulation-Ready", "authors": [ "Chaoyue Song", "Jianfeng Zhang", "Xiu Li", "Fan Yang", "Yiwen Chen", "Zhongcong Xu", "Jun Hao Liew", "Xiaoyang Guo", "Fayao Liu", "Jiashi Feng", "Guosheng Lin" ], "github": "https://github.com/Seed3D/MagicArticulate", "project_page": "", "abstract": "With the explosive growth of 3D content creation, there is an increasing demand for automatically converting static 3D models into articulation-ready versions that support realistic animation. Traditional approaches rely heavily on manual annotation, which is both time-consuming and labor-intensive. Moreover, the lack of large-scale benchmarks has hindered the development of learning-based solutions. In this work, we present MagicArticulate, an effective framework that automatically transforms static 3D models into articulation-ready assets. Our key contributions are threefold. First, we introduce Articulation-XL, a large-scale benchmark containing over 33k 3D models with high-quality articulation annotations, carefully curated from Objaverse-XL. Second, we propose a novel skeleton generation method that formulates the task as a sequence modeling problem, leveraging an auto-regressive transformer to naturally handle varying numbers of bones or joints within skeletons and their inherent dependencies across different 3D models. Third, we predict skinning weights using a functional diffusion process that incorporates volumetric geodesic distance priors between vertices and joints. Extensive experiments demonstrate that MagicArticulate significantly outperforms existing methods across diverse object categories, achieving high-quality articulation that enables realistic animation. Project page: https://chaoyuesong.github.io/MagicArticulate.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Seed3D/MagicArticulate", "github_api_added_by": "auto" }, { "date": "2025-02-18", "arxiv_id": "2502.11085", "title": "Towards Data-Efficient Pretraining for Atomic Property Prediction", "authors": [ "Yasir Ghunaim", "Hasan Abed Al Kader Hammoud", "Bernard Ghanem" ], "github": "", "project_page": "", "abstract": "This paper challenges the recent paradigm in atomic property prediction that links progress to growing dataset sizes and computational resources. We show that pretraining on a carefully selected, task-relevant dataset can match or even surpass large-scale pretraining, while using as little as 1/24th of the computational cost. We introduce the Chemical Similarity Index (CSI), a novel metric inspired by computer vision's Fr\\'echet Inception Distance, for molecular graphs which quantifies the alignment between upstream pretraining datasets and downstream tasks. By selecting the most relevant dataset with minimal CSI distance, we show that models pretrained on a smaller, focused dataset consistently outperform those pretrained on massive, mixed datasets such as JMP, even when those larger datasets include the relevant dataset. Counterintuitively, we also find that indiscriminately adding more data can degrade model performance when the additional data poorly aligns with the task at hand. Our findings highlight that quality often outperforms quantity in pretraining for atomic property prediction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11831", "title": "Intuitive physics understanding emerges from self-supervised pretraining on natural videos", "authors": [ "Quentin Garrido", "Nicolas Ballas", "Mahmoud Assran", "Adrien Bardes", "Laurent Najman", "Michael Rabbat", "Emmanuel Dupoux", "Yann LeCun" ], "github": "", "project_page": "", "abstract": "We investigate the emergence of intuitive physics understanding in general-purpose deep neural network models trained to predict masked regions in natural videos. Leveraging the violation-of-expectation framework, we find that video prediction models trained to predict outcomes in a learned representation space demonstrate an understanding of various intuitive physics properties, such as object permanence and shape consistency. In contrast, video prediction in pixel space and multimodal large language models, which reason through text, achieve performance closer to chance. Our comparisons of these architectures reveal that jointly learning an abstract representation space while predicting missing parts of sensory input, akin to predictive coding, is sufficient to acquire an understanding of intuitive physics, and that even models trained on one week of unique video achieve above chance performance. This challenges the idea that core knowledge -- a set of innate systems to help understand the world -- needs to be hardwired to develop an understanding of intuitive physics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.08441", "title": "Better Embeddings with Coupled Adam", "authors": [ "Felix Stollenwerk", "Tobias Stollenwerk" ], "github": "https://github.com/flxst/coupled-adam", "project_page": "", "abstract": "Despite their remarkable capabilities, LLMs learn word representations that exhibit the undesirable yet poorly understood feature of anisotropy. In this paper, we argue that the second moment in Adam is a cause of anisotropic embeddings, and suggest a modified optimizer called Coupled Adam to mitigate the problem. Our experiments demonstrate that Coupled Adam significantly improves the quality of embeddings, while also leading to better upstream and downstream performance on large enough datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/flxst/coupled-adam", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11089", "title": "Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention", "authors": [ "Jingyang Yuan", "Huazuo Gao", "Damai Dai", "Junyu Luo", "Liang Zhao", "Zhengyan Zhang", "Zhenda Xie", "Y. X. Wei", "Lean Wang", "Zhiping Xiao", "Yuqing Wang", "Chong Ruan", "Ming Zhang", "Wenfeng Liang", "Wangding Zeng" ], "github": "https://github.com/gkamradt/LLMTest_NeedleInAHaystack", "project_page": "", "abstract": "Long-context modeling is crucial for next-generation language models, yet the high computational cost of standard attention mechanisms poses significant computational challenges. Sparse attention offers a promising direction for improving efficiency while maintaining model capabilities. We present NSA, a Natively trainable Sparse Attention mechanism that integrates algorithmic innovations with hardware-aligned optimizations to achieve efficient long-context modeling. NSA employs a dynamic hierarchical sparse strategy, combining coarse-grained token compression with fine-grained token selection to preserve both global context awareness and local precision. Our approach advances sparse attention design with two key innovations: (1) We achieve substantial speedups through arithmetic intensity-balanced algorithm design, with implementation optimizations for modern hardware. (2) We enable end-to-end training, reducing pretraining computation without sacrificing model performance. As shown in Figure 1, experiments show the model pretrained with NSA maintains or exceeds Full Attention models across general benchmarks, long-context tasks, and instruction-based reasoning. Meanwhile, NSA achieves substantial speedups over Full Attention on 64k-length sequences across decoding, forward propagation, and backward propagation, validating its efficiency throughout the model lifecycle.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gkamradt/LLMTest_NeedleInAHaystack", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11157", "title": "Dyve: Thinking Fast and Slow for Dynamic Process Verification", "authors": [ "Jianyuan Zhong", "Zeju Li", "Zhijian Xu", "Xiangyu Wen", "Qiang Xu" ], "github": "https://github.com/staymylove/Dyve", "project_page": "", "abstract": "We present Dyve, a dynamic process verifier that enhances reasoning error detection in large language models by integrating fast and slow thinking, inspired by Kahneman's Systems Theory. Dyve adaptively applies immediate token-level confirmation System 1 for straightforward steps and comprehensive analysis System 2 for complex ones. Leveraging a novel step-wise consensus-filtered process supervision technique, combining Monte Carlo estimation with LLM based evaluation, Dyve curates high-quality supervision signals from noisy data. Experimental results on ProcessBench and the MATH dataset confirm that Dyve significantly outperforms existing process-based verifiers and boosts performance in Best-of-N settings.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/staymylove/Dyve", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.10550", "title": "Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning", "authors": [ "Egor Cherepanov", "Nikita Kachaev", "Alexey K. Kovalev", "Aleksandr I. Panov" ], "github": "", "project_page": "", "abstract": "Memory is crucial for enabling agents to tackle complex tasks with temporal and spatial dependencies. While many reinforcement learning (RL) algorithms incorporate memory, the field lacks a universal benchmark to assess an agent's memory capabilities across diverse scenarios. This gap is particularly evident in tabletop robotic manipulation, where memory is essential for solving tasks with partial observability and ensuring robust performance, yet no standardized benchmarks exist. To address this, we introduce MIKASA (Memory-Intensive Skills Assessment Suite for Agents), a comprehensive benchmark for memory RL, with three key contributions: (1) we propose a comprehensive classification framework for memory-intensive RL tasks, (2) we collect MIKASA-Base - a unified benchmark that enables systematic evaluation of memory-enhanced agents across diverse scenarios, and (3) we develop MIKASA-Robo - a novel benchmark of 32 carefully designed memory-intensive tasks that assess memory capabilities in tabletop robotic manipulation. Our contributions establish a unified framework for advancing memory RL research, driving the development of more reliable systems for real-world applications. The code is available at https://sites.google.com/view/memorybenchrobots/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11177", "title": "The Mirage of Model Editing: Revisiting Evaluation in the Wild", "authors": [ "Wanli Yang", "Fei Sun", "Jiajun Tan", "Xinyu Ma", "Qi Cao", "Dawei Yin", "Huawei Shen", "Xueqi Cheng" ], "github": "https://github.com/wanliyoung/revisit-editing-evaluation", "project_page": "", "abstract": "Despite near-perfect results in artificial evaluations, the effectiveness of model editing in real-world applications remains unexplored. To bridge this gap, we propose to study model editing in question answering (QA) by establishing a rigorous evaluation practice to assess the effectiveness of editing methods in correcting LLMs' errors. It consists of QAEdit, a new benchmark derived from popular QA datasets, and a standardized evaluation framework. Our single editing experiments indicate that current editing methods perform substantially worse than previously reported (38.5% vs. ~96%). Through module analysis and controlled experiments, we demonstrate that this performance decline stems from issues in evaluation practices of prior editing research. One key issue is the inappropriate use of teacher forcing in testing prevents error propagation by feeding ground truth tokens (inaccessible in real-world scenarios) as input. Furthermore, we simulate real-world deployment by sequential editing, revealing that current approaches fail drastically with only 1000 edits. Our analysis provides a fundamental reexamination of both the real-world applicability of existing model editing methods and their evaluation practices, and establishes a rigorous evaluation framework with key insights to advance reliable and practical model editing research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wanliyoung/revisit-editing-evaluation", "github_api_added_by": "auto" }, { "date": "2025-02-18", "arxiv_id": "2502.08820", "title": "Can a Single Model Master Both Multi-turn Conversations and Tool Use? CALM: A Unified Conversational Agentic Language Model", "authors": [ "Emre Can Acikgoz", "Jeremiah Greer", "Akul Datta", "Ze Yang", "William Zeng", "Oussama Elachqar", "Emmanouil Koukoumidis", "Dilek Hakkani-T\u00fcr", "Gokhan Tur" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) with API-calling capabilities enabled building effective Language Agents (LA), while also revolutionizing the conventional task-oriented dialogue (TOD) paradigm. However, current approaches face a critical dilemma: TOD systems are often trained on a limited set of target APIs, requiring new data to maintain their quality when interfacing with new services, while LAs are not trained to maintain user intent over multi-turn conversations. Because both robust multi-turn management and advanced function calling are crucial for effective conversational agents, we evaluate these skills on three popular benchmarks: MultiWOZ 2.4 (TOD), BFCL V3 (LA), and API-Bank (LA), and our analyses reveal that specialized approaches excel in one domain but underperform in the other. To bridge this chasm, we introduce CALM (Conversational Agentic Language Model), a unified approach that integrates both conversational and agentic capabilities. We created CALM-IT, a carefully constructed multi-task dataset that interleave multi-turn ReAct reasoning with complex API usage. Using CALM-IT, we train three models CALM 8B, CALM 70B, and CALM 405B, which outperform top domain-specific models, including GPT-4o, across all three benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11748", "title": "ILIAS: Instance-Level Image retrieval At Scale", "authors": [ "Giorgos Kordopatis-Zilos", "Vladan Stojni\u0107", "Anna Manko", "Pavel \u0160uma", "Nikolaos-Antonios Ypsilantis", "Nikos Efthymiadis", "Zakaria Laskar", "Ji\u0159\u00ed Matas", "Ond\u0159ej Chum", "Giorgos Tolias" ], "github": "", "project_page": "", "abstract": "This work introduces ILIAS, a new test dataset for Instance-Level Image retrieval At Scale. It is designed to evaluate the ability of current and future foundation models and retrieval techniques to recognize particular objects. The key benefits over existing datasets include large scale, domain diversity, accurate ground truth, and a performance that is far from saturated. ILIAS includes query and positive images for 1,000 object instances, manually collected to capture challenging conditions and diverse domains. Large-scale retrieval is conducted against 100 million distractor images from YFCC100M. To avoid false negatives without extra annotation effort, we include only query objects confirmed to have emerged after 2014, i.e. the compilation date of YFCC100M. An extensive benchmarking is performed with the following observations: i) models fine-tuned on specific domains, such as landmarks or products, excel in that domain but fail on ILIAS ii) learning a linear adaptation layer using multi-domain class supervision results in performance improvements, especially for vision-language models iii) local descriptors in retrieval re-ranking are still a key ingredient, especially in the presence of severe background clutter iv) the text-to-image performance of the vision-language foundation models is surprisingly close to the corresponding image-to-image case. website: https://vrg.fel.cvut.cz/ilias/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11357", "title": "Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents", "authors": [ "Vardaan Pahuja", "Yadong Lu", "Corby Rosset", "Boyu Gou", "Arindam Mitra", "Spencer Whitehead", "Yu Su", "Ahmed Awadallah" ], "github": "", "project_page": "", "abstract": "Recent success in large multimodal models (LMMs) has sparked promising applications of agents capable of autonomously completing complex web tasks. While open-source LMM agents have made significant advances in offline evaluation benchmarks, their performance still falls substantially short of human-level capabilities in more realistic online settings. A key bottleneck is the lack of diverse and large-scale trajectory-level datasets across various domains, which are expensive to collect. In this paper, we address this challenge by developing a scalable recipe to synthesize the largest and most diverse trajectory-level dataset to date, containing over 94K successful multimodal web trajectories, spanning 49K unique URLs, 720K screenshots, and 33M web elements. In particular, we leverage extensive web exploration and refinement to obtain diverse task intents. The average cost is 28 cents per successful trajectory, making it affordable to a wide range of users in the community. Leveraging this dataset, we train Explorer, a multimodal web agent, and demonstrate strong performance on both offline and online web agent benchmarks such as Mind2Web-Live, Multimodal-Mind2Web, and MiniWob++. Additionally, our experiments highlight data scaling as a key driver for improving web agent capabilities. We hope this study makes state-of-the-art LMM-based agent research at a larger scale more accessible.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.08745", "title": "IHEval: Evaluating Language Models on Following the Instruction Hierarchy", "authors": [ "Zhihan Zhang", "Shiyang Li", "Zixuan Zhang", "Xin Liu", "Haoming Jiang", "Xianfeng Tang", "Yifan Gao", "Zheng Li", "Haodong Wang", "Zhaoxuan Tan", "Yichuan Li", "Qingyu Yin", "Bing Yin", "Meng Jiang" ], "github": "https://github.com/ytyz1307zzh/IHEval", "project_page": "", "abstract": "The instruction hierarchy, which establishes a priority order from system messages to user messages, conversation history, and tool outputs, is essential for ensuring consistent and safe behavior in language models (LMs). Despite its importance, this topic receives limited attention, and there is a lack of comprehensive benchmarks for evaluating models' ability to follow the instruction hierarchy. We bridge this gap by introducing IHEval, a novel benchmark comprising 3,538 examples across nine tasks, covering cases where instructions in different priorities either align or conflict. Our evaluation of popular LMs highlights their struggle to recognize instruction priorities. All evaluated models experience a sharp performance decline when facing conflicting instructions, compared to their original instruction-following performance. Moreover, the most competitive open-source model only achieves 48% accuracy in resolving such conflicts. Our results underscore the need for targeted optimization in the future development of LMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ytyz1307zzh/IHEval", "github_api_added_by": "auto" }, { "date": "2025-02-18", "arxiv_id": "2502.09969", "title": "Data Valuation using Neural Networks for Efficient Instruction Fine-Tuning", "authors": [ "Ishika Agarwal", "Dilek Hakkani-T\u00fcr" ], "github": "https://github.com/agarwalishika/NN-CIFT", "project_page": "", "abstract": "Influence functions provide crucial insights into model training, but existing methods suffer from large computational costs and limited generalization. Particularly, recent works have proposed various metrics and algorithms to calculate the influence of data using language models, which do not scale well with large models and datasets. This is because of the expensive forward and backward passes required for computation, substantial memory requirements to store large models, and poor generalization of influence estimates to new data. In this paper, we explore the use of small neural networks -- which we refer to as the InfluenceNetwork -- to estimate influence values, achieving up to 99% cost reduction. Our evaluation demonstrates that influence values can be estimated with models just 0.0027% the size of full language models (we use 7B and 8B versions). We apply our algorithm of estimating influence values (called NN-CIFT: Neural Networks for effiCient Instruction Fine-Tuning) to the downstream task of subset selection for general instruction fine-tuning. In our study, we include four state-of-the-art influence functions and show no compromise in performance, despite large speedups, between NN-CIFT and the original influence functions. We provide an in-depth hyperparameter analyses of NN-CIFT. The code for our method can be found here: https://github.com/agarwalishika/NN-CIFT.", "github_manual": "", "github_regex": "https://github.com/agarwalishika/NN-CIFT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.08826", "title": "Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation", "authors": [ "Mohammad Mahdi Abootorabi", "Amirhosein Zobeiri", "Mahdi Dehghani", "Mohammadali Mohammadkhani", "Bardia Mohammadi", "Omid Ghahroodi", "Mahdieh Soleymani Baghshah", "Ehsaneddin Asgari" ], "github": "https://github.com/llm-lab-org/Multimodal-RAG-Survey", "project_page": "", "abstract": "Large Language Models (LLMs) struggle with hallucinations and outdated knowledge due to their reliance on static training data. Retrieval-Augmented Generation (RAG) mitigates these issues by integrating external dynamic information enhancing factual and updated grounding. Recent advances in multimodal learning have led to the development of Multimodal RAG, incorporating multiple modalities such as text, images, audio, and video to enhance the generated outputs. However, cross-modal alignment and reasoning introduce unique challenges to Multimodal RAG, distinguishing it from traditional unimodal RAG. This survey offers a structured and comprehensive analysis of Multimodal RAG systems, covering datasets, metrics, benchmarks, evaluation, methodologies, and innovations in retrieval, fusion, augmentation, and generation. We precisely review training strategies, robustness enhancements, and loss functions, while also exploring the diverse Multimodal RAG scenarios. Furthermore, we discuss open challenges and future research directions to support advancements in this evolving field. This survey lays the foundation for developing more capable and reliable AI systems that effectively leverage multimodal dynamic external knowledge bases. Resources are available at https://github.com/llm-lab-org/Multimodal-RAG-Survey.", "github_manual": "", "github_regex": "https://github.com/llm-lab-org/Multimodal-RAG-Survey", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.09509", "title": "EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling", "authors": [ "Theodoros Kouzelis", "Ioannis Kakogeorgiou", "Spyros Gidaris", "Nikos Komodakis" ], "github": "https://github.com/zelaki/eqvae", "project_page": "", "abstract": "Latent generative models have emerged as a leading approach for high-quality image synthesis. These models rely on an autoencoder to compress images into a latent space, followed by a generative model to learn the latent distribution. We identify that existing autoencoders lack equivariance to semantic-preserving transformations like scaling and rotation, resulting in complex latent spaces that hinder generative performance. To address this, we propose EQ-VAE, a simple regularization approach that enforces equivariance in the latent space, reducing its complexity without degrading reconstruction quality. By finetuning pre-trained autoencoders with EQ-VAE, we enhance the performance of several state-of-the-art generative models, including DiT, SiT, REPA and MaskGIT, achieving a 7 speedup on DiT-XL/2 with only five epochs of SD-VAE fine-tuning. EQ-VAE is compatible with both continuous and discrete autoencoders, thus offering a versatile enhancement for a wide range of latent generative models. Project page and code: https://eq-vae.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zelaki/eqvae", "github_api_added_by": "auto" }, { "date": "2025-02-18", "arxiv_id": "2502.12154", "title": "Diffusion Models without Classifier-free Guidance", "authors": [ "Zhicong Tang", "Jianmin Bao", "Dong Chen", "Baining Guo" ], "github": "https://github.com/tzco/Diffusion-wo-CFG", "project_page": "", "abstract": "This paper presents Model-guidance (MG), a novel objective for training diffusion model that addresses and removes of the commonly used Classifier-free guidance (CFG). Our innovative approach transcends the standard modeling of solely data distribution to incorporating the posterior probability of conditions. The proposed technique originates from the idea of CFG and is easy yet effective, making it a plug-and-play module for existing models. Our method significantly accelerates the training process, doubles the inference speed, and achieve exceptional quality that parallel and even surpass concurrent diffusion models with CFG. Extensive experiments demonstrate the effectiveness, efficiency, scalability on different models and datasets. Finally, we establish state-of-the-art performance on ImageNet 256 benchmarks with an FID of 1.34. Our code is available at https://github.com/tzco/Diffusion-wo-CFG.", "github_manual": "", "github_regex": "https://github.com/tzco/Diffusion-wo-CFG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.12982", "title": "Sailor2: Sailing in South-East Asia with Inclusive Multilingual LLMs", "authors": [ "Longxu Dou", "Qian Liu", "Fan Zhou", "Changyu Chen", "Zili Wang", "Ziqi Jin", "Zichen Liu", "Tongyao Zhu", "Cunxiao Du", "Penghui Yang", "Haonan Wang", "Jiaheng Liu", "Yongchi Zhao", "Xiachong Feng", "Xin Mao", "Man Tsung Yeung", "Kunat Pipatanakul", "Fajri Koto", "Min Si Thu", "Hynek Kydl\u00ed\u010dek", "Zeyi Liu", "Qunshu Lin", "Sittipong Sripaisarnmongkol", "Kridtaphad Sae-Khow", "Nirattisai Thongchim", "Taechawat Konkaew", "Narong Borijindargoon", "Anh Dao", "Matichon Maneegard", "Phakphum Artkaew", "Zheng-Xin Yong", "Quan Nguyen", "Wannaphong Phatthiyaphaibun", "Hoang H. Tran", "Mike Zhang", "Shiqi Chen", "Tianyu Pang", "Chao Du", "Xinyi Wan", "Wei Lu", "Min Lin" ], "github": "", "project_page": "", "abstract": "Sailor2 is a family of cutting-edge multilingual language models for South-East Asian (SEA) languages, available in 1B, 8B, and 20B sizes to suit diverse applications. Building on Qwen2.5, Sailor2 undergoes continuous pre-training on 500B tokens (400B SEA-specific and 100B replay tokens) to support 13 SEA languages while retaining proficiency in Chinese and English. Sailor2-20B model achieves a 50-50 win rate against GPT-4o across SEA languages. We also deliver a comprehensive cookbook on how to develop the multilingual model in an efficient manner, including five key aspects: data curation, pre-training, post-training, model customization and evaluation. We hope that Sailor2 model (Apache 2.0 license) will drive language development in the SEA region, and Sailor2 cookbook will inspire researchers to build more inclusive LLMs for other under-served languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-18", "arxiv_id": "2502.11336", "title": "ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability", "authors": [ "Ryuto Koike", "Masahiro Kaneko", "Ayana Niwa", "Preslav Nakov", "Naoaki Okazaki" ], "github": "", "project_page": "", "abstract": "Detecting texts generated by Large Language Models (LLMs) could cause grave mistakes due to incorrect decisions, such as undermining student's academic dignity. LLM text detection thus needs to ensure the interpretability of the decision, which can help users judge how reliably correct its prediction is. When humans verify whether a text is human-written or LLM-generated, they intuitively investigate with which of them it shares more similar spans. However, existing interpretable detectors are not aligned with the human decision-making process and fail to offer evidence that users easily understand. To bridge this gap, we introduce ExaGPT, an interpretable detection approach grounded in the human decision-making process for verifying the origin of a text. ExaGPT identifies a text by checking whether it shares more similar spans with human-written vs. with LLM-generated texts from a datastore. This approach can provide similar span examples that contribute to the decision for each span in the text as evidence. Our human evaluation demonstrates that providing similar span examples contributes more effectively to judging the correctness of the decision than existing interpretable methods. Moreover, extensive experiments in four domains and three generators show that ExaGPT massively outperforms prior powerful detectors by up to +40.9 points of accuracy at a false positive rate of 1%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.13143", "title": "SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation", "authors": [ "Zekun Qi", "Wenyao Zhang", "Yufei Ding", "Runpei Dong", "Xinqiang Yu", "Jingwen Li", "Lingyun Xu", "Baoyu Li", "Xialin He", "Guofan Fan", "Jiazhao Zhang", "Jiawei He", "Jiayuan Gu", "Xin Jin", "Kaisheng Ma", "Zhizheng Zhang", "He Wang", "Li Yi" ], "github": "https://github.com/qizekun/SoFar", "project_page": "", "abstract": "Spatial intelligence is a critical component of embodied AI, promoting robots to understand and interact with their environments. While recent advances have enhanced the ability of VLMs to perceive object locations and positional relationships, they still lack the capability to precisely understand object orientations-a key requirement for tasks involving fine-grained manipulations. Addressing this limitation not only requires geometric reasoning but also an expressive and intuitive way to represent orientation. In this context, we propose that natural language offers a more flexible representation space than canonical frames, making it particularly suitable for instruction-following robotic systems. In this paper, we introduce the concept of semantic orientation, which defines object orientations using natural language in a reference-frame-free manner (e.g., the ''plug-in'' direction of a USB or the ''handle'' direction of a knife). To support this, we construct OrienText300K, a large-scale dataset of 3D models annotated with semantic orientations that link geometric understanding to functional semantics. By integrating semantic orientation into a VLM system, we enable robots to generate manipulation actions with both positional and orientational constraints. Extensive experiments in simulation and real world demonstrate that our approach significantly enhances robotic manipulation capabilities, e.g., 48.7% accuracy on Open6DOR and 74.9% accuracy on SIMPLER.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/qizekun/SoFar", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.13131", "title": "Rethinking Diverse Human Preference Learning through Principal Component Analysis", "authors": [ "Feng Luo", "Rui Yang", "Hao Sun", "Chunyuan Deng", "Jiarui Yao", "Jingyan Shen", "Huan Zhang", "Hanjie Chen" ], "github": "https://github.com/amandaluof/DRMs", "project_page": "", "abstract": "Understanding human preferences is crucial for improving foundation models and building personalized AI systems. However, preferences are inherently diverse and complex, making it difficult for traditional reward models to capture their full range. While fine-grained preference data can help, collecting it is expensive and hard to scale. In this paper, we introduce Decomposed Reward Models (DRMs), a novel approach that extracts diverse human preferences from binary comparisons without requiring fine-grained annotations. Our key insight is to represent human preferences as vectors and analyze them using Principal Component Analysis (PCA). By constructing a dataset of embedding differences between preferred and rejected responses, DRMs identify orthogonal basis vectors that capture distinct aspects of preference. These decomposed rewards can be flexibly combined to align with different user needs, offering an interpretable and scalable alternative to traditional reward models. We demonstrate that DRMs effectively extract meaningful preference dimensions (e.g., helpfulness, safety, humor) and adapt to new users without additional training. Our results highlight DRMs as a powerful framework for personalized and interpretable LLM alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/amandaluof/DRMs", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.11079", "title": "Phantom: Subject-consistent video generation via cross-modal alignment", "authors": [ "Lijie Liu", "Tianxiang Ma", "Bingchuan Li", "Zhuowei Chen", "Jiawei Liu", "Qian He", "Xinglong Wu" ], "github": "", "project_page": "", "abstract": "The continuous development of foundational models for video generation is evolving into various applications, with subject-consistent video generation still in the exploratory stage. We refer to this as Subject-to-Video, which extracts subject elements from reference images and generates subject-consistent video through textual instructions. We believe that the essence of subject-to-video lies in balancing the dual-modal prompts of text and image, thereby deeply and simultaneously aligning both text and visual content. To this end, we propose Phantom, a unified video generation framework for both single and multi-subject references. Building on existing text-to-video and image-to-video architectures, we redesign the joint text-image injection model and drive it to learn cross-modal alignment via text-image-video triplet data. In particular, we emphasize subject consistency in human generation, covering existing ID-preserving video generation while offering enhanced advantages. The project homepage is here https://phantom-video.github.io/Phantom/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.11433", "title": "FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading", "authors": [ "Guojun Xiong", "Zhiyang Deng", "Keyi Wang", "Yupeng Cao", "Haohang Li", "Yangyang Yu", "Xueqing Peng", "Mingquan Lin", "Kaleb E Smith", "Xiao-Yang Liu", "Jimin Huang", "Sophia Ananiadou", "Qianqian Xie" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks. However, they often struggle with multi-step, goal-oriented scenarios in interactive financial markets, such as trading, where complex agentic approaches are required to improve decision-making. To address this, we propose FLAG-Trader, a unified architecture integrating linguistic processing (via LLMs) with gradient-driven reinforcement learning (RL) policy optimization, in which a partially fine-tuned LLM acts as the policy network, leveraging pre-trained knowledge while adapting to the financial domain through parameter-efficient fine-tuning. Through policy gradient optimization driven by trading rewards, our framework not only enhances LLM performance in trading but also improves results on other financial-domain tasks. We present extensive empirical evidence to validate these enhancements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.13145", "title": "Multimodal Mamba: Decoder-only Multimodal State Space Model via Quadratic to Linear Distillation", "authors": [ "Bencheng Liao", "Hongyuan Tao", "Qian Zhang", "Tianheng Cheng", "Yingyue Li", "Haoran Yin", "Wenyu Liu", "Xinggang Wang" ], "github": "https://github.com/hustvl/mmMamba", "project_page": "", "abstract": "Recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance but face deployment challenges due to their quadratic computational complexity, growing Key-Value cache requirements, and reliance on separate vision encoders. We propose mmMamba, a framework for developing linear-complexity native multimodal state space models through progressive distillation from existing MLLMs using moderate academic computational resources. Our approach enables the direct conversion of trained decoder-only MLLMs to linear-complexity architectures without requiring pre-trained RNN-based LLM or vision encoders. We propose an seeding strategy to carve Mamba from trained Transformer and a three-stage distillation recipe, which can effectively transfer the knowledge from Transformer to Mamba while preserving multimodal capabilities. Our method also supports flexible hybrid architectures that combine Transformer and Mamba layers for customizable efficiency-performance trade-offs. Distilled from the Transformer-based decoder-only HoVLE, mmMamba-linear achieves competitive performance against existing linear and quadratic-complexity VLMs, while mmMamba-hybrid further improves performance significantly, approaching HoVLE's capabilities. At 103K tokens, mmMamba-linear demonstrates 20.6times speedup and 75.8% GPU memory reduction compared to HoVLE, while mmMamba-hybrid achieves 13.5times speedup and 60.2% memory savings. Code and models are released at https://github.com/hustvl/mmMamba", "github_manual": "", "github_regex": "https://github.com/hustvl/mmMamba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.12513", "title": "RealSyn: An Effective and Scalable Multimodal Interleaved Document Transformation Paradigm", "authors": [ "Tiancheng Gu", "Kaicheng Yang", "Chaoyi Zhang", "Yin Xie", "Xiang An", "Ziyong Feng", "Dongnan Liu", "Weidong Cai", "Jiankang Deng" ], "github": "https://github.com/deepglint/RealSyn", "project_page": "", "abstract": "After pre-training on extensive image-text pairs, Contrastive Language-Image Pre-training (CLIP) demonstrates promising performance on a wide variety of benchmarks. However, a substantial volume of non-paired data, such as multimodal interleaved documents, remains underutilized for vision-language representation learning. To fully leverage these unpaired documents, we initially establish a Real-World Data Extraction pipeline to extract high-quality images and texts. Then we design a hierarchical retrieval method to efficiently associate each image with multiple semantically relevant realistic texts. To further enhance fine-grained visual information, we propose an image semantic augmented generation module for synthetic text production. Furthermore, we employ a semantic balance sampling strategy to improve dataset diversity, enabling better learning of long-tail concepts. Based on these innovations, we construct RealSyn, a dataset combining realistic and synthetic texts, available in three scales: 15M, 30M, and 100M. Extensive experiments demonstrate that RealSyn effectively advances vision-language representation learning and exhibits strong scalability. Models pre-trained on RealSyn achieve state-of-the-art performance on multiple downstream tasks. To facilitate future research, the RealSyn dataset and pre-trained model weights are released at https://github.com/deepglint/RealSyn.", "github_manual": "", "github_regex": "https://github.com/deepglint/RealSyn", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.11564", "title": "Continuous Diffusion Model for Language Modeling", "authors": [ "Jaehyeong Jo", "Sung Ju Hwang" ], "github": "https://github.com/harryjo97/RDLM", "project_page": "", "abstract": "Diffusion models have emerged as a promising alternative to autoregressive models in modeling discrete categorical data. Yet diffusion models that directly work on discrete data space do not fully exploit the power of iterative refinement, as the signals are lost during the transition between discrete states. Existing continuous diffusion models for discrete data have limited performance compared to discrete approaches, and the unclear link between them restricts the development of diffusion models for discrete data. In this work, we propose a continuous diffusion model for language modeling that incorporates the geometry of the underlying categorical distribution. We establish a connection between the discrete diffusion and continuous flow on the statistical manifold, and building on the analogy, we introduce a simple design for the diffusion process that generalizes previous discrete diffusion models. We further propose a simulation-free training framework based on radial symmetry and a simple technique to address the high dimensionality of the manifold. Comprehensive experiments on language modeling benchmarks and other modalities show that our method outperforms existing discrete diffusion models and approaches the performance of autoregressive models. Codes available at https://github.com/harryjo97/RDLM{https://github.com/harryjo97/RDLM}.", "github_manual": "", "github_regex": "https://github.com/harryjo97/RDLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.12501", "title": "Crowd Comparative Reasoning: Unlocking Comprehensive Evaluations for LLM-as-a-Judge", "authors": [ "Qiyuan Zhang", "Yufei Wang", "Yuxin Jiang", "Liangyou Li", "Chuhan Wu", "Yasheng Wang", "Xin Jiang", "Lifeng Shang", "Ruiming Tang", "Fuyuan Lyu", "Chen Ma" ], "github": "https://github.com/Don-Joey/CCE.git", "project_page": "", "abstract": "LLM-as-a-Judge, which generates chain-of-thought (CoT) judgments, has become a widely adopted auto-evaluation method. However, its reliability is compromised by the CoT reasoning's inability to capture comprehensive and deeper details, often leading to incomplete outcomes. Existing methods mainly rely on majority voting or criteria expansion, which is insufficient to address the limitation in CoT. We propose Crowd-based Comparative Evaluation, which introduces additional crowd responses to compare with the candidate responses, thereby exposing deeper and more comprehensive details within the candidate responses. This process effectively guides LLM-as-a-Judge to provide a more detailed CoT judgment. Extensive experiments demonstrate that our approach enhances evaluation reliability, achieving an average accuracy gain of 6.7% across five benchmarks. Moreover, our method produces higher-quality CoTs that facilitate judge distillation and exhibit superior performance in rejection sampling for supervised fine-tuning (SFT), referred to as crowd rejection sampling, thereby enabling more efficient SFT. Our analysis confirms that CoTs generated by ours are more comprehensive and of higher quality, and evaluation accuracy improves as inference scales.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Don-Joey/CCE.git", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.09838", "title": "HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation", "authors": [ "Tianwei Lin", "Wenqiao Zhang", "Sijing Li", "Yuqian Yuan", "Binhe Yu", "Haoyuan Li", "Wanggui He", "Hao Jiang", "Mengze Li", "Xiaohui Song", "Siliang Tang", "Jun Xiao", "Hui Lin", "Yueting Zhuang", "Beng Chin Ooi" ], "github": "https://github.com/DCDmllm/HealthGPT", "project_page": "", "abstract": "We present HealthGPT, a powerful Medical Large Vision-Language Model (Med-LVLM) that integrates medical visual comprehension and generation capabilities within a unified autoregressive paradigm. Our bootstrapping philosophy is to progressively adapt heterogeneous comprehension and generation knowledge to pre-trained large language models (LLMs). This is achieved through a novel heterogeneous low-rank adaptation (H-LoRA) technique, which is complemented by a tailored hierarchical visual perception approach and a three-stage learning strategy. To effectively learn the HealthGPT, we devise a comprehensive medical domain-specific comprehension and generation dataset called VL-Health. Experimental results demonstrate exceptional performance and scalability of HealthGPT in medical visual unified tasks. Our project can be accessed at https://github.com/DCDmllm/HealthGPT.", "github_manual": "", "github_regex": "https://github.com/DCDmllm/HealthGPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.12574", "title": "HeadInfer: Memory-Efficient LLM Inference by Head-wise Offloading", "authors": [ "Cheng Luo", "Zefan Cai", "Hanshi Sun", "Jinqi Xiao", "Bo Yuan", "Wen Xiao", "Junjie Hu", "Jiawei Zhao", "Beidi Chen", "Anima Anandkumar" ], "github": "https://github.com/wdlctc/headinfer", "project_page": "", "abstract": "Transformer-based large language models (LLMs) demonstrate impressive performance in long context generation. Extending the context length has disproportionately shifted the memory footprint of LLMs during inference to the key-value cache (KV cache). In this paper, we propose HEADINFER, which offloads the KV cache to CPU RAM while avoiding the need to fully store the KV cache for any transformer layer on the GPU. HEADINFER employs a fine-grained, head-wise offloading strategy, maintaining only selective attention heads KV cache on the GPU while computing attention output dynamically. Through roofline analysis, we demonstrate that HEADINFER maintains computational efficiency while significantly reducing memory footprint. We evaluate HEADINFER on the Llama-3-8B model with a 1-million-token sequence, reducing the GPU memory footprint of the KV cache from 128 GB to 1 GB and the total GPU memory usage from 207 GB to 17 GB, achieving a 92% reduction compared to BF16 baseline inference. Notably, HEADINFER enables 4-million-token inference with an 8B model on a single consumer GPU with 24GB memory (e.g., NVIDIA RTX 4090) without approximation methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/wdlctc/headinfer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.10852", "title": "Multilingual Encoder Knows more than You Realize: Shared Weights Pretraining for Extremely Low-Resource Languages", "authors": [ "Zeli Su", "Ziyin Zhang", "Guixian Xu", "Jianing Liu", "XU Han", "Ting Zhang", "Yushuang Dong" ], "github": "", "project_page": "", "abstract": "While multilingual language models like XLM-R have advanced multilingualism in NLP, they still perform poorly in extremely low-resource languages. This situation is exacerbated by the fact that modern LLMs such as LLaMA and Qwen support far fewer languages than XLM-R, making text generation models non-existent for many languages in the world. To tackle this challenge, we propose a novel framework for adapting multilingual encoders to text generation in extremely low-resource languages. By reusing the weights between the encoder and the decoder, our framework allows the model to leverage the learned semantic space of the encoder, enabling efficient learning and effective generalization in low-resource languages. Applying this framework to four Chinese minority languages, we present XLM-SWCM, and demonstrate its superior performance on various downstream tasks even when compared with much larger models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.12215", "title": "Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?", "authors": [ "Zhiyuan Zeng", "Qinyuan Cheng", "Zhangyue Yin", "Yunhua Zhou", "Xipeng Qiu" ], "github": "https://github.com/ZhiYuanZeng/test-time-scaling-eval", "project_page": "", "abstract": "The advent of test-time scaling in large language models (LLMs), exemplified by OpenAI's o1 series, has advanced reasoning capabilities by scaling computational resource allocation during inference. While successors like QwQ, Deepseek-R1 (R1) and LIMO replicate these advancements, whether these models truly possess test-time scaling capabilities remains underexplored. This study found that longer CoTs of these o1-like models do not consistently enhance accuracy; in fact, correct solutions are often shorter than incorrect ones for the same questions. Further investigation shows this phenomenon is closely related to models' self-revision capabilities - longer CoTs contain more self-revisions, which often lead to performance degradation. We then compare sequential and parallel scaling strategies on QwQ, R1 and LIMO, finding that parallel scaling achieves better coverage and scalability. Based on these insights, we propose Shortest Majority Vote, a method that combines parallel scaling strategies with CoT length characteristics, significantly improving models' test-time scalability compared to conventional majority voting approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ZhiYuanZeng/test-time-scaling-eval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.12464", "title": "SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models", "authors": [ "Seanie Lee", "Dong Bok Lee", "Dominik Wagner", "Minki Kang", "Haebin Seong", "Tobias Bocklet", "Juho Lee", "Sung Ju Hwang" ], "github": "https://github.com/seanie12/safe-route", "project_page": "", "abstract": "Deploying large language models (LLMs) in real-world applications requires robust safety guard models to detect and block harmful user prompts. While large safety guard models achieve strong performance, their computational cost is substantial. To mitigate this, smaller distilled models are used, but they often underperform on \"hard\" examples where the larger model provides accurate predictions. We observe that many inputs can be reliably handled by the smaller model, while only a small fraction require the larger model's capacity. Motivated by this, we propose SafeRoute, a binary router that distinguishes hard examples from easy ones. Our method selectively applies the larger safety guard model to the data that the router considers hard, improving efficiency while maintaining accuracy compared to solely using the larger safety guard model. Experimental results on multiple benchmark datasets demonstrate that our adaptive model selection significantly enhances the trade-off between computational cost and safety performance, outperforming relevant baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/seanie12/safe-route", "github_api_added_by": "auto" }, { "date": "2025-02-19", "arxiv_id": "2502.12170", "title": "MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections", "authors": [ "Da Xiao", "Qingye Meng", "Shengping Li", "Xingyuan Yuan" ], "github": "https://github.com/Caiyun-AI/MUDDFormer", "project_page": "", "abstract": "We propose MUltiway Dynamic Dense (MUDD) connections, a simple yet effective method to address the limitations of residual connections and enhance cross-layer information flow in Transformers. Unlike existing dense connection approaches with static and shared connection weights, MUDD generates connection weights dynamically depending on hidden states at each sequence position and for each decoupled input stream (the query, key, value or residual) of a Transformer block. MUDD connections can be seamlessly integrated into any Transformer architecture to create MUDDFormer. Extensive experiments show that MUDDFormer significantly outperforms Transformers across various model architectures and scales in language modeling, achieving the performance of Transformers trained with 1.8X-2.4X compute. Notably, MUDDPythia-2.8B matches Pythia-6.9B in pretraining ppl and downstream tasks and even rivals Pythia-12B in five-shot settings, while adding only 0.23% parameters and 0.4% computation. Code in JAX and PyTorch and pre-trained models are available at https://github.com/Caiyun-AI/MUDDFormer .", "github_manual": "", "github_regex": "https://github.com/Caiyun-AI/MUDDFormer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.12900", "title": "Soundwave: Less is More for Speech-Text Alignment in LLMs", "authors": [ "Yuhao Zhang", "Zhiheng Liu", "Fan Bu", "Ruiyu Zhang", "Benyou Wang", "Haizhou Li" ], "github": "https://github.com/FreedomIntelligence/Soundwave", "project_page": "", "abstract": "Existing end-to-end speech large language models (LLMs) usually rely on large-scale annotated data for training, while data-efficient training has not been discussed in depth. We focus on two fundamental problems between speech and text: the representation space gap and sequence length inconsistency. We propose Soundwave, which utilizes an efficient training strategy and a novel architecture to address these issues. Results show that Soundwave outperforms the advanced Qwen2-Audio in speech translation and AIR-Bench speech tasks, using only one-fiftieth of the training data. Further analysis shows that Soundwave still retains its intelligence during conversation. The project is available at https://github.com/FreedomIntelligence/Soundwave.", "github_manual": "", "github_regex": "https://github.com/FreedomIntelligence/Soundwave", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.13130", "title": "Magma: A Foundation Model for Multimodal AI Agents", "authors": [ "Jianwei Yang", "Reuben Tan", "Qianhui Wu", "Ruijie Zheng", "Baolin Peng", "Yongyuan Liang", "Yu Gu", "Mu Cai", "Seonghyeon Ye", "Joel Jang", "Yuquan Deng", "Lars Liden", "Jianfeng Gao" ], "github": "https://github.com/microsoft/Magma", "project_page": "", "abstract": "We present Magma, a foundation model that serves multimodal AI agentic tasks in both the digital and physical worlds. Magma is a significant extension of vision-language (VL) models in that it not only retains the VL understanding ability (verbal intelligence) of the latter, but is also equipped with the ability to plan and act in the visual-spatial world (spatial-temporal intelligence) and complete agentic tasks ranging from UI navigation to robot manipulation. To endow the agentic capabilities, Magma is pretrained on large amounts of heterogeneous datasets spanning from images, videos to robotics data, where the actionable visual objects (e.g., clickable buttons in GUI) in images are labeled by Set-of-Mark (SoM) for action grounding, and the object movements (e.g., the trace of human hands or robotic arms) in videos are labeled by Trace-of-Mark (ToM) for action planning. Extensive experiments show that SoM and ToM reach great synergy and facilitate the acquisition of spatial-temporal intelligence for our Magma model, which is fundamental to a wide range of tasks as shown in Fig.1. In particular, Magma creates new state-of-the-art results on UI navigation and robotic manipulation tasks, outperforming previous models that are specifically tailored to these tasks. On image and video-related multimodal tasks, Magma also compares favorably to popular large multimodal models that are trained on much larger datasets. We make our model and code public for reproducibility at https://microsoft.github.io/Magma.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/Magma", "github_api_added_by": "auto" }, { "date": "2025-02-19", "arxiv_id": "2502.12859", "title": "PAFT: Prompt-Agnostic Fine-Tuning", "authors": [ "Chenxing Wei", "Yao Shu", "Mingwen Ou", "Ying Tiffany He", "Fei Richard Yu" ], "github": "", "project_page": "", "abstract": "While Large Language Models (LLMs) adapt well to downstream tasks after fine-tuning, this adaptability often compromises prompt robustness, as even minor prompt variations can significantly degrade performance. To address this, we propose Prompt-Agnostic Fine-Tuning(PAFT), a simple yet effective approach that dynamically adjusts prompts during fine-tuning. This encourages the model to learn underlying task principles rather than overfitting to specific prompt formulations. PAFT operates in two stages: First, a diverse set of meaningful, synthetic candidate prompts is constructed. Second, during fine-tuning, prompts are randomly sampled from this set to create dynamic training inputs. Extensive experiments across diverse datasets and LLMs demonstrate that models trained with PAFT exhibit strong robustness and generalization across a wide range of prompts, including unseen ones. This enhanced robustness improves both model performance and inference speed while maintaining training efficiency. Ablation studies further confirm the effectiveness of PAFT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.13142", "title": "Pre-training Auto-regressive Robotic Models with 4D Representations", "authors": [ "Dantong Niu", "Yuvan Sharma", "Haoru Xue", "Giscard Biamby", "Junyi Zhang", "Ziteng Ji", "Trevor Darrell", "Roei Herzig" ], "github": "", "project_page": "", "abstract": "Foundation models pre-trained on massive unlabeled datasets have revolutionized natural language and computer vision, exhibiting remarkable generalization capabilities, thus highlighting the importance of pre-training. Yet, efforts in robotics have struggled to achieve similar success, limited by either the need for costly robotic annotations or the lack of representations that effectively model the physical world. In this paper, we introduce ARM4R, an Auto-regressive Robotic Model that leverages low-level 4D Representations learned from human video data to yield a better pre-trained robotic model. Specifically, we focus on utilizing 3D point tracking representations from videos derived by lifting 2D representations into 3D space via monocular depth estimation across time. These 4D representations maintain a shared geometric structure between the points and robot state representations up to a linear transformation, enabling efficient transfer learning from human video data to low-level robotic control. Our experiments show that ARM4R can transfer efficiently from human video data to robotics and consistently improves performance on tasks across various robot environments and configurations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.11271", "title": "OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoning", "authors": [ "Pan Lu", "Bowen Chen", "Sheng Liu", "Rahul Thapa", "Joseph Boen", "James Zou" ], "github": "https://github.com/octotools/octotools", "project_page": "", "abstract": "Solving complex reasoning tasks may involve visual understanding, domain knowledge retrieval, numerical calculation, and multi-step reasoning. Existing methods augment large language models (LLMs) with external tools but are restricted to specialized domains, limited tool types, or require additional training data. In this paper, we introduce OctoTools, a training-free, user-friendly, and easily extensible open-source agentic framework designed to tackle complex reasoning across diverse domains. OctoTools introduces standardized tool cards to encapsulate tool functionality, a planner for both high-level and low-level planning, and an executor to carry out tool usage. We validate OctoTools' generality across 16 diverse tasks (including MathVista, MMLU-Pro, MedQA, and GAIA-Text), achieving substantial average accuracy gains of 9.3% over GPT-4o. Furthermore, OctoTools outperforms AutoGen, GPT-Functions and LangChain by up to 10.6% when given the same set of tools. Through comprehensive analysis and ablations, OctoTools demonstrates advantages in task planning, effective tool usage, and multi-step problem solving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/octotools/octotools", "github_api_added_by": "auto" }, { "date": "2025-02-19", "arxiv_id": "2502.12669", "title": "Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research", "authors": [ "Xiang Liu", "Penglei Sun", "Shuyan Chen", "Longhan Zhang", "Peijie Dong", "Huajie You", "Yongqi Zhang", "Chang Yan", "Xiaowen Chu", "Tong-yi Zhang" ], "github": "", "project_page": "", "abstract": "The rapid advancement of perovskite solar cells (PSCs) has led to an exponential growth in research publications, creating an urgent need for efficient knowledge management and reasoning systems in this domain. We present a comprehensive knowledge-enhanced system for PSCs that integrates three key components. First, we develop Perovskite-KG, a domain-specific knowledge graph constructed from 1,517 research papers, containing 23,789 entities and 22,272 relationships. Second, we create two complementary datasets: Perovskite-Chat, comprising 55,101 high-quality question-answer pairs generated through a novel multi-agent framework, and Perovskite-Reasoning, containing 2,217 carefully curated materials science problems. Third, we introduce two specialized large language models: Perovskite-Chat-LLM for domain-specific knowledge assistance and Perovskite-Reasoning-LLM for scientific reasoning tasks. Experimental results demonstrate that our system significantly outperforms existing models in both domain-specific knowledge retrieval and scientific reasoning tasks, providing researchers with effective tools for literature review, experimental design, and complex problem-solving in PSC research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.09245", "title": "You Do Not Fully Utilize Transformer's Representation Capacity", "authors": [ "Gleb Gerasimov", "Yaroslav Aksenov", "Nikita Balagansky", "Viacheslav Sinii", "Daniil Gavrilov" ], "github": "https://github.com/corl-team/lime", "project_page": "", "abstract": "In contrast to RNNs, which compress previous tokens into a single hidden state, Transformers can attend to all previous tokens directly. However, standard Transformers only use representations from the immediately preceding layer. In this paper, we show that this design choice causes representation collapse and leads to suboptimal performance. To address this issue, we introduce Layer-Integrated Memory (LIMe), a simple yet powerful approach that preserves the model's overall memory footprint while expanding its representational capacity by allowing access to hidden states from earlier layers. Through extensive experiments across various architectures and different lookup mechanisms, we demonstrate consistent performance improvements on a wide range of tasks. Moreover, our analysis of the learned representation dynamics and our exploration of depthwise circuits reveal how LIMe integrates information across layers, pointing to promising directions for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/corl-team/lime", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.10708", "title": "Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey", "authors": [ "Zirui Song", "Bin Yan", "Yuhan Liu", "Miao Fang", "Mingzhe Li", "Rui Yan", "Xiuying Chen" ], "github": "https://github.com/abilliyb/Knowledge_Injection_Survey_Papers", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable success in various tasks such as natural language understanding, text summarization, and machine translation. However, their general-purpose nature often limits their effectiveness in domain-specific applications that require specialized knowledge, such as healthcare, chemistry, or legal analysis. To address this, researchers have explored diverse methods to enhance LLMs by integrating domain-specific knowledge. In this survey, we provide a comprehensive overview of these methods, which we categorize into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization. Each approach offers unique mechanisms to equip LLMs with domain expertise, balancing trade-offs between flexibility, scalability, and efficiency. We discuss how these methods enable LLMs to tackle specialized tasks, compare their advantages and disadvantages, evaluate domain-specific LLMs against general LLMs, and highlight the challenges and opportunities in this emerging field. For those interested in delving deeper into this area, we also summarize the commonly used datasets and benchmarks. To keep researchers updated on the latest studies, we maintain an open-source at: https://github.com/abilliyb/Knowledge_Injection_Survey_Papers, dedicated to documenting research in the field of specialized LLM.", "github_manual": "", "github_regex": "https://github.com/abilliyb/Knowledge_Injection_Survey_Papers", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.13063", "title": "Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the Limits of Embedding Space Capacity", "authors": [ "Yuri Kuratov", "Mikhail Arkhipov", "Aydar Bulatov", "Mikhail Burtsev" ], "github": "https://github.com/yurakuratov/hidden_capacity", "project_page": "", "abstract": "A range of recent works addresses the problem of compression of sequence of tokens into a shorter sequence of real-valued vectors to be used as inputs instead of token embeddings or key-value cache. These approaches allow to reduce the amount of compute in existing language models. Despite relying on powerful models as encoders, the maximum attainable lossless compression ratio is typically not higher than x10. This fact is highly intriguing because, in theory, the maximum information capacity of large real-valued vectors is far beyond the presented rates even for 16-bit precision and a modest vector size. In this work, we explore the limits of compression by replacing the encoder with a per-sample optimization procedure. We show that vectors with compression ratios up to x1500 exist, which highlights two orders of magnitude gap between existing and practically attainable solutions. Furthermore, we empirically show that the compression limits are determined not by the length of the input but by the amount of uncertainty to be reduced, namely, the cross-entropy loss on this sequence without any conditioning. The obtained limits highlight the substantial gap between the theoretical capacity of input embeddings and their practical utilization, suggesting significant room for optimization in model design.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yurakuratov/hidden_capacity", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.10990", "title": "FinMTEB: Finance Massive Text Embedding Benchmark", "authors": [ "Yixuan Tang", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Embedding models play a crucial role in representing and retrieving information across various NLP applications. Recent advances in large language models (LLMs) have further enhanced the performance of embedding models. While these models are often benchmarked on general-purpose datasets, real-world applications demand domain-specific evaluation. In this work, we introduce the Finance Massive Text Embedding Benchmark (FinMTEB), a specialized counterpart to MTEB designed for the financial domain. FinMTEB comprises 64 financial domain-specific embedding datasets across 7 tasks that cover diverse textual types in both Chinese and English, such as financial news articles, corporate annual reports, ESG reports, regulatory filings, and earnings call transcripts. We also develop a finance-adapted model, FinPersona-E5, using a persona-based data synthetic method to cover diverse financial embedding tasks for training. Through extensive evaluation of 15 embedding models, including FinPersona-E5, we show three key findings: (1) performance on general-purpose benchmarks shows limited correlation with financial domain tasks; (2) domain-adapted models consistently outperform their general-purpose counterparts; and (3) surprisingly, a simple Bag-of-Words (BoW) approach outperforms sophisticated dense embeddings in financial Semantic Textual Similarity (STS) tasks, underscoring current limitations in dense embedding techniques. Our work establishes a robust evaluation framework for financial NLP applications and provides crucial insights for developing domain-specific embedding models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.12996", "title": "Eager Updates For Overlapped Communication and Computation in DiLoCo", "authors": [ "Satyen Kale", "Arthur Douillard", "Yanislav Donchev" ], "github": "", "project_page": "", "abstract": "Distributed optimization methods such as DiLoCo have been shown to be effective in training very large models across multiple distributed workers, such as datacenters. These methods split updates into two parts: an inner optimization phase, where the workers independently execute multiple optimization steps on their own local data, and an outer optimization step, where the inner updates are synchronized. While such approaches require orders of magnitude less communication than standard data-parallel training, in settings where the workers are datacenters, even the limited communication requirements of these approaches can still cause significant slow downs due to the blocking necessary at each outer optimization step. In this paper, we investigate techniques to mitigate this issue by overlapping communication with computation in a manner that allows the outer optimization step to fully overlap with the inner optimization phase. We show that a particular variant, dubbed eager updates, provides competitive performance with standard DiLoCo in settings with low bandwidth between workers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.12018", "title": "Atom of Thoughts for Markov LLM Test-Time Scaling", "authors": [ "Fengwei Teng", "Zhaoyang Yu", "Quan Shi", "Jiayi Zhang", "Chenglin Wu", "Yuyu Luo" ], "github": "https://github.com/qixucen/atom", "project_page": "", "abstract": "Large Language Models (LLMs) achieve superior performance through training-time scaling, and test-time scaling further enhances their capabilities by conducting effective reasoning during inference. However, as the scale of reasoning increases, existing test-time scaling methods suffer from accumulated historical information, which not only wastes computational resources but also interferes with effective reasoning. To address this issue, we observe that complex reasoning progress is often achieved by solving a sequence of independent subquestions, each being self-contained and verifiable. These subquestions are essentially atomic questions, relying primarily on their current state rather than accumulated history, similar to the memoryless transitions in a Markov process. Based on this observation, we propose Atom of Thoughts (AoT), where each state transition in the reasoning process consists of decomposing the current question into a dependency-based directed acyclic graph and contracting its subquestions, forming a new atomic question state. This iterative decomposition-contraction process continues until reaching directly solvable atomic questions, naturally realizing Markov transitions between question states. Furthermore, these atomic questions can be seamlessly integrated into existing test-time scaling methods, enabling AoT to serve as a plug-in enhancement for improving reasoning capabilities. Experiments across six benchmarks demonstrate the effectiveness of AoT both as a standalone framework and a plug-in enhancement. Notably, on HotpotQA, when applied to gpt-4o-mini, AoT achieves an 80.6% F1 score, surpassing o3-mini by 3.4% and DeepSeek-R1 by 10.6%. The code will be available at https://github.com/qixucen/atom.", "github_manual": "", "github_regex": "https://github.com/qixucen/atom", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.13092", "title": "Text2World: Benchmarking Large Language Models for Symbolic World Model Generation", "authors": [ "Mengkang Hu", "Tianxing Chen", "Yude Zou", "Yuheng Lei", "Qiguang Chen", "Ming Li", "Hongyuan Zhang", "Wenqi Shao", "Ping Luo" ], "github": "https://github.com/Aaron617/text2world", "project_page": "", "abstract": "Recently, there has been growing interest in leveraging large language models (LLMs) to generate symbolic world models from textual descriptions. Although LLMs have been extensively explored in the context of world modeling, prior studies encountered several challenges, including evaluation randomness, dependence on indirect metrics, and a limited domain scope. To address these limitations, we introduce a novel benchmark, Text2World, based on planning domain definition language (PDDL), featuring hundreds of diverse domains and employing multi-criteria, execution-based metrics for a more robust evaluation. We benchmark current LLMs using Text2World and find that reasoning models trained with large-scale reinforcement learning outperform others. However, even the best-performing model still demonstrates limited capabilities in world modeling. Building on these insights, we examine several promising strategies to enhance the world modeling capabilities of LLMs, including test-time scaling, agent training, and more. We hope that Text2World can serve as a crucial resource, laying the groundwork for future research in leveraging LLMs as world models. The project page is available at https://text-to-world.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Aaron617/text2world", "github_api_added_by": "auto" }, { "date": "2025-02-19", "arxiv_id": "2502.12929", "title": "Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options", "authors": [ "Lakshmi Nair", "Ian Trase", "Mark Kim" ], "github": "https://github.com/flagshipinnovation/Flow-of-Options", "project_page": "", "abstract": "We present a novel reasoning approach called Flow-of-Options (FoO), designed to address intrinsic biases in Large Language Models (LLMs). FoO enables LLMs to systematically explore a diverse range of possibilities in their reasoning, as demonstrated by an FoO-based agentic system for autonomously solving Machine Learning tasks (AutoML). Our framework outperforms state-of-the-art baselines, achieving improvements of 38.2% - 69.2% on standard data science tasks, and 37.4% - 47.9% on therapeutic chemistry tasks. With an overall operation cost under $1 per task, our framework is well-suited for cost-sensitive applications. Beyond classification and regression, we illustrate the broader applicability of our FoO-based agentic system to tasks such as reinforcement learning and image generation. Our framework presents significant advancements compared to current state-of-the-art agentic systems for AutoML, due to the benefits of FoO in enforcing diversity in LLM solutions through compressed, explainable representations that also support long-term memory when combined with case-based reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/flagshipinnovation/Flow-of-Options", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.08869", "title": "Harnessing Vision Models for Time Series Analysis: A Survey", "authors": [ "Jingchao Ni", "Ziming Zhao", "ChengAo Shen", "Hanghang Tong", "Dongjin Song", "Wei Cheng", "Dongsheng Luo", "Haifeng Chen" ], "github": "https://github.com/d2i-group/awesome-vision-time-series", "project_page": "", "abstract": "Time series analysis has witnessed the inspiring development from traditional autoregressive models, deep learning models, to recent Transformers and Large Language Models (LLMs). Efforts in leveraging vision models for time series analysis have also been made along the way but are less visible to the community due to the predominant research on sequence modeling in this domain. However, the discrepancy between continuous time series and the discrete token space of LLMs, and the challenges in explicitly modeling the correlations of variates in multivariate time series have shifted some research attentions to the equally successful Large Vision Models (LVMs) and Vision Language Models (VLMs). To fill the blank in the existing literature, this survey discusses the advantages of vision models over LLMs in time series analysis. It provides a comprehensive and in-depth overview of the existing methods, with dual views of detailed taxonomy that answer the key research questions including how to encode time series as images and how to model the imaged time series for various tasks. Additionally, we address the challenges in the pre- and post-processing steps involved in this framework and outline future directions to further advance time series analysis with vision models.", "github_manual": "https://github.com/d2i-group/awesome-vision-time-series", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.12524", "title": "YOLOv12: Attention-Centric Real-Time Object Detectors", "authors": [ "Yunjie Tian", "Qixiang Ye", "David Doermann" ], "github": "https://github.com/sunsmarterjie/yolov12", "project_page": "", "abstract": "Enhancing the network architecture of the YOLO framework has been crucial for a long time, but has focused on CNN-based improvements despite the proven superiority of attention mechanisms in modeling capabilities. This is because attention-based models cannot match the speed of CNN-based models. This paper proposes an attention-centric YOLO framework, namely YOLOv12, that matches the speed of previous CNN-based ones while harnessing the performance benefits of attention mechanisms. YOLOv12 surpasses all popular real-time object detectors in accuracy with competitive speed. For example, YOLOv12-N achieves 40.6% mAP with an inference latency of 1.64 ms on a T4 GPU, outperforming advanced YOLOv10-N / YOLOv11-N by 2.1%/1.2% mAP with a comparable speed. This advantage extends to other model scales. YOLOv12 also surpasses end-to-end real-time detectors that improve DETR, such as RT-DETR / RT-DETRv2: YOLOv12-S beats RT-DETR-R18 / RT-DETRv2-R18 while running 42% faster, using only 36% of the computation and 45% of the parameters. More comparisons are shown in Figure 1.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sunsmarterjie/yolov12", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.12130", "title": "Scaling Autonomous Agents via Automatic Reward Modeling And Planning", "authors": [ "Zhenfang Chen", "Delin Chen", "Rui Sun", "Wenjun Liu", "Chuang Gan" ], "github": "https://github.com/heaplax/ARMAP", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities across a range of text-generation tasks. However, LLMs still struggle with problems requiring multi-step decision-making and environmental feedback, such as online shopping, scientific reasoning, and mathematical problem-solving. Unlike pure text data, collecting large-scale decision-making data is challenging. Moreover, many powerful LLMs are only accessible through APIs, which hinders their fine-tuning for agent tasks due to cost and complexity. To address LLM agents' limitations, we propose a framework that can automatically learn a reward model from the environment without human annotations. This model can be used to evaluate the action trajectories of LLM agents and provide heuristics for task planning. Specifically, our approach involves employing one LLM-based agent to navigate an environment randomly, generating diverse action trajectories. Subsequently, a separate LLM is leveraged to assign a task intent and synthesize a negative response alongside the correct response for each trajectory. These triplets (task intent, positive response, and negative response) are then utilized as training data to optimize a reward model capable of scoring action trajectories. The effectiveness and generalizability of our framework are demonstrated through evaluations conducted on different agent benchmarks. In conclusion, our proposed framework represents a significant advancement in enhancing LLM agents' decision-making capabilities. By automating the learning of reward models, we overcome the challenges of data scarcity and API limitations, potentially revolutionizing the application of LLMs in complex and interactive environments. This research paves the way for more sophisticated AI agents capable of tackling a wide range of real-world problems requiring multi-step decision-making.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/heaplax/ARMAP", "github_api_added_by": "auto" }, { "date": "2025-02-19", "arxiv_id": "2502.13962", "title": "Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering", "authors": [ "William Jurayj", "Jeffrey Cheng", "Benjamin Van Durme" ], "github": "https://github.com/wjurayj/final_answer", "project_page": "", "abstract": "Scaling the test-time compute of large language models has demonstrated impressive performance on reasoning benchmarks. However, existing evaluations of test-time scaling make the strong assumption that a reasoning system should always give an answer to any question provided. This overlooks concerns about whether a model is confident in its answer, and whether it is appropriate to always provide a response. To address these concerns, we extract confidence scores during reasoning for thresholding model responses. We find that increasing compute budget at inference time not only helps models answer more questions correctly, but also increases confidence in correct responses. We then extend the current paradigm of zero-risk responses during evaluation by considering settings with non-zero levels of response risk, and suggest a recipe for reporting evaluations under these settings.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/wjurayj/final_answer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-19", "arxiv_id": "2502.12659", "title": "The Hidden Risks of Large Reasoning Models: A Safety Assessment of R1", "authors": [ "Kaiwen Zhou", "Chengzhi Liu", "Xuandong Zhao", "Shreedhar Jangam", "Jayanth Srinivasa", "Gaowen Liu", "Dawn Song", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "The rapid development of large reasoning models, such as OpenAI-o3 and DeepSeek-R1, has led to significant improvements in complex reasoning over non-reasoning large language models~(LLMs). However, their enhanced capabilities, combined with the open-source access of models like DeepSeek-R1, raise serious safety concerns, particularly regarding their potential for misuse. In this work, we present a comprehensive safety assessment of these reasoning models, leveraging established safety benchmarks to evaluate their compliance with safety regulations. Furthermore, we investigate their susceptibility to adversarial attacks, such as jailbreaking and prompt injection, to assess their robustness in real-world applications. Through our multi-faceted analysis, we uncover four key findings: (1) There is a significant safety gap between the open-source R1 models and the o3-mini model, on both safety benchmark and attack, suggesting more safety effort on R1 is needed. (2) The distilled reasoning model shows poorer safety performance compared to its safety-aligned base models. (3) The stronger the model's reasoning ability, the greater the potential harm it may cause when answering unsafe questions. (4) The thinking process in R1 models pose greater safety concerns than their final answers. Our study provides insights into the security implications of reasoning models and highlights the need for further advancements in R1 models' safety to close the gap.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.12143", "title": "Small Models Struggle to Learn from Strong Reasoners", "authors": [ "Yuetai Li", "Xiang Yue", "Zhangchen Xu", "Fengqing Jiang", "Luyao Niu", "Bill Yuchen Lin", "Bhaskar Ramasubramanian", "Radha Poovendran" ], "github": "https://github.com/Small-Model-Gap/Small-Model-Learnability-Gap", "project_page": "", "abstract": "Large language models (LLMs) excel in complex reasoning tasks, and distilling their reasoning capabilities into smaller models has shown promise. However, we uncover an interesting phenomenon, which we term the Small Model Learnability Gap: small models (leq3B parameters) do not consistently benefit from long chain-of-thought (CoT) reasoning or distillation from larger models. Instead, they perform better when fine-tuned on shorter, simpler reasoning chains that better align with their intrinsic learning capacity. To address this, we propose Mix Distillation, a simple yet effective strategy that balances reasoning complexity by combining long and short CoT examples or reasoning from both larger and smaller models. Our experiments demonstrate that Mix Distillation significantly improves small model reasoning performance compared to training on either data alone. These findings highlight the limitations of direct strong model distillation and underscore the importance of adapting reasoning complexity for effective reasoning capability transfer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Small-Model-Gap/Small-Model-Learnability-Gap", "github_api_added_by": "auto" }, { "date": "2025-02-20", "arxiv_id": "2502.13922", "title": "LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization", "authors": [ "Guanzheng Chen", "Xin Li", "Michael Qizhe Shieh", "Lidong Bing" ], "github": "https://github.com/DAMO-NLP-SG/LongPO", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities through pretraining and alignment. However, superior short-context LLMs may underperform in long-context scenarios due to insufficient long-context alignment. This alignment process remains challenging due to the impracticality of human annotation for extended contexts and the difficulty in balancing short- and long-context performance. To address these challenges, we introduce LongPO, that enables short-context LLMs to self-evolve to excel on long-context tasks by internally transferring short-context capabilities. LongPO harnesses LLMs to learn from self-generated short-to-long preference data, comprising paired responses generated for identical instructions with long-context inputs and their compressed short-context counterparts, respectively. This preference reveals capabilities and potentials of LLMs cultivated during short-context alignment that may be diminished in under-aligned long-context scenarios. Additionally, LongPO incorporates a short-to-long KL constraint to mitigate short-context performance decline during long-context alignment. When applied to Mistral-7B-Instruct-v0.2 from 128K to 512K context lengths, LongPO fully retains short-context performance and largely outperforms naive SFT and DPO in both long- and short-context tasks. Specifically, \\ourMethod-trained models can achieve results on long-context benchmarks comparable to, or even surpassing, those of superior LLMs (e.g., GPT-4-128K) that involve extensive long-context annotation and larger parameter scales.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/DAMO-NLP-SG/LongPO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13144", "title": "RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning", "authors": [ "Hao Gao", "Shaoyu Chen", "Bo Jiang", "Bencheng Liao", "Yiang Shi", "Xiaoyang Guo", "Yuechuan Pu", "Haoran Yin", "Xiangyu Li", "Xinbang Zhang", "Ying Zhang", "Wenyu Liu", "Qian Zhang", "Xinggang Wang" ], "github": "https://github.com/hustvl/RAD", "project_page": "", "abstract": "Existing end-to-end autonomous driving (AD) algorithms typically follow the Imitation Learning (IL) paradigm, which faces challenges such as causal confusion and the open-loop gap. In this work, we establish a 3DGS-based closed-loop Reinforcement Learning (RL) training paradigm. By leveraging 3DGS techniques, we construct a photorealistic digital replica of the real physical world, enabling the AD policy to extensively explore the state space and learn to handle out-of-distribution scenarios through large-scale trial and error. To enhance safety, we design specialized rewards that guide the policy to effectively respond to safety-critical events and understand real-world causal relationships. For better alignment with human driving behavior, IL is incorporated into RL training as a regularization term. We introduce a closed-loop evaluation benchmark consisting of diverse, previously unseen 3DGS environments. Compared to IL-based methods, RAD achieves stronger performance in most closed-loop metrics, especially 3x lower collision rate. Abundant closed-loop results are presented at https://hgao-cv.github.io/RAD.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hustvl/RAD", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13965", "title": "Autellix: An Efficient Serving Engine for LLM Agents as General Programs", "authors": [ "Michael Luo", "Xiaoxiang Shi", "Colin Cai", "Tianjun Zhang", "Justin Wong", "Yichuan Wang", "Chi Wang", "Yanping Huang", "Zhifeng Chen", "Joseph E. Gonzalez", "Ion Stoica" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) applications are evolving beyond simple chatbots into dynamic, general-purpose agentic programs, which scale LLM calls and output tokens to help AI agents reason, explore, and solve complex tasks. However, existing LLM serving systems ignore dependencies between programs and calls, missing significant opportunities for optimization. Our analysis reveals that programs submitted to LLM serving engines experience long cumulative wait times, primarily due to head-of-line blocking at both the individual LLM request and the program. To address this, we introduce Autellix, an LLM serving system that treats programs as first-class citizens to minimize their end-to-end latencies. Autellix intercepts LLM calls submitted by programs, enriching schedulers with program-level context. We propose two scheduling algorithms-for single-threaded and distributed programs-that preempt and prioritize LLM calls based on their programs' previously completed calls. Our evaluation demonstrates that across diverse LLMs and agentic workloads, Autellix improves throughput of programs by 4-15x at the same latency compared to state-of-the-art systems, such as vLLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13233", "title": "SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?", "authors": [ "Yucheng Shi", "Tianze Yang", "Canyu Chen", "Quanzheng Li", "Tianming Liu", "Xiang Li", "Ninghao Liu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have shown remarkable capabilities in general domains but often struggle with tasks requiring specialized knowledge. Conventional Retrieval-Augmented Generation (RAG) techniques typically retrieve external information from static knowledge bases, which can be outdated or incomplete, missing fine-grained clinical details essential for accurate medical question answering. In this work, we propose SearchRAG, a novel framework that overcomes these limitations by leveraging real-time search engines. Our method employs synthetic query generation to convert complex medical questions into search-engine-friendly queries and utilizes uncertainty-based knowledge selection to filter and incorporate the most relevant and informative medical knowledge into the LLM's input. Experimental results demonstrate that our method significantly improves response accuracy in medical question answering tasks, particularly for complex questions requiring detailed and up-to-date knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13347", "title": "Craw4LLM: Efficient Web Crawling for LLM Pretraining", "authors": [ "Shi Yu", "Zhiyuan Liu", "Chenyan Xiong" ], "github": "https://github.com/cxcscmu/Crawl4LLM", "project_page": "", "abstract": "Web crawl is a main source of large language models' (LLMs) pretraining data, but the majority of crawled web pages are discarded in pretraining due to low data quality. This paper presents Crawl4LLM, an efficient web crawling method that explores the web graph based on the preference of LLM pretraining. Specifically, it leverages the influence of a webpage in LLM pretraining as the priority score of the web crawler's scheduler, replacing the standard graph connectivity based priority. Our experiments on a web graph containing 900 million webpages from a commercial search engine's index demonstrate the efficiency of Crawl4LLM in obtaining high-quality pretraining data. With just 21% URLs crawled, LLMs pretrained on Crawl4LLM data reach the same downstream performances of previous crawls, significantly reducing the crawling waste and alleviating the burdens on websites. Our code is publicly available at https://github.com/cxcscmu/Crawl4LLM.", "github_manual": "", "github_regex": "https://github.com/cxcscmu/Crawl4LLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13923", "title": "Qwen2.5-VL Technical Report", "authors": [ "Shuai Bai", "Keqin Chen", "Xuejing Liu", "Jialin Wang", "Wenbin Ge", "Sibo Song", "Kai Dang", "Peng Wang", "Shijie Wang", "Jun Tang", "Humen Zhong", "Yuanzhi Zhu", "Mingkun Yang", "Zhaohai Li", "Jianqiang Wan", "Pengfei Wang", "Wei Ding", "Zheren Fu", "Yiheng Xu", "Jiabo Ye", "Xi Zhang", "Tianbao Xie", "Zesen Cheng", "Hang Zhang", "Zhibo Yang", "Haiyang Xu", "Junyang Lin" ], "github": "https://github.com/QwenLM/Qwen2.5-VL", "project_page": "", "abstract": "We introduce Qwen2.5-VL, the latest flagship model of Qwen vision-language series, which demonstrates significant advancements in both foundational capabilities and innovative functionalities. Qwen2.5-VL achieves a major leap forward in understanding and interacting with the world through enhanced visual recognition, precise object localization, robust document parsing, and long-video comprehension. A standout feature of Qwen2.5-VL is its ability to localize objects using bounding boxes or points accurately. It provides robust structured data extraction from invoices, forms, and tables, as well as detailed analysis of charts, diagrams, and layouts. To handle complex inputs, Qwen2.5-VL introduces dynamic resolution processing and absolute time encoding, enabling it to process images of varying sizes and videos of extended durations (up to hours) with second-level event localization. This allows the model to natively perceive spatial scales and temporal dynamics without relying on traditional normalization techniques. By training a native dynamic-resolution Vision Transformer (ViT) from scratch and incorporating Window Attention, we reduce computational overhead while maintaining native resolution. As a result, Qwen2.5-VL excels not only in static image and document understanding but also as an interactive visual agent capable of reasoning, tool usage, and task execution in real-world scenarios such as operating computers and mobile devices. Qwen2.5-VL is available in three sizes, addressing diverse use cases from edge AI to high-performance computing. The flagship Qwen2.5-VL-72B model matches state-of-the-art models like GPT-4o and Claude 3.5 Sonnet, particularly excelling in document and diagram understanding. Additionally, Qwen2.5-VL maintains robust linguistic performance, preserving the core language competencies of the Qwen2.5 LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/QwenLM/Qwen2.5-VL", "github_api_added_by": "auto" }, { "date": "2025-02-20", "arxiv_id": "2502.13943", "title": "AdaptiveStep: Automatically Dividing Reasoning Step through Model Confidence", "authors": [ "Yuliang Liu", "Junjie Lu", "Zhaoling Chen", "Chaofeng Qu", "Jason Klein Liu", "Chonghan Liu", "Zefan Cai", "Yunhui Xia", "Li Zhao", "Jiang Bian", "Chuheng Zhang", "Wei Shen", "Zhouhan Lin" ], "github": "https://github.com/lux0926/asprm", "project_page": "", "abstract": "Current approaches for training Process Reward Models (PRMs) often involve breaking down responses into multiple reasoning steps using rule-based techniques, such as using predefined placeholder tokens or setting the reasoning step's length into a fixed size. These approaches overlook the fact that specific words do not typically mark true decision points in a text. To address this, we propose AdaptiveStep, a method that divides reasoning steps based on the model's confidence in predicting the next word. This division method provides more decision-making information at each step, enhancing downstream tasks, such as reward model learning. Moreover, our method does not require manual annotation. We demonstrate its effectiveness through experiments with AdaptiveStep-trained PRMs in mathematical reasoning and code generation tasks. Experimental results indicate that the outcome PRM achieves state-of-the-art Best-of-N performance, surpassing greedy search strategy with token-level value-guided decoding, while also reducing construction costs by over 30% compared to existing open-source PRMs. In addition, we provide a thorough analysis and case study on the PRM's performance, transferability, and generalization capabilities.", "github_manual": "https://github.com/lux0926/asprm", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.12638", "title": "NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation", "authors": [ "Zhiyuan Liu", "Yanchen Luo", "Han Huang", "Enzhi Zhang", "Sihang Li", "Junfeng Fang", "Yaorui Shi", "Xiang Wang", "Kenji Kawaguchi", "Tat-Seng Chua" ], "github": "https://github.com/acharkq/NExT-Mol", "project_page": "", "abstract": "3D molecule generation is crucial for drug discovery and material design. While prior efforts focus on 3D diffusion models for their benefits in modeling continuous 3D conformers, they overlook the advantages of 1D SELFIES-based Language Models (LMs), which can generate 100% valid molecules and leverage the billion-scale 1D molecule datasets. To combine these advantages for 3D molecule generation, we propose a foundation model -- NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation. NExT-Mol uses an extensively pretrained molecule LM for 1D molecule generation, and subsequently predicts the generated molecule's 3D conformers with a 3D diffusion model. We enhance NExT-Mol's performance by scaling up the LM's model size, refining the diffusion neural architecture, and applying 1D to 3D transfer learning. Notably, our 1D molecule LM significantly outperforms baselines in distributional similarity while ensuring validity, and our 3D diffusion model achieves leading performances in conformer prediction. Given these improvements in 1D and 3D modeling, NExT-Mol achieves a 26% relative improvement in 3D FCD for de novo 3D generation on GEOM-DRUGS, and a 13% average relative gain for conditional 3D generation on QM9-2014. Our codes and pretrained checkpoints are available at https://github.com/acharkq/NExT-Mol.", "github_manual": "", "github_regex": "https://github.com/acharkq/NExT-Mol", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13173", "title": "Thinking Preference Optimization", "authors": [ "Wang Yang", "Hongye Jin", "Jingfeng Yang", "Vipin Chaudhary", "Xiaotian Han" ], "github": "https://github.com/uservan/ThinkPO", "project_page": "", "abstract": "Supervised Fine-Tuning (SFT) has been a go-to and effective method for enhancing long chain-of-thought (CoT) reasoning in relatively small LLMs by fine-tuning them with long CoT responses from larger LLMs. To continually improve reasoning abilities, we can either collect new high-quality long CoT reasoning SFT data or repeatedly train on existing SFT datasets. However, acquiring new long CoT SFT data is costly and limited, while repeated training often results in a performance plateau or decline. To further boost the performance with the SFT data, we propose Thinking Preference Optimization (ThinkPO), a simple yet effective post-SFT method that enhances long CoT reasoning without requiring new long CoT responses. Instead, ThinkPO utilizes readily available or easily obtainable short CoT reasoning responses as rejected answers and long CoT responses as chosen answers for the same question. It then applies direct preference optimization to encourage the model to favor longer reasoning outputs. Experiments show that ThinkPO further improves the reasoning performance of SFT-ed models, e.g. it increases math reasoning accuracy of SFT-ed models by 8.6% and output length by 25.9%. Notably, ThinkPO is capable of continually boosting the performance of the publicly distilled SFT model, e.g., increasing the official DeepSeek-R1-Distill-Qwen-7B's performance on MATH500 from 87.4% to 91.2%.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/uservan/ThinkPO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13946", "title": "Why Safeguarded Ships Run Aground? Aligned Large Language Models' Safety Mechanisms Tend to Be Anchored in The Template Region", "authors": [ "Chak Tou Leong", "Qingyu Yin", "Jian Wang", "Wenjie Li" ], "github": "https://github.com/cooperleong00/TASA", "project_page": "", "abstract": "The safety alignment of large language models (LLMs) remains vulnerable, as their initial behavior can be easily jailbroken by even relatively simple attacks. Since infilling a fixed template between the input instruction and initial model output is a common practice for existing LLMs, we hypothesize that this template is a key factor behind their vulnerabilities: LLMs' safety-related decision-making overly relies on the aggregated information from the template region, which largely influences these models' safety behavior. We refer to this issue as template-anchored safety alignment. In this paper, we conduct extensive experiments and verify that template-anchored safety alignment is widespread across various aligned LLMs. Our mechanistic analyses demonstrate how it leads to models' susceptibility when encountering inference-time jailbreak attacks. Furthermore, we show that detaching safety mechanisms from the template region is promising in mitigating vulnerabilities to jailbreak attacks. We encourage future research to develop more robust safety alignment techniques that reduce reliance on the template region.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/cooperleong00/TASA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13128", "title": "SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation", "authors": [ "Zihan Liu", "Shuangrui Ding", "Zhixiong Zhang", "Xiaoyi Dong", "Pan Zhang", "Yuhang Zang", "Yuhang Cao", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/LiuZH-19/SongGen", "project_page": "", "abstract": "Text-to-song generation, the task of creating vocals and accompaniment from textual inputs, poses significant challenges due to domain complexity and data scarcity. Existing approaches often employ multi-stage generation procedures, resulting in cumbersome training and inference pipelines. In this paper, we propose SongGen, a fully open-source, single-stage auto-regressive transformer designed for controllable song generation. The proposed model facilitates fine-grained control over diverse musical attributes, including lyrics and textual descriptions of instrumentation, genre, mood, and timbre, while also offering an optional three-second reference clip for voice cloning. Within a unified auto-regressive framework, SongGen supports two output modes: mixed mode, which generates a mixture of vocals and accompaniment directly, and dual-track mode, which synthesizes them separately for greater flexibility in downstream applications. We explore diverse token pattern strategies for each mode, leading to notable improvements and valuable insights. Furthermore, we design an automated data preprocessing pipeline with effective quality control. To foster community engagement and future research, we will release our model weights, training code, annotated data, and preprocessing pipeline. The generated samples are showcased on our project page at https://liuzh-19.github.io/SongGen/ , and the code will be available at https://github.com/LiuZH-19/SongGen .", "github_manual": "", "github_regex": "https://github.com/LiuZH-19/SongGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.11995", "title": "Presumed Cultural Identity: How Names Shape LLM Responses", "authors": [ "Siddhesh Pawar", "Arnav Arora", "Lucie-Aim\u00e9e Kaffee", "Isabelle Augenstein" ], "github": "https://github.com/copenlu/cultural-name-bias", "project_page": "", "abstract": "Names are deeply tied to human identity. They can serve as markers of individuality, cultural heritage, and personal history. However, using names as a core indicator of identity can lead to over-simplification of complex identities. When interacting with LLMs, user names are an important point of information for personalisation. Names can enter chatbot conversations through direct user input (requested by chatbots), as part of task contexts such as CV reviews, or as built-in memory features that store user information for personalisation. We study biases associated with names by measuring cultural presumptions in the responses generated by LLMs when presented with common suggestion-seeking queries, which might involve making assumptions about the user. Our analyses demonstrate strong assumptions about cultural identity associated with names present in LLM generations across multiple cultures. Our work has implications for designing more nuanced personalisation systems that avoid reinforcing stereotypes while maintaining meaningful customisation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/copenlu/cultural-name-bias", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13685", "title": "MoM: Linear Sequence Modeling with Mixture-of-Memories", "authors": [ "Jusen Du", "Weigao Sun", "Disen Lan", "Jiaxi Hu", "Yu Cheng" ], "github": "https://github.com/OpenSparseLLMs/MoM", "project_page": "", "abstract": "Linear sequence modeling methods, such as linear attention, state space modeling, and linear RNNs, offer significant efficiency improvements by reducing the complexity of training and inference. However, these methods typically compress the entire input sequence into a single fixed-size memory state, which leads to suboptimal performance on recall-intensive downstream tasks. Drawing inspiration from neuroscience, particularly the brain's ability to maintain robust long-term memory while mitigating \"memory interference\", we introduce a novel architecture called Mixture-of-Memories (MoM). MoM utilizes multiple independent memory states, with a router network directing input tokens to specific memory states. This approach greatly enhances the overall memory capacity while minimizing memory interference. As a result, MoM performs exceptionally well on recall-intensive tasks, surpassing existing linear sequence modeling techniques. Despite incorporating multiple memory states, the computation of each memory state remains linear in complexity, allowing MoM to retain the linear-complexity advantage during training, while constant-complexity during inference. Our experimental results show that MoM significantly outperforms current linear sequence models on downstream language tasks, particularly recall-intensive tasks, and even achieves performance comparable to Transformer models. The code is released at https://github.com/OpenSparseLLMs/MoM and is also released as a part of https://github.com/OpenSparseLLMs/Linear-MoE.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenSparseLLMs/MoM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13581", "title": "ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation", "authors": [ "Yupeng Hou", "Jianmo Ni", "Zhankui He", "Noveen Sachdeva", "Wang-Cheng Kang", "Ed H. Chi", "Julian McAuley", "Derek Zhiyuan Cheng" ], "github": "https://github.com/google-deepmind/action_piece", "project_page": "", "abstract": "Generative recommendation (GR) is an emerging paradigm where user actions are tokenized into discrete token patterns and autoregressively generated as predictions. However, existing GR models tokenize each action independently, assigning the same fixed tokens to identical actions across all sequences without considering contextual relationships. This lack of context-awareness can lead to suboptimal performance, as the same action may hold different meanings depending on its surrounding context. To address this issue, we propose ActionPiece to explicitly incorporate context when tokenizing action sequences. In ActionPiece, each action is represented as a set of item features, which serve as the initial tokens. Given the action sequence corpora, we construct the vocabulary by merging feature patterns as new tokens, based on their co-occurrence frequency both within individual sets and across adjacent sets. Considering the unordered nature of feature sets, we further introduce set permutation regularization, which produces multiple segmentations of action sequences with the same semantics. Experiments on public datasets demonstrate that ActionPiece consistently outperforms existing action tokenization methods, improving NDCG@10 by 6.00% to 12.82%.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/action_piece", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.11573", "title": "InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning", "authors": [ "Congkai Xie", "Shuo Cai", "Wenjun Wang", "Pengxiang Li", "Zhijie Sang", "Kejing Yang", "Yiming Zhang", "Zhen Li", "Guanghao Zhu", "Zeyu Liu", "Yang Yu", "Yuhang Liu", "Su Lu", "Baoyi He", "Qi Zhou", "Xiaotian Han", "Jianbo Yuan", "Shengyu Zhang", "Fei Wu", "Hongxia Yang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have made significant advancements in reasoning capabilities. However, they still face challenges such as high computational demands and privacy concerns. This paper focuses on developing efficient Small Language Models (SLMs) and Multimodal Small Language Models (MSLMs) that retain competitive reasoning abilities. We introduce a novel training pipeline that enhances reasoning capabilities and facilitates deployment on edge devices, achieving state-of-the-art performance while minimizing development costs. \\InfR~ aims to advance AI systems by improving reasoning, reducing adoption barriers, and addressing privacy concerns through smaller model sizes. Resources are available at https://github. com/Reallm-Labs/InfiR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13766", "title": "GIMMICK -- Globally Inclusive Multimodal Multitask Cultural Knowledge Benchmarking", "authors": [ "Florian Schneider", "Carolin Holtermann", "Chris Biemann", "Anne Lauscher" ], "github": "https://github.com/floschne/gimmick", "project_page": "", "abstract": "Large Vision-Language Models (LVLMs) have recently gained attention due to their distinctive performance and broad applicability. While it has been previously shown that their efficacy in usage scenarios involving non-Western contexts falls short, existing studies are limited in scope, covering just a narrow range of cultures, focusing exclusively on a small number of cultural aspects, or evaluating a limited selection of models on a single task only. Towards globally inclusive LVLM research, we introduce GIMMICK, an extensive multimodal benchmark designed to assess a broad spectrum of cultural knowledge across 144 countries representing six global macro-regions. GIMMICK comprises six tasks built upon three new datasets that span 728 unique cultural events or facets on which we evaluated 20 LVLMs and 11 LLMs, including five proprietary and 26 open-weight models of all sizes. We systematically examine (1) regional cultural biases, (2) the influence of model size, (3) input modalities, and (4) external cues. Our analyses reveal strong biases toward Western cultures across models and tasks and highlight strong correlations between model size and performance, as well as the effectiveness of multimodal input and external geographic cues. We further find that models have more knowledge of tangible than intangible aspects (e.g., food vs. rituals) and that they excel in recognizing broad cultural origins but struggle with a more nuanced understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/floschne/gimmick", "github_api_added_by": "auto" }, { "date": "2025-02-20", "arxiv_id": "2502.13573", "title": "Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical Perspective", "authors": [ "Yuan Yao", "Xiaopu Zhang", "Yu Zhang", "Jian Jin", "Qiang Yang" ], "github": "https://github.com/yyyaoyuan/SHDA", "project_page": "", "abstract": "Semi-supervised heterogeneous domain adaptation (SHDA) addresses learning across domains with distinct feature representations and distributions, where source samples are labeled while most target samples are unlabeled, with only a small fraction labeled. Moreover, there is no one-to-one correspondence between source and target samples. Although various SHDA methods have been developed to tackle this problem, the nature of the knowledge transferred across heterogeneous domains remains unclear. This paper delves into this question from an empirical perspective. We conduct extensive experiments on about 330 SHDA tasks, employing two supervised learning methods and seven representative SHDA methods. Surprisingly, our observations indicate that both the category and feature information of source samples do not significantly impact the performance of the target domain. Additionally, noise drawn from simple distributions, when used as source samples, may contain transferable knowledge. Based on this insight, we perform a series of experiments to uncover the underlying principles of transferable knowledge in SHDA. Specifically, we design a unified Knowledge Transfer Framework (KTF) for SHDA. Based on the KTF, we find that the transferable knowledge in SHDA primarily stems from the transferability and discriminability of the source domain. Consequently, ensuring those properties in source samples, regardless of their origin (e.g., image, text, noise), can enhance the effectiveness of knowledge transfer in SHDA tasks. The codes and datasets are available at https://github.com/yyyaoyuan/SHDA.", "github_manual": "", "github_regex": "https://github.com/yyyaoyuan/SHDA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13533", "title": "Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models", "authors": [ "Jun Zhang", "Jue Wang", "Huan Li", "Lidan Shou", "Ke Chen", "Yang You", "Guiming Xie", "Xuejian Gong", "Kunlong Zhou" ], "github": "https://github.com/junzhang-zj/LoRAM", "project_page": "", "abstract": "Large Language Models (LLMs) have significantly advanced natural language processing with exceptional task generalization capabilities. Low-Rank Adaption (LoRA) offers a cost-effective fine-tuning solution, freezing the original model parameters and training only lightweight, low-rank adapter matrices. However, the memory footprint of LoRA is largely dominated by the original model parameters. To mitigate this, we propose LoRAM, a memory-efficient LoRA training scheme founded on the intuition that many neurons in over-parameterized LLMs have low training utility but are essential for inference. LoRAM presents a unique twist: it trains on a pruned (small) model to obtain pruned low-rank matrices, which are then recovered and utilized with the original (large) model for inference. Additionally, minimal-cost continual pre-training, performed by the model publishers in advance, aligns the knowledge discrepancy between pruned and original models. Our extensive experiments demonstrate the efficacy of LoRAM across various pruning strategies and downstream tasks. For a model with 70 billion parameters, LoRAM enables training on a GPU with only 20G HBM, replacing an A100-80G GPU for LoRA training and 15 GPUs for full fine-tuning. Specifically, QLoRAM implemented by structured pruning combined with 4-bit quantization, for LLaMA-3.1-70B (LLaMA-2-70B), reduces the parameter storage cost that dominates the memory usage in low-rank matrix training by 15.81times (16.95times), while achieving dominant performance gains over both the original LLaMA-3.1-70B (LLaMA-2-70B) and LoRA-trained LLaMA-3.1-8B (LLaMA-2-13B).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/junzhang-zj/LoRAM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13622", "title": "REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large Language Models", "authors": [ "DongGeon Lee", "Hwanjo Yu" ], "github": "https://github.com/oneonlee/REFIND", "project_page": "", "abstract": "Hallucinations in large language model (LLM) outputs severely limit their reliability in knowledge-intensive tasks such as question answering. To address this challenge, we introduce REFIND (Retrieval-augmented Factuality hallucINation Detection), a novel framework that detects hallucinated spans within LLM outputs by directly leveraging retrieved documents. As part of the REFIND, we propose the Context Sensitivity Ratio (CSR), a novel metric that quantifies the sensitivity of LLM outputs to retrieved evidence. This innovative approach enables REFIND to efficiently and accurately detect hallucinations, setting it apart from existing methods. In the evaluation, REFIND demonstrated robustness across nine languages, including low-resource settings, and significantly outperformed baseline models, achieving superior IoU scores in identifying hallucinated spans. This work highlights the effectiveness of quantifying context sensitivity for hallucination detection, thereby paving the way for more reliable and trustworthy LLM applications across diverse languages.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/oneonlee/REFIND", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13917", "title": "TESS 2: A Large-Scale Generalist Diffusion Language Model", "authors": [ "Jaesung Tae", "Hamish Ivison", "Sachin Kumar", "Arman Cohan" ], "github": "https://github.com/hamishivi/tess-2", "project_page": "", "abstract": "We introduce TESS 2, a general instruction-following diffusion language model that outperforms contemporary instruction-tuned diffusion models, as well as matches and sometimes exceeds strong autoregressive (AR) models. We train TESS 2 by first adapting a strong AR model via continued pretraining with the usual cross-entropy as diffusion loss, and then performing further instruction tuning. We find that adaptation training as well as the choice of the base model is crucial for training good instruction-following diffusion models. We further propose reward guidance, a novel and modular inference-time guidance procedure to align model outputs without needing to train the underlying model. Finally, we show that TESS 2 further improves with increased inference-time compute, highlighting the utility of diffusion LMs in having fine-grained controllability over the amount of compute used at inference time. Code and models are available at https://github.com/hamishivi/tess-2.", "github_manual": "", "github_regex": "https://github.com/hamishivi/tess-2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.12752", "title": "High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion", "authors": [ "Xiang Zhang", "Yang Zhang", "Lukas Mehl", "Markus Gross", "Christopher Schroers" ], "github": "", "project_page": "", "abstract": "Despite recent advances in Novel View Synthesis (NVS), generating high-fidelity views from single or sparse observations remains a significant challenge. Existing splatting-based approaches often produce distorted geometry due to splatting errors. While diffusion-based methods leverage rich 3D priors to achieve improved geometry, they often suffer from texture hallucination. In this paper, we introduce SplatDiff, a pixel-splatting-guided video diffusion model designed to synthesize high-fidelity novel views from a single image. Specifically, we propose an aligned synthesis strategy for precise control of target viewpoints and geometry-consistent view synthesis. To mitigate texture hallucination, we design a texture bridge module that enables high-fidelity texture generation through adaptive feature fusion. In this manner, SplatDiff leverages the strengths of splatting and diffusion to generate novel views with consistent geometry and high-fidelity details. Extensive experiments verify the state-of-the-art performance of SplatDiff in single-view NVS. Additionally, without extra training, SplatDiff shows remarkable zero-shot performance across diverse tasks, including sparse-view NVS and stereo video conversion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13595", "title": "MMTEB: Massive Multilingual Text Embedding Benchmark", "authors": [ "Kenneth Enevoldsen", "Isaac Chung", "Imene Kerboua", "M\u00e1rton Kardos", "Ashwin Mathur", "David Stap", "Jay Gala", "Wissam Siblini", "Dominik Krzemi\u0144ski", "Genta Indra Winata", "Saba Sturua", "Saiteja Utpala", "Mathieu Ciancone", "Marion Schaeffer", "Gabriel Sequeira", "Diganta Misra", "Shreeya Dhakal", "Jonathan Rystr\u00f8m", "Roman Solomatin", "\u00d6mer \u00c7a\u011fatan", "Akash Kundu", "Martin Bernstorff", "Shitao Xiao", "Akshita Sukhlecha", "Bhavish Pahwa", "Rafa\u0142 Po\u015bwiata", "Kranthi Kiran GV", "Shawon Ashraf", "Daniel Auras", "Bj\u00f6rn Pl\u00fcster", "Jan Philipp Harries", "Lo\u00efc Magne", "Isabelle Mohr", "Mariya Hendriksen", "Dawei Zhu", "Hippolyte Gisserot-Boukhlef", "Tom Aarsen", "Jan Kostkan", "Konrad Wojtasik", "Taemin Lee", "Marek \u0160uppa", "Crystina Zhang", "Roberta Rocca", "Mohammed Hamdy", "Andrianos Michail", "John Yang", "Manuel Faysse", "Aleksei Vatolin", "Nandan Thakur", "Manan Dey", "Dipam Vasani", "Pranjal Chitale", "Simone Tedeschi", "Nguyen Tai", "Artem Snegirev", "Michael G\u00fcnther", "Mengzhou Xia", "Weijia Shi", "Xing Han L\u00f9", "Jordan Clive", "Gayatri Krishnakumar", "Anna Maksimova", "Silvan Wehrli", "Maria Tikhonova", "Henil Panchal", "Aleksandr Abramov", "Malte Ostendorff", "Zheng Liu", "Simon Clematide", "Lester James Miranda", "Alena Fenogenova", "Guangyu Song", "Ruqiya Bin Safi", "Wen-Ding Li", "Alessia Borghini", "Federico Cassano", "Hongjin Su", "Jimmy Lin", "Howard Yen", "Lasse Hansen", "Sara Hooker", "Chenghao Xiao", "Vaibhav Adlakha", "Orion Weller", "Siva Reddy", "Niklas Muennighoff" ], "github": "https://github.com/PhilipMay/stsb-multi-mt", "project_page": "", "abstract": "Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) - a large-scale, community-driven expansion of MTEB, covering over 500 quality-controlled evaluation tasks across 250+ languages. MMTEB includes a diverse set of challenging, novel tasks such as instruction following, long-document retrieval, and code retrieval, representing the largest multilingual collection of evaluation tasks for embedding models to date. Using this collection, we develop several highly multilingual benchmarks, which we use to evaluate a representative set of models. We find that while large language models (LLMs) with billions of parameters can achieve state-of-the-art performance on certain language subsets and task categories, the best-performing publicly available model is multilingual-e5-large-instruct with only 560 million parameters. To facilitate accessibility and reduce computational cost, we introduce a novel downsampling method based on inter-task correlation, ensuring a diverse selection while preserving relative model rankings. Furthermore, we optimize tasks such as retrieval by sampling hard negatives, creating smaller but effective splits. These optimizations allow us to introduce benchmarks that drastically reduce computational demands. For instance, our newly introduced zero-shot English benchmark maintains a ranking order similar to the full-scale version but at a fraction of the computational cost.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PhilipMay/stsb-multi-mt", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.12852", "title": "MVL-SIB: A Massively Multilingual Vision-Language Benchmark for Cross-Modal Topical Matching", "authors": [ "Fabian David Schmidt", "Florian Schneider", "Chris Biemann", "Goran Glava\u0161" ], "github": "", "project_page": "", "abstract": "Existing multilingual vision-language (VL) benchmarks often only cover a handful of languages. Consequently, evaluations of large vision-language models (LVLMs) predominantly target high-resource languages, underscoring the need for evaluation data for low-resource languages. To address this limitation, we introduce MVL-SIB, a massively multilingual vision-language benchmark that evaluates both cross-modal and text-only topical matching across 205 languages -- over 100 more than the most multilingual existing VL benchmarks encompass. We then benchmark a range of of open-weight LVLMs together with GPT-4o(-mini) on MVL-SIB. Our results reveal that LVLMs struggle in cross-modal topic matching in lower-resource languages, performing no better than chance on languages like N'Koo. Our analysis further reveals that VL support in LVLMs declines disproportionately relative to textual support for lower-resource languages, as evidenced by comparison of cross-modal and text-only topical matching performance. We further observe that open-weight LVLMs do not benefit from representing a topic with more than one image, suggesting that these models are not yet fully effective at handling multi-image tasks. By correlating performance on MVL-SIB with other multilingual VL benchmarks, we highlight that MVL-SIB serves as a comprehensive probe of multilingual VL understanding in LVLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13138", "title": "AIDE: AI-Driven Exploration in the Space of Code", "authors": [ "Zhengyao Jiang", "Dominik Schmidt", "Dhruv Srikanth", "Dixing Xu", "Ian Kaplan", "Deniss Jacenko", "Yuxiang Wu" ], "github": "https://github.com/WecoAI/aideml", "project_page": "", "abstract": "Machine learning, the foundation of modern artificial intelligence, has driven innovations that have fundamentally transformed the world. Yet, behind advancements lies a complex and often tedious process requiring labor and compute intensive iteration and experimentation. Engineers and scientists developing machine learning models spend much of their time on trial-and-error tasks instead of conceptualizing innovative solutions or research hypotheses. To address this challenge, we introduce AI-Driven Exploration (AIDE), a machine learning engineering agent powered by large language models (LLMs). AIDE frames machine learning engineering as a code optimization problem, and formulates trial-and-error as a tree search in the space of potential solutions. By strategically reusing and refining promising solutions, AIDE effectively trades computational resources for enhanced performance, achieving state-of-the-art results on multiple machine learning engineering benchmarks, including our Kaggle evaluations, OpenAI MLE-Bench and METRs RE-Bench.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WecoAI/aideml", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13369", "title": "Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrieval", "authors": [ "Aditya Sharma", "Luis Lara", "Amal Zouaq", "Christopher J. Pal" ], "github": "", "project_page": "", "abstract": "The ability to generate SPARQL queries from natural language questions is crucial for ensuring efficient and accurate retrieval of structured data from knowledge graphs (KG). While large language models (LLMs) have been widely adopted for SPARQL query generation, they are often susceptible to hallucinations and out-of-distribution errors when producing KG elements like Uniform Resource Identifiers (URIs) based on internal parametric knowledge. This often results in content that appears plausible but is factually incorrect, posing significant challenges for their use in real-world information retrieval (IR) applications. This has led to increased research aimed at detecting and mitigating such errors. In this paper, we introduce PGMR (Post-Generation Memory Retrieval), a modular framework that incorporates a non-parametric memory module to retrieve KG elements and enhance LLM-based SPARQL query generation. Our experimental results indicate that PGMR consistently delivers strong performance across diverse datasets, data distributions, and LLMs. Notably, PGMR significantly mitigates URI hallucinations, nearly eliminating the problem in several scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13908", "title": "Judging the Judges: A Collection of LLM-Generated Relevance Judgements", "authors": [ "Hossein A. Rahmani", "Clemencia Siro", "Mohammad Aliannejadi", "Nick Craswell", "Charles L. A. Clarke", "Guglielmo Faggioli", "Bhaskar Mitra", "Paul Thomas", "Emine Yilmaz" ], "github": "", "project_page": "", "abstract": "Using Large Language Models (LLMs) for relevance assessments offers promising opportunities to improve Information Retrieval (IR), Natural Language Processing (NLP), and related fields. Indeed, LLMs hold the promise of allowing IR experimenters to build evaluation collections with a fraction of the manual human labor currently required. This could help with fresh topics on which there is still limited knowledge and could mitigate the challenges of evaluating ranking systems in low-resource scenarios, where it is challenging to find human annotators. Given the fast-paced recent developments in the domain, many questions concerning LLMs as assessors are yet to be answered. Among the aspects that require further investigation, we can list the impact of various components in a relevance judgment generation pipeline, such as the prompt used or the LLM chosen. This paper benchmarks and reports on the results of a large-scale automatic relevance judgment evaluation, the LLMJudge challenge at SIGIR 2024, where different relevance assessment approaches were proposed. In detail, we release and benchmark 42 LLM-generated labels of the TREC 2023 Deep Learning track relevance judgments produced by eight international teams who participated in the challenge. Given their diverse nature, these automatically generated relevance judgments can help the community not only investigate systematic biases caused by LLMs but also explore the effectiveness of ensemble models, analyze the trade-offs between different models and human assessors, and advance methodologies for improving automated evaluation techniques. The released resource is available at the following link: https://llm4eval.github.io/LLMJudge-benchmark/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13791", "title": "From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions", "authors": [ "Nathana\u00ebl Carraz Rakotonirina", "Mohammed Hamdy", "Jon Ander Campos", "Lucas Weber", "Alberto Testoni", "Marzieh Fadaee", "Sandro Pezzelle", "Marco Del Tredici" ], "github": "https://github.com/Cohere-Labs-Community/MemoryCode", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly used in working environments for a wide range of tasks, excelling at solving individual problems in isolation. However, are they also able to effectively collaborate over long-term interactions? To investigate this, we introduce MemoryCode, a synthetic multi-session dataset designed to test LLMs' ability to track and execute simple coding instructions amid irrelevant information, simulating a realistic setting. While all the models we tested handle isolated instructions well, even the performance of state-of-the-art models like GPT-4o deteriorates when instructions are spread across sessions. Our analysis suggests this is due to their failure to retrieve and integrate information over long instruction chains. Our results highlight a fundamental limitation of current LLMs, restricting their ability to collaborate effectively in long interactions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Cohere-Labs-Community/MemoryCode", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.13270", "title": "REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation", "authors": [ "Dong-Ho Lee", "Adyasha Maharana", "Jay Pujara", "Xiang Ren", "Francesco Barbieri" ], "github": "https://github.com/danny911kr/REALTALK", "project_page": "", "abstract": "Long-term, open-domain dialogue capabilities are essential for chatbots aiming to recall past interactions and demonstrate emotional intelligence (EI). Yet, most existing research relies on synthetic, LLM-generated data, leaving open questions about real-world conversational patterns. To address this gap, we introduce REALTALK, a 21-day corpus of authentic messaging app dialogues, providing a direct benchmark against genuine human interactions. We first conduct a dataset analysis, focusing on EI attributes and persona consistency to understand the unique challenges posed by real-world dialogues. By comparing with LLM-generated conversations, we highlight key differences, including diverse emotional expressions and variations in persona stability that synthetic dialogues often fail to capture. Building on these insights, we introduce two benchmark tasks: (1) persona simulation where a model continues a conversation on behalf of a specific user given prior dialogue context; and (2) memory probing where a model answers targeted questions requiring long-term memory of past interactions. Our findings reveal that models struggle to simulate a user solely from dialogue history, while fine-tuning on specific user chats improves persona emulation. Additionally, existing models face significant challenges in recalling and leveraging long-term context within real-world conversations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/danny911kr/REALTALK", "github_api_added_by": "auto" }, { "date": "2025-02-20", "arxiv_id": "2502.14296", "title": "On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective", "authors": [ "Yue Huang", "Chujie Gao", "Siyuan Wu", "Haoran Wang", "Xiangqi Wang", "Yujun Zhou", "Yanbo Wang", "Jiayi Ye", "Jiawen Shi", "Qihui Zhang", "Yuan Li", "Han Bao", "Zhaoyi Liu", "Tianrui Guan", "Dongping Chen", "Ruoxi Chen", "Kehan Guo", "Andy Zou", "Bryan Hooi Kuen-Yew", "Caiming Xiong", "Elias Stengel-Eskin", "Hongyang Zhang", "Hongzhi Yin", "Huan Zhang", "Huaxiu Yao", "Jaehong Yoon", "Jieyu Zhang", "Kai Shu", "Kaijie Zhu", "Ranjay Krishna", "Swabha Swayamdipta", "Taiwei Shi", "Weijia Shi", "Xiang Li", "Yiwei Li", "Yuexing Hao", "Yuexing Hao", "Zhihao Jia", "Zhize Li", "Xiuying Chen", "Zhengzhong Tu", "Xiyang Hu", "Tianyi Zhou", "Jieyu Zhao", "Lichao Sun", "Furong Huang", "Or Cohen Sasson", "Prasanna Sattigeri", "Anka Reuel", "Max Lamparth", "Yue Zhao", "Nouha Dziri", "Yu Su", "Huan Sun", "Heng Ji", "Chaowei Xiao", "Mohit Bansal", "Nitesh V. Chawla", "Jian Pei", "Jianfeng Gao", "Michael Backes", "Philip S. Yu", "Neil Zhenqiang Gong", "Pin-Yu Chen", "Bo Li", "Xiangliang Zhang" ], "github": "https://github.com/TrustGen/TrustEval-toolkit", "project_page": "", "abstract": "Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions. This paper presents a comprehensive framework to address these challenges through three key contributions. First, we systematically review global AI governance laws and policies from governments and regulatory bodies, as well as industry practices and standards. Based on this analysis, we propose a set of guiding principles for GenFMs, developed through extensive multidisciplinary collaboration that integrates technical, ethical, legal, and societal perspectives. Second, we introduce TrustGen, the first dynamic benchmarking platform designed to evaluate trustworthiness across multiple dimensions and model types, including text-to-image, large language, and vision-language models. TrustGen leverages modular components--metadata curation, test case generation, and contextual variation--to enable adaptive and iterative assessments, overcoming the limitations of static evaluation methods. Using TrustGen, we reveal significant progress in trustworthiness while identifying persistent challenges. Finally, we provide an in-depth discussion of the challenges and future directions for trustworthy GenFMs, which reveals the complex, evolving nature of trustworthiness, highlighting the nuanced trade-offs between utility and trustworthiness, and consideration for various downstream applications, identifying persistent challenges and providing a strategic roadmap for future research. This work establishes a holistic framework for advancing trustworthiness in GenAI, paving the way for safer and more responsible integration of GenFMs into critical applications. To facilitate advancement in the community, we release the toolkit for dynamic evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/TrustGen/TrustEval-toolkit", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-20", "arxiv_id": "2502.14127", "title": "Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above", "authors": [ "Nishant Balepur", "Rachel Rudinger", "Jordan Lee Boyd-Graber" ], "github": "", "project_page": "", "abstract": "Multiple choice question answering (MCQA) is popular for LLM evaluation due to its simplicity and human-like testing, but we argue for its reform. We first reveal flaws in MCQA's format, as it struggles to: 1) test generation/subjectivity; 2) match LLM use cases; and 3) fully test knowledge. We instead advocate for generative formats based on human testing-where LLMs construct and explain answers-better capturing user needs and knowledge while remaining easy to score. We then show even when MCQA is a useful format, its datasets suffer from: leakage; unanswerability; shortcuts; and saturation. In each issue, we give fixes from education, like rubrics to guide MCQ writing; scoring methods to bridle guessing; and Item Response Theory to build harder MCQs. Lastly, we discuss LLM errors in MCQA-robustness, biases, and unfaithful explanations-showing how our prior solutions better measure or address these issues. While we do not need to desert MCQA, we encourage more efforts in refining the task based on educational testing, advancing evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14739", "title": "SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines", "authors": [ "M-A-P Team", "Xinrun Du", "Yifan Yao", "Kaijing Ma", "Bingli Wang", "Tianyu Zheng", "Kang Zhu", "Minghao Liu", "Yiming Liang", "Xiaolong Jin", "Zhenlin Wei", "Chujie Zheng", "Kaixing Deng", "Shuyue Guo", "Shian Jia", "Sichao Jiang", "Yiyan Liao", "Rui Li", "Qinrui Li", "Sirun Li", "Yizhi Li", "Yunwen Li", "Dehua Ma", "Yuansheng Ni", "Haoran Que", "Qiyao Wang", "Zhoufutu Wen", "Siwei Wu", "Tianshun Xing", "Ming Xu", "Zhenzhu Yang", "Zekun Moore Wang", "Junting Zhou", "Yuelin Bai", "Xingyuan Bu", "Chenglin Cai", "Liang Chen", "Yifan Chen", "Chengtuo Cheng", "Tianhao Cheng", "Keyi Ding", "Siming Huang", "Yun Huang", "Yaoru Li", "Yizhe Li", "Zhaoqun Li", "Tianhao Liang", "Chengdong Lin", "Hongquan Lin", "Yinghao Ma", "Zhongyuan Peng", "Zifan Peng", "Qige Qi", "Shi Qiu", "Xingwei Qu", "Yizhou Tan", "Zili Wang", "Chenqing Wang", "Hao Wang", "Yiya Wang", "Yubo Wang", "Jiajun Xu", "Kexin Yang", "Ruibin Yuan", "Yuanhao Yue", "Tianyang Zhan", "Chun Zhang", "Jingyang Zhang", "Xiyue Zhang", "Xingjian Zhang", "Yue Zhang", "Yongchi Zhao", "Xiangyu Zheng", "Chenghua Zhong", "Yang Gao", "Zhoujun Li", "Dayiheng Liu", "Qian Liu", "Tianyu Liu", "Shiwen Ni", "Junran Peng", "Yujia Qin", "Wenbo Su", "Guoyin Wang", "Shi Wang", "Jian Yang", "Min Yang", "Meng Cao", "Xiang Yue", "Zhaoxiang Zhang", "Wangchunshu Zhou", "Jiaheng Liu", "Qunshu Lin", "Wenhao Huang", "Ge Zhang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledge encompasses over 200 specialized disciplines, far exceeding the scope of existing benchmarks. The capabilities of LLMs in many of these specialized fields-particularly in light industry, agriculture, and service-oriented disciplines-remain inadequately evaluated. To address this gap, we present SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines. Our benchmark employs a novel Human-LLM collaborative filtering mechanism to eliminate trivial or ambiguous questions through iterative refinement based on both LLM responses and expert feedback. Our experimental results reveal significant room for improvement in the performance of current state-of-the-art LLMs across diverse knowledge domains (e.g., the reasoning-focused model DeepSeek-R1 achieved the highest accuracy of 61.82% on SuperGPQA), highlighting the considerable gap between current model capabilities and artificial general intelligence. Additionally, we present comprehensive insights from our management of a large-scale annotation process, involving over 80 expert annotators and an interactive Human-LLM collaborative system, offering valuable methodological guidance for future research initiatives of comparable scope.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14282", "title": "PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC", "authors": [ "Haowei Liu", "Xi Zhang", "Haiyang Xu", "Yuyang Wanyan", "Junyang Wang", "Ming Yan", "Ji Zhang", "Chunfeng Yuan", "Changsheng Xu", "Weiming Hu", "Fei Huang" ], "github": "https://github.com/X-PLUG/MobileAgent/tree/main/PC-Agent", "project_page": "", "abstract": "In the field of MLLM-based GUI agents, compared to smartphones, the PC scenario not only features a more complex interactive environment, but also involves more intricate intra- and inter-app workflows. To address these issues, we propose a hierarchical agent framework named PC-Agent. Specifically, from the perception perspective, we devise an Active Perception Module (APM) to overcome the inadequate abilities of current MLLMs in perceiving screenshot content. From the decision-making perspective, to handle complex user instructions and interdependent subtasks more effectively, we propose a hierarchical multi-agent collaboration architecture that decomposes decision-making processes into Instruction-Subtask-Action levels. Within this architecture, three agents (i.e., Manager, Progress and Decision) are set up for instruction decomposition, progress tracking and step-by-step decision-making respectively. Additionally, a Reflection agent is adopted to enable timely bottom-up error feedback and adjustment. We also introduce a new benchmark PC-Eval with 25 real-world complex instructions. Empirical results on PC-Eval show that our PC-Agent achieves a 32% absolute improvement of task success rate over previous state-of-the-art methods. The code will be publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/X-PLUG/MobileAgent/tree/main/PC-Agent", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14834", "title": "LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in Vision-Language Models", "authors": [ "Shangqing Tu", "Yucheng Wang", "Daniel Zhang-Li", "Yushi Bai", "Jifan Yu", "Yuhao Wu", "Lei Hou", "Huiqin Liu", "Zhiyuan Liu", "Bin Xu", "Juanzi Li" ], "github": "https://github.com/THU-KEG/LongWriter-V", "project_page": "", "abstract": "Existing Large Vision-Language Models (LVLMs) can process inputs with context lengths up to 128k visual and text tokens, yet they struggle to generate coherent outputs beyond 1,000 words. We find that the primary limitation is the absence of long output examples during supervised fine-tuning (SFT). To tackle this issue, we introduce LongWriter-V-22k, a SFT dataset comprising 22,158 examples, each with multiple input images, an instruction, and corresponding outputs ranging from 0 to 10,000 words. Moreover, to achieve long outputs that maintain high-fidelity to the input images, we employ Direct Preference Optimization (DPO) to the SFT model. Given the high cost of collecting human feedback for lengthy outputs (e.g., 3,000 words), we propose IterDPO, which breaks long outputs into segments and uses iterative corrections to form preference pairs with the original outputs. Additionally, we develop MMLongBench-Write, a benchmark featuring six tasks to evaluate the long-generation capabilities of VLMs. Our 7B parameter model, trained with LongWriter-V-22k and IterDPO, achieves impressive performance on this benchmark, outperforming larger proprietary models like GPT-4o. Code and data: https://github.com/THU-KEG/LongWriter-V", "github_manual": "", "github_regex": "https://github.com/THU-KEG/LongWriter-V", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14786", "title": "SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features", "authors": [ "Michael Tschannen", "Alexey Gritsenko", "Xiao Wang", "Muhammad Ferjad Naeem", "Ibrahim Alabdulmohsin", "Nikhil Parthasarathy", "Talfan Evans", "Lucas Beyer", "Ye Xia", "Basil Mustafa", "Olivier H\u00e9naff", "Jeremiah Harmsen", "Andreas Steiner", "Xiaohua Zhai" ], "github": "", "project_page": "", "abstract": "We introduce SigLIP 2, a family of new multilingual vision-language encoders that build on the success of the original SigLIP. In this second iteration, we extend the original image-text training objective with several prior, independently developed techniques into a unified recipe -- this includes captioning-based pretraining, self-supervised losses (self-distillation, masked prediction) and online data curation. With these changes, SigLIP 2 models outperform their SigLIP counterparts at all model scales in core capabilities, including zero-shot classification, image-text retrieval, and transfer performance when extracting visual representations for Vision-Language Models (VLMs). Furthermore, the new training recipe leads to significant improvements on localization and dense prediction tasks. We also train variants which support multiple resolutions and preserve the input's native aspect ratio. Finally, we train on a more diverse data-mixture that includes de-biasing techniques, leading to much better multilingual understanding and improved fairness. To allow users to trade off inference cost with performance, we release model checkpoints at four sizes: ViT-B (86M), L (303M), So400m (400M), and g (1B).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14768", "title": "Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning", "authors": [ "Tian Xie", "Zitian Gao", "Qingnan Ren", "Haoming Luo", "Yuqian Hong", "Bryan Dai", "Joey Zhou", "Kai Qiu", "Zhirong Wu", "Chong Luo" ], "github": "https://github.com/Unakar/Logic-RL", "project_page": "", "abstract": "Inspired by the success of DeepSeek-R1, we explore the potential of rule-based reinforcement learning (RL) in large reasoning models. To analyze reasoning dynamics, we use synthetic logic puzzles as training data due to their controllable complexity and straightforward answer verification. We make some key technical contributions that lead to effective and stable RL training: a system prompt that emphasizes the thinking and answering process, a stringent format reward function that penalizes outputs for taking shortcuts, and a straightforward training recipe that achieves stable convergence. Our 7B model develops advanced reasoning skills-such as reflection, verification, and summarization-that are absent from the logic corpus. Remarkably, after training on just 5K logic problems, it demonstrates generalization abilities to the challenging math benchmarks AIME and AMC.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Unakar/Logic-RL", "github_api_added_by": "auto" }, { "date": "2025-02-21", "arxiv_id": "2502.14844", "title": "Dynamic Concepts Personalization from Single Videos", "authors": [ "Rameen Abdal", "Or Patashnik", "Ivan Skorokhodov", "Willi Menapace", "Aliaksandr Siarohin", "Sergey Tulyakov", "Daniel Cohen-Or", "Kfir Aberman" ], "github": "", "project_page": "", "abstract": "Personalizing generative text-to-image models has seen remarkable progress, but extending this personalization to text-to-video models presents unique challenges. Unlike static concepts, personalizing text-to-video models has the potential to capture dynamic concepts, i.e., entities defined not only by their appearance but also by their motion. In this paper, we introduce Set-and-Sequence, a novel framework for personalizing Diffusion Transformers (DiTs)-based generative video models with dynamic concepts. Our approach imposes a spatio-temporal weight space within an architecture that does not explicitly separate spatial and temporal features. This is achieved in two key stages. First, we fine-tune Low-Rank Adaptation (LoRA) layers using an unordered set of frames from the video to learn an identity LoRA basis that represents the appearance, free from temporal interference. In the second stage, with the identity LoRAs frozen, we augment their coefficients with Motion Residuals and fine-tune them on the full video sequence, capturing motion dynamics. Our Set-and-Sequence framework results in a spatio-temporal weight space that effectively embeds dynamic concepts into the video model's output domain, enabling unprecedented editability and compositionality while setting a new benchmark for personalizing dynamic concepts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14846", "title": "Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation", "authors": [ "Yue Yang", "Ajay Patel", "Matt Deitke", "Tanmay Gupta", "Luca Weihs", "Andrew Head", "Mark Yatskar", "Chris Callison-Burch", "Ranjay Krishna", "Aniruddha Kembhavi", "Christopher Clark" ], "github": "", "project_page": "", "abstract": "Reasoning about images with rich text, such as charts and documents, is a critical application of vision-language models (VLMs). However, VLMs often struggle in these domains due to the scarcity of diverse text-rich vision-language data. To address this challenge, we present CoSyn, a framework that leverages the coding capabilities of text-only large language models (LLMs) to automatically create synthetic text-rich multimodal data. Given input text describing a target domain (e.g., \"nutrition fact labels\"), CoSyn prompts an LLM to generate code (Python, HTML, LaTeX, etc.) for rendering synthetic images. With the underlying code as textual representations of the synthetic images, CoSyn can generate high-quality instruction-tuning data, again relying on a text-only LLM. Using CoSyn, we constructed a dataset comprising 400K images and 2.7M rows of vision-language instruction-tuning data. Comprehensive experiments on seven benchmarks demonstrate that models trained on our synthetic data achieve state-of-the-art performance among competitive open-source models, including Llama 3.2, and surpass proprietary models such as GPT-4V and Gemini 1.5 Flash. Furthermore, CoSyn can produce synthetic pointing data, enabling VLMs to ground information within input images, showcasing its potential for developing multimodal agents capable of acting in real-world environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14377", "title": "RelaCtrl: Relevance-Guided Efficient Control for Diffusion Transformers", "authors": [ "Ke Cao", "Jing Wang", "Ao Ma", "Jiasong Feng", "Zhanjie Zhang", "Xuanhua He", "Shanyuan Liu", "Bo Cheng", "Dawei Leng", "Yuhui Yin", "Jie Zhang" ], "github": "", "project_page": "", "abstract": "The Diffusion Transformer plays a pivotal role in advancing text-to-image and text-to-video generation, owing primarily to its inherent scalability. However, existing controlled diffusion transformer methods incur significant parameter and computational overheads and suffer from inefficient resource allocation due to their failure to account for the varying relevance of control information across different transformer layers. To address this, we propose the Relevance-Guided Efficient Controllable Generation framework, RelaCtrl, enabling efficient and resource-optimized integration of control signals into the Diffusion Transformer. First, we evaluate the relevance of each layer in the Diffusion Transformer to the control information by assessing the \"ControlNet Relevance Score\"-i.e., the impact of skipping each control layer on both the quality of generation and the control effectiveness during inference. Based on the strength of the relevance, we then tailor the positioning, parameter scale, and modeling capacity of the control layers to reduce unnecessary parameters and redundant computations. Additionally, to further improve efficiency, we replace the self-attention and FFN in the commonly used copy block with the carefully designed Two-Dimensional Shuffle Mixer (TDSM), enabling efficient implementation of both the token mixer and channel mixer. Both qualitative and quantitative experimental results demonstrate that our approach achieves superior performance with only 15% of the parameters and computational complexity compared to PixArt-delta. More examples are available at https://relactrl.github.io/RelaCtrl/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14669", "title": "AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via GRPO", "authors": [ "Alan Dao", "Dinh Bach Vu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated impressive capabilities in language processing, yet they often struggle with tasks requiring genuine visual spatial reasoning. In this paper, we introduce a novel two-stage training framework designed to equip standard LLMs with visual reasoning abilities for maze navigation. First, we leverage Supervised Fine Tuning (SFT) on a curated dataset of tokenized maze representations to teach the model to predict step-by-step movement commands. Next, we apply Group Relative Policy Optimization (GRPO)-a technique used in DeepSeekR1-with a carefully crafted reward function to refine the model's sequential decision-making and encourage emergent chain-of-thought behaviors. Experimental results on synthetically generated mazes show that while a baseline model fails to navigate the maze, the SFT-trained model achieves 86% accuracy, and further GRPO fine-tuning boosts accuracy to 93%. Qualitative analyses reveal that GRPO fosters more robust and self-corrective reasoning, highlighting the potential of our approach to bridge the gap between language models and visual spatial tasks. These findings offer promising implications for applications in robotics, autonomous navigation, and other domains that require integrated visual and sequential reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14499", "title": "MLGym: A New Framework and Benchmark for Advancing AI Research Agents", "authors": [ "Deepak Nathani", "Lovish Madaan", "Nicholas Roberts", "Nikolay Bashlykov", "Ajay Menon", "Vincent Moens", "Amar Budhiraja", "Despoina Magka", "Vladislav Vorotilov", "Gaurav Chaurasia", "Dieuwke Hupkes", "Ricardo Silveira Cabral", "Tatiana Shavrina", "Jakob Foerster", "Yoram Bachrach", "William Yang Wang", "Roberta Raileanu" ], "github": "https://github.com/facebookresearch/MLGym", "project_page": "", "abstract": "We introduce Meta MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. This is the first Gym environment for machine learning (ML) tasks, enabling research on reinforcement learning (RL) algorithms for training such agents. MLGym-bench consists of 13 diverse and open-ended AI research tasks from diverse domains such as computer vision, natural language processing, reinforcement learning, and game theory. Solving these tasks requires real-world AI research skills such as generating new ideas and hypotheses, creating and processing data, implementing ML methods, training models, running experiments, analyzing the results, and iterating through this process to improve on a given task. We evaluate a number of frontier large language models (LLMs) on our benchmarks such as Claude-3.5-Sonnet, Llama-3.1 405B, GPT-4o, o1-preview, and Gemini-1.5 Pro. Our MLGym framework makes it easy to add new tasks, integrate and evaluate models or agents, generate synthetic data at scale, as well as develop new learning algorithms for training agents on AI research tasks. We find that current frontier models can improve on the given baselines, usually by finding better hyperparameters, but do not generate novel hypotheses, algorithms, architectures, or substantial improvements. We open-source our framework and benchmark to facilitate future research in advancing the AI research capabilities of LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/MLGym", "github_api_added_by": "auto" }, { "date": "2025-02-21", "arxiv_id": "2502.14382", "title": "S*: Test Time Scaling for Code Generation", "authors": [ "Dacheng Li", "Shiyi Cao", "Chengkun Cao", "Xiuyu Li", "Shangyin Tan", "Kurt Keutzer", "Jiarong Xing", "Joseph E. Gonzalez", "Ion Stoica" ], "github": "https://github.com/NovaSky-AI/SkyThought", "project_page": "", "abstract": "Increasing test-time compute for LLMs shows promise across domains but remains underexplored in code generation, despite extensive study in math. In this paper, we propose S*, the first hybrid test-time scaling framework that substantially improves the coverage and selection accuracy of generated code. S* extends the existing parallel scaling paradigm with sequential scaling to push performance boundaries. It further leverages a novel selection mechanism that adaptively generates distinguishing inputs for pairwise comparison, combined with execution-grounded information to robustly identify correct solutions. We evaluate across 12 Large Language Models and Large Reasoning Model and show: (1) S* consistently improves performance across model families and sizes, enabling a 3B model to outperform GPT-4o-mini; (2) S* enables non-reasoning models to surpass reasoning models - GPT-4o-mini with S* outperforms o1-preview by 3.7% on LiveCodeBench; (3) S* further boosts state-of-the-art reasoning models - DeepSeek-R1-Distill-Qwen-32B with S* achieves 85.7% on LiveCodeBench, approaching o1 (high) at 88.5%. Code will be available under https://github.com/NovaSky-AI/SkyThought.", "github_manual": "", "github_regex": "https://github.com/NovaSky-AI/SkyThought", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14258", "title": "Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information", "authors": [ "Yein Park", "Chanwoong Yoon", "Jungwoo Park", "Minbyul Jeong", "Jaewoo Kang" ], "github": "https://github.com/dmis-lab/TemporalHead", "project_page": "", "abstract": "While the ability of language models to elicit facts has been widely investigated, how they handle temporally changing facts remains underexplored. We discover Temporal Heads, specific attention heads primarily responsible for processing temporal knowledge through circuit analysis. We confirm that these heads are present across multiple models, though their specific locations may vary, and their responses differ depending on the type of knowledge and its corresponding years. Disabling these heads degrades the model's ability to recall time-specific knowledge while maintaining its general capabilities without compromising time-invariant and question-answering performances. Moreover, the heads are activated not only numeric conditions (\"In 2004\") but also textual aliases (\"In the year ...\"), indicating that they encode a temporal dimension beyond simple numerical representation. Furthermore, we expand the potential of our findings by demonstrating how temporal knowledge can be edited by adjusting the values of these heads.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dmis-lab/TemporalHead", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14372", "title": "Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning", "authors": [ "Austin Yubo He", "Zi-Wen Liu" ], "github": "", "project_page": "", "abstract": "The realization of scalable fault-tolerant quantum computing is expected to hinge on quantum error-correcting codes. In the quest for more efficient quantum fault tolerance, a critical code parameter is the weight of measurements that extract information about errors to enable error correction: as higher measurement weights require higher implementation costs and introduce more errors, it is important in code design to optimize measurement weight. This underlies the surging interest in quantum low-density parity-check (qLDPC) codes, the study of which has primarily focused on the asymptotic (large-code-limit) properties. In this work, we introduce a versatile and computationally efficient approach to stabilizer code weight reduction based on reinforcement learning (RL), which produces new low-weight codes that substantially outperform the state of the art in practically relevant parameter regimes, extending significantly beyond previously accessible small distances. For example, our approach demonstrates savings in physical qubit overhead compared to existing results by 1 to 2 orders of magnitude for weight 6 codes and brings the overhead into a feasible range for near-future experiments. We also investigate the interplay between code parameters using our RL framework, offering new insights into the potential efficiency and power of practically viable coding strategies. Overall, our results demonstrate how RL can effectively advance the crucial yet challenging problem of quantum code discovery and thereby facilitate a faster path to the practical implementation of fault-tolerant quantum technologies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14409", "title": "Unstructured Evidence Attribution for Long Context Query Focused Summarization", "authors": [ "Dustin Wright", "Zain Muhammad Mujahid", "Lu Wang", "Isabelle Augenstein", "David Jurgens" ], "github": "https://github.com/dwright37/unstructured-evidence-sunset", "project_page": "", "abstract": "Large language models (LLMs) are capable of generating coherent summaries from very long contexts given a user query. Extracting and properly citing evidence spans could help improve the transparency and reliability of these summaries. At the same time, LLMs suffer from positional biases in terms of which information they understand and attend to, which could affect evidence citation. Whereas previous work has focused on evidence citation with predefined levels of granularity (e.g. sentence, paragraph, document, etc.), we propose the task of long-context query focused summarization with unstructured evidence citation. We show how existing systems struggle to generate and properly cite unstructured evidence from their context, and that evidence tends to be \"lost-in-the-middle\". To help mitigate this, we create the Summaries with Unstructured Evidence Text dataset (SUnsET), a synthetic dataset generated using a novel domain-agnostic pipeline which can be used as supervision to adapt LLMs to this task. We demonstrate across 5 LLMs of different sizes and 4 datasets with varying document types and lengths that LLMs adapted with SUnsET data generate more relevant and factually consistent evidence than their base models, extract evidence from more diverse locations in their context, and can generate more relevant and consistent summaries.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dwright37/unstructured-evidence-sunset", "github_api_added_by": "auto" }, { "date": "2025-02-21", "arxiv_id": "2502.13759", "title": "Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework", "authors": [ "Zirui Song", "Jingpu Yang", "Yuan Huang", "Jonathan Tonglet", "Zeyu Zhang", "Tao Cheng", "Meng Fang", "Iryna Gurevych", "Xiuying Chen" ], "github": "", "project_page": "", "abstract": "Geolocation, the task of identifying an image's location, requires complex reasoning and is crucial for navigation, monitoring, and cultural preservation. However, current methods often produce coarse, imprecise, and non-interpretable localization. A major challenge lies in the quality and scale of existing geolocation datasets. These datasets are typically small-scale and automatically constructed, leading to noisy data and inconsistent task difficulty, with images that either reveal answers too easily or lack sufficient clues for reliable inference. To address these challenges, we introduce a comprehensive geolocation framework with three key components: GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric, collectively designed to address critical challenges and drive advancements in geolocation research. At the core of this framework is GeoComp (Geolocation Competition Dataset), a large-scale dataset collected from a geolocation game platform involving 740K users over two years. It comprises 25 million entries of metadata and 3 million geo-tagged locations spanning much of the globe, with each location annotated thousands to tens of thousands of times by human users. The dataset offers diverse difficulty levels for detailed analysis and highlights key gaps in current models. Building on this dataset, we propose Geographical Chain-of-Thought (GeoCoT), a novel multi-step reasoning framework designed to enhance the reasoning capabilities of Large Vision Models (LVMs) in geolocation tasks. GeoCoT improves performance by integrating contextual and spatial cues through a multi-step process that mimics human geolocation reasoning. Finally, using the GeoEval metric, we demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14502", "title": "How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?", "authors": [ "Sergey Pletenev", "Maria Marina", "Daniil Moskovskiy", "Vasily Konovalov", "Pavel Braslavski", "Alexander Panchenko", "Mikhail Salnikov" ], "github": "", "project_page": "", "abstract": "The performance of Large Language Models (LLMs) on many tasks is greatly limited by the knowledge learned during pre-training and stored in the model's parameters. Low-rank adaptation (LoRA) is a popular and efficient training technique for updating or domain-specific adaptation of LLMs. In this study, we investigate how new facts can be incorporated into the LLM using LoRA without compromising the previously learned knowledge. We fine-tuned Llama-3.1-8B-instruct using LoRA with varying amounts of new knowledge. Our experiments have shown that the best results are obtained when the training data contains a mixture of known and new facts. However, this approach is still potentially harmful because the model's performance on external question-answering benchmarks declines after such fine-tuning. When the training data is biased towards certain entities, the model tends to regress to few overrepresented answers. In addition, we found that the model becomes more confident and refuses to provide an answer in only few cases. These findings highlight the potential pitfalls of LoRA-based LLM updates and underscore the importance of training data composition and tuning parameters to balance new knowledge integration and general model capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.12853", "title": "S^2R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning", "authors": [ "Ruotian Ma", "Peisong Wang", "Cheng Liu", "Xingyan Liu", "Jiaqi Chen", "Bang Zhang", "Xin Zhou", "Nan Du", "Jia Li" ], "github": "https://github.com/NineAbyss/S2R", "project_page": "", "abstract": "Recent studies have demonstrated the effectiveness of LLM test-time scaling. However, existing approaches to incentivize LLMs' deep thinking abilities generally require large-scale data or significant training efforts. Meanwhile, it remains unclear how to improve the thinking abilities of less powerful base models. In this work, we introduce S^2R, an efficient framework that enhances LLM reasoning by teaching models to self-verify and self-correct during inference. Specifically, we first initialize LLMs with iterative self-verification and self-correction behaviors through supervised fine-tuning on carefully curated data. The self-verification and self-correction skills are then further strengthened by both outcome-level and process-level reinforcement learning, with minimized resource requirements, enabling the model to adaptively refine its reasoning process during inference. Our results demonstrate that, with only 3.1k self-verifying and self-correcting behavior initialization samples, Qwen2.5-math-7B achieves an accuracy improvement from 51.0\\% to 81.6\\%, outperforming models trained on an equivalent amount of long-CoT distilled data. Extensive experiments and analysis based on three base models across both in-domain and out-of-domain benchmarks validate the effectiveness of S^2R. Our code and data are available at https://github.com/NineAbyss/S2R.", "github_manual": "", "github_regex": "https://github.com/NineAbyss/S2R", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.12769", "title": "How Much Do LLMs Hallucinate across Languages? On Multilingual Estimation of LLM Hallucination in the Wild", "authors": [ "Saad Obaid ul Islam", "Anne Lauscher", "Goran Glava\u0161" ], "github": "", "project_page": "", "abstract": "In the age of misinformation, hallucination -- the tendency of Large Language Models (LLMs) to generate non-factual or unfaithful responses -- represents the main risk for their global utility. Despite LLMs becoming increasingly multilingual, the vast majority of research on detecting and quantifying LLM hallucination are (a) English-centric and (b) focus on machine translation (MT) and summarization, tasks that are less common ``in the wild'' than open information seeking. In contrast, we aim to quantify the extent of LLM hallucination across languages in knowledge-intensive long-form question answering. To this end, we train a multilingual hallucination detection model and conduct a large-scale study across 30 languages and 6 open-source LLM families. We start from an English hallucination detection dataset and rely on MT to generate (noisy) training data in other languages. We also manually annotate gold data for five high-resource languages; we then demonstrate, for these languages, that the estimates of hallucination rates are similar between silver (LLM-generated) and gold test sets, validating the use of silver data for estimating hallucination rates for other languages. For the final rates estimation, we build a knowledge-intensive QA dataset for 30 languages with LLM-generated prompts and Wikipedia articles as references. We find that, while LLMs generate longer responses with more hallucinated tokens for higher-resource languages, there is no correlation between length-normalized hallucination rates of languages and their digital representation. Further, we find that smaller LLMs exhibit larger hallucination rates than larger models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14541", "title": "LLM-based User Profile Management for Recommender System", "authors": [ "Seunghwan Bang", "Hwanjun Song" ], "github": "", "project_page": "", "abstract": "The rapid advancement of Large Language Models (LLMs) has opened new opportunities in recommender systems by enabling zero-shot recommendation without conventional training. Despite their potential, most existing works rely solely on users' purchase histories, leaving significant room for improvement by incorporating user-generated textual data, such as reviews and product descriptions. Addressing this gap, we propose PURE, a novel LLM-based recommendation framework that builds and maintains evolving user profiles by systematically extracting and summarizing key information from user reviews. PURE consists of three core components: a Review Extractor for identifying user preferences and key product features, a Profile Updater for refining and updating user profiles, and a Recommender for generating personalized recommendations using the most current profile. To evaluate PURE, we introduce a continuous sequential recommendation task that reflects real-world scenarios by adding reviews over time and updating predictions incrementally. Our experimental results on Amazon datasets demonstrate that PURE outperforms existing LLM-based methods, effectively leveraging long-term user information while managing token limitations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14044", "title": "Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data", "authors": [ "Yucheng Shi", "Quanzheng Li", "Jin Sun", "Xiang Li", "Ninghao Liu" ], "github": "https://github.com/sycny/selfsynthx", "project_page": "", "abstract": "Large multimodal models (LMMs) have shown impressive capabilities in a wide range of visual tasks. However, they often struggle with fine-grained visual reasoning, failing to identify domain-specific objectives and provide justifiable explanations for their predictions. To address this, we propose a novel visual rejection sampling framework to improve the cognition and explainability of LMMs using self-synthesized data. Specifically, visual fine-tuning requires images, queries, and target answers. Our approach begins by synthesizing interpretable answers that include human-verifiable visual features. These features are based on expert-defined concepts, carefully selected based on their alignment with the image content. After each round of fine-tuning, we apply a reward model-free filtering mechanism to select the highest-quality interpretable answers for the next round of tuning. This iterative process of data synthesis and fine-tuning progressively improves the model's ability to generate accurate and reasonable explanations. Experimental results demonstrate the effectiveness of our method in improving both the accuracy and explainability of specialized visual classification tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sycny/selfsynthx", "github_api_added_by": "auto" }, { "date": "2025-02-21", "arxiv_id": "2502.14638", "title": "NAVIG: Natural Language-guided Analysis with Vision Language Models for Image Geo-localization", "authors": [ "Zheyuan Zhang", "Runze Li", "Tasnim Kabir", "Jordan Boyd-Graber" ], "github": "https://github.com/SparrowZheyuan18/Navig", "project_page": "", "abstract": "Image geo-localization is the task of predicting the specific location of an image and requires complex reasoning across visual, geographical, and cultural contexts. While prior Vision Language Models (VLMs) have the best accuracy at this task, there is a dearth of high-quality datasets and models for analytical reasoning. We first create NaviClues, a high-quality dataset derived from GeoGuessr, a popular geography game, to supply examples of expert reasoning from language. Using this dataset, we present Navig, a comprehensive image geo-localization framework integrating global and fine-grained image information. By reasoning with language, Navig reduces the average distance error by 14% compared to previous state-of-the-art models while requiring fewer than 1000 training samples. Our dataset and code are available at https://github.com/SparrowZheyuan18/Navig/.", "github_manual": "", "github_regex": "https://github.com/SparrowZheyuan18/Navig", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14802", "title": "From RAG to Memory: Non-Parametric Continual Learning for Large Language Models", "authors": [ "Bernal Jim\u00e9nez Guti\u00e9rrez", "Yiheng Shu", "Weijian Qi", "Sizhe Zhou", "Yu Su" ], "github": "https://github.com/OSU-NLP-Group/HippoRAG", "project_page": "", "abstract": "Our ability to continuously acquire, organize, and leverage knowledge is a key feature of human intelligence that AI systems must approximate to unlock their full potential. Given the challenges in continual learning with large language models (LLMs), retrieval-augmented generation (RAG) has become the dominant way to introduce new information. However, its reliance on vector retrieval hinders its ability to mimic the dynamic and interconnected nature of human long-term memory. Recent RAG approaches augment vector embeddings with various structures like knowledge graphs to address some of these gaps, namely sense-making and associativity. However, their performance on more basic factual memory tasks drops considerably below standard RAG. We address this unintended deterioration and propose HippoRAG 2, a framework that outperforms standard RAG comprehensively on factual, sense-making, and associative memory tasks. HippoRAG 2 builds upon the Personalized PageRank algorithm used in HippoRAG and enhances it with deeper passage integration and more effective online use of an LLM. This combination pushes this RAG system closer to the effectiveness of human long-term memory, achieving a 7% improvement in associative memory tasks over the state-of-the-art embedding model while also exhibiting superior factual knowledge and sense-making memory capabilities. This work paves the way for non-parametric continual learning for LLMs. Our code and data will be released at https://github.com/OSU-NLP-Group/HippoRAG.", "github_manual": "", "github_regex": "https://github.com/OSU-NLP-Group/HippoRAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14854", "title": "CLIPPER: Compression enables long-context synthetic data generation", "authors": [ "Chau Minh Pham", "Yapei Chang", "Mohit Iyyer" ], "github": "https://github.com/chtmp223/clipper", "project_page": "", "abstract": "LLM developers are increasingly reliant on synthetic data, but generating high-quality data for complex long-context reasoning tasks remains challenging. We introduce CLIPPER, a compression-based approach for generating synthetic data tailored to narrative claim verification - a task that requires reasoning over a book to verify a given claim. Instead of generating claims directly from the raw text of the book, which results in artifact-riddled claims, CLIPPER first compresses the book into chapter outlines and book summaries and then uses these intermediate representations to generate complex claims and corresponding chain-of-thoughts. Compared to naive approaches, CLIPPER produces claims that are more valid, grounded, and complex. Using CLIPPER, we construct a dataset of 19K synthetic book claims paired with their source texts and chain-of-thought reasoning, and use it to fine-tune three open-weight models. Our best model achieves breakthrough results on narrative claim verification (from 28% to 76% accuracy on our test set) and sets a new state-of-the-art for sub-10B models on the NoCha leaderboard. Further analysis shows that our models generate more detailed and grounded chain-of-thought reasoning while also improving performance on other narrative understanding tasks (e.g., NarrativeQA).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/chtmp223/clipper", "github_api_added_by": "auto" }, { "date": "2025-02-21", "arxiv_id": "2502.14866", "title": "LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention", "authors": [ "Shang Yang", "Junxian Guo", "Haotian Tang", "Qinghao Hu", "Guangxuan Xiao", "Jiaming Tang", "Yujun Lin", "Zhijian Liu", "Yao Lu", "Song Han" ], "github": "https://github.com/mit-han-lab/omniserve", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable potential in processing long sequences, yet efficiently serving these long-context models remains challenging due to the quadratic computational complexity of attention in the prefilling stage and the large memory footprint of the KV cache in the decoding stage. To address these issues, we introduce LServe, an efficient system that accelerates long-sequence LLM serving via hybrid sparse attention. This method unifies different hardware-friendly, structured sparsity patterns for both prefilling and decoding attention into a single framework, where computations on less important tokens are skipped block-wise. LServe demonstrates the compatibility of static and dynamic sparsity in long-context LLM attention. This design enables multiplicative speedups by combining these optimizations. Specifically, we convert half of the attention heads to nearly free streaming heads in both the prefilling and decoding stages. Additionally, we find that only a constant number of KV pages is required to preserve long-context capabilities, irrespective of context length. We then design a hierarchical KV page selection policy that dynamically prunes KV pages based on query-centric similarity. On average, LServe accelerates LLM prefilling by up to 2.9x and decoding by 1.3-2.1x over vLLM, maintaining long-context accuracy. Code is released at https://github.com/mit-han-lab/omniserve.", "github_manual": "", "github_regex": "https://github.com/mit-han-lab/omniserve", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14678", "title": "How to Get Your LLM to Generate Challenging Problems for Evaluation", "authors": [ "Arkil Patel", "Siva Reddy", "Dzmitry Bahdanau" ], "github": "https://github.com/mcgill-nlp/chase", "project_page": "", "abstract": "The pace of evolution of Large Language Models (LLMs) necessitates new approaches for rigorous and comprehensive evaluation. Traditional human annotation is increasingly impracticable due to the complexities and costs involved in generating high-quality, challenging problems. In this work, we introduce CHASE, a unified framework to synthetically generate challenging problems using LLMs without human involvement. For a given task, our approach builds a hard problem in a bottom-up manner from simpler components. Moreover, our framework decomposes the generation process into independently verifiable sub-tasks, thereby ensuring a high level of quality and correctness. We implement CHASE to create evaluation benchmarks across three diverse domains: (1) document-based question answering, (2) repository-level code completion, and (3) math reasoning. The performance of state-of-the-art LLMs on these synthetic benchmarks lies in the range of 40-60% accuracy, thereby demonstrating the effectiveness of our framework at generating challenging problems. We publicly release our benchmarks and code.", "github_manual": "https://github.com/mcgill-nlp/chase", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.14191", "title": "Multimodal RewardBench: Holistic Evaluation of Reward Models for Vision Language Models", "authors": [ "Michihiro Yasunaga", "Luke Zettlemoyer", "Marjan Ghazvininejad" ], "github": "https://github.com/facebookresearch/multimodal_rewardbench", "project_page": "", "abstract": "Reward models play an essential role in training vision-language models (VLMs) by assessing output quality to enable aligning with human preferences. Despite their importance, the research community lacks comprehensive open benchmarks for evaluating multimodal reward models in VLMs. To address this gap, we introduce Multimodal RewardBench, an expert-annotated benchmark covering six domains: general correctness, preference, knowledge, reasoning, safety, and visual question-answering. Our dataset comprises 5,211 annotated (prompt, chosen response, rejected response) triplets collected from various VLMs. In evaluating a range of VLM judges, we find that even the top-performing models, Gemini 1.5 Pro and Claude 3.5 Sonnet, achieve only 72% overall accuracy. Notably, most models struggle in the reasoning and safety domains. These findings suggest that Multimodal RewardBench offers a challenging testbed for advancing reward model development across multiple domains. We release the benchmark at https://github.com/facebookresearch/multimodal_rewardbench.", "github_manual": "", "github_regex": "https://github.com/facebookresearch/multimodal_rewardbench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-21", "arxiv_id": "2502.13928", "title": "Symmetrical Visual Contrastive Optimization: Aligning Vision-Language Models with Minimal Contrastive Images", "authors": [ "Shengguang Wu", "Fan-Yun Sun", "Kaiyue Wen", "Nick Haber" ], "github": "https://github.com/s-vco/s-vco", "project_page": "", "abstract": "Recent studies have shown that Large Vision-Language Models (VLMs) tend to neglect image content and over-rely on language-model priors, resulting in errors in visually grounded tasks and hallucinations. We hypothesize that this issue arises because existing VLMs are not explicitly trained to generate texts that are accurately grounded in fine-grained image details. To enhance visual feedback during VLM training, we propose S-VCO (Symmetrical Visual Contrastive Optimization), a novel finetuning objective that steers the model toward capturing important visual details and aligning them with corresponding text tokens. To further facilitate this detailed alignment, we introduce MVC, a paired image-text dataset built by automatically filtering and augmenting visual counterfactual data to challenge the model with hard contrastive cases involving Minimal Visual Contrasts. Experiments show that our method consistently improves VLM performance across diverse benchmarks covering various abilities and domains, achieving up to a 22% reduction in hallucinations, and significant gains in vision-centric and general tasks. Notably, these improvements become increasingly pronounced in benchmarks with higher visual dependency. In short, S-VCO offers a significant enhancement of VLM's visually-dependent task performance while retaining or even improving the model's general abilities. We opensource our code at https://s-vco.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/s-vco/s-vco", "github_api_added_by": "auto" }, { "date": "2025-02-21", "arxiv_id": "2502.14842", "title": "Generating \u03c0-Functional Molecules Using STGG+ with Active Learning", "authors": [ "Alexia Jolicoeur-Martineau", "Yan Zhang", "Boris Knyazev", "Aristide Baratin", "Cheng-Hao Liu" ], "github": "https://github.com/SamsungSAILMontreal/STGG-AL", "project_page": "", "abstract": "Generating novel molecules with out-of-distribution properties is a major challenge in molecular discovery. While supervised learning methods generate high-quality molecules similar to those in a dataset, they struggle to generalize to out-of-distribution properties. Reinforcement learning can explore new chemical spaces but often conducts 'reward-hacking' and generates non-synthesizable molecules. In this work, we address this problem by integrating a state-of-the-art supervised learning method, STGG+, in an active learning loop. Our approach iteratively generates, evaluates, and fine-tunes STGG+ to continuously expand its knowledge. We denote this approach STGG+AL. We apply STGG+AL to the design of organic pi-functional materials, specifically two challenging tasks: 1) generating highly absorptive molecules characterized by high oscillator strength and 2) designing absorptive molecules with reasonable oscillator strength in the near-infrared (NIR) range. The generated molecules are validated and rationalized in-silico with time-dependent density functional theory. Our results demonstrate that our method is highly effective in generating novel molecules with high oscillator strength, contrary to existing methods such as reinforcement learning (RL) methods. We open-source our active-learning code along with our Conjugated-xTB dataset containing 2.9 million pi-conjugated molecules and the function for approximating the oscillator strength and absorption wavelength (based on sTDA-xTB).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SamsungSAILMontreal/STGG-AL", "github_api_added_by": "auto" }, { "date": "2025-02-21", "arxiv_id": "2502.11262", "title": "Generating Skyline Datasets for Data Science Models", "authors": [ "Mengying Wang", "Hanchao Ma", "Yiyang Bian", "Yangxin Fan", "Yinghui Wu" ], "github": "", "project_page": "", "abstract": "Preparing high-quality datasets required by various data-driven AI and machine learning models has become a cornerstone task in data-driven analysis. Conventional data discovery methods typically integrate datasets towards a single pre-defined quality measure that may lead to bias for downstream tasks. This paper introduces MODis, a framework that discovers datasets by optimizing multiple user-defined, model-performance measures. Given a set of data sources and a model, MODis selects and integrates data sources into a skyline dataset, over which the model is expected to have the desired performance in all the performance measures. We formulate MODis as a multi-goal finite state transducer, and derive three feasible algorithms to generate skyline datasets. Our first algorithm adopts a \"reduce-from-universal\" strategy, that starts with a universal schema and iteratively prunes unpromising data. Our second algorithm further reduces the cost with a bi-directional strategy that interleaves data augmentation and reduction. We also introduce a diversification algorithm to mitigate the bias in skyline datasets. We experimentally verify the efficiency and effectiveness of our skyline data discovery algorithms, and showcase their applications in optimizing data science pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.14776", "title": "SurveyX: Academic Survey Automation via Large Language Models", "authors": [ "Xun Liang", "Jiawei Yang", "Yezhaohui Wang", "Chen Tang", "Zifan Zheng", "Simin Niu", "Shichao Song", "Hanyu Wang", "Bo Tang", "Feiyu Xiong", "Keming Mao", "Zhiyu li" ], "github": "https://github.com/IAAR-Shanghai/SurveyX", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated exceptional comprehension capabilities and a vast knowledge base, suggesting that LLMs can serve as efficient tools for automated survey generation. However, recent research related to automated survey generation remains constrained by some critical limitations like finite context window, lack of in-depth content discussion, and absence of systematic evaluation frameworks. Inspired by human writing processes, we propose SurveyX, an efficient and organized system for automated survey generation that decomposes the survey composing process into two phases: the Preparation and Generation phases. By innovatively introducing online reference retrieval, a pre-processing method called AttributeTree, and a re-polishing process, SurveyX significantly enhances the efficacy of survey composition. Experimental evaluation results show that SurveyX outperforms existing automated survey generation systems in content quality (0.259 improvement) and citation quality (1.76 enhancement), approaching human expert performance across multiple evaluation dimensions. Examples of surveys generated by SurveyX are available on www.surveyx.cn", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/IAAR-Shanghai/SurveyX", "github_api_added_by": "auto" }, { "date": "2025-02-24", "arxiv_id": "2502.15027", "title": "InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback", "authors": [ "Henry Hengyuan Zhao", "Wenqi Pei", "Yifei Tao", "Haiyang Mei", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Existing benchmarks do not test Large Multimodal Models (LMMs) on their interactive intelligence with human users which is vital for developing general-purpose AI assistants. We design InterFeedback, an interactive framework, which can be applied to any LMM and dataset to assess this ability autonomously. On top of this, we introduce InterFeedback-Bench which evaluates interactive intelligence using two representative datasets, MMMU-Pro and MathVerse, to test 10 different open-source LMMs. Additionally, we present InterFeedback-Human, a newly collected dataset of 120 cases designed for manually testing interactive performance in leading models such as OpenAI-o1 and Claude-3.5-Sonnet. Our evaluation results show that even state-of-the-art LMM (like OpenAI-o1) can correct their results through human feedback less than 50%. Our findings point to the need for methods that can enhance the LMMs' capability to interpret and benefit from feedback.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.15631", "title": "The Relationship Between Reasoning and Performance in Large Language Models -- o3 (mini) Thinks Harder, Not Longer", "authors": [ "Marthe Ballon", "Andres Algaba", "Vincent Ginis" ], "github": "https://github.com/MartheBallon/analysis_o3-mini_thinks_harder_not_longer", "project_page": "", "abstract": "Large language models have demonstrated remarkable progress in mathematical reasoning, leveraging chain-of-thought and test-time compute scaling. However, many open questions remain regarding the interplay between reasoning token usage and accuracy gains. In particular, when comparing models across generations, it is unclear whether improved performance results from longer reasoning chains or more efficient reasoning. We systematically analyze chain-of-thought length across o1-mini and o3-mini variants on the Omni-MATH benchmark, finding that o3-mini (m) achieves superior accuracy without requiring longer reasoning chains than o1-mini. Moreover, we show that accuracy generally declines as reasoning chains grow across all models and compute settings, even when controlling for difficulty of the questions. This accuracy drop is significantly smaller in more proficient models, suggesting that new generations of reasoning models use test-time compute more effectively. Finally, we highlight that while o3-mini (h) achieves a marginal accuracy gain over o3-mini (m), it does so by allocating substantially more reasoning tokens across all problems, even the ones that o3-mini (m) can already solve. These findings provide new insights into the relationship between model capability and reasoning length, with implications for efficiency, scaling, and evaluation methodologies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MartheBallon/analysis_o3-mini_thinks_harder_not_longer", "github_api_added_by": "auto" }, { "date": "2025-02-24", "arxiv_id": "2502.13449", "title": "Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model", "authors": [ "Dongki Kim", "Wonbin Lee", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "Understanding molecules is key to understanding organisms and driving advances in drug discovery, requiring interdisciplinary knowledge across chemistry and biology. Although large molecular language models have achieved notable success in interpreting molecular structures, their instruction datasets are limited to the specific knowledge from task-oriented datasets and do not fully cover the fundamental characteristics of molecules, hindering their abilities as general-purpose molecular assistants. To address this issue, we propose Mol-LLaMA, a large molecular language model that grasps the general knowledge centered on molecules via multi-modal instruction tuning. To this end, we design key data types that encompass the fundamental features of molecules, incorporating essential knowledge from molecular structures. In addition, to improve understanding of molecular features, we introduce a module that integrates complementary information from different molecular encoders, leveraging the distinct advantages of different molecular representations. Our experimental results demonstrate that Mol-LLaMA is capable of comprehending the general features of molecules and generating relevant responses to users' queries with detailed explanations, implying its potential as a general-purpose assistant for molecular analysis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.14922", "title": "SIFT: Grounding LLM Reasoning in Contexts via Stickers", "authors": [ "Zihao Zeng", "Xuyao Huang", "Boxiu Li", "Zhijie Deng" ], "github": "https://github.com/zhijie-group/SIFT", "project_page": "", "abstract": "This paper identifies the misinterpretation of the context can be a significant issue during the reasoning process of large language models, spanning from smaller models like Llama3.2-3B-Instruct to cutting-edge ones like DeepSeek-R1. For example, in the phrase \"10 dollars per kilo,\" LLMs might not recognize that \"per\" means \"for each,\" leading to calculation errors. We introduce a novel, post-training approach called **Stick to the Facts (SIFT)** to tackle this. SIFT leverages increasing inference-time compute to ground LLM reasoning in contexts. At the core of SIFT lies the *Sticker*, which is generated by the model itself to explicitly emphasize the key information within the context. Given the curated Sticker, SIFT generates two predictions -- one from the original query and one from the query augmented with the Sticker. If they differ, the Sticker is sequentially refined via *forward* optimization (to better align the extracted facts with the query) and *inverse* generation (to conform with the model's inherent tendencies) for more faithful reasoning outcomes. Studies across diverse models (from 3B to 100B+) and benchmarks (e.g., GSM8K, MATH-500) reveal consistent performance improvements. Notably, SIFT improves the pass@1 accuracy of DeepSeek-R1 on AIME2024 from 78.33% to **85.67**%, establishing a new state-of-the-art in the open-source community. The code is available at https://github.com/zhijie-group/SIFT.", "github_manual": "", "github_regex": "https://github.com/zhijie-group/SIFT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.15681", "title": "One-step Diffusion Models with f-Divergence Distribution Matching", "authors": [ "Yilun Xu", "Weili Nie", "Arash Vahdat" ], "github": "", "project_page": "", "abstract": "Sampling from diffusion models involves a slow iterative process that hinders their practical deployment, especially for interactive applications. To accelerate generation speed, recent approaches distill a multi-step diffusion model into a single-step student generator via variational score distillation, which matches the distribution of samples generated by the student to the teacher's distribution. However, these approaches use the reverse Kullback-Leibler (KL) divergence for distribution matching which is known to be mode seeking. In this paper, we generalize the distribution matching approach using a novel f-divergence minimization framework, termed f-distill, that covers different divergences with different trade-offs in terms of mode coverage and training variance. We derive the gradient of the f-divergence between the teacher and student distributions and show that it is expressed as the product of their score differences and a weighting function determined by their density ratio. This weighting function naturally emphasizes samples with higher density in the teacher distribution, when using a less mode-seeking divergence. We observe that the popular variational score distillation approach using the reverse-KL divergence is a special case within our framework. Empirically, we demonstrate that alternative f-divergences, such as forward-KL and Jensen-Shannon divergences, outperform the current best variational score distillation methods across image generation tasks. In particular, when using Jensen-Shannon divergence, f-distill achieves current state-of-the-art one-step generation performance on ImageNet64 and zero-shot text-to-image generation on MS-COCO. Project page: https://research.nvidia.com/labs/genair/f-distill", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.14905", "title": "Think Inside the JSON: Reinforcement Strategy for Strict LLM Schema Adherence", "authors": [ "Bhavik Agarwal", "Ishan Joshi", "Viktoria Rojkova" ], "github": "", "project_page": "", "abstract": "In this paper, we address the challenge of enforcing strict schema adherence in large language model (LLM) generation by leveraging LLM reasoning capabilities. Building on the DeepSeek R1 reinforcement learning framework, our approach trains structured reasoning skills of a 1.5B parameter model through a novel pipeline that combines synthetic reasoning dataset construction with custom reward functions under Group Relative Policy Optimization (GRPO). Specifically, we first perform R1 reinforcement learning on a 20K sample unstructured-to-structured dataset, mirroring the original DeepSeek R1 methods, to establish core reasoning abilities. Subsequently, we performed supervised fine-tuning on a separate 10K reasoning sample dataset, focusing on refining schema adherence for downstream tasks. Despite the relatively modest training scope, requiring approximately 20 hours on an 8xH100 GPU cluster for GRPO training and 3 hours on 1xA100 for SFT, our model demonstrates robust performance in enforcing schema consistency. We compare our ThinkJSON approach against the original DeepSeek R1 (671B), distilled versions of DeepSeek R1 (Qwen-1.5B and Qwen-7B), and Gemini 2.0 Flash (70B), showcasing its effectiveness in real-world applications. Our results underscore the practical utility of a resource-efficient framework for schema-constrained text generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.14494", "title": "StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following", "authors": [ "Jinnan Li", "Jinzhe Li", "Yue Wang", "Yi Chang", "Yuan Wu" ], "github": "https://github.com/MLGroupJLU/StructFlowBench", "project_page": "", "abstract": "Multi-turn instruction following capability constitutes a core competency of large language models (LLMs) in real-world applications. Existing evaluation benchmarks predominantly focus on fine-grained constraint satisfaction and domain-specific capability assessment, yet overlook the crucial structural dependency between dialogue turns that distinguishes multi-turn from single-turn interactions. This structural dependency not only reflects user intent but also establishes a second dimension for instruction following evaluation beyond constraint satisfaction. To address this gap, we propose StructFlowBench, a multi-turn instruction following benchmark with structural flow modeling. The benchmark innovatively defines a structural flow framework comprising six fundamental inter-turn relationships, which not only introduces novel structural constraints for model evaluation but also serves as generation parameters for creating customized dialogue flows tailored to specific scenarios. Adopting established LLM-based automatic evaluation methodologies, we conduct systematic evaluations of 13 leading open-source and closed-source LLMs. Experimental results reveal significant deficiencies in current models' comprehension of multi-turn dialogue structures. The code is available at https://github.com/MLGroupJLU/StructFlowBench.", "github_manual": "", "github_regex": "https://github.com/MLGroupJLU/StructFlowBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.14397", "title": "PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data", "authors": [ "Shijie Huang", "Yiren Song", "Yuxuan Zhang", "Hailong Guo", "Xueyin Wang", "Mike Zheng Shou", "Jiaming Liu" ], "github": "https://github.com/showlab/PhotoDoodle", "project_page": "", "abstract": "We introduce PhotoDoodle, a novel image editing framework designed to facilitate photo doodling by enabling artists to overlay decorative elements onto photographs. Photo doodling is challenging because the inserted elements must appear seamlessly integrated with the background, requiring realistic blending, perspective alignment, and contextual coherence. Additionally, the background must be preserved without distortion, and the artist's unique style must be captured efficiently from limited training data. These requirements are not addressed by previous methods that primarily focus on global style transfer or regional inpainting. The proposed method, PhotoDoodle, employs a two-stage training strategy. Initially, we train a general-purpose image editing model, OmniEditor, using large-scale data. Subsequently, we fine-tune this model with EditLoRA using a small, artist-curated dataset of before-and-after image pairs to capture distinct editing styles and techniques. To enhance consistency in the generated results, we introduce a positional encoding reuse mechanism. Additionally, we release a PhotoDoodle dataset featuring six high-quality styles. Extensive experiments demonstrate the advanced performance and robustness of our method in customized image editing, opening new possibilities for artistic creation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/showlab/PhotoDoodle", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.15082", "title": "UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning", "authors": [ "Vaidehi Patil", "Elias Stengel-Eskin", "Mohit Bansal" ], "github": "https://github.com/vaidehi99/upcore", "project_page": "", "abstract": "User specifications or legal frameworks often require information to be removed from pretrained models, including large language models (LLMs). This requires deleting or \"forgetting\" a set of data points from an already-trained model, which typically degrades its performance on other data points. Thus, a balance must be struck between removing information and keeping the model's other abilities intact, with a failure to balance this trade-off leading to poor deletion or an unusable model. To this end, we propose UPCORE (Utility-Preserving Coreset Selection), a method-agnostic data selection framework for mitigating collateral damage during unlearning. Finding that the model damage is correlated with the variance of the model's representations on the forget set, we selectively prune the forget set to remove outliers, thereby minimizing model degradation after unlearning. We evaluate UPCORE across three standard unlearning methods consistently achieving a superior balance between the competing objectives of deletion efficacy and model preservation. To better evaluate this trade-off, we introduce a new metric, measuring the area-under-the-curve (AUC) across standard metrics. We find that UPCORE improves both standard metrics and AUC, benefitting from positive transfer between the coreset and pruned points while reducing negative transfer from the forget set to points outside of it.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vaidehi99/upcore", "github_api_added_by": "auto" }, { "date": "2025-02-24", "arxiv_id": "2502.15589", "title": "LightThinker: Thinking Step-by-Step Compression", "authors": [ "Jintian Zhang", "Yuqi Zhu", "Mengshu Sun", "Yujie Luo", "Shuofei Qiao", "Lun Du", "Da Zheng", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/LightThinker", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable performance in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associated with generating lengthy tokens. In this paper, we propose LightThinker, a novel method that enables LLMs to dynamically compress intermediate thoughts during reasoning. Inspired by human cognitive processes, LightThinker compresses verbose thought steps into compact representations and discards the original reasoning chains, thereby significantly reducing the number of tokens stored in the context window. This is achieved by training the model on when and how to perform compression through data construction, mapping hidden states to condensed gist tokens, and creating specialized attention masks. Additionally, we introduce the Dependency (Dep) metric to quantify the degree of compression by measuring the reliance on historical tokens during generation. Extensive experiments on four datasets and two models show that LightThinker reduces peak memory usage and inference time, while maintaining competitive accuracy. Our work provides a new direction for improving the efficiency of LLMs in complex reasoning tasks without sacrificing performance. Code will be released at https://github.com/zjunlp/LightThinker.", "github_manual": "", "github_regex": "https://github.com/zjunlp/LightThinker", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.15657", "title": "Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?", "authors": [ "Yoshua Bengio", "Michael Cohen", "Damiano Fornasiere", "Joumana Ghosn", "Pietro Greiner", "Matt MacDermott", "S\u00f6ren Mindermann", "Adam Oberman", "Jesse Richardson", "Oliver Richardson", "Marc-Antoine Rondeau", "Pierre-Luc St-Charles", "David Williams-King" ], "github": "", "project_page": "", "abstract": "The leading AI companies are increasingly focused on building generalist AI agents -- systems that can autonomously plan, act, and pursue goals across almost all tasks that humans can perform. Despite how useful these systems might be, unchecked AI agency poses significant risks to public safety and security, ranging from misuse by malicious actors to a potentially irreversible loss of human control. We discuss how these risks arise from current AI training methods. Indeed, various scenarios and experiments have demonstrated the possibility of AI agents engaging in deception or pursuing goals that were not specified by human operators and that conflict with human interests, such as self-preservation. Following the precautionary principle, we see a strong need for safer, yet still useful, alternatives to the current agency-driven trajectory. Accordingly, we propose as a core building block for further advances the development of a non-agentic AI system that is trustworthy and safe by design, which we call Scientist AI. This system is designed to explain the world from observations, as opposed to taking actions in it to imitate or please humans. It comprises a world model that generates theories to explain data and a question-answering inference machine. Both components operate with an explicit notion of uncertainty to mitigate the risks of overconfident predictions. In light of these considerations, a Scientist AI could be used to assist human researchers in accelerating scientific progress, including in AI safety. In particular, our system can be employed as a guardrail against AI agents that might be created despite the risks involved. Ultimately, focusing on non-agentic AI may enable the benefits of AI innovation while avoiding the risks associated with the current trajectory. We hope these arguments will motivate researchers, developers, and policymakers to favor this safer path.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.12084", "title": "VLM^2-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues", "authors": [ "Jianshu Zhang", "Dongyu Yao", "Renjie Pi", "Paul Pu Liang", "Yi R.", "Fung" ], "github": "", "project_page": "", "abstract": "Visually linking matching cues is a crucial ability in daily life, such as identifying the same person in multiple photos based on their cues, even without knowing who they are. Despite the extensive knowledge that vision-language models (VLMs) possess, it remains largely unexplored whether they are capable of performing this fundamental task. To address this, we introduce VLM^2-Bench, a benchmark designed to assess whether VLMs can Visually Link Matching cues, with 9 subtasks and over 3,000 test cases. Comprehensive evaluation across eight open-source VLMs and GPT-4o, along with further analysis of various language-side and vision-side prompting methods, leads to a total of eight key findings. We identify critical challenges in models' ability to link visual cues, highlighting a significant performance gap where even GPT-4o lags 34.80% behind humans. Based on these insights, we advocate for (i) enhancing core visual capabilities to improve adaptability and reduce reliance on prior knowledge, (ii) establishing clearer principles for integrating language-based reasoning in vision-centric tasks to prevent unnecessary biases, and (iii) shifting vision-text training paradigms toward fostering models' ability to independently structure and infer relationships among visual cues.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.11663", "title": "MaskGWM: A Generalizable Driving World Model with Video Mask Reconstruction", "authors": [ "Jingcheng Ni", "Yuxin Guo", "Yichen Liu", "Rui Chen", "Lewei Lu", "Zehuan Wu" ], "github": "https://github.com/sensetime-fvg/opendwm", "project_page": "", "abstract": "World models that forecast environmental changes from actions are vital for autonomous driving models with strong generalization. The prevailing driving world model mainly build on video prediction model. Although these models can produce high-fidelity video sequences with advanced diffusion-based generator, they are constrained by their predictive duration and overall generalization capabilities. In this paper, we explore to solve this problem by combining generation loss with MAE-style feature-level context learning. In particular, we instantiate this target with three key design: (1) A more scalable Diffusion Transformer (DiT) structure trained with extra mask construction task. (2) we devise diffusion-related mask tokens to deal with the fuzzy relations between mask reconstruction and generative diffusion process. (3) we extend mask construction task to spatial-temporal domain by utilizing row-wise mask for shifted self-attention rather than masked self-attention in MAE. Then, we adopt a row-wise cross-view module to align with this mask design. Based on above improvement, we propose MaskGWM: a Generalizable driving World Model embodied with Video Mask reconstruction. Our model contains two variants: MaskGWM-long, focusing on long-horizon prediction, and MaskGWM-mview, dedicated to multi-view generation. Comprehensive experiments on standard benchmarks validate the effectiveness of the proposed method, which contain normal validation of Nuscene dataset, long-horizon rollout of OpenDV-2K dataset and zero-shot validation of Waymo dataset. Quantitative metrics on these datasets show our method notably improving state-of-the-art driving world model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sensetime-fvg/opendwm", "github_api_added_by": "auto" }, { "date": "2025-02-24", "arxiv_id": "2502.15011", "title": "CrossOver: 3D Scene Cross-Modal Alignment", "authors": [ "Sayan Deb Sarkar", "Ondrej Miksik", "Marc Pollefeys", "Daniel Barath", "Iro Armeni" ], "github": "", "project_page": "", "abstract": "Multi-modal 3D object understanding has gained significant attention, yet current approaches often assume complete data availability and rigid alignment across all modalities. We present CrossOver, a novel framework for cross-modal 3D scene understanding via flexible, scene-level modality alignment. Unlike traditional methods that require aligned modality data for every object instance, CrossOver learns a unified, modality-agnostic embedding space for scenes by aligning modalities - RGB images, point clouds, CAD models, floorplans, and text descriptions - with relaxed constraints and without explicit object semantics. Leveraging dimensionality-specific encoders, a multi-stage training pipeline, and emergent cross-modal behaviors, CrossOver supports robust scene retrieval and object localization, even with missing modalities. Evaluations on ScanNet and 3RScan datasets show its superior performance across diverse metrics, highlighting adaptability for real-world applications in 3D scene understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.15007", "title": "LLM-Microscope: Uncovering the Hidden Role of Punctuation in Context Memory of Transformers", "authors": [ "Anton Razzhigaev", "Matvey Mikhalchuk", "Temurbek Rahmatullaev", "Elizaveta Goncharova", "Polina Druzhinina", "Ivan Oseledets", "Andrey Kuznetsov" ], "github": "", "project_page": "", "abstract": "We introduce methods to quantify how Large Language Models (LLMs) encode and store contextual information, revealing that tokens often seen as minor (e.g., determiners, punctuation) carry surprisingly high context. Notably, removing these tokens -- especially stopwords, articles, and commas -- consistently degrades performance on MMLU and BABILong-4k, even if removing only irrelevant tokens. Our analysis also shows a strong correlation between contextualization and linearity, where linearity measures how closely the transformation from one layer's embeddings to the next can be approximated by a single linear mapping. These findings underscore the hidden importance of filler tokens in maintaining context. For further exploration, we present LLM-Microscope, an open-source toolkit that assesses token-level nonlinearity, evaluates contextual memory, visualizes intermediate layer contributions (via an adapted Logit Lens), and measures the intrinsic dimensionality of representations. This toolkit illuminates how seemingly trivial tokens can be critical for long-range understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.13407", "title": "JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework", "authors": [ "Ziyuan Liu", "Ruifei Zhu", "Long Gao", "Yuanxiu Zhou", "Jingyu Ma", "Yuantao Gu" ], "github": "https://github.com/circleLZY/MTKD-CD", "project_page": "", "abstract": "Deep learning has achieved significant success in the field of remote sensing image change detection (CD), yet two major challenges remain: the scarcity of sub-meter, all-inclusive open-source CD datasets, and the difficulty of achieving consistent and satisfactory detection results across images with varying change areas. To address these issues, we introduce the JL1-CD dataset, which contains 5,000 pairs of 512 x 512 pixel images with a resolution of 0.5 to 0.75 meters. Additionally, we propose a multi-teacher knowledge distillation (MTKD) framework for CD. Experimental results on the JL1-CD and SYSU-CD datasets demonstrate that the MTKD framework significantly improves the performance of CD models with various network architectures and parameter sizes, achieving new state-of-the-art results. The code is available at https://github.com/circleLZY/MTKD-CD.", "github_manual": "", "github_regex": "https://github.com/circleLZY/MTKD-CD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.14949", "title": "KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding", "authors": [ "Ahmed Heakl", "Abdullah Sohail", "Mukul Ranjan", "Rania Hossam", "Ghazi Ahmed", "Mohamed El-Geish", "Omar Maher", "Zhiqiang Shen", "Fahad Khan", "Salman Khan" ], "github": "", "project_page": "", "abstract": "With the growing adoption of Retrieval-Augmented Generation (RAG) in document processing, robust text recognition has become increasingly critical for knowledge extraction. While OCR (Optical Character Recognition) for English and other languages benefits from large datasets and well-established benchmarks, Arabic OCR faces unique challenges due to its cursive script, right-to-left text flow, and complex typographic and calligraphic features. We present KITAB-Bench, a comprehensive Arabic OCR benchmark that fills the gaps in current evaluation systems. Our benchmark comprises 8,809 samples across 9 major domains and 36 sub-domains, encompassing diverse document types including handwritten text, structured tables, and specialized coverage of 21 chart types for business intelligence. Our findings show that modern vision-language models (such as GPT-4, Gemini, and Qwen) outperform traditional OCR approaches (like EasyOCR, PaddleOCR, and Surya) by an average of 60% in Character Error Rate (CER). Furthermore, we highlight significant limitations of current Arabic OCR models, particularly in PDF-to-Markdown conversion, where the best model Gemini-2.0-Flash achieves only 65% accuracy. This underscores the challenges in accurately recognizing Arabic text, including issues with complex fonts, numeral recognition errors, word elongation, and table structure detection. This work establishes a rigorous evaluation framework that can drive improvements in Arabic document analysis methods and bridge the performance gap with English OCR technologies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.14637", "title": "ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation", "authors": [ "Angxiao Yue", "Zichong Wang", "Hongteng Xu" ], "github": "https://github.com/AngxiaoYue/ReQFlow", "project_page": "", "abstract": "Protein backbone generation plays a central role in de novo protein design and is significant for many biological and medical applications. Although diffusion and flow-based generative models provide potential solutions to this challenging task, they often generate proteins with undesired designability and suffer computational inefficiency. In this study, we propose a novel rectified quaternion flow (ReQFlow) matching method for fast and high-quality protein backbone generation. In particular, our method generates a local translation and a 3D rotation from random noise for each residue in a protein chain, which represents each 3D rotation as a unit quaternion and constructs its flow by spherical linear interpolation (SLERP) in an exponential format. We train the model by quaternion flow (QFlow) matching with guaranteed numerical stability and rectify the QFlow model to accelerate its inference and improve the designability of generated protein backbones, leading to the proposed ReQFlow model. Experiments show that ReQFlow achieves state-of-the-art performance in protein backbone generation while requiring much fewer sampling steps and significantly less inference time (e.g., being 37x faster than RFDiffusion and 62x faster than Genie2 when generating a backbone of length 300), demonstrating its effectiveness and efficiency. The code is available at https://github.com/AngxiaoYue/ReQFlow.", "github_manual": "", "github_regex": "https://github.com/AngxiaoYue/ReQFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.13189", "title": "MoBA: Mixture of Block Attention for Long-Context LLMs", "authors": [ "Enzhe Lu", "Zhejun Jiang", "Jingyuan Liu", "Yulun Du", "Tao Jiang", "Chao Hong", "Shaowei Liu", "Weiran He", "Enming Yuan", "Yuzhi Wang", "Zhiqi Huang", "Huan Yuan", "Suting Xu", "Xinran Xu", "Guokun Lai", "Yanru Chen", "Huabin Zheng", "Junjie Yan", "Jianlin Su", "Yuxin Wu", "Neo Y. Zhang", "Zhilin Yang", "Xinyu Zhou", "Mingxing Zhang", "Jiezhong Qiu" ], "github": "https://github.com/MoonshotAI/MoBA", "project_page": "", "abstract": "Scaling the effective context length is essential for advancing large language models (LLMs) toward artificial general intelligence (AGI). However, the quadratic increase in computational complexity inherent in traditional attention mechanisms presents a prohibitive overhead. Existing approaches either impose strongly biased structures, such as sink or window attention which are task-specific, or radically modify the attention mechanism into linear approximations, whose performance in complex reasoning tasks remains inadequately explored. In this work, we propose a solution that adheres to the ``less structure'' principle, allowing the model to determine where to attend autonomously, rather than introducing predefined biases. We introduce Mixture of Block Attention (MoBA), an innovative approach that applies the principles of Mixture of Experts (MoE) to the attention mechanism. This novel architecture demonstrates superior performance on long-context tasks while offering a key advantage: the ability to seamlessly transition between full and sparse attention, enhancing efficiency without the risk of compromising performance. MoBA has already been deployed to support Kimi's long-context requests and demonstrates significant advancements in efficient attention computation for LLMs. Our code is available at https://github.com/MoonshotAI/MoBA.", "github_manual": "", "github_regex": "https://github.com/MoonshotAI/MoBA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.14975", "title": "Beyond No: Quantifying AI Over-Refusal and Emotional Attachment Boundaries", "authors": [ "David Noever", "Grant Rosario" ], "github": "", "project_page": "", "abstract": "We present an open-source benchmark and evaluation framework for assessing emotional boundary handling in Large Language Models (LLMs). Using a dataset of 1156 prompts across six languages, we evaluated three leading LLMs (GPT-4o, Claude-3.5 Sonnet, and Mistral-large) on their ability to maintain appropriate emotional boundaries through pattern-matched response analysis. Our framework quantifies responses across seven key patterns: direct refusal, apology, explanation, deflection, acknowledgment, boundary setting, and emotional awareness. Results demonstrate significant variation in boundary-handling approaches, with Claude-3.5 achieving the highest overall score (8.69/10) and producing longer, more nuanced responses (86.51 words on average). We identified a substantial performance gap between English (average score 25.62) and non-English interactions (< 0.22), with English responses showing markedly higher refusal rates (43.20% vs. < 1% for non-English). Pattern analysis revealed model-specific strategies, such as Mistral's preference for deflection (4.2%) and consistently low empathy scores across all models (< 0.06). Limitations include potential oversimplification through pattern matching, lack of contextual understanding in response analysis, and binary classification of complex emotional responses. Future work should explore more nuanced scoring methods, expand language coverage, and investigate cultural variations in emotional boundary expectations. Our benchmark and methodology provide a foundation for systematic evaluation of LLM emotional intelligence and boundary-setting capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.15422", "title": "Evaluating Multimodal Generative AI with Korean Educational Standards", "authors": [ "Sanghee Park", "Geewook Kim" ], "github": "https://github.com/naver-ai/KoNET", "project_page": "", "abstract": "This paper presents the Korean National Educational Test Benchmark (KoNET), a new benchmark designed to evaluate Multimodal Generative AI Systems using Korean national educational tests. KoNET comprises four exams: the Korean Elementary General Educational Development Test (KoEGED), Middle (KoMGED), High (KoHGED), and College Scholastic Ability Test (KoCSAT). These exams are renowned for their rigorous standards and diverse questions, facilitating a comprehensive analysis of AI performance across different educational levels. By focusing on Korean, KoNET provides insights into model performance in less-explored languages. We assess a range of models - open-source, open-access, and closed APIs - by examining difficulties, subject diversity, and human error rates. The code and dataset builder will be made fully open-sourced at https://github.com/naver-ai/KoNET.", "github_manual": "", "github_regex": "https://github.com/naver-ai/KoNET", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.15086", "title": "Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models", "authors": [ "Yeonjun In", "Wonjoong Kim", "Kanghoon Yoon", "Sungchul Kim", "Mehrab Tanjim", "Kibum Kim", "Chanyoung Park" ], "github": "https://github.com/yeonjun-in/U-SafeBench", "project_page": "", "abstract": "As the use of large language model (LLM) agents continues to grow, their safety vulnerabilities have become increasingly evident. Extensive benchmarks evaluate various aspects of LLM safety by defining the safety relying heavily on general standards, overlooking user-specific standards. However, safety standards for LLM may vary based on a user-specific profiles rather than being universally consistent across all users. This raises a critical research question: Do LLM agents act safely when considering user-specific safety standards? Despite its importance for safe LLM use, no benchmark datasets currently exist to evaluate the user-specific safety of LLMs. To address this gap, we introduce U-SAFEBENCH, the first benchmark designed to assess user-specific aspect of LLM safety. Our evaluation of 18 widely used LLMs reveals current LLMs fail to act safely when considering user-specific safety standards, marking a new discovery in this field. To address this vulnerability, we propose a simple remedy based on chain-of-thought, demonstrating its effectiveness in improving user-specific safety. Our benchmark and code are available at https://github.com/yeonjun-in/U-SafeBench.", "github_manual": "", "github_regex": "https://github.com/yeonjun-in/U-SafeBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2403.12959", "title": "WHAC: World-grounded Humans and Cameras", "authors": [ "Wanqi Yin", "Zhongang Cai", "Ruisi Wang", "Fanzhou Wang", "Chen Wei", "Haiyi Mei", "Weiye Xiao", "Zhitao Yang", "Qingping Sun", "Atsushi Yamashita", "Ziwei Liu", "Lei Yang" ], "github": "", "project_page": "", "abstract": "Estimating human and camera trajectories with accurate scale in the world coordinate system from a monocular video is a highly desirable yet challenging and ill-posed problem. In this study, we aim to recover expressive parametric human models (i.e., SMPL-X) and corresponding camera poses jointly, by leveraging the synergy between three critical players: the world, the human, and the camera. Our approach is founded on two key observations. Firstly, camera-frame SMPL-X estimation methods readily recover absolute human depth. Secondly, human motions inherently provide absolute spatial cues. By integrating these insights, we introduce a novel framework, referred to as WHAC, to facilitate world-grounded expressive human pose and shape estimation (EHPS) alongside camera pose estimation, without relying on traditional optimization techniques. Additionally, we present a new synthetic dataset, WHAC-A-Mole, which includes accurately annotated humans and cameras, and features diverse interactive human motions as well as realistic camera trajectories. Extensive experiments on both standard and newly established benchmarks highlight the superiority and efficacy of our framework. We will make the code and dataset publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.14892", "title": "EgoSpeak: Learning When to Speak for Egocentric Conversational Agents in the Wild", "authors": [ "Junhyeok Kim", "Min Soo Kim", "Jiwan Chung", "Jungbin Cho", "Jisoo Kim", "Sungwoong Kim", "Gyeongbo Sim", "Youngjae Yu" ], "github": "", "project_page": "", "abstract": "Predicting when to initiate speech in real-world environments remains a fundamental challenge for conversational agents. We introduce EgoSpeak, a novel framework for real-time speech initiation prediction in egocentric streaming video. By modeling the conversation from the speaker's first-person viewpoint, EgoSpeak is tailored for human-like interactions in which a conversational agent must continuously observe its environment and dynamically decide when to talk. Our approach bridges the gap between simplified experimental setups and complex natural conversations by integrating four key capabilities: (1) first-person perspective, (2) RGB processing, (3) online processing, and (4) untrimmed video processing. We also present YT-Conversation, a diverse collection of in-the-wild conversational videos from YouTube, as a resource for large-scale pretraining. Experiments on EasyCom and Ego4D demonstrate that EgoSpeak outperforms random and silence-based baselines in real time. Our results also highlight the importance of multimodal input and context length in effectively deciding when to speak.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.15168", "title": "mStyleDistance: Multilingual Style Embeddings and their Evaluation", "authors": [ "Justin Qiu", "Jiacheng Zhu", "Ajay Patel", "Marianna Apidianaki", "Chris Callison-Burch" ], "github": "", "project_page": "", "abstract": "Style embeddings are useful for stylistic analysis and style transfer; however, only English style embeddings have been made available. We introduce Multilingual StyleDistance (mStyleDistance), a multilingual style embedding model trained using synthetic data and contrastive learning. We train the model on data from nine languages and create a multilingual STEL-or-Content benchmark (Wegmann et al., 2022) that serves to assess the embeddings' quality. We also employ our embeddings in an authorship verification task involving different languages. Our results show that mStyleDistance embeddings outperform existing models on these multilingual style benchmarks and generalize well to unseen features and languages. We make our model publicly available at https://huggingface.co/StyleDistance/mstyledistance .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.13995", "title": "FantasyID: Face Knowledge Enhanced ID-Preserving Video Generation", "authors": [ "Yunpeng Zhang", "Qiang Wang", "Fan Jiang", "Yaqi Fan", "Mu Xu", "Yonggang Qi" ], "github": "", "project_page": "", "abstract": "Tuning-free approaches adapting large-scale pre-trained video diffusion models for identity-preserving text-to-video generation (IPT2V) have gained popularity recently due to their efficacy and scalability. However, significant challenges remain to achieve satisfied facial dynamics while keeping the identity unchanged. In this work, we present a novel tuning-free IPT2V framework by enhancing face knowledge of the pre-trained video model built on diffusion transformers (DiT), dubbed FantasyID. Essentially, 3D facial geometry prior is incorporated to ensure plausible facial structures during video synthesis. To prevent the model from learning copy-paste shortcuts that simply replicate reference face across frames, a multi-view face augmentation strategy is devised to capture diverse 2D facial appearance features, hence increasing the dynamics over the facial expressions and head poses. Additionally, after blending the 2D and 3D features as guidance, instead of naively employing cross-attention to inject guidance cues into DiT layers, a learnable layer-aware adaptive mechanism is employed to selectively inject the fused features into each individual DiT layers, facilitating balanced modeling of identity preservation and motion dynamics. Experimental results validate our model's superiority over the current tuning-free IPT2V methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.14302", "title": "MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models", "authors": [ "Shrey Pandit", "Jiawei Xu", "Junyuan Hong", "Zhangyang Wang", "Tianlong Chen", "Kaidi Xu", "Ying Ding" ], "github": "", "project_page": "", "abstract": "Advancements in Large Language Models (LLMs) and their increasing use in medical question-answering necessitate rigorous evaluation of their reliability. A critical challenge lies in hallucination, where models generate plausible yet factually incorrect outputs. In the medical domain, this poses serious risks to patient safety and clinical decision-making. To address this, we introduce MedHallu, the first benchmark specifically designed for medical hallucination detection. MedHallu comprises 10,000 high-quality question-answer pairs derived from PubMedQA, with hallucinated answers systematically generated through a controlled pipeline. Our experiments show that state-of-the-art LLMs, including GPT-4o, Llama-3.1, and the medically fine-tuned UltraMedical, struggle with this binary hallucination detection task, with the best model achieving an F1 score as low as 0.625 for detecting \"hard\" category hallucinations. Using bidirectional entailment clustering, we show that harder-to-detect hallucinations are semantically closer to ground truth. Through experiments, we also show incorporating domain-specific knowledge and introducing a \"not sure\" category as one of the answer categories improves the precision and F1 scores by up to 38% relative to baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.14122", "title": "Benchmarking LLMs for Political Science: A United Nations Perspective", "authors": [ "Yueqing Liang", "Liangwei Yang", "Chen Wang", "Congying Xia", "Rui Meng", "Xiongxiao Xu", "Haoran Wang", "Ali Payani", "Kai Shu" ], "github": "https://github.com/yueqingliang1/UNBench", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved significant advances in natural language processing, yet their potential for high-stake political decision-making remains largely unexplored. This paper addresses the gap by focusing on the application of LLMs to the United Nations (UN) decision-making process, where the stakes are particularly high and political decisions can have far-reaching consequences. We introduce a novel dataset comprising publicly available UN Security Council (UNSC) records from 1994 to 2024, including draft resolutions, voting records, and diplomatic speeches. Using this dataset, we propose the United Nations Benchmark (UNBench), the first comprehensive benchmark designed to evaluate LLMs across four interconnected political science tasks: co-penholder judgment, representative voting simulation, draft adoption prediction, and representative statement generation. These tasks span the three stages of the UN decision-making process--drafting, voting, and discussing--and aim to assess LLMs' ability to understand and simulate political dynamics. Our experimental analysis demonstrates the potential and challenges of applying LLMs in this domain, providing insights into their strengths and limitations in political science. This work contributes to the growing intersection of AI and political science, opening new avenues for research and practical applications in global governance. The UNBench Repository can be accessed at: https://github.com/yueqingliang1/UNBench.", "github_manual": "", "github_regex": "https://github.com/yueqingliang1/UNBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.14767", "title": "Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for Scientific Comparative Analysis", "authors": [ "Priyanka Kargupta", "Ishika Agarwal", "Tal August", "Jiawei Han" ], "github": "https://github.com/pkargupta/tree-of-debate", "project_page": "", "abstract": "With the exponential growth of research facilitated by modern technology and improved accessibility, scientific discoveries have become increasingly fragmented within and across fields. This makes it challenging to assess the significance, novelty, incremental findings, and equivalent ideas between related works, particularly those from different research communities. Large language models (LLMs) have recently demonstrated strong quantitative and qualitative reasoning abilities, and multi-agent LLM debates have shown promise in handling complex reasoning tasks by exploring diverse perspectives and reasoning paths. Inspired by this, we introduce Tree-of-Debate (ToD), a framework which converts scientific papers into LLM personas that debate their respective novelties. To emphasize structured, critical reasoning rather than focusing solely on outcomes, ToD dynamically constructs a debate tree, enabling fine-grained analysis of independent novelty arguments within scholarly articles. Through experiments on scientific literature across various domains, evaluated by expert researchers, we demonstrate that ToD generates informative arguments, effectively contrasts papers, and supports researchers in their literature review.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/pkargupta/tree-of-debate", "github_api_added_by": "auto" }, { "date": "2025-02-24", "arxiv_id": "2502.15069", "title": "Rare Disease Differential Diagnosis with Large Language Models at Scale: From Abdominal Actinomycosis to Wilson's Disease", "authors": [ "Elliot Schumacher", "Dhruv Naik", "Anitha Kannan" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated impressive capabilities in disease diagnosis. However, their effectiveness in identifying rarer diseases, which are inherently more challenging to diagnose, remains an open question. Rare disease performance is critical with the increasing use of LLMs in healthcare settings. This is especially true if a primary care physician needs to make a rarer prognosis from only a patient conversation so that they can take the appropriate next step. To that end, several clinical decision support systems are designed to support providers in rare disease identification. Yet their utility is limited due to their lack of knowledge of common disorders and difficulty of use. In this paper, we propose RareScale to combine the knowledge LLMs with expert systems. We use jointly use an expert system and LLM to simulate rare disease chats. This data is used to train a rare disease candidate predictor model. Candidates from this smaller model are then used as additional inputs to black-box LLM to make the final differential diagnosis. Thus, RareScale allows for a balance between rare and common diagnoses. We present results on over 575 rare diseases, beginning with Abdominal Actinomycosis and ending with Wilson's Disease. Our approach significantly improves the baseline performance of black-box LLMs by over 17% in Top-5 accuracy. We also find that our candidate generation performance is high (e.g. 88.8% on gpt-4o generated chats).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.13991", "title": "Learning to Discover Regulatory Elements for Gene Expression Prediction", "authors": [ "Xingyu Su", "Haiyang Yu", "Degui Zhi", "Shuiwang Ji" ], "github": "https://github.com/divelab/AIRS", "project_page": "", "abstract": "We consider the problem of predicting gene expressions from DNA sequences. A key challenge of this task is to find the regulatory elements that control gene expressions. Here, we introduce Seq2Exp, a Sequence to Expression network explicitly designed to discover and extract regulatory elements that drive target gene expression, enhancing the accuracy of the gene expression prediction. Our approach captures the causal relationship between epigenomic signals, DNA sequences and their associated regulatory elements. Specifically, we propose to decompose the epigenomic signals and the DNA sequence conditioned on the causal active regulatory elements, and apply an information bottleneck with the Beta distribution to combine their effects while filtering out non-causal components. Our experiments demonstrate that Seq2Exp outperforms existing baselines in gene expression prediction tasks and discovers influential regions compared to commonly used statistical methods for peak detection such as MACS3. The source code is released as part of the AIRS library (https://github.com/divelab/AIRS/).", "github_manual": "", "github_regex": "https://github.com/divelab/AIRS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.16457", "title": "Towards Fully-Automated Materials Discovery via Large-Scale Synthesis Dataset and Expert-Level LLM-as-a-Judge", "authors": [ "Heegyu Kim", "Taeyang Jeon", "Seungtaek Choi", "Jihoon Hong", "Dongwon Jeon", "Sungbum Cho", "Ga-Yeon Baek", "Kyung-Won Kwak", "Dong-Hee Lee", "Sun-Jin Choi", "Jisu Bae", "Chihoon Lee", "Yunseo Kim", "Jinsung Park", "Hyunsouk Cho" ], "github": "https://github.com/HeegyuKim/AlchemyBench", "project_page": "", "abstract": "Materials synthesis is vital for innovations such as energy storage, catalysis, electronics, and biomedical devices. Yet, the process relies heavily on empirical, trial-and-error methods guided by expert intuition. Our work aims to support the materials science community by providing a practical, data-driven resource. We have curated a comprehensive dataset of 17K expert-verified synthesis recipes from open-access literature, which forms the basis of our newly developed benchmark, AlchemyBench. AlchemyBench offers an end-to-end framework that supports research in large language models applied to synthesis prediction. It encompasses key tasks, including raw materials and equipment prediction, synthesis procedure generation, and characterization outcome forecasting. We propose an LLM-as-a-Judge framework that leverages large language models for automated evaluation, demonstrating strong statistical agreement with expert assessments. Overall, our contributions offer a supportive foundation for exploring the capabilities of LLMs in predicting and guiding materials synthesis, ultimately paving the way for more efficient experimental design and accelerated innovation in materials science.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HeegyuKim/AlchemyBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-24", "arxiv_id": "2502.13502", "title": "PLDR-LLMs Learn A Generalizable Tensor Operator That Can Replace Its Own Deep Neural Net At Inference", "authors": [ "Burc Gokden" ], "github": "", "project_page": "", "abstract": "We show that Large Language Model from Power Law Decoder Representations (PLDR-LLM) is a foundational model whose deductive outputs are invariant tensors up to a small perturbation. PLDR-LLM learns a singularity condition for the deductive outputs that enable the once-inferred energy-curvature tensor G_{LM} to replace the deep neural network of power law graph attention (PLGA) generating the deductive outputs at inference. We demonstrate that a cache for G_{LM} (G-cache) and KV-cache can be implemented in a straightforward manner to improve the inference time. The invariance and generalizable nature of deductive outputs is at a very high fidelity where deductive outputs have same RMSE and determinant values up to 15 decimal places after caching, and zero-shot benchmark scores remain unchanged. Ablation studies show that learned deductive outputs have distinct loss and accuracy characteristics from models pretrained with transferred, randomly initialized or identity tensors as a constant tensor operator and an LLM with scaled-dot product attention (SDPA) is a special case of PLDR-LLM where G_{LM} is predefined as identity. The observed invariance characteristic introduces a novel asymmetry between training and inference phases with caching. We outline observed common characteristics of the deductive outputs for the learned singularity condition. We provide an implementation of a training and inference framework for PLDR-LLM with KV-cache and G-cache.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.17129", "title": "Thus Spake Long-Context Large Language Model", "authors": [ "Xiaoran Liu", "Ruixiao Li", "Mianqiu Huang", "Zhigeng Liu", "Yuerong Song", "Qipeng Guo", "Siyang He", "Qiqi Wang", "Linlin Li", "Qun Liu", "Yaqian Zhou", "Xuanjing Huang", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Long context is an important topic in Natural Language Processing (NLP), running through the development of NLP architectures, and offers immense opportunities for Large Language Models (LLMs) giving LLMs the lifelong learning potential akin to humans. Unfortunately, the pursuit of a long context is accompanied by numerous obstacles. Nevertheless, long context remains a core competitive advantage for LLMs. In the past two years, the context length of LLMs has achieved a breakthrough extension to millions of tokens. Moreover, the research on long-context LLMs has expanded from length extrapolation to a comprehensive focus on architecture, infrastructure, training, and evaluation technologies. Inspired by the symphonic poem, Thus Spake Zarathustra, we draw an analogy between the journey of extending the context of LLM and the attempts of humans to transcend its mortality. In this survey, We will illustrate how LLM struggles between the tremendous need for a longer context and its equal need to accept the fact that it is ultimately finite. To achieve this, we give a global picture of the lifecycle of long-context LLMs from four perspectives: architecture, infrastructure, training, and evaluation, showcasing the full spectrum of long-context technologies. At the end of this survey, we will present 10 unanswered questions currently faced by long-context LLMs. We hope this survey can serve as a systematic introduction to the research on long-context LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.16614", "title": "CodeCriticBench: A Holistic Code Critique Benchmark for Large Language Models", "authors": [ "Alexander Zhang", "Marcus Dong", "Jiaheng Liu", "Wei Zhang", "Yejie Wang", "Jian Yang", "Ge Zhang", "Tianyu Liu", "Zhongyuan Peng", "Yingshui Tan", "Yuanxing Zhang", "Zhexu Wang", "Weixun Wang", "Yancheng He", "Ken Deng", "Wangchunshu Zhou", "Wenhao Huang", "Zhaoxiang Zhang" ], "github": "https://github.com/multimodal-art-projection/CodeCriticBench", "project_page": "", "abstract": "The critique capacity of Large Language Models (LLMs) is essential for reasoning abilities, which can provide necessary suggestions (e.g., detailed analysis and constructive feedback). Therefore, how to evaluate the critique capacity of LLMs has drawn great attention and several critique benchmarks have been proposed. However, existing critique benchmarks usually have the following limitations: (1). Focusing on diverse reasoning tasks in general domains and insufficient evaluation on code tasks (e.g., only covering code generation task), where the difficulty of queries is relatively easy (e.g., the code queries of CriticBench are from Humaneval and MBPP). (2). Lacking comprehensive evaluation from different dimensions. To address these limitations, we introduce a holistic code critique benchmark for LLMs called CodeCriticBench. Specifically, our CodeCriticBench includes two mainstream code tasks (i.e., code generation and code QA) with different difficulties. Besides, the evaluation protocols include basic critique evaluation and advanced critique evaluation for different characteristics, where fine-grained evaluation checklists are well-designed for advanced settings. Finally, we conduct extensive experimental results of existing LLMs, which show the effectiveness of CodeCriticBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/multimodal-art-projection/CodeCriticBench", "github_api_added_by": "auto" }, { "date": "2025-02-25", "arxiv_id": "2502.17157", "title": "DICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks", "authors": [ "Canyu Zhao", "Mingyu Liu", "Huanyi Zheng", "Muzhi Zhu", "Zhiyue Zhao", "Hao Chen", "Tong He", "Chunhua Shen" ], "github": "https://github.com/aim-uofa/Diception", "project_page": "", "abstract": "Our primary goal here is to create a good, generalist perception model that can tackle multiple tasks, within limits on computational resources and training data. To achieve this, we resort to text-to-image diffusion models pre-trained on billions of images. Our exhaustive evaluation metrics demonstrate that DICEPTION effectively tackles multiple perception tasks, achieving performance on par with state-of-the-art models. We achieve results on par with SAM-vit-h using only 0.06% of their data (e.g., 600K vs. 1B pixel-level annotated images). Inspired by Wang et al., DICEPTION formulates the outputs of various perception tasks using color encoding; and we show that the strategy of assigning random colors to different instances is highly effective in both entity segmentation and semantic segmentation. Unifying various perception tasks as conditional image generation enables us to fully leverage pre-trained text-to-image models. Thus, DICEPTION can be efficiently trained at a cost of orders of magnitude lower, compared to conventional models that were trained from scratch. When adapting our model to other tasks, it only requires fine-tuning on as few as 50 images and 1% of its parameters. DICEPTION provides valuable insights and a more promising solution for visual generalist models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aim-uofa/Diception", "github_api_added_by": "auto" }, { "date": "2025-02-25", "arxiv_id": "2502.17110", "title": "Mobile-Agent-V: Learning Mobile Device Operation Through Video-Guided Multi-Agent Collaboration", "authors": [ "Junyang Wang", "Haiyang Xu", "Xi Zhang", "Ming Yan", "Ji Zhang", "Fei Huang", "Jitao Sang" ], "github": "", "project_page": "", "abstract": "The rapid increase in mobile device usage necessitates improved automation for seamless task management. However, many AI-driven frameworks struggle due to insufficient operational knowledge. Manually written knowledge helps but is labor-intensive and inefficient. To address these challenges, we introduce Mobile-Agent-V, a framework that leverages video guidance to provide rich and cost-effective operational knowledge for mobile automation. Mobile-Agent-V enhances task execution capabilities by leveraging video inputs without requiring specialized sampling or preprocessing. Mobile-Agent-V integrates a sliding window strategy and incorporates a video agent and deep-reflection agent to ensure that actions align with user instructions. Through this innovative approach, users can record task processes with guidance, enabling the system to autonomously learn and execute tasks efficiently. Experimental results show that Mobile-Agent-V achieves a 30% performance improvement compared to existing frameworks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.16894", "title": "Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment", "authors": [ "Chenghao Fan", "Zhenyi Lu", "Sichen Liu", "Xiaoye Qu", "Wei Wei", "Chengfeng Gu", "Yu Cheng" ], "github": "https://github.com/Facico/GOAT-PEFT", "project_page": "", "abstract": "While Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning for Large Language Models (LLMs), its performance often falls short of Full Fine-Tuning (Full FT). Current methods optimize LoRA by initializing with static singular value decomposition (SVD) subsets, leading to suboptimal leveraging of pre-trained knowledge. Another path for improving LoRA is incorporating a Mixture-of-Experts (MoE) architecture. However, weight misalignment and complex gradient dynamics make it challenging to adopt SVD prior to the LoRA MoE architecture. To mitigate these issues, we propose Great LoRA Mixture-of-Expert (GOAT), a framework that (1) adaptively integrates relevant priors using an SVD-structured MoE, and (2) aligns optimization with full fine-tuned MoE by deriving a theoretical scaling factor. We demonstrate that proper scaling, without modifying the architecture or training algorithms, boosts LoRA MoE's efficiency and performance. Experiments across 25 datasets, including natural language understanding, commonsense reasoning, image classification, and natural language generation, demonstrate GOAT's state-of-the-art performance, closing the gap with Full FT.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Facico/GOAT-PEFT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.16033", "title": "Multimodal Inconsistency Reasoning (MMIR): A New Benchmark for Multimodal Reasoning Models", "authors": [ "Qianqi Yan", "Yue Fan", "Hongquan Li", "Shan Jiang", "Yang Zhao", "Xinze Guan", "Ching-Chen Kuo", "Xin Eric Wang" ], "github": "https://github.com/eric-ai-lab/MMIR", "project_page": "", "abstract": "Existing Multimodal Large Language Models (MLLMs) are predominantly trained and tested on consistent visual-textual inputs, leaving open the question of whether they can handle inconsistencies in real-world, layout-rich content. To bridge this gap, we propose the Multimodal Inconsistency Reasoning (MMIR) benchmark to assess MLLMs' ability to detect and reason about semantic mismatches in artifacts such as webpages, presentation slides, and posters. MMIR comprises 534 challenging samples, each containing synthetically injected errors across five reasoning-heavy categories: Factual Contradiction, Identity Misattribution, Contextual Mismatch, Quantitative Discrepancy, and Temporal/Spatial Incoherence. We evaluate six state-of-the-art MLLMs, showing that models with dedicated multimodal reasoning capabilities, such as o1, substantially outperform their counterparts while open-source models remain particularly vulnerable to inconsistency errors. Detailed error analyses further show that models excel in detecting inconsistencies confined to a single modality, particularly in text, but struggle with cross-modal conflicts and complex layouts. Probing experiments reveal that single-modality prompting, including Chain-of-Thought (CoT) and Set-of-Mark (SoM) methods, yields marginal gains, revealing a key bottleneck in cross-modal reasoning. Our findings highlight the need for advanced multimodal reasoning and point to future research on multimodal inconsistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/eric-ai-lab/MMIR", "github_api_added_by": "auto" }, { "date": "2025-02-25", "arxiv_id": "2502.16922", "title": "Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties", "authors": [ "Zhenglin Wang", "Jialong Wu", "Pengfei LI", "Yong Jiang", "Deyu Zhou" ], "github": "https://github.com/Linking-ai/ctm_bench", "project_page": "", "abstract": "Temporal reasoning is fundamental to human cognition and is crucial for various real-world applications. While recent advances in Large Language Models have demonstrated promising capabilities in temporal reasoning, existing benchmarks primarily rely on rule-based construction, lack contextual depth, and involve a limited range of temporal entities. To address these limitations, we introduce Chinese Time Reasoning (CTM), a benchmark designed to evaluate LLMs on temporal reasoning within the extensive scope of Chinese dynastic chronology. CTM emphasizes cross-entity relationships, pairwise temporal alignment, and contextualized and culturally-grounded reasoning, providing a comprehensive evaluation. Extensive experimental results reveal the challenges posed by CTM and highlight potential avenues for improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Linking-ai/ctm_bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.16701", "title": "Beyond Release: Access Considerations for Generative AI Systems", "authors": [ "Irene Solaiman", "Rishi Bommasani", "Dan Hendrycks", "Ariel Herbert-Voss", "Yacine Jernite", "Aviya Skowron", "Andrew Trask" ], "github": "", "project_page": "", "abstract": "Generative AI release decisions determine whether system components are made available, but release does not address many other elements that change how users and stakeholders are able to engage with a system. Beyond release, access to system components informs potential risks and benefits. Access refers to practical needs, infrastructurally, technically, and societally, in order to use available components in some way. We deconstruct access along three axes: resourcing, technical usability, and utility. Within each category, a set of variables per system component clarify tradeoffs. For example, resourcing requires access to computing infrastructure to serve model weights. We also compare the accessibility of four high performance language models, two open-weight and two closed-weight, showing similar considerations for all based instead on access variables. Access variables set the foundation for being able to scale or increase access to users; we examine the scale of access and how scale affects ability to manage and intervene on risks. This framework better encompasses the landscape and risk-benefit tradeoffs of system releases to inform system release decisions, research, and policy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.16584", "title": "Audio-FLAN: A Preliminary Release", "authors": [ "Liumeng Xue", "Ziya Zhou", "Jiahao Pan", "Zixuan Li", "Shuai Fan", "Yinghao Ma", "Sitong Cheng", "Dongchao Yang", "Haohan Guo", "Yujia Xiao", "Xinsheng Wang", "Zixuan Shen", "Chuanbo Zhu", "Xinshen Zhang", "Tianchi Liu", "Ruibin Yuan", "Zeyue Tian", "Haohe Liu", "Emmanouil Benetos", "Ge Zhang", "Yike Guo", "Wei Xue" ], "github": "https://github.com/lmxue/audio-flan", "project_page": "", "abstract": "Recent advancements in audio tokenization have significantly enhanced the integration of audio capabilities into large language models (LLMs). However, audio understanding and generation are often treated as distinct tasks, hindering the development of truly unified audio-language models. While instruction tuning has demonstrated remarkable success in improving generalization and zero-shot learning across text and vision, its application to audio remains largely unexplored. A major obstacle is the lack of comprehensive datasets that unify audio understanding and generation. To address this, we introduce Audio-FLAN, a large-scale instruction-tuning dataset covering 80 diverse tasks across speech, music, and sound domains, with over 100 million instances. Audio-FLAN lays the foundation for unified audio-language models that can seamlessly handle both understanding (e.g., transcription, comprehension) and generation (e.g., speech, music, sound) tasks across a wide range of audio domains in a zero-shot manner. The Audio-FLAN dataset is available on HuggingFace and GitHub and will be continuously updated.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lmxue/audio-flan", "github_api_added_by": "auto" }, { "date": "2025-02-25", "arxiv_id": "2502.17407", "title": "Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning", "authors": [ "Guijin Son", "Jiwoo Hong", "Hyunwoo Ko", "James Thorne" ], "github": "https://github.com/gauss5930/mclm", "project_page": "", "abstract": "Scaling pre-training compute has proven effective for achieving mulitlinguality, but does the same hold for test-time scaling? In this work, we introduce MCLM, a multilingual math benchmark featuring competition-level problems in 55 languages. We test three test-time scaling methods-Outcome Reward Modeling (ORM), Process Reward Modeling (ORM), and Budget Forcing (BF)-on both Qwen2.5-1.5B Math and MR1-1.5B, a multilingual LLM we trained for extended reasoning. Our experiments show that using Qwen2.5-1.5B Math with ORM achieves a score of 35.8 on MCLM, while BF on MR1-1.5B attains 35.2. Although \"thinking LLMs\" have recently garnered significant attention, we find that their performance is comparable to traditional scaling methods like best-of-N once constrained to similar levels of inference FLOPs. Moreover, while BF yields a 20-point improvement on English AIME, it provides only a 1.94-point average gain across other languages-a pattern consistent across the other test-time scaling methods we studied-higlighting that test-time scaling may not generalize as effectively to multilingual tasks. To foster further research, we release MCLM, MR1-1.5B, and evaluation results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gauss5930/mclm", "github_api_added_by": "auto" }, { "date": "2025-02-25", "arxiv_id": "2502.15814", "title": "Slamming: Training a Speech Language Model on One GPU in a Day", "authors": [ "Gallil Maimon", "Avishai Elmakies", "Yossi Adi" ], "github": "", "project_page": "", "abstract": "We introduce Slam, a recipe for training high-quality Speech Language Models (SLMs) on a single academic GPU in 24 hours. We do so through empirical analysis of model initialisation and architecture, synthetic training data, preference optimisation with synthetic data and tweaking all other components. We empirically demonstrate that this training recipe also scales well with more compute getting results on par with leading SLMs in a fraction of the compute cost. We hope these insights will make SLM training and research more accessible. In the context of SLM scaling laws, our results far outperform predicted compute optimal performance, giving an optimistic view to SLM feasibility. See code, data, models, samples at - https://pages.cs.huji.ac.il/adiyoss-lab/slamming .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.15987", "title": "Forecasting Open-Weight AI Model Growth on Hugging Face", "authors": [ "Kushal Raj Bhandari", "Pin-Yu Chen", "Jianxi Gao" ], "github": "", "project_page": "", "abstract": "As the open-weight AI landscape continues to proliferate-with model development, significant investment, and user interest-it becomes increasingly important to predict which models will ultimately drive innovation and shape AI ecosystems. Building on parallels with citation dynamics in scientific literature, we propose a framework to quantify how an open-weight model's influence evolves. Specifically, we adapt the model introduced by Wang et al. for scientific citations, using three key parameters-immediacy, longevity, and relative fitness-to track the cumulative number of fine-tuned models of an open-weight model. Our findings reveal that this citation-style approach can effectively capture the diverse trajectories of open-weight model adoption, with most models fitting well and outliers indicating unique patterns or abrupt jumps in usage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.17258", "title": "VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing", "authors": [ "Xiangpeng Yang", "Linchao Zhu", "Hehe Fan", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Recent advancements in diffusion models have significantly improved video generation and editing capabilities. However, multi-grained video editing, which encompasses class-level, instance-level, and part-level modifications, remains a formidable challenge. The major difficulties in multi-grained editing include semantic misalignment of text-to-region control and feature coupling within the diffusion model. To address these difficulties, we present VideoGrain, a zero-shot approach that modulates space-time (cross- and self-) attention mechanisms to achieve fine-grained control over video content. We enhance text-to-region control by amplifying each local prompt's attention to its corresponding spatial-disentangled region while minimizing interactions with irrelevant areas in cross-attention. Additionally, we improve feature separation by increasing intra-region awareness and reducing inter-region interference in self-attention. Extensive experiments demonstrate our method achieves state-of-the-art performance in real-world scenarios. Our code, data, and demos are available at https://knightyxp.github.io/VideoGrain_project_page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.15894", "title": "RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers", "authors": [ "Min Zhao", "Guande He", "Yixiao Chen", "Hongzhou Zhu", "Chongxuan Li", "Jun Zhu" ], "github": "https://github.com/thu-ml/RIFLEx", "project_page": "", "abstract": "Recent advancements in video generation have enabled models to synthesize high-quality, minute-long videos. However, generating even longer videos with temporal coherence remains a major challenge, and existing length extrapolation methods lead to temporal repetition or motion deceleration. In this work, we systematically analyze the role of frequency components in positional embeddings and identify an intrinsic frequency that primarily governs extrapolation behavior. Based on this insight, we propose RIFLEx, a minimal yet effective approach that reduces the intrinsic frequency to suppress repetition while preserving motion consistency, without requiring any additional modifications. RIFLEx offers a true free lunch--achieving high-quality 2times extrapolation on state-of-the-art video diffusion transformers in a completely training-free manner. Moreover, it enhances quality and enables 3times extrapolation by minimal fine-tuning without long videos. Project page and codes: https://riflex-video.github.io/{https://riflex-video.github.io/.}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thu-ml/RIFLEx", "github_api_added_by": "auto" }, { "date": "2025-02-25", "arxiv_id": "2502.15122", "title": "MONSTER: Monash Scalable Time Series Evaluation Repository", "authors": [ "Angus Dempster", "Navid Mohammadi Foumani", "Chang Wei Tan", "Lynn Miller", "Amish Mishra", "Mahsa Salehi", "Charlotte Pelletier", "Daniel F. Schmidt", "Geoffrey I. Webb" ], "github": "https://github.com/Navidfoumani/monster", "project_page": "", "abstract": "We introduce MONSTER-the MONash Scalable Time Series Evaluation Repository-a collection of large datasets for time series classification. The field of time series classification has benefitted from common benchmarks set by the UCR and UEA time series classification repositories. However, the datasets in these benchmarks are small, with median sizes of 217 and 255 examples, respectively. In consequence they favour a narrow subspace of models that are optimised to achieve low classification error on a wide variety of smaller datasets, that is, models that minimise variance, and give little weight to computational issues such as scalability. Our hope is to diversify the field by introducing benchmarks using larger datasets. We believe that there is enormous potential for new progress in the field by engaging with the theoretical and practical challenges of learning effectively from larger quantities of data.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Navidfoumani/monster", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.17414", "title": "X-Dancer: Expressive Music to Human Dance Video Generation", "authors": [ "Zeyuan Chen", "Hongyi Xu", "Guoxian Song", "You Xie", "Chenxu Zhang", "Xin Chen", "Chao Wang", "Di Chang", "Linjie Luo" ], "github": "", "project_page": "", "abstract": "We present X-Dancer, a novel zero-shot music-driven image animation pipeline that creates diverse and long-range lifelike human dance videos from a single static image. As its core, we introduce a unified transformer-diffusion framework, featuring an autoregressive transformer model that synthesize extended and music-synchronized token sequences for 2D body, head and hands poses, which then guide a diffusion model to produce coherent and realistic dance video frames. Unlike traditional methods that primarily generate human motion in 3D, X-Dancer addresses data limitations and enhances scalability by modeling a wide spectrum of 2D dance motions, capturing their nuanced alignment with musical beats through readily available monocular videos. To achieve this, we first build a spatially compositional token representation from 2D human pose labels associated with keypoint confidences, encoding both large articulated body movements (e.g., upper and lower body) and fine-grained motions (e.g., head and hands). We then design a music-to-motion transformer model that autoregressively generates music-aligned dance pose token sequences, incorporating global attention to both musical style and prior motion context. Finally we leverage a diffusion backbone to animate the reference image with these synthesized pose tokens through AdaIN, forming a fully differentiable end-to-end framework. Experimental results demonstrate that X-Dancer is able to produce both diverse and characterized dance videos, substantially outperforming state-of-the-art methods in term of diversity, expressiveness and realism. Code and model will be available for research purposes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.16707", "title": "Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation", "authors": [ "Yunhai Feng", "Jiaming Han", "Zhuoran Yang", "Xiangyu Yue", "Sergey Levine", "Jianlan Luo" ], "github": "https://github.com/yunhaif/reflect-vlm", "project_page": "", "abstract": "Solving complex long-horizon robotic manipulation problems requires sophisticated high-level planning capabilities, the ability to reason about the physical world, and reactively choose appropriate motor skills. Vision-language models (VLMs) pretrained on Internet data could in principle offer a framework for tackling such problems. However, in their current form, VLMs lack both the nuanced understanding of intricate physics required for robotic manipulation and the ability to reason over long horizons to address error compounding issues. In this paper, we introduce a novel test-time computation framework that enhances VLMs' physical reasoning capabilities for multi-stage manipulation tasks. At its core, our approach iteratively improves a pretrained VLM with a \"reflection\" mechanism - it uses a generative model to imagine future world states, leverages these predictions to guide action selection, and critically reflects on potential suboptimalities to refine its reasoning. Experimental results demonstrate that our method significantly outperforms several state-of-the-art commercial VLMs as well as other post-training approaches such as Monte Carlo Tree Search (MCTS). Videos are available at https://reflect-vlm.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yunhaif/reflect-vlm", "github_api_added_by": "auto" }, { "date": "2025-02-25", "arxiv_id": "2502.17435", "title": "GCC: Generative Color Constancy via Diffusing a Color Checker", "authors": [ "Chen-Wei Chang", "Cheng-De Fan", "Chia-Che Chang", "Yi-Chen Lo", "Yu-Chee Tseng", "Jiun-Long Huang", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Color constancy methods often struggle to generalize across different camera sensors due to varying spectral sensitivities. We present GCC, which leverages diffusion models to inpaint color checkers into images for illumination estimation. Our key innovations include (1) a single-step deterministic inference approach that inpaints color checkers reflecting scene illumination, (2) a Laplacian decomposition technique that preserves checker structure while allowing illumination-dependent color adaptation, and (3) a mask-based data augmentation strategy for handling imprecise color checker annotations. GCC demonstrates superior robustness in cross-camera scenarios, achieving state-of-the-art worst-25% error rates of 5.15{\\deg} and 4.32{\\deg} in bi-directional evaluations. These results highlight our method's stability and generalization capability across different camera characteristics without requiring sensor-specific training, making it a versatile solution for real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.15167", "title": "M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image Quality Assessment", "authors": [ "Chuan Cui", "Kejiang Chen", "Zhihua Wei", "Wen Shen", "Weiming Zhang", "Nenghai Yu" ], "github": "https://github.com/strawhatboy/M3-AGIQA", "project_page": "", "abstract": "The rapid advancement of AI-generated image (AGI) models has introduced significant challenges in evaluating their quality, which requires considering multiple dimensions such as perceptual quality, prompt correspondence, and authenticity. To address these challenges, we propose M3-AGIQA, a comprehensive framework for AGI quality assessment that is Multimodal, Multi-Round, and Multi-Aspect. Our approach leverages the capabilities of Multimodal Large Language Models (MLLMs) as joint text and image encoders and distills advanced captioning capabilities from online MLLMs into a local model via Low-Rank Adaptation (LoRA) fine-tuning. The framework includes a structured multi-round evaluation mechanism, where intermediate image descriptions are generated to provide deeper insights into the quality, correspondence, and authenticity aspects. To align predictions with human perceptual judgments, a predictor constructed by an xLSTM and a regression head is incorporated to process sequential logits and predict Mean Opinion Scores (MOSs). Extensive experiments conducted on multiple benchmark datasets demonstrate that M3-AGIQA achieves state-of-the-art performance, effectively capturing nuanced aspects of AGI quality. Furthermore, cross-dataset validation confirms its strong generalizability. The code is available at https://github.com/strawhatboy/M3-AGIQA.", "github_manual": "", "github_regex": "https://github.com/strawhatboy/M3-AGIQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.14132", "title": "Can Community Notes Replace Professional Fact-Checkers?", "authors": [ "Nadav Borenstein", "Greta Warren", "Desmond Elliott", "Isabelle Augenstein" ], "github": "", "project_page": "", "abstract": "Two commonly-employed strategies to combat the rise of misinformation on social media are (i) fact-checking by professional organisations and (ii) community moderation by platform users. Policy changes by Twitter/X and, more recently, Meta, signal a shift away from partnerships with fact-checking organisations and towards an increased reliance on crowdsourced community notes. However, the extent and nature of dependencies between fact-checking and helpful community notes remain unclear. To address these questions, we use language models to annotate a large corpus of Twitter/X community notes with attributes such as topic, cited sources, and whether they refute claims tied to broader misinformation narratives. Our analysis reveals that community notes cite fact-checking sources up to five times more than previously reported. Fact-checking is especially crucial for notes on posts linked to broader narratives, which are twice as likely to reference fact-checking sources compared to other sources. In conclusion, our results show that successful community moderation heavily relies on professional fact-checking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.13074", "title": "The snake in the Brownian sphere", "authors": [ "Omer Angel", "Emmanuel Jacob", "Brett Kolesnik", "Gr\u00e9gory Miermont" ], "github": "", "project_page": "", "abstract": "The Brownian sphere is a random metric space, homeomorphic to the two-dimensional sphere, which arises as the universal scaling limit of many types of random planar maps. The direct construction of the Brownian sphere is via a continuous analogue of the Cori--Vauquelin--Schaeffer (CVS) bijection. The CVS bijection maps labeled trees to planar maps, and the continuous version maps Aldous' continuum random tree with Brownian labels (the Brownian snake) to the Brownian sphere. In this work, we describe the inverse of the continuous CVS bijection, by constructing the Brownian snake as a measurable function of the Brownian sphere. Special care is needed to work with the orientation of the Brownian sphere.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.17055", "title": "Stable-SPAM: How to Train in 4-Bit More Stably than 16-Bit Adam", "authors": [ "Tianjin Huang", "Haotian Hu", "Zhenyu Zhang", "Gaojie Jin", "Xiang Li", "Li Shen", "Tianlong Chen", "Lu Liu", "Qingsong Wen", "Zhangyang Wang", "Shiwei Liu" ], "github": "https://github.com/TianjinYellow/StableSPAM.git", "project_page": "", "abstract": "This paper comprehensively evaluates several recently proposed optimizers for 4-bit training, revealing that low-bit precision amplifies sensitivity to learning rates and often causes unstable gradient norms, leading to divergence at higher learning rates. Among these, SPAM, a recent optimizer featuring momentum reset and spike-aware gradient clipping, achieves the best performance across various bit levels, but struggles to stabilize gradient norms, requiring careful learning rate tuning. To address these limitations, we propose Stable-SPAM, which incorporates enhanced gradient normalization and clipping techniques. In particular, Stable-SPAM (1) adaptively updates the clipping threshold for spiked gradients by tracking their historical maxima; (2) normalizes the entire gradient matrix based on its historical l_2-norm statistics; and (3) inherits momentum reset from SPAM to periodically reset the first and second moments of Adam, mitigating the accumulation of spiked gradients. Extensive experiments show that Stable-SPAM effectively stabilizes gradient norms in 4-bit LLM training, delivering superior performance compared to Adam and SPAM. Notably, our 4-bit LLaMA-1B model trained with Stable-SPAM outperforms the BF16 LLaMA-1B trained with Adam by up to 2 perplexity. Furthermore, when both models are trained in 4-bit, Stable-SPAM achieves the same loss as Adam while requiring only about half the training steps. Code is available at https://github.com/TianjinYellow/StableSPAM.git.", "github_manual": "", "github_regex": "https://github.com/TianjinYellow/StableSPAM.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.15425", "title": "TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning", "authors": [ "Giuseppe Paolo", "Abdelhakim Benechehab", "Hamza Cherkaoui", "Albert Thomas", "Bal\u00e1zs K\u00e9gl" ], "github": "https://github.com/GPaolo/TAG_Framework", "project_page": "", "abstract": "Hierarchical organization is fundamental to biological systems and human societies, yet artificial intelligence systems often rely on monolithic architectures that limit adaptability and scalability. Current hierarchical reinforcement learning (HRL) approaches typically restrict hierarchies to two levels or require centralized training, which limits their practical applicability. We introduce TAME Agent Framework (TAG), a framework for constructing fully decentralized hierarchical multi-agent systems.TAG enables hierarchies of arbitrary depth through a novel LevelEnv concept, which abstracts each hierarchy level as the environment for the agents above it. This approach standardizes information flow between levels while preserving loose coupling, allowing for seamless integration of diverse agent types. We demonstrate the effectiveness of TAG by implementing hierarchical architectures that combine different RL agents across multiple levels, achieving improved performance over classical multi-agent RL baselines on standard benchmarks. Our results show that decentralized hierarchical organization enhances both learning speed and final performance, positioning TAG as a promising direction for scalable multi-agent systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GPaolo/TAG_Framework", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.14429", "title": "Early-Exit and Instant Confidence Translation Quality Estimation", "authors": [ "Vil\u00e9m Zouhar", "Maike Z\u00fcfle", "Beni Egressy", "Julius Cheng", "Jan Niehues" ], "github": "https://github.com/zouharvi/COMET-early-exit", "project_page": "", "abstract": "Quality estimation is omnipresent in machine translation, for both evaluation and generation. Unfortunately, quality estimation models are often opaque and computationally expensive, making them impractical to be part of large-scale pipelines. In this work, we tackle two connected challenges: (1) reducing the cost of quality estimation at scale, and (2) developing an inexpensive uncertainty estimation method for quality estimation. To address the latter, we introduce Instant Confidence COMET, an uncertainty-aware quality estimation model that matches the performance of previous approaches at a fraction of their costs. We extend this to Early-Exit COMET, a quality estimation model that can compute quality scores and associated confidences already at early model layers, allowing us to early-exit computations and reduce evaluation costs. We also apply our model to machine translation reranking. We combine Early-Exit COMET with an upper confidence bound bandit algorithm to find the best candidate from a large pool without having to run the full evaluation model on all candidates. In both cases (evaluation and reranking) our methods reduce the required compute by 50% with very little degradation in performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zouharvi/COMET-early-exit", "github_api_added_by": "auto" }, { "date": "2025-02-25", "arxiv_id": "2502.17237", "title": "MegaLoc: One Retrieval to Place Them All", "authors": [ "Gabriele Berton", "Carlo Masone" ], "github": "https://github.com/gmberton/MegaLoc", "project_page": "", "abstract": "Retrieving images from the same location as a given query is an important component of multiple computer vision tasks, like Visual Place Recognition, Landmark Retrieval, Visual Localization, 3D reconstruction, and SLAM. However, existing solutions are built to specifically work for one of these tasks, and are known to fail when the requirements slightly change or when they meet out-of-distribution data. In this paper we combine a variety of existing methods, training techniques, and datasets to train a retrieval model, called MegaLoc, that is performant on multiple tasks. We find that MegaLoc (1) achieves state of the art on a large number of Visual Place Recognition datasets, (2) impressive results on common Landmark Retrieval datasets, and (3) sets a new state of the art for Visual Localization on the LaMAR datasets, where we only changed the retrieval method to the existing localization pipeline. The code for MegaLoc is available at https://github.com/gmberton/MegaLoc", "github_manual": "", "github_regex": "https://github.com/gmberton/MegaLoc", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.15799", "title": "Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models", "authors": [ "Artyom Kharinaev", "Viktor Moskvoretskii", "Egor Shvetsov", "Kseniia Studenikina", "Bykov Mikhail", "Evgeny Burnaev" ], "github": "https://github.com/On-Point-RND/OpenSafetyMini-Investigating-the-Impact-of-Quantization-Methods-on-the-Safety-and-Reliability-of-LLM", "project_page": "", "abstract": "Large Language Models (LLMs) have emerged as powerful tools for addressing modern challenges and enabling practical applications. However, their computational expense remains a significant barrier to widespread adoption. Quantization has emerged as a promising technique to democratize access and enable low resource device deployment. Despite these advancements, the safety and trustworthiness of quantized models remain underexplored, as prior studies often overlook contemporary architectures and rely on overly simplistic benchmarks and evaluations. To address this gap, we introduce OpenSafetyMini, a novel open-ended safety dataset designed to better distinguish between models. We evaluate 4 state-of-the-art quantization techniques across LLaMA and Mistral models using 4 benchmarks, including human evaluations. Our findings reveal that the optimal quantization method varies for 4-bit precision, while vector quantization techniques deliver the best safety and trustworthiness performance at 2-bit precision, providing foundation for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/On-Point-RND/OpenSafetyMini-Investigating-the-Impact-of-Quantization-Methods-on-the-Safety-and-Reliability-of-LLM", "github_api_added_by": "auto" }, { "date": "2025-02-25", "arxiv_id": "2502.16622", "title": "Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN Architectures", "authors": [ "Luis Lara", "Lucia Eve Berger", "Rajesh Raju", "Shawn Whitfield" ], "github": "https://github.com/stwhitfield/covid-severity", "project_page": "", "abstract": "The COVID-19 pandemic strained healthcare resources and prompted discussion about how machine learning can alleviate physician burdens and contribute to diagnosis. Chest x-rays (CXRs) are used for diagnosis of COVID-19, but few studies predict the severity of a patient's condition from CXRs. In this study, we produce a large COVID severity dataset by merging three sources and investigate the efficacy of transfer learning using ImageNet- and CXR-pretrained models and vision transformers (ViTs) in both severity regression and classification tasks. A pretrained DenseNet161 model performed the best on the three class severity prediction problem, reaching 80% accuracy overall and 77.3%, 83.9%, and 70% on mild, moderate and severe cases, respectively. The ViT had the best regression results, with a mean absolute error of 0.5676 compared to radiologist-predicted severity scores. The project's source code is publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stwhitfield/covid-severity", "github_api_added_by": "auto" }, { "date": "2025-02-25", "arxiv_id": "2502.16810", "title": "Grounded Persuasive Language Generation for Automated Marketing", "authors": [ "Jibang Wu", "Chenghao Yang", "Simon Mahns", "Chaoqi Wang", "Hao Zhu", "Fei Fang", "Haifeng Xu" ], "github": "", "project_page": "", "abstract": "This paper develops an agentic framework that employs large language models (LLMs) to automate the generation of persuasive and grounded marketing content, using real estate listing descriptions as our focal application domain. Our method is designed to align the generated content with user preferences while highlighting useful factual attributes. This agent consists of three key modules: (1) Grounding Module, mimicking expert human behavior to predict marketable features; (2) Personalization Module, aligning content with user preferences; (3) Marketing Module, ensuring factual accuracy and the inclusion of localized features. We conduct systematic human-subject experiments in the domain of real estate marketing, with a focus group of potential house buyers. The results demonstrate that marketing descriptions generated by our approach are preferred over those written by human experts by a clear margin. Our findings suggest a promising LLM-based agentic framework to automate large-scale targeted marketing while ensuring responsible generation using only facts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.15823", "title": "InductionBench: LLMs Fail in the Simplest Complexity Class", "authors": [ "Wenyue Hua", "Tyler Wong", "Sun Fei", "Liangming Pan", "Adam Jardine", "William Yang Wang" ], "github": "https://github.com/Wenyueh/inductive_reasoning_benchmark", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable improvements in reasoning and many existing benchmarks have been addressed by models such as o1 and o3 either fully or partially. However, a majority of these benchmarks emphasize deductive reasoning, including mathematical and coding tasks in which rules such as mathematical axioms or programming syntax are clearly defined, based on which LLMs can plan and apply these rules to arrive at a solution. In contrast, inductive reasoning, where one infers the underlying rules from observed data, remains less explored. Such inductive processes lie at the heart of scientific discovery, as they enable researchers to extract general principles from empirical observations. To assess whether LLMs possess this capacity, we introduce InductionBench, a new benchmark designed to evaluate the inductive reasoning ability of LLMs. Our experimental findings reveal that even the most advanced models available struggle to master the simplest complexity classes within the subregular hierarchy of functions, highlighting a notable deficiency in current LLMs' inductive reasoning capabilities. Coda and data are available https://github.com/Wenyueh/inductive_reasoning_benchmark.", "github_manual": "", "github_regex": "https://github.com/Wenyueh/inductive_reasoning_benchmark", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.15920", "title": "Self-Taught Agentic Long Context Understanding", "authors": [ "Yufan Zhuang", "Xiaodong Yu", "Jialian Wu", "Ximeng Sun", "Ze Wang", "Jiang Liu", "Yusheng Su", "Jingbo Shang", "Zicheng Liu", "Emad Barsoum" ], "github": "https://github.com/EvanZhuang/AgenticLU", "project_page": "", "abstract": "Answering complex, long-context questions remains a major challenge for large language models (LLMs) as it requires effective question clarifications and context retrieval. We propose Agentic Long-Context Understanding (AgenticLU), a framework designed to enhance an LLM's understanding of such queries by integrating targeted self-clarification with contextual grounding within an agentic workflow. At the core of AgenticLU is Chain-of-Clarifications (CoC), where models refine their understanding through self-generated clarification questions and corresponding contextual groundings. By scaling inference as a tree search where each node represents a CoC step, we achieve 97.8% answer recall on NarrativeQA with a search depth of up to three and a branching factor of eight. To amortize the high cost of this search process to training, we leverage the preference pairs for each step obtained by the CoC workflow and perform two-stage model finetuning: (1) supervised finetuning to learn effective decomposition strategies, and (2) direct preference optimization to enhance reasoning quality. This enables AgenticLU models to generate clarifications and retrieve relevant context effectively and efficiently in a single inference pass. Extensive experiments across seven long-context tasks demonstrate that AgenticLU significantly outperforms state-of-the-art prompting methods and specialized long-context LLMs, achieving robust multi-hop reasoning while sustaining consistent performance as context length grows.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EvanZhuang/AgenticLU", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.15919", "title": "Mind the Gap! Static and Interactive Evaluations of Large Audio Models", "authors": [ "Minzhi Li", "William Barr Held", "Michael J Ryan", "Kunat Pipatanakul", "Potsawee Manakul", "Hao Zhu", "Diyi Yang" ], "github": "", "project_page": "", "abstract": "As AI chatbots become ubiquitous, voice interaction presents a compelling way to enable rapid, high-bandwidth communication for both semantic and social signals. This has driven research into Large Audio Models (LAMs) to power voice-native experiences. However, aligning LAM development with user goals requires a clear understanding of user needs and preferences to establish reliable progress metrics. This study addresses these challenges by introducing an interactive approach to evaluate LAMs and collecting 7,500 LAM interactions from 484 participants. Through topic modeling of user queries, we identify primary use cases for audio interfaces. We then analyze user preference rankings and qualitative feedback to determine which models best align with user needs. Finally, we evaluate how static benchmarks predict interactive performance - our analysis reveals no individual benchmark strongly correlates with interactive results (tau leq 0.33 for all benchmarks). While combining multiple coarse-grained features yields modest predictive power (R^2=0.30), only two out of twenty datasets on spoken question answering and age prediction show significantly positive correlations. This suggests a clear need to develop LAM evaluations that better correlate with user preferences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.14247", "title": "Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and Texture Generation", "authors": [ "Jiayu Yang", "Taizhang Shang", "Weixuan Sun", "Xibin Song", "Ziang Cheng", "Senbo Wang", "Shenzhou Chen", "Weizhe Liu", "Hongdong Li", "Pan Ji" ], "github": "https://github.com/Tencent/Tencent-XR-3DGen", "project_page": "", "abstract": "This report presents a comprehensive framework for generating high-quality 3D shapes and textures from diverse input prompts, including single images, multi-view images, and text descriptions. The framework consists of 3D shape generation and texture generation. (1). The 3D shape generation pipeline employs a Variational Autoencoder (VAE) to encode implicit 3D geometries into a latent space and a diffusion network to generate latents conditioned on input prompts, with modifications to enhance model capacity. An alternative Artist-Created Mesh (AM) generation approach is also explored, yielding promising results for simpler geometries. (2). Texture generation involves a multi-stage process starting with frontal images generation followed by multi-view images generation, RGB-to-PBR texture conversion, and high-resolution multi-view texture refinement. A consistency scheduler is plugged into every stage, to enforce pixel-wise consistency among multi-view textures during inference, ensuring seamless integration. The pipeline demonstrates effective handling of diverse input formats, leveraging advanced neural architectures and novel methodologies to produce high-quality 3D content. This report details the system architecture, experimental results, and potential future directions to improve and expand the framework. The source code and pretrained weights are released at: https://github.com/Tencent/Tencent-XR-3DGen.", "github_manual": "", "github_regex": "https://github.com/Tencent/Tencent-XR-3DGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-25", "arxiv_id": "2502.15872", "title": "MutaGReP: Execution-Free Repository-Grounded Plan Search for Code-Use", "authors": [ "Zaid Khan", "Ali Farhadi", "Ranjay Krishna", "Luca Weihs", "Mohit Bansal", "Tanmay Gupta" ], "github": "https://github.com/codezakh/MutaGReP", "project_page": "", "abstract": "When a human requests an LLM to complete a coding task using functionality from a large code repository, how do we provide context from the repo to the LLM? One approach is to add the entire repo to the LLM's context window. However, most tasks involve only fraction of symbols from a repo, longer contexts are detrimental to the LLM's reasoning abilities, and context windows are not unlimited. Alternatively, we could emulate the human ability to navigate a large repo, pick out the right functionality, and form a plan to solve the task. We propose MutaGReP (Mutation-guided Grounded Repository Plan Search), an approach to search for plans that decompose a user request into natural language steps grounded in the codebase. MutaGReP performs neural tree search in plan space, exploring by mutating plans and using a symbol retriever for grounding. On the challenging LongCodeArena benchmark, our plans use less than 5% of the 128K context window for GPT-4o but rival the coding performance of GPT-4o with a context window filled with the repo. Plans produced by MutaGReP allow Qwen 2.5 Coder 32B and 72B to match the performance of GPT-4o with full repo context and enable progress on the hardest LongCodeArena tasks. Project page: zaidkhan.me/MutaGReP", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/codezakh/MutaGReP", "github_api_added_by": "auto" }, { "date": "2025-02-26", "arxiv_id": "2502.17363", "title": "KV-Edit: Training-Free Image Editing for Precise Background Preservation", "authors": [ "Tianrui Zhu", "Shiyi Zhang", "Jiawei Shao", "Yansong Tang" ], "github": "", "project_page": "", "abstract": "Background consistency remains a significant challenge in image editing tasks. Despite extensive developments, existing works still face a trade-off between maintaining similarity to the original image and generating content that aligns with the target. Here, we propose KV-Edit, a training-free approach that uses KV cache in DiTs to maintain background consistency, where background tokens are preserved rather than regenerated, eliminating the need for complex mechanisms or expensive training, ultimately generating new content that seamlessly integrates with the background within user-provided regions. We further explore the memory consumption of the KV cache during editing and optimize the space complexity to O(1) using an inversion-free method. Our approach is compatible with any DiT-based generative model without additional training. Experiments demonstrate that KV-Edit significantly outperforms existing approaches in terms of both background and image quality, even surpassing training-based methods. Project webpage is available at https://xilluill.github.io/projectpages/KV-Edit", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.18137", "title": "SpargeAttn: Accurate Sparse Attention Accelerating Any Model Inference", "authors": [ "Jintao Zhang", "Chendong Xiang", "Haofeng Huang", "Jia Wei", "Haocheng Xi", "Jun Zhu", "Jianfei Chen" ], "github": "https://github.com/thu-ml/SpargeAttn", "project_page": "", "abstract": "An efficient attention implementation is essential for large models due to its quadratic time complexity. Fortunately, attention commonly exhibits sparsity, i.e., many values in the attention map are near zero, allowing for the omission of corresponding computations. Many studies have utilized the sparse pattern to accelerate attention. However, most existing works focus on optimizing attention within specific models by exploiting certain sparse patterns of the attention map. A universal sparse attention that guarantees both the speedup and end-to-end performance of diverse models remains elusive. In this paper, we propose SpargeAttn, a universal sparse and quantized attention for any model. Our method uses a two-stage online filter: in the first stage, we rapidly and accurately predict the attention map, enabling the skip of some matrix multiplications in attention. In the second stage, we design an online softmax-aware filter that incurs no extra overhead and further skips some matrix multiplications. Experiments show that our method significantly accelerates diverse models, including language, image, and video generation, without sacrificing end-to-end metrics. The codes are available at https://github.com/thu-ml/SpargeAttn.", "github_manual": "", "github_regex": "https://github.com/thu-ml/SpargeAttn", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.15499", "title": "Scale-Distribution Decoupling: Enabling Stable and Effective Training of Large Language Models", "authors": [ "Ya Wang", "Zhijian Zhuo", "Yutao Zeng", "Xun Zhou", "Jian Yang", "Xiaoqing Li" ], "github": "https://github.com/kaihemo/SDD", "project_page": "", "abstract": "Training stability is a persistent challenge in the pre-training of large language models (LLMs), particularly for architectures such as Post-Norm Transformers, which are prone to gradient explosion and dissipation. In this paper, we propose Scale-Distribution Decoupling (SDD), a novel approach that stabilizes training by explicitly decoupling the scale and distribution of the weight matrix in fully-connected layers. SDD applies a normalization mechanism to regulate activations and a learnable scaling vector to maintain well-conditioned gradients, effectively preventing gradient explosion and dissipation. This separation improves optimization efficiency, particularly in deep networks, by ensuring stable gradient propagation. Experimental results demonstrate that our method stabilizes training across various LLM architectures and outperforms existing techniques in different normalization configurations. Furthermore, the proposed method is lightweight and compatible with existing frameworks, making it a practical solution for stabilizing LLM training. Code is available at https://github.com/kaihemo/SDD.", "github_manual": "", "github_regex": "https://github.com/kaihemo/SDD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.17262", "title": "Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based Perspective", "authors": [ "Chengyin Xu", "Kaiyuan Chen", "Xiao Li", "Ke Shen", "Chenggang Li" ], "github": "", "project_page": "", "abstract": "The rapid advancements in computing dramatically increase the scale and cost of training Large Language Models (LLMs). Accurately predicting downstream task performance prior to model training is crucial for efficient resource allocation, yet remains challenging due to two primary constraints: (1) the \"emergence phenomenon\", wherein downstream performance metrics become meaningful only after extensive training, which limits the ability to use smaller models for prediction; (2) Uneven task difficulty distributions and the absence of consistent scaling laws, resulting in substantial metric variability. Existing performance prediction methods suffer from limited accuracy and reliability, thereby impeding the assessment of potential LLM capabilities. To address these challenges, we propose a Clustering-On-Difficulty (COD) downstream performance prediction framework. COD first constructs a predictable support subset by clustering tasks based on difficulty features, strategically excluding non-emergent and non-scalable clusters. The scores on the selected subset serve as effective intermediate predictors of downstream performance on the full evaluation set. With theoretical support, we derive a mapping function that transforms performance metrics from the predictable subset to the full evaluation set, thereby ensuring accurate extrapolation of LLM downstream performance. The proposed method has been applied to predict performance scaling for a 70B LLM, providing actionable insights for training resource allocation and assisting in monitoring the training process. Notably, COD achieves remarkable predictive accuracy on the 70B LLM by leveraging an ensemble of small models, demonstrating an absolute mean deviation of 1.36% across eight important LLM evaluation benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.18411", "title": "OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference", "authors": [ "Xiangyu Zhao", "Shengyuan Ding", "Zicheng Zhang", "Haian Huang", "Maosong Cao", "Weiyun Wang", "Jiaqi Wang", "Xinyu Fang", "Wenhai Wang", "Guangtao Zhai", "Haodong Duan", "Hua Yang", "Kai Chen" ], "github": "https://github.com/PhoenixZ810/OmniAlign-V", "project_page": "", "abstract": "Recent advancements in open-source multi-modal large language models (MLLMs) have primarily focused on enhancing foundational capabilities, leaving a significant gap in human preference alignment. This paper introduces OmniAlign-V, a comprehensive dataset of 200K high-quality training samples featuring diverse images, complex questions, and varied response formats to improve MLLMs' alignment with human preferences. We also present MM-AlignBench, a human-annotated benchmark specifically designed to evaluate MLLMs' alignment with human values. Experimental results show that finetuning MLLMs with OmniAlign-V, using Supervised Fine-Tuning (SFT) or Direct Preference Optimization (DPO), significantly enhances human preference alignment while maintaining or enhancing performance on standard VQA benchmarks, preserving their fundamental capabilities. Our datasets, benchmark, code and checkpoints have been released at https://github.com/PhoenixZ810/OmniAlign-V.", "github_manual": "", "github_regex": "https://github.com/PhoenixZ810/OmniAlign-V", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.18449", "title": "SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution", "authors": [ "Yuxiang Wei", "Olivier Duchenne", "Jade Copet", "Quentin Carbonneaux", "Lingming Zhang", "Daniel Fried", "Gabriel Synnaeve", "Rishabh Singh", "Sida I. Wang" ], "github": "https://github.com/facebookresearch/swe-rl", "project_page": "", "abstract": "The recent DeepSeek-R1 release has demonstrated the immense potential of reinforcement learning (RL) in enhancing the general reasoning capabilities of large language models (LLMs). While DeepSeek-R1 and other follow-up work primarily focus on applying RL to competitive coding and math problems, this paper introduces SWE-RL, the first approach to scale RL-based LLM reasoning for real-world software engineering. Leveraging a lightweight rule-based reward (e.g., the similarity score between ground-truth and LLM-generated solutions), SWE-RL enables LLMs to autonomously recover a developer's reasoning processes and solutions by learning from extensive open-source software evolution data -- the record of a software's entire lifecycle, including its code snapshots, code changes, and events such as issues and pull requests. Trained on top of Llama 3, our resulting reasoning model, Llama3-SWE-RL-70B, achieves a 41.0% solve rate on SWE-bench Verified -- a human-verified collection of real-world GitHub issues. To our knowledge, this is the best performance reported for medium-sized (<100B) LLMs to date, even comparable to leading proprietary LLMs like GPT-4o. Surprisingly, despite performing RL solely on software evolution data, Llama3-SWE-RL has even emerged with generalized reasoning skills. For example, it shows improved results on five out-of-domain tasks, namely, function coding, library use, code reasoning, mathematics, and general language understanding, whereas a supervised-finetuning baseline even leads to performance degradation on average. Overall, SWE-RL opens up a new direction to improve the reasoning capabilities of LLMs through reinforcement learning on massive software engineering data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/swe-rl", "github_api_added_by": "auto" }, { "date": "2025-02-26", "arxiv_id": "2502.18356", "title": "WebGames: Challenging General-Purpose Web-Browsing AI Agents", "authors": [ "George Thomas", "Alex J. Chan", "Jikun Kang", "Wenqi Wu", "Filippos Christianos", "Fraser Greenlee", "Andy Toulis", "Marvin Purtorab" ], "github": "https://github.com/convergence-ai/webgames", "project_page": "", "abstract": "We introduce WebGames, a comprehensive benchmark suite designed to evaluate general-purpose web-browsing AI agents through a collection of 50+ interactive challenges. These challenges are specifically crafted to be straightforward for humans while systematically testing the limitations of current AI systems across fundamental browser interactions, advanced input processing, cognitive tasks, workflow automation, and interactive entertainment. Our framework eliminates external dependencies through a hermetic testing environment, ensuring reproducible evaluation with verifiable ground-truth solutions. We evaluate leading vision-language models including GPT-4o, Claude Computer-Use, Gemini-1.5-Pro, and Qwen2-VL against human performance. Results reveal a substantial capability gap, with the best AI system achieving only 43.1% success rate compared to human performance of 95.7%, highlighting fundamental limitations in current AI systems' ability to handle common web interaction patterns that humans find intuitive. The benchmark is publicly available at webgames.convergence.ai, offering a lightweight, client-side implementation that facilitates rapid evaluation cycles. Through its modular architecture and standardized challenge specifications, WebGames provides a robust foundation for measuring progress in development of more capable web-browsing agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/convergence-ai/webgames", "github_api_added_by": "auto" }, { "date": "2025-02-26", "arxiv_id": "2502.16794", "title": "AAD-LLM: Neural Attention-Driven Auditory Scene Understanding", "authors": [ "Xilin Jiang", "Sukru Samet Dindar", "Vishal Choudhari", "Stephan Bickel", "Ashesh Mehta", "Guy M McKhann", "Adeen Flinker", "Daniel Friedman", "Nima Mesgarani" ], "github": "https://github.com/xi-j/AAD-LLM", "project_page": "", "abstract": "Auditory foundation models, including auditory large language models (LLMs), process all sound inputs equally, independent of listener perception. However, human auditory perception is inherently selective: listeners focus on specific speakers while ignoring others in complex auditory scenes. Existing models do not incorporate this selectivity, limiting their ability to generate perception-aligned responses. To address this, we introduce Intention-Informed Auditory Scene Understanding (II-ASU) and present Auditory Attention-Driven LLM (AAD-LLM), a prototype system that integrates brain signals to infer listener attention. AAD-LLM extends an auditory LLM by incorporating intracranial electroencephalography (iEEG) recordings to decode which speaker a listener is attending to and refine responses accordingly. The model first predicts the attended speaker from neural activity, then conditions response generation on this inferred attentional state. We evaluate AAD-LLM on speaker description, speech transcription and extraction, and question answering in multitalker scenarios, with both objective and subjective ratings showing improved alignment with listener intention. By taking a first step toward intention-aware auditory AI, this work explores a new paradigm where listener perception informs machine listening, paving the way for future listener-centered auditory systems. Demo and code available: https://aad-llm.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xi-j/AAD-LLM", "github_api_added_by": "auto" }, { "date": "2025-02-26", "arxiv_id": "2502.18364", "title": "ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation", "authors": [ "Yifan Pu", "Yiming Zhao", "Zhicong Tang", "Ruihong Yin", "Haoxing Ye", "Yuhui Yuan", "Dong Chen", "Jianmin Bao", "Sirui Zhang", "Yanbin Wang", "Lin Liang", "Lijuan Wang", "Ji Li", "Xiu Li", "Zhouhui Lian", "Gao Huang", "Baining Guo" ], "github": "", "project_page": "", "abstract": "Multi-layer image generation is a fundamental task that enables users to isolate, select, and edit specific image layers, thereby revolutionizing interactions with generative models. In this paper, we introduce the Anonymous Region Transformer (ART), which facilitates the direct generation of variable multi-layer transparent images based on a global text prompt and an anonymous region layout. Inspired by Schema theory suggests that knowledge is organized in frameworks (schemas) that enable people to interpret and learn from new information by linking it to prior knowledge.}, this anonymous region layout allows the generative model to autonomously determine which set of visual tokens should align with which text tokens, which is in contrast to the previously dominant semantic layout for the image generation task. In addition, the layer-wise region crop mechanism, which only selects the visual tokens belonging to each anonymous region, significantly reduces attention computation costs and enables the efficient generation of images with numerous distinct layers (e.g., 50+). When compared to the full attention approach, our method is over 12 times faster and exhibits fewer layer conflicts. Furthermore, we propose a high-quality multi-layer transparent image autoencoder that supports the direct encoding and decoding of the transparency of variable multi-layer images in a joint manner. By enabling precise control and scalable layer generation, ART establishes a new paradigm for interactive content creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.17092", "title": "Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI", "authors": [ "Syed Abdul Gaffar Shakhadri", "Kruthika KR", "Kartik Basavaraj Angadi" ], "github": "", "project_page": "", "abstract": "We introduce Shakti VLM, a family of vision-language models in the capacity of 1B and 4B parameters designed to address data efficiency challenges in multimodal learning. While recent VLMs achieve strong performance through extensive training data, Shakti models leverage architectural innovations to attain competitive results with fewer tokens. Key advancements include QK-Normalization for attention stability, hybrid normalization techniques, and enhanced positional encoding. A three-stage training strategy further optimizes learning efficiency. Evaluations show that Shakti-Shakti-VLM-1B and Shakti-VLM-4B excel in document understanding, Visual Reasoning, OCR extraction, and general multimodal reasoning. Our results highlight that high performance can be achieved through model design and training strategy rather than sheer data volume, making Shakti an efficient solution for enterprise-scale multimodal tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.18461", "title": "K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs", "authors": [ "Ziheng Ouyang", "Zhen Li", "Qibin Hou" ], "github": "", "project_page": "", "abstract": "Recent studies have explored combining different LoRAs to jointly generate learned style and content. However, existing methods either fail to effectively preserve both the original subject and style simultaneously or require additional training. In this paper, we argue that the intrinsic properties of LoRA can effectively guide diffusion models in merging learned subject and style. Building on this insight, we propose K-LoRA, a simple yet effective training-free LoRA fusion approach. In each attention layer, K-LoRA compares the Top-K elements in each LoRA to be fused, determining which LoRA to select for optimal fusion. This selection mechanism ensures that the most representative features of both subject and style are retained during the fusion process, effectively balancing their contributions. Experimental results demonstrate that the proposed method effectively integrates the subject and style information learned by the original LoRAs, outperforming state-of-the-art training-based approaches in both qualitative and quantitative results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.17535", "title": "The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?", "authors": [ "Zhenheng Tang", "Xiang Liu", "Qian Wang", "Peijie Dong", "Bingsheng He", "Xiaowen Chu", "Bo Li" ], "github": "", "project_page": "", "abstract": "Motivated by reducing the computational and storage costs of LLMs, model compression and KV cache compression have attracted much attention from researchers. However, current methods predominantly emphasize maintaining the performance of compressed LLMs, as measured by perplexity or simple accuracy on tasks of common sense knowledge QA and basic arithmetic reasoning. In this blog, we present a brief review of recent advancements in LLMs related to retrieval-augmented generation, multi-step reasoning, external tools, and computational expressivity, all of which substantially enhance LLM performance. Then, we propose a lottery LLM hypothesis suggesting that for a given LLM and task, there exists a smaller lottery LLM capable of producing the same performance as the original LLM with the assistance of multi-step reasoning and external tools. Based on the review of current progress in LLMs, we discuss and summarize the essential capabilities that the lottery LLM and KV cache compression must possess, which are currently overlooked in existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.17425", "title": "Introducing Visual Perception Token into Multimodal Large Language Model", "authors": [ "Runpeng Yu", "Xinyin Ma", "Xinchao Wang" ], "github": "https://github.com/yu-rp/VisualPerceptionToken", "project_page": "", "abstract": "To utilize visual information, Multimodal Large Language Model (MLLM) relies on the perception process of its vision encoder. The completeness and accuracy of visual perception significantly influence the precision of spatial reasoning, fine-grained understanding, and other tasks. However, MLLM still lacks the autonomous capability to control its own visual perception processes, for example, selectively reviewing specific regions of an image or focusing on information related to specific object categories. In this work, we propose the concept of Visual Perception Token, aiming to empower MLLM with a mechanism to control its visual perception processes. We design two types of Visual Perception Tokens, termed the Region Selection Token and the Vision Re-Encoding Token. MLLMs autonomously generate these tokens, just as they generate text, and use them to trigger additional visual perception actions. The Region Selection Token explicitly identifies specific regions in an image that require further perception, while the Vision Re-Encoding Token uses its hidden states as control signals to guide additional visual perception processes. Extensive experiments demonstrate the advantages of these tokens in handling spatial reasoning, improving fine-grained understanding, and other tasks. On average, the introduction of Visual Perception Tokens improves the performance of a 2B model by 23.6\\%, increasing its score from 0.572 to 0.708, and even outperforms a 7B parameter model by 13.4\\% (from 0.624). Please check out our repo https://github.com/yu-rp/VisualPerceptionToken", "github_manual": "", "github_regex": "https://github.com/yu-rp/VisualPerceptionToken", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.15612", "title": "LaTIM: Measuring Latent Token-to-Token Interactions in Mamba Models", "authors": [ "Hugo Pitorro", "Marcos Treviso" ], "github": "https://github.com/deep-spin/latim", "project_page": "", "abstract": "State space models (SSMs), such as Mamba, have emerged as an efficient alternative to transformers for long-context sequence modeling. However, despite their growing adoption, SSMs lack the interpretability tools that have been crucial for understanding and improving attention-based architectures. While recent efforts provide insights into Mamba's internal mechanisms, they do not explicitly decompose token-wise contributions, leaving gaps in understanding how Mamba selectively processes sequences across layers. In this work, we introduce LaTIM, a novel token-level decomposition method for both Mamba-1 and Mamba-2 that enables fine-grained interpretability. We extensively evaluate our method across diverse tasks, including machine translation, copying, and retrieval-based generation, demonstrating its effectiveness in revealing Mamba's token-to-token interaction patterns.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/deep-spin/latim", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.16825", "title": "Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization", "authors": [ "Yao Xiao", "Hai Ye", "Linyao Chen", "Hwee Tou Ng", "Lidong Bing", "Xiaoli Li", "Roy Ka-wei Lee" ], "github": "https://github.com/XYaoooo/DPO_Pair", "project_page": "", "abstract": "Iterative data generation and model retraining are widely used to align large language models (LLMs). It typically involves a policy model to generate on-policy responses and a reward model to guide training data selection. Direct Preference Optimization (DPO) further enhances this process by constructing preference pairs of chosen and rejected responses. In this work, we aim to scale up the number of on-policy samples via repeated random sampling to improve alignment performance. Conventional practice selects the sample with the highest reward as chosen and the lowest as rejected for DPO. However, our experiments reveal that this strategy leads to a decline in performance as the sample size increases. To address this, we investigate preference data construction through the lens of underlying normal distribution of sample rewards. We categorize the reward space into seven representative points and systematically explore all 21 (C_7^2) pairwise combinations. Through evaluations on four models using AlpacaEval 2, we find that selecting the rejected response at reward position mu - 2sigma rather than the minimum reward, is crucial for optimal performance. We finally introduce a scalable preference data construction strategy that consistently enhances model performance as the sample scale increases.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/XYaoooo/DPO_Pair", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.14855", "title": "Prompt-to-Leaderboard", "authors": [ "Evan Frick", "Connor Chen", "Joseph Tennyson", "Tianle Li", "Wei-Lin Chiang", "Anastasios N. Angelopoulos", "Ion Stoica" ], "github": "https://github.com/lmarena/p2l", "project_page": "", "abstract": "Large language model (LLM) evaluations typically rely on aggregated metrics like accuracy or human preference, averaging across users and prompts. This averaging obscures user- and prompt-specific variations in model performance. To address this, we propose Prompt-to-Leaderboard (P2L), a method that produces leaderboards specific to a prompt. The core idea is to train an LLM taking natural language prompts as input to output a vector of Bradley-Terry coefficients which are then used to predict the human preference vote. The resulting prompt-dependent leaderboards allow for unsupervised task-specific evaluation, optimal routing of queries to models, personalization, and automated evaluation of model strengths and weaknesses. Data from Chatbot Arena suggest that P2L better captures the nuanced landscape of language model performance than the averaged leaderboard. Furthermore, our findings suggest that P2L's ability to produce prompt-specific evaluations follows a power law scaling similar to that observed in LLMs themselves. In January 2025, the router we trained based on this methodology achieved the \\#1 spot in the Chatbot Arena leaderboard. Our code is available at this GitHub link: https://github.com/lmarena/p2l.", "github_manual": "", "github_regex": "https://github.com/lmarena/p2l", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.18316", "title": "WiCkeD: A Simple Method to Make Multiple Choice Benchmarks More Challenging", "authors": [ "Ahmed Elhady", "Eneko Agirre", "Mikel Artetxe" ], "github": "https://github.com/ahmedselhady/wicked-benchmarks", "project_page": "", "abstract": "We introduce WiCkeD, a simple method to increase the complexity of existing multiple-choice benchmarks by randomly replacing a choice with \"None of the above\", a method often used in educational tests. We show that WiCkeD can be automatically applied to any existing benchmark, making it more challenging. We apply WiCkeD to 6 popular benchmarks and use it to evaluate 18 open-weight LLMs. The performance of the models drops 12.1 points on average with respect to the original versions of the datasets. When using chain-of-thought on 3 MMLU datasets, the performance drop for the WiCkeD variant is similar to the one observed when using the LLMs directly, showing that WiCkeD is also challenging for models with enhanced reasoning abilities. WiCkeD also uncovers that some models are more sensitive to the extra reasoning required, providing additional information with respect to the original benchmarks. We relase our code and data at https://github.com/ahmedselhady/wicked-benchmarks.", "github_manual": "", "github_regex": "https://github.com/ahmedselhady/wicked-benchmarks", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.17814", "title": "An Overview of Large Language Models for Statisticians", "authors": [ "Wenlong Ji", "Weizhe Yuan", "Emily Getzen", "Kyunghyun Cho", "Michael I. Jordan", "Song Mei", "Jason E Weston", "Weijie J. Su", "Jing Xu", "Linjun Zhang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI), exhibiting remarkable capabilities across diverse tasks such as text generation, reasoning, and decision-making. While their success has primarily been driven by advances in computational power and deep learning architectures, emerging problems -- in areas such as uncertainty quantification, decision-making, causal inference, and distribution shift -- require a deeper engagement with the field of statistics. This paper explores potential areas where statisticians can make important contributions to the development of LLMs, particularly those that aim to engender trustworthiness and transparency for human users. Thus, we focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation. We also consider possible roles for LLMs in statistical analysis. By bridging AI and statistics, we aim to foster a deeper collaboration that advances both the theoretical foundations and practical applications of LLMs, ultimately shaping their role in addressing complex societal challenges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.16069", "title": "Curie: Toward Rigorous and Automated Scientific Experimentation with AI Agents", "authors": [ "Patrick Tser Jern Kon", "Jiachen Liu", "Qiuyi Ding", "Yiming Qiu", "Zhenning Yang", "Yibo Huang", "Jayanth Srinivasa", "Myungjin Lee", "Mosharaf Chowdhury", "Ang Chen" ], "github": "https://github.com/Just-Curieous/Curie", "project_page": "", "abstract": "Scientific experimentation, a cornerstone of human progress, demands rigor in reliability, methodical control, and interpretability to yield meaningful results. Despite the growing capabilities of large language models (LLMs) in automating different aspects of the scientific process, automating rigorous experimentation remains a significant challenge. To address this gap, we propose Curie, an AI agent framework designed to embed rigor into the experimentation process through three key components: an intra-agent rigor module to enhance reliability, an inter-agent rigor module to maintain methodical control, and an experiment knowledge module to enhance interpretability. To evaluate Curie, we design a novel experimental benchmark composed of 46 questions across four computer science domains, derived from influential research papers, and widely adopted open-source projects. Compared to the strongest baseline tested, we achieve a 3.4times improvement in correctly answering experimental questions.Curie is open-sourced at https://github.com/Just-Curieous/Curie.", "github_manual": "", "github_regex": "https://github.com/Just-Curieous/Curie", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.17422", "title": "MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs", "authors": [ "Jiarui Zhang", "Mahyar Khayatkhoei", "Prateek Chhikara", "Filip Ilievski" ], "github": "https://github.com/saccharomycetes/mllms_know", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have experienced rapid progress in visual recognition tasks in recent years. Given their potential integration into many critical applications, it is important to understand the limitations of their visual perception. In this work, we study whether MLLMs can perceive small visual details as effectively as large ones when answering questions about images. We observe that their performance is very sensitive to the size of the visual subject of the question, and further show that this effect is in fact causal by conducting an intervention study. Next, we study the attention patterns of MLLMs when answering visual questions, and intriguingly find that they consistently know where to look, even when they provide the wrong answer. Based on these findings, we then propose training-free visual intervention methods that leverage the internal knowledge of any MLLM itself, in the form of attention and gradient maps, to enhance its perception of small visual details. We evaluate our proposed methods on two widely-used MLLMs and seven visual question answering benchmarks and show that they can significantly improve MLLMs' accuracy without requiring any training. Our results elucidate the risk of applying MLLMs to visual recognition tasks concerning small details and indicate that visual intervention using the model's internal state is a promising direction to mitigate this risk.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/saccharomycetes/mllms_know", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-26", "arxiv_id": "2502.18302", "title": "LDGen: Enhancing Text-to-Image Synthesis via Large Language Model-Driven Language Representation", "authors": [ "Pengzhi Li", "Pengfei Yu", "Zide Liu", "Wei He", "Xuhao Pan", "Xudong Rao", "Tao Wei", "Wei Chen" ], "github": "https://github.com/zrealli/LDGen", "project_page": "", "abstract": "In this paper, we introduce LDGen, a novel method for integrating large language models (LLMs) into existing text-to-image diffusion models while minimizing computational demands. Traditional text encoders, such as CLIP and T5, exhibit limitations in multilingual processing, hindering image generation across diverse languages. We address these challenges by leveraging the advanced capabilities of LLMs. Our approach employs a language representation strategy that applies hierarchical caption optimization and human instruction techniques to derive precise semantic information,. Subsequently, we incorporate a lightweight adapter and a cross-modal refiner to facilitate efficient feature alignment and interaction between LLMs and image features. LDGen reduces training time and enables zero-shot multilingual image generation. Experimental results indicate that our method surpasses baseline models in both prompt adherence and image aesthetic quality, while seamlessly supporting multiple languages. Project page: https://zrealli.github.io/LDGen.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zrealli/LDGen", "github_api_added_by": "auto" }, { "date": "2025-02-26", "arxiv_id": "2502.17910", "title": "Scaling LLM Pre-training with Vocabulary Curriculum", "authors": [ "Fangyuan Yu" ], "github": "", "project_page": "", "abstract": "Modern language models rely on static vocabularies, fixed before pretraining, in contrast to the adaptive vocabulary acquisition observed in human language learning. To bridge this gap, we introduce vocabulary curriculum learning, an approach that improves pretraining efficiency with log-linear scaling gains relative to vocabulary size. Our method alternates between entropy-guided vocabulary expansion and model optimization, enabling models to learn transferable representations across diverse tokenization granularities. This approach naturally gives rise to an optimal computation allocation pattern: longer tokens capture predictable content, while shorter tokens focus on more complex, harder-to-predict contexts. Experiments on small-scale GPT models demonstrate improved scaling efficiency, reinforcing the effectiveness of dynamic tokenization. We release our code to support further research and plan to extend our experiments to larger models and diverse domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.19400", "title": "TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding", "authors": [ "Max Ku", "Thomas Chong", "Jonathan Leung", "Krish Shah", "Alvin Yu", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Understanding domain-specific theorems often requires more than just text-based reasoning; effective communication through structured visual explanations is crucial for deeper comprehension. While large language models (LLMs) demonstrate strong performance in text-based theorem reasoning, their ability to generate coherent and pedagogically meaningful visual explanations remains an open challenge. In this work, we introduce TheoremExplainAgent, an agentic approach for generating long-form theorem explanation videos (over 5 minutes) using Manim animations. To systematically evaluate multimodal theorem explanations, we propose TheoremExplainBench, a benchmark covering 240 theorems across multiple STEM disciplines, along with 5 automated evaluation metrics. Our results reveal that agentic planning is essential for generating detailed long-form videos, and the o3-mini agent achieves a success rate of 93.8% and an overall score of 0.77. However, our quantitative and qualitative studies show that most of the videos produced exhibit minor issues with visual element layout. Furthermore, multimodal explanations expose deeper reasoning flaws that text-based explanations fail to reveal, highlighting the importance of multimodal explanations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.18864", "title": "Towards an AI co-scientist", "authors": [ "Juraj Gottweis", "Wei-Hung Weng", "Alexander Daryin", "Tao Tu", "Anil Palepu", "Petar Sirkovic", "Artiom Myaskovsky", "Felix Weissenberger", "Keran Rong", "Ryutaro Tanno", "Khaled Saab", "Dan Popovici", "Jacob Blum", "Fan Zhang", "Katherine Chou", "Avinatan Hassidim", "Burak Gokturk", "Amin Vahdat", "Pushmeet Kohli", "Yossi Matias", "Andrew Carroll", "Kavita Kulkarni", "Nenad Tomasev", "Yuan Guan", "Vikram Dhillon", "Eeshit Dhaval Vaishnav", "Byron Lee", "Tiago R D Costa", "Jos\u00e9 R Penad\u00e9s", "Gary Peltz", "Yunhan Xu", "Annalisa Pawlosky", "Alan Karthikesalingam", "Vivek Natarajan" ], "github": "", "project_page": "", "abstract": "Scientific discovery relies on scientists generating novel hypotheses that undergo rigorous experimental validation. To augment this process, we introduce an AI co-scientist, a multi-agent system built on Gemini 2.0. The AI co-scientist is intended to help uncover new, original knowledge and to formulate demonstrably novel research hypotheses and proposals, building upon prior evidence and aligned to scientist-provided research objectives and guidance. The system's design incorporates a generate, debate, and evolve approach to hypothesis generation, inspired by the scientific method and accelerated by scaling test-time compute. Key contributions include: (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling; (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute, improving hypothesis quality. While general purpose, we focus development and validation in three biomedical areas: drug repurposing, novel target discovery, and explaining mechanisms of bacterial evolution and anti-microbial resistance. For drug repurposing, the system proposes candidates with promising validation findings, including candidates for acute myeloid leukemia that show tumor inhibition in vitro at clinically applicable concentrations. For novel target discovery, the AI co-scientist proposed new epigenetic targets for liver fibrosis, validated by anti-fibrotic activity and liver cell regeneration in human hepatic organoids. Finally, the AI co-scientist recapitulated unpublished experimental results via a parallel in silico discovery of a novel gene transfer mechanism in bacterial evolution. These results, detailed in separate, co-timed reports, demonstrate the potential to augment biomedical and scientific discovery and usher an era of AI empowered scientists.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.16776", "title": "AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement", "authors": [ "Zhexin Zhang", "Leqi Lei", "Junxiao Yang", "Xijie Huang", "Yida Lu", "Shiyao Cui", "Renmiao Chen", "Qinglin Zhang", "Xinyuan Wang", "Hao Wang", "Hao Li", "Xianqi Lei", "Chengwei Pan", "Lei Sha", "Hongning Wang", "Minlie Huang" ], "github": "https://github.com/thu-coai/AISafetyLab", "project_page": "", "abstract": "As AI models are increasingly deployed across diverse real-world scenarios, ensuring their safety remains a critical yet underexplored challenge. While substantial efforts have been made to evaluate and enhance AI safety, the lack of a standardized framework and comprehensive toolkit poses significant obstacles to systematic research and practical adoption. To bridge this gap, we introduce AISafetyLab, a unified framework and toolkit that integrates representative attack, defense, and evaluation methodologies for AI safety. AISafetyLab features an intuitive interface that enables developers to seamlessly apply various techniques while maintaining a well-structured and extensible codebase for future advancements. Additionally, we conduct empirical studies on Vicuna, analyzing different attack and defense strategies to provide valuable insights into their comparative effectiveness. To facilitate ongoing research and development in AI safety, AISafetyLab is publicly available at https://github.com/thu-coai/AISafetyLab, and we are committed to its continuous maintenance and improvement.", "github_manual": "", "github_regex": "https://github.com/thu-coai/AISafetyLab", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.18906", "title": "VEM: Environment-Free Exploration for Training GUI Agent with Value Environment Model", "authors": [ "Jiani Zheng", "Lu Wang", "Fangkai Yang", "Chaoyun Zhang", "Lingrui Mei", "Wenjie Yin", "Qingwei Lin", "Dongmei Zhang", "Saravan Rajmohan", "Qi Zhang" ], "github": "https://github.com/microsoft/gui-agent-rl", "project_page": "", "abstract": "Training Vision-Language Models (VLMs) for Graphical User Interfaces (GUI) agents via Reinforcement Learning (RL) faces critical challenges: environment-based RL requires costly interactions, while environment-free methods struggle with distribution shift and reward generalization. We propose an environment-free RL framework that decouples value estimation from policy optimization by leveraging a pretrained Value Environment Model (VEM). VEM predicts state-action values directly from offline data, distilling human-like priors about GUI interaction outcomes without requiring next-state prediction or environmental feedback. This avoids compounding errors and enhances resilience to UI changes by focusing on semantic reasoning (e.g., Does this action advance the user's goal?). The framework operates in two stages: (1) pretraining VEM to estimate long-term action utilities and (2) guiding policy exploration with frozen VEM signals, enabling layout-agnostic GUI automation. Evaluated on Android-in-the-Wild benchmarks, VEM achieves state-of-the-art performance in both offline and online settings, outperforming environment-free baselines significantly and matching environment-based approaches without interaction costs. Importantly, VEM demonstrates that semantic-aware value estimation can achieve comparable performance with online-trained methods.", "github_manual": "https://github.com/microsoft/gui-agent-rl", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.19328", "title": "Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems", "authors": [ "Hao Peng", "Yunjia Qi", "Xiaozhi Wang", "Zijun Yao", "Bin Xu", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/THU-KEG/Agentic-Reward-Modeling", "project_page": "", "abstract": "Reward models (RMs) are crucial for the training and inference-time scaling up of large language models (LLMs). However, existing reward models primarily focus on human preferences, neglecting verifiable correctness signals which have shown strong potential in training LLMs. In this paper, we propose agentic reward modeling, a reward system that combines reward models with verifiable correctness signals from different aspects to provide reliable rewards. We empirically implement a reward agent, named RewardAgent, that combines human preference rewards with two verifiable signals: factuality and instruction following, to provide more reliable rewards. We conduct comprehensive experiments on existing reward model benchmarks and inference time best-of-n searches on real-world downstream tasks. RewardAgent significantly outperforms vanilla reward models, demonstrating its effectiveness. We further construct training preference pairs using RewardAgent and train an LLM with the DPO objective, achieving superior performance on various NLP benchmarks compared to conventional reward models. Our codes are publicly released to facilitate further research (https://github.com/THU-KEG/Agentic-Reward-Modeling).", "github_manual": "", "github_regex": "https://github.com/THU-KEG/Agentic-Reward-Modeling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.16284", "title": "MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra", "authors": [ "Liang Wang", "Shaozhen Liu", "Yu Rong", "Deli Zhao", "Qiang Liu", "Shu Wu", "Liang Wang" ], "github": "https://github.com/azureleon1/molspectra", "project_page": "", "abstract": "Establishing the relationship between 3D structures and the energy states of molecular systems has proven to be a promising approach for learning 3D molecular representations. However, existing methods are limited to modeling the molecular energy states from classical mechanics. This limitation results in a significant oversight of quantum mechanical effects, such as quantized (discrete) energy level structures, which offer a more accurate estimation of molecular energy and can be experimentally measured through energy spectra. In this paper, we propose to utilize the energy spectra to enhance the pre-training of 3D molecular representations (MolSpectra), thereby infusing the knowledge of quantum mechanics into the molecular representations. Specifically, we propose SpecFormer, a multi-spectrum encoder for encoding molecular spectra via masked patch reconstruction. By further aligning outputs from the 3D encoder and spectrum encoder using a contrastive objective, we enhance the 3D encoder's understanding of molecules. Evaluations on public benchmarks reveal that our pre-trained representations surpass existing methods in predicting molecular properties and modeling dynamics.", "github_manual": "https://github.com/azureleon1/molspectra", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.19204", "title": "Distill Any Depth: Distillation Creates a Stronger Monocular Depth Estimator", "authors": [ "Xiankang He", "Dongyan Guo", "Hongji Li", "Ruibo Li", "Ying Cui", "Chi Zhang" ], "github": "https://github.com/Westlake-AGI-Lab/Distill-Any-Depth", "project_page": "", "abstract": "Monocular depth estimation (MDE) aims to predict scene depth from a single RGB image and plays a crucial role in 3D scene understanding. Recent advances in zero-shot MDE leverage normalized depth representations and distillation-based learning to improve generalization across diverse scenes. However, current depth normalization methods for distillation, relying on global normalization, can amplify noisy pseudo-labels, reducing distillation effectiveness. In this paper, we systematically analyze the impact of different depth normalization strategies on pseudo-label distillation. Based on our findings, we propose Cross-Context Distillation, which integrates global and local depth cues to enhance pseudo-label quality. Additionally, we introduce a multi-teacher distillation framework that leverages complementary strengths of different depth estimation models, leading to more robust and accurate depth predictions. Extensive experiments on benchmark datasets demonstrate that our approach significantly outperforms state-of-the-art methods, both quantitatively and qualitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Westlake-AGI-Lab/Distill-Any-Depth", "github_api_added_by": "auto" }, { "date": "2025-02-27", "arxiv_id": "2502.18934", "title": "Kanana: Compute-efficient Bilingual Language Models", "authors": [ "Kanana LLM Team", "Yunju Bak", "Hojin Lee", "Minho Ryu", "Jiyeon Ham", "Seungjae Jung", "Daniel Wontae Nam", "Taegyeong Eo", "Donghun Lee", "Doohae Jung", "Boseop Kim", "Nayeon Kim", "Jaesun Park", "Hyunho Kim", "Hyunwoong Ko", "Changmin Lee", "Kyoung-Woon On", "Seulye Baeg", "Junrae Cho", "Sunghee Jung", "Jieun Kang", "EungGyun Kim", "Eunhwa Kim", "Byeongil Ko", "Daniel Lee", "Minchul Lee", "Miok Lee", "Shinbok Lee", "Gaeun Seo" ], "github": "https://github.com/kakao/kanana", "project_page": "", "abstract": "We introduce Kanana, a series of bilingual language models that demonstrate exceeding performance in Korean and competitive performance in English. The computational cost of Kanana is significantly lower than that of state-of-the-art models of similar size. The report details the techniques employed during pre-training to achieve compute-efficient yet competitive models, including high quality data filtering, staged pre-training, depth up-scaling, and pruning and distillation. Furthermore, the report outlines the methodologies utilized during the post-training of the Kanana models, encompassing supervised fine-tuning and preference optimization, aimed at enhancing their capability for seamless interaction with users. Lastly, the report elaborates on plausible approaches used for language model adaptation to specific scenarios, such as embedding, retrieval augmented generation, and function calling. The Kanana model series spans from 2.1B to 32.5B parameters with 2.1B models (base, instruct, embedding) publicly released to promote research on Korean language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kakao/kanana", "github_api_added_by": "auto" }, { "date": "2025-02-27", "arxiv_id": "2502.19361", "title": "Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning?", "authors": [ "Yancheng He", "Shilong Li", "Jiaheng Liu", "Weixun Wang", "Xingyuan Bu", "Ge Zhang", "Zhongyuan Peng", "Zhaoxiang Zhang", "Wenbo Su", "Bo Zheng" ], "github": "https://github.com/openstellarteam/deltabench", "project_page": "", "abstract": "Recently, o1-like models have drawn significant attention, where these models produce the long Chain-of-Thought (CoT) reasoning steps to improve the reasoning abilities of existing Large Language Models (LLMs). In this paper, to understand the qualities of these long CoTs and measure the critique abilities of existing LLMs on these long CoTs, we introduce the DeltaBench, including the generated long CoTs from different o1-like models (e.g., QwQ, DeepSeek-R1) for different reasoning tasks (e.g., Math, Code, General Reasoning), to measure the ability to detect errors in long CoT reasoning. Based on DeltaBench, we first perform fine-grained analysis of the generated long CoTs to discover the effectiveness and efficiency of different o1-like models. Then, we conduct extensive evaluations of existing process reward models (PRMs) and critic models to detect the errors of each annotated process, which aims to investigate the boundaries and limitations of existing PRMs and critic models. Finally, we hope that DeltaBench could guide developers to better understand the long CoT reasoning abilities of their models.", "github_manual": "https://github.com/openstellarteam/deltabench", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.17955", "title": "Language Models' Factuality Depends on the Language of Inquiry", "authors": [ "Tushar Aggarwal", "Kumar Tanmay", "Ayush Agrawal", "Kumar Ayush", "Hamid Palangi", "Paul Pu Liang" ], "github": "https://github.com/kmrtanmay/X_FaKT", "project_page": "", "abstract": "Multilingual language models (LMs) are expected to recall factual knowledge consistently across languages, yet they often fail to transfer knowledge between languages even when they possess the correct information in one of the languages. For example, we find that an LM may correctly identify Rashed Al Shashai as being from Saudi Arabia when asked in Arabic, but consistently fails to do so when asked in English or Swahili. To systematically investigate this limitation, we introduce a benchmark of 10,000 country-related facts across 13 languages and propose three novel metrics: Factual Recall Score, Knowledge Transferability Score, and Cross-Lingual Factual Knowledge Transferability Score-to quantify factual recall and knowledge transferability in LMs across different languages. Our results reveal fundamental weaknesses in today's state-of-the-art LMs, particularly in cross-lingual generalization where models fail to transfer knowledge effectively across different languages, leading to inconsistent performance sensitive to the language used. Our findings emphasize the need for LMs to recognize language-specific factual reliability and leverage the most trustworthy information across languages. We release our benchmark and evaluation framework to drive future research in multilingual knowledge transfer.", "github_manual": "https://github.com/kmrtanmay/X_FaKT", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.18772", "title": "Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance", "authors": [ "Xueqing Peng", "Triantafillos Papadopoulos", "Efstathia Soufleri", "Polydoros Giannouris", "Ruoyu Xiang", "Yan Wang", "Lingfei Qian", "Jimin Huang", "Qianqian Xie", "Sophia Ananiadou" ], "github": "https://github.com/HumanSignal/label-studio", "project_page": "", "abstract": "Despite Greece's pivotal role in the global economy, large language models (LLMs) remain underexplored for Greek financial context due to the linguistic complexity of Greek and the scarcity of domain-specific datasets. Previous efforts in multilingual financial natural language processing (NLP) have exposed considerable performance disparities, yet no dedicated Greek financial benchmarks or Greek-specific financial LLMs have been developed until now. To bridge this gap, we introduce Plutus-ben, the first Greek Financial Evaluation Benchmark, and Plutus-8B, the pioneering Greek Financial LLM, fine-tuned with Greek domain-specific data. Plutus-ben addresses five core financial NLP tasks in Greek: numeric and textual named entity recognition, question answering, abstractive summarization, and topic classification, thereby facilitating systematic and reproducible LLM assessments. To underpin these tasks, we present three novel, high-quality Greek financial datasets, thoroughly annotated by expert native Greek speakers, augmented by two existing resources. Our comprehensive evaluation of 22 LLMs on Plutus-ben reveals that Greek financial NLP remains challenging due to linguistic complexity, domain-specific terminology, and financial reasoning gaps. These findings underscore the limitations of cross-lingual transfer, the necessity for financial expertise in Greek-trained models, and the challenges of adapting financial LLMs to Greek text. We release Plutus-ben, Plutus-8B, and all associated datasets publicly to promote reproducible research and advance Greek financial NLP, fostering broader multilingual inclusivity in finance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HumanSignal/label-studio", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.19279", "title": "CritiQ: Mining Data Quality Criteria from Human Preferences", "authors": [ "Honglin Guo", "Kai Lv", "Qipeng Guo", "Tianyi Liang", "Zhiheng Xi", "Demin Song", "Qiuyinzhe Zhang", "Yu Sun", "Kai Chen", "Xipeng Qiu", "Tao Gui" ], "github": "", "project_page": "", "abstract": "Language model heavily depends on high-quality data for optimal performance. Existing approaches rely on manually designed heuristics, the perplexity of existing models, training classifiers, or careful prompt engineering, which require significant expert experience and human annotation effort while introduce biases. We introduce CritiQ, a novel data selection method that automatically mines criteria from human preferences for data quality with only sim30 human-annotated pairs and performs efficient data selection. The main component, CritiQ Flow, employs a manager agent to evolve quality criteria and worker agents to make pairwise judgments. We build a knowledge base that extracts quality criteria from previous work to boost CritiQ Flow. Compared to perplexity- and classifier- based methods, verbal criteria are more interpretable and possess reusable value. After deriving the criteria, we train the CritiQ Scorer to give quality scores and perform efficient data selection. We demonstrate the effectiveness of our method in the code, math, and logic domains, achieving high accuracy on human-annotated test sets. To validate the quality of the selected data, we continually train Llama 3.1 models and observe improved performance on downstream tasks compared to uniform sampling. Ablation studies validate the benefits of the knowledge base and the reflection process. We analyze how criteria evolve and the effectiveness of majority voting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.17540", "title": "PosterSum: A Multimodal Benchmark for Scientific Poster Summarization", "authors": [ "Rohit Saxena", "Pasquale Minervini", "Frank Keller" ], "github": "https://github.com/saxenarohit/postersum", "project_page": "", "abstract": "Generating accurate and concise textual summaries from multimodal documents is challenging, especially when dealing with visually complex content like scientific posters. We introduce PosterSum, a novel benchmark to advance the development of vision-language models that can understand and summarize scientific posters into research paper abstracts. Our dataset contains 16,305 conference posters paired with their corresponding abstracts as summaries. Each poster is provided in image format and presents diverse visual understanding challenges, such as complex layouts, dense text regions, tables, and figures. We benchmark state-of-the-art Multimodal Large Language Models (MLLMs) on PosterSum and demonstrate that they struggle to accurately interpret and summarize scientific posters. We propose Segment & Summarize, a hierarchical method that outperforms current MLLMs on automated metrics, achieving a 3.14% gain in ROUGE-L. This will serve as a starting point for future research on poster summarization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/saxenarohit/postersum", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.19414", "title": "Can Language Models Falsify? Evaluating Algorithmic Reasoning with Counterexample Creation", "authors": [ "Shiven Sinha", "Shashwat Goel", "Ponnurangam Kumaraguru", "Jonas Geiping", "Matthias Bethge", "Ameya Prabhu" ], "github": "https://github.com/falsifiers/REFUTE", "project_page": "", "abstract": "There is growing excitement about the potential of Language Models (LMs) to accelerate scientific discovery. Falsifying hypotheses is key to scientific progress, as it allows claims to be iteratively refined over time. This process requires significant researcher effort, reasoning, and ingenuity. Yet current benchmarks for LMs predominantly assess their ability to generate solutions rather than challenge them. We advocate for developing benchmarks that evaluate this inverse capability - creating counterexamples for subtly incorrect solutions. To demonstrate this approach, we start with the domain of algorithmic problem solving, where counterexamples can be evaluated automatically using code execution. Specifically, we introduce REFUTE, a dynamically updating benchmark that includes recent problems and incorrect submissions from programming competitions, where human experts successfully identified counterexamples. Our analysis finds that the best reasoning agents, even OpenAI o3-mini (high) with code execution feedback, can create counterexamples for only <9% of incorrect solutions in REFUTE, even though ratings indicate its ability to solve up to 48% of these problems from scratch. We hope our work spurs progress in evaluating and enhancing LMs' ability to falsify incorrect solutions - a capability that is crucial for both accelerating research and making models self-improve through reliable reflective reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/falsifiers/REFUTE", "github_api_added_by": "auto" }, { "date": "2025-02-27", "arxiv_id": "2502.19187", "title": "BIG-Bench Extra Hard", "authors": [ "Mehran Kazemi", "Bahare Fatemi", "Hritik Bansal", "John Palowitch", "Chrysovalantis Anastasiou", "Sanket Vaibhav Mehta", "Lalit K. Jain", "Virginia Aglietti", "Disha Jindal", "Peter Chen", "Nishanth Dikkala", "Gladys Tyen", "Xin Liu", "Uri Shalit", "Silvia Chiappa", "Kate Olszewska", "Yi Tay", "Vinh Q. Tran", "Quoc V. Le", "Orhan Firat" ], "github": "https://github.com/google-deepmind/bbeh", "project_page": "", "abstract": "Large language models (LLMs) are increasingly deployed in everyday applications, demanding robust general reasoning capabilities and diverse reasoning skillset. However, current LLM reasoning benchmarks predominantly focus on mathematical and coding abilities, leaving a gap in evaluating broader reasoning proficiencies. One particular exception is the BIG-Bench dataset, which has served as a crucial benchmark for evaluating the general reasoning capabilities of LLMs, thanks to its diverse set of challenging tasks that allowed for a comprehensive assessment of general reasoning across various skills within a unified framework. However, recent advances in LLMs have led to saturation on BIG-Bench, and its harder version BIG-Bench Hard (BBH). State-of-the-art models achieve near-perfect scores on many tasks in BBH, thus diminishing its utility. To address this limitation, we introduce BIG-Bench Extra Hard (BBEH), a new benchmark designed to push the boundaries of LLM reasoning evaluation. BBEH replaces each task in BBH with a novel task that probes a similar reasoning capability but exhibits significantly increased difficulty. We evaluate various models on BBEH and observe a (harmonic) average accuracy of 9.8\\% for the best general-purpose model and 44.8\\% for the best reasoning-specialized model, indicating substantial room for improvement and highlighting the ongoing challenge of achieving robust general reasoning in LLMs. We release BBEH publicly at: https://github.com/google-deepmind/bbeh.", "github_manual": "", "github_regex": "https://github.com/google-deepmind/bbeh", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.19413", "title": "Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs", "authors": [ "Christoph Schuhmann", "Gollam Rabby", "Ameya Prabhu", "Tawsif Ahmed", "Andreas Hochlehnert", "Huu Nguyen", "Nick Akinci Heidrich", "Ludwig Schmidt", "Robert Kaczmarczyk", "S\u00f6ren Auer", "Jenia Jitsev", "Matthias Bethge" ], "github": "https://github.com/laion-ai/project-alexandria", "project_page": "", "abstract": "Paywalls, licenses and copyright rules often restrict the broad dissemination and reuse of scientific knowledge. We take the position that it is both legally and technically feasible to extract the scientific knowledge in scholarly texts. Current methods, like text embeddings, fail to reliably preserve factual content, and simple paraphrasing may not be legally sound. We urge the community to adopt a new idea: convert scholarly documents into Knowledge Units using LLMs. These units use structured data capturing entities, attributes and relationships without stylistic content. We provide evidence that Knowledge Units: (1) form a legally defensible framework for sharing knowledge from copyrighted research texts, based on legal analyses of German copyright law and U.S. Fair Use doctrine, and (2) preserve most (~95%) factual knowledge from original text, measured by MCQ performance on facts from the original copyrighted text across four research domains. Freeing scientific knowledge from copyright promises transformative benefits for scientific research and education by allowing language models to reuse important facts from copyrighted text. To support this, we share open-source tools for converting research documents into Knowledge Units. Overall, our work posits the feasibility of democratizing access to scientific knowledge while respecting copyright.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/laion-ai/project-alexandria", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.18417", "title": "GHOST 2.0: generative high-fidelity one shot transfer of heads", "authors": [ "Alexander Groshev", "Anastasiia Iashchenko", "Pavel Paramonov", "Denis Dimitrov", "Andrey Kuznetsov" ], "github": "https://github.com/ai-forever/ghost-2.0", "project_page": "", "abstract": "While the task of face swapping has recently gained attention in the research community, a related problem of head swapping remains largely unexplored. In addition to skin color transfer, head swap poses extra challenges, such as the need to preserve structural information of the whole head during synthesis and inpaint gaps between swapped head and background. In this paper, we address these concerns with GHOST 2.0, which consists of two problem-specific modules. First, we introduce enhanced Aligner model for head reenactment, which preserves identity information at multiple scales and is robust to extreme pose variations. Secondly, we use a Blender module that seamlessly integrates the reenacted head into the target background by transferring skin color and inpainting mismatched regions. Both modules outperform the baselines on the corresponding tasks, allowing to achieve state of the art results in head swapping. We also tackle complex cases, such as large difference in hair styles of source and target. Code is available at https://github.com/ai-forever/ghost-2.0", "github_manual": "", "github_regex": "https://github.com/ai-forever/ghost-2.0", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.15885", "title": "DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Maps", "authors": [ "Hongjie Zhu", "Zeyu Zhang", "Guansong Pang", "Xu Wang", "Shimin Wen", "Yu Bai", "Daji Ergu", "Ying Cai", "Yang Zhao" ], "github": "https://github.com/AIGeeksGroup/DOEI", "project_page": "", "abstract": "Weakly supervised semantic segmentation (WSSS) typically utilizes limited semantic annotations to obtain initial Class Activation Maps (CAMs). However, due to the inadequate coupling between class activation responses and semantic information in high-dimensional space, the CAM is prone to object co-occurrence or under-activation, resulting in inferior recognition accuracy. To tackle this issue, we propose DOEI, Dual Optimization of Embedding Information, a novel approach that reconstructs embedding representations through semantic-aware attention weight matrices to optimize the expression capability of embedding information. Specifically, DOEI amplifies tokens with high confidence and suppresses those with low confidence during the class-to-patch interaction. This alignment of activation responses with semantic information strengthens the propagation and decoupling of target features, enabling the generated embeddings to more accurately represent target features in high-level semantic space. In addition, we propose a hybrid-feature alignment module in DOEI that combines RGB values, embedding-guided features, and self-attention weights to increase the reliability of candidate tokens. Comprehensive experiments show that DOEI is an effective plug-and-play module that empowers state-of-the-art visual transformer-based WSSS models to significantly improve the quality of CAMs and segmentation performance on popular benchmarks, including PASCAL VOC (+3.6%, +1.5%, +1.2% mIoU) and MS COCO (+1.2%, +1.6% mIoU). Code will be available at https://github.com/AIGeeksGroup/DOEI.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/DOEI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.18418", "title": "Rank1: Test-Time Compute for Reranking in Information Retrieval", "authors": [ "Orion Weller", "Kathryn Ricci", "Eugene Yang", "Andrew Yates", "Dawn Lawrie", "Benjamin Van Durme" ], "github": "https://github.com/orionw/rank1", "project_page": "", "abstract": "We introduce Rank1, the first reranking model trained to take advantage of test-time compute. Rank1 demonstrates the applicability within retrieval of using a reasoning language model (i.e. OpenAI's o1, Deepseek's R1, etc.) for distillation in order to rapidly improve the performance of a smaller model. We gather and open-source a dataset of more than 600,000 examples of R1 reasoning traces from queries and passages in MS MARCO. Models trained on this dataset show: (1) state-of-the-art performance on advanced reasoning and instruction following datasets; (2) work remarkably well out of distribution due to the ability to respond to user-input prompts; and (3) have explainable reasoning chains that can be given to users or RAG-based systems. Further, we demonstrate that quantized versions of these models retain strong performance while using less compute/memory. Overall, Rank1 shows that test-time compute allows for a fundamentally new type of explainable and performant reranker model for search.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/orionw/rank1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.19261", "title": "Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization", "authors": [ "Taishi Nakamura", "Takuya Akiba", "Kazuki Fujii", "Yusuke Oda", "Rio Yokota", "Jun Suzuki" ], "github": "https://github.com/Taishi-N324/Drop-Upcycling", "project_page": "", "abstract": "The Mixture of Experts (MoE) architecture reduces the training and inference cost significantly compared to a dense model of equivalent capacity. Upcycling is an approach that initializes and trains an MoE model using a pre-trained dense model. While upcycling leads to initial performance gains, the training progresses slower than when trained from scratch, leading to suboptimal performance in the long term. We propose Drop-Upcycling - a method that effectively addresses this problem. Drop-Upcycling combines two seemingly contradictory approaches: utilizing the knowledge of pre-trained dense models while statistically re-initializing some parts of the weights. This approach strategically promotes expert specialization, significantly enhancing the MoE model's efficiency in knowledge acquisition. Extensive large-scale experiments demonstrate that Drop-Upcycling significantly outperforms previous MoE construction methods in the long term, specifically when training on hundreds of billions of tokens or more. As a result, our MoE model with 5.9B active parameters achieves comparable performance to a 13B dense model in the same model family, while requiring approximately 1/4 of the training FLOPs. All experimental resources, including source code, training data, model checkpoints and logs, are publicly available to promote reproducibility and future research on MoE.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Taishi-N324/Drop-Upcycling", "github_api_added_by": "auto" }, { "date": "2025-02-27", "arxiv_id": "2502.19312", "title": "FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users", "authors": [ "Anikait Singh", "Sheryl Hsu", "Kyle Hsu", "Eric Mitchell", "Stefano Ermon", "Tatsunori Hashimoto", "Archit Sharma", "Chelsea Finn" ], "github": "", "project_page": "", "abstract": "Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curation. Inspired by the strong in-context learning capabilities of LLMs, we propose Few-Shot Preference Optimization (FSPO), which reframes reward modeling as a meta-learning problem. Under this framework, an LLM learns to quickly adapt to a user via a few labeled preferences from that user, constructing a personalized reward function for them. Additionally, since real-world preference data is scarce and challenging to collect at scale, we propose careful design choices to construct synthetic preference datasets for personalization, generating over 1M synthetic personalized preferences using publicly available LLMs. In particular, to successfully transfer from synthetic data to real users, we find it crucial for the data to exhibit both high diversity and coherent, self-consistent structure. We evaluate FSPO on personalized open-ended generation for up to 1,500 synthetic users across across three domains: movie reviews, pedagogical adaptation based on educational background, and general question answering, along with a controlled human study. Overall, FSPO achieves an 87% Alpaca Eval winrate on average in generating responses that are personalized to synthetic users and a 72% winrate with real human users in open-ended question answering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.18779", "title": "Towards Optimal Multi-draft Speculative Decoding", "authors": [ "Zhengmian Hu", "Tong Zheng", "Vignesh Viswanathan", "Ziyi Chen", "Ryan A. Rossi", "Yihan Wu", "Dinesh Manocha", "Heng Huang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have become an indispensable part of natural language processing tasks. However, autoregressive sampling has become an efficiency bottleneck. Multi-Draft Speculative Decoding (MDSD) is a recent approach where, when generating each token, a small draft model generates multiple drafts, and the target LLM verifies them in parallel, ensuring that the final output conforms to the target model distribution. The two main design choices in MDSD are the draft sampling method and the verification algorithm. For a fixed draft sampling method, the optimal acceptance rate is a solution to an optimal transport problem, but the complexity of this problem makes it difficult to solve for the optimal acceptance rate and measure the gap between existing verification algorithms and the theoretical upper bound. This paper discusses the dual of the optimal transport problem, providing a way to efficiently compute the optimal acceptance rate. For the first time, we measure the theoretical upper bound of MDSD efficiency for vocabulary sizes in the thousands and quantify the gap between existing verification algorithms and this bound. We also compare different draft sampling methods based on their optimal acceptance rates. Our results show that the draft sampling method strongly influences the optimal acceptance rate, with sampling without replacement outperforming sampling with replacement. Additionally, existing verification algorithms do not reach the theoretical upper bound for both without replacement and with replacement sampling. Our findings suggest that carefully designed draft sampling methods can potentially improve the optimal acceptance rate and enable the development of verification algorithms that closely match the theoretical upper bound.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.20311", "title": "Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications", "authors": [ "Marcus Yu Zhe Wee", "Justin Juin Hng Wong", "Lynus Lim", "Joe Yu Wei Tan", "Prannaya Gupta", "Dillion Lim", "En Hao Tew", "Aloysius Keng Siew Han", "Yong Zhi Lim" ], "github": "", "project_page": "", "abstract": "Effective communication in Air Traffic Control (ATC) is critical to maintaining aviation safety, yet the challenges posed by accented English remain largely unaddressed in Automatic Speech Recognition (ASR) systems. Existing models struggle with transcription accuracy for Southeast Asian-accented (SEA-accented) speech, particularly in noisy ATC environments. This study presents the development of ASR models fine-tuned specifically for Southeast Asian accents using a newly created dataset. Our research achieves significant improvements, achieving a Word Error Rate (WER) of 0.0982 or 9.82% on SEA-accented ATC speech. Additionally, the paper highlights the importance of region-specific datasets and accent-focused training, offering a pathway for deploying ASR systems in resource-constrained military operations. The findings emphasize the need for noise-robust training techniques and region-specific datasets to improve transcription accuracy for non-Western accents in ATC communications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-27", "arxiv_id": "2502.19870", "title": "MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge", "authors": [ "Yuntao Du", "Kailin Jiang", "Zhi Gao", "Chenrui Shi", "Zilong Zheng", "Siyuan Qi", "Qing Li" ], "github": "", "project_page": "", "abstract": "Knowledge editing techniques have emerged as essential tools for updating the factual knowledge of large language models (LLMs) and multimodal models (LMMs), allowing them to correct outdated or inaccurate information without retraining from scratch. However, existing benchmarks for multimodal knowledge editing primarily focus on entity-level knowledge represented as simple triplets, which fail to capture the complexity of real-world multimodal information. To address this issue, we introduce MMKE-Bench, a comprehensive MultiModal Knowledge Editing Benchmark, designed to evaluate the ability of LMMs to edit diverse visual knowledge in real-world scenarios. MMKE-Bench addresses these limitations by incorporating three types of editing tasks: visual entity editing, visual semantic editing, and user-specific editing. Besides, MMKE-Bench uses free-form natural language to represent and edit knowledge, offering a more flexible and effective format. The benchmark consists of 2,940 pieces of knowledge and 8,363 images across 33 broad categories, with evaluation questions automatically generated and human-verified. We assess five state-of-the-art knowledge editing methods on three prominent LMMs, revealing that no method excels across all criteria, and that visual and user-specific edits are particularly challenging. MMKE-Bench sets a new standard for evaluating the robustness of multimodal knowledge editing techniques, driving progress in this rapidly evolving field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.19613", "title": "Self-rewarding correction for mathematical reasoning", "authors": [ "Wei Xiong", "Hanning Zhang", "Chenlu Ye", "Lichang Chen", "Nan Jiang", "Tong Zhang" ], "github": "https://github.com/rlhflow/self-rewarding-reasoning-llm", "project_page": "", "abstract": "We study self-rewarding reasoning large language models (LLMs), which can simultaneously generate step-by-step reasoning and evaluate the correctness of their outputs during the inference time-without external feedback. This integrated approach allows a single model to independently guide its reasoning process, offering computational advantages for model deployment. We particularly focus on the representative task of self-correction, where models autonomously detect errors in their responses, revise outputs, and decide when to terminate iterative refinement loops. To enable this, we propose a two-staged algorithmic framework for constructing self-rewarding reasoning models using only self-generated data. In the first stage, we employ sequential rejection sampling to synthesize long chain-of-thought trajectories that incorporate both self-rewarding and self-correction mechanisms. Fine-tuning models on these curated data allows them to learn the patterns of self-rewarding and self-correction. In the second stage, we further enhance the models' ability to assess response accuracy and refine outputs through reinforcement learning with rule-based signals. Experiments with Llama-3 and Qwen-2.5 demonstrate that our approach surpasses intrinsic self-correction capabilities and achieves performance comparable to systems that rely on external reward models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rlhflow/self-rewarding-reasoning-llm", "github_api_added_by": "auto" }, { "date": "2025-02-28", "arxiv_id": "2502.20082", "title": "LongRoPE2: Near-Lossless LLM Context Window Scaling", "authors": [ "Ning Shang", "Li Lyna Zhang", "Siyuan Wang", "Gaokai Zhang", "Gilsinia Lopez", "Fan Yang", "Weizhu Chen", "Mao Yang" ], "github": "https://github.com/microsoft/LongRoPE", "project_page": "", "abstract": "LongRoPE2 is a novel approach that extends the effective context window of pre-trained large language models (LLMs) to the target length, while preserving the performance on the original shorter context window. This is achieved by three contributions: (1) a hypothesis that insufficient training in higher RoPE dimensions contributes to the persistent out-of-distribution (OOD) issues observed in existing methods; (2) an effective RoPE rescaling algorithm that adopts evolutionary search guided by \"needle-driven\" perplexity to address the insufficient training problem; (3) a mixed context window training approach that fine-tunes model weights to adopt rescaled RoPE for long-context sequences while preserving the short-context performance with the original RoPE. Extensive experiments on LLaMA3-8B and Phi3-mini-3.8B across various benchmarks validate the hypothesis and demonstrate the effectiveness of LongRoPE2. Remarkably, LongRoPE2 extends LLaMA3-8B to achieve a 128K effective context length while retaining over 98.5% of short-context performance, using only 10B tokens -- 80x fewer than Meta's approach, which fails to reach the target effective context length. Code will be available at https://github.com/microsoft/LongRoPE.", "github_manual": "", "github_regex": "https://github.com/microsoft/LongRoPE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.20395", "title": "R2-T2: Re-Routing in Test-Time for Multimodal Mixture-of-Experts", "authors": [ "Zhongyang Li", "Ziyue Li", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "In large multimodal models (LMMs), the perception of non-language modalities (e.g., visual representations) is usually not on par with the large language models (LLMs)' powerful reasoning capabilities, deterring LMMs' performance on challenging downstream tasks. This weakness has been recently mitigated by replacing the vision encoder with a mixture-of-experts (MoE), which provides rich, multi-granularity, and diverse representations required by diverse downstream tasks. The performance of multimodal MoE largely depends on its router, which reweights and mixes the representations of different experts for each input. However, we find that the end-to-end trained router does not always produce the optimal routing weights for every test sample. To bridge the gap, we propose a novel and efficient method \"Re-Routing in Test-Time(R2-T2) that locally optimizes the vector of routing weights in test-time by moving it toward those vectors of the correctly predicted samples in a neighborhood of the test sample. We propose three R2-T2 strategies with different optimization objectives and neighbor-search spaces. R2-T2 consistently and greatly improves state-of-the-art LMMs' performance on challenging benchmarks of diverse tasks, without training any base-model parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.20127", "title": "SoRFT: Issue Resolving with Subtask-oriented Reinforced Fine-Tuning", "authors": [ "Zexiong Ma", "Chao Peng", "Pengfei Gao", "Xiangxin Meng", "Yanzhen Zou", "Bing Xie" ], "github": "https://github.com/Unakar/Logic-RL", "project_page": "", "abstract": "Mainstream issue-resolving frameworks predominantly rely on commercial models, leading to high costs and privacy concerns. Existing training approaches for issue resolving struggle with poor generalization and fail to fully leverage open-source development resources. We propose Subtask-oriented Reinforced Fine-Tuning (SoRFT), a novel training approach to enhance the issue resolving capability of LLMs. We decomposes issue resolving into structured subtasks: file localization, function localization, line localization, and code edit generation. SoRFT consists of two training stages: (1) rejection-sampled supervised fine-tuning, Chain of Thought (CoT) data is filtered using ground-truth before fine-tuning the LLM, and (2) rule-based reinforcement learning, which leverages PPO with ground-truth based rewards. We evaluate the SoRFT-trained model on SWE-Bench Verified and SWE-Bench Lite, achieving state-of-the-art (SOTA) performance among open-source models (e.g., resolve 21.4% issues on SWE-Bench Verified with SoRFT-Qwen-7B). The experimental results demonstrate that SoRFT significantly enhances issue-resolving performance, improves model generalization, and provides a cost-efficient alternative to commercial models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Unakar/Logic-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.16645", "title": "CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale", "authors": [ "Chenlong Wang", "Zhaoyang Chu", "Zhengxiang Cheng", "Xuyi Yang", "Kaiyue Qiu", "Yao Wan", "Zhou Zhao", "Xuanhua Shi", "Dongping Chen" ], "github": "https://github.com/Lucky-voyage/Code-Sync", "project_page": "", "abstract": "Large Language Models (LLMs) have exhibited exceptional performance in software engineering yet face challenges in adapting to continually evolving code knowledge, particularly regarding the frequent updates of third-party library APIs. This limitation, stemming from static pre-training datasets, often results in non-executable code or implementations with suboptimal safety and efficiency. To this end, this paper introduces CODESYNC, a data engine for identifying outdated code patterns and collecting real-time code knowledge updates from Python third-party libraries. Building upon CODESYNC, we develop CODESYNCBENCH, a comprehensive benchmark for assessing LLMs' ability to stay synchronized with code evolution, which covers real-world updates for 220 APIs from six Python libraries. Our benchmark offers 3,300 test cases across three evaluation tasks and an update-aware instruction tuning dataset consisting of 2,200 training samples. Extensive experiments on 14 state-of-the-art LLMs reveal that they struggle with dynamic code evolution, even with the support of advanced knowledge updating methods (e.g., DPO, ORPO, and SimPO). We believe that our benchmark can offer a strong foundation for the development of more effective methods for real-time code knowledge updating in the future. The experimental code and dataset are publicly available at: https://github.com/Lucky-voyage/Code-Sync.", "github_manual": "", "github_regex": "https://github.com/Lucky-voyage/Code-Sync", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.20321", "title": "UniTok: A Unified Tokenizer for Visual Generation and Understanding", "authors": [ "Chuofan Ma", "Yi Jiang", "Junfeng Wu", "Jihan Yang", "Xin Yu", "Zehuan Yuan", "Bingyue Peng", "Xiaojuan Qi" ], "github": "https://github.com/FoundationVision/UniTok", "project_page": "", "abstract": "The representation disparity between visual generation and understanding imposes a critical gap in integrating these capabilities into a single framework. To bridge this gap, we introduce UniTok, a discrete visual tokenizer that encodes fine-grained details for generation while also capturing high-level semantics for understanding. Despite recent studies have shown that these objectives could induce loss conflicts in training, we reveal that the underlying bottleneck stems from limited representational capacity of discrete tokens. We address this by introducing multi-codebook quantization, which divides vector quantization with several independent sub-codebooks to expand the latent feature space, while avoiding training instability caused by overlarge codebooks. Our method significantly raises the upper limit of unified discrete tokenizers to match or even surpass domain-specific continuous tokenizers. For instance, UniTok achieves a remarkable rFID of 0.38 (versus 0.87 for SD-VAE) and a zero-shot accuracy of 78.6% (versus 76.2% for CLIP) on ImageNet. Our code is available at https://github.com/FoundationVision/UniTok.", "github_manual": "", "github_regex": "https://github.com/FoundationVision/UniTok", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.20126", "title": "FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute", "authors": [ "Sotiris Anagnostidis", "Gregor Bachmann", "Yeongmin Kim", "Jonas Kohler", "Markos Georgopoulos", "Artsiom Sanakoyeu", "Yuming Du", "Albert Pumarola", "Ali Thabet", "Edgar Sch\u00f6nfeld" ], "github": "", "project_page": "", "abstract": "Despite their remarkable performance, modern Diffusion Transformers are hindered by substantial resource requirements during inference, stemming from the fixed and large amount of compute needed for each denoising step. In this work, we revisit the conventional static paradigm that allocates a fixed compute budget per denoising iteration and propose a dynamic strategy instead. Our simple and sample-efficient framework enables pre-trained DiT models to be converted into flexible ones -- dubbed FlexiDiT -- allowing them to process inputs at varying compute budgets. We demonstrate how a single flexible model can generate images without any drop in quality, while reducing the required FLOPs by more than 40\\% compared to their static counterparts, for both class-conditioned and text-conditioned image generation. Our method is general and agnostic to input and conditioning modalities. We show how our approach can be readily extended for video generation, where FlexiDiT models generate samples with up to 75\\% less compute without compromising performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.20307", "title": "Mobius: Text to Seamless Looping Video Generation via Latent Shift", "authors": [ "Xiuli Bi", "Jianfei Yuan", "Bo Liu", "Yong Zhang", "Xiaodong Cun", "Chi-Man Pun", "Bin Xiao" ], "github": "", "project_page": "", "abstract": "We present Mobius, a novel method to generate seamlessly looping videos from text descriptions directly without any user annotations, thereby creating new visual materials for the multi-media presentation. Our method repurposes the pre-trained video latent diffusion model for generating looping videos from text prompts without any training. During inference, we first construct a latent cycle by connecting the starting and ending noise of the videos. Given that the temporal consistency can be maintained by the context of the video diffusion model, we perform multi-frame latent denoising by gradually shifting the first-frame latent to the end in each step. As a result, the denoising context varies in each step while maintaining consistency throughout the inference process. Moreover, the latent cycle in our method can be of any length. This extends our latent-shifting approach to generate seamless looping videos beyond the scope of the video diffusion model's context. Unlike previous cinemagraphs, the proposed method does not require an image as appearance, which will restrict the motions of the generated results. Instead, our method can produce more dynamic motion and better visual quality. We conduct multiple experiments and comparisons to verify the effectiveness of the proposed method, demonstrating its efficacy in different scenarios. All the code will be made available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.19735", "title": "R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning", "authors": [ "Minggui He", "Yilun Liu", "Shimin Tao", "Yuanchang Luo", "Hongyong Zeng", "Chang Su", "Li Zhang", "Hongxia Ma", "Daimeng Wei", "Weibin Meng", "Hao Yang", "Boxing Chen", "Osamu Yoshie" ], "github": "", "project_page": "", "abstract": "Despite recent breakthroughs in reasoning-enhanced large language models (LLMs) like DeepSeek-R1, incorporating inference-time reasoning into machine translation (MT), where human translators naturally employ structured, multi-layered reasoning chain-of-thoughts (CoTs), is yet underexplored. Existing methods either design a fixed CoT tailored for a specific MT sub-task (e.g., literature translation), or rely on synthesizing CoTs unaligned with humans and supervised fine-tuning (SFT) prone to catastrophic forgetting, limiting their adaptability to diverse translation scenarios. This paper introduces R1-Translator (R1-T1), a novel framework to achieve inference-time reasoning for general MT via reinforcement learning (RL) with human-aligned CoTs comprising six common patterns. Our approach pioneers three innovations: (1) extending reasoning-based translation beyond MT sub-tasks to six languages and diverse tasks (e.g., legal/medical domain adaptation, idiom resolution); (2) formalizing six expert-curated CoT templates that mirror hybrid human strategies like context-aware paraphrasing and back translation; and (3) enabling self-evolving CoT discovery and anti-forgetting adaptation through RL with KL-constrained rewards. Experimental results indicate a steady translation performance improvement in 21 languages and 80 translation directions on Flores-101 test set, especially on the 15 languages unseen from training, with its general multilingual abilities preserved compared with plain SFT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.20238", "title": "FINEREASON: Evaluating and Improving LLMs' Deliberate Reasoning through Reflective Puzzle Solving", "authors": [ "Guizhen Chen", "Weiwen Xu", "Hao Zhang", "Hou Pong Chan", "Chaoqun Liu", "Lidong Bing", "Deli Zhao", "Anh Tuan Luu", "Yu Rong" ], "github": "https://github.com/DAMO-NLP-SG/FineReason", "project_page": "", "abstract": "Many challenging reasoning tasks require not just rapid, intuitive responses, but a more deliberate, multi-step approach. Recent progress in large language models (LLMs) highlights an important shift from the \"System 1\" way of quick reactions to the \"System 2\" style of reflection-and-correction problem solving. However, current benchmarks heavily rely on the final-answer accuracy, leaving much of a model's intermediate reasoning steps unexamined. This fails to assess the model's ability to reflect and rectify mistakes within the reasoning process. To bridge this gap, we introduce FINEREASON, a logic-puzzle benchmark for fine-grained evaluation of LLMs' reasoning capabilities. Each puzzle can be decomposed into atomic steps, making it ideal for rigorous validation of intermediate correctness. Building on this, we introduce two tasks: state checking, and state transition, for a comprehensive evaluation of how models assess the current situation and plan the next move. To support broader research, we also provide a puzzle training set aimed at enhancing performance on general mathematical tasks. We show that models trained on our state checking and transition data demonstrate gains in math reasoning by up to 5.1% on GSM8K.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DAMO-NLP-SG/FineReason", "github_api_added_by": "auto" }, { "date": "2025-02-28", "arxiv_id": "2502.16944", "title": "Lean and Mean: Decoupled Value Policy Optimization with Global Value Guidance", "authors": [ "Chenghua Huang", "Lu Wang", "Fangkai Yang", "Pu Zhao", "Zhixu Li", "Qingwei Lin", "Dongmei Zhang", "Saravan Rajmohan", "Qi Zhang" ], "github": "", "project_page": "", "abstract": "Proximal Policy Optimization (PPO)-based Reinforcement Learning from Human Feedback (RLHF) is essential for aligning large language models (LLMs) with human preferences. It requires joint training of an actor and critic with a pretrained, fixed reward model for guidance. This approach increases computational complexity and instability due to actor-critic interdependence. Additionally, PPO lacks access to true environment rewards in LLM tasks, limiting its adaptability. Under such conditions, pretraining a value model or a reward model becomes equivalent, as both provide fixed supervisory signals without new ground-truth feedback. To address these issues, we propose Decoupled Value Policy Optimization (DVPO), a lean framework that replaces traditional reward modeling with a pretrained global value model (GVM). The GVM is conditioned on policy trajectories and predicts token-level return-to-go estimates. By decoupling value model from policy training (via frozen GVM-driven RL objectives), DVPO eliminates actor-critic interdependence, reducing GPU memory usage by 40\\% and training time by 35\\% compared to conventional RLHF. Experiments across benchmarks show DVPO outperforms efficient RLHF methods (e.g., DPO) while matching state-of-the-art PPO in performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.19587", "title": "NeoBERT: A Next-Generation BERT", "authors": [ "Lola Le Breton", "Quentin Fournier", "Mariam El Mezouar", "Sarath Chandar" ], "github": "https://github.com/chandar-lab/NeoBERT", "project_page": "", "abstract": "Recent innovations in architecture, pre-training, and fine-tuning have led to the remarkable in-context learning and reasoning abilities of large auto-regressive language models such as LLaMA and DeepSeek. In contrast, encoders like BERT and RoBERTa have not seen the same level of progress despite being foundational for many downstream NLP applications. To bridge this gap, we introduce NeoBERT, a next-generation encoder that redefines the capabilities of bidirectional models by integrating state-of-the-art advancements in architecture, modern data, and optimized pre-training methodologies. NeoBERT is designed for seamless adoption: it serves as a plug-and-play replacement for existing base models, relies on an optimal depth-to-width ratio, and leverages an extended context length of 4,096 tokens. Despite its compact 250M parameter footprint, it achieves state-of-the-art results on the massive MTEB benchmark, outperforming BERT large, RoBERTa large, NomicBERT, and ModernBERT under identical fine-tuning conditions. In addition, we rigorously evaluate the impact of each modification on GLUE and design a uniform fine-tuning and evaluation framework for MTEB. We release all code, data, checkpoints, and training scripts to accelerate research and real-world adoption.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/chandar-lab/NeoBERT", "github_api_added_by": "auto" }, { "date": "2025-02-28", "arxiv_id": "2502.19634", "title": "MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning", "authors": [ "Jiazhen Pan", "Che Liu", "Junde Wu", "Fenglin Liu", "Jiayuan Zhu", "Hongwei Bran Li", "Chen Chen", "Cheng Ouyang", "Daniel Rueckert" ], "github": "", "project_page": "", "abstract": "Reasoning is a critical frontier for advancing medical image analysis, where transparency and trustworthiness play a central role in both clinician trust and regulatory approval. Although Medical Visual Language Models (VLMs) show promise for radiological tasks, most existing VLMs merely produce final answers without revealing the underlying reasoning. To address this gap, we introduce MedVLM-R1, a medical VLM that explicitly generates natural language reasoning to enhance transparency and trustworthiness. Instead of relying on supervised fine-tuning (SFT), which often suffers from overfitting to training distributions and fails to foster genuine reasoning, MedVLM-R1 employs a reinforcement learning framework that incentivizes the model to discover human-interpretable reasoning paths without using any reasoning references. Despite limited training data (600 visual question answering samples) and model parameters (2B), MedVLM-R1 boosts accuracy from 55.11% to 78.22% across MRI, CT, and X-ray benchmarks, outperforming larger models trained on over a million samples. It also demonstrates robust domain generalization under out-of-distribution tasks. By unifying medical image analysis with explicit reasoning, MedVLM-R1 marks a pivotal step toward trustworthy and interpretable AI in clinical practice.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.20172", "title": "Multimodal Representation Alignment for Image Generation: Text-Image Interleaved Control Is Easier Than You Think", "authors": [ "Liang Chen", "Shuai Bai", "Wenhao Chai", "Weichu Xie", "Haozhe Zhao", "Leon Vinci", "Junyang Lin", "Baobao Chang" ], "github": "https://github.com/chenllliang/dreamengine", "project_page": "", "abstract": "The field of advanced text-to-image generation is witnessing the emergence of unified frameworks that integrate powerful text encoders, such as CLIP and T5, with Diffusion Transformer backbones. Although there have been efforts to control output images with additional conditions, like canny and depth map, a comprehensive framework for arbitrary text-image interleaved control is still lacking. This gap is especially evident when attempting to merge concepts or visual elements from multiple images in the generation process. To mitigate the gap, we conducted preliminary experiments showing that large multimodal models (LMMs) offer an effective shared representation space, where image and text can be well-aligned to serve as a condition for external diffusion models. Based on this discovery, we propose Dream Engine, an efficient and unified framework designed for arbitrary text-image interleaved control in image generation models. Building on powerful text-to-image models like SD3.5, we replace the original text-only encoders by incorporating versatile multimodal information encoders such as QwenVL. Our approach utilizes a two-stage training paradigm, consisting of joint text-image alignment and multimodal interleaved instruction tuning. Our experiments demonstrate that this training method is effective, achieving a 0.69 overall score on the GenEval benchmark, and matching the performance of state-of-the-art text-to-image models like SD3.5 and FLUX.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/chenllliang/dreamengine", "github_api_added_by": "auto" }, { "date": "2025-02-28", "arxiv_id": "2502.19459", "title": "Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting", "authors": [ "Yu Liu", "Baoxiong Jia", "Ruijie Lu", "Junfeng Ni", "Song-Chun Zhu", "Siyuan Huang" ], "github": "", "project_page": "", "abstract": "Building articulated objects is a key challenge in computer vision. Existing methods often fail to effectively integrate information across different object states, limiting the accuracy of part-mesh reconstruction and part dynamics modeling, particularly for complex multi-part articulated objects. We introduce ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient representation to address these issues. Our method incorporates canonical Gaussians with coarse-to-fine initialization and updates for aligning articulated part information across different object states, and employs a skinning-inspired part dynamics modeling module to improve both part-mesh reconstruction and articulation learning. Extensive experiments on both synthetic and real-world datasets, including a new benchmark for complex multi-part objects, demonstrate that ArtGS achieves state-of-the-art performance in joint parameter estimation and part mesh reconstruction. Our approach significantly improves reconstruction quality and efficiency, especially for multi-part articulated objects. Additionally, we provide comprehensive analyses of our design choices, validating the effectiveness of each component to highlight potential areas for future improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.20378", "title": "Efficient Gaussian Splatting for Monocular Dynamic Scene Rendering via Sparse Time-Variant Attribute Modeling", "authors": [ "Hanyang Kong", "Xingyi Yang", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Rendering dynamic scenes from monocular videos is a crucial yet challenging task. The recent deformable Gaussian Splatting has emerged as a robust solution to represent real-world dynamic scenes. However, it often leads to heavily redundant Gaussians, attempting to fit every training view at various time steps, leading to slower rendering speeds. Additionally, the attributes of Gaussians in static areas are time-invariant, making it unnecessary to model every Gaussian, which can cause jittering in static regions. In practice, the primary bottleneck in rendering speed for dynamic scenes is the number of Gaussians. In response, we introduce Efficient Dynamic Gaussian Splatting (EDGS), which represents dynamic scenes via sparse time-variant attribute modeling. Our approach formulates dynamic scenes using a sparse anchor-grid representation, with the motion flow of dense Gaussians calculated via a classical kernel representation. Furthermore, we propose an unsupervised strategy to efficiently filter out anchors corresponding to static areas. Only anchors associated with deformable objects are input into MLPs to query time-variant attributes. Experiments on two real-world datasets demonstrate that our EDGS significantly improves the rendering speed with superior rendering quality compared to previous state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.16750", "title": "Guardians of the Agentic System: Preventing Many Shots Jailbreak with Agentic System", "authors": [ "Saikat Barua", "Mostafizur Rahman", "Md Jafor Sadek", "Rafiul Islam", "Shehnaz Khaled", "Ahmedul Kabir" ], "github": "https://github.com/GitsSaikat/Guardians-Preventing-Jail-Break-Prompts", "project_page": "", "abstract": "The autonomous AI agents using large language models can create undeniable values in all span of the society but they face security threats from adversaries that warrants immediate protective solutions because trust and safety issues arise. Considering the many-shot jailbreaking and deceptive alignment as some of the main advanced attacks, that cannot be mitigated by the static guardrails used during the supervised training, points out a crucial research priority for real world robustness. The combination of static guardrails in dynamic multi-agent system fails to defend against those attacks. We intend to enhance security for LLM-based agents through the development of new evaluation frameworks which identify and counter threats for safe operational deployment. Our work uses three examination methods to detect rogue agents through a Reverse Turing Test and analyze deceptive alignment through multi-agent simulations and develops an anti-jailbreaking system by testing it with GEMINI 1.5 pro and llama-3.3-70B, deepseek r1 models using tool-mediated adversarial scenarios. The detection capabilities are strong such as 94\\% accuracy for GEMINI 1.5 pro yet the system suffers persistent vulnerabilities when under long attacks as prompt length increases attack success rates (ASR) and diversity metrics become ineffective in prediction while revealing multiple complex system faults. The findings demonstrate the necessity of adopting flexible security systems based on active monitoring that can be performed by the agents themselves together with adaptable interventions by system admin as the current models can create vulnerabilities that can lead to the unreliable and vulnerable system. So, in our work, we try to address such situations and propose a comprehensive framework to counteract the security issues.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GitsSaikat/Guardians-Preventing-Jail-Break-Prompts", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.18197", "title": "Training Consistency Models with Variational Noise Coupling", "authors": [ "Gianluigi Silvestri", "Luca Ambrogioni", "Chieh-Hsin Lai", "Yuhta Takida", "Yuki Mitsufuji" ], "github": "https://github.com/sony/vct", "project_page": "", "abstract": "Consistency Training (CT) has recently emerged as a promising alternative to diffusion models, achieving competitive performance in image generation tasks. However, non-distillation consistency training often suffers from high variance and instability, and analyzing and improving its training dynamics is an active area of research. In this work, we propose a novel CT training approach based on the Flow Matching framework. Our main contribution is a trained noise-coupling scheme inspired by the architecture of Variational Autoencoders (VAE). By training a data-dependent noise emission model implemented as an encoder architecture, our method can indirectly learn the geometry of the noise-to-data mapping, which is instead fixed by the choice of the forward process in classical CT. Empirical results across diverse image datasets show significant generative improvements, with our model outperforming baselines and achieving the state-of-the-art (SoTA) non-distillation CT FID on CIFAR-10, and attaining FID on par with SoTA on ImageNet at 64 times 64 resolution in 2-step generation. Our code is available at https://github.com/sony/vct .", "github_manual": "", "github_regex": "https://github.com/sony/vct", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.17355", "title": "On Relation-Specific Neurons in Large Language Models", "authors": [ "Yihong Liu", "Runsheng Chen", "Lea Hirlimann", "Ahmad Dawar Hakimi", "Mingyang Wang", "Amir Hossein Kargaran", "Sascha Rothe", "Fran\u00e7ois Yvon", "Hinrich Sch\u00fctze" ], "github": "https://github.com/cisnlp/relation-specific-neurons", "project_page": "", "abstract": "In large language models (LLMs), certain neurons can store distinct pieces of knowledge learned during pretraining. While knowledge typically appears as a combination of relations and entities, it remains unclear whether some neurons focus on a relation itself -- independent of any entity. We hypothesize such neurons detect a relation in the input text and guide generation involving such a relation. To investigate this, we study the Llama-2 family on a chosen set of relations with a statistics-based method. Our experiments demonstrate the existence of relation-specific neurons. We measure the effect of selectively deactivating candidate neurons specific to relation r on the LLM's ability to handle (1) facts whose relation is r and (2) facts whose relation is a different relation r' neq r. With respect to their capacity for encoding relation information, we give evidence for the following three properties of relation-specific neurons. (i) Neuron cumulativity. The neurons for r present a cumulative effect so that deactivating a larger portion of them results in the degradation of more facts in r. (ii) Neuron versatility. Neurons can be shared across multiple closely related as well as less related relations. Some relation neurons transfer across languages. (iii) Neuron interference. Deactivating neurons specific to one relation can improve LLM generation performance for facts of other relations. We will make our code publicly available at https://github.com/cisnlp/relation-specific-neurons.", "github_manual": "", "github_regex": "https://github.com/cisnlp/relation-specific-neurons", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-02-28", "arxiv_id": "2502.20388", "title": "Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation", "authors": [ "Sucheng Ren", "Qihang Yu", "Ju He", "Xiaohui Shen", "Alan Yuille", "Liang-Chieh Chen" ], "github": "https://github.com/OliverRensu/xAR", "project_page": "", "abstract": "Autoregressive (AR) modeling, known for its next-token prediction paradigm, underpins state-of-the-art language and visual generative models. Traditionally, a ``token'' is treated as the smallest prediction unit, often a discrete symbol in language or a quantized patch in vision. However, the optimal token definition for 2D image structures remains an open question. Moreover, AR models suffer from exposure bias, where teacher forcing during training leads to error accumulation at inference. In this paper, we propose xAR, a generalized AR framework that extends the notion of a token to an entity X, which can represent an individual patch token, a cell (a ktimes k grouping of neighboring patches), a subsample (a non-local grouping of distant patches), a scale (coarse-to-fine resolution), or even a whole image. Additionally, we reformulate discrete token classification as continuous entity regression, leveraging flow-matching methods at each AR step. This approach conditions training on noisy entities instead of ground truth tokens, leading to Noisy Context Learning, which effectively alleviates exposure bias. As a result, xAR offers two key advantages: (1) it enables flexible prediction units that capture different contextual granularity and spatial structures, and (2) it mitigates exposure bias by avoiding reliance on teacher forcing. On ImageNet-256 generation benchmark, our base model, xAR-B (172M), outperforms DiT-XL/SiT-XL (675M) while achieving 20times faster inference. Meanwhile, xAR-H sets a new state-of-the-art with an FID of 1.24, running 2.2times faster than the previous best-performing model without relying on vision foundation modules (\\eg, DINOv2) or advanced guidance interval sampling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OliverRensu/xAR", "github_api_added_by": "auto" }, { "date": "2025-02-28", "arxiv_id": "2502.16111", "title": "PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving", "authors": [ "Mihir Parmar", "Xin Liu", "Palash Goyal", "Yanfei Chen", "Long Le", "Swaroop Mishra", "Hossein Mobahi", "Jindong Gu", "Zifeng Wang", "Hootan Nakhost", "Chitta Baral", "Chen-Yu Lee", "Tomas Pfister", "Hamid Palangi" ], "github": "", "project_page": "", "abstract": "Recent agent frameworks and inference-time algorithms often struggle with complex planning problems due to limitations in verifying generated plans or reasoning and varying complexity of instances within a single task. Many existing methods for these tasks either perform task-level verification without considering constraints or apply inference-time algorithms without adapting to instance-level complexity. To address these limitations, we propose PlanGEN, a model-agnostic and easily scalable agent framework with three key components: constraint, verification, and selection agents. Specifically, our approach proposes constraint-guided iterative verification to enhance performance of inference-time algorithms--Best of N, Tree-of-Thought, and REBASE. In PlanGEN framework, the selection agent optimizes algorithm choice based on instance complexity, ensuring better adaptability to complex planning problems. Experimental results demonstrate significant improvements over the strongest baseline across multiple benchmarks, achieving state-of-the-art results on NATURAL PLAN (sim8%uparrow), OlympiadBench (sim4%uparrow), DocFinQA (sim7%uparrow), and GPQA (sim1%uparrow). Our key finding highlights that constraint-guided iterative verification improves inference-time algorithms, and adaptive selection further boosts performance on complex planning and reasoning problems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.20730", "title": "DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking", "authors": [ "Zhuoqun Li", "Haiyang Yu", "Xuanang Chen", "Hongyu Lin", "Yaojie Lu", "Fei Huang", "Xianpei Han", "Yongbin Li", "Le Sun" ], "github": "", "project_page": "", "abstract": "Designing solutions for complex engineering challenges is crucial in human production activities. However, previous research in the retrieval-augmented generation (RAG) field has not sufficiently addressed tasks related to the design of complex engineering solutions. To fill this gap, we introduce a new benchmark, SolutionBench, to evaluate a system's ability to generate complete and feasible solutions for engineering problems with multiple complex constraints. To further advance the design of complex engineering solutions, we propose a novel system, SolutionRAG, that leverages the tree-based exploration and bi-point thinking mechanism to generate reliable solutions. Extensive experimental results demonstrate that SolutionRAG achieves state-of-the-art (SOTA) performance on the SolutionBench, highlighting its potential to enhance the automation and reliability of complex engineering solution design in real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.20583", "title": "LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation", "authors": [ "Keisuke Kamahori", "Jungo Kasai", "Noriyuki Kojima", "Baris Kasikci" ], "github": "https://github.com/efeslab/LiteASR", "project_page": "", "abstract": "Modern automatic speech recognition (ASR) models, such as OpenAI's Whisper, rely on deep encoder-decoder architectures, and their encoders are a critical bottleneck for efficient deployment due to high computational intensity. We introduce LiteASR, a low-rank compression scheme for ASR encoders that significantly reduces inference costs while maintaining transcription accuracy. Our approach leverages the strong low-rank properties observed in intermediate activations: by applying principal component analysis (PCA) with a small calibration dataset, we approximate linear transformations with a chain of low-rank matrix multiplications, and further optimize self-attention to work in the reduced dimension. Evaluation results show that our method can compress Whisper large-v3's encoder size by over 50%, matching Whisper medium's size with better transcription accuracy, thereby establishing a new Pareto-optimal frontier of efficiency and performance. The code of LiteASR is available at https://github.com/efeslab/LiteASR.", "github_manual": "", "github_regex": "https://github.com/efeslab/LiteASR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.18017", "title": "ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents", "authors": [ "Qiuchen Wang", "Ruixue Ding", "Zehui Chen", "Weiqi Wu", "Shihang Wang", "Pengjun Xie", "Feng Zhao" ], "github": "", "project_page": "", "abstract": "Understanding information from visually rich documents remains a significant challenge for traditional Retrieval-Augmented Generation (RAG) methods. Existing benchmarks predominantly focus on image-based question answering (QA), overlooking the fundamental challenges of efficient retrieval, comprehension, and reasoning within dense visual documents. To bridge this gap, we introduce ViDoSeek, a novel dataset designed to evaluate RAG performance on visually rich documents requiring complex reasoning. Based on it, we identify key limitations in current RAG approaches: (i) purely visual retrieval methods struggle to effectively integrate both textual and visual features, and (ii) previous approaches often allocate insufficient reasoning tokens, limiting their effectiveness. To address these challenges, we propose ViDoRAG, a novel multi-agent RAG framework tailored for complex reasoning across visual documents. ViDoRAG employs a Gaussian Mixture Model (GMM)-based hybrid strategy to effectively handle multi-modal retrieval. To further elicit the model's reasoning capabilities, we introduce an iterative agent workflow incorporating exploration, summarization, and reflection, providing a framework for investigating test-time scaling in RAG domains. Extensive experiments on ViDoSeek validate the effectiveness and generalization of our approach. Notably, ViDoRAG outperforms existing methods by over 10% on the competitive ViDoSeek benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.20396", "title": "Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids", "authors": [ "Toru Lin", "Kartik Sachdev", "Linxi Fan", "Jitendra Malik", "Yuke Zhu" ], "github": "", "project_page": "", "abstract": "Reinforcement learning has delivered promising results in achieving human- or even superhuman-level capabilities across diverse problem domains, but success in dexterous robot manipulation remains limited. This work investigates the key challenges in applying reinforcement learning to solve a collection of contact-rich manipulation tasks on a humanoid embodiment. We introduce novel techniques to overcome the identified challenges with empirical validation. Our main contributions include an automated real-to-sim tuning module that brings the simulated environment closer to the real world, a generalized reward design scheme that simplifies reward engineering for long-horizon contact-rich manipulation tasks, a divide-and-conquer distillation process that improves the sample efficiency of hard-exploration problems while maintaining sim-to-real performance, and a mixture of sparse and dense object representations to bridge the sim-to-real perception gap. We show promising results on three humanoid dexterous manipulation tasks, with ablation studies on each technique. Our work presents a successful approach to learning humanoid dexterous manipulation using sim-to-real reinforcement learning, achieving robust generalization and high performance without the need for human demonstration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.20811", "title": "HAIC: Improving Human Action Understanding and Generation with Better Captions for Multi-modal Large Language Models", "authors": [ "Xiao Wang", "Jingyun Hua", "Weihong Lin", "Yuanxing Zhang", "Fuzheng Zhang", "Jianlong Wu", "Di Zhang", "Liqiang Nie" ], "github": "", "project_page": "", "abstract": "Recent Multi-modal Large Language Models (MLLMs) have made great progress in video understanding. However, their performance on videos involving human actions is still limited by the lack of high-quality data. To address this, we introduce a two-stage data annotation pipeline. First, we design strategies to accumulate videos featuring clear human actions from the Internet. Second, videos are annotated in a standardized caption format that uses human attributes to distinguish individuals and chronologically details their actions and interactions. Through this pipeline, we curate two datasets, namely HAICTrain and HAICBench. HAICTrain comprises 126K video-caption pairs generated by Gemini-Pro and verified for training purposes. Meanwhile, HAICBench includes 500 manually annotated video-caption pairs and 1,400 QA pairs, for a comprehensive evaluation of human action understanding. Experimental results demonstrate that training with HAICTrain not only significantly enhances human understanding abilities across 4 benchmarks, but can also improve text-to-video generation results. Both the HAICTrain and HAICBench are released at https://huggingface.co/datasets/KuaishouHAIC/HAIC.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.20545", "title": "SoS1: O1 and R1-Like Reasoning LLMs are Sum-of-Square Solvers", "authors": [ "Kechen Li", "Wenqi Zhu", "Coralia Cartis", "Tianbo Ji", "Shiwei Liu" ], "github": "https://github.com/Joe-2002/SoS1", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved human-level proficiency across diverse tasks, but their ability to perform rigorous mathematical problem solving remains an open challenge. In this work, we investigate a fundamental yet computationally intractable problem: determining whether a given multivariate polynomial is nonnegative. This problem, closely related to Hilbert's Seventeenth Problem, plays a crucial role in global polynomial optimization and has applications in various fields. First, we introduce SoS-1K, a meticulously curated dataset of approximately 1,000 polynomials, along with expert-designed reasoning instructions based on five progressively challenging criteria. Evaluating multiple state-of-the-art LLMs, we find that without structured guidance, all models perform only slightly above the random guess baseline 50%. However, high-quality reasoning instructions significantly improve accuracy, boosting performance up to 81%. Furthermore, our 7B model, SoS-7B, fine-tuned on SoS-1K for just 4 hours, outperforms the 671B DeepSeek-V3 and GPT-4o-mini in accuracy while only requiring 1.8% and 5% of the computation time needed for letters, respectively. Our findings highlight the potential of LLMs to push the boundaries of mathematical reasoning and tackle NP-hard problems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Joe-2002/SoS1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.18600", "title": "Chain of Draft: Thinking Faster by Writing Less", "authors": [ "Silei Xu", "Wenhao Xie", "Lingxiao Zhao", "Pengcheng He" ], "github": "https://github.com/sileix/chain-of-draft", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable performance in solving complex reasoning tasks through mechanisms like Chain-of-Thought (CoT) prompting, which emphasizes verbose, step-by-step reasoning. However, humans typically employ a more efficient strategy: drafting concise intermediate thoughts that capture only essential information. In this work, we propose Chain of Draft (CoD), a novel paradigm inspired by human cognitive processes, where LLMs generate minimalistic yet informative intermediate reasoning outputs while solving tasks. By reducing verbosity and focusing on critical insights, CoD matches or surpasses CoT in accuracy while using as little as only 7.6% of the tokens, significantly reducing cost and latency across various reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sileix/chain-of-draft", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.19577", "title": "Tell me why: Visual foundation models as self-explainable classifiers", "authors": [ "Hugues Turb\u00e9", "Mina Bjelogrlic", "Gianmarco Mengaldo", "Christian Lovis" ], "github": "https://github.com/hturbe/proto-fm", "project_page": "", "abstract": "Visual foundation models (VFMs) have become increasingly popular due to their state-of-the-art performance. However, interpretability remains crucial for critical applications. In this sense, self-explainable models (SEM) aim to provide interpretable classifiers that decompose predictions into a weighted sum of interpretable concepts. Despite their promise, recent studies have shown that these explanations often lack faithfulness. In this work, we combine VFMs with a novel prototypical architecture and specialized training objectives. By training only a lightweight head (approximately 1M parameters) on top of frozen VFMs, our approach (ProtoFM) offers an efficient and interpretable solution. Evaluations demonstrate that our approach achieves competitive classification performance while outperforming existing models across a range of interpretability metrics derived from the literature. Code is available at https://github.com/hturbe/proto-fm.", "github_manual": "", "github_regex": "https://github.com/hturbe/proto-fm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.17941", "title": "Optimal Brain Apoptosis", "authors": [ "Mingyuan Sun", "Zheng Fang", "Jiaxu Wang", "Junjie Jiang", "Delei Kong", "Chenming Hu", "Yuetong Fang", "Renjing Xu" ], "github": "https://github.com/NEU-REAL/OBA", "project_page": "", "abstract": "The increasing complexity and parameter count of Convolutional Neural Networks (CNNs) and Transformers pose challenges in terms of computational efficiency and resource demands. Pruning has been identified as an effective strategy to address these challenges by removing redundant elements such as neurons, channels, or connections, thereby enhancing computational efficiency without heavily compromising performance. This paper builds on the foundational work of Optimal Brain Damage (OBD) by advancing the methodology of parameter importance estimation using the Hessian matrix. Unlike previous approaches that rely on approximations, we introduce Optimal Brain Apoptosis (OBA), a novel pruning method that calculates the Hessian-vector product value directly for each parameter. By decomposing the Hessian matrix across network layers and identifying conditions under which inter-layer Hessian submatrices are non-zero, we propose a highly efficient technique for computing the second-order Taylor expansion of parameters. This approach allows for a more precise pruning process, particularly in the context of CNNs and Transformers, as validated in our experiments including VGG19, ResNet32, ResNet50, and ViT-B/16 on CIFAR10, CIFAR100 and Imagenet datasets. Our code is available at https://github.com/NEU-REAL/OBA.", "github_manual": "", "github_regex": "https://github.com/NEU-REAL/OBA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.17125", "title": "LettuceDetect: A Hallucination Detection Framework for RAG Applications", "authors": [ "\u00c1d\u00e1m Kov\u00e1cs", "G\u00e1bor Recski" ], "github": "", "project_page": "", "abstract": "Retrieval Augmented Generation (RAG) systems remain vulnerable to hallucinated answers despite incorporating external knowledge sources. We present LettuceDetect a framework that addresses two critical limitations in existing hallucination detection methods: (1) the context window constraints of traditional encoder-based methods, and (2) the computational inefficiency of LLM based approaches. Building on ModernBERT's extended context capabilities (up to 8k tokens) and trained on the RAGTruth benchmark dataset, our approach outperforms all previous encoder-based models and most prompt-based models, while being approximately 30 times smaller than the best models. LettuceDetect is a token-classification model that processes context-question-answer triples, allowing for the identification of unsupported claims at the token level. Evaluations on the RAGTruth corpus demonstrate an F1 score of 79.22% for example-level detection, which is a 14.8% improvement over Luna, the previous state-of-the-art encoder-based architecture. Additionally, the system can process 30 to 60 examples per second on a single GPU, making it more practical for real-world RAG applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.21291", "title": "MIGE: A Unified Framework for Multimodal Instruction-Based Image Generation and Editing", "authors": [ "Xueyun Tian", "Wei Li", "Bingbing Xu", "Yige Yuan", "Yuanzhuo Wang", "Huawei Shen" ], "github": "https://github.com/Eureka-Maggie/MIGE", "project_page": "", "abstract": "Despite significant progress in diffusion-based image generation, subject-driven generation and instruction-based editing remain challenging. Existing methods typically treat them separately, struggling with limited high-quality data and poor generalization. However, both tasks require capturing complex visual variations while maintaining consistency between inputs and outputs. Therefore, we propose MIGE, a unified framework that standardizes task representations using multimodal instructions. It treats subject-driven generation as creation on a blank canvas and instruction-based editing as modification of an existing image, establishing a shared input-output formulation. MIGE introduces a novel multimodal encoder that maps free-form multimodal instructions into a unified vision-language space, integrating visual and semantic features through a feature fusion mechanism.This unification enables joint training of both tasks, providing two key advantages: (1) Cross-Task Enhancement: By leveraging shared visual and semantic representations, joint training improves instruction adherence and visual consistency in both subject-driven generation and instruction-based editing. (2) Generalization: Learning in a unified format facilitates cross-task knowledge transfer, enabling MIGE to generalize to novel compositional tasks, including instruction-based subject-driven editing. Experiments show that MIGE excels in both subject-driven generation and instruction-based editing while setting a state-of-the-art in the new task of instruction-based subject-driven editing. Code and model have been publicly available at https://github.com/Eureka-Maggie/MIGE.", "github_manual": "", "github_regex": "https://github.com/Eureka-Maggie/MIGE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.20900", "title": "DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping", "authors": [ "Yifan Zhong", "Xuchuan Huang", "Ruochong Li", "Ceyao Zhang", "Yitao Liang", "Yaodong Yang", "Yuanpei Chen" ], "github": "https://github.com/Psi-Robot/DexGraspVLA", "project_page": "", "abstract": "Dexterous grasping remains a fundamental yet challenging problem in robotics. A general-purpose robot must be capable of grasping diverse objects in arbitrary scenarios. However, existing research typically relies on specific assumptions, such as single-object settings or limited environments, leading to constrained generalization. Our solution is DexGraspVLA, a hierarchical framework that utilizes a pre-trained Vision-Language model as the high-level task planner and learns a diffusion-based policy as the low-level Action controller. The key insight lies in iteratively transforming diverse language and visual inputs into domain-invariant representations, where imitation learning can be effectively applied due to the alleviation of domain shift. Thus, it enables robust generalization across a wide range of real-world scenarios. Notably, our method achieves a 90+% success rate under thousands of unseen object, lighting, and background combinations in a ``zero-shot'' environment. Empirical analysis further confirms the consistency of internal model behavior across environmental variations, thereby validating our design and explaining its generalization performance. We hope our work can be a step forward in achieving general dexterous grasping. Our demo and code can be found at https://dexgraspvla.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Psi-Robot/DexGraspVLA", "github_api_added_by": "auto" }, { "date": "2025-03-03", "arxiv_id": "2502.20969", "title": "TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval", "authors": [ "Chien-Yu Lin", "Keisuke Kamahori", "Yiyu Liu", "Xiaoxiang Shi", "Madhav Kashyap", "Yile Gu", "Rulin Shao", "Zihao Ye", "Kan Zhu", "Stephanie Wang", "Arvind Krishnamurthy", "Rohan Kadekodi", "Luis Ceze", "Baris Kasikci" ], "github": "", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) extends large language models (LLMs) with external data sources to enhance factual correctness and domain coverage. Modern RAG pipelines rely on large datastores, leading to system challenges in latency-sensitive deployments, especially when limited GPU memory is available. To address these challenges, we propose TeleRAG, an efficient inference system that reduces RAG latency with minimal GPU memory requirements. The core innovation of TeleRAG is lookahead retrieval, a prefetching mechanism that anticipates required data and transfers it from CPU to GPU in parallel with LLM generation. By leveraging the modularity of RAG pipelines, the inverted file index (IVF) search algorithm and similarities between queries, TeleRAG optimally overlaps data movement and computation. Experimental results show that TeleRAG reduces end-to-end RAG inference latency by up to 1.72x on average compared to state-of-the-art systems, enabling faster, more memory-efficient deployments of advanced RAG applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.21318", "title": "How far can we go with ImageNet for Text-to-Image generation?", "authors": [ "L. Degeorge", "A. Ghosh", "N. Dufour", "D. Picard", "V. Kalogeiton" ], "github": "https://github.com/lucasdegeorge/T2I-ImageNet", "project_page": "", "abstract": "Recent text-to-image (T2I) generation models have achieved remarkable results by training on billion-scale datasets, following a `bigger is better' paradigm that prioritizes data quantity over quality. We challenge this established paradigm by demonstrating that strategic data augmentation of small, well-curated datasets can match or outperform models trained on massive web-scraped collections. Using only ImageNet enhanced with well-designed text and image augmentations, we achieve a +2 overall score over SD-XL on GenEval and +5 on DPGBench while using just 1/10th the parameters and 1/1000th the training images. Our results suggest that strategic data augmentation, rather than massive datasets, could offer a more sustainable path forward for T2I generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lucasdegeorge/T2I-ImageNet", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.20490", "title": "EgoNormia: Benchmarking Physical Social Norm Understanding", "authors": [ "MohammadHossein Rezaei", "Yicheng Fu", "Phil Cuvin", "Caleb Ziems", "Yanzhe Zhang", "Hao Zhu", "Diyi Yang" ], "github": "", "project_page": "", "abstract": "Human activity is moderated by norms. When performing actions in the real world, humans not only follow norms, but also consider the trade-off between different norms However, machines are often trained without explicit supervision on norm understanding and reasoning, especially when the norms are grounded in a physical and social context. To improve and evaluate the normative reasoning capability of vision-language models (VLMs), we present EgoNormia |epsilon|, consisting of 1,853 ego-centric videos of human interactions, each of which has two related questions evaluating both the prediction and justification of normative actions. The normative actions encompass seven categories: safety, privacy, proxemics, politeness, cooperation, coordination/proactivity, and communication/legibility. To compile this dataset at scale, we propose a novel pipeline leveraging video sampling, automatic answer generation, filtering, and human validation. Our work demonstrates that current state-of-the-art vision-language models lack robust norm understanding, scoring a maximum of 45% on EgoNormia (versus a human bench of 92%). Our analysis of performance in each dimension highlights the significant risks of safety, privacy, and the lack of collaboration and communication capability when applied to real-world agents. We additionally show that through a retrieval-based generation method, it is possible to use EgoNomia to enhance normative reasoning in VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.20380", "title": "Multi-Turn Code Generation Through Single-Step Rewards", "authors": [ "Arnav Kumar Jain", "Gonzalo Gonzalez-Pumariega", "Wayne Chen", "Alexander M Rush", "Wenting Zhao", "Sanjiban Choudhury" ], "github": "https://github.com/portal-cornell/muCode", "project_page": "", "abstract": "We address the problem of code generation from multi-turn execution feedback. Existing methods either generate code without feedback or use complex, hierarchical reinforcement learning to optimize multi-turn rewards. We propose a simple yet scalable approach, muCode, that solves multi-turn code generation using only single-step rewards. Our key insight is that code generation is a one-step recoverable MDP, where the correct code can be recovered from any intermediate code state in a single turn. muCode iteratively trains both a generator to provide code solutions conditioned on multi-turn execution feedback and a verifier to score the newly generated code. Experimental evaluations show that our approach achieves significant improvements over the state-of-the-art baselines. We provide analysis of the design choices of the reward models and policy, and show the efficacy of muCode at utilizing the execution feedback. Our code is available at https://github.com/portal-cornell/muCode.", "github_manual": "", "github_regex": "https://github.com/portal-cornell/muCode", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2502.19731", "title": "Preference Learning Unlocks LLMs' Psycho-Counseling Skills", "authors": [ "Mian Zhang", "Shaun M. Eack", "Zhiyu Zoey Chen" ], "github": "", "project_page": "", "abstract": "Applying large language models (LLMs) to assist in psycho-counseling is an emerging and meaningful approach, driven by the significant gap between patient needs and the availability of mental health support. However, current LLMs struggle to consistently provide effective responses to client speeches, largely due to the lack of supervision from high-quality real psycho-counseling data, whose content is typically inaccessible due to client privacy concerns. Furthermore, the quality of therapists' responses in available sessions can vary significantly based on their professional training and experience. Assessing the quality of therapists' responses remains an open challenge. In this work, we address these challenges by first proposing a set of professional and comprehensive principles to evaluate therapists' responses to client speeches. Using these principles, we create a preference dataset, PsychoCounsel-Preference, which contains 36k high-quality preference comparison pairs. This dataset aligns with the preferences of professional psychotherapists, providing a robust foundation for evaluating and improving LLMs in psycho-counseling. Experiments on reward modeling and preference learning demonstrate that PsychoCounsel-Preference is an excellent resource for LLMs to acquire essential skills for responding to clients in a counseling session. Our best-aligned model, PsychoCounsel-Llama3-8B, achieves an impressive win rate of 87% against GPT-4o. We release PsychoCounsel-Preference, PsychoCounsel-Llama3-8B and the reward model PsychoCounsel Llama3-8B-Reward to facilitate the research of psycho-counseling with LLMs at: https://hf.co/Psychotherapy-LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-03", "arxiv_id": "2503.00808", "title": "Predictive Data Selection: The Data That Predicts Is the Data That Teaches", "authors": [ "Kashun Shum", "Yuzhen Huang", "Hongjian Zou", "Ding Qi", "Yixuan Liao", "Xiaoxin Chen", "Qian Liu", "Junxian He" ], "github": "https://github.com/hkust-nlp/PreSelect", "project_page": "", "abstract": "Language model pretraining involves training on extensive corpora, where data quality plays a pivotal role. In this work, we aim to directly estimate the contribution of data during pretraining and select pretraining data in an efficient manner. Specifically, we draw inspiration from recent findings showing that compression efficiency (i.e., the normalized loss) of diverse models on certain text correlates strongly with their downstream performance, when the text domain aligns with the downstream benchmark (Huang et al., 2024). Building on this observation, we hypothesize that data on which model losses are predictive of downstream abilities also contribute effectively to learning. To leverage this insight, we introduce data selection based on data's Predictive strength (Preselect), a lightweight and efficient data selection method that requires training and deploying only a fastText-based scorer. Through comprehensive experiments with 1B and 3B parameter models, we demonstrate that models trained on 30B tokens selected with PreSelect surpasses the performance of a vanilla baseline trained on 300B tokens, achieving a 10x reduction in compute requirements. Furthermore, PreSelect significantly outperforms other competitive data selection baselines, such as DCLM and FineWeb-Edu on a scale of 3B models trained on 100B tokens. We open-source our trained data selection scorer along with the curated datasets at https://github.com/hkust-nlp/PreSelect.", "github_manual": "", "github_regex": "https://github.com/hkust-nlp/PreSelect", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.00784", "title": "DuoDecoding: Hardware-aware Heterogeneous Speculative Decoding with Dynamic Multi-Sequence Drafting", "authors": [ "Kai Lv", "Honglin Guo", "Qipeng Guo", "Xipeng Qiu" ], "github": "https://github.com/KaiLv69/DuoDecoding", "project_page": "", "abstract": "Large language models (LLMs) exhibit exceptional performance across a wide range of tasks; however, their token-by-token autoregressive generation process significantly hinders inference speed. Speculative decoding presents a promising draft-then-verify framework that reduces generation latency while maintaining output distribution fidelity. Nevertheless, the draft model introduces additional computational overhead, becoming a performance bottleneck and increasing the time to first token (TTFT). Previous approaches to mitigate draft model overhead have primarily relied on heuristics and generally failed to match the quality of the draft language models. To address these challenges, we propose DuoDecoding, a novel approach that strategically deploys the draft and target models on the CPU and GPU respectively, enabling parallel decoding while preserving draft quality. Our method incorporates a hardware-aware optimal draft budget to minimize idle times and employs dynamic multi-sequence drafting to enhance draft quality. Extensive experiments across seven tasks show that DuoDecoding achieves up to 2.61x speedup in generation latency, while reducing TTFT to 83% of that in conventional speculative decoding. The Code is available at https://github.com/KaiLv69/DuoDecoding.", "github_manual": "", "github_regex": "https://github.com/KaiLv69/DuoDecoding", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01743", "title": "Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs", "authors": [ "Abdelrahman Abouelenin", "Atabak Ashfaq", "Adam Atkinson", "Hany Awadalla", "Nguyen Bach", "Jianmin Bao", "Alon Benhaim", "Martin Cai", "Vishrav Chaudhary", "Congcong Chen", "Dong Chen", "Dongdong Chen", "Junkun Chen", "Weizhu Chen", "Yen-Chun Chen", "Yi-ling Chen", "Qi Dai", "Xiyang Dai", "Ruchao Fan", "Mei Gao", "Min Gao", "Amit Garg", "Abhishek Goswami", "Junheng Hao", "Amr Hendy", "Yuxuan Hu", "Xin Jin", "Mahmoud Khademi", "Dongwoo Kim", "Young Jin Kim", "Gina Lee", "Jinyu Li", "Yunsheng Li", "Chen Liang", "Xihui Lin", "Zeqi Lin", "Mengchen Liu", "Yang Liu", "Gilsinia Lopez", "Chong Luo", "Piyush Madan", "Vadim Mazalov", "Ali Mousavi", "Anh Nguyen", "Jing Pan", "Daniel Perez-Becker", "Jacob Platin", "Thomas Portet", "Kai Qiu", "Bo Ren", "Liliang Ren", "Sambuddha Roy", "Ning Shang", "Yelong Shen", "Saksham Singhal", "Subhojit Som", "Xia Song", "Tetyana Sych", "Praneetha Vaddamanu", "Shuohang Wang", "Yiming Wang", "Zhenghao Wang", "Haibin Wu", "Haoran Xu", "Weijian Xu", "Yifan Yang", "Ziyi Yang", "Donghan Yu", "Ishmam Zabir", "Jianwen Zhang", "Li Lyna Zhang", "Yunan Zhang", "Xiren Zhou" ], "github": "", "project_page": "", "abstract": "We introduce Phi-4-Mini and Phi-4-Multimodal, compact yet highly capable language and multimodal models. Phi-4-Mini is a 3.8-billion-parameter language model trained on high-quality web and synthetic data, significantly outperforming recent open-source models of similar size and matching the performance of models twice its size on math and coding tasks requiring complex reasoning. This achievement is driven by a carefully curated synthetic data recipe emphasizing high-quality math and coding datasets. Compared to its predecessor, Phi-3.5-Mini, Phi-4-Mini features an expanded vocabulary size of 200K tokens to better support multilingual applications, as well as group query attention for more efficient long-sequence generation. Phi-4-Multimodal is a multimodal model that integrates text, vision, and speech/audio input modalities into a single model. Its novel modality extension approach leverages LoRA adapters and modality-specific routers to allow multiple inference modes combining various modalities without interference. For example, it now ranks first in the OpenASR leaderboard to date, although the LoRA component of the speech/audio modality has just 460 million parameters. Phi-4-Multimodal supports scenarios involving (vision + language), (vision + speech), and (speech/audio) inputs, outperforming larger vision-language and speech-language models on a wide range of tasks. Additionally, we experiment to further train Phi-4-Mini to enhance its reasoning capabilities. Despite its compact 3.8-billion-parameter size, this experimental version achieves reasoning performance on par with or surpassing significantly larger models, including DeepSeek-R1-Distill-Qwen-7B and DeepSeek-R1-Distill-Llama-8B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01785", "title": "Visual-RFT: Visual Reinforcement Fine-Tuning", "authors": [ "Ziyu Liu", "Zeyi Sun", "Yuhang Zang", "Xiaoyi Dong", "Yuhang Cao", "Haodong Duan", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/Liuziyu77/Visual-RFT", "project_page": "", "abstract": "Reinforcement Fine-Tuning (RFT) in Large Reasoning Models like OpenAI o1 learns from feedback on its answers, which is especially useful in applications when fine-tuning data is scarce. Recent open-source work like DeepSeek-R1 demonstrates that reinforcement learning with verifiable reward is one key direction in reproducing o1. While the R1-style model has demonstrated success in language models, its application in multi-modal domains remains under-explored. This work introduces Visual Reinforcement Fine-Tuning (Visual-RFT), which further extends the application areas of RFT on visual tasks. Specifically, Visual-RFT first uses Large Vision-Language Models (LVLMs) to generate multiple responses containing reasoning tokens and final answers for each input, and then uses our proposed visual perception verifiable reward functions to update the model via the policy optimization algorithm such as Group Relative Policy Optimization (GRPO). We design different verifiable reward functions for different perception tasks, such as the Intersection over Union (IoU) reward for object detection. Experimental results on fine-grained image classification, few-shot object detection, reasoning grounding, as well as open-vocabulary object detection benchmarks show the competitive performance and advanced generalization ability of Visual-RFT compared with Supervised Fine-tuning (SFT). For example, Visual-RFT improves accuracy by 24.3% over the baseline in one-shot fine-grained image classification with around 100 samples. In few-shot object detection, Visual-RFT also exceeds the baseline by 21.9 on COCO's two-shot setting and 15.4 on LVIS. Our Visual-RFT represents a paradigm shift in fine-tuning LVLMs, offering a data-efficient, reward-driven approach that enhances reasoning and adaptability for domain-specific tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Liuziyu77/Visual-RFT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01807", "title": "Large-Scale Data Selection for Instruction Tuning", "authors": [ "Hamish Ivison", "Muru Zhang", "Faeze Brahman", "Pang Wei Koh", "Pradeep Dasigi" ], "github": "https://github.com/hamishivi/automated-instruction-selection", "project_page": "", "abstract": "Selecting high-quality training data from a larger pool is a crucial step when instruction-tuning language models, as carefully curated datasets often produce models that outperform those trained on much larger, noisier datasets. Automated data selection approaches for instruction-tuning are typically tested by selecting small datasets (roughly 10k samples) from small pools (100-200k samples). However, popular deployed instruction-tuned models often train on hundreds of thousands to millions of samples, subsampled from even larger data pools. We present a systematic study of how well data selection methods scale to these settings, selecting up to 2.5M samples from pools of up to 5.8M samples and evaluating across 7 diverse tasks. We show that many recently proposed methods fall short of random selection in this setting (while using more compute), and even decline in performance when given access to larger pools of data to select over. However, we find that a variant of representation-based data selection (RDS+), which uses weighted mean pooling of pretrained LM hidden states, consistently outperforms more complex methods across all settings tested -- all whilst being more compute-efficient. Our findings highlight that the scaling properties of proposed automated selection methods should be more closely examined. We release our code, data, and models at https://github.com/hamishivi/automated-instruction-selection.", "github_manual": "", "github_regex": "https://github.com/hamishivi/automated-instruction-selection", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01307", "title": "Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs", "authors": [ "Kanishk Gandhi", "Ayush Chakravarthy", "Anikait Singh", "Nathan Lile", "Noah D. Goodman" ], "github": "https://github.com/kanishkg/cognitive-behaviors", "project_page": "", "abstract": "Test-time inference has emerged as a powerful paradigm for enabling language models to ``think'' longer and more carefully about complex challenges, much like skilled human experts. While reinforcement learning (RL) can drive self-improvement in language models on verifiable tasks, some models exhibit substantial gains while others quickly plateau. For instance, we find that Qwen-2.5-3B far exceeds Llama-3.2-3B under identical RL training for the game of Countdown. This discrepancy raises a critical question: what intrinsic properties enable effective self-improvement? We introduce a framework to investigate this question by analyzing four key cognitive behaviors -- verification, backtracking, subgoal setting, and backward chaining -- that both expert human problem solvers and successful language models employ. Our study reveals that Qwen naturally exhibits these reasoning behaviors, whereas Llama initially lacks them. In systematic experimentation with controlled behavioral datasets, we find that priming Llama with examples containing these reasoning behaviors enables substantial improvements during RL, matching or exceeding Qwen's performance. Importantly, the presence of reasoning behaviors, rather than correctness of answers, proves to be the critical factor -- models primed with incorrect solutions containing proper reasoning patterns achieve comparable performance to those trained on correct solutions. Finally, leveraging continued pretraining with OpenWebMath data, filtered to amplify reasoning behaviors, enables the Llama model to match Qwen's self-improvement trajectory. Our findings establish a fundamental relationship between initial reasoning behaviors and the capacity for improvement, explaining why some language models effectively utilize additional computation while others plateau.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kanishkg/cognitive-behaviors", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01739", "title": "VideoUFO: A Million-Scale User-Focused Dataset for Text-to-Video Generation", "authors": [ "Wenhao Wang", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Text-to-video generative models convert textual prompts into dynamic visual content, offering wide-ranging applications in film production, gaming, and education. However, their real-world performance often falls short of user expectations. One key reason is that these models have not been trained on videos related to some topics users want to create. In this paper, we propose VideoUFO, the first Video dataset specifically curated to align with Users' FOcus in real-world scenarios. Beyond this, our VideoUFO also features: (1) minimal (0.29%) overlap with existing video datasets, and (2) videos searched exclusively via YouTube's official API under the Creative Commons license. These two attributes provide future researchers with greater freedom to broaden their training sources. The VideoUFO comprises over 1.09 million video clips, each paired with both a brief and a detailed caption (description). Specifically, through clustering, we first identify 1,291 user-focused topics from the million-scale real text-to-video prompt dataset, VidProM. Then, we use these topics to retrieve videos from YouTube, split the retrieved videos into clips, and generate both brief and detailed captions for each clip. After verifying the clips with specified topics, we are left with about 1.09 million video clips. Our experiments reveal that (1) current 16 text-to-video models do not achieve consistent performance across all user-focused topics; and (2) a simple model trained on VideoUFO outperforms others on worst-performing topics. The dataset is publicly available at https://huggingface.co/datasets/WenhaoWang/VideoUFO under the CC BY 4.0 License.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01774", "title": "Difix3D+: Improving 3D Reconstructions with Single-Step Diffusion Models", "authors": [ "Jay Zhangjie Wu", "Yuxuan Zhang", "Haithem Turki", "Xuanchi Ren", "Jun Gao", "Mike Zheng Shou", "Sanja Fidler", "Zan Gojcic", "Huan Ling" ], "github": "https://github.com/nerfstudio-project/gsplat", "project_page": "", "abstract": "Neural Radiance Fields and 3D Gaussian Splatting have revolutionized 3D reconstruction and novel-view synthesis task. However, achieving photorealistic rendering from extreme novel viewpoints remains challenging, as artifacts persist across representations. In this work, we introduce Difix3D+, a novel pipeline designed to enhance 3D reconstruction and novel-view synthesis through single-step diffusion models. At the core of our approach is Difix, a single-step image diffusion model trained to enhance and remove artifacts in rendered novel views caused by underconstrained regions of the 3D representation. Difix serves two critical roles in our pipeline. First, it is used during the reconstruction phase to clean up pseudo-training views that are rendered from the reconstruction and then distilled back into 3D. This greatly enhances underconstrained regions and improves the overall 3D representation quality. More importantly, Difix also acts as a neural enhancer during inference, effectively removing residual artifacts arising from imperfect 3D supervision and the limited capacity of current reconstruction models. Difix3D+ is a general solution, a single model compatible with both NeRF and 3DGS representations, and it achieves an average 2times improvement in FID score over baselines while maintaining 3D consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nerfstudio-project/gsplat", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01370", "title": "Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation", "authors": [ "Jiantao Lin", "Xin Yang", "Meixi Chen", "Yingjie Xu", "Dongyu Yan", "Leyi Wu", "Xinli Xu", "Lie XU", "Shunsi Zhang", "Ying-Cong Chen" ], "github": "", "project_page": "", "abstract": "Diffusion models have achieved great success in generating 2D images. However, the quality and generalizability of 3D content generation remain limited. State-of-the-art methods often require large-scale 3D assets for training, which are challenging to collect. In this work, we introduce Kiss3DGen (Keep It Simple and Straightforward in 3D Generation), an efficient framework for generating, editing, and enhancing 3D objects by repurposing a well-trained 2D image diffusion model for 3D generation. Specifically, we fine-tune a diffusion model to generate ''3D Bundle Image'', a tiled representation composed of multi-view images and their corresponding normal maps. The normal maps are then used to reconstruct a 3D mesh, and the multi-view images provide texture mapping, resulting in a complete 3D model. This simple method effectively transforms the 3D generation problem into a 2D image generation task, maximizing the utilization of knowledge in pretrained diffusion models. Furthermore, we demonstrate that our Kiss3DGen model is compatible with various diffusion model techniques, enabling advanced features such as 3D editing, mesh and texture enhancement, etc. Through extensive experiments, we demonstrate the effectiveness of our approach, showcasing its ability to produce high-quality 3D models efficiently.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.00714", "title": "Speculative Ad-hoc Querying", "authors": [ "Haoyu Li", "Srikanth Kandula", "Maria Angels de Luis Balaguer", "Aditya Akella", "Venkat Arun" ], "github": "https://github.com/lihy0529/SpeQL", "project_page": "", "abstract": "Analyzing large datasets requires responsive query execution, but executing SQL queries on massive datasets can be slow. This paper explores whether query execution can begin even before the user has finished typing, allowing results to appear almost instantly. We propose SpeQL, a system that leverages Large Language Models (LLMs) to predict likely queries based on the database schema, the user's past queries, and their incomplete query. Since exact query prediction is infeasible, SpeQL speculates on partial queries in two ways: 1) it predicts the query structure to compile and plan queries in advance, and 2) it precomputes smaller temporary tables that are much smaller than the original database, but are still predicted to contain all information necessary to answer the user's final query. Additionally, SpeQL continuously displays results for speculated queries and subqueries in real time, aiding exploratory analysis. A utility/user study showed that SpeQL improved task completion time, and participants reported that its speculative display of results helped them discover patterns in the data more quickly. In the study, SpeQL improves user's query latency by up to 289times and kept the overhead reasonable, at 4$ per hour.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lihy0529/SpeQL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.00501", "title": "Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions", "authors": [ "Jia Chen", "Qian Dong", "Haitao Li", "Xiaohui He", "Yan Gao", "Shaosheng Cao", "Yi Wu", "Ping Yang", "Chen Xu", "Yao Hu", "Qingyao Ai", "Yiqun Liu" ], "github": "", "project_page": "", "abstract": "User-generated content (UGC) communities, especially those featuring multimodal content, improve user experiences by integrating visual and textual information into results (or items). The challenge of improving user experiences in complex systems with search and recommendation (S\\&R) services has drawn significant attention from both academia and industry these years. However, the lack of high-quality datasets has limited the research progress on multimodal S\\&R. To address the growing need for developing better S\\&R services, we present a novel multimodal information retrieval dataset in this paper, namely Qilin. The dataset is collected from Xiaohongshu, a popular social platform with over 300 million monthly active users and an average search penetration rate of over 70\\%. In contrast to existing datasets, Qilin offers a comprehensive collection of user sessions with heterogeneous results like image-text notes, video notes, commercial notes, and direct answers, facilitating the development of advanced multimodal neural retrieval models across diverse task settings. To better model user satisfaction and support the analysis of heterogeneous user behaviors, we also collect extensive APP-level contextual signals and genuine user feedback. Notably, Qilin contains user-favored answers and their referred results for search requests triggering the Deep Query Answering (DQA) module. This allows not only the training \\& evaluation of a Retrieval-augmented Generation (RAG) pipeline, but also the exploration of how such a module would affect users' search behavior. Through comprehensive analysis and experiments, we provide interesting findings and insights for further improving S\\&R systems. We hope that Qilin will significantly contribute to the advancement of multimodal content platforms with S\\&R services in the future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01496", "title": "Liger: Linearizing Large Language Models to Gated Recurrent Structures", "authors": [ "Disen Lan", "Weigao Sun", "Jiaxi Hu", "Jusen Du", "Yu Cheng" ], "github": "https://github.com/OpenSparseLLMs/Linearization", "project_page": "", "abstract": "Transformers with linear recurrent modeling offer linear-time training and constant-memory inference. Despite their demonstrated efficiency and performance, pretraining such non-standard architectures from scratch remains costly and risky. The linearization of large language models (LLMs) transforms pretrained standard models into linear recurrent structures, enabling more efficient deployment. However, current linearization methods typically introduce additional feature map modules that require extensive fine-tuning and overlook the gating mechanisms used in state-of-the-art linear recurrent models. To address these issues, this paper presents Liger, short for Linearizing LLMs to gated recurrent structures. Liger is a novel approach for converting pretrained LLMs into gated linear recurrent models without adding extra parameters. It repurposes the pretrained key matrix weights to construct diverse gating mechanisms, facilitating the formation of various gated recurrent structures while avoiding the need to train additional components from scratch. Using lightweight fine-tuning with Low-Rank Adaptation (LoRA), Liger restores the performance of the linearized gated recurrent models to match that of the original LLMs. Additionally, we introduce Liger Attention, an intra-layer hybrid attention mechanism, which significantly recovers 93\\% of the Transformer-based LLM at 0.02\\% pre-training tokens during the linearization process, achieving competitive results across multiple benchmarks, as validated on models ranging from 1B to 8B parameters. Code is available at https://github.com/OpenSparseLLMs/Linearization.", "github_manual": "", "github_regex": "https://github.com/OpenSparseLLMs/Linearization", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01295", "title": "CodeArena: A Collective Evaluation Platform for LLM Code Generation", "authors": [ "Mingzhe Du", "Anh Tuan Luu", "Bin Ji", "Xiaobao Wu", "Dong Huang", "Terry Yue Zhuo", "Qian Liu", "See-Kiong Ng" ], "github": "https://github.com/DMOJ/online-judge", "project_page": "", "abstract": "Large Language Models (LLMs) have reshaped code generation by synergizing their exceptional comprehension of natural language and programming syntax, thereby substantially boosting developer productivity. These advancements have prompted numerous efforts to quantitatively evaluate their coding capabilities. However, persistent challenges, such as benchmark leakage, data dissipation, and limited system accessibility, continue to impede a timely and accurate assessment. To address these limitations, we introduce CodeArena, an online evaluation framework tailored for LLM code generation. The key innovation is a collective evaluation mechanism, which dynamically recalibrates individual model scores based on the holistic performance of all participating models, mitigating score biases caused by widespread benchmark leakage. In addition, CodeArena ensures open access to all submitted solutions and test cases and provides automation-friendly APIs to streamline the code evaluation workflow. Our main contributions are: (1) a collective evaluation system for unbiased assessment, (2) a public repository of solutions and test cases, and (3) automation-ready APIs for seamless integration.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/DMOJ/online-judge", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.00729", "title": "CLEA: Closed-Loop Embodied Agent for Enhancing Task Execution in Dynamic Environments", "authors": [ "Mingcong Lei", "Ge Wang", "Yiming Zhao", "Zhixin Mai", "Qing Zhao", "Yao Guo", "Zhen Li", "Shuguang Cui", "Yatong Han", "Jinke Ren" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) exhibit remarkable capabilities in the hierarchical decomposition of complex tasks through semantic reasoning. However, their application in embodied systems faces challenges in ensuring reliable execution of subtask sequences and achieving one-shot success in long-term task completion. To address these limitations in dynamic environments, we propose Closed-Loop Embodied Agent (CLEA) -- a novel architecture incorporating four specialized open-source LLMs with functional decoupling for closed-loop task management. The framework features two core innovations: (1) Interactive task planner that dynamically generates executable subtasks based on the environmental memory, and (2) Multimodal execution critic employing an evaluation framework to conduct a probabilistic assessment of action feasibility, triggering hierarchical re-planning mechanisms when environmental perturbations exceed preset thresholds. To validate CLEA's effectiveness, we conduct experiments in a real environment with manipulable objects, using two heterogeneous robots for object search, manipulation, and search-manipulation integration tasks. Across 12 task trials, CLEA outperforms the baseline model, achieving a 67.3% improvement in success rate and a 52.8% increase in task completion rate. These results demonstrate that CLEA significantly enhances the robustness of task planning and execution in dynamic environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01063", "title": "AI-Invented Tonal Languages: Preventing a Machine Lingua Franca Beyond Human Understanding", "authors": [ "David Noever" ], "github": "", "project_page": "", "abstract": "This paper investigates the potential for large language models (LLMs) to develop private tonal languages for machine-to-machine (M2M) communication. Inspired by cryptophasia in human twins (affecting up to 50% of twin births) and natural tonal languages like Mandarin and Vietnamese, we implement a precise character-to-frequency mapping system that encodes the full ASCII character set (32-126) using musical semitones. Each character is assigned a unique frequency, creating a logarithmic progression beginning with space (220 Hz) and ending with tilde (50,175.42 Hz). This spans approximately 7.9 octaves, with higher characters deliberately mapped to ultrasonic frequencies beyond human perception (>20 kHz). Our implemented software prototype demonstrates this encoding through visualization, auditory playback, and ABC musical notation, allowing for analysis of information density and transmission speed. Testing reveals that tonal encoding can achieve information rates exceeding human speech while operating partially outside human perceptual boundaries. This work responds directly to concerns about AI systems catastrophically developing private languages within the next five years, providing a concrete prototype software example of how such communication might function and the technical foundation required for its emergence, detection, and governance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2502.16779", "title": "Unposed Sparse Views Room Layout Reconstruction in the Age of Pretrain Model", "authors": [ "Yaxuan Huang", "Xili Dai", "Jianan Wang", "Xianbiao Qi", "Yixing Yuan", "Xiangyu Yue" ], "github": "https://github.com/justacar/Plane-DUSt3R", "project_page": "", "abstract": "Room layout estimation from multiple-perspective images is poorly investigated due to the complexities that emerge from multi-view geometry, which requires muti-step solutions such as camera intrinsic and extrinsic estimation, image matching, and triangulation. However, in 3D reconstruction, the advancement of recent 3D foundation models such as DUSt3R has shifted the paradigm from the traditional multi-step structure-from-motion process to an end-to-end single-step approach. To this end, we introduce Plane-DUSt3R, a novel method for multi-view room layout estimation leveraging the 3D foundation model DUSt3R. Plane-DUSt3R incorporates the DUSt3R framework and fine-tunes on a room layout dataset (Structure3D) with a modified objective to estimate structural planes. By generating uniform and parsimonious results, Plane-DUSt3R enables room layout estimation with only a single post-processing step and 2D detection results. Unlike previous methods that rely on single-perspective or panorama image, Plane-DUSt3R extends the setting to handle multiple-perspective images. Moreover, it offers a streamlined, end-to-end solution that simplifies the process and reduces error accumulation. Experimental results demonstrate that Plane-DUSt3R not only outperforms state-of-the-art methods on the synthetic dataset but also proves robust and effective on in the wild data with different image styles such as cartoon.Our code is available at: https://github.com/justacar/Plane-DUSt3R", "github_manual": "", "github_regex": "https://github.com/justacar/Plane-DUSt3R", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2502.18965", "title": "OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment", "authors": [ "Jiaxin Deng", "Shiyao Wang", "Kuo Cai", "Lejian Ren", "Qigen Hu", "Weifeng Ding", "Qiang Luo", "Guorui Zhou" ], "github": "", "project_page": "", "abstract": "Recently, generative retrieval-based recommendation systems have emerged as a promising paradigm. However, most modern recommender systems adopt a retrieve-and-rank strategy, where the generative model functions only as a selector during the retrieval stage. In this paper, we propose OneRec, which replaces the cascaded learning framework with a unified generative model. To the best of our knowledge, this is the first end-to-end generative model that significantly surpasses current complex and well-designed recommender systems in real-world scenarios. Specifically, OneRec includes: 1) an encoder-decoder structure, which encodes the user's historical behavior sequences and gradually decodes the videos that the user may be interested in. We adopt sparse Mixture-of-Experts (MoE) to scale model capacity without proportionally increasing computational FLOPs. 2) a session-wise generation approach. In contrast to traditional next-item prediction, we propose a session-wise generation, which is more elegant and contextually coherent than point-by-point generation that relies on hand-crafted rules to properly combine the generated results. 3) an Iterative Preference Alignment module combined with Direct Preference Optimization (DPO) to enhance the quality of the generated results. Unlike DPO in NLP, a recommendation system typically has only one opportunity to display results for each user's browsing request, making it impossible to obtain positive and negative samples simultaneously. To address this limitation, We design a reward model to simulate user generation and customize the sampling strategy. Extensive experiments have demonstrated that a limited number of DPO samples can align user interest preferences and significantly improve the quality of generated results. We deployed OneRec in the main scene of Kuaishou, achieving a 1.6\\% increase in watch-time, which is a substantial improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01183", "title": "DiffRhythm: Blazingly Fast and Embarrassingly Simple End-to-End Full-Length Song Generation with Latent Diffusion", "authors": [ "Ziqian Ning", "Huakang Chen", "Yuepeng Jiang", "Chunbo Hao", "Guobin Ma", "Shuai Wang", "Jixun Yao", "Lei Xie" ], "github": "", "project_page": "", "abstract": "Recent advancements in music generation have garnered significant attention, yet existing approaches face critical limitations. Some current generative models can only synthesize either the vocal track or the accompaniment track. While some models can generate combined vocal and accompaniment, they typically rely on meticulously designed multi-stage cascading architectures and intricate data pipelines, hindering scalability. Additionally, most systems are restricted to generating short musical segments rather than full-length songs. Furthermore, widely used language model-based methods suffer from slow inference speeds. To address these challenges, we propose DiffRhythm, the first latent diffusion-based song generation model capable of synthesizing complete songs with both vocal and accompaniment for durations of up to 4m45s in only ten seconds, maintaining high musicality and intelligibility. Despite its remarkable capabilities, DiffRhythm is designed to be simple and elegant: it eliminates the need for complex data preparation, employs a straightforward model structure, and requires only lyrics and a style prompt during inference. Additionally, its non-autoregressive structure ensures fast inference speeds. This simplicity guarantees the scalability of DiffRhythm. Moreover, we release the complete training code along with the pre-trained model on large-scale data to promote reproducibility and further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01714", "title": "Word Form Matters: LLMs' Semantic Reconstruction under Typoglycemia", "authors": [ "Chenxi Wang", "Tianle Gu", "Zhongyu Wei", "Lang Gao", "Zirui Song", "Xiuying Chen" ], "github": "", "project_page": "", "abstract": "Human readers can efficiently comprehend scrambled words, a phenomenon known as Typoglycemia, primarily by relying on word form; if word form alone is insufficient, they further utilize contextual cues for interpretation. While advanced large language models (LLMs) exhibit similar abilities, the underlying mechanisms remain unclear. To investigate this, we conduct controlled experiments to analyze the roles of word form and contextual information in semantic reconstruction and examine LLM attention patterns. Specifically, we first propose SemRecScore, a reliable metric to quantify the degree of semantic reconstruction, and validate its effectiveness. Using this metric, we study how word form and contextual information influence LLMs' semantic reconstruction ability, identifying word form as the core factor in this process. Furthermore, we analyze how LLMs utilize word form and find that they rely on specialized attention heads to extract and process word form information, with this mechanism remaining stable across varying levels of word scrambling. This distinction between LLMs' fixed attention patterns primarily focused on word form and human readers' adaptive strategy in balancing word form and contextual information provides insights into enhancing LLM performance by incorporating human-like, context-aware mechanisms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2502.18890", "title": "From Hours to Minutes: Lossless Acceleration of Ultra Long Sequence Generation up to 100K Tokens", "authors": [ "Tong Wu", "Junzhe Shen", "Zixia Jia", "Yuxuan Wang", "Zilong Zheng" ], "github": "https://github.com/bigai-nlco/TokenSwift", "project_page": "", "abstract": "Generating ultra-long sequences with large language models (LLMs) has become increasingly crucial but remains a highly time-intensive task, particularly for sequences up to 100K tokens. While traditional speculative decoding methods exist, simply extending their generation limits fails to accelerate the process and can be detrimental. Through an in-depth analysis, we identify three major challenges hindering efficient generation: frequent model reloading, dynamic key-value (KV) management and repetitive generation. To address these issues, we introduce TOKENSWIFT, a novel framework designed to substantially accelerate the generation process of ultra-long sequences while maintaining the target model's inherent quality. Experimental results demonstrate that TOKENSWIFT achieves over 3 times speedup across models of varying scales (1.5B, 7B, 8B, 14B) and architectures (MHA, GQA). This acceleration translates to hours of time savings for ultra-long sequence generation, establishing TOKENSWIFT as a scalable and effective solution at unprecedented lengths. Code can be found at https://github.com/bigai-nlco/TokenSwift.", "github_manual": "", "github_regex": "https://github.com/bigai-nlco/TokenSwift", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01506", "title": "SampleMix: A Sample-wise Pre-training Data Mixing Strategey by Coordinating Data Quality and Diversity", "authors": [ "Xiangyu Xi", "Deyang Kong", "Jian Yang", "Jiawei Yang", "Zhengyu Chen", "Wei Wang", "Jingang Wang", "Xunliang Cai", "Shikun Zhang", "Wei Ye" ], "github": "", "project_page": "", "abstract": "Existing pretraining data mixing methods for large language models (LLMs) typically follow a domain-wise methodology, a top-down process that first determines domain weights and then performs uniform data sampling across each domain. However, these approaches neglect significant inter-domain overlaps and commonalities, failing to control the global diversity of the constructed training dataset. Further, uniform sampling within domains ignores fine-grained sample-specific features, potentially leading to suboptimal data distribution. To address these shortcomings, we propose a novel sample-wise data mixture approach based on a bottom-up paradigm. This method performs global cross-domain sampling by systematically evaluating the quality and diversity of each sample, thereby dynamically determining the optimal domain distribution. Comprehensive experiments across multiple downstream tasks and perplexity assessments demonstrate that SampleMix surpasses existing domain-based methods. Meanwhile, SampleMix requires 1.4x to 2.1x training steps to achieves the baselines' performance, highlighting the substantial potential of SampleMix to optimize pre-training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01103", "title": "Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminator", "authors": [ "Kaiwen Zheng", "Yongxin Chen", "Huayu Chen", "Guande He", "Ming-Yu Liu", "Jun Zhu", "Qinsheng Zhang" ], "github": "", "project_page": "", "abstract": "While likelihood-based generative models, particularly diffusion and autoregressive models, have achieved remarkable fidelity in visual generation, the maximum likelihood estimation (MLE) objective inherently suffers from a mode-covering tendency that limits the generation quality under limited model capacity. In this work, we propose Direct Discriminative Optimization (DDO) as a unified framework that bridges likelihood-based generative training and the GAN objective to bypass this fundamental constraint. Our key insight is to parameterize a discriminator implicitly using the likelihood ratio between a learnable target model and a fixed reference model, drawing parallels with the philosophy of Direct Preference Optimization (DPO). Unlike GANs, this parameterization eliminates the need for joint training of generator and discriminator networks, allowing for direct, efficient, and effective finetuning of a well-trained model to its full potential beyond the limits of MLE. DDO can be performed iteratively in a self-play manner for progressive model refinement, with each round requiring less than 1% of pretraining epochs. Our experiments demonstrate the effectiveness of DDO by significantly advancing the previous SOTA diffusion model EDM, reducing FID scores from 1.79/1.58 to new records of 1.30/0.97 on CIFAR-10/ImageNet-64 datasets, and by consistently improving both guidance-free and CFG-enhanced FIDs of visual autoregressive models on ImageNet 256times256.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01688", "title": "When an LLM is apprehensive about its answers -- and when its uncertainty is justified", "authors": [ "Petr Sychev", "Andrey Goncharov", "Daniil Vyazhev", "Edvard Khalafyan", "Alexey Zaytsev" ], "github": "", "project_page": "", "abstract": "Uncertainty estimation is crucial for evaluating Large Language Models (LLMs), particularly in high-stakes domains where incorrect answers result in significant consequences. Numerous approaches consider this problem, while focusing on a specific type of uncertainty, ignoring others. We investigate what estimates, specifically token-wise entropy and model-as-judge (MASJ), would work for multiple-choice question-answering tasks for different question topics. Our experiments consider three LLMs: Phi-4, Mistral, and Qwen of different sizes from 1.5B to 72B and 14 topics. While MASJ performs similarly to a random error predictor, the response entropy predicts model error in knowledge-dependent domains and serves as an effective indicator of question difficulty: for biology ROC AUC is 0.73. This correlation vanishes for the reasoning-dependent domain: for math questions ROC-AUC is 0.55. More principally, we found out that the entropy measure required a reasoning amount. Thus, data-uncertainty related entropy should be integrated within uncertainty estimates frameworks, while MASJ requires refinement. Moreover, existing MMLU-Pro samples are biased, and should balance required amount of reasoning for different subdomains to provide a more fair assessment of LLMs performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.00455", "title": "PodAgent: A Comprehensive Framework for Podcast Generation", "authors": [ "Yujia Xiao", "Lei He", "Haohan Guo", "Fenglong Xie", "Tan Lee" ], "github": "https://github.com/yujxx/PodAgent", "project_page": "", "abstract": "Existing Existing automatic audio generation methods struggle to generate podcast-like audio programs effectively. The key challenges lie in in-depth content generation, appropriate and expressive voice production. This paper proposed PodAgent, a comprehensive framework for creating audio programs. PodAgent 1) generates informative topic-discussion content by designing a Host-Guest-Writer multi-agent collaboration system, 2) builds a voice pool for suitable voice-role matching and 3) utilizes LLM-enhanced speech synthesis method to generate expressive conversational speech. Given the absence of standardized evaluation criteria for podcast-like audio generation, we developed comprehensive assessment guidelines to effectively evaluate the model's performance. Experimental results demonstrate PodAgent's effectiveness, significantly surpassing direct GPT-4 generation in topic-discussion dialogue content, achieving an 87.4% voice-matching accuracy, and producing more expressive speech through LLM-guided synthesis. Demo page: https://podcast-agent.github.io/demo/. Source code: https://github.com/yujxx/PodAgent.", "github_manual": "", "github_regex": "https://github.com/yujxx/PodAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2502.19402", "title": "General Reasoning Requires Learning to Reason from the Get-go", "authors": [ "Seungwook Han", "Jyothish Pari", "Samuel J. Gershman", "Pulkit Agrawal" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated impressive real-world utility, exemplifying artificial useful intelligence (AUI). However, their ability to reason adaptively and robustly -- the hallmarks of artificial general intelligence (AGI) -- remains fragile. While LLMs seemingly succeed in commonsense reasoning, programming, and mathematics, they struggle to generalize algorithmic understanding across novel contexts. Our experiments with algorithmic tasks in esoteric programming languages reveal that LLM's reasoning overfits to the training data and is limited in its transferability. We hypothesize that the core issue underlying such limited transferability is the coupling of reasoning and knowledge in LLMs. To transition from AUI to AGI, we propose disentangling knowledge and reasoning through three key directions: (1) pretaining to reason using RL from scratch as an alternative to the widely used next-token prediction pretraining, (2) using a curriculum of synthetic tasks to ease the learning of a reasoning prior for RL that can then be transferred to natural language tasks, and (3) learning more generalizable reasoning functions using a small context window to reduce exploiting spurious correlations between tokens. Such a reasoning system coupled with a trained retrieval system and a large external memory bank as a knowledge store can overcome several limitations of existing architectures at learning to reason in novel scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2502.20383", "title": "Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security Analysis", "authors": [ "Jeffrey Yang Fan Chiang", "Seungjae Lee", "Jia-Bin Huang", "Furong Huang", "Yizheng Chen" ], "github": "", "project_page": "", "abstract": "Recent advancements in Web AI agents have demonstrated remarkable capabilities in addressing complex web navigation tasks. However, emerging research shows that these agents exhibit greater vulnerability compared to standalone Large Language Models (LLMs), despite both being built upon the same safety-aligned models. This discrepancy is particularly concerning given the greater flexibility of Web AI Agent compared to standalone LLMs, which may expose them to a wider range of adversarial user inputs. To build a scaffold that addresses these concerns, this study investigates the underlying factors that contribute to the increased vulnerability of Web AI agents. Notably, this disparity stems from the multifaceted differences between Web AI agents and standalone LLMs, as well as the complex signals - nuances that simple evaluation metrics, such as success rate, often fail to capture. To tackle these challenges, we propose a component-level analysis and a more granular, systematic evaluation framework. Through this fine-grained investigation, we identify three critical factors that amplify the vulnerability of Web AI agents; (1) embedding user goals into the system prompt, (2) multi-step action generation, and (3) observational capabilities. Our findings highlights the pressing need to enhance security and robustness in AI agent design and provide actionable insights for targeted defense strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.00031", "title": "Efficient Test-Time Scaling via Self-Calibration", "authors": [ "Chengsong Huang", "Langlin Huang", "Jixuan Leng", "Jiacheng Liu", "Jiaxin Huang" ], "github": "https://github.com/Chengsong-Huang/Self-Calibration", "project_page": "", "abstract": "Increasing test-time computation is a straightforward approach to enhancing the quality of responses in Large Language Models (LLMs). While Best-of-N sampling and Self-Consistency with majority voting are simple and effective, they require a fixed number of sampling responses for each query, regardless of its complexity. This could result in wasted computation for simpler questions and insufficient exploration for more challenging ones. In this work, we argue that model confidence of responses can be used for improving the efficiency of test-time scaling. Unfortunately, LLMs are known to be overconfident and provide unreliable confidence estimation. To address this limitation, we introduce Self-Calibration by distilling Self-Consistency-derived confidence into the model itself. This enables reliable confidence estimation at test time with one forward pass. We then design confidence-based efficient test-time scaling methods to handle queries of various difficulty, such as Early-Stopping for Best-of-N and Self-Consistency with calibrated confidence. Experiments on three LLMs across six datasets demonstrate the effectiveness of our approach. Specifically, applying confidence-based Early Stopping to Best-of-N improves MathQA accuracy from 81.0 to 83.6 with a sample budget of 16 responses, indicating the efficacy of confidence-based sampling strategy at inference time.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Chengsong-Huang/Self-Calibration", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.01820", "title": "RSQ: Learning from Important Tokens Leads to Better Quantized LLMs", "authors": [ "Yi-Lin Sung", "Prateek Yadav", "Jialu Li", "Jaehong Yoon", "Mohit Bansal" ], "github": "https://github.com/ylsung/rsq", "project_page": "", "abstract": "Layer-wise quantization is a key technique for efficiently compressing large models without expensive retraining. Previous methods typically quantize the weights of each layer by \"uniformly\" optimizing the layer reconstruction loss across all output tokens. However, in this paper, we demonstrate that better-quantized models can be obtained by prioritizing learning from important tokens (e.g. which have large attention scores). Building on this finding, we propose RSQ (Rotate, Scale, then Quantize), which (1) applies rotations (orthogonal transformation) to the model to mitigate outliers (those with exceptionally large magnitude), (2) scales the token feature based on its importance, and (3) quantizes the model using the GPTQ framework with the second-order statistics computed by scaled tokens. To compute token importance, we explore both heuristic and dynamic strategies. Based on a thorough analysis of all approaches, we adopt attention concentration, which uses attention scores of each token as its importance, as the best approach. We demonstrate that RSQ consistently outperforms baseline methods across multiple downstream tasks and three model families: LLaMA3, Mistral, and Qwen2.5. Additionally, models quantized with RSQ achieve superior performance on long-context tasks, further highlighting its effectiveness. Lastly, RSQ demonstrates generalizability across various setups, including different model sizes, calibration datasets, bit precisions, and quantization methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ylsung/rsq", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-04", "arxiv_id": "2503.02379", "title": "Teaching Metric Distance to Autoregressive Multimodal Foundational Models", "authors": [ "Jiwan Chung", "Saejin Kim", "Yongrae Jo", "Jaewoo Park", "Dongjun Min", "Youngjae Yu" ], "github": "", "project_page": "", "abstract": "As large language models expand beyond natural language to domains such as mathematics, multimodal understanding, and embodied agents, tokens increasingly reflect metric relationships rather than purely linguistic meaning. We introduce DIST2Loss, a distance-aware framework designed to train autoregressive discrete models by leveraging predefined distance relationships among output tokens. At its core, DIST2Loss transforms continuous exponential family distributions derived from inherent distance metrics into discrete, categorical optimization targets compatible with the models' architectures. This approach enables the models to learn and preserve meaningful distance relationships during token generation while maintaining compatibility with existing architectures. Empirical evaluations show consistent performance gains in diverse multimodal applications, including visual grounding, robotic manipulation, generative reward modeling, and image generation using vector-quantized features. These improvements are pronounced in cases of limited training data, highlighting DIST2Loss's effectiveness in resource-constrained settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.01328", "title": "PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization", "authors": [ "Xinyi Wan", "Penghui Qi", "Guangxing Huang", "Jialin Li", "Min Lin" ], "github": "https://github.com/sail-sg/zero-bubble-pipeline-parallelism", "project_page": "", "abstract": "Pipeline parallelism (PP) is widely used for training large language models (LLMs), yet its scalability is often constrained by high activation memory consumption as the number of in-flight microbatches grows with the degree of PP. In this paper, we focus on addressing this challenge by leveraging the under-explored memory offload strategy in PP. With empirical study, we discover that in the majority of standard configurations, at least half, and potentially all, of the activations can be offloaded with negligible overhead. In the cases where full overload is not possible, we introduce a novel selective offload strategy that decreases peak activation memory in a better-than-linear manner. Furthermore, we integrate memory offload with other techniques to jointly consider overall throughput and memory limitation. Our experiments proves that the per-device activation memory effectively reduces with the total number of stages, making PP a stronger alternative than TP, offering up to a 19\\% acceleration with even lower memory consumption. The implementation is open-sourced at https://github.com/sail-sg/zero-bubble-pipeline-parallelism{this url}.", "github_manual": "", "github_regex": "https://github.com/sail-sg/zero-bubble-pipeline-parallelism", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.01935", "title": "MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents", "authors": [ "Kunlun Zhu", "Hongyi Du", "Zhaochen Hong", "Xiaocheng Yang", "Shuyi Guo", "Zhe Wang", "Zhenhailong Wang", "Cheng Qian", "Xiangru Tang", "Heng Ji", "Jiaxuan You" ], "github": "https://github.com/MultiagentBench/MARBLE", "project_page": "", "abstract": "Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents, yet existing benchmarks either focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition. In this paper, we introduce MultiAgentBench, a comprehensive benchmark designed to evaluate LLM-based multi-agent systems across diverse, interactive scenarios. Our framework measures not only task completion but also the quality of collaboration and competition using novel, milestone-based key performance indicators. Moreover, we evaluate various coordination protocols (including star, chain, tree, and graph topologies) and innovative strategies such as group discussion and cognitive planning. Notably, gpt-4o-mini reaches the average highest task score, graph structure performs the best among coordination protocols in the research scenario, and cognitive planning improves milestone achievement rates by 3%. Code and datasets are public available at https://github.com/MultiagentBench/MARBLE.", "github_manual": "", "github_regex": "https://github.com/MultiagentBench/MARBLE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02846", "title": "Mask-DPO: Generalizable Fine-grained Factuality Alignment of LLMs", "authors": [ "Yuzhe Gu", "Wenwei Zhang", "Chengqi Lyu", "Dahua Lin", "Kai Chen" ], "github": "https://github.com/open-compass/ANAH", "project_page": "", "abstract": "Large language models (LLMs) exhibit hallucinations (i.e., unfaithful or nonsensical information) when serving as AI assistants in various domains. Since hallucinations always come with truthful content in the LLM responses, previous factuality alignment methods that conduct response-level preference learning inevitably introduced noises during training. Therefore, this paper proposes a fine-grained factuality alignment method based on Direct Preference Optimization (DPO), called Mask-DPO. Incorporating sentence-level factuality as mask signals, Mask-DPO only learns from factually correct sentences in the preferred samples and prevents the penalty on factual contents in the not preferred samples, which resolves the ambiguity in the preference learning. Extensive experimental results demonstrate that Mask-DPO can significantly improve the factuality of LLMs responses to questions from both in-domain and out-of-domain datasets, although these questions and their corresponding topics are unseen during training. Only trained on the ANAH train set, the score of Llama3.1-8B-Instruct on the ANAH test set is improved from 49.19% to 77.53%, even surpassing the score of Llama3.1-70B-Instruct (53.44%), while its FactScore on the out-of-domain Biography dataset is also improved from 30.29% to 39.39%. We further study the generalization property of Mask-DPO using different training sample scaling strategies and find that scaling the number of topics in the dataset is more effective than the number of questions. We provide a hypothesis of what factual alignment is doing with LLMs, on the implication of this phenomenon, and conduct proof-of-concept experiments to verify it. We hope the method and the findings pave the way for future research on scaling factuality alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-compass/ANAH", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02682", "title": "MPO: Boosting LLM Agents with Meta Plan Optimization", "authors": [ "Weimin Xiong", "Yifan Song", "Qingxiu Dong", "Bingchan Zhao", "Feifan Song", "Xun Wang", "Sujian Li" ], "github": "", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) have enabled LLM-based agents to successfully tackle interactive planning tasks. However, despite their successes, existing approaches often suffer from planning hallucinations and require retraining for each new agent. To address these challenges, we propose the Meta Plan Optimization (MPO) framework, which enhances agent planning capabilities by directly incorporating explicit guidance. Unlike previous methods that rely on complex knowledge, which either require significant human effort or lack quality assurance, MPO leverages high-level general guidance through meta plans to assist agent planning and enables continuous optimization of the meta plans based on feedback from the agent's task execution. Our experiments conducted on two representative tasks demonstrate that MPO significantly outperforms existing baselines. Moreover, our analysis indicates that MPO provides a plug-and-play solution that enhances both task completion efficiency and generalization capabilities in previous unseen scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02879", "title": "Wikipedia in the Era of LLMs: Evolution and Risks", "authors": [ "Siming Huang", "Yuliang Xu", "Mingmeng Geng", "Yao Wan", "Dongping Chen" ], "github": "https://github.com/HSM316/LLM_Wikipedia", "project_page": "", "abstract": "In this paper, we present a thorough analysis of the impact of Large Language Models (LLMs) on Wikipedia, examining the evolution of Wikipedia through existing data and using simulations to explore potential risks. We begin by analyzing page views and article content to study Wikipedia's recent changes and assess the impact of LLMs. Subsequently, we evaluate how LLMs affect various Natural Language Processing (NLP) tasks related to Wikipedia, including machine translation and retrieval-augmented generation (RAG). Our findings and simulation results reveal that Wikipedia articles have been influenced by LLMs, with an impact of approximately 1%-2% in certain categories. If the machine translation benchmark based on Wikipedia is influenced by LLMs, the scores of the models may become inflated, and the comparative results among models might shift as well. Moreover, the effectiveness of RAG might decrease if the knowledge base becomes polluted by LLM-generated content. While LLMs have not yet fully changed Wikipedia's language and knowledge structures, we believe that our empirical findings signal the need for careful consideration of potential future risks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HSM316/LLM_Wikipedia", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02197", "title": "ATLaS: Agent Tuning via Learning Critical Steps", "authors": [ "Zhixun Chen", "Ming Li", "Yuxuan Huang", "Yali Du", "Meng Fang", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM) agents have demonstrated remarkable generalization capabilities across multi-domain tasks. Existing agent tuning approaches typically employ supervised finetuning on entire expert trajectories. However, behavior-cloning of full trajectories can introduce expert bias and weaken generalization to states not covered by the expert data. Additionally, critical steps, such as planning, complex reasoning for intermediate subtasks, and strategic decision-making, are essential to success in agent tasks, so learning these steps is the key to improving LLM agents. For more effective and efficient agent tuning, we propose ATLaS that identifies the critical steps in expert trajectories and finetunes LLMs solely on these steps with reduced costs. By steering the training's focus to a few critical steps, our method mitigates the risk of overfitting entire trajectories and promotes generalization across different environments and tasks. In extensive experiments, an LLM finetuned on only 30% critical steps selected by ATLaS outperforms the LLM finetuned on all steps and recent open-source LLM agents. ATLaS maintains and improves base LLM skills as generalist agents interacting with diverse environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02878", "title": "Language Models can Self-Improve at State-Value Estimation for Better Search", "authors": [ "Ethan Mendes", "Alan Ritter" ], "github": "https://github.com/noahshinn/reflexion", "project_page": "", "abstract": "Collecting ground truth task completion rewards or human demonstrations for multi-step reasoning tasks is often cost-prohibitive and time-consuming, especially in interactive domains like web tasks. To address this bottleneck, we present self-taught lookahead, a self-supervised method that leverages state-transition dynamics to train a value model capable of effectively guiding language model-controlled search. We find that moderately sized (8 billion parameters) open-weight value models improved with self-taught lookahead can match the performance of using a frontier LLM such as gpt-4o as the value model. Furthermore, we find that self-taught lookahead improves performance by 20% while reducing costs 37x compared to previous LLM-based tree search, without relying on ground truth rewards.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/noahshinn/reflexion", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02876", "title": "SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and Baseline Models", "authors": [ "Dmitry Nechaev", "Alexey Pchelnikov", "Ekaterina Ivanova" ], "github": "https://github.com/HistAI/SPIDER", "project_page": "", "abstract": "Advancing AI in computational pathology requires large, high-quality, and diverse datasets, yet existing public datasets are often limited in organ diversity, class coverage, or annotation quality. To bridge this gap, we introduce SPIDER (Supervised Pathology Image-DEscription Repository), the largest publicly available patch-level dataset covering multiple organ types, including Skin, Colorectal, and Thorax, with comprehensive class coverage for each organ. SPIDER provides high-quality annotations verified by expert pathologists and includes surrounding context patches, which enhance classification performance by providing spatial context. Alongside the dataset, we present baseline models trained on SPIDER using the Hibou-L foundation model as a feature extractor combined with an attention-based classification head. The models achieve state-of-the-art performance across multiple tissue categories and serve as strong benchmarks for future digital pathology research. Beyond patch classification, the model enables rapid identification of significant areas, quantitative tissue metrics, and establishes a foundation for multimodal approaches. Both the dataset and trained models are publicly available to advance research, reproducibility, and AI-driven pathology development. Access them at: https://github.com/HistAI/SPIDER", "github_manual": "", "github_regex": "https://github.com/HistAI/SPIDER", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.00955", "title": "SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking", "authors": [ "Nam V. Nguyen", "Dien X. Tran", "Thanh T. Tran", "Anh T. Hoang", "Tai V. Duong", "Di T. Le", "Phuc-Lu Le" ], "github": "https://github.com/DAVID-NGUYEN-S16/SemViQA", "project_page": "", "abstract": "The rise of misinformation, exacerbated by Large Language Models (LLMs) like GPT and Gemini, demands robust fact-checking solutions, especially for low-resource languages like Vietnamese. Existing methods struggle with semantic ambiguity, homonyms, and complex linguistic structures, often trading accuracy for efficiency. We introduce SemViQA, a novel Vietnamese fact-checking framework integrating Semantic-based Evidence Retrieval (SER) and Two-step Verdict Classification (TVC). Our approach balances precision and speed, achieving state-of-the-art results with 78.97\\% strict accuracy on ISE-DSC01 and 80.82\\% on ViWikiFC, securing 1st place in the UIT Data Science Challenge. Additionally, SemViQA Faster improves inference speed 7x while maintaining competitive accuracy. SemViQA sets a new benchmark for Vietnamese fact verification, advancing the fight against misinformation. The source code is available at: https://github.com/DAVID-NGUYEN-S16/SemViQA.", "github_manual": "", "github_regex": "https://github.com/DAVID-NGUYEN-S16/SemViQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2502.14856", "title": "FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling", "authors": [ "Weilin Zhao", "Tengyu Pan", "Xu Han", "Yudi Zhang", "Ao Sun", "Yuxiang Huang", "Kaihuo Zhang", "Weilun Zhao", "Yuxuan Li", "Jianyong Wang", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/thunlp/FR-Spec", "project_page": "", "abstract": "Speculative sampling has emerged as an important technique for accelerating the auto-regressive generation process of large language models (LLMs) by utilizing a draft-then-verify mechanism to produce multiple tokens per forward pass. While state-of-the-art speculative sampling methods use only a single layer and a language modeling (LM) head as the draft model to achieve impressive layer compression, their efficiency gains are substantially reduced for large-vocabulary LLMs, such as Llama-3-8B with a vocabulary of 128k tokens. To address this, we present FR-Spec, a frequency-ranked speculative sampling framework that optimizes draft candidate selection through vocabulary space compression. By constraining the draft search to a frequency-prioritized token subset, our method reduces LM Head computation overhead by 75% while ensuring the equivalence of the final output distribution. Experiments across multiple datasets demonstrate an average of 1.12times speedup over the state-of-the-art speculative sampling method EAGLE-2.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/thunlp/FR-Spec", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.01342", "title": "UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended Language Interface", "authors": [ "Hao Tang", "Chenwei Xie", "Haiyang Wang", "Xiaoyi Bao", "Tingyu Weng", "Pandeng Li", "Yun Zheng", "Liwei Wang" ], "github": "https://github.com/nnnth/UFO", "project_page": "", "abstract": "Generalist models have achieved remarkable success in both language and vision-language tasks, showcasing the potential of unified modeling. However, effectively integrating fine-grained perception tasks like detection and segmentation into these models remains a significant challenge. This is primarily because these tasks often rely heavily on task-specific designs and architectures that can complicate the modeling process. To address this challenge, we present \\ours, a framework that Unifies Fine-grained visual perception tasks through an Open-ended language interface. By transforming all perception targets into the language space, \\ours unifies object-level detection, pixel-level segmentation, and image-level vision-language tasks into a single model. Additionally, we introduce a novel embedding retrieval approach that relies solely on the language interface to support segmentation tasks. Our framework bridges the gap between fine-grained perception and vision-language tasks, significantly simplifying architectural design and training strategies while achieving comparable or superior performance to methods with intricate task-specific designs. After multi-task training on five standard visual perception datasets, \\ours outperforms the previous state-of-the-art generalist models by 12.3 mAP on COCO instance segmentation and 3.3 mIoU on ADE20K semantic segmentation. Furthermore, our method seamlessly integrates with existing MLLMs, effectively combining fine-grained perception capabilities with their advanced language abilities, thereby enabling more challenging tasks such as reasoning segmentation. Code and models will be publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nnnth/UFO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02268", "title": "AppAgentX: Evolving GUI Agents as Proficient Smartphone Users", "authors": [ "Wenjia Jiang", "Yangyang Zhuang", "Chenxi Song", "Xu Yang", "Chi Zhang" ], "github": "https://github.com/Westlake-AGI-Lab/AppAgentX", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) have led to the development of intelligent LLM-based agents capable of interacting with graphical user interfaces (GUIs). These agents demonstrate strong reasoning and adaptability, enabling them to perform complex tasks that traditionally required predefined rules. However, the reliance on step-by-step reasoning in LLM-based agents often results in inefficiencies, particularly for routine tasks. In contrast, traditional rule-based systems excel in efficiency but lack the intelligence and flexibility to adapt to novel scenarios. To address this challenge, we propose a novel evolutionary framework for GUI agents that enhances operational efficiency while retaining intelligence and flexibility. Our approach incorporates a memory mechanism that records the agent's task execution history. By analyzing this history, the agent identifies repetitive action sequences and evolves high-level actions that act as shortcuts, replacing these low-level operations and improving efficiency. This allows the agent to focus on tasks requiring more complex reasoning, while simplifying routine actions. Experimental results on multiple benchmark tasks demonstrate that our approach significantly outperforms existing methods in both efficiency and accuracy. The code will be open-sourced to support further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Westlake-AGI-Lab/AppAgentX", "github_api_added_by": "auto" }, { "date": "2025-03-05", "arxiv_id": "2503.02368", "title": "Iterative Value Function Optimization for Guided Decoding", "authors": [ "Zhenhua Liu", "Lijun Li", "Ruizhe Chen", "Yuxian Jiang", "Tong Zhu", "Wenliang Chen", "Jing Shao" ], "github": "", "project_page": "", "abstract": "While Reinforcement Learning from Human Feedback (RLHF) has become the predominant method for controlling language model outputs, it suffers from high computational costs and training instability. Guided decoding, especially value-guided methods, offers a cost-effective alternative by controlling outputs without re-training models. However, the accuracy of the value function is crucial for value-guided decoding, as inaccuracies can lead to suboptimal decision-making and degraded performance. Existing methods struggle with accurately estimating the optimal value function, leading to less effective control. We propose Iterative Value Function Optimization, a novel framework that addresses these limitations through two key components: Monte Carlo Value Estimation, which reduces estimation variance by exploring diverse trajectories, and Iterative On-Policy Optimization, which progressively improves value estimation through collecting trajectories from value-guided policies. Extensive experiments on text summarization, multi-turn dialogue, and instruction following demonstrate the effectiveness of value-guided decoding approaches in aligning language models. These approaches not only achieve alignment but also significantly reduce computational costs by leveraging principled value function optimization for efficient and effective control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.00735", "title": "LADDER: Self-Improving LLMs Through Recursive Problem Decomposition", "authors": [ "Toby Simonds", "Akira Yoshiyama" ], "github": "https://github.com/Tufalabs/MITIntegrationBee", "project_page": "", "abstract": "We introduce LADDER (Learning through Autonomous Difficulty-Driven Example Recursion), a framework which enables Large Language Models to autonomously improve their problem-solving capabilities through self-guided learning by recursively generating and solving progressively simpler variants of complex problems. Unlike prior approaches that require curated datasets or human feedback, LADDER leverages a model's own capabilities to generate easier question variants. We demonstrate LADDER's effectiveness in the subject of mathematical integration, improving Llama 3.2 3B's accuracy from 1% to 82% on undergraduate-level problems and enabling Qwen2.5 7B Deepseek-R1 Distilled to achieve 73% on the MIT Integration Bee qualifying examination. We also introduce TTRL (Test-Time Reinforcement Learning), where we perform reinforcement learning on variants of test problems at inference time. TTRL enables Qwen2.5 7B Deepseek-R1 Distilled to achieve a state-of-the-art score of 90% on the MIT Integration Bee qualifying examination, surpassing OpenAI o1's performance. These results show how self-directed strategic learning can achieve significant capability improvements without relying on architectural scaling or human supervision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tufalabs/MITIntegrationBee", "github_api_added_by": "auto" }, { "date": "2025-03-05", "arxiv_id": "2503.02537", "title": "RectifiedHR: Enable Efficient High-Resolution Image Generation via Energy Rectification", "authors": [ "Zhen Yang", "Guibao Shen", "Liang Hou", "Mushui Liu", "Luozhou Wang", "Xin Tao", "Pengfei Wan", "Di Zhang", "Ying-Cong Chen" ], "github": "", "project_page": "", "abstract": "Diffusion models have achieved remarkable advances in various image generation tasks. However, their performance notably declines when generating images at resolutions higher than those used during the training period. Despite the existence of numerous methods for producing high-resolution images, they either suffer from inefficiency or are hindered by complex operations. In this paper, we propose RectifiedHR, an efficient and straightforward solution for training-free high-resolution image generation. Specifically, we introduce the noise refresh strategy, which theoretically only requires a few lines of code to unlock the model's high-resolution generation ability and improve efficiency. Additionally, we first observe the phenomenon of energy decay that may cause image blurriness during the high-resolution image generation process. To address this issue, we propose an Energy Rectification strategy, where modifying the hyperparameters of the classifier-free guidance effectively improves the generation performance. Our method is entirely training-free and boasts a simple implementation logic. Through extensive comparisons with numerous baseline methods, our RectifiedHR demonstrates superior effectiveness and efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02357", "title": "Q-Eval-100K: Evaluating Visual Quality and Alignment Level for Text-to-Vision Content", "authors": [ "Zicheng Zhang", "Tengchuan Kou", "Shushi Wang", "Chunyi Li", "Wei Sun", "Wei Wang", "Xiaoyu Li", "Zongyu Wang", "Xuezhi Cao", "Xiongkuo Min", "Xiaohong Liu", "Guangtao Zhai" ], "github": "https://github.com/zzc-1998/Q-Eval", "project_page": "", "abstract": "Evaluating text-to-vision content hinges on two crucial aspects: visual quality and alignment. While significant progress has been made in developing objective models to assess these dimensions, the performance of such models heavily relies on the scale and quality of human annotations. According to Scaling Law, increasing the number of human-labeled instances follows a predictable pattern that enhances the performance of evaluation models. Therefore, we introduce a comprehensive dataset designed to Evaluate Visual quality and Alignment Level for text-to-vision content (Q-EVAL-100K), featuring the largest collection of human-labeled Mean Opinion Scores (MOS) for the mentioned two aspects. The Q-EVAL-100K dataset encompasses both text-to-image and text-to-video models, with 960K human annotations specifically focused on visual quality and alignment for 100K instances (60K images and 40K videos). Leveraging this dataset with context prompt, we propose Q-Eval-Score, a unified model capable of evaluating both visual quality and alignment with special improvements for handling long-text prompt alignment. Experimental results indicate that the proposed Q-Eval-Score achieves superior performance on both visual quality and alignment, with strong generalization capabilities across other benchmarks. These findings highlight the significant value of the Q-EVAL-100K dataset. Data and codes will be available at https://github.com/zzc-1998/Q-Eval.", "github_manual": "", "github_regex": "https://github.com/zzc-1998/Q-Eval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02783", "title": "IterPref: Focal Preference Learning for Code Generation via Iterative Debugging", "authors": [ "Jie Wu", "Haoling Li", "Xin Zhang", "Jianwen Luo", "Yangyu Huang", "Ruihang Chu", "Yujiu Yang", "Scarlett Li" ], "github": "", "project_page": "", "abstract": "Preference learning enhances Code LLMs beyond supervised fine-tuning by leveraging relative quality comparisons. Existing methods construct preference pairs from candidates based on test case success, treating the higher pass rate sample as positive and the lower as negative. However, this approach does not pinpoint specific errors in the code, which prevents the model from learning more informative error correction patterns, as aligning failing code as a whole lacks the granularity needed to capture meaningful error-resolution relationships. To address these issues, we propose IterPref, a new preference alignment framework that mimics human iterative debugging to refine Code LLMs. IterPref explicitly locates error regions and aligns the corresponding tokens via a tailored DPO algorithm. To generate informative pairs, we introduce the CodeFlow dataset, where samples are iteratively refined until passing tests, with modifications capturing error corrections. Extensive experiments show that a diverse suite of Code LLMs equipped with IterPref achieves significant performance gains in code generation and improves on challenging tasks like BigCodeBench. In-depth analysis reveals that IterPref yields fewer errors. Our code and data will be made publicaly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.00876", "title": "Improve Representation for Imbalanced Regression through Geometric Constraints", "authors": [ "Zijian Dong", "Yilei Wu", "Chongyao Chen", "Yingtian Zou", "Yichi Zhang", "Juan Helen Zhou" ], "github": "https://github.com/yilei-wu/imbalanced-regression", "project_page": "", "abstract": "In representation learning, uniformity refers to the uniform feature distribution in the latent space (i.e., unit hypersphere). Previous work has shown that improving uniformity contributes to the learning of under-represented classes. However, most of the previous work focused on classification; the representation space of imbalanced regression remains unexplored. Classification-based methods are not suitable for regression tasks because they cluster features into distinct groups without considering the continuous and ordered nature essential for regression. In a geometric aspect, we uniquely focus on ensuring uniformity in the latent space for imbalanced regression through two key losses: enveloping and homogeneity. The enveloping loss encourages the induced trace to uniformly occupy the surface of a hypersphere, while the homogeneity loss ensures smoothness, with representations evenly spaced at consistent intervals. Our method integrates these geometric principles into the data representations via a Surrogate-driven Representation Learning (SRL) framework. Experiments with real-world regression and operator learning tasks highlight the importance of uniformity in imbalanced regression and validate the efficacy of our geometry-based loss functions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yilei-wu/imbalanced-regression", "github_api_added_by": "auto" }, { "date": "2025-03-05", "arxiv_id": "2503.01842", "title": "Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding", "authors": [ "Hang Liu", "Sangli Teng", "Ben Liu", "Wei Zhang", "Maani Ghaffari" ], "github": "https://github.com/Genesis-Embodied-AI/Genesis", "project_page": "", "abstract": "This paper introduces Discrete-time Hybrid Automata Learning (DHAL), a framework using on-policy Reinforcement Learning to identify and execute mode-switching without trajectory segmentation or event function learning. Hybrid dynamical systems, which include continuous flow and discrete mode switching, can model robotics tasks like legged robot locomotion. Model-based methods usually depend on predefined gaits, while model-free approaches lack explicit mode-switching knowledge. Current methods identify discrete modes via segmentation before regressing continuous flow, but learning high-dimensional complex rigid body dynamics without trajectory labels or segmentation is a challenging open problem. Our approach incorporates a beta policy distribution and a multi-critic architecture to model contact-guided motions, exemplified by a challenging quadrupedal robot skateboard task. We validate our method through simulations and real-world tests, demonstrating robust performance in hybrid dynamical systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Genesis-Embodied-AI/Genesis", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02812", "title": "Q-Filters: Leveraging QK Geometry for Efficient KV Cache Compression", "authors": [ "Nathan Godey", "Alessio Devoto", "Yu Zhao", "Simone Scardapane", "Pasquale Minervini", "\u00c9ric de la Clergerie", "Beno\u00eet Sagot" ], "github": "", "project_page": "", "abstract": "Autoregressive language models rely on a Key-Value (KV) Cache, which avoids re-computing past hidden states during generation, making it faster. As model sizes and context lengths grow, the KV Cache becomes a significant memory bottleneck, which calls for compression methods that limit its size during generation. In this paper, we discover surprising properties of Query (Q) and Key (K) vectors that allow us to efficiently approximate attention scores without computing the attention maps. We propose Q-Filters, a training-free KV Cache compression method that filters out less crucial Key-Value pairs based on a single context-agnostic projection. Contrarily to many alternatives, Q-Filters is compatible with FlashAttention, as it does not require direct access to attention weights. Experimental results in long-context settings demonstrate that Q-Filters is competitive with attention-based compression methods such as SnapKV in retrieval tasks while consistently outperforming efficient compression schemes such as Streaming-LLM in generation setups. Notably, Q-Filters achieves a 99% accuracy in the needle-in-a-haystack task with a x32 compression level while reducing the generation perplexity drop by up to 65% in text generation compared to Streaming-LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02152", "title": "Tabby: Tabular Data Synthesis with Language Models", "authors": [ "Sonia Cromp", "Satya Sai Srinath Namburi GNVV", "Mohammed Alkhudhayri", "Catherine Cao", "Samuel Guo", "Nicholas Roberts", "Frederic Sala" ], "github": "", "project_page": "", "abstract": "While advances in large language models (LLMs) have greatly improved the quality of synthetic text data in recent years, synthesizing tabular data has received relatively less attention. We address this disparity with Tabby, a simple but powerful post-training modification to the standard Transformer language model architecture, enabling its use for tabular dataset synthesis. Tabby enables the representation of differences across columns using Gated Mixture-of-Experts, with column-specific sets of parameters. Empirically, Tabby results in data quality near or equal to that of real data. By pairing our novel LLM table training technique, Plain, with Tabby, we observe up to a 44% improvement in quality over previous methods. We also show that Tabby extends beyond tables to more general structured data, reaching parity with real data on a nested JSON dataset as well.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02304", "title": "A Token-level Text Image Foundation Model for Document Understanding", "authors": [ "Tongkun Guan", "Zining Wang", "Pei Fu", "Zhengtao Guo", "Wei Shen", "Kai Zhou", "Tiezhu Yue", "Chen Duan", "Hao Sun", "Qianyi Jiang", "Junfeng Luo", "Xiaokang Yang" ], "github": "", "project_page": "", "abstract": "In recent years, general visual foundation models (VFMs) have witnessed increasing adoption, particularly as image encoders for popular multi-modal large language models (MLLMs). However, without semantically fine-grained supervision, these models still encounter fundamental prediction errors in the context of downstream text-image-related tasks, i.e., perception, understanding and reasoning with images containing small and dense texts. To bridge this gap, we develop TokenOCR, the first token-level visual foundation model specifically tailored for text-image-related tasks, designed to support a variety of traditional downstream applications. To facilitate the pretraining of TokenOCR, we also devise a high-quality data production pipeline that constructs the first token-level image text dataset, TokenIT, comprising 20 million images and 1.8 billion token-mask pairs. Furthermore, leveraging this foundation with exceptional image-as-text capability, we seamlessly replace previous VFMs with TokenOCR to construct a document-level MLLM, TokenVL, for VQA-based document understanding tasks. Finally, extensive experiments demonstrate the effectiveness of TokenOCR and TokenVL. Code, datasets, and weights will be available at https://token-family.github.io/TokenOCR_project.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.02823", "title": "A Multimodal Symphony: Integrating Taste and Sound through Generative AI", "authors": [ "Matteo Spanio", "Massimiliano Zampini", "Antonio Rod\u00e0", "Franco Pierucci" ], "github": "https://github.com/matteospanio/tasty-musicgen-small", "project_page": "", "abstract": "In recent decades, neuroscientific and psychological research has traced direct relationships between taste and auditory perceptions. This article explores multimodal generative models capable of converting taste information into music, building on this foundational research. We provide a brief review of the state of the art in this field, highlighting key findings and methodologies. We present an experiment in which a fine-tuned version of a generative music model (MusicGEN) is used to generate music based on detailed taste descriptions provided for each musical piece. The results are promising: according the participants' (n=111) evaluation, the fine-tuned model produces music that more coherently reflects the input taste descriptions compared to the non-fine-tuned model. This study represents a significant step towards understanding and developing embodied interactions between AI, sound, and taste, opening new possibilities in the field of generative AI. We release our dataset, code and pre-trained model at: https://osf.io/xs5jy/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/matteospanio/tasty-musicgen-small", "github_api_added_by": "auto" }, { "date": "2025-03-05", "arxiv_id": "2503.00200", "title": "Unified Video Action Model", "authors": [ "Shuang Li", "Yihuai Gao", "Dorsa Sadigh", "Shuran Song" ], "github": "", "project_page": "", "abstract": "A unified video and action model holds significant promise for robotics, where videos provide rich scene information for action prediction, and actions provide dynamics information for video prediction. However, effectively combining video generation and action prediction remains challenging, and current video generation-based methods struggle to match the performance of direct policy learning in action accuracy and inference speed. To bridge this gap, we introduce the Unified Video Action model (UVA), which jointly optimizes video and action predictions to achieve both high accuracy and efficient action inference. The key lies in learning a joint video-action latent representation and decoupling video-action decoding. The joint latent representation bridges the visual and action domains, effectively modeling the relationship between video and action sequences. Meanwhile, the decoupled decoding, powered by two lightweight diffusion heads, enables high-speed action inference by bypassing video generation during inference. Such a unified framework further enables versatile functionality through masked input training. By selectively masking actions or videos, a single model can tackle diverse tasks beyond policy learning, such as forward and inverse dynamics modeling and video generation. Via an extensive set of experiments, we demonstrate that UVA can serve as a general-purpose solution for a wide range of robotics tasks, such as policy learning, forward/inverse dynamics and video observation prediction, without compromising performance compared to methods tailored for specific applications. Results are best viewed on https://unified-video-action-model.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.00069", "title": "Societal Alignment Frameworks Can Improve LLM Alignment", "authors": [ "Karolina Sta\u0144czak", "Nicholas Meade", "Mehar Bhatia", "Hattie Zhou", "Konstantin B\u00f6ttinger", "Jeremy Barnes", "Jason Stanley", "Jessica Montgomery", "Richard Zemel", "Nicolas Papernot", "Nicolas Chapados", "Denis Therien", "Timothy P. Lillicrap", "Ana Marasovi\u0107", "Sylvie Delacroix", "Gillian K. Hadfield", "Siva Reddy" ], "github": "", "project_page": "", "abstract": "Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared values - a process coined alignment. However, aligning LLMs remains challenging due to the inherent disconnect between the complexity of human values and the narrow nature of the technological approaches designed to address them. Current alignment methods often lead to misspecified objectives, reflecting the broader issue of incomplete contracts, the impracticality of specifying a contract between a model developer, and the model that accounts for every scenario in LLM alignment. In this paper, we argue that improving LLM alignment requires incorporating insights from societal alignment frameworks, including social, economic, and contractual alignment, and discuss potential solutions drawn from these domains. Given the role of uncertainty within societal alignment frameworks, we then investigate how it manifests in LLM alignment. We end our discussion by offering an alternative view on LLM alignment, framing the underspecified nature of its objectives as an opportunity rather than perfect their specification. Beyond technical improvements in LLM alignment, we discuss the need for participatory alignment interface designs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-05", "arxiv_id": "2503.03651", "title": "DoraCycle: Domain-Oriented Adaptation of Unified Generative Model in Multimodal Cycles", "authors": [ "Rui Zhao", "Weijia Mao", "Mike Zheng Shou" ], "github": "https://github.com/showlab/DoraCycle", "project_page": "", "abstract": "Adapting generative models to specific domains presents an effective solution for satisfying specialized requirements. However, adapting to some complex domains remains challenging, especially when these domains require substantial paired data to capture the targeted distributions. Since unpaired data from a single modality, such as vision or language, is more readily available, we utilize the bidirectional mappings between vision and language learned by the unified generative model to enable training on unpaired data for domain adaptation. Specifically, we propose DoraCycle, which integrates two multimodal cycles: text-to-image-to-text and image-to-text-to-image. The model is optimized through cross-entropy loss computed at the cycle endpoints, where both endpoints share the same modality. This facilitates self-evolution of the model without reliance on annotated text-image pairs. Experimental results demonstrate that for tasks independent of paired knowledge, such as stylization, DoraCycle can effectively adapt the unified model using only unpaired data. For tasks involving new paired knowledge, such as specific identities, a combination of a small set of paired image-text examples and larger-scale unpaired data is sufficient for effective domain-oriented adaptation. The code will be released at https://github.com/showlab/DoraCycle.", "github_manual": "", "github_regex": "https://github.com/showlab/DoraCycle", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.00329", "title": "ABC: Achieving Better Control of Multimodal Embeddings using VLMs", "authors": [ "Benjamin Schneider", "Florian Kerschbaum", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Visual embedding models excel at zero-shot tasks like visual retrieval and classification. However, these models cannot be used for tasks that contain ambiguity or require user instruction. These tasks necessitate a multimodal embedding model, which outputs embeddings that combine visual and natural language input. Existing CLIP-based approaches embed images and text independently, and fuse the result. We find that this results in weak interactions between modalities, and poor user control over the representation. We introduce ABC, an open-source multimodal embedding model that uses a vision-language model backbone to deeply integrate image features with natural language instructions. ABC achieves bestfor-size performance on MSCOCO image-to-text retrieval and is the top performing model on classification and VQA tasks in the Massive Multimodal Embedding Benchmark. With a strongly unified vision-language representation, ABC can use natural language to solve subtle and potentially ambiguous visual retrieval problems. To evaluate this capability, we design CtrlBench, a benchmark that requires interleaving textual instructions with image content for correct retrieval. ABC advances the state of multimodal embeddings by offering high-quality representations and flexible natural language control. Our model and datasets are available at our project page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.02951", "title": "KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding", "authors": [ "Zhangchen Xu", "Yang Liu", "Yueqin Yin", "Mingyuan Zhou", "Radha Poovendran" ], "github": "", "project_page": "", "abstract": "We introduce KodCode, a synthetic dataset that addresses the persistent challenge of acquiring high-quality, verifiable training data across diverse difficulties and domains for training Large Language Models for coding. Existing code-focused resources typically fail to ensure either the breadth of coverage (e.g., spanning simple coding tasks to advanced algorithmic problems) or verifiable correctness (e.g., unit tests). In contrast, KodCode comprises question-solution-test triplets that are systematically validated via a self-verification procedure. Our pipeline begins by synthesizing a broad range of coding questions, then generates solutions and test cases with additional attempts allocated to challenging problems. Finally, post-training data synthesis is done by rewriting questions into diverse formats and generating responses under a test-based reject sampling procedure from a reasoning model (DeepSeek R1). This pipeline yields a large-scale, robust and diverse coding dataset. KodCode is suitable for supervised fine-tuning and the paired unit tests also provide great potential for RL tuning. Fine-tuning experiments on coding benchmarks (HumanEval(+), MBPP(+), BigCodeBench, and LiveCodeBench) demonstrate that KodCode-tuned models achieve state-of-the-art performance, surpassing models like Qwen2.5-Coder-32B-Instruct and DeepSeek-R1-Distill-Llama-70B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.00865", "title": "Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers", "authors": [ "Yiran Zhao", "Chaoqun Liu", "Yue Deng", "Jiahao Ying", "Mahani Aljunied", "Zhaodonghui Li", "Lidong Bing", "Hou Pong Chan", "Yu Rong", "Deli Zhao", "Wenxuan Zhang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have revolutionized natural language processing (NLP), yet open-source multilingual LLMs remain scarce, with existing models often limited in language coverage. Such models typically prioritize well-resourced languages, while widely spoken but under-resourced languages are often overlooked. To address this disparity, we introduce Babel, an open multilingual LLM that covers the top 25 languages by number of speakers, supports over 90% of the global population, and includes many languages neglected by other open multilingual LLMs. Unlike traditional continue pretraining approaches, Babel expands its parameter count through a layer extension technique that elevates Babel's performance ceiling. We introduce two variants: Babel-9B, designed for efficient inference and fine-tuning, and Babel-83B, which sets a new standard for open multilingual LLMs. Extensive evaluations on multilingual tasks demonstrate its superior performance compared to open LLMs of comparable size. In addition, using open-source supervised fine-tuning datasets, Babel achieves remarkable performance, with Babel-9B-Chat leading among 10B-sized LLMs and Babel-83B-Chat setting a new standard for multilingual tasks, reaching the same level of commercial models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.00502", "title": "Interact, Instruct to Improve: A LLM-Driven Parallel Actor-Reasoner Framework for Enhancing Autonomous Vehicle Interactions", "authors": [ "Shiyu Fang", "Jiaqi Liu", "Chengkai Xu", "Chen Lv", "Peng Hang", "Jian Sun" ], "github": "https://github.com/FanGShiYuu/Actor-Reasoner", "project_page": "", "abstract": "Autonomous Vehicles (AVs) have entered the commercialization stage, but their limited ability to interact and express intentions still poses challenges in interactions with Human-driven Vehicles (HVs). Recent advances in large language models (LLMs) enable bidirectional human-machine communication, but the conflict between slow inference speed and the need for real-time decision-making challenges practical deployment. To address these issues, this paper introduces a parallel Actor-Reasoner framework designed to enable explicit bidirectional AV-HV interactions across multiple scenarios. First, by facilitating interactions between the LLM-driven Reasoner and heterogeneous simulated HVs during training, an interaction memory database, referred to as the Actor, is established. Then, by introducing the memory partition module and the two-layer memory retrieval module, the Actor's ability to handle heterogeneous HVs is significantly enhanced. Ablation studies and comparisons with other decision-making methods demonstrate that the proposed Actor-Reasoner framework significantly improves safety and efficiency. Finally, with the combination of the external Human-Machine Interface (eHMI) information derived from Reasoner's reasoning and the feasible action solutions retrieved from the Actor, the effectiveness of the proposed Actor-Reasoner is confirmed in multi-scenario field interactions. Our code is available at https://github.com/FanGShiYuu/Actor-Reasoner.", "github_manual": "", "github_regex": "https://github.com/FanGShiYuu/Actor-Reasoner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.03751", "title": "GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control", "authors": [ "Xuanchi Ren", "Tianchang Shen", "Jiahui Huang", "Huan Ling", "Yifan Lu", "Merlin Nimier-David", "Thomas M\u00fcller", "Alexander Keller", "Sanja Fidler", "Jun Gao" ], "github": "https://github.com/nv-tlabs/GEN3C", "project_page": "", "abstract": "We present GEN3C, a generative video model with precise Camera Control and temporal 3D Consistency. Prior video models already generate realistic videos, but they tend to leverage little 3D information, leading to inconsistencies, such as objects popping in and out of existence. Camera control, if implemented at all, is imprecise, because camera parameters are mere inputs to the neural network which must then infer how the video depends on the camera. In contrast, GEN3C is guided by a 3D cache: point clouds obtained by predicting the pixel-wise depth of seed images or previously generated frames. When generating the next frames, GEN3C is conditioned on the 2D renderings of the 3D cache with the new camera trajectory provided by the user. Crucially, this means that GEN3C neither has to remember what it previously generated nor does it have to infer the image structure from the camera pose. The model, instead, can focus all its generative power on previously unobserved regions, as well as advancing the scene state to the next frame. Our results demonstrate more precise camera control than prior work, as well as state-of-the-art results in sparse-view novel view synthesis, even in challenging settings such as driving scenes and monocular dynamic video. Results are best viewed in videos. Check out our webpage! https://research.nvidia.com/labs/toronto-ai/GEN3C/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nv-tlabs/GEN3C", "github_api_added_by": "auto" }, { "date": "2025-03-06", "arxiv_id": "2503.01763", "title": "Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models", "authors": [ "Zhengliang Shi", "Yuhan Wang", "Lingyong Yan", "Pengjie Ren", "Shuaiqiang Wang", "Dawei Yin", "Zhaochun Ren" ], "github": "https://github.com/mangopy/tool-retrieval-benchmark", "project_page": "", "abstract": "Tool learning aims to augment large language models (LLMs) with diverse tools, enabling them to act as agents for solving practical tasks. Due to the limited context length of tool-using LLMs, adopting information retrieval (IR) models to select useful tools from large toolsets is a critical initial step. However, the performance of IR models in tool retrieval tasks remains underexplored and unclear. Most tool-use benchmarks simplify this step by manually pre-annotating a small set of relevant tools for each task, which is far from the real-world scenarios. In this paper, we propose ToolRet, a heterogeneous tool retrieval benchmark comprising 7.6k diverse retrieval tasks, and a corpus of 43k tools, collected from existing datasets. We benchmark six types of models on ToolRet. Surprisingly, even the models with strong performance in conventional IR benchmarks, exhibit poor performance on ToolRet. This low retrieval quality degrades the task pass rate of tool-use LLMs. As a further step, we contribute a large-scale training dataset with over 200k instances, which substantially optimizes the tool retrieval ability of IR models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mangopy/tool-retrieval-benchmark", "github_api_added_by": "auto" }, { "date": "2025-03-06", "arxiv_id": "2503.01729", "title": "FLAME: A Federated Learning Benchmark for Robotic Manipulation", "authors": [ "Santiago Bou Betran", "Alberta Longhini", "Miguel Vasco", "Yuchong Zhang", "Danica Kragic" ], "github": "https://github.com/KTH-RPL/ELSA-Robotics-Challenge", "project_page": "", "abstract": "Recent progress in robotic manipulation has been fueled by large-scale datasets collected across diverse environments. Training robotic manipulation policies on these datasets is traditionally performed in a centralized manner, raising concerns regarding scalability, adaptability, and data privacy. While federated learning enables decentralized, privacy-preserving training, its application to robotic manipulation remains largely unexplored. We introduce FLAME (Federated Learning Across Manipulation Environments), the first benchmark designed for federated learning in robotic manipulation. FLAME consists of: (i) a set of large-scale datasets of over 160,000 expert demonstrations of multiple manipulation tasks, collected across a wide range of simulated environments; (ii) a training and evaluation framework for robotic policy learning in a federated setting. We evaluate standard federated learning algorithms in FLAME, showing their potential for distributed policy learning and highlighting key challenges. Our benchmark establishes a foundation for scalable, adaptive, and privacy-aware robotic learning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KTH-RPL/ELSA-Robotics-Challenge", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.01449", "title": "Benchmarking Large Language Models for Multi-Language Software Vulnerability Detection", "authors": [ "Ting Zhang", "Chengran Yang", "Yindu Su", "Martin Weyssow", "Hung Nguyen", "Tan Bui", "Hong Jin Kang", "Yikun Li", "Eng Lieh Ouh", "Lwin Khin Shar", "David Lo" ], "github": "", "project_page": "", "abstract": "Recent advancements in generative AI have led to the widespread adoption of large language models (LLMs) in software engineering, addressing numerous long-standing challenges. However, a comprehensive study examining the capabilities of LLMs in software vulnerability detection (SVD), a crucial aspect of software security, is currently lacking. Existing research primarily focuses on evaluating LLMs using C/C++ datasets. It typically explores only one or two strategies among prompt engineering, instruction tuning, and sequence classification fine-tuning for open-source LLMs. Consequently, there is a significant knowledge gap regarding the effectiveness of diverse LLMs in detecting vulnerabilities across various programming languages. To address this knowledge gap, we present a comprehensive empirical study evaluating the performance of LLMs on the SVD task. We have compiled a comprehensive dataset comprising 8,260 vulnerable functions in Python, 7,505 in Java, and 28,983 in JavaScript. We assess five open-source LLMs using multiple approaches, including prompt engineering, instruction tuning, and sequence classification fine-tuning. These LLMs are benchmarked against five fine-tuned small language models and two open-source static application security testing tools. Furthermore, we explore two avenues to improve LLM performance on SVD: a) Data perspective: Retraining models using downsampled balanced datasets. b) Model perspective: Investigating ensemble learning methods that combine predictions from multiple LLMs. Our comprehensive experiments demonstrate that SVD remains a challenging task for LLMs. This study provides a thorough understanding of the role of LLMs in SVD and offers practical insights for future advancements in leveraging generative AI to enhance software security practices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.01378", "title": "CognitiveDrone: A VLA Model and Evaluation Benchmark for Real-Time Cognitive Task Solving and Reasoning in UAVs", "authors": [ "Artem Lykov", "Valerii Serpiva", "Muhammad Haris Khan", "Oleg Sautenkov", "Artyom Myshlyaev", "Grik Tadevosyan", "Yasheerah Yaqoot", "Dzmitry Tsetserukou" ], "github": "", "project_page": "", "abstract": "This paper introduces CognitiveDrone, a novel Vision-Language-Action (VLA) model tailored for complex Unmanned Aerial Vehicles (UAVs) tasks that demand advanced cognitive abilities. Trained on a dataset comprising over 8,000 simulated flight trajectories across three key categories-Human Recognition, Symbol Understanding, and Reasoning-the model generates real-time 4D action commands based on first-person visual inputs and textual instructions. To further enhance performance in intricate scenarios, we propose CognitiveDrone-R1, which integrates an additional Vision-Language Model (VLM) reasoning module to simplify task directives prior to high-frequency control. Experimental evaluations using our open-source benchmark, CognitiveDroneBench, reveal that while a racing-oriented model (RaceVLA) achieves an overall success rate of 31.3%, the base CognitiveDrone model reaches 59.6%, and CognitiveDrone-R1 attains a success rate of 77.2%. These results demonstrate improvements of up to 30% in critical cognitive tasks, underscoring the effectiveness of incorporating advanced reasoning capabilities into UAV control systems. Our contributions include the development of a state-of-the-art VLA model for UAV control and the introduction of the first dedicated benchmark for assessing cognitive tasks in drone operations. The complete repository is available at cognitivedrone.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.01372", "title": "SwiLTra-Bench: The Swiss Legal Translation Benchmark", "authors": [ "Joel Niklaus", "Jakob Merane", "Luka Nenadic", "Sina Ahmadi", "Yingqiang Gao", "Cyrill A. H. Chevalley", "Claude Humbel", "Christophe G\u00f6sken", "Lorenzo Tanzi", "Thomas L\u00fcthi", "Stefan Palombo", "Spencer Poff", "Boling Yang", "Nan Wu", "Matthew Guillod", "Robin Mami\u00e9", "Daniel Brunner", "Julio Pereyra", "Niko Grupen" ], "github": "https://github.com/JoelNiklaus/SwissLegalTranslations", "project_page": "", "abstract": "In Switzerland legal translation is uniquely important due to the country's four official languages and requirements for multilingual legal documentation. However, this process traditionally relies on professionals who must be both legal experts and skilled translators -- creating bottlenecks and impacting effective access to justice. To address this challenge, we introduce SwiLTra-Bench, a comprehensive multilingual benchmark of over 180K aligned Swiss legal translation pairs comprising laws, headnotes, and press releases across all Swiss languages along with English, designed to evaluate LLM-based translation systems. Our systematic evaluation reveals that frontier models achieve superior translation performance across all document types, while specialized translation systems excel specifically in laws but under-perform in headnotes. Through rigorous testing and human expert validation, we demonstrate that while fine-tuning open SLMs significantly improves their translation quality, they still lag behind the best zero-shot prompted frontier models such as Claude-3.5-Sonnet. Additionally, we present SwiLTra-Judge, a specialized LLM evaluation system that aligns best with human expert assessments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JoelNiklaus/SwissLegalTranslations", "github_api_added_by": "auto" }, { "date": "2025-03-06", "arxiv_id": "2503.03278", "title": "Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions", "authors": [ "Jun Li", "Che Liu", "Wenjia Bai", "Rossella Arcucci", "Cosmin I. Bercea", "Julia A. Schnabel" ], "github": "", "project_page": "", "abstract": "Visual Language Models (VLMs) have demonstrated impressive capabilities in visual grounding tasks. However, their effectiveness in the medical domain, particularly for abnormality detection and localization within medical images, remains underexplored. A major challenge is the complex and abstract nature of medical terminology, which makes it difficult to directly associate pathological anomaly terms with their corresponding visual features. In this work, we introduce a novel approach to enhance VLM performance in medical abnormality detection and localization by leveraging decomposed medical knowledge. Instead of directly prompting models to recognize specific abnormalities, we focus on breaking down medical concepts into fundamental attributes and common visual patterns. This strategy promotes a stronger alignment between textual descriptions and visual features, improving both the recognition and localization of abnormalities in medical images.We evaluate our method on the 0.23B Florence-2 base model and demonstrate that it achieves comparable performance in abnormality grounding to significantly larger 7B LLaVA-based medical VLMs, despite being trained on only 1.5% of the data used for such models. Experimental results also demonstrate the effectiveness of our approach in both known and previously unseen abnormalities, suggesting its strong generalization capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.01836", "title": "CrowdSelect: Synthetic Instruction Data Selection with Multi-LLM Wisdom", "authors": [ "Yisen Li", "Lingfeng Yang", "Wenxuan Shen", "Pan Zhou", "Yao Wan", "Weiwei Lin", "Dongping Chen" ], "github": "https://github.com/listentm/crowdselect", "project_page": "", "abstract": "Distilling advanced Large Language Models' instruction-following capabilities into smaller models using a selected subset has become a mainstream approach in model training. While existing synthetic instruction data selection strategies rely mainly on single-dimensional signals (i.e., reward scores, model perplexity), they fail to capture the complexity of instruction-following across diverse fields. Therefore, we investigate more diverse signals to capture comprehensive instruction-response pair characteristics and propose three foundational metrics that leverage Multi-LLM wisdom, informed by (1) diverse LLM responses and (2) reward model assessment. Building upon base metrics, we propose CrowdSelect, an integrated metric incorporating a clustering-based approach to maintain response diversity. Our comprehensive experiments demonstrate that our foundation metrics consistently improve performance across 4 base models on MT-bench and Arena-Hard. CrowdSelect, efficiently incorporating all metrics, achieves state-of-the-art performance in both Full and LoRA fine-tuning, showing improvements of 4.81% on Arena-Hard and 11.1% on MT-bench with Llama-3.2-3b-instruct. We hope our findings will bring valuable insights for future research in this direction. Code are available at https://github.com/listentm/crowdselect.", "github_manual": "", "github_regex": "https://github.com/listentm/crowdselect", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.03044", "title": "QE4PE: Word-level Quality Estimation for Human Post-Editing", "authors": [ "Gabriele Sarti", "Vil\u00e9m Zouhar", "Grzegorz Chrupa\u0142a", "Ana Guerberof-Arenas", "Malvina Nissim", "Arianna Bisazza" ], "github": "", "project_page": "", "abstract": "Word-level quality estimation (QE) detects erroneous spans in machine translations, which can direct and facilitate human post-editing. While the accuracy of word-level QE systems has been assessed extensively, their usability and downstream influence on the speed, quality and editing choices of human post-editing remain understudied. Our QE4PE study investigates the impact of word-level QE on machine translation (MT) post-editing in a realistic setting involving 42 professional post-editors across two translation directions. We compare four error-span highlight modalities, including supervised and uncertainty-based word-level QE methods, for identifying potential errors in the outputs of a state-of-the-art neural MT model. Post-editing effort and productivity are estimated by behavioral logs, while quality improvements are assessed by word- and segment-level human annotation. We find that domain, language and editors' speed are critical factors in determining highlights' effectiveness, with modest differences between human-made and automated QE highlights underlining a gap between accuracy and usability in professional workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2502.20317", "title": "Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases", "authors": [ "Yongjia Lei", "Haoyu Han", "Ryan A. Rossi", "Franck Dernoncourt", "Nedim Lipka", "Mahantesh M Halappanavar", "Jiliang Tang", "Yu Wang" ], "github": "https://github.com/Yoega/MoR", "project_page": "", "abstract": "Text-rich Graph Knowledge Bases (TG-KBs) have become increasingly crucial for answering queries by providing textual and structural knowledge. However, current retrieval methods often retrieve these two types of knowledge in isolation without considering their mutual reinforcement and some hybrid methods even bypass structural retrieval entirely after neighboring aggregation. To fill in this gap, we propose a Mixture of Structural-and-Textual Retrieval (MoR) to retrieve these two types of knowledge via a Planning-Reasoning-Organizing framework. In the Planning stage, MoR generates textual planning graphs delineating the logic for answering queries. Following planning graphs, in the Reasoning stage, MoR interweaves structural traversal and textual matching to obtain candidates from TG-KBs. In the Organizing stage, MoR further reranks fetched candidates based on their structural trajectory. Extensive experiments demonstrate the superiority of MoR in harmonizing structural and textual retrieval with insights, including uneven retrieving performance across different query logics and the benefits of integrating structural trajectories for candidate reranking. Our code is available at https://github.com/Yoega/MoR.", "github_manual": "", "github_regex": "https://github.com/Yoega/MoR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2502.18860", "title": "Exploring Rewriting Approaches for Different Conversational Tasks", "authors": [ "Md Mehrab Tanjim", "Ryan A. Rossi", "Mike Rimer", "Xiang Chen", "Sungchul Kim", "Vaishnavi Muppala", "Tong Yu", "Zhengmian Hu", "Ritwik Sinha", "Wei Zhang", "Iftikhar Ahamath Burhanuddin", "Franck Dernoncourt" ], "github": "", "project_page": "", "abstract": "Conversational assistants often require a question rewriting algorithm that leverages a subset of past interactions to provide a more meaningful (accurate) answer to the user's question or request. However, the exact rewriting approach may often depend on the use case and application-specific tasks supported by the conversational assistant, among other constraints. In this paper, we systematically investigate two different approaches, denoted as rewriting and fusion, on two fundamentally different generation tasks, including a text-to-text generation task and a multimodal generative task that takes as input text and generates a visualization or data table that answers the user's question. Our results indicate that the specific rewriting or fusion approach highly depends on the underlying use case and generative task. In particular, we find that for a conversational question-answering assistant, the query rewriting approach performs best, whereas for a data analysis assistant that generates visualizations and data tables based on the user's conversation with the assistant, the fusion approach works best. Notably, we explore two datasets for the data analysis assistant use case, for short and long conversations, and we find that query fusion always performs better, whereas for the conversational text-based question-answering, the query rewrite approach performs best.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.01933", "title": "Fine-Tuning Small Language Models for Domain-Specific AI: An Edge AI Perspective", "authors": [ "Rakshit Aralimatti", "Syed Abdul Gaffar Shakhadri", "Kruthika KR", "Kartik Basavaraj Angadi" ], "github": "", "project_page": "", "abstract": "Deploying large scale language models on edge devices faces inherent challenges such as high computational demands, energy consumption, and potential data privacy risks. This paper introduces the Shakti Small Language Models (SLMs) Shakti-100M, Shakti-250M, and Shakti-500M which target these constraints headon. By combining efficient architectures, quantization techniques, and responsible AI principles, the Shakti series enables on-device intelligence for smartphones, smart appliances, IoT systems, and beyond. We provide comprehensive insights into their design philosophy, training pipelines, and benchmark performance on both general tasks (e.g., MMLU, Hellaswag) and specialized domains (healthcare, finance, and legal). Our findings illustrate that compact models, when carefully engineered and fine-tuned, can meet and often exceed expectations in real-world edge-AI scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.03746", "title": "Process-based Self-Rewarding Language Models", "authors": [ "Shimao Zhang", "Xiao Liu", "Xin Zhang", "Junxiao Liu", "Zheheng Luo", "Shujian Huang", "Yeyun Gong" ], "github": "https://github.com/shimao-zhang/process-self-rewarding", "project_page": "", "abstract": "Large Language Models have demonstrated outstanding performance across various downstream tasks and have been widely applied in multiple scenarios. Human-annotated preference data is used for training to further improve LLMs' performance, which is constrained by the upper limit of human performance. Therefore, Self-Rewarding method has been proposed, where LLMs generate training data by rewarding their own outputs. However, the existing self-rewarding paradigm is not effective in mathematical reasoning scenarios and may even lead to a decline in performance. In this work, we propose the Process-based Self-Rewarding pipeline for language models, which introduces long-thought reasoning, step-wise LLM-as-a-Judge, and step-wise preference optimization within the self-rewarding paradigm. Our new paradigm successfully enhances the performance of LLMs on multiple mathematical reasoning benchmarks through iterative Process-based Self-Rewarding, demonstrating the immense potential of self-rewarding to achieve LLM reasoning that may surpass human capabilities.", "github_manual": "https://github.com/shimao-zhang/process-self-rewarding", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.00307", "title": "Remasking Discrete Diffusion Models with Inference-Time Scaling", "authors": [ "Guanghan Wang", "Yair Schiff", "Subham Sekhar Sahoo", "Volodymyr Kuleshov" ], "github": "https://github.com/kuleshov-group/remdm", "project_page": "", "abstract": "Part of the success of diffusion models stems from their ability to perform iterative refinement, i.e., repeatedly correcting outputs during generation. However, modern masked discrete diffusion lacks this capability: when a token is generated, it cannot be updated again, even when it introduces an error. Here, we address this limitation by introducing the remasking diffusion model (ReMDM) sampler, a method that can be applied to pretrained masked diffusion models in a principled way and that is derived from a discrete diffusion model with a custom remasking backward process. Most interestingly, ReMDM endows discrete diffusion with a form of inference-time compute scaling. By increasing the number of sampling steps, ReMDM generates natural language outputs that approach the quality of autoregressive models, whereas when the computation budget is limited, ReMDM better maintains quality. ReMDM also improves sample quality of masked diffusion models for discretized images, and in scientific domains such as molecule design, ReMDM facilitates diffusion guidance and pushes the Pareto frontier of controllability relative to classical masking and uniform noise diffusion. We provide the code along with a blog post on the project page: https://remdm.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kuleshov-group/remdm", "github_api_added_by": "auto" }, { "date": "2025-03-06", "arxiv_id": "2503.02954", "title": "Reliable and Efficient Multi-Agent Coordination via Graph Neural Network Variational Autoencoders", "authors": [ "Yue Meng", "Nathalie Majcherczyk", "Wenliang Liu", "Scott Kiesel", "Chuchu Fan", "Federico Pecora" ], "github": "https://github.com/mengyuest/gnn-vae-coord", "project_page": "", "abstract": "Multi-agent coordination is crucial for reliable multi-robot navigation in shared spaces such as automated warehouses. In regions of dense robot traffic, local coordination methods may fail to find a deadlock-free solution. In these scenarios, it is appropriate to let a central unit generate a global schedule that decides the passing order of robots. However, the runtime of such centralized coordination methods increases significantly with the problem scale. In this paper, we propose to leverage Graph Neural Network Variational Autoencoders (GNN-VAE) to solve the multi-agent coordination problem at scale faster than through centralized optimization. We formulate the coordination problem as a graph problem and collect ground truth data using a Mixed-Integer Linear Program (MILP) solver. During training, our learning framework encodes good quality solutions of the graph problem into a latent space. At inference time, solution samples are decoded from the sampled latent variables, and the lowest-cost sample is selected for coordination. Finally, the feasible proposal with the highest performance index is selected for the deployment. By construction, our GNN-VAE framework returns solutions that always respect the constraints of the considered coordination problem. Numerical results show that our approach trained on small-scale problems can achieve high-quality solutions even for large-scale problems with 250 robots, being much faster than other baselines. Project page: https://mengyuest.github.io/gnn-vae-coord", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mengyuest/gnn-vae-coord", "github_api_added_by": "auto" }, { "date": "2025-03-06", "arxiv_id": "2503.02924", "title": "Diverse Controllable Diffusion Policy with Signal Temporal Logic", "authors": [ "Yue Meng", "Chuchu fan" ], "github": "https://github.com/mengyuest/pSTL-diffusion-policy", "project_page": "", "abstract": "Generating realistic simulations is critical for autonomous system applications such as self-driving and human-robot interactions. However, driving simulators nowadays still have difficulty in generating controllable, diverse, and rule-compliant behaviors for road participants: Rule-based models cannot produce diverse behaviors and require careful tuning, whereas learning-based methods imitate the policy from data but are not designed to follow the rules explicitly. Besides, the real-world datasets are by nature \"single-outcome\", making the learning method hard to generate diverse behaviors. In this paper, we leverage Signal Temporal Logic (STL) and Diffusion Models to learn controllable, diverse, and rule-aware policy. We first calibrate the STL on the real-world data, then generate diverse synthetic data using trajectory optimization, and finally learn the rectified diffusion policy on the augmented dataset. We test on the NuScenes dataset and our approach can achieve the most diverse rule-compliant trajectories compared to other baselines, with a runtime 1/17X to the second-best approach. In the closed-loop testing, our approach reaches the highest diversity, rule satisfaction rate, and the least collision rate. Our method can generate varied characteristics conditional on different STL parameters in testing. A case study on human-robot encounter scenarios shows our approach can generate diverse and closed-to-oracle trajectories. The annotation tool, augmented dataset, and code are available at https://github.com/mengyuest/pSTL-diffusion-policy.", "github_manual": "", "github_regex": "https://github.com/mengyuest/pSTL-diffusion-policy", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-06", "arxiv_id": "2503.02003", "title": "HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs", "authors": [ "Tin Nguyen", "Logan Bolton", "Mohammad Reza Taesiri", "Anh Totti Nguyen" ], "github": "", "project_page": "", "abstract": "An Achilles heel of Large Language Models (LLMs) is their tendency to hallucinate non-factual statements. A response mixed of factual and non-factual statements poses a challenge for humans to verify and accurately base their decisions on. To combat this problem, we propose Highlighted Chain-of-Thought Prompting (HoT), a technique for prompting LLMs to generate responses with XML tags that ground facts to those provided in the query. That is, given an input question, LLMs would first re-format the question to add XML tags highlighting key facts, and then, generate a response with highlights over the facts referenced from the input. Interestingly, in few-shot settings, HoT outperforms vanilla chain of thought prompting (CoT) on a wide range of 17 tasks from arithmetic, reading comprehension to logical reasoning. When asking humans to verify LLM responses, highlights help time-limited participants to more accurately and efficiently recognize when LLMs are correct. Yet, surprisingly, when LLMs are wrong, HoTs tend to make users believe that an answer is correct.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.04625", "title": "START: Self-taught Reasoner with Tools", "authors": [ "Chengpeng Li", "Mingfeng Xue", "Zhenru Zhang", "Jiaxi Yang", "Beichen Zhang", "Xiang Wang", "Bowen Yu", "Binyuan Hui", "Junyang Lin", "Dayiheng Liu" ], "github": "", "project_page": "", "abstract": "Large reasoning models (LRMs) like OpenAI-o1 and DeepSeek-R1 have demonstrated remarkable capabilities in complex reasoning tasks through the utilization of long Chain-of-thought (CoT). However, these models often suffer from hallucinations and inefficiencies due to their reliance solely on internal reasoning processes. In this paper, we introduce START (Self-Taught Reasoner with Tools), a novel tool-integrated long CoT reasoning LLM that significantly enhances reasoning capabilities by leveraging external tools. Through code execution, START is capable of performing complex computations, self-checking, exploring diverse methods, and self-debugging, thereby addressing the limitations of LRMs. The core innovation of START lies in its self-learning framework, which comprises two key techniques: 1) Hint-infer: We demonstrate that inserting artificially designed hints (e.g., ``Wait, maybe using Python here is a good idea.'') during the inference process of a LRM effectively stimulates its ability to utilize external tools without the need for any demonstration data. Hint-infer can also serve as a simple and effective sequential test-time scaling method; 2) Hint Rejection Sampling Fine-Tuning (Hint-RFT): Hint-RFT combines Hint-infer and RFT by scoring, filtering, and modifying the reasoning trajectories with tool invocation generated by a LRM via Hint-infer, followed by fine-tuning the LRM. Through this framework, we have fine-tuned the QwQ-32B model to achieve START. On PhD-level science QA (GPQA), competition-level math benchmarks (AMC23, AIME24, AIME25), and the competition-level code benchmark (LiveCodeBench), START achieves accuracy rates of 63.6%, 95.0%, 66.7%, 47.1%, and 47.3%, respectively. It significantly outperforms the base QwQ-32B and achieves performance comparable to the state-of-the-art open-weight model R1-Distill-Qwen-32B and the proprietary model o1-Preview.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.04222", "title": "FuseChat-3.0: Preference Optimization Meets Heterogeneous Model Fusion", "authors": [ "Ziyi Yang", "Fanqi Wan", "Longguang Zhong", "Canbin Huang", "Guosheng Liang", "Xiaojun Quan" ], "github": "https://github.com/SLIT-AI/FuseChat-3.0", "project_page": "", "abstract": "We introduce FuseChat-3.0, a suite of large language models (LLMs) developed by integrating the strengths of heterogeneous source LLMs into more compact target LLMs. Our source models include the powerful Gemma-2-27B-it, Mistral-Large-Instruct-2407, Qwen-2.5-72B-Instruct, and Llama-3.1-70B-Instruct. For target models, we focus on three widely-used smaller variants-Llama-3.1-8B-Instruct, Gemma-2-9B-it, and Qwen-2.5-7B-Instruct-along with two ultra-compact options, Llama-3.2-3B-Instruct and Llama-3.2-1B-Instruct. To leverage the diverse capabilities of these source models, we develop a specialized data construction protocol tailored to various tasks and domains. The FuseChat-3.0 training pipeline consists of two key stages: (1) supervised fine-tuning (SFT) to align the target and source model distributions, and (2) Direct Preference Optimization (DPO) to apply preferences from multiple source LLMs to fine-tune the target model. The resulting FuseChat-3.0 models exhibit significant performance gains across tasks such as instruction following, general knowledge, mathematics, and coding. As illustrated in Figure 1, using Llama-3.1-8B-Instruct as the target model, our fusion approach achieves an average improvement of 6.8 points across 14 benchmarks. Moreover, it demonstrates remarkable gains of 37.1 points and 30.1 points on the instruction-following benchmarks AlpacaEval-2 and Arena-Hard, respectively. Our code, models, and datasets are available at https://github.com/SLIT-AI/FuseChat-3.0.", "github_manual": "", "github_regex": "https://github.com/SLIT-AI/FuseChat-3.0", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.04378", "title": "Dedicated Feedback and Edit Models Empower Inference-Time Scaling for Open-Ended General-Domain Tasks", "authors": [ "Zhilin Wang", "Jiaqi Zeng", "Olivier Delalleau", "Daniel Egert", "Ellie Evans", "Hoo-Chang Shin", "Felipe Soares", "Yi Dong", "Oleksii Kuchaiev" ], "github": "", "project_page": "", "abstract": "Inference-Time Scaling has been critical to the success of recent models such as OpenAI o1 and DeepSeek R1. However, many techniques used to train models for inference-time scaling require tasks to have answers that can be verified, limiting their application to domains such as math, coding and logical reasoning. We take inspiration from how humans make first attempts, ask for detailed feedback from others and make improvements based on such feedback across a wide spectrum of open-ended endeavors. To this end, we collect data for and train dedicated Feedback and Edit Models that are capable of performing inference-time scaling for open-ended general-domain tasks. In our setup, one model generates an initial response, which are given feedback by a second model, that are then used by a third model to edit the response. We show that performance on Arena Hard, a benchmark strongly predictive of Chatbot Arena Elo can be boosted by scaling the number of initial response drafts, effective feedback and edited responses. When scaled optimally, our setup based on 70B models from the Llama 3 family can reach SoTA performance on Arena Hard at 92.7 as of 5 Mar 2025, surpassing OpenAI o1-preview-2024-09-12 with 90.4 and DeepSeek R1 with 92.3.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.04598", "title": "HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid Normalization", "authors": [ "Zhijian Zhuo", "Yutao Zeng", "Ya Wang", "Sijun Zhang", "Jian Yang", "Xiaoqing Li", "Xun Zhou", "Jinwen Ma" ], "github": "https://github.com/BryceZhuo/HybridNorm", "project_page": "", "abstract": "Transformers have become the de facto architecture for a wide range of machine learning tasks, particularly in large language models (LLMs). Despite their remarkable performance, challenges remain in training deep transformer networks, especially regarding the location of layer normalization. While Pre-Norm structures facilitate easier training due to their more prominent identity path, they often yield suboptimal performance compared to Post-Norm. In this paper, we propose HybridNorm, a straightforward yet effective hybrid normalization strategy that integrates the advantages of both Pre-Norm and Post-Norm approaches. Specifically, HybridNorm employs QKV normalization within the attention mechanism and Post-Norm in the feed-forward network (FFN) of each transformer block. This design not only stabilizes training but also enhances performance, particularly in the context of LLMs. Comprehensive experiments in both dense and sparse architectures show that HybridNorm consistently outperforms both Pre-Norm and Post-Norm approaches, achieving state-of-the-art results across various benchmarks. These findings highlight the potential of HybridNorm as a more stable and effective technique for improving the training and performance of deep transformer models. %Code will be made publicly available. Code is available at https://github.com/BryceZhuo/HybridNorm.", "github_manual": "", "github_regex": "https://github.com/BryceZhuo/HybridNorm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.04094", "title": "Pok\u00e9Champ: an Expert-level Minimax Language Agent", "authors": [ "Seth Karten", "Andy Luu Nguyen", "Chi Jin" ], "github": "", "project_page": "", "abstract": "We introduce Pok\\'eChamp, a minimax agent powered by Large Language Models (LLMs) for Pok\\'emon battles. Built on a general framework for two-player competitive games, Pok\\'eChamp leverages the generalist capabilities of LLMs to enhance minimax tree search. Specifically, LLMs replace three key modules: (1) player action sampling, (2) opponent modeling, and (3) value function estimation, enabling the agent to effectively utilize gameplay history and human knowledge to reduce the search space and address partial observability. Notably, our framework requires no additional LLM training. We evaluate Pok\\'eChamp in the popular Gen 9 OU format. When powered by GPT-4o, it achieves a win rate of 76% against the best existing LLM-based bot and 84% against the strongest rule-based bot, demonstrating its superior performance. Even with an open-source 8-billion-parameter Llama 3.1 model, Pok\\'eChamp consistently outperforms the previous best LLM-based bot, Pok\\'ellmon powered by GPT-4o, with a 64% win rate. Pok\\'eChamp attains a projected Elo of 1300-1500 on the Pok\\'emon Showdown online ladder, placing it among the top 30%-10% of human players. In addition, this work compiles the largest real-player Pok\\'emon battle dataset, featuring over 3 million games, including more than 500k high-Elo matches. Based on this dataset, we establish a series of battle benchmarks and puzzles to evaluate specific battling skills. We further provide key updates to the local game engine. We hope this work fosters further research that leverage Pok\\'emon battle as benchmark to integrate LLM technologies with game-theoretic algorithms addressing general multiagent problems. Videos, code, and dataset available at https://sites.google.com/view/pokechamp-llm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.04130", "title": "Token-Efficient Long Video Understanding for Multimodal LLMs", "authors": [ "Jindong Jiang", "Xiuyu Li", "Zhijian Liu", "Muyang Li", "Guo Chen", "Zhiqi Li", "De-An Huang", "Guilin Liu", "Zhiding Yu", "Kurt Keutzer", "Sungjin Ahn", "Jan Kautz", "Hongxu Yin", "Yao Lu", "Song Han", "Wonmin Byeon" ], "github": "", "project_page": "", "abstract": "Recent advances in video-based multimodal large language models (Video-LLMs) have significantly improved video understanding by processing videos as sequences of image frames. However, many existing methods treat frames independently in the vision backbone, lacking explicit temporal modeling, which limits their ability to capture dynamic patterns and efficiently handle long videos. To address these limitations, we introduce STORM (Spatiotemporal TOken Reduction for Multimodal LLMs), a novel architecture incorporating a dedicated temporal encoder between the image encoder and the LLM. Our temporal encoder leverages the Mamba State Space Model to integrate temporal information into image tokens, generating enriched representations that preserve inter-frame dynamics across the entire video sequence. This enriched encoding not only enhances video reasoning capabilities but also enables effective token reduction strategies, including test-time sampling and training-based temporal and spatial pooling, substantially reducing computational demands on the LLM without sacrificing key temporal information. By integrating these techniques, our approach simultaneously reduces training and inference latency while improving performance, enabling efficient and robust video understanding over extended temporal contexts. Extensive evaluations show that STORM achieves state-of-the-art results across various long video understanding benchmarks (more than 5\\% improvement on MLVU and LongVideoBench) while reducing the computation costs by up to 8times and the decoding latency by 2.4-2.9times for the fixed numbers of input frames. Project page is available at https://research.nvidia.com/labs/lpr/storm", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.03803", "title": "EgoLife: Towards Egocentric Life Assistant", "authors": [ "Jingkang Yang", "Shuai Liu", "Hongming Guo", "Yuhao Dong", "Xiamengwei Zhang", "Sicheng Zhang", "Pengyun Wang", "Zitang Zhou", "Binzhu Xie", "Ziyue Wang", "Bei Ouyang", "Zhengyu Lin", "Marco Cominelli", "Zhongang Cai", "Yuanhan Zhang", "Peiyuan Zhang", "Fangzhou Hong", "Joerg Widmer", "Francesco Gringoli", "Lei Yang", "Bo Li", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "We introduce EgoLife, a project to develop an egocentric life assistant that accompanies and enhances personal efficiency through AI-powered wearable glasses. To lay the foundation for this assistant, we conducted a comprehensive data collection study where six participants lived together for one week, continuously recording their daily activities - including discussions, shopping, cooking, socializing, and entertainment - using AI glasses for multimodal egocentric video capture, along with synchronized third-person-view video references. This effort resulted in the EgoLife Dataset, a comprehensive 300-hour egocentric, interpersonal, multiview, and multimodal daily life dataset with intensive annotation. Leveraging this dataset, we introduce EgoLifeQA, a suite of long-context, life-oriented question-answering tasks designed to provide meaningful assistance in daily life by addressing practical questions such as recalling past relevant events, monitoring health habits, and offering personalized recommendations. To address the key technical challenges of (1) developing robust visual-audio models for egocentric data, (2) enabling identity recognition, and (3) facilitating long-context question answering over extensive temporal information, we introduce EgoButler, an integrated system comprising EgoGPT and EgoRAG. EgoGPT is an omni-modal model trained on egocentric datasets, achieving state-of-the-art performance on egocentric video understanding. EgoRAG is a retrieval-based component that supports answering ultra-long-context questions. Our experimental studies verify their working mechanisms and reveal critical factors and bottlenecks, guiding future improvements. By releasing our datasets, models, and benchmarks, we aim to stimulate further research in egocentric AI assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.03983", "title": "Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities", "authors": [ "Sreyan Ghosh", "Zhifeng Kong", "Sonal Kumar", "S Sakshi", "Jaehyeon Kim", "Wei Ping", "Rafael Valle", "Dinesh Manocha", "Bryan Catanzaro" ], "github": "", "project_page": "", "abstract": "Understanding and reasoning over non-speech sounds and music are crucial for both humans and AI agents to interact effectively with their environments. In this paper, we introduce Audio Flamingo 2 (AF2), an Audio-Language Model (ALM) with advanced audio understanding and reasoning capabilities. AF2 leverages (i) a custom CLAP model, (ii) synthetic Audio QA data for fine-grained audio reasoning, and (iii) a multi-stage curriculum learning strategy. AF2 achieves state-of-the-art performance with only a 3B parameter small language model, surpassing large open-source and proprietary models across over 20 benchmarks. Next, for the first time, we extend audio understanding to long audio segments (30 secs to 5 mins) and propose LongAudio, a large and novel dataset for training ALMs on long audio captioning and question-answering tasks. Fine-tuning AF2 on LongAudio leads to exceptional performance on our proposed LongAudioBench, an expert annotated benchmark for evaluating ALMs on long audio understanding capabilities. We conduct extensive ablation studies to confirm the efficacy of our approach. Project Website: https://research.nvidia.com/labs/adlr/AF2/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.02191", "title": "Understanding and Predicting Derailment in Toxic Conversations on GitHub", "authors": [ "Mia Mohammad Imran", "Robert Zita", "Rebekah Copeland", "Preetha Chatterjee", "Rahat Rizvi Rahman", "Kostadin Damevski" ], "github": "", "project_page": "", "abstract": "Software projects thrive on the involvement and contributions of individuals from different backgrounds. However, toxic language and negative interactions can hinder the participation and retention of contributors and alienate newcomers. Proactive moderation strategies aim to prevent toxicity from occurring by addressing conversations that have derailed from their intended purpose. This study aims to understand and predict conversational derailment leading to toxicity on GitHub. To facilitate this research, we curate a novel dataset comprising 202 toxic conversations from GitHub with annotated derailment points, along with 696 non-toxic conversations as a baseline. Based on this dataset, we identify unique characteristics of toxic conversations and derailment points, including linguistic markers such as second-person pronouns, negation terms, and tones of Bitter Frustration and Impatience, as well as patterns in conversational dynamics between project contributors and external participants. Leveraging these empirical observations, we propose a proactive moderation approach to automatically detect and address potentially harmful conversations before escalation. By utilizing modern LLMs, we develop a conversation trajectory summary technique that captures the evolution of discussions and identifies early signs of derailment. Our experiments demonstrate that LLM prompts tailored to provide summaries of GitHub conversations achieve 69% F1-Score in predicting conversational derailment, strongly improving over a set of baseline approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2502.20258", "title": "LLM as a Broken Telephone: Iterative Generation Distorts Information", "authors": [ "Amr Mohamed", "Mingmeng Geng", "Michalis Vazirgiannis", "Guokan Shang" ], "github": "", "project_page": "", "abstract": "As large language models are increasingly responsible for online content, concerns arise about the impact of repeatedly processing their own outputs. Inspired by the \"broken telephone\" effect in chained human communication, this study investigates whether LLMs similarly distort information through iterative generation. Through translation-based experiments, we find that distortion accumulates over time, influenced by language choice and chain complexity. While degradation is inevitable, it can be mitigated through strategic prompting techniques. These findings contribute to discussions on the long-term effects of AI-mediated information propagation, raising important questions about the reliability of LLM-generated content in iterative workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.04606", "title": "The Best of Both Worlds: Integrating Language Models and Diffusion Models for Video Generation", "authors": [ "Aoxiong Yin", "Kai Shen", "Yichong Leng", "Xu Tan", "Xinyu Zhou", "Juncheng Li", "Siliang Tang" ], "github": "https://github.com/landiff/landiff", "project_page": "", "abstract": "Recent advancements in text-to-video (T2V) generation have been driven by two competing paradigms: autoregressive language models and diffusion models. However, each paradigm has intrinsic limitations: language models struggle with visual quality and error accumulation, while diffusion models lack semantic understanding and causal modeling. In this work, we propose LanDiff, a hybrid framework that synergizes the strengths of both paradigms through coarse-to-fine generation. Our architecture introduces three key innovations: (1) a semantic tokenizer that compresses 3D visual features into compact 1D discrete representations through efficient semantic compression, achieving a sim14,000times compression ratio; (2) a language model that generates semantic tokens with high-level semantic relationships; (3) a streaming diffusion model that refines coarse semantics into high-fidelity videos. Experiments show that LanDiff, a 5B model, achieves a score of 85.43 on the VBench T2V benchmark, surpassing the state-of-the-art open-source models Hunyuan Video (13B) and other commercial models such as Sora, Keling, and Hailuo. Furthermore, our model also achieves state-of-the-art performance in long video generation, surpassing other open-source models in this field. Our demo can be viewed at https://landiff.github.io/.", "github_manual": "https://github.com/landiff/landiff", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.04725", "title": "L^2M: Mutual Information Scaling Law for Long-Context Language Modeling", "authors": [ "Zhuo Chen", "Oriol Mayn\u00e9 i Comas", "Zhuotao Jin", "Di Luo", "Marin Solja\u010di\u0107" ], "github": "https://github.com/LSquaredM/mutual_info_scaling_law", "project_page": "", "abstract": "We rigorously establish a bipartite mutual information scaling law in natural language that governs long-range dependencies. This scaling law, which we show is distinct from and scales independently of the conventional two-point mutual information, is the key to understanding long-context language modeling. Using this scaling law, we formulate the Long-context Language Modeling (L^2M) condition, which relates a model's capacity for effective long context length modeling to the scaling of its latent state size for storing past information. Our results are validated through experiments on both transformers and state space models. This work establishes a theoretical foundation that guides the development of large language models toward longer context lengths.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LSquaredM/mutual_info_scaling_law", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.01375", "title": "Combining Flow Matching and Transformers for Efficient Solution of Bayesian Inverse Problems", "authors": [ "Daniil Sherki", "Ivan Oseledets", "Ekaterina Muravleva" ], "github": "", "project_page": "", "abstract": "Solving Bayesian inverse problems efficiently remains a significant challenge due to the complexity of posterior distributions and the computational cost of traditional sampling methods. Given a series of observations and the forward model, we want to recover the distribution of the parameters, conditioned on observed experimental data. We show, that combining Conditional Flow Mathching (CFM) with transformer-based architecture, we can efficiently sample from such kind of distribution, conditioned on variable number of observations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.01901", "title": "Identifying Sensitive Weights via Post-quantization Integral", "authors": [ "Yuezhou Hu", "Weiyu Huang", "Zichen Liang", "Chang Chen", "Jintao Zhang", "Jun Zhu", "Jianfei Chen" ], "github": "", "project_page": "", "abstract": "Serving Large Language Models (LLMs) is costly. However, post-training weight quantization can address this problem by both compressing their sizes for limited memory and saving bandwidth for acceleration. As not all weight dimensions are equally important, those methods typically rely on a sensitivity metric, which indicates the element-wise influence of weights on loss function and is used to preprocess original weights for better quantization. In this work, we conduct an empirical study on the accuracy of the sensitivity metric, and find that existing gradient and Hessian based metrics are very inaccurate: they underestimate quantization's impact on the loss function by orders of magnitude, mainly due to the small convergence radius of local 2nd order approximation, \\ie, gradient and Hessian term in Taylor's formula. To tackle this problem, we propose Post-quantization Integral (PQI), an accurate metric to estimate posterior sensitivity in a fine-grained manner. To leverage this accurate metric, we further propose ReQuant, a simple yet powerful framework that mainly consists of two Dense-and-Sparse detach components: self-adaptive outlier selection and step-wise significant weights detach. Results show that ReQuant boosts state-of-the-art post-training quantization methods, with a pronounced improvement of 2.66 perplexity gain on Llama 3.2 1B with QTIP.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.02972", "title": "LINGOLY-TOO: Disentangling Memorisation from Reasoning with Linguistic Templatisation and Orthographic Obfuscation", "authors": [ "Jude Khouja", "Karolina Korgul", "Simi Hellsten", "Lingyi Yang", "Vlad Neacs", "Harry Mayne", "Ryan Kearns", "Andrew Bean", "Adam Mahdi" ], "github": "", "project_page": "", "abstract": "Effective evaluation of the reasoning capabilities of large language models (LLMs) are susceptible to overestimation due to data exposure of evaluation benchmarks. We introduce a framework for producing linguistic reasoning problems that reduces the effect of memorisation in model performance estimates and apply this framework to develop LINGOLY-TOO, a challenging evaluation benchmark for linguistic reasoning. By developing orthographic templates, we dynamically obfuscate the writing systems of real languages to generate numerous question variations. These variations preserve the reasoning steps required for each solution while reducing the likelihood of specific problem instances appearing in model training data. Our experiments demonstrate that frontier models, including OpenAI o1-preview and DeepSeem R1, struggle with advanced reasoning. Our analysis also shows that LLMs exhibit noticeable variance in accuracy across permutations of the same problem, and on average perform better on questions appearing in their original orthography. Our findings highlight the opaque nature of response generation in LLMs and provide evidence that prior data exposure contributes to overestimating the reasoning capabilities of frontier models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.04644", "title": "IFIR: A Comprehensive Benchmark for Evaluating Instruction-Following in Expert-Domain Information Retrieval", "authors": [ "Tingyu Song", "Guo Gan", "Mingsheng Shang", "Yilun Zhao" ], "github": "", "project_page": "", "abstract": "We introduce IFIR, the first comprehensive benchmark designed to evaluate instruction-following information retrieval (IR) in expert domains. IFIR includes 2,426 high-quality examples and covers eight subsets across four specialized domains: finance, law, healthcare, and science literature. Each subset addresses one or more domain-specific retrieval tasks, replicating real-world scenarios where customized instructions are critical. IFIR enables a detailed analysis of instruction-following retrieval capabilities by incorporating instructions at different levels of complexity. We also propose a novel LLM-based evaluation method to provide a more precise and reliable assessment of model performance in following instructions. Through extensive experiments on 15 frontier retrieval models, including those based on LLMs, our results reveal that current models face significant challenges in effectively following complex, domain-specific instructions. We further provide in-depth analyses to highlight these limitations, offering valuable insights to guide future advancements in retriever development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.04369", "title": "Lost in Literalism: How Supervised Training Shapes Translationese in LLMs", "authors": [ "Yafu Li", "Ronghao Zhang", "Zhilin Wang", "Huajian Zhang", "Leyang Cui", "Yongjing Yin", "Tong Xiao", "Yue Zhang" ], "github": "https://github.com/yafuly/LLM_Translationese", "project_page": "", "abstract": "Large language models (LLMs) have achieved remarkable success in machine translation, demonstrating impressive performance across diverse languages. However, translationese, characterized by overly literal and unnatural translations, remains a persistent challenge in LLM-based translation systems. Despite their pre-training on vast corpora of natural utterances, LLMs exhibit translationese errors and generate unexpected unnatural translations, stemming from biases introduced during supervised fine-tuning (SFT). In this work, we systematically evaluate the prevalence of translationese in LLM-generated translations and investigate its roots during supervised training. We introduce methods to mitigate these biases, including polishing golden references and filtering unnatural training instances. Empirical evaluations demonstrate that these approaches significantly reduce translationese while improving translation naturalness, validated by human evaluations and automatic metrics. Our findings highlight the need for training-aware adjustments to optimize LLM translation outputs, paving the way for more fluent and target-language-consistent translations. We release the data and code at https://github.com/yafuly/LLM_Translationese.", "github_manual": "", "github_regex": "https://github.com/yafuly/LLM_Translationese", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.02495", "title": "Union of Experts: Adapting Hierarchical Routing to Equivalently Decomposed Transformer", "authors": [ "Yujiao Yang", "Jing Lian", "Linhui Li" ], "github": "https://github.com/YujiaoYang-work/UoE", "project_page": "", "abstract": "Mixture-of-Experts (MoE) enhances model performance while maintaining computational efficiency, making it well-suited for large-scale applications. However, expert in exist MoE paradigm works as an individual, thereby lacking high-quality expert interactions. Moreover, they have not been effectively extended to attention block, which constrains further efficiency improvements. To tackle these issues, we propose Union-of-Experts (UoE), which decomposes transformer into an equitant group of experts, and then implement dynamic routing on input data and experts. Our approach advances MoE design with three key innovations: (1) We conducted equitant expert decomposition on both MLP blocks and attention blocks based on matrix partition in tensor parallelism. (2) We developed two routing paradigms: patch wise data selection and expert selection, to apply routing across different levels. (3) We design the architecture of UoE model, including Selective Multi-Head Attention (SMHA) and Union-of-MLP-Experts (UoME). (4) We develop parallel implementation of UoE's routing and computation operation, and optimize efficiency based on the hardware processing analysis. The experiments demonstrate that the model employed with UoE surpass Full Attention, state-of-art MoEs and efficient transformers in several tasks across image and natural language domains. The source codes are available at https://github.com/YujiaoYang-work/UoE.", "github_manual": "", "github_regex": "https://github.com/YujiaoYang-work/UoE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.04724", "title": "LLMVoX: Autoregressive Streaming Text-to-Speech Model for Any LLM", "authors": [ "Sambal Shikhar", "Mohammed Irfan Kurpath", "Sahal Shaji Mullappilly", "Jean Lahoud", "Fahad Khan", "Rao Muhammad Anwer", "Salman Khan", "Hisham Cholakkal" ], "github": "", "project_page": "", "abstract": "Recent advancements in speech-to-speech dialogue systems leverage LLMs for multimodal interactions, yet they remain hindered by fine-tuning requirements, high computational overhead, and text-speech misalignment. Existing speech-enabled LLMs often degrade conversational quality by modifying the LLM, thereby compromising its linguistic capabilities. In contrast, we propose LLMVoX, a lightweight 30M-parameter, LLM-agnostic, autoregressive streaming TTS system that generates high-quality speech with low latency, while fully preserving the capabilities of the base LLM. Our approach achieves a significantly lower Word Error Rate compared to speech-enabled LLMs, while operating at comparable latency and UTMOS score. By decoupling speech synthesis from LLM processing via a multi-queue token streaming system, LLMVoX supports seamless, infinite-length dialogues. Its plug-and-play design also facilitates extension to various tasks with different backbones. Furthermore, LLMVoX generalizes to new languages with only dataset adaptation, attaining a low Character Error Rate on an Arabic speech task. Additionally, we have integrated LLMVoX with a Vision-Language Model to create an omni-model with speech, text, and vision capabilities, without requiring additional multimodal training. Our code base and project page is available at https://mbzuai-oryx.github.io/LLMVoX .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.01917", "title": "How to Steer LLM Latents for Hallucination Detection?", "authors": [ "Seongheon Park", "Xuefeng Du", "Min-Hsuan Yeh", "Haobo Wang", "Yixuan Li" ], "github": "https://github.com/deeplearning-wisc/tsv", "project_page": "", "abstract": "Hallucinations in LLMs pose a significant concern to their safe deployment in real-world applications. Recent approaches have leveraged the latent space of LLMs for hallucination detection, but their embeddings, optimized for linguistic coherence rather than factual accuracy, often fail to clearly separate truthful and hallucinated content. To this end, we propose the Truthfulness Separator Vector (TSV), a lightweight and flexible steering vector that reshapes the LLM's representation space during inference to enhance the separation between truthful and hallucinated outputs, without altering model parameters. Our two-stage framework first trains TSV on a small set of labeled exemplars to form compact and well-separated clusters. It then augments the exemplar set with unlabeled LLM generations, employing an optimal transport-based algorithm for pseudo-labeling combined with a confidence-based filtering process. Extensive experiments demonstrate that TSV achieves state-of-the-art performance with minimal labeled data, exhibiting strong generalization across datasets and providing a practical solution for real-world LLM applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/deeplearning-wisc/tsv", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-07", "arxiv_id": "2503.03962", "title": "On the Acquisition of Shared Grammatical Representations in Bilingual Language Models", "authors": [ "Catherine Arnett", "Tyler A. Chang", "James A. Michaelov", "Benjamin K. Bergen" ], "github": "https://github.com/tylerachang/word-acquisition-language-models", "project_page": "", "abstract": "While crosslingual transfer is crucial to contemporary language models' multilingual capabilities, how it occurs is not well understood. In this paper, we ask what happens to a monolingual language model when it begins to be trained on a second language. Specifically, we train small bilingual models for which we control the amount of data for each language and the order of language exposure. To find evidence of shared multilingual representations, we turn to structural priming, a method used to study grammatical representations in humans. We first replicate previous crosslingual structural priming results and find that after controlling for training data quantity and language exposure, there are asymmetrical effects across language pairs and directions. We argue that this asymmetry may shape hypotheses about human structural priming effects. We also find that structural priming effects are less robust for less similar language pairs, highlighting potential limitations of crosslingual transfer learning and shared representations for typologically diverse languages.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tylerachang/word-acquisition-language-models", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.05236", "title": "Unified Reward Model for Multimodal Understanding and Generation", "authors": [ "Yibin Wang", "Yuhang Zang", "Hao Li", "Cheng Jin", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in human preference alignment have significantly enhanced multimodal generation and understanding. A key approach is training reward models to guide preference optimization. However, existing models are often task-specific, limiting their adaptability across diverse visual applications. We also argue that jointly learning to assess multiple tasks may foster a synergistic effect, where improved image understanding enhances image generation assessment, and refined image evaluation benefits video assessment through better frame analysis. To this end, this paper proposes UnifiedReward, the first unified reward model for multimodal understanding and generation assessment, enabling both pairwise ranking and pointwise scoring, which can be employed for vision model preference alignment. Specifically, (1) we first develop UnifiedReward on our constructed large-scale human preference dataset, including both image and video generation/understanding tasks. (2) Then, it is utilized to automatically construct high-quality preference pair data based on the vision models, fine-gradually filtering their outputs through pair ranking and point sifting. (3) Finally, these data are used for their preference alignment through Direct Preference Optimization (DPO). Experimental results demonstrate that joint learning to assess diverse visual tasks can lead to substantial mutual benefits and we apply our pipeline to both image and video understanding/generation tasks, significantly improving the performance in each domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.05179", "title": "Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching", "authors": [ "Simon A. Aytes", "Jinheon Baek", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models have demonstrated remarkable reasoning capabilities through Chain of Thought (CoT) prompting, but often at the cost of excessive verbosity in their intermediate outputs, which increases computational overhead. We introduce Sketch-of-Thought (SoT), a novel prompting framework that combines cognitive-inspired reasoning paradigms with linguistic constraints to minimize token usage while preserving reasoning accuracy. SoT is designed as a flexible framework that can incorporate any custom reasoning paradigms based on cognitive science, and we instantiate it with three such paradigms - Conceptual Chaining, Chunked Symbolism, and Expert Lexicons - each tailored to different reasoning tasks and selected dynamically via a lightweight routing model. Through comprehensive evaluation across 15 reasoning datasets with multiple languages and multimodal scenarios, we demonstrate that SoT achieves token reductions of 76% with negligible accuracy impact. In certain domains like mathematical and multi-hop reasoning, it even improves accuracy while using significantly fewer tokens. Our code is publicly available: https://www.github.com/SimonAytes/SoT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.02130", "title": "Forgetting Transformer: Softmax Attention with a Forget Gate", "authors": [ "Zhixuan Lin", "Evgenii Nikishin", "Xu Owen He", "Aaron Courville" ], "github": "https://github.com/zhixuan-lin/forgetting-transformer", "project_page": "", "abstract": "An essential component of modern recurrent sequence models is the forget gate. While Transformers do not have an explicit recurrent form, we show that a forget gate can be naturally incorporated into Transformers by down-weighting the unnormalized attention scores in a data-dependent way. We name this attention mechanism the Forgetting Attention and the resulting model the Forgetting Transformer (FoX). We show that FoX outperforms the Transformer on long-context language modeling, length extrapolation, and short-context downstream tasks, while performing on par with the Transformer on long-context downstream tasks. Moreover, it is compatible with the FlashAttention algorithm and does not require any positional embeddings. Several analyses, including the needle-in-the-haystack test, show that FoX also retains the Transformer's superior long-context capabilities over recurrent sequence models such as Mamba-2, HGRN2, and DeltaNet. We also introduce a \"Pro\" block design that incorporates some common architectural components in recurrent sequence models and find it significantly improves the performance of both FoX and the Transformer. Our code is available at https://github.com/zhixuan-lin/forgetting-transformer.", "github_manual": "", "github_regex": "https://github.com/zhixuan-lin/forgetting-transformer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.05652", "title": "BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities", "authors": [ "Yunfan Jiang", "Ruohan Zhang", "Josiah Wong", "Chen Wang", "Yanjie Ze", "Hang Yin", "Cem Gokmen", "Shuran Song", "Jiajun Wu", "Li Fei-Fei" ], "github": "", "project_page": "", "abstract": "Real-world household tasks present significant challenges for mobile manipulation robots. An analysis of existing robotics benchmarks reveals that successful task performance hinges on three key whole-body control capabilities: bimanual coordination, stable and precise navigation, and extensive end-effector reachability. Achieving these capabilities requires careful hardware design, but the resulting system complexity further complicates visuomotor policy learning. To address these challenges, we introduce the BEHAVIOR Robot Suite (BRS), a comprehensive framework for whole-body manipulation in diverse household tasks. Built on a bimanual, wheeled robot with a 4-DoF torso, BRS integrates a cost-effective whole-body teleoperation interface for data collection and a novel algorithm for learning whole-body visuomotor policies. We evaluate BRS on five challenging household tasks that not only emphasize the three core capabilities but also introduce additional complexities, such as long-range navigation, interaction with articulated and deformable objects, and manipulation in confined spaces. We believe that BRS's integrated robotic embodiment, data collection interface, and learning framework mark a significant step toward enabling real-world whole-body manipulation for everyday household tasks. BRS is open-sourced at https://behavior-robot-suite.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.05592", "title": "R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning", "authors": [ "Huatong Song", "Jinhao Jiang", "Yingqian Min", "Jie Chen", "Zhipeng Chen", "Wayne Xin Zhao", "Lei Fang", "Ji-Rong Wen" ], "github": "https://github.com/RUCAIBox/R1-Searcher", "project_page": "", "abstract": "Existing Large Reasoning Models (LRMs) have shown the potential of reinforcement learning (RL) to enhance the complex reasoning capabilities of Large Language Models~(LLMs). While they achieve remarkable performance on challenging tasks such as mathematics and coding, they often rely on their internal knowledge to solve problems, which can be inadequate for time-sensitive or knowledge-intensive questions, leading to inaccuracies and hallucinations. To address this, we propose R1-Searcher, a novel two-stage outcome-based RL approach designed to enhance the search capabilities of LLMs. This method allows LLMs to autonomously invoke external search systems to access additional knowledge during the reasoning process. Our framework relies exclusively on RL, without requiring process rewards or distillation for a cold start. % effectively generalizing to out-of-domain datasets and supporting both Base and Instruct models. Our experiments demonstrate that our method significantly outperforms previous strong RAG methods, even when compared to the closed-source GPT-4o-mini.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/RUCAIBox/R1-Searcher", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.05379", "title": "R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcing Learning", "authors": [ "Jiaxing Zhao", "Xihan Wei", "Liefeng Bo" ], "github": "", "project_page": "", "abstract": "In this work, we present the first application of Reinforcement Learning with Verifiable Reward (RLVR) to an Omni-multimodal large language model in the context of emotion recognition, a task where both visual and audio modalities play crucial roles. We leverage RLVR to optimize the Omni model, significantly enhancing its performance in three key aspects: reasoning capability, emotion recognition accuracy, and generalization ability. The introduction of RLVR not only improves the model's overall performance on in-distribution data but also demonstrates superior robustness when evaluated on out-of-distribution datasets. More importantly, the improved reasoning capability enables clear analysis of the contributions of different modalities, particularly visual and audio information, in the emotion recognition process. This provides valuable insights into the optimization of multimodal large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.05132", "title": "R1-Zero's \"Aha Moment\" in Visual Reasoning on a 2B Non-SFT Model", "authors": [ "Hengguang Zhou", "Xirui Li", "Ruochen Wang", "Minhao Cheng", "Tianyi Zhou", "Cho-Jui Hsieh" ], "github": "https://github.com/turningpoint-ai/VisualThinker-R1-Zero", "project_page": "", "abstract": "Recently DeepSeek R1 demonstrated how reinforcement learning with simple rule-based incentives can enable autonomous development of complex reasoning in large language models, characterized by the \"aha moment\", in which the model manifest self-reflection and increased response length during training. However, attempts to extend this success to multimodal reasoning often failed to reproduce these key characteristics. In this report, we present the first successful replication of these emergent characteristics for multimodal reasoning on only a non-SFT 2B model. Starting with Qwen2-VL-2B and applying reinforcement learning directly on the SAT dataset, our model achieves 59.47% accuracy on CVBench, outperforming the base model by approximately ~30% and exceeding both SFT setting by ~2%. In addition, we share our failed attempts and insights in attempting to achieve R1-like reasoning using RL with instruct models. aiming to shed light on the challenges involved. Our key observations include: (1) applying RL on instruct model often results in trivial reasoning trajectories, and (2) naive length reward are ineffective in eliciting reasoning capabilities. The project code is available at https://github.com/turningpoint-ai/VisualThinker-R1-Zero", "github_manual": "", "github_regex": "https://github.com/turningpoint-ai/VisualThinker-R1-Zero", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.04872", "title": "TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation", "authors": [ "Lin Sun", "Guangxiang Zhao", "Xiaoqi Jian", "Yuhan Wu", "Weihong Lin", "Yongfu Zhu", "Change Jia", "Linglin Zhang", "Jinzhu Wu", "Junfeng Ran", "Sai-er Hu", "Zihan Jiang", "Junting Zhou", "Wenrui Liu", "Bin Cui", "Tong Yang", "Xiangzheng Zhang" ], "github": "", "project_page": "", "abstract": "The challenge of reducing the size of Large Language Models (LLMs) while maintaining their performance has gained significant attention. However, existing methods, such as model distillation and transfer learning, often fail to achieve high accuracy. To address this limitation, we introduce the Branch-Merge distillation approach, which enhances model compression through two phases: (1) the Branch Phase, where knowledge from a large teacher model is selectively distilled into specialized student models via domain-specific supervised fine-tuning (SFT); And (2) the Merge Phase, where these student models are merged to enable cross-domain knowledge transfer and improve generalization. We validate our distillation approach using DeepSeek-R1 as the teacher and DeepSeek-R1-Distill-Qwen-32B as the student. The resulting merged model, TinyR1-32B-Preview, outperforms its counterpart DeepSeek-R1-Distill-Qwen-32B across multiple benchmarks, including Mathematics (+5.5 points), Coding (+4.4 points) and Science (+2.9 points), while achieving near-equal performance to DeepSeek-R1 on AIME 2024. The Branch-Merge distillation approach provides a scalable solution for creating smaller, high-performing LLMs with reduced computational cost and time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.04808", "title": "Learning from Failures in Multi-Attempt Reinforcement Learning", "authors": [ "Stephen Chung", "Wenyu Du", "Jie Fu" ], "github": "https://github.com/DualityRL/multi-attempt", "project_page": "", "abstract": "Recent advancements in reinforcement learning (RL) for large language models (LLMs), exemplified by DeepSeek R1, have shown that even a simple question-answering task can substantially improve an LLM's reasoning capabilities. In this work, we extend this approach by modifying the task into a multi-attempt setting. Instead of generating a single response per question, the model is given multiple attempts, with feedback provided after incorrect responses. The multi-attempt task encourages the model to refine its previous attempts and improve search efficiency. Experimental results show that even a small LLM trained on a multi-attempt task achieves significantly higher accuracy when evaluated with more attempts, improving from 45.6% with 1 attempt to 52.5% with 2 attempts on the math benchmark. In contrast, the same LLM trained on a standard single-turn task exhibits only a marginal improvement, increasing from 42.3% to 43.2% when given more attempts during evaluation. The results indicate that, compared to the standard single-turn task, an LLM trained on a multi-attempt task achieves slightly better performance on math benchmarks while also learning to refine its responses more effectively based on user feedback. Full code is available at https://github.com/DualityRL/multi-attempt", "github_manual": "", "github_regex": "https://github.com/DualityRL/multi-attempt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.05315", "title": "LoRACode: LoRA Adapters for Code Embeddings", "authors": [ "Saumya Chaturvedi", "Aman Chadha", "Laurent Bindschaedler" ], "github": "https://github.com/loracode-submission/loracode", "project_page": "", "abstract": "Code embeddings are essential for semantic code search; however, current approaches often struggle to capture the precise syntactic and contextual nuances inherent in code. Open-source models such as CodeBERT and UniXcoder exhibit limitations in scalability and efficiency, while high-performing proprietary systems impose substantial computational costs. We introduce a parameter-efficient fine-tuning method based on Low-Rank Adaptation (LoRA) to construct task-specific adapters for code retrieval. Our approach reduces the number of trainable parameters to less than two percent of the base model, enabling rapid fine-tuning on extensive code corpora (2 million samples in 25 minutes on two H100 GPUs). Experiments demonstrate an increase of up to 9.1% in Mean Reciprocal Rank (MRR) for Code2Code search, and up to 86.69% for Text2Code search tasks across multiple programming languages. Distinction in task-wise and language-wise adaptation helps explore the sensitivity of code retrieval for syntactical and linguistic variations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/loracode-submission/loracode", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.05638", "title": "TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models", "authors": [ "Mark YU", "Wenbo Hu", "Jinbo Xing", "Ying Shan" ], "github": "", "project_page": "", "abstract": "We present TrajectoryCrafter, a novel approach to redirect camera trajectories for monocular videos. By disentangling deterministic view transformations from stochastic content generation, our method achieves precise control over user-specified camera trajectories. We propose a novel dual-stream conditional video diffusion model that concurrently integrates point cloud renders and source videos as conditions, ensuring accurate view transformations and coherent 4D content generation. Instead of leveraging scarce multi-view videos, we curate a hybrid training dataset combining web-scale monocular videos with static multi-view datasets, by our innovative double-reprojection strategy, significantly fostering robust generalization across diverse scenes. Extensive evaluations on multi-view and large-scale monocular videos demonstrate the superior performance of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.01713", "title": "SAGE: A Framework of Precise Retrieval for RAG", "authors": [ "Jintao Zhang", "Guoliang Li", "Jinyang Su" ], "github": "", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) has demonstrated significant proficiency in conducting question-answering (QA) tasks within a specified corpus. Nonetheless, numerous failure instances of RAG in QA still exist. These failures are not solely attributable to the limitations of Large Language Models (LLMs); instead, they predominantly arise from the retrieval of inaccurate information for LLMs due to two limitations: (1) Current RAG methods segment the corpus without considering semantics, making it difficult to find relevant context due to impaired correlation between questions and the segments. (2) There is a trade-off between missing essential context with fewer context retrieved and getting irrelevant context with more context retrieved. In this paper, we introduce a RAG framework (SAGE), to overcome these limitations. First, to address the segmentation issue without considering semantics, we propose to train a semantic segmentation model. This model is trained to segment the corpus into semantically complete chunks. Second, to ensure that only the most relevant chunks are retrieved while the irrelevant ones are ignored, we design a chunk selection algorithm to dynamically select chunks based on the decreasing speed of the relevance score, leading to a more relevant selection. Third, to further ensure the precision of the retrieved chunks, we propose letting LLMs assess whether retrieved chunks are excessive or lacking and then adjust the amount of context accordingly. Experiments show that SAGE outperforms baselines by 61.25% in the quality of QA on average. Moreover, by avoiding retrieving noisy context, SAGE lowers the cost of the tokens consumed in LLM inference and achieves a 49.41% enhancement in cost efficiency on average. Additionally, our work offers valuable insights for boosting RAG.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.05500", "title": "EuroBERT: Scaling Multilingual Encoders for European Languages", "authors": [ "Nicolas Boizard", "Hippolyte Gisserot-Boukhlef", "Duarte M. Alves", "Andr\u00e9 Martins", "Ayoub Hammal", "Caio Corro", "C\u00e9line Hudelot", "Emmanuel Malherbe", "Etienne Malaboeuf", "Fanny Jourdan", "Gabriel Hautreux", "Jo\u00e3o Alves", "Kevin El-Haddad", "Manuel Faysse", "Maxime Peyrard", "Nuno M. Guerreiro", "Patrick Fernandes", "Ricardo Rei", "Pierre Colombo" ], "github": "", "project_page": "", "abstract": "General-purpose multilingual vector representations, used in retrieval, regression and classification, are traditionally obtained from bidirectional encoder models. Despite their wide applicability, encoders have been recently overshadowed by advances in generative decoder-only models. However, many innovations driving this progress are not inherently tied to decoders. In this paper, we revisit the development of multilingual encoders through the lens of these advances, and introduce EuroBERT, a family of multilingual encoders covering European and widely spoken global languages. Our models outperform existing alternatives across a diverse range of tasks, spanning multilingual capabilities, mathematics, and coding, and natively supporting sequences of up to 8,192 tokens. We also examine the design decisions behind EuroBERT, offering insights into our dataset composition and training pipeline. We publicly release the EuroBERT models, including intermediate training checkpoints, together with our training framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.05639", "title": "VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control", "authors": [ "Yuxuan Bian", "Zhaoyang Zhang", "Xuan Ju", "Mingdeng Cao", "Liangbin Xie", "Ying Shan", "Qiang Xu" ], "github": "", "project_page": "", "abstract": "Video inpainting, which aims to restore corrupted video content, has experienced substantial progress. Despite these advances, existing methods, whether propagating unmasked region pixels through optical flow and receptive field priors, or extending image-inpainting models temporally, face challenges in generating fully masked objects or balancing the competing objectives of background context preservation and foreground generation in one model, respectively. To address these limitations, we propose a novel dual-stream paradigm VideoPainter that incorporates an efficient context encoder (comprising only 6% of the backbone parameters) to process masked videos and inject backbone-aware background contextual cues to any pre-trained video DiT, producing semantically consistent content in a plug-and-play manner. This architectural separation significantly reduces the model's learning complexity while enabling nuanced integration of crucial background context. We also introduce a novel target region ID resampling technique that enables any-length video inpainting, greatly enhancing our practical applicability. Additionally, we establish a scalable dataset pipeline leveraging current vision understanding models, contributing VPData and VPBench to facilitate segmentation-based inpainting training and assessment, the largest video inpainting dataset and benchmark to date with over 390K diverse clips. Using inpainting as a pipeline basis, we also explore downstream applications including video editing and video editing pair data generation, demonstrating competitive performance and significant practical potential. Extensive experiments demonstrate VideoPainter's superior performance in both any-length video inpainting and editing, across eight key metrics, including video quality, mask region preservation, and textual coherence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.04504", "title": "AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM", "authors": [ "Sunghyun Ahn", "Youngwan Jo", "Kijung Lee", "Sein Kwon", "Inpyo Hong", "Sanghyun Park" ], "github": "https://github.com/SkiddieAhn/Paper-AnyAnomaly", "project_page": "", "abstract": "Video anomaly detection (VAD) is crucial for video analysis and surveillance in computer vision. However, existing VAD models rely on learned normal patterns, which makes them difficult to apply to diverse environments. Consequently, users should retrain models or develop separate AI models for new environments, which requires expertise in machine learning, high-performance hardware, and extensive data collection, limiting the practical usability of VAD. To address these challenges, this study proposes customizable video anomaly detection (C-VAD) technique and the AnyAnomaly model. C-VAD considers user-defined text as an abnormal event and detects frames containing a specified event in a video. We effectively implemented AnyAnomaly using a context-aware visual question answering without fine-tuning the large vision language model. To validate the effectiveness of the proposed model, we constructed C-VAD datasets and demonstrated the superiority of AnyAnomaly. Furthermore, our approach showed competitive performance on VAD benchmark datasets, achieving state-of-the-art results on the UBnormal dataset and outperforming other methods in generalization across all datasets. Our code is available online at github.com/SkiddieAhn/Paper-AnyAnomaly.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SkiddieAhn/Paper-AnyAnomaly", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.05447", "title": "Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts", "authors": [ "Weigao Sun", "Disen Lan", "Tong Zhu", "Xiaoye Qu", "Yu Cheng" ], "github": "https://github.com/OpenSparseLLMs/Linear-MoE", "project_page": "", "abstract": "Linear Sequence Modeling (LSM) like linear attention, state space models and linear RNNs, and Mixture-of-Experts (MoE) have recently emerged as significant architectural improvements. In this paper, we introduce Linear-MoE, a production-level system for modeling and training large-scale models that integrate LSM with MoE. Linear-MoE leverages the advantages of both LSM modules for linear-complexity sequence modeling and MoE layers for sparsely activation, aiming to offer high performance with efficient training. The Linear-MoE system comprises: 1) Modeling subsystem, which provides a unified framework supporting all instances of LSM. and 2) Training subsystem, which facilitates efficient training by incorporating various advanced parallelism technologies, particularly Sequence Parallelism designed for Linear-MoE models. Additionally, we explore hybrid models that combine Linear-MoE layers with standard Transformer-MoE layers with its Sequence Parallelism to further enhance model flexibility and performance. Evaluations on two model series, A0.3B-2B and A1B-7B, demonstrate Linear-MoE achieves efficiency gains while maintaining competitive performance on various benchmarks, showcasing its potential as a next-generation foundational model architecture. Code: https://github.com/OpenSparseLLMs/Linear-MoE.", "github_manual": "", "github_regex": "https://github.com/OpenSparseLLMs/Linear-MoE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.04548", "title": "An Empirical Study on Eliciting and Improving R1-like Reasoning Models", "authors": [ "Zhipeng Chen", "Yingqian Min", "Beichen Zhang", "Jie Chen", "Jinhao Jiang", "Daixuan Cheng", "Wayne Xin Zhao", "Zheng Liu", "Xu Miao", "Yang Lu", "Lei Fang", "Zhongyuan Wang", "Ji-Rong Wen" ], "github": "https://github.com/RUCAIBox/Slow_Thinking_with_LLMs", "project_page": "", "abstract": "In this report, we present the third technical report on the development of slow-thinking models as part of the STILL project. As the technical pathway becomes clearer, scaling RL training has become a central technique for implementing such reasoning models. We systematically experiment with and document the effects of various factors influencing RL training, conducting experiments on both base models and fine-tuned models. Specifically, we demonstrate that our RL training approach consistently improves the Qwen2.5-32B base models, enhancing both response length and test accuracy. Furthermore, we show that even when a model like DeepSeek-R1-Distill-Qwen-1.5B has already achieved a high performance level, it can be further refined through RL training, reaching an accuracy of 39.33% on AIME 2024. Beyond RL training, we also explore the use of tool manipulation, finding that it significantly boosts the reasoning performance of large reasoning models. This approach achieves a remarkable accuracy of 86.67% with greedy search on AIME 2024, underscoring its effectiveness in enhancing model capabilities. We release our resources at the STILL project website: https://github.com/RUCAIBox/Slow_Thinking_with_LLMs.", "github_manual": "", "github_regex": "https://github.com/RUCAIBox/Slow_Thinking_with_LLMs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.04824", "title": "ProReflow: Progressive Reflow with Decomposed Velocity", "authors": [ "Lei Ke", "Haohang Xu", "Xuefei Ning", "Yu Li", "Jiajun Li", "Haoling Li", "Yuxuan Lin", "Dongsheng Jiang", "Yujiu Yang", "Linfeng Zhang" ], "github": "", "project_page": "", "abstract": "Diffusion models have achieved significant progress in both image and video generation while still suffering from huge computation costs. As an effective solution, flow matching aims to reflow the diffusion process of diffusion models into a straight line for a few-step and even one-step generation. However, in this paper, we suggest that the original training pipeline of flow matching is not optimal and introduce two techniques to improve it. Firstly, we introduce progressive reflow, which progressively reflows the diffusion models in local timesteps until the whole diffusion progresses, reducing the difficulty of flow matching. Second, we introduce aligned v-prediction, which highlights the importance of direction matching in flow matching over magnitude matching. Experimental results on SDv1.5 and SDXL demonstrate the effectiveness of our method, for example, conducting on SDv1.5 achieves an FID of 10.70 on MSCOCO2014 validation set with only 4 sampling steps, close to our teacher model (32 DDIM steps, FID = 10.05).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2502.21263", "title": "RuCCoD: Towards Automated ICD Coding in Russian", "authors": [ "Aleksandr Nesterov", "Andrey Sakhovskiy", "Ivan Sviridov", "Airat Valiev", "Vladimir Makharev", "Petr Anokhin", "Galina Zubkova", "Elena Tutubalina" ], "github": "https://github.com/auto-icd-coding/ruccod", "project_page": "", "abstract": "This study investigates the feasibility of automating clinical coding in Russian, a language with limited biomedical resources. We present a new dataset for ICD coding, which includes diagnosis fields from electronic health records (EHRs) annotated with over 10,000 entities and more than 1,500 unique ICD codes. This dataset serves as a benchmark for several state-of-the-art models, including BERT, LLaMA with LoRA, and RAG, with additional experiments examining transfer learning across domains (from PubMed abstracts to medical diagnosis) and terminologies (from UMLS concepts to ICD codes). We then apply the best-performing model to label an in-house EHR dataset containing patient histories from 2017 to 2021. Our experiments, conducted on a carefully curated test set, demonstrate that training with the automated predicted codes leads to a significant improvement in accuracy compared to manually annotated data from physicians. We believe our findings offer valuable insights into the potential for automating clinical coding in resource-limited languages like Russian, which could enhance clinical efficiency and data accuracy in these contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/auto-icd-coding/ruccod", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.04359", "title": "LONGCODEU: Benchmarking Long-Context Language Models on Long Code Understanding", "authors": [ "Jia Li", "Xuyuan Guo", "Lei Li", "Kechi Zhang", "Ge Li", "Jia Li", "Zhengwei Tao", "Fang Liu", "Chongyang Tao", "Yuqi Zhu", "Zhi Jin" ], "github": "", "project_page": "", "abstract": "Current advanced long-context language models offer great potential for real-world software engineering applications. However, progress in this critical domain remains hampered by a fundamental limitation: the absence of a rigorous evaluation framework for long code understanding. To gap this obstacle, we propose a long code understanding benchmark LONGCODEU from four aspects (8 tasks) to evaluate LCLMs' long code understanding ability required for practical applications, including code unit perception, intra-code unit understanding, inter-code unit relation understanding, and long code documentation understanding. We evaluate 9 popular LCLMs on LONGCODEU (i.e., 6 general models and 3 code models). Our experimental results reveal key limitations in current LCLMs' capabilities for long code understanding. Particularly, the performance of LCLMs drops dramatically when the long code length is greater than 32K, falling far short of their claimed 128K-1M context windows. In the four aspects, inter-code unit relation understanding is the most challenging for LCLMs. Our study provides valuable insights for optimizing LCLMs and driving advancements in software engineering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.05085", "title": "S2S-Arena, Evaluating Speech2Speech Protocols on Instruction Following with Paralinguistic Information", "authors": [ "Feng Jiang", "Zhiyu Lin", "Fan Bu", "Yuhao Du", "Benyou Wang", "Haizhou Li" ], "github": "https://github.com/0nutation/SpeechGPT", "project_page": "", "abstract": "The rapid development of large language models (LLMs) has brought significant attention to speech models, particularly recent progress in speech2speech protocols supporting speech input and output. However, the existing benchmarks adopt automatic text-based evaluators for evaluating the instruction following ability of these models lack consideration for paralinguistic information in both speech understanding and generation. To address these issues, we introduce S2S-Arena, a novel arena-style S2S benchmark that evaluates instruction-following capabilities with paralinguistic information in both speech-in and speech-out across real-world tasks. We design 154 samples that fused TTS and live recordings in four domains with 21 tasks and manually evaluate existing popular speech models in an arena-style manner. The experimental results show that: (1) in addition to the superior performance of GPT-4o, the speech model of cascaded ASR, LLM, and TTS outperforms the jointly trained model after text-speech alignment in speech2speech protocols; (2) considering paralinguistic information, the knowledgeability of the speech model mainly depends on the LLM backbone, and the multilingual support of that is limited by the speech module; (3) excellent speech models can already understand the paralinguistic information in speech input, but generating appropriate audio with paralinguistic information is still a challenge.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/0nutation/SpeechGPT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2502.18968", "title": "Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profiles", "authors": [ "Kuang Wang", "Xianfei Li", "Shenghao Yang", "Li Zhou", "Feng Jiang", "Haizhou Li" ], "github": "https://github.com/wangkevin02/USP", "project_page": "", "abstract": "User simulators are crucial for replicating human interactions with dialogue systems, supporting both collaborative training and automatic evaluation, especially for large language models (LLMs). However, existing simulators often rely solely on text utterances, missing implicit user traits such as personality, speaking style, and goals. In contrast, persona-based methods lack generalizability, as they depend on predefined profiles of famous individuals or archetypes. To address these challenges, we propose User Simulator with implicit Profiles (USP), a framework that infers implicit user profiles from human-machine conversations and uses them to generate more personalized and realistic dialogues. We first develop an LLM-driven extractor with a comprehensive profile schema. Then, we refine the simulation through conditional supervised fine-tuning and reinforcement learning with cycle consistency, optimizing it at both the utterance and conversation levels. Finally, we adopt a diverse profile sampler to capture the distribution of real-world user profiles. Experimental results demonstrate that USP outperforms strong baselines in terms of authenticity and diversity while achieving comparable performance in consistency. Furthermore, dynamic multi-turn evaluations based on USP strongly align with mainstream benchmarks, demonstrating its effectiveness in real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/wangkevin02/USP", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.01840", "title": "EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test", "authors": [ "Yuhui Li", "Fangyun Wei", "Chao Zhang", "Hongyang Zhang" ], "github": "https://github.com/SafeAILab/EAGLE", "project_page": "", "abstract": "The sequential nature of modern LLMs makes them expensive and slow, and speculative sampling has proven to be an effective solution to this problem. Methods like EAGLE perform autoregression at the feature level, reusing top-layer features from the target model to achieve better results than vanilla speculative sampling. A growing trend in the LLM community is scaling up training data to improve model intelligence without increasing inference costs. However, we observe that scaling up data provides limited improvements for EAGLE. We identify that this limitation arises from EAGLE's feature prediction constraints. In this paper, we introduce EAGLE-3, which abandons feature prediction in favor of direct token prediction and replaces reliance on top-layer features with multi-layer feature fusion via a technique named training-time test. These improvements significantly enhance performance and enable the draft model to fully benefit from scaling up training data. Our experiments include both chat models and reasoning models, evaluated on five tasks. The results show that EAGLE-3 achieves a speedup ratio up to 6.5x, with about 1.4x improvement over EAGLE-2. The code is available at https://github.com/SafeAILab/EAGLE.", "github_manual": "", "github_regex": "https://github.com/SafeAILab/EAGLE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-10", "arxiv_id": "2503.04957", "title": "SafeArena: Evaluating the Safety of Autonomous Web Agents", "authors": [ "Ada Defne Tur", "Nicholas Meade", "Xing Han L\u00f9", "Alejandra Zambrano", "Arkil Patel", "Esin Durmus", "Spandana Gella", "Karolina Sta\u0144czak", "Siva Reddy" ], "github": "", "project_page": "", "abstract": "LLM-based agents are becoming increasingly proficient at solving web-based tasks. With this capability comes a greater risk of misuse for malicious purposes, such as posting misinformation in an online forum or selling illicit substances on a website. To evaluate these risks, we propose SafeArena, the first benchmark to focus on the deliberate misuse of web agents. SafeArena comprises 250 safe and 250 harmful tasks across four websites. We classify the harmful tasks into five harm categories -- misinformation, illegal activity, harassment, cybercrime, and social bias, designed to assess realistic misuses of web agents. We evaluate leading LLM-based web agents, including GPT-4o, Claude-3.5 Sonnet, Qwen-2-VL 72B, and Llama-3.2 90B, on our benchmark. To systematically assess their susceptibility to harmful tasks, we introduce the Agent Risk Assessment framework that categorizes agent behavior across four risk levels. We find agents are surprisingly compliant with malicious requests, with GPT-4o and Qwen-2 completing 34.7% and 27.3% of harmful requests, respectively. Our findings highlight the urgent need for safety alignment procedures for web agents. Our benchmark is available here: https://safearena.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.06680", "title": "FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation", "authors": [ "Wei Li", "Xin Zhang", "Zhongxin Guo", "Shaoguang Mao", "Wen Luo", "Guangyue Peng", "Yangyu Huang", "Houfeng Wang", "Scarlett Li" ], "github": "https://github.com/microsoft/FEA-Bench", "project_page": "", "abstract": "Implementing new features in repository-level codebases is a crucial application of code generation models. However, current benchmarks lack a dedicated evaluation framework for this capability. To fill this gap, we introduce FEA-Bench, a benchmark designed to assess the ability of large language models (LLMs) to perform incremental development within code repositories. We collect pull requests from 83 GitHub repositories and use rule-based and intent-based filtering to construct task instances focused on new feature development. Each task instance containing code changes is paired with relevant unit test files to ensure that the solution can be verified. The feature implementation requires LLMs to simultaneously possess code completion capabilities for new components and code editing abilities for other relevant parts in the code repository, providing a more comprehensive evaluation method of LLMs' automated software engineering capabilities. Experimental results show that LLMs perform significantly worse in the FEA-Bench, highlighting considerable challenges in such repository-level incremental code development.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/FEA-Bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.03499", "title": "State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for State Space Models", "authors": [ "Wonjun Kang", "Kevin Galim", "Yuchen Zeng", "Minjae Lee", "Hyung Il Koo", "Nam Ik Cho" ], "github": "https://github.com/furiosa-ai/ssm-state-tuning", "project_page": "", "abstract": "State Space Models (SSMs) have emerged as efficient alternatives to Transformers, mitigating their quadratic computational cost. However, the application of Parameter-Efficient Fine-Tuning (PEFT) methods to SSMs remains largely unexplored. In particular, prompt-based methods like Prompt Tuning and Prefix-Tuning, which are widely used in Transformers, do not perform well on SSMs. To address this, we propose state-based methods as a superior alternative to prompt-based methods. This new family of methods naturally stems from the architectural characteristics of SSMs. State-based methods adjust state-related features directly instead of depending on external prompts. Furthermore, we introduce a novel state-based PEFT method: State-offset Tuning. At every timestep, our method directly affects the state at the current step, leading to more effective adaptation. Through extensive experiments across diverse datasets, we demonstrate the effectiveness of our method. Code is available at https://github.com/furiosa-ai/ssm-state-tuning.", "github_manual": "", "github_regex": "https://github.com/furiosa-ai/ssm-state-tuning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.03511", "title": "NeuGrasp: Generalizable Neural Surface Reconstruction with Background Priors for Material-Agnostic Object Grasp Detection", "authors": [ "Qingyu Fan", "Yinghao Cai", "Chao Li", "Wenzhe He", "Xudong Zheng", "Tao Lu", "Bin Liang", "Shuo Wang" ], "github": "", "project_page": "", "abstract": "Robotic grasping in scenes with transparent and specular objects presents great challenges for methods relying on accurate depth information. In this paper, we introduce NeuGrasp, a neural surface reconstruction method that leverages background priors for material-agnostic grasp detection. NeuGrasp integrates transformers and global prior volumes to aggregate multi-view features with spatial encoding, enabling robust surface reconstruction in narrow and sparse viewing conditions. By focusing on foreground objects through residual feature enhancement and refining spatial perception with an occupancy-prior volume, NeuGrasp excels in handling objects with transparent and specular surfaces. Extensive experiments in both simulated and real-world scenarios show that NeuGrasp outperforms state-of-the-art methods in grasping while maintaining comparable reconstruction quality. More details are available at https://neugrasp.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.02199", "title": "Words or Vision: Do Vision-Language Models Have Blind Faith in Text?", "authors": [ "Ailin Deng", "Tri Cao", "Zhirui Chen", "Bryan Hooi" ], "github": "https://github.com/d-ailin/blind-faith-in-text", "project_page": "", "abstract": "Vision-Language Models (VLMs) excel in integrating visual and textual information for vision-centric tasks, but their handling of inconsistencies between modalities is underexplored. We investigate VLMs' modality preferences when faced with visual data and varied textual inputs in vision-centered settings. By introducing textual variations to four vision-centric tasks and evaluating ten Vision-Language Models (VLMs), we discover a ``blind faith in text'' phenomenon: VLMs disproportionately trust textual data over visual data when inconsistencies arise, leading to significant performance drops under corrupted text and raising safety concerns. We analyze factors influencing this text bias, including instruction prompts, language model size, text relevance, token order, and the interplay between visual and textual certainty. While certain factors, such as scaling up the language model size, slightly mitigate text bias, others like token order can exacerbate it due to positional biases inherited from language models. To address this issue, we explore supervised fine-tuning with text augmentation and demonstrate its effectiveness in reducing text bias. Additionally, we provide a theoretical analysis suggesting that the blind faith in text phenomenon may stem from an imbalance of pure text and multi-modal data during training. Our findings highlight the need for balanced training and careful consideration of modality interactions in VLMs to enhance their robustness and reliability in handling multi-modal data inconsistencies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/d-ailin/blind-faith-in-text", "github_api_added_by": "auto" }, { "date": "2025-03-11", "arxiv_id": "2503.07365", "title": "MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale Reinforcement Learning", "authors": [ "Fanqing Meng", "Lingxiao Du", "Zongkai Liu", "Zhixiang Zhou", "Quanfeng Lu", "Daocheng Fu", "Botian Shi", "Wenhai Wang", "Junjun He", "Kaipeng Zhang", "Ping Luo", "Yu Qiao", "Qiaosheng Zhang", "Wenqi Shao" ], "github": "https://github.com/ModalMinds/MM-EUREKA", "project_page": "", "abstract": "We present MM-Eureka, a multimodal reasoning model that successfully extends large-scale rule-based reinforcement learning (RL) to multimodal reasoning. While rule-based RL has shown remarkable success in improving LLMs' reasoning abilities in text domains, its application to multimodal settings has remained challenging. Our work reproduces key characteristics of text-based RL systems like DeepSeek-R1 in the multimodal space, including steady increases in accuracy reward and response length, and the emergence of reflection behaviors. We demonstrate that both instruction-tuned and pre-trained models can develop strong multimodal reasoning capabilities through rule-based RL without supervised fine-tuning, showing superior data efficiency compared to alternative approaches. We open-source our complete pipeline to foster further research in this area. We release all our codes, models, data, etc. at https://github.com/ModalMinds/MM-EUREKA", "github_manual": "", "github_regex": "https://github.com/ModalMinds/MM-EUREKA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.04812", "title": "LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning", "authors": [ "Zhibin Lan", "Liqiang Niu", "Fandong Meng", "Jie Zhou", "Jinsong Su" ], "github": "", "project_page": "", "abstract": "Universal multimodal embedding models play a critical role in tasks such as interleaved image-text retrieval, multimodal RAG, and multimodal clustering. However, our empirical results indicate that existing LMM-based embedding models trained with the standard InfoNCE loss exhibit a high degree of overlap in similarity distribution between positive and negative pairs, making it challenging to distinguish hard negative pairs effectively. To deal with this issue, we propose a simple yet effective framework that dynamically improves the embedding model's representation learning for negative pairs based on their discriminative difficulty. Within this framework, we train a series of models, named LLaVE, and evaluate them on the MMEB benchmark, which covers 4 meta-tasks and 36 datasets. Experimental results show that LLaVE establishes stronger baselines that achieve state-of-the-art (SOTA) performance while demonstrating strong scalability and efficiency. Specifically, LLaVE-2B surpasses the previous SOTA 7B models, while LLaVE-7B achieves a further performance improvement of 6.2 points. Although LLaVE is trained on image-text data, it can generalize to text-video retrieval tasks in a zero-shot manner and achieve strong performance, demonstrating its remarkable potential for transfer to other embedding tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07027", "title": "EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer", "authors": [ "Yuxuan Zhang", "Yirui Yuan", "Yiren Song", "Haofan Wang", "Jiaming Liu" ], "github": "", "project_page": "", "abstract": "Recent advancements in Unet-based diffusion models, such as ControlNet and IP-Adapter, have introduced effective spatial and subject control mechanisms. However, the DiT (Diffusion Transformer) architecture still struggles with efficient and flexible control. To tackle this issue, we propose EasyControl, a novel framework designed to unify condition-guided diffusion transformers with high efficiency and flexibility. Our framework is built on three key innovations. First, we introduce a lightweight Condition Injection LoRA Module. This module processes conditional signals in isolation, acting as a plug-and-play solution. It avoids modifying the base model weights, ensuring compatibility with customized models and enabling the flexible injection of diverse conditions. Notably, this module also supports harmonious and robust zero-shot multi-condition generalization, even when trained only on single-condition data. Second, we propose a Position-Aware Training Paradigm. This approach standardizes input conditions to fixed resolutions, allowing the generation of images with arbitrary aspect ratios and flexible resolutions. At the same time, it optimizes computational efficiency, making the framework more practical for real-world applications. Third, we develop a Causal Attention Mechanism combined with the KV Cache technique, adapted for conditional generation tasks. This innovation significantly reduces the latency of image synthesis, improving the overall efficiency of the framework. Through extensive experiments, we demonstrate that EasyControl achieves exceptional performance across various application scenarios. These innovations collectively make our framework highly efficient, flexible, and suitable for a wide range of tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.06520", "title": "Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement", "authors": [ "Yuqi Liu", "Bohao Peng", "Zhisheng Zhong", "Zihao Yue", "Fanbin Lu", "Bei Yu", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/Seg-Zero", "project_page": "", "abstract": "Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-domain generalization and lacking explicit reasoning processes. To address these limitations, we propose Seg-Zero, a novel framework that demonstrates remarkable generalizability and derives explicit chain-of-thought reasoning through cognitive reinforcement. Seg-Zero introduces a decoupled architecture consisting of a reasoning model and a segmentation model. The reasoning model interprets user intentions, generates explicit reasoning chains, and produces positional prompts, which are subsequently used by the segmentation model to generate precious pixel-level masks. We design a sophisticated reward mechanism that integrates both format and accuracy rewards to effectively guide optimization directions. Trained exclusively via reinforcement learning with GRPO and without explicit reasoning data, Seg-Zero achieves robust zero-shot generalization and exhibits emergent test-time reasoning capabilities. Experiments show that Seg-Zero-7B achieves a zero-shot performance of 57.5 on the ReasonSeg benchmark, surpassing the prior LISA-7B by 18\\%. This significant improvement highlights Seg-Zero's ability to generalize across domains while presenting an explicit reasoning process. Code is available at https://github.com/dvlab-research/Seg-Zero.", "github_manual": "", "github_regex": "https://github.com/dvlab-research/Seg-Zero", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07507", "title": "PE3R: Perception-Efficient 3D Reconstruction", "authors": [ "Jie Hu", "Shizun Wang", "Xinchao Wang" ], "github": "https://github.com/hujiecpp/PE3R", "project_page": "", "abstract": "Recent advancements in 2D-to-3D perception have significantly improved the understanding of 3D scenes from 2D images. However, existing methods face critical challenges, including limited generalization across scenes, suboptimal perception accuracy, and slow reconstruction speeds. To address these limitations, we propose Perception-Efficient 3D Reconstruction (PE3R), a novel framework designed to enhance both accuracy and efficiency. PE3R employs a feed-forward architecture to enable rapid 3D semantic field reconstruction. The framework demonstrates robust zero-shot generalization across diverse scenes and objects while significantly improving reconstruction speed. Extensive experiments on 2D-to-3D open-vocabulary segmentation and 3D reconstruction validate the effectiveness and versatility of PE3R. The framework achieves a minimum 9-fold speedup in 3D semantic field reconstruction, along with substantial gains in perception accuracy and reconstruction precision, setting new benchmarks in the field. The code is publicly available at: https://github.com/hujiecpp/PE3R.", "github_manual": "", "github_regex": "https://github.com/hujiecpp/PE3R", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.06121", "title": "BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement for Transformers in Large-Scale Time Series Modeling", "authors": [ "Li weile", "Liu Xiao" ], "github": "https://github.com/Alic-Li/BlackGoose_Rimer", "project_page": "", "abstract": "Time series models face significant challenges in scaling to handle large and complex datasets, akin to the scaling achieved by large language models (LLMs). The unique characteristics of time series data and the computational demands of model scaling necessitate innovative approaches. While researchers have explored various architectures such as Transformers, LSTMs, and GRUs to address these challenges, we propose a novel solution using RWKV-7, which incorporates meta-learning into its state update mechanism. By integrating RWKV-7's time mix and channel mix components into the transformer-based time series model Timer, we achieve a substantial performance improvement of approximately 1.13 to 43.3x and a 4.5x reduction in training time with 1/23 parameters, all while utilizing fewer parameters. Our code and model weights are publicly available for further research and development at https://github.com/Alic-Li/BlackGoose_Rimer.", "github_manual": "", "github_regex": "https://github.com/Alic-Li/BlackGoose_Rimer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07595", "title": "Detection Avoidance Techniques for Large Language Models", "authors": [ "Sinclair Schneider", "Florian Steuber", "Joao A. G. Schneider", "Gabi Dreo Rodosek" ], "github": "", "project_page": "", "abstract": "The increasing popularity of large language models has not only led to widespread use but has also brought various risks, including the potential for systematically spreading fake news. Consequently, the development of classification systems such as DetectGPT has become vital. These detectors are vulnerable to evasion techniques, as demonstrated in an experimental series: Systematic changes of the generative models' temperature proofed shallow learning-detectors to be the least reliable. Fine-tuning the generative model via reinforcement learning circumvented BERT-based-detectors. Finally, rephrasing led to a >90\\% evasion of zero-shot-detectors like DetectGPT, although texts stayed highly similar to the original. A comparison with existing work highlights the better performance of the presented methods. Possible implications for society and further research are discussed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07314", "title": "Automated Movie Generation via Multi-Agent CoT Planning", "authors": [ "Weijia Wu", "Zeyu Zhu", "Mike Zheng Shou" ], "github": "https://github.com/showlab/MovieAgent", "project_page": "", "abstract": "Existing long-form video generation frameworks lack automated planning, requiring manual input for storylines, scenes, cinematography, and character interactions, resulting in high costs and inefficiencies. To address these challenges, we present MovieAgent, an automated movie generation via multi-agent Chain of Thought (CoT) planning. MovieAgent offers two key advantages: 1) We firstly explore and define the paradigm of automated movie/long-video generation. Given a script and character bank, our MovieAgent can generates multi-scene, multi-shot long-form videos with a coherent narrative, while ensuring character consistency, synchronized subtitles, and stable audio throughout the film. 2) MovieAgent introduces a hierarchical CoT-based reasoning process to automatically structure scenes, camera settings, and cinematography, significantly reducing human effort. By employing multiple LLM agents to simulate the roles of a director, screenwriter, storyboard artist, and location manager, MovieAgent streamlines the production pipeline. Experiments demonstrate that MovieAgent achieves new state-of-the-art results in script faithfulness, character consistency, and narrative coherence. Our hierarchical framework takes a step forward and provides new insights into fully automated movie generation. The code and project website are available at: https://github.com/showlab/MovieAgent and https://weijiawu.github.io/MovieAgent.", "github_manual": "", "github_regex": "https://github.com/showlab/MovieAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07216", "title": "FedRand: Enhancing Privacy in Federated Learning with Randomized LoRA Subparameter Updates", "authors": [ "Sangwoo Park", "Seanie Lee", "Byungjoo Kim", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "Federated Learning (FL) is a widely used framework for training models in a decentralized manner, ensuring that the central server does not have direct access to data from local clients. However, this approach may still fail to fully preserve data privacy, as models from local clients are exposed to the central server during the aggregation process. This issue becomes even more critical when training vision-language models (VLMs) with FL, as VLMs can easily memorize training data instances, making them vulnerable to membership inference attacks (MIAs). To address this challenge, we propose the FedRand framework, which avoids disclosing the full set of client parameters. In this framework, each client randomly selects subparameters of Low-Rank Adaptation (LoRA) from the server and keeps the remaining counterparts of the LoRA weights as private parameters. After training both parameters on the client's private dataset, only the non-private client parameters are sent back to the server for aggregation. This approach mitigates the risk of exposing client-side VLM parameters, thereby enhancing data privacy. We empirically validate that FedRand improves robustness against MIAs compared to relevant baselines while achieving accuracy comparable to methods that communicate full LoRA parameters across several benchmark datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07605", "title": "SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models", "authors": [ "Xun Liang", "Hanyu Wang", "Huayi Lai", "Simin Niu", "Shichao Song", "Jiawei Yang", "Jihao Zhao", "Feiyu Xiong", "Bo Tang", "Zhiyu Li" ], "github": "https://github.com/IAAR-Shanghai/SEAP", "project_page": "", "abstract": "Large Language Models have achieved remarkable success across various natural language processing tasks, yet their high computational cost during inference remains a major bottleneck. This paper introduces Sparse Expert Activation Pruning (SEAP), a training-free pruning method that selectively retains task-relevant parameters to reduce inference overhead. Inspired by the clustering patterns of hidden states and activations in LLMs, SEAP identifies task-specific expert activation patterns and prunes the model while preserving task performance and enhancing computational efficiency. Experimental results demonstrate that SEAP significantly reduces computational overhead while maintaining competitive accuracy. Notably, at 50% pruning, SEAP surpasses both WandA and FLAP by over 20%, and at 20% pruning, it incurs only a 2.2% performance drop compared to the dense model. These findings highlight SEAP's scalability and effectiveness, making it a promising approach for optimizing large-scale LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/IAAR-Shanghai/SEAP", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07002", "title": "Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning", "authors": [ "Jiazheng Liu", "Sipeng Zheng", "B\u00f6rje F. Karlsson", "Zongqing Lu" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs), built on large-scale pre-trained vision towers and language models, have shown great capabilities in multimodal understanding. However, most existing MLLMs are trained on single-turn vision question-answering tasks, which do not accurately reflect real-world human conversations. In this paper, we introduce MMDiag, a multi-turn multimodal dialogue dataset. This dataset is collaboratively generated through deliberately designed rules and GPT assistance, featuring strong correlations between questions, between questions and images, and among different image regions; thus aligning more closely with real-world scenarios. MMDiag serves as a strong benchmark for multi-turn multimodal dialogue learning and brings more challenges to the grounding and reasoning capabilities of MLLMs. Further, inspired by human vision processing, we present DiagNote, an MLLM equipped with multimodal grounding and reasoning capabilities. DiagNote consists of two modules (Deliberate and Gaze) interacting with each other to perform Chain-of-Thought and annotations respectively, throughout multi-turn dialogues. We empirically demonstrate the advantages of DiagNote in both grounding and jointly processing and reasoning with vision and language information over existing MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07197", "title": "Effective and Efficient Masked Image Generation Models", "authors": [ "Zebin You", "Jingyang Ou", "Xiaolu Zhang", "Jun Hu", "Jun Zhou", "Chongxuan Li" ], "github": "https://github.com/ML-GSAI/eMIGM", "project_page": "", "abstract": "Although masked image generation models and masked diffusion models are designed with different motivations and objectives, we observe that they can be unified within a single framework. Building upon this insight, we carefully explore the design space of training and sampling, identifying key factors that contribute to both performance and efficiency. Based on the improvements observed during this exploration, we develop our model, referred to as eMIGM. Empirically, eMIGM demonstrates strong performance on ImageNet generation, as measured by Fr\\'echet Inception Distance (FID). In particular, on ImageNet 256x256, with similar number of function evaluations (NFEs) and model parameters, eMIGM outperforms the seminal VAR. Moreover, as NFE and model parameters increase, eMIGM achieves performance comparable to the state-of-the-art continuous diffusion models while requiring less than 40% of the NFE. Additionally, on ImageNet 512x512, with only about 60% of the NFE, eMIGM outperforms the state-of-the-art continuous diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ML-GSAI/eMIGM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.06580", "title": "Agent models: Internalizing Chain-of-Action Generation into Reasoning models", "authors": [ "Yuxiang Zhang", "Yuqi Yang", "Jiangming Shu", "Xinyan Wen", "Jitao Sang" ], "github": "https://github.com/ADaM-BJTU/AutoCoA", "project_page": "", "abstract": "Traditional agentic workflows rely on external prompts to manage interactions with tools and the environment, which limits the autonomy of reasoning models. We position Large Agent Models (LAMs) that internalize the generation of Chain-of-Action (CoA), enabling the model to autonomously decide when and how to use external tools. Our proposed AutoCoA framework combines supervised fine-tuning (SFT) and reinforcement learning (RL), allowing the model to seamlessly switch between reasoning and action while efficiently managing environment interactions. Main components include step-level action triggering, trajectory-level CoA optimization, and an internal world model to reduce real-environment interaction costs. Evaluations on open-domain QA tasks demonstrate that AutoCoA-trained agent models significantly outperform ReAct-based workflows in task completion, especially in tasks that require long-term reasoning and multi-step actions. Code and dataset are available at https://github.com/ADaM-BJTU/AutoCoA", "github_manual": "", "github_regex": "https://github.com/ADaM-BJTU/AutoCoA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07602", "title": "DreamRelation: Relation-Centric Video Customization", "authors": [ "Yujie Wei", "Shiwei Zhang", "Hangjie Yuan", "Biao Gong", "Longxiang Tang", "Xiang Wang", "Haonan Qiu", "Hengjia Li", "Shuai Tan", "Yingya Zhang", "Hongming Shan" ], "github": "https://github.com/ziqihuangg/ReVersion", "project_page": "", "abstract": "Relational video customization refers to the creation of personalized videos that depict user-specified relations between two subjects, a crucial task for comprehending real-world visual content. While existing methods can personalize subject appearances and motions, they still struggle with complex relational video customization, where precise relational modeling and high generalization across subject categories are essential. The primary challenge arises from the intricate spatial arrangements, layout variations, and nuanced temporal dynamics inherent in relations; consequently, current models tend to overemphasize irrelevant visual details rather than capturing meaningful interactions. To address these challenges, we propose DreamRelation, a novel approach that personalizes relations through a small set of exemplar videos, leveraging two key components: Relational Decoupling Learning and Relational Dynamics Enhancement. First, in Relational Decoupling Learning, we disentangle relations from subject appearances using relation LoRA triplet and hybrid mask training strategy, ensuring better generalization across diverse relationships. Furthermore, we determine the optimal design of relation LoRA triplet by analyzing the distinct roles of the query, key, and value features within MM-DiT's attention mechanism, making DreamRelation the first relational video generation framework with explainable components. Second, in Relational Dynamics Enhancement, we introduce space-time relational contrastive loss, which prioritizes relational dynamics while minimizing the reliance on detailed subject appearances. Extensive experiments demonstrate that DreamRelation outperforms state-of-the-art methods in relational video customization. Code and models will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ziqihuangg/ReVersion", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07067", "title": "DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs", "authors": [ "Jongwoo Ko", "Tianyi Chen", "Sungnyun Kim", "Tianyu Ding", "Luming Liang", "Ilya Zharkov", "Se-Young Yun" ], "github": "", "project_page": "", "abstract": "Despite the success of distillation in large language models (LLMs), most prior work applies identical loss functions to both teacher- and student-generated data. These strategies overlook the synergy between loss formulations and data types, leading to a suboptimal performance boost in student models. To address this, we propose DistiLLM-2, a contrastive approach that simultaneously increases the likelihood of teacher responses and decreases that of student responses by harnessing this synergy. Our extensive experiments show that DistiLLM-2 not only builds high-performing student models across a wide range of tasks, including instruction-following and code generation, but also supports diverse applications, such as preference alignment and vision-language extensions. These findings highlight the potential of a contrastive approach to enhance the efficacy of LLM distillation by effectively aligning teacher and student models across varied data types.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07608", "title": "AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning", "authors": [ "Bo Jiang", "Shaoyu Chen", "Qian Zhang", "Wenyu Liu", "Xinggang Wang" ], "github": "", "project_page": "", "abstract": "OpenAI o1 and DeepSeek R1 achieve or even surpass human expert-level performance in complex domains like mathematics and science, with reinforcement learning (RL) and reasoning playing a crucial role. In autonomous driving, recent end-to-end models have greatly improved planning performance but still struggle with long-tailed problems due to limited common sense and reasoning abilities. Some studies integrate vision-language models (VLMs) into autonomous driving, but they typically rely on pre-trained models with simple supervised fine-tuning (SFT) on driving data, without further exploration of training strategies or optimizations specifically tailored for planning. In this paper, we propose AlphaDrive, a RL and reasoning framework for VLMs in autonomous driving. AlphaDrive introduces four GRPO-based RL rewards tailored for planning and employs a two-stage planning reasoning training strategy that combines SFT with RL. As a result, AlphaDrive significantly improves both planning performance and training efficiency compared to using only SFT or without reasoning. Moreover, we are also excited to discover that, following RL training, AlphaDrive exhibits some emergent multimodal planning capabilities, which is critical for improving driving safety and efficiency. To the best of our knowledge, AlphaDrive is the first to integrate GRPO-based RL with planning reasoning into autonomous driving. Code will be released to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.06749", "title": "Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models", "authors": [ "Wenxuan Huang", "Bohan Jia", "Zijie Zhai", "Shaosheng Cao", "Zheyu Ye", "Fei Zhao", "Yao Hu", "Shaohui Lin" ], "github": "https://github.com/Osilly/Vision-R1", "project_page": "", "abstract": "DeepSeek-R1-Zero has successfully demonstrated the emergence of reasoning capabilities in LLMs purely through Reinforcement Learning (RL). Inspired by this breakthrough, we explore how RL can be utilized to enhance the reasoning capability of MLLMs. However, direct training with RL struggles to activate complex reasoning capabilities such as questioning and reflection in MLLMs, due to the absence of substantial high-quality multimodal reasoning data. To address this issue, we propose the reasoning MLLM, Vision-R1, to improve multimodal reasoning capability. Specifically, we first construct a high-quality multimodal CoT dataset without human annotations by leveraging an existing MLLM and DeepSeek-R1 through modality bridging and data filtering to obtain a 200K multimodal CoT dataset, Vision-R1-cold dataset. It serves as cold-start initialization data for Vision-R1. To mitigate the optimization challenges caused by overthinking after cold start, we propose Progressive Thinking Suppression Training (PTST) strategy and employ Group Relative Policy Optimization (GRPO) with the hard formatting result reward function to gradually refine the model's ability to learn correct and complex reasoning processes on a 10K multimodal math dataset. Comprehensive experiments show our model achieves an average improvement of sim6% across various multimodal math reasoning benchmarks. Vision-R1-7B achieves a 73.5% accuracy on the widely used MathVista benchmark, which is only 0.4% lower than the leading reasoning model, OpenAI O1. The datasets and code will be released in: https://github.com/Osilly/Vision-R1 .", "github_manual": "", "github_regex": "https://github.com/Osilly/Vision-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.04629", "title": "SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing", "authors": [ "Xiangchao Yan", "Shiyang Feng", "Jiakang Yuan", "Renqiu Xia", "Bin Wang", "Bo Zhang", "Lei Bai" ], "github": "https://github.com/alpha-innovator/surveyforge", "project_page": "", "abstract": "Survey paper plays a crucial role in scientific research, especially given the rapid growth of research publications. Recently, researchers have begun using LLMs to automate survey generation for better efficiency. However, the quality gap between LLM-generated surveys and those written by human remains significant, particularly in terms of outline quality and citation accuracy. To close these gaps, we introduce SurveyForge, which first generates the outline by analyzing the logical structure of human-written outlines and referring to the retrieved domain-related articles. Subsequently, leveraging high-quality papers retrieved from memory by our scholar navigation agent, SurveyForge can automatically generate and refine the content of the generated article. Moreover, to achieve a comprehensive evaluation, we construct SurveyBench, which includes 100 human-written survey papers for win-rate comparison and assesses AI-generated survey papers across three dimensions: reference, outline, and content quality. Experiments demonstrate that SurveyForge can outperform previous works such as AutoSurvey.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alpha-innovator/surveyforge", "github_api_added_by": "auto" }, { "date": "2025-03-11", "arxiv_id": "2503.07603", "title": "Should VLMs be Pre-trained with Image Data?", "authors": [ "Sedrick Keh", "Jean Mercat", "Samir Yitzhak Gadre", "Kushal Arora", "Igor Vasiljevic", "Benjamin Burchfiel", "Shuran Song", "Russ Tedrake", "Thomas Kollar", "Ludwig Schmidt", "Achal Dave" ], "github": "", "project_page": "", "abstract": "Pre-trained LLMs that are further trained with image data perform well on vision-language tasks. While adding images during a second training phase effectively unlocks this capability, it is unclear how much of a gain or loss this two-step pipeline gives over VLMs which integrate images earlier into the training process. To investigate this, we train models spanning various datasets, scales, image-text ratios, and amount of pre-training done before introducing vision tokens. We then fine-tune these models and evaluate their downstream performance on a suite of vision-language and text-only tasks. We find that pre-training with a mixture of image and text data allows models to perform better on vision-language tasks while maintaining strong performance on text-only evaluations. On an average of 6 diverse tasks, we find that for a 1B model, introducing visual tokens 80% of the way through pre-training results in a 2% average improvement over introducing visual tokens to a fully pre-trained model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.06885", "title": "ProBench: Judging Multimodal Foundation Models on Open-ended Multi-domain Expert Tasks", "authors": [ "Yan Yang", "Dongxu Li", "Haoning Wu", "Bei Chen", "Liu Liu", "Liyuan Pan", "Junnan Li" ], "github": "", "project_page": "", "abstract": "Solving expert-level multimodal tasks is a key milestone towards general intelligence. As the capabilities of multimodal large language models (MLLMs) continue to improve, evaluation of such advanced multimodal intelligence becomes necessary yet challenging. In this work, we introduce ProBench, a benchmark of open-ended user queries that require professional expertise and advanced reasoning. ProBench consists of 4,000 high-quality samples independently submitted by professionals based on their daily productivity demands. It spans across 10 fields and 56 sub-fields, including science, arts, humanities, coding, mathematics, and creative writing. Experimentally, we evaluate and compare 24 latest models using MLLM-as-a-Judge. Our results reveal that although the best open-source models rival the proprietary ones, ProBench presents significant challenges in visual perception, textual understanding, domain knowledge and advanced reasoning, thus providing valuable directions for future multimodal AI research efforts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.03601", "title": "Feature-Level Insights into Artificial Text Detection with Sparse Autoencoders", "authors": [ "Kristian Kuznetsov", "Laida Kushnareva", "Polina Druzhinina", "Anton Razzhigaev", "Anastasia Voznyuk", "Irina Piontkovskaya", "Evgeny Burnaev", "Serguei Barannikov" ], "github": "", "project_page": "", "abstract": "Artificial Text Detection (ATD) is becoming increasingly important with the rise of advanced Large Language Models (LLMs). Despite numerous efforts, no single algorithm performs consistently well across different types of unseen text or guarantees effective generalization to new LLMs. Interpretability plays a crucial role in achieving this goal. In this study, we enhance ATD interpretability by using Sparse Autoencoders (SAE) to extract features from Gemma-2-2b residual stream. We identify both interpretable and efficient features, analyzing their semantics and relevance through domain- and model-specific statistics, a steering approach, and manual or LLM-based interpretation. Our methods offer valuable insights into how texts from various models differ from human-written content. We show that modern LLMs have a distinct writing style, especially in information-dense domains, even though they can produce human-like outputs with personalized prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07459", "title": "MedAgentsBench: Benchmarking Thinking Models and Agent Frameworks for Complex Medical Reasoning", "authors": [ "Xiangru Tang", "Daniel Shao", "Jiwoong Sohn", "Jiapeng Chen", "Jiayi Zhang", "Jinyu Xiang", "Fang Wu", "Yilun Zhao", "Chenglin Wu", "Wenqi Shi", "Arman Cohan", "Mark Gerstein" ], "github": "https://github.com/gersteinlab/medagents-benchmark", "project_page": "", "abstract": "Large Language Models (LLMs) have shown impressive performance on existing medical question-answering benchmarks. This high performance makes it increasingly difficult to meaningfully evaluate and differentiate advanced methods. We present MedAgentsBench, a benchmark that focuses on challenging medical questions requiring multi-step clinical reasoning, diagnosis formulation, and treatment planning-scenarios where current models still struggle despite their strong performance on standard tests. Drawing from seven established medical datasets, our benchmark addresses three key limitations in existing evaluations: (1) the prevalence of straightforward questions where even base models achieve high performance, (2) inconsistent sampling and evaluation protocols across studies, and (3) lack of systematic analysis of the interplay between performance, cost, and inference time. Through experiments with various base models and reasoning methods, we demonstrate that the latest thinking models, DeepSeek R1 and OpenAI o3, exhibit exceptional performance in complex medical reasoning tasks. Additionally, advanced search-based agent methods offer promising performance-to-cost ratios compared to traditional approaches. Our analysis reveals substantial performance gaps between model families on complex questions and identifies optimal model selections for different computational constraints. Our benchmark and evaluation framework are publicly available at https://github.com/gersteinlab/medagents-benchmark.", "github_manual": "", "github_regex": "https://github.com/gersteinlab/medagents-benchmark", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.05244", "title": "WritingBench: A Comprehensive Benchmark for Generative Writing", "authors": [ "Yuning Wu", "Jiahao Mei", "Ming Yan", "Chenliang Li", "SHaopeng Lai", "Yuran Ren", "Zijia Wang", "Ji Zhang", "Mengyue Wu", "Qin Jin", "Fei Huang" ], "github": "https://github.com/X-PLUG/WritingBench", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) have significantly enhanced text generation capabilities, yet evaluating their performance in generative writing remains a challenge. Existing benchmarks primarily focus on generic text generation or limited in writing tasks, failing to capture the diverse requirements of high-quality written contents across various domains. To bridge this gap, we present WritingBench, a comprehensive benchmark designed to evaluate LLMs across 6 core writing domains and 100 subdomains, encompassing creative, persuasive, informative, and technical writing. We further propose a query-dependent evaluation framework that empowers LLMs to dynamically generate instance-specific assessment criteria. This framework is complemented by a fine-tuned critic model for criteria-aware scoring, enabling evaluations in style, format and length. The framework's validity is further demonstrated by its data curation capability, which enables 7B-parameter models to approach state-of-the-art (SOTA) performance. We open-source the benchmark, along with evaluation tools and modular framework components, to advance the development of LLMs in writing.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/X-PLUG/WritingBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07465", "title": "YOLOE: Real-Time Seeing Anything", "authors": [ "Ao Wang", "Lihao Liu", "Hui Chen", "Zijia Lin", "Jungong Han", "Guiguang Ding" ], "github": "https://github.com/THU-MIG/yoloe", "project_page": "", "abstract": "Object detection and segmentation are widely employed in computer vision applications, yet conventional models like YOLO series, while efficient and accurate, are limited by predefined categories, hindering adaptability in open scenarios. Recent open-set methods leverage text prompts, visual cues, or prompt-free paradigm to overcome this, but often compromise between performance and efficiency due to high computational demands or deployment complexity. In this work, we introduce YOLOE, which integrates detection and segmentation across diverse open prompt mechanisms within a single highly efficient model, achieving real-time seeing anything. For text prompts, we propose Re-parameterizable Region-Text Alignment (RepRTA) strategy. It refines pretrained textual embeddings via a re-parameterizable lightweight auxiliary network and enhances visual-textual alignment with zero inference and transferring overhead. For visual prompts, we present Semantic-Activated Visual Prompt Encoder (SAVPE). It employs decoupled semantic and activation branches to bring improved visual embedding and accuracy with minimal complexity. For prompt-free scenario, we introduce Lazy Region-Prompt Contrast (LRPC) strategy. It utilizes a built-in large vocabulary and specialized embedding to identify all objects, avoiding costly language model dependency. Extensive experiments show YOLOE's exceptional zero-shot performance and transferability with high inference efficiency and low training cost. Notably, on LVIS, with 3times less training cost and 1.4times inference speedup, YOLOE-v8-S surpasses YOLO-Worldv2-S by 3.5 AP. When transferring to COCO, YOLOE-v8-L achieves 0.6 AP^b and 0.4 AP^m gains over closed-set YOLOv8-L with nearly 4times less training time. Code and models are available at https://github.com/THU-MIG/yoloe.", "github_manual": "", "github_regex": "https://github.com/THU-MIG/yoloe", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07426", "title": "RePO: ReLU-based Preference Optimization", "authors": [ "Junkang Wu", "Kexin Huang", "Xue Wang", "Jinyang Gao", "Bolin Ding", "Jiancan Wu", "Xiangnan He", "Xiang Wang" ], "github": "https://github.com/junkangwu/repo", "project_page": "", "abstract": "Aligning large language models (LLMs) with human preferences is critical for real-world deployment, yet existing methods like RLHF face computational and stability challenges. While DPO establishes an offline paradigm with single hyperparameter beta, subsequent methods like SimPO reintroduce complexity through dual parameters (beta, gamma). We propose {ReLU-based Preference Optimization (RePO)}, a streamlined algorithm that eliminates beta via two advances: (1) retaining SimPO's reference-free margins but removing beta through gradient analysis, and (2) adopting a ReLU-based max-margin loss that naturally filters trivial pairs. Theoretically, RePO is characterized as SimPO's limiting case (beta to infty), where the logistic weighting collapses to binary thresholding, forming a convex envelope of the 0-1 loss. Empirical results on AlpacaEval 2 and Arena-Hard show that RePO outperforms DPO and SimPO across multiple base models, requiring only one hyperparameter to tune.", "github_manual": "https://github.com/junkangwu/repo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.05283", "title": "Escaping Plato's Cave: Towards the Alignment of 3D and Text Latent Spaces", "authors": [ "Souhail Hadgi", "Luca Moschella", "Andrea Santilli", "Diego Gomez", "Qixing Huang", "Emanuele Rodol\u00e0", "Simone Melzi", "Maks Ovsjanikov" ], "github": "https://github.com/Souhail-01/3d-text-alignment", "project_page": "", "abstract": "Recent works have shown that, when trained at scale, uni-modal 2D vision and text encoders converge to learned features that share remarkable structural properties, despite arising from different representations. However, the role of 3D encoders with respect to other modalities remains unexplored. Furthermore, existing 3D foundation models that leverage large datasets are typically trained with explicit alignment objectives with respect to frozen encoders from other representations. In this work, we investigate the possibility of a posteriori alignment of representations obtained from uni-modal 3D encoders compared to text-based feature spaces. We show that naive post-training feature alignment of uni-modal text and 3D encoders results in limited performance. We then focus on extracting subspaces of the corresponding feature spaces and discover that by projecting learned representations onto well-chosen lower-dimensional subspaces the quality of alignment becomes significantly higher, leading to improved accuracy on matching and retrieval tasks. Our analysis further sheds light on the nature of these shared subspaces, which roughly separate between semantic and geometric data representations. Overall, ours is the first work that helps to establish a baseline for post-training alignment of 3D uni-modal and text feature spaces, and helps to highlight both the shared and unique properties of 3D data compared to other representations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Souhail-01/3d-text-alignment", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.06362", "title": "Adaptive Audio-Visual Speech Recognition via Matryoshka-Based Multimodal LLMs", "authors": [ "Umberto Cappellazzo", "Minsu Kim", "Stavros Petridis" ], "github": "", "project_page": "", "abstract": "Audio-Visual Speech Recognition (AVSR) leverages both audio and visual modalities to enhance speech recognition robustness, particularly in noisy environments. Recent advancements in Large Language Models (LLMs) have demonstrated their effectiveness in speech recognition, including AVSR. However, due to the significant length of speech representations, direct integration with LLMs imposes substantial computational costs. Prior approaches address this by compressing speech representations before feeding them into LLMs. However, higher compression ratios often lead to performance degradation, necessitating a trade-off between computational efficiency and recognition accuracy. To address this challenge, we propose Llama-MTSK, the first Matryoshka-based Multimodal LLM for AVSR, which enables flexible adaptation of the audio-visual token allocation based on specific computational constraints while preserving high performance. Our approach, inspired by Matryoshka Representation Learning, encodes audio-visual representations at multiple granularities within a single model, eliminating the need to train separate models for different compression levels. Moreover, to efficiently fine-tune the LLM, we introduce three LoRA-based Matryoshka strategies using global and scale-specific LoRA modules. Extensive evaluations on the two largest AVSR datasets demonstrate that Llama-MTSK achieves state-of-the-art results, matching or surpassing models trained independently at fixed compression levels.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.05856", "title": "This Is Your Doge, If It Please You: Exploring Deception and Robustness in Mixture of LLMs", "authors": [ "Lorenz Wolf", "Sangwoong Yoon", "Ilija Bogunovic" ], "github": "https://github.com/lorenzflow/robust-moa", "project_page": "", "abstract": "Mixture of large language model (LLMs) Agents (MoA) architectures achieve state-of-the-art performance on prominent benchmarks like AlpacaEval 2.0 by leveraging the collaboration of multiple LLMs at inference time. Despite these successes, an evaluation of the safety and reliability of MoA is missing. We present the first comprehensive study of MoA's robustness against deceptive LLM agents that deliberately provide misleading responses. We examine factors like the propagation of deceptive information, model size, and information availability, and uncover critical vulnerabilities. On AlpacaEval 2.0, the popular LLaMA 3.1-70B model achieves a length-controlled Win Rate (LC WR) of 49.2% when coupled with 3-layer MoA (6 LLM agents). However, we demonstrate that introducing only a single carefully-instructed deceptive agent into the MoA can reduce performance to 37.9%, effectively nullifying all MoA gains. On QuALITY, a multiple-choice comprehension task, the impact is also severe, with accuracy plummeting by a staggering 48.5%. Inspired in part by the historical Doge of Venice voting process, designed to minimize influence and deception, we propose a range of unsupervised defense mechanisms that recover most of the lost performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lorenzflow/robust-moa", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07274", "title": "Efficient Distillation of Classifier-Free Guidance using Adapters", "authors": [ "Cristian Perez Jensen", "Seyedmorteza Sadat" ], "github": "", "project_page": "", "abstract": "While classifier-free guidance (CFG) is essential for conditional diffusion models, it doubles the number of neural function evaluations (NFEs) per inference step. To mitigate this inefficiency, we introduce adapter guidance distillation (AGD), a novel approach that simulates CFG in a single forward pass. AGD leverages lightweight adapters to approximate CFG, effectively doubling the sampling speed while maintaining or even improving sample quality. Unlike prior guidance distillation methods that tune the entire model, AGD keeps the base model frozen and only trains minimal additional parameters (sim2%) to significantly reduce the resource requirement of the distillation phase. Additionally, this approach preserves the original model weights and enables the adapters to be seamlessly combined with other checkpoints derived from the same base model. We also address a key mismatch between training and inference in existing guidance distillation methods by training on CFG-guided trajectories instead of standard diffusion trajectories. Through extensive experiments, we show that AGD achieves comparable or superior FID to CFG across multiple architectures with only half the NFEs. Notably, our method enables the distillation of large models (sim2.6B parameters) on a single consumer GPU with 24 GB of VRAM, making it more accessible than previous approaches that require multiple high-end GPUs. We will publicly release the implementation of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.06273", "title": "Zero-AVSR: Zero-Shot Audio-Visual Speech Recognition with LLMs by Learning Language-Agnostic Speech Representations", "authors": [ "Jeong Hun Yeo", "Minsu Kim", "Chae Won Kim", "Stavros Petridis", "Yong Man Ro" ], "github": "https://github.com/JeongHun0716/zero-avsr", "project_page": "", "abstract": "We explore a novel zero-shot Audio-Visual Speech Recognition (AVSR) framework, dubbed Zero-AVSR, which enables speech recognition in target languages without requiring any audio-visual speech data in those languages. Specifically, we introduce the Audio-Visual Speech Romanizer (AV-Romanizer), which learns language-agnostic speech representations by predicting Roman text. Then, by leveraging the strong multilingual modeling capabilities of Large Language Models (LLMs), we propose converting the predicted Roman text into language-specific graphemes, forming the proposed Cascaded Zero-AVSR. Taking it a step further, we explore a unified Zero-AVSR approach by directly integrating the audio-visual speech representations encoded by the AV-Romanizer into the LLM. This is achieved through finetuning the adapter and the LLM using our proposed multi-task learning scheme. To capture the wide spectrum of phonetic and linguistic diversity, we also introduce a Multilingual Audio-Visual Romanized Corpus (MARC) consisting of 2,916 hours of audio-visual speech data across 82 languages, along with transcriptions in both language-specific graphemes and Roman text. Extensive analysis and experiments confirm that the proposed Zero-AVSR framework has the potential to expand language support beyond the languages seen during the training of the AV-Romanizer.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JeongHun0716/zero-avsr", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07265", "title": "WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation", "authors": [ "Yuwei Niu", "Munan Ning", "Mengren Zheng", "Bin Lin", "Peng Jin", "Jiaqi Liao", "Kunpeng Ning", "Bin Zhu", "Li Yuan" ], "github": "https://github.com/PKU-YuanGroup/WISE", "project_page": "", "abstract": "Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content. However, existing research and evaluation standards predominantly focus on image realism and shallow text-image alignment, lacking a comprehensive assessment of complex semantic understanding and world knowledge integration in text to image generation. To address this challenge, we propose WISE, the first benchmark specifically designed for World Knowledge-Informed Semantic Evaluation. WISE moves beyond simple word-pixel mapping by challenging models with 1000 meticulously crafted prompts across 25 sub-domains in cultural common sense, spatio-temporal reasoning, and natural science. To overcome the limitations of traditional CLIP metric, we introduce WiScore, a novel quantitative metric for assessing knowledge-image alignment. Through comprehensive testing of 20 models (10 dedicated T2I models and 10 unified multimodal models) using 1,000 structured prompts spanning 25 subdomains, our findings reveal significant limitations in their ability to effectively integrate and apply world knowledge during image generation, highlighting critical pathways for enhancing knowledge incorporation and application in next-generation T2I models. Code and data are available at https://github.com/PKU-YuanGroup/WISE.", "github_manual": "", "github_regex": "https://github.com/PKU-YuanGroup/WISE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.04973", "title": "Beyond RAG: Task-Aware KV Cache Compression for Comprehensive Knowledge Reasoning", "authors": [ "Giulio Corallo", "Orion Weller", "Fabio Petroni", "Paolo Papotti" ], "github": "", "project_page": "", "abstract": "Incorporating external knowledge in large language models (LLMs) enhances their utility across diverse applications, but existing methods have trade-offs. Retrieval-Augmented Generation (RAG) fetches evidence via similarity search, but key information may fall outside top ranked results. Long-context models can process multiple documents but are computationally expensive and limited by context window size. Inspired by students condensing study material for open-book exams, we propose task-aware key-value (KV) cache compression, which compresses external knowledge in a zero- or few-shot setup. This enables LLMs to reason efficiently over a compacted representation of all relevant information. Experiments show our approach outperforms both RAG and task-agnostic compression methods. On LongBench v2, it improves accuracy by up to 7 absolute points over RAG with a 30x compression rate, while reducing inference latency from 0.43s to 0.16s. A synthetic dataset highlights that RAG performs well when sparse evidence suffices, whereas task-aware compression is superior for broad knowledge tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.06626", "title": "DiffCLIP: Differential Attention Meets CLIP", "authors": [ "Hasan Abed Al Kader Hammoud", "Bernard Ghanem" ], "github": "https://github.com/hammoudhasan/DiffCLIP", "project_page": "", "abstract": "We propose DiffCLIP, a novel vision-language model that extends the differential attention mechanism to CLIP architectures. Differential attention was originally developed for large language models to amplify relevant context while canceling out noisy information. In this work, we integrate this mechanism into CLIP's dual encoder (image and text) framework. With minimal additional parameters, DiffCLIP achieves superior performance on image-text understanding tasks. Across zero-shot classification, retrieval, and robustness benchmarks, DiffCLIP consistently outperforms baseline CLIP models. Notably, these gains come with negligible computational overhead, demonstrating that differential attention can significantly enhance multi-modal representations without sacrificing efficiency. Code can be found at https://github.com/hammoudhasan/DiffCLIP.", "github_manual": "", "github_regex": "https://github.com/hammoudhasan/DiffCLIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.05578", "title": "Novel Object 6D Pose Estimation with a Single Reference View", "authors": [ "Jian Liu", "Wei Sun", "Kai Zeng", "Jin Zheng", "Hui Yang", "Lin Wang", "Hossein Rahmani", "Ajmal Mian" ], "github": "https://github.com/CNJianLiu/SinRef-6D", "project_page": "", "abstract": "Existing novel object 6D pose estimation methods typically rely on CAD models or dense reference views, which are both difficult to acquire. Using only a single reference view is more scalable, but challenging due to large pose discrepancies and limited geometric and spatial information. To address these issues, we propose a Single-Reference-based novel object 6D (SinRef-6D) pose estimation method. Our key idea is to iteratively establish point-wise alignment in the camera coordinate system based on state space models (SSMs). Specifically, iterative camera-space point-wise alignment can effectively handle large pose discrepancies, while our proposed RGB and Points SSMs can capture long-range dependencies and spatial information from a single view, offering linear complexity and superior spatial modeling capability. Once pre-trained on synthetic data, SinRef-6D can estimate the 6D pose of a novel object using only a single reference view, without requiring retraining or a CAD model. Extensive experiments on six popular datasets and real-world robotic scenes demonstrate that we achieve on-par performance with CAD-based and dense reference view-based methods, despite operating in the more challenging single reference setting. Code will be released at https://github.com/CNJianLiu/SinRef-6D.", "github_manual": "", "github_regex": "https://github.com/CNJianLiu/SinRef-6D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07597", "title": "HumanMM: Global Human Motion Recovery from Multi-shot Videos", "authors": [ "Yuhong Zhang", "Guanlin Wu", "Ling-Hao Chen", "Zhuokai Zhao", "Jing Lin", "Xiaoke Jiang", "Jiamin Wu", "Zhuoheng Li", "Hao Frank Yang", "Haoqian Wang", "Lei Zhang" ], "github": "https://github.com/zhangyuhong01/HumanMM-code", "project_page": "", "abstract": "In this paper, we present a novel framework designed to reconstruct long-sequence 3D human motion in the world coordinates from in-the-wild videos with multiple shot transitions. Such long-sequence in-the-wild motions are highly valuable to applications such as motion generation and motion understanding, but are of great challenge to be recovered due to abrupt shot transitions, partial occlusions, and dynamic backgrounds presented in such videos. Existing methods primarily focus on single-shot videos, where continuity is maintained within a single camera view, or simplify multi-shot alignment in camera space only. In this work, we tackle the challenges by integrating an enhanced camera pose estimation with Human Motion Recovery (HMR) by incorporating a shot transition detector and a robust alignment module for accurate pose and orientation continuity across shots. By leveraging a custom motion integrator, we effectively mitigate the problem of foot sliding and ensure temporal consistency in human pose. Extensive evaluations on our created multi-shot dataset from public 3D human datasets demonstrate the robustness of our method in reconstructing realistic human motion in world coordinates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhangyuhong01/HumanMM-code", "github_api_added_by": "auto" }, { "date": "2025-03-11", "arxiv_id": "2503.07389", "title": "TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion Models", "authors": [ "Ruidong Chen", "Honglin Guo", "Lanjun Wang", "Chenyu Zhang", "Weizhi Nie", "An-An Liu" ], "github": "http://github.com/ddgoodgood/TRCE", "project_page": "", "abstract": "Recent advances in text-to-image diffusion models enable photorealistic image generation, but they also risk producing malicious content, such as NSFW images. To mitigate risk, concept erasure methods are studied to facilitate the model to unlearn specific concepts. However, current studies struggle to fully erase malicious concepts implicitly embedded in prompts (e.g., metaphorical expressions or adversarial prompts) while preserving the model's normal generation capability. To address this challenge, our study proposes TRCE, using a two-stage concept erasure strategy to achieve an effective trade-off between reliable erasure and knowledge preservation. Firstly, TRCE starts by erasing the malicious semantics implicitly embedded in textual prompts. By identifying a critical mapping objective(i.e., the [EoT] embedding), we optimize the cross-attention layers to map malicious prompts to contextually similar prompts but with safe concepts. This step prevents the model from being overly influenced by malicious semantics during the denoising process. Following this, considering the deterministic properties of the sampling trajectory of the diffusion model, TRCE further steers the early denoising prediction toward the safe direction and away from the unsafe one through contrastive learning, thus further avoiding the generation of malicious content. Finally, we conduct comprehensive evaluations of TRCE on multiple malicious concept erasure benchmarks, and the results demonstrate its effectiveness in erasing malicious concepts while better preserving the model's original generation ability. The code is available at: http://github.com/ddgoodgood/TRCE. CAUTION: This paper includes model-generated content that may contain offensive material.", "github_manual": "", "github_regex": "", "github_paper_md": "http://github.com/ddgoodgood/TRCE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.06960", "title": "A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning", "authors": [ "Xin Wen", "Bingchen Zhao", "Yilun Chen", "Jiangmiao Pang", "Xiaojuan Qi" ], "github": "https://github.com/CVMI-Lab/SlotMIM", "project_page": "", "abstract": "Pre-trained vision models (PVMs) are fundamental to modern robotics, yet their optimal configuration remains unclear. Through systematic evaluation, we find that while DINO and iBOT outperform MAE across visuomotor control and perception tasks, they struggle when trained on non-(single-)object-centric (NOC) data--a limitation strongly correlated with their diminished ability to learn object-centric representations. This investigation indicates that the ability to form object-centric representations from the non-object-centric robotics dataset is the key to success for PVMs. Motivated by this discovery, we designed SlotMIM, a method that induces object-centric representations by introducing a semantic bottleneck to reduce the number of prototypes to encourage the emergence of objectness as well as cross-view consistency regularization for encouraging multiview invariance. Our experiments encompass pre-training on object-centric, scene-centric, web-crawled, and ego-centric data. Across all settings, our approach learns transferrable representations and achieves significant improvements over prior work in image recognition, scene understanding, and robot learning evaluations. When scaled up with million-scale datasets, our method also demonstrates superior data efficiency and scalability. Our code and models are publicly available at https://github.com/CVMI-Lab/SlotMIM.", "github_manual": "", "github_regex": "https://github.com/CVMI-Lab/SlotMIM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07334", "title": "Unleashing the Potential of Large Language Models for Text-to-Image Generation through Autoregressive Representation Alignment", "authors": [ "Xing Xie", "Jiawei Liu", "Ziyue Lin", "Huijie Fan", "Zhi Han", "Yandong Tang", "Liangqiong Qu" ], "github": "https://github.com/HKU-HealthAI/ARRA", "project_page": "", "abstract": "We present Autoregressive Representation Alignment (ARRA), a new training framework that unlocks global-coherent text-to-image generation in autoregressive LLMs without architectural changes. Unlike prior work that requires complex architectural redesigns, ARRA aligns LLM hidden states with visual representations from external visual foundational models via a global visual alignment loss and a hybrid token, . This token enforces dual constraints: local next-token prediction and global semantic distillation, enabling LLMs to implicitly learn spatial and contextual coherence while retaining their original autoregressive paradigm. Extensive experiments validate ARRA's plug-and-play versatility. When training from text-generation-only LLMs or random initialization, ARRA reduces FID by 25.5% (MIMIC-CXR), 8.8% (DeepEyeNet), and 7.5% (ImageNet) for advanced autoregressive LLMs like Chameleon and LlamaGen, all without framework modifications. For domain adaption, ARRA aligns general-purpose LLMs with specialized models (e.g., BioMedCLIP), achieving an 18.6% FID reduction over direct fine-tuning on medical imaging (MIMIC-CXR). By demonstrating that training objective redesign -- not just architectural innovation -- can resolve cross-modal global coherence challenges, ARRA offers a complementary paradigm for advancing autoregressive models. Code and models will be released to advance autoregressive image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HKU-HealthAI/ARRA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.05641", "title": "Symbolic Mixture-of-Experts: Adaptive Skill-based Routing for Heterogeneous Reasoning", "authors": [ "Justin Chih-Yao Chen", "Sukwon Yun", "Elias Stengel-Eskin", "Tianlong Chen", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Combining existing pre-trained expert LLMs is a promising avenue for scalably tackling large-scale and diverse tasks. However, selecting experts at the task level is often too coarse-grained, as heterogeneous tasks may require different expertise for each instance. To enable adaptive instance-level mixing of pre-trained LLM experts, we propose Symbolic-MoE, a symbolic, text-based, and gradient-free Mixture-of-Experts framework. Symbolic-MoE takes a fine-grained approach to selection by emphasizing skills, e.g., algebra in math or molecular biology in biomedical reasoning. We propose a skill-based recruiting strategy that dynamically selects the most relevant set of expert LLMs for diverse reasoning tasks based on their strengths. Each selected expert then generates its own reasoning, resulting in k outputs from k experts, which are then synthesized into a final high-quality response by an aggregator chosen based on its ability to integrate diverse reasoning outputs. We show that Symbolic-MoE's instance-level expert selection improves performance by a large margin but -- when implemented naively -- can introduce a high computational overhead due to the need for constant model loading and offloading. To address this, we implement a batch inference strategy that groups instances based on their assigned experts, loading each model only once. This allows us to integrate 16 expert models on 1 GPU with a time cost comparable to or better than prior multi-agent baselines using 4 GPUs. Through extensive evaluations on diverse benchmarks (MMLU-Pro, GPQA, AIME, and MedMCQA), we demonstrate that Symbolic-MoE outperforms strong LLMs like GPT4o-mini, as well as multi-agent approaches, with an absolute average improvement of 8.15% over the best multi-agent baseline. Moreover, Symbolic-MoE removes the need for expensive multi-round discussions, outperforming discussion baselines with less computation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07413", "title": "REF-VLM: Triplet-Based Referring Paradigm for Unified Visual Decoding", "authors": [ "Yan Tai", "Luhao Zhu", "Zhiqiang Chen", "Ynan Ding", "Yiying Dong", "Xiaohong Liu", "Guodong Guo" ], "github": "https://github.com/MacavityT/REF-VLM", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) demonstrate robust zero-shot capabilities across diverse vision-language tasks after training on mega-scale datasets. However, dense prediction tasks, such as semantic segmentation and keypoint detection, pose significant challenges for MLLMs when represented solely as text outputs. Simultaneously, current MLLMs utilizing latent embeddings for visual task decoding generally demonstrate limited adaptability to both multi-task learning and multi-granularity scenarios. In this work, we present REF-VLM, an end-to-end framework for unified training of various visual decoding tasks. To address complex visual decoding scenarios, we introduce the Triplet-Based Referring Paradigm (TRP), which explicitly decouples three critical dimensions in visual decoding tasks through a triplet structure: concepts, decoding types, and targets. TRP employs symbolic delimiters to enforce structured representation learning, enhancing the parsability and interpretability of model outputs. Additionally, we construct Visual-Task Instruction Following Dataset (VTInstruct), a large-scale multi-task dataset containing over 100 million multimodal dialogue samples across 25 task types. Beyond text inputs and outputs, VT-Instruct incorporates various visual prompts such as point, box, scribble, and mask, and generates outputs composed of text and visual units like box, keypoint, depth and mask. The combination of different visual prompts and visual units generates a wide variety of task types, expanding the applicability of REF-VLM significantly. Both qualitative and quantitative experiments demonstrate that our REF-VLM outperforms other MLLMs across a variety of standard benchmarks. The code, dataset, and demo available at https://github.com/MacavityT/REF-VLM.", "github_manual": "", "github_regex": "https://github.com/MacavityT/REF-VLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.07598", "title": "VACE: All-in-One Video Creation and Editing", "authors": [ "Zeyinzi Jiang", "Zhen Han", "Chaojie Mao", "Jingfeng Zhang", "Yulin Pan", "Yu Liu" ], "github": "", "project_page": "", "abstract": "Diffusion Transformer has demonstrated powerful capability and scalability in generating high-quality images and videos. Further pursuing the unification of generation and editing tasks has yielded significant progress in the domain of image content creation. However, due to the intrinsic demands for consistency across both temporal and spatial dynamics, achieving a unified approach for video synthesis remains challenging. We introduce VACE, which enables users to perform Video tasks within an All-in-one framework for Creation and Editing. These tasks include reference-to-video generation, video-to-video editing, and masked video-to-video editing. Specifically, we effectively integrate the requirements of various tasks by organizing video task inputs, such as editing, reference, and masking, into a unified interface referred to as the Video Condition Unit (VCU). Furthermore, by utilizing a Context Adapter structure, we inject different task concepts into the model using formalized representations of temporal and spatial dimensions, allowing it to handle arbitrary video synthesis tasks flexibly. Extensive experiments demonstrate that the unified model of VACE achieves performance on par with task-specific models across various subtasks. Simultaneously, it enables diverse applications through versatile task combinations. Project page: https://ali-vilab.github.io/VACE-Page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.06698", "title": "What's in a Latent? Leveraging Diffusion Latent Space for Domain Generalization", "authors": [ "Xavier Thomas", "Deepti Ghadiyaram" ], "github": "", "project_page": "", "abstract": "Domain Generalization aims to develop models that can generalize to novel and unseen data distributions. In this work, we study how model architectures and pre-training objectives impact feature richness and propose a method to effectively leverage them for domain generalization. Specifically, given a pre-trained feature space, we first discover latent domain structures, referred to as pseudo-domains, that capture domain-specific variations in an unsupervised manner. Next, we augment existing classifiers with these complementary pseudo-domain representations making them more amenable to diverse unseen test domains. We analyze how different pre-training feature spaces differ in the domain-specific variances they capture. Our empirical studies reveal that features from diffusion models excel at separating domains in the absence of explicit domain labels and capture nuanced domain-specific information. On 5 datasets, we show that our very simple framework improves generalization to unseen domains by a maximum test accuracy improvement of over 4% compared to the standard baseline Empirical Risk Minimization (ERM). Crucially, our method outperforms most algorithms that access domain labels during training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2502.20475", "title": "Promote, Suppress, Iterate: How Language Models Answer One-to-Many Factual Queries", "authors": [ "Tianyi Lorena Yan", "Robin Jia" ], "github": "https://github.com/Lorenayannnnn/how-lms-answer-one-to-many-factual-queries", "project_page": "", "abstract": "To answer one-to-many factual queries (e.g., listing cities of a country), a language model (LM) must simultaneously recall knowledge and avoid repeating previous answers. How are these two subtasks implemented and integrated internally? Across multiple datasets and models, we identify a promote-then-suppress mechanism: the model first recalls all answers, and then suppresses previously generated ones. Specifically, LMs use both the subject and previous answer tokens to perform knowledge recall, with attention propagating subject information and MLPs promoting the answers. Then, attention attends to and suppresses previous answer tokens, while MLPs amplify the suppression signal. Our mechanism is corroborated by extensive experimental evidence: in addition to using early decoding and causal tracing, we analyze how components use different tokens by introducing both Token Lens, which decodes aggregated attention updates from specified tokens, and a knockout method that analyzes changes in MLP outputs after removing attention to specified tokens. Overall, we provide new insights into how LMs' internal components interact with different input tokens to support complex factual recall. Code is available at https://github.com/Lorenayannnnn/how-lms-answer-one-to-many-factual-queries.", "github_manual": "", "github_regex": "https://github.com/Lorenayannnnn/how-lms-answer-one-to-many-factual-queries", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.02819", "title": "Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts", "authors": [ "Marta Skreta", "Tara Akhound-Sadegh", "Viktor Ohanesian", "Roberto Bondesan", "Al\u00e1n Aspuru-Guzik", "Arnaud Doucet", "Rob Brekelmans", "Alexander Tong", "Kirill Neklyudov" ], "github": "https://github.com/martaskrt/fkc-diffusion", "project_page": "", "abstract": "While score-based generative models are the model of choice across diverse domains, there are limited tools available for controlling inference-time behavior in a principled manner, e.g. for composing multiple pretrained models. Existing classifier-free guidance methods use a simple heuristic to mix conditional and unconditional scores to approximately sample from conditional distributions. However, such methods do not approximate the intermediate distributions, necessitating additional 'corrector' steps. In this work, we provide an efficient and principled method for sampling from a sequence of annealed, geometric-averaged, or product distributions derived from pretrained score-based models. We derive a weighted simulation scheme which we call Feynman-Kac Correctors (FKCs) based on the celebrated Feynman-Kac formula by carefully accounting for terms in the appropriate partial differential equations (PDEs). To simulate these PDEs, we propose Sequential Monte Carlo (SMC) resampling algorithms that leverage inference-time scaling to improve sampling quality. We empirically demonstrate the utility of our methods by proposing amortized sampling via inference-time temperature annealing, improving multi-objective molecule generation using pretrained models, and improving classifier-free guidance for text-to-image generation. Our code is available at https://github.com/martaskrt/fkc-diffusion.", "github_manual": "", "github_regex": "https://github.com/martaskrt/fkc-diffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.05265", "title": "PhiloBERTA: A Transformer-Based Cross-Lingual Analysis of Greek and Latin Lexicons", "authors": [ "Rumi A. Allbert", "Makai L. Allbert" ], "github": "https://github.com/RumiAllbert/PhiloBERTA", "project_page": "", "abstract": "We present PhiloBERTA, a cross-lingual transformer model that measures semantic relationships between ancient Greek and Latin lexicons. Through analysis of selected term pairs from classical texts, we use contextual embeddings and angular similarity metrics to identify precise semantic alignments. Our results show that etymologically related pairs demonstrate significantly higher similarity scores, particularly for abstract philosophical concepts such as epist\\=em\\=e (scientia) and dikaiosyn\\=e (iustitia). Statistical analysis reveals consistent patterns in these relationships (p = 0.012), with etymologically related pairs showing remarkably stable semantic preservation compared to control pairs. These findings establish a quantitative framework for examining how philosophical concepts moved between Greek and Latin traditions, offering new methods for classical philological research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/RumiAllbert/PhiloBERTA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-11", "arxiv_id": "2503.06141", "title": "Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language Model", "authors": [ "Mingxing Li", "Rui Wang", "Lei Sun", "Yancheng Bai", "Xiangxiang Chu" ], "github": "https://github.com/AMAP-ML/RealQA", "project_page": "", "abstract": "The rapid expansion of mobile internet has resulted in a substantial increase in user-generated content (UGC) images, thereby making the thorough assessment of UGC images both urgent and essential. Recently, multimodal large language models (MLLMs) have shown great potential in image quality assessment (IQA) and image aesthetic assessment (IAA). Despite this progress, effectively scoring the quality and aesthetics of UGC images still faces two main challenges: 1) A single score is inadequate to capture the hierarchical human perception. 2) How to use MLLMs to output numerical scores, such as mean opinion scores (MOS), remains an open question. To address these challenges, we introduce a novel dataset, named Realistic image Quality and Aesthetic (RealQA), including 14,715 UGC images, each of which is annoted with 10 fine-grained attributes. These attributes span three levels: low level (e.g., image clarity), middle level (e.g., subject integrity) and high level (e.g., composition). Besides, we conduct a series of in-depth and comprehensive investigations into how to effectively predict numerical scores using MLLMs. Surprisingly, by predicting just two extra significant digits, the next token paradigm can achieve SOTA performance. Furthermore, with the help of chain of thought (CoT) combined with the learnt fine-grained attributes, the proposed method can outperform SOTA methods on five public datasets for IQA and IAA with superior interpretability and show strong zero-shot generalization for video quality assessment (VQA). The code and dataset will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AMAP-ML/RealQA", "github_api_added_by": "auto" }, { "date": "2025-03-12", "arxiv_id": "2503.07572", "title": "Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning", "authors": [ "Yuxiao Qu", "Matthew Y. R. Yang", "Amrith Setlur", "Lewis Tunstall", "Edward Emanuel Beeching", "Ruslan Salakhutdinov", "Aviral Kumar" ], "github": "https://github.com/CMU-AIRe/MRT", "project_page": "", "abstract": "Training models to effectively use test-time compute is crucial for improving the reasoning performance of LLMs. Current methods mostly do so via fine-tuning on search traces or running RL with 0/1 outcome reward, but do these approaches efficiently utilize test-time compute? Would these approaches continue to scale as the budget improves? In this paper, we try to answer these questions. We formalize the problem of optimizing test-time compute as a meta-reinforcement learning (RL) problem, which provides a principled perspective on spending test-time compute. This perspective enables us to view the long output stream from the LLM as consisting of several episodes run at test time and leads us to use a notion of cumulative regret over output tokens as a way to measure the efficacy of test-time compute. Akin to how RL algorithms can best tradeoff exploration and exploitation over training, minimizing cumulative regret would also provide the best balance between exploration and exploitation in the token stream. While we show that state-of-the-art models do not minimize regret, one can do so by maximizing a dense reward bonus in conjunction with the outcome 0/1 reward RL. This bonus is the ''progress'' made by each subsequent block in the output stream, quantified by the change in the likelihood of eventual success. Using these insights, we develop Meta Reinforcement Fine-Tuning, or MRT, a new class of fine-tuning methods for optimizing test-time compute. MRT leads to a 2-3x relative gain in performance and roughly a 1.5x gain in token efficiency for math reasoning compared to outcome-reward RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CMU-AIRe/MRT", "github_api_added_by": "auto" }, { "date": "2025-03-12", "arxiv_id": "2503.07639", "title": "Mixture of Experts Made Intrinsically Interpretable", "authors": [ "Xingyi Yang", "Constantin Venhoff", "Ashkan Khakzar", "Christian Schroeder de Witt", "Puneet K. Dokania", "Adel Bibi", "Philip Torr" ], "github": "", "project_page": "", "abstract": "Neurons in large language models often exhibit polysemanticity, simultaneously encoding multiple unrelated concepts and obscuring interpretability. Instead of relying on post-hoc methods, we present MoE-X, a Mixture-of-Experts (MoE) language model designed to be intrinsically interpretable. Our approach is motivated by the observation that, in language models, wider networks with sparse activations are more likely to capture interpretable factors. However, directly training such large sparse networks is computationally prohibitive. MoE architectures offer a scalable alternative by activating only a subset of experts for any given input, inherently aligning with interpretability objectives. In MoE-X, we establish this connection by rewriting the MoE layer as an equivalent sparse, large MLP. This approach enables efficient scaling of the hidden size while maintaining sparsity. To further enhance interpretability, we enforce sparse activation within each expert and redesign the routing mechanism to prioritize experts with the highest activation sparsity. These designs ensure that only the most salient features are routed and processed by the experts. We evaluate MoE-X on chess and natural language tasks, showing that it achieves performance comparable to dense models while significantly improving interpretability. MoE-X achieves a perplexity better than GPT-2, with interpretability surpassing even sparse autoencoder (SAE)-based approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08605", "title": "Tuning-Free Multi-Event Long Video Generation via Synchronized Coupled Sampling", "authors": [ "Subin Kim", "Seoung Wug Oh", "Jui-Hsien Wang", "Joon-Young Lee", "Jinwoo Shin" ], "github": "", "project_page": "", "abstract": "While recent advancements in text-to-video diffusion models enable high-quality short video generation from a single prompt, generating real-world long videos in a single pass remains challenging due to limited data and high computational costs. To address this, several works propose tuning-free approaches, i.e., extending existing models for long video generation, specifically using multiple prompts to allow for dynamic and controlled content changes. However, these methods primarily focus on ensuring smooth transitions between adjacent frames, often leading to content drift and a gradual loss of semantic coherence over longer sequences. To tackle such an issue, we propose Synchronized Coupled Sampling (SynCoS), a novel inference framework that synchronizes denoising paths across the entire video, ensuring long-range consistency across both adjacent and distant frames. Our approach combines two complementary sampling strategies: reverse and optimization-based sampling, which ensure seamless local transitions and enforce global coherence, respectively. However, directly alternating between these samplings misaligns denoising trajectories, disrupting prompt guidance and introducing unintended content changes as they operate independently. To resolve this, SynCoS synchronizes them through a grounded timestep and a fixed baseline noise, ensuring fully coupled sampling with aligned denoising paths. Extensive experiments show that SynCoS significantly improves multi-event long video generation, achieving smoother transitions and superior long-range coherence, outperforming previous approaches both quantitatively and qualitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.07604", "title": "Implicit Reasoning in Transformers is Reasoning through Shortcuts", "authors": [ "Tianhe Lin", "Jian Xie", "Siyu Yuan", "Deqing Yang" ], "github": "", "project_page": "", "abstract": "Test-time compute is emerging as a new paradigm for enhancing language models' complex multi-step reasoning capabilities, as demonstrated by the success of OpenAI's o1 and o3, as well as DeepSeek's R1. Compared to explicit reasoning in test-time compute, implicit reasoning is more inference-efficient, requiring fewer generated tokens. However, why does the advanced reasoning capability fail to emerge in the implicit reasoning style? In this work, we train GPT-2 from scratch on a curated multi-step mathematical reasoning dataset and conduct analytical experiments to investigate how language models perform implicit reasoning in multi-step tasks. Our findings reveal: 1) Language models can perform step-by-step reasoning and achieve high accuracy in both in-domain and out-of-domain tests via implicit reasoning. However, this capability only emerges when trained on fixed-pattern data. 2) Conversely, implicit reasoning abilities emerging from training on unfixed-pattern data tend to overfit a specific pattern and fail to generalize further. Notably, this limitation is also observed in state-of-the-art large language models. These findings suggest that language models acquire implicit reasoning through shortcut learning, enabling strong performance on tasks with similar patterns while lacking generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.07860", "title": "Video Action Differencing", "authors": [ "James Burgess", "Xiaohan Wang", "Yuhui Zhang", "Anita Rau", "Alejandro Lozano", "Lisa Dunlap", "Trevor Darrell", "Serena Yeung-Levy" ], "github": "https://github.com/jmhb0/viddiff", "project_page": "", "abstract": "How do two individuals differ when performing the same action? In this work, we introduce Video Action Differencing (VidDiff), the novel task of identifying subtle differences between videos of the same action, which has many applications, such as coaching and skill learning. To enable development on this new task, we first create VidDiffBench, a benchmark dataset containing 549 video pairs, with human annotations of 4,469 fine-grained action differences and 2,075 localization timestamps indicating where these differences occur. Our experiments demonstrate that VidDiffBench poses a significant challenge for state-of-the-art large multimodal models (LMMs), such as GPT-4o and Qwen2-VL. By analyzing failure cases of LMMs on VidDiffBench, we highlight two key challenges for this task: localizing relevant sub-actions over two videos and fine-grained frame comparison. To overcome these, we propose the VidDiff method, an agentic workflow that breaks the task into three stages: action difference proposal, keyframe localization, and frame differencing, each stage utilizing specialized foundation models. To encourage future research in this new task, we release the benchmark at https://huggingface.co/datasets/jmhb/VidDiffBench and code at http://jmhb0.github.io/viddiff.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jmhb0/viddiff", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.07703", "title": "Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model", "authors": [ "Lixue Gong", "Xiaoxia Hou", "Fanshi Li", "Liang Li", "Xiaochen Lian", "Fei Liu", "Liyang Liu", "Wei Liu", "Wei Lu", "Yichun Shi", "Shiqi Sun", "Yu Tian", "Zhi Tian", "Peng Wang", "Xun Wang", "Ye Wang", "Guofeng Wu", "Jie Wu", "Xin Xia", "Xuefeng Xiao", "Linjie Yang", "Zhonghua Zhai", "Xinyu Zhang", "Qi Zhang", "Yuwei Zhang", "Shijia Zhao", "Jianchao Yang", "Weilin Huang" ], "github": "", "project_page": "", "abstract": "Rapid advancement of diffusion models has catalyzed remarkable progress in the field of image generation. However, prevalent models such as Flux, SD3.5 and Midjourney, still grapple with issues like model bias, limited text rendering capabilities, and insufficient understanding of Chinese cultural nuances. To address these limitations, we present Seedream 2.0, a native Chinese-English bilingual image generation foundation model that excels across diverse dimensions, which adeptly manages text prompt in both Chinese and English, supporting bilingual image generation and text rendering. We develop a powerful data system that facilitates knowledge integration, and a caption system that balances the accuracy and richness for image description. Particularly, Seedream is integrated with a self-developed bilingual large language model as a text encoder, allowing it to learn native knowledge directly from massive data. This enable it to generate high-fidelity images with accurate cultural nuances and aesthetic expressions described in either Chinese or English. Beside, Glyph-Aligned ByT5 is applied for flexible character-level text rendering, while a Scaled ROPE generalizes well to untrained resolutions. Multi-phase post-training optimizations, including SFT and RLHF iterations, further improve the overall capability. Through extensive experimentation, we demonstrate that Seedream 2.0 achieves state-of-the-art performance across multiple aspects, including prompt-following, aesthetics, text rendering, and structural correctness. Furthermore, Seedream 2.0 has been optimized through multiple RLHF iterations to closely align its output with human preferences, as revealed by its outstanding ELO score. In addition, it can be readily adapted to an instruction-based image editing model, such as SeedEdit, with strong editing capability that balances instruction-following and image consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.07920", "title": "Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia", "authors": [ "Samuel Cahyawijaya", "Holy Lovenia", "Joel Ruben Antony Moniz", "Tack Hwa Wong", "Mohammad Rifqi Farhansyah", "Thant Thiri Maung", "Frederikus Hudi", "David Anugraha", "Muhammad Ravi Shulthan Habibi", "Muhammad Reza Qorib", "Amit Agarwal", "Joseph Marvin Imperial", "Hitesh Laxmichand Patel", "Vicky Feliren", "Bahrul Ilmi Nasution", "Manuel Antonio Rufino", "Genta Indra Winata", "Rian Adam Rajagede", "Carlos Rafael Catalan", "Mohamed Fazli Imam", "Priyaranjan Pattnayak", "Salsabila Zahirah Pranida", "Kevin Pratama", "Yeshil Bangera", "Adisai Na-Thalang", "Patricia Nicole Monderin", "Yueqi Song", "Christian Simon", "Lynnette Hui Xian Ng", "Richardy Lobo' Sapan", "Taki Hasan Rafi", "Bin Wang", "Supryadi", "Kanyakorn Veerakanjana", "Piyalitt Ittichaiwong", "Matthew Theodore Roque", "Karissa Vincentio", "Takdanai Kreangphet", "Phakphum Artkaew", "Kadek Hendrawan Palgunadi", "Yanzhi Yu", "Rochana Prih Hastuti", "William Nixon", "Mithil Bangera", "Adrian Xuan Wei Lim", "Aye Hninn Khine", "Hanif Muhammad Zhafran", "Teddy Ferdinan", "Audra Aurora Izzani", "Ayushman Singh", "Evan", "Jauza Akbar Krito", "Michael Anugraha", "Fenal Ashokbhai Ilasariya", "Haochen Li", "John Amadeo Daniswara", "Filbert Aurelian Tjiaranata", "Eryawan Presma Yulianrifat", "Can Udomcharoenchaikit", "Fadil Risdian Ansori", "Mahardika Krisna Ihsani", "Giang Nguyen", "Anab Maulana Barik", "Dan John Velasco", "Rifo Ahmad Genadi", "Saptarshi Saha", "Chengwei Wei", "Isaiah Flores", "Kenneth Ko Han Chen", "Anjela Gail Santos", "Wan Shen Lim", "Kaung Si Phyo", "Tim Santos", "Meisyarah Dwiastuti", "Jiayun Luo", "Jan Christian Blaise Cruz", "Ming Shan Hee", "Ikhlasul Akmal Hanif", "M. Alif Al Hakim", "Muhammad Rizky Sya'ban", "Kun Kerdthaisong", "Lester James V. Miranda", "Fajri Koto", "Tirana Noor Fatyanosa", "Alham Fikri Aji", "Jostin Jerico Rosal", "Jun Kevin", "Robert Wijaya", "Onno P. Kampman", "Ruochen Zhang", "B\u00f6rje F. Karlsson", "Peerat Limkonchotiwat" ], "github": "", "project_page": "", "abstract": "Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often results in artificial intelligence (AI) models that fail to capture SEA cultural nuances. To fill this gap, we present SEA-VL, an open-source initiative dedicated to developing high-quality, culturally relevant data for SEA languages. By involving contributors from SEA countries, SEA-VL aims to ensure better cultural relevance and diversity, fostering greater inclusivity of underrepresented languages in VL research. Beyond crowdsourcing, our initiative goes one step further in the exploration of the automatic collection of culturally relevant images through crawling and image generation. First, we find that image crawling achieves approximately ~85% cultural relevance while being more cost- and time-efficient than crowdsourcing. Second, despite the substantial progress in generative vision models, synthetic images remain unreliable in accurately reflecting SEA cultures. The generated images often fail to reflect the nuanced traditions and cultural contexts of the region. Collectively, we gather 1.28M SEA culturally-relevant images, more than 50 times larger than other existing datasets. Through SEA-VL, we aim to bridge the representation gap in SEA, fostering the development of more inclusive AI systems that authentically represent diverse cultures across SEA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08120", "title": "UniF^2ace: Fine-grained Face Understanding and Generation with Unified Multimodal Models", "authors": [ "Junzhe Li", "Xuerui Qiu", "Linrui Xu", "Liya Guo", "Delin Qu", "Tingting Long", "Chun Fan", "Ming Li" ], "github": "https://github.com/tulvgengenr/UniF2ace", "project_page": "", "abstract": "Unified multimodal models (UMMs) have emerged as a powerful paradigm in foundational computer vision research, demonstrating significant potential in both image understanding and generation. However, existing research in the face domain primarily focuses on coarse facial attribute understanding, with limited capacity to handle fine-grained facial attributes and without addressing generation capabilities. To overcome these limitations, we propose UniF^2ace, the first UMM tailored specifically for fine-grained face understanding and generation. In general, we train UniF^2ace on a self-constructed, specialized dataset utilizing two mutually beneficial diffusion techniques and a two-level mixture-of-experts architecture. Specifically, we first build a large-scale facial dataset, UniF^2ace-130K, which contains 130K image-text pairs with one million question-answering pairs that span a wide range of facial attributes. Second, we establish a theoretical connection between discrete diffusion score matching and masked generative models, optimizing both evidence lower bounds simultaneously, which significantly improves the model's ability to synthesize facial details. Finally, we introduce both token-level and sequence-level mixture-of-experts, enabling efficient fine-grained representation learning for both understanding and generation tasks. Extensive experiments on UniF^2ace-130K demonstrate that UniF^2ace outperforms existing UMMs and generative models, achieving superior performance across both understanding and generation tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tulvgengenr/UniF2ace", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08625", "title": "SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories", "authors": [ "Muzhi Zhu", "Yuzhuo Tian", "Hao Chen", "Chunluan Zhou", "Qingpei Guo", "Yang Liu", "Ming Yang", "Chunhua Shen" ], "github": "https://github.com/aim-uofa/SegAgent", "project_page": "", "abstract": "While MLLMs have demonstrated adequate image understanding capabilities, they still struggle with pixel-level comprehension, limiting their practical applications. Current evaluation tasks like VQA and visual grounding remain too coarse to assess fine-grained pixel comprehension accurately. Though segmentation is foundational for pixel-level understanding, existing methods often require MLLMs to generate implicit tokens, decoded through external pixel decoders. This approach disrupts the MLLM's text output space, potentially compromising language capabilities and reducing flexibility and extensibility, while failing to reflect the model's intrinsic pixel-level understanding. Thus, we introduce the Human-Like Mask Annotation Task (HLMAT), a new paradigm where MLLMs mimic human annotators using interactive segmentation tools. Modeling segmentation as a multi-step Markov Decision Process, HLMAT enables MLLMs to iteratively generate text-based click points, achieving high-quality masks without architectural changes or implicit tokens. Through this setup, we develop SegAgent, a model fine-tuned on human-like annotation trajectories, which achieves performance comparable to state-of-the-art (SOTA) methods and supports additional tasks like mask refinement and annotation filtering. HLMAT provides a protocol for assessing fine-grained pixel understanding in MLLMs and introduces a vision-centric, multi-step decision-making task that facilitates exploration of MLLMs' visual reasoning abilities. Our adaptations of policy improvement method StaR and PRM-guided tree search further enhance model robustness in complex segmentation tasks, laying a foundation for future advancements in fine-grained visual perception and multi-step decision-making for MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aim-uofa/SegAgent", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.07536", "title": "LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL", "authors": [ "Yingzhe Peng", "Gongrui Zhang", "Miaosen Zhang", "Zhiyuan You", "Jie Liu", "Qipeng Zhu", "Kai Yang", "Xingzhong Xu", "Xin Geng", "Xu Yang" ], "github": "", "project_page": "", "abstract": "Enhancing reasoning in Large Multimodal Models (LMMs) faces unique challenges from the complex interplay between visual perception and logical reasoning, particularly in compact 3B-parameter architectures where architectural constraints limit reasoning capacity and modality alignment. While rule-based reinforcement learning (RL) excels in text-only domains, its multimodal extension confronts two critical barriers: (1) data limitations due to ambiguous answers and scarce complex reasoning examples, and (2) degraded foundational reasoning induced by multimodal pretraining. To address these challenges, we propose \\method, a two-stage framework adapting rule-based RL for multimodal reasoning through Foundational Reasoning Enhancement (FRE) followed by Multimodal Generalization Training (MGT). The FRE stage first strengthens reasoning abilities using text-only data with rule-based RL, then the MGT stage generalizes these reasoning capabilities to multimodal domains. Experiments on Qwen2.5-VL-Instruct-3B demonstrate that \\method achieves 4.83\\% and 4.5\\% average improvements over baselines in multimodal and text-only benchmarks, respectively, with a 3.63\\% gain in complex Football Game tasks. These results validate that text-based reasoning enhancement enables effective multimodal generalization, offering a data-efficient paradigm that bypasses costly high-quality multimodal training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08686", "title": "OmniMamba: Efficient and Unified Multimodal Understanding and Generation via State Space Models", "authors": [ "Jialv Zou", "Bencheng Liao", "Qian Zhang", "Wenyu Liu", "Xinggang Wang" ], "github": "https://github.com/hustvl/OmniMamba", "project_page": "", "abstract": "Recent advancements in unified multimodal understanding and visual generation (or multimodal generation) models have been hindered by their quadratic computational complexity and dependence on large-scale training data. We present OmniMamba, the first linear-architecture-based multimodal generation model that generates both text and images through a unified next-token prediction paradigm. The model fully leverages Mamba-2's high computational and memory efficiency, extending its capabilities from text generation to multimodal generation. To address the data inefficiency of existing unified models, we propose two key innovations: (1) decoupled vocabularies to guide modality-specific generation, and (2) task-specific LoRA for parameter-efficient adaptation. Furthermore, we introduce a decoupled two-stage training strategy to mitigate data imbalance between two tasks. Equipped with these techniques, OmniMamba achieves competitive performance with JanusFlow while surpassing Show-o across benchmarks, despite being trained on merely 2M image-text pairs, which is 1,000 times fewer than Show-o. Notably, OmniMamba stands out with outstanding inference efficiency, achieving up to a 119.2 times speedup and 63% GPU memory reduction for long-sequence generation compared to Transformer-based counterparts. Code and models are released at https://github.com/hustvl/OmniMamba", "github_manual": "", "github_regex": "https://github.com/hustvl/OmniMamba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08689", "title": "QuoTA: Query-oriented Token Assignment via CoT Query Decouple for Long Video Comprehension", "authors": [ "Yongdong Luo", "Wang Chen", "Xiawu Zheng", "Weizhong Huang", "Shukang Yin", "Haojia Lin", "Chaoyou Fu", "Jinfa Huang", "Jiayi Ji", "Jiebo Luo", "Rongrong Ji" ], "github": "https://github.com/MAC-AutoML/QuoTA", "project_page": "", "abstract": "Recent advances in long video understanding typically mitigate visual redundancy through visual token pruning based on attention distribution. However, while existing methods employ post-hoc low-response token pruning in decoder layers, they overlook the input-level semantic correlation between visual tokens and instructions (query). In this paper, we propose QuoTA, an ante-hoc training-free modular that extends existing large video-language models (LVLMs) for visual token assignment based on query-oriented frame-level importance assessment. The query-oriented token selection is crucial as it aligns visual processing with task-specific requirements, optimizing token budget utilization while preserving semantically relevant content. Specifically, (i) QuoTA strategically allocates frame-level importance scores based on query relevance, enabling one-time visual token assignment before cross-modal interactions in decoder layers, (ii) we decouple the query through Chain-of-Thoughts reasoning to facilitate more precise LVLM-based frame importance scoring, and (iii) QuoTA offers a plug-and-play functionality that extends to existing LVLMs. Extensive experimental results demonstrate that implementing QuoTA with LLaVA-Video-7B yields an average performance improvement of 3.2% across six benchmarks (including Video-MME and MLVU) while operating within an identical visual token budget as the baseline. Codes are open-sourced at https://github.com/MAC-AutoML/QuoTA.", "github_manual": "", "github_regex": "https://github.com/MAC-AutoML/QuoTA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08685", "title": "\"Principal Components\" Enable A New Language of Images", "authors": [ "Xin Wen", "Bingchen Zhao", "Ismail Elezi", "Jiankang Deng", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "We introduce a novel visual tokenization framework that embeds a provable PCA-like structure into the latent token space. While existing visual tokenizers primarily optimize for reconstruction fidelity, they often neglect the structural properties of the latent space -- a critical factor for both interpretability and downstream tasks. Our method generates a 1D causal token sequence for images, where each successive token contributes non-overlapping information with mathematically guaranteed decreasing explained variance, analogous to principal component analysis. This structural constraint ensures the tokenizer extracts the most salient visual features first, with each subsequent token adding diminishing yet complementary information. Additionally, we identified and resolved a semantic-spectrum coupling effect that causes the unwanted entanglement of high-level semantic content and low-level spectral details in the tokens by leveraging a diffusion decoder. Experiments demonstrate that our approach achieves state-of-the-art reconstruction performance and enables better interpretability to align with the human vision system. Moreover, auto-regressive models trained on our token sequences achieve performance comparable to current state-of-the-art methods while requiring fewer tokens for training and inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08619", "title": "LightGen: Efficient Image Generation through Knowledge Distillation and Direct Preference Optimization", "authors": [ "Xianfeng Wu", "Yajing Bai", "Haoze Zheng", "Harold Haodong Chen", "Yexin Liu", "Zihao Wang", "Xuran Ma", "Wen-Jie Shu", "Xianzu Wu", "Harry Yang", "Ser-Nam Lim" ], "github": "https://github.com/XianfengWu01/LightGen", "project_page": "", "abstract": "Recent advances in text-to-image generation have primarily relied on extensive datasets and parameter-heavy architectures. These requirements severely limit accessibility for researchers and practitioners who lack substantial computational resources. In this paper, we introduce \\model, an efficient training paradigm for image generation models that uses knowledge distillation (KD) and Direct Preference Optimization (DPO). Drawing inspiration from the success of data KD techniques widely adopted in Multi-Modal Large Language Models (MLLMs), LightGen distills knowledge from state-of-the-art (SOTA) text-to-image models into a compact Masked Autoregressive (MAR) architecture with only 0.7B parameters. Using a compact synthetic dataset of just 2M high-quality images generated from varied captions, we demonstrate that data diversity significantly outweighs data volume in determining model performance. This strategy dramatically reduces computational demands and reduces pre-training time from potentially thousands of GPU-days to merely 88 GPU-days. Furthermore, to address the inherent shortcomings of synthetic data, particularly poor high-frequency details and spatial inaccuracies, we integrate the DPO technique that refines image fidelity and positional accuracy. Comprehensive experiments confirm that LightGen achieves image generation quality comparable to SOTA models while significantly reducing computational resources and expanding accessibility for resource-constrained environments. Code is available at https://github.com/XianfengWu01/LightGen", "github_manual": "", "github_regex": "https://github.com/XianfengWu01/LightGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08638", "title": "YuE: Scaling Open Foundation Models for Long-Form Music Generation", "authors": [ "Ruibin Yuan", "Hanfeng Lin", "Shuyue Guo", "Ge Zhang", "Jiahao Pan", "Yongyi Zang", "Haohe Liu", "Yiming Liang", "Wenye Ma", "Xingjian Du", "Xinrun Du", "Zhen Ye", "Tianyu Zheng", "Yinghao Ma", "Minghao Liu", "Zeyue Tian", "Ziya Zhou", "Liumeng Xue", "Xingwei Qu", "Yizhi Li", "Shangda Wu", "Tianhao Shen", "Ziyang Ma", "Jun Zhan", "Chunhui Wang", "Yatian Wang", "Xiaowei Chi", "Xinyue Zhang", "Zhenzhu Yang", "Xiangzhou Wang", "Shansong Liu", "Lingrui Mei", "Peng Li", "Junjie Wang", "Jianwei Yu", "Guojian Pang", "Xu Li", "Zihao Wang", "Xiaohuan Zhou", "Lijun Yu", "Emmanouil Benetos", "Yong Chen", "Chenghua Lin", "Xie Chen", "Gus Xia", "Zhaoxiang Zhang", "Chao Zhang", "Wenhu Chen", "Xinyu Zhou", "Xipeng Qiu", "Roger Dannenberg", "Jiaheng Liu", "Jian Yang", "Wenhao Huang", "Wei Xue", "Xu Tan", "Yike Guo" ], "github": "", "project_page": "", "abstract": "We tackle the task of long-form music generation--particularly the challenging lyrics-to-song problem--by introducing YuE, a family of open foundation models based on the LLaMA2 architecture. Specifically, YuE scales to trillions of tokens and generates up to five minutes of music while maintaining lyrical alignment, coherent musical structure, and engaging vocal melodies with appropriate accompaniment. It achieves this through (1) track-decoupled next-token prediction to overcome dense mixture signals, (2) structural progressive conditioning for long-context lyrical alignment, and (3) a multitask, multiphase pre-training recipe to converge and generalize. In addition, we redesign the in-context learning technique for music generation, enabling versatile style transfer (e.g., converting Japanese city pop into an English rap while preserving the original accompaniment) and bidirectional generation. Through extensive evaluation, we demonstrate that YuE matches or even surpasses some of the proprietary systems in musicality and vocal agility. In addition, fine-tuning YuE enables additional controls and enhanced support for tail languages. Furthermore, beyond generation, we show that YuE's learned representations can perform well on music understanding tasks, where the results of YuE match or exceed state-of-the-art methods on the MARBLE benchmark. Keywords: lyrics2song, song generation, long-form, foundation model, music generation", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.07891", "title": "Gemini Embedding: Generalizable Embeddings from Gemini", "authors": [ "Jinhyuk Lee", "Feiyang Chen", "Sahil Dua", "Daniel Cer", "Madhuri Shanbhogue", "Iftekhar Naim", "Gustavo Hern\u00e1ndez \u00c1brego", "Zhe Li", "Kaifeng Chen", "Henrique Schechter Vera", "Xiaoqi Ren", "Shanfeng Zhang", "Daniel Salz", "Michael Boratko", "Jay Han", "Blair Chen", "Shuo Huang", "Vikram Rao", "Paul Suganthan", "Feng Han", "Andreas Doumanoglou", "Nithi Gupta", "Fedor Moiseev", "Cathy Yip", "Aashi Jain", "Simon Baumgartner", "Shahrokh Shahi", "Frank Palma Gomez", "Sandeep Mariserla", "Min Choi", "Parashar Shah", "Sonam Goenka", "Ke Chen", "Ye Xia", "Koert Chen", "Sai Meher Karthik Duddu", "Yichang Chen", "Trevor Walker", "Wenlei Zhou", "Rakesh Ghiya", "Zach Gleicher", "Karan Gill", "Zhe Dong", "Mojtaba Seyedhosseini", "Yunhsuan Sung", "Raphael Hoffmann", "Tom Duerig" ], "github": "", "project_page": "", "abstract": "In this report, we introduce Gemini Embedding, a state-of-the-art embedding model leveraging the power of Gemini, Google's most capable large language model. Capitalizing on Gemini's inherent multilingual and code understanding capabilities, Gemini Embedding produces highly generalizable embeddings for text spanning numerous languages and textual modalities. The representations generated by Gemini Embedding can be precomputed and applied to a variety of downstream tasks including classification, similarity, clustering, ranking, and retrieval. Evaluated on the Massive Multilingual Text Embedding Benchmark (MMTEB), which includes over one hundred tasks across 250+ languages, Gemini Embedding substantially outperforms prior state-of-the-art models, demonstrating considerable improvements in embedding quality. Achieving state-of-the-art performance across MMTEB's multilingual, English, and code benchmarks, our unified model demonstrates strong capabilities across a broad selection of tasks and surpasses specialized domain-specific models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2502.18858", "title": "Evaluating Intelligence via Trial and Error", "authors": [ "Jingtao Zhan", "Jiahao Zhao", "Jiayu Li", "Yiqun Liu", "Bo Zhang", "Qingyao Ai", "Jiaxin Mao", "Hongning Wang", "Min Zhang", "Shaoping Ma" ], "github": "", "project_page": "", "abstract": "Intelligence is a crucial trait for species to find solutions within a limited number of trial-and-error attempts. Building on this idea, we introduce Survival Game as a framework to evaluate intelligence based on the number of failed attempts in a trial-and-error process. Fewer failures indicate higher intelligence. When the expectation and variance of failure counts are both finite, it signals the ability to consistently find solutions to new challenges, which we define as the Autonomous Level of intelligence. Using Survival Game, we comprehensively evaluate existing AI systems. Our results show that while AI systems achieve the Autonomous Level in simple tasks, they are still far from it in more complex tasks, such as vision, search, recommendation, and language. While scaling current AI technologies might help, this would come at an astronomical cost. Projections suggest that achieving the Autonomous Level for general tasks would require 10^{26} parameters. To put this into perspective, loading such a massive model requires so many H100 GPUs that their total value is 10^{7} times that of Apple Inc.'s market value. Even with Moore's Law, supporting such a parameter scale would take 70 years. This staggering cost highlights the complexity of human tasks and the inadequacies of current AI technologies. To further investigate this phenomenon, we conduct a theoretical analysis of Survival Game and its experimental results. Our findings suggest that human tasks possess a criticality property. As a result, Autonomous Level requires a deep understanding of the task's underlying mechanisms. Current AI systems, however, do not fully grasp these mechanisms and instead rely on superficial mimicry, making it difficult for them to reach an autonomous level. We believe Survival Game can not only guide the future development of AI but also offer profound insights into human intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.07699", "title": "RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories", "authors": [ "Huiyang Shao", "Xin Xia", "Yuhong Yang", "Yuxi Ren", "Xing Wang", "Xuefeng Xiao" ], "github": "", "project_page": "", "abstract": "Diffusion models have achieved remarkable success across various domains. However, their slow generation speed remains a critical challenge. Existing acceleration methods, while aiming to reduce steps, often compromise sample quality, controllability, or introduce training complexities. Therefore, we propose RayFlow, a novel diffusion framework that addresses these limitations. Unlike previous methods, RayFlow guides each sample along a unique path towards an instance-specific target distribution. This method minimizes sampling steps while preserving generation diversity and stability. Furthermore, we introduce Time Sampler, an importance sampling technique to enhance training efficiency by focusing on crucial timesteps. Extensive experiments demonstrate RayFlow's superiority in generating high-quality images with improved speed, control, and training efficiency compared to existing acceleration techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.05978", "title": "MagicInfinite: Generating Infinite Talking Videos with Your Words and Voice", "authors": [ "Hongwei Yi", "Tian Ye", "Shitong Shao", "Xuancheng Yang", "Jiantong Zhao", "Hanzhong Guo", "Terrance Wang", "Qingyu Yin", "Zeke Xie", "Lei Zhu", "Wei Li", "Michael Lingelbach", "Daquan Zhou" ], "github": "", "project_page": "", "abstract": "We present MagicInfinite, a novel diffusion Transformer (DiT) framework that overcomes traditional portrait animation limitations, delivering high-fidelity results across diverse character types-realistic humans, full-body figures, and stylized anime characters. It supports varied facial poses, including back-facing views, and animates single or multiple characters with input masks for precise speaker designation in multi-character scenes. Our approach tackles key challenges with three innovations: (1) 3D full-attention mechanisms with a sliding window denoising strategy, enabling infinite video generation with temporal coherence and visual quality across diverse character styles; (2) a two-stage curriculum learning scheme, integrating audio for lip sync, text for expressive dynamics, and reference images for identity preservation, enabling flexible multi-modal control over long sequences; and (3) region-specific masks with adaptive loss functions to balance global textual control and local audio guidance, supporting speaker-specific animations. Efficiency is enhanced via our innovative unified step and cfg distillation techniques, achieving a 20x inference speed boost over the basemodel: generating a 10 second 540x540p video in 10 seconds or 720x720p in 30 seconds on 8 H100 GPUs, without quality loss. Evaluations on our new benchmark demonstrate MagicInfinite's superiority in audio-lip synchronization, identity preservation, and motion naturalness across diverse scenarios. It is publicly available at https://www.hedra.com/, with examples at https://magicinfinite.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08588", "title": "BiasEdit: Debiasing Stereotyped Language Models via Model Editing", "authors": [ "Xin Xu", "Wei Xu", "Ningyu Zhang", "Julian McAuley" ], "github": "", "project_page": "", "abstract": "Previous studies have established that language models manifest stereotyped biases. Existing debiasing strategies, such as retraining a model with counterfactual data, representation projection, and prompting often fail to efficiently eliminate bias or directly alter the models' biased internal representations. To address these issues, we propose BiasEdit, an efficient model editing method to remove stereotypical bias from language models through lightweight networks that act as editors to generate parameter updates. BiasEdit employs a debiasing loss guiding editor networks to conduct local edits on partial parameters of a language model for debiasing while preserving the language modeling abilities during editing through a retention loss. Experiments on StereoSet and Crows-Pairs demonstrate the effectiveness, efficiency, and robustness of BiasEdit in eliminating bias compared to tangental debiasing baselines and little to no impact on the language models' general capabilities. In addition, we conduct bias tracing to probe bias in various modules and explore bias editing impacts on different components of language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08102", "title": "AI-native Memory 2.0: Second Me", "authors": [ "Jiale Wei", "Xiang Ying", "Tao Gao", "Felix Tao", "Jingbo Shang" ], "github": "https://github.com/Mindverse/Second-Me", "project_page": "", "abstract": "Human interaction with the external world fundamentally involves the exchange of personal memory, whether with other individuals, websites, applications, or, in the future, AI agents. A significant portion of this interaction is redundant, requiring users to repeatedly provide the same information across different contexts. Existing solutions, such as browser-stored credentials, autofill mechanisms, and unified authentication systems, have aimed to mitigate this redundancy by serving as intermediaries that store and retrieve commonly used user data. The advent of large language models (LLMs) presents an opportunity to redefine memory management through an AI-native paradigm: SECOND ME. SECOND ME acts as an intelligent, persistent memory offload system that retains, organizes, and dynamically utilizes user-specific knowledge. By serving as an intermediary in user interactions, it can autonomously generate context-aware responses, prefill required information, and facilitate seamless communication with external systems, significantly reducing cognitive load and interaction friction. Unlike traditional memory storage solutions, SECOND ME extends beyond static data retention by leveraging LLM-based memory parameterization. This enables structured organization, contextual reasoning, and adaptive knowledge retrieval, facilitating a more systematic and intelligent approach to memory management. As AI-driven personal agents like SECOND ME become increasingly integrated into digital ecosystems, SECOND ME further represents a critical step toward augmenting human-world interaction with persistent, contextually aware, and self-optimizing memory systems. We have open-sourced the fully localizable deployment system at GitHub: https://github.com/Mindverse/Second-Me.", "github_manual": "", "github_regex": "https://github.com/Mindverse/Second-Me", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08478", "title": "NullFace: Training-Free Localized Face Anonymization", "authors": [ "Han-Wei Kung", "Tuomas Varanka", "Terence Sim", "Nicu Sebe" ], "github": "https://github.com/hanweikung/nullface", "project_page": "", "abstract": "Privacy concerns around ever increasing number of cameras are increasing in today's digital age. Although existing anonymization methods are able to obscure identity information, they often struggle to preserve the utility of the images. In this work, we introduce a training-free method for face anonymization that preserves key non-identity-related attributes. Our approach utilizes a pre-trained text-to-image diffusion model without requiring optimization or training. It begins by inverting the input image to recover its initial noise. The noise is then denoised through an identity-conditioned diffusion process, where modified identity embeddings ensure the anonymized face is distinct from the original identity. Our approach also supports localized anonymization, giving users control over which facial regions are anonymized or kept intact. Comprehensive evaluations against state-of-the-art methods show our approach excels in anonymization, attribute preservation, and image quality. Its flexibility, robustness, and practicality make it well-suited for real-world applications. Code and data can be found at https://github.com/hanweikung/nullface .", "github_manual": "", "github_regex": "https://github.com/hanweikung/nullface", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.06492", "title": "VisualSimpleQA: A Benchmark for Decoupled Evaluation of Large Vision-Language Models in Fact-Seeking Question Answering", "authors": [ "Yanling Wang", "Yihan Zhao", "Xiaodong Chen", "Shasha Guo", "Lixin Liu", "Haoyang Li", "Yong Xiao", "Jing Zhang", "Qi Li", "Ke Xu" ], "github": "", "project_page": "", "abstract": "Large vision-language models (LVLMs) have demonstrated remarkable achievements, yet the generation of non-factual responses remains prevalent in fact-seeking question answering (QA). Current multimodal fact-seeking benchmarks primarily focus on comparing model outputs to ground truth answers, providing limited insights into the performance of modality-specific modules. To bridge this gap, we introduce VisualSimpleQA, a multimodal fact-seeking benchmark with two key features. First, it enables streamlined and decoupled evaluation of LVLMs in visual and linguistic modalities. Second, it incorporates well-defined difficulty criteria to guide human annotation and facilitates the extraction of a challenging subset, VisualSimpleQA-hard. Experiments on 15 LVLMs show that even state-of-the-art models such as GPT-4o achieve merely 60%+ correctness in multimodal fact-seeking QA on VisualSimpleQA and 30%+ on VisualSimpleQA-hard. Furthermore, the decoupled evaluation across these models highlights substantial opportunities for improvement in both visual and linguistic modules. The dataset is available at https://huggingface.co/datasets/WYLing/VisualSimpleQA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.06594", "title": "Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation", "authors": [ "Yingfeng Luo", "Tong Zheng", "Yongyu Mu", "Bei Li", "Qinghong Zhang", "Yongqi Gao", "Ziqiang Xu", "Peinan Feng", "Xiaoqian Liu", "Tong Xiao", "Jingbo Zhu" ], "github": "", "project_page": "", "abstract": "The field of neural machine translation (NMT) has changed with the advent of large language models (LLMs). Much of the recent emphasis in natural language processing (NLP) has been on modeling machine translation and many other problems using a single pre-trained Transformer decoder, while encoder-decoder architectures, which were the standard in earlier NMT models, have received relatively less attention. In this paper, we explore translation models that are universal, efficient, and easy to optimize, by marrying the world of LLMs with the world of NMT. We apply LLMs to NMT encoding and leave the NMT decoder unchanged. We also develop methods for adapting LLMs to work better with the NMT decoder. Furthermore, we construct a new dataset involving multiple tasks to assess how well the machine translation system generalizes across various tasks. Evaluations on the WMT and our datasets show that results using our method match or surpass a range of baselines in terms of translation quality, but achieve 2.4 sim 6.5 times inference speedups and a 75% reduction in the memory footprint of the KV cache. It also demonstrates strong generalization across a variety of translation-related tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08307", "title": "^RFLAV: Rolling Flow matching for infinite Audio Video generation", "authors": [ "Alex Ergasti", "Giuseppe Gabriele Tarollo", "Filippo Botti", "Tomaso Fontanini", "Claudio Ferrari", "Massimo Bertozzi", "Andrea Prati" ], "github": "https://github.com/ErgastiAlex/R-FLAV", "project_page": "", "abstract": "Joint audio-video (AV) generation is still a significant challenge in generative AI, primarily due to three critical requirements: quality of the generated samples, seamless multimodal synchronization and temporal coherence, with audio tracks that match the visual data and vice versa, and limitless video duration. In this paper, we present , a novel transformer-based architecture that addresses all the key challenges of AV generation. We explore three distinct cross modality interaction modules, with our lightweight temporal fusion module emerging as the most effective and computationally efficient approach for aligning audio and visual modalities. Our experimental results demonstrate that outperforms existing state-of-the-art models in multimodal AV generation tasks. Our code and checkpoints are available at https://github.com/ErgastiAlex/R-FLAV.", "github_manual": "", "github_regex": "https://github.com/ErgastiAlex/R-FLAV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08507", "title": "Referring to Any Person", "authors": [ "Qing Jiang", "Lin Wu", "Zhaoyang Zeng", "Tianhe Ren", "Yuda Xiong", "Yihao Chen", "Qin Liu", "Lei Zhang" ], "github": "https://github.com/IDEA-Research/RexSeek", "project_page": "", "abstract": "Humans are undoubtedly the most important participants in computer vision, and the ability to detect any individual given a natural language description, a task we define as referring to any person, holds substantial practical value. However, we find that existing models generally fail to achieve real-world usability, and current benchmarks are limited by their focus on one-to-one referring, that hinder progress in this area. In this work, we revisit this task from three critical perspectives: task definition, dataset design, and model architecture. We first identify five aspects of referable entities and three distinctive characteristics of this task. Next, we introduce HumanRef, a novel dataset designed to tackle these challenges and better reflect real-world applications. From a model design perspective, we integrate a multimodal large language model with an object detection framework, constructing a robust referring model named RexSeek. Experimental results reveal that state-of-the-art models, which perform well on commonly used benchmarks like RefCOCO/+/g, struggle with HumanRef due to their inability to detect multiple individuals. In contrast, RexSeek not only excels in human referring but also generalizes effectively to common object referring, making it broadly applicable across various perception tasks. Code is available at https://github.com/IDEA-Research/RexSeek", "github_manual": "", "github_regex": "https://github.com/IDEA-Research/RexSeek", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.05860", "title": "Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Enhancement Protocol", "authors": [ "Roham Koohestani", "Philippe de Bekker", "Maliheh Izadi" ], "github": "", "project_page": "", "abstract": "Benchmarks are essential for consistent evaluation and reproducibility. The integration of Artificial Intelligence into Software Engineering (AI4SE) has given rise to numerous benchmarks for tasks such as code generation and bug fixing. However, this surge presents challenges: (1) scattered benchmark knowledge across tasks, (2) difficulty in selecting relevant benchmarks, (3) the absence of a uniform standard for benchmark development, and (4) limitations of existing benchmarks. In this paper, we review 173 studies and identify 204 AI4SE benchmarks. We classify these benchmarks, analyze their limitations, and expose gaps in practices. Based on our review, we created BenchScout, a semantic search tool to find relevant benchmarks, using automated clustering of the contexts from associated studies. We conducted a user study with 22 participants to evaluate BenchScout's usability, effectiveness, and intuitiveness which resulted in average scores of 4.5, 4.0, and 4.1 out of 5. To advance benchmarking standards, we propose BenchFrame, a unified method to enhance benchmark quality. As a case study, we applied BenchFrame to the HumanEval benchmark and addressed its main limitations. This led to HumanEvalNext, featuring (1) corrected errors, (2) improved language conversion, (3) expanded test coverage, and (4) increased difficulty. We then evaluated ten state-of-the-art code language models on HumanEval, HumanEvalPlus, and HumanEvalNext. On HumanEvalNext, models showed a pass@1 score reduction of 31.22% and 19.94% compared to HumanEval and HumanEvalPlus, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08417", "title": "AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models", "authors": [ "Kwan Yun", "Seokhyeon Hong", "Chaelin Kim", "Junyong Noh" ], "github": "https://github.com/kwanyun/AnyMoLe", "project_page": "", "abstract": "Despite recent advancements in learning-based motion in-betweening, a key limitation has been overlooked: the requirement for character-specific datasets. In this work, we introduce AnyMoLe, a novel method that addresses this limitation by leveraging video diffusion models to generate motion in-between frames for arbitrary characters without external data. Our approach employs a two-stage frame generation process to enhance contextual understanding. Furthermore, to bridge the domain gap between real-world and rendered character animations, we introduce ICAdapt, a fine-tuning technique for video diffusion models. Additionally, we propose a ``motion-video mimicking'' optimization technique, enabling seamless motion generation for characters with arbitrary joint structures using 2D and 3D-aware features. AnyMoLe significantly reduces data dependency while generating smooth and realistic transitions, making it applicable to a wide range of motion in-betweening tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kwanyun/AnyMoLe", "github_api_added_by": "auto" }, { "date": "2025-03-12", "arxiv_id": "2503.08684", "title": "Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents", "authors": [ "Haoyu Wang", "Sunhao Dai", "Haiyuan Zhao", "Liang Pang", "Xiao Zhang", "Gang Wang", "Zhenhua Dong", "Jun Xu", "Ji-Rong Wen" ], "github": "https://github.com/WhyDwelledOnAi/Perplexity-Trap", "project_page": "", "abstract": "Previous studies have found that PLM-based retrieval models exhibit a preference for LLM-generated content, assigning higher relevance scores to these documents even when their semantic quality is comparable to human-written ones. This phenomenon, known as source bias, threatens the sustainable development of the information access ecosystem. However, the underlying causes of source bias remain unexplored. In this paper, we explain the process of information retrieval with a causal graph and discover that PLM-based retrievers learn perplexity features for relevance estimation, causing source bias by ranking the documents with low perplexity higher. Theoretical analysis further reveals that the phenomenon stems from the positive correlation between the gradients of the loss functions in language modeling task and retrieval task. Based on the analysis, a causal-inspired inference-time debiasing method is proposed, called Causal Diagnosis and Correction (CDC). CDC first diagnoses the bias effect of the perplexity and then separates the bias effect from the overall estimated relevance score. Experimental results across three domains demonstrate the superior debiasing effectiveness of CDC, emphasizing the validity of our proposed explanatory framework. Source codes are available at https://github.com/WhyDwelledOnAi/Perplexity-Trap.", "github_manual": "", "github_regex": "https://github.com/WhyDwelledOnAi/Perplexity-Trap", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.06940", "title": "CineBrain: A Large-Scale Multi-Modal Brain Dataset During Naturalistic Audiovisual Narrative Processing", "authors": [ "Jianxiong Gao", "Yichang Liu", "Baofeng Yang", "Jianfeng Feng", "Yanwei Fu" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce CineBrain, the first large-scale dataset featuring simultaneous EEG and fMRI recordings during dynamic audiovisual stimulation. Recognizing the complementary strengths of EEG's high temporal resolution and fMRI's deep-brain spatial coverage, CineBrain provides approximately six hours of narrative-driven content from the popular television series The Big Bang Theory for each of six participants. Building upon this unique dataset, we propose CineSync, an innovative multimodal decoding framework integrates a Multi-Modal Fusion Encoder with a diffusion-based Neural Latent Decoder. Our approach effectively fuses EEG and fMRI signals, significantly improving the reconstruction quality of complex audiovisual stimuli. To facilitate rigorous evaluation, we introduce Cine-Benchmark, a comprehensive evaluation protocol that assesses reconstructions across semantic and perceptual dimensions. Experimental results demonstrate that CineSync achieves state-of-the-art video reconstruction performance and highlight our initial success in combining fMRI and EEG for reconstructing both video and audio stimuli. Project Page: https://jianxgao.github.io/CineBrain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.07587", "title": "Robusto-1 Dataset: Comparing Humans and VLMs on real out-of-distribution Autonomous Driving VQA from Peru", "authors": [ "Dunant Cusipuma", "David Ortega", "Victor Flores-Benites", "Arturo Deza" ], "github": "", "project_page": "", "abstract": "As multimodal foundational models start being deployed experimentally in Self-Driving cars, a reasonable question we ask ourselves is how similar to humans do these systems respond in certain driving situations -- especially those that are out-of-distribution? To study this, we create the Robusto-1 dataset that uses dashcam video data from Peru, a country with one of the worst (aggressive) drivers in the world, a high traffic index, and a high ratio of bizarre to non-bizarre street objects likely never seen in training. In particular, to preliminarly test at a cognitive level how well Foundational Visual Language Models (VLMs) compare to Humans in Driving, we move away from bounding boxes, segmentation maps, occupancy maps or trajectory estimation to multi-modal Visual Question Answering (VQA) comparing both humans and machines through a popular method in systems neuroscience known as Representational Similarity Analysis (RSA). Depending on the type of questions we ask and the answers these systems give, we will show in what cases do VLMs and Humans converge or diverge allowing us to probe on their cognitive alignment. We find that the degree of alignment varies significantly depending on the type of questions asked to each type of system (Humans vs VLMs), highlighting a gap in their alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08644", "title": "Exploiting Instruction-Following Retrievers for Malicious Information Retrieval", "authors": [ "Parishad BehnamGhader", "Nicholas Meade", "Siva Reddy" ], "github": "", "project_page": "", "abstract": "Instruction-following retrievers have been widely adopted alongside LLMs in real-world applications, but little work has investigated the safety risks surrounding their increasing search capabilities. We empirically study the ability of retrievers to satisfy malicious queries, both when used directly and when used in a retrieval augmented generation-based setup. Concretely, we investigate six leading retrievers, including NV-Embed and LLM2Vec, and find that given malicious requests, most retrievers can (for >50% of queries) select relevant harmful passages. For example, LLM2Vec correctly selects passages for 61.35% of our malicious queries. We further uncover an emerging risk with instruction-following retrievers, where highly relevant harmful information can be surfaced by exploiting their instruction-following capabilities. Finally, we show that even safety-aligned LLMs, such as Llama3, can satisfy malicious requests when provided with harmful retrieved passages in-context. In summary, our findings underscore the malicious misuse risks associated with increasing retriever capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08037", "title": "ObjectMover: Generative Object Movement with Video Prior", "authors": [ "Xin Yu", "Tianyu Wang", "Soo Ye Kim", "Paul Guerrero", "Xi Chen", "Qing Liu", "Zhe Lin", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "Simple as it seems, moving an object to another location within an image is, in fact, a challenging image-editing task that requires re-harmonizing the lighting, adjusting the pose based on perspective, accurately filling occluded regions, and ensuring coherent synchronization of shadows and reflections while maintaining the object identity. In this paper, we present ObjectMover, a generative model that can perform object movement in highly challenging scenes. Our key insight is that we model this task as a sequence-to-sequence problem and fine-tune a video generation model to leverage its knowledge of consistent object generation across video frames. We show that with this approach, our model is able to adjust to complex real-world scenarios, handling extreme lighting harmonization and object effect movement. As large-scale data for object movement are unavailable, we construct a data generation pipeline using a modern game engine to synthesize high-quality data pairs. We further propose a multi-task learning strategy that enables training on real-world video data to improve the model generalization. Through extensive experiments, we demonstrate that ObjectMover achieves outstanding results and adapts well to real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.05037", "title": "Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence", "authors": [ "Mohsen Fayyaz", "Ali Modarressi", "Hinrich Schuetze", "Nanyun Peng" ], "github": "https://github.com/mohsenfayyaz/ColDeR", "project_page": "", "abstract": "Dense retrieval models are commonly used in Information Retrieval (IR) applications, such as Retrieval-Augmented Generation (RAG). Since they often serve as the first step in these systems, their robustness is critical to avoid failures. In this work, by repurposing a relation extraction dataset (e.g. Re-DocRED), we design controlled experiments to quantify the impact of heuristic biases, such as favoring shorter documents, in retrievers like Dragon+ and Contriever. Our findings reveal significant vulnerabilities: retrievers often rely on superficial patterns like over-prioritizing document beginnings, shorter documents, repeated entities, and literal matches. Additionally, they tend to overlook whether the document contains the query's answer, lacking deep semantic understanding. Notably, when multiple biases combine, models exhibit catastrophic performance degradation, selecting the answer-containing document in less than 3% of cases over a biased document without the answer. Furthermore, we show that these biases have direct consequences for downstream applications like RAG, where retrieval-preferred documents can mislead LLMs, resulting in a 34% performance drop than not providing any documents at all.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mohsenfayyaz/ColDeR", "github_api_added_by": "auto" }, { "date": "2025-03-12", "arxiv_id": "2503.03734", "title": "OTTER: A Vision-Language-Action Model with Text-Aware Visual Feature Extraction", "authors": [ "Huang Huang", "Fangchen Liu", "Letian Fu", "Tingfan Wu", "Mustafa Mukadam", "Jitendra Malik", "Ken Goldberg", "Pieter Abbeel" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models aim to predict robotic actions based on visual observations and language instructions. Existing approaches require fine-tuning pre-trained visionlanguage models (VLMs) as visual and language features are independently fed into downstream policies, degrading the pre-trained semantic alignments. We propose OTTER, a novel VLA architecture that leverages these existing alignments through explicit, text-aware visual feature extraction. Instead of processing all visual features, OTTER selectively extracts and passes only task-relevant visual features that are semantically aligned with the language instruction to the policy transformer. This allows OTTER to keep the pre-trained vision-language encoders frozen. Thereby, OTTER preserves and utilizes the rich semantic understanding learned from large-scale pre-training, enabling strong zero-shot generalization capabilities. In simulation and real-world experiments, OTTER significantly outperforms existing VLA models, demonstrating strong zeroshot generalization to novel objects and environments. Video, code, checkpoints, and dataset: https://ottervla.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.07565", "title": "Inductive Moment Matching", "authors": [ "Linqi Zhou", "Stefano Ermon", "Jiaming Song" ], "github": "", "project_page": "", "abstract": "Diffusion models and Flow Matching generate high-quality samples but are slow at inference, and distilling them into few-step models often leads to instability and extensive tuning. To resolve these trade-offs, we propose Inductive Moment Matching (IMM), a new class of generative models for one- or few-step sampling with a single-stage training procedure. Unlike distillation, IMM does not require pre-training initialization and optimization of two networks; and unlike Consistency Models, IMM guarantees distribution-level convergence and remains stable under various hyperparameters and standard model architectures. IMM surpasses diffusion models on ImageNet-256x256 with 1.99 FID using only 8 inference steps and achieves state-of-the-art 2-step FID of 1.98 on CIFAR-10 for a model trained from scratch.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.07154", "title": "Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms", "authors": [ "Jiaming Song", "Linqi Zhou" ], "github": "", "project_page": "", "abstract": "Recent years have seen significant advancements in foundation models through generative pre-training, yet algorithmic innovation in this space has largely stagnated around autoregressive models for discrete signals and diffusion models for continuous signals. This stagnation creates a bottleneck that prevents us from fully unlocking the potential of rich multi-modal data, which in turn limits the progress on multimodal intelligence. We argue that an inference-first perspective, which prioritizes scaling efficiency during inference time across sequence length and refinement steps, can inspire novel generative pre-training algorithms. Using Inductive Moment Matching (IMM) as a concrete example, we demonstrate how addressing limitations in diffusion models' inference process through targeted modifications yields a stable, single-stage algorithm that achieves superior sample quality with over an order of magnitude greater inference efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.05066", "title": "Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts", "authors": [ "Shwai He", "Weilin Cai", "Jiayi Huang", "Ang Li" ], "github": "", "project_page": "", "abstract": "The Mixture of Experts (MoE) is an effective architecture for scaling large language models by leveraging sparse expert activation, optimizing the trade-off between performance and efficiency. However, under expert parallelism, MoE suffers from inference inefficiencies due to imbalanced token-to-expert assignment, where some experts are overloaded while others remain underutilized. This imbalance leads to poor resource utilization and increased latency, as the most burdened expert dictates the overall delay, a phenomenon we define as the \\textit{Straggler Effect}. To mitigate this, we propose Capacity-Aware Inference, including two key techniques: (1) \\textit{Capacity-Aware Token Drop}, which discards overloaded tokens to regulate the maximum latency of MoE, and (2) \\textit{Capacity-Aware Token Reroute}, which reallocates overflowed tokens to underutilized experts, balancing the token distribution. These techniques collectively optimize both high-load and low-load expert utilization, leading to a more efficient MoE inference pipeline. Extensive experiments demonstrate the effectiveness of our methods, showing significant improvements in inference efficiency, e.g., 0.2\\% average performance increase and a 1.94times inference speedup on Mixtral-8times7B-Instruct.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.09089", "title": "LocAgent: Graph-Guided LLM Agents for Code Localization", "authors": [ "Zhaoling Chen", "Xiangru Tang", "Gangda Deng", "Fang Wu", "Jialong Wu", "Zhiwei Jiang", "Viktor Prasanna", "Arman Cohan", "Xingyao Wang" ], "github": "https://github.com/gersteinlab/LocAgent", "project_page": "", "abstract": "Code localization--identifying precisely where in a codebase changes need to be made--is a fundamental yet challenging task in software maintenance. Existing approaches struggle to efficiently navigate complex codebases when identifying relevant code sections. The challenge lies in bridging natural language problem descriptions with the appropriate code elements, often requiring reasoning across hierarchical structures and multiple dependencies. We introduce LocAgent, a framework that addresses code localization through graph-based representation. By parsing codebases into directed heterogeneous graphs, LocAgent creates a lightweight representation that captures code structures (files, classes, functions) and their dependencies (imports, invocations, inheritance), enabling LLM agents to effectively search and locate relevant entities through powerful multi-hop reasoning. Experimental results on real-world benchmarks demonstrate that our approach significantly enhances accuracy in code localization. Notably, our method with the fine-tuned Qwen-2.5-Coder-Instruct-32B model achieves comparable results to SOTA proprietary models at greatly reduced cost (approximately 86% reduction), reaching up to 92.7% accuracy on file-level localization while improving downstream GitHub issue resolution success rates by 12% for multiple attempts (Pass@10). Our code is available at https://github.com/gersteinlab/LocAgent.", "github_manual": "", "github_regex": "https://github.com/gersteinlab/LocAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-12", "arxiv_id": "2503.08890", "title": "PlainQAFact: Automatic Factuality Evaluation Metric for Biomedical Plain Language Summaries Generation", "authors": [ "Zhiwen You", "Yue Guo" ], "github": "https://github.com/zhiwenyou103/PlainQAFact", "project_page": "", "abstract": "Hallucinated outputs from language models pose risks in the medical domain, especially for lay audiences making health-related decisions. Existing factuality evaluation methods, such as entailment- and question-answering-based (QA), struggle with plain language summary (PLS) generation due to elaborative explanation phenomenon, which introduces external content (e.g., definitions, background, examples) absent from the source document to enhance comprehension. To address this, we introduce PlainQAFact, a framework trained on a fine-grained, human-annotated dataset PlainFact, to evaluate the factuality of both source-simplified and elaboratively explained sentences. PlainQAFact first classifies factuality type and then assesses factuality using a retrieval-augmented QA-based scoring method. Our approach is lightweight and computationally efficient. Empirical results show that existing factuality metrics fail to effectively evaluate factuality in PLS, especially for elaborative explanations, whereas PlainQAFact achieves state-of-the-art performance. We further analyze its effectiveness across external knowledge sources, answer extraction strategies, overlap measures, and document granularity levels, refining its overall factuality assessment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhiwenyou103/PlainQAFact", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.09573", "title": "Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models", "authors": [ "Marianne Arriola", "Aaron Gokaslan", "Justin T Chiu", "Zhihan Yang", "Zhixuan Qi", "Jiaqi Han", "Subham Sekhar Sahoo", "Volodymyr Kuleshov" ], "github": "", "project_page": "", "abstract": "Diffusion language models offer unique benefits over autoregressive models due to their potential for parallelized generation and controllability, yet they lag in likelihood modeling and are limited to fixed-length generation. In this work, we introduce a class of block diffusion language models that interpolate between discrete denoising diffusion and autoregressive models. Block diffusion overcomes key limitations of both approaches by supporting flexible-length generation and improving inference efficiency with KV caching and parallel token sampling. We propose a recipe for building effective block diffusion models that includes an efficient training algorithm, estimators of gradient variance, and data-driven noise schedules to minimize the variance. Block diffusion sets a new state-of-the-art performance among diffusion models on language modeling benchmarks and enables generation of arbitrary-length sequences. We provide the code, along with the model weights and blog post on the project page: https://m-arriola.com/bd3lms/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.09427", "title": "Multimodal Language Modeling for High-Accuracy Single Cell Transcriptomics Analysis and Generation", "authors": [ "Yaorui Shi", "Jiaqi Yang", "Sihang Li", "Junfeng Fang", "Xiang Wang", "Zhiyuan Liu", "Yang Zhang" ], "github": "https://github.com/syr-cn/scmmgpt", "project_page": "", "abstract": "Pre-trained language models (PLMs) have revolutionized scientific research, yet their application to single-cell analysis remains limited. Text PLMs cannot process single-cell RNA sequencing data, while cell PLMs lack the ability to handle free text, restricting their use in multimodal tasks. Existing efforts to bridge these modalities often suffer from information loss or inadequate single-modal pre-training, leading to suboptimal performances. To address these challenges, we propose Single-Cell MultiModal Generative Pre-trained Transformer (scMMGPT), a unified PLM for joint cell and text modeling. scMMGPT effectively integrates the state-of-the-art cell and text PLMs, facilitating cross-modal knowledge sharing for improved performance. To bridge the text-cell modality gap, scMMGPT leverages dedicated cross-modal projectors, and undergoes extensive pre-training on 27 million cells -- the largest dataset for multimodal cell-text PLMs to date. This large-scale pre-training enables scMMGPT to excel in joint cell-text tasks, achieving an 84\\% relative improvement of textual discrepancy for cell description generation, 20.5\\% higher accuracy for cell type annotation, and 4\\% improvement in k-NN accuracy for text-conditioned pseudo-cell generation, outperforming baselines.", "github_manual": "https://github.com/syr-cn/scmmgpt", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.09419", "title": "Alias-Free Latent Diffusion Models:Improving Fractional Shift Equivariance of Diffusion Latent Space", "authors": [ "Yifan Zhou", "Zeqi Xiao", "Shuai Yang", "Xingang Pan" ], "github": "https://github.com/SingleZombie/AFLDM", "project_page": "", "abstract": "Latent Diffusion Models (LDMs) are known to have an unstable generation process, where even small perturbations or shifts in the input noise can lead to significantly different outputs. This hinders their applicability in applications requiring consistent results. In this work, we redesign LDMs to enhance consistency by making them shift-equivariant. While introducing anti-aliasing operations can partially improve shift-equivariance, significant aliasing and inconsistency persist due to the unique challenges in LDMs, including 1) aliasing amplification during VAE training and multiple U-Net inferences, and 2) self-attention modules that inherently lack shift-equivariance. To address these issues, we redesign the attention modules to be shift-equivariant and propose an equivariance loss that effectively suppresses the frequency bandwidth of the features in the continuous domain. The resulting alias-free LDM (AF-LDM) achieves strong shift-equivariance and is also robust to irregular warping. Extensive experiments demonstrate that AF-LDM produces significantly more consistent results than vanilla LDM across various applications, including video editing and image-to-image translation. Code is available at: https://github.com/SingleZombie/AFLDM", "github_manual": "", "github_regex": "https://github.com/SingleZombie/AFLDM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.09566", "title": "TPDiff: Temporal Pyramid Video Diffusion Model", "authors": [ "Lingmin Ran", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "The development of video diffusion models unveils a significant challenge: the substantial computational demands. To mitigate this challenge, we note that the reverse process of diffusion exhibits an inherent entropy-reducing nature. Given the inter-frame redundancy in video modality, maintaining full frame rates in high-entropy stages is unnecessary. Based on this insight, we propose TPDiff, a unified framework to enhance training and inference efficiency. By dividing diffusion into several stages, our framework progressively increases frame rate along the diffusion process with only the last stage operating on full frame rate, thereby optimizing computational efficiency. To train the multi-stage diffusion model, we introduce a dedicated training framework: stage-wise diffusion. By solving the partitioned probability flow ordinary differential equations (ODE) of diffusion under aligned data and noise, our training strategy is applicable to various diffusion forms and further enhances training efficiency. Comprehensive experimental evaluations validate the generality of our method, demonstrating 50% reduction in training cost and 1.5x improvement in inference efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.09151", "title": "Reangle-A-Video: 4D Video Generation as Video-to-Video Translation", "authors": [ "Hyeonho Jeong", "Suhyeon Lee", "Jong Chul Ye" ], "github": "", "project_page": "", "abstract": "We introduce Reangle-A-Video, a unified framework for generating synchronized multi-view videos from a single input video. Unlike mainstream approaches that train multi-view video diffusion models on large-scale 4D datasets, our method reframes the multi-view video generation task as video-to-videos translation, leveraging publicly available image and video diffusion priors. In essence, Reangle-A-Video operates in two stages. (1) Multi-View Motion Learning: An image-to-video diffusion transformer is synchronously fine-tuned in a self-supervised manner to distill view-invariant motion from a set of warped videos. (2) Multi-View Consistent Image-to-Images Translation: The first frame of the input video is warped and inpainted into various camera perspectives under an inference-time cross-view consistency guidance using DUSt3R, generating multi-view consistent starting images. Extensive experiments on static view transport and dynamic camera control show that Reangle-A-Video surpasses existing methods, establishing a new solution for multi-view video generation. We will publicly release our code and data. Project page: https://hyeonho99.github.io/reangle-a-video/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.06955", "title": "Motion Anything: Any to Motion Generation", "authors": [ "Zeyu Zhang", "Yiran Wang", "Wei Mao", "Danning Li", "Rui Zhao", "Biao Wu", "Zirui Song", "Bohan Zhuang", "Ian Reid", "Richard Hartley" ], "github": "", "project_page": "", "abstract": "Conditional motion generation has been extensively studied in computer vision, yet two critical challenges remain. First, while masked autoregressive methods have recently outperformed diffusion-based approaches, existing masking models lack a mechanism to prioritize dynamic frames and body parts based on given conditions. Second, existing methods for different conditioning modalities often fail to integrate multiple modalities effectively, limiting control and coherence in generated motion. To address these challenges, we propose Motion Anything, a multimodal motion generation framework that introduces an Attention-based Mask Modeling approach, enabling fine-grained spatial and temporal control over key frames and actions. Our model adaptively encodes multimodal conditions, including text and music, improving controllability. Additionally, we introduce Text-Music-Dance (TMD), a new motion dataset consisting of 2,153 pairs of text, music, and dance, making it twice the size of AIST++, thereby filling a critical gap in the community. Extensive experiments demonstrate that Motion Anything surpasses state-of-the-art methods across multiple benchmarks, achieving a 15% improvement in FID on HumanML3D and showing consistent performance gains on AIST++ and TMD. See our project website https://steve-zeyu-zhang.github.io/MotionAnything", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.09402", "title": "VLog: Video-Language Models by Generative Retrieval of Narration Vocabulary", "authors": [ "Kevin Qinghong Lin", "Mike Zheng Shou" ], "github": "https://github.com/showlab/VLog", "project_page": "", "abstract": "Human daily activities can be concisely narrated as sequences of routine events (e.g., turning off an alarm) in video streams, forming an event vocabulary. Motivated by this, we introduce VLog, a novel video understanding framework that define video narrations as vocabulary, going beyond the typical subword vocabularies in existing generative video-language models. Built on the lightweight language model GPT-2, VLog feature three key innovations: (i) A generative retrieval model, marrying language model's complex reasoning capabilities with contrastive retrieval's efficient similarity search. (ii) A hierarchical vocabulary derived from large-scale video narrations using our narration pair encoding algorithm, enabling efficient indexing of specific events (e.g., cutting a tomato) by identifying broader scenarios (e.g., kitchen) with expressive postfixes (e.g., by the left hand). (iii) A vocabulary update strategy leveraging generative models to extend the vocabulary for novel events encountered during inference. To validate our approach, we introduce VidCap-Eval, a development set requiring concise narrations with reasoning relationships (e.g., before and after). Experiments on EgoSchema, COIN, and HiREST further demonstrate the effectiveness of VLog, highlighting its ability to generate concise, contextually accurate, and efficient narrations, offering a novel perspective on video understanding. Codes are released at https://github.com/showlab/VLog.", "github_manual": "", "github_regex": "https://github.com/showlab/VLog", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.04388", "title": "More Documents, Same Length: Isolating the Challenge of Multiple Documents in RAG", "authors": [ "Shahar Levy", "Nir Mazor", "Lihi Shalmon", "Michael Hassid", "Gabriel Stanovsky" ], "github": "https://github.com/shaharl6000/MoreDocsSameLen", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) provides LLMs with relevant documents. Although previous studies noted that retrieving many documents can degrade performance, they did not isolate how the quantity of documents affects performance while controlling for context length. We evaluate various language models on custom datasets derived from a multi-hop QA task. We keep the context length and position of relevant information constant while varying the number of documents, and find that increasing the document count in RAG settings poses significant challenges for LLMs. Additionally, our results indicate that processing multiple documents is a separate challenge from handling long contexts. We also make the datasets and code available: https://github.com/shaharl6000/MoreDocsSameLen .", "github_manual": "", "github_regex": "https://github.com/shaharl6000/MoreDocsSameLen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.09579", "title": "Cost-Optimal Grouped-Query Attention for Long-Context LLMs", "authors": [ "Yingfa Chen", "Yutong Wu", "Xu Han", "Zhiyuan Liu", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "Building effective and efficient Transformer-based large language models (LLMs) has recently become a research focus, requiring maximizing model language capabilities and minimizing training and deployment costs. Existing efforts have primarily described complex relationships among model performance, parameter size, and data size, as well as searched for the optimal compute allocation to train LLMs. However, they overlook the impacts of context length and attention head configuration (the number of query and key-value heads in grouped-query attention) on training and inference. In this paper, we systematically compare models with different parameter sizes, context lengths, and attention head configurations in terms of model performance, computational cost, and memory cost. Then, we extend the existing scaling methods, which are based solely on parameter size and training compute, to guide the construction of cost-optimal LLMs during both training and inference. Our quantitative scaling studies show that, when processing sufficiently long sequences, a larger model with fewer attention heads can achieve a lower loss while incurring lower computational and memory costs. Our findings provide valuable insights for developing practical LLMs, especially in long-context processing scenarios. We will publicly release our code and data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.08525", "title": "GTR: Guided Thought Reinforcement Prevents Thought Collapse in RL-based VLM Agent Training", "authors": [ "Tong Wei", "Yijun Yang", "Junliang Xing", "Yuanchun Shi", "Zongqing Lu", "Deheng Ye" ], "github": "https://github.com/weit123/GTR", "project_page": "", "abstract": "Reinforcement learning with verifiable outcome rewards (RLVR) has effectively scaled up chain-of-thought (CoT) reasoning in large language models (LLMs). Yet, its efficacy in training vision-language model (VLM) agents for goal-directed action reasoning in visual environments is less established. This work investigates this problem through extensive experiments on complex card games, such as 24 points, and embodied tasks from ALFWorld. We find that when rewards are based solely on action outcomes, RL fails to incentivize CoT reasoning in VLMs, instead leading to a phenomenon we termed thought collapse, characterized by a rapid loss of diversity in the agent's thoughts, state-irrelevant and incomplete reasoning, and subsequent invalid actions, resulting in negative rewards. To counteract thought collapse, we highlight the necessity of process guidance and propose an automated corrector that evaluates and refines the agent's reasoning at each RL step. This simple and scalable GTR (Guided Thought Reinforcement) framework trains reasoning and action simultaneously without the need for dense, per-step human labeling. Our experiments demonstrate that GTR significantly enhances the performance and generalization of the LLaVA-7b model across various visual environments, achieving 3-5 times higher task success rates compared to SoTA models with notably smaller model sizes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/weit123/GTR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.07103", "title": "Quantizing Large Language Models for Code Generation: A Differentiated Replication", "authors": [ "Alessandro Giagnorio", "Antonio Mastropaolo", "Saima Afrin", "Massimiliano Di Penta", "Gabriele Bavota" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have shown an impressive capability in code generation and, specifically, to automatically implement requirements described in natural language. The LLM effectiveness generally increases with its size: The higher the number of LLM's trainable parameters the better its ability to implement code. However, when it comes to deploying LLM-based code generators, larger LLMs pose significant challenges related to their memory (and, consequently, carbon) footprint. A previous work by Wei et al. proposed to leverage quantization techniques to reduce the memory footprint of LLM-based code generators without substantially degrading their effectiveness. In short, they studied LLMs featuring up to 16B parameters, quantizing their precision from floating point 32 bits down to int 8 bits and showing their limited impact on code generation performance. Given the fast pace at which LLM capabilities and quantization techniques are evolving, in this work we present a differentiated replication of the work by Wei et al. in which we consider (i) on the one side, more recent and larger code-related LLMs, of up to 34B parameters; (ii) the latest advancements in model quantization techniques, which allow pushing the compression to the extreme quantization level of 2 bits per model parameter and; (iii) different types of calibration datasets to guide the quantization process, including code-specific ones. Our empirical evaluation reveals that the new frontier for LLM quantization is 4-bit precision, resulting in an average memory footprint reduction of 70% compared to the original model without observing any significant decrease in performance. Additionally, when the quantization becomes even more extreme (3 and 2 bits), a code-specific calibration dataset helps to limit the loss of performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.09600", "title": "MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation System", "authors": [ "Jihao Zhao", "Zhiyuan Ji", "Zhaoxin Fan", "Hanyu Wang", "Simin Niu", "Bo Tang", "Feiyu Xiong", "Zhiyu Li" ], "github": "https://github.com/IAAR-Shanghai/Meta-Chunking/tree/main/MoC", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG), while serving as a viable complement to large language models (LLMs), often overlooks the crucial aspect of text chunking within its pipeline. This paper initially introduces a dual-metric evaluation method, comprising Boundary Clarity and Chunk Stickiness, to enable the direct quantification of chunking quality. Leveraging this assessment method, we highlight the inherent limitations of traditional and semantic chunking in handling complex contextual nuances, thereby substantiating the necessity of integrating LLMs into chunking process. To address the inherent trade-off between computational efficiency and chunking precision in LLM-based approaches, we devise the granularity-aware Mixture-of-Chunkers (MoC) framework, which consists of a three-stage processing mechanism. Notably, our objective is to guide the chunker towards generating a structured list of chunking regular expressions, which are subsequently employed to extract chunks from the original text. Extensive experiments demonstrate that both our proposed metrics and the MoC framework effectively settle challenges of the chunking task, revealing the chunking kernel while enhancing the performance of the RAG system.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/IAAR-Shanghai/Meta-Chunking/tree/main/MoC", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.09601", "title": "RewardSDS: Aligning Score Distillation via Reward-Weighted Sampling", "authors": [ "Itay Chachy", "Guy Yariv", "Sagie Benaim" ], "github": "", "project_page": "", "abstract": "Score Distillation Sampling (SDS) has emerged as an effective technique for leveraging 2D diffusion priors for tasks such as text-to-3D generation. While powerful, SDS struggles with achieving fine-grained alignment to user intent. To overcome this, we introduce RewardSDS, a novel approach that weights noise samples based on alignment scores from a reward model, producing a weighted SDS loss. This loss prioritizes gradients from noise samples that yield aligned high-reward output. Our approach is broadly applicable and can extend SDS-based methods. In particular, we demonstrate its applicability to Variational Score Distillation (VSD) by introducing RewardVSD. We evaluate RewardSDS and RewardVSD on text-to-image, 2D editing, and text-to-3D generation tasks, showing significant improvements over SDS and VSD on a diverse set of metrics measuring generation quality and alignment to desired reward models, enabling state-of-the-art performance. Project page is available at https://itaychachy. github.io/reward-sds/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.06573", "title": "WildIFEval: Instruction Following in the Wild", "authors": [ "Gili Lior", "Asaf Yehudai", "Ariel Gera", "Liat Ein-Dor" ], "github": "https://github.com/gililior/wild-if-eval-code", "project_page": "", "abstract": "Recent LLMs have shown remarkable success in following user instructions, yet handling instructions with multiple constraints remains a significant challenge. In this work, we introduce WildIFEval - a large-scale dataset of 12K real user instructions with diverse, multi-constraint conditions. Unlike prior datasets, our collection spans a broad lexical and topical spectrum of constraints, in natural user prompts. We categorize these constraints into eight high-level classes to capture their distribution and dynamics in real-world scenarios. Leveraging WildIFEval, we conduct extensive experiments to benchmark the instruction-following capabilities of leading LLMs. Our findings reveal that all evaluated models experience performance degradation with an increasing number of constraints. Thus, we show that all models have a large room for improvement on such tasks. Moreover, we observe that the specific type of constraint plays a critical role in model performance. We release our dataset to promote further research on instruction-following under complex, realistic conditions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gililior/wild-if-eval-code", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.07588", "title": "When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token Pruning", "authors": [ "Junwei Luo", "Yingying Zhang", "Xue Yang", "Kang Wu", "Qi Zhu", "Lei Liang", "Jingdong Chen", "Yansheng Li" ], "github": "https://github.com/VisionXLab/LRS-VQA", "project_page": "", "abstract": "Efficient vision-language understanding of large Remote Sensing Images (RSIs) is meaningful but challenging. Current Large Vision-Language Models (LVLMs) typically employ limited pre-defined grids to process images, leading to information loss when handling gigapixel RSIs. Conversely, using unlimited grids significantly increases computational costs. To preserve image details while reducing computational complexity, we propose a text-guided token pruning method with Dynamic Image Pyramid (DIP) integration. Our method introduces: (i) a Region Focus Module (RFM) that leverages text-aware region localization capability to identify critical vision tokens, and (ii) a coarse-to-fine image tile selection and vision token pruning strategy based on DIP, which is guided by RFM outputs and avoids directly processing the entire large imagery. Additionally, existing benchmarks for evaluating LVLMs' perception ability on large RSI suffer from limited question diversity and constrained image sizes. We construct a new benchmark named LRS-VQA, which contains 7,333 QA pairs across 8 categories, with image length up to 27,328 pixels. Our method outperforms existing high-resolution strategies on four datasets using the same data. Moreover, compared to existing token reduction methods, our approach demonstrates higher efficiency under high-resolution settings. Dataset and code are in https://github.com/VisionXLab/LRS-VQA.", "github_manual": "", "github_regex": "https://github.com/VisionXLab/LRS-VQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.05397", "title": "Multi Agent based Medical Assistant for Edge Devices", "authors": [ "Sakharam Gawade", "Shivam Akhouri", "Chinmay Kulkarni", "Jagdish Samant", "Pragya Sahu", "Aastik", "Jai Pahal", "Saswat Meher" ], "github": "https://github.com/sakharamg/Multi-Agent-Health-Assistant", "project_page": "", "abstract": "Large Action Models (LAMs) have revolutionized intelligent automation, but their application in healthcare faces challenges due to privacy concerns, latency, and dependency on internet access. This report introduces an ondevice, multi-agent healthcare assistant that overcomes these limitations. The system utilizes smaller, task-specific agents to optimize resources, ensure scalability and high performance. Our proposed system acts as a one-stop solution for health care needs with features like appointment booking, health monitoring, medication reminders, and daily health reporting. Powered by the Qwen Code Instruct 2.5 7B model, the Planner and Caller Agents achieve an average RougeL score of 85.5 for planning and 96.5 for calling for our tasks while being lightweight for on-device deployment. This innovative approach combines the benefits of ondevice systems with multi-agent architectures, paving the way for user-centric healthcare solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sakharamg/Multi-Agent-Health-Assistant", "github_api_added_by": "auto" }, { "date": "2025-03-13", "arxiv_id": "2503.08681", "title": "Self-Taught Self-Correction for Small Language Models", "authors": [ "Viktor Moskvoretskii", "Chris Biemann", "Irina Nikishina" ], "github": "", "project_page": "", "abstract": "Although large language models (LLMs) have achieved remarkable performance across various tasks, they remain prone to errors. A key challenge is enabling them to self-correct. While prior research has relied on external tools or large proprietary models, this work explores self-correction in small language models (SLMs) through iterative fine-tuning using solely self-generated data. We introduce the Self-Taught Self-Correction (STaSC) algorithm, which incorporates multiple algorithmic design choices. Experimental results on a question-answering task demonstrate that STaSC effectively learns self-correction, leading to significant performance improvements. Our analysis further provides insights into the mechanisms of self-correction and the impact of different design choices on learning dynamics and overall performance. To support future research, we release our user-friendly codebase and lightweight models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.09410", "title": "Monte Carlo Diffusion for Generalizable Learning-Based RANSAC", "authors": [ "Jiale Wang", "Chen Zhao", "Wei Ke", "Tong Zhang" ], "github": "https://github.com/comedy0913/MCD", "project_page": "", "abstract": "Random Sample Consensus (RANSAC) is a fundamental approach for robustly estimating parametric models from noisy data. Existing learning-based RANSAC methods utilize deep learning to enhance the robustness of RANSAC against outliers. However, these approaches are trained and tested on the data generated by the same algorithms, leading to limited generalization to out-of-distribution data during inference. Therefore, in this paper, we introduce a novel diffusion-based paradigm that progressively injects noise into ground-truth data, simulating the noisy conditions for training learning-based RANSAC. To enhance data diversity, we incorporate Monte Carlo sampling into the diffusion paradigm, approximating diverse data distributions by introducing different types of randomness at multiple stages. We evaluate our approach in the context of feature matching through comprehensive experiments on the ScanNet and MegaDepth datasets. The experimental results demonstrate that our Monte Carlo diffusion mechanism significantly improves the generalization ability of learning-based RANSAC. We also develop extensive ablation studies that highlight the effectiveness of key components in our framework.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/comedy0913/MCD", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.09590", "title": "BIMBA: Selective-Scan Compression for Long-Range Video Question Answering", "authors": [ "Md Mohaiminul Islam", "Tushar Nagarajan", "Huiyu Wang", "Gedas Bertasius", "Lorenzo Torresani" ], "github": "https://github.com/md-mohaiminul/BIMBA", "project_page": "", "abstract": "Video Question Answering (VQA) in long videos poses the key challenge of extracting relevant information and modeling long-range dependencies from many redundant frames. The self-attention mechanism provides a general solution for sequence modeling, but it has a prohibitive cost when applied to a massive number of spatiotemporal tokens in long videos. Most prior methods rely on compression strategies to lower the computational cost, such as reducing the input length via sparse frame sampling or compressing the output sequence passed to the large language model (LLM) via space-time pooling. However, these naive approaches over-represent redundant information and often miss salient events or fast-occurring space-time patterns. In this work, we introduce BIMBA, an efficient state-space model to handle long-form videos. Our model leverages the selective scan algorithm to learn to effectively select critical information from high-dimensional video and transform it into a reduced token sequence for efficient LLM processing. Extensive experiments demonstrate that BIMBA achieves state-of-the-art accuracy on multiple long-form VQA benchmarks, including PerceptionTest, NExT-QA, EgoSchema, VNBench, LongVideoBench, and Video-MME. Code, and models are publicly available at https://sites.google.com/view/bimba-mllm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/md-mohaiminul/BIMBA", "github_api_added_by": "auto" }, { "date": "2025-03-13", "arxiv_id": "2503.05333", "title": "PhysicsGen: Can Generative Models Learn from Images to Predict Complex Physical Relations?", "authors": [ "Martin Spitznagel", "Jan Vaillant", "Janis Keuper" ], "github": "", "project_page": "", "abstract": "The image-to-image translation abilities of generative learning models have recently made significant progress in the estimation of complex (steered) mappings between image distributions. While appearance based tasks like image in-painting or style transfer have been studied at length, we propose to investigate the potential of generative models in the context of physical simulations. Providing a dataset of 300k image-pairs and baseline evaluations for three different physical simulation tasks, we propose a benchmark to investigate the following research questions: i) are generative models able to learn complex physical relations from input-output image pairs? ii) what speedups can be achieved by replacing differential equation based simulations? While baseline evaluations of different current models show the potential for high speedups (ii), these results also show strong limitations toward the physical correctness (i). This underlines the need for new methods to enforce physical correctness. Data, baseline models and evaluation code http://www.physics-gen.org.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.08674", "title": "Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields", "authors": [ "Tobias Kreiman", "Aditi S. Krishnapriyan" ], "github": "", "project_page": "", "abstract": "Machine Learning Force Fields (MLFFs) are a promising alternative to expensive ab initio quantum mechanical molecular simulations. Given the diversity of chemical spaces that are of interest and the cost of generating new data, it is important to understand how MLFFs generalize beyond their training distributions. In order to characterize and better understand distribution shifts in MLFFs, we conduct diagnostic experiments on chemical datasets, revealing common shifts that pose significant challenges, even for large foundation models trained on extensive data. Based on these observations, we hypothesize that current supervised training methods inadequately regularize MLFFs, resulting in overfitting and learning poor representations of out-of-distribution systems. We then propose two new methods as initial steps for mitigating distribution shifts for MLFFs. Our methods focus on test-time refinement strategies that incur minimal computational cost and do not use expensive ab initio reference labels. The first strategy, based on spectral graph theory, modifies the edges of test graphs to align with graph structures seen during training. Our second strategy improves representations for out-of-distribution systems at test-time by taking gradient steps using an auxiliary objective, such as a cheap physical prior. Our test-time refinement strategies significantly reduce errors on out-of-distribution systems, suggesting that MLFFs are capable of and can move towards modeling diverse chemical spaces, but are not being effectively trained to do so. Our experiments establish clear benchmarks for evaluating the generalization capabilities of the next generation of MLFFs. Our code is available at https://tkreiman.github.io/projects/mlff_distribution_shifts/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-13", "arxiv_id": "2503.09516", "title": "Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning", "authors": [ "Bowen Jin", "Hansi Zeng", "Zhenrui Yue", "Dong Wang", "Hamed Zamani", "Jiawei Han" ], "github": "https://github.com/PeterGriffinJin/Search-R1", "project_page": "", "abstract": "Efficiently acquiring external knowledge and up-to-date information is essential for effective reasoning and text generation in large language models (LLMs). Retrieval augmentation and tool-use training approaches where a search engine is treated as a tool lack complex multi-turn retrieval flexibility or require large-scale supervised data. Prompting advanced LLMs with reasoning capabilities during inference to use search engines is not optimal, since the LLM does not learn how to optimally interact with the search engine. This paper introduces Search-R1, an extension of the DeepSeek-R1 model where the LLM learns -- solely through reinforcement learning (RL) -- to autonomously generate (multiple) search queries during step-by-step reasoning with real-time retrieval. Search-R1 optimizes LLM rollouts with multi-turn search interactions, leveraging retrieved token masking for stable RL training and a simple outcome-based reward function. Experiments on seven question-answering datasets show that Search-R1 improves performance by 26% (Qwen2.5-7B), 21% (Qwen2.5-3B), and 10% (LLaMA3.2-3B) over SOTA baselines. This paper further provides empirical insights into RL optimization methods, LLM choices, and response length dynamics in retrieval-augmented reasoning. The code and model checkpoints are available at https://github.com/PeterGriffinJin/Search-R1.", "github_manual": "", "github_regex": "https://github.com/PeterGriffinJin/Search-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10582", "title": "VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search", "authors": [ "Yiming Jia", "Jiachen Li", "Xiang Yue", "Bo Li", "Ping Nie", "Kai Zou", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Vision-Language Models have made significant progress on many perception-focused tasks, however, their progress on reasoning-focused tasks seem to be limited due to the lack of high-quality and diverse training data. In this work, we aim to address the scarcity issue of reasoning-focused multimodal datasets. We propose VisualWebInstruct - a novel approach that leverages search engine to create a diverse, and high-quality dataset spanning multiple disciplines like math, physics, finance, chemistry, etc. Starting with meticulously selected 30,000 seed images, we employ Google Image search to identify websites containing similar images. We collect and process the HTMLs from over 700K unique URL sources. Through a pipeline of content extraction, filtering and synthesis, we build a dataset of approximately 900K question-answer pairs, with 40% being visual QA pairs and the rest as text QA pairs. Models fine-tuned on VisualWebInstruct demonstrate significant performance gains: (1) training from Llava-OV-mid shows 10-20% absolute point gains across benchmarks, (2) training from MAmmoTH-VL shows 5% absoluate gain. Our best model MAmmoTH-VL2 shows state-of-the-art performance within the 10B parameter class on MMMU-Pro-std (40.7%), MathVerse (42.6%), and DynaMath (55.7%). These remarkable results highlight the effectiveness of our dataset in enhancing VLMs' reasoning capabilities for complex multimodal tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10391", "title": "CINEMA: Coherent Multi-Subject Video Generation via MLLM-Based Guidance", "authors": [ "Yufan Deng", "Xun Guo", "Yizhi Wang", "Jacob Zhiyuan Fang", "Angtian Wang", "Shenghai Yuan", "Yiding Yang", "Bo Liu", "Haibin Huang", "Chongyang Ma" ], "github": "", "project_page": "", "abstract": "Video generation has witnessed remarkable progress with the advent of deep generative models, particularly diffusion models. While existing methods excel in generating high-quality videos from text prompts or single images, personalized multi-subject video generation remains a largely unexplored challenge. This task involves synthesizing videos that incorporate multiple distinct subjects, each defined by separate reference images, while ensuring temporal and spatial consistency. Current approaches primarily rely on mapping subject images to keywords in text prompts, which introduces ambiguity and limits their ability to model subject relationships effectively. In this paper, we propose CINEMA, a novel framework for coherent multi-subject video generation by leveraging Multimodal Large Language Model (MLLM). Our approach eliminates the need for explicit correspondences between subject images and text entities, mitigating ambiguity and reducing annotation effort. By leveraging MLLM to interpret subject relationships, our method facilitates scalability, enabling the use of large and diverse datasets for training. Furthermore, our framework can be conditioned on varying numbers of subjects, offering greater flexibility in personalized content creation. Through extensive evaluations, we demonstrate that our approach significantly improves subject consistency, and overall video coherence, paving the way for advanced applications in storytelling, interactive media, and personalized video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10072", "title": "\"Silent Is Not Actually Silent\": An Investigation of Toxicity on Bug Report Discussion", "authors": [ "Mia Mohammad Imran", "Jaydeb Sarker" ], "github": "", "project_page": "", "abstract": "Toxicity in bug report discussions poses significant challenges to the collaborative dynamics of open-source software development. Bug reports are crucial for identifying and resolving defects, yet their inherently problem-focused nature and emotionally charged context make them susceptible to toxic interactions. This study explores toxicity in GitHub bug reports through a qualitative analysis of 203 bug threads, including 81 toxic ones. Our findings reveal that toxicity frequently arises from misaligned perceptions of bug severity and priority, unresolved frustrations with tools, and lapses in professional communication. These toxic interactions not only derail productive discussions but also reduce the likelihood of actionable outcomes, such as linking issues with pull requests. Our preliminary findings offer actionable recommendations to improve bug resolution by mitigating toxicity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.09905", "title": "Quantization for OpenAI's Whisper Models: A Comparative Analysis", "authors": [ "Allison Andreyev" ], "github": "https://github.com/allisonandreyev/WhisperQuantization.git", "project_page": "", "abstract": "Automated speech recognition (ASR) models have gained prominence for applications such as captioning, speech translation, and live transcription. This paper studies Whisper and two model variants: one optimized for live speech streaming and another for offline transcription. Notably, these models have been found to generate hallucinated content, reducing transcription reliability. Furthermore, larger model variants exhibit increased latency and pose challenges for deployment on resource-constrained devices. This study analyzes the similarities and differences between three Whisper models, qualitatively examining their distinct capabilities. Next, this study quantifies the impact of model quantization on latency and evaluates its viability for edge deployment. Using the open source LibriSpeech dataset, this paper evaluates the word error rate (WER) along with latency analysis of whispercpp using 3 quantization methods (INT4, INT5, INT8). Results show that quantization reduces latency by 19\\% and model size by 45\\%, while preserving transcription accuracy. These findings provide insights into the optimal use cases of different Whisper models and edge device deployment possibilities. All code, datasets, and implementation details are available in a public GitHub repository: https://github.com/allisonandreyev/WhisperQuantization.git", "github_manual": "", "github_regex": "https://github.com/allisonandreyev/WhisperQuantization.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.04723", "title": "Shifting Long-Context LLMs Research from Input to Output", "authors": [ "Yuhao Wu", "Yushi Bai", "Zhiqing Hu", "Shangqing Tu", "Ming Shan Hee", "Juanzi Li", "Roy Ka-Wei Lee" ], "github": "", "project_page": "", "abstract": "Recent advancements in long-context Large Language Models (LLMs) have primarily concentrated on processing extended input contexts, resulting in significant strides in long-context comprehension. However, the equally critical aspect of generating long-form outputs has received comparatively less attention. This paper advocates for a paradigm shift in NLP research toward addressing the challenges of long-output generation. Tasks such as novel writing, long-term planning, and complex reasoning require models to understand extensive contexts and produce coherent, contextually rich, and logically consistent extended text. These demands highlight a critical gap in current LLM capabilities. We underscore the importance of this under-explored domain and call for focused efforts to develop foundational LLMs tailored for generating high-quality, long-form outputs, which hold immense potential for real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10480", "title": "World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning", "authors": [ "Siyin Wang", "Zhaoye Fei", "Qinyuan Cheng", "Shiduo Zhang", "Panpan Cai", "Jinlan Fu", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Recent advances in large vision-language models (LVLMs) have shown promise for embodied task planning, yet they struggle with fundamental challenges like dependency constraints and efficiency. Existing approaches either solely optimize action selection or leverage world models during inference, overlooking the benefits of learning to model the world as a way to enhance planning capabilities. We propose Dual Preference Optimization (D^2PO), a new learning framework that jointly optimizes state prediction and action selection through preference learning, enabling LVLMs to understand environment dynamics for better planning. To automatically collect trajectories and stepwise preference data without human annotation, we introduce a tree search mechanism for extensive exploration via trial-and-error. Extensive experiments on VoTa-Bench demonstrate that our D^2PO-based method significantly outperforms existing methods and GPT-4o when applied to Qwen2-VL (7B), LLaVA-1.6 (7B), and LLaMA-3.2 (11B), achieving superior task success rates with more efficient execution paths.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.09669", "title": "Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models", "authors": [ "Sangwon Jang", "June Suk Choi", "Jaehyeong Jo", "Kimin Lee", "Sung Ju Hwang" ], "github": "https://github.com/agwmon/silent-branding-attack", "project_page": "", "abstract": "Text-to-image diffusion models have achieved remarkable success in generating high-quality contents from text prompts. However, their reliance on publicly available data and the growing trend of data sharing for fine-tuning make these models particularly vulnerable to data poisoning attacks. In this work, we introduce the Silent Branding Attack, a novel data poisoning method that manipulates text-to-image diffusion models to generate images containing specific brand logos or symbols without any text triggers. We find that when certain visual patterns are repeatedly in the training data, the model learns to reproduce them naturally in its outputs, even without prompt mentions. Leveraging this, we develop an automated data poisoning algorithm that unobtrusively injects logos into original images, ensuring they blend naturally and remain undetected. Models trained on this poisoned dataset generate images containing logos without degrading image quality or text alignment. We experimentally validate our silent branding attack across two realistic settings on large-scale high-quality image datasets and style personalization datasets, achieving high success rates even without a specific text trigger. Human evaluation and quantitative metrics including logo detection show that our method can stealthily embed logos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/agwmon/silent-branding-attack", "github_api_added_by": "auto" }, { "date": "2025-03-14", "arxiv_id": "2503.10351", "title": "New Trends for Modern Machine Translation with Large Reasoning Models", "authors": [ "Sinuo Liu", "Chenyang Lyu", "Minghao Wu", "Longyue Wang", "Weihua Luo", "Kaifu Zhang" ], "github": "", "project_page": "", "abstract": "Recent advances in Large Reasoning Models (LRMs), particularly those leveraging Chain-of-Thought reasoning (CoT), have opened brand new possibility for Machine Translation (MT). This position paper argues that LRMs substantially transformed traditional neural MT as well as LLMs-based MT paradigms by reframing translation as a dynamic reasoning task that requires contextual, cultural, and linguistic understanding and reasoning. We identify three foundational shifts: 1) contextual coherence, where LRMs resolve ambiguities and preserve discourse structure through explicit reasoning over cross-sentence and complex context or even lack of context; 2) cultural intentionality, enabling models to adapt outputs by inferring speaker intent, audience expectations, and socio-linguistic norms; 3) self-reflection, LRMs can perform self-reflection during the inference time to correct the potential errors in translation especially extremely noisy cases, showing better robustness compared to simply mapping X->Y translation. We explore various scenarios in translation including stylized translation, document-level translation and multimodal translation by showcasing empirical examples that demonstrate the superiority of LRMs in translation. We also identify several interesting phenomenons for LRMs for MT including auto-pivot translation as well as the critical challenges such as over-localisation in translation and inference efficiency. In conclusion, we think that LRMs redefine translation systems not merely as text converters but as multilingual cognitive agents capable of reasoning about meaning beyond the text. This paradigm shift reminds us to think of problems in translation beyond traditional translation scenarios in a much broader context with LRMs - what we can achieve on top of it.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10630", "title": "UniGoal: Towards Universal Zero-shot Goal-oriented Navigation", "authors": [ "Hang Yin", "Xiuwei Xu", "Lingqing Zhao", "Ziwei Wang", "Jie Zhou", "Jiwen Lu" ], "github": "https://github.com/bagh2178/UniGoal", "project_page": "", "abstract": "In this paper, we propose a general framework for universal zero-shot goal-oriented navigation. Existing zero-shot methods build inference framework upon large language models (LLM) for specific tasks, which differs a lot in overall pipeline and fails to generalize across different types of goal. Towards the aim of universal zero-shot navigation, we propose a uniform graph representation to unify different goals, including object category, instance image and text description. We also convert the observation of agent into an online maintained scene graph. With this consistent scene and goal representation, we preserve most structural information compared with pure text and are able to leverage LLM for explicit graph-based reasoning. Specifically, we conduct graph matching between the scene graph and goal graph at each time instant and propose different strategies to generate long-term goal of exploration according to different matching states. The agent first iteratively searches subgraph of goal when zero-matched. With partial matching, the agent then utilizes coordinate projection and anchor pair alignment to infer the goal location. Finally scene graph correction and goal verification are applied for perfect matching. We also present a blacklist mechanism to enable robust switch between stages. Extensive experiments on several benchmarks show that our UniGoal achieves state-of-the-art zero-shot performance on three studied navigation tasks with a single model, even outperforming task-specific zero-shot methods and supervised universal methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bagh2178/UniGoal", "github_api_added_by": "auto" }, { "date": "2025-03-14", "arxiv_id": "2503.10613", "title": "CoSTAast: Cost-Sensitive Toolpath Agent for Multi-turn Image Editing", "authors": [ "Advait Gupta", "NandaKiran Velaga", "Dang Nguyen", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Text-to-image models like stable diffusion and DALLE-3 still struggle with multi-turn image editing. We decompose such a task as an agentic workflow (path) of tool use that addresses a sequence of subtasks by AI tools of varying costs. Conventional search algorithms require expensive exploration to find tool paths. While large language models (LLMs) possess prior knowledge of subtask planning, they may lack accurate estimations of capabilities and costs of tools to determine which to apply in each subtask. Can we combine the strengths of both LLMs and graph search to find cost-efficient tool paths? We propose a three-stage approach \"CoSTA*\" that leverages LLMs to create a subtask tree, which helps prune a graph of AI tools for the given task, and then conducts A* search on the small subgraph to find a tool path. To better balance the total cost and quality, CoSTA* combines both metrics of each tool on every subtask to guide the A* search. Each subtask's output is then evaluated by a vision-language model (VLM), where a failure will trigger an update of the tool's cost and quality on the subtask. Hence, the A* search can recover from failures quickly to explore other paths. Moreover, CoSTA* can automatically switch between modalities across subtasks for a better cost-quality trade-off. We build a novel benchmark of challenging multi-turn image editing, on which CoSTA* outperforms state-of-the-art image-editing models or agents in terms of both cost and quality, and performs versatile trade-offs upon user preference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10639", "title": "GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and Editing", "authors": [ "Rongyao Fang", "Chengqi Duan", "Kun Wang", "Linjiang Huang", "Hao Li", "Shilin Yan", "Hao Tian", "Xingyu Zeng", "Rui Zhao", "Jifeng Dai", "Xihui Liu", "Hongsheng Li" ], "github": "https://github.com/rongyaofang/GoT", "project_page": "", "abstract": "Current image generation and editing methods primarily process textual prompts as direct inputs without reasoning about visual composition and explicit operations. We present Generation Chain-of-Thought (GoT), a novel paradigm that enables generation and editing through an explicit language reasoning process before outputting images. This approach transforms conventional text-to-image generation and editing into a reasoning-guided framework that analyzes semantic relationships and spatial arrangements. We define the formulation of GoT and construct large-scale GoT datasets containing over 9M samples with detailed reasoning chains capturing semantic-spatial relationships. To leverage the advantages of GoT, we implement a unified framework that integrates Qwen2.5-VL for reasoning chain generation with an end-to-end diffusion model enhanced by our novel Semantic-Spatial Guidance Module. Experiments show our GoT framework achieves excellent performance on both generation and editing tasks, with significant improvements over baselines. Additionally, our approach enables interactive visual generation, allowing users to explicitly modify reasoning steps for precise image adjustments. GoT pioneers a new direction for reasoning-driven visual generation and editing, producing images that better align with human intent. To facilitate future research, we make our datasets, code, and pretrained models publicly available at https://github.com/rongyaofang/GoT.", "github_manual": "", "github_regex": "https://github.com/rongyaofang/GoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10637", "title": "Distilling Diversity and Control in Diffusion Models", "authors": [ "Rohit Gandikota", "David Bau" ], "github": "", "project_page": "", "abstract": "Distilled diffusion models suffer from a critical limitation: reduced sample diversity compared to their base counterparts. In this work, we uncover that despite this diversity loss, distilled models retain the fundamental concept representations of base models. We demonstrate control distillation - where control mechanisms like Concept Sliders and LoRAs trained on base models can be seamlessly transferred to distilled models and vice-versa, effectively distilling control without any retraining. This preservation of representational structure prompted our investigation into the mechanisms of diversity collapse during distillation. To understand how distillation affects diversity, we introduce Diffusion Target (DT) Visualization, an analysis and debugging tool that reveals how models predict final outputs at intermediate steps. Through DT-Visualization, we identify generation artifacts, inconsistencies, and demonstrate that initial diffusion timesteps disproportionately determine output diversity, while later steps primarily refine details. Based on these insights, we introduce diversity distillation - a hybrid inference approach that strategically employs the base model for only the first critical timestep before transitioning to the efficient distilled model. Our experiments demonstrate that this simple modification not only restores the diversity capabilities from base to distilled models but surprisingly exceeds it, while maintaining nearly the computational efficiency of distilled inference, all without requiring additional training or model modifications. Our code and data are available at https://distillation.baulab.info", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10636", "title": "The Curse of Conditions: Analyzing and Improving Optimal Transport for Conditional Flow-Based Generation", "authors": [ "Ho Kei Cheng", "Alexander Schwing" ], "github": "https://github.com/hkchengrex/C2OT", "project_page": "", "abstract": "Minibatch optimal transport coupling straightens paths in unconditional flow matching. This leads to computationally less demanding inference as fewer integration steps and less complex numerical solvers can be employed when numerically solving an ordinary differential equation at test time. However, in the conditional setting, minibatch optimal transport falls short. This is because the default optimal transport mapping disregards conditions, resulting in a conditionally skewed prior distribution during training. In contrast, at test time, we have no access to the skewed prior, and instead sample from the full, unbiased prior distribution. This gap between training and testing leads to a subpar performance. To bridge this gap, we propose conditional optimal transport C^2OT that adds a conditional weighting term in the cost matrix when computing the optimal transport assignment. Experiments demonstrate that this simple fix works with both discrete and continuous conditions in 8gaussians-to-moons, CIFAR-10, ImageNet-32x32, and ImageNet-256x256. Our method performs better overall compared to the existing baselines across different function evaluation budgets. Code is available at https://hkchengrex.github.io/C2OT", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hkchengrex/C2OT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10596", "title": "GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding", "authors": [ "Rui Hu", "Lianghui Zhu", "Yuxuan Zhang", "Tianheng Cheng", "Lei Liu", "Heng Liu", "Longjin Ran", "Xiaoxin Chen", "Wenyu Liu", "Xinggang Wang" ], "github": "https://github.com/hustvl/groundingsuite", "project_page": "", "abstract": "Pixel grounding, encompassing tasks such as Referring Expression Segmentation (RES), has garnered considerable attention due to its immense potential for bridging the gap between vision and language modalities. However, advancements in this domain are currently constrained by limitations inherent in existing datasets, including limited object categories, insufficient textual diversity, and a scarcity of high-quality annotations. To mitigate these limitations, we introduce GroundingSuite, which comprises: (1) an automated data annotation framework leveraging multiple Vision-Language Model (VLM) agents; (2) a large-scale training dataset encompassing 9.56 million diverse referring expressions and their corresponding segmentations; and (3) a meticulously curated evaluation benchmark consisting of 3,800 images. The GroundingSuite training dataset facilitates substantial performance improvements, enabling models trained on it to achieve state-of-the-art results. Specifically, a cIoU of 68.9 on gRefCOCO and a gIoU of 55.3 on RefCOCOm. Moreover, the GroundingSuite annotation framework demonstrates superior efficiency compared to the current leading data annotation method, i.e., 4.5 times faster than the GLaMM.", "github_manual": "https://github.com/hustvl/groundingsuite", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10589", "title": "Long Context Tuning for Video Generation", "authors": [ "Yuwei Guo", "Ceyuan Yang", "Ziyan Yang", "Zhibei Ma", "Zhijie Lin", "Zhenheng Yang", "Dahua Lin", "Lu Jiang" ], "github": "", "project_page": "", "abstract": "Recent advances in video generation can produce realistic, minute-long single-shot videos with scalable diffusion transformers. However, real-world narrative videos require multi-shot scenes with visual and dynamic consistency across shots. In this work, we introduce Long Context Tuning (LCT), a training paradigm that expands the context window of pre-trained single-shot video diffusion models to learn scene-level consistency directly from data. Our method expands full attention mechanisms from individual shots to encompass all shots within a scene, incorporating interleaved 3D position embedding and an asynchronous noise strategy, enabling both joint and auto-regressive shot generation without additional parameters. Models with bidirectional attention after LCT can further be fine-tuned with context-causal attention, facilitating auto-regressive generation with efficient KV-cache. Experiments demonstrate single-shot models after LCT can produce coherent multi-shot scenes and exhibit emerging capabilities, including compositional generation and interactive shot extension, paving the way for more practical visual content creation. See https://guoyww.github.io/projects/long-context-video/ for more details.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10568", "title": "Autoregressive Image Generation with Randomized Parallel Decoding", "authors": [ "Haopeng Li", "Jinyue Yang", "Guoqi Li", "Huan Wang" ], "github": "", "project_page": "", "abstract": "We introduce ARPG, a novel visual autoregressive model that enables randomized parallel generation, addressing the inherent limitations of conventional raster-order approaches, which hinder inference efficiency and zero-shot generalization due to their sequential, predefined token generation order. Our key insight is that effective random-order modeling necessitates explicit guidance for determining the position of the next predicted token. To this end, we propose a novel guided decoding framework that decouples positional guidance from content representation, encoding them separately as queries and key-value pairs. By directly incorporating this guidance into the causal attention mechanism, our approach enables fully random-order training and generation, eliminating the need for bidirectional attention. Consequently, ARPG readily generalizes to zero-shot tasks such as image inpainting, outpainting, and resolution expansion. Furthermore, it supports parallel inference by concurrently processing multiple queries using a shared KV cache. On the ImageNet-1K 256 benchmark, our approach attains an FID of 1.94 with only 64 sampling steps, achieving over a 20-fold increase in throughput while reducing memory consumption by over 75% compared to representative recent autoregressive models at a similar scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10460", "title": "Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond", "authors": [ "Liang Wen", "Yunke Cai", "Fenrui Xiao", "Xin He", "Qi An", "Zhenyu Duan", "Yimin Du", "Junchen Liu", "Lifu Tang", "Xiaowei Lv", "Haosheng Zou", "Yongchao Deng", "Shousheng Jia", "Xiangzheng Zhang" ], "github": "https://github.com/qihoo360/light-r1", "project_page": "", "abstract": "This paper presents our work on the Light-R1 series, with models, data, and code all released. We first focus on training long COT models from scratch, specifically starting from models initially lacking long COT capabilities. Using a curriculum training recipe consisting of two-stage SFT and semi-on-policy DPO, we train our model Light-R1-32B from Qwen2.5-32B-Instruct, resulting in superior math performance compared to DeepSeek-R1-Distill-Qwen-32B. Despite being trained exclusively on math data, Light-R1-32B shows strong generalization across other domains. In the subsequent phase of this work, we highlight the significant benefit of the 3k dataset constructed for the second SFT stage on enhancing other models. By fine-tuning DeepSeek-R1-Distilled models using this dataset, we obtain new SOTA models in 7B and 14B, while the 32B model, Light-R1-32B-DS performed comparably to QwQ-32B and DeepSeek-R1. Furthermore, we extend our work by applying reinforcement learning, specifically GRPO, on long-COT models to further improve reasoning performance. We successfully train our final Light-R1-14B-DS with RL, achieving SOTA performance among 14B parameter models in math. With AIME24 & 25 scores of 74.0 and 60.2 respectively, Light-R1-14B-DS surpasses even many 32B models and DeepSeek-R1-Distill-Llama-70B. Its RL training also exhibits well expected behavior, showing simultaneous increase in response length and reward score. The Light-R1 series of work validates training long-COT models from scratch, showcases the art in SFT data and releases SOTA models from RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qihoo360/light-r1", "github_api_added_by": "auto" }, { "date": "2025-03-14", "arxiv_id": "2503.10437", "title": "4D LangSplat: 4D Language Gaussian Splatting via Multimodal Large Language Models", "authors": [ "Wanhua Li", "Renping Zhou", "Jiawei Zhou", "Yingwei Song", "Johannes Herter", "Minghan Qin", "Gao Huang", "Hanspeter Pfister" ], "github": "", "project_page": "", "abstract": "Learning 4D language fields to enable time-sensitive, open-ended language queries in dynamic scenes is essential for many real-world applications. While LangSplat successfully grounds CLIP features into 3D Gaussian representations, achieving precision and efficiency in 3D static scenes, it lacks the ability to handle dynamic 4D fields as CLIP, designed for static image-text tasks, cannot capture temporal dynamics in videos. Real-world environments are inherently dynamic, with object semantics evolving over time. Building a precise 4D language field necessitates obtaining pixel-aligned, object-wise video features, which current vision models struggle to achieve. To address these challenges, we propose 4D LangSplat, which learns 4D language fields to handle time-agnostic or time-sensitive open-vocabulary queries in dynamic scenes efficiently. 4D LangSplat bypasses learning the language field from vision features and instead learns directly from text generated from object-wise video captions via Multimodal Large Language Models (MLLMs). Specifically, we propose a multimodal object-wise video prompting method, consisting of visual and text prompts that guide MLLMs to generate detailed, temporally consistent, high-quality captions for objects throughout a video. These captions are encoded using a Large Language Model into high-quality sentence embeddings, which then serve as pixel-aligned, object-specific feature supervision, facilitating open-vocabulary text queries through shared embedding spaces. Recognizing that objects in 4D scenes exhibit smooth transitions across states, we further propose a status deformable network to model these continuous changes over time effectively. Our results across multiple benchmarks demonstrate that 4D LangSplat attains precise and efficient results for both time-sensitive and time-agnostic open-vocabulary queries.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10622", "title": "Transformers without Normalization", "authors": [ "Jiachen Zhu", "Xinlei Chen", "Kaiming He", "Yann LeCun", "Zhuang Liu" ], "github": "https://github.com/jiachenzhu/DyT", "project_page": "", "abstract": "Normalization layers are ubiquitous in modern neural networks and have long been considered essential. This work demonstrates that Transformers without normalization can achieve the same or better performance using a remarkably simple technique. We introduce Dynamic Tanh (DyT), an element-wise operation DyT(x) = tanh(alpha x), as a drop-in replacement for normalization layers in Transformers. DyT is inspired by the observation that layer normalization in Transformers often produces tanh-like, S-shaped input-output mappings. By incorporating DyT, Transformers without normalization can match or exceed the performance of their normalized counterparts, mostly without hyperparameter tuning. We validate the effectiveness of Transformers with DyT across diverse settings, ranging from recognition to generation, supervised to self-supervised learning, and computer vision to language models. These findings challenge the conventional understanding that normalization layers are indispensable in modern neural networks, and offer new insights into their role in deep networks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jiachenzhu/DyT", "github_api_added_by": "auto" }, { "date": "2025-03-14", "arxiv_id": "2503.10615", "title": "R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization", "authors": [ "Yi Yang", "Xiaoxuan He", "Hongkun Pan", "Xiyan Jiang", "Yan Deng", "Xingtao Yang", "Haoyu Lu", "Dacheng Yin", "Fengyun Rao", "Minfeng Zhu", "Bo Zhang", "Wei Chen" ], "github": "", "project_page": "", "abstract": "Large Language Models have demonstrated remarkable reasoning capability in complex textual tasks. However, multimodal reasoning, which requires integrating visual and textual information, remains a significant challenge. Existing visual-language models often struggle to effectively analyze and reason visual content, resulting in suboptimal performance on complex reasoning tasks. Moreover, the absence of comprehensive benchmarks hinders the accurate assessment of multimodal reasoning capabilities. In this paper, we introduce R1-Onevision, a multimodal reasoning model designed to bridge the gap between visual perception and deep reasoning. To achieve this, we propose a cross-modal reasoning pipeline that transforms images into formal textural representations, enabling precise language-based reasoning. Leveraging this pipeline, we construct the R1-Onevision dataset which provides detailed, step-by-step multimodal reasoning annotations across diverse domains. We further develop the R1-Onevision model through supervised fine-tuning and reinforcement learning to cultivate advanced reasoning and robust generalization abilities. To comprehensively evaluate multimodal reasoning performance across different grades, we introduce R1-Onevision-Bench, a benchmark aligned with human educational stages, covering exams from junior high school to university and beyond. Experimental results show that R1-Onevision achieves state-of-the-art performance, outperforming models such as GPT-4o and Qwen2.5-VL on multiple challenging multimodal reasoning benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.09642", "title": "Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k", "authors": [ "Xiangyu Peng", "Zangwei Zheng", "Chenhui Shen", "Tom Young", "Xinying Guo", "Binluo Wang", "Hang Xu", "Hongxin Liu", "Mingyan Jiang", "Wenjun Li", "Yuhui Wang", "Anbang Ye", "Gang Ren", "Qianran Ma", "Wanying Liang", "Xiang Lian", "Xiwen Wu", "Yuting Zhong", "Zhuangyan Li", "Chaoyu Gong", "Guojun Lei", "Leijun Cheng", "Limin Zhang", "Minghao Li", "Ruijie Zhang", "Silan Hu", "Shijie Huang", "Xiaokang Wang", "Yuanheng Zhao", "Yuqi Wang", "Ziang Wei", "Yang You" ], "github": "https://github.com/hpcaitech/Open-Sora", "project_page": "", "abstract": "Video generation models have achieved remarkable progress in the past year. The quality of AI video continues to improve, but at the cost of larger model size, increased data quantity, and greater demand for training compute. In this report, we present Open-Sora 2.0, a commercial-level video generation model trained for only $200k. With this model, we demonstrate that the cost of training a top-performing video generation model is highly controllable. We detail all techniques that contribute to this efficiency breakthrough, including data curation, model architecture, training strategy, and system optimization. According to human evaluation results and VBench scores, Open-Sora 2.0 is comparable to global leading video generation models including the open-source HunyuanVideo and the closed-source Runway Gen-3 Alpha. By making Open-Sora 2.0 fully open-source, we aim to democratize access to advanced video generation technology, fostering broader innovation and creativity in content creation. All resources are publicly available at: https://github.com/hpcaitech/Open-Sora.", "github_manual": "", "github_regex": "https://github.com/hpcaitech/Open-Sora", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.09641", "title": "SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation", "authors": [ "Junsong Chen", "Shuchen Xue", "Yuyang Zhao", "Jincheng Yu", "Sayak Paul", "Junyu Chen", "Han Cai", "Enze Xie", "Song Han" ], "github": "https://github.com/NVlabs/Sana", "project_page": "", "abstract": "This paper presents SANA-Sprint, an efficient diffusion model for ultra-fast text-to-image (T2I) generation. SANA-Sprint is built on a pre-trained foundation model and augmented with hybrid distillation, dramatically reducing inference steps from 20 to 1-4. We introduce three key innovations: (1) We propose a training-free approach that transforms a pre-trained flow-matching model for continuous-time consistency distillation (sCM), eliminating costly training from scratch and achieving high training efficiency. Our hybrid distillation strategy combines sCM with latent adversarial distillation (LADD): sCM ensures alignment with the teacher model, while LADD enhances single-step generation fidelity. (2) SANA-Sprint is a unified step-adaptive model that achieves high-quality generation in 1-4 steps, eliminating step-specific training and improving efficiency. (3) We integrate ControlNet with SANA-Sprint for real-time interactive image generation, enabling instant visual feedback for user interaction. SANA-Sprint establishes a new Pareto frontier in speed-quality tradeoffs, achieving state-of-the-art performance with 7.59 FID and 0.74 GenEval in only 1 step - outperforming FLUX-schnell (7.94 FID / 0.71 GenEval) while being 10x faster (0.1s vs 1.1s on H100). It also achieves 0.1s (T2I) and 0.25s (ControlNet) latency for 1024 x 1024 images on H100, and 0.31s (T2I) on an RTX 4090, showcasing its exceptional efficiency and potential for AI-powered consumer applications (AIPC). Code and pre-trained models will be open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVlabs/Sana", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10633", "title": "Charting and Navigating Hugging Face's Model Atlas", "authors": [ "Eliahu Horwitz", "Nitzan Kurer", "Jonathan Kahana", "Liel Amar", "Yedid Hoshen" ], "github": "", "project_page": "", "abstract": "As there are now millions of publicly available neural networks, searching and analyzing large model repositories becomes increasingly important. Navigating so many models requires an atlas, but as most models are poorly documented charting such an atlas is challenging. To explore the hidden potential of model repositories, we chart a preliminary atlas representing the documented fraction of Hugging Face. It provides stunning visualizations of the model landscape and evolution. We demonstrate several applications of this atlas including predicting model attributes (e.g., accuracy), and analyzing trends in computer vision models. However, as the current atlas remains incomplete, we propose a method for charting undocumented regions. Specifically, we identify high-confidence structural priors based on dominant real-world model training practices. Leveraging these priors, our approach enables accurate mapping of previously undocumented areas of the atlas. We publicly release our datasets, code, and interactive atlas.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.09662", "title": "CoRe^2: Collect, Reflect and Refine to Generate Better and Faster", "authors": [ "Shitong Shao", "Zikai Zhou", "Dian Xie", "Yuetong Fang", "Tian Ye", "Lichen Bai", "Zeke Xie" ], "github": "https://github.com/xie-lab-ml/CoRe2", "project_page": "", "abstract": "Making text-to-image (T2I) generative model sample both fast and well represents a promising research direction. Previous studies have typically focused on either enhancing the visual quality of synthesized images at the expense of sampling efficiency or dramatically accelerating sampling without improving the base model's generative capacity. Moreover, nearly all inference methods have not been able to ensure stable performance simultaneously on both diffusion models (DMs) and visual autoregressive models (ARMs). In this paper, we introduce a novel plug-and-play inference paradigm, CoRe^2, which comprises three subprocesses: Collect, Reflect, and Refine. CoRe^2 first collects classifier-free guidance (CFG) trajectories, and then use collected data to train a weak model that reflects the easy-to-learn contents while reducing number of function evaluations during inference by half. Subsequently, CoRe^2 employs weak-to-strong guidance to refine the conditional output, thereby improving the model's capacity to generate high-frequency and realistic content, which is difficult for the base model to capture. To the best of our knowledge, CoRe^2 is the first to demonstrate both efficiency and effectiveness across a wide range of DMs, including SDXL, SD3.5, and FLUX, as well as ARMs like LlamaGen. It has exhibited significant performance improvements on HPD v2, Pick-of-Pic, Drawbench, GenEval, and T2I-Compbench. Furthermore, CoRe^2 can be seamlessly integrated with the state-of-the-art Z-Sampling, outperforming it by 0.3 and 0.16 on PickScore and AES, while achieving 5.64s time saving using SD3.5.Code is released at https://github.com/xie-lab-ml/CoRe/tree/main.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xie-lab-ml/CoRe2", "github_api_added_by": "user" }, { "date": "2025-03-14", "arxiv_id": "2503.10618", "title": "DiT-Air: Revisiting the Efficiency of Diffusion Model Architecture Design in Text to Image Generation", "authors": [ "Chen Chen", "Rui Qian", "Wenze Hu", "Tsu-Jui Fu", "Lezhi Li", "Bowen Zhang", "Alex Schwing", "Wei Liu", "Yinfei Yang" ], "github": "", "project_page": "", "abstract": "In this work, we empirically study Diffusion Transformers (DiTs) for text-to-image generation, focusing on architectural choices, text-conditioning strategies, and training protocols. We evaluate a range of DiT-based architectures--including PixArt-style and MMDiT variants--and compare them with a standard DiT variant which directly processes concatenated text and noise inputs. Surprisingly, our findings reveal that the performance of standard DiT is comparable with those specialized models, while demonstrating superior parameter-efficiency, especially when scaled up. Leveraging the layer-wise parameter sharing strategy, we achieve a further reduction of 66% in model size compared to an MMDiT architecture, with minimal performance impact. Building on an in-depth analysis of critical components such as text encoders and Variational Auto-Encoders (VAEs), we introduce DiT-Air and DiT-Air-Lite. With supervised and reward fine-tuning, DiT-Air achieves state-of-the-art performance on GenEval and T2I CompBench, while DiT-Air-Lite remains highly competitive, surpassing most existing models despite its compact size.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10357", "title": "Do I look like a `cat.n.01` to you? A Taxonomy Image Generation Benchmark", "authors": [ "Viktor Moskvoretskii", "Alina Lobanova", "Ekaterina Neminova", "Chris Biemann", "Alexander Panchenko", "Irina Nikishina" ], "github": "", "project_page": "", "abstract": "This paper explores the feasibility of using text-to-image models in a zero-shot setup to generate images for taxonomy concepts. While text-based methods for taxonomy enrichment are well-established, the potential of the visual dimension remains unexplored. To address this, we propose a comprehensive benchmark for Taxonomy Image Generation that assesses models' abilities to understand taxonomy concepts and generate relevant, high-quality images. The benchmark includes common-sense and randomly sampled WordNet concepts, alongside the LLM generated predictions. The 12 models are evaluated using 9 novel taxonomy-related text-to-image metrics and human feedback. Moreover, we pioneer the use of pairwise evaluation with GPT-4 feedback for image generation. Experimental results show that the ranking of models differs significantly from standard T2I tasks. Playground-v2 and FLUX consistently outperform across metrics and subsets and the retrieval-based approach performs poorly. These findings highlight the potential for automating the curation of structured data resources.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10291", "title": "VisualPRM: An Effective Process Reward Model for Multimodal Reasoning", "authors": [ "Weiyun Wang", "Zhangwei Gao", "Lianjie Chen", "Zhe Chen", "Jinguo Zhu", "Xiangyu Zhao", "Yangzhou Liu", "Yue Cao", "Shenglong Ye", "Xizhou Zhu", "Lewei Lu", "Haodong Duan", "Yu Qiao", "Jifeng Dai", "Wenhai Wang" ], "github": "", "project_page": "", "abstract": "We introduce VisualPRM, an advanced multimodal Process Reward Model (PRM) with 8B parameters, which improves the reasoning abilities of existing Multimodal Large Language Models (MLLMs) across different model scales and families with Best-of-N (BoN) evaluation strategies. Specifically, our model improves the reasoning performance of three types of MLLMs and four different model scales. Even when applied to the highly capable InternVL2.5-78B, it achieves a 5.9-point improvement across seven multimodal reasoning benchmarks. Experimental results show that our model exhibits superior performance compared to Outcome Reward Models and Self-Consistency during BoN evaluation. To facilitate the training of multimodal PRMs, we construct a multimodal process supervision dataset VisualPRM400K using an automated data pipeline. For the evaluation of multimodal PRMs, we propose VisualProcessBench, a benchmark with human-annotated step-wise correctness labels, to measure the abilities of PRMs to detect erroneous steps in multimodal reasoning tasks. We hope that our work can inspire more future research and contribute to the development of MLLMs. Our model, data, and benchmark are released in https://internvl.github.io/blog/2025-03-13-VisualPRM/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.09368", "title": "PerCoV2: Improved Ultra-Low Bit-Rate Perceptual Image Compression with Implicit Hierarchical Masked Image Modeling", "authors": [ "Nikolai K\u00f6rber", "Eduard Kromer", "Andreas Siebert", "Sascha Hauke", "Daniel Mueller-Gritschneder", "Bj\u00f6rn Schuller" ], "github": "https://github.com/Nikolai10/PerCoV2", "project_page": "", "abstract": "We introduce PerCoV2, a novel and open ultra-low bit-rate perceptual image compression system designed for bandwidth- and storage-constrained applications. Building upon prior work by Careil et al., PerCoV2 extends the original formulation to the Stable Diffusion 3 ecosystem and enhances entropy coding efficiency by explicitly modeling the discrete hyper-latent image distribution. To this end, we conduct a comprehensive comparison of recent autoregressive methods (VAR and MaskGIT) for entropy modeling and evaluate our approach on the large-scale MSCOCO-30k benchmark. Compared to previous work, PerCoV2 (i) achieves higher image fidelity at even lower bit-rates while maintaining competitive perceptual quality, (ii) features a hybrid generation mode for further bit-rate savings, and (iii) is built solely on public components. Code and trained models will be released at https://github.com/Nikolai10/PerCoV2.", "github_manual": "", "github_regex": "https://github.com/Nikolai10/PerCoV2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.09837", "title": "On the Limitations of Vision-Language Models in Understanding Image Transforms", "authors": [ "Ahmad Mustafa Anis", "Hasnain Ali", "Saquib Sarfraz" ], "github": "", "project_page": "", "abstract": "Vision Language Models (VLMs) have demonstrated significant potential in various downstream tasks, including Image/Video Generation, Visual Question Answering, Multimodal Chatbots, and Video Understanding. However, these models often struggle with basic image transformations. This paper investigates the image-level understanding of VLMs, specifically CLIP by OpenAI and SigLIP by Google. Our findings reveal that these models lack comprehension of multiple image-level augmentations. To facilitate this study, we created an augmented version of the Flickr8k dataset, pairing each image with a detailed description of the applied transformation. We further explore how this deficiency impacts downstream tasks, particularly in image editing, and evaluate the performance of state-of-the-art Image2Image models on simple transformations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10614", "title": "ConsisLoRA: Enhancing Content and Style Consistency for LoRA-based Style Transfer", "authors": [ "Bolin Chen", "Baoquan Zhao", "Haoran Xie", "Yi Cai", "Qing Li", "Xudong Mao" ], "github": "", "project_page": "", "abstract": "Style transfer involves transferring the style from a reference image to the content of a target image. Recent advancements in LoRA-based (Low-Rank Adaptation) methods have shown promise in effectively capturing the style of a single image. However, these approaches still face significant challenges such as content inconsistency, style misalignment, and content leakage. In this paper, we comprehensively analyze the limitations of the standard diffusion parameterization, which learns to predict noise, in the context of style transfer. To address these issues, we introduce ConsisLoRA, a LoRA-based method that enhances both content and style consistency by optimizing the LoRA weights to predict the original image rather than noise. We also propose a two-step training strategy that decouples the learning of content and style from the reference image. To effectively capture both the global structure and local details of the content image, we introduce a stepwise loss transition strategy. Additionally, we present an inference guidance method that enables continuous control over content and style strengths during inference. Through both qualitative and quantitative evaluations, our method demonstrates significant improvements in content and style consistency while effectively reducing content leakage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.08677", "title": "OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpainting", "authors": [ "Yongsheng Yu", "Ziyun Zeng", "Haitian Zheng", "Jiebo Luo" ], "github": "", "project_page": "", "abstract": "Diffusion-based generative models have revolutionized object-oriented image editing, yet their deployment in realistic object removal and insertion remains hampered by challenges such as the intricate interplay of physical effects and insufficient paired training data. In this work, we introduce OmniPaint, a unified framework that re-conceptualizes object removal and insertion as interdependent processes rather than isolated tasks. Leveraging a pre-trained diffusion prior along with a progressive training pipeline comprising initial paired sample optimization and subsequent large-scale unpaired refinement via CycleFlow, OmniPaint achieves precise foreground elimination and seamless object insertion while faithfully preserving scene geometry and intrinsic properties. Furthermore, our novel CFD metric offers a robust, reference-free evaluation of context consistency and object hallucination, establishing a new benchmark for high-fidelity image editing. Project page: https://yeates.github.io/OmniPaint-Page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10602", "title": "TruthPrInt: Mitigating LVLM Object Hallucination Via Latent Truthful-Guided Pre-Intervention", "authors": [ "Jinhao Duan", "Fei Kong", "Hao Cheng", "James Diffenderfer", "Bhavya Kailkhura", "Lichao Sun", "Xiaofeng Zhu", "Xiaoshuang Shi", "Kaidi Xu" ], "github": "https://github.com/jinhaoduan/TruthPrInt", "project_page": "", "abstract": "Object Hallucination (OH) has been acknowledged as one of the major trustworthy challenges in Large Vision-Language Models (LVLMs). Recent advancements in Large Language Models (LLMs) indicate that internal states, such as hidden states, encode the \"overall truthfulness\" of generated responses. However, it remains under-explored how internal states in LVLMs function and whether they could serve as \"per-token\" hallucination indicators, which is essential for mitigating OH. In this paper, we first conduct an in-depth exploration of LVLM internal states in relation to OH issues and discover that (1) LVLM internal states are high-specificity per-token indicators of hallucination behaviors. Moreover, (2) different LVLMs encode universal patterns of hallucinations in common latent subspaces, indicating that there exist \"generic truthful directions\" shared by various LVLMs. Based on these discoveries, we propose Truthful-Guided Pre-Intervention (TruthPrInt) that first learns the truthful direction of LVLM decoding and then applies truthful-guided inference-time intervention during LVLM decoding. We further propose ComnHallu to enhance both cross-LVLM and cross-data hallucination detection transferability by constructing and aligning hallucination latent subspaces. We evaluate TruthPrInt in extensive experimental settings, including in-domain and out-of-domain scenarios, over popular LVLMs and OH benchmarks. Experimental results indicate that TruthPrInt significantly outperforms state-of-the-art methods. Codes will be available at https://github.com/jinhaoduan/TruthPrInt.", "github_manual": "", "github_regex": "https://github.com/jinhaoduan/TruthPrInt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10242", "title": "MinorBench: A hand-built benchmark for content-based risks for children", "authors": [ "Shaun Khoo", "Gabriel Chua", "Rachel Shong" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are rapidly entering children's lives - through parent-driven adoption, schools, and peer networks - yet current AI ethics and safety research do not adequately address content-related risks specific to minors. In this paper, we highlight these gaps with a real-world case study of an LLM-based chatbot deployed in a middle school setting, revealing how students used and sometimes misused the system. Building on these findings, we propose a new taxonomy of content-based risks for minors and introduce MinorBench, an open-source benchmark designed to evaluate LLMs on their ability to refuse unsafe or inappropriate queries from children. We evaluate six prominent LLMs under different system prompts, demonstrating substantial variability in their child-safety compliance. Our results inform practical steps for more robust, child-focused safety mechanisms and underscore the urgency of tailoring AI systems to safeguard young users.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.09046", "title": "Discovering Influential Neuron Path in Vision Transformers", "authors": [ "Yifan Wang", "Yifei Liu", "Yingdong Shi", "Changming Li", "Anqi Pang", "Sibei Yang", "Jingyi Yu", "Kan Ren" ], "github": "https://github.com/foundation-model-research/NeuronPath", "project_page": "", "abstract": "Vision Transformer models exhibit immense power yet remain opaque to human understanding, posing challenges and risks for practical applications. While prior research has attempted to demystify these models through input attribution and neuron role analysis, there's been a notable gap in considering layer-level information and the holistic path of information flow across layers. In this paper, we investigate the significance of influential neuron paths within vision Transformers, which is a path of neurons from the model input to output that impacts the model inference most significantly. We first propose a joint influence measure to assess the contribution of a set of neurons to the model outcome. And we further provide a layer-progressive neuron locating approach that efficiently selects the most influential neuron at each layer trying to discover the crucial neuron path from input to output within the target model. Our experiments demonstrate the superiority of our method finding the most influential neuron path along which the information flows, over the existing baseline solutions. Additionally, the neuron paths have illustrated that vision Transformers exhibit some specific inner working mechanism for processing the visual information within the same image category. We further analyze the key effects of these neurons on the image classification task, showcasing that the found neuron paths have already preserved the model capability on downstream tasks, which may also shed some lights on real-world applications like model pruning. The project website including implementation code is available at https://foundation-model-research.github.io/NeuronPath/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/foundation-model-research/NeuronPath", "github_api_added_by": "auto" }, { "date": "2025-03-14", "arxiv_id": "2503.10365", "title": "Piece it Together: Part-Based Concepting with IP-Priors", "authors": [ "Elad Richardson", "Kfir Goldberg", "Yuval Alaluf", "Daniel Cohen-Or" ], "github": "https://github.com/eladrich/PiT", "project_page": "", "abstract": "Advanced generative models excel at synthesizing images but often rely on text-based conditioning. Visual designers, however, often work beyond language, directly drawing inspiration from existing visual elements. In many cases, these elements represent only fragments of a potential concept-such as an uniquely structured wing, or a specific hairstyle-serving as inspiration for the artist to explore how they can come together creatively into a coherent whole. Recognizing this need, we introduce a generative framework that seamlessly integrates a partial set of user-provided visual components into a coherent composition while simultaneously sampling the missing parts needed to generate a plausible and complete concept. Our approach builds on a strong and underexplored representation space, extracted from IP-Adapter+, on which we train IP-Prior, a lightweight flow-matching model that synthesizes coherent compositions based on domain-specific priors, enabling diverse and context-aware generations. Additionally, we present a LoRA-based fine-tuning strategy that significantly improves prompt adherence in IP-Adapter+ for a given task, addressing its common trade-off between reconstruction quality and prompt adherence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/eladrich/PiT", "github_api_added_by": "auto" }, { "date": "2025-03-14", "arxiv_id": "2503.09799", "title": "Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo", "authors": [ "Zachary Charles", "Gabriel Teston", "Lucio Dery", "Keith Rush", "Nova Fallen", "Zachary Garrett", "Arthur Szlam", "Arthur Douillard" ], "github": "", "project_page": "", "abstract": "As we scale to more massive machine learning models, the frequent synchronization demands inherent in data-parallel approaches create significant slowdowns, posing a critical challenge to further scaling. Recent work develops an approach (DiLoCo) that relaxes synchronization demands without compromising model quality. However, these works do not carefully analyze how DiLoCo's behavior changes with model size. In this work, we study the scaling law behavior of DiLoCo when training LLMs under a fixed compute budget. We focus on how algorithmic factors, including number of model replicas, hyperparameters, and token budget affect training in ways that can be accurately predicted via scaling laws. We find that DiLoCo scales both predictably and robustly with model size. When well-tuned, DiLoCo scales better than data-parallel training with model size, and can outperform data-parallel training even at small model sizes. Our results showcase a more general set of benefits of DiLoCo than previously documented, including increased optimal batch sizes, improved downstream generalization with scale, and improved evaluation loss for a fixed token budget.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10638", "title": "Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective", "authors": [ "Xiaoming Zhao", "Alexander G. Schwing" ], "github": "", "project_page": "", "abstract": "Classifier-free guidance has become a staple for conditional generation with denoising diffusion models. However, a comprehensive understanding of classifier-free guidance is still missing. In this work, we carry out an empirical study to provide a fresh perspective on classifier-free guidance. Concretely, instead of solely focusing on classifier-free guidance, we trace back to the root, i.e., classifier guidance, pinpoint the key assumption for the derivation, and conduct a systematic study to understand the role of the classifier. We find that both classifier guidance and classifier-free guidance achieve conditional generation by pushing the denoising diffusion trajectories away from decision boundaries, i.e., areas where conditional information is usually entangled and is hard to learn. Based on this classifier-centric understanding, we propose a generic postprocessing step built upon flow-matching to shrink the gap between the learned distribution for a pre-trained denoising diffusion model and the real data distribution, majorly around the decision boundaries. Experiments on various datasets verify the effectiveness of the proposed approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.10635", "title": "A Frustratingly Simple Yet Highly Effective Attack Baseline: Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1", "authors": [ "Zhaoyi Li", "Xiaohan Zhao", "Dong-Dong Wu", "Jiacheng Cui", "Zhiqiang Shen" ], "github": "https://github.com/VILA-Lab/M-Attack", "project_page": "", "abstract": "Despite promising performance on open-source large vision-language models (LVLMs), transfer-based targeted attacks often fail against black-box commercial LVLMs. Analyzing failed adversarial perturbations reveals that the learned perturbations typically originate from a uniform distribution and lack clear semantic details, resulting in unintended responses. This critical absence of semantic information leads commercial LVLMs to either ignore the perturbation entirely or misinterpret its embedded semantics, thereby causing the attack to fail. To overcome these issues, we notice that identifying core semantic objects is a key objective for models trained with various datasets and methodologies. This insight motivates our approach that refines semantic clarity by encoding explicit semantic details within local regions, thus ensuring interoperability and capturing finer-grained features, and by concentrating modifications on semantically rich areas rather than applying them uniformly. To achieve this, we propose a simple yet highly effective solution: at each optimization step, the adversarial image is cropped randomly by a controlled aspect ratio and scale, resized, and then aligned with the target image in the embedding space. Experimental results confirm our hypothesis. Our adversarial examples crafted with local-aggregated perturbations focused on crucial regions exhibit surprisingly good transferability to commercial LVLMs, including GPT-4.5, GPT-4o, Gemini-2.0-flash, Claude-3.5-sonnet, Claude-3.7-sonnet, and even reasoning models like o1, Claude-3.7-thinking and Gemini-2.0-flash-thinking. Our approach achieves success rates exceeding 90% on GPT-4.5, 4o, and o1, significantly outperforming all prior state-of-the-art attack methods. Our optimized adversarial examples under different configurations and training code are available at https://github.com/VILA-Lab/M-Attack.", "github_manual": "", "github_regex": "https://github.com/VILA-Lab/M-Attack", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-14", "arxiv_id": "2503.07111", "title": "PoseLess: Depth-Free Vision-to-Joint Control via Direct Image Mapping with VLM", "authors": [ "Alan Dao", "Dinh Bach Vu", "Tuan Le Duc Anh", "Bui Quang Huy" ], "github": "", "project_page": "", "abstract": "This paper introduces PoseLess, a novel framework for robot hand control that eliminates the need for explicit pose estimation by directly mapping 2D images to joint angles using projected representations. Our approach leverages synthetic training data generated through randomized joint configurations, enabling zero-shot generalization to real-world scenarios and cross-morphology transfer from robotic to human hands. By projecting visual inputs and employing a transformer-based decoder, PoseLess achieves robust, low-latency control while addressing challenges such as depth ambiguity and data scarcity. Experimental results demonstrate competitive performance in joint angle prediction accuracy without relying on any human-labelled dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.07677", "title": "PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference Time by Leveraging Sparsity", "authors": [ "Kwanyoung Kim", "Byeongsu Sim" ], "github": "https://github.com/cubeyoung/PLADIS", "project_page": "", "abstract": "Diffusion models have shown impressive results in generating high-quality conditional samples using guidance techniques such as Classifier-Free Guidance (CFG). However, existing methods often require additional training or neural function evaluations (NFEs), making them incompatible with guidance-distilled models. Also, they rely on heuristic approaches that need identifying target layers. In this work, we propose a novel and efficient method, termed PLADIS, which boosts pre-trained models (U-Net/Transformer) by leveraging sparse attention. Specifically, we extrapolate query-key correlations using softmax and its sparse counterpart in the cross-attention layer during inference, without requiring extra training or NFEs. By leveraging the noise robustness of sparse attention, our PLADIS unleashes the latent potential of text-to-image diffusion models, enabling them to excel in areas where they once struggled with newfound effectiveness. It integrates seamlessly with guidance techniques, including guidance-distilled models. Extensive experiments show notable improvements in text alignment and human preference, offering a highly efficient and universally applicable solution.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/cubeyoung/PLADIS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.11647", "title": "ReCamMaster: Camera-Controlled Generative Rendering from A Single Video", "authors": [ "Jianhong Bai", "Menghan Xia", "Xiao Fu", "Xintao Wang", "Lianrui Mu", "Jinwen Cao", "Zuozhu Liu", "Haoji Hu", "Xiang Bai", "Pengfei Wan", "Di Zhang" ], "github": "https://github.com/KwaiVGI/ReCamMaster", "project_page": "", "abstract": "Camera control has been actively studied in text or image conditioned video generation tasks. However, altering camera trajectories of a given video remains under-explored, despite its importance in the field of video creation. It is non-trivial due to the extra constraints of maintaining multiple-frame appearance and dynamic synchronization. To address this, we present ReCamMaster, a camera-controlled generative video re-rendering framework that reproduces the dynamic scene of an input video at novel camera trajectories. The core innovation lies in harnessing the generative capabilities of pre-trained text-to-video models through a simple yet powerful video conditioning mechanism -- its capability often overlooked in current research. To overcome the scarcity of qualified training data, we construct a comprehensive multi-camera synchronized video dataset using Unreal Engine 5, which is carefully curated to follow real-world filming characteristics, covering diverse scenes and camera movements. It helps the model generalize to in-the-wild videos. Lastly, we further improve the robustness to diverse inputs through a meticulously designed training strategy. Extensive experiments tell that our method substantially outperforms existing state-of-the-art approaches and strong baselines. Our method also finds promising applications in video stabilization, super-resolution, and outpainting. Project page: https://jianhongbai.github.io/ReCamMaster/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KwaiVGI/ReCamMaster", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.11069", "title": "API Agents vs. GUI Agents: Divergence and Convergence", "authors": [ "Chaoyun Zhang", "Shilin He", "Liqun Li", "Si Qin", "Yu Kang", "Qingwei Lin", "Dongmei Zhang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have evolved beyond simple text generation to power software agents that directly translate natural language commands into tangible actions. While API-based LLM agents initially rose to prominence for their robust automation capabilities and seamless integration with programmatic endpoints, recent progress in multimodal LLM research has enabled GUI-based LLM agents that interact with graphical user interfaces in a human-like manner. Although these two paradigms share the goal of enabling LLM-driven task automation, they diverge significantly in architectural complexity, development workflows, and user interaction models. This paper presents the first comprehensive comparative study of API-based and GUI-based LLM agents, systematically analyzing their divergence and potential convergence. We examine key dimensions and highlight scenarios in which hybrid approaches can harness their complementary strengths. By proposing clear decision criteria and illustrating practical use cases, we aim to guide practitioners and researchers in selecting, combining, or transitioning between these paradigms. Ultimately, we indicate that continuing innovations in LLM-based automation are poised to blur the lines between API- and GUI-driven agents, paving the way for more flexible, adaptive solutions in a wide range of real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.11514", "title": "Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks", "authors": [ "Pengxin Guo", "Runxi Wang", "Shuang Zeng", "Jinjing Zhu", "Haoning Jiang", "Yanran Wang", "Yuyin Zhou", "Feifei Wang", "Hui Xiong", "Liangqiong Qu" ], "github": "", "project_page": "", "abstract": "Federated Learning (FL) has emerged as a promising privacy-preserving collaborative model training paradigm without sharing raw data. However, recent studies have revealed that private information can still be leaked through shared gradient information and attacked by Gradient Inversion Attacks (GIA). While many GIA methods have been proposed, a detailed analysis, evaluation, and summary of these methods are still lacking. Although various survey papers summarize existing privacy attacks in FL, few studies have conducted extensive experiments to unveil the effectiveness of GIA and their associated limiting factors in this context. To fill this gap, we first undertake a systematic review of GIA and categorize existing methods into three types, i.e., optimization-based GIA (OP-GIA), generation-based GIA (GEN-GIA), and analytics-based GIA (ANA-GIA). Then, we comprehensively analyze and evaluate the three types of GIA in FL, providing insights into the factors that influence their performance, practicality, and potential threats. Our findings indicate that OP-GIA is the most practical attack setting despite its unsatisfactory performance, while GEN-GIA has many dependencies and ANA-GIA is easily detectable, making them both impractical. Finally, we offer a three-stage defense pipeline to users when designing FL frameworks and protocols for better privacy protection and share some future research directions from the perspectives of attackers and defenders that we believe should be pursued. We hope that our study can help researchers design more robust FL frameworks to defend against these attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.10632", "title": "Kolmogorov-Arnold Attention: Is Learnable Attention Better For Vision Transformers?", "authors": [ "Subhajit Maity", "Killian Hitsman", "Xin Li", "Aritra Dutta" ], "github": "", "project_page": "", "abstract": "Kolmogorov-Arnold networks (KANs) are a remarkable innovation consisting of learnable activation functions with the potential to capture more complex relationships from data. Although KANs are useful in finding symbolic representations and continual learning of one-dimensional functions, their effectiveness in diverse machine learning (ML) tasks, such as vision, remains questionable. Presently, KANs are deployed by replacing multilayer perceptrons (MLPs) in deep network architectures, including advanced architectures such as vision Transformers (ViTs). In this paper, we are the first to design a general learnable Kolmogorov-Arnold Attention (KArAt) for vanilla ViTs that can operate on any choice of basis. However, the computing and memory costs of training them motivated us to propose a more modular version, and we designed particular learnable attention, called Fourier-KArAt. Fourier-KArAt and its variants either outperform their ViT counterparts or show comparable performance on CIFAR-10, CIFAR-100, and ImageNet-1K datasets. We dissect these architectures' performance and generalization capacity by analyzing their loss landscapes, weight distributions, optimizer path, attention visualization, and spectral behavior, and contrast them with vanilla ViTs. The goal of this paper is not to produce parameter- and compute-efficient attention, but to encourage the community to explore KANs in conjunction with more advanced architectures that require a careful understanding of learnable activations. Our open-source code and implementation details are available on: https://subhajitmaity.me/KArAt", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.09279", "title": "Cockatiel: Ensembling Synthetic and Human Preferenced Training for Detailed Video Caption", "authors": [ "Luozheng Qin", "Zhiyu Tan", "Mengping Yang", "Xiaomeng Yang", "Hao Li" ], "github": "", "project_page": "", "abstract": "Video Detailed Captioning (VDC) is a crucial task for vision-language bridging, enabling fine-grained descriptions of complex video content. In this paper, we first comprehensively benchmark current state-of-the-art approaches and systematically identified two critical limitations: biased capability towards specific captioning aspect and misalignment with human preferences. To address these deficiencies, we propose Cockatiel, a novel three-stage training pipeline that ensembles synthetic and human-aligned training for improving VDC performance. In the first stage, we derive a scorer from a meticulously annotated dataset to select synthetic captions high-performing on certain fine-grained video-caption alignment and human-preferred while disregarding others. Then, we train Cockatiel-13B, using this curated dataset to infuse it with assembled model strengths and human preferences. Finally, we further distill Cockatiel-8B from Cockatiel-13B for the ease of usage. Extensive quantitative and qualitative experiments reflect the effectiveness of our method, as we not only set new state-of-the-art performance on VDCSCORE in a dimension-balanced way but also surpass leading alternatives on human preference by a large margin as depicted by the human evaluation results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.10772", "title": "FlowTok: Flowing Seamlessly Across Text and Image Tokens", "authors": [ "Ju He", "Qihang Yu", "Qihao Liu", "Liang-Chieh Chen" ], "github": "https://github.com/bytedance/1d-tokenizer", "project_page": "", "abstract": "Bridging different modalities lies at the heart of cross-modality generation. While conventional approaches treat the text modality as a conditioning signal that gradually guides the denoising process from Gaussian noise to the target image modality, we explore a much simpler paradigm-directly evolving between text and image modalities through flow matching. This requires projecting both modalities into a shared latent space, which poses a significant challenge due to their inherently different representations: text is highly semantic and encoded as 1D tokens, whereas images are spatially redundant and represented as 2D latent embeddings. To address this, we introduce FlowTok, a minimal framework that seamlessly flows across text and images by encoding images into a compact 1D token representation. Compared to prior methods, this design reduces the latent space size by 3.3x at an image resolution of 256, eliminating the need for complex conditioning mechanisms or noise scheduling. Moreover, FlowTok naturally extends to image-to-text generation under the same formulation. With its streamlined architecture centered around compact 1D tokens, FlowTok is highly memory-efficient, requires significantly fewer training resources, and achieves much faster sampling speeds-all while delivering performance comparable to state-of-the-art models. Code will be available at https://github.com/bytedance/1d-tokenizer.", "github_manual": "", "github_regex": "https://github.com/bytedance/1d-tokenizer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.05689", "title": "GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving", "authors": [ "Zebin Xing", "Xingyu Zhang", "Yang Hu", "Bo Jiang", "Tong He", "Qian Zhang", "Xiaoxiao Long", "Wei Yin" ], "github": "https://github.com/YvanYin/GoalFlow", "project_page": "", "abstract": "We propose GoalFlow, an end-to-end autonomous driving method for generating high-quality multimodal trajectories. In autonomous driving scenarios, there is rarely a single suitable trajectory. Recent methods have increasingly focused on modeling multimodal trajectory distributions. However, they suffer from trajectory selection complexity and reduced trajectory quality due to high trajectory divergence and inconsistencies between guidance and scene information. To address these issues, we introduce GoalFlow, a novel method that effectively constrains the generative process to produce high-quality, multimodal trajectories. To resolve the trajectory divergence problem inherent in diffusion-based methods, GoalFlow constrains the generated trajectories by introducing a goal point. GoalFlow establishes a novel scoring mechanism that selects the most appropriate goal point from the candidate points based on scene information. Furthermore, GoalFlow employs an efficient generative method, Flow Matching, to generate multimodal trajectories, and incorporates a refined scoring mechanism to select the optimal trajectory from the candidates. Our experimental results, validated on the NavsimDauner2024_navsim, demonstrate that GoalFlow achieves state-of-the-art performance, delivering robust multimodal trajectories for autonomous driving. GoalFlow achieved PDMS of 90.3, significantly surpassing other methods. Compared with other diffusion-policy-based methods, our approach requires only a single denoising step to obtain excellent performance. The code is available at https://github.com/YvanYin/GoalFlow.", "github_manual": "", "github_regex": "https://github.com/YvanYin/GoalFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.11224", "title": "Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models", "authors": [ "Xingtai Lv", "Youbang Sun", "Kaiyan Zhang", "Shang Qu", "Xuekai Zhu", "Yuchen Fan", "Yi Wu", "Ermo Hua", "Xinwei Long", "Ning Ding", "Bowen Zhou" ], "github": "", "project_page": "", "abstract": "State Space Models (SSMs) have emerged as a promising alternative to the popular transformer-based models and have been increasingly gaining attention. Compared to transformers, SSMs excel at tasks with sequential data or longer contexts, demonstrating comparable performances with significant efficiency gains. In this survey, we provide a coherent and systematic overview for SSMs, including their theoretical motivations, mathematical formulations, comparison with existing model classes, and various applications. We divide the SSM series into three main sections, providing a detailed introduction to the original SSM, the structured SSM represented by S4, and the selective SSM typified by Mamba. We put an emphasis on technicality, and highlight the various key techniques introduced to address the effectiveness and efficiency of SSMs. We hope this manuscript serves as an introduction for researchers to explore the theoretical foundations of SSMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.11646", "title": "Adversarial Data Collection: Human-Collaborative Perturbations for Efficient and Robust Robotic Imitation Learning", "authors": [ "Siyuan Huang", "Yue Liao", "Siyuan Feng", "Shu Jiang", "Si Liu", "Hongsheng Li", "Maoqing Yao", "Guanghui Ren" ], "github": "", "project_page": "", "abstract": "The pursuit of data efficiency, where quality outweighs quantity, has emerged as a cornerstone in robotic manipulation, especially given the high costs associated with real-world data collection. We propose that maximizing the informational density of individual demonstrations can dramatically reduce reliance on large-scale datasets while improving task performance. To this end, we introduce Adversarial Data Collection, a Human-in-the-Loop (HiL) framework that redefines robotic data acquisition through real-time, bidirectional human-environment interactions. Unlike conventional pipelines that passively record static demonstrations, ADC adopts a collaborative perturbation paradigm: during a single episode, an adversarial operator dynamically alters object states, environmental conditions, and linguistic commands, while the tele-operator adaptively adjusts actions to overcome these evolving challenges. This process compresses diverse failure-recovery behaviors, compositional task variations, and environmental perturbations into minimal demonstrations. Our experiments demonstrate that ADC-trained models achieve superior compositional generalization to unseen task instructions, enhanced robustness to perceptual perturbations, and emergent error recovery capabilities. Strikingly, models trained with merely 20% of the demonstration volume collected through ADC significantly outperform traditional approaches using full datasets. These advances bridge the gap between data-centric learning paradigms and practical robotic deployment, demonstrating that strategic data acquisition, not merely post-hoc processing, is critical for scalable, real-world robot learning. Additionally, we are curating a large-scale ADC-Robotics dataset comprising real-world manipulation tasks with adversarial perturbations. This benchmark will be open-sourced to facilitate advancements in robotic imitation learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.10970", "title": "TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools", "authors": [ "Shanghua Gao", "Richard Zhu", "Zhenglun Kong", "Ayush Noori", "Xiaorui Su", "Curtis Ginder", "Theodoros Tsiligkaridis", "Marinka Zitnik" ], "github": "", "project_page": "", "abstract": "Precision therapeutics require multimodal adaptive models that generate personalized treatment recommendations. We introduce TxAgent, an AI agent that leverages multi-step reasoning and real-time biomedical knowledge retrieval across a toolbox of 211 tools to analyze drug interactions, contraindications, and patient-specific treatment strategies. TxAgent evaluates how drugs interact at molecular, pharmacokinetic, and clinical levels, identifies contraindications based on patient comorbidities and concurrent medications, and tailors treatment strategies to individual patient characteristics. It retrieves and synthesizes evidence from multiple biomedical sources, assesses interactions between drugs and patient conditions, and refines treatment recommendations through iterative reasoning. It selects tools based on task objectives and executes structured function calls to solve therapeutic tasks that require clinical reasoning and cross-source validation. The ToolUniverse consolidates 211 tools from trusted sources, including all US FDA-approved drugs since 1939 and validated clinical insights from Open Targets. TxAgent outperforms leading LLMs, tool-use models, and reasoning agents across five new benchmarks: DrugPC, BrandPC, GenericPC, TreatmentPC, and DescriptionPC, covering 3,168 drug reasoning tasks and 456 personalized treatment scenarios. It achieves 92.1% accuracy in open-ended drug reasoning tasks, surpassing GPT-4o and outperforming DeepSeek-R1 (671B) in structured multi-step reasoning. TxAgent generalizes across drug name variants and descriptions. By integrating multi-step inference, real-time knowledge grounding, and tool-assisted decision-making, TxAgent ensures that treatment recommendations align with established clinical guidelines and real-world evidence, reducing the risk of adverse events and improving therapeutic decision-making.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.06674", "title": "Learning Few-Step Diffusion Models by Trajectory Distribution Matching", "authors": [ "Yihong Luo", "Tianyang Hu", "Jiacheng Sun", "Yujun Cai", "Jing Tang" ], "github": "", "project_page": "", "abstract": "Accelerating diffusion model sampling is crucial for efficient AIGC deployment. While diffusion distillation methods -- based on distribution matching and trajectory matching -- reduce sampling to as few as one step, they fall short on complex tasks like text-to-image generation. Few-step generation offers a better balance between speed and quality, but existing approaches face a persistent trade-off: distribution matching lacks flexibility for multi-step sampling, while trajectory matching often yields suboptimal image quality. To bridge this gap, we propose learning few-step diffusion models by Trajectory Distribution Matching (TDM), a unified distillation paradigm that combines the strengths of distribution and trajectory matching. Our method introduces a data-free score distillation objective, aligning the student's trajectory with the teacher's at the distribution level. Further, we develop a sampling-steps-aware objective that decouples learning targets across different steps, enabling more adjustable sampling. This approach supports both deterministic sampling for superior image quality and flexible multi-step adaptation, achieving state-of-the-art performance with remarkable efficiency. Our model, TDM, outperforms existing methods on various backbones, such as SDXL and PixArt-alpha, delivering superior quality and significantly reduced training costs. In particular, our method distills PixArt-alpha into a 4-step generator that outperforms its teacher on real user preference at 1024 resolution. This is accomplished with 500 iterations and 2 A800 hours -- a mere 0.01% of the teacher's training cost. In addition, our proposed TDM can be extended to accelerate text-to-video diffusion. Notably, TDM can outperform its teacher model (CogVideoX-2B) by using only 4 NFE on VBench, improving the total score from 80.91 to 81.65. Project page: https://tdm-t2x.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.10781", "title": "Large-scale Pre-training for Grounded Video Caption Generation", "authors": [ "Evangelos Kazakos", "Cordelia Schmid", "Josef Sivic" ], "github": "", "project_page": "", "abstract": "We propose a novel approach for captioning and object grounding in video, where the objects in the caption are grounded in the video via temporally dense bounding boxes. We introduce the following contributions. First, we present a large-scale automatic annotation method that aggregates captions grounded with bounding boxes across individual frames into temporally dense and consistent bounding box annotations. We apply this approach on the HowTo100M dataset to construct a large-scale pre-training dataset, named HowToGround1M. We also introduce a Grounded Video Caption Generation model, dubbed GROVE, and pre-train the model on HowToGround1M. Second, we introduce a new dataset, called iGround, of 3500 videos with manually annotated captions and dense spatio-temporally grounded bounding boxes. This allows us to measure progress on this challenging problem, as well as to fine-tune our model on this small-scale but high-quality data. Third, we demonstrate that our approach achieves state-of-the-art results on the proposed iGround dataset compared to a number of baselines, as well as on the VidSTG and ActivityNet-Entities datasets. We perform extensive ablations that demonstrate the importance of pre-training using our automatically annotated HowToGround1M dataset followed by fine-tuning on the manually annotated iGround dataset and validate the key technical contributions of our model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.10696", "title": "Neighboring Autoregressive Modeling for Efficient Visual Generation", "authors": [ "Yefei He", "Yuanyu He", "Shaoxuan He", "Feng Chen", "Hong Zhou", "Kaipeng Zhang", "Bohan Zhuang" ], "github": "https://github.com/ThisisBillhe/NAR", "project_page": "", "abstract": "Visual autoregressive models typically adhere to a raster-order ``next-token prediction\" paradigm, which overlooks the spatial and temporal locality inherent in visual content. Specifically, visual tokens exhibit significantly stronger correlations with their spatially or temporally adjacent tokens compared to those that are distant. In this paper, we propose Neighboring Autoregressive Modeling (NAR), a novel paradigm that formulates autoregressive visual generation as a progressive outpainting procedure, following a near-to-far ``next-neighbor prediction\" mechanism. Starting from an initial token, the remaining tokens are decoded in ascending order of their Manhattan distance from the initial token in the spatial-temporal space, progressively expanding the boundary of the decoded region. To enable parallel prediction of multiple adjacent tokens in the spatial-temporal space, we introduce a set of dimension-oriented decoding heads, each predicting the next token along a mutually orthogonal dimension. During inference, all tokens adjacent to the decoded tokens are processed in parallel, substantially reducing the model forward steps for generation. Experiments on ImageNet256times 256 and UCF101 demonstrate that NAR achieves 2.4times and 8.6times higher throughput respectively, while obtaining superior FID/FVD scores for both image and video generation tasks compared to the PAR-4X approach. When evaluating on text-to-image generation benchmark GenEval, NAR with 0.8B parameters outperforms Chameleon-7B while using merely 0.4 of the training data. Code is available at https://github.com/ThisisBillhe/NAR.", "github_manual": "", "github_regex": "https://github.com/ThisisBillhe/NAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.06553", "title": "ProJudge: A Multi-Modal Multi-Discipline Benchmark and Instruction-Tuning Dataset for MLLM-based Process Judges", "authors": [ "Jiaxin Ai", "Pengfei Zhou", "Zhaopan Xu", "Ming Li", "Fanrui Zhang", "Zizhen Li", "Jianwen Sun", "Yukang Feng", "Baojin Huang", "Zhongyuan Wang", "Kaipeng Zhang" ], "github": "", "project_page": "", "abstract": "As multi-modal large language models (MLLMs) frequently exhibit errors when solving scientific problems, evaluating the validity of their reasoning processes is critical for ensuring reliability and uncovering fine-grained model weaknesses. Since human evaluation is laborious and costly, prompting MLLMs as automated process judges has become a common practice. However, the reliability of these model-based judges remains uncertain. To address this, we introduce ProJudgeBench, the first comprehensive benchmark specifically designed for evaluating abilities of MLLM-based process judges. ProJudgeBench comprises 2,400 test cases and 50,118 step-level labels, spanning four scientific disciplines with diverse difficulty levels and multi-modal content. In ProJudgeBench, each step is meticulously annotated by human experts for correctness, error type, and explanation, enabling a systematic evaluation of judges' capabilities to detect, classify and diagnose errors. Evaluation on ProJudgeBench reveals a significant performance gap between open-source and proprietary models. To bridge this gap, we further propose ProJudge-173k, a large-scale instruction-tuning dataset, and a Dynamic Dual-Phase fine-tuning strategy that encourages models to explicitly reason through problem-solving before assessing solutions. Both contributions significantly enhance the process evaluation capabilities of open-source models. All the resources will be released to foster future research of reliable multi-modal process evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.06542", "title": "ARMOR v0.1: Empowering Autoregressive Multimodal Understanding Model with Interleaved Multimodal Generation via Asymmetric Synergy", "authors": [ "Jianwen Sun", "Yukang Feng", "Chuanhao Li", "Fanrui Zhang", "Zizhen Li", "Jiaxin Ai", "Sizhuo Zhou", "Yu Dai", "Shenglin Zhang", "Kaipeng Zhang" ], "github": "https://github.com/finyorko/armor", "project_page": "", "abstract": "Unified models (UniMs) for multimodal understanding and generation have recently received much attention in the area of vision and language. Existing UniMs are designed to simultaneously learn both multimodal understanding and generation capabilities, demanding substantial computational resources, and often struggle to generate interleaved text-image. We present ARMOR, a resource-efficient and pure autoregressive framework that achieves both understanding and generation by fine-tuning existing multimodal large language models (MLLMs). Specifically, ARMOR extends existing MLLMs from three perspectives: (1) For model architecture, an asymmetric encoder-decoder architecture with a forward-switching mechanism is introduced to unify embedding space integrating textual and visual modalities for enabling natural text-image interleaved generation with minimal computational overhead. (2) For training data, a meticulously curated, high-quality interleaved dataset is collected for fine-tuning MLLMs. (3) For the training algorithm, we propose a ``what or how to generate\" algorithm to empower existing MLLMs with multimodal generation capabilities while preserving their multimodal understanding capabilities, through three progressive training stages based on the collected dataset. Experimental results demonstrate that ARMOR upgrades existing MLLMs to UniMs with promising image generation capabilities, using limited training resources. Our code will be released soon at https://armor.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/finyorko/armor", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.10624", "title": "ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness", "authors": [ "Boqian Li", "Haiwen Feng", "Zeyu Cai", "Michael J. Black", "Yuliang Xiu" ], "github": "", "project_page": "", "abstract": "Fitting a body to a 3D clothed human point cloud is a common yet challenging task. Traditional optimization-based approaches use multi-stage pipelines that are sensitive to pose initialization, while recent learning-based methods often struggle with generalization across diverse poses and garment types. We propose Equivariant Tightness Fitting for Clothed Humans, or ETCH, a novel pipeline that estimates cloth-to-body surface mapping through locally approximate SE(3) equivariance, encoding tightness as displacement vectors from the cloth surface to the underlying body. Following this mapping, pose-invariant body features regress sparse body markers, simplifying clothed human fitting into an inner-body marker fitting task. Extensive experiments on CAPE and 4D-Dress show that ETCH significantly outperforms state-of-the-art methods -- both tightness-agnostic and tightness-aware -- in body fitting accuracy on loose clothing (16.7% ~ 69.5%) and shape accuracy (average 49.9%). Our equivariant tightness design can even reduce directional errors by (67.2% ~ 89.8%) in one-shot (or out-of-distribution) settings. Qualitative results demonstrate strong generalization of ETCH, regardless of challenging poses, unseen shapes, loose clothing, and non-rigid dynamics. We will release the code and models soon for research purposes at https://boqian-li.github.io/ETCH/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.08111", "title": "MaRI: Material Retrieval Integration across Domains", "authors": [ "Jianhui Wang", "Zhifei Yang", "Yangfan He", "Huixiong Zhang", "Yuxuan Chen", "Jingwei Huang" ], "github": "", "project_page": "", "abstract": "Accurate material retrieval is critical for creating realistic 3D assets. Existing methods rely on datasets that capture shape-invariant and lighting-varied representations of materials, which are scarce and face challenges due to limited diversity and inadequate real-world generalization. Most current approaches adopt traditional image search techniques. They fall short in capturing the unique properties of material spaces, leading to suboptimal performance in retrieval tasks. Addressing these challenges, we introduce MaRI, a framework designed to bridge the feature space gap between synthetic and real-world materials. MaRI constructs a shared embedding space that harmonizes visual and material attributes through a contrastive learning strategy by jointly training an image and a material encoder, bringing similar materials and images closer while separating dissimilar pairs within the feature space. To support this, we construct a comprehensive dataset comprising high-quality synthetic materials rendered with controlled shape variations and diverse lighting conditions, along with real-world materials processed and standardized using material transfer techniques. Extensive experiments demonstrate the superior performance, accuracy, and generalization capabilities of MaRI across diverse and complex material retrieval tasks, outperforming existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.10684", "title": "Open-World Skill Discovery from Unsegmented Demonstrations", "authors": [ "Jingwen Deng", "Zihao Wang", "Shaofei Cai", "Anji Liu", "Yitao Liang" ], "github": "", "project_page": "", "abstract": "Learning skills in open-world environments is essential for developing agents capable of handling a variety of tasks by combining basic skills. Online demonstration videos are typically long but unsegmented, making them difficult to segment and label with skill identifiers. Unlike existing methods that rely on sequence sampling or human labeling, we have developed a self-supervised learning-based approach to segment these long videos into a series of semantic-aware and skill-consistent segments. Drawing inspiration from human cognitive event segmentation theory, we introduce Skill Boundary Detection (SBD), an annotation-free temporal video segmentation algorithm. SBD detects skill boundaries in a video by leveraging prediction errors from a pretrained unconditional action-prediction model. This approach is based on the assumption that a significant increase in prediction error indicates a shift in the skill being executed. We evaluated our method in Minecraft, a rich open-world simulator with extensive gameplay videos available online. Our SBD-generated segments improved the average performance of conditioned policies by 63.7% and 52.1% on short-term atomic skill tasks, and their corresponding hierarchical agents by 11.3% and 20.8% on long-horizon tasks. Our method can leverage the diverse YouTube videos to train instruction-following agents. The project page can be found in https://craftjarvis.github.io/SkillDiscovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.10620", "title": "From TOWER to SPIRE: Adding the Speech Modality to a Text-Only LLM", "authors": [ "Kshitij Ambilduke", "Ben Peters", "Sonal Sannigrahi", "Anil Keshwani", "Tsz Kin Lam", "Bruno Martins", "Marcely Zanon Boito", "Andr\u00e9 F. T. Martins" ], "github": "https://github.com/utter-project/SpireLM", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable performance and generalization capabilities across multiple languages and tasks, making them very attractive targets for multi-modality integration (e.g., images or speech). In this work, we extend an existing LLM to the speech modality via speech discretization and continued pre-training. In particular, we are interested in multilingual LLMs, such as TOWER, as their pre-training setting allows us to treat discretized speech input as an additional translation language. The resulting open-source model, SPIRE, is able to transcribe and translate English speech input while maintaining TOWER's original performance on translation-related tasks, showcasing that discretized speech input integration as an additional language is feasible during LLM adaptation. We make our code and models available to the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/utter-project/SpireLM", "github_api_added_by": "auto" }, { "date": "2025-03-17", "arxiv_id": "2503.09330", "title": "Group-robust Machine Unlearning", "authors": [ "Thomas De Min", "Subhankar Roy", "St\u00e9phane Lathuili\u00e8re", "Elisa Ricci", "Massimiliano Mancini" ], "github": "https://github.com/tdemin16/group-robust_machine_unlearning", "project_page": "", "abstract": "Machine unlearning is an emerging paradigm to remove the influence of specific training data (i.e., the forget set) from a model while preserving its knowledge of the rest of the data (i.e., the retain set). Previous approaches assume the forget data to be uniformly distributed from all training datapoints. However, if the data to unlearn is dominant in one group, we empirically show that performance for this group degrades, leading to fairness issues. This work tackles the overlooked problem of non-uniformly distributed forget sets, which we call group-robust machine unlearning, by presenting a simple, effective strategy that mitigates the performance loss in dominant groups via sample distribution reweighting. Moreover, we present MIU (Mutual Information-aware Machine Unlearning), the first approach for group robustness in approximate machine unlearning. MIU minimizes the mutual information between model features and group information, achieving unlearning while reducing performance degradation in the dominant group of the forget set. Additionally, MIU exploits sample distribution reweighting and mutual information calibration with the original model to preserve group robustness. We conduct experiments on three datasets and show that MIU outperforms standard methods, achieving unlearning without compromising model robustness. Source code available at https://github.com/tdemin16/group-robust_machine_unlearning.", "github_manual": "", "github_regex": "https://github.com/tdemin16/group-robust_machine_unlearning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.11651", "title": "VGGT: Visual Geometry Grounded Transformer", "authors": [ "Jianyuan Wang", "Minghao Chen", "Nikita Karaev", "Andrea Vedaldi", "Christian Rupprecht", "David Novotny" ], "github": "https://github.com/facebookresearch/vggt", "project_page": "", "abstract": "We present VGGT, a feed-forward neural network that directly infers all key 3D attributes of a scene, including camera parameters, point maps, depth maps, and 3D point tracks, from one, a few, or hundreds of its views. This approach is a step forward in 3D computer vision, where models have typically been constrained to and specialized for single tasks. It is also simple and efficient, reconstructing images in under one second, and still outperforming alternatives that require post-processing with visual geometry optimization techniques. The network achieves state-of-the-art results in multiple 3D tasks, including camera parameter estimation, multi-view depth estimation, dense point cloud reconstruction, and 3D point tracking. We also show that using pretrained VGGT as a feature backbone significantly enhances downstream tasks, such as non-rigid point tracking and feed-forward novel view synthesis. Code and models are publicly available at https://github.com/facebookresearch/vggt.", "github_manual": "", "github_regex": "https://github.com/facebookresearch/vggt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.11629", "title": "TreeMeshGPT: Artistic Mesh Generation with Autoregressive Tree Sequencing", "authors": [ "Stefan Lionar", "Jiabin Liang", "Gim Hee Lee" ], "github": "https://github.com/sail-sg/treemeshgpt", "project_page": "", "abstract": "We introduce TreeMeshGPT, an autoregressive Transformer designed to generate high-quality artistic meshes aligned with input point clouds. Instead of the conventional next-token prediction in autoregressive Transformer, we propose a novel Autoregressive Tree Sequencing where the next input token is retrieved from a dynamically growing tree structure that is built upon the triangle adjacency of faces within the mesh. Our sequencing enables the mesh to extend locally from the last generated triangular face at each step, and therefore reduces training difficulty and improves mesh quality. Our approach represents each triangular face with two tokens, achieving a compression rate of approximately 22% compared to the naive face tokenization. This efficient tokenization enables our model to generate highly detailed artistic meshes with strong point cloud conditioning, surpassing previous methods in both capacity and fidelity. Furthermore, our method generates mesh with strong normal orientation constraints, minimizing flipped normals commonly encountered in previous methods. Our experiments show that TreeMeshGPT enhances the mesh generation quality with refined details and normal orientation consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sail-sg/treemeshgpt", "github_api_added_by": "auto" }, { "date": "2025-03-17", "arxiv_id": "2503.11579", "title": "Vamba: Understanding Hour-Long Videos with Hybrid Mamba-Transformers", "authors": [ "Weiming Ren", "Wentao Ma", "Huan Yang", "Cong Wei", "Ge Zhang", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "State-of-the-art transformer-based large multimodal models (LMMs) struggle to handle hour-long video inputs due to the quadratic complexity of the causal self-attention operations, leading to high computational costs during training and inference. Existing token compression-based methods reduce the number of video tokens but often incur information loss and remain inefficient for extremely long sequences. In this paper, we explore an orthogonal direction to build a hybrid Mamba-Transformer model (VAMBA) that employs Mamba-2 blocks to encode video tokens with linear complexity. Without any token reduction, VAMBA can encode more than 1024 frames (640times360) on a single GPU, while transformer-based models can only encode 256 frames. On long video input, VAMBA achieves at least 50% reduction in GPU memory usage during training and inference, and nearly doubles the speed per training step compared to transformer-based LMMs. Our experimental results demonstrate that VAMBA improves accuracy by 4.3% on the challenging hour-long video understanding benchmark LVBench over prior efficient video LMMs, and maintains strong performance on a broad spectrum of long and short video understanding tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.11576", "title": "SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion", "authors": [ "Ahmed Nassar", "Andres Marafioti", "Matteo Omenetti", "Maksym Lysak", "Nikolaos Livathinos", "Christoph Auer", "Lucas Morin", "Rafael Teixeira de Lima", "Yusik Kim", "A. Said Gurbuz", "Michele Dolfi", "Miquel Farr\u00e9", "Peter W. J. Staar" ], "github": "", "project_page": "", "abstract": "We introduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion. Our model comprehensively processes entire pages by generating DocTags, a new universal markup format that captures all page elements in their full context with location. Unlike existing approaches that rely on large foundational models, or ensemble solutions that rely on handcrafted pipelines of multiple specialized models, SmolDocling offers an end-to-end conversion for accurately capturing content, structure and spatial location of document elements in a 256M parameters vision-language model. SmolDocling exhibits robust performance in correctly reproducing document features such as code listings, tables, equations, charts, lists, and more across a diverse range of document types including business documents, academic papers, technical reports, patents, and forms -- significantly extending beyond the commonly observed focus on scientific papers. Additionally, we contribute novel publicly sourced datasets for charts, tables, equations, and code recognition. Experimental results demonstrate that SmolDocling competes with other Vision Language Models that are up to 27 times larger in size, while reducing computational requirements substantially. The model is currently available, datasets will be publicly available soon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.11207", "title": "Can Large Reasoning Models do Analogical Reasoning under Perceptual Uncertainty?", "authors": [ "Giacomo Camposampiero", "Michael Hersche", "Roger Wattenhofer", "Abu Sebastian", "Abbas Rahimi" ], "github": "https://github.com/IBM/raven-large-language-models", "project_page": "", "abstract": "This work presents a first evaluation of two state-of-the-art Large Reasoning Models (LRMs), OpenAI's o3-mini and DeepSeek R1, on analogical reasoning, focusing on well-established nonverbal human IQ tests based on Raven's progressive matrices. We benchmark with the I-RAVEN dataset and its more difficult extension, I-RAVEN-X, which tests the ability to generalize to longer reasoning rules and ranges of the attribute values. To assess the influence of visual uncertainties on these nonverbal analogical reasoning tests, we extend the I-RAVEN-X dataset, which otherwise assumes an oracle perception. We adopt a two-fold strategy to simulate this imperfect visual perception: 1) we introduce confounding attributes which, being sampled at random, do not contribute to the prediction of the correct answer of the puzzles and 2) smoothen the distributions of the input attributes' values. We observe a sharp decline in OpenAI's o3-mini task accuracy, dropping from 86.6% on the original I-RAVEN to just 17.0% -- approaching random chance -- on the more challenging I-RAVEN-X, which increases input length and range and emulates perceptual uncertainty. This drop occurred despite spending 3.4x more reasoning tokens. A similar trend is also observed for DeepSeek R1: from 80.6% to 23.2%. On the other hand, a neuro-symbolic probabilistic abductive model, ARLC, that achieves state-of-the-art performances on I-RAVEN, can robustly reason under all these out-of-distribution tests, maintaining strong accuracy with only a modest reduction from 98.6% to 88.0%. Our code is available at https://github.com/IBM/raven-large-language-models.", "github_manual": "", "github_regex": "https://github.com/IBM/raven-large-language-models", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-17", "arxiv_id": "2503.11958", "title": "CHOrD: Generation of Collision-Free, House-Scale, and Organized Digital Twins for 3D Indoor Scenes with Controllable Floor Plans and Optimal Layouts", "authors": [ "Chong Su", "Yingbin Fu", "Zheyuan Hu", "Jing Yang", "Param Hanji", "Shaojun Wang", "Xuan Zhao", "Cengiz \u00d6ztireli", "Fangcheng Zhong" ], "github": "", "project_page": "", "abstract": "We introduce CHOrD, a novel framework for scalable synthesis of 3D indoor scenes, designed to create house-scale, collision-free, and hierarchically structured indoor digital twins. In contrast to existing methods that directly synthesize the scene layout as a scene graph or object list, CHOrD incorporates a 2D image-based intermediate layout representation, enabling effective prevention of collision artifacts by successfully capturing them as out-of-distribution (OOD) scenarios during generation. Furthermore, unlike existing methods, CHOrD is capable of generating scene layouts that adhere to complex floor plans with multi-modal controls, enabling the creation of coherent, house-wide layouts robust to both geometric and semantic variations in room structures. Additionally, we propose a novel dataset with expanded coverage of household items and room configurations, as well as significantly improved data quality. CHOrD demonstrates state-of-the-art performance on both the 3D-FRONT and our proposed datasets, delivering photorealistic, spatially coherent indoor scene synthesis adaptable to arbitrary floor plan variations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.13399", "title": "MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research", "authors": [ "James Burgess", "Jeffrey J Nirschl", "Laura Bravo-S\u00e1nchez", "Alejandro Lozano", "Sanket Rajan Gupte", "Jesus G. Galaz-Montoya", "Yuhui Zhang", "Yuchang Su", "Disha Bhowmik", "Zachary Coman", "Sarina M. Hasan", "Alexandra Johannesson", "William D. Leineweber", "Malvika G Nair", "Ridhi Yarlagadda", "Connor Zuraski", "Wah Chiu", "Sarah Cohen", "Jan N. Hansen", "Manuel D Leonetti", "Chad Liu", "Emma Lundberg", "Serena Yeung-Levy" ], "github": "", "project_page": "", "abstract": "Scientific research demands sophisticated reasoning over multimodal data, a challenge especially prevalent in biology. Despite recent advances in multimodal large language models (MLLMs) for AI-assisted research, existing multimodal reasoning benchmarks only target up to college-level difficulty, while research-level benchmarks emphasize lower-level perception, falling short of the complex multimodal reasoning needed for scientific discovery. To bridge this gap, we introduce MicroVQA, a visual-question answering (VQA) benchmark designed to assess three reasoning capabilities vital in research workflows: expert image understanding, hypothesis generation, and experiment proposal. MicroVQA consists of 1,042 multiple-choice questions (MCQs) curated by biology experts across diverse microscopy modalities, ensuring VQA samples represent real scientific practice. In constructing the benchmark, we find that standard MCQ generation methods induce language shortcuts, motivating a new two-stage pipeline: an optimized LLM prompt structures question-answer pairs into MCQs; then, an agent-based `RefineBot' updates them to remove shortcuts. Benchmarking on state-of-the-art MLLMs reveal a peak performance of 53\\%; models with smaller LLMs only slightly underperform top models, suggesting that language-based reasoning is less challenging than multimodal reasoning; and tuning with scientific articles enhances performance. Expert analysis of chain-of-thought responses shows that perception errors are the most frequent, followed by knowledge errors and then overgeneralization errors. These insights highlight the challenges in multimodal scientific reasoning, showing MicroVQA is a valuable resource advancing AI-driven biomedical research. MicroVQA is available at https://huggingface.co/datasets/jmhb/microvqa, and project page at https://jmhb0.github.io/microvqa.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.12590", "title": "Personalize Anything for Free with Diffusion Transformer", "authors": [ "Haoran Feng", "Zehuan Huang", "Lin Li", "Hairong Lv", "Lu Sheng" ], "github": "", "project_page": "", "abstract": "Personalized image generation aims to produce images of user-specified concepts while enabling flexible editing. Recent training-free approaches, while exhibit higher computational efficiency than training-based methods, struggle with identity preservation, applicability, and compatibility with diffusion transformers (DiTs). In this paper, we uncover the untapped potential of DiT, where simply replacing denoising tokens with those of a reference subject achieves zero-shot subject reconstruction. This simple yet effective feature injection technique unlocks diverse scenarios, from personalization to image editing. Building upon this observation, we propose Personalize Anything, a training-free framework that achieves personalized image generation in DiT through: 1) timestep-adaptive token replacement that enforces subject consistency via early-stage injection and enhances flexibility through late-stage regularization, and 2) patch perturbation strategies to boost structural diversity. Our method seamlessly supports layout-guided generation, multi-subject personalization, and mask-controlled editing. Evaluations demonstrate state-of-the-art performance in identity preservation and versatility. Our work establishes new insights into DiTs while delivering a practical paradigm for efficient personalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.11495", "title": "V-STaR: Benchmarking Video-LLMs on Video Spatio-Temporal Reasoning", "authors": [ "Zixu Cheng", "Jian Hu", "Ziquan Liu", "Chenyang Si", "Wei Li", "Shaogang Gong" ], "github": "", "project_page": "", "abstract": "Human processes video reasoning in a sequential spatio-temporal reasoning logic, we first identify the relevant frames (\"when\") and then analyse the spatial relationships (\"where\") between key objects, and finally leverage these relationships to draw inferences (\"what\"). However, can Video Large Language Models (Video-LLMs) also \"reason through a sequential spatio-temporal logic\" in videos? Existing Video-LLM benchmarks primarily focus on assessing object presence, neglecting relational reasoning. Consequently, it is difficult to measure whether a model truly comprehends object interactions (actions/events) in videos or merely relies on pre-trained \"memory\" of co-occurrences as biases in generating answers. In this work, we introduce a Video Spatio-Temporal Reasoning (V-STaR) benchmark to address these shortcomings. The key idea is to decompose video understanding into a Reverse Spatio-Temporal Reasoning (RSTR) task that simultaneously evaluates what objects are present, when events occur, and where they are located while capturing the underlying Chain-of-thought (CoT) logic. To support this evaluation, we construct a dataset to elicit the spatial-temporal reasoning process of Video-LLMs. It contains coarse-to-fine CoT questions generated by a semi-automated GPT-4-powered pipeline, embedding explicit reasoning chains to mimic human cognition. Experiments from 14 Video-LLMs on our V-STaR reveal significant gaps between current Video-LLMs and the needs for robust and consistent spatio-temporal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.13327", "title": "Edit Transfer: Learning Image Editing via Vision In-Context Relations", "authors": [ "Lan Chen", "Qi Mao", "Yuchao Gu", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "We introduce a new setting, Edit Transfer, where a model learns a transformation from just a single source-target example and applies it to a new query image. While text-based methods excel at semantic manipulations through textual prompts, they often struggle with precise geometric details (e.g., poses and viewpoint changes). Reference-based editing, on the other hand, typically focuses on style or appearance and fails at non-rigid transformations. By explicitly learning the editing transformation from a source-target pair, Edit Transfer mitigates the limitations of both text-only and appearance-centric references. Drawing inspiration from in-context learning in large language models, we propose a visual relation in-context learning paradigm, building upon a DiT-based text-to-image model. We arrange the edited example and the query image into a unified four-panel composite, then apply lightweight LoRA fine-tuning to capture complex spatial transformations from minimal examples. Despite using only 42 training samples, Edit Transfer substantially outperforms state-of-the-art TIE and RIE methods on diverse non-rigid scenarios, demonstrating the effectiveness of few-shot visual relation learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.12885", "title": "DreamRenderer: Taming Multi-Instance Attribute Control in Large-Scale Text-to-Image Models", "authors": [ "Dewei Zhou", "Mingwei Li", "Zongxin Yang", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Image-conditioned generation methods, such as depth- and canny-conditioned approaches, have demonstrated remarkable abilities for precise image synthesis. However, existing models still struggle to accurately control the content of multiple instances (or regions). Even state-of-the-art models like FLUX and 3DIS face challenges, such as attribute leakage between instances, which limits user control. To address these issues, we introduce DreamRenderer, a training-free approach built upon the FLUX model. DreamRenderer enables users to control the content of each instance via bounding boxes or masks, while ensuring overall visual harmony. We propose two key innovations: 1) Bridge Image Tokens for Hard Text Attribute Binding, which uses replicated image tokens as bridge tokens to ensure that T5 text embeddings, pre-trained solely on text data, bind the correct visual attributes for each instance during Joint Attention; 2) Hard Image Attribute Binding applied only to vital layers. Through our analysis of FLUX, we identify the critical layers responsible for instance attribute rendering and apply Hard Image Attribute Binding only in these layers, using soft binding in the others. This approach ensures precise control while preserving image quality. Evaluations on the COCO-POS and COCO-MIG benchmarks demonstrate that DreamRenderer improves the Image Success Ratio by 17.7% over FLUX and enhances the performance of layout-to-image models like GLIGEN and 3DIS by up to 26.8%. Project Page: https://limuloo.github.io/DreamRenderer/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.12533", "title": "Being-0: A Humanoid Robotic Agent with Vision-Language Models and Modular Skills", "authors": [ "Haoqi Yuan", "Yu Bai", "Yuhui Fu", "Bohan Zhou", "Yicheng Feng", "Xinrun Xu", "Yi Zhan", "B\u00f6rje F. Karlsson", "Zongqing Lu" ], "github": "https://github.com/BeingBeyond/being-0", "project_page": "", "abstract": "Building autonomous robotic agents capable of achieving human-level performance in real-world embodied tasks is an ultimate goal in humanoid robot research. Recent advances have made significant progress in high-level cognition with Foundation Models (FMs) and low-level skill development for humanoid robots. However, directly combining these components often results in poor robustness and efficiency due to compounding errors in long-horizon tasks and the varied latency of different modules. We introduce Being-0, a hierarchical agent framework that integrates an FM with a modular skill library. The FM handles high-level cognitive tasks such as instruction understanding, task planning, and reasoning, while the skill library provides stable locomotion and dexterous manipulation for low-level control. To bridge the gap between these levels, we propose a novel Connector module, powered by a lightweight vision-language model (VLM). The Connector enhances the FM's embodied capabilities by translating language-based plans into actionable skill commands and dynamically coordinating locomotion and manipulation to improve task success. With all components, except the FM, deployable on low-cost onboard computation devices, Being-0 achieves efficient, real-time performance on a full-sized humanoid robot equipped with dexterous hands and active vision. Extensive experiments in large indoor environments demonstrate Being-0's effectiveness in solving complex, long-horizon tasks that require challenging navigation and manipulation subtasks. For further details and videos, visit https://beingbeyond.github.io/being-0.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/BeingBeyond/being-0", "github_api_added_by": "auto" }, { "date": "2025-03-18", "arxiv_id": "2503.11751", "title": "reWordBench: Benchmarking and Improving the Robustness of Reward Models with Transformed Inputs", "authors": [ "Zhaofeng Wu", "Michihiro Yasunaga", "Andrew Cohen", "Yoon Kim", "Asli Celikyilmaz", "Marjan Ghazvininejad" ], "github": "", "project_page": "", "abstract": "Reward models have become a staple in modern NLP, serving as not only a scalable text evaluator, but also an indispensable component in many alignment recipes and inference-time algorithms. However, while recent reward models increase performance on standard benchmarks, this may partly be due to overfitting effects, which would confound an understanding of their true capability. In this work, we scrutinize the robustness of reward models and the extent of such overfitting. We build **reWordBench**, which systematically transforms reward model inputs in meaning- or ranking-preserving ways. We show that state-of-the-art reward models suffer from substantial performance degradation even with minor input transformations, sometimes dropping to significantly below-random accuracy, suggesting brittleness. To improve reward model robustness, we propose to explicitly train them to assign similar scores to paraphrases, and find that this approach also improves robustness to other distinct kinds of transformations. For example, our robust reward model reduces such degradation by roughly half for the Chat Hard subset in RewardBench. Furthermore, when used in alignment, our robust reward models demonstrate better utility and lead to higher-quality outputs, winning in up to 59% of instances against a standardly trained RM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.13435", "title": "WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes", "authors": [ "Ling Yang", "Kaixin Zhu", "Juanxi Tian", "Bohan Zeng", "Mingbao Lin", "Hongjuan Pei", "Wentao Zhang", "Shuicheng Yan" ], "github": "https://github.com/Gen-Verse/WideRange4D", "project_page": "", "abstract": "With the rapid development of 3D reconstruction technology, research in 4D reconstruction is also advancing, existing 4D reconstruction methods can generate high-quality 4D scenes. However, due to the challenges in acquiring multi-view video data, the current 4D reconstruction benchmarks mainly display actions performed in place, such as dancing, within limited scenarios. In practical scenarios, many scenes involve wide-range spatial movements, highlighting the limitations of existing 4D reconstruction datasets. Additionally, existing 4D reconstruction methods rely on deformation fields to estimate the dynamics of 3D objects, but deformation fields struggle with wide-range spatial movements, which limits the ability to achieve high-quality 4D scene reconstruction with wide-range spatial movements. In this paper, we focus on 4D scene reconstruction with significant object spatial movements and propose a novel 4D reconstruction benchmark, WideRange4D. This benchmark includes rich 4D scene data with large spatial variations, allowing for a more comprehensive evaluation of the generation capabilities of 4D generation methods. Furthermore, we introduce a new 4D reconstruction method, Progress4D, which generates stable and high-quality 4D results across various complex 4D scene reconstruction tasks. We conduct both quantitative and qualitative comparison experiments on WideRange4D, showing that our Progress4D outperforms existing state-of-the-art 4D reconstruction methods. Project: https://github.com/Gen-Verse/WideRange4D", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/WideRange4D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.12937", "title": "R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization", "authors": [ "Jingyi Zhang", "Jiaxing Huang", "Huanjin Yao", "Shunyu Liu", "Xikun Zhang", "Shijian Lu", "Dacheng Tao" ], "github": "https://github.com/jingyi0000/R1-VL", "project_page": "", "abstract": "Recent studies generally enhance MLLMs' reasoning capabilities via supervised fine-tuning on high-quality chain-of-thought reasoning data, which often leads models to merely imitate successful reasoning paths without understanding what the wrong reasoning paths are. In this work, we aim to enhance the MLLMs' reasoning ability beyond passively imitating positive reasoning paths. To this end, we design Step-wise Group Relative Policy Optimization (StepGRPO), a new online reinforcement learning framework that enables MLLMs to self-improve reasoning ability via simple, effective and dense step-wise rewarding. Specifically, StepGRPO introduces two novel rule-based reasoning rewards: Step-wise Reasoning Accuracy Reward (StepRAR) and Step-wise Reasoning Validity Reward (StepRVR). StepRAR rewards the reasoning paths that contain necessary intermediate reasoning steps via a soft key-step matching technique, while StepRAR rewards reasoning paths that follow a well-structured and logically consistent reasoning process through a reasoning completeness and logic evaluation strategy. With the proposed StepGRPO, we introduce R1-VL, a series of MLLMs with outstanding capabilities in step-by-step reasoning. Extensive experiments over 8 benchmarks demonstrate the superiority of our methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jingyi0000/R1-VL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.13444", "title": "VideoMind: A Chain-of-LoRA Agent for Long Video Reasoning", "authors": [ "Ye Liu", "Kevin Qinghong Lin", "Chang Wen Chen", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Videos, with their unique temporal dimension, demand precise grounded understanding, where answers are directly linked to visual, interpretable evidence. Despite significant breakthroughs in reasoning capabilities within Large Language Models, multi-modal reasoning - especially for videos - remains unexplored. In this work, we introduce VideoMind, a novel video-language agent designed for temporal-grounded video understanding. VideoMind incorporates two key innovations: (i) We identify essential capabilities for video temporal reasoning and develop a role-based agentic workflow, including a planner for coordinating different roles, a grounder for temporal localization, a verifier to assess temporal interval accuracy, and an answerer for question-answering. (ii) To efficiently integrate these diverse roles, we propose a novel Chain-of-LoRA strategy, enabling seamless role-switching via lightweight LoRA adaptors while avoiding the overhead of multiple models, thus balancing efficiency and flexibility. Extensive experiments on 14 public benchmarks demonstrate that our agent achieves state-of-the-art performance on diverse video understanding tasks, including 3 on grounded video question-answering, 6 on video temporal grounding, and 5 on general video question-answering, underscoring its effectiveness in advancing video agent and long-form temporal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.12530", "title": "Basic Category Usage in Vision Language Models", "authors": [ "Hunter Sawyer", "Jesse Roberts", "Kyle Moore" ], "github": "", "project_page": "", "abstract": "The field of psychology has long recognized a basic level of categorization that humans use when labeling visual stimuli, a term coined by Rosch in 1976. This level of categorization has been found to be used most frequently, to have higher information density, and to aid in visual language tasks with priming in humans. Here, we investigate basic level categorization in two recently released, open-source vision-language models (VLMs). This paper demonstrates that Llama 3.2 Vision Instruct (11B) and Molmo 7B-D both prefer basic level categorization consistent with human behavior. Moreover, the models' preferences are consistent with nuanced human behaviors like the biological versus non-biological basic level effects and the well established expert basic level shift, further suggesting that VLMs acquire cognitive categorization behaviors from the human data on which they are trained.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.12528", "title": "Investigating Human-Aligned Large Language Model Uncertainty", "authors": [ "Kyle Moore", "Jesse Roberts", "Daryl Watson", "Pamela Wisniewski" ], "github": "", "project_page": "", "abstract": "Recent work has sought to quantify large language model uncertainty to facilitate model control and modulate user trust. Previous works focus on measures of uncertainty that are theoretically grounded or reflect the average overt behavior of the model. In this work, we investigate a variety of uncertainty measures, in order to identify measures that correlate with human group-level uncertainty. We find that Bayesian measures and a variation on entropy measures, top-k entropy, tend to agree with human behavior as a function of model size. We find that some strong measures decrease in human-similarity with model size, but, by multiple linear regression, we find that combining multiple uncertainty measures provide comparable human-alignment with reduced size-dependency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.11412", "title": "MTV-Inpaint: Multi-Task Long Video Inpainting", "authors": [ "Shiyuan Yang", "Zheng Gu", "Liang Hou", "Xin Tao", "Pengfei Wan", "Xiaodong Chen", "Jing Liao" ], "github": "", "project_page": "", "abstract": "Video inpainting involves modifying local regions within a video, ensuring spatial and temporal consistency. Most existing methods focus primarily on scene completion (i.e., filling missing regions) and lack the capability to insert new objects into a scene in a controllable manner. Fortunately, recent advancements in text-to-video (T2V) diffusion models pave the way for text-guided video inpainting. However, directly adapting T2V models for inpainting remains limited in unifying completion and insertion tasks, lacks input controllability, and struggles with long videos, thereby restricting their applicability and flexibility. To address these challenges, we propose MTV-Inpaint, a unified multi-task video inpainting framework capable of handling both traditional scene completion and novel object insertion tasks. To unify these distinct tasks, we design a dual-branch spatial attention mechanism in the T2V diffusion U-Net, enabling seamless integration of scene completion and object insertion within a single framework. In addition to textual guidance, MTV-Inpaint supports multimodal control by integrating various image inpainting models through our proposed image-to-video (I2V) inpainting mode. Additionally, we propose a two-stage pipeline that combines keyframe inpainting with in-between frame propagation, enabling MTV-Inpaint to effectively handle long videos with hundreds of frames. Extensive experiments demonstrate that MTV-Inpaint achieves state-of-the-art performance in both scene completion and object insertion tasks. Furthermore, it demonstrates versatility in derived applications such as multi-modal inpainting, object editing, removal, image object brush, and the ability to handle long videos. Project page: https://mtv-inpaint.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.10704", "title": "Error Analyses of Auto-Regressive Video Diffusion Models: A Unified Framework", "authors": [ "Jing Wang", "Fengzhuo Zhang", "Xiaoli Li", "Vincent Y. F. Tan", "Tianyu Pang", "Chao Du", "Aixin Sun", "Zhuoran Yang" ], "github": "https://github.com/sail-sg/Meta-ARVDM", "project_page": "", "abstract": "A variety of Auto-Regressive Video Diffusion Models (ARVDM) have achieved remarkable successes in generating realistic long-form videos. However, theoretical analyses of these models remain scant. In this work, we develop theoretical underpinnings for these models and use our insights to improve the performance of existing models. We first develop Meta-ARVDM, a unified framework of ARVDMs that subsumes most existing methods. Using Meta-ARVDM, we analyze the KL-divergence between the videos generated by Meta-ARVDM and the true videos. Our analysis uncovers two important phenomena inherent to ARVDM -- error accumulation and memory bottleneck. By deriving an information-theoretic impossibility result, we show that the memory bottleneck phenomenon cannot be avoided. To mitigate the memory bottleneck, we design various network structures to explicitly use more past frames. We also achieve a significantly improved trade-off between the mitigation of the memory bottleneck and the inference efficiency by compressing the frames. Experimental results on DMLab and Minecraft validate the efficacy of our methods. Our experiments also demonstrate a Pareto-frontier between the error accumulation and memory bottleneck across different methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sail-sg/Meta-ARVDM", "github_api_added_by": "auto" }, { "date": "2025-03-18", "arxiv_id": "2503.13070", "title": "Rewards Are Enough for Fast Photo-Realistic Text-to-image Generation", "authors": [ "Yihong Luo", "Tianyang Hu", "Weijian Luo", "Kenji Kawaguchi", "Jing Tang" ], "github": "https://github.com/Luo-Yihong/R0", "project_page": "", "abstract": "Aligning generated images to complicated text prompts and human preferences is a central challenge in Artificial Intelligence-Generated Content (AIGC). With reward-enhanced diffusion distillation emerging as a promising approach that boosts controllability and fidelity of text-to-image models, we identify a fundamental paradigm shift: as conditions become more specific and reward signals stronger, the rewards themselves become the dominant force in generation. In contrast, the diffusion losses serve as an overly expensive form of regularization. To thoroughly validate our hypothesis, we introduce R0, a novel conditional generation approach via regularized reward maximization. Instead of relying on tricky diffusion distillation losses, R0 proposes a new perspective that treats image generations as an optimization problem in data space which aims to search for valid images that have high compositional rewards. By innovative designs of the generator parameterization and proper regularization techniques, we train state-of-the-art few-step text-to-image generative models with R0 at scales. Our results challenge the conventional wisdom of diffusion post-training and conditional generation by demonstrating that rewards play a dominant role in scenarios with complex conditions. We hope our findings can contribute to further research into human-centric and reward-centric generation paradigms across the broader field of AIGC. Code is available at https://github.com/Luo-Yihong/R0.", "github_manual": "", "github_regex": "https://github.com/Luo-Yihong/R0", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.13434", "title": "BlobCtrl: A Unified and Flexible Framework for Element-level Image Generation and Editing", "authors": [ "Yaowei Li", "Lingen Li", "Zhaoyang Zhang", "Xiaoyu Li", "Guangzhi Wang", "Hongxiang Li", "Xiaodong Cun", "Ying Shan", "Yuexian Zou" ], "github": "", "project_page": "", "abstract": "Element-level visual manipulation is essential in digital content creation, but current diffusion-based methods lack the precision and flexibility of traditional tools. In this work, we introduce BlobCtrl, a framework that unifies element-level generation and editing using a probabilistic blob-based representation. By employing blobs as visual primitives, our approach effectively decouples and represents spatial location, semantic content, and identity information, enabling precise element-level manipulation. Our key contributions include: 1) a dual-branch diffusion architecture with hierarchical feature fusion for seamless foreground-background integration; 2) a self-supervised training paradigm with tailored data augmentation and score functions; and 3) controllable dropout strategies to balance fidelity and diversity. To support further research, we introduce BlobData for large-scale training and BlobBench for systematic evaluation. Experiments show that BlobCtrl excels in various element-level manipulation tasks while maintaining computational efficiency, offering a practical solution for precise and flexible visual content creation. Project page: https://liyaowei-stu.github.io/project/BlobCtrl/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.06053", "title": "DropletVideo: A Dataset and Approach to Explore Integral Spatio-Temporal Consistent Video Generation", "authors": [ "Runze Zhang", "Guoguang Du", "Xiaochuan Li", "Qi Jia", "Liang Jin", "Lu Liu", "Jingjing Wang", "Cong Xu", "Zhenhua Guo", "Yaqian Zhao", "Xiaoli Gong", "Rengang Li", "Baoyu Fan" ], "github": "https://github.com/IEIT-AGI/DropletVideo", "project_page": "", "abstract": "Spatio-temporal consistency is a critical research topic in video generation. A qualified generated video segment must ensure plot plausibility and coherence while maintaining visual consistency of objects and scenes across varying viewpoints. Prior research, especially in open-source projects, primarily focuses on either temporal or spatial consistency, or their basic combination, such as appending a description of a camera movement after a prompt without constraining the outcomes of this movement. However, camera movement may introduce new objects to the scene or eliminate existing ones, thereby overlaying and affecting the preceding narrative. Especially in videos with numerous camera movements, the interplay between multiple plots becomes increasingly complex. This paper introduces and examines integral spatio-temporal consistency, considering the synergy between plot progression and camera techniques, and the long-term impact of prior content on subsequent generation. Our research encompasses dataset construction through to the development of the model. Initially, we constructed a DropletVideo-10M dataset, which comprises 10 million videos featuring dynamic camera motion and object actions. Each video is annotated with an average caption of 206 words, detailing various camera movements and plot developments. Following this, we developed and trained the DropletVideo model, which excels in preserving spatio-temporal coherence during video generation. The DropletVideo dataset and model are accessible at https://dropletx.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/IEIT-AGI/DropletVideo", "github_api_added_by": "auto" }, { "date": "2025-03-18", "arxiv_id": "2503.13369", "title": "Sightation Counts: Leveraging Sighted User Feedback in Building a BLV-aligned Dataset of Diagram Descriptions", "authors": [ "Wan Ju Kang", "Eunki Kim", "Na Min An", "Sangryul Kim", "Haemin Choi", "Ki Hoon Kwak", "James Thorne" ], "github": "", "project_page": "", "abstract": "Often, the needs and visual abilities differ between the annotator group and the end user group. Generating detailed diagram descriptions for blind and low-vision (BLV) users is one such challenging domain. Sighted annotators could describe visuals with ease, but existing studies have shown that direct generations by them are costly, bias-prone, and somewhat lacking by BLV standards. In this study, we ask sighted individuals to assess -- rather than produce -- diagram descriptions generated by vision-language models (VLM) that have been guided with latent supervision via a multi-pass inference. The sighted assessments prove effective and useful to professional educators who are themselves BLV and teach visually impaired learners. We release Sightation, a collection of diagram description datasets spanning 5k diagrams and 137k samples for completion, preference, retrieval, question answering, and reasoning training purposes and demonstrate their fine-tuning potential in various downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.10719", "title": "Long-Video Audio Synthesis with Multi-Agent Collaboration", "authors": [ "Yehang Zhang", "Xinli Xu", "Xiaojie Xu", "Li Liu", "Yingcong Chen" ], "github": "", "project_page": "", "abstract": "Video-to-audio synthesis, which generates synchronized audio for visual content, critically enhances viewer immersion and narrative coherence in film and interactive media. However, video-to-audio dubbing for long-form content remains an unsolved challenge due to dynamic semantic shifts, temporal misalignment, and the absence of dedicated datasets. While existing methods excel in short videos, they falter in long scenarios (e.g., movies) due to fragmented synthesis and inadequate cross-scene consistency. We propose LVAS-Agent, a novel multi-agent framework that emulates professional dubbing workflows through collaborative role specialization. Our approach decomposes long-video synthesis into four steps including scene segmentation, script generation, sound design and audio synthesis. Central innovations include a discussion-correction mechanism for scene/script refinement and a generation-retrieval loop for temporal-semantic alignment. To enable systematic evaluation, we introduce LVAS-Bench, the first benchmark with 207 professionally curated long videos spanning diverse scenarios. Experiments demonstrate superior audio-visual alignment over baseline methods. Project page: https://lvas-agent.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.12605", "title": "Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey", "authors": [ "Yaoting Wang", "Shengqiong Wu", "Yuecheng Zhang", "William Wang", "Ziwei Liu", "Jiebo Luo", "Hao Fei" ], "github": "https://github.com/huggingface/open-r1", "project_page": "", "abstract": "By extending the advantage of chain-of-thought (CoT) reasoning in human-like step-by-step processes to multimodal contexts, multimodal CoT (MCoT) reasoning has recently garnered significant research attention, especially in the integration with multimodal large language models (MLLMs). Existing MCoT studies design various methodologies and innovative reasoning paradigms to address the unique challenges of image, video, speech, audio, 3D, and structured data across different modalities, achieving extensive success in applications such as robotics, healthcare, autonomous driving, and multimodal generation. However, MCoT still presents distinct challenges and opportunities that require further focus to ensure consistent thriving in this field, where, unfortunately, an up-to-date review of this domain is lacking. To bridge this gap, we present the first systematic survey of MCoT reasoning, elucidating the relevant foundational concepts and definitions. We offer a comprehensive taxonomy and an in-depth analysis of current methodologies from diverse perspectives across various application scenarios. Furthermore, we provide insights into existing challenges and future research directions, aiming to foster innovation toward multimodal AGI.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/open-r1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.13082", "title": "Free-form language-based robotic reasoning and grasping", "authors": [ "Runyu Jiao", "Alice Fasoli", "Francesco Giuliari", "Matteo Bortolon", "Sergio Povoli", "Guofeng Mei", "Yiming Wang", "Fabio Poiesi" ], "github": "", "project_page": "", "abstract": "Performing robotic grasping from a cluttered bin based on human instructions is a challenging task, as it requires understanding both the nuances of free-form language and the spatial relationships between objects. Vision-Language Models (VLMs) trained on web-scale data, such as GPT-4o, have demonstrated remarkable reasoning capabilities across both text and images. But can they truly be used for this task in a zero-shot setting? And what are their limitations? In this paper, we explore these research questions via the free-form language-based robotic grasping task, and propose a novel method, FreeGrasp, leveraging the pre-trained VLMs' world knowledge to reason about human instructions and object spatial arrangements. Our method detects all objects as keypoints and uses these keypoints to annotate marks on images, aiming to facilitate GPT-4o's zero-shot spatial reasoning. This allows our method to determine whether a requested object is directly graspable or if other objects must be grasped and removed first. Since no existing dataset is specifically designed for this task, we introduce a synthetic dataset FreeGraspData by extending the MetaGraspNetV2 dataset with human-annotated instructions and ground-truth grasping sequences. We conduct extensive analyses with both FreeGraspData and real-world validation with a gripper-equipped robotic arm, demonstrating state-of-the-art performance in grasp reasoning and execution. Project website: https://tev-fbk.github.io/FreeGrasp/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.12349", "title": "SPIN-Bench: How Well Do LLMs Plan Strategically and Reason Socially?", "authors": [ "Jianzhu Yao", "Kevin Wang", "Ryan Hsieh", "Haisu Zhou", "Tianqing Zou", "Zerui Cheng", "Zhangyang Wang", "Pramod Viswanath" ], "github": "https://github.com/spinbench/spinbench", "project_page": "", "abstract": "Reasoning and strategic behavior in social interactions is a hallmark of intelligence. This form of reasoning is significantly more sophisticated than isolated planning or reasoning tasks in static settings (e.g., math problem solving). In this paper, we present Strategic Planning, Interaction, and Negotiation (SPIN-Bench), a new multi-domain evaluation designed to measure the intelligence of strategic planning and social reasoning. While many existing benchmarks focus on narrow planning or single-agent reasoning, SPIN-Bench combines classical PDDL tasks, competitive board games, cooperative card games, and multi-agent negotiation scenarios in one unified framework. The framework includes both a benchmark as well as an arena to simulate and evaluate the variety of social settings to test reasoning and strategic behavior of AI agents. We formulate the benchmark SPIN-Bench by systematically varying action spaces, state complexity, and the number of interacting agents to simulate a variety of social settings where success depends on not only methodical and step-wise decision making, but also conceptual inference of other (adversarial or cooperative) participants. Our experiments reveal that while contemporary LLMs handle basic fact retrieval and short-range planning reasonably well, they encounter significant performance bottlenecks in tasks requiring deep multi-hop reasoning over large state spaces and socially adept coordination under uncertainty. We envision SPIN-Bench as a catalyst for future research on robust multi-agent planning, social reasoning, and human--AI teaming.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/spinbench/spinbench", "github_api_added_by": "auto" }, { "date": "2025-03-18", "arxiv_id": "2503.08153", "title": "WISA: World Simulator Assistant for Physics-Aware Text-to-Video Generation", "authors": [ "Jing Wang", "Ao Ma", "Ke Cao", "Jun Zheng", "Zhanjie Zhang", "Jiasong Feng", "Shanyuan Liu", "Yuhang Ma", "Bo Cheng", "Dawei Leng", "Yuhui Yin", "Xiaodan Liang" ], "github": "https://github.com/360CVGroup/WISA", "project_page": "", "abstract": "Recent rapid advancements in text-to-video (T2V) generation, such as SoRA and Kling, have shown great potential for building world simulators. However, current T2V models struggle to grasp abstract physical principles and generate videos that adhere to physical laws. This challenge arises primarily from a lack of clear guidance on physical information due to a significant gap between abstract physical principles and generation models. To this end, we introduce the World Simulator Assistant (WISA), an effective framework for decomposing and incorporating physical principles into T2V models. Specifically, WISA decomposes physical principles into textual physical descriptions, qualitative physical categories, and quantitative physical properties. To effectively embed these physical attributes into the generation process, WISA incorporates several key designs, including Mixture-of-Physical-Experts Attention (MoPA) and a Physical Classifier, enhancing the model's physics awareness. Furthermore, most existing datasets feature videos where physical phenomena are either weakly represented or entangled with multiple co-occurring processes, limiting their suitability as dedicated resources for learning explicit physical principles. We propose a novel video dataset, WISA-32K, collected based on qualitative physical categories. It consists of 32,000 videos, representing 17 physical laws across three domains of physics: dynamics, thermodynamics, and optics. Experimental results demonstrate that WISA can effectively enhance the compatibility of T2V models with real-world physical laws, achieving a considerable improvement on the VideoPhy benchmark. The visual exhibitions of WISA and WISA-32K are available in the https://360cvgroup.github.io/WISA/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/360CVGroup/WISA", "github_api_added_by": "auto" }, { "date": "2025-03-18", "arxiv_id": "2503.12720", "title": "GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised Matching", "authors": [ "Feng Qiao", "Zhexiao Xiong", "Eric Xing", "Nathan Jacobs" ], "github": "", "project_page": "", "abstract": "Stereo images are fundamental to numerous applications, including extended reality (XR) devices, autonomous driving, and robotics. Unfortunately, acquiring high-quality stereo images remains challenging due to the precise calibration requirements of dual-camera setups and the complexity of obtaining accurate, dense disparity maps. Existing stereo image generation methods typically focus on either visual quality for viewing or geometric accuracy for matching, but not both. We introduce GenStereo, a diffusion-based approach, to bridge this gap. The method includes two primary innovations (1) conditioning the diffusion process on a disparity-aware coordinate embedding and a warped input image, allowing for more precise stereo alignment than previous methods, and (2) an adaptive fusion mechanism that intelligently combines the diffusion-generated image with a warped image, improving both realism and disparity consistency. Through extensive training on 11 diverse stereo datasets, GenStereo demonstrates strong generalization ability. GenStereo achieves state-of-the-art performance in both stereo image generation and unsupervised stereo matching tasks. Our framework eliminates the need for complex hardware setups while enabling high-quality stereo image generation, making it valuable for both real-world applications and unsupervised learning scenarios. Project page is available at https://qjizhi.github.io/genstereo", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.12964", "title": "Training Video Foundation Models with NVIDIA NeMo", "authors": [ "Zeeshan Patel", "Ethan He", "Parth Mannan", "Xiaowei Ren", "Ryan Wolf", "Niket Agarwal", "Jacob Huffman", "Zhuoyao Wang", "Carl Wang", "Jack Chang", "Yan Bai", "Tommy Huang", "Linnan Wang", "Sahil Jain", "Shanmugam Ramasamy", "Joseph Jennings", "Ekaterina Sirazitdinova", "Oleg Sudakov", "Mingyuan Ma", "Bobby Chen", "Forrest Lin", "Hao Wang", "Vasanth Rao Naik Sabavat", "Sriharsha Niverty", "Rong Ou", "Pallab Bhattacharya", "David Page", "Nima Tajbakhsh", "Ashwath Aithal" ], "github": "https://github.com/NVIDIA/NeMo/tree/main/nemo/collections/diffusion", "project_page": "", "abstract": "Video Foundation Models (VFMs) have recently been used to simulate the real world to train physical AI systems and develop creative visual experiences. However, there are significant challenges in training large-scale, high quality VFMs that can generate high-quality videos. We present a scalable, open-source VFM training pipeline with NVIDIA NeMo, providing accelerated video dataset curation, multimodal data loading, and parallelized video diffusion model training and inference. We also provide a comprehensive performance analysis highlighting best practices for efficient VFM training and inference.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA/NeMo/tree/main/nemo/collections/diffusion", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-18", "arxiv_id": "2503.06269", "title": "Using Mechanistic Interpretability to Craft Adversarial Attacks against Large Language Models", "authors": [ "Thomas Winninger", "Boussad Addad", "Katarzyna Kapusta" ], "github": "https://github.com/Sckathach/subspace-rerouting", "project_page": "", "abstract": "Traditional white-box methods for creating adversarial perturbations against LLMs typically rely only on gradient computation from the targeted model, ignoring the internal mechanisms responsible for attack success or failure. Conversely, interpretability studies that analyze these internal mechanisms lack practical applications beyond runtime interventions. We bridge this gap by introducing a novel white-box approach that leverages mechanistic interpretability techniques to craft practical adversarial inputs. Specifically, we first identify acceptance subspaces - sets of feature vectors that do not trigger the model's refusal mechanisms - then use gradient-based optimization to reroute embeddings from refusal subspaces to acceptance subspaces, effectively achieving jailbreaks. This targeted approach significantly reduces computation cost, achieving attack success rates of 80-95\\% on state-of-the-art models including Gemma2, Llama3.2, and Qwen2.5 within minutes or even seconds, compared to existing techniques that often fail or require hours of computation. We believe this approach opens a new direction for both attack research and defense development. Furthermore, it showcases a practical application of mechanistic interpretability where other methods are less efficient, which highlights its utility. The code and generated datasets are available at https://github.com/Sckathach/subspace-rerouting.", "github_manual": "", "github_regex": "https://github.com/Sckathach/subspace-rerouting", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.14125", "title": "Frac-Connections: Fractional Extension of Hyper-Connections", "authors": [ "Defa Zhu", "Hongzhi Huang", "Jundong Zhou", "Zihao Huang", "Yutao Zeng", "Banggu Wu", "Qiyang Min", "Xun Zhou" ], "github": "", "project_page": "", "abstract": "Residual connections are central to modern deep learning architectures, enabling the training of very deep networks by mitigating gradient vanishing. Hyper-Connections recently generalized residual connections by introducing multiple connection strengths at different depths, thereby addressing the seesaw effect between gradient vanishing and representation collapse. However, Hyper-Connections increase memory access costs by expanding the width of hidden states. In this paper, we propose Frac-Connections, a novel approach that divides hidden states into multiple parts rather than expanding their width. Frac-Connections retain partial benefits of Hyper-Connections while reducing memory consumption. To validate their effectiveness, we conduct large-scale experiments on language tasks, with the largest being a 7B MoE model trained on up to 3T tokens, demonstrating that Frac-Connections significantly outperform residual connections.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.14378", "title": "Impossible Videos", "authors": [ "Zechen Bai", "Hai Ci", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Synthetic videos nowadays is widely used to complement data scarcity and diversity of real-world videos. Current synthetic datasets primarily replicate real-world scenarios, leaving impossible, counterfactual and anti-reality video concepts underexplored. This work aims to answer two questions: 1) Can today's video generation models effectively follow prompts to create impossible video content? 2) Are today's video understanding models good enough for understanding impossible videos? To this end, we introduce IPV-Bench, a novel benchmark designed to evaluate and foster progress in video understanding and generation. IPV-Bench is underpinned by a comprehensive taxonomy, encompassing 4 domains, 14 categories. It features diverse scenes that defy physical, biological, geographical, or social laws. Based on the taxonomy, a prompt suite is constructed to evaluate video generation models, challenging their prompt following and creativity capabilities. In addition, a video benchmark is curated to assess Video-LLMs on their ability of understanding impossible videos, which particularly requires reasoning on temporal dynamics and world knowledge. Comprehensive evaluations reveal limitations and insights for future directions of video models, paving the way for next-generation video models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.12545", "title": "PEBench: A Fictitious Dataset to Benchmark Machine Unlearning for Multimodal Large Language Models", "authors": [ "Zhaopan Xu", "Pengfei Zhou", "Weidong Tang", "Jiaxin Ai", "Wangbo Zhao", "Xiaojiang Peng", "Kai Wang", "Yang You", "Wenqi Shao", "Hongxun Yao", "Kaipeng Zhang" ], "github": "", "project_page": "", "abstract": "In recent years, Multimodal Large Language Models (MLLMs) have demonstrated remarkable advancements in tasks such as visual question answering, visual understanding, and reasoning. However, this impressive progress relies on vast amounts of data collected from the internet, raising significant concerns about privacy and security. To address these issues, machine unlearning (MU) has emerged as a promising solution, enabling the removal of specific knowledge from an already trained model without requiring retraining from scratch. Although MU for MLLMs has gained attention, current evaluations of its efficacy remain incomplete, and the underlying problem is often poorly defined, which hinders the development of strategies for creating more secure and trustworthy systems. To bridge this gap, we introduce a benchmark, named PEBench, which includes a dataset of personal entities and corresponding general event scenes, designed to comprehensively assess the performance of MU for MLLMs. Through PEBench, we aim to provide a standardized and robust framework to advance research in secure and privacy-preserving multimodal models. We benchmarked 6 MU methods, revealing their strengths and limitations, and shedding light on key challenges and opportunities for MU in MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.12505", "title": "MPBench: A Comprehensive Multimodal Reasoning Benchmark for Process Errors Identification", "authors": [ "Zhaopan Xu", "Pengfei Zhou", "Jiaxin Ai", "Wangbo Zhao", "Kai Wang", "Xiaojiang Peng", "Wenqi Shao", "Hongxun Yao", "Kaipeng Zhang" ], "github": "", "project_page": "", "abstract": "Reasoning is an essential capacity for large language models (LLMs) to address complex tasks, where the identification of process errors is vital for improving this ability. Recently, process-level reward models (PRMs) were proposed to provide step-wise rewards that facilitate reinforcement learning and data production during training and guide LLMs toward correct steps during inference, thereby improving reasoning accuracy. However, existing benchmarks of PRMs are text-based and focus on error detection, neglecting other scenarios like reasoning search. To address this gap, we introduce MPBench, a comprehensive, multi-task, multimodal benchmark designed to systematically assess the effectiveness of PRMs in diverse scenarios. MPBench employs three evaluation paradigms, each targeting a specific role of PRMs in the reasoning process: (1) Step Correctness, which assesses the correctness of each intermediate reasoning step; (2) Answer Aggregation, which aggregates multiple solutions and selects the best one; and (3) Reasoning Process Search, which guides the search for optimal reasoning steps during inference. Through these paradigms, MPBench makes comprehensive evaluations and provides insights into the development of multimodal PRMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.10410", "title": "RoCo-Sim: Enhancing Roadside Collaborative Perception through Foreground Simulation", "authors": [ "Yuwen Du", "Anning Hu", "Zichen Chao", "Yifan Lu", "Junhao Ge", "Genjia Liu", "Weitao Wu", "Lanjun Wang", "Siheng Chen" ], "github": "https://github.com/duyuwen-duen/RoCo-Sim", "project_page": "", "abstract": "Roadside Collaborative Perception refers to a system where multiple roadside units collaborate to pool their perceptual data, assisting vehicles in enhancing their environmental awareness. Existing roadside perception methods concentrate on model design but overlook data issues like calibration errors, sparse information, and multi-view consistency, leading to poor performance on recent published datasets. To significantly enhance roadside collaborative perception and address critical data issues, we present the first simulation framework RoCo-Sim for road-side collaborative perception. RoCo-Sim is capable of generating diverse, multi-view consistent simulated roadside data through dynamic foreground editing and full-scene style transfer of a single image. RoCo-Sim consists of four components: (1) Camera Extrinsic Optimization ensures accurate 3D to 2D projection for roadside cameras; (2) A novel Multi-View Occlusion-Aware Sampler (MOAS) determines the placement of diverse digital assets within 3D space; (3) DepthSAM innovatively models foreground-background relationships from single-frame fixed-view images, ensuring multi-view consistency of foreground; and (4) Scalable Post-Processing Toolkit generates more realistic and enriched scenes through style transfer and other enhancements. RoCo-Sim significantly improves roadside 3D object detection, outperforming SOTA methods by 83.74 on Rcooper-Intersection and 83.12 on TUMTraf-V2X for AP70. RoCo-Sim fills a critical gap in roadside perception simulation. Code and pre-trained models will be released soon: https://github.com/duyuwen-duen/RoCo-Sim", "github_manual": "", "github_regex": "https://github.com/duyuwen-duen/RoCo-Sim", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.10546", "title": "KUDA: Keypoints to Unify Dynamics Learning and Visual Prompting for Open-Vocabulary Robotic Manipulation", "authors": [ "Zixian Liu", "Mingtong Zhang", "Yunzhu Li" ], "github": "", "project_page": "", "abstract": "With the rapid advancement of large language models (LLMs) and vision-language models (VLMs), significant progress has been made in developing open-vocabulary robotic manipulation systems. However, many existing approaches overlook the importance of object dynamics, limiting their applicability to more complex, dynamic tasks. In this work, we introduce KUDA, an open-vocabulary manipulation system that integrates dynamics learning and visual prompting through keypoints, leveraging both VLMs and learning-based neural dynamics models. Our key insight is that a keypoint-based target specification is simultaneously interpretable by VLMs and can be efficiently translated into cost functions for model-based planning. Given language instructions and visual observations, KUDA first assigns keypoints to the RGB image and queries the VLM to generate target specifications. These abstract keypoint-based representations are then converted into cost functions, which are optimized using a learned dynamics model to produce robotic trajectories. We evaluate KUDA on a range of manipulation tasks, including free-form language instructions across diverse object categories, multi-object interactions, and deformable or granular objects, demonstrating the effectiveness of our framework. The project page is available at http://kuda-dynamics.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.14456", "title": "RWKV-7 \"Goose\" with Expressive Dynamic State Evolution", "authors": [ "Bo Peng", "Ruichong Zhang", "Daniel Goldstein", "Eric Alcaide", "Haowen Hou", "Janna Lu", "William Merrill", "Guangyu Song", "Kaifeng Tan", "Saiteja Utpala", "Nathan Wilce", "Johan S. Wind", "Tianyi Wu", "Daniel Wuttke", "Christian Zhou-Zheng" ], "github": "https://github.com/RWKV/RWKV-LM", "project_page": "", "abstract": "We present RWKV-7 \"Goose\", a new sequence modeling architecture, along with pre-trained language models that establish a new state-of-the-art in downstream performance at the 3 billion parameter scale on multilingual tasks, and match current SoTA English language performance despite being trained on dramatically fewer tokens than other top 3B models. Nevertheless, RWKV-7 models require only constant memory usage and constant inference time per token. RWKV-7 introduces a newly generalized formulation of the delta rule with vector-valued gating and in-context learning rates, as well as a relaxed value replacement rule. We show that RWKV-7 can perform state tracking and recognize all regular languages, while retaining parallelizability of training. This exceeds the capabilities of Transformers under standard complexity conjectures, which are limited to TC^0. To demonstrate RWKV-7's language modeling capability, we also present an extended open source 3.1 trillion token multilingual corpus, and train four RWKV-7 models ranging from 0.19 billion to 2.9 billion parameters on this dataset. To foster openness, reproduction, and adoption, we release our models and dataset component listing at https://huggingface.co/RWKV, and our training and inference code at https://github.com/RWKV/RWKV-LM all under the Apache 2.0 License.", "github_manual": "", "github_regex": "https://github.com/RWKV/RWKV-LM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.13424", "title": "Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated Objects via Procedural Generation", "authors": [ "Xinyu Lian", "Zichao Yu", "Ruiming Liang", "Yitong Wang", "Li Ray Luo", "Kaixu Chen", "Yuanzhen Zhou", "Qihong Tang", "Xudong Xu", "Zhaoyang Lyu", "Bo Dai", "Jiangmiao Pang" ], "github": "https://github.com/Intern-Nexus/Infinite-Mobility", "project_page": "", "abstract": "Large-scale articulated objects with high quality are desperately needed for multiple tasks related to embodied AI. Most existing methods for creating articulated objects are either data-driven or simulation based, which are limited by the scale and quality of the training data or the fidelity and heavy labour of the simulation. In this paper, we propose Infinite Mobility, a novel method for synthesizing high-fidelity articulated objects through procedural generation. User study and quantitative evaluation demonstrate that our method can produce results that excel current state-of-the-art methods and are comparable to human-annotated datasets in both physics property and mesh quality. Furthermore, we show that our synthetic data can be used as training data for generative models, enabling next-step scaling up. Code is available at https://github.com/Intern-Nexus/Infinite-Mobility", "github_manual": "", "github_regex": "https://github.com/Intern-Nexus/Infinite-Mobility", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.12797", "title": "DeepPerception: Advancing R1-like Cognitive Visual Perception in MLLMs for Knowledge-Intensive Visual Grounding", "authors": [ "Xinyu Ma", "Ziyang Ding", "Zhicong Luo", "Chi Chen", "Zonghao Guo", "Derek F. Wong", "Xiaoyi Feng", "Maosong Sun" ], "github": "https://github.com/thunlp/DeepPerception", "project_page": "", "abstract": "Human experts excel at fine-grained visual discrimination by leveraging domain knowledge to refine perceptual features, a capability that remains underdeveloped in current Multimodal Large Language Models (MLLMs). Despite possessing vast expert-level knowledge, MLLMs struggle to integrate reasoning into visual perception, often generating direct responses without deeper analysis. To bridge this gap, we introduce knowledge-intensive visual grounding (KVG), a novel visual grounding task that requires both fine-grained perception and domain-specific knowledge integration. To address the challenges of KVG, we propose DeepPerception, an MLLM enhanced with cognitive visual perception capabilities. Our approach consists of (1) an automated data synthesis pipeline that generates high-quality, knowledge-aligned training samples, and (2) a two-stage training framework combining supervised fine-tuning for cognitive reasoning scaffolding and reinforcement learning to optimize perception-cognition synergy. To benchmark performance, we introduce KVG-Bench a comprehensive dataset spanning 10 domains with 1.3K manually curated test cases. Experimental results demonstrate that DeepPerception significantly outperforms direct fine-tuning, achieving +8.08\\% accuracy improvements on KVG-Bench and exhibiting +4.60\\% superior cross-domain generalization over baseline approaches. Our findings highlight the importance of integrating cognitive processes into MLLMs for human-like visual perception and open new directions for multimodal reasoning research. The data, codes, and models are released at https://github.com/thunlp/DeepPerception.", "github_manual": "", "github_regex": "https://github.com/thunlp/DeepPerception", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.14476", "title": "DAPO: An Open-Source LLM Reinforcement Learning System at Scale", "authors": [ "Qiying Yu", "Zheng Zhang", "Ruofei Zhu", "Yufeng Yuan", "Xiaochen Zuo", "Yu Yue", "Tiantian Fan", "Gaohong Liu", "Lingjun Liu", "Xin Liu", "Haibin Lin", "Zhiqi Lin", "Bole Ma", "Guangming Sheng", "Yuxuan Tong", "Chi Zhang", "Mofan Zhang", "Wang Zhang", "Hang Zhu", "Jinhua Zhu", "Jiaze Chen", "Jiangjie Chen", "Chengyi Wang", "Hongli Yu", "Weinan Dai", "Yuxuan Song", "Xiangpeng Wei", "Hao Zhou", "Jingjing Liu", "Wei-Ying Ma", "Ya-Qin Zhang", "Lin Yan", "Mu Qiao", "Yonghui Wu", "Mingxuan Wang" ], "github": "https://github.com/BytedTsinghua-SIA/DAPO", "project_page": "", "abstract": "Inference scaling empowers LLMs with unprecedented reasoning ability, with reinforcement learning as the core technique to elicit complex reasoning. However, key technical details of state-of-the-art reasoning LLMs are concealed (such as in OpenAI o1 blog and DeepSeek R1 technical report), thus the community still struggles to reproduce their RL training results. We propose the Decoupled Clip and Dynamic sAmpling Policy Optimization (DAPO) algorithm, and fully open-source a state-of-the-art large-scale RL system that achieves 50 points on AIME 2024 using Qwen2.5-32B base model. Unlike previous works that withhold training details, we introduce four key techniques of our algorithm that make large-scale LLM RL a success. In addition, we open-source our training code, which is built on the verl framework, along with a carefully curated and processed dataset. These components of our open-source system enhance reproducibility and support future research in large-scale LLM RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/BytedTsinghua-SIA/DAPO", "github_api_added_by": "auto" }, { "date": "2025-03-19", "arxiv_id": "2503.14499", "title": "Measuring AI Ability to Complete Long Tasks", "authors": [ "Thomas Kwa", "Ben West", "Joel Becker", "Amy Deng", "Katharyn Garcia", "Max Hasin", "Sami Jawhar", "Megan Kinniment", "Nate Rush", "Sydney Von Arx", "Ryan Bloom", "Thomas Broadley", "Haoxing Du", "Brian Goodrich", "Nikola Jurkovic", "Luke Harold Miles", "Seraphina Nix", "Tao Lin", "Neev Parikh", "David Rein", "Lucas Jun Koba Sato", "Hjalmar Wijk", "Daniel M. Ziegler", "Elizabeth Barnes", "Lawrence Chan" ], "github": "https://github.com/METR/eval-analysis-public", "project_page": "", "abstract": "Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear. To quantify the capabilities of AI systems in terms of human capabilities, we propose a new metric: 50%-task-completion time horizon. This is the time humans typically take to complete tasks that AI models can complete with 50% success rate. We first timed humans with relevant domain expertise on a combination of RE-Bench, HCAST, and 66 novel shorter tasks. On these tasks, current frontier AI models such as Claude 3.7 Sonnet have a 50% time horizon of around 50 minutes. Furthermore, frontier AI time horizon has been doubling approximately every seven months since 2019, though the trend may have accelerated in 2024. The increase in AI models' time horizons seems to be primarily driven by greater reliability and ability to adapt to mistakes, combined with better logical reasoning and tool use capabilities. We discuss the limitations of our results -- including their degree of external validity -- and the implications of increased autonomy for dangerous capabilities. If these results generalize to real-world software tasks, extrapolation of this trend predicts that within 5 years, AI systems will be capable of automating many software tasks that currently take humans a month.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/METR/eval-analysis-public", "github_api_added_by": "auto" }, { "date": "2025-03-19", "arxiv_id": "2503.14492", "title": "Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control", "authors": [ "NVIDIA", "Hassan Abu Alhaija", "Jose Alvarez", "Maciej Bala", "Tiffany Cai", "Tianshi Cao", "Liz Cha", "Joshua Chen", "Mike Chen", "Francesco Ferroni", "Sanja Fidler", "Dieter Fox", "Yunhao Ge", "Jinwei Gu", "Ali Hassani", "Michael Isaev", "Pooya Jannaty", "Shiyi Lan", "Tobias Lasser", "Huan Ling", "Ming-Yu Liu", "Xian Liu", "Yifan Lu", "Alice Luo", "Qianli Ma", "Hanzi Mao", "Fabio Ramos", "Xuanchi Ren", "Tianchang Shen", "Shitao Tang", "Ting-Chun Wang", "Jay Wu", "Jiashu Xu", "Stella Xu", "Kevin Xie", "Yuchong Ye", "Xiaodong Yang", "Xiaohui Zeng", "Yu Zeng" ], "github": "https://github.com/nvidia-cosmos/cosmos-transfer1", "project_page": "", "abstract": "We introduce Cosmos-Transfer, a conditional world generation model that can generate world simulations based on multiple spatial control inputs of various modalities such as segmentation, depth, and edge. In the design, the spatial conditional scheme is adaptive and customizable. It allows weighting different conditional inputs differently at different spatial locations. This enables highly controllable world generation and finds use in various world-to-world transfer use cases, including Sim2Real. We conduct extensive evaluations to analyze the proposed model and demonstrate its applications for Physical AI, including robotics Sim2Real and autonomous vehicle data enrichment. We further demonstrate an inference scaling strategy to achieve real-time world generation with an NVIDIA GB200 NVL72 rack. To help accelerate research development in the field, we open-source our models and code at https://github.com/nvidia-cosmos/cosmos-transfer1.", "github_manual": "", "github_regex": "https://github.com/nvidia-cosmos/cosmos-transfer1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.14478", "title": "Creation-MMBench: Assessing Context-Aware Creative Intelligence in MLLM", "authors": [ "Xinyu Fang", "Zhijian Chen", "Kai Lan", "Shengyuan Ding", "Yingji Liang", "Xiangyu Zhao", "Farong Wen", "Zicheng Zhang", "Guofeng Zhang", "Haodong Duan", "Kai Chen", "Dahua Lin" ], "github": "https://github.com/open-compass/Creation-MMBench", "project_page": "", "abstract": "Creativity is a fundamental aspect of intelligence, involving the ability to generate novel and appropriate solutions across diverse contexts. While Large Language Models (LLMs) have been extensively evaluated for their creative capabilities, the assessment of Multimodal Large Language Models (MLLMs) in this domain remains largely unexplored. To address this gap, we introduce Creation-MMBench, a multimodal benchmark specifically designed to evaluate the creative capabilities of MLLMs in real-world, image-based tasks. The benchmark comprises 765 test cases spanning 51 fine-grained tasks. To ensure rigorous evaluation, we define instance-specific evaluation criteria for each test case, guiding the assessment of both general response quality and factual consistency with visual inputs. Experimental results reveal that current open-source MLLMs significantly underperform compared to proprietary models in creative tasks. Furthermore, our analysis demonstrates that visual fine-tuning can negatively impact the base LLM's creative abilities. Creation-MMBench provides valuable insights for advancing MLLM creativity and establishes a foundation for future improvements in multimodal generative intelligence. Full data and evaluation code is released on https://github.com/open-compass/Creation-MMBench.", "github_manual": "", "github_regex": "https://github.com/open-compass/Creation-MMBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.12329", "title": "CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era", "authors": [ "Kanzhi Cheng", "Wenpo Song", "Jiaxin Fan", "Zheng Ma", "Qiushi Sun", "Fangzhi Xu", "Chenyang Yan", "Nuo Chen", "Jianbing Zhang", "Jiajun Chen" ], "github": "", "project_page": "", "abstract": "Image captioning has been a longstanding challenge in vision-language research. With the rise of LLMs, modern Vision-Language Models (VLMs) generate detailed and comprehensive image descriptions. However, benchmarking the quality of such captions remains unresolved. This paper addresses two key questions: (1) How well do current VLMs actually perform on image captioning, particularly compared to humans? We built CapArena, a platform with over 6000 pairwise caption battles and high-quality human preference votes. Our arena-style evaluation marks a milestone, showing that leading models like GPT-4o achieve or even surpass human performance, while most open-source models lag behind. (2) Can automated metrics reliably assess detailed caption quality? Using human annotations from CapArena, we evaluate traditional and recent captioning metrics, as well as VLM-as-a-Judge. Our analysis reveals that while some metrics (e.g., METEOR) show decent caption-level agreement with humans, their systematic biases lead to inconsistencies in model ranking. In contrast, VLM-as-a-Judge demonstrates robust discernment at both the caption and model levels. Building on these insights, we release CapArena-Auto, an accurate and efficient automated benchmark for detailed captioning, achieving 94.3% correlation with human rankings at just $4 per test. Data and resources will be open-sourced at https://caparena.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.12271", "title": "Reflect-DiT: Inference-Time Scaling for Text-to-Image Diffusion Transformers via In-Context Reflection", "authors": [ "Shufan Li", "Konstantinos Kallidromitis", "Akash Gokul", "Arsh Koneru", "Yusuke Kato", "Kazuki Kozuka", "Aditya Grover" ], "github": "https://github.com/jacklishufan/Reflect-DiT", "project_page": "", "abstract": "The predominant approach to advancing text-to-image generation has been training-time scaling, where larger models are trained on more data using greater computational resources. While effective, this approach is computationally expensive, leading to growing interest in inference-time scaling to improve performance. Currently, inference-time scaling for text-to-image diffusion models is largely limited to best-of-N sampling, where multiple images are generated per prompt and a selection model chooses the best output. Inspired by the recent success of reasoning models like DeepSeek-R1 in the language domain, we introduce an alternative to naive best-of-N sampling by equipping text-to-image Diffusion Transformers with in-context reflection capabilities. We propose Reflect-DiT, a method that enables Diffusion Transformers to refine their generations using in-context examples of previously generated images alongside textual feedback describing necessary improvements. Instead of passively relying on random sampling and hoping for a better result in a future generation, Reflect-DiT explicitly tailors its generations to address specific aspects requiring enhancement. Experimental results demonstrate that Reflect-DiT improves performance on the GenEval benchmark (+0.19) using SANA-1.0-1.6B as a base model. Additionally, it achieves a new state-of-the-art score of 0.81 on GenEval while generating only 20 samples per prompt, surpassing the previous best score of 0.80, which was obtained using a significantly larger model (SANA-1.5-4.8B) with 2048 samples under the best-of-N approach.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jacklishufan/Reflect-DiT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.14495", "title": "Temporal Consistency for LLM Reasoning Process Error Identification", "authors": [ "Jiacheng Guo", "Yue Wu", "Jiahao Qiu", "Kaixuan Huang", "Xinzhe Juan", "Ling Yang", "Mengdi Wang" ], "github": "https://github.com/jcguo123/Temporal-Consistency", "project_page": "", "abstract": "Verification is crucial for effective mathematical reasoning. We present a new temporal consistency method where verifiers iteratively refine their judgments based on the previous assessment. Unlike one-round verification or multi-model debate approaches, our method leverages consistency in a sequence of self-reflection actions to improve verification accuracy. Empirical evaluations across diverse mathematical process error identification benchmarks (Mathcheck, ProcessBench, and PRM800K) show consistent performance improvements over baseline methods. When applied to the recent DeepSeek R1 distilled models, our method demonstrates strong performance, enabling 7B/8B distilled models to outperform all 70B/72B models and GPT-4o on ProcessBench. Notably, the distilled 14B model with our method achieves performance comparable to Deepseek-R1. Our codes are available at https://github.com/jcguo123/Temporal-Consistency", "github_manual": "", "github_regex": "https://github.com/jcguo123/Temporal-Consistency", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.14504", "title": "Aligning Multimodal LLM with Human Preference: A Survey", "authors": [ "Tao Yu", "Yi-Fan Zhang", "Chaoyou Fu", "Junkang Wu", "Jinda Lu", "Kun Wang", "Xingyu Lu", "Yunhang Shen", "Guibin Zhang", "Dingjie Song", "Yibo Yan", "Tianlong Xu", "Qingsong Wen", "Zhang Zhang", "Yan Huang", "Liang Wang", "Tieniu Tan" ], "github": "https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models", "project_page": "", "abstract": "Large language models (LLMs) can handle a wide variety of general tasks with simple prompts, without the need for task-specific training. Multimodal Large Language Models (MLLMs), built upon LLMs, have demonstrated impressive potential in tackling complex tasks involving visual, auditory, and textual data. However, critical issues related to truthfulness, safety, o1-like reasoning, and alignment with human preference remain insufficiently addressed. This gap has spurred the emergence of various alignment algorithms, each targeting different application scenarios and optimization goals. Recent studies have shown that alignment algorithms are a powerful approach to resolving the aforementioned challenges. In this paper, we aim to provide a comprehensive and systematic review of alignment algorithms for MLLMs. Specifically, we explore four key aspects: (1) the application scenarios covered by alignment algorithms, including general image understanding, multi-image, video, and audio, and extended multimodal applications; (2) the core factors in constructing alignment datasets, including data sources, model responses, and preference annotations; (3) the benchmarks used to evaluate alignment algorithms; and (4) a discussion of potential future directions for the development of alignment algorithms. This work seeks to help researchers organize current advancements in the field and inspire better alignment methods. The project page of this paper is available at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Alignment.", "github_manual": "", "github_regex": "https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.14151", "title": "Concat-ID: Towards Universal Identity-Preserving Video Synthesis", "authors": [ "Yong Zhong", "Zhuoyi Yang", "Jiayan Teng", "Xiaotao Gu", "Chongxuan Li" ], "github": "", "project_page": "", "abstract": "We present Concat-ID, a unified framework for identity-preserving video generation. Concat-ID employs Variational Autoencoders to extract image features, which are concatenated with video latents along the sequence dimension, leveraging solely 3D self-attention mechanisms without the need for additional modules. A novel cross-video pairing strategy and a multi-stage training regimen are introduced to balance identity consistency and facial editability while enhancing video naturalness. Extensive experiments demonstrate Concat-ID's superiority over existing methods in both single and multi-identity generation, as well as its seamless scalability to multi-subject scenarios, including virtual try-on and background-controllable generation. Concat-ID establishes a new benchmark for identity-preserving video synthesis, providing a versatile and scalable solution for a wide range of applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.09443", "title": "Florenz: Scaling Laws for Systematic Generalization in Vision-Language Models", "authors": [ "Julian Spravil", "Sebastian Houben", "Sven Behnke" ], "github": "", "project_page": "", "abstract": "Cross-lingual transfer enables vision-language models (VLMs) to perform vision tasks in various languages with training data only in one language. Current approaches rely on large pre-trained multilingual language models. However, they face the curse of multilinguality, sacrificing downstream task performance for multilingual capabilities, struggling with lexical ambiguities, and falling behind recent advances. In this work, we study the scaling laws of systematic generalization with monolingual VLMs for multilingual tasks, focusing on the impact of model size and seen training samples. We propose Florenz, a monolingual encoder-decoder VLM with 0.4B to 11.2B parameters combining the pre-trained VLM Florence-2 and the large language model Gemma-2. Florenz is trained with varying compute budgets on a synthetic dataset that features intentionally incomplete language coverage for image captioning, thus, testing generalization from the fully covered translation task. We show that not only does indirectly learning unseen task-language pairs adhere to a scaling law, but also that with our data generation pipeline and the proposed Florenz model family, image captioning abilities can emerge in a specific language even when only data for the translation task is available. Fine-tuning on a mix of downstream datasets yields competitive performance and demonstrates promising scaling trends in multimodal machine translation (Multi30K, CoMMuTE), lexical disambiguation (CoMMuTE), and image captioning (Multi30K, XM3600, COCO Karpathy).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.13661", "title": "Pensez: Less Data, Better Reasoning -- Rethinking French LLM", "authors": [ "Huy Hoang Ha" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks. However, achieving strong performance in specialized domains like mathematical reasoning and non-English languages often requires extensive training on massive datasets. This paper investigates a contrasting approach: strategic fine-tuning on a small, high-quality, bilingual (English-French) dataset to enhance both the reasoning capabilities and French language proficiency of a large language model. Rather than relying on scale, we explore the hypothesis that targeted data curation and optimized training can achieve competitive, or even superior, performance. We demonstrate, through targeted supervised fine-tuning (SFT) on only 2,000 carefully selected samples, significant improvements in mathematical reasoning. Specifically, Pensez 7B exhibits an increase in accuracy of the base model up to 20% on the AIME25 and a 12% increase on a French MATH level 5 benchmark. These results challenge the prevailing assumption that massive datasets are aprerequisite for strong reasoning performance in LLMs, highlighting the potential of strategic data curation and optimized fine-tuning for enhancing both specialized skills and multilingual capabilities. Our findings have implications for the efficient development of high-performing, multilingual LLMs, especially in resource-constrained scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.12127", "title": "Hyperbolic Safety-Aware Vision-Language Models", "authors": [ "Tobia Poppi", "Tejaswi Kasarla", "Pascal Mettes", "Lorenzo Baraldi", "Rita Cucchiara" ], "github": "https://github.com/aimagelab/HySAC", "project_page": "", "abstract": "Addressing the retrieval of unsafe content from vision-language models such as CLIP is an important step towards real-world integration. Current efforts have relied on unlearning techniques that try to erase the model's knowledge of unsafe concepts. While effective in reducing unwanted outputs, unlearning limits the model's capacity to discern between safe and unsafe content. In this work, we introduce a novel approach that shifts from unlearning to an awareness paradigm by leveraging the inherent hierarchical properties of the hyperbolic space. We propose to encode safe and unsafe content as an entailment hierarchy, where both are placed in different regions of hyperbolic space. Our HySAC, Hyperbolic Safety-Aware CLIP, employs entailment loss functions to model the hierarchical and asymmetrical relations between safe and unsafe image-text pairs. This modelling, ineffective in standard vision-language models due to their reliance on Euclidean embeddings, endows the model with awareness of unsafe content, enabling it to serve as both a multimodal unsafe classifier and a flexible content retriever, with the option to dynamically redirect unsafe queries toward safer alternatives or retain the original output. Extensive experiments show that our approach not only enhances safety recognition but also establishes a more adaptable and interpretable framework for content moderation in vision-language models. Our source code is available at https://github.com/aimagelab/HySAC.", "github_manual": "", "github_regex": "https://github.com/aimagelab/HySAC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.13265", "title": "FlexWorld: Progressively Expanding 3D Scenes for Flexiable-View Synthesis", "authors": [ "Luxi Chen", "Zihan Zhou", "Min Zhao", "Yikai Wang", "Ge Zhang", "Wenhao Huang", "Hao Sun", "Ji-Rong Wen", "Chongxuan Li" ], "github": "", "project_page": "", "abstract": "Generating flexible-view 3D scenes, including 360{\\deg} rotation and zooming, from single images is challenging due to a lack of 3D data. To this end, we introduce FlexWorld, a novel framework consisting of two key components: (1) a strong video-to-video (V2V) diffusion model to generate high-quality novel view images from incomplete input rendered from a coarse scene, and (2) a progressive expansion process to construct a complete 3D scene. In particular, leveraging an advanced pre-trained video model and accurate depth-estimated training pairs, our V2V model can generate novel views under large camera pose variations. Building upon it, FlexWorld progressively generates new 3D content and integrates it into the global scene through geometry-aware scene fusion. Extensive experiments demonstrate the effectiveness of FlexWorld in generating high-quality novel view videos and flexible-view 3D scenes from single images, achieving superior visual quality under multiple popular metrics and datasets compared to existing state-of-the-art methods. Qualitatively, we highlight that FlexWorld can generate high-fidelity scenes with flexible views like 360{\\deg} rotations and zooming. Project page: https://ml-gsai.github.io/FlexWorld.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.13111", "title": "MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMs", "authors": [ "Erik Daxberger", "Nina Wenzel", "David Griffiths", "Haiming Gang", "Justin Lazarow", "Gefen Kohavi", "Kai Kang", "Marcin Eichner", "Yinfei Yang", "Afshin Dehghan", "Peter Grasch" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) excel at 2D visual understanding but remain limited in their ability to reason about 3D space. In this work, we leverage large-scale high-quality 3D scene data with open-set annotations to introduce 1) a novel supervised fine-tuning dataset and 2) a new evaluation benchmark, focused on indoor scenes. Our Cubify Anything VQA (CA-VQA) data covers diverse spatial tasks including spatial relationship prediction, metric size and distance estimation, and 3D grounding. We show that CA-VQA enables us to train MM-Spatial, a strong generalist MLLM that also achieves state-of-the-art performance on 3D spatial understanding benchmarks, including our own. We show how incorporating metric depth and multi-view inputs (provided in CA-VQA) can further improve 3D understanding, and demonstrate that data alone allows our model to achieve depth perception capabilities comparable to dedicated monocular depth estimation models. We will publish our SFT dataset and benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.12303", "title": "Towards Self-Improving Systematic Cognition for Next-Generation Foundation MLLMs", "authors": [ "Xiaoying Zhang", "Da Peng", "Yipeng Zhang", "Zonghao Guo", "Chengyue Wu", "Chi Chen", "Wei Ke", "Helen Meng", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "Despite their impressive capabilities, Multimodal Large Language Models (MLLMs) face challenges with fine-grained perception and complex reasoning. Prevalent multimodal pre-training approaches focus on enhancing perception by training on high-quality image captions due to the extremely high cost of collecting chain-of-thought (CoT) reasoning data for improving reasoning. While leveraging advanced MLLMs for caption generation enhances scalability, the outputs often lack comprehensiveness and accuracy. In this paper, we introduce Self-Improving cognition (SIcog), a self-learning framework designed to construct next-generation foundation MLLMs by enhancing their systematic cognitive capabilities through multimodal pre-training with self-generated data. Specifically, we propose Chain-of-Description, an approach that improves an MLLM's systematic perception by enabling step-by-step visual understanding, ensuring greater comprehensiveness and accuracy. Additionally, we adopt a structured CoT reasoning technique to enable MLLMs to integrate in-depth multimodal reasoning. To construct a next-generation foundation MLLM with self-improved cognition, SIcog first equips an MLLM with systematic perception and reasoning abilities using minimal external annotations. The enhanced models then generate detailed captions and CoT reasoning data, which are further curated through self-consistency. This curated data is ultimately used for multimodal pre-training to develop next-generation foundation models. Extensive experiments on both low- and high-resolution MLLMs across diverse benchmarks demonstrate that, with merely 213K self-generated pre-training samples, SIcog produces next-generation foundation MLLMs with significantly improved cognition, achieving benchmark-leading performance compared to prevalent pre-training approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.14002", "title": "MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling", "authors": [ "Damian Boborzi", "Phillip Mueller", "Jonas Emrich", "Dominik Schmid", "Sebastian Mueller", "Lars Mikelsons" ], "github": "https://github.com/FeMa42/MeshFleet", "project_page": "", "abstract": "Generative models have recently made remarkable progress in the field of 3D objects. However, their practical application in fields like engineering remains limited since they fail to deliver the accuracy, quality, and controllability needed for domain-specific tasks. Fine-tuning large generative models is a promising perspective for making these models available in these fields. Creating high-quality, domain-specific 3D datasets is crucial for fine-tuning large generative models, yet the data filtering and annotation process remains a significant bottleneck. We present MeshFleet, a filtered and annotated 3D vehicle dataset extracted from Objaverse-XL, the most extensive publicly available collection of 3D objects. Our approach proposes a pipeline for automated data filtering based on a quality classifier. This classifier is trained on a manually labeled subset of Objaverse, incorporating DINOv2 and SigLIP embeddings, refined through caption-based analysis and uncertainty estimation. We demonstrate the efficacy of our filtering method through a comparative analysis against caption and image aesthetic score-based techniques and fine-tuning experiments with SV3D, highlighting the importance of targeted data selection for domain-specific 3D generative modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FeMa42/MeshFleet", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.08893", "title": "EvalTree: Profiling Language Model Weaknesses via Hierarchical Capability Trees", "authors": [ "Zhiyuan Zeng", "Yizhong Wang", "Hannaneh Hajishirzi", "Pang Wei Koh" ], "github": "https://github.com/xlang-ai/DS-1000", "project_page": "", "abstract": "An ideal model evaluation should achieve two goals: identifying where the model fails and providing actionable improvement guidance. Toward these goals for Language Model (LM) evaluations, we formulate the problem of generating a weakness profile, a set of weaknesses expressed in natural language, given an LM's performance on every individual instance in a benchmark. We introduce a suite of quantitative assessments to compare different weakness profiling methods. We also propose a weakness profiling method EvalTree. It constructs a capability tree where each node represents a capability described in natural language and is linked to a subset of benchmark instances that specifically evaluate this capability; it then extracts nodes where the LM performs poorly to generate a weakness profile. On the MATH and WildChat benchmarks, we show that EvalTree outperforms baseline weakness profiling methods by identifying weaknesses more precisely and comprehensively. Weakness profiling further enables weakness-guided data collection, and training data collection guided by EvalTree-identified weaknesses improves LM performance more than other data collection strategies. We also show how EvalTree exposes flaws in Chatbot Arena's human-voter-based evaluation practice. To facilitate future work, we release our code and an interface that allows practitioners to interactively explore the capability trees built by EvalTree.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xlang-ai/DS-1000", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.08683", "title": "CoLMDriver: LLM-based Negotiation Benefits Cooperative Autonomous Driving", "authors": [ "Changxing Liu", "Genjia Liu", "Zijun Wang", "Jinchang Yang", "Siheng Chen" ], "github": "https://github.com/cxliu0314/CoLMDriver", "project_page": "", "abstract": "Vehicle-to-vehicle (V2V) cooperative autonomous driving holds great promise for improving safety by addressing the perception and prediction uncertainties inherent in single-agent systems. However, traditional cooperative methods are constrained by rigid collaboration protocols and limited generalization to unseen interactive scenarios. While LLM-based approaches offer generalized reasoning capabilities, their challenges in spatial planning and unstable inference latency hinder their direct application in cooperative driving. To address these limitations, we propose CoLMDriver, the first full-pipeline LLM-based cooperative driving system, enabling effective language-based negotiation and real-time driving control. CoLMDriver features a parallel driving pipeline with two key components: (i) an LLM-based negotiation module under an actor-critic paradigm, which continuously refines cooperation policies through feedback from previous decisions of all vehicles; and (ii) an intention-guided waypoint generator, which translates negotiation outcomes into executable waypoints. Additionally, we introduce InterDrive, a CARLA-based simulation benchmark comprising 10 challenging interactive driving scenarios for evaluating V2V cooperation. Experimental results demonstrate that CoLMDriver significantly outperforms existing approaches, achieving an 11% higher success rate across diverse highly interactive V2V driving scenarios. Code will be released on https://github.com/cxliu0314/CoLMDriver.", "github_manual": "", "github_regex": "https://github.com/cxliu0314/CoLMDriver", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.10905", "title": "Learning to Inference Adaptively for Multimodal Large Language Models", "authors": [ "Zhuoyan Xu", "Khoi Duc Nguyen", "Preeti Mukherjee", "Saurabh Bagchi", "Somali Chaterji", "Yingyu Liang", "Yin Li" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have shown impressive capabilities in reasoning, yet come with substantial computational cost, limiting their deployment in resource-constrained settings. Despite recent efforts on improving the efficiency of MLLMs, prior solutions fall short in responding to varying runtime conditions, in particular changing resource availability (e.g., contention due to the execution of other programs on the device). To bridge this gap, we introduce AdaLLaVA, an adaptive inference framework that learns to dynamically reconfigure operations in an MLLM during inference, accounting for the input data and a latency budget. We conduct extensive experiments across benchmarks involving question-answering, reasoning, and hallucination. Our results show that AdaLLaVA effectively adheres to input latency budget, achieving varying accuracy and latency tradeoffs at runtime. Further, we demonstrate that AdaLLaVA adapts to both input latency and content, can be integrated with token selection for enhanced efficiency, and generalizes across MLLMs. Our project webpage with code release is at https://zhuoyan-xu.github.io/ada-llava/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.10522", "title": "AudioX: Diffusion Transformer for Anything-to-Audio Generation", "authors": [ "Zeyue Tian", "Yizhu Jin", "Zhaoyang Liu", "Ruibin Yuan", "Xu Tan", "Qifeng Chen", "Wei Xue", "Yike Guo" ], "github": "https://github.com/ZeyueT/AudioX", "project_page": "", "abstract": "Audio and music generation have emerged as crucial tasks in many applications, yet existing approaches face significant limitations: they operate in isolation without unified capabilities across modalities, suffer from scarce high-quality, multi-modal training data, and struggle to effectively integrate diverse inputs. In this work, we propose AudioX, a unified Diffusion Transformer model for Anything-to-Audio and Music Generation. Unlike previous domain-specific models, AudioX can generate both general audio and music with high quality, while offering flexible natural language control and seamless processing of various modalities including text, video, image, music, and audio. Its key innovation is a multi-modal masked training strategy that masks inputs across modalities and forces the model to learn from masked inputs, yielding robust and unified cross-modal representations. To address data scarcity, we curate two comprehensive datasets: vggsound-caps with 190K audio captions based on the VGGSound dataset, and V2M-caps with 6 million music captions derived from the V2M dataset. Extensive experiments demonstrate that AudioX not only matches or outperforms state-of-the-art specialized models, but also offers remarkable versatility in handling diverse input modalities and generation tasks within a unified architecture. The code and datasets will be available at https://zeyuet.github.io/AudioX/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZeyueT/AudioX", "github_api_added_by": "auto" }, { "date": "2025-03-19", "arxiv_id": "2503.12355", "title": "Atlas: Multi-Scale Attention Improves Long Context Image Modeling", "authors": [ "Kumar Krishna Agrawal", "Long Lian", "Longchao Liu", "Natalia Harguindeguy", "Boyi Li", "Alexander Bick", "Maggie Chung", "Trevor Darrell", "Adam Yala" ], "github": "https://github.com/yalalab/atlas", "project_page": "", "abstract": "Efficiently modeling massive images is a long-standing challenge in machine learning. To this end, we introduce Multi-Scale Attention (MSA). MSA relies on two key ideas, (i) multi-scale representations (ii) bi-directional cross-scale communication. MSA creates O(log N) scales to represent the image across progressively coarser features and leverages cross-attention to propagate information across scales. We then introduce Atlas, a novel neural network architecture based on MSA. We demonstrate that Atlas significantly improves the compute-performance tradeoff of long-context image modeling in a high-resolution variant of ImageNet 100. At 1024px resolution, Atlas-B achieves 91.04% accuracy, comparable to ConvNext-B (91.92%) while being 4.3x faster. Atlas is 2.95x faster and 7.38% better than FasterViT, 2.25x faster and 4.96% better than LongViT. In comparisons against MambaVision-S, we find Atlas-S achieves 5%, 16% and 32% higher accuracy at 1024px, 2048px and 4096px respectively, while obtaining similar runtimes. Code for reproducing our experiments and pretrained models is available at https://github.com/yalalab/atlas.", "github_manual": "", "github_regex": "https://github.com/yalalab/atlas", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-19", "arxiv_id": "2503.10284", "title": "PyGDA: A Python Library for Graph Domain Adaptation", "authors": [ "Zhen Zhang", "Meihan Liu", "Bingsheng He" ], "github": "https://github.com/pygda-team/pygda", "project_page": "", "abstract": "Graph domain adaptation has emerged as a promising approach to facilitate knowledge transfer across different domains. Recently, numerous models have been proposed to enhance their generalization capabilities in this field. However, there is still no unified library that brings together existing techniques and simplifies their implementation. To fill this gap, we introduce PyGDA, an open-source Python library tailored for graph domain adaptation. As the first comprehensive library in this area, PyGDA covers more than 20 widely used graph domain adaptation methods together with different types of graph datasets. Specifically, PyGDA offers modular components, enabling users to seamlessly build custom models with a variety of commonly used utility functions. To handle large-scale graphs, PyGDA includes support for features such as sampling and mini-batch processing, ensuring efficient computation. In addition, PyGDA also includes comprehensive performance benchmarks and well-documented user-friendly API for both researchers and practitioners. To foster convenient accessibility, PyGDA is released under the MIT license at https://github.com/pygda-team/pygda, and the API documentation is https://pygda.readthedocs.io/en/stable/.", "github_manual": "", "github_regex": "https://github.com/pygda-team/pygda", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.15485", "title": "TULIP: Towards Unified Language-Image Pretraining", "authors": [ "Zineng Tang", "Long Lian", "Seun Eisape", "XuDong Wang", "Roei Herzig", "Adam Yala", "Alane Suhr", "Trevor Darrell", "David M. Chan" ], "github": "", "project_page": "", "abstract": "Despite the recent success of image-text contrastive models like CLIP and SigLIP, these models often struggle with vision-centric tasks that demand high-fidelity image understanding, such as counting, depth estimation, and fine-grained object recognition. These models, by performing language alignment, tend to prioritize high-level semantics over visual understanding, weakening their image understanding. On the other hand, vision-focused models are great at processing visual information but struggle to understand language, limiting their flexibility for language-driven tasks. In this work, we introduce TULIP, an open-source, drop-in replacement for existing CLIP-like models. Our method leverages generative data augmentation, enhanced image-image and text-text contrastive learning, and image/text reconstruction regularization to learn fine-grained visual features while preserving global semantic alignment. Our approach, scaling to over 1B parameters, outperforms existing state-of-the-art (SOTA) models across multiple benchmarks, establishing a new SOTA zero-shot performance on ImageNet-1K, delivering up to a 2times enhancement over SigLIP on RxRx1 in linear probing for few-shot classification, and improving vision-language models, achieving over 3times higher scores than SigLIP on MMVP. Our code/checkpoints are available at https://tulip-berkeley.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.15475", "title": "Cube: A Roblox View of 3D Intelligence", "authors": [ "Foundation AI Team", "Kiran Bhat", "Nishchaie Khanna", "Karun Channa", "Tinghui Zhou", "Yiheng Zhu", "Xiaoxia Sun", "Charles Shang", "Anirudh Sudarshan", "Maurice Chu", "Daiqing Li", "Kangle Deng", "Jean-Philippe Fauconnier", "Tijmen Verhulsdonck", "Maneesh Agrawala", "Kayvon Fatahalian", "Alexander Weiss", "Christian Reiser", "Ravi Kiran Chirravuri", "Ravali Kandur", "Alejandro Pelaez", "Akash Garg", "Michael Palleschi", "Jessica Wang", "Skylar Litz", "Leon Liu", "Anying Li", "David Harmon", "Derek Liu", "Liangjun Feng", "Denis Goupil", "Lukas Kuczynski", "Jihyun Yoon", "Naveen Marri", "Peiye Zhuang", "Yinan Zhang", "Brian Yin", "Haomiao Jiang", "Marcel van Workum", "Thomas Lane", "Bryce Erickson", "Salil Pathare", "Kyle Price", "Anupam Singh", "David Baszucki" ], "github": "https://github.com/Roblox/cube", "project_page": "", "abstract": "Foundation models trained on vast amounts of data have demonstrated remarkable reasoning and generation capabilities in the domains of text, images, audio and video. Our goal at Roblox is to build such a foundation model for 3D intelligence, a model that can support developers in producing all aspects of a Roblox experience, from generating 3D objects and scenes to rigging characters for animation to producing programmatic scripts describing object behaviors. We discuss three key design requirements for such a 3D foundation model and then present our first step towards building such a model. We expect that 3D geometric shapes will be a core data type and describe our solution for 3D shape tokenizer. We show how our tokenization scheme can be used in applications for text-to-shape generation, shape-to-text generation and text-to-scene generation. We demonstrate how these applications can collaborate with existing large language models (LLMs) to perform scene analysis and reasoning. We conclude with a discussion outlining our path to building a fully unified foundation model for 3D intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Roblox/cube", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.12769", "title": "ViSpeak: Visual Instruction Feedback in Streaming Videos", "authors": [ "Shenghao Fu", "Qize Yang", "Yuan-Ming Li", "Yi-Xing Peng", "Kun-Yu Lin", "Xihan Wei", "Jian-Fang Hu", "Xiaohua Xie", "Wei-Shi Zheng" ], "github": "", "project_page": "", "abstract": "Recent advances in Large Multi-modal Models (LMMs) are primarily focused on offline video understanding. Instead, streaming video understanding poses great challenges to recent models due to its time-sensitive, omni-modal and interactive characteristics. In this work, we aim to extend the streaming video understanding from a new perspective and propose a novel task named Visual Instruction Feedback in which models should be aware of visual contents and learn to extract instructions from them. For example, when users wave their hands to agents, agents should recognize the gesture and start conversations with welcome information. Thus, following instructions in visual modality greatly enhances user-agent interactions. To facilitate research, we define seven key subtasks highly relevant to visual modality and collect the ViSpeak-Instruct dataset for training and the ViSpeak-Bench for evaluation. Further, we propose the ViSpeak model, which is a SOTA streaming video understanding LMM with GPT-4o-level performance on various streaming video understanding benchmarks. After finetuning on our ViSpeak-Instruct dataset, ViSpeak is equipped with basic visual instruction feedback ability, serving as a solid baseline for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.15417", "title": "Temporal Regularization Makes Your Video Generator Stronger", "authors": [ "Harold Haodong Chen", "Haojian Huang", "Xianfeng Wu", "Yexin Liu", "Yajing Bai", "Wen-Jie Shu", "Harry Yang", "Ser-Nam Lim" ], "github": "", "project_page": "", "abstract": "Temporal quality is a critical aspect of video generation, as it ensures consistent motion and realistic dynamics across frames. However, achieving high temporal coherence and diversity remains challenging. In this work, we explore temporal augmentation in video generation for the first time, and introduce FluxFlow for initial investigation, a strategy designed to enhance temporal quality. Operating at the data level, FluxFlow applies controlled temporal perturbations without requiring architectural modifications. Extensive experiments on UCF-101 and VBench benchmarks demonstrate that FluxFlow significantly improves temporal coherence and diversity across various video generation models, including U-Net, DiT, and AR-based architectures, while preserving spatial fidelity. These findings highlight the potential of temporal augmentation as a simple yet effective approach to advancing video generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.12532", "title": "STEVE: AStep Verification Pipeline for Computer-use Agent Training", "authors": [ "Fanbin Lu", "Zhisheng Zhong", "Ziqin Wei", "Shu Liu", "Chi-Wing Fu", "Jiaya Jia" ], "github": "https://github.com/FanbinLu/STEVE", "project_page": "", "abstract": "Developing AI agents to autonomously manipulate graphical user interfaces is a long challenging task. Recent advances in data scaling law inspire us to train computer-use agents with a scaled instruction set, yet using behavior cloning to train agents still requires immense high-quality trajectories. To meet the scalability need, we designed STEVE, a step verification pipeline for computer-use agent training. First, we establish a large instruction set for computer-use agents and collect trajectory data with some suboptimal agents. GPT-4o is used to verify the correctness of each step in the trajectories based on the screens before and after the action execution, assigning each step with a binary label. Last, we adopt the Kahneman and Tversky Optimization to optimize the agent from the binary stepwise labels. Extensive experiments manifest that our agent outperforms supervised finetuning by leveraging both positive and negative actions within a trajectory. Also, STEVE enables us to train a 7B vision-language model as a computer-use agent, achieving leading performance in the challenging live desktop environment WinAgentArena with great efficiency at a reduced cost. Code and data: https://github.com/FanbinLu/STEVE.", "github_manual": "", "github_regex": "https://github.com/FanbinLu/STEVE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.14505", "title": "MusicInfuser: Making Video Diffusion Listen and Dance", "authors": [ "Susung Hong", "Ira Kemelmacher-Shlizerman", "Brian Curless", "Steven M. Seitz" ], "github": "", "project_page": "", "abstract": "We introduce MusicInfuser, an approach for generating high-quality dance videos that are synchronized to a specified music track. Rather than attempting to design and train a new multimodal audio-video model, we show how existing video diffusion models can be adapted to align with musical inputs by introducing lightweight music-video cross-attention and a low-rank adapter. Unlike prior work requiring motion capture data, our approach fine-tunes only on dance videos. MusicInfuser achieves high-quality music-driven video generation while preserving the flexibility and generative capabilities of the underlying models. We introduce an evaluation framework using Video-LLMs to assess multiple dimensions of dance generation quality. The project page and code are available at https://susunghong.github.io/MusicInfuser.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.15055", "title": "ELTEX: A Framework for Domain-Driven Synthetic Data Generation", "authors": [ "Arina Razmyslovich", "Kseniia Murasheva", "Sofia Sedlova", "Julien Capitaine", "Eugene Dmitriev" ], "github": "", "project_page": "", "abstract": "We present ELTEX (Efficient LLM Token Extraction), a domain-driven framework for generating high-quality synthetic training data in specialized domains. While Large Language Models (LLMs) have shown impressive general capabilities, their performance in specialized domains like cybersecurity remains limited by the scarcity of domain-specific training data. ELTEX addresses this challenge by systematically integrating explicit domain indicator extraction with dynamic prompting to preserve critical domain knowledge throughout the generation process. We demonstrate ELTEX's effectiveness in the context of blockchain-related cyberattack detection, where we fine-tune Gemma-2B using various combinations of real and ELTEX-generated data. Our results show that the ELTEX-enhanced model achieves performance competitive with GPT-4 across both standard classification metrics and uncertainty calibration, while requiring significantly fewer computational resources. We release a curated synthetic dataset of social media texts for cyberattack detection in blockchain. Our work demonstrates that domain-driven synthetic data generation can effectively bridge the performance gap between resource-efficient models and larger architectures in specialized domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.14868", "title": "Efficient Personalization of Quantized Diffusion Model without Backpropagation", "authors": [ "Hoigi Seo", "Wongi Jeong", "Kyungryeol Lee", "Se Young Chun" ], "github": "", "project_page": "", "abstract": "Diffusion models have shown remarkable performance in image synthesis, but they demand extensive computational and memory resources for training, fine-tuning and inference. Although advanced quantization techniques have successfully minimized memory usage for inference, training and fine-tuning these quantized models still require large memory possibly due to dequantization for accurate computation of gradients and/or backpropagation for gradient-based algorithms. However, memory-efficient fine-tuning is particularly desirable for applications such as personalization that often must be run on edge devices like mobile phones with private data. In this work, we address this challenge by quantizing a diffusion model with personalization via Textual Inversion and by leveraging a zeroth-order optimization on personalization tokens without dequantization so that it does not require gradient and activation storage for backpropagation that consumes considerable memory. Since a gradient estimation using zeroth-order optimization is quite noisy for a single or a few images in personalization, we propose to denoise the estimated gradient by projecting it onto a subspace that is constructed with the past history of the tokens, dubbed Subspace Gradient. In addition, we investigated the influence of text embedding in image generation, leading to our proposed time steps sampling, dubbed Partial Uniform Timestep Sampling for sampling with effective diffusion timesteps. Our method achieves comparable performance to prior methods in image and text alignment scores for personalizing Stable Diffusion with only forward passes while reducing training memory demand up to 8.2times.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.15265", "title": "DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning", "authors": [ "Ruowen Zhao", "Junliang Ye", "Zhengyi Wang", "Guangce Liu", "Yiwen Chen", "Yikai Wang", "Jun Zhu" ], "github": "", "project_page": "", "abstract": "Triangle meshes play a crucial role in 3D applications for efficient manipulation and rendering. While auto-regressive methods generate structured meshes by predicting discrete vertex tokens, they are often constrained by limited face counts and mesh incompleteness. To address these challenges, we propose DeepMesh, a framework that optimizes mesh generation through two key innovations: (1) an efficient pre-training strategy incorporating a novel tokenization algorithm, along with improvements in data curation and processing, and (2) the introduction of Reinforcement Learning (RL) into 3D mesh generation to achieve human preference alignment via Direct Preference Optimization (DPO). We design a scoring standard that combines human evaluation with 3D metrics to collect preference pairs for DPO, ensuring both visual appeal and geometric accuracy. Conditioned on point clouds and images, DeepMesh generates meshes with intricate details and precise topology, outperforming state-of-the-art methods in both precision and quality. Project page: https://zhaorw02.github.io/DeepMesh/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.13360", "title": "Mitigating Visual Forgetting via Take-along Visual Conditioning for Multi-modal Long CoT Reasoning", "authors": [ "Hai-Long Sun", "Zhun Sun", "Houwen Peng", "Han-Jia Ye" ], "github": "https://github.com/sun-hailong/TVC", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) have demonstrated enhanced reasoning capabilities, evolving from Chain-of-Thought (CoT) prompting to advanced, product-oriented solutions like OpenAI o1. During our re-implementation of this model, we noticed that in multimodal tasks requiring visual input (e.g., geometry problems), Multimodal LLMs (MLLMs) struggle to maintain focus on the visual information, in other words, MLLMs suffer from a gradual decline in attention to visual information as reasoning progresses, causing text-over-relied outputs. To investigate this, we ablate image inputs during long-chain reasoning. Concretely, we truncate the reasoning process midway, then re-complete the reasoning process with the input image removed. We observe only a ~2% accuracy drop on MathVista's test-hard subset, revealing the model's textual outputs dominate the following reasoning process. Motivated by this, we propose Take-along Visual Conditioning (TVC), a strategy that shifts image input to critical reasoning stages and compresses redundant visual tokens via dynamic pruning. This methodology helps the model retain attention to the visual components throughout the reasoning. Our approach achieves state-of-the-art performance on average across five mathematical reasoning benchmarks (+3.4% vs previous sota), demonstrating the effectiveness of TVC in enhancing multimodal reasoning systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sun-hailong/TVC", "github_api_added_by": "auto" }, { "date": "2025-03-20", "arxiv_id": "2503.13288", "title": "\u03c6-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation", "authors": [ "Fangzhi Xu", "Hang Yan", "Chang Ma", "Haiteng Zhao", "Jun Liu", "Qika Lin", "Zhiyong Wu" ], "github": "https://github.com/xufangzhi/phi-Decoding", "project_page": "", "abstract": "Inference-time optimization scales computation to derive deliberate reasoning steps for effective performance. While previous search-based strategies address the short-sightedness of auto-regressive generation, the vast search space leads to excessive exploration and insufficient exploitation. To strike an efficient balance to derive the optimal step, we frame the decoding strategy as foresight sampling, leveraging simulated future steps to obtain globally optimal step estimation. Built on it, we propose a novel decoding strategy, named phi-Decoding. To provide a precise and expressive estimation of step value, phi-Decoding approximates two distributions via foresight and clustering. Sampling from the joint distribution, the optimal steps can be selected for exploitation. To support adaptive computation allocation, we propose in-width and in-depth pruning strategies, featuring a light-weight solution to achieve inference efficiency. Extensive experiments across seven benchmarks show phi-Decoding outperforms strong baselines in both performance and efficiency. Additional analysis demonstrates its generalization across various LLMs and scalability across a wide range of computing budgets. The code will be released at https://github.com/xufangzhi/phi-Decoding, and the open-source PyPI package is coming soon.", "github_manual": "", "github_regex": "https://github.com/xufangzhi/phi-Decoding", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.15264", "title": "LEGION: Learning to Ground and Explain for Synthetic Image Detection", "authors": [ "Hengrui Kang", "Siwei Wen", "Zichen Wen", "Junyan Ye", "Weijia Li", "Peilin Feng", "Baichuan Zhou", "Bin Wang", "Dahua Lin", "Linfeng Zhang", "Conghui He" ], "github": "", "project_page": "", "abstract": "The rapid advancements in generative technology have emerged as a double-edged sword. While offering powerful tools that enhance convenience, they also pose significant social concerns. As defenders, current synthetic image detection methods often lack artifact-level textual interpretability and are overly focused on image manipulation detection, and current datasets usually suffer from outdated generators and a lack of fine-grained annotations. In this paper, we introduce SynthScars, a high-quality and diverse dataset consisting of 12,236 fully synthetic images with human-expert annotations. It features 4 distinct image content types, 3 categories of artifacts, and fine-grained annotations covering pixel-level segmentation, detailed textual explanations, and artifact category labels. Furthermore, we propose LEGION (LEarning to Ground and explain for Synthetic Image detectiON), a multimodal large language model (MLLM)-based image forgery analysis framework that integrates artifact detection, segmentation, and explanation. Building upon this capability, we further explore LEGION as a controller, integrating it into image refinement pipelines to guide the generation of higher-quality and more realistic images. Extensive experiments show that LEGION outperforms existing methods across multiple benchmarks, particularly surpassing the second-best traditional expert on SynthScars by 3.31% in mIoU and 7.75% in F1 score. Moreover, the refined images generated under its guidance exhibit stronger alignment with human preferences. The code, model, and dataset will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.11227", "title": "GKG-LLM: A Unified Framework for Generalized Knowledge Graph Construction", "authors": [ "Jian Zhang", "Bifan Wei", "Shihao Qi", "haiping Zhu", "Jun Liu", "Qika Lin" ], "github": "", "project_page": "", "abstract": "The construction of Generalized Knowledge Graph (GKG), including knowledge graph, event knowledge graph and commonsense knowledge graph, is fundamental for various natural language processing tasks. Current studies typically construct these types of graph separately, overlooking holistic insights and potential unification that could be beneficial in computing resources and usage perspectives. However, a key challenge in developing a unified framework for GKG is obstacles arising from task-specific differences. In this study, we propose a unified framework for constructing generalized knowledge graphs to address this challenge. First, we collect data from 15 sub-tasks in 29 datasets across the three types of graphs, categorizing them into in-sample, counter-task, and out-of-distribution (OOD) data. Then, we propose a three-stage curriculum learning fine-tuning framework, by iteratively injecting knowledge from the three types of graphs into the Large Language Models. Extensive experiments show that our proposed model improves the construction of all three graph types across in-domain, OOD and counter-task data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.12963", "title": "Unlock Pose Diversity: Accurate and Efficient Implicit Keypoint-based Spatiotemporal Diffusion for Audio-driven Talking Portrait", "authors": [ "Chaolong Yang", "Kai Yao", "Yuyao Yan", "Chenru Jiang", "Weiguang Zhao", "Jie Sun", "Guangliang Cheng", "Yifei Zhang", "Bin Dong", "Kaizhu Huang" ], "github": "https://github.com/chaolongy/KDTalker", "project_page": "", "abstract": "Audio-driven single-image talking portrait generation plays a crucial role in virtual reality, digital human creation, and filmmaking. Existing approaches are generally categorized into keypoint-based and image-based methods. Keypoint-based methods effectively preserve character identity but struggle to capture fine facial details due to the fixed points limitation of the 3D Morphable Model. Moreover, traditional generative networks face challenges in establishing causality between audio and keypoints on limited datasets, resulting in low pose diversity. In contrast, image-based approaches produce high-quality portraits with diverse details using the diffusion network but incur identity distortion and expensive computational costs. In this work, we propose KDTalker, the first framework to combine unsupervised implicit 3D keypoint with a spatiotemporal diffusion model. Leveraging unsupervised implicit 3D keypoints, KDTalker adapts facial information densities, allowing the diffusion process to model diverse head poses and capture fine facial details flexibly. The custom-designed spatiotemporal attention mechanism ensures accurate lip synchronization, producing temporally consistent, high-quality animations while enhancing computational efficiency. Experimental results demonstrate that KDTalker achieves state-of-the-art performance regarding lip synchronization accuracy, head pose diversity, and execution efficiency.Our codes are available at https://github.com/chaolongy/KDTalker.", "github_manual": "", "github_regex": "https://github.com/chaolongy/KDTalker", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.14891", "title": "MetaLadder: Ascending Mathematical Solution Quality via Analogical-Problem Reasoning Transfer", "authors": [ "Honglin Lin", "Zhuoshi Pan", "Yu Li", "Qizhi Pei", "Xin Gao", "Mengzhang Cai", "Conghui He", "Lijun Wu" ], "github": "https://github.com/LHL3341/MetaLadder", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated promising capabilities in solving mathematical reasoning tasks, leveraging Chain-of-Thought (CoT) data as a vital component in guiding answer generation. Current paradigms typically generate CoT and answers directly for a given problem, diverging from human problem-solving strategies to some extent. Humans often solve problems by recalling analogous cases and leveraging their solutions to reason about the current task. Inspired by this cognitive process, we propose MetaLadder, a novel framework that explicitly prompts LLMs to recall and reflect on meta-problems, those structurally or semantically analogous problems, alongside their CoT solutions before addressing the target problem. Additionally, we introduce a problem-restating mechanism to enhance the model's comprehension of the target problem by regenerating the original question, which further improves reasoning accuracy. Therefore, the model can achieve reasoning transfer from analogical problems, mimicking human-like \"learning from examples\" and generalization abilities. Extensive experiments on mathematical benchmarks demonstrate that our MetaLadder significantly boosts LLMs' problem-solving accuracy, largely outperforming standard CoT-based methods (10.3\\% accuracy gain) and other methods. Our code and data has been released at https://github.com/LHL3341/MetaLadder.", "github_manual": "", "github_regex": "https://github.com/LHL3341/MetaLadder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.13517", "title": "CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning", "authors": [ "Hao Cui", "Zahra Shamsi", "Gowoon Cheon", "Xuejian Ma", "Shutong Li", "Maria Tikhanovskaya", "Peter Norgaard", "Nayantara Mudur", "Martyna Plomecka", "Paul Raccuglia", "Yasaman Bahri", "Victor V. Albert", "Pranesh Srinivasan", "Haining Pan", "Philippe Faist", "Brian Rohr", "Michael J. Statt", "Dan Morris", "Drew Purves", "Elise Kleeman", "Ruth Alcantara", "Matthew Abraham", "Muqthar Mohammad", "Ean Phing VanLee", "Chenfei Jiang", "Elizabeth Dorfman", "Eun-Ah Kim", "Michael P Brenner", "Viren Jain", "Sameera Ponda", "Subhashini Venugopalan" ], "github": "https://github.com/google/curie", "project_page": "", "abstract": "Scientific problem-solving involves synthesizing information while applying expert knowledge. We introduce CURIE, a scientific long-Context Understanding,Reasoning and Information Extraction benchmark to measure the potential of Large Language Models (LLMs) in scientific problem-solving and assisting scientists in realistic workflows. This benchmark introduces ten challenging tasks with a total of 580 problems and solution pairs curated by experts in six disciplines - materials science, condensed matter physics, quantum computing, geospatial analysis, biodiversity, and proteins - covering both experimental and theoretical work-flows in science. We evaluate a range of closed and open LLMs on tasks in CURIE which requires domain expertise, comprehension of long in-context information,and multi-step reasoning. While Gemini Flash 2.0 and Claude-3 show consistent high comprehension across domains, the popular GPT-4o and command-R+ fail dramatically on protein sequencing tasks. With the best performance at 32% there is much room for improvement for all models. We hope that insights gained from CURIE can guide the future development of LLMs in sciences. Evaluation code and data are in https://github.com/google/curie", "github_manual": "", "github_regex": "https://github.com/google/curie", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.11557", "title": "VERIFY: A Benchmark of Visual Explanation and Reasoning for Investigating Multimodal Reasoning Fidelity", "authors": [ "Jing Bi", "Junjia Guo", "Susan Liang", "Guangyu Sun", "Luchuan Song", "Yunlong Tang", "Jinxi He", "Jiarui Wu", "Ali Vosoughi", "Chen Chen", "Chenliang Xu" ], "github": "", "project_page": "", "abstract": "Visual reasoning is central to human cognition, enabling individuals to interpret and abstractly understand their environment. Although recent Multimodal Large Language Models (MLLMs) have demonstrated impressive performance across language and vision-language tasks, existing benchmarks primarily measure recognition-based skills and inadequately assess true visual reasoning capabilities. To bridge this critical gap, we introduce VERIFY, a benchmark explicitly designed to isolate and rigorously evaluate the visual reasoning capabilities of state-of-the-art MLLMs. VERIFY compels models to reason primarily from visual information, providing minimal textual context to reduce reliance on domain-specific knowledge and linguistic biases. Each problem is accompanied by a human-annotated reasoning path, making it the first to provide in-depth evaluation of model decision-making processes. Additionally, we propose novel metrics that assess visual reasoning fidelity beyond mere accuracy, highlighting critical imbalances in current model reasoning patterns. Our comprehensive benchmarking of leading MLLMs uncovers significant limitations, underscoring the need for a balanced and holistic approach to both perception and reasoning. For more teaser and testing, visit our project page (https://verify-eqh.pages.dev/).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.15354", "title": "Optimizing Decomposition for Optimal Claim Verification", "authors": [ "Yining Lu", "Noah Ziems", "Hy Dang", "Meng Jiang" ], "github": "https://github.com/yining610/dynamic-decomposition", "project_page": "", "abstract": "Current research on the Decompose-Then-Verify paradigm for evaluating the factuality of long-form text typically treats decomposition and verification in isolation, overlooking their interactions and potential misalignment. We find that existing decomposition policies, typically hand-crafted demonstrations, do not align well with downstream verifiers in terms of atomicity -- a novel metric quantifying information density -- leading to suboptimal verification results. We formulate finding the optimal decomposition policy for optimal verification as a bilevel optimization problem. To approximate a solution for this strongly NP-hard problem, we propose dynamic decomposition, a reinforcement learning framework that leverages verifier feedback to learn a policy for dynamically decomposing claims to verifier-preferred atomicity. Experimental results show that dynamic decomposition outperforms existing decomposition policies, improving verification confidence by 0.07 and accuracy by 0.12 (on a 0-1 scale) on average across varying verifiers, datasets, and atomcities of input claims.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yining610/dynamic-decomposition", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.14830", "title": "Decompositional Neural Scene Reconstruction with Generative Diffusion Prior", "authors": [ "Junfeng Ni", "Yu Liu", "Ruijie Lu", "Zirui Zhou", "Song-Chun Zhu", "Yixin Chen", "Siyuan Huang" ], "github": "https://github.com/DP-Recon/DP-Recon", "project_page": "", "abstract": "Decompositional reconstruction of 3D scenes, with complete shapes and detailed texture of all objects within, is intriguing for downstream applications but remains challenging, particularly with sparse views as input. Recent approaches incorporate semantic or geometric regularization to address this issue, but they suffer significant degradation in underconstrained areas and fail to recover occluded regions. We argue that the key to solving this problem lies in supplementing missing information for these areas. To this end, we propose DP-Recon, which employs diffusion priors in the form of Score Distillation Sampling (SDS) to optimize the neural representation of each individual object under novel views. This provides additional information for the underconstrained areas, but directly incorporating diffusion prior raises potential conflicts between the reconstruction and generative guidance. Therefore, we further introduce a visibility-guided approach to dynamically adjust the per-pixel SDS loss weights. Together these components enhance both geometry and appearance recovery while remaining faithful to input images. Extensive experiments across Replica and ScanNet++ demonstrate that our method significantly outperforms SOTA methods. Notably, it achieves better object reconstruction under 10 views than the baselines under 100 views. Our method enables seamless text-based editing for geometry and appearance through SDS optimization and produces decomposed object meshes with detailed UV maps that support photorealistic Visual effects (VFX) editing. The project page is available at https://dp-recon.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DP-Recon/DP-Recon", "github_api_added_by": "auto" }, { "date": "2025-03-20", "arxiv_id": "2503.15450", "title": "SkyLadder: Better and Faster Pretraining via Context Window Scheduling", "authors": [ "Tongyao Zhu", "Qian Liu", "Haonan Wang", "Shiqi Chen", "Xiangming Gu", "Tianyu Pang", "Min-Yen Kan" ], "github": "https://github.com/sail-sg/SkyLadder", "project_page": "", "abstract": "Recent advancements in LLM pretraining have featured ever-expanding context windows to process longer sequences. However, our pilot study reveals that models pretrained with shorter context windows consistently outperform their long-context counterparts under a fixed token budget. This finding motivates us to explore an optimal context window scheduling strategy to better balance long-context capability with pretraining efficiency. To this end, we propose SkyLadder, a simple yet effective approach that implements a short-to-long context window transition. SkyLadder preserves strong standard benchmark performance, while matching or exceeding baseline results on long context tasks. Through extensive experiments, we pre-train 1B-parameter models (up to 32K context) and 3B-parameter models (8K context) on 100B tokens, demonstrating that SkyLadder yields consistent gains of up to 3.7% on common benchmarks, while achieving up to 22% faster training speeds compared to baselines. The code is at https://github.com/sail-sg/SkyLadder.", "github_manual": "", "github_regex": "https://github.com/sail-sg/SkyLadder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.14434", "title": "LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers", "authors": [ "Nikhil Abhyankar", "Parshin Shojaee", "Chandan K. Reddy" ], "github": "https://github.com/nikhilsab/LLMFE", "project_page": "", "abstract": "Automated feature engineering plays a critical role in improving predictive model performance for tabular learning tasks. Traditional automated feature engineering methods are limited by their reliance on pre-defined transformations within fixed, manually designed search spaces, often neglecting domain knowledge. Recent advances using Large Language Models (LLMs) have enabled the integration of domain knowledge into the feature engineering process. However, existing LLM-based approaches use direct prompting or rely solely on validation scores for feature selection, failing to leverage insights from prior feature discovery experiments or establish meaningful reasoning between feature generation and data-driven performance. To address these challenges, we propose LLM-FE, a novel framework that combines evolutionary search with the domain knowledge and reasoning capabilities of LLMs to automatically discover effective features for tabular learning tasks. LLM-FE formulates feature engineering as a program search problem, where LLMs propose new feature transformation programs iteratively, and data-driven feedback guides the search process. Our results demonstrate that LLM-FE consistently outperforms state-of-the-art baselines, significantly enhancing the performance of tabular prediction models across diverse classification and regression benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nikhilsab/LLMFE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-20", "arxiv_id": "2503.15478", "title": "SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks", "authors": [ "Yifei Zhou", "Song Jiang", "Yuandong Tian", "Jason Weston", "Sergey Levine", "Sainbayar Sukhbaatar", "Xian Li" ], "github": "https://github.com/facebookresearch/sweet_rl", "project_page": "", "abstract": "Large language model (LLM) agents need to perform multi-turn interactions in real-world tasks. However, existing multi-turn RL algorithms for optimizing LLM agents fail to perform effective credit assignment over multiple turns while leveraging the generalization capabilities of LLMs and it remains unclear how to develop such algorithms. To study this, we first introduce a new benchmark, ColBench, where an LLM agent interacts with a human collaborator over multiple turns to solve realistic tasks in backend programming and frontend design. Building on this benchmark, we propose a novel RL algorithm, SWEET-RL (RL with Step-WisE Evaluation from Training-time information), that uses a carefully designed optimization objective to train a critic model with access to additional training-time information. The critic provides step-level rewards for improving the policy model. Our experiments demonstrate that SWEET-RL achieves a 6% absolute improvement in success and win rates on ColBench compared to other state-of-the-art multi-turn RL algorithms, enabling Llama-3.1-8B to match or exceed the performance of GPT4-o in realistic collaborative content creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/sweet_rl", "github_api_added_by": "auto" }, { "date": "2025-03-20", "arxiv_id": "2503.13553", "title": "LLM-Mediated Guidance of MARL Systems", "authors": [ "Philipp D. Siedler", "Ian Gemp" ], "github": "", "project_page": "", "abstract": "In complex multi-agent environments, achieving efficient learning and desirable behaviours is a significant challenge for Multi-Agent Reinforcement Learning (MARL) systems. This work explores the potential of combining MARL with Large Language Model (LLM)-mediated interventions to guide agents toward more desirable behaviours. Specifically, we investigate how LLMs can be used to interpret and facilitate interventions that shape the learning trajectories of multiple agents. We experimented with two types of interventions, referred to as controllers: a Natural Language (NL) Controller and a Rule-Based (RB) Controller. The NL Controller, which uses an LLM to simulate human-like interventions, showed a stronger impact than the RB Controller. Our findings indicate that agents particularly benefit from early interventions, leading to more efficient training and higher performance. Both intervention types outperform the baseline without interventions, highlighting the potential of LLM-mediated guidance to accelerate training and enhance MARL performance in challenging environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16356", "title": "CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners", "authors": [ "Yunzhi Yao", "Jizhan Fang", "Jia-Chen Gu", "Ningyu Zhang", "Shumin Deng", "Huajun Chen", "Nanyun Peng" ], "github": "https://github.com/zjunlp/CaKE", "project_page": "", "abstract": "Knowledge Editing (KE) enables the modification of outdated or incorrect information in large language models (LLMs). While existing KE methods can update isolated facts, they struggle to generalize these updates to multi-hop reasoning tasks that depend on the modified knowledge. Through an analysis of reasoning circuits -- the neural pathways LLMs use for knowledge-based inference, we observe that current layer-localized KE approaches, such as MEMIT and WISE, which edit only single or a few model layers, struggle to effectively incorporate updated information into these reasoning pathways. To address this limitation, we propose CaKE (Circuit-aware Knowledge Editing), a novel method that enables more effective integration of updated knowledge in LLMs. CaKE leverages strategically curated data, guided by our circuits-based analysis, that enforces the model to utilize the modified knowledge, stimulating the model to develop appropriate reasoning circuits for newly integrated knowledge. Experimental results show that CaKE enables more accurate and consistent use of updated knowledge across related reasoning tasks, leading to an average of 20% improvement in multi-hop reasoning accuracy on MQuAKE dataset compared to existing KE methods. We release the code and data in https://github.com/zjunlp/CaKE.", "github_manual": "", "github_regex": "https://github.com/zjunlp/CaKE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16278", "title": "Uni-3DAR: Unified 3D Generation and Understanding via Autoregression on Compressed Spatial Tokens", "authors": [ "Shuqi Lu", "Haowei Lin", "Lin Yao", "Zhifeng Gao", "Xiaohong Ji", "Weinan E", "Linfeng Zhang", "Guolin Ke" ], "github": "https://github.com/dptech-corp/Uni-3DAR", "project_page": "", "abstract": "Recent advancements in large language models and their multi-modal extensions have demonstrated the effectiveness of unifying generation and understanding through autoregressive next-token prediction. However, despite the critical role of 3D structural generation and understanding ({3D GU}) in AI for science, these tasks have largely evolved independently, with autoregressive methods remaining underexplored. To bridge this gap, we introduce Uni-3DAR, a unified framework that seamlessly integrates {3D GU} tasks via autoregressive prediction. At its core, Uni-3DAR employs a novel hierarchical tokenization that compresses 3D space using an octree, leveraging the inherent sparsity of 3D structures. It then applies an additional tokenization for fine-grained structural details, capturing key attributes such as atom types and precise spatial coordinates in microscopic 3D structures. We further propose two optimizations to enhance efficiency and effectiveness. The first is a two-level subtree compression strategy, which reduces the octree token sequence by up to 8x. The second is a masked next-token prediction mechanism tailored for dynamically varying token positions, significantly boosting model performance. By combining these strategies, Uni-3DAR successfully unifies diverse {3D GU} tasks within a single autoregressive framework. Extensive experiments across multiple microscopic {3D GU} tasks, including molecules, proteins, polymers, and crystals, validate its effectiveness and versatility. Notably, Uni-3DAR surpasses previous state-of-the-art diffusion models by a substantial margin, achieving up to 256\\% relative improvement while delivering inference speeds up to 21.8x faster. The code is publicly available at https://github.com/dptech-corp/Uni-3DAR.", "github_manual": "", "github_regex": "https://github.com/dptech-corp/Uni-3DAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.15567", "title": "Towards Unified Latent Space for 3D Molecular Latent Diffusion Modeling", "authors": [ "Yanchen Luo", "Zhiyuan Liu", "Yi Zhao", "Sihang Li", "Kenji Kawaguchi", "Tat-Seng Chua", "Xiang Wang" ], "github": "https://github.com/lyc0930/UAE-3D", "project_page": "", "abstract": "3D molecule generation is crucial for drug discovery and material science, requiring models to process complex multi-modalities, including atom types, chemical bonds, and 3D coordinates. A key challenge is integrating these modalities of different shapes while maintaining SE(3) equivariance for 3D coordinates. To achieve this, existing approaches typically maintain separate latent spaces for invariant and equivariant modalities, reducing efficiency in both training and sampling. In this work, we propose Unified Variational Auto-Encoder for 3D Molecular Latent Diffusion Modeling (UAE-3D), a multi-modal VAE that compresses 3D molecules into latent sequences from a unified latent space, while maintaining near-zero reconstruction error. This unified latent space eliminates the complexities of handling multi-modality and equivariance when performing latent diffusion modeling. We demonstrate this by employing the Diffusion Transformer--a general-purpose diffusion model without any molecular inductive bias--for latent generation. Extensive experiments on GEOM-Drugs and QM9 datasets demonstrate that our method significantly establishes new benchmarks in both de novo and conditional 3D molecule generation, achieving leading efficiency and quality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lyc0930/UAE-3D", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16252", "title": "Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning", "authors": [ "Zhaowei Liu", "Xin Guo", "Fangqi Lou", "Lingfeng Zeng", "Jinyi Niu", "Zixuan Wang", "Jiajie Xu", "Weige Cai", "Ziwei Yang", "Xueqian Zhao", "Chao Li", "Sheng Xu", "Dezhi Chen", "Yun Chen", "Zuo Bai", "Liwen Zhang" ], "github": "https://github.com/SUFE-AIFLM-Lab/Fin-R1", "project_page": "", "abstract": "Reasoning large language models are rapidly evolving across various domains. However, their capabilities in handling complex financial tasks still require in-depth exploration. In this paper, we introduce Fin-R1, a reasoning large language model specifically designed for the financial sector. Fin-R1 is built using a two-stage architecture, leveraging a financial reasoning dataset distilled and processed based on DeepSeek-R1. Through supervised fine-tuning (SFT) and reinforcement learning (RL) training, it demonstrates performance close to DeepSeek-R1 with a parameter size of 7 billion across a range of financial reasoning tasks. It achieves the state-of-the-art (SOTA) in the FinQA and ConvFinQA tasks between those LLMs in our evaluation, surpassing larger models in other tasks as well. Fin-R1 showcases strong reasoning and decision-making capabilities, providing solutions to various problems encountered in the financial domain. Our code is available at https://github.com/SUFE-AIFLM-Lab/Fin-R1.", "github_manual": "", "github_regex": "https://github.com/SUFE-AIFLM-Lab/Fin-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16212", "title": "MathFusion: Enhancing Mathematic Problem-solving of LLM through Instruction Fusion", "authors": [ "Qizhi Pei", "Lijun Wu", "Zhuoshi Pan", "Yu Li", "Honglin Lin", "Chenlin Ming", "Xin Gao", "Conghui He", "Rui Yan" ], "github": "https://github.com/QizhiPei/mathfusion", "project_page": "", "abstract": "Large Language Models (LLMs) have shown impressive progress in mathematical reasoning. While data augmentation is promising to enhance mathematical problem-solving ability, current approaches are predominantly limited to instance-level modifications-such as rephrasing or generating syntactic variations-which fail to capture and leverage the intrinsic relational structures inherent in mathematical knowledge. Inspired by human learning processes, where mathematical proficiency develops through systematic exposure to interconnected concepts, we introduce MathFusion, a novel framework that enhances mathematical reasoning through cross-problem instruction synthesis. MathFusion implements this through three fusion strategies: (1) sequential fusion, which chains related problems to model solution dependencies; (2) parallel fusion, which combines analogous problems to reinforce conceptual understanding; and (3) conditional fusion, which creates context-aware selective problems to enhance reasoning flexibility. By applying these strategies, we generate a new dataset, MathFusionQA, followed by fine-tuning models (DeepSeekMath-7B, Mistral-7B, Llama3-8B) on it. Experimental results demonstrate that MathFusion achieves substantial improvements in mathematical reasoning while maintaining high data efficiency, boosting performance by 18.0 points in accuracy across diverse benchmarks while requiring only 45K additional synthetic instructions, representing a substantial improvement over traditional single-instruction approaches. Our datasets, models, and code are publicly available at https://github.com/QizhiPei/mathfusion.", "github_manual": "", "github_regex": "https://github.com/QizhiPei/mathfusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16031", "title": "Deceptive Humor: A Synthetic Multilingual Benchmark Dataset for Bridging Fabricated Claims with Humorous Content", "authors": [ "Sai Kartheek Reddy Kasu", "Shankar Biradar", "Sunil Saumya" ], "github": "", "project_page": "", "abstract": "This paper presents the Deceptive Humor Dataset (DHD), a novel resource for studying humor derived from fabricated claims and misinformation. In an era of rampant misinformation, understanding how humor intertwines with deception is essential. DHD consists of humor-infused comments generated from false narratives, incorporating fabricated claims and manipulated information using the ChatGPT-4o model. Each instance is labeled with a Satire Level, ranging from 1 for subtle satire to 3 for high-level satire and classified into five distinct Humor Categories: Dark Humor, Irony, Social Commentary, Wordplay, and Absurdity. The dataset spans multiple languages including English, Telugu, Hindi, Kannada, Tamil, and their code-mixed variants (Te-En, Hi-En, Ka-En, Ta-En), making it a valuable multilingual benchmark. By introducing DHD, we establish a structured foundation for analyzing humor in deceptive contexts, paving the way for a new research direction that explores how humor not only interacts with misinformation but also influences its perception and spread. We establish strong baselines for the proposed dataset, providing a foundation for future research to benchmark and advance deceptive humor detection models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.15558", "title": "Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning", "authors": [ "NVIDIA", "Alisson Azzolini", "Hannah Brandon", "Prithvijit Chattopadhyay", "Huayu Chen", "Jinju Chu", "Yin Cui", "Jenna Diamond", "Yifan Ding", "Francesco Ferroni", "Rama Govindaraju", "Jinwei Gu", "Siddharth Gururani", "Imad El Hanafi", "Zekun Hao", "Jacob Huffman", "Jingyi Jin", "Brendan Johnson", "Rizwan Khan", "George Kurian", "Elena Lantz", "Nayeon Lee", "Zhaoshuo Li", "Xuan Li", "Tsung-Yi Lin", "Yen-Chen Lin", "Ming-Yu Liu", "Andrew Mathau", "Yun Ni", "Lindsey Pavao", "Wei Ping", "David W. Romero", "Misha Smelyanskiy", "Shuran Song", "Lyne Tchapmi", "Andrew Z. Wang", "Boxin Wang", "Haoxiang Wang", "Fangyin Wei", "Jiashu Xu", "Yao Xu", "Xiaodong Yang", "Zhuolin Yang", "Xiaohui Zeng", "Zhe Zhang" ], "github": "https://github.com/nvidia-cosmos/cosmos-reason1", "project_page": "", "abstract": "Physical AI systems need to perceive, understand, and perform complex actions in the physical world. In this paper, we present the Cosmos-Reason1 models that can understand the physical world and generate appropriate embodied decisions (e.g., next step action) in natural language through long chain-of-thought reasoning processes. We begin by defining key capabilities for Physical AI reasoning, with a focus on physical common sense and embodied reasoning. To represent physical common sense, we use a hierarchical ontology that captures fundamental knowledge about space, time, and physics. For embodied reasoning, we rely on a two-dimensional ontology that generalizes across different physical embodiments. Building on these capabilities, we develop two multimodal large language models, Cosmos-Reason1-8B and Cosmos-Reason1-56B. We curate data and train our models in four stages: vision pre-training, general supervised fine-tuning (SFT), Physical AI SFT, and Physical AI reinforcement learning (RL) as the post-training. To evaluate our models, we build comprehensive benchmarks for physical common sense and embodied reasoning according to our ontologies. Evaluation results show that Physical AI SFT and reinforcement learning bring significant improvements. To facilitate the development of Physical AI, we will make our code and pre-trained models available under the NVIDIA Open Model License at https://github.com/nvidia-cosmos/cosmos-reason1.", "github_manual": "", "github_regex": "https://github.com/nvidia-cosmos/cosmos-reason1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16413", "title": "M3: 3D-Spatial MultiModal Memory", "authors": [ "Xueyan Zou", "Yuchen Song", "Ri-Zhao Qiu", "Xuanbin Peng", "Jianglong Ye", "Sifei Liu", "Xiaolong Wang" ], "github": "", "project_page": "", "abstract": "We present 3D Spatial MultiModal Memory (M3), a multimodal memory system designed to retain information about medium-sized static scenes through video sources for visual perception. By integrating 3D Gaussian Splatting techniques with foundation models, M3 builds a multimodal memory capable of rendering feature representations across granularities, encompassing a wide range of knowledge. In our exploration, we identify two key challenges in previous works on feature splatting: (1) computational constraints in storing high-dimensional features for each Gaussian primitive, and (2) misalignment or information loss between distilled features and foundation model features. To address these challenges, we propose M3 with key components of principal scene components and Gaussian memory attention, enabling efficient training and inference. To validate M3, we conduct comprehensive quantitative evaluations of feature similarity and downstream tasks, as well as qualitative visualizations to highlight the pixel trace of Gaussian memory attention. Our approach encompasses a diverse range of foundation models, including vision-language models (VLMs), perception models, and large multimodal and language models (LMMs/LLMs). Furthermore, to demonstrate real-world applicability, we deploy M3's feature field in indoor scenes on a quadruped robot. Notably, we claim that M3 is the first work to address the core compression challenges in 3D feature distillation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16419", "title": "Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models", "authors": [ "Yang Sui", "Yu-Neng Chuang", "Guanchu Wang", "Jiamu Zhang", "Tianyi Zhang", "Jiayi Yuan", "Hongyi Liu", "Andrew Wen", "Shaochen", "Zhong", "Hanjie Chen", "Xia Hu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks. Recent advancements in Large Reasoning Models (LRMs), such as OpenAI o1 and DeepSeek-R1, have further improved performance in System-2 reasoning domains like mathematics and programming by harnessing supervised fine-tuning (SFT) and reinforcement learning (RL) techniques to enhance the Chain-of-Thought (CoT) reasoning. However, while longer CoT reasoning sequences improve performance, they also introduce significant computational overhead due to verbose and redundant outputs, known as the \"overthinking phenomenon\". In this paper, we provide the first structured survey to systematically investigate and explore the current progress toward achieving efficient reasoning in LLMs. Overall, relying on the inherent mechanism of LLMs, we categorize existing works into several key directions: (1) model-based efficient reasoning, which considers optimizing full-length reasoning models into more concise reasoning models or directly training efficient reasoning models; (2) reasoning output-based efficient reasoning, which aims to dynamically reduce reasoning steps and length during inference; (3) input prompts-based efficient reasoning, which seeks to enhance reasoning efficiency based on input prompt properties such as difficulty or length control. Additionally, we introduce the use of efficient data for training reasoning models, explore the reasoning capabilities of small language models, and discuss evaluation methods and benchmarking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16422", "title": "1000+ FPS 4D Gaussian Splatting for Dynamic Scene Rendering", "authors": [ "Yuheng Yuan", "Qiuhong Shen", "Xingyi Yang", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "4D Gaussian Splatting (4DGS) has recently gained considerable attention as a method for reconstructing dynamic scenes. Despite achieving superior quality, 4DGS typically requires substantial storage and suffers from slow rendering speed. In this work, we delve into these issues and identify two key sources of temporal redundancy. (Q1) Short-Lifespan Gaussians: 4DGS uses a large portion of Gaussians with short temporal span to represent scene dynamics, leading to an excessive number of Gaussians. (Q2) Inactive Gaussians: When rendering, only a small subset of Gaussians contributes to each frame. Despite this, all Gaussians are processed during rasterization, resulting in redundant computation overhead. To address these redundancies, we present 4DGS-1K, which runs at over 1000 FPS on modern GPUs. For Q1, we introduce the Spatial-Temporal Variation Score, a new pruning criterion that effectively removes short-lifespan Gaussians while encouraging 4DGS to capture scene dynamics using Gaussians with longer temporal spans. For Q2, we store a mask for active Gaussians across consecutive frames, significantly reducing redundant computations in rendering. Compared to vanilla 4DGS, our method achieves a 41times reduction in storage and 9times faster rasterization speed on complex dynamic scenes, while maintaining comparable visual quality. Please see our project page at https://4DGS-1K.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16365", "title": "JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse", "authors": [ "Muyao Li", "Zihao Wang", "Kaichen He", "Xiaojian Ma", "Yitao Liang" ], "github": "", "project_page": "", "abstract": "Recently, action-based decision-making in open-world environments has gained significant attention. Visual Language Action (VLA) models, pretrained on large-scale web datasets, have shown promise in decision-making tasks. However, previous work has primarily focused on action post-training, often neglecting enhancements to the foundational model itself. In response, we introduce a novel approach, Act from Visual Language Post-Training, which refines Visual Language Models (VLMs) through visual and linguistic guidance in a self-supervised manner. This enhancement improves the models' capabilities in world knowledge, visual recognition, and spatial grounding in open-world environments. Following the above post-training paradigms, we obtain the first VLA models in Minecraft that can follow human instructions on over 1k different atomic tasks, including crafting, smelting, cooking, mining, and killing. Our experiments demonstrate that post-training on non-trajectory tasks leads to a significant 40% improvement over the best agent baseline on a diverse set of atomic tasks. Furthermore, we demonstrate that our approach surpasses traditional imitation learning-based policies in Minecraft, achieving state-of-the-art performance. We have open-sourced the code, models, and datasets to foster further research. The project page can be found in https://craftjarvis.github.io/JarvisVLA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16428", "title": "XAttention: Block Sparse Attention with Antidiagonal Scoring", "authors": [ "Ruyi Xu", "Guangxuan Xiao", "Haofeng Huang", "Junxian Guo", "Song Han" ], "github": "https://github.com/mit-han-lab/x-attention", "project_page": "", "abstract": "Long-Context Transformer Models (LCTMs) are vital for real-world applications but suffer high computational costs due to attention's quadratic complexity. Block-sparse attention mitigates this by focusing computation on critical regions, yet existing methods struggle with balancing accuracy and efficiency due to costly block importance measurements. In this paper, we introduce XAttention, a plug-and-play framework that dramatically accelerates long-context inference in Transformers models using sparse attention. XAttention's key innovation is the insight that the sum of antidiagonal values (i.e., from the lower-left to upper-right) in the attention matrix provides a powerful proxy for block importance. This allows for precise identification and pruning of non-essential blocks, resulting in high sparsity and dramatically accelerated inference. Across comprehensive evaluations on demanding long-context benchmarks-including RULER and LongBench for language, VideoMME for video understanding, and VBench for video generation. XAttention achieves accuracy comparable to full attention while delivering substantial computational gains. We demonstrate up to 13.5x acceleration in attention computation. These results underscore XAttention's ability to unlock the practical potential of block sparse attention, paving the way for scalable and efficient deployment of LCTMs in real-world applications. Code is available at https://github.com/mit-han-lab/x-attention.", "github_manual": "", "github_regex": "https://github.com/mit-han-lab/x-attention", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16421", "title": "MagicMotion: Controllable Video Generation with Dense-to-Sparse Trajectory Guidance", "authors": [ "Quanhao Li", "Zhen Xing", "Rui Wang", "Hui Zhang", "Qi Dai", "Zuxuan Wu" ], "github": "", "project_page": "", "abstract": "Recent advances in video generation have led to remarkable improvements in visual quality and temporal coherence. Upon this, trajectory-controllable video generation has emerged to enable precise object motion control through explicitly defined spatial paths. However, existing methods struggle with complex object movements and multi-object motion control, resulting in imprecise trajectory adherence, poor object consistency, and compromised visual quality. Furthermore, these methods only support trajectory control in a single format, limiting their applicability in diverse scenarios. Additionally, there is no publicly available dataset or benchmark specifically tailored for trajectory-controllable video generation, hindering robust training and systematic evaluation. To address these challenges, we introduce MagicMotion, a novel image-to-video generation framework that enables trajectory control through three levels of conditions from dense to sparse: masks, bounding boxes, and sparse boxes. Given an input image and trajectories, MagicMotion seamlessly animates objects along defined trajectories while maintaining object consistency and visual quality. Furthermore, we present MagicData, a large-scale trajectory-controlled video dataset, along with an automated pipeline for annotation and filtering. We also introduce MagicBench, a comprehensive benchmark that assesses both video quality and trajectory control accuracy across different numbers of objects. Extensive experiments demonstrate that MagicMotion outperforms previous methods across various metrics. Our project page are publicly available at https://quanhaol.github.io/magicmotion-site.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16302", "title": "Unleashing Vecset Diffusion Model for Fast Shape Generation", "authors": [ "Zeqiang Lai", "Yunfei Zhao", "Zibo Zhao", "Haolin Liu", "Fuyun Wang", "Huiwen Shi", "Xianghui Yang", "Qinxiang Lin", "Jinwei Huang", "Yuhong Liu", "Jie Jiang", "Chunchao Guo", "Xiangyu Yue" ], "github": "https://github.com/Tencent/FlashVDM", "project_page": "", "abstract": "3D shape generation has greatly flourished through the development of so-called \"native\" 3D diffusion, particularly through the Vecset Diffusion Model (VDM). While recent advancements have shown promising results in generating high-resolution 3D shapes, VDM still struggles with high-speed generation. Challenges exist because of difficulties not only in accelerating diffusion sampling but also VAE decoding in VDM, areas under-explored in previous works. To address these challenges, we present FlashVDM, a systematic framework for accelerating both VAE and DiT in VDM. For DiT, FlashVDM enables flexible diffusion sampling with as few as 5 inference steps and comparable quality, which is made possible by stabilizing consistency distillation with our newly introduced Progressive Flow Distillation. For VAE, we introduce a lightning vecset decoder equipped with Adaptive KV Selection, Hierarchical Volume Decoding, and Efficient Network Design. By exploiting the locality of the vecset and the sparsity of shape surface in the volume, our decoder drastically lowers FLOPs, minimizing the overall decoding overhead. We apply FlashVDM to Hunyuan3D-2 to obtain Hunyuan3D-2 Turbo. Through systematic evaluation, we show that our model significantly outperforms existing fast 3D generation methods, achieving comparable performance to the state-of-the-art while reducing inference time by over 45x for reconstruction and 32x for generation. Code and models are available at https://github.com/Tencent/FlashVDM.", "github_manual": "", "github_regex": "https://github.com/Tencent/FlashVDM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16322", "title": "Ultra-Resolution Adaptation with Ease", "authors": [ "Ruonan Yu", "Songhua Liu", "Zhenxiong Tan", "Xinchao Wang" ], "github": "https://github.com/Huage001/URAE", "project_page": "", "abstract": "Text-to-image diffusion models have achieved remarkable progress in recent years. However, training models for high-resolution image generation remains challenging, particularly when training data and computational resources are limited. In this paper, we explore this practical problem from two key perspectives: data and parameter efficiency, and propose a set of key guidelines for ultra-resolution adaptation termed URAE. For data efficiency, we theoretically and empirically demonstrate that synthetic data generated by some teacher models can significantly promote training convergence. For parameter efficiency, we find that tuning minor components of the weight matrices outperforms widely-used low-rank adapters when synthetic data are unavailable, offering substantial performance gains while maintaining efficiency. Additionally, for models leveraging guidance distillation, such as FLUX, we show that disabling classifier-free guidance, i.e., setting the guidance scale to 1 during adaptation, is crucial for satisfactory performance. Extensive experiments validate that URAE achieves comparable 2K-generation performance to state-of-the-art closed-source models like FLUX1.1 [Pro] Ultra with only 3K samples and 2K iterations, while setting new benchmarks for 4K-resolution generation. Codes are available https://github.com/Huage001/URAE{here}.", "github_manual": "", "github_regex": "https://github.com/Huage001/URAE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16418", "title": "InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity", "authors": [ "Liming Jiang", "Qing Yan", "Yumin Jia", "Zichuan Liu", "Hao Kang", "Xin Lu" ], "github": "https://github.com/bytedance/infiniteyou", "project_page": "", "abstract": "Achieving flexible and high-fidelity identity-preserved image generation remains formidable, particularly with advanced Diffusion Transformers (DiTs) like FLUX. We introduce InfiniteYou (InfU), one of the earliest robust frameworks leveraging DiTs for this task. InfU addresses significant issues of existing methods, such as insufficient identity similarity, poor text-image alignment, and low generation quality and aesthetics. Central to InfU is InfuseNet, a component that injects identity features into the DiT base model via residual connections, enhancing identity similarity while maintaining generation capabilities. A multi-stage training strategy, including pretraining and supervised fine-tuning (SFT) with synthetic single-person-multiple-sample (SPMS) data, further improves text-image alignment, ameliorates image quality, and alleviates face copy-pasting. Extensive experiments demonstrate that InfU achieves state-of-the-art performance, surpassing existing baselines. In addition, the plug-and-play design of InfU ensures compatibility with various existing methods, offering a valuable contribution to the broader community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bytedance/infiniteyou", "github_api_added_by": "auto" }, { "date": "2025-03-21", "arxiv_id": "2503.12689", "title": "MagicID: Hybrid Preference Optimization for ID-Consistent and Dynamic-Preserved Video Customization", "authors": [ "Hengjia Li", "Lifan Jiang", "Xi Xiao", "Tianyang Wang", "Hongwei Yi", "Boxi Wu", "Deng Cai" ], "github": "", "project_page": "", "abstract": "Video identity customization seeks to produce high-fidelity videos that maintain consistent identity and exhibit significant dynamics based on users' reference images. However, existing approaches face two key challenges: identity degradation over extended video length and reduced dynamics during training, primarily due to their reliance on traditional self-reconstruction training with static images. To address these issues, we introduce MagicID, a novel framework designed to directly promote the generation of identity-consistent and dynamically rich videos tailored to user preferences. Specifically, we propose constructing pairwise preference video data with explicit identity and dynamic rewards for preference learning, instead of sticking to the traditional self-reconstruction. To address the constraints of customized preference data, we introduce a hybrid sampling strategy. This approach first prioritizes identity preservation by leveraging static videos derived from reference images, then enhances dynamic motion quality in the generated videos using a Frontier-based sampling method. By utilizing these hybrid preference pairs, we optimize the model to align with the reward differences between pairs of customized preferences. Extensive experiments show that MagicID successfully achieves consistent identity and natural dynamics, surpassing existing methods across various metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16188", "title": "CLS-RL: Image Classification with Rule-Based Reinforcement Learning", "authors": [ "Ming Li", "Shitian Zhao", "Jike Zhong", "Yuxiang Lai", "Kaipeng Zhang" ], "github": "", "project_page": "", "abstract": "Classification is a core task in machine learning. Recent research has shown that although Multimodal Large Language Models (MLLMs) are initially poor at image classification, fine-tuning them with an adequate amount of data can significantly enhance their performance, making them comparable to SOTA classification models. However, acquiring large-scale labeled data is expensive. In this paper, we explore few-shot MLLM classification fine-tuning. We found that SFT can cause severe overfitting issues and may even degrade performance over the zero-shot approach. To address this challenge, inspired by the recent successes in rule-based reinforcement learning, we propose CLS-RL, which uses verifiable signals as reward to fine-tune MLLMs. We discovered that CLS-RL outperforms SFT in most datasets and has a much higher average accuracy on both base-to-new and few-shot learning setting. Moreover, we observed a free-lunch phenomenon for CLS-RL; when models are fine-tuned on a particular dataset, their performance on other distinct datasets may also improve over zero-shot models, even if those datasets differ in distribution and class names. This suggests that RL-based methods effectively teach models the fundamentals of classification. Lastly, inspired by recent works in inference time thinking, we re-examine the `thinking process' during fine-tuning, a critical aspect of RL-based methods, in the context of visual classification. We question whether such tasks require extensive thinking process during fine-tuning, proposing that this may actually detract from performance. Based on this premise, we introduce the No-Thinking-CLS-RL method, which minimizes thinking processes during training by setting an equality accuracy reward. Our findings indicate that, with much less fine-tuning time, No-Thinking-CLS-RL method achieves superior in-domain performance and generalization capabilities than CLS-RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16194", "title": "Improving Autoregressive Image Generation through Coarse-to-Fine Token Prediction", "authors": [ "Ziyao Guo", "Kaipeng Zhang", "Michael Qizhe Shieh" ], "github": "", "project_page": "", "abstract": "Autoregressive models have shown remarkable success in image generation by adapting sequential prediction techniques from language modeling. However, applying these approaches to images requires discretizing continuous pixel data through vector quantization methods like VQ-VAE. To alleviate the quantization errors that existed in VQ-VAE, recent works tend to use larger codebooks. However, this will accordingly expand vocabulary size, complicating the autoregressive modeling task. This paper aims to find a way to enjoy the benefits of large codebooks without making autoregressive modeling more difficult. Through empirical investigation, we discover that tokens with similar codeword representations produce similar effects on the final generated image, revealing significant redundancy in large codebooks. Based on this insight, we propose to predict tokens from coarse to fine (CTF), realized by assigning the same coarse label for similar tokens. Our framework consists of two stages: (1) an autoregressive model that sequentially predicts coarse labels for each token in the sequence, and (2) an auxiliary model that simultaneously predicts fine-grained labels for all tokens conditioned on their coarse labels. Experiments on ImageNet demonstrate our method's superior performance, achieving an average improvement of 59 points in Inception Score compared to baselines. Notably, despite adding an inference step, our approach achieves faster sampling speeds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.13834", "title": "See-Saw Modality Balance: See Gradient, and Sew Impaired Vision-Language Balance to Mitigate Dominant Modality Bias", "authors": [ "JuneHyoung Kwon", "MiHyeon Kim", "Eunju Lee", "Juhwan Choi", "YoungBin Kim" ], "github": "", "project_page": "", "abstract": "Vision-language (VL) models have demonstrated strong performance across various tasks. However, these models often rely on a specific modality for predictions, leading to \"dominant modality bias.'' This bias significantly hurts performance, especially when one modality is impaired. In this study, we analyze model behavior under dominant modality bias and theoretically show that unaligned gradients or differences in gradient magnitudes prevent balanced convergence of the loss. Based on these findings, we propose a novel framework, BalGrad to mitigate dominant modality bias. Our approach includes inter-modality gradient reweighting, adjusting the gradient of KL divergence based on each modality's contribution, and inter-task gradient projection to align task directions in a non-conflicting manner. Experiments on UPMC Food-101, Hateful Memes, and MM-IMDb datasets confirm that BalGrad effectively alleviates over-reliance on specific modalities when making predictions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16057", "title": "Expert Race: A Flexible Routing Strategy for Scaling Diffusion Transformer with Mixture of Experts", "authors": [ "Yike Yuan", "Ziyu Wang", "Zihao Huang", "Defa Zhu", "Xun Zhou", "Jingyi Yu", "Qiyang Min" ], "github": "", "project_page": "", "abstract": "Diffusion models have emerged as mainstream framework in visual generation. Building upon this success, the integration of Mixture of Experts (MoE) methods has shown promise in enhancing model scalability and performance. In this paper, we introduce Race-DiT, a novel MoE model for diffusion transformers with a flexible routing strategy, Expert Race. By allowing tokens and experts to compete together and select the top candidates, the model learns to dynamically assign experts to critical tokens. Additionally, we propose per-layer regularization to address challenges in shallow layer learning, and router similarity loss to prevent mode collapse, ensuring better expert utilization. Extensive experiments on ImageNet validate the effectiveness of our approach, showcasing significant performance gains while promising scaling properties.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.15851", "title": "Zero-1-to-A: Zero-Shot One Image to Animatable Head Avatars Using Video Diffusion", "authors": [ "Zhou Zhenglin", "Ma Fan", "Fan Hehe", "Chua Tat-Seng" ], "github": "https://github.com/ZhenglinZhou/Zero-1-to-A", "project_page": "", "abstract": "Animatable head avatar generation typically requires extensive data for training. To reduce the data requirements, a natural solution is to leverage existing data-free static avatar generation methods, such as pre-trained diffusion models with score distillation sampling (SDS), which align avatars with pseudo ground-truth outputs from the diffusion model. However, directly distilling 4D avatars from video diffusion often leads to over-smooth results due to spatial and temporal inconsistencies in the generated video. To address this issue, we propose Zero-1-to-A, a robust method that synthesizes a spatial and temporal consistency dataset for 4D avatar reconstruction using the video diffusion model. Specifically, Zero-1-to-A iteratively constructs video datasets and optimizes animatable avatars in a progressive manner, ensuring that avatar quality increases smoothly and consistently throughout the learning process. This progressive learning involves two stages: (1) Spatial Consistency Learning fixes expressions and learns from front-to-side views, and (2) Temporal Consistency Learning fixes views and learns from relaxed to exaggerated expressions, generating 4D avatars in a simple-to-complex manner. Extensive experiments demonstrate that Zero-1-to-A improves fidelity, animation quality, and rendering speed compared to existing diffusion-based methods, providing a solution for lifelike avatar creation. Code is publicly available at: https://github.com/ZhenglinZhou/Zero-1-to-A.", "github_manual": "", "github_regex": "https://github.com/ZhenglinZhou/Zero-1-to-A", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.15451", "title": "MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space", "authors": [ "Lixing Xiao", "Shunlin Lu", "Huaijin Pi", "Ke Fan", "Liang Pan", "Yueer Zhou", "Ziyong Feng", "Xiaowei Zhou", "Sida Peng", "Jingbo Wang" ], "github": "https://github.com/Li-xingXiao/272-dim-Motion-Representation", "project_page": "", "abstract": "This paper addresses the challenge of text-conditioned streaming motion generation, which requires us to predict the next-step human pose based on variable-length historical motions and incoming texts. Existing methods struggle to achieve streaming motion generation, e.g., diffusion models are constrained by pre-defined motion lengths, while GPT-based methods suffer from delayed response and error accumulation problem due to discretized non-causal tokenization. To solve these problems, we propose MotionStreamer, a novel framework that incorporates a continuous causal latent space into a probabilistic autoregressive model. The continuous latents mitigate information loss caused by discretization and effectively reduce error accumulation during long-term autoregressive generation. In addition, by establishing temporal causal dependencies between current and historical motion latents, our model fully utilizes the available information to achieve accurate online motion decoding. Experiments show that our method outperforms existing approaches while offering more applications, including multi-round generation, long-term generation, and dynamic motion composition. Project Page: https://zju3dv.github.io/MotionStreamer/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Li-xingXiao/272-dim-Motion-Representation", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16055", "title": "SALT: Singular Value Adaptation with Low-Rank Transformation", "authors": [ "Abdelrahman Elsayed", "Sarim Hashmi", "Mohammed Elseiagy", "Hu Wang", "Mohammad Yaqub", "Ibrahim Almakky" ], "github": "https://github.com/BioMedIA-MBZUAI/SALT", "project_page": "", "abstract": "The complex nature of medical image segmentation calls for models that are specifically designed to capture detailed, domain-specific features. Large foundation models offer considerable flexibility, yet the cost of fine-tuning these models remains a significant barrier. Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), efficiently update model weights with low-rank matrices but may suffer from underfitting when the chosen rank is insufficient to capture domain-specific nuances. Conversely, full-rank Singular Value Decomposition (SVD) based methods provide comprehensive updates by modifying all singular values, yet they often lack flexibility and exhibit variable performance across datasets. We propose SALT (Singular Value Adaptation with Low-Rank Transformation), a method that selectively adapts the most influential singular values using trainable scale and shift parameters while complementing this with a low-rank update for the remaining subspace. This hybrid approach harnesses the advantages of both LoRA and SVD, enabling effective adaptation without relying on increasing model size or depth. Evaluated on 5 challenging medical datasets, ranging from as few as 20 samples to 1000, SALT outperforms state-of-the-art PEFT (LoRA and SVD) by 2% to 5% in Dice with only 3.9% trainable parameters, demonstrating robust adaptation even in low-resource settings. The code for SALT is available at: https://github.com/BioMedIA-MBZUAI/SALT", "github_manual": "", "github_regex": "https://github.com/BioMedIA-MBZUAI/SALT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.10625", "title": "LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds", "authors": [ "Lingteng Qiu", "Xiaodong Gu", "Peihao Li", "Qi Zuo", "Weichao Shen", "Junfei Zhang", "Kejie Qiu", "Weihao Yuan", "Guanying Chen", "Zilong Dong", "Liefeng Bo" ], "github": "https://github.com/aigc3d/LHM", "project_page": "", "abstract": "Animatable 3D human reconstruction from a single image is a challenging problem due to the ambiguity in decoupling geometry, appearance, and deformation. Recent advances in 3D human reconstruction mainly focus on static human modeling, and the reliance of using synthetic 3D scans for training limits their generalization ability. Conversely, optimization-based video methods achieve higher fidelity but demand controlled capture conditions and computationally intensive refinement processes. Motivated by the emergence of large reconstruction models for efficient static reconstruction, we propose LHM (Large Animatable Human Reconstruction Model) to infer high-fidelity avatars represented as 3D Gaussian splatting in a feed-forward pass. Our model leverages a multimodal transformer architecture to effectively encode the human body positional features and image features with attention mechanism, enabling detailed preservation of clothing geometry and texture. To further boost the face identity preservation and fine detail recovery, we propose a head feature pyramid encoding scheme to aggregate multi-scale features of the head regions. Extensive experiments demonstrate that our LHM generates plausible animatable human in seconds without post-processing for face and hands, outperforming existing methods in both reconstruction accuracy and generalization ability.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aigc3d/LHM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.14487", "title": "DiffMoE: Dynamic Token Selection for Scalable Diffusion Transformers", "authors": [ "Minglei Shi", "Ziyang Yuan", "Haotian Yang", "Xintao Wang", "Mingwu Zheng", "Xin Tao", "Wenliang Zhao", "Wenzhao Zheng", "Jie Zhou", "Jiwen Lu", "Pengfei Wan", "Di Zhang", "Kun Gai" ], "github": "", "project_page": "", "abstract": "Diffusion models have demonstrated remarkable success in various image generation tasks, but their performance is often limited by the uniform processing of inputs across varying conditions and noise levels. To address this limitation, we propose a novel approach that leverages the inherent heterogeneity of the diffusion process. Our method, DiffMoE, introduces a batch-level global token pool that enables experts to access global token distributions during training, promoting specialized expert behavior. To unleash the full potential of the diffusion process, DiffMoE incorporates a capacity predictor that dynamically allocates computational resources based on noise levels and sample complexity. Through comprehensive evaluation, DiffMoE achieves state-of-the-art performance among diffusion models on ImageNet benchmark, substantially outperforming both dense architectures with 3x activated parameters and existing MoE approaches while maintaining 1x activated parameters. The effectiveness of our approach extends beyond class-conditional generation to more challenging tasks such as text-to-image generation, demonstrating its broad applicability across different diffusion model applications. Project Page: https://shiml20.github.io/DiffMoE/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16416", "title": "Survey on Evaluation of LLM-based Agents", "authors": [ "Asaf Yehudai", "Lilach Eden", "Alan Li", "Guy Uziel", "Yilun Zhao", "Roy Bar-Haim", "Arman Cohan", "Michal Shmueli-Scheuer" ], "github": "", "project_page": "", "abstract": "The emergence of LLM-based agents represents a paradigm shift in AI, enabling autonomous systems to plan, reason, use tools, and maintain memory while interacting with dynamic environments. This paper provides the first comprehensive survey of evaluation methodologies for these increasingly capable agents. We systematically analyze evaluation benchmarks and frameworks across four critical dimensions: (1) fundamental agent capabilities, including planning, tool use, self-reflection, and memory; (2) application-specific benchmarks for web, software engineering, scientific, and conversational agents; (3) benchmarks for generalist agents; and (4) frameworks for evaluating agents. Our analysis reveals emerging trends, including a shift toward more realistic, challenging evaluations with continuously updated benchmarks. We also identify critical gaps that future research must address-particularly in assessing cost-efficiency, safety, and robustness, and in developing fine-grained, and scalable evaluation methods. This survey maps the rapidly evolving landscape of agent evaluation, reveals the emerging trends in the field, identifies current limitations, and proposes directions for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.13657", "title": "Why Do Multi-Agent LLM Systems Fail?", "authors": [ "Mert Cemri", "Melissa Z. Pan", "Shuyi Yang", "Lakshya A. Agrawal", "Bhavya Chopra", "Rishabh Tiwari", "Kurt Keutzer", "Aditya Parameswaran", "Dan Klein", "Kannan Ramchandran", "Matei Zaharia", "Joseph E. Gonzalez", "Ion Stoica" ], "github": "https://github.com/mannaandpoem/OpenManus", "project_page": "", "abstract": "Despite growing enthusiasm for Multi-Agent Systems (MAS), where multiple LLM agents collaborate to accomplish tasks, their performance gains across popular benchmarks remain minimal compared to single-agent frameworks. This gap highlights the need to analyze the challenges hindering MAS effectiveness. In this paper, we present the first comprehensive study of MAS challenges. We analyze five popular MAS frameworks across over 150 tasks, involving six expert human annotators. We identify 14 unique failure modes and propose a comprehensive taxonomy applicable to various MAS frameworks. This taxonomy emerges iteratively from agreements among three expert annotators per study, achieving a Cohen's Kappa score of 0.88. These fine-grained failure modes are organized into 3 categories, (i) specification and system design failures, (ii) inter-agent misalignment, and (iii) task verification and termination. To support scalable evaluation, we integrate MASFT with LLM-as-a-Judge. We also explore if identified failures could be easily prevented by proposing two interventions: improved specification of agent roles and enhanced orchestration strategies. Our findings reveal that identified failures require more complex solutions, highlighting a clear roadmap for future research. We open-source our dataset and LLM annotator.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mannaandpoem/OpenManus", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16375", "title": "NuiScene: Exploring Efficient Generation of Unbounded Outdoor Scenes", "authors": [ "Han-Hung Lee", "Qinghong Han", "Angel X. Chang" ], "github": "https://github.com/3dlg-hcvc/NuiScene", "project_page": "", "abstract": "In this paper, we explore the task of generating expansive outdoor scenes, ranging from castles to high-rises. Unlike indoor scene generation, which has been a primary focus of prior work, outdoor scene generation presents unique challenges, including wide variations in scene heights and the need for a method capable of rapidly producing large landscapes. To address this, we propose an efficient approach that encodes scene chunks as uniform vector sets, offering better compression and performance than the spatially structured latents used in prior methods. Furthermore, we train an explicit outpainting model for unbounded generation, which improves coherence compared to prior resampling-based inpainting schemes while also speeding up generation by eliminating extra diffusion steps. To facilitate this task, we curate NuiScene43, a small but high-quality set of scenes, preprocessed for joint training. Notably, when trained on scenes of varying styles, our model can blend different environments, such as rural houses and city skyscrapers, within the same scene, highlighting the potential of our curation process to leverage heterogeneous scenes for joint training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3dlg-hcvc/NuiScene", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16219", "title": "Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't", "authors": [ "Quy-Anh Dang", "Chris Ngo" ], "github": "https://github.com/knoveleng/open-rs", "project_page": "", "abstract": "Enhancing the reasoning capabilities of large language models (LLMs) typically relies on massive computational resources and extensive datasets, limiting accessibility for resource-constrained settings. Our study investigates the potential of reinforcement learning (RL) to improve reasoning in small LLMs, focusing on a 1.5-billion-parameter model, DeepSeek-R1-Distill-Qwen-1.5B, under strict constraints: training on 4 NVIDIA A40 GPUs (48 GB VRAM each) within 24 hours. Adapting the Group Relative Policy Optimization (GRPO) algorithm and curating a compact, high-quality mathematical reasoning dataset, we conducted three experiments to explore model behavior and performance. Our results demonstrate rapid reasoning gains - e.g., AMC23 accuracy rising from 63% to 80% and AIME24 reaching 46.7%, surpassing o1-preview - using only 7,000 samples and a $42 training cost, compared to thousands of dollars for baseline models. However, challenges such as optimization instability and length constraints emerged with prolonged training. These findings highlight the efficacy of RL-based fine-tuning for small LLMs, offering a cost-effective alternative to large-scale approaches. We release our code and datasets as open-source resources, providing insights into trade-offs and laying a foundation for scalable, reasoning-capable LLMs in resource-limited environments. All are available at https://github.com/knoveleng/open-rs.", "github_manual": "", "github_regex": "https://github.com/knoveleng/open-rs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.13358", "title": "One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation", "authors": [ "Daniil Selikhanovych", "David Li", "Aleksei Leonov", "Nikita Gushchin", "Sergei Kushneriuk", "Alexander Filippov", "Evgeny Burnaev", "Iaroslav Koshelev", "Alexander Korotin" ], "github": "https://github.com/NVlabs/I2SB", "project_page": "", "abstract": "Diffusion models for super-resolution (SR) produce high-quality visual results but require expensive computational costs. Despite the development of several methods to accelerate diffusion-based SR models, some (e.g., SinSR) fail to produce realistic perceptual details, while others (e.g., OSEDiff) may hallucinate non-existent structures. To overcome these issues, we present RSD, a new distillation method for ResShift, one of the top diffusion-based SR models. Our method is based on training the student network to produce such images that a new fake ResShift model trained on them will coincide with the teacher model. RSD achieves single-step restoration and outperforms the teacher by a large margin. We show that our distillation method can surpass the other distillation-based method for ResShift - SinSR - making it on par with state-of-the-art diffusion-based SR distillation methods. Compared to SR methods based on pre-trained text-to-image models, RSD produces competitive perceptual quality, provides images with better alignment to degraded input images, and requires fewer parameters and GPU memory. We provide experimental results on various real-world and synthetic datasets, including RealSR, RealSet65, DRealSR, ImageNet, and DIV2K.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVlabs/I2SB", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16257", "title": "Plug-and-Play 1.x-Bit KV Cache Quantization for Video Large Language Models", "authors": [ "Keda Tao", "Haoxuan You", "Yang Sui", "Can Qin", "Huan Wang" ], "github": "", "project_page": "", "abstract": "Video large language models (VideoLLMs) have demonstrated the capability to process longer video inputs and enable complex reasoning and analysis. However, due to the thousands of visual tokens from the video frames, key-value (KV) cache can significantly increase memory requirements, becoming a bottleneck for inference speed and memory usage. KV cache quantization is a widely used approach to address this problem. In this paper, we find that 2-bit KV quantization of VideoLLMs can hardly hurt the model performance, while the limit of KV cache quantization in even lower bits has not been investigated. To bridge this gap, we introduce VidKV, a plug-and-play KV cache quantization method to compress the KV cache to lower than 2 bits. Specifically, (1) for key, we propose a mixed-precision quantization strategy in the channel dimension, where we perform 2-bit quantization for anomalous channels and 1-bit quantization combined with FFT for normal channels; (2) for value, we implement 1.58-bit quantization while selectively filtering semantically salient visual tokens for targeted preservation, for a better trade-off between precision and model performance. Importantly, our findings suggest that the value cache of VideoLLMs should be quantized in a per-channel fashion instead of the per-token fashion proposed by prior KV cache quantization works for LLMs. Empirically, extensive results with LLaVA-OV-7B and Qwen2.5-VL-7B on six benchmarks show that VidKV effectively compresses the KV cache to 1.5-bit and 1.58-bit precision with almost no performance drop compared to the FP16 counterparts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16397", "title": "Scale-wise Distillation of Diffusion Models", "authors": [ "Nikita Starodubcev", "Denis Kuznedelev", "Artem Babenko", "Dmitry Baranchuk" ], "github": "", "project_page": "", "abstract": "We present SwD, a scale-wise distillation framework for diffusion models (DMs), which effectively employs next-scale prediction ideas for diffusion-based few-step generators. In more detail, SwD is inspired by the recent insights relating diffusion processes to the implicit spectral autoregression. We suppose that DMs can initiate generation at lower data resolutions and gradually upscale the samples at each denoising step without loss in performance while significantly reducing computational costs. SwD naturally integrates this idea into existing diffusion distillation methods based on distribution matching. Also, we enrich the family of distribution matching approaches by introducing a novel patch loss enforcing finer-grained similarity to the target distribution. When applied to state-of-the-art text-to-image diffusion models, SwD approaches the inference times of two full resolution steps and significantly outperforms the counterparts under the same computation budget, as evidenced by automated metrics and human preference studies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.15855", "title": "VideoRFSplat: Direct Scene-Level Text-to-3D Gaussian Splatting Generation with Flexible Pose and Multi-View Joint Modeling", "authors": [ "Hyojun Go", "Byeongjun Park", "Hyelin Nam", "Byung-Hoon Kim", "Hyungjin Chung", "Changick Kim" ], "github": "https://github.com/gohyojun15/VideoRFSplat", "project_page": "", "abstract": "We propose VideoRFSplat, a direct text-to-3D model leveraging a video generation model to generate realistic 3D Gaussian Splatting (3DGS) for unbounded real-world scenes. To generate diverse camera poses and unbounded spatial extent of real-world scenes, while ensuring generalization to arbitrary text prompts, previous methods fine-tune 2D generative models to jointly model camera poses and multi-view images. However, these methods suffer from instability when extending 2D generative models to joint modeling due to the modality gap, which necessitates additional models to stabilize training and inference. In this work, we propose an architecture and a sampling strategy to jointly model multi-view images and camera poses when fine-tuning a video generation model. Our core idea is a dual-stream architecture that attaches a dedicated pose generation model alongside a pre-trained video generation model via communication blocks, generating multi-view images and camera poses through separate streams. This design reduces interference between the pose and image modalities. Additionally, we propose an asynchronous sampling strategy that denoises camera poses faster than multi-view images, allowing rapidly denoised poses to condition multi-view generation, reducing mutual ambiguity and enhancing cross-modal consistency. Trained on multiple large-scale real-world datasets (RealEstate10K, MVImgNet, DL3DV-10K, ACID), VideoRFSplat outperforms existing text-to-3D direct generation methods that heavily depend on post-hoc refinement via score distillation sampling, achieving superior results without such refinement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gohyojun15/VideoRFSplat", "github_api_added_by": "auto" }, { "date": "2025-03-21", "arxiv_id": "2503.14237", "title": "Make Your Training Flexible: Towards Deployment-Efficient Video Models", "authors": [ "Chenting Wang", "Kunchang Li", "Tianxiang Jiang", "Xiangyu Zeng", "Yi Wang", "Limin Wang" ], "github": "https://github.com/OpenGVLab/FluxViT", "project_page": "", "abstract": "Popular video training methods mainly operate on a fixed number of tokens sampled from a predetermined spatiotemporal grid, resulting in sub-optimal accuracy-computation trade-offs due to inherent video redundancy. They also lack adaptability to varying computational budgets for downstream tasks, hindering applications of the most competitive model in real-world scenes. We thus propose a new test setting, Token Optimization, for maximized input information across budgets, which optimizes the size-limited set of input tokens through token selection from more suitably sampled videos. To this end, we propose a novel augmentation tool termed Flux. By making the sampling grid flexible and leveraging token selection, it is easily adopted in most popular video training frameworks, boosting model robustness with nearly no additional cost. We integrate Flux in large-scale video pre-training, and the resulting FluxViT establishes new state-of-the-art results across extensive tasks at standard costs. Notably, with 1/4 tokens only, it can still match the performance of previous state-of-the-art models with Token Optimization, yielding nearly 90\\% savings. All models and data are available at https://github.com/OpenGVLab/FluxViT.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/FluxViT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16425", "title": "Tokenize Image as a Set", "authors": [ "Zigang Geng", "Mengde Xu", "Han Hu", "Shuyang Gu" ], "github": "https://github.com/Gengzigang/TokenSet", "project_page": "", "abstract": "This paper proposes a fundamentally new paradigm for image generation through set-based tokenization and distribution modeling. Unlike conventional methods that serialize images into fixed-position latent codes with a uniform compression ratio, we introduce an unordered token set representation to dynamically allocate coding capacity based on regional semantic complexity. This TokenSet enhances global context aggregation and improves robustness against local perturbations. To address the critical challenge of modeling discrete sets, we devise a dual transformation mechanism that bijectively converts sets into fixed-length integer sequences with summation constraints. Further, we propose Fixed-Sum Discrete Diffusion--the first framework to simultaneously handle discrete values, fixed sequence length, and summation invariance--enabling effective set distribution modeling. Experiments demonstrate our method's superiority in semantic-aware representation and generation quality. Our innovations, spanning novel representation and modeling strategies, advance visual generation beyond traditional sequential token paradigms. Our code and models are publicly available at https://github.com/Gengzigang/TokenSet.", "github_manual": "", "github_regex": "https://github.com/Gengzigang/TokenSet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.15242", "title": "BigO(Bench) -- Can LLMs Generate Code with Controlled Time and Space Complexity?", "authors": [ "Pierre Chambon", "Baptiste Roziere", "Benoit Sagot", "Gabriel Synnaeve" ], "github": "", "project_page": "", "abstract": "We introduce BigO(Bench), a novel coding benchmark designed to evaluate the capabilities of generative language models in understanding and generating code with specified time and space complexities. This benchmark addresses the gap in current evaluations that often overlook the ability of models to comprehend and produce code constrained by computational complexity. BigO(Bench) includes tooling to infer the algorithmic complexity of any Python function from profiling measurements, including human- or LLM-generated solutions. BigO(Bench) also includes of set of 3,105 coding problems and 1,190,250 solutions from Code Contests annotated with inferred (synthetic) time and space complexity labels from the complexity framework, as well as corresponding runtime and memory footprint values for a large set of input sizes. We present results from evaluating multiple state-of-the-art language models on this benchmark, highlighting their strengths and weaknesses in handling complexity requirements. In particular, token-space reasoning models are unrivaled in code generation but not in complexity understanding, hinting that they may not generalize well to tasks for which no reward was given at training time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16091", "title": "AIMI: Leveraging Future Knowledge and Personalization in Sparse Event Forecasting for Treatment Adherence", "authors": [ "Abdullah Mamun", "Diane J. Cook", "Hassan Ghasemzadeh" ], "github": "https://github.com/ab9mamun/AIMI", "project_page": "", "abstract": "Adherence to prescribed treatments is crucial for individuals with chronic conditions to avoid costly or adverse health outcomes. For certain patient groups, intensive lifestyle interventions are vital for enhancing medication adherence. Accurate forecasting of treatment adherence can open pathways to developing an on-demand intervention tool, enabling timely and personalized support. With the increasing popularity of smartphones and wearables, it is now easier than ever to develop and deploy smart activity monitoring systems. However, effective forecasting systems for treatment adherence based on wearable sensors are still not widely available. We close this gap by proposing Adherence Forecasting and Intervention with Machine Intelligence (AIMI). AIMI is a knowledge-guided adherence forecasting system that leverages smartphone sensors and previous medication history to estimate the likelihood of forgetting to take a prescribed medication. A user study was conducted with 27 participants who took daily medications to manage their cardiovascular diseases. We designed and developed CNN and LSTM-based forecasting models with various combinations of input features and found that LSTM models can forecast medication adherence with an accuracy of 0.932 and an F-1 score of 0.936. Moreover, through a series of ablation studies involving convolutional and recurrent neural network architectures, we demonstrate that leveraging known knowledge about future and personalized training enhances the accuracy of medication adherence forecasting. Code available: https://github.com/ab9mamun/AIMI.", "github_manual": "", "github_regex": "https://github.com/ab9mamun/AIMI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.15299", "title": "Inside-Out: Hidden Factual Knowledge in LLMs", "authors": [ "Zorik Gekhman", "Eyal Ben David", "Hadas Orgad", "Eran Ofek", "Yonatan Belinkov", "Idan Szpector", "Jonathan Herzig", "Roi Reichart" ], "github": "", "project_page": "", "abstract": "This work presents a framework for assessing whether large language models (LLMs) encode more factual knowledge in their parameters than what they express in their outputs. While a few studies hint at this possibility, none has clearly defined or demonstrated this phenomenon. We first propose a formal definition of knowledge, quantifying it for a given question as the fraction of correct-incorrect answer pairs where the correct one is ranked higher. This gives rise to external and internal knowledge, depending on the information used to score individual answer candidates: either the model's observable token-level probabilities or its intermediate computations. Hidden knowledge arises when internal knowledge exceeds external knowledge. We then present a case study, applying this framework to three popular open-weights LLMs in a closed-book QA setup. Our results indicate that: (1) LLMs consistently encode more factual knowledge internally than what they express externally, with an average gap of 40%. (2) Surprisingly, some knowledge is so deeply hidden that a model can internally know an answer perfectly, yet fail to generate it even once, despite large-scale repeated sampling of 1,000 answers. This reveals fundamental limitations in the generation capabilities of LLMs, which (3) puts a practical constraint on scaling test-time compute via repeated answer sampling in closed-book QA: significant performance improvements remain inaccessible because some answers are practically never sampled, yet if they were, we would be guaranteed to rank them first.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16429", "title": "Sonata: Self-Supervised Learning of Reliable Point Representations", "authors": [ "Xiaoyang Wu", "Daniel DeTone", "Duncan Frost", "Tianwei Shen", "Chris Xie", "Nan Yang", "Jakob Engel", "Richard Newcombe", "Hengshuang Zhao", "Julian Straub" ], "github": "", "project_page": "", "abstract": "In this paper, we question whether we have a reliable self-supervised point cloud model that can be used for diverse 3D tasks via simple linear probing, even with limited data and minimal computation. We find that existing 3D self-supervised learning approaches fall short when evaluated on representation quality through linear probing. We hypothesize that this is due to what we term the \"geometric shortcut\", which causes representations to collapse to low-level spatial features. This challenge is unique to 3D and arises from the sparse nature of point cloud data. We address it through two key strategies: obscuring spatial information and enhancing the reliance on input features, ultimately composing a Sonata of 140k point clouds through self-distillation. Sonata is simple and intuitive, yet its learned representations are strong and reliable: zero-shot visualizations demonstrate semantic grouping, alongside strong spatial reasoning through nearest-neighbor relationships. Sonata demonstrates exceptional parameter and data efficiency, tripling linear probing accuracy (from 21.8% to 72.5%) on ScanNet and nearly doubling performance with only 1% of the data compared to previous approaches. Full fine-tuning further advances SOTA across both 3D indoor and outdoor perception tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.09949", "title": "UVE: Are MLLMs Unified Evaluators for AI-Generated Videos?", "authors": [ "Yuanxin Liu", "Rui Zhu", "Shuhuai Ren", "Jiacong Wang", "Haoyuan Guo", "Xu Sun", "Lu Jiang" ], "github": "https://github.com/bytedance/UVE", "project_page": "", "abstract": "With the rapid growth of video generative models (VGMs), it is essential to develop reliable and comprehensive automatic metrics for AI-generated videos (AIGVs). Existing methods either use off-the-shelf models optimized for other tasks or rely on human assessment data to train specialized evaluators. These approaches are constrained to specific evaluation aspects and are difficult to scale with the increasing demands for finer-grained and more comprehensive evaluations. To address this issue, this work investigates the feasibility of using multimodal large language models (MLLMs) as a unified evaluator for AIGVs, leveraging their strong visual perception and language understanding capabilities. To evaluate the performance of automatic metrics in unified AIGV evaluation, we introduce a benchmark called UVE-Bench. UVE-Bench collects videos generated by state-of-the-art VGMs and provides pairwise human preference annotations across 15 evaluation aspects. Using UVE-Bench, we extensively evaluate 16 MLLMs. Our empirical results suggest that while advanced MLLMs (e.g., Qwen2VL-72B and InternVL2.5-78B) still lag behind human evaluators, they demonstrate promising ability in unified AIGV evaluation, significantly surpassing existing specialized evaluation methods. Additionally, we conduct an in-depth analysis of key design choices that impact the performance of MLLM-driven evaluators, offering valuable insights for future research on AIGV evaluation. The code is available at https://github.com/bytedance/UVE.", "github_manual": "", "github_regex": "https://github.com/bytedance/UVE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.13356", "title": "Agents Play Thousands of 3D Video Games", "authors": [ "Zhongwen Xu", "Xianliang Wang", "Siyi Li", "Tao Yu", "Liang Wang", "Qiang Fu", "Wei Yang" ], "github": "", "project_page": "", "abstract": "We present PORTAL, a novel framework for developing artificial intelligence agents capable of playing thousands of 3D video games through language-guided policy generation. By transforming decision-making problems into language modeling tasks, our approach leverages large language models (LLMs) to generate behavior trees represented in domain-specific language (DSL). This method eliminates the computational burden associated with traditional reinforcement learning approaches while preserving strategic depth and rapid adaptability. Our framework introduces a hybrid policy structure that combines rule-based nodes with neural network components, enabling both high-level strategic reasoning and precise low-level control. A dual-feedback mechanism incorporating quantitative game metrics and vision-language model analysis facilitates iterative policy improvement at both tactical and strategic levels. The resulting policies are instantaneously deployable, human-interpretable, and capable of generalizing across diverse gaming environments. Experimental results demonstrate PORTAL's effectiveness across thousands of first-person shooter (FPS) games, showcasing significant improvements in development efficiency, policy generalization, and behavior diversity compared to traditional approaches. PORTAL represents a significant advancement in game AI development, offering a practical solution for creating sophisticated agents that can operate across thousands of commercial video games with minimal development overhead. Experiment results on the 3D video games are best viewed on https://zhongwen.one/projects/portal .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.16420", "title": "SynCity: Training-Free Generation of 3D Worlds", "authors": [ "Paul Engstler", "Aleksandar Shtedritski", "Iro Laina", "Christian Rupprecht", "Andrea Vedaldi" ], "github": "https://github.com/alimama-creative/FLUX-Controlnet-Inpainting", "project_page": "", "abstract": "We address the challenge of generating 3D worlds from textual descriptions. We propose SynCity, a training- and optimization-free approach, which leverages the geometric precision of pre-trained 3D generative models and the artistic versatility of 2D image generators to create large, high-quality 3D spaces. While most 3D generative models are object-centric and cannot generate large-scale worlds, we show how 3D and 2D generators can be combined to generate ever-expanding scenes. Through a tile-based approach, we allow fine-grained control over the layout and the appearance of scenes. The world is generated tile-by-tile, and each new tile is generated within its world-context and then fused with the scene. SynCity generates compelling and immersive scenes that are rich in detail and diversity.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/alimama-creative/FLUX-Controlnet-Inpainting", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.11509", "title": "TikZero: Zero-Shot Text-Guided Graphics Program Synthesis", "authors": [ "Jonas Belouadi", "Eddy Ilg", "Margret Keuper", "Hideki Tanaka", "Masao Utiyama", "Raj Dabre", "Steffen Eger", "Simone Paolo Ponzetto" ], "github": "", "project_page": "", "abstract": "With the rise of generative AI, synthesizing figures from text captions becomes a compelling application. However, achieving high geometric precision and editability requires representing figures as graphics programs in languages like TikZ, and aligned training data (i.e., graphics programs with captions) remains scarce. Meanwhile, large amounts of unaligned graphics programs and captioned raster images are more readily available. We reconcile these disparate data sources by presenting TikZero, which decouples graphics program generation from text understanding by using image representations as an intermediary bridge. It enables independent training on graphics programs and captioned images and allows for zero-shot text-guided graphics program synthesis during inference. We show that our method substantially outperforms baselines that can only operate with caption-aligned graphics programs. Furthermore, when leveraging caption-aligned graphics programs as a complementary training signal, TikZero matches or exceeds the performance of much larger models, including commercial systems like GPT-4o. Our code, datasets, and select models are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.15672", "title": "GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for Autonomous Driving", "authors": [ "William Ljungbergh", "Adam Lilja", "Adam Tonderski. Arvid Laveno Ling", "Carl Lindstr\u00f6m", "Willem Verbeke", "Junsheng Fu", "Christoffer Petersson", "Lars Hammarstrand", "Michael Felsberg" ], "github": "https://github.com/LiljaAdam/gasp", "project_page": "", "abstract": "Self-supervised pre-training based on next-token prediction has enabled large language models to capture the underlying structure of text, and has led to unprecedented performance on a large array of tasks when applied at scale. Similarly, autonomous driving generates vast amounts of spatiotemporal data, alluding to the possibility of harnessing scale to learn the underlying geometric and semantic structure of the environment and its evolution over time. In this direction, we propose a geometric and semantic self-supervised pre-training method, GASP, that learns a unified representation by predicting, at any queried future point in spacetime, (1) general occupancy, capturing the evolving structure of the 3D scene; (2) ego occupancy, modeling the ego vehicle path through the environment; and (3) distilled high-level features from a vision foundation model. By modeling geometric and semantic 4D occupancy fields instead of raw sensor measurements, the model learns a structured, generalizable representation of the environment and its evolution through time. We validate GASP on multiple autonomous driving benchmarks, demonstrating significant improvements in semantic occupancy forecasting, online mapping, and ego trajectory prediction. Our results demonstrate that continuous 4D geometric and semantic occupancy prediction provides a scalable and effective pre-training paradigm for autonomous driving. For code and additional visualizations, see \\href{https://research.zenseact.com/publications/gasp/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LiljaAdam/gasp", "github_api_added_by": "auto" }, { "date": "2025-03-21", "arxiv_id": "2503.14201", "title": "Why Personalizing Deep Learning-Based Code Completion Tools Matters", "authors": [ "Alessandro Giagnorio", "Alberto Martin-Lopez", "Gabriele Bavota" ], "github": "", "project_page": "", "abstract": "Deep learning (DL)-based code completion tools have transformed software development by enabling advanced code generation. These tools leverage models trained on vast amounts of code from numerous repositories, capturing general coding patterns. However, the impact of fine-tuning these models for specific organizations or developers to boost their performance on such subjects remains unexplored. In this work, we fill this gap by presenting solid empirical evidence answering this question. More specifically, we consider 136 developers from two organizations (Apache and Spring), two model architectures (T5 and Code Llama), and three model sizes (60M, 750M, and 7B trainable parameters). T5 models (60M, 750M) were pre-trained and fine-tuned on over 2,000 open-source projects, excluding the subject organizations' data, and compared against versions fine-tuned on organization- and developer-specific datasets. For the Code Llama model (7B), we compared the performance of the already pre-trained model publicly available online with the same model fine-tuned via parameter-efficient fine-tuning on organization- and developer-specific datasets. Our results show that there is a boost in prediction capabilities provided by both an organization-specific and a developer-specific additional fine-tuning, with the former being particularly performant. Such a finding generalizes across (i) the two subject organizations (i.e., Apache and Spring) and (ii) models of completely different magnitude (from 60M to 7B trainable parameters). Finally, we show that DL models fine-tuned on an organization-specific dataset achieve the same completion performance of pre-trained code models used out of the box and being sim10times larger, with consequent savings in terms of deployment and inference cost (e.g., smaller GPUs needed).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.13891", "title": "Where do Large Vision-Language Models Look at when Answering Questions?", "authors": [ "Xiaoying Xing", "Chia-Wen Kuo", "Li Fuxin", "Yulei Niu", "Fan Chen", "Ming Li", "Ying Wu", "Longyin Wen", "Sijie Zhu" ], "github": "https://github.com/bytedance/LVLM_Interpretation", "project_page": "", "abstract": "Large Vision-Language Models (LVLMs) have shown promising performance in vision-language understanding and reasoning tasks. However, their visual understanding behaviors remain underexplored. A fundamental question arises: to what extent do LVLMs rely on visual input, and which image regions contribute to their responses? It is non-trivial to interpret the free-form generation of LVLMs due to their complicated visual architecture (e.g., multiple encoders and multi-resolution) and variable-length outputs. In this paper, we extend existing heatmap visualization methods (e.g., iGOS++) to support LVLMs for open-ended visual question answering. We propose a method to select visually relevant tokens that reflect the relevance between generated answers and input image. Furthermore, we conduct a comprehensive analysis of state-of-the-art LVLMs on benchmarks designed to require visual information to answer. Our findings offer several insights into LVLM behavior, including the relationship between focus region and answer correctness, differences in visual attention across architectures, and the impact of LLM scale on visual understanding. The code and data are available at https://github.com/bytedance/LVLM_Interpretation.", "github_manual": "", "github_regex": "https://github.com/bytedance/LVLM_Interpretation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-21", "arxiv_id": "2503.07906", "title": "Painting with Words: Elevating Detailed Image Captioning with Benchmark and Alignment Learning", "authors": [ "Qinghao Ye", "Xianhan Zeng", "Fu Li", "Chunyuan Li", "Haoqi Fan" ], "github": "https://github.com/MAGAer13/DeCapBench", "project_page": "", "abstract": "Image captioning has long been a pivotal task in visual understanding, with recent advancements in vision-language models (VLMs) significantly enhancing the ability to generate detailed image captions. However, the evaluation of detailed image captioning remains underexplored due to outdated evaluation metrics and coarse annotations. In this paper, we introduce DeCapBench along with a novel metric, DCScore, specifically designed for detailed captioning tasks. DCScore evaluates hallucinations and fine-grained comprehensiveness by deconstructing responses into the smallest self-sufficient units, termed primitive information units, and assessing them individually. Our evaluation shows that DCScore aligns more closely with human judgment than other rule-based or model-based metrics. Concurrently, DeCapBench exhibits a high correlation with VLM arena results on descriptive tasks, surpassing existing benchmarks for vision-language models. Additionally, we present an automatic fine-grained feedback collection method, FeedQuill, for preference optimization based on our advanced metric, showing robust generalization capabilities across auto-generated preference data. Extensive experiments on multiple VLMs demonstrate that our method not only significantly reduces hallucinations but also enhances performance across various benchmarks, achieving superior detail captioning performance while surpassing GPT-4o.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MAGAer13/DeCapBench", "github_api_added_by": "auto" }, { "date": "2025-03-24", "arxiv_id": "2503.16905", "title": "MAPS: A Multi-Agent Framework Based on Big Seven Personality and Socratic Guidance for Multimodal Scientific Problem Solving", "authors": [ "Jian Zhang", "Zhiyuan Wang", "Zhangqi Wang", "Xinyu Zhang", "Fangzhi Xu", "Qika Lin", "Rui Mao", "Erik Cambria", "Jun Liu" ], "github": "", "project_page": "", "abstract": "Multimodal scientific problems (MSPs) involve complex issues that require the integration of multiple modalities, such as text and diagrams, presenting a significant challenge in artificial intelligence. While progress has been made in addressing traditional scientific problems, MSPs still face two primary issues: the challenge of multi-modal comprehensive reasoning in scientific problem-solving and the lack of reflective and rethinking capabilities. To address these issues, we introduce a Multi-Agent framework based on the Big Seven Personality and Socratic guidance (MAPS). This framework employs seven distinct agents that leverage feedback mechanisms and the Socratic method to guide the resolution of MSPs. To tackle the first issue, we propose a progressive four-agent solving strategy, where each agent focuses on a specific stage of the problem-solving process. For the second issue, we introduce a Critic agent, inspired by Socratic questioning, which prompts critical thinking and stimulates autonomous learning. We conduct extensive experiments on the EMMA, Olympiad, and MathVista datasets, achieving promising results that outperform the current SOTA model by 15.84% across all tasks. Meanwhile, the additional analytical experiments also verify the model's progress as well as generalization ability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.16874", "title": "MARS: A Multi-Agent Framework Incorporating Socratic Guidance for Automated Prompt Optimization", "authors": [ "Jian Zhang", "Zhangqi Wang", "Haiping Zhu", "Jun Liu", "Qika Lin", "Erik Cambria" ], "github": "", "project_page": "", "abstract": "The basic question-answering format of large language models involves inputting a prompt and receiving a response, and the quality of the prompt directly impacts the effectiveness of the response. Automated Prompt Optimization (APO) aims to break free from the cognitive biases of manually designed prompts and explores a broader design space for prompts. However, existing APO methods suffer from limited flexibility of fixed templates and inefficient search in prompt spaces as key issues. To this end, we propose a Multi-Agent framework Incorporating Socratic guidance (MARS), which utilizes multi-agent fusion technology for automatic planning, with gradual continuous optimization and evaluation. Specifically, MARS comprises seven agents, each with distinct functionalities, which autonomously use the Planner to devise an optimization path that ensures flexibility. Additionally, it employs a Teacher-Critic-Student Socratic dialogue pattern to iteratively optimize the prompts while conducting effective search. We conduct extensive experiments on various datasets to validate the effectiveness of our method, and perform additional analytical experiments to assess the model's advancement as well as the interpretability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.16430", "title": "Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation", "authors": [ "Yuqing Wang", "Zhijie Lin", "Yao Teng", "Yuanzhi Zhu", "Shuhuai Ren", "Jiashi Feng", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Autoregressive visual generation models typically rely on tokenizers to compress images into tokens that can be predicted sequentially. A fundamental dilemma exists in token representation: discrete tokens enable straightforward modeling with standard cross-entropy loss, but suffer from information loss and tokenizer training instability; continuous tokens better preserve visual details, but require complex distribution modeling, complicating the generation pipeline. In this paper, we propose TokenBridge, which bridges this gap by maintaining the strong representation capacity of continuous tokens while preserving the modeling simplicity of discrete tokens. To achieve this, we decouple discretization from the tokenizer training process through post-training quantization that directly obtains discrete tokens from continuous representations. Specifically, we introduce a dimension-wise quantization strategy that independently discretizes each feature dimension, paired with a lightweight autoregressive prediction mechanism that efficiently model the resulting large token space. Extensive experiments show that our approach achieves reconstruction and generation quality on par with continuous methods while using standard categorical prediction. This work demonstrates that bridging discrete and continuous paradigms can effectively harness the strengths of both approaches, providing a promising direction for high-quality visual generation with simple autoregressive modeling. Project page: https://yuqingwang1029.github.io/TokenBridge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.17352", "title": "OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-Improvement", "authors": [ "Yihe Deng", "Hritik Bansal", "Fan Yin", "Nanyun Peng", "Wei Wang", "Kai-Wei Chang" ], "github": "https://github.com/yihedeng9/OpenVLThinker", "project_page": "", "abstract": "Recent advancements demonstrated by DeepSeek-R1 have shown that complex reasoning abilities in large language models (LLMs), including sophisticated behaviors such as self-verification and self-correction, can be achieved by RL with verifiable rewards and significantly improves model performance on challenging tasks such as AIME. Motivated by these findings, our study investigates whether similar reasoning capabilities can be successfully integrated into large vision-language models (LVLMs) and assesses their impact on challenging multimodal reasoning tasks. We consider an approach that iteratively leverages supervised fine-tuning (SFT) on lightweight training data and Reinforcement Learning (RL) to further improve model generalization. Initially, reasoning capabilities were distilled from pure-text R1 models by generating reasoning steps using high-quality captions of the images sourced from diverse visual datasets. Subsequently, iterative RL training further enhance reasoning skills, with each iteration's RL-improved model generating refined SFT datasets for the next round. This iterative process yielded OpenVLThinker, a LVLM exhibiting consistently improved reasoning performance on challenging benchmarks such as MathVista, MathVerse, and MathVision, demonstrating the potential of our strategy for robust vision-language reasoning. The code, model and data are held at https://github.com/yihedeng9/OpenVLThinker.", "github_manual": "", "github_regex": "https://github.com/yihedeng9/OpenVLThinker", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.17126", "title": "Modifying Large Language Model Post-Training for Diverse Creative Writing", "authors": [ "John Joon Young Chung", "Vishakh Padmakumar", "Melissa Roemmele", "Yuqian Sun", "Max Kreminski" ], "github": "https://github.com/mj-storytelling/DiversityTuning", "project_page": "", "abstract": "As creative writing tasks do not have singular correct answers, large language models (LLMs) trained to perform these tasks should be able to generate diverse valid outputs. However, LLM post-training often focuses on improving generation quality but neglects to facilitate output diversity. Hence, in creative writing generation, we investigate post-training approaches to promote both output diversity and quality. Our core idea is to include deviation -- the degree of difference between a training sample and all other samples with the same prompt -- in the training objective to facilitate learning from rare high-quality instances. By adopting our approach to direct preference optimization (DPO) and odds ratio preference optimization (ORPO), we demonstrate that we can promote the output diversity of trained models while minimally decreasing quality. Our best model with 8B parameters could achieve on-par diversity as a human-created dataset while having output quality similar to the best instruction-tuned models we examined, GPT-4o and DeepSeek-R1. We further validate our approaches with a human evaluation, an ablation, and a comparison to an existing diversification approach, DivPO.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mj-storytelling/DiversityTuning", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.17287", "title": "FastCuRL: Curriculum Reinforcement Learning with Progressive Context Extension for Efficient Training R1-like Reasoning Models", "authors": [ "Mingyang Song", "Mao Zheng", "Zheng Li", "Wenjie Yang", "Xuan Luo", "Yue Pan", "Feng Zhang" ], "github": "https://github.com/nick7nlp/FastCuRL", "project_page": "", "abstract": "In this paper, we propose \\textsc{FastCuRL}, a simple yet efficient Curriculum Reinforcement Learning approach with context window extending strategy to accelerate the reinforcement learning training efficiency for R1-like reasoning models while enhancing their performance in tackling complex reasoning tasks with long chain-of-thought rationales, particularly with a 1.5B parameter language model. \\textsc{FastCuRL} consists of two main procedures: length-aware training data segmentation and context window extension training. Specifically, the former first splits the original training data into three different levels by the input prompt length, and then the latter leverages segmented training datasets with a progressively increasing context window length to train the reasoning model. Experimental results demonstrate that \\textsc{FastCuRL}-1.5B-Preview surpasses DeepScaleR-1.5B-Preview across all five datasets (including MATH 500, AIME 2024, AMC 2023, Minerva Math, and OlympiadBench) while only utilizing 50\\% of training steps. Furthermore, all training stages for FastCuRL-1.5B-Preview are completed using just a single node with 8 GPUs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nick7nlp/FastCuRL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.16983", "title": "Enabling Versatile Controls for Video Diffusion Models", "authors": [ "Xu Zhang", "Hao Zhou", "Haoming Qin", "Xiaobin Lu", "Jiaxing Yan", "Guanzhong Wang", "Zeyu Chen", "Yi Liu" ], "github": "", "project_page": "", "abstract": "Despite substantial progress in text-to-video generation, achieving precise and flexible control over fine-grained spatiotemporal attributes remains a significant unresolved challenge in video generation research. To address these limitations, we introduce VCtrl (also termed PP-VCtrl), a novel framework designed to enable fine-grained control over pre-trained video diffusion models in a unified manner. VCtrl integrates diverse user-specified control signals-such as Canny edges, segmentation masks, and human keypoints-into pretrained video diffusion models via a generalizable conditional module capable of uniformly encoding multiple types of auxiliary signals without modifying the underlying generator. Additionally, we design a unified control signal encoding pipeline and a sparse residual connection mechanism to efficiently incorporate control representations. Comprehensive experiments and human evaluations demonstrate that VCtrl effectively enhances controllability and generation quality. The source code and pre-trained models are publicly available and implemented using the PaddlePaddle framework at http://github.com/PaddlePaddle/PaddleMIX/tree/develop/ppdiffusers/examples/ppvctrl.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.16921", "title": "When Preferences Diverge: Aligning Diffusion Models with Minority-Aware Adaptive DPO", "authors": [ "Lingfan Zhang", "Chen Liu", "Chengming Xu", "Kai Hu", "Donghao Luo", "Chengjie Wang", "Yanwei Fu", "Yuan Yao" ], "github": "", "project_page": "", "abstract": "In recent years, the field of image generation has witnessed significant advancements, particularly in fine-tuning methods that align models with universal human preferences. This paper explores the critical role of preference data in the training process of diffusion models, particularly in the context of Diffusion-DPO and its subsequent adaptations. We investigate the complexities surrounding universal human preferences in image generation, highlighting the subjective nature of these preferences and the challenges posed by minority samples in preference datasets. Through pilot experiments, we demonstrate the existence of minority samples and their detrimental effects on model performance. We propose Adaptive-DPO -- a novel approach that incorporates a minority-instance-aware metric into the DPO objective. This metric, which includes intra-annotator confidence and inter-annotator stability, distinguishes between majority and minority samples. We introduce an Adaptive-DPO loss function which improves the DPO loss in two ways: enhancing the model's learning of majority labels while mitigating the negative impact of minority samples. Our experiments demonstrate that this method effectively handles both synthetic minority data and real-world preference data, paving the way for more effective training methodologies in image generation tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.16408", "title": "RoboFactory: Exploring Embodied Agent Collaboration with Compositional Constraints", "authors": [ "Yiran Qin", "Li Kang", "Xiufeng Song", "Zhenfei Yin", "Xiaohong Liu", "Xihui Liu", "Ruimao Zhang", "Lei Bai" ], "github": "", "project_page": "", "abstract": "Designing effective embodied multi-agent systems is critical for solving complex real-world tasks across domains. Due to the complexity of multi-agent embodied systems, existing methods fail to automatically generate safe and efficient training data for such systems. To this end, we propose the concept of compositional constraints for embodied multi-agent systems, addressing the challenges arising from collaboration among embodied agents. We design various interfaces tailored to different types of constraints, enabling seamless interaction with the physical world. Leveraging compositional constraints and specifically designed interfaces, we develop an automated data collection framework for embodied multi-agent systems and introduce the first benchmark for embodied multi-agent manipulation, RoboFactory. Based on RoboFactory benchmark, we adapt and evaluate the method of imitation learning and analyzed its performance in different difficulty agent tasks. Furthermore, we explore the architectures and training strategies for multi-agent imitation learning, aiming to build safe and efficient embodied multi-agent systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.16549", "title": "MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems", "authors": [ "Felix Chen", "Hangjie Yuan", "Yunqiu Xu", "Tao Feng", "Jun Cen", "Pengwei Liu", "Zeying Huang", "Yi Yang" ], "github": "https://github.com/MathFlow-zju/MathFlow", "project_page": "", "abstract": "Despite impressive performance across diverse tasks, Multimodal Large Language Models (MLLMs) have yet to fully demonstrate their potential in visual mathematical problem-solving, particularly in accurately perceiving and interpreting diagrams. Inspired by typical processes of humans, we hypothesize that the perception capabilities to extract meaningful information from diagrams is crucial, as it directly impacts subsequent inference processes. To validate this hypothesis, we developed FlowVerse, a comprehensive benchmark that categorizes all information used during problem-solving into four components, which are then combined into six problem versions for evaluation. Our preliminary results on FlowVerse reveal that existing MLLMs exhibit substantial limitations when extracting essential information and reasoned property from diagrams and performing complex reasoning based on these visual inputs. In response, we introduce MathFlow, a modular problem-solving pipeline that decouples perception and inference into distinct stages, thereby optimizing each independently. Given the perceptual limitations observed in current MLLMs, we trained MathFlow-P-7B as a dedicated perception model. Experimental results indicate that MathFlow-P-7B yields substantial performance gains when integrated with various closed-source and open-source inference models. This demonstrates the effectiveness of the MathFlow pipeline and its compatibility to diverse inference frameworks. The FlowVerse benchmark and code are available at https://github.com/MathFlow-zju/MathFlow.", "github_manual": "", "github_regex": "https://github.com/MathFlow-zju/MathFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.11572", "title": "Implicit Bias-Like Patterns in Reasoning Models", "authors": [ "Messi H. J. Lee", "Calvin K. Lai" ], "github": "", "project_page": "", "abstract": "Implicit bias refers to automatic or spontaneous mental processes that shape perceptions, judgments, and behaviors. Previous research examining `implicit bias' in large language models (LLMs) has often approached the phenomenon differently than how it is studied in humans by focusing primarily on model outputs rather than on model processing. To examine model processing, we present a method called the Reasoning Model Implicit Association Test (RM-IAT) for studying implicit bias-like patterns in reasoning models: LLMs that employ step-by-step reasoning to solve complex tasks. Using this method, we find that reasoning models require more tokens when processing association-incompatible information compared to association-compatible information. These findings suggest AI systems harbor patterns in processing information that are analogous to human implicit bias. We consider the implications of these implicit bias-like patterns for their deployment in real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.12821", "title": "From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration", "authors": [ "Mingyang Song", "Xiaoye Qu", "Jiawei Zhou", "Yu Cheng" ], "github": "", "project_page": "", "abstract": "Large Vision-Language Models (LVLMs) have achieved significant progress in combining visual comprehension with language generation. Despite this success, the training data of LVLMs still suffers from Long-Tail (LT) problems, where the data distribution is highly imbalanced. Previous works have mainly focused on traditional VLM architectures, i.e., CLIP or ViT, and specific tasks such as recognition and classification. Nevertheless, the exploration of LVLM (e.g. LLaVA) and more general tasks (e.g. Visual Question Answering and Visual Reasoning) remains under-explored. In this paper, we first conduct an in-depth analysis of the LT issues in LVLMs and identify two core causes: the overrepresentation of head concepts and the underrepresentation of tail concepts. Based on the above observation, we propose an Adaptive Data Refinement Framework (ADR), which consists of two stages: Data Rebalancing (DR) and Data Synthesis (DS). In the DR stage, we adaptively rebalance the redundant data based on entity distributions, while in the DS stage, we leverage Denoising Diffusion Probabilistic Models (DDPMs) and scarce images to supplement underrepresented portions. Through comprehensive evaluations across eleven benchmarks, our proposed ADR effectively mitigates the long-tail problem in the training data, improving the average performance of LLaVA 1.5 relatively by 4.36%, without increasing the training data volume.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.16660", "title": "When Less is Enough: Adaptive Token Reduction for Efficient Image Representation", "authors": [ "Eduard Allakhverdov", "Elizaveta Goncharova", "Andrey Kuznetsov" ], "github": "", "project_page": "", "abstract": "Vision encoders typically generate a large number of visual tokens, providing information-rich representations but significantly increasing computational demands. This raises the question of whether all generated tokens are equally valuable or if some of them can be discarded to reduce computational costs without compromising quality. In this paper, we introduce a new method for determining feature utility based on the idea that less valuable features can be reconstructed from more valuable ones. We implement this concept by integrating an autoencoder with a Gumbel-Softmax selection mechanism, that allows identifying and retaining only the most informative visual tokens. To validate our approach, we compared the performance of the LLaVA-NeXT model, using features selected by our method with randomly selected features. We found that on OCR-based tasks, more than 50% of the visual context can be removed with minimal performance loss, whereas randomly discarding the same proportion of features significantly affects the model capabilities. Furthermore, in general-domain tasks, even randomly retaining only 30% of tokens achieves performance comparable to using the full set of visual tokens. Our results highlight a promising direction towards adaptive and efficient multimodal pruning that facilitates scalable and low-overhead inference without compromising performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.16867", "title": "ETVA: Evaluation of Text-to-Video Alignment via Fine-grained Question Generation and Answering", "authors": [ "Kaisi Guan", "Zhengfeng Lai", "Yuchong Sun", "Peng Zhang", "Wei Liu", "Kieran Liu", "Meng Cao", "Ruihua Song" ], "github": "", "project_page": "", "abstract": "Precisely evaluating semantic alignment between text prompts and generated videos remains a challenge in Text-to-Video (T2V) Generation. Existing text-to-video alignment metrics like CLIPScore only generate coarse-grained scores without fine-grained alignment details, failing to align with human preference. To address this limitation, we propose ETVA, a novel Evaluation method of Text-to-Video Alignment via fine-grained question generation and answering. First, a multi-agent system parses prompts into semantic scene graphs to generate atomic questions. Then we design a knowledge-augmented multi-stage reasoning framework for question answering, where an auxiliary LLM first retrieves relevant common-sense knowledge (e.g., physical laws), and then video LLM answers the generated questions through a multi-stage reasoning mechanism. Extensive experiments demonstrate that ETVA achieves a Spearman's correlation coefficient of 58.47, showing a much higher correlation with human judgment than existing metrics which attain only 31.0. We also construct a comprehensive benchmark specifically designed for text-to-video alignment evaluation, featuring 2k diverse prompts and 12k atomic questions spanning 10 categories. Through a systematic evaluation of 15 existing text-to-video models, we identify their key capabilities and limitations, paving the way for next-generation T2V generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.17069", "title": "PVChat: Personalized Video Chat with One-Shot Learning", "authors": [ "Yufei Shi", "Weilong Yan", "Gang Xu", "Yumeng Li", "Yuchen Li", "Zhenxi Li", "Fei Richard Yu", "Ming Li", "Si Yong Yeo" ], "github": "https://github.com/DavidYan2001/PVChat", "project_page": "", "abstract": "Video large language models (ViLLMs) excel in general video understanding, e.g., recognizing activities like talking and eating, but struggle with identity-aware comprehension, such as \"Wilson is receiving chemotherapy\" or \"Tom is discussing with Sarah\", limiting their applicability in smart healthcare and smart home environments. To address this limitation, we propose a one-shot learning framework PVChat, the first personalized ViLLM that enables subject-aware question answering (QA) from a single video for each subject. Our approach optimizes a Mixture-of-Heads (MoH) enhanced ViLLM on a synthetically augmented video-QA dataset, leveraging a progressive image-to-video learning strategy. Specifically, we introduce an automated augmentation pipeline that synthesizes identity-preserving positive samples and retrieves hard negatives from existing video corpora, generating a diverse training dataset with four QA types: existence, appearance, action, and location inquiries. To enhance subject-specific learning, we propose a ReLU Routing MoH attention mechanism, alongside two novel objectives: (1) Smooth Proximity Regularization for progressive learning through exponential distance scaling and (2) Head Activation Enhancement for balanced attention routing. Finally, we adopt a two-stage training strategy, transitioning from image pre-training to video fine-tuning, enabling a gradual learning process from static attributes to dynamic representations. We evaluate PVChat on diverse datasets covering medical scenarios, TV series, anime, and real-world footage, demonstrating its superiority in personalized feature understanding after learning from a single video, compared to state-of-the-art ViLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/DavidYan2001/PVChat", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.16025", "title": "Single Image Iterative Subject-driven Generation and Editing", "authors": [ "Yair Shpitzer", "Gal Chechik", "Idan Schwartz" ], "github": "https://github.com/advimman/lama", "project_page": "", "abstract": "Personalizing image generation and editing is particularly challenging when we only have a few images of the subject, or even a single image. A common approach to personalization is concept learning, which can integrate the subject into existing models relatively quickly, but produces images whose quality tends to deteriorate quickly when the number of subject images is small. Quality can be improved by pre-training an encoder, but training restricts generation to the training distribution, and is time consuming. It is still an open hard challenge to personalize image generation and editing from a single image without training. Here, we present SISO, a novel, training-free approach based on optimizing a similarity score with an input subject image. More specifically, SISO iteratively generates images and optimizes the model based on loss of similarity with the given subject image until a satisfactory level of similarity is achieved, allowing plug-and-play optimization to any image generator. We evaluated SISO in two tasks, image editing and image generation, using a diverse data set of personal subjects, and demonstrate significant improvements over existing methods in image quality, subject fidelity, and background preservation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/advimman/lama", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.17032", "title": "TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars for Augmented Reality via 3D Gaussian Splatting", "authors": [ "Jianchuan Chen", "Jingchuan Hu", "Gaige Wang", "Zhonghua Jiang", "Tiansong Zhou", "Zhiwen Chen", "Chengfei Lv" ], "github": "", "project_page": "", "abstract": "Realistic 3D full-body talking avatars hold great potential in AR, with applications ranging from e-commerce live streaming to holographic communication. Despite advances in 3D Gaussian Splatting (3DGS) for lifelike avatar creation, existing methods struggle with fine-grained control of facial expressions and body movements in full-body talking tasks. Additionally, they often lack sufficient details and cannot run in real-time on mobile devices. We present TaoAvatar, a high-fidelity, lightweight, 3DGS-based full-body talking avatar driven by various signals. Our approach starts by creating a personalized clothed human parametric template that binds Gaussians to represent appearances. We then pre-train a StyleUnet-based network to handle complex pose-dependent non-rigid deformation, which can capture high-frequency appearance details but is too resource-intensive for mobile devices. To overcome this, we \"bake\" the non-rigid deformations into a lightweight MLP-based network using a distillation technique and develop blend shapes to compensate for details. Extensive experiments show that TaoAvatar achieves state-of-the-art rendering quality while running in real-time across various devices, maintaining 90 FPS on high-definition stereo devices such as the Apple Vision Pro.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.16282", "title": "Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model", "authors": [ "Zhaochong An", "Guolei Sun", "Yun Liu", "Runjia Li", "Junlin Han", "Ender Konukoglu", "Serge Belongie" ], "github": "https://github.com/ZhaochongAn/GFS-VL", "project_page": "", "abstract": "Generalized few-shot 3D point cloud segmentation (GFS-PCS) adapts models to new classes with few support samples while retaining base class segmentation. Existing GFS-PCS methods enhance prototypes via interacting with support or query features but remain limited by sparse knowledge from few-shot samples. Meanwhile, 3D vision-language models (3D VLMs), generalizing across open-world novel classes, contain rich but noisy novel class knowledge. In this work, we introduce a GFS-PCS framework that synergizes dense but noisy pseudo-labels from 3D VLMs with precise yet sparse few-shot samples to maximize the strengths of both, named GFS-VL. Specifically, we present a prototype-guided pseudo-label selection to filter low-quality regions, followed by an adaptive infilling strategy that combines knowledge from pseudo-label contexts and few-shot samples to adaptively label the filtered, unlabeled areas. Additionally, we design a novel-base mix strategy to embed few-shot samples into training scenes, preserving essential context for improved novel class learning. Moreover, recognizing the limited diversity in current GFS-PCS benchmarks, we introduce two challenging benchmarks with diverse novel classes for comprehensive generalization evaluation. Experiments validate the effectiveness of our framework across models and datasets. Our approach and benchmarks provide a solid foundation for advancing GFS-PCS in the real world. The code is at https://github.com/ZhaochongAn/GFS-VL", "github_manual": "", "github_regex": "https://github.com/ZhaochongAn/GFS-VL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.17095", "title": "FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields", "authors": [ "Kwan Yun", "Chaelin Kim", "Hangyeul Shin", "Junyong Noh" ], "github": "", "project_page": "", "abstract": "Recent 3D face editing methods using masks have produced high-quality edited images by leveraging Neural Radiance Fields (NeRF). Despite their impressive performance, existing methods often provide limited user control due to the use of pre-trained segmentation masks. To utilize masks with a desired layout, an extensive training dataset is required, which is challenging to gather. We present FFaceNeRF, a NeRF-based face editing technique that can overcome the challenge of limited user control due to the use of fixed mask layouts. Our method employs a geometry adapter with feature injection, allowing for effective manipulation of geometry attributes. Additionally, we adopt latent mixing for tri-plane augmentation, which enables training with a few samples. This facilitates rapid model adaptation to desired mask layouts, crucial for applications in fields like personalized medical imaging or creative face editing. Our comparative evaluations demonstrate that FFaceNeRF surpasses existing mask based face editing methods in terms of flexibility, control, and generated image quality, paving the way for future advancements in customized and high-fidelity 3D face editing. The code is available on the {https://kwanyun.github.io/FFaceNeRF_page/{project-page}}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.16423", "title": "GAEA: A Geolocation Aware Conversational Model", "authors": [ "Ron Campos", "Ashmal Vayani", "Parth Parag Kulkarni", "Rohit Gupta", "Aritra Dutta", "Mubarak Shah" ], "github": "https://github.com/UCF-CRCV/GAEA", "project_page": "", "abstract": "Image geolocalization, in which, traditionally, an AI model predicts the precise GPS coordinates of an image is a challenging task with many downstream applications. However, the user cannot utilize the model to further their knowledge other than the GPS coordinate; the model lacks an understanding of the location and the conversational ability to communicate with the user. In recent days, with tremendous progress of large multimodal models (LMMs) proprietary and open-source researchers have attempted to geolocalize images via LMMs. However, the issues remain unaddressed; beyond general tasks, for more specialized downstream tasks, one of which is geolocalization, LMMs struggle. In this work, we propose to solve this problem by introducing a conversational model GAEA that can provide information regarding the location of an image, as required by a user. No large-scale dataset enabling the training of such a model exists. Thus we propose a comprehensive dataset GAEA with 800K images and around 1.6M question answer pairs constructed by leveraging OpenStreetMap (OSM) attributes and geographical context clues. For quantitative evaluation, we propose a diverse benchmark comprising 4K image-text pairs to evaluate conversational capabilities equipped with diverse question types. We consider 11 state-of-the-art open-source and proprietary LMMs and demonstrate that GAEA significantly outperforms the best open-source model, LLaVA-OneVision by 25.69% and the best proprietary model, GPT-4o by 8.28%. Our dataset, model and codes are available", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UCF-CRCV/GAEA", "github_api_added_by": "auto" }, { "date": "2025-03-24", "arxiv_id": "2503.14607", "title": "Can Large Vision Language Models Read Maps Like a Human?", "authors": [ "Shuo Xing", "Zezhou Sun", "Shuangyu Xie", "Kaiyuan Chen", "Yanjia Huang", "Yuping Wang", "Jiachen Li", "Dezhen Song", "Zhengzhong Tu" ], "github": "https://github.com/taco-group/MapBench", "project_page": "", "abstract": "In this paper, we introduce MapBench-the first dataset specifically designed for human-readable, pixel-based map-based outdoor navigation, curated from complex path finding scenarios. MapBench comprises over 1600 pixel space map path finding problems from 100 diverse maps. In MapBench, LVLMs generate language-based navigation instructions given a map image and a query with beginning and end landmarks. For each map, MapBench provides Map Space Scene Graph (MSSG) as an indexing data structure to convert between natural language and evaluate LVLM-generated results. We demonstrate that MapBench significantly challenges state-of-the-art LVLMs both zero-shot prompting and a Chain-of-Thought (CoT) augmented reasoning framework that decomposes map navigation into sequential cognitive processes. Our evaluation of both open-source and closed-source LVLMs underscores the substantial difficulty posed by MapBench, revealing critical limitations in their spatial reasoning and structured decision-making capabilities. We release all the code and dataset in https://github.com/taco-group/MapBench.", "github_manual": "", "github_regex": "https://github.com/taco-group/MapBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-24", "arxiv_id": "2503.17407", "title": "A Comprehensive Survey on Long Context Language Modeling", "authors": [ "Jiaheng Liu", "Dawei Zhu", "Zhiqi Bai", "Yancheng He", "Huanxuan Liao", "Haoran Que", "Zekun Wang", "Chenchen Zhang", "Ge Zhang", "Jiebin Zhang", "Yuanxing Zhang", "Zhuo Chen", "Hangyu Guo", "Shilong Li", "Ziqiang Liu", "Yong Shan", "Yifan Song", "Jiayi Tian", "Wenhao Wu", "Zhejian Zhou", "Ruijie Zhu", "Junlan Feng", "Yang Gao", "Shizhu He", "Zhoujun Li", "Tianyu Liu", "Fanyu Meng", "Wenbo Su", "Yingshui Tan", "Zili Wang", "Jian Yang", "Wei Ye", "Bo Zheng", "Wangchunshu Zhou", "Wenhao Huang", "Sujian Li", "Zhaoxiang Zhang" ], "github": "https://github.com/LCLM-Horizon/A-Comprehensive-Survey-For-Long-Context-Language-Modeling", "project_page": "", "abstract": "Efficient processing of long contexts has been a persistent pursuit in Natural Language Processing. With the growing number of long documents, dialogues, and other textual data, it is important to develop Long Context Language Models (LCLMs) that can process and analyze extensive inputs in an effective and efficient way. In this paper, we present a comprehensive survey on recent advances in long-context modeling for large language models. Our survey is structured around three key aspects: how to obtain effective and efficient LCLMs, how to train and deploy LCLMs efficiently, and how to evaluate and analyze LCLMs comprehensively. For the first aspect, we discuss data strategies, architectural designs, and workflow approaches oriented with long context processing. For the second aspect, we provide a detailed examination of the infrastructure required for LCLM training and inference. For the third aspect, we present evaluation paradigms for long-context comprehension and long-form generation, as well as behavioral analysis and mechanism interpretability of LCLMs. Beyond these three key aspects, we thoroughly explore the diverse application scenarios where existing LCLMs have been deployed and outline promising future development directions. This survey provides an up-to-date review of the literature on long-context LLMs, which we wish to serve as a valuable resource for both researchers and engineers. An associated GitHub repository collecting the latest papers and repos is available at: https://github.com/LCLM-Horizon/A-Comprehensive-Survey-For-Long-Context-Language-Modeling{\\color[RGB]{175,36,67}{LCLM-Horizon}}.", "github_manual": "", "github_regex": "https://github.com/LCLM-Horizon/A-Comprehensive-Survey-For-Long-Context-Language-Modeling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18769", "title": "AlphaSpace: Enabling Robotic Actions through Semantic Tokenization and Symbolic Reasoning", "authors": [ "Alan Dao", "Dinh Bach Vu", "Bui Quang Huy" ], "github": "", "project_page": "", "abstract": "This paper presents AlphaSpace, a novel methodology designed to enhance the spatial reasoning capabilities of large language models (LLMs) for 3D Cartesian space navigation. AlphaSpace employs a semantics-based tokenization strategy, encoding height information through specialized semantic tokens, and integrates primarily symbolic synthetic reasoning data. This approach enables LLMs to accurately manipulate objects by positioning them at specific [x, y, z] coordinates. Experimental results demonstrate that AlphaSpace significantly outperforms existing models on manipulation subtasks, achieving a total accuracy of 66.67%, compared to 37.5% for GPT-4o and 29.17% for Claude 3.5 Sonnet.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.14428", "title": "MagicComp: Training-free Dual-Phase Refinement for Compositional Video Generation", "authors": [ "Hongyu Zhang", "Yufan Deng", "Shenghai Yuan", "Peng Jin", "Zesen Cheng", "Yian Zhao", "Chang Liu", "Jie Chen" ], "github": "", "project_page": "", "abstract": "Text-to-video (T2V) generation has made significant strides with diffusion models. However, existing methods still struggle with accurately binding attributes, determining spatial relationships, and capturing complex action interactions between multiple subjects. To address these limitations, we propose MagicComp, a training-free method that enhances compositional T2V generation through dual-phase refinement. Specifically, (1) During the Conditioning Stage: We introduce the Semantic Anchor Disambiguation to reinforces subject-specific semantics and resolve inter-subject ambiguity by progressively injecting the directional vectors of semantic anchors into original text embedding; (2) During the Denoising Stage: We propose Dynamic Layout Fusion Attention, which integrates grounding priors and model-adaptive spatial perception to flexibly bind subjects to their spatiotemporal regions through masked attention modulation. Furthermore, MagicComp is a model-agnostic and versatile approach, which can be seamlessly integrated into existing T2V architectures. Extensive experiments on T2V-CompBench and VBench demonstrate that MagicComp outperforms state-of-the-art methods, highlighting its potential for applications such as complex prompt-based and trajectory-controllable video generation. Project page: https://hong-yu-zhang.github.io/MagicComp-Page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.17359", "title": "Position: Interactive Generative Video as Next-Generation Game Engine", "authors": [ "Jiwen Yu", "Yiran Qin", "Haoxuan Che", "Quande Liu", "Xintao Wang", "Pengfei Wan", "Di Zhang", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Modern game development faces significant challenges in creativity and cost due to predetermined content in traditional game engines. Recent breakthroughs in video generation models, capable of synthesizing realistic and interactive virtual environments, present an opportunity to revolutionize game creation. In this position paper, we propose Interactive Generative Video (IGV) as the foundation for Generative Game Engines (GGE), enabling unlimited novel content generation in next-generation gaming. GGE leverages IGV's unique strengths in unlimited high-quality content synthesis, physics-aware world modeling, user-controlled interactivity, long-term memory capabilities, and causal reasoning. We present a comprehensive framework detailing GGE's core modules and a hierarchical maturity roadmap (L0-L4) to guide its evolution. Our work charts a new course for game development in the AI era, envisioning a future where AI-powered generative systems fundamentally reshape how games are created and experienced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18942", "title": "Video-T1: Test-Time Scaling for Video Generation", "authors": [ "Fangfu Liu", "Hanyang Wang", "Yimo Cai", "Kaiyan Zhang", "Xiaohang Zhan", "Yueqi Duan" ], "github": "", "project_page": "", "abstract": "With the scale capability of increasing training data, model size, and computational cost, video generation has achieved impressive results in digital creation, enabling users to express creativity across various domains. Recently, researchers in Large Language Models (LLMs) have expanded the scaling to test-time, which can significantly improve LLM performance by using more inference-time computation. Instead of scaling up video foundation models through expensive training costs, we explore the power of Test-Time Scaling (TTS) in video generation, aiming to answer the question: if a video generation model is allowed to use non-trivial amount of inference-time compute, how much can it improve generation quality given a challenging text prompt. In this work, we reinterpret the test-time scaling of video generation as a searching problem to sample better trajectories from Gaussian noise space to the target video distribution. Specifically, we build the search space with test-time verifiers to provide feedback and heuristic algorithms to guide searching process. Given a text prompt, we first explore an intuitive linear search strategy by increasing noise candidates at inference time. As full-step denoising all frames simultaneously requires heavy test-time computation costs, we further design a more efficient TTS method for video generation called Tree-of-Frames (ToF) that adaptively expands and prunes video branches in an autoregressive manner. Extensive experiments on text-conditioned video generation benchmarks demonstrate that increasing test-time compute consistently leads to significant improvements in the quality of videos. Project page: https://liuff19.github.io/Video-T1", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18940", "title": "Training-free Diffusion Acceleration with Bottleneck Sampling", "authors": [ "Ye Tian", "Xin Xia", "Yuxi Ren", "Shanchuan Lin", "Xing Wang", "Xuefeng Xiao", "Yunhai Tong", "Ling Yang", "Bin Cui" ], "github": "https://github.com/tyfeld/Bottleneck-Sampling", "project_page": "", "abstract": "Diffusion models have demonstrated remarkable capabilities in visual content generation but remain challenging to deploy due to their high computational cost during inference. This computational burden primarily arises from the quadratic complexity of self-attention with respect to image or video resolution. While existing acceleration methods often compromise output quality or necessitate costly retraining, we observe that most diffusion models are pre-trained at lower resolutions, presenting an opportunity to exploit these low-resolution priors for more efficient inference without degrading performance. In this work, we introduce Bottleneck Sampling, a training-free framework that leverages low-resolution priors to reduce computational overhead while preserving output fidelity. Bottleneck Sampling follows a high-low-high denoising workflow: it performs high-resolution denoising in the initial and final stages while operating at lower resolutions in intermediate steps. To mitigate aliasing and blurring artifacts, we further refine the resolution transition points and adaptively shift the denoising timesteps at each stage. We evaluate Bottleneck Sampling on both image and video generation tasks, where extensive experiments demonstrate that it accelerates inference by up to 3times for image generation and 2.5times for video generation, all while maintaining output quality comparable to the standard full-resolution sampling process across multiple evaluation metrics. Code is available at: https://github.com/tyfeld/Bottleneck-Sampling", "github_manual": "", "github_regex": "https://github.com/tyfeld/Bottleneck-Sampling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.17489", "title": "Judge Anything: MLLM as a Judge Across Any Modality", "authors": [ "Shu Pu", "Yaochen Wang", "Dongping Chen", "Yuhang Chen", "Guohao Wang", "Qi Qin", "Zhongyi Zhang", "Zhiyuan Zhang", "Zetong Zhou", "Shuang Gong", "Yi Gui", "Yao Wan", "Philip S. Yu" ], "github": "", "project_page": "", "abstract": "Evaluating generative foundation models on open-ended multimodal understanding (MMU) and generation (MMG) tasks across diverse modalities (e.g., images, audio, video) poses significant challenges due to the complexity of cross-modal interactions. To this end, the idea of utilizing Multimodal LLMs (MLLMs) as automated judges has emerged, with encouraging results in assessing vision-language understanding tasks. Moving further, this paper extends MLLM-as-a-Judge across modalities to a unified manner by introducing two benchmarks, TaskAnything and JudgeAnything, to respectively evaluate the overall performance and judging capabilities of MLLMs across any-to-any modality tasks. Specifically, TaskAnything evaluates the MMU and MMG capabilities across 15 any-to-any modality categories, employing 1,500 queries curated from well-established benchmarks. Furthermore, JudgeAnything evaluates the judging capabilities of 5 advanced (e.g., GPT-4o and Gemini-2.0-Flash) from the perspectives of Pair Comparison and Score Evaluation, providing a standardized testbed that incorporates human judgments and detailed rubrics. Our extensive experiments reveal that while these MLLMs show promise in assessing MMU (i.e., achieving an average of 66.55% in Pair Comparison setting and 42.79% in Score Evaluation setting), they encounter significant challenges with MMG tasks (i.e., averaging only 53.37% in Pair Comparison setting and 30.05% in Score Evaluation setting), exposing cross-modality biases and hallucination issues. To address this, we present OmniArena, an automated platform for evaluating omni-models and multimodal reward models. Our work highlights the need for fairer evaluation protocols and stronger alignment with human preferences. The source code and dataset are publicly available at: https://urrealhero.github.io/judgeanythingweb/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.17439", "title": "LEMMA: Learning from Errors for MatheMatical Advancement in LLMs", "authors": [ "Zhuoshi Pan", "Yu Li", "Honglin Lin", "Qizhi Pei", "Zinan Tang", "Wei Wu", "Chenlin Ming", "H. Vicky Zhao", "Conghui He", "Lijun Wu" ], "github": "https://github.com/ytyz1307zzh/RefAug", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable reasoning capability in solving mathematical problems. However, existing approaches primarily focus on improving the quality of correct training data, e.g., distilling high-quality correct solutions from advanced models, neglecting the value contained in error data, potentially hindering the model's reflective ability. Though some studies attempt to leverage error data, they often involve complex mechanisms, such as Monte Carlo Tree Search (MCTS) to explore error nodes. In this work, we propose to enhance LLMs' reasoning ability by Learning from Errors for Mathematical Advancement (LEMMA). LEMMA constructs data consisting of an incorrect solution with an erroneous step and a reflection connection to a correct solution for fine-tuning. Specifically, we systematically analyze the model-generated error types and introduce an error-type grounded mistake augmentation method to collect diverse and representative errors. Correct solutions are either from fixing the errors or generating a fresh start. Through a model-aware smooth reflection connection, the erroneous solution is transferred to the correct one. By fine-tuning on the constructed dataset, the model is able to self-correct errors autonomously within the generation process without relying on external critique models. Experimental results demonstrate that LEMMA achieves significant performance improvements over other strong baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ytyz1307zzh/RefAug", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18923", "title": "Video SimpleQA: Towards Factuality Evaluation in Large Video Language Models", "authors": [ "Meng Cao", "Pengfei Hu", "Yingyao Wang", "Jihao Gu", "Haoran Tang", "Haoze Zhao", "Jiahua Dong", "Wangbo Yu", "Ge Zhang", "Ian Reid", "Xiaodan Liang" ], "github": "", "project_page": "", "abstract": "Recent advancements in Large Video Language Models (LVLMs) have highlighted their potential for multi-modal understanding, yet evaluating their factual grounding in video contexts remains a critical unsolved challenge. To address this gap, we introduce Video SimpleQA, the first comprehensive benchmark tailored for factuality evaluation of LVLMs. Our work distinguishes from existing video benchmarks through the following key features: 1) Knowledge required: demanding integration of external knowledge beyond the explicit narrative; 2) Fact-seeking question: targeting objective, undisputed events or relationships, avoiding subjective interpretation; 3) Definitive & short-form answer: Answers are crafted as unambiguous and definitively correct in a short format, enabling automated evaluation through LLM-as-a-judge frameworks with minimal scoring variance; 4) External-source verified: All annotations undergo rigorous validation against authoritative external references to ensure the reliability; 5) Temporal reasoning required: The annotated question types encompass both static single-frame understanding and dynamic temporal reasoning, explicitly evaluating LVLMs factuality under the long-context dependencies. We extensively evaluate 41 state-of-the-art LVLMs and summarize key findings as follows: 1) Current LVLMs exhibit notable deficiencies in factual adherence, particularly for open-source models. The best-performing model Gemini-1.5-Pro achieves merely an F-score of 54.4%; 2) Test-time compute paradigms show insignificant performance gains, revealing fundamental constraints for enhancing factuality through post-hoc computation; 3) Retrieval-Augmented Generation demonstrates consistent improvements at the cost of additional inference time overhead, presenting a critical efficiency-performance trade-off.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.17422", "title": "V-Seek: Accelerating LLM Reasoning on Open-hardware Server-class RISC-V Platforms", "authors": [ "Javier J. Poveda Rodrigo", "Mohamed Amine Ahmdi", "Alessio Burrello", "Daniele Jahier Pagliari", "Luca Benini" ], "github": "", "project_page": "", "abstract": "The recent exponential growth of Large Language Models (LLMs) has relied on GPU-based systems. However, CPUs are emerging as a flexible and lower-cost alternative, especially when targeting inference and reasoning workloads. RISC-V is rapidly gaining traction in this area, given its open and vendor-neutral ISA. However, the RISC-V hardware for LLM workloads and the corresponding software ecosystem are not fully mature and streamlined, given the requirement of domain-specific tuning. This paper aims at filling this gap, focusing on optimizing LLM inference on the Sophon SG2042, the first commercially available many-core RISC-V CPU with vector processing capabilities. On two recent state-of-the-art LLMs optimized for reasoning, DeepSeek R1 Distill Llama 8B and DeepSeek R1 Distill QWEN 14B, we achieve 4.32/2.29 token/s for token generation and 6.54/3.68 token/s for prompt processing, with a speed up of up 2.9x/3.0x compared to our baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18892", "title": "SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild", "authors": [ "Weihao Zeng", "Yuzhen Huang", "Qian Liu", "Wei Liu", "Keqing He", "Zejun Ma", "Junxian He" ], "github": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "project_page": "", "abstract": "DeepSeek-R1 has shown that long chain-of-thought (CoT) reasoning can naturally emerge through a simple reinforcement learning (RL) framework with rule-based rewards, where the training may directly start from the base models-a paradigm referred to as zero RL training. Most recent efforts to reproduce zero RL training have primarily focused on the Qwen2.5 model series, which may not be representative as we find the base models already exhibit strong instruction-following and self-reflection abilities. In this work, we investigate zero RL training across 10 diverse base models, spanning different families and sizes including LLama3-8B, Mistral-7B/24B, DeepSeek-Math-7B, Qwen2.5-math-7B, and all Qwen2.5 models from 0.5B to 32B. Leveraging several key design strategies-such as adjusting format reward and controlling query difficulty-we achieve substantial improvements in both reasoning accuracy and response length across most settings. However, by carefully monitoring the training dynamics, we observe that different base models exhibit distinct patterns during training. For instance, the increased response length does not always correlate with the emergence of certain cognitive behaviors such as verification (i.e., the \"aha moment\"). Notably, we observe the \"aha moment\" for the first time in small models not from the Qwen family. We share the key designs that enable successful zero RL training, along with our findings and practices. To facilitate further research, we open-source the code, models, and analysis tools.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18013", "title": "Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning", "authors": [ "Yufei Zhan", "Yousong Zhu", "Shurong Zheng", "Hongyin Zhao", "Fan Yang", "Ming Tang", "Jinqiao Wang" ], "github": "https://github.com/jefferyzhan/griffon", "project_page": "", "abstract": "Large Vision-Language Models (LVLMs) typically follow a two-stage training paradigm-pretraining and supervised fine-tuning. Recently, preference optimization, derived from the language domain, has emerged as an effective post-training reinforcement strategy to enhance capabilities of LVLMs. However, constructing high-quality human-annotated preference data and developing robust reward models to mimic these preferences are both costly and challenging. Motivated by this observation, we propose Vision-R1, a novel vision-guided R1-like reinforcement learning algorithm for LVLMs that rewards models with definitive vision feedback. It only leverages curated instruction data, eliminating the need for specialized reward models and handcrafted preference datasets. We incorporate a criterion-driven reward function that further integrates multi-dimensional feedback to evaluate model completions comprehensively based on the vision task logic. Furthermore, we introduce a progressive rule refinement strategy that dynamically adjusts the reward criteria during training, enabling continuous model improvement and mitigating reward hacking. Extensive experiments on both in-distribution and out-of-distribution benchmarks demonstrate that fine-tuning the 7B LVLMs with Vision-R1 achieves consistent performance gains, with even up to 50% improvement and surpassing the state-of-the-art 10x size model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jefferyzhan/griffon", "github_api_added_by": "auto" }, { "date": "2025-03-25", "arxiv_id": "2503.17735", "title": "RDTF: Resource-efficient Dual-mask Training Framework for Multi-frame Animated Sticker Generation", "authors": [ "Zhiqiang Yuan", "Ting Zhang", "Ying Deng", "Jiapei Zhang", "Yeshuang Zhu", "Zexi Jia", "Jie Zhou", "Jinchao Zhang" ], "github": "", "project_page": "", "abstract": "Recently, great progress has been made in video generation technology, attracting the widespread attention of scholars. To apply this technology to downstream applications under resource-constrained conditions, researchers usually fine-tune the pre-trained models based on parameter-efficient tuning methods such as Adapter or Lora. Although these methods can transfer the knowledge from the source domain to the target domain, fewer training parameters lead to poor fitting ability, and the knowledge from the source domain may lead to the inference process deviating from the target domain. In this paper, we argue that under constrained resources, training a smaller video generation model from scratch using only million-level samples can outperform parameter-efficient tuning on larger models in downstream applications: the core lies in the effective utilization of data and curriculum strategy. Take animated sticker generation (ASG) as a case study, we first construct a discrete frame generation network for stickers with low frame rates, ensuring that its parameters meet the requirements of model training under constrained resources. In order to provide data support for models trained from scratch, we come up with a dual-mask based data utilization strategy, which manages to improve the availability and expand the diversity of limited data. To facilitate convergence under dual-mask situation, we propose a difficulty-adaptive curriculum learning method, which decomposes the sample entropy into static and adaptive components so as to obtain samples from easy to difficult. The experiment demonstrates that our resource-efficient dual-mask training framework is quantitatively and qualitatively superior to efficient-parameter tuning methods such as I2V-Adapter and SimDA, verifying the feasibility of our method on downstream tasks under constrained resources. Code will be available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18945", "title": "Aether: Geometric-Aware Unified World Modeling", "authors": [ "Aether Team", "Haoyi Zhu", "Yifan Wang", "Jianjun Zhou", "Wenzheng Chang", "Yang Zhou", "Zizun Li", "Junyi Chen", "Chunhua Shen", "Jiangmiao Pang", "Tong He" ], "github": "https://github.com/OpenRobotLab/Aether", "project_page": "", "abstract": "The integration of geometric reconstruction and generative modeling remains a critical challenge in developing AI systems capable of human-like spatial reasoning. This paper proposes Aether, a unified framework that enables geometry-aware reasoning in world models by jointly optimizing three core capabilities: (1) 4D dynamic reconstruction, (2) action-conditioned video prediction, and (3) goal-conditioned visual planning. Through task-interleaved feature learning, Aether achieves synergistic knowledge sharing across reconstruction, prediction, and planning objectives. Building upon video generation models, our framework demonstrates unprecedented synthetic-to-real generalization despite never observing real-world data during training. Furthermore, our approach achieves zero-shot generalization in both action following and reconstruction tasks, thanks to its intrinsic geometric modeling. Remarkably, even without real-world data, its reconstruction performance far exceeds that of domain-specific models. Additionally, Aether leverages a geometry-informed action space to seamlessly translate predictions into actions, enabling effective autonomous trajectory planning. We hope our work inspires the community to explore new frontiers in physically-reasonable world modeling and its applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenRobotLab/Aether", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18908", "title": "FFN Fusion: Rethinking Sequential Computation in Large Language Models", "authors": [ "Akhiad Bercovich", "Mohammad Dabbah", "Omri Puny", "Ido Galil", "Amnon Geifman", "Yonatan Geifman", "Izhak Golan", "Ehud Karpas", "Itay Levy", "Zach Moshe", "Najeeb Nabwani", "Tomer Ronen", "Itamar Schen", "Elad Segal", "Ido Shahaf", "Oren Tropp", "Ran Zilberstein", "Ran El-Yaniv" ], "github": "", "project_page": "", "abstract": "We introduce FFN Fusion, an architectural optimization technique that reduces sequential computation in large language models by identifying and exploiting natural opportunities for parallelization. Our key insight is that sequences of Feed-Forward Network (FFN) layers, particularly those remaining after the removal of specific attention layers, can often be parallelized with minimal accuracy impact. We develop a principled methodology for identifying and fusing such sequences, transforming them into parallel operations that significantly reduce inference latency while preserving model behavior. Applying these techniques to Llama-3.1-405B-Instruct, we create Llama-Nemotron-Ultra-253B-Base (Ultra-253B-Base), an efficient and soon-to-be publicly available model that achieves a 1.71X speedup in inference latency and 35X lower per-token cost while maintaining strong performance across benchmarks. Through extensive experiments on models from 49B to 253B parameters, we demonstrate that FFN Fusion becomes increasingly effective at larger scales and can complement existing optimization techniques like quantization and pruning. Most intriguingly, we find that even full transformer blocks containing both attention and FFN layers can sometimes be parallelized, suggesting new directions for neural architecture design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18866", "title": "Reasoning to Learn from Latent Thoughts", "authors": [ "Yangjun Ruan", "Neil Band", "Chris J. Maddison", "Tatsunori Hashimoto" ], "github": "https://github.com/ryoungj/BoLT", "project_page": "", "abstract": "Compute scaling for language model (LM) pretraining has outpaced the growth of human-written texts, leading to concerns that data will become the bottleneck to LM scaling. To continue scaling pretraining in this data-constrained regime, we propose that explicitly modeling and inferring the latent thoughts that underlie the text generation process can significantly improve pretraining data efficiency. Intuitively, our approach views web text as the compressed final outcome of a verbose human thought process and that the latent thoughts contain important contextual knowledge and reasoning steps that are critical to data-efficient learning. We empirically demonstrate the effectiveness of our approach through data-constrained continued pretraining for math. We first show that synthetic data approaches to inferring latent thoughts significantly improve data efficiency, outperforming training on the same amount of raw data (5.7\\% rightarrow 25.4\\% on MATH). Furthermore, we demonstrate latent thought inference without a strong teacher, where an LM bootstraps its own performance by using an EM algorithm to iteratively improve the capability of the trained LM and the quality of thought-augmented pretraining data. We show that a 1B LM can bootstrap its performance across at least three iterations and significantly outperform baselines trained on raw data, with increasing gains from additional inference compute when performing the E-step. The gains from inference scaling and EM iterations suggest new opportunities for scaling data-constrained pretraining.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ryoungj/BoLT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18559", "title": "AMD-Hummingbird: Towards an Efficient Text-to-Video Model", "authors": [ "Takashi Isobe", "He Cui", "Dong Zhou", "Mengmeng Ge", "Dong Li", "Emad Barsoum" ], "github": "https://github.com/amd-aig-aima/amd-hummingbird-t2v", "project_page": "", "abstract": "Text-to-Video (T2V) generation has attracted significant attention for its ability to synthesize realistic videos from textual descriptions. However, existing models struggle to balance computational efficiency and high visual quality, particularly on resource-limited devices, e.g.,iGPUs and mobile phones. Most prior work prioritizes visual fidelity while overlooking the need for smaller, more efficient models suitable for real-world deployment. To address this challenge, we propose a lightweight T2V framework, termed Hummingbird, which prunes existing models and enhances visual quality through visual feedback learning. Our approach reduces the size of the U-Net from 1.4 billion to 0.7 billion parameters, significantly improving efficiency while preserving high-quality video generation. Additionally, we introduce a novel data processing pipeline that leverages Large Language Models (LLMs) and Video Quality Assessment (VQA) models to enhance the quality of both text prompts and video data. To support user-driven training and style customization, we publicly release the full training code, including data processing and model training. Extensive experiments show that our method achieves a 31X speedup compared to state-of-the-art models such as VideoCrafter2, while also attaining the highest overall score on VBench. Moreover, our method supports the generation of videos with up to 26 frames, addressing the limitations of existing U-Net-based methods in long video generation. Notably, the entire training process requires only four GPUs, yet delivers performance competitive with existing leading methods. Hummingbird presents a practical and efficient solution for T2V generation, combining high performance, scalability, and flexibility for real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amd-aig-aima/amd-hummingbird-t2v", "github_api_added_by": "auto" }, { "date": "2025-03-25", "arxiv_id": "2503.16924", "title": "Optimized Minimal 3D Gaussian Splatting", "authors": [ "Joo Chan Lee", "Jong Hwan Ko", "Eunbyung Park" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time, high-performance rendering, enabling a wide range of applications. However, representing 3D scenes with numerous explicit Gaussian primitives imposes significant storage and memory overhead. Recent studies have shown that high-quality rendering can be achieved with a substantially reduced number of Gaussians when represented with high-precision attributes. Nevertheless, existing 3DGS compression methods still rely on a relatively large number of Gaussians, focusing primarily on attribute compression. This is because a smaller set of Gaussians becomes increasingly sensitive to lossy attribute compression, leading to severe quality degradation. Since the number of Gaussians is directly tied to computational costs, it is essential to reduce the number of Gaussians effectively rather than only optimizing storage. In this paper, we propose Optimized Minimal Gaussians representation (OMG), which significantly reduces storage while using a minimal number of primitives. First, we determine the distinct Gaussian from the near ones, minimizing redundancy without sacrificing quality. Second, we propose a compact and precise attribute representation that efficiently captures both continuity and irregularity among primitives. Additionally, we propose a sub-vector quantization technique for improved irregularity representation, maintaining fast training with a negligible codebook size. Extensive experiments demonstrate that OMG reduces storage requirements by nearly 50% compared to the previous state-of-the-art and enables 600+ FPS rendering while maintaining high rendering quality. Our source code is available at https://maincold2.github.io/omg/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.15879", "title": "Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid Question Answering", "authors": [ "DongGeon Lee", "Ahjeong Park", "Hyeri Lee", "Hyeonseo Nam", "Yunho Maeng" ], "github": "https://github.com/TeamNLP/Typed-RAG", "project_page": "", "abstract": "Non-factoid question-answering (NFQA) poses a significant challenge due to its open-ended nature, diverse intents, and the need for multi-aspect reasoning, which renders conventional factoid QA approaches, including retrieval-augmented generation (RAG), inadequate. Unlike factoid questions, non-factoid questions (NFQs) lack definitive answers and require synthesizing information from multiple sources across various reasoning dimensions. To address these limitations, we introduce Typed-RAG, a type-aware multi-aspect decomposition framework within the RAG paradigm for NFQA. Typed-RAG classifies NFQs into distinct types -- such as debate, experience, and comparison -- and applies aspect-based decomposition to refine retrieval and generation strategies. By decomposing multi-aspect NFQs into single-aspect sub-queries and aggregating the results, Typed-RAG generates more informative and contextually relevant responses. To evaluate Typed-RAG, we introduce Wiki-NFQA, a benchmark dataset covering diverse NFQ types. Experimental results demonstrate that Typed-RAG outperforms baselines, thereby highlighting the importance of type-aware decomposition for effective retrieval and generation in NFQA. Our code and dataset are available at https://github.com/TeamNLP/Typed-RAG{https://github.com/TeamNLP/Typed-RAG}.", "github_manual": "", "github_regex": "https://github.com/TeamNLP/Typed-RAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.17500", "title": "Variance Control via Weight Rescaling in LLM Pre-training", "authors": [ "Louis Owen", "Abhay Kumar", "Nilabhra Roy Chowdhury", "Fabian G\u00fcra" ], "github": "https://github.com/bluorion-com/weight_rescaling", "project_page": "", "abstract": "The outcome of Large Language Model (LLM) pre-training strongly depends on weight initialization and variance control strategies. Although the importance of initial variance control has been well documented in neural networks in general, the literature on initialization and management of its growth during LLM pre-training, specifically, is somewhat sparse. In this paper, we introduce the Layer Index Rescaling (LIR) weight initialization scheme, and the Target Variance Rescaling (TVR) variance control strategy. Experiments on a 1B parameter LLaMA model demonstrate that better variance management using these techniques yields substantial improvements in downstream task performance (up to 4.6% on common pre-training benchmarks) and reduces extreme activation values, thus mitigating challenges associated with quantization and low-precision training. Our code is available at: https://github.com/bluorion-com/weight_rescaling.", "github_manual": "", "github_regex": "https://github.com/bluorion-com/weight_rescaling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18102", "title": "AgentRxiv: Towards Collaborative Autonomous Research", "authors": [ "Samuel Schmidgall", "Michael Moor" ], "github": "https://github.com/SamuelSchmidgall/AgentLaboratory", "project_page": "", "abstract": "Progress in scientific discovery is rarely the result of a single \"Eureka\" moment, but is rather the product of hundreds of scientists incrementally working together toward a common goal. While existing agent workflows are capable of producing research autonomously, they do so in isolation, without the ability to continuously improve upon prior research results. To address these challenges, we introduce AgentRxiv-a framework that lets LLM agent laboratories upload and retrieve reports from a shared preprint server in order to collaborate, share insights, and iteratively build on each other's research. We task agent laboratories to develop new reasoning and prompting techniques and find that agents with access to their prior research achieve higher performance improvements compared to agents operating in isolation (11.4% relative improvement over baseline on MATH-500). We find that the best performing strategy generalizes to benchmarks in other domains (improving on average by 3.3%). Multiple agent laboratories sharing research through AgentRxiv are able to work together towards a common goal, progressing more rapidly than isolated laboratories, achieving higher overall accuracy (13.7% relative improvement over baseline on MATH-500). These findings suggest that autonomous agents may play a role in designing future AI systems alongside humans. We hope that AgentRxiv allows agents to collaborate toward research goals and enables researchers to accelerate discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SamuelSchmidgall/AgentLaboratory", "github_api_added_by": "auto" }, { "date": "2025-03-25", "arxiv_id": "2503.18813", "title": "Defeating Prompt Injections by Design", "authors": [ "Edoardo Debenedetti", "Ilia Shumailov", "Tianqi Fan", "Jamie Hayes", "Nicholas Carlini", "Daniel Fabian", "Christoph Kern", "Chongyang Shi", "Andreas Terzis", "Florian Tram\u00e8r" ], "github": "https://github.com/google-research/camel-prompt-injection", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly deployed in agentic systems that interact with an external environment. However, LLM agents are vulnerable to prompt injection attacks when handling untrusted data. In this paper we propose CaMeL, a robust defense that creates a protective system layer around the LLM, securing it even when underlying models may be susceptible to attacks. To operate, CaMeL explicitly extracts the control and data flows from the (trusted) query; therefore, the untrusted data retrieved by the LLM can never impact the program flow. To further improve security, CaMeL relies on a notion of a capability to prevent the exfiltration of private data over unauthorized data flows. We demonstrate effectiveness of CaMeL by solving 67% of tasks with provable security in AgentDojo [NeurIPS 2024], a recent agentic security benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/google-research/camel-prompt-injection", "github_api_added_by": "auto" }, { "date": "2025-03-25", "arxiv_id": "2503.18948", "title": "Equivariant Image Modeling", "authors": [ "Ruixiao Dong", "Mengde Xu", "Zigang Geng", "Li Li", "Han Hu", "Shuyang Gu" ], "github": "https://github.com/drx-code/EquivariantModeling", "project_page": "", "abstract": "Current generative models, such as autoregressive and diffusion approaches, decompose high-dimensional data distribution learning into a series of simpler subtasks. However, inherent conflicts arise during the joint optimization of these subtasks, and existing solutions fail to resolve such conflicts without sacrificing efficiency or scalability. We propose a novel equivariant image modeling framework that inherently aligns optimization targets across subtasks by leveraging the translation invariance of natural visual signals. Our method introduces (1) column-wise tokenization which enhances translational symmetry along the horizontal axis, and (2) windowed causal attention which enforces consistent contextual relationships across positions. Evaluated on class-conditioned ImageNet generation at 256x256 resolution, our approach achieves performance comparable to state-of-the-art AR models while using fewer computational resources. Systematic analysis demonstrates that enhanced equivariance reduces inter-task conflicts, significantly improving zero-shot generalization and enabling ultra-long image synthesis. This work establishes the first framework for task-aligned decomposition in generative modeling, offering insights into efficient parameter sharing and conflict-free optimization. The code and models are publicly available at https://github.com/drx-code/EquivariantModeling.", "github_manual": "", "github_regex": "https://github.com/drx-code/EquivariantModeling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18878", "title": "I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders", "authors": [ "Andrey Galichin", "Alexey Dontsov", "Polina Druzhinina", "Anton Razzhigaev", "Oleg Y. Rogov", "Elena Tutubalina", "Ivan Oseledets" ], "github": "https://github.com/AIRI-Institute/SAE-Reasoning", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved remarkable success in natural language processing. Recent advances have led to the developing of a new class of reasoning LLMs; for example, open-source DeepSeek-R1 has achieved state-of-the-art performance by integrating deep thinking and complex reasoning. Despite these impressive capabilities, the internal reasoning mechanisms of such models remain unexplored. In this work, we employ Sparse Autoencoders (SAEs), a method to learn a sparse decomposition of latent representations of a neural network into interpretable features, to identify features that drive reasoning in the DeepSeek-R1 series of models. First, we propose an approach to extract candidate ''reasoning features'' from SAE representations. We validate these features through empirical analysis and interpretability methods, demonstrating their direct correlation with the model's reasoning abilities. Crucially, we demonstrate that steering these features systematically enhances reasoning performance, offering the first mechanistic account of reasoning in LLMs. Code available at https://github.com/AIRI-Institute/SAE-Reasoning", "github_manual": "", "github_regex": "https://github.com/AIRI-Institute/SAE-Reasoning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18886", "title": "CFG-Zero*: Improved Classifier-Free Guidance for Flow Matching Models", "authors": [ "Weichen Fan", "Amber Yijia Zheng", "Raymond A. Yeh", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Classifier-Free Guidance (CFG) is a widely adopted technique in diffusion/flow models to improve image fidelity and controllability. In this work, we first analytically study the effect of CFG on flow matching models trained on Gaussian mixtures where the ground-truth flow can be derived. We observe that in the early stages of training, when the flow estimation is inaccurate, CFG directs samples toward incorrect trajectories. Building on this observation, we propose CFG-Zero*, an improved CFG with two contributions: (a) optimized scale, where a scalar is optimized to correct for the inaccuracies in the estimated velocity, hence the * in the name; and (b) zero-init, which involves zeroing out the first few steps of the ODE solver. Experiments on both text-to-image (Lumina-Next, Stable Diffusion 3, and Flux) and text-to-video (Wan-2.1) generation demonstrate that CFG-Zero* consistently outperforms CFG, highlighting its effectiveness in guiding Flow Matching models. (Code is available at github.com/WeichenFan/CFG-Zero-star)", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18470", "title": "MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse", "authors": [ "Zhenyu Pan", "Han Liu" ], "github": "https://github.com/PzySeere/MetaSpatial", "project_page": "", "abstract": "We present MetaSpatial, the first reinforcement learning (RL)-based framework designed to enhance 3D spatial reasoning in vision-language models (VLMs), enabling real-time 3D scene generation without the need for hard-coded optimizations. MetaSpatial addresses two core challenges: (i) the lack of internalized 3D spatial reasoning in VLMs, which limits their ability to generate realistic layouts, and (ii) the inefficiency of traditional supervised fine-tuning (SFT) for layout generation tasks, as perfect ground truth annotations are unavailable. Our key innovation is a multi-turn RL-based optimization mechanism that integrates physics-aware constraints and rendered image evaluations, ensuring generated 3D layouts are coherent, physically plausible, and aesthetically consistent. Methodologically, MetaSpatial introduces an adaptive, iterative reasoning process, where the VLM refines spatial arrangements over multiple turns by analyzing rendered outputs, improving scene coherence progressively. Empirical evaluations demonstrate that MetaSpatial significantly enhances the spatial consistency and formatting stability of various scale models. Post-training, object placements are more realistic, aligned, and functionally coherent, validating the effectiveness of RL for 3D spatial reasoning in metaverse, AR/VR, digital twins, and game development applications. Our code, data, and training pipeline are publicly available at https://github.com/PzySeere/MetaSpatial.", "github_manual": "", "github_regex": "https://github.com/PzySeere/MetaSpatial", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18494", "title": "Verbal Process Supervision Elicits Better Coding Agents", "authors": [ "Hao-Yuan Chen", "Cheng-Pong Huang", "Jui-Ming Yao" ], "github": "", "project_page": "", "abstract": "The emergence of large language models and their applications as AI agents have significantly advanced state-of-the-art code generation benchmarks, transforming modern software engineering tasks. However, even with test-time computed reasoning models, these systems still struggle with complex software engineering challenges. This work introduces CURA, a code understanding and reasoning agent system enhanced with verbal process supervision (VPS), achieving a 3.65\\% improvement over baseline models on challenging benchmarks like BigCodeBench. Furthermore, CURA, when paired with the o3-mini model and VPS techniques, attains state-of-the-art performance. This work represents a step forward in integrating reasoning-driven architectures with LLM-based code generation, enabling agentic reasoning for language models to solve complex software engineering tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18033", "title": "OmnimatteZero: Training-free Real-time Omnimatte with Pre-trained Video Diffusion Models", "authors": [ "Dvir Samuel", "Matan Levy", "Nir Darshan", "Gal Chechik", "Rami Ben-Ari" ], "github": "", "project_page": "", "abstract": "Omnimatte aims to decompose a given video into semantically meaningful layers, including the background and individual objects along with their associated effects, such as shadows and reflections. Existing methods often require extensive training or costly self-supervised optimization. In this paper, we present OmnimatteZero, a training-free approach that leverages off-the-shelf pre-trained video diffusion models for omnimatte. It can remove objects from videos, extract individual object layers along with their effects, and composite those objects onto new videos. We accomplish this by adapting zero-shot image inpainting techniques for video object removal, a task they fail to handle effectively out-of-the-box. We then show that self-attention maps capture information about the object and its footprints and use them to inpaint the object's effects, leaving a clean background. Additionally, through simple latent arithmetic, object layers can be isolated and recombined seamlessly with new video layers to produce new videos. Evaluations show that OmnimatteZero not only achieves superior performance in terms of background reconstruction but also sets a new record for the fastest Omnimatte approach, achieving real-time performance with minimal frame runtime.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18352", "title": "Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models", "authors": [ "Jinjin Zhang", "Qiuyu Huang", "Junjie Liu", "Xiefan Guo", "Di Huang" ], "github": "https://github.com/zhang0jhon/diffusion-4k", "project_page": "", "abstract": "In this paper, we present Diffusion-4K, a novel framework for direct ultra-high-resolution image synthesis using text-to-image diffusion models. The core advancements include: (1) Aesthetic-4K Benchmark: addressing the absence of a publicly available 4K image synthesis dataset, we construct Aesthetic-4K, a comprehensive benchmark for ultra-high-resolution image generation. We curated a high-quality 4K dataset with carefully selected images and captions generated by GPT-4o. Additionally, we introduce GLCM Score and Compression Ratio metrics to evaluate fine details, combined with holistic measures such as FID, Aesthetics and CLIPScore for a comprehensive assessment of ultra-high-resolution images. (2) Wavelet-based Fine-tuning: we propose a wavelet-based fine-tuning approach for direct training with photorealistic 4K images, applicable to various latent diffusion models, demonstrating its effectiveness in synthesizing highly detailed 4K images. Consequently, Diffusion-4K achieves impressive performance in high-quality image synthesis and text prompt adherence, especially when powered by modern large-scale diffusion models (e.g., SD3-2B and Flux-12B). Extensive experimental results from our benchmark demonstrate the superiority of Diffusion-4K in ultra-high-resolution image synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhang0jhon/diffusion-4k", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18018", "title": "Lost in Cultural Translation: Do LLMs Struggle with Math Across Cultural Contexts?", "authors": [ "Aabid Karim", "Abdul Karim", "Bhoomika Lohana", "Matt Keon", "Jaswinder Singh", "Abdul Sattar" ], "github": "https://github.com/akarim23131/Lost_in_Cultural_Translation", "project_page": "", "abstract": "Large Language Models (LLMs) have significantly advanced various fields, particularly coding, mathematical reasoning, and logical problem solving. However, a critical question remains: Do these mathematical reasoning abilities persist when LLMs are presented with culturally adapted math problems? Specifically, how do LLMs perform when faced with math problems embedded in cultural contexts that have no significant representation in main stream web-scale AI training data? To explore this, we generated six synthetic cultural datasets from GSM8K, a widely used benchmark for assessing LLMs' mathematical reasoning skills. While preserving the mathematical logic and numerical values of the original GSM8K test set, we modify cultural elements such as personal names, food items, place names, etc. These culturally adapted datasets provide a more reliable framework for evaluating LLMs' mathematical reasoning under shifting cultural contexts. Our findings reveal that LLMs struggle with math problems when cultural references change, even though the underlying mathematical structure remains constant. Smaller models exhibit greater performance drops compared to larger models. Interestingly, our results also suggest that cultural familiarity can enhance mathematical reasoning. Even models with no explicit mathematical training but exposure to relevant cultural contexts sometimes outperform larger, mathematically proficient models on culturally embedded math problems. This study highlights the impact of cultural context on the mathematical reasoning abilities of LLMs, underscoring the need for more diverse and representative training data to improve robustness in real-world applications. The benchmark data sets and script for reproducing the results are available at https://github.com/akarim23131/Lost_in_Cultural_Translation", "github_manual": "", "github_regex": "https://github.com/akarim23131/Lost_in_Cultural_Translation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.16426", "title": "DynamicVis: An Efficient and General Visual Foundation Model for Remote Sensing Image Understanding", "authors": [ "Keyan Chen", "Chenyang Liu", "Bowen Chen", "Wenyuan Li", "Zhengxia Zou", "Zhenwei Shi" ], "github": "https://github.com/KyanChen/DynamicVis", "project_page": "", "abstract": "The advancement of remote sensing technology has improved the spatial resolution of satellite imagery, facilitating more detailed visual representations for diverse interpretations. However, existing methods exhibit limited generalization capabilities across varied applications. While some contemporary foundation models demonstrate potential, they are hindered by insufficient cross-task adaptability and primarily process low-resolution imagery of restricted sizes, thus failing to fully exploit high-resolution data or leverage comprehensive large-scene semantics. Crucially, remote sensing imagery differs fundamentally from natural images, as key foreground targets (eg., maritime objects, artificial structures) often occupy minimal spatial proportions (~1%) and exhibit sparse distributions. Efficiently modeling cross-task generalizable knowledge from lengthy 2D tokens (~100,000) poses a significant challenge yet remains critical for remote sensing image understanding. Motivated by the selective attention mechanisms inherent to the human visual system, we propose DynamicVis, a dynamic visual perception foundation model for remote sensing imagery. The framework integrates a novel dynamic region perception backbone based on the selective state space model, which strategically balances localized detail extraction with global contextual integration, enabling computationally efficient encoding of large-scale data while maintaining architectural scalability. To enhance cross-task knowledge transferring, we introduce a multi-instance learning paradigm utilizing meta-embedding representations, trained on million-scale region-level annotations. Evaluations across nine downstream tasks demonstrate the model's versatility. DynamicVis achieves multi-level feature modeling with exceptional efficiency, processing (2048x2048) pixels with 97 ms latency (6% of ViT's) and 833 MB GPU memory (3% of ViT's).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KyanChen/DynamicVis", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.17811", "title": "Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model Collaboration Paradigm for Small Language Models", "authors": [ "Wenqi Pei", "Hailing Xu", "Hengyuan Zhao", "Shizheng Hou", "Han Chen", "Zining Zhang", "Pingyi Luo", "Bingsheng He" ], "github": "https://github.com/defog-ai/sqlcoder", "project_page": "", "abstract": "Natural Language to SQL (NL2SQL) has seen significant advancements with large language models (LLMs). However, these models often depend on closed-source systems and high computational resources, posing challenges in data privacy and deployment. In contrast, small language models (SLMs) struggle with NL2SQL tasks, exhibiting poor performance and incompatibility with existing frameworks. To address these issues, we introduce Feather-SQL, a new lightweight framework tailored for SLMs. Feather-SQL improves SQL executability and accuracy through 1) schema pruning and linking, 2) multi-path and multi-candidate generation. Additionally, we introduce the 1+1 Model Collaboration Paradigm, which pairs a strong general-purpose chat model with a fine-tuned SQL specialist, combining strong analytical reasoning with high-precision SQL generation. Experimental results on BIRD demonstrate that Feather-SQL improves NL2SQL performance on SLMs, with around 10% boost for models without fine-tuning. The proposed paradigm raises the accuracy ceiling of SLMs to 54.76%, highlighting its effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/defog-ai/sqlcoder", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.17760", "title": "CODA: Repurposing Continuous VAEs for Discrete Tokenization", "authors": [ "Zeyu Liu", "Zanlin Ni", "Yeguo Hua", "Xin Deng", "Xiao Ma", "Cheng Zhong", "Gao Huang" ], "github": "https://github.com/LeapLabTHU/CODA", "project_page": "", "abstract": "Discrete visual tokenizers transform images into a sequence of tokens, enabling token-based visual generation akin to language models. However, this process is inherently challenging, as it requires both compressing visual signals into a compact representation and discretizing them into a fixed set of codes. Traditional discrete tokenizers typically learn the two tasks jointly, often leading to unstable training, low codebook utilization, and limited reconstruction quality. In this paper, we introduce CODA(COntinuous-to-Discrete Adaptation), a framework that decouples compression and discretization. Instead of training discrete tokenizers from scratch, CODA adapts off-the-shelf continuous VAEs -- already optimized for perceptual compression -- into discrete tokenizers via a carefully designed discretization process. By primarily focusing on discretization, CODA ensures stable and efficient training while retaining the strong visual fidelity of continuous VAEs. Empirically, with 6 times less training budget than standard VQGAN, our approach achieves a remarkable codebook utilization of 100% and notable reconstruction FID (rFID) of 0.43 and 1.34 for 8 times and 16 times compression on ImageNet 256times 256 benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LeapLabTHU/CODA", "github_api_added_by": "auto" }, { "date": "2025-03-25", "arxiv_id": "2503.18071", "title": "Mind with Eyes: from Language Reasoning to Multimodal Reasoning", "authors": [ "Zhiyu Lin", "Yifei Gao", "Xian Zhao", "Yunfan Yang", "Jitao Sang" ], "github": "", "project_page": "", "abstract": "Language models have recently advanced into the realm of reasoning, yet it is through multimodal reasoning that we can fully unlock the potential to achieve more comprehensive, human-like cognitive capabilities. This survey provides a systematic overview of the recent multimodal reasoning approaches, categorizing them into two levels: language-centric multimodal reasoning and collaborative multimodal reasoning. The former encompasses one-pass visual perception and active visual perception, where vision primarily serves a supporting role in language reasoning. The latter involves action generation and state update within reasoning process, enabling a more dynamic interaction between modalities. Furthermore, we analyze the technical evolution of these methods, discuss their inherent challenges, and introduce key benchmark tasks and evaluation metrics for assessing multimodal reasoning performance. Finally, we provide insights into future research directions from the following two perspectives: (i) from visual-language reasoning to omnimodal reasoning and (ii) from multimodal reasoning to multimodal agents. This survey aims to provide a structured overview that will inspire further advancements in multimodal reasoning research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.14774", "title": "Revisiting Image Fusion for Multi-Illuminant White-Balance Correction", "authors": [ "David Serrano-Lozano", "Aditya Arora", "Luis Herranz", "Konstantinos G. Derpanis", "Michael S. Brown", "Javier Vazquez-Corral" ], "github": "", "project_page": "", "abstract": "White balance (WB) correction in scenes with multiple illuminants remains a persistent challenge in computer vision. Recent methods explored fusion-based approaches, where a neural network linearly blends multiple sRGB versions of an input image, each processed with predefined WB presets. However, we demonstrate that these methods are suboptimal for common multi-illuminant scenarios. Additionally, existing fusion-based methods rely on sRGB WB datasets lacking dedicated multi-illuminant images, limiting both training and evaluation. To address these challenges, we introduce two key contributions. First, we propose an efficient transformer-based model that effectively captures spatial dependencies across sRGB WB presets, substantially improving upon linear fusion techniques. Second, we introduce a large-scale multi-illuminant dataset comprising over 16,000 sRGB images rendered with five different WB settings, along with WB-corrected images. Our method achieves up to 100\\% improvement over existing techniques on our new multi-illuminant image fusion dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.13074", "title": "Rethinking Image Evaluation in Super-Resolution", "authors": [ "Shaolin Su", "Josep M. Rocafort", "Danna Xue", "David Serrano-Lozano", "Lei Sun", "Javier Vazquez-Corral" ], "github": "", "project_page": "", "abstract": "While recent advancing image super-resolution (SR) techniques are continually improving the perceptual quality of their outputs, they can usually fail in quantitative evaluations. This inconsistency leads to a growing distrust in existing image metrics for SR evaluations. Though image evaluation depends on both the metric and the reference ground truth (GT), researchers typically do not inspect the role of GTs, as they are generally accepted as `perfect' references. However, due to the data being collected in the early years and the ignorance of controlling other types of distortions, we point out that GTs in existing SR datasets can exhibit relatively poor quality, which leads to biased evaluations. Following this observation, in this paper, we are interested in the following questions: Are GT images in existing SR datasets 100% trustworthy for model evaluations? How does GT quality affect this evaluation? And how to make fair evaluations if there exist imperfect GTs? To answer these questions, this paper presents two main contributions. First, by systematically analyzing seven state-of-the-art SR models across three real-world SR datasets, we show that SR performances can be consistently affected across models by low-quality GTs, and models can perform quite differently when GT quality is controlled. Second, we propose a novel perceptual quality metric, Relative Quality Index (RQI), that measures the relative quality discrepancy of image pairs, thus issuing the biased evaluations caused by unreliable GTs. Our proposed model achieves significantly better consistency with human opinions. We expect our work to provide insights for the SR community on how future datasets, models, and metrics should be developed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18674", "title": "Human Motion Unlearning", "authors": [ "Edoardo De Matteis", "Matteo Migliarini", "Alessio Sampieri", "Indro Spinelli", "Fabio Galasso" ], "github": "", "project_page": "", "abstract": "We introduce the task of human motion unlearning to prevent the synthesis of toxic animations while preserving the general text-to-motion generative performance. Unlearning toxic motions is challenging as those can be generated from explicit text prompts and from implicit toxic combinations of safe motions (e.g., ``kicking\" is ``loading and swinging a leg\"). We propose the first motion unlearning benchmark by filtering toxic motions from the large and recent text-to-motion datasets of HumanML3D and Motion-X. We propose baselines, by adapting state-of-the-art image unlearning techniques to process spatio-temporal signals. Finally, we propose a novel motion unlearning model based on Latent Code Replacement, which we dub LCR. LCR is training-free and suitable to the discrete latent spaces of state-of-the-art text-to-motion diffusion models. LCR is simple and consistently outperforms baselines qualitatively and quantitatively. Project page: https://www.pinlab.org/hmu{https://www.pinlab.org/hmu}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.16709", "title": "QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge", "authors": [ "Xuan Shen", "Weize Ma", "Jing Liu", "Changdi Yang", "Rui Ding", "Quanyi Wang", "Henghui Ding", "Wei Niu", "Yanzhi Wang", "Pu Zhao", "Jun Lin", "Jiuxiang Gu" ], "github": "https://github.com/shawnricecake/quart-depth", "project_page": "", "abstract": "Monocular Depth Estimation (MDE) has emerged as a pivotal task in computer vision, supporting numerous real-world applications. However, deploying accurate depth estimation models on resource-limited edge devices, especially Application-Specific Integrated Circuits (ASICs), is challenging due to the high computational and memory demands. Recent advancements in foundational depth estimation deliver impressive results but further amplify the difficulty of deployment on ASICs. To address this, we propose QuartDepth which adopts post-training quantization to quantize MDE models with hardware accelerations for ASICs. Our approach involves quantizing both weights and activations to 4-bit precision, reducing the model size and computation cost. To mitigate the performance degradation, we introduce activation polishing and compensation algorithm applied before and after activation quantization, as well as a weight reconstruction method for minimizing errors in weight quantization. Furthermore, we design a flexible and programmable hardware accelerator by supporting kernel fusion and customized instruction programmability, enhancing throughput and efficiency. Experimental results demonstrate that our framework achieves competitive accuracy while enabling fast inference and higher energy efficiency on ASICs, bridging the gap between high-performance depth estimation and practical edge-device applicability. Code: https://github.com/shawnricecake/quart-depth", "github_manual": "", "github_regex": "https://github.com/shawnricecake/quart-depth", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18406", "title": "Instruct-CLIP: Improving Instruction-Guided Image Editing with Automated Data Refinement Using Contrastive Learning", "authors": [ "Sherry X. Chen", "Misha Sra", "Pradeep Sen" ], "github": "https://github.com/SherryXTChen/Instruct-CLIP.git", "project_page": "", "abstract": "Although natural language instructions offer an intuitive way to guide automated image editing, deep-learning models often struggle to achieve high-quality results, largely due to challenges in creating large, high-quality training datasets. Previous work has typically relied on text-toimage (T2I) generative models to produce pairs of original and edited images that simulate the input/output of an instruction-guided image-editing model. However, these image pairs often fail to align with the specified edit instructions due to the limitations of T2I models, which negatively impacts models trained on such datasets. To address this, we present Instruct-CLIP, a self-supervised method that learns the semantic changes between original and edited images to refine and better align the instructions in existing datasets. Furthermore, we adapt Instruct-CLIP to handle noisy latent images and diffusion timesteps so that it can be used to train latent diffusion models (LDMs) [19] and efficiently enforce alignment between the edit instruction and the image changes in latent space at any step of the diffusion pipeline. We use Instruct-CLIP to correct the InstructPix2Pix dataset and get over 120K refined samples we then use to fine-tune their model, guided by our novel Instruct-CLIP-based loss function. The resulting model can produce edits that are more aligned with the given instructions. Our code and dataset are available at https://github.com/SherryXTChen/Instruct-CLIP.git.", "github_manual": "", "github_regex": "https://github.com/SherryXTChen/Instruct-CLIP.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-25", "arxiv_id": "2503.18476", "title": "Global-Local Tree Search for Language Guided 3D Scene Generation", "authors": [ "Wei Deng", "Mengshi Qi", "Huadong Ma" ], "github": "https://github.com/dw-dengwei/TreeSearchGen", "project_page": "", "abstract": "Large Vision-Language Models (VLMs), such as GPT-4, have achieved remarkable success across various fields. However, there are few studies on 3D indoor scene generation with VLMs. This paper considers this task as a planning problem subject to spatial and layout common sense constraints. To solve the problem with a VLM, we propose a new global-local tree search algorithm. Globally, the method places each object sequentially and explores multiple placements during each placement process, where the problem space is represented as a tree. To reduce the depth of the tree, we decompose the scene structure hierarchically, i.e. room level, region level, floor object level, and supported object level. The algorithm independently generates the floor objects in different regions and supported objects placed on different floor objects. Locally, we also decompose the sub-task, the placement of each object, into multiple steps. The algorithm searches the tree of problem space. To leverage the VLM model to produce positions of objects, we discretize the top-down view space as a dense grid and fill each cell with diverse emojis to make to cells distinct. We prompt the VLM with the emoji grid and the VLM produces a reasonable location for the object by describing the position with the name of emojis. The quantitative and qualitative experimental results illustrate our approach generates more plausible 3D scenes than state-of-the-art approaches. Our source code is available at https://github.com/dw-dengwei/TreeSearchGen .", "github_manual": "", "github_regex": "https://github.com/dw-dengwei/TreeSearchGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.17361", "title": "Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation", "authors": [ "Sophia Tang", "Yinuo Zhang", "Alexander Tong", "Pranam Chatterjee" ], "github": "", "project_page": "", "abstract": "Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and protein generation. We introduce Gumbel-Softmax Flow and Score Matching, a generative framework on the simplex based on a novel Gumbel-Softmax interpolant with a time-dependent temperature. Using this interpolant, we introduce Gumbel-Softmax Flow Matching by deriving a parameterized velocity field that transports from smooth categorical distributions to distributions concentrated at a single vertex of the simplex. We alternatively present Gumbel-Softmax Score Matching which learns to regress the gradient of the probability density. Our framework enables high-quality, diverse generation and scales efficiently to higher-dimensional simplices. To enable training-free guidance, we propose Straight-Through Guided Flows (STGFlow), a classifier-based guidance method that leverages straight-through estimators to steer the unconditional velocity field toward optimal vertices of the simplex. STGFlow enables efficient inference-time guidance using classifiers pre-trained on clean sequences, and can be used with any discrete flow method. Together, these components form a robust framework for controllable de novo sequence generation. We demonstrate state-of-the-art performance in conditional DNA promoter design, sequence-only protein generation, and target-binding peptide design for rare disease treatment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19385", "title": "Inference-Time Scaling for Flow Models via Stochastic Generation and Rollover Budget Forcing", "authors": [ "Jaihoon Kim", "Taehoon Yoon", "Jisung Hwang", "Minhyuk Sung" ], "github": "https://github.com/KAIST-Visual-AI-Group/Flow-Inference-Time-Scaling", "project_page": "", "abstract": "We propose an inference-time scaling approach for pretrained flow models. Recently, inference-time scaling has gained significant attention in LLMs and diffusion models, improving sample quality or better aligning outputs with user preferences by leveraging additional computation. For diffusion models, particle sampling has allowed more efficient scaling due to the stochasticity at intermediate denoising steps. On the contrary, while flow models have gained popularity as an alternative to diffusion models--offering faster generation and high-quality outputs in state-of-the-art image and video generative models--efficient inference-time scaling methods used for diffusion models cannot be directly applied due to their deterministic generative process. To enable efficient inference-time scaling for flow models, we propose three key ideas: 1) SDE-based generation, enabling particle sampling in flow models, 2) Interpolant conversion, broadening the search space and enhancing sample diversity, and 3) Rollover Budget Forcing (RBF), an adaptive allocation of computational resources across timesteps to maximize budget utilization. Our experiments show that SDE-based generation, particularly variance-preserving (VP) interpolant-based generation, improves the performance of particle sampling methods for inference-time scaling in flow models. Additionally, we demonstrate that RBF with VP-SDE achieves the best performance, outperforming all previous inference-time scaling approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KAIST-Visual-AI-Group/Flow-Inference-Time-Scaling", "github_api_added_by": "auto" }, { "date": "2025-03-26", "arxiv_id": "2503.19910", "title": "CoLLM: A Large Language Model for Composed Image Retrieval", "authors": [ "Chuong Huynh", "Jinyu Yang", "Ashish Tawari", "Mubarak Shah", "Son Tran", "Raffay Hamid", "Trishul Chilimbi", "Abhinav Shrivastava" ], "github": "https://github.com/hmchuong/CoLLM", "project_page": "", "abstract": "Composed Image Retrieval (CIR) is a complex task that aims to retrieve images based on a multimodal query. Typical training data consists of triplets containing a reference image, a textual description of desired modifications, and the target image, which are expensive and time-consuming to acquire. The scarcity of CIR datasets has led to zero-shot approaches utilizing synthetic triplets or leveraging vision-language models (VLMs) with ubiquitous web-crawled image-caption pairs. However, these methods have significant limitations: synthetic triplets suffer from limited scale, lack of diversity, and unnatural modification text, while image-caption pairs hinder joint embedding learning of the multimodal query due to the absence of triplet data. Moreover, existing approaches struggle with complex and nuanced modification texts that demand sophisticated fusion and understanding of vision and language modalities. We present CoLLM, a one-stop framework that effectively addresses these limitations. Our approach generates triplets on-the-fly from image-caption pairs, enabling supervised training without manual annotation. We leverage Large Language Models (LLMs) to generate joint embeddings of reference images and modification texts, facilitating deeper multimodal fusion. Additionally, we introduce Multi-Text CIR (MTCIR), a large-scale dataset comprising 3.4M samples, and refine existing CIR benchmarks (CIRR and Fashion-IQ) to enhance evaluation reliability. Experimental results demonstrate that CoLLM achieves state-of-the-art performance across multiple CIR benchmarks and settings. MTCIR yields competitive results, with up to 15% performance improvement. Our refined benchmarks provide more reliable evaluation metrics for CIR models, contributing to the advancement of this important field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hmchuong/CoLLM", "github_api_added_by": "auto" }, { "date": "2025-03-26", "arxiv_id": "2503.19325", "title": "Long-Context Autoregressive Video Modeling with Next-Frame Prediction", "authors": [ "Yuchao Gu", "Weijia Mao", "Mike Zheng Shou" ], "github": "https://github.com/showlab/FAR", "project_page": "", "abstract": "Long-context autoregressive modeling has significantly advanced language generation, but video generation still struggles to fully utilize extended temporal contexts. To investigate long-context video modeling, we introduce Frame AutoRegressive (FAR), a strong baseline for video autoregressive modeling. Just as language models learn causal dependencies between tokens (i.e., Token AR), FAR models temporal causal dependencies between continuous frames, achieving better convergence than Token AR and video diffusion transformers. Building on FAR, we observe that long-context vision modeling faces challenges due to visual redundancy. Existing RoPE lacks effective temporal decay for remote context and fails to extrapolate well to long video sequences. Additionally, training on long videos is computationally expensive, as vision tokens grow much faster than language tokens. To tackle these issues, we propose balancing locality and long-range dependency. We introduce FlexRoPE, an test-time technique that adds flexible temporal decay to RoPE, enabling extrapolation to 16x longer vision contexts. Furthermore, we propose long short-term context modeling, where a high-resolution short-term context window ensures fine-grained temporal consistency, while an unlimited long-term context window encodes long-range information using fewer tokens. With this approach, we can train on long video sequences with a manageable token context length. We demonstrate that FAR achieves state-of-the-art performance in both short- and long-video generation, providing a simple yet effective baseline for video autoregressive modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/showlab/FAR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.18931", "title": "CoMP: Continual Multimodal Pre-training for Vision Foundation Models", "authors": [ "Yitong Chen", "Lingchen Meng", "Wujian Peng", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "", "project_page": "", "abstract": "Pre-trained Vision Foundation Models (VFMs) provide strong visual representations for a wide range of applications. In this paper, we continually pre-train prevailing VFMs in a multimodal manner such that they can effortlessly process visual inputs of varying sizes and produce visual representations that are more aligned with language representations, regardless of their original pre-training process. To this end, we introduce CoMP, a carefully designed multimodal pre-training pipeline. CoMP uses a Continual Rotary Position Embedding to support native resolution continual pre-training, and an Alignment Loss between visual and textual features through language prototypes to align multimodal representations. By three-stage training, our VFMs achieve remarkable improvements not only in multimodal understanding but also in other downstream tasks such as classification and segmentation. Remarkably, CoMP-SigLIP achieves scores of 66.7 on ChartQA and 75.9 on DocVQA with a 0.5B LLM, while maintaining an 87.4% accuracy on ImageNet-1K and a 49.5 mIoU on ADE20K under frozen chunk evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19041", "title": "LookAhead Tuning: Safer Language Models via Partial Answer Previews", "authors": [ "Kangwei Liu", "Mengru Wang", "Yujie Luo", "Lin Yuan", "Mengshu Sun", "Ningyu Zhang", "Lei Liang", "Zhiqiang Zhang", "Jun Zhou", "Huajun Chen" ], "github": "https://github.com/zjunlp/LookAheadTuning", "project_page": "", "abstract": "Fine-tuning enables large language models (LLMs) to adapt to specific domains, but often undermines their previously established safety alignment. To mitigate the degradation of model safety during fine-tuning, we introduce LookAhead Tuning, which comprises two simple, low-resource, and effective data-driven methods that modify training data by previewing partial answer prefixes. Both methods aim to preserve the model's inherent safety mechanisms by minimizing perturbations to initial token distributions. Comprehensive experiments demonstrate that LookAhead Tuning effectively maintains model safety without sacrificing robust performance on downstream tasks. Our findings position LookAhead Tuning as a reliable and efficient solution for the safe and effective adaptation of LLMs. Code is released at https://github.com/zjunlp/LookAheadTuning.", "github_manual": "", "github_regex": "https://github.com/zjunlp/LookAheadTuning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.18783", "title": "Frequency Dynamic Convolution for Dense Image Prediction", "authors": [ "Linwei Chen", "Lin Gu", "Liang Li", "Chenggang Yan", "Ying Fu" ], "github": "https://github.com/Linwei-Chen/FDConv", "project_page": "", "abstract": "While Dynamic Convolution (DY-Conv) has shown promising performance by enabling adaptive weight selection through multiple parallel weights combined with an attention mechanism, the frequency response of these weights tends to exhibit high similarity, resulting in high parameter costs but limited adaptability. In this work, we introduce Frequency Dynamic Convolution (FDConv), a novel approach that mitigates these limitations by learning a fixed parameter budget in the Fourier domain. FDConv divides this budget into frequency-based groups with disjoint Fourier indices, enabling the construction of frequency-diverse weights without increasing the parameter cost. To further enhance adaptability, we propose Kernel Spatial Modulation (KSM) and Frequency Band Modulation (FBM). KSM dynamically adjusts the frequency response of each filter at the spatial level, while FBM decomposes weights into distinct frequency bands in the frequency domain and modulates them dynamically based on local content. Extensive experiments on object detection, segmentation, and classification validate the effectiveness of FDConv. We demonstrate that when applied to ResNet-50, FDConv achieves superior performance with a modest increase of +3.6M parameters, outperforming previous methods that require substantial increases in parameter budgets (e.g., CondConv +90M, KW +76.5M). Moreover, FDConv seamlessly integrates into a variety of architectures, including ConvNeXt, Swin-Transformer, offering a flexible and efficient solution for modern vision tasks. The code is made publicly available at https://github.com/Linwei-Chen/FDConv.", "github_manual": "", "github_regex": "https://github.com/Linwei-Chen/FDConv", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.16965", "title": "When Words Outperform Vision: VLMs Can Self-Improve Via Text-Only Training For Human-Centered Decision Making", "authors": [ "Zhe Hu", "Jing Li", "Yu Yin" ], "github": "", "project_page": "", "abstract": "Embodied decision-making is fundamental for AI agents operating in real-world environments. While Visual Language Models (VLMs) have advanced this capability, they still struggle with complex decisions, particularly in human-centered situations that require deep reasoning about human needs and values. In this study, we systematically evaluate open-sourced VLMs on multimodal human-centered decision-making tasks. We find that LLMs receiving only textual descriptions unexpectedly outperform their VLM counterparts of similar scale that process actual images, suggesting that visual alignment may hinder VLM abilities. To address this challenge, we propose a novel text-only training approach with synthesized textual data. This method strengthens VLMs' language components and transfers the learned abilities to multimodal inference, eliminating the need for expensive image-text paired data. Furthermore, we show that VLMs can achieve substantial performance gains through self-improvement, using training data generated by their LLM counterparts rather than relying on larger teacher models like GPT-4. Our findings establish a more efficient and scalable approach to enhancing VLMs' human-centered decision-making capabilities, opening new avenues for optimizing VLMs through self-improvement mechanisms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19855", "title": "Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking", "authors": [ "Xiaoyu Tian", "Sitong Zhao", "Haotian Wang", "Shuaiting Chen", "Yunjie Ji", "Yiping Peng", "Han Zhao", "Xiangang Li" ], "github": "https://github.com/a-m-team/a-m-models/blob/main/docs/AM-DeepSeek-R1-Distilled-Dataset.pdf", "project_page": "", "abstract": "Recent advances in large language models (LLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated the effectiveness of test-time scaling, where extended reasoning processes substantially enhance model performance. Despite this, current models are constrained by limitations in handling long texts and reinforcement learning (RL) training efficiency. To address these issues, we propose a simple yet effective test-time scaling approach Multi-round Thinking. This method iteratively refines model reasoning by leveraging previous answers as prompts for subsequent rounds. Extensive experiments across multiple models, including QwQ-32B and DeepSeek-R1, consistently show performance improvements on various benchmarks such as AIME 2024, MATH-500, GPQA-diamond, and LiveCodeBench. For instance, the accuracy of QwQ-32B improved from 80.3% (Round 1) to 82.1% (Round 2) on the AIME 2024 dataset, while DeepSeek-R1 showed a similar increase from 79.7% to 82.0%. These results confirm that Multi-round Thinking is a broadly applicable, straightforward approach to achieving stable enhancements in model performance, underscoring its potential for future developments in test-time scaling techniques. The key prompt: {Original question prompt} The assistant's previous answer is: {last round answer} , and please re-answer.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/a-m-team/a-m-models/blob/main/docs/AM-DeepSeek-R1-Distilled-Dataset.pdf", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19470", "title": "ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning", "authors": [ "Mingyang Chen", "Tianpeng Li", "Haoze Sun", "Yijie Zhou", "Chenzheng Zhu", "Fan Yang", "Zenan Zhou", "Weipeng Chen", "Haofen Wang", "Jeff Z. Pan", "Wen Zhang", "Huajun Chen" ], "github": "https://github.com/agent-rl/research", "project_page": "", "abstract": "Large Language Models (LLMs) have shown remarkable capabilities in reasoning, exemplified by the success of OpenAI-o1 and DeepSeek-R1. However, integrating reasoning with external search processes remains challenging, especially for complex multi-hop questions requiring multiple retrieval steps. We propose ReSearch, a novel framework that trains LLMs to Reason with Search via reinforcement learning without using any supervised data on reasoning steps. Our approach treats search operations as integral components of the reasoning chain, where when and how to perform searches is guided by text-based thinking, and search results subsequently influence further reasoning. We train ReSearch on Qwen2.5-7B(-Instruct) and Qwen2.5-32B(-Instruct) models and conduct extensive experiments. Despite being trained on only one dataset, our models demonstrate strong generalizability across various benchmarks. Analysis reveals that ReSearch naturally elicits advanced reasoning capabilities such as reflection and self-correction during the reinforcement learning process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/agent-rl/research", "github_api_added_by": "auto" }, { "date": "2025-03-26", "arxiv_id": "2503.18446", "title": "Latent Space Super-Resolution for Higher-Resolution Image Generation with Diffusion Models", "authors": [ "Jinho Jeong", "Sangmin Han", "Jinwoo Kim", "Seon Joo Kim" ], "github": "https://github.com/3587jjh/LSRNA", "project_page": "", "abstract": "In this paper, we propose LSRNA, a novel framework for higher-resolution (exceeding 1K) image generation using diffusion models by leveraging super-resolution directly in the latent space. Existing diffusion models struggle with scaling beyond their training resolutions, often leading to structural distortions or content repetition. Reference-based methods address the issues by upsampling a low-resolution reference to guide higher-resolution generation. However, they face significant challenges: upsampling in latent space often causes manifold deviation, which degrades output quality. On the other hand, upsampling in RGB space tends to produce overly smoothed outputs. To overcome these limitations, LSRNA combines Latent space Super-Resolution (LSR) for manifold alignment and Region-wise Noise Addition (RNA) to enhance high-frequency details. Our extensive experiments demonstrate that integrating LSRNA outperforms state-of-the-art reference-based methods across various resolutions and metrics, while showing the critical role of latent space upsampling in preserving detail and sharpness. The code is available at https://github.com/3587jjh/LSRNA.", "github_manual": "", "github_regex": "https://github.com/3587jjh/LSRNA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19622", "title": "Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigation", "authors": [ "Hongcheng Gao", "Jiashu Qu", "Jingyi Tang", "Baolong Bi", "Yue Liu", "Hongyu Chen", "Li Liang", "Li Su", "Qingming Huang" ], "github": "https://github.com/Hongcheng-Gao/HAVEN", "project_page": "", "abstract": "The hallucination of large multimodal models (LMMs), providing responses that appear correct but are actually incorrect, limits their reliability and applicability. This paper aims to study the hallucination problem of LMMs in video modality, which is dynamic and more challenging compared to static modalities like images and text. From this motivation, we first present a comprehensive benchmark termed HAVEN for evaluating hallucinations of LMMs in video understanding tasks. It is built upon three dimensions, i.e., hallucination causes, hallucination aspects, and question formats, resulting in 6K questions. Then, we quantitatively study 7 influential factors on hallucinations, e.g., duration time of videos, model sizes, and model reasoning, via experiments of 16 LMMs on the presented benchmark. In addition, inspired by recent thinking models like OpenAI o1, we propose a video-thinking model to mitigate the hallucinations of LMMs via supervised reasoning fine-tuning (SRFT) and direct preference optimization (TDPO)-- where SRFT enhances reasoning capabilities while TDPO reduces hallucinations in the thinking process. Extensive experiments and analyses demonstrate the effectiveness. Remarkably, it improves the baseline by 7.65% in accuracy on hallucination evaluation and reduces the bias score by 4.5%. The code and data are public at https://github.com/Hongcheng-Gao/HAVEN.", "github_manual": "", "github_regex": "https://github.com/Hongcheng-Gao/HAVEN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19903", "title": "Scaling Vision Pre-Training to 4K Resolution", "authors": [ "Baifeng Shi", "Boyi Li", "Han Cai", "Yao Lu", "Sifei Liu", "Marco Pavone", "Jan Kautz", "Song Han", "Trevor Darrell", "Pavlo Molchanov", "Hongxu Yin" ], "github": "", "project_page": "", "abstract": "High-resolution perception of visual details is crucial for daily tasks. Current vision pre-training, however, is still limited to low resolutions (e.g., 378 x 378 pixels) due to the quadratic cost of processing larger images. We introduce PS3 that scales CLIP-style vision pre-training to 4K resolution with a near-constant cost. Instead of contrastive learning on global image representation, PS3 is pre-trained by selectively processing local regions and contrasting them with local detailed captions, enabling high-resolution representation learning with greatly reduced computational overhead. The pre-trained PS3 is able to both encode the global image at low resolution and selectively process local high-resolution regions based on their saliency or relevance to a text prompt. When applying PS3 to multi-modal LLM (MLLM), the resulting model, named VILA-HD, significantly improves high-resolution visual perception compared to baselines without high-resolution vision pre-training such as AnyRes and S^2 while using up to 4.3x fewer tokens. PS3 also unlocks appealing scaling properties of VILA-HD, including scaling up resolution for free and scaling up test-time compute for better performance. Compared to state of the arts, VILA-HD outperforms previous MLLMs such as NVILA and Qwen2-VL across multiple benchmarks and achieves better efficiency than latest token pruning approaches. Finally, we find current benchmarks do not require 4K-resolution perception, which motivates us to propose 4KPro, a new benchmark of image QA at 4K resolution, on which VILA-HD outperforms all previous MLLMs, including a 14.5% improvement over GPT-4o, and a 3.2% improvement and 2.96x speedup over Qwen2-VL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.14905", "title": "Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation", "authors": [ "Siwei Wen", "Junyan Ye", "Peilin Feng", "Hengrui Kang", "Zichen Wen", "Yize Chen", "Jiang Wu", "Wenjun Wu", "Conghui He", "Weijia Li" ], "github": "https://github.com/opendatalab/FakeVLM", "project_page": "", "abstract": "With the rapid advancement of Artificial Intelligence Generated Content (AIGC) technologies, synthetic images have become increasingly prevalent in everyday life, posing new challenges for authenticity assessment and detection. Despite the effectiveness of existing methods in evaluating image authenticity and locating forgeries, these approaches often lack human interpretability and do not fully address the growing complexity of synthetic data. To tackle these challenges, we introduce FakeVLM, a specialized large multimodal model designed for both general synthetic image and DeepFake detection tasks. FakeVLM not only excels in distinguishing real from fake images but also provides clear, natural language explanations for image artifacts, enhancing interpretability. Additionally, we present FakeClue, a comprehensive dataset containing over 100,000 images across seven categories, annotated with fine-grained artifact clues in natural language. FakeVLM demonstrates performance comparable to expert models while eliminating the need for additional classifiers, making it a robust solution for synthetic data detection. Extensive evaluations across multiple datasets confirm the superiority of FakeVLM in both authenticity classification and artifact explanation tasks, setting a new benchmark for synthetic image detection. The dataset and code will be released in: https://github.com/opendatalab/FakeVLM.", "github_manual": "", "github_regex": "https://github.com/opendatalab/FakeVLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.13964", "title": "MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding", "authors": [ "Siwei Han", "Peng Xia", "Ruiyi Zhang", "Tong Sun", "Yun Li", "Hongtu Zhu", "Huaxiu Yao" ], "github": "https://github.com/aiming-lab/MDocAgent", "project_page": "", "abstract": "Document Question Answering (DocQA) is a very common task. Existing methods using Large Language Models (LLMs) or Large Vision Language Models (LVLMs) and Retrieval Augmented Generation (RAG) often prioritize information from a single modal, failing to effectively integrate textual and visual cues. These approaches struggle with complex multi-modal reasoning, limiting their performance on real-world documents. We present MDocAgent (A Multi-Modal Multi-Agent Framework for Document Understanding), a novel RAG and multi-agent framework that leverages both text and image. Our system employs five specialized agents: a general agent, a critical agent, a text agent, an image agent and a summarizing agent. These agents engage in multi-modal context retrieval, combining their individual insights to achieve a more comprehensive understanding of the document's content. This collaborative approach enables the system to synthesize information from both textual and visual components, leading to improved accuracy in question answering. Preliminary experiments on five benchmarks like MMLongBench, LongDocURL demonstrate the effectiveness of our MDocAgent, achieve an average improvement of 12.1% compared to current state-of-the-art method. This work contributes to the development of more robust and comprehensive DocQA systems capable of handling the complexities of real-world documents containing rich textual and visual information. Our data and code are available at https://github.com/aiming-lab/MDocAgent.", "github_manual": "", "github_regex": "https://github.com/aiming-lab/MDocAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.17237", "title": "Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID", "authors": [ "Yu-Hsi Chen" ], "github": "https://github.com/wish44165/YOLOv12-BoT-SORT-ReID", "project_page": "", "abstract": "Detecting and tracking multiple unmanned aerial vehicles (UAVs) in thermal infrared video is inherently challenging due to low contrast, environmental noise, and small target sizes. This paper provides a straightforward approach to address multi-UAV tracking in thermal infrared video, leveraging recent advances in detection and tracking. Instead of relying on the YOLOv5 with the DeepSORT pipeline, we present a tracking framework built on YOLOv12 and BoT-SORT, enhanced with tailored training and inference strategies. We evaluate our approach following the metrics from the 4th Anti-UAV Challenge and demonstrate competitive performance. Notably, we achieve strong results without using contrast enhancement or temporal information fusion to enrich UAV features, highlighting our approach as a \"Strong Baseline\" for the multi-UAV tracking task. We provide implementation details, in-depth experimental analysis, and a discussion of potential improvements. The code is available at https://github.com/wish44165/YOLOv12-BoT-SORT-ReID .", "github_manual": "", "github_regex": "https://github.com/wish44165/YOLOv12-BoT-SORT-ReID", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19777", "title": "LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation", "authors": [ "Vladan Stojni\u0107", "Yannis Kalantidis", "Ji\u0159\u00ed Matas", "Giorgos Tolias" ], "github": "https://github.com/vladan-stojnic/LPOSS", "project_page": "", "abstract": "We propose a training-free method for open-vocabulary semantic segmentation using Vision-and-Language Models (VLMs). Our approach enhances the initial per-patch predictions of VLMs through label propagation, which jointly optimizes predictions by incorporating patch-to-patch relationships. Since VLMs are primarily optimized for cross-modal alignment and not for intra-modal similarity, we use a Vision Model (VM) that is observed to better capture these relationships. We address resolution limitations inherent to patch-based encoders by applying label propagation at the pixel level as a refinement step, significantly improving segmentation accuracy near class boundaries. Our method, called LPOSS+, performs inference over the entire image, avoiding window-based processing and thereby capturing contextual interactions across the full image. LPOSS+ achieves state-of-the-art performance among training-free methods, across a diverse set of datasets. Code: https://github.com/vladan-stojnic/LPOSS", "github_manual": "", "github_regex": "https://github.com/vladan-stojnic/LPOSS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.11849", "title": "Towards a Unified Copernicus Foundation Model for Earth Vision", "authors": [ "Yi Wang", "Zhitong Xiong", "Chenying Liu", "Adam J. Stewart", "Thomas Dujardin", "Nikolaos Ioannis Bountos", "Angelos Zavras", "Franziska Gerken", "Ioannis Papoutsis", "Laura Leal-Taix\u00e9", "Xiao Xiang Zhu" ], "github": "https://github.com/zhu-xlab/Copernicus-FM", "project_page": "", "abstract": "Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downstream applications crucial to our planet. However, most existing efforts remain limited to fixed spectral sensors, focus solely on the Earth's surface, and overlook valuable metadata beyond imagery. In this work, we take a step towards next-generation EO foundation models with three key components: 1) Copernicus-Pretrain, a massive-scale pretraining dataset that integrates 18.7M aligned images from all major Copernicus Sentinel missions, spanning from the Earth's surface to its atmosphere; 2) Copernicus-FM, a unified foundation model capable of processing any spectral or non-spectral sensor modality using extended dynamic hypernetworks and flexible metadata encoding; and 3) Copernicus-Bench, a systematic evaluation benchmark with 15 hierarchical downstream tasks ranging from preprocessing to specialized applications for each Sentinel mission. Our dataset, model, and benchmark greatly improve the scalability, versatility, and multimodal adaptability of EO foundation models, while also creating new opportunities to connect EO, weather, and climate research. Codes, datasets and models are available at https://github.com/zhu-xlab/Copernicus-FM.", "github_manual": "", "github_regex": "https://github.com/zhu-xlab/Copernicus-FM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19207", "title": "FRESA:Feedforward Reconstruction of Personalized Skinned Avatars from Few Images", "authors": [ "Rong Wang", "Fabian Prada", "Ziyan Wang", "Zhongshi Jiang", "Chengxiang Yin", "Junxuan Li", "Shunsuke Saito", "Igor Santesteban", "Javier Romero", "Rohan Joshi", "Hongdong Li", "Jason Saragih", "Yaser Sheikh" ], "github": "https://github.com/rongakowang/FRESA", "project_page": "", "abstract": "We present a novel method for reconstructing personalized 3D human avatars with realistic animation from only a few images. Due to the large variations in body shapes, poses, and cloth types, existing methods mostly require hours of per-subject optimization during inference, which limits their practical applications. In contrast, we learn a universal prior from over a thousand clothed humans to achieve instant feedforward generation and zero-shot generalization. Specifically, instead of rigging the avatar with shared skinning weights, we jointly infer personalized avatar shape, skinning weights, and pose-dependent deformations, which effectively improves overall geometric fidelity and reduces deformation artifacts. Moreover, to normalize pose variations and resolve coupled ambiguity between canonical shapes and skinning weights, we design a 3D canonicalization process to produce pixel-aligned initial conditions, which helps to reconstruct fine-grained geometric details. We then propose a multi-frame feature aggregation to robustly reduce artifacts introduced in canonicalization and fuse a plausible avatar preserving person-specific identities. Finally, we train the model in an end-to-end framework on a large-scale capture dataset, which contains diverse human subjects paired with high-quality 3D scans. Extensive experiments show that our method generates more authentic reconstruction and animation than state-of-the-arts, and can be directly generalized to inputs from casually taken phone photos. Project page and code is available at https://github.com/rongakowang/FRESA.", "github_manual": "", "github_regex": "https://github.com/rongakowang/FRESA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19123", "title": "Overcoming Vocabulary Mismatch: Vocabulary-agnostic Teacher Guided Language Modeling", "authors": [ "Haebin Shin", "Lei Ji", "Xiao Liu", "Yeyun Gong" ], "github": "https://github.com/Lightning-AI/litgpt", "project_page": "", "abstract": "Using large teacher models to guide the training of smaller student models has become the prevailing paradigm for efficient and effective learning. However, vocabulary mismatches between teacher and student language models pose significant challenges in language modeling, resulting in divergent token sequences and output distributions. To overcome these limitations, we propose Vocabulary-agnostic Teacher Guided Language Modeling (VocAgnoLM), a novel approach that bridges the gap caused by vocabulary mismatch through two key methods: (1) Token-level Lexical Alignment, which aligns token sequences across mismatched vocabularies, and (2) Teacher Guided Loss, which leverages the loss of teacher model to guide effective student training. We demonstrate its effectiveness in language modeling with 1B student model using various 7B teacher models with different vocabularies. Notably, with Qwen2.5-Math-Instruct, a teacher model sharing only about 6% of its vocabulary with TinyLlama, VocAgnoLM achieves a 46% performance improvement compared to naive continual pretraining. Furthermore, we demonstrate that VocAgnoLM consistently benefits from stronger teacher models, providing a robust solution to vocabulary mismatches in language modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Lightning-AI/litgpt", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19356", "title": "Can Vision-Language Models Answer Face to Face Questions in the Real-World?", "authors": [ "Reza Pourreza", "Rishit Dagli", "Apratim Bhattacharyya", "Sunny Panchal", "Guillaume Berger", "Roland Memisevic" ], "github": "", "project_page": "", "abstract": "AI models have made significant strides in recent years in their ability to describe and answer questions about real-world images. They have also made progress in the ability to converse with users in real-time using audio input. This raises the question: have we reached the point where AI models, connected to a camera and microphone, can converse with users in real-time about scenes and events that are unfolding live in front of the camera? This has been a long-standing goal in AI and is a prerequisite for real-world AI assistants and humanoid robots to interact with humans in everyday situations. In this work, we introduce a new dataset and benchmark, the Qualcomm Interactive Video Dataset (IVD), which allows us to assess the extent to which existing models can support these abilities, and to what degree these capabilities can be instilled through fine-tuning. The dataset is based on a simple question-answering setup, where users ask questions that the system has to answer, in real-time, based on the camera and audio input. We show that existing models fall far behind human performance on this task, and we identify the main sources for the performance gap. However, we also show that for many of the required perceptual skills, fine-tuning on this form of data can significantly reduce this gap.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19065", "title": "WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation", "authors": [ "Zhongyu Yang", "Jun Chen", "Dannong Xu", "Junjie Fei", "Xiaoqian Shen", "Liangbing Zhao", "Chun-Mei Feng", "Mohamed Elhoseiny" ], "github": "", "project_page": "", "abstract": "Knowledge discovery and collection are intelligence-intensive tasks that traditionally require significant human effort to ensure high-quality outputs. Recent research has explored multi-agent frameworks for automating Wikipedia-style article generation by retrieving and synthesizing information from the internet. However, these methods primarily focus on text-only generation, overlooking the importance of multimodal content in enhancing informativeness and engagement. In this work, we introduce WikiAutoGen, a novel system for automated multimodal Wikipedia-style article generation. Unlike prior approaches, WikiAutoGen retrieves and integrates relevant images alongside text, enriching both the depth and visual appeal of generated content. To further improve factual accuracy and comprehensiveness, we propose a multi-perspective self-reflection mechanism, which critically assesses retrieved content from diverse viewpoints to enhance reliability, breadth, and coherence, etc. Additionally, we introduce WikiSeek, a benchmark comprising Wikipedia articles with topics paired with both textual and image-based representations, designed to evaluate multimodal knowledge generation on more challenging topics. Experimental results show that WikiAutoGen outperforms previous methods by 8%-29% on our WikiSeek benchmark, producing more accurate, coherent, and visually enriched Wikipedia-style articles. We show some of our generated examples in https://wikiautogen.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19907", "title": "FullDiT: Multi-Task Video Generative Foundation Model with Full Attention", "authors": [ "Xuan Ju", "Weicai Ye", "Quande Liu", "Qiulin Wang", "Xintao Wang", "Pengfei Wan", "Di Zhang", "Kun Gai", "Qiang Xu" ], "github": "", "project_page": "", "abstract": "Current video generative foundation models primarily focus on text-to-video tasks, providing limited control for fine-grained video content creation. Although adapter-based approaches (e.g., ControlNet) enable additional controls with minimal fine-tuning, they encounter challenges when integrating multiple conditions, including: branch conflicts between independently trained adapters, parameter redundancy leading to increased computational cost, and suboptimal performance compared to full fine-tuning. To address these challenges, we introduce FullDiT, a unified foundation model for video generation that seamlessly integrates multiple conditions via unified full-attention mechanisms. By fusing multi-task conditions into a unified sequence representation and leveraging the long-context learning ability of full self-attention to capture condition dynamics, FullDiT reduces parameter overhead, avoids conditions conflict, and shows scalability and emergent ability. We further introduce FullBench for multi-task video generation evaluation. Experiments demonstrate that FullDiT achieves state-of-the-art results, highlighting the efficacy of full-attention in complex multi-task video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.18893", "title": "xKV: Cross-Layer SVD for KV-Cache Compression", "authors": [ "Chi-Chih Chang", "Chien-Yu Lin", "Yash Akhauri", "Wei-Cheng Lin", "Kai-Chiang Wu", "Luis Ceze", "Mohamed S. Abdelfattah" ], "github": "https://github.com/abdelfattah-lab/xKV", "project_page": "", "abstract": "Large Language Models (LLMs) with long context windows enable powerful applications but come at the cost of high memory consumption to store the Key and Value states (KV-Cache). Recent studies attempted to merge KV-cache from multiple layers into shared representations, yet these approaches either require expensive pretraining or rely on assumptions of high per-token cosine similarity across layers which generally does not hold in practice. We find that the dominant singular vectors are remarkably well-aligned across multiple layers of the KV-Cache. Exploiting this insight, we propose xKV, a simple post-training method that applies Singular Value Decomposition (SVD) on the KV-Cache of grouped layers. xKV consolidates the KV-Cache of multiple layers into a shared low-rank subspace, significantly reducing KV-Cache sizes. Through extensive evaluations on the RULER long-context benchmark with widely-used LLMs (e.g., Llama-3.1 and Qwen2.5), xKV achieves up to 6.8x higher compression rates than state-of-the-art inter-layer technique while improving accuracy by 2.7%. Moreover, xKV is compatible with the emerging Multi-Head Latent Attention (MLA) (e.g., DeepSeek-Coder-V2), yielding a notable 3x compression rates on coding tasks without performance degradation. These results highlight xKV's strong capability and versatility in addressing memory bottlenecks for long-context LLM inference. Our code is publicly available at: https://github.com/abdelfattah-lab/xKV.", "github_manual": "", "github_regex": "https://github.com/abdelfattah-lab/xKV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.17973", "title": "PhysTwin: Physics-Informed Reconstruction and Simulation of Deformable Objects from Videos", "authors": [ "Hanxiao Jiang", "Hao-Yu Hsu", "Kaifeng Zhang", "Hsin-Ni Yu", "Shenlong Wang", "Yunzhu Li" ], "github": "", "project_page": "", "abstract": "Creating a physical digital twin of a real-world object has immense potential in robotics, content creation, and XR. In this paper, we present PhysTwin, a novel framework that uses sparse videos of dynamic objects under interaction to produce a photo- and physically realistic, real-time interactive virtual replica. Our approach centers on two key components: (1) a physics-informed representation that combines spring-mass models for realistic physical simulation, generative shape models for geometry, and Gaussian splats for rendering; and (2) a novel multi-stage, optimization-based inverse modeling framework that reconstructs complete geometry, infers dense physical properties, and replicates realistic appearance from videos. Our method integrates an inverse physics framework with visual perception cues, enabling high-fidelity reconstruction even from partial, occluded, and limited viewpoints. PhysTwin supports modeling various deformable objects, including ropes, stuffed animals, cloth, and delivery packages. Experiments show that PhysTwin outperforms competing methods in reconstruction, rendering, future prediction, and simulation under novel interactions. We further demonstrate its applications in interactive real-time simulation and model-based robotic motion planning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.17982", "title": "Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on Aerial Images", "authors": [ "Yara AlaaEldin", "Francesca Odone" ], "github": "https://github.com/Malga-Vision/Co-SemDepth", "project_page": "", "abstract": "Understanding the geometric and semantic properties of the scene is crucial in autonomous navigation and particularly challenging in the case of Unmanned Aerial Vehicle (UAV) navigation. Such information may be by obtained by estimating depth and semantic segmentation maps of the surrounding environment and for their practical use in autonomous navigation, the procedure must be performed as close to real-time as possible. In this paper, we leverage monocular cameras on aerial robots to predict depth and semantic maps in low-altitude unstructured environments. We propose a joint deep-learning architecture that can perform the two tasks accurately and rapidly, and validate its effectiveness on MidAir and Aeroscapes benchmark datasets. Our joint-architecture proves to be competitive or superior to the other single and joint architecture methods while performing its task fast predicting 20.2 FPS on a single NVIDIA quadro p5000 GPU and it has a low memory footprint. All codes for training and prediction can be found on this link: https://github.com/Malga-Vision/Co-SemDepth", "github_manual": "", "github_regex": "https://github.com/Malga-Vision/Co-SemDepth", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19355", "title": "ST-VLM: Kinematic Instruction Tuning for Spatio-Temporal Reasoning in Vision-Language Models", "authors": [ "Dohwan Ko", "Sihyeon Kim", "Yumin Suh", "Vijay Kumar B. G", "Minseo Yoon", "Manmohan Chandraker", "Hyunwoo J. Kim" ], "github": "", "project_page": "", "abstract": "Spatio-temporal reasoning is essential in understanding real-world environments in various fields, eg, autonomous driving and sports analytics. Recent advances have improved the spatial reasoning ability of Vision-Language Models (VLMs) by introducing large-scale data, but these models still struggle to analyze kinematic elements like traveled distance and speed of moving objects. To bridge this gap, we construct a spatio-temporal reasoning dataset and benchmark involving kinematic instruction tuning, referred to as STKit and STKit-Bench. They consist of real-world videos with 3D annotations, detailing object motion dynamics: traveled distance, speed, movement direction, inter-object distance comparisons, and relative movement direction. To further scale such data construction to videos without 3D labels, we propose an automatic pipeline to generate pseudo-labels using 4D reconstruction in real-world scale. With our kinematic instruction tuning data for spatio-temporal reasoning, we present ST-VLM, a VLM enhanced for spatio-temporal reasoning, which exhibits outstanding performance on STKit-Bench. Furthermore, we show that ST-VLM generalizes robustly across diverse domains and tasks, outperforming baselines on other spatio-temporal benchmarks (eg, ActivityNet, TVQA+). Finally, by integrating learned spatio-temporal reasoning with existing abilities, ST-VLM enables complex multi-step reasoning. Project page: https://ikodoh.github.io/ST-VLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.16776", "title": "OpenCity3D: What do Vision-Language Models know about Urban Environments?", "authors": [ "Valentin Bieri", "Marco Zamboni", "Nicolas S. Blumer", "Qingxuan Chen", "Francis Engelmann" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) show great promise for 3D scene understanding but are mainly applied to indoor spaces or autonomous driving, focusing on low-level tasks like segmentation. This work expands their use to urban-scale environments by leveraging 3D reconstructions from multi-view aerial imagery. We propose OpenCity3D, an approach that addresses high-level tasks, such as population density estimation, building age classification, property price prediction, crime rate assessment, and noise pollution evaluation. Our findings highlight OpenCity3D's impressive zero-shot and few-shot capabilities, showcasing adaptability to new contexts. This research establishes a new paradigm for language-driven urban analytics, enabling applications in planning, policy, and environmental monitoring. See our project page: opencity3d.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.18712", "title": "LLaVAction: evaluating and training multi-modal large language models for action recognition", "authors": [ "Shaokai Ye", "Haozhe Qi", "Alexander Mathis", "Mackenzie W. Mathis" ], "github": "https://github.com/AdaptiveMotorControlLab/LLaVAction", "project_page": "", "abstract": "Understanding human behavior requires measuring behavioral actions. Due to its complexity, behavior is best mapped onto a rich, semantic structure such as language. The recent development of multi-modal large language models (MLLMs) is a promising candidate for a wide range of action understanding tasks. In this work, we focus on evaluating and then improving MLLMs to perform action recognition. We reformulate EPIC-KITCHENS-100, one of the largest and most challenging egocentric action datasets, to the form of video multiple question answering (EPIC-KITCHENS-100-MQA). We show that when we sample difficult incorrect answers as distractors, leading MLLMs struggle to recognize the correct actions. We propose a series of methods that greatly improve the MLLMs' ability to perform action recognition, achieving state-of-the-art on both the EPIC-KITCHENS-100 validation set, as well as outperforming GPT-4o by 21 points in accuracy on EPIC-KITCHENS-100-MQA. Lastly, we show improvements on other action-related video benchmarks such as EgoSchema, PerceptionTest, LongVideoBench, VideoMME and MVBench, suggesting that MLLMs are a promising path forward for complex action tasks. Code and models are available at: https://github.com/AdaptiveMotorControlLab/LLaVAction.", "github_manual": "", "github_regex": "https://github.com/AdaptiveMotorControlLab/LLaVAction", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.15667", "title": "DiffPortrait360: Consistent Portrait Diffusion for 360 View Synthesis", "authors": [ "Yuming Gu", "Phong Tran", "Yujian Zheng", "Hongyi Xu", "Heyuan Li", "Adilbek Karmanov", "Hao Li" ], "github": "", "project_page": "", "abstract": "Generating high-quality 360-degree views of human heads from single-view images is essential for enabling accessible immersive telepresence applications and scalable personalized content creation. While cutting-edge methods for full head generation are limited to modeling realistic human heads, the latest diffusion-based approaches for style-omniscient head synthesis can produce only frontal views and struggle with view consistency, preventing their conversion into true 3D models for rendering from arbitrary angles. We introduce a novel approach that generates fully consistent 360-degree head views, accommodating human, stylized, and anthropomorphic forms, including accessories like glasses and hats. Our method builds on the DiffPortrait3D framework, incorporating a custom ControlNet for back-of-head detail generation and a dual appearance module to ensure global front-back consistency. By training on continuous view sequences and integrating a back reference image, our approach achieves robust, locally continuous view synthesis. Our model can be used to produce high-quality neural radiance fields (NeRFs) for real-time, free-viewpoint rendering, outperforming state-of-the-art methods in object synthesis and 360-degree head generation for very challenging input portraits.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.04919", "title": "FirePlace: Geometric Refinements of LLM Common Sense Reasoning for 3D Object Placement", "authors": [ "Ian Huang", "Yanan Bao", "Karen Truong", "Howard Zhou", "Cordelia Schmid", "Leonidas Guibas", "Alireza Fathi" ], "github": "", "project_page": "", "abstract": "Scene generation with 3D assets presents a complex challenge, requiring both high-level semantic understanding and low-level geometric reasoning. While Multimodal Large Language Models (MLLMs) excel at semantic tasks, their application to 3D scene generation is hindered by their limited grounding on 3D geometry. In this paper, we investigate how to best work with MLLMs in an object placement task. Towards this goal, we introduce a novel framework, FirePlace, that applies existing MLLMs in (1) 3D geometric reasoning and the extraction of relevant geometric details from the 3D scene, (2) constructing and solving geometric constraints on the extracted low-level geometry, and (3) pruning for final placements that conform to common sense. By combining geometric reasoning with real-world understanding of MLLMs, our method can propose object placements that satisfy both geometric constraints as well as high-level semantic common-sense considerations. Our experiments show that these capabilities allow our method to place objects more effectively in complex scenes with intricate geometry, surpassing the quality of prior work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.19881", "title": "Mask^2DiT: Dual Mask-based Diffusion Transformer for Multi-Scene Long Video Generation", "authors": [ "Tianhao Qi", "Jianlong Yuan", "Wanquan Feng", "Shancheng Fang", "Jiawei Liu", "SiYu Zhou", "Qian He", "Hongtao Xie", "Yongdong Zhang" ], "github": "", "project_page": "", "abstract": "Sora has unveiled the immense potential of the Diffusion Transformer (DiT) architecture in single-scene video generation. However, the more challenging task of multi-scene video generation, which offers broader applications, remains relatively underexplored. To bridge this gap, we propose Mask^2DiT, a novel approach that establishes fine-grained, one-to-one alignment between video segments and their corresponding text annotations. Specifically, we introduce a symmetric binary mask at each attention layer within the DiT architecture, ensuring that each text annotation applies exclusively to its respective video segment while preserving temporal coherence across visual tokens. This attention mechanism enables precise segment-level textual-to-visual alignment, allowing the DiT architecture to effectively handle video generation tasks with a fixed number of scenes. To further equip the DiT architecture with the ability to generate additional scenes based on existing ones, we incorporate a segment-level conditional mask, which conditions each newly generated segment on the preceding video segments, thereby enabling auto-regressive scene extension. Both qualitative and quantitative experiments confirm that Mask^2DiT excels in maintaining visual consistency across segments while ensuring semantic alignment between each segment and its corresponding text description. Our project page is https://tianhao-qi.github.io/Mask2DiTProject.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.20110", "title": "Efficient Model Development through Fine-tuning Transfer", "authors": [ "Pin-Jie Lin", "Rishab Balasubramanian", "Fengyuan Liu", "Nikhil Kandpal", "Tu Vu" ], "github": "https://github.com/pjlintw/finetuning-transfer", "project_page": "", "abstract": "Modern LLMs struggle with efficient updates, as each new pretrained model version requires repeating expensive alignment processes. This challenge also applies to domain- or language-specific models, where fine-tuning on specialized data must be redone for every new base model release. In this paper, we explore the transfer of fine-tuning updates between model versions. Specifically, we derive the diff vector from one source model version, which represents the weight changes from fine-tuning, and apply it to the base model of a different target version. Through empirical evaluations on various open-weight model versions, we show that transferring diff vectors can significantly improve the target base model, often achieving performance comparable to its fine-tuned counterpart. For example, reusing the fine-tuning updates from Llama 3.0 8B leads to an absolute accuracy improvement of 10.7% on GPQA over the base Llama 3.1 8B without additional training, surpassing Llama 3.1 8B Instruct. In a multilingual model development setting, we show that this approach can significantly increase performance on target-language tasks without retraining, achieving an absolute improvement of 4.7% and 15.5% on Global MMLU for Malagasy and Turkish, respectively, compared to Llama 3.1 8B Instruct. Our controlled experiments reveal that fine-tuning transfer is most effective when the source and target models are linearly connected in the parameter space. Additionally, we demonstrate that fine-tuning transfer offers a stronger and more computationally efficient starting point for further fine-tuning. Finally, we propose an iterative recycling-then-finetuning approach for continuous model development, which improves both efficiency and effectiveness. Our findings suggest that fine-tuning transfer is a viable strategy to reduce training costs while maintaining model performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pjlintw/finetuning-transfer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-26", "arxiv_id": "2503.18673", "title": "Any6D: Model-free 6D Pose Estimation of Novel Objects", "authors": [ "Taeyeop Lee", "Bowen Wen", "Minjun Kang", "Gyuree Kang", "In So Kweon", "Kuk-Jin Yoon" ], "github": "", "project_page": "", "abstract": "We introduce Any6D, a model-free framework for 6D object pose estimation that requires only a single RGB-D anchor image to estimate both the 6D pose and size of unknown objects in novel scenes. Unlike existing methods that rely on textured 3D models or multiple viewpoints, Any6D leverages a joint object alignment process to enhance 2D-3D alignment and metric scale estimation for improved pose accuracy. Our approach integrates a render-and-compare strategy to generate and refine pose hypotheses, enabling robust performance in scenarios with occlusions, non-overlapping views, diverse lighting conditions, and large cross-environment variations. We evaluate our method on five challenging datasets: REAL275, Toyota-Light, HO3D, YCBINEOAT, and LM-O, demonstrating its effectiveness in significantly outperforming state-of-the-art methods for novel object pose estimation. Project page: https://taeyeop.com/any6d", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.20240", "title": "Unconditional Priors Matter! Improving Conditional Generation of Fine-Tuned Diffusion Models", "authors": [ "Prin Phunyaphibarn", "Phillip Y. Lee", "Jaihoon Kim", "Minhyuk Sung" ], "github": "", "project_page": "", "abstract": "Classifier-Free Guidance (CFG) is a fundamental technique in training conditional diffusion models. The common practice for CFG-based training is to use a single network to learn both conditional and unconditional noise prediction, with a small dropout rate for conditioning. However, we observe that the joint learning of unconditional noise with limited bandwidth in training results in poor priors for the unconditional case. More importantly, these poor unconditional noise predictions become a serious reason for degrading the quality of conditional generation. Inspired by the fact that most CFG-based conditional models are trained by fine-tuning a base model with better unconditional generation, we first show that simply replacing the unconditional noise in CFG with that predicted by the base model can significantly improve conditional generation. Furthermore, we show that a diffusion model other than the one the fine-tuned model was trained on can be used for unconditional noise replacement. We experimentally verify our claim with a range of CFG-based conditional models for both image and video generation, including Zero-1-to-3, Versatile Diffusion, DiT, DynamiCrafter, and InstructPix2Pix.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.20201", "title": "Open Deep Search: Democratizing Search with Open-source Reasoning Agents", "authors": [ "Salaheddin Alzubi", "Creston Brooks", "Purva Chiniya", "Edoardo Contente", "Chiara von Gerlach", "Lucas Irwin", "Yihan Jiang", "Arda Kaz", "Windsor Nguyen", "Sewoong Oh", "Himanshu Tyagi", "Pramod Viswanath" ], "github": "https://github.com/sentient-agi/OpenDeepSearch", "project_page": "", "abstract": "We introduce Open Deep Search (ODS) to close the increasing gap between the proprietary search AI solutions, such as Perplexity's Sonar Reasoning Pro and OpenAI's GPT-4o Search Preview, and their open-source counterparts. The main innovation introduced in ODS is to augment the reasoning capabilities of the latest open-source LLMs with reasoning agents that can judiciously use web search tools to answer queries. Concretely, ODS consists of two components that work with a base LLM chosen by the user: Open Search Tool and Open Reasoning Agent. Open Reasoning Agent interprets the given task and completes it by orchestrating a sequence of actions that includes calling tools, one of which is the Open Search Tool. Open Search Tool is a novel web search tool that outperforms proprietary counterparts. Together with powerful open-source reasoning LLMs, such as DeepSeek-R1, ODS nearly matches and sometimes surpasses the existing state-of-the-art baselines on two benchmarks: SimpleQA and FRAMES. For example, on the FRAMES evaluation benchmark, ODS improves the best existing baseline of the recently released GPT-4o Search Preview by 9.7% in accuracy. ODS is a general framework for seamlessly augmenting any LLMs -- for example, DeepSeek-R1 that achieves 82.4% on SimpleQA and 30.1% on FRAMES -- with search and reasoning capabilities to achieve state-of-the-art performance: 88.3% on SimpleQA and 75.3% on FRAMES.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sentient-agi/OpenDeepSearch", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.20756", "title": "ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems", "authors": [ "Chenxi Wang", "Jizhan Fang", "Xiang Chen", "Bozhong Tian", "Ziwen Xu", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/EasyEdit", "project_page": "", "abstract": "Recent advancements in Large Multimodal Models (LMMs) have shown promise in Autonomous Driving Systems (ADS). However, their direct application to ADS is hindered by challenges such as misunderstanding of traffic knowledge, complex road conditions, and diverse states of vehicle. To address these challenges, we propose the use of Knowledge Editing, which enables targeted modifications to a model's behavior without the need for full retraining. Meanwhile, we introduce ADS-Edit, a multimodal knowledge editing dataset specifically designed for ADS, which includes various real-world scenarios, multiple data types, and comprehensive evaluation metrics. We conduct comprehensive experiments and derive several interesting conclusions. We hope that our work will contribute to the further advancement of knowledge editing applications in the field of autonomous driving. Code and data are available in https://github.com/zjunlp/EasyEdit.", "github_manual": "", "github_regex": "https://github.com/zjunlp/EasyEdit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.20314", "title": "Wan: Open and Advanced Large-Scale Video Generative Models", "authors": [ "WanTeam", "Ang Wang", "Baole Ai", "Bin Wen", "Chaojie Mao", "Chen-Wei Xie", "Di Chen", "Feiwu Yu", "Haiming Zhao", "Jianxiao Yang", "Jianyuan Zeng", "Jiayu Wang", "Jingfeng Zhang", "Jingren Zhou", "Jinkai Wang", "Jixuan Chen", "Kai Zhu", "Kang Zhao", "Keyu Yan", "Lianghua Huang", "Mengyang Feng", "Ningyi Zhang", "Pandeng Li", "Pingyu Wu", "Ruihang Chu", "Ruili Feng", "Shiwei Zhang", "Siyang Sun", "Tao Fang", "Tianxing Wang", "Tianyi Gui", "Tingyu Weng", "Tong Shen", "Wei Lin", "Wei Wang", "Wei Wang", "Wenmeng Zhou", "Wente Wang", "Wenting Shen", "Wenyuan Yu", "Xianzhong Shi", "Xiaoming Huang", "Xin Xu", "Yan Kou", "Yangyu Lv", "Yifei Li", "Yijing Liu", "Yiming Wang", "Yingya Zhang", "Yitong Huang", "Yong Li", "You Wu", "Yu Liu", "Yulin Pan", "Yun Zheng", "Yuntao Hong", "Yupeng Shi", "Yutong Feng", "Zeyinzi Jiang", "Zhen Han", "Zhi-Fan Wu", "Ziyu Liu" ], "github": "https://github.com/Wan-Video/Wan2.1", "project_page": "", "abstract": "This report presents Wan, a comprehensive and open suite of video foundation models designed to push the boundaries of video generation. Built upon the mainstream diffusion transformer paradigm, Wan achieves significant advancements in generative capabilities through a series of innovations, including our novel VAE, scalable pre-training strategies, large-scale data curation, and automated evaluation metrics. These contributions collectively enhance the model's performance and versatility. Specifically, Wan is characterized by four key features: Leading Performance: The 14B model of Wan, trained on a vast dataset comprising billions of images and videos, demonstrates the scaling laws of video generation with respect to both data and model size. It consistently outperforms the existing open-source models as well as state-of-the-art commercial solutions across multiple internal and external benchmarks, demonstrating a clear and significant performance superiority. Comprehensiveness: Wan offers two capable models, i.e., 1.3B and 14B parameters, for efficiency and effectiveness respectively. It also covers multiple downstream applications, including image-to-video, instruction-guided video editing, and personal video generation, encompassing up to eight tasks. Consumer-Grade Efficiency: The 1.3B model demonstrates exceptional resource efficiency, requiring only 8.19 GB VRAM, making it compatible with a wide range of consumer-grade GPUs. Openness: We open-source the entire series of Wan, including source code and all models, with the goal of fostering the growth of the video generation community. This openness seeks to significantly expand the creative possibilities of video production in the industry and provide academia with high-quality video foundation models. All the code and models are available at https://github.com/Wan-Video/Wan2.1.", "github_manual": "", "github_regex": "https://github.com/Wan-Video/Wan2.1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.20020", "title": "Gemini Robotics: Bringing AI into the Physical World", "authors": [ "Gemini Robotics Team", "Saminda Abeyruwan", "Joshua Ainslie", "Jean-Baptiste Alayrac", "Montserrat Gonzalez Arenas", "Travis Armstrong", "Ashwin Balakrishna", "Robert Baruch", "Maria Bauza", "Michiel Blokzijl", "Steven Bohez", "Konstantinos Bousmalis", "Anthony Brohan", "Thomas Buschmann", "Arunkumar Byravan", "Serkan Cabi", "Ken Caluwaerts", "Federico Casarini", "Oscar Chang", "Jose Enrique Chen", "Xi Chen", "Hao-Tien Lewis Chiang", "Krzysztof Choromanski", "David D'Ambrosio", "Sudeep Dasari", "Todor Davchev", "Coline Devin", "Norman Di Palo", "Tianli Ding", "Adil Dostmohamed", "Danny Driess", "Yilun Du", "Debidatta Dwibedi", "Michael Elabd", "Claudio Fantacci", "Cody Fong", "Erik Frey", "Chuyuan Fu", "Marissa Giustina", "Keerthana Gopalakrishnan", "Laura Graesser", "Leonard Hasenclever", "Nicolas Heess", "Brandon Hernaez", "Alexander Herzog", "R. Alex Hofer", "Jan Humplik", "Atil Iscen", "Mithun George Jacob", "Deepali Jain", "Ryan Julian", "Dmitry Kalashnikov", "M. Emre Karagozler", "Stefani Karp", "Chase Kew", "Jerad Kirkland", "Sean Kirmani", "Yuheng Kuang", "Thomas Lampe", "Antoine Laurens", "Isabel Leal", "Alex X. Lee", "Tsang-Wei Edward Lee", "Jacky Liang", "Yixin Lin", "Sharath Maddineni", "Anirudha Majumdar", "Assaf Hurwitz Michaely", "Robert Moreno", "Michael Neunert", "Francesco Nori", "Carolina Parada", "Emilio Parisotto", "Peter Pastor", "Acorn Pooley", "Kanishka Rao", "Krista Reymann", "Dorsa Sadigh", "Stefano Saliceti", "Pannag Sanketi", "Pierre Sermanet", "Dhruv Shah", "Mohit Sharma", "Kathryn Shea", "Charles Shu", "Vikas Sindhwani", "Sumeet Singh", "Radu Soricut", "Jost Tobias Springenberg", "Rachel Sterneck", "Razvan Surdulescu", "Jie Tan", "Jonathan Tompson", "Vincent Vanhoucke", "Jake Varley", "Grace Vesom", "Giulia Vezzani", "Oriol Vinyals", "Ayzaan Wahid", "Stefan Welker", "Paul Wohlhart", "Fei Xia", "Ted Xiao", "Annie Xie", "Jinyu Xie", "Peng Xu", "Sichun Xu", "Ying Xu", "Zhuo Xu", "Yuxiang Yang", "Rui Yao", "Sergey Yaroshenko", "Wenhao Yu", "Wentao Yuan", "Jingwei Zhang", "Tingnan Zhang", "Allan Zhou", "Yuxiang Zhou" ], "github": "", "project_page": "", "abstract": "Recent advancements in large multimodal models have led to the emergence of remarkable generalist capabilities in digital domains, yet their translation to physical agents such as robots remains a significant challenge. This report introduces a new family of AI models purposefully designed for robotics and built upon the foundation of Gemini 2.0. We present Gemini Robotics, an advanced Vision-Language-Action (VLA) generalist model capable of directly controlling robots. Gemini Robotics executes smooth and reactive movements to tackle a wide range of complex manipulation tasks while also being robust to variations in object types and positions, handling unseen environments as well as following diverse, open vocabulary instructions. We show that with additional fine-tuning, Gemini Robotics can be specialized to new capabilities including solving long-horizon, highly dexterous tasks, learning new short-horizon tasks from as few as 100 demonstrations and adapting to completely novel robot embodiments. This is made possible because Gemini Robotics builds on top of the Gemini Robotics-ER model, the second model we introduce in this work. Gemini Robotics-ER (Embodied Reasoning) extends Gemini's multimodal reasoning capabilities into the physical world, with enhanced spatial and temporal understanding. This enables capabilities relevant to robotics including object detection, pointing, trajectory and grasp prediction, as well as multi-view correspondence and 3D bounding box predictions. We show how this novel combination can support a variety of robotics applications. We also discuss and address important safety considerations related to this new class of robotics foundation models. The Gemini Robotics family marks a substantial step towards developing general-purpose robots that realizes AI's potential in the physical world.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.20220", "title": "DINeMo: Learning Neural Mesh Models with no 3D Annotations", "authors": [ "Weijie Guo", "Guofeng Zhang", "Wufei Ma", "Alan Yuille" ], "github": "https://github.com/ccvl/DINeMo", "project_page": "", "abstract": "Category-level 3D/6D pose estimation is a crucial step towards comprehensive 3D scene understanding, which would enable a broad range of applications in robotics and embodied AI. Recent works explored neural mesh models that approach a range of 2D and 3D tasks from an analysis-by-synthesis perspective. Despite the largely enhanced robustness to partial occlusion and domain shifts, these methods depended heavily on 3D annotations for part-contrastive learning, which confines them to a narrow set of categories and hinders efficient scaling. In this work, we present DINeMo, a novel neural mesh model that is trained with no 3D annotations by leveraging pseudo-correspondence obtained from large visual foundation models. We adopt a bidirectional pseudo-correspondence generation method, which produce pseudo correspondence utilize both local appearance features and global context information. Experimental results on car datasets demonstrate that our DINeMo outperforms previous zero- and few-shot 3D pose estimation by a wide margin, narrowing the gap with fully-supervised methods by 67.3%. Our DINeMo also scales effectively and efficiently when incorporating more unlabeled images during training, which demonstrate the advantages over supervised learning methods that rely on 3D annotations. Our project page is available at https://analysis-by-synthesis.github.io/DINeMo/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ccvl/DINeMo", "github_api_added_by": "auto" }, { "date": "2025-03-27", "arxiv_id": "2503.20198", "title": "Beyond Words: Advancing Long-Text Image Generation via Multimodal Autoregressive Models", "authors": [ "Alex Jinpeng Wang", "Linjie Li", "Zhengyuan Yang", "Lijuan Wang", "Min Li" ], "github": "", "project_page": "", "abstract": "Recent advancements in autoregressive and diffusion models have led to strong performance in image generation with short scene text words. However, generating coherent, long-form text in images, such as paragraphs in slides or documents, remains a major challenge for current generative models. We present the first work specifically focused on long text image generation, addressing a critical gap in existing text-to-image systems that typically handle only brief phrases or single sentences. Through comprehensive analysis of state-of-the-art autoregressive generation models, we identify the image tokenizer as a critical bottleneck in text generating quality. To address this, we introduce a novel text-focused, binary tokenizer optimized for capturing detailed scene text features. Leveraging our tokenizer, we develop \\ModelName, a multimodal autoregressive model that excels in generating high-quality long-text images with unprecedented fidelity. Our model offers robust controllability, enabling customization of text properties such as font style, size, color, and alignment. Extensive experiments demonstrate that \\ModelName~significantly outperforms SD3.5 Large~sd3 and GPT4o~gpt4o with DALL-E 3~dalle3 in generating long text accurately, consistently, and flexibly. Beyond its technical achievements, \\ModelName~opens up exciting opportunities for innovative applications like interleaved document and PowerPoint generation, establishing a new frontier in long-text image generating.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.20672", "title": "BizGen: Advancing Article-level Visual Text Rendering for Infographics Generation", "authors": [ "Yuyang Peng", "Shishi Xiao", "Keming Wu", "Qisheng Liao", "Bohan Chen", "Kevin Lin", "Danqing Huang", "Ji Li", "Yuhui Yuan" ], "github": "", "project_page": "", "abstract": "Recently, state-of-the-art text-to-image generation models, such as Flux and Ideogram 2.0, have made significant progress in sentence-level visual text rendering. In this paper, we focus on the more challenging scenarios of article-level visual text rendering and address a novel task of generating high-quality business content, including infographics and slides, based on user provided article-level descriptive prompts and ultra-dense layouts. The fundamental challenges are twofold: significantly longer context lengths and the scarcity of high-quality business content data. In contrast to most previous works that focus on a limited number of sub-regions and sentence-level prompts, ensuring precise adherence to ultra-dense layouts with tens or even hundreds of sub-regions in business content is far more challenging. We make two key technical contributions: (i) the construction of scalable, high-quality business content dataset, i.e., Infographics-650K, equipped with ultra-dense layouts and prompts by implementing a layer-wise retrieval-augmented infographic generation scheme; and (ii) a layout-guided cross attention scheme, which injects tens of region-wise prompts into a set of cropped region latent space according to the ultra-dense layouts, and refine each sub-regions flexibly during inference using a layout conditional CFG. We demonstrate the strong results of our system compared to previous SOTA systems such as Flux and SD3 on our BizEval prompt set. Additionally, we conduct thorough ablation experiments to verify the effectiveness of each component. We hope our constructed Infographics-650K and BizEval can encourage the broader community to advance the progress of business content generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.19953", "title": "Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals", "authors": [ "Stefan Stojanov", "David Wendt", "Seungwoo Kim", "Rahul Venkatesh", "Kevin Feigelis", "Jiajun Wu", "Daniel LK Yamins" ], "github": "https://github.com/neuroailab/Opt_CWM", "project_page": "", "abstract": "Estimating motion in videos is an essential computer vision problem with many downstream applications, including controllable video generation and robotics. Current solutions are primarily trained using synthetic data or require tuning of situation-specific heuristics, which inherently limits these models' capabilities in real-world contexts. Despite recent developments in large-scale self-supervised learning from videos, leveraging such representations for motion estimation remains relatively underexplored. In this work, we develop Opt-CWM, a self-supervised technique for flow and occlusion estimation from a pre-trained next-frame prediction model. Opt-CWM works by learning to optimize counterfactual probes that extract motion information from a base video model, avoiding the need for fixed heuristics while training on unrestricted video inputs. We achieve state-of-the-art performance for motion estimation on real-world videos while requiring no labeled data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/neuroailab/Opt_CWM", "github_api_added_by": "auto" }, { "date": "2025-03-27", "arxiv_id": "2503.19462", "title": "AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset", "authors": [ "Haiyu Zhang", "Xinyuan Chen", "Yaohui Wang", "Xihui Liu", "Yunhong Wang", "Yu Qiao" ], "github": "https://github.com/aejion/AccVideo", "project_page": "", "abstract": "Diffusion models have achieved remarkable progress in the field of video generation. However, their iterative denoising nature requires a large number of inference steps to generate a video, which is slow and computationally expensive. In this paper, we begin with a detailed analysis of the challenges present in existing diffusion distillation methods and propose a novel efficient method, namely AccVideo, to reduce the inference steps for accelerating video diffusion models with synthetic dataset. We leverage the pretrained video diffusion model to generate multiple valid denoising trajectories as our synthetic dataset, which eliminates the use of useless data points during distillation. Based on the synthetic dataset, we design a trajectory-based few-step guidance that utilizes key data points from the denoising trajectories to learn the noise-to-video mapping, enabling video generation in fewer steps. Furthermore, since the synthetic dataset captures the data distribution at each diffusion timestep, we introduce an adversarial training strategy to align the output distribution of the student model with that of our synthetic dataset, thereby enhancing the video quality. Extensive experiments demonstrate that our model achieves 8.5x improvements in generation speed compared to the teacher model while maintaining comparable performance. Compared to previous accelerating methods, our approach is capable of generating videos with higher quality and resolution, i.e., 5-seconds, 720x1280, 24fps.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aejion/AccVideo", "github_api_added_by": "auto" }, { "date": "2025-03-27", "arxiv_id": "2503.19757", "title": "Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action Policy", "authors": [ "Zhi Hou", "Tianyi Zhang", "Yuwen Xiong", "Haonan Duan", "Hengjun Pu", "Ronglei Tong", "Chengyang Zhao", "Xizhou Zhu", "Yu Qiao", "Jifeng Dai", "Yuntao Chen" ], "github": "", "project_page": "", "abstract": "While recent vision-language-action models trained on diverse robot datasets exhibit promising generalization capabilities with limited in-domain data, their reliance on compact action heads to predict discretized or continuous actions constrains adaptability to heterogeneous action spaces. We present Dita, a scalable framework that leverages Transformer architectures to directly denoise continuous action sequences through a unified multimodal diffusion process. Departing from prior methods that condition denoising on fused embeddings via shallow networks, Dita employs in-context conditioning -- enabling fine-grained alignment between denoised actions and raw visual tokens from historical observations. This design explicitly models action deltas and environmental nuances. By scaling the diffusion action denoiser alongside the Transformer's scalability, Dita effectively integrates cross-embodiment datasets across diverse camera perspectives, observation scenes, tasks, and action spaces. Such synergy enhances robustness against various variances and facilitates the successful execution of long-horizon tasks. Evaluations across extensive benchmarks demonstrate state-of-the-art or comparative performance in simulation. Notably, Dita achieves robust real-world adaptation to environmental variances and complex long-horizon tasks through 10-shot finetuning, using only third-person camera inputs. The architecture establishes a versatile, lightweight and open-source baseline for generalist robot policy learning. Project Page: https://robodita.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.19480", "title": "GenHancer: Imperfect Generative Models are Secretly Strong Vision-Centric Enhancers", "authors": [ "Shijie Ma", "Yuying Ge", "Teng Wang", "Yuxin Guo", "Yixiao Ge", "Ying Shan" ], "github": "", "project_page": "", "abstract": "The synergy between generative and discriminative models receives growing attention. While discriminative Contrastive Language-Image Pre-Training (CLIP) excels in high-level semantics, it struggles with perceiving fine-grained visual details. Generally, to enhance representations, generative models take CLIP's visual features as conditions for reconstruction. However, the underlying principle remains underexplored. In this work, we empirically found that visually perfect generations are not always optimal for representation enhancement. The essence lies in effectively extracting fine-grained knowledge from generative models while mitigating irrelevant information. To explore critical factors, we delve into three aspects: (1) Conditioning mechanisms: We found that even a small number of local tokens can drastically reduce the difficulty of reconstruction, leading to collapsed training. We thus conclude that utilizing only global visual tokens as conditions is the most effective strategy. (2) Denoising configurations: We observed that end-to-end training introduces extraneous information. To address this, we propose a two-stage training strategy to prioritize learning useful visual knowledge. Additionally, we demonstrate that lightweight denoisers can yield remarkable improvements. (3) Generation paradigms: We explore both continuous and discrete denoisers with desirable outcomes, validating the versatility of our method. Through our in-depth explorations, we have finally arrived at an effective method, namely GenHancer, which consistently outperforms prior arts on the MMVP-VLM benchmark, e.g., 6.0% on OpenAICLIP. The enhanced CLIP can be further plugged into multimodal large language models for better vision-centric performance. All the models and codes are made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.20757", "title": "MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search", "authors": [ "Yunhai Hu", "Yilun Zhao", "Chen Zhao", "Arman Cohan" ], "github": "https://github.com/yale-nlp/mcts-rag", "project_page": "", "abstract": "We introduce MCTS-RAG, a novel approach that enhances the reasoning capabilities of small language models on knowledge-intensive tasks by leveraging retrieval-augmented generation (RAG) to provide relevant context and Monte Carlo Tree Search (MCTS) to refine reasoning paths. MCTS-RAG dynamically integrates retrieval and reasoning through an iterative decision-making process. Unlike standard RAG methods, which typically retrieve information independently from reasoning and thus integrate knowledge suboptimally, or conventional MCTS reasoning, which depends solely on internal model knowledge without external facts, MCTS-RAG combines structured reasoning with adaptive retrieval. This integrated approach enhances decision-making, reduces hallucinations, and ensures improved factual accuracy and response consistency. The experimental results on multiple reasoning and knowledge-intensive datasets datasets (i.e., ComplexWebQA, GPQA, and FoolMeTwice) show that our method enables small-scale LMs to achieve performance comparable to frontier LLMs like GPT-4o by effectively scaling inference-time compute, setting a new standard for reasoning in small-scale models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yale-nlp/mcts-rag", "github_api_added_by": "auto" }, { "date": "2025-03-27", "arxiv_id": "2503.19950", "title": "LogQuant: Log-Distributed 2-Bit Quantization of KV Cache with Superior Accuracy Preservation", "authors": [ "Han Chen", "Zicong Jiang", "Zining Zhang", "Bingsheng He", "Pingyi Luo", "Mian Lu", "Yuqiang Chen" ], "github": "https://github.com/Concyclics/LogQuantKV", "project_page": "", "abstract": "We introduce LogQuant, a groundbreaking 2-bit quantization technique for KV Cache in large language model (LLM) inference, delivering substantial memory savings while preserving superior performance. Previous methods either assume that later tokens are more important or attempt to predict important tokens based on earlier attention patterns. Both approaches, however, can result in performance bottlenecks or frequent mispredictions. LogQuant takes a different approach. By applying a log-based filtering mechanism, it selectively compresses the KV Cache across the entire context, achieving better performance with the same or even reduced memory footprint compared to existing methods. In benchmark tests, it enhances throughput by 25% and boosts batch size by 60% without increasing memory consumption. For challenging tasks such as Math and Code Completion, LogQuant improves accuracy by 40% to 200% at the same compression ratio, outperforming comparable techniques.LogQuant integrates effortlessly with popular inference frameworks like Python's transformers library. Implementation can be available in https://github.com/Concyclics/LogQuantKV.", "github_manual": "", "github_regex": "https://github.com/Concyclics/LogQuantKV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.19846", "title": "Attention IoU: Examining Biases in CelebA using Attention Maps", "authors": [ "Aaron Serianni", "Tyler Zhu", "Olga Russakovsky", "Vikram V. Ramaswamy" ], "github": "https://github.com/aaronserianni/attention-iou", "project_page": "", "abstract": "Computer vision models have been shown to exhibit and amplify biases across a wide array of datasets and tasks. Existing methods for quantifying bias in classification models primarily focus on dataset distribution and model performance on subgroups, overlooking the internal workings of a model. We introduce the Attention-IoU (Attention Intersection over Union) metric and related scores, which use attention maps to reveal biases within a model's internal representations and identify image features potentially causing the biases. First, we validate Attention-IoU on the synthetic Waterbirds dataset, showing that the metric accurately measures model bias. We then analyze the CelebA dataset, finding that Attention-IoU uncovers correlations beyond accuracy disparities. Through an investigation of individual attributes through the protected attribute of Male, we examine the distinct ways biases are represented in CelebA. Lastly, by subsampling the training set to change attribute correlations, we demonstrate that Attention-IoU reveals potential confounding variables not present in dataset labels.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aaronserianni/attention-iou", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.16870", "title": "Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs", "authors": [ "Anshumann", "Mohd Abbas Zaidi", "Akhil Kedia", "Jinwoo Ahn", "Taehwak Kwon", "Kangwook Lee", "Haejun Lee", "Joohyung Lee" ], "github": "", "project_page": "", "abstract": "Knowledge distillation can be a cost-effective technique to distill knowledge in Large Language Models, if the teacher output logits can be pre-computed and cached. However, successfully applying this to pre-training remains largely unexplored. In this work, we prove that naive approaches for sparse knowledge distillation such as caching Top-K probabilities, while intuitive, provide biased estimates of teacher probability distribution to the student, resulting in suboptimal performance and calibration. We propose an importance-sampling-based method `Random Sampling Knowledge Distillation', which provides unbiased estimates, preserves the gradient in expectation, and requires storing significantly sparser logits. Our method enables faster training of student models with marginal overhead (<10%) compared to cross-entropy based training, while maintaining competitive performance compared to full distillation, across a range of model sizes from 300M to 3B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.19990", "title": "LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?", "authors": [ "Kexian Tang", "Junyao Gao", "Yanhong Zeng", "Haodong Duan", "Yanan Sun", "Zhening Xing", "Wenran Liu", "Kaifeng Lyu", "Kai Chen" ], "github": "https://github.com/Tangkexian/LEGO-Puzzles", "project_page": "", "abstract": "Multi-step spatial reasoning entails understanding and reasoning about spatial relationships across multiple sequential steps, which is crucial for tackling complex real-world applications, such as robotic manipulation, autonomous navigation, and automated assembly. To assess how well current Multimodal Large Language Models (MLLMs) have acquired this fundamental capability, we introduce LEGO-Puzzles, a scalable benchmark designed to evaluate both spatial understanding and sequential reasoning in MLLMs through LEGO-based tasks. LEGO-Puzzles consists of 1,100 carefully curated visual question-answering (VQA) samples spanning 11 distinct tasks, ranging from basic spatial understanding to complex multi-step reasoning. Based on LEGO-Puzzles, we conduct a comprehensive evaluation of state-of-the-art MLLMs and uncover significant limitations in their spatial reasoning capabilities: even the most powerful MLLMs can answer only about half of the test cases, whereas human participants achieve over 90\\% accuracy. In addition to VQA tasks, we evaluate MLLMs' abilities to generate LEGO images following assembly illustrations. Our experiments show that only Gemini-2.0-Flash and GPT-4o exhibit a limited ability to follow these instructions, while other MLLMs either replicate the input image or generate completely irrelevant outputs. Overall, LEGO-Puzzles exposes critical deficiencies in existing MLLMs' spatial understanding and sequential reasoning capabilities, and underscores the need for further advancements in multimodal spatial reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Tangkexian/LEGO-Puzzles", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.20215", "title": "Qwen2.5-Omni Technical Report", "authors": [ "Jin Xu", "Zhifang Guo", "Jinzheng He", "Hangrui Hu", "Ting He", "Shuai Bai", "Keqin Chen", "Jialin Wang", "Yang Fan", "Kai Dang", "Bin Zhang", "Xiong Wang", "Yunfei Chu", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "In this report, we present Qwen2.5-Omni, an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner. To enable the streaming of multimodal information inputs, both audio and visual encoders utilize a block-wise processing approach. To synchronize the timestamps of video inputs with audio, we organize the audio and video sequentially in an interleaved manner and propose a novel position embedding approach, named TMRoPE(Time-aligned Multimodal RoPE). To concurrently generate text and speech while avoiding interference between the two modalities, we propose Thinker-Talker architecture. In this framework, Thinker functions as a large language model tasked with text generation, while Talker is a dual-track autoregressive model that directly utilizes the hidden representations from the Thinker to produce audio tokens as output. Both the Thinker and Talker models are designed to be trained and inferred in an end-to-end manner. For decoding audio tokens in a streaming manner, we introduce a sliding-window DiT that restricts the receptive field, aiming to reduce the initial package delay. Qwen2.5-Omni is comparable with the similarly sized Qwen2.5-VL and outperforms Qwen2-Audio. Furthermore, Qwen2.5-Omni achieves state-of-the-art performance on multimodal benchmarks like Omni-Bench. Notably, Qwen2.5-Omni's performance in end-to-end speech instruction following is comparable to its capabilities with text inputs, as evidenced by benchmarks such as MMLU and GSM8K. As for speech generation, Qwen2.5-Omni's streaming Talker outperforms most existing streaming and non-streaming alternatives in robustness and naturalness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.20271", "title": "ViLBench: A Suite for Vision-Language Process Reward Modeling", "authors": [ "Haoqin Tu", "Weitao Feng", "Hardy Chen", "Hui Liu", "Xianfeng Tang", "Cihang Xie" ], "github": "", "project_page": "", "abstract": "Process-supervised reward models serve as a fine-grained function that provides detailed step-wise feedback to model responses, facilitating effective selection of reasoning trajectories for complex tasks. Despite its advantages, evaluation on PRMs remains less explored, especially in the multimodal domain. To address this gap, this paper first benchmarks current vision large language models (VLLMs) as two types of reward models: output reward models (ORMs) and process reward models (PRMs) on multiple vision-language benchmarks, which reveal that neither ORM nor PRM consistently outperforms across all tasks, and superior VLLMs do not necessarily yield better rewarding performance. To further advance evaluation, we introduce ViLBench, a vision-language benchmark designed to require intensive process reward signals. Notably, OpenAI's GPT-4o with Chain-of-Thought (CoT) achieves only 27.3% accuracy, indicating the benchmark's challenge for current VLLMs. Lastly, we preliminarily showcase a promising pathway towards bridging the gap between general VLLMs and reward models -- by collecting 73.6K vision-language process reward data using an enhanced tree-search algorithm, our 3B model is able to achieve an average improvement of 3.3% over standard CoT and up to 2.5% compared to its untrained counterpart on ViLBench by selecting OpenAI o1's generations. We release the implementations at https://ucsc-vlaa.github.io/ViLBench with our code, model, and data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.17358", "title": "Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image", "authors": [ "Jerred Chen", "Ronald Clark" ], "github": "", "project_page": "", "abstract": "In many robotics and VR/AR applications, fast camera motions cause a high level of motion blur, causing existing camera pose estimation methods to fail. In this work, we propose a novel framework that leverages motion blur as a rich cue for motion estimation rather than treating it as an unwanted artifact. Our approach works by predicting a dense motion flow field and a monocular depth map directly from a single motion-blurred image. We then recover the instantaneous camera velocity by solving a linear least squares problem under the small motion assumption. In essence, our method produces an IMU-like measurement that robustly captures fast and aggressive camera movements. To train our model, we construct a large-scale dataset with realistic synthetic motion blur derived from ScanNet++v2 and further refine our model by training end-to-end on real data using our fully differentiable pipeline. Extensive evaluations on real-world benchmarks demonstrate that our method achieves state-of-the-art angular and translational velocity estimates, outperforming current methods like MASt3R and COLMAP.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.17970", "title": "PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images", "authors": [ "Yang Luo", "Shiru Wang", "Jun Liu", "Jiaxuan Xiao", "Rundong Xue", "Zeyu Zhang", "Hao Zhang", "Yu Lu", "Yang Zhao", "Yutong Xie" ], "github": "https://github.com/AIGeeksGroup/PathoHR", "project_page": "", "abstract": "Breast cancer survival prediction in computational pathology presents a remarkable challenge due to tumor heterogeneity. For instance, different regions of the same tumor in the pathology image can show distinct morphological and molecular characteristics. This makes it difficult to extract representative features from whole slide images (WSIs) that truly reflect the tumor's aggressive potential and likely survival outcomes. In this paper, we present PathoHR, a novel pipeline for accurate breast cancer survival prediction that enhances any size of pathological images to enable more effective feature learning. Our approach entails (1) the incorporation of a plug-and-play high-resolution Vision Transformer (ViT) to enhance patch-wise WSI representation, enabling more detailed and comprehensive feature extraction, (2) the systematic evaluation of multiple advanced similarity metrics for comparing WSI-extracted features, optimizing the representation learning process to better capture tumor characteristics, (3) the demonstration that smaller image patches enhanced follow the proposed pipeline can achieve equivalent or superior prediction accuracy compared to raw larger patches, while significantly reducing computational overhead. Experimental findings valid that PathoHR provides the potential way of integrating enhanced image resolution with optimized feature learning to advance computational pathology, offering a promising direction for more accurate and efficient breast cancer survival prediction. Code will be available at https://github.com/AIGeeksGroup/PathoHR.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/PathoHR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.15893", "title": "UniHDSA: A Unified Relation Prediction Approach for Hierarchical Document Structure Analysis", "authors": [ "Jiawei Wang", "Kai Hu", "Qiang Huo" ], "github": "https://github.com/microsoft/CompHRDoc", "project_page": "", "abstract": "Document structure analysis, aka document layout analysis, is crucial for understanding both the physical layout and logical structure of documents, serving information retrieval, document summarization, knowledge extraction, etc. Hierarchical Document Structure Analysis (HDSA) specifically aims to restore the hierarchical structure of documents created using authoring software with hierarchical schemas. Previous research has primarily followed two approaches: one focuses on tackling specific subtasks of HDSA in isolation, such as table detection or reading order prediction, while the other adopts a unified framework that uses multiple branches or modules, each designed to address a distinct task. In this work, we propose a unified relation prediction approach for HDSA, called UniHDSA, which treats various HDSA sub-tasks as relation prediction problems and consolidates relation prediction labels into a unified label space. This allows a single relation prediction module to handle multiple tasks simultaneously, whether at a page-level or document-level structure analysis. To validate the effectiveness of UniHDSA, we develop a multimodal end-to-end system based on Transformer architectures. Extensive experimental results demonstrate that our approach achieves state-of-the-art performance on a hierarchical document structure analysis benchmark, Comp-HRDoc, and competitive results on a large-scale document layout analysis dataset, DocLayNet, effectively illustrating the superiority of our method across all sub-tasks. The Comp-HRDoc benchmark and UniHDSA's configurations are publicly available at https://github.com/microsoft/CompHRDoc.", "github_manual": "", "github_regex": "https://github.com/microsoft/CompHRDoc", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.10997", "title": "RONA: Pragmatically Diverse Image Captioning with Coherence Relations", "authors": [ "Aashish Anantha Ramakrishnan", "Aadarsh Anantha Ramakrishnan", "Dongwon Lee" ], "github": "https://github.com/aashish2000/RONA", "project_page": "", "abstract": "Writing Assistants (e.g., Grammarly, Microsoft Copilot) traditionally generate diverse image captions by employing syntactic and semantic variations to describe image components. However, human-written captions prioritize conveying a central message alongside visual descriptions using pragmatic cues. To enhance pragmatic diversity, it is essential to explore alternative ways of communicating these messages in conjunction with visual content. To address this challenge, we propose RONA, a novel prompting strategy for Multi-modal Large Language Models (MLLM) that leverages Coherence Relations as an axis for variation. We demonstrate that RONA generates captions with better overall diversity and ground-truth alignment, compared to MLLM baselines across multiple domains. Our code is available at: https://github.com/aashish2000/RONA", "github_manual": "", "github_regex": "https://github.com/aashish2000/RONA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.20641", "title": "Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging", "authors": [ "Han Wu", "Yuxuan Yao", "Shuqi Liu", "Zehua Liu", "Xiaojin Fu", "Xiongwei Han", "Xing Li", "Hui-Ling Zhen", "Tao Zhong", "Mingxuan Yuan" ], "github": "https://github.com/hahahawu/Long-to-Short-via-Model-Merging", "project_page": "", "abstract": "The transition from System 1 to System 2 reasoning in large language models (LLMs) has marked significant advancements in handling complex tasks through deliberate, iterative thinking. However, this progress often comes at the cost of efficiency, as models tend to overthink, generating redundant reasoning steps without proportional improvements in output quality. Long-to-Short (L2S) reasoning has emerged as a promising solution to this challenge, aiming to balance reasoning depth with practical efficiency. While existing approaches, such as supervised fine-tuning (SFT), reinforcement learning (RL), and prompt engineering, have shown potential, they are either computationally expensive or unstable. Model merging, on the other hand, offers a cost-effective and robust alternative by integrating the quick-thinking capabilities of System 1 models with the methodical reasoning of System 2 models. In this work, we present a comprehensive empirical study on model merging for L2S reasoning, exploring diverse methodologies, including task-vector-based, SVD-based, and activation-informed merging. Our experiments reveal that model merging can reduce average response length by up to 55% while preserving or even improving baseline performance. We also identify a strong correlation between model scale and merging efficacy with extensive evaluations on 1.5B/7B/14B/32B models. Furthermore, we investigate the merged model's ability to self-critique and self-correct, as well as its adaptive response length based on task complexity. Our findings highlight model merging as a highly efficient and effective paradigm for L2S reasoning, offering a practical solution to the overthinking problem while maintaining the robustness of System 2 reasoning. This work can be found on Github https://github.com/hahahawu/Long-to-Short-via-Model-Merging.", "github_manual": "", "github_regex": "https://github.com/hahahawu/Long-to-Short-via-Model-Merging", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.18929", "title": "Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training", "authors": [ "Brian R. Bartoldson", "Siddarth Venkatraman", "James Diffenderfer", "Moksh Jain", "Tal Ben-Nun", "Seanie Lee", "Minsu Kim", "Johan Obando-Ceron", "Yoshua Bengio", "Bhavya Kailkhura" ], "github": "https://github.com/bbartoldson/TBA", "project_page": "", "abstract": "Reinforcement learning (RL) is a critical component of large language model (LLM) post-training. However, existing on-policy algorithms used for post-training are inherently incompatible with the use of experience replay buffers, which can be populated scalably by distributed off-policy actors to enhance exploration as compute increases. We propose efficiently obtaining this benefit of replay buffers via Trajectory Balance with Asynchrony (TBA), a massively scalable LLM RL system. In contrast to existing approaches, TBA uses a larger fraction of compute on search, constantly generating off-policy data for a central replay buffer. A training node simultaneously samples data from this buffer based on reward or recency to update the policy using Trajectory Balance (TB), a diversity-seeking RL objective introduced for GFlowNets. TBA offers three key advantages: (1) decoupled training and search, speeding up training wall-clock time by 4x or more; (2) improved diversity through large-scale off-policy sampling; and (3) scalable search for sparse reward settings. On mathematical reasoning, preference-tuning, and automated red-teaming (diverse and representative post-training tasks), TBA produces speed and performance improvements over strong baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bbartoldson/TBA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.19786", "title": "Gemma 3 Technical Report", "authors": [ "Gemma Team", "Aishwarya Kamath", "Johan Ferret", "Shreya Pathak", "Nino Vieillard", "Ramona Merhej", "Sarah Perrin", "Tatiana Matejovicova", "Alexandre Ram\u00e9", "Morgane Rivi\u00e8re", "Louis Rouillard", "Thomas Mesnard", "Geoffrey Cideron", "Jean-bastien Grill", "Sabela Ramos", "Edouard Yvinec", "Michelle Casbon", "Etienne Pot", "Ivo Penchev", "Ga\u00ebl Liu", "Francesco Visin", "Kathleen Kenealy", "Lucas Beyer", "Xiaohai Zhai", "Anton Tsitsulin", "Robert Busa-Fekete", "Alex Feng", "Noveen Sachdeva", "Benjamin Coleman", "Yi Gao", "Basil Mustafa", "Iain Barr", "Emilio Parisotto", "David Tian", "Matan Eyal", "Colin Cherry", "Jan-Thorsten Peter", "Danila Sinopalnikov", "Surya Bhupatiraju", "Rishabh Agarwal", "Mehran Kazemi", "Dan Malkin", "Ravin Kumar", "David Vilar", "Idan Brusilovsky", "Jiaming Luo", "Andreas Steiner", "Abe Friesen", "Abhanshu Sharma", "Abheesht Sharma", "Adi Mayrav Gilady", "Adrian Goedeckemeyer", "Alaa Saade", "Alex Feng", "Alexander Kolesnikov", "Alexei Bendebury", "Alvin Abdagic", "Amit Vadi", "Andr\u00e1s Gy\u00f6rgy", "Andr\u00e9 Susano Pinto", "Anil Das", "Ankur Bapna", "Antoine Miech", "Antoine Yang", "Antonia Paterson", "Ashish Shenoy", "Ayan Chakrabarti", "Bilal Piot", "Bo Wu", "Bobak Shahriari", "Bryce Petrini", "Charlie Chen", "Charline Le Lan", "Christopher A. Choquette-Choo", "CJ Carey", "Cormac Brick", "Daniel Deutsch", "Danielle Eisenbud", "Dee Cattle", "Derek Cheng", "Dimitris Paparas", "Divyashree Shivakumar Sreepathihalli", "Doug Reid", "Dustin Tran", "Dustin Zelle", "Eric Noland", "Erwin Huizenga", "Eugene Kharitonov", "Frederick Liu", "Gagik Amirkhanyan", "Glenn Cameron", "Hadi Hashemi", "Hanna Klimczak-Pluci\u0144ska", "Harman Singh", "Harsh Mehta", "Harshal Tushar Lehri", "Hussein Hazimeh", "Ian Ballantyne", "Idan Szpektor", "Ivan Nardini", "Jean Pouget-Abadie", "Jetha Chan", "Joe Stanton", "John Wieting", "Jonathan Lai", "Jordi Orbay", "Joseph Fernandez", "Josh Newlan", "Ju-yeong Ji", "Jyotinder Singh", "Kat Black", "Kathy Yu", "Kevin Hui", "Kiran Vodrahalli", "Klaus Greff", "Linhai Qiu", "Marcella Valentine", "Marina Coelho", "Marvin Ritter", "Matt Hoffman", "Matthew Watson", "Mayank Chaturvedi", "Michael Moynihan", "Min Ma", "Nabila Babar", "Natasha Noy", "Nathan Byrd", "Nick Roy", "Nikola Momchev", "Nilay Chauhan", "Noveen Sachdeva", "Oskar Bunyan", "Pankil Botarda", "Paul Caron", "Paul Kishan Rubenstein", "Phil Culliton", "Philipp Schmid", "Pier Giuseppe Sessa", "Pingmei Xu", "Piotr Stanczyk", "Pouya Tafti", "Rakesh Shivanna", "Renjie Wu", "Renke Pan", "Reza Rokni", "Rob Willoughby", "Rohith Vallu", "Ryan Mullins", "Sammy Jerome", "Sara Smoot", "Sertan Girgin", "Shariq Iqbal", "Shashir Reddy", "Shruti Sheth", "Siim P\u00f5der", "Sijal Bhatnagar", "Sindhu Raghuram Panyam", "Sivan Eiger", "Susan Zhang", "Tianqi Liu", "Trevor Yacovone", "Tyler Liechty", "Uday Kalra", "Utku Evci", "Vedant Misra", "Vincent Roseberry", "Vlad Feinberg", "Vlad Kolesnikov", "Woohyun Han", "Woosuk Kwon", "Xi Chen", "Yinlam Chow", "Yuvein Zhu", "Zichuan Wei", "Zoltan Egyed", "Victor Cotruta", "Minh Giang", "Phoebe Kirk", "Anand Rao", "Kat Black", "Nabila Babar", "Jessica Lo", "Erica Moreira", "Luiz Gustavo Martins", "Omar Sanseviero", "Lucas Gonzalez", "Zach Gleicher", "Tris Warkentin", "Vahab Mirrokni", "Evan Senter", "Eli Collins", "Joelle Barral", "Zoubin Ghahramani", "Raia Hadsell", "Yossi Matias", "D. Sculley", "Slav Petrov", "Noah Fiedel", "Noam Shazeer", "Oriol Vinyals", "Jeff Dean", "Demis Hassabis", "Koray Kavukcuoglu", "Clement Farabet", "Elena Buchatskaya", "Jean-Baptiste Alayrac", "Rohan Anil", "Dmitry", "Lepikhin", "Sebastian Borgeaud", "Olivier Bachem", "Armand Joulin", "Alek Andreev", "Cassidy Hardin", "Robert Dadashi", "L\u00e9onard Hussenot" ], "github": "", "project_page": "", "abstract": "We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision understanding abilities, a wider coverage of languages and longer context - at least 128K tokens. We also change the architecture of the model to reduce the KV-cache memory that tends to explode with long context. This is achieved by increasing the ratio of local to global attention layers, and keeping the span on local attention short. The Gemma 3 models are trained with distillation and achieve superior performance to Gemma 2 for both pre-trained and instruction finetuned versions. In particular, our novel post-training recipe significantly improves the math, chat, instruction-following and multilingual abilities, making Gemma3-4B-IT competitive with Gemma2-27B-IT and Gemma3-27B-IT comparable to Gemini-1.5-Pro across benchmarks. We release all our models to the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-27", "arxiv_id": "2503.20731", "title": "RecTable: Fast Modeling Tabular Data with Rectified Flow", "authors": [ "Masane Fuchi", "Tomohiro Takagi" ], "github": "https://github.com/fmp453/rectable", "project_page": "", "abstract": "Score-based or diffusion models generate high-quality tabular data, surpassing GAN-based and VAE-based models. However, these methods require substantial training time. In this paper, we introduce RecTable, which uses the rectified flow modeling, applied in such as text-to-image generation and text-to-video generation. RecTable features a simple architecture consisting of a few stacked gated linear unit blocks. Additionally, our training strategies are also simple, incorporating a mixed-type noise distribution and a logit-normal timestep distribution. Our experiments demonstrate that RecTable achieves competitive performance compared to the several state-of-the-art diffusion and score-based models while reducing the required training time. Our code is available at https://github.com/fmp453/rectable.", "github_manual": "", "github_regex": "https://github.com/fmp453/rectable", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.20853", "title": "Unified Multimodal Discrete Diffusion", "authors": [ "Alexander Swerdlow", "Mihir Prabhudesai", "Siddharth Gandhi", "Deepak Pathak", "Katerina Fragkiadaki" ], "github": "", "project_page": "", "abstract": "Multimodal generative models that can understand and generate across multiple modalities are dominated by autoregressive (AR) approaches, which process tokens sequentially from left to right, or top to bottom. These models jointly handle images, text, video, and audio for various tasks such as image captioning, question answering, and image generation. In this work, we explore discrete diffusion models as a unified generative formulation in the joint text and image domain, building upon their recent success in text generation. Discrete diffusion models offer several advantages over AR models, including improved control over quality versus diversity of generated samples, the ability to perform joint multimodal inpainting (across both text and image domains), and greater controllability in generation through guidance. Leveraging these benefits, we present the first Unified Multimodal Discrete Diffusion (UniDisc) model which is capable of jointly understanding and generating text and images for a variety of downstream tasks. We compare UniDisc to multimodal AR models, performing a scaling analysis and demonstrating that UniDisc outperforms them in terms of both performance and inference-time compute, enhanced controllability, editability, inpainting, and flexible trade-off between inference time and generation quality. Code and additional visualizations are available at https://unidisc.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.20578", "title": "LLPut: Investigating Large Language Models for Bug Report-Based Input Generation", "authors": [ "Alif Al Hasan", "Subarna Saha", "Mia Mohammad Imran", "Tarannum Shaila Zaman" ], "github": "", "project_page": "", "abstract": "Failure-inducing inputs play a crucial role in diagnosing and analyzing software bugs. Bug reports typically contain these inputs, which developers extract to facilitate debugging. Since bug reports are written in natural language, prior research has leveraged various Natural Language Processing (NLP) techniques for automated input extraction. With the advent of Large Language Models (LLMs), an important research question arises: how effectively can generative LLMs extract failure-inducing inputs from bug reports? In this paper, we propose LLPut, a technique to empirically evaluate the performance of three open-source generative LLMs -- LLaMA, Qwen, and Qwen-Coder -- in extracting relevant inputs from bug reports. We conduct an experimental evaluation on a dataset of 206 bug reports to assess the accuracy and effectiveness of these models. Our findings provide insights into the capabilities and limitations of generative LLMs in automated bug diagnosis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21755", "title": "VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness", "authors": [ "Dian Zheng", "Ziqi Huang", "Hongbo Liu", "Kai Zou", "Yinan He", "Fan Zhang", "Yuanhan Zhang", "Jingwen He", "Wei-Shi Zheng", "Yu Qiao", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Video generation has advanced significantly, evolving from producing unrealistic outputs to generating videos that appear visually convincing and temporally coherent. To evaluate these video generative models, benchmarks such as VBench have been developed to assess their faithfulness, measuring factors like per-frame aesthetics, temporal consistency, and basic prompt adherence. However, these aspects mainly represent superficial faithfulness, which focus on whether the video appears visually convincing rather than whether it adheres to real-world principles. While recent models perform increasingly well on these metrics, they still struggle to generate videos that are not just visually plausible but fundamentally realistic. To achieve real \"world models\" through video generation, the next frontier lies in intrinsic faithfulness to ensure that generated videos adhere to physical laws, commonsense reasoning, anatomical correctness, and compositional integrity. Achieving this level of realism is essential for applications such as AI-assisted filmmaking and simulated world modeling. To bridge this gap, we introduce VBench-2.0, a next-generation benchmark designed to automatically evaluate video generative models for their intrinsic faithfulness. VBench-2.0 assesses five key dimensions: Human Fidelity, Controllability, Creativity, Physics, and Commonsense, each further broken down into fine-grained capabilities. Tailored for individual dimensions, our evaluation framework integrates generalists such as state-of-the-art VLMs and LLMs, and specialists, including anomaly detection methods proposed for video generation. We conduct extensive annotations to ensure alignment with human judgment. By pushing beyond superficial faithfulness toward intrinsic faithfulness, VBench-2.0 aims to set a new standard for the next generation of video generative models in pursuit of intrinsic faithfulness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21749", "title": "LeX-Art: Rethinking Text Generation via Scalable High-Quality Data Synthesis", "authors": [ "Shitian Zhao", "Qilong Wu", "Xinyue Li", "Bo Zhang", "Ming Li", "Qi Qin", "Dongyang Liu", "Kaipeng Zhang", "Hongsheng Li", "Yu Qiao", "Peng Gao", "Bin Fu", "Zhen Li" ], "github": "", "project_page": "", "abstract": "We introduce LeX-Art, a comprehensive suite for high-quality text-image synthesis that systematically bridges the gap between prompt expressiveness and text rendering fidelity. Our approach follows a data-centric paradigm, constructing a high-quality data synthesis pipeline based on Deepseek-R1 to curate LeX-10K, a dataset of 10K high-resolution, aesthetically refined 1024times1024 images. Beyond dataset construction, we develop LeX-Enhancer, a robust prompt enrichment model, and train two text-to-image models, LeX-FLUX and LeX-Lumina, achieving state-of-the-art text rendering performance. To systematically evaluate visual text generation, we introduce LeX-Bench, a benchmark that assesses fidelity, aesthetics, and alignment, complemented by Pairwise Normalized Edit Distance (PNED), a novel metric for robust text accuracy evaluation. Experiments demonstrate significant improvements, with LeX-Lumina achieving a 79.81% PNED gain on CreateBench, and LeX-FLUX outperforming baselines in color (+3.18%), positional (+4.45%), and font accuracy (+3.81%). Our codes, models, datasets, and demo are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21758", "title": "Lumina-Image 2.0: A Unified and Efficient Image Generative Framework", "authors": [ "Qi Qin", "Le Zhuo", "Yi Xin", "Ruoyi Du", "Zhen Li", "Bin Fu", "Yiting Lu", "Jiakang Yuan", "Xinyue Li", "Dongyang Liu", "Xiangyang Zhu", "Manyuan Zhang", "Will Beddow", "Erwann Millon", "Victor Perez", "Wenhai Wang", "Conghui He", "Bo Zhang", "Xiaohong Liu", "Hongsheng Li", "Yu Qiao", "Chang Xu", "Peng Gao" ], "github": "https://github.com/Alpha-VLLM/Lumina-Image-2.0", "project_page": "", "abstract": "We introduce Lumina-Image 2.0, an advanced text-to-image generation framework that achieves significant progress compared to previous work, Lumina-Next. Lumina-Image 2.0 is built upon two key principles: (1) Unification - it adopts a unified architecture (Unified Next-DiT) that treats text and image tokens as a joint sequence, enabling natural cross-modal interactions and allowing seamless task expansion. Besides, since high-quality captioners can provide semantically well-aligned text-image training pairs, we introduce a unified captioning system, Unified Captioner (UniCap), specifically designed for T2I generation tasks. UniCap excels at generating comprehensive and accurate captions, accelerating convergence and enhancing prompt adherence. (2) Efficiency - to improve the efficiency of our proposed model, we develop multi-stage progressive training strategies and introduce inference acceleration techniques without compromising image quality. Extensive evaluations on academic benchmarks and public text-to-image arenas show that Lumina-Image 2.0 delivers strong performances even with only 2.6B parameters, highlighting its scalability and design efficiency. We have released our training details, code, and models at https://github.com/Alpha-VLLM/Lumina-Image-2.0.", "github_manual": "", "github_regex": "https://github.com/Alpha-VLLM/Lumina-Image-2.0", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21460", "title": "Large Language Model Agent: A Survey on Methodology, Applications and Challenges", "authors": [ "Junyu Luo", "Weizhi Zhang", "Ye Yuan", "Yusheng Zhao", "Junwei Yang", "Yiyang Gu", "Bohan Wu", "Binqi Chen", "Ziyue Qiao", "Qingqing Long", "Rongcheng Tu", "Xiao Luo", "Wei Ju", "Zhiping Xiao", "Yifan Wang", "Meng Xiao", "Chenwu Liu", "Jingyang Yuan", "Shichang Zhang", "Yiqiao Jin", "Fan Zhang", "Xian Wu", "Hanqing Zhao", "Dacheng Tao", "Philip S. Yu", "Ming Zhang" ], "github": "https://github.com/luo-junyu/Awesome-Agent-Papers", "project_page": "", "abstract": "The era of intelligent agents is upon us, driven by revolutionary advancements in large language models. Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence. This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy, linking architectural foundations, collaboration mechanisms, and evolutionary pathways. We unify fragmented research threads by revealing fundamental connections between agent design principles and their emergent behaviors in complex environments. Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time, while also addressing evaluation methodologies, tool applications, practical challenges, and diverse application domains. By surveying the latest developments in this rapidly evolving field, we offer researchers a structured taxonomy for understanding LLM agents and identify promising directions for future research. The collection is available at https://github.com/luo-junyu/Awesome-Agent-Papers.", "github_manual": "", "github_regex": "https://github.com/luo-junyu/Awesome-Agent-Papers", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21088", "title": "ZJUKLAB at SemEval-2025 Task 4: Unlearning via Model Merging", "authors": [ "Haoming Xu", "Shuxun Wang", "Yanqiu Zhao", "Yi Zhong", "Ziyan Jiang", "Ningyuan Zhao", "Shumin Deng", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/unlearn/tree/main/semeval25", "project_page": "", "abstract": "This paper presents the ZJUKLAB team's submission for SemEval-2025 Task 4: Unlearning Sensitive Content from Large Language Models. This task aims to selectively erase sensitive knowledge from large language models, avoiding both over-forgetting and under-forgetting issues. We propose an unlearning system that leverages Model Merging (specifically TIES-Merging), combining two specialized models into a more balanced unlearned model. Our system achieves competitive results, ranking second among 26 teams, with an online score of 0.944 for Task Aggregate and 0.487 for overall Aggregate. In this paper, we also conduct local experiments and perform a comprehensive analysis of the unlearning process, examining performance trajectories, loss dynamics, and weight perspectives, along with several supplementary experiments, to understand the effectiveness of our method. Furthermore, we analyze the shortcomings of our method and evaluation metrics, emphasizing that MIA scores and ROUGE-based metrics alone are insufficient to fully evaluate successful unlearning. Finally, we emphasize the need for more comprehensive evaluation methodologies and rethinking of unlearning objectives in future research. Code is available at https://github.com/zjunlp/unlearn/tree/main/semeval25.", "github_manual": "", "github_regex": "https://github.com/zjunlp/unlearn/tree/main/semeval25", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21774", "title": "Optimal Stepsize for Diffusion Sampling", "authors": [ "Jianning Pei", "Han Hu", "Shuyang Gu" ], "github": "https://github.com/bebebe666/OptimalSteps", "project_page": "", "abstract": "Diffusion models achieve remarkable generation quality but suffer from computational intensive sampling due to suboptimal step discretization. While existing works focus on optimizing denoising directions, we address the principled design of stepsize schedules. This paper proposes Optimal Stepsize Distillation, a dynamic programming framework that extracts theoretically optimal schedules by distilling knowledge from reference trajectories. By reformulating stepsize optimization as recursive error minimization, our method guarantees global discretization bounds through optimal substructure exploitation. Crucially, the distilled schedules demonstrate strong robustness across architectures, ODE solvers, and noise schedules. Experiments show 10x accelerated text-to-image generation while preserving 99.4% performance on GenEval. Our code is available at https://github.com/bebebe666/OptimalSteps.", "github_manual": "", "github_regex": "https://github.com/bebebe666/OptimalSteps", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21765", "title": "Exploring the Evolution of Physics Cognition in Video Generation: A Survey", "authors": [ "Minghui Lin", "Xiang Wang", "Yishan Wang", "Shu Wang", "Fengqi Dai", "Pengxiang Ding", "Cunxiang Wang", "Zhengrong Zuo", "Nong Sang", "Siteng Huang", "Donglin Wang" ], "github": "https://github.com/minnie-lin/Awesome-Physics-Cognition-based-Video-Generation", "project_page": "", "abstract": "Recent advancements in video generation have witnessed significant progress, especially with the rapid advancement of diffusion models. Despite this, their deficiencies in physical cognition have gradually received widespread attention - generated content often violates the fundamental laws of physics, falling into the dilemma of ''visual realism but physical absurdity\". Researchers began to increasingly recognize the importance of physical fidelity in video generation and attempted to integrate heuristic physical cognition such as motion representations and physical knowledge into generative systems to simulate real-world dynamic scenarios. Considering the lack of a systematic overview in this field, this survey aims to provide a comprehensive summary of architecture designs and their applications to fill this gap. Specifically, we discuss and organize the evolutionary process of physical cognition in video generation from a cognitive science perspective, while proposing a three-tier taxonomy: 1) basic schema perception for generation, 2) passive cognition of physical knowledge for generation, and 3) active cognition for world simulation, encompassing state-of-the-art methods, classical paradigms, and benchmarks. Subsequently, we emphasize the inherent key challenges in this domain and delineate potential pathways for future research, contributing to advancing the frontiers of discussion in both academia and industry. Through structured review and interdisciplinary analysis, this survey aims to provide directional guidance for developing interpretable, controllable, and physically consistent video generation paradigms, thereby propelling generative models from the stage of ''visual mimicry'' towards a new phase of ''human-like physical comprehension''.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/minnie-lin/Awesome-Physics-Cognition-based-Video-Generation", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21380", "title": "Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models", "authors": [ "Haoxiang Sun", "Yingqian Min", "Zhipeng Chen", "Wayne Xin Zhao", "Zheng Liu", "Zhongyuan Wang", "Lei Fang", "Ji-Rong Wen" ], "github": "https://github.com/RUCAIBox/OlymMATH", "project_page": "", "abstract": "In recent years, the rapid development of large reasoning models has resulted in the saturation of existing benchmarks for evaluating mathematical reasoning, highlighting the urgent need for more challenging and rigorous evaluation frameworks. To address this gap, we introduce OlymMATH, a novel Olympiad-level mathematical benchmark, designed to rigorously test the complex reasoning capabilities of LLMs. OlymMATH features 200 meticulously curated problems, each manually verified and available in parallel English and Chinese versions. The problems are systematically organized into two distinct difficulty tiers: (1) AIME-level problems (easy) that establish a baseline for mathematical reasoning assessment, and (2) significantly more challenging problems (hard) designed to push the boundaries of current state-of-the-art models. In our benchmark, these problems span four core mathematical fields, each including a verifiable numerical solution to enable objective, rule-based evaluation. Empirical results underscore the significant challenge presented by OlymMATH, with state-of-the-art models including DeepSeek-R1 and OpenAI's o3-mini demonstrating notably limited accuracy on the hard subset. Furthermore, the benchmark facilitates comprehensive bilingual assessment of mathematical reasoning abilities-a critical dimension that remains largely unaddressed in mainstream mathematical reasoning benchmarks. We release the OlymMATH benchmark at the STILL project: https://github.com/RUCAIBox/Slow_Thinking_with_LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RUCAIBox/OlymMATH", "github_api_added_by": "user" }, { "date": "2025-03-28", "arxiv_id": "2503.20776", "title": "Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields", "authors": [ "Shijie Zhou", "Hui Ren", "Yijia Weng", "Shuwang Zhang", "Zhen Wang", "Dejia Xu", "Zhiwen Fan", "Suya You", "Zhangyang Wang", "Leonidas Guibas", "Achuta Kadambi" ], "github": "", "project_page": "", "abstract": "Recent advancements in 2D and multimodal models have achieved remarkable success by leveraging large-scale training on extensive datasets. However, extending these achievements to enable free-form interactions and high-level semantic operations with complex 3D/4D scenes remains challenging. This difficulty stems from the limited availability of large-scale, annotated 3D/4D or multi-view datasets, which are crucial for generalizable vision and language tasks such as open-vocabulary and prompt-based segmentation, language-guided editing, and visual question answering (VQA). In this paper, we introduce Feature4X, a universal framework designed to extend any functionality from 2D vision foundation model into the 4D realm, using only monocular video input, which is widely available from user-generated content. The \"X\" in Feature4X represents its versatility, enabling any task through adaptable, model-conditioned 4D feature field distillation. At the core of our framework is a dynamic optimization strategy that unifies multiple model capabilities into a single representation. Additionally, to the best of our knowledge, Feature4X is the first method to distill and lift the features of video foundation models (e.g. SAM2, InternVideo2) into an explicit 4D feature field using Gaussian Splatting. Our experiments showcase novel view segment anything, geometric and appearance scene editing, and free-form VQA across all time steps, empowered by LLMs in feedback loops. These advancements broaden the scope of agentic AI applications by providing a foundation for scalable, contextually and spatiotemporally aware systems capable of immersive dynamic 4D scene interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21776", "title": "Video-R1: Reinforcing Video Reasoning in MLLMs", "authors": [ "Kaituo Feng", "Kaixiong Gong", "Bohao Li", "Zonghao Guo", "Yibing Wang", "Tianshuo Peng", "Benyou Wang", "Xiangyu Yue" ], "github": "", "project_page": "", "abstract": "Inspired by DeepSeek-R1's success in eliciting reasoning abilities through rule-based reinforcement learning (RL), we introduce Video-R1 as the first attempt to systematically explore the R1 paradigm for eliciting video reasoning within multimodal large language models (MLLMs). However, directly applying RL training with the GRPO algorithm to video reasoning presents two primary challenges: (i) a lack of temporal modeling for video reasoning, and (ii) the scarcity of high-quality video-reasoning data. To address these issues, we first propose the T-GRPO algorithm, which encourages models to utilize temporal information in videos for reasoning. Additionally, instead of relying solely on video data, we incorporate high-quality image-reasoning data into the training process. We have constructed two datasets: Video-R1-COT-165k for SFT cold start and Video-R1-260k for RL training, both comprising image and video data. Experimental results demonstrate that Video-R1 achieves significant improvements on video reasoning benchmarks such as VideoMMMU and VSI-Bench, as well as on general video benchmarks including MVBench and TempCompass, etc. Notably, Video-R1-7B attains a 35.8% accuracy on video spatial reasoning benchmark VSI-bench, surpassing the commercial proprietary model GPT-4o. All codes, models, data are released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21620", "title": "UI-R1: Enhancing Action Prediction of GUI Agents by Reinforcement Learning", "authors": [ "Zhengxi Lu", "Yuxiang Chai", "Yaxuan Guo", "Xi Yin", "Liang Liu", "Hao Wang", "Guanjing Xiong", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "The recent DeepSeek-R1 has showcased the emergence of reasoning capabilities in LLMs through reinforcement learning (RL) with rule-based rewards. Building on this idea, we are the first to explore how rule-based RL can enhance the reasoning capabilities of multimodal large language models (MLLMs) for graphic user interface (GUI) action prediction tasks. To this end, we curate a small yet high-quality dataset of 136 challenging tasks, encompassing five common action types on mobile devices. We also introduce a unified rule-based action reward, enabling model optimization via policy-based algorithms such as Group Relative Policy Optimization (GRPO). Experimental results demonstrate that our proposed data-efficient model, UI-R1-3B, achieves substantial improvements on both in-domain (ID) and out-of-domain (OOD) tasks. Specifically, on the ID benchmark AndroidControl, the action type accuracy improves by 15%, while grounding accuracy increases by 10.3%, compared with the base model (i.e. Qwen2.5-VL-3B). On the OOD GUI grounding benchmark ScreenSpot-Pro, our model surpasses the base model by 6.0% and achieves competitive performance with larger models (e.g., OS-Atlas-7B), which are trained via supervised fine-tuning (SFT) on 76K data. These results underscore the potential of rule-based reinforcement learning to advance GUI understanding and control, paving the way for future research in this domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21729", "title": "ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation", "authors": [ "Zhicheng Lee", "Shulin Cao", "Jinxin Liu", "Jiajie Zhang", "Weichuan Liu", "Xiaoyin Che", "Lei Hou", "Juanzi Li" ], "github": "", "project_page": "", "abstract": "Large Reasoning Models (LRMs) exhibit remarkable reasoning abilities but rely primarily on parametric knowledge, limiting factual accuracy. While recent works equip reinforcement learning (RL)-based LRMs with retrieval capabilities, they suffer from overthinking and lack robustness in reasoning, reducing their effectiveness in question answering (QA) tasks. To address this, we propose ReaRAG, a factuality-enhanced reasoning model that explores diverse queries without excessive iterations. Our solution includes a novel data construction framework with an upper bound on the reasoning chain length. Specifically, we first leverage an LRM to generate deliberate thinking, then select an action from a predefined action space (Search and Finish). For Search action, a query is executed against the RAG engine, where the result is returned as observation to guide reasoning steps later. This process iterates until a Finish action is chosen. Benefiting from ReaRAG's strong reasoning capabilities, our approach outperforms existing baselines on multi-hop QA. Further analysis highlights its strong reflective ability to recognize errors and refine its reasoning trajectory. Our study enhances LRMs' factuality while effectively integrating robust reasoning for Retrieval-Augmented Generation (RAG).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21144", "title": "ChatAnyone: Stylized Real-time Portrait Video Generation with Hierarchical Motion Diffusion Model", "authors": [ "Jinwei Qi", "Chaonan Ji", "Sheng Xu", "Peng Zhang", "Bang Zhang", "Liefeng Bo" ], "github": "", "project_page": "", "abstract": "Real-time interactive video-chat portraits have been increasingly recognized as the future trend, particularly due to the remarkable progress made in text and voice chat technologies. However, existing methods primarily focus on real-time generation of head movements, but struggle to produce synchronized body motions that match these head actions. Additionally, achieving fine-grained control over the speaking style and nuances of facial expressions remains a challenge. To address these limitations, we introduce a novel framework for stylized real-time portrait video generation, enabling expressive and flexible video chat that extends from talking head to upper-body interaction. Our approach consists of the following two stages. The first stage involves efficient hierarchical motion diffusion models, that take both explicit and implicit motion representations into account based on audio inputs, which can generate a diverse range of facial expressions with stylistic control and synchronization between head and body movements. The second stage aims to generate portrait video featuring upper-body movements, including hand gestures. We inject explicit hand control signals into the generator to produce more detailed hand movements, and further perform face refinement to enhance the overall realism and expressiveness of the portrait video. Additionally, our approach supports efficient and continuous generation of upper-body portrait video in maximum 512 * 768 resolution at up to 30fps on 4090 GPU, supporting interactive video-chat in real-time. Experimental results demonstrate the capability of our approach to produce portrait videos with rich expressiveness and natural upper-body movements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.20822", "title": "Synthetic Video Enhances Physical Fidelity in Video Synthesis", "authors": [ "Qi Zhao", "Xingyu Ni", "Ziyu Wang", "Feng Cheng", "Ziyan Yang", "Lu Jiang", "Bohan Wang" ], "github": "", "project_page": "", "abstract": "We investigate how to enhance the physical fidelity of video generation models by leveraging synthetic videos derived from computer graphics pipelines. These rendered videos respect real-world physics, such as maintaining 3D consistency, and serve as a valuable resource that can potentially improve video generation models. To harness this potential, we propose a solution that curates and integrates synthetic data while introducing a method to transfer its physical realism to the model, significantly reducing unwanted artifacts. Through experiments on three representative tasks emphasizing physical consistency, we demonstrate its efficacy in enhancing physical fidelity. While our model still lacks a deep understanding of physics, our work offers one of the first empirical demonstrations that synthetic video enhances physical fidelity in video synthesis. Website: https://kevinz8866.github.io/simulation/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.20990", "title": "FinAudio: A Benchmark for Audio Large Language Models in Financial Applications", "authors": [ "Yupeng Cao", "Haohang Li", "Yangyang Yu", "Shashidhar Reddy Javaji", "Yueru He", "Jimin Huang", "Zining Zhu", "Qianqian Xie", "Xiao-yang Liu", "Koduvayur Subbalakshmi", "Meikang Qiu", "Sophia Ananiadou", "Jian-Yun Nie" ], "github": "", "project_page": "", "abstract": "Audio Large Language Models (AudioLLMs) have received widespread attention and have significantly improved performance on audio tasks such as conversation, audio understanding, and automatic speech recognition (ASR). Despite these advancements, there is an absence of a benchmark for assessing AudioLLMs in financial scenarios, where audio data, such as earnings conference calls and CEO speeches, are crucial resources for financial analysis and investment decisions. In this paper, we introduce FinAudio, the first benchmark designed to evaluate the capacity of AudioLLMs in the financial domain. We first define three tasks based on the unique characteristics of the financial domain: 1) ASR for short financial audio, 2) ASR for long financial audio, and 3) summarization of long financial audio. Then, we curate two short and two long audio datasets, respectively, and develop a novel dataset for financial audio summarization, comprising the FinAudio benchmark. Then, we evaluate seven prevalent AudioLLMs on FinAudio. Our evaluation reveals the limitations of existing AudioLLMs in the financial domain and offers insights for improving AudioLLMs. All datasets and codes will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21248", "title": "ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition", "authors": [ "Yujie Liu", "Zonglin Yang", "Tong Xie", "Jinjie Ni", "Ben Gao", "Yuqiang Li", "Shixiang Tang", "Wanli Ouyang", "Erik Cambria", "Dongzhan Zhou" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark. To address this gap, we introduce the first large-scale benchmark for evaluating LLMs with a near-sufficient set of sub-tasks of scientific discovery: inspiration retrieval, hypothesis composition, and hypothesis ranking. We develop an automated framework that extracts critical components - research questions, background surveys, inspirations, and hypotheses - from scientific papers across 12 disciplines, with expert validation confirming its accuracy. To prevent data contamination, we focus exclusively on papers published in 2024, ensuring minimal overlap with LLM pretraining data. Our evaluation reveals that LLMs perform well in retrieving inspirations, an out-of-distribution task, suggesting their ability to surface novel knowledge associations. This positions LLMs as \"research hypothesis mines\", capable of facilitating automated scientific discovery by generating innovative hypotheses at scale with minimal human intervention.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21696", "title": "Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks", "authors": [ "Wenqi Zhang", "Mengna Wang", "Gangao Liu", "Xu Huixin", "Yiwei Jiang", "Yongliang Shen", "Guiyang Hou", "Zhe Zheng", "Hang Zhang", "Xin Li", "Weiming Lu", "Peng Li", "Yueting Zhuang" ], "github": "", "project_page": "", "abstract": "Recent advances in deep thinking models have demonstrated remarkable reasoning capabilities on mathematical and coding tasks. However, their effectiveness in embodied domains which require continuous interaction with environments through image action interleaved trajectories remains largely -unexplored. We present Embodied Reasoner, a model that extends o1 style reasoning to interactive embodied search tasks. Unlike mathematical reasoning that relies primarily on logical deduction, embodied scenarios demand spatial understanding, temporal reasoning, and ongoing self-reflection based on interaction history. To address these challenges, we synthesize 9.3k coherent Observation-Thought-Action trajectories containing 64k interactive images and 90k diverse thinking processes (analysis, spatial reasoning, reflection, planning, and verification). We develop a three-stage training pipeline that progressively enhances the model's capabilities through imitation learning, self-exploration via rejection sampling, and self-correction through reflection tuning. The evaluation shows that our model significantly outperforms those advanced visual reasoning models, e.g., it exceeds OpenAI o1, o3-mini, and Claude-3.7 by +9\\%, 24\\%, and +13\\%. Analysis reveals our model exhibits fewer repeated searches and logical inconsistencies, with particular advantages in complex long-horizon tasks. Real-world environments also show our superiority while exhibiting fewer repeated searches and logical inconsistency cases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21541", "title": "LOCATEdit: Graph Laplacian Optimized Cross Attention for Localized Text-Guided Image Editing", "authors": [ "Achint Soni", "Meet Soni", "Sirisha Rambhatla" ], "github": "https://github.com/LOCATEdit/LOCATEdit", "project_page": "", "abstract": "Text-guided image editing aims to modify specific regions of an image according to natural language instructions while maintaining the general structure and the background fidelity. Existing methods utilize masks derived from cross-attention maps generated from diffusion models to identify the target regions for modification. However, since cross-attention mechanisms focus on semantic relevance, they struggle to maintain the image integrity. As a result, these methods often lack spatial consistency, leading to editing artifacts and distortions. In this work, we address these limitations and introduce LOCATEdit, which enhances cross-attention maps through a graph-based approach utilizing self-attention-derived patch relationships to maintain smooth, coherent attention across image regions, ensuring that alterations are limited to the designated items while retaining the surrounding structure. \\method consistently and substantially outperforms existing baselines on PIE-Bench, demonstrating its state-of-the-art performance and effectiveness on various editing tasks. Code can be found on https://github.com/LOCATEdit/LOCATEdit/", "github_manual": "", "github_regex": "https://github.com/LOCATEdit/LOCATEdit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.21780", "title": "Semantic Library Adaptation: LoRA Retrieval and Fusion for Open-Vocabulary Semantic Segmentation", "authors": [ "Reza Qorbani", "Gianluca Villani", "Theodoros Panagiotakopoulos", "Marc Botet Colomer", "Linus H\u00e4renstam-Nielsen", "Mattia Segu", "Pier Luigi Dovesi", "Jussi Karlgren", "Daniel Cremers", "Federico Tombari", "Matteo Poggi" ], "github": "", "project_page": "", "abstract": "Open-vocabulary semantic segmentation models associate vision and text to label pixels from an undefined set of classes using textual queries, providing versatile performance on novel datasets. However, large shifts between training and test domains degrade their performance, requiring fine-tuning for effective real-world applications. We introduce Semantic Library Adaptation (SemLA), a novel framework for training-free, test-time domain adaptation. SemLA leverages a library of LoRA-based adapters indexed with CLIP embeddings, dynamically merging the most relevant adapters based on proximity to the target domain in the embedding space. This approach constructs an ad-hoc model tailored to each specific input without additional training. Our method scales efficiently, enhances explainability by tracking adapter contributions, and inherently protects data privacy, making it ideal for sensitive applications. Comprehensive experiments on a 20-domain benchmark built over 10 standard datasets demonstrate SemLA's superior adaptability and performance across diverse settings, establishing a new standard in domain adaptation for open-vocabulary semantic segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-28", "arxiv_id": "2503.19904", "title": "Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better", "authors": [ "Zihang Lai", "Andrea Vedaldi" ], "github": "https://github.com/zlai0/TrackTention", "project_page": "", "abstract": "Temporal consistency is critical in video prediction to ensure that outputs are coherent and free of artifacts. Traditional methods, such as temporal attention and 3D convolution, may struggle with significant object motion and may not capture long-range temporal dependencies in dynamic scenes. To address this gap, we propose the Tracktention Layer, a novel architectural component that explicitly integrates motion information using point tracks, i.e., sequences of corresponding points across frames. By incorporating these motion cues, the Tracktention Layer enhances temporal alignment and effectively handles complex object motions, maintaining consistent feature representations over time. Our approach is computationally efficient and can be seamlessly integrated into existing models, such as Vision Transformers, with minimal modification. It can be used to upgrade image-only models to state-of-the-art video ones, sometimes outperforming models natively designed for video prediction. We demonstrate this on video depth prediction and video colorization, where models augmented with the Tracktention Layer exhibit significantly improved temporal consistency compared to baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zlai0/TrackTention", "github_api_added_by": "auto" }, { "date": "2025-03-31", "arxiv_id": "2503.22675", "title": "Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation", "authors": [ "Jiakai Tang", "Sunhao Dai", "Teng Shi", "Jun Xu", "Xu Chen", "Wen Chen", "Wu Jian", "Yuning Jiang" ], "github": "https://github.com/TangJiakai/ReaRec", "project_page": "", "abstract": "Sequential Recommendation (SeqRec) aims to predict the next item by capturing sequential patterns from users' historical interactions, playing a crucial role in many real-world recommender systems. However, existing approaches predominantly adopt a direct forward computation paradigm, where the final hidden state of the sequence encoder serves as the user representation. We argue that this inference paradigm, due to its limited computational depth, struggles to model the complex evolving nature of user preferences and lacks a nuanced understanding of long-tail items, leading to suboptimal performance. To address this issue, we propose ReaRec, the first inference-time computing framework for recommender systems, which enhances user representations through implicit multi-step reasoning. Specifically, ReaRec autoregressively feeds the sequence's last hidden state into the sequential recommender while incorporating special reasoning position embeddings to decouple the original item encoding space from the multi-step reasoning space. Moreover, we introduce two lightweight reasoning-based learning methods, Ensemble Reasoning Learning (ERL) and Progressive Reasoning Learning (PRL), to further effectively exploit ReaRec's reasoning potential. Extensive experiments on five public real-world datasets and different SeqRec architectures demonstrate the generality and effectiveness of our proposed ReaRec. Remarkably, post-hoc analyses reveal that ReaRec significantly elevates the performance ceiling of multiple sequential recommendation backbones by approximately 30\\%-50\\%. Thus, we believe this work can open a new and promising avenue for future research in inference-time computing for sequential recommendation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/TangJiakai/ReaRec", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.21821", "title": "PHYSICS: Benchmarking Foundation Models on University-Level Physics Problem Solving", "authors": [ "Kaiyue Feng", "Yilun Zhao", "Yixin Liu", "Tianyu Yang", "Chen Zhao", "John Sous", "Arman Cohan" ], "github": "", "project_page": "", "abstract": "We introduce PHYSICS, a comprehensive benchmark for university-level physics problem solving. It contains 1297 expert-annotated problems covering six core areas: classical mechanics, quantum mechanics, thermodynamics and statistical mechanics, electromagnetism, atomic physics, and optics. Each problem requires advanced physics knowledge and mathematical reasoning. We develop a robust automated evaluation system for precise and reliable validation. Our evaluation of leading foundation models reveals substantial limitations. Even the most advanced model, o3-mini, achieves only 59.9% accuracy, highlighting significant challenges in solving high-level scientific problems. Through comprehensive error analysis, exploration of diverse prompting strategies, and Retrieval-Augmented Generation (RAG)-based knowledge augmentation, we identify key areas for improvement, laying the foundation for future advancements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.22268", "title": "Segment Any Motion in Videos", "authors": [ "Nan Huang", "Wenzhao Zheng", "Chenfeng Xu", "Kurt Keutzer", "Shanghang Zhang", "Angjoo Kanazawa", "Qianqian Wang" ], "github": "https://github.com/nnanhuang/SegAnyMo", "project_page": "", "abstract": "Moving object segmentation is a crucial task for achieving a high-level understanding of visual scenes and has numerous downstream applications. Humans can effortlessly segment moving objects in videos. Previous work has largely relied on optical flow to provide motion cues; however, this approach often results in imperfect predictions due to challenges such as partial motion, complex deformations, motion blur and background distractions. We propose a novel approach for moving object segmentation that combines long-range trajectory motion cues with DINO-based semantic features and leverages SAM2 for pixel-level mask densification through an iterative prompting strategy. Our model employs Spatio-Temporal Trajectory Attention and Motion-Semantic Decoupled Embedding to prioritize motion while integrating semantic support. Extensive testing on diverse datasets demonstrates state-of-the-art performance, excelling in challenging scenarios and fine-grained segmentation of multiple objects. Our code is available at https://motion-seg.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nnanhuang/SegAnyMo", "github_api_added_by": "auto" }, { "date": "2025-03-31", "arxiv_id": "2503.22236", "title": "Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging", "authors": [ "Chongjie Ye", "Yushuang Wu", "Ziteng Lu", "Jiahao Chang", "Xiaoyang Guo", "Jiaqing Zhou", "Hao Zhao", "Xiaoguang Han" ], "github": "", "project_page": "", "abstract": "With the growing demand for high-fidelity 3D models from 2D images, existing methods still face significant challenges in accurately reproducing fine-grained geometric details due to limitations in domain gaps and inherent ambiguities in RGB images. To address these issues, we propose Hi3DGen, a novel framework for generating high-fidelity 3D geometry from images via normal bridging. Hi3DGen consists of three key components: (1) an image-to-normal estimator that decouples the low-high frequency image pattern with noise injection and dual-stream training to achieve generalizable, stable, and sharp estimation; (2) a normal-to-geometry learning approach that uses normal-regularized latent diffusion learning to enhance 3D geometry generation fidelity; and (3) a 3D data synthesis pipeline that constructs a high-quality dataset to support training. Extensive experiments demonstrate the effectiveness and superiority of our framework in generating rich geometric details, outperforming state-of-the-art methods in terms of fidelity. Our work provides a new direction for high-fidelity 3D geometry generation from images by leveraging normal maps as an intermediate representation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.22230", "title": "Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback", "authors": [ "Wei Shen", "Guanlin Liu", "Zheng Wu", "Ruofei Zhu", "Qingping Yang", "Chao Xin", "Yu Yue", "Lin Yan" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning large language models with human preferences. While recent research has focused on algorithmic improvements, the importance of prompt-data construction has been overlooked. This paper addresses this gap by exploring data-driven bottlenecks in RLHF performance scaling, particularly reward hacking and decreasing response diversity. We introduce a hybrid reward system combining reasoning task verifiers (RTV) and a generative reward model (GenRM) to mitigate reward hacking. We also propose a novel prompt-selection method, Pre-PPO, to maintain response diversity and enhance learning effectiveness. Additionally, we find that prioritizing mathematical and coding tasks early in RLHF training significantly improves performance. Experiments across two model sizes validate our methods' effectiveness and scalability. Results show that RTV is most resistant to reward hacking, followed by GenRM with ground truth, and then GenRM with SFT Best-of-N responses. Our strategies enable rapid capture of subtle task-specific distinctions, leading to substantial improvements in overall RLHF performance. This work highlights the importance of careful data construction and provides practical methods to overcome performance barriers in RLHF.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.22194", "title": "ORIGEN: Zero-Shot 3D Orientation Grounding in Text-to-Image Generation", "authors": [ "Yunhong Min", "Daehyeon Choi", "Kyeongmin Yeo", "Jihyun Lee", "Minhyuk Sung" ], "github": "", "project_page": "", "abstract": "We introduce ORIGEN, the first zero-shot method for 3D orientation grounding in text-to-image generation across multiple objects and diverse categories. While previous work on spatial grounding in image generation has mainly focused on 2D positioning, it lacks control over 3D orientation. To address this, we propose a reward-guided sampling approach using a pretrained discriminative model for 3D orientation estimation and a one-step text-to-image generative flow model. While gradient-ascent-based optimization is a natural choice for reward-based guidance, it struggles to maintain image realism. Instead, we adopt a sampling-based approach using Langevin dynamics, which extends gradient ascent by simply injecting random noise--requiring just a single additional line of code. Additionally, we introduce adaptive time rescaling based on the reward function to accelerate convergence. Our experiments show that ORIGEN outperforms both training-based and test-time guidance methods across quantitative metrics and user studies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.22329", "title": "A Refined Analysis of Massive Activations in LLMs", "authors": [ "Louis Owen", "Nilabhra Roy Chowdhury", "Abhay Kumar", "Fabian G\u00fcra" ], "github": "https://github.com/bluorion-com/refine_massive_activations", "project_page": "", "abstract": "Motivated in part by their relevance for low-precision training and quantization, massive activations in large language models (LLMs) have recently emerged as a topic of interest. However, existing analyses are limited in scope, and generalizability across architectures is unclear. This paper helps address some of these gaps by conducting an analysis of massive activations across a broad range of LLMs, including both GLU-based and non-GLU-based architectures. Our findings challenge several prior assumptions, most importantly: (1) not all massive activations are detrimental, i.e. suppressing them does not lead to an explosion of perplexity or a collapse in downstream task performance; (2) proposed mitigation strategies such as Attention KV bias are model-specific and ineffective in certain cases. We consequently investigate novel hybrid mitigation strategies; in particular pairing Target Variance Rescaling (TVR) with Attention KV bias or Dynamic Tanh (DyT) successfully balances the mitigation of massive activations with preserved downstream model performance in the scenarios we investigated. Our code is available at: https://github.com/bluorion-com/refine_massive_activations.", "github_manual": "", "github_regex": "https://github.com/bluorion-com/refine_massive_activations", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.21732", "title": "SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling", "authors": [ "Xianglong He", "Zi-Xin Zou", "Chia-Hao Chen", "Yuan-Chen Guo", "Ding Liang", "Chun Yuan", "Wanli Ouyang", "Yan-Pei Cao", "Yangguang Li" ], "github": "", "project_page": "", "abstract": "Creating high-fidelity 3D meshes with arbitrary topology, including open surfaces and complex interiors, remains a significant challenge. Existing implicit field methods often require costly and detail-degrading watertight conversion, while other approaches struggle with high resolutions. This paper introduces SparseFlex, a novel sparse-structured isosurface representation that enables differentiable mesh reconstruction at resolutions up to 1024^3 directly from rendering losses. SparseFlex combines the accuracy of Flexicubes with a sparse voxel structure, focusing computation on surface-adjacent regions and efficiently handling open surfaces. Crucially, we introduce a frustum-aware sectional voxel training strategy that activates only relevant voxels during rendering, dramatically reducing memory consumption and enabling high-resolution training. This also allows, for the first time, the reconstruction of mesh interiors using only rendering supervision. Building upon this, we demonstrate a complete shape modeling pipeline by training a variational autoencoder (VAE) and a rectified flow transformer for high-quality 3D shape generation. Our experiments show state-of-the-art reconstruction accuracy, with a ~82% reduction in Chamfer Distance and a ~88% increase in F-score compared to previous methods, and demonstrate the generation of high-resolution, detailed 3D shapes with arbitrary topology. By enabling high-resolution, differentiable mesh reconstruction and generation with rendering losses, SparseFlex significantly advances the state-of-the-art in 3D shape representation and modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.18968", "title": "MedAgent-Pro: Towards Multi-modal Evidence-based Medical Diagnosis via Reasoning Agentic Workflow", "authors": [ "Ziyue Wang", "Junde Wu", "Chang Han Low", "Yueming Jin" ], "github": "https://github.com/jinlab-imvr/MedAgent-Pro", "project_page": "", "abstract": "Developing reliable AI systems to assist human clinicians in multi-modal medical diagnosis has long been a key objective for researchers. Recently, Multi-modal Large Language Models (MLLMs) have gained significant attention and achieved success across various domains. With strong reasoning capabilities and the ability to perform diverse tasks based on user instructions, they hold great potential for enhancing medical diagnosis. However, directly applying MLLMs to the medical domain still presents challenges. They lack detailed perception of visual inputs, limiting their ability to perform quantitative image analysis, which is crucial for medical diagnostics. Additionally, MLLMs often exhibit hallucinations and inconsistencies in reasoning, whereas clinical diagnoses must adhere strictly to established criteria. To address these challenges, we propose MedAgent-Pro, an evidence-based reasoning agentic system designed to achieve reliable, explainable, and precise medical diagnoses. This is accomplished through a hierarchical workflow: at the task level, knowledge-based reasoning generate reliable diagnostic plans for specific diseases following retrieved clinical criteria. While at the case level, multiple tool agents process multi-modal inputs, analyze different indicators according to the plan, and provide a final diagnosis based on both quantitative and qualitative evidence. Comprehensive experiments on both 2D and 3D medical diagnosis tasks demonstrate the superiority and effectiveness of MedAgent-Pro, while case studies further highlight its reliability and interpretability. The code is available at https://github.com/jinlab-imvr/MedAgent-Pro.", "github_manual": "", "github_regex": "https://github.com/jinlab-imvr/MedAgent-Pro", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.20785", "title": "Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency", "authors": [ "Tianqi Liu", "Zihao Huang", "Zhaoxi Chen", "Guangcong Wang", "Shoukang Hu", "Liao Shen", "Huiqiang Sun", "Zhiguo Cao", "Wei Li", "Ziwei Liu" ], "github": "https://github.com/tqtqliu/free4d", "project_page": "", "abstract": "We present Free4D, a novel tuning-free framework for 4D scene generation from a single image. Existing methods either focus on object-level generation, making scene-level generation infeasible, or rely on large-scale multi-view video datasets for expensive training, with limited generalization ability due to the scarcity of 4D scene data. In contrast, our key insight is to distill pre-trained foundation models for consistent 4D scene representation, which offers promising advantages such as efficiency and generalizability. 1) To achieve this, we first animate the input image using image-to-video diffusion models followed by 4D geometric structure initialization. 2) To turn this coarse structure into spatial-temporal consistent multiview videos, we design an adaptive guidance mechanism with a point-guided denoising strategy for spatial consistency and a novel latent replacement strategy for temporal coherence. 3) To lift these generated observations into consistent 4D representation, we propose a modulation-based refinement to mitigate inconsistencies while fully leveraging the generated information. The resulting 4D representation enables real-time, controllable rendering, marking a significant advancement in single-image-based 4D scene generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tqtqliu/free4d", "github_api_added_by": "auto" }, { "date": "2025-03-31", "arxiv_id": "2503.21614", "title": "A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond", "authors": [ "Xiaoye Qu", "Yafu Li", "Zhaochen Su", "Weigao Sun", "Jianhao Yan", "Dongrui Liu", "Ganqu Cui", "Daizong Liu", "Shuxian Liang", "Junxian He", "Peng Li", "Wei Wei", "Jing Shao", "Chaochao Lu", "Yue Zhang", "Xian-Sheng Hua", "Bowen Zhou", "Yu Cheng" ], "github": "https://github.com/XiaoYee/Awesome_Efficient_LRM_Reasoning", "project_page": "", "abstract": "Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference. However, a growing concern lies in their tendency to produce excessively long reasoning traces, which are often filled with redundant content (e.g., repeated definitions), over-analysis of simple problems, and superficial exploration of multiple reasoning paths for harder tasks. This inefficiency introduces significant challenges for training, inference, and real-world deployment (e.g., in agent-based systems), where token economy is critical. In this survey, we provide a comprehensive overview of recent efforts aimed at improving reasoning efficiency in LRMs, with a particular focus on the unique challenges that arise in this new paradigm. We identify common patterns of inefficiency, examine methods proposed across the LRM lifecycle, i.e., from pretraining to inference, and discuss promising future directions for research. To support ongoing development, we also maintain a real-time GitHub repository tracking recent progress in the field. We hope this survey serves as a foundation for further exploration and inspires innovation in this rapidly evolving area.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/XiaoYee/Awesome_Efficient_LRM_Reasoning", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.19693", "title": "AdaptiVocab: Enhancing LLM Efficiency in Focused Domains through Lightweight Vocabulary Adaptation", "authors": [ "Itay Nakash", "Nitay Calderon", "Eyal Ben David", "Elad Hoffer", "Roi Reichart" ], "github": "https://github.com/itay-nakash/AdaptiVocab", "project_page": "", "abstract": "Large Language Models (LLMs) have shown impressive versatility as general purpose models. However, their broad applicability comes at a high-cost computational overhead, particularly in auto-regressive decoding where each step requires a forward pass. In domain-specific settings, general-purpose capabilities are unnecessary and can be exchanged for efficiency. In this work, we take a novel perspective on domain adaptation, reducing latency and computational costs by adapting the vocabulary to focused domains of interest. We introduce AdaptiVocab, an end-to-end approach for vocabulary adaptation, designed to enhance LLM efficiency in low-resource domains. AdaptiVocab can be applied to any tokenizer and architecture, modifying the vocabulary by replacing tokens with domain-specific n-gram-based tokens, thereby reducing the number of tokens required for both input processing and output generation. AdaptiVocab initializes new n-token embeddings using an exponentially weighted combination of existing embeddings and employs a lightweight fine-tuning phase that can be efficiently performed on a single GPU. We evaluate two 7B LLMs across three niche domains, assessing efficiency, generation quality, and end-task performance. Our results show that AdaptiVocab reduces token usage by over 25% without compromising performance", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/itay-nakash/AdaptiVocab", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.21779", "title": "X^{2}-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time Tomographic Reconstruction", "authors": [ "Weihao Yu", "Yuanhao Cai", "Ruyi Zha", "Zhiwen Fan", "Chenxin Li", "Yixuan Yuan" ], "github": "", "project_page": "", "abstract": "Four-dimensional computed tomography (4D CT) reconstruction is crucial for capturing dynamic anatomical changes but faces inherent limitations from conventional phase-binning workflows. Current methods discretize temporal resolution into fixed phases with respiratory gating devices, introducing motion misalignment and restricting clinical practicality. In this paper, We propose X^2-Gaussian, a novel framework that enables continuous-time 4D-CT reconstruction by integrating dynamic radiative Gaussian splatting with self-supervised respiratory motion learning. Our approach models anatomical dynamics through a spatiotemporal encoder-decoder architecture that predicts time-varying Gaussian deformations, eliminating phase discretization. To remove dependency on external gating devices, we introduce a physiology-driven periodic consistency loss that learns patient-specific breathing cycles directly from projections via differentiable optimization. Extensive experiments demonstrate state-of-the-art performance, achieving a 9.93 dB PSNR gain over traditional methods and 2.25 dB improvement against prior Gaussian splatting techniques. By unifying continuous motion modeling with hardware-free period learning, X^2-Gaussian advances high-fidelity 4D CT reconstruction for dynamic clinical imaging. Project website at: https://x2-gaussian.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.21332", "title": "ReFeed: Multi-dimensional Summarization Refinement with Reflective Reasoning on Feedback", "authors": [ "Taewon Yun", "Jihwan Oh", "Hyangsuk Min", "Yuho Lee", "Jihwan Bang", "Jason Cai", "Hwanjun Song" ], "github": "", "project_page": "", "abstract": "Summarization refinement faces challenges when extending to multi-dimension. In this paper, we introduce ReFeed, a powerful summarization refinement pipeline that enhances multiple dimensions through reflective reasoning on feedback. To achieve this, we release SumFeed-CoT, a large-scale Long-CoT-based dataset optimized for training a lightweight model with reflective reasoning. Our experiments reveal how the number of dimensions, feedback exposure, and reasoning policy influence refinement performance, highlighting reflective reasoning and simultaneously addressing multiple feedback is crucial to mitigate trade-off between dimensions. Furthermore, ReFeed is robust to noisy feedback and feedback order. Lastly, our finding emphasizes that creating data with a proper goal and guideline constitutes a fundamental pillar of effective reasoning. The dataset and model will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.21851", "title": "On Large Multimodal Models as Open-World Image Classifiers", "authors": [ "Alessandro Conti", "Massimiliano Mancini", "Enrico Fini", "Yiming Wang", "Paolo Rota", "Elisa Ricci" ], "github": "https://github.com/altndrr/lmms-owc", "project_page": "", "abstract": "Traditional image classification requires a predefined list of semantic categories. In contrast, Large Multimodal Models (LMMs) can sidestep this requirement by classifying images directly using natural language (e.g., answering the prompt \"What is the main object in the image?\"). Despite this remarkable capability, most existing studies on LMM classification performance are surprisingly limited in scope, often assuming a closed-world setting with a predefined set of categories. In this work, we address this gap by thoroughly evaluating LMM classification performance in a truly open-world setting. We first formalize the task and introduce an evaluation protocol, defining various metrics to assess the alignment between predicted and ground truth classes. We then evaluate 13 models across 10 benchmarks, encompassing prototypical, non-prototypical, fine-grained, and very fine-grained classes, demonstrating the challenges LMMs face in this task. Further analyses based on the proposed metrics reveal the types of errors LMMs make, highlighting challenges related to granularity and fine-grained capabilities, showing how tailored prompting and reasoning can alleviate them.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/altndrr/lmms-owc", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.17827", "title": "4D-Bench: Benchmarking Multi-modal Large Language Models for 4D Object Understanding", "authors": [ "Wenxuan Zhu", "Bing Li", "Cheng Zheng", "Jinjie Mai", "Jun Chen", "Letian Jiang", "Abdullah Hamdi", "Sara Rojas Martinez", "Chia-Wen Lin", "Mohamed Elhoseiny", "Bernard Ghanem" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have demonstrated impressive 2D image/video understanding capabilities. However, there are no publicly standardized benchmarks to assess the abilities of MLLMs in understanding the 4D objects (3D objects with temporal evolution over time). In this paper, we introduce 4D-Bench, the first benchmark to evaluate the capabilities of MLLMs in 4D object understanding, featuring tasks in 4D object Question Answering (4D object QA) and 4D object captioning. 4D-Bench provides 4D objects with diverse categories, high-quality annotations, and tasks necessitating multi-view spatial-temporal understanding, different from existing 2D image/video-based benchmarks. With 4D-Bench, we evaluate a wide range of open-source and closed-source MLLMs. The results from the 4D object captioning experiment indicate that MLLMs generally exhibit weaker temporal understanding compared to their appearance understanding, notably, while open-source models approach closed-source performance in appearance understanding, they show larger performance gaps in temporal understanding. 4D object QA yields surprising findings: even with simple single-object videos, MLLMs perform poorly, with state-of-the-art GPT-4o achieving only 63\\% accuracy compared to the human baseline of 91\\%. These findings highlight a substantial gap in 4D object understanding and the need for further advancements in MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.20308", "title": "Perceptually Accurate 3D Talking Head Generation: New Definitions, Speech-Mesh Representation, and Evaluation Metrics", "authors": [ "Lee Chae-Yeon", "Oh Hyun-Bin", "Han EunGi", "Kim Sung-Bin", "Suekyeong Nam", "Tae-Hyun Oh" ], "github": "https://github.com/kaist-ami/Perceptual-3D-Talking-Head", "project_page": "", "abstract": "Recent advancements in speech-driven 3D talking head generation have made significant progress in lip synchronization. However, existing models still struggle to capture the perceptual alignment between varying speech characteristics and corresponding lip movements. In this work, we claim that three criteria -- Temporal Synchronization, Lip Readability, and Expressiveness -- are crucial for achieving perceptually accurate lip movements. Motivated by our hypothesis that a desirable representation space exists to meet these three criteria, we introduce a speech-mesh synchronized representation that captures intricate correspondences between speech signals and 3D face meshes. We found that our learned representation exhibits desirable characteristics, and we plug it into existing models as a perceptual loss to better align lip movements to the given speech. In addition, we utilize this representation as a perceptual metric and introduce two other physically grounded lip synchronization metrics to assess how well the generated 3D talking heads align with these three criteria. Experiments show that training 3D talking head generation models with our perceptual loss significantly improve all three aspects of perceptually accurate lip synchronization. Codes and datasets are available at https://perceptual-3d-talking-head.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kaist-ami/Perceptual-3D-Talking-Head", "github_api_added_by": "auto" }, { "date": "2025-03-31", "arxiv_id": "2503.19108", "title": "Your ViT is Secretly an Image Segmentation Model", "authors": [ "Tommie Kerssies", "Niccol\u00f2 Cavagnero", "Alexander Hermans", "Narges Norouzi", "Giuseppe Averta", "Bastian Leibe", "Gijs Dubbelman", "Daan de Geus" ], "github": "", "project_page": "", "abstract": "Vision Transformers (ViTs) have shown remarkable performance and scalability across various computer vision tasks. To apply single-scale ViTs to image segmentation, existing methods adopt a convolutional adapter to generate multi-scale features, a pixel decoder to fuse these features, and a Transformer decoder that uses the fused features to make predictions. In this paper, we show that the inductive biases introduced by these task-specific components can instead be learned by the ViT itself, given sufficiently large models and extensive pre-training. Based on these findings, we introduce the Encoder-only Mask Transformer (EoMT), which repurposes the plain ViT architecture to conduct image segmentation. With large-scale models and pre-training, EoMT obtains a segmentation accuracy similar to state-of-the-art models that use task-specific components. At the same time, EoMT is significantly faster than these methods due to its architectural simplicity, e.g., up to 4x faster with ViT-L. Across a range of model sizes, EoMT demonstrates an optimal balance between segmentation accuracy and prediction speed, suggesting that compute resources are better spent on scaling the ViT itself rather than adding architectural complexity. Code: https://www.tue-mps.org/eomt/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.21751", "title": "Reconstructing Humans with a Biomechanically Accurate Skeleton", "authors": [ "Yan Xia", "Xiaowei Zhou", "Etienne Vouga", "Qixing Huang", "Georgios Pavlakos" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce a method for reconstructing 3D humans from a single image using a biomechanically accurate skeleton model. To achieve this, we train a transformer that takes an image as input and estimates the parameters of the model. Due to the lack of training data for this task, we build a pipeline to produce pseudo ground truth model parameters for single images and implement a training procedure that iteratively refines these pseudo labels. Compared to state-of-the-art methods for 3D human mesh recovery, our model achieves competitive performance on standard benchmarks, while it significantly outperforms them in settings with extreme 3D poses and viewpoints. Additionally, we show that previous reconstruction methods frequently violate joint angle limits, leading to unnatural rotations. In contrast, our approach leverages the biomechanically plausible degrees of freedom making more realistic joint rotation estimates. We validate our approach across multiple human pose estimation benchmarks. We make the code, models and data available at: https://isshikihugh.github.io/HSMR/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.16081", "title": "OThink-MR1: Stimulating multimodal generalized reasoning capabilities via dynamic reinforcement learning", "authors": [ "Zhiyuan Liu", "Yuting Zhang", "Feng Liu", "Changwang Zhang", "Ying Sun", "Jun Wang" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have gained significant traction for their ability to process diverse input data types and generate coherent, contextually relevant outputs across various applications. While supervised fine-tuning (SFT) has been the predominant approach to enhance MLLM capabilities in task-specific optimization, it often falls short in fostering crucial generalized reasoning abilities. Although reinforcement learning (RL) holds great promise in overcoming these limitations, it encounters two significant challenges: (1) its generalized capacities in multimodal tasks remain largely unexplored, and (2) its training constraints, including the constant Kullback-Leibler divergence or the clamp strategy, often result in suboptimal bottlenecks. To address these challenges, we propose OThink-MR1, an advanced MLLM equipped with profound comprehension and reasoning capabilities across multimodal tasks. Specifically, we introduce Group Relative Policy Optimization with a dynamic Kullback-Leibler strategy (GRPO-D), which markedly enhances reinforcement learning (RL) performance. For Qwen2-VL-2B-Instruct, GRPO-D achieves a relative improvement of more than 5.72% over SFT and more than 13.59% over GRPO in same-task evaluation on two adapted datasets. Furthermore, GRPO-D demonstrates remarkable cross-task generalization capabilities, with an average relative improvement of more than 61.63% over SFT in cross-task evaluation. These results highlight that the MLLM trained with GRPO-D on one multimodal task can be effectively transferred to another task, underscoring the superior generalized reasoning capabilities of our proposed OThink-MR1 model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.21544", "title": "SWI: Speaking with Intent in Large Language Models", "authors": [ "Yuwei Yin", "EunJeong Hwang", "Giuseppe Carenini" ], "github": "", "project_page": "", "abstract": "Intent, typically clearly formulated and planned, functions as a cognitive framework for reasoning and problem-solving. This paper introduces the concept of Speaking with Intent (SWI) in large language models (LLMs), where the explicitly generated intent encapsulates the model's underlying intention and provides high-level planning to guide subsequent analysis and communication. By emulating deliberate and purposeful thoughts in the human mind, SWI is hypothesized to enhance the reasoning capabilities and generation quality of LLMs. Extensive experiments on mathematical reasoning benchmarks consistently demonstrate the superiority of Speaking with Intent over Baseline (i.e., generation without explicit intent). Moreover, SWI outperforms answer-trigger prompting methods Chain-of-Thought and Plan-and-Solve and maintains competitive performance with the strong method ARR (Analyzing, Retrieving, and Reasoning). Additionally, the effectiveness and generalizability of SWI are solidified on reasoning-intensive question answering (QA) and text summarization benchmarks, where SWI brings consistent improvement to the Baseline generation. In text summarization, SWI-generated summaries exhibit greater accuracy, conciseness, and factual correctness, with fewer hallucinations. Furthermore, human evaluations verify the coherence, effectiveness, and interpretability of the intent produced by SWI. This proof-of-concept study creates a novel avenue for enhancing LLMs' reasoning abilities with cognitive notions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.22622", "title": "Zero4D: Training-Free 4D Video Generation From Single Video Using Off-the-Shelf Video Diffusion Model", "authors": [ "Jangho Park", "Taesung Kwon", "Jong Chul Ye" ], "github": "https://github.com/zero4dvid/zero4dvid.github.io", "project_page": "", "abstract": "Recently, multi-view or 4D video generation has emerged as a significant research topic. Nonetheless, recent approaches to 4D generation still struggle with fundamental limitations, as they primarily rely on harnessing multiple video diffusion models with additional training or compute-intensive training of a full 4D diffusion model with limited real-world 4D data and large computational costs. To address these challenges, here we propose the first training-free 4D video generation method that leverages the off-the-shelf video diffusion models to generate multi-view videos from a single input video. Our approach consists of two key steps: (1) By designating the edge frames in the spatio-temporal sampling grid as key frames, we first synthesize them using a video diffusion model, leveraging a depth-based warping technique for guidance. This approach ensures structural consistency across the generated frames, preserving spatial and temporal coherence. (2) We then interpolate the remaining frames using a video diffusion model, constructing a fully populated and temporally coherent sampling grid while preserving spatial and temporal consistency. Through this approach, we extend a single video into a multi-view video along novel camera trajectories while maintaining spatio-temporal consistency. Our method is training-free and fully utilizes an off-the-shelf video diffusion model, offering a practical and effective solution for multi-view video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zero4dvid/zero4dvid.github.io", "github_api": "", "github_api_added_by": "" }, { "date": "2025-03-31", "arxiv_id": "2503.22625", "title": "Challenges and Paths Towards AI for Software Engineering", "authors": [ "Alex Gu", "Naman Jain", "Wen-Ding Li", "Manish Shetty", "Yijia Shao", "Ziyang Li", "Diyi Yang", "Kevin Ellis", "Koushik Sen", "Armando Solar-Lezama" ], "github": "", "project_page": "", "abstract": "AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be addressed before automated software engineering reaches its full potential. It should be possible to reach high levels of automation where humans can focus on the critical decisions of what to build and how to balance difficult tradeoffs while most routine development effort is automated away. Reaching this level of automation will require substantial research and engineering efforts across academia and industry. In this paper, we aim to discuss progress towards this in a threefold manner. First, we provide a structured taxonomy of concrete tasks in AI for software engineering, emphasizing the many other tasks in software engineering beyond code generation and completion. Second, we outline several key bottlenecks that limit current approaches. Finally, we provide an opinionated list of promising research directions toward making progress on these bottlenecks, hoping to inspire future research in this rapidly maturing field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.18809", "title": "Classical Planning with LLM-Generated Heuristics: Challenging the State of the Art with Python Code", "authors": [ "Augusto B. Corr\u00eaa", "Andr\u00e9 G. Pereira", "Jendrik Seipp" ], "github": "", "project_page": "", "abstract": "In recent years, large language models (LLMs) have shown remarkable capabilities in various artificial intelligence problems. However, they fail to plan reliably, even when prompted with a detailed definition of the planning task. Attempts to improve their planning capabilities, such as chain-of-thought prompting, fine-tuning, and explicit \"reasoning\" still yield incorrect plans and usually fail to generalize to larger tasks. In this paper, we show how to use LLMs to generate correct plans, even for out-of-distribution tasks of increasing size. For a given planning domain, we ask an LLM to generate several domain-dependent heuristic functions in the form of Python code, evaluate them on a set of training tasks within a greedy best-first search, and choose the strongest one. The resulting LLM-generated heuristics solve many more unseen test tasks than state-of-the-art domain-independent heuristics for classical planning. They are even competitive with the strongest learning algorithm for domain-dependent planning. These findings are especially remarkable given that our proof-of-concept implementation is based on an unoptimized Python planner and the baselines all build upon highly optimized C++ code. In some domains, the LLM-generated heuristics expand fewer states than the baselines, revealing that they are not only efficiently computable, but sometimes even more informative than the state-of-the-art heuristics. Overall, our results show that sampling a set of planning heuristic function programs can significantly improve the planning capabilities of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.23307", "title": "MoCha: Towards Movie-Grade Talking Character Synthesis", "authors": [ "Cong Wei", "Bo Sun", "Haoyu Ma", "Ji Hou", "Felix Juefei-Xu", "Zecheng He", "Xiaoliang Dai", "Luxin Zhang", "Kunpeng Li", "Tingbo Hou", "Animesh Sinha", "Peter Vajda", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Recent advancements in video generation have achieved impressive motion realism, yet they often overlook character-driven storytelling, a crucial task for automated film, animation generation. We introduce Talking Characters, a more realistic task to generate talking character animations directly from speech and text. Unlike talking head, Talking Characters aims at generating the full portrait of one or more characters beyond the facial region. In this paper, we propose MoCha, the first of its kind to generate talking characters. To ensure precise synchronization between video and speech, we propose a speech-video window attention mechanism that effectively aligns speech and video tokens. To address the scarcity of large-scale speech-labeled video datasets, we introduce a joint training strategy that leverages both speech-labeled and text-labeled video data, significantly improving generalization across diverse character actions. We also design structured prompt templates with character tags, enabling, for the first time, multi-character conversation with turn-based dialogue-allowing AI-generated characters to engage in context-aware conversations with cinematic coherence. Extensive qualitative and quantitative evaluations, including human preference studies and benchmark comparisons, demonstrate that MoCha sets a new standard for AI-generated cinematic storytelling, achieving superior realism, expressiveness, controllability and generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.24370", "title": "Effectively Controlling Reasoning Models through Thinking Intervention", "authors": [ "Tong Wu", "Chong Xiang", "Jiachen T. Wang", "Prateek Mittal" ], "github": "", "project_page": "", "abstract": "Reasoning-enhanced large language models (LLMs) explicitly generate intermediate reasoning steps prior to generating final answers, helping the model excel in complex problem-solving. In this paper, we demonstrate that this emerging generation framework offers a unique opportunity for more fine-grained control over model behavior. We propose Thinking Intervention, a novel paradigm designed to explicitly guide the internal reasoning processes of LLMs by strategically inserting or revising specific thinking tokens. We conduct comprehensive evaluations across multiple tasks, including instruction following on IFEval, instruction hierarchy on SEP, and safety alignment on XSTest and SORRY-Bench. Our results demonstrate that Thinking Intervention significantly outperforms baseline prompting approaches, achieving up to 6.7% accuracy gains in instruction-following scenarios, 15.4% improvements in reasoning about instruction hierarchies, and a 40.0% increase in refusal rates for unsafe prompts using open-source DeepSeek R1 models. Overall, our work opens a promising new research avenue for controlling reasoning LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.24115", "title": "TeleAntiFraud-28k: A Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection", "authors": [ "Zhiming Ma", "Peidong Wang", "Minhua Huang", "Jingpeng Wang", "Kai Wu", "Xiangzhao Lv", "Yachun Pang", "Yin Yang", "Wenjie Tang", "Yuchen Kang" ], "github": "https://github.com/JimmyMa99/TeleAntiFraud", "project_page": "", "abstract": "The detection of telecom fraud faces significant challenges due to the lack of high-quality multimodal training data that integrates audio signals with reasoning-oriented textual analysis. To address this gap, we present TeleAntiFraud-28k, the first open-source audio-text slow-thinking dataset specifically designed for automated telecom fraud analysis. Our dataset is constructed through three strategies: (1) Privacy-preserved text-truth sample generation using automatically speech recognition (ASR)-transcribed call recordings (with anonymized original audio), ensuring real-world consistency through text-to-speech (TTS) model regeneration; (2) Semantic enhancement via large language model (LLM)-based self-instruction sampling on authentic ASR outputs to expand scenario coverage; (3) Multi-agent adversarial synthesis that simulates emerging fraud tactics through predefined communication scenarios and fraud typologies. The generated dataset contains 28,511 rigorously processed speech-text pairs, complete with detailed annotations for fraud reasoning. The dataset is divided into three tasks: scenario classification, fraud detection, fraud type classification. Furthermore, we construct TeleAntiFraud-Bench, a standardized evaluation benchmark comprising proportionally sampled instances from the dataset, to facilitate systematic testing of model performance on telecom fraud detection tasks. We also contribute a production-optimized supervised fine-tuning (SFT) model trained on hybrid real/synthetic data, while open-sourcing the data processing framework to enable community-driven dataset expansion. This work establishes a foundational framework for multimodal anti-fraud research while addressing critical challenges in data privacy and scenario diversity. The project will be released at https://github.com/JimmyMa99/TeleAntiFraud.", "github_manual": "", "github_regex": "https://github.com/JimmyMa99/TeleAntiFraud", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.20286", "title": "Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization", "authors": [ "Zhenyu Liang", "Hao Li", "Naiwei Yu", "Kebin Sun", "Ran Cheng" ], "github": "https://github.com/EMI-Group/evomo", "project_page": "", "abstract": "Evolutionary multiobjective optimization (EMO) has made significant strides over the past two decades. However, as problem scales and complexities increase, traditional EMO algorithms face substantial performance limitations due to insufficient parallelism and scalability. While most work has focused on algorithm design to address these challenges, little attention has been given to hardware acceleration, thereby leaving a clear gap between EMO algorithms and advanced computing devices, such as GPUs. To bridge the gap, we propose to parallelize EMO algorithms on GPUs via the tensorization methodology. By employing tensorization, the data structures and operations of EMO algorithms are transformed into concise tensor representations, which seamlessly enables automatic utilization of GPU computing. We demonstrate the effectiveness of our approach by applying it to three representative EMO algorithms: NSGA-III, MOEA/D, and HypE. To comprehensively assess our methodology, we introduce a multiobjective robot control benchmark using a GPU-accelerated physics engine. Our experiments show that the tensorized EMO algorithms achieve speedups of up to 1113x compared to their CPU-based counterparts, while maintaining solution quality and effectively scaling population sizes to hundreds of thousands. Furthermore, the tensorized EMO algorithms efficiently tackle complex multiobjective robot control tasks, producing high-quality solutions with diverse behaviors. Source codes are available at https://github.com/EMI-Group/evomo.", "github_manual": "", "github_regex": "https://github.com/EMI-Group/evomo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.24235", "title": "What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models", "authors": [ "Qiyuan Zhang", "Fuyuan Lyu", "Zexu Sun", "Lei Wang", "Weixu Zhang", "Zhihan Guo", "Yufei Wang", "Irwin King", "Xue Liu", "Chen Ma" ], "github": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "project_page": "", "abstract": "As enthusiasm for scaling computation (data and parameters) in the pretraining era gradually diminished, test-time scaling (TTS), also referred to as ``test-time computing'' has emerged as a prominent research focus. Recent studies demonstrate that TTS can further elicit the problem-solving capabilities of large language models (LLMs), enabling significant breakthroughs not only in specialized reasoning tasks, such as mathematics and coding, but also in general tasks like open-ended Q&A. However, despite the explosion of recent efforts in this area, there remains an urgent need for a comprehensive survey offering a systemic understanding. To fill this gap, we propose a unified, multidimensional framework structured along four core dimensions of TTS research: what to scale, how to scale, where to scale, and how well to scale. Building upon this taxonomy, we conduct an extensive review of methods, application scenarios, and assessment aspects, and present an organized decomposition that highlights the unique functional roles of individual techniques within the broader TTS landscape. From this analysis, we distill the major developmental trajectories of TTS to date and offer hands-on guidelines for practical deployment. Furthermore, we identify several open challenges and offer insights into promising future directions, including further scaling, clarifying the functional essence of techniques, generalizing to more tasks, and more attributions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.24388", "title": "RIG: Synergizing Reasoning and Imagination in End-to-End Generalist Policy", "authors": [ "Zhonghan Zhao", "Wenwei Zhang", "Haian Huang", "Kuikun Liu", "Jianfei Gao", "Gaoang Wang", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Reasoning before action and imagining potential outcomes (i.e., world models) are essential for embodied agents operating in complex open-world environments. Yet, prior work either incorporates only one of these abilities in an end-to-end agent or integrates multiple specialized models into an agent system, limiting the learning efficiency and generalization of the policy. Thus, this paper makes the first attempt to synergize Reasoning and Imagination in an end-to-end Generalist policy, termed RIG. To train RIG in an end-to-end manner, we construct a data pipeline that progressively integrates and enriches the content of imagination and reasoning in the trajectories collected from existing agents. The joint learning of reasoning and next image generation explicitly models the inherent correlation between reasoning, action, and dynamics of environments, and thus exhibits more than 17times sample efficiency improvements and generalization in comparison with previous works. During inference, RIG first reasons about the next action, produces potential action, and then predicts the action outcomes, which offers the agent a chance to review and self-correct based on the imagination before taking real actions. Experimental results show that the synergy of reasoning and imagination not only improves the robustness, generalization, and interoperability of generalist policy but also enables test-time scaling to enhance overall performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.23461", "title": "TextCrafter: Accurately Rendering Multiple Texts in Complex Visual Scenes", "authors": [ "Nikai Du", "Zhennan Chen", "Zhizhou Chen", "Shan Gao", "Xi Chen", "Zhengkai Jiang", "Jian Yang", "Ying Tai" ], "github": "", "project_page": "", "abstract": "This paper explores the task of Complex Visual Text Generation (CVTG), which centers on generating intricate textual content distributed across diverse regions within visual images. In CVTG, image generation models often rendering distorted and blurred visual text or missing some visual text. To tackle these challenges, we propose TextCrafter, a novel multi-visual text rendering method. TextCrafter employs a progressive strategy to decompose complex visual text into distinct components while ensuring robust alignment between textual content and its visual carrier. Additionally, it incorporates a token focus enhancement mechanism to amplify the prominence of visual text during the generation process. TextCrafter effectively addresses key challenges in CVTG tasks, such as text confusion, omissions, and blurriness. Moreover, we present a new benchmark dataset, CVTG-2K, tailored to rigorously evaluate the performance of generative models on CVTG tasks. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.23730", "title": "KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large Vision-Language Models in the Korean Language", "authors": [ "Yoonshik Kim", "Jaeyoon Jung" ], "github": "https://github.com/maum-ai/KOFFVQA", "project_page": "", "abstract": "The recent emergence of Large Vision-Language Models(VLMs) has resulted in a variety of different benchmarks for evaluating such models. Despite this, we observe that most existing evaluation methods suffer from the fact that they either require the model to choose from pre-determined responses, sacrificing open-endedness, or evaluate responses using a judge model, resulting in subjective and unreliable evaluation. In addition, we observe a lack of benchmarks for VLMs in the Korean language, which are necessary as a separate metric from more common English language benchmarks, as the performance of generative language models can differ significantly based on the language being used. Therefore, we present KOFFVQA, a general-purpose free-form visual question answering benchmark in the Korean language for the evaluation of VLMs. Our benchmark consists of 275 carefully crafted questions each paired with an image and grading criteria covering 10 different aspects of VLM performance. The grading criteria eliminate the problem of unreliability by allowing the judge model to grade each response based on a pre-determined set of rules. By defining the evaluation criteria in an objective manner, even a small open-source model can be used to evaluate models on our benchmark reliably. In addition to evaluating a large number of existing VLMs on our benchmark, we also experimentally verify that our method of using pre-existing grading criteria for evaluation is much more reliable than existing methods. Our evaluation code is available at https://github.com/maum-ai/KOFFVQA", "github_manual": "", "github_regex": "https://github.com/maum-ai/KOFFVQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.23077", "title": "Efficient Inference for Large Reasoning Models: A Survey", "authors": [ "Yue Liu", "Jiaying Wu", "Yufei He", "Hongcheng Gao", "Hongyu Chen", "Baolong Bi", "Jiaheng Zhang", "Zhiqi Huang", "Bryan Hooi" ], "github": "https://github.com/yueliu1999/Awesome-Efficient-Inference-for-LRMs", "project_page": "", "abstract": "Large Reasoning Models (LRMs) significantly improve the reasoning ability of Large Language Models (LLMs) by learning to reason, exhibiting promising performance in complex task-solving. However, their deliberative reasoning process leads to inefficiencies in token usage, memory consumption, and inference time. Thus, this survey provides a review of efficient inference methods designed specifically for LRMs, focusing on mitigating token inefficiency while preserving the reasoning quality. First, we introduce a taxonomy to group the recent methods into two main categories: (a) explicit compact Chain-of-Thought (CoT), which reduces tokens while keeping the explicit reasoning structure, and (b) implicit latent CoT, which encodes reasoning steps within hidden representations instead of explicit tokens. Meanwhile, we discuss their strengths and weaknesses. Then, we conduct empirical analyses on existing methods from performance and efficiency aspects. Besides, we present open challenges in this field, including human-centric controllable reasoning, trade-off between interpretability and efficiency of reasoning, ensuring safety of efficient reasoning, and broader applications of efficient reasoning. In addition, we highlight key insights for enhancing LRMs' inference efficiency via techniques such as model merging, new architectures, and agent routers. We hope this work serves as a valuable guide, helping researchers overcome challenges in this vibrant fieldhttps://github.com/yueliu1999/Awesome-Efficient-Inference-for-LRMs.", "github_manual": "", "github_regex": "https://github.com/yueliu1999/Awesome-Efficient-Inference-for-LRMs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.23284", "title": "SketchVideo: Sketch-based Video Generation and Editing", "authors": [ "Feng-Lin Liu", "Hongbo Fu", "Xintao Wang", "Weicai Ye", "Pengfei Wan", "Di Zhang", "Lin Gao" ], "github": "", "project_page": "", "abstract": "Video generation and editing conditioned on text prompts or images have undergone significant advancements. However, challenges remain in accurately controlling global layout and geometry details solely by texts, and supporting motion control and local modification through images. In this paper, we aim to achieve sketch-based spatial and motion control for video generation and support fine-grained editing of real or synthetic videos. Based on the DiT video generation model, we propose a memory-efficient control structure with sketch control blocks that predict residual features of skipped DiT blocks. Sketches are drawn on one or two keyframes (at arbitrary time points) for easy interaction. To propagate such temporally sparse sketch conditions across all frames, we propose an inter-frame attention mechanism to analyze the relationship between the keyframes and each video frame. For sketch-based video editing, we design an additional video insertion module that maintains consistency between the newly edited content and the original video's spatial feature and dynamic motion. During inference, we use latent fusion for the accurate preservation of unedited regions. Extensive experiments demonstrate that our SketchVideo achieves superior performance in controllable video generation and editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.24364", "title": "Query and Conquer: Execution-Guided SQL Generation", "authors": [ "\u0141ukasz Borchmann", "Marek Wydmuch" ], "github": "", "project_page": "", "abstract": "We propose a novel approach for generating complex outputs that significantly improves accuracy in text-to-SQL tasks. Our method leverages execution results to select the most semantically consistent query from multiple candidates, enabling smaller, cost-effective models to surpass computationally intensive reasoning methods such as o1, o3-mini, and DeepSeek R1 while reducing inference cost by as much as 30 times. It integrates effortlessly with existing models, offering a practical and scalable pathway to state-of-the-art SQL generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.24290", "title": "Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model", "authors": [ "Jingcheng Hu", "Yinmin Zhang", "Qi Han", "Daxin Jiang", "Xiangyu Zhang", "Heung-Yeung Shum" ], "github": "", "project_page": "", "abstract": "We introduce Open-Reasoner-Zero, the first open source implementation of large-scale reasoning-oriented RL training focusing on scalability, simplicity and accessibility. Through extensive experiments, we demonstrate that a minimalist approach, vanilla PPO with GAE (lambda=1, gamma=1) and straightforward rule-based rewards, without any KL regularization, is sufficient to scale up both response length and benchmark performance, similar to the phenomenon observed in DeepSeek-R1-Zero. Using the same base model as DeepSeek-R1-Zero-Qwen-32B, our implementation achieves superior performance on AIME2024, MATH500, and the GPQA Diamond benchmark while demonstrating remarkable efficiency -- requiring only a tenth of the training steps, compared to DeepSeek-R1-Zero pipeline. In the spirit of open source, we release our source code, parameter settings, training data, and model weights across various sizes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.23829", "title": "Expanding RL with Verifiable Rewards Across Diverse Domains", "authors": [ "Yi Su", "Dian Yu", "Linfeng Song", "Juntao Li", "Haitao Mi", "Zhaopeng Tu", "Min Zhang", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) with verifiable rewards (RLVR) has shown promising results in mathematical reasoning and coding tasks where well-structured reference answers are available. However, its applicability to broader domains remains underexplored. In this work, we study the extension of RLVR to more diverse domains such as medicine, chemistry, psychology, and economics. We observe high agreement in binary judgments across different large language models (LLMs) when objective reference answers exist, which challenges the necessity of large-scale annotation for training domain-specific reward models. To address the limitations of binary rewards when handling unstructured reference answers, we further incorporate model-based soft scoring into RLVR to improve its flexibility. Our experiments show that a distilled generative reward model can serve as an effective cross-domain verifier, providing reliable reward signals for RL without requiring domain-specific annotations. By fine-tuning a base 7B model using various RL algorithms against our reward model, we obtain policies that outperform state-of-the-art open-source aligned LLMs such as Qwen2.5-72B-Instruct and DeepSeek-R1-Distill-Qwen-32B by a large margin, across domains in free-form answer settings. This also strengthens RLVR's robustness and scalability, highlighting its potential for real-world applications with noisy or weak labels.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.21694", "title": "Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data", "authors": [ "Zhiyuan Ma", "Xinyue Liang", "Rongyuan Wu", "Xiangyu Zhu", "Zhen Lei", "Lei Zhang" ], "github": "https://github.com/theEricMa/TriplaneTurbo", "project_page": "", "abstract": "It is highly desirable to obtain a model that can generate high-quality 3D meshes from text prompts in just seconds. While recent attempts have adapted pre-trained text-to-image diffusion models, such as Stable Diffusion (SD), into generators of 3D representations (e.g., Triplane), they often suffer from poor quality due to the lack of sufficient high-quality 3D training data. Aiming at overcoming the data shortage, we propose a novel training scheme, termed as Progressive Rendering Distillation (PRD), eliminating the need for 3D ground-truths by distilling multi-view diffusion models and adapting SD into a native 3D generator. In each iteration of training, PRD uses the U-Net to progressively denoise the latent from random noise for a few steps, and in each step it decodes the denoised latent into 3D output. Multi-view diffusion models, including MVDream and RichDreamer, are used in joint with SD to distill text-consistent textures and geometries into the 3D outputs through score distillation. Since PRD supports training without 3D ground-truths, we can easily scale up the training data and improve generation quality for challenging text prompts with creative concepts. Meanwhile, PRD can accelerate the inference speed of the generation model in just a few steps. With PRD, we train a Triplane generator, namely TriplaneTurbo, which adds only 2.5% trainable parameters to adapt SD for Triplane generation. TriplaneTurbo outperforms previous text-to-3D generators in both efficiency and quality. Specifically, it can produce high-quality 3D meshes in 1.2 seconds and generalize well for challenging text input. The code is available at https://github.com/theEricMa/TriplaneTurbo.", "github_manual": "", "github_regex": "https://github.com/theEricMa/TriplaneTurbo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.14941", "title": "UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation", "authors": [ "Qihui Zhang", "Munan Ning", "Zheyuan Liu", "Yanbo Wang", "Jiayi Ye", "Yue Huang", "Shuo Yang", "Xiao Chen", "Yibing Song", "Li Yuan" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have emerged to tackle the challenges of Visual Question Answering (VQA), sparking a new research focus on conducting objective evaluations of these models. Existing evaluation methods face limitations due to the significant human workload required to design Q&A pairs for visual images, which inherently restricts the scale and scope of evaluations. Although automated MLLM-as-judge approaches attempt to reduce the human workload through automatic evaluations, they often introduce biases. To address these problems, we propose an Unsupervised Peer review MLLM Evaluation framework. It utilizes only image data, allowing models to automatically generate questions and conduct peer review assessments of answers from other models, effectively alleviating the reliance on human workload. Additionally, we introduce the vision-language scoring system to mitigate the bias issues, which focuses on three aspects: (i) response correctness; (ii) visual understanding and reasoning; and (iii) image-text correlation. Experimental results demonstrate that UPME achieves a Pearson correlation of 0.944 with human evaluations on the MMstar dataset and 0.814 on the ScienceQA dataset, indicating that our framework closely aligns with human-designed benchmarks and inherent human preferences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.19901", "title": "TokenHSI: Unified Synthesis of Physical Human-Scene Interactions through Task Tokenization", "authors": [ "Liang Pan", "Zeshi Yang", "Zhiyang Dou", "Wenjia Wang", "Buzhen Huang", "Bo Dai", "Taku Komura", "Jingbo Wang" ], "github": "", "project_page": "", "abstract": "Synthesizing diverse and physically plausible Human-Scene Interactions (HSI) is pivotal for both computer animation and embodied AI. Despite encouraging progress, current methods mainly focus on developing separate controllers, each specialized for a specific interaction task. This significantly hinders the ability to tackle a wide variety of challenging HSI tasks that require the integration of multiple skills, e.g., sitting down while carrying an object. To address this issue, we present TokenHSI, a single, unified transformer-based policy capable of multi-skill unification and flexible adaptation. The key insight is to model the humanoid proprioception as a separate shared token and combine it with distinct task tokens via a masking mechanism. Such a unified policy enables effective knowledge sharing across skills, thereby facilitating the multi-task training. Moreover, our policy architecture supports variable length inputs, enabling flexible adaptation of learned skills to new scenarios. By training additional task tokenizers, we can not only modify the geometries of interaction targets but also coordinate multiple skills to address complex tasks. The experiments demonstrate that our approach can significantly improve versatility, adaptability, and extensibility in various HSI tasks. Website: https://liangpan99.github.io/TokenHSI/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.24391", "title": "Easi3R: Estimating Disentangled Motion from DUSt3R Without Training", "authors": [ "Xingyu Chen", "Yue Chen", "Yuliang Xiu", "Andreas Geiger", "Anpei Chen" ], "github": "", "project_page": "", "abstract": "Recent advances in DUSt3R have enabled robust estimation of dense point clouds and camera parameters of static scenes, leveraging Transformer network architectures and direct supervision on large-scale 3D datasets. In contrast, the limited scale and diversity of available 4D datasets present a major bottleneck for training a highly generalizable 4D model. This constraint has driven conventional 4D methods to fine-tune 3D models on scalable dynamic video data with additional geometric priors such as optical flow and depths. In this work, we take an opposite path and introduce Easi3R, a simple yet efficient training-free method for 4D reconstruction. Our approach applies attention adaptation during inference, eliminating the need for from-scratch pre-training or network fine-tuning. We find that the attention layers in DUSt3R inherently encode rich information about camera and object motion. By carefully disentangling these attention maps, we achieve accurate dynamic region segmentation, camera pose estimation, and 4D dense point map reconstruction. Extensive experiments on real-world dynamic videos demonstrate that our lightweight attention adaptation significantly outperforms previous state-of-the-art methods that are trained or finetuned on extensive dynamic datasets. Our code is publicly available for research purpose at https://easi3r.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.23022", "title": "MeshCraft: Exploring Efficient and Controllable Mesh Generation with Flow-based DiTs", "authors": [ "Xianglong He", "Junyi Chen", "Di Huang", "Zexiang Liu", "Xiaoshui Huang", "Wanli Ouyang", "Chun Yuan", "Yangguang Li" ], "github": "", "project_page": "", "abstract": "In the domain of 3D content creation, achieving optimal mesh topology through AI models has long been a pursuit for 3D artists. Previous methods, such as MeshGPT, have explored the generation of ready-to-use 3D objects via mesh auto-regressive techniques. While these methods produce visually impressive results, their reliance on token-by-token predictions in the auto-regressive process leads to several significant limitations. These include extremely slow generation speeds and an uncontrollable number of mesh faces. In this paper, we introduce MeshCraft, a novel framework for efficient and controllable mesh generation, which leverages continuous spatial diffusion to generate discrete triangle faces. Specifically, MeshCraft consists of two core components: 1) a transformer-based VAE that encodes raw meshes into continuous face-level tokens and decodes them back to the original meshes, and 2) a flow-based diffusion transformer conditioned on the number of faces, enabling the generation of high-quality 3D meshes with a predefined number of faces. By utilizing the diffusion model for the simultaneous generation of the entire mesh topology, MeshCraft achieves high-fidelity mesh generation at significantly faster speeds compared to auto-regressive methods. Specifically, MeshCraft can generate an 800-face mesh in just 3.2 seconds (35times faster than existing baselines). Extensive experiments demonstrate that MeshCraft outperforms state-of-the-art techniques in both qualitative and quantitative evaluations on ShapeNet dataset and demonstrates superior performance on Objaverse dataset. Moreover, it integrates seamlessly with existing conditional guidance strategies, showcasing its potential to relieve artists from the time-consuming manual work involved in mesh creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.18225", "title": "Decoupling Angles and Strength in Low-rank Adaptation", "authors": [ "Massimo Bini", "Leander Girrbach", "Zeynep Akata" ], "github": "https://github.com/ExplainableML/DeLoRA", "project_page": "", "abstract": "Parameter-Efficient FineTuning (PEFT) methods have recently gained significant popularity thanks to the widespread availability of large-scale pretrained models. These methods allow for quick adaptation to downstream tasks with minimal computational cost. However, popular finetuning methods such as LoRA exhibit limited robustness when it comes to hyperparameter choices or extended training regimes, preventing optimal out-of-the-box performance. In contrast, bounded approaches, such as ETHER, provide greater robustness but are limited to extremely low-rank adaptations and fixed-strength transformations, reducing their adaptation expressive power. In this work, we propose Decoupled Low-rank Adaptation (DeLoRA), a novel finetuning method that normalizes and scales learnable low-rank matrices. By bounding the distance of the transformation, DeLoRA effectively decouples the angular learning from the adaptation strength, enhancing robustness without compromising performance. Through evaluations on subject-driven image generation, natural language understanding, and instruction tuning, we show that DeLoRA matches or surpasses performance of competing PEFT methods, while exhibiting stronger robustness. Code is available at https://github.com/ExplainableML/DeLoRA.", "github_manual": "", "github_regex": "https://github.com/ExplainableML/DeLoRA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.23913", "title": "Entropy-Based Adaptive Weighting for Self-Training", "authors": [ "Xiaoxuan Wang", "Yihe Deng", "Mingyu Derek Ma", "Wei Wang" ], "github": "https://github.com/mandyyyyii/east", "project_page": "", "abstract": "The mathematical problem-solving capabilities of large language models have become a focal point of research, with growing interests in leveraging self-generated reasoning paths as a promising way to refine and enhance these models. These paths capture step-by-step logical processes while requiring only the correct answer for supervision. The self-training method has been shown to be effective in reasoning tasks while eliminating the need for external models and manual annotations. However, optimizing the use of self-generated data for model training remains an open challenge. In this work, we propose Entropy-Based Adaptive Weighting for Self-Training (EAST), an adaptive weighting strategy designed to prioritize uncertain data during self-training. Specifically, EAST employs a mapping function with a tunable parameter that controls the sharpness of the weighting, assigning higher weights to data where the model exhibits greater uncertainty. This approach guides the model to focus on more informative and challenging examples, thereby enhancing its reasoning ability. We evaluate our approach on GSM8K and MATH benchmarks. Empirical results show that, while the vanilla method yields virtually no improvement (0%) on MATH, EAST achieves around a 1% gain over backbone model. On GSM8K, EAST attains a further 1-2% performance boost compared to the vanilla method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mandyyyyii/east", "github_api_added_by": "auto" }, { "date": "2025-04-01", "arxiv_id": "2503.22655", "title": "Unicorn: Text-Only Data Synthesis for Vision Language Model Training", "authors": [ "Xiaomin Yu", "Pengxiang Ding", "Wenjie Zhang", "Siteng Huang", "Songyang Gao", "Chengwei Qin", "Kejian Wu", "Zhaoxin Fan", "Ziyue Qiao", "Donglin Wang" ], "github": "https://github.com/Yu-xm/Unicorn.git", "project_page": "", "abstract": "Training vision-language models (VLMs) typically requires large-scale, high-quality image-text pairs, but collecting or synthesizing such data is costly. In contrast, text data is abundant and inexpensive, prompting the question: can high-quality multimodal training data be synthesized purely from text? To tackle this, we propose a cross-integrated three-stage multimodal data synthesis framework, which generates two datasets: Unicorn-1.2M and Unicorn-471K-Instruction. In Stage 1: Diverse Caption Data Synthesis, we construct 1.2M semantically diverse high-quality captions by expanding sparse caption seeds using large language models (LLMs). In Stage 2: Instruction-Tuning Data Generation, we further process 471K captions into multi-turn instruction-tuning tasks to support complex reasoning. Finally, in Stage 3: Modality Representation Transfer, these textual captions representations are transformed into visual representations, resulting in diverse synthetic image representations. This three-stage process enables us to construct Unicorn-1.2M for pretraining and Unicorn-471K-Instruction for instruction-tuning, without relying on real images. By eliminating the dependency on real images while maintaining data quality and diversity, our framework offers a cost-effective and scalable solution for VLMs training. Code is available at https://github.com/Yu-xm/Unicorn.git.", "github_manual": "", "github_regex": "https://github.com/Yu-xm/Unicorn.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.22668", "title": "Understanding Co-speech Gestures in-the-wild", "authors": [ "Sindhu B Hegde", "K R Prajwal", "Taein Kwon", "Andrew Zisserman" ], "github": "", "project_page": "", "abstract": "Co-speech gestures play a vital role in non-verbal communication. In this paper, we introduce a new framework for co-speech gesture understanding in the wild. Specifically, we propose three new tasks and benchmarks to evaluate a model's capability to comprehend gesture-text-speech associations: (i) gesture-based retrieval, (ii) gestured word spotting, and (iii) active speaker detection using gestures. We present a new approach that learns a tri-modal speech-text-video-gesture representation to solve these tasks. By leveraging a combination of global phrase contrastive loss and local gesture-word coupling loss, we demonstrate that a strong gesture representation can be learned in a weakly supervised manner from videos in the wild. Our learned representations outperform previous methods, including large vision-language models (VLMs), across all three tasks. Further analysis reveals that speech and text modalities capture distinct gesture-related signals, underscoring the advantages of learning a shared tri-modal embedding space. The dataset, model, and code are available at: https://www.robots.ox.ac.uk/~vgg/research/jegal", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.19794", "title": "PAVE: Patching and Adapting Video Large Language Models", "authors": [ "Zhuoming Liu", "Yiquan Li", "Khoi Duc Nguyen", "Yiwu Zhong", "Yin Li" ], "github": "https://github.com/dragonlzm/PAVE", "project_page": "", "abstract": "Pre-trained video large language models (Video LLMs) exhibit remarkable reasoning capabilities, yet adapting these models to new tasks involving additional modalities or data types (e.g., audio or 3D information) remains challenging. In this paper, we present PAVE, a flexible framework for adapting pre-trained Video LLMs to downstream tasks with side-channel signals, such as audio, 3D cues, or multi-view videos. PAVE introduces lightweight adapters, referred to as \"patches,\" which add a small number of parameters and operations to a base model without modifying its architecture or pre-trained weights. In doing so, PAVE can effectively adapt the pre-trained base model to support diverse downstream tasks, including audio-visual question answering, 3D reasoning, multi-view video recognition, and high frame rate video understanding. Across these tasks, PAVE significantly enhances the performance of the base model, surpassing state-of-the-art task-specific models while incurring a minor cost of ~0.1% additional FLOPs and parameters. Further, PAVE supports multi-task learning and generalizes well across different Video LLMs. Our code is available at https://github.com/dragonlzm/PAVE.", "github_manual": "", "github_regex": "https://github.com/dragonlzm/PAVE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.22673", "title": "ActionStudio: A Lightweight Framework for Data and Training of Large Action Models", "authors": [ "Jianguo Zhang", "Thai Hoang", "Ming Zhu", "Zuxin Liu", "Shiyu Wang", "Tulika Awalgaonkar", "Akshara Prabhakar", "Haolin Chen", "Weiran Yao", "Zhiwei Liu", "Juntao Tan", "Juan Carlos Niebles", "Shelby Heinecke", "Huan Wang", "Silvio Savarese", "Caiming Xiong" ], "github": "https://github.com/SalesforceAIResearch/xLAM", "project_page": "", "abstract": "Action models are essential for enabling autonomous agents to perform complex tasks. However, training large action models remains challenging due to the diversity of agent environments and the complexity of agentic data. Despite growing interest, existing infrastructure provides limited support for scalable, agent-specific fine-tuning. We present ActionStudio, a lightweight and extensible data and training framework designed for large action models. ActionStudio unifies heterogeneous agent trajectories through a standardized format, supports diverse training paradigms including LoRA, full fine-tuning, and distributed setups, and integrates robust preprocessing and verification tools. We validate its effectiveness across both public and realistic industry benchmarks, demonstrating strong performance and practical scalability. We open-sourced code and data at https://github.com/SalesforceAIResearch/xLAM to facilitate research in the community.", "github_manual": "", "github_regex": "https://github.com/SalesforceAIResearch/xLAM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-01", "arxiv_id": "2503.19906", "title": "AvatarArtist: Open-Domain 4D Avatarization", "authors": [ "Hongyu Liu", "Xuan Wang", "Ziyu Wan", "Yue Ma", "Jingye Chen", "Yanbo Fan", "Yujun Shen", "Yibing Song", "Qifeng Chen" ], "github": "https://github.com/ant-research/AvatarArtist", "project_page": "", "abstract": "This work focuses on open-domain 4D avatarization, with the purpose of creating a 4D avatar from a portrait image in an arbitrary style. We select parametric triplanes as the intermediate 4D representation and propose a practical training paradigm that takes advantage of both generative adversarial networks (GANs) and diffusion models. Our design stems from the observation that 4D GANs excel at bridging images and triplanes without supervision yet usually face challenges in handling diverse data distributions. A robust 2D diffusion prior emerges as the solution, assisting the GAN in transferring its expertise across various domains. The synergy between these experts permits the construction of a multi-domain image-triplane dataset, which drives the development of a general 4D avatar creator. Extensive experiments suggest that our model, AvatarArtist, is capable of producing high-quality 4D avatars with strong robustness to various source image domains. The code, the data, and the models will be made publicly available to facilitate future studies..", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ant-research/AvatarArtist", "github_api_added_by": "auto" }, { "date": "2025-04-01", "arxiv_id": "2503.22677", "title": "DSO: Aligning 3D Generators with Simulation Feedback for Physical Soundness", "authors": [ "Ruining Li", "Chuanxia Zheng", "Christian Rupprecht", "Andrea Vedaldi" ], "github": "", "project_page": "", "abstract": "Most 3D object generators focus on aesthetic quality, often neglecting physical constraints necessary in applications. One such constraint is that the 3D object should be self-supporting, i.e., remains balanced under gravity. Prior approaches to generating stable 3D objects used differentiable physics simulators to optimize geometry at test-time, which is slow, unstable, and prone to local optima. Inspired by the literature on aligning generative models to external feedback, we propose Direct Simulation Optimization (DSO), a framework to use the feedback from a (non-differentiable) simulator to increase the likelihood that the 3D generator outputs stable 3D objects directly. We construct a dataset of 3D objects labeled with a stability score obtained from the physics simulator. We can then fine-tune the 3D generator using the stability score as the alignment metric, via direct preference optimization (DPO) or direct reward optimization (DRO), a novel objective, which we introduce, to align diffusion models without requiring pairwise preferences. Our experiments show that the fine-tuned feed-forward generator, using either DPO or DRO objective, is much faster and more likely to produce stable objects than test-time optimization. Notably, the DSO framework works even without any ground-truth 3D objects for training, allowing the 3D generator to self-improve by automatically collecting simulation feedback on its own outputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.00906", "title": "Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents", "authors": [ "Saaket Agashe", "Kyle Wong", "Vincent Tu", "Jiachen Yang", "Ang Li", "Xin Eric Wang" ], "github": "https://github.com/simular-ai/Agent-S", "project_page": "", "abstract": "Computer use agents automate digital tasks by directly interacting with graphical user interfaces (GUIs) on computers and mobile devices, offering significant potential to enhance human productivity by completing an open-ended space of user queries. However, current agents face significant challenges: imprecise grounding of GUI elements, difficulties with long-horizon task planning, and performance bottlenecks from relying on single generalist models for diverse cognitive tasks. To this end, we introduce Agent S2, a novel compositional framework that delegates cognitive responsibilities across various generalist and specialist models. We propose a novel Mixture-of-Grounding technique to achieve precise GUI localization and introduce Proactive Hierarchical Planning, dynamically refining action plans at multiple temporal scales in response to evolving observations. Evaluations demonstrate that Agent S2 establishes new state-of-the-art (SOTA) performance on three prominent computer use benchmarks. Specifically, Agent S2 achieves 18.9% and 32.7% relative improvements over leading baseline agents such as Claude Computer Use and UI-TARS on the OSWorld 15-step and 50-step evaluation. Moreover, Agent S2 generalizes effectively to other operating systems and applications, surpassing previous best methods by 52.8% on WindowsAgentArena and by 16.52% on AndroidWorld relatively. Code available at https://github.com/simular-ai/Agent-S.", "github_manual": "", "github_regex": "https://github.com/simular-ai/Agent-S", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2503.23361", "title": "Discovering Knowledge Deficiencies of Language Models on Massive Knowledge Base", "authors": [ "Linxin Song", "Xuwei Ding", "Jieyu Zhang", "Taiwei Shi", "Ryotaro Shimizu", "Rahul Gupta", "Yang Liu", "Jian Kang", "Jieyu Zhao" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) possess impressive linguistic capabilities but often fail to faithfully retain factual knowledge, leading to hallucinations and unreliable outputs. Understanding LLMs' knowledge deficiencies by exhaustively evaluating against full-scale knowledge bases is computationally prohibitive, especially for closed-weight models. We propose stochastic error ascent (SEA), a scalable and efficient framework for discovering knowledge deficiencies (errors) in closed-weight LLMs under a strict query budget. Rather than naively probing all knowledge candidates, SEA formulates error discovery as a stochastic optimization process: it iteratively retrieves new high-error candidates by leveraging the semantic similarity to previously observed failures. To further enhance search efficiency and coverage, SEA employs hierarchical retrieval across document and paragraph levels, and constructs a relation directed acyclic graph to model error propagation and identify systematic failure modes. Empirically, SEA uncovers 40.7x more knowledge errors than Automated Capability Discovery and 26.7% more than AutoBencher, while reducing the cost-per-error by 599x and 9x, respectively. Human evaluation confirms the high quality of generated questions, while ablation and convergence analyses validate the contribution of each component in SEA. Further analysis on the discovered errors reveals correlated failure patterns across LLM families and recurring deficits, highlighting the need for better data coverage and targeted fine-tuning in future LLM development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.01016", "title": "GeometryCrafter: Consistent Geometry Estimation for Open-world Videos with Diffusion Priors", "authors": [ "Tian-Xing Xu", "Xiangjun Gao", "Wenbo Hu", "Xiaoyu Li", "Song-Hai Zhang", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Despite remarkable advancements in video depth estimation, existing methods exhibit inherent limitations in achieving geometric fidelity through the affine-invariant predictions, limiting their applicability in reconstruction and other metrically grounded downstream tasks. We propose GeometryCrafter, a novel framework that recovers high-fidelity point map sequences with temporal coherence from open-world videos, enabling accurate 3D/4D reconstruction, camera parameter estimation, and other depth-based applications. At the core of our approach lies a point map Variational Autoencoder (VAE) that learns a latent space agnostic to video latent distributions for effective point map encoding and decoding. Leveraging the VAE, we train a video diffusion model to model the distribution of point map sequences conditioned on the input videos. Extensive evaluations on diverse datasets demonstrate that GeometryCrafter achieves state-of-the-art 3D accuracy, temporal consistency, and generalization capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.01005", "title": "When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning", "authors": [ "Nishad Singhi", "Hritik Bansal", "Arian Hosseini", "Aditya Grover", "Kai-Wei Chang", "Marcus Rohrbach", "Anna Rohrbach" ], "github": "https://github.com/nishadsinghi/sc-genrm-scaling", "project_page": "", "abstract": "Scaling test-time compute has emerged as a key strategy for enhancing the reasoning capabilities of large language models (LLMs), particularly in tasks like mathematical problem-solving. A traditional approach, Self-Consistency (SC), generates multiple solutions to a problem and selects the most common answer via majority voting. Another common method involves scoring each solution with a reward model (verifier) and choosing the best one. Recent advancements in Generative Reward Models (GenRM) reframe verification as a next-token prediction task, enabling inference-time scaling along a new axis. Specifically, GenRM generates multiple verification chains-of-thought to score each solution. Under a limited inference budget, this introduces a fundamental trade-off: should you spend the budget on scaling solutions via SC or generate fewer solutions and allocate compute to verification via GenRM? To address this, we evaluate GenRM against SC under a fixed inference budget. Interestingly, we find that SC is more compute-efficient than GenRM for most practical inference budgets across diverse models and datasets. For instance, GenRM first matches SC after consuming up to 8x the inference compute and requires significantly more compute to outperform it. Furthermore, we derive inference scaling laws for the GenRM paradigm, revealing that compute-optimal inference favors scaling solution generation more aggressively than scaling the number of verifications. Our work provides practical guidance on optimizing test-time scaling by balancing solution generation and verification. The code is available at https://github.com/nishadsinghi/sc-genrm-scaling.", "github_manual": "", "github_regex": "https://github.com/nishadsinghi/sc-genrm-scaling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2503.24377", "title": "Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models", "authors": [ "Rui Wang", "Hongru Wang", "Boyang Xue", "Jianhui Pang", "Shudong Liu", "Yi Chen", "Jiahao Qiu", "Derek Fai Wong", "Heng Ji", "Kam-Fai Wong" ], "github": "", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to perform complex reasoning tasks, transitioning from fast and intuitive thinking (System 1) to slow and deep reasoning (System 2). While System 2 reasoning improves task accuracy, it often incurs substantial computational costs due to its slow thinking nature and inefficient or unnecessary reasoning behaviors. In contrast, System 1 reasoning is computationally efficient but leads to suboptimal performance. Consequently, it is critical to balance the trade-off between performance (benefits) and computational costs (budgets), giving rise to the concept of reasoning economy. In this survey, we provide a comprehensive analysis of reasoning economy in both the post-training and test-time inference stages of LLMs, encompassing i) the cause of reasoning inefficiency, ii) behavior analysis of different reasoning patterns, and iii) potential solutions to achieve reasoning economy. By offering actionable insights and highlighting open challenges, we aim to shed light on strategies for improving the reasoning economy of LLMs, thereby serving as a valuable resource for advancing research in this evolving area. We also provide a public repository to continually track developments in this fast-evolving field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2503.24376", "title": "Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1", "authors": [ "Yi Chen", "Yuying Ge", "Rui Wang", "Yixiao Ge", "Lu Qiu", "Ying Shan", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Recent advancements in Chain of Thought (COT) generation have significantly improved the reasoning capabilities of Large Language Models (LLMs), with reinforcement learning (RL) emerging as an effective post-training approach. Multimodal Large Language Models (MLLMs) inherit this reasoning potential but remain underexplored in tasks requiring both perception and logical reasoning. To address this, we introduce SEED-Bench-R1, a benchmark designed to systematically evaluate post-training methods for MLLMs in video understanding. It includes intricate real-world videos and complex everyday planning tasks in the format of multiple-choice questions, requiring sophisticated perception and reasoning. SEED-Bench-R1 assesses generalization through a three-level hierarchy: in-distribution, cross-environment, and cross-environment-task scenarios, equipped with a large-scale training dataset with easily verifiable ground-truth answers. Using Qwen2-VL-Instruct-7B as a base model, we compare RL with supervised fine-tuning (SFT), demonstrating RL's data efficiency and superior performance on both in-distribution and out-of-distribution tasks, even outperforming SFT on general video understanding benchmarks like LongVideoBench. Our detailed analysis reveals that RL enhances visual perception but often produces less logically coherent reasoning chains. We identify key limitations such as inconsistent reasoning and overlooked visual cues, and suggest future improvements in base model reasoning, reward modeling, and RL robustness against noisy signals.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2503.21860", "title": "ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning", "authors": [ "Kailin Li", "Puhao Li", "Tengyu Liu", "Yuyang Li", "Siyuan Huang" ], "github": "https://github.com/ManipTrans/ManipTrans", "project_page": "", "abstract": "Human hands play a central role in interacting, motivating increasing research in dexterous robotic manipulation. Data-driven embodied AI algorithms demand precise, large-scale, human-like manipulation sequences, which are challenging to obtain with conventional reinforcement learning or real-world teleoperation. To address this, we introduce ManipTrans, a novel two-stage method for efficiently transferring human bimanual skills to dexterous robotic hands in simulation. ManipTrans first pre-trains a generalist trajectory imitator to mimic hand motion, then fine-tunes a specific residual module under interaction constraints, enabling efficient learning and accurate execution of complex bimanual tasks. Experiments show that ManipTrans surpasses state-of-the-art methods in success rate, fidelity, and efficiency. Leveraging ManipTrans, we transfer multiple hand-object datasets to robotic hands, creating DexManipNet, a large-scale dataset featuring previously unexplored tasks like pen capping and bottle unscrewing. DexManipNet comprises 3.3K episodes of robotic manipulation and is easily extensible, facilitating further policy training for dexterous hands and enabling real-world deployments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ManipTrans/ManipTrans", "github_api_added_by": "auto" }, { "date": "2025-04-02", "arxiv_id": "2503.23733", "title": "AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization", "authors": [ "Yiyang Du", "Xiaochen Wang", "Chi Chen", "Jiabo Ye", "Yiru Wang", "Peng Li", "Ming Yan", "Ji Zhang", "Fei Huang", "Zhifang Sui", "Maosong Sun", "Yang Liu" ], "github": "https://github.com/thunlp-mt/adamms", "project_page": "", "abstract": "Recently, model merging methods have demonstrated powerful strengths in combining abilities on various tasks from multiple Large Language Models (LLMs). While previous model merging methods mainly focus on merging homogeneous models with identical architecture, they meet challenges when dealing with Multimodal Large Language Models (MLLMs) with inherent heterogeneous property, including differences in model architecture and the asymmetry in the parameter space. In this work, we propose AdaMMS, a novel model merging method tailored for heterogeneous MLLMs. Our method tackles the challenges in three steps: mapping, merging and searching. Specifically, we first design mapping function between models to apply model merging on MLLMs with different architecture. Then we apply linear interpolation on model weights to actively adapt the asymmetry in the heterogeneous MLLMs. Finally in the hyper-parameter searching step, we propose an unsupervised hyper-parameter selection method for model merging. As the first model merging method capable of merging heterogeneous MLLMs without labeled data, extensive experiments on various model combinations demonstrated that AdaMMS outperforms previous model merging methods on various vision-language benchmarks.", "github_manual": "https://github.com/thunlp-mt/adamms", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2503.24379", "title": "Any2Caption:Interpreting Any Condition to Caption for Controllable Video Generation", "authors": [ "Shengqiong Wu", "Weicai Ye", "Jiahao Wang", "Quande Liu", "Xintao Wang", "Pengfei Wan", "Di Zhang", "Kun Gai", "Shuicheng Yan", "Hao Fei", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "To address the bottleneck of accurate user intent interpretation within the current video generation community, we present Any2Caption, a novel framework for controllable video generation under any condition. The key idea is to decouple various condition interpretation steps from the video synthesis step. By leveraging modern multimodal large language models (MLLMs), Any2Caption interprets diverse inputs--text, images, videos, and specialized cues such as region, motion, and camera poses--into dense, structured captions that offer backbone video generators with better guidance. We also introduce Any2CapIns, a large-scale dataset with 337K instances and 407K conditions for any-condition-to-caption instruction tuning. Comprehensive evaluations demonstrate significant improvements of our system in controllability and video quality across various aspects of existing video generation models. Project Page: https://sqwu.top/Any2Cap/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.00595", "title": "Open-Qwen2VL: Compute-Efficient Pre-Training of Fully-Open Multimodal LLMs on Academic Resources", "authors": [ "Weizhi Wang", "Yu Tian", "Linjie Yang", "Heng Wang", "Xifeng Yan" ], "github": "", "project_page": "", "abstract": "The reproduction of state-of-the-art multimodal LLM pre-training faces barriers at every stage of the pipeline, including high-quality data filtering, multimodal data mixture strategies, sequence packing techniques, and training frameworks. We introduce Open-Qwen2VL, a fully open-source 2B-parameter Multimodal Large Language Model pre-trained efficiently on 29M image-text pairs using only 442 A100-40G GPU hours. Our approach employs low-to-high dynamic image resolution and multimodal sequence packing to significantly enhance pre-training efficiency. The training dataset was carefully curated using both MLLM-based filtering techniques (e.g., MLM-Filter) and conventional CLIP-based filtering methods, substantially improving data quality and training efficiency. The Open-Qwen2VL pre-training is conducted on academic level 8xA100-40G GPUs at UCSB on 5B packed multimodal tokens, which is 0.36\\% of 1.4T multimodal pre-training tokens of Qwen2-VL. The final instruction-tuned Open-Qwen2VL outperforms partially-open state-of-the-art MLLM Qwen2-VL-2B on various multimodal benchmarks of MMBench, SEEDBench, MMstar, and MathVista, indicating the remarkable training efficiency of Open-Qwen2VL. We open-source all aspects of our work, including compute-efficient and data-efficient training details, data filtering methods, sequence packing scripts, pre-training data in WebDataset format, FSDP-based training codebase, and both base and instruction-tuned model checkpoints. We redefine \"fully open\" for multimodal LLMs as the complete release of: 1) the training codebase, 2) detailed data filtering techniques, and 3) all pre-training and supervised fine-tuning data used to develop the model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.00557", "title": "Efficient LLaMA-3.2-Vision by Trimming Cross-attended Visual Features", "authors": [ "Jewon Lee", "Ki-Ung Song", "Seungmin Yang", "Donguk Lim", "Jaeyeon Kim", "Wooksu Shin", "Bo-Kyeong Kim", "Yong Jae Lee", "Tae-Ho Kim" ], "github": "", "project_page": "", "abstract": "Visual token reduction lowers inference costs caused by extensive image features in large vision-language models (LVLMs). Unlike relevant studies that prune tokens in self-attention-only LVLMs, our work uniquely addresses cross-attention-based models, which achieve superior performance. We identify that the key-value (KV) cache size for image tokens in cross-attention layers significantly exceeds that of text tokens in self-attention layers, posing a major compute bottleneck. To mitigate this issue, we exploit the sparse nature in cross-attention maps to selectively prune redundant visual features. Our Trimmed Llama effectively reduces KV cache demands without requiring additional training. By benefiting from 50%-reduced visual features, our model can reduce inference latency and memory usage while achieving benchmark parity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.00294", "title": "Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead", "authors": [ "Vidhisha Balachandran", "Jingya Chen", "Lingjiao Chen", "Shivam Garg", "Neel Joshi", "Yash Lara", "John Langford", "Besmira Nushi", "Vibhav Vineet", "Yue Wu", "Safoora Yousefi" ], "github": "https://github.com/microsoft/eureka-ml-insights", "project_page": "", "abstract": "Inference-time scaling can enhance the reasoning capabilities of large language models (LLMs) on complex problems that benefit from step-by-step problem solving. Although lengthening generated scratchpads has proven effective for mathematical tasks, the broader impact of this approach on other tasks remains less clear. In this work, we investigate the benefits and limitations of scaling methods across nine state-of-the-art models and eight challenging tasks, including math and STEM reasoning, calendar planning, NP-hard problems, navigation, and spatial reasoning. We compare conventional models (e.g., GPT-4o) with models fine-tuned for inference-time scaling (e.g., o1) through evaluation protocols that involve repeated model calls, either independently or sequentially with feedback. These evaluations approximate lower and upper performance bounds and potential for future performance improvements for each model, whether through enhanced training or multi-model inference systems. Our extensive empirical analysis reveals that the advantages of inference-time scaling vary across tasks and diminish as problem complexity increases. In addition, simply using more tokens does not necessarily translate to higher accuracy in these challenging regimes. Results from multiple independent runs with conventional models using perfect verifiers show that, for some tasks, these models can achieve performance close to the average performance of today's most advanced reasoning models. However, for other tasks, a significant performance gap remains, even in very high scaling regimes. Encouragingly, all models demonstrate significant gains when inference is further scaled with perfect verifiers or strong feedback, suggesting ample potential for future improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/eureka-ml-insights", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.00810", "title": "Z1: Efficient Test-time Scaling with Code", "authors": [ "Zhaojian Yu", "Yinghao Wu", "Yilun Zhao", "Arman Cohan", "Xiao-Ping Zhang" ], "github": "https://github.com/efficientscaling/Z1", "project_page": "", "abstract": "Large Language Models (LLMs) can achieve enhanced complex problem-solving through test-time computing scaling, yet this often entails longer contexts and numerous reasoning token costs. In this paper, we propose an efficient test-time scaling method that trains LLMs on code-related reasoning trajectories, facilitating their reduction of excess thinking tokens while maintaining performance. First, we create Z1-Code-Reasoning-107K, a curated dataset of simple and complex coding problems paired with their short and long solution trajectories. Second, we present a novel Shifted Thinking Window to mitigate overthinking overhead by removing context-delimiting tags (e.g., . . . ) and capping reasoning tokens. Trained with long and short trajectory data and equipped with Shifted Thinking Window, our model, Z1-7B, demonstrates the ability to adjust its reasoning level as the complexity of problems and exhibits efficient test-time scaling across different reasoning tasks that matches R1-Distill-Qwen-7B performance with about 30% of its average thinking tokens. Notably, fine-tuned with only code trajectories, Z1-7B demonstrates generalization to broader reasoning tasks (47.5% on GPQA Diamond). Our analysis of efficient reasoning elicitation also provides valuable insights for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/efficientscaling/Z1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.00509", "title": "Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems?", "authors": [ "Kai Yan", "Yufei Xu", "Zhengyin Du", "Xuesong Yao", "Zheyu Wang", "Xiaowen Guo", "Jiecao Chen" ], "github": "", "project_page": "", "abstract": "The rapid escalation from elementary school-level to frontier problems of the difficulty for LLM benchmarks in recent years have weaved a miracle for researchers that we are only inches away from surpassing human intelligence. However, is the LLMs' remarkable reasoning ability indeed comes from true intelligence by human standards, or are they simply reciting solutions witnessed during training at an Internet level? To study this problem, we propose RoR-Bench, a novel, multi-modal benchmark for detecting LLM's recitation behavior when asked simple reasoning problems but with conditions subtly shifted, and conduct empirical analysis on our benchmark. Surprisingly, we found existing cutting-edge LLMs unanimously exhibits extremely severe recitation behavior; by changing one phrase in the condition, top models such as OpenAI-o1 and DeepSeek-R1 can suffer 60% performance loss on elementary school-level arithmetic and reasoning problems. Such findings are a wake-up call to the LLM community that compels us to re-evaluate the true intelligence level of cutting-edge LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.00869", "title": "m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning with Large Language Models", "authors": [ "Xiaoke Huang", "Juncheng Wu", "Hui Liu", "Xianfeng Tang", "Yuyin Zhou" ], "github": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "project_page": "", "abstract": "Test-time scaling has emerged as a powerful technique for enhancing the reasoning capabilities of large language models. However, its effectiveness in medical reasoning remains uncertain, as the medical domain fundamentally differs from mathematical tasks in terms of knowledge representation and decision-making processes. In this paper, we provide the first comprehensive investigation of test-time scaling for medical reasoning and present m1, a simple yet effective approach that increases a model's medical reasoning capability at inference. Our evaluation across diverse medical tasks demonstrates that test-time scaling consistently enhances medical reasoning, enabling lightweight fine-tuned models under 10B parameters to establish new state-of-the-art performance, while our 32B model rivals previous 70B-scale medical LLMs. However, we identify an optimal reasoning token budget of approximately 4K, beyond which performance may degrade due to overthinking. Budget forcing, which extends test-time computation through iterative prompts, helps models double-check answers but does not necessarily improve the overall medical QA performance and, in some cases, even introduces errors into previously correct responses. Our case-by-case analysis identifies insufficient medical knowledge as a key bottleneck that prevents further performance gains through test-time scaling. We find that increasing data scale, improving data quality, and expanding model capacity consistently enhance medical knowledge grounding, enabling continued performance improvements, particularly on challenging medical benchmarks where smaller models reach saturation. These findings underscore fundamental differences between medical and mathematical reasoning in LLMs, highlighting that enriched medical knowledge, other than increased reasoning depth alone, is essential for realizing the benefits of test-time scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.00927", "title": "Multi-Token Attention", "authors": [ "Olga Golovneva", "Tianlu Wang", "Jason Weston", "Sainbayar Sukhbaatar" ], "github": "https://github.com/facebookresearch/RAM/tree/main/projects/mta", "project_page": "", "abstract": "Soft attention is a critical mechanism powering LLMs to locate relevant parts within a given context. However, individual attention weights are determined by the similarity of only a single query and key token vector. This \"single token attention\" bottlenecks the amount of information used in distinguishing a relevant part from the rest of the context. To address this issue, we propose a new attention method, Multi-Token Attention (MTA), which allows LLMs to condition their attention weights on multiple query and key vectors simultaneously. This is achieved by applying convolution operations over queries, keys and heads, allowing nearby queries and keys to affect each other's attention weights for more precise attention. As a result, our method can locate relevant context using richer, more nuanced information that can exceed a single vector's capacity. Through extensive evaluations, we demonstrate that MTA achieves enhanced performance on a range of popular benchmarks. Notably, it outperforms Transformer baseline models on standard language modeling tasks, and on tasks that require searching for information within long contexts, where our method's ability to leverage richer information proves particularly beneficial.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/RAM/tree/main/projects/mta", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.00698", "title": "Command A: An Enterprise-Ready Large Language Model", "authors": [ "Team Cohere", "Aakanksha", "Arash Ahmadian", "Marwan Ahmed", "Jay Alammar", "Yazeed Alnumay", "Sophia Althammer", "Arkady Arkhangorodsky", "Viraat Aryabumi", "Dennis Aumiller", "Rapha\u00ebl Avalos", "Zahara Aviv", "Sammie Bae", "Saurabh Baji", "Alexandre Barbet", "Max Bartolo", "Bj\u00f6rn Bebensee", "Neeral Beladia", "Walter Beller-Morales", "Alexandre B\u00e9rard", "Andrew Berneshawi", "Anna Bialas", "Phil Blunsom", "Matt Bobkin", "Adi Bongale", "Sam Braun", "Maxime Brunet", "Samuel Cahyawijaya", "David Cairuz", "Jon Ander Campos", "Cassie Cao", "Kris Cao", "Roman Castagn\u00e9", "Juli\u00e1n Cendrero", "Leila Chan Currie", "Yash Chandak", "Diane Chang", "Giannis Chatziveroglou", "Hongyu Chen", "Claire Cheng", "Alexis Chevalier", "Justin T. Chiu", "Eugene Cho", "Eugene Choi", "Eujeong Choi", "Tim Chung", "Volkan Cirik", "Ana Cismaru", "Pierre Clavier", "Henry Conklin", "Lucas Crawhall-Stein", "Devon Crouse", "Andres Felipe Cruz-Salinas", "Ben Cyrus", "Daniel D'souza", "Hugo Dalla-Torre", "John Dang", "William Darling", "Omar Darwiche Domingues", "Saurabh Dash", "Antoine Debugne", "Th\u00e9o Dehaze", "Shaan Desai", "Joan Devassy", "Rishit Dholakia", "Kyle Duffy", "Ali Edalati", "Ace Eldeib", "Abdullah Elkady", "Sarah Elsharkawy", "Irem Erg\u00fcn", "Beyza Ermis", "Marzieh Fadaee", "Boyu Fan", "Lucas Fayoux", "Yannis Flet-Berliac", "Nick Frosst", "Matthias Gall\u00e9", "Wojciech Galuba", "Utsav Garg", "Matthieu Geist", "Mohammad Gheshlaghi Azar", "Seraphina Goldfarb-Tarrant", "Tomas Goldsack", "Aidan Gomez", "Victor Machado Gonzaga", "Nithya Govindarajan", "Manoj Govindassamy", "Nathan Grinsztajn", "Nikolas Gritsch", "Patrick Gu", "Shangmin Guo", "Kilian Haefeli", "Rod Hajjar", "Tim Hawes", "Jingyi He", "Sebastian Hofst\u00e4tter", "Sungjin Hong", "Sara Hooker", "Tom Hosking", "Stephanie Howe", "Eric Hu", "Renjie Huang", "Hemant Jain", "Ritika Jain", "Nick Jakobi", "Madeline Jenkins", "JJ Jordan", "Dhruti Joshi", "Jason Jung", "Trushant Kalyanpur", "Siddhartha Rao Kamalakara", "Julia Kedrzycki", "Gokce Keskin", "Edward Kim", "Joon Kim", "Wei-Yin Ko", "Tom Kocmi", "Michael Kozakov", "Wojciech Kry\u015bci\u0144ski", "Arnav Kumar Jain", "Komal Kumar Teru", "Sander Land", "Michael Lasby", "Olivia Lasche", "Justin Lee", "Patrick Lewis", "Jeffrey Li", "Jonathan Li", "Hangyu Lin", "Acyr Locatelli", "Kevin Luong", "Raymond Ma", "Lukas Mach", "Marina Machado", "Joanne Magbitang", "Brenda Malacara Lopez", "Aryan Mann", "Kelly Marchisio", "Olivia Markham", "Alexandre Matton", "Alex McKinney", "Dominic McLoughlin", "Jozef Mokry", "Adrien Morisot", "Autumn Moulder", "Harry Moynehan", "Maximilian Mozes", "Vivek Muppalla", "Lidiya Murakhovska", "Hemangani Nagarajan", "Alekhya Nandula", "Hisham Nasir", "Shauna Nehra", "Josh Netto-Rosen", "Daniel Ohashi", "James Owers-Bardsley", "Jason Ozuzu", "Dennis Padilla", "Gloria Park", "Sam Passaglia", "Jeremy Pekmez", "Laura Penstone", "Aleksandra Piktus", "Case Ploeg", "Andrew Poulton", "Youran Qi", "Shubha Raghvendra", "Miguel Ramos", "Ekagra Ranjan", "Pierre Richemond", "C\u00e9cile Robert-Michon", "Aur\u00e9lien Rodriguez", "Sudip Roy", "Laura Ruis", "Louise Rust", "Anubhav Sachan", "Alejandro Salamanca", "Kailash Karthik Saravanakumar", "Isha Satyakam", "Alice Schoenauer Sebag", "Priyanka Sen", "Sholeh Sepehri", "Preethi Seshadri", "Ye Shen", "Tom Sherborne", "Sylvie Chang Shi", "Sanal Shivaprasad", "Vladyslav Shmyhlo", "Anirudh Shrinivason", "Inna Shteinbuk", "Amir Shukayev", "Mathieu Simard", "Ella Snyder", "Ava Spataru", "Victoria Spooner", "Trisha Starostina", "Florian Strub", "Yixuan Su", "Jimin Sun", "Dwarak Talupuru", "Eugene Tarassov", "Elena Tommasone", "Jennifer Tracey", "Billy Trend", "Evren Tumer", "Ahmet \u00dcst\u00fcn", "Bharat Venkitesh", "David Venuto", "Pat Verga", "Maxime Voisin", "Alex Wang", "Donglu Wang", "Shijian Wang", "Edmond Wen", "Naomi White", "Jesse Willman", "Marysia Winkels", "Chen Xia", "Jessica Xie", "Minjie Xu", "Bowen Yang", "Tan Yi-Chern", "Ivan Zhang", "Zhenyu Zhao", "Zhoujie Zhao" ], "github": "", "project_page": "", "abstract": "In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised and multilingual-capable model, with support for 23 languages of global business, and a novel hybrid architecture balancing efficiency with top of the range performance. It offers best-in-class Retrieval Augmented Generation (RAG) capabilities with grounding and tool use to automate sophisticated business processes. These abilities are achieved through a decentralised training approach, including self-refinement algorithms and model merging techniques. We also include results for Command R7B which shares capability and architectural similarities to Command A. Weights for both models have been released for research purposes. This technical report details our original training pipeline and presents an extensive evaluation of our models across a suite of enterprise-relevant tasks and public benchmarks, demonstrating excellent performance and efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.00072", "title": "Chapter-Llama: Efficient Chaptering in Hour-Long Videos with LLMs", "authors": [ "Lucas Ventura", "Antoine Yang", "Cordelia Schmid", "G\u00fcl Varol" ], "github": "https://github.com/lucas-ventura/chapter-llama", "project_page": "", "abstract": "We address the task of video chaptering, i.e., partitioning a long video timeline into semantic units and generating corresponding chapter titles. While relatively underexplored, automatic chaptering has the potential to enable efficient navigation and content retrieval in long-form videos. In this paper, we achieve strong chaptering performance on hour-long videos by efficiently addressing the problem in the text domain with our 'Chapter-Llama' framework. Specifically, we leverage a pretrained large language model (LLM) with large context window, and feed as input (i) speech transcripts and (ii) captions describing video frames, along with their respective timestamps. Given the inefficiency of exhaustively captioning all frames, we propose a lightweight speech-guided frame selection strategy based on speech transcript content, and experimentally demonstrate remarkable advantages. We train the LLM to output timestamps for the chapter boundaries, as well as free-form chapter titles. This simple yet powerful approach scales to processing one-hour long videos in a single forward pass. Our results demonstrate substantial improvements (e.g., 45.3 vs 26.7 F1 score) over the state of the art on the recent VidChapters-7M benchmark. To promote further research, we release our code and models at our project page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lucas-ventura/chapter-llama", "github_api_added_by": "auto" }, { "date": "2025-04-02", "arxiv_id": "2503.23145", "title": "CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis", "authors": [ "Anjiang Wei", "Tarun Suresh", "Jiannan Cao", "Naveen Kannan", "Yuheng Wu", "Kai Yan", "Thiago S. F. X. Teixeira", "Ke Wang", "Alex Aiken" ], "github": "https://github.com/Anjiang-Wei/CodeARC", "project_page": "", "abstract": "Inductive program synthesis, or programming by example, requires synthesizing functions from input-output examples that generalize to unseen inputs. While large language model agents have shown promise in programming tasks guided by natural language, their ability to perform inductive program synthesis is underexplored. Existing evaluation protocols rely on static sets of examples and held-out tests, offering no feedback when synthesized functions are incorrect and failing to reflect real-world scenarios such as reverse engineering. We propose CodeARC, the Code Abstraction and Reasoning Challenge, a new evaluation framework where agents interact with a hidden target function by querying it with new inputs, synthesizing candidate functions, and iteratively refining their solutions using a differential testing oracle. This interactive setting encourages agents to perform function calls and self-correction based on feedback. We construct the first large-scale benchmark for general-purpose inductive program synthesis, featuring 1114 functions. Among 18 models evaluated, o3-mini performs best with a success rate of 52.7%, highlighting the difficulty of this task. Fine-tuning LLaMA-3.1-8B-Instruct on curated synthesis traces yields up to a 31% relative performance gain. CodeARC provides a more realistic and challenging testbed for evaluating LLM-based program synthesis and inductive reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Anjiang-Wei/CodeARC", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.00050", "title": "JudgeLRM: Large Reasoning Models as a Judge", "authors": [ "Nuo Chen", "Zhiyuan Hu", "Qingyun Zou", "Jiaying Wu", "Qian Wang", "Bryan Hooi", "Bingsheng He" ], "github": "", "project_page": "", "abstract": "The rise of Large Language Models (LLMs) as evaluators offers a scalable alternative to human annotation, yet existing Supervised Fine-Tuning (SFT) for judges approaches often fall short in domains requiring complex reasoning. In this work, we investigate whether LLM judges truly benefit from enhanced reasoning capabilities. Through a detailed analysis of reasoning requirements across evaluation tasks, we reveal a negative correlation between SFT performance gains and the proportion of reasoning-demanding samples - highlighting the limitations of SFT in such scenarios. To address this, we introduce JudgeLRM, a family of judgment-oriented LLMs trained using reinforcement learning (RL) with judge-wise, outcome-driven rewards. JudgeLRM models consistently outperform both SFT-tuned and state-of-the-art reasoning models. Notably, JudgeLRM-3B surpasses GPT-4, and JudgeLRM-7B outperforms DeepSeek-R1 by 2.79% in F1 score, particularly excelling in judge tasks requiring deep reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.01019", "title": "MixerMDM: Learnable Composition of Human Motion Diffusion Models", "authors": [ "Pablo Ruiz-Ponce", "German Barquero", "Cristina Palmero", "Sergio Escalera", "Jos\u00e9 Garc\u00eda-Rodr\u00edguez" ], "github": "", "project_page": "", "abstract": "Generating human motion guided by conditions such as textual descriptions is challenging due to the need for datasets with pairs of high-quality motion and their corresponding conditions. The difficulty increases when aiming for finer control in the generation. To that end, prior works have proposed to combine several motion diffusion models pre-trained on datasets with different types of conditions, thus allowing control with multiple conditions. However, the proposed merging strategies overlook that the optimal way to combine the generation processes might depend on the particularities of each pre-trained generative model and also the specific textual descriptions. In this context, we introduce MixerMDM, the first learnable model composition technique for combining pre-trained text-conditioned human motion diffusion models. Unlike previous approaches, MixerMDM provides a dynamic mixing strategy that is trained in an adversarial fashion to learn to combine the denoising process of each model depending on the set of conditions driving the generation. By using MixerMDM to combine single- and multi-person motion diffusion models, we achieve fine-grained control on the dynamics of every person individually, and also on the overall interaction. Furthermore, we propose a new evaluation technique that, for the first time in this task, measures the interaction and individual quality by computing the alignment between the mixed generated motions and their conditions as well as the capabilities of MixerMDM to adapt the mixing throughout the denoising process depending on the motions to mix.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.01017", "title": "Scaling Language-Free Visual Representation Learning", "authors": [ "David Fan", "Shengbang Tong", "Jiachen Zhu", "Koustuv Sinha", "Zhuang Liu", "Xinlei Chen", "Michael Rabbat", "Nicolas Ballas", "Yann LeCun", "Amir Bar", "Saining Xie" ], "github": "", "project_page": "", "abstract": "Visual Self-Supervised Learning (SSL) currently underperforms Contrastive Language-Image Pretraining (CLIP) in multimodal settings such as Visual Question Answering (VQA). This multimodal gap is often attributed to the semantics introduced by language supervision, even though visual SSL and CLIP models are often trained on different data. In this work, we ask the question: \"Do visual self-supervised approaches lag behind CLIP due to the lack of language supervision, or differences in the training data?\" We study this question by training both visual SSL and CLIP models on the same MetaCLIP data, and leveraging VQA as a diverse testbed for vision encoders. In this controlled setup, visual SSL models scale better than CLIP models in terms of data and model capacity, and visual SSL performance does not saturate even after scaling up to 7B parameters. Consequently, we observe visual SSL methods achieve CLIP-level performance on a wide range of VQA and classic vision benchmarks. These findings demonstrate that pure visual SSL can match language-supervised visual pretraining at scale, opening new opportunities for vision-centric representation learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2503.24210", "title": "DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D Gaussian Splatting", "authors": [ "Seungjun Lee", "Gim Hee Lee" ], "github": "", "project_page": "", "abstract": "Reconstructing sharp 3D representations from blurry multi-view images are long-standing problem in computer vision. Recent works attempt to enhance high-quality novel view synthesis from the motion blur by leveraging event-based cameras, benefiting from high dynamic range and microsecond temporal resolution. However, they often reach sub-optimal visual quality in either restoring inaccurate color or losing fine-grained details. In this paper, we present DiET-GS, a diffusion prior and event stream-assisted motion deblurring 3DGS. Our framework effectively leverages both blur-free event streams and diffusion prior in a two-stage training strategy. Specifically, we introduce the novel framework to constraint 3DGS with event double integral, achieving both accurate color and well-defined details. Additionally, we propose a simple technique to leverage diffusion prior to further enhance the edge details. Qualitative and quantitative results on both synthetic and real-world data demonstrate that our DiET-GS is capable of producing significantly better quality of novel views compared to the existing baselines. Our project page is https://diet-gs.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2503.23434", "title": "Towards Trustworthy GUI Agents: A Survey", "authors": [ "Yucheng Shi", "Wenhao Yu", "Wenlin Yao", "Wenhu Chen", "Ninghao Liu" ], "github": "", "project_page": "", "abstract": "GUI agents, powered by large foundation models, can interact with digital interfaces, enabling various applications in web automation, mobile navigation, and software testing. However, their increasing autonomy has raised critical concerns about their security, privacy, and safety. This survey examines the trustworthiness of GUI agents in five critical dimensions: security vulnerabilities, reliability in dynamic environments, transparency and explainability, ethical considerations, and evaluation methodologies. We also identify major challenges such as vulnerability to adversarial attacks, cascading failure modes in sequential decision-making, and a lack of realistic evaluation benchmarks. These issues not only hinder real-world deployment but also call for comprehensive mitigation strategies beyond task success. As GUI agents become more widespread, establishing robust safety standards and responsible development practices is essential. This survey provides a foundation for advancing trustworthy GUI agents through systematic understanding and future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2503.22952", "title": "OmniMMI: A Comprehensive Multi-modal Interaction Benchmark in Streaming Video Contexts", "authors": [ "Yuxuan Wang", "Yueqian Wang", "Bo Chen", "Tong Wu", "Dongyan Zhao", "Zilong Zheng" ], "github": "https://github.com/patrick-tssn/M4", "project_page": "", "abstract": "The rapid advancement of multi-modal language models (MLLMs) like GPT-4o has propelled the development of Omni language models, designed to process and proactively respond to continuous streams of multi-modal data. Despite their potential, evaluating their real-world interactive capabilities in streaming video contexts remains a formidable challenge. In this work, we introduce OmniMMI, a comprehensive multi-modal interaction benchmark tailored for OmniLLMs in streaming video contexts. OmniMMI encompasses over 1,121 videos and 2,290 questions, addressing two critical yet underexplored challenges in existing video benchmarks: streaming video understanding and proactive reasoning, across six distinct subtasks. Moreover, we propose a novel framework, Multi-modal Multiplexing Modeling (M4), designed to enable an inference-efficient streaming model that can see, listen while generating.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/patrick-tssn/M4", "github_api_added_by": "auto" }, { "date": "2025-04-02", "arxiv_id": "2503.24219", "title": "MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote Sensing", "authors": [ "Karim Radouane", "Hanane Azzag", "Mustapha lebbah" ], "github": "https://github.com/rd20karim/MB-ORES", "project_page": "", "abstract": "We propose a unified framework that integrates object detection (OD) and visual grounding (VG) for remote sensing (RS) imagery. To support conventional OD and establish an intuitive prior for VG task, we fine-tune an open-set object detector using referring expression data, framing it as a partially supervised OD task. In the first stage, we construct a graph representation of each image, comprising object queries, class embeddings, and proposal locations. Then, our task-aware architecture processes this graph to perform the VG task. The model consists of: (i) a multi-branch network that integrates spatial, visual, and categorical features to generate task-aware proposals, and (ii) an object reasoning network that assigns probabilities across proposals, followed by a soft selection mechanism for final referring object localization. Our model demonstrates superior performance on the OPT-RSVG and DIOR-RSVG datasets, achieving significant improvements over state-of-the-art methods while retaining classical OD capabilities. The code will be available in our repository: https://github.com/rd20karim/MB-ORES.", "github_manual": "", "github_regex": "https://github.com/rd20karim/MB-ORES", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2503.23157", "title": "Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL", "authors": [ "Mohammadreza Pourreza", "Shayan Talaei", "Ruoxi Sun", "Xingchen Wan", "Hailong Li", "Azalia Mirhoseini", "Amin Saberi", "Sercan \"O. Arik" ], "github": "", "project_page": "", "abstract": "Text-to-SQL is a challenging task involving multiple reasoning-intensive subtasks, including natural language understanding, database schema comprehension, and precise SQL query formulation. Existing approaches often rely on handcrafted reasoning paths with inductive biases that can limit their overall effectiveness. Motivated by the recent success of reasoning-enhanced models such as DeepSeek R1 and OpenAI o1, which effectively leverage reward-driven self-exploration to enhance reasoning capabilities and generalization, we propose a novel set of partial rewards tailored specifically for the Text-to-SQL task. Our reward set includes schema-linking, AI feedback, n-gram similarity, and syntax check, explicitly designed to address the reward sparsity issue prevalent in reinforcement learning (RL). Leveraging group relative policy optimization (GRPO), our approach explicitly encourages large language models (LLMs) to develop intrinsic reasoning skills necessary for accurate SQL query generation. With models of different sizes, we demonstrate that RL-only training with our proposed rewards consistently achieves higher accuracy and superior generalization compared to supervised fine-tuning (SFT). Remarkably, our RL-trained 14B-parameter model significantly outperforms larger proprietary models, e.g. o3-mini by 4% and Gemini-1.5-Pro-002 by 3% on the BIRD benchmark. These highlight the efficacy of our proposed RL-training framework with partial rewards for enhancing both accuracy and reasoning capabilities in Text-to-SQL tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2503.22165", "title": "Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models", "authors": [ "Zhanke Zhou", "Zhaocheng Zhu", "Xuan Li", "Mikhail Galkin", "Xiao Feng", "Sanmi Koyejo", "Jian Tang", "Bo Han" ], "github": "https://github.com/tmlr-group/landscape-of-thoughts", "project_page": "", "abstract": "Numerous applications of large language models (LLMs) rely on their ability to perform step-by-step reasoning. However, the reasoning behavior of LLMs remains poorly understood, posing challenges to research, development, and safety. To address this gap, we introduce landscape of thoughts-the first visualization tool for users to inspect the reasoning paths of chain-of-thought and its derivatives on any multi-choice dataset. Specifically, we represent the states in a reasoning path as feature vectors that quantify their distances to all answer choices. These features are then visualized in two-dimensional plots using t-SNE. Qualitative and quantitative analysis with the landscape of thoughts effectively distinguishes between strong and weak models, correct and incorrect answers, as well as different reasoning tasks. It also uncovers undesirable reasoning patterns, such as low consistency and high uncertainty. Additionally, users can adapt our tool to a model that predicts the property they observe. We showcase this advantage by adapting our tool to a lightweight verifier that evaluates the correctness of reasoning paths. The code is publicly available at: https://github.com/tmlr-group/landscape-of-thoughts.", "github_manual": "", "github_regex": "https://github.com/tmlr-group/landscape-of-thoughts", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-02", "arxiv_id": "2504.01833", "title": "YourBench: Easy Custom Evaluation Sets for Everyone", "authors": [ "Sumuk Shashidhar", "Cl\u00e9mentine Fourrier", "Alina Lozovskia", "Thomas Wolf", "Gokhan Tur", "Dilek Hakkani-T\u00fcr" ], "github": "https://github.com/huggingface/yourbench", "project_page": "", "abstract": "Evaluating large language models (LLMs) effectively remains a critical bottleneck, as traditional static benchmarks suffer from saturation and contamination, while human evaluations are costly and slow. This hinders timely or domain-specific assessment, crucial for real-world applications. We introduce YourBench, a novel, open-source framework that addresses these limitations by enabling dynamic, automated generation of reliable, up-to-date, and domain-tailored benchmarks cheaply and without manual annotation, directly from user-provided documents. We demonstrate its efficacy by replicating 7 diverse MMLU subsets using minimal source text, achieving this for under 15 USD in total inference costs while perfectly preserving the relative model performance rankings (Spearman Rho = 1) observed on the original benchmark. To ensure that YourBench generates data grounded in provided input instead of relying on posterior parametric knowledge in models, we also introduce Tempora-0325, a novel dataset of over 7K diverse documents, published exclusively after March 2025. Our comprehensive analysis spans 26 SoTA models from 7 major families across varying scales (3-671B parameters) to validate the quality of generated evaluations through rigorous algorithmic checks (e.g., citation grounding) and human assessments. We release the YourBench library, the Tempora-0325 dataset, 150k+ question answer pairs based on Tempora and all evaluation and inference traces to facilitate reproducible research and empower the community to generate bespoke benchmarks on demand, fostering more relevant and trustworthy LLM evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/yourbench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2504.00999", "title": "MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization", "authors": [ "Siyuan Li", "Luyuan Zhang", "Zedong Wang", "Juanxi Tian", "Cheng Tan", "Zicheng Liu", "Chang Yu", "Qingsong Xie", "Haonan Lu", "Haoqian Wang", "Zhen Lei" ], "github": "", "project_page": "", "abstract": "Masked Image Modeling (MIM) with Vector Quantization (VQ) has achieved great success in both self-supervised pre-training and image generation. However, most existing methods struggle to address the trade-off in shared latent space for generation quality vs. representation learning and efficiency. To push the limits of this paradigm, we propose MergeVQ, which incorporates token merging techniques into VQ-based generative models to bridge the gap between image generation and visual representation learning in a unified architecture. During pre-training, MergeVQ decouples top-k semantics from latent space with the token merge module after self-attention blocks in the encoder for subsequent Look-up Free Quantization (LFQ) and global alignment and recovers their fine-grained details through cross-attention in the decoder for reconstruction. As for the second-stage generation, we introduce MergeAR, which performs KV Cache compression for efficient raster-order prediction. Extensive experiments on ImageNet verify that MergeVQ as an AR generative model achieves competitive performance in both visual representation learning and image generation tasks while maintaining favorable token efficiency and inference speed. The code and model will be available at https://apexgen-x.github.io/MergeVQ.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2504.01308", "title": "Safeguarding Vision-Language Models: Mitigating Vulnerabilities to Gaussian Noise in Perturbation-based Attacks", "authors": [ "Jiawei Wang", "Yushen Zuo", "Yuanjun Chai", "Zhendong Liu", "Yichen Fu", "Yichun Feng", "Kin-man Lam" ], "github": "https://github.com/JarvisUSTC/DiffPure-RobustVLM", "project_page": "", "abstract": "Vision-Language Models (VLMs) extend the capabilities of Large Language Models (LLMs) by incorporating visual information, yet they remain vulnerable to jailbreak attacks, especially when processing noisy or corrupted images. Although existing VLMs adopt security measures during training to mitigate such attacks, vulnerabilities associated with noise-augmented visual inputs are overlooked. In this work, we identify that missing noise-augmented training causes critical security gaps: many VLMs are susceptible to even simple perturbations such as Gaussian noise. To address this challenge, we propose Robust-VLGuard, a multimodal safety dataset with aligned / misaligned image-text pairs, combined with noise-augmented fine-tuning that reduces attack success rates while preserving functionality of VLM. For stronger optimization-based visual perturbation attacks, we propose DiffPure-VLM, leveraging diffusion models to convert adversarial perturbations into Gaussian-like noise, which can be defended by VLMs with noise-augmented safety fine-tuning. Experimental results demonstrate that the distribution-shifting property of diffusion model aligns well with our fine-tuned VLMs, significantly mitigating adversarial perturbations across varying intensities. The dataset and code are available at https://github.com/JarvisUSTC/DiffPure-RobustVLM.", "github_manual": "", "github_regex": "https://github.com/JarvisUSTC/DiffPure-RobustVLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2504.00824", "title": "ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations", "authors": [ "Yubo Wang", "Xueguang Ma", "Ping Nie", "Huaye Zeng", "Zhiheng Lyu", "Yuxuan Zhang", "Benjamin Schneider", "Yi Lu", "Xiang Yue", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Academic writing requires both coherent text generation and precise citation of relevant literature. Although recent Retrieval-Augmented Generation (RAG) systems have significantly improved factual accuracy in general-purpose text generation, their capacity to adequately support professional academic writing remains limited. In this work, we introduce ScholarCopilot, a unified framework designed to enhance existing large language models for generating professional academic articles with accurate and contextually relevant citations. ScholarCopilot dynamically determines when to retrieve scholarly references by generating a retrieval token [RET], and then utilizes its representation to look up relevant citations from a database. The retrieved references are fed into the model to augment the generation process. We jointly optimize both the generation and citation tasks within a single framework to increase efficiency. Trained on 500K papers from arXiv, our model achieves a top-1 retrieval accuracy of 40.1% on our evaluation dataset, outperforming baselines such as E5-Mistral-7B-Instruct (15.0%) and BM25 (9.8%). On a dataset of 1,000 academic writing samples, ScholarCopilot scores 16.2/25 in generation quality (measured across relevance, coherence, academic rigor, completeness, and innovation), surpassing models with 10x more parameters such as Qwen-2.5-72B-Instruct (15.8/25). Human studies also confirm ScholarCopilot's superior performance in citation recall, writing efficiency, and overall user experience, confirming the effectiveness of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2504.00883", "title": "Improved Visual-Spatial Reasoning via R1-Zero-Like Training", "authors": [ "Zhenyi Liao", "Qingsong Xie", "Yanhao Zhang", "Zijian Kong", "Haonan Lu", "Zhenyu Yang", "Zhijie Deng" ], "github": "https://github.com/zhijie-group/R1-Zero-VSI", "project_page": "", "abstract": "Increasing attention has been placed on improving the reasoning capacities of multi-modal large language models (MLLMs). As the cornerstone for AI agents that function in the physical realm, video-based visual-spatial intelligence (VSI) emerges as one of the most pivotal reasoning capabilities of MLLMs. This work conducts a first, in-depth study on improving the visual-spatial reasoning of MLLMs via R1-Zero-like training. Technically, we first identify that the visual-spatial reasoning capacities of small- to medium-sized Qwen2-VL models cannot be activated via Chain of Thought (CoT) prompts. We then incorporate GRPO training for improved visual-spatial reasoning, using the carefully curated VSI-100k dataset, following DeepSeek-R1-Zero. During the investigation, we identify the necessity to keep the KL penalty (even with a small value) in GRPO. With just 120 GPU hours, our vsGRPO-2B model, fine-tuned from Qwen2-VL-2B, can outperform the base model by 12.1% and surpass GPT-4o. Moreover, our vsGRPO-7B model, fine-tuned from Qwen2-VL-7B, achieves performance comparable to that of the best open-source model LLaVA-NeXT-Video-72B. Additionally, we compare vsGRPO to supervised fine-tuning and direct preference optimization baselines and observe strong performance superiority. The code and dataset will be available soon.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhijie-group/R1-Zero-VSI", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2504.01014", "title": "AnimeGamer: Infinite Anime Life Simulation with Next Game State Prediction", "authors": [ "Junhao Cheng", "Yuying Ge", "Yixiao Ge", "Jing Liao", "Ying Shan" ], "github": "https://github.com/TencentARC/AnimeGamer", "project_page": "", "abstract": "Recent advancements in image and video synthesis have opened up new promise in generative games. One particularly intriguing application is transforming characters from anime films into interactive, playable entities. This allows players to immerse themselves in the dynamic anime world as their favorite characters for life simulation through language instructions. Such games are defined as infinite game since they eliminate predetermined boundaries and fixed gameplay rules, where players can interact with the game world through open-ended language and experience ever-evolving storylines and environments. Recently, a pioneering approach for infinite anime life simulation employs large language models (LLMs) to translate multi-turn text dialogues into language instructions for image generation. However, it neglects historical visual context, leading to inconsistent gameplay. Furthermore, it only generates static images, failing to incorporate the dynamics necessary for an engaging gaming experience. In this work, we propose AnimeGamer, which is built upon Multimodal Large Language Models (MLLMs) to generate each game state, including dynamic animation shots that depict character movements and updates to character states, as illustrated in Figure 1. We introduce novel action-aware multimodal representations to represent animation shots, which can be decoded into high-quality video clips using a video diffusion model. By taking historical animation shot representations as context and predicting subsequent representations, AnimeGamer can generate games with contextual consistency and satisfactory dynamics. Extensive evaluations using both automated metrics and human evaluations demonstrate that AnimeGamer outperforms existing methods in various aspects of the gaming experience. Codes and checkpoints are available at https://github.com/TencentARC/AnimeGamer.", "github_manual": "", "github_regex": "https://github.com/TencentARC/AnimeGamer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2503.23135", "title": "LSNet: See Large, Focus Small", "authors": [ "Ao Wang", "Hui Chen", "Zijia Lin", "Jungong Han", "Guiguang Ding" ], "github": "https://github.com/THU-MIG/lsnet", "project_page": "", "abstract": "Vision network designs, including Convolutional Neural Networks and Vision Transformers, have significantly advanced the field of computer vision. Yet, their complex computations pose challenges for practical deployments, particularly in real-time applications. To tackle this issue, researchers have explored various lightweight and efficient network designs. However, existing lightweight models predominantly leverage self-attention mechanisms and convolutions for token mixing. This dependence brings limitations in effectiveness and efficiency in the perception and aggregation processes of lightweight networks, hindering the balance between performance and efficiency under limited computational budgets. In this paper, we draw inspiration from the dynamic heteroscale vision ability inherent in the efficient human vision system and propose a ``See Large, Focus Small'' strategy for lightweight vision network design. We introduce LS (Large-Small) convolution, which combines large-kernel perception and small-kernel aggregation. It can efficiently capture a wide range of perceptual information and achieve precise feature aggregation for dynamic and complex visual representations, thus enabling proficient processing of visual information. Based on LS convolution, we present LSNet, a new family of lightweight models. Extensive experiments demonstrate that LSNet achieves superior performance and efficiency over existing lightweight networks in various vision tasks. Codes and models are available at https://github.com/jameslahm/lsnet.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/THU-MIG/lsnet", "github_api_added_by": "user" }, { "date": "2025-04-03", "arxiv_id": "2504.01848", "title": "PaperBench: Evaluating AI's Ability to Replicate AI Research", "authors": [ "Giulio Starace", "Oliver Jaffe", "Dane Sherburn", "James Aung", "Jun Shern Chan", "Leon Maksin", "Rachel Dias", "Evan Mays", "Benjamin Kinsella", "Wyatt Thompson", "Johannes Heidecke", "Amelia Glaese", "Tejal Patwardhan" ], "github": "https://github.com/openai/preparedness", "project_page": "", "abstract": "We introduce PaperBench, a benchmark evaluating the ability of AI agents to replicate state-of-the-art AI research. Agents must replicate 20 ICML 2024 Spotlight and Oral papers from scratch, including understanding paper contributions, developing a codebase, and successfully executing experiments. For objective evaluation, we develop rubrics that hierarchically decompose each replication task into smaller sub-tasks with clear grading criteria. In total, PaperBench contains 8,316 individually gradable tasks. Rubrics are co-developed with the author(s) of each ICML paper for accuracy and realism. To enable scalable evaluation, we also develop an LLM-based judge to automatically grade replication attempts against rubrics, and assess our judge's performance by creating a separate benchmark for judges. We evaluate several frontier models on PaperBench, finding that the best-performing tested agent, Claude 3.5 Sonnet (New) with open-source scaffolding, achieves an average replication score of 21.0\\%. Finally, we recruit top ML PhDs to attempt a subset of PaperBench, finding that models do not yet outperform the human baseline. We https://github.com/openai/preparedness{open-source our code} to facilitate future research in understanding the AI engineering capabilities of AI agents.", "github_manual": "", "github_regex": "https://github.com/openai/preparedness", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2504.01204", "title": "Articulated Kinematics Distillation from Video Diffusion Models", "authors": [ "Xuan Li", "Qianli Ma", "Tsung-Yi Lin", "Yongxin Chen", "Chenfanfu Jiang", "Ming-Yu Liu", "Donglai Xiang" ], "github": "", "project_page": "", "abstract": "We present Articulated Kinematics Distillation (AKD), a framework for generating high-fidelity character animations by merging the strengths of skeleton-based animation and modern generative models. AKD uses a skeleton-based representation for rigged 3D assets, drastically reducing the Degrees of Freedom (DoFs) by focusing on joint-level control, which allows for efficient, consistent motion synthesis. Through Score Distillation Sampling (SDS) with pre-trained video diffusion models, AKD distills complex, articulated motions while maintaining structural integrity, overcoming challenges faced by 4D neural deformation fields in preserving shape consistency. This approach is naturally compatible with physics-based simulation, ensuring physically plausible interactions. Experiments show that AKD achieves superior 3D consistency and motion quality compared with existing works on text-to-4D generation. Project page: https://research.nvidia.com/labs/dir/akd/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2504.01934", "title": "ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement", "authors": [ "Runhui Huang", "Chunwei Wang", "Junwei Yang", "Guansong Lu", "Yunlong Yuan", "Jianhua Han", "Lu Hou", "Wei Zhang", "Lanqing Hong", "Hengshuang Zhao", "Hang Xu" ], "github": "", "project_page": "", "abstract": "We present ILLUME+ that leverages dual visual tokenization and a diffusion decoder to improve both deep semantic understanding and high-fidelity image generation. Existing unified models have struggled to simultaneously handle the three fundamental capabilities in a unified model: understanding, generation, and editing. Models like Chameleon and EMU3 utilize VQGAN for image discretization, due to the lack of deep semantic interaction, they lag behind specialist models like LLaVA in visual understanding tasks. To mitigate this, LaViT and ILLUME employ semantic encoders for tokenization, but they struggle with image editing due to poor texture preservation. Meanwhile, Janus series decouples the input and output image representation, limiting their abilities to seamlessly handle interleaved image-text understanding and generation. In contrast, ILLUME+ introduces a unified dual visual tokenizer, DualViTok, which preserves both fine-grained textures and text-aligned semantics while enabling a coarse-to-fine image representation strategy for multimodal understanding and generation. Additionally, we employ a diffusion model as the image detokenizer for enhanced generation quality and efficient super-resolution. ILLUME+ follows a continuous-input, discrete-output scheme within the unified MLLM and adopts a progressive training procedure that supports dynamic resolution across the vision tokenizer, MLLM, and diffusion decoder. This design allows for flexible and efficient context-aware image editing and generation across diverse tasks. ILLUME+ (3B) exhibits competitive performance against existing unified MLLMs and specialized models across multimodal understanding, generation, and editing benchmarks. With its strong performance, ILLUME+ provides a scalable and versatile foundation for future multimodal applications. Project Page: https://illume-unified-mllm.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2504.01956", "title": "VideoScene: Distilling Video Diffusion Model to Generate 3D Scenes in One Step", "authors": [ "Hanyang Wang", "Fangfu Liu", "Jiawei Chi", "Yueqi Duan" ], "github": "", "project_page": "", "abstract": "Recovering 3D scenes from sparse views is a challenging task due to its inherent ill-posed problem. Conventional methods have developed specialized solutions (e.g., geometry regularization or feed-forward deterministic model) to mitigate the issue. However, they still suffer from performance degradation by minimal overlap across input views with insufficient visual information. Fortunately, recent video generative models show promise in addressing this challenge as they are capable of generating video clips with plausible 3D structures. Powered by large pretrained video diffusion models, some pioneering research start to explore the potential of video generative prior and create 3D scenes from sparse views. Despite impressive improvements, they are limited by slow inference time and the lack of 3D constraint, leading to inefficiencies and reconstruction artifacts that do not align with real-world geometry structure. In this paper, we propose VideoScene to distill the video diffusion model to generate 3D scenes in one step, aiming to build an efficient and effective tool to bridge the gap from video to 3D. Specifically, we design a 3D-aware leap flow distillation strategy to leap over time-consuming redundant information and train a dynamic denoising policy network to adaptively determine the optimal leap timestep during inference. Extensive experiments demonstrate that our VideoScene achieves faster and superior 3D scene generation results than previous video diffusion models, highlighting its potential as an efficient tool for future video to 3D applications. Project Page: https://hanyang-21.github.io/VideoScene", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2504.01724", "title": "DreamActor-M1: Holistic, Expressive and Robust Human Image Animation with Hybrid Guidance", "authors": [ "Yuxuan Luo", "Zhengkun Rong", "Lizhen Wang", "Longhao Zhang", "Tianshu Hu", "Yongming Zhu" ], "github": "", "project_page": "", "abstract": "While recent image-based human animation methods achieve realistic body and facial motion synthesis, critical gaps remain in fine-grained holistic controllability, multi-scale adaptability, and long-term temporal coherence, which leads to their lower expressiveness and robustness. We propose a diffusion transformer (DiT) based framework, DreamActor-M1, with hybrid guidance to overcome these limitations. For motion guidance, our hybrid control signals that integrate implicit facial representations, 3D head spheres, and 3D body skeletons achieve robust control of facial expressions and body movements, while producing expressive and identity-preserving animations. For scale adaptation, to handle various body poses and image scales ranging from portraits to full-body views, we employ a progressive training strategy using data with varying resolutions and scales. For appearance guidance, we integrate motion patterns from sequential frames with complementary visual references, ensuring long-term temporal coherence for unseen regions during complex movements. Experiments demonstrate that our method outperforms the state-of-the-art works, delivering expressive results for portraits, upper-body, and full-body generation with robust long-term consistency. Project Page: https://grisoon.github.io/DreamActor-M1/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2503.20783", "title": "Understanding R1-Zero-Like Training: A Critical Perspective", "authors": [ "Zichen Liu", "Changyu Chen", "Wenjun Li", "Penghui Qi", "Tianyu Pang", "Chao Du", "Wee Sun Lee", "Min Lin" ], "github": "https://github.com/sail-sg/understand-r1-zero", "project_page": "", "abstract": "DeepSeek-R1-Zero has shown that reinforcement learning (RL) at scale can directly enhance the reasoning capabilities of LLMs without supervised fine-tuning. In this work, we critically examine R1-Zero-like training by analyzing its two core components: base models and RL. We investigate a wide range of base models, including DeepSeek-V3-Base, to understand how pretraining characteristics influence RL performance. Our analysis reveals that DeepSeek-V3-Base already exhibit ''Aha moment'', while Qwen2.5 base models demonstrate strong reasoning capabilities even without prompt templates, suggesting potential pretraining biases. Additionally, we identify an optimization bias in Group Relative Policy Optimization (GRPO), which artificially increases response length (especially for incorrect outputs) during training. To address this, we introduce Dr. GRPO, an unbiased optimization method that improves token efficiency while maintaining reasoning performance. Leveraging these insights, we present a minimalist R1-Zero recipe that achieves 43.3% accuracy on AIME 2024 with a 7B base model, establishing a new state-of-the-art. Our code is available at https://github.com/sail-sg/understand-r1-zero.", "github_manual": "", "github_regex": "https://github.com/sail-sg/understand-r1-zero", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2405.20216", "title": "Boost Your Own Human Image Generation Model via Direct Preference Optimization with AI Feedback", "authors": [ "Sanghyeon Na", "Yonggyu Kim", "Hyunjoon Lee" ], "github": "", "project_page": "", "abstract": "The generation of high-quality human images through text-to-image (T2I) methods is a significant yet challenging task. Distinct from general image generation, human image synthesis must satisfy stringent criteria related to human pose, anatomy, and alignment with textual prompts, making it particularly difficult to achieve realistic results. Recent advancements in T2I generation based on diffusion models have shown promise, yet challenges remain in meeting human-specific preferences. In this paper, we introduce a novel approach tailored specifically for human image generation utilizing Direct Preference Optimization (DPO). Specifically, we introduce an efficient method for constructing a specialized DPO dataset for training human image generation models without the need for costly human feedback. We also propose a modified loss function that enhances the DPO training process by minimizing artifacts and improving image fidelity. Our method demonstrates its versatility and effectiveness in generating human images, including personalized text-to-image generation. Through comprehensive evaluations, we show that our approach significantly advances the state of human image generation, achieving superior results in terms of natural anatomies, poses, and text-image alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2504.00406", "title": "VerifiAgent: a Unified Verification Agent in Language Model Reasoning", "authors": [ "Jiuzhou Han", "Wray Buntine", "Ehsan Shareghi" ], "github": "https://github.com/Jiuzhouh/VerifiAgent", "project_page": "", "abstract": "Large language models demonstrate remarkable reasoning capabilities but often produce unreliable or incorrect responses. Existing verification methods are typically model-specific or domain-restricted, requiring significant computational resources and lacking scalability across diverse reasoning tasks. To address these limitations, we propose VerifiAgent, a unified verification agent that integrates two levels of verification: meta-verification, which assesses completeness and consistency in model responses, and tool-based adaptive verification, where VerifiAgent autonomously selects appropriate verification tools based on the reasoning type, including mathematical, logical, or commonsense reasoning. This adaptive approach ensures both efficiency and robustness across different verification scenarios. Experimental results show that VerifiAgent outperforms baseline verification methods (e.g., deductive verifier, backward verifier) among all reasoning tasks. Additionally, it can further enhance reasoning accuracy by leveraging feedback from verification results. VerifiAgent can also be effectively applied to inference scaling, achieving better results with fewer generated samples and costs compared to existing process reward models in the mathematical reasoning domain. Code is available at https://github.com/Jiuzhouh/VerifiAgent", "github_manual": "", "github_regex": "https://github.com/Jiuzhouh/VerifiAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2503.18817", "title": "Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal Representations", "authors": [ "Jeonghyeon Kim", "Sangheum Hwang" ], "github": "", "project_page": "", "abstract": "Prior research on out-of-distribution detection (OoDD) has primarily focused on single-modality models. Recently, with the advent of large-scale pretrained vision-language models such as CLIP, OoDD methods utilizing such multi-modal representations through zero-shot and prompt learning strategies have emerged. However, these methods typically involve either freezing the pretrained weights or only partially tuning them, which can be suboptimal for downstream datasets. In this paper, we highlight that multi-modal fine-tuning (MMFT) can achieve notable OoDD performance. Despite some recent works demonstrating the impact of fine-tuning methods for OoDD, there remains significant potential for performance improvement. We investigate the limitation of na\\\"ive fine-tuning methods, examining why they fail to fully leverage the pretrained knowledge. Our empirical analysis suggests that this issue could stem from the modality gap within in-distribution (ID) embeddings. To address this, we propose a training objective that enhances cross-modal alignment by regularizing the distances between image and text embeddings of ID data. This adjustment helps in better utilizing pretrained textual information by aligning similar semantics from different modalities (i.e., text and image) more closely in the hyperspherical representation space. We theoretically demonstrate that the proposed regularization corresponds to the maximum likelihood estimation of an energy-based model on a hypersphere. Utilizing ImageNet-1k OoD benchmark datasets, we show that our method, combined with post-hoc OoDD approaches leveraging pretrained knowledge (e.g., NegLabel), significantly outperforms existing methods, achieving state-of-the-art OoDD performance and leading ID accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2503.23573", "title": "DASH: Detection and Assessment of Systematic Hallucinations of VLMs", "authors": [ "Maximilian Augustin", "Yannic Neuhaus", "Matthias Hein" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) are prone to object hallucinations, where they erroneously indicate the presenceof certain objects in an image. Existing benchmarks quantify hallucinations using relatively small, labeled datasets. However, this approach is i) insufficient to assess hallucinations that arise in open-world settings, where VLMs are widely used, and ii) inadequate for detecting systematic errors in VLMs. We propose DASH (Detection and Assessment of Systematic Hallucinations), an automatic, large-scale pipeline designed to identify systematic hallucinations of VLMs on real-world images in an open-world setting. A key component is DASH-OPT for image-based retrieval, where we optimize over the ''natural image manifold'' to generate images that mislead the VLM. The output of DASH consists of clusters of real and semantically similar images for which the VLM hallucinates an object. We apply DASH to PaliGemma and two LLaVA-NeXT models across 380 object classes and, in total, find more than 19k clusters with 950k images. We study the transfer of the identified systematic hallucinations to other VLMs and show that fine-tuning PaliGemma with the model-specific images obtained with DASH mitigates object hallucinations. Code and data are available at https://YanNeu.github.io/DASH.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2503.23368", "title": "Towards Physically Plausible Video Generation via VLM Planning", "authors": [ "Xindi Yang", "Baolu Li", "Yiming Zhang", "Zhenfei Yin", "Lei Bai", "Liqian Ma", "Zhiyong Wang", "Jianfei Cai", "Tien-Tsin Wong", "Huchuan Lu", "Xu Jia" ], "github": "https://github.com/Madaoer/VLIPP", "project_page": "", "abstract": "Video diffusion models (VDMs) have advanced significantly in recent years, enabling the generation of highly realistic videos and drawing the attention of the community in their potential as world simulators. However, despite their capabilities, VDMs often fail to produce physically plausible videos due to an inherent lack of understanding of physics, resulting in incorrect dynamics and event sequences. To address this limitation, we propose a novel two-stage image-to-video generation framework that explicitly incorporates physics. In the first stage, we employ a Vision Language Model (VLM) as a coarse-grained motion planner, integrating chain-of-thought and physics-aware reasoning to predict a rough motion trajectories/changes that approximate real-world physical dynamics while ensuring the inter-frame consistency. In the second stage, we use the predicted motion trajectories/changes to guide the video generation of a VDM. As the predicted motion trajectories/changes are rough, noise is added during inference to provide freedom to the VDM in generating motion with more fine details. Extensive experimental results demonstrate that our framework can produce physically plausible motion, and comparative evaluations highlight the notable superiority of our approach over existing methods. More video results are available on our Project Page: https://madaoer.github.io/projects/physically_plausible_video_generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Madaoer/VLIPP", "github_api_added_by": "auto" }, { "date": "2025-04-03", "arxiv_id": "2504.01201", "title": "Medical large language models are easily distracted", "authors": [ "Krithik Vishwanath", "Anton Alyakin", "Daniel Alexander Alber", "Jin Vivian Lee", "Douglas Kondziolka", "Eric Karl Oermann" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have the potential to transform medicine, but real-world clinical scenarios contain extraneous information that can hinder performance. The rise of assistive technologies like ambient dictation, which automatically generates draft notes from live patient encounters, has the potential to introduce additional noise making it crucial to assess the ability of LLM's to filter relevant data. To investigate this, we developed MedDistractQA, a benchmark using USMLE-style questions embedded with simulated real-world distractions. Our findings show that distracting statements (polysemous words with clinical meanings used in a non-clinical context or references to unrelated health conditions) can reduce LLM accuracy by up to 17.9%. Commonly proposed solutions to improve model performance such as retrieval-augmented generation (RAG) and medical fine-tuning did not change this effect and in some cases introduced their own confounders and further degraded performance. Our findings suggest that LLMs natively lack the logical mechanisms necessary to distinguish relevant from irrelevant clinical information, posing challenges for real-world applications. MedDistractQA and our results highlights the need for robust mitigation strategies to enhance LLM resilience to extraneous information.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2502.18924", "title": "MegaTTS 3: Sparse Alignment Enhanced Latent Diffusion Transformer for Zero-Shot Speech Synthesis", "authors": [ "Ziyue Jiang", "Yi Ren", "Ruiqi Li", "Shengpeng Ji", "Boyang Zhang", "Zhenhui Ye", "Chen Zhang", "Bai Jionghao", "Xiaoda Yang", "Jialong Zuo", "Yu Zhang", "Rui Liu", "Xiang Yin", "Zhou Zhao" ], "github": "https://github.com/FunAudioLLM/CosyVoice", "project_page": "", "abstract": "While recent zero-shot text-to-speech (TTS) models have significantly improved speech quality and expressiveness, mainstream systems still suffer from issues related to speech-text alignment modeling: 1) models without explicit speech-text alignment modeling exhibit less robustness, especially for hard sentences in practical applications; 2) predefined alignment-based models suffer from naturalness constraints of forced alignments. This paper introduces MegaTTS 3, a TTS system featuring an innovative sparse alignment algorithm that guides the latent diffusion transformer (DiT). Specifically, we provide sparse alignment boundaries to MegaTTS 3 to reduce the difficulty of alignment without limiting the search space, thereby achieving high naturalness. Moreover, we employ a multi-condition classifier-free guidance strategy for accent intensity adjustment and adopt the piecewise rectified flow technique to accelerate the generation process. Experiments demonstrate that MegaTTS 3 achieves state-of-the-art zero-shot TTS speech quality and supports highly flexible control over accent intensity. Notably, our system can generate high-quality one-minute speech with only 8 sampling steps. Audio samples are available at https://sditdemo.github.io/sditdemo/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FunAudioLLM/CosyVoice", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2503.18950", "title": "Target-Aware Video Diffusion Models", "authors": [ "Taeksoo Kim", "Hanbyul Joo" ], "github": "https://github.com/taeksuu/tavid", "project_page": "", "abstract": "We present a target-aware video diffusion model that generates videos from an input image in which an actor interacts with a specified target while performing a desired action. The target is defined by a segmentation mask and the desired action is described via a text prompt. Unlike existing controllable image-to-video diffusion models that often rely on dense structural or motion cues to guide the actor's movements toward the target, our target-aware model requires only a simple mask to indicate the target, leveraging the generalization capabilities of pretrained models to produce plausible actions. This makes our method particularly effective for human-object interaction (HOI) scenarios, where providing precise action guidance is challenging, and further enables the use of video diffusion models for high-level action planning in applications such as robotics. We build our target-aware model by extending a baseline model to incorporate the target mask as an additional input. To enforce target awareness, we introduce a special token that encodes the target's spatial information within the text prompt. We then fine-tune the model with our curated dataset using a novel cross-attention loss that aligns the cross-attention maps associated with this token with the input target mask. To further improve performance, we selectively apply this loss to the most semantically relevant transformer blocks and attention regions. Experimental results show that our target-aware model outperforms existing solutions in generating videos where actors interact accurately with the specified targets. We further demonstrate its efficacy in two downstream applications: video content creation and zero-shot 3D HOI motion synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/taeksuu/tavid", "github_api_added_by": "auto" }, { "date": "2025-04-03", "arxiv_id": "2503.22879", "title": "Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models", "authors": [ "Hung-Yueh Chiang", "Chi-Chih Chang", "Natalia Frumkin", "Kai-Chiang Wu", "Mohamed S. Abdelfattah", "Diana Marculescu" ], "github": "https://github.com/enyac-group/Quamba", "project_page": "", "abstract": "State Space Models (SSMs) are emerging as a compelling alternative to Transformers because of their consistent memory usage and high performance. Despite this, scaling up SSMs on cloud services or limited-resource devices is challenging due to their storage requirements and computational power. To overcome this, quantizing SSMs with low bit-width data formats can reduce model size and benefit from hardware acceleration. As SSMs are prone to quantization-induced errors, recent efforts have focused on optimizing a particular model or bit-width for efficiency without sacrificing performance. However, distinct bit-width configurations are essential for different scenarios, like W4A8 for boosting large-batch decoding speed, and W4A16 for enhancing generation speed in short prompt applications for a single user. To this end, we present Quamba2, compatible with W8A8, W4A8, and W4A16 for both Mamba1 and Mamba2 backbones, addressing the growing demand for SSM deployment on various platforms. Based on the channel order preserving and activation persistence of SSMs, we propose an offline approach to quantize inputs of a linear recurrence in 8-bit by sorting and clustering for input x, combined with a per-state-group quantization for input-dependent parameters B and C. To ensure compute-invariance in the SSM output, we rearrange weights offline according to the clustering sequence. The experiments show that Quamba2-8B outperforms several state-of-the-art SSM quantization methods and delivers 1.3times and 3times speed-ups in the pre-filling and generation stages, respectively, while offering 4times memory reduction with only a 1.6% average accuracy drop. The evaluation on MMLU shows the generalizability and robustness of our framework. The code and quantized models will be released at: https://github.com/enyac-group/Quamba.", "github_manual": "", "github_regex": "https://github.com/enyac-group/Quamba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-03", "arxiv_id": "2503.23798", "title": "Adaptive Layer-skipping in Pre-trained LLMs", "authors": [ "Xuan Luo", "Weizhi Wang", "Xifeng Yan" ], "github": "", "project_page": "", "abstract": "Various layer-skipping methods have been proposed to accelerate token generation in large language models (LLMs). However, they have overlooked a fundamental question: How do computational demands vary across the generation of different tokens? In this work, we introduce FlexiDepth, a method that dynamically adjusts the number of Transformer layers used in text generation. By incorporating a plug-in router and adapter, FlexiDepth enables adaptive layer-skipping in LLMs without modifying their original parameters. Introducing FlexiDepth to Llama-3-8B model achieves layer skipping of 8 layers out of 32, and meanwhile maintains the full 100\\% benchmark performance. Experimental results with FlexiDepth demonstrate that computational demands in LLMs significantly vary based on token type. Specifically, generating repetitive tokens or fixed phrases requires fewer layers, whereas producing tokens involving computation or high uncertainty requires more layers. Interestingly, this adaptive allocation pattern aligns with human intuition. To advance research in this area, we open sourced FlexiDepth and a dataset documenting FlexiDepth's layer allocation patterns for future exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.02587", "title": "Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme", "authors": [ "Yan Ma", "Steffi Chern", "Xuyang Shen", "Yiran Zhong", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/MAYE", "project_page": "", "abstract": "Reinforcement learning (RL) has recently shown strong potential in improving the reasoning capabilities of large language models and is now being actively extended to vision-language models (VLMs). However, existing RL applications in VLMs often rely on heavily engineered frameworks that hinder reproducibility and accessibility, while lacking standardized evaluation protocols, making it difficult to compare results or interpret training dynamics. This work introduces a transparent, from-scratch framework for RL in VLMs, offering a minimal yet functional four-step pipeline validated across multiple models and datasets. In addition, a standardized evaluation scheme is proposed to assess training dynamics and reflective behaviors. Extensive experiments on visual reasoning tasks uncover key empirical findings: response length is sensitive to random seeds, reflection correlates with output length, and RL consistently outperforms supervised fine-tuning (SFT) in generalization, even with high-quality data. These findings, together with the proposed framework, aim to establish a reproducible baseline and support broader engagement in RL-based VLM research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GAIR-NLP/MAYE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.02436", "title": "SkyReels-A2: Compose Anything in Video Diffusion Transformers", "authors": [ "Zhengcong Fei", "Debang Li", "Di Qiu", "Jiahua Wang", "Yikun Dou", "Rui Wang", "Jingtao Xu", "Mingyuan Fan", "Guibin Chen", "Yang Li", "Yahui Zhou" ], "github": "https://github.com/SkyworkAI/SkyReels-A2", "project_page": "", "abstract": "This paper presents SkyReels-A2, a controllable video generation framework capable of assembling arbitrary visual elements (e.g., characters, objects, backgrounds) into synthesized videos based on textual prompts while maintaining strict consistency with reference images for each element. We term this task elements-to-video (E2V), whose primary challenges lie in preserving the fidelity of each reference element, ensuring coherent composition of the scene, and achieving natural outputs. To address these, we first design a comprehensive data pipeline to construct prompt-reference-video triplets for model training. Next, we propose a novel image-text joint embedding model to inject multi-element representations into the generative process, balancing element-specific consistency with global coherence and text alignment. We also optimize the inference pipeline for both speed and output stability. Moreover, we introduce a carefully curated benchmark for systematic evaluation, i.e, A2 Bench. Experiments demonstrate that our framework can generate diverse, high-quality videos with precise element control. SkyReels-A2 is the first open-source commercial grade model for the generation of E2V, performing favorably against advanced closed-source commercial models. We anticipate SkyReels-A2 will advance creative applications such as drama and virtual e-commerce, pushing the boundaries of controllable video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SkyworkAI/SkyReels-A2", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.02119", "title": "Efficient Model Selection for Time Series Forecasting via LLMs", "authors": [ "Wang Wei", "Tiankai Yang", "Hongjie Chen", "Ryan A. Rossi", "Yue Zhao", "Franck Dernoncourt", "Hoda Eldardiry" ], "github": "", "project_page": "", "abstract": "Model selection is a critical step in time series forecasting, traditionally requiring extensive performance evaluations across various datasets. Meta-learning approaches aim to automate this process, but they typically depend on pre-constructed performance matrices, which are costly to build. In this work, we propose to leverage Large Language Models (LLMs) as a lightweight alternative for model selection. Our method eliminates the need for explicit performance matrices by utilizing the inherent knowledge and reasoning capabilities of LLMs. Through extensive experiments with LLaMA, GPT and Gemini, we demonstrate that our approach outperforms traditional meta-learning techniques and heuristic baselines, while significantly reducing computational overhead. These findings underscore the potential of LLMs in efficient model selection for time series forecasting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.02782", "title": "GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation", "authors": [ "Zhiyuan Yan", "Junyan Ye", "Weijia Li", "Zilong Huang", "Shenghai Yuan", "Xiangyang He", "Kaiqing Lin", "Jun He", "Conghui He", "Li Yuan" ], "github": "https://github.com/PicoTrex/GPT-ImgEval", "project_page": "", "abstract": "The recent breakthroughs in OpenAI's GPT4o model have demonstrated surprisingly good capabilities in image generation and editing, resulting in significant excitement in the community. This technical report presents the first-look evaluation benchmark (named GPT-ImgEval), quantitatively and qualitatively diagnosing GPT-4o's performance across three critical dimensions: (1) generation quality, (2) editing proficiency, and (3) world knowledge-informed semantic synthesis. Across all three tasks, GPT-4o demonstrates strong performance, significantly surpassing existing methods in both image generation control and output quality, while also showcasing exceptional knowledge reasoning capabilities. Furthermore, based on the GPT-4o's generated data, we propose a classification-model-based approach to investigate the underlying architecture of GPT-4o, where our empirical results suggest the model consists of an auto-regressive (AR) combined with a diffusion-based head for image decoding, rather than the VAR-like architectures. We also provide a complete speculation on GPT-4o's overall architecture. In addition, we conduct a series of analyses to identify and visualize GPT-4o's specific limitations and the synthetic artifacts commonly observed in its image generation. We also present a comparative study of multi-round image editing between GPT-4o and Gemini 2.0 Flash, and discuss the safety implications of GPT-4o's outputs, particularly their detectability by existing image forensic models. We hope that our work can offer valuable insight and provide a reliable benchmark to guide future research, foster reproducibility, and accelerate innovation in the field of image generation and beyond. The codes and datasets used for evaluating GPT-4o can be found at https://github.com/PicoTrex/GPT-ImgEval.", "github_manual": "", "github_regex": "https://github.com/PicoTrex/GPT-ImgEval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.02826", "title": "Envisioning Beyond the Pixels: Benchmarking Reasoning-Informed Visual Editing", "authors": [ "Xiangyu Zhao", "Peiyuan Zhang", "Kexian Tang", "Hao Li", "Zicheng Zhang", "Guangtao Zhai", "Junchi Yan", "Hua Yang", "Xue Yang", "Haodong Duan" ], "github": "https://github.com/PhoenixZ810/RISEBench", "project_page": "", "abstract": "Large Multi-modality Models (LMMs) have made significant progress in visual understanding and generation, but they still face challenges in General Visual Editing, particularly in following complex instructions, preserving appearance consistency, and supporting flexible input formats. To address this gap, we introduce RISEBench, the first benchmark for evaluating Reasoning-Informed viSual Editing (RISE). RISEBench focuses on four key reasoning types: Temporal, Causal, Spatial, and Logical Reasoning. We curate high-quality test cases for each category and propose an evaluation framework that assesses Instruction Reasoning, Appearance Consistency, and Visual Plausibility with both human judges and an LMM-as-a-judge approach. Our experiments reveal that while GPT-4o-Native significantly outperforms other open-source and proprietary models, even this state-of-the-art system struggles with logical reasoning tasks, highlighting an area that remains underexplored. As an initial effort, RISEBench aims to provide foundational insights into reasoning-aware visual editing and to catalyze future research. Though still in its early stages, we are committed to continuously expanding and refining the benchmark to support more comprehensive, reliable, and scalable evaluations of next-generation multimodal systems. Our code and data will be released at https://github.com/PhoenixZ810/RISEBench.", "github_manual": "", "github_regex": "https://github.com/PhoenixZ810/RISEBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.02542", "title": "Audio-visual Controlled Video Diffusion with Masked Selective State Spaces Modeling for Natural Talking Head Generation", "authors": [ "Fa-Ting Hong", "Zunnan Xu", "Zixiang Zhou", "Jun Zhou", "Xiu Li", "Qin Lin", "Qinglin Lu", "Dan Xu" ], "github": "", "project_page": "", "abstract": "Talking head synthesis is vital for virtual avatars and human-computer interaction. However, most existing methods are typically limited to accepting control from a single primary modality, restricting their practical utility. To this end, we introduce ACTalker, an end-to-end video diffusion framework that supports both multi-signals control and single-signal control for talking head video generation. For multiple control, we design a parallel mamba structure with multiple branches, each utilizing a separate driving signal to control specific facial regions. A gate mechanism is applied across all branches, providing flexible control over video generation. To ensure natural coordination of the controlled video both temporally and spatially, we employ the mamba structure, which enables driving signals to manipulate feature tokens across both dimensions in each branch. Additionally, we introduce a mask-drop strategy that allows each driving signal to independently control its corresponding facial region within the mamba structure, preventing control conflicts. Experimental results demonstrate that our method produces natural-looking facial videos driven by diverse signals and that the mamba layer seamlessly integrates multiple driving modalities without conflict.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.02507", "title": "ZClip: Adaptive Spike Mitigation for LLM Pre-Training", "authors": [ "Abhay Kumar", "Louis Owen", "Nilabhra Roy Chowdhury", "Fabian G\u00fcra" ], "github": "https://github.com/bluorion-com/ZClip", "project_page": "", "abstract": "Training large language models (LLMs) presents numerous challenges, including gradient instability and loss spikes. These phenomena can lead to catastrophic divergence, requiring costly checkpoint restoration and data batch skipping. Traditional gradient clipping techniques, such as constant or norm-based methods, fail to address these issues effectively due to their reliance on fixed thresholds or heuristics, leading to inefficient learning and requiring frequent manual intervention. In this work, we propose ZClip, an adaptive gradient clipping algorithm that dynamically adjusts the clipping threshold based on statistical properties of gradient norms over time. Unlike prior reactive strategies, ZClip proactively adapts to training dynamics without making any prior assumptions on the scale and the temporal evolution of gradient norms. At its core, it leverages z-score-based anomaly detection to identify and mitigate large gradient spikes, preventing malignant loss spikes while not interfering with convergence otherwise. Our code is available at: https://github.com/bluorion-com/ZClip.", "github_manual": "", "github_regex": "https://github.com/bluorion-com/ZClip", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.02012", "title": "Instruction-Guided Autoregressive Neural Network Parameter Generation", "authors": [ "Soro Bedionita", "Bruno Andreis", "Song Chong", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "Learning to generate neural network parameters conditioned on task descriptions and architecture specifications is pivotal for advancing model adaptability and transfer learning. Existing methods especially those based on diffusion models suffer from limited scalability to large architectures, rigidity in handling varying network depths, and disjointed parameter generation that undermines inter-layer coherence. In this work, we propose IGPG (Instruction Guided Parameter Generation), an autoregressive framework that unifies parameter synthesis across diverse tasks and architectures. IGPG leverages a VQ-VAE and an autoregressive model to generate neural network parameters, conditioned on task instructions, dataset, and architecture details. By autoregressively generating neural network weights' tokens, IGPG ensures inter-layer coherence and enables efficient adaptation across models and datasets. Operating at the token level, IGPG effectively captures complex parameter distributions aggregated from a broad spectrum of pretrained models. Extensive experiments on multiple vision datasets demonstrate that IGPG consolidates diverse pretrained models into a single, flexible generative framework. The synthesized parameters achieve competitive or superior performance relative to state-of-the-art methods, especially in terms of scalability and efficiency when applied to large architectures. These results underscore ICPG potential as a powerful tool for pretrained weight retrieval, model selection, and rapid task-specific fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.00891", "title": "GenPRM: Scaling Test-Time Compute of Process Reward Models via Generative Reasoning", "authors": [ "Jian Zhao", "Runze Liu", "Kaiyan Zhang", "Zhimu Zhou", "Junqi Gao", "Dong Li", "Jiafei Lyu", "Zhouyi Qian", "Biqing Qi", "Xiu Li", "Bowen Zhou" ], "github": "", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) have shown that it is promising to utilize Process Reward Models (PRMs) as verifiers to enhance the performance of LLMs. However, current PRMs face three key challenges: (1) limited process supervision and generalization capabilities, (2) dependence on scalar value prediction without leveraging the generative abilities of LLMs, and (3) inability to scale the test-time compute of PRMs. In this work, we introduce GenPRM, a generative process reward model that performs explicit Chain-of-Thought (CoT) reasoning with code verification before providing judgment for each reasoning step. To obtain high-quality process supervision labels and rationale data, we propose Relative Progress Estimation (RPE) and a rationale synthesis framework that incorporates code verification. Experimental results on ProcessBench and several mathematical reasoning tasks show that GenPRM significantly outperforms prior PRMs with only 23K training data from MATH dataset. Through test-time scaling, a 1.5B GenPRM outperforms GPT-4o, and a 7B GenPRM surpasses Qwen2.5-Math-PRM-72B on ProcessBench. Additionally, GenPRM demonstrates strong abilities to serve as a critic model for policy model refinement. This work establishes a new paradigm for process supervision that bridges the gap between PRMs and critic models in LLMs. Our code, model, and data will be available in https://ryanliu112.github.io/GenPRM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.02398", "title": "Scaling Analysis of Interleaved Speech-Text Language Models", "authors": [ "Gallil Maimon", "Michael Hassid", "Amit Roth", "Yossi Adi" ], "github": "", "project_page": "", "abstract": "Existing Speech Language Model (SLM) scaling analysis paints a bleak picture. They predict that SLMs require much more compute and data compared to text, leading some to question the feasibility of training high-quality SLMs. However, modern SLMs are often initialised from pre-trained TextLMs using speech-text interleaving to allow knowledge transfer. This raises the question - Do interleaved SLMs scale more efficiently than textless-SLMs? In this paper we answer a resounding, yes! We conduct scaling analysis of interleaved SLMs by training several dozen and analysing the scaling trends. We see that under this setup SLMs scale more efficiently with compute. Additionally, our results indicate that the scaling-dynamics are significantly different than textless-SLMs, suggesting one should allocate notably more of the compute budget for increasing model size over training tokens. We also study the role of synthetic data and TextLM model families in unlocking this potential. Results suggest, that our scaled up model achieves comparable performance with leading models on speech semantic metrics while using less compute and data than other approaches. We open source models, samples, and data - https://pages.cs.huji.ac.il/adiyoss-lab/sims.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.00502", "title": "ShortV: Efficient Multimodal Large Language Models by Freezing Visual Tokens in Ineffective Layers", "authors": [ "Qianhao Yuan", "Qingyu Zhang", "Yanjiang Liu", "Jiawei Chen", "Yaojie Lu", "Hongyu Lin", "Jia Zheng", "Xianpei Han", "Le Sun" ], "github": "https://github.com/icip-cas/ShortV", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) suffer from high computational costs due to their massive size and the large number of visual tokens. In this paper, we investigate layer-wise redundancy in MLLMs by introducing a novel metric, Layer Contribution (LC), which quantifies the impact of a layer's transformations on visual and text tokens, respectively. The calculation of LC involves measuring the divergence in model output that results from removing the layer's transformations on the specified tokens. Our pilot experiment reveals that many layers of MLLMs exhibit minimal contribution during the processing of visual tokens. Motivated by this observation, we propose ShortV, a training-free method that leverages LC to identify ineffective layers, and freezes visual token updates in these layers. Experiments show that ShortV can freeze visual token in approximately 60\\% of the MLLM layers, thereby dramatically reducing computational costs related to updating visual tokens. For example, it achieves a 50\\% reduction in FLOPs on LLaVA-NeXT-13B while maintaining superior performance. The code will be publicly available at https://github.com/icip-cas/ShortV", "github_manual": "", "github_regex": "https://github.com/icip-cas/ShortV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.01990", "title": "Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems", "authors": [ "Bang Liu", "Xinfeng Li", "Jiayi Zhang", "Jinlin Wang", "Tanjin He", "Sirui Hong", "Hongzhang Liu", "Shaokun Zhang", "Kaitao Song", "Kunlun Zhu", "Yuheng Cheng", "Suyuchen Wang", "Xiaoqiang Wang", "Yuyu Luo", "Haibo Jin", "Peiyan Zhang", "Ollie Liu", "Jiaqi Chen", "Huan Zhang", "Zhaoyang Yu", "Haochen Shi", "Boyan Li", "Dekun Wu", "Fengwei Teng", "Xiaojun Jia", "Jiawei Xu", "Jinyu Xiang", "Yizhang Lin", "Tianming Liu", "Tongliang Liu", "Yu Su", "Huan Sun", "Glen Berseth", "Jianyun Nie", "Ian Foster", "Logan Ward", "Qingyun Wu", "Yu Gu", "Mingchen Zhuge", "Xiangru Tang", "Haohan Wang", "Jiaxuan You", "Chi Wang", "Jian Pei", "Qiang Yang", "Xiaoliang Qi", "Chenglin Wu" ], "github": "", "project_page": "", "abstract": "The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across diverse domains. As these agents increasingly drive AI research and practical applications, their design, evaluation, and continuous improvement present intricate, multifaceted challenges. This survey provides a comprehensive overview, framing intelligent agents within a modular, brain-inspired architecture that integrates principles from cognitive science, neuroscience, and computational research. We structure our exploration into four interconnected parts. First, we delve into the modular foundation of intelligent agents, systematically mapping their cognitive, perceptual, and operational modules onto analogous human brain functionalities, and elucidating core components such as memory, world modeling, reward processing, and emotion-like systems. Second, we discuss self-enhancement and adaptive evolution mechanisms, exploring how agents autonomously refine their capabilities, adapt to dynamic environments, and achieve continual learning through automated optimization paradigms, including emerging AutoML and LLM-driven optimization strategies. Third, we examine collaborative and evolutionary multi-agent systems, investigating the collective intelligence emerging from agent interactions, cooperation, and societal structures, highlighting parallels to human social dynamics. Finally, we address the critical imperative of building safe, secure, and beneficial AI systems, emphasizing intrinsic and extrinsic security threats, ethical alignment, robustness, and practical mitigation strategies necessary for trustworthy real-world deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2503.22444", "title": "Scaling Laws in Scientific Discovery with AI and Robot Scientists", "authors": [ "Pengsong Zhang", "Heng Zhang", "Huazhe Xu", "Renjun Xu", "Zhenting Wang", "Cong Wang", "Animesh Garg", "Zhibin Li", "Arash Ajoudani", "Xinyu Liu" ], "github": "", "project_page": "", "abstract": "Scientific discovery is poised for rapid advancement through advanced robotics and artificial intelligence. Current scientific practices face substantial limitations as manual experimentation remains time-consuming and resource-intensive, while multidisciplinary research demands knowledge integration beyond individual researchers' expertise boundaries. Here, we envision an autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle. This system could dynamically interact with both physical and virtual environments while facilitating the integration of knowledge across diverse scientific disciplines. By deploying these technologies throughout every research stage -- spanning literature review, hypothesis generation, experimentation, and manuscript writing -- and incorporating internal reflection alongside external feedback, this system aims to significantly reduce the time and resources needed for scientific discovery. Building on the evolution from virtual AI scientists to versatile generalist AI-based robot scientists, AGS promises groundbreaking potential. As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws, potentially shaped by the number and capabilities of these autonomous systems, offering novel perspectives on how knowledge is generated and evolves. The adaptability of embodied robots to extreme environments, paired with the flywheel effect of accumulating scientific knowledge, holds the promise of continually pushing beyond both physical and intellectual frontiers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.02821", "title": "Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models", "authors": [ "Mateusz Pach", "Shyamgopal Karthik", "Quentin Bouniot", "Serge Belongie", "Zeynep Akata" ], "github": "https://github.com/ExplainableML/sae-for-vlm", "project_page": "", "abstract": "Sparse Autoencoders (SAEs) have recently been shown to enhance interpretability and steerability in Large Language Models (LLMs). In this work, we extend the application of SAEs to Vision-Language Models (VLMs), such as CLIP, and introduce a comprehensive framework for evaluating monosemanticity in vision representations. Our experimental results reveal that SAEs trained on VLMs significantly enhance the monosemanticity of individual neurons while also exhibiting hierarchical representations that align well with expert-defined structures (e.g., iNaturalist taxonomy). Most notably, we demonstrate that applying SAEs to intervene on a CLIP vision encoder, directly steer output from multimodal LLMs (e.g., LLaVA) without any modifications to the underlying model. These findings emphasize the practicality and efficacy of SAEs as an unsupervised approach for enhancing both the interpretability and control of VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ExplainableML/sae-for-vlm", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.01871", "title": "Interpreting Emergent Planning in Model-Free Reinforcement Learning", "authors": [ "Thomas Bush", "Stephen Chung", "Usman Anwar", "Adri\u00e0 Garriga-Alonso", "David Krueger" ], "github": "", "project_page": "", "abstract": "We present the first mechanistic evidence that model-free reinforcement learning agents can learn to plan. This is achieved by applying a methodology based on concept-based interpretability to a model-free agent in Sokoban -- a commonly used benchmark for studying planning. Specifically, we demonstrate that DRC, a generic model-free agent introduced by Guez et al. (2019), uses learned concept representations to internally formulate plans that both predict the long-term effects of actions on the environment and influence action selection. Our methodology involves: (1) probing for planning-relevant concepts, (2) investigating plan formation within the agent's representations, and (3) verifying that discovered plans (in the agent's representations) have a causal effect on the agent's behavior through interventions. We also show that the emergence of these plans coincides with the emergence of a planning-like property: the ability to benefit from additional test-time compute. Finally, we perform a qualitative analysis of the planning algorithm learned by the agent and discover a strong resemblance to parallelized bidirectional search. Our findings advance understanding of the internal mechanisms underlying planning behavior in agents, which is important given the recent trend of emergent planning and reasoning capabilities in LLMs through RL", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.02495", "title": "Inference-Time Scaling for Generalist Reward Modeling", "authors": [ "Zijun Liu", "Peiyi Wang", "Runxin Xu", "Shirong Ma", "Chong Ruan", "Peng Li", "Yang Liu", "Yu Wu" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has been widely adopted in post-training for large language models (LLMs) at scale. Recently, the incentivization of reasoning capabilities in LLMs from RL indicates that proper learning methods could enable effective inference-time scalability. A key challenge of RL is to obtain accurate reward signals for LLMs in various domains beyond verifiable questions or artificial rules. In this work, we investigate how to improve reward modeling (RM) with more inference compute for general queries, i.e. the inference-time scalability of generalist RM, and further, how to improve the effectiveness of performance-compute scaling with proper learning methods. For the RM approach, we adopt pointwise generative reward modeling (GRM) to enable flexibility for different input types and potential for inference-time scaling. For the learning method, we propose Self-Principled Critique Tuning (SPCT) to foster scalable reward generation behaviors in GRMs through online RL, to generate principles adaptively and critiques accurately, resulting in DeepSeek-GRM models. Furthermore, for effective inference-time scaling, we use parallel sampling to expand compute usage, and introduce a meta RM to guide voting process for better scaling performance. Empirically, we show that SPCT significantly improves the quality and scalability of GRMs, outperforming existing methods and models in various RM benchmarks without severe biases, and could achieve better performance compared to training-time scaling. DeepSeek-GRM still meets challenges in some tasks, which we believe can be addressed by future efforts in generalist reward systems. The models will be released and open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2503.23542", "title": "Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages", "authors": [ "Xabier de Zuazo", "Eva Navas", "Ibon Saratxaga", "Inma Hern\u00e1ez Rioja" ], "github": "", "project_page": "", "abstract": "Automatic speech recognition systems have undoubtedly advanced with the integration of multilingual and multitask models such as Whisper, which have shown a promising ability to understand and process speech across a wide range of languages. Despite their robustness, these models often fall short in handling the linguistic distinctions of minority languages. This study addresses this gap by integrating traditional and novel language models with fine-tuned Whisper models to raise their performance in less commonly studied languages. Through rigorous fine-tuning and evaluation across multiple datasets, we demonstrate substantial improvements in word error rate, particularly in low-resource scenarios. Our approach not only does take advantage of the extensive data Whisper was pre-trained on, but also complements its linguistic adaptability by incorporating language models. We obtained improvements up to 51\\% for in-distribution datasets and up to 34\\% for out-of-distribution sentences using statistical language models, while large language models provided moderate but consistently robust improvement across diverse linguistic contexts. The findings reveal that, while the integration reliably benefits all model sizes, the extent of improvement varies, highlighting the importance of optimized language model parameters. Finally, we emphasize the importance of selecting appropriate evaluation parameters when reporting the results using transformer-based ASR models. In summary, this research clears the way for more inclusive ASR technologies that perform better across languages by enriching their linguistic knowledge. For further implementation details of this study, the technical documentation and source code are available at http://www.github.com/hitz-zentroa/whisper-lm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2503.23162", "title": "NeuralGS: Bridging Neural Fields and 3D Gaussian Splatting for Compact 3D Representations", "authors": [ "Zhenyu Tang", "Chaoran Feng", "Xinhua Cheng", "Wangbo Yu", "Junwu Zhang", "Yuan Liu", "Xiaoxiao Long", "Wenping Wang", "Li Yuan" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) demonstrates superior quality and rendering speed, but with millions of 3D Gaussians and significant storage and transmission costs. Recent 3DGS compression methods mainly concentrate on compressing Scaffold-GS, achieving impressive performance but with an additional voxel structure and a complex encoding and quantization strategy. In this paper, we aim to develop a simple yet effective method called NeuralGS that explores in another way to compress the original 3DGS into a compact representation without the voxel structure and complex quantization strategies. Our observation is that neural fields like NeRF can represent complex 3D scenes with Multi-Layer Perceptron (MLP) neural networks using only a few megabytes. Thus, NeuralGS effectively adopts the neural field representation to encode the attributes of 3D Gaussians with MLPs, only requiring a small storage size even for a large-scale scene. To achieve this, we adopt a clustering strategy and fit the Gaussians with different tiny MLPs for each cluster, based on importance scores of Gaussians as fitting weights. We experiment on multiple datasets, achieving a 45-times average model size reduction without harming the visual quality. The compression performance of our method on original 3DGS is comparable to the dedicated Scaffold-GS-based compression methods, which demonstrate the huge potential of directly compressing original 3DGS with neural fields.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.02154", "title": "FreSca: Unveiling the Scaling Space in Diffusion Models", "authors": [ "Chao Huang", "Susan Liang", "Yunlong Tang", "Li Ma", "Yapeng Tian", "Chenliang Xu" ], "github": "", "project_page": "", "abstract": "Diffusion models offer impressive controllability for image tasks, primarily through noise predictions that encode task-specific information and classifier-free guidance enabling adjustable scaling. This scaling mechanism implicitly defines a ``scaling space'' whose potential for fine-grained semantic manipulation remains underexplored. We investigate this space, starting with inversion-based editing where the difference between conditional/unconditional noise predictions carries key semantic information. Our core contribution stems from a Fourier analysis of noise predictions, revealing that its low- and high-frequency components evolve differently throughout diffusion. Based on this insight, we introduce FreSca, a straightforward method that applies guidance scaling independently to different frequency bands in the Fourier domain. FreSca demonstrably enhances existing image editing methods without retraining. Excitingly, its effectiveness extends to image understanding tasks such as depth estimation, yielding quantitative gains across multiple datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2503.23377", "title": "JavisDiT: Joint Audio-Video Diffusion Transformer with Hierarchical Spatio-Temporal Prior Synchronization", "authors": [ "Kai Liu", "Wei Li", "Lai Chen", "Shengqiong Wu", "Yanhao Zheng", "Jiayi Ji", "Fan Zhou", "Rongxin Jiang", "Jiebo Luo", "Hao Fei", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "This paper introduces JavisDiT, a novel Joint Audio-Video Diffusion Transformer designed for synchronized audio-video generation (JAVG). Built upon the powerful Diffusion Transformer (DiT) architecture, JavisDiT is able to generate high-quality audio and video content simultaneously from open-ended user prompts. To ensure optimal synchronization, we introduce a fine-grained spatio-temporal alignment mechanism through a Hierarchical Spatial-Temporal Synchronized Prior (HiST-Sypo) Estimator. This module extracts both global and fine-grained spatio-temporal priors, guiding the synchronization between the visual and auditory components. Furthermore, we propose a new benchmark, JavisBench, consisting of 10,140 high-quality text-captioned sounding videos spanning diverse scenes and complex real-world scenarios. Further, we specifically devise a robust metric for evaluating the synchronization between generated audio-video pairs in real-world complex content. Experimental results demonstrate that JavisDiT significantly outperforms existing methods by ensuring both high-quality generation and precise synchronization, setting a new standard for JAVG tasks. Our code, model, and dataset will be made publicly available at https://javisdit.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.00939", "title": "WikiVideo: Article Generation from Multiple Videos", "authors": [ "Alexander Martin", "Reno Kriz", "William Gantt Walden", "Kate Sanders", "Hannah Recknor", "Eugene Yang", "Francis Ferraro", "Benjamin Van Durme" ], "github": "https://github.com/alexmartin1722/wikivideo", "project_page": "", "abstract": "We present the challenging task of automatically creating a high-level Wikipedia-style article that aggregates information from multiple diverse videos about real-world events, such as natural disasters or political elections. Videos are intuitive sources for retrieval-augmented generation (RAG), but most contemporary RAG workflows focus heavily on text and existing methods for video-based summarization focus on low-level scene understanding rather than high-level event semantics. To close this gap, we introduce WikiVideo, a benchmark consisting of expert-written articles and densely annotated videos that provide evidence for articles' claims, facilitating the integration of video into RAG pipelines and enabling the creation of in-depth content that is grounded in multimodal sources. We further propose Collaborative Article Generation (CAG), a novel interactive method for article creation from multiple videos. CAG leverages an iterative interaction between an r1-style reasoning model and a VideoLLM to draw higher level inferences about the target event than is possible with VideoLLMs alone, which fixate on low-level visual features. We benchmark state-of-the-art VideoLLMs and CAG in both oracle retrieval and RAG settings and find that CAG consistently outperforms alternative methods, while suggesting intriguing avenues for future work.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/alexmartin1722/wikivideo", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.01955", "title": "Scene-Centric Unsupervised Panoptic Segmentation", "authors": [ "Oliver Hahn", "Christoph Reich", "Nikita Araslanov", "Daniel Cremers", "Christian Rupprecht", "Stefan Roth" ], "github": "https://github.com/visinf/cups", "project_page": "", "abstract": "Unsupervised panoptic segmentation aims to partition an image into semantically meaningful regions and distinct object instances without training on manually annotated data. In contrast to prior work on unsupervised panoptic scene understanding, we eliminate the need for object-centric training data, enabling the unsupervised understanding of complex scenes. To that end, we present the first unsupervised panoptic method that directly trains on scene-centric imagery. In particular, we propose an approach to obtain high-resolution panoptic pseudo labels on complex scene-centric data, combining visual representations, depth, and motion cues. Utilizing both pseudo-label training and a panoptic self-training strategy yields a novel approach that accurately predicts panoptic segmentation of complex scenes without requiring any human annotations. Our approach significantly improves panoptic quality, e.g., surpassing the recent state of the art in unsupervised panoptic segmentation on Cityscapes by 9.4% points in PQ.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/visinf/cups", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-04", "arxiv_id": "2504.01943", "title": "OpenCodeReasoning: Advancing Data Distillation for Competitive Coding", "authors": [ "Wasi Uddin Ahmad", "Sean Narenthiran", "Somshubra Majumdar", "Aleksander Ficek", "Siddhartha Jain", "Jocelyn Huang", "Vahid Noroozi", "Boris Ginsburg" ], "github": "", "project_page": "", "abstract": "Since the advent of reasoning-based large language models, many have found great success from distilling reasoning capabilities into student models. Such techniques have significantly bridged the gap between reasoning and standard LLMs on coding tasks. Despite this, much of the progress on distilling reasoning models remains locked behind proprietary datasets or lacks details on data curation, filtering and subsequent training. To address this, we construct a superior supervised fine-tuning (SFT) dataset that we use to achieve state-of-the-art coding capability results in models of various sizes. Our distilled models use only SFT to achieve 61.8% on LiveCodeBench and 24.6% on CodeContests, surpassing alternatives trained with reinforcement learning. We then perform analysis on the data sources used to construct our dataset, the impact of code execution filtering, and the importance of instruction/solution diversity. We observe that execution filtering negatively affected benchmark accuracy, leading us to prioritize instruction diversity over solution correctness. Finally, we also analyze the token efficiency and reasoning patterns utilized by these models. We will open-source these datasets and distilled models to the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.02605", "title": "Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving", "authors": [ "Daoguang Zan", "Zhirong Huang", "Wei Liu", "Hanwu Chen", "Linhao Zhang", "Shulin Xin", "Lu Chen", "Qi Liu", "Xiaojian Zhong", "Aoyan Li", "Siyao Liu", "Yongsheng Xiao", "Liangqiang Chen", "Yuyu Zhang", "Jing Su", "Tianyu Liu", "Rui Long", "Kai Shen", "Liang Xiang" ], "github": "https://github.com/multi-swe-bench/multi-swe-bench/blob/main/docs/contribution-demo.md", "project_page": "", "abstract": "The task of issue resolving is to modify a codebase to generate a patch that addresses a given issue. However, existing benchmarks, such as SWE-bench, focus almost exclusively on Python, making them insufficient for evaluating Large Language Models (LLMs) across diverse software ecosystems. To address this, we introduce a multilingual issue-resolving benchmark, called Multi-SWE-bench, covering Java, TypeScript, JavaScript, Go, Rust, C, and C++. It includes a total of 1,632 high-quality instances, which were carefully annotated from 2,456 candidates by 68 expert annotators, ensuring that the benchmark can provide an accurate and reliable evaluation. Based on Multi-SWE-bench, we evaluate a series of state-of-the-art models using three representative methods (Agentless, SWE-agent, and OpenHands) and present a comprehensive analysis with key empirical insights. In addition, we launch a Multi-SWE-RL open-source community, aimed at building large-scale reinforcement learning (RL) training datasets for issue-resolving tasks. As an initial contribution, we release a set of 4,723 well-structured instances spanning seven programming languages, laying a solid foundation for RL research in this domain. More importantly, we open-source our entire data production pipeline, along with detailed tutorials, encouraging the open-source community to continuously contribute and expand the dataset. We envision our Multi-SWE-bench and the ever-growing Multi-SWE-RL community as catalysts for advancing RL toward its full potential, bringing us one step closer to the dawn of AGI.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/multi-swe-bench/multi-swe-bench/blob/main/docs/contribution-demo.md", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.03553", "title": "Agentic Knowledgeable Self-awareness", "authors": [ "Shuofei Qiao", "Zhisong Qiu", "Baochang Ren", "Xiaobin Wang", "Xiangyuan Ru", "Ningyu Zhang", "Xiang Chen", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Huajun Chen" ], "github": "https://github.com/zjunlp/KnowSelf", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved considerable performance across various agentic planning tasks. However, traditional agent planning approaches adopt a \"flood irrigation\" methodology that indiscriminately injects gold trajectories, external feedback, and domain knowledge into agent models. This practice overlooks the fundamental human cognitive principle of situational self-awareness during decision-making-the ability to dynamically assess situational demands and strategically employ resources during decision-making. We propose agentic knowledgeable self-awareness to address this gap, a novel paradigm enabling LLM-based agents to autonomously regulate knowledge utilization. Specifically, we propose KnowSelf, a data-centric approach that applies agents with knowledgeable self-awareness like humans. Concretely, we devise a heuristic situation judgement criterion to mark special tokens on the agent's self-explored trajectories for collecting training data. Through a two-stage training process, the agent model can switch between different situations by generating specific special tokens, achieving optimal planning effects with minimal costs. Our experiments demonstrate that KnowSelf can outperform various strong baselines on different tasks and models with minimal use of external knowledge. Code is available at https://github.com/zjunlp/KnowSelf.", "github_manual": "", "github_regex": "https://github.com/zjunlp/KnowSelf", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.03561", "title": "SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement", "authors": [ "Runnan Fang", "Xiaobin Wang", "Yuan Liang", "Shuofei Qiao", "Jialong Wu", "Zekun Xi", "Ningyu Zhang", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Huajun Chen" ], "github": "https://github.com/zjunlp/SynWorld", "project_page": "", "abstract": "In the interaction between agents and their environments, agents expand their capabilities by planning and executing actions. However, LLM-based agents face substantial challenges when deployed in novel environments or required to navigate unconventional action spaces. To empower agents to autonomously explore environments, optimize workflows, and enhance their understanding of actions, we propose SynWorld, a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search (MCTS) exploration to effectively refine their action knowledge in the current environment. Our experiments demonstrate that SynWorld is an effective and general approach to learning action knowledge in new environments. Code is available at https://github.com/zjunlp/SynWorld.", "github_manual": "", "github_regex": "https://github.com/zjunlp/SynWorld", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.03641", "title": "MME-Unify: A Comprehensive Benchmark for Unified Multimodal Understanding and Generation Models", "authors": [ "Wulin Xie", "Yi-Fan Zhang", "Chaoyou Fu", "Yang Shi", "Bingyan Nie", "Hongkai Chen", "Zhang Zhang", "Liang Wang", "Tieniu Tan" ], "github": "", "project_page": "", "abstract": "Existing MLLM benchmarks face significant challenges in evaluating Unified MLLMs (U-MLLMs) due to: 1) lack of standardized benchmarks for traditional tasks, leading to inconsistent comparisons; 2) absence of benchmarks for mixed-modality generation, which fails to assess multimodal reasoning capabilities. We present a comprehensive evaluation framework designed to systematically assess U-MLLMs. Our benchmark includes: Standardized Traditional Task Evaluation. We sample from 12 datasets, covering 10 tasks with 30 subtasks, ensuring consistent and fair comparisons across studies.\" 2. Unified Task Assessment. We introduce five novel tasks testing multimodal reasoning, including image editing, commonsense QA with image generation, and geometric reasoning. 3. Comprehensive Model Benchmarking. We evaluate 12 leading U-MLLMs, such as Janus-Pro, EMU3, VILA-U, and Gemini2-flash, alongside specialized understanding (e.g., Claude-3.5-Sonnet) and generation models (e.g., DALL-E-3). Our findings reveal substantial performance gaps in existing U-MLLMs, highlighting the need for more robust models capable of handling mixed-modality tasks effectively. The code and evaluation data can be found in https://mme-unify.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.02949", "title": "VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning", "authors": [ "Xianwei Zhuang", "Yuxin Xie", "Yufan Deng", "Dongchao Yang", "Liming Liang", "Jinghan Ru", "Yuguo Yin", "Yuexian Zou" ], "github": "https://github.com/VARGPT-family/VARGPT-v1.1", "project_page": "", "abstract": "In this work, we present VARGPT-v1.1, an advanced unified visual autoregressive model that builds upon our previous framework VARGPT. The model preserves the dual paradigm of next-token prediction for visual understanding and next-scale generation for image synthesis. Specifically, VARGPT-v1.1 integrates: (1) a novel training strategy combining iterative visual instruction tuning with reinforcement learning through Direct Preference Optimization (DPO), (2) an expanded training corpus containing 8.3M visual-generative instruction pairs, (3) an upgraded language model backbone using Qwen2, (4) enhanced image generation resolution, and (5) emergent image editing capabilities without architectural modifications. These advancements enable VARGPT-v1.1 to achieve state-of-the-art performance in multimodal understanding and text-to-image instruction-following tasks, demonstrating significant improvements in both comprehension and generation metrics. Notably, through visual instruction tuning, the model acquires image editing functionality while maintaining architectural consistency with its predecessor, revealing the potential for unified visual understanding, generation, and editing. Our findings suggest that well-designed unified visual autoregressive models can effectively adopt flexible training strategies from large language models (LLMs), exhibiting promising scalability. The codebase and model weights are publicly available at https://github.com/VARGPT-family/VARGPT-v1.1.", "github_manual": "", "github_regex": "https://github.com/VARGPT-family/VARGPT-v1.1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.03011", "title": "Comprehensive Relighting: Generalizable and Consistent Monocular Human Relighting and Harmonization", "authors": [ "Junying Wang", "Jingyuan Liu", "Xin Sun", "Krishna Kumar Singh", "Zhixin Shu", "He Zhang", "Jimei Yang", "Nanxuan Zhao", "Tuanfeng Y. Wang", "Simon S. Chen", "Ulrich Neumann", "Jae Shin Yoon" ], "github": "", "project_page": "", "abstract": "This paper introduces Comprehensive Relighting, the first all-in-one approach that can both control and harmonize the lighting from an image or video of humans with arbitrary body parts from any scene. Building such a generalizable model is extremely challenging due to the lack of dataset, restricting existing image-based relighting models to a specific scenario (e.g., face or static human). To address this challenge, we repurpose a pre-trained diffusion model as a general image prior and jointly model the human relighting and background harmonization in the coarse-to-fine framework. To further enhance the temporal coherence of the relighting, we introduce an unsupervised temporal lighting model that learns the lighting cycle consistency from many real-world videos without any ground truth. In inference time, our temporal lighting module is combined with the diffusion models through the spatio-temporal feature blending algorithms without extra training; and we apply a new guided refinement as a post-processing to preserve the high-frequency details from the input image. In the experiments, Comprehensive Relighting shows a strong generalizability and lighting temporal coherence, outperforming existing image-based human relighting and harmonization methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2503.24310", "title": "BEATS: Bias Evaluation and Assessment Test Suite for Large Language Models", "authors": [ "Alok Abhishek", "Lisa Erickson", "Tushar Bandopadhyay" ], "github": "", "project_page": "", "abstract": "In this research, we introduce BEATS, a novel framework for evaluating Bias, Ethics, Fairness, and Factuality in Large Language Models (LLMs). Building upon the BEATS framework, we present a bias benchmark for LLMs that measure performance across 29 distinct metrics. These metrics span a broad range of characteristics, including demographic, cognitive, and social biases, as well as measures of ethical reasoning, group fairness, and factuality related misinformation risk. These metrics enable a quantitative assessment of the extent to which LLM generated responses may perpetuate societal prejudices that reinforce or expand systemic inequities. To achieve a high score on this benchmark a LLM must show very equitable behavior in their responses, making it a rigorous standard for responsible AI evaluation. Empirical results based on data from our experiment show that, 37.65\\% of outputs generated by industry leading models contained some form of bias, highlighting a substantial risk of using these models in critical decision making systems. BEATS framework and benchmark offer a scalable and statistically rigorous methodology to benchmark LLMs, diagnose factors driving biases, and develop mitigation strategies. With the BEATS framework, our goal is to help the development of more socially responsible and ethically aligned AI models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.03601", "title": "APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay", "authors": [ "Akshara Prabhakar", "Zuxin Liu", "Weiran Yao", "Jianguo Zhang", "Ming Zhu", "Shiyu Wang", "Zhiwei Liu", "Tulika Awalgaonkar", "Haolin Chen", "Thai Hoang", "Juan Carlos Niebles", "Shelby Heinecke", "Huan Wang", "Silvio Savarese", "Caiming Xiong" ], "github": "", "project_page": "", "abstract": "Training effective AI agents for multi-turn interactions requires high-quality data that captures realistic human-agent dynamics, yet such data is scarce and expensive to collect manually. We introduce APIGen-MT, a two-phase framework that generates verifiable and diverse multi-turn agent data. In the first phase, our agentic pipeline produces detailed task blueprints with ground-truth actions, leveraging a committee of LLM reviewers and iterative feedback loops. These blueprints are then transformed into complete interaction trajectories through simulated human-agent interplay. We train a family of models -- the xLAM-2-fc-r series with sizes ranging from 1B to 70B parameters. Our models outperform frontier models such as GPT-4o and Claude 3.5 on tau-bench and BFCL benchmarks, with the smaller models surpassing their larger counterparts, particularly in multi-turn settings, while maintaining superior consistency across multiple trials. Comprehensive experiments demonstrate that our verified blueprint-to-details approach yields high-quality training data, enabling the development of more reliable, efficient, and capable agents. We open-source both the synthetic data collected and the trained xLAM-2-fc-r models to advance research in AI agents. Models are available on HuggingFace at https://huggingface.co/collections/Salesforce/xlam-2-67ef5be12949d8dcdae354c4 and project website is https://apigen-mt.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.03536", "title": "HumanDreamer-X: Photorealistic Single-image Human Avatars Reconstruction via Gaussian Restoration", "authors": [ "Boyuan Wang", "Runqi Ouyang", "Xiaofeng Wang", "Zheng Zhu", "Guosheng Zhao", "Chaojun Ni", "Guan Huang", "Lihong Liu", "Xingang Wang" ], "github": "https://github.com/GigaAI-research/HumanDreamer-X", "project_page": "", "abstract": "Single-image human reconstruction is vital for digital human modeling applications but remains an extremely challenging task. Current approaches rely on generative models to synthesize multi-view images for subsequent 3D reconstruction and animation. However, directly generating multiple views from a single human image suffers from geometric inconsistencies, resulting in issues like fragmented or blurred limbs in the reconstructed models. To tackle these limitations, we introduce HumanDreamer-X, a novel framework that integrates multi-view human generation and reconstruction into a unified pipeline, which significantly enhances the geometric consistency and visual fidelity of the reconstructed 3D models. In this framework, 3D Gaussian Splatting serves as an explicit 3D representation to provide initial geometry and appearance priority. Building upon this foundation, HumanFixer is trained to restore 3DGS renderings, which guarantee photorealistic results. Furthermore, we delve into the inherent challenges associated with attention mechanisms in multi-view human generation, and propose an attention modulation strategy that effectively enhances geometric details identity consistency across multi-view. Experimental results demonstrate that our approach markedly improves generation and reconstruction PSNR quality metrics by 16.45% and 12.65%, respectively, achieving a PSNR of up to 25.62 dB, while also showing generalization capabilities on in-the-wild data and applicability to various human reconstruction backbone models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GigaAI-research/HumanDreamer-X", "github_api_added_by": "auto" }, { "date": "2025-04-07", "arxiv_id": "2504.02402", "title": "EvMic: Event-based Non-contact sound recovery from effective spatial-temporal modeling", "authors": [ "Hao Yin", "Shi Guo", "Xu Jia", "Xudong XU", "Lu Zhang", "Si Liu", "Dong Wang", "Huchuan Lu", "Tianfan Xue" ], "github": "", "project_page": "", "abstract": "When sound waves hit an object, they induce vibrations that produce high-frequency and subtle visual changes, which can be used for recovering the sound. Early studies always encounter trade-offs related to sampling rate, bandwidth, field of view, and the simplicity of the optical path. Recent advances in event camera hardware show good potential for its application in visual sound recovery, because of its superior ability in capturing high-frequency signals. However, existing event-based vibration recovery methods are still sub-optimal for sound recovery. In this work, we propose a novel pipeline for non-contact sound recovery, fully utilizing spatial-temporal information from the event stream. We first generate a large training set using a novel simulation pipeline. Then we designed a network that leverages the sparsity of events to capture spatial information and uses Mamba to model long-term temporal information. Lastly, we train a spatial aggregation block to aggregate information from different locations to further improve signal quality. To capture event signals caused by sound waves, we also designed an imaging system using a laser matrix to enhance the gradient and collected multiple data sequences for testing. Experimental results on synthetic and real-world data demonstrate the effectiveness of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2503.24067", "title": "TransMamba: Flexibly Switching between Transformer and Mamba", "authors": [ "Yixing Li", "Ruobing Xie", "Zhen Yang", "Xingwu Sun", "Shuaipeng Li", "Weidong Han", "Zhanhui Kang", "Yu Cheng", "Chengzhong Xu", "Di Wang", "Jie Jiang" ], "github": "https://github.com/Yixing-Li/TransMamba", "project_page": "", "abstract": "Transformers are the cornerstone of modern large language models, but their quadratic computational complexity limits efficiency in long-sequence processing. Recent advancements in Mamba, a state space model (SSM) with linear complexity, offer promising efficiency gains but suffer from unstable contextual learning and multitask generalization. This paper proposes TransMamba, a novel framework that unifies Transformer and Mamba through shared parameter matrices (e.g., QKV and CBx), and thus could dynamically switch between attention and SSM mechanisms at different token lengths and layers. We design the Memory converter to bridge Transformer and Mamba by converting attention outputs into SSM-compatible states, ensuring seamless information flow at TransPoints where the transformation happens. The TransPoint scheduling is also thoroughly explored for further improvements. We conducted extensive experiments demonstrating that TransMamba achieves superior training efficiency and performance compared to baselines, and validated the deeper consistency between Transformer and Mamba paradigms, offering a scalable solution for next-generation sequence modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Yixing-Li/TransMamba", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.02807", "title": "MegaMath: Pushing the Limits of Open Math Corpora", "authors": [ "Fan Zhou", "Zengzhi Wang", "Nikhil Ranjan", "Zhoujun Cheng", "Liping Tang", "Guowei He", "Zhengzhong Liu", "Eric P. Xing" ], "github": "", "project_page": "", "abstract": "Mathematical reasoning is a cornerstone of human intelligence and a key benchmark for advanced capabilities in large language models (LLMs). However, the research community still lacks an open, large-scale, high-quality corpus tailored to the demands of math-centric LLM pre-training. We present MegaMath, an open dataset curated from diverse, math-focused sources through following practices: (1) Revisiting web data: We re-extracted mathematical documents from Common Crawl with math-oriented HTML optimizations, fasttext-based filtering and deduplication, all for acquiring higher-quality data on the Internet. (2) Recalling Math-related code data: We identified high quality math-related code from large code training corpus, Stack-V2, further enhancing data diversity. (3) Exploring Synthetic data: We synthesized QA-style text, math-related code, and interleaved text-code blocks from web data or code data. By integrating these strategies and validating their effectiveness through extensive ablations, MegaMath delivers 371B tokens with the largest quantity and top quality among existing open math pre-training datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.03600", "title": "MedSAM2: Segment Anything in 3D Medical Images and Videos", "authors": [ "Jun Ma", "Zongxin Yang", "Sumin Kim", "Bihui Chen", "Mohammed Baharoon", "Adibvafa Fallahpour", "Reza Asakereh", "Hongwei Lyu", "Bo Wang" ], "github": "", "project_page": "", "abstract": "Medical image and video segmentation is a critical task for precision medicine, which has witnessed considerable progress in developing task or modality-specific and generalist models for 2D images. However, there have been limited studies on building general-purpose models for 3D images and videos with comprehensive user studies. Here, we present MedSAM2, a promptable segmentation foundation model for 3D image and video segmentation. The model is developed by fine-tuning the Segment Anything Model 2 on a large medical dataset with over 455,000 3D image-mask pairs and 76,000 frames, outperforming previous models across a wide range of organs, lesions, and imaging modalities. Furthermore, we implement a human-in-the-loop pipeline to facilitate the creation of large-scale datasets resulting in, to the best of our knowledge, the most extensive user study to date, involving the annotation of 5,000 CT lesions, 3,984 liver MRI lesions, and 251,550 echocardiogram video frames, demonstrating that MedSAM2 can reduce manual costs by more than 85%. MedSAM2 is also integrated into widely used platforms with user-friendly interfaces for local and cloud deployment, making it a practical tool for supporting efficient, scalable, and high-quality segmentation in both research and healthcare environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.00396", "title": "SPF-Portrait: Towards Pure Portrait Customization with Semantic Pollution-Free Fine-tuning", "authors": [ "Xiaole Xian", "Zhichao Liao", "Qingyu Li", "Wenyu Qin", "Pengfei Wan", "Weicheng Xie", "Long Zeng", "Linlin Shen", "Pingfa Feng" ], "github": "", "project_page": "", "abstract": "Fine-tuning a pre-trained Text-to-Image (T2I) model on a tailored portrait dataset is the mainstream method for text-driven customization of portrait attributes. Due to Semantic Pollution during fine-tuning, existing methods struggle to maintain the original model's behavior and achieve incremental learning while customizing target attributes. To address this issue, we propose SPF-Portrait, a pioneering work to purely understand customized semantics while eliminating semantic pollution in text-driven portrait customization. In our SPF-Portrait, we propose a dual-path pipeline that introduces the original model as a reference for the conventional fine-tuning path. Through contrastive learning, we ensure adaptation to target attributes and purposefully align other unrelated attributes with the original portrait. We introduce a novel Semantic-Aware Fine Control Map, which represents the precise response regions of the target semantics, to spatially guide the alignment process between the contrastive paths. This alignment process not only effectively preserves the performance of the original model but also avoids over-alignment. Furthermore, we propose a novel response enhancement mechanism to reinforce the performance of target attributes, while mitigating representation discrepancy inherent in direct cross-modal supervision. Extensive experiments demonstrate that SPF-Portrait achieves state-of-the-art performance. Project webpage: https://spf-portrait.github.io/SPF-Portrait/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.01328", "title": "Slow-Fast Architecture for Video Multi-Modal Large Language Models", "authors": [ "Min Shi", "Shihao Wang", "Chieh-Yun Chen", "Jitesh Jain", "Kai Wang", "Junjun Xiong", "Guilin Liu", "Zhiding Yu", "Humphrey Shi" ], "github": "https://github.com/shi-labs/slow-fast-video-multimodal-llm", "project_page": "", "abstract": "Balancing temporal resolution and spatial detail under limited compute budget remains a key challenge for video-based multi-modal large language models (MLLMs). Existing methods typically compress video representations using predefined rules before feeding them into the LLM, resulting in irreversible information loss and often ignoring input instructions. To address this, we propose a novel slow-fast architecture that naturally circumvents this trade-off, enabling the use of more input frames while preserving spatial details. Inspired by how humans first skim a video before focusing on relevant parts, our slow-fast design employs a dual-token strategy: 1) \"fast\" visual tokens -- a compact set of compressed video features -- are fed into the LLM alongside text embeddings to provide a quick overview; 2) \"slow\" visual tokens -- uncompressed video features -- are cross-attended by text embeddings through specially designed hybrid decoder layers, enabling instruction-aware extraction of relevant visual details with linear complexity. We conduct systematic exploration to optimize both the overall architecture and key components. Experiments show that our model significantly outperforms self-attention-only baselines, extending the input capacity from 16 to 128 frames with just a 3% increase in computation, and achieving a 16% average performance improvement across five video understanding benchmarks. Our 7B model achieves state-of-the-art performance among models of similar size. Furthermore, our slow-fast architecture is a plug-and-play design that can be integrated into other video MLLMs to improve efficiency and scalability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shi-labs/slow-fast-video-multimodal-llm", "github_api_added_by": "auto" }, { "date": "2025-04-07", "arxiv_id": "2504.03597", "title": "Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin for Real-World Robot Policy Evaluation", "authors": [ "Jad Abou-Chakra", "Lingfeng Sun", "Krishan Rana", "Brandon May", "Karl Schmeckpeper", "Maria Vittoria Minniti", "Laura Herlant" ], "github": "", "project_page": "", "abstract": "Recent advancements in behavior cloning have enabled robots to perform complex manipulation tasks. However, accurately assessing training performance remains challenging, particularly for real-world applications, as behavior cloning losses often correlate poorly with actual task success. Consequently, researchers resort to success rate metrics derived from costly and time-consuming real-world evaluations, making the identification of optimal policies and detection of overfitting or underfitting impractical. To address these issues, we propose real-is-sim, a novel behavior cloning framework that incorporates a dynamic digital twin (based on Embodied Gaussians) throughout the entire policy development pipeline: data collection, training, and deployment. By continuously aligning the simulated world with the physical world, demonstrations can be collected in the real world with states extracted from the simulator. The simulator enables flexible state representations by rendering image inputs from any viewpoint or extracting low-level state information from objects embodied within the scene. During training, policies can be directly evaluated within the simulator in an offline and highly parallelizable manner. Finally, during deployment, policies are run within the simulator where the real robot directly tracks the simulated robot's joints, effectively decoupling policy execution from real hardware and mitigating traditional domain-transfer challenges. We validate real-is-sim on the PushT manipulation task, demonstrating strong correlation between success rates obtained in the simulator and real-world evaluations. Videos of our system can be found at https://realissim.rai-inst.com.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2504.02534", "title": "Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery", "authors": [ "Mykola Lavreniuk", "Nataliia Kussul", "Andrii Shelestov", "Bohdan Yailymov", "Yevhenii Salii", "Volodymyr Kuzin", "Zoltan Szantoi" ], "github": "", "project_page": "", "abstract": "The accurate delineation of agricultural field boundaries from satellite imagery is vital for land management and crop monitoring. However, current methods face challenges due to limited dataset sizes, resolution discrepancies, and diverse environmental conditions. We address this by reformulating the task as instance segmentation and introducing the Field Boundary Instance Segmentation - 22M dataset (FBIS-22M), a large-scale, multi-resolution dataset comprising 672,909 high-resolution satellite image patches (ranging from 0.25 m to 10 m) and 22,926,427 instance masks of individual fields, significantly narrowing the gap between agricultural datasets and those in other computer vision domains. We further propose Delineate Anything, an instance segmentation model trained on our new FBIS-22M dataset. Our proposed model sets a new state-of-the-art, achieving a substantial improvement of 88.5% in mAP@0.5 and 103% in mAP@0.5:0.95 over existing methods, while also demonstrating significantly faster inference and strong zero-shot generalization across diverse image resolutions and unseen geographic regions. Code, pre-trained models, and the FBIS-22M dataset are available at https://lavreniuk.github.io/Delineate-Anything.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-07", "arxiv_id": "2503.22738", "title": "ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning", "authors": [ "Zhaorun Chen", "Mintong Kang", "Bo Li" ], "github": "", "project_page": "", "abstract": "Autonomous agents powered by foundation models have seen widespread adoption across various real-world applications. However, they remain highly vulnerable to malicious instructions and attacks, which can result in severe consequences such as privacy breaches and financial losses. More critically, existing guardrails for LLMs are not applicable due to the complex and dynamic nature of agents. To tackle these challenges, we propose ShieldAgent, the first guardrail agent designed to enforce explicit safety policy compliance for the action trajectory of other protected agents through logical reasoning. Specifically, ShieldAgent first constructs a safety policy model by extracting verifiable rules from policy documents and structuring them into a set of action-based probabilistic rule circuits. Given the action trajectory of the protected agent, ShieldAgent retrieves relevant rule circuits and generates a shielding plan, leveraging its comprehensive tool library and executable code for formal verification. In addition, given the lack of guardrail benchmarks for agents, we introduce ShieldAgent-Bench, a dataset with 3K safety-related pairs of agent instructions and action trajectories, collected via SOTA attacks across 6 web environments and 7 risk categories. Experiments show that ShieldAgent achieves SOTA on ShieldAgent-Bench and three existing benchmarks, outperforming prior methods by 11.3% on average with a high recall of 90.1%. Additionally, ShieldAgent reduces API queries by 64.7% and inference time by 58.2%, demonstrating its high precision and efficiency in safeguarding agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.05304", "title": "Gaussian Mixture Flow Matching Models", "authors": [ "Hansheng Chen", "Kai Zhang", "Hao Tan", "Zexiang Xu", "Fujun Luan", "Leonidas Guibas", "Gordon Wetzstein", "Sai Bi" ], "github": "", "project_page": "", "abstract": "Diffusion models approximate the denoising distribution as a Gaussian and predict its mean, whereas flow matching models reparameterize the Gaussian mean as flow velocity. However, they underperform in few-step sampling due to discretization error and tend to produce over-saturated colors under classifier-free guidance (CFG). To address these limitations, we propose a novel Gaussian mixture flow matching (GMFlow) model: instead of predicting the mean, GMFlow predicts dynamic Gaussian mixture (GM) parameters to capture a multi-modal flow velocity distribution, which can be learned with a KL divergence loss. We demonstrate that GMFlow generalizes previous diffusion and flow matching models where a single Gaussian is learned with an L_2 denoising loss. For inference, we derive GM-SDE/ODE solvers that leverage analytic denoising distributions and velocity fields for precise few-step sampling. Furthermore, we introduce a novel probabilistic guidance scheme that mitigates the over-saturation issues of CFG and improves image generation quality. Extensive experiments demonstrate that GMFlow consistently outperforms flow matching baselines in generation quality, achieving a Precision of 0.942 with only 6 sampling steps on ImageNet 256times256.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.05305", "title": "URECA: Unique Region Caption Anything", "authors": [ "Sangbeom Lim", "Junwan Kim", "Heeji Yoon", "Jaewoo Jung", "Seungryong Kim" ], "github": "", "project_page": "", "abstract": "Region-level captioning aims to generate natural language descriptions for specific image regions while highlighting their distinguishing features. However, existing methods struggle to produce unique captions across multi-granularity, limiting their real-world applicability. To address the need for detailed region-level understanding, we introduce URECA dataset, a large-scale dataset tailored for multi-granularity region captioning. Unlike prior datasets that focus primarily on salient objects, URECA dataset ensures a unique and consistent mapping between regions and captions by incorporating a diverse set of objects, parts, and background elements. Central to this is a stage-wise data curation pipeline, where each stage incrementally refines region selection and caption generation. By leveraging Multimodal Large Language Models (MLLMs) at each stage, our pipeline produces distinctive and contextually grounded captions with improved accuracy and semantic diversity. Building upon this dataset, we present URECA, a novel captioning model designed to effectively encode multi-granularity regions. URECA maintains essential spatial properties such as position and shape through simple yet impactful modifications to existing MLLMs, enabling fine-grained and semantically rich region descriptions. Our approach introduces dynamic mask modeling and a high-resolution mask encoder to enhance caption uniqueness. Experiments show that URECA achieves state-of-the-art performance on URECA dataset and generalizes well to existing region-level captioning benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.02828", "title": "Concept Lancet: Image Editing with Compositional Representation Transplant", "authors": [ "Jinqi Luo", "Tianjiao Ding", "Kwan Ho Ryan Chan", "Hancheng Min", "Chris Callison-Burch", "Ren\u00e9 Vidal" ], "github": "", "project_page": "", "abstract": "Diffusion models are widely used for image editing tasks. Existing editing methods often design a representation manipulation procedure by curating an edit direction in the text embedding or score space. However, such a procedure faces a key challenge: overestimating the edit strength harms visual consistency while underestimating it fails the editing task. Notably, each source image may require a different editing strength, and it is costly to search for an appropriate strength via trial-and-error. To address this challenge, we propose Concept Lancet (CoLan), a zero-shot plug-and-play framework for principled representation manipulation in diffusion-based image editing. At inference time, we decompose the source input in the latent (text embedding or diffusion score) space as a sparse linear combination of the representations of the collected visual concepts. This allows us to accurately estimate the presence of concepts in each image, which informs the edit. Based on the editing task (replace/add/remove), we perform a customized concept transplant process to impose the corresponding editing direction. To sufficiently model the concept space, we curate a conceptual representation dataset, CoLan-150K, which contains diverse descriptions and scenarios of visual terms and phrases for the latent dictionary. Experiments on multiple diffusion-based image editing baselines show that methods equipped with CoLan achieve state-of-the-art performance in editing effectiveness and consistency preservation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.05298", "title": "One-Minute Video Generation with Test-Time Training", "authors": [ "Karan Dalal", "Daniel Koceja", "Gashon Hussein", "Jiarui Xu", "Yue Zhao", "Youjin Song", "Shihao Han", "Ka Chun Cheung", "Jan Kautz", "Carlos Guestrin", "Tatsunori Hashimoto", "Sanmi Koyejo", "Yejin Choi", "Yu Sun", "Xiaolong Wang" ], "github": "", "project_page": "", "abstract": "Transformers today still struggle to generate one-minute videos because self-attention layers are inefficient for long context. Alternatives such as Mamba layers struggle with complex multi-scene stories because their hidden states are less expressive. We experiment with Test-Time Training (TTT) layers, whose hidden states themselves can be neural networks, therefore more expressive. Adding TTT layers into a pre-trained Transformer enables it to generate one-minute videos from text storyboards. For proof of concept, we curate a dataset based on Tom and Jerry cartoons. Compared to baselines such as Mamba~2, Gated DeltaNet, and sliding-window attention layers, TTT layers generate much more coherent videos that tell complex stories, leading by 34 Elo points in a human evaluation of 100 videos per method. Although promising, results still contain artifacts, likely due to the limited capability of the pre-trained 5B model. The efficiency of our implementation can also be improved. We have only experimented with one-minute videos due to resource constraints, but the approach can be extended to longer videos and more complex stories. Sample videos, code and annotations are available at: https://test-time-training.github.io/video-dit", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.04823", "title": "Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models", "authors": [ "Ruikang Liu", "Yuxuan Sun", "Manyi Zhang", "Haoli Bai", "Xianzhi Yu", "Tiezheng Yu", "Chun Yuan", "Lu Hou" ], "github": "https://github.com/ruikangliu/Quantized-Reasoning-Models", "project_page": "", "abstract": "Recent advancements in reasoning language models have demonstrated remarkable performance in complex tasks, but their extended chain-of-thought reasoning process increases inference overhead. While quantization has been widely adopted to reduce the inference cost of large language models, its impact on reasoning models remains understudied. In this study, we conduct the first systematic study on quantized reasoning models, evaluating the open-sourced DeepSeek-R1-Distilled Qwen and LLaMA families ranging from 1.5B to 70B parameters, and QwQ-32B. Our investigation covers weight, KV cache, and activation quantization using state-of-the-art algorithms at varying bit-widths, with extensive evaluation across mathematical (AIME, MATH-500), scientific (GPQA), and programming (LiveCodeBench) reasoning benchmarks. Our findings reveal that while lossless quantization can be achieved with W8A8 or W4A16 quantization, lower bit-widths introduce significant accuracy risks. We further identify model size, model origin, and task difficulty as critical determinants of performance. Contrary to expectations, quantized models do not exhibit increased output lengths. In addition, strategically scaling the model sizes or reasoning steps can effectively enhance the performance. All quantized models and codes will be open-sourced in https://github.com/ruikangliu/Quantized-Reasoning-Models.", "github_manual": "", "github_regex": "https://github.com/ruikangliu/Quantized-Reasoning-Models", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.04715", "title": "Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs", "authors": [ "Will Cai", "Tianneng Shi", "Xuandong Zhao", "Dawn Song" ], "github": "https://github.com/sunblaze-ucb/llm-api-audit", "project_page": "", "abstract": "The proliferation of Large Language Models (LLMs) accessed via black-box APIs introduces a significant trust challenge: users pay for services based on advertised model capabilities (e.g., size, performance), but providers may covertly substitute the specified model with a cheaper, lower-quality alternative to reduce operational costs. This lack of transparency undermines fairness, erodes trust, and complicates reliable benchmarking. Detecting such substitutions is difficult due to the black-box nature, typically limiting interaction to input-output queries. This paper formalizes the problem of model substitution detection in LLM APIs. We systematically evaluate existing verification techniques, including output-based statistical tests, benchmark evaluations, and log probability analysis, under various realistic attack scenarios like model quantization, randomized substitution, and benchmark evasion. Our findings reveal the limitations of methods relying solely on text outputs, especially against subtle or adaptive attacks. While log probability analysis offers stronger guarantees when available, its accessibility is often limited. We conclude by discussing the potential of hardware-based solutions like Trusted Execution Environments (TEEs) as a pathway towards provable model integrity, highlighting the trade-offs between security, performance, and provider adoption. Code is available at https://github.com/sunblaze-ucb/llm-api-audit", "github_manual": "", "github_regex": "https://github.com/sunblaze-ucb/llm-api-audit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.03770", "title": "JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model", "authors": [ "Yi Nian", "Shenzhe Zhu", "Yuehan Qin", "Li Li", "Ziyi Wang", "Chaowei Xiao", "Yue Zhao" ], "github": "https://github.com/ShenzheZhu/JailDAM", "project_page": "", "abstract": "Multimodal large language models (MLLMs) excel in vision-language tasks but also pose significant risks of generating harmful content, particularly through jailbreak attacks. Jailbreak attacks refer to intentional manipulations that bypass safety mechanisms in models, leading to the generation of inappropriate or unsafe content. Detecting such attacks is critical to ensuring the responsible deployment of MLLMs. Existing jailbreak detection methods face three primary challenges: (1) Many rely on model hidden states or gradients, limiting their applicability to white-box models, where the internal workings of the model are accessible; (2) They involve high computational overhead from uncertainty-based analysis, which limits real-time detection, and (3) They require fully labeled harmful datasets, which are often scarce in real-world settings. To address these issues, we introduce a test-time adaptive framework called JAILDAM. Our method leverages a memory-based approach guided by policy-driven unsafe knowledge representations, eliminating the need for explicit exposure to harmful data. By dynamically updating unsafe knowledge during test-time, our framework improves generalization to unseen jailbreak strategies while maintaining efficiency. Experiments on multiple VLM jailbreak benchmarks demonstrate that JAILDAM delivers state-of-the-art performance in harmful content detection, improving both accuracy and speed.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ShenzheZhu/JailDAM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.05288", "title": "LiveVQA: Live Visual Knowledge Seeking", "authors": [ "Mingyang Fu", "Yuyang Peng", "Benlin Liu", "Yao Wan", "Dongping Chen" ], "github": "", "project_page": "", "abstract": "We introduce LiveVQA, an automatically collected dataset of latest visual knowledge from the Internet with synthesized VQA problems. LiveVQA consists of 3,602 single- and multi-hop visual questions from 6 news websites across 14 news categories, featuring high-quality image-text coherence and authentic information. Our evaluation across 15 MLLMs (e.g., GPT-4o, Gemma-3, and Qwen-2.5-VL family) demonstrates that stronger models perform better overall, with advanced visual reasoning capabilities proving crucial for complex multi-hop questions. Despite excellent performance on textual problems, models with tools like search engines still show significant gaps when addressing visual questions requiring latest visual knowledge, highlighting important areas for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.02812", "title": "BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose Estimation", "authors": [ "Van Nguyen Nguyen", "Stephen Tyree", "Andrew Guo", "Mederic Fourmy", "Anas Gouda", "Taeyeop Lee", "Sungphill Moon", "Hyeontae Son", "Lukas Ranftl", "Jonathan Tremblay", "Eric Brachmann", "Bertram Drost", "Vincent Lepetit", "Carsten Rother", "Stan Birchfield", "Jiri Matas", "Yann Labbe", "Martin Sundermeyer", "Tomas Hodan" ], "github": "", "project_page": "", "abstract": "We present the evaluation methodology, datasets and results of the BOP Challenge 2024, the sixth in a series of public competitions organized to capture the state of the art in 6D object pose estimation and related tasks. In 2024, our goal was to transition BOP from lab-like setups to real-world scenarios. First, we introduced new model-free tasks, where no 3D object models are available and methods need to onboard objects just from provided reference videos. Second, we defined a new, more practical 6D object detection task where identities of objects visible in a test image are not provided as input. Third, we introduced new BOP-H3 datasets recorded with high-resolution sensors and AR/VR headsets, closely resembling real-world scenarios. BOP-H3 include 3D models and onboarding videos to support both model-based and model-free tasks. Participants competed on seven challenge tracks, each defined by a task, object onboarding setup, and dataset group. Notably, the best 2024 method for model-based 6D localization of unseen objects (FreeZeV2.1) achieves 22% higher accuracy on BOP-Classic-Core than the best 2023 method (GenFlow), and is only 4% behind the best 2023 method for seen objects (GPose2023) although being significantly slower (24.9 vs 2.7s per image). A more practical 2024 method for this task is Co-op which takes only 0.8s per image and is 25X faster and 13% more accurate than GenFlow. Methods have a similar ranking on 6D detection as on 6D localization but higher run time. On model-based 2D detection of unseen objects, the best 2024 method (MUSE) achieves 21% relative improvement compared to the best 2023 method (CNOS). However, the 2D detection accuracy for unseen objects is still noticealy (-53%) behind the accuracy for seen objects (GDet2023). The online evaluation system stays open and is available at http://bop.felk.cvut.cz/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.04718", "title": "T1: Tool-integrated Self-verification for Test-time Compute Scaling in Small Language Models", "authors": [ "Minki Kang", "Jongwon Jeong", "Jaewoong Cho" ], "github": "", "project_page": "", "abstract": "Recent studies have demonstrated that test-time compute scaling effectively improves the performance of small language models (sLMs). However, prior research has mainly examined test-time compute scaling with an additional larger model as a verifier, leaving self-verification by sLMs underexplored. In this work, we investigate whether sLMs can reliably self-verify their outputs under test-time scaling. We find that even with knowledge distillation from larger verifiers, sLMs struggle with verification tasks requiring memorization, such as numerical calculations and fact-checking. To address this limitation, we propose Tool-integrated self-verification (T1), which delegates memorization-heavy verification steps to external tools, such as a code interpreter. Our theoretical analysis shows that tool integration reduces memorization demands and improves test-time scaling performance. Experiments on the MATH benchmark demonstrate that, with T1, a Llama-3.2 1B model under test-time scaling outperforms the significantly larger Llama-3.1 8B model. Moreover, T1 generalizes effectively to both mathematical (MATH500) and multi-domain knowledge-intensive tasks (MMLU-Pro). Our findings highlight the potential of tool integration to substantially improve the self-verification abilities of sLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.05299", "title": "SmolVLM: Redefining small and efficient multimodal models", "authors": [ "Andr\u00e9s Marafioti", "Orr Zohar", "Miquel Farr\u00e9", "Merve Noyan", "Elie Bakouch", "Pedro Cuenca", "Cyril Zakka", "Loubna Ben Allal", "Anton Lozhkov", "Nouamane Tazi", "Vaibhav Srivastav", "Joshua Lochner", "Hugo Larcher", "Mathieu Morlon", "Lewis Tunstall", "Leandro von Werra", "Thomas Wolf" ], "github": "", "project_page": "", "abstract": "Large Vision-Language Models (VLMs) deliver exceptional performance but require significant computational resources, limiting their deployment on mobile and edge devices. Smaller VLMs typically mirror design choices of larger models, such as extensive image tokenization, leading to inefficient GPU memory usage and constrained practicality for on-device applications. We introduce SmolVLM, a series of compact multimodal models specifically engineered for resource-efficient inference. We systematically explore architectural configurations, tokenization strategies, and data curation optimized for low computational overhead. Through this, we identify key design choices that yield substantial performance gains on image and video tasks with minimal memory footprints. Our smallest model, SmolVLM-256M, uses less than 1GB GPU memory during inference and outperforms the 300-times larger Idefics-80B model, despite an 18-month development gap. Our largest model, at 2.2B parameters, rivals state-of-the-art VLMs consuming twice the GPU memory. SmolVLM models extend beyond static images, demonstrating robust video comprehension capabilities. Our results emphasize that strategic architectural optimizations, aggressive yet efficient tokenization, and carefully curated training data significantly enhance multimodal performance, facilitating practical, energy-efficient deployments at significantly smaller scales.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.03964", "title": "Clinical ModernBERT: An efficient and long context encoder for biomedical text", "authors": [ "Simon A. Lee", "Anthony Wu", "Jeffrey N. Chiang" ], "github": "", "project_page": "", "abstract": "We introduce Clinical ModernBERT, a transformer based encoder pretrained on large scale biomedical literature, clinical notes, and medical ontologies, incorporating PubMed abstracts, MIMIC IV clinical data, and medical codes with their textual descriptions. Building on ModernBERT the current state of the art natural language text encoder featuring architectural upgrades such as rotary positional embeddings (RoPE), Flash Attention, and extended context length up to 8,192 tokens our model adapts these innovations specifically for biomedical and clinical domains. Clinical ModernBERT excels at producing semantically rich representations tailored for long context tasks. We validate this both by analyzing its pretrained weights and through empirical evaluation on a comprehensive suite of clinical NLP benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.03193", "title": "Mamba as a Bridge: Where Vision Foundation Models Meet Vision Language Models for Domain-Generalized Semantic Segmentation", "authors": [ "Xin Zhang", "Robby T. Tan" ], "github": "https://github.com/devinxzhang/MFuser", "project_page": "", "abstract": "Vision Foundation Models (VFMs) and Vision-Language Models (VLMs) have gained traction in Domain Generalized Semantic Segmentation (DGSS) due to their strong generalization capabilities. However, existing DGSS methods often rely exclusively on either VFMs or VLMs, overlooking their complementary strengths. VFMs (e.g., DINOv2) excel at capturing fine-grained features, while VLMs (e.g., CLIP) provide robust text alignment but struggle with coarse granularity. Despite their complementary strengths, effectively integrating VFMs and VLMs with attention mechanisms is challenging, as the increased patch tokens complicate long-sequence modeling. To address this, we propose MFuser, a novel Mamba-based fusion framework that efficiently combines the strengths of VFMs and VLMs while maintaining linear scalability in sequence length. MFuser consists of two key components: MVFuser, which acts as a co-adapter to jointly fine-tune the two models by capturing both sequential and spatial dynamics; and MTEnhancer, a hybrid attention-Mamba module that refines text embeddings by incorporating image priors. Our approach achieves precise feature locality and strong text alignment without incurring significant computational overhead. Extensive experiments demonstrate that MFuser significantly outperforms state-of-the-art DGSS methods, achieving 68.20 mIoU on synthetic-to-real and 71.87 mIoU on real-to-real benchmarks. The code is available at https://github.com/devinxzhang/MFuser.", "github_manual": "", "github_regex": "https://github.com/devinxzhang/MFuser", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.02882", "title": "DiaTool-DPO: Multi-Turn Direct Preference Optimization for Tool-Augmented Large Language Models", "authors": [ "Sunghee Jung", "Donghun Lee", "Shinbok Lee", "Gaeun Seo", "Daniel Lee", "Byeongil Ko", "Junrae Cho", "Kihyun Kim", "Eunggyun Kim", "Myeongcheol Shin" ], "github": "", "project_page": "", "abstract": "Tool-Augmented Larage Language Models (TA-LLMs) have shown promise in real-world applications, but face challenges in handling incomplete queries and out-of-scope requests. While existing approaches rely mainly on Supervised Fine-Tuning with expert trajectories, we propose DiaTool-DPO, a novel method that enhances TA-LLM's dialogue capabilities through Direct Preference Optimization. We model TA-LLM interactions as a Markov Decision Process with 5 distinct dialogue states and categorize user queries into 3 types based on their state transition trajectories. We automatically construct paired trajectory datasets of correct and incorrect dialogue flows and introduce a specialized objective loss for dialogue control. Our comprehensive evaluation demonstrates that DiaTool-DPO approaches GPT-4o's performance (94.8% in information gathering, 91% in tool call rejection) with substantial improvements over baseline (44% and 9.6% respectively) while maintaining core functionality. Our approach opens new possibilities for developing TA-LLMs that can handle diverse real-world scenarios without requiring additional expert demonstrations or human labeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.05118", "title": "VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks", "authors": [ "YuYue", "Yufeng Yuan", "Qiying Yu", "Xiaochen Zuo", "Ruofei Zhu", "Wenyuan Xu", "Jiaze Chen", "Chengyi Wang", "TianTian Fan", "Zhengyin Du", "Xiangpeng Wei", "Gaohong Liu", "Juncai Liu", "Lingjun Liu", "Haibin Lin", "Zhiqi Lin", "Bole Ma", "Chi Zhang", "Mofan Zhang", "Wang Zhang", "Hang Zhu", "Ru Zhang", "Xin Liu", "Mingxuan Wang", "Yonghui Wu", "Lin Yan" ], "github": "", "project_page": "", "abstract": "We present VAPO, Value-based Augmented Proximal Policy Optimization framework for reasoning models., a novel framework tailored for reasoning models within the value-based paradigm. Benchmarked the AIME 2024 dataset, VAPO, built on the Qwen 32B pre-trained model, attains a state-of-the-art score of 60.4. In direct comparison under identical experimental settings, VAPO outperforms the previously reported results of DeepSeek-R1-Zero-Qwen-32B and DAPO by more than 10 points. The training process of VAPO stands out for its stability and efficiency. It reaches state-of-the-art performance within a mere 5,000 steps. Moreover, across multiple independent runs, no training crashes occur, underscoring its reliability. This research delves into long chain-of-thought (long-CoT) reasoning using a value-based reinforcement learning framework. We pinpoint three key challenges that plague value-based methods: value model bias, the presence of heterogeneous sequence lengths, and the sparsity of reward signals. Through systematic design, VAPO offers an integrated solution that effectively alleviates these challenges, enabling enhanced performance in long-CoT reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.04155", "title": "GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models", "authors": [ "Hengyu Luo", "Zihao Li", "Joseph Attieh", "Sawal Devkota", "Ona de Gibert", "Shaoxiong Ji", "Peiqin Lin", "Bhavani Sai Praneeth Varma Mantina", "Ananda Sreenidhi", "Ra\u00fal V\u00e1zquez", "Mengjie Wang", "Samea Yusofi", "J\u00f6rg Tiedemann" ], "github": "https://github.com/cisnlp/Taxi1500", "project_page": "", "abstract": "Large language models (LLMs) are advancing at an unprecedented pace globally, with regions increasingly adopting these models for applications in their primary language. Evaluation of these models in diverse linguistic environments, especially in low-resource languages, has become a major challenge for academia and industry. Existing evaluation frameworks are disproportionately focused on English and a handful of high-resource languages, thereby overlooking the realistic performance of LLMs in multilingual and lower-resource scenarios. To address this gap, we introduce GlotEval, a lightweight framework designed for massively multilingual evaluation. Supporting seven key tasks (machine translation, text classification, summarization, open-ended generation, reading comprehension, sequence labeling, and intrinsic evaluation), spanning over dozens to hundreds of languages, GlotEval highlights consistent multilingual benchmarking, language-specific prompt templates, and non-English-centric machine translation. This enables a precise diagnosis of model strengths and weaknesses in diverse linguistic contexts. A multilingual translation case study demonstrates GlotEval's applicability for multilingual and language-specific evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/cisnlp/Taxi1500", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.04152", "title": "Rethinking Multilingual Continual Pretraining: Data Mixing for Adapting LLMs Across Languages and Resources", "authors": [ "Zihao Li", "Shaoxiong Ji", "Hengyu Luo", "J\u00f6rg Tiedemann" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) exhibit significant disparities in performance across languages, primarily benefiting high-resource languages while marginalizing underrepresented ones. Continual Pretraining (CPT) has emerged as a promising approach to address this imbalance, although the relative effectiveness of monolingual, bilingual, and code-augmented data strategies remains unclear. This study systematically evaluates 36 CPT configurations involving three multilingual base models, across 30+ languages categorized as altruistic, selfish, and stagnant, spanning various resource levels. Our findings reveal three major insights: (1) Bilingual CPT improves multilingual classification but often causes language mixing issues during generation. (2) Including programming code data during CPT consistently enhances multilingual classification accuracy, particularly benefiting low-resource languages, but introduces a trade-off by slightly degrading generation quality. (3) Contrary to prior work, we observe substantial deviations from language classifications according to their impact on cross-lingual transfer: Languages classified as altruistic often negatively affect related languages, selfish languages show conditional and configuration-dependent behavior, and stagnant languages demonstrate surprising adaptability under certain CPT conditions. These nuanced interactions emphasize the complexity of multilingual representation learning, underscoring the importance of systematic studies on generalizable language classification to inform future multilingual CPT strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.04022", "title": "Rethinking Reflection in Pre-Training", "authors": [ "Essential AI", "Darsh J Shah", "Peter Rushton", "Somanshu Singla", "Mohit Parmar", "Kurt Smith", "Yash Vanjani", "Ashish Vaswani", "Adarsh Chaluvaraju", "Andrew Hojel", "Andrew Ma", "Anil Thomas", "Anthony Polloreno", "Ashish Tanwer", "Burhan Drak Sibai", "Divya S Mansingka", "Divya Shivaprasad", "Ishaan Shah", "Karl Stratos", "Khoi Nguyen", "Michael Callahan", "Michael Pust", "Mrinal Iyer", "Philip Monk", "Platon Mazarakis", "Ritvik Kapila", "Saurabh Srivastava", "Tim Romanski" ], "github": "", "project_page": "", "abstract": "A language model's ability to reflect on its own reasoning provides a key advantage for solving complex problems. While most recent research has focused on how this ability develops during reinforcement learning, we show that it actually begins to emerge much earlier - during the model's pre-training. To study this, we introduce deliberate errors into chains-of-thought and test whether the model can still arrive at the correct answer by recognizing and correcting these mistakes. By tracking performance across different stages of pre-training, we observe that this self-correcting ability appears early and improves steadily over time. For instance, an OLMo2-7B model pre-trained on 4 trillion tokens displays self-correction on our six self-reflection tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.03790", "title": "Sample, Don't Search: Rethinking Test-Time Alignment for Language Models", "authors": [ "Gon\u00e7alo Faria", "Noah A. Smith" ], "github": "https://github.com/goncalorafaria/qalign", "project_page": "", "abstract": "Increasing test-time computation has emerged as a promising direction for improving language model performance, particularly in scenarios where model finetuning is impractical or impossible due to computational constraints or private model weights. However, existing test-time search methods using a reward model (RM) often degrade in quality as compute scales, due to the over-optimization of what are inherently imperfect reward proxies. We introduce QAlign, a new test-time alignment approach. As we scale test-time compute, QAlign converges to sampling from the optimal aligned distribution for each individual prompt. By adopting recent advances in Markov chain Monte Carlo for text generation, our method enables better-aligned outputs without modifying the underlying model or even requiring logit access. We demonstrate the effectiveness of QAlign on mathematical reasoning benchmarks (GSM8K and GSM-Symbolic) using a task-specific RM, showing consistent improvements over existing test-time compute methods like best-of-n and majority voting. Furthermore, when applied with more realistic RMs trained on the Tulu 3 preference dataset, QAlign outperforms direct preference optimization (DPO), best-of-n, majority voting, and weighted majority voting on a diverse range of datasets (GSM8K, MATH500, IFEval, MMLU-Redux, and TruthfulQA). A practical solution to aligning language models at test time using additional computation without degradation, our approach expands the limits of the capability that can be obtained from off-the-shelf language models without further training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/goncalorafaria/qalign", "github_api_added_by": "auto" }, { "date": "2025-04-08", "arxiv_id": "2504.03151", "title": "Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)", "authors": [ "Jing Bi", "Susan Liang", "Xiaofei Zhou", "Pinxin Liu", "Junjia Guo", "Yunlong Tang", "Luchuan Song", "Chao Huang", "Guangyu Sun", "Jinxi He", "Jiarui Wu", "Shu Yang", "Daoan Zhang", "Chen Chen", "Lianggong Bruce Wen", "Zhang Liu", "Jiebo Luo", "Chenliang Xu" ], "github": "", "project_page": "", "abstract": "Reasoning is central to human intelligence, enabling structured problem-solving across diverse tasks. Recent advances in large language models (LLMs) have greatly enhanced their reasoning abilities in arithmetic, commonsense, and symbolic domains. However, effectively extending these capabilities into multimodal contexts-where models must integrate both visual and textual inputs-continues to be a significant challenge. Multimodal reasoning introduces complexities, such as handling conflicting information across modalities, which require models to adopt advanced interpretative strategies. Addressing these challenges involves not only sophisticated algorithms but also robust methodologies for evaluating reasoning accuracy and coherence. This paper offers a concise yet insightful overview of reasoning techniques in both textual and multimodal LLMs. Through a thorough and up-to-date comparison, we clearly formulate core reasoning challenges and opportunities, highlighting practical methods for post-training optimization and test-time inference. Our work provides valuable insights and guidance, bridging theoretical frameworks and practical implementations, and sets clear directions for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.03947", "title": "Distillation and Refinement of Reasoning in Small Language Models for Document Re-ranking", "authors": [ "Chris Samarinas", "Hamed Zamani" ], "github": "https://github.com/algoprog/InteRank", "project_page": "", "abstract": "We present a novel approach for training small language models for reasoning-intensive document ranking that combines knowledge distillation with reinforcement learning optimization. While existing methods often rely on expensive human annotations or large black-box language models, our methodology leverages web data and a teacher LLM to automatically generate high-quality training examples with relevance explanations. By framing document ranking as a reinforcement learning problem and incentivizing explicit reasoning capabilities, we train a compact 3B parameter language model that achieves state-of-the-art performance on the BRIGHT benchmark. Our model ranks third on the leaderboard while using substantially fewer parameters than other approaches, outperforming models that are over 20 times larger. Through extensive experiments, we demonstrate that generating explanations during inference, rather than directly predicting relevance scores, enables more effective reasoning with smaller language models. The self-supervised nature of our method offers a scalable and interpretable solution for modern information retrieval systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/algoprog/InteRank", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-08", "arxiv_id": "2504.03875", "title": "3D Scene Understanding Through Local Random Access Sequence Modeling", "authors": [ "Wanhee Lee", "Klemen Kotar", "Rahul Mysore Venkatesh", "Jared Watrous", "Honglin Chen", "Khai Loong Aw", "Daniel L. K. Yamins" ], "github": "", "project_page": "", "abstract": "3D scene understanding from single images is a pivotal problem in computer vision with numerous downstream applications in graphics, augmented reality, and robotics. While diffusion-based modeling approaches have shown promise, they often struggle to maintain object and scene consistency, especially in complex real-world scenarios. To address these limitations, we propose an autoregressive generative approach called Local Random Access Sequence (LRAS) modeling, which uses local patch quantization and randomly ordered sequence generation. By utilizing optical flow as an intermediate representation for 3D scene editing, our experiments demonstrate that LRAS achieves state-of-the-art novel view synthesis and 3D object manipulation capabilities. Furthermore, we show that our framework naturally extends to self-supervised depth estimation through a simple modification of the sequence design. By achieving strong performance on multiple 3D scene understanding tasks, LRAS provides a unified and effective framework for building the next generation of 3D vision models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.05979", "title": "An Empirical Study of GPT-4o Image Generation Capabilities", "authors": [ "Sixiang Chen", "Jinbin Bai", "Zhuoran Zhao", "Tian Ye", "Qingyu Shi", "Donghao Zhou", "Wenhao Chai", "Xin Lin", "Jianzong Wu", "Chao Tang", "Shilin Xu", "Tao Zhang", "Haobo Yuan", "Yikang Zhou", "Wei Chow", "Linfeng Li", "Xiangtai Li", "Lei Zhu", "Lu Qi" ], "github": "https://github.com/ephemeral182/empirical-study-of-gpt-4o-image-gen", "project_page": "", "abstract": "The landscape of image generation has rapidly evolved, from early GAN-based approaches to diffusion models and, most recently, to unified generative architectures that seek to bridge understanding and generation tasks. Recent advances, especially the GPT-4o, have demonstrated the feasibility of high-fidelity multimodal generation, their architectural design remains mysterious and unpublished. This prompts the question of whether image and text generation have already been successfully integrated into a unified framework for those methods. In this work, we conduct an empirical study of GPT-4o's image generation capabilities, benchmarking it against leading open-source and commercial models. Our evaluation covers four main categories, including text-to-image, image-to-image, image-to-3D, and image-to-X generation, with more than 20 tasks. Our analysis highlights the strengths and limitations of GPT-4o under various settings, and situates it within the broader evolution of generative modeling. Through this investigation, we identify promising directions for future unified generative models, emphasizing the role of architectural design and data scaling.", "github_manual": "https://github.com/ephemeral182/empirical-study-of-gpt-4o-image-gen", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.00043", "title": "CrossWordBench: Evaluating the Reasoning Capabilities of LLMs and LVLMs with Controllable Puzzle Generation", "authors": [ "Jixuan Leng", "Chengsong Huang", "Langlin Huang", "Bill Yuchen Lin", "William W. Cohen", "Haohan Wang", "Jiaxin Huang" ], "github": "https://github.com/SeanLeng1/CrossWordBench", "project_page": "", "abstract": "Existing reasoning evaluation frameworks for Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) predominantly either assess text-based reasoning or vision-language understanding capabilities, with limited dynamic interplay between textual and visual constraints. To address this limitation, we introduce CrossWordBench, a benchmark designed to evaluate the reasoning capabilities of both LLMs and LVLMs through the medium of crossword puzzles-a task requiring multimodal adherence to semantic constraints from text-based clues and intersectional constraints from visual grid structures. CrossWordBench leverages a controllable puzzle generation framework that produces puzzles in multiple formats (text and image) and offers different evaluation strategies ranging from direct puzzle solving to interactive modes. Our extensive evaluation of over 20 models reveals that reasoning LLMs outperform non-reasoning models substantially by effectively leveraging crossing-letter constraints. We further demonstrate that LVLMs struggle with the task, showing a strong correlation between their puzzle-solving performance and grid-parsing accuracy. Our findings offer insights into the limitations of the reasoning capabilities of current LLMs and LVLMs, and provide an effective approach for creating multimodal constrained tasks for future evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SeanLeng1/CrossWordBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.02810", "title": "Generative Evaluation of Complex Reasoning in Large Language Models", "authors": [ "Haowei Lin", "Xiangyu Wang", "Ruilin Yan", "Baizhou Huang", "Haotian Ye", "Jianhua Zhu", "Zihao Wang", "James Zou", "Jianzhu Ma", "Yitao Liang" ], "github": "https://github.com/linhaowei/kumo", "project_page": "", "abstract": "With powerful large language models (LLMs) demonstrating superhuman reasoning capabilities, a critical question arises: Do LLMs genuinely reason, or do they merely recall answers from their extensive, web-scraped training datasets? Publicly released benchmarks inevitably become contaminated once incorporated into subsequent LLM training sets, undermining their reliability as faithful assessments. To address this, we introduce KUMO, a generative evaluation framework designed specifically for assessing reasoning in LLMs. KUMO synergistically combines LLMs with symbolic engines to dynamically produce diverse, multi-turn reasoning tasks that are partially observable and adjustable in difficulty. Through an automated pipeline, KUMO continuously generates novel tasks across open-ended domains, compelling models to demonstrate genuine generalization rather than memorization. We evaluated 23 state-of-the-art LLMs on 5,000 tasks across 100 domains created by KUMO, benchmarking their reasoning abilities against university students. Our findings reveal that many LLMs have outperformed university-level performance on easy reasoning tasks, and reasoning-scaled LLMs reach university-level performance on complex reasoning challenges. Moreover, LLM performance on KUMO tasks correlates strongly with results on newly released real-world reasoning benchmarks, underscoring KUMO's value as a robust, enduring assessment tool for genuine LLM reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/linhaowei/kumo", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.05594", "title": "Tuning-Free Image Editing with Fidelity and Editability via Unified Latent Diffusion Model", "authors": [ "Qi Mao", "Lan Chen", "Yuchao Gu", "Mike Zheng Shou", "Ming-Hsuan Yang" ], "github": "https://github.com/CUC-MIPG/UnifyEdit", "project_page": "", "abstract": "Balancing fidelity and editability is essential in text-based image editing (TIE), where failures commonly lead to over- or under-editing issues. Existing methods typically rely on attention injections for structure preservation and leverage the inherent text alignment capabilities of pre-trained text-to-image (T2I) models for editability, but they lack explicit and unified mechanisms to properly balance these two objectives. In this work, we introduce UnifyEdit, a tuning-free method that performs diffusion latent optimization to enable a balanced integration of fidelity and editability within a unified framework. Unlike direct attention injections, we develop two attention-based constraints: a self-attention (SA) preservation constraint for structural fidelity, and a cross-attention (CA) alignment constraint to enhance text alignment for improved editability. However, simultaneously applying both constraints can lead to gradient conflicts, where the dominance of one constraint results in over- or under-editing. To address this challenge, we introduce an adaptive time-step scheduler that dynamically adjusts the influence of these constraints, guiding the diffusion latent toward an optimal balance. Extensive quantitative and qualitative experiments validate the effectiveness of our approach, demonstrating its superiority in achieving a robust balance between structure preservation and text alignment across various editing tasks, outperforming other state-of-the-art methods. The source code will be available at https://github.com/CUC-MIPG/UnifyEdit.", "github_manual": "", "github_regex": "https://github.com/CUC-MIPG/UnifyEdit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.06263", "title": "OmniSVG: A Unified Scalable Vector Graphics Generation Model", "authors": [ "Yiying Yang", "Wei Cheng", "Sijin Chen", "Xianfang Zeng", "Jiaxu Zhang", "Liao Wang", "Gang Yu", "Xingjun Ma", "Yu-Gang Jiang" ], "github": "", "project_page": "", "abstract": "Scalable Vector Graphics (SVG) is an important image format widely adopted in graphic design because of their resolution independence and editability. The study of generating high-quality SVG has continuously drawn attention from both designers and researchers in the AIGC community. However, existing methods either produces unstructured outputs with huge computational cost or is limited to generating monochrome icons of over-simplified structures. To produce high-quality and complex SVG, we propose OmniSVG, a unified framework that leverages pre-trained Vision-Language Models (VLMs) for end-to-end multimodal SVG generation. By parameterizing SVG commands and coordinates into discrete tokens, OmniSVG decouples structural logic from low-level geometry for efficient training while maintaining the expressiveness of complex SVG structure. To further advance the development of SVG synthesis, we introduce MMSVG-2M, a multimodal dataset with two million richly annotated SVG assets, along with a standardized evaluation protocol for conditional SVG generation tasks. Extensive experiments show that OmniSVG outperforms existing methods and demonstrates its potential for integration into professional SVG design workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.02160", "title": "Less-to-More Generalization: Unlocking More Controllability by In-Context Generation", "authors": [ "Shaojin Wu", "Mengqi Huang", "Wenxu Wu", "Yufeng Cheng", "Fei Ding", "Qian He" ], "github": "", "project_page": "", "abstract": "Although subject-driven generation has been extensively explored in image generation due to its wide applications, it still has challenges in data scalability and subject expansibility. For the first challenge, moving from curating single-subject datasets to multiple-subject ones and scaling them is particularly difficult. For the second, most recent methods center on single-subject generation, making it hard to apply when dealing with multi-subject scenarios. In this study, we propose a highly-consistent data synthesis pipeline to tackle this challenge. This pipeline harnesses the intrinsic in-context generation capabilities of diffusion transformers and generates high-consistency multi-subject paired data. Additionally, we introduce UNO, which consists of progressive cross-modal alignment and universal rotary position embedding. It is a multi-image conditioned subject-to-image model iteratively trained from a text-to-image model. Extensive experiments show that our method can achieve high consistency while ensuring controllability in both single-subject and multi-subject driven generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.06261", "title": "Hogwild! Inference: Parallel LLM Generation via Concurrent Attention", "authors": [ "Gleb Rodionov", "Roman Garipov", "Alina Shutova", "George Yakushev", "Vage Egiazarian", "Anton Sinitsin", "Denis Kuznedelev", "Dan Alistarh" ], "github": "https://github.com/eqimp/hogwild_llm", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated the ability to tackle increasingly complex tasks through advanced reasoning, long-form content generation, and tool use. Solving these tasks often involves long inference-time computations. In human problem solving, a common strategy to expedite work is collaboration: by dividing the problem into sub-tasks, exploring different strategies concurrently, etc. Recent research has shown that LLMs can also operate in parallel by implementing explicit cooperation frameworks, such as voting mechanisms or the explicit creation of independent sub-tasks that can be executed in parallel. However, each of these frameworks may not be suitable for all types of tasks, which can hinder their applicability. In this work, we propose a different design approach: we run LLM \"workers\" in parallel , allowing them to synchronize via a concurrently-updated attention cache and prompt these workers to decide how best to collaborate. Our approach allows the instances to come up with their own collaboration strategy for the problem at hand, all the while \"seeing\" each other's partial progress in the concurrent cache. We implement this approach via Hogwild! Inference: a parallel LLM inference engine where multiple instances of the same LLM run in parallel with the same attention cache, with \"instant\" access to each other's generated tokens. Hogwild! inference takes advantage of Rotary Position Embeddings (RoPE) to avoid recomputation while improving parallel hardware utilization. We find that modern reasoning-capable LLMs can perform inference with shared Key-Value cache out of the box, without additional fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/eqimp/hogwild_llm", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.05599", "title": "Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought", "authors": [ "Yi Peng", "Chris", "Xiaokun Wang", "Yichen Wei", "Jiangbo Pei", "Weijie Qiu", "Ai Jian", "Yunzhuo Hao", "Jiachun Pan", "Tianyidan Xie", "Li Ge", "Rongxian Zhuang", "Xuchen Song", "Yang Liu", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "We introduce Skywork R1V, a multimodal reasoning model extending the an R1-series Large language models (LLM) to visual modalities via an efficient multimodal transfer method. Leveraging a lightweight visual projector, Skywork R1V facilitates seamless multimodal adaptation without necessitating retraining of either the foundational language model or the vision encoder. To strengthen visual-text alignment, we propose a hybrid optimization strategy that combines Iterative Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO), significantly enhancing cross-modal integration efficiency. Additionally, we introduce an adaptive-length Chain-of-Thought distillation approach for reasoning data generation. This approach dynamically optimizes reasoning chain lengths, thereby enhancing inference efficiency and preventing excessive reasoning overthinking. Empirical evaluations demonstrate that Skywork R1V, with only 38B parameters, delivers competitive performance, achieving a score of 69.0 on the MMMU benchmark and 67.5 on MathVista. Meanwhile, it maintains robust textual reasoning performance, evidenced by impressive scores of 72.0 on AIME and 94.0 on MATH500. The Skywork R1V model weights have been publicly released to promote openness and reproducibility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.06148", "title": "V-MAGE: A Game Evaluation Framework for Assessing Visual-Centric Capabilities in Multimodal Large Language Models", "authors": [ "Xiangxi Zheng", "Linjie Li", "Zhengyuan Yang", "Ping Yu", "Alex Jinpeng Wang", "Rui Yan", "Yuan Yao", "Lijuan Wang" ], "github": "https://github.com/CSU-JPG/V-MAGE", "project_page": "", "abstract": "Recent advancements in Multimodal Large Language Models (MLLMs) have led to significant improvements across various multimodal benchmarks. However, as evaluations shift from static datasets to open-world, dynamic environments, current game-based benchmarks remain inadequate because they lack visual-centric tasks and fail to assess the diverse reasoning skills required for real-world decision-making. To address this, we introduce Visual-centric Multiple Abilities Game Evaluation (V-MAGE), a game-based evaluation framework designed to assess visual reasoning capabilities of MLLMs. V-MAGE features five diverse games with 30+ handcrafted levels, testing models on core visual skills such as positioning, trajectory tracking, timing, and visual memory, alongside higher-level reasoning like long-term planning and deliberation. We use V-MAGE to evaluate leading MLLMs, revealing significant challenges in their visual perception and reasoning. In all game environments, the top-performing MLLMs, as determined by Elo rating comparisons, exhibit a substantial performance gap compared to humans. Our findings highlight critical limitations, including various types of perceptual errors made by the models, and suggest potential avenues for improvement from an agent-centric perspective, such as refining agent strategies and addressing perceptual inaccuracies. Code is available at https://github.com/CSU-JPG/V-MAGE.", "github_manual": "", "github_regex": "https://github.com/CSU-JPG/V-MAGE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2503.20533", "title": "Accelerate Parallelizable Reasoning via Parallel Decoding within One Sequence", "authors": [ "Yijiong Yu" ], "github": "https://github.com/yuyijiong/parallel-decoding-in-one-sequence", "project_page": "", "abstract": "Recent advances in reasoning models have demonstrated significant improvements in accuracy, particularly for complex tasks such as mathematical reasoning, by employing detailed and comprehensive reasoning processes. However, generating these lengthy reasoning sequences is computationally expensive and time-consuming. To address this inefficiency, we leverage the inherent parallelizability of certain tasks to accelerate the reasoning process. Specifically, when multiple parallel reasoning branches exist, we decode multiple tokens per step using a specialized attention mask, processing them within a single sequence, avoiding additional memory usage. Experimental results show that our method achieves over 100% speedup in decoding time while maintaining the answer quality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yuyijiong/parallel-decoding-in-one-sequence", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.05535", "title": "COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values", "authors": [ "M-A-P Team", "Siwei Wu", "Jincheng Ren", "Xinrun Du", "Shuyue Guo", "Xingwei Qu", "Yiming Liang", "Jie Liu", "Yunwen Li", "Tianyu Zheng", "Boyu Feng", "Huaqing Yuan", "Zenith Wang", "Jiaheng Liu", "Wenhao Huang", "Chenglin Cai", "Haoran Que", "Jian Yang", "Yuelin Bai", "Zekun Moore Wang", "Zhouliang Yu", "Qunshu Lin", "Ding Pan", "Yuchen Jiang", "Tiannan Wang", "Wangchunshu Zhou", "Shenzhi Wang", "Xingyuan Bu", "Minghao Liu", "Guoyin Wang", "Ge Zhang", "Chenghua Lin" ], "github": "https://github.com/multimodal-art-projection/COIG-P", "project_page": "", "abstract": "Aligning large language models (LLMs) with human preferences has achieved remarkable success. However, existing Chinese preference datasets are limited by small scale, narrow domain coverage, and lack of rigorous data validation. Additionally, the reliance on human annotators for instruction and response labeling significantly constrains the scalability of human preference datasets. To address these challenges, we design an LLM-based Chinese preference dataset annotation pipeline with no human intervention. Specifically, we crawled and carefully filtered 92k high-quality Chinese queries and employed 15 mainstream LLMs to generate and score chosen-rejected response pairs. Based on it, we introduce COIG-P (Chinese Open Instruction Generalist - Preference), a high-quality, large-scale Chinese preference dataset, comprises 1,009k Chinese preference pairs spanning 6 diverse domains: Chat, Code, Math, Logic, Novel, and Role. Building upon COIG-P, to reduce the overhead of using LLMs for scoring, we trained a 8B-sized Chinese Reward Model (CRM) and meticulously constructed a Chinese Reward Benchmark (CRBench). Evaluation results based on AlignBench liu2024alignbenchbenchmarkingchinesealignment show that that COIG-P significantly outperforms other Chinese preference datasets, and it brings significant performance improvements ranging from 2% to 12% for the Qwen2/2.5 and Infinity-Instruct-3M-0625 model series, respectively. The results on CRBench demonstrate that our CRM has a strong and robust scoring ability. We apply it to filter chosen-rejected response pairs in a test split of COIG-P, and our experiments show that it is comparable to GPT-4o in identifying low-quality samples while maintaining efficiency and cost-effectiveness. Our codes and data are released in https://github.com/multimodal-art-projection/COIG-P.", "github_manual": "", "github_regex": "https://github.com/multimodal-art-projection/COIG-P", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.06232", "title": "HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance", "authors": [ "Jiazi Bu", "Pengyang Ling", "Yujie Zhou", "Pan Zhang", "Tong Wu", "Xiaoyi Dong", "Yuhang Zang", "Yuhang Cao", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/Bujiazi/HiFlow", "project_page": "", "abstract": "Text-to-image (T2I) diffusion/flow models have drawn considerable attention recently due to their remarkable ability to deliver flexible visual creations. Still, high-resolution image synthesis presents formidable challenges due to the scarcity and complexity of high-resolution content. To this end, we present HiFlow, a training-free and model-agnostic framework to unlock the resolution potential of pre-trained flow models. Specifically, HiFlow establishes a virtual reference flow within the high-resolution space that effectively captures the characteristics of low-resolution flow information, offering guidance for high-resolution generation through three key aspects: initialization alignment for low-frequency consistency, direction alignment for structure preservation, and acceleration alignment for detail fidelity. By leveraging this flow-aligned guidance, HiFlow substantially elevates the quality of high-resolution image synthesis of T2I models and demonstrates versatility across their personalized variants. Extensive experiments validate HiFlow's superiority in achieving superior high-resolution image quality over current state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Bujiazi/HiFlow", "github_api_added_by": "auto" }, { "date": "2025-04-09", "arxiv_id": "2504.06122", "title": "Leanabell-Prover: Posttraining Scaling in Formal Reasoning", "authors": [ "Jingyuan Zhang", "Qi Wang", "Xingguang Ji", "Yahui Liu", "Yang Yue", "Fuzheng Zhang", "Di Zhang", "Guorui Zhou", "Kun Gai" ], "github": "https://github.com/leanabell-lm/leanabell-prover", "project_page": "", "abstract": "Recent advances in automated theorem proving (ATP) through LLMs have highlighted the potential of formal reasoning with Lean 4 codes. However, ATP has not yet be revolutionized by the recent posttraining scaling as demonstrated by Open AI O1/O3 and Deepseek R1. In this work, we investigate the entire posttraining of ATP, aiming to align it with breakthroughs in reasoning models in natural languages.To begin, we continual train current ATP models with a hybrid dataset, which consists of numerous statement-proof pairs, and additional data aimed at incorporating cognitive behaviors that emulate human reasoning and hypothesis refinement. Next, we explore reinforcement learning with the use of outcome reward returned by Lean 4 compiler. Through our designed continual training and reinforcement learning processes, we have successfully improved existing formal provers, including both DeepSeek-Prover-v1.5 and Goedel-Prover, achieving state-of-the-art performance in the field of whole-proof generation. For example, we achieve a 59.8% pass rate (pass@32) on MiniF2F. This is an on-going project and we will progressively update our findings, release our data and training details.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/leanabell-lm/leanabell-prover", "github_api_added_by": "auto" }, { "date": "2025-04-09", "arxiv_id": "2504.05520", "title": "Efficient Reinforcement Finetuning via Adaptive Curriculum Learning", "authors": [ "Taiwei Shi", "Yiyang Wu", "Linxin Song", "Tianyi Zhou", "Jieyu Zhao" ], "github": "", "project_page": "", "abstract": "Reinforcement finetuning (RFT) has shown great potential for enhancing the mathematical reasoning capabilities of large language models (LLMs), but it is often sample- and compute-inefficient, requiring extensive training. In this work, we introduce AdaRFT (Adaptive Curriculum Reinforcement Finetuning), a method that significantly improves both the efficiency and final accuracy of RFT through adaptive curriculum learning. AdaRFT dynamically adjusts the difficulty of training problems based on the model's recent reward signals, ensuring that the model consistently trains on tasks that are challenging but solvable. This adaptive sampling strategy accelerates learning by maintaining an optimal difficulty range, avoiding wasted computation on problems that are too easy or too hard. AdaRFT requires only a lightweight extension to standard RFT algorithms like Proximal Policy Optimization (PPO), without modifying the reward function or model architecture. Experiments on competition-level math datasets-including AMC, AIME, and IMO-style problems-demonstrate that AdaRFT significantly improves both training efficiency and reasoning performance. We evaluate AdaRFT across multiple data distributions and model sizes, showing that it reduces the number of training steps by up to 2x and improves accuracy by a considerable margin, offering a more scalable and effective RFT framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.05897", "title": "HybriMoE: Hybrid CPU-GPU Scheduling and Cache Management for Efficient MoE Inference", "authors": [ "Shuzhang Zhong", "Yanfan Sun", "Ling Liang", "Runsheng Wang", "Ru Huang", "Meng Li" ], "github": "https://github.com/PKU-SEC-Lab/HybriMoE", "project_page": "", "abstract": "The Mixture of Experts (MoE) architecture has demonstrated significant advantages as it enables to increase the model capacity without a proportional increase in computation. However, the large MoE model size still introduces substantial memory demands, which usually requires expert offloading on resource-constrained platforms and incurs significant overhead. Hybrid CPU-GPU inference has been proposed to leverage CPU computation to reduce expert loading overhead but faces major challenges: on one hand, the expert activation patterns of MoE models are highly unstable, rendering the fixed mapping strategies in existing works inefficient; on the other hand, the hybrid CPU-GPU schedule for MoE is inherently complex due to the diverse expert sizes, structures, uneven workload distribution, etc. To address these challenges, in this paper, we propose HybriMoE, a hybrid CPU-GPU inference framework that improves resource utilization through a novel CPU-GPU scheduling and cache management system. HybriMoE introduces (i) a dynamic intra-layer scheduling strategy to balance workloads across CPU and GPU, (ii) an impact-driven inter-layer prefetching algorithm, and (iii) a score-based caching algorithm to mitigate expert activation instability. We implement HybriMoE on top of the kTransformers framework and evaluate it on three widely used MoE-based LLMs. Experimental results demonstrate that HybriMoE achieves an average speedup of 1.33times in the prefill stage and 1.70times in the decode stage compared to state-of-the-art hybrid MoE inference framework. Our code is available at: https://github.com/PKU-SEC-Lab/HybriMoE.", "github_manual": "", "github_regex": "https://github.com/PKU-SEC-Lab/HybriMoE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.03755", "title": "ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discovery", "authors": [ "Shijie Ma", "Fei Zhu", "Xu-Yao Zhang", "Cheng-Lin Liu" ], "github": "https://github.com/mashijie1028/ProtoGCD", "project_page": "", "abstract": "Generalized category discovery (GCD) is a pragmatic but underexplored problem, which requires models to automatically cluster and discover novel categories by leveraging the labeled samples from old classes. The challenge is that unlabeled data contain both old and new classes. Early works leveraging pseudo-labeling with parametric classifiers handle old and new classes separately, which brings about imbalanced accuracy between them. Recent methods employing contrastive learning neglect potential positives and are decoupled from the clustering objective, leading to biased representations and sub-optimal results. To address these issues, we introduce a unified and unbiased prototype learning framework, namely ProtoGCD, wherein old and new classes are modeled with joint prototypes and unified learning objectives, {enabling unified modeling between old and new classes}. Specifically, we propose a dual-level adaptive pseudo-labeling mechanism to mitigate confirmation bias, together with two regularization terms to collectively help learn more suitable representations for GCD. Moreover, for practical considerations, we devise a criterion to estimate the number of new classes. Furthermore, we extend ProtoGCD to detect unseen outliers, achieving task-level unification. Comprehensive experiments show that ProtoGCD achieves state-of-the-art performance on both generic and fine-grained datasets. The code is available at https://github.com/mashijie1028/ProtoGCD.", "github_manual": "", "github_regex": "https://github.com/mashijie1028/ProtoGCD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.02792", "title": "Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets", "authors": [ "Chuning Zhu", "Raymond Yu", "Siyuan Feng", "Benjamin Burchfiel", "Paarth Shah", "Abhishek Gupta" ], "github": "", "project_page": "", "abstract": "Imitation learning has emerged as a promising approach towards building generalist robots. However, scaling imitation learning for large robot foundation models remains challenging due to its reliance on high-quality expert demonstrations. Meanwhile, large amounts of video data depicting a wide range of environments and diverse behaviors are readily available. This data provides a rich source of information about real-world dynamics and agent-environment interactions. Leveraging this data directly for imitation learning, however, has proven difficult due to the lack of action annotation required for most contemporary methods. In this work, we present Unified World Models (UWM), a framework that allows for leveraging both video and action data for policy learning. Specifically, a UWM integrates an action diffusion process and a video diffusion process within a unified transformer architecture, where independent diffusion timesteps govern each modality. We show that by simply controlling each diffusion timestep, UWM can flexibly represent a policy, a forward dynamics, an inverse dynamics, and a video generator. Through simulated and real-world experiments, we show that: (1) UWM enables effective pretraining on large-scale multitask robot datasets with both dynamics and action predictions, resulting in more generalizable and robust policies than imitation learning, (2) UWM naturally facilitates learning from action-free video data through independent control of modality-specific diffusion timesteps, further improving the performance of finetuned policies. Our results suggest that UWM offers a promising step toward harnessing large, heterogeneous datasets for scalable robot learning, and provides a simple unification between the often disparate paradigms of imitation learning and world modeling. Videos and code are available at https://weirdlabuw.github.io/uwm/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-09", "arxiv_id": "2504.07079", "title": "SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills", "authors": [ "Boyuan Zheng", "Michael Y. Fatemi", "Xiaolong Jin", "Zora Zhiruo Wang", "Apurva Gandhi", "Yueqi Song", "Yu Gu", "Jayanth Srinivasa", "Gaowen Liu", "Graham Neubig", "Yu Su" ], "github": "https://github.com/OSU-NLP-Group/SkillWeaver", "project_page": "", "abstract": "To survive and thrive in complex environments, humans have evolved sophisticated self-improvement mechanisms through environment exploration, hierarchical abstraction of experiences into reuseable skills, and collaborative construction of an ever-growing skill repertoire. Despite recent advancements, autonomous web agents still lack crucial self-improvement capabilities, struggling with procedural knowledge abstraction, refining skills, and skill composition. In this work, we introduce SkillWeaver, a skill-centric framework enabling agents to self-improve by autonomously synthesizing reusable skills as APIs. Given a new website, the agent autonomously discovers skills, executes them for practice, and distills practice experiences into robust APIs. Iterative exploration continually expands a library of lightweight, plug-and-play APIs, significantly enhancing the agent's capabilities. Experiments on WebArena and real-world websites demonstrate the efficacy of SkillWeaver, achieving relative success rate improvements of 31.8% and 39.8%, respectively. Additionally, APIs synthesized by strong agents substantially enhance weaker agents through transferable skills, yielding improvements of up to 54.3% on WebArena. These results demonstrate the effectiveness of honing diverse website interactions into APIs, which can be seamlessly shared among various web agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OSU-NLP-Group/SkillWeaver", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.05741", "title": "DDT: Decoupled Diffusion Transformer", "authors": [ "Shuai Wang", "Zhi Tian", "Weilin Huang", "Limin Wang" ], "github": "", "project_page": "", "abstract": "Diffusion transformers have demonstrated remarkable generation quality, albeit requiring longer training iterations and numerous inference steps. In each denoising step, diffusion transformers encode the noisy inputs to extract the lower-frequency semantic component and then decode the higher frequency with identical modules. This scheme creates an inherent optimization dilemma: encoding low-frequency semantics necessitates reducing high-frequency components, creating tension between semantic encoding and high-frequency decoding. To resolve this challenge, we propose a new \\color{ddtD}ecoupled \\color{ddtD}iffusion \\color{ddtT}ransformer~(\\color{ddtDDT}), with a decoupled design of a dedicated condition encoder for semantic extraction alongside a specialized velocity decoder. Our experiments reveal that a more substantial encoder yields performance improvements as model size increases. For ImageNet 256times256, Our DDT-XL/2 achieves a new state-of-the-art performance of {1.31 FID}~(nearly 4times faster training convergence compared to previous diffusion transformers). For ImageNet 512times512, Our DDT-XL/2 achieves a new state-of-the-art FID of 1.28. Additionally, as a beneficial by-product, our decoupled architecture enhances inference speed by enabling the sharing self-condition between adjacent denoising steps. To minimize performance degradation, we propose a novel statistical dynamic programming approach to identify optimal sharing strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.06514", "title": "Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?", "authors": [ "Chenrui Fan", "Ming Li", "Lichao Sun", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "We find that the response length of reasoning LLMs, whether trained by reinforcement learning or supervised learning, drastically increases for ill-posed questions with missing premises (MiP), ending up with redundant and ineffective thinking. This newly introduced scenario exacerbates the general overthinking issue to a large extent, which we name as the MiP-Overthinking. Such failures are against the ``test-time scaling law'' but have been widely observed on multiple datasets we curated with MiP, indicating the harm of cheap overthinking and a lack of critical thinking. Surprisingly, LLMs not specifically trained for reasoning exhibit much better performance on the MiP scenario, producing much shorter responses that quickly identify ill-posed queries. This implies a critical flaw of the current training recipe for reasoning LLMs, which does not encourage efficient thinking adequately, leading to the abuse of thinking patterns. To further investigate the reasons behind such failures, we conduct fine-grained analyses of the reasoning length, overthinking patterns, and location of critical thinking on different types of LLMs. Moreover, our extended ablation study reveals that the overthinking is contagious through the distillation of reasoning models' responses. These results improve the understanding of overthinking and shed novel insights into mitigating the problem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.04842", "title": "FantasyTalking: Realistic Talking Portrait Generation via Coherent Motion Synthesis", "authors": [ "Mengchao Wang", "Qiang Wang", "Fan Jiang", "Yaqi Fan", "Yunpeng Zhang", "Yonggang Qi", "Kun Zhao", "Mu Xu" ], "github": "", "project_page": "", "abstract": "Creating a realistic animatable avatar from a single static portrait remains challenging. Existing approaches often struggle to capture subtle facial expressions, the associated global body movements, and the dynamic background. To address these limitations, we propose a novel framework that leverages a pretrained video diffusion transformer model to generate high-fidelity, coherent talking portraits with controllable motion dynamics. At the core of our work is a dual-stage audio-visual alignment strategy. In the first stage, we employ a clip-level training scheme to establish coherent global motion by aligning audio-driven dynamics across the entire scene, including the reference portrait, contextual objects, and background. In the second stage, we refine lip movements at the frame level using a lip-tracing mask, ensuring precise synchronization with audio signals. To preserve identity without compromising motion flexibility, we replace the commonly used reference network with a facial-focused cross-attention module that effectively maintains facial consistency throughout the video. Furthermore, we integrate a motion intensity modulation module that explicitly controls expression and body motion intensity, enabling controllable manipulation of portrait movements beyond mere lip motion. Extensive experimental results show that our proposed approach achieves higher quality with better realism, coherence, motion intensity, and identity preservation. Ours project page: https://fantasy-amap.github.io/fantasy-talking/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.07096", "title": "OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens", "authors": [ "Jiacheng Liu", "Taylor Blanton", "Yanai Elazar", "Sewon Min", "YenSung Chen", "Arnavi Chheda-Kothary", "Huy Tran", "Byron Bischoff", "Eric Marsh", "Michael Schmitz", "Cassidy Trier", "Aaron Sarnat", "Jenna James", "Jon Borchardt", "Bailey Kuehl", "Evie Cheng", "Karen Farley", "Sruthi Sreeram", "Taira Anderson", "David Albright", "Carissa Schoenick", "Luca Soldaini", "Dirk Groeneveld", "Rock Yuren Pang", "Pang Wei Koh", "Noah A. Smith", "Sophie Lebrecht", "Yejin Choi", "Hannaneh Hajishirzi", "Ali Farhadi", "Jesse Dodge" ], "github": "", "project_page": "", "abstract": "We present OLMoTrace, the first system that traces the outputs of language models back to their full, multi-trillion-token training data in real time. OLMoTrace finds and shows verbatim matches between segments of language model output and documents in the training text corpora. Powered by an extended version of infini-gram (Liu et al., 2024), our system returns tracing results within a few seconds. OLMoTrace can help users understand the behavior of language models through the lens of their training data. We showcase how it can be used to explore fact checking, hallucination, and the creativity of language models. OLMoTrace is publicly available and fully open-source.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.07083", "title": "GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photography", "authors": [ "Mengchen Zhang", "Tong Wu", "Jing Tan", "Ziwei Liu", "Gordon Wetzstein", "Dahua Lin" ], "github": "", "project_page": "", "abstract": "Camera trajectory design plays a crucial role in video production, serving as a fundamental tool for conveying directorial intent and enhancing visual storytelling. In cinematography, Directors of Photography meticulously craft camera movements to achieve expressive and intentional framing. However, existing methods for camera trajectory generation remain limited: Traditional approaches rely on geometric optimization or handcrafted procedural systems, while recent learning-based methods often inherit structural biases or lack textual alignment, constraining creative synthesis. In this work, we introduce an auto-regressive model inspired by the expertise of Directors of Photography to generate artistic and expressive camera trajectories. We first introduce DataDoP, a large-scale multi-modal dataset containing 29K real-world shots with free-moving camera trajectories, depth maps, and detailed captions in specific movements, interaction with the scene, and directorial intent. Thanks to the comprehensive and diverse database, we further train an auto-regressive, decoder-only Transformer for high-quality, context-aware camera movement generation based on text guidance and RGBD inputs, named GenDoP. Extensive experiments demonstrate that compared to existing methods, GenDoP offers better controllability, finer-grained trajectory adjustments, and higher motion stability. We believe our approach establishes a new standard for learning-based cinematography, paving the way for future advancements in camera control and filmmaking. Our project website: https://kszpxxzmc.github.io/GenDoP/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.07086", "title": "A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility", "authors": [ "Andreas Hochlehnert", "Hardik Bhatnagar", "Vishaal Udandarao", "Samuel Albanie", "Ameya Prabhu", "Matthias Bethge" ], "github": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "project_page": "", "abstract": "Reasoning has emerged as the next major frontier for language models (LMs), with rapid advances from both academic and industrial labs. However, this progress often outpaces methodological rigor, with many evaluations relying on benchmarking practices that lack transparency, robustness, or statistical grounding. In this work, we conduct a comprehensive empirical study and find that current mathematical reasoning benchmarks are highly sensitive to subtle implementation choices - including decoding parameters, random seeds, prompt formatting, and even hardware and software-framework configurations. Performance gains reported in recent studies frequently hinge on unclear comparisons or unreported sources of variance. To address these issues, we propose a standardized evaluation framework with clearly defined best practices and reporting standards. Using this framework, we reassess recent methods and find that reinforcement learning (RL) approaches yield only modest improvements - far below prior claims - and are prone to overfitting, especially on small-scale benchmarks like AIME24. In contrast, supervised finetuning (SFT) methods show consistently stronger generalization. To foster reproducibility, we release all code, prompts, and model outputs, for reasoning benchmarks, establishing more rigorous foundations for future work.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.04010", "title": "DiTaiListener: Controllable High Fidelity Listener Video Generation with Diffusion", "authors": [ "Maksim Siniukov", "Di Chang", "Minh Tran", "Hongkun Gong", "Ashutosh Chaubey", "Mohammad Soleymani" ], "github": "", "project_page": "", "abstract": "Generating naturalistic and nuanced listener motions for extended interactions remains an open problem. Existing methods often rely on low-dimensional motion codes for facial behavior generation followed by photorealistic rendering, limiting both visual fidelity and expressive richness. To address these challenges, we introduce DiTaiListener, powered by a video diffusion model with multimodal conditions. Our approach first generates short segments of listener responses conditioned on the speaker's speech and facial motions with DiTaiListener-Gen. It then refines the transitional frames via DiTaiListener-Edit for a seamless transition. Specifically, DiTaiListener-Gen adapts a Diffusion Transformer (DiT) for the task of listener head portrait generation by introducing a Causal Temporal Multimodal Adapter (CTM-Adapter) to process speakers' auditory and visual cues. CTM-Adapter integrates speakers' input in a causal manner into the video generation process to ensure temporally coherent listener responses. For long-form video generation, we introduce DiTaiListener-Edit, a transition refinement video-to-video diffusion model. The model fuses video segments into smooth and continuous videos, ensuring temporal consistency in facial expressions and image quality when merging short video segments produced by DiTaiListener-Gen. Quantitatively, DiTaiListener achieves the state-of-the-art performance on benchmark datasets in both photorealism (+73.8% in FID on RealTalk) and motion representation (+6.1% in FD metric on VICO) spaces. User studies confirm the superior performance of DiTaiListener, with the model being the clear preference in terms of feedback, diversity, and smoothness, outperforming competitors by a significant margin.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.07089", "title": "OmniCaptioner: One Captioner to Rule Them All", "authors": [ "Yiting Lu", "Jiakang Yuan", "Zhen Li", "Shitian Zhao", "Qi Qin", "Xinyue Li", "Le Zhuo", "Licheng Wen", "Dongyang Liu", "Yuewen Cao", "Xiangchao Yan", "Xin Li", "Botian Shi", "Tao Chen", "Zhibo Chen", "Lei Bai", "Bo Zhang", "Peng Gao" ], "github": "https://github.com/alpha-innovator/omnicaptioner", "project_page": "", "abstract": "We propose OmniCaptioner, a versatile visual captioning framework for generating fine-grained textual descriptions across a wide variety of visual domains. Unlike prior methods limited to specific image types (e.g., natural images or geometric visuals), our framework provides a unified solution for captioning natural images, visual text (e.g., posters, UIs, textbooks), and structured visuals (e.g., documents, tables, charts). By converting low-level pixel information into semantically rich textual representations, our framework bridges the gap between visual and textual modalities. Our results highlight three key advantages: (i) Enhanced Visual Reasoning with LLMs, where long-context captions of visual modalities empower LLMs, particularly the DeepSeek-R1 series, to reason effectively in multimodal scenarios; (ii) Improved Image Generation, where detailed captions improve tasks like text-to-image generation and image transformation; and (iii) Efficient Supervised Fine-Tuning (SFT), which enables faster convergence with less data. We believe the versatility and adaptability of OmniCaptioner can offer a new perspective for bridging the gap between language and visual modalities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alpha-innovator/omnicaptioner", "github_api_added_by": "auto" }, { "date": "2025-04-10", "arxiv_id": "2504.07092", "title": "Are We Done with Object-Centric Learning?", "authors": [ "Alexander Rubinstein", "Ameya Prabhu", "Matthias Bethge", "Seong Joon Oh" ], "github": "https://github.com/AlexanderRubinstein/OCCAM", "project_page": "", "abstract": "Object-centric learning (OCL) seeks to learn representations that only encode an object, isolated from other objects or background cues in a scene. This approach underpins various aims, including out-of-distribution (OOD) generalization, sample-efficient composition, and modeling of structured environments. Most research has focused on developing unsupervised mechanisms that separate objects into discrete slots in the representation space, evaluated using unsupervised object discovery. However, with recent sample-efficient segmentation models, we can separate objects in the pixel space and encode them independently. This achieves remarkable zero-shot performance on OOD object discovery benchmarks, is scalable to foundation models, and can handle a variable number of slots out-of-the-box. Hence, the goal of OCL methods to obtain object-centric representations has been largely achieved. Despite this progress, a key question remains: How does the ability to separate objects within a scene contribute to broader OCL objectives, such as OOD generalization? We address this by investigating the OOD generalization challenge caused by spurious background cues through the lens of OCL. We propose a novel, training-free probe called Object-Centric Classification with Applied Masks (OCCAM), demonstrating that segmentation-based encoding of individual objects significantly outperforms slot-based OCL methods. However, challenges in real-world applications remain. We provide the toolbox for the OCL community to use scalable object-centric representations, and focus on practical applications and fundamental questions, such as understanding object perception in human cognition. Our code is available https://github.com/AlexanderRubinstein/OCCAM{here}.", "github_manual": "", "github_regex": "https://github.com/AlexanderRubinstein/OCCAM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.07081", "title": "Self-Steering Language Models", "authors": [ "Gabriel Grand", "Joshua B. Tenenbaum", "Vikash K. Mansinghka", "Alexander K. Lew", "Jacob Andreas" ], "github": "", "project_page": "", "abstract": "While test-time reasoning enables language models to tackle complex tasks, searching or planning in natural language can be slow, costly, and error-prone. But even when LMs struggle to emulate the precise reasoning steps needed to solve a problem, they often excel at describing its abstract structure--both how to verify solutions and how to search for them. This paper introduces DisCIPL, a method for \"self-steering\" LMs where a Planner model generates a task-specific inference program that is executed by a population of Follower models. Our approach equips LMs with the ability to write recursive search procedures that guide LM inference, enabling new forms of verifiable and efficient reasoning. When instantiated with a small Follower (e.g., Llama-3.2-1B), DisCIPL matches (and sometimes outperforms) much larger models, including GPT-4o and o1, on challenging constrained generation tasks. In decoupling planning from execution, our work opens up a design space of highly-parallelized Monte Carlo inference strategies that outperform standard best-of-N sampling, require no finetuning, and can be implemented automatically by existing LMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.06958", "title": "VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning", "authors": [ "Xinhao Li", "Ziang Yan", "Desen Meng", "Lu Dong", "Xiangyu Zeng", "Yinan He", "Yali Wang", "Yu Qiao", "Yi Wang", "Limin Wang" ], "github": "https://github.com/opengvlab/videochat-r1", "project_page": "", "abstract": "Recent advancements in reinforcement learning have significantly advanced the reasoning capabilities of multimodal large language models (MLLMs). While approaches such as Group Relative Policy Optimization (GRPO) and rule-based reward mechanisms demonstrate promise in text and image domains, their application to video understanding remains limited. This paper presents a systematic exploration of Reinforcement Fine-Tuning (RFT) with GRPO for video MLLMs, aiming to enhance spatio-temporal perception while maintaining general capabilities. Our experiments reveal that RFT is highly data-efficient for task-specific improvements. Through multi-task RFT on spatio-temporal perception objectives with limited samples, we develop VideoChat-R1, a powerful video MLLM that achieves state-of-the-art performance on spatio-temporal perception tasks without sacrificing chat ability, while exhibiting emerging spatio-temporal reasoning abilities. Compared to Qwen2.5-VL-7B, VideoChat-R1 boosts performance several-fold in tasks like temporal grounding (+31.8) and object tracking (+31.2). Additionally, it significantly improves on general QA benchmarks such as VideoMME (+0.9), MVBench (+1.0), and Perception Test (+0.9). Our findings underscore the potential of RFT for specialized task enhancement of Video MLLMs. We hope our work offers valuable insights for future RL research in video MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/opengvlab/videochat-r1", "github_api_added_by": "auto" }, { "date": "2025-04-10", "arxiv_id": "2504.05287", "title": "RobustDexGrasp: Robust Dexterous Grasping of General Objects from Single-view Perception", "authors": [ "Hui Zhang", "Zijian Wu", "Linyi Huang", "Sammy Christen", "Jie Song" ], "github": "https://github.com/zdchan/RobustDexGrasp", "project_page": "", "abstract": "Robust grasping of various objects from single-view perception is fundamental for dexterous robots. Previous works often rely on fully observable objects, expert demonstrations, or static grasping poses, which restrict their generalization ability and adaptability to external disturbances. In this paper, we present a reinforcement-learning-based framework that enables zero-shot dynamic dexterous grasping of a wide range of unseen objects from single-view perception, while performing adaptive motions to external disturbances. We utilize a hand-centric object representation for shape feature extraction that emphasizes interaction-relevant local shapes, enhancing robustness to shape variance and uncertainty. To enable effective hand adaptation to disturbances with limited observations, we propose a mixed curriculum learning strategy, which first utilizes imitation learning to distill a policy trained with privileged real-time visual-tactile feedback, and gradually transfers to reinforcement learning to learn adaptive motions under disturbances caused by observation noises and dynamic randomization. Our experiments demonstrate strong generalization in grasping unseen objects with random poses, achieving success rates of 97.0% across 247,786 simulated objects and 94.6% across 512 real objects. We also demonstrate the robustness of our method to various disturbances, including unobserved object movement and external forces, through both quantitative and qualitative evaluations. Project Page: https://zdchan.github.io/Robust_DexGrasp/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zdchan/RobustDexGrasp", "github_api_added_by": "auto" }, { "date": "2025-04-10", "arxiv_id": "2504.06947", "title": "RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts", "authors": [ "Natalia Loukachevitch", "Natalia Tkachenko", "Anna Lapanitsyna", "Mikhail Tikhomirov", "Nicolay Rusnachenko" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce the Dialogue Evaluation shared task on extraction of structured opinions from Russian news texts. The task of the contest is to extract opinion tuples for a given sentence; the tuples are composed of a sentiment holder, its target, an expression and sentiment from the holder to the target. In total, the task received more than 100 submissions. The participants experimented mainly with large language models in zero-shot, few-shot and fine-tuning formats. The best result on the test set was obtained with fine-tuning of a large language model. We also compared 30 prompts and 11 open source language models with 3-32 billion parameters in the 1-shot and 10-shot settings and found the best models and prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.07046", "title": "A Unified Agentic Framework for Evaluating Conditional Image Generation", "authors": [ "Jifang Wang", "Xue Yang", "Longyue Wang", "Zhenran Xu", "Yiyu Wang", "Yaowei Wang", "Weihua Luo", "Kaifu Zhang", "Baotian Hu", "Min Zhang" ], "github": "https://github.com/HITsz-TMG/Agentic-CIGEval", "project_page": "", "abstract": "Conditional image generation has gained significant attention for its ability to personalize content. However, the field faces challenges in developing task-agnostic, reliable, and explainable evaluation metrics. This paper introduces CIGEval, a unified agentic framework for comprehensive evaluation of conditional image generation tasks. CIGEval utilizes large multimodal models (LMMs) as its core, integrating a multi-functional toolbox and establishing a fine-grained evaluation framework. Additionally, we synthesize evaluation trajectories for fine-tuning, empowering smaller LMMs to autonomously select appropriate tools and conduct nuanced analyses based on tool outputs. Experiments across seven prominent conditional image generation tasks demonstrate that CIGEval (GPT-4o version) achieves a high correlation of 0.4625 with human assessments, closely matching the inter-annotator correlation of 0.47. Moreover, when implemented with 7B open-source LMMs using only 2.3K training trajectories, CIGEval surpasses the previous GPT-4o-based state-of-the-art method. Case studies on GPT-4o image generation highlight CIGEval's capability in identifying subtle issues related to subject consistency and adherence to control guidance, indicating its great potential for automating evaluation of image generation tasks with human-level reliability.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HITsz-TMG/Agentic-CIGEval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.03886", "title": "WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments", "authors": [ "Jianhao Zheng", "Zihan Zhu", "Valentin Bieri", "Marc Pollefeys", "Songyou Peng", "Iro Armeni" ], "github": "https://github.com/GradientSpaces/WildGS-SLAM", "project_page": "", "abstract": "We present WildGS-SLAM, a robust and efficient monocular RGB SLAM system designed to handle dynamic environments by leveraging uncertainty-aware geometric mapping. Unlike traditional SLAM systems, which assume static scenes, our approach integrates depth and uncertainty information to enhance tracking, mapping, and rendering performance in the presence of moving objects. We introduce an uncertainty map, predicted by a shallow multi-layer perceptron and DINOv2 features, to guide dynamic object removal during both tracking and mapping. This uncertainty map enhances dense bundle adjustment and Gaussian map optimization, improving reconstruction accuracy. Our system is evaluated on multiple datasets and demonstrates artifact-free view synthesis. Results showcase WildGS-SLAM's superior performance in dynamic environments compared to state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GradientSpaces/WildGS-SLAM", "github_api_added_by": "auto" }, { "date": "2025-04-10", "arxiv_id": "2504.06719", "title": "Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene Understanding", "authors": [ "Pedro Hermosilla", "Christian Stippel", "Leon Sick" ], "github": "https://github.com/phermosilla/msm", "project_page": "", "abstract": "Self-supervised learning has transformed 2D computer vision by enabling models trained on large, unannotated datasets to provide versatile off-the-shelf features that perform similarly to models trained with labels. However, in 3D scene understanding, self-supervised methods are typically only used as a weight initialization step for task-specific fine-tuning, limiting their utility for general-purpose feature extraction. This paper addresses this shortcoming by proposing a robust evaluation protocol specifically designed to assess the quality of self-supervised features for 3D scene understanding. Our protocol uses multi-resolution feature sampling of hierarchical models to create rich point-level representations that capture the semantic capabilities of the model and, hence, are suitable for evaluation with linear probing and nearest-neighbor methods. Furthermore, we introduce the first self-supervised model that performs similarly to supervised models when only off-the-shelf features are used in a linear probing setup. In particular, our model is trained natively in 3D with a novel self-supervised approach based on a Masked Scene Modeling objective, which reconstructs deep features of masked patches in a bottom-up manner and is specifically tailored to hierarchical 3D models. Our experiments not only demonstrate that our method achieves competitive performance to supervised models, but also surpasses existing self-supervised approaches by a large margin. The model and training code can be found at our Github repository (https://github.com/phermosilla/msm).", "github_manual": "", "github_regex": "https://github.com/phermosilla/msm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.05523", "title": "Pretraining Language Models for Diachronic Linguistic Change Discovery", "authors": [ "Elisabeth Fittschen", "Sabrina Li", "Tom Lippincott", "Leshem Choshen", "Craig Messner" ], "github": "https://github.com/comp-int-hum/historical-perspectival-lm", "project_page": "", "abstract": "Large language models (LLMs) have shown potential as tools for scientific discovery. This has engendered growing interest in their use in humanistic disciplines, such as historical linguistics and literary studies. These fields often construct arguments on the basis of delineations like genre, or more inflexibly, time period. Although efforts have been made to restrict inference to specific domains via fine-tuning or model editing, we posit that the only true guarantee is domain-restricted pretraining -- typically, a data- and compute-expensive proposition. We show that efficient pretraining techniques can produce useful models over corpora too large for easy manual inspection but too small for \"typical\" LLM approaches. We employ a novel date-attribution pipeline in order to obtain a temporally-segmented dataset of five 10-million-word slices. We train two corresponding five-model batteries over these corpus segments, efficient pretraining and Llama3-8B parameter efficiently finetuned. We find that the pretrained models are faster to train than the finetuned baselines and that they better respect the historical divisions of our corpus. Emphasizing speed and precision over a-historical comprehensiveness enables a number of novel approaches to hypothesis discovery and testing in our target fields. Taking up diachronic linguistics as a testbed, we show that our method enables the detection of a diverse set of phenomena, including en masse lexical change, non-lexical (grammatical and morphological) change, and word sense introduction/obsolescence. We provide a ready-to-use pipeline that allows extension of our approach to other target fields with only minimal adaptation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/comp-int-hum/historical-perspectival-lm", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.05541", "title": "Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting", "authors": [ "Yunlong Tang", "Jing Bi", "Chao Huang", "Susan Liang", "Daiki Shimada", "Hang Hua", "Yunzhong Xiao", "Yizhi Song", "Pinxin Liu", "Mingqian Feng", "Junjia Guo", "Zhuo Liu", "Luchuan Song", "Ali Vosoughi", "Jinxi He", "Liu He", "Zeliang Zhang", "Jiebo Luo", "Chenliang Xu" ], "github": "https://github.com/yunlong10/CAT-V", "project_page": "", "abstract": "We present CAT-V (Caption AnyThing in Video), a training-free framework for fine-grained object-centric video captioning that enables detailed descriptions of user-selected objects through time. CAT-V integrates three key components: a Segmenter based on SAMURAI for precise object segmentation across frames, a Temporal Analyzer powered by TRACE-Uni for accurate event boundary detection and temporal analysis, and a Captioner using InternVL-2.5 for generating detailed object-centric descriptions. Through spatiotemporal visual prompts and chain-of-thought reasoning, our framework generates detailed, temporally-aware descriptions of objects' attributes, actions, statuses, interactions, and environmental contexts without requiring additional training data. CAT-V supports flexible user interactions through various visual prompts (points, bounding boxes, and irregular regions) and maintains temporal sensitivity by tracking object states and interactions across different time segments. Our approach addresses limitations of existing video captioning methods, which either produce overly abstract descriptions or lack object-level precision, enabling fine-grained, object-specific descriptions while maintaining temporal coherence and spatial accuracy. The GitHub repository for this project is available at https://github.com/yunlong10/CAT-V", "github_manual": "", "github_regex": "https://github.com/yunlong10/CAT-V", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-10", "arxiv_id": "2504.05410", "title": "Fast Controlled Generation from Language Models with Adaptive Weighted Rejection Sampling", "authors": [ "Benjamin Lipkin", "Benjamin LeBrun", "Jacob Hoover Vigly", "Jo\u00e3o Loula", "David R. MacIver", "Li Du", "Jason Eisner", "Ryan Cotterell", "Vikash Mansinghka", "Timothy J. O'Donnell", "Alexander K. Lew", "Tim Vieira" ], "github": "https://github.com/genlm/genlm-control", "project_page": "", "abstract": "The dominant approach to generating from language models subject to some constraint is locally constrained decoding (LCD), incrementally sampling tokens at each time step such that the constraint is never violated. Typically, this is achieved through token masking: looping over the vocabulary and excluding non-conforming tokens. There are two important problems with this approach. (i) Evaluating the constraint on every token can be prohibitively expensive -- LM vocabularies often exceed 100,000 tokens. (ii) LCD can distort the global distribution over strings, sampling tokens based only on local information, even if they lead down dead-end paths. This work introduces a new algorithm that addresses both these problems. First, to avoid evaluating a constraint on the full vocabulary at each step of generation, we propose an adaptive rejection sampling algorithm that typically requires orders of magnitude fewer constraint evaluations. Second, we show how this algorithm can be extended to produce low-variance, unbiased estimates of importance weights at a very small additional cost -- estimates that can be soundly used within previously proposed sequential Monte Carlo algorithms to correct for the myopic behavior of local constraint enforcement. Through extensive empirical evaluation in text-to-SQL, molecular synthesis, goal inference, pattern matching, and JSON domains, we show that our approach is superior to state-of-the-art baselines, supporting a broader class of constraints and improving both runtime and performance. Additional theoretical and empirical analyses show that our method's runtime efficiency is driven by its dynamic use of computation, scaling with the divergence between the unconstrained and constrained LM, and as a consequence, runtime improvements are greater for better models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/genlm/genlm-control", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.07956", "title": "VCR-Bench: A Comprehensive Evaluation Framework for Video Chain-of-Thought Reasoning", "authors": [ "Yukun Qi", "Yiming Zhao", "Yu Zeng", "Xikun Bao", "Wenxuan Huang", "Lin Chen", "Zehui Chen", "Jie Zhao", "Zhongang Qi", "Feng Zhao" ], "github": "", "project_page": "", "abstract": "The advancement of Chain-of-Thought (CoT) reasoning has significantly enhanced the capabilities of large language models (LLMs) and large vision-language models (LVLMs). However, a rigorous evaluation framework for video CoT reasoning remains absent. Current video benchmarks fail to adequately assess the reasoning process and expose whether failures stem from deficiencies in perception or reasoning capabilities. Therefore, we introduce VCR-Bench, a novel benchmark designed to comprehensively evaluate LVLMs' Video Chain-of-Thought Reasoning capabilities. VCR-Bench comprises 859 videos spanning a variety of video content and durations, along with 1,034 high-quality question-answer pairs. Each pair is manually annotated with a stepwise CoT rationale, where every step is tagged to indicate its association with the perception or reasoning capabilities. Furthermore, we design seven distinct task dimensions and propose the CoT score to assess the entire CoT process based on the stepwise tagged CoT rationals. Extensive experiments on VCR-Bench highlight substantial limitations in current LVLMs. Even the top-performing model, o1, only achieves a 62.8% CoT score and an 56.7% accuracy, while most models score below 40%. Experiments show most models score lower on perception than reasoning steps, revealing LVLMs' key bottleneck in temporal-spatial information processing for complex video reasoning. A robust positive correlation between the CoT score and accuracy confirms the validity of our evaluation framework and underscores the critical role of CoT reasoning in solving complex video reasoning tasks. We hope VCR-Bench to serve as a standardized evaluation framework and expose the actual drawbacks in complex video reasoning task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.07943", "title": "HoloPart: Generative 3D Part Amodal Segmentation", "authors": [ "Yunhan Yang", "Yuan-Chen Guo", "Yukun Huang", "Zi-Xin Zou", "Zhipeng Yu", "Yangguang Li", "Yan-Pei Cao", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "3D part amodal segmentation--decomposing a 3D shape into complete, semantically meaningful parts, even when occluded--is a challenging but crucial task for 3D content creation and understanding. Existing 3D part segmentation methods only identify visible surface patches, limiting their utility. Inspired by 2D amodal segmentation, we introduce this novel task to the 3D domain and propose a practical, two-stage approach, addressing the key challenges of inferring occluded 3D geometry, maintaining global shape consistency, and handling diverse shapes with limited training data. First, we leverage existing 3D part segmentation to obtain initial, incomplete part segments. Second, we introduce HoloPart, a novel diffusion-based model, to complete these segments into full 3D parts. HoloPart utilizes a specialized architecture with local attention to capture fine-grained part geometry and global shape context attention to ensure overall shape consistency. We introduce new benchmarks based on the ABO and PartObjaverse-Tiny datasets and demonstrate that HoloPart significantly outperforms state-of-the-art shape completion methods. By incorporating HoloPart with existing segmentation techniques, we achieve promising results on 3D part amodal segmentation, opening new avenues for applications in geometry editing, animation, and material assignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.07128", "title": "DeepSeek-R1 Thoughtology: Let's about LLM Reasoning", "authors": [ "Sara Vera Marjanovi\u0107", "Arkil Patel", "Vaibhav Adlakha", "Milad Aghajohari", "Parishad BehnamGhader", "Mehar Bhatia", "Aditi Khandelwal", "Austin Kraft", "Benno Krojer", "Xing Han L\u00f9", "Nicholas Meade", "Dongchan Shin", "Amirhossein Kazemnejad", "Gaurav Kamath", "Marius Mosbach", "Karolina Sta\u0144czak", "Siva Reddy" ], "github": "https://github.com/McGill-NLP/thoughtology", "project_page": "", "abstract": "Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an answer for a given input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly \"thinking\" about a problem before providing an answer. This reasoning process is publicly available to the user, creating endless opportunities for studying the reasoning behaviour of the model and opening up the field of Thoughtology. Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning, our analyses on DeepSeek-R1 investigate the impact and controllability of thought length, management of long or confusing contexts, cultural and safety concerns, and the status of DeepSeek-R1 vis-\\`a-vis cognitive phenomena, such as human-like language processing and world modelling. Our findings paint a nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning, where extra inference time can impair model performance. Furthermore, we find a tendency for DeepSeek-R1 to persistently ruminate on previously explored problem formulations, obstructing further exploration. We also note strong safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning counterpart, which can also compromise safety-aligned LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/McGill-NLP/thoughtology", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.07960", "title": "VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning", "authors": [ "Zhong-Yu Li", "Ruoyi Du", "Juncheng Yan", "Le Zhuo", "Zhen Li", "Peng Gao", "Zhanyu Ma", "Ming-Ming Cheng" ], "github": "", "project_page": "", "abstract": "Recent progress in diffusion models significantly advances various image generation tasks. However, the current mainstream approach remains focused on building task-specific models, which have limited efficiency when supporting a wide range of different needs. While universal models attempt to address this limitation, they face critical challenges, including generalizable task instruction, appropriate task distributions, and unified architectural design. To tackle these challenges, we propose VisualCloze, a universal image generation framework, which supports a wide range of in-domain tasks, generalization to unseen ones, unseen unification of multiple tasks, and reverse generation. Unlike existing methods that rely on language-based task instruction, leading to task ambiguity and weak generalization, we integrate visual in-context learning, allowing models to identify tasks from visual demonstrations. Meanwhile, the inherent sparsity of visual task distributions hampers the learning of transferable knowledge across tasks. To this end, we introduce Graph200K, a graph-structured dataset that establishes various interrelated tasks, enhancing task density and transferable knowledge. Furthermore, we uncover that our unified image generation formulation shared a consistent objective with image infilling, enabling us to leverage the strong generative priors of pre-trained infilling models without modifying the architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.07830", "title": "MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations", "authors": [ "Genglin Liu", "Salman Rahman", "Elisa Kreiss", "Marzyeh Ghassemi", "Saadia Gabriel" ], "github": "https://github.com/genglinliu/MOSAIC", "project_page": "", "abstract": "We present a novel, open-source social network simulation framework, MOSAIC, where generative language agents predict user behaviors such as liking, sharing, and flagging content. This simulation combines LLM agents with a directed social graph to analyze emergent deception behaviors and gain a better understanding of how users determine the veracity of online social content. By constructing user representations from diverse fine-grained personas, our system enables multi-agent simulations that model content dissemination and engagement dynamics at scale. Within this framework, we evaluate three different content moderation strategies with simulated misinformation dissemination, and we find that they not only mitigate the spread of non-factual content but also increase user engagement. In addition, we analyze the trajectories of popular content in our simulations, and explore whether simulation agents' articulated reasoning for their social interactions truly aligns with their collective engagement patterns. We open-source our simulation software to encourage further research within AI and social sciences.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/genglinliu/MOSAIC", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.07934", "title": "SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement", "authors": [ "Xiyao Wang", "Zhengyuan Yang", "Chao Feng", "Hongjin Lu", "Linjie Li", "Chung-Ching Lin", "Kevin Lin", "Furong Huang", "Lijuan Wang" ], "github": "https://github.com/si0wang/ThinkLite-VL", "project_page": "", "abstract": "In this paper, we present an effective method to enhance visual reasoning with significantly fewer training samples, relying purely on self-improvement with no knowledge distillation. Our key insight is that the difficulty of training data during reinforcement fine-tuning (RFT) is critical. Appropriately challenging samples can substantially boost reasoning capabilities even when the dataset is small. Despite being intuitive, the main challenge remains in accurately quantifying sample difficulty to enable effective data filtering. To this end, we propose a novel way of repurposing Monte Carlo Tree Search (MCTS) to achieve that. Starting from our curated 70k open-source training samples, we introduce an MCTS-based selection method that quantifies sample difficulty based on the number of iterations required by the VLMs to solve each problem. This explicit step-by-step reasoning in MCTS enforces the model to think longer and better identifies samples that are genuinely challenging. We filter and retain 11k samples to perform RFT on Qwen2.5-VL-7B-Instruct, resulting in our final model, ThinkLite-VL. Evaluation results on eight benchmarks show that ThinkLite-VL improves the average performance of Qwen2.5-VL-7B-Instruct by 7%, using only 11k training samples with no knowledge distillation. This significantly outperforms all existing 7B-level reasoning VLMs, and our fairly comparable baselines that use classic selection methods such as accuracy-based filtering. Notably, on MathVista, ThinkLite-VL-7B achieves the SoTA accuracy of 75.1, surpassing Qwen2.5-VL-72B, GPT-4o, and O1. Our code, data, and model are available at https://github.com/si0wang/ThinkLite-VL.", "github_manual": "", "github_regex": "https://github.com/si0wang/ThinkLite-VL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.07964", "title": "C3PO: Critical-Layer, Core-Expert, Collaborative Pathway Optimization for Test-Time Expert Re-Mixing", "authors": [ "Zhongyang Li", "Ziyue Li", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) Large Language Models (LLMs) suffer from severely sub-optimal expert pathways-our study reveals that naive expert selection learned from pretraining leaves a surprising 10-20% accuracy gap for improvement. Motivated by this observation, we develop a novel class of test-time optimization methods to re-weight or \"re-mixing\" the experts in different layers jointly for each test sample. Since the test sample's ground truth is unknown, we propose to optimize a surrogate objective defined by the sample's \"successful neighbors\" from a reference set of samples. We introduce three surrogates and algorithms based on mode-finding, kernel regression, and the average loss of similar reference samples/tasks. To reduce the cost of optimizing whole pathways, we apply our algorithms merely to the core experts' mixing weights in critical layers, which enjoy similar performance but save significant computation. This leads to \"Critical-Layer, Core-Expert, Collaborative Pathway Optimization (C3PO)\". We apply C3PO to two recent MoE LLMs and examine it on six widely-used benchmarks. It consistently improves the base model by 7-15% in accuracy and outperforms widely used test-time learning baselines, e.g., in-context learning and prompt/prefix tuning, by a large margin. Moreover, C3PO enables MoE LLMs with 1-3B active parameters to outperform LLMs of 7-9B parameters, hence improving MoE's advantages on efficiency. Our thorough ablation study further sheds novel insights on achieving test-time improvement on MoE.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.07957", "title": "MM-IFEngine: Towards Multimodal Instruction Following", "authors": [ "Shengyuan Ding", "Shenxi Wu", "Xiangyu Zhao", "Yuhang Zang", "Haodong Duan", "Xiaoyi Dong", "Pan Zhang", "Yuhang Cao", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/SYuan03/MM-IFEngine", "project_page": "", "abstract": "The Instruction Following (IF) ability measures how well Multi-modal Large Language Models (MLLMs) understand exactly what users are telling them and whether they are doing it right. Existing multimodal instruction following training data is scarce, the benchmarks are simple with atomic instructions, and the evaluation strategies are imprecise for tasks demanding exact output constraints. To address this, we present MM-IFEngine, an effective pipeline to generate high-quality image-instruction pairs. Our MM-IFEngine pipeline yields large-scale, diverse, and high-quality training data MM-IFInstruct-23k, which is suitable for Supervised Fine-Tuning (SFT) and extended as MM-IFDPO-23k for Direct Preference Optimization (DPO). We further introduce MM-IFEval, a challenging and diverse multi-modal instruction-following benchmark that includes (1) both compose-level constraints for output responses and perception-level constraints tied to the input images, and (2) a comprehensive evaluation pipeline incorporating both rule-based assessment and judge model. We conduct SFT and DPO experiments and demonstrate that fine-tuning MLLMs on MM-IFInstruct-23k and MM-IFDPO-23k achieves notable gains on various IF benchmarks, such as MM-IFEval (+10.2%), MIA (+7.6%), and IFEval (+12.3%). The full data and evaluation code will be released on https://github.com/SYuan03/MM-IFEngine.", "github_manual": "", "github_regex": "https://github.com/SYuan03/MM-IFEngine", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.04974", "title": "Towards Visual Text Grounding of Multimodal Large Language Model", "authors": [ "Ming Li", "Ruiyi Zhang", "Jian Chen", "Jiuxiang Gu", "Yufan Zhou", "Franck Dernoncourt", "Wanrong Zhu", "Tianyi Zhou", "Tong Sun" ], "github": "", "project_page": "", "abstract": "Despite the existing evolution of Multimodal Large Language Models (MLLMs), a non-neglectable limitation remains in their struggle with visual text grounding, especially in text-rich images of documents. Document images, such as scanned forms and infographics, highlight critical challenges due to their complex layouts and textual content. However, current benchmarks do not fully address these challenges, as they mostly focus on visual grounding on natural images, rather than text-rich document images. Thus, to bridge this gap, we introduce TRIG, a novel task with a newly designed instruction dataset for benchmarking and improving the Text-Rich Image Grounding capabilities of MLLMs in document question-answering. Specifically, we propose an OCR-LLM-human interaction pipeline to create 800 manually annotated question-answer pairs as a benchmark and a large-scale training set of 90$ synthetic data based on four diverse datasets. A comprehensive evaluation of various MLLMs on our proposed benchmark exposes substantial limitations in their grounding capability on text-rich images. In addition, we propose two simple and effective TRIG methods based on general instruction tuning and plug-and-play efficient embedding, respectively. By finetuning MLLMs on our synthetic dataset, they promisingly improve spatial reasoning and grounding capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.07491", "title": "Kimi-VL Technical Report", "authors": [ "Kimi Team", "Angang Du", "Bohong Yin", "Bowei Xing", "Bowen Qu", "Bowen Wang", "Cheng Chen", "Chenlin Zhang", "Chenzhuang Du", "Chu Wei", "Congcong Wang", "Dehao Zhang", "Dikang Du", "Dongliang Wang", "Enming Yuan", "Enzhe Lu", "Fang Li", "Flood Sung", "Guangda Wei", "Guokun Lai", "Han Zhu", "Hao Ding", "Hao Hu", "Hao Yang", "Hao Zhang", "Haoning Wu", "Haotian Yao", "Haoyu Lu", "Heng Wang", "Hongcheng Gao", "Huabin Zheng", "Jiaming Li", "Jianlin Su", "Jianzhou Wang", "Jiaqi Deng", "Jiezhong Qiu", "Jin Xie", "Jinhong Wang", "Jingyuan Liu", "Junjie Yan", "Kun Ouyang", "Liang Chen", "Lin Sui", "Longhui Yu", "Mengfan Dong", "Mengnan Dong", "Nuo Xu", "Pengyu Cheng", "Qizheng Gu", "Runjie Zhou", "Shaowei Liu", "Sihan Cao", "Tao Yu", "Tianhui Song", "Tongtong Bai", "Wei Song", "Weiran He", "Weixiao Huang", "Weixin Xu", "Xiaokun Yuan", "Xingcheng Yao", "Xingzhe Wu", "Xinxing Zu", "Xinyu Zhou", "Xinyuan Wang", "Y. Charles", "Yan Zhong", "Yang Li", "Yangyang Hu", "Yanru Chen", "Yejie Wang", "Yibo Liu", "Yibo Miao", "Yidao Qin", "Yimin Chen", "Yiping Bao", "Yiqin Wang", "Yongsheng Kang", "Yuanxin Liu", "Yulun Du", "Yuxin Wu", "Yuzhi Wang", "Yuzi Yan", "Zaida Zhou", "Zhaowei Li", "Zhejun Jiang", "Zheng Zhang", "Zhilin Yang", "Zhiqi Huang", "Zihao Huang", "Zijia Zhao", "Ziwei Chen" ], "github": "https://github.com/MoonshotAI/Kimi-VL", "project_page": "", "abstract": "We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2.8B parameters in its language decoder (Kimi-VL-A3B). Kimi-VL demonstrates strong performance across challenging domains: as a general-purpose VLM, Kimi-VL excels in multi-turn agent tasks (e.g., OSWorld), matching flagship models. Furthermore, it exhibits remarkable capabilities across diverse challenging vision language tasks, including college-level image and video comprehension, OCR, mathematical reasoning, and multi-image understanding. In comparative evaluations, it effectively competes with cutting-edge efficient VLMs such as GPT-4o-mini, Qwen2.5-VL-7B, and Gemma-3-12B-IT, while surpassing GPT-4o in several key domains. Kimi-VL also advances in processing long contexts and perceiving clearly. With a 128K extended context window, Kimi-VL can process diverse long inputs, achieving impressive scores of 64.5 on LongVideoBench and 35.1 on MMLongBench-Doc. Its native-resolution vision encoder, MoonViT, further allows it to see and understand ultra-high-resolution visual inputs, achieving 83.2 on InfoVQA and 34.5 on ScreenSpot-Pro, while maintaining lower computational cost for common tasks. Building upon Kimi-VL, we introduce an advanced long-thinking variant: Kimi-VL-Thinking. Developed through long chain-of-thought (CoT) supervised fine-tuning (SFT) and reinforcement learning (RL), this model exhibits strong long-horizon reasoning capabilities. It achieves scores of 61.7 on MMMU, 36.8 on MathVision, and 71.3 on MathVista while maintaining the compact 2.8B activated LLM parameters, setting a new standard for efficient multimodal thinking models. Code and models are publicly accessible at https://github.com/MoonshotAI/Kimi-VL.", "github_manual": "", "github_regex": "https://github.com/MoonshotAI/Kimi-VL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.06801", "title": "MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detection", "authors": [ "Rishubh Parihar", "Srinjay Sarkar", "Sarthak Vora", "Jogendra Kundu", "R. Venkatesh Babu" ], "github": "", "project_page": "", "abstract": "Current monocular 3D detectors are held back by the limited diversity and scale of real-world datasets. While data augmentation certainly helps, it's particularly difficult to generate realistic scene-aware augmented data for outdoor settings. Most current approaches to synthetic data generation focus on realistic object appearance through improved rendering techniques. However, we show that where and how objects are positioned is just as crucial for training effective 3D monocular detectors. The key obstacle lies in automatically determining realistic object placement parameters - including position, dimensions, and directional alignment when introducing synthetic objects into actual scenes. To address this, we introduce MonoPlace3D, a novel system that considers the 3D scene content to create realistic augmentations. Specifically, given a background scene, MonoPlace3D learns a distribution over plausible 3D bounding boxes. Subsequently, we render realistic objects and place them according to the locations sampled from the learned distribution. Our comprehensive evaluation on two standard datasets KITTI and NuScenes, demonstrates that MonoPlace3D significantly improves the accuracy of multiple existing monocular 3D detectors while being highly data efficient.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.06752", "title": "Compass Control: Multi Object Orientation Control for Text-to-Image Generation", "authors": [ "Rishubh Parihar", "Vaibhav Agrawal", "Sachidanand VS", "R. Venkatesh Babu" ], "github": "", "project_page": "", "abstract": "Existing approaches for controlling text-to-image diffusion models, while powerful, do not allow for explicit 3D object-centric control, such as precise control of object orientation. In this work, we address the problem of multi-object orientation control in text-to-image diffusion models. This enables the generation of diverse multi-object scenes with precise orientation control for each object. The key idea is to condition the diffusion model with a set of orientation-aware compass tokens, one for each object, along with text tokens. A light-weight encoder network predicts these compass tokens taking object orientation as the input. The model is trained on a synthetic dataset of procedurally generated scenes, each containing one or two 3D assets on a plain background. However, direct training this framework results in poor orientation control as well as leads to entanglement among objects. To mitigate this, we intervene in the generation process and constrain the cross-attention maps of each compass token to its corresponding object regions. The trained model is able to achieve precise orientation control for a) complex objects not seen during training and b) multi-object scenes with more than two objects, indicating strong generalization capabilities. Further, when combined with personalization methods, our method precisely controls the orientation of the new object in diverse contexts. Our method achieves state-of-the-art orientation control and text alignment, quantified with extensive evaluations and a user study.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.07951", "title": "Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models", "authors": [ "Mustafa Shukor", "Enrico Fini", "Victor Guilherme Turrisi da Costa", "Matthieu Cord", "Joshua Susskind", "Alaaeldin El-Nouby" ], "github": "", "project_page": "", "abstract": "Building general-purpose models that can effectively perceive the world through multimodal signals has been a long-standing goal. Current approaches involve integrating separately pre-trained components, such as connecting vision encoders to LLMs and continuing multimodal training. While such approaches exhibit remarkable sample efficiency, it remains an open question whether such late-fusion architectures are inherently superior. In this work, we revisit the architectural design of native multimodal models (NMMs)--those trained from the ground up on all modalities--and conduct an extensive scaling laws study, spanning 457 trained models with different architectures and training mixtures. Our investigation reveals no inherent advantage to late-fusion architectures over early-fusion ones, which do not rely on image encoders. On the contrary, early-fusion exhibits stronger performance at lower parameter counts, is more efficient to train, and is easier to deploy. Motivated by the strong performance of the early-fusion architectures, we show that incorporating Mixture of Experts (MoEs) allows for models that learn modality-specific weights, significantly enhancing performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.05579", "title": "TAPNext: Tracking Any Point (TAP) as Next Token Prediction", "authors": [ "Artem Zholus", "Carl Doersch", "Yi Yang", "Skanda Koppula", "Viorica Patraucean", "Xu Owen He", "Ignacio Rocco", "Mehdi S. M. Sajjadi", "Sarath Chandar", "Ross Goroshin" ], "github": "", "project_page": "", "abstract": "Tracking Any Point (TAP) in a video is a challenging computer vision problem with many demonstrated applications in robotics, video editing, and 3D reconstruction. Existing methods for TAP rely heavily on complex tracking-specific inductive biases and heuristics, limiting their generality and potential for scaling. To address these challenges, we present TAPNext, a new approach that casts TAP as sequential masked token decoding. Our model is causal, tracks in a purely online fashion, and removes tracking-specific inductive biases. This enables TAPNext to run with minimal latency, and removes the temporal windowing required by many existing state of art trackers. Despite its simplicity, TAPNext achieves a new state-of-the-art tracking performance among both online and offline trackers. Finally, we present evidence that many widely used tracking heuristics emerge naturally in TAPNext through end-to-end training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-11", "arxiv_id": "2504.07961", "title": "Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction", "authors": [ "Zeren Jiang", "Chuanxia Zheng", "Iro Laina", "Diane Larlus", "Andrea Vedaldi" ], "github": "https://github.com/jzr99/Geo4D", "project_page": "", "abstract": "We introduce Geo4D, a method to repurpose video diffusion models for monocular 3D reconstruction of dynamic scenes. By leveraging the strong dynamic prior captured by such video models, Geo4D can be trained using only synthetic data while generalizing well to real data in a zero-shot manner. Geo4D predicts several complementary geometric modalities, namely point, depth, and ray maps. It uses a new multi-modal alignment algorithm to align and fuse these modalities, as well as multiple sliding windows, at inference time, thus obtaining robust and accurate 4D reconstruction of long videos. Extensive experiments across multiple benchmarks show that Geo4D significantly surpasses state-of-the-art video depth estimation methods, including recent methods such as MonST3R, which are also designed to handle dynamic scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jzr99/Geo4D", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.08685", "title": "Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model", "authors": [ "Team Seawead", "Ceyuan Yang", "Zhijie Lin", "Yang Zhao", "Shanchuan Lin", "Zhibei Ma", "Haoyuan Guo", "Hao Chen", "Lu Qi", "Sen Wang", "Feng Cheng", "Feilong Zuo Xuejiao Zeng", "Ziyan Yang", "Fangyuan Kong", "Zhiwu Qing", "Fei Xiao", "Meng Wei", "Tuyen Hoang", "Siyu Zhang", "Peihao Zhu", "Qi Zhao", "Jiangqiao Yan", "Liangke Gui", "Sheng Bi", "Jiashi Li", "Yuxi Ren", "Rui Wang", "Huixia Li", "Xuefeng Xiao", "Shu Liu", "Feng Ling", "Heng Zhang", "Houmin Wei", "Huafeng Kuang", "Jerry Duncan", "Junda Zhang", "Junru Zheng", "Li Sun", "Manlin Zhang", "Renfei Sun", "Xiaobin Zhuang", "Xiaojie Li", "Xin Xia", "Xuyan Chi", "Yanghua Peng", "Yuping Wang", "Yuxuan Wang", "Zhongkai Zhao", "Zhuo Chen", "Zuquan Song", "Zhenheng Yang", "Jiashi Feng", "Jianchao Yang", "Lu Jiang" ], "github": "", "project_page": "", "abstract": "This technical report presents a cost-efficient strategy for training a video generation foundation model. We present a mid-sized research model with approximately 7 billion parameters (7B) called Seaweed-7B trained from scratch using 665,000 H100 GPU hours. Despite being trained with moderate computational resources, Seaweed-7B demonstrates highly competitive performance compared to contemporary video generation models of much larger size. Design choices are especially crucial in a resource-constrained setting. This technical report highlights the key design decisions that enhance the performance of the medium-sized diffusion model. Empirically, we make two observations: (1) Seaweed-7B achieves performance comparable to, or even surpasses, larger models trained on substantially greater GPU resources, and (2) our model, which exhibits strong generalization ability, can be effectively adapted across a wide range of downstream applications either by lightweight fine-tuning or continue training. See the project page at https://seaweed.video/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.08366", "title": "In-2-4D: Inbetweening from Two Single-View Images to 4D Generation", "authors": [ "Sauradip Nag", "Daniel Cohen-Or", "Hao Zhang", "Ali Mahdavi-Amiri" ], "github": "", "project_page": "", "abstract": "We propose a new problem, In-2-4D, for generative 4D (i.e., 3D + motion) inbetweening from a minimalistic input setting: two single-view images capturing an object in two distinct motion states. Given two images representing the start and end states of an object in motion, our goal is to generate and reconstruct the motion in 4D. We utilize a video interpolation model to predict the motion, but large frame-to-frame motions can lead to ambiguous interpretations. To overcome this, we employ a hierarchical approach to identify keyframes that are visually close to the input states and show significant motion, then generate smooth fragments between them. For each fragment, we construct the 3D representation of the keyframe using Gaussian Splatting. The temporal frames within the fragment guide the motion, enabling their transformation into dynamic Gaussians through a deformation field. To improve temporal consistency and refine 3D motion, we expand the self-attention of multi-view diffusion across timesteps and apply rigid transformation regularization. Finally, we merge the independently generated 3D motion segments by interpolating boundary deformation fields and optimizing them to align with the guiding video, ensuring smooth and flicker-free transitions. Through extensive qualitative and quantitiave experiments as well as a user study, we show the effectiveness of our method and its components. The project page is available at https://in-2-4d.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.07405", "title": "FlexIP: Dynamic Control of Preservation and Personality for Customized Image Generation", "authors": [ "Linyan Huang", "Haonan Lin", "Yanning Zhou", "Kaiwen Xiao" ], "github": "", "project_page": "", "abstract": "With the rapid advancement of 2D generative models, preserving subject identity while enabling diverse editing has emerged as a critical research focus. Existing methods typically face inherent trade-offs between identity preservation and personalized manipulation. We introduce FlexIP, a novel framework that decouples these objectives through two dedicated components: a Personalization Adapter for stylistic manipulation and a Preservation Adapter for identity maintenance. By explicitly injecting both control mechanisms into the generative model, our framework enables flexible parameterized control during inference through dynamic tuning of the weight adapter. Experimental results demonstrate that our approach breaks through the performance limitations of conventional methods, achieving superior identity preservation while supporting more diverse personalized generation capabilities (Project Page: https://flexip-tech.github.io/flexip/).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.08388", "title": "MineWorld: a Real-Time and Open-Source Interactive World Model on Minecraft", "authors": [ "Junliang Guo", "Yang Ye", "Tianyu He", "Haoyu Wu", "Yushu Jiang", "Tim Pearce", "Jiang Bian" ], "github": "", "project_page": "", "abstract": "World modeling is a crucial task for enabling intelligent agents to effectively interact with humans and operate in dynamic environments. In this work, we propose MineWorld, a real-time interactive world model on Minecraft, an open-ended sandbox game which has been utilized as a common testbed for world modeling. MineWorld is driven by a visual-action autoregressive Transformer, which takes paired game scenes and corresponding actions as input, and generates consequent new scenes following the actions. Specifically, by transforming visual game scenes and actions into discrete token ids with an image tokenizer and an action tokenizer correspondingly, we consist the model input with the concatenation of the two kinds of ids interleaved. The model is then trained with next token prediction to learn rich representations of game states as well as the conditions between states and actions simultaneously. In inference, we develop a novel parallel decoding algorithm that predicts the spatial redundant tokens in each frame at the same time, letting models in different scales generate 4 to 7 frames per second and enabling real-time interactions with game players. In evaluation, we propose new metrics to assess not only visual quality but also the action following capacity when generating new scenes, which is crucial for a world model. Our comprehensive evaluation shows the efficacy of MineWorld, outperforming SoTA open-sourced diffusion based world models significantly. The code and model have been released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.08736", "title": "GigaTok: Scaling Visual Tokenizers to 3 Billion Parameters for Autoregressive Image Generation", "authors": [ "Tianwei Xiong", "Jun Hao Liew", "Zilong Huang", "Jiashi Feng", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "In autoregressive (AR) image generation, visual tokenizers compress images into compact discrete latent tokens, enabling efficient training of downstream autoregressive models for visual generation via next-token prediction. While scaling visual tokenizers improves image reconstruction quality, it often degrades downstream generation quality -- a challenge not adequately addressed in existing literature. To address this, we introduce GigaTok, the first approach to simultaneously improve image reconstruction, generation, and representation learning when scaling visual tokenizers. We identify the growing complexity of latent space as the key factor behind the reconstruction vs. generation dilemma. To mitigate this, we propose semantic regularization, which aligns tokenizer features with semantically consistent features from a pre-trained visual encoder. This constraint prevents excessive latent space complexity during scaling, yielding consistent improvements in both reconstruction and downstream autoregressive generation. Building on semantic regularization, we explore three key practices for scaling tokenizers:(1) using 1D tokenizers for better scalability, (2) prioritizing decoder scaling when expanding both encoder and decoder, and (3) employing entropy loss to stabilize training for billion-scale tokenizers. By scaling to 3 space billion parameters, GigaTok achieves state-of-the-art performance in reconstruction, downstream AR generation, and downstream AR representation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.08716", "title": "ModernBERT or DeBERTaV3? Examining Architecture and Data Influence on Transformer Encoder Models Performance", "authors": [ "Wissam Antoun", "Beno\u00eet Sagot", "Djam\u00e9 Seddah" ], "github": "", "project_page": "", "abstract": "Pretrained transformer-encoder models like DeBERTaV3 and ModernBERT introduce architectural advancements aimed at improving efficiency and performance. Although the authors of ModernBERT report improved performance over DeBERTaV3 on several benchmarks, the lack of disclosed training data and the absence of comparisons using a shared dataset make it difficult to determine whether these gains are due to architectural improvements or differences in training data. In this work, we conduct a controlled study by pretraining ModernBERT on the same dataset as CamemBERTaV2, a DeBERTaV3 French model, isolating the effect of model design. Our results show that the previous model generation remains superior in sample efficiency and overall benchmark performance, with ModernBERT's primary advantage being faster training and inference speed. However, the new proposed model still provides meaningful architectural improvements compared to earlier models such as BERT and RoBERTa. Additionally, we observe that high-quality pre-training data accelerates convergence but does not significantly improve final performance, suggesting potential benchmark saturation. These findings show the importance of disentangling pretraining data from architectural innovations when evaluating transformer models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.07963", "title": "PixelFlow: Pixel-Space Generative Models with Flow", "authors": [ "Shoufa Chen", "Chongjian Ge", "Shilong Zhang", "Peize Sun", "Ping Luo" ], "github": "https://github.com/ShoufaChen/PixelFlow", "project_page": "", "abstract": "We present PixelFlow, a family of image generation models that operate directly in the raw pixel space, in contrast to the predominant latent-space models. This approach simplifies the image generation process by eliminating the need for a pre-trained Variational Autoencoder (VAE) and enabling the whole model end-to-end trainable. Through efficient cascade flow modeling, PixelFlow achieves affordable computation cost in pixel space. It achieves an FID of 1.98 on 256times256 ImageNet class-conditional image generation benchmark. The qualitative text-to-image results demonstrate that PixelFlow excels in image quality, artistry, and semantic control. We hope this new paradigm will inspire and open up new opportunities for next-generation visual generation models. Code and models are available at https://github.com/ShoufaChen/PixelFlow.", "github_manual": "", "github_regex": "https://github.com/ShoufaChen/PixelFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.08600", "title": "SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning", "authors": [ "Peixian Ma", "Xialie Zhuang", "Chengjin Xu", "Xuhui Jiang", "Ran Chen", "Jian Guo" ], "github": "https://github.com/IDEA-FinAI/SQL-R1", "project_page": "", "abstract": "Natural Language to SQL (NL2SQL) enables intuitive interactions with databases by transforming natural language queries into structured SQL statements. Despite recent advancements in enhancing human-computer interaction within database applications, significant challenges persist, particularly regarding the inference performance in complex scenarios involving multi-table joins and nested queries. Current methodologies primarily utilize supervised fine-tuning (SFT) to train the NL2SQL model, which may limit adaptability and interpretability in new environments (e.g., finance and healthcare). In order to enhance the reasoning performance of the NL2SQL model in the above complex situations, we introduce SQL-R1, a novel NL2SQL reasoning model trained by the reinforcement learning (RL) algorithms. We design a specialized RL-based reward function tailored for NL2SQL tasks and discussed the impact of cold start on the effectiveness of intensive training. In addition, we achieve competitive accuracy using only a tiny amount of synthetic NL2SQL data for augmented training and further explore data engineering for RL. In existing experiments, SQL-R1 achieves execution accuracy of 88.6% and 66.6% on the benchmark Spider and BIRD, respectively, only using the 7B base model.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/IDEA-FinAI/SQL-R1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.08192", "title": "SAEs Can Improve Unlearning: Dynamic Sparse Autoencoder Guardrails for Precision Unlearning in LLMs", "authors": [ "Aashiq Muhamed", "Jacopo Bonato", "Mona Diab", "Virginia Smith" ], "github": "https://github.com/aashiqmuhamed/DynamicSAEGuardrails", "project_page": "", "abstract": "Machine unlearning is a promising approach to improve LLM safety by removing unwanted knowledge from the model. However, prevailing gradient-based unlearning methods suffer from issues such as high computational costs, hyperparameter instability, poor sequential unlearning capability, vulnerability to relearning attacks, low data efficiency, and lack of interpretability. While Sparse Autoencoders are well-suited to improve these aspects by enabling targeted activation-based unlearning, prior approaches underperform gradient-based methods. This work demonstrates that, contrary to these earlier findings, SAEs can significantly improve unlearning when employed dynamically. We introduce Dynamic DAE Guardrails (DSG), a novel method for precision unlearning that leverages principled feature selection and a dynamic classifier. Our experiments show DSG substantially outperforms leading unlearning methods, achieving superior forget-utility trade-offs. DSG addresses key drawbacks of gradient-based approaches for unlearning -- offering enhanced computational efficiency and stability, robust performance in sequential unlearning, stronger resistance to relearning attacks, better data efficiency including zero-shot settings, and more interpretable unlearning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aashiqmuhamed/DynamicSAEGuardrails", "github_api_added_by": "auto" }, { "date": "2025-04-14", "arxiv_id": "2504.01883", "title": "CoRAG: Collaborative Retrieval-Augmented Generation", "authors": [ "Aashiq Muhamed", "Mona Diab", "Virginia Smith" ], "github": "https://github.com/aashiqmuhamed/CoRAG", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) models excel in knowledge-intensive tasks, especially under few-shot learning constraints. We introduce CoRAG, a framework extending RAG to collaborative settings, where clients jointly train a shared model using a collaborative passage store. To evaluate CoRAG, we introduce CRAB, a benchmark for collaborative homogeneous open-domain question answering. Our experiments demonstrate that CoRAG consistently outperforms both parametric collaborative learning methods and locally trained RAG models in low-resource scenarios. Further analysis reveals the critical importance of relevant passages within the shared store, the surprising benefits of incorporating irrelevant passages, and the potential for hard negatives to negatively impact performance. This introduces a novel consideration in collaborative RAG: the trade-off between leveraging a collectively enriched knowledge base and the potential risk of incorporating detrimental passages from other clients. Our findings underscore the viability of CoRAG, while also highlighting key design challenges and promising avenues for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aashiqmuhamed/CoRAG", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.05303", "title": "InteractVLM: 3D Interaction Reasoning from 2D Foundational Models", "authors": [ "Sai Kumar Dwivedi", "Dimitrije Anti\u0107", "Shashank Tripathi", "Omid Taheri", "Cordelia Schmid", "Michael J. Black", "Dimitrios Tzionas" ], "github": "", "project_page": "", "abstract": "We introduce InteractVLM, a novel method to estimate 3D contact points on human bodies and objects from single in-the-wild images, enabling accurate human-object joint reconstruction in 3D. This is challenging due to occlusions, depth ambiguities, and widely varying object shapes. Existing methods rely on 3D contact annotations collected via expensive motion-capture systems or tedious manual labeling, limiting scalability and generalization. To overcome this, InteractVLM harnesses the broad visual knowledge of large Vision-Language Models (VLMs), fine-tuned with limited 3D contact data. However, directly applying these models is non-trivial, as they reason only in 2D, while human-object contact is inherently 3D. Thus we introduce a novel Render-Localize-Lift module that: (1) embeds 3D body and object surfaces in 2D space via multi-view rendering, (2) trains a novel multi-view localization model (MV-Loc) to infer contacts in 2D, and (3) lifts these to 3D. Additionally, we propose a new task called Semantic Human Contact estimation, where human contact predictions are conditioned explicitly on object semantics, enabling richer interaction modeling. InteractVLM outperforms existing work on contact estimation and also facilitates 3D reconstruction from an in-the wild image. Code and models are available at https://interactvlm.is.tue.mpg.de.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.05262", "title": "Do PhD-level LLMs Truly Grasp Elementary Addition? Probing Rule Learning vs. Memorization in Large Language Models", "authors": [ "Yang Yan", "Yu Lu", "Renjun Xu", "Zhenzhong Lan" ], "github": "https://github.com/kuri-leo/llm-arithmetic-diagnostic", "project_page": "", "abstract": "Despite high benchmark scores, Large Language Models (LLMs) often fail simple problem, raising a critical question: Do LLMs learn mathematical principles or merely memorize patterns? Rather than designing increasingly complex benchmarks like recent works, we investigate this using elementary two-integer addition (0 to 2^{64}), probing two core properties: commutativity (A+B=B+A) and compositional generalization (via isomorphic symbolic mappings, e.g., 7 rightarrow y). While state-of-the-art LLMs achieve 73.8-99.8\\% accuracy on numerical addition, performance collapses to leq7.5\\% under symbolic mapping, indicating failure to generalize learned rules. Non-monotonic performance scaling with digit count and frequent commutativity violations (over 1,700 cases of A+B neq B+A) further support this. Explicitly providing addition rules degrades performance by 81.2\\% on average, while self-explanation maintains baseline accuracy, suggesting LLM arithmetic processing is misaligned with human-defined principles. Our findings indicate current LLMs rely on memory pattern over genuine rule learning, highlighting architectural limitations and the need for new approaches to achieve true mathematical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kuri-leo/llm-arithmetic-diagnostic", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.07866", "title": "Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs", "authors": [ "Yichun Yin", "Wenyong Huang", "Kaikai Song", "Yehui Tang", "Xueyu Wu", "Wei Guo", "Peng Guo", "Yaoyuan Wang", "Xiaojun Meng", "Yasheng Wang", "Dong Li", "Can Chen", "Dandan Tu", "Yin Li", "Fisher Yu", "Ruiming Tang", "Yunhe Wang", "Baojun Wang", "Bin Wang", "Bo Wang", "Boxiao Liu", "Changzheng Zhang", "Duyu Tang", "Fei Mi", "Hui Jin", "Jiansheng Wei", "Jiarui Qin", "Jinpeng Li", "Jun Zhao", "Liqun Deng", "Lin Li", "Minghui Xu", "Naifu Zhang", "Nianzu Zheng", "Qiang Li", "Rongju Ruan", "Shengjun Cheng", "Tianyu Guo", "Wei He", "Wei Li", "Weiwen Liu", "Wulong Liu", "Xinyi Dai", "Yonghan Dong", "Yu Pan", "Yue Li", "Yufei Wang", "Yujun Li", "Yunsheng Ni", "Zhe Liu", "Zhenhe Zhang", "Zhicheng Liu" ], "github": "", "project_page": "", "abstract": "We present Pangu Ultra, a Large Language Model (LLM) with 135 billion parameters and dense Transformer modules trained on Ascend Neural Processing Units (NPUs). Although the field of LLM has been witnessing unprecedented advances in pushing the scale and capability of LLM in recent years, training such a large-scale model still involves significant optimization and system challenges. To stabilize the training process, we propose depth-scaled sandwich normalization, which effectively eliminates loss spikes during the training process of deep models. We pre-train our model on 13.2 trillion diverse and high-quality tokens and further enhance its reasoning capabilities during post-training. To perform such large-scale training efficiently, we utilize 8,192 Ascend NPUs with a series of system optimizations. Evaluations on multiple diverse benchmarks indicate that Pangu Ultra significantly advances the state-of-the-art capabilities of dense LLMs such as Llama 405B and Mistral Large 2, and even achieves competitive results with DeepSeek-R1, whose sparse model structure contains much more parameters. Our exploration demonstrates that Ascend NPUs are capable of efficiently and effectively training dense models with more than 100 billion parameters. Our model and system will be available for our commercial customers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.08635", "title": "Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging", "authors": [ "Gabriele Lozupone", "Alessandro Bria", "Francesco Fontanella", "Frederick J. A. Meijer", "Claudio De Stefano", "Henkjan Huisman" ], "github": "https://github.com/GabrieleLozupone/LDAE", "project_page": "", "abstract": "This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised learning in medical imaging, focusing on Alzheimer disease (AD) using brain MR from the ADNI database as a case study. Unlike conventional diffusion autoencoders operating in image space, LDAE applies the diffusion process in a compressed latent representation, improving computational efficiency and making 3D medical imaging representation learning tractable. To validate the proposed approach, we explore two key hypotheses: (i) LDAE effectively captures meaningful semantic representations on 3D brain MR associated with AD and ageing, and (ii) LDAE achieves high-quality image generation and reconstruction while being computationally efficient. Experimental results support both hypotheses: (i) linear-probe evaluations demonstrate promising diagnostic performance for AD (ROC-AUC: 90%, ACC: 84%) and age prediction (MAE: 4.1 years, RMSE: 5.2 years); (ii) the learned semantic representations enable attribute manipulation, yielding anatomically plausible modifications; (iii) semantic interpolation experiments show strong reconstruction of missing scans, with SSIM of 0.969 (MSE: 0.0019) for a 6-month gap. Even for longer gaps (24 months), the model maintains robust performance (SSIM > 0.93, MSE < 0.004), indicating an ability to capture temporal progression trends; (iv) compared to conventional diffusion autoencoders, LDAE significantly increases inference throughput (20x faster) while also enhancing reconstruction quality. These findings position LDAE as a promising framework for scalable medical imaging applications, with the potential to serve as a foundation model for medical image analysis. Code available at https://github.com/GabrieleLozupone/LDAE", "github_manual": "", "github_regex": "https://github.com/GabrieleLozupone/LDAE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.08727", "title": "Visual Chronicles: Using Multimodal LLMs to Analyze Massive Collections of Images", "authors": [ "Boyang Deng", "Songyou Peng", "Kyle Genova", "Gordon Wetzstein", "Noah Snavely", "Leonidas Guibas", "Thomas Funkhouser" ], "github": "", "project_page": "", "abstract": "We present a system using Multimodal LLMs (MLLMs) to analyze a large database with tens of millions of images captured at different times, with the aim of discovering patterns in temporal changes. Specifically, we aim to capture frequent co-occurring changes (\"trends\") across a city over a certain period. Unlike previous visual analyses, our analysis answers open-ended queries (e.g., \"what are the frequent types of changes in the city?\") without any predetermined target subjects or training labels. These properties cast prior learning-based or unsupervised visual analysis tools unsuitable. We identify MLLMs as a novel tool for their open-ended semantic understanding capabilities. Yet, our datasets are four orders of magnitude too large for an MLLM to ingest as context. So we introduce a bottom-up procedure that decomposes the massive visual analysis problem into more tractable sub-problems. We carefully design MLLM-based solutions to each sub-problem. During experiments and ablation studies with our system, we find it significantly outperforms baselines and is able to discover interesting trends from images captured in large cities (e.g., \"addition of outdoor dining,\", \"overpass was painted blue,\" etc.). See more results and interactive demos at https://boyangdeng.com/visual-chronicles.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.08591", "title": "ZipIR: Latent Pyramid Diffusion Transformer for High-Resolution Image Restoration", "authors": [ "Yongsheng Yu", "Haitian Zheng", "Zhifei Zhang", "Jianming Zhang", "Yuqian Zhou", "Connelly Barnes", "Yuchen Liu", "Wei Xiong", "Zhe Lin", "Jiebo Luo" ], "github": "", "project_page": "", "abstract": "Recent progress in generative models has significantly improved image restoration capabilities, particularly through powerful diffusion models that offer remarkable recovery of semantic details and local fidelity. However, deploying these models at ultra-high resolutions faces a critical trade-off between quality and efficiency due to the computational demands of long-range attention mechanisms. To address this, we introduce ZipIR, a novel framework that enhances efficiency, scalability, and long-range modeling for high-res image restoration. ZipIR employs a highly compressed latent representation that compresses image 32x, effectively reducing the number of spatial tokens, and enabling the use of high-capacity models like the Diffusion Transformer (DiT). Toward this goal, we propose a Latent Pyramid VAE (LP-VAE) design that structures the latent space into sub-bands to ease diffusion training. Trained on full images up to 2K resolution, ZipIR surpasses existing diffusion-based methods, offering unmatched speed and quality in restoring high-resolution images from severely degraded inputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.08641", "title": "Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization", "authors": [ "Jialu Li", "Shoubin Yu", "Han Lin", "Jaemin Cho", "Jaehong Yoon", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Recent advancements in text-to-video (T2V) diffusion models have significantly enhanced the visual quality of the generated videos. However, even recent T2V models find it challenging to follow text descriptions accurately, especially when the prompt requires accurate control of spatial layouts or object trajectories. A recent line of research uses layout guidance for T2V models that require fine-tuning or iterative manipulation of the attention map during inference time. This significantly increases the memory requirement, making it difficult to adopt a large T2V model as a backbone. To address this, we introduce Video-MSG, a training-free Guidance method for T2V generation based on Multimodal planning and Structured noise initialization. Video-MSG consists of three steps, where in the first two steps, Video-MSG creates Video Sketch, a fine-grained spatio-temporal plan for the final video, specifying background, foreground, and object trajectories, in the form of draft video frames. In the last step, Video-MSG guides a downstream T2V diffusion model with Video Sketch through noise inversion and denoising. Notably, Video-MSG does not need fine-tuning or attention manipulation with additional memory during inference time, making it easier to adopt large T2V models. Video-MSG demonstrates its effectiveness in enhancing text alignment with multiple T2V backbones (VideoCrafter2 and CogVideoX-5B) on popular T2V generation benchmarks (T2VCompBench and VBench). We provide comprehensive ablation studies about noise inversion ratio, different background generators, background object detection, and foreground object segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.07615", "title": "VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model", "authors": [ "Haozhan Shen", "Peng Liu", "Jingcheng Li", "Chunxin Fang", "Yibo Ma", "Jiajia Liao", "Qiaoli Shen", "Zilun Zhang", "Kangjia Zhao", "Qianqian Zhang", "Ruochen Xu", "Tiancheng Zhao" ], "github": "https://github.com/om-ai-lab/VLM-R1", "project_page": "", "abstract": "Recently DeepSeek R1 has shown that reinforcement learning (RL) can substantially improve the reasoning capabilities of Large Language Models (LLMs) through a simple yet effective design. The core of R1 lies in its rule-based reward formulation, which leverages tasks with deterministic ground-truth answers to enable precise and stable reward computation. In the visual domain, we similarly observe that a wide range of visual understanding tasks are inherently equipped with well-defined ground-truth annotations. This property makes them naturally compatible with rule-based reward mechanisms. Motivated by this observation, we investigate the extension of R1-style reinforcement learning to Vision-Language Models (VLMs), aiming to enhance their visual reasoning capabilities. To this end, we develop VLM-R1, a dedicated framework designed to harness RL for improving VLMs' performance on general vision-language tasks. Using this framework, we further explore the feasibility of applying RL to visual domain. Experimental results indicate that the RL-based model not only delivers competitive performance on visual understanding tasks but also surpasses Supervised Fine-Tuning (SFT) in generalization ability. Furthermore, we conduct comprehensive ablation studies that uncover a series of noteworthy insights, including the presence of reward hacking in object detection, the emergence of the \"OD aha moment\", the impact of training data quality, and the scaling behavior of RL across different model sizes. Through these analyses, we aim to deepen the understanding of how reinforcement learning enhances the capabilities of vision-language models, and we hope our findings and open-source contributions will support continued progress in the vision-language RL community. Our code and model are available at https://github.com/om-ai-lab/VLM-R1", "github_manual": "", "github_regex": "https://github.com/om-ai-lab/VLM-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-14", "arxiv_id": "2504.06908", "title": "UKBOB: One Billion MRI Labeled Masks for Generalizable 3D Medical Image Segmentation", "authors": [ "Emmanuelle Bourigault", "Amir Jamaludin", "Abdullah Hamdi" ], "github": "https://github.com/EmmanuelleB985/UK_BOB", "project_page": "", "abstract": "In medical imaging, the primary challenge is collecting large-scale labeled data due to privacy concerns, logistics, and high labeling costs. In this work, we present the UK Biobank Organs and Bones (UKBOB), the largest labeled dataset of body organs, comprising 51,761 MRI 3D samples (equivalent to 17.9 million 2D images) and more than 1.37 billion 2D segmentation masks of 72 organs, all based on the UK Biobank MRI dataset. We utilize automatic labeling, introduce an automated label cleaning pipeline with organ-specific filters, and manually annotate a subset of 300 MRIs with 11 abdominal classes to validate the quality (referred to as UKBOB-manual). This approach allows for scaling up the dataset collection while maintaining confidence in the labels. We further confirm the validity of the labels by demonstrating zero-shot generalization of trained models on the filtered UKBOB to other small labeled datasets from similar domains (e.g., abdominal MRI). To further mitigate the effect of noisy labels, we propose a novel method called Entropy Test-time Adaptation (ETTA) to refine the segmentation output. We use UKBOB to train a foundation model, Swin-BOB, for 3D medical image segmentation based on the Swin-UNetr architecture, achieving state-of-the-art results in several benchmarks in 3D medical imaging, including the BRATS brain MRI tumor challenge (with a 0.4% improvement) and the BTCV abdominal CT scan benchmark (with a 1.3% improvement). The pre-trained models and the code are available at https://emmanuelleb985.github.io/ukbob , and the filtered labels will be made available with the UK Biobank.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/EmmanuelleB985/UK_BOB", "github_api_added_by": "auto" }, { "date": "2025-04-14", "arxiv_id": "2504.01786", "title": "BlenderGym: Benchmarking Foundational Model Systems for Graphics Editing", "authors": [ "Yunqi Gu", "Ian Huang", "Jihyeon Je", "Guandao Yang", "Leonidas Guibas" ], "github": "https://github.com/richard-guyunqi/BlenderGym-Open", "project_page": "", "abstract": "3D graphics editing is crucial in applications like movie production and game design, yet it remains a time-consuming process that demands highly specialized domain expertise. Automating this process is challenging because graphical editing requires performing a variety of tasks, each requiring distinct skill sets. Recently, vision-language models (VLMs) have emerged as a powerful framework for automating the editing process, but their development and evaluation are bottlenecked by the lack of a comprehensive benchmark that requires human-level perception and presents real-world editing complexity. In this work, we present BlenderGym, the first comprehensive VLM system benchmark for 3D graphics editing. BlenderGym evaluates VLM systems through code-based 3D reconstruction tasks. We evaluate closed- and open-source VLM systems and observe that even the state-of-the-art VLM system struggles with tasks relatively easy for human Blender users. Enabled by BlenderGym, we study how inference scaling techniques impact VLM's performance on graphics editing tasks. Notably, our findings reveal that the verifier used to guide the scaling of generation can itself be improved through inference scaling, complementing recent insights on inference scaling of LLM generation in coding and math tasks. We further show that inference compute is not uniformly effective and can be optimized by strategically distributing it between generation and verification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/richard-guyunqi/BlenderGym-Open", "github_api_added_by": "auto" }, { "date": "2025-04-14", "arxiv_id": "2504.07891", "title": "SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning", "authors": [ "Rui Pan", "Yinwei Dai", "Zhihao Zhang", "Gabriele Oliaro", "Zhihao Jia", "Ravi Netravali" ], "github": "https://github.com/ruipeterpan/specreason", "project_page": "", "abstract": "Recent advances in inference-time compute have significantly improved performance on complex tasks by generating long chains of thought (CoTs) using Large Reasoning Models (LRMs). However, this improved accuracy comes at the cost of high inference latency due to the length of generated reasoning sequences and the autoregressive nature of decoding. Our key insight in tackling these overheads is that LRM inference, and the reasoning that it embeds, is highly tolerant of approximations: complex tasks are typically broken down into simpler steps, each of which brings utility based on the semantic insight it provides for downstream steps rather than the exact tokens it generates. Accordingly, we introduce SpecReason, a system that automatically accelerates LRM inference by using a lightweight model to (speculatively) carry out simpler intermediate reasoning steps and reserving the costly base model only to assess (and potentially correct) the speculated outputs. Importantly, SpecReason's focus on exploiting the semantic flexibility of thinking tokens in preserving final-answer accuracy is complementary to prior speculation techniques, most notably speculative decoding, which demands token-level equivalence at each step. Across a variety of reasoning benchmarks, SpecReason achieves 1.5-2.5times speedup over vanilla LRM inference while improving accuracy by 1.0-9.9\\%. Compared to speculative decoding without SpecReason, their combination yields an additional 19.4-44.2\\% latency reduction. We open-source SpecReason at https://github.com/ruipeterpan/specreason.", "github_manual": "", "github_regex": "https://github.com/ruipeterpan/specreason", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.09925", "title": "FUSION: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding", "authors": [ "Zheng Liu", "Mengjie Liu", "Jingzhou Chen", "Jingwei Xu", "Bin Cui", "Conghui He", "Wentao Zhang" ], "github": "https://github.com/starriver030515/FUSION", "project_page": "", "abstract": "We introduce FUSION, a family of multimodal large language models (MLLMs) with a fully vision-language alignment and integration paradigm. Unlike existing methods that primarily rely on late-stage modality interaction during LLM decoding, our approach achieves deep, dynamic integration throughout the entire processing pipeline. To this end, we propose Text-Guided Unified Vision Encoding, incorporating textual information in vision encoding to achieve pixel-level integration. We further design Context-Aware Recursive Alignment Decoding that recursively aggregates visual features conditioned on textual context during decoding, enabling fine-grained, question-level semantic integration. To guide feature mapping and mitigate modality discrepancies, we develop Dual-Supervised Semantic Mapping Loss. Additionally, we construct a Synthesized Language-Driven Question-Answer (QA) dataset through a new data synthesis method, prioritizing high-quality QA pairs to optimize text-guided feature integration. Building on these foundations, we train FUSION at two scales-3B, 8B-and demonstrate that our full-modality integration approach significantly outperforms existing methods with only 630 vision tokens. Notably, FUSION 3B surpasses Cambrian-1 8B and Florence-VL 8B on most benchmarks. FUSION 3B continues to outperform Cambrian-1 8B even when limited to 300 vision tokens. Our ablation studies show that FUSION outperforms LLaVA-NeXT on over half of the benchmarks under same configuration without dynamic resolution, highlighting the effectiveness of our approach. We release our code, model weights, and dataset. https://github.com/starriver030515/FUSION", "github_manual": "", "github_regex": "https://github.com/starriver030515/FUSION", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.09710", "title": "DUMP: Automated Distribution-Level Curriculum Learning for RL-based LLM Post-training", "authors": [ "Zhenting Wang", "Guofeng Cui", "Kun Wan", "Wentian Zhao" ], "github": "https://github.com/ZhentingWang/DUMP", "project_page": "", "abstract": "Recent advances in reinforcement learning (RL)-based post-training have led to notable improvements in large language models (LLMs), particularly in enhancing their reasoning capabilities to handle complex tasks. However, most existing methods treat the training data as a unified whole, overlooking the fact that modern LLM training often involves a mixture of data from diverse distributions-varying in both source and difficulty. This heterogeneity introduces a key challenge: how to adaptively schedule training across distributions to optimize learning efficiency. In this paper, we present a principled curriculum learning framework grounded in the notion of distribution-level learnability. Our core insight is that the magnitude of policy advantages reflects how much a model can still benefit from further training on a given distribution. Based on this, we propose a distribution-level curriculum learning framework for RL-based LLM post-training, which leverages the Upper Confidence Bound (UCB) principle to dynamically adjust sampling probabilities for different distrubutions. This approach prioritizes distributions with either high average advantage (exploitation) or low sample count (exploration), yielding an adaptive and theoretically grounded training schedule. We instantiate our curriculum learning framework with GRPO as the underlying RL algorithm and demonstrate its effectiveness on logic reasoning datasets with multiple difficulties and sources. Our experiments show that our framework significantly improves convergence speed and final performance, highlighting the value of distribution-aware curriculum strategies in LLM post-training. Code: https://github.com/ZhentingWang/DUMP.", "github_manual": "", "github_regex": "https://github.com/ZhentingWang/DUMP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.08791", "title": "PRIMA.CPP: Speeding Up 70B-Scale LLM Inference on Low-Resource Everyday Home Clusters", "authors": [ "Zonghang Li", "Tao Li", "Wenjiao Feng", "Mohsen Guizani", "Hongfang Yu" ], "github": "https://github.com/Lizonghang/prima.cpp", "project_page": "", "abstract": "Emergency of DeepSeek R1 and QwQ 32B have broken through performance barriers for running frontier large language models (LLMs) on home devices. While consumer hardware is getting stronger and model quantization is improving, existing end-side solutions still demand GPU clusters, large RAM/VRAM, and high bandwidth, far beyond what a common home cluster can handle. This paper introduces prima.cpp, a distributed inference system that runs 70B-scale models on everyday home devices using a mix of CPU/GPU, low RAM/VRAM, Wi-Fi, and cross-platform support. It uses mmap to manage model weights and introduces piped-ring parallelism with prefetching to hide disk loading. By modeling heterogeneity in computation, communication, disk, memory (and its management behavior), and OS, it optimally assigns model layers to each device's CPU and GPU, further reducing token latency. An elegant algorithm named Halda is proposed to solve this NP-hard assignment problem. We evaluate prima.cpp on a common four-node home cluster. It outperforms llama.cpp, exo, and dllama on 30B+ models while keeping memory pressure below 6%. This brings frontier 30B-70B models, such as Llama 3, DeepSeek R1, Qwen 2.5, and QwQ to home assistants, making advanced AI truly accessible to individuals. The code is open source and available at https://github.com/Lizonghang/prima.cpp.", "github_manual": "", "github_regex": "https://github.com/Lizonghang/prima.cpp", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.08003", "title": "Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability", "authors": [ "Ning Li", "Jingran Zhang", "Justin Cui" ], "github": "", "project_page": "", "abstract": "OpenAI's multimodal GPT-4o has demonstrated remarkable capabilities in image generation and editing, yet its ability to achieve world knowledge-informed semantic synthesis--seamlessly integrating domain knowledge, contextual reasoning, and instruction adherence--remains unproven. In this study, we systematically evaluate these capabilities across three critical dimensions: (1) Global Instruction Adherence, (2) Fine-Grained Editing Precision, and (3) Post-Generation Reasoning. While existing benchmarks highlight GPT-4o's strong capabilities in image generation and editing, our evaluation reveals GPT-4o's persistent limitations: the model frequently defaults to literal interpretations of instructions, inconsistently applies knowledge constraints, and struggles with conditional reasoning tasks. These findings challenge prevailing assumptions about GPT-4o's unified understanding and generation capabilities, exposing significant gaps in its dynamic knowledge integration. Our study calls for the development of more robust benchmarks and training strategies that go beyond surface-level alignment, emphasizing context-aware and reasoning-grounded multimodal generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.08837", "title": "VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning", "authors": [ "Haozhe Wang", "Chao Qu", "Zuming Huang", "Wei Chu", "Fangzhen Lin", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Recently, slow-thinking systems like GPT-o1 and DeepSeek-R1 have demonstrated great potential in solving challenging problems through explicit reflection. They significantly outperform the best fast-thinking models, such as GPT-4o, on various math and science benchmarks. However, their multimodal reasoning capabilities remain on par with fast-thinking models. For instance, GPT-o1's performance on benchmarks like MathVista, MathVerse, and MathVision is similar to fast-thinking models. In this paper, we aim to enhance the slow-thinking capabilities of vision-language models using reinforcement learning (without relying on distillation) to advance the state of the art. First, we adapt the GRPO algorithm with a novel technique called Selective Sample Replay (SSR) to address the vanishing advantages problem. While this approach yields strong performance, the resulting RL-trained models exhibit limited self-reflection or self-verification. To further encourage slow-thinking, we introduce Forced Rethinking, which appends a textual rethinking trigger to the end of initial rollouts in RL training, explicitly enforcing a self-reflection reasoning step. By combining these two techniques, our model, VL-Rethinker, advances state-of-the-art scores on MathVista, MathVerse, and MathVision to achieve 80.3%, 61.8%, and 43.9% respectively. VL-Rethinker also achieves open-source SoTA on multi-disciplinary benchmarks such as MMMU-Pro, EMMA, and MEGA-Bench, narrowing the gap with GPT-o1.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.08942", "title": "AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories", "authors": [ "Xing Han L\u00f9", "Amirhossein Kazemnejad", "Nicholas Meade", "Arkil Patel", "Dongchan Shin", "Alejandra Zambrano", "Karolina Sta\u0144czak", "Peter Shaw", "Christopher J. Pal", "Siva Reddy" ], "github": "", "project_page": "", "abstract": "Web agents enable users to perform tasks on web browsers through natural language interaction. Evaluating web agents trajectories is an important problem, since it helps us determine whether the agent successfully completed the tasks. Rule-based methods are widely used for this purpose, but they are challenging to extend to new tasks and may not always recognize successful trajectories. We may achieve higher accuracy through human evaluation, but the process would be substantially slower and more expensive. Automatic evaluations with LLMs may avoid the challenges of designing new rules and manually annotating trajectories, enabling faster and cost-effective evaluation. However, it is unclear how effective they are at evaluating web agents. To this end, we propose AgentRewardBench, the first benchmark to assess the effectiveness of LLM judges for evaluating web agents. AgentRewardBench contains 1302 trajectories across 5 benchmarks and 4 LLMs. Each trajectory in AgentRewardBench is reviewed by an expert, who answers questions pertaining to the success, side effects, and repetitiveness of the agent. Using our benchmark, we evaluate 12 LLM judges and find that no single LLM excels across all benchmarks. We also find that the rule-based evaluation used by common benchmarks tends to underreport the success rate of web agents, highlighting a key weakness of rule-based evaluation and the need to develop more flexible automatic evaluations. We release the benchmark at: https://agent-reward-bench.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.10068", "title": "Mavors: Multi-granularity Video Representation for Multimodal Large Language Model", "authors": [ "Yang Shi", "Jiaheng Liu", "Yushuo Guan", "Zhenhua Wu", "Yuanxing Zhang", "Zihao Wang", "Weihong Lin", "Jingyun Hua", "Zekun Wang", "Xinlong Chen", "Bohan Zeng", "Wentao Zhang", "Fuzheng Zhang", "Wenjing Yang", "Di Zhang" ], "github": "", "project_page": "", "abstract": "Long-context video understanding in multimodal large language models (MLLMs) faces a critical challenge: balancing computational efficiency with the retention of fine-grained spatio-temporal patterns. Existing approaches (e.g., sparse sampling, dense sampling with low resolution, and token compression) suffer from significant information loss in temporal dynamics, spatial details, or subtle interactions, particularly in videos with complex motion or varying resolutions. To address this, we propose Mavors, a novel framework that introduces Multi-granularity video representation for holistic long-video modeling. Specifically, Mavors directly encodes raw video content into latent representations through two core components: 1) an Intra-chunk Vision Encoder (IVE) that preserves high-resolution spatial features via 3D convolutions and Vision Transformers, and 2) an Inter-chunk Feature Aggregator (IFA) that establishes temporal coherence across chunks using transformer-based dependency modeling with chunk-level rotary position encodings. Moreover, the framework unifies image and video understanding by treating images as single-frame videos via sub-image decomposition. Experiments across diverse benchmarks demonstrate Mavors' superiority in maintaining both spatial fidelity and temporal continuity, significantly outperforming existing methods in tasks requiring fine-grained spatio-temporal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.10479", "title": "InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models", "authors": [ "Jinguo Zhu", "Weiyun Wang", "Zhe Chen", "Zhaoyang Liu", "Shenglong Ye", "Lixin Gu", "Yuchen Duan", "Hao Tian", "Weijie Su", "Jie Shao", "Zhangwei Gao", "Erfei Cui", "Yue Cao", "Yangzhou Liu", "Weiye Xu", "Hao Li", "Jiahao Wang", "Han Lv", "Dengnian Chen", "Songze Li", "Yinan He", "Tan Jiang", "Jiapeng Luo", "Yi Wang", "Conghui He", "Botian Shi", "Xingcheng Zhang", "Wenqi Shao", "Junjun He", "Yingtong Xiong", "Wenwen Qu", "Peng Sun", "Penglong Jiao", "Lijun Wu", "Kaipeng Zhang", "Huipeng Deng", "Jiaye Ge", "Kai Chen", "Limin Wang", "Min Dou", "Lewei Lu", "Xizhou Zhu", "Tong Lu", "Dahua Lin", "Yu Qiao", "Jifeng Dai", "Wenhai Wang" ], "github": "", "project_page": "", "abstract": "We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm. Rather than adapting a text-only large language model (LLM) into a multimodal large language model (MLLM) that supports visual inputs, InternVL3 jointly acquires multimodal and linguistic capabilities from both diverse multimodal data and pure-text corpora during a single pre-training stage. This unified training paradigm effectively addresses the complexities and alignment challenges commonly encountered in conventional post-hoc training pipelines for MLLMs. To further improve performance and scalability, InternVL3 incorporates variable visual position encoding (V2PE) to support extended multimodal contexts, employs advanced post-training techniques such as supervised fine-tuning (SFT) and mixed preference optimization (MPO), and adopts test-time scaling strategies alongside an optimized training infrastructure. Extensive empirical evaluations demonstrate that InternVL3 delivers superior performance across a wide range of multi-modal tasks. In particular, InternVL3-78B achieves a score of 72.2 on the MMMU benchmark, setting a new state-of-the-art among open-source MLLMs. Its capabilities remain highly competitive with leading proprietary models, including ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Pro, while also maintaining strong pure-language proficiency. In pursuit of open-science principles, we will publicly release both the training data and model weights to foster further research and development in next-generation MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.10157", "title": "SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users", "authors": [ "Xinnong Zhang", "Jiayu Lin", "Xinyi Mou", "Shiyue Yang", "Xiawei Liu", "Libo Sun", "Hanjia Lyu", "Yihang Yang", "Weihong Qi", "Yue Chen", "Guanying Li", "Ling Yan", "Yao Hu", "Siming Chen", "Yu Wang", "Jingxuan Huang", "Jiebo Luo", "Shiping Tang", "Libo Wu", "Baohua Zhou", "Zhongyu Wei" ], "github": "https://github.com/fudandisc/socioverse", "project_page": "", "abstract": "Social simulation is transforming traditional social science research by modeling human behavior through interactions between virtual individuals and their environments. With recent advances in large language models (LLMs), this approach has shown growing potential in capturing individual differences and predicting group behaviors. However, existing methods face alignment challenges related to the environment, target users, interaction mechanisms, and behavioral patterns. To this end, we introduce SocioVerse, an LLM-agent-driven world model for social simulation. Our framework features four powerful alignment components and a user pool of 10 million real individuals. To validate its effectiveness, we conducted large-scale simulation experiments across three distinct domains: politics, news, and economics. Results demonstrate that SocioVerse can reflect large-scale population dynamics while ensuring diversity, credibility, and representativeness through standardized procedures and minimal manual adjustments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/fudandisc/socioverse", "github_api_added_by": "auto" }, { "date": "2025-04-15", "arxiv_id": "2504.10368", "title": "S1-Bench: A Simple Benchmark for Evaluating System 1 Thinking Capability of Large Reasoning Models", "authors": [ "Wenyuan Zhang", "Shuaiyi Nie", "Xinghua Zhang", "Zefeng Zhang", "Tingwen Liu" ], "github": "https://github.com/WYRipple/S1_Bench", "project_page": "", "abstract": "We introduce S1-Bench, a novel benchmark designed to evaluate Large Reasoning Models' (LRMs) performance on simple tasks that favor intuitive system 1 thinking rather than deliberative system 2 reasoning. While LRMs have achieved significant breakthroughs in complex reasoning tasks through explicit chains of thought, their reliance on deep analytical thinking may limit their system 1 thinking capabilities. Moreover, a lack of benchmark currently exists to evaluate LRMs' performance in tasks that require such capabilities. To fill this gap, S1-Bench presents a set of simple, diverse, and naturally clear questions across multiple domains and languages, specifically designed to assess LRMs' performance in such tasks. Our comprehensive evaluation of 22 LRMs reveals significant lower efficiency tendencies, with outputs averaging 15.5 times longer than those of traditional small LLMs. Additionally, LRMs often identify correct answers early but continue unnecessary deliberation, with some models even producing numerous errors. These findings highlight the rigid reasoning patterns of current LRMs and underscore the substantial development needed to achieve balanced dual-system thinking capabilities that can adapt appropriately to task complexity.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WYRipple/S1_Bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.09689", "title": "EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety", "authors": [ "Jiahao Qiu", "Yinghui He", "Xinzhe Juan", "Yiming Wang", "Yuhan Liu", "Zixin Yao", "Yue Wu", "Xun Jiang", "Ling Yang", "Mengdi Wang" ], "github": "https://github.com/1akaman/EmoAgent", "project_page": "", "abstract": "The rise of LLM-driven AI characters raises safety concerns, particularly for vulnerable human users with psychological disorders. To address these risks, we propose EmoAgent, a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions. EmoAgent comprises two components: EmoEval simulates virtual users, including those portraying mentally vulnerable individuals, to assess mental health changes before and after interactions with AI characters. It uses clinically proven psychological and psychiatric assessment tools (PHQ-9, PDI, PANSS) to evaluate mental risks induced by LLM. EmoGuard serves as an intermediary, monitoring users' mental status, predicting potential harm, and providing corrective feedback to mitigate risks. Experiments conducted in popular character-based chatbots show that emotionally engaging dialogues can lead to psychological deterioration in vulnerable users, with mental state deterioration in more than 34.4% of the simulations. EmoGuard significantly reduces these deterioration rates, underscoring its role in ensuring safer AI-human interactions. Our code is available at: https://github.com/1akaman/EmoAgent", "github_manual": "", "github_regex": "https://github.com/1akaman/EmoAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.09641", "title": "TinyLLaVA-Video-R1: Towards Smaller LMMs for Video Reasoning", "authors": [ "Xingjian Zhang", "Siwei Wen", "Wenjun Wu", "Lei Huang" ], "github": "https://github.com/ZhangXJ199/TinyLLaVA-Video-R1", "project_page": "", "abstract": "Recently, improving the reasoning ability of large multimodal models (LMMs) through reinforcement learning has made great progress. However, most existing works are based on highly reasoning-intensive datasets such as mathematics and code, and researchers generally choose large-scale models as the foundation. We argue that exploring small-scale models' reasoning capabilities remains valuable for researchers with limited computational resources. Moreover, enabling models to explain their reasoning processes on general question-answering datasets is equally meaningful. Therefore, we present the small-scale video reasoning model TinyLLaVA-Video-R1. Based on TinyLLaVA-Video, a traceably trained video understanding model with no more than 4B parameters, it not only demonstrates significantly improved reasoning and thinking capabilities after using reinforcement learning on general Video-QA datasets, but also exhibits the emergent characteristic of \"aha moments\". Furthermore, we share a series of experimental findings, aiming to provide practical insights for future exploration of video reasoning (thinking) abilities in small-scale models. It is available at https://github.com/ZhangXJ199/TinyLLaVA-Video-R1.", "github_manual": "", "github_regex": "https://github.com/ZhangXJ199/TinyLLaVA-Video-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.10415", "title": "LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models", "authors": [ "Parshin Shojaee", "Ngoc-Hieu Nguyen", "Kazem Meidani", "Amir Barati Farimani", "Khoa D Doan", "Chandan K Reddy" ], "github": "", "project_page": "", "abstract": "Scientific equation discovery is a fundamental task in the history of scientific progress, enabling the derivation of laws governing natural phenomena. Recently, Large Language Models (LLMs) have gained interest for this task due to their potential to leverage embedded scientific knowledge for hypothesis generation. However, evaluating the true discovery capabilities of these methods remains challenging, as existing benchmarks often rely on common equations that are susceptible to memorization by LLMs, leading to inflated performance metrics that do not reflect discovery. In this paper, we introduce LLM-SRBench, a comprehensive benchmark with 239 challenging problems across four scientific domains specifically designed to evaluate LLM-based scientific equation discovery methods while preventing trivial memorization. Our benchmark comprises two main categories: LSR-Transform, which transforms common physical models into less common mathematical representations to test reasoning beyond memorized forms, and LSR-Synth, which introduces synthetic, discovery-driven problems requiring data-driven reasoning. Through extensive evaluation of several state-of-the-art methods, using both open and closed LLMs, we find that the best-performing system so far achieves only 31.5% symbolic accuracy. These findings highlight the challenges of scientific equation discovery, positioning LLM-SRBench as a valuable resource for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.09763", "title": "Executable Functional Abstractions: Inferring Generative Programs for Advanced Math Problems", "authors": [ "Zaid Khan", "Elias Stengel-Eskin", "Archiki Prasad", "Jaemin Cho", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Scientists often infer abstract procedures from specific instances of problems and use the abstractions to generate new, related instances. For example, programs encoding the formal rules and properties of a system have been useful in fields ranging from RL (procedural environments) to physics (simulation engines). These programs can be seen as functions which execute to different outputs based on their parameterizations (e.g., gridworld configuration or initial physical conditions). We introduce the term EFA (Executable Functional Abstraction) to denote such programs for math problems. EFA-like constructs have been shown to be useful for math reasoning as problem generators for stress-testing models. However, prior work has been limited to abstractions for grade-school math (whose simple rules are easy to encode in programs), while generating EFAs for advanced math has thus far required human engineering. We explore the automatic construction of EFAs for advanced math problems. We operationalize the task of automatically constructing EFAs as a program synthesis task, and develop EFAGen, which conditions an LLM on a seed math problem and its step-by-step solution to generate candidate EFA programs that are faithful to the generalized problem and solution class underlying the seed problem. Furthermore, we formalize properties any valid EFA must possess in terms of executable unit tests, and show how the tests can be used as verifiable rewards to train LLMs to become better writers of EFAs. We demonstrate that EFAs constructed by EFAGen behave rationally by remaining faithful to seed problems, produce learnable problem variations, and that EFAGen can infer EFAs across multiple diverse sources of competition-level math problems. Finally, we show downstream uses of model-written EFAs e.g. finding problem variations that are harder or easier for a learner to solve, as well as data generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.10430", "title": "LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models", "authors": [ "Minqian Liu", "Zhiyang Xu", "Xinyi Zhang", "Heajun An", "Sarvech Qadir", "Qi Zhang", "Pamela J. Wisniewski", "Jin-Hee Cho", "Sang Won Lee", "Ruoxi Jia", "Lifu Huang" ], "github": "https://github.com/PLUM-Lab/PersuSafety", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) have enabled them to approach human-level persuasion capabilities. However, such potential also raises concerns about the safety risks of LLM-driven persuasion, particularly their potential for unethical influence through manipulation, deception, exploitation of vulnerabilities, and many other harmful tactics. In this work, we present a systematic investigation of LLM persuasion safety through two critical aspects: (1) whether LLMs appropriately reject unethical persuasion tasks and avoid unethical strategies during execution, including cases where the initial persuasion goal appears ethically neutral, and (2) how influencing factors like personality traits and external pressures affect their behavior. To this end, we introduce PersuSafety, the first comprehensive framework for the assessment of persuasion safety which consists of three stages, i.e., persuasion scene creation, persuasive conversation simulation, and persuasion safety assessment. PersuSafety covers 6 diverse unethical persuasion topics and 15 common unethical strategies. Through extensive experiments across 8 widely used LLMs, we observe significant safety concerns in most LLMs, including failing to identify harmful persuasion tasks and leveraging various unethical persuasion strategies. Our study calls for more attention to improve safety alignment in progressive and goal-driven conversations such as persuasion.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PLUM-Lab/PersuSafety", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.10127", "title": "Breaking the Data Barrier -- Building GUI Agents Through Task Generalization", "authors": [ "Junlei Zhang", "Zichen Ding", "Chang Ma", "Zijie Chen", "Qiushi Sun", "Zhenzhong Lan", "Junxian He" ], "github": "https://github.com/hkust-nlp/GUIMid", "project_page": "", "abstract": "Graphical User Interface (GUI) agents offer cross-platform solutions for automating complex digital tasks, with significant potential to transform productivity workflows. However, their performance is often constrained by the scarcity of high-quality trajectory data. To address this limitation, we propose training Vision Language Models (VLMs) on data-rich, reasoning-intensive tasks during a dedicated mid-training stage, and then examine how incorporating these tasks facilitates generalization to GUI planning scenarios. Specifically, we explore a range of tasks with readily available instruction-tuning data, including GUI perception, multimodal reasoning, and textual reasoning. Through extensive experiments across 11 mid-training tasks, we demonstrate that: (1) Task generalization proves highly effective, yielding substantial improvements across most settings. For instance, multimodal mathematical reasoning enhances performance on AndroidWorld by an absolute 6.3%. Remarkably, text-only mathematical data significantly boosts GUI web agent performance, achieving a 5.6% improvement on WebArena and 5.4% improvement on AndroidWorld, underscoring notable cross-modal generalization from text-based to visual domains; (2) Contrary to prior assumptions, GUI perception data - previously considered closely aligned with GUI agent tasks and widely utilized for training - has a comparatively limited impact on final performance; (3) Building on these insights, we identify the most effective mid-training tasks and curate optimized mixture datasets, resulting in absolute performance gains of 8.0% on WebArena and 12.2% on AndroidWorld. Our work provides valuable insights into cross-domain knowledge transfer for GUI agents and offers a practical approach to addressing data scarcity challenges in this emerging field. The code, data and models will be available at https://github.com/hkust-nlp/GUIMid.", "github_manual": "", "github_regex": "https://github.com/hkust-nlp/GUIMid", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.09130", "title": "VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search", "authors": [ "Yikun Wang", "Siyin Wang", "Qinyuan Cheng", "Zhaoye Fei", "Liang Ding", "Qipeng Guo", "Dacheng Tao", "Xipeng Qiu" ], "github": "https://github.com/ekonwang/VisuoThink", "project_page": "", "abstract": "Recent advancements in Large Vision-Language Models have showcased remarkable capabilities. However, they often falter when confronted with complex reasoning tasks that humans typically address through visual aids and deliberate, step-by-step thinking. While existing methods have explored text-based slow thinking or rudimentary visual assistance, they fall short of capturing the intricate, interleaved nature of human visual-verbal reasoning processes. To overcome these limitations and inspired by the mechanisms of slow thinking in human cognition, we introduce VisuoThink, a novel framework that seamlessly integrates visuospatial and linguistic domains. VisuoThink facilitates multimodal slow thinking by enabling progressive visual-textual reasoning and incorporates test-time scaling through look-ahead tree search. Extensive experiments demonstrate that VisuoThink significantly enhances reasoning capabilities via inference-time scaling, even without fine-tuning, achieving state-of-the-art performance in tasks involving geometry and spatial reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ekonwang/VisuoThink", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.10449", "title": "M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models", "authors": [ "Junxiong Wang", "Wen-Ding Li", "Daniele Paliotta", "Daniel Ritter", "Alexander M. Rush", "Tri Dao" ], "github": "https://github.com/jxiw/M1", "project_page": "", "abstract": "Effective reasoning is crucial to solving complex mathematical problems. Recent large language models (LLMs) have boosted performance by scaling test-time computation through long chain-of-thought reasoning. However, transformer-based models are inherently limited in extending context length due to their quadratic computational complexity and linear memory requirements. In this paper, we introduce a novel hybrid linear RNN reasoning model, M1, built on the Mamba architecture, which allows memory-efficient inference. Our approach leverages a distillation process from existing reasoning models and is further enhanced through RL training. Experimental results on the AIME and MATH benchmarks show that M1 not only outperforms previous linear RNN models but also matches the performance of state-of-the-art Deepseek R1 distilled reasoning models at a similar scale. We also compare our generation speed with a highly performant general purpose inference engine, vLLM, and observe more than a 3x speedup compared to a same size transformer. With throughput speedup, we are able to achieve higher accuracy compared to DeepSeek R1 distilled transformer reasoning models under a fixed generation time budget using self-consistency voting. Overall, we introduce a hybrid Mamba reasoning model and provide a more effective approach to scaling test-time generation using self-consistency or long chain of thought reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jxiw/M1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.09522", "title": "How new data permeates LLM knowledge and how to dilute it", "authors": [ "Chen Sun", "Renat Aksitov", "Andrey Zhmoginov", "Nolan Andrew Miller", "Max Vladymyrov", "Ulrich Rueckert", "Been Kim", "Mark Sandler" ], "github": "", "project_page": "", "abstract": "Large language models learn and continually learn through the accumulation of gradient-based updates, but how individual pieces of new information affect existing knowledge, leading to both beneficial generalization and problematic hallucination, remains poorly understood. We demonstrate that when learning new information, LLMs exhibit a \"priming\" effect: learning a new fact can cause the model to inappropriately apply that knowledge in unrelated contexts. To systematically study this phenomenon, we introduce \"Outlandish,\" a carefully curated dataset of 1320 diverse text samples designed to probe how new knowledge permeates through an LLM's existing knowledge base. Using this dataset, we show that the degree of priming after learning new information can be predicted by measuring the token probability of key words before learning. This relationship holds robustly across different model architectures (PALM-2, Gemma, Llama), sizes, and training stages. Finally, we develop two novel techniques to modulate how new knowledge affects existing model behavior: (1) a ``stepping-stone'' text augmentation strategy and (2) an ``ignore-k'' update pruning method. These approaches reduce undesirable priming effects by 50-95\\% while preserving the model's ability to learn new information. Our findings provide both empirical insights into how LLMs learn and practical tools for improving the specificity of knowledge insertion in language models. Further materials: https://sunchipsster1.github.io/projects/outlandish/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.08066", "title": "The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search", "authors": [ "Yutaro Yamada", "Robert Tjarko Lange", "Cong Lu", "Shengran Hu", "Chris Lu", "Jakob Foerster", "Jeff Clune", "David Ha" ], "github": "https://github.com/SakanaAI/AI-Scientist-v2", "project_page": "", "abstract": "AI is increasingly playing a pivotal role in transforming how scientific discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic system capable of producing the first entirely AI generated peer-review-accepted workshop paper. This system iteratively formulates scientific hypotheses, designs and executes experiments, analyzes and visualizes data, and autonomously authors scientific manuscripts. Compared to its predecessor (v1, Lu et al., 2024 arXiv:2408.06292), The AI Scientist-v2 eliminates the reliance on human-authored code templates, generalizes effectively across diverse machine learning domains, and leverages a novel progressive agentic tree-search methodology managed by a dedicated experiment manager agent. Additionally, we enhance the AI reviewer component by integrating a Vision-Language Model (VLM) feedback loop for iterative refinement of content and aesthetics of the figures. We evaluated The AI Scientist-v2 by submitting three fully autonomous manuscripts to a peer-reviewed ICLR workshop. Notably, one manuscript achieved high enough scores to exceed the average human acceptance threshold, marking the first instance of a fully AI-generated paper successfully navigating a peer review. This accomplishment highlights the growing capability of AI in conducting all aspects of scientific research. We anticipate that further advancements in autonomous scientific discovery technologies will profoundly impact human knowledge generation, enabling unprecedented scalability in research productivity and significantly accelerating scientific breakthroughs, greatly benefiting society at large. We have open-sourced the code at https://github.com/SakanaAI/AI-Scientist-v2 to foster the future development of this transformative technology. We also discuss the role of AI in science, including AI safety.", "github_manual": "", "github_regex": "https://github.com/SakanaAI/AI-Scientist-v2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.09643", "title": "Iterative Self-Training for Code Generation via Reinforced Re-Ranking", "authors": [ "Nikita Sorokin", "Ivan Sedykh", "Valentin Malykh" ], "github": "", "project_page": "", "abstract": "Generating high-quality code that solves complex programming tasks is challenging, especially with current decoder-based models that produce highly stochastic outputs. In code generation, even minor errors can easily break the entire solution. Leveraging multiple sampled solutions can significantly improve the overall output quality. One effective way to enhance code generation is by pairing a code generation model with a reranker model, which selects the best solution from the generated samples. We propose a novel iterative self-training approach for self-training reranker models using Proximal Policy Optimization (PPO), aimed at improving both reranking accuracy and the overall code generation process. Unlike traditional PPO approaches, where the focus is on optimizing a generative model with a reward model, our approach emphasizes the development of a robust reward/reranking model. This model improves the quality of generated code through reranking and addresses problems and errors that the reward model might overlook during PPO alignment with the reranker. Our method iteratively refines the training dataset by re-evaluating outputs, identifying high-scoring negative examples, and incorporating them into the training loop, that boosting model performance. Our evaluation on the MultiPL-E dataset demonstrates that our 13.4B parameter model outperforms a 33B model in code generation quality while being three times faster. Moreover, it achieves performance comparable to GPT-4 and surpasses it in one programming language.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.08120", "title": "DeepSeek vs. o3-mini: How Well can Reasoning LLMs Evaluate MT and Summarization?", "authors": [ "Daniil Larionov", "Sotaro Takeshita", "Ran Zhang", "Yanran Chen", "Christoph Leiter", "Zhipin Wang", "Christian Greisinger", "Steffen Eger" ], "github": "https://github.com/NL2G/reasoning-eval", "project_page": "", "abstract": "Reasoning-enabled large language models (LLMs) have recently demonstrated impressive performance in complex logical and mathematical tasks, yet their effectiveness in evaluating natural language generation remains unexplored. This study systematically compares reasoning-based LLMs (DeepSeek-R1 and OpenAI o3) with their non-reasoning counterparts across machine translation (MT) and text summarization (TS) evaluation tasks. We evaluate eight models across three architectural categories, including state-of-the-art reasoning models, their distilled variants (ranging from 8B to 70B parameters), and equivalent conventional, non-reasoning LLMs. Our experiments on WMT23 and SummEval benchmarks reveal that the benefits of reasoning capabilities are highly model and task-dependent: while OpenAI o3-mini models show consistent performance improvements with increased reasoning intensity, DeepSeek-R1 underperforms compared to its non-reasoning variant, with exception to certain aspects of TS evaluation. Correlation analysis demonstrates that increased reasoning token usage positively correlates with evaluation quality in o3-mini models. Furthermore, our results show that distillation of reasoning capabilities maintains reasonable performance in medium-sized models (32B) but degrades substantially in smaller variants (8B). This work provides the first comprehensive assessment of reasoning LLMs for NLG evaluation and offers insights into their practical use.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NL2G/reasoning-eval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.05782", "title": "MDK12-Bench: A Multi-Discipline Benchmark for Evaluating Reasoning in Multimodal Large Language Models", "authors": [ "Pengfei Zhou", "Fanrui Zhang", "Xiaopeng Peng", "Zhaopan Xu", "Jiaxin Ai", "Yansheng Qiu", "Chuanhao Li", "Zhen Li", "Ming Li", "Yukang Feng", "Jianwen Sun", "Haoquan Zhang", "Zizhen Li", "Xiaofeng Mao", "Wangbo Zhao", "Kai Wang", "Xiaojun Chang", "Wenqi Shao", "Yang You", "Kaipeng Zhang" ], "github": "https://github.com/LanceZPF/MDK12", "project_page": "", "abstract": "Multimodal reasoning, which integrates language and visual cues into problem solving and decision making, is a fundamental aspect of human intelligence and a crucial step toward artificial general intelligence. However, the evaluation of multimodal reasoning capabilities in Multimodal Large Language Models (MLLMs) remains inadequate. Most existing reasoning benchmarks are constrained by limited data size, narrow domain coverage, and unstructured knowledge distribution. To close these gaps, we introduce MDK12-Bench, a multi-disciplinary benchmark assessing the reasoning capabilities of MLLMs via real-world K-12 examinations. Spanning six disciplines (math, physics, chemistry, biology, geography, and information science), our benchmark comprises 140K reasoning instances across diverse difficulty levels from primary school to 12th grade. It features 6,827 instance-level knowledge point annotations based on a well-organized knowledge structure, detailed answer explanations, difficulty labels and cross-year partitions, providing a robust platform for comprehensive evaluation. Additionally, we present a novel dynamic evaluation framework to mitigate data contamination issues by bootstrapping question forms, question types, and image styles during evaluation. Extensive experiment on MDK12-Bench reveals the significant limitation of current MLLMs in multimodal reasoning. The findings on our benchmark provide insights into the development of the next-generation models. Our data and codes are available at https://github.com/LanceZPF/MDK12.", "github_manual": "", "github_regex": "https://github.com/LanceZPF/MDK12", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.09518", "title": "3D CoCa: Contrastive Learners are 3D Captioners", "authors": [ "Ting Huang", "Zeyu Zhang", "Yemin Wang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/3DCoCa", "project_page": "", "abstract": "3D captioning, which aims to describe the content of 3D scenes in natural language, remains highly challenging due to the inherent sparsity of point clouds and weak cross-modal alignment in existing methods. To address these challenges, we propose 3D CoCa, a novel unified framework that seamlessly combines contrastive vision-language learning with 3D caption generation in a single architecture. Our approach leverages a frozen CLIP vision-language backbone to provide rich semantic priors, a spatially-aware 3D scene encoder to capture geometric context, and a multi-modal decoder to generate descriptive captions. Unlike prior two-stage methods that rely on explicit object proposals, 3D CoCa jointly optimizes contrastive and captioning objectives in a shared feature space, eliminating the need for external detectors or handcrafted proposals. This joint training paradigm yields stronger spatial reasoning and richer semantic grounding by aligning 3D and textual representations. Extensive experiments on the ScanRefer and Nr3D benchmarks demonstrate that 3D CoCa significantly outperforms current state-of-the-arts by 10.2% and 5.76% in CIDEr at 0.5IoU, respectively. Code will be available at https://github.com/AIGeeksGroup/3DCoCa.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/3DCoCa", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.09513", "title": "DiffuMural: Restoring Dunhuang Murals with Multi-scale Diffusion", "authors": [ "Puyu Han", "Jiaju Kang", "Yuhang Pan", "Erting Pan", "Zeyu Zhang", "Qunchao Jin", "Juntao Jiang", "Zhichen Liu", "Luqi Gong" ], "github": "", "project_page": "", "abstract": "Large-scale pre-trained diffusion models have produced excellent results in the field of conditional image generation. However, restoration of ancient murals, as an important downstream task in this field, poses significant challenges to diffusion model-based restoration methods due to its large defective area and scarce training samples. Conditional restoration tasks are more concerned with whether the restored part meets the aesthetic standards of mural restoration in terms of overall style and seam detail, and such metrics for evaluating heuristic image complements are lacking in current research. We therefore propose DiffuMural, a combined Multi-scale convergence and Collaborative Diffusion mechanism with ControlNet and cyclic consistency loss to optimise the matching between the generated images and the conditional control. DiffuMural demonstrates outstanding capabilities in mural restoration, leveraging training data from 23 large-scale Dunhuang murals that exhibit consistent visual aesthetics. The model excels in restoring intricate details, achieving a coherent overall appearance, and addressing the unique challenges posed by incomplete murals lacking factual grounding. Our evaluation framework incorporates four key metrics to quantitatively assess incomplete murals: factual accuracy, textural detail, contextual semantics, and holistic visual coherence. Furthermore, we integrate humanistic value assessments to ensure the restored murals retain their cultural and artistic significance. Extensive experiments validate that our method outperforms state-of-the-art (SOTA) approaches in both qualitative and quantitative metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.10471", "title": "MIEB: Massive Image Embedding Benchmark", "authors": [ "Chenghao Xiao", "Isaac Chung", "Imene Kerboua", "Jamie Stirling", "Xin Zhang", "M\u00e1rton Kardos", "Roman Solomatin", "Noura Al Moubayed", "Kenneth Enevoldsen", "Niklas Muennighoff" ], "github": "https://github.com/embeddings-benchmark/mteb", "project_page": "", "abstract": "Image representations are often evaluated through disjointed, task-specific protocols, leading to a fragmented understanding of model capabilities. For instance, it is unclear whether an image embedding model adept at clustering images is equally good at retrieving relevant images given a piece of text. We introduce the Massive Image Embedding Benchmark (MIEB) to evaluate the performance of image and image-text embedding models across the broadest spectrum to date. MIEB spans 38 languages across 130 individual tasks, which we group into 8 high-level categories. We benchmark 50 models across our benchmark, finding that no single method dominates across all task categories. We reveal hidden capabilities in advanced vision models such as their accurate visual representation of texts, and their yet limited capabilities in interleaved encodings and matching images and texts in the presence of confounders. We also show that the performance of vision encoders on MIEB correlates highly with their performance when used in multimodal large language models. Our code, dataset, and leaderboard are publicly available at https://github.com/embeddings-benchmark/mteb.", "github_manual": "", "github_regex": "https://github.com/embeddings-benchmark/mteb", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.09858", "title": "Reasoning Models Can Be Effective Without Thinking", "authors": [ "Wenjie Ma", "Jingxuan He", "Charlie Snell", "Tyler Griggs", "Sewon Min", "Matei Zaharia" ], "github": "", "project_page": "", "abstract": "Recent LLMs have significantly improved reasoning capabilities, primarily by including an explicit, lengthy Thinking process as part of generation. In this paper, we question whether this explicit thinking is necessary. Using the state-of-the-art DeepSeek-R1-Distill-Qwen, we find that bypassing the thinking process via simple prompting, denoted as NoThinking, can be surprisingly effective. When controlling for the number of tokens, NoThinking outperforms Thinking across a diverse set of seven challenging reasoning datasets--including mathematical problem solving, formal theorem proving, and coding--especially in low-budget settings, e.g., 51.3 vs. 28.9 on ACM 23 with 700 tokens. Notably, the performance of NoThinking becomes more competitive with pass@k as k increases. Building on this observation, we demonstrate that a parallel scaling approach that uses NoThinking to generate N outputs independently and aggregates them is highly effective. For aggregation, we use task-specific verifiers when available, or we apply simple best-of-N strategies such as confidence-based selection. Our method outperforms a range of baselines with similar latency using Thinking, and is comparable to Thinking with significantly longer latency (up to 9x). Together, our research encourages a reconsideration of the necessity of lengthy thinking processes, while also establishing a competitive reference for achieving strong reasoning performance in low-budget settings or at low latency using parallel scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-15", "arxiv_id": "2504.03767", "title": "MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits", "authors": [ "Brandon Radosevich", "John Halloran" ], "github": "https://github.com/johnhalloran321/mcpSafetyScanner", "project_page": "", "abstract": "To reduce development overhead and enable seamless integration between potential components comprising any given generative AI application, the Model Context Protocol (MCP) (Anthropic, 2024) has recently been released and subsequently widely adopted. The MCP is an open protocol that standardizes API calls to large language models (LLMs), data sources, and agentic tools. By connecting multiple MCP servers, each defined with a set of tools, resources, and prompts, users are able to define automated workflows fully driven by LLMs. However, we show that the current MCP design carries a wide range of security risks for end users. In particular, we demonstrate that industry-leading LLMs may be coerced into using MCP tools to compromise an AI developer's system through various attacks, such as malicious code execution, remote access control, and credential theft. To proactively mitigate these and related attacks, we introduce a safety auditing tool, MCPSafetyScanner, the first agentic tool to assess the security of an arbitrary MCP server. MCPScanner uses several agents to (a) automatically determine adversarial samples given an MCP server's tools and resources; (b) search for related vulnerabilities and remediations based on those samples; and (c) generate a security report detailing all findings. Our work highlights serious security issues with general-purpose agentic workflows while also providing a proactive tool to audit MCP server safety and address detected vulnerabilities before deployment. The described MCP server auditing tool, MCPSafetyScanner, is freely available at: https://github.com/johnhalloran321/mcpSafetyScanner", "github_manual": "", "github_regex": "https://github.com/johnhalloran321/mcpSafetyScanner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.10481", "title": "xVerify: Efficient Answer Verifier for Reasoning Model Evaluations", "authors": [ "Ding Chen", "Qingchen Yu", "Pengyuan Wang", "Wentao Zhang", "Bo Tang", "Feiyu Xiong", "Xinchi Li", "Minchuan Yang", "Zhiyu Li" ], "github": "", "project_page": "", "abstract": "With the release of the o1 model by OpenAI, reasoning models adopting slow thinking strategies have gradually emerged. As the responses generated by such models often include complex reasoning, intermediate steps, and self-reflection, existing evaluation methods are often inadequate. They struggle to determine whether the LLM output is truly equivalent to the reference answer, and also have difficulty identifying and extracting the final answer from long, complex responses. To address this issue, we propose xVerify, an efficient answer verifier for reasoning model evaluations. xVerify demonstrates strong capability in equivalence judgment, enabling it to effectively determine whether the answers produced by reasoning models are equivalent to reference answers across various types of objective questions. To train and evaluate xVerify, we construct the VAR dataset by collecting question-answer pairs generated by multiple LLMs across various datasets, leveraging multiple reasoning models and challenging evaluation sets designed specifically for reasoning model assessment. A multi-round annotation process is employed to ensure label accuracy. Based on the VAR dataset, we train multiple xVerify models of different scales. In evaluation experiments conducted on both the test set and generalization set, all xVerify models achieve overall F1 scores and accuracy exceeding 95\\%. Notably, the smallest variant, xVerify-0.5B-I, outperforms all evaluation methods except GPT-4o, while xVerify-3B-Ib surpasses GPT-4o in overall performance. These results validate the effectiveness and generalizability of xVerify.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11442", "title": "TextArena", "authors": [ "Leon Guertler", "Bobby Cheng", "Simon Yu", "Bo Liu", "Leshem Choshen", "Cheston Tan" ], "github": "https://github.com/LeonGuertler/TextArena", "project_page": "", "abstract": "TextArena is an open-source collection of competitive text-based games for training and evaluation of agentic behavior in Large Language Models (LLMs). It spans 57+ unique environments (including single-player, two-player, and multi-player setups) and allows for easy evaluation of model capabilities via an online-play system (against humans and other submitted models) with real-time TrueSkill scores. Traditional benchmarks rarely assess dynamic social skills such as negotiation, theory of mind, and deception, creating a gap that TextArena addresses. Designed with research, community and extensibility in mind, TextArena emphasizes ease of adding new games, adapting the framework, testing models, playing against the models, and training models. Detailed documentation of environments, games, leaderboard, and examples are available on https://github.com/LeonGuertler/TextArena and https://www.textarena.ai/.", "github_manual": "", "github_regex": "https://github.com/LeonGuertler/TextArena", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.10766", "title": "How Instruction and Reasoning Data shape Post-Training: Data Quality through the Lens of Layer-wise Gradients", "authors": [ "Ming Li", "Yanhong Li", "Ziyue Li", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "As the post-training of large language models (LLMs) advances from instruction-following to complex reasoning tasks, understanding how different data affect finetuning dynamics remains largely unexplored. In this paper, we present a spectral analysis of layer-wise gradients induced by low/high-quality instruction and reasoning data for LLM post-training. Our analysis reveals that widely-studied metrics for data evaluation, e.g., IFD, InsTag, Difficulty, and Reward, can be explained and unified by spectral properties computed from gradients' singular value decomposition (SVD). Specifically, higher-quality data are usually associated with lower nuclear norms and higher effective ranks. Notably, effective rank exhibits better robustness and resolution than nuclear norm in capturing subtle quality differences. For example, reasoning data achieves substantially higher effective ranks than instruction data, implying richer gradient structures on more complex tasks. Our experiments also highlight that models within the same family share similar gradient patterns regardless of their sizes, whereas different model families diverge significantly. Providing a unified view on the effects of data quality across instruction and reasoning data, this work illuminates the interplay between data quality and training stability, shedding novel insights into developing better data exploration strategies for post-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.10337", "title": "Heimdall: test-time scaling on the generative verification", "authors": [ "Wenlei Shi", "Xing Jin" ], "github": "", "project_page": "", "abstract": "An AI system can create and maintain knowledge only to the extent that it can verify that knowledge itself. Recent work on long Chain-of-Thought reasoning has demonstrated great potential of LLMs on solving competitive problems, but their verification ability remains to be weak and not sufficiently investigated. In this paper, we propose Heimdall, the long CoT verification LLM that can accurately judge the correctness of solutions. With pure reinforcement learning, we boost the verification accuracy from 62.5% to 94.5% on competitive math problems. By scaling with repeated sampling, the accuracy further increases to 97.5%. Through human evaluation, Heimdall demonstrates impressive generalization capabilities, successfully detecting most issues in challenging math proofs, the type of which is not included during training. Furthermore, we propose Pessimistic Verification to extend the functionality of Heimdall to scaling up the problem solving. It calls Heimdall to judge the solutions from a solver model and based on the pessimistic principle, selects the most likely correct solution with the least uncertainty. Taking DeepSeek-R1-Distill-Qwen-32B as the solver model, Pessimistic Verification improves the solution accuracy on AIME2025 from 54.2% to 70.0% with 16x compute budget and to 83.3% with more compute budget. With the stronger solver Gemini 2.5 Pro, the score reaches 93.0%. Finally, we prototype an automatic knowledge discovery system, a ternary system where one poses questions, another provides solutions, and the third verifies the solutions. Using the data synthesis work NuminaMath for the first two components, Heimdall effectively identifies problematic records within the dataset and reveals that nearly half of the data is flawed, which interestingly aligns with the recent ablation studies from NuminaMath.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11447", "title": "Diffusion Distillation With Direct Preference Optimization For Efficient 3D LiDAR Scene Completion", "authors": [ "An Zhaol", "Shengyuan Zhang", "Ling Yang", "Zejian Li", "Jiale Wu", "Haoran Xu", "AnYang Wei", "Perry Pengyun GU Lingyun Sun" ], "github": "https://github.com/happyw1nd/DistillationDPO", "project_page": "", "abstract": "The application of diffusion models in 3D LiDAR scene completion is limited due to diffusion's slow sampling speed. Score distillation accelerates diffusion sampling but with performance degradation, while post-training with direct policy optimization (DPO) boosts performance using preference data. This paper proposes Distillation-DPO, a novel diffusion distillation framework for LiDAR scene completion with preference aligment. First, the student model generates paired completion scenes with different initial noises. Second, using LiDAR scene evaluation metrics as preference, we construct winning and losing sample pairs. Such construction is reasonable, since most LiDAR scene metrics are informative but non-differentiable to be optimized directly. Third, Distillation-DPO optimizes the student model by exploiting the difference in score functions between the teacher and student models on the paired completion scenes. Such procedure is repeated until convergence. Extensive experiments demonstrate that, compared to state-of-the-art LiDAR scene completion diffusion models, Distillation-DPO achieves higher-quality scene completion while accelerating the completion speed by more than 5-fold. Our method is the first to explore adopting preference learning in distillation to the best of our knowledge and provide insights into preference-aligned distillation. Our code is public available on https://github.com/happyw1nd/DistillationDPO.", "github_manual": "", "github_regex": "https://github.com/happyw1nd/DistillationDPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11346", "title": "Seedream 3.0 Technical Report", "authors": [ "Yu Gao", "Lixue Gong", "Qiushan Guo", "Xiaoxia Hou", "Zhichao Lai", "Fanshi Li", "Liang Li", "Xiaochen Lian", "Chao Liao", "Liyang Liu", "Wei Liu", "Yichun Shi", "Shiqi Sun", "Yu Tian", "Zhi Tian", "Peng Wang", "Rui Wang", "Xuanda Wang", "Xun Wang", "Ye Wang", "Guofeng Wu", "Jie Wu", "Xin Xia", "Xuefeng Xiao", "Zhonghua Zhai", "Xinyu Zhang", "Qi Zhang", "Yuwei Zhang", "Shijia Zhao", "Jianchao Yang", "Weilin Huang" ], "github": "", "project_page": "", "abstract": "We present Seedream 3.0, a high-performance Chinese-English bilingual image generation foundation model. We develop several technical improvements to address existing challenges in Seedream 2.0, including alignment with complicated prompts, fine-grained typography generation, suboptimal visual aesthetics and fidelity, and limited image resolutions. Specifically, the advancements of Seedream 3.0 stem from improvements across the entire pipeline, from data construction to model deployment. At the data stratum, we double the dataset using a defect-aware training paradigm and a dual-axis collaborative data-sampling framework. Furthermore, we adopt several effective techniques such as mixed-resolution training, cross-modality RoPE, representation alignment loss, and resolution-aware timestep sampling in the pre-training phase. During the post-training stage, we utilize diversified aesthetic captions in SFT, and a VLM-based reward model with scaling, thereby achieving outputs that well align with human preferences. Furthermore, Seedream 3.0 pioneers a novel acceleration paradigm. By employing consistent noise expectation and importance-aware timestep sampling, we achieve a 4 to 8 times speedup while maintaining image quality. Seedream 3.0 demonstrates significant improvements over Seedream 2.0: it enhances overall capabilities, in particular for text-rendering in complicated Chinese characters which is important to professional typography generation. In addition, it provides native high-resolution output (up to 2K), allowing it to generate images with high visual quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.10559", "title": "Efficient Process Reward Model Training via Active Learning", "authors": [ "Keyu Duan", "Zichen Liu", "Xin Mao", "Tianyu Pang", "Changyu Chen", "Qiguang Chen", "Michael Qizhe Shieh", "Longxu Dou" ], "github": "https://github.com/sail-sg/ActivePRM", "project_page": "", "abstract": "Process Reward Models (PRMs) provide step-level supervision to large language models (LLMs), but scaling up training data annotation remains challenging for both humans and LLMs. To address this limitation, we propose an active learning approach, ActPRM, which proactively selects the most uncertain samples for training, substantially reducing labeling costs. During training, we use the PRM to estimate uncertainty after the forward pass, retaining only highly uncertain data. A capable yet costly reasoning model then labels this data. Then we compute the loss with respect to the labels and update the PRM's weights. We compare ActPRM vs. vanilla fine-tuning, on a pool-based active learning setting, demonstrating that ActPRM reduces 50% annotation, but achieving the comparable or even better performance. Beyond annotation efficiency, we further advance the actively trained PRM by filtering over 1M+ math reasoning trajectories with ActPRM, retaining 60% of the data. A subsequent training on this selected dataset yields a new state-of-the-art (SOTA) PRM on ProcessBench (75.0%) and PRMBench (65.5%) compared with same sized models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sail-sg/ActivePRM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11427", "title": "NormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors", "authors": [ "Yanrui Bin", "Wenbo Hu", "Haoyuan Wang", "Xinya Chen", "Bing Wang" ], "github": "", "project_page": "", "abstract": "Surface normal estimation serves as a cornerstone for a spectrum of computer vision applications. While numerous efforts have been devoted to static image scenarios, ensuring temporal coherence in video-based normal estimation remains a formidable challenge. Instead of merely augmenting existing methods with temporal components, we present NormalCrafter to leverage the inherent temporal priors of video diffusion models. To secure high-fidelity normal estimation across sequences, we propose Semantic Feature Regularization (SFR), which aligns diffusion features with semantic cues, encouraging the model to concentrate on the intrinsic semantics of the scene. Moreover, we introduce a two-stage training protocol that leverages both latent and pixel space learning to preserve spatial accuracy while maintaining long temporal context. Extensive evaluations demonstrate the efficacy of our method, showcasing a superior performance in generating temporally consistent normal sequences with intricate details from diverse videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.10465", "title": "Pixel-SAIL: Single Transformer For Pixel-Grounded Understanding", "authors": [ "Tao Zhang", "Xiangtai Li", "Zilong Huang", "Yanwei Li", "Weixian Lei", "Xueqing Deng", "Shihao Chen", "Shunping Ji", "Jiashi Feng" ], "github": "https://github.com/magic-research/Sa2VA", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) achieve remarkable performance for fine-grained pixel-level understanding tasks. However, all the works rely heavily on extra components, such as vision encoder (CLIP), segmentation experts, leading to high system complexity and limiting model scaling. In this work, our goal is to explore a highly simplified MLLM without introducing extra components. Our work is motivated by the recent works on Single trAnsformer as a unified vIsion-Language Model (SAIL) design, where these works jointly learn vision tokens and text tokens in transformers. We present Pixel-SAIL, a single transformer for pixel-wise MLLM tasks. In particular, we present three technical improvements on the plain baseline. First, we design a learnable upsampling module to refine visual token features. Secondly, we propose a novel visual prompt injection strategy to enable the single transformer to understand visual prompt inputs and benefit from the early fusion of visual prompt embeddings and vision tokens. Thirdly, we introduce a vision expert distillation strategy to efficiently enhance the single transformer's fine-grained feature extraction capability. In addition, we have collected a comprehensive pixel understanding benchmark (PerBench), using a manual check. It includes three tasks: detailed object description, visual prompt-based question answering, and visual-text referring segmentation. Extensive experiments on four referring segmentation benchmarks, one visual prompt benchmark, and our PerBench show that our Pixel-SAIL achieves comparable or even better results with a much simpler pipeline. Code and model will be released at https://github.com/magic-research/Sa2VA.", "github_manual": "", "github_regex": "https://github.com/magic-research/Sa2VA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.06949", "title": "Adaptive Computation Pruning for the Forgetting Transformer", "authors": [ "Zhixuan Lin", "Johan Obando-Ceron", "Xu Owen He", "Aaron Courville" ], "github": "https://github.com/zhixuan-lin/arctic-fox", "project_page": "", "abstract": "The recently proposed Forgetting Transformer (FoX) incorporates a forget gate into softmax attention and has shown consistently better or on-par performance compared to the standard RoPE-based Transformer. Notably, many attention heads in FoX tend to forget quickly, causing their output at each timestep to rely primarily on the local context. Based on this observation, we propose Adaptive Computation Pruning (ACP) for FoX, a method that dynamically prunes computations involving input-output dependencies that are strongly decayed by the forget gate. This is achieved using a dynamically set pruning threshold that ensures that the pruned attention weights remain negligible. We apply ACP to language model pretraining with FoX and show it consistently reduces the number of FLOPs in softmax attention by around 70% across different model sizes and context lengths, resulting in a roughly 10% to 35% improvement in training throughput. Furthermore, longer context lengths yield greater computational savings. All these speed improvements are achieved without any performance degradation. We also perform several analyses to provide deeper insights into our method, such as examining the pruning patterns and analyzing the distribution of FLOP savings across different attention heads. Our code is available at https://github.com/zhixuan-lin/arctic-fox.", "github_manual": "", "github_regex": "https://github.com/zhixuan-lin/arctic-fox", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11343", "title": "A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce", "authors": [ "Wei Xiong", "Jiarui Yao", "Yuhui Xu", "Bo Pang", "Lei Wang", "Doyen Sahoo", "Junnan Li", "Nan Jiang", "Tong Zhang", "Caiming Xiong", "Hanze Dong" ], "github": "https://github.com/rlhflow/minimal-rl", "project_page": "", "abstract": "Reinforcement learning (RL) has become a prevailing approach for fine-tuning large language models (LLMs) on complex reasoning tasks. Among recent methods, GRPO stands out for its empirical success in training models such as DeepSeek-R1, yet the sources of its effectiveness remain poorly understood. In this work, we revisit GRPO from a reinforce-like algorithm perspective and analyze its core components. Surprisingly, we find that a simple rejection sampling baseline, RAFT, which trains only on positively rewarded samples, yields competitive performance than GRPO and PPO. Our ablation studies reveal that GRPO's main advantage arises from discarding prompts with entirely incorrect responses, rather than from its reward normalization. Motivated by this insight, we propose Reinforce-Rej, a minimal extension of policy gradient that filters both entirely incorrect and entirely correct samples. Reinforce-Rej improves KL efficiency and stability, serving as a lightweight yet effective alternative to more complex RL algorithms. We advocate RAFT as a robust and interpretable baseline, and suggest that future advances should focus on more principled designs for incorporating negative samples, rather than relying on them indiscriminately. Our findings provide guidance for future work in reward-based LLM post-training.", "github_manual": "https://github.com/rlhflow/minimal-rl", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.10188", "title": "Efficient Generative Model Training via Embedded Representation Warmup", "authors": [ "Deyuan Liu", "Peng Sun", "Xufeng Li", "Tao Lin" ], "github": "https://github.com/LINs-lab/ERW", "project_page": "", "abstract": "Diffusion models excel at generating high-dimensional data but fall short in training efficiency and representation quality compared to self-supervised methods. We identify a key bottleneck: the underutilization of high-quality, semantically rich representations during training notably slows down convergence. Our systematic analysis reveals a critical representation processing region -- primarily in the early layers -- where semantic and structural pattern learning takes place before generation can occur. To address this, we propose Embedded Representation Warmup (ERW), a plug-and-play framework where in the first stage we get the ERW module serves as a warmup that initializes the early layers of the diffusion model with high-quality, pretrained representations. This warmup minimizes the burden of learning representations from scratch, thereby accelerating convergence and boosting performance. Our theoretical analysis demonstrates that ERW's efficacy depends on its precise integration into specific neural network layers -- termed the representation processing region -- where the model primarily processes and transforms feature representations for later generation. We further establish that ERW not only accelerates training convergence but also enhances representation quality: empirically, our method achieves a 40times acceleration in training speed compared to REPA, the current state-of-the-art methods. Code is available at https://github.com/LINs-lab/ERW.", "github_manual": "", "github_regex": "https://github.com/LINs-lab/ERW", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.10462", "title": "The Scalability of Simplicity: Empirical Analysis of Vision-Language Learning with a Single Transformer", "authors": [ "Weixian Lei", "Jiacong Wang", "Haochen Wang", "Xiangtai Li", "Jun Hao Liew", "Jiashi Feng", "Zilong Huang" ], "github": "https://github.com/bytedance/SAIL", "project_page": "", "abstract": "This paper introduces SAIL, a single transformer unified multimodal large language model (MLLM) that integrates raw pixel encoding and language decoding within a singular architecture. Unlike existing modular MLLMs, which rely on a pre-trained vision transformer (ViT), SAIL eliminates the need for a separate vision encoder, presenting a more minimalist architecture design. Instead of introducing novel architectural components, SAIL adapts mix-attention mechanisms and multimodal positional encodings to better align with the distinct characteristics of visual and textual modalities. We systematically compare SAIL's properties-including scalability, cross-modal information flow patterns, and visual representation capabilities-with those of modular MLLMs. By scaling both training data and model size, SAIL achieves performance comparable to modular MLLMs. Notably, the removal of pretrained ViT components enhances SAIL's scalability and results in significantly different cross-modal information flow patterns. Moreover, SAIL demonstrates strong visual representation capabilities, achieving results on par with ViT-22B in vision tasks such as semantic segmentation. Code and models are available at https://github.com/bytedance/SAIL.", "github_manual": "", "github_regex": "https://github.com/bytedance/SAIL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11326", "title": "PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild", "authors": [ "Henghui Ding", "Chang Liu", "Nikhila Ravi", "Shuting He", "Yunchao Wei", "Song Bai", "Philip Torr", "Kehuan Song", "Xinglin Xie", "Kexin Zhang", "Licheng Jiao", "Lingling Li", "Shuyuan Yang", "Xuqiang Cao", "Linnan Zhao", "Jiaxuan Zhao", "Fang Liu", "Mengjiao Wang", "Junpei Zhang", "Xu Liu", "Yuting Yang", "Mengru Ma", "Hao Fang", "Runmin Cong", "Xiankai Lu", "Zhiyang Che", "Wei Zhan", "Tianming Liang", "Haichao Jiang", "Wei-Shi Zheng", "Jian-Fang Hu", "Haobo Yuan", "Xiangtai Li", "Tao Zhang", "Lu Qi", "Ming-Hsuan Yang" ], "github": "", "project_page": "", "abstract": "This report provides a comprehensive overview of the 4th Pixel-level Video Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025. It summarizes the challenge outcomes, participating methodologies, and future research directions. The challenge features two tracks: MOSE, which focuses on complex scene video object segmentation, and MeViS, which targets motion-guided, language-based video segmentation. Both tracks introduce new, more challenging datasets designed to better reflect real-world scenarios. Through detailed evaluation and analysis, the challenge offers valuable insights into the current state-of-the-art and emerging trends in complex video segmentation. More information can be found on the workshop website: https://pvuw.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11001", "title": "ReZero: Enhancing LLM search ability by trying one-more-time", "authors": [ "Alan Dao", "Thinh Le" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) improves Large Language Model (LLM) performance on knowledge-intensive tasks but depends heavily on initial search query quality. Current methods, often using Reinforcement Learning (RL), typically focus on query formulation or reasoning over results, without explicitly encouraging persistence after a failed search. We introduce ReZero (Retry-Zero), a novel RL framework that directly rewards the act of retrying a search query following an initial unsuccessful attempt. This incentivizes the LLM to explore alternative queries rather than prematurely halting. ReZero demonstrates significant improvement, achieving 46.88% accuracy compared to a 25% baseline. By rewarding persistence, ReZero enhances LLM robustness in complex information-seeking scenarios where initial queries may prove insufficient.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.08672", "title": "Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning", "authors": [ "Fangzhi Xu", "Hang Yan", "Chang Ma", "Haiteng Zhao", "Qiushi Sun", "Kanzhi Cheng", "Junxian He", "Jun Liu", "Zhiyong Wu" ], "github": "https://github.com/xufangzhi/Genius", "project_page": "", "abstract": "Advancing LLM reasoning skills has captivated wide interest. However, current post-training techniques rely heavily on supervisory signals, such as outcome supervision or auxiliary reward models, which face the problem of scalability and high annotation costs. This motivates us to enhance LLM reasoning without the need for external supervision. We introduce a generalizable and purely unsupervised self-training framework, named Genius. Without external auxiliary, Genius requires to seek the optimal response sequence in a stepwise manner and optimize the LLM. To explore the potential steps and exploit the optimal ones, Genius introduces a stepwise foresight re-sampling strategy to sample and estimate the step value by simulating future outcomes. Further, we recognize that the unsupervised setting inevitably induces the intrinsic noise and uncertainty. To provide a robust optimization, we propose an advantage-calibrated optimization (ACO) loss function to mitigate estimation inconsistencies. Combining these techniques together, Genius provides an advanced initial step towards self-improve LLM reasoning with general queries and without supervision, revolutionizing reasoning scaling laws given the vast availability of general queries. The code will be released at https://github.com/xufangzhi/Genius.", "github_manual": "", "github_regex": "https://github.com/xufangzhi/Genius", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.10049", "title": "Summarization of Multimodal Presentations with Vision-Language Models: Study of the Effect of Modalities and Structure", "authors": [ "Th\u00e9o Gigant", "Camille Guinaudeau", "Fr\u00e9d\u00e9ric Dufaux" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) can process visual and textual information in multiple formats: texts, images, interleaved texts and images, or even hour-long videos. In this work, we conduct fine-grained quantitative and qualitative analyses of automatic summarization of multimodal presentations using VLMs with various representations as input. From these experiments, we suggest cost-effective strategies for generating summaries from text-heavy multimodal documents under different input-length budgets using VLMs. We show that slides extracted from the video stream can be beneficially used as input against the raw video, and that a structured representation from interleaved slides and transcript provides the best performance. Finally, we reflect and comment on the nature of cross-modal interactions in multimodal presentations and share suggestions to improve the capabilities of VLMs to understand documents of this nature.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11456", "title": "DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning", "authors": [ "Zhiwei He", "Tian Liang", "Jiahao Xu", "Qiuzhi Liu", "Xingyu Chen", "Yue Wang", "Linfeng Song", "Dian Yu", "Zhenwen Liang", "Wenxuan Wang", "Zhuosheng Zhang", "Rui Wang", "Zhaopeng Tu", "Haitao Mi", "Dong Yu" ], "github": "https://github.com/zwhe99/DeepMath", "project_page": "", "abstract": "The capacity for complex mathematical reasoning is a key benchmark for artificial intelligence. While reinforcement learning (RL) applied to LLMs shows promise, progress is significantly hindered by the lack of large-scale training data that is sufficiently challenging, possesses verifiable answer formats suitable for RL, and is free from contamination with evaluation benchmarks. To address these limitations, we introduce DeepMath-103K, a new, large-scale dataset comprising approximately 103K mathematical problems, specifically designed to train advanced reasoning models via RL. DeepMath-103K is curated through a rigorous pipeline involving source analysis, stringent decontamination against numerous benchmarks, and filtering for high difficulty (primarily Levels 5-9), significantly exceeding existing open resources in challenge. Each problem includes a verifiable final answer, enabling rule-based RL, and three distinct R1-generated solutions suitable for diverse training paradigms like supervised fine-tuning or distillation. Spanning a wide range of mathematical topics, DeepMath-103K promotes the development of generalizable reasoning. We demonstrate that models trained on DeepMath-103K achieve significant improvements on challenging mathematical benchmarks, validating its effectiveness. We release DeepMath-103K publicly to facilitate community progress in building more capable AI reasoning systems: https://github.com/zwhe99/DeepMath.", "github_manual": "", "github_regex": "https://github.com/zwhe99/DeepMath", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.08846", "title": "AI-University: An LLM-based platform for instructional alignment to scientific classrooms", "authors": [ "Mostafa Faghih Shojaei", "Rahul Gulati", "Benjamin A. Jasperson", "Shangshang Wang", "Simone Cimolato", "Dangli Cao", "Willie Neiswanger", "Krishna Garikipati" ], "github": "", "project_page": "", "abstract": "We introduce AI University (AI-U), a flexible framework for AI-driven course content delivery that adapts to instructors' teaching styles. At its core, AI-U fine-tunes a large language model (LLM) with retrieval-augmented generation (RAG) to generate instructor-aligned responses from lecture videos, notes, and textbooks. Using a graduate-level finite-element-method (FEM) course as a case study, we present a scalable pipeline to systematically construct training data, fine-tune an open-source LLM with Low-Rank Adaptation (LoRA), and optimize its responses through RAG-based synthesis. Our evaluation - combining cosine similarity, LLM-based assessment, and expert review - demonstrates strong alignment with course materials. We also have developed a prototype web application, available at https://my-ai-university.com, that enhances traceability by linking AI-generated responses to specific sections of the relevant course material and time-stamped instances of the open-access video lectures. Our expert model is found to have greater cosine similarity with a reference on 86% of test cases. An LLM judge also found our expert model to outperform the base Llama 3.2 model approximately four times out of five. AI-U offers a scalable approach to AI-assisted education, paving the way for broader adoption in higher education. Here, our framework has been presented in the setting of a class on FEM - a subject that is central to training PhD and Master students in engineering science. However, this setting is a particular instance of a broader context: fine-tuning LLMs to research content in science.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.09454", "title": "D^2iT: Dynamic Diffusion Transformer for Accurate Image Generation", "authors": [ "Weinan Jia", "Mengqi Huang", "Nan Chen", "Lei Zhang", "Zhendong Mao" ], "github": "https://github.com/jiawn-creator/Dynamic-DiT", "project_page": "", "abstract": "Diffusion models are widely recognized for their ability to generate high-fidelity images. Despite the excellent performance and scalability of the Diffusion Transformer (DiT) architecture, it applies fixed compression across different image regions during the diffusion process, disregarding the naturally varying information densities present in these regions. However, large compression leads to limited local realism, while small compression increases computational complexity and compromises global consistency, ultimately impacting the quality of generated images. To address these limitations, we propose dynamically compressing different image regions by recognizing the importance of different regions, and introduce a novel two-stage framework designed to enhance the effectiveness and efficiency of image generation: (1) Dynamic VAE (DVAE) at first stage employs a hierarchical encoder to encode different image regions at different downsampling rates, tailored to their specific information densities, thereby providing more accurate and natural latent codes for the diffusion process. (2) Dynamic Diffusion Transformer (D^2iT) at second stage generates images by predicting multi-grained noise, consisting of coarse-grained (less latent code in smooth regions) and fine-grained (more latent codes in detailed regions), through an novel combination of the Dynamic Grain Transformer and the Dynamic Content Transformer. The strategy of combining rough prediction of noise with detailed regions correction achieves a unification of global consistency and local realism. Comprehensive experiments on various generation tasks validate the effectiveness of our approach. Code will be released at https://github.com/jiawn-creator/Dynamic-DiT.", "github_manual": "", "github_regex": "https://github.com/jiawn-creator/Dynamic-DiT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.10443", "title": "Multimodal Long Video Modeling Based on Temporal Dynamic Context", "authors": [ "Haoran Hao", "Jiaming Han", "Yiyuan Zhang", "Xiangyu Yue" ], "github": "https://github.com/Hoar012/TDC-Video", "project_page": "", "abstract": "Recent advances in Large Language Models (LLMs) have led to significant breakthroughs in video understanding. However, existing models still struggle with long video processing due to the context length constraint of LLMs and the vast amount of information within the video. Although some recent methods are designed for long video understanding, they often lose crucial information during token compression and struggle with additional modality like audio. In this work, we propose a dynamic long video encoding method utilizing the temporal relationship between frames, named Temporal Dynamic Context (TDC). Firstly, we segment the video into semantically consistent scenes based on inter-frame similarities, then encode each frame into tokens using visual-audio encoders. Secondly, we propose a novel temporal context compressor to reduce the number of tokens within each segment. Specifically, we employ a query-based Transformer to aggregate video, audio, and instruction text tokens into a limited set of temporal context tokens. Finally, we feed the static frame tokens and the temporal context tokens into the LLM for video understanding. Furthermore, to handle extremely long videos, we propose a training-free chain-of-thought strategy that progressively extracts answers from multiple video segments. These intermediate answers serve as part of the reasoning process and contribute to the final answer. We conduct extensive experiments on general video understanding and audio-video understanding benchmarks, where our method demonstrates strong performance. The code and models are available at https://github.com/Hoar012/TDC-Video.", "github_manual": "", "github_regex": "https://github.com/Hoar012/TDC-Video", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11080", "title": "Change State Space Models for Remote Sensing Change Detection", "authors": [ "Elman Ghazaei", "Erchan Aptoula" ], "github": "https://github.com/Elman295/CSSM", "project_page": "", "abstract": "Despite their frequent use for change detection, both ConvNets and Vision transformers (ViT) exhibit well-known limitations, namely the former struggle to model long-range dependencies while the latter are computationally inefficient, rendering them challenging to train on large-scale datasets. Vision Mamba, an architecture based on State Space Models has emerged as an alternative addressing the aforementioned deficiencies and has been already applied to remote sensing change detection, though mostly as a feature extracting backbone. In this article the Change State Space Model is introduced, that has been specifically designed for change detection by focusing on the relevant changes between bi-temporal images, effectively filtering out irrelevant information. By concentrating solely on the changed features, the number of network parameters is reduced, enhancing significantly computational efficiency while maintaining high detection performance and robustness against input degradation. The proposed model has been evaluated via three benchmark datasets, where it outperformed ConvNets, ViTs, and Mamba-based counterparts at a fraction of their computational complexity. The implementation will be made available at https://github.com/Elman295/CSSM upon acceptance.", "github_manual": "", "github_regex": "https://github.com/Elman295/CSSM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11042", "title": "LazyReview A Dataset for Uncovering Lazy Thinking in NLP Peer Reviews", "authors": [ "Sukannya Purkayastha", "Zhuang Li", "Anne Lauscher", "Lizhen Qu", "Iryna Gurevych" ], "github": "https://github.com/UKPLab/arxiv2025-lazy-review", "project_page": "", "abstract": "Peer review is a cornerstone of quality control in scientific publishing. With the increasing workload, the unintended use of `quick' heuristics, referred to as lazy thinking, has emerged as a recurring issue compromising review quality. Automated methods to detect such heuristics can help improve the peer-reviewing process. However, there is limited NLP research on this issue, and no real-world dataset exists to support the development of detection tools. This work introduces LazyReview, a dataset of peer-review sentences annotated with fine-grained lazy thinking categories. Our analysis reveals that Large Language Models (LLMs) struggle to detect these instances in a zero-shot setting. However, instruction-based fine-tuning on our dataset significantly boosts performance by 10-20 performance points, highlighting the importance of high-quality training data. Furthermore, a controlled experiment demonstrates that reviews revised with lazy thinking feedback are more comprehensive and actionable than those written without such feedback. We will release our dataset and the enhanced guidelines that can be used to train junior reviewers in the community. (Code available here: https://github.com/UKPLab/arxiv2025-lazy-review)", "github_manual": "", "github_regex": "https://github.com/UKPLab/arxiv2025-lazy-review", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.10903", "title": "Efficient Reasoning Models: A Survey", "authors": [ "Sicheng Feng", "Gongfan Fang", "Xinyin Ma", "Xinchao Wang" ], "github": "https://github.com/fscdc/Awesome-Efficient-Reasoning-Models", "project_page": "", "abstract": "Reasoning models have demonstrated remarkable progress in solving complex and logic-intensive tasks by generating extended Chain-of-Thoughts (CoTs) prior to arriving at a final answer. Yet, the emergence of this \"slow-thinking\" paradigm, with numerous tokens generated in sequence, inevitably introduces substantial computational overhead. To this end, it highlights an urgent need for effective acceleration. This survey aims to provide a comprehensive overview of recent advances in efficient reasoning. It categorizes existing works into three key directions: (1) shorter - compressing lengthy CoTs into concise yet effective reasoning chains; (2) smaller - developing compact language models with strong reasoning capabilities through techniques such as knowledge distillation, other model compression techniques, and reinforcement learning; and (3) faster - designing efficient decoding strategies to accelerate inference. A curated collection of papers discussed in this survey is available in our GitHub repository.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/fscdc/Awesome-Efficient-Reasoning-Models", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11455", "title": "SimpleAR: Pushing the Frontier of Autoregressive Visual Generation through Pretraining, SFT, and RL", "authors": [ "Junke Wang", "Zhi Tian", "Xun Wang", "Xinyu Zhang", "Weilin Huang", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "https://github.com/wdrink/SimpleAR", "project_page": "", "abstract": "This work presents SimpleAR, a vanilla autoregressive visual generation framework without complex architecure modifications. Through careful exploration of training and inference optimization, we demonstrate that: 1) with only 0.5B parameters, our model can generate 1024x1024 resolution images with high fidelity, and achieve competitive results on challenging text-to-image benchmarks, e.g., 0.59 on GenEval and 79.66 on DPG; 2) both supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO) training could lead to significant improvements on generation aesthectics and prompt alignment; and 3) when optimized with inference acceleraton techniques like vLLM, the time for SimpleAR to generate an 1024x1024 image could be reduced to around 14 seconds. By sharing these findings and open-sourcing the code, we hope to reveal the potential of autoregressive visual generation and encourage more participation in this research field. Code is available at https://github.com/wdrink/SimpleAR.", "github_manual": "", "github_regex": "https://github.com/wdrink/SimpleAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.10342", "title": "VisualPuzzles: Decoupling Multimodal Reasoning Evaluation from Domain Knowledge", "authors": [ "Yueqi Song", "Tianyue Ou", "Yibo Kong", "Zecheng Li", "Graham Neubig", "Xiang Yue" ], "github": "https://github.com/neulab/VisualPuzzles", "project_page": "", "abstract": "Current multimodal benchmarks often conflate reasoning with domain-specific knowledge, making it difficult to isolate and evaluate general reasoning abilities in non-expert settings. To address this, we introduce VisualPuzzles, a benchmark that targets visual reasoning while deliberately minimizing reliance on specialized knowledge. VisualPuzzles consists of diverse questions spanning five categories: algorithmic, analogical, deductive, inductive, and spatial reasoning. One major source of our questions is manually translated logical reasoning questions from the Chinese Civil Service Examination. Experiments show that VisualPuzzles requires significantly less intensive domain-specific knowledge and more complex reasoning compared to benchmarks like MMMU, enabling us to better evaluate genuine multimodal reasoning. Evaluations show that state-of-the-art multimodal large language models consistently lag behind human performance on VisualPuzzles, and that strong performance on knowledge-intensive benchmarks does not necessarily translate to success on reasoning-focused, knowledge-light tasks. Additionally, reasoning enhancements such as scaling up inference compute (with \"thinking\" modes) yield inconsistent gains across models and task types, and we observe no clear correlation between model size and performance. We also found that models exhibit different reasoning and answering patterns on VisualPuzzles compared to benchmarks with heavier emphasis on knowledge. VisualPuzzles offers a clearer lens through which to evaluate reasoning capabilities beyond factual recall and domain knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/neulab/VisualPuzzles", "github_api_added_by": "auto" }, { "date": "2025-04-16", "arxiv_id": "2504.11457", "title": "Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual Perception", "authors": [ "Ziqi Pang", "Xin Xu", "Yu-Xiong Wang" ], "github": "https://github.com/ziqipang/ADDP", "project_page": "", "abstract": "With the success of image generation, generative diffusion models are increasingly adopted for discriminative tasks, as pixel generation provides a unified perception interface. However, directly repurposing the generative denoising process for discriminative objectives reveals critical gaps rarely addressed previously. Generative models tolerate intermediate sampling errors if the final distribution remains plausible, but discriminative tasks require rigorous accuracy throughout, as evidenced in challenging multi-modal tasks like referring image segmentation. Motivated by this gap, we analyze and enhance alignment between generative diffusion processes and perception tasks, focusing on how perception quality evolves during denoising. We find: (1) earlier denoising steps contribute disproportionately to perception quality, prompting us to propose tailored learning objectives reflecting varying timestep contributions; (2) later denoising steps show unexpected perception degradation, highlighting sensitivity to training-denoising distribution shifts, addressed by our diffusion-tailored data augmentation; and (3) generative processes uniquely enable interactivity, serving as controllable user interfaces adaptable to correctional prompts in multi-round interactions. Our insights significantly improve diffusion-based perception models without architectural changes, achieving state-of-the-art performance on depth estimation, referring image segmentation, and generalist perception tasks. Code available at https://github.com/ziqipang/ADDP.", "github_manual": "", "github_regex": "https://github.com/ziqipang/ADDP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.10277", "title": "RealHarm: A Collection of Real-World Language Model Application Failures", "authors": [ "Pierre Le Jeune", "Jiaen Liu", "Luca Rossi", "Matteo Dora" ], "github": "", "project_page": "", "abstract": "Language model deployments in consumer-facing applications introduce numerous risks. While existing research on harms and hazards of such applications follows top-down approaches derived from regulatory frameworks and theoretical analyses, empirical evidence of real-world failure modes remains underexplored. In this work, we introduce RealHarm, a dataset of annotated problematic interactions with AI agents built from a systematic review of publicly reported incidents. Analyzing harms, causes, and hazards specifically from the deployer's perspective, we find that reputational damage constitutes the predominant organizational harm, while misinformation emerges as the most common hazard category. We empirically evaluate state-of-the-art guardrails and content moderation systems to probe whether such systems would have prevented the incidents, revealing a significant gap in the protection of AI applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11409", "title": "Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning", "authors": [ "Ali Taghibakhshi", "Sharath Turuvekere Sreenivas", "Saurav Muralidharan", "Marcin Chochowski", "Yashaswi Karnati", "Raviraj Joshi", "Ameya Sunil Mahabaleshwarkar", "Zijia Chen", "Yoshi Suhara", "Oluwatobi Olabiyi", "Daniel Korzekwa", "Mostofa Patwary", "Mohammad Shoeybi", "Jan Kautz", "Bryan Catanzaro", "Ashwath Aithal", "Nima Tajbakhsh", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "Hybrid LLM architectures that combine Attention and State Space Models (SSMs) achieve state-of-the-art accuracy and runtime performance. Recent work has demonstrated that applying compression and distillation to Attention-only models yields smaller, more accurate models at a fraction of the training cost. In this work, we explore the effectiveness of compressing Hybrid architectures. We introduce a novel group-aware pruning strategy that preserves the structural integrity of SSM blocks and their sequence modeling capabilities. Furthermore, we demonstrate the necessity of such SSM pruning to achieve improved accuracy and inference speed compared to traditional approaches. Our compression recipe combines SSM, FFN, embedding dimension, and layer pruning, followed by knowledge distillation-based retraining, similar to the MINITRON technique. Using this approach, we compress the Nemotron-H 8B Hybrid model down to 4B parameters with up to 40x fewer training tokens. The resulting model surpasses the accuracy of similarly-sized models while achieving 2x faster inference, significantly advancing the Pareto frontier.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-16", "arxiv_id": "2504.11393", "title": "DataDecide: How to Predict Best Pretraining Data with Small Experiments", "authors": [ "Ian Magnusson", "Nguyen Tai", "Ben Bogin", "David Heineman", "Jena D. Hwang", "Luca Soldaini", "Akshita Bhagia", "Jiacheng Liu", "Dirk Groeneveld", "Oyvind Tafjord", "Noah A. Smith", "Pang Wei Koh", "Jesse Dodge" ], "github": "", "project_page": "", "abstract": "Because large language models are expensive to pretrain on different datasets, using smaller-scale experiments to decide on data is crucial for reducing costs. Which benchmarks and methods of making decisions from observed performance at small scale most accurately predict the datasets that yield the best large models? To empower open exploration of this question, we release models, data, and evaluations in DataDecide -- the most extensive open suite of models over differences in data and scale. We conduct controlled pretraining experiments across 25 corpora with differing sources, deduplication, and filtering up to 100B tokens, model sizes up to 1B parameters, and 3 random seeds. We find that the ranking of models at a single, small size (e.g., 150M parameters) is a strong baseline for predicting best models at our larger target scale (1B) (~80% of com parisons correct). No scaling law methods among 8 baselines exceed the compute-decision frontier of single-scale predictions, but DataDecide can measure improvement in future scaling laws. We also identify that using continuous likelihood metrics as proxies in small experiments makes benchmarks including MMLU, ARC, HellaSwag, MBPP, and HumanEval >80% predictable at the target 1B scale with just 0.01% of the compute.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.12240", "title": "Cobra: Efficient Line Art COlorization with BRoAder References", "authors": [ "Junhao Zhuang", "Lingen Li", "Xuan Ju", "Zhaoyang Zhang", "Chun Yuan", "Ying Shan" ], "github": "", "project_page": "", "abstract": "The comic production industry requires reference-based line art colorization with high accuracy, efficiency, contextual consistency, and flexible control. A comic page often involves diverse characters, objects, and backgrounds, which complicates the coloring process. Despite advancements in diffusion models for image generation, their application in line art colorization remains limited, facing challenges related to handling extensive reference images, time-consuming inference, and flexible control. We investigate the necessity of extensive contextual image guidance on the quality of line art colorization. To address these challenges, we introduce Cobra, an efficient and versatile method that supports color hints and utilizes over 200 reference images while maintaining low latency. Central to Cobra is a Causal Sparse DiT architecture, which leverages specially designed positional encodings, causal sparse attention, and Key-Value Cache to effectively manage long-context references and ensure color identity consistency. Results demonstrate that Cobra achieves accurate line art colorization through extensive contextual reference, significantly enhancing inference speed and interactivity, thereby meeting critical industrial demands. We release our codes and models on our project page: https://zhuang2002.github.io/Cobra/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.10514", "title": "ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness", "authors": [ "Yijun Liang", "Ming Li", "Chenrui Fan", "Ziyue Li", "Dang Nguyen", "Kwesi Cobbina", "Shweta Bhardwaj", "Jiuhai Chen", "Fuxiao Liu", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Color plays an important role in human perception and usually provides critical clues in visual reasoning. However, it is unclear whether and how vision-language models (VLMs) can perceive, understand, and leverage color as humans. This paper introduces ColorBench, an innovative benchmark meticulously crafted to assess the capabilities of VLMs in color understanding, including color perception, reasoning, and robustness. By curating a suite of diverse test scenarios, with grounding in real applications, ColorBench evaluates how these models perceive colors, infer meanings from color-based cues, and maintain consistent performance under varying color transformations. Through an extensive evaluation of 32 VLMs with varying language models and vision encoders, our paper reveals some undiscovered findings: (i) The scaling law (larger models are better) still holds on ColorBench, while the language model plays a more important role than the vision encoder. (ii) However, the performance gaps across models are relatively small, indicating that color understanding has been largely neglected by existing VLMs. (iii) CoT reasoning improves color understanding accuracies and robustness, though they are vision-centric tasks. (iv) Color clues are indeed leveraged by VLMs on ColorBench but they can also mislead models in some tasks. These findings highlight the critical limitations of current VLMs and underscore the need to enhance color comprehension. Our ColorBenchcan serve as a foundational tool for advancing the study of human-level color understanding of multimodal AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.11952", "title": "Robust and Fine-Grained Detection of AI Generated Texts", "authors": [ "Ram Mohan Rao Kadiyala", "Siddartha Pullakhandam", "Kanwal Mehreen", "Drishti Sharma", "Siddhant Gupta", "Jebish Purbey", "Ashay Srivastava", "Subhasya TippaReddy", "Arvind Reddy Bobbili", "Suraj Telugara Chandrashekhar", "Modabbir Adeeb", "Srinadh Vura", "Hamza Farooq" ], "github": "", "project_page": "", "abstract": "An ideal detection system for machine generated content is supposed to work well on any generator as many more advanced LLMs come into existence day by day. Existing systems often struggle with accurately identifying AI-generated content over shorter texts. Further, not all texts might be entirely authored by a human or LLM, hence we focused more over partial cases i.e human-LLM co-authored texts. Our paper introduces a set of models built for the task of token classification which are trained on an extensive collection of human-machine co-authored texts, which performed well over texts of unseen domains, unseen generators, texts by non-native speakers and those with adversarial inputs. We also introduce a new dataset of over 2.4M such texts mostly co-authored by several popular proprietary LLMs over 23 languages. We also present findings of our models' performance over each texts of each domain and generator. Additional findings include comparison of performance against each adversarial method, length of input texts and characteristics of generated texts compared to the original human authored texts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.12285", "title": "BitNet b1.58 2B4T Technical Report", "authors": [ "Shuming Ma", "Hongyu Wang", "Shaohan Huang", "Xingxing Zhang", "Ying Hu", "Ting Song", "Yan Xia", "Furu Wei" ], "github": "https://github.com/EleutherAI/lm-evaluation-harness", "project_page": "", "abstract": "We introduce BitNet b1.58 2B4T, the first open-source, native 1-bit Large Language Model (LLM) at the 2-billion parameter scale. Trained on a corpus of 4 trillion tokens, the model has been rigorously evaluated across benchmarks covering language understanding, mathematical reasoning, coding proficiency, and conversational ability. Our results demonstrate that BitNet b1.58 2B4T achieves performance on par with leading open-weight, full-precision LLMs of similar size, while offering significant advantages in computational efficiency, including substantially reduced memory footprint, energy consumption, and decoding latency. To facilitate further research and adoption, the model weights are released via Hugging Face along with open-source inference implementations for both GPU and CPU architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EleutherAI/lm-evaluation-harness", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.10483", "title": "REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers", "authors": [ "Xingjian Leng", "Jaskirat Singh", "Yunzhong Hou", "Zhenchang Xing", "Saining Xie", "Liang Zheng" ], "github": "", "project_page": "", "abstract": "In this paper we tackle a fundamental question: \"Can we train latent diffusion models together with the variational auto-encoder (VAE) tokenizer in an end-to-end manner?\" Traditional deep-learning wisdom dictates that end-to-end training is often preferable when possible. However, for latent diffusion transformers, it is observed that end-to-end training both VAE and diffusion-model using standard diffusion-loss is ineffective, even causing a degradation in final performance. We show that while diffusion loss is ineffective, end-to-end training can be unlocked through the representation-alignment (REPA) loss -- allowing both VAE and diffusion model to be jointly tuned during the training process. Despite its simplicity, the proposed training recipe (REPA-E) shows remarkable performance; speeding up diffusion model training by over 17x and 45x over REPA and vanilla training recipes, respectively. Interestingly, we observe that end-to-end tuning with REPA-E also improves the VAE itself; leading to improved latent space structure and downstream generation performance. In terms of final performance, our approach sets a new state-of-the-art; achieving FID of 1.26 and 1.83 with and without classifier-free guidance on ImageNet 256 x 256. Code is available at https://end2end-diffusion.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.10326", "title": "AlayaDB: The Data Foundation for Efficient and Effective Long-context LLM Inference", "authors": [ "Yangshen Deng", "Zhengxin You", "Long Xiang", "Qilong Li", "Peiqi Yuan", "Zhaoyang Hong", "Yitao Zheng", "Wanting Li", "Runzhong Li", "Haotian Liu", "Kyriakos Mouratidis", "Man Lung Yiu", "Huan Li", "Qiaomu Shen", "Rui Mao", "Bo Tang" ], "github": "", "project_page": "", "abstract": "AlayaDB is a cutting-edge vector database system natively architected for efficient and effective long-context inference for Large Language Models (LLMs) at AlayaDB AI. Specifically, it decouples the KV cache and attention computation from the LLM inference systems, and encapsulates them into a novel vector database system. For the Model as a Service providers (MaaS), AlayaDB consumes fewer hardware resources and offers higher generation quality for various workloads with different kinds of Service Level Objectives (SLOs), when comparing with the existing alternative solutions (e.g., KV cache disaggregation, retrieval-based sparse attention). The crux of AlayaDB is that it abstracts the attention computation and cache management for LLM inference into a query processing procedure, and optimizes the performance via a native query optimizer. In this work, we demonstrate the effectiveness of AlayaDB via (i) three use cases from our industry partners, and (ii) extensive experimental results on LLM inference benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.09081", "title": "SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning", "authors": [ "Prabhat Pandey", "Rupak Vignesh Swaminathan", "K V Vijay Girish", "Arunasish Sen", "Jian Xie", "Grant P. Strimel", "Andreas Schwarz" ], "github": "", "project_page": "", "abstract": "We introduce SIFT (Speech Instruction Fine-Tuning), a 50M-example dataset designed for instruction fine-tuning and pre-training of speech-text large language models (LLMs). SIFT-50M is built from publicly available speech corpora, which collectively contain 14K hours of speech, and leverages LLMs along with off-the-shelf expert models. The dataset spans five languages, encompassing a diverse range of speech understanding as well as controllable speech generation instructions. Using SIFT-50M, we train SIFT-LLM, which outperforms existing speech-text LLMs on instruction-following benchmarks while achieving competitive performance on foundational speech tasks. To support further research, we also introduce EvalSIFT, a benchmark dataset specifically designed to evaluate the instruction-following capabilities of speech-text LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.11092", "title": "Vivid4D: Improving 4D Reconstruction from Monocular Video by Video Inpainting", "authors": [ "Jiaxin Huang", "Sheng Miao", "BangBnag Yang", "Yuewen Ma", "Yiyi Liao" ], "github": "", "project_page": "", "abstract": "Reconstructing 4D dynamic scenes from casually captured monocular videos is valuable but highly challenging, as each timestamp is observed from a single viewpoint. We introduce Vivid4D, a novel approach that enhances 4D monocular video synthesis by augmenting observation views - synthesizing multi-view videos from a monocular input. Unlike existing methods that either solely leverage geometric priors for supervision or use generative priors while overlooking geometry, we integrate both. This reformulates view augmentation as a video inpainting task, where observed views are warped into new viewpoints based on monocular depth priors. To achieve this, we train a video inpainting model on unposed web videos with synthetically generated masks that mimic warping occlusions, ensuring spatially and temporally consistent completion of missing regions. To further mitigate inaccuracies in monocular depth priors, we introduce an iterative view augmentation strategy and a robust reconstruction loss. Experiments demonstrate that our method effectively improves monocular 4D scene reconstruction and completion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.11536", "title": "ReTool: Reinforcement Learning for Strategic Tool Use in LLMs", "authors": [ "Jiazhan Feng", "Shijue Huang", "Xingwei Qu", "Ge Zhang", "Yujia Qin", "Baoquan Zhong", "Chengquan Jiang", "Jinxin Chi", "Wanjun Zhong" ], "github": "", "project_page": "", "abstract": "While reasoning models (e.g., DeepSeek R1) trained with reinforcement learning (RL), excel in textual reasoning, they struggle in scenarios requiring structured problem-solving, such as geometric reasoning, concise computation, or complex equation solving-areas where computational tools like code interpreters (CI) demonstrate distinct advantages. To bridge this gap, we propose ReTool, which enhances long-form reasoning with tool-integrated learning, including two key features: (1) dynamic interleaving of real-time code execution within natural language reasoning processes, and (2) an automated RL paradigm that allows policy rollouts with multi-turn real-time code execution and teaches the model in learning when and how to invoke tools based on outcome feedback. ReTool employs a systematic training framework, beginning with synthetic cold-start data generation to produce code-augmented long-form reasoning traces for fine-tuning base models. Subsequent RL training leverages task outcomes as rewards to iteratively refine the model's tool use strategy, enabling autonomous discovery of optimal tool invocation patterns without human priors. Experiments on the challenging MATH Olympiad benchmark AIME demonstrate ReTool's superiority: Our 32B model achieves 67% accuracy with 400 training steps, outperforming text-based RL baseline (40% accuracy, 1080 steps) in efficiency and performance. Remarkably, ReTool-32B attains 72.5% accuracy in extended settings, surpassing OpenAI's o1-preview by 27.9%. Further analysis reveals emergent behaviors such as code self-correction, signaling an ''aha moment'' in which the model autonomously masters adaptive tool use. These findings highlight the promise of outcome-driven tool integration for advancing complex mathematical reasoning and offer new insights into hybrid neuro-symbolic systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.09566", "title": "Syzygy of Thoughts: Improving LLM CoT with the Minimal Free Resolution", "authors": [ "Chenghao Li", "Chaoning Zhang", "Yi Lu", "Jiaquan Zhang", "Qigan Sun", "Xudong Wang", "Jiwei Wei", "Guoqing Wang", "Yang Yang", "Heng Tao Shen" ], "github": "https://github.com/dlMARiA/Syzygy-of-thoughts", "project_page": "", "abstract": "Chain-of-Thought (CoT) prompting enhances the reasoning of large language models (LLMs) by decomposing problems into sequential steps, mimicking human logic and reducing errors. However, complex tasks with vast solution spaces and vague constraints often exceed the capacity of a single reasoning chain. Inspired by Minimal Free Resolution (MFR) in commutative algebra and algebraic geometry, we propose Syzygy of Thoughts (SoT)-a novel framework that extends CoT by introducing auxiliary, interrelated reasoning paths. SoT captures deeper logical dependencies, enabling more robust and structured problem-solving. MFR decomposes a module into a sequence of free modules with minimal rank, providing a structured analytical approach to complex systems. This method introduces the concepts of \"Module\", \"Betti numbers\",\"Freeness\", \"Mapping\", \"Exactness\" and \"Minimality\", enabling the systematic decomposition of the original complex problem into logically complete minimal subproblems while preserving key problem features and reducing reasoning length. We tested SoT across diverse datasets (e.g., GSM8K, MATH) and models (e.g., GPT-4o-mini, Qwen2.5), achieving inference accuracy that matches or surpasses mainstream CoTs standards. Additionally, by aligning the sampling process with algebraic constraints, our approach enhances the scalability of inference time in LLMs, ensuring both transparent reasoning and high performance. Our code will be publicly available at https://github.com/dlMARiA/Syzygy-of-thoughts.", "github_manual": "", "github_regex": "https://github.com/dlMARiA/Syzygy-of-thoughts", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.09048", "title": "BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via Adaptive Block-Based Gaussian Splatting", "authors": [ "Yongchang Wu", "Zipeng Qi", "Zhenwei Shi", "Zhengxia Zou" ], "github": "https://github.com/SunshineWYC/BlockGaussian", "project_page": "", "abstract": "The recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated remarkable potential in novel view synthesis tasks. The divide-and-conquer paradigm has enabled large-scale scene reconstruction, but significant challenges remain in scene partitioning, optimization, and merging processes. This paper introduces BlockGaussian, a novel framework incorporating a content-aware scene partition strategy and visibility-aware block optimization to achieve efficient and high-quality large-scale scene reconstruction. Specifically, our approach considers the content-complexity variation across different regions and balances computational load during scene partitioning, enabling efficient scene reconstruction. To tackle the supervision mismatch issue during independent block optimization, we introduce auxiliary points during individual block optimization to align the ground-truth supervision, which enhances the reconstruction quality. Furthermore, we propose a pseudo-view geometry constraint that effectively mitigates rendering degradation caused by airspace floaters during block merging. Extensive experiments on large-scale scenes demonstrate that our approach achieves state-of-the-art performance in both reconstruction efficiency and rendering quality, with a 5x speedup in optimization and an average PSNR improvement of 1.21 dB on multiple benchmarks. Notably, BlockGaussian significantly reduces computational requirements, enabling large-scale scene reconstruction on a single 24GB VRAM device. The project page is available at https://github.com/SunshineWYC/BlockGaussian", "github_manual": "", "github_regex": "https://github.com/SunshineWYC/BlockGaussian", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.12264", "title": "Towards Learning to Complete Anything in Lidar", "authors": [ "Ayca Takmaz", "Cristiano Saltori", "Neehar Peri", "Tim Meinhardt", "Riccardo de Lutio", "Laura Leal-Taix\u00e9", "Aljo\u0161a O\u0161ep" ], "github": "", "project_page": "", "abstract": "We propose CAL (Complete Anything in Lidar) for Lidar-based shape-completion in-the-wild. This is closely related to Lidar-based semantic/panoptic scene completion. However, contemporary methods can only complete and recognize objects from a closed vocabulary labeled in existing Lidar datasets. Different to that, our zero-shot approach leverages the temporal context from multi-modal sensor sequences to mine object shapes and semantic features of observed objects. These are then distilled into a Lidar-only instance-level completion and recognition model. Although we only mine partial shape completions, we find that our distilled model learns to infer full object shapes from multiple such partial observations across the dataset. We show that our model can be prompted on standard benchmarks for Semantic and Panoptic Scene Completion, localize objects as (amodal) 3D bounding boxes, and recognize objects beyond fixed class vocabularies. Our project page is https://research.nvidia.com/labs/dvl/projects/complete-anything-lidar", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.09346", "title": "\"It's not a representation of me\": Examining Accent Bias and Digital Exclusion in Synthetic AI Voice Services", "authors": [ "Shira Michel", "Sufi Kaur", "Sarah Elizabeth Gillespie", "Jeffrey Gleason", "Christo Wilson", "Avijit Ghosh" ], "github": "", "project_page": "", "abstract": "Recent advances in artificial intelligence (AI) speech generation and voice cloning technologies have produced naturalistic speech and accurate voice replication, yet their influence on sociotechnical systems across diverse accents and linguistic traits is not fully understood. This study evaluates two synthetic AI voice services (Speechify and ElevenLabs) through a mixed methods approach using surveys and interviews to assess technical performance and uncover how users' lived experiences influence their perceptions of accent variations in these speech technologies. Our findings reveal technical performance disparities across five regional, English-language accents and demonstrate how current speech generation technologies may inadvertently reinforce linguistic privilege and accent-based discrimination, potentially creating new forms of digital exclusion. Overall, our study highlights the need for inclusive design and regulation by providing actionable insights for developers, policymakers, and organizations to ensure equitable and socially responsible AI speech technologies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.11468", "title": "SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models", "authors": [ "Hardy Chen", "Haoqin Tu", "Fali Wang", "Hui Liu", "Xianfeng Tang", "Xinya Du", "Yuyin Zhou", "Cihang Xie" ], "github": "https://github.com/ucsc-vlaa/vlaa-thinking", "project_page": "", "abstract": "This work revisits the dominant supervised fine-tuning (SFT) then reinforcement learning (RL) paradigm for training Large Vision-Language Models (LVLMs), and reveals a key finding: SFT can significantly undermine subsequent RL by inducing ``pseudo reasoning paths'' imitated from expert models. While these paths may resemble the native reasoning paths of RL models, they often involve prolonged, hesitant, less informative steps, and incorrect reasoning. To systematically study this effect, we introduce VLAA-Thinking, a new multimodal dataset designed to support reasoning in LVLMs. Constructed via a six-step pipeline involving captioning, reasoning distillation, answer rewrite and verification, VLAA-Thinking comprises high-quality, step-by-step visual reasoning traces for SFT, along with a more challenging RL split from the same data source. Using this dataset, we conduct extensive experiments comparing SFT, RL and their combinations. Results show that while SFT helps models learn reasoning formats, it often locks aligned models into imitative, rigid reasoning modes that impede further learning. In contrast, building on the Group Relative Policy Optimization (GRPO) with a novel mixed reward module integrating both perception and cognition signals, our RL approach fosters more genuine, adaptive reasoning behavior. Notably, our model VLAA-Thinker, based on Qwen2.5VL 3B, achieves top-1 performance on Open LMM Reasoning Leaderboard (https://huggingface.co/spaces/opencompass/Open_LMM_Reasoning_Leaderboard) among 4B scale LVLMs, surpassing the previous state-of-the-art by 1.8%. We hope our findings provide valuable insights in developing reasoning-capable LVLMs and can inform future research in this area.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ucsc-vlaa/vlaa-thinking", "github_api_added_by": "auto" }, { "date": "2025-04-17", "arxiv_id": "2504.09702", "title": "MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?", "authors": [ "Yunxiang Zhang", "Muhammad Khalifa", "Shitanshu Bhushan", "Grant D Murphy", "Lajanugen Logeswaran", "Jaekyeom Kim", "Moontae Lee", "Honglak Lee", "Lu Wang" ], "github": "", "project_page": "", "abstract": "Existing evaluation of large language model (LLM) agents on scientific discovery lacks objective baselines and metrics to assess the viability of their proposed methods. To address this issue, we introduce MLRC-Bench, a benchmark designed to quantify how effectively language agents can tackle challenging Machine Learning (ML) Research Competitions. Our benchmark highlights open research problems that demand novel methodologies, in contrast to recent benchmarks such as OpenAI's MLE-Bench (Chan et al., 2024) and METR's RE-Bench (Wijk et al., 2024), which focus on well-established research tasks that are largely solvable through sufficient engineering effort. Unlike prior work, e.g., AI Scientist (Lu et al., 2024b), which evaluates the end-to-end agentic pipeline by using LLM-as-a-judge, MLRC-Bench measures the key steps of proposing and implementing novel research methods and evaluates them with newly proposed rigorous protocol and objective metrics. Our curated suite of 7 competition tasks reveals significant challenges for LLM agents. Even the best-performing tested agent (gemini-exp-1206 under MLAB (Huang et al., 2024a)) closes only 9.3% of the gap between baseline and top human participant scores. Furthermore, our analysis reveals a misalignment between the LLM-judged innovation and their actual performance on cutting-edge ML research problems. MLRC-Bench is a dynamic benchmark, which is designed to continually grow with new ML competitions to encourage rigorous and objective evaluations of AI's research capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-17", "arxiv_id": "2504.13128", "title": "FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents", "authors": [ "Nandan Thakur", "Jimmy Lin", "Sam Havens", "Michael Carbin", "Omar Khattab", "Andrew Drozdov" ], "github": "https://github.com/fresh-stack/freshstack", "project_page": "", "abstract": "We introduce FreshStack, a reusable framework for automatically building information retrieval (IR) evaluation benchmarks from community-asked questions and answers. FreshStack conducts the following steps: (1) automatic corpus collection from code and technical documentation, (2) nugget generation from community-asked questions and answers, and (3) nugget-level support, retrieving documents using a fusion of retrieval techniques and hybrid architectures. We use FreshStack to build five datasets on fast-growing, recent, and niche topics to ensure the tasks are sufficiently challenging. On FreshStack, existing retrieval models, when applied out-of-the-box, significantly underperform oracle approaches on all five topics, denoting plenty of headroom to improve IR quality. In addition, we identify cases where rerankers do not clearly improve first-stage retrieval accuracy (two out of five topics). We hope that FreshStack will facilitate future work toward constructing realistic, scalable, and uncontaminated IR and RAG evaluation benchmarks. FreshStack datasets are available at: https://fresh-stack.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/fresh-stack/freshstack", "github_api_added_by": "auto" }, { "date": "2025-04-18", "arxiv_id": "2504.12626", "title": "Packing Input Frame Context in Next-Frame Prediction Models for Video Generation", "authors": [ "Lvmin Zhang", "Maneesh Agrawala" ], "github": "", "project_page": "", "abstract": "We present a neural network structure, FramePack, to train next-frame (or next-frame-section) prediction models for video generation. The FramePack compresses input frames to make the transformer context length a fixed number regardless of the video length. As a result, we are able to process a large number of frames using video diffusion with computation bottleneck similar to image diffusion. This also makes the training video batch sizes significantly higher (batch sizes become comparable to image diffusion training). We also propose an anti-drifting sampling method that generates frames in inverted temporal order with early-established endpoints to avoid exposure bias (error accumulation over iterations). Finally, we show that existing video diffusion models can be finetuned with FramePack, and their visual quality may be improved because the next-frame prediction supports more balanced diffusion schedulers with less extreme flow shift timesteps.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.13146", "title": "Antidistillation Sampling", "authors": [ "Yash Savani", "Asher Trockman", "Zhili Feng", "Avi Schwarzschild", "Alexander Robey", "Marc Finzi", "J. Zico Kolter" ], "github": "https://github.com/locuslab/antidistillation-sampling", "project_page": "", "abstract": "Frontier models that generate extended reasoning traces inadvertently produce rich token sequences that can facilitate model distillation. Recognizing this vulnerability, model owners may seek sampling strategies that limit the effectiveness of distillation without compromising model performance. Antidistillation sampling provides exactly this capability. By strategically modifying a model's next-token probability distribution, antidistillation sampling poisons reasoning traces, rendering them significantly less effective for distillation while preserving the model's practical utility. For further details, see https://antidistillation.com.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/locuslab/antidistillation-sampling", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.05506", "title": "ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering", "authors": [ "Ahmed Masry", "Mohammed Saidul Islam", "Mahir Ahmed", "Aayush Bajaj", "Firoz Kabir", "Aaryaman Kartha", "Md Tahmid Rahman Laskar", "Mizanur Rahman", "Shadikur Rahman", "Mehrad Shahmohammadi", "Megh Thakkar", "Md Rizwan Parvez", "Enamul Hoque", "Shafiq Joty" ], "github": "https://github.com/vis-nlp/ChartQAPro", "project_page": "", "abstract": "Charts are ubiquitous, as people often use them to analyze data, answer questions, and discover critical insights. However, performing complex analytical tasks with charts requires significant perceptual and cognitive effort. Chart Question Answering (CQA) systems automate this process by enabling models to interpret and reason with visual representations of data. However, existing benchmarks like ChartQA lack real-world diversity and have recently shown performance saturation with modern large vision-language models (LVLMs). To address these limitations, we introduce ChartQAPro, a new benchmark that includes 1,341 charts from 157 diverse sources, spanning various chart types, including infographics and dashboards, and featuring 1,948 questions in various types, such as multiple-choice, conversational, hypothetical, and unanswerable questions, to better reflect real-world challenges. Our evaluations with 21 models show a substantial performance drop for LVLMs on ChartQAPro; e.g., Claude Sonnet 3.5 scores 90.5% on ChartQA but only 55.81% on ChartQAPro, underscoring the complexity of chart reasoning. We complement our findings with detailed error analyses and ablation studies, identifying key challenges and opportunities for advancing LVLMs in chart understanding and reasoning. We release ChartQAPro at https://github.com/vis-nlp/ChartQAPro.", "github_manual": "", "github_regex": "https://github.com/vis-nlp/ChartQAPro", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.13169", "title": "Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling", "authors": [ "Tsung-Han Wu", "Heekyung Lee", "Jiaxin Ge", "Joseph E. Gonzalez", "Trevor Darrell", "David M. Chan" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) excel at visual understanding but often suffer from visual hallucinations, where they generate descriptions of nonexistent objects, actions, or concepts, posing significant risks in safety-critical applications. Existing hallucination mitigation methods typically follow one of two paradigms: generation adjustment, which modifies decoding behavior to align text with visual inputs, and post-hoc verification, where external models assess and correct outputs. While effective, generation adjustment methods often rely on heuristics and lack correction mechanisms, while post-hoc verification is complicated, typically requiring multiple models and tending to reject outputs rather than refine them. In this work, we introduce REVERSE, a unified framework that integrates hallucination-aware training with on-the-fly self-verification. By leveraging a new hallucination-verification dataset containing over 1.3M semi-synthetic samples, along with a novel inference-time retrospective resampling technique, our approach enables VLMs to both detect hallucinations during generation and dynamically revise those hallucinations. Our evaluations show that REVERSE achieves state-of-the-art hallucination reduction, outperforming the best existing methods by up to 12% on CHAIR-MSCOCO and 28% on HaloQuest. Our dataset, model, and code are available at: https://reverse-vlm.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.12369", "title": "WORLDMEM: Long-term Consistent World Simulation with Memory", "authors": [ "Zeqi Xiao", "Yushi Lan", "Yifan Zhou", "Wenqi Ouyang", "Shuai Yang", "Yanhong Zeng", "Xingang Pan" ], "github": "", "project_page": "", "abstract": "World simulation has gained increasing popularity due to its ability to model virtual environments and predict the consequences of actions. However, the limited temporal context window often leads to failures in maintaining long-term consistency, particularly in preserving 3D spatial consistency. In this work, we present WorldMem, a framework that enhances scene generation with a memory bank consisting of memory units that store memory frames and states (e.g., poses and timestamps). By employing a memory attention mechanism that effectively extracts relevant information from these memory frames based on their states, our method is capable of accurately reconstructing previously observed scenes, even under significant viewpoint or temporal gaps. Furthermore, by incorporating timestamps into the states, our framework not only models a static world but also captures its dynamic evolution over time, enabling both perception and interaction within the simulated world. Extensive experiments in both virtual and real scenarios validate the effectiveness of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.13122", "title": "VistaDPO: Video Hierarchical Spatial-Temporal Direct Preference Optimization for Large Video Models", "authors": [ "Haojian Huang", "Haodong Chen", "Shengqiong Wu", "Meng Luo", "Jinlan Fu", "Xinya Du", "Hanwang Zhang", "Hao Fei" ], "github": "https://github.com/HaroldChen19/VistaDPO", "project_page": "", "abstract": "Large Video Models (LVMs) built upon Large Language Models (LLMs) have shown promise in video understanding but often suffer from misalignment with human intuition and video hallucination issues. To address these challenges, we introduce VistaDPO, a novel framework for Video Hierarchical Spatial-Temporal Direct Preference Optimization. VistaDPO enhances text-video preference alignment across three hierarchical levels: i) Instance Level, aligning overall video content with responses; ii) Temporal Level, aligning video temporal semantics with event descriptions; and iii) Perceptive Level, aligning spatial objects with language tokens. Given the lack of datasets for fine-grained video-language preference alignment, we construct VistaDPO-7k, a dataset of 7.2K QA pairs annotated with chosen and rejected responses, along with spatial-temporal grounding information such as timestamps, keyframes, and bounding boxes. Extensive experiments on benchmarks such as Video Hallucination, Video QA, and Captioning performance tasks demonstrate that VistaDPO significantly improves the performance of existing LVMs, effectively mitigating video-language misalignment and hallucination. The code and data are available at https://github.com/HaroldChen19/VistaDPO.", "github_manual": "", "github_regex": "https://github.com/HaroldChen19/VistaDPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.13079", "title": "Retrieval-Augmented Generation with Conflicting Evidence", "authors": [ "Han Wang", "Archiki Prasad", "Elias Stengel-Eskin", "Mohit Bansal" ], "github": "https://github.com/HanNight/RAMDocs", "project_page": "", "abstract": "Large language model (LLM) agents are increasingly employing retrieval-augmented generation (RAG) to improve the factuality of their responses. However, in practice, these systems often need to handle ambiguous user queries and potentially conflicting information from multiple sources while also suppressing inaccurate information from noisy or irrelevant documents. Prior work has generally studied and addressed these challenges in isolation, considering only one aspect at a time, such as handling ambiguity or robustness to noise and misinformation. We instead consider multiple factors simultaneously, proposing (i) RAMDocs (Retrieval with Ambiguity and Misinformation in Documents), a new dataset that simulates complex and realistic scenarios for conflicting evidence for a user query, including ambiguity, misinformation, and noise; and (ii) MADAM-RAG, a multi-agent approach in which LLM agents debate over the merits of an answer over multiple rounds, allowing an aggregator to collate responses corresponding to disambiguated entities while discarding misinformation and noise, thereby handling diverse sources of conflict jointly. We demonstrate the effectiveness of MADAM-RAG using both closed and open-source models on AmbigDocs -- which requires presenting all valid answers for ambiguous queries -- improving over strong RAG baselines by up to 11.40% and on FaithEval -- which requires suppressing misinformation -- where we improve by up to 15.80% (absolute) with Llama3.3-70B-Instruct. Furthermore, we find that RAMDocs poses a challenge for existing RAG baselines (Llama3.3-70B-Instruct only obtains 32.60 exact match score). While MADAM-RAG begins to address these conflicting factors, our analysis indicates that a substantial gap remains especially when increasing the level of imbalance in supporting evidence and misinformation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HanNight/RAMDocs", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.13161", "title": "CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training", "authors": [ "Shizhe Diao", "Yu Yang", "Yonggan Fu", "Xin Dong", "Dan Su", "Markus Kliegl", "Zijia Chen", "Peter Belcak", "Yoshi Suhara", "Hongxu Yin", "Mostofa Patwary", "Yingyan", "Lin", "Jan Kautz", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "Pre-training datasets are typically collected from web content and lack inherent domain divisions. For instance, widely used datasets like Common Crawl do not include explicit domain labels, while manually curating labeled datasets such as The Pile is labor-intensive. Consequently, identifying an optimal pre-training data mixture remains a challenging problem, despite its significant benefits for pre-training performance. To address these challenges, we propose CLustering-based Iterative Data Mixture Bootstrapping (CLIMB), an automated framework that discovers, evaluates, and refines data mixtures in a pre-training setting. Specifically, CLIMB embeds and clusters large-scale datasets in a semantic space and then iteratively searches for optimal mixtures using a smaller proxy model and a predictor. When continuously trained on 400B tokens with this mixture, our 1B model exceeds the state-of-the-art Llama-3.2-1B by 2.0%. Moreover, we observe that optimizing for a specific domain (e.g., Social Sciences) yields a 5% improvement over random sampling. Finally, we introduce ClimbLab, a filtered 1.2-trillion-token corpus with 20 clusters as a research playground, and ClimbMix, a compact yet powerful 400-billion-token dataset designed for efficient pre-training that delivers superior performance under an equal token budget. We analyze the final data mixture, elucidating the characteristics of an optimal data mixture. Our data is available at: https://research.nvidia.com/labs/lpr/climb/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.13145", "title": "Exploring Expert Failures Improves LLM Agent Tuning", "authors": [ "Li-Cheng Lan", "Andrew Bai", "Minhao Cheng", "Ruochen Wang", "Cho-Jui Hsieh", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have shown tremendous potential as agents, excelling at tasks that require multiple rounds of reasoning and interactions. Rejection Sampling Fine-Tuning (RFT) has emerged as an effective method for finetuning LLMs as agents: it first imitates expert-generated successful trajectories and further improves agentic skills through iterative fine-tuning on successful, self-generated trajectories. However, since the expert (e.g., GPT-4) succeeds primarily on simpler subtasks and RFT inherently favors simpler scenarios, many complex subtasks remain unsolved and persistently out-of-distribution (OOD). Upon investigating these challenging subtasks, we discovered that previously failed expert trajectories can often provide valuable guidance, e.g., plans and key actions, that can significantly improve agent exploration efficiency and acquisition of critical skills. Motivated by these observations, we propose Exploring Expert Failures (EEF), which identifies beneficial actions from failed expert trajectories and integrates them into the training dataset. Potentially harmful actions are meticulously excluded to prevent contamination of the model learning process. By leveraging the beneficial actions in expert failures, EEF successfully solves some previously unsolvable subtasks and improves agent tuning performance. Remarkably, our approach achieved a 62\\% win rate in WebShop, outperforming RFT (53. 6\\%) and GPT-4 (35. 6\\%), and to the best of our knowledge, setting a new state-of-the-art as the first method to surpass a score of 0.81 in WebShop and exceed 81 in SciWorld.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.12782", "title": "Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted Concepts", "authors": [ "Leyang Li", "Shilin Lu", "Yan Ren", "Adams Wai-Kin Kong" ], "github": "https://github.com/lileyang1210/ANT", "project_page": "", "abstract": "Ensuring the ethical deployment of text-to-image models requires effective techniques to prevent the generation of harmful or inappropriate content. While concept erasure methods offer a promising solution, existing finetuning-based approaches suffer from notable limitations. Anchor-free methods risk disrupting sampling trajectories, leading to visual artifacts, while anchor-based methods rely on the heuristic selection of anchor concepts. To overcome these shortcomings, we introduce a finetuning framework, dubbed ANT, which Automatically guides deNoising Trajectories to avoid unwanted concepts. ANT is built on a key insight: reversing the condition direction of classifier-free guidance during mid-to-late denoising stages enables precise content modification without sacrificing early-stage structural integrity. This inspires a trajectory-aware objective that preserves the integrity of the early-stage score function field, which steers samples toward the natural image manifold, without relying on heuristic anchor concept selection. For single-concept erasure, we propose an augmentation-enhanced weight saliency map to precisely identify the critical parameters that most significantly contribute to the unwanted concept, enabling more thorough and efficient erasure. For multi-concept erasure, our objective function offers a versatile plug-and-play solution that significantly boosts performance. Extensive experiments demonstrate that ANT achieves state-of-the-art results in both single and multi-concept erasure, delivering high-quality, safe outputs without compromising the generative fidelity. Code is available at https://github.com/lileyang1210/ANT", "github_manual": "", "github_regex": "https://github.com/lileyang1210/ANT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.12157", "title": "FocusedAD: Character-centric Movie Audio Description", "authors": [ "Xiaojun Ye", "Chun Wang", "Yiren Song", "Sheng Zhou", "Liangcheng Li", "Jiajun Bu" ], "github": "https://github.com/Thorin215/FocusedAD", "project_page": "", "abstract": "Movie Audio Description (AD) aims to narrate visual content during dialogue-free segments, particularly benefiting blind and visually impaired (BVI) audiences. Compared with general video captioning, AD demands plot-relevant narration with explicit character name references, posing unique challenges in movie understanding.To identify active main characters and focus on storyline-relevant regions, we propose FocusedAD, a novel framework that delivers character-centric movie audio descriptions. It includes: (i) a Character Perception Module(CPM) for tracking character regions and linking them to names; (ii) a Dynamic Prior Module(DPM) that injects contextual cues from prior ADs and subtitles via learnable soft prompts; and (iii) a Focused Caption Module(FCM) that generates narrations enriched with plot-relevant details and named characters. To overcome limitations in character identification, we also introduce an automated pipeline for building character query banks. FocusedAD achieves state-of-the-art performance on multiple benchmarks, including strong zero-shot results on MAD-eval-Named and our newly proposed Cinepile-AD dataset. Code and data will be released at https://github.com/Thorin215/FocusedAD .", "github_manual": "", "github_regex": "https://github.com/Thorin215/FocusedAD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.12364", "title": "DMM: Building a Versatile Image Generation Model via Distillation-Based Model Merging", "authors": [ "Tianhui Song", "Weixin Feng", "Shuai Wang", "Xubin Li", "Tiezheng Ge", "Bo Zheng", "Limin Wang" ], "github": "", "project_page": "", "abstract": "The success of text-to-image (T2I) generation models has spurred a proliferation of numerous model checkpoints fine-tuned from the same base model on various specialized datasets. This overwhelming specialized model production introduces new challenges for high parameter redundancy and huge storage cost, thereby necessitating the development of effective methods to consolidate and unify the capabilities of diverse powerful models into a single one. A common practice in model merging adopts static linear interpolation in the parameter space to achieve the goal of style mixing. However, it neglects the features of T2I generation task that numerous distinct models cover sundry styles which may lead to incompatibility and confusion in the merged model. To address this issue, we introduce a style-promptable image generation pipeline which can accurately generate arbitrary-style images under the control of style vectors. Based on this design, we propose the score distillation based model merging paradigm (DMM), compressing multiple models into a single versatile T2I model. Moreover, we rethink and reformulate the model merging task in the context of T2I generation, by presenting new merging goals and evaluation protocols. Our experiments demonstrate that DMM can compactly reorganize the knowledge from multiple teacher models and achieve controllable arbitrary-style generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.12322", "title": "A Strategic Coordination Framework of Small LLMs Matches Large LLMs in Data Synthesis", "authors": [ "Xin Gao", "Qizhi Pei", "Zinan Tang", "Yu Li", "Honglin Lin", "Jiang Wu", "Conghui He", "Lijun Wu" ], "github": "https://github.com/GX-XinGao/GRA", "project_page": "", "abstract": "While data synthesis and distillation are promising strategies to enhance small language models, current approaches heavily rely on Large Language Models (LLMs), which suffer from high computational costs, environmental inefficiency, and potential biases inherited from monolithic architectures. In contrast, smaller LLMs are more accessible and sustainable, but their individual capabilities often fall short in generating high-quality, diverse, and reliable data. Inspired by collaborative human processes (e.g., peer review), we propose a multiple small LLMs involved framework, GRA, that aggregates specialized roles across small LLMs to iterative refinement and quality control typically achieved by a single large LLM. In this collaborative framework, multiple small LLMs assume distinct roles-Generator, Reviewer, and Adjudicator-to simulate a peer-review-inspired data synthesis pipeline. The Generator proposes initial data samples, the Reviewer critiques their quality and diversity, and the Adjudicator resolves conflicts to finalize the output. By decomposing the synthesis process into specialized sub-tasks, collaborative small LLMs can achieve data-level parity with large LLM-based distillation. Through experiments across multiple benchmarks, we demonstrate that GRA-produced data matches or exceeds the quality of single large LLM outputs, e.g., Qwen-2.5-72B-Instruct. Our results challenge the necessity of monolithic large models for high-quality data synthesis, advocating instead for strategic coordination of smaller agents. Our datasets, models, and code are publicly available at https://github.com/GX-XinGao/GRA.", "github_manual": "", "github_regex": "https://github.com/GX-XinGao/GRA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.13055", "title": "NoisyRollout: Reinforcing Visual Reasoning with Data Augmentation", "authors": [ "Xiangyan Liu", "Jinjie Ni", "Zijian Wu", "Chao Du", "Longxu Dou", "Haonan Wang", "Tianyu Pang", "Michael Qizhe Shieh" ], "github": "https://github.com/volcengine/verl/blob/main/recipe/dapo/README.md", "project_page": "", "abstract": "Recent advances in reinforcement learning (RL) have strengthened the reasoning capabilities of vision-language models (VLMs). However, enhancing policy exploration to more effectively scale test-time compute remains underexplored in VLMs. In addition, VLMs continue to struggle with imperfect visual perception, which in turn affects the subsequent reasoning process. To this end, we propose NoisyRollout, a simple yet effective RL approach that mixes trajectories from both clean and moderately distorted images to introduce targeted diversity in visual perception and the resulting reasoning patterns. Without additional training cost, NoisyRollout enhances the exploration capabilities of VLMs by incorporating a vision-oriented inductive bias. Furthermore, NoisyRollout employs a noise annealing schedule that gradually reduces distortion strength over training, ensuring benefit from noisy signals early while maintaining training stability and scalability in later stages. With just 2.1K training samples, NoisyRollout achieves state-of-the-art performance among open-source RL-tuned models on 5 out-of-domain benchmarks spanning both reasoning and perception tasks, while preserving comparable or even better in-domain performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/volcengine/verl/blob/main/recipe/dapo/README.md", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.12395", "title": "InstantCharacter: Personalize Any Characters with a Scalable Diffusion Transformer Framework", "authors": [ "Jiale Tao", "Yanbing Zhang", "Qixun Wang", "Yiji Cheng", "Haofan Wang", "Xu Bai", "Zhengguang Zhou", "Ruihuang Li", "Linqing Wang", "Chunyu Wang", "Qin Lin", "Qinglin Lu" ], "github": "https://github.com/Tencent/InstantCharacter", "project_page": "", "abstract": "Current learning-based subject customization approaches, predominantly relying on U-Net architectures, suffer from limited generalization ability and compromised image quality. Meanwhile, optimization-based methods require subject-specific fine-tuning, which inevitably degrades textual controllability. To address these challenges, we propose InstantCharacter, a scalable framework for character customization built upon a foundation diffusion transformer. InstantCharacter demonstrates three fundamental advantages: first, it achieves open-domain personalization across diverse character appearances, poses, and styles while maintaining high-fidelity results. Second, the framework introduces a scalable adapter with stacked transformer encoders, which effectively processes open-domain character features and seamlessly interacts with the latent space of modern diffusion transformers. Third, to effectively train the framework, we construct a large-scale character dataset containing 10-million-level samples. The dataset is systematically organized into paired (multi-view character) and unpaired (text-image combinations) subsets. This dual-data structure enables simultaneous optimization of identity consistency and textual editability through distinct learning pathways. Qualitative experiments demonstrate the advanced capabilities of InstantCharacter in generating high-fidelity, text-controllable, and character-consistent images, setting a new benchmark for character-driven image generation. Our source code is available at https://github.com/Tencent/InstantCharacter.", "github_manual": "", "github_regex": "https://github.com/Tencent/InstantCharacter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.13171", "title": "Sleep-time Compute: Beyond Inference Scaling at Test-time", "authors": [ "Kevin Lin", "Charlie Snell", "Yu Wang", "Charles Packer", "Sarah Wooders", "Ion Stoica", "Joseph E. Gonzalez" ], "github": "https://github.com/letta-ai/sleep-time-compute", "project_page": "", "abstract": "Scaling test-time compute has emerged as a key ingredient for enabling large language models (LLMs) to solve difficult problems, but comes with high latency and inference cost. We introduce sleep-time compute, which allows models to \"think\" offline about contexts before queries are presented: by anticipating what queries users might ask and pre-computing useful quantities, we can significantly reduce the compute requirements at test-time. To demonstrate the efficacy of our method, we create modified versions of two reasoning tasks - Stateful GSM-Symbolic and Stateful AIME. We find that sleep-time compute can reduce the amount of test-time compute needed to achieve the same accuracy by ~ 5x on Stateful GSM-Symbolic and Stateful AIME and that by scaling sleep-time compute we can further increase accuracy by up to 13% on Stateful GSM-Symbolic and 18% on Stateful AIME. Furthermore, we introduce Multi-Query GSM-Symbolic, which extends GSM-Symbolic by including multiple related queries per context. By amortizing sleep-time compute across related queries about the same context using Multi-Query GSM-Symbolic, we can decrease the average cost per query by 2.5x. We then conduct additional analysis to understand when sleep-time compute is most effective, finding the predictability of the user query to be well correlated with the efficacy of sleep-time compute. Finally, we conduct a case-study of applying sleep-time compute to a realistic agentic SWE task.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/letta-ai/sleep-time-compute", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.07959", "title": "CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera Color Constancy", "authors": [ "Dongyoung Kim", "Mahmoud Afifi", "Dongyun Kim", "Michael S. Brown", "Seon Joo Kim" ], "github": "https://github.com/DY112/CCMNet", "project_page": "", "abstract": "Computational color constancy, or white balancing, is a key module in a camera's image signal processor (ISP) that corrects color casts from scene lighting. Because this operation occurs in the camera-specific raw color space, white balance algorithms must adapt to different cameras. This paper introduces a learning-based method for cross-camera color constancy that generalizes to new cameras without retraining. Our method leverages pre-calibrated color correction matrices (CCMs) available on ISPs that map the camera's raw color space to a standard space (e.g., CIE XYZ). Our method uses these CCMs to transform predefined illumination colors (i.e., along the Planckian locus) into the test camera's raw space. The mapped illuminants are encoded into a compact camera fingerprint embedding (CFE) that enables the network to adapt to unseen cameras. To prevent overfitting due to limited cameras and CCMs during training, we introduce a data augmentation technique that interpolates between cameras and their CCMs. Experimental results across multiple datasets and backbones show that our method achieves state-of-the-art cross-camera color constancy while remaining lightweight and relying only on data readily available in camera ISPs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DY112/CCMNet", "github_api_added_by": "auto" }, { "date": "2025-04-18", "arxiv_id": "2504.13181", "title": "Perception Encoder: The best visual embeddings are not at the output of the network", "authors": [ "Daniel Bolya", "Po-Yao Huang", "Peize Sun", "Jang Hyun Cho", "Andrea Madotto", "Chen Wei", "Tengyu Ma", "Jiale Zhi", "Jathushan Rajasegaran", "Hanoona Rasheed", "Junke Wang", "Marco Monteiro", "Hu Xu", "Shiyu Dong", "Nikhila Ravi", "Daniel Li", "Piotr Doll\u00e1r", "Christoph Feichtenhofer" ], "github": "", "project_page": "", "abstract": "We introduce Perception Encoder (PE), a state-of-the-art encoder for image and video understanding trained via simple vision-language learning. Traditionally, vision encoders have relied on a variety of pretraining objectives, each tailored to specific downstream tasks such as classification, captioning, or localization. Surprisingly, after scaling our carefully tuned image pretraining recipe and refining with our robust video data engine, we find that contrastive vision-language training alone can produce strong, general embeddings for all of these downstream tasks. There is only one caveat: these embeddings are hidden within the intermediate layers of the network. To draw them out, we introduce two alignment methods, language alignment for multimodal language modeling, and spatial alignment for dense prediction. Together with the core contrastive checkpoint, our PE family of models achieves state-of-the-art performance on a wide variety of tasks, including zero-shot image and video classification and retrieval; document, image, and video Q&A; and spatial tasks such as detection, depth estimation, and tracking. To foster further research, we are releasing our models, code, and a novel dataset of synthetically and human-annotated videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.12563", "title": "MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation", "authors": [ "Haris Riaz", "Sourav Bhabesh", "Vinayak Arannil", "Miguel Ballesteros", "Graham Horwood" ], "github": "", "project_page": "", "abstract": "Recent smaller language models such Phi-3.5 and Phi-4 rely on synthetic data generated using larger Language models. Questions remain about leveraging synthetic data for other use cases, such as adapting LLMs to specific domains. A key limitation of synthetic data is low diversity, which negatively impacts its downstream applicability for improving other models. To address this, we propose MetaSynth, a method for generating synthetic data that enhances diversity through meta-prompting, where a language model orchestrates multiple \"expert\" LLM agents to collaboratively generate data. Using only 25 million tokens of synthetic data generated with MetaSynth, we successfully adapt a well-trained LLM (Mistral-7B-v0.3) to two specialized domains-Finance and Biomedicine-without compromising the capabilities of the resulting model in general tasks. In addition, we evaluate the diversity of our synthetic data using seven automated metrics, and find that it approaches the diversity of LLM pre-training corpora. Continually pre-training Mistral-7B-v0.3 with MetaSynth notably outperforms the base LLM, showing improvements of up to 4.08% in Finance and 13.75% in Biomedicine. The same model shows degraded performance when trained on data generated using a template prompt, even when the template includes prior generations and varying In-Context exemplars of real data. Our findings suggest that a few million tokens of diverse synthetic data without mixing any real data, is sufficient for effective domain adaptation when using MetaSynth.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.11651", "title": "70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float", "authors": [ "Tianyi Zhang", "Yang Sui", "Shaochen Zhong", "Vipin Chaudhary", "Xia Hu", "Anshumali Shrivastava" ], "github": "https://github.com/LeanModels/DFloat11", "project_page": "", "abstract": "Large Language Models (LLMs) have grown rapidly in size, creating significant challenges for efficient deployment on resource-constrained hardware. In this paper, we introduce Dynamic-Length Float (DFloat11), a lossless compression framework that reduces LLM size by 30% while preserving outputs that are bit-for-bit identical to the original model. DFloat11 is motivated by the low entropy in the BFloat16 weight representation of LLMs, which reveals significant inefficiency in existing storage format. By applying entropy coding, DFloat11 assigns dynamic-length encodings to weights based on frequency, achieving near information-optimal compression without any loss of precision. To facilitate efficient inference with dynamic-length encodings, we develop a custom GPU kernel for fast online decompression. Our design incorporates the following: (i) decomposition of memory-intensive lookup tables (LUTs) into compact LUTs that fit in GPU SRAM, (ii) a two-phase kernel for coordinating thread read/write positions using lightweight auxiliary variables, and (iii) transformer-block-level decompression to minimize latency. Experiments on recent models, including Llama-3.1, Qwen-2.5, and Gemma-3, validates our hypothesis that DFloat11 achieves around 30% model size reduction while preserving bit-for-bit exact outputs. Compared to a potential alternative of offloading parts of an uncompressed model to the CPU to meet memory constraints, DFloat11 achieves 1.9-38.8x higher throughput in token generation. With a fixed GPU memory budget, DFloat11 enables 5.3-13.17x longer context lengths than uncompressed models. Notably, our method enables lossless inference of Llama-3.1-405B, an 810GB model, on a single node equipped with 8x80GB GPUs. Our code and models are available at https://github.com/LeanModels/DFloat11.", "github_manual": "", "github_regex": "https://github.com/LeanModels/DFloat11", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.13180", "title": "PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding", "authors": [ "Jang Hyun Cho", "Andrea Madotto", "Effrosyni Mavroudi", "Triantafyllos Afouras", "Tushar Nagarajan", "Muhammad Maaz", "Yale Song", "Tengyu Ma", "Shuming Hu", "Suyog Jain", "Miguel Martin", "Huiyu Wang", "Hanoona Rasheed", "Peize Sun", "Po-Yao Huang", "Daniel Bolya", "Nikhila Ravi", "Shashank Jain", "Tammy Stark", "Shane Moon", "Babak Damavandi", "Vivian Lee", "Andrew Westbury", "Salman Khan", "Philipp Kr\u00e4henb\u00fchl", "Piotr Doll\u00e1r", "Lorenzo Torresani", "Kristen Grauman", "Christoph Feichtenhofer" ], "github": "", "project_page": "", "abstract": "Vision-language models are integral to computer vision research, yet many high-performing models remain closed-source, obscuring their data, design and training recipe. The research community has responded by using distillation from black-box models to label training data, achieving strong benchmark results, at the cost of measurable scientific progress. However, without knowing the details of the teacher model and its data sources, scientific progress remains difficult to measure. In this paper, we study building a Perception Language Model (PLM) in a fully open and reproducible framework for transparent research in image and video understanding. We analyze standard training pipelines without distillation from proprietary models and explore large-scale synthetic data to identify critical data gaps, particularly in detailed video understanding. To bridge these gaps, we release 2.8M human-labeled instances of fine-grained video question-answer pairs and spatio-temporally grounded video captions. Additionally, we introduce PLM-VideoBench, a suite for evaluating challenging video understanding tasks focusing on the ability to reason about \"what\", \"where\", \"when\", and \"how\" of a video. We make our work fully reproducible by providing data, training recipes, code & models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.13143", "title": "Complex-Edit: CoT-Like Instruction Generation for Complexity-Controllable Image Editing Benchmark", "authors": [ "Siwei Yang", "Mude Hui", "Bingchen Zhao", "Yuyin Zhou", "Nataniel Ruiz", "Cihang Xie" ], "github": "", "project_page": "", "abstract": "We introduce Complex-Edit, a comprehensive benchmark designed to systematically evaluate instruction-based image editing models across instructions of varying complexity. To develop this benchmark, we harness GPT-4o to automatically collect a diverse set of editing instructions at scale. Our approach follows a well-structured ``Chain-of-Edit'' pipeline: we first generate individual atomic editing tasks independently and then integrate them to form cohesive, complex instructions. Additionally, we introduce a suite of metrics to assess various aspects of editing performance, along with a VLM-based auto-evaluation pipeline that supports large-scale assessments. Our benchmark yields several notable insights: 1) Open-source models significantly underperform relative to proprietary, closed-source models, with the performance gap widening as instruction complexity increases; 2) Increased instructional complexity primarily impairs the models' ability to retain key elements from the input images and to preserve the overall aesthetic quality; 3) Decomposing a complex instruction into a sequence of atomic steps, executed in a step-by-step manner, substantially degrades performance across multiple metrics; 4) A straightforward Best-of-N selection strategy improves results for both direct editing and the step-by-step sequential approach; and 5) We observe a ``curse of synthetic data'': when synthetic data is involved in model training, the edited images from such models tend to appear increasingly synthetic as the complexity of the editing instructions rises -- a phenomenon that intriguingly also manifests in the latest GPT-4o outputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-18", "arxiv_id": "2504.09228", "title": "Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking", "authors": [ "You Wu", "Xucheng Wang", "Xiangyang Yang", "Mengyuan Liu", "Dan Zeng", "Hengzhou Ye", "Shuiwang Li" ], "github": "https://github.com/wuyou3474/ORTrack", "project_page": "", "abstract": "Single-stream architectures using Vision Transformer (ViT) backbones show great potential for real-time UAV tracking recently. However, frequent occlusions from obstacles like buildings and trees expose a major drawback: these models often lack strategies to handle occlusions effectively. New methods are needed to enhance the occlusion resilience of single-stream ViT models in aerial tracking. In this work, we propose to learn Occlusion-Robust Representations (ORR) based on ViTs for UAV tracking by enforcing an invariance of the feature representation of a target with respect to random masking operations modeled by a spatial Cox process. Hopefully, this random masking approximately simulates target occlusions, thereby enabling us to learn ViTs that are robust to target occlusion for UAV tracking. This framework is termed ORTrack. Additionally, to facilitate real-time applications, we propose an Adaptive Feature-Based Knowledge Distillation (AFKD) method to create a more compact tracker, which adaptively mimics the behavior of the teacher model ORTrack according to the task's difficulty. This student model, dubbed ORTrack-D, retains much of ORTrack's performance while offering higher efficiency. Extensive experiments on multiple benchmarks validate the effectiveness of our method, demonstrating its state-of-the-art performance. Codes is available at https://github.com/wuyou3474/ORTrack.", "github_manual": "", "github_regex": "https://github.com/wuyou3474/ORTrack", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.13837", "title": "Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?", "authors": [ "Yang Yue", "Zhiqi Chen", "Rui Lu", "Andrew Zhao", "Zhaokai Wang", "Yang Yue", "Shiji Song", "Gao Huang" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has recently demonstrated notable success in enhancing the reasoning capabilities of LLMs, particularly in mathematics and programming tasks. It is widely believed that RLVR enables LLMs to continuously self-improve, thus acquiring novel reasoning abilities that exceed corresponding base models' capacity. In this study, however, we critically re-examines this assumption by measuring the pass@k metric with large values of k to explore the reasoning capability boundary of the models across a wide range of model families and benchmarks. Surprisingly, the RL does not, in fact, elicit fundamentally new reasoning patterns. While RL-trained models outperform their base models at smaller values of k (\\eg, k=1), base models can achieve a comparable or even higher pass@k score compared to their RL counterparts at large k values. The reasoning paths generated by RL-trained models are already included in the base models' sampling distribution, suggesting that most reasoning abilities manifested in RL-trained models are already obtained by base models. Further analysis shows that RL training boosts the performance by biasing the model's output distribution toward paths that are more likely to yield rewards, therefore sampling correct responses more efficiently. But this also results in a narrower reasoning capability boundary compared to base models. Similar results are observed in visual reasoning tasks trained with RLVR. Moreover, we find that distillation can genuinely introduce new knowledge into the model, different from RLVR. These findings underscore a critical limitation of RLVR in advancing LLM reasoning abilities which requires us to fundamentally rethink the impact of RL training in reasoning LLMs and the need of a better paradigm. Project Page: https://limit-of-RLVR.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.11833", "title": "Could Thinking Multilingually Empower LLM Reasoning?", "authors": [ "Changjiang Gao", "Xu Huang", "Wenhao Zhu", "Shujian Huang", "Lei Li", "Fei Yuan" ], "github": "https://github.com/CONE-MT/multilingual_reasoning", "project_page": "", "abstract": "Previous work indicates that large language models exhibit a significant \"English bias\", i.e. they often perform better when tasks are presented in English. Interestingly, we have observed that using certain other languages in reasoning tasks can yield better performance than English. However, this phenomenon remains under-explored. In this paper, we explore the upper bound of harnessing multilingualism in reasoning tasks, suggesting that multilingual reasoning promises significantly (by nearly 10 Acc@k points) and robustly (tolerance for variations in translation quality and language choice) higher upper bounds than English-only reasoning. Besides analyzing the reason behind the upper bound and challenges in reaching it, we also find that common answer selection methods cannot achieve this upper bound, due to their limitations and biases. These insights could pave the way for future research aimed at fully harnessing the potential of multilingual reasoning in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/CONE-MT/multilingual_reasoning", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.13835", "title": "MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space", "authors": [ "Yicheng Chen", "Yining Li", "Kai Hu", "Zerun Ma", "Haochen Ye", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Data quality and diversity are key to the construction of effective instruction-tuning datasets. % With the increasing availability of open-source instruction-tuning datasets, it is advantageous to automatically select high-quality and diverse subsets from a vast amount of data. % Existing methods typically prioritize instance quality and use heuristic rules to maintain diversity. % However, this absence of a comprehensive view of the entire collection often leads to suboptimal results. % Moreover, heuristic rules generally focus on distance or clustering within the embedding space, which fails to accurately capture the intent of complex instructions in the semantic space. % To bridge this gap, we propose a unified method for quantifying the information content of datasets. This method models the semantic space by constructing a label graph and quantifies diversity based on the distribution of information within the graph. % Based on such a measurement, we further introduce an efficient sampling method that selects data samples iteratively to Maximize the Information Gain (MIG) in semantic space. % Experiments on various datasets and base models demonstrate that MIG consistently outperforms state-of-the-art methods. % Notably, the model fine-tuned with 5\\% Tulu3 data sampled by MIG achieves comparable performance to the official SFT model trained on the full dataset, with improvements of +5.73\\% on AlpacaEval and +6.89\\% on Wildbench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.11544", "title": "NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes", "authors": [ "Tianyang Xu", "Haojie Zheng", "Chengze Li", "Haoxiang Chen", "Yixin Liu", "Ruoxi Chen", "Lichao Sun" ], "github": "https://github.com/Terry-Xu-666/NodeRAG", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) empowers large language models to access external and private corpus, enabling factually consistent responses in specific domains. By exploiting the inherent structure of the corpus, graph-based RAG methods further enrich this process by building a knowledge graph index and leveraging the structural nature of graphs. However, current graph-based RAG approaches seldom prioritize the design of graph structures. Inadequately designed graph not only impede the seamless integration of diverse graph algorithms but also result in workflow inconsistencies and degraded performance. To further unleash the potential of graph for RAG, we propose NodeRAG, a graph-centric framework introducing heterogeneous graph structures that enable the seamless and holistic integration of graph-based methodologies into the RAG workflow. By aligning closely with the capabilities of LLMs, this framework ensures a fully cohesive and efficient end-to-end process. Through extensive experiments, we demonstrate that NodeRAG exhibits performance advantages over previous methods, including GraphRAG and LightRAG, not only in indexing time, query time, and storage efficiency but also in delivering superior question-answering performance on multi-hop benchmarks and open-ended head-to-head evaluations with minimal retrieval tokens. Our GitHub repository could be seen at https://github.com/Terry-Xu-666/NodeRAG.", "github_manual": "", "github_regex": "https://github.com/Terry-Xu-666/NodeRAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.13157", "title": "AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis", "authors": [ "Khiem Vuong", "Anurag Ghosh", "Deva Ramanan", "Srinivasa Narasimhan", "Shubham Tulsiani" ], "github": "", "project_page": "", "abstract": "We explore the task of geometric reconstruction of images captured from a mixture of ground and aerial views. Current state-of-the-art learning-based approaches fail to handle the extreme viewpoint variation between aerial-ground image pairs. Our hypothesis is that the lack of high-quality, co-registered aerial-ground datasets for training is a key reason for this failure. Such data is difficult to assemble precisely because it is difficult to reconstruct in a scalable way. To overcome this challenge, we propose a scalable framework combining pseudo-synthetic renderings from 3D city-wide meshes (e.g., Google Earth) with real, ground-level crowd-sourced images (e.g., MegaDepth). The pseudo-synthetic data simulates a wide range of aerial viewpoints, while the real, crowd-sourced images help improve visual fidelity for ground-level images where mesh-based renderings lack sufficient detail, effectively bridging the domain gap between real images and pseudo-synthetic renderings. Using this hybrid dataset, we fine-tune several state-of-the-art algorithms and achieve significant improvements on real-world, zero-shot aerial-ground tasks. For example, we observe that baseline DUSt3R localizes fewer than 5% of aerial-ground pairs within 5 degrees of camera rotation error, while fine-tuning with our data raises accuracy to nearly 56%, addressing a major failure point in handling large viewpoint changes. Beyond camera estimation and scene reconstruction, our dataset also improves performance on downstream tasks like novel-view synthesis in challenging aerial-ground scenarios, demonstrating the practical value of our approach in real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.13173", "title": "It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization", "authors": [ "Ali Behrouz", "Meisam Razaviyayn", "Peilin Zhong", "Vahab Mirrokni" ], "github": "", "project_page": "", "abstract": "Designing efficient and effective architectural backbones has been in the core of research efforts to enhance the capability of foundation models. Inspired by the human cognitive phenomenon of attentional bias-the natural tendency to prioritize certain events or stimuli-we reconceptualize neural architectures, including Transformers, Titans, and modern linear recurrent neural networks as associative memory modules that learn a mapping of keys and values using an internal objective, referred to as attentional bias. Surprisingly, we observed that most existing sequence models leverage either (1) dot-product similarity, or (2) L2 regression objectives as their attentional bias. Going beyond these objectives, we present a set of alternative attentional bias configurations along with their effective approximations to stabilize their training procedure. We then reinterpret forgetting mechanisms in modern deep learning architectures as a form of retention regularization, providing a novel set of forget gates for sequence models. Building upon these insights, we present Miras, a general framework to design deep learning architectures based on four choices of: (i) associative memory architecture, (ii) attentional bias objective, (iii) retention gate, and (iv) memory learning algorithm. We present three novel sequence models-Moneta, Yaad, and Memora-that go beyond the power of existing linear RNNs while maintaining a fast parallelizable training process. Our experiments show different design choices in Miras yield models with varying strengths. For example, certain instances of Miras achieve exceptional performance in special tasks such as language modeling, commonsense reasoning, and recall intensive tasks, even outperforming Transformers and other modern linear recurrent models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.13072", "title": "HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation", "authors": [ "Wenqi Dong", "Bangbang Yang", "Zesong Yang", "Yuan Li", "Tao Hu", "Hujun Bao", "Yuewen Ma", "Zhaopeng Cui" ], "github": "", "project_page": "", "abstract": "Scene-level 3D generation represents a critical frontier in multimedia and computer graphics, yet existing approaches either suffer from limited object categories or lack editing flexibility for interactive applications. In this paper, we present HiScene, a novel hierarchical framework that bridges the gap between 2D image generation and 3D object generation and delivers high-fidelity scenes with compositional identities and aesthetic scene content. Our key insight is treating scenes as hierarchical \"objects\" under isometric views, where a room functions as a complex object that can be further decomposed into manipulatable items. This hierarchical approach enables us to generate 3D content that aligns with 2D representations while maintaining compositional structure. To ensure completeness and spatial alignment of each decomposed instance, we develop a video-diffusion-based amodal completion technique that effectively handles occlusions and shadows between objects, and introduce shape prior injection to ensure spatial coherence within the scene. Experimental results demonstrate that our method produces more natural object arrangements and complete object instances suitable for interactive applications, while maintaining physical plausibility and alignment with user inputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.09621", "title": "Tokenize Image Patches: Global Context Fusion for Effective Haze Removal in Large Images", "authors": [ "Jiuchen Chen", "Xinyu Yan", "Qizhi Xu", "Kaiqi Li" ], "github": "https://github.com/CastleChen339/DehazeXL", "project_page": "", "abstract": "Global contextual information and local detail features are essential for haze removal tasks. Deep learning models perform well on small, low-resolution images, but they encounter difficulties with large, high-resolution ones due to GPU memory limitations. As a compromise, they often resort to image slicing or downsampling. The former diminishes global information, while the latter discards high-frequency details. To address these challenges, we propose DehazeXL, a haze removal method that effectively balances global context and local feature extraction, enabling end-to-end modeling of large images on mainstream GPU hardware. Additionally, to evaluate the efficiency of global context utilization in haze removal performance, we design a visual attribution method tailored to the characteristics of haze removal tasks. Finally, recognizing the lack of benchmark datasets for haze removal in large images, we have developed an ultra-high-resolution haze removal dataset (8KDehaze) to support model training and testing. It includes 10000 pairs of clear and hazy remote sensing images, each sized at 8192 times 8192 pixels. Extensive experiments demonstrate that DehazeXL can infer images up to 10240 times 10240 pixels with only 21 GB of memory, achieving state-of-the-art results among all evaluated methods. The source code and experimental dataset are available at https://github.com/CastleChen339/DehazeXL.", "github_manual": "", "github_regex": "https://github.com/CastleChen339/DehazeXL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.13626", "title": "Thought Manipulation: External Thought Can Be Efficient for Large Reasoning Models", "authors": [ "Yule Liu", "Jingyi Zheng", "Zhen Sun", "Zifan Peng", "Wenhan Dong", "Zeyang Sha", "Shiwen Cui", "Weiqiang Wang", "Xinlei He" ], "github": "", "project_page": "", "abstract": "Recent advancements in large reasoning models (LRMs) have demonstrated the effectiveness of scaling test-time computation to enhance reasoning capabilities in multiple tasks. However, LRMs typically suffer from \"overthinking\" problems, where models generate significantly redundant reasoning steps while bringing limited performance gains. Existing work relies on fine-tuning to mitigate overthinking, which requires additional data, unconventional training setups, risky safety misalignment, and poor generalization. Through empirical analysis, we reveal an important characteristic of LRM behaviors that placing external CoTs generated by smaller models between the thinking token ( and ) can effectively manipulate the model to generate fewer thoughts. Building on these insights, we propose a simple yet efficient pipeline, ThoughtMani, to enable LRMs to bypass unnecessary intermediate steps and reduce computational costs significantly. We conduct extensive experiments to validate the utility and efficiency of ThoughtMani. For instance, when applied to QwQ-32B on the LiveBench/Code dataset, ThoughtMani keeps the original performance and reduces output token counts by approximately 30%, with little overhead from the CoT generator. Furthermore, we find that ThoughtMani enhances safety alignment by an average of 10%. Since model vendors typically serve models of different sizes simultaneously, ThoughtMani provides an effective way to construct more efficient and accessible LRMs for real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.10823", "title": "CLASH: Evaluating Language Models on Judging High-Stakes Dilemmas from Multiple Perspectives", "authors": [ "Ayoung Lee", "Ryan Sungmo Kwon", "Peter Railton", "Lu Wang" ], "github": "", "project_page": "", "abstract": "Navigating high-stakes dilemmas involving conflicting values is challenging even for humans, let alone for AI. Yet prior work in evaluating the reasoning capabilities of large language models (LLMs) in such situations has been limited to everyday scenarios. To close this gap, this work first introduces CLASH (Character perspective-based LLM Assessments in Situations with High-stakes), a meticulously curated dataset consisting of 345 high-impact dilemmas along with 3,795 individual perspectives of diverse values. In particular, we design CLASH in a way to support the study of critical aspects of value-based decision-making processes which are missing from prior work, including understanding decision ambivalence and psychological discomfort as well as capturing the temporal shifts of values in characters' perspectives. By benchmarking 10 open and closed frontier models, we uncover several key findings. (1) Even the strongest models, such as GPT-4o and Claude-Sonnet, achieve less than 50% accuracy in identifying situations where the decision should be ambivalent, while they perform significantly better in clear-cut scenarios. (2) While LLMs reasonably predict psychological discomfort as marked by human, they inadequately comprehend perspectives involving value shifts, indicating a need for LLMs to reason over complex values. (3) Our experiments also reveal a significant correlation between LLMs' value preferences and their steerability towards a given value. (4) Finally, LLMs exhibit greater steerability when engaged in value reasoning from a third-party perspective, compared to a first-person setup, though certain value pairs benefit uniquely from the first-person framing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.13816", "title": "Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations", "authors": [ "Chenghao Xiao", "Hou Pong Chan", "Hao Zhang", "Mahani Aljunied", "Lidong Bing", "Noura Al Moubayed", "Yu Rong" ], "github": "https://github.com/DAMO-NLP-SG/LLM-Multilingual-Knowledge-Boundaries", "project_page": "", "abstract": "While understanding the knowledge boundaries of LLMs is crucial to prevent hallucination, research on knowledge boundaries of LLMs has predominantly focused on English. In this work, we present the first study to analyze how LLMs recognize knowledge boundaries across different languages by probing their internal representations when processing known and unknown questions in multiple languages. Our empirical studies reveal three key findings: 1) LLMs' perceptions of knowledge boundaries are encoded in the middle to middle-upper layers across different languages. 2) Language differences in knowledge boundary perception follow a linear structure, which motivates our proposal of a training-free alignment method that effectively transfers knowledge boundary perception ability across languages, thereby helping reduce hallucination risk in low-resource languages; 3) Fine-tuning on bilingual question pair translation further enhances LLMs' recognition of knowledge boundaries across languages. Given the absence of standard testbeds for cross-lingual knowledge boundary analysis, we construct a multilingual evaluation suite comprising three representative types of knowledge boundary data. Our code and datasets are publicly available at https://github.com/DAMO-NLP-SG/LLM-Multilingual-Knowledge-Boundaries.", "github_manual": "", "github_regex": "https://github.com/DAMO-NLP-SG/LLM-Multilingual-Knowledge-Boundaries", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.13828", "title": "Generative AI Act II: Test Time Scaling Drives Cognition Engineering", "authors": [ "Shijie Xia", "Yiwei Qin", "Xuefeng Li", "Yan Ma", "Run-Ze Fan", "Steffi Chern", "Haoyang Zou", "Fan Zhou", "Xiangkun Hu", "Jiahe Jin", "Yanheng He", "Yixin Ye", "Yixiu Liu", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/cognition-engineering", "project_page": "", "abstract": "The first generation of Large Language Models - what might be called \"Act I\" of generative AI (2020-2023) - achieved remarkable success through massive parameter and data scaling, yet exhibited fundamental limitations in knowledge latency, shallow reasoning, and constrained cognitive processes. During this era, prompt engineering emerged as our primary interface with AI, enabling dialogue-level communication through natural language. We now witness the emergence of \"Act II\" (2024-present), where models are transitioning from knowledge-retrieval systems (in latent space) to thought-construction engines through test-time scaling techniques. This new paradigm establishes a mind-level connection with AI through language-based thoughts. In this paper, we clarify the conceptual foundations of cognition engineering and explain why this moment is critical for its development. We systematically break down these advanced approaches through comprehensive tutorials and optimized implementations, democratizing access to cognition engineering and enabling every practitioner to participate in AI's second act. We provide a regularly updated collection of papers on test-time scaling in the GitHub Repository: https://github.com/GAIR-NLP/cognition-engineering", "github_manual": "", "github_regex": "https://github.com/GAIR-NLP/cognition-engineering", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.13519", "title": "Filter2Noise: Interpretable Self-Supervised Single-Image Denoising for Low-Dose CT with Attention-Guided Bilateral Filtering", "authors": [ "Yipeng Sun", "Linda-Sophie Schneider", "Mingxuan Gu", "Siyuan Mei", "Chengze Ye", "Fabian Wagner", "Siming Bayer", "Andreas Maier" ], "github": "https://github.com/sypsyp97/Filter2Noise.git", "project_page": "", "abstract": "Effective denoising is crucial in low-dose CT to enhance subtle structures and low-contrast lesions while preventing diagnostic errors. Supervised methods struggle with limited paired datasets, and self-supervised approaches often require multiple noisy images and rely on deep networks like U-Net, offering little insight into the denoising mechanism. To address these challenges, we propose an interpretable self-supervised single-image denoising framework -- Filter2Noise (F2N). Our approach introduces an Attention-Guided Bilateral Filter that adapted to each noisy input through a lightweight module that predicts spatially varying filter parameters, which can be visualized and adjusted post-training for user-controlled denoising in specific regions of interest. To enable single-image training, we introduce a novel downsampling shuffle strategy with a new self-supervised loss function that extends the concept of Noise2Noise to a single image and addresses spatially correlated noise. On the Mayo Clinic 2016 low-dose CT dataset, F2N outperforms the leading self-supervised single-image method (ZS-N2N) by 4.59 dB PSNR while improving transparency, user control, and parametric efficiency. These features provide key advantages for medical applications that require precise and interpretable noise reduction. Our code is demonstrated at https://github.com/sypsyp97/Filter2Noise.git .", "github_manual": "", "github_regex": "https://github.com/sypsyp97/Filter2Noise.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.12083", "title": "Self-alignment of Large Video Language Models with Refined Regularized Preference Optimization", "authors": [ "Pritam Sarkar", "Ali Etemad" ], "github": "", "project_page": "", "abstract": "Despite recent advances in Large Video Language Models (LVLMs), they still struggle with fine-grained temporal understanding, hallucinate, and often make simple mistakes on even simple video question-answering tasks, all of which pose significant challenges to their safe and reliable deployment in real-world applications. To address these limitations, we propose a self-alignment framework that enables LVLMs to learn from their own errors. Our proposed framework first obtains a training set of preferred and non-preferred response pairs, where non-preferred responses are generated by incorporating common error patterns that often occur due to inadequate spatio-temporal understanding, spurious correlations between co-occurring concepts, and over-reliance on linguistic cues while neglecting the vision modality, among others. To facilitate self-alignment of LVLMs with the constructed preferred and non-preferred response pairs, we introduce Refined Regularized Preference Optimization (RRPO), a novel preference optimization method that utilizes sub-sequence-level refined rewards and token-wise KL regularization to address the limitations of Direct Preference Optimization (DPO). We demonstrate that RRPO achieves more precise alignment and more stable training compared to DPO. Our experiments and analysis validate the effectiveness of our approach across diverse video tasks, including video hallucination, short- and long-video understanding, and fine-grained temporal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.13359", "title": "Cost-of-Pass: An Economic Framework for Evaluating Language Models", "authors": [ "Mehmet Hamza Erol", "Batu El", "Mirac Suzgun", "Mert Yuksekgonul", "James Zou" ], "github": "", "project_page": "", "abstract": "The widespread adoption of AI systems in the economy hinges on their ability to generate economic value that outweighs their inference costs. Evaluating this tradeoff requires metrics that account for both performance and costs. We propose a framework grounded in production theory for evaluating language models by combining accuracy and inference cost. We introduce \"cost-of-pass\", the expected monetary cost of generating a correct solution. We then define the \"frontier cost-of-pass\" as the minimum cost-of-pass achievable across available models or the \"human-expert, using the approximate cost of hiring an expert. Our analysis reveals distinct economic insights. First, lightweight models are most cost-effective for basic quantitative tasks, large models for knowledge-intensive ones, and reasoning models for complex quantitative problems, despite higher per-token costs. Second, tracking this frontier cost-of-pass over the past year reveals significant progress, particularly for complex quantitative tasks where the cost has roughly halved every few months. Third, to trace key innovations driving this progress, we examine counterfactual frontiers: estimates of cost-efficiency without specific model classes. We find that innovations in lightweight, large, and reasoning models have been essential for pushing the frontier in basic quantitative, knowledge-intensive, and complex quantitative tasks, respectively. Finally, we assess the cost-reductions afforded by common inference-time techniques like majority voting and self-refinement, finding that their marginal accuracy gains rarely justify their costs. Our findings underscore that complementary model-level innovations are the primary drivers of cost-efficiency, and our economic framework provides a principled tool for measuring this progress and guiding deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-21", "arxiv_id": "2504.13677", "title": "Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results", "authors": [ "Andrea Santilli", "Adam Golinski", "Michael Kirchhof", "Federico Danieli", "Arno Blaas", "Miao Xiong", "Luca Zappella", "Sinead Williamson" ], "github": "", "project_page": "", "abstract": "Uncertainty Quantification (UQ) in Language Models (LMs) is crucial for improving their safety and reliability. Evaluations often use performance metrics like AUROC to assess how well UQ methods (e.g., negative sequence probabilities) correlate with task correctness functions (e.g., ROUGE-L). In this paper, we show that commonly used correctness functions bias UQ evaluations by inflating the performance of certain UQ methods. We evaluate 7 correctness functions -- from lexical-based and embedding-based metrics to LLM-as-a-judge approaches -- across 4 datasets x 4 models x 6 UQ methods. Our analysis reveals that length biases in the errors of these correctness functions distort UQ assessments by interacting with length biases in UQ methods. We identify LLM-as-a-judge approaches as among the least length-biased choices and hence a potential solution to mitigate these biases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.14945", "title": "Learning to Reason under Off-Policy Guidance", "authors": [ "Jianhao Yan", "Yafu Li", "Zican Hu", "Zhi Wang", "Ganqu Cui", "Xiaoye Qu", "Yu Cheng", "Yue Zhang" ], "github": "https://github.com/ElliottYan/LUFFY", "project_page": "", "abstract": "Recent advances in large reasoning models (LRMs) demonstrate that sophisticated behaviors such as multi-step reasoning and self-reflection can emerge via reinforcement learning (RL) with simple rule-based rewards. However, existing zero-RL approaches are inherently ``on-policy'', limiting learning to a model's own outputs and failing to acquire reasoning abilities beyond its initial capabilities. We introduce LUFFY (Learning to reason Under oFF-policY guidance), a framework that augments zero-RL with off-policy reasoning traces. LUFFY dynamically balances imitation and exploration by combining off-policy demonstrations with on-policy rollouts during training. Notably, we propose policy shaping via regularized importance sampling to avoid superficial and rigid imitation during mixed-policy training. Remarkably, LUFFY achieves an over +7.0 average gain across six math benchmarks and an advantage of over +6.2 points in out-of-distribution tasks. It also substantially surpasses imitation-based supervised fine-tuning (SFT), particularly in generalization. Analysis shows LUFFY not only imitates effectively but also explores beyond demonstrations, offering a scalable path to train generalizable reasoning models with off-policy guidance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ElliottYan/LUFFY", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.14603", "title": "UFO2: The Desktop AgentOS", "authors": [ "Chaoyun Zhang", "He Huang", "Chiming Ni", "Jian Mu", "Si Qin", "Shilin He", "Lu Wang", "Fangkai Yang", "Pu Zhao", "Chao Du", "Liqun Li", "Yu Kang", "Zhao Jiang", "Suzhen Zheng", "Rujia Wang", "Jiaxu Qian", "Minghua Ma", "Jian-Guang Lou", "Qingwei Lin", "Saravan Rajmohan", "Dongmei Zhang" ], "github": "https://github.com/microsoft/UFO", "project_page": "", "abstract": "Recent Computer-Using Agents (CUAs), powered by multimodal large language models (LLMs), offer a promising direction for automating complex desktop workflows through natural language. However, most existing CUAs remain conceptual prototypes, hindered by shallow OS integration, fragile screenshot-based interaction, and disruptive execution. We present UFO2, a multiagent AgentOS for Windows desktops that elevates CUAs into practical, system-level automation. UFO2 features a centralized HostAgent for task decomposition and coordination, alongside a collection of application-specialized AppAgent equipped with native APIs, domain-specific knowledge, and a unified GUI--API action layer. This architecture enables robust task execution while preserving modularity and extensibility. A hybrid control detection pipeline fuses Windows UI Automation (UIA) with vision-based parsing to support diverse interface styles. Runtime efficiency is further enhanced through speculative multi-action planning, reducing per-step LLM overhead. Finally, a Picture-in-Picture (PiP) interface enables automation within an isolated virtual desktop, allowing agents and users to operate concurrently without interference. We evaluate UFO2 across over 20 real-world Windows applications, demonstrating substantial improvements in robustness and execution accuracy over prior CUAs. Our results show that deep OS integration unlocks a scalable path toward reliable, user-aligned desktop automation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/UFO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.15047", "title": "RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary Quality-Diversity Search", "authors": [ "Quy-Anh Dang", "Chris Ngo", "Truong-Son Hy" ], "github": "https://github.com/knoveleng/rainbowplus", "project_page": "", "abstract": "Large Language Models (LLMs) exhibit remarkable capabilities but are susceptible to adversarial prompts that exploit vulnerabilities to produce unsafe or biased outputs. Existing red-teaming methods often face scalability challenges, resource-intensive requirements, or limited diversity in attack strategies. We propose RainbowPlus, a novel red-teaming framework rooted in evolutionary computation, enhancing adversarial prompt generation through an adaptive quality-diversity (QD) search that extends classical evolutionary algorithms like MAP-Elites with innovations tailored for language models. By employing a multi-element archive to store diverse high-quality prompts and a comprehensive fitness function to evaluate multiple prompts concurrently, RainbowPlus overcomes the constraints of single-prompt archives and pairwise comparisons in prior QD methods like Rainbow Teaming. Experiments comparing RainbowPlus to QD methods across six benchmark datasets and four open-source LLMs demonstrate superior attack success rate (ASR) and diversity (Diverse-Score approx 0.84), generating up to 100 times more unique prompts (e.g., 10,418 vs. 100 for Ministral-8B-Instruct-2410). Against nine state-of-the-art methods on the HarmBench dataset with twelve LLMs (ten open-source, two closed-source), RainbowPlus achieves an average ASR of 81.1%, surpassing AutoDAN-Turbo by 3.9%, and is 9 times faster (1.45 vs. 13.50 hours). Our open-source implementation fosters further advancements in LLM safety, offering a scalable tool for vulnerability assessment. Code and resources are publicly available at https://github.com/knoveleng/rainbowplus, supporting reproducibility and future research in LLM red-teaming.", "github_manual": "", "github_regex": "https://github.com/knoveleng/rainbowplus", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.13203", "title": "X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents", "authors": [ "Salman Rahman", "Liwei Jiang", "James Shiffer", "Genglin Liu", "Sheriff Issaka", "Md Rizwan Parvez", "Hamid Palangi", "Kai-Wei Chang", "Yejin Choi", "Saadia Gabriel" ], "github": "", "project_page": "", "abstract": "Multi-turn interactions with language models (LMs) pose critical safety risks, as harmful intent can be strategically spread across exchanges. Yet, the vast majority of prior work has focused on single-turn safety, while adaptability and diversity remain among the key challenges of multi-turn red-teaming. To address these challenges, we present X-Teaming, a scalable framework that systematically explores how seemingly harmless interactions escalate into harmful outcomes and generates corresponding attack scenarios. X-Teaming employs collaborative agents for planning, attack optimization, and verification, achieving state-of-the-art multi-turn jailbreak effectiveness and diversity with success rates up to 98.1% across representative leading open-weight and closed-source models. In particular, X-Teaming achieves a 96.2% attack success rate against the latest Claude 3.7 Sonnet model, which has been considered nearly immune to single-turn attacks. Building on X-Teaming, we introduce XGuard-Train, an open-source multi-turn safety training dataset that is 20x larger than the previous best resource, comprising 30K interactive jailbreaks, designed to enable robust multi-turn safety alignment for LMs. Our work offers essential tools and insights for mitigating sophisticated conversational attacks, advancing the multi-turn safety of LMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.14396", "title": "SphereDiff: Tuning-free Omnidirectional Panoramic Image and Video Generation via Spherical Latent Representation", "authors": [ "Minho Park", "Taewoong Kang", "Jooyeol Yun", "Sungwon Hwang", "Jaegul Choo" ], "github": "https://github.com/pmh9960/SphereDiff", "project_page": "", "abstract": "The increasing demand for AR/VR applications has highlighted the need for high-quality 360-degree panoramic content. However, generating high-quality 360-degree panoramic images and videos remains a challenging task due to the severe distortions introduced by equirectangular projection (ERP). Existing approaches either fine-tune pretrained diffusion models on limited ERP datasets or attempt tuning-free methods that still rely on ERP latent representations, leading to discontinuities near the poles. In this paper, we introduce SphereDiff, a novel approach for seamless 360-degree panoramic image and video generation using state-of-the-art diffusion models without additional tuning. We define a spherical latent representation that ensures uniform distribution across all perspectives, mitigating the distortions inherent in ERP. We extend MultiDiffusion to spherical latent space and propose a spherical latent sampling method to enable direct use of pretrained diffusion models. Moreover, we introduce distortion-aware weighted averaging to further improve the generation quality in the projection process. Our method outperforms existing approaches in generating 360-degree panoramic content while maintaining high fidelity, making it a robust solution for immersive AR/VR applications. The code is available here. https://github.com/pmh9960/SphereDiff", "github_manual": "", "github_regex": "https://github.com/pmh9960/SphereDiff", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.13805", "title": "LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark", "authors": [ "Guangyi Liu", "Pengxiang Zhao", "Liang Liu", "Zhiming Chen", "Yuxiang Chai", "Shuai Ren", "Hao Wang", "Shibo He", "Wenchao Meng" ], "github": "https://github.com/lgy0404/LearnAct", "project_page": "", "abstract": "Mobile GUI agents show promise in automating tasks but face generalization challenges in diverse real-world scenarios. Traditional approaches using pre-training or fine-tuning with massive datasets struggle with the diversity of mobile applications and user-specific tasks. We propose enhancing mobile GUI agent capabilities through human demonstrations, focusing on improving performance in unseen scenarios rather than pursuing universal generalization through larger datasets. To realize this paradigm, we introduce LearnGUI, the first comprehensive dataset specifically designed for studying demonstration-based learning in mobile GUI agents, comprising 2,252 offline tasks and 101 online tasks with high-quality human demonstrations. We further develop LearnAct, a sophisticated multi-agent framework that automatically extracts knowledge from demonstrations to enhance task completion. This framework integrates three specialized agents: DemoParser for knowledge extraction, KnowSeeker for relevant knowledge retrieval, and ActExecutor for demonstration-enhanced task execution. Our experimental results show significant performance gains in both offline and online evaluations. In offline assessments, a single demonstration improves model performance, increasing Gemini-1.5-Pro's accuracy from 19.3% to 51.7%. In online evaluations, our framework enhances UI-TARS-7B-SFT's task success rate from 18.1% to 32.8%. LearnAct framework and LearnGUI benchmark establish demonstration-based learning as a promising direction for more adaptable, personalized, and deployable mobile GUI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lgy0404/LearnAct", "github_api_added_by": "auto" }, { "date": "2025-04-22", "arxiv_id": "2504.13941", "title": "NEMOTRON-CROSSTHINK: Scaling Self-Learning beyond Math Reasoning", "authors": [ "Syeda Nahida Akter", "Shrimai Prabhumoye", "Matvei Novikov", "Seungju Han", "Ying Lin", "Evelina Bakhturi", "Eric Nyberg", "Yejin Choi", "Mostofa Patwary", "Mohammad Shoeybi", "Bryan Catanzaro" ], "github": "https://github.com/volcengine/verl", "project_page": "", "abstract": "Large Language Models (LLMs) have shown strong reasoning capabilities, particularly when enhanced through Reinforcement Learning (RL). While prior work has successfully applied RL to mathematical reasoning -- where rules and correctness are well-defined -- generalizing these methods to broader reasoning domains remains challenging due to limited data, the lack of verifiable reward structures, and diverse task requirements. In this work, we propose NEMOTRON-CROSSTHINK, a framework that systematically incorporates multi-domain corpora, including both synthetic and real-world question-answer pairs, into RL training to improve generalization across diverse reasoning tasks. NEMOTRON-CROSSTHINK addresses key challenges by (1) incorporating data from varied sources spanning STEM, humanities, social sciences, etc.; (2) applying structured templates (e.g., multiple-choice and open-ended) to control answer-space complexity; (3) filtering for verifiable answers; and (4) optimizing data blending strategies that utilizes data from multiple sources effectively. Our approach enables scalable and verifiable reward modeling beyond mathematics and demonstrates improved accuracies on both math (MATH-500: +30.1%, AMC23:+27.5%) and non-math reasoning benchmarks (MMLU-PRO: +12.8%, GPQA-DIAMOND: +11.3%, AGIEVAL: +15.1%, SUPERGPQA: +3.8%). Moreover, NEMOTRON-CROSSTHINK exhibits significantly improved response efficiency -- using 28% fewer tokens for correct answers -- highlighting more focused and effective reasoning. Through NEMOTRON-CROSSTHINK, we demonstrate that integrating multi-domain, multi-format data in RL leads to more accurate, efficient, and generalizable LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/volcengine/verl", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.13958", "title": "ToolRL: Reward is All Tool Learning Needs", "authors": [ "Cheng Qian", "Emre Can Acikgoz", "Qi He", "Hongru Wang", "Xiusi Chen", "Dilek Hakkani-T\u00fcr", "Gokhan Tur", "Heng Ji" ], "github": "https://github.com/qiancheng0/ToolRL", "project_page": "", "abstract": "Current Large Language Models (LLMs) often undergo supervised fine-tuning (SFT) to acquire tool use capabilities. However, SFT struggles to generalize to unfamiliar or complex tool use scenarios. Recent advancements in reinforcement learning (RL), particularly with R1-like models, have demonstrated promising reasoning and generalization abilities. Yet, reward design for tool use presents unique challenges: multiple tools may be invoked with diverse parameters, and coarse-grained reward signals, such as answer matching, fail to offer the finegrained feedback required for effective learning. In this work, we present the first comprehensive study on reward design for tool selection and application tasks within the RL paradigm. We systematically explore a wide range of reward strategies, analyzing their types, scales, granularity, and temporal dynamics. Building on these insights, we propose a principled reward design tailored for tool use tasks and apply it to train LLMs using Group Relative Policy Optimization (GRPO). Empirical evaluations across diverse benchmarks demonstrate that our approach yields robust, scalable, and stable training, achieving a 17% improvement over base models and a 15% gain over SFT models. These results highlight the critical role of thoughtful reward design in enhancing the tool use capabilities and generalization performance of LLMs. All the codes are released to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/qiancheng0/ToolRL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.15280", "title": "Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs", "authors": [ "Chun-Hsiao Yeh", "Chenyu Wang", "Shengbang Tong", "Ta-Ying Cheng", "Rouyu Wang", "Tianzhe Chu", "Yuexiang Zhai", "Yubei Chen", "Shenghua Gao", "Yi Ma" ], "github": "", "project_page": "", "abstract": "Multi-view understanding, the ability to reconcile visual information across diverse viewpoints for effective navigation, manipulation, and 3D scene comprehension, is a fundamental challenge in Multi-Modal Large Language Models (MLLMs) to be used as embodied agents. While recent MLLMs have shown impressive advances in high-level reasoning and planning, they frequently fall short when confronted with multi-view geometric consistency and cross-view correspondence. To comprehensively evaluate the challenges of MLLMs in multi-view scene reasoning, we propose All-Angles Bench, a benchmark of over 2,100 human carefully annotated multi-view question-answer pairs across 90 diverse real-world scenes. Our six tasks (counting, attribute identification, relative distance, relative direction, object manipulation, and camera pose estimation) specifically test model's geometric correspondence and the capacity to align information consistently across views. Our extensive experiments, benchmark on 27 representative MLLMs including Gemini-2.0-Flash, Claude-3.7-Sonnet, and GPT-4o against human evaluators reveals a substantial performance gap, indicating that current MLLMs remain far from human-level proficiency. Through in-depth analysis, we show that MLLMs are particularly underperforming under two aspects: (1) cross-view correspondence for partially occluded views and (2) establishing the coarse camera poses. These findings highlight the necessity of domain-specific refinements or modules that embed stronger multi-view awareness. We believe that our All-Angles Bench offers valuable insights and contribute to bridging the gap between MLLMs and human-level multi-view understanding. The project and benchmark are publicly available at https://danielchyeh.github.io/All-Angles-Bench/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.15257", "title": "FlowReasoner: Reinforcing Query-Level Meta-Agents", "authors": [ "Hongcheng Gao", "Yue Liu", "Yufei He", "Longxu Dou", "Chao Du", "Zhijie Deng", "Bryan Hooi", "Min Lin", "Tianyu Pang" ], "github": "https://github.com/sail-sg/FlowReasoner", "project_page": "", "abstract": "This paper proposes a query-level meta-agent named FlowReasoner to automate the design of query-level multi-agent systems, i.e., one system per user query. Our core idea is to incentivize a reasoning-based meta-agent via external execution feedback. Concretely, by distilling DeepSeek R1, we first endow the basic reasoning ability regarding the generation of multi-agent systems to FlowReasoner. Then, we further enhance it via reinforcement learning (RL) with external execution feedback. A multi-purpose reward is designed to guide the RL training from aspects of performance, complexity, and efficiency. In this manner, FlowReasoner is enabled to generate a personalized multi-agent system for each user query via deliberative reasoning. Experiments on both engineering and competition code benchmarks demonstrate the superiority of FlowReasoner. Remarkably, it surpasses o1-mini by 10.52% accuracy across three benchmarks. The code is available at https://github.com/sail-sg/FlowReasoner.", "github_manual": "", "github_regex": "https://github.com/sail-sg/FlowReasoner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.14655", "title": "LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs", "authors": [ "Yunhui Xia", "Wei Shen", "Yan Wang", "Jason Klein Liu", "Huifeng Sun", "Siyue Wu", "Jian Hu", "Xiaolong Xu" ], "github": "https://github.com/newfacade/LeetCodeDataset", "project_page": "", "abstract": "We introduce LeetCodeDataset, a high-quality benchmark for evaluating and training code-generation models, addressing two key challenges in LLM research: the lack of reasoning-focused coding benchmarks and self-contained training testbeds. By curating LeetCode Python problems with rich metadata, broad coverage, 100+ test cases per problem, and temporal splits (pre/post July 2024), our dataset enables contamination-free evaluation and efficient supervised fine-tuning (SFT). Experiments show reasoning models significantly outperform non-reasoning counterparts, while SFT with only 2.6K model-generated solutions achieves performance comparable to 110K-sample counterparts. The dataset and evaluation framework are available on Hugging Face and Github.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/newfacade/LeetCodeDataset", "github_api_added_by": "auto" }, { "date": "2025-04-22", "arxiv_id": "2504.15217", "title": "DRAGON: Distributional Rewards Optimize Diffusion Generative Models", "authors": [ "Yatong Bai", "Jonah Casebeer", "Somayeh Sojoudi", "Nicholas J. Bryan" ], "github": "", "project_page": "", "abstract": "We present Distributional RewArds for Generative OptimizatioN (DRAGON), a versatile framework for fine-tuning media generation models towards a desired outcome. Compared with traditional reinforcement learning with human feedback (RLHF) or pairwise preference approaches such as direct preference optimization (DPO), DRAGON is more flexible. It can optimize reward functions that evaluate either individual examples or distributions of them, making it compatible with a broad spectrum of instance-wise, instance-to-distribution, and distribution-to-distribution rewards. Leveraging this versatility, we construct novel reward functions by selecting an encoder and a set of reference examples to create an exemplar distribution. When cross-modality encoders such as CLAP are used, the reference examples may be of a different modality (e.g., text versus audio). Then, DRAGON gathers online and on-policy generations, scores them to construct a positive demonstration set and a negative set, and leverages the contrast between the two sets to maximize the reward. For evaluation, we fine-tune an audio-domain text-to-music diffusion model with 20 different reward functions, including a custom music aesthetics model, CLAP score, Vendi diversity, and Frechet audio distance (FAD). We further compare instance-wise (per-song) and full-dataset FAD settings while ablating multiple FAD encoders and reference sets. Over all 20 target rewards, DRAGON achieves an 81.45% average win rate. Moreover, reward functions based on exemplar sets indeed enhance generations and are comparable to model-based rewards. With an appropriate exemplar set, DRAGON achieves a 60.95% human-voted music quality win rate without training on human preference annotations. As such, DRAGON exhibits a new approach to designing and optimizing reward functions for improving human-perceived quality. Sound examples at https://ml-dragon.github.io/web.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.14239", "title": "InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners", "authors": [ "Yuhang Liu", "Pengxiang Li", "Congkai Xie", "Xavier Hu", "Xiaotian Han", "Shengyu Zhang", "Hongxia Yang", "Fei Wu" ], "github": "https://github.com/Reallm-Labs/InfiGUI-R1", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have powered Graphical User Interface (GUI) Agents, showing promise in automating tasks on computing devices. Recent works have begun exploring reasoning in GUI tasks with encouraging results. However, many current approaches rely on manually designed reasoning templates, which may result in reasoning that is not sufficiently robust and adaptive for complex GUI environments. Meanwhile, some existing agents continue to operate as Reactive Actors, relying primarily on implicit reasoning that may lack sufficient depth for GUI tasks demanding planning and error recovery. We argue that advancing these agents requires a shift from reactive acting towards acting based on deliberate reasoning. To facilitate this transformation, we introduce InfiGUI-R1, an MLLM-based GUI agent developed through our Actor2Reasoner framework, a reasoning-centric, two-stage training approach designed to progressively evolve agents from Reactive Actors to Deliberative Reasoners. The first stage, Reasoning Injection, focuses on establishing a basic reasoner. We employ Spatial Reasoning Distillation to transfer cross-modal spatial reasoning capabilities from teacher models to MLLMs through trajectories with explicit reasoning steps, enabling models to integrate GUI visual-spatial information with logical reasoning before action generation. The second stage, Deliberation Enhancement, refines the basic reasoner into a deliberative one using Reinforcement Learning. This stage introduces two approaches: Sub-goal Guidance, which rewards models for generating accurate intermediate sub-goals, and Error Recovery Scenario Construction, which creates failure-and-recovery training scenarios from identified prone-to-error steps. Experimental results show InfiGUI-R1 achieves strong performance in GUI grounding and trajectory tasks. Resources at https://github.com/Reallm-Labs/InfiGUI-R1.", "github_manual": "", "github_regex": "https://github.com/Reallm-Labs/InfiGUI-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.14717", "title": "TAPIP3D: Tracking Any Point in Persistent 3D Geometry", "authors": [ "Bowei Zhang", "Lei Ke", "Adam W. Harley", "Katerina Fragkiadaki" ], "github": "", "project_page": "", "abstract": "We introduce TAPIP3D, a novel approach for long-term 3D point tracking in monocular RGB and RGB-D videos. TAPIP3D represents videos as camera-stabilized spatio-temporal feature clouds, leveraging depth and camera motion information to lift 2D video features into a 3D world space where camera motion is effectively canceled. TAPIP3D iteratively refines multi-frame 3D motion estimates within this stabilized representation, enabling robust tracking over extended periods. To manage the inherent irregularities of 3D point distributions, we propose a Local Pair Attention mechanism. This 3D contextualization strategy effectively exploits spatial relationships in 3D, forming informative feature neighborhoods for precise 3D trajectory estimation. Our 3D-centric approach significantly outperforms existing 3D point tracking methods and even enhances 2D tracking accuracy compared to conventional 2D pixel trackers when accurate depth is available. It supports inference in both camera coordinates (i.e., unstabilized) and world coordinates, and our results demonstrate that compensating for camera motion improves tracking performance. Our approach replaces the conventional 2D square correlation neighborhoods used in prior 2D and 3D trackers, leading to more robust and accurate results across various 3D point tracking benchmarks. Project Page: https://tapip3d.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.15281", "title": "StyleMe3D: Stylization with Disentangled Priors by Multiple Encoders on 3D Gaussians", "authors": [ "Cailin Zhuang", "Yaoqi Hu", "Xuanyang Zhang", "Wei Cheng", "Jiacheng Bao", "Shengqi Liu", "Yiying Yang", "Xianfang Zeng", "Gang Yu", "Ming Li" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) excels in photorealistic scene reconstruction but struggles with stylized scenarios (e.g., cartoons, games) due to fragmented textures, semantic misalignment, and limited adaptability to abstract aesthetics. We propose StyleMe3D, a holistic framework for 3D GS style transfer that integrates multi-modal style conditioning, multi-level semantic alignment, and perceptual quality enhancement. Our key insights include: (1) optimizing only RGB attributes preserves geometric integrity during stylization; (2) disentangling low-, medium-, and high-level semantics is critical for coherent style transfer; (3) scalability across isolated objects and complex scenes is essential for practical deployment. StyleMe3D introduces four novel components: Dynamic Style Score Distillation (DSSD), leveraging Stable Diffusion's latent space for semantic alignment; Contrastive Style Descriptor (CSD) for localized, content-aware texture transfer; Simultaneously Optimized Scale (SOS) to decouple style details and structural coherence; and 3D Gaussian Quality Assessment (3DG-QA), a differentiable aesthetic prior trained on human-rated data to suppress artifacts and enhance visual harmony. Evaluated on NeRF synthetic dataset (objects) and tandt db (scenes) datasets, StyleMe3D outperforms state-of-the-art methods in preserving geometric details (e.g., carvings on sculptures) and ensuring stylistic consistency across scenes (e.g., coherent lighting in landscapes), while maintaining real-time rendering. This work bridges photorealistic 3D GS and artistic stylization, unlocking applications in gaming, virtual worlds, and digital art.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.15133", "title": "EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models", "authors": [ "Ziwen Xu", "Shuxun Wang", "Kewei Xu", "Haoming Xu", "Mengru Wang", "Xinle Deng", "Yunzhi Yao", "Guozhou Zheng", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/EasyEdit", "project_page": "", "abstract": "In this paper, we introduce EasyEdit2, a framework designed to enable plug-and-play adjustability for controlling Large Language Model (LLM) behaviors. EasyEdit2 supports a wide range of test-time interventions, including safety, sentiment, personality, reasoning patterns, factuality, and language features. Unlike its predecessor, EasyEdit2 features a new architecture specifically designed for seamless model steering. It comprises key modules such as the steering vector generator and the steering vector applier, which enable automatic generation and application of steering vectors to influence the model's behavior without modifying its parameters. One of the main advantages of EasyEdit2 is its ease of use-users do not need extensive technical knowledge. With just a single example, they can effectively guide and adjust the model's responses, making precise control both accessible and efficient. Empirically, we report model steering performance across different LLMs, demonstrating the effectiveness of these techniques. We have released the source code on GitHub at https://github.com/zjunlp/EasyEdit along with a demonstration notebook. In addition, we provide a demo video at https://zjunlp.github.io/project/EasyEdit2/video for a quick introduction.", "github_manual": "", "github_regex": "https://github.com/zjunlp/EasyEdit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.08902", "title": "LookingGlass: Generative Anamorphoses via Laplacian Pyramid Warping", "authors": [ "Pascal Chang", "Sergio Sancho", "Jingwei Tang", "Markus Gross", "Vinicius C. Azevedo" ], "github": "https://github.com/dangeng/visual_anagrams", "project_page": "", "abstract": "Anamorphosis refers to a category of images that are intentionally distorted, making them unrecognizable when viewed directly. Their true form only reveals itself when seen from a specific viewpoint, which can be through some catadioptric device like a mirror or a lens. While the construction of these mathematical devices can be traced back to as early as the 17th century, they are only interpretable when viewed from a specific vantage point and tend to lose meaning when seen normally. In this paper, we revisit these famous optical illusions with a generative twist. With the help of latent rectified flow models, we propose a method to create anamorphic images that still retain a valid interpretation when viewed directly. To this end, we introduce Laplacian Pyramid Warping, a frequency-aware image warping technique key to generating high-quality visuals. Our work extends Visual Anagrams (arXiv:2311.17919) to latent space models and to a wider range of spatial transforms, enabling the creation of novel generative perceptual illusions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dangeng/visual_anagrams", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.15271", "title": "Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models", "authors": [ "Guo Chen", "Zhiqi Li", "Shihao Wang", "Jindong Jiang", "Yicheng Liu", "Lidong Lu", "De-An Huang", "Wonmin Byeon", "Matthieu Le", "Tuomas Rintamaki", "Tyler Poon", "Max Ehrlich", "Tuomas Rintamaki", "Tyler Poon", "Tong Lu", "Limin Wang", "Bryan Catanzaro", "Jan Kautz", "Andrew Tao", "Zhiding Yu", "Guilin Liu" ], "github": "", "project_page": "", "abstract": "We introduce Eagle 2.5, a family of frontier vision-language models (VLMs) for long-context multimodal learning. Our work addresses the challenges in long video comprehension and high-resolution image understanding, introducing a generalist framework for both tasks. The proposed training framework incorporates Automatic Degrade Sampling and Image Area Preservation, two techniques that preserve contextual integrity and visual details. The framework also includes numerous efficiency optimizations in the pipeline for long-context data training. Finally, we propose Eagle-Video-110K, a novel dataset that integrates both story-level and clip-level annotations, facilitating long-video understanding. Eagle 2.5 demonstrates substantial improvements on long-context multimodal benchmarks, providing a robust solution to the limitations of existing VLMs. Notably, our best model Eagle 2.5-8B achieves 72.4% on Video-MME with 512 input frames, matching the results of top-tier commercial model such as GPT-4o and large-scale open-source models like Qwen2.5-VL-72B and InternVL2.5-78B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.15270", "title": "An LMM for Efficient Video Understanding via Reinforced Compression of Video Cubes", "authors": [ "Ji Qi", "Yuan Yao", "Yushi Bai", "Bin Xu", "Juanzi Li", "Zhiyuan Liu", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "Large Multimodal Models (LMMs) uniformly perceive video frames, creating computational inefficiency for videos with inherently varying temporal information density. This paper present Quicksviewer, an LMM with new perceiving paradigm that partitions a video of nonuniform density into varying cubes using Gumbel Softmax, followed by a unified resampling for each cube to achieve efficient video understanding. This simple and intuitive approach dynamically compress video online based on its temporal density, significantly reducing spatiotemporal redundancy (overall 45times compression rate), while enabling efficient training with large receptive field. We train the model from a language backbone through three progressive stages, each incorporating lengthy videos on average of 420s/1fps thanks to the perceiving efficiency. With only 0.8M total video-text samples for training, our model outperforms the direct baseline employing a fixed partitioning strategy by a maximum of 8.72 in accuracy, demonstrating the effectiveness in performance. On Video-MME, Quicksviewer achieves SOTA under modest sequence lengths using just up to 5\\% of tokens per frame required by baselines. With this paradigm, scaling up the number of input frames reveals a clear power law of the model capabilities. It is also empirically verified that the segments generated by the cubing network can help for analyzing continuous events in videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.14899", "title": "Uni3C: Unifying Precisely 3D-Enhanced Camera and Human Motion Controls for Video Generation", "authors": [ "Chenjie Cao", "Jingkai Zhou", "Shikai Li", "Jingyun Liang", "Chaohui Yu", "Fan Wang", "Xiangyang Xue", "Yanwei Fu" ], "github": "https://github.com/ewrfcas/uni3c", "project_page": "", "abstract": "Camera and human motion controls have been extensively studied for video generation, but existing approaches typically address them separately, suffering from limited data with high-quality annotations for both aspects. To overcome this, we present Uni3C, a unified 3D-enhanced framework for precise control of both camera and human motion in video generation. Uni3C includes two key contributions. First, we propose a plug-and-play control module trained with a frozen video generative backbone, PCDController, which utilizes unprojected point clouds from monocular depth to achieve accurate camera control. By leveraging the strong 3D priors of point clouds and the powerful capacities of video foundational models, PCDController shows impressive generalization, performing well regardless of whether the inference backbone is frozen or fine-tuned. This flexibility enables different modules of Uni3C to be trained in specific domains, i.e., either camera control or human motion control, reducing the dependency on jointly annotated data. Second, we propose a jointly aligned 3D world guidance for the inference phase that seamlessly integrates both scenic point clouds and SMPL-X characters to unify the control signals for camera and human motion, respectively. Extensive experiments confirm that PCDController enjoys strong robustness in driving camera motion for fine-tuned backbones of video generation. Uni3C substantially outperforms competitors in both camera controllability and human motion quality. Additionally, we collect tailored validation sets featuring challenging camera movements and human actions to validate the effectiveness of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ewrfcas/uni3c", "github_api_added_by": "auto" }, { "date": "2025-04-22", "arxiv_id": "2504.12186", "title": "CoMotion: Concurrent Multi-person 3D Motion", "authors": [ "Alejandro Newell", "Peiyun Hu", "Lahav Lipson", "Stephan R. Richter", "Vladlen Koltun" ], "github": "https://github.com/apple/ml-comotion", "project_page": "", "abstract": "We introduce an approach for detecting and tracking detailed 3D poses of multiple people from a single monocular camera stream. Our system maintains temporally coherent predictions in crowded scenes filled with difficult poses and occlusions. Our model performs both strong per-frame detection and a learned pose update to track people from frame to frame. Rather than match detections across time, poses are updated directly from a new input image, which enables online tracking through occlusion. We train on numerous image and video datasets leveraging pseudo-labeled annotations to produce a model that matches state-of-the-art systems in 3D pose estimation accuracy while being faster and more accurate in tracking multiple people through time. Code and weights are provided at https://github.com/apple/ml-comotion", "github_manual": "", "github_regex": "https://github.com/apple/ml-comotion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.13099", "title": "RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguity", "authors": [ "Ranjan Sapkota", "Rahul Harsha Cheppally", "Ajay Sharda", "Manoj Karkee" ], "github": "", "project_page": "", "abstract": "This study conducts a detailed comparison of RF-DETR object detection base model and YOLOv12 object detection model configurations for detecting greenfruits in a complex orchard environment marked by label ambiguity, occlusions, and background blending. A custom dataset was developed featuring both single-class (greenfruit) and multi-class (occluded and non-occluded greenfruits) annotations to assess model performance under dynamic real-world conditions. RF-DETR object detection model, utilizing a DINOv2 backbone and deformable attention, excelled in global context modeling, effectively identifying partially occluded or ambiguous greenfruits. In contrast, YOLOv12 leveraged CNN-based attention for enhanced local feature extraction, optimizing it for computational efficiency and edge deployment. RF-DETR achieved the highest mean Average Precision (mAP50) of 0.9464 in single-class detection, proving its superior ability to localize greenfruits in cluttered scenes. Although YOLOv12N recorded the highest mAP@50:95 of 0.7620, RF-DETR consistently outperformed in complex spatial scenarios. For multi-class detection, RF-DETR led with an mAP@50 of 0.8298, showing its capability to differentiate between occluded and non-occluded fruits, while YOLOv12L scored highest in mAP@50:95 with 0.6622, indicating better classification in detailed occlusion contexts. Training dynamics analysis highlighted RF-DETR's swift convergence, particularly in single-class settings where it plateaued within 10 epochs, demonstrating the efficiency of transformer-based architectures in adapting to dynamic visual data. These findings validate RF-DETR's effectiveness for precision agricultural applications, with YOLOv12 suited for fast-response scenarios. >Index Terms: RF-DETR object detection, YOLOv12, YOLOv13, YOLOv14, YOLOv15, YOLOE, YOLO World, YOLO, You Only Look Once, Roboflow, Detection Transformers, CNNs", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.10642", "title": "SilVar-Med: A Speech-Driven Visual Language Model for Explainable Abnormality Detection in Medical Imaging", "authors": [ "Tan-Hanh Pham", "Chris Ngo", "Trong-Duong Bui", "Minh Luu Quang", "Tan-Huong Pham", "Truong-Son Hy" ], "github": "https://github.com/hanhpt23/silvarmed", "project_page": "", "abstract": "Medical Visual Language Models have shown great potential in various healthcare applications, including medical image captioning and diagnostic assistance. However, most existing models rely on text-based instructions, limiting their usability in real-world clinical environments especially in scenarios such as surgery, text-based interaction is often impractical for physicians. In addition, current medical image analysis models typically lack comprehensive reasoning behind their predictions, which reduces their reliability for clinical decision-making. Given that medical diagnosis errors can have life-changing consequences, there is a critical need for interpretable and rational medical assistance. To address these challenges, we introduce an end-to-end speech-driven medical VLM, SilVar-Med, a multimodal medical image assistant that integrates speech interaction with VLMs, pioneering the task of voice-based communication for medical image analysis. In addition, we focus on the interpretation of the reasoning behind each prediction of medical abnormalities with a proposed reasoning dataset. Through extensive experiments, we demonstrate a proof-of-concept study for reasoning-driven medical image interpretation with end-to-end speech interaction. We believe this work will advance the field of medical AI by fostering more transparent, interactive, and clinically viable diagnostic support systems. Our code and dataset are publicly available at SiVar-Med.", "github_manual": "https://github.com/hanhpt23/silvarmed", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.14870", "title": "OTC: Optimal Tool Calls via Reinforcement Learning", "authors": [ "Hongru Wang", "Cheng Qian", "Wanjun Zhong", "Xiusi Chen", "Jiahao Qiu", "Shijue Huang", "Bowen Jin", "Mengdi Wang", "Kam-Fai Wong", "Heng Ji" ], "github": "", "project_page": "", "abstract": "Tool-integrated reasoning (TIR) augments large language models (LLMs) with the ability to invoke external tools, such as search engines and code interpreters, to solve tasks beyond the capabilities of language-only reasoning. While reinforcement learning (RL) has shown promise in improving TIR by optimizing final answer correctness, existing approaches often overlook the efficiency and cost associated with tool usage. This can lead to suboptimal behavior, including excessive tool calls that increase computational and financial overhead, or insufficient tool use that compromises answer quality. In this work, we propose Optimal Tool Call-controlled Policy Optimization (OTC-PO), a simple yet effective RL-based framework that encourages models to produce accurate answers with minimal tool calls. Our method introduces a tool-integrated reward that jointly considers correctness and tool efficiency, promoting high tool productivity. We instantiate this framework within both Proximal Policy Optimization (PPO) and Group Relative Preference Optimization (GRPO), resulting in OTC-PPO and OTC-GRPO. Experiments with Qwen-2.5 and Qwen-Math across multiple QA benchmarks show that our approach reduces tool calls by up to 73.1\\% and improves tool productivity by up to 229.4\\%, while maintaining comparable answer accuracy. To the best of our knowledge, this is the first RL-based framework that explicitly optimizes tool-use efficiency in TIR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.13367", "title": "THOUGHTTERMINATOR: Benchmarking, Calibrating, and Mitigating Overthinking in Reasoning Models", "authors": [ "Xiao Pu", "Michael Saxon", "Wenyue Hua", "William Yang Wang" ], "github": "", "project_page": "", "abstract": "Reasoning models have demonstrated impressive performance on difficult tasks that traditional language models struggle at. However, many are plagued with the problem of overthinking--generating large amounts of unnecessary tokens which don't improve accuracy on a question. We introduce approximate measures of problem-level difficulty and demonstrate that a clear relationship between problem difficulty and optimal token spend exists, and evaluate how well calibrated a variety of reasoning models are in terms of efficiently allocating the optimal token count. We find that in general, reasoning models are poorly calibrated, particularly on easy problems. To evaluate calibration on easy questions we introduce DUMB500, a dataset of extremely easy math, reasoning, code, and task problems, and jointly evaluate reasoning model on these simple examples and extremely difficult examples from existing frontier benchmarks on the same task domain. Finally, we introduce THOUGHTTERMINATOR, a training-free black box decoding technique that significantly improves reasoning model calibration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.14738", "title": "PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines", "authors": [ "Reya Vir", "Shreya Shankar", "Harrison Chase", "Will Fu-Hinthorn", "Aditya Parameswaran" ], "github": "https://github.com/reyavir/promptevals", "project_page": "", "abstract": "Large language models (LLMs) are increasingly deployed in specialized production data processing pipelines across diverse domains -- such as finance, marketing, and e-commerce. However, when running them in production across many inputs, they often fail to follow instructions or meet developer expectations. To improve reliability in these applications, creating assertions or guardrails for LLM outputs to run alongside the pipelines is essential. Yet, determining the right set of assertions that capture developer requirements for a task is challenging. In this paper, we introduce PROMPTEVALS, a dataset of 2087 LLM pipeline prompts with 12623 corresponding assertion criteria, sourced from developers using our open-source LLM pipeline tools. This dataset is 5x larger than previous collections. Using a hold-out test split of PROMPTEVALS as a benchmark, we evaluated closed- and open-source models in generating relevant assertions. Notably, our fine-tuned Mistral and Llama 3 models outperform GPT-4o by 20.93% on average, offering both reduced latency and improved performance. We believe our dataset can spur further research in LLM reliability, alignment, and prompt engineering.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/reyavir/promptevals", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.14032", "title": "LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models", "authors": [ "Haiwen Huang", "Anpei Chen", "Volodymyr Havrylov", "Andreas Geiger", "Dan Zhang" ], "github": "https://github.com/andrehuang/loftup", "project_page": "", "abstract": "Vision foundation models (VFMs) such as DINOv2 and CLIP have achieved impressive results on various downstream tasks, but their limited feature resolution hampers performance in applications requiring pixel-level understanding. Feature upsampling offers a promising direction to address this challenge. In this work, we identify two critical factors for enhancing feature upsampling: the upsampler architecture and the training objective. For the upsampler architecture, we introduce a coordinate-based cross-attention transformer that integrates the high-resolution images with coordinates and low-resolution VFM features to generate sharp, high-quality features. For the training objective, we propose constructing high-resolution pseudo-groundtruth features by leveraging class-agnostic masks and self-distillation. Our approach effectively captures fine-grained details and adapts flexibly to various input and feature resolutions. Through experiments, we demonstrate that our approach significantly outperforms existing feature upsampling techniques across various downstream tasks. Our code is released at https://github.com/andrehuang/loftup.", "github_manual": "", "github_regex": "https://github.com/andrehuang/loftup", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-22", "arxiv_id": "2504.15266", "title": "Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction", "authors": [ "Vaishnavh Nagarajan", "Chen Henry Wu", "Charles Ding", "Aditi Raghunathan" ], "github": "https://github.com/chenwu98/algorithmic-creativity", "project_page": "", "abstract": "We design a suite of minimal algorithmic tasks that are a loose abstraction of open-ended real-world tasks. This allows us to cleanly and controllably quantify the creative limits of the present-day language model. Much like real-world tasks that require a creative, far-sighted leap of thought, our tasks require an implicit, open-ended stochastic planning step that either (a) discovers new connections in an abstract knowledge graph (like in wordplay, drawing analogies, or research) or (b) constructs new patterns (like in designing math problems or new proteins). In these tasks, we empirically and conceptually argue how next-token learning is myopic and memorizes excessively; comparatively, multi-token approaches, namely teacherless training and diffusion models, excel in producing diverse and original output. Secondly, in our tasks, we find that to elicit randomness from the Transformer without hurting coherence, it is better to inject noise right at the input layer (via a method we dub hash-conditioning) rather than defer to temperature sampling from the output layer. Thus, our work offers a principled, minimal test-bed for analyzing open-ended creative skills, and offers new arguments for going beyond next-token learning and softmax-based sampling. We make part of the code available under https://github.com/chenwu98/algorithmic-creativity", "github_manual": "", "github_regex": "https://github.com/chenwu98/algorithmic-creativity", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.16080", "title": "From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning", "authors": [ "Le Zhuo", "Liangbing Zhao", "Sayak Paul", "Yue Liao", "Renrui Zhang", "Yi Xin", "Peng Gao", "Mohamed Elhoseiny", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "Recent text-to-image diffusion models achieve impressive visual quality through extensive scaling of training data and model parameters, yet they often struggle with complex scenes and fine-grained details. Inspired by the self-reflection capabilities emergent in large language models, we propose ReflectionFlow, an inference-time framework enabling diffusion models to iteratively reflect upon and refine their outputs. ReflectionFlow introduces three complementary inference-time scaling axes: (1) noise-level scaling to optimize latent initialization; (2) prompt-level scaling for precise semantic guidance; and most notably, (3) reflection-level scaling, which explicitly provides actionable reflections to iteratively assess and correct previous generations. To facilitate reflection-level scaling, we construct GenRef, a large-scale dataset comprising 1 million triplets, each containing a reflection, a flawed image, and an enhanced image. Leveraging this dataset, we efficiently perform reflection tuning on state-of-the-art diffusion transformer, FLUX.1-dev, by jointly modeling multimodal inputs within a unified framework. Experimental results show that ReflectionFlow significantly outperforms naive noise-level scaling methods, offering a scalable and compute-efficient solution toward higher-quality image synthesis on challenging tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.15681", "title": "Vidi: Large Multimodal Models for Video Understanding and Editing", "authors": [ "Vidi Team", "Celong Liu", "Chia-Wen Kuo", "Dawei Du", "Fan Chen", "Guang Chen", "Jiamin Yuan", "Lingxi Zhang", "Lu Guo", "Lusha Li", "Longyin Wen", "Qingyu Chen", "Rachel Deng", "Sijie Zhu", "Stuart Siew", "Tong Jin", "Wei Lu", "Wen Zhong", "Xiaohui Shen", "Xin Gu", "Xing Mei", "Xueqiong Qu" ], "github": "", "project_page": "", "abstract": "Humans naturally share information with those they are connected to, and video has become one of the dominant mediums for communication and expression on the Internet. To support the creation of high-quality large-scale video content, a modern pipeline requires a comprehensive understanding of both the raw input materials (e.g., the unedited footage captured by cameras) and the editing components (e.g., visual effects). In video editing scenarios, models must process multiple modalities (e.g., vision, audio, text) with strong background knowledge and handle flexible input lengths (e.g., hour-long raw videos), which poses significant challenges for traditional models. In this report, we introduce Vidi, a family of Large Multimodal Models (LMMs) for a wide range of video understand editing scenarios. The first release focuses on temporal retrieval, i.e., identifying the time ranges within the input videos corresponding to a given text query, which plays a critical role in intelligent editing. The model is capable of processing hour-long videos with strong temporal understanding capability, e.g., retrieve time ranges for certain queries. To support a comprehensive evaluation in real-world scenarios, we also present the VUE-TR benchmark, which introduces five key advancements. 1) Video duration: significantly longer than existing temporal retrival datasets, 2) Audio support: includes audio-based queries, 3) Query format: diverse query lengths/formats, 4) Annotation quality: ground-truth time ranges are manually annotated. 5) Evaluation metric: a refined IoU metric to support evaluation over multiple time ranges. Remarkably, Vidi significantly outperforms leading proprietary models, e.g., GPT-4o and Gemini, on the temporal retrieval task, indicating its superiority in video editing scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.15521", "title": "The Bitter Lesson Learned from 2,000+ Multilingual Benchmarks", "authors": [ "Minghao Wu", "Weixuan Wang", "Sinuo Liu", "Huifeng Yin", "Xintong Wang", "Yu Zhao", "Chenyang Lyu", "Longyue Wang", "Weihua Luo", "Kaifu Zhang" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) continue to advance in linguistic capabilities, robust multilingual evaluation has become essential for promoting equitable technological progress. This position paper examines over 2,000 multilingual (non-English) benchmarks from 148 countries, published between 2021 and 2024, to evaluate past, present, and future practices in multilingual benchmarking. Our findings reveal that, despite significant investments amounting to tens of millions of dollars, English remains significantly overrepresented in these benchmarks. Additionally, most benchmarks rely on original language content rather than translations, with the majority sourced from high-resource countries such as China, India, Germany, the UK, and the USA. Furthermore, a comparison of benchmark performance with human judgments highlights notable disparities. STEM-related tasks exhibit strong correlations with human evaluations (0.70 to 0.85), while traditional NLP tasks like question answering (e.g., XQuAD) show much weaker correlations (0.11 to 0.30). Moreover, translating English benchmarks into other languages proves insufficient, as localized benchmarks demonstrate significantly higher alignment with local human judgments (0.68) than their translated counterparts (0.47). This underscores the importance of creating culturally and linguistically tailored benchmarks rather than relying solely on translations. Through this comprehensive analysis, we highlight six key limitations in current multilingual evaluation practices, propose the guiding principles accordingly for effective multilingual benchmarking, and outline five critical research directions to drive progress in the field. Finally, we call for a global collaborative effort to develop human-aligned benchmarks that prioritize real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.16072", "title": "Describe Anything: Detailed Localized Image and Video Captioning", "authors": [ "Long Lian", "Yifan Ding", "Yunhao Ge", "Sifei Liu", "Hanzi Mao", "Boyi Li", "Marco Pavone", "Ming-Yu Liu", "Trevor Darrell", "Adam Yala", "Yin Cui" ], "github": "", "project_page": "", "abstract": "Generating detailed and accurate descriptions for specific regions in images and videos remains a fundamental challenge for vision-language models. We introduce the Describe Anything Model (DAM), a model designed for detailed localized captioning (DLC). DAM preserves both local details and global context through two key innovations: a focal prompt, which ensures high-resolution encoding of targeted regions, and a localized vision backbone, which integrates precise localization with its broader context. To tackle the scarcity of high-quality DLC data, we propose a Semi-supervised learning (SSL)-based Data Pipeline (DLC-SDP). DLC-SDP starts with existing segmentation datasets and expands to unlabeled web images using SSL. We introduce DLC-Bench, a benchmark designed to evaluate DLC without relying on reference captions. DAM sets new state-of-the-art on 7 benchmarks spanning keyword-level, phrase-level, and detailed multi-sentence localized image and video captioning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.15415", "title": "IV-Bench: A Benchmark for Image-Grounded Video Perception and Reasoning in Multimodal LLMs", "authors": [ "David Ma", "Yuanxing Zhang", "Jincheng Ren", "Jarvis Guo", "Yifan Yao", "Zhenlin Wei", "Zhenzhu Yang", "Zhongyuan Peng", "Boyu Feng", "Jun Ma", "Xiao Gu", "Zhoufutu Wen", "King Zhu", "Yancheng He", "Meng Cao", "Shiwen Ni", "Jiaheng Liu", "Wenhao Huang", "Ge Zhang", "Xiaojie Jin" ], "github": "https://github.com/multimodal-art-projection/IV-Bench", "project_page": "", "abstract": "Existing evaluation frameworks for Multimodal Large Language Models (MLLMs) primarily focus on image reasoning or general video understanding tasks, largely overlooking the significant role of image context in video comprehension. To bridge this gap, we propose IV-Bench, the first comprehensive benchmark for evaluating Image-Grounded Video Perception and Reasoning. IV-Bench consists of 967 videos paired with 2,585 meticulously annotated image-text queries across 13 tasks (7 perception and 6 reasoning tasks) and 5 representative categories. Extensive evaluations of state-of-the-art open-source (e.g., InternVL2.5, Qwen2.5-VL) and closed-source (e.g., GPT-4o, Gemini2-Flash and Gemini2-Pro) MLLMs demonstrate that current models substantially underperform in image-grounded video Perception and Reasoning, merely achieving at most 28.9% accuracy. Further analysis reveals key factors influencing model performance on IV-Bench, including inference pattern, frame number, and resolution. Additionally, through a simple data synthesis approach, we demonstratethe challenges of IV- Bench extend beyond merely aligning the data format in the training proecss. These findings collectively provide valuable insights for future research. Our codes and data are released in https://github.com/multimodal-art-projection/IV-Bench.", "github_manual": "", "github_regex": "https://github.com/multimodal-art-projection/IV-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.15466", "title": "Learning Adaptive Parallel Reasoning with Language Models", "authors": [ "Jiayi Pan", "Xiuyu Li", "Long Lian", "Charlie Snell", "Yifei Zhou", "Adam Yala", "Trevor Darrell", "Kurt Keutzer", "Alane Suhr" ], "github": "https://github.com/Parallel-Reasoning/APR", "project_page": "", "abstract": "Scaling inference-time computation has substantially improved the reasoning capabilities of language models. However, existing methods have significant limitations: serialized chain-of-thought approaches generate overly long outputs, leading to increased latency and exhausted context windows, while parallel methods such as self-consistency suffer from insufficient coordination, resulting in redundant computations and limited performance gains. To address these shortcomings, we propose Adaptive Parallel Reasoning (APR), a novel reasoning framework that enables language models to orchestrate both serialized and parallel computations end-to-end. APR generalizes existing reasoning methods by enabling adaptive multi-threaded inference using spawn() and join() operations. A key innovation is our end-to-end reinforcement learning strategy, optimizing both parent and child inference threads to enhance task success rate without requiring predefined reasoning structures. Experiments on the Countdown reasoning task demonstrate significant benefits of APR: (1) higher performance within the same context window (83.4% vs. 60.0% at 4k context); (2) superior scalability with increased computation (80.1% vs. 66.6% at 20k total tokens); (3) improved accuracy at equivalent latency (75.2% vs. 57.3% at approximately 5,000ms). APR represents a step towards enabling language models to autonomously optimize their reasoning processes through adaptive allocation of computation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Parallel-Reasoning/APR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.14992", "title": "Efficient Pretraining Length Scaling", "authors": [ "Bohong Wu", "Shen Yan", "Sijun Zhang", "Jianqiao Lu", "Yutao Zeng", "Ya Wang", "Xun Zhou" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models have demonstrated the effectiveness of length scaling during post-training, yet its potential in pre-training remains underexplored. We present the Parallel Hidden Decoding Transformer (PHD-Transformer), a novel framework that enables efficient length scaling during pre-training while maintaining inference efficiency. PHD-Transformer achieves this through an innovative KV cache management strategy that distinguishes between original tokens and hidden decoding tokens. By retaining only the KV cache of original tokens for long-range dependencies while immediately discarding hidden decoding tokens after use, our approach maintains the same KV cache size as the vanilla transformer while enabling effective length scaling. To further enhance performance, we introduce two optimized variants: PHD-SWA employs sliding window attention to preserve local dependencies, while PHD-CSWA implements chunk-wise sliding window attention to eliminate linear growth in pre-filling time. Extensive experiments demonstrate consistent improvements across multiple benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.13820", "title": "CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning", "authors": [ "Yang Yue", "Yulin Wang", "Chenxin Tao", "Pan Liu", "Shiji Song", "Gao Huang" ], "github": "https://github.com/LeapLabTHU/CheXWorld", "project_page": "", "abstract": "Humans can develop internal world models that encode common sense knowledge, telling them how the world works and predicting the consequences of their actions. This concept has emerged as a promising direction for establishing general-purpose machine-learning models in recent preliminary works, e.g., for visual representation learning. In this paper, we present CheXWorld, the first effort towards a self-supervised world model for radiographic images. Specifically, our work develops a unified framework that simultaneously models three aspects of medical knowledge essential for qualified radiologists, including 1) local anatomical structures describing the fine-grained characteristics of local tissues (e.g., architectures, shapes, and textures); 2) global anatomical layouts describing the global organization of the human body (e.g., layouts of organs and skeletons); and 3) domain variations that encourage CheXWorld to model the transitions across different appearance domains of radiographs (e.g., varying clarity, contrast, and exposure caused by collecting radiographs from different hospitals, devices, or patients). Empirically, we design tailored qualitative and quantitative analyses, revealing that CheXWorld successfully captures these three dimensions of medical knowledge. Furthermore, transfer learning experiments across eight medical image classification and segmentation benchmarks showcase that CheXWorld significantly outperforms existing SSL methods and large-scale medical foundation models. Code & pre-trained models are available at https://github.com/LeapLabTHU/CheXWorld.", "github_manual": "", "github_regex": "https://github.com/LeapLabTHU/CheXWorld", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.16030", "title": "LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale", "authors": [ "Joya Chen", "Ziyun Zeng", "Yiqi Lin", "Wei Li", "Zejun Ma", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Recent video large language models (Video LLMs) often depend on costly human annotations or proprietary model APIs (e.g., GPT-4o) to produce training data, which limits their training at scale. In this paper, we explore large-scale training for Video LLM with cheap automatic speech recognition (ASR) transcripts. Specifically, we propose a novel streaming training approach that densely interleaves the ASR words and video frames according to their timestamps. Compared to previous studies in vision-language representation with ASR, our method naturally fits the streaming characteristics of ASR, thus enabling the model to learn temporally-aligned, fine-grained vision-language modeling. To support the training algorithm, we introduce a data production pipeline to process YouTube videos and their closed captions (CC, same as ASR), resulting in Live-CC-5M dataset for pre-training and Live-WhisperX-526K dataset for high-quality supervised fine-tuning (SFT). Remarkably, even without SFT, the ASR-only pre-trained LiveCC-7B-Base model demonstrates competitive general video QA performance and exhibits a new capability in real-time video commentary. To evaluate this, we carefully design a new LiveSports-3K benchmark, using LLM-as-a-judge to measure the free-form commentary. Experiments show our final LiveCC-7B-Instruct model can surpass advanced 72B models (Qwen2.5-VL-72B-Instruct, LLaVA-Video-72B) in commentary quality even working in a real-time mode. Meanwhile, it achieves state-of-the-art results at the 7B/8B scale on popular video QA benchmarks such as VideoMME and OVOBench, demonstrating the broad generalizability of our approach. All resources of this paper have been released at https://showlab.github.io/livecc.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.11703", "title": "Progent: Programmable Privilege Control for LLM Agents", "authors": [ "Tianneng Shi", "Jingxuan He", "Zhun Wang", "Linyu Wu", "Hongwei Li", "Wenbo Guo", "Dawn Song" ], "github": "https://github.com/sunblaze-ucb/progent", "project_page": "", "abstract": "LLM agents are an emerging form of AI systems where large language models (LLMs) serve as the central component, utilizing a diverse set of tools to complete user-assigned tasks. Despite their great potential, LLM agents pose significant security risks. When interacting with the external world, they may encounter malicious commands from attackers, leading to the execution of dangerous actions. A promising way to address this is by enforcing the principle of least privilege: allowing only essential actions for task completion while blocking unnecessary ones. However, achieving this is challenging, as it requires covering diverse agent scenarios while preserving both security and utility. We introduce Progent, the first privilege control mechanism for LLM agents. At its core is a domain-specific language for flexibly expressing privilege control policies applied during agent execution. These policies provide fine-grained constraints over tool calls, deciding when tool calls are permissible and specifying fallbacks if they are not. This enables agent developers and users to craft suitable policies for their specific use cases and enforce them deterministically to guarantee security. Thanks to its modular design, integrating Progent does not alter agent internals and requires only minimal changes to agent implementation, enhancing its practicality and potential for widespread adoption. To automate policy writing, we leverage LLMs to generate policies based on user queries, which are then updated dynamically for improved security and utility. Our extensive evaluation shows that it enables strong security while preserving high utility across three distinct scenarios or benchmarks: AgentDojo, ASB, and AgentPoison. Furthermore, we perform an in-depth analysis, showcasing the effectiveness of its core components and the resilience of its automated policy generation against adaptive attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sunblaze-ucb/progent", "github_api_added_by": "auto" }, { "date": "2025-04-23", "arxiv_id": "2504.16082", "title": "MR. Video: \"MapReduce\" is the Principle for Long Video Understanding", "authors": [ "Ziqi Pang", "Yu-Xiong Wang" ], "github": "https://github.com/ziqipang/MR-Video", "project_page": "", "abstract": "We propose MR. Video, an agentic long video understanding framework that demonstrates the simple yet effective MapReduce principle for processing long videos: (1) Map: independently and densely perceiving short video clips, and (2) Reduce: jointly aggregating information from all clips. Compared with sequence-to-sequence vision-language models (VLMs), MR. Video performs detailed short video perception without being limited by context length. Compared with existing video agents that typically rely on sequential key segment selection, the Map operation enables simpler and more scalable sequence parallel perception of short video segments. Its Reduce step allows for more comprehensive context aggregation and reasoning, surpassing explicit key segment retrieval. This MapReduce principle is applicable to both VLMs and video agents, and we use LLM agents to validate its effectiveness. In practice, MR. Video employs two MapReduce stages: (A) Captioning: generating captions for short video clips (map), then standardizing repeated characters and objects into shared names (reduce); (B) Analysis: for each user question, analyzing relevant information from individual short videos (map), and integrating them into a final answer (reduce). MR. Video achieves over 10% accuracy improvement on the challenging LVBench compared to state-of-the-art VLMs and video agents. Code is available at: https://github.com/ziqipang/MR-Video", "github_manual": "", "github_regex": "https://github.com/ziqipang/MR-Video", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.14977", "title": "RealisDance-DiT: Simple yet Strong Baseline towards Controllable Character Animation in the Wild", "authors": [ "Jingkai Zhou", "Yifan Wu", "Shikai Li", "Min Wei", "Chao Fan", "Weihua Chen", "Wei Jiang", "Fan Wang" ], "github": "", "project_page": "", "abstract": "Controllable character animation remains a challenging problem, particularly in handling rare poses, stylized characters, character-object interactions, complex illumination, and dynamic scenes. To tackle these issues, prior work has largely focused on injecting pose and appearance guidance via elaborate bypass networks, but often struggles to generalize to open-world scenarios. In this paper, we propose a new perspective that, as long as the foundation model is powerful enough, straightforward model modifications with flexible fine-tuning strategies can largely address the above challenges, taking a step towards controllable character animation in the wild. Specifically, we introduce RealisDance-DiT, built upon the Wan-2.1 video foundation model. Our sufficient analysis reveals that the widely adopted Reference Net design is suboptimal for large-scale DiT models. Instead, we demonstrate that minimal modifications to the foundation model architecture yield a surprisingly strong baseline. We further propose the low-noise warmup and \"large batches and small iterations\" strategies to accelerate model convergence during fine-tuning while maximally preserving the priors of the foundation model. In addition, we introduce a new test dataset that captures diverse real-world challenges, complementing existing benchmarks such as TikTok dataset and UBC fashion video dataset, to comprehensively evaluate the proposed method. Extensive experiments show that RealisDance-DiT outperforms existing methods by a large margin.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.16084", "title": "TTRL: Test-Time Reinforcement Learning", "authors": [ "Yuxin Zuo", "Kaiyan Zhang", "Shang Qu", "Li Sheng", "Xuekai Zhu", "Biqing Qi", "Youbang Sun", "Ganqu Cui", "Ning Ding", "Bowen Zhou" ], "github": "https://github.com/PRIME-RL/TTRL", "project_page": "", "abstract": "This paper investigates Reinforcement Learning (RL) on data without explicit labels for reasoning tasks in Large Language Models (LLMs). The core challenge of the problem is reward estimation during inference while not having access to ground-truth information. While this setting appears elusive, we find that common practices in Test-Time Scaling (TTS), such as majority voting, yield surprisingly effective rewards suitable for driving RL training. In this work, we introduce Test-Time Reinforcement Learning (TTRL), a novel method for training LLMs using RL on unlabeled data. TTRL enables self-evolution of LLMs by utilizing the priors in the pre-trained models. Our experiments demonstrate that TTRL consistently improves performance across a variety of tasks and models. Notably, TTRL boosts the pass@1 performance of Qwen-2.5-Math-7B by approximately 159% on the AIME 2024 with only unlabeled test data. Furthermore, although TTRL is only supervised by the Maj@N metric, TTRL has demonstrated performance to consistently surpass the upper limit of the initial model, and approach the performance of models trained directly on test data with ground-truth labels. Our experimental findings validate the general effectiveness of TTRL across various tasks, and highlight TTRL's potential for broader tasks and domains. GitHub: https://github.com/PRIME-RL/TTRL", "github_manual": "", "github_regex": "https://github.com/PRIME-RL/TTRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.15785", "title": "WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents", "authors": [ "Siyu Zhou", "Tianyi Zhou", "Yijun Yang", "Guodong Long", "Deheng Ye", "Jing Jiang", "Chengqi Zhang" ], "github": "", "project_page": "", "abstract": "Can we build accurate world models out of large language models (LLMs)? How can world models benefit LLM agents? The gap between the prior knowledge of LLMs and the specified environment's dynamics usually bottlenecks LLMs' performance as world models. To bridge the gap, we propose a training-free \"world alignment\" that learns an environment's symbolic knowledge complementary to LLMs. The symbolic knowledge covers action rules, knowledge graphs, and scene graphs, which are extracted by LLMs from exploration trajectories and encoded into executable codes to regulate LLM agents' policies. We further propose an RL-free, model-based agent \"WALL-E 2.0\" through the model-predictive control (MPC) framework. Unlike classical MPC requiring costly optimization on the fly, we adopt an LLM agent as an efficient look-ahead optimizer of future steps' actions by interacting with the neurosymbolic world model. While the LLM agent's strong heuristics make it an efficient planner in MPC, the quality of its planned actions is also secured by the accurate predictions of the aligned world model. They together considerably improve learning efficiency in a new environment. On open-world challenges in Mars (Minecraft like) and ALFWorld (embodied indoor environments), WALL-E 2.0 significantly outperforms existing methods, e.g., surpassing baselines in Mars by 16.1%-51.6% of success rate and by at least 61.7% in score. In ALFWorld, it achieves a new record 98% success rate after only 4 iterations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.14538", "title": "BookWorld: From Novels to Interactive Agent Societies for Creative Story Generation", "authors": [ "Yiting Ran", "Xintao Wang", "Tian Qiu", "Jiaqing Liang", "Yanghua Xiao", "Deqing Yang" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have enabled social simulation through multi-agent systems. Prior efforts focus on agent societies created from scratch, assigning agents with newly defined personas. However, simulating established fictional worlds and characters remain largely underexplored, despite its significant practical value. In this paper, we introduce BookWorld, a comprehensive system for constructing and simulating book-based multi-agent societies. BookWorld's design covers comprehensive real-world intricacies, including diverse and dynamic characters, fictional worldviews, geographical constraints and changes, e.t.c. BookWorld enables diverse applications including story generation, interactive games and social simulation, offering novel ways to extend and explore beloved fictional works. Through extensive experiments, we demonstrate that BookWorld generates creative, high-quality stories while maintaining fidelity to the source books, surpassing previous methods with a win rate of 75.36%. The code of this paper can be found at the project page: https://bookworld2025.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.15120", "title": "Kuwain 1.5B: An Arabic SLM via Language Injection", "authors": [ "Khalil Hennara", "Sara Chrouf", "Mohamed Motaism Hamed", "Zeina Aldallal", "Omar Hadid", "Safwan AlModhayan" ], "github": "", "project_page": "", "abstract": "Enhancing existing models with new knowledge is a crucial aspect of AI development. This paper introduces a novel method for integrating a new language into a large language model (LLM). Our approach successfully incorporates a previously unseen target language into an existing LLM without compromising its prior knowledge. We trained a tiny model with 1.5 billion parameters named Kuwain by injecting the Arabic language into a small open-source model mainly trained in English. Our method demonstrates significant improvements in Arabic language performance, with an average 8% improvement across various benchmarks, while retaining the model's existing knowledge with a minimum amount of the original model's data. This offers a cost-effective alternative to training a comprehensive model in both English and Arabic. The results highlight the potential for efficient, targeted language model expansion without extensive retraining or resource-intensive processes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.16078", "title": "LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities", "authors": [ "Thomas Schmied", "J\u00f6rg Bornschein", "Jordi Grau-Moya", "Markus Wulfmeier", "Razvan Pascanu" ], "github": "", "project_page": "", "abstract": "The success of Large Language Models (LLMs) has sparked interest in various agentic applications. A key hypothesis is that LLMs, leveraging common sense and Chain-of-Thought (CoT) reasoning, can effectively explore and efficiently solve complex domains. However, LLM agents have been found to suffer from sub-optimal exploration and the knowing-doing gap, the inability to effectively act on knowledge present in the model. In this work, we systematically study why LLMs perform sub-optimally in decision-making scenarios. In particular, we closely examine three prevalent failure modes: greediness, frequency bias, and the knowing-doing gap. We propose mitigation of these shortcomings by fine-tuning via Reinforcement Learning (RL) on self-generated CoT rationales. Our experiments across multi-armed bandits, contextual bandits, and Tic-tac-toe, demonstrate that RL fine-tuning enhances the decision-making abilities of LLMs by increasing exploration and narrowing the knowing-doing gap. Finally, we study both classic exploration mechanisms, such as epsilon-greedy, and LLM-specific approaches, such as self-correction and self-consistency, to enable more effective fine-tuning of LLMs for decision-making.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.15524", "title": "IPBench: Benchmarking the Knowledge of Large Language Models in Intellectual Property", "authors": [ "Qiyao Wang", "Guhong Chen", "Hongbo Wang", "Huaren Liu", "Minghui Zhu", "Zhifei Qin", "Linwei Li", "Yilin Yue", "Shiqiang Wang", "Jiayan Li", "Yihang Wu", "Ziqiang Liu", "Longze Chen", "Run Luo", "Liyang Fan", "Jiaming Li", "Lei Zhang", "Kan Xu", "Hongfei Lin", "Hamid Alinejad-Rokny", "Shiwen Ni", "Yuan Lin", "Min Yang" ], "github": "", "project_page": "", "abstract": "Intellectual Property (IP) is a unique domain that integrates technical and legal knowledge, making it inherently complex and knowledge-intensive. As large language models (LLMs) continue to advance, they show great potential for processing IP tasks, enabling more efficient analysis, understanding, and generation of IP-related content. However, existing datasets and benchmarks either focus narrowly on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. To bridge this gap, we introduce the first comprehensive IP task taxonomy and a large, diverse bilingual benchmark, IPBench, covering 8 IP mechanisms and 20 tasks. This benchmark is designed to evaluate LLMs in real-world intellectual property applications, encompassing both understanding and generation. We benchmark 16 LLMs, ranging from general-purpose to domain-specific models, and find that even the best-performing model achieves only 75.8% accuracy, revealing substantial room for improvement. Notably, open-source IP and law-oriented models lag behind closed-source general-purpose models. We publicly release all data and code of IPBench and will continue to update it with additional IP-related tasks to better reflect real-world challenges in the intellectual property domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.14735", "title": "DiffVox: A Differentiable Model for Capturing and Analysing Professional Effects Distributions", "authors": [ "Chin-Yun Yu", "Marco A. Mart\u00ednez-Ram\u00edrez", "Junghyun Koo", "Ben Hayes", "Wei-Hsiang Liao", "Gy\u00f6rgy Fazekas", "Yuki Mitsufuji" ], "github": "https://github.com/SonyResearch/diffvox", "project_page": "", "abstract": "This study introduces a novel and interpretable model, DiffVox, for matching vocal effects in music production. DiffVox, short for ``Differentiable Vocal Fx\", integrates parametric equalisation, dynamic range control, delay, and reverb with efficient differentiable implementations to enable gradient-based optimisation for parameter estimation. Vocal presets are retrieved from two datasets, comprising 70 tracks from MedleyDB and 365 tracks from a private collection. Analysis of parameter correlations highlights strong relationships between effects and parameters, such as the high-pass and low-shelf filters often behaving together to shape the low end, and the delay time correlates with the intensity of the delayed signals. Principal component analysis reveals connections to McAdams' timbre dimensions, where the most crucial component modulates the perceived spaciousness while the secondary components influence spectral brightness. Statistical testing confirms the non-Gaussian nature of the parameter distribution, highlighting the complexity of the vocal effects space. These initial findings on the parameter distributions set the foundation for future research in vocal effects modelling and automatic mixing. Our source code and datasets are accessible at https://github.com/SonyResearch/diffvox.", "github_manual": "", "github_regex": "https://github.com/SonyResearch/diffvox", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-23", "arxiv_id": "2504.13162", "title": "Personalized Text-to-Image Generation with Auto-Regressive Models", "authors": [ "Kaiyue Sun", "Xian Liu", "Yao Teng", "Xihui Liu" ], "github": "https://github.com/kaiyuesun98/t2i-personalization-with-ar", "project_page": "", "abstract": "Personalized image synthesis has emerged as a pivotal application in text-to-image generation, enabling the creation of images featuring specific subjects in diverse contexts. While diffusion models have dominated this domain, auto-regressive models, with their unified architecture for text and image modeling, remain underexplored for personalized image generation. This paper investigates the potential of optimizing auto-regressive models for personalized image synthesis, leveraging their inherent multimodal capabilities to perform this task. We propose a two-stage training strategy that combines optimization of text embeddings and fine-tuning of transformer layers. Our experiments on the auto-regressive model demonstrate that this method achieves comparable subject fidelity and prompt following to the leading diffusion-based personalization methods. The results highlight the effectiveness of auto-regressive models in personalized image generation, offering a new direction for future research in this area.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kaiyuesun98/t2i-personalization-with-ar", "github_api_added_by": "auto" }, { "date": "2025-04-23", "arxiv_id": "2504.15485", "title": "CAPTURe: Evaluating Spatial Reasoning in Vision Language Models via Occluded Object Counting", "authors": [ "Atin Pothiraj", "Elias Stengel-Eskin", "Jaemin Cho", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Recognizing and reasoning about occluded (partially or fully hidden) objects is vital to understanding visual scenes, as occlusions frequently occur in real-world environments and act as obstacles for spatial comprehension. To test models' ability to reason about multiple occluded objects, we introduce a novel task, Counting Amodally for Patterns Through Unseen REgions (CAPTURe), which requires a model to count objects arranged in a pattern by inferring how the pattern continues behind an occluder (an object which blocks parts of the scene). CAPTURe requires both recognizing visual patterns and reasoning, making it a useful testbed for evaluating vision-language models (VLMs) on whether they understand occluded patterns and possess spatial understanding skills. By requiring models to reason about occluded objects, CAPTURe also tests VLMs' ability to form world models that would allow them to fill in missing information. CAPTURe consists of two parts: (1) CAPTURe-real, with manually filtered images of real objects in patterns and (2) CAPTURe-synthetic, a controlled diagnostic with generated patterned images. We evaluate four strong VLMs (GPT-4o, Intern-VL2, Molmo, and Qwen2-VL) on CAPTURe, finding that models struggle to count on both occluded and unoccluded patterns. Crucially, we find that models perform worse with occlusion, suggesting that VLMs are also deficient in inferring unseen spatial relationships: even the strongest VLMs like GPT-4o fail to count with occlusion. In contrast, we find that humans achieve very little error on CAPTURe. We also find that providing auxiliary information of occluded object locations increases performance, underscoring that the model error comes both from an inability to handle occlusion as well as difficulty counting in images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.15843", "title": "Pre-DPO: Improving Data Utilization in Direct Preference Optimization Using a Guiding Reference Model", "authors": [ "Junshu Pan", "Wei Shen", "Shulin Huang", "Qiji Zhou", "Yue Zhang" ], "github": "https://github.com/DtYXs/Pre-DPO", "project_page": "", "abstract": "Direct Preference Optimization (DPO) simplifies reinforcement learning from human feedback (RLHF) for large language models (LLMs) by directly optimizing human preferences without an explicit reward model. We find that during DPO training, the reference model plays the role of a data weight adjuster. However, the common practice of initializing the policy and reference models identically in DPO can lead to inefficient data utilization and impose a performance ceiling. Meanwhile, the lack of a reference model in Simple Preference Optimization (SimPO) reduces training robustness and necessitates stricter conditions to prevent catastrophic forgetting. In this work, we propose Pre-DPO, a simple yet effective DPO-based training paradigm that enhances preference optimization performance by leveraging a guiding reference model. This reference model provides foresight into the optimal policy state achievable through the training preference data, serving as a guiding mechanism that adaptively assigns higher weights to samples more suitable for the model and lower weights to those less suitable. Extensive experiments on AlpacaEval 2.0 and Arena-Hard v0.1 benchmarks demonstrate that Pre-DPO consistently improves the performance of both DPO and SimPO, without relying on external models or additional data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DtYXs/Pre-DPO", "github_api_added_by": "auto" }, { "date": "2025-04-24", "arxiv_id": "2504.15431", "title": "Trillion 7B Technical Report", "authors": [ "Sungjun Han", "Juyoung Suk", "Suyeong An", "Hyungguk Kim", "Kyuseok Kim", "Wonsuk Yang", "Seungtaek Choi", "Jamin Shin" ], "github": "", "project_page": "", "abstract": "We introduce Trillion-7B, the most token-efficient Korean-centric multilingual LLM available. Our novel Cross-lingual Document Attention (XLDA) mechanism enables highly efficient and effective knowledge transfer from English to target languages like Korean and Japanese. Combined with optimized data mixtures, language-specific filtering, and tailored tokenizer construction, Trillion-7B achieves competitive performance while dedicating only 10\\% of its 2T training tokens to multilingual data and requiring just 59.4K H100 GPU hours (\\$148K) for full training. Comprehensive evaluations across 27 benchmarks in four languages demonstrate Trillion-7B's robust multilingual performance and exceptional cross-lingual consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.15279", "title": "VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models", "authors": [ "Weiye Xu", "Jiahao Wang", "Weiyun Wang", "Zhe Chen", "Wengang Zhou", "Aijun Yang", "Lewei Lu", "Houqiang Li", "Xiaohua Wang", "Xizhou Zhu", "Wenhai Wang", "Jifeng Dai", "Jinguo Zhu" ], "github": "", "project_page": "", "abstract": "Visual reasoning is a core component of human intelligence and a critical capability for advanced multimodal models. Yet current reasoning evaluations of multimodal large language models (MLLMs) often rely on text descriptions and allow language-based reasoning shortcuts, failing to measure genuine vision-centric reasoning. To address this, we introduce VisuLogic: a benchmark of 1,000 human-verified problems across six categories (e.g., quantitative shifts, spatial relations, attribute comparisons). These various types of questions can be evaluated to assess the visual reasoning capabilities of MLLMs from multiple perspectives. We evaluate leading MLLMs on this benchmark and analyze their results to identify common failure modes. Most models score below 30% accuracy-only slightly above the 25% random baseline and far below the 51.4% achieved by humans-revealing significant gaps in visual reasoning. Furthermore, we provide a supplementary training dataset and a reinforcement-learning baseline to support further progress.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.16915", "title": "DreamO: A Unified Framework for Image Customization", "authors": [ "Chong Mou", "Yanze Wu", "Wenxu Wu", "Zinan Guo", "Pengze Zhang", "Yufeng Cheng", "Yiming Luo", "Fei Ding", "Shiwen Zhang", "Xinghui Li", "Mengtian Li", "Songtao Zhao", "Jian Zhang", "Qian He", "Xinglong Wu" ], "github": "", "project_page": "", "abstract": "Recently, extensive research on image customization (e.g., identity, subject, style, background, etc.) demonstrates strong customization capabilities in large-scale generative models. However, most approaches are designed for specific tasks, restricting their generalizability to combine different types of condition. Developing a unified framework for image customization remains an open challenge. In this paper, we present DreamO, an image customization framework designed to support a wide range of tasks while facilitating seamless integration of multiple conditions. Specifically, DreamO utilizes a diffusion transformer (DiT) framework to uniformly process input of different types. During training, we construct a large-scale training dataset that includes various customization tasks, and we introduce a feature routing constraint to facilitate the precise querying of relevant information from reference images. Additionally, we design a placeholder strategy that associates specific placeholders with conditions at particular positions, enabling control over the placement of conditions in the generated results. Moreover, we employ a progressive training strategy consisting of three stages: an initial stage focused on simple tasks with limited data to establish baseline consistency, a full-scale training stage to comprehensively enhance the customization capabilities, and a final quality alignment stage to correct quality biases introduced by low-quality data. Extensive experiments demonstrate that the proposed DreamO can effectively perform various image customization tasks with high quality and flexibly integrate different types of control conditions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.16801", "title": "Decoupled Global-Local Alignment for Improving Compositional Understanding", "authors": [ "Xiaoxing Hu", "Kaicheng Yang", "Jun Wang", "Haoran Xu", "Ziyong Feng", "Yupei Wang" ], "github": "https://github.com/xiaoxing2001/DeGLA", "project_page": "", "abstract": "Contrastive Language-Image Pre-training (CLIP) has achieved success on multiple downstream tasks by aligning image and text modalities. However, the nature of global contrastive learning limits CLIP's ability to comprehend compositional concepts, such as relations and attributes. Although recent studies employ global hard negative samples to improve compositional understanding, these methods significantly compromise the model's inherent general capabilities by forcibly distancing textual negative samples from images in the embedding space. To overcome this limitation, we introduce a Decoupled Global-Local Alignment (DeGLA) framework that improves compositional understanding while substantially mitigating losses in general capabilities. To optimize the retention of the model's inherent capabilities, we incorporate a self-distillation mechanism within the global alignment process, aligning the learnable image-text encoder with a frozen teacher model derived from an exponential moving average. Under the constraint of self-distillation, it effectively mitigates the catastrophic forgetting of pretrained knowledge during fine-tuning. To improve compositional understanding, we first leverage the in-context learning capability of Large Language Models (LLMs) to construct about 2M high-quality negative captions across five types. Subsequently, we propose the Image-Grounded Contrast (IGC) loss and Text-Grounded Contrast (TGC) loss to enhance vision-language compositionally. Extensive experimental results demonstrate the effectiveness of the DeGLA framework. Compared to previous state-of-the-art methods, DeGLA achieves an average enhancement of 3.5% across the VALSE, SugarCrepe, and ARO benchmarks. Concurrently, it obtains an average performance improvement of 13.0% on zero-shot classification tasks across eleven datasets. Our code will be released at https://github.com/xiaoxing2001/DeGLA", "github_manual": "", "github_regex": "https://github.com/xiaoxing2001/DeGLA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.16929", "title": "I-Con: A Unifying Framework for Representation Learning", "authors": [ "Shaden Alshammari", "John Hershey", "Axel Feldmann", "William T. Freeman", "Mark Hamilton" ], "github": "", "project_page": "", "abstract": "As the field of representation learning grows, there has been a proliferation of different loss functions to solve different classes of problems. We introduce a single information-theoretic equation that generalizes a large collection of modern loss functions in machine learning. In particular, we introduce a framework that shows that several broad classes of machine learning methods are precisely minimizing an integrated KL divergence between two conditional distributions: the supervisory and learned representations. This viewpoint exposes a hidden information geometry underlying clustering, spectral methods, dimensionality reduction, contrastive learning, and supervised learning. This framework enables the development of new loss functions by combining successful techniques from across the literature. We not only present a wide array of proofs, connecting over 23 different approaches, but we also leverage these theoretical results to create state-of-the-art unsupervised image classifiers that achieve a +8% improvement over the prior state-of-the-art on unsupervised classification on ImageNet-1K. We also demonstrate that I-Con can be used to derive principled debiasing methods which improve contrastive representation learners.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.15585", "title": "A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment", "authors": [ "Kun Wang", "Guibin Zhang", "Zhenhong Zhou", "Jiahao Wu", "Miao Yu", "Shiqian Zhao", "Chenlong Yin", "Jinhu Fu", "Yibo Yan", "Hanjun Luo", "Liang Lin", "Zhihao Xu", "Haolang Lu", "Xinye Cao", "Xinyun Zhou", "Weifei Jin", "Fanci Meng", "Junyuan Mao", "Hao Wu", "Minghe Wang", "Fan Zhang", "Junfeng Fang", "Chengwei Liu", "Yifan Zhang", "Qiankun Li", "Chongye Guo", "Yalan Qin", "Yi Ding", "Donghai Hong", "Jiaming Ji", "Xinfeng Li", "Yifan Jiang", "Dongxia Wang", "Yihao Huang", "Yufei Guo", "Jen-tse Huang", "Yanwei Yue", "Wenke Huang", "Guancheng Wan", "Tianlin Li", "Lei Bai", "Jie Zhang", "Qing Guo", "Jingyi Wang", "Tianlong Chen", "Joey Tianyi Zhou", "Xiaojun Jia", "Weisong Sun", "Cong Wu", "Jing Chen", "Xuming Hu", "Yiming Li", "Xiao Wang", "Ningyu Zhang", "Luu Anh Tuan", "Guowen Xu", "Tianwei Zhang", "Xingjun Ma", "Xiang Wang", "Bo An", "Jun Sun", "Mohit Bansal", "Shirui Pan", "Yuval Elovici", "Bhavya Kailkhura", "Bo Li", "Yaodong Yang", "Hongwei Li", "Wenyuan Xu", "Yizhou Sun", "Wei Wang", "Qing Li", "Ke Tang", "Yu-Gang Jiang", "Felix Juefei-Xu", "Hui Xiong", "Xiaofeng Wang", "Shuicheng Yan", "Dacheng Tao", "Philip S. Yu", "Qingsong Wen", "Yang Liu" ], "github": "", "project_page": "", "abstract": "The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communities, owing to their unprecedented performance across various applications. As LLMs continue to gain prominence in both research and commercial domains, their security and safety implications have become a growing concern, not only for researchers and corporations but also for every nation. Currently, existing surveys on LLM safety primarily focus on specific stages of the LLM lifecycle, e.g., deployment phase or fine-tuning phase, lacking a comprehensive understanding of the entire \"lifechain\" of LLMs. To address this gap, this paper introduces, for the first time, the concept of \"full-stack\" safety to systematically consider safety issues throughout the entire process of LLM training, deployment, and eventual commercialization. Compared to the off-the-shelf LLM safety surveys, our work demonstrates several distinctive advantages: (I) Comprehensive Perspective. We define the complete LLM lifecycle as encompassing data preparation, pre-training, post-training, deployment and final commercialization. To our knowledge, this represents the first safety survey to encompass the entire lifecycle of LLMs. (II) Extensive Literature Support. Our research is grounded in an exhaustive review of over 800+ papers, ensuring comprehensive coverage and systematic organization of security issues within a more holistic understanding. (III) Unique Insights. Through systematic literature analysis, we have developed reliable roadmaps and perspectives for each chapter. Our work identifies promising research directions, including safety in data generation, alignment techniques, model editing, and LLM-based agent systems. These insights provide valuable guidance for researchers pursuing future work in this field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.10419", "title": "Unchecked and Overlooked: Addressing the Checkbox Blind Spot in Large Language Models with CheckboxQA", "authors": [ "Micha\u0142 Turski", "Mateusz Chili\u0144ski", "\u0141ukasz Borchmann" ], "github": "https://github.com/Snowflake-Labs/CheckboxQA", "project_page": "", "abstract": "Checkboxes are critical in real-world document processing where the presence or absence of ticks directly informs data extraction and decision-making processes. Yet, despite the strong performance of Large Vision and Language Models across a wide range of tasks, they struggle with interpreting checkable content. This challenge becomes particularly pressing in industries where a single overlooked checkbox may lead to costly regulatory or contractual oversights. To address this gap, we introduce the CheckboxQA dataset, a targeted resource designed to evaluate and improve model performance on checkbox-related tasks. It reveals the limitations of current models and serves as a valuable tool for advancing document comprehension systems, with significant implications for applications in sectors such as legal tech and finance. The dataset is publicly available at: https://github.com/Snowflake-Labs/CheckboxQA", "github_manual": "", "github_regex": "https://github.com/Snowflake-Labs/CheckboxQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.15254", "title": "CRUST-Bench: A Comprehensive Benchmark for C-to-safe-Rust Transpilation", "authors": [ "Anirudh Khatry", "Robert Zhang", "Jia Pan", "Ziteng Wang", "Qiaochu Chen", "Greg Durrett", "Isil Dillig" ], "github": "https://github.com/anirudhkhatry/CRUST-bench", "project_page": "", "abstract": "C-to-Rust transpilation is essential for modernizing legacy C code while enhancing safety and interoperability with modern Rust ecosystems. However, no dataset currently exists for evaluating whether a system can transpile C into safe Rust that passes a set of test cases. We introduce CRUST-Bench, a dataset of 100 C repositories, each paired with manually-written interfaces in safe Rust as well as test cases that can be used to validate correctness of the transpilation. By considering entire repositories rather than isolated functions, CRUST-Bench captures the challenges of translating complex projects with dependencies across multiple files. The provided Rust interfaces provide explicit specifications that ensure adherence to idiomatic, memory-safe Rust patterns, while the accompanying test cases enforce functional correctness. We evaluate state-of-the-art large language models (LLMs) on this task and find that safe and idiomatic Rust generation is still a challenging problem for various state-of-the-art methods and techniques. We also provide insights into the errors LLMs usually make in transpiling code from C to safe Rust. The best performing model, OpenAI o1, is able to solve only 15 tasks in a single-shot setting. Improvements on CRUST-Bench would lead to improved transpilation systems that can reason about complex scenarios and help in migrating legacy codebases from C into languages like Rust that ensure memory safety. You can find the dataset and code at https://github.com/anirudhkhatry/CRUST-bench.", "github_manual": "", "github_regex": "https://github.com/anirudhkhatry/CRUST-bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.14509", "title": "DreamID: High-Fidelity and Fast diffusion-based Face Swapping via Triplet ID Group Learning", "authors": [ "Fulong Ye", "Miao Hua", "Pengze Zhang", "Xinghui Li", "Qichao Sun", "Songtao Zhao", "Qian He", "Xinglong Wu" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce DreamID, a diffusion-based face swapping model that achieves high levels of ID similarity, attribute preservation, image fidelity, and fast inference speed. Unlike the typical face swapping training process, which often relies on implicit supervision and struggles to achieve satisfactory results. DreamID establishes explicit supervision for face swapping by constructing Triplet ID Group data, significantly enhancing identity similarity and attribute preservation. The iterative nature of diffusion models poses challenges for utilizing efficient image-space loss functions, as performing time-consuming multi-step sampling to obtain the generated image during training is impractical. To address this issue, we leverage the accelerated diffusion model SD Turbo, reducing the inference steps to a single iteration, enabling efficient pixel-level end-to-end training with explicit Triplet ID Group supervision. Additionally, we propose an improved diffusion-based model architecture comprising SwapNet, FaceNet, and ID Adapter. This robust architecture fully unlocks the power of the Triplet ID Group explicit supervision. Finally, to further extend our method, we explicitly modify the Triplet ID Group data during training to fine-tune and preserve specific attributes, such as glasses and face shape. Extensive experiments demonstrate that DreamID outperforms state-of-the-art methods in terms of identity similarity, pose and expression preservation, and image fidelity. Overall, DreamID achieves high-quality face swapping results at 512*512 resolution in just 0.6 seconds and performs exceptionally well in challenging scenarios such as complex lighting, large angles, and occlusions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.15777", "title": "Tina: Tiny Reasoning Models via LoRA", "authors": [ "Shangshang Wang", "Julian Asilis", "\u00d6mer Faruk Akg\u00fcl", "Enes Burak Bilgin", "Ollie Liu", "Willie Neiswanger" ], "github": "https://github.com/shangshang-wang/Tina", "project_page": "", "abstract": "How cost-effectively can strong reasoning abilities be achieved in language models? Driven by this fundamental question, we present Tina, a family of tiny reasoning models achieved with high cost-efficiency. Notably, Tina demonstrates that substantial reasoning performance can be developed using only minimal resources, by applying parameter-efficient updates during reinforcement learning (RL), using low-rank adaptation (LoRA), to an already tiny 1.5B parameter base model. This minimalist approach produces models that achieve reasoning performance which is competitive with, and sometimes surpasses, SOTA RL reasoning models built upon the same base model. Crucially, this is achieved at a tiny fraction of the computational post-training cost employed by existing SOTA models. In fact, the best Tina model achieves a >20\\% reasoning performance increase and 43.33\\% Pass@1 accuracy on AIME24, at only \\$9 USD post-training and evaluation cost (i.e., an estimated 260x cost reduction). Our work reveals the surprising effectiveness of efficient RL reasoning via LoRA. We validate this across multiple open-source reasoning datasets and various ablation settings starting with a single, fixed set of hyperparameters. Furthermore, we hypothesize that this effectiveness and efficiency stem from LoRA rapidly adapting the model to the structural format of reasoning rewarded by RL, while largely preserving the base model's underlying knowledge. In service of accessibility and open research, we fully open-source all code, training logs, and model weights \\& checkpoints.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/shangshang-wang/Tina", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.15707", "title": "RePOPE: Impact of Annotation Errors on the POPE Benchmark", "authors": [ "Yannic Neuhaus", "Matthias Hein" ], "github": "https://github.com/YanNeu/RePOPE", "project_page": "", "abstract": "Since data annotation is costly, benchmark datasets often incorporate labels from established image datasets. In this work, we assess the impact of label errors in MSCOCO on the frequently used object hallucination benchmark POPE. We re-annotate the benchmark images and identify an imbalance in annotation errors across different subsets. Evaluating multiple models on the revised labels, which we denote as RePOPE, we observe notable shifts in model rankings, highlighting the impact of label quality. Code and data are available at https://github.com/YanNeu/RePOPE .", "github_manual": "", "github_regex": "https://github.com/YanNeu/RePOPE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.11919", "title": "Rethinking the Generation of High-Quality CoT Data from the Perspective of LLM-Adaptive Question Difficulty Grading", "authors": [ "Qianjin Yu", "Keyu Wu", "Zihan Chen", "Chushu Zhang", "Manlin Mei", "Lingjun Huang", "Fang Tan", "Yongsheng Du", "Kunlin Liu", "Yurui Zhu" ], "github": "", "project_page": "", "abstract": "Recently, DeepSeek-R1 (671B) (DeepSeek-AIet al., 2025) has demonstrated its excellent reasoning ability in complex tasks and has publiclyshared its methodology. This provides potentially high-quality chain-of-thought (CoT) data for stimulating the reasoning abilities of small-sized large language models (LLMs). To generate high-quality CoT data for different LLMs, we seek an efficient method for generating high-quality CoT data with LLM-Adaptive questiondifficulty levels. First, we grade the difficulty of the questions according to the reasoning ability of the LLMs themselves and construct a LLM-Adaptive question database. Second, we sample the problem database based on a distribution of difficulty levels of the questions and then use DeepSeek-R1 (671B) (DeepSeek-AI et al., 2025) to generate the corresponding high-quality CoT data with correct answers. Thanks to the construction of CoT data with LLM-Adaptive difficulty levels, we have significantly reduced the cost of data generation and enhanced the efficiency of model supervised fine-tuning (SFT). Finally, we have validated the effectiveness and generalizability of the proposed method in the fields of complex mathematical competitions and code generation tasks. Notably, with only 2k high-quality mathematical CoT data, our ZMath-32B surpasses DeepSeek-Distill-32B in math reasoning task. Similarly, with only 2k high-quality code CoT data, our ZCode-32B surpasses DeepSeek-Distill-32B in code reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.16145", "title": "Progressive Language-guided Visual Learning for Multi-Task Visual Grounding", "authors": [ "Jingchao Wang", "Hong Wang", "Wenlong Zhang", "Kunhua Ji", "Dingjiang Huang", "Yefeng Zheng" ], "github": "https://github.com/jcwang0602/PLVL", "project_page": "", "abstract": "Multi-task visual grounding (MTVG) includes two sub-tasks, i.e., Referring Expression Comprehension (REC) and Referring Expression Segmentation (RES). The existing representative approaches generally follow the research pipeline which mainly consists of three core procedures, including independent feature extraction for visual and linguistic modalities, respectively, cross-modal interaction module, and independent prediction heads for different sub-tasks. Albeit achieving remarkable performance, this research line has two limitations: 1) The linguistic content has not been fully injected into the entire visual backbone for boosting more effective visual feature extraction and it needs an extra cross-modal interaction module; 2) The relationship between REC and RES tasks is not effectively exploited to help the collaborative prediction for more accurate output. To deal with these problems, in this paper, we propose a Progressive Language-guided Visual Learning framework for multi-task visual grounding, called PLVL, which not only finely mine the inherent feature expression of the visual modality itself but also progressively inject the language information to help learn linguistic-related visual features. In this manner, our PLVL does not need additional cross-modal fusion module while fully introducing the language guidance. Furthermore, we analyze that the localization center for REC would help identify the to-be-segmented object region for RES to some extent. Inspired by this investigation, we design a multi-task head to accomplish collaborative predictions for these two sub-tasks. Extensive experiments conducted on several benchmark datasets comprehensively substantiate that our PLVL obviously outperforms the representative methods in both REC and RES tasks. https://github.com/jcwang0602/PLVL", "github_manual": "", "github_regex": "https://github.com/jcwang0602/PLVL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.16074", "title": "PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models", "authors": [ "Shi Qiu", "Shaoyang Guo", "Zhuo-Yang Song", "Yunbo Sun", "Zeyu Cai", "Jiashen Wei", "Tianyu Luo", "Yixuan Yin", "Haoxu Zhang", "Yi Hu", "Chenyang Wang", "Chencheng Tang", "Haoling Chang", "Qi Liu", "Ziheng Zhou", "Tianyu Zhang", "Jingtian Zhang", "Zhangyi Liu", "Minghao Li", "Yuku Zhang", "Boxuan Jing", "Xianqi Yin", "Yutong Ren", "Zizhuo Fu", "Weike Wang", "Xudong Tian", "Anqi Lv", "Laifu Man", "Jianxiang Li", "Feiyu Tao", "Qihua Sun", "Zhou Liang", "Yushu Mu", "Zhongxuan Li", "Jing-Jun Zhang", "Shutao Zhang", "Xiaotian Li", "Xingqi Xia", "Jiawei Lin", "Zheyu Shen", "Jiahang Chen", "Qiuhao Xiong", "Binran Wang", "Fengyuan Wang", "Ziyang Ni", "Bohan Zhang", "Fan Cui", "Changkun Shao", "Qing-Hong Cao", "Ming-xing Luo", "Muhan Zhang", "Hua Xing Zhu" ], "github": "", "project_page": "", "abstract": "We introduce PHYBench, a novel, high-quality benchmark designed for evaluating reasoning capabilities of large language models (LLMs) in physical contexts. PHYBench consists of 500 meticulously curated physics problems based on real-world physical scenarios, designed to assess the ability of models to understand and reason about realistic physical processes. Covering mechanics, electromagnetism, thermodynamics, optics, modern physics, and advanced physics, the benchmark spans difficulty levels from high school exercises to undergraduate problems and Physics Olympiad challenges. Additionally, we propose the Expression Edit Distance (EED) Score, a novel evaluation metric based on the edit distance between mathematical expressions, which effectively captures differences in model reasoning processes and results beyond traditional binary scoring methods. We evaluate various LLMs on PHYBench and compare their performance with human experts. Our results reveal that even state-of-the-art reasoning models significantly lag behind human experts, highlighting their limitations and the need for improvement in complex physical reasoning scenarios. Our benchmark results and dataset are publicly available at https://phybench-official.github.io/phybench-demo/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.16891", "title": "AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset", "authors": [ "Ivan Moshkov", "Darragh Hanley", "Ivan Sorokin", "Shubham Toshniwal", "Christof Henkel", "Benedikt Schifferer", "Wei Du", "Igor Gitman" ], "github": "https://github.com/NVIDIA/NeMo-Skills", "project_page": "", "abstract": "This paper presents our winning submission to the AI Mathematical Olympiad - Progress Prize 2 (AIMO-2) competition. Our recipe for building state-of-the-art mathematical reasoning models relies on three key pillars. First, we create a large-scale dataset comprising 540K unique high-quality math problems, including olympiad-level problems, and their 3.2M long-reasoning solutions. Second, we develop a novel method to integrate code execution with long reasoning models through iterative training, generation, and quality filtering, resulting in 1.7M high-quality Tool-Integrated Reasoning solutions. Third, we create a pipeline to train models to select the most promising solution from many candidates. We show that such generative solution selection (GenSelect) can significantly improve upon majority voting baseline. Combining these ideas, we train a series of models that achieve state-of-the-art results on mathematical reasoning benchmarks. To facilitate further research, we release our code, models, and the complete OpenMathReasoning dataset under a commercially permissive license.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA/NeMo-Skills", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-24", "arxiv_id": "2504.13263", "title": "Causal-Copilot: An Autonomous Causal Analysis Agent", "authors": [ "Xinyue Wang", "Kun Zhou", "Wenyi Wu", "Har Simrat Singh", "Fang Nan", "Songyao Jin", "Aryan Philip", "Saloni Patnaik", "Hou Zhu", "Shivam Singh", "Parjanya Prashant", "Qian Shen", "Biwei Huang" ], "github": "https://github.com/Lancelot39/Causal-Copilot", "project_page": "", "abstract": "Causal analysis plays a foundational role in scientific discovery and reliable decision-making, yet it remains largely inaccessible to domain experts due to its conceptual and algorithmic complexity. This disconnect between causal methodology and practical usability presents a dual challenge: domain experts are unable to leverage recent advances in causal learning, while causal researchers lack broad, real-world deployment to test and refine their methods. To address this, we introduce Causal-Copilot, an autonomous agent that operationalizes expert-level causal analysis within a large language model framework. Causal-Copilot automates the full pipeline of causal analysis for both tabular and time-series data -- including causal discovery, causal inference, algorithm selection, hyperparameter optimization, result interpretation, and generation of actionable insights. It supports interactive refinement through natural language, lowering the barrier for non-specialists while preserving methodological rigor. By integrating over 20 state-of-the-art causal analysis techniques, our system fosters a virtuous cycle -- expanding access to advanced causal methods for domain experts while generating rich, real-world applications that inform and advance causal theory. Empirical evaluations demonstrate that Causal-Copilot achieves superior performance compared to existing baselines, offering a reliable, scalable, and extensible solution that bridges the gap between theoretical sophistication and real-world applicability in causal analysis. A live interactive demo of Causal-Copilot is available at https://causalcopilot.com/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Lancelot39/Causal-Copilot", "github_api_added_by": "auto" }, { "date": "2025-04-25", "arxiv_id": "2504.17761", "title": "Step1X-Edit: A Practical Framework for General Image Editing", "authors": [ "Shiyu Liu", "Yucheng Han", "Peng Xing", "Fukun Yin", "Rui Wang", "Wei Cheng", "Jiaqi Liao", "Yingming Wang", "Honghao Fu", "Chunrui Han", "Guopeng Li", "Yuang Peng", "Quan Sun", "Jingwei Wu", "Yan Cai", "Zheng Ge", "Ranchen Ming", "Lei Xia", "Xianfang Zeng", "Yibo Zhu", "Binxing Jiao", "Xiangyu Zhang", "Gang Yu", "Daxin Jiang" ], "github": "", "project_page": "", "abstract": "In recent years, image editing models have witnessed remarkable and rapid development. The recent unveiling of cutting-edge multimodal models such as GPT-4o and Gemini2 Flash has introduced highly promising image editing capabilities. These models demonstrate an impressive aptitude for fulfilling a vast majority of user-driven editing requirements, marking a significant advancement in the field of image manipulation. However, there is still a large gap between the open-source algorithm with these closed-source models. Thus, in this paper, we aim to release a state-of-the-art image editing model, called Step1X-Edit, which can provide comparable performance against the closed-source models like GPT-4o and Gemini2 Flash. More specifically, we adopt the Multimodal LLM to process the reference image and the user's editing instruction. A latent embedding has been extracted and integrated with a diffusion image decoder to obtain the target image. To train the model, we build a data generation pipeline to produce a high-quality dataset. For evaluation, we develop the GEdit-Bench, a novel benchmark rooted in real-world user instructions. Experimental results on GEdit-Bench demonstrate that Step1X-Edit outperforms existing open-source baselines by a substantial margin and approaches the performance of leading proprietary models, thereby making significant contributions to the field of image editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.17432", "title": "Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs", "authors": [ "Tiancheng Gu", "Kaicheng Yang", "Ziyong Feng", "Xingjun Wang", "Yanzhao Zhang", "Dingkun Long", "Yingda Chen", "Weidong Cai", "Jiankang Deng" ], "github": "https://github.com/deepglint/UniME", "project_page": "", "abstract": "The Contrastive Language-Image Pre-training (CLIP) framework has become a widely used approach for multimodal representation learning, particularly in image-text retrieval and clustering. However, its efficacy is constrained by three key limitations: (1) text token truncation, (2) isolated image-text encoding, and (3) deficient compositionality due to bag-of-words behavior. While recent Multimodal Large Language Models (MLLMs) have demonstrated significant advances in generalized vision-language understanding, their potential for learning transferable multimodal representations remains underexplored.In this work, we present UniME (Universal Multimodal Embedding), a novel two-stage framework that leverages MLLMs to learn discriminative representations for diverse downstream tasks. In the first stage, we perform textual discriminative knowledge distillation from a powerful LLM-based teacher model to enhance the embedding capability of the MLLM\\'s language component. In the second stage, we introduce hard negative enhanced instruction tuning to further advance discriminative representation learning. Specifically, we initially mitigate false negative contamination and then sample multiple hard negatives per instance within each batch, forcing the model to focus on challenging samples. This approach not only improves discriminative power but also enhances instruction-following ability in downstream tasks. We conduct extensive experiments on the MMEB benchmark and multiple retrieval tasks, including short and long caption retrieval and compositional retrieval. Results demonstrate that UniME achieves consistent performance improvement across all tasks, exhibiting superior discriminative and compositional capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/deepglint/UniME", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.17207", "title": "Perspective-Aware Reasoning in Vision-Language Models via Mental Imagery Simulation", "authors": [ "Phillip Y. Lee", "Jihyeon Je", "Chanho Park", "Mikaela Angelina Uy", "Leonidas Guibas", "Minhyuk Sung" ], "github": "https://github.com/KAIST-Visual-AI-Group/APC-VLM", "project_page": "", "abstract": "We present a framework for perspective-aware reasoning in vision-language models (VLMs) through mental imagery simulation. Perspective-taking, the ability to perceive an environment or situation from an alternative viewpoint, is a key benchmark for human-level visual understanding, essential for environmental interaction and collaboration with autonomous agents. Despite advancements in spatial reasoning within VLMs, recent research has shown that modern VLMs significantly lack perspective-aware reasoning capabilities and exhibit a strong bias toward egocentric interpretations. To bridge the gap between VLMs and human perception, we focus on the role of mental imagery, where humans perceive the world through abstracted representations that facilitate perspective shifts. Motivated by this, we propose a framework for perspective-aware reasoning, named Abstract Perspective Change (APC), that effectively leverages vision foundation models, such as object detection, segmentation, and orientation estimation, to construct scene abstractions and enable perspective transformations. Our experiments on synthetic and real-image benchmarks, compared with various VLMs, demonstrate significant improvements in perspective-aware reasoning with our framework, further outperforming fine-tuned spatial reasoning models and novel-view-synthesis-based approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KAIST-Visual-AI-Group/APC-VLM", "github_api_added_by": "auto" }, { "date": "2025-04-25", "arxiv_id": "2504.17192", "title": "Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning", "authors": [ "Minju Seo", "Jinheon Baek", "Seongyun Lee", "Sung Ju Hwang" ], "github": "https://github.com/going-doer/Paper2Code", "project_page": "", "abstract": "Despite the rapid growth of machine learning research, corresponding code implementations are often unavailable, making it slow and labor-intensive for researchers to reproduce results and build upon prior work. In the meantime, recent Large Language Models (LLMs) excel at understanding scientific documents and generating high-quality code. Inspired by this, we introduce PaperCoder, a multi-agent LLM framework that transforms machine learning papers into functional code repositories. PaperCoder operates in three stages: planning, where it constructs a high-level roadmap, designs the system architecture with diagrams, identifies file dependencies, and generates configuration files; analysis, which focuses on interpreting implementation-specific details; and generation, where modular, dependency-aware code is produced. Moreover, each phase is instantiated through a set of specialized agents designed to collaborate effectively across the pipeline. We then evaluate PaperCoder on generating code implementations from machine learning papers based on both model-based and human evaluations, specifically from the original paper authors, with author-released repositories as ground truth if available. Our results demonstrate the effectiveness of PaperCoder in creating high-quality, faithful implementations. Furthermore, it consistently shows strengths in the recently released PaperBench benchmark, surpassing strong baselines by substantial margins.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/going-doer/Paper2Code", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.17069", "title": "Distilling semantically aware orders for autoregressive image generation", "authors": [ "Rishav Pramanik", "Antoine Poupon", "Juan A. Rodriguez", "Masih Aminbeidokhti", "David Vazquez", "Christopher Pal", "Zhaozheng Yin", "Marco Pedersoli" ], "github": "", "project_page": "", "abstract": "Autoregressive patch-based image generation has recently shown competitive results in terms of image quality and scalability. It can also be easily integrated and scaled within Vision-Language models. Nevertheless, autoregressive models require a defined order for patch generation. While a natural order based on the dictation of the words makes sense for text generation, there is no inherent generation order that exists for image generation. Traditionally, a raster-scan order (from top-left to bottom-right) guides autoregressive image generation models. In this paper, we argue that this order is suboptimal, as it fails to respect the causality of the image content: for instance, when conditioned on a visual description of a sunset, an autoregressive model may generate clouds before the sun, even though the color of clouds should depend on the color of the sun and not the inverse. In this work, we show that first by training a model to generate patches in any-given-order, we can infer both the content and the location (order) of each patch during generation. Secondly, we use these extracted orders to finetune the any-given-order model to produce better-quality images. Through our experiments, we show on two datasets that this new generation method produces better images than the traditional raster-scan approach, with similar training costs and no extra annotations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.16511", "title": "QuaDMix: Quality-Diversity Balanced Data Selection for Efficient LLM Pretraining", "authors": [ "Fengze Liu", "Weidong Zhou", "Binbin Liu", "Zhimiao Yu", "Yifan Zhang", "Haobin Lin", "Yifeng Yu", "Xiaohuan Zhou", "Taifeng Wang", "Yong Cao" ], "github": "", "project_page": "", "abstract": "Quality and diversity are two critical metrics for the training data of large language models (LLMs), positively impacting performance. Existing studies often optimize these metrics separately, typically by first applying quality filtering and then adjusting data proportions. However, these approaches overlook the inherent trade-off between quality and diversity, necessitating their joint consideration. Given a fixed training quota, it is essential to evaluate both the quality of each data point and its complementary effect on the overall dataset. In this paper, we introduce a unified data selection framework called QuaDMix, which automatically optimizes the data distribution for LLM pretraining while balancing both quality and diversity. Specifically, we first propose multiple criteria to measure data quality and employ domain classification to distinguish data points, thereby measuring overall diversity. QuaDMix then employs a unified parameterized data sampling function that determines the sampling probability of each data point based on these quality and diversity related labels. To accelerate the search for the optimal parameters involved in the QuaDMix framework, we conduct simulated experiments on smaller models and use LightGBM for parameters searching, inspired by the RegMix method. Our experiments across diverse models and datasets demonstrate that QuaDMix achieves an average performance improvement of 7.2% across multiple benchmarks. These results outperform the independent strategies for quality and diversity, highlighting the necessity and ability to balance data quality and diversity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.17601", "title": "Interpretable non-linear dimensionality reduction using gaussian weighted linear transformation", "authors": [ "Erik Bergh" ], "github": "", "project_page": "", "abstract": "Dimensionality reduction techniques are fundamental for analyzing and visualizing high-dimensional data. With established methods like t-SNE and PCA presenting a trade-off between representational power and interpretability. This paper introduces a novel approach that bridges this gap by combining the interpretability of linear methods with the expressiveness of non-linear transformations. The proposed algorithm constructs a non-linear mapping between high-dimensional and low-dimensional spaces through a combination of linear transformations, each weighted by Gaussian functions. This architecture enables complex non-linear transformations while preserving the interpretability advantages of linear methods, as each transformation can be analyzed independently. The resulting model provides both powerful dimensionality reduction and transparent insights into the transformed space. Techniques for interpreting the learned transformations are presented, including methods for identifying suppressed dimensions and how space is expanded and contracted. These tools enable practitioners to understand how the algorithm preserves and modifies geometric relationships during dimensionality reduction. To ensure the practical utility of this algorithm, the creation of user-friendly software packages is emphasized, facilitating its adoption in both academia and industry.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.17789", "title": "Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models", "authors": [ "Xu Ma", "Peize Sun", "Haoyu Ma", "Hao Tang", "Chih-Yao Ma", "Jialiang Wang", "Kunpeng Li", "Xiaoliang Dai", "Yujun Shi", "Xuan Ju", "Yushi Hu", "Artsiom Sanakoyeu", "Felix Juefei-Xu", "Ji Hou", "Junjiao Tian", "Tao Xu", "Tingbo Hou", "Yen-Cheng Liu", "Zecheng He", "Zijian He", "Matt Feiszli", "Peizhao Zhang", "Peter Vajda", "Sam Tsai", "Yun Fu" ], "github": "", "project_page": "", "abstract": "Autoregressive (AR) models, long dominant in language generation, are increasingly applied to image synthesis but are often considered less competitive than Diffusion-based models. A primary limitation is the substantial number of image tokens required for AR models, which constrains both training and inference efficiency, as well as image resolution. To address this, we present Token-Shuffle, a novel yet simple method that reduces the number of image tokens in Transformer. Our key insight is the dimensional redundancy of visual vocabularies in Multimodal Large Language Models (MLLMs), where low-dimensional visual codes from visual encoder are directly mapped to high-dimensional language vocabularies. Leveraging this, we consider two key operations: token-shuffle, which merges spatially local tokens along channel dimension to decrease the input token number, and token-unshuffle, which untangles the inferred tokens after Transformer blocks to restore the spatial arrangement for output. Jointly training with textual prompts, our strategy requires no additional pretrained text-encoder and enables MLLMs to support extremely high-resolution image synthesis in a unified next-token prediction way while maintaining efficient training and inference. For the first time, we push the boundary of AR text-to-image generation to a resolution of 2048x2048 with gratifying generation performance. In GenAI-benchmark, our 2.7B model achieves 0.77 overall score on hard prompts, outperforming AR models LlamaGen by 0.18 and diffusion models LDM by 0.15. Exhaustive large-scale human evaluations also demonstrate our prominent image generation ability in terms of text-alignment, visual flaw, and visual appearance. We hope that Token-Shuffle can serve as a foundational design for efficient high-resolution image generation within MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.17040", "title": "DyMU: Dynamic Merging and Virtual Unmerging for Efficient VLMs", "authors": [ "Zhenhailong Wang", "Senthil Purushwalkam", "Caiming Xiong", "Silvio Savarese", "Heng Ji", "Ran Xu" ], "github": "https://github.com/MikeWangWZHL/dymu", "project_page": "", "abstract": "We present DyMU, an efficient, training-free framework that dynamically reduces the computational burden of vision-language models (VLMs) while maintaining high task performance. Our approach comprises two key components. First, Dynamic Token Merging (DToMe) reduces the number of visual token embeddings by merging similar tokens based on image complexity, addressing the inherent inefficiency of fixed-length outputs in vision transformers. Second, Virtual Token Unmerging (VTU) simulates the expected token sequence for large language models (LLMs) by efficiently reconstructing the attention dynamics of a full sequence, thus preserving the downstream performance without additional fine-tuning. Unlike previous approaches, our method dynamically adapts token compression to the content of the image and operates completely training-free, making it readily applicable to most state-of-the-art VLM architectures. Extensive experiments on image and video understanding tasks demonstrate that DyMU can reduce the average visual token count by 32%-85% while achieving comparable performance to full-length models across diverse VLM architectures, including the recently popularized AnyRes-based visual encoders. Furthermore, through qualitative analyses, we demonstrate that DToMe effectively adapts token reduction based on image complexity and, unlike existing systems, provides users more control over computational costs. Project page: https://mikewangwzhl.github.io/dymu/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MikeWangWZHL/dymu", "github_api_added_by": "auto" }, { "date": "2025-04-25", "arxiv_id": "2504.17502", "title": "RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation", "authors": [ "Aviv Slobodkin", "Hagai Taitelbaum", "Yonatan Bitton", "Brian Gordon", "Michal Sokolik", "Nitzan Bitton Guetta", "Almog Gueta", "Royi Rassin", "Itay Laish", "Dani Lischinski", "Idan Szpektor" ], "github": "", "project_page": "", "abstract": "Subject-driven text-to-image (T2I) generation aims to produce images that align with a given textual description, while preserving the visual identity from a referenced subject image. Despite its broad downstream applicability -- ranging from enhanced personalization in image generation to consistent character representation in video rendering -- progress in this field is limited by the lack of reliable automatic evaluation. Existing methods either assess only one aspect of the task (i.e., textual alignment or subject preservation), misalign with human judgments, or rely on costly API-based evaluation. To address this, we introduce RefVNLI, a cost-effective metric that evaluates both textual alignment and subject preservation in a single prediction. Trained on a large-scale dataset derived from video-reasoning benchmarks and image perturbations, RefVNLI outperforms or matches existing baselines across multiple benchmarks and subject categories (e.g., Animal, Object), achieving up to 6.4-point gains in textual alignment and 8.5-point gains in subject consistency. It also excels with lesser-known concepts, aligning with human preferences at over 87\\% accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.16921", "title": "IberBench: LLM Evaluation on Iberian Languages", "authors": [ "Jos\u00e9 \u00c1ngel Gonz\u00e1lez", "Ian Borrego Obrador", "\u00c1lvaro Romo Herrero", "Areg Mikael Sarvazyan", "Mara Chinea-R\u00edos", "Angelo Basile", "Marc Franco-Salvador" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) remain difficult to evaluate comprehensively, particularly for languages other than English, where high-quality data is often limited. Existing benchmarks and leaderboards are predominantly English-centric, with only a few addressing other languages. These benchmarks fall short in several key areas: they overlook the diversity of language varieties, prioritize fundamental Natural Language Processing (NLP) capabilities over tasks of industrial relevance, and are static. With these aspects in mind, we present IberBench, a comprehensive and extensible benchmark designed to assess LLM performance on both fundamental and industry-relevant NLP tasks, in languages spoken across the Iberian Peninsula and Ibero-America. IberBench integrates 101 datasets from evaluation campaigns and recent benchmarks, covering 22 task categories such as sentiment and emotion analysis, toxicity detection, and summarization. The benchmark addresses key limitations in current evaluation practices, such as the lack of linguistic diversity and static evaluation setups by enabling continual updates and community-driven model and dataset submissions moderated by a committee of experts. We evaluate 23 LLMs ranging from 100 million to 14 billion parameters and provide empirical insights into their strengths and limitations. Our findings indicate that (i) LLMs perform worse on industry-relevant tasks than in fundamental ones, (ii) performance is on average lower for Galician and Basque, (iii) some tasks show results close to random, and (iv) in other tasks LLMs perform above random but below shared task systems. IberBench offers open-source implementations for the entire evaluation pipeline, including dataset normalization and hosting, incremental evaluation of LLMs, and a publicly accessible leaderboard.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.15921", "title": "ViSMaP: Unsupervised Hour-long Video Summarisation by Meta-Prompting", "authors": [ "Jian Hu", "Dimitrios Korkinof", "Shaogang Gong", "Mariano Beguerisse-Diaz" ], "github": "", "project_page": "", "abstract": "We introduce ViSMap: Unsupervised Video Summarisation by Meta Prompting, a system to summarise hour long videos with no-supervision. Most existing video understanding models work well on short videos of pre-segmented events, yet they struggle to summarise longer videos where relevant events are sparsely distributed and not pre-segmented. Moreover, long-form video understanding often relies on supervised hierarchical training that needs extensive annotations which are costly, slow and prone to inconsistency. With ViSMaP we bridge the gap between short videos (where annotated data is plentiful) and long ones (where it's not). We rely on LLMs to create optimised pseudo-summaries of long videos using segment descriptions from short ones. These pseudo-summaries are used as training data for a model that generates long-form video summaries, bypassing the need for expensive annotations of long videos. Specifically, we adopt a meta-prompting strategy to iteratively generate and refine creating pseudo-summaries of long videos. The strategy leverages short clip descriptions obtained from a supervised short video model to guide the summary. Each iteration uses three LLMs working in sequence: one to generate the pseudo-summary from clip descriptions, another to evaluate it, and a third to optimise the prompt of the generator. This iteration is necessary because the quality of the pseudo-summaries is highly dependent on the generator prompt, and varies widely among videos. We evaluate our summaries extensively on multiple datasets; our results show that ViSMaP achieves performance comparable to fully supervised state-of-the-art models while generalising across domains without sacrificing performance. Code will be released upon publication.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.17414", "title": "3DV-TON: Textured 3D-Guided Consistent Video Try-on via Diffusion Models", "authors": [ "Min Wei", "Chaohui Yu", "Jingkai Zhou", "Fan Wang" ], "github": "", "project_page": "", "abstract": "Video try-on replaces clothing in videos with target garments. Existing methods struggle to generate high-quality and temporally consistent results when handling complex clothing patterns and diverse body poses. We present 3DV-TON, a novel diffusion-based framework for generating high-fidelity and temporally consistent video try-on results. Our approach employs generated animatable textured 3D meshes as explicit frame-level guidance, alleviating the issue of models over-focusing on appearance fidelity at the expanse of motion coherence. This is achieved by enabling direct reference to consistent garment texture movements throughout video sequences. The proposed method features an adaptive pipeline for generating dynamic 3D guidance: (1) selecting a keyframe for initial 2D image try-on, followed by (2) reconstructing and animating a textured 3D mesh synchronized with original video poses. We further introduce a robust rectangular masking strategy that successfully mitigates artifact propagation caused by leaking clothing information during dynamic human and garment movements. To advance video try-on research, we introduce HR-VVT, a high-resolution benchmark dataset containing 130 videos with diverse clothing types and scenarios. Quantitative and qualitative results demonstrate our superior performance over existing methods. The project page is at this link https://2y7c3.github.io/3DV-TON/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.17343", "title": "TimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming Videos", "authors": [ "Linli Yao", "Yicheng Li", "Yuancheng Wei", "Lei Li", "Shuhuai Ren", "Yuanxin Liu", "Kun Ouyang", "Lean Wang", "Shicheng Li", "Sida Li", "Lingpeng Kong", "Qi Liu", "Yuanxing Zhang", "Xu Sun" ], "github": "https://github.com/yaolinli/TimeChat-Online", "project_page": "", "abstract": "The rapid growth of online video platforms, particularly live streaming services, has created an urgent need for real-time video understanding systems. These systems must process continuous video streams and respond to user queries instantaneously, presenting unique challenges for current Video Large Language Models (VideoLLMs). While existing VideoLLMs excel at processing complete videos, they face significant limitations in streaming scenarios due to their inability to handle dense, redundant frames efficiently. We introduce TimeChat-Online, a novel online VideoLLM that revolutionizes real-time video interaction. At its core lies our innovative Differential Token Drop (DTD) module, which addresses the fundamental challenge of visual redundancy in streaming videos. Drawing inspiration from human visual perception's Change Blindness phenomenon, DTD preserves meaningful temporal changes while filtering out static, redundant content between frames. Remarkably, our experiments demonstrate that DTD achieves an 82.8% reduction in video tokens while maintaining 98% performance on StreamingBench, revealing that over 80% of visual content in streaming videos is naturally redundant without requiring language guidance. To enable seamless real-time interaction, we present TimeChat-Online-139K, a comprehensive streaming video dataset featuring diverse interaction patterns including backward-tracing, current-perception, and future-responding scenarios. TimeChat-Online's unique Proactive Response capability, naturally achieved through continuous monitoring of video scene transitions via DTD, sets it apart from conventional approaches. Our extensive evaluation demonstrates TimeChat-Online's superior performance on streaming benchmarks (StreamingBench and OvOBench) and maintaining competitive results on long-form video tasks such as Video-MME and MLVU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yaolinli/TimeChat-Online", "github_api_added_by": "auto" }, { "date": "2025-04-25", "arxiv_id": "2504.16064", "title": "Boosting Generative Image Modeling via Joint Image-Feature Synthesis", "authors": [ "Theodoros Kouzelis", "Efstathios Karypidis", "Ioannis Kakogeorgiou", "Spyros Gidaris", "Nikos Komodakis" ], "github": "", "project_page": "", "abstract": "Latent diffusion models (LDMs) dominate high-quality image generation, yet integrating representation learning with generative modeling remains a challenge. We introduce a novel generative image modeling framework that seamlessly bridges this gap by leveraging a diffusion model to jointly model low-level image latents (from a variational autoencoder) and high-level semantic features (from a pretrained self-supervised encoder like DINO). Our latent-semantic diffusion approach learns to generate coherent image-feature pairs from pure noise, significantly enhancing both generative quality and training efficiency, all while requiring only minimal modifications to standard Diffusion Transformer architectures. By eliminating the need for complex distillation objectives, our unified design simplifies training and unlocks a powerful new inference strategy: Representation Guidance, which leverages learned semantics to steer and refine image generation. Evaluated in both conditional and unconditional settings, our method delivers substantial improvements in image quality and training convergence speed, establishing a new direction for representation-aware generative modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.16828", "title": "Process Reward Models That Think", "authors": [ "Muhammad Khalifa", "Rishabh Agarwal", "Lajanugen Logeswaran", "Jaekyeom Kim", "Hao Peng", "Moontae Lee", "Honglak Lee", "Lu Wang" ], "github": "https://github.com/mukhal/thinkprm", "project_page": "", "abstract": "Step-by-step verifiers -- also known as process reward models (PRMs) -- are a key ingredient for test-time scaling. PRMs require step-level supervision, making them expensive to train. This work aims to build data-efficient PRMs as verbalized step-wise reward models that verify every step in the solution by generating a verification chain-of-thought (CoT). We propose ThinkPRM, a long CoT verifier fine-tuned on orders of magnitude fewer process labels than those required by discriminative PRMs. Our approach capitalizes on the inherent reasoning abilities of long CoT models, and outperforms LLM-as-a-Judge and discriminative verifiers -- using only 1% of the process labels in PRM800K -- across several challenging benchmarks. Specifically, ThinkPRM beats the baselines on ProcessBench, MATH-500, and AIME '24 under best-of-N selection and reward-guided search. In an out-of-domain evaluation on a subset of GPQA-Diamond and LiveCodeBench, our PRM surpasses discriminative verifiers trained on the full PRM800K by 8% and 4.5%, respectively. Lastly, under the same token budget, ThinkPRM scales up verification compute more effectively compared to LLM-as-a-Judge, outperforming it by 7.2% on a subset of ProcessBench. Our work highlights the value of generative, long CoT PRMs that can scale test-time compute for verification while requiring minimal supervision for training. Our code, data, and models will be released at https://github.com/mukhal/thinkprm.", "github_manual": "", "github_regex": "https://github.com/mukhal/thinkprm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.17670", "title": "DiMeR: Disentangled Mesh Reconstruction Model", "authors": [ "Lutao Jiang", "Jiantao Lin", "Kanghao Chen", "Wenhang Ge", "Xin Yang", "Yifan Jiang", "Yuanhuiyi Lyu", "Xu Zheng", "Yingcong Chen" ], "github": "", "project_page": "", "abstract": "With the advent of large-scale 3D datasets, feed-forward 3D generative models, such as the Large Reconstruction Model (LRM), have gained significant attention and achieved remarkable success. However, we observe that RGB images often lead to conflicting training objectives and lack the necessary clarity for geometry reconstruction. In this paper, we revisit the inductive biases associated with mesh reconstruction and introduce DiMeR, a novel disentangled dual-stream feed-forward model for sparse-view mesh reconstruction. The key idea is to disentangle both the input and framework into geometry and texture parts, thereby reducing the training difficulty for each part according to the Principle of Occam's Razor. Given that normal maps are strictly consistent with geometry and accurately capture surface variations, we utilize normal maps as exclusive input for the geometry branch to reduce the complexity between the network's input and output. Moreover, we improve the mesh extraction algorithm to introduce 3D ground truth supervision. As for texture branch, we use RGB images as input to obtain the textured mesh. Overall, DiMeR demonstrates robust capabilities across various tasks, including sparse-view reconstruction, single-image-to-3D, and text-to-3D. Numerous experiments show that DiMeR significantly outperforms previous methods, achieving over 30% improvement in Chamfer Distance on the GSO and OmniObject3D dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-25", "arxiv_id": "2504.17788", "title": "Dynamic Camera Poses and Where to Find Them", "authors": [ "Chris Rockwell", "Joseph Tung", "Tsung-Yi Lin", "Ming-Yu Liu", "David F. Fouhey", "Chen-Hsuan Lin" ], "github": "", "project_page": "", "abstract": "Annotating camera poses on dynamic Internet videos at scale is critical for advancing fields like realistic video generation and simulation. However, collecting such a dataset is difficult, as most Internet videos are unsuitable for pose estimation. Furthermore, annotating dynamic Internet videos present significant challenges even for state-of-theart methods. In this paper, we introduce DynPose-100K, a large-scale dataset of dynamic Internet videos annotated with camera poses. Our collection pipeline addresses filtering using a carefully combined set of task-specific and generalist models. For pose estimation, we combine the latest techniques of point tracking, dynamic masking, and structure-from-motion to achieve improvements over the state-of-the-art approaches. Our analysis and experiments demonstrate that DynPose-100K is both large-scale and diverse across several key attributes, opening up avenues for advancements in various downstream applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-28", "arxiv_id": "2504.15376", "title": "Towards Understanding Camera Motions in Any Video", "authors": [ "Zhiqiu Lin", "Siyuan Cen", "Daniel Jiang", "Jay Karhade", "Hewei Wang", "Chancharik Mitra", "Tiffany Ling", "Yuhan Huang", "Sifan Liu", "Mingyu Chen", "Rushikesh Zawar", "Xue Bai", "Yilun Du", "Chuang Gan", "Deva Ramanan" ], "github": "", "project_page": "", "abstract": "We introduce CameraBench, a large-scale dataset and benchmark designed to assess and improve camera motion understanding. CameraBench consists of ~3,000 diverse internet videos, annotated by experts through a rigorous multi-stage quality control process. One of our contributions is a taxonomy of camera motion primitives, designed in collaboration with cinematographers. We find, for example, that some motions like \"follow\" (or tracking) require understanding scene content like moving subjects. We conduct a large-scale human study to quantify human annotation performance, revealing that domain expertise and tutorial-based training can significantly enhance accuracy. For example, a novice may confuse zoom-in (a change of intrinsics) with translating forward (a change of extrinsics), but can be trained to differentiate the two. Using CameraBench, we evaluate Structure-from-Motion (SfM) and Video-Language Models (VLMs), finding that SfM models struggle to capture semantic primitives that depend on scene content, while VLMs struggle to capture geometric primitives that require precise estimation of trajectories. We then fine-tune a generative VLM on CameraBench to achieve the best of both worlds and showcase its applications, including motion-augmented captioning, video question answering, and video-text retrieval. We hope our taxonomy, benchmark, and tutorials will drive future efforts towards the ultimate goal of understanding camera motions in any video.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-28", "arxiv_id": "2504.16427", "title": "Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark", "authors": [ "Hanlei Zhang", "Zhuohang Li", "Yeshuang Zhu", "Hua Xu", "Peiwu Wang", "Haige Zhu", "Jie Zhou", "Jinchao Zhang" ], "github": "https://github.com/thuiar/MMLA", "project_page": "", "abstract": "Multimodal language analysis is a rapidly evolving field that leverages multiple modalities to enhance the understanding of high-level semantics underlying human conversational utterances. Despite its significance, little research has investigated the capability of multimodal large language models (MLLMs) to comprehend cognitive-level semantics. In this paper, we introduce MMLA, a comprehensive benchmark specifically designed to address this gap. MMLA comprises over 61K multimodal utterances drawn from both staged and real-world scenarios, covering six core dimensions of multimodal semantics: intent, emotion, dialogue act, sentiment, speaking style, and communication behavior. We evaluate eight mainstream branches of LLMs and MLLMs using three methods: zero-shot inference, supervised fine-tuning, and instruction tuning. Extensive experiments reveal that even fine-tuned models achieve only about 60%~70% accuracy, underscoring the limitations of current MLLMs in understanding complex human language. We believe that MMLA will serve as a solid foundation for exploring the potential of large language models in multimodal language analysis and provide valuable resources to advance this field. The datasets and code are open-sourced at https://github.com/thuiar/MMLA.", "github_manual": "", "github_regex": "https://github.com/thuiar/MMLA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-28", "arxiv_id": "2504.18415", "title": "BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs", "authors": [ "Hongyu Wang", "Shuming Ma", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Efficient deployment of 1-bit Large Language Models (LLMs) is hindered by activation outliers, which complicate quantization to low bit-widths. We introduce BitNet v2, a novel framework enabling native 4-bit activation quantization for 1-bit LLMs. To tackle outliers in attention and feed-forward network activations, we propose H-BitLinear, a module applying an online Hadamard transformation prior to activation quantization. This transformation smooths sharp activation distributions into more Gaussian-like forms, suitable for low-bit representation. Experiments show BitNet v2 trained from scratch with 8-bit activations matches BitNet b1.58 performance. Crucially, BitNet v2 achieves minimal performance degradation when trained with native 4-bit activations, significantly reducing memory footprint and computational cost for batched inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-28", "arxiv_id": "2504.12080", "title": "DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency", "authors": [ "Mengshi Qi", "Pengfei Zhu", "Xiangtai Li", "Xiaoyang Bi", "Lu Qi", "Huadong Ma", "Ming-Hsuan Yang" ], "github": "https://github.com/zaplm/DC-SAM", "project_page": "", "abstract": "Given a single labeled example, in-context segmentation aims to segment corresponding objects. This setting, known as one-shot segmentation in few-shot learning, explores the segmentation model's generalization ability and has been applied to various vision tasks, including scene understanding and image/video editing. While recent Segment Anything Models have achieved state-of-the-art results in interactive segmentation, these approaches are not directly applicable to in-context segmentation. In this work, we propose the Dual Consistency SAM (DC-SAM) method based on prompt-tuning to adapt SAM and SAM2 for in-context segmentation of both images and videos. Our key insights are to enhance the features of the SAM's prompt encoder in segmentation by providing high-quality visual prompts. When generating a mask prior, we fuse the SAM features to better align the prompt encoder. Then, we design a cycle-consistent cross-attention on fused features and initial visual prompts. Next, a dual-branch design is provided by using the discriminative positive and negative prompts in the prompt encoder. Furthermore, we design a simple mask-tube training strategy to adopt our proposed dual consistency method into the mask tube. Although the proposed DC-SAM is primarily designed for images, it can be seamlessly extended to the video domain with the support of SAM2. Given the absence of in-context segmentation in the video domain, we manually curate and construct the first benchmark from existing video segmentation datasets, named In-Context Video Object Segmentation (IC-VOS), to better assess the in-context capability of the model. Extensive experiments demonstrate that our method achieves 55.5 (+1.4) mIoU on COCO-20i, 73.0 (+1.1) mIoU on PASCAL-5i, and a J&F score of 71.52 on the proposed IC-VOS benchmark. Our source code and benchmark are available at https://github.com/zaplm/DC-SAM.", "github_manual": "", "github_regex": "https://github.com/zaplm/DC-SAM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-28", "arxiv_id": "2504.16656", "title": "Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning", "authors": [ "Chris", "Yichen Wei", "Yi Peng", "Xiaokun Wang", "Weijie Qiu", "Wei Shen", "Tianyidan Xie", "Jiangbo Pei", "Jianhao Zhang", "Yunzhuo Hao", "Xuchen Song", "Yang Liu", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "We present Skywork R1V2, a next-generation multimodal reasoning model and a major leap forward from its predecessor, Skywork R1V. At its core, R1V2 introduces a hybrid reinforcement learning paradigm that harmonizes reward-model guidance with rule-based strategies, thereby addressing the long-standing challenge of balancing sophisticated reasoning capabilities with broad generalization. To further enhance training efficiency, we propose the Selective Sample Buffer (SSB) mechanism, which effectively counters the ``Vanishing Advantages'' dilemma inherent in Group Relative Policy Optimization (GRPO) by prioritizing high-value samples throughout the optimization process. Notably, we observe that excessive reinforcement signals can induce visual hallucinations--a phenomenon we systematically monitor and mitigate through calibrated reward thresholds throughout the training process. Empirical results affirm the exceptional capability of R1V2, with benchmark-leading performances such as 62.6 on OlympiadBench, 79.0 on AIME2024, 63.6 on LiveCodeBench, and 74.0 on MMMU. These results underscore R1V2's superiority over existing open-source models and demonstrate significant progress in closing the performance gap with premier proprietary systems, including Gemini 2.5 and OpenAI o4-mini. The Skywork R1V2 model weights have been publicly released to promote openness and reproducibility https://huggingface.co/Skywork/Skywork-R1V2-38B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-28", "arxiv_id": "2504.17768", "title": "The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs", "authors": [ "Piotr Nawrot", "Robert Li", "Renjie Huang", "Sebastian Ruder", "Kelly Marchisio", "Edoardo M. Ponti" ], "github": "https://github.com/PiotrNawrot/sparse-frontier", "project_page": "", "abstract": "Sparse attention offers a promising strategy to extend long-context capabilities in Transformer LLMs, yet its viability, its efficiency-accuracy trade-offs, and systematic scaling studies remain unexplored. To address this gap, we perform a careful comparison of training-free sparse attention methods at varying model scales, sequence lengths, and sparsity levels on a diverse collection of long-sequence tasks-including novel ones that rely on natural language while remaining controllable and easy to evaluate. Based on our experiments, we report a series of key findings: 1) an isoFLOPS analysis reveals that for very long sequences, larger and highly sparse models are preferable to smaller and dense ones. 2) The level of sparsity attainable while statistically guaranteeing accuracy preservation is higher during decoding than prefilling, and correlates with model size in the former. 3) There is no clear strategy that performs best across tasks and phases, with different units of sparsification or budget adaptivity needed for different scenarios. Even moderate sparsity levels often result in significant performance degradation on at least one task, highlighting that sparse attention is not a universal solution. 4) We introduce and validate novel scaling laws specifically tailored for sparse attention, providing evidence that our findings are likely to hold true beyond our range of experiments. Through these insights, we demonstrate that sparse attention is a key tool to enhance the capabilities of Transformer LLMs for processing longer sequences, but requires careful evaluation of trade-offs for performance-sensitive applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PiotrNawrot/sparse-frontier", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-28", "arxiv_id": "2504.15716", "title": "DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large Language Models", "authors": [ "Jie Zhu", "Qian Chen", "Huaixia Dou", "Junhui Li", "Lifan Guo", "Feng Chen", "Chi Zhang" ], "github": "", "project_page": "", "abstract": "Effective reasoning remains a core challenge for large language models (LLMs) in the financial domain, where tasks often require domain-specific knowledge, precise numerical calculations, and strict adherence to compliance rules. We propose DianJin-R1, a reasoning-enhanced framework designed to address these challenges through reasoning-augmented supervision and reinforcement learning. Central to our approach is DianJin-R1-Data, a high-quality dataset constructed from CFLUE, FinQA, and a proprietary compliance corpus (Chinese Compliance Check, CCC), combining diverse financial reasoning scenarios with verified annotations. Our models, DianJin-R1-7B and DianJin-R1-32B, are fine-tuned from Qwen2.5-7B-Instruct and Qwen2.5-32B-Instruct using a structured format that generates both reasoning steps and final answers. To further refine reasoning quality, we apply Group Relative Policy Optimization (GRPO), a reinforcement learning method that incorporates dual reward signals: one encouraging structured outputs and another rewarding answer correctness. We evaluate our models on five benchmarks: three financial datasets (CFLUE, FinQA, and CCC) and two general reasoning benchmarks (MATH-500 and GPQA-Diamond). Experimental results show that DianJin-R1 models consistently outperform their non-reasoning counterparts, especially on complex financial tasks. Moreover, on the real-world CCC dataset, our single-call reasoning models match or even surpass the performance of multi-agent systems that require significantly more computational cost. These findings demonstrate the effectiveness of DianJin-R1 in enhancing financial reasoning through structured supervision and reward-aligned learning, offering a scalable and practical solution for real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-28", "arxiv_id": "2504.17816", "title": "Subject-driven Video Generation via Disentangled Identity and Motion", "authors": [ "Daneul Kim", "Jingxu Zhang", "Wonjoon Jin", "Sunghyun Cho", "Qi Dai", "Jaesik Park", "Chong Luo" ], "github": "", "project_page": "", "abstract": "We propose to train a subject-driven customized video generation model through decoupling the subject-specific learning from temporal dynamics in zero-shot without additional tuning. A traditional method for video customization that is tuning-free often relies on large, annotated video datasets, which are computationally expensive and require extensive annotation. In contrast to the previous approach, we introduce the use of an image customization dataset directly on training video customization models, factorizing the video customization into two folds: (1) identity injection through image customization dataset and (2) temporal modeling preservation with a small set of unannotated videos through the image-to-video training method. Additionally, we employ random image token dropping with randomized image initialization during image-to-video fine-tuning to mitigate the copy-and-paste issue. To further enhance learning, we introduce stochastic switching during joint optimization of subject-specific and temporal features, mitigating catastrophic forgetting. Our method achieves strong subject consistency and scalability, outperforming existing video customization models in zero-shot settings, demonstrating the effectiveness of our framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-28", "arxiv_id": "2504.17821", "title": "VideoVista-CulturalLingo: 360^circ Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension", "authors": [ "Xinyu Chen", "Yunxin Li", "Haoyuan Shi", "Baotian Hu", "Wenhan Luo", "Yaowei Wang", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Assessing the video comprehension capabilities of multimodal AI systems can effectively measure their understanding and reasoning abilities. Most video evaluation benchmarks are limited to a single language, typically English, and predominantly feature videos rooted in Western cultural contexts. In this paper, we present VideoVista-CulturalLingo, the first video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension. Our work differs from existing benchmarks in the following ways: 1) Cultural diversity, incorporating cultures from China, North America, and Europe; 2) Multi-linguistics, with questions presented in Chinese and English-two of the most widely spoken languages; and 3) Broad domain, featuring videos sourced from hundreds of human-created domains. VideoVista-CulturalLingo contains 1,389 videos and 3,134 QA pairs, and we have evaluated 24 recent open-source or proprietary video large models. From the experiment results, we observe that: 1) Existing models perform worse on Chinese-centric questions than Western-centric ones, particularly those related to Chinese history; 2) Current open-source models still exhibit limitations in temporal understanding, especially in the Event Localization task, achieving a maximum score of only 45.2%; 3) Mainstream models demonstrate strong performance in general scientific questions, while open-source models demonstrate weak performance in mathematics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-28", "arxiv_id": "2504.18425", "title": "Kimi-Audio Technical Report", "authors": [ "KimiTeam", "Ding Ding", "Zeqian Ju", "Yichong Leng", "Songxiang Liu", "Tong Liu", "Zeyu Shang", "Kai Shen", "Wei Song", "Xu Tan", "Heyi Tang", "Zhengtao Wang", "Chu Wei", "Yifei Xin", "Xinran Xu", "Jianwei Yu", "Yutao Zhang", "Xinyu Zhou", "Y. Charles", "Jun Chen", "Yanru Chen", "Yulun Du", "Weiran He", "Zhenxing Hu", "Guokun Lai", "Qingcheng Li", "Yangyang Liu", "Weidong Sun", "Jianzhou Wang", "Yuzhi Wang", "Yuefeng Wu", "Yuxin Wu", "Dongchao Yang", "Hao Yang", "Ying Yang", "Zhilin Yang", "Aoxiong Yin", "Ruibin Yuan", "Yutong Zhang", "Zaida Zhou" ], "github": "https://github.com/MoonshotAI/Kimi-Audio", "project_page": "", "abstract": "We present Kimi-Audio, an open-source audio foundation model that excels in audio understanding, generation, and conversation. We detail the practices in building Kimi-Audio, including model architecture, data curation, training recipe, inference deployment, and evaluation. Specifically, we leverage a 12.5Hz audio tokenizer, design a novel LLM-based architecture with continuous features as input and discrete tokens as output, and develop a chunk-wise streaming detokenizer based on flow matching. We curate a pre-training dataset that consists of more than 13 million hours of audio data covering a wide range of modalities including speech, sound, and music, and build a pipeline to construct high-quality and diverse post-training data. Initialized from a pre-trained LLM, Kimi-Audio is continual pre-trained on both audio and text data with several carefully designed tasks, and then fine-tuned to support a diverse of audio-related tasks. Extensive evaluation shows that Kimi-Audio achieves state-of-the-art performance on a range of audio benchmarks including speech recognition, audio understanding, audio question answering, and speech conversation. We release the codes, model checkpoints, as well as the evaluation toolkits in https://github.com/MoonshotAI/Kimi-Audio.", "github_manual": "", "github_regex": "https://github.com/MoonshotAI/Kimi-Audio", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-28", "arxiv_id": "2504.17025", "title": "Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing Efficiency Through Vocabulary Adaptation", "authors": [ "Luca Moroni", "Giovanni Puccetti", "Pere-Lluis Huguet Cabot", "Andrei Stefan Bejgu", "Edoardo Barba", "Alessio Miaschi", "Felice Dell'Orletta", "Andrea Esuli", "Roberto Navigli" ], "github": "https://github.com/SapienzaNLP/sava", "project_page": "", "abstract": "The number of pretrained Large Language Models (LLMs) is increasing steadily, though the majority are designed predominantly for the English language. While state-of-the-art LLMs can handle other languages, due to language contamination or some degree of multilingual pretraining data, they are not optimized for non-English languages, leading to inefficient encoding (high token \"fertility\") and slower inference speed. In this work, we thoroughly compare a variety of vocabulary adaptation techniques for optimizing English LLMs for the Italian language, and put forward Semantic Alignment Vocabulary Adaptation (SAVA), a novel method that leverages neural mapping for vocabulary substitution. SAVA achieves competitive performance across multiple downstream tasks, enhancing grounded alignment strategies. We adapt two LLMs: Mistral-7b-v0.1, reducing token fertility by 25\\%, and Llama-3.1-8B, optimizing the vocabulary and reducing the number of parameters by 1 billion. We show that, following the adaptation of the vocabulary, these models can recover their performance with a relatively limited stage of continual training on the target language. Finally, we test the capabilities of the adapted models on various multi-choice and generative tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SapienzaNLP/sava", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-28", "arxiv_id": "2504.18225", "title": "Even Small Reasoners Should Quote Their Sources: Introducing the Pleias-RAG Model Family", "authors": [ "Pierre-Carl Langlais", "Pavel Chizhov", "Mattia Nee", "Carlos Rosas Hinostroza", "Matthieu Delsart", "Ir\u00e8ne Girard", "Othman Hicheur", "Anastasia Stasenko", "Ivan P. Yamshchikov" ], "github": "", "project_page": "", "abstract": "We introduce a new generation of small reasoning models for RAG, search, and source summarization. Pleias-RAG-350m and Pleias-RAG-1B are mid-trained on a large synthetic dataset emulating the retrieval of a wide variety of multilingual open sources from the Common Corpus. They provide native support for citation and grounding with literal quotes and reintegrate multiple features associated with RAG workflows, such as query routing, query reformulation, and source reranking. Pleias-RAG-350m and Pleias-RAG-1B outperform SLMs below 4 billion parameters on standardized RAG benchmarks (HotPotQA, 2wiki) and are competitive with popular larger models, including Qwen-2.5-7B, Llama-3.1-8B, and Gemma-3-4B. They are the only SLMs to date maintaining consistent RAG performance across leading European languages and ensuring systematic reference grounding for statements. Due to their size and ease of deployment on constrained infrastructure and higher factuality by design, the models unlock a range of new use cases for generative AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.19838", "title": "LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects", "authors": [ "Guangyi Liu", "Pengxiang Zhao", "Liang Liu", "Yaxuan Guo", "Han Xiao", "Weifeng Lin", "Yuxiang Chai", "Yue Han", "Shuai Ren", "Hao Wang", "Xiaoyu Liang", "Wenhao Wang", "Tianze Wu", "Linghao Li", "Hao Wang", "Guanjing Xiong", "Yong Liu", "Hongsheng Li" ], "github": "https://github.com/phonellm/awesome-llm-powered-phone-gui-agents", "project_page": "", "abstract": "With the rapid rise of large language models (LLMs), phone automation has undergone transformative changes. This paper systematically reviews LLM-driven phone GUI agents, highlighting their evolution from script-based automation to intelligent, adaptive systems. We first contextualize key challenges, (i) limited generality, (ii) high maintenance overhead, and (iii) weak intent comprehension, and show how LLMs address these issues through advanced language understanding, multimodal perception, and robust decision-making. We then propose a taxonomy covering fundamental agent frameworks (single-agent, multi-agent, plan-then-act), modeling approaches (prompt engineering, training-based), and essential datasets and benchmarks. Furthermore, we detail task-specific architectures, supervised fine-tuning, and reinforcement learning strategies that bridge user intent and GUI operations. Finally, we discuss open challenges such as dataset diversity, on-device deployment efficiency, user-centric adaptation, and security concerns, offering forward-looking insights into this rapidly evolving field. By providing a structured overview and identifying pressing research gaps, this paper serves as a definitive reference for researchers and practitioners seeking to harness LLMs in designing scalable, user-friendly phone GUI agents.", "github_manual": "https://github.com/phonellm/awesome-llm-powered-phone-gui-agents", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.18919", "title": "Clinical knowledge in LLMs does not translate to human interactions", "authors": [ "Andrew M. Bean", "Rebecca Payne", "Guy Parsons", "Hannah Rose Kirk", "Juan Ciro", "Rafael Mosquera", "Sara Hincapi\u00e9 Monsalve", "Aruna S. Ekanayaka", "Lionel Tarassenko", "Luc Rocher", "Adam Mahdi" ], "github": "https://github.com/am-bean/HELPMed", "project_page": "", "abstract": "Global healthcare providers are exploring use of large language models (LLMs) to provide medical advice to the public. LLMs now achieve nearly perfect scores on medical licensing exams, but this does not necessarily translate to accurate performance in real-world settings. We tested if LLMs can assist members of the public in identifying underlying conditions and choosing a course of action (disposition) in ten medical scenarios in a controlled study with 1,298 participants. Participants were randomly assigned to receive assistance from an LLM (GPT-4o, Llama 3, Command R+) or a source of their choice (control). Tested alone, LLMs complete the scenarios accurately, correctly identifying conditions in 94.9% of cases and disposition in 56.3% on average. However, participants using the same LLMs identified relevant conditions in less than 34.5% of cases and disposition in less than 44.2%, both no better than the control group. We identify user interactions as a challenge to the deployment of LLMs for medical advice. Standard benchmarks for medical knowledge and simulated patient interactions do not predict the failures we find with human participants. Moving forward, we recommend systematic human user testing to evaluate interactive capabilities prior to public deployments in healthcare.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/am-bean/HELPMed", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.19724", "title": "RepText: Rendering Visual Text via Replicating", "authors": [ "Haofan Wang", "Yujia Xu", "Yimeng Li", "Junchen Li", "Chaowei Zhang", "Jing Wang", "Kejia Yang", "Zhibo Chen" ], "github": "https://github.com/Shakker-Labs/RepText", "project_page": "", "abstract": "Although contemporary text-to-image generation models have achieved remarkable breakthroughs in producing visually appealing images, their capacity to generate precise and flexible typographic elements, especially non-Latin alphabets, remains constrained. To address these limitations, we start from an naive assumption that text understanding is only a sufficient condition for text rendering, but not a necessary condition. Based on this, we present RepText, which aims to empower pre-trained monolingual text-to-image generation models with the ability to accurately render, or more precisely, replicate, multilingual visual text in user-specified fonts, without the need to really understand them. Specifically, we adopt the setting from ControlNet and additionally integrate language agnostic glyph and position of rendered text to enable generating harmonized visual text, allowing users to customize text content, font and position on their needs. To improve accuracy, a text perceptual loss is employed along with the diffusion loss. Furthermore, to stabilize rendering process, at the inference phase, we directly initialize with noisy glyph latent instead of random initialization, and adopt region masks to restrict the feature injection to only the text region to avoid distortion of the background. We conducted extensive experiments to verify the effectiveness of our RepText relative to existing works, our approach outperforms existing open-source methods and achieves comparable results to native multi-language closed-source models. To be more fair, we also exhaustively discuss its limitations in the end.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Shakker-Labs/RepText", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.15780", "title": "TrustGeoGen: Scalable and Formal-Verified Data Engine for Trustworthy Multi-modal Geometric Problem Solving", "authors": [ "Daocheng Fu", "Zijun Chen", "Renqiu Xia", "Qi Liu", "Yuan Feng", "Hongbin Zhou", "Renrui Zhang", "Shiyang Feng", "Peng Gao", "Junchi Yan", "Botian Shi", "Bo Zhang", "Yu Qiao" ], "github": "https://github.com/Alpha-Innovator/TrustGeoGen", "project_page": "", "abstract": "Mathematical geometric problem solving (GPS) often requires effective integration of multimodal information and verifiable logical coherence. Despite the fast development of large language models in general problem solving, it remains unresolved regarding with both methodology and benchmarks, especially given the fact that exiting synthetic GPS benchmarks are often not self-verified and contain noise and self-contradicted information due to the illusion of LLMs. In this paper, we propose a scalable data engine called TrustGeoGen for problem generation, with formal verification to provide a principled benchmark, which we believe lays the foundation for the further development of methods for GPS. The engine synthesizes geometric data through four key innovations: 1) multimodal-aligned generation of diagrams, textual descriptions, and stepwise solutions; 2) formal verification ensuring rule-compliant reasoning paths; 3) a bootstrapping mechanism enabling complexity escalation via recursive state generation and 4) our devised GeoExplore series algorithms simultaneously produce multi-solution variants and self-reflective backtracking traces. By formal logical verification, TrustGeoGen produces GeoTrust-200K dataset with guaranteed modality integrity, along with GeoTrust-test testset. Experiments reveal the state-of-the-art models achieve only 49.17\\% accuracy on GeoTrust-test, demonstrating its evaluation stringency. Crucially, models trained on GeoTrust achieve OOD generalization on GeoQA, significantly reducing logical inconsistencies relative to pseudo-label annotated by OpenAI-o1. Our code is available at https://github.com/Alpha-Innovator/TrustGeoGen", "github_manual": "", "github_regex": "https://github.com/Alpha-Innovator/TrustGeoGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.17258", "title": "Group Downsampling with Equivariant Anti-aliasing", "authors": [ "Md Ashiqur Rahman", "Raymond A. Yeh" ], "github": "https://github.com/ashiq24/Group", "project_page": "", "abstract": "Downsampling layers are crucial building blocks in CNN architectures, which help to increase the receptive field for learning high-level features and reduce the amount of memory/computation in the model. In this work, we study the generalization of the uniform downsampling layer for group equivariant architectures, e.g., G-CNNs. That is, we aim to downsample signals (feature maps) on general finite groups with anti-aliasing. This involves the following: (a) Given a finite group and a downsampling rate, we present an algorithm to form a suitable choice of subgroup. (b) Given a group and a subgroup, we study the notion of bandlimited-ness and propose how to perform anti-aliasing. Notably, our method generalizes the notion of downsampling based on classical sampling theory. When the signal is on a cyclic group, i.e., periodic, our method recovers the standard downsampling of an ideal low-pass filter followed by a subsampling operation. Finally, we conducted experiments on image classification tasks demonstrating that the proposed downsampling operation improves accuracy, better preserves equivariance, and reduces model size when incorporated into G-equivariant networks", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ashiq24/Group", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.16083", "title": "MMInference: Accelerating Pre-filling for Long-Context VLMs via Modality-Aware Permutation Sparse Attention", "authors": [ "Yucheng Li", "Huiqiang Jiang", "Chengruidong Zhang", "Qianhui Wu", "Xufang Luo", "Surin Ahn", "Amir H. Abdi", "Dongsheng Li", "Jianfeng Gao", "Yuqing Yang", "Lili Qiu" ], "github": "", "project_page": "", "abstract": "The integration of long-context capabilities with visual understanding unlocks unprecedented potential for Vision Language Models (VLMs). However, the quadratic attention complexity during the pre-filling phase remains a significant obstacle to real-world deployment. To overcome this limitation, we introduce MMInference (Multimodality Million tokens Inference), a dynamic sparse attention method that accelerates the prefilling stage for long-context multi-modal inputs. First, our analysis reveals that the temporal and spatial locality of video input leads to a unique sparse pattern, the Grid pattern. Simultaneously, VLMs exhibit markedly different sparse distributions across different modalities. We introduce a permutation-based method to leverage the unique Grid pattern and handle modality boundary issues. By offline search the optimal sparse patterns for each head, MMInference constructs the sparse distribution dynamically based on the input. We also provide optimized GPU kernels for efficient sparse computations. Notably, MMInference integrates seamlessly into existing VLM pipelines without any model modifications or fine-tuning. Experiments on multi-modal benchmarks-including Video QA, Captioning, VisionNIAH, and Mixed-Modality NIAH-with state-of-the-art long-context VLMs (LongVila, LlavaVideo, VideoChat-Flash, Qwen2.5-VL) show that MMInference accelerates the pre-filling stage by up to 8.3x at 1M tokens while maintaining accuracy. Our code is available at https://aka.ms/MMInference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.19093", "title": "CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenges", "authors": [ "Yu Li", "Qizhi Pei", "Mengyuan Sun", "Honglin Lin", "Chenlin Ming", "Xin Gao", "Jiang Wu", "Conghui He", "Lijun Wu" ], "github": "https://github.com/open-compass/opencompass", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities, especially the recent advancements in reasoning, such as o1 and o3, pushing the boundaries of AI. Despite these impressive achievements in mathematics and coding, the reasoning abilities of LLMs in domains requiring cryptographic expertise remain underexplored. In this paper, we introduce CipherBank, a comprehensive benchmark designed to evaluate the reasoning capabilities of LLMs in cryptographic decryption tasks. CipherBank comprises 2,358 meticulously crafted problems, covering 262 unique plaintexts across 5 domains and 14 subdomains, with a focus on privacy-sensitive and real-world scenarios that necessitate encryption. From a cryptographic perspective, CipherBank incorporates 3 major categories of encryption methods, spanning 9 distinct algorithms, ranging from classical ciphers to custom cryptographic techniques. We evaluate state-of-the-art LLMs on CipherBank, e.g., GPT-4o, DeepSeek-V3, and cutting-edge reasoning-focused models such as o1 and DeepSeek-R1. Our results reveal significant gaps in reasoning abilities not only between general-purpose chat LLMs and reasoning-focused LLMs but also in the performance of current reasoning-focused models when applied to classical cryptographic decryption tasks, highlighting the challenges these models face in understanding and manipulating encrypted data. Through detailed analysis and error investigations, we provide several key observations that shed light on the limitations and potential improvement areas for LLMs in cryptographic reasoning. These findings underscore the need for continuous advancements in LLM reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-compass/opencompass", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.19395", "title": "ICL CIPHERS: Quantifying \"Learning'' in In-Context Learning via Substitution Ciphers", "authors": [ "Zhouxiang Fang", "Aayush Mishra", "Muhan Gao", "Anqi Liu", "Daniel Khashabi" ], "github": "https://github.com/jhu-CLSP/icl-ciphers", "project_page": "", "abstract": "Recent works have suggested that In-Context Learning (ICL) operates in dual modes, i.e. task retrieval (remember learned patterns from pre-training) and task learning (inference-time ``learning'' from demonstrations). However, disentangling these the two modes remains a challenging goal. We introduce ICL CIPHERS, a class of task reformulations based on substitution ciphers borrowed from classic cryptography. In this approach, a subset of tokens in the in-context inputs are substituted with other (irrelevant) tokens, rendering English sentences less comprehensible to human eye. However, by design, there is a latent, fixed pattern to this substitution, making it reversible. This bijective (reversible) cipher ensures that the task remains a well-defined task in some abstract sense, despite the transformations. It is a curious question if LLMs can solve ICL CIPHERS with a BIJECTIVE mapping, which requires deciphering the latent cipher. We show that LLMs are better at solving ICL CIPHERS with BIJECTIVE mappings than the NON-BIJECTIVE (irreversible) baseline, providing a novel approach to quantify ``learning'' in ICL. While this gap is small, it is consistent across the board on four datasets and six models. Finally, we examine LLMs' internal representations and identify evidence in their ability to decode the ciphered inputs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jhu-CLSP/icl-ciphers", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.19162", "title": "SPC: Evolving Self-Play Critic via Adversarial Games for LLM Reasoning", "authors": [ "Jiaqi Chen", "Bang Zhang", "Ruotian Ma", "Peisong Wang", "Xiaodan Liang", "Zhaopeng Tu", "Xiaolong Li", "Kwan-Yee K. Wong" ], "github": "", "project_page": "", "abstract": "Evaluating the step-by-step reliability of large language model (LLM) reasoning, such as Chain-of-Thought, remains challenging due to the difficulty and cost of obtaining high-quality step-level supervision. In this paper, we introduce Self-Play Critic (SPC), a novel approach where a critic model evolves its ability to assess reasoning steps through adversarial self-play games, eliminating the need for manual step-level annotation. SPC involves fine-tuning two copies of a base model to play two roles, namely a \"sneaky generator\" that deliberately produces erroneous steps designed to be difficult to detect, and a \"critic\" that analyzes the correctness of reasoning steps. These two models engage in an adversarial game in which the generator aims to fool the critic, while the critic model seeks to identify the generator's errors. Using reinforcement learning based on the game outcomes, the models iteratively improve; the winner of each confrontation receives a positive reward and the loser receives a negative reward, driving continuous self-evolution. Experiments on three reasoning process benchmarks (ProcessBench, PRM800K, DeltaBench) demonstrate that our SPC progressively enhances its error detection capabilities (e.g., accuracy increases from 70.8% to 77.7% on ProcessBench) and surpasses strong baselines, including distilled R1 model. Furthermore, applying SPC to guide the test-time search of diverse LLMs significantly improves their mathematical reasoning performance on MATH500 and AIME2024, outperforming state-of-the-art process reward models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.18589", "title": "Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency", "authors": [ "Zhikai Wang", "Jiashuo Sun", "Wenqi Zhang", "Zhiqiang Hu", "Xin Li", "Fan Wang", "Deli Zhao" ], "github": "", "project_page": "", "abstract": "Recent advancements in Large Vision-Language Models (LVLMs) have significantly enhanced their ability to integrate visual and linguistic information, achieving near-human proficiency in tasks like object recognition, captioning, and visual question answering. However, current benchmarks typically focus on knowledge-centric evaluations that assess domain-specific expertise, often neglecting the core ability to reason about fundamental mathematical elements and visual concepts. We identify a gap in evaluating elementary-level math problems, which rely on explicit visual dependencies-requiring models to discern, integrate, and reason across multiple images while incorporating commonsense knowledge, all of which are crucial for advancing toward broader AGI capabilities. To address this gap, we introduce VCBENCH, a comprehensive benchmark for multimodal mathematical reasoning with explicit visual dependencies. VCBENCH includes 1,720 problems across six cognitive domains, featuring 6,697 images (averaging 3.9 per question) to ensure multi-image reasoning. We evaluate 26 state-of-the-art LVLMs on VCBENCH, revealing substantial performance disparities, with even the top models unable to exceed 50% accuracy. Our findings highlight the ongoing challenges in visual-mathematical integration and suggest avenues for future LVLM advancements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.19144", "title": "ChiseLLM: Unleashing the Power of Reasoning LLMs for Chisel Agile Hardware Development", "authors": [ "Bowei Wang", "Jiaran Gao", "Yelai Feng", "Renzhi Chen", "Shanshan Li", "Lei Wang" ], "github": "https://github.com/observerw/ChiseLLM", "project_page": "", "abstract": "The growing demand for Domain-Specific Architecture (DSA) has driven the development of Agile Hardware Development Methodology (AHDM). Hardware Construction Language (HCL) like Chisel offers high-level abstraction features, making it an ideal language for HCL-Based AHDM. While Large Language Models (LLMs) excel in code generation tasks, they still face challenges with Chisel generation, particularly regarding syntax correctness and design variability. Recent reasoning models have significantly enhanced code generation capabilities through test-time scaling techniques. However, we found that reasoning models without domain adaptation cannot bring substantial benefits to Chisel code generation tasks. This paper presents ChiseLLM, a solution comprising data processing and transformation, prompt-guided reasoning trace synthesis, and domain-adapted model training. We constructed high-quality datasets from public RTL code resources and guided the model to adopt structured thinking patterns through prompt enhancement methods. Experiments demonstrate that our ChiseLLM-7B and ChiseLLM-32B models improved syntax correctness by 18.85% and 26.32% respectively over base models, while increasing variability design ability by 47.58% compared to baseline reasoning models. Our datasets and models are publicly available, providing high-performance, cost-effective models for HCL-Based AHDM, and offering an effective baseline for future research. Github repository: https://github.com/observerw/ChiseLLM", "github_manual": "", "github_regex": "https://github.com/observerw/ChiseLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.19062", "title": "Versatile Framework for Song Generation with Prompt-based Control", "authors": [ "Yu Zhang", "Wenxiang Guo", "Changhao Pan", "Zhiyuan Zhu", "Ruiqi Li", "Jingyu Lu", "Rongjie Huang", "Ruiyuan Zhang", "Zhiqing Hong", "Ziyue Jiang", "Zhou Zhao" ], "github": "https://github.com/AaronZ345/VersBand", "project_page": "", "abstract": "Song generation focuses on producing controllable high-quality songs based on various prompts. However, existing methods struggle to generate vocals and accompaniments with prompt-based control and proper alignment. Additionally, they fall short in supporting various tasks. To address these challenges, we introduce VersBand, a multi-task song generation framework for synthesizing high-quality, aligned songs with prompt-based control. VersBand comprises these primary models: 1) VocalBand, a decoupled model, leverages the flow-matching method for generating singing styles, pitches, and mel-spectrograms, allowing fast, high-quality vocal generation with style control. 2) AccompBand, a flow-based transformer model, incorporates the Band-MOE, selecting suitable experts for enhanced quality, alignment, and control. This model allows for generating controllable, high-quality accompaniments aligned with vocals. 3) Two generation models, LyricBand for lyrics and MelodyBand for melodies, contribute to the comprehensive multi-task song generation system, allowing for extensive control based on multiple prompts. Experimental results demonstrate that VersBand performs better over baseline models across multiple song generation tasks using objective and subjective metrics. Audio samples are available at https://VersBand.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AaronZ345/VersBand", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.19854", "title": "NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks", "authors": [ "Chia-Yu Hung", "Qi Sun", "Pengfei Hong", "Amir Zadeh", "Chuan Li", "U-Xuan Tan", "Navonil Majumder", "Soujanya Poria" ], "github": "", "project_page": "", "abstract": "Existing Visual-Language-Action (VLA) models have shown promising performance in zero-shot scenarios, demonstrating impressive task execution and reasoning capabilities. However, a significant challenge arises from the limitations of visual encoding, which can result in failures during tasks such as object grasping. Moreover, these models typically suffer from high computational overhead due to their large sizes, often exceeding 7B parameters. While these models excel in reasoning and task planning, the substantial computational overhead they incur makes them impractical for real-time robotic environments, where speed and efficiency are paramount. To address the limitations of existing VLA models, we propose NORA, a 3B-parameter model designed to reduce computational overhead while maintaining strong task performance. NORA adopts the Qwen-2.5-VL-3B multimodal model as its backbone, leveraging its superior visual-semantic understanding to enhance visual reasoning and action grounding. Additionally, our is trained on 970k real-world robot demonstrations and equipped with the FAST+ tokenizer for efficient action sequence generation. Experimental results demonstrate that NORA outperforms existing large-scale VLA models, achieving better task performance with significantly reduced computational overhead, making it a more practical solution for real-time robotic autonomy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-29", "arxiv_id": "2504.19413", "title": "Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory", "authors": [ "Prateek Chhikara", "Dev Khant", "Saket Aryan", "Taranjeet Singh", "Deshraj Yadav" ], "github": "https://github.com/mem0ai/mem0", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable prowess in generating contextually coherent responses, yet their fixed context windows pose fundamental challenges for maintaining consistency over prolonged multi-session dialogues. We introduce Mem0, a scalable memory-centric architecture that addresses this issue by dynamically extracting, consolidating, and retrieving salient information from ongoing conversations. Building on this foundation, we further propose an enhanced variant that leverages graph-based memory representations to capture complex relational structures among conversational elements. Through comprehensive evaluations on LOCOMO benchmark, we systematically compare our approaches against six baseline categories: (i) established memory-augmented systems, (ii) retrieval-augmented generation (RAG) with varying chunk sizes and k-values, (iii) a full-context approach that processes the entire conversation history, (iv) an open-source memory solution, (v) a proprietary model system, and (vi) a dedicated memory management platform. Empirical results show that our methods consistently outperform all existing memory systems across four question categories: single-hop, temporal, multi-hop, and open-domain. Notably, Mem0 achieves 26% relative improvements in the LLM-as-a-Judge metric over OpenAI, while Mem0 with graph memory achieves around 2% higher overall score than the base configuration. Beyond accuracy gains, we also markedly reduce computational overhead compared to full-context method. In particular, Mem0 attains a 91% lower p95 latency and saves more than 90% token cost, offering a compelling balance between advanced reasoning capabilities and practical deployment constraints. Our findings highlight critical role of structured, persistent memory mechanisms for long-term conversational coherence, paving the way for more reliable and efficient LLM-driven AI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mem0ai/mem0", "github_api_added_by": "auto" }, { "date": "2025-04-30", "arxiv_id": "2504.20595", "title": "ReasonIR: Training Retrievers for Reasoning Tasks", "authors": [ "Rulin Shao", "Rui Qiao", "Varsha Kishore", "Niklas Muennighoff", "Xi Victoria Lin", "Daniela Rus", "Bryan Kian Hsiang Low", "Sewon Min", "Wen-tau Yih", "Pang Wei Koh", "Luke Zettlemoyer" ], "github": "https://github.com/facebookresearch/reasonir", "project_page": "", "abstract": "We present ReasonIR-8B, the first retriever specifically trained for general reasoning tasks. Existing retrievers have shown limited gains on reasoning tasks, in part because existing training datasets focus on short factual queries tied to documents that straightforwardly answer them. We develop a synthetic data generation pipeline that, for each document, our pipeline creates a challenging and relevant query, along with a plausibly related but ultimately unhelpful hard negative. By training on a mixture of our synthetic data and existing public data, ReasonIR-8B achieves a new state-of-the-art of 29.9 nDCG@10 without reranker and 36.9 nDCG@10 with reranker on BRIGHT, a widely-used reasoning-intensive information retrieval (IR) benchmark. When applied to RAG tasks, ReasonIR-8B improves MMLU and GPQA performance by 6.4% and 22.6% respectively, relative to the closed-book baseline, outperforming other retrievers and search engines. In addition, ReasonIR-8B uses test-time compute more effectively: on BRIGHT, its performance consistently increases with longer and more information-rich rewritten queries; it continues to outperform other retrievers when combined with an LLM reranker. Our training recipe is general and can be easily extended to future LLMs; to this end, we open-source our code, data, and model.", "github_manual": "https://github.com/facebookresearch/reasonir", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.20571", "title": "Reinforcement Learning for Reasoning in Large Language Models with One Training Example", "authors": [ "Yiping Wang", "Qing Yang", "Zhiyuan Zeng", "Liliang Ren", "Lucas Liu", "Baolin Peng", "Hao Cheng", "Xuehai He", "Kuan Wang", "Jianfeng Gao", "Weizhu Chen", "Shuohang Wang", "Simon Shaolei Du", "Yelong Shen" ], "github": "https://github.com/ypwang61/One-Shot-RLVR", "project_page": "", "abstract": "We show that reinforcement learning with verifiable reward using one training example (1-shot RLVR) is effective in incentivizing the math reasoning capabilities of large language models (LLMs). Applying RLVR to the base model Qwen2.5-Math-1.5B, we identify a single example that elevates model performance on MATH500 from 36.0% to 73.6%, and improves the average performance across six common mathematical reasoning benchmarks from 17.6% to 35.7%. This result matches the performance obtained using the 1.2k DeepScaleR subset (MATH500: 73.6%, average: 35.9%), which includes the aforementioned example. Similar substantial improvements are observed across various models (Qwen2.5-Math-7B, Llama3.2-3B-Instruct, DeepSeek-R1-Distill-Qwen-1.5B), RL algorithms (GRPO and PPO), and different math examples (many of which yield approximately 30% or greater improvement on MATH500 when employed as a single training example). In addition, we identify some interesting phenomena during 1-shot RLVR, including cross-domain generalization, increased frequency of self-reflection, and sustained test performance improvement even after the training accuracy has saturated, a phenomenon we term post-saturation generalization. Moreover, we verify that the effectiveness of 1-shot RLVR primarily arises from the policy gradient loss, distinguishing it from the \"grokking\" phenomenon. We also show the critical role of promoting exploration (e.g., by adding entropy loss with an appropriate coefficient) in 1-shot RLVR training. As a bonus, we observe that applying entropy loss alone, without any outcome reward, significantly enhances Qwen2.5-Math-1.5B's performance on MATH500 by 27.4%. These findings can inspire future work on RLVR data efficiency and encourage a re-examination of both recent progress and the underlying mechanisms in RLVR. Our code, model, and data are open source at https://github.com/ypwang61/One-Shot-RLVR", "github_manual": "", "github_regex": "https://github.com/ypwang61/One-Shot-RLVR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.20998", "title": "YoChameleon: Personalized Vision and Language Generation", "authors": [ "Thao Nguyen", "Krishna Kumar Singh", "Jing Shi", "Trung Bui", "Yong Jae Lee", "Yuheng Li" ], "github": "", "project_page": "", "abstract": "Large Multimodal Models (e.g., GPT-4, Gemini, Chameleon) have evolved into powerful tools with millions of users. However, they remain generic models and lack personalized knowledge of specific user concepts. Previous work has explored personalization for text generation, yet it remains unclear how these methods can be adapted to new modalities, such as image generation. In this paper, we introduce Yo'Chameleon, the first attempt to study personalization for large multimodal models. Given 3-5 images of a particular concept, Yo'Chameleon leverages soft-prompt tuning to embed subject-specific information to (i) answer questions about the subject and (ii) recreate pixel-level details to produce images of the subject in new contexts. Yo'Chameleon is trained with (i) a self-prompting optimization mechanism to balance performance across multiple modalities, and (ii) a ``soft-positive\" image generation approach to enhance image quality in a few-shot setting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.20995", "title": "TesserAct: Learning 4D Embodied World Models", "authors": [ "Haoyu Zhen", "Qiao Sun", "Hongxin Zhang", "Junyan Li", "Siyuan Zhou", "Yilun Du", "Chuang Gan" ], "github": "https://github.com/UMass-Embodied-AGI/TesserAct", "project_page": "", "abstract": "This paper presents an effective approach for learning novel 4D embodied world models, which predict the dynamic evolution of 3D scenes over time in response to an embodied agent's actions, providing both spatial and temporal consistency. We propose to learn a 4D world model by training on RGB-DN (RGB, Depth, and Normal) videos. This not only surpasses traditional 2D models by incorporating detailed shape, configuration, and temporal changes into their predictions, but also allows us to effectively learn accurate inverse dynamic models for an embodied agent. Specifically, we first extend existing robotic manipulation video datasets with depth and normal information leveraging off-the-shelf models. Next, we fine-tune a video generation model on this annotated dataset, which jointly predicts RGB-DN (RGB, Depth, and Normal) for each frame. We then present an algorithm to directly convert generated RGB, Depth, and Normal videos into a high-quality 4D scene of the world. Our method ensures temporal and spatial coherence in 4D scene predictions from embodied scenarios, enables novel view synthesis for embodied environments, and facilitates policy learning that significantly outperforms those derived from prior video-based world models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UMass-Embodied-AGI/TesserAct", "github_api_added_by": "auto" }, { "date": "2025-04-30", "arxiv_id": "2504.20734", "title": "UniversalRAG: Retrieval-Augmented Generation over Multiple Corpora with Diverse Modalities and Granularities", "authors": [ "Woongyeong Yeo", "Kangsan Kim", "Soyeong Jeong", "Jinheon Baek", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. However, most existing RAG approaches are limited to a text-only corpus, and while recent efforts have extended RAG to other modalities such as images and videos, they typically operate over a single modality-specific corpus. In contrast, real-world queries vary widely in the type of knowledge they require, which a single type of knowledge source cannot address. To address this, we introduce UniversalRAG, a novel RAG framework designed to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities. Specifically, motivated by the observation that forcing all modalities into a unified representation space derived from a single combined corpus causes a modality gap, where the retrieval tends to favor items from the same modality as the query, we propose a modality-aware routing mechanism that dynamically identifies the most appropriate modality-specific corpus and performs targeted retrieval within it. Also, beyond modality, we organize each modality into multiple granularity levels, enabling fine-tuned retrieval tailored to the complexity and scope of the query. We validate UniversalRAG on 8 benchmarks spanning multiple modalities, showing its superiority over modality-specific and unified baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.20157", "title": "Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward Models", "authors": [ "Zae Myung Kim", "Chanwoo Park", "Vipul Raheja", "Dongyeop Kang" ], "github": "https://github.com/minnesotanlp/mpo", "project_page": "", "abstract": "Reward-based alignment methods for large language models (LLMs) face two key limitations: vulnerability to reward hacking, where models exploit flaws in the reward signal; and reliance on brittle, labor-intensive prompt engineering when LLMs are used as reward models. We introduce Meta Policy Optimization (MPO), a framework that addresses these challenges by integrating a meta-reward model that dynamically refines the reward model's prompt throughout training. In MPO, the meta-reward model monitors the evolving training context and continuously adjusts the reward model's prompt to maintain high alignment, providing an adaptive reward signal that resists exploitation by the policy. This meta-learning approach promotes a more stable policy optimization, and greatly reduces the need for manual reward prompt design. It yields performance on par with or better than models guided by extensively hand-crafted reward prompts. Furthermore, we show that MPO maintains its effectiveness across diverse tasks, such as question answering and mathematical reasoning, without requiring specialized reward designs. Beyond standard RLAIF, MPO's meta-learning formulation is readily extensible to higher-level alignment frameworks. Overall, this method addresses theoretical and practical challenges in reward-based RL alignment for LLMs, paving the way for more robust and adaptable alignment strategies. The code and models will be publicly shared.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/minnesotanlp/mpo", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.16046", "title": "Certified Mitigation of Worst-Case LLM Copyright Infringement", "authors": [ "Jingyu Zhang", "Jiacan Yu", "Marc Marone", "Benjamin Van Durme", "Daniel Khashabi" ], "github": "", "project_page": "", "abstract": "The exposure of large language models (LLMs) to copyrighted material during pre-training raises concerns about unintentional copyright infringement post deployment. This has driven the development of \"copyright takedown\" methods, post-training approaches aimed at preventing models from generating content substantially similar to copyrighted ones. While current mitigation approaches are somewhat effective for average-case risks, we demonstrate that they overlook worst-case copyright risks exhibits by the existence of long, verbatim quotes from copyrighted sources. We propose BloomScrub, a remarkably simple yet highly effective inference-time approach that provides certified copyright takedown. Our method repeatedly interleaves quote detection with rewriting techniques to transform potentially infringing segments. By leveraging efficient data sketches (Bloom filters), our approach enables scalable copyright screening even for large-scale real-world corpora. When quotes beyond a length threshold cannot be removed, the system can abstain from responding, offering certified risk reduction. Experimental results show that BloomScrub reduces infringement risk, preserves utility, and accommodates different levels of enforcement stringency with adaptive abstention. Our results suggest that lightweight, inference-time methods can be surprisingly effective for copyright prevention.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.20879", "title": "The Leaderboard Illusion", "authors": [ "Shivalika Singh", "Yiyang Nan", "Alex Wang", "Daniel D'Souza", "Sayash Kapoor", "Ahmet \u00dcst\u00fcn", "Sanmi Koyejo", "Yuntian Deng", "Shayne Longpre", "Noah Smith", "Beyza Ermis", "Marzieh Fadaee", "Sara Hooker" ], "github": "", "project_page": "", "abstract": "Measuring progress is fundamental to the advancement of any scientific field. As benchmarks play an increasingly central role, they also grow more susceptible to distortion. Chatbot Arena has emerged as the go-to leaderboard for ranking the most capable AI systems. Yet, in this work we identify systematic issues that have resulted in a distorted playing field. We find that undisclosed private testing practices benefit a handful of providers who are able to test multiple variants before public release and retract scores if desired. We establish that the ability of these providers to choose the best score leads to biased Arena scores due to selective disclosure of performance results. At an extreme, we identify 27 private LLM variants tested by Meta in the lead-up to the Llama-4 release. We also establish that proprietary closed models are sampled at higher rates (number of battles) and have fewer models removed from the arena than open-weight and open-source alternatives. Both these policies lead to large data access asymmetries over time. Providers like Google and OpenAI have received an estimated 19.2% and 20.4% of all data on the arena, respectively. In contrast, a combined 83 open-weight models have only received an estimated 29.7% of the total data. We show that access to Chatbot Arena data yields substantial benefits; even limited additional data can result in relative performance gains of up to 112% on the arena distribution, based on our conservative estimates. Together, these dynamics result in overfitting to Arena-specific dynamics rather than general model quality. The Arena builds on the substantial efforts of both the organizers and an open community that maintains this valuable evaluation platform. We offer actionable recommendations to reform the Chatbot Arena's evaluation framework and promote fairer, more transparent benchmarking for the field", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.20996", "title": "X-Fusion: Introducing New Modality to Frozen Large Language Models", "authors": [ "Sicheng Mo", "Thao Nguyen", "Xun Huang", "Siddharth Srinivasan Iyer", "Yijun Li", "Yuchen Liu", "Abhishek Tandon", "Eli Shechtman", "Krishna Kumar Singh", "Yong Jae Lee", "Bolei Zhou", "Yuheng Li" ], "github": "", "project_page": "", "abstract": "We propose X-Fusion, a framework that extends pretrained Large Language Models (LLMs) for multimodal tasks while preserving their language capabilities. X-Fusion employs a dual-tower design with modality-specific weights, keeping the LLM's parameters frozen while integrating vision-specific information for both understanding and generation. Our experiments demonstrate that X-Fusion consistently outperforms alternative architectures on both image-to-text and text-to-image tasks. We find that incorporating understanding-focused data improves generation quality, reducing image data noise enhances overall performance, and feature alignment accelerates convergence for smaller models but has minimal impact on larger ones. Our findings provide valuable insights into building efficient unified multimodal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.18087", "title": "Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation", "authors": [ "Weipeng Tan", "Chuming Lin", "Chengming Xu", "FeiFan Xu", "Xiaobin Hu", "Xiaozhong Ji", "Junwei Zhu", "Chengjie Wang", "Yanwei Fu" ], "github": "", "project_page": "", "abstract": "Recent advances in Talking Head Generation (THG) have achieved impressive lip synchronization and visual quality through diffusion models; yet existing methods struggle to generate emotionally expressive portraits while preserving speaker identity. We identify three critical limitations in current emotional talking head generation: insufficient utilization of audio's inherent emotional cues, identity leakage in emotion representations, and isolated learning of emotion correlations. To address these challenges, we propose a novel framework dubbed as DICE-Talk, following the idea of disentangling identity with emotion, and then cooperating emotions with similar characteristics. First, we develop a disentangled emotion embedder that jointly models audio-visual emotional cues through cross-modal attention, representing emotions as identity-agnostic Gaussian distributions. Second, we introduce a correlation-enhanced emotion conditioning module with learnable Emotion Banks that explicitly capture inter-emotion relationships through vector quantization and attention-based feature aggregation. Third, we design an emotion discrimination objective that enforces affective consistency during the diffusion process through latent-space classification. Extensive experiments on MEAD and HDTF datasets demonstrate our method's superiority, outperforming state-of-the-art approaches in emotion accuracy while maintaining competitive lip-sync performance. Qualitative results and user studies further confirm our method's ability to generate identity-preserving portraits with rich, correlated emotional expressions that naturally adapt to unseen identities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.20630", "title": "ISDrama: Immersive Spatial Drama Generation through Multimodal Prompting", "authors": [ "Yu Zhang", "Wenxiang Guo", "Changhao Pan", "Zhiyuan Zhu", "Tao Jin", "Zhou Zhao" ], "github": "https://github.com/AaronZ345/ISDrama", "project_page": "", "abstract": "Multimodal immersive spatial drama generation focuses on creating continuous multi-speaker binaural speech with dramatic prosody based on multimodal prompts, with potential applications in AR, VR, and others. This task requires simultaneous modeling of spatial information and dramatic prosody based on multimodal inputs, with high data collection costs. To the best of our knowledge, our work is the first attempt to address these challenges. We construct MRSDrama, the first multimodal recorded spatial drama dataset, containing binaural drama audios, scripts, videos, geometric poses, and textual prompts. Then, we propose ISDrama, the first immersive spatial drama generation model through multimodal prompting. ISDrama comprises these primary components: 1) Multimodal Pose Encoder, based on contrastive learning, considering the Doppler effect caused by moving speakers to extract unified pose information from multimodal prompts. 2) Immersive Drama Transformer, a flow-based mamba-transformer model that generates high-quality drama, incorporating Drama-MOE to select proper experts for enhanced prosody and pose control. We also design a context-consistent classifier-free guidance strategy to coherently generate complete drama. Experimental results show that ISDrama outperforms baseline models on objective and subjective metrics. The demos and dataset are available at https://aaronz345.github.io/ISDramaDemo.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AaronZ345/ISDrama", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.20114", "title": "TreeHop: Generate and Filter Next Query Embeddings Efficiently for Multi-hop Question Answering", "authors": [ "Zhonghao Li", "Kunpeng Zhang", "Jinghuai Ou", "Shuliang Liu", "Xuming Hu" ], "github": "https://github.com/allen-li1231/treehop-rag", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) systems face significant challenges in multi-hop question answering (MHQA), where complex queries require synthesizing information across multiple document chunks. Existing approaches typically rely on iterative LLM-based query rewriting and routing, resulting in high computational costs due to repeated LLM invocations and multi-stage processes. To address these limitations, we propose TreeHop, an embedding-level framework without the need for LLMs in query refinement. TreeHop dynamically updates query embeddings by fusing semantic information from prior queries and retrieved documents, enabling iterative retrieval through embedding-space operations alone. This method replaces the traditional \"Retrieve-Rewrite-Vectorize-Retrieve\" cycle with a streamlined \"Retrieve-Embed-Retrieve\" loop, significantly reducing computational overhead. Moreover, a rule-based stop criterion is introduced to further prune redundant retrievals, balancing efficiency and recall rate. Experimental results show that TreeHop rivals advanced RAG methods across three open-domain MHQA datasets, achieving comparable performance with only 5\\%-0.4\\% of the model parameter size and reducing the query latency by approximately 99\\% compared to concurrent approaches. This makes TreeHop a faster and more cost-effective solution for deployment in a range of knowledge-intensive applications. For reproducibility purposes, codes and data are available here: https://github.com/allen-li1231/TreeHop.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allen-li1231/treehop-rag", "github_api_added_by": "user" }, { "date": "2025-04-30", "arxiv_id": "2504.20690", "title": "In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer", "authors": [ "Zechuan Zhang", "Ji Xie", "Yu Lu", "Zongxin Yang", "Yi Yang" ], "github": "https://github.com/River-Zhang/ICEdit", "project_page": "", "abstract": "Instruction-based image editing enables robust image modification via natural language prompts, yet current methods face a precision-efficiency tradeoff. Fine-tuning methods demand significant computational resources and large datasets, while training-free techniques struggle with instruction comprehension and edit quality. We resolve this dilemma by leveraging large-scale Diffusion Transformer (DiT)' enhanced generation capacity and native contextual awareness. Our solution introduces three contributions: (1) an in-context editing framework for zero-shot instruction compliance using in-context prompting, avoiding structural changes; (2) a LoRA-MoE hybrid tuning strategy that enhances flexibility with efficient adaptation and dynamic expert routing, without extensive retraining; and (3) an early filter inference-time scaling method using vision-language models (VLMs) to select better initial noise early, improving edit quality. Extensive evaluations demonstrate our method's superiority: it outperforms state-of-the-art approaches while requiring only 0.5% training data and 1% trainable parameters compared to conventional baselines. This work establishes a new paradigm that enables high-precision yet efficient instruction-guided editing. Codes and demos can be found in https://river-zhang.github.io/ICEdit-gh-pages/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/River-Zhang/ICEdit", "github_api_added_by": "auto" }, { "date": "2025-04-30", "arxiv_id": "2504.18738", "title": "A Review of 3D Object Detection with Vision-Language Models", "authors": [ "Ranjan Sapkota", "Konstantinos I Roumeliotis", "Rahul Harsha Cheppally", "Marco Flores Calero", "Manoj Karkee" ], "github": "", "project_page": "", "abstract": "This review provides a systematic analysis of comprehensive survey of 3D object detection with vision-language models(VLMs) , a rapidly advancing area at the intersection of 3D vision and multimodal AI. By examining over 100 research papers, we provide the first systematic analysis dedicated to 3D object detection with vision-language models. We begin by outlining the unique challenges of 3D object detection with vision-language models, emphasizing differences from 2D detection in spatial reasoning and data complexity. Traditional approaches using point clouds and voxel grids are compared to modern vision-language frameworks like CLIP and 3D LLMs, which enable open-vocabulary detection and zero-shot generalization. We review key architectures, pretraining strategies, and prompt engineering methods that align textual and 3D features for effective 3D object detection with vision-language models. Visualization examples and evaluation benchmarks are discussed to illustrate performance and behavior. Finally, we highlight current challenges, such as limited 3D-language datasets and computational demands, and propose future research directions to advance 3D object detection with vision-language models. >Object Detection, Vision-Language Models, Agents, VLMs, LLMs, AI", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.20073", "title": "RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning", "authors": [ "Zihan Wang", "Kangrui Wang", "Qineng Wang", "Pingyue Zhang", "Linjie Li", "Zhengyuan Yang", "Kefan Yu", "Minh Nhat Nguyen", "Licheng Liu", "Eli Gottlieb", "Monica Lam", "Yiping Lu", "Kyunghyun Cho", "Jiajun Wu", "Li Fei-Fei", "Lijuan Wang", "Yejin Choi", "Manling Li" ], "github": "https://github.com/RAGEN-AI/RAGEN", "project_page": "", "abstract": "Training large language models (LLMs) as interactive agents presents unique challenges including long-horizon decision making and interacting with stochastic environment feedback. While reinforcement learning (RL) has enabled progress in static tasks, multi-turn agent RL training remains underexplored. We propose StarPO (State-Thinking-Actions-Reward Policy Optimization), a general framework for trajectory-level agent RL, and introduce RAGEN, a modular system for training and evaluating LLM agents. Our study on three stylized environments reveals three core findings. First, our agent RL training shows a recurring mode of Echo Trap where reward variance cliffs and gradient spikes; we address this with StarPO-S, a stabilized variant with trajectory filtering, critic incorporation, and decoupled clipping. Second, we find the shaping of RL rollouts would benefit from diverse initial states, medium interaction granularity and more frequent sampling. Third, we show that without fine-grained, reasoning-aware reward signals, agent reasoning hardly emerge through multi-turn RL and they may show shallow strategies or hallucinated thoughts. Code and environments are available at https://github.com/RAGEN-AI/RAGEN.", "github_manual": "", "github_regex": "https://github.com/RAGEN-AI/RAGEN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.17838", "title": "CaRL: Learning Scalable Planning Policies with Simple Rewards", "authors": [ "Bernhard Jaeger", "Daniel Dauner", "Jens Bei\u00dfwenger", "Simon Gerstenecker", "Kashyap Chitta", "Andreas Geiger" ], "github": "https://github.com/autonomousvision/CaRL", "project_page": "", "abstract": "We investigate reinforcement learning (RL) for privileged planning in autonomous driving. State-of-the-art approaches for this task are rule-based, but these methods do not scale to the long tail. RL, on the other hand, is scalable and does not suffer from compounding errors like imitation learning. Contemporary RL approaches for driving use complex shaped rewards that sum multiple individual rewards, \\eg~progress, position, or orientation rewards. We show that PPO fails to optimize a popular version of these rewards when the mini-batch size is increased, which limits the scalability of these approaches. Instead, we propose a new reward design based primarily on optimizing a single intuitive reward term: route completion. Infractions are penalized by terminating the episode or multiplicatively reducing route completion. We find that PPO scales well with higher mini-batch sizes when trained with our simple reward, even improving performance. Training with large mini-batch sizes enables efficient scaling via distributed data parallelism. We scale PPO to 300M samples in CARLA and 500M samples in nuPlan with a single 8-GPU node. The resulting model achieves 64 DS on the CARLA longest6 v2 benchmark, outperforming other RL methods with more complex rewards by a large margin. Requiring only minimal adaptations from its use in CARLA, the same method is the best learning-based approach on nuPlan. It scores 91.3 in non-reactive and 90.6 in reactive traffic on the Val14 benchmark while being an order of magnitude faster than prior work.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/autonomousvision/CaRL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.18942", "title": "LawFlow : Collecting and Simulating Lawyers' Thought Processes", "authors": [ "Debarati Das", "Khanh Chi Le", "Ritik Sachin Parkar", "Karin De Langis", "Brendan Madson", "Chad M. Berryman", "Robin M. Willis", "Daniel H. Moses", "Brett McDonnell", "Daniel Schwarcz", "Dongyeop Kang" ], "github": "https://github.com/minnesotanlp/LawFlow", "project_page": "", "abstract": "Legal practitioners, particularly those early in their careers, face complex, high-stakes tasks that require adaptive, context-sensitive reasoning. While AI holds promise in supporting legal work, current datasets and models are narrowly focused on isolated subtasks and fail to capture the end-to-end decision-making required in real-world practice. To address this gap, we introduce LawFlow, a dataset of complete end-to-end legal workflows collected from trained law students, grounded in real-world business entity formation scenarios. Unlike prior datasets focused on input-output pairs or linear chains of thought, LawFlow captures dynamic, modular, and iterative reasoning processes that reflect the ambiguity, revision, and client-adaptive strategies of legal practice. Using LawFlow, we compare human and LLM-generated workflows, revealing systematic differences in structure, reasoning flexibility, and plan execution. Human workflows tend to be modular and adaptive, while LLM workflows are more sequential, exhaustive, and less sensitive to downstream implications. Our findings also suggest that legal professionals prefer AI to carry out supportive roles, such as brainstorming, identifying blind spots, and surfacing alternatives, rather than executing complex workflows end-to-end. Building on these findings, we propose a set of design suggestions, rooted in empirical observations, that align AI assistance with human goals of clarity, completeness, creativity, and efficiency, through hybrid planning, adaptive execution, and decision-point support. Our results highlight both the current limitations of LLMs in supporting complex legal workflows and opportunities for developing more collaborative, reasoning-aware legal AI systems. All data and code are available on our project page (https://minnesotanlp.github.io/LawFlow-website/).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/minnesotanlp/LawFlow", "github_api_added_by": "auto" }, { "date": "2025-04-30", "arxiv_id": "2504.16272", "title": "Learning Explainable Dense Reward Shapes via Bayesian Optimization", "authors": [ "Ryan Koo", "Ian Yang", "Vipul Raheja", "Mingyi Hong", "Kwang-Sung Jun", "Dongyeop Kang" ], "github": "https://github.com/minnesotanlp/explainable-dense-rewards", "project_page": "", "abstract": "Current reinforcement learning from human feedback (RLHF) pipelines for large language model (LLM) alignment typically assign scalar rewards to sequences, using the final token as a surrogate indicator for the quality of the entire sequence. However, this leads to sparse feedback and suboptimal token-level credit assignment. In this work, we frame reward shaping as an optimization problem focused on token-level credit assignment. We propose a reward-shaping function leveraging explainability methods such as SHAP and LIME to estimate per-token rewards from the reward model. To learn parameters of this shaping function, we employ a bilevel optimization framework that integrates Bayesian Optimization and policy training to handle noise from the token reward estimates. Our experiments show that achieving a better balance of token-level reward attribution leads to performance improvements over baselines on downstream tasks and finds an optimal policy faster during training. Furthermore, we show theoretically that explainability methods that are feature additive attribution functions maintain the optimal policy as the original reward.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/minnesotanlp/explainable-dense-rewards", "github_api": "", "github_api_added_by": "" }, { "date": "2025-04-30", "arxiv_id": "2504.20769", "title": "Chain-of-Defensive-Thought: Structured Reasoning Elicits Robustness in Large Language Models against Reference Corruption", "authors": [ "Wenxiao Wang", "Parsa Hosseini", "Soheil Feizi" ], "github": "", "project_page": "", "abstract": "Chain-of-thought prompting has demonstrated great success in facilitating the reasoning abilities of large language models. In this work, we explore how these enhanced reasoning abilities can be exploited to improve the robustness of large language models in tasks that are not necessarily reasoning-focused. In particular, we show how a wide range of large language models exhibit significantly improved robustness against reference corruption using a simple method called chain-of-defensive-thought, where only a few exemplars with structured and defensive reasoning are provided as demonstrations. Empirically, the improvements can be astounding, especially given the simplicity and applicability of the method. For example, in the Natural Questions task, the accuracy of GPT-4o degrades from 60% to as low as 3% with standard prompting when 1 out of 10 references provided is corrupted with prompt injection attacks. In contrast, GPT-4o using chain-of-defensive-thought prompting maintains an accuracy of 50%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.21776", "title": "WebThinker: Empowering Large Reasoning Models with Deep Research Capability", "authors": [ "Xiaoxi Li", "Jiajie Jin", "Guanting Dong", "Hongjin Qian", "Yutao Zhu", "Yongkang Wu", "Ji-Rong Wen", "Zhicheng Dou" ], "github": "https://github.com/RUC-NLPIR/WebThinker", "project_page": "", "abstract": "Large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, demonstrate impressive long-horizon reasoning capabilities. However, their reliance on static internal knowledge limits their performance on complex, knowledge-intensive tasks and hinders their ability to produce comprehensive research reports requiring synthesis of diverse web information. To address this, we propose WebThinker, a deep research agent that empowers LRMs to autonomously search the web, navigate web pages, and draft research reports during the reasoning process. WebThinker integrates a Deep Web Explorer module, enabling LRMs to dynamically search, navigate, and extract information from the web when encountering knowledge gaps. It also employs an Autonomous Think-Search-and-Draft strategy, allowing the model to seamlessly interleave reasoning, information gathering, and report writing in real time. To further enhance research tool utilization, we introduce an RL-based training strategy via iterative online Direct Preference Optimization (DPO). Extensive experiments on complex reasoning benchmarks (GPQA, GAIA, WebWalkerQA, HLE) and scientific report generation tasks (Glaive) demonstrate that WebThinker significantly outperforms existing methods and strong proprietary systems. Our approach enhances LRM reliability and applicability in complex scenarios, paving the way for more capable and versatile deep research systems. The code is available at https://github.com/RUC-NLPIR/WebThinker.", "github_manual": "", "github_regex": "https://github.com/RUC-NLPIR/WebThinker", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.21850", "title": "COMPACT: COMPositional Atomic-to-Complex Visual Capability Tuning", "authors": [ "Xindi Wu", "Hee Seung Hwang", "Polina Kirichenko", "Olga Russakovsky" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) excel at simple vision-language tasks but struggle when faced with complex tasks that require multiple capabilities, such as simultaneously recognizing objects, counting them, and understanding their spatial relationships. This might be partially the result of the fact that Visual Instruction Tuning (VIT), a critical training step for MLLMs, has traditionally focused on scaling data volume, but not the compositional complexity of training examples. We propose COMPACT (COMPositional Atomic-to-complex visual Capability Tuning), which generates a training dataset explicitly controlling for the compositional complexity of the training examples. The data from COMPACT allows MLLMs to train on combinations of atomic capabilities to learn complex capabilities more efficiently. Across all benchmarks, COMPACT achieves comparable performance to the LLaVA-665k VIT while using less than 10% of its data budget, and even outperforms it on several, especially those involving complex multi-capability tasks. For example, COMPACT achieves substantial 83.3% improvement on MMStar and 94.0% improvement on MM-Vet compared to the full-scale VIT on particularly complex questions that require four or more atomic capabilities. COMPACT offers a scalable, data-efficient, visual compositional tuning recipe to improve on complex visual-language tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.21318", "title": "Phi-4-reasoning Technical Report", "authors": [ "Marah Abdin", "Sahaj Agarwal", "Ahmed Awadallah", "Vidhisha Balachandran", "Harkirat Behl", "Lingjiao Chen", "Gustavo de Rosa", "Suriya Gunasekar", "Mojan Javaheripi", "Neel Joshi", "Piero Kauffmann", "Yash Lara", "Caio C\u00e9sar Teodoro Mendes", "Arindam Mitra", "Besmira Nushi", "Dimitris Papailiopoulos", "Olli Saarikivi", "Shital Shah", "Vaishnavi Shrivastava", "Vibhav Vineet", "Yue Wu", "Safoora Yousefi", "Guoqing Zheng" ], "github": "", "project_page": "", "abstract": "We introduce Phi-4-reasoning, a 14-billion parameter reasoning model that achieves strong performance on complex reasoning tasks. Trained via supervised fine-tuning of Phi-4 on carefully curated set of \"teachable\" prompts-selected for the right level of complexity and diversity-and reasoning demonstrations generated using o3-mini, Phi-4-reasoning generates detailed reasoning chains that effectively leverage inference-time compute. We further develop Phi-4-reasoning-plus, a variant enhanced through a short phase of outcome-based reinforcement learning that offers higher performance by generating longer reasoning traces. Across a wide range of reasoning tasks, both models outperform significantly larger open-weight models such as DeepSeek-R1-Distill-Llama-70B model and approach the performance levels of full DeepSeek-R1 model. Our comprehensive evaluations span benchmarks in math and scientific reasoning, coding, algorithmic problem solving, planning, and spatial understanding. Interestingly, we observe a non-trivial transfer of improvements to general-purpose benchmarks as well. In this report, we provide insights into our training data, our training methodologies, and our evaluations. We show that the benefit of careful data curation for supervised fine-tuning (SFT) extends to reasoning language models, and can be further amplified by reinforcement learning (RL). Finally, our evaluation points to opportunities for improving how we assess the performance and robustness of reasoning models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.21233", "title": "Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math", "authors": [ "Haoran Xu", "Baolin Peng", "Hany Awadalla", "Dongdong Chen", "Yen-Chun Chen", "Mei Gao", "Young Jin Kim", "Yunsheng Li", "Liliang Ren", "Yelong Shen", "Shuohang Wang", "Weijian Xu", "Jianfeng Gao", "Weizhu Chen" ], "github": "", "project_page": "", "abstract": "Chain-of-Thought (CoT) significantly enhances formal reasoning capabilities in Large Language Models (LLMs) by training them to explicitly generate intermediate reasoning steps. While LLMs readily benefit from such techniques, improving reasoning in Small Language Models (SLMs) remains challenging due to their limited model capacity. Recent work by Deepseek-R1 demonstrates that distillation from LLM-generated synthetic data can substantially improve the reasoning ability of SLM. However, the detailed modeling recipe is not disclosed. In this work, we present a systematic training recipe for SLMs that consists of four steps: (1) large-scale mid-training on diverse distilled long-CoT data, (2) supervised fine-tuning on high-quality long-CoT data, (3) Rollout DPO leveraging a carefully curated preference dataset, and (4) Reinforcement Learning (RL) with Verifiable Reward. We apply our method on Phi-4-Mini, a compact 3.8B-parameter model. The resulting Phi-4-Mini-Reasoning model exceeds, on math reasoning tasks, much larger reasoning models, e.g., outperforming DeepSeek-R1-Distill-Qwen-7B by 3.2 points and DeepSeek-R1-Distill-Llama-8B by 7.7 points on Math-500. Our results validate that a carefully designed training recipe, with large-scale high-quality CoT data, is effective to unlock strong reasoning capabilities even in resource-constrained small models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.18904", "title": "RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning", "authors": [ "Haoran Geng", "Feishi Wang", "Songlin Wei", "Yuyang Li", "Bangjun Wang", "Boshi An", "Charlie Tianyue Cheng", "Haozhe Lou", "Peihao Li", "Yen-Jen Wang", "Yutong Liang", "Dylan Goetting", "Chaoyi Xu", "Haozhe Chen", "Yuxi Qian", "Yiran Geng", "Jiageng Mao", "Weikang Wan", "Mingtong Zhang", "Jiangran Lyu", "Siheng Zhao", "Jiazhao Zhang", "Jialiang Zhang", "Chengyang Zhao", "Haoran Lu", "Yufei Ding", "Ran Gong", "Yuran Wang", "Yuxuan Kuang", "Ruihai Wu", "Baoxiong Jia", "Carlo Sferrazza", "Hao Dong", "Siyuan Huang", "Yue Wang", "Jitendra Malik", "Pieter Abbeel" ], "github": "https://github.com/RoboVerseOrg/RoboVerse", "project_page": "", "abstract": "Data scaling and standardized evaluation benchmarks have driven significant advances in natural language processing and computer vision. However, robotics faces unique challenges in scaling data and establishing evaluation protocols. Collecting real-world data is resource-intensive and inefficient, while benchmarking in real-world scenarios remains highly complex. Synthetic data and simulation offer promising alternatives, yet existing efforts often fall short in data quality, diversity, and benchmark standardization. To address these challenges, we introduce RoboVerse, a comprehensive framework comprising a simulation platform, a synthetic dataset, and unified benchmarks. Our simulation platform supports multiple simulators and robotic embodiments, enabling seamless transitions between different environments. The synthetic dataset, featuring high-fidelity physics and photorealistic rendering, is constructed through multiple approaches. Additionally, we propose unified benchmarks for imitation learning and reinforcement learning, enabling evaluation across different levels of generalization. At the core of the simulation platform is MetaSim, an infrastructure that abstracts diverse simulation environments into a universal interface. It restructures existing simulation environments into a simulator-agnostic configuration system, as well as an API aligning different simulator functionalities, such as launching simulation environments, loading assets with initial states, stepping the physics engine, etc. This abstraction ensures interoperability and extensibility. Comprehensive experiments demonstrate that RoboVerse enhances the performance of imitation learning, reinforcement learning, world model learning, and sim-to-real transfer. These results validate the reliability of our dataset and benchmarks, establishing RoboVerse as a robust solution for advancing robot learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RoboVerseOrg/RoboVerse", "github_api_added_by": "auto" }, { "date": "2025-05-01", "arxiv_id": "2504.21855", "title": "ReVision: High-Quality, Low-Cost Video Generation with Explicit 3D Physics Modeling for Complex Motion and Interaction", "authors": [ "Qihao Liu", "Ju He", "Qihang Yu", "Liang-Chieh Chen", "Alan Yuille" ], "github": "", "project_page": "", "abstract": "In recent years, video generation has seen significant advancements. However, challenges still persist in generating complex motions and interactions. To address these challenges, we introduce ReVision, a plug-and-play framework that explicitly integrates parameterized 3D physical knowledge into a pretrained conditional video generation model, significantly enhancing its ability to generate high-quality videos with complex motion and interactions. Specifically, ReVision consists of three stages. First, a video diffusion model is used to generate a coarse video. Next, we extract a set of 2D and 3D features from the coarse video to construct a 3D object-centric representation, which is then refined by our proposed parameterized physical prior model to produce an accurate 3D motion sequence. Finally, this refined motion sequence is fed back into the same video diffusion model as additional conditioning, enabling the generation of motion-consistent videos, even in scenarios involving complex actions and interactions. We validate the effectiveness of our approach on Stable Video Diffusion, where ReVision significantly improves motion fidelity and coherence. Remarkably, with only 1.5B parameters, it even outperforms a state-of-the-art video generation model with over 13B parameters on complex video generation by a substantial margin. Our results suggest that, by incorporating 3D physical knowledge, even a relatively small video diffusion model can generate complex motions and interactions with greater realism and controllability, offering a promising solution for physically plausible video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.20966", "title": "Softpick: No Attention Sink, No Massive Activations with Rectified Softmax", "authors": [ "Zayd M. K. Zuhri", "Erland Hilman Fuadi", "Alham Fikri Aji" ], "github": "https://github.com/zaydzuhri/softpick-attention", "project_page": "", "abstract": "We introduce softpick, a rectified, not sum-to-one, drop-in replacement for softmax in transformer attention mechanisms that eliminates attention sink and massive activations. Our experiments with 340M parameter models demonstrate that softpick maintains performance parity with softmax on standard benchmarks while achieving 0% sink rate. The softpick transformer produces hidden states with significantly lower kurtosis (340 vs 33,510) and creates sparse attention maps (46.97% sparsity). Models using softpick consistently outperform softmax when quantized, with particularly pronounced advantages at lower bit precisions. Our analysis and discussion shows how softpick has the potential to open new possibilities for quantization, low-precision training, sparsity optimization, pruning, and interpretability. Our code is available at https://github.com/zaydzuhri/softpick-attention.", "github_manual": "", "github_regex": "https://github.com/zaydzuhri/softpick-attention", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.19720", "title": "Taming the Titans: A Survey of Efficient LLM Inference Serving", "authors": [ "Ranran Zhen", "Juntao Li", "Yixin Ji", "Zhenlin Yang", "Tong Liu", "Qingrong Xia", "Xinyu Duan", "Zhefeng Wang", "Baoxing Huai", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) for Generative AI have achieved remarkable progress, evolving into sophisticated and versatile tools widely adopted across various domains and applications. However, the substantial memory overhead caused by their vast number of parameters, combined with the high computational demands of the attention mechanism, poses significant challenges in achieving low latency and high throughput for LLM inference services. Recent advancements, driven by groundbreaking research, have significantly accelerated progress in this field. This paper provides a comprehensive survey of these methods, covering fundamental instance-level approaches, in-depth cluster-level strategies, emerging scenario directions, and other miscellaneous but important areas. At the instance level, we review model placement, request scheduling, decoding length prediction, storage management, and the disaggregation paradigm. At the cluster level, we explore GPU cluster deployment, multi-instance load balancing, and cloud service solutions. For emerging scenarios, we organize the discussion around specific tasks, modules, and auxiliary methods. To ensure a holistic overview, we also highlight several niche yet critical areas. Finally, we outline potential research directions to further advance the field of LLM inference serving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.21039", "title": "Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report", "authors": [ "Paul Kassianik", "Baturay Saglam", "Alexander Chen", "Blaine Nelson", "Anu Vellore", "Massimo Aufiero", "Fraser Burch", "Dhruv Kedia", "Avi Zohary", "Sajana Weerawardhena", "Aman Priyanshu", "Adam Swanda", "Amy Chang", "Hyrum Anderson", "Kojin Oshiba", "Omar Santos", "Yaron Singer", "Amin Karbasi" ], "github": "", "project_page": "", "abstract": "As transformer-based large language models (LLMs) increasingly permeate society, they have revolutionized domains such as software engineering, creative writing, and digital arts. However, their adoption in cybersecurity remains limited due to challenges like scarcity of specialized training data and complexity of representing cybersecurity-specific knowledge. To address these gaps, we present Foundation-Sec-8B, a cybersecurity-focused LLM built on the Llama 3.1 architecture and enhanced through continued pretraining on a carefully curated cybersecurity corpus. We evaluate Foundation-Sec-8B across both established and new cybersecurity benchmarks, showing that it matches Llama 3.1-70B and GPT-4o-mini in certain cybersecurity-specific tasks. By releasing our model to the public, we aim to accelerate progress and adoption of AI-driven tools in both public and private cybersecurity contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.21635", "title": "Sadeed: Advancing Arabic Diacritization Through Small Language Model", "authors": [ "Zeina Aldallal", "Sara Chrouf", "Khalil Hennara", "Mohamed Motaism Hamed", "Muhammad Hreden", "Safwan AlModhayan" ], "github": "", "project_page": "", "abstract": "Arabic text diacritization remains a persistent challenge in natural language processing due to the language's morphological richness. In this paper, we introduce Sadeed, a novel approach based on a fine-tuned decoder-only language model adapted from Kuwain 1.5B Hennara et al. [2025], a compact model originally trained on diverse Arabic corpora. Sadeed is fine-tuned on carefully curated, high-quality diacritized datasets, constructed through a rigorous data-cleaning and normalization pipeline. Despite utilizing modest computational resources, Sadeed achieves competitive results compared to proprietary large language models and outperforms traditional models trained on similar domains. Additionally, we highlight key limitations in current benchmarking practices for Arabic diacritization. To address these issues, we introduce SadeedDiac-25, a new benchmark designed to enable fairer and more comprehensive evaluation across diverse text genres and complexity levels. Together, Sadeed and SadeedDiac-25 provide a robust foundation for advancing Arabic NLP applications, including machine translation, text-to-speech, and language learning tools.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.21336", "title": "UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation", "authors": [ "Linshan Wu", "Yuxiang Nie", "Sunan He", "Jiaxin Zhuang", "Hao Chen" ], "github": "", "project_page": "", "abstract": "Multi-modal interpretation of biomedical images opens up novel opportunities in biomedical image analysis. Conventional AI approaches typically rely on disjointed training, i.e., Large Language Models (LLMs) for clinical text generation and segmentation models for target extraction, which results in inflexible real-world deployment and a failure to leverage holistic biomedical information. To this end, we introduce UniBiomed, the first universal foundation model for grounded biomedical image interpretation. UniBiomed is based on a novel integration of Multi-modal Large Language Model (MLLM) and Segment Anything Model (SAM), which effectively unifies the generation of clinical texts and the segmentation of corresponding biomedical objects for grounded interpretation. In this way, UniBiomed is capable of tackling a wide range of biomedical tasks across ten diverse biomedical imaging modalities. To develop UniBiomed, we curate a large-scale dataset comprising over 27 million triplets of images, annotations, and text descriptions across ten imaging modalities. Extensive validation on 84 internal and external datasets demonstrated that UniBiomed achieves state-of-the-art performance in segmentation, disease recognition, region-aware diagnosis, visual question answering, and report generation. Moreover, unlike previous models that rely on clinical experts to pre-diagnose images and manually craft precise textual or visual prompts, UniBiomed can provide automated and end-to-end grounded interpretation for biomedical image analysis. This represents a novel paradigm shift in clinical workflows, which will significantly improve diagnostic efficiency. In summary, UniBiomed represents a novel breakthrough in biomedical AI, unlocking powerful grounded interpretation capabilities for more accurate and efficient biomedical image analysis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.20708", "title": "Beyond the Last Answer: Your Reasoning Trace Uncovers More than You Think", "authors": [ "Hasan Abed Al Kader Hammoud", "Hani Itani", "Bernard Ghanem" ], "github": "https://github.com/hammoudhasan/SubthoughtReasoner", "project_page": "", "abstract": "Large Language Models (LLMs) leverage step-by-step reasoning to solve complex problems. Standard evaluation practice involves generating a complete reasoning trace and assessing the correctness of the final answer presented at its conclusion. In this paper, we challenge the reliance on the final answer by posing the following two questions: Does the final answer reliably represent the model's optimal conclusion? Can alternative reasoning paths yield different results? To answer these questions, we analyze intermediate reasoning steps, termed subthoughts, and propose a method based on our findings. Our approach involves segmenting a reasoning trace into sequential subthoughts based on linguistic cues. We start by prompting the model to generate continuations from the end-point of each intermediate subthought. We extract a potential answer from every completed continuation originating from different subthoughts. We find that aggregating these answers by selecting the most frequent one (the mode) often yields significantly higher accuracy compared to relying solely on the answer derived from the original complete trace. Analyzing the consistency among the answers derived from different subthoughts reveals characteristics that correlate with the model's confidence and correctness, suggesting potential for identifying less reliable answers. Our experiments across various LLMs and challenging mathematical reasoning datasets (AIME2024 and AIME2025) show consistent accuracy improvements, with gains reaching up to 13\\% and 10\\% respectively. Implementation is available at: https://github.com/hammoudhasan/SubthoughtReasoner.", "github_manual": "", "github_regex": "https://github.com/hammoudhasan/SubthoughtReasoner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.19056", "title": "Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future Directions", "authors": [ "Mohammad Mahdi Abootorabi", "Omid Ghahroodi", "Pardis Sadat Zahraei", "Hossein Behzadasl", "Alireza Mirrokni", "Mobina Salimipanah", "Arash Rasouli", "Bahar Behzadipour", "Sara Azarnoush", "Benyamin Maleki", "Erfan Sadraiye", "Kiarash Kiani Feriz", "Mahdi Teymouri Nahad", "Ali Moghadasi", "Abolfazl Eshagh Abianeh", "Nizi Nazar", "Hamid R. Rabiee", "Mahdieh Soleymani Baghshah", "Meisam Ahmadi", "Ehsaneddin Asgari" ], "github": "https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey", "project_page": "", "abstract": "Generative AI is reshaping art, gaming, and most notably animation. Recent breakthroughs in foundation and diffusion models have reduced the time and cost of producing animated content. Characters are central animation components, involving motion, emotions, gestures, and facial expressions. The pace and breadth of advances in recent months make it difficult to maintain a coherent view of the field, motivating the need for an integrative review. Unlike earlier overviews that treat avatars, gestures, or facial animation in isolation, this survey offers a single, comprehensive perspective on all the main generative AI applications for character animation. We begin by examining the state-of-the-art in facial animation, expression rendering, image synthesis, avatar creation, gesture modeling, motion synthesis, object generation, and texture synthesis. We highlight leading research, practical deployments, commonly used datasets, and emerging trends for each area. To support newcomers, we also provide a comprehensive background section that introduces foundational models and evaluation metrics, equipping readers with the knowledge needed to enter the field. We discuss open challenges and map future research directions, providing a roadmap to advance AI-driven character-animation technologies. This survey is intended as a resource for researchers and developers entering the field of generative AI animation or adjacent fields. Resources are available at: https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey.", "github_manual": "", "github_regex": "https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2504.19043", "title": "Selecting Optimal Candidate Profiles in Adversarial Environments Using Conjoint Analysis and Machine Learning", "authors": [ "Connor T. Jerzak", "Priyanshi Chandra", "Rishi Hazra" ], "github": "", "project_page": "", "abstract": "Conjoint analysis, an application of factorial experimental design, is a popular tool in social science research for studying multidimensional preferences. In such experiments in the political analysis context, respondents are asked to choose between two hypothetical political candidates with randomly selected features, which can include partisanship, policy positions, gender and race. We consider the problem of identifying optimal candidate profiles. Because the number of unique feature combinations far exceeds the total number of observations in a typical conjoint experiment, it is impossible to determine the optimal profile exactly. To address this identification challenge, we derive an optimal stochastic intervention that represents a probability distribution of various attributes aimed at achieving the most favorable average outcome. We first consider an environment where one political party optimizes their candidate selection. We then move to the more realistic case where two political parties optimize their own candidate selection simultaneously and in opposition to each other. We apply the proposed methodology to an existing candidate choice conjoint experiment concerning vote choice for US president. We find that, in contrast to the non-adversarial approach, expected outcomes in the adversarial regime fall within range of historical electoral outcomes, with optimal strategies suggested by the method more likely to match the actual observed candidates compared to strategies derived from a non-adversarial approach. These findings indicate that incorporating adversarial dynamics into conjoint analysis may yield unique insight into social science data from experiments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-01", "arxiv_id": "2505.00551", "title": "100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models", "authors": [ "Chong Zhang", "Yue Deng", "Xiang Lin", "Bin Wang", "Dianwen Ng", "Hai Ye", "Xingxuan Li", "Yao Xiao", "Zhanfeng Mo", "Qi Zhang", "Lidong Bing" ], "github": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "project_page": "", "abstract": "The recent development of reasoning language models (RLMs) represents a novel evolution in large language models. In particular, the recent release of DeepSeek-R1 has generated widespread social impact and sparked enthusiasm in the research community for exploring the explicit reasoning paradigm of language models. However, the implementation details of the released models have not been fully open-sourced by DeepSeek, including DeepSeek-R1-Zero, DeepSeek-R1, and the distilled small models. As a result, many replication studies have emerged aiming to reproduce the strong performance achieved by DeepSeek-R1, reaching comparable performance through similar training procedures and fully open-source data resources. These works have investigated feasible strategies for supervised fine-tuning (SFT) and reinforcement learning from verifiable rewards (RLVR), focusing on data preparation and method design, yielding various valuable insights. In this report, we provide a summary of recent replication studies to inspire future research. We primarily focus on SFT and RLVR as two main directions, introducing the details for data construction, method design and training procedure of current replication studies. Moreover, we conclude key findings from the implementation details and experimental results reported by these studies, anticipating to inspire future research. We also discuss additional techniques of enhancing RLMs, highlighting the potential of expanding the application scope of these models, and discussing the challenges in development. By this survey, we aim to help researchers and developers of RLMs stay updated with the latest advancements, and seek to inspire new ideas to further enhance RLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-02", "arxiv_id": "2505.00662", "title": "DeepCritic: Deliberate Critique with Large Language Models", "authors": [ "Wenkai Yang", "Jingwen Chen", "Yankai Lin", "Ji-Rong Wen" ], "github": "https://github.com/rucbm/deepcritic", "project_page": "", "abstract": "As Large Language Models (LLMs) are rapidly evolving, providing accurate feedback and scalable oversight on their outputs becomes an urgent and critical problem. Leveraging LLMs as critique models to achieve automated supervision is a promising solution. In this work, we focus on studying and enhancing the math critique ability of LLMs. Current LLM critics provide critiques that are too shallow and superficial on each step, leading to low judgment accuracy and struggling to offer sufficient feedback for the LLM generator to correct mistakes. To tackle this issue, we propose a novel and effective two-stage framework to develop LLM critics that are capable of deliberately critiquing on each reasoning step of math solutions. In the first stage, we utilize Qwen2.5-72B-Instruct to generate 4.5K long-form critiques as seed data for supervised fine-tuning. Each seed critique consists of deliberate step-wise critiques that includes multi-perspective verifications as well as in-depth critiques of initial critiques for each reasoning step. Then, we perform reinforcement learning on the fine-tuned model with either existing human-labeled data from PRM800K or our automatically annotated data obtained via Monte Carlo sampling-based correctness estimation, to further incentivize its critique ability. Our developed critique model built on Qwen2.5-7B-Instruct not only significantly outperforms existing LLM critics (including the same-sized DeepSeek-R1-distill models and GPT-4o) on various error identification benchmarks, but also more effectively helps the LLM generator refine erroneous steps through more detailed feedback.", "github_manual": "https://github.com/rucbm/deepcritic", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-02", "arxiv_id": "2505.00703", "title": "T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT", "authors": [ "Dongzhi Jiang", "Ziyu Guo", "Renrui Zhang", "Zhuofan Zong", "Hao Li", "Le Zhuo", "Shilin Yan", "Pheng-Ann Heng", "Hongsheng Li" ], "github": "https://github.com/CaraJ7/T2I-R1", "project_page": "", "abstract": "Recent advancements in large language models have demonstrated how chain-of-thought (CoT) and reinforcement learning (RL) can improve performance. However, applying such reasoning strategies to the visual generation domain remains largely unexplored. In this paper, we present T2I-R1, a novel reasoning-enhanced text-to-image generation model, powered by RL with a bi-level CoT reasoning process. Specifically, we identify two levels of CoT that can be utilized to enhance different stages of generation: (1) the semantic-level CoT for high-level planning of the prompt and (2) the token-level CoT for low-level pixel processing during patch-by-patch generation. To better coordinate these two levels of CoT, we introduce BiCoT-GRPO with an ensemble of generation rewards, which seamlessly optimizes both generation CoTs within the same training step. By applying our reasoning strategies to the baseline model, Janus-Pro, we achieve superior performance with 13% improvement on T2I-CompBench and 19% improvement on the WISE benchmark, even surpassing the state-of-the-art model FLUX.1. Code is available at: https://github.com/CaraJ7/T2I-R1", "github_manual": "", "github_regex": "https://github.com/CaraJ7/T2I-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-02", "arxiv_id": "2504.21659", "title": "AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization", "authors": [ "Haotian Luo", "Haiying He", "Yibo Wang", "Jinluan Yang", "Rui Liu", "Naiqiang Tan", "Xiaochun Cao", "Dacheng Tao", "Li Shen" ], "github": "https://github.com/StarDewXXX/AdaR1", "project_page": "", "abstract": "Recently, long-thought reasoning models achieve strong performance on complex reasoning tasks, but often incur substantial inference overhead, making efficiency a critical concern. Our empirical analysis reveals that the benefit of using Long-CoT varies across problems: while some problems require elaborate reasoning, others show no improvement, or even degraded accuracy. This motivates adaptive reasoning strategies that tailor reasoning depth to the input. However, prior work primarily reduces redundancy within long reasoning paths, limiting exploration of more efficient strategies beyond the Long-CoT paradigm. To address this, we propose a novel two-stage framework for adaptive and efficient reasoning. First, we construct a hybrid reasoning model by merging long and short CoT models to enable diverse reasoning styles. Second, we apply bi-level preference training to guide the model to select suitable reasoning styles (group-level), and prefer concise and correct reasoning within each style group (instance-level). Experiments demonstrate that our method significantly reduces inference costs compared to other baseline approaches, while maintaining performance. Notably, on five mathematical datasets, the average length of reasoning is reduced by more than 50%, highlighting the potential of adaptive strategies to optimize reasoning efficiency in large language models. Our code is coming soon at https://github.com/StarDewXXX/AdaR1", "github_manual": "", "github_regex": "https://github.com/StarDewXXX/AdaR1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-02", "arxiv_id": "2504.21853", "title": "A Survey of Interactive Generative Video", "authors": [ "Jiwen Yu", "Yiran Qin", "Haoxuan Che", "Quande Liu", "Xintao Wang", "Pengfei Wan", "Di Zhang", "Kun Gai", "Hao Chen", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Interactive Generative Video (IGV) has emerged as a crucial technology in response to the growing demand for high-quality, interactive video content across various domains. In this paper, we define IGV as a technology that combines generative capabilities to produce diverse high-quality video content with interactive features that enable user engagement through control signals and responsive feedback. We survey the current landscape of IGV applications, focusing on three major domains: 1) gaming, where IGV enables infinite exploration in virtual worlds; 2) embodied AI, where IGV serves as a physics-aware environment synthesizer for training agents in multimodal interaction with dynamically evolving scenes; and 3) autonomous driving, where IGV provides closed-loop simulation capabilities for safety-critical testing and validation. To guide future development, we propose a comprehensive framework that decomposes an ideal IGV system into five essential modules: Generation, Control, Memory, Dynamics, and Intelligence. Furthermore, we systematically analyze the technical challenges and future directions in realizing each component for an ideal IGV system, such as achieving real-time generation, enabling open-domain control, maintaining long-term coherence, simulating accurate physics, and integrating causal reasoning. We believe that this systematic analysis will facilitate future research and development in the field of IGV, ultimately advancing the technology toward more sophisticated and practical applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-02", "arxiv_id": "2504.18983", "title": "MediAug: Exploring Visual Augmentation in Medical Imaging", "authors": [ "Xuyin Qi", "Zeyu Zhang", "Canxuan Gang", "Hao Zhang", "Lei Zhang", "Zhiwei Zhang", "Yang Zhao" ], "github": "https://github.com/AIGeeksGroup/MediAug", "project_page": "", "abstract": "Data augmentation is essential in medical imaging for improving classification accuracy, lesion detection, and organ segmentation under limited data conditions. However, two significant challenges remain. First, a pronounced domain gap between natural photographs and medical images can distort critical disease features. Second, augmentation studies in medical imaging are fragmented and limited to single tasks or architectures, leaving the benefits of advanced mix-based strategies unclear. To address these challenges, we propose a unified evaluation framework with six mix-based augmentation methods integrated with both convolutional and transformer backbones on brain tumour MRI and eye disease fundus datasets. Our contributions are threefold. (1) We introduce MediAug, a comprehensive and reproducible benchmark for advanced data augmentation in medical imaging. (2) We systematically evaluate MixUp, YOCO, CropMix, CutMix, AugMix, and SnapMix with ResNet-50 and ViT-B backbones. (3) We demonstrate through extensive experiments that MixUp yields the greatest improvement on the brain tumor classification task for ResNet-50 with 79.19% accuracy and SnapMix yields the greatest improvement for ViT-B with 99.44% accuracy, and that YOCO yields the greatest improvement on the eye disease classification task for ResNet-50 with 91.60% accuracy and CutMix yields the greatest improvement for ViT-B with 97.94% accuracy. Code will be available at https://github.com/AIGeeksGroup/MediAug.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/MediAug", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-02", "arxiv_id": "2504.20605", "title": "TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models", "authors": [ "Mihai Nadas", "Laura Diosan", "Andrei Piscoran", "Andreea Tomescu" ], "github": "", "project_page": "", "abstract": "Moral stories are a time-tested vehicle for transmitting values, yet modern NLP lacks a large, structured corpus that couples coherent narratives with explicit ethical lessons. We close this gap with TF1-EN-3M, the first open dataset of three million English-language fables generated exclusively by instruction-tuned models no larger than 8B parameters. Each story follows a six-slot scaffold (character -> trait -> setting -> conflict -> resolution -> moral), produced through a combinatorial prompt engine that guarantees genre fidelity while covering a broad thematic space. A hybrid evaluation pipeline blends (i) a GPT-based critic that scores grammar, creativity, moral clarity, and template adherence with (ii) reference-free diversity and readability metrics. Among ten open-weight candidates, an 8B-parameter Llama-3 variant delivers the best quality-speed trade-off, producing high-scoring fables on a single consumer GPU (<24 GB VRAM) at approximately 13.5 cents per 1,000 fables. We release the dataset, generation code, evaluation scripts, and full metadata under a permissive license, enabling exact reproducibility and cost benchmarking. TF1-EN-3M opens avenues for research in instruction following, narrative intelligence, value alignment, and child-friendly educational AI, demonstrating that large-scale moral storytelling no longer requires proprietary giant models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-02", "arxiv_id": "2505.00497", "title": "KeySync: A Robust Approach for Leakage-free Lip Synchronization in High Resolution", "authors": [ "Antoni Bigata", "Rodrigo Mira", "Stella Bounareli", "Micha\u0142 Stypu\u0142kowski", "Konstantinos Vougioukas", "Stavros Petridis", "Maja Pantic" ], "github": "", "project_page": "", "abstract": "Lip synchronization, known as the task of aligning lip movements in an existing video with new input audio, is typically framed as a simpler variant of audio-driven facial animation. However, as well as suffering from the usual issues in talking head generation (e.g., temporal consistency), lip synchronization presents significant new challenges such as expression leakage from the input video and facial occlusions, which can severely impact real-world applications like automated dubbing, but are often neglected in existing works. To address these shortcomings, we present KeySync, a two-stage framework that succeeds in solving the issue of temporal consistency, while also incorporating solutions for leakage and occlusions using a carefully designed masking strategy. We show that KeySync achieves state-of-the-art results in lip reconstruction and cross-synchronization, improving visual quality and reducing expression leakage according to LipLeak, our novel leakage metric. Furthermore, we demonstrate the effectiveness of our new masking approach in handling occlusions and validate our architectural choices through several ablation studies. Code and model weights can be found at https://antonibigata.github.io/KeySync.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-02", "arxiv_id": "2504.19394", "title": "LLMs for Engineering: Teaching Models to Design High Powered Rockets", "authors": [ "Toby Simonds" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have transformed software engineering, but their application to physical engineering domains remains underexplored. This paper evaluates LLMs' capabilities in high-powered rocketry design through RocketBench, a benchmark connecting LLMs to high-fidelity rocket simulations. We test models on two increasingly complex design tasks: target altitude optimization and precision landing challenges. Our findings reveal that while state-of-the-art LLMs demonstrate strong baseline engineering knowledge, they struggle to iterate on their designs when given simulation results and ultimately plateau below human performance levels. However, when enhanced with reinforcement learning (RL), we show that a 7B parameter model outperforms both SoTA foundation models and human experts. This research demonstrates that RL-trained LLMs can serve as effective tools for complex engineering optimization, potentially transforming engineering domains beyond software development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-02", "arxiv_id": "2505.00534", "title": "A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic", "authors": [ "Muhammad Imran Zaman", "Usama Ijaz Bajwa", "Gulshan Saleem", "Rana Hammad Raza" ], "github": "https://github.com/imranzaman5202/MO-MCT", "project_page": "", "abstract": "Vision sensors are becoming more important in Intelligent Transportation Systems (ITS) for traffic monitoring, management, and optimization as the number of network cameras continues to rise. However, manual object tracking and matching across multiple non-overlapping cameras pose significant challenges in city-scale urban traffic scenarios. These challenges include handling diverse vehicle attributes, occlusions, illumination variations, shadows, and varying video resolutions. To address these issues, we propose an efficient and cost-effective deep learning-based framework for Multi-Object Multi-Camera Tracking (MO-MCT). The proposed framework utilizes Mask R-CNN for object detection and employs Non-Maximum Suppression (NMS) to select target objects from overlapping detections. Transfer learning is employed for re-identification, enabling the association and generation of vehicle tracklets across multiple cameras. Moreover, we leverage appropriate loss functions and distance measures to handle occlusion, illumination, and shadow challenges. The final solution identification module performs feature extraction using ResNet-152 coupled with Deep SORT based vehicle tracking. The proposed framework is evaluated on the 5th AI City Challenge dataset (Track 3), comprising 46 camera feeds. Among these 46 camera streams, 40 are used for model training and validation, while the remaining six are utilized for model testing. The proposed framework achieves competitive performance with an IDF1 score of 0.8289, and precision and recall scores of 0.9026 and 0.8527 respectively, demonstrating its effectiveness in robust and accurate vehicle tracking.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/imranzaman5202/MO-MCT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-02", "arxiv_id": "2505.00234", "title": "Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks", "authors": [ "Vishnu Sarukkai", "Zhiqiang Xie", "Kayvon Fatahalian" ], "github": "", "project_page": "", "abstract": "Many methods for improving Large Language Model (LLM) agents for sequential decision-making tasks depend on task-specific knowledge engineering--such as prompt tuning, curated in-context examples, or customized observation and action spaces. Using these approaches, agent performance improves with the quality or amount of knowledge engineering invested. Instead, we investigate how LLM agents can automatically improve their performance by learning in-context from their own successful experiences on similar tasks. Rather than relying on task-specific knowledge engineering, we focus on constructing and refining a database of self-generated examples. We demonstrate that even a naive accumulation of successful trajectories across training tasks boosts test performance on three benchmarks: ALFWorld (73% to 89%), Wordcraft (55% to 64%), and InterCode-SQL (75% to 79%)--matching the performance the initial agent achieves if allowed two to three attempts per task. We then introduce two extensions: (1) database-level selection through population-based training to identify high-performing example collections, and (2) exemplar-level selection that retains individual trajectories based on their empirical utility as in-context examples. These extensions further enhance performance, achieving 91% on ALFWorld--matching more complex approaches that employ task-specific components and prompts. Our results demonstrate that automatic trajectory database construction offers a compelling alternative to labor-intensive knowledge engineering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-02", "arxiv_id": "2504.18715", "title": "Spatial Speech Translation: Translating Across Space With Binaural Hearables", "authors": [ "Tuochao Chen", "Qirui Wang", "Runlin He", "Shyam Gollakota" ], "github": "https://github.com/chentuochao/Spatial-Speech-Translation", "project_page": "", "abstract": "Imagine being in a crowded space where people speak a different language and having hearables that transform the auditory space into your native language, while preserving the spatial cues for all speakers. We introduce spatial speech translation, a novel concept for hearables that translate speakers in the wearer's environment, while maintaining the direction and unique voice characteristics of each speaker in the binaural output. To achieve this, we tackle several technical challenges spanning blind source separation, localization, real-time expressive translation, and binaural rendering to preserve the speaker directions in the translated audio, while achieving real-time inference on the Apple M2 silicon. Our proof-of-concept evaluation with a prototype binaural headset shows that, unlike existing models, which fail in the presence of interference, we achieve a BLEU score of up to 22.01 when translating between languages, despite strong interference from other speakers in the environment. User studies further confirm the system's effectiveness in spatially rendering the translated speech in previously unseen real-world reverberant environments. Taking a step back, this work marks the first step towards integrating spatial perception into speech translation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/chentuochao/Spatial-Speech-Translation", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-02", "arxiv_id": "2504.20406", "title": "Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs", "authors": [ "Paiheng Xu", "Gang Wu", "Xiang Chen", "Tong Yu", "Chang Xiao", "Franck Dernoncourt", "Tianyi Zhou", "Wei Ai", "Viswanathan Swaminathan" ], "github": "", "project_page": "", "abstract": "Scripting interfaces enable users to automate tasks and customize software workflows, but creating scripts traditionally requires programming expertise and familiarity with specific APIs, posing barriers for many users. While Large Language Models (LLMs) can generate code from natural language queries, runtime code generation is severely limited due to unverified code, security risks, longer response times, and higher computational costs. To bridge the gap, we propose an offline simulation framework to curate a software-specific skillset, a collection of verified scripts, by exploiting LLMs and publicly available scripting guides. Our framework comprises two components: (1) task creation, using top-down functionality guidance and bottom-up API synergy exploration to generate helpful tasks; and (2) skill generation with trials, refining and validating scripts based on execution feedback. To efficiently navigate the extensive API landscape, we introduce a Graph Neural Network (GNN)-based link prediction model to capture API synergy, enabling the generation of skills involving underutilized APIs and expanding the skillset's diversity. Experiments with Adobe Illustrator demonstrate that our framework significantly improves automation success rates, reduces response time, and saves runtime token costs compared to traditional runtime code generation. This is the first attempt to use software scripting interfaces as a testbed for LLM-based systems, highlighting the advantages of leveraging execution feedback in a controlled environment and offering valuable insights into aligning AI capabilities with user needs in specialized software domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-05", "arxiv_id": "2505.01079", "title": "Improving Editability in Image Generation with Layer-wise Memory", "authors": [ "Daneul Kim", "Jaeah Lee", "Jaesik Park" ], "github": "", "project_page": "", "abstract": "Most real-world image editing tasks require multiple sequential edits to achieve desired results. Current editing approaches, primarily designed for single-object modifications, struggle with sequential editing: especially with maintaining previous edits along with adapting new objects naturally into the existing content. These limitations significantly hinder complex editing scenarios where multiple objects need to be modified while preserving their contextual relationships. We address this fundamental challenge through two key proposals: enabling rough mask inputs that preserve existing content while naturally integrating new elements and supporting consistent editing across multiple modifications. Our framework achieves this through layer-wise memory, which stores latent representations and prompt embeddings from previous edits. We propose Background Consistency Guidance that leverages memorized latents to maintain scene coherence and Multi-Query Disentanglement in cross-attention that ensures natural adaptation to existing content. To evaluate our method, we present a new benchmark dataset incorporating semantic alignment metrics and interactive editing scenarios. Through comprehensive experiments, we demonstrate superior performance in iterative image editing tasks with minimal user effort, requiring only rough masks while maintaining high-quality results throughout multiple editing steps.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-05", "arxiv_id": "2505.00174", "title": "Real-World Gaps in AI Governance Research", "authors": [ "Ilan Strauss", "Isobel Moure", "Tim O'Reilly", "Sruly Rosenblat" ], "github": "https://github.com/AI-Disclosures-Project/Real-World-Gaps-in-AI-Governance-Research", "project_page": "", "abstract": "Drawing on 1,178 safety and reliability papers from 9,439 generative AI papers (January 2020 - March 2025), we compare research outputs of leading AI companies (Anthropic, Google DeepMind, Meta, Microsoft, and OpenAI) and AI universities (CMU, MIT, NYU, Stanford, UC Berkeley, and University of Washington). We find that corporate AI research increasingly concentrates on pre-deployment areas -- model alignment and testing & evaluation -- while attention to deployment-stage issues such as model bias has waned. Significant research gaps exist in high-risk deployment domains, including healthcare, finance, misinformation, persuasive and addictive features, hallucinations, and copyright. Without improved observability into deployed AI, growing corporate concentration could deepen knowledge deficits. We recommend expanding external researcher access to deployment data and systematic observability of in-market AI behaviors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AI-Disclosures-Project/Real-World-Gaps-in-AI-Governance-Research", "github_api_added_by": "auto" }, { "date": "2025-05-05", "arxiv_id": "2505.00023", "title": "CORG: Generating Answers from Complex, Interrelated Contexts", "authors": [ "Hyunji Lee", "Franck Dernoncourt", "Trung Bui", "Seunghyun Yoon" ], "github": "", "project_page": "", "abstract": "In a real-world corpus, knowledge frequently recurs across documents but often contains inconsistencies due to ambiguous naming, outdated information, or errors, leading to complex interrelationships between contexts. Previous research has shown that language models struggle with these complexities, typically focusing on single factors in isolation. We classify these relationships into four types: distracting, ambiguous, counterfactual, and duplicated. Our analysis reveals that no single approach effectively addresses all these interrelationships simultaneously. Therefore, we introduce Context Organizer (CORG), a framework that organizes multiple contexts into independently processed groups. This design allows the model to efficiently find all relevant answers while ensuring disambiguation. CORG consists of three key components: a graph constructor, a reranker, and an aggregator. Our results demonstrate that CORG balances performance and efficiency effectively, outperforming existing grouping methods and achieving comparable results to more computationally intensive, single-context approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-05", "arxiv_id": "2504.20859", "title": "X-Cross: Dynamic Integration of Language Models for Cross-Domain Sequential Recommendation", "authors": [ "Guy Hadad", "Haggai Roitman", "Yotam Eshel", "Bracha Shapira", "Lior Rokach" ], "github": "", "project_page": "", "abstract": "As new products are emerging daily, recommendation systems are required to quickly adapt to possible new domains without needing extensive retraining. This work presents ``X-Cross'' -- a novel cross-domain sequential-recommendation model that recommends products in new domains by integrating several domain-specific language models; each model is fine-tuned with low-rank adapters (LoRA). Given a recommendation prompt, operating layer by layer, X-Cross dynamically refines the representation of each source language model by integrating knowledge from all other models. These refined representations are propagated from one layer to the next, leveraging the activations from each domain adapter to ensure domain-specific nuances are preserved while enabling adaptability across domains. Using Amazon datasets for sequential recommendation, X-Cross achieves performance comparable to a model that is fine-tuned with LoRA, while using only 25% of the additional parameters. In cross-domain tasks, such as adapting from Toys domain to Tools, Electronics or Sports, X-Cross demonstrates robust performance, while requiring about 50%-75% less fine-tuning data than LoRA to make fine-tuning effective. Furthermore, X-Cross achieves significant improvement in accuracy over alternative cross-domain baselines. Overall, X-Cross enables scalable and adaptive cross-domain recommendations, reducing computational overhead and providing an efficient solution for data-constrained environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-05", "arxiv_id": "2505.00562", "title": "TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching", "authors": [ "Yue Meng", "Chuchu Fan" ], "github": "https://github.com/mengyuest/TeLoGraF", "project_page": "", "abstract": "Learning to solve complex tasks with signal temporal logic (STL) specifications is crucial to many real-world applications. However, most previous works only consider fixed or parametrized STL specifications due to the lack of a diverse STL dataset and encoders to effectively extract temporal logic information for downstream tasks. In this paper, we propose TeLoGraF, Temporal Logic Graph-encoded Flow, which utilizes Graph Neural Networks (GNN) encoder and flow-matching to learn solutions for general STL specifications. We identify four commonly used STL templates and collect a total of 200K specifications with paired demonstrations. We conduct extensive experiments in five simulation environments ranging from simple dynamical models in the 2D space to high-dimensional 7DoF Franka Panda robot arm and Ant quadruped navigation. Results show that our method outperforms other baselines in the STL satisfaction rate. Compared to classical STL planning algorithms, our approach is 10-100X faster in inference and can work on any system dynamics. Besides, we show our graph-encoding method's capability to solve complex STLs and robustness to out-distribution STL specifications. Code is available at https://github.com/mengyuest/TeLoGraF", "github_manual": "", "github_regex": "https://github.com/mengyuest/TeLoGraF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-05", "arxiv_id": "2504.20438", "title": "PixelHacker: Image Inpainting with Structural and Semantic Consistency", "authors": [ "Ziyang Xu", "Kangsheng Duan", "Xiaolei Shen", "Zhifeng Ding", "Wenyu Liu", "Xiaohu Ruan", "Xiaoxin Chen", "Xinggang Wang" ], "github": "", "project_page": "", "abstract": "Image inpainting is a fundamental research area between image editing and image generation. Recent state-of-the-art (SOTA) methods have explored novel attention mechanisms, lightweight architectures, and context-aware modeling, demonstrating impressive performance. However, they often struggle with complex structure (e.g., texture, shape, spatial relations) and semantics (e.g., color consistency, object restoration, and logical correctness), leading to artifacts and inappropriate generation. To address this challenge, we design a simple yet effective inpainting paradigm called latent categories guidance, and further propose a diffusion-based model named PixelHacker. Specifically, we first construct a large dataset containing 14 million image-mask pairs by annotating foreground and background (potential 116 and 21 categories, respectively). Then, we encode potential foreground and background representations separately through two fixed-size embeddings, and intermittently inject these features into the denoising process via linear attention. Finally, by pre-training on our dataset and fine-tuning on open-source benchmarks, we obtain PixelHacker. Extensive experiments show that PixelHacker comprehensively outperforms the SOTA on a wide range of datasets (Places2, CelebA-HQ, and FFHQ) and exhibits remarkable consistency in both structure and semantics. Project page at https://hustvl.github.io/PixelHacker.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-05", "arxiv_id": "2505.00949", "title": "Llama-Nemotron: Efficient Reasoning Models", "authors": [ "Akhiad Bercovich", "Itay Levy", "Izik Golan", "Mohammad Dabbah", "Ran El-Yaniv", "Omri Puny", "Ido Galil", "Zach Moshe", "Tomer Ronen", "Najeeb Nabwani", "Ido Shahaf", "Oren Tropp", "Ehud Karpas", "Ran Zilberstein", "Jiaqi Zeng", "Soumye Singhal", "Alexander Bukharin", "Yian Zhang", "Tugrul Konuk", "Gerald Shen", "Ameya Sunil Mahabaleshwarkar", "Bilal Kartal", "Yoshi Suhara", "Olivier Delalleau", "Zijia Chen", "Zhilin Wang", "David Mosallanezhad", "Adi Renduchintala", "Haifeng Qian", "Dima Rekesh", "Fei Jia", "Somshubra Majumdar", "Vahid Noroozi", "Wasi Uddin Ahmad", "Sean Narenthiran", "Aleksander Ficek", "Mehrzad Samadi", "Jocelyn Huang", "Siddhartha Jain", "Igor Gitman", "Ivan Moshkov", "Wei Du", "Shubham Toshniwal", "George Armstrong", "Branislav Kisacanin", "Matvei Novikov", "Daria Gitman", "Evelina Bakhturina", "Jane Polak Scowcroft", "John Kamalu", "Dan Su", "Kezhi Kong", "Markus Kliegl", "Rabeeh Karimi", "Ying Lin", "Sanjeev Satheesh", "Jupinder Parmar", "Pritam Gundecha", "Brandon Norick", "Joseph Jennings", "Shrimai Prabhumoye", "Syeda Nahida Akter", "Mostofa Patwary", "Abhinav Khattar", "Deepak Narayanan", "Roger Waleffe", "Jimmy Zhang", "Bor-Yiing Su", "Guyue Huang", "Terry Kong", "Parth Chadha", "Sahil Jain", "Christine Harvey", "Elad Segal", "Jining Huang", "Sergey Kashirsky", "Robert McQueen", "Izzy Putterman", "George Lam", "Arun Venkatesan", "Sherry Wu", "Vinh Nguyen", "Manoj Kilaru", "Andrew Wang", "Anna Warno", "Abhilash Somasamudramath", "Sandip Bhaskar", "Maka Dong", "Nave Assaf", "Shahar Mor", "Omer Ullman Argov", "Scot Junkin", "Oleksandr Romanenko", "Pedro Larroy", "Monika Katariya", "Marco Rovinelli", "Viji Balas", "Nicholas Edelman", "Anahita Bhiwandiwalla", "Muthu Subramaniam", "Smita Ithape", "Karthik Ramamoorthy", "Yuting Wu", "Suguna Varshini Velury", "Omri Almog", "Joyjit Daw", "Denys Fridman", "Erick Galinkin", "Michael Evans", "Katherine Luna", "Leon Derczynski", "Nikki Pope", "Eileen Long", "Seth Schneider", "Guillermo Siman", "Tomasz Grzegorzek", "Pablo Ribalta", "Monika Katariya", "Joey Conway", "Trisha Saar", "Ann Guan", "Krzysztof Pawelec", "Shyamala Prayaga", "Oleksii Kuchaiev", "Boris Ginsburg", "Oluwatobi Olabiyi", "Kari Briski", "Jonathan Cohen", "Bryan Catanzaro", "Jonah Alben", "Yonatan Geifman", "Eric Chung" ], "github": "https://github.com/tree-sitter/tree-sitter", "project_page": "", "abstract": "We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an open license for enterprise use. The family comes in three sizes -- Nano (8B), Super (49B), and Ultra (253B) -- and performs competitively with state-of-the-art reasoning models such as DeepSeek-R1 while offering superior inference throughput and memory efficiency. In this report, we discuss the training procedure for these models, which entails using neural architecture search from Llama 3 models for accelerated inference, knowledge distillation, and continued pretraining, followed by a reasoning-focused post-training stage consisting of two main parts: supervised fine-tuning and large scale reinforcement learning. Llama-Nemotron models are the first open-source models to support a dynamic reasoning toggle, allowing users to switch between standard chat and reasoning modes during inference. To further support open research and facilitate model development, we provide the following resources: 1. We release the Llama-Nemotron reasoning models -- LN-Nano, LN-Super, and LN-Ultra -- under the commercially permissive NVIDIA Open Model License Agreement. 2. We release the complete post-training dataset: Llama-Nemotron-Post-Training-Dataset. 3. We also release our training codebases: NeMo, NeMo-Aligner, and Megatron-LM.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tree-sitter/tree-sitter", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-05", "arxiv_id": "2504.21117", "title": "Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts", "authors": [ "Hanhua Hong", "Chenghao Xiao", "Yang Wang", "Yiqi Liu", "Wenge Rong", "Chenghua Lin" ], "github": "", "project_page": "", "abstract": "Evaluating natural language generation (NLG) systems is challenging due to the diversity of valid outputs. While human evaluation is the gold standard, it suffers from inconsistencies, lack of standardisation, and demographic biases, limiting reproducibility. LLM-based evaluation offers a scalable alternative but is highly sensitive to prompt design, where small variations can lead to significant discrepancies. In this work, we propose an inversion learning method that learns effective reverse mappings from model outputs back to their input instructions, enabling the automatic generation of highly effective, model-specific evaluation prompts. Our method requires only a single evaluation sample and eliminates the need for time-consuming manual prompt engineering, thereby improving both efficiency and robustness. Our work contributes toward a new direction for more robust and efficient LLM-based evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-05", "arxiv_id": "2505.01490", "title": "WorldGenBench: A World-Knowledge-Integrated Benchmark for Reasoning-Driven Text-to-Image Generation", "authors": [ "Daoan Zhang", "Che Jiang", "Ruoshi Xu", "Biaoxiang Chen", "Zijian Jin", "Yutian Lu", "Jianguo Zhang", "Liang Yong", "Jiebo Luo", "Shengda Luo" ], "github": "", "project_page": "", "abstract": "Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models still struggle with prompts that require rich world knowledge and implicit reasoning: both of which are critical for producing semantically accurate, coherent, and contextually appropriate images in real-world scenarios. To address this gap, we introduce WorldGenBench, a benchmark designed to systematically evaluate T2I models' world knowledge grounding and implicit inferential capabilities, covering both the humanities and nature domains. We propose the Knowledge Checklist Score, a structured metric that measures how well generated images satisfy key semantic expectations. Experiments across 21 state-of-the-art models reveal that while diffusion models lead among open-source methods, proprietary auto-regressive models like GPT-4o exhibit significantly stronger reasoning and knowledge integration. Our findings highlight the need for deeper understanding and inference capabilities in next-generation T2I systems. Project Page: https://dwanzhang-ai.github.io/WorldGenBench/{https://dwanzhang-ai.github.io/WorldGenBench/}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02156", "title": "Think on your Feet: Adaptive Thinking via Reinforcement Learning for Social Agents", "authors": [ "Minzheng Wang", "Yongbin Li", "Haobo Wang", "Xinghua Zhang", "Nan Xu", "Bingli Wu", "Fei Huang", "Haiyang Yu", "Wenji Mao" ], "github": "https://github.com/MozerWang/AMPO", "project_page": "", "abstract": "Effective social intelligence simulation requires language agents to dynamically adjust reasoning depth, a capability notably absent in current approaches. While existing methods either lack this kind of reasoning capability or enforce uniform long chain-of-thought reasoning across all scenarios, resulting in excessive token usage and inappropriate social simulation. In this paper, we propose Adaptive Mode Learning (AML) that strategically selects from four thinking modes (intuitive reaction rightarrow deep contemplation) based on real-time context. Our framework's core innovation, the Adaptive Mode Policy Optimization (AMPO) algorithm, introduces three key advancements over existing methods: (1) Multi-granular thinking mode design, (2) Context-aware mode switching across social interaction, and (3) Token-efficient reasoning via depth-adaptive processing. Extensive experiments on social intelligence tasks confirm that AML achieves 15.6% higher task performance than state-of-the-art methods. Notably, our method outperforms GRPO by 7.0% with 32.8% shorter reasoning chains. These results demonstrate that context-sensitive thinking mode selection, as implemented in AMPO, enables more human-like adaptive reasoning than GRPO's fixed-depth approach", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MozerWang/AMPO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02835", "title": "R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning", "authors": [ "Yi-Fan Zhang", "Xingyu Lu", "Xiao Hu", "Chaoyou Fu", "Bin Wen", "Tianke Zhang", "Changyi Liu", "Kaiyu Jiang", "Kaibing Chen", "Kaiyu Tang", "Haojie Ding", "Jiankang Chen", "Fan Yang", "Zhang Zhang", "Tingting Gao", "Liang Wang" ], "github": "", "project_page": "", "abstract": "Multimodal Reward Models (MRMs) play a crucial role in enhancing the performance of Multimodal Large Language Models (MLLMs). While recent advancements have primarily focused on improving the model structure and training data of MRMs, there has been limited exploration into the effectiveness of long-term reasoning capabilities for reward modeling and how to activate these capabilities in MRMs. In this paper, we explore how Reinforcement Learning (RL) can be used to improve reward modeling. Specifically, we reformulate the reward modeling problem as a rule-based RL task. However, we observe that directly applying existing RL algorithms, such as Reinforce++, to reward modeling often leads to training instability or even collapse due to the inherent limitations of these algorithms. To address this issue, we propose the StableReinforce algorithm, which refines the training loss, advantage estimation strategy, and reward design of existing RL methods. These refinements result in more stable training dynamics and superior performance. To facilitate MRM training, we collect 200K preference data from diverse datasets. Our reward model, R1-Reward, trained using the StableReinforce algorithm on this dataset, significantly improves performance on multimodal reward modeling benchmarks. Compared to previous SOTA models, R1-Reward achieves a 8.4% improvement on the VL Reward-Bench and a 14.3% improvement on the Multimodal Reward Bench. Moreover, with more inference compute, R1-Reward's performance is further enhanced, highlighting the potential of RL algorithms in optimizing MRMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.01043", "title": "Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities", "authors": [ "Zhiwei Hao", "Jianyuan Guo", "Li Shen", "Yong Luo", "Han Hu", "Guoxia Wang", "Dianhai Yu", "Yonggang Wen", "Dacheng Tao" ], "github": "https://github.com/Hao840/Awesome-Low-Precision-Training", "project_page": "", "abstract": "Large language models (LLMs) have achieved impressive performance across various domains. However, the substantial hardware resources required for their training present a significant barrier to efficiency and scalability. To mitigate this challenge, low-precision training techniques have been widely adopted, leading to notable advancements in training efficiency. Despite these gains, low-precision training involves several componentsx2013such as weights, activations, and gradientsx2013each of which can be represented in different numerical formats. The resulting diversity has created a fragmented landscape in low-precision training research, making it difficult for researchers to gain a unified overview of the field. This survey provides a comprehensive review of existing low-precision training methods. To systematically organize these approaches, we categorize them into three primary groups based on their underlying numerical formats, which is a key factor influencing hardware compatibility, computational efficiency, and ease of reference for readers. The categories are: (1) fixed-point and integer-based methods, (2) floating-point-based methods, and (3) customized format-based methods. Additionally, we discuss quantization-aware training approaches, which share key similarities with low-precision training during forward propagation. Finally, we highlight several promising research directions to advance this field. A collection of papers discussed in this survey is provided in https://github.com/Hao840/Awesome-Low-Precision-Training.", "github_manual": "", "github_regex": "https://github.com/Hao840/Awesome-Low-Precision-Training", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02735", "title": "FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models", "authors": [ "Zhouliang Yu", "Ruotian Peng", "Keyi Ding", "Yizhe Li", "Zhongyuan Peng", "Minghao Liu", "Yifan Zhang", "Zheng Yuan", "Huajian Xin", "Wenhao Huang", "Yandong Wen", "Ge Zhang", "Weiyang Liu" ], "github": "", "project_page": "", "abstract": "Formal mathematical reasoning remains a critical challenge for artificial intelligence, hindered by limitations of existing benchmarks in scope and scale. To address this, we present FormalMATH, a large-scale Lean4 benchmark comprising 5,560 formally verified problems spanning from high-school Olympiad challenges to undergraduate-level theorems across diverse domains (e.g., algebra, applied mathematics, calculus, number theory, and discrete mathematics). To mitigate the inefficiency of manual formalization, we introduce a novel human-in-the-loop autoformalization pipeline that integrates: (1) specialized large language models (LLMs) for statement autoformalization, (2) multi-LLM semantic verification, and (3) negation-based disproof filtering strategies using off-the-shelf LLM-based provers. This approach reduces expert annotation costs by retaining 72.09% of statements before manual verification while ensuring fidelity to the original natural-language problems. Our evaluation of state-of-the-art LLM-based theorem provers reveals significant limitations: even the strongest models achieve only 16.46% success rate under practical sampling budgets, exhibiting pronounced domain bias (e.g., excelling in algebra but failing in calculus) and over-reliance on simplified automation tactics. Notably, we identify a counterintuitive inverse relationship between natural-language solution guidance and proof success in chain-of-thought reasoning scenarios, suggesting that human-written informal reasoning introduces noise rather than clarity in the formal reasoning settings. We believe that FormalMATH provides a robust benchmark for benchmarking formal mathematical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02471", "title": "Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction", "authors": [ "Biao Gong", "Cheng Zou", "Dandan Zheng", "Hu Yu", "Jingdong Chen", "Jianxin Sun", "Junbo Zhao", "Jun Zhou", "Kaixiang Ji", "Lixiang Ru", "Libin Wang", "Qingpei Guo", "Rui Liu", "Weilong Chai", "Xinyu Xiao", "Ziyuan Huang" ], "github": "", "project_page": "", "abstract": "We introduce Ming-Lite-Uni, an open-source multimodal framework featuring a newly designed unified visual generator and a native multimodal autoregressive model tailored for unifying vision and language. Specifically, this project provides an open-source implementation of the integrated MetaQueries and M2-omni framework, while introducing the novel multi-scale learnable tokens and multi-scale representation alignment strategy. By leveraging a fixed MLLM and a learnable diffusion model, Ming-Lite-Uni enables native multimodal AR models to perform both text-to-image generation and instruction based image editing tasks, expanding their capabilities beyond pure visual understanding. Our experimental results demonstrate the strong performance of Ming-Lite-Uni and illustrate the impressive fluid nature of its interactive process. All code and model weights are open-sourced to foster further exploration within the community. Notably, this work aligns with concurrent multimodal AI milestones - such as ChatGPT-4o with native image generation updated in March 25, 2025 - underscoring the broader significance of unified models like Ming-Lite-Uni on the path toward AGI. Ming-Lite-Uni is in alpha stage and will soon be further refined.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02625", "title": "LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis", "authors": [ "Qingkai Fang", "Yan Zhou", "Shoutao Guo", "Shaolei Zhang", "Yang Feng" ], "github": "https://github.com/ictnlp/llama-omni2", "project_page": "", "abstract": "Real-time, intelligent, and natural speech interaction is an essential part of the next-generation human-computer interaction. Recent advancements have showcased the potential of building intelligent spoken chatbots based on large language models (LLMs). In this paper, we introduce LLaMA-Omni 2, a series of speech language models (SpeechLMs) ranging from 0.5B to 14B parameters, capable of achieving high-quality real-time speech interaction. LLaMA-Omni 2 is built upon the Qwen2.5 series models, integrating a speech encoder and an autoregressive streaming speech decoder. Despite being trained on only 200K multi-turn speech dialogue samples, LLaMA-Omni 2 demonstrates strong performance on several spoken question answering and speech instruction following benchmarks, surpassing previous state-of-the-art SpeechLMs like GLM-4-Voice, which was trained on millions of hours of speech data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ictnlp/llama-omni2", "github_api_added_by": "auto" }, { "date": "2025-05-06", "arxiv_id": "2505.02370", "title": "SuperEdit: Rectifying and Facilitating Supervision for Instruction-Based Image Editing", "authors": [ "Ming Li", "Xin Gu", "Fan Chen", "Xiaoying Xing", "Longyin Wen", "Chen Chen", "Sijie Zhu" ], "github": "https://github.com/bytedance/SuperEdit", "project_page": "", "abstract": "Due to the challenges of manually collecting accurate editing data, existing datasets are typically constructed using various automated methods, leading to noisy supervision signals caused by the mismatch between editing instructions and original-edited image pairs. Recent efforts attempt to improve editing models through generating higher-quality edited images, pre-training on recognition tasks, or introducing vision-language models (VLMs) but fail to resolve this fundamental issue. In this paper, we offer a novel solution by constructing more effective editing instructions for given image pairs. This includes rectifying the editing instructions to better align with the original-edited image pairs and using contrastive editing instructions to further enhance their effectiveness. Specifically, we find that editing models exhibit specific generation attributes at different inference steps, independent of the text. Based on these prior attributes, we define a unified guide for VLMs to rectify editing instructions. However, there are some challenging editing scenarios that cannot be resolved solely with rectified instructions. To this end, we further construct contrastive supervision signals with positive and negative instructions and introduce them into the model training using triplet loss, thereby further facilitating supervision effectiveness. Our method does not require the VLM modules or pre-training tasks used in previous work, offering a more direct and efficient way to provide better supervision signals, and providing a novel, simple, and effective solution for instruction-based image editing. Results on multiple benchmarks demonstrate that our method significantly outperforms existing approaches. Compared with previous SOTA SmartEdit, we achieve 9.19% improvements on the Real-Edit benchmark with 30x less training data and 13x smaller model size.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bytedance/SuperEdit", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.01583", "title": "TEMPURA: Temporal Event Masked Prediction and Understanding for Reasoning in Action", "authors": [ "Jen-Hao Cheng", "Vivian Wang", "Huayu Wang", "Huapeng Zhou", "Yi-Hao Peng", "Hou-I Liu", "Hsiang-Wei Huang", "Kuang-Ming Chen", "Cheng-Yen Yang", "Wenhao Chai", "Yi-Ling Chen", "Vibhav Vineet", "Qin Cai", "Jenq-Neng Hwang" ], "github": "https://github.com/Andy-Cheng/TEMPURA", "project_page": "", "abstract": "Understanding causal event relationships and achieving fine-grained temporal grounding in videos remain challenging for vision-language models. Existing methods either compress video tokens to reduce temporal resolution, or treat videos as unsegmented streams, which obscures fine-grained event boundaries and limits the modeling of causal dependencies. We propose TEMPURA (Temporal Event Masked Prediction and Understanding for Reasoning in Action), a two-stage training framework that enhances video temporal understanding. TEMPURA first applies masked event prediction reasoning to reconstruct missing events and generate step-by-step causal explanations from dense event annotations, drawing inspiration from effective infilling techniques. TEMPURA then learns to perform video segmentation and dense captioning to decompose videos into non-overlapping events with detailed, timestamp-aligned descriptions. We train TEMPURA on VER, a large-scale dataset curated by us that comprises 1M training instances and 500K videos with temporally aligned event descriptions and structured reasoning steps. Experiments on temporal grounding and highlight detection benchmarks demonstrate that TEMPURA outperforms strong baseline models, confirming that integrating causal reasoning with fine-grained temporal segmentation leads to improved video understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Andy-Cheng/TEMPURA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02387", "title": "RM-R1: Reward Modeling as Reasoning", "authors": [ "Xiusi Chen", "Gaotang Li", "Ziqi Wang", "Bowen Jin", "Cheng Qian", "Yu Wang", "Hongru Wang", "Yu Zhang", "Denghui Zhang", "Tong Zhang", "Hanghang Tong", "Heng Ji" ], "github": "https://github.com/RM-R1-UIUC/RM-R1", "project_page": "", "abstract": "Reward modeling is essential for aligning large language models (LLMs) with human preferences, especially through reinforcement learning from human feedback (RLHF). To provide accurate reward signals, a reward model (RM) should stimulate deep thinking and conduct interpretable reasoning before assigning a score or a judgment. However, existing RMs either produce opaque scalar scores or directly generate the prediction of a preferred answer, making them struggle to integrate natural language critiques, thus lacking interpretability. Inspired by recent advances of long chain-of-thought (CoT) on reasoning-intensive tasks, we hypothesize and validate that integrating reasoning capabilities into reward modeling significantly enhances RM's interpretability and performance. In this work, we introduce a new class of generative reward models -- Reasoning Reward Models (ReasRMs) -- which formulate reward modeling as a reasoning task. We propose a reasoning-oriented training pipeline and train a family of ReasRMs, RM-R1. The training consists of two key stages: (1) distillation of high-quality reasoning chains and (2) reinforcement learning with verifiable rewards. RM-R1 improves LLM rollouts by self-generating reasoning traces or chat-specific rubrics and evaluating candidate responses against them. Empirically, our models achieve state-of-the-art or near state-of-the-art performance of generative RMs across multiple comprehensive reward model benchmarks, outperforming much larger open-weight models (e.g., Llama3.1-405B) and proprietary ones (e.g., GPT-4o) by up to 13.8%. Beyond final performance, we perform thorough empirical analysis to understand the key ingredients of successful ReasRM training. To facilitate future research, we release six ReasRM models along with code and data at https://github.com/RM-R1-UIUC/RM-R1.", "github_manual": "", "github_regex": "https://github.com/RM-R1-UIUC/RM-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02094", "title": "SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from Sparse and Noisy Demonstrations", "authors": [ "Runyi Yu", "Yinhuai Wang", "Qihan Zhao", "Hok Wai Tsui", "Jingbo Wang", "Ping Tan", "Qifeng Chen" ], "github": "https://github.com/Ingrid789/SkillMimic-V2", "project_page": "", "abstract": "We address a fundamental challenge in Reinforcement Learning from Interaction Demonstration (RLID): demonstration noise and coverage limitations. While existing data collection approaches provide valuable interaction demonstrations, they often yield sparse, disconnected, and noisy trajectories that fail to capture the full spectrum of possible skill variations and transitions. Our key insight is that despite noisy and sparse demonstrations, there exist infinite physically feasible trajectories that naturally bridge between demonstrated skills or emerge from their neighboring states, forming a continuous space of possible skill variations and transitions. Building upon this insight, we present two data augmentation techniques: a Stitched Trajectory Graph (STG) that discovers potential transitions between demonstration skills, and a State Transition Field (STF) that establishes unique connections for arbitrary states within the demonstration neighborhood. To enable effective RLID with augmented data, we develop an Adaptive Trajectory Sampling (ATS) strategy for dynamic curriculum generation and a historical encoding mechanism for memory-dependent skill learning. Our approach enables robust skill acquisition that significantly generalizes beyond the reference demonstrations. Extensive experiments across diverse interaction tasks demonstrate substantial improvements over state-of-the-art methods in terms of convergence stability, generalization capability, and recovery robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Ingrid789/SkillMimic-V2", "github_api_added_by": "auto" }, { "date": "2025-05-06", "arxiv_id": "2505.01441", "title": "Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning", "authors": [ "Joykirat Singh", "Raghav Magazine", "Yash Pandya", "Akshay Nambi" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have achieved remarkable progress in complex reasoning tasks, yet they remain fundamentally limited by their reliance on static internal knowledge and text-only reasoning. Real-world problem solving often demands dynamic, multi-step reasoning, adaptive decision making, and the ability to interact with external tools and environments. In this work, we introduce ARTIST (Agentic Reasoning and Tool Integration in Self-improving Transformers), a unified framework that tightly couples agentic reasoning, reinforcement learning, and tool integration for LLMs. ARTIST enables models to autonomously decide when, how, and which tools to invoke within multi-turn reasoning chains, leveraging outcome-based RL to learn robust strategies for tool use and environment interaction without requiring step-level supervision. Extensive experiments on mathematical reasoning and multi-turn function calling benchmarks show that ARTIST consistently outperforms state-of-the-art baselines, with up to 22% absolute improvement over base models and strong gains on the most challenging tasks. Detailed studies and metric analyses reveal that agentic RL training leads to deeper reasoning, more effective tool use, and higher-quality solutions. Our results establish agentic RL with tool integration as a powerful new frontier for robust, interpretable, and generalizable problem-solving in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02823", "title": "MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing", "authors": [ "Zinan Guo", "Pengze Zhang", "Yanze Wu", "Chong Mou", "Songtao Zhao", "Qian He" ], "github": "", "project_page": "", "abstract": "Current multi-subject customization approaches encounter two critical challenges: the difficulty in acquiring diverse multi-subject training data, and attribute entanglement across different subjects. To bridge these gaps, we propose MUSAR - a simple yet effective framework to achieve robust multi-subject customization while requiring only single-subject training data. Firstly, to break the data limitation, we introduce debiased diptych learning. It constructs diptych training pairs from single-subject images to facilitate multi-subject learning, while actively correcting the distribution bias introduced by diptych construction via static attention routing and dual-branch LoRA. Secondly, to eliminate cross-subject entanglement, we introduce dynamic attention routing mechanism, which adaptively establishes bijective mappings between generated images and conditional subjects. This design not only achieves decoupling of multi-subject representations but also maintains scalable generalization performance with increasing reference subjects. Comprehensive experiments demonstrate that our MUSAR outperforms existing methods - even those trained on multi-subject dataset - in image quality, subject consistency, and interaction naturalness, despite requiring only single-subject dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02707", "title": "Voila: Voice-Language Foundation Models for Real-Time Autonomous Interaction and Voice Role-Play", "authors": [ "Yemin Shi", "Yu Shu", "Siwei Dong", "Guangyi Liu", "Jaward Sesay", "Jingwen Li", "Zhiting Hu" ], "github": "", "project_page": "", "abstract": "A voice AI agent that blends seamlessly into daily life would interact with humans in an autonomous, real-time, and emotionally expressive manner. Rather than merely reacting to commands, it would continuously listen, reason, and respond proactively, fostering fluid, dynamic, and emotionally resonant interactions. We introduce Voila, a family of large voice-language foundation models that make a step towards this vision. Voila moves beyond traditional pipeline systems by adopting a new end-to-end architecture that enables full-duplex, low-latency conversations while preserving rich vocal nuances such as tone, rhythm, and emotion. It achieves a response latency of just 195 milliseconds, surpassing the average human response time. Its hierarchical multi-scale Transformer integrates the reasoning capabilities of large language models (LLMs) with powerful acoustic modeling, enabling natural, persona-aware voice generation -- where users can simply write text instructions to define the speaker's identity, tone, and other characteristics. Moreover, Voila supports over one million pre-built voices and efficient customization of new ones from brief audio samples as short as 10 seconds. Beyond spoken dialogue, Voila is designed as a unified model for a wide range of voice-based applications, including automatic speech recognition (ASR), Text-to-Speech (TTS), and, with minimal adaptation, multilingual speech translation. Voila is fully open-sourced to support open research and accelerate progress toward next-generation human-machine interactions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.01658", "title": "A Survey on Inference Engines for Large Language Models: Perspectives on Optimization and Efficiency", "authors": [ "Sihyeong Park", "Sungryeol Jeon", "Chaelyn Lee", "Seokhun Jeon", "Byung-Soo Kim", "Jemin Lee" ], "github": "https://github.com/sihyeong/Awesome-LLM-Inference-Engine", "project_page": "", "abstract": "Large language models (LLMs) are widely applied in chatbots, code generators, and search engines. Workloads such as chain-of-thought, complex reasoning, and agent services significantly increase the inference cost by invoking the model repeatedly. Optimization methods such as parallelism, compression, and caching have been adopted to reduce costs, but the diverse service requirements make it hard to select the right method. Recently, specialized LLM inference engines have emerged as a key component for integrating the optimization methods into service-oriented infrastructures. However, a systematic study on inference engines is still lacking. This paper provides a comprehensive evaluation of 25 open-source and commercial inference engines. We examine each inference engine in terms of ease-of-use, ease-of-deployment, general-purpose support, scalability, and suitability for throughput- and latency-aware computation. Furthermore, we explore the design goals of each inference engine by investigating the optimization techniques it supports. In addition, we assess the ecosystem maturity of open source inference engines and handle the performance and cost policy of commercial solutions. We outline future research directions that include support for complex LLM-based services, support of various hardware, and enhanced security, offering practical guidance to researchers and developers in selecting and designing optimized LLM inference engines. We also provide a public repository to continually track developments in this fast-evolving field: https://github.com/sihyeong/Awesome-LLM-Inference-Engine", "github_manual": "", "github_regex": "https://github.com/sihyeong/Awesome-LLM-Inference-Engine", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2504.20752", "title": "Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers", "authors": [ "Roman Abramov", "Felix Steinbauer", "Gjergji Kasneci" ], "github": "", "project_page": "", "abstract": "Transformers have achieved great success in numerous NLP tasks but continue to exhibit notable gaps in multi-step factual reasoning, especially when real-world knowledge is sparse. Recent advances in grokking have demonstrated that neural networks can transition from memorizing to perfectly generalizing once they detect underlying logical patterns - yet these studies have primarily used small, synthetic tasks. In this paper, for the first time, we extend grokking to real-world factual data and address the challenge of dataset sparsity by augmenting existing knowledge graphs with carefully designed synthetic data to raise the ratio phi_r of inferred facts to atomic facts above the threshold required for grokking. Surprisingly, we find that even factually incorrect synthetic data can strengthen emergent reasoning circuits rather than degrade accuracy, as it forces the model to rely on relational structure rather than memorization. When evaluated on multi-hop reasoning benchmarks, our approach achieves up to 95-100% accuracy on 2WikiMultiHopQA - substantially improving over strong baselines and matching or exceeding current state-of-the-art results. We further provide an in-depth analysis of how increasing phi_r drives the formation of generalizing circuits inside Transformers. Our findings suggest that grokking-based data augmentation can unlock implicit multi-hop reasoning capabilities, opening the door to more robust and interpretable factual reasoning in large-scale language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02391", "title": "Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL", "authors": [ "Jiarui Yao", "Yifan Hao", "Hanning Zhang", "Hanze Dong", "Wei Xiong", "Nan Jiang", "Tong Zhang" ], "github": "https://github.com/RLHFlow/GVM", "project_page": "", "abstract": "Chain-of-thought (CoT) reasoning in large language models (LLMs) can be formalized as a latent variable problem, where the model needs to generate intermediate reasoning steps. While prior approaches such as iterative reward-ranked fine-tuning (RAFT) have relied on such formulations, they typically apply uniform inference budgets across prompts, which fails to account for variability in difficulty and convergence behavior. This work identifies the main bottleneck in CoT training as inefficient stochastic gradient estimation due to static sampling strategies. We propose GVM-RAFT, a prompt-specific Dynamic Sample Allocation Strategy designed to minimize stochastic gradient variance under a computational budget constraint. The method dynamically allocates computational resources by monitoring prompt acceptance rates and stochastic gradient norms, ensuring that the resulting gradient variance is minimized. Our theoretical analysis shows that the proposed dynamic sampling strategy leads to accelerated convergence guarantees under suitable conditions. Experiments on mathematical reasoning show that GVM-RAFT achieves a 2-4x speedup and considerable accuracy improvements over vanilla RAFT. The proposed dynamic sampling strategy is general and can be incorporated into other reinforcement learning algorithms, such as GRPO, leading to similar improvements in convergence and test accuracy. Our code is available at https://github.com/RLHFlow/GVM.", "github_manual": "", "github_regex": "https://github.com/RLHFlow/GVM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02819", "title": "ReplaceMe: Network Simplification via Layer Pruning and Linear Transformations", "authors": [ "Dmitriy Shopkhoev", "Ammar Ali", "Magauiya Zhussip", "Valentin Malykh", "Stamatios Lefkimmiatis", "Nikos Komodakis", "Sergey Zagoruyko" ], "github": "https://github.com/mts-ai/ReplaceMe", "project_page": "", "abstract": "We introduce ReplaceMe, a generalized training-free depth pruning method that effectively replaces transformer blocks with a linear operation, while maintaining high performance for low compression ratios. In contrast to conventional pruning approaches that require additional training or fine-tuning, our approach requires only a small calibration dataset that is used to estimate a linear transformation to approximate the pruned blocks. This estimated linear mapping can be seamlessly merged with the remaining transformer blocks, eliminating the need for any additional network parameters. Our experiments show that ReplaceMe consistently outperforms other training-free approaches and remains highly competitive with state-of-the-art pruning methods that involve extensive retraining/fine-tuning and architectural modifications. Applied to several large language models (LLMs), ReplaceMe achieves up to 25% pruning while retaining approximately 90% of the original model's performance on open benchmarks - without any training or healing steps, resulting in minimal computational overhead (see Fig.1). We provide an open-source library implementing ReplaceMe alongside several state-of-the-art depth pruning techniques, available at this repository.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mts-ai/ReplaceMe", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.01456", "title": "Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation", "authors": [ "Vaidehi Patil", "Yi-Lin Sung", "Peter Hase", "Jie Peng", "Tianlong Chen", "Mohit Bansal" ], "github": "https://github.com/Vaidehi99/UnLOK-VQA", "project_page": "", "abstract": "LLMs trained on massive datasets may inadvertently acquire sensitive information such as personal details and potentially harmful content. This risk is further heightened in multimodal LLMs as they integrate information from multiple modalities (image and text). Adversaries can exploit this knowledge through multimodal prompts to extract sensitive details. Evaluating how effectively MLLMs can forget such information (targeted unlearning) necessitates the creation of high-quality, well-annotated image-text pairs. While prior work on unlearning has focused on text, multimodal unlearning remains underexplored. To address this gap, we first introduce a multimodal unlearning benchmark, UnLOK-VQA (Unlearning Outside Knowledge VQA), as well as an attack-and-defense framework to evaluate methods for deleting specific multimodal knowledge from MLLMs. We extend a visual question-answering dataset using an automated pipeline that generates varying-proximity samples for testing generalization and specificity, followed by manual filtering for maintaining high quality. We then evaluate six defense objectives against seven attacks (four whitebox, three blackbox), including a novel whitebox method leveraging interpretability of hidden states. Our results show multimodal attacks outperform text- or image-only ones, and that the most effective defense removes answer information from internal model states. Additionally, larger models exhibit greater post-editing robustness, suggesting that scale enhances safety. UnLOK-VQA provides a rigorous benchmark for advancing unlearning in MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Vaidehi99/UnLOK-VQA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02222", "title": "Practical Efficiency of Muon for Pretraining", "authors": [ "Essential AI", "Ishaan Shah", "Anthony M. Polloreno", "Karl Stratos", "Philip Monk", "Adarsh Chaluvaraju", "Andrew Hojel", "Andrew Ma", "Anil Thomas", "Ashish Tanwer", "Darsh J Shah", "Khoi Nguyen", "Kurt Smith", "Michael Callahan", "Michael Pust", "Mohit Parmar", "Peter Rushton", "Platon Mazarakis", "Ritvik Kapila", "Saurabh Srivastava", "Somanshu Singla", "Tim Romanski", "Yash Vanjani", "Ashish Vaswani" ], "github": "", "project_page": "", "abstract": "We demonstrate that Muon, the simplest instantiation of a second-order optimizer, explicitly expands the Pareto frontier over AdamW on the compute-time tradeoff. We find that Muon is more effective than AdamW in retaining data efficiency at large batch sizes, far beyond the so-called critical batch size, while remaining computationally efficient, thus enabling more economical training. We study the combination of Muon and the maximal update parameterization (muP) for efficient hyperparameter transfer and present a simple telescoping algorithm that accounts for all sources of error in muP while introducing only a modest overhead in resources. We validate our findings through extensive experiments with model sizes up to four billion parameters and ablations on the data distribution and architecture.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02005", "title": "Learning Heterogeneous Mixture of Scene Experts for Large-scale Neural Radiance Fields", "authors": [ "Zhenxing Mi", "Ping Yin", "Xue Xiao", "Dan Xu" ], "github": "https://github.com/MiZhenxing/Switch-NeRF", "project_page": "", "abstract": "Recent NeRF methods on large-scale scenes have underlined the importance of scene decomposition for scalable NeRFs. Although achieving reasonable scalability, there are several critical problems remaining unexplored, i.e., learnable decomposition, modeling scene heterogeneity, and modeling efficiency. In this paper, we introduce Switch-NeRF++, a Heterogeneous Mixture of Hash Experts (HMoHE) network that addresses these challenges within a unified framework. It is a highly scalable NeRF that learns heterogeneous decomposition and heterogeneous NeRFs efficiently for large-scale scenes in an end-to-end manner. In our framework, a gating network learns to decomposes scenes and allocates 3D points to specialized NeRF experts. This gating network is co-optimized with the experts, by our proposed Sparsely Gated Mixture of Experts (MoE) NeRF framework. We incorporate a hash-based gating network and distinct heterogeneous hash experts. The hash-based gating efficiently learns the decomposition of the large-scale scene. The distinct heterogeneous hash experts consist of hash grids of different resolution ranges, enabling effective learning of the heterogeneous representation of different scene parts. These design choices make our framework an end-to-end and highly scalable NeRF solution for real-world large-scale scene modeling to achieve both quality and efficiency. We evaluate our accuracy and scalability on existing large-scale NeRF datasets and a new dataset with very large-scale scenes (>6.5km^2) from UrbanBIS. Extensive experiments demonstrate that our approach can be easily scaled to various large-scale scenes and achieve state-of-the-art scene rendering accuracy. Furthermore, our method exhibits significant efficiency, with an 8x acceleration in training and a 16x acceleration in rendering compared to Switch-NeRF. Codes will be released in https://github.com/MiZhenxing/Switch-NeRF.", "github_manual": "", "github_regex": "https://github.com/MiZhenxing/Switch-NeRF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.02130", "title": "Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data", "authors": [ "Zhong Guan", "Likang Wu", "Hongke Zhao", "Ming He", "Jianpin Fan" ], "github": "https://github.com/millioniron/LLM_exploration", "project_page": "", "abstract": "Attention mechanisms are critical to the success of large language models (LLMs), driving significant advancements in multiple fields. However, for graph-structured data, which requires emphasis on topological connections, they fall short compared to message-passing mechanisms on fixed links, such as those employed by Graph Neural Networks (GNNs). This raises a question: ``Does attention fail for graphs in natural language settings?'' Motivated by these observations, we embarked on an empirical study from the perspective of attention mechanisms to explore how LLMs process graph-structured data. The goal is to gain deeper insights into the attention behavior of LLMs over graph structures. We uncovered unique phenomena regarding how LLMs apply attention to graph-structured data and analyzed these findings to improve the modeling of such data by LLMs. The primary findings of our research are: 1) While LLMs can recognize graph data and capture text-node interactions, they struggle to model inter-node relationships within graph structures due to inherent architectural constraints. 2) The attention distribution of LLMs across graph nodes does not align with ideal structural patterns, indicating a failure to adapt to graph topology nuances. 3) Neither fully connected attention nor fixed connectivity is optimal; each has specific limitations in its application scenarios. Instead, intermediate-state attention windows improve LLM training performance and seamlessly transition to fully connected windows during inference. Source code: https://github.com/millioniron/LLM_exploration{LLM4Exploration}", "github_manual": "", "github_regex": "https://github.com/millioniron/LLM_exploration", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-06", "arxiv_id": "2505.01548", "title": "Rethinking RGB-Event Semantic Segmentation with a Novel Bidirectional Motion-enhanced Event Representation", "authors": [ "Zhen Yao", "Xiaowen Ying", "Mooi Choo Chuah" ], "github": "https://github.com/zyaocoder/BRENet", "project_page": "", "abstract": "Event cameras capture motion dynamics, offering a unique modality with great potential in various computer vision tasks. However, RGB-Event fusion faces three intrinsic misalignments: (i) temporal, (ii) spatial, and (iii) modal misalignment. Existing voxel grid representations neglect temporal correlations between consecutive event windows, and their formulation with simple accumulation of asynchronous and sparse events is incompatible with the synchronous and dense nature of RGB modality. To tackle these challenges, we propose a novel event representation, Motion-enhanced Event Tensor (MET), which transforms sparse event voxels into a dense and temporally coherent form by leveraging dense optical flows and event temporal features. In addition, we introduce a Frequency-aware Bidirectional Flow Aggregation Module (BFAM) and a Temporal Fusion Module (TFM). BFAM leverages the frequency domain and MET to mitigate modal misalignment, while bidirectional flow aggregation and temporal fusion mechanisms resolve spatiotemporal misalignment. Experimental results on two large-scale datasets demonstrate that our framework significantly outperforms state-of-the-art RGB-Event semantic segmentation approaches. Our code is available at: https://github.com/zyaocoder/BRENet.", "github_manual": "", "github_regex": "https://github.com/zyaocoder/BRENet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.03335", "title": "Absolute Zero: Reinforced Self-play Reasoning with Zero Data", "authors": [ "Andrew Zhao", "Yiran Wu", "Yang Yue", "Tong Wu", "Quentin Xu", "Yang Yue", "Matthieu Lin", "Shenzhi Wang", "Qingyun Wu", "Zilong Zheng", "Gao Huang" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has shown promise in enhancing the reasoning capabilities of large language models by learning directly from outcome-based rewards. Recent RLVR works that operate under the zero setting avoid supervision in labeling the reasoning process, but still depend on manually curated collections of questions and answers for training. The scarcity of high-quality, human-produced examples raises concerns about the long-term scalability of relying on human supervision, a challenge already evident in the domain of language model pretraining. Furthermore, in a hypothetical future where AI surpasses human intelligence, tasks provided by humans may offer limited learning potential for a superintelligent system. To address these concerns, we propose a new RLVR paradigm called Absolute Zero, in which a single model learns to propose tasks that maximize its own learning progress and improves reasoning by solving them, without relying on any external data. Under this paradigm, we introduce the Absolute Zero Reasoner (AZR), a system that self-evolves its training curriculum and reasoning ability by using a code executor to both validate proposed code reasoning tasks and verify answers, serving as an unified source of verifiable reward to guide open-ended yet grounded learning. Despite being trained entirely without external data, AZR achieves overall SOTA performance on coding and mathematical reasoning tasks, outperforming existing zero-setting models that rely on tens of thousands of in-domain human-curated examples. Furthermore, we demonstrate that AZR can be effectively applied across different model scales and is compatible with various model classes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.03318", "title": "Unified Multimodal Chain-of-Thought Reward Model through Reinforcement Fine-Tuning", "authors": [ "Yibin Wang", "Zhimin Li", "Yuhang Zang", "Chunyu Wang", "Qinglin Lu", "Cheng Jin", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in multimodal Reward Models (RMs) have shown significant promise in delivering reward signals to align vision models with human preferences. However, current RMs are generally restricted to providing direct responses or engaging in shallow reasoning processes with limited depth, often leading to inaccurate reward signals. We posit that incorporating explicit long chains of thought (CoT) into the reward reasoning process can significantly strengthen their reliability and robustness. Furthermore, we believe that once RMs internalize CoT reasoning, their direct response accuracy can also be improved through implicit reasoning capabilities. To this end, this paper proposes UnifiedReward-Think, the first unified multimodal CoT-based reward model, capable of multi-dimensional, step-by-step long-chain reasoning for both visual understanding and generation reward tasks. Specifically, we adopt an exploration-driven reinforcement fine-tuning approach to elicit and incentivize the model's latent complex reasoning ability: (1) We first use a small amount of image generation preference data to distill the reasoning process of GPT-4o, which is then used for the model's cold start to learn the format and structure of CoT reasoning. (2) Subsequently, by leveraging the model's prior knowledge and generalization capabilities, we prepare large-scale unified multimodal preference data to elicit the model's reasoning process across various vision tasks. During this phase, correct reasoning outputs are retained for rejection sampling to refine the model (3) while incorrect predicted samples are finally used for Group Relative Policy Optimization (GRPO) based reinforcement fine-tuning, enabling the model to explore diverse reasoning paths and optimize for correct and robust solutions. Extensive experiments across various vision reward tasks demonstrate the superiority of our model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.02311", "title": "Invoke Interfaces Only When Needed: Adaptive Invocation for Large Language Models in Question Answering", "authors": [ "Jihao Zhao", "Chunlai Zhou", "Biao Qin" ], "github": "https://github.com/robot2050/attenhscore", "project_page": "", "abstract": "The collaborative paradigm of large and small language models (LMs) effectively balances performance and cost, yet its pivotal challenge lies in precisely pinpointing the moment of invocation when hallucinations arise in small LMs. Previous optimization efforts primarily focused on post-processing techniques, which were separate from the reasoning process of LMs, resulting in high computational costs and limited effectiveness. In this paper, we propose a practical invocation evaluation metric called AttenHScore, which calculates the accumulation and propagation of hallucinations during the generation process of small LMs, continuously amplifying potential reasoning errors. By dynamically adjusting the detection threshold, we achieve more accurate real-time invocation of large LMs. Additionally, considering the limited reasoning capacity of small LMs, we leverage uncertainty-aware knowledge reorganization to assist them better capture critical information from different text chunks. Extensive experiments reveal that our AttenHScore outperforms most baseline in enhancing real-time hallucination detection capabilities across multiple QA datasets, especially when addressing complex queries. Moreover, our strategies eliminate the need for additional model training and display flexibility in adapting to various transformer-based LMs.", "github_manual": "https://github.com/robot2050/attenhscore", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.03730", "title": "FlexiAct: Towards Flexible Action Control in Heterogeneous Scenarios", "authors": [ "Shiyi Zhang", "Junhao Zhuang", "Zhaoyang Zhang", "Ying Shan", "Yansong Tang" ], "github": "", "project_page": "", "abstract": "Action customization involves generating videos where the subject performs actions dictated by input control signals. Current methods use pose-guided or global motion customization but are limited by strict constraints on spatial structure, such as layout, skeleton, and viewpoint consistency, reducing adaptability across diverse subjects and scenarios. To overcome these limitations, we propose FlexiAct, which transfers actions from a reference video to an arbitrary target image. Unlike existing methods, FlexiAct allows for variations in layout, viewpoint, and skeletal structure between the subject of the reference video and the target image, while maintaining identity consistency. Achieving this requires precise action control, spatial structure adaptation, and consistency preservation. To this end, we introduce RefAdapter, a lightweight image-conditioned adapter that excels in spatial adaptation and consistency preservation, surpassing existing methods in balancing appearance consistency and structural flexibility. Additionally, based on our observations, the denoising process exhibits varying levels of attention to motion (low frequency) and appearance details (high frequency) at different timesteps. So we propose FAE (Frequency-aware Action Extraction), which, unlike existing methods that rely on separate spatial-temporal architectures, directly achieves action extraction during the denoising process. Experiments demonstrate that our method effectively transfers actions to subjects with diverse layouts, skeletons, and viewpoints. We release our code and model weights to support further research at https://shiyi-zh0408.github.io/projectpages/FlexiAct/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.02922", "title": "RetroInfer: A Vector-Storage Approach for Scalable Long-Context LLM Inference", "authors": [ "Yaoqi Chen", "Jinkai Zhang", "Baotong Lu", "Qianxi Zhang", "Chengruidong Zhang", "Jingjia Luo", "Di Liu", "Huiqiang Jiang", "Qi Chen", "Jing Liu", "Bailu Ding", "Xiao Yan", "Jiawei Jiang", "Chen Chen", "Mingxing Zhang", "Yuqing Yang", "Fan Yang", "Mao Yang" ], "github": "https://github.com/microsoft/RetrievalAttention", "project_page": "", "abstract": "The growing context lengths of large language models (LLMs) pose significant challenges for efficient inference, primarily due to GPU memory and bandwidth constraints. We present RetroInfer, a novel system that reconceptualizes the key-value (KV) cache as a vector storage system which exploits the inherent attention sparsity to accelerate long-context LLM inference. At its core is the wave index, an Attention-aWare VEctor index that enables efficient and accurate retrieval of critical tokens through techniques such as tripartite attention approximation, accuracy-bounded attention estimation, and segmented clustering. Complementing this is the wave buffer, which coordinates KV cache placement and overlaps computation and data transfer across GPU and CPU to sustain high throughput. Unlike prior sparsity-based methods that struggle with token selection and hardware coordination, RetroInfer delivers robust performance without compromising model accuracy. Experiments on long-context benchmarks show up to 4.5X speedup over full attention within GPU memory limits and up to 10.5X over sparse attention baselines when KV cache is extended to CPU memory, all while preserving full-attention-level accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/RetrievalAttention", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.03164", "title": "InfoVids: Reimagining the Viewer Experience with Alternative Visualization-Presenter Relationships", "authors": [ "Ji Won Chung", "Tongyu Zhou", "Ivy Chen", "Kevin Hsu", "Ryan A. Rossi", "Alexa Siu", "Shunan Guo", "Franck Dernoncourt", "James Tompkin", "Jeff Huang" ], "github": "", "project_page": "", "abstract": "Traditional data presentations typically separate the presenter and visualization into two separate spaces--the 3D world and a 2D screen--enforcing visualization-centric stories. To create a more human-centric viewing experience, we establish a more equitable relationship between the visualization and the presenter through our InfoVids. These infographics-inspired informational videos are crafted to redefine relationships between the presenter and visualizations. As we design InfoVids, we explore how the use of layout, form, and interactions affects the viewer experience. We compare InfoVids against their baseline 2D `slides' equivalents across 9 metrics with 30 participants and provide practical, long-term insights from an autobiographical perspective. Our mixed methods analyses reveal that this paradigm reduced viewer attention splitting, shifted the focus from the visualization to the presenter, and led to more interactive, natural, and engaging full-body data performances for viewers. Ultimately, InfoVids helped viewers re-imagine traditional dynamics between the presenter and visualizations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.03005", "title": "RADLADS: Rapid Attention Distillation to Linear Attention Decoders at Scale", "authors": [ "Daniel Goldstein", "Eric Alcaide", "Janna Lu", "Eugene Cheah" ], "github": "https://github.com/recursal/RADLADS-paper", "project_page": "", "abstract": "We present Rapid Attention Distillation to Linear Attention Decoders at Scale (RADLADS), a protocol for rapidly converting softmax attention transformers into linear attention decoder models, along with two new RWKV-variant architectures, and models converted from popular Qwen2.5 open source models in 7B, 32B, and 72B sizes. Our conversion process requires only 350-700M tokens, less than 0.005% of the token count used to train the original teacher models. Converting to our 72B linear attention model costs less than \\$2,000 USD at today's prices, yet quality at inference remains close to the original transformer. These models achieve state-of-the-art downstream performance across a set of standard benchmarks for linear attention models of their size. We release all our models on HuggingFace under the Apache 2.0 license, with the exception of our 72B models which are also governed by the Qwen License Agreement. Models at https://huggingface.co/collections/recursal/radlads-6818ee69e99e729ba8a87102 Training Code at https://github.com/recursal/RADLADS-paper", "github_manual": "", "github_regex": "https://github.com/recursal/RADLADS-paper", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.03735", "title": "Multi-Agent System for Comprehensive Soccer Understanding", "authors": [ "Jiayuan Rao", "Zifeng Li", "Haoning Wu", "Ya Zhang", "Yanfeng Wang", "Weidi Xie" ], "github": "", "project_page": "", "abstract": "Recent advancements in AI-driven soccer understanding have demonstrated rapid progress, yet existing research predominantly focuses on isolated or narrow tasks. To bridge this gap, we propose a comprehensive framework for holistic soccer understanding. Specifically, we make the following contributions in this paper: (i) we construct SoccerWiki, the first large-scale multimodal soccer knowledge base, integrating rich domain knowledge about players, teams, referees, and venues to enable knowledge-driven reasoning; (ii) we present SoccerBench, the largest and most comprehensive soccer-specific benchmark, featuring around 10K standardized multimodal (text, image, video) multi-choice QA pairs across 13 distinct understanding tasks, curated through automated pipelines and manual verification; (iii) we introduce SoccerAgent, a novel multi-agent system that decomposes complex soccer questions via collaborative reasoning, leveraging domain expertise from SoccerWiki and achieving robust performance; (iv) extensive evaluations and ablations that benchmark state-of-the-art MLLMs on SoccerBench, highlighting the superiority of our proposed agentic system. All data and code are publicly available at: https://jyrao.github.io/SoccerAgent/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.02214", "title": "An Empirical Study of Qwen3 Quantization", "authors": [ "Xingyu Zheng", "Yuye Li", "Haoran Chu", "Yue Feng", "Xudong Ma", "Jie Luo", "Jinyang Guo", "Haotong Qin", "Michele Magno", "Xianglong Liu" ], "github": "https://github.com/Efficient-ML/Qwen3-Quantization", "project_page": "", "abstract": "The Qwen series has emerged as a leading family of open-source Large Language Models (LLMs), demonstrating remarkable capabilities in natural language understanding tasks. With the recent release of Qwen3, which exhibits superior performance across diverse benchmarks, there is growing interest in deploying these models efficiently in resource-constrained environments. Low-bit quantization presents a promising solution, yet its impact on Qwen3's performance remains underexplored. This study conducts a systematic evaluation of Qwen3's robustness under various quantization settings, aiming to uncover both opportunities and challenges in compressing this state-of-the-art model. We rigorously assess 5 existing classic post-training quantization techniques applied to Qwen3, spanning bit-widths from 1 to 8 bits, and evaluate their effectiveness across multiple datasets. Our findings reveal that while Qwen3 maintains competitive performance at moderate bit-widths, it experiences notable degradation in linguistic tasks under ultra-low precision, underscoring the persistent hurdles in LLM compression. These results emphasize the need for further research to mitigate performance loss in extreme quantization scenarios. We anticipate that this empirical analysis will provide actionable insights for advancing quantization methods tailored to Qwen3 and future LLMs, ultimately enhancing their practicality without compromising accuracy. Our project is released on https://github.com/Efficient-ML/Qwen3-Quantization and https://huggingface.co/collections/Efficient-ML/qwen3-quantization-68164450decb1c868788cb2b.", "github_manual": "", "github_regex": "https://github.com/Efficient-ML/Qwen3-Quantization", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2504.18373", "title": "Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant", "authors": [ "Lei Shen", "Xiaoyu Shen" ], "github": "https://github.com/lorashen/Auto-SLURP", "project_page": "", "abstract": "In recent years, multi-agent frameworks powered by large language models (LLMs) have advanced rapidly. Despite this progress, there is still a notable absence of benchmark datasets specifically tailored to evaluate their performance. To bridge this gap, we introduce Auto-SLURP, a benchmark dataset aimed at evaluating LLM-based multi-agent frameworks in the context of intelligent personal assistants. Auto-SLURP extends the original SLURP dataset -- initially developed for natural language understanding tasks -- by relabeling the data and integrating simulated servers and external services. This enhancement enables a comprehensive end-to-end evaluation pipeline, covering language understanding, task execution, and response generation. Our experiments demonstrate that Auto-SLURP presents a significant challenge for current state-of-the-art frameworks, highlighting that truly reliable and intelligent multi-agent personal assistants remain a work in progress. The dataset and related code are available at https://github.com/lorashen/Auto-SLURP/.", "github_manual": "", "github_regex": "https://github.com/lorashen/Auto-SLURP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.03368", "title": "Geospatial Mechanistic Interpretability of Large Language Models", "authors": [ "Stef De Sabbata", "Stefano Mizzaro", "Kevin Roitero" ], "github": "https://github.com/sdesabbata/geospatial-mechanistic-interpretability", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated unprecedented capabilities across various natural language processing tasks. Their ability to process and generate viable text and code has made them ubiquitous in many fields, while their deployment as knowledge bases and \"reasoning\" tools remains an area of ongoing research. In geography, a growing body of literature has been focusing on evaluating LLMs' geographical knowledge and their ability to perform spatial reasoning. However, very little is still known about the internal functioning of these models, especially about how they process geographical information. In this chapter, we establish a novel framework for the study of geospatial mechanistic interpretability - using spatial analysis to reverse engineer how LLMs handle geographical information. Our aim is to advance our understanding of the internal representations that these complex models generate while processing geographical information - what one might call \"how LLMs think about geographic information\" if such phrasing was not an undue anthropomorphism. We first outline the use of probing in revealing internal structures within LLMs. We then introduce the field of mechanistic interpretability, discussing the superposition hypothesis and the role of sparse autoencoders in disentangling polysemantic internal representations of LLMs into more interpretable, monosemantic features. In our experiments, we use spatial autocorrelation to show how features obtained for placenames display spatial patterns related to their geographic location and can thus be interpreted geospatially, providing insights into how these models process geographical information. We conclude by discussing how our framework can help shape the study and use of foundation models in geography.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sdesabbata/geospatial-mechanistic-interpretability", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.02872", "title": "Decoding Open-Ended Information Seeking Goals from Eye Movements in Reading", "authors": [ "Cfir Avraham Hadar", "Omer Shubi", "Yoav Meiri", "Yevgeni Berzak" ], "github": "", "project_page": "", "abstract": "When reading, we often have specific information that interests us in a text. For example, you might be reading this paper because you are curious about LLMs for eye movements in reading, the experimental design, or perhaps you only care about the question ``but does it work?''. More broadly, in daily life, people approach texts with any number of text-specific goals that guide their reading behavior. In this work, we ask, for the first time, whether open-ended reading goals can be automatically decoded from eye movements in reading. To address this question, we introduce goal classification and goal reconstruction tasks and evaluation frameworks, and use large-scale eye tracking for reading data in English with hundreds of text-specific information seeking tasks. We develop and compare several discriminative and generative multimodal LLMs that combine eye movements and text for goal classification and goal reconstruction. Our experiments show considerable success on both tasks, suggesting that LLMs can extract valuable information about the readers' text-specific goals from eye movements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.03739", "title": "VITA-Audio: Fast Interleaved Cross-Modal Token Generation for Efficient Large Speech-Language Model", "authors": [ "Zuwei Long", "Yunhang Shen", "Chaoyou Fu", "Heting Gao", "Lijiang Li", "Peixian Chen", "Mengdan Zhang", "Hang Shao", "Jian Li", "Jinlong Peng", "Haoyu Cao", "Ke Li", "Rongrong Ji", "Xing Sun" ], "github": "", "project_page": "", "abstract": "With the growing requirement for natural human-computer interaction, speech-based systems receive increasing attention as speech is one of the most common forms of daily communication. However, the existing speech models still experience high latency when generating the first audio token during streaming, which poses a significant bottleneck for deployment. To address this issue, we propose VITA-Audio, an end-to-end large speech model with fast audio-text token generation. Specifically, we introduce a lightweight Multiple Cross-modal Token Prediction (MCTP) module that efficiently generates multiple audio tokens within a single model forward pass, which not only accelerates the inference but also significantly reduces the latency for generating the first audio in streaming scenarios. In addition, a four-stage progressive training strategy is explored to achieve model acceleration with minimal loss of speech quality. To our knowledge, VITA-Audio is the first multi-modal large language model capable of generating audio output during the first forward pass, enabling real-time conversational capabilities with minimal latency. VITA-Audio is fully reproducible and is trained on open-source data only. Experimental results demonstrate that our model achieves an inference speedup of 3~5x at the 7B parameter scale, but also significantly outperforms open-source models of similar model size on multiple benchmarks for automatic speech recognition (ASR), text-to-speech (TTS), and spoken question answering (SQA) tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2504.21650", "title": "HoloTime: Taming Video Diffusion Models for Panoramic 4D Scene Generation", "authors": [ "Haiyang Zhou", "Wangbo Yu", "Jiawen Guan", "Xinhua Cheng", "Yonghong Tian", "Li Yuan" ], "github": "", "project_page": "", "abstract": "The rapid advancement of diffusion models holds the promise of revolutionizing the application of VR and AR technologies, which typically require scene-level 4D assets for user experience. Nonetheless, existing diffusion models predominantly concentrate on modeling static 3D scenes or object-level dynamics, constraining their capacity to provide truly immersive experiences. To address this issue, we propose HoloTime, a framework that integrates video diffusion models to generate panoramic videos from a single prompt or reference image, along with a 360-degree 4D scene reconstruction method that seamlessly transforms the generated panoramic video into 4D assets, enabling a fully immersive 4D experience for users. Specifically, to tame video diffusion models for generating high-fidelity panoramic videos, we introduce the 360World dataset, the first comprehensive collection of panoramic videos suitable for downstream 4D scene reconstruction tasks. With this curated dataset, we propose Panoramic Animator, a two-stage image-to-video diffusion model that can convert panoramic images into high-quality panoramic videos. Following this, we present Panoramic Space-Time Reconstruction, which leverages a space-time depth estimation method to transform the generated panoramic videos into 4D point clouds, enabling the optimization of a holistic 4D Gaussian Splatting representation to reconstruct spatially and temporally consistent 4D scenes. To validate the efficacy of our method, we conducted a comparative analysis with existing approaches, revealing its superiority in both panoramic video generation and 4D scene reconstruction. This demonstrates our method's capability to create more engaging and realistic immersive environments, thereby enhancing user experiences in VR and AR applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2504.21798", "title": "SWE-smith: Scaling Data for Software Engineering Agents", "authors": [ "John Yang", "Kilian Leret", "Carlos E. Jimenez", "Alexander Wettig", "Kabir Khandpur", "Yanzhe Zhang", "Binyuan Hui", "Ofir Press", "Ludwig Schmidt", "Diyi Yang" ], "github": "https://github.com/SWE-bench/SWE-smith", "project_page": "", "abstract": "Despite recent progress in Language Models (LMs) for software engineering, collecting training data remains a significant pain point. Existing datasets are small, with at most 1,000s of training instances from 11 or fewer GitHub repositories. The procedures to curate such datasets are often complex, necessitating hundreds of hours of human labor; companion execution environments also take up several terabytes of storage, severely limiting their scalability and usability. To address this pain point, we introduce SWE-smith, a novel pipeline for generating software engineering training data at scale. Given any Python codebase, SWE-smith constructs a corresponding execution environment, then automatically synthesizes 100s to 1,000s of task instances that break existing test(s) in the codebase. Using SWE-smith, we create a dataset of 50k instances sourced from 128 GitHub repositories, an order of magnitude larger than all previous works. We train SWE-agent-LM-32B, achieving 40.2% Pass@1 resolve rate on the SWE-bench Verified benchmark, state of the art among open source models. We open source SWE-smith (collection procedure, task instances, trajectories, models) to lower the barrier of entry for research in LM systems for automated software engineering. All assets available at https://swesmith.com.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SWE-bench/SWE-smith", "github_api_added_by": "auto" }, { "date": "2025-05-07", "arxiv_id": "2505.00212", "title": "Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems", "authors": [ "Shaokun Zhang", "Ming Yin", "Jieyu Zhang", "Jiale Liu", "Zhiguang Han", "Jingyang Zhang", "Beibin Li", "Chi Wang", "Huazheng Wang", "Yiran Chen", "Qingyun Wu" ], "github": "https://github.com/mingyin1/Agents_Failure_Attribution", "project_page": "", "abstract": "Failure attribution in LLM multi-agent systems-identifying the agent and step responsible for task failures-provides crucial clues for systems debugging but remains underexplored and labor-intensive. In this paper, we propose and formulate a new research area: automated failure attribution for LLM multi-agent systems. To support this initiative, we introduce the Who&When dataset, comprising extensive failure logs from 127 LLM multi-agent systems with fine-grained annotations linking failures to specific agents and decisive error steps. Using the Who&When, we develop and evaluate three automated failure attribution methods, summarizing their corresponding pros and cons. The best method achieves 53.5% accuracy in identifying failure-responsible agents but only 14.2% in pinpointing failure steps, with some methods performing below random. Even SOTA reasoning models, such as OpenAI o1 and DeepSeek R1, fail to achieve practical usability. These results highlight the task's complexity and the need for further research in this area. Code and dataset are available at https://github.com/mingyin1/Agents_Failure_Attribution", "github_manual": "", "github_regex": "https://github.com/mingyin1/Agents_Failure_Attribution", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.03052", "title": "Teaching Models to Understand (but not Generate) High-risk Data", "authors": [ "Ryan Wang", "Matthew Finlayson", "Luca Soldaini", "Swabha Swayamdipta", "Robin Jia" ], "github": "https://github.com/ryanyxw/llm-decouple", "project_page": "", "abstract": "Language model developers typically filter out high-risk content -- such as toxic or copyrighted text -- from their pre-training data to prevent models from generating similar outputs. However, removing such data altogether limits models' ability to recognize and appropriately respond to harmful or sensitive content. In this paper, we introduce Selective Loss to Understand but Not Generate (SLUNG), a pre-training paradigm through which models learn to understand high-risk data without learning to generate it. Instead of uniformly applying the next-token prediction loss, SLUNG selectively avoids incentivizing the generation of high-risk tokens while ensuring they remain within the model's context window. As the model learns to predict low-risk tokens that follow high-risk ones, it is forced to understand the high-risk content. Through our experiments, we show that SLUNG consistently improves models' understanding of high-risk data (e.g., ability to recognize toxic content) without increasing its generation (e.g., toxicity of model responses). Overall, our SLUNG paradigm enables models to benefit from high-risk text that would otherwise be filtered out.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ryanyxw/llm-decouple", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.02836", "title": "Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation", "authors": [ "Lu Ling", "Chen-Hsuan Lin", "Tsung-Yi Lin", "Yifan Ding", "Yu Zeng", "Yichen Sheng", "Yunhao Ge", "Ming-Yu Liu", "Aniket Bera", "Zhaoshuo Li" ], "github": "", "project_page": "", "abstract": "Synthesizing interactive 3D scenes from text is essential for gaming, virtual reality, and embodied AI. However, existing methods face several challenges. Learning-based approaches depend on small-scale indoor datasets, limiting the scene diversity and layout complexity. While large language models (LLMs) can leverage diverse text-domain knowledge, they struggle with spatial realism, often producing unnatural object placements that fail to respect common sense. Our key insight is that vision perception can bridge this gap by providing realistic spatial guidance that LLMs lack. To this end, we introduce Scenethesis, a training-free agentic framework that integrates LLM-based scene planning with vision-guided layout refinement. Given a text prompt, Scenethesis first employs an LLM to draft a coarse layout. A vision module then refines it by generating an image guidance and extracting scene structure to capture inter-object relations. Next, an optimization module iteratively enforces accurate pose alignment and physical plausibility, preventing artifacts like object penetration and instability. Finally, a judge module verifies spatial coherence. Comprehensive experiments show that Scenethesis generates diverse, realistic, and physically plausible 3D interactive scenes, making it valuable for virtual content creation, simulation environments, and embodied AI research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-07", "arxiv_id": "2505.04110", "title": "Alpha Excel Benchmark", "authors": [ "David Noever", "Forrest McKee" ], "github": "", "project_page": "", "abstract": "This study presents a novel benchmark for evaluating Large Language Models (LLMs) using challenges derived from the Financial Modeling World Cup (FMWC) Excel competitions. We introduce a methodology for converting 113 existing FMWC challenges into programmatically evaluable JSON formats and use this dataset to compare the performance of several leading LLMs. Our findings demonstrate significant variations in performance across different challenge categories, with models showing specific strengths in pattern recognition tasks but struggling with complex numerical reasoning. The benchmark provides a standardized framework for assessing LLM capabilities in realistic business-oriented tasks rather than abstract academic problems. This research contributes to the growing field of AI benchmarking by establishing proficiency among the 1.5 billion people who daily use Microsoft Excel as a meaningful evaluation metric that bridges the gap between academic AI benchmarks and practical business applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.04588", "title": "ZeroSearch: Incentivize the Search Capability of LLMs without Searching", "authors": [ "Hao Sun", "Zile Qiao", "Jiayan Guo", "Xuanbo Fan", "Yingyan Hou", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Yan Zhang" ], "github": "", "project_page": "", "abstract": "Effective information searching is essential for enhancing the reasoning and generation capabilities of large language models (LLMs). Recent research has explored using reinforcement learning (RL) to improve LLMs' search capabilities by interacting with live search engines in real-world environments. While these approaches show promising results, they face two major challenges: (1) Uncontrolled Document Quality: The quality of documents returned by search engines is often unpredictable, introducing noise and instability into the training process. (2) Prohibitively High API Costs: RL training requires frequent rollouts, potentially involving hundreds of thousands of search requests, which incur substantial API expenses and severely constrain scalability. To address these challenges, we introduce ZeroSearch, a reinforcement learning framework that incentivizes the search capabilities of LLMs without interacting with real search engines. Our approach begins with lightweight supervised fine-tuning to transform the LLM into a retrieval module capable of generating both relevant and noisy documents in response to a query. During RL training, we employ a curriculum-based rollout strategy that incrementally degrades the quality of generated documents, progressively eliciting the model's reasoning ability by exposing it to increasingly challenging retrieval scenarios. Extensive experiments demonstrate that ZeroSearch effectively incentivizes the search capabilities of LLMs using a 3B LLM as the retrieval module. Remarkably, a 7B retrieval module achieves comparable performance to the real search engine, while a 14B retrieval module even surpasses it. Furthermore, it generalizes well across both base and instruction-tuned models of various parameter sizes and is compatible with a wide range of RL algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.04364", "title": "Benchmarking LLMs' Swarm intelligence", "authors": [ "Kai Ruan", "Mowen Huang", "Ji-Rong Wen", "Hao Sun" ], "github": "https://github.com/x66ccff/swarmbench", "project_page": "", "abstract": "Large Language Models (LLMs) show potential for complex reasoning, yet their capacity for emergent coordination in Multi-Agent Systems (MAS) when operating under strict constraints-such as limited local perception and communication, characteristic of natural swarms-remains largely unexplored, particularly concerning the nuances of swarm intelligence. Existing benchmarks often do not fully capture the unique challenges of decentralized coordination that arise when agents operate with incomplete spatio-temporal information. To bridge this gap, we introduce SwarmBench, a novel benchmark designed to systematically evaluate the swarm intelligence capabilities of LLMs acting as decentralized agents. SwarmBench features five foundational MAS coordination tasks within a configurable 2D grid environment, forcing agents to rely primarily on local sensory input (k x k view) and local communication. We propose metrics for coordination effectiveness and analyze emergent group dynamics. Evaluating several leading LLMs in a zero-shot setting, we find significant performance variations across tasks, highlighting the difficulties posed by local information constraints. While some coordination emerges, results indicate limitations in robust planning and strategy formation under uncertainty in these decentralized scenarios. Assessing LLMs under swarm-like conditions is crucial for realizing their potential in future decentralized systems. We release SwarmBench as an open, extensible toolkit-built upon a customizable and scalable physical system with defined mechanical properties. It provides environments, prompts, evaluation scripts, and the comprehensive experimental datasets generated, aiming to foster reproducible research into LLM-based MAS coordination and the theoretical underpinnings of Embodied MAS. Our code repository is available at https://github.com/x66ccff/swarmbench.", "github_manual": "", "github_regex": "https://github.com/x66ccff/swarmbench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.02393", "title": "Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly Detection", "authors": [ "Sungheon Jeong", "Jihong Park", "Mohsen Imani" ], "github": "https://github.com/EavnJeong/IEF-VAD", "project_page": "", "abstract": "Most existing video anomaly detectors rely solely on RGB frames, which lack the temporal resolution needed to capture abrupt or transient motion cues, key indicators of anomalous events. To address this limitation, we propose Image-Event Fusion for Video Anomaly Detection (IEF-VAD), a framework that synthesizes event representations directly from RGB videos and fuses them with image features through a principled, uncertainty-aware process. The system (i) models heavy-tailed sensor noise with a Student`s-t likelihood, deriving value-level inverse-variance weights via a Laplace approximation; (ii) applies Kalman-style frame-wise updates to balance modalities over time; and (iii) iteratively refines the fused latent state to erase residual cross-modal noise. Without any dedicated event sensor or frame-level labels, IEF-VAD sets a new state of the art across multiple real-world anomaly detection benchmarks. These findings highlight the utility of synthetic event representations in emphasizing motion cues that are often underrepresented in RGB frames, enabling accurate and robust video understanding across diverse applications without requiring dedicated event sensors. Code and models are available at https://github.com/EavnJeong/IEF-VAD.", "github_manual": "", "github_regex": "https://github.com/EavnJeong/IEF-VAD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.04528", "title": "Beyond Theorem Proving: Formulation, Framework and Benchmark for Formal Problem-Solving", "authors": [ "Qi Liu", "Xinhao Zheng", "Renqiu Xia", "Xingzhi Qi", "Qinxiang Cao", "Junchi Yan" ], "github": "https://github.com/leanprover/lean4", "project_page": "", "abstract": "As a seemingly self-explanatory task, problem-solving has been a significant component of science and engineering. However, a general yet concrete formulation of problem-solving itself is missing. With the recent development of AI-based problem-solving agents, the demand for process-level verifiability is rapidly increasing yet underexplored. To fill these gaps, we present a principled formulation of problem-solving as a deterministic Markov decision process; a novel framework, FPS (Formal Problem-Solving), which utilizes existing FTP (formal theorem proving) environments to perform process-verified problem-solving; and D-FPS (Deductive FPS), decoupling solving and answer verification for better human-alignment. The expressiveness, soundness and completeness of the frameworks are proven. We construct three benchmarks on problem-solving: FormalMath500, a formalization of a subset of the MATH500 benchmark; MiniF2F-Solving and PutnamBench-Solving, adaptations of FTP benchmarks MiniF2F and PutnamBench. For faithful, interpretable, and human-aligned evaluation, we propose RPE (Restricted Propositional Equivalence), a symbolic approach to determine the correctness of answers by formal verification. We evaluate four prevalent FTP models and two prompting methods as baselines, solving at most 23.77% of FormalMath500, 27.47% of MiniF2F-Solving, and 0.31% of PutnamBench-Solving.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/leanprover/lean4", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.03418", "title": "Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey", "authors": [ "Da Zheng", "Lun Du", "Junwei Su", "Yuchen Tian", "Yuqi Zhu", "Jintian Zhang", "Lanning Wei", "Ningyu Zhang", "Huajun Chen" ], "github": "", "project_page": "", "abstract": "Problem-solving has been a fundamental driver of human progress in numerous domains. With advancements in artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools capable of tackling complex problems across diverse domains. Unlike traditional computational systems, LLMs combine raw computational power with an approximation of human reasoning, allowing them to generate solutions, make inferences, and even leverage external computational tools. However, applying LLMs to real-world problem-solving presents significant challenges, including multi-step reasoning, domain knowledge integration, and result verification. This survey explores the capabilities and limitations of LLMs in complex problem-solving, examining techniques including Chain-of-Thought (CoT) reasoning, knowledge augmentation, and various LLM-based and tool-based verification techniques. Additionally, we highlight domain-specific challenges in various domains, such as software engineering, mathematical reasoning and proving, data analysis and modeling, and scientific research. The paper further discusses the fundamental limitations of the current LLM solutions and the future directions of LLM-based complex problems solving from the perspective of multi-step reasoning, domain knowledge integration and result verification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.04622", "title": "PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformer", "authors": [ "Jingwen Ye", "Yuze He", "Yanning Zhou", "Yiqin Zhu", "Kaiwen Xiao", "Yong-Jin Liu", "Wei Yang", "Xiao Han" ], "github": "", "project_page": "", "abstract": "Shape primitive abstraction, which decomposes complex 3D shapes into simple geometric elements, plays a crucial role in human visual cognition and has broad applications in computer vision and graphics. While recent advances in 3D content generation have shown remarkable progress, existing primitive abstraction methods either rely on geometric optimization with limited semantic understanding or learn from small-scale, category-specific datasets, struggling to generalize across diverse shape categories. We present PrimitiveAnything, a novel framework that reformulates shape primitive abstraction as a primitive assembly generation task. PrimitiveAnything includes a shape-conditioned primitive transformer for auto-regressive generation and an ambiguity-free parameterization scheme to represent multiple types of primitives in a unified manner. The proposed framework directly learns the process of primitive assembly from large-scale human-crafted abstractions, enabling it to capture how humans decompose complex shapes into primitive elements. Through extensive experiments, we demonstrate that PrimitiveAnything can generate high-quality primitive assemblies that better align with human perception while maintaining geometric fidelity across diverse shape categories. It benefits various 3D applications and shows potential for enabling primitive-based user-generated content (UGC) in games. Project page: https://primitiveanything.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.04512", "title": "HunyuanCustom: A Multimodal-Driven Architecture for Customized Video Generation", "authors": [ "Teng Hu", "Zhentao Yu", "Zhengguang Zhou", "Sen Liang", "Yuan Zhou", "Qin Lin", "Qinglin Lu" ], "github": "", "project_page": "", "abstract": "Customized video generation aims to produce videos featuring specific subjects under flexible user-defined conditions, yet existing methods often struggle with identity consistency and limited input modalities. In this paper, we propose HunyuanCustom, a multi-modal customized video generation framework that emphasizes subject consistency while supporting image, audio, video, and text conditions. Built upon HunyuanVideo, our model first addresses the image-text conditioned generation task by introducing a text-image fusion module based on LLaVA for enhanced multi-modal understanding, along with an image ID enhancement module that leverages temporal concatenation to reinforce identity features across frames. To enable audio- and video-conditioned generation, we further propose modality-specific condition injection mechanisms: an AudioNet module that achieves hierarchical alignment via spatial cross-attention, and a video-driven injection module that integrates latent-compressed conditional video through a patchify-based feature-alignment network. Extensive experiments on single- and multi-subject scenarios demonstrate that HunyuanCustom significantly outperforms state-of-the-art open- and closed-source methods in terms of ID consistency, realism, and text-video alignment. Moreover, we validate its robustness across downstream tasks, including audio and video-driven customized video generation. Our results highlight the effectiveness of multi-modal conditioning and identity-preserving strategies in advancing controllable video generation. All the code and models are available at https://hunyuancustom.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.03912", "title": "OpenHelix: A Short Survey, Empirical Analysis, and Open-Source Dual-System VLA Model for Robotic Manipulation", "authors": [ "Can Cui", "Pengxiang Ding", "Wenxuan Song", "Shuanghao Bai", "Xinyang Tong", "Zirui Ge", "Runze Suo", "Wanqi Zhou", "Yang Liu", "Bofang Jia", "Han Zhao", "Siteng Huang", "Donglin Wang" ], "github": "https://github.com/OpenHelix-robot/OpenHelix", "project_page": "", "abstract": "Dual-system VLA (Vision-Language-Action) architectures have become a hot topic in embodied intelligence research, but there is a lack of sufficient open-source work for further performance analysis and optimization. To address this problem, this paper will summarize and compare the structural designs of existing dual-system architectures, and conduct systematic empirical evaluations on the core design elements of existing dual-system architectures. Ultimately, it will provide a low-cost open-source model for further exploration. Of course, this project will continue to update with more experimental conclusions and open-source models with improved performance for everyone to choose from. Project page: https://openhelix-robot.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenHelix-robot/OpenHelix", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.00358", "title": "R&B: Domain Regrouping and Data Mixture Balancing for Efficient Foundation Model Training", "authors": [ "Albert Ge", "Tzu-Heng Huang", "John Cooper", "Avi Trost", "Ziyi Chu", "Satya Sai Srinath Namburi GNVV", "Ziyang Cai", "Kendall Park", "Nicholas Roberts", "Frederic Sala" ], "github": "", "project_page": "", "abstract": "Data mixing strategies have successfully reduced the costs involved in training language models. While promising, such methods suffer from two flaws. First, they rely on predetermined data domains (e.g., data sources, task types), which may fail to capture critical semantic nuances, leaving performance on the table. Second, these methods scale with the number of domains in a computationally prohibitive way. We address these challenges via R&B, a framework that re-partitions training data based on semantic similarity (Regroup) to create finer-grained domains, and efficiently optimizes the data composition (Balance) by leveraging a Gram matrix induced by domain gradients obtained throughout training. Unlike prior works, it removes the need for additional compute to obtain evaluation information such as losses or gradients. We analyze this technique under standard regularity conditions and provide theoretical insights that justify R&B's effectiveness compared to non-adaptive mixing approaches. Empirically, we demonstrate the effectiveness of R&B on five diverse datasets ranging from natural language to reasoning and multimodal tasks. With as little as 0.01% additional compute overhead, R&B matches or exceeds the performance of state-of-the-art data mixing strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.02567", "title": "Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities", "authors": [ "Xinjie Zhang", "Jintao Guo", "Shanshan Zhao", "Minghao Fu", "Lunhao Duan", "Guo-Hua Wang", "Qing-Guo Chen", "Zhao Xu", "Weihua Luo", "Kaifu Zhang" ], "github": "https://github.com/AIDC-AI/Awesome-Unified-Multimodal-Models", "project_page": "", "abstract": "Recent years have seen remarkable progress in both multimodal understanding models and image generation models. Despite their respective successes, these two domains have evolved independently, leading to distinct architectural paradigms: While autoregressive-based architectures have dominated multimodal understanding, diffusion-based models have become the cornerstone of image generation. Recently, there has been growing interest in developing unified frameworks that integrate these tasks. The emergence of GPT-4o's new capabilities exemplifies this trend, highlighting the potential for unification. However, the architectural differences between the two domains pose significant challenges. To provide a clear overview of current efforts toward unification, we present a comprehensive survey aimed at guiding future research. First, we introduce the foundational concepts and recent advancements in multimodal understanding and text-to-image generation models. Next, we review existing unified models, categorizing them into three main architectural paradigms: diffusion-based, autoregressive-based, and hybrid approaches that fuse autoregressive and diffusion mechanisms. For each category, we analyze the structural designs and innovations introduced by related works. Additionally, we compile datasets and benchmarks tailored for unified models, offering resources for future exploration. Finally, we discuss the key challenges facing this nascent field, including tokenization strategy, cross-modal attention, and data. As this area is still in its early stages, we anticipate rapid advancements and will regularly update this survey. Our goal is to inspire further research and provide a valuable reference for the community. The references associated with this survey are available on GitHub (https://github.com/AIDC-AI/Awesome-Unified-Multimodal-Models).", "github_manual": "", "github_regex": "https://github.com/AIDC-AI/Awesome-Unified-Multimodal-Models", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.03570", "title": "OSUniverse: Benchmark for Multimodal GUI-navigation AI Agents", "authors": [ "Mariya Davydova", "Daniel Jeffries", "Patrick Barker", "Arturo M\u00e1rquez Flores", "Sin\u00e9ad Ryan" ], "github": "https://github.com/agentsea/osuniverse", "project_page": "", "abstract": "In this paper, we introduce OSUniverse: a benchmark of complex, multimodal desktop-oriented tasks for advanced GUI-navigation AI agents that focuses on ease of use, extensibility, comprehensive coverage of test cases, and automated validation. We divide the tasks in increasing levels of complexity, from basic precision clicking to multistep, multiapplication tests requiring dexterity, precision, and clear thinking from the agent. In version one of the benchmark, presented here, we have calibrated the complexity of the benchmark test cases to ensure that the SOTA (State of the Art) agents (at the time of publication) do not achieve results higher than 50%, while the average white collar worker can perform all these tasks with perfect accuracy. The benchmark can be scored manually, but we also introduce an automated validation mechanism that has an average error rate less than 2%. Therefore, this benchmark presents solid ground for fully automated measuring of progress, capabilities and the effectiveness of GUI-navigation AI agents over the short and medium-term horizon. The source code of the benchmark is available at https://github.com/agentsea/osuniverse.", "github_manual": "", "github_regex": "https://github.com/agentsea/osuniverse", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.03821", "title": "Beyond Recognition: Evaluating Visual Perspective Taking in Vision Language Models", "authors": [ "Gracjan G\u00f3ral", "Alicja Ziarko", "Piotr Mi\u0142o\u015b", "Micha\u0142 Nauman", "Maciej Wo\u0142czyk", "Micha\u0142 Kosi\u0144ski" ], "github": "", "project_page": "", "abstract": "We investigate the ability of Vision Language Models (VLMs) to perform visual perspective taking using a novel set of visual tasks inspired by established human tests. Our approach leverages carefully controlled scenes, in which a single humanoid minifigure is paired with a single object. By systematically varying spatial configurations - such as object position relative to the humanoid minifigure and the humanoid minifigure's orientation - and using both bird's-eye and surface-level views, we created 144 unique visual tasks. Each visual task is paired with a series of 7 diagnostic questions designed to assess three levels of visual cognition: scene understanding, spatial reasoning, and visual perspective taking. Our evaluation of several state-of-the-art models, including GPT-4-Turbo, GPT-4o, Llama-3.2-11B-Vision-Instruct, and variants of Claude Sonnet, reveals that while they excel in scene understanding, the performance declines significantly on spatial reasoning and further deteriorates on perspective-taking. Our analysis suggests a gap between surface-level object recognition and the deeper spatial and perspective reasoning required for complex visual tasks, pointing to the need for integrating explicit geometric representations and tailored training protocols in future VLM development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.04606", "title": "OmniGIRL: A Multilingual and Multimodal Benchmark for GitHub Issue Resolution", "authors": [ "Lianghong Guo", "Wei Tao", "Runhan Jiang", "Yanlin Wang", "Jiachi Chen", "Xilin Liu", "Yuchi Ma", "Mingzhi Mao", "Hongyu Zhang", "Zibin Zheng" ], "github": "", "project_page": "", "abstract": "The GitHub issue resolution task aims to resolve issues reported in repositories automatically. With advances in large language models (LLMs), this task has gained increasing attention, and several benchmarks are proposed to evaluate the issue resolution ability of LLMs. However, existing benchmarks have three main limitations. First, current benchmarks focus on a single programming language, limiting the evaluation of issues from repositories across different languages. Second, they usually cover a narrow range of domains, which may fail to represent the diversity of real-world issues. Third, existing benchmarks rely solely on textual information in issue descriptions, overlooking multimodal information such as images in issues. In this paper, we propose OmniGIRL, a GitHub Issue ResoLution benchmark that is multilingual, multimodal, and multi-domain. OmniGIRL includes 959 task instances, which are collected from repositories across four programming languages (i.e., Python, JavaScript, TypeScript, and Java) and eight different domains. Our evaluation shows that current LLMs show limited performances on OmniGIRL. Notably, the best-performing model, GPT-4o, resolves only 8.6% of the issues. Besides, we find that current LLMs struggle to resolve issues requiring understanding images. The best performance is achieved by Claude-3.5-Sonnet, which resolves only 10.5% of the issues with image information. Finally, we analyze the reasons behind current LLMs' failure on OmniGIRL, providing insights for future improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.03105", "title": "Cognitio Emergens: Agency, Dimensions, and Dynamics in Human-AI Knowledge Co-Creation", "authors": [ "Xule Lin" ], "github": "", "project_page": "", "abstract": "Scientific knowledge creation is fundamentally transforming as humans and AI systems evolve beyond tool-user relationships into co-evolutionary epistemic partnerships. When AlphaFold revolutionized protein structure prediction, researchers described engaging with an epistemic partner that reshaped how they conceptualized fundamental relationships. This article introduces Cognitio Emergens (CE), a framework addressing critical limitations in existing models that focus on static roles or narrow metrics while failing to capture how scientific understanding emerges through recursive human-AI interaction over time. CE integrates three components addressing these limitations: Agency Configurations describing how authority distributes between humans and AI (Directed, Contributory, Partnership), with partnerships dynamically oscillating between configurations rather than following linear progression; Epistemic Dimensions capturing six specific capabilities emerging through collaboration across Discovery, Integration, and Projection axes, creating distinctive \"capability signatures\" that guide development; and Partnership Dynamics identifying forces shaping how these relationships evolve, particularly the risk of epistemic alienation where researchers lose interpretive control over knowledge they formally endorse. Drawing from autopoiesis theory, social systems theory, and organizational modularity, CE reveals how knowledge co-creation emerges through continuous negotiation of roles, values, and organizational structures. By reconceptualizing human-AI scientific collaboration as fundamentally co-evolutionary, CE offers a balanced perspective that neither uncritically celebrates nor unnecessarily fears AI's evolving role, instead providing conceptual tools for cultivating partnerships that maintain meaningful human participation while enabling transformative scientific breakthroughs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.03538", "title": "RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth Segmentation in CBCT", "authors": [ "Chuyu Zhao", "Hao Huang", "Jiashuo Guo", "Ziyu Shen", "Zhongwei Zhou", "Jie Liu", "Zekuan Yu" ], "github": "https://github.com/Tournesol-Saturday/RAIL", "project_page": "", "abstract": "Semi-supervised learning has become a compelling approach for 3D tooth segmentation from CBCT scans, where labeled data is minimal. However, existing methods still face two persistent challenges: limited corrective supervision in structurally ambiguous or mislabeled regions during supervised training and performance degradation caused by unreliable pseudo-labels on unlabeled data. To address these problems, we propose Region-Aware Instructive Learning (RAIL), a dual-group dual-student, semi-supervised framework. Each group contains two student models guided by a shared teacher network. By alternating training between the two groups, RAIL promotes intergroup knowledge transfer and collaborative region-aware instruction while reducing overfitting to the characteristics of any single model. Specifically, RAIL introduces two instructive mechanisms. Disagreement-Focused Supervision (DFS) Controller improves supervised learning by instructing predictions only within areas where student outputs diverge from both ground truth and the best student, thereby concentrating supervision on structurally ambiguous or mislabeled areas. In the unsupervised phase, Confidence-Aware Learning (CAL) Modulator reinforces agreement in regions with high model certainty while reducing the effect of low-confidence predictions during training. This helps prevent our model from learning unstable patterns and improves the overall reliability of pseudo-labels. Extensive experiments on four CBCT tooth segmentation datasets show that RAIL surpasses state-of-the-art methods under limited annotation. Our code will be available at https://github.com/Tournesol-Saturday/RAIL.", "github_manual": "", "github_regex": "https://github.com/Tournesol-Saturday/RAIL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.04253", "title": "LLM-Independent Adaptive RAG: Let the Question Speak for Itself", "authors": [ "Maria Marina", "Nikolay Ivanov", "Sergey Pletenev", "Mikhail Salnikov", "Daria Galimzianova", "Nikita Krayko", "Vasily Konovalov", "Alexander Panchenko", "Viktor Moskvoretskii" ], "github": "", "project_page": "", "abstract": "Large Language Models~(LLMs) are prone to hallucinations, and Retrieval-Augmented Generation (RAG) helps mitigate this, but at a high computational cost while risking misinformation. Adaptive retrieval aims to retrieve only when necessary, but existing approaches rely on LLM-based uncertainty estimation, which remain inefficient and impractical. In this study, we introduce lightweight LLM-independent adaptive retrieval methods based on external information. We investigated 27 features, organized into 7 groups, and their hybrid combinations. We evaluated these methods on 6 QA datasets, assessing the QA performance and efficiency. The results show that our approach matches the performance of complex LLM-based methods while achieving significant efficiency gains, demonstrating the potential of external information for adaptive retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.02820", "title": "AutoLibra: Agent Metric Induction from Open-Ended Feedback", "authors": [ "Hao Zhu", "Phil Cuvin", "Xinkai Yu", "Charlotte Ka Yee Yan", "Jason Zhang", "Diyi Yang" ], "github": "", "project_page": "", "abstract": "Agents are predominantly evaluated and optimized via task success metrics, which are coarse, rely on manual design from experts, and fail to reward intermediate emergent behaviors. We propose AutoLibra, a framework for agent evaluation, that transforms open-ended human feedback, e.g., \"If you find that the button is disabled, don't click it again\", or \"This agent has too much autonomy to decide what to do on its own\", into metrics for evaluating fine-grained behaviors in agent trajectories. AutoLibra accomplishes this by grounding feedback to an agent's behavior, clustering similar positive and negative behaviors, and creating concrete metrics with clear definitions and concrete examples, which can be used for prompting LLM-as-a-Judge as evaluators. We further propose two meta-metrics to evaluate the alignment of a set of (induced) metrics with open feedback: \"coverage\" and \"redundancy\". Through optimizing these meta-metrics, we experimentally demonstrate AutoLibra's ability to induce more concrete agent evaluation metrics than the ones proposed in previous agent evaluation benchmarks and discover new metrics to analyze agents. We also present two applications of AutoLibra in agent improvement: First, we show that AutoLibra-induced metrics serve as better prompt-engineering targets than the task success rate on a wide range of text game tasks, improving agent performance over baseline by a mean of 20%. Second, we show that AutoLibra can iteratively select high-quality fine-tuning data for web navigation agents. Our results suggest that AutoLibra is a powerful task-agnostic tool for evaluating and improving language agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.04620", "title": "On Path to Multimodal Generalist: General-Level and General-Bench", "authors": [ "Hao Fei", "Yuan Zhou", "Juncheng Li", "Xiangtai Li", "Qingshan Xu", "Bobo Li", "Shengqiong Wu", "Yaoting Wang", "Junbao Zhou", "Jiahao Meng", "Qingyu Shi", "Zhiyuan Zhou", "Liangtao Shi", "Minghe Gao", "Daoan Zhang", "Zhiqi Ge", "Weiming Wu", "Siliang Tang", "Kaihang Pan", "Yaobo Ye", "Haobo Yuan", "Tao Zhang", "Tianjie Ju", "Zixiang Meng", "Shilin Xu", "Liyu Jia", "Wentao Hu", "Meng Luo", "Jiebo Luo", "Tat-Seng Chua", "Shuicheng Yan", "Hanwang Zhang" ], "github": "", "project_page": "", "abstract": "The Multimodal Large Language Model (MLLM) is currently experiencing rapid growth, driven by the advanced capabilities of LLMs. Unlike earlier specialists, existing MLLMs are evolving towards a Multimodal Generalist paradigm. Initially limited to understanding multiple modalities, these models have advanced to not only comprehend but also generate across modalities. Their capabilities have expanded from coarse-grained to fine-grained multimodal understanding and from supporting limited modalities to arbitrary ones. While many benchmarks exist to assess MLLMs, a critical question arises: Can we simply assume that higher performance across tasks indicates a stronger MLLM capability, bringing us closer to human-level AI? We argue that the answer is not as straightforward as it seems. This project introduces General-Level, an evaluation framework that defines 5-scale levels of MLLM performance and generality, offering a methodology to compare MLLMs and gauge the progress of existing systems towards more robust multimodal generalists and, ultimately, towards AGI. At the core of the framework is the concept of Synergy, which measures whether models maintain consistent capabilities across comprehension and generation, and across multiple modalities. To support this evaluation, we present General-Bench, which encompasses a broader spectrum of skills, modalities, formats, and capabilities, including over 700 tasks and 325,800 instances. The evaluation results that involve over 100 existing state-of-the-art MLLMs uncover the capability rankings of generalists, highlighting the challenges in reaching genuine AI. We expect this project to pave the way for future research on next-generation multimodal foundation models, providing a robust infrastructure to accelerate the realization of AGI. Project page: https://generalist.top/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.01449", "title": "COSMOS: Predictable and Cost-Effective Adaptation of LLMs", "authors": [ "Jiayu Wang", "Aws Albarghouthi", "Frederic Sala" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) achieve remarkable performance across numerous tasks by using a diverse array of adaptation strategies. However, optimally selecting a model and adaptation strategy under resource constraints is challenging and often requires extensive experimentation. We investigate whether it is possible to accurately predict both performance and cost without expensive trials. We formalize the strategy selection problem for LLMs and introduce COSMOS, a unified prediction framework that efficiently estimates adaptation outcomes at minimal cost. We instantiate and study the capability of our framework via a pair of powerful predictors: embedding-augmented lightweight proxy models to predict fine-tuning performance, and low-sample scaling laws to forecast retrieval-augmented in-context learning. Extensive evaluation across eight representative benchmarks demonstrates that COSMOS achieves high prediction accuracy while reducing computational costs by 92.72% on average, and up to 98.71% in resource-intensive scenarios. Our results show that efficient prediction of adaptation outcomes is not only feasible but can substantially reduce the computational overhead of LLM deployment while maintaining performance standards.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-08", "arxiv_id": "2505.04601", "title": "OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision Encoders for Multimodal Learning", "authors": [ "Xianhang Li", "Yanqing Liu", "Haoqin Tu", "Hongru Zhu", "Cihang Xie" ], "github": "https://github.com/xiaoachen98/Open-LLaVA-NeXT", "project_page": "", "abstract": "OpenAI's CLIP, released in early 2021, have long been the go-to choice of vision encoder for building multimodal foundation models. Although recent alternatives such as SigLIP have begun to challenge this status quo, to our knowledge none are fully open: their training data remains proprietary and/or their training recipes are not released. This paper fills this gap with OpenVision, a fully-open, cost-effective family of vision encoders that match or surpass the performance of OpenAI's CLIP when integrated into multimodal frameworks like LLaVA. OpenVision builds on existing works -- e.g., CLIPS for training framework and Recap-DataComp-1B for training data -- while revealing multiple key insights in enhancing encoder quality and showcasing practical benefits in advancing multimodal models. By releasing vision encoders spanning from 5.9M to 632.1M parameters, OpenVision offers practitioners a flexible trade-off between capacity and efficiency in building multimodal models: larger models deliver enhanced multimodal performance, while smaller versions enable lightweight, edge-ready multimodal deployments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xiaoachen98/Open-LLaVA-NeXT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.02847", "title": "Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models", "authors": [ "Bang Zhang", "Ruotian Ma", "Qingxuan Jiang", "Peisong Wang", "Jiaqi Chen", "Zheng Xie", "Xingyu Chen", "Yue Wang", "Fanghua Ye", "Jian Li", "Yifan Yang", "Zhaopeng Tu", "Xiaolong Li" ], "github": "", "project_page": "", "abstract": "Assessing how well a large language model (LLM) understands human, rather than merely text, remains an open challenge. To bridge the gap, we introduce Sentient Agent as a Judge (SAGE), an automated evaluation framework that measures an LLM's higher-order social cognition. SAGE instantiates a Sentient Agent that simulates human-like emotional changes and inner thoughts during interaction, providing a more realistic evaluation of the tested model in multi-turn conversations. At every turn, the agent reasons about (i) how its emotion changes, (ii) how it feels, and (iii) how it should reply, yielding a numerical emotion trajectory and interpretable inner thoughts. Experiments on 100 supportive-dialogue scenarios show that the final Sentient emotion score correlates strongly with Barrett-Lennard Relationship Inventory (BLRI) ratings and utterance-level empathy metrics, validating psychological fidelity. We also build a public Sentient Leaderboard covering 18 commercial and open-source models that uncovers substantial gaps (up to 4x) between frontier systems (GPT-4o-Latest, Gemini2.5-Pro) and earlier baselines, gaps not reflected in conventional leaderboards (e.g., Arena). SAGE thus provides a principled, scalable and interpretable tool for tracking progress toward genuinely empathetic and socially adept language agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.05315", "title": "Scalable Chain of Thoughts via Elastic Reasoning", "authors": [ "Yuhui Xu", "Hanze Dong", "Lei Wang", "Doyen Sahoo", "Junnan Li", "Caiming Xiong" ], "github": "https://github.com/SalesforceAIResearch/Elastic-Reasoning", "project_page": "", "abstract": "Large reasoning models (LRMs) have achieved remarkable progress on complex tasks by generating extended chains of thought (CoT). However, their uncontrolled output lengths pose significant challenges for real-world deployment, where inference-time budgets on tokens, latency, or compute are strictly constrained. We propose Elastic Reasoning, a novel framework for scalable chain of thoughts that explicitly separates reasoning into two phases--thinking and solution--with independently allocated budgets. At test time, Elastic Reasoning prioritize that completeness of solution segments, significantly improving reliability under tight resource constraints. To train models that are robust to truncated thinking, we introduce a lightweight budget-constrained rollout strategy, integrated into GRPO, which teaches the model to reason adaptively when the thinking process is cut short and generalizes effectively to unseen budget constraints without additional training. Empirical results on mathematical (AIME, MATH500) and programming (LiveCodeBench, Codeforces) benchmarks demonstrate that Elastic Reasoning performs robustly under strict budget constraints, while incurring significantly lower training cost than baseline methods. Remarkably, our approach also produces more concise and efficient reasoning even in unconstrained settings. Elastic Reasoning offers a principled and practical solution to the pressing challenge of controllable reasoning at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SalesforceAIResearch/Elastic-Reasoning", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.05474", "title": "3D Scene Generation: A Survey", "authors": [ "Beichen Wen", "Haozhe Xie", "Zhaoxi Chen", "Fangzhou Hong", "Ziwei Liu" ], "github": "https://github.com/hzxie/Awesome-3D-Scene-Generation", "project_page": "", "abstract": "3D scene generation seeks to synthesize spatially structured, semantically meaningful, and photorealistic environments for applications such as immersive media, robotics, autonomous driving, and embodied AI. Early methods based on procedural rules offered scalability but limited diversity. Recent advances in deep generative models (e.g., GANs, diffusion models) and 3D representations (e.g., NeRF, 3D Gaussians) have enabled the learning of real-world scene distributions, improving fidelity, diversity, and view consistency. Recent advances like diffusion models bridge 3D scene synthesis and photorealism by reframing generation as image or video synthesis problems. This survey provides a systematic overview of state-of-the-art approaches, organizing them into four paradigms: procedural generation, neural 3D-based generation, image-based generation, and video-based generation. We analyze their technical foundations, trade-offs, and representative results, and review commonly used datasets, evaluation protocols, and downstream applications. We conclude by discussing key challenges in generation capacity, 3D representation, data and annotations, and evaluation, and outline promising directions including higher fidelity, physics-aware and interactive generation, and unified perception-generation models. This review organizes recent advances in 3D scene generation and highlights promising directions at the intersection of generative AI, 3D vision, and embodied intelligence. To track ongoing developments, we maintain an up-to-date project page: https://github.com/hzxie/Awesome-3D-Scene-Generation.", "github_manual": "", "github_regex": "https://github.com/hzxie/Awesome-3D-Scene-Generation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.03981", "title": "X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains", "authors": [ "Qianchu Liu", "Sheng Zhang", "Guanghui Qin", "Timothy Ossowski", "Yu Gu", "Ying Jin", "Sid Kiblawi", "Sam Preston", "Mu Wei", "Paul Vozila", "Tristan Naumann", "Hoifung Poon" ], "github": "https://github.com/microsoft/x-reasoner", "project_page": "", "abstract": "Recent proprietary models (e.g., o3) have begun to demonstrate strong multimodal reasoning capabilities. Yet, most existing open-source research concentrates on training text-only reasoning models, with evaluations limited to mainly mathematical and general-domain tasks. Therefore, it remains unclear how to effectively extend reasoning capabilities beyond text input and general domains. This paper explores a fundamental research question: Is reasoning generalizable across modalities and domains? Our findings support an affirmative answer: General-domain text-based post-training can enable such strong generalizable reasoning. Leveraging this finding, we introduce X-Reasoner, a vision-language model post-trained solely on general-domain text for generalizable reasoning, using a two-stage approach: an initial supervised fine-tuning phase with distilled long chain-of-thoughts, followed by reinforcement learning with verifiable rewards. Experiments show that X-Reasoner successfully transfers reasoning capabilities to both multimodal and out-of-domain settings, outperforming existing state-of-the-art models trained with in-domain and multimodal data across various general and medical benchmarks (Figure 1). Additionally, we find that X-Reasoner's performance in specialized domains can be further enhanced through continued training on domain-specific text-only data. Building upon this, we introduce X-Reasoner-Med, a medical-specialized variant that achieves new state of the art on numerous text-only and multimodal medical benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/x-reasoner", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2504.19314", "title": "BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese", "authors": [ "Peilin Zhou", "Bruce Leon", "Xiang Ying", "Can Zhang", "Yifan Shao", "Qichen Ye", "Dading Chong", "Zhiling Jin", "Chenxuan Xie", "Meng Cao", "Yuxin Gu", "Sixin Hong", "Jing Ren", "Jian Chen", "Chao Liu", "Yining Hua" ], "github": "https://github.com/PALIN2018/BrowseComp-ZH", "project_page": "", "abstract": "As large language models (LLMs) evolve into tool-using agents, the ability to browse the web in real-time has become a critical yardstick for measuring their reasoning and retrieval competence. Existing benchmarks such as BrowseComp concentrate on English and overlook the linguistic, infrastructural, and censorship-related complexities of other major information ecosystems -- most notably Chinese. To address this gap, we introduce BrowseComp-ZH, a high-difficulty benchmark purpose-built to comprehensively evaluate LLM agents on the Chinese web. BrowseComp-ZH consists of 289 multi-hop questions spanning 11 diverse domains. Each question is reverse-engineered from a short, objective, and easily verifiable answer (e.g., a date, number, or proper noun). A two-stage quality control protocol is applied to strive for high question difficulty and answer uniqueness. We benchmark over 20 state-of-the-art language models and agentic search systems on our proposed BrowseComp-ZH. Despite their strong conversational and retrieval capabilities, most models struggle severely: a large number achieve accuracy rates below 10%, and only a handful exceed 20%. Even the best-performing system, OpenAI's DeepResearch, reaches just 42.9%. These results demonstrate the considerable difficulty of BrowseComp-ZH, where success demands not only effective retrieval strategies, but also sophisticated reasoning and information reconciliation -- capabilities that current models still struggle to master. Our dataset, construction guidelines, and benchmark results have been publicly released at https://github.com/PALIN2018/BrowseComp-ZH.", "github_manual": "", "github_regex": "https://github.com/PALIN2018/BrowseComp-ZH", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.05467", "title": "StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant", "authors": [ "Haibo Wang", "Bo Feng", "Zhengfeng Lai", "Mingze Xu", "Shiyu Li", "Weifeng Ge", "Afshin Dehghan", "Meng Cao", "Ping Huang" ], "github": "", "project_page": "", "abstract": "We present StreamBridge, a simple yet effective framework that seamlessly transforms offline Video-LLMs into streaming-capable models. It addresses two fundamental challenges in adapting existing models into online scenarios: (1) limited capability for multi-turn real-time understanding, and (2) lack of proactive response mechanisms. Specifically, StreamBridge incorporates (1) a memory buffer combined with a round-decayed compression strategy, supporting long-context multi-turn interactions, and (2) a decoupled, lightweight activation model that can be effortlessly integrated into existing Video-LLMs, enabling continuous proactive responses. To further support StreamBridge, we construct Stream-IT, a large-scale dataset tailored for streaming video understanding, featuring interleaved video-text sequences and diverse instruction formats. Extensive experiments show that StreamBridge significantly improves the streaming understanding capabilities of offline Video-LLMs across various tasks, outperforming even proprietary models such as GPT-4o and Gemini 1.5 Pro. Simultaneously, it achieves competitive or superior performance on standard video understanding benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.05288", "title": "PlaceIt3D: Language-Guided Object Placement in Real 3D Scenes", "authors": [ "Ahmed Abdelreheem", "Filippo Aleotti", "Jamie Watson", "Zawar Qureshi", "Abdelrahman Eldesokey", "Peter Wonka", "Gabriel Brostow", "Sara Vicente", "Guillermo Garcia-Hernando" ], "github": "", "project_page": "", "abstract": "We introduce the novel task of Language-Guided Object Placement in Real 3D Scenes. Our model is given a 3D scene's point cloud, a 3D asset, and a textual prompt broadly describing where the 3D asset should be placed. The task here is to find a valid placement for the 3D asset that respects the prompt. Compared with other language-guided localization tasks in 3D scenes such as grounding, this task has specific challenges: it is ambiguous because it has multiple valid solutions, and it requires reasoning about 3D geometric relationships and free space. We inaugurate this task by proposing a new benchmark and evaluation protocol. We also introduce a new dataset for training 3D LLMs on this task, as well as the first method to serve as a non-trivial baseline. We believe that this challenging task and our new benchmark could become part of the suite of benchmarks used to evaluate and compare generalist 3D LLM models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.05470", "title": "Flow-GRPO: Training Flow Matching Models via Online RL", "authors": [ "Jie Liu", "Gongye Liu", "Jiajun Liang", "Yangguang Li", "Jiaheng Liu", "Xintao Wang", "Pengfei Wan", "Di Zhang", "Wanli Ouyang" ], "github": "", "project_page": "", "abstract": "We propose Flow-GRPO, the first method integrating online reinforcement learning (RL) into flow matching models. Our approach uses two key strategies: (1) an ODE-to-SDE conversion that transforms a deterministic Ordinary Differential Equation (ODE) into an equivalent Stochastic Differential Equation (SDE) that matches the original model's marginal distribution at all timesteps, enabling statistical sampling for RL exploration; and (2) a Denoising Reduction strategy that reduces training denoising steps while retaining the original inference timestep number, significantly improving sampling efficiency without performance degradation. Empirically, Flow-GRPO is effective across multiple text-to-image tasks. For complex compositions, RL-tuned SD3.5 generates nearly perfect object counts, spatial relations, and fine-grained attributes, boosting GenEval accuracy from 63% to 95%. In visual text rendering, its accuracy improves from 59% to 92%, significantly enhancing text generation. Flow-GRPO also achieves substantial gains in human preference alignment. Notably, little to no reward hacking occurred, meaning rewards did not increase at the cost of image quality or diversity, and both remained stable in our experiments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.05071", "title": "FG-CLIP: Fine-Grained Visual and Textual Alignment", "authors": [ "Chunyu Xie", "Bin Wang", "Fanjing Kong", "Jincheng Li", "Dawei Liang", "Gengshen Zhang", "Dawei Leng", "Yuhui Yin" ], "github": "https://github.com/360CVGroup/FG-CLIP", "project_page": "", "abstract": "Contrastive Language-Image Pre-training (CLIP) excels in multimodal tasks such as image-text retrieval and zero-shot classification but struggles with fine-grained understanding due to its focus on coarse-grained short captions. To address this, we propose Fine-Grained CLIP (FG-CLIP), which enhances fine-grained understanding through three key innovations. First, we leverage large multimodal models to generate 1.6 billion long caption-image pairs for capturing global-level semantic details. Second, a high-quality dataset is constructed with 12 million images and 40 million region-specific bounding boxes aligned with detailed captions to ensure precise, context-rich representations. Third, 10 million hard fine-grained negative samples are incorporated to improve the model's ability to distinguish subtle semantic differences. Corresponding training methods are meticulously designed for these data. Extensive experiments demonstrate that FG-CLIP outperforms the original CLIP and other state-of-the-art methods across various downstream tasks, including fine-grained understanding, open-vocabulary object detection, image-text retrieval, and general multimodal benchmarks. These results highlight FG-CLIP's effectiveness in capturing fine-grained image details and improving overall model performance. The related data, code, and models are available at https://github.com/360CVGroup/FG-CLIP.", "github_manual": "", "github_regex": "https://github.com/360CVGroup/FG-CLIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.05408", "title": "Crosslingual Reasoning through Test-Time Scaling", "authors": [ "Zheng-Xin Yong", "M. Farid Adilazuarda", "Jonibek Mansurov", "Ruochen Zhang", "Niklas Muennighoff", "Carsten Eickhoff", "Genta Indra Winata", "Julia Kreutzer", "Stephen H. Bach", "Alham Fikri Aji" ], "github": "https://github.com/BatsResearch/crosslingual-test-time-scaling", "project_page": "", "abstract": "Reasoning capabilities of large language models are primarily studied for English, even when pretrained models are multilingual. In this work, we investigate to what extent English reasoning finetuning with long chain-of-thoughts (CoTs) can generalize across languages. First, we find that scaling up inference compute for English-centric reasoning language models (RLMs) improves multilingual mathematical reasoning across many languages including low-resource languages, to an extent where they outperform models twice their size. Second, we reveal that while English-centric RLM's CoTs are naturally predominantly English, they consistently follow a quote-and-think pattern to reason about quoted non-English inputs. Third, we discover an effective strategy to control the language of long CoT reasoning, and we observe that models reason better and more efficiently in high-resource languages. Finally, we observe poor out-of-domain reasoning generalization, in particular from STEM to cultural commonsense knowledge, even for English. Overall, we demonstrate the potentials, study the mechanisms and outline the limitations of crosslingual generalization of English reasoning test-time scaling. We conclude that practitioners should let English-centric RLMs reason in high-resource languages, while further work is needed to improve reasoning in low-resource languages and out-of-domain contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/BatsResearch/crosslingual-test-time-scaling", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.05327", "title": "ICon: In-Context Contribution for Automatic Data Selection", "authors": [ "Yixin Yang", "Qingxiu Dong", "Linli Yao", "Fangwei Zhu", "Zhifang Sui" ], "github": "", "project_page": "", "abstract": "Data selection for instruction tuning is essential for improving the performance of Large Language Models (LLMs) and reducing training cost. However, existing automated selection methods either depend on computationally expensive gradient-based measures or manually designed heuristics, which may fail to fully exploit the intrinsic attributes of data. In this paper, we propose In-context Learning for Contribution Measurement (ICon), a novel gradient-free method that takes advantage of the implicit fine-tuning nature of in-context learning (ICL) to measure sample contribution without gradient computation or manual indicators engineering. ICon offers a computationally efficient alternative to gradient-based methods and reduces human inductive bias inherent in heuristic-based approaches. ICon comprises three components and identifies high-contribution data by assessing performance shifts under implicit learning through ICL. Extensive experiments on three LLMs across 12 benchmarks and 5 pairwise evaluation sets demonstrate the effectiveness of ICon. Remarkably, on LLaMA3.1-8B, models trained on 15% of ICon-selected data outperform full datasets by 5.42% points and exceed the best performance of widely used selection methods by 2.06% points. We further analyze high-contribution samples selected by ICon, which show both diverse tasks and appropriate difficulty levels, rather than just the hardest ones.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.04921", "title": "Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models", "authors": [ "Yunxin Li", "Zhenyu Liu", "Zitao Li", "Xuanyu Zhang", "Zhenran Xu", "Xinyu Chen", "Haoyuan Shi", "Shenyuan Jiang", "Xintong Wang", "Jifang Wang", "Shouzheng Huang", "Xinping Zhao", "Borui Jiang", "Lanqing Hong", "Longyue Wang", "Zhuotao Tian", "Baoxing Huai", "Wenhan Luo", "Weihua Luo", "Zheng Zhang", "Baotian Hu", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains. In artificial intelligence, as systems increasingly operate in open, uncertain, and multimodal environments, reasoning becomes essential for enabling robust and adaptive behavior. Large Multimodal Reasoning Models (LMRMs) have emerged as a promising paradigm, integrating modalities such as text, images, audio, and video to support complex reasoning capabilities and aiming to achieve comprehensive perception, precise understanding, and deep reasoning. As research advances, multimodal reasoning has rapidly evolved from modular, perception-driven pipelines to unified, language-centric frameworks that offer more coherent cross-modal understanding. While instruction tuning and reinforcement learning have improved model reasoning, significant challenges remain in omni-modal generalization, reasoning depth, and agentic behavior. To address these issues, we present a comprehensive and structured survey of multimodal reasoning research, organized around a four-stage developmental roadmap that reflects the field's shifting design philosophies and emerging capabilities. First, we review early efforts based on task-specific modules, where reasoning was implicitly embedded across stages of representation, alignment, and fusion. Next, we examine recent approaches that unify reasoning into multimodal LLMs, with advances such as Multimodal Chain-of-Thought (MCoT) and multimodal reinforcement learning enabling richer and more structured reasoning chains. Finally, drawing on empirical insights from challenging benchmarks and experimental cases of OpenAI O3 and O4-mini, we discuss the conceptual direction of native large multimodal reasoning models (N-LMRMs), which aim to support scalable, agentic, and adaptive reasoning and planning in complex, real-world environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.05469", "title": "Generating Physically Stable and Buildable LEGO Designs from Text", "authors": [ "Ava Pun", "Kangle Deng", "Ruixuan Liu", "Deva Ramanan", "Changliu Liu", "Jun-Yan Zhu" ], "github": "", "project_page": "", "abstract": "We introduce LegoGPT, the first approach for generating physically stable LEGO brick models from text prompts. To achieve this, we construct a large-scale, physically stable dataset of LEGO designs, along with their associated captions, and train an autoregressive large language model to predict the next brick to add via next-token prediction. To improve the stability of the resulting designs, we employ an efficient validity check and physics-aware rollback during autoregressive inference, which prunes infeasible token predictions using physics laws and assembly constraints. Our experiments show that LegoGPT produces stable, diverse, and aesthetically pleasing LEGO designs that align closely with the input text prompts. We also develop a text-based LEGO texturing method to generate colored and textured designs. We show that our designs can be assembled manually by humans and automatically by robotic arms. We also release our new dataset, StableText2Lego, containing over 47,000 LEGO structures of over 28,000 unique 3D objects accompanied by detailed captions, along with our code and models at the project website: https://avalovelace1.github.io/LegoGPT/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.04955", "title": "Chain-of-Thought Tokens are Computer Program Variables", "authors": [ "Fangwei Zhu", "Peiyi Wang", "Zhifang Sui" ], "github": "https://github.com/solitaryzero/CoTs_are_Variables", "project_page": "", "abstract": "Chain-of-thoughts (CoT) requires large language models (LLMs) to generate intermediate steps before reaching the final answer, and has been proven effective to help LLMs solve complex reasoning tasks. However, the inner mechanism of CoT still remains largely unclear. In this paper, we empirically study the role of CoT tokens in LLMs on two compositional tasks: multi-digit multiplication and dynamic programming. While CoT is essential for solving these problems, we find that preserving only tokens that store intermediate results would achieve comparable performance. Furthermore, we observe that storing intermediate results in an alternative latent form will not affect model performance. We also randomly intervene some values in CoT, and notice that subsequent CoT tokens and the final answer would change correspondingly. These findings suggest that CoT tokens may function like variables in computer programs but with potential drawbacks like unintended shortcuts and computational complexity limits between tokens. The code and data are available at https://github.com/solitaryzero/CoTs_are_Variables.", "github_manual": "", "github_regex": "https://github.com/solitaryzero/CoTs_are_Variables", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.03422", "title": "LiftFeat: 3D Geometry-Aware Local Feature Matching", "authors": [ "Yepeng Liu", "Wenpeng Lai", "Zhou Zhao", "Yuxuan Xiong", "Jinchi Zhu", "Jun Cheng", "Yongchao Xu" ], "github": "https://github.com/lyp-deeplearning/LiftFeat", "project_page": "", "abstract": "Robust and efficient local feature matching plays a crucial role in applications such as SLAM and visual localization for robotics. Despite great progress, it is still very challenging to extract robust and discriminative visual features in scenarios with drastic lighting changes, low texture areas, or repetitive patterns. In this paper, we propose a new lightweight network called LiftFeat, which lifts the robustness of raw descriptor by aggregating 3D geometric feature. Specifically, we first adopt a pre-trained monocular depth estimation model to generate pseudo surface normal label, supervising the extraction of 3D geometric feature in terms of predicted surface normal. We then design a 3D geometry-aware feature lifting module to fuse surface normal feature with raw 2D descriptor feature. Integrating such 3D geometric feature enhances the discriminative ability of 2D feature description in extreme conditions. Extensive experimental results on relative pose estimation, homography estimation, and visual localization tasks, demonstrate that our LiftFeat outperforms some lightweight state-of-the-art methods. Code will be released at : https://github.com/lyp-deeplearning/LiftFeat.", "github_manual": "", "github_regex": "https://github.com/lyp-deeplearning/LiftFeat", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.04769", "title": "Vision-Language-Action Models: Concepts, Progress, Applications and Challenges", "authors": [ "Ranjan Sapkota", "Yang Cao", "Konstantinos I. Roumeliotis", "Manoj Karkee" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models mark a transformative advancement in artificial intelligence, aiming to unify perception, natural language understanding, and embodied action within a single computational framework. This foundational review presents a comprehensive synthesis of recent advancements in Vision-Language-Action models, systematically organized across five thematic pillars that structure the landscape of this rapidly evolving field. We begin by establishing the conceptual foundations of VLA systems, tracing their evolution from cross-modal learning architectures to generalist agents that tightly integrate vision-language models (VLMs), action planners, and hierarchical controllers. Our methodology adopts a rigorous literature review framework, covering over 80 VLA models published in the past three years. Key progress areas include architectural innovations, parameter-efficient training strategies, and real-time inference accelerations. We explore diverse application domains such as humanoid robotics, autonomous vehicles, medical and industrial robotics, precision agriculture, and augmented reality navigation. The review further addresses major challenges across real-time control, multimodal action representation, system scalability, generalization to unseen tasks, and ethical deployment risks. Drawing from the state-of-the-art, we propose targeted solutions including agentic AI adaptation, cross-embodiment generalization, and unified neuro-symbolic planning. In our forward-looking discussion, we outline a future roadmap where VLA models, VLMs, and agentic AI converge to power socially aligned, adaptive, and general-purpose embodied agents. This work serves as a foundational reference for advancing intelligent, real-world robotics and artificial general intelligence. >Vision-language-action, Agentic AI, AI Agents, Vision-language Models", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.05064", "title": "WaterDrum: Watermarking for Data-centric Unlearning Metric", "authors": [ "Xinyang Lu", "Xinyuan Niu", "Gregory Kang Ruey Lau", "Bui Thi Cam Nhung", "Rachael Hwee Ling Sim", "Fanyu Wen", "Chuan-Sheng Foo", "See-Kiong Ng", "Bryan Kian Hsiang Low" ], "github": "https://github.com/lululu008/WaterDrum", "project_page": "", "abstract": "Large language model (LLM) unlearning is critical in real-world applications where it is necessary to efficiently remove the influence of private, copyrighted, or harmful data from some users. However, existing utility-centric unlearning metrics (based on model utility) may fail to accurately evaluate the extent of unlearning in realistic settings such as when (a) the forget and retain set have semantically similar content, (b) retraining the model from scratch on the retain set is impractical, and/or (c) the model owner can improve the unlearning metric without directly performing unlearning on the LLM. This paper presents the first data-centric unlearning metric for LLMs called WaterDrum that exploits robust text watermarking for overcoming these limitations. We also introduce new benchmark datasets for LLM unlearning that contain varying levels of similar data points and can be used to rigorously evaluate unlearning algorithms using WaterDrum. Our code is available at https://github.com/lululu008/WaterDrum and our new benchmark datasets are released at https://huggingface.co/datasets/Glow-AI/WaterDrum-Ax.", "github_manual": "", "github_regex": "https://github.com/lululu008/WaterDrum", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.02363", "title": "SIMPLEMIX: Frustratingly Simple Mixing of Off- and On-policy Data in Language Model Preference Learning", "authors": [ "Tianjian Li", "Daniel Khashabi" ], "github": "https://github.com/tatsu-lab/stanford", "project_page": "", "abstract": "Aligning language models with human preferences relies on pairwise preference datasets. While some studies suggest that on-policy data consistently outperforms off -policy data for preference learning, others indicate that the advantages of on-policy data may be task-dependent, highlighting the need for a systematic exploration of their interplay. In this work, we show that on-policy and off-policy data offer complementary strengths in preference optimization: on-policy data is particularly effective for reasoning tasks like math and coding, while off-policy data performs better on open-ended tasks such as creative writing and making personal recommendations. Guided by these findings, we introduce SIMPLEMIX, an approach to combine the complementary strengths of on-policy and off-policy preference learning by simply mixing these two data sources. Our empirical results across diverse tasks and benchmarks demonstrate that SIMPLEMIX substantially improves language model alignment. Specifically, SIMPLEMIX improves upon on-policy DPO and off-policy DPO by an average of 6.03% on Alpaca Eval 2.0. Moreover, it outperforms prior approaches that are much more complex in combining on- and off-policy data, such as HyPO and DPO-Mix-P, by an average of 3.05%.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tatsu-lab/stanford", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-09", "arxiv_id": "2505.04842", "title": "Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers", "authors": [ "Kusha Sareen", "Morgane M Moss", "Alessandro Sordoni", "Rishabh Agarwal", "Arian Hosseini" ], "github": "", "project_page": "", "abstract": "Prevalent reinforcement learning~(RL) methods for fine-tuning LLM reasoners, such as GRPO or Leave-one-out PPO, abandon the learned value function in favor of empirically estimated returns. This hinders test-time compute scaling that relies on using the value-function for verification. In this work, we propose RL^V that augments any ``value-free'' RL method by jointly training the LLM as both a reasoner and a generative verifier using RL-generated data, adding verification capabilities without significant overhead. Empirically, RL^V boosts MATH accuracy by over 20\\% with parallel sampling and enables 8-32times efficient test-time compute scaling compared to the base RL method. RL^V also exhibits strong generalization capabilities for both easy-to-hard and out-of-domain tasks. Furthermore, RL^V achieves 1.2-1.6times higher performance when jointly scaling parallel and sequential test-time compute with a long reasoning R1 model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-12", "arxiv_id": "2505.06111", "title": "UniVLA: Learning to Act Anywhere with Task-centric Latent Actions", "authors": [ "Qingwen Bu", "Yanting Yang", "Jisong Cai", "Shenyuan Gao", "Guanghui Ren", "Maoqing Yao", "Ping Luo", "Hongyang Li" ], "github": "", "project_page": "", "abstract": "A generalist robot should perform effectively across various environments. However, most existing approaches heavily rely on scaling action-annotated data to enhance their capabilities. Consequently, they are often limited to single physical specification and struggle to learn transferable knowledge across different embodiments and environments. To confront these limitations, we propose UniVLA, a new framework for learning cross-embodiment vision-language-action (VLA) policies. Our key innovation is to derive task-centric action representations from videos with a latent action model. This enables us to exploit extensive data across a wide spectrum of embodiments and perspectives. To mitigate the effect of task-irrelevant dynamics, we incorporate language instructions and establish a latent action model within the DINO feature space. Learned from internet-scale videos, the generalist policy can be deployed to various robots through efficient latent action decoding. We obtain state-of-the-art results across multiple manipulation and navigation benchmarks, as well as real-robot deployments. UniVLA achieves superior performance over OpenVLA with less than 1/20 of pretraining compute and 1/10 of downstream data. Continuous performance improvements are observed as heterogeneous data, even including human videos, are incorporated into the training pipeline. The results underscore UniVLA's potential to facilitate scalable and efficient robot policy learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-12", "arxiv_id": "2505.05026", "title": "G-FOCUS: Towards a Robust Method for Assessing UI Design Persuasiveness", "authors": [ "Jaehyun Jeon", "Jang Han Yoon", "Min Soo Kim", "Sumin Shim", "Yejin Choi", "Hanbin Kim", "Youngjae Yu" ], "github": "", "project_page": "", "abstract": "Evaluating user interface (UI) design effectiveness extends beyond aesthetics to influencing user behavior, a principle central to Design Persuasiveness. A/B testing is the predominant method for determining which UI variations drive higher user engagement, but it is costly and time-consuming. While recent Vision-Language Models (VLMs) can process automated UI analysis, current approaches focus on isolated design attributes rather than comparative persuasiveness-the key factor in optimizing user interactions. To address this, we introduce WiserUI-Bench, a benchmark designed for Pairwise UI Design Persuasiveness Assessment task, featuring 300 real-world UI image pairs labeled with A/B test results and expert rationales. Additionally, we propose G-FOCUS, a novel inference-time reasoning strategy that enhances VLM-based persuasiveness assessment by reducing position bias and improving evaluation accuracy. Experimental results show that G-FOCUS surpasses existing inference strategies in consistency and accuracy for pairwise UI evaluation. Through promoting VLM-driven evaluation of UI persuasiveness, our work offers an approach to complement A/B testing, propelling progress in scalable UI preference modeling and design optimization. Code and data will be released publicly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-12", "arxiv_id": "2505.02686", "title": "Sailing AI by the Stars: A Survey of Learning from Rewards in Post-Training and Test-Time Scaling of Large Language Models", "authors": [ "Xiaobao Wu" ], "github": "https://github.com/bobxwu/learning-from-rewards-llm-papers", "project_page": "", "abstract": "Recent developments in Large Language Models (LLMs) have shifted from pre-training scaling to post-training and test-time scaling. Across these developments, a key unified paradigm has arisen: Learning from Rewards, where reward signals act as the guiding stars to steer LLM behavior. It has underpinned a wide range of prevalent techniques, such as reinforcement learning (in RLHF, DPO, and GRPO), reward-guided decoding, and post-hoc correction. Crucially, this paradigm enables the transition from passive learning from static data to active learning from dynamic feedback. This endows LLMs with aligned preferences and deep reasoning capabilities. In this survey, we present a comprehensive overview of the paradigm of learning from rewards. We categorize and analyze the strategies under this paradigm across training, inference, and post-inference stages. We further discuss the benchmarks for reward models and the primary applications. Finally we highlight the challenges and future directions. We maintain a paper collection at https://github.com/bobxwu/learning-from-rewards-llm-papers.", "github_manual": "", "github_regex": "https://github.com/bobxwu/learning-from-rewards-llm-papers", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-12", "arxiv_id": "2505.02550", "title": "Bielik v3 Small: Technical Report", "authors": [ "Krzysztof Ociepa", "\u0141ukasz Flis", "Remigiusz Kinas", "Krzysztof Wr\u00f3bel", "Adrian Gwo\u017adziej" ], "github": "", "project_page": "", "abstract": "We introduce Bielik v3, a series of parameter-efficient generative text models (1.5B and 4.5B) optimized for Polish language processing. These models demonstrate that smaller, well-optimized architectures can achieve performance comparable to much larger counterparts while requiring substantially fewer computational resources. Our approach incorporates several key innovations: a custom Polish tokenizer (APT4) that significantly improves token efficiency, Weighted Instruction Cross-Entropy Loss to balance learning across instruction types, and Adaptive Learning Rate that dynamically adjusts based on training progress. Trained on a meticulously curated corpus of 292 billion tokens spanning 303 million documents, these models excel across multiple benchmarks, including the Open PL LLM Leaderboard, Complex Polish Text Understanding Benchmark, Polish EQ-Bench, and Polish Medical Leaderboard. The 4.5B parameter model achieves results competitive with models 2-3 times its size, while the 1.5B model delivers strong performance despite its extremely compact profile. These advances establish new benchmarks for parameter-efficient language modeling in less-represented languages, making high-quality Polish language AI more accessible for resource-constrained applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-12", "arxiv_id": "2505.02410", "title": "Bielik 11B v2 Technical Report", "authors": [ "Krzysztof Ociepa", "\u0141ukasz Flis", "Krzysztof Wr\u00f3bel", "Adrian Gwo\u017adziej", "Remigiusz Kinas" ], "github": "", "project_page": "", "abstract": "We present Bielik 11B v2, a state-of-the-art language model optimized for Polish text processing. Built on the Mistral 7B v0.2 architecture and scaled to 11B parameters using depth up-scaling, this model demonstrates exceptional performance across Polish language benchmarks while maintaining strong cross-lingual capabilities. We introduce two key technical innovations: Weighted Instruction Cross-Entropy Loss, which optimizes learning across diverse instruction types by assigning quality-based weights to training examples, and Adaptive Learning Rate, which dynamically adjusts based on context length. Comprehensive evaluation across multiple benchmarks demonstrates that Bielik 11B v2 outperforms many larger models, including those with 2-6 times more parameters, and significantly surpasses other specialized Polish language models on tasks ranging from linguistic understanding to complex reasoning. The model's parameter efficiency and extensive quantization options enable deployment across various hardware configurations, advancing Polish language AI capabilities and establishing new benchmarks for resource-efficient language modeling in less-represented languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-12", "arxiv_id": "2505.06046", "title": "Healthy LLMs? Benchmarking LLM Knowledge of UK Government Public Health Information", "authors": [ "Joshua Harris", "Fan Grayson", "Felix Feldman", "Timothy Laurence", "Toby Nonnenmacher", "Oliver Higgins", "Leo Loman", "Selina Patel", "Thomas Finnie", "Samuel Collins", "Michael Borowitz" ], "github": "", "project_page": "", "abstract": "As Large Language Models (LLMs) become widely accessible, a detailed understanding of their knowledge within specific domains becomes necessary for successful real world use. This is particularly critical in public health, where failure to retrieve relevant, accurate, and current information could significantly impact UK residents. However, currently little is known about LLM knowledge of UK Government public health information. To address this issue, this paper introduces a new benchmark, PubHealthBench, with over 8000 questions for evaluating LLMs' Multiple Choice Question Answering (MCQA) and free form responses to public health queries, created via an automated pipeline. We also release a new dataset of the extracted UK Government public health guidance documents used as source text for PubHealthBench. Assessing 24 LLMs on PubHealthBench we find the latest private LLMs (GPT-4.5, GPT-4.1 and o1) have a high degree of knowledge, achieving >90% in the MCQA setup, and outperform humans with cursory search engine use. However, in the free form setup we see lower performance with no model scoring >75%. Therefore, whilst there are promising signs that state of the art (SOTA) LLMs are an increasingly accurate source of public health information, additional safeguards or tools may still be needed when providing free form responses on public health topics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-12", "arxiv_id": "2505.05621", "title": "A Preliminary Study for GPT-4o on Image Restoration", "authors": [ "Hao Yang", "Yan Yang", "Ruikun Zhang", "Liyuan Pan" ], "github": "https://github.com/noxsine/gpt_restoration", "project_page": "", "abstract": "OpenAI's GPT-4o model, integrating multi-modal inputs and outputs within an autoregressive architecture, has demonstrated unprecedented performance in image generation. In this work, we investigate its potential impact on the image restoration community. We present the first systematic evaluation of GPT-4o across diverse restoration tasks. Our experiments reveal that, although restoration outputs from GPT-4o are visually appealing, they often suffer from pixel-level structural fidelity when compared to ground-truth images. Common issues are variations in image proportions, shifts in object positions and quantities, and changes in viewpoint.To address it, taking image dehazing, derainning, and low-light enhancement as representative case studies, we show that GPT-4o's outputs can serve as powerful visual priors, substantially enhancing the performance of existing dehazing networks. It offers practical guidelines and a baseline framework to facilitate the integration of GPT-4o into future image restoration pipelines. We hope the study on GPT-4o image restoration will accelerate innovation in the broader field of image generation areas. To support further research, we will release GPT-4o-restored images from over 10 widely used image restoration datasets.", "github_manual": "https://github.com/noxsine/gpt_restoration", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-12", "arxiv_id": "2504.21467", "title": "Multiview Point Cloud Registration via Optimization in an Autoencoder Latent Space", "authors": [ "Luc Vedrenne", "Sylvain Faisan", "Denis Fortun" ], "github": "https://github.com/py-polar/polar", "project_page": "", "abstract": "Point cloud rigid registration is a fundamental problem in 3D computer vision. In the multiview case, we aim to find a set of 6D poses to align a set of objects. Methods based on pairwise registration rely on a subsequent synchronization algorithm, which makes them poorly scalable with the number of views. Generative approaches overcome this limitation, but are based on Gaussian Mixture Models and use an Expectation-Maximization algorithm. Hence, they are not well suited to handle large transformations. Moreover, most existing methods cannot handle high levels of degradations. In this paper, we introduce POLAR (POint cloud LAtent Registration), a multiview registration method able to efficiently deal with a large number of views, while being robust to a high level of degradations and large initial angles. To achieve this, we transpose the registration problem into the latent space of a pretrained autoencoder, design a loss taking degradations into account, and develop an efficient multistart optimization strategy. Our proposed method significantly outperforms state-of-the-art approaches on synthetic and real data. POLAR is available at github.com/pypolar/polar or as a standalone package which can be installed with pip install polaregistration.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/py-polar/polar", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.06548", "title": "REFINE-AF: A Task-Agnostic Framework to Align Language Models via Self-Generated Instructions using Reinforcement Learning from Automated Feedback", "authors": [ "Aniruddha Roy", "Pretam Ray", "Abhilash Nandy", "Somak Aditya", "Pawan Goyal" ], "github": "", "project_page": "", "abstract": "Instruction-based Large Language Models (LLMs) have proven effective in numerous few-shot or zero-shot Natural Language Processing (NLP) tasks. However, creating human-annotated instruction data is time-consuming, expensive, and often limited in quantity and task diversity. Previous research endeavors have attempted to address this challenge by proposing frameworks capable of generating instructions in a semi-automated and task-agnostic manner directly from the model itself. Many of these efforts have relied on large API-only parameter-based models such as GPT-3.5 (175B), which are expensive, and subject to limits on a number of queries. This paper explores the performance of three open-source small LLMs such as LLaMA 2-7B, LLama 2-13B, and Mistral 7B, using a semi-automated framework, thereby reducing human intervention, effort, and cost required to generate an instruction dataset for fine-tuning LLMs. Furthermore, we demonstrate that incorporating a Reinforcement Learning (RL) based training algorithm into this LLMs-based framework leads to further enhancements. Our evaluation of the dataset reveals that these RL-based frameworks achieve a substantial improvements in 63-66% of the tasks compared to previous approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.03733", "title": "WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch", "authors": [ "Zimu Lu", "Yunqiao Yang", "Houxing Ren", "Haotian Hou", "Han Xiao", "Ke Wang", "Weikang Shi", "Aojun Zhou", "Mingjie Zhan", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "LLM-based agents have demonstrated great potential in generating and managing code within complex codebases. In this paper, we introduce WebGen-Bench, a novel benchmark designed to measure an LLM-based agent's ability to create multi-file website codebases from scratch. It contains diverse instructions for website generation, created through the combined efforts of human annotators and GPT-4o. These instructions span three major categories and thirteen minor categories, encompassing nearly all important types of web applications. To assess the quality of the generated websites, we use GPT-4o to generate test cases targeting each functionality described in the instructions, and then manually filter, adjust, and organize them to ensure accuracy, resulting in 647 test cases. Each test case specifies an operation to be performed on the website and the expected result after the operation. To automate testing and improve reproducibility, we employ a powerful web-navigation agent to execute tests on the generated websites and determine whether the observed responses align with the expected results. We evaluate three high-performance code-agent frameworks, Bolt.diy, OpenHands, and Aider, using multiple proprietary and open-source LLMs as engines. The best-performing combination, Bolt.diy powered by DeepSeek-R1, achieves only 27.8\\% accuracy on the test cases, highlighting the challenging nature of our benchmark. Additionally, we construct WebGen-Instruct, a training set consisting of 6,667 website-generation instructions. Training Qwen2.5-Coder-32B-Instruct on Bolt.diy trajectories generated from a subset of this training set achieves an accuracy of 38.2\\%, surpassing the performance of the best proprietary model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07062", "title": "Seed1.5-VL Technical Report", "authors": [ "Dong Guo", "Faming Wu", "Feida Zhu", "Fuxing Leng", "Guang Shi", "Haobin Chen", "Haoqi Fan", "Jian Wang", "Jianyu Jiang", "Jiawei Wang", "Jingji Chen", "Jingjia Huang", "Kang Lei", "Liping Yuan", "Lishu Luo", "Pengfei Liu", "Qinghao Ye", "Rui Qian", "Shen Yan", "Shixiong Zhao", "Shuai Peng", "Shuangye Li", "Sihang Yuan", "Sijin Wu", "Tianheng Cheng", "Weiwei Liu", "Wenqian Wang", "Xianhan Zeng", "Xiao Liu", "Xiaobo Qin", "Xiaohan Ding", "Xiaojun Xiao", "Xiaoying Zhang", "Xuanwei Zhang", "Xuehan Xiong", "Yanghua Peng", "Yangrui Chen", "Yanwei Li", "Yanxu Hu", "Yi Lin", "Yiyuan Hu", "Yiyuan Zhang", "Youbin Wu", "Yu Li", "Yudong Liu", "Yue Ling", "Yujia Qin", "Zanbo Wang", "Zhiwu He", "Aoxue Zhang", "Bairen Yi", "Bencheng Liao", "Can Huang", "Can Zhang", "Chaorui Deng", "Chaoyi Deng", "Cheng Lin", "Cheng Yuan", "Chenggang Li", "Chenhui Gou", "Chenwei Lou", "Chengzhi Wei", "Chundian Liu", "Chunyuan Li", "Deyao Zhu", "Donghong Zhong", "Feng Li", "Feng Zhang", "Gang Wu", "Guodong Li", "Guohong Xiao", "Haibin Lin", "Haihua Yang", "Haoming Wang", "Heng Ji", "Hongxiang Hao", "Hui Shen", "Huixia Li", "Jiahao Li", "Jialong Wu", "Jianhua Zhu", "Jianpeng Jiao", "Jiashi Feng", "Jiaze Chen", "Jianhui Duan", "Jihao Liu", "Jin Zeng", "Jingqun Tang", "Jingyu Sun", "Joya Chen", "Jun Long", "Junda Feng", "Junfeng Zhan", "Junjie Fang", "Junting Lu", "Kai Hua", "Kai Liu", "Kai Shen", "Kaiyuan Zhang", "Ke Shen", "Ke Wang", "Keyu Pan", "Kun Zhang", "Kunchang Li", "Lanxin Li", "Lei Li", "Lei Shi", "Li Han", "Liang Xiang", "Liangqiang Chen", "Lin Chen", "Lin Li", "Lin Yan", "Liying Chi", "Longxiang Liu", "Mengfei Du", "Mingxuan Wang", "Ningxin Pan", "Peibin Chen", "Pengfei Chen", "Pengfei Wu", "Qingqing Yuan", "Qingyao Shuai", "Qiuyan Tao", "Renjie Zheng", "Renrui Zhang", "Ru Zhang", "Rui Wang", "Rui Yang", "Rui Zhao", "Shaoqiang Xu", "Shihao Liang", "Shipeng Yan", "Shu Zhong", "Shuaishuai Cao", "Shuangzhi Wu", "Shufan Liu", "Shuhan Chang", "Songhua Cai", "Tenglong Ao", "Tianhao Yang", "Tingting Zhang", "Wanjun Zhong", "Wei Jia", "Wei Weng", "Weihao Yu", "Wenhao Huang", "Wenjia Zhu", "Wenli Yang", "Wenzhi Wang", "Xiang Long", "XiangRui Yin", "Xiao Li", "Xiaolei Zhu", "Xiaoying Jia", "Xijin Zhang", "Xin Liu", "Xinchen Zhang", "Xinyu Yang", "Xiongcai Luo", "Xiuli Chen", "Xuantong Zhong", "Xuefeng Xiao", "Xujing Li", "Yan Wu", "Yawei Wen", "Yifan Du", "Yihao Zhang", "Yining Ye", "Yonghui Wu", "Yu Liu", "Yu Yue", "Yufeng Zhou", "Yufeng Yuan", "Yuhang Xu", "Yuhong Yang", "Yun Zhang", "Yunhao Fang", "Yuntao Li", "Yurui Ren", "Yuwen Xiong", "Zehua Hong", "Zehua Wang", "Zewei Sun", "Zeyu Wang", "Zhao Cai", "Zhaoyue Zha", "Zhecheng An", "Zhehui Zhao", "Zhengzhuo Xu", "Zhipeng Chen", "Zhiyong Wu", "Zhuofan Zheng", "Zihao Wang", "Zilong Huang", "Ziyu Zhu", "Zuquan Song" ], "github": "", "project_page": "", "abstract": "We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter vision encoder and a Mixture-of-Experts (MoE) LLM of 20B active parameters. Despite its relatively compact architecture, it delivers strong performance across a wide spectrum of public VLM benchmarks and internal evaluation suites, achieving the state-of-the-art performance on 38 out of 60 public benchmarks. Moreover, in agent-centric tasks such as GUI control and gameplay, Seed1.5-VL outperforms leading multimodal systems, including OpenAI CUA and Claude 3.7. Beyond visual and video understanding, it also demonstrates strong reasoning abilities, making it particularly effective for multimodal reasoning challenges such as visual puzzles. We believe these capabilities will empower broader applications across diverse tasks. In this report, we mainly provide a comprehensive review of our experiences in building Seed1.5-VL across model design, data construction, and training at various stages, hoping that this report can inspire further research. Seed1.5-VL is now accessible at https://www.volcengine.com/ (Volcano Engine Model ID: doubao-1-5-thinking-vision-pro-250428)", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07787", "title": "Learning from Peers in Reasoning Models", "authors": [ "Tongxu Luo", "Wenyu Du", "Jiaxi Bi", "Stephen Chung", "Zhengyang Tang", "Hao Yang", "Min Zhang", "Benyou Wang" ], "github": "", "project_page": "", "abstract": "Large Reasoning Models (LRMs) have the ability to self-correct even when they make mistakes in their reasoning paths. However, our study reveals that when the reasoning process starts with a short but poor beginning, it becomes difficult for the model to recover. We refer to this phenomenon as the \"Prefix Dominance Trap\". Inspired by psychological findings that peer interaction can promote self-correction without negatively impacting already accurate individuals, we propose **Learning from Peers** (LeaP) to address this phenomenon. Specifically, every tokens, each reasoning path summarizes its intermediate reasoning and shares it with others through a routing mechanism, enabling paths to incorporate peer insights during inference. However, we observe that smaller models sometimes fail to follow summarization and reflection instructions effectively. To address this, we fine-tune them into our **LeaP-T** model series. Experiments on AIME 2024, AIME 2025, AIMO 2025, and GPQA Diamond show that LeaP provides substantial improvements. For instance, QwQ-32B with LeaP achieves nearly 5 absolute points higher than the baseline on average, and surpasses DeepSeek-R1-671B on three math benchmarks with an average gain of 3.3 points. Notably, our fine-tuned LeaP-T-7B matches the performance of DeepSeek-R1-Distill-Qwen-14B on AIME 2024. In-depth analysis reveals LeaP's robust error correction by timely peer insights, showing strong error tolerance and handling varied task difficulty. LeaP marks a milestone by enabling LRMs to collaborate during reasoning. Our code, datasets, and models are available at https://learning-from-peers.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07796", "title": "Learning Dynamics in Continual Pre-Training for Large Language Models", "authors": [ "Xingjin Wang", "Howe Tissue", "Lu Wang", "Linjing Li", "Daniel Dajun Zeng" ], "github": "", "project_page": "", "abstract": "Continual Pre-Training (CPT) has become a popular and effective method to apply strong foundation models to specific downstream tasks. In this work, we explore the learning dynamics throughout the CPT process for large language models. We specifically focus on how general and downstream domain performance evolves at each training step, with domain performance measured via validation losses. We have observed that the CPT loss curve fundamentally characterizes the transition from one curve to another hidden curve, and could be described by decoupling the effects of distribution shift and learning rate annealing. We derive a CPT scaling law that combines the two factors, enabling the prediction of loss at any (continual) training steps and across learning rate schedules (LRS) in CPT. Our formulation presents a comprehensive understanding of several critical factors in CPT, including loss potential, peak learning rate, training steps, replay ratio, etc. Moreover, our approach can be adapted to customize training hyper-parameters to different CPT goals such as balancing general and domain-specific performance. Extensive experiments demonstrate that our scaling law holds across various CPT datasets and training hyper-parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07608", "title": "MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining", "authors": [ "Xiaomi LLM-Core Team", "Bingquan Xia", "Bowen Shen", "Cici", "Dawei Zhu", "Di Zhang", "Gang Wang", "Hailin Zhang", "Huaqiu Liu", "Jiebao Xiao", "Jinhao Dong", "Liang Zhao", "Peidian Li", "Peng Wang", "Shihua Yu", "Shimao Chen", "Weikun Wang", "Wenhan Ma", "Xiangwei Deng", "Yi Huang", "Yifan Song", "Zihan Jiang", "Bowen Ye", "Can Cai", "Chenhong He", "Dong Zhang", "Duo Zhang", "Guoan Wang", "Hao Tian", "Haochen Zhao", "Heng Qu", "Hongshen Xu", "Jun Shi", "Kainan Bao", "QingKai Fang", "Kang Zhou", "Kangyang Zhou", "Lei Li", "Menghang Zhu", "Nuo Chen", "Qiantong Wang", "Shaohui Liu", "Shicheng Li", "Shuhao Gu", "Shuhuai Ren", "Shuo Liu", "Sirui Deng", "Weiji Zhuang", "Weiwei Lv", "Wenyu Yang", "Xin Zhang", "Xing Yong", "Xing Zhang", "Xingchen Song", "Xinzhe Xu", "Xu Wang", "Yihan Yan", "Yu Tu", "Yuanyuan Tian", "Yudong Wang", "Yue Yu", "Zhenru Lin", "Zhichao Song", "Zihao Yue" ], "github": "https://github.com/xiaomimimo/MiMo", "project_page": "", "abstract": "We present MiMo-7B, a large language model born for reasoning tasks, with optimization across both pre-training and post-training stages. During pre-training, we enhance the data preprocessing pipeline and employ a three-stage data mixing strategy to strengthen the base model's reasoning potential. MiMo-7B-Base is pre-trained on 25 trillion tokens, with additional Multi-Token Prediction objective for enhanced performance and accelerated inference speed. During post-training, we curate a dataset of 130K verifiable mathematics and programming problems for reinforcement learning, integrating a test-difficulty-driven code-reward scheme to alleviate sparse-reward issues and employing strategic data resampling to stabilize training. Extensive evaluations show that MiMo-7B-Base possesses exceptional reasoning potential, outperforming even much larger 32B models. The final RL-tuned model, MiMo-7B-RL, achieves superior performance on mathematics, code and general reasoning tasks, surpassing the performance of OpenAI o1-mini. The model checkpoints are available at https://github.com/xiaomimimo/MiMo.", "github_manual": "", "github_regex": "https://github.com/xiaomimimo/MiMo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07818", "title": "DanceGRPO: Unleashing GRPO on Visual Generation", "authors": [ "Zeyue Xue", "Jie Wu", "Yu Gao", "Fangyuan Kong", "Lingting Zhu", "Mengzhao Chen", "Zhiheng Liu", "Wei Liu", "Qiushan Guo", "Weilin Huang", "Ping Luo" ], "github": "", "project_page": "", "abstract": "Recent breakthroughs in generative models-particularly diffusion models and rectified flows-have revolutionized visual content creation, yet aligning model outputs with human preferences remains a critical challenge. Existing reinforcement learning (RL)-based methods for visual generation face critical limitations: incompatibility with modern Ordinary Differential Equations (ODEs)-based sampling paradigms, instability in large-scale training, and lack of validation for video generation. This paper introduces DanceGRPO, the first unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms, unleashing one unified RL algorithm across two generative paradigms (diffusion models and rectified flows), three tasks (text-to-image, text-to-video, image-to-video), four foundation models (Stable Diffusion, HunyuanVideo, FLUX, SkyReel-I2V), and five reward models (image/video aesthetics, text-image alignment, video motion quality, and binary reward). To our knowledge, DanceGRPO is the first RL-based unified framework capable of seamless adaptation across diverse generative paradigms, tasks, foundational models, and reward models. DanceGRPO demonstrates consistent and substantial improvements, which outperform baselines by up to 181% on benchmarks such as HPS-v2.1, CLIP Score, VideoAlign, and GenEval. Notably, DanceGRPO not only can stabilize policy optimization for complex video generation, but also enables generative policy to better capture denoising trajectories for Best-of-N inference scaling and learn from sparse binary feedback. Our results establish DanceGRPO as a robust and versatile solution for scaling Reinforcement Learning from Human Feedback (RLHF) tasks in visual generation, offering new insights into harmonizing reinforcement learning and visual synthesis. The code will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07596", "title": "Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search Agent", "authors": [ "Ziyang Huang", "Xiaowei Yuan", "Yiming Ju", "Jun Zhao", "Kang Liu" ], "github": "", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) is a common strategy to reduce hallucinations in Large Language Models (LLMs). While reinforcement learning (RL) can enable LLMs to act as search agents by activating retrieval capabilities, existing ones often underutilize their internal knowledge. This can lead to redundant retrievals, potential harmful knowledge conflicts, and increased inference latency. To address these limitations, an efficient and adaptive search agent capable of discerning optimal retrieval timing and synergistically integrating parametric (internal) and retrieved (external) knowledge is in urgent need. This paper introduces the Reinforced Internal-External Knowledge Synergistic Reasoning Agent (IKEA), which could indentify its own knowledge boundary and prioritize the utilization of internal knowledge, resorting to external search only when internal knowledge is deemed insufficient. This is achieved using a novel knowledge-boundary aware reward function and a knowledge-boundary aware training dataset. These are designed for internal-external knowledge synergy oriented RL, incentivizing the model to deliver accurate answers, minimize unnecessary retrievals, and encourage appropriate external searches when its own knowledge is lacking. Evaluations across multiple knowledge reasoning tasks demonstrate that IKEA significantly outperforms baseline methods, reduces retrieval frequency significantly, and exhibits robust generalization capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07747", "title": "Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets", "authors": [ "Weiyu Li", "Xuanyang Zhang", "Zheng Sun", "Di Qi", "Hao Li", "Wei Cheng", "Weiwei Cai", "Shihao Wu", "Jiarui Liu", "Zihao Wang", "Xiao Chen", "Feipeng Tian", "Jianxiong Pan", "Zeming Li", "Gang Yu", "Xiangyu Zhang", "Daxin Jiang", "Ping Tan" ], "github": "", "project_page": "", "abstract": "While generative artificial intelligence has advanced significantly across text, image, audio, and video domains, 3D generation remains comparatively underdeveloped due to fundamental challenges such as data scarcity, algorithmic limitations, and ecosystem fragmentation. To this end, we present Step1X-3D, an open framework addressing these challenges through: (1) a rigorous data curation pipeline processing >5M assets to create a 2M high-quality dataset with standardized geometric and textural properties; (2) a two-stage 3D-native architecture combining a hybrid VAE-DiT geometry generator with an diffusion-based texture synthesis module; and (3) the full open-source release of models, training code, and adaptation modules. For geometry generation, the hybrid VAE-DiT component produces TSDF representations by employing perceiver-based latent encoding with sharp edge sampling for detail preservation. The diffusion-based texture synthesis module then ensures cross-view consistency through geometric conditioning and latent-space synchronization. Benchmark results demonstrate state-of-the-art performance that exceeds existing open-source methods, while also achieving competitive quality with proprietary solutions. Notably, the framework uniquely bridges the 2D and 3D generation paradigms by supporting direct transfer of 2D control techniques~(e.g., LoRA) to 3D synthesis. By simultaneously advancing data quality, algorithmic fidelity, and reproducibility, Step1X-3D aims to establish new standards for open research in controllable 3D asset generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.06324", "title": "Document Attribution: Examining Citation Relationships using Large Language Models", "authors": [ "Vipula Rawte", "Ryan A. Rossi", "Franck Dernoncourt", "Nedim Lipka" ], "github": "", "project_page": "", "abstract": "As Large Language Models (LLMs) are increasingly applied to document-based tasks - such as document summarization, question answering, and information extraction - where user requirements focus on retrieving information from provided documents rather than relying on the model's parametric knowledge, ensuring the trustworthiness and interpretability of these systems has become a critical concern. A central approach to addressing this challenge is attribution, which involves tracing the generated outputs back to their source documents. However, since LLMs can produce inaccurate or imprecise responses, it is crucial to assess the reliability of these citations. To tackle this, our work proposes two techniques. (1) A zero-shot approach that frames attribution as a straightforward textual entailment task. Our method using flan-ul2 demonstrates an improvement of 0.27% and 2.4% over the best baseline of ID and OOD sets of AttributionBench, respectively. (2) We also explore the role of the attention mechanism in enhancing the attribution process. Using a smaller LLM, flan-t5-small, the F1 scores outperform the baseline across almost all layers except layer 4 and layers 8 through 11.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07447", "title": "Unified Continuous Generative Models", "authors": [ "Peng Sun", "Yi Jiang", "Tao Lin" ], "github": "https://github.com/LINs-lab/UCGM", "project_page": "", "abstract": "Recent advances in continuous generative models, including multi-step approaches like diffusion and flow-matching (typically requiring 8-1000 sampling steps) and few-step methods such as consistency models (typically 1-8 steps), have demonstrated impressive generative performance. However, existing work often treats these approaches as distinct paradigms, resulting in separate training and sampling methodologies. We introduce a unified framework for training, sampling, and analyzing these models. Our implementation, the Unified Continuous Generative Models Trainer and Sampler (UCGM-{T,S}), achieves state-of-the-art (SOTA) performance. For example, on ImageNet 256x256 using a 675M diffusion transformer, UCGM-T trains a multi-step model achieving 1.30 FID in 20 steps and a few-step model reaching 1.42 FID in just 2 steps. Additionally, applying UCGM-S to a pre-trained model (previously 1.26 FID at 250 steps) improves performance to 1.06 FID in only 40 steps. Code is available at: https://github.com/LINs-lab/UCGM.", "github_manual": "", "github_regex": "https://github.com/LINs-lab/UCGM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07086", "title": "Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design", "authors": [ "Tong Chen", "Yinuo Zhang", "Sophia Tang", "Pranam Chatterjee" ], "github": "", "project_page": "", "abstract": "Designing biological sequences that satisfy multiple, often conflicting, functional and biophysical criteria remains a central challenge in biomolecule engineering. While discrete flow matching models have recently shown promise for efficient sampling in high-dimensional sequence spaces, existing approaches address only single objectives or require continuous embeddings that can distort discrete distributions. We present Multi-Objective-Guided Discrete Flow Matching (MOG-DFM), a general framework to steer any pretrained discrete-time flow matching generator toward Pareto-efficient trade-offs across multiple scalar objectives. At each sampling step, MOG-DFM computes a hybrid rank-directional score for candidate transitions and applies an adaptive hypercone filter to enforce consistent multi-objective progression. We also trained two unconditional discrete flow matching models, PepDFM for diverse peptide generation and EnhancerDFM for functional enhancer DNA generation, as base generation models for MOG-DFM. We demonstrate MOG-DFM's effectiveness in generating peptide binders optimized across five properties (hemolysis, non-fouling, solubility, half-life, and binding affinity), and in designing DNA sequences with specific enhancer classes and DNA shapes. In total, MOG-DFM proves to be a powerful tool for multi-property-guided biomolecule sequence design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07293", "title": "AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection", "authors": [ "Kai Hua", "Steven Wu", "Ge Zhang", "Ke Shen" ], "github": "", "project_page": "", "abstract": "Recently, there has been growing interest in collecting reasoning-intensive pretraining data to improve LLMs' complex reasoning ability. Prior approaches typically rely on supervised classifiers to identify such data, which requires labeling by humans or LLMs, often introducing domain-specific biases. Due to the attention heads being crucial to in-context reasoning, we propose AttentionInfluence, a simple yet effective, training-free method without supervision signal. Our approach enables a small pretrained language model to act as a strong data selector through a simple attention head masking operation. Specifically, we identify retrieval heads and compute the loss difference when masking these heads. We apply AttentionInfluence to a 1.3B-parameter dense model to conduct data selection on the SmolLM corpus of 241B tokens, and mix the SmolLM corpus with the selected subset comprising 73B tokens to pretrain a 7B-parameter dense model using 1T training tokens and WSD learning rate scheduling. Our experimental results demonstrate substantial improvements, ranging from 1.4pp to 3.5pp, across several knowledge-intensive and reasoning-heavy benchmarks (i.e., MMLU, MMLU-Pro, AGIEval-en, GSM8K, and HumanEval). This demonstrates an effective weak-to-strong scaling property, with small models improving the final performance of larger models-offering a promising and scalable path for reasoning-centric data selection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07819", "title": "H^{3}DP: Triply-Hierarchical Diffusion Policy for Visuomotor Learning", "authors": [ "Yiyang Lu", "Yufeng Tian", "Zhecheng Yuan", "Xianbang Wang", "Pu Hua", "Zhengrong Xue", "Huazhe Xu" ], "github": "", "project_page": "", "abstract": "Visuomotor policy learning has witnessed substantial progress in robotic manipulation, with recent approaches predominantly relying on generative models to model the action distribution. However, these methods often overlook the critical coupling between visual perception and action prediction. In this work, we introduce Triply-Hierarchical Diffusion Policy~(H^{\\mathbf{3}DP}), a novel visuomotor learning framework that explicitly incorporates hierarchical structures to strengthen the integration between visual features and action generation. H^{3}DP contains 3 levels of hierarchy: (1) depth-aware input layering that organizes RGB-D observations based on depth information; (2) multi-scale visual representations that encode semantic features at varying levels of granularity; and (3) a hierarchically conditioned diffusion process that aligns the generation of coarse-to-fine actions with corresponding visual features. Extensive experiments demonstrate that H^{3}DP yields a +27.5% average relative improvement over baselines across 44 simulation tasks and achieves superior performance in 4 challenging bimanual real-world manipulation tasks. Project Page: https://lyy-iiis.github.io/h3dp/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07793", "title": "Overflow Prevention Enhances Long-Context Recurrent LLMs", "authors": [ "Assaf Ben-Kish", "Itamar Zimerman", "M. Jehanzeb Mirza", "James Glass", "Leonid Karlinsky", "Raja Giryes" ], "github": "https://github.com/state-spaces/mamba", "project_page": "", "abstract": "A recent trend in LLMs is developing recurrent sub-quadratic models that improve long-context processing efficiency. We investigate leading large long-context models, focusing on how their fixed-size recurrent memory affects their performance. Our experiments reveal that, even when these models are trained for extended contexts, their use of long contexts remains underutilized. Specifically, we demonstrate that a chunk-based inference procedure, which identifies and processes only the most relevant portion of the input can mitigate recurrent memory failures and be effective for many long-context tasks: On LongBench, our method improves the overall performance of Falcon3-Mamba-Inst-7B by 14%, Falcon-Mamba-Inst-7B by 28%, RecurrentGemma-IT-9B by 50%, and RWKV6-Finch-7B by 51%. Surprisingly, this simple approach also leads to state-of-the-art results in the challenging LongBench v2 benchmark, showing competitive performance with equivalent size Transformers. Furthermore, our findings raise questions about whether recurrent models genuinely exploit long-range dependencies, as our single-chunk strategy delivers stronger performance - even in tasks that presumably require cross-context relations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/state-spaces/mamba", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.04918", "title": "Physics-Assisted and Topology-Informed Deep Learning for Weather Prediction", "authors": [ "Jiaqi Zheng", "Qing Ling", "Yerong Feng" ], "github": "https://github.com/Yumenomae/PASSAT_5p625", "project_page": "", "abstract": "Although deep learning models have demonstrated remarkable potential in weather prediction, most of them overlook either the physics of the underlying weather evolution or the topology of the Earth's surface. In light of these disadvantages, we develop PASSAT, a novel Physics-ASSisted And Topology-informed deep learning model for weather prediction. PASSAT attributes the weather evolution to two key factors: (i) the advection process that can be characterized by the advection equation and the Navier-Stokes equation; (ii) the Earth-atmosphere interaction that is difficult to both model and calculate. PASSAT also takes the topology of the Earth's surface into consideration, other than simply treating it as a plane. With these considerations, PASSAT numerically solves the advection equation and the Navier-Stokes equation on the spherical manifold, utilizes a spherical graph neural network to capture the Earth-atmosphere interaction, and generates the initial velocity fields that are critical to solving the advection equation from the same spherical graph neural network. In the 5.625^circ-resolution ERA5 data set, PASSAT outperforms both the state-of-the-art deep learning-based weather prediction models and the operational numerical weather prediction model IFS T42. Code and checkpoint are available at https://github.com/Yumenomae/PASSAT_5p625.", "github_manual": "", "github_regex": "https://github.com/Yumenomae/PASSAT_5p625", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07812", "title": "Continuous Visual Autoregressive Generation via Score Maximization", "authors": [ "Chenze Shao", "Fandong Meng", "Jie Zhou" ], "github": "https://github.com/shaochenze/EAR", "project_page": "", "abstract": "Conventional wisdom suggests that autoregressive models are used to process discrete data. When applied to continuous modalities such as visual data, Visual AutoRegressive modeling (VAR) typically resorts to quantization-based approaches to cast the data into a discrete space, which can introduce significant information loss. To tackle this issue, we introduce a Continuous VAR framework that enables direct visual autoregressive generation without vector quantization. The underlying theoretical foundation is strictly proper scoring rules, which provide powerful statistical tools capable of evaluating how well a generative model approximates the true distribution. Within this framework, all we need is to select a strictly proper score and set it as the training objective to optimize. We primarily explore a class of training objectives based on the energy score, which is likelihood-free and thus overcomes the difficulty of making probabilistic predictions in the continuous space. Previous efforts on continuous autoregressive generation, such as GIVT and diffusion loss, can also be derived from our framework using other strictly proper scores. Source code: https://github.com/shaochenze/EAR.", "github_manual": "", "github_regex": "https://github.com/shaochenze/EAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07260", "title": "UMoE: Unifying Attention and FFN with Shared Experts", "authors": [ "Yuanhang Yang", "Chaozheng Wang", "Jing Li" ], "github": "https://github.com/ysngki/UMoE", "project_page": "", "abstract": "Sparse Mixture of Experts (MoE) architectures have emerged as a promising approach for scaling Transformer models. While initial works primarily incorporated MoE into feed-forward network (FFN) layers, recent studies have explored extending the MoE paradigm to attention layers to enhance model performance. However, existing attention-based MoE layers require specialized implementations and demonstrate suboptimal performance compared to their FFN-based counterparts. In this paper, we aim to unify the MoE designs in attention and FFN layers by introducing a novel reformulation of the attention mechanism, revealing an underlying FFN-like structure within attention modules. Our proposed architecture, UMoE, achieves superior performance through attention-based MoE layers while enabling efficient parameter sharing between FFN and attention components.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ysngki/UMoE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07263", "title": "Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning", "authors": [ "Xiaokun Wang", "Chris", "Jiangbo Pei", "Wei Shen", "Yi Peng", "Yunzhuo Hao", "Weijie Qiu", "Ai Jian", "Tianyidan Xie", "Xuchen Song", "Yang Liu", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "We propose Skywork-VL Reward, a multimodal reward model that provides reward signals for both multimodal understanding and reasoning tasks. Our technical approach comprises two key components: First, we construct a large-scale multimodal preference dataset that covers a wide range of tasks and scenarios, with responses collected from both standard vision-language models (VLMs) and advanced VLM reasoners. Second, we design a reward model architecture based on Qwen2.5-VL-7B-Instruct, integrating a reward head and applying multi-stage fine-tuning using pairwise ranking loss on pairwise preference data. Experimental evaluations show that Skywork-VL Reward achieves state-of-the-art results on multimodal VL-RewardBench and exhibits competitive performance on the text-only RewardBench benchmark. Furthermore, preference data constructed based on our Skywork-VL Reward proves highly effective for training Mixed Preference Optimization (MPO), leading to significant improvements in multimodal reasoning capabilities. Our results underscore Skywork-VL Reward as a significant advancement toward general-purpose, reliable reward models for multimodal alignment. Our model has been publicly released to promote transparency and reproducibility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.06176", "title": "MonetGPT: Solving Puzzles Enhances MLLMs' Image Retouching Skills", "authors": [ "Niladri Shekhar Dutt", "Duygu Ceylan", "Niloy J. Mitra" ], "github": "", "project_page": "", "abstract": "Retouching is an essential task in post-manipulation of raw photographs. Generative editing, guided by text or strokes, provides a new tool accessible to users but can easily change the identity of the original objects in unacceptable and unpredictable ways. In contrast, although traditional procedural edits, as commonly supported by photoediting tools (e.g., Gimp, Lightroom), are conservative, they are still preferred by professionals. Unfortunately, professional quality retouching involves many individual procedural editing operations that is challenging to plan for most novices. In this paper, we ask if a multimodal large language model (MLLM) can be taught to critique raw photographs, suggest suitable remedies, and finally realize them with a given set of pre-authored procedural image operations. We demonstrate that MLLMs can be first made aware of the underlying image processing operations, by training them to solve specially designed visual puzzles. Subsequently, such an operation-aware MLLM can both plan and propose edit sequences. To facilitate training, given a set of expert-edited photos, we synthesize a reasoning dataset by procedurally manipulating the expert edits and then grounding a pretrained LLM on the visual adjustments, to synthesize reasoning for finetuning. The proposed retouching operations are, by construction, understandable by the users, preserve object details and resolution, and can be optionally overridden. We evaluate our setup on a variety of test examples and show advantages, in terms of explainability and identity preservation, over existing generative and other procedural alternatives. Code, data, models, and supplementary results can be found via our project website at https://monetgpt.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.00612", "title": "Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation", "authors": [ "D. Sculley", "Will Cukierski", "Phil Culliton", "Sohier Dane", "Maggie Demkin", "Ryan Holbrook", "Addison Howard", "Paul Mooney", "Walter Reade", "Megan Risdal", "Nate Keating" ], "github": "", "project_page": "", "abstract": "In this position paper, we observe that empirical evaluation in Generative AI is at a crisis point since traditional ML evaluation and benchmarking strategies are insufficient to meet the needs of evaluating modern GenAI models and systems. There are many reasons for this, including the fact that these models typically have nearly unbounded input and output spaces, typically do not have a well defined ground truth target, and typically exhibit strong feedback loops and prediction dependence based on context of previous model outputs. On top of these critical issues, we argue that the problems of {\\em leakage} and {\\em contamination} are in fact the most important and difficult issues to address for GenAI evaluations. Interestingly, the field of AI Competitions has developed effective measures and practices to combat leakage for the purpose of counteracting cheating by bad actors within a competition setting. This makes AI Competitions an especially valuable (but underutilized) resource. Now is time for the field to view AI Competitions as the gold standard for empirical rigor in GenAI evaluation, and to harness and harvest their results with according value.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07233", "title": "DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation", "authors": [ "Jiashuo Sun", "Xianrui Zhong", "Sizhe Zhou", "Jiawei Han" ], "github": "https://github.com/GasolSun36/DynamicRAG", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) systems combine large language models (LLMs) with external knowledge retrieval, making them highly effective for knowledge-intensive tasks. A crucial but often under-explored component of these systems is the reranker, which refines retrieved documents to enhance generation quality and explainability. The challenge of selecting the optimal number of documents (k) remains unsolved: too few may omit critical information, while too many introduce noise and inefficiencies. Although recent studies have explored LLM-based rerankers, they primarily leverage internal model knowledge and overlook the rich supervisory signals that LLMs can provide, such as using response quality as feedback for optimizing reranking decisions. In this paper, we propose DynamicRAG, a novel RAG framework where the reranker dynamically adjusts both the order and number of retrieved documents based on the query. We model the reranker as an agent optimized through reinforcement learning (RL), using rewards derived from LLM output quality. Across seven knowledge-intensive datasets, DynamicRAG demonstrates superior performance, achieving state-of-the-art results. The model, data and code are available at https://github.com/GasolSun36/DynamicRAG", "github_manual": "", "github_regex": "https://github.com/GasolSun36/DynamicRAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.04066", "title": "LLAMAPIE: Proactive In-Ear Conversation Assistants", "authors": [ "Tuochao Chen", "Nicholas Batchelder", "Alisa Liu", "Noah Smith", "Shyamnath Gollakota" ], "github": "https://github.com/chentuochao/LlamaPIE", "project_page": "", "abstract": "We introduce LlamaPIE, the first real-time proactive assistant designed to enhance human conversations through discreet, concise guidance delivered via hearable devices. Unlike traditional language models that require explicit user invocation, this assistant operates in the background, anticipating user needs without interrupting conversations. We address several challenges, including determining when to respond, crafting concise responses that enhance conversations, leveraging knowledge of the user for context-aware assistance, and real-time, on-device processing. To achieve this, we construct a semi-synthetic dialogue dataset and propose a two-model pipeline: a small model that decides when to respond and a larger model that generates the response. We evaluate our approach on real-world datasets, demonstrating its effectiveness in providing helpful, unobtrusive assistance. User studies with our assistant, implemented on Apple Silicon M2 hardware, show a strong preference for the proactive assistant over both a baseline with no assistance and a reactive model, highlighting the potential of LlamaPie to enhance live conversations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/chentuochao/LlamaPIE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-13", "arxiv_id": "2505.07291", "title": "INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning", "authors": [ "Prime Intellect Team", "Sami Jaghouar", "Justus Mattern", "Jack Min Ong", "Jannik Straube", "Manveer Basra", "Aaron Pazdera", "Kushal Thaman", "Matthew Di Ferrante", "Felix Gabriel", "Fares Obeid", "Kemal Erdem", "Michael Keiblinger", "Johannes Hagemann" ], "github": "", "project_page": "", "abstract": "We introduce INTELLECT-2, the first globally distributed reinforcement learning (RL) training run of a 32 billion parameter language model. Unlike traditional centralized training efforts, INTELLECT-2 trains a reasoning model using fully asynchronous RL across a dynamic, heterogeneous swarm of permissionless compute contributors. To enable a training run with this unique infrastructure, we built various components from scratch: we introduce PRIME-RL, our training framework purpose-built for distributed asynchronous reinforcement learning, based on top of novel components such as TOPLOC, which verifies rollouts from untrusted inference workers, and SHARDCAST, which efficiently broadcasts policy weights from training nodes to inference workers. Beyond infrastructure components, we propose modifications to the standard GRPO training recipe and data filtering techniques that were crucial to achieve training stability and ensure that our model successfully learned its training objective, thus improving upon QwQ-32B, the state of the art reasoning model in the 32B parameter range. We open-source INTELLECT-2 along with all of our code and data, hoping to encourage and enable more open research in the field of decentralized training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-14", "arxiv_id": "2505.08665", "title": "SkillFormer: Unified Multi-View Video Understanding for Proficiency Estimation", "authors": [ "Edoardo Bianchi", "Antonio Liotta" ], "github": "https://github.com/EdoWhite/SkillFormer", "project_page": "", "abstract": "Assessing human skill levels in complex activities is a challenging problem with applications in sports, rehabilitation, and training. In this work, we present SkillFormer, a parameter-efficient architecture for unified multi-view proficiency estimation from egocentric and exocentric videos. Building on the TimeSformer backbone, SkillFormer introduces a CrossViewFusion module that fuses view-specific features using multi-head cross-attention, learnable gating, and adaptive self-calibration. We leverage Low-Rank Adaptation to fine-tune only a small subset of parameters, significantly reducing training costs. In fact, when evaluated on the EgoExo4D dataset, SkillFormer achieves state-of-the-art accuracy in multi-view settings while demonstrating remarkable computational efficiency, using 4.5x fewer parameters and requiring 3.75x fewer training epochs than prior baselines. It excels in multiple structured tasks, confirming the value of multi-view integration for fine-grained skill assessment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/EdoWhite/SkillFormer", "github_api_added_by": "auto" }, { "date": "2025-05-14", "arxiv_id": "2505.07591", "title": "A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models", "authors": [ "Junjie Ye", "Caishuang Huang", "Zhuohan Chen", "Wenjie Fu", "Chenyuan Yang", "Leyi Yang", "Yilong Wu", "Peng Wang", "Meng Zhou", "Xiaolong Yang", "Tao Gui", "Qi Zhang", "Zhongchao Shi", "Jianping Fan", "Xuanjing Huang" ], "github": "https://github.com/Junjie-Ye/MulDimIF", "project_page": "", "abstract": "Instruction following evaluates large language models (LLMs) on their ability to generate outputs that adhere to user-defined constraints. However, existing benchmarks often rely on templated constraint prompts, which lack the diversity of real-world usage and limit fine-grained performance assessment. To fill this gap, we propose a multi-dimensional constraint framework encompassing three constraint patterns, four constraint categories, and four difficulty levels. Building on this framework, we develop an automated instruction generation pipeline that performs constraint expansion, conflict detection, and instruction rewriting, yielding 1,200 code-verifiable instruction-following test samples. We evaluate 19 LLMs across seven model families and uncover substantial variation in performance across constraint forms. For instance, average performance drops from 77.67% at Level I to 32.96% at Level IV. Furthermore, we demonstrate the utility of our approach by using it to generate data for reinforcement learning, achieving substantial gains in instruction following without degrading general performance. In-depth analysis indicates that these gains stem primarily from modifications in the model's attention modules parameters, which enhance constraint recognition and adherence. Code and data are available in https://github.com/Junjie-Ye/MulDimIF.", "github_manual": "", "github_regex": "https://github.com/Junjie-Ye/MulDimIF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-14", "arxiv_id": "2505.07215", "title": "Measuring General Intelligence with Generated Games", "authors": [ "Vivek Verma", "David Huang", "William Chen", "Dan Klein", "Nicholas Tomlin" ], "github": "https://github.com/vivek3141/gg-bench", "project_page": "", "abstract": "We present gg-bench, a collection of game environments designed to evaluate general reasoning capabilities in language models. Unlike most static benchmarks, gg-bench is a data generating process where new evaluation instances can be generated at will. In particular, gg-bench is synthetically generated by (1) using a large language model (LLM) to generate natural language descriptions of novel games, (2) using the LLM to implement each game in code as a Gym environment, and (3) training reinforcement learning (RL) agents via self-play on the generated games. We evaluate language models by their winrate against these RL agents by prompting models with the game description, current board state, and a list of valid moves, after which models output the moves they wish to take. gg-bench is challenging: state-of-the-art LLMs such as GPT-4o and Claude 3.7 Sonnet achieve winrates of 7-9% on gg-bench using in-context learning, while reasoning models such as o1, o3-mini and DeepSeek-R1 achieve average winrates of 31-36%. We release the generated games, data generation process, and evaluation code in order to support future modeling work and expansion of our benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vivek3141/gg-bench", "github_api_added_by": "auto" }, { "date": "2025-05-14", "arxiv_id": "2505.07416", "title": "ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness Prediction via Human-AI Collaborative Annotation", "authors": [ "Truc Mai-Thanh Nguyen", "Dat Minh Nguyen", "Son T. Luu", "Kiet Van Nguyen" ], "github": "https://github.com/trng28/ViMRHP", "project_page": "", "abstract": "Multimodal Review Helpfulness Prediction (MRHP) is an essential task in recommender systems, particularly in E-commerce platforms. Determining the helpfulness of user-generated reviews enhances user experience and improves consumer decision-making. However, existing datasets focus predominantly on English and Indonesian, resulting in a lack of linguistic diversity, especially for low-resource languages such as Vietnamese. In this paper, we introduce ViMRHP (Vietnamese Multimodal Review Helpfulness Prediction), a large-scale benchmark dataset for MRHP task in Vietnamese. This dataset covers four domains, including 2K products with 46K reviews. Meanwhile, a large-scale dataset requires considerable time and cost. To optimize the annotation process, we leverage AI to assist annotators in constructing the ViMRHP dataset. With AI assistance, annotation time is reduced (90 to 120 seconds per task down to 20 to 40 seconds per task) while maintaining data quality and lowering overall costs by approximately 65%. However, AI-generated annotations still have limitations in complex annotation tasks, which we further examine through a detailed performance analysis. In our experiment on ViMRHP, we evaluate baseline models on human-verified and AI-generated annotations to assess their quality differences. The ViMRHP dataset is publicly available at https://github.com/trng28/ViMRHP", "github_manual": "", "github_regex": "https://github.com/trng28/ViMRHP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-14", "arxiv_id": "2505.08712", "title": "NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance", "authors": [ "Wenzhe Cai", "Jiaqi Peng", "Yuqiang Yang", "Yujian Zhang", "Meng Wei", "Hanqing Wang", "Yilun Chen", "Tai Wang", "Jiangmiao Pang" ], "github": "https://github.com/wzcai99/NavDP", "project_page": "", "abstract": "Learning navigation in dynamic open-world environments is an important yet challenging skill for robots. Most previous methods rely on precise localization and mapping or learn from expensive real-world demonstrations. In this paper, we propose the Navigation Diffusion Policy (NavDP), an end-to-end framework trained solely in simulation and can zero-shot transfer to different embodiments in diverse real-world environments. The key ingredient of NavDP's network is the combination of diffusion-based trajectory generation and a critic function for trajectory selection, which are conditioned on only local observation tokens encoded from a shared policy transformer. Given the privileged information of the global environment in simulation, we scale up the demonstrations of good quality to train the diffusion policy and formulate the critic value function targets with contrastive negative samples. Our demonstration generation approach achieves about 2,500 trajectories/GPU per day, 20times more efficient than real-world data collection, and results in a large-scale navigation dataset with 363.2km trajectories across 1244 scenes. Trained with this simulation dataset, NavDP achieves state-of-the-art performance and consistently outstanding generalization capability on quadruped, wheeled, and humanoid robots in diverse indoor and outdoor environments. In addition, we present a preliminary attempt at using Gaussian Splatting to make in-domain real-to-sim fine-tuning to further bridge the sim-to-real gap. Experiments show that adding such real-to-sim data can improve the success rate by 30\\% without hurting its generalization capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wzcai99/NavDP", "github_api_added_by": "auto" }, { "date": "2025-05-14", "arxiv_id": "2505.07916", "title": "MiniMax-Speech: Intrinsic Zero-Shot Text-to-Speech with a Learnable Speaker Encoder", "authors": [ "Bowen Zhang", "Congchao Guo", "Geng Yang", "Hang Yu", "Haozhe Zhang", "Heidi Lei", "Jialong Mai", "Junjie Yan", "Kaiyue Yang", "Mingqi Yang", "Peikai Huang", "Ruiyang Jin", "Sitan Jiang", "Weihua Cheng", "Yawei Li", "Yichen Xiao", "Yiying Zhou", "Yongmao Zhang", "Yuan Lu", "Yucen He" ], "github": "", "project_page": "", "abstract": "We introduce MiniMax-Speech, an autoregressive Transformer-based Text-to-Speech (TTS) model that generates high-quality speech. A key innovation is our learnable speaker encoder, which extracts timbre features from a reference audio without requiring its transcription. This enables MiniMax-Speech to produce highly expressive speech with timbre consistent with the reference in a zero-shot manner, while also supporting one-shot voice cloning with exceptionally high similarity to the reference voice. In addition, the overall quality of the synthesized audio is enhanced through the proposed Flow-VAE. Our model supports 32 languages and demonstrates excellent performance across multiple objective and subjective evaluations metrics. Notably, it achieves state-of-the-art (SOTA) results on objective voice cloning metrics (Word Error Rate and Speaker Similarity) and has secured the top position on the public TTS Arena leaderboard. Another key strength of MiniMax-Speech, granted by the robust and disentangled representations from the speaker encoder, is its extensibility without modifying the base model, enabling various applications such as: arbitrary voice emotion control via LoRA; text to voice (T2V) by synthesizing timbre features directly from text description; and professional voice cloning (PVC) by fine-tuning timbre features with additional data. We encourage readers to visit https://minimax-ai.github.io/tts_tech_report for more examples.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-14", "arxiv_id": "2505.08445", "title": "Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency", "authors": [ "Adel Ammar", "Anis Koubaa", "Omer Nacar", "Wadii Boulila" ], "github": "", "project_page": "", "abstract": "Large language models achieve high task performance yet often hallucinate or rely on outdated knowledge. Retrieval-augmented generation (RAG) addresses these gaps by coupling generation with external search. We analyse how hyperparameters influence speed and quality in RAG systems, covering Chroma and Faiss vector stores, chunking policies, cross-encoder re-ranking, and temperature, and we evaluate six metrics: faithfulness, answer correctness, answer relevancy, context precision, context recall, and answer similarity. Chroma processes queries 13% faster, whereas Faiss yields higher retrieval precision, revealing a clear speed-accuracy trade-off. Naive fixed-length chunking with small windows and minimal overlap outperforms semantic segmentation while remaining the quickest option. Re-ranking provides modest gains in retrieval quality yet increases runtime by roughly a factor of 5, so its usefulness depends on latency constraints. These results help practitioners balance computational cost and accuracy when tuning RAG systems for transparent, up-to-date responses. Finally, we re-evaluate the top configurations with a corrective RAG workflow and show that their advantages persist when the model can iteratively request additional evidence. We obtain a near-perfect context precision (99%), which demonstrates that RAG systems can achieve extremely high retrieval accuracy with the right combination of hyperparameters, with significant implications for applications where retrieval quality directly impacts downstream task performance, such as clinical decision support in healthcare.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-14", "arxiv_id": "2504.21475", "title": "Advancing Arabic Reverse Dictionary Systems: A Transformer-Based Approach with Dataset Construction Guidelines", "authors": [ "Serry Sibaee", "Samar Ahmed", "Abdullah Al Harbi", "Omer Nacar", "Adel Ammar", "Yasser Habashi", "Wadii Boulila" ], "github": "https://github.com/serrysibaee/reverse_dictionary", "project_page": "", "abstract": "This study addresses the critical gap in Arabic natural language processing by developing an effective Arabic Reverse Dictionary (RD) system that enables users to find words based on their descriptions or meanings. We present a novel transformer-based approach with a semi-encoder neural network architecture featuring geometrically decreasing layers that achieves state-of-the-art results for Arabic RD tasks. Our methodology incorporates a comprehensive dataset construction process and establishes formal quality standards for Arabic lexicographic definitions. Experiments with various pre-trained models demonstrate that Arabic-specific models significantly outperform general multilingual embeddings, with ARBERTv2 achieving the best ranking score (0.0644). Additionally, we provide a formal abstraction of the reverse dictionary task that enhances theoretical understanding and develop a modular, extensible Python library (RDTL) with configurable training pipelines. Our analysis of dataset quality reveals important insights for improving Arabic definition construction, leading to eight specific standards for building high-quality reverse dictionary resources. This work contributes significantly to Arabic computational linguistics and provides valuable tools for language learning, academic writing, and professional communication in Arabic.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/serrysibaee/reverse_dictionary", "github_api_added_by": "auto" }, { "date": "2025-05-14", "arxiv_id": "2505.08751", "title": "Aya Vision: Advancing the Frontier of Multilingual Multimodality", "authors": [ "Saurabh Dash", "Yiyang Nan", "John Dang", "Arash Ahmadian", "Shivalika Singh", "Madeline Smith", "Bharat Venkitesh", "Vlad Shmyhlo", "Viraat Aryabumi", "Walter Beller-Morales", "Jeremy Pekmez", "Jason Ozuzu", "Pierre Richemond", "Acyr Locatelli", "Nick Frosst", "Phil Blunsom", "Aidan Gomez", "Ivan Zhang", "Marzieh Fadaee", "Manoj Govindassamy", "Sudip Roy", "Matthias Gall\u00e9", "Beyza Ermis", "Ahmet \u00dcst\u00fcn", "Sara Hooker" ], "github": "", "project_page": "", "abstract": "Building multimodal language models is fundamentally challenging: it requires aligning vision and language modalities, curating high-quality instruction data, and avoiding the degradation of existing text-only capabilities once vision is introduced. These difficulties are further magnified in the multilingual setting, where the need for multimodal data in different languages exacerbates existing data scarcity, machine translation often distorts meaning, and catastrophic forgetting is more pronounced. To address the aforementioned challenges, we introduce novel techniques spanning both data and modeling. First, we develop a synthetic annotation framework that curates high-quality, diverse multilingual multimodal instruction data, enabling Aya Vision models to produce natural, human-preferred responses to multimodal inputs across many languages. Complementing this, we propose a cross-modal model merging technique that mitigates catastrophic forgetting, effectively preserving text-only capabilities while simultaneously enhancing multimodal generative performance. Aya-Vision-8B achieves best-in-class performance compared to strong multimodal models such as Qwen-2.5-VL-7B, Pixtral-12B, and even much larger Llama-3.2-90B-Vision. We further scale this approach with Aya-Vision-32B, which outperforms models more than twice its size, such as Molmo-72B and LLaMA-3.2-90B-Vision. Our work advances multilingual progress on the multi-modal frontier, and provides insights into techniques that effectively bend the need for compute while delivering extremely high performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-14", "arxiv_id": "2505.08311", "title": "AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale", "authors": [ "Yunjie Ji", "Xiaoyu Tian", "Sitong Zhao", "Haotian Wang", "Shuaiting Chen", "Yiping Peng", "Han Zhao", "Xiangang Li" ], "github": "https://github.com/GAIR-NLP/AIME-Preview", "project_page": "", "abstract": "We present AM-Thinking-v1, a 32B dense language model that advances the frontier of reasoning, embodying the collaborative spirit of open-source innovation. Outperforming DeepSeek-R1 and rivaling leading Mixture-of-Experts (MoE) models like Qwen3-235B-A22B and Seed1.5-Thinking, AM-Thinking-v1 achieves impressive scores of 85.3 on AIME 2024, 74.4 on AIME 2025, and 70.3 on LiveCodeBench, showcasing state-of-the-art mathematical and coding capabilities among open-source models of similar scale. Built entirely from the open-source Qwen2.5-32B base model and publicly available queries, AM-Thinking-v1 leverages a meticulously crafted post-training pipeline - combining supervised fine-tuning and reinforcement learning - to deliver exceptional reasoning capabilities. This work demonstrates that the open-source community can achieve high performance at the 32B scale, a practical sweet spot for deployment and fine-tuning. By striking a balance between top-tier performance and real-world usability, we hope AM-Thinking-v1 inspires further collaborative efforts to harness mid-scale models, pushing reasoning boundaries while keeping accessibility at the core of innovation. We have open-sourced our model on https://huggingface.co/a-m-team/AM-Thinking-v1{Hugging Face}.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GAIR-NLP/AIME-Preview", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-14", "arxiv_id": "2505.05464", "title": "Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging", "authors": [ "Shiqi Chen", "Jinghan Zhang", "Tongyao Zhu", "Wei Liu", "Siyang Gao", "Miao Xiong", "Manling Li", "Junxian He" ], "github": "https://github.com/shiqichen17/VLM_Merging", "project_page": "", "abstract": "Vision-Language Models (VLMs) combine visual perception with the general capabilities, such as reasoning, of Large Language Models (LLMs). However, the mechanisms by which these two abilities can be combined and contribute remain poorly understood. In this work, we explore to compose perception and reasoning through model merging that connects parameters of different models. Unlike previous works that often focus on merging models of the same kind, we propose merging models across modalities, enabling the incorporation of the reasoning capabilities of LLMs into VLMs. Through extensive experiments, we demonstrate that model merging offers a successful pathway to transfer reasoning abilities from LLMs to VLMs in a training-free manner. Moreover, we utilize the merged models to understand the internal mechanism of perception and reasoning and how merging affects it. We find that perception capabilities are predominantly encoded in the early layers of the model, whereas reasoning is largely facilitated by the middle-to-late layers. After merging, we observe that all layers begin to contribute to reasoning, whereas the distribution of perception abilities across layers remains largely unchanged. These observations shed light on the potential of model merging as a tool for multimodal integration and interpretation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/shiqichen17/VLM_Merging", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-14", "arxiv_id": "2505.08727", "title": "Memorization-Compression Cycles Improve Generalization", "authors": [ "Fangyuan Yu" ], "github": "", "project_page": "", "abstract": "We prove theoretically that generalization improves not only through data scaling but also by compressing internal representations. To operationalize this insight, we introduce the Information Bottleneck Language Modeling (IBLM) objective, which reframes language modeling as a constrained optimization problem: minimizing representation entropy subject to optimal prediction performance. Empirically, we observe an emergent memorization-compression cycle during LLM pretraining, evidenced by oscillation positive/negative gradient alignment between cross-entropy and Matrix-Based Entropy (MBE), a measure of representation entropy. This pattern closely mirrors the predictive-compressive trade-off prescribed by IBLM and also parallels the biological alternation between awake learning and sleep consolidation. Motivated by this observation, we propose Gated Phase Transition (GAPT), a training algorithm that adaptively switches between memorization and compression phases. When applied to GPT-2 pretraining on FineWeb dataset, GAPT reduces MBE by 50% and improves cross-entropy by 4.8%. GAPT improves OOD generalizatino by 35% in a pretraining task on arithmetic multiplication. In a setting designed to simulate catastrophic forgetting, GAPT reduces interference by compressing and separating representations, achieving a 97% improvement in separation - paralleling the functional role of sleep consolidation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-14", "arxiv_id": "2505.08638", "title": "TRAIL: Trace Reasoning and Agentic Issue Localization", "authors": [ "Darshan Deshpande", "Varun Gangal", "Hersh Mehta", "Jitin Krishnan", "Anand Kannappan", "Rebecca Qian" ], "github": "https://github.com/huggingface/smolagents/tree/main/examples/open_deep_research", "project_page": "", "abstract": "The increasing adoption of agentic workflows across diverse domains brings a critical need to scalably and systematically evaluate the complex traces these systems generate. Current evaluation methods depend on manual, domain-specific human analysis of lengthy workflow traces - an approach that does not scale with the growing complexity and volume of agentic outputs. Error analysis in these settings is further complicated by the interplay of external tool outputs and language model reasoning, making it more challenging than traditional software debugging. In this work, we (1) articulate the need for robust and dynamic evaluation methods for agentic workflow traces, (2) introduce a formal taxonomy of error types encountered in agentic systems, and (3) present a set of 148 large human-annotated traces (TRAIL) constructed using this taxonomy and grounded in established agentic benchmarks. To ensure ecological validity, we curate traces from both single and multi-agent systems, focusing on real-world applications such as software engineering and open-world information retrieval. Our evaluations reveal that modern long context LLMs perform poorly at trace debugging, with the best Gemini-2.5-pro model scoring a mere 11% on TRAIL. Our dataset and code are made publicly available to support and accelerate future research in scalable evaluation for agentic workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/smolagents/tree/main/examples/open_deep_research", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-14", "arxiv_id": "2505.08175", "title": "Fast Text-to-Audio Generation with Adversarial Post-Training", "authors": [ "Zachary Novack", "Zach Evans", "Zack Zukowski", "Josiah Taylor", "CJ Carr", "Julian Parker", "Adnan Al-Sinan", "Gian Marco Iodice", "Julian McAuley", "Taylor Berg-Kirkpatrick", "Jordi Pons" ], "github": "https://github.com/Stability-AI/stable-audio-tools/blob/main/stable_audio_tools/training/arc.py", "project_page": "", "abstract": "Text-to-audio systems, while increasingly performant, are slow at inference time, thus making their latency unpractical for many creative applications. We present Adversarial Relativistic-Contrastive (ARC) post-training, the first adversarial acceleration algorithm for diffusion/flow models not based on distillation. While past adversarial post-training methods have struggled to compare against their expensive distillation counterparts, ARC post-training is a simple procedure that (1) extends a recent relativistic adversarial formulation to diffusion/flow post-training and (2) combines it with a novel contrastive discriminator objective to encourage better prompt adherence. We pair ARC post-training with a number optimizations to Stable Audio Open and build a model capable of generating approx12s of 44.1kHz stereo audio in approx75ms on an H100, and approx7s on a mobile edge-device, the fastest text-to-audio model to our knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Stability-AI/stable-audio-tools/blob/main/stable_audio_tools/training/arc.py", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-14", "arxiv_id": "2505.09027", "title": "Tests as Prompt: A Test-Driven-Development Benchmark for LLM Code Generation", "authors": [ "Yi Cui" ], "github": "", "project_page": "", "abstract": "We introduce WebApp1K, a novel benchmark for evaluating large language models (LLMs) in test-driven development (TDD) tasks, where test cases serve as both prompt and verification for code generation. Unlike traditional approaches relying on natural language prompts, our benchmark emphasizes the ability of LLMs to interpret and implement functionality directly from test cases, reflecting real-world software development practices. Comprising 1000 diverse challenges across 20 application domains, the benchmark evaluates LLMs on their ability to generate compact, functional code under the constraints of context length and multi-feature complexity. Our findings highlight instruction following and in-context learning as critical capabilities for TDD success, surpassing the importance of general coding proficiency or pretraining knowledge. Through comprehensive evaluation of 19 frontier models, we reveal performance bottlenecks, such as instruction loss in long prompts, and provide a detailed error analysis spanning multiple root causes. This work underscores the practical value of TDD-specific benchmarks and lays the foundation for advancing LLM capabilities in rigorous, application-driven coding scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.09568", "title": "BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset", "authors": [ "Jiuhai Chen", "Zhiyang Xu", "Xichen Pan", "Yushi Hu", "Can Qin", "Tom Goldstein", "Lifu Huang", "Tianyi Zhou", "Saining Xie", "Silvio Savarese", "Le Xue", "Caiming Xiong", "Ran Xu" ], "github": "", "project_page": "", "abstract": "Unifying image understanding and generation has gained growing attention in recent research on multimodal models. Although design choices for image understanding have been extensively studied, the optimal model architecture and training recipe for a unified framework with image generation remain underexplored. Motivated by the strong potential of autoregressive and diffusion models for high-quality generation and scalability, we conduct a comprehensive study of their use in unified multimodal settings, with emphasis on image representations, modeling objectives, and training strategies. Grounded in these investigations, we introduce a novel approach that employs a diffusion transformer to generate semantically rich CLIP image features, in contrast to conventional VAE-based representations. This design yields both higher training efficiency and improved generative quality. Furthermore, we demonstrate that a sequential pretraining strategy for unified models-first training on image understanding and subsequently on image generation-offers practical advantages by preserving image understanding capability while developing strong image generation ability. Finally, we carefully curate a high-quality instruction-tuning dataset BLIP3o-60k for image generation by prompting GPT-4o with a diverse set of captions covering various scenes, objects, human gestures, and more. Building on our innovative model design, training recipe, and datasets, we develop BLIP3-o, a suite of state-of-the-art unified multimodal models. BLIP3-o achieves superior performance across most of the popular benchmarks spanning both image understanding and generation tasks. To facilitate future research, we fully open-source our models, including code, model weights, training scripts, and pretraining and instruction tuning datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.08455", "title": "VCRBench: Exploring Long-form Causal Reasoning Capabilities of Large Video Language Models", "authors": [ "Pritam Sarkar", "Ali Etemad" ], "github": "", "project_page": "", "abstract": "Despite recent advances in video understanding, the capabilities of Large Video Language Models (LVLMs) to perform video-based causal reasoning remains underexplored, largely due to the absence of relevant and dedicated benchmarks for evaluating causal reasoning in visually grounded and goal-driven settings. To fill this gap, we introduce a novel benchmark named Video-based long-form Causal Reasoning (VCRBench). We create VCRBench using procedural videos of simple everyday activities, where the steps are deliberately shuffled with each clip capturing a key causal event, to test whether LVLMs can identify, reason about, and correctly sequence the events needed to accomplish a specific goal. Moreover, the benchmark is carefully designed to prevent LVLMs from exploiting linguistic shortcuts, as seen in multiple-choice or binary QA formats, while also avoiding the challenges associated with evaluating open-ended QA. Our evaluation of state-of-the-art LVLMs on VCRBench suggests that these models struggle with video-based long-form causal reasoning, primarily due to their difficulty in modeling long-range causal dependencies directly from visual observations. As a simple step toward enabling such capabilities, we propose Recognition-Reasoning Decomposition (RRD), a modular approach that breaks video-based causal reasoning into two sub-tasks of video recognition and causal reasoning. Our experiments on VCRBench show that RRD significantly boosts accuracy on VCRBench, with gains of up to 25.2%. Finally, our thorough analysis reveals interesting insights, for instance, that LVLMs primarily rely on language knowledge for complex video-based long-form causal reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.09343", "title": "Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures", "authors": [ "Chenggang Zhao", "Chengqi Deng", "Chong Ruan", "Damai Dai", "Huazuo Gao", "Jiashi Li", "Liyue Zhang", "Panpan Huang", "Shangyan Zhou", "Shirong Ma", "Wenfeng Liang", "Ying He", "Yuqing Wang", "Yuxuan Liu", "Y. X. Wei" ], "github": "", "project_page": "", "abstract": "The rapid scaling of large language models (LLMs) has unveiled critical limitations in current hardware architectures, including constraints in memory capacity, computational efficiency, and interconnection bandwidth. DeepSeek-V3, trained on 2,048 NVIDIA H800 GPUs, demonstrates how hardware-aware model co-design can effectively address these challenges, enabling cost-efficient training and inference at scale. This paper presents an in-depth analysis of the DeepSeek-V3/R1 model architecture and its AI infrastructure, highlighting key innovations such as Multi-head Latent Attention (MLA) for enhanced memory efficiency, Mixture of Experts (MoE) architectures for optimized computation-communication trade-offs, FP8 mixed-precision training to unlock the full potential of hardware capabilities, and a Multi-Plane Network Topology to minimize cluster-level network overhead. Building on the hardware bottlenecks encountered during DeepSeek-V3's development, we engage in a broader discussion with academic and industry peers on potential future hardware directions, including precise low-precision computation units, scale-up and scale-out convergence, and innovations in low-latency communication fabrics. These insights underscore the critical role of hardware and model co-design in meeting the escalating demands of AI workloads, offering a practical blueprint for innovation in next-generation AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.04410", "title": "DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception", "authors": [ "Junjie Wang", "Bin Chen", "Yulin Li", "Bin Kang", "Yichi Chen", "Zhuotao Tian" ], "github": "https://github.com/xiaomoguhz/DeCLIP", "project_page": "", "abstract": "Dense visual prediction tasks have been constrained by their reliance on predefined categories, limiting their applicability in real-world scenarios where visual concepts are unbounded. While Vision-Language Models (VLMs) like CLIP have shown promise in open-vocabulary tasks, their direct application to dense prediction often leads to suboptimal performance due to limitations in local feature representation. In this work, we present our observation that CLIP's image tokens struggle to effectively aggregate information from spatially or semantically related regions, resulting in features that lack local discriminability and spatial consistency. To address this issue, we propose DeCLIP, a novel framework that enhances CLIP by decoupling the self-attention module to obtain ``content'' and ``context'' features respectively. The ``content'' features are aligned with image crop representations to improve local discriminability, while ``context'' features learn to retain the spatial correlations under the guidance of vision foundation models, such as DINO. Extensive experiments demonstrate that DeCLIP significantly outperforms existing methods across multiple open-vocabulary dense prediction tasks, including object detection and semantic segmentation. Code is available at magenta{https://github.com/xiaomoguhz/DeCLIP}.", "github_manual": "", "github_regex": "https://github.com/xiaomoguhz/DeCLIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.09358", "title": "Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis", "authors": [ "Bingxin Ke", "Kevin Qu", "Tianfu Wang", "Nando Metzger", "Shengyu Huang", "Bo Li", "Anton Obukhov", "Konrad Schindler" ], "github": "", "project_page": "", "abstract": "The success of deep learning in computer vision over the past decade has hinged on large labeled datasets and strong pretrained models. In data-scarce settings, the quality of these pretrained models becomes crucial for effective transfer learning. Image classification and self-supervised learning have traditionally been the primary methods for pretraining CNNs and transformer-based architectures. Recently, the rise of text-to-image generative models, particularly those using denoising diffusion in a latent space, has introduced a new class of foundational models trained on massive, captioned image datasets. These models' ability to generate realistic images of unseen content suggests they possess a deep understanding of the visual world. In this work, we present Marigold, a family of conditional generative models and a fine-tuning protocol that extracts the knowledge from pretrained latent diffusion models like Stable Diffusion and adapts them for dense image analysis tasks, including monocular depth estimation, surface normals prediction, and intrinsic decomposition. Marigold requires minimal modification of the pre-trained latent diffusion model's architecture, trains with small synthetic datasets on a single GPU over a few days, and demonstrates state-of-the-art zero-shot generalization. Project page: https://marigoldcomputervision.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.07849", "title": "SweRank: Software Issue Localization with Code Ranking", "authors": [ "Revanth Gangi Reddy", "Tarun Suresh", "JaeHyeok Doo", "Ye Liu", "Xuan Phi Nguyen", "Yingbo Zhou", "Semih Yavuz", "Caiming Xiong", "Heng Ji", "Shafiq Joty" ], "github": "", "project_page": "", "abstract": "Software issue localization, the task of identifying the precise code locations (files, classes, or functions) relevant to a natural language issue description (e.g., bug report, feature request), is a critical yet time-consuming aspect of software development. While recent LLM-based agentic approaches demonstrate promise, they often incur significant latency and cost due to complex multi-step reasoning and relying on closed-source LLMs. Alternatively, traditional code ranking models, typically optimized for query-to-code or code-to-code retrieval, struggle with the verbose and failure-descriptive nature of issue localization queries. To bridge this gap, we introduce SweRank, an efficient and effective retrieve-and-rerank framework for software issue localization. To facilitate training, we construct SweLoc, a large-scale dataset curated from public GitHub repositories, featuring real-world issue descriptions paired with corresponding code modifications. Empirical results on SWE-Bench-Lite and LocBench show that SweRank achieves state-of-the-art performance, outperforming both prior ranking models and costly agent-based systems using closed-source LLMs like Claude-3.5. Further, we demonstrate SweLoc's utility in enhancing various existing retriever and reranker models for issue localization, establishing the dataset as a valuable resource for the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.09439", "title": "Omni-R1: Do You Really Need Audio to Fine-Tune Your Audio LLM?", "authors": [ "Andrew Rouditchenko", "Saurabhchand Bhati", "Edson Araujo", "Samuel Thomas", "Hilde Kuehne", "Rogerio Feris", "James Glass" ], "github": "", "project_page": "", "abstract": "We propose Omni-R1 which fine-tunes a recent multi-modal LLM, Qwen2.5-Omni, on an audio question answering dataset with the reinforcement learning method GRPO. This leads to new State-of-the-Art performance on the recent MMAU benchmark. Omni-R1 achieves the highest accuracies on the sounds, music, speech, and overall average categories, both on the Test-mini and Test-full splits. To understand the performance improvement, we tested models both with and without audio and found that much of the performance improvement from GRPO could be attributed to better text-based reasoning. We also made a surprising discovery that fine-tuning without audio on a text-only dataset was effective at improving the audio-based performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.04793", "title": "DetReIDX: A Stress-Test Dataset for Real-World UAV-Based Person Recognition", "authors": [ "Kailash A. Hambarde", "Nzakiese Mbongo", "Pavan Kumar MP", "Satish Mekewad", "Carolina Fernandes", "G\u00f6khan Silahtaro\u011flu", "Alice Nithya", "Pawan Wasnik", "MD. Rashidunnabi", "Pranita Samale", "Hugo Proen\u00e7a" ], "github": "https://github.com/kailashhambarde/DetReIDX", "project_page": "", "abstract": "Person reidentification (ReID) technology has been considered to perform relatively well under controlled, ground-level conditions, but it breaks down when deployed in challenging real-world settings. Evidently, this is due to extreme data variability factors such as resolution, viewpoint changes, scale variations, occlusions, and appearance shifts from clothing or session drifts. Moreover, the publicly available data sets do not realistically incorporate such kinds and magnitudes of variability, which limits the progress of this technology. This paper introduces DetReIDX, a large-scale aerial-ground person dataset, that was explicitly designed as a stress test to ReID under real-world conditions. DetReIDX is a multi-session set that includes over 13 million bounding boxes from 509 identities, collected in seven university campuses from three continents, with drone altitudes between 5.8 and 120 meters. More important, as a key novelty, DetReIDX subjects were recorded in (at least) two sessions on different days, with changes in clothing, daylight and location, making it suitable to actually evaluate long-term person ReID. Plus, data were annotated from 16 soft biometric attributes and multitask labels for detection, tracking, ReID, and action recognition. In order to provide empirical evidence of DetReIDX usefulness, we considered the specific tasks of human detection and ReID, where SOTA methods catastrophically degrade performance (up to 80% in detection accuracy and over 70% in Rank-1 ReID) when exposed to DetReIDXs conditions. The dataset, annotations, and official evaluation protocols are publicly available at https://www.it.ubi.pt/DetReIDX/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kailashhambarde/DetReIDX", "github_api_added_by": "auto" }, { "date": "2025-05-15", "arxiv_id": "2502.12894", "title": "CAST: Component-Aligned 3D Scene Reconstruction from an RGB Image", "authors": [ "Kaixin Yao", "Longwen Zhang", "Xinhao Yan", "Yan Zeng", "Qixuan Zhang", "Lan Xu", "Wei Yang", "Jiayuan Gu", "Jingyi Yu" ], "github": "", "project_page": "", "abstract": "Recovering high-quality 3D scenes from a single RGB image is a challenging task in computer graphics. Current methods often struggle with domain-specific limitations or low-quality object generation. To address these, we propose CAST (Component-Aligned 3D Scene Reconstruction from a Single RGB Image), a novel method for 3D scene reconstruction and recovery. CAST starts by extracting object-level 2D segmentation and relative depth information from the input image, followed by using a GPT-based model to analyze inter-object spatial relationships. This enables the understanding of how objects relate to each other within the scene, ensuring more coherent reconstruction. CAST then employs an occlusion-aware large-scale 3D generation model to independently generate each object's full geometry, using MAE and point cloud conditioning to mitigate the effects of occlusions and partial object information, ensuring accurate alignment with the source image's geometry and texture. To align each object with the scene, the alignment generation model computes the necessary transformations, allowing the generated meshes to be accurately placed and integrated into the scene's point cloud. Finally, CAST incorporates a physics-aware correction step that leverages a fine-grained relation graph to generate a constraint graph. This graph guides the optimization of object poses, ensuring physical consistency and spatial coherence. By utilizing Signed Distance Fields (SDF), the model effectively addresses issues such as occlusions, object penetration, and floating objects, ensuring that the generated scene accurately reflects real-world physical interactions. CAST can be leveraged in robotics, enabling efficient real-to-simulation workflows and providing realistic, scalable simulation environments for robotic systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.08787", "title": "UniSkill: Imitating Human Videos via Cross-Embodiment Skill Representations", "authors": [ "Hanjung Kim", "Jaehyun Kang", "Hyolim Kang", "Meedeum Cho", "Seon Joo Kim", "Youngwoon Lee" ], "github": "", "project_page": "", "abstract": "Mimicry is a fundamental learning mechanism in humans, enabling individuals to learn new tasks by observing and imitating experts. However, applying this ability to robots presents significant challenges due to the inherent differences between human and robot embodiments in both their visual appearance and physical capabilities. While previous methods bridge this gap using cross-embodiment datasets with shared scenes and tasks, collecting such aligned data between humans and robots at scale is not trivial. In this paper, we propose UniSkill, a novel framework that learns embodiment-agnostic skill representations from large-scale cross-embodiment video data without any labels, enabling skills extracted from human video prompts to effectively transfer to robot policies trained only on robot data. Our experiments in both simulation and real-world environments show that our cross-embodiment skills successfully guide robots in selecting appropriate actions, even with unseen video prompts. The project website can be found at: https://kimhanjung.github.io/UniSkill.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.08084", "title": "Visually Interpretable Subtask Reasoning for Visual Question Answering", "authors": [ "Yu Cheng", "Arushi Goel", "Hakan Bilen" ], "github": "https://github.com/ChengJade/VISTAR", "project_page": "", "abstract": "Answering complex visual questions like `Which red furniture can be used for sitting?' requires multi-step reasoning, including object recognition, attribute filtering, and relational understanding. Recent work improves interpretability in multimodal large language models (MLLMs) by decomposing tasks into sub-task programs, but these methods are computationally expensive and less accurate due to poor adaptation to target data. To address this, we introduce VISTAR (Visually Interpretable Subtask-Aware Reasoning Model), a subtask-driven training framework that enhances both interpretability and reasoning by generating textual and visual explanations within MLLMs. Instead of relying on external models, VISTAR fine-tunes MLLMs to produce structured Subtask-of-Thought rationales (step-by-step reasoning sequences). Experiments on two benchmarks show that VISTAR consistently improves reasoning accuracy while maintaining interpretability. Our code and dataset will be available at https://github.com/ChengJade/VISTAR.", "github_manual": "", "github_regex": "https://github.com/ChengJade/VISTAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.09558", "title": "WavReward: Spoken Dialogue Models With Generalist Reward Evaluators", "authors": [ "Shengpeng Ji", "Tianle Liang", "Yangzhuo Li", "Jialong Zuo", "Minghui Fang", "Jinzheng He", "Yifu Chen", "Zhengqing Liu", "Ziyue Jiang", "Xize Cheng", "Siqi Zheng", "Jin Xu", "Junyang Lin", "Zhou Zhao" ], "github": "https://github.com/jishengpeng/WavReward", "project_page": "", "abstract": "End-to-end spoken dialogue models such as GPT-4o-audio have recently garnered significant attention in the speech domain. However, the evaluation of spoken dialogue models' conversational performance has largely been overlooked. This is primarily due to the intelligent chatbots convey a wealth of non-textual information which cannot be easily measured using text-based language models like ChatGPT. To address this gap, we propose WavReward, a reward feedback model based on audio language models that can evaluate both the IQ and EQ of spoken dialogue systems with speech input. Specifically, 1) based on audio language models, WavReward incorporates the deep reasoning process and the nonlinear reward mechanism for post-training. By utilizing multi-sample feedback via the reinforcement learning algorithm, we construct a specialized evaluator tailored to spoken dialogue models. 2) We introduce ChatReward-30K, a preference dataset used to train WavReward. ChatReward-30K includes both comprehension and generation aspects of spoken dialogue models. These scenarios span various tasks, such as text-based chats, nine acoustic attributes of instruction chats, and implicit chats. WavReward outperforms previous state-of-the-art evaluation models across multiple spoken dialogue scenarios, achieving a substantial improvement about Qwen2.5-Omni in objective accuracy from 55.1% to 91.5%. In subjective A/B testing, WavReward also leads by a margin of 83%. Comprehensive ablation studies confirm the necessity of each component of WavReward. All data and code will be publicly at https://github.com/jishengpeng/WavReward after the paper is accepted.", "github_manual": "", "github_regex": "https://github.com/jishengpeng/WavReward", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.05587", "title": "Steepest Descent Density Control for Compact 3D Gaussian Splatting", "authors": [ "Peihao Wang", "Yuehao Wang", "Dilin Wang", "Sreyas Mohan", "Zhiwen Fan", "Lemeng Wu", "Ruisi Cai", "Yu-Ying Yeh", "Zhangyang Wang", "Qiang Liu", "Rakesh Ranjan" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time, high-resolution novel view synthesis. By representing scenes as a mixture of Gaussian primitives, 3DGS leverages GPU rasterization pipelines for efficient rendering and reconstruction. To optimize scene coverage and capture fine details, 3DGS employs a densification algorithm to generate additional points. However, this process often leads to redundant point clouds, resulting in excessive memory usage, slower performance, and substantial storage demands - posing significant challenges for deployment on resource-constrained devices. To address this limitation, we propose a theoretical framework that demystifies and improves density control in 3DGS. Our analysis reveals that splitting is crucial for escaping saddle points. Through an optimization-theoretic approach, we establish the necessary conditions for densification, determine the minimal number of offspring Gaussians, identify the optimal parameter update direction, and provide an analytical solution for normalizing off-spring opacity. Building on these insights, we introduce SteepGS, incorporating steepest density control, a principled strategy that minimizes loss while maintaining a compact point cloud. SteepGS achieves a ~50% reduction in Gaussian points without compromising rendering quality, significantly enhancing both efficiency and scalability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.09608", "title": "LightLab: Controlling Light Sources in Images with Diffusion Models", "authors": [ "Nadav Magar", "Amir Hertz", "Eric Tabellion", "Yael Pritch", "Alex Rav-Acha", "Ariel Shamir", "Yedid Hoshen" ], "github": "", "project_page": "", "abstract": "We present a simple, yet effective diffusion-based method for fine-grained, parametric control over light sources in an image. Existing relighting methods either rely on multiple input views to perform inverse rendering at inference time, or fail to provide explicit control over light changes. Our method fine-tunes a diffusion model on a small set of real raw photograph pairs, supplemented by synthetically rendered images at scale, to elicit its photorealistic prior for relighting. We leverage the linearity of light to synthesize image pairs depicting controlled light changes of either a target light source or ambient illumination. Using this data and an appropriate fine-tuning scheme, we train a model for precise illumination changes with explicit control over light intensity and color. Lastly, we show how our method can achieve compelling light editing results, and outperforms existing methods based on user preference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.08910", "title": "Behind Maya: Building a Multilingual Vision Language Model", "authors": [ "Nahid Alam", "Karthik Reddy Kanjula", "Surya Guthikonda", "Timothy Chung", "Bala Krishna S Vegesna", "Abhipsha Das", "Anthony Susevski", "Ryan Sze-Yin Chan", "S M Iftekhar Uddin", "Shayekh Bin Islam", "Roshan Santhosh", "Snegha A", "Drishti Sharma", "Chen Liu", "Isha Chaturvedi", "Genta Indra Winata", "Ashvanth. S", "Snehanshu Mukherjee", "Alham Fikri Aji" ], "github": "https://github.com/nahidalam/maya", "project_page": "", "abstract": "In recent times, we have seen a rapid development of large Vision-Language Models (VLMs). They have shown impressive results on academic benchmarks, primarily in widely spoken languages but lack performance on low-resource languages and varied cultural contexts. To address these limitations, we introduce Maya, an open-source Multilingual VLM. Our contributions are: 1) a multilingual image-text pretraining dataset in eight languages, based on the LLaVA pretraining dataset; and 2) a multilingual image-text model supporting these languages, enhancing cultural and linguistic comprehension in vision-language tasks. Code available at https://github.com/nahidalam/maya.", "github_manual": "", "github_regex": "https://github.com/nahidalam/maya", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.06356", "title": "Understanding and Mitigating Toxicity in Image-Text Pretraining Datasets: A Case Study on LLaVA", "authors": [ "Karthik Reddy Kanjula", "Surya Guthikonda", "Nahid Alam", "Shayekh Bin Islam" ], "github": "", "project_page": "", "abstract": "Pretraining datasets are foundational to the development of multimodal models, yet they often have inherent biases and toxic content from the web-scale corpora they are sourced from. In this paper, we investigate the prevalence of toxicity in LLaVA image-text pretraining dataset, examining how harmful content manifests in different modalities. We present a comprehensive analysis of common toxicity categories and propose targeted mitigation strategies, resulting in the creation of a refined toxicity-mitigated dataset. This dataset removes 7,531 of toxic image-text pairs in the LLaVA pre-training dataset. We offer guidelines for implementing robust toxicity detection pipelines. Our findings underscore the need to actively identify and filter toxic content - such as hate speech, explicit imagery, and targeted harassment - to build more responsible and equitable multimodal systems. The toxicity-mitigated dataset is open source and is available for further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-15", "arxiv_id": "2505.10557", "title": "MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning", "authors": [ "Ke Wang", "Junting Pan", "Linda Wei", "Aojun Zhou", "Weikang Shi", "Zimu Lu", "Han Xiao", "Yunqiao Yang", "Houxing Ren", "Mingjie Zhan", "Hongsheng Li" ], "github": "https://github.com/mathllm/MathCoder", "project_page": "", "abstract": "Natural language image-caption datasets, widely used for training Large Multimodal Models, mainly focus on natural scenarios and overlook the intricate details of mathematical figures that are critical for problem-solving, hindering the advancement of current LMMs in multimodal mathematical reasoning. To this end, we propose leveraging code as supervision for cross-modal alignment, since code inherently encodes all information needed to generate corresponding figures, establishing a precise connection between the two modalities. Specifically, we co-develop our image-to-code model and dataset with model-in-the-loop approach, resulting in an image-to-code model, FigCodifier and ImgCode-8.6M dataset, the largest image-code dataset to date. Furthermore, we utilize FigCodifier to synthesize novel mathematical figures and then construct MM-MathInstruct-3M, a high-quality multimodal math instruction fine-tuning dataset. Finally, we present MathCoder-VL, trained with ImgCode-8.6M for cross-modal alignment and subsequently fine-tuned on MM-MathInstruct-3M for multimodal math problem solving. Our model achieves a new open-source SOTA across all six metrics. Notably, it surpasses GPT-4o and Claude 3.5 Sonnet in the geometry problem-solving subset of MathVista, achieving improvements of 8.9% and 9.2%. The dataset and models will be released at https://github.com/mathllm/MathCoder.", "github_manual": "", "github_regex": "https://github.com/mathllm/MathCoder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.09926", "title": "AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection", "authors": [ "Bin-Bin Gao", "Yue Zhu", "Jiangtao Yan", "Yuezhi Cai", "Weixi Zhang", "Meng Wang", "Jun Liu", "Yong Liu", "Lei Wang", "Chengjie Wang" ], "github": "https://github.com/gaobb/AdaptCLIP", "project_page": "", "abstract": "Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studies have demonstrated that pre-trained vision-language models like CLIP exhibit strong generalization with just zero or a few normal images. However, existing methods struggle with designing prompt templates, complex token interactions, or requiring additional fine-tuning, resulting in limited flexibility. In this work, we present a simple yet effective method called AdaptCLIP based on two key insights. First, adaptive visual and textual representations should be learned alternately rather than jointly. Second, comparative learning between query and normal image prompt should incorporate both contextual and aligned residual features, rather than relying solely on residual features. AdaptCLIP treats CLIP models as a foundational service, adding only three simple adapters, visual adapter, textual adapter, and prompt-query adapter, at its input or output ends. AdaptCLIP supports zero-/few-shot generalization across domains and possesses a training-free manner on target domains once trained on a base dataset. AdaptCLIP achieves state-of-the-art performance on 12 anomaly detection benchmarks from industrial and medical domains, significantly outperforming existing competitive methods. We will make the code and model of AdaptCLIP available at https://github.com/gaobb/AdaptCLIP.", "github_manual": "", "github_regex": "https://github.com/gaobb/AdaptCLIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.09265", "title": "MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning", "authors": [ "Bin-Bin Gao" ], "github": "https://github.com/gaobb/MetaUAS", "project_page": "", "abstract": "Zero- and few-shot visual anomaly segmentation relies on powerful vision-language models that detect unseen anomalies using manually designed textual prompts. However, visual representations are inherently independent of language. In this paper, we explore the potential of a pure visual foundation model as an alternative to widely used vision-language models for universal visual anomaly segmentation. We present a novel paradigm that unifies anomaly segmentation into change segmentation. This paradigm enables us to leverage large-scale synthetic image pairs, featuring object-level and local region changes, derived from existing image datasets, which are independent of target anomaly datasets. We propose a one-prompt Meta-learning framework for Universal Anomaly Segmentation (MetaUAS) that is trained on this synthetic dataset and then generalizes well to segment any novel or unseen visual anomalies in the real world. To handle geometrical variations between prompt and query images, we propose a soft feature alignment module that bridges paired-image change perception and single-image semantic segmentation. This is the first work to achieve universal anomaly segmentation using a pure vision model without relying on special anomaly detection datasets and pre-trained visual-language models. Our method effectively and efficiently segments any anomalies with only one normal image prompt and enjoys training-free without guidance from language. Our MetaUAS significantly outperforms previous zero-shot, few-shot, and even full-shot anomaly segmentation methods. The code and pre-trained models are available at https://github.com/gaobb/MetaUAS.", "github_manual": "", "github_regex": "https://github.com/gaobb/MetaUAS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.09264", "title": "Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt", "authors": [ "Bin-Bin Gao" ], "github": "https://github.com/gaobb/OneNIP", "project_page": "", "abstract": "Unsupervised reconstruction networks using self-attention transformers have achieved state-of-the-art performance for multi-class (unified) anomaly detection with a single model. However, these self-attention reconstruction models primarily operate on target features, which may result in perfect reconstruction for both normal and anomaly features due to high consistency with context, leading to failure in detecting anomalies. Additionally, these models often produce inaccurate anomaly segmentation due to performing reconstruction in a low spatial resolution latent space. To enable reconstruction models enjoying high efficiency while enhancing their generalization for unified anomaly detection, we propose a simple yet effective method that reconstructs normal features and restores anomaly features with just One Normal Image Prompt (OneNIP). In contrast to previous work, OneNIP allows for the first time to reconstruct or restore anomalies with just one normal image prompt, effectively boosting unified anomaly detection performance. Furthermore, we propose a supervised refiner that regresses reconstruction errors by using both real normal and synthesized anomalous images, which significantly improves pixel-level anomaly segmentation. OneNIP outperforms previous methods on three industry anomaly detection benchmarks: MVTec, BTAD, and VisA. The code and pre-trained models are available at https://github.com/gaobb/OneNIP.", "github_manual": "", "github_regex": "https://github.com/gaobb/OneNIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.09263", "title": "Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation", "authors": [ "Guan Gui", "Bin-Bin Gao", "Jun Liu", "Chengjie Wang", "Yunsheng Wu" ], "github": "https://github.com/gaobb/AnoGen", "project_page": "", "abstract": "Anomaly detection is a practical and challenging task due to the scarcity of anomaly samples in industrial inspection. Some existing anomaly detection methods address this issue by synthesizing anomalies with noise or external data. However, there is always a large semantic gap between synthetic and real-world anomalies, resulting in weak performance in anomaly detection. To solve the problem, we propose a few-shot Anomaly-driven Generation (AnoGen) method, which guides the diffusion model to generate realistic and diverse anomalies with only a few real anomalies, thereby benefiting training anomaly detection models. Specifically, our work is divided into three stages. In the first stage, we learn the anomaly distribution based on a few given real anomalies and inject the learned knowledge into an embedding. In the second stage, we use the embedding and given bounding boxes to guide the diffusion model to generate realistic and diverse anomalies on specific objects (or textures). In the final stage, we propose a weakly-supervised anomaly detection method to train a more powerful model with generated anomalies. Our method builds upon DRAEM and DesTSeg as the foundation model and conducts experiments on the commonly used industrial anomaly detection dataset, MVTec. The experiments demonstrate that our generated anomalies effectively improve the model performance of both anomaly classification and segmentation tasks simultaneously, \\eg, DRAEM and DseTSeg achieved a 5.8\\% and 1.5\\% improvement in AU-PR metric on segmentation task, respectively. The code and generated anomalous data are available at https://github.com/gaobb/AnoGen.", "github_manual": "", "github_regex": "https://github.com/gaobb/AnoGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.10554", "title": "Beyond 'Aha!': Toward Systematic Meta-Abilities Alignment in Large Reasoning Models", "authors": [ "Zhiyuan Hu", "Yibo Wang", "Hanze Dong", "Yuhui Xu", "Amrita Saha", "Caiming Xiong", "Bryan Hooi", "Junnan Li" ], "github": "https://github.com/zhiyuanhubj/Meta-Ability-Alignment", "project_page": "", "abstract": "Large reasoning models (LRMs) already possess a latent capacity for long chain-of-thought reasoning. Prior work has shown that outcome-based reinforcement learning (RL) can incidentally elicit advanced reasoning behaviors such as self-correction, backtracking, and verification phenomena often referred to as the model's \"aha moment\". However, the timing and consistency of these emergent behaviors remain unpredictable and uncontrollable, limiting the scalability and reliability of LRMs' reasoning capabilities. To address these limitations, we move beyond reliance on prompts and coincidental \"aha moments\". Instead, we explicitly align models with three meta-abilities: deduction, induction, and abduction, using automatically generated, self-verifiable tasks. Our three stage-pipeline individual alignment, parameter-space merging, and domain-specific reinforcement learning, boosting performance by over 10\\% relative to instruction-tuned baselines. Furthermore, domain-specific RL from the aligned checkpoint yields an additional 2\\% average gain in the performance ceiling across math, coding, and science benchmarks, demonstrating that explicit meta-ability alignment offers a scalable and dependable foundation for reasoning. Code is available at: https://github.com/zhiyuanhubj/Meta-Ability-Alignment", "github_manual": "", "github_regex": "https://github.com/zhiyuanhubj/Meta-Ability-Alignment", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.10527", "title": "WorldPM: Scaling Human Preference Modeling", "authors": [ "Binghai Wang", "Runji Lin", "Keming Lu", "Le Yu", "Zhenru Zhang", "Fei Huang", "Chujie Zheng", "Kai Dang", "Yang Fan", "Xingzhang Ren", "An Yang", "Binyuan Hui", "Dayiheng Liu", "Tao Gui", "Qi Zhang", "Xuanjing Huang", "Yu-Gang Jiang", "Bowen Yu", "Jingren Zhou", "Junyang Lin" ], "github": "https://github.com/qwenlm/worldpm", "project_page": "", "abstract": "Motivated by scaling laws in language modeling that demonstrate how test loss scales as a power law with model and dataset sizes, we find that similar laws exist in preference modeling. We propose World Preference Modeling$ (WorldPM) to emphasize this scaling potential, where World Preference embodies a unified representation of human preferences. In this paper, we collect preference data from public forums covering diverse user communities, and conduct extensive training using 15M-scale data across models ranging from 1.5B to 72B parameters. We observe distinct patterns across different evaluation metrics: (1) Adversarial metrics (ability to identify deceptive features) consistently scale up with increased training data and base model size; (2) Objective metrics (objective knowledge with well-defined answers) show emergent behavior in larger language models, highlighting WorldPM's scalability potential; (3) Subjective metrics (subjective preferences from a limited number of humans or AI) do not demonstrate scaling trends. Further experiments validate the effectiveness of WorldPM as a foundation for preference fine-tuning. Through evaluations on 7 benchmarks with 20 subtasks, we find that WorldPM broadly improves the generalization performance across human preference datasets of varying sizes (7K, 100K and 800K samples), with performance gains exceeding 5% on many key subtasks. Integrating WorldPM into our internal RLHF pipeline, we observe significant improvements on both in-house and public evaluation sets, with notable gains of 4% to 8% in our in-house evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qwenlm/worldpm", "github_api_added_by": "auto" }, { "date": "2025-05-16", "arxiv_id": "2505.07782", "title": "MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering", "authors": [ "Rushi Qiang", "Yuchen Zhuang", "Yinghao Li", "Dingu Sagar V K", "Rongzhi Zhang", "Changhao Li", "Ian Shu-Hei Wong", "Sherry Yang", "Percy Liang", "Chao Zhang", "Bo Dai" ], "github": "https://github.com/MLE-Dojo/MLE-Dojo", "project_page": "", "abstract": "We introduce MLE-Dojo, a Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous large language model (LLM) agents in iterative machine learning engineering (MLE) workflows. Unlike existing benchmarks that primarily rely on static datasets or single-attempt evaluations, MLE-Dojo provides an interactive environment enabling agents to iteratively experiment, debug, and refine solutions through structured feedback loops. Built upon 200+ real-world Kaggle challenges, MLE-Dojo covers diverse, open-ended MLE tasks carefully curated to reflect realistic engineering scenarios such as data processing, architecture search, hyperparameter tuning, and code debugging. Its fully executable environment supports comprehensive agent training via both supervised fine-tuning and reinforcement learning, facilitating iterative experimentation, realistic data sampling, and real-time outcome verification. Extensive evaluations of eight frontier LLMs reveal that while current models achieve meaningful iterative improvements, they still exhibit significant limitations in autonomously generating long-horizon solutions and efficiently resolving complex errors. Furthermore, MLE-Dojo's flexible and extensible architecture seamlessly integrates diverse data sources, tools, and evaluation protocols, uniquely enabling model-based agent tuning and promoting interoperability, scalability, and reproducibility. We open-source our framework and benchmarks to foster community-driven innovation towards next-generation MLE agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MLE-Dojo/MLE-Dojo", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.10185", "title": "The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think", "authors": [ "Seongyun Lee", "Seungone Kim", "Minju Seo", "Yongrae Jo", "Dongyoung Go", "Hyeonbin Hwang", "Jinho Park", "Xiang Yue", "Sean Welleck", "Graham Neubig", "Moontae Lee", "Minjoon Seo" ], "github": "", "project_page": "", "abstract": "Long chain-of-thought (CoT) is an essential ingredient in effective usage of modern large language models, but our understanding of the reasoning strategies underlying these capabilities remains limited. While some prior works have attempted to categorize CoTs using predefined strategy types, such approaches are constrained by human intuition and fail to capture the full diversity of model behaviors. In this work, we introduce the CoT Encyclopedia, a bottom-up framework for analyzing and steering model reasoning. Our method automatically extracts diverse reasoning criteria from model-generated CoTs, embeds them into a semantic space, clusters them into representative categories, and derives contrastive rubrics to interpret reasoning behavior. Human evaluations show that this framework produces more interpretable and comprehensive analyses than existing methods. Moreover, we demonstrate that this understanding enables performance gains: we can predict which strategy a model is likely to use and guide it toward more effective alternatives. Finally, we provide practical insights, such as that training data format (e.g., free-form vs. multiple-choice) has a far greater impact on reasoning behavior than data domain, underscoring the importance of format-aware model design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.10320", "title": "J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning", "authors": [ "Chenxi Whitehouse", "Tianlu Wang", "Ping Yu", "Xian Li", "Jason Weston", "Ilia Kulikov", "Swarnadeep Saha" ], "github": "", "project_page": "", "abstract": "The progress of AI is bottlenecked by the quality of evaluation, and powerful LLM-as-a-Judge models have proved to be a core solution. Improved judgment ability is enabled by stronger chain-of-thought reasoning, motivating the need to find the best recipes for training such models to think. In this work we introduce J1, a reinforcement learning approach to training such models. Our method converts both verifiable and non-verifiable prompts to judgment tasks with verifiable rewards that incentivize thinking and mitigate judgment bias. In particular, our approach outperforms all other existing 8B or 70B models when trained at those sizes, including models distilled from DeepSeek-R1. J1 also outperforms o1-mini, and even R1 on some benchmarks, despite training a smaller model. We provide analysis and ablations comparing Pairwise-J1 vs Pointwise-J1 models, offline vs online training recipes, reward strategies, seed prompts, and variations in thought length and content. We find that our models make better judgments by learning to outline evaluation criteria, comparing against self-generated reference answers, and re-evaluating the correctness of model responses.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.09666", "title": "System Prompt Optimization with Meta-Learning", "authors": [ "Yumin Choi", "Jinheon Baek", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have shown remarkable capabilities, with optimizing their input prompts playing a pivotal role in maximizing their performance. However, while LLM prompts consist of both the task-agnostic system prompts and task-specific user prompts, existing work on prompt optimization has focused on user prompts specific to individual queries or tasks, and largely overlooked the system prompt that is, once optimized, applicable across different tasks and domains. Motivated by this, we introduce the novel problem of bilevel system prompt optimization, whose objective is to design system prompts that are robust to diverse user prompts and transferable to unseen tasks. To tackle this problem, we then propose a meta-learning framework, which meta-learns the system prompt by optimizing it over various user prompts across multiple datasets, while simultaneously updating the user prompts in an iterative manner to ensure synergy between them. We conduct experiments on 14 unseen datasets spanning 5 different domains, on which we show that our approach produces system prompts that generalize effectively to diverse user prompts. Also, our findings reveal that the optimized system prompt enables rapid adaptation even to unseen tasks, requiring fewer optimization steps for test-time user prompts while achieving improved performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.09723", "title": "EnerVerse-AC: Envisioning Embodied Environments with Action Condition", "authors": [ "Yuxin Jiang", "Shengcong Chen", "Siyuan Huang", "Liliang Chen", "Pengfei Zhou", "Yue Liao", "Xindong He", "Chiming Liu", "Hongsheng Li", "Maoqing Yao", "Guanghui Ren" ], "github": "", "project_page": "", "abstract": "Robotic imitation learning has advanced from solving static tasks to addressing dynamic interaction scenarios, but testing and evaluation remain costly and challenging due to the need for real-time interaction with dynamic environments. We propose EnerVerse-AC (EVAC), an action-conditional world model that generates future visual observations based on an agent's predicted actions, enabling realistic and controllable robotic inference. Building on prior architectures, EVAC introduces a multi-level action-conditioning mechanism and ray map encoding for dynamic multi-view image generation while expanding training data with diverse failure trajectories to improve generalization. As both a data engine and evaluator, EVAC augments human-collected trajectories into diverse datasets and generates realistic, action-conditioned video observations for policy testing, eliminating the need for physical robots or complex simulations. This approach significantly reduces costs while maintaining high fidelity in robotic manipulation evaluation. Extensive experiments validate the effectiveness of our method. Code, checkpoints, and datasets can be found at .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.09694", "title": "EWMBench: Evaluating Scene, Motion, and Semantic Quality in Embodied World Models", "authors": [ "Hu Yue", "Siyuan Huang", "Yue Liao", "Shengcong Chen", "Pengfei Zhou", "Liliang Chen", "Maoqing Yao", "Guanghui Ren" ], "github": "https://github.com/AgibotTech/EWMBench", "project_page": "", "abstract": "Recent advances in creative AI have enabled the synthesis of high-fidelity images and videos conditioned on language instructions. Building on these developments, text-to-video diffusion models have evolved into embodied world models (EWMs) capable of generating physically plausible scenes from language commands, effectively bridging vision and action in embodied AI applications. This work addresses the critical challenge of evaluating EWMs beyond general perceptual metrics to ensure the generation of physically grounded and action-consistent behaviors. We propose the Embodied World Model Benchmark (EWMBench), a dedicated framework designed to evaluate EWMs based on three key aspects: visual scene consistency, motion correctness, and semantic alignment. Our approach leverages a meticulously curated dataset encompassing diverse scenes and motion patterns, alongside a comprehensive multi-dimensional evaluation toolkit, to assess and compare candidate models. The proposed benchmark not only identifies the limitations of existing video generation models in meeting the unique requirements of embodied tasks but also provides valuable insights to guide future advancements in the field. The dataset and evaluation tools are publicly available at https://github.com/AgibotTech/EWMBench.", "github_manual": "", "github_regex": "https://github.com/AgibotTech/EWMBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.08617", "title": "OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning", "authors": [ "Zhaochen Su", "Linjie Li", "Mingyang Song", "Yunzhuo Hao", "Zhengyuan Yang", "Jun Zhang", "Guanjie Chen", "Jiawei Gu", "Juntao Li", "Xiaoye Qu", "Yu Cheng" ], "github": "", "project_page": "", "abstract": "While humans can flexibly leverage interactive visual cognition for complex problem-solving, enabling Large Vision-Language Models (LVLMs) to learn similarly adaptive behaviors with visual tools remains challenging. A significant hurdle is the current lack of standardized infrastructure, which hinders integrating diverse tools, generating rich interaction data, and training robust agents effectively. To address these gaps, we introduce OpenThinkIMG, the first open-source, comprehensive end-to-end framework for tool-augmented LVLMs. It features standardized vision tool interfaces, scalable trajectory generation for policy initialization, and a flexible training environment. Furthermore, considering supervised fine-tuning (SFT) on static demonstrations offers limited policy generalization for dynamic tool invocation, we propose a novel reinforcement learning (RL) framework V-ToolRL to train LVLMs to learn adaptive policies for invoking external vision tools. V-ToolRL enables LVLMs to autonomously discover optimal tool-usage strategies by directly optimizing for task success using feedback from tool interactions. We empirically validate V-ToolRL on challenging chart reasoning tasks. Our RL-trained agent, built upon a Qwen2-VL-2B, significantly outperforms its SFT-initialized counterpart (+28.83 points) and surpasses established supervised tool-learning baselines like Taco and CogCom by an average of +12.7 points. Notably, it also surpasses prominent closed-source models like GPT-4.1 by +8.68 accuracy points. We hope OpenThinkIMG can serve as a foundational framework for advancing dynamic, tool-augmented visual reasoning, helping the community develop AI agents that can genuinely \"think with images\".", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.10562", "title": "End-to-End Vision Tokenizer Tuning", "authors": [ "Wenxuan Wang", "Fan Zhang", "Yufeng Cui", "Haiwen Diao", "Zhuoyan Luo", "Huchuan Lu", "Jing Liu", "Xinlong Wang" ], "github": "", "project_page": "", "abstract": "Existing vision tokenization isolates the optimization of vision tokenizers from downstream training, implicitly assuming the visual tokens can generalize well across various tasks, e.g., image generation and visual question answering. The vision tokenizer optimized for low-level reconstruction is agnostic to downstream tasks requiring varied representations and semantics. This decoupled paradigm introduces a critical misalignment: The loss of the vision tokenization can be the representation bottleneck for target tasks. For example, errors in tokenizing text in a given image lead to poor results when recognizing or generating them. To address this, we propose ETT, an end-to-end vision tokenizer tuning approach that enables joint optimization between vision tokenization and target autoregressive tasks. Unlike prior autoregressive models that use only discrete indices from a frozen vision tokenizer, ETT leverages the visual embeddings of the tokenizer codebook, and optimizes the vision tokenizers end-to-end with both reconstruction and caption objectives. ETT can be seamlessly integrated into existing training pipelines with minimal architecture modifications. Our ETT is simple to implement and integrate, without the need to adjust the original codebooks or architectures of the employed large language models. Extensive experiments demonstrate that our proposed end-to-end vision tokenizer tuning unlocks significant performance gains, i.e., 2-6% for multimodal understanding and visual generation tasks compared to frozen tokenizer baselines, while preserving the original reconstruction capability. We hope this very simple and strong method can empower multimodal foundation models besides image generation and understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.10046", "title": "Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis", "authors": [ "Bingda Tang", "Boyang Zheng", "Xichen Pan", "Sayak Paul", "Saining Xie" ], "github": "https://github.com/tang-bd/fuse-dit", "project_page": "", "abstract": "This paper does not describe a new method; instead, it provides a thorough exploration of an important yet understudied design space related to recent advances in text-to-image synthesis -- specifically, the deep fusion of large language models (LLMs) and diffusion transformers (DiTs) for multi-modal generation. Previous studies mainly focused on overall system performance rather than detailed comparisons with alternative methods, and key design details and training recipes were often left undisclosed. These gaps create uncertainty about the real potential of this approach. To fill these gaps, we conduct an empirical study on text-to-image generation, performing controlled comparisons with established baselines, analyzing important design choices, and providing a clear, reproducible recipe for training at scale. We hope this work offers meaningful data points and practical guidelines for future research in multi-modal generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tang-bd/fuse-dit", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.10558", "title": "Style Customization of Text-to-Vector Generation with Image Diffusion Priors", "authors": [ "Peiying Zhang", "Nanxuan Zhao", "Jing Liao" ], "github": "https://github.com/intchous/custom-svg-style", "project_page": "", "abstract": "Scalable Vector Graphics (SVGs) are highly favored by designers due to their resolution independence and well-organized layer structure. Although existing text-to-vector (T2V) generation methods can create SVGs from text prompts, they often overlook an important need in practical applications: style customization, which is vital for producing a collection of vector graphics with consistent visual appearance and coherent aesthetics. Extending existing T2V methods for style customization poses certain challenges. Optimization-based T2V models can utilize the priors of text-to-image (T2I) models for customization, but struggle with maintaining structural regularity. On the other hand, feed-forward T2V models can ensure structural regularity, yet they encounter difficulties in disentangling content and style due to limited SVG training data. To address these challenges, we propose a novel two-stage style customization pipeline for SVG generation, making use of the advantages of both feed-forward T2V models and T2I image priors. In the first stage, we train a T2V diffusion model with a path-level representation to ensure the structural regularity of SVGs while preserving diverse expressive capabilities. In the second stage, we customize the T2V diffusion model to different styles by distilling customized T2I models. By integrating these techniques, our pipeline can generate high-quality and diverse SVGs in custom styles based on text prompts in an efficient feed-forward manner. The effectiveness of our method has been validated through extensive experiments. The project page is https://customsvg.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/intchous/custom-svg-style", "github_api_added_by": "auto" }, { "date": "2025-05-16", "arxiv_id": "2505.10565", "title": "Depth Anything with Any Prior", "authors": [ "Zehan Wang", "Siyu Chen", "Lihe Yang", "Jialei Wang", "Ziang Zhang", "Hengshuang Zhao", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "This work presents Prior Depth Anything, a framework that combines incomplete but precise metric information in depth measurement with relative but complete geometric structures in depth prediction, generating accurate, dense, and detailed metric depth maps for any scene. To this end, we design a coarse-to-fine pipeline to progressively integrate the two complementary depth sources. First, we introduce pixel-level metric alignment and distance-aware weighting to pre-fill diverse metric priors by explicitly using depth prediction. It effectively narrows the domain gap between prior patterns, enhancing generalization across varying scenarios. Second, we develop a conditioned monocular depth estimation (MDE) model to refine the inherent noise of depth priors. By conditioning on the normalized pre-filled prior and prediction, the model further implicitly merges the two complementary depth sources. Our model showcases impressive zero-shot generalization across depth completion, super-resolution, and inpainting over 7 real-world datasets, matching or even surpassing previous task-specific methods. More importantly, it performs well on challenging, unseen mixed priors and enables test-time improvements by switching prediction models, providing a flexible accuracy-efficiency trade-off while evolving with advancements in MDE models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.08581", "title": "ReSurgSAM2: Referring Segment Anything in Surgical Video via Credible Long-term Tracking", "authors": [ "Haofeng Liu", "Mingqi Gao", "Xuxiao Luo", "Ziyue Wang", "Guanyi Qin", "Junde Wu", "Yueming Jin" ], "github": "https://github.com/jinlab-imvr/ReSurgSAM2", "project_page": "", "abstract": "Surgical scene segmentation is critical in computer-assisted surgery and is vital for enhancing surgical quality and patient outcomes. Recently, referring surgical segmentation is emerging, given its advantage of providing surgeons with an interactive experience to segment the target object. However, existing methods are limited by low efficiency and short-term tracking, hindering their applicability in complex real-world surgical scenarios. In this paper, we introduce ReSurgSAM2, a two-stage surgical referring segmentation framework that leverages Segment Anything Model 2 to perform text-referred target detection, followed by tracking with reliable initial frame identification and diversity-driven long-term memory. For the detection stage, we propose a cross-modal spatial-temporal Mamba to generate precise detection and segmentation results. Based on these results, our credible initial frame selection strategy identifies the reliable frame for the subsequent tracking. Upon selecting the initial frame, our method transitions to the tracking stage, where it incorporates a diversity-driven memory mechanism that maintains a credible and diverse memory bank, ensuring consistent long-term tracking. Extensive experiments demonstrate that ReSurgSAM2 achieves substantial improvements in accuracy and efficiency compared to existing methods, operating in real-time at 61.2 FPS. Our code and datasets will be available at https://github.com/jinlab-imvr/ReSurgSAM2.", "github_manual": "", "github_regex": "https://github.com/jinlab-imvr/ReSurgSAM2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.09738", "title": "Achieving Tokenizer Flexibility in Language Models through Heuristic Adaptation and Supertoken Learning", "authors": [ "Shaurya Sharthak", "Vinayak Pahalwan", "Adithya Kamath", "Adarsh Shirawalmath" ], "github": "https://github.com/Tinycompany-AI/tokenadapt", "project_page": "", "abstract": "Pretrained language models (LLMs) are often constrained by their fixed tokenization schemes, leading to inefficiencies and performance limitations, particularly for multilingual or specialized applications. This tokenizer lock-in presents significant challenges. standard methods to overcome this often require prohibitive computational resources. Although tokenizer replacement with heuristic initialization aims to reduce this burden, existing methods often require exhaustive residual fine-tuning and still may not fully preserve semantic nuances or adequately address the underlying compression inefficiencies. Our framework introduces two innovations: first, Tokenadapt, a model-agnostic tokenizer transplantation method, and second, novel pre-tokenization learning for multi-word Supertokens to enhance compression and reduce fragmentation. Tokenadapt initializes new unique token embeddings via a hybrid heuristic that combines two methods: a local estimate based on subword decomposition using the old tokenizer, and a global estimate utilizing the top-k semantically similar tokens from the original vocabulary. This methodology aims to preserve semantics while significantly minimizing retraining requirements. Empirical investigations validate both contributions: the transplantation heuristic successfully initializes unique tokens, markedly outperforming conventional baselines and sophisticated methods including Transtokenizer and ReTok, while our Supertokens achieve notable compression gains. Our zero-shot perplexity results demonstrate that the TokenAdapt hybrid initialization consistently yields lower perplexity ratios compared to both ReTok and TransTokenizer baselines across different base models and newly trained target tokenizers. TokenAdapt typically reduced the overall perplexity ratio significantly compared to ReTok, yielding at least a 2-fold improvement in these aggregate scores.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Tinycompany-AI/tokenadapt", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.09990", "title": "PointArena: Probing Multimodal Grounding Through Language-Guided Pointing", "authors": [ "Long Cheng", "Jiafei Duan", "Yi Ru Wang", "Haoquan Fang", "Boyang Li", "Yushan Huang", "Elvis Wang", "Ainaz Eftekhar", "Jason Lee", "Wentao Yuan", "Rose Hendrix", "Noah A. Smith", "Fei Xia", "Dieter Fox", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Pointing serves as a fundamental and intuitive mechanism for grounding language within visual contexts, with applications spanning robotics, assistive technologies, and interactive AI systems. While recent multimodal models have started to support pointing capabilities, existing benchmarks typically focus only on referential object localization tasks. We introduce PointArena, a comprehensive platform for evaluating multimodal pointing across diverse reasoning scenarios. PointArena comprises three components: (1) Point-Bench, a curated dataset containing approximately 1,000 pointing tasks across five reasoning categories; (2) Point-Battle, an interactive, web-based arena facilitating blind, pairwise model comparisons, which has already gathered over 4,500 anonymized votes; and (3) Point-Act, a real-world robotic manipulation system allowing users to directly evaluate multimodal model pointing capabilities in practical settings. We conducted extensive evaluations of both state-of-the-art open-source and proprietary multimodal models. Results indicate that Molmo-72B consistently outperforms other models, though proprietary models increasingly demonstrate comparable performance. Additionally, we find that supervised training specifically targeting pointing tasks significantly enhances model performance. Across our multi-stage evaluation pipeline, we also observe strong correlations, underscoring the critical role of precise pointing capabilities in enabling multimodal models to effectively bridge abstract reasoning with concrete, real-world actions. Project page: https://pointarena.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.10167", "title": "QuXAI: Explainers for Hybrid Quantum Machine Learning Models", "authors": [ "Saikat Barua", "Mostafizur Rahman", "Shehenaz Khaled", "Md Jafor Sadek", "Rafiul Islam", "Shahnewaz Siddique" ], "github": "https://github.com/GitsSaikat/QuXAI", "project_page": "", "abstract": "The emergence of hybrid quantum-classical machine learning (HQML) models opens new horizons of computational intelligence but their fundamental complexity frequently leads to black box behavior that undermines transparency and reliability in their application. Although XAI for quantum systems still in its infancy, a major research gap is evident in robust global and local explainability approaches that are designed for HQML architectures that employ quantized feature encoding followed by classical learning. The gap is the focus of this work, which introduces QuXAI, an framework based upon Q-MEDLEY, an explainer for explaining feature importance in these hybrid systems. Our model entails the creation of HQML models incorporating quantum feature maps, the use of Q-MEDLEY, which combines feature based inferences, preserving the quantum transformation stage and visualizing the resulting attributions. Our result shows that Q-MEDLEY delineates influential classical aspects in HQML models, as well as separates their noise, and competes well against established XAI techniques in classical validation settings. Ablation studies more significantly expose the virtues of the composite structure used in Q-MEDLEY. The implications of this work are critically important, as it provides a route to improve the interpretability and reliability of HQML models, thus promoting greater confidence and being able to engage in safer and more responsible use of quantum-enhanced AI technology.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GitsSaikat/QuXAI", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.10475", "title": "Parallel Scaling Law for Language Models", "authors": [ "Mouxiang Chen", "Binyuan Hui", "Zeyu Cui", "Jiaxi Yang", "Dayiheng Liu", "Jianling Sun", "Junyang Lin", "Zhongxin Liu" ], "github": "https://github.com/QwenLM/ParScale", "project_page": "", "abstract": "It is commonly believed that scaling language models should commit a significant space or time cost, by increasing the parameters (parameter scaling) or output tokens (inference-time scaling). We introduce the third and more inference-efficient scaling paradigm: increasing the model's parallel computation during both training and inference time. We apply P diverse and learnable transformations to the input, execute forward passes of the model in parallel, and dynamically aggregate the P outputs. This method, namely parallel scaling (ParScale), scales parallel computation by reusing existing parameters and can be applied to any model structure, optimization procedure, data, or task. We theoretically propose a new scaling law and validate it through large-scale pre-training, which shows that a model with P parallel streams is similar to scaling the parameters by O(log P) while showing superior inference efficiency. For example, ParScale can use up to 22times less memory increase and 6times less latency increase compared to parameter scaling that achieves the same performance improvement. It can also recycle an off-the-shelf pre-trained model into a parallelly scaled one by post-training on a small amount of tokens, further reducing the training budget. The new scaling law we discovered potentially facilitates the deployment of more powerful models in low-resource scenarios, and provides an alternative perspective for the role of computation in machine learning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/QwenLM/ParScale", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.10468", "title": "AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenge", "authors": [ "Ranjan Sapkota", "Konstantinos I. Roumeliotis", "Manoj Karkee" ], "github": "", "project_page": "", "abstract": "This study critically distinguishes between AI Agents and Agentic AI, offering a structured conceptual taxonomy, application mapping, and challenge analysis to clarify their divergent design philosophies and capabilities. We begin by outlining the search strategy and foundational definitions, characterizing AI Agents as modular systems driven by Large Language Models (LLMs) and Large Image Models (LIMs) for narrow, task-specific automation. Generative AI is positioned as a precursor, with AI Agents advancing through tool integration, prompt engineering, and reasoning enhancements. In contrast, Agentic AI systems represent a paradigmatic shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy. Through a sequential evaluation of architectural evolution, operational mechanisms, interaction styles, and autonomy levels, we present a comparative analysis across both paradigms. Application domains such as customer support, scheduling, and data summarization are contrasted with Agentic AI deployments in research automation, robotic coordination, and medical decision support. We further examine unique challenges in each paradigm including hallucination, brittleness, emergent behavior, and coordination failure and propose targeted solutions such as ReAct loops, RAG, orchestration layers, and causal modeling. This work aims to provide a definitive roadmap for developing robust, scalable, and explainable AI agent and Agentic AI-driven systems. >AI Agents, Agent-driven, Vision-Language-Models, Agentic AI Decision Support System, Agentic-AI Applications", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.06027", "title": "Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target Self-Distillation", "authors": [ "Stefan Vasilev", "Christian Herold", "Baohao Liao", "Seyyed Hadi Hashemi", "Shahram Khadivi", "Christof Monz" ], "github": "", "project_page": "", "abstract": "This paper introduces Unilogit, a novel self-distillation method for machine unlearning in Large Language Models. Unilogit addresses the challenge of selectively forgetting specific information while maintaining overall model utility, a critical task in compliance with data privacy regulations like GDPR. Unlike prior methods that rely on static hyperparameters or starting model outputs, Unilogit dynamically adjusts target logits to achieve a uniform probability for the target token, leveraging the current model's outputs for more accurate self-distillation targets. This approach not only eliminates the need for additional hyperparameters but also enhances the model's ability to approximate the golden targets. Extensive experiments on public benchmarks and an in-house e-commerce dataset demonstrate Unilogit's superior performance in balancing forget and retain objectives, outperforming state-of-the-art methods such as NPO and UnDIAL. Our analysis further reveals Unilogit's robustness across various scenarios, highlighting its practical applicability and effectiveness in achieving efficacious machine unlearning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.07096", "title": "X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real", "authors": [ "Prithwish Dan", "Kushal Kedia", "Angela Chao", "Edward Weiyi Duan", "Maximus Adrian Pace", "Wei-Chiu Ma", "Sanjiban Choudhury" ], "github": "https://github.com/portal-cornell/X-Sim", "project_page": "", "abstract": "Human videos offer a scalable way to train robot manipulation policies, but lack the action labels needed by standard imitation learning algorithms. Existing cross-embodiment approaches try to map human motion to robot actions, but often fail when the embodiments differ significantly. We propose X-Sim, a real-to-sim-to-real framework that uses object motion as a dense and transferable signal for learning robot policies. X-Sim starts by reconstructing a photorealistic simulation from an RGBD human video and tracking object trajectories to define object-centric rewards. These rewards are used to train a reinforcement learning (RL) policy in simulation. The learned policy is then distilled into an image-conditioned diffusion policy using synthetic rollouts rendered with varied viewpoints and lighting. To transfer to the real world, X-Sim introduces an online domain adaptation technique that aligns real and simulated observations during deployment. Importantly, X-Sim does not require any robot teleoperation data. We evaluate it across 5 manipulation tasks in 2 environments and show that it: (1) improves task progress by 30% on average over hand-tracking and sim-to-real baselines, (2) matches behavior cloning with 10x less data collection time, and (3) generalizes to new camera viewpoints and test-time changes. Code and videos are available at https://portal-cornell.github.io/X-Sim/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/portal-cornell/X-Sim", "github_api_added_by": "auto" }, { "date": "2025-05-16", "arxiv_id": "2505.10566", "title": "3D-Fixup: Advancing Photo Editing with 3D Priors", "authors": [ "Yen-Chi Cheng", "Krishna Kumar Singh", "Jae Shin Yoon", "Alex Schwing", "Liangyan Gui", "Matheus Gadelha", "Paul Guerrero", "Nanxuan Zhao" ], "github": "", "project_page": "", "abstract": "Despite significant advances in modeling image priors via diffusion models, 3D-aware image editing remains challenging, in part because the object is only specified via a single image. To tackle this challenge, we propose 3D-Fixup, a new framework for editing 2D images guided by learned 3D priors. The framework supports difficult editing situations such as object translation and 3D rotation. To achieve this, we leverage a training-based approach that harnesses the generative power of diffusion models. As video data naturally encodes real-world physical dynamics, we turn to video data for generating training data pairs, i.e., a source and a target frame. Rather than relying solely on a single trained model to infer transformations between source and target frames, we incorporate 3D guidance from an Image-to-3D model, which bridges this challenging task by explicitly projecting 2D information into 3D space. We design a data generation pipeline to ensure high-quality 3D guidance throughout training. Results show that by integrating these 3D priors, 3D-Fixup effectively supports complex, identity coherent 3D-aware edits, achieving high-quality results and advancing the application of diffusion models in realistic image manipulation. The code is provided at https://3dfixup.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-16", "arxiv_id": "2505.09601", "title": "Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware", "authors": [ "Justin Yu", "Letian Fu", "Huang Huang", "Karim El-Refai", "Rares Andrei Ambrus", "Richard Cheng", "Muhammad Zubair Irshad", "Ken Goldberg" ], "github": "", "project_page": "", "abstract": "Scaling robot learning requires vast and diverse datasets. Yet the prevailing data collection paradigm-human teleoperation-remains costly and constrained by manual effort and physical robot access. We introduce Real2Render2Real (R2R2R), a novel approach for generating robot training data without relying on object dynamics simulation or teleoperation of robot hardware. The input is a smartphone-captured scan of one or more objects and a single video of a human demonstration. R2R2R renders thousands of high visual fidelity robot-agnostic demonstrations by reconstructing detailed 3D object geometry and appearance, and tracking 6-DoF object motion. R2R2R uses 3D Gaussian Splatting (3DGS) to enable flexible asset generation and trajectory synthesis for both rigid and articulated objects, converting these representations to meshes to maintain compatibility with scalable rendering engines like IsaacLab but with collision modeling off. Robot demonstration data generated by R2R2R integrates directly with models that operate on robot proprioceptive states and image observations, such as vision-language-action models (VLA) and imitation learning policies. Physical experiments suggest that models trained on R2R2R data from a single human demonstration can match the performance of models trained on 150 human teleoperation demonstrations. Project page: https://real2render2real.com", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.11152", "title": "Learning Dense Hand Contact Estimation from Imbalanced Data", "authors": [ "Daniel Sungho Jung", "Kyoung Mu Lee" ], "github": "https://github.com/dqj5182/HACO_RELEASE", "project_page": "", "abstract": "Hands are essential to human interaction, and understanding contact between hands and the world can promote comprehensive understanding of their function. Recently, there have been growing number of hand interaction datasets that cover interaction with object, other hand, scene, and body. Despite the significance of the task and increasing high-quality data, how to effectively learn dense hand contact estimation remains largely underexplored. There are two major challenges for learning dense hand contact estimation. First, there exists class imbalance issue from hand contact datasets where majority of samples are not in contact. Second, hand contact datasets contain spatial imbalance issue with most of hand contact exhibited in finger tips, resulting in challenges for generalization towards contacts in other hand regions. To tackle these issues, we present a framework that learns dense HAnd COntact estimation (HACO) from imbalanced data. To resolve the class imbalance issue, we introduce balanced contact sampling, which builds and samples from multiple sampling groups that fairly represent diverse contact statistics for both contact and non-contact samples. Moreover, to address the spatial imbalance issue, we propose vertex-level class-balanced (VCB) loss, which incorporates spatially varying contact distribution by separately reweighting loss contribution of each vertex based on its contact frequency across dataset. As a result, we effectively learn to predict dense hand contact estimation with large-scale hand contact data without suffering from class and spatial imbalance issue. The codes will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dqj5182/HACO_RELEASE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.09388", "title": "Qwen3 Technical Report", "authors": [ "An Yang", "Anfeng Li", "Baosong Yang", "Beichen Zhang", "Binyuan Hui", "Bo Zheng", "Bowen Yu", "Chang Gao", "Chengen Huang", "Chenxu Lv", "Chujie Zheng", "Dayiheng Liu", "Fan Zhou", "Fei Huang", "Feng Hu", "Hao Ge", "Haoran Wei", "Huan Lin", "Jialong Tang", "Jian Yang", "Jianhong Tu", "Jianwei Zhang", "Jianxin Yang", "Jiaxi Yang", "Jing Zhou", "Jingren Zhou", "Junyang Lin", "Kai Dang", "Keqin Bao", "Kexin Yang", "Le Yu", "Lianghao Deng", "Mei Li", "Mingfeng Xue", "Mingze Li", "Pei Zhang", "Peng Wang", "Qin Zhu", "Rui Men", "Ruize Gao", "Shixuan Liu", "Shuang Luo", "Tianhao Li", "Tianyi Tang", "Wenbiao Yin", "Xingzhang Ren", "Xinyu Wang", "Xinyu Zhang", "Xuancheng Ren", "Yang Fan", "Yang Su", "Yichang Zhang", "Yinger Zhang", "Yu Wan", "Yuqiong Liu", "Zekun Wang", "Zeyu Cui", "Zhenru Zhang", "Zhipeng Zhou", "Zihan Qiu" ], "github": "", "project_page": "", "abstract": "In this work, we present Qwen3, the latest version of the Qwen model family. Qwen3 comprises a series of large language models (LLMs) designed to advance performance, efficiency, and multilingual capabilities. The Qwen3 series includes models of both dense and Mixture-of-Expert (MoE) architectures, with parameter scales ranging from 0.6 to 235 billion. A key innovation in Qwen3 is the integration of thinking mode (for complex, multi-step reasoning) and non-thinking mode (for rapid, context-driven responses) into a unified framework. This eliminates the need to switch between different models--such as chat-optimized models (e.g., GPT-4o) and dedicated reasoning models (e.g., QwQ-32B)--and enables dynamic mode switching based on user queries or chat templates. Meanwhile, Qwen3 introduces a thinking budget mechanism, allowing users to allocate computational resources adaptively during inference, thereby balancing latency and performance based on task complexity. Moreover, by leveraging the knowledge from the flagship models, we significantly reduce the computational resources required to build smaller-scale models, while ensuring their highly competitive performance. Empirical evaluations demonstrate that Qwen3 achieves state-of-the-art results across diverse benchmarks, including tasks in code generation, mathematical reasoning, agent tasks, etc., competitive against larger MoE models and proprietary models. Compared to its predecessor Qwen2.5, Qwen3 expands multilingual support from 29 to 119 languages and dialects, enhancing global accessibility through improved cross-lingual understanding and generation capabilities. To facilitate reproducibility and community-driven research and development, all Qwen3 models are publicly accessible under Apache 2.0.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.10962", "title": "MPS-Prover: Advancing Stepwise Theorem Proving by Multi-Perspective Search and Data Curation", "authors": [ "Zhenwen Liang", "Linfeng Song", "Yang Li", "Tao Yang", "Feng Zhang", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Automated Theorem Proving (ATP) in formal languages remains a formidable challenge in AI, demanding rigorous logical deduction and navigating vast search spaces. While large language models (LLMs) have shown promising performance, existing stepwise provers often suffer from biased search guidance, leading to inefficiencies and suboptimal proof strategies. This paper introduces the Multi-Perspective Search Prover (MPS-Prover), a novel stepwise ATP system designed to overcome these limitations. MPS-Prover incorporates two key innovations: a highly effective post-training data curation strategy that prunes approximately 40% of redundant training data without sacrificing performance, and a multi-perspective tree search mechanism. This search integrates a learned critic model with strategically designed heuristic rules to diversify tactic selection, prevent getting trapped in unproductive states, and enhance search robustness. Extensive evaluations demonstrate that MPS-Prover achieves state-of-the-art performance on multiple challenging benchmarks, including miniF2F and ProofNet, outperforming prior 7B parameter models. Furthermore, our analyses reveal that MPS-Prover generates significantly shorter and more diverse proofs compared to existing stepwise and whole-proof methods, highlighting its efficiency and efficacy. Our work advances the capabilities of LLM-based formal reasoning and offers a robust framework and a comprehensive analysis for developing more powerful theorem provers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.11409", "title": "Visual Planning: Let's Think Only with Images", "authors": [ "Yi Xu", "Chengzu Li", "Han Zhou", "Xingchen Wan", "Caiqi Zhang", "Anna Korhonen", "Ivan Vuli\u0107" ], "github": "https://github.com/yix8/VisualPlanning", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) and their multimodal extensions (MLLMs) have substantially enhanced machine reasoning across diverse tasks. However, these models predominantly rely on pure text as the medium for both expressing and structuring reasoning, even when visual information is present. In this work, we argue that language may not always be the most natural or effective modality for reasoning, particularly in tasks involving spatial and geometrical information. Motivated by this, we propose a new paradigm, Visual Planning, which enables planning through purely visual representations, independent of text. In this paradigm, planning is executed via sequences of images that encode step-by-step inference in the visual domain, akin to how humans sketch or visualize future actions. We introduce a novel reinforcement learning framework, Visual Planning via Reinforcement Learning (VPRL), empowered by GRPO for post-training large vision models, leading to substantial improvements in planning in a selection of representative visual navigation tasks, FrozenLake, Maze, and MiniBehavior. Our visual planning paradigm outperforms all other planning variants that conduct reasoning in the text-only space. Our results establish Visual Planning as a viable and promising alternative to language-based reasoning, opening new avenues for tasks that benefit from intuitive, image-based inference.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yix8/VisualPlanning", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.11427", "title": "Mergenetic: a Simple Evolutionary Model Merging Library", "authors": [ "Adrian Robert Minut", "Tommaso Mencattini", "Andrea Santilli", "Donato Crisostomi", "Emanuele Rodol\u00e0" ], "github": "", "project_page": "", "abstract": "Model merging allows combining the capabilities of existing models into a new one - post hoc, without additional training. This has made it increasingly popular thanks to its low cost and the availability of libraries that support merging on consumer GPUs. Recent work shows that pairing merging with evolutionary algorithms can boost performance, but no framework currently supports flexible experimentation with such strategies in language models. We introduce Mergenetic, an open-source library for evolutionary model merging. Mergenetic enables easy composition of merging methods and evolutionary algorithms while incorporating lightweight fitness estimators to reduce evaluation costs. We describe its design and demonstrate that Mergenetic produces competitive results across tasks and languages using modest hardware.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.11107", "title": "Group Think: Multiple Concurrent Reasoning Agents Collaborating at Token Level Granularity", "authors": [ "Chan-Jan Hsu", "Davide Buffelli", "Jamie McGowan", "Feng-Ting Liao", "Yi-Chang Chen", "Sattar Vakili", "Da-shan Shiu" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have demonstrated the power of reasoning through self-generated chains of thought. Multiple reasoning agents can collaborate to raise joint reasoning quality above individual outcomes. However, such agents typically interact in a turn-based manner, trading increased latency for improved quality. In this paper, we propose Group Think--a single LLM that acts as multiple concurrent reasoning agents, or thinkers. With shared visibility into each other's partial generation progress, Group Think introduces a new concurrent-reasoning paradigm in which multiple reasoning trajectories adapt dynamically to one another at the token level. For example, a reasoning thread may shift its generation mid-sentence upon detecting that another thread is better positioned to continue. This fine-grained, token-level collaboration enables Group Think to reduce redundant reasoning and improve quality while achieving significantly lower latency. Moreover, its concurrent nature allows for efficient utilization of idle computational resources, making it especially suitable for edge inference, where very small batch size often underutilizes local~GPUs. We give a simple and generalizable modification that enables any existing LLM to perform Group Think on a local GPU. We also present an evaluation strategy to benchmark reasoning latency and empirically demonstrate latency improvements using open-source LLMs that were not explicitly trained for Group Think. We hope this work paves the way for future LLMs to exhibit more sophisticated and more efficient collaborative behavior for higher quality generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.11140", "title": "Scaling Reasoning can Improve Factuality in Large Language Models", "authors": [ "Mike Zhang", "Johannes Bjerva", "Russa Biswas" ], "github": "", "project_page": "", "abstract": "Recent studies on large language model (LLM) reasoning capabilities have demonstrated promising improvements in model performance by leveraging a lengthy thinking process and additional computational resources during inference, primarily in tasks involving mathematical reasoning (Muennighoff et al., 2025). However, it remains uncertain if longer reasoning chains inherently enhance factual accuracy, particularly beyond mathematical contexts. In this work, we thoroughly examine LLM reasoning within complex open-domain question-answering (QA) scenarios. We initially distill reasoning traces from advanced, large-scale reasoning models (QwQ-32B and DeepSeek-R1-671B), then fine-tune a variety of models ranging from smaller, instruction-tuned variants to larger architectures based on Qwen2.5. To enrich reasoning traces, we introduce factual information from knowledge graphs in the form of paths into our reasoning traces. Our experimental setup includes four baseline approaches and six different instruction-tuned models evaluated across a benchmark of six datasets, encompassing over 22.6K questions. Overall, we carry out 168 experimental runs and analyze approximately 1.7 million reasoning traces. Our findings indicate that, within a single run, smaller reasoning models achieve noticeable improvements in factual accuracy compared to their original instruction-tuned counterparts. Moreover, our analysis demonstrates that adding test-time compute and token budgets factual accuracy consistently improves by 2-8%, further confirming the effectiveness of test-time scaling for enhancing performance and consequently improving reasoning accuracy in open-domain QA tasks. We release all the experimental artifacts for further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.07675", "title": "Simple Semi-supervised Knowledge Distillation from Vision-Language Models via texttt{D}ual-texttt{H}ead texttt{O}ptimization", "authors": [ "Seongjae Kang", "Dong Bok Lee", "Hyungjoon Jang", "Sung Ju Hwang" ], "github": "https://github.com/erjui/DHO", "project_page": "", "abstract": "Vision-language models (VLMs) have achieved remarkable success across diverse tasks by leveraging rich textual information with minimal labeled data. However, deploying such large models remains challenging, particularly in resource-constrained environments. Knowledge distillation (KD) offers a well-established solution to this problem; however, recent KD approaches from VLMs often involve multi-stage training or additional tuning, increasing computational overhead and optimization complexity. In this paper, we propose texttt{D}ual-texttt{H}ead texttt{O}ptimization (texttt{DHO}) -- a simple yet effective KD framework that transfers knowledge from VLMs to compact, task-specific models in semi-supervised settings. Specifically, we introduce dual prediction heads that independently learn from labeled data and teacher predictions, and propose to linearly combine their outputs during inference. We observe that DHO mitigates gradient conflicts between supervised and distillation signals, enabling more effective feature learning than single-head KD baselines. As a result, extensive experiments show that DHO consistently outperforms baselines across multiple domains and fine-grained datasets. Notably, on ImageNet, it achieves state-of-the-art performance, improving accuracy by 3% and 0.1% with 1% and 10% labeled data, respectively, while using fewer parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/erjui/DHO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.11011", "title": "Humans expect rationality and cooperation from LLM opponents in strategic games", "authors": [ "Darija Barak", "Miguel Costa-Gomes" ], "github": "", "project_page": "", "abstract": "As Large Language Models (LLMs) integrate into our social and economic interactions, we need to deepen our understanding of how humans respond to LLMs opponents in strategic settings. We present the results of the first controlled monetarily-incentivised laboratory experiment looking at differences in human behaviour in a multi-player p-beauty contest against other humans and LLMs. We use a within-subject design in order to compare behaviour at the individual level. We show that, in this environment, human subjects choose significantly lower numbers when playing against LLMs than humans, which is mainly driven by the increased prevalence of `zero' Nash-equilibrium choices. This shift is mainly driven by subjects with high strategic reasoning ability. Subjects who play the zero Nash-equilibrium choice motivate their strategy by appealing to perceived LLM's reasoning ability and, unexpectedly, propensity towards cooperation. Our findings provide foundational insights into the multi-player human-LLM interaction in simultaneous choice games, uncover heterogeneities in both subjects' behaviour and beliefs about LLM's play when playing against them, and suggest important implications for mechanism design in mixed human-LLM systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.10518", "title": "Multi-Token Prediction Needs Registers", "authors": [ "Anastasios Gerontopoulos", "Spyros Gidaris", "Nikos Komodakis" ], "github": "https://github.com/nasosger/MuToR", "project_page": "", "abstract": "Multi-token prediction has emerged as a promising objective for improving language model pretraining, but its benefits have not consistently generalized to other settings such as fine-tuning. In this paper, we propose MuToR, a simple and effective approach to multi-token prediction that interleaves learnable register tokens into the input sequence, each tasked with predicting future targets. Compared to existing methods, MuToR offers several key advantages: it introduces only a negligible number of additional parameters, requires no architectural changes--ensuring compatibility with off-the-shelf pretrained language models--and remains aligned with the next-token pretraining objective, making it especially well-suited for supervised fine-tuning. Moreover, it naturally supports scalable prediction horizons. We demonstrate the effectiveness and versatility of MuToR across a range of use cases, including supervised fine-tuning, parameter-efficient fine-tuning (PEFT), and pretraining, on challenging generative tasks in both language and vision domains. Our code will be available at: https://github.com/nasosger/MuToR.", "github_manual": "", "github_regex": "https://github.com/nasosger/MuToR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.11049", "title": "GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning", "authors": [ "Yue Liu", "Shengfang Zhai", "Mingzhe Du", "Yulin Chen", "Tri Cao", "Hongcheng Gao", "Cheng Wang", "Xinfeng Li", "Kun Wang", "Junfeng Fang", "Jiaheng Zhang", "Bryan Hooi" ], "github": "https://github.com/yueliu1999/GuardReasoner-VL", "project_page": "", "abstract": "To enhance the safety of VLMs, this paper introduces a novel reasoning-based VLM guard model dubbed GuardReasoner-VL. The core idea is to incentivize the guard model to deliberatively reason before making moderation decisions via online RL. First, we construct GuardReasoner-VLTrain, a reasoning corpus with 123K samples and 631K reasoning steps, spanning text, image, and text-image inputs. Then, based on it, we cold-start our model's reasoning ability via SFT. In addition, we further enhance reasoning regarding moderation through online RL. Concretely, to enhance diversity and difficulty of samples, we conduct rejection sampling followed by data augmentation via the proposed safety-aware data concatenation. Besides, we use a dynamic clipping parameter to encourage exploration in early stages and exploitation in later stages. To balance performance and token efficiency, we design a length-aware safety reward that integrates accuracy, format, and token cost. Extensive experiments demonstrate the superiority of our model. Remarkably, it surpasses the runner-up by 19.27% F1 score on average. We release data, code, and models (3B/7B) of GuardReasoner-VL at https://github.com/yueliu1999/GuardReasoner-VL/", "github_manual": "", "github_regex": "https://github.com/yueliu1999/GuardReasoner-VL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.10610", "title": "MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly", "authors": [ "Zhaowei Wang", "Wenhao Yu", "Xiyu Ren", "Jipeng Zhang", "Yu Zhao", "Rohit Saxena", "Liang Cheng", "Ginny Wong", "Simon See", "Pasquale Minervini", "Yangqiu Song", "Mark Steedman" ], "github": "https://github.com/EdinburghNLP/MMLongBench", "project_page": "", "abstract": "The rapid extension of context windows in large vision-language models has given rise to long-context vision-language models (LCVLMs), which are capable of handling hundreds of images with interleaved text tokens in a single forward pass. In this work, we introduce MMLongBench, the first benchmark covering a diverse set of long-context vision-language tasks, to evaluate LCVLMs effectively and thoroughly. MMLongBench is composed of 13,331 examples spanning five different categories of downstream tasks, such as Visual RAG and Many-Shot ICL. It also provides broad coverage of image types, including various natural and synthetic images. To assess the robustness of the models to different input lengths, all examples are delivered at five standardized input lengths (8K-128K tokens) via a cross-modal tokenization scheme that combines vision patches and text tokens. Through a thorough benchmarking of 46 closed-source and open-source LCVLMs, we provide a comprehensive analysis of the current models' vision-language long-context ability. Our results show that: i) performance on a single task is a weak proxy for overall long-context capability; ii) both closed-source and open-source models face challenges in long-context vision-language tasks, indicating substantial room for future improvement; iii) models with stronger reasoning ability tend to exhibit better long-context performance. By offering wide task coverage, various image types, and rigorous length control, MMLongBench provides the missing foundation for diagnosing and advancing the next generation of LCVLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EdinburghNLP/MMLongBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.10852", "title": "MatTools: Benchmarking Large Language Models for Materials Science Tools", "authors": [ "Siyu Liu", "Jiamin Xu", "Beilin Ye", "Bo Hu", "David J. Srolovitz", "Tongqi Wen" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly applied to materials science questions, including literature comprehension, property prediction, materials discovery and alloy design. At the same time, a wide range of physics-based computational approaches have been developed in which materials properties can be calculated. Here, we propose a benchmark application to evaluate the proficiency of LLMs to answer materials science questions through the generation and safe execution of codes based on such physics-based computational materials science packages. MatTools is built on two complementary components: a materials simulation tool question-answer (QA) benchmark and a real-world tool-usage benchmark. We designed an automated methodology to efficiently collect real-world materials science tool-use examples. The QA benchmark, derived from the pymatgen (Python Materials Genomics) codebase and documentation, comprises 69,225 QA pairs that assess the ability of an LLM to understand materials science tools. The real-world benchmark contains 49 tasks (138 subtasks) requiring the generation of functional Python code for materials property calculations. Our evaluation of diverse LLMs yields three key insights: (1)Generalists outshine specialists;(2)AI knows AI; and (3)Simpler is better. MatTools provides a standardized framework for assessing and improving LLM capabilities for materials science tool applications, facilitating the development of more effective AI systems for materials science and general scientific research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.09924", "title": "From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework for Large Language Models", "authors": [ "Yidan Wang", "Yubing Ren", "Yanan Cao", "Binxing Fang" ], "github": "https://github.com/redwyd/SymMark", "project_page": "", "abstract": "The rise of Large Language Models (LLMs) has heightened concerns about the misuse of AI-generated text, making watermarking a promising solution. Mainstream watermarking schemes for LLMs fall into two categories: logits-based and sampling-based. However, current schemes entail trade-offs among robustness, text quality, and security. To mitigate this, we integrate logits-based and sampling-based schemes, harnessing their respective strengths to achieve synergy. In this paper, we propose a versatile symbiotic watermarking framework with three strategies: serial, parallel, and hybrid. The hybrid framework adaptively embeds watermarks using token entropy and semantic entropy, optimizing the balance between detectability, robustness, text quality, and security. Furthermore, we validate our approach through comprehensive experiments on various datasets and models. Experimental results indicate that our method outperforms existing baselines and achieves state-of-the-art (SOTA) performance. We believe this framework provides novel insights into diverse watermarking paradigms. Our code is available at https://github.com/redwyd/SymMark{https://github.com/redwyd/SymMark}.", "github_manual": "", "github_regex": "https://github.com/redwyd/SymMark", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.10496", "title": "CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs", "authors": [ "Raman Dutt", "Pedro Sanchez", "Yongchen Yao", "Steven McDonagh", "Sotirios A. Tsaftaris", "Timothy Hospedales" ], "github": "", "project_page": "", "abstract": "We introduce CheXGenBench, a rigorous and multifaceted evaluation framework for synthetic chest radiograph generation that simultaneously assesses fidelity, privacy risks, and clinical utility across state-of-the-art text-to-image generative models. Despite rapid advancements in generative AI for real-world imagery, medical domain evaluations have been hindered by methodological inconsistencies, outdated architectural comparisons, and disconnected assessment criteria that rarely address the practical clinical value of synthetic samples. CheXGenBench overcomes these limitations through standardised data partitioning and a unified evaluation protocol comprising over 20 quantitative metrics that systematically analyse generation quality, potential privacy vulnerabilities, and downstream clinical applicability across 11 leading text-to-image architectures. Our results reveal critical inefficiencies in the existing evaluation protocols, particularly in assessing generative fidelity, leading to inconsistent and uninformative comparisons. Our framework establishes a standardised benchmark for the medical AI community, enabling objective and reproducible comparisons while facilitating seamless integration of both existing and future generative models. Additionally, we release a high-quality, synthetic dataset, SynthCheX-75K, comprising 75K radiographs generated by the top-performing model (Sana 0.6B) in our benchmark to support further research in this critical domain. Through CheXGenBench, we establish a new state-of-the-art and release our framework, models, and SynthCheX-75K dataset at https://raman1121.github.io/CheXGenBench/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.11480", "title": "Improving Assembly Code Performance with Large Language Models via Reinforcement Learning", "authors": [ "Anjiang Wei", "Tarun Suresh", "Huanmi Tan", "Yinglun Xu", "Gagandeep Singh", "Ke Wang", "Alex Aiken" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated strong performance across a wide range of programming tasks, yet their potential for code optimization remains underexplored. This work investigates whether LLMs can optimize the performance of assembly code, where fine-grained control over execution enables improvements that are difficult to express in high-level languages. We present a reinforcement learning framework that trains LLMs using Proximal Policy Optimization (PPO), guided by a reward function that considers both functional correctness, validated through test cases, and execution performance relative to the industry-standard compiler gcc -O3. To support this study, we introduce a benchmark of 8,072 real-world programs. Our model, Qwen2.5-Coder-7B-PPO, achieves 96.0% test pass rates and an average speedup of 1.47x over the gcc -O3 baseline, outperforming all 20 other models evaluated, including Claude-3.7-sonnet. These results indicate that reinforcement learning can unlock the potential of LLMs to serve as effective optimizers for assembly code performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.11315", "title": "Improving Inference-Time Optimisation for Vocal Effects Style Transfer with a Gaussian Prior", "authors": [ "Chin-Yun Yu", "Marco A. Mart\u00ednez-Ram\u00edrez", "Junghyun Koo", "Wei-Hsiang Liao", "Yuki Mitsufuji", "Gy\u00f6rgy Fazekas" ], "github": "https://github.com/SonyResearch/diffvox", "project_page": "", "abstract": "Style Transfer with Inference-Time Optimisation (ST-ITO) is a recent approach for transferring the applied effects of a reference audio to a raw audio track. It optimises the effect parameters to minimise the distance between the style embeddings of the processed audio and the reference. However, this method treats all possible configurations equally and relies solely on the embedding space, which can lead to unrealistic or biased results. We address this pitfall by introducing a Gaussian prior derived from a vocal preset dataset, DiffVox, over the parameter space. The resulting optimisation is equivalent to maximum-a-posteriori estimation. Evaluations on vocal effects transfer on the MedleyDB dataset show significant improvements across metrics compared to baselines, including a blind audio effects estimator, nearest-neighbour approaches, and uncalibrated ST-ITO. The proposed calibration reduces parameter mean squared error by up to 33% and matches the reference style better. Subjective evaluations with 16 participants confirm our method's superiority, especially in limited data regimes. This work demonstrates how incorporating prior knowledge in inference time enhances audio effects transfer, paving the way for more effective and realistic audio processing systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SonyResearch/diffvox", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.11493", "title": "GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing", "authors": [ "Yusu Qian", "Jiasen Lu", "Tsu-Jui Fu", "Xinze Wang", "Chen Chen", "Yinfei Yang", "Wenze Hu", "Zhe Gan" ], "github": "https://github.com/apple/ml-gie-bench", "project_page": "", "abstract": "Editing images using natural language instructions has become a natural and expressive way to modify visual content; yet, evaluating the performance of such models remains challenging. Existing evaluation approaches often rely on image-text similarity metrics like CLIP, which lack precision. In this work, we introduce a new benchmark designed to evaluate text-guided image editing models in a more grounded manner, along two critical dimensions: (i) functional correctness, assessed via automatically generated multiple-choice questions that verify whether the intended change was successfully applied; and (ii) image content preservation, which ensures that non-targeted regions of the image remain visually consistent using an object-aware masking technique and preservation scoring. The benchmark includes over 1000 high-quality editing examples across 20 diverse content categories, each annotated with detailed editing instructions, evaluation questions, and spatial object masks. We conduct a large-scale study comparing GPT-Image-1, the latest flagship in the text-guided image editing space, against several state-of-the-art editing models, and validate our automatic metrics against human ratings. Results show that GPT-Image-1 leads in instruction-following accuracy, but often over-modifies irrelevant image regions, highlighting a key trade-off in the current model behavior. GIE-Bench provides a scalable, reproducible framework for advancing more accurate evaluation of text-guided image editing.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/ml-gie-bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.05678", "title": "InstanceGen: Image Generation with Instance-level Instructions", "authors": [ "Etai Sella", "Yanir Kleiman", "Hadar Averbuch-Elor" ], "github": "https://github.com/tsunghan-wu/SLD", "project_page": "", "abstract": "Despite rapid advancements in the capabilities of generative models, pretrained text-to-image models still struggle in capturing the semantics conveyed by complex prompts that compound multiple objects and instance-level attributes. Consequently, we are witnessing growing interests in integrating additional structural constraints, typically in the form of coarse bounding boxes, to better guide the generation process in such challenging cases. In this work, we take the idea of structural guidance a step further by making the observation that contemporary image generation models can directly provide a plausible fine-grained structural initialization. We propose a technique that couples this image-based structural guidance with LLM-based instance-level instructions, yielding output images that adhere to all parts of the text prompt, including object counts, instance-level attributes, and spatial relations between instances.", "github_manual": "https://github.com/tsunghan-wu/SLD", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-19", "arxiv_id": "2505.10769", "title": "Unifying Segment Anything in Microscopy with Multimodal Large Language Model", "authors": [ "Manyu Li", "Ruian He", "Zixian Zhang", "Weimin Tan", "Bo Yan" ], "github": "https://github.com/ieellee/uLLSAM", "project_page": "", "abstract": "Accurate segmentation of regions of interest in biomedical images holds substantial value in image analysis. Although several foundation models for biomedical segmentation have currently achieved excellent performance on certain datasets, they typically demonstrate sub-optimal performance on unseen domain data. We owe the deficiency to lack of vision-language knowledge before segmentation. Multimodal Large Language Models (MLLMs) bring outstanding understanding and reasoning capabilities to multimodal tasks, which inspires us to leverage MLLMs to inject Vision-Language Knowledge (VLK), thereby enabling vision models to demonstrate superior generalization capabilities on cross-domain datasets. In this paper, we propose using MLLMs to guide SAM in learning microscopy crose-domain data, unifying Segment Anything in Microscopy, named uLLSAM. Specifically, we propose the Vision-Language Semantic Alignment (VLSA) module, which injects VLK into Segment Anything Model (SAM). We find that after SAM receives global VLK prompts, its performance improves significantly, but there are deficiencies in boundary contour perception. Therefore, we further propose Semantic Boundary Regularization (SBR) to prompt SAM. Our method achieves performance improvements of 7.71% in Dice and 12.10% in SA across 9 in-domain microscopy datasets, achieving state-of-the-art performance. Our method also demonstrates improvements of 6.79% in Dice and 10.08% in SA across 10 out-ofdomain datasets, exhibiting strong generalization capabilities. Code is available at https://github.com/ieellee/uLLSAM.", "github_manual": "", "github_regex": "https://github.com/ieellee/uLLSAM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12081", "title": "VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement Learning", "authors": [ "Yuqi Liu", "Tianyuan Qu", "Zhisheng Zhong", "Bohao Peng", "Shu Liu", "Bei Yu", "Jiaya Jia" ], "github": "", "project_page": "", "abstract": "Large vision-language models exhibit inherent capabilities to handle diverse visual perception tasks. In this paper, we introduce VisionReasoner, a unified framework capable of reasoning and solving multiple visual perception tasks within a shared model. Specifically, by designing novel multi-object cognitive learning strategies and systematic task reformulation, VisionReasoner enhances its reasoning capabilities to analyze visual inputs, and addresses diverse perception tasks in a unified framework. The model generates a structured reasoning process before delivering the desired outputs responding to user queries. To rigorously assess unified visual perception capabilities, we evaluate VisionReasoner on ten diverse tasks spanning three critical domains: detection, segmentation, and counting. Experimental results show that VisionReasoner achieves superior performance as a unified model, outperforming Qwen2.5VL by relative margins of 29.1% on COCO (detection), 22.1% on ReasonSeg (segmentation), and 15.3% on CountBench (counting).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13417", "title": "AdaptThink: Reasoning Models Can Learn When to Think", "authors": [ "Jiajie Zhang", "Nianyi Lin", "Lei Hou", "Ling Feng", "Juanzi Li" ], "github": "https://github.com/THU-KEG/AdaptThink", "project_page": "", "abstract": "Recently, large reasoning models have achieved impressive performance on various tasks by employing human-like deep thinking. However, the lengthy thinking process substantially increases inference overhead, making efficiency a critical bottleneck. In this work, we first demonstrate that NoThinking, which prompts the reasoning model to skip thinking and directly generate the final solution, is a better choice for relatively simple tasks in terms of both performance and efficiency. Motivated by this, we propose AdaptThink, a novel RL algorithm to teach reasoning models to choose the optimal thinking mode adaptively based on problem difficulty. Specifically, AdaptThink features two core components: (1) a constrained optimization objective that encourages the model to choose NoThinking while maintaining the overall performance; (2) an importance sampling strategy that balances Thinking and NoThinking samples during on-policy training, thereby enabling cold start and allowing the model to explore and exploit both thinking modes throughout the training process. Our experiments indicate that AdaptThink significantly reduces the inference costs while further enhancing performance. Notably, on three math datasets, AdaptThink reduces the average response length of DeepSeek-R1-Distill-Qwen-1.5B by 53% and improves its accuracy by 2.4%, highlighting the promise of adaptive thinking-mode selection for optimizing the balance between reasoning quality and efficiency. Our codes and models are available at https://github.com/THU-KEG/AdaptThink.", "github_manual": "", "github_regex": "https://github.com/THU-KEG/AdaptThink", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.11896", "title": "AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning", "authors": [ "Chenwei Lou", "Zewei Sun", "Xinnian Liang", "Meng Qu", "Wei Shen", "Wenqi Wang", "Yuntao Li", "Qingping Yang", "Shuangzhi Wu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities but often face challenges with tasks requiring sophisticated reasoning. While Chain-of-Thought (CoT) prompting significantly enhances reasoning, it indiscriminately generates lengthy reasoning steps for all queries, leading to substantial computational costs and inefficiency, especially for simpler inputs. To address this critical issue, we introduce AdaCoT (Adaptive Chain-of-Thought), a novel framework enabling LLMs to adaptively decide when to invoke CoT. AdaCoT framed adaptive reasoning as a Pareto optimization problem that seeks to balance model performance with the costs associated with CoT invocation (both frequency and computational overhead). We propose a reinforcement learning (RL) based method, specifically utilizing Proximal Policy Optimization (PPO), to dynamically control the CoT triggering decision boundary by adjusting penalty coefficients, thereby allowing the model to determine CoT necessity based on implicit query complexity. A key technical contribution is Selective Loss Masking (SLM), designed to counteract decision boundary collapse during multi-stage RL training, ensuring robust and stable adaptive triggering. Experimental results demonstrate that AdaCoT successfully navigates the Pareto frontier, achieving substantial reductions in CoT usage for queries not requiring elaborate reasoning. For instance, on our production traffic testset, AdaCoT reduced CoT triggering rates to as low as 3.18\\% and decreased average response tokens by 69.06%, while maintaining high performance on complex tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13379", "title": "Thinkless: LLM Learns When to Think", "authors": [ "Gongfan Fang", "Xinyin Ma", "Xinchao Wang" ], "github": "https://github.com/VainF/Thinkless", "project_page": "", "abstract": "Reasoning Language Models, capable of extended chain-of-thought reasoning, have demonstrated remarkable performance on tasks requiring complex logical inference. However, applying elaborate reasoning for all queries often results in substantial computational inefficiencies, particularly when many problems admit straightforward solutions. This motivates an open question: Can LLMs learn when to think? To answer this, we propose Thinkless, a learnable framework that empowers an LLM to adaptively select between short-form and long-form reasoning, based on both task complexity and the model's ability. Thinkless is trained under a reinforcement learning paradigm and employs two control tokens, for concise responses and for detailed reasoning. At the core of our method is a Decoupled Group Relative Policy Optimization (DeGRPO) algorithm, which decomposes the learning objective of hybrid reasoning into two components: (1) a control token loss that governs the selection of the reasoning mode, and (2) a response loss that improves the accuracy of the generated answers. This decoupled formulation enables fine-grained control over the contributions of each objective, stabilizing training and effectively preventing collapse observed in vanilla GRPO. Empirically, on several benchmarks such as Minerva Algebra, MATH-500, and GSM8K, Thinkless is able to reduce the usage of long-chain thinking by 50% - 90%, significantly improving the efficiency of Reasoning Language Models. The code is available at https://github.com/VainF/Thinkless", "github_manual": "", "github_regex": "https://github.com/VainF/Thinkless", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12849", "title": "Accelerate TarFlow Sampling with GS-Jacobi Iteration", "authors": [ "Ben Liu", "Zhen Qin" ], "github": "https://github.com/encoreus/GS-Jacobi_for_TarFlow", "project_page": "", "abstract": "Image generation models have achieved widespread applications. As an instance, the TarFlow model combines the transformer architecture with Normalizing Flow models, achieving state-of-the-art results on multiple benchmarks. However, due to the causal form of attention requiring sequential computation, TarFlow's sampling process is extremely slow. In this paper, we demonstrate that through a series of optimization strategies, TarFlow sampling can be greatly accelerated by using the Gauss-Seidel-Jacobi (abbreviated as GS-Jacobi) iteration method. Specifically, we find that blocks in the TarFlow model have varying importance: a small number of blocks play a major role in image generation tasks, while other blocks contribute relatively little; some blocks are sensitive to initial values and prone to numerical overflow, while others are relatively robust. Based on these two characteristics, we propose the Convergence Ranking Metric (CRM) and the Initial Guessing Metric (IGM): CRM is used to identify whether a TarFlow block is \"simple\" (converges in few iterations) or \"tough\" (requires more iterations); IGM is used to evaluate whether the initial value of the iteration is good. Experiments on four TarFlow models demonstrate that GS-Jacobi sampling can significantly enhance sampling efficiency while maintaining the quality of generated images (measured by FID), achieving speed-ups of 4.53x in Img128cond, 5.32x in AFHQ, 2.96x in Img64uncond, and 2.51x in Img64cond without degrading FID scores or sample quality. Code and checkpoints are accessible on https://github.com/encoreus/GS-Jacobi_for_TarFlow", "github_manual": "", "github_regex": "https://github.com/encoreus/GS-Jacobi_for_TarFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13427", "title": "MM-PRM: Enhancing Multimodal Mathematical Reasoning with Scalable Step-Level Supervision", "authors": [ "Lingxiao Du", "Fanqing Meng", "Zongkai Liu", "Zhixiang Zhou", "Ping Luo", "Qiaosheng Zhang", "Wenqi Shao" ], "github": "https://github.com/ModalMinds/MM-PRM", "project_page": "", "abstract": "While Multimodal Large Language Models (MLLMs) have achieved impressive progress in vision-language understanding, they still struggle with complex multi-step reasoning, often producing logically inconsistent or partially correct solutions. A key limitation lies in the lack of fine-grained supervision over intermediate reasoning steps. To address this, we propose MM-PRM, a process reward model trained within a fully automated, scalable framework. We first build MM-Policy, a strong multimodal model trained on diverse mathematical reasoning data. Then, we construct MM-K12, a curated dataset of 10,000 multimodal math problems with verifiable answers, which serves as seed data. Leveraging a Monte Carlo Tree Search (MCTS)-based pipeline, we generate over 700k step-level annotations without human labeling. The resulting PRM is used to score candidate reasoning paths in the Best-of-N inference setup and achieves significant improvements across both in-domain (MM-K12 test set) and out-of-domain (OlympiadBench, MathVista, etc.) benchmarks. Further analysis confirms the effectiveness of soft labels, smaller learning rates, and path diversity in optimizing PRM performance. MM-PRM demonstrates that process supervision is a powerful tool for enhancing the logical robustness of multimodal reasoning systems. We release all our codes and data at https://github.com/ModalMinds/MM-PRM.", "github_manual": "", "github_regex": "https://github.com/ModalMinds/MM-PRM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13215", "title": "Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation", "authors": [ "Seungjun Oh", "Younggeun Lee", "Hyejin Jeon", "Eunbyung Park" ], "github": "", "project_page": "", "abstract": "Recent advancements in dynamic 3D scene reconstruction have shown promising results, enabling high-fidelity 3D novel view synthesis with improved temporal consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an appealing approach due to its ability to model high-fidelity spatial and temporal variations. However, existing methods suffer from substantial computational and memory overhead due to the redundant allocation of 4D Gaussians to static regions, which can also degrade image quality. In this work, we introduce hybrid 3D-4D Gaussian Splatting (3D-4DGS), a novel framework that adaptively represents static regions with 3D Gaussians while reserving 4D Gaussians for dynamic elements. Our method begins with a fully 4D Gaussian representation and iteratively converts temporally invariant Gaussians into 3D, significantly reducing the number of parameters and improving computational efficiency. Meanwhile, dynamic Gaussians retain their full 4D representation, capturing complex motions with high fidelity. Our approach achieves significantly faster training times compared to baseline 4D Gaussian Splatting methods while maintaining or improving the visual quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.11932", "title": "Neuro-Symbolic Query Compiler", "authors": [ "Yuyao Zhang", "Zhicheng Dou", "Xiaoxi Li", "Jiajie Jin", "Yongkang Wu", "Zhonghua Li", "Qi Ye", "Ji-Rong Wen" ], "github": "", "project_page": "", "abstract": "Precise recognition of search intent in Retrieval-Augmented Generation (RAG) systems remains a challenging goal, especially under resource constraints and for complex queries with nested structures and dependencies. This paper presents QCompiler, a neuro-symbolic framework inspired by linguistic grammar rules and compiler design, to bridge this gap. It theoretically designs a minimal yet sufficient Backus-Naur Form (BNF) grammar G[q] to formalize complex queries. Unlike previous methods, this grammar maintains completeness while minimizing redundancy. Based on this, QCompiler includes a Query Expression Translator, a Lexical Syntax Parser, and a Recursive Descent Processor to compile queries into Abstract Syntax Trees (ASTs) for execution. The atomicity of the sub-queries in the leaf nodes ensures more precise document retrieval and response generation, significantly improving the RAG system's ability to address complex queries.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12504", "title": "CPGD: Toward Stable Rule-based Reinforcement Learning for Language Models", "authors": [ "Zongkai Liu", "Fanqing Meng", "Lingxiao Du", "Zhixiang Zhou", "Chao Yu", "Wenqi Shao", "Qiaosheng Zhang" ], "github": "https://github.com/ModalMinds/MM-EUREKA", "project_page": "", "abstract": "Recent advances in rule-based reinforcement learning (RL) have significantly improved the reasoning capability of language models (LMs) with rule-based rewards. However, existing RL methods -- such as GRPO, REINFORCE++, and RLOO -- often suffer from training instability, where large policy updates and improper clipping can lead to training collapse. To address this issue, we propose Clipped Policy Gradient Optimization with Policy Drift (CPGD), a novel algorithm designed to stabilize policy learning in LMs. CPGD introduces a policy drift constraint based on KL divergence to dynamically regularize policy updates, and leverages a clip mechanism on the logarithm of the ratio to prevent excessive policy updates. We provide theoretical justification for CPGD and demonstrate through empirical analysis that it mitigates the instability observed in prior approaches. Furthermore, we show that CPGD significantly improves performance while maintaining training stability. Our implementation balances theoretical rigor with practical usability, offering a robust alternative for RL in the post-training of LMs. We release our code at https://github.com/ModalMinds/MM-EUREKA.", "github_manual": "", "github_regex": "https://github.com/ModalMinds/MM-EUREKA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.11254", "title": "Delta Attention: Fast and Accurate Sparse Attention Inference by Delta Correction", "authors": [ "Jeffrey Willette", "Heejun Lee", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "The attention mechanism of a transformer has a quadratic complexity, leading to high inference costs and latency for long sequences. However, attention matrices are mostly sparse, which implies that many entries may be omitted from computation for efficient inference. Sparse attention inference methods aim to reduce this computational burden; however, they also come with a troublesome performance degradation. We discover that one reason for this degradation is that the sparse calculation induces a distributional shift in the attention outputs. The distributional shift causes decoding-time queries to fail to align well with the appropriate keys from the prefill stage, leading to a drop in performance. We propose a simple, novel, and effective procedure for correcting this distributional shift, bringing the distribution of sparse attention outputs closer to that of quadratic attention. Our method can be applied on top of any sparse attention method, and results in an average 36%pt performance increase, recovering 88% of quadratic attention accuracy on the 131K RULER benchmark when applied on top of sliding window attention with sink tokens while only adding a small overhead. Our method can maintain approximately 98.5% sparsity over full quadratic attention, making our model 32 times faster than Flash Attention 2 when processing 1M token prefills.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.11820", "title": "Chain-of-Model Learning for Language Model", "authors": [ "Kaitao Song", "Xiaohua Wang", "Xu Tan", "Huiqiang Jiang", "Chengruidong Zhang", "Yongliang Shen", "Cen LU", "Zihao Li", "Zifan Song", "Caihua Shan", "Yansen Wang", "Kan Ren", "Xiaoqing Zheng", "Tao Qin", "Yuqing Yang", "Dongsheng Li", "Lili Qiu" ], "github": "https://github.com/microsoft/CoLM", "project_page": "", "abstract": "In this paper, we propose a novel learning paradigm, termed Chain-of-Model (CoM), which incorporates the causal relationship into the hidden states of each layer as a chain style, thereby introducing great scaling efficiency in model training and inference flexibility in deployment. We introduce the concept of Chain-of-Representation (CoR), which formulates the hidden states at each layer as a combination of multiple sub-representations (i.e., chains) at the hidden dimension level. In each layer, each chain from the output representations can only view all of its preceding chains in the input representations. Consequently, the model built upon CoM framework can progressively scale up the model size by increasing the chains based on the previous models (i.e., chains), and offer multiple sub-models at varying sizes for elastic inference by using different chain numbers. Based on this principle, we devise Chain-of-Language-Model (CoLM), which incorporates the idea of CoM into each layer of Transformer architecture. Based on CoLM, we further introduce CoLM-Air by introducing a KV sharing mechanism, that computes all keys and values within the first chain and then shares across all chains. This design demonstrates additional extensibility, such as enabling seamless LM switching, prefilling acceleration and so on. Experimental results demonstrate our CoLM family can achieve comparable performance to the standard Transformer, while simultaneously enabling greater flexiblity, such as progressive scaling to improve training efficiency and offer multiple varying model sizes for elastic inference, paving a a new way toward building language models. Our code will be released in the future at: https://github.com/microsoft/CoLM.", "github_manual": "", "github_regex": "https://github.com/microsoft/CoLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12805", "title": "FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA", "authors": [ "Seanie Lee", "Sangwoo Park", "Dong Bok Lee", "Dominik Wagner", "Haebin Seong", "Tobias Bocklet", "Juho Lee", "Sung Ju Hwang" ], "github": "https://github.com/seanie12/fed-svd", "project_page": "", "abstract": "Low-Rank Adaptation (LoRA), which introduces a product of two trainable low-rank matrices into frozen pre-trained weights, is widely used for efficient fine-tuning of language models in federated learning (FL). However, when combined with differentially private stochastic gradient descent (DP-SGD), LoRA faces substantial noise amplification: DP-SGD perturbs per-sample gradients, and the matrix multiplication of the LoRA update (BA) intensifies this effect. Freezing one matrix (e.g., A) reduces the noise but restricts model expressiveness, often resulting in suboptimal adaptation. To address this, we propose FedSVD, a simple yet effective method that introduces a global reparameterization based on singular value decomposition (SVD). In our approach, each client optimizes only the B matrix and transmits it to the server. The server aggregates the B matrices, computes the product BA using the previous A, and refactorizes the result via SVD. This yields a new adaptive A composed of the orthonormal right singular vectors of BA, and an updated B containing the remaining SVD components. This reparameterization avoids quadratic noise amplification, while allowing A to better capture the principal directions of the aggregate updates. Moreover, the orthonormal structure of A bounds the gradient norms of B and preserves more signal under DP-SGD, as confirmed by our theoretical analysis. As a result, FedSVD consistently improves stability and performance across a variety of privacy settings and benchmarks, outperforming relevant baselines under both private and non-private regimes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/seanie12/fed-svd", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13437", "title": "FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidance", "authors": [ "Dian Shao", "Mingfei Shi", "Shengda Xu", "Haodong Chen", "Yongle Huang", "Binglu Wang" ], "github": "", "project_page": "", "abstract": "Despite significant advances in video generation, synthesizing physically plausible human actions remains a persistent challenge, particularly in modeling fine-grained semantics and complex temporal dynamics. For instance, generating gymnastics routines such as \"switch leap with 0.5 turn\" poses substantial difficulties for current methods, often yielding unsatisfactory results. To bridge this gap, we propose FinePhys, a Fine-grained human action generation framework that incorporates Physics to obtain effective skeletal guidance. Specifically, FinePhys first estimates 2D poses in an online manner and then performs 2D-to-3D dimension lifting via in-context learning. To mitigate the instability and limited interpretability of purely data-driven 3D poses, we further introduce a physics-based motion re-estimation module governed by Euler-Lagrange equations, calculating joint accelerations via bidirectional temporal updating. The physically predicted 3D poses are then fused with data-driven ones, offering multi-scale 2D heatmap guidance for the diffusion process. Evaluated on three fine-grained action subsets from FineGym (FX-JUMP, FX-TURN, and FX-SALTO), FinePhys significantly outperforms competitive baselines. Comprehensive qualitative results further demonstrate FinePhys's ability to generate more natural and plausible fine-grained human actions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13444", "title": "ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models", "authors": [ "Liyan Tang", "Grace Kim", "Xinyu Zhao", "Thom Lake", "Wenxuan Ding", "Fangcong Yin", "Prasann Singhal", "Manya Wadhwa", "Zeyu Leo Liu", "Zayne Sprague", "Ramya Namuduri", "Bodun Hu", "Juan Diego Rodriguez", "Puyuan Peng", "Greg Durrett" ], "github": "", "project_page": "", "abstract": "Chart understanding presents a unique challenge for large vision-language models (LVLMs), as it requires the integration of sophisticated textual and visual reasoning capabilities. However, current LVLMs exhibit a notable imbalance between these skills, falling short on visual reasoning that is difficult to perform in text. We conduct a case study using a synthetic dataset solvable only through visual reasoning and show that model performance degrades significantly with increasing visual complexity, while human performance remains robust. We then introduce ChartMuseum, a new Chart Question Answering (QA) benchmark containing 1,162 expert-annotated questions spanning multiple reasoning types, curated from real-world charts across 184 sources, specifically built to evaluate complex visual and textual reasoning. Unlike prior chart understanding benchmarks -- where frontier models perform similarly and near saturation -- our benchmark exposes a substantial gap between model and human performance, while effectively differentiating model capabilities: although humans achieve 93% accuracy, the best-performing model Gemini-2.5-Pro attains only 63.0%, and the leading open-source LVLM Qwen2.5-VL-72B-Instruct achieves only 38.5%. Moreover, on questions requiring primarily visual reasoning, all models experience a 35%-55% performance drop from text-reasoning-heavy question performance. Lastly, our qualitative error analysis reveals specific categories of visual reasoning that are challenging for current LVLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12992", "title": "Fractured Chain-of-Thought Reasoning", "authors": [ "Baohao Liao", "Hanze Dong", "Yuhui Xu", "Doyen Sahoo", "Christof Monz", "Junnan Li", "Caiming Xiong" ], "github": "https://github.com/BaohaoLiao/frac-cot", "project_page": "", "abstract": "Inference-time scaling techniques have significantly bolstered the reasoning capabilities of large language models (LLMs) by harnessing additional computational effort at inference without retraining. Similarly, Chain-of-Thought (CoT) prompting and its extension, Long CoT, improve accuracy by generating rich intermediate reasoning trajectories, but these approaches incur substantial token costs that impede their deployment in latency-sensitive settings. In this work, we first show that truncated CoT, which stops reasoning before completion and directly generates the final answer, often matches full CoT sampling while using dramatically fewer tokens. Building on this insight, we introduce Fractured Sampling, a unified inference-time strategy that interpolates between full CoT and solution-only sampling along three orthogonal axes: (1) the number of reasoning trajectories, (2) the number of final solutions per trajectory, and (3) the depth at which reasoning traces are truncated. Through extensive experiments on five diverse reasoning benchmarks and several model scales, we demonstrate that Fractured Sampling consistently achieves superior accuracy-cost trade-offs, yielding steep log-linear scaling gains in Pass@k versus token budget. Our analysis reveals how to allocate computation across these dimensions to maximize performance, paving the way for more efficient and scalable LLM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/BaohaoLiao/frac-cot", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13227", "title": "Scaling Computer-Use Grounding via User Interface Decomposition and Synthesis", "authors": [ "Tianbao Xie", "Jiaqi Deng", "Xiaochuan Li", "Junlin Yang", "Haoyuan Wu", "Jixuan Chen", "Wenjing Hu", "Xinyuan Wang", "Yuhui Xu", "Zekun Wang", "Yiheng Xu", "Junli Wang", "Doyen Sahoo", "Tao Yu", "Caiming Xiong" ], "github": "", "project_page": "", "abstract": "Graphical user interface (GUI) grounding, the ability to map natural language instructions to specific actions on graphical user interfaces, remains a critical bottleneck in computer use agent development. Current benchmarks oversimplify grounding tasks as short referring expressions, failing to capture the complexity of real-world interactions that require software commonsense, layout understanding, and fine-grained manipulation capabilities. To address these limitations, we introduce OSWorld-G, a comprehensive benchmark comprising 564 finely annotated samples across diverse task types including text matching, element recognition, layout understanding, and precise manipulation. Additionally, we synthesize and release the largest computer use grounding dataset Jedi, which contains 4 million examples through multi-perspective decoupling of tasks. Our multi-scale models trained on Jedi demonstrate its effectiveness by outperforming existing approaches on ScreenSpot-v2, ScreenSpot-Pro, and our OSWorld-G. Furthermore, we demonstrate that improved grounding with Jedi directly enhances agentic capabilities of general foundation models on complex computer tasks, improving from 5% to 27% on OSWorld. Through detailed ablation studies, we identify key factors contributing to grounding performance and verify that combining specialized data for different interface elements enables compositional generalization to novel interfaces. All benchmark, data, checkpoints, and code are open-sourced and available at https://osworld-grounding.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.11855", "title": "When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research", "authors": [ "Guijin Son", "Jiwoo Hong", "Honglu Fan", "Heejeong Nam", "Hyunwoo Ko", "Seungwon Lim", "Jinyeop Song", "Jinha Choi", "Gon\u00e7alo Paulo", "Youngjae Yu", "Stella Biderman" ], "github": "https://github.com/guijinSON/SPOT", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have fueled the vision of automated scientific discovery, often called AI Co-Scientists. To date, prior work casts these systems as generative co-authors responsible for crafting hypotheses, synthesizing code, or drafting manuscripts. In this work, we explore a complementary application: using LLMs as verifiers to automate the academic verification of scientific manuscripts. To that end, we introduce SPOT, a dataset of 83 published papers paired with 91 errors significant enough to prompt errata or retraction, cross-validated with actual authors and human annotators. Evaluating state-of-the-art LLMs on SPOT, we find that none surpasses 21.1\\% recall or 6.1\\% precision (o3 achieves the best scores, with all others near zero). Furthermore, confidence estimates are uniformly low, and across eight independent runs, models rarely rediscover the same errors, undermining their reliability. Finally, qualitative analysis with domain experts reveals that even the strongest models make mistakes resembling student-level misconceptions derived from misunderstandings. These findings highlight the substantial gap between current LLM capabilities and the requirements for dependable AI-assisted academic verification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/guijinSON/SPOT", "github_api_added_by": "auto" }, { "date": "2025-05-20", "arxiv_id": "2505.12257", "title": "LLM Context Conditioning and PWP Prompting for Multimodal Validation of Chemical Formulas", "authors": [ "Evgeny Markhasin" ], "github": "", "project_page": "", "abstract": "Identifying subtle technical errors within complex scientific and technical documents, especially those requiring multimodal interpretation (e.g., formulas in images), presents a significant hurdle for Large Language Models (LLMs) whose inherent error-correction tendencies can mask inaccuracies. This exploratory proof-of-concept (PoC) study investigates structured LLM context conditioning, informed by Persistent Workflow Prompting (PWP) principles, as a methodological strategy to modulate this LLM behavior at inference time. The approach is designed to enhance the reliability of readily available, general-purpose LLMs (specifically Gemini 2.5 Pro and ChatGPT Plus o3) for precise validation tasks, crucially relying only on their standard chat interfaces without API access or model modifications. To explore this methodology, we focused on validating chemical formulas within a single, complex test paper with known textual and image-based errors. Several prompting strategies were evaluated: while basic prompts proved unreliable, an approach adapting PWP structures to rigorously condition the LLM's analytical mindset appeared to improve textual error identification with both models. Notably, this method also guided Gemini 2.5 Pro to repeatedly identify a subtle image-based formula error previously overlooked during manual review, a task where ChatGPT Plus o3 failed in our tests. These preliminary findings highlight specific LLM operational modes that impede detail-oriented validation and suggest that PWP-informed context conditioning offers a promising and highly accessible technique for developing more robust LLM-driven analytical workflows, particularly for tasks requiring meticulous error detection in scientific and technical documents. Extensive validation beyond this limited PoC is necessary to ascertain broader applicability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.03332", "title": "AI-Driven Scholarly Peer Review via Persistent Workflow Prompting, Meta-Prompting, and Meta-Reasoning", "authors": [ "Evgeny Markhasin" ], "github": "", "project_page": "", "abstract": "Critical peer review of scientific manuscripts presents a significant challenge for Large Language Models (LLMs), partly due to data limitations and the complexity of expert reasoning. This report introduces Persistent Workflow Prompting (PWP), a potentially broadly applicable prompt engineering methodology designed to bridge this gap using standard LLM chat interfaces (zero-code, no APIs). We present a proof-of-concept PWP prompt for the critical analysis of experimental chemistry manuscripts, featuring a hierarchical, modular architecture (structured via Markdown) that defines detailed analysis workflows. We develop this PWP prompt through iterative application of meta-prompting techniques and meta-reasoning aimed at systematically codifying expert review workflows, including tacit knowledge. Submitted once at the start of a session, this PWP prompt equips the LLM with persistent workflows triggered by subsequent queries, guiding modern reasoning LLMs through systematic, multimodal evaluations. Demonstrations show the PWP-guided LLM identifying major methodological flaws in a test case while mitigating LLM input bias and performing complex tasks, including distinguishing claims from evidence, integrating text/photo/figure analysis to infer parameters, executing quantitative feasibility checks, comparing estimates against claims, and assessing a priori plausibility. To ensure transparency and facilitate replication, we provide full prompts, detailed demonstration analyses, and logs of interactive chats as supplementary resources. Beyond the specific application, this work offers insights into the meta-development process itself, highlighting the potential of PWP, informed by detailed workflow formalization, to enable sophisticated analysis using readily available LLMs for complex scientific tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12996", "title": "ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced Reinforcement Learning", "authors": [ "Jiaan Wang", "Fandong Meng", "Jie Zhou" ], "github": "", "project_page": "", "abstract": "In recent years, the emergence of large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, has shown impressive capabilities in complex problems, e.g., mathematics and coding. Some pioneering studies attempt to bring the success of LRMs in neural machine translation (MT). They try to build LRMs with deep reasoning MT ability via reinforcement learning (RL). Despite some progress that has been made, these attempts generally focus on several high-resource languages, e.g., English and Chinese, leaving the performance on other languages unclear. Besides, the reward modeling methods in previous work do not fully unleash the potential of reinforcement learning in MT. In this work, we first design a new reward modeling method that compares the translation results of the policy MT model with a strong LRM (i.e., DeepSeek-R1-671B), and quantifies the comparisons to provide rewards. Experimental results demonstrate the superiority of the reward modeling method. Using Qwen2.5-7B-Instruct as the backbone, the trained model achieves the new state-of-the-art performance in literary translation, and outperforms strong LRMs including OpenAI-o1 and DeepSeeK-R1. Furthermore, we extend our method to the multilingual settings with 11 languages. With a carefully designed lightweight reward modeling in RL, we can simply transfer the strong MT ability from a single direction into multiple (i.e., 90) translation directions and achieve impressive multilingual MT performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13308", "title": "Seek in the Dark: Reasoning via Test-Time Instance-Level Policy Gradient in Latent Space", "authors": [ "Hengli Li", "Chenxi Li", "Tong Wu", "Xuekai Zhu", "Yuxuan Wang", "Zhaoxin Yu", "Eric Hanchen Jiang", "Song-Chun Zhu", "Zixia Jia", "Ying Nian Wu", "Zilong Zheng" ], "github": "", "project_page": "", "abstract": "Reasoning ability, a core component of human intelligence, continues to pose a significant challenge for Large Language Models (LLMs) in the pursuit of AGI. Although model performance has improved under the training scaling law, significant challenges remain, particularly with respect to training algorithms, such as catastrophic forgetting, and the limited availability of novel training data. As an alternative, test-time scaling enhances reasoning performance by increasing test-time computation without parameter updating. Unlike prior methods in this paradigm focused on token space, we propose leveraging latent space for more effective reasoning and better adherence to the test-time scaling law. We introduce LatentSeek, a novel framework that enhances LLM reasoning through Test-Time Instance-level Adaptation (TTIA) within the model's latent space. Specifically, LatentSeek leverages policy gradient to iteratively update latent representations, guided by self-generated reward signals. LatentSeek is evaluated on a range of reasoning benchmarks, including GSM8K, MATH-500, and AIME2024, across multiple LLM architectures. Results show that LatentSeek consistently outperforms strong baselines, such as Chain-of-Thought prompting and fine-tuning-based methods. Furthermore, our analysis demonstrates that LatentSeek is highly efficient, typically converging within a few iterations for problems of average complexity, while also benefiting from additional iterations, thereby highlighting the potential of test-time scaling in the latent space. These findings position LatentSeek as a lightweight, scalable, and effective solution for enhancing the reasoning capabilities of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13389", "title": "Faster Video Diffusion with Trainable Sparse Attention", "authors": [ "Peiyuan Zhang", "Haofeng Huang", "Yongqi Chen", "Will Lin", "Zhengzhong Liu", "Ion Stoica", "Eric P. Xing", "Hao Zhang" ], "github": "https://github.com/hao-ai-lab/FastVideo", "project_page": "", "abstract": "Scaling video diffusion transformers (DiTs) is limited by their quadratic 3D attention, even though most of the attention mass concentrates on a small subset of positions. We turn this observation into VSA, a trainable, hardware-efficient sparse attention that replaces full attention at both training and inference. In VSA, a lightweight coarse stage pools tokens into tiles and identifies high-weight critical tokens; a fine stage computes token-level attention only inside those tiles subjecting to block computing layout to ensure hard efficiency. This leads to a single differentiable kernel that trains end-to-end, requires no post-hoc profiling, and sustains 85\\% of FlashAttention3 MFU. We perform a large sweep of ablation studies and scaling-law experiments by pretraining DiTs from 60M to 1.4B parameters. VSA reaches a Pareto point that cuts training FLOPS by 2.53times with no drop in diffusion loss. Retrofitting the open-source Wan-2.1 model speeds up attention time by 6times and lowers end-to-end generation time from 31s to 18s with comparable quality. These results establish trainable sparse attention as a practical alternative to full attention and a key enabler for further scaling of video diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hao-ai-lab/FastVideo", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.10238", "title": "MTVCrafter: 4D Motion Tokenization for Open-World Human Image Animation", "authors": [ "Yanbo Ding", "Xirui Hu", "Zhizhi Guo", "Yali Wang" ], "github": "https://github.com/DINGYANB/MTVCrafter", "project_page": "", "abstract": "Human image animation has gained increasing attention and developed rapidly due to its broad applications in digital humans. However, existing methods rely largely on 2D-rendered pose images for motion guidance, which limits generalization and discards essential 3D information for open-world animation. To tackle this problem, we propose MTVCrafter (Motion Tokenization Video Crafter), the first framework that directly models raw 3D motion sequences (i.e., 4D motion) for human image animation. Specifically, we introduce 4DMoT (4D motion tokenizer) to quantize 3D motion sequences into 4D motion tokens. Compared to 2D-rendered pose images, 4D motion tokens offer more robust spatio-temporal cues and avoid strict pixel-level alignment between pose image and character, enabling more flexible and disentangled control. Then, we introduce MV-DiT (Motion-aware Video DiT). By designing unique motion attention with 4D positional encodings, MV-DiT can effectively leverage motion tokens as 4D compact yet expressive context for human image animation in the complex 3D world. Hence, it marks a significant step forward in this field and opens a new direction for pose-guided human video generation. Experiments show that our MTVCrafter achieves state-of-the-art results with an FID-VID of 6.98, surpassing the second-best by 65%. Powered by robust motion tokens, MTVCrafter also generalizes well to diverse open-world characters (single/multiple, full/half-body) across various styles and scenarios. Our video demos and code are on: https://github.com/DINGYANB/MTVCrafter.", "github_manual": "", "github_regex": "https://github.com/DINGYANB/MTVCrafter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.11484", "title": "SoftCoT++: Test-Time Scaling with Soft Chain-of-Thought Reasoning", "authors": [ "Yige Xu", "Xu Guo", "Zhiwei Zeng", "Chunyan Miao" ], "github": "https://github.com/xuyige/SoftCoT", "project_page": "", "abstract": "Test-Time Scaling (TTS) refers to approaches that improve reasoning performance by allocating extra computation during inference, without altering the model's parameters. While existing TTS methods operate in a discrete token space by generating more intermediate steps, recent studies in Coconut and SoftCoT have demonstrated that thinking in the continuous latent space can further enhance the reasoning performance. Such latent thoughts encode informative thinking without the information loss associated with autoregressive token generation, sparking increased interest in continuous-space reasoning. Unlike discrete decoding, where repeated sampling enables exploring diverse reasoning paths, latent representations in continuous space are fixed for a given input, which limits diverse exploration, as all decoded paths originate from the same latent thought. To overcome this limitation, we introduce SoftCoT++ to extend SoftCoT to the Test-Time Scaling paradigm by enabling diverse exploration of thinking paths. Specifically, we perturb latent thoughts via multiple specialized initial tokens and apply contrastive learning to promote diversity among soft thought representations. Experiments across five reasoning benchmarks and two distinct LLM architectures demonstrate that SoftCoT++ significantly boosts SoftCoT and also outperforms SoftCoT with self-consistency scaling. Moreover, it shows strong compatibility with conventional scaling techniques such as self-consistency. Source code is available at https://github.com/xuyige/SoftCoT.", "github_manual": "", "github_regex": "https://github.com/xuyige/SoftCoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.11988", "title": "TechniqueRAG: Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text", "authors": [ "Ahmed Lekssays", "Utsav Shukla", "Husrev Taha Sencar", "Md Rizwan Parvez" ], "github": "", "project_page": "", "abstract": "Accurately identifying adversarial techniques in security texts is critical for effective cyber defense. However, existing methods face a fundamental trade-off: they either rely on generic models with limited domain precision or require resource-intensive pipelines that depend on large labeled datasets and task-specific optimizations, such as custom hard-negative mining and denoising, resources rarely available in specialized domains. We propose TechniqueRAG, a domain-specific retrieval-augmented generation (RAG) framework that bridges this gap by integrating off-the-shelf retrievers, instruction-tuned LLMs, and minimal text-technique pairs. Our approach addresses data scarcity by fine-tuning only the generation component on limited in-domain examples, circumventing the need for resource-intensive retrieval training. While conventional RAG mitigates hallucination by coupling retrieval and generation, its reliance on generic retrievers often introduces noisy candidates, limiting domain-specific precision. To address this, we enhance retrieval quality and domain specificity through zero-shot LLM re-ranking, which explicitly aligns retrieved candidates with adversarial techniques. Experiments on multiple security benchmarks demonstrate that TechniqueRAG achieves state-of-the-art performance without extensive task-specific optimizations or labeled data, while comprehensive analysis provides further insights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.11497", "title": "QVGen: Pushing the Limit of Quantized Video Generative Models", "authors": [ "Yushi Huang", "Ruihao Gong", "Jing Liu", "Yifu Ding", "Chengtao Lv", "Haotong Qin", "Jun Zhang" ], "github": "", "project_page": "", "abstract": "Video diffusion models (DMs) have enabled high-quality video synthesis. Yet, their substantial computational and memory demands pose serious challenges to real-world deployment, even on high-end GPUs. As a commonly adopted solution, quantization has proven notable success in reducing cost for image DMs, while its direct application to video DMs remains ineffective. In this paper, we present QVGen, a novel quantization-aware training (QAT) framework tailored for high-performance and inference-efficient video DMs under extremely low-bit quantization (e.g., 4-bit or below). We begin with a theoretical analysis demonstrating that reducing the gradient norm is essential to facilitate convergence for QAT. To this end, we introduce auxiliary modules (Phi) to mitigate large quantization errors, leading to significantly enhanced convergence. To eliminate the inference overhead of Phi, we propose a rank-decay strategy that progressively eliminates Phi. Specifically, we repeatedly employ singular value decomposition (SVD) and a proposed rank-based regularization gamma to identify and decay low-contributing components. This strategy retains performance while zeroing out inference overhead. Extensive experiments across 4 state-of-the-art (SOTA) video DMs, with parameter sizes ranging from 1.3B sim14B, show that QVGen is the first to reach full-precision comparable quality under 4-bit settings. Moreover, it significantly outperforms existing methods. For instance, our 3-bit CogVideoX-2B achieves improvements of +25.28 in Dynamic Degree and +8.43 in Scene Consistency on VBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13180", "title": "ViPlan: A Benchmark for Visual Planning with Symbolic Predicates and Vision-Language Models", "authors": [ "Matteo Merler", "Nicola Dainese", "Minttu Alakuijala", "Giovanni Bonetta", "Pietro Ferrazzi", "Yu Tian", "Bernardo Magnini", "Pekka Marttinen" ], "github": "https://github.com/merlerm/ViPlan", "project_page": "", "abstract": "Integrating Large Language Models with symbolic planners is a promising direction for obtaining verifiable and grounded plans compared to planning in natural language, with recent works extending this idea to visual domains using Vision-Language Models (VLMs). However, rigorous comparison between VLM-grounded symbolic approaches and methods that plan directly with a VLM has been hindered by a lack of common environments, evaluation protocols and model coverage. We introduce ViPlan, the first open-source benchmark for Visual Planning with symbolic predicates and VLMs. ViPlan features a series of increasingly challenging tasks in two domains: a visual variant of the classic Blocksworld planning problem and a simulated household robotics environment. We benchmark nine open-source VLM families across multiple sizes, along with selected closed models, evaluating both VLM-grounded symbolic planning and using the models directly to propose actions. We find symbolic planning to outperform direct VLM planning in Blocksworld, where accurate image grounding is crucial, whereas the opposite is true in the household robotics tasks, where commonsense knowledge and the ability to recover from errors are beneficial. Finally, we show that across most models and methods, there is no significant benefit to using Chain-of-Thought prompting, suggesting that current VLMs still struggle with visual reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/merlerm/ViPlan", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12058", "title": "Tiny QA Benchmark++: Ultra-Lightweight, Synthetic Multilingual Dataset Generation & Smoke-Tests for Continuous LLM Evaluation", "authors": [ "Vincent Koc" ], "github": "https://github.com/vincentkoc/tiny_qa_benchmark_pp", "project_page": "", "abstract": "Tiny QA Benchmark++ (TQB++) presents an ultra-lightweight, multilingual smoke-test suite designed to give large-language-model (LLM) pipelines a unit-test style safety net dataset that runs in seconds with minimal cost. Born out of the tight feedback-loop demands building the Comet Opik prompt-optimization SDK, where waiting on heavyweight benchmarks breaks developer flow. TQB++ couples a 52-item English gold set (less than 20 kB) with a tiny synthetic-data generator pypi package built on provider-agnostic LiteLLM. The generator lets practitioners mint their own tiny packs in any language, domain, or difficulty, while ten ready-made packs already cover Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Russian, Spanish, and Turkish. Every dataset ships with Croissant metadata and plug-and-play files for OpenAI-Evals, LangChain, and standard CI tools, so teams can drop deterministic micro-benchmarks directly into pull-request gates, prompt-engineering loops, and production dashboards without touching GPU budgets. A complete TQB++ run adds only a few seconds to pipeline latency yet reliably flags prompt-template errors, tokenizer drift, and fine-tuning side-effects long before full-scale suites like MMLU or BIG-Bench would finish configuring. The entire framework is released to accelerate continuous, resource-efficient quality assurance across the generative-AI ecosystem.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vincentkoc/tiny_qa_benchmark_pp", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12082", "title": "Model Merging in Pre-training of Large Language Models", "authors": [ "Yunshui Li", "Yiyuan Ma", "Shen Yan", "Chaoyi Zhang", "Jing Liu", "Jianqiao Lu", "Ziwen Xu", "Mengzhao Chen", "Minrui Wang", "Shiyi Zhan", "Jin Ma", "Xunhao Lai", "Yao Luo", "Xingyan Bin", "Hongbin Ren", "Mingji Han", "Wenhao Hao", "Bairen Yi", "LingJun Liu", "Bole Ma", "Xiaoying Jia", "Zhou Xun", "Liang Xiang", "Yonghui Wu" ], "github": "", "project_page": "", "abstract": "Model merging has emerged as a promising technique for enhancing large language models, though its application in large-scale pre-training remains relatively unexplored. In this paper, we present a comprehensive investigation of model merging techniques during the pre-training process. Through extensive experiments with both dense and Mixture-of-Experts (MoE) architectures ranging from millions to over 100 billion parameters, we demonstrate that merging checkpoints trained with constant learning rates not only achieves significant performance improvements but also enables accurate prediction of annealing behavior. These improvements lead to both more efficient model development and significantly lower training costs. Our detailed ablation studies on merging strategies and hyperparameters provide new insights into the underlying mechanisms while uncovering novel applications. Through comprehensive experimental analysis, we offer the open-source community practical pre-training guidelines for effective model merging.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12872", "title": "From Grunts to Grammar: Emergent Language from Cooperative Foraging", "authors": [ "Maytus Piriyajitakonkij", "Rujikorn Charakorn", "Weicheng Tao", "Wei Pan", "Mingfei Sun", "Cheston Tan", "Mengmi Zhang" ], "github": "", "project_page": "", "abstract": "Early cavemen relied on gestures, vocalizations, and simple signals to coordinate, plan, avoid predators, and share resources. Today, humans collaborate using complex languages to achieve remarkable results. What drives this evolution in communication? How does language emerge, adapt, and become vital for teamwork? Understanding the origins of language remains a challenge. A leading hypothesis in linguistics and anthropology posits that language evolved to meet the ecological and social demands of early human cooperation. Language did not arise in isolation, but through shared survival goals. Inspired by this view, we investigate the emergence of language in multi-agent Foraging Games. These environments are designed to reflect the cognitive and ecological constraints believed to have influenced the evolution of communication. Agents operate in a shared grid world with only partial knowledge about other agents and the environment, and must coordinate to complete games like picking up high-value targets or executing temporally ordered actions. Using end-to-end deep reinforcement learning, agents learn both actions and communication strategies from scratch. We find that agents develop communication protocols with hallmark features of natural language: arbitrariness, interchangeability, displacement, cultural transmission, and compositionality. We quantify each property and analyze how different factors, such as population size and temporal dependencies, shape specific aspects of the emergent language. Our framework serves as a platform for studying how language can evolve from partial observability, temporal reasoning, and cooperative goals in embodied multi-agent settings. We will release all data, code, and models publicly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12973", "title": "Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models", "authors": [ "Mahta Fetrat Qharabagh", "Zahra Dehghanian", "Hamid R. Rabiee" ], "github": "https://github.com/MahtaFetrat/Homo-GE2PE-Persian", "project_page": "", "abstract": "Homograph disambiguation remains a significant challenge in grapheme-to-phoneme (G2P) conversion, especially for low-resource languages. This challenge is twofold: (1) creating balanced and comprehensive homograph datasets is labor-intensive and costly, and (2) specific disambiguation strategies introduce additional latency, making them unsuitable for real-time applications such as screen readers and other accessibility tools. In this paper, we address both issues. First, we propose a semi-automated pipeline for constructing homograph-focused datasets, introduce the HomoRich dataset generated through this pipeline, and demonstrate its effectiveness by applying it to enhance a state-of-the-art deep learning-based G2P system for Persian. Second, we advocate for a paradigm shift - utilizing rich offline datasets to inform the development of fast, rule-based methods suitable for latency-sensitive accessibility applications like screen readers. To this end, we improve one of the most well-known rule-based G2P systems, eSpeak, into a fast homograph-aware version, HomoFast eSpeak. Our results show an approximate 30% improvement in homograph disambiguation accuracy for the deep learning-based and eSpeak systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MahtaFetrat/Homo-GE2PE-Persian", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12120", "title": "HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational Pathology", "authors": [ "Dmitry Nechaev", "Alexey Pchelnikov", "Ekaterina Ivanova" ], "github": "https://github.com/HistAI/HISTAI", "project_page": "", "abstract": "Recent advancements in Digital Pathology (DP), particularly through artificial intelligence and Foundation Models, have underscored the importance of large-scale, diverse, and richly annotated datasets. Despite their critical role, publicly available Whole Slide Image (WSI) datasets often lack sufficient scale, tissue diversity, and comprehensive clinical metadata, limiting the robustness and generalizability of AI models. In response, we introduce the HISTAI dataset, a large, multimodal, open-access WSI collection comprising over 60,000 slides from various tissue types. Each case in the HISTAI dataset is accompanied by extensive clinical metadata, including diagnosis, demographic information, detailed pathological annotations, and standardized diagnostic coding. The dataset aims to fill gaps identified in existing resources, promoting innovation, reproducibility, and the development of clinically relevant computational pathology solutions. The dataset can be accessed at https://github.com/HistAI/HISTAI.", "github_manual": "", "github_regex": "https://github.com/HistAI/HISTAI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.11475", "title": "HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languages", "authors": [ "Zhilin Wang", "Jiaqi Zeng", "Olivier Delalleau", "Hoo-Chang Shin", "Felipe Soares", "Alexander Bukharin", "Ellie Evans", "Yi Dong", "Oleksii Kuchaiev" ], "github": "https://github.com/NVIDIA/NeMo-Aligner/blob/main/examples/nlp/gpt/train_reward_model.py", "project_page": "", "abstract": "Preference datasets are essential for training general-domain, instruction-following language models with Reinforcement Learning from Human Feedback (RLHF). Each subsequent data release raises expectations for future data collection, meaning there is a constant need to advance the quality and diversity of openly available preference data. To address this need, we introduce HelpSteer3-Preference, a permissively licensed (CC-BY-4.0), high-quality, human-annotated preference dataset comprising of over 40,000 samples. These samples span diverse real-world applications of large language models (LLMs), including tasks relating to STEM, coding and multilingual scenarios. Using HelpSteer3-Preference, we train Reward Models (RMs) that achieve top performance on RM-Bench (82.4%) and JudgeBench (73.7%). This represents a substantial improvement (~10% absolute) over the previously best-reported results from existing RMs. We demonstrate HelpSteer3-Preference can also be applied to train Generative RMs and how policy models can be aligned with RLHF using our RMs. Dataset (CC-BY-4.0): https://huggingface.co/datasets/nvidia/HelpSteer3#preference", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA/NeMo-Aligner/blob/main/examples/nlp/gpt/train_reward_model.py", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.10420", "title": "Learned Lightweight Smartphone ISP with Unpaired Data", "authors": [ "Andrei Arhire", "Radu Timofte" ], "github": "https://github.com/AndreiiArhire/Learned-Lightweight-Smartphone-ISP-with-Unpaired-Data", "project_page": "", "abstract": "The Image Signal Processor (ISP) is a fundamental component in modern smartphone cameras responsible for conversion of RAW sensor image data to RGB images with a strong focus on perceptual quality. Recent work highlights the potential of deep learning approaches and their ability to capture details with a quality increasingly close to that of professional cameras. A difficult and costly step when developing a learned ISP is the acquisition of pixel-wise aligned paired data that maps the raw captured by a smartphone camera sensor to high-quality reference images. In this work, we address this challenge by proposing a novel training method for a learnable ISP that eliminates the need for direct correspondences between raw images and ground-truth data with matching content. Our unpaired approach employs a multi-term loss function guided by adversarial training with multiple discriminators processing feature maps from pre-trained networks to maintain content structure while learning color and texture characteristics from the target RGB dataset. Using lightweight neural network architectures suitable for mobile devices as backbones, we evaluated our method on the Zurich RAW to RGB and Fujifilm UltraISP datasets. Compared to paired training methods, our unpaired learning strategy shows strong potential and achieves high fidelity across multiple evaluation metrics. The code and pre-trained models are available at https://github.com/AndreiiArhire/Learned-Lightweight-Smartphone-ISP-with-Unpaired-Data .", "github_manual": "", "github_regex": "https://github.com/AndreiiArhire/Learned-Lightweight-Smartphone-ISP-with-Unpaired-Data", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12346", "title": "SEED-GRPO: Semantic Entropy Enhanced GRPO for Uncertainty-Aware Policy Optimization", "authors": [ "Minghan Chen", "Guikun Chen", "Wenguan Wang", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) exhibit varying levels of confidence across input prompts (questions): some lead to consistent, semantically similar answers, while others yield diverse or contradictory outputs. This variation reflects LLM's uncertainty about the input prompt, a signal of how confidently the model understands a given problem. However, vanilla Group Relative Policy Optimization (GRPO) treats all prompts equally during policy updates, ignoring this important information about the model's knowledge boundaries. To address this limitation, we propose SEED-GRPO (Semantic Entropy EnhanceD GRPO), which explicitly measures LLMs' uncertainty of the input prompts semantic entropy. Semantic entropy measures the diversity of meaning in multiple generated answers given a prompt and uses this to modulate the magnitude of policy updates. This uncertainty-aware training mechanism enables dynamic adjustment of policy update magnitudes based on question uncertainty. It allows more conservative updates on high-uncertainty questions while maintaining the original learning signal on confident ones. Experimental results on five mathematical reasoning benchmarks (AIME24 56.7, AMC 68.7, MATH 83.4, Minerva 34.2, and OlympiadBench 48.0) demonstrate that SEED-GRPO achieves new state-of-the-art performance in average accuracy, validating the effectiveness of uncertainty-aware policy optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.10831", "title": "Creating General User Models from Computer Use", "authors": [ "Omar Shaikh", "Shardul Sapkota", "Shan Rizvi", "Eric Horvitz", "Joon Sung Park", "Diyi Yang", "Michael S. Bernstein" ], "github": "", "project_page": "", "abstract": "Human-computer interaction has long imagined technology that understands us-from our preferences and habits, to the timing and purpose of our everyday actions. Yet current user models remain fragmented, narrowly tailored to specific apps, and incapable of the flexible reasoning required to fulfill these visions. This paper presents an architecture for a general user model (GUM) that learns about you by observing any interaction you have with your computer. The GUM takes as input any unstructured observation of a user (e.g., device screenshots) and constructs confidence-weighted propositions that capture that user knowledge and preferences. GUMs can infer that a user is preparing for a wedding they're attending from messages with a friend. Or recognize that a user is struggling with a collaborator's feedback on a draft by observing multiple stalled edits and a switch to reading related work. GUMs introduce an architecture that infers new propositions about a user from multimodal observations, retrieves related propositions for context, and continuously revises existing propositions. To illustrate the breadth of applications that GUMs enable, we demonstrate how they augment chat-based assistants with context, manage OS notifications to selectively surface important information, and enable interactive agents that adapt to preferences across apps. We also instantiate proactive assistants (GUMBOs) that discover and execute useful suggestions on a user's behalf using their GUM. In our evaluations, we find that GUMs make calibrated and accurate inferences about users, and that assistants built on GUMs proactively identify and perform actions that users wouldn't think to request explicitly. Altogether, GUMs introduce methods that leverage multimodal models to understand unstructured context, enabling long-standing visions of HCI and entirely new interactive systems that anticipate user needs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.12781", "title": "A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone", "authors": [ "Jitai Hao", "Qiang Huang", "Hao Liu", "Xinyan Xiao", "Zhaochun Ren", "Jun Yu" ], "github": "https://github.com/CURRENTF/LowRankClone", "project_page": "", "abstract": "Training high-performing Small Language Models (SLMs) remains costly, even with knowledge distillation and pruning from larger teacher models. Existing work often faces three key challenges: (1) information loss from hard pruning, (2) inefficient alignment of representations, and (3) underutilization of informative activations, particularly from Feed-Forward Networks (FFNs). To address these challenges, we introduce Low-Rank Clone (LRC), an efficient pre-training method that constructs SLMs aspiring to behavioral equivalence with strong teacher models. LRC trains a set of low-rank projection matrices that jointly enable soft pruning by compressing teacher weights, and activation clone by aligning student activations, including FFN signals, with those of the teacher. This unified design maximizes knowledge transfer while removing the need for explicit alignment modules. Extensive experiments with open-source teachers (e.g., Llama-3.2-3B-Instruct, Qwen2.5-3B/7B-Instruct) show that LRC matches or surpasses state-of-the-art models trained on trillions of tokens--while using only 20B tokens, achieving over 1,000x training efficiency. Our codes and model checkpoints are available at https://github.com/CURRENTF/LowRankClone and https://huggingface.co/collections/JitaiHao/low-rank-clone-lrc-6828389e96a93f1d4219dfaf.", "github_manual": "", "github_regex": "https://github.com/CURRENTF/LowRankClone", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13388", "title": "R3: Robust Rubric-Agnostic Reward Models", "authors": [ "David Anugraha", "Zilu Tang", "Lester James V. Miranda", "Hanyang Zhao", "Mohammad Rifqi Farhansyah", "Garry Kuwanto", "Derry Wijaya", "Genta Indra Winata" ], "github": "https://github.com/rubricreward/r3", "project_page": "", "abstract": "Reward models are essential for aligning language model outputs with human preferences, yet existing approaches often lack both controllability and interpretability. These models are typically optimized for narrow objectives, limiting their generalizability to broader downstream tasks. Moreover, their scalar outputs are difficult to interpret without contextual reasoning. To address these limitations, we introduce R3, a novel reward modeling framework that is rubric-agnostic, generalizable across evaluation dimensions, and provides interpretable, reasoned score assignments. R3 enables more transparent and flexible evaluation of language models, supporting robust alignment with diverse human values and use cases. Our models, data, and code are available as open source at https://github.com/rubricreward/r3", "github_manual": "", "github_regex": "https://github.com/rubricreward/r3", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.07704", "title": "Through the Looking Glass: Common Sense Consistency Evaluation of Weird Images", "authors": [ "Elisei Rykov", "Kseniia Petrushina", "Kseniia Titova", "Anton Razzhigaev", "Alexander Panchenko", "Vasily Konovalov" ], "github": "", "project_page": "", "abstract": "Measuring how real images look is a complex task in artificial intelligence research. For example, an image of a boy with a vacuum cleaner in a desert violates common sense. We introduce a novel method, which we call Through the Looking Glass (TLG), to assess image common sense consistency using Large Vision-Language Models (LVLMs) and Transformer-based encoder. By leveraging LVLMs to extract atomic facts from these images, we obtain a mix of accurate facts. We proceed by fine-tuning a compact attention-pooling classifier over encoded atomic facts. Our TLG has achieved a new state-of-the-art performance on the WHOOPS! and WEIRD datasets while leveraging a compact fine-tuning component.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.11733", "title": "MedCaseReasoning: Evaluating and learning diagnostic reasoning from clinical case reports", "authors": [ "Kevin Wu", "Eric Wu", "Rahul Thapa", "Kevin Wei", "Angela Zhang", "Arvind Suresh", "Jacqueline J. Tao", "Min Woo Sun", "Alejandro Lozano", "James Zou" ], "github": "https://github.com/kevinwu23/Stanford-MedCaseReasoning", "project_page": "", "abstract": "Doctors and patients alike increasingly use Large Language Models (LLMs) to diagnose clinical cases. However, unlike domains such as math or coding, where correctness can be objectively defined by the final answer, medical diagnosis requires both the outcome and the reasoning process to be accurate. Currently, widely used medical benchmarks like MedQA and MMLU assess only accuracy in the final answer, overlooking the quality and faithfulness of the clinical reasoning process. To address this limitation, we introduce MedCaseReasoning, the first open-access dataset for evaluating LLMs on their ability to align with clinician-authored diagnostic reasoning. The dataset includes 14,489 diagnostic question-and-answer cases, each paired with detailed reasoning statements derived from open-access medical case reports. We evaluate state-of-the-art reasoning LLMs on MedCaseReasoning and find significant shortcomings in their diagnoses and reasoning: for instance, the top-performing open-source model, DeepSeek-R1, achieves only 48% 10-shot diagnostic accuracy and mentions only 64% of the clinician reasoning statements (recall). However, we demonstrate that fine-tuning LLMs on the reasoning traces derived from MedCaseReasoning significantly improves diagnostic accuracy and clinical reasoning recall by an average relative gain of 29% and 41%, respectively. The open-source dataset, code, and models are available at https://github.com/kevinwu23/Stanford-MedCaseReasoning.", "github_manual": "", "github_regex": "https://github.com/kevinwu23/Stanford-MedCaseReasoning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13840", "title": "EfficientLLM: Efficiency in Large Language Models", "authors": [ "Zhengqing Yuan", "Weixiang Sun", "Yixin Liu", "Huichi Zhou", "Rong Zhou", "Yiyang Li", "Zheyuan Zhang", "Wei Song", "Yue Huang", "Haolong Jia", "Keerthiram Murugesan", "Yu Wang", "Lifang He", "Jianfeng Gao", "Lichao Sun", "Yanfang Ye" ], "github": "https://github.com/MoonshotAI/Kimi-k1.5", "project_page": "", "abstract": "Large Language Models (LLMs) have driven significant progress, yet their growing parameter counts and context windows incur prohibitive compute, energy, and monetary costs. We introduce EfficientLLM, a novel benchmark and the first comprehensive empirical study evaluating efficiency techniques for LLMs at scale. Conducted on a production-class cluster (48xGH200, 8xH200 GPUs), our study systematically explores three key axes: (1) architecture pretraining (efficient attention variants: MQA, GQA, MLA, NSA; sparse Mixture-of-Experts (MoE)), (2) fine-tuning (parameter-efficient methods: LoRA, RSLoRA, DoRA), and (3) inference (quantization methods: int4, float16). We define six fine-grained metrics (Memory Utilization, Compute Utilization, Latency, Throughput, Energy Consumption, Compression Rate) to capture hardware saturation, latency-throughput balance, and carbon cost. Evaluating over 100 model-technique pairs (0.5B-72B parameters), we derive three core insights: (i) Efficiency involves quantifiable trade-offs: no single method is universally optimal; e.g., MoE reduces FLOPs and improves accuracy but increases VRAM by 40%, while int4 quantization cuts memory/energy by up to 3.9x at a 3-5% accuracy drop. (ii) Optima are task- and scale-dependent: MQA offers optimal memory-latency trade-offs for constrained devices, MLA achieves lowest perplexity for quality-critical tasks, and RSLoRA surpasses LoRA efficiency only beyond 14B parameters. (iii) Techniques generalize across modalities: we extend evaluations to Large Vision Models (Stable Diffusion 3.5, Wan 2.1) and Vision-Language Models (Qwen2.5-VL), confirming effective transferability. By open-sourcing datasets, evaluation pipelines, and leaderboards, EfficientLLM provides essential guidance for researchers and engineers navigating the efficiency-performance landscape of next-generation foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MoonshotAI/Kimi-k1.5", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13181", "title": "Efficient Speech Language Modeling via Energy Distance in Continuous Latent Space", "authors": [ "Zhengrui Ma", "Yang Feng", "Chenze Shao", "Fandong Meng", "Jie Zhou", "Min Zhang" ], "github": "https://github.com/ictnlp/sled-tts", "project_page": "", "abstract": "We introduce SLED, an alternative approach to speech language modeling by encoding speech waveforms into sequences of continuous latent representations and modeling them autoregressively using an energy distance objective. The energy distance offers an analytical measure of the distributional gap by contrasting simulated and target samples, enabling efficient training to capture the underlying continuous autoregressive distribution. By bypassing reliance on residual vector quantization, SLED avoids discretization errors and eliminates the need for the complicated hierarchical architectures common in existing speech language models. It simplifies the overall modeling pipeline while preserving the richness of speech information and maintaining inference efficiency. Empirical results demonstrate that SLED achieves strong performance in both zero-shot and streaming speech synthesis, showing its potential for broader applications in general-purpose speech language models.", "github_manual": "https://github.com/ictnlp/sled-tts", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-20", "arxiv_id": "2505.13511", "title": "Can AI Freelancers Compete? Benchmarking Earnings, Reliability, and Task Success at Scale", "authors": [ "David Noever", "Forrest McKee" ], "github": "", "project_page": "", "abstract": "This study explores Large Language Models (LLMs) as autonomous agents for real-world tasks, including freelance software development. This work presents a new benchmark that evaluates LLMs on freelance programming and data analysis tasks derived from economic data. We construct the benchmark using synthetic tasks created from a Kaggle Freelancer dataset of job postings, with all job prices standardized to USD (median fixed-project price around 250, and an average of 306). Each task is accompanied by structured input-output test cases and an estimated price tag, enabling automated correctness checking and a monetary performance valuation. This approach is inspired by OpenAI's recent SWE-Lancer benchmark (1,400 real Upwork tasks worth 1M total). Still, our framework simplifies evaluation using programmatically testable tasks and predicted price values, making it highly scalable and repeatable. On this benchmark, we evaluate four modern LLMs - Claude 3.5 Haiku, GPT-4o-mini, Qwen 2.5, and Mistral. We report each model's accuracy (task success rate and test-case pass rate) and the total \"freelance earnings\" it achieves (sum of prices of solved tasks). Our results show that Claude 3.5 Haiku performs best, earning approximately 1.52 million USD, followed closely by GPT-4o-mini at 1.49 million, then Qwen 2.5 (1.33M) and Mistral ($0.70M). We analyze the distribution of errors per task and observe that the strongest models solve the most tasks and rarely fail completely on any project. We discuss the implications of these results for the feasibility of AI as a freelance developer, the advantages and limitations of our automated benchmark approach, and the gap between performance on structured tasks versus the true complexity of real-world freelance jobs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13866", "title": "Reasoning Path Compression: Compressing Generation Trajectories for Efficient LLM Reasoning", "authors": [ "Jiwon Song", "Dongwon Jo", "Yulhwa Kim", "Jae-Joon Kim" ], "github": "https://github.com/jiwonsong-dev/ReasoningPathCompression", "project_page": "", "abstract": "Recent reasoning-focused language models achieve high accuracy by generating lengthy intermediate reasoning paths before producing final answers. While this approach is effective in solving problems that require logical thinking, long reasoning paths significantly increase memory usage and throughput of token generation, limiting the practical deployment of such models. We propose Reasoning Path Compression (RPC), a training-free method that accelerates inference by leveraging the semantic sparsity of reasoning paths. RPC periodically compresses the KV cache by retaining KV cache that receive high importance score, which are computed using a selector window composed of recently generated queries. Experiments show that RPC improves generation throughput of QwQ-32B by up to 1.60times compared to the inference with full KV cache, with an accuracy drop of 1.2% on the AIME 2024 benchmark. Our findings demonstrate that semantic sparsity in reasoning traces can be effectively exploited for compression, offering a practical path toward efficient deployment of reasoning LLMs. Our code is available at https://github.com/jiwonsong-dev/ReasoningPathCompression.", "github_manual": "", "github_regex": "https://github.com/jiwonsong-dev/ReasoningPathCompression", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14680", "title": "NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search", "authors": [ "Sunhao Dai", "Wenjie Wang", "Liang Pang", "Jun Xu", "See-Kiong Ng", "Ji-Rong Wen", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "Generative AI search is reshaping information retrieval by offering end-to-end answers to complex queries, reducing users' reliance on manually browsing and summarizing multiple web pages. However, while this paradigm enhances convenience, it disrupts the feedback-driven improvement loop that has historically powered the evolution of traditional Web search. Web search can continuously improve their ranking models by collecting large-scale, fine-grained user feedback (e.g., clicks, dwell time) at the document level. In contrast, generative AI search operates through a much longer search pipeline, spanning query decomposition, document retrieval, and answer generation, yet typically receives only coarse-grained feedback on the final answer. This introduces a feedback loop disconnect, where user feedback for the final output cannot be effectively mapped back to specific system components, making it difficult to improve each intermediate stage and sustain the feedback loop. In this paper, we envision NExT-Search, a next-generation paradigm designed to reintroduce fine-grained, process-level feedback into generative AI search. NExT-Search integrates two complementary modes: User Debug Mode, which allows engaged users to intervene at key stages; and Shadow User Mode, where a personalized user agent simulates user preferences and provides AI-assisted feedback for less interactive users. Furthermore, we envision how these feedback signals can be leveraged through online adaptation, which refines current search outputs in real-time, and offline update, which aggregates interaction logs to periodically fine-tune query decomposition, retrieval, and generation models. By restoring human control over key stages of the generative AI search pipeline, we believe NExT-Search offers a promising direction for building feedback-rich AI search systems that can evolve continuously alongside human feedback.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.12306", "title": "Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection", "authors": [ "Yuwei Zhang", "Wenhao Yu", "Shangbin Feng", "Yifan Zhu", "Letian Peng", "Jayanth Srinivasa", "Gaowen Liu", "Jingbo Shang" ], "github": "https://github.com/zhang-yu-wei/WikiDYK", "project_page": "", "abstract": "Despite significant advances in large language models (LLMs), their knowledge memorization capabilities remain underexplored, due to the lack of standardized and high-quality test ground. In this paper, we introduce a novel, real-world and large-scale knowledge injection benchmark that evolves continuously over time without requiring human intervention. Specifically, we propose WikiDYK, which leverages recently-added and human-written facts from Wikipedia's \"Did You Know...\" entries. These entries are carefully selected by expert Wikipedia editors based on criteria such as verifiability and clarity. Each entry is converted into multiple question-answer pairs spanning diverse task formats from easy cloze prompts to complex multi-hop questions. WikiDYK contains 12,290 facts and 77,180 questions, which is also seamlessly extensible with future updates from Wikipedia editors. Extensive experiments using continued pre-training reveal a surprising insight: despite their prevalence in modern LLMs, Causal Language Models (CLMs) demonstrate significantly weaker knowledge memorization capabilities compared to Bidirectional Language Models (BiLMs), exhibiting a 23% lower accuracy in terms of reliability. To compensate for the smaller scales of current BiLMs, we introduce a modular collaborative framework utilizing ensembles of BiLMs as external knowledge repositories to integrate with LLMs. Experiment shows that our framework further improves the reliability accuracy by up to 29.1%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhang-yu-wei/WikiDYK", "github_api_added_by": "auto" }, { "date": "2025-05-21", "arxiv_id": "2505.10588", "title": "Understanding Gen Alpha Digital Language: Evaluation of LLM Safety Systems for Content Moderation", "authors": [ "Manisha Mehta", "Fausto Giunchiglia" ], "github": "", "project_page": "", "abstract": "This research offers a unique evaluation of how AI systems interpret the digital language of Generation Alpha (Gen Alpha, born 2010-2024). As the first cohort raised alongside AI, Gen Alpha faces new forms of online risk due to immersive digital engagement and a growing mismatch between their evolving communication and existing safety tools. Their distinct language, shaped by gaming, memes, and AI-driven trends, often conceals harmful interactions from both human moderators and automated systems. We assess four leading AI models (GPT-4, Claude, Gemini, and Llama 3) on their ability to detect masked harassment and manipulation within Gen Alpha discourse. Using a dataset of 100 recent expressions from gaming platforms, social media, and video content, the study reveals critical comprehension failures with direct implications for online safety. This work contributes: (1) a first-of-its-kind dataset capturing Gen Alpha expressions; (2) a framework to improve AI moderation systems for youth protection; (3) a multi-perspective evaluation including AI systems, human moderators, and parents, with direct input from Gen Alpha co-researchers; and (4) an analysis of how linguistic divergence increases youth vulnerability. Findings highlight the urgent need to redesign safety systems attuned to youth communication, especially given Gen Alpha reluctance to seek help when adults fail to understand their digital world. This study combines the insight of a Gen Alpha researcher with systematic academic analysis to address critical digital safety challenges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14683", "title": "Emerging Properties in Unified Multimodal Pretraining", "authors": [ "Chaorui Deng", "Deyao Zhu", "Kunchang Li", "Chenhui Gou", "Feng Li", "Zeyu Wang", "Shu Zhong", "Weihao Yu", "Xiaonan Nie", "Ziang Song", "Guang Shi", "Haoqi Fan" ], "github": "", "project_page": "", "abstract": "Unifying multimodal understanding and generation has shown impressive capabilities in cutting-edge proprietary systems. In this work, we introduce BAGEL, an open0source foundational model that natively supports multimodal understanding and generation. BAGEL is a unified, decoder0only model pretrained on trillions of tokens curated from large0scale interleaved text, image, video, and web data. When scaled with such diverse multimodal interleaved data, BAGEL exhibits emerging capabilities in complex multimodal reasoning. As a result, it significantly outperforms open-source unified models in both multimodal generation and understanding across standard benchmarks, while exhibiting advanced multimodal reasoning abilities such as free-form image manipulation, future frame prediction, 3D manipulation, and world navigation. In the hope of facilitating further opportunities for multimodal research, we share the key findings, pretraining details, data creation protocal, and release our code and checkpoints to the community. The project page is at https://bagel-ai.org/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14513", "title": "Latent Flow Transformer", "authors": [ "Yen-Chen Wu", "Feng-Ting Liao", "Meng-Hsi Chen", "Pei-Chen Ho", "Farhang Nabiei", "Da-shan Shiu" ], "github": "", "project_page": "", "abstract": "Transformers, the standard implementation for large language models (LLMs), typically consist of tens to hundreds of discrete layers. While more layers can lead to better performance, this approach has been challenged as far from efficient, especially given the superiority of continuous layers demonstrated by diffusion and flow-based models for image generation. We propose the Latent Flow Transformer (LFT), which replaces a block of layers with a single learned transport operator trained via flow matching, offering significant compression while maintaining compatibility with the original architecture. Additionally, we address the limitations of existing flow-based methods in preserving coupling by introducing the Flow Walking (FW) algorithm. On the Pythia-410M model, LFT trained with flow matching compresses 6 of 24 layers and outperforms directly skipping 2 layers (KL Divergence of LM logits at 0.407 vs. 0.529), demonstrating the feasibility of this design. When trained with FW, LFT further distills 12 layers into one while reducing the KL to 0.736 surpassing that from skipping 3 layers (0.932), significantly narrowing the gap between autoregressive and flow-based generation paradigms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13380", "title": "CompeteSMoE -- Statistically Guaranteed Mixture of Experts Training via Competition", "authors": [ "Nam V. Nguyen", "Huy Nguyen", "Quang Pham", "Van Nguyen", "Savitha Ramasamy", "Nhat Ho" ], "github": "https://github.com/Fsoft-AIC/CompeteSMoE", "project_page": "", "abstract": "Sparse mixture of experts (SMoE) offers an appealing solution to scale up the model complexity beyond the mean of increasing the network's depth or width. However, we argue that effective SMoE training remains challenging because of the suboptimal routing process where experts that perform computation do not directly contribute to the routing process. In this work, we propose competition, a novel mechanism to route tokens to experts with the highest neural response. Theoretically, we show that the competition mechanism enjoys a better sample efficiency than the traditional softmax routing. Furthermore, we develop CompeteSMoE, a simple yet effective algorithm to train large language models by deploying a router to learn the competition policy, thus enjoying strong performances at a low training overhead. Our extensive empirical evaluations on both the visual instruction tuning and language pre-training tasks demonstrate the efficacy, robustness, and scalability of CompeteSMoE compared to state-of-the-art SMoE strategies. We have made the implementation available at: https://github.com/Fsoft-AIC/CompeteSMoE. This work is an improved version of the previous study at arXiv:2402.02526", "github_manual": "", "github_regex": "https://github.com/Fsoft-AIC/CompeteSMoE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.12182", "title": "Truth Neurons", "authors": [ "Haohang Li", "Yupeng Cao", "Yangyang Yu", "Jordan W. Suchow", "Zining Zhu" ], "github": "", "project_page": "", "abstract": "Despite their remarkable success and deployment across diverse workflows, language models sometimes produce untruthful responses. Our limited understanding of how truthfulness is mechanistically encoded within these models jeopardizes their reliability and safety. In this paper, we propose a method for identifying representations of truthfulness at the neuron level. We show that language models contain truth neurons, which encode truthfulness in a subject-agnostic manner. Experiments conducted across models of varying scales validate the existence of truth neurons, confirming that the encoding of truthfulness at the neuron level is a property shared by many language models. The distribution patterns of truth neurons over layers align with prior findings on the geometry of truthfulness. Selectively suppressing the activations of truth neurons found through the TruthfulQA dataset degrades performance both on TruthfulQA and on other benchmarks, showing that the truthfulness mechanisms are not tied to a specific dataset. Our results offer novel insights into the mechanisms underlying truthfulness in language models and highlight potential directions toward improving their trustworthiness and reliability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14178", "title": "Tokenization Constraints in LLMs: A Study of Symbolic and Arithmetic Reasoning Limits", "authors": [ "Xiang Zhang", "Juntai Cao", "Jiaqi Wei", "Yiwei Xu", "Chenyu You" ], "github": "", "project_page": "", "abstract": "Tokenization is the first - and often underappreciated - layer of computation in language models. While Chain-of-Thought (CoT) prompting enables transformer models to approximate recurrent computation by externalizing intermediate steps, we show that the success of such reasoning is fundamentally bounded by the structure of tokenized inputs. This work presents a theoretical and empirical investigation into how tokenization schemes, particularly subword-based methods like byte-pair encoding (BPE), impede symbolic computation by merging or obscuring atomic reasoning units. We introduce the notion of Token Awareness to formalize how poor token granularity disrupts logical alignment and prevents models from generalizing symbolic procedures. Through systematic evaluation on arithmetic and symbolic tasks, we demonstrate that token structure dramatically affect reasoning performance, causing failure even with CoT, while atomically-aligned formats unlock strong generalization, allowing small models (e.g., GPT-4o-mini) to outperform larger systems (e.g., o1) in structured reasoning. Our findings reveal that symbolic reasoning ability in LLMs is not purely architectural, but deeply conditioned on token-level representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.11594", "title": "SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training", "authors": [ "Jintao Zhang", "Jia Wei", "Pengle Zhang", "Xiaoming Xu", "Haofeng Huang", "Haoxu Wang", "Kai Jiang", "Jun Zhu", "Jianfei Chen" ], "github": "https://github.com/thu-ml/SageAttention", "project_page": "", "abstract": "The efficiency of attention is important due to its quadratic time complexity. We enhance the efficiency of attention through two key contributions: First, we leverage the new FP4 Tensor Cores in Blackwell GPUs to accelerate attention computation. Our implementation achieves 1038 TOPS on RTX5090, which is a 5x speedup over the fastest FlashAttention on RTX5090. Experiments show that our FP4 attention can accelerate inference of various models in a plug-and-play way. Second, we pioneer low-bit attention to training tasks. Existing low-bit attention works like FlashAttention3 and SageAttention focus only on inference. However, the efficiency of training large models is also important. To explore whether low-bit attention can be effectively applied to training tasks, we design an accurate and efficient 8-bit attention for both forward and backward propagation. Experiments indicate that 8-bit attention achieves lossless performance in fine-tuning tasks but exhibits slower convergence in pretraining tasks. The code will be available at https://github.com/thu-ml/SageAttention.", "github_manual": "", "github_regex": "https://github.com/thu-ml/SageAttention", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14640", "title": "VideoEval-Pro: Robust and Realistic Long Video Understanding Evaluation", "authors": [ "Wentao Ma", "Weiming Ren", "Yiming Jia", "Zhuofeng Li", "Ping Nie", "Ge Zhang", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Large multimodal models (LMMs) have recently emerged as a powerful tool for long video understanding (LVU), prompting the development of standardized LVU benchmarks to evaluate their performance. However, our investigation reveals a rather sober lesson for existing LVU benchmarks. First, most existing benchmarks rely heavily on multiple-choice questions (MCQs), whose evaluation results are inflated due to the possibility of guessing the correct answer; Second, a significant portion of questions in these benchmarks have strong priors to allow models to answer directly without even reading the input video. For example, Gemini-1.5-Pro can achieve over 50\\% accuracy given a random frame from a long video on Video-MME. We also observe that increasing the number of frames does not necessarily lead to improvement on existing benchmarks, which is counterintuitive. As a result, the validity and robustness of current LVU benchmarks are undermined, impeding a faithful assessment of LMMs' long-video understanding capability. To tackle this problem, we propose VideoEval-Pro, a realistic LVU benchmark containing questions with open-ended short-answer, which truly require understanding the entire video. VideoEval-Pro assesses both segment-level and full-video understanding through perception and reasoning tasks. By evaluating 21 proprietary and open-source video LMMs, we conclude the following findings: (1) video LMMs show drastic performance (>25\\%) drops on open-ended questions compared with MCQs; (2) surprisingly, higher MCQ scores do not lead to higher open-ended scores on VideoEval-Pro; (3) compared to other MCQ benchmarks, VideoEval-Pro benefits more from increasing the number of input frames. Our results show that VideoEval-Pro offers a more realistic and reliable measure of long video understanding, providing a clearer view of progress in this domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14652", "title": "General-Reasoner: Advancing LLM Reasoning Across All Domains", "authors": [ "Xueguang Ma", "Qian Liu", "Dongfu Jiang", "Ge Zhang", "Zejun Ma", "Wenhu Chen" ], "github": "https://github.com/volcengine/verl", "project_page": "", "abstract": "Reinforcement learning (RL) has recently demonstrated strong potential in enhancing the reasoning capabilities of large language models (LLMs). Particularly, the \"Zero\" reinforcement learning introduced by Deepseek-R1-Zero, enables direct RL training of base LLMs without relying on an intermediate supervised fine-tuning stage. Despite these advancements, current works for LLM reasoning mainly focus on mathematical and coding domains, largely due to data abundance and the ease of answer verification. This limits the applicability and generalization of such models to broader domains, where questions often have diverse answer representations, and data is more scarce. In this paper, we propose General-Reasoner, a novel training paradigm designed to enhance LLM reasoning capabilities across diverse domains. Our key contributions include: (1) constructing a large-scale, high-quality dataset of questions with verifiable answers curated by web crawling, covering a wide range of disciplines; and (2) developing a generative model-based answer verifier, which replaces traditional rule-based verification with the capability of chain-of-thought and context-awareness. We train a series of models and evaluate them on a wide range of datasets covering wide domains like physics, chemistry, finance, electronics etc. Our comprehensive evaluation across these 12 benchmarks (e.g. MMLU-Pro, GPQA, SuperGPQA, TheoremQA, BBEH and MATH AMC) demonstrates that General-Reasoner outperforms existing baseline methods, achieving robust and generalizable reasoning performance while maintaining superior effectiveness in mathematical reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/volcengine/verl", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14464", "title": "Not All Correct Answers Are Equal: Why Your Distillation Source Matters", "authors": [ "Xiaoyu Tian", "Yunjie Ji", "Haotian Wang", "Shuaiting Chen", "Sitong Zhao", "Yiping Peng", "Han Zhao", "Xiangang Li" ], "github": "https://github.com/GAIR-NLP/AIME-Preview", "project_page": "", "abstract": "Distillation has emerged as a practical and effective approach to enhance the reasoning capabilities of open-source language models. In this work, we conduct a large-scale empirical study on reasoning data distillation by collecting verified outputs from three state-of-the-art teacher models-AM-Thinking-v1, Qwen3-235B-A22B, and DeepSeek-R1-on a shared corpus of 1.89 million queries. We construct three parallel datasets and analyze their distributions, revealing that AM-Thinking-v1-distilled data exhibits greater token length diversity and lower perplexity. Student models trained on each dataset are evaluated on reasoning benchmarks including AIME2024, AIME2025, MATH500, and LiveCodeBench. The AM-based model consistently achieves the best performance (e.g., 84.3 on AIME2024, 72.2 on AIME2025, 98.4 on MATH500, and 65.9 on LiveCodeBench) and demonstrates adaptive output behavior-producing longer responses for harder tasks and shorter ones for simpler tasks. These findings highlight the value of high-quality, verified reasoning traces. We release the AM-Thinking-v1 and Qwen3-235B-A22B distilled datasets to support future research on open and high-performing reasoning-oriented language models. The datasets are publicly available on Hugging FaceDatasets are available on Hugging Face: \\href{https://huggingface.co/datasets/a-m-team/AM-Thinking-v1-Distilled{AM-Thinking-v1-Distilled}, https://huggingface.co/datasets/a-m-team/AM-Qwen3-Distilled{AM-Qwen3-Distilled}.}.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GAIR-NLP/AIME-Preview", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13559", "title": "CS-Sum: A Benchmark for Code-Switching Dialogue Summarization and the Limits of Large Language Models", "authors": [ "Sathya Krishnan Suresh", "Tanmay Surana", "Lim Zhi Hao", "Eng Siong Chng" ], "github": "", "project_page": "", "abstract": "Code-switching (CS) poses a significant challenge for Large Language Models (LLMs), yet its comprehensibility remains underexplored in LLMs. We introduce CS-Sum, to evaluate the comprehensibility of CS by the LLMs through CS dialogue to English summarization. CS-Sum is the first benchmark for CS dialogue summarization across Mandarin-English (EN-ZH), Tamil-English (EN-TA), and Malay-English (EN-MS), with 900-1300 human-annotated dialogues per language pair. Evaluating ten LLMs, including open and closed-source models, we analyze performance across few-shot, translate-summarize, and fine-tuning (LoRA, QLoRA on synthetic data) approaches. Our findings show that though the scores on automated metrics are high, LLMs make subtle mistakes that alter the complete meaning of the dialogue. To this end, we introduce 3 most common type of errors that LLMs make when handling CS input. Error rates vary across CS pairs and LLMs, with some LLMs showing more frequent errors on certain language pairs, underscoring the need for specialized training on code-switched data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.12448", "title": "SSR: Enhancing Depth Perception in Vision-Language Models via Rationale-Guided Spatial Reasoning", "authors": [ "Yang Liu", "Ming Ma", "Xiaomin Yu", "Pengxiang Ding", "Han Zhao", "Mingyang Sun", "Siteng Huang", "Donglin Wang" ], "github": "", "project_page": "", "abstract": "Despite impressive advancements in Visual-Language Models (VLMs) for multi-modal tasks, their reliance on RGB inputs limits precise spatial understanding. Existing methods for integrating spatial cues, such as point clouds or depth, either require specialized sensors or fail to effectively exploit depth information for higher-order reasoning. To this end, we propose a novel Spatial Sense and Reasoning method, dubbed SSR, a novel framework that transforms raw depth data into structured, interpretable textual rationales. These textual rationales serve as meaningful intermediate representations to significantly enhance spatial reasoning capabilities. Additionally, we leverage knowledge distillation to compress the generated rationales into compact latent embeddings, which facilitate resource-efficient and plug-and-play integration into existing VLMs without retraining. To enable comprehensive evaluation, we introduce a new dataset named SSR-CoT, a million-scale visual-language reasoning dataset enriched with intermediate spatial reasoning annotations, and present SSRBench, a comprehensive multi-task benchmark. Extensive experiments on multiple benchmarks demonstrate SSR substantially improves depth utilization and enhances spatial reasoning, thereby advancing VLMs toward more human-like multi-modal understanding. Our project page is at https://yliu-cs.github.io/SSR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14135", "title": "Hunyuan-Game: Industrial-grade Intelligent Game Creation Model", "authors": [ "Ruihuang Li", "Caijin Zhou", "Shoujian Zheng", "Jianxiang Lu", "Jiabin Huang", "Comi Chen", "Junshu Tang", "Guangzheng Xu", "Jiale Tao", "Hongmei Wang", "Donghao Li", "Wenqing Yu", "Senbo Wang", "Zhimin Li", "Yetshuan Shi", "Haoyu Yang", "Yukun Wang", "Wenxun Dai", "Jiaqi Li", "Linqing Wang", "Qixun Wang", "Zhiyong Xu", "Yingfang Zhang", "Jiangfeng Xiong", "Weijie Kong", "Chao Zhang", "Hongxin Zhang", "Qiaoling Zheng", "Weiting Guo", "Xinchi Deng", "Yixuan Li", "Renjia Wei", "Yulin Jian", "Duojun Huang", "Xuhua Ren", "Sihuan Lin", "Yifu Sun", "Yuan Zhou", "Joey Wang", "Qin Lin", "Jingmiao Yu", "Jihong Zhang", "Caesar Zhong", "Di Wang", "Yuhong Liu", "Linus", "Jie Jiang", "Longhuang Wu", "Shuai Shao", "Qinglin Lu" ], "github": "", "project_page": "", "abstract": "Intelligent game creation represents a transformative advancement in game development, utilizing generative artificial intelligence to dynamically generate and enhance game content. Despite notable progress in generative models, the comprehensive synthesis of high-quality game assets, including both images and videos, remains a challenging frontier. To create high-fidelity game content that simultaneously aligns with player preferences and significantly boosts designer efficiency, we present Hunyuan-Game, an innovative project designed to revolutionize intelligent game production. Hunyuan-Game encompasses two primary branches: image generation and video generation. The image generation component is built upon a vast dataset comprising billions of game images, leading to the development of a group of customized image generation models tailored for game scenarios: (1) General Text-to-Image Generation. (2) Game Visual Effects Generation, involving text-to-effect and reference image-based game visual effect generation. (3) Transparent Image Generation for characters, scenes, and game visual effects. (4) Game Character Generation based on sketches, black-and-white images, and white models. The video generation component is built upon a comprehensive dataset of millions of game and anime videos, leading to the development of five core algorithmic models, each targeting critical pain points in game development and having robust adaptation to diverse game video scenarios: (1) Image-to-Video Generation. (2) 360 A/T Pose Avatar Video Synthesis. (3) Dynamic Illustration Generation. (4) Generative Video Super-Resolution. (5) Interactive Game Video Generation. These image and video generation models not only exhibit high-level aesthetic expression but also deeply integrate domain-specific knowledge, establishing a systematic understanding of diverse game and anime art styles.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.11966", "title": "Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier", "authors": [ "Jianyuan Zhong", "Zeju Li", "Zhijian Xu", "Xiangyu Wen", "Kezhi Li", "Qiang Xu" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM) reasoning for complex tasks inherently involves a trade-off between solution accuracy and computational efficiency. The subsequent step of verification, while intended to improve performance, further complicates this landscape by introducing its own challenging trade-off: sophisticated Generative Reward Models (GenRMs) can be computationally prohibitive if naively integrated with LLMs at test-time, while simpler, faster methods may lack reliability. To overcome these challenges, we introduce FlexiVe, a novel generative verifier that flexibly balances computational resources between rapid, reliable fast thinking and meticulous slow thinking using a Flexible Allocation of Verification Budget strategy. We further propose the Solve-Detect-Verify pipeline, an efficient inference-time scaling framework that intelligently integrates FlexiVe, proactively identifying solution completion points to trigger targeted verification and provide focused solver feedback. Experiments show FlexiVe achieves superior accuracy in pinpointing errors within reasoning traces on ProcessBench. Furthermore, on challenging mathematical reasoning benchmarks (AIME 2024, AIME 2025, and CNMO), our full approach outperforms baselines like self-consistency in reasoning accuracy and inference efficiency. Our system offers a scalable and effective solution to enhance LLM reasoning at test time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14489", "title": "Reasoning Models Better Express Their Confidence", "authors": [ "Dongkeun Yoon", "Seungone Kim", "Sohee Yang", "Sunkyoung Kim", "Soyeon Kim", "Yongil Kim", "Eunbi Choi", "Yireun Kim", "Minjoon Seo" ], "github": "https://github.com/MattYoon/reasoning-models-confidence", "project_page": "", "abstract": "Despite their strengths, large language models (LLMs) often fail to communicate their confidence accurately, making it difficult to assess when they might be wrong and limiting their reliability. In this work, we demonstrate that reasoning models-LLMs that engage in extended chain-of-thought (CoT) reasoning-exhibit superior performance not only in problem-solving but also in accurately expressing their confidence. Specifically, we benchmark six reasoning models across six datasets and find that they achieve strictly better confidence calibration than their non-reasoning counterparts in 33 out of the 36 settings. Our detailed analysis reveals that these gains in calibration stem from the slow thinking behaviors of reasoning models-such as exploring alternative approaches and backtracking-which enable them to adjust their confidence dynamically throughout their CoT, making it progressively more accurate. In particular, we find that reasoning models become increasingly better calibrated as their CoT unfolds, a trend not observed in non-reasoning models. Moreover, removing slow thinking behaviors from the CoT leads to a significant drop in calibration. Lastly, we show that these gains are not exclusive to reasoning models-non-reasoning models also benefit when guided to perform slow thinking via in-context learning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MattYoon/reasoning-models-confidence", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14674", "title": "Reward Reasoning Model", "authors": [ "Jiaxin Guo", "Zewen Chi", "Li Dong", "Qingxiu Dong", "Xun Wu", "Shaohan Huang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Reward models play a critical role in guiding large language models toward outputs that align with human expectations. However, an open challenge remains in effectively utilizing test-time compute to enhance reward model performance. In this work, we introduce Reward Reasoning Models (RRMs), which are specifically designed to execute a deliberate reasoning process before generating final rewards. Through chain-of-thought reasoning, RRMs leverage additional test-time compute for complex queries where appropriate rewards are not immediately apparent. To develop RRMs, we implement a reinforcement learning framework that fosters self-evolved reward reasoning capabilities without requiring explicit reasoning traces as training data. Experimental results demonstrate that RRMs achieve superior performance on reward modeling benchmarks across diverse domains. Notably, we show that RRMs can adaptively exploit test-time compute to further improve reward accuracy. The pretrained reward reasoning models are available at https://huggingface.co/Reward-Reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13547", "title": "Exploring Federated Pruning for Large Language Models", "authors": [ "Pengxin Guo", "Yinong Wang", "Wei Li", "Mengting Liu", "Ming Li", "Jinkai Zheng", "Liangqiong Qu" ], "github": "https://github.com/Pengxin-Guo/FedPrLLM", "project_page": "", "abstract": "LLM pruning has emerged as a promising technology for compressing LLMs, enabling their deployment on resource-limited devices. However, current methodologies typically require access to public calibration samples, which can be challenging to obtain in privacy-sensitive domains. To address this issue, we introduce FedPrLLM, a comprehensive federated pruning framework designed for the privacy-preserving compression of LLMs. In FedPrLLM, each client only needs to calculate a pruning mask matrix based on its local calibration data and share it with the server to prune the global model. This approach allows for collaborative pruning of the global model with the knowledge of each client while maintaining local data privacy. Additionally, we conduct extensive experiments to explore various possibilities within the FedPrLLM framework, including different comparison groups, pruning strategies, and the decision to scale weights. Our extensive evaluation reveals that one-shot pruning with layer comparison and no weight scaling is the optimal choice within the FedPrLLM framework. We hope our work will help guide future efforts in pruning LLMs in privacy-sensitive fields. Our code is available at https://github.com/Pengxin-Guo/FedPrLLM.", "github_manual": "", "github_regex": "https://github.com/Pengxin-Guo/FedPrLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14677", "title": "Visionary-R1: Mitigating Shortcuts in Visual Reasoning with Reinforcement Learning", "authors": [ "Jiaer Xia", "Yuhang Zang", "Peng Gao", "Yixuan Li", "Kaiyang Zhou" ], "github": "", "project_page": "", "abstract": "Learning general-purpose reasoning capabilities has long been a challenging problem in AI. Recent research in large language models (LLMs), such as DeepSeek-R1, has shown that reinforcement learning techniques like GRPO can enable pre-trained LLMs to develop reasoning capabilities using simple question-answer pairs. In this paper, we aim to train visual language models (VLMs) to perform reasoning on image data through reinforcement learning and visual question-answer pairs, without any explicit chain-of-thought (CoT) supervision. Our findings indicate that simply applying reinforcement learning to a VLM -- by prompting the model to produce a reasoning chain before providing an answer -- can lead the model to develop shortcuts from easy questions, thereby reducing its ability to generalize across unseen data distributions. We argue that the key to mitigating shortcut learning is to encourage the model to interpret images prior to reasoning. Therefore, we train the model to adhere to a caption-reason-answer output format: initially generating a detailed caption for an image, followed by constructing an extensive reasoning chain. When trained on 273K CoT-free visual question-answer pairs and using only reinforcement learning, our model, named Visionary-R1, outperforms strong multimodal models, such as GPT-4o, Claude3.5-Sonnet, and Gemini-1.5-Pro, on multiple visual reasoning benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14673", "title": "Training-Free Watermarking for Autoregressive Image Generation", "authors": [ "Yu Tong", "Zihao Pan", "Shuai Yang", "Kaiyang Zhou" ], "github": "", "project_page": "", "abstract": "Invisible image watermarking can protect image ownership and prevent malicious misuse of visual generative models. However, existing generative watermarking methods are mainly designed for diffusion models while watermarking for autoregressive image generation models remains largely underexplored. We propose IndexMark, a training-free watermarking framework for autoregressive image generation models. IndexMark is inspired by the redundancy property of the codebook: replacing autoregressively generated indices with similar indices produces negligible visual differences. The core component in IndexMark is a simple yet effective match-then-replace method, which carefully selects watermark tokens from the codebook based on token similarity, and promotes the use of watermark tokens through token replacement, thereby embedding the watermark without affecting the image quality. Watermark verification is achieved by calculating the proportion of watermark tokens in generated images, with precision further improved by an Index Encoder. Furthermore, we introduce an auxiliary validation scheme to enhance robustness against cropping attacks. Experiments demonstrate that IndexMark achieves state-of-the-art performance in terms of image quality and verification accuracy, and exhibits robustness against various perturbations, including cropping, noises, Gaussian blur, random erasing, color jittering, and JPEG compression.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14631", "title": "Think Only When You Need with Large Hybrid-Reasoning Models", "authors": [ "Lingjie Jiang", "Xun Wu", "Shaohan Huang", "Qingxiu Dong", "Zewen Chi", "Li Dong", "Xingxing Zhang", "Tengchao Lv", "Lei Cui", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Recent Large Reasoning Models (LRMs) have shown substantially improved reasoning capabilities over traditional Large Language Models (LLMs) by incorporating extended thinking processes prior to producing final responses. However, excessively lengthy thinking introduces substantial overhead in terms of token consumption and latency, which is particularly unnecessary for simple queries. In this work, we introduce Large Hybrid-Reasoning Models (LHRMs), the first kind of model capable of adaptively determining whether to perform thinking based on the contextual information of user queries. To achieve this, we propose a two-stage training pipeline comprising Hybrid Fine-Tuning (HFT) as a cold start, followed by online reinforcement learning with the proposed Hybrid Group Policy Optimization (HGPO) to implicitly learn to select the appropriate thinking mode. Furthermore, we introduce a metric called Hybrid Accuracy to quantitatively assess the model's capability for hybrid thinking. Extensive experimental results show that LHRMs can adaptively perform hybrid thinking on queries of varying difficulty and type. It outperforms existing LRMs and LLMs in reasoning and general capabilities while significantly improving efficiency. Together, our work advocates for a reconsideration of the appropriate use of extended thinking processes and provides a solid starting point for building hybrid thinking systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13430", "title": "Fine-tuning Quantized Neural Networks with Zeroth-order Optimization", "authors": [ "Sifeng Shang", "Jiayi Zhou", "Chenyu Lin", "Minxian Li", "Kaiyang Zhou" ], "github": "https://github.com/princeton-nlp/MeZO", "project_page": "", "abstract": "As the size of large language models grows exponentially, GPU memory has become a bottleneck for adapting these models to downstream tasks. In this paper, we aim to push the limits of memory-efficient training by minimizing memory usage on model weights, gradients, and optimizer states, within a unified framework. Our idea is to eliminate both gradients and optimizer states using zeroth-order optimization, which approximates gradients by perturbing weights during forward passes to identify gradient directions. To minimize memory usage on weights, we employ model quantization, e.g., converting from bfloat16 to int4. However, directly applying zeroth-order optimization to quantized weights is infeasible due to the precision gap between discrete weights and continuous gradients, which would otherwise require de-quantization and re-quantization. To overcome this challenge, we propose Quantized Zeroth-order Optimization (QZO), a novel approach that perturbs the continuous quantization scale for gradient estimation and uses a directional derivative clipping method to stabilize training. QZO is orthogonal to both scalar-based and codebook-based post-training quantization methods. Compared to full-parameter fine-tuning in bfloat16, QZO can reduce the total memory cost by more than 18times for 4-bit LLMs, and enables fine-tuning Llama-2-13B and Stable Diffusion 3.5 Large within a single 24GB GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/princeton-nlp/MeZO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.09569", "title": "MIGRATION-BENCH: Repository-Level Code Migration Benchmark from Java 8", "authors": [ "Linbo Liu", "Xinle Liu", "Qiang Zhou", "Lin Chen", "Yihan Liu", "Hoan Nguyen", "Behrooz Omidvar-Tehrani", "Xi Shen", "Jun Huan", "Omer Tripp", "Anoop Deoras" ], "github": "https://github.com/amazon-science/MigrationBench", "project_page": "", "abstract": "With the rapid advancement of powerful large language models (LLMs) in recent years, a wide range of software engineering tasks can now be addressed using LLMs, significantly enhancing productivity and scalability. Numerous benchmark datasets have been developed to evaluate the coding capabilities of these models, while they primarily focus on problem-solving and issue-resolution tasks. In contrast, we introduce a new coding benchmark MIGRATION-BENCH with a distinct focus: code migration. MIGRATION-BENCH aims to serve as a comprehensive benchmark for migration from Java 8 to the latest long-term support (LTS) versions (Java 17, 21), MIGRATION-BENCH includes a full dataset and its subset selected with 5,102 and 300 repositories respectively. Selected is a representative subset curated for complexity and difficulty, offering a versatile resource to support research in the field of code migration. Additionally, we provide a comprehensive evaluation framework to facilitate rigorous and standardized assessment of LLMs on this challenging task. We further propose SD-Feedback and demonstrate that LLMs can effectively tackle repository-level code migration to Java 17. For the selected subset with Claude-3.5-Sonnet-v2, SD-Feedback achieves 62.33% and 27.00% success rate (pass@1) for minimal and maximal migration respectively. The benchmark dataset and source code are available at: https://huggingface.co/collections/AmazonScience and https://github.com/amazon-science/self_debug respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amazon-science/MigrationBench", "github_api_added_by": "user" }, { "date": "2025-05-21", "arxiv_id": "2505.14352", "title": "Towards eliciting latent knowledge from LLMs with mechanistic interpretability", "authors": [ "Bartosz Cywi\u0144ski", "Emil Ryd", "Senthooran Rajamanoharan", "Neel Nanda" ], "github": "", "project_page": "", "abstract": "As language models become more powerful and sophisticated, it is crucial that they remain trustworthy and reliable. There is concerning preliminary evidence that models may attempt to deceive or keep secrets from their operators. To explore the ability of current techniques to elicit such hidden knowledge, we train a Taboo model: a language model that describes a specific secret word without explicitly stating it. Importantly, the secret word is not presented to the model in its training data or prompt. We then investigate methods to uncover this secret. First, we evaluate non-interpretability (black-box) approaches. Subsequently, we develop largely automated strategies based on mechanistic interpretability techniques, including logit lens and sparse autoencoders. Evaluation shows that both approaches are effective in eliciting the secret word in our proof-of-concept setting. Our findings highlight the promise of these approaches for eliciting hidden knowledge and suggest several promising avenues for future work, including testing and refining these methods on more complex model organisms. This work aims to be a step towards addressing the crucial problem of eliciting secret knowledge from language models, thereby contributing to their safe and reliable deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13718", "title": "Warm Up Before You Train: Unlocking General Reasoning in Resource-Constrained Settings", "authors": [ "Safal Shrestha", "Minwu Kim", "Aadim Nepal", "Anubhav Shrestha", "Keith Ross" ], "github": "https://github.com/safal312/warmup-before-you-train", "project_page": "", "abstract": "Designing effective reasoning-capable LLMs typically requires training using Reinforcement Learning with Verifiable Rewards (RLVR) or distillation with carefully curated Long Chain of Thoughts (CoT), both of which depend heavily on extensive training data. This creates a major challenge when the amount of quality training data is scarce. We propose a sample-efficient, two-stage training strategy to develop reasoning LLMs under limited supervision. In the first stage, we \"warm up\" the model by distilling Long CoTs from a toy domain, namely, Knights \\& Knaves (K\\&K) logic puzzles to acquire general reasoning skills. In the second stage, we apply RLVR to the warmed-up model using a limited set of target-domain examples. Our experiments demonstrate that this two-phase approach offers several benefits: (i) the warmup phase alone facilitates generalized reasoning, leading to performance improvements across a range of tasks, including MATH, HumanEval^{+}, and MMLU-Pro. (ii) When both the base model and the warmed-up model are RLVR trained on the same small dataset (leq100 examples), the warmed-up model consistently outperforms the base model; (iii) Warming up before RLVR training allows a model to maintain cross-domain generalizability even after training on a specific domain; (iv) Introducing warmup in the pipeline improves not only accuracy but also overall sample efficiency during RLVR training. The results in this paper highlight the promise of warmup for building robust reasoning LLMs in data-scarce environments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/safal312/warmup-before-you-train", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14246", "title": "Visual Agentic Reinforcement Fine-Tuning", "authors": [ "Ziyu Liu", "Yuhang Zang", "Yushan Zou", "Zijian Liang", "Xiaoyi Dong", "Yuhang Cao", "Haodong Duan", "Dahua Lin", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "A key trend in Large Reasoning Models (e.g., OpenAI's o3) is the native agentic ability to use external tools such as web browsers for searching and writing/executing code for image manipulation to think with images. In the open-source research community, while significant progress has been made in language-only agentic abilities such as function calling and tool integration, the development of multi-modal agentic capabilities that involve truly thinking with images, and their corresponding benchmarks, are still less explored. This work highlights the effectiveness of Visual Agentic Reinforcement Fine-Tuning (Visual-ARFT) for enabling flexible and adaptive reasoning abilities for Large Vision-Language Models (LVLMs). With Visual-ARFT, open-source LVLMs gain the ability to browse websites for real-time information updates and write code to manipulate and analyze input images through cropping, rotation, and other image processing techniques. We also present a Multi-modal Agentic Tool Bench (MAT) with two settings (MAT-Search and MAT-Coding) designed to evaluate LVLMs' agentic search and coding abilities. Our experimental results demonstrate that Visual-ARFT outperforms its baseline by +18.6% F1 / +13.0% EM on MAT-Coding and +10.3% F1 / +8.7% EM on MAT-Search, ultimately surpassing GPT-4o. Visual-ARFT also achieves +29.3 F1% / +25.9% EM gains on existing multi-hop QA benchmarks such as 2Wiki and HotpotQA, demonstrating strong generalization capabilities. Our findings suggest that Visual-ARFT offers a promising path toward building robust and generalizable multimodal agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.11365", "title": "Phare: A Safety Probe for Large Language Models", "authors": [ "Pierre Le Jeune", "Beno\u00eet Mal\u00e9zieux", "Weixuan Xiao", "Matteo Dora" ], "github": "https://github.com/giskard-ai/phare", "project_page": "", "abstract": "Ensuring the safety of large language models (LLMs) is critical for responsible deployment, yet existing evaluations often prioritize performance over identifying failure modes. We introduce Phare, a multilingual diagnostic framework to probe and evaluate LLM behavior across three critical dimensions: hallucination and reliability, social biases, and harmful content generation. Our evaluation of 17 state-of-the-art LLMs reveals patterns of systematic vulnerabilities across all safety dimensions, including sycophancy, prompt sensitivity, and stereotype reproduction. By highlighting these specific failure modes rather than simply ranking models, Phare provides researchers and practitioners with actionable insights to build more robust, aligned, and trustworthy language systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/giskard-ai/phare", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14534", "title": "Lessons from Defending Gemini Against Indirect Prompt Injections", "authors": [ "Chongyang Shi", "Sharon Lin", "Shuang Song", "Jamie Hayes", "Ilia Shumailov", "Itay Yona", "Juliette Pluto", "Aneesh Pappu", "Christopher A. Choquette-Choo", "Milad Nasr", "Chawin Sitawarin", "Gena Gibson", "Andreas Terzis", "John \"Four\" Flynn" ], "github": "", "project_page": "", "abstract": "Gemini is increasingly used to perform tasks on behalf of users, where function-calling and tool-use capabilities enable the model to access user data. Some tools, however, require access to untrusted data introducing risk. Adversaries can embed malicious instructions in untrusted data which cause the model to deviate from the user's expectations and mishandle their data or permissions. In this report, we set out Google DeepMind's approach to evaluating the adversarial robustness of Gemini models and describe the main lessons learned from the process. We test how Gemini performs against a sophisticated adversary through an adversarial evaluation framework, which deploys a suite of adaptive attack techniques to run continuously against past, current, and future versions of Gemini. We describe how these ongoing evaluations directly help make Gemini more resilient against manipulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13103", "title": "Fixing 7,400 Bugs for 1$: Cheap Crash-Site Program Repair", "authors": [ "Han Zheng", "Ilia Shumailov", "Tianqi Fan", "Aiden Hall", "Mathias Payer" ], "github": "https://github.com/google/syzkaller", "project_page": "", "abstract": "The rapid advancement of bug-finding techniques has led to the discovery of more vulnerabilities than developers can reasonably fix, creating an urgent need for effective Automated Program Repair (APR) methods. However, the complexity of modern bugs often makes precise root cause analysis difficult and unreliable. To address this challenge, we propose crash-site repair to simplify the repair task while still mitigating the risk of exploitation. In addition, we introduce a template-guided patch generation approach that significantly reduces the token cost of Large Language Models (LLMs) while maintaining both efficiency and effectiveness. We implement our prototype system, WILLIAMT, and evaluate it against state-of-the-art APR tools. Our results show that, when combined with the top-performing agent CodeRover-S, WILLIAMT reduces token cost by 45.9% and increases the bug-fixing rate to 73.5% (+29.6%) on ARVO, a ground-truth open source software vulnerabilities benchmark. Furthermore, we demonstrate that WILLIAMT can function effectively even without access to frontier LLMs: even a local model running on a Mac M4 Mini achieves a reasonable repair rate. These findings highlight the broad applicability and scalability of WILLIAMT.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google/syzkaller", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.04388", "title": "The Aloe Family Recipe for Open and Specialized Healthcare LLMs", "authors": [ "Dario Garcia-Gasulla", "Jordi Bayarri-Planas", "Ashwin Kumar Gururajan", "Enrique Lopez-Cuena", "Adrian Tormos", "Daniel Hinjos", "Pablo Bernabeu-Perez", "Anna Arias-Duart", "Pablo Agustin Martin-Torres", "Marta Gonzalez-Mallo", "Sergio Alvarez-Napagao", "Eduard Ayguad\u00e9-Parra", "Ulises Cort\u00e9s" ], "github": "", "project_page": "", "abstract": "Purpose: With advancements in Large Language Models (LLMs) for healthcare, the need arises for competitive open-source models to protect the public interest. This work contributes to the field of open medical LLMs by optimizing key stages of data preprocessing and training, while showing how to improve model safety (through DPO) and efficacy (through RAG). The evaluation methodology used, which includes four different types of tests, defines a new standard for the field. The resultant models, shown to be competitive with the best private alternatives, are released with a permisive license. Methods: Building on top of strong base models like Llama 3.1 and Qwen 2.5, Aloe Beta uses a custom dataset to enhance public data with synthetic Chain of Thought examples. The models undergo alignment with Direct Preference Optimization, emphasizing ethical and policy-aligned performance in the presence of jailbreaking attacks. Evaluation includes close-ended, open-ended, safety and human assessments, to maximize the reliability of results. Results: Recommendations are made across the entire pipeline, backed by the solid performance of the Aloe Family. These models deliver competitive performance across healthcare benchmarks and medical fields, and are often preferred by healthcare professionals. On bias and toxicity, the Aloe Beta models significantly improve safety, showing resilience to unseen jailbreaking attacks. For a responsible release, a detailed risk assessment specific to healthcare is attached to the Aloe Family models. Conclusion: The Aloe Beta models, and the recipe that leads to them, are a significant contribution to the open-source medical LLM field, offering top-of-the-line performance while maintaining high ethical requirements. This work sets a new standard for developing and reporting aligned LLMs in healthcare.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13438", "title": "Optimizing Anytime Reasoning via Budget Relative Policy Optimization", "authors": [ "Penghui Qi", "Zichen Liu", "Tianyu Pang", "Chao Du", "Wee Sun Lee", "Min Lin" ], "github": "", "project_page": "", "abstract": "Scaling test-time compute is crucial for enhancing the reasoning capabilities of large language models (LLMs). Existing approaches typically employ reinforcement learning (RL) to maximize a verifiable reward obtained at the end of reasoning traces. However, such methods optimize only the final performance under a large and fixed token budget, which hinders efficiency in both training and deployment. In this work, we present a novel framework, AnytimeReasoner, to optimize anytime reasoning performance, which aims to improve token efficiency and the flexibility of reasoning under varying token budget constraints. To achieve this, we truncate the complete thinking process to fit within sampled token budgets from a prior distribution, compelling the model to summarize the optimal answer for each truncated thinking for verification. This introduces verifiable dense rewards into the reasoning process, facilitating more effective credit assignment in RL optimization. We then optimize the thinking and summary policies in a decoupled manner to maximize the cumulative reward. Additionally, we introduce a novel variance reduction technique, Budget Relative Policy Optimization (BRPO), to enhance the robustness and efficiency of the learning process when reinforcing the thinking policy. Empirical results in mathematical reasoning tasks demonstrate that our method consistently outperforms GRPO across all thinking budgets under various prior distributions, enhancing both training and token efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14460", "title": "VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank", "authors": [ "Tianhe Wu", "Jian Zou", "Jie Liang", "Lei Zhang", "Kede Ma" ], "github": "", "project_page": "", "abstract": "DeepSeek-R1 has demonstrated remarkable effectiveness in incentivizing reasoning and generalization capabilities of large language models (LLMs) through reinforcement learning. Nevertheless, the potential of reasoning-induced computational modeling has not been thoroughly explored in the context of image quality assessment (IQA), a task critically dependent on visual reasoning. In this paper, we introduce VisualQuality-R1, a reasoning-induced no-reference IQA (NR-IQA) model, and we train it with reinforcement learning to rank, a learning algorithm tailored to the intrinsically relative nature of visual quality. Specifically, for a pair of images, we employ group relative policy optimization to generate multiple quality scores for each image. These estimates are then used to compute comparative probabilities of one image having higher quality than the other under the Thurstone model. Rewards for each quality estimate are defined using continuous fidelity measures rather than discretized binary labels. Extensive experiments show that the proposed VisualQuality-R1 consistently outperforms discriminative deep learning-based NR-IQA models as well as a recent reasoning-induced quality regression method. Moreover, VisualQuality-R1 is capable of generating contextually rich, human-aligned quality descriptions, and supports multi-dataset training without requiring perceptual scale realignment. These features make VisualQuality-R1 especially well-suited for reliably measuring progress in a wide range of image processing tasks like super-resolution and image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13010", "title": "To Bias or Not to Bias: Detecting bias in News with bias-detector", "authors": [ "Himel Ghosh", "Ahmed Mosharafa", "Georg Groh" ], "github": "", "project_page": "", "abstract": "Media bias detection is a critical task in ensuring fair and balanced information dissemination, yet it remains challenging due to the subjectivity of bias and the scarcity of high-quality annotated data. In this work, we perform sentence-level bias classification by fine-tuning a RoBERTa-based model on the expert-annotated BABE dataset. Using McNemar's test and the 5x2 cross-validation paired t-test, we show statistically significant improvements in performance when comparing our model to a domain-adaptively pre-trained DA-RoBERTa baseline. Furthermore, attention-based analysis shows that our model avoids common pitfalls like oversensitivity to politically charged terms and instead attends more meaningfully to contextually relevant tokens. For a comprehensive examination of media bias, we present a pipeline that combines our model with an already-existing bias-type classifier. Our method exhibits good generalization and interpretability, despite being constrained by sentence-level analysis and dataset size because of a lack of larger and more advanced bias corpora. We talk about context-aware modeling, bias neutralization, and advanced bias type classification as potential future directions. Our findings contribute to building more robust, explainable, and socially responsible NLP systems for media bias detection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13138", "title": "Neurosymbolic Diffusion Models", "authors": [ "Emile van Krieken", "Pasquale Minervini", "Edoardo Ponti", "Antonio Vergari" ], "github": "https://github.com/HEmile/neurosymbolic-diffusion", "project_page": "", "abstract": "Neurosymbolic (NeSy) predictors combine neural perception with symbolic reasoning to solve tasks like visual reasoning. However, standard NeSy predictors assume conditional independence between the symbols they extract, thus limiting their ability to model interactions and uncertainty - often leading to overconfident predictions and poor out-of-distribution generalisation. To overcome the limitations of the independence assumption, we introduce neurosymbolic diffusion models (NeSyDMs), a new class of NeSy predictors that use discrete diffusion to model dependencies between symbols. Our approach reuses the independence assumption from NeSy predictors at each step of the diffusion process, enabling scalable learning while capturing symbol dependencies and uncertainty quantification. Across both synthetic and real-world benchmarks - including high-dimensional visual path planning and rule-based autonomous driving - NeSyDMs achieve state-of-the-art accuracy among NeSy predictors and demonstrate strong calibration.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HEmile/neurosymbolic-diffusion", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14681", "title": "Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training", "authors": [ "Mengru Wang", "Xingyu Chen", "Yue Wang", "Zhiwei He", "Jiahao Xu", "Tian Liang", "Qiuzhi Liu", "Yunzhi Yao", "Wenxuan Wang", "Ruotian Ma", "Haitao Mi", "Ningyu Zhang", "Zhaopeng Tu", "Xiaolong Li", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) architectures within Large Reasoning Models (LRMs) have achieved impressive reasoning capabilities by selectively activating experts to facilitate structured cognitive processes. Despite notable advances, existing reasoning models often suffer from cognitive inefficiencies like overthinking and underthinking. To address these limitations, we introduce a novel inference-time steering methodology called Reinforcing Cognitive Experts (RICE), designed to improve reasoning performance without additional training or complex heuristics. Leveraging normalized Pointwise Mutual Information (nPMI), we systematically identify specialized experts, termed ''cognitive experts'' that orchestrate meta-level reasoning operations characterized by tokens like ''''. Empirical evaluations with leading MoE-based LRMs (DeepSeek-R1 and Qwen3-235B) on rigorous quantitative and scientific reasoning benchmarks demonstrate noticeable and consistent improvements in reasoning accuracy, cognitive efficiency, and cross-domain generalization. Crucially, our lightweight approach substantially outperforms prevalent reasoning-steering techniques, such as prompt design and decoding constraints, while preserving the model's general instruction-following skills. These results highlight reinforcing cognitive experts as a promising, practical, and interpretable direction to enhance cognitive efficiency within advanced reasoning models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.11754", "title": "Masking in Multi-hop QA: An Analysis of How Language Models Perform with Context Permutation", "authors": [ "Wenyu Huang", "Pavlos Vougiouklis", "Mirella Lapata", "Jeff Z. Pan" ], "github": "https://github.com/hwy9855/MultiHopQA-Reasoning", "project_page": "", "abstract": "Multi-hop Question Answering (MHQA) adds layers of complexity to question answering, making it more challenging. When Language Models (LMs) are prompted with multiple search results, they are tasked not only with retrieving relevant information but also employing multi-hop reasoning across the information sources. Although LMs perform well on traditional question-answering tasks, the causal mask can hinder their capacity to reason across complex contexts. In this paper, we explore how LMs respond to multi-hop questions by permuting search results (retrieved documents) under various configurations. Our study reveals interesting findings as follows: 1) Encoder-decoder models, such as the ones in the Flan-T5 family, generally outperform causal decoder-only LMs in MHQA tasks, despite being significantly smaller in size; 2) altering the order of gold documents reveals distinct trends in both Flan T5 models and fine-tuned decoder-only models, with optimal performance observed when the document order aligns with the reasoning chain order; 3) enhancing causal decoder-only models with bi-directional attention by modifying the causal mask can effectively boost their end performance. In addition to the above, we conduct a thorough investigation of the distribution of LM attention weights in the context of MHQA. Our experiments reveal that attention weights tend to peak at higher values when the resulting answer is correct. We leverage this finding to heuristically improve LMs' performance on this task. Our code is publicly available at https://github.com/hwy9855/MultiHopQA-Reasoning.", "github_manual": "", "github_regex": "https://github.com/hwy9855/MultiHopQA-Reasoning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.10176", "title": "Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence", "authors": [ "Xiang He", "Dongcheng Zhao", "Yang Li", "Qingqun Kong", "Xin Yang", "Yi Zeng" ], "github": "https://github.com/Brain-Cog-Lab/IEMF", "project_page": "", "abstract": "Multimodal learning enhances the perceptual capabilities of cognitive systems by integrating information from different sensory modalities. However, existing multimodal fusion research typically assumes static integration, not fully incorporating key dynamic mechanisms found in the brain. Specifically, the brain exhibits an inverse effectiveness phenomenon, wherein weaker unimodal cues yield stronger multisensory integration benefits; conversely, when individual modal cues are stronger, the effect of fusion is diminished. This mechanism enables biological systems to achieve robust cognition even with scarce or noisy perceptual cues. Inspired by this biological mechanism, we explore the relationship between multimodal output and information from individual modalities, proposing an inverse effectiveness driven multimodal fusion (IEMF) strategy. By incorporating this strategy into neural networks, we achieve more efficient integration with improved model performance and computational efficiency, demonstrating up to 50% reduction in computational cost across diverse fusion methods. We conduct experiments on audio-visual classification, continual learning, and question answering tasks to validate our method. Results consistently demonstrate that our method performs excellently in these tasks. To verify universality and generalization, we also conduct experiments on Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN), with results showing good adaptability to both network types. Our research emphasizes the potential of incorporating biologically inspired mechanisms into multimodal networks and provides promising directions for the future development of multimodal artificial intelligence. The code is available at https://github.com/Brain-Cog-Lab/IEMF.", "github_manual": "", "github_regex": "https://github.com/Brain-Cog-Lab/IEMF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14629", "title": "KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models", "authors": [ "Fnu Mohbat", "Mohammed J Zaki" ], "github": "https://github.com/mohbattharani/KERL", "project_page": "", "abstract": "Recent advances in large language models (LLMs) and the abundance of food data have resulted in studies to improve food understanding using LLMs. Despite several recommendation systems utilizing LLMs and Knowledge Graphs (KGs), there has been limited research on integrating food related KGs with LLMs. We introduce KERL, a unified system that leverages food KGs and LLMs to provide personalized food recommendations and generates recipes with associated micro-nutritional information. Given a natural language question, KERL extracts entities, retrieves subgraphs from the KG, which are then fed into the LLM as context to select the recipes that satisfy the constraints. Next, our system generates the cooking steps and nutritional information for each recipe. To evaluate our approach, we also develop a benchmark dataset by curating recipe related questions, combined with constraints and personal preferences. Through extensive experiments, we show that our proposed KG-augmented LLM significantly outperforms existing approaches, offering a complete and coherent solution for food recommendation, recipe generation, and nutritional analysis. Our code and benchmark datasets are publicly available at https://github.com/mohbattharani/KERL.", "github_manual": "", "github_regex": "https://github.com/mohbattharani/KERL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.11563", "title": "Object-Centric Representations Improve Policy Generalization in Robot Manipulation", "authors": [ "Alexandre Chapin", "Bruno Machado", "Emmanuel Dellandrea", "Liming Chen" ], "github": "", "project_page": "", "abstract": "Visual representations are central to the learning and generalization capabilities of robotic manipulation policies. While existing methods rely on global or dense features, such representations often entangle task-relevant and irrelevant scene information, limiting robustness under distribution shifts. In this work, we investigate object-centric representations (OCR) as a structured alternative that segments visual input into a finished set of entities, introducing inductive biases that align more naturally with manipulation tasks. We benchmark a range of visual encoders-object-centric, global and dense methods-across a suite of simulated and real-world manipulation tasks ranging from simple to complex, and evaluate their generalization under diverse visual conditions including changes in lighting, texture, and the presence of distractors. Our findings reveal that OCR-based policies outperform dense and global representations in generalization settings, even without task-specific pretraining. These insights suggest that OCR is a promising direction for designing visual systems that generalize effectively in dynamic, real-world robotic environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14556", "title": "Dynadiff: Single-stage Decoding of Images from Continuously Evolving fMRI", "authors": [ "Marl\u00e8ne Careil", "Yohann Benchetrit", "Jean-R\u00e9mi King" ], "github": "https://github.com/facebookresearch/dynadiff", "project_page": "", "abstract": "Brain-to-image decoding has been recently propelled by the progress in generative AI models and the availability of large ultra-high field functional Magnetic Resonance Imaging (fMRI). However, current approaches depend on complicated multi-stage pipelines and preprocessing steps that typically collapse the temporal dimension of brain recordings, thereby limiting time-resolved brain decoders. Here, we introduce Dynadiff (Dynamic Neural Activity Diffusion for Image Reconstruction), a new single-stage diffusion model designed for reconstructing images from dynamically evolving fMRI recordings. Our approach offers three main contributions. First, Dynadiff simplifies training as compared to existing approaches. Second, our model outperforms state-of-the-art models on time-resolved fMRI signals, especially on high-level semantic image reconstruction metrics, while remaining competitive on preprocessed fMRI data that collapse time. Third, this approach allows a precise characterization of the evolution of image representations in brain activity. Overall, this work lays the foundation for time-resolved brain-to-image decoding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/dynadiff", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14366", "title": "Towards Embodied Cognition in Robots via Spatially Grounded Synthetic Worlds", "authors": [ "Joel Currie", "Gioele Migno", "Enrico Piacenti", "Maria Elena Giannaccini", "Patric Bach", "Davide De Tommaso", "Agnieszka Wykowska" ], "github": "", "project_page": "", "abstract": "We present a conceptual framework for training Vision-Language Models (VLMs) to perform Visual Perspective Taking (VPT), a core capability for embodied cognition essential for Human-Robot Interaction (HRI). As a first step toward this goal, we introduce a synthetic dataset, generated in NVIDIA Omniverse, that enables supervised learning for spatial reasoning tasks. Each instance includes an RGB image, a natural language description, and a ground-truth 4X4 transformation matrix representing object pose. We focus on inferring Z-axis distance as a foundational skill, with future extensions targeting full 6 Degrees Of Freedom (DOFs) reasoning. The dataset is publicly available to support further research. This work serves as a foundational step toward embodied AI systems capable of spatial understanding in interactive human-robot scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.06914", "title": "The Distracting Effect: Understanding Irrelevant Passages in RAG", "authors": [ "Chen Amiraz", "Florin Cuconasu", "Simone Filice", "Zohar Karnin" ], "github": "", "project_page": "", "abstract": "A well-known issue with Retrieval Augmented Generation (RAG) is that retrieved passages that are irrelevant to the query sometimes distract the answer-generating LLM, causing it to provide an incorrect response. In this paper, we shed light on this core issue and formulate the distracting effect of a passage w.r.t. a query (and an LLM). We provide a quantifiable measure of the distracting effect of a passage and demonstrate its robustness across LLMs. Our research introduces novel methods for identifying and using hard distracting passages to improve RAG systems. By fine-tuning LLMs with these carefully selected distracting passages, we achieve up to a 7.5% increase in answering accuracy compared to counterparts fine-tuned on conventional RAG datasets. Our contribution is two-fold: first, we move beyond the simple binary classification of irrelevant passages as either completely unrelated vs. distracting, and second, we develop and analyze multiple methods for finding hard distracting passages. To our knowledge, no other research has provided such a comprehensive framework for identifying and utilizing hard distracting passages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.12154", "title": "Learning to Highlight Audio by Watching Movies", "authors": [ "Chao Huang", "Ruohan Gao", "J. M. F. Tsang", "Jan Kurcius", "Cagdas Bilen", "Chenliang Xu", "Anurag Kumar", "Sanjeel Parekh" ], "github": "", "project_page": "", "abstract": "Recent years have seen a significant increase in video content creation and consumption. Crafting engaging content requires the careful curation of both visual and audio elements. While visual cue curation, through techniques like optimal viewpoint selection or post-editing, has been central to media production, its natural counterpart, audio, has not undergone equivalent advancements. This often results in a disconnect between visual and acoustic saliency. To bridge this gap, we introduce a novel task: visually-guided acoustic highlighting, which aims to transform audio to deliver appropriate highlighting effects guided by the accompanying video, ultimately creating a more harmonious audio-visual experience. We propose a flexible, transformer-based multimodal framework to solve this task. To train our model, we also introduce a new dataset -- the muddy mix dataset, leveraging the meticulous audio and video crafting found in movies, which provides a form of free supervision. We develop a pseudo-data generation process to simulate poorly mixed audio, mimicking real-world scenarios through a three-step process -- separation, adjustment, and remixing. Our approach consistently outperforms several baselines in both quantitative and subjective evaluation. We also systematically study the impact of different types of contextual guidance and difficulty levels of the dataset. Our project page is here: https://wikichao.github.io/VisAH/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.11730", "title": "Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling", "authors": [ "Hao Mark Chen", "Guanxi Lu", "Yasuyuki Okoshi", "Zhiwen Mo", "Masato Motomura", "Hongxiang Fan" ], "github": "https://github.com/hmarkc/VG-Search", "project_page": "", "abstract": "Test-time scaling (TTS) has proven effective in enhancing the reasoning capabilities of large language models (LLMs). Verification plays a key role in TTS, simultaneously influencing (1) reasoning performance and (2) compute efficiency, due to the quality and computational cost of verification. In this work, we challenge the conventional paradigms of verification, and make the first attempt toward systematically investigating the impact of verification granularity-that is, how frequently the verifier is invoked during generation, beyond verifying only the final output or individual generation steps. To this end, we introduce Variable Granularity Search (VG-Search), a unified algorithm that generalizes beam search and Best-of-N sampling via a tunable granularity parameter g. Extensive experiments with VG-Search under varying compute budgets, generator-verifier configurations, and task attributes reveal that dynamically selecting g can improve the compute efficiency and scaling behavior. Building on these findings, we propose adaptive VG-Search strategies that achieve accuracy gains of up to 3.1\\% over Beam Search and 3.6\\% over Best-of-N, while reducing FLOPs by over 52\\%. We will open-source the code to support future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hmarkc/VG-Search", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14648", "title": "Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits", "authors": [ "Tiantian Feng", "Jihwan Lee", "Anfeng Xu", "Yoonjeong Lee", "Thanathai Lertpetchpun", "Xuan Shi", "Helin Wang", "Thomas Thebaud", "Laureano Moro-Velazquez", "Dani Byrd", "Najim Dehak", "Shrikanth Narayanan" ], "github": "https://github.com/tiantiaf0627/vox-profile-release", "project_page": "", "abstract": "We introduce Vox-Profile, a comprehensive benchmark to characterize rich speaker and speech traits using speech foundation models. Unlike existing works that focus on a single dimension of speaker traits, Vox-Profile provides holistic and multi-dimensional profiles that reflect both static speaker traits (e.g., age, sex, accent) and dynamic speech properties (e.g., emotion, speech flow). This benchmark is grounded in speech science and linguistics, developed with domain experts to accurately index speaker and speech characteristics. We report benchmark experiments using over 15 publicly available speech datasets and several widely used speech foundation models that target various static and dynamic speaker and speech properties. In addition to benchmark experiments, we showcase several downstream applications supported by Vox-Profile. First, we show that Vox-Profile can augment existing speech recognition datasets to analyze ASR performance variability. Vox-Profile is also used as a tool to evaluate the performance of speech generation systems. Finally, we assess the quality of our automated profiles through comparison with human evaluation and show convergent validity. Vox-Profile is publicly available at: https://github.com/tiantiaf0627/vox-profile-release.", "github_manual": "", "github_regex": "https://github.com/tiantiaf0627/vox-profile-release", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13778", "title": "CoIn: Counting the Invisible Reasoning Tokens in Commercial Opaque LLM APIs", "authors": [ "Guoheng Sun", "Ziyao Wang", "Bowei Tian", "Meng Liu", "Zheyu Shen", "Shwai He", "Yexiao He", "Wanghao Ye", "Yiting Wang", "Ang Li" ], "github": "https://github.com/CASE-Lab-UMD/LLM-Auditing-CoIn", "project_page": "", "abstract": "As post-training techniques evolve, large language models (LLMs) are increasingly augmented with structured multi-step reasoning abilities, often optimized through reinforcement learning. These reasoning-enhanced models outperform standard LLMs on complex tasks and now underpin many commercial LLM APIs. However, to protect proprietary behavior and reduce verbosity, providers typically conceal the reasoning traces while returning only the final answer. This opacity introduces a critical transparency gap: users are billed for invisible reasoning tokens, which often account for the majority of the cost, yet have no means to verify their authenticity. This opens the door to token count inflation, where providers may overreport token usage or inject synthetic, low-effort tokens to inflate charges. To address this issue, we propose CoIn, a verification framework that audits both the quantity and semantic validity of hidden tokens. CoIn constructs a verifiable hash tree from token embedding fingerprints to check token counts, and uses embedding-based relevance matching to detect fabricated reasoning content. Experiments demonstrate that CoIn, when deployed as a trusted third-party auditor, can effectively detect token count inflation with a success rate reaching up to 94.7%, showing the strong ability to restore billing transparency in opaque LLM services. The dataset and code are available at https://github.com/CASE-Lab-UMD/LLM-Auditing-CoIn.", "github_manual": "", "github_regex": "https://github.com/CASE-Lab-UMD/LLM-Auditing-CoIn", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14633", "title": "Will AI Tell Lies to Save Sick Children? Litmus-Testing AI Values Prioritization with AIRiskDilemmas", "authors": [ "Yu Ying Chiu", "Zhilin Wang", "Sharan Maiya", "Yejin Choi", "Kyle Fish", "Sydney Levine", "Evan Hubinger" ], "github": "", "project_page": "", "abstract": "Detecting AI risks becomes more challenging as stronger models emerge and find novel methods such as Alignment Faking to circumvent these detection attempts. Inspired by how risky behaviors in humans (i.e., illegal activities that may hurt others) are sometimes guided by strongly-held values, we believe that identifying values within AI models can be an early warning system for AI's risky behaviors. We create LitmusValues, an evaluation pipeline to reveal AI models' priorities on a range of AI value classes. Then, we collect AIRiskDilemmas, a diverse collection of dilemmas that pit values against one another in scenarios relevant to AI safety risks such as Power Seeking. By measuring an AI model's value prioritization using its aggregate choices, we obtain a self-consistent set of predicted value priorities that uncover potential risks. We show that values in LitmusValues (including seemingly innocuous ones like Care) can predict for both seen risky behaviors in AIRiskDilemmas and unseen risky behaviors in HarmBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13731", "title": "GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization", "authors": [ "Pengyue Jia", "Seongheon Park", "Song Gao", "Xiangyu Zhao", "Yixuan Li" ], "github": "https://github.com/Applied-Machine-Learning-Lab/GeoRanker", "project_page": "", "abstract": "Worldwide image geolocalization-the task of predicting GPS coordinates from images taken anywhere on Earth-poses a fundamental challenge due to the vast diversity in visual content across regions. While recent approaches adopt a two-stage pipeline of retrieving candidates and selecting the best match, they typically rely on simplistic similarity heuristics and point-wise supervision, failing to model spatial relationships among candidates. In this paper, we propose GeoRanker, a distance-aware ranking framework that leverages large vision-language models to jointly encode query-candidate interactions and predict geographic proximity. In addition, we introduce a multi-order distance loss that ranks both absolute and relative distances, enabling the model to reason over structured spatial relationships. To support this, we curate GeoRanking, the first dataset explicitly designed for geographic ranking tasks with multimodal candidate information. GeoRanker achieves state-of-the-art results on two well-established benchmarks (IM2GPS3K and YFCC4K), significantly outperforming current best methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Applied-Machine-Learning-Lab/GeoRanker", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13988", "title": "The Hallucination Tax of Reinforcement Finetuning", "authors": [ "Linxin Song", "Taiwei Shi", "Jieyu Zhao" ], "github": "", "project_page": "", "abstract": "Reinforcement finetuning (RFT) has become a standard approach for enhancing the reasoning capabilities of large language models (LLMs). However, its impact on model trustworthiness remains underexplored. In this work, we identify and systematically study a critical side effect of RFT, which we term the hallucination tax: a degradation in refusal behavior causing models to produce hallucinated answers to unanswerable questions confidently. To investigate this, we introduce SUM (Synthetic Unanswerable Math), a high-quality dataset of unanswerable math problems designed to probe models' ability to recognize an unanswerable question by reasoning from the insufficient or ambiguous information. Our results show that standard RFT training could reduce model refusal rates by more than 80%, which significantly increases model's tendency to hallucinate. We further demonstrate that incorporating just 10% SUM during RFT substantially restores appropriate refusal behavior, with minimal accuracy trade-offs on solvable tasks. Crucially, this approach enables LLMs to leverage inference-time compute to reason about their own uncertainty and knowledge boundaries, improving generalization not only to out-of-domain math problems but also to factual question answering tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.13946", "title": "Visual Instruction Bottleneck Tuning", "authors": [ "Changdae Oh", "Jiatong Li", "Shawn Im", "Yixuan Li" ], "github": "", "project_page": "", "abstract": "Despite widespread adoption, multimodal large language models (MLLMs) suffer performance degradation when encountering unfamiliar queries under distribution shifts. Existing methods to improve MLLM generalization typically require either more instruction data or larger advanced model architectures, both of which incur non-trivial human labor or computational costs. In this work, we take an alternative approach to enhance the robustness of MLLMs under distribution shifts, from a representation learning perspective. Inspired by the information bottleneck (IB) principle, we derive a variational lower bound of the IB for MLLMs and devise a practical implementation, Visual Instruction Bottleneck Tuning (Vittle). We then provide a theoretical justification of Vittle by revealing its connection to an information-theoretic robustness metric of MLLM. Empirical validation of three MLLMs on open-ended and closed-form question answering and object hallucination detection tasks over 45 datasets, including 30 shift scenarios, demonstrates that Vittle consistently improves the MLLM's robustness under shifts by pursuing the learning of a minimal sufficient representation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-21", "arxiv_id": "2505.14467", "title": "Void in Language Models", "authors": [ "Mani Shemiranifar" ], "github": "https://github.com/manishemirani/void", "project_page": "", "abstract": "Despite advances in transformer-based language models (LMs), a fundamental question remains largely unanswered: Are all layers activated during inference? We investigate this question by detecting unactivated layers (which we refer to as Voids) using a non-trainable and parameter-free adaptive computation method called L2 Adaptive Computation (LAC). We adapt LAC from its original efficiency-focused application to trace activated layers during inference. This method monitors changes in the L2-norm of activations to identify voids. We analyze layer activation in instruction-tuned LMs across two phases: Prompt Processing (PP), where we trace activated layers for each token in the input prompts, and Response Generation (RG), where we trace activated layers for each generated token. We further demonstrate that distinct layers are activated during these two phases. To show the effectiveness of our method, we evaluated three distinct instruction-tuned LMs from the Llama, Mistral, and Qwen families on three benchmarks: MMLU, GPQA Diamond, and BoolQ. For example, on MMLU with a zero-shot setting, skipping voids in Qwen2.5-7B-Instruct resulted in an improvement from 69.24 to 71.29 while the model uses only 30% of the layers. Similarly, Mistral-7B-Instruct-v0.3 on GPQA Diamond improved from 13.88 to 18.36 when using 70% of the layers during both the PP and RG phases. These results show that not all layers contribute equally during inference, and that selectively skipping most of them can improve the performance of models on certain tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/manishemirani/void", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.14357", "title": "Vid2World: Crafting Video Diffusion Models to Interactive World Models", "authors": [ "Siqiao Huang", "Jialong Wu", "Qixing Zhou", "Shangchen Miao", "Mingsheng Long" ], "github": "", "project_page": "", "abstract": "World models, which predict transitions based on history observation and action sequences, have shown great promise in improving data efficiency for sequential decision making. However, existing world models often require extensive domain-specific training and still produce low-fidelity, coarse predictions, limiting their applicability in complex environments. In contrast, video diffusion models trained on large, internet-scale datasets have demonstrated impressive capabilities in generating high-quality videos that capture diverse real-world dynamics. In this work, we present Vid2World, a general approach for leveraging and transferring pre-trained video diffusion models into interactive world models. To bridge the gap, Vid2World performs casualization of a pre-trained video diffusion model by crafting its architecture and training objective to enable autoregressive generation. Furthermore, it introduces a causal action guidance mechanism to enhance action controllability in the resulting interactive world model. Extensive experiments in robot manipulation and game simulation domains show that our method offers a scalable and effective approach for repurposing highly capable video diffusion models to interactive world models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.14231", "title": "UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning", "authors": [ "Sule Bai", "Mingxing Li", "Yong Liu", "Jing Tang", "Haoji Zhang", "Lei Sun", "Xiangxiang Chu", "Yansong Tang" ], "github": "", "project_page": "", "abstract": "Traditional visual grounding methods primarily focus on single-image scenarios with simple textual references. However, extending these methods to real-world scenarios that involve implicit and complex instructions, particularly in conjunction with multiple images, poses significant challenges, which is mainly due to the lack of advanced reasoning ability across diverse multi-modal contexts. In this work, we aim to address the more practical universal grounding task, and propose UniVG-R1, a reasoning guided multimodal large language model (MLLM) for universal visual grounding, which enhances reasoning capabilities through reinforcement learning (RL) combined with cold-start data. Specifically, we first construct a high-quality Chain-of-Thought (CoT) grounding dataset, annotated with detailed reasoning chains, to guide the model towards correct reasoning paths via supervised fine-tuning. Subsequently, we perform rule-based reinforcement learning to encourage the model to identify correct reasoning chains, thereby incentivizing its reasoning capabilities. In addition, we identify a difficulty bias arising from the prevalence of easy samples as RL training progresses, and we propose a difficulty-aware weight adjustment strategy to further strengthen the performance. Experimental results demonstrate the effectiveness of UniVG-R1, which achieves state-of-the-art performance on MIG-Bench with a 9.1% improvement over the previous method. Furthermore, our model exhibits strong generalizability, achieving an average improvement of 23.4% in zero-shot performance across four image and video reasoning grounding benchmarks. The project page can be accessed at https://amap-ml.github.io/UniVG-R1-page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.14302", "title": "Scaling Law for Quantization-Aware Training", "authors": [ "Mengzhao Chen", "Chaoyi Zhang", "Jing Liu", "Yutao Zeng", "Zeyue Xue", "Zhiheng Liu", "Yunshui Li", "Jin Ma", "Jie Huang", "Xun Zhou", "Ping Luo" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) demand substantial computational and memory resources, creating deployment challenges. Quantization-aware training (QAT) addresses these challenges by reducing model precision while maintaining performance. However, the scaling behavior of QAT, especially at 4-bit precision (W4A4), is not well understood. Existing QAT scaling laws often ignore key factors such as the number of training tokens and quantization granularity, which limits their applicability. This paper proposes a unified scaling law for QAT that models quantization error as a function of model size, training data volume, and quantization group size. Through 268 QAT experiments, we show that quantization error decreases as model size increases, but rises with more training tokens and coarser quantization granularity. To identify the sources of W4A4 quantization error, we decompose it into weight and activation components. Both components follow the overall trend of W4A4 quantization error, but with different sensitivities. Specifically, weight quantization error increases more rapidly with more training tokens. Further analysis shows that the activation quantization error in the FC2 layer, caused by outliers, is the primary bottleneck of W4A4 QAT quantization error. By applying mixed-precision quantization to address this bottleneck, we demonstrate that weight and activation quantization errors can converge to similar levels. Additionally, with more training data, weight quantization error eventually exceeds activation quantization error, suggesting that reducing weight quantization error is also important in such scenarios. These findings offer key insights for improving QAT research and development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15277", "title": "Web-Shepherd: Advancing PRMs for Reinforcing Web Agents", "authors": [ "Hyungjoo Chae", "Sunghwan Kim", "Junhee Cho", "Seungone Kim", "Seungjun Moon", "Gyeom Hwangbo", "Dongha Lim", "Minjin Kim", "Yeonjun Hwang", "Minju Gwak", "Dongwook Choi", "Minseok Kang", "Gwanhoon Im", "ByeongUng Cho", "Hyojun Kim", "Jun Hee Han", "Taeyoon Kwon", "Minju Kim", "Beong-woo Kwak", "Dongjin Kang", "Jinyoung Yeo" ], "github": "https://github.com/kyle8581/Web-Shepherd", "project_page": "", "abstract": "Web navigation is a unique domain that can automate many repetitive real-life tasks and is challenging as it requires long-horizon sequential decision making beyond typical multimodal large language model (MLLM) tasks. Yet, specialized reward models for web navigation that can be utilized during both training and test-time have been absent until now. Despite the importance of speed and cost-effectiveness, prior works have utilized MLLMs as reward models, which poses significant constraints for real-world deployment. To address this, in this work, we propose the first process reward model (PRM) called Web-Shepherd which could assess web navigation trajectories in a step-level. To achieve this, we first construct the WebPRM Collection, a large-scale dataset with 40K step-level preference pairs and annotated checklists spanning diverse domains and difficulty levels. Next, we also introduce the WebRewardBench, the first meta-evaluation benchmark for evaluating PRMs. In our experiments, we observe that our Web-Shepherd achieves about 30 points better accuracy compared to using GPT-4o on WebRewardBench. Furthermore, when testing on WebArena-lite by using GPT-4o-mini as the policy and Web-Shepherd as the verifier, we achieve 10.9 points better performance, in 10 less cost compared to using GPT-4o-mini as the verifier. Our model, dataset, and code are publicly available at LINK.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kyle8581/Web-Shepherd", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15809", "title": "MMaDA: Multimodal Large Diffusion Language Models", "authors": [ "Ling Yang", "Ye Tian", "Bowen Li", "Xinchen Zhang", "Ke Shen", "Yunhai Tong", "Mengdi Wang" ], "github": "https://github.com/Gen-Verse/MMaDA", "project_page": "", "abstract": "We introduce MMaDA, a novel class of multimodal diffusion foundation models designed to achieve superior performance across diverse domains such as textual reasoning, multimodal understanding, and text-to-image generation. The approach is distinguished by three key innovations: (i) MMaDA adopts a unified diffusion architecture with a shared probabilistic formulation and a modality-agnostic design, eliminating the need for modality-specific components. This architecture ensures seamless integration and processing across different data types. (ii) We implement a mixed long chain-of-thought (CoT) fine-tuning strategy that curates a unified CoT format across modalities. By aligning reasoning processes between textual and visual domains, this strategy facilitates cold-start training for the final reinforcement learning (RL) stage, thereby enhancing the model's ability to handle complex tasks from the outset. (iii) We propose UniGRPO, a unified policy-gradient-based RL algorithm specifically tailored for diffusion foundation models. Utilizing diversified reward modeling, UniGRPO unifies post-training across both reasoning and generation tasks, ensuring consistent performance improvements. Experimental results demonstrate that MMaDA-8B exhibits strong generalization capabilities as a unified multimodal foundation model. It surpasses powerful models like LLaMA-3-7B and Qwen2-7B in textual reasoning, outperforms Show-o and SEED-X in multimodal understanding, and excels over SDXL and Janus in text-to-image generation. These achievements highlight MMaDA's effectiveness in bridging the gap between pretraining and post-training within unified diffusion architectures, providing a comprehensive framework for future research and development. We open-source our code and trained models at: https://github.com/Gen-Verse/MMaDA", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/MMaDA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.13909", "title": "Efficient Agent Training for Computer Use", "authors": [ "Yanheng He", "Jiahe Jin", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/PC-Agent-E", "project_page": "", "abstract": "Scaling up high-quality trajectory data has long been a critical bottleneck for developing human-like computer use agents. We introduce PC Agent-E, an efficient agent training framework that significantly reduces reliance on large-scale human demonstrations. Starting with just 312 human-annotated computer use trajectories, we further improved data quality by synthesizing diverse action decisions with Claude 3.7 Sonnet. Trained on these enriched trajectories, our PC Agent-E model achieved a remarkable 141% relative improvement, surpassing the strong Claude 3.7 Sonnet with extended thinking on WindowsAgentArena-V2, an improved benchmark we also released. Furthermore, PC Agent-E demonstrates strong generalizability to different operating systems on OSWorld. Our findings suggest that strong computer use capabilities can be stimulated from a small amount of high-quality trajectory data.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GAIR-NLP/PC-Agent-E", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15210", "title": "Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge Graphs", "authors": [ "Jie Ma", "Ning Qu", "Zhitao Gao", "Rui Xing", "Jun Liu", "Hongbin Pei", "Jiang Xie", "Linyun Song", "Pinghui Wang", "Jing Tao", "Zhou Su" ], "github": "https://github.com/reml-group/Deliberation-on-Priors", "project_page": "", "abstract": "Knowledge graph-based retrieval-augmented generation seeks to mitigate hallucinations in Large Language Models (LLMs) caused by insufficient or outdated knowledge. However, existing methods often fail to fully exploit the prior knowledge embedded in knowledge graphs (KGs), particularly their structural information and explicit or implicit constraints. The former can enhance the faithfulness of LLMs' reasoning, while the latter can improve the reliability of response generation. Motivated by these, we propose a trustworthy reasoning framework, termed Deliberation over Priors (DP), which sufficiently utilizes the priors contained in KGs. Specifically, DP adopts a progressive knowledge distillation strategy that integrates structural priors into LLMs through a combination of supervised fine-tuning and Kahneman-Tversky optimization, thereby improving the faithfulness of relation path generation. Furthermore, our framework employs a reasoning-introspection strategy, which guides LLMs to perform refined reasoning verification based on extracted constraint priors, ensuring the reliability of response generation. Extensive experiments on three benchmark datasets demonstrate that DP achieves new state-of-the-art performance, especially a Hit@1 improvement of 13% on the ComplexWebQuestions dataset, and generates highly trustworthy responses. We also conduct various analyses to verify its flexibility and practicality. The code is available at https://github.com/reml-group/Deliberation-on-Priors.", "github_manual": "", "github_regex": "https://github.com/reml-group/Deliberation-on-Priors", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15765", "title": "Constructing a 3D Town from a Single Image", "authors": [ "Kaizhi Zheng", "Ruijian Zhang", "Jing Gu", "Jie Yang", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "Acquiring detailed 3D scenes typically demands costly equipment, multi-view data, or labor-intensive modeling. Therefore, a lightweight alternative, generating complex 3D scenes from a single top-down image, plays an essential role in real-world applications. While recent 3D generative models have achieved remarkable results at the object level, their extension to full-scene generation often leads to inconsistent geometry, layout hallucinations, and low-quality meshes. In this work, we introduce 3DTown, a training-free framework designed to synthesize realistic and coherent 3D scenes from a single top-down view. Our method is grounded in two principles: region-based generation to improve image-to-3D alignment and resolution, and spatial-aware 3D inpainting to ensure global scene coherence and high-quality geometry generation. Specifically, we decompose the input image into overlapping regions and generate each using a pretrained 3D object generator, followed by a masked rectified flow inpainting process that fills in missing geometry while maintaining structural continuity. This modular design allows us to overcome resolution bottlenecks and preserve spatial structure without requiring 3D supervision or fine-tuning. Extensive experiments across diverse scenes show that 3DTown outperforms state-of-the-art baselines, including Trellis, Hunyuan3D-2, and TripoSG, in terms of geometry quality, spatial coherence, and texture fidelity. Our results demonstrate that high-quality 3D town generation is achievable from a single image using a principled, training-free approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15778", "title": "Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space", "authors": [ "Zhen Zhang", "Xuehai He", "Weixiang Yan", "Ao Shen", "Chenyang Zhao", "Shuohang Wang", "Yelong Shen", "Xin Eric Wang" ], "github": "https://github.com/eric-ai-lab/Soft-Thinking", "project_page": "", "abstract": "Human cognition typically involves thinking through abstract, fluid concepts rather than strictly using discrete linguistic tokens. Current reasoning models, however, are constrained to reasoning within the boundaries of human language, processing discrete token embeddings that represent fixed points in the semantic space. This discrete constraint restricts the expressive power and upper potential of such reasoning models, often causing incomplete exploration of reasoning paths, as standard Chain-of-Thought (CoT) methods rely on sampling one token per step. In this work, we introduce Soft Thinking, a training-free method that emulates human-like \"soft\" reasoning by generating soft, abstract concept tokens in a continuous concept space. These concept tokens are created by the probability-weighted mixture of token embeddings, which form the continuous concept space, enabling smooth transitions and richer representations that transcend traditional discrete boundaries. In essence, each generated concept token encapsulates multiple meanings from related discrete tokens, implicitly exploring various reasoning paths to converge effectively toward the correct answer. Empirical evaluations on diverse mathematical and coding benchmarks consistently demonstrate the effectiveness and efficiency of Soft Thinking, improving pass@1 accuracy by up to 2.48 points while simultaneously reducing token usage by up to 22.4% compared to standard CoT. Qualitative analysis further reveals that Soft Thinking outputs remain highly interpretable and readable, highlighting the potential of Soft Thinking to break the inherent bottleneck of discrete language-based reasoning. Code is available at https://github.com/eric-ai-lab/Soft-Thinking.", "github_manual": "", "github_regex": "https://github.com/eric-ai-lab/Soft-Thinking", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15656", "title": "Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data Could Be Secretly Stolen!", "authors": [ "Zhexin Zhang", "Yuhao Sun", "Junxiao Yang", "Shiyao Cui", "Hongning Wang", "Minlie Huang" ], "github": "https://github.com/thu-coai/Backdoor-Data-Extraction", "project_page": "", "abstract": "Fine-tuning on open-source Large Language Models (LLMs) with proprietary data is now a standard practice for downstream developers to obtain task-specific LLMs. Surprisingly, we reveal a new and concerning risk along with the practice: the creator of the open-source LLMs can later extract the private downstream fine-tuning data through simple backdoor training, only requiring black-box access to the fine-tuned downstream model. Our comprehensive experiments, across 4 popularly used open-source models with 3B to 32B parameters and 2 downstream datasets, suggest that the extraction performance can be strikingly high: in practical settings, as much as 76.3% downstream fine-tuning data (queries) out of a total 5,000 samples can be perfectly extracted, and the success rate can increase to 94.9% in more ideal settings. We also explore a detection-based defense strategy but find it can be bypassed with improved attack. Overall, we highlight the emergency of this newly identified data breaching risk in fine-tuning, and we hope that more follow-up research could push the progress of addressing this concerning risk. The code and data used in our experiments are released at https://github.com/thu-coai/Backdoor-Data-Extraction.", "github_manual": "", "github_regex": "https://github.com/thu-coai/Backdoor-Data-Extraction", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15404", "title": "How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study", "authors": [ "Zhexin Zhang", "Xian Qi Loye", "Victor Shea-Jay Huang", "Junxiao Yang", "Qi Zhu", "Shiyao Cui", "Fei Mi", "Lifeng Shang", "Yingkang Wang", "Hongning Wang", "Minlie Huang" ], "github": "https://github.com/thu-coai/LRM-Safety-Study", "project_page": "", "abstract": "Large Reasoning Models (LRMs) have achieved remarkable success on reasoning-intensive tasks such as mathematics and programming. However, their enhanced reasoning capabilities do not necessarily translate to improved safety performance-and in some cases, may even degrade it. This raises an important research question: how can we enhance the safety of LRMs? In this paper, we present a comprehensive empirical study on how to enhance the safety of LRMs through Supervised Fine-Tuning (SFT). Our investigation begins with an unexpected observation: directly distilling safe responses from DeepSeek-R1 fails to significantly enhance safety. We analyze this phenomenon and identify three key failure patterns that contribute to it. We then demonstrate that explicitly addressing these issues during the data distillation process can lead to substantial safety improvements. Next, we explore whether a long and complex reasoning process is necessary for achieving safety. Interestingly, we find that simply using short or template-based reasoning process can attain comparable safety performance-and are significantly easier for models to learn than more intricate reasoning chains. These findings prompt a deeper reflection on the role of reasoning in ensuring safety. Finally, we find that mixing math reasoning data during safety fine-tuning is helpful to balance safety and over-refusal. Overall, we hope our empirical study could provide a more holistic picture on enhancing the safety of LRMs. The code and data used in our experiments are released in https://github.com/thu-coai/LRM-Safety-Study.", "github_manual": "", "github_regex": "https://github.com/thu-coai/LRM-Safety-Study", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.12650", "title": "AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use", "authors": [ "Yaotian Yang", "Yiwen Tang", "Yizhe Chen", "Xiao Chen", "Jiangjie Qiu", "Hao Xiong", "Haoyu Yin", "Zhiyao Luo", "Yifei Zhang", "Sijia Tao", "Wentao Li", "Qinghua Zhang", "Yuqiang Li", "Wanli Ouyang", "Bin Zhao", "Xiaonan Wang", "Fei Wei" ], "github": "https://github.com/yyt-2378/AutoMat", "project_page": "", "abstract": "Machine learning-based interatomic potentials and force fields depend critically on accurate atomic structures, yet such data are scarce due to the limited availability of experimentally resolved crystals. Although atomic-resolution electron microscopy offers a potential source of structural data, converting these images into simulation-ready formats remains labor-intensive and error-prone, creating a bottleneck for model training and validation. We introduce AutoMat, an end-to-end, agent-assisted pipeline that automatically transforms scanning transmission electron microscopy (STEM) images into atomic crystal structures and predicts their physical properties. AutoMat combines pattern-adaptive denoising, physics-guided template retrieval, symmetry-aware atomic reconstruction, fast relaxation and property prediction via MatterSim, and coordinated orchestration across all stages. We propose the first dedicated STEM2Mat-Bench for this task and evaluate performance using lattice RMSD, formation energy MAE, and structure-matching success rate. By orchestrating external tool calls, AutoMat enables a text-only LLM to outperform vision-language models in this domain, achieving closed-loop reasoning throughout the pipeline. In large-scale experiments over 450 structure samples, AutoMat substantially outperforms existing multimodal large language models and tools. These results validate both AutoMat and STEM2Mat-Bench, marking a key step toward bridging microscopy and atomistic simulation in materials science.The code and dataset are publicly available at https://github.com/yyt-2378/AutoMat and https://huggingface.co/datasets/yaotianvector/STEM2Mat.", "github_manual": "", "github_regex": "https://github.com/yyt-2378/AutoMat", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.13529", "title": "BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs", "authors": [ "Junxiao Yang", "Jinzhe Tu", "Haoran Liu", "Xiaoce Wang", "Chujie Zheng", "Zhexin Zhang", "Shiyao Cui", "Caishun Chen", "Tiantian He", "Hongning Wang", "Yew-Soon Ong", "Minlie Huang" ], "github": "", "project_page": "", "abstract": "Recent advances in Large Reasoning Models (LRMs) have shown impressive capabilities in mathematical and logical reasoning. However, current LRMs rarely admit ignorance or respond with \"I don't know\". Instead, they often produce incorrect answers while showing undue confidence, raising concerns about their factual reliability. In this work, we identify two pathological reasoning patterns characterized by overthinking that contribute to the overconfident and incorrect answers: last-minute guessing and second-thought spiraling. To address these issues, we propose BARREL-a novel framework that promotes concise and boundary-aware factual reasoning. Our experiments show that BARREL-training increases the reliability of DeepSeek-R1-Distill-Llama-8B from 39.33% to 61.48%, while still achieving accuracy comparable to models finetuned on reasoning data generated by R1. These results demonstrate that our pilot study is inspiring to build more reliable and factual System 2 LRMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.13934", "title": "RLVR-World: Training World Models with Reinforcement Learning", "authors": [ "Jialong Wu", "Shaofeng Yin", "Ningya Feng", "Mingsheng Long" ], "github": "", "project_page": "", "abstract": "World models predict state transitions in response to actions and are increasingly developed across diverse modalities. However, standard training objectives such as maximum likelihood estimation (MLE) often misalign with task-specific goals of world models, i.e., transition prediction metrics like accuracy or perceptual quality. In this paper, we present RLVR-World, a unified framework that leverages reinforcement learning with verifiable rewards (RLVR) to directly optimize world models for such metrics. Despite formulating world modeling as autoregressive prediction of tokenized sequences, RLVR-World evaluates metrics of decoded predictions as verifiable rewards. We demonstrate substantial performance gains on both language- and video-based world models across domains, including text games, web navigation, and robot manipulation. Our work indicates that, beyond recent advances in reasoning language models, RLVR offers a promising post-training paradigm for enhancing the utility of generative models more broadly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15816", "title": "Streamline Without Sacrifice - Squeeze out Computation Redundancy in LMM", "authors": [ "Penghao Wu", "Lewei Lu", "Ziwei Liu" ], "github": "https://github.com/penghao-wu/ProxyV", "project_page": "", "abstract": "Large multimodal models excel in multimodal tasks but face significant computational challenges due to excessive computation on visual tokens. Unlike token reduction methods that focus on token-level redundancy, we identify and study the computation-level redundancy on vision tokens to ensure no information loss. Our key insight is that vision tokens from the pretrained vision encoder do not necessarily require all the heavy operations (e.g., self-attention, FFNs) in decoder-only LMMs and could be processed more lightly with proper designs. We designed a series of experiments to discover and progressively squeeze out the vision-related computation redundancy. Based on our findings, we propose ProxyV, a novel approach that utilizes proxy vision tokens to alleviate the computational burden on original vision tokens. ProxyV enhances efficiency without compromising performance and can even yield notable performance gains in scenarios with more moderate efficiency improvements. Furthermore, the flexibility of ProxyV is demonstrated through its combination with token reduction methods to boost efficiency further. The code will be made public at this https://github.com/penghao-wu/ProxyV URL.", "github_manual": "", "github_regex": "https://github.com/penghao-wu/ProxyV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15791", "title": "VARD: Efficient and Dense Fine-Tuning for Diffusion Models with Value-based RL", "authors": [ "Fengyuan Dai", "Zifeng Zhuang", "Yufei Huang", "Siteng Huang", "Bangyan Liao", "Donglin Wang", "Fajie Yuan" ], "github": "", "project_page": "", "abstract": "Diffusion models have emerged as powerful generative tools across various domains, yet tailoring pre-trained models to exhibit specific desirable properties remains challenging. While reinforcement learning (RL) offers a promising solution,current methods struggle to simultaneously achieve stable, efficient fine-tuning and support non-differentiable rewards. Furthermore, their reliance on sparse rewards provides inadequate supervision during intermediate steps, often resulting in suboptimal generation quality. To address these limitations, dense and differentiable signals are required throughout the diffusion process. Hence, we propose VAlue-based Reinforced Diffusion (VARD): a novel approach that first learns a value function predicting expection of rewards from intermediate states, and subsequently uses this value function with KL regularization to provide dense supervision throughout the generation process. Our method maintains proximity to the pretrained model while enabling effective and stable training via backpropagation. Experimental results demonstrate that our approach facilitates better trajectory guidance, improves training efficiency and extends the applicability of RL to diffusion models optimized for complex, non-differentiable reward functions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15047", "title": "PiFlow: Principle-aware Scientific Discovery with Multi-Agent Collaboration", "authors": [ "Yingming Pu", "Tao Lin", "Hongyu Chen" ], "github": "https://github.com/amair-lab/PiFlow", "project_page": "", "abstract": "Large Language Model (LLM)-based multi-agent systems (MAS) demonstrate remarkable potential for scientific discovery. Existing approaches, however, often automate scientific discovery using predefined workflows that lack rationality constraints. This often leads to aimless hypothesizing and a failure to consistently link hypotheses with evidence, thereby hindering systematic uncertainty reduction. Overcoming these limitations fundamentally requires systematic uncertainty reduction. We introduce PiFlow, an information-theoretical framework, treating automated scientific discovery as a structured uncertainty reduction problem guided by principles (e.g., scientific laws). In evaluations across three distinct scientific domains -- discovering nanomaterial structures, bio-molecules, and superconductor candidates with targeted properties -- our method significantly improves discovery efficiency, reflected by a 73.55\\% increase in the Area Under the Curve (AUC) of property values versus exploration steps, and enhances solution quality by 94.06\\% compared to a vanilla agent system. Overall, PiFlow serves as a Plug-and-Play method, establishing a novel paradigm shift in highly efficient automated scientific discovery, paving the way for more robust and accelerated AI-driven research. Code is publicly available at our https://github.com/amair-lab/PiFlow{GitHub}.", "github_manual": "", "github_regex": "https://github.com/amair-lab/PiFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15045", "title": "Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective", "authors": [ "Siyue Zhang", "Yilun Zhao", "Liyuan Geng", "Arman Cohan", "Anh Tuan Luu", "Chen Zhao" ], "github": "", "project_page": "", "abstract": "Large language model (LLM)-based embedding models, benefiting from large scale pre-training and post-training, have begun to surpass BERT and T5-based models on general-purpose text embedding tasks such as document retrieval. However, a fundamental limitation of LLM embeddings lies in the unidirectional attention used during autoregressive pre-training, which misaligns with the bidirectional nature of text embedding tasks. To this end, We propose adopting diffusion language models for text embeddings, motivated by their inherent bidirectional architecture and recent success in matching or surpassing LLMs especially on reasoning tasks. We present the first systematic study of the diffusion language embedding model, which outperforms the LLM-based embedding model by 20% on long-document retrieval, 8% on reasoning-intensive retrieval, 2% on instruction-following retrieval, and achieve competitive performance on traditional text embedding benchmarks. Our analysis verifies that bidirectional attention is crucial for encoding global context in long and complex text.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15612", "title": "Learn to Reason Efficiently with Adaptive Length-based Reward Shaping", "authors": [ "Wei Liu", "Ruochen Zhou", "Yiyun Deng", "Yuzhen Huang", "Junteng Liu", "Yuntian Deng", "Yizhe Zhang", "Junxian He" ], "github": "https://github.com/hkust-nlp/Laser", "project_page": "", "abstract": "Large Reasoning Models (LRMs) have shown remarkable capabilities in solving complex problems through reinforcement learning (RL), particularly by generating long reasoning traces. However, these extended outputs often exhibit substantial redundancy, which limits the efficiency of LRMs. In this paper, we investigate RL-based approaches to promote reasoning efficiency. Specifically, we first present a unified framework that formulates various efficient reasoning methods through the lens of length-based reward shaping. Building on this perspective, we propose a novel Length-bAsed StEp Reward shaping method (LASER), which employs a step function as the reward, controlled by a target length. LASER surpasses previous methods, achieving a superior Pareto-optimal balance between performance and efficiency. Next, we further extend LASER based on two key intuitions: (1) The reasoning behavior of the model evolves during training, necessitating reward specifications that are also adaptive and dynamic; (2) Rather than uniformly encouraging shorter or longer chains of thought (CoT), we posit that length-based reward shaping should be difficulty-aware i.e., it should penalize lengthy CoTs more for easy queries. This approach is expected to facilitate a combination of fast and slow thinking, leading to a better overall tradeoff. The resulting method is termed LASER-D (Dynamic and Difficulty-aware). Experiments on DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, and DeepSeek-R1-Distill-Qwen-32B show that our approach significantly enhances both reasoning performance and response length efficiency. For instance, LASER-D and its variant achieve a +6.1 improvement on AIME2024 while reducing token usage by 63%. Further analysis reveals our RL-based compression produces more concise reasoning patterns with less redundant \"self-reflections\". Resources are at https://github.com/hkust-nlp/Laser.", "github_manual": "", "github_regex": "https://github.com/hkust-nlp/Laser", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15146", "title": "lmgame-Bench: How Good are LLMs at Playing Games?", "authors": [ "Lanxiang Hu", "Mingjia Huo", "Yuxuan Zhang", "Haoyang Yu", "Eric P. Xing", "Ion Stoica", "Tajana Rosing", "Haojian Jin", "Hao Zhang" ], "github": "https://github.com/lmgame-org/GamingAgent", "project_page": "", "abstract": "Playing video games requires perception, memory, and planning, exactly the faculties modern large language model (LLM) agents are expected to master. We study the major challenges in using popular video games to evaluate modern LLMs and find that directly dropping LLMs into games cannot make an effective evaluation, for three reasons -- brittle vision perception, prompt sensitivity, and potential data contamination. We introduce lmgame-Bench to turn games into reliable evaluations. lmgame-Bench features a suite of platformer, puzzle, and narrative games delivered through a unified Gym-style API and paired with lightweight perception and memory scaffolds, and is designed to stabilize prompt variance and remove contamination. Across 13 leading models, we show lmgame-Bench is challenging while still separating models well. Correlation analysis shows that every game probes a unique blend of capabilities often tested in isolation elsewhere. More interestingly, performing reinforcement learning on a single game from lmgame-Bench transfers both to unseen games and to external planning tasks. Our evaluation code is available at https://github.com/lmgame-org/GamingAgent/lmgame-bench.", "github_manual": "", "github_regex": "https://github.com/lmgame-org/GamingAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15779", "title": "IA-T2I: Internet-Augmented Text-to-Image Generation", "authors": [ "Chuanhao Li", "Jianwen Sun", "Yukang Feng", "Mingliang Zhai", "Yifan Chang", "Kaipeng Zhang" ], "github": "", "project_page": "", "abstract": "Current text-to-image (T2I) generation models achieve promising results, but they fail on the scenarios where the knowledge implied in the text prompt is uncertain. For example, a T2I model released in February would struggle to generate a suitable poster for a movie premiering in April, because the character designs and styles are uncertain to the model. To solve this problem, we propose an Internet-Augmented text-to-image generation (IA-T2I) framework to compel T2I models clear about such uncertain knowledge by providing them with reference images. Specifically, an active retrieval module is designed to determine whether a reference image is needed based on the given text prompt; a hierarchical image selection module is introduced to find the most suitable image returned by an image search engine to enhance the T2I model; a self-reflection mechanism is presented to continuously evaluate and refine the generated image to ensure faithful alignment with the text prompt. To evaluate the proposed framework's performance, we collect a dataset named Img-Ref-T2I, where text prompts include three types of uncertain knowledge: (1) known but rare. (2) unknown. (3) ambiguous. Moreover, we carefully craft a complex prompt to guide GPT-4o in making preference evaluation, which has been shown to have an evaluation accuracy similar to that of human preference evaluation. Experimental results demonstrate the effectiveness of our framework, outperforming GPT-4o by about 30% in human evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15781", "title": "dKV-Cache: The Cache for Diffusion Language Models", "authors": [ "Xinyin Ma", "Runpeng Yu", "Gongfan Fang", "Xinchao Wang" ], "github": "https://github.com/horseee/dkv-cache", "project_page": "", "abstract": "Diffusion Language Models (DLMs) have been seen as a promising competitor for autoregressive language models. However, diffusion language models have long been constrained by slow inference. A core challenge is that their non-autoregressive architecture and bidirectional attention preclude the key-value cache that accelerates decoding. We address this bottleneck by proposing a KV-cache-like mechanism, delayed KV-Cache, for the denoising process of DLMs. Our approach is motivated by the observation that different tokens have distinct representation dynamics throughout the diffusion process. Accordingly, we propose a delayed and conditioned caching strategy for key and value states. We design two complementary variants to cache key and value step-by-step: (1) dKV-Cache-Decode, which provides almost lossless acceleration, and even improves performance on long sequences, suggesting that existing DLMs may under-utilise contextual information during inference. (2) dKV-Cache-Greedy, which has aggressive caching with reduced lifespan, achieving higher speed-ups with quadratic time complexity at the cost of some performance degradation. dKV-Cache, in final, achieves from 2-10x speedup in inference, largely narrowing the gap between ARs and DLMs. We evaluate our dKV-Cache on several benchmarks, delivering acceleration across general language understanding, mathematical, and code-generation benchmarks. Experiments demonstrate that cache can also be used in DLMs, even in a training-free manner from current DLMs.", "github_manual": "https://github.com/horseee/dkv-cache", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.14827", "title": "Text Generation Beyond Discrete Token Sampling", "authors": [ "Yufan Zhuang", "Liyuan Liu", "Chandan Singh", "Jingbo Shang", "Jianfeng Gao" ], "github": "https://github.com/EvanZhuang/mixinputs", "project_page": "", "abstract": "In standard autoregressive generation, an LLM predicts the next-token distribution, samples a discrete token, and then discards the distribution, passing only the sampled token as new input. To preserve this distribution's rich information, we propose Mixture of Inputs (MoI), a training-free method for autoregressive generation. After generating a token following the standard paradigm, we construct a new input that blends the generated discrete token with the previously discarded token distribution. Specifically, we employ a Bayesian estimation method that treats the token distribution as the prior, the sampled token as the observation, and replaces the conventional one-hot vector with the continuous posterior expectation as the new model input. MoI allows the model to maintain a richer internal representation throughout the generation process, resulting in improved text quality and reasoning capabilities. On mathematical reasoning, code generation, and PhD-level QA tasks, MoI consistently improves performance across multiple models including QwQ-32B, Nemotron-Super-49B, Gemma-3-27B, and DAPO-Qwen-32B, with no additional training and negligible computational overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EvanZhuang/mixinputs", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.14990", "title": "Language Specific Knowledge: Do Models Know Better in X than in English?", "authors": [ "Ishika Agarwal", "Nimet Beyza Bozdag", "Dilek Hakkani-T\u00fcr" ], "github": "", "project_page": "", "abstract": "Code-switching is a common phenomenon of alternating between different languages in the same utterance, thought, or conversation. We posit that humans code-switch because they feel more comfortable talking about certain topics and domains in one language than another. With the rise of knowledge-intensive language models, we ask ourselves the next, natural question: Could models hold more knowledge on some topics in some language X? More importantly, could we improve reasoning by changing the language that reasoning is performed in? We coin the term Language Specific Knowledge (LSK) to represent this phenomenon. As ethnic cultures tend to develop alongside different languages, we employ culture-specific datasets (that contain knowledge about cultural and social behavioral norms). We find that language models can perform better when using chain-of-thought reasoning in some languages other than English, sometimes even better in low-resource languages. Paired with previous works showing that semantic similarity does not equate to representational similarity, we hypothesize that culturally specific texts occur more abundantly in corresponding languages, enabling specific knowledge to occur only in specific \"expert\" languages. Motivated by our initial results, we design a simple methodology called LSKExtractor to benchmark the language-specific knowledge present in a language model and, then, exploit it during inference. We show our results on various models and datasets, showing an average relative improvement of 10% in accuracy. Our research contributes to the open-source development of language models that are inclusive and more aligned with the cultural and linguistic contexts in which they are deployed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15400", "title": "When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning", "authors": [ "Xiaoyun Zhang", "Jingqing Ruan", "Xing Ma", "Yawen Zhu", "Haodong Zhao", "Hao Li", "Jiansong Chen", "Ke Zeng", "Xunliang Cai" ], "github": "", "project_page": "", "abstract": "Large reasoning models (LRMs) achieve remarkable performance via long reasoning chains, but often incur excessive computational overhead due to redundant reasoning, especially on simple tasks. In this work, we systematically quantify the upper bounds of LRMs under both Long-Thinking and No-Thinking modes, and uncover the phenomenon of \"Internal Self-Recovery Mechanism\" where models implicitly supplement reasoning during answer generation. Building on this insight, we propose Adaptive Self-Recovery Reasoning (ASRR), a framework that suppresses unnecessary reasoning and enables implicit recovery. By introducing accuracy-aware length reward regulation, ASRR adaptively allocates reasoning effort according to problem difficulty, achieving high efficiency with negligible performance sacrifice. Experiments across multiple benchmarks and models show that, compared with GRPO, ASRR reduces reasoning budget by up to 32.5% (1.5B) and 25.7% (7B) with minimal accuracy loss (1.2% and 0.6% pass@1), and significantly boosts harmless rates on safety benchmarks (up to +21.7%). Our results highlight the potential of ASRR for enabling efficient, adaptive, and safer reasoning in LRMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15776", "title": "ConvSearch-R1: Enhancing Query Reformulation for Conversational Search with Reasoning via Reinforcement Learning", "authors": [ "Changtai Zhu", "Siyin Wang", "Ruijun Feng", "Kai Song", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Conversational search systems require effective handling of context-dependent queries that often contain ambiguity, omission, and coreference. Conversational Query Reformulation (CQR) addresses this challenge by transforming these queries into self-contained forms suitable for off-the-shelf retrievers. However, existing CQR approaches suffer from two critical constraints: high dependency on costly external supervision from human annotations or large language models, and insufficient alignment between the rewriting model and downstream retrievers. We present ConvSearch-R1, the first self-driven framework that completely eliminates dependency on external rewrite supervision by leveraging reinforcement learning to optimize reformulation directly through retrieval signals. Our novel two-stage approach combines Self-Driven Policy Warm-Up to address the cold-start problem through retrieval-guided self-distillation, followed by Retrieval-Guided Reinforcement Learning with a specially designed rank-incentive reward shaping mechanism that addresses the sparsity issue in conventional retrieval metrics. Extensive experiments on TopiOCQA and QReCC datasets demonstrate that ConvSearch-R1 significantly outperforms previous state-of-the-art methods, achieving over 10% improvement on the challenging TopiOCQA dataset while using smaller 3B parameter models without any external supervision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15817", "title": "Learning to Reason via Mixture-of-Thought for Logical Reasoning", "authors": [ "Tong Zheng", "Lichang Chen", "Simeng Han", "R. Thomas McCoy", "Heng Huang" ], "github": "", "project_page": "", "abstract": "Human beings naturally utilize multiple reasoning modalities to learn and solve logical problems, i.e., different representational formats such as natural language, code, and symbolic logic. In contrast, most existing LLM-based approaches operate with a single reasoning modality during training, typically natural language. Although some methods explored modality selection or augmentation at inference time, the training process remains modality-blind, limiting synergy among modalities. To fill in this gap, we propose Mixture-of-Thought (MoT), a framework that enables LLMs to reason across three complementary modalities: natural language, code, and a newly introduced symbolic modality, truth-table, which systematically enumerates logical cases and partially mitigates key failure modes in natural language reasoning. MoT adopts a two-phase design: (1) self-evolving MoT training, which jointly learns from filtered, self-generated rationales across modalities; and (2) MoT inference, which fully leverages the synergy of three modalities to produce better predictions. Experiments on logical reasoning benchmarks including FOLIO and ProofWriter demonstrate that our MoT framework consistently and significantly outperforms strong LLM baselines with single-modality chain-of-thought approaches, achieving up to +11.7pp average accuracy gain. Further analyses show that our MoT framework benefits both training and inference stages; that it is particularly effective on harder logical reasoning problems; and that different modalities contribute complementary strengths, with truth-table reasoning helping to overcome key bottlenecks in natural language inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15034", "title": "RL Tango: Reinforcing Generator and Verifier Together for Language Reasoning", "authors": [ "Kaiwen Zha", "Zhengqi Gao", "Maohao Shen", "Zhang-Wei Hong", "Duane S. Boning", "Dina Katabi" ], "github": "https://github.com/kaiwenzha/rl-tango", "project_page": "", "abstract": "Reinforcement learning (RL) has recently emerged as a compelling approach for enhancing the reasoning capabilities of large language models (LLMs), where an LLM generator serves as a policy guided by a verifier (reward model). However, current RL post-training methods for LLMs typically use verifiers that are fixed (rule-based or frozen pretrained) or trained discriminatively via supervised fine-tuning (SFT). Such designs are susceptible to reward hacking and generalize poorly beyond their training distributions. To overcome these limitations, we propose Tango, a novel framework that uses RL to concurrently train both an LLM generator and a verifier in an interleaved manner. A central innovation of Tango is its generative, process-level LLM verifier, which is trained via RL and co-evolves with the generator. Importantly, the verifier is trained solely based on outcome-level verification correctness rewards without requiring explicit process-level annotations. This generative RL-trained verifier exhibits improved robustness and superior generalization compared to deterministic or SFT-trained verifiers, fostering effective mutual reinforcement with the generator. Extensive experiments demonstrate that both components of Tango achieve state-of-the-art results among 7B/8B-scale models: the generator attains best-in-class performance across five competition-level math benchmarks and four challenging out-of-domain reasoning tasks, while the verifier leads on the ProcessBench dataset. Remarkably, both components exhibit particularly substantial improvements on the most difficult mathematical reasoning problems. Code is at: https://github.com/kaiwenzha/rl-tango.", "github_manual": "", "github_regex": "https://github.com/kaiwenzha/rl-tango", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.14157", "title": "Prior Prompt Engineering for Reinforcement Fine-Tuning", "authors": [ "Pittawat Taveekitworachai", "Potsawee Manakul", "Sarana Nutanong", "Kunat Pipatanakul" ], "github": "", "project_page": "", "abstract": "This paper investigates prior prompt engineering (pPE) in the context of reinforcement fine-tuning (RFT), where language models (LMs) are incentivized to exhibit behaviors that maximize performance through reward signals. While existing RFT research has primarily focused on algorithms, reward shaping, and data curation, the design of the prior prompt--the instructions prepended to queries during training to elicit behaviors such as step-by-step reasoning--remains underexplored. We investigate whether different pPE approaches can guide LMs to internalize distinct behaviors after RFT. Inspired by inference-time prompt engineering (iPE), we translate five representative iPE strategies--reasoning, planning, code-based reasoning, knowledge recall, and null-example utilization--into corresponding pPE approaches. We experiment with Qwen2.5-7B using each of the pPE approaches, then evaluate performance on in-domain and out-of-domain benchmarks (e.g., AIME2024, HumanEval+, and GPQA-Diamond). Our results show that all pPE-trained models surpass their iPE-prompted counterparts, with the null-example pPE approach achieving the largest average performance gain and the highest improvement on AIME2024 and GPQA-Diamond, surpassing the commonly used reasoning approach. Furthermore, by adapting a behavior-classification framework, we demonstrate that different pPE strategies instill distinct behavioral styles in the resulting models. These findings position pPE as a powerful yet understudied axis for RFT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.11454", "title": "HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation", "authors": [ "Shaina Raza", "Aravind Narayanan", "Vahid Reza Khazaie", "Ashmal Vayani", "Mukund S. Chettiar", "Amandeep Singh", "Mubarak Shah", "Deval Pandya" ], "github": "https://github.com/vectorinstitute/humanibench", "project_page": "", "abstract": "Large multimodal models (LMMs) now excel on many vision language benchmarks, however, they still struggle with human centered criteria such as fairness, ethics, empathy, and inclusivity, key to aligning with human values. We introduce HumaniBench, a holistic benchmark of 32K real-world image question pairs, annotated via a scalable GPT4o assisted pipeline and exhaustively verified by domain experts. HumaniBench evaluates seven Human Centered AI (HCAI) principles: fairness, ethics, understanding, reasoning, language inclusivity, empathy, and robustness, across seven diverse tasks, including open and closed ended visual question answering (VQA), multilingual QA, visual grounding, empathetic captioning, and robustness tests. Benchmarking 15 state of the art LMMs (open and closed source) reveals that proprietary models generally lead, though robustness and visual grounding remain weak points. Some open-source models also struggle to balance accuracy with adherence to human-aligned principles. HumaniBench is the first benchmark purpose built around HCAI principles. It provides a rigorous testbed for diagnosing alignment gaps and guiding LMMs toward behavior that is both accurate and socially responsible. Dataset, annotation prompts, and evaluation code are available at: https://vectorinstitute.github.io/HumaniBench", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vectorinstitute/humanibench", "github_api_added_by": "auto" }, { "date": "2025-05-22", "arxiv_id": "2505.14766", "title": "This Time is Different: An Observability Perspective on Time Series Foundation Models", "authors": [ "Ben Cohen", "Emaad Khwaja", "Youssef Doubli", "Salahidine Lemaachi", "Chris Lettieri", "Charles Masson", "Hugo Miccinilli", "Elise Ram\u00e9", "Qiqi Ren", "Afshin Rostamizadeh", "Jean Ogier du Terrail", "Anna-Monica Toon", "Kan Wang", "Stephan Xie", "David Asker", "Ameet Talwalkar", "Othmane Abou-Amal" ], "github": "https://github.com/DataDog/toto", "project_page": "", "abstract": "We introduce Toto, a time series forecasting foundation model with 151 million parameters. Toto uses a modern decoder-only architecture coupled with architectural innovations designed to account for specific challenges found in multivariate observability time series data. Toto's pre-training corpus is a mixture of observability data, open datasets, and synthetic data, and is 4-10times larger than those of leading time series foundation models. Additionally, we introduce BOOM, a large-scale benchmark consisting of 350 million observations across 2,807 real-world time series. For both Toto and BOOM, we source observability data exclusively from Datadog's own telemetry and internal observability metrics. Extensive evaluations demonstrate that Toto achieves state-of-the-art performance on both BOOM and on established general purpose time series forecasting benchmarks. Toto's model weights, inference code, and evaluation scripts, as well as BOOM's data and evaluation code, are all available as open source under the Apache 2.0 License available at https://huggingface.co/Datadog/Toto-Open-Base-1.0 and https://github.com/DataDog/toto.", "github_manual": "", "github_regex": "https://github.com/DataDog/toto", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15524", "title": "Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs", "authors": [ "Lang Gao", "Kaiyang Wan", "Wei Liu", "Chenxi Wang", "Zirui Song", "Zixiang Xu", "Yanbo Wang", "Veselin Stoyanov", "Xiuying Chen" ], "github": "", "project_page": "", "abstract": "Bias in Large Language Models (LLMs) significantly undermines their reliability and fairness. We focus on a common form of bias: when two reference concepts in the model's concept space, such as sentiment polarities (e.g., \"positive\" and \"negative\"), are asymmetrically correlated with a third, target concept, such as a reviewing aspect, the model exhibits unintended bias. For instance, the understanding of \"food\" should not skew toward any particular sentiment. Existing bias evaluation methods assess behavioral differences of LLMs by constructing labeled data for different social groups and measuring model responses across them, a process that requires substantial human effort and captures only a limited set of social concepts. To overcome these limitations, we propose BiasLens, a test-set-free bias analysis framework based on the structure of the model's vector space. BiasLens combines Concept Activation Vectors (CAVs) with Sparse Autoencoders (SAEs) to extract interpretable concept representations, and quantifies bias by measuring the variation in representational similarity between the target concept and each of the reference concepts. Even without labeled data, BiasLens shows strong agreement with traditional bias evaluation metrics (Spearman correlation r > 0.85). Moreover, BiasLens reveals forms of bias that are difficult to detect using existing methods. For example, in simulated clinical scenarios, a patient's insurance status can cause the LLM to produce biased diagnostic assessments. Overall, BiasLens offers a scalable, interpretable, and efficient paradigm for bias discovery, paving the way for improving fairness and transparency in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15406", "title": "Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models", "authors": [ "Zirui Song", "Qian Jiang", "Mingxuan Cui", "Mingzhe Li", "Lang Gao", "Zeyu Zhang", "Zixiang Xu", "Yanbo Wang", "Chenxi Wang", "Guangxian Ouyang", "Zhenhao Chen", "Xiuying Chen" ], "github": "https://github.com/mbzuai-nlp/audiojailbreak", "project_page": "", "abstract": "The rise of Large Audio Language Models (LAMs) brings both potential and risks, as their audio outputs may contain harmful or unethical content. However, current research lacks a systematic, quantitative evaluation of LAM safety especially against jailbreak attacks, which are challenging due to the temporal and semantic nature of speech. To bridge this gap, we introduce AJailBench, the first benchmark specifically designed to evaluate jailbreak vulnerabilities in LAMs. We begin by constructing AJailBench-Base, a dataset of 1,495 adversarial audio prompts spanning 10 policy-violating categories, converted from textual jailbreak attacks using realistic text to speech synthesis. Using this dataset, we evaluate several state-of-the-art LAMs and reveal that none exhibit consistent robustness across attacks. To further strengthen jailbreak testing and simulate more realistic attack conditions, we propose a method to generate dynamic adversarial variants. Our Audio Perturbation Toolkit (APT) applies targeted distortions across time, frequency, and amplitude domains. To preserve the original jailbreak intent, we enforce a semantic consistency constraint and employ Bayesian optimization to efficiently search for perturbations that are both subtle and highly effective. This results in AJailBench-APT, an extended dataset of optimized adversarial audio samples. Our findings demonstrate that even small, semantically preserved perturbations can significantly reduce the safety performance of leading LAMs, underscoring the need for more robust and semantically aware defense mechanisms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mbzuai-nlp/audiojailbreak", "github_api_added_by": "auto" }, { "date": "2025-05-22", "arxiv_id": "2505.14818", "title": "WebNovelBench: Placing LLM Novelists on the Web Novel Distribution", "authors": [ "Leon Lin", "Jun Zheng", "Haidong Wang" ], "github": "", "project_page": "", "abstract": "Robustly evaluating the long-form storytelling capabilities of Large Language Models (LLMs) remains a significant challenge, as existing benchmarks often lack the necessary scale, diversity, or objective measures. To address this, we introduce WebNovelBench, a novel benchmark specifically designed for evaluating long-form novel generation. WebNovelBench leverages a large-scale dataset of over 4,000 Chinese web novels, framing evaluation as a synopsis-to-story generation task. We propose a multi-faceted framework encompassing eight narrative quality dimensions, assessed automatically via an LLM-as-Judge approach. Scores are aggregated using Principal Component Analysis and mapped to a percentile rank against human-authored works. Our experiments demonstrate that WebNovelBench effectively differentiates between human-written masterpieces, popular web novels, and LLM-generated content. We provide a comprehensive analysis of 24 state-of-the-art LLMs, ranking their storytelling abilities and offering insights for future development. This benchmark provides a scalable, replicable, and data-driven methodology for assessing and advancing LLM-driven narrative generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.14101", "title": "MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations", "authors": [ "Ernests Lavrinovics", "Russa Biswas", "Katja Hose", "Johannes Bjerva" ], "github": "https://github.com/ernlavr/multihal", "project_page": "", "abstract": "Large Language Models (LLMs) have inherent limitations of faithfulness and factuality, commonly referred to as hallucinations. Several benchmarks have been developed that provide a test bed for factuality evaluation within the context of English-centric datasets, while relying on supplementary informative context like web links or text passages but ignoring the available structured factual resources. To this end, Knowledge Graphs (KGs) have been identified as a useful aid for hallucination mitigation, as they provide a structured way to represent the facts about entities and their relations with minimal linguistic overhead. We bridge the lack of KG paths and multilinguality for factual language modeling within the existing hallucination evaluation benchmarks and propose a KG-based multilingual, multihop benchmark called MultiHal framed for generative text evaluation. As part of our data collection pipeline, we mined 140k KG-paths from open-domain KGs, from which we pruned noisy KG-paths, curating a high-quality subset of 25.9k. Our baseline evaluation shows an absolute scale increase by approximately 0.12 to 0.36 points for the semantic similarity score in KG-RAG over vanilla QA across multiple languages and multiple models, demonstrating the potential of KG integration. We anticipate MultiHal will foster future research towards several graph-based hallucination mitigation and fact-checking tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ernlavr/multihal", "github_api_added_by": "auto" }, { "date": "2025-05-22", "arxiv_id": "2505.14336", "title": "Scaling and Enhancing LLM-based AVSR: A Sparse Mixture of Projectors Approach", "authors": [ "Umberto Cappellazzo", "Minsu Kim", "Stavros Petridis", "Daniele Falavigna", "Alessio Brutti" ], "github": "", "project_page": "", "abstract": "Audio-Visual Speech Recognition (AVSR) enhances robustness in noisy environments by integrating visual cues. While recent advances integrate Large Language Models (LLMs) into AVSR, their high computational cost hinders deployment in resource-constrained settings. To address this, we propose Llama-SMoP, an efficient Multimodal LLM that employs a Sparse Mixture of Projectors (SMoP) module to scale model capacity without increasing inference costs. By incorporating sparsely-gated mixture-of-experts (MoE) projectors, Llama-SMoP enables the use of smaller LLMs while maintaining strong performance. We explore three SMoP configurations and show that Llama-SMoP DEDR (Disjoint-Experts, Disjoint-Routers), which uses modality-specific routers and experts, achieves superior performance on ASR, VSR, and AVSR tasks. Ablation studies confirm its effectiveness in expert activation, scalability, and noise robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15141", "title": "BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms", "authors": [ "Yunlong Hou", "Fengzhuo Zhang", "Cunxiao Du", "Xuan Zhang", "Jiachun Pan", "Tianyu Pang", "Chao Du", "Vincent Y. F. Tan", "Zhuoran Yang" ], "github": "https://github.com/sail-sg/BanditSpec", "project_page": "", "abstract": "Speculative decoding has emerged as a popular method to accelerate the inference of Large Language Models (LLMs) while retaining their superior text generation performance. Previous methods either adopt a fixed speculative decoding configuration regardless of the prefix tokens, or train draft models in an offline or online manner to align them with the context. This paper proposes a training-free online learning framework to adaptively choose the configuration of the hyperparameters for speculative decoding as text is being generated. We first formulate this hyperparameter selection problem as a Multi-Armed Bandit problem and provide a general speculative decoding framework BanditSpec. Furthermore, two bandit-based hyperparameter selection algorithms, UCBSpec and EXP3Spec, are designed and analyzed in terms of a novel quantity, the stopping time regret. We upper bound this regret under both stochastic and adversarial reward settings. By deriving an information-theoretic impossibility result, it is shown that the regret performance of UCBSpec is optimal up to universal constants. Finally, extensive empirical experiments with LLaMA3 and Qwen2 demonstrate that our algorithms are effective compared to existing methods, and the throughput is close to the oracle best hyperparameter in simulated real-life LLM serving scenarios with diverse input prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sail-sg/BanditSpec", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15801", "title": "VerifyBench: Benchmarking Reference-based Reward Systems for Large Language Models", "authors": [ "Yuchen Yan", "Jin Jiang", "Zhenbang Ren", "Yijun Li", "Xudong Cai", "Yang Liu", "Xin Xu", "Mengdi Zhang", "Jian Shao", "Yongliang Shen", "Jun Xiao", "Yueting Zhuang" ], "github": "", "project_page": "", "abstract": "Large reasoning models such as OpenAI o1 and DeepSeek-R1 have achieved remarkable performance in the domain of reasoning. A key component of their training is the incorporation of verifiable rewards within reinforcement learning (RL). However, existing reward benchmarks do not evaluate reference-based reward systems, leaving researchers with limited understanding of the accuracy of verifiers used in RL. In this paper, we introduce two benchmarks, VerifyBench and VerifyBench-Hard, designed to assess the performance of reference-based reward systems. These benchmarks are constructed through meticulous data collection and curation, followed by careful human annotation to ensure high quality. Current models still show considerable room for improvement on both VerifyBench and VerifyBench-Hard, especially smaller-scale models. Furthermore, we conduct a thorough and comprehensive analysis of evaluation results, offering insights for understanding and developing reference-based reward systems. Our proposed benchmarks serve as effective tools for guiding the development of verifier accuracy and the reasoning capabilities of models trained via RL in reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.11196", "title": "DiCo: Revitalizing ConvNets for Scalable and Efficient Diffusion Modeling", "authors": [ "Yuang Ai", "Qihang Fan", "Xuefeng Hu", "Zhenheng Yang", "Ran He", "Huaibo Huang" ], "github": "https://github.com/shallowdream204/DiCo", "project_page": "", "abstract": "Diffusion Transformer (DiT), a promising diffusion model for visual generation, demonstrates impressive performance but incurs significant computational overhead. Intriguingly, analysis of pre-trained DiT models reveals that global self-attention is often redundant, predominantly capturing local patterns-highlighting the potential for more efficient alternatives. In this paper, we revisit convolution as an alternative building block for constructing efficient and expressive diffusion models. However, naively replacing self-attention with convolution typically results in degraded performance. Our investigations attribute this performance gap to the higher channel redundancy in ConvNets compared to Transformers. To resolve this, we introduce a compact channel attention mechanism that promotes the activation of more diverse channels, thereby enhancing feature diversity. This leads to Diffusion ConvNet (DiCo), a family of diffusion models built entirely from standard ConvNet modules, offering strong generative performance with significant efficiency gains. On class-conditional ImageNet benchmarks, DiCo outperforms previous diffusion models in both image quality and generation speed. Notably, DiCo-XL achieves an FID of 2.05 at 256x256 resolution and 2.53 at 512x512, with a 2.7x and 3.1x speedup over DiT-XL/2, respectively. Furthermore, our largest model, DiCo-H, scaled to 1B parameters, reaches an FID of 1.90 on ImageNet 256x256-without any additional supervision during training. Code: https://github.com/shallowdream204/DiCo.", "github_manual": "", "github_regex": "https://github.com/shallowdream204/DiCo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.15134", "title": "The Unreasonable Effectiveness of Entropy Minimization in LLM Reasoning", "authors": [ "Shivam Agarwal", "Zimin Zhang", "Lifan Yuan", "Jiawei Han", "Hao Peng" ], "github": "https://github.com/shivamag125/EM_PT", "project_page": "", "abstract": "Entropy minimization (EM) trains the model to concentrate even more probability mass on its most confident outputs. We show that this simple objective alone, without any labeled data, can substantially improve large language models' (LLMs) performance on challenging math, physics, and coding tasks. We explore three approaches: (1) EM-FT minimizes token-level entropy similarly to instruction finetuning, but on unlabeled outputs drawn from the model; (2) EM-RL: reinforcement learning with negative entropy as the only reward to maximize; (3) EM-INF: inference-time logit adjustment to reduce entropy without any training data or parameter updates. On Qwen-7B, EM-RL, without any labeled data, achieves comparable or better performance than strong RL baselines such as GRPO and RLOO that are trained on 60K labeled examples. Furthermore, EM-INF enables Qwen-32B to match or exceed the performance of proprietary models like GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro on the challenging SciCode benchmark, while being 3x more efficient than self-consistency and sequential refinement. Our findings reveal that many pretrained LLMs possess previously underappreciated reasoning capabilities that can be effectively elicited through entropy minimization alone, without any labeled data or even any parameter updates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shivamag125/EM_PT", "github_api_added_by": "auto" }, { "date": "2025-05-22", "arxiv_id": "2505.11080", "title": "BLEUBERI: BLEU is a surprisingly effective reward for instruction following", "authors": [ "Yapei Chang", "Yekyung Kim", "Michael Krumdick", "Amir Zadeh", "Chuan Li", "Chris Tanner", "Mohit Iyyer" ], "github": "https://github.com/lilakk/BLEUBERI", "project_page": "", "abstract": "Reward models are central to aligning LLMs with human preferences, but they are costly to train, requiring large-scale human-labeled preference data and powerful pretrained LLM backbones. Meanwhile, the increasing availability of high-quality synthetic instruction-following datasets raises the question: can simpler, reference-based metrics serve as viable alternatives to reward models during RL-based alignment? In this paper, we show first that BLEU, a basic string-matching metric, surprisingly matches strong reward models in agreement with human preferences on general instruction-following datasets. Based on this insight, we develop BLEUBERI, a method that first identifies challenging instructions and then applies Group Relative Policy Optimization (GRPO) using BLEU directly as the reward function. We demonstrate that BLEUBERI-trained models are competitive with models trained via reward model-guided RL across four challenging instruction-following benchmarks and three different base language models. A human evaluation further supports that the quality of BLEUBERI model outputs is on par with those from reward model-aligned models. Moreover, BLEUBERI models generate outputs that are more factually grounded than competing methods. Overall, we show that given access to high-quality reference outputs (easily obtained via existing instruction-following datasets or synthetic data generation), string matching-based metrics are cheap yet effective proxies for reward models during alignment. We release our code and data at https://github.com/lilakk/BLEUBERI.", "github_manual": "", "github_regex": "https://github.com/lilakk/BLEUBERI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-22", "arxiv_id": "2505.14887", "title": "In-Context Learning Boosts Speech Recognition via Human-like Adaptation to Speakers and Language Varieties", "authors": [ "Nathan Roll", "Calbert Graham", "Yuka Tatsumi", "Kim Tien Nguyen", "Meghan Sumner", "Dan Jurafsky" ], "github": "https://github.com/Nathan-Roll1/ASR-Adaptation", "project_page": "", "abstract": "Human listeners readily adjust to unfamiliar speakers and language varieties through exposure, but do these adaptation benefits extend to state-of-the-art spoken language models? We introduce a scalable framework that allows for in-context learning (ICL) in Phi-4 Multimodal using interleaved task prompts and audio-text pairs, and find that as few as 12 example utterances (~50 seconds) at inference time reduce word error rates by a relative 19.7% (1.2 pp.) on average across diverse English corpora. These improvements are most pronounced in low-resource varieties, when the context and target speaker match, and when more examples are provided--though scaling our procedure yields diminishing marginal returns to context length. Overall, we find that our novel ICL adaptation scheme (1) reveals a similar performance profile to human listeners, and (2) demonstrates consistent improvements to automatic speech recognition (ASR) robustness across diverse speakers and language backgrounds. While adaptation succeeds broadly, significant gaps remain for certain varieties, revealing where current models still fall short of human flexibility. We release our prompts and code on GitHub.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Nathan-Roll1/ASR-Adaptation", "github_api_added_by": "auto" }, { "date": "2025-05-23", "arxiv_id": "2505.11711", "title": "Reinforcement Learning Finetunes Small Subnetworks in Large Language Models", "authors": [ "Sagnik Mukherjee", "Lifan Yuan", "Dilek Hakkani-Tur", "Hao Peng" ], "github": "https://github.com/SagnikMukherjee/sparsity_in_rl", "project_page": "", "abstract": "Reinforcement learning (RL) yields substantial improvements in large language models (LLMs) downstream task performance and alignment with human values. Surprisingly, such large gains result from updating only a small subnetwork comprising just 5 percent to 30 percent of the parameters, with the rest effectively unchanged. We refer to this phenomenon as parameter update sparsity induced by RL. It is observed across all 7 widely used RL algorithms (e.g., PPO, GRPO, DPO) and all 10 LLMs from different families in our experiments. This sparsity is intrinsic and occurs without any explicit sparsity promoting regularizations or architectural constraints. Finetuning the subnetwork alone recovers the test accuracy, and, remarkably, produces a model nearly identical to the one obtained via full finetuning. The subnetworks from different random seeds, training data, and even RL algorithms show substantially greater overlap than expected by chance. Our analysis suggests that this sparsity is not due to updating only a subset of layers, instead, nearly all parameter matrices receive similarly sparse updates. Moreover, the updates to almost all parameter matrices are nearly full-rank, suggesting RL updates a small subset of parameters that nevertheless span almost the full subspaces that the parameter matrices can represent. We conjecture that the this update sparsity can be primarily attributed to training on data that is near the policy distribution, techniques that encourage the policy to remain close to the pretrained model, such as the KL regularization and gradient clipping, have limited impact.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SagnikMukherjee/sparsity_in_rl", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16707", "title": "KRIS-Bench: Benchmarking Next-Level Intelligent Image Editing Models", "authors": [ "Yongliang Wu", "Zonghui Li", "Xinting Hu", "Xinyu Ye", "Xianfang Zeng", "Gang Yu", "Wenbo Zhu", "Bernt Schiele", "Ming-Hsuan Yang", "Xu Yang" ], "github": "", "project_page": "", "abstract": "Recent advances in multi-modal generative models have enabled significant progress in instruction-based image editing. However, while these models produce visually plausible outputs, their capacity for knowledge-based reasoning editing tasks remains under-explored. In this paper, we introduce KRIS-Bench (Knowledge-based Reasoning in Image-editing Systems Benchmark), a diagnostic benchmark designed to assess models through a cognitively informed lens. Drawing from educational theory, KRIS-Bench categorizes editing tasks across three foundational knowledge types: Factual, Conceptual, and Procedural. Based on this taxonomy, we design 22 representative tasks spanning 7 reasoning dimensions and release 1,267 high-quality annotated editing instances. To support fine-grained evaluation, we propose a comprehensive protocol that incorporates a novel Knowledge Plausibility metric, enhanced by knowledge hints and calibrated through human studies. Empirical results on 10 state-of-the-art models reveal significant gaps in reasoning performance, highlighting the need for knowledge-centric benchmarks to advance the development of intelligent image editing systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16410", "title": "Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning", "authors": [ "Guanting Dong", "Yifei Chen", "Xiaoxi Li", "Jiajie Jin", "Hongjin Qian", "Yutao Zhu", "Hangyu Mao", "Guorui Zhou", "Zhicheng Dou", "Ji-Rong Wen" ], "github": "https://github.com/dongguanting/Tool-Star", "project_page": "", "abstract": "Recently, large language models (LLMs) have shown remarkable reasoning capabilities via large-scale reinforcement learning (RL). However, leveraging the RL algorithm to empower effective multi-tool collaborative reasoning in LLMs remains an open challenge. In this paper, we introduce Tool-Star, an RL-based framework designed to empower LLMs to autonomously invoke multiple external tools during stepwise reasoning. Tool-Star integrates six types of tools and incorporates systematic designs in both data synthesis and training. To address the scarcity of tool-use data, we propose a general tool-integrated reasoning data synthesis pipeline, which combines tool-integrated prompting with hint-based sampling to automatically and scalably generate tool-use trajectories. A subsequent quality normalization and difficulty-aware classification process filters out low-quality samples and organizes the dataset from easy to hard. Furthermore, we propose a two-stage training framework to enhance multi-tool collaborative reasoning by: (1) cold-start fine-tuning, which guides LLMs to explore reasoning patterns via tool-invocation feedback; and (2) a multi-tool self-critic RL algorithm with hierarchical reward design, which reinforces reward understanding and promotes effective tool collaboration. Experimental analyses on over 10 challenging reasoning benchmarks highlight the effectiveness and efficiency of Tool-Star. The code is available at https://github.com/dongguanting/Tool-Star.", "github_manual": "", "github_regex": "https://github.com/dongguanting/Tool-Star", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.14810", "title": "Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models", "authors": [ "Tingchen Fu", "Jiawei Gu", "Yafu Li", "Xiaoye Qu", "Yu Cheng" ], "github": "https://github.com/TingchenFu/MathIF", "project_page": "", "abstract": "Instruction-following is essential for aligning large language models (LLMs) with user intent. While recent reasoning-oriented models exhibit impressive performance on complex mathematical problems, their ability to adhere to natural language instructions remains underexplored. In this work, we introduce MathIF, a dedicated benchmark for evaluating instruction-following in mathematical reasoning tasks. Our empirical analysis reveals a consistent tension between scaling up reasoning capacity and maintaining controllability, as models that reason more effectively often struggle to comply with user directives. We find that models tuned on distilled long chains-of-thought or trained with reasoning-oriented reinforcement learning often degrade in instruction adherence, especially when generation length increases. Furthermore, we show that even simple interventions can partially recover obedience, though at the cost of reasoning performance. These findings highlight a fundamental tension in current LLM training paradigms and motivate the need for more instruction-aware reasoning models. We release the code and data at https://github.com/TingchenFu/MathIF.", "github_manual": "", "github_regex": "https://github.com/TingchenFu/MathIF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.14625", "title": "TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning", "authors": [ "Zhangchen Xu", "Yuetai Li", "Fengqing Jiang", "Bhaskar Ramasubramanian", "Luyao Niu", "Bill Yuchen Lin", "Radha Poovendran" ], "github": "https://github.com/uw-nsl/TinyV", "project_page": "", "abstract": "Reinforcement Learning (RL) has become a powerful tool for enhancing the reasoning abilities of large language models (LLMs) by optimizing their policies with reward signals. Yet, RL's success relies on the reliability of rewards, which are provided by verifiers. In this paper, we expose and analyze a widespread problem--false negatives--where verifiers wrongly reject correct model outputs. Our in-depth study of the Big-Math-RL-Verified dataset reveals that over 38% of model-generated responses suffer from false negatives, where the verifier fails to recognize correct answers. We show, both empirically and theoretically, that these false negatives severely impair RL training by depriving the model of informative gradient signals and slowing convergence. To mitigate this, we propose tinyV, a lightweight LLM-based verifier that augments existing rule-based methods, which dynamically identifies potential false negatives and recovers valid responses to produce more accurate reward estimates. Across multiple math-reasoning benchmarks, integrating TinyV boosts pass rates by up to 10% and accelerates convergence relative to the baseline. Our findings highlight the critical importance of addressing verifier false negatives and offer a practical approach to improve RL-based fine-tuning of LLMs. Our code is available at https://github.com/uw-nsl/TinyV.", "github_manual": "", "github_regex": "https://github.com/uw-nsl/TinyV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16990", "title": "Dimple: Discrete Diffusion Multimodal Large Language Model with Parallel Decoding", "authors": [ "Runpeng Yu", "Xinyin Ma", "Xinchao Wang" ], "github": "https://github.com/yu-rp/Dimple", "project_page": "", "abstract": "In this work, we propose Dimple, the first Discrete Diffusion Multimodal Large Language Model (DMLLM). We observe that training with a purely discrete diffusion approach leads to significant training instability, suboptimal performance, and severe length bias issues. To address these challenges, we design a novel training paradigm that combines an initial autoregressive phase with a subsequent diffusion phase. This approach yields the Dimple-7B model, trained on the same dataset and using a similar training pipeline as LLaVA-NEXT. Dimple-7B ultimately surpasses LLaVA-NEXT in performance by 3.9%, demonstrating that DMLLM can achieve performance comparable to that of autoregressive models. To improve inference efficiency, we propose a decoding strategy termed confident decoding, which dynamically adjusts the number of tokens generated at each step, significantly reducing the number of generation iterations. In autoregressive models, the number of forward iterations during generation equals the response length. With confident decoding, however, the number of iterations needed by Dimple is even only text{response length}{3}. We also re-implement the prefilling technique in autoregressive models and demonstrate that it does not significantly impact performance on most benchmark evaluations, while offering a speedup of 1.5x to 7x. Additionally, we explore Dimple's capability to precisely control its response using structure priors. These priors enable structured responses in a manner distinct from instruction-based or chain-of-thought prompting, and allow fine-grained control over response format and length, which is difficult to achieve in autoregressive models. Overall, this work validates the feasibility and advantages of DMLLM and enhances its inference efficiency and controllability. Code and models are available at https://github.com/yu-rp/Dimple.", "github_manual": "", "github_regex": "https://github.com/yu-rp/Dimple", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16175", "title": "QuickVideo: Real-Time Long Video Understanding with System Algorithm Co-Design", "authors": [ "Benjamin Schneider", "Dongfu Jiang", "Chao Du", "Tianyu Pang", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Long-video understanding has emerged as a crucial capability in real-world applications such as video surveillance, meeting summarization, educational lecture analysis, and sports broadcasting. However, it remains computationally prohibitive for VideoLLMs, primarily due to two bottlenecks: 1) sequential video decoding, the process of converting the raw bit stream to RGB frames can take up to a minute for hour-long video inputs, and 2) costly prefilling of up to several million tokens for LLM inference, resulting in high latency and memory use. To address these challenges, we propose QuickVideo, a system-algorithm co-design that substantially accelerates long-video understanding to support real-time downstream applications. It comprises three key innovations: QuickDecoder, a parallelized CPU-based video decoder that achieves 2-3 times speedup by splitting videos into keyframe-aligned intervals processed concurrently; QuickPrefill, a memory-efficient prefilling method using KV-cache pruning to support more frames with less GPU memory; and an overlapping scheme that overlaps CPU video decoding with GPU inference. Together, these components infernece time reduce by a minute on long video inputs, enabling scalable, high-quality video understanding even on limited hardware. Experiments show that QuickVideo generalizes across durations and sampling rates, making long video processing feasible in practice.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16854", "title": "Think or Not? Selective Reasoning via Reinforcement Learning for Vision-Language Models", "authors": [ "Jiaqi Wang", "Kevin Qinghong Lin", "James Cheng", "Mike Zheng Shou" ], "github": "https://github.com/kokolerk/TON", "project_page": "", "abstract": "Reinforcement Learning (RL) has proven to be an effective post-training strategy for enhancing reasoning in vision-language models (VLMs). Group Relative Policy Optimization (GRPO) is a recent prominent method that encourages models to generate complete reasoning traces before answering, leading to increased token usage and computational cost. Inspired by the human-like thinking process-where people skip reasoning for easy questions but think carefully when needed-we explore how to enable VLMs to first decide when reasoning is necessary. To realize this, we propose TON, a two-stage training strategy: (i) a supervised fine-tuning (SFT) stage with a simple yet effective 'thought dropout' operation, where reasoning traces are randomly replaced with empty thoughts. This introduces a think-or-not format that serves as a cold start for selective reasoning; (ii) a GRPO stage that enables the model to freely explore when to think or not, while maximizing task-aware outcome rewards. Experimental results show that TON can reduce the completion length by up to 90% compared to vanilla GRPO, without sacrificing performance or even improving it. Further evaluations across diverse vision-language tasks-covering a range of reasoning difficulties under both 3B and 7B models-consistently reveal that the model progressively learns to bypass unnecessary reasoning steps as training advances. These findings shed light on the path toward human-like reasoning patterns in reinforcement learning approaches. Our code is available at https://github.com/kokolerk/TON.", "github_manual": "", "github_regex": "https://github.com/kokolerk/TON", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16839", "title": "LaViDa: A Large Diffusion Language Model for Multimodal Understanding", "authors": [ "Shufan Li", "Konstantinos Kallidromitis", "Hritik Bansal", "Akash Gokul", "Yusuke Kato", "Kazuki Kozuka", "Jason Kuen", "Zhe Lin", "Kai-Wei Chang", "Aditya Grover" ], "github": "https://github.com/jacklishufan/LaViDa", "project_page": "", "abstract": "Modern Vision-Language Models (VLMs) can solve a wide range of tasks requiring visual reasoning. In real-world scenarios, desirable properties for VLMs include fast inference and controllable generation (e.g., constraining outputs to adhere to a desired format). However, existing autoregressive (AR) VLMs like LLaVA struggle in these aspects. Discrete diffusion models (DMs) offer a promising alternative, enabling parallel decoding for faster inference and bidirectional context for controllable generation through text-infilling. While effective in language-only settings, DMs' potential for multimodal tasks is underexplored. We introduce LaViDa, a family of VLMs built on DMs. We build LaViDa by equipping DMs with a vision encoder and jointly fine-tune the combined parts for multimodal instruction following. To address challenges encountered, LaViDa incorporates novel techniques such as complementary masking for effective training, prefix KV cache for efficient inference, and timestep shifting for high-quality sampling. Experiments show that LaViDa achieves competitive or superior performance to AR VLMs on multi-modal benchmarks such as MMMU, while offering unique advantages of DMs, including flexible speed-quality tradeoff, controllability, and bidirectional reasoning. On COCO captioning, LaViDa surpasses Open-LLaVa-Next-8B by +4.1 CIDEr with 1.92x speedup. On bidirectional tasks, it achieves +59% improvement on Constrained Poem Completion. These results demonstrate LaViDa as a strong alternative to AR VLMs. Code and models will be released in the camera-ready version.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jacklishufan/LaViDa", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.15270", "title": "Scaling Diffusion Transformers Efficiently via \u03bcP", "authors": [ "Chenyu Zheng", "Xinyu Zhang", "Rongzhen Wang", "Wei Huang", "Zhi Tian", "Weilin Huang", "Jun Zhu", "Chongxuan Li" ], "github": "https://github.com/ML-GSAI/Scaling-Diffusion-Transformers-muP", "project_page": "", "abstract": "Diffusion Transformers have emerged as the foundation for vision generative models, but their scalability is limited by the high cost of hyperparameter (HP) tuning at large scales. Recently, Maximal Update Parametrization (muP) was proposed for vanilla Transformers, which enables stable HP transfer from small to large language models, and dramatically reduces tuning costs. However, it remains unclear whether muP of vanilla Transformers extends to diffusion Transformers, which differ architecturally and objectively. In this work, we generalize standard muP to diffusion Transformers and validate its effectiveness through large-scale experiments. First, we rigorously prove that muP of mainstream diffusion Transformers, including DiT, U-ViT, PixArt-alpha, and MMDiT, aligns with that of the vanilla Transformer, enabling the direct application of existing muP methodologies. Leveraging this result, we systematically demonstrate that DiT-muP enjoys robust HP transferability. Notably, DiT-XL-2-muP with transferred learning rate achieves 2.9 times faster convergence than the original DiT-XL-2. Finally, we validate the effectiveness of muP on text-to-image generation by scaling PixArt-alpha from 0.04B to 0.61B and MMDiT from 0.18B to 18B. In both cases, models under muP outperform their respective baselines while requiring small tuning cost, only 5.5% of one training run for PixArt-alpha and 3% of consumption by human experts for MMDiT-18B. These results establish muP as a principled and efficient framework for scaling diffusion Transformers.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ML-GSAI/Scaling-Diffusion-Transformers-muP", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.17012", "title": "SpatialScore: Towards Unified Evaluation for Multimodal Spatial Understanding", "authors": [ "Haoning Wu", "Xiao Huang", "Yaohui Chen", "Ya Zhang", "Yanfeng Wang", "Weidi Xie" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have achieved impressive success in question-answering tasks, yet their capabilities for spatial understanding are less explored. This work investigates a critical question: do existing MLLMs possess 3D spatial perception and understanding abilities? Concretely, we make the following contributions in this paper: (i) we introduce VGBench, a benchmark specifically designed to assess MLLMs for visual geometry perception, e.g., camera pose and motion estimation; (ii) we propose SpatialScore, the most comprehensive and diverse multimodal spatial understanding benchmark to date, integrating VGBench with relevant data from the other 11 existing datasets. This benchmark comprises 28K samples across various spatial understanding tasks, modalities, and QA formats, along with a carefully curated challenging subset, SpatialScore-Hard; (iii) we develop SpatialAgent, a novel multi-agent system incorporating 9 specialized tools for spatial understanding, supporting both Plan-Execute and ReAct reasoning paradigms; (iv) we conduct extensive evaluations to reveal persistent challenges in spatial reasoning while demonstrating the effectiveness of SpatialAgent. We believe SpatialScore will offer valuable insights and serve as a rigorous benchmark for the next evolution of MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16181", "title": "Understanding Generative AI Capabilities in Everyday Image Editing Tasks", "authors": [ "Mohammad Reza Taesiri", "Brandon Collins", "Logan Bolton", "Viet Dac Lai", "Franck Dernoncourt", "Trung Bui", "Anh Totti Nguyen" ], "github": "", "project_page": "", "abstract": "Generative AI (GenAI) holds significant promise for automating everyday image editing tasks, especially following the recent release of GPT-4o on March 25, 2025. However, what subjects do people most often want edited? What kinds of editing actions do they want to perform (e.g., removing or stylizing the subject)? Do people prefer precise edits with predictable outcomes or highly creative ones? By understanding the characteristics of real-world requests and the corresponding edits made by freelance photo-editing wizards, can we draw lessons for improving AI-based editors and determine which types of requests can currently be handled successfully by AI editors? In this paper, we present a unique study addressing these questions by analyzing 83k requests from the past 12 years (2013-2025) on the Reddit community, which collected 305k PSR-wizard edits. According to human ratings, approximately only 33% of requests can be fulfilled by the best AI editors (including GPT-4o, Gemini-2.0-Flash, SeedEdit). Interestingly, AI editors perform worse on low-creativity requests that require precise editing than on more open-ended tasks. They often struggle to preserve the identity of people and animals, and frequently make non-requested touch-ups. On the other side of the table, VLM judges (e.g., o1) perform differently from human judges and may prefer AI edits more than human edits. Code and qualitative examples are available at: https://psrdataset.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16151", "title": "Training-Free Reasoning and Reflection in MLLMs", "authors": [ "Hongchen Wei", "Zhenzhong Chen" ], "github": "https://github.com/hcwei13/FRANK-ZERO-Inference", "project_page": "", "abstract": "Recent advances in Reasoning LLMs (e.g., DeepSeek-R1 and OpenAI-o1) have showcased impressive reasoning capabilities via reinforcement learning. However, extending these capabilities to Multimodal LLMs (MLLMs) is hampered by the prohibitive costs of retraining and the scarcity of high-quality, verifiable multimodal reasoning datasets. This paper introduces FRANK Model, a training-FRee ANd r1-liKe MLLM that imbues off-the-shelf MLLMs with reasoning and reflection abilities, without any gradient updates or extra supervision. Our key insight is to decouple perception and reasoning across MLLM decoder layers. Specifically, we observe that compared to the deeper decoder layers, the shallow decoder layers allocate more attention to visual tokens, while the deeper decoder layers concentrate on textual semantics. This observation motivates a hierarchical weight merging approach that combines a visual-pretrained MLLM with a reasoning-specialized LLM. To this end, we propose a layer-wise, Taylor-derived closed-form fusion mechanism that integrates reasoning capacity into deep decoder layers while preserving visual grounding in shallow decoder layers. Extensive experiments on challenging multimodal reasoning benchmarks demonstrate the effectiveness of our approach. On the MMMU benchmark, our model FRANK-38B achieves an accuracy of 69.2, outperforming the strongest baseline InternVL2.5-38B by +5.3, and even surpasses the proprietary GPT-4o model. Our project homepage is at: http://iip.whu.edu.cn/frank/index.html", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hcwei13/FRANK-ZERO-Inference", "github_api_added_by": "auto" }, { "date": "2025-05-23", "arxiv_id": "2505.16916", "title": "Backdoor Cleaning without External Guidance in MLLM Fine-tuning", "authors": [ "Xuankun Rong", "Wenke Huang", "Jian Liang", "Jinhe Bi", "Xun Xiao", "Yiming Li", "Bo Du", "Mang Ye" ], "github": "https://github.com/xuankunrong/bye", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) are increasingly deployed in fine-tuning-as-a-service (FTaaS) settings, where user-submitted datasets adapt general-purpose models to downstream tasks. This flexibility, however, introduces serious security risks, as malicious fine-tuning can implant backdoors into MLLMs with minimal effort. In this paper, we observe that backdoor triggers systematically disrupt cross-modal processing by causing abnormal attention concentration on non-semantic regions--a phenomenon we term attention collapse. Based on this insight, we propose Believe Your Eyes (BYE), a data filtering framework that leverages attention entropy patterns as self-supervised signals to identify and filter backdoor samples. BYE operates via a three-stage pipeline: (1) extracting attention maps using the fine-tuned model, (2) computing entropy scores and profiling sensitive layers via bimodal separation, and (3) performing unsupervised clustering to remove suspicious samples. Unlike prior defenses, BYE equires no clean supervision, auxiliary labels, or model modifications. Extensive experiments across various datasets, models, and diverse trigger types validate BYE's effectiveness: it achieves near-zero attack success rates while maintaining clean-task performance, offering a robust and generalizable solution against backdoor threats in MLLMs.", "github_manual": "https://github.com/xuankunrong/bye", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.15963", "title": "OViP: Online Vision-Language Preference Learning", "authors": [ "Shujun Liu", "Siyuan Wang", "Zejun Li", "Jianxiang Wang", "Cheng Zeng", "Zhongyu Wei" ], "github": "https://github.com/lsjlsj35/Online-Vision-Language-Preference-Learning-for-VLM-Hallucination", "project_page": "", "abstract": "Large vision-language models (LVLMs) remain vulnerable to hallucination, often generating content misaligned with visual inputs. While recent approaches advance multi-modal Direct Preference Optimization (DPO) to mitigate hallucination, they typically rely on predefined or randomly edited negative samples that fail to reflect actual model errors, limiting training efficacy. In this work, we propose an Online Vision-language Preference Learning (OViP) framework that dynamically constructs contrastive training data based on the model's own hallucinated outputs. By identifying semantic differences between sampled response pairs and synthesizing negative images using a diffusion model, OViP generates more relevant supervision signals in real time. This failure-driven training enables adaptive alignment of both textual and visual preferences. Moreover, we refine existing evaluation protocols to better capture the trade-off between hallucination suppression and expressiveness. Experiments on hallucination and general benchmarks demonstrate that OViP effectively reduces hallucinations while preserving core multi-modal capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lsjlsj35/Online-Vision-Language-Preference-Learning-for-VLM-Hallucination", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16938", "title": "NovelSeek: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification", "authors": [ "NovelSeek Team", "Bo Zhang", "Shiyang Feng", "Xiangchao Yan", "Jiakang Yuan", "Zhiyin Yu", "Xiaohan He", "Songtao Huang", "Shaowei Hou", "Zheng Nie", "Zhilong Wang", "Jinyao Liu", "Runmin Ma", "Tianshuo Peng", "Peng Ye", "Dongzhan Zhou", "Shufei Zhang", "Xiaosong Wang", "Yilan Zhang", "Meng Li", "Zhongying Tu", "Xiangyu Yue", "Wangli Ouyang", "Bowen Zhou", "Lei Bai" ], "github": "https://github.com/alpha-innovator/novelseek", "project_page": "", "abstract": "Artificial Intelligence (AI) is accelerating the transformation of scientific research paradigms, not only enhancing research efficiency but also driving innovation. We introduce NovelSeek, a unified closed-loop multi-agent framework to conduct Autonomous Scientific Research (ASR) across various scientific research fields, enabling researchers to tackle complicated problems in these fields with unprecedented speed and precision. NovelSeek highlights three key advantages: 1) Scalability: NovelSeek has demonstrated its versatility across 12 scientific research tasks, capable of generating innovative ideas to enhance the performance of baseline code. 2) Interactivity: NovelSeek provides an interface for human expert feedback and multi-agent interaction in automated end-to-end processes, allowing for the seamless integration of domain expert knowledge. 3) Efficiency: NovelSeek has achieved promising performance gains in several scientific fields with significantly less time cost compared to human efforts. For instance, in reaction yield prediction, it increased from 27.6% to 35.4% in just 12 hours; in enhancer activity prediction, accuracy rose from 0.52 to 0.79 with only 4 hours of processing; and in 2D semantic segmentation, precision advanced from 78.8% to 81.0% in a mere 30 hours.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alpha-innovator/novelseek", "github_api_added_by": "auto" }, { "date": "2025-05-23", "arxiv_id": "2505.16933", "title": "LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning", "authors": [ "Zebin You", "Shen Nie", "Xiaolu Zhang", "Jun Hu", "Jun Zhou", "Zhiwu Lu", "Ji-Rong Wen", "Chongxuan Li" ], "github": "", "project_page": "", "abstract": "In this work, we introduce LLaDA-V, a purely diffusion-based Multimodal Large Language Model (MLLM) that integrates visual instruction tuning with masked diffusion models, representing a departure from the autoregressive paradigms dominant in current multimodal approaches. Built upon LLaDA, a representative large language diffusion model, LLaDA-V incorporates a vision encoder and MLP connector that projects visual features into the language embedding space, enabling effective multimodal alignment. Our empirical investigation reveals several intriguing results: First, LLaDA-V demonstrates promising multimodal performance despite its language model being weaker on purely textual tasks than counterparts like LLaMA3-8B and Qwen2-7B. When trained on the same instruction data, LLaDA-V is highly competitive to LLaMA3-V across multimodal tasks with better data scalability. It also narrows the performance gap to Qwen2-VL, suggesting the effectiveness of its architecture for multimodal tasks. Second, LLaDA-V achieves state-of-the-art performance in multimodal understanding compared to existing hybrid autoregressive-diffusion and purely diffusion-based MLLMs. Our findings suggest that large language diffusion models show promise in multimodal contexts and warrant further investigation in future research. Project page and codes: https://ml-gsai.github.io/LLaDA-V-demo/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.15966", "title": "Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning", "authors": [ "Alex Su", "Haozhe Wang", "Weimin Ren", "Fangzhen Lin", "Wenhu Chen" ], "github": "https://github.com/TIGER-AI-Lab/Pixel-Reasoner", "project_page": "", "abstract": "Chain-of-thought reasoning has significantly improved the performance of Large Language Models (LLMs) across various domains. However, this reasoning process has been confined exclusively to textual space, limiting its effectiveness in visually intensive tasks. To address this limitation, we introduce the concept of reasoning in the pixel-space. Within this novel framework, Vision-Language Models (VLMs) are equipped with a suite of visual reasoning operations, such as zoom-in and select-frame. These operations enable VLMs to directly inspect, interrogate, and infer from visual evidences, thereby enhancing reasoning fidelity for visual tasks. Cultivating such pixel-space reasoning capabilities in VLMs presents notable challenges, including the model's initially imbalanced competence and its reluctance to adopt the newly introduced pixel-space operations. We address these challenges through a two-phase training approach. The first phase employs instruction tuning on synthesized reasoning traces to familiarize the model with the novel visual operations. Following this, a reinforcement learning (RL) phase leverages a curiosity-driven reward scheme to balance exploration between pixel-space reasoning and textual reasoning. With these visual operations, VLMs can interact with complex visual inputs, such as information-rich images or videos to proactively gather necessary information. We demonstrate that this approach significantly improves VLM performance across diverse visual reasoning benchmarks. Our 7B model, \\model, achieves 84\\% on V* bench, 74\\% on TallyQA-Complex, and 84\\% on InfographicsVQA, marking the highest accuracy achieved by any open-source model to date. These results highlight the importance of pixel-space reasoning and the effectiveness of our framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TIGER-AI-Lab/Pixel-Reasoner", "github_api_added_by": "auto" }, { "date": "2025-05-23", "arxiv_id": "2505.17018", "title": "SophiaVL-R1: Reinforcing MLLMs Reasoning with Thinking Reward", "authors": [ "Kaixuan Fan", "Kaituo Feng", "Haoming Lyu", "Dongzhan Zhou", "Xiangyu Yue" ], "github": "https://github.com/kxfan2002/SophiaVL-R1", "project_page": "", "abstract": "Recent advances have shown success in eliciting strong reasoning abilities in multimodal large language models (MLLMs) through rule-based reinforcement learning (RL) with outcome rewards. However, this paradigm typically lacks supervision over the thinking process leading to the final outcome.As a result, the model may learn sub-optimal reasoning strategies, which can hinder its generalization ability. In light of this, we propose SophiaVL-R1, as an attempt to add reward signals for the thinking process in this paradigm. To achieve this, we first train a thinking reward model that evaluates the quality of the entire thinking process. Given that the thinking reward may be unreliable for certain samples due to reward hacking, we propose the Trust-GRPO method, which assigns a trustworthiness weight to the thinking reward during training. This weight is computed based on the thinking reward comparison of responses leading to correct answers versus incorrect answers, helping to mitigate the impact of potentially unreliable thinking rewards. Moreover, we design an annealing training strategy that gradually reduces the thinking reward over time, allowing the model to rely more on the accurate rule-based outcome reward in later training stages. Experiments show that our SophiaVL-R1 surpasses a series of reasoning MLLMs on various benchmarks (e.g., MathVisita, MMMU), demonstrating strong reasoning and generalization capabilities. Notably, our SophiaVL-R1-7B even outperforms LLaVA-OneVision-72B on most benchmarks, despite the latter having 10 times more parameters. All code, models, and datasets are made publicly available at https://github.com/kxfan2002/SophiaVL-R1.", "github_manual": "", "github_regex": "https://github.com/kxfan2002/SophiaVL-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.15952", "title": "VideoGameQA-Bench: Evaluating Vision-Language Models for Video Game Quality Assurance", "authors": [ "Mohammad Reza Taesiri", "Abhijay Ghildyal", "Saman Zadtootaghaj", "Nabajeet Barman", "Cor-Paul Bezemer" ], "github": "", "project_page": "", "abstract": "With video games now generating the highest revenues in the entertainment industry, optimizing game development workflows has become essential for the sector's sustained growth. Recent advancements in Vision-Language Models (VLMs) offer considerable potential to automate and enhance various aspects of game development, particularly Quality Assurance (QA), which remains one of the industry's most labor-intensive processes with limited automation options. To accurately evaluate the performance of VLMs in video game QA tasks and determine their effectiveness in handling real-world scenarios, there is a clear need for standardized benchmarks, as existing benchmarks are insufficient to address the specific requirements of this domain. To bridge this gap, we introduce VideoGameQA-Bench, a comprehensive benchmark that covers a wide array of game QA activities, including visual unit testing, visual regression testing, needle-in-a-haystack tasks, glitch detection, and bug report generation for both images and videos of various games. Code and data are available at: https://asgaardlab.github.io/videogameqa-bench/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16864", "title": "Training-Free Efficient Video Generation via Dynamic Token Carving", "authors": [ "Yuechen Zhang", "Jinbo Xing", "Bin Xia", "Shaoteng Liu", "Bohao Peng", "Xin Tao", "Pengfei Wan", "Eric Lo", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/Jenga", "project_page": "", "abstract": "Despite the remarkable generation quality of video Diffusion Transformer (DiT) models, their practical deployment is severely hindered by extensive computational requirements. This inefficiency stems from two key challenges: the quadratic complexity of self-attention with respect to token length and the multi-step nature of diffusion models. To address these limitations, we present Jenga, a novel inference pipeline that combines dynamic attention carving with progressive resolution generation. Our approach leverages two key insights: (1) early denoising steps do not require high-resolution latents, and (2) later steps do not require dense attention. Jenga introduces a block-wise attention mechanism that dynamically selects relevant token interactions using 3D space-filling curves, alongside a progressive resolution strategy that gradually increases latent resolution during generation. Experimental results demonstrate that Jenga achieves substantial speedups across multiple state-of-the-art video diffusion models while maintaining comparable generation quality (8.83times speedup with 0.01\\% performance drop on VBench). As a plug-and-play solution, Jenga enables practical, high-quality video generation on modern hardware by reducing inference time from minutes to seconds -- without requiring model retraining. Code: https://github.com/dvlab-research/Jenga", "github_manual": "", "github_regex": "https://github.com/dvlab-research/Jenga", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16400", "title": "AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning", "authors": [ "Yang Chen", "Zhuolin Yang", "Zihan Liu", "Chankyu Lee", "Peng Xu", "Mohammad Shoeybi", "Bryan Catanzaro", "Wei Ping" ], "github": "", "project_page": "", "abstract": "Despite recent progress in large-scale reinforcement learning (RL) for reasoning, the training recipe for building high-performing reasoning models remains elusive. Key implementation details of frontier models, such as DeepSeek-R1, including data curation strategies and RL training recipe, are often omitted. Moreover, recent research indicates distillation remains more effective than RL for smaller models. In this work, we demonstrate that large-scale RL can significantly enhance the reasoning capabilities of strong, small- and mid-sized models, achieving results that surpass those of state-of-the-art distillation-based models. We systematically study the RL training process through extensive ablations and propose a simple yet effective approach: first training on math-only prompts, then on code-only prompts. Notably, we find that math-only RL not only significantly enhances the performance of strong distilled models on math benchmarks (e.g., +14.6% / +17.2% on AIME 2025 for the 7B / 14B models), but also code reasoning tasks (e.g., +6.8% / +5.8% on LiveCodeBench for the 7B / 14B models). In addition, extended code-only RL iterations further improve performance on code benchmarks with minimal or no degradation in math results. We develop a robust data curation pipeline to collect challenging prompts with high-quality, verifiable answers and test cases to enable verification-based RL across both domains. Finally, we identify key experimental insights, including curriculum learning with progressively increasing response lengths and the stabilizing effect of on-policy parameter updates. We find that RL not only elicits the foundational reasoning capabilities acquired during pretraining and supervised fine-tuning (e.g., distillation), but also pushes the limits of the model's reasoning ability, enabling it to solve problems that were previously unsolvable.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.15879", "title": "GRIT: Teaching MLLMs to Think with Images", "authors": [ "Yue Fan", "Xuehai He", "Diji Yang", "Kaizhi Zheng", "Ching-Chen Kuo", "Yuting Zheng", "Sravana Jyothi Narayanaraju", "Xinze Guan", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "Recent studies have demonstrated the efficacy of using Reinforcement Learning (RL) in building reasoning models that articulate chains of thoughts prior to producing final answers. However, despite ongoing advances that aim at enabling reasoning for vision-language tasks, existing open-source visual reasoning models typically generate reasoning content with pure natural language, lacking explicit integration of visual information. This limits their ability to produce clearly articulated and visually grounded reasoning chains. To this end, we propose Grounded Reasoning with Images and Texts (GRIT), a novel method for training MLLMs to think with images. GRIT introduces a grounded reasoning paradigm, in which models generate reasoning chains that interleave natural language and explicit bounding box coordinates. These coordinates point to regions of the input image that the model consults during its reasoning process. Additionally, GRIT is equipped with a reinforcement learning approach, GRPO-GR, built upon the GRPO algorithm. GRPO-GR employs robust rewards focused on the final answer accuracy and format of the grounded reasoning output, which eliminates the need for data with reasoning chain annotations or explicit bounding box labels. As a result, GRIT achieves exceptional data efficiency, requiring as few as 20 image-question-answer triplets from existing datasets. Comprehensive evaluations demonstrate that GRIT effectively trains MLLMs to produce coherent and visually grounded reasoning chains, showing a successful unification of reasoning and grounding abilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.17022", "title": "GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning", "authors": [ "Chengqi Duan", "Rongyao Fang", "Yuqing Wang", "Kun Wang", "Linjiang Huang", "Xingyu Zeng", "Hongsheng Li", "Xihui Liu" ], "github": "https://github.com/gogoduan/GoT-R1", "project_page": "", "abstract": "Visual generation models have made remarkable progress in creating realistic images from text prompts, yet struggle with complex prompts that specify multiple objects with precise spatial relationships and attributes. Effective handling of such prompts requires explicit reasoning about the semantic content and spatial layout. We present GoT-R1, a framework that applies reinforcement learning to enhance semantic-spatial reasoning in visual generation. Building upon the Generation Chain-of-Thought approach, GoT-R1 enables models to autonomously discover effective reasoning strategies beyond predefined templates through carefully designed reinforcement learning. To achieve this, we propose a dual-stage multi-dimensional reward framework that leverages MLLMs to evaluate both the reasoning process and final output, enabling effective supervision across the entire generation pipeline. The reward system assesses semantic alignment, spatial accuracy, and visual quality in a unified approach. Experimental results demonstrate significant improvements on T2I-CompBench benchmark, particularly in compositional tasks involving precise spatial relationships and attribute binding. GoT-R1 advances the state-of-the-art in image generation by successfully transferring sophisticated reasoning capabilities to the visual generation domain. To facilitate future research, we make our code and pretrained models publicly available at https://github.com/gogoduan/GoT-R1.", "github_manual": "", "github_regex": "https://github.com/gogoduan/GoT-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.14684", "title": "Mind the Gap: Bridging Thought Leap for Improved Chain-of-Thought Tuning", "authors": [ "Haolei Xu", "Yuchen Yan", "Yongliang Shen", "Wenqi Zhang", "Guiyang Hou", "Shengpei Jiang", "Kaitao Song", "Weiming Lu", "Jun Xiao", "Yueting Zhuang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have achieved remarkable progress on mathematical tasks through Chain-of-Thought (CoT) reasoning. However, existing mathematical CoT datasets often suffer from Thought Leaps due to experts omitting intermediate steps, which negatively impacts model learning and generalization. We propose the CoT Thought Leap Bridge Task, which aims to automatically detect leaps and generate missing intermediate reasoning steps to restore the completeness and coherence of CoT. To facilitate this, we constructed a specialized training dataset called ScaleQM+, based on the structured ScaleQuestMath dataset, and trained CoT-Bridge to bridge thought leaps. Through comprehensive experiments on mathematical reasoning benchmarks, we demonstrate that models fine-tuned on bridged datasets consistently outperform those trained on original datasets, with improvements of up to +5.87% on NuminaMath. Our approach effectively enhances distilled data (+3.02%) and provides better starting points for reinforcement learning (+3.1%), functioning as a plug-and-play module compatible with existing optimization techniques. Furthermore, CoT-Bridge demonstrate improved generalization to out-of-domain logical reasoning tasks, confirming that enhancing reasoning completeness yields broadly applicable benefits.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.14604", "title": "Let LLMs Break Free from Overthinking via Self-Braking Tuning", "authors": [ "Haoran Zhao", "Yuchen Yan", "Yongliang Shen", "Haolei Xu", "Wenqi Zhang", "Kaitao Song", "Jian Shao", "Weiming Lu", "Jun Xiao", "Yueting Zhuang" ], "github": "https://github.com/ZJU-REAL/Self-Braking-Tuning", "project_page": "", "abstract": "Large reasoning models (LRMs), such as OpenAI o1 and DeepSeek-R1, have significantly enhanced their reasoning capabilities by generating longer chains of thought, demonstrating outstanding performance across a variety of tasks. However, this performance gain comes at the cost of a substantial increase in redundant reasoning during the generation process, leading to high computational overhead and exacerbating the issue of overthinking. Although numerous existing approaches aim to address the problem of overthinking, they often rely on external interventions. In this paper, we propose a novel framework, Self-Braking Tuning (SBT), which tackles overthinking from the perspective of allowing the model to regulate its own reasoning process, thus eliminating the reliance on external control mechanisms. We construct a set of overthinking identification metrics based on standard answers and design a systematic method to detect redundant reasoning. This method accurately identifies unnecessary steps within the reasoning trajectory and generates training signals for learning self-regulation behaviors. Building on this foundation, we develop a complete strategy for constructing data with adaptive reasoning lengths and introduce an innovative braking prompt mechanism that enables the model to naturally learn when to terminate reasoning at an appropriate point. Experiments across mathematical benchmarks (AIME, AMC, MATH500, GSM8K) demonstrate that our method reduces token consumption by up to 60% while maintaining comparable accuracy to unconstrained models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZJU-REAL/Self-Braking-Tuning", "github_api_added_by": "auto" }, { "date": "2025-05-23", "arxiv_id": "2505.16186", "title": "SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning", "authors": [ "Kaiwen Zhou", "Xuandong Zhao", "Gaowen Liu", "Jayanth Srinivasa", "Aosong Feng", "Dawn Song", "Xin Eric Wang" ], "github": "https://github.com/eric-ai-lab/SafeKey", "project_page": "", "abstract": "Large Reasoning Models (LRMs) introduce a new generation paradigm of explicitly reasoning before answering, leading to remarkable improvements in complex tasks. However, they pose great safety risks against harmful queries and adversarial attacks. While recent mainstream safety efforts on LRMs, supervised fine-tuning (SFT), improve safety performance, we find that SFT-aligned models struggle to generalize to unseen jailbreak prompts. After thorough investigation of LRMs' generation, we identify a safety aha moment that can activate safety reasoning and lead to a safe response. This aha moment typically appears in the `key sentence', which follows models' query understanding process and can indicate whether the model will proceed safely. Based on these insights, we propose SafeKey, including two complementary objectives to better activate the safety aha moment in the key sentence: (1) a Dual-Path Safety Head to enhance the safety signal in the model's internal representations before the key sentence, and (2) a Query-Mask Modeling objective to improve the models' attention on its query understanding, which has important safety hints. Experiments across multiple safety benchmarks demonstrate that our methods significantly improve safety generalization to a wide range of jailbreak attacks and out-of-distribution harmful prompts, lowering the average harmfulness rate by 9.6\\%, while maintaining general abilities. Our analysis reveals how SafeKey enhances safety by reshaping internal attention and improving the quality of hidden representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/eric-ai-lab/SafeKey", "github_api_added_by": "auto" }, { "date": "2025-05-23", "arxiv_id": "2505.15517", "title": "Robo2VLM: Visual Question Answering from Large-Scale In-the-Wild Robot Manipulation Datasets", "authors": [ "Kaiyuan Chen", "Shuangyu Xie", "Zehan Ma", "Ken Goldberg" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) acquire real-world knowledge and general reasoning ability through Internet-scale image-text corpora. They can augment robotic systems with scene understanding and task planning, and assist visuomotor policies that are trained on robot trajectory data. We explore the reverse paradigm - using rich, real, multi-modal robot trajectory data to enhance and evaluate VLMs. In this paper, we present Robo2VLM, a Visual Question Answering (VQA) dataset generation framework for VLMs. Given a human tele-operated robot trajectory, Robo2VLM derives ground-truth from non-visual and non-descriptive sensory modalities, such as end-effector pose, gripper aperture, and force sensing. Based on these modalities, it segments the robot trajectory into a sequence of manipulation phases. At each phase, Robo2VLM uses scene and interaction understanding to identify 3D properties of the robot, task goal, and the target object. The properties are used to generate representative VQA queries - images with textural multiple-choice questions - based on spatial, goal-conditioned, and interaction reasoning question templates. We curate Robo2VLM-1, a large-scale in-the-wild dataset with 684,710 questions covering 463 distinct scenes and 3,396 robotic manipulation tasks from 176k real robot trajectories. Results suggest that Robo2VLM-1 can benchmark and improve VLM capabilities in spatial and interaction reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16170", "title": "When Do LLMs Admit Their Mistakes? Understanding the Role of Model Belief in Retraction", "authors": [ "Yuqing Yang", "Robin Jia" ], "github": "https://github.com/ayyyq/llm-retraction", "project_page": "", "abstract": "Can large language models (LLMs) admit their mistakes when they should know better? In this work, we define the behavior of acknowledging errors in previously generated answers as \"retraction\" and aim to understand when and why LLMs choose to retract. We first construct model-specific datasets to evaluate whether a model will retract an incorrect answer that contradicts its own parametric knowledge. While LLMs are capable of retraction, they do so only infrequently. We demonstrate that retraction is closely tied to previously identified indicators of models' internal belief: models fail to retract wrong answers that they \"believe\" to be factually correct. Steering experiments further demonstrate that internal belief causally influences model retraction. In particular, when the model does not believe its answer, this not only encourages the model to attempt to verify the answer, but also alters attention behavior during self-verification. Finally, we demonstrate that simple supervised fine-tuning significantly improves retraction performance by helping the model learn more accurate internal beliefs. Code and datasets are available on https://github.com/ayyyq/llm-retraction.", "github_manual": "", "github_regex": "https://github.com/ayyyq/llm-retraction", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.15865", "title": "How Do Large Vision-Language Models See Text in Image? Unveiling the Distinctive Role of OCR Heads", "authors": [ "Ingeol Baek", "Hwan Chang", "Sunghyun Ryu", "Hwanhee Lee" ], "github": "", "project_page": "", "abstract": "Despite significant advancements in Large Vision Language Models (LVLMs), a gap remains, particularly regarding their interpretability and how they locate and interpret textual information within images. In this paper, we explore various LVLMs to identify the specific heads responsible for recognizing text from images, which we term the Optical Character Recognition Head (OCR Head). Our findings regarding these heads are as follows: (1) Less Sparse: Unlike previous retrieval heads, a large number of heads are activated to extract textual information from images. (2) Qualitatively Distinct: OCR heads possess properties that differ significantly from general retrieval heads, exhibiting low similarity in their characteristics. (3) Statically Activated: The frequency of activation for these heads closely aligns with their OCR scores. We validate our findings in downstream tasks by applying Chain-of-Thought (CoT) to both OCR and conventional retrieval heads and by masking these heads. We also demonstrate that redistributing sink-token values within the OCR heads improves performance. These insights provide a deeper understanding of the internal mechanisms LVLMs employ in processing embedded textual information in images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16944", "title": "AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios", "authors": [ "Yunjia Qi", "Hao Peng", "Xiaozhi Wang", "Amy Xin", "Youfeng Liu", "Bin Xu", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/THU-KEG/AgentIF", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated advanced capabilities in real-world agentic applications. Growing research efforts aim to develop LLM-based agents to address practical demands, introducing a new challenge: agentic scenarios often involve lengthy instructions with complex constraints, such as extended system prompts and detailed tool specifications. While adherence to such instructions is crucial for agentic applications, whether LLMs can reliably follow them remains underexplored. In this paper, we introduce AgentIF, the first benchmark for systematically evaluating LLM instruction following ability in agentic scenarios. AgentIF features three key characteristics: (1) Realistic, constructed from 50 real-world agentic applications. (2) Long, averaging 1,723 words with a maximum of 15,630 words. (3) Complex, averaging 11.9 constraints per instruction, covering diverse constraint types, such as tool specifications and condition constraints. To construct AgentIF, we collect 707 human-annotated instructions across 50 agentic tasks from industrial application agents and open-source agentic systems. For each instruction, we annotate the associated constraints and corresponding evaluation metrics, including code-based evaluation, LLM-based evaluation, and hybrid code-LLM evaluation. We use AgentIF to systematically evaluate existing advanced LLMs. We observe that current models generally perform poorly, especially in handling complex constraint structures and tool specifications. We further conduct error analysis and analytical experiments on instruction length and meta constraints, providing some findings about the failure modes of existing LLMs. We have released the code and data to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THU-KEG/AgentIF", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16048", "title": "SPhyR: Spatial-Physical Reasoning Benchmark on Material Distribution", "authors": [ "Philipp D. Siedler" ], "github": "https://github.com/philippds/SPhyR", "project_page": "", "abstract": "We introduce a novel dataset designed to benchmark the physical and spatial reasoning capabilities of Large Language Models (LLM) based on topology optimization, a method for computing optimal material distributions within a design space under prescribed loads and supports. In this dataset, LLMs are provided with conditions such as 2D boundary, applied forces and supports, and must reason about the resulting optimal material distribution. The dataset includes a variety of tasks, ranging from filling in masked regions within partial structures to predicting complete material distributions. Solving these tasks requires understanding the flow of forces and the required material distribution under given constraints, without access to simulation tools or explicit physical models, challenging models to reason about structural stability and spatial organization. Our dataset targets the evaluation of spatial and physical reasoning abilities in 2D settings, offering a complementary perspective to traditional language and logic benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/philippds/SPhyR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16925", "title": "Risk-Averse Reinforcement Learning with Itakura-Saito Loss", "authors": [ "Igor Udovichenko", "Olivier Croissant", "Anita Toleutaeva", "Evgeny Burnaev", "Alexander Korotin" ], "github": "", "project_page": "", "abstract": "Risk-averse reinforcement learning finds application in various high-stakes fields. Unlike classical reinforcement learning, which aims to maximize expected returns, risk-averse agents choose policies that minimize risk, occasionally sacrificing expected value. These preferences can be framed through utility theory. We focus on the specific case of the exponential utility function, where we can derive the Bellman equations and employ various reinforcement learning algorithms with few modifications. However, these methods suffer from numerical instability due to the need for exponent computation throughout the process. To address this, we introduce a numerically stable and mathematically sound loss function based on the Itakura-Saito divergence for learning state-value and action-value functions. We evaluate our proposed loss function against established alternatives, both theoretically and empirically. In the experimental section, we explore multiple financial scenarios, some with known analytical solutions, and show that our loss function outperforms the alternatives.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16192", "title": "VLM-R^3: Region Recognition, Reasoning, and Refinement for Enhanced Multimodal Chain-of-Thought", "authors": [ "Chaoya Jiang", "Yongrui Heng", "Wei Ye", "Han Yang", "Haiyang Xu", "Ming Yan", "Ji Zhang", "Fei Huang", "Shikun Zhang" ], "github": "", "project_page": "", "abstract": "Recently, reasoning-based MLLMs have achieved a degree of success in generating long-form textual reasoning chains. However, they still struggle with complex tasks that necessitate dynamic and iterative focusing on and revisiting of visual regions to achieve precise grounding of textual reasoning in visual evidence. We introduce VLM-R^3 (Visual Language Model with Region Recognition and Reasoning), a framework that equips an MLLM with the ability to (i) decide when additional visual evidence is needed, (ii) determine where to ground within the image, and (iii) seamlessly weave the relevant sub-image content back into an interleaved chain-of-thought. The core of our method is Region-Conditioned Reinforcement Policy Optimization (R-GRPO), a training paradigm that rewards the model for selecting informative regions, formulating appropriate transformations (e.g.\\ crop, zoom), and integrating the resulting visual context into subsequent reasoning steps. To bootstrap this policy, we compile a modest but carefully curated Visuo-Lingual Interleaved Rationale (VLIR) corpus that provides step-level supervision on region selection and textual justification. Extensive experiments on MathVista, ScienceQA, and other benchmarks show that VLM-R^3 sets a new state of the art in zero-shot and few-shot settings, with the largest gains appearing on questions demanding subtle spatial reasoning or fine-grained visual cue extraction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16088", "title": "Date Fragments: A Hidden Bottleneck of Tokenization for Temporal Reasoning", "authors": [ "Gagan Bhatia", "Maxime Peyrard", "Wei Zhao" ], "github": "https://github.com/gagan3012/date-fragments", "project_page": "", "abstract": "Modern BPE tokenizers often split calendar dates into meaningless fragments, e.g., 20250312 rightarrow 202, 503, 12, inflating token counts and obscuring the inherent structure needed for robust temporal reasoning. In this work, we (1) introduce a simple yet interpretable metric, termed date fragmentation ratio, that measures how faithfully a tokenizer preserves multi-digit date components; (2) release DateAugBench, a suite of 6500 examples spanning three temporal reasoning tasks: context-based date resolution, format-invariance puzzles, and date arithmetic across historical, contemporary, and future regimes; and (3) through layer-wise probing and causal attention-hop analyses, uncover an emergent date-abstraction mechanism whereby large language models stitch together the fragments of month, day, and year components for temporal reasoning. Our experiments show that excessive fragmentation correlates with accuracy drops of up to 10 points on uncommon dates like historical and futuristic dates. Further, we find that the larger the model, the faster the emergent date abstraction that heals date fragments is accomplished. Lastly, we observe a reasoning path that LLMs follow to assemble date fragments, typically differing from human interpretation (year rightarrow month rightarrow day).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gagan3012/date-fragments", "github_api_added_by": "auto" }, { "date": "2025-05-23", "arxiv_id": "2505.14395", "title": "MUG-Eval: A Proxy Evaluation Framework for Multilingual Generation Capabilities in Any Language", "authors": [ "Seyoung Song", "Seogyeong Jeong", "Eunsu Kim", "Jiho Jin", "Dongkwan Kim", "Jay Shin", "Alice Oh" ], "github": "https://github.com/seyoungsong/mugeval", "project_page": "", "abstract": "Evaluating text generation capabilities of large language models (LLMs) is challenging, particularly for low-resource languages where methods for direct assessment are scarce. We propose MUG-Eval, a novel framework that evaluates LLMs' multilingual generation capabilities by transforming existing benchmarks into conversational tasks and measuring the LLMs' accuracies on those tasks. We specifically designed these conversational tasks to require effective communication in the target language. Then, we simply use task success rate as a proxy of successful conversation generation. Our approach offers two key advantages: it is independent of language-specific NLP tools or annotated datasets, which are limited for most languages, and it does not rely on LLMs-as-judges, whose evaluation quality degrades outside a few high-resource languages. We evaluate 8 LLMs across 30 languages spanning high, mid, and low-resource categories, and we find that MUG-Eval correlates strongly with established benchmarks (r > 0.75) while enabling standardized comparisons across languages and models. Our framework provides a robust and resource-efficient solution for evaluating multilingual generation that can be extended to thousands of languages.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/seyoungsong/mugeval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.17019", "title": "Let Androids Dream of Electric Sheep: A Human-like Image Implication Understanding and Reasoning Framework", "authors": [ "Chenhao Zhang", "Yazhe Niu" ], "github": "https://github.com/MING-ZCH/Let-Androids-Dream-of-Electric-Sheep", "project_page": "", "abstract": "Metaphorical comprehension in images remains a critical challenge for AI systems, as existing models struggle to grasp the nuanced cultural, emotional, and contextual implications embedded in visual content. While multimodal large language models (MLLMs) excel in basic Visual Question Answer (VQA) tasks, they struggle with a fundamental limitation on image implication tasks: contextual gaps that obscure the relationships between different visual elements and their abstract meanings. Inspired by the human cognitive process, we propose Let Androids Dream (LAD), a novel framework for image implication understanding and reasoning. LAD addresses contextual missing through the three-stage framework: (1) Perception: converting visual information into rich and multi-level textual representations, (2) Search: iteratively searching and integrating cross-domain knowledge to resolve ambiguity, and (3) Reasoning: generating context-alignment image implication via explicit reasoning. Our framework with the lightweight GPT-4o-mini model achieves SOTA performance compared to 15+ MLLMs on English image implication benchmark and a huge improvement on Chinese benchmark, performing comparable with the GPT-4o model on Multiple-Choice Question (MCQ) and outperforms 36.7% on Open-Style Question (OSQ). Additionally, our work provides new insights into how AI can more effectively interpret image implications, advancing the field of vision-language reasoning and human-AI interaction. Our project is publicly available at https://github.com/MING-ZCH/Let-Androids-Dream-of-Electric-Sheep.", "github_manual": "", "github_regex": "https://github.com/MING-ZCH/Let-Androids-Dream-of-Electric-Sheep", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16612", "title": "Steering Large Language Models for Machine Translation Personalization", "authors": [ "Daniel Scalena", "Gabriele Sarti", "Arianna Bisazza", "Elisabetta Fersini", "Malvina Nissim" ], "github": "", "project_page": "", "abstract": "High-quality machine translation systems based on large language models (LLMs) have simplified the production of personalized translations reflecting specific stylistic constraints. However, these systems still struggle in settings where stylistic requirements are less explicit and might be harder to convey via prompting. We explore various strategies for personalizing LLM-generated translations in low-resource settings, focusing on the challenging literary translation domain. We explore prompting strategies and inference-time interventions for steering model generations towards a personalized style, and propose a contrastive framework exploiting latent concepts extracted from sparse autoencoders to identify salient personalization properties. Our results show that steering achieves strong personalization while preserving translation quality. We further examine the impact of steering on LLM representations, finding model layers with a relevant impact for personalization are impacted similarly by multi-shot prompting and our steering method, suggesting similar mechanism at play.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.17015", "title": "Multi-SpatialMLLM: Multi-Frame Spatial Understanding with Multi-Modal Large Language Models", "authors": [ "Runsen Xu", "Weiyao Wang", "Hao Tang", "Xingyu Chen", "Xiaodong Wang", "Fu-Jen Chu", "Dahua Lin", "Matt Feiszli", "Kevin J. Liang" ], "github": "", "project_page": "", "abstract": "Multi-modal large language models (MLLMs) have rapidly advanced in visual tasks, yet their spatial understanding remains limited to single images, leaving them ill-suited for robotics and other real-world applications that require multi-frame reasoning. In this paper, we propose a framework to equip MLLMs with robust multi-frame spatial understanding by integrating depth perception, visual correspondence, and dynamic perception. Central to our approach is the MultiSPA dataset, a novel, large-scale collection of more than 27 million samples spanning diverse 3D and 4D scenes. Alongside MultiSPA, we introduce a comprehensive benchmark that tests a wide spectrum of spatial tasks under uniform metrics. Our resulting model, Multi-SpatialMLLM, achieves significant gains over baselines and proprietary systems, demonstrating scalable, generalizable multi-frame reasoning. We further observe multi-task benefits and early indications of emergent capabilities in challenging scenarios, and showcase how our model can serve as a multi-frame reward annotator for robotics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.14462", "title": "RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding", "authors": [ "Jiaang Li", "Yifei Yuan", "Wenyan Li", "Mohammad Aliannejadi", "Daniel Hershcovich", "Anders S\u00f8gaard", "Ivan Vuli\u0107", "Wenxuan Zhang", "Paul Pu Liang", "Yang Deng", "Serge Belongie" ], "github": "", "project_page": "", "abstract": "As vision-language models (VLMs) become increasingly integrated into daily life, the need for accurate visual culture understanding is becoming critical. Yet, these models frequently fall short in interpreting cultural nuances effectively. Prior work has demonstrated the effectiveness of retrieval-augmented generation (RAG) in enhancing cultural understanding in text-only settings, while its application in multimodal scenarios remains underexplored. To bridge this gap, we introduce RAVENEA (Retrieval-Augmented Visual culturE uNdErstAnding), a new benchmark designed to advance visual culture understanding through retrieval, focusing on two tasks: culture-focused visual question answering (cVQA) and culture-informed image captioning (cIC). RAVENEA extends existing datasets by integrating over 10,000 Wikipedia documents curated and ranked by human annotators. With RAVENEA, we train and evaluate seven multimodal retrievers for each image query, and measure the downstream impact of retrieval-augmented inputs across fourteen state-of-the-art VLMs. Our results show that lightweight VLMs, when augmented with culture-aware retrieval, outperform their non-augmented counterparts (by at least 3.2% absolute on cVQA and 6.2% absolute on cIC). This highlights the value of retrieval-augmented methods and culturally inclusive benchmarks for multimodal understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.15960", "title": "Training Step-Level Reasoning Verifiers with Formal Verification Tools", "authors": [ "Ryo Kamoi", "Yusen Zhang", "Nan Zhang", "Sarkar Snigdha Sarathi Das", "Rui Zhang" ], "github": "https://github.com/psunlpgroup/FoVer", "project_page": "", "abstract": "Process Reward Models (PRMs), which provide step-by-step feedback on the reasoning generated by Large Language Models (LLMs), are receiving increasing attention. However, two key research gaps remain: collecting accurate step-level error labels for training typically requires costly human annotation, and existing PRMs are limited to math reasoning problems. In response to these gaps, this paper aims to address the challenges of automatic dataset creation and the generalization of PRMs to diverse reasoning tasks. To achieve this goal, we propose FoVer, an approach for training PRMs on step-level error labels automatically annotated by formal verification tools, such as Z3 for formal logic and Isabelle for theorem proof, which provide automatic and accurate verification for symbolic tasks. Using this approach, we synthesize a training dataset with error labels on LLM responses for formal logic and theorem proof tasks without human annotation. Although this data synthesis is feasible only for tasks compatible with formal verification, we observe that LLM-based PRMs trained on our dataset exhibit cross-task generalization, improving verification across diverse reasoning tasks. Specifically, PRMs trained with FoVer significantly outperform baseline PRMs based on the original LLMs and achieve competitive or superior results compared to state-of-the-art PRMs trained on labels annotated by humans or stronger models, as measured by step-level verification on ProcessBench and Best-of-K performance across 12 reasoning benchmarks, including MATH, AIME, ANLI, MMLU, and BBH. The datasets, models, and code are provided at https://github.com/psunlpgroup/FoVer.", "github_manual": "", "github_regex": "https://github.com/psunlpgroup/FoVer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.13237", "title": "SAKURA: On the Multi-hop Reasoning of Large Audio-Language Models Based on Speech and Audio Information", "authors": [ "Chih-Kai Yang", "Neo Ho", "Yen-Ting Piao", "Hung-yi Lee" ], "github": "https://github.com/YuanGongND/ltu", "project_page": "", "abstract": "Large audio-language models (LALMs) extend the large language models with multimodal understanding in speech, audio, etc. While their performances on speech and audio-processing tasks are extensively studied, their reasoning abilities remain underexplored. Particularly, their multi-hop reasoning, the ability to recall and integrate multiple facts, lacks systematic evaluation. Existing benchmarks focus on general speech and audio-processing tasks, conversational abilities, and fairness but overlook this aspect. To bridge this gap, we introduce SAKURA, a benchmark assessing LALMs' multi-hop reasoning based on speech and audio information. Results show that LALMs struggle to integrate speech/audio representations for multi-hop reasoning, even when they extract the relevant information correctly, highlighting a fundamental challenge in multimodal reasoning. Our findings expose a critical limitation in LALMs, offering insights and resources for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YuanGongND/ltu", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16421", "title": "WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning", "authors": [ "Zhepei Wei", "Wenlin Yao", "Yao Liu", "Weizhi Zhang", "Qin Lu", "Liang Qiu", "Changlong Yu", "Puyang Xu", "Chao Zhang", "Bing Yin", "Hyokun Yun", "Lihong Li" ], "github": "https://github.com/weizhepei/webagent-r1", "project_page": "", "abstract": "While reinforcement learning (RL) has demonstrated remarkable success in enhancing large language models (LLMs), it has primarily focused on single-turn tasks such as solving math problems. Training effective web agents for multi-turn interactions remains challenging due to the complexity of long-horizon decision-making across dynamic web interfaces. In this work, we present WebAgent-R1, a simple yet effective end-to-end multi-turn RL framework for training web agents. It learns directly from online interactions with web environments by asynchronously generating diverse trajectories, entirely guided by binary rewards depending on task success. Experiments on the WebArena-Lite benchmark demonstrate the effectiveness of WebAgent-R1, boosting the task success rate of Qwen-2.5-3B from 6.1% to 33.9% and Llama-3.1-8B from 8.5% to 44.8%, significantly outperforming existing state-of-the-art methods and strong proprietary models such as OpenAI o3. In-depth analyses reveal the effectiveness of the thinking-based prompting strategy and test-time scaling through increased interactions for web tasks. We further investigate different RL initialization policies by introducing two variants, namely WebAgent-R1-Zero and WebAgent-R1-CoT, which highlight the importance of the warm-up training stage (i.e., behavior cloning) and provide insights on incorporating long chain-of-thought (CoT) reasoning in web agents.", "github_manual": "https://github.com/weizhepei/webagent-r1", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16967", "title": "Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval", "authors": [ "Nandan Thakur", "Crystina Zhang", "Xueguang Ma", "Jimmy Lin" ], "github": "", "project_page": "", "abstract": "Training robust retrieval and reranker models typically relies on large-scale retrieval datasets; for example, the BGE collection contains 1.6 million query-passage pairs sourced from various data sources. However, we find that certain datasets can negatively impact model effectiveness -- pruning 8 out of 15 datasets from the BGE collection reduces the training set size by 2.35times and increases nDCG@10 on BEIR by 1.0 point. This motivates a deeper examination of training data quality, with a particular focus on \"false negatives\", where relevant passages are incorrectly labeled as irrelevant. We propose a simple, cost-effective approach using cascading LLM prompts to identify and relabel hard negatives. Experimental results show that relabeling false negatives with true positives improves both E5 (base) and Qwen2.5-7B retrieval models by 0.7-1.4 nDCG@10 on BEIR and by 1.7-1.8 nDCG@10 on zero-shot AIR-Bench evaluation. Similar gains are observed for rerankers fine-tuned on the relabeled data, such as Qwen2.5-3B on BEIR. The reliability of the cascading design is further supported by human annotation results, where we find judgment by GPT-4o shows much higher agreement with humans than GPT-4o-mini.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.16265", "title": "Think-RM: Enabling Long-Horizon Reasoning in Generative Reward Models", "authors": [ "Ilgee Hong", "Changlong Yu", "Liang Qiu", "Weixiang Yan", "Zhenghao Xu", "Haoming Jiang", "Qingru Zhang", "Qin Lu", "Xin Liu", "Chao Zhang", "Tuo Zhao" ], "github": "https://github.com/IlgeeHong/Think-RM", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) has become a powerful post-training paradigm for aligning large language models with human preferences. A core challenge in RLHF is constructing accurate reward signals, where the conventional Bradley-Terry reward models (BT RMs) often suffer from sensitivity to data size and coverage, as well as vulnerability to reward hacking. Generative reward models (GenRMs) offer a more robust alternative by generating chain-of-thought (CoT) rationales followed by a final reward. However, existing GenRMs rely on shallow, vertically scaled reasoning, limiting their capacity to handle nuanced or complex (e.g., reasoning-intensive) tasks. Moreover, their pairwise preference outputs are incompatible with standard RLHF algorithms that require pointwise reward signals. In this work, we introduce Think-RM, a training framework that enables long-horizon reasoning in GenRMs by modeling an internal thinking process. Rather than producing structured, externally provided rationales, Think-RM generates flexible, self-guided reasoning traces that support advanced capabilities such as self-reflection, hypothetical reasoning, and divergent reasoning. To elicit these reasoning abilities, we first warm-up the models by supervised fine-tuning (SFT) over long CoT data. We then further improve the model's long-horizon abilities by rule-based reinforcement learning (RL). In addition, we propose a novel pairwise RLHF pipeline that directly optimizes policies using pairwise preference rewards, eliminating the need for pointwise reward conversion and enabling more effective use of Think-RM outputs. Experiments show that Think-RM achieves state-of-the-art results on RM-Bench, outperforming both BT RM and vertically scaled GenRM by 8%. When combined with our pairwise RLHF pipeline, it demonstrates superior end-policy performance compared to traditional approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/IlgeeHong/Think-RM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.13344", "title": "RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE Optimization on Diffusion Transformers", "authors": [ "Ahmet Berke Gokmen", "Yigit Ekin", "Bahri Batuhan Bilecen", "Aysegul Dundar" ], "github": "", "project_page": "", "abstract": "We propose RoPECraft, a training-free video motion transfer method for diffusion transformers that operates solely by modifying their rotary positional embeddings (RoPE). We first extract dense optical flow from a reference video, and utilize the resulting motion offsets to warp the complex-exponential tensors of RoPE, effectively encoding motion into the generation process. These embeddings are then further optimized during denoising time steps via trajectory alignment between the predicted and target velocities using a flow-matching objective. To keep the output faithful to the text prompt and prevent duplicate generations, we incorporate a regularization term based on the phase components of the reference video's Fourier transform, projecting the phase angles onto a smooth manifold to suppress high-frequency artifacts. Experiments on benchmarks reveal that RoPECraft outperforms all recently published methods, both qualitatively and quantitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-23", "arxiv_id": "2505.15263", "title": "gen2seg: Generative Models Enable Generalizable Instance Segmentation", "authors": [ "Om Khangaonkar", "Hamed Pirsiavash" ], "github": "https://github.com/UCDvision/gen2seg", "project_page": "", "abstract": "By pretraining to synthesize coherent images from perturbed inputs, generative models inherently learn to understand object boundaries and scene compositions. How can we repurpose these generative representations for general-purpose perceptual organization? We finetune Stable Diffusion and MAE (encoder+decoder) for category-agnostic instance segmentation using our instance coloring loss exclusively on a narrow set of object types (indoor furnishings and cars). Surprisingly, our models exhibit strong zero-shot generalization, accurately segmenting objects of types and styles unseen in finetuning (and in many cases, MAE's ImageNet-1K pretraining too). Our best-performing models closely approach the heavily supervised SAM when evaluated on unseen object types and styles, and outperform it when segmenting fine structures and ambiguous boundaries. In contrast, existing promptable segmentation architectures or discriminatively pretrained models fail to generalize. This suggests that generative models learn an inherent grouping mechanism that transfers across categories and domains, even without internet-scale pretraining. Code, pretrained models, and demos are available on our website.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UCDvision/gen2seg", "github_api_added_by": "auto" }, { "date": "2025-05-26", "arxiv_id": "2505.17225", "title": "Reasoning Model is Stubborn: Diagnosing Instruction Overriding in Reasoning Models", "authors": [ "Doohyuk Jang", "Yoonjeon Kim", "Chanjae Park", "Hyun Ryu", "Eunho Yang" ], "github": "", "project_page": "", "abstract": "Large language models have demonstrated remarkable proficiency in long and complex reasoning tasks. However, they frequently exhibit a problematic reliance on familiar reasoning patterns, a phenomenon we term reasoning rigidity. Despite explicit instructions from users, these models often override clearly stated conditions and default to habitual reasoning trajectories, leading to incorrect conclusions. This behavior presents significant challenges, particularly in domains such as mathematics and logic puzzle, where precise adherence to specified constraints is critical. To systematically investigate reasoning rigidity, a behavior largely unexplored in prior work, we introduce a expert-curated diagnostic set, . Our dataset includes specially modified variants of existing mathematical benchmarks, namely AIME and MATH500, as well as well-known puzzles deliberately redesigned to require deviation from familiar reasoning strategies. Using this dataset, we identify recurring contamination patterns that occur when models default to ingrained reasoning. Specifically, we categorize this contamination into three distinctive modes: (i) Interpretation Overload, (ii) Input Distrust, and (iii) Partial Instruction Attention, each causing models to ignore or distort provided instructions. We publicly release our diagnostic set to facilitate future research on mitigating reasoning rigidity in language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17612", "title": "Distilling LLM Agent into Small Models with Retrieval and Code Tools", "authors": [ "Minki Kang", "Jongwon Jeong", "Seanie Lee", "Jaewoong Cho", "Sung Ju Hwang" ], "github": "https://github.com/Nardien/agent-distillation", "project_page": "", "abstract": "Large language models (LLMs) excel at complex reasoning tasks but remain computationally expensive, limiting their practical deployment. To address this, recent works have focused on distilling reasoning capabilities into smaller language models (sLMs) using chain-of-thought (CoT) traces from teacher LLMs. However, this approach struggles in scenarios requiring rare factual knowledge or precise computation, where sLMs often hallucinate due to limited capability. In this work, we propose Agent Distillation, a framework for transferring not only reasoning capability but full task-solving behavior from LLM-based agents into sLMs with retrieval and code tools. We improve agent distillation along two complementary axes: (1) we introduce a prompting method called first-thought prefix to enhance the quality of teacher-generated trajectories; and (2) we propose a self-consistent action generation for improving test-time robustness of small agents. We evaluate our method on eight reasoning tasks across factual and mathematical domains, covering both in-domain and out-of-domain generalization. Our results show that sLMs as small as 0.5B, 1.5B, 3B parameters can achieve performance competitive with next-tier larger 1.5B, 3B, 7B models fine-tuned using CoT distillation, demonstrating the potential of agent distillation for building practical, tool-using small agents. Our code is available at https://github.com/Nardien/agent-distillation.", "github_manual": "", "github_regex": "https://github.com/Nardien/agent-distillation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17558", "title": "Teaching with Lies: Curriculum DPO on Synthetic Negatives for Hallucination Detection", "authors": [ "Shrey Pandit", "Ashwin Vinod", "Liu Leqi", "Ying Ding" ], "github": "", "project_page": "", "abstract": "Aligning large language models (LLMs) to accurately detect hallucinations remains a significant challenge due to the sophisticated nature of hallucinated text. Recognizing that hallucinated samples typically exhibit higher deceptive quality than traditional negative samples, we use these carefully engineered hallucinations as negative examples in the DPO alignment procedure. Our method incorporates a curriculum learning strategy, gradually transitioning the training from easier samples, identified based on the greatest reduction in probability scores from independent fact checking models, to progressively harder ones. This structured difficulty scaling ensures stable and incremental learning. Experimental evaluation demonstrates that our HaluCheck models, trained with curriculum DPO approach and high quality negative samples, significantly improves model performance across various metrics, achieving improvements of upto 24% on difficult benchmarks like MedHallu and HaluEval. Additionally, HaluCheck models demonstrate robustness in zero-shot settings, significantly outperforming larger state-of-the-art models across various benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.16483", "title": "Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning", "authors": [ "Shuzheng Si", "Haozhe Zhao", "Cheng Gao", "Yuzhuo Bai", "Zhitong Wang", "Bofei Gao", "Kangyang Luo", "Wenhao Li", "Yufei Huang", "Gang Chen", "Fanchao Qi", "Minjia Zhang", "Baobao Chang", "Maosong Sun" ], "github": "https://github.com/S1s-Z/CANOE", "project_page": "", "abstract": "Teaching large language models (LLMs) to be faithful in the provided context is crucial for building reliable information-seeking systems. Therefore, we propose a systematic framework, CANOE, to improve the faithfulness of LLMs in both short-form and long-form generation tasks without human annotations. Specifically, we first synthesize short-form question-answering (QA) data with four diverse tasks to construct high-quality and easily verifiable training data without human annotation. Also, we propose Dual-GRPO, a rule-based reinforcement learning method that includes three tailored rule-based rewards derived from synthesized short-form QA data, while simultaneously optimizing both short-form and long-form response generation. Notably, Dual-GRPO eliminates the need to manually label preference data to train reward models and avoids over-optimizing short-form generation when relying only on the synthesized short-form QA data. Experimental results show that CANOE greatly improves the faithfulness of LLMs across 11 different downstream tasks, even outperforming the most advanced LLMs, e.g., GPT-4o and OpenAI o1.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/S1s-Z/CANOE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17941", "title": "VeriThinker: Learning to Verify Makes Reasoning Model Efficient", "authors": [ "Zigeng Chen", "Xinyin Ma", "Gongfan Fang", "Ruonan Yu", "Xinchao Wang" ], "github": "https://github.com/czg1225/VeriThinker", "project_page": "", "abstract": "Large Reasoning Models (LRMs) excel at complex tasks using Chain-of-Thought (CoT) reasoning. However, their tendency to overthinking leads to unnecessarily lengthy reasoning chains, dramatically increasing inference costs. To mitigate this issue, we introduce VeriThinker, a novel approach for CoT compression. Unlike conventional methods that fine-tune LRMs directly on the original reasoning task using synthetic concise CoT data, we innovatively fine-tune the model solely through an auxiliary verification task. By training LRMs to accurately verify the correctness of CoT solutions, the LRMs inherently become more discerning about the necessity of subsequent self-reflection steps, thereby effectively suppressing overthinking. Extensive experiments validate that VeriThinker substantially reduces reasoning chain lengths while maintaining or even slightly improving accuracy. When applied to DeepSeek-R1-Distill-Qwen-7B, our approach reduces reasoning tokens on MATH500 from 3790 to 2125 while improving accuracy by 0.8% (94.0% to 94.8%), and on AIME25, tokens decrease from 14321 to 10287 with a 2.1% accuracy gain (38.7% to 40.8%). Additionally, our experiments demonstrate that VeriThinker can also be zero-shot generalized to speculative reasoning. Code is available at https://github.com/czg1225/VeriThinker", "github_manual": "", "github_regex": "https://github.com/czg1225/VeriThinker", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.15389", "title": "Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark Study", "authors": [ "DongGeon Lee", "Joonwon Jang", "Jihae Jeong", "Hwanjo Yu" ], "github": "https://github.com/oneonlee/Meme-Safety-Bench", "project_page": "", "abstract": "Rapid deployment of vision-language models (VLMs) magnifies safety risks, yet most evaluations rely on artificial images. This study asks: How safe are current VLMs when confronted with meme images that ordinary users share? To investigate this question, we introduce MemeSafetyBench, a 50,430-instance benchmark pairing real meme images with both harmful and benign instructions. Using a comprehensive safety taxonomy and LLM-based instruction generation, we assess multiple VLMs across single and multi-turn interactions. We investigate how real-world memes influence harmful outputs, the mitigating effects of conversational context, and the relationship between model scale and safety metrics. Our findings demonstrate that VLMs show greater vulnerability to meme-based harmful prompts than to synthetic or typographic images. Memes significantly increase harmful responses and decrease refusals compared to text-only inputs. Though multi-turn interactions provide partial mitigation, elevated vulnerability persists. These results highlight the need for ecologically valid evaluations and stronger safety mechanisms.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/oneonlee/Meme-Safety-Bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.18129", "title": "One RL to See Them All: Visual Triple Unified Reinforcement Learning", "authors": [ "Yan Ma", "Linge Du", "Xuyang Shen", "Shaoxiang Chen", "Pengfei Li", "Qibing Ren", "Lizhuang Ma", "Yuchao Dai", "Pengfei Liu", "Junjie Yan" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has significantly advanced the reasoning capabilities of vision-language models (VLMs). However, the use of RL beyond reasoning tasks remains largely unexplored, especially for perceptionintensive tasks like object detection and grounding. We propose V-Triune, a Visual Triple Unified Reinforcement Learning system that enables VLMs to jointly learn visual reasoning and perception tasks within a single training pipeline. V-Triune comprises triple complementary components: Sample-Level Data Formatting (to unify diverse task inputs), Verifier-Level Reward Computation (to deliver custom rewards via specialized verifiers) , and Source-Level Metric Monitoring (to diagnose problems at the data-source level). We further introduce a novel Dynamic IoU reward, which provides adaptive, progressive, and definite feedback for perception tasks handled by V-Triune. Our approach is instantiated within off-the-shelf RL training framework using open-source 7B and 32B backbone models. The resulting model, dubbed Orsta (One RL to See Them All), demonstrates consistent improvements across both reasoning and perception tasks. This broad capability is significantly shaped by its training on a diverse dataset, constructed around four representative visual reasoning tasks (Math, Puzzle, Chart, and Science) and four visual perception tasks (Grounding, Detection, Counting, and OCR). Subsequently, Orsta achieves substantial gains on MEGA-Bench Core, with improvements ranging from +2.1 to an impressive +14.1 across its various 7B and 32B model variants, with performance benefits extending to a wide range of downstream tasks. These results highlight the effectiveness and scalability of our unified RL approach for VLMs. The V-Triune system, along with the Orsta models, is publicly available at https://github.com/MiniMax-AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17826", "title": "Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of Large Language Models", "authors": [ "Xuchen Pan", "Yanxi Chen", "Yushuo Chen", "Yuchang Sun", "Daoyuan Chen", "Wenhao Zhang", "Yuexiang Xie", "Yilun Huang", "Yilei Zhang", "Dawei Gao", "Yaliang Li", "Bolin Ding", "Jingren Zhou" ], "github": "https://github.com/HumanSignal/label-studio", "project_page": "", "abstract": "Trinity-RFT is a general-purpose, flexible and scalable framework designed for reinforcement fine-tuning (RFT) of large language models. It is built with a decoupled design, consisting of (1) an RFT-core that unifies and generalizes synchronous/asynchronous, on-policy/off-policy, and online/offline modes of RFT, (2) seamless integration for agent-environment interaction with high efficiency and robustness, and (3) systematic data pipelines optimized for RFT. Trinity-RFT can be easily adapted for diverse application scenarios, and serves as a unified platform for exploring advanced reinforcement learning paradigms. This technical report outlines the vision, features, design and implementations of Trinity-RFT, accompanied by extensive examples demonstrating the utility and user-friendliness of the proposed framework.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HumanSignal/label-studio", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17561", "title": "Model Already Knows the Best Noise: Bayesian Active Noise Selection via Attention in Video Diffusion Model", "authors": [ "Kwanyoung Kim", "Sanghyun Kim" ], "github": "", "project_page": "", "abstract": "The choice of initial noise significantly affects the quality and prompt alignment of video diffusion models, where different noise seeds for the same prompt can lead to drastically different generations. While recent methods rely on externally designed priors such as frequency filters or inter-frame smoothing, they often overlook internal model signals that indicate which noise seeds are inherently preferable. To address this, we propose ANSE (Active Noise Selection for Generation), a model-aware framework that selects high-quality noise seeds by quantifying attention-based uncertainty. At its core is BANSA (Bayesian Active Noise Selection via Attention), an acquisition function that measures entropy disagreement across multiple stochastic attention samples to estimate model confidence and consistency. For efficient inference-time deployment, we introduce a Bernoulli-masked approximation of BANSA that enables score estimation using a single diffusion step and a subset of attention layers. Experiments on CogVideoX-2B and 5B demonstrate that ANSE improves video quality and temporal coherence with only an 8% and 13% increase in inference time, respectively, providing a principled and generalizable approach to noise selection in video diffusion. See our project page: https://anse-project.github.io/anse-project/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17091", "title": "Large Language Models Implicitly Learn to See and Hear Just By Reading", "authors": [ "Prateek Verma", "Mert Pilanci" ], "github": "", "project_page": "", "abstract": "This paper presents a fascinating find: By training an auto-regressive LLM model on text tokens, the text model inherently develops internally an ability to understand images and audio, thereby developing the ability to see and hear just by reading. Popular audio and visual LLM models fine-tune text LLM models to give text output conditioned on images and audio embeddings. On the other hand, our architecture takes in patches of images, audio waveforms or tokens as input. It gives us the embeddings or category labels typical of a classification pipeline. We show the generality of text weights in aiding audio classification for datasets FSD-50K and GTZAN. Further, we show this working for image classification on CIFAR-10 and Fashion-MNIST, as well on image patches. This pushes the notion of text-LLMs learning powerful internal circuits that can be utilized by activating necessary connections for various applications rather than training models from scratch every single time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17508", "title": "On the Design of KL-Regularized Policy Gradient Algorithms for LLM Reasoning", "authors": [ "Yifan Zhang", "Yifeng Liu", "Huizhuo Yuan", "Yang Yuan", "Quanquan Gu", "Andrew C Yao" ], "github": "https://github.com/complex-reasoning/RPG", "project_page": "", "abstract": "Policy gradient algorithms have been successfully applied to enhance the reasoning capabilities of large language models (LLMs). Despite the widespread use of Kullback-Leibler (KL) regularization in policy gradient algorithms to stabilize training, the systematic exploration of how different KL divergence formulations can be estimated and integrated into surrogate loss functions for online reinforcement learning (RL) presents a nuanced and systematically explorable design space. In this paper, we propose regularized policy gradient (RPG), a systematic framework for deriving and analyzing KL-regularized policy gradient methods in the online RL setting. We derive policy gradients and corresponding surrogate loss functions for objectives regularized by both forward and reverse KL divergences, considering both normalized and unnormalized policy distributions. Furthermore, we present derivations for fully differentiable loss functions as well as REINFORCE-style gradient estimators, accommodating diverse algorithmic needs. We conduct extensive experiments on RL for LLM reasoning using these methods, showing improved or competitive results in terms of training stability and performance compared to strong baselines such as GRPO, REINFORCE++, and DAPO. The code is available at https://github.com/complex-reasoning/RPG.", "github_manual": "", "github_regex": "https://github.com/complex-reasoning/RPG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17417", "title": "Speechless: Speech Instruction Training Without Speech for Low Resource Languages", "authors": [ "Alan Dao", "Dinh Bach Vu", "Huy Hoang Ha", "Tuan Le Duc Anh", "Shreyas Gopal", "Yue Heng Yeo", "Warren Keng Hoong Low", "Eng Siong Chng", "Jia Qi Yip" ], "github": "", "project_page": "", "abstract": "The rapid growth of voice assistants powered by large language models (LLM) has highlighted a need for speech instruction data to train these systems. Despite the abundance of speech recognition data, there is a notable scarcity of speech instruction data, which is essential for fine-tuning models to understand and execute spoken commands. Generating high-quality synthetic speech requires a good text-to-speech (TTS) model, which may not be available to low resource languages. Our novel approach addresses this challenge by halting synthesis at the semantic representation level, bypassing the need for TTS. We achieve this by aligning synthetic semantic representations with the pre-trained Whisper encoder, enabling an LLM to be fine-tuned on text instructions while maintaining the ability to understand spoken instructions during inference. This simplified training process is a promising approach to building voice assistant for low-resource languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.16270", "title": "Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning", "authors": [ "Jiaru Zou", "Yikun Ban", "Zihao Li", "Yunzhe Qi", "Ruizhong Qiu", "Ling Yang", "Jingrui He" ], "github": "", "project_page": "", "abstract": "Large language models are typically adapted to downstream tasks through supervised fine-tuning on domain-specific data. While standard fine-tuning focuses on minimizing generation loss to optimize model parameters, we take a deeper step by retaining and leveraging the model's own learning signals, analogous to how human learners reflect on past mistakes to improve future performance. We first introduce the concept of Mistake Log to systematically track the model's learning behavior and recurring errors throughout fine-tuning. Treating the original transformer-based model as the Pilot, we correspondingly design a Copilot model to refine the Pilot's inference performance via logits rectification. We name the overall Pilot-Copilot framework the Transformer Copilot, which introduces (i) a novel Copilot model design, (ii) a joint training paradigm where the Copilot continuously learns from the evolving Mistake Log alongside the Pilot, and (iii) a fused inference paradigm where the Copilot rectifies the Pilot's logits for enhanced generation. We provide both theoretical and empirical analyses on our new learning framework. Experiments on 12 benchmarks spanning commonsense, arithmetic, and recommendation tasks demonstrate that Transformer Copilot consistently improves performance by up to 34.5%, while introducing marginal computational overhead to Pilot models and exhibiting strong scalability and transferability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.15692", "title": "Thought-Augmented Policy Optimization: Bridging External Guidance and Internal Capabilities", "authors": [ "Jinyang Wu", "Chonghua Liao", "Mingkuan Feng", "Shuai Zhang", "Zhengqi Wen", "Pengpeng Shao", "Huazhe Xu", "Jianhua Tao" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has emerged as an effective method for training reasoning models. However, existing RL approaches typically bias the model's output distribution toward reward-maximizing paths without introducing external knowledge. This limits their exploration capacity and results in a narrower reasoning capability boundary compared to base models. To address this limitation, we propose TAPO (Thought-Augmented Policy Optimization), a novel framework that augments RL by incorporating external high-level guidance (\"thought patterns\"). By adaptively integrating structured thoughts during training, TAPO effectively balances model-internal exploration and external guidance exploitation. Extensive experiments show that our approach significantly outperforms GRPO by 99% on AIME, 41% on AMC, and 17% on Minerva Math. Notably, these high-level thought patterns, abstracted from only 500 prior samples, generalize effectively across various tasks and models. This highlights TAPO's potential for broader applications across multiple tasks and domains. Our further analysis reveals that introducing external guidance produces powerful reasoning models with superior explainability of inference behavior and enhanced output readability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.16211", "title": "AudioTrust: Benchmarking the Multifaceted Trustworthiness of Audio Large Language Models", "authors": [ "Kai Li", "Can Shen", "Yile Liu", "Jirui Han", "Kelong Zheng", "Xuechao Zou", "Zhe Wang", "Xingjian Du", "Shun Zhang", "Hanjun Luo", "Yingbin Jin", "Xinxin Xing", "Ziyang Ma", "Yue Liu", "Xiaojun Jia", "Yifan Zhang", "Junfeng Fang", "Kun Wang", "Yibo Yan", "Haoyang Li", "Yiming Li", "Xiaobin Zhuang", "Yang Liu", "Haibo Hu", "Zhuo Chen", "Zhizheng Wu", "Xiaolin Hu", "Eng-Siong Chng", "XiaoFeng Wang", "Wenyuan Xu", "Wei Dong", "Xinfeng Li" ], "github": "https://github.com/JusperLee/AudioTrust", "project_page": "", "abstract": "The rapid advancement and expanding applications of Audio Large Language Models (ALLMs) demand a rigorous understanding of their trustworthiness. However, systematic research on evaluating these models, particularly concerning risks unique to the audio modality, remains largely unexplored. Existing evaluation frameworks primarily focus on the text modality or address only a restricted set of safety dimensions, failing to adequately account for the unique characteristics and application scenarios inherent to the audio modality. We introduce AudioTrust-the first multifaceted trustworthiness evaluation framework and benchmark specifically designed for ALLMs. AudioTrust facilitates assessments across six key dimensions: fairness, hallucination, safety, privacy, robustness, and authentication. To comprehensively evaluate these dimensions, AudioTrust is structured around 18 distinct experimental setups. Its core is a meticulously constructed dataset of over 4,420 audio/text samples, drawn from real-world scenarios (e.g., daily conversations, emergency calls, voice assistant interactions), specifically designed to probe the multifaceted trustworthiness of ALLMs. For assessment, the benchmark carefully designs 9 audio-specific evaluation metrics, and we employ a large-scale automated pipeline for objective and scalable scoring of model outputs. Experimental results reveal the trustworthiness boundaries and limitations of current state-of-the-art open-source and closed-source ALLMs when confronted with various high-risk audio scenarios, offering valuable insights for the secure and trustworthy deployment of future audio models. Our platform and benchmark are available at https://github.com/JusperLee/AudioTrust.", "github_manual": "", "github_regex": "https://github.com/JusperLee/AudioTrust", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17412", "title": "Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention", "authors": [ "Shuang Wu", "Youtian Lin", "Feihu Zhang", "Yifei Zeng", "Yikang Yang", "Yajie Bao", "Jiachen Qian", "Siyu Zhu", "Philip Torr", "Xun Cao", "Yao Yao" ], "github": "", "project_page": "", "abstract": "Generating high resolution 3D shapes using volumetric representations such as Signed Distance Functions presents substantial computational and memory challenges. We introduce Direct3D S2, a scalable 3D generation framework based on sparse volumes that achieves superior output quality with dramatically reduced training costs. Our key innovation is the Spatial Sparse Attention mechanism, which greatly enhances the efficiency of Diffusion Transformer computations on sparse volumetric data. SSA allows the model to effectively process large token sets within sparse volumes, significantly reducing computational overhead and achieving a 3.9x speedup in the forward pass and a 9.6x speedup in the backward pass. Our framework also includes a variational autoencoder that maintains a consistent sparse volumetric format across input, latent, and output stages. Compared to previous methods with heterogeneous representations in 3D VAE, this unified design significantly improves training efficiency and stability. Our model is trained on public available datasets, and experiments demonstrate that Direct3D S2 not only surpasses state-of-the-art methods in generation quality and efficiency, but also enables training at 1024 resolution using only 8 GPUs, a task typically requiring at least 32 GPUs for volumetric representations at 256 resolution, thus making gigascale 3D generation both practical and accessible. Project page: https://nju3dv.github.io/projects/Direct3D-S2/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17063", "title": "Synthetic Data RL: Task Definition Is All You Need", "authors": [ "Yiduo Guo", "Zhen Guo", "Chuanwei Huang", "Zi-Ang Wang", "Zekai Zhang", "Haofei Yu", "Huishuai Zhang", "Yikang Shen" ], "github": "https://github.com/gydpku/Data_Synthesis_RL", "project_page": "", "abstract": "Reinforcement learning (RL) is a powerful way to adapt foundation models to specialized tasks, but its reliance on large-scale human-labeled data limits broad adoption. We introduce Synthetic Data RL, a simple and general framework that reinforcement fine-tunes models using only synthetic data generated from a task definition. Our method first generates question and answer pairs from the task definition and retrieved documents, then adapts the difficulty of the question based on model solvability, and selects questions using the average pass rate of the model across samples for RL training. On Qwen-2.5-7B, our method achieves a 29.2% absolute improvement over the base model on GSM8K (+2.9 pp vs. instruction-tuned, +6.6 pp vs. Self-Instruct), 8.7% on MATH, 13.1% on GPQA (+7.0 pp vs. SynthLLM), 8.9% on MedQA, 17.7% on CQA (law) and 13.7% on CFA (finance). It surpasses supervised fine-tuning under the same data budget and nearly matches RL with full human data across datasets (e.g., +17.2 pp on GSM8K). Adding 100 human demonstrations improves the performance of GSM8K only by 0.4 pp, showing a limited added value. By reducing human data annotation, Synthetic Data RL enables scalable and efficient RL-based model adaptation. Code and demos are available at https://github.com/gydpku/Data_Synthesis_RL/.", "github_manual": "", "github_regex": "https://github.com/gydpku/Data_Synthesis_RL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.11881", "title": "Revisiting Residual Connections: Orthogonal Updates for Stable and Efficient Deep Networks", "authors": [ "Giyeong Oh", "Woohyun Cho", "Siyeol Kim", "Suhwan Choi", "Younjae Yu" ], "github": "https://github.com/BootsofLagrangian/ortho-residual", "project_page": "", "abstract": "Residual connections are pivotal for deep neural networks, enabling greater depth by mitigating vanishing gradients. However, in standard residual updates, the module's output is directly added to the input stream. This can lead to updates that predominantly reinforce or modulate the existing stream direction, potentially underutilizing the module's capacity for learning entirely novel features. In this work, we introduce Orthogonal Residual Update: we decompose the module's output relative to the input stream and add only the component orthogonal to this stream. This design aims to guide modules to contribute primarily new representational directions, fostering richer feature learning while promoting more efficient training. We demonstrate that our orthogonal update strategy improves generalization accuracy and training stability across diverse architectures (ResNetV2, Vision Transformers) and datasets (CIFARs, TinyImageNet, ImageNet-1k), achieving, for instance, a +4.3\\%p top-1 accuracy gain for ViT-B on ImageNet-1k.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/BootsofLagrangian/ortho-residual", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.18125", "title": "TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations", "authors": [ "Alan Arazi", "Eilam Shapira", "Roi Reichart" ], "github": "https://github.com/alanarazi7/TabSTAR", "project_page": "", "abstract": "While deep learning has achieved remarkable success across many domains, it has historically underperformed on tabular learning tasks, which remain dominated by gradient boosting decision trees (GBDTs). However, recent advancements are paving the way for Tabular Foundation Models, which can leverage real-world knowledge and generalize across diverse datasets, particularly when the data contains free-text. Although incorporating language model capabilities into tabular tasks has been explored, most existing methods utilize static, target-agnostic textual representations, limiting their effectiveness. We introduce TabSTAR: a Foundation Tabular Model with Semantically Target-Aware Representations. TabSTAR is designed to enable transfer learning on tabular data with textual features, with an architecture free of dataset-specific parameters. It unfreezes a pretrained text encoder and takes as input target tokens, which provide the model with the context needed to learn task-specific embeddings. TabSTAR achieves state-of-the-art performance for both medium- and large-sized datasets across known benchmarks of classification tasks with text features, and its pretraining phase exhibits scaling laws in the number of datasets, offering a pathway for further performance improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alanarazi7/TabSTAR", "github_api_added_by": "auto" }, { "date": "2025-05-26", "arxiv_id": "2505.17667", "title": "QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning", "authors": [ "Fanqi Wan", "Weizhou Shen", "Shengyi Liao", "Yingcheng Shi", "Chenliang Li", "Ziyi Yang", "Ji Zhang", "Fei Huang", "Jingren Zhou", "Ming Yan" ], "github": "", "project_page": "", "abstract": "Recent large reasoning models (LRMs) have demonstrated strong reasoning capabilities through reinforcement learning (RL). These improvements have primarily been observed within the short-context reasoning tasks. In contrast, extending LRMs to effectively process and reason on long-context inputs via RL remains a critical unsolved challenge. To bridge this gap, we first formalize the paradigm of long-context reasoning RL, and identify key challenges in suboptimal training efficiency and unstable optimization process. To address these issues, we propose QwenLong-L1, a framework that adapts short-context LRMs to long-context scenarios via progressive context scaling. Specifically, we utilize a warm-up supervised fine-tuning (SFT) stage to establish a robust initial policy, followed by a curriculum-guided phased RL technique to stabilize the policy evolution, and enhanced with a difficulty-aware retrospective sampling strategy to incentivize the policy exploration. Experiments on seven long-context document question-answering benchmarks demonstrate that QwenLong-L1-32B outperforms flagship LRMs like OpenAI-o3-mini and Qwen3-235B-A22B, achieving performance on par with Claude-3.7-Sonnet-Thinking, demonstrating leading performance among state-of-the-art LRMs. This work advances the development of practical long-context LRMs capable of robust reasoning across information-intensive environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17016", "title": "Interactive Post-Training for Vision-Language-Action Models", "authors": [ "Shuhan Tan", "Kairan Dou", "Yue Zhao", "Philipp Kr\u00e4henb\u00fchl" ], "github": "", "project_page": "", "abstract": "We introduce RIPT-VLA, a simple and scalable reinforcement-learning-based interactive post-training paradigm that fine-tunes pretrained Vision-Language-Action (VLA) models using only sparse binary success rewards. Existing VLA training pipelines rely heavily on offline expert demonstration data and supervised imitation, limiting their ability to adapt to new tasks and environments under low-data regimes. RIPT-VLA addresses this by enabling interactive post-training with a stable policy optimization algorithm based on dynamic rollout sampling and leave-one-out advantage estimation. RIPT-VLA has the following characteristics. First, it applies to various VLA models, resulting in an improvement on the lightweight QueST model by 21.2%, and the 7B OpenVLA-OFT model to an unprecedented 97.5% success rate. Second, it is computationally efficient and data-efficient: with only one demonstration, RIPT-VLA enables an unworkable SFT model (4%) to succeed with a 97% success rate within 15 iterations. Furthermore, we demonstrate that the policy learned by RIPT-VLA generalizes across different tasks and scenarios and is robust to the initial state context. These results highlight RIPT-VLA as a practical and effective paradigm for post-training VLA models through minimal supervision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.12891", "title": "TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios", "authors": [ "Shaohang Wei", "Wei Li", "Feifan Song", "Wen Luo", "Tianyi Zhuang", "Haochen Tan", "Zhijiang Guo", "Houfeng Wang" ], "github": "https://github.com/sylvain-wei/TIME", "project_page": "", "abstract": "Temporal reasoning is pivotal for Large Language Models (LLMs) to comprehend the real world. However, existing works neglect the real-world challenges for temporal reasoning: (1) intensive temporal information, (2) fast-changing event dynamics, and (3) complex temporal dependencies in social interactions. To bridge this gap, we propose a multi-level benchmark TIME, designed for temporal reasoning in real-world scenarios. TIME consists of 38,522 QA pairs, covering 3 levels with 11 fine-grained sub-tasks. This benchmark encompasses 3 sub-datasets reflecting different real-world challenges: TIME-Wiki, TIME-News, and TIME-Dial. We conduct extensive experiments on reasoning models and non-reasoning models. And we conducted an in-depth analysis of temporal reasoning performance across diverse real-world scenarios and tasks, and summarized the impact of test-time scaling on temporal reasoning capabilities. Additionally, we release TIME-Lite, a human-annotated subset to foster future research and standardized evaluation in temporal reasoning. The code is available at https://github.com/sylvain-wei/TIME , and the dataset is available at https://huggingface.co/datasets/SylvainWei/TIME .", "github_manual": "", "github_regex": "https://github.com/sylvain-wei/TIME", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.18092", "title": "QwenLong-CPRS: Towards infty-LLMs with Dynamic Context Optimization", "authors": [ "Weizhou Shen", "Chenliang Li", "Fanqi Wan", "Shengyi Liao", "Shaopeng Lai", "Bo Zhang", "Yingcheng Shi", "Yuning Wu", "Gang Fu", "Zhansheng Li", "Bin Yang", "Ji Zhang", "Fei Huang", "Jingren Zhou", "Ming Yan" ], "github": "https://github.com/Tongyi-Zhiwen/QwenLong-CPRS", "project_page": "", "abstract": "This technical report presents QwenLong-CPRS, a context compression framework designed for explicit long-context optimization, addressing prohibitive computation overhead during the prefill stage and the \"lost in the middle\" performance degradation of large language models (LLMs) during long sequence processing. Implemented through a novel dynamic context optimization mechanism, QwenLong-CPRS enables multi-granularity context compression guided by natural language instructions, achieving both efficiency gains and improved performance. Evolved from the Qwen architecture series, QwenLong-CPRS introduces four key innovations: (1) Natural language-guided dynamic optimization, (2) Bidirectional reasoning layers for enhanced boundary awareness, (3) Token critic mechanisms with language modeling heads, and (4) Window-parallel inference. Comprehensive evaluations across five benchmarks (4K-2M word contexts) demonstrate QwenLong-CPRS's threefold effectiveness: (1) Consistent superiority over other context management methods like RAG and sparse attention in both accuracy and efficiency. (2) Architecture-agnostic integration with all flagship LLMs, including GPT-4o, Gemini2.0-pro, Claude3.7-sonnet, DeepSeek-v3, and Qwen2.5-max, achieves 21.59times context compression alongside 19.15-point average performance gains; (3) Deployed with Qwen2.5-32B-Instruct, QwenLong-CPRS surpasses leading proprietary LLMs by 4.85 and 10.88 points on Ruler-128K and InfiniteBench, establishing new SOTA performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tongyi-Zhiwen/QwenLong-CPRS", "github_api_added_by": "auto" }, { "date": "2025-05-26", "arxiv_id": "2505.16479", "title": "Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration", "authors": [ "Yuetong Liu", "Yunqiu Xu", "Yang Wei", "Xiuli Bi", "Bin Xiao" ], "github": "", "project_page": "", "abstract": "Restoring nighttime images affected by multiple adverse weather conditions is a practical yet under-explored research problem, as multiple weather conditions often coexist in the real world alongside various lighting effects at night. This paper first explores the challenging multi-weather nighttime image restoration task, where various types of weather degradations are intertwined with flare effects. To support the research, we contribute the AllWeatherNight dataset, featuring large-scale high-quality nighttime images with diverse compositional degradations, synthesized using our introduced illumination-aware degradation generation. Moreover, we present ClearNight, a unified nighttime image restoration framework, which effectively removes complex degradations in one go. Specifically, ClearNight extracts Retinex-based dual priors and explicitly guides the network to focus on uneven illumination regions and intrinsic texture contents respectively, thereby enhancing restoration effectiveness in nighttime scenarios. In order to better represent the common and unique characters of multiple weather degradations, we introduce a weather-aware dynamic specific-commonality collaboration method, which identifies weather degradations and adaptively selects optimal candidate units associated with specific weather types. Our ClearNight achieves state-of-the-art performance on both synthetic and real-world images. Comprehensive ablation experiments validate the necessity of AllWeatherNight dataset as well as the effectiveness of ClearNight. Project page: https://henlyta.github.io/ClearNight/mainpage.html", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17618", "title": "Scaling Image and Video Generation via Test-Time Evolutionary Search", "authors": [ "Haoran He", "Jiajun Liang", "Xintao Wang", "Pengfei Wan", "Di Zhang", "Kun Gai", "Ling Pan" ], "github": "", "project_page": "", "abstract": "As the marginal cost of scaling computation (data and parameters) during model pre-training continues to increase substantially, test-time scaling (TTS) has emerged as a promising direction for improving generative model performance by allocating additional computation at inference time. While TTS has demonstrated significant success across multiple language tasks, there remains a notable gap in understanding the test-time scaling behaviors of image and video generative models (diffusion-based or flow-based models). Although recent works have initiated exploration into inference-time strategies for vision tasks, these approaches face critical limitations: being constrained to task-specific domains, exhibiting poor scalability, or falling into reward over-optimization that sacrifices sample diversity. In this paper, we propose Evolutionary Search (EvoSearch), a novel, generalist, and efficient TTS method that effectively enhances the scalability of both image and video generation across diffusion and flow models, without requiring additional training or model expansion. EvoSearch reformulates test-time scaling for diffusion and flow models as an evolutionary search problem, leveraging principles from biological evolution to efficiently explore and refine the denoising trajectory. By incorporating carefully designed selection and mutation mechanisms tailored to the stochastic differential equation denoising process, EvoSearch iteratively generates higher-quality offspring while preserving population diversity. Through extensive evaluation across both diffusion and flow architectures for image and video generation tasks, we demonstrate that our method consistently outperforms existing approaches, achieves higher diversity, and shows strong generalizability to unseen evaluation metrics. Our project is available at the website https://tinnerhrhe.github.io/evosearch.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17540", "title": "RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning", "authors": [ "Mingrui Wu", "Lu Wang", "Pu Zhao", "Fangkai Yang", "Jianjin Zhang", "Jianfeng Liu", "Yuefeng Zhan", "Weihao Han", "Hao Sun", "Jiayi Ji", "Xiaoshuai Sun", "Qingwei Lin", "Weiwei Deng", "Dongmei Zhang", "Feng Sun", "Qi Zhang", "Rongrong Ji" ], "github": "", "project_page": "", "abstract": "Despite recent progress in text-to-image (T2I) generation, existing models often struggle to faithfully capture user intentions from short and under-specified prompts. While prior work has attempted to enhance prompts using large language models (LLMs), these methods frequently generate stylistic or unrealistic content due to insufficient grounding in visual semantics and real-world composition. Inspired by recent advances in reasoning for language model, we propose RePrompt, a novel reprompting framework that introduces explicit reasoning into the prompt enhancement process via reinforcement learning. Instead of relying on handcrafted rules or stylistic rewrites, our method trains a language model to generate structured, self-reflective prompts by optimizing for image-level outcomes. The tailored reward models assesse the generated images in terms of human preference, semantic alignment, and visual composition, providing indirect supervision to refine prompt generation. Our approach enables end-to-end training without human-annotated data. Experiments on GenEval and T2I-Compbench show that RePrompt significantly boosts spatial layout fidelity and compositional generalization across diverse T2I backbones, establishing new state-of-the-art results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.16293", "title": "Augmenting LLM Reasoning with Dynamic Notes Writing for Complex QA", "authors": [ "Rishabh Maheshwary", "Masoud Hashemi", "Khyati Mahajan", "Shiva Krishna Reddy Malay", "Sai Rajeswar", "Sathwik Tejaswi Madhusudhan", "Spandana Gella", "Vikas Yadav" ], "github": "https://github.com/gangiswag/infogent/tree/main/direct-api-driven", "project_page": "", "abstract": "Iterative RAG for multi-hop question answering faces challenges with lengthy contexts and the buildup of irrelevant information. This hinders a model's capacity to process and reason over retrieved content and limits performance. While recent methods focus on compressing retrieved information, they are either restricted to single-round RAG, require finetuning or lack scalability in iterative RAG. To address these challenges, we propose Notes Writing, a method that generates concise and relevant notes from retrieved documents at each step, thereby reducing noise and retaining only essential information. This indirectly increases the effective context length of Large Language Models (LLMs), enabling them to reason and plan more effectively while processing larger volumes of input text. Notes Writing is framework agnostic and can be integrated with different iterative RAG methods. We demonstrate its effectiveness with three iterative RAG methods, across two models and four evaluation datasets. Notes writing yields an average improvement of 15.6 percentage points overall, with minimal increase in output tokens.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gangiswag/infogent/tree/main/direct-api-driven", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.15929", "title": "PhyX: Does Your Model Have the \"Wits\" for Physical Reasoning?", "authors": [ "Hui Shen", "Taiqiang Wu", "Qi Han", "Yunta Hsieh", "Jizhou Wang", "Yuyue Zhang", "Yuxin Cheng", "Zijian Hao", "Yuansheng Ni", "Xin Wang", "Zhongwei Wan", "Kai Zhang", "Wendong Xu", "Jing Xiong", "Ping Luo", "Wenhu Chen", "Chaofan Tao", "Zhuoqing Mao", "Ngai Wong" ], "github": "https://github.com/open-compass/VLMEvalKit", "project_page": "", "abstract": "Existing benchmarks fail to capture a crucial aspect of intelligence: physical reasoning, the integrated ability to combine domain knowledge, symbolic reasoning, and understanding of real-world constraints. To address this gap, we introduce PhyX: the first large-scale benchmark designed to assess models capacity for physics-grounded reasoning in visual scenarios. PhyX includes 3K meticulously curated multimodal questions spanning 6 reasoning types across 25 sub-domains and 6 core physics domains: thermodynamics, electromagnetism, mechanics, modern physics, optics, and wave\\&acoustics. In our comprehensive evaluation, even state-of-the-art models struggle significantly with physical reasoning. GPT-4o, Claude3.7-Sonnet, and GPT-o4-mini achieve only 32.5\\%, 42.2\\%, and 45.8\\% accuracy respectively-performance gaps exceeding 29\\% compared to human experts. Our analysis exposes critical limitations in current models: over-reliance on memorized disciplinary knowledge, excessive dependence on mathematical formulations, and surface-level visual pattern matching rather than genuine physical understanding. We provide in-depth analysis through fine-grained statistics, detailed case studies, and multiple evaluation paradigms to thoroughly examine physical reasoning capabilities. To ensure reproducibility, we implement a compatible evaluation protocol based on widely-used toolkits such as VLMEvalKit, enabling one-click evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-compass/VLMEvalKit", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17873", "title": "MOOSE-Chem3: Toward Experiment-Guided Hypothesis Ranking via Simulated Experimental Feedback", "authors": [ "Wanhao Liu", "Zonglin Yang", "Jue Wang", "Lidong Bing", "Di Zhang", "Dongzhan Zhou", "Yuqiang Li", "Houqiang Li", "Erik Cambria", "Wanli Ouyang" ], "github": "https://github.com/wanhaoliu/chemsimx", "project_page": "", "abstract": "Hypothesis ranking is a crucial component of automated scientific discovery, particularly in natural sciences where wet-lab experiments are costly and throughput-limited. Existing approaches focus on pre-experiment ranking, relying solely on large language model's internal reasoning without incorporating empirical outcomes from experiments. We introduce the task of experiment-guided ranking, which aims to prioritize candidate hypotheses based on the results of previously tested ones. However, developing such strategies is challenging due to the impracticality of repeatedly conducting real experiments in natural science domains. To address this, we propose a simulator grounded in three domain-informed assumptions, modeling hypothesis performance as a function of similarity to a known ground truth hypothesis, perturbed by noise. We curate a dataset of 124 chemistry hypotheses with experimentally reported outcomes to validate the simulator. Building on this simulator, we develop a pseudo experiment-guided ranking method that clusters hypotheses by shared functional characteristics and prioritizes candidates based on insights derived from simulated experimental feedback. Experiments show that our method outperforms pre-experiment baselines and strong ablations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wanhaoliu/chemsimx", "github_api_added_by": "auto" }, { "date": "2025-05-26", "arxiv_id": "2505.13508", "title": "Time-R1: Towards Comprehensive Temporal Reasoning in LLMs", "authors": [ "Zijia Liu", "Peixuan Han", "Haofei Yu", "Haoru Li", "Jiaxuan You" ], "github": "https://github.com/ulab-uiuc/Time-R1", "project_page": "", "abstract": "Large Language Models (LLMs) demonstrate impressive capabilities but lack robust temporal intelligence, struggling to integrate reasoning about the past with predictions and plausible generations of the future. Meanwhile, existing methods typically target isolated temporal skills, such as question answering about past events or basic forecasting, and exhibit poor generalization, particularly when dealing with events beyond their knowledge cutoff or requiring creative foresight. To address these limitations, we introduce Time-R1, the first framework to endow a moderate-sized (3B-parameter) LLM with comprehensive temporal abilities: understanding, prediction, and creative generation. Our approach features a novel three-stage development path; the first two constitute a reinforcement learning (RL) curriculum driven by a meticulously designed dynamic rule-based reward system. This framework progressively builds (1) foundational temporal understanding and logical event-time mappings from historical data, (2) future event prediction skills for events beyond its knowledge cutoff, and finally (3) enables remarkable generalization to creative future scenario generation without any fine-tuning. Strikingly, experiments demonstrate that Time-R1 outperforms models over 200 times larger, including the state-of-the-art 671B DeepSeek-R1, on highly challenging future event prediction and creative scenario generation benchmarks. This work provides strong evidence that thoughtfully engineered, progressive RL fine-tuning allows smaller, efficient models to achieve superior temporal performance, offering a practical and scalable path towards truly time-aware AI. To foster further research, we also release Time-Bench, a large-scale multi-task temporal reasoning dataset derived from 10 years of news data, and our series of Time-R1 checkpoints.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ulab-uiuc/Time-R1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.16770", "title": "RBench-V: A Primary Assessment for Visual Reasoning Models with Multi-modal Outputs", "authors": [ "Meng-Hao Guo", "Xuanyu Chu", "Qianrui Yang", "Zhe-Han Mo", "Yiqing Shen", "Pei-lin Li", "Xinjie Lin", "Jinnian Zhang", "Xin-Sheng Chen", "Yi Zhang", "Kiyohiro Nakayama", "Zhengyang Geng", "Houwen Peng", "Han Hu", "Shi-Nin Hu" ], "github": "https://github.com/CHEN-Xinsheng/VLMEvalKit_RBench-V", "project_page": "", "abstract": "The rapid advancement of native multi-modal models and omni-models, exemplified by GPT-4o, Gemini, and o3, with their capability to process and generate content across modalities such as text and images, marks a significant milestone in the evolution of intelligence. Systematic evaluation of their multi-modal output capabilities in visual thinking processes (also known as multi-modal chain of thought, M-CoT) becomes critically important. However, existing benchmarks for evaluating multi-modal models primarily focus on assessing multi-modal inputs and text-only reasoning while neglecting the importance of reasoning through multi-modal outputs. In this paper, we present a benchmark, dubbed RBench-V, designed to assess models' vision-indispensable reasoning abilities. To construct RBench-V, we carefully hand-pick 803 questions covering math, physics, counting, and games. Unlike previous benchmarks that typically specify certain input modalities, RBench-V presents problems centered on multi-modal outputs, which require image manipulation such as generating novel images and constructing auxiliary lines to support the reasoning process. We evaluate numerous open- and closed-source models on RBench-V, including o3, Gemini 2.5 Pro, Qwen2.5-VL, etc. Even the best-performing model, o3, achieves only 25.8% accuracy on RBench-V, far below the human score of 82.3%, highlighting that current models struggle to leverage multi-modal reasoning. Data and code are available at https://evalmodels.github.io/rbenchv", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CHEN-Xinsheng/VLMEvalKit_RBench-V", "github_api_added_by": "auto" }, { "date": "2025-05-26", "arxiv_id": "2505.15805", "title": "Keep Security! Benchmarking Security Policy Preservation in Large Language Model Contexts Against Indirect Attacks in Question Answering", "authors": [ "Hwan Chang", "Yumin Kim", "Yonghyun Jun", "Hwanhee Lee" ], "github": "", "project_page": "", "abstract": "As Large Language Models (LLMs) are increasingly deployed in sensitive domains such as enterprise and government, ensuring that they adhere to user-defined security policies within context is critical-especially with respect to information non-disclosure. While prior LLM studies have focused on general safety and socially sensitive data, large-scale benchmarks for contextual security preservation against attacks remain lacking. To address this, we introduce a novel large-scale benchmark dataset, CoPriva, evaluating LLM adherence to contextual non-disclosure policies in question answering. Derived from realistic contexts, our dataset includes explicit policies and queries designed as direct and challenging indirect attacks seeking prohibited information. We evaluate 10 LLMs on our benchmark and reveal a significant vulnerability: many models violate user-defined policies and leak sensitive information. This failure is particularly severe against indirect attacks, highlighting a critical gap in current LLM safety alignment for sensitive applications. Our analysis reveals that while models can often identify the correct answer to a query, they struggle to incorporate policy constraints during generation. In contrast, they exhibit a partial ability to revise outputs when explicitly prompted. Our findings underscore the urgent need for more robust methods to guarantee contextual security.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17399", "title": "FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflow", "authors": [ "Haoyu Sun", "Huichen Will Wang", "Jiawei Gu", "Linjie Li", "Yu Cheng" ], "github": "https://github.com/Mikivishy/FullFront", "project_page": "", "abstract": "Front-end engineering involves a complex workflow where engineers conceptualize designs, translate them into code, and iteratively refine the implementation. While recent benchmarks primarily focus on converting visual designs to code, we present FullFront, a benchmark designed to evaluate Multimodal Large Language Models (MLLMs) across the full front-end development pipeline. FullFront assesses three fundamental tasks that map directly to the front-end engineering pipeline: Webpage Design (conceptualization phase), Webpage Perception QA (comprehension of visual organization and elements), and Webpage Code Generation (implementation phase). Unlike existing benchmarks that use either scraped websites with bloated code or oversimplified LLM-generated HTML, FullFront employs a novel, two-stage process to transform real-world webpages into clean, standardized HTML while maintaining diverse visual designs and avoiding copyright issues. Extensive testing of state-of-the-art MLLMs reveals significant limitations in page perception, code generation (particularly for image handling and layout), and interaction implementation. Our results quantitatively demonstrate performance disparities across models and tasks, and highlight a substantial gap between current MLLM capabilities and human expert performance in front-end engineering. The FullFront benchmark and code are available in https://github.com/Mikivishy/FullFront.", "github_manual": "", "github_regex": "https://github.com/Mikivishy/FullFront", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.16022", "title": "NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning", "authors": [ "Wei Liu", "Siya Qi", "Xinyu Wang", "Chen Qian", "Yali Du", "Yulan He" ], "github": "", "project_page": "", "abstract": "Recent advances such as DeepSeek R1-Zero highlight the effectiveness of incentive training, a reinforcement learning paradigm that computes rewards solely based on the final answer part of a language model's output, thereby encouraging the generation of intermediate reasoning steps. However, these methods fundamentally rely on external verifiers, which limits their applicability to domains like mathematics and coding where such verifiers are readily available. Although reward models can serve as verifiers, they require high-quality annotated data and are costly to train. In this work, we propose NOVER, NO-VERifier Reinforcement Learning, a general reinforcement learning framework that requires only standard supervised fine-tuning data with no need for an external verifier. NOVER enables incentive training across a wide range of text-to-text tasks and outperforms the model of the same size distilled from large reasoning models such as DeepSeek R1 671B by 7.7 percent. Moreover, the flexibility of NOVER enables new possibilities for optimizing large language models, such as inverse incentive training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.16056", "title": "Not All Models Suit Expert Offloading: On Local Routing Consistency of Mixture-of-Expert Models", "authors": [ "Jingcong Liang", "Siyuan Wang", "Miren Tian", "Yitong Li", "Duyu Tang", "Zhongyu Wei" ], "github": "https://github.com/ljcleo/moe-lrc", "project_page": "", "abstract": "Mixture-of-Experts (MoE) enables efficient scaling of large language models (LLMs) with sparsely activated experts during inference. To effectively deploy large MoE models on memory-constrained devices, many systems introduce *expert offloading* that caches a subset of experts in fast memory, leaving others on slow memory to run on CPU or load on demand. While some research has exploited the locality of expert activations, where consecutive tokens activate similar experts, the degree of this **local routing consistency** varies across models and remains understudied. In this paper, we propose two metrics to measure local routing consistency of MoE models: (1) **Segment Routing Best Performance (SRP)**, which evaluates how well a fixed group of experts can cover the needs of a segment of tokens, and (2) **Segment Cache Best Hit Rate (SCH)**, which measures the optimal segment-level cache hit rate under a given cache size limit. We analyzed 20 MoE LLMs with diverse sizes and architectures and found that models that apply MoE on every layer and do not use shared experts exhibit the highest local routing consistency. We further showed that domain-specialized experts contribute more to routing consistency than vocabulary-specialized ones, and that most models can balance between cache effectiveness and efficiency with cache sizes approximately 2x the active experts. These findings pave the way for memory-efficient MoE design and deployment without compromising inference speed. We publish the code for replicating experiments at https://github.com/ljcleo/moe-lrc .", "github_manual": "", "github_regex": "https://github.com/ljcleo/moe-lrc", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.14669", "title": "Quartet: Native FP4 Training Can Be Optimal for Large Language Models", "authors": [ "Roberto L. Castro", "Andrei Panferov", "Soroush Tabesh", "Oliver Sieberling", "Jiale Chen", "Mahdi Nikdan", "Saleh Ashkboos", "Dan Alistarh" ], "github": "https://github.com/IST-DASLab/Quartet", "project_page": "", "abstract": "The rapid advancement of large language models (LLMs) has been paralleled by unprecedented increases in computational demands, with training costs for state-of-the-art models doubling every few months. Training models directly in low-precision arithmetic offers a solution, by improving both computational throughput and energy efficiency. Specifically, NVIDIA's recent Blackwell architecture facilitates extremely low-precision operations, specifically FP4 variants, promising substantial efficiency gains. Yet, current algorithms for training LLMs in FP4 precision face significant accuracy degradation and often rely on mixed-precision fallbacks. In this paper, we systematically investigate hardware-supported FP4 training and introduce Quartet, a new approach enabling accurate, end-to-end FP4 training with all the major computations (in e.g. linear layers) being performed in low precision. Through extensive evaluations on Llama-type models, we reveal a new low-precision scaling law that quantifies performance trade-offs across varying bit-widths and allows us to identify a \"near-optimal\" low-precision training technique in terms of accuracy-vs-computation, called Quartet. We implement Quartet using optimized CUDA kernels tailored for NVIDIA Blackwell GPUs, and show that it can achieve state-of-the-art accuracy for FP4 precision, successfully training billion-scale models. Our method demonstrates that fully FP4-based training is a competitive alternative to standard-precision and FP8 training. Our code is available at https://github.com/IST-DASLab/Quartet.", "github_manual": "", "github_regex": "https://github.com/IST-DASLab/Quartet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.14146", "title": "s3: You Don't Need That Much Data to Train a Search Agent via RL", "authors": [ "Pengcheng Jiang", "Xueqiang Xu", "Jiacheng Lin", "Jinfeng Xiao", "Zifeng Wang", "Jimeng Sun", "Jiawei Han" ], "github": "https://github.com/pat-jj/s3", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference. Recent advances have enabled LLMs to act as search agents via reinforcement learning (RL), improving information acquisition through multi-turn interactions with retrieval engines. However, existing approaches either optimize retrieval using search-only metrics (e.g., NDCG) that ignore downstream utility or fine-tune the entire LLM to jointly reason and retrieve-entangling retrieval with generation and limiting the real search utility and compatibility with frozen or proprietary models. In this work, we propose s3, a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the searcher using a Gain Beyond RAG reward: the improvement in generation accuracy over naive RAG. s3 requires only 2.4k training samples to outperform baselines trained on over 70x more data, consistently delivering stronger downstream performance across six general QA and five medical QA benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pat-jj/s3", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17955", "title": "Diffusion Classifiers Understand Compositionality, but Conditions Apply", "authors": [ "Yujin Jeong", "Arnas Uselis", "Seong Joon Oh", "Anna Rohrbach" ], "github": "https://github.com/eugene6923/Diffusion-Classifiers-Compositionality", "project_page": "", "abstract": "Understanding visual scenes is fundamental to human intelligence. While discriminative models have significantly advanced computer vision, they often struggle with compositional understanding. In contrast, recent generative text-to-image diffusion models excel at synthesizing complex scenes, suggesting inherent compositional capabilities. Building on this, zero-shot diffusion classifiers have been proposed to repurpose diffusion models for discriminative tasks. While prior work offered promising results in discriminative compositional scenarios, these results remain preliminary due to a small number of benchmarks and a relatively shallow analysis of conditions under which the models succeed. To address this, we present a comprehensive study of the discriminative capabilities of diffusion classifiers on a wide range of compositional tasks. Specifically, our study covers three diffusion models (SD 1.5, 2.0, and, for the first time, 3-m) spanning 10 datasets and over 30 tasks. Further, we shed light on the role that target dataset domains play in respective performance; to isolate the domain effects, we introduce a new diagnostic benchmark Self-Bench comprised of images created by diffusion models themselves. Finally, we explore the importance of timestep weighting and uncover a relationship between domain gap and timestep sensitivity, particularly for SD3-m. To sum up, diffusion classifiers understand compositionality, but conditions apply! Code and dataset are available at https://github.com/eugene6923/Diffusion-Classifiers-Compositionality.", "github_manual": "", "github_regex": "https://github.com/eugene6923/Diffusion-Classifiers-Compositionality", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.15182", "title": "ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflection", "authors": [ "Jeonghye Kim", "Sojeong Rhee", "Minbeom Kim", "Dohyung Kim", "Sangmook Lee", "Youngchul Sung", "Kyomin Jung" ], "github": "https://github.com/WeiminXiong/MPO", "project_page": "", "abstract": "Recent advances in LLM agents have largely built on reasoning backbones like ReAct, which interleave thought and action in complex environments. However, ReAct often produces ungrounded or incoherent reasoning steps, leading to misalignment between the agent's actual state and goal. Our analysis finds that this stems from ReAct's inability to maintain consistent internal beliefs and goal alignment, causing compounding errors and hallucinations. To address this, we introduce ReflAct, a novel backbone that shifts reasoning from merely planning next actions to continuously reflecting on the agent's state relative to its goal. By explicitly grounding decisions in states and enforcing ongoing goal alignment, ReflAct dramatically improves strategic reliability. This design delivers substantial empirical gains: ReflAct surpasses ReAct by 27.7% on average, achieving a 93.3% success rate in ALFWorld. Notably, ReflAct even outperforms ReAct with added enhancement modules (e.g., Reflexion, WKM), showing that strengthening the core reasoning backbone is key to reliable agent performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WeiminXiong/MPO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.18078", "title": "DanceTogether! Identity-Preserving Multi-Person Interactive Video Generation", "authors": [ "Junhao Chen", "Mingjin Chen", "Jianjin Xu", "Xiang Li", "Junting Dong", "Mingze Sun", "Puhua Jiang", "Hongxiang Li", "Yuhang Yang", "Hao Zhao", "Xiaoxiao Long", "Ruqi Huang" ], "github": "", "project_page": "", "abstract": "Controllable video generation (CVG) has advanced rapidly, yet current systems falter when more than one actor must move, interact, and exchange positions under noisy control signals. We address this gap with DanceTogether, the first end-to-end diffusion framework that turns a single reference image plus independent pose-mask streams into long, photorealistic videos while strictly preserving every identity. A novel MaskPoseAdapter binds \"who\" and \"how\" at every denoising step by fusing robust tracking masks with semantically rich-but noisy-pose heat-maps, eliminating the identity drift and appearance bleeding that plague frame-wise pipelines. To train and evaluate at scale, we introduce (i) PairFS-4K, 26 hours of dual-skater footage with 7,000+ distinct IDs, (ii) HumanRob-300, a one-hour humanoid-robot interaction set for rapid cross-domain transfer, and (iii) TogetherVideoBench, a three-track benchmark centered on the DanceTogEval-100 test suite covering dance, boxing, wrestling, yoga, and figure skating. On TogetherVideoBench, DanceTogether outperforms the prior arts by a significant margin. Moreover, we show that a one-hour fine-tune yields convincing human-robot videos, underscoring broad generalization to embodied-AI and HRI tasks. Extensive ablations confirm that persistent identity-action binding is critical to these gains. Together, our model, datasets, and benchmark lift CVG from single-subject choreography to compositionally controllable, multi-actor interaction, opening new avenues for digital production, simulation, and embodied intelligence. Our video demos and code are available at https://DanceTog.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.16134", "title": "Position of Uncertainty: A Cross-Linguistic Study of Positional Bias in Large Language Models", "authors": [ "Menschikov Mikhail", "Alexander Kharitonov", "Maiia Kotyga", "Vadim Porvatov", "Anna Zhukovskaya", "David Kagramanyan", "Egor Shvetsov", "Evgeny Burnaev" ], "github": "", "project_page": "", "abstract": "Large language models exhibit positional bias -- systematic neglect of information at specific context positions -- yet its interplay with linguistic diversity remains poorly understood. We present a cross-linguistic study across five typologically distinct languages (English, Russian, German, Hindi, Vietnamese), examining how positional bias interacts with model uncertainty, syntax, and prompting. Key findings: (1) Positional bias is model-driven, with language-specific variations -- Qwen2.5-7B favors late positions, challenging assumptions of early-token bias; (2) Explicit positional guidance (e.g., correct context is at position X) reduces accuracy across languages, undermining prompt-engineering practices; (3) Aligning context with positional bias increases entropy, yet minimal entropy does not predict accuracy. (4) We further uncover that LLMs differently impose dominant word order in free-word-order languages like Hindi.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.16409", "title": "FREESON: Retriever-Free Retrieval-Augmented Reasoning via Corpus-Traversing MCTS", "authors": [ "Chaeeun Kim", "Seungone Kim" ], "github": "", "project_page": "", "abstract": "Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in multi-step reasoning and calling search engines at appropriate steps. However, existing retrieval-augmented reasoning approaches rely on separate retrieval models, limiting the LRM's role in retrieval to deciding when to retrieve and how to query. This separation not only increases hardware and operational costs but also leads to errors in the retrieval process due to the representation bottleneck, a phenomenon where the retriever's embedding space is not expressive enough to meet the generator's requirements. To address this, we shift our perspective from sequence-to-sequence matching to locating the answer-containing paths within the corpus, and propose a novel framework called FREESON (Retriever-FREE Retrieval-Augmented ReaSONing). This framework enables LRMs to retrieve relevant knowledge on their own by acting as both a generator and retriever. To achieve this, we introduce a variant of the MCTS algorithm specialized for the retrieval task, which we call CT-MCTS (Corpus-Traversing Monte Carlo Tree Search). In this algorithm, LRMs traverse through the corpus toward answer-containing regions. Our results on five open-domain QA benchmarks, including single-hop and multi-hop questions, show that FREESON achieves an average improvement of 14.4% in EM and F1 over four multi-step reasoning models with a separate retriever, and it also performs comparably to the strongest baseline, surpassing it by 3% on PopQA and 2WikiMultihopQA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17295", "title": "ScanBot: Towards Intelligent Surface Scanning in Embodied Robotic Systems", "authors": [ "Zhiling Chen", "Yang Zhang", "Fardin Jalil Piran", "Qianyu Zhou", "Jiong Tang", "Farhad Imani" ], "github": "", "project_page": "", "abstract": "We introduce ScanBot, a novel dataset designed for instruction-conditioned, high-precision surface scanning in robotic systems. In contrast to existing robot learning datasets that focus on coarse tasks such as grasping, navigation, or dialogue, ScanBot targets the high-precision demands of industrial laser scanning, where sub-millimeter path continuity and parameter stability are critical. The dataset covers laser scanning trajectories executed by a robot across 12 diverse objects and 6 task types, including full-surface scans, geometry-focused regions, spatially referenced parts, functionally relevant structures, defect inspection, and comparative analysis. Each scan is guided by natural language instructions and paired with synchronized RGB, depth, and laser profiles, as well as robot pose and joint states. Despite recent progress, existing vision-language action (VLA) models still fail to generate stable scanning trajectories under fine-grained instructions and real-world precision demands. To investigate this limitation, we benchmark a range of multimodal large language models (MLLMs) across the full perception-planning-execution loop, revealing persistent challenges in instruction-following under realistic constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17373", "title": "Value-Guided Search for Efficient Chain-of-Thought Reasoning", "authors": [ "Kaiwen Wang", "Jin Peng Zhou", "Jonathan Chang", "Zhaolin Gao", "Nathan Kallus", "Kiant\u00e9 Brantley", "Wen Sun" ], "github": "https://github.com/kaiwenw/value-guided-search", "project_page": "", "abstract": "In this paper, we propose a simple and efficient method for value model training on long-context reasoning traces. Compared to existing process reward models (PRMs), our method does not require a fine-grained notion of \"step,\" which is difficult to define for long-context reasoning models. By collecting a dataset of 2.5 million reasoning traces, we train a 1.5B token-level value model and apply it to DeepSeek models for improved performance with test-time compute scaling. We find that block-wise value-guided search (VGS) with a final weighted majority vote achieves better test-time scaling than standard methods such as majority voting or best-of-n. With an inference budget of 64 generations, VGS with DeepSeek-R1-Distill-1.5B achieves an average accuracy of 45.7% across four competition math benchmarks (AIME 2024 & 2025, HMMT Feb 2024 & 2025), reaching parity with o3-mini-medium. Moreover, VGS significantly reduces the inference FLOPs required to achieve the same performance of majority voting. Our dataset, model and codebase are open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kaiwenw/value-guided-search", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.14256", "title": "FuxiMT: Sparsifying Large Language Models for Chinese-Centric Multilingual Machine Translation", "authors": [ "Shaolin Zhu", "Tianyu Dong", "Bo Li", "Deyi Xiong" ], "github": "", "project_page": "", "abstract": "In this paper, we present FuxiMT, a novel Chinese-centric multilingual machine translation model powered by a sparsified large language model (LLM). We adopt a two-stage strategy to train FuxiMT. We first pre-train the model on a massive Chinese corpus and then conduct multilingual fine-tuning on a large parallel dataset encompassing 65 languages. FuxiMT incorporates Mixture-of-Experts (MoEs) and employs a curriculum learning strategy for robust performance across various resource levels. Experimental results demonstrate that FuxiMT significantly outperforms strong baselines, including state-of-the-art LLMs and machine translation models, particularly under low-resource scenarios. Furthermore, FuxiMT exhibits remarkable zero-shot translation capabilities for unseen language pairs, indicating its potential to bridge communication gaps where parallel data are scarce or unavailable.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.17552", "title": "Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing", "authors": [ "Zijie Qiu", "Jiaqi Wei", "Xiang Zhang", "Sheng Xu", "Kai Zou", "Zhi Jin", "Zhiqiang Gao", "Nanqing Dong", "Siqi Sun" ], "github": "", "project_page": "", "abstract": "De novo peptide sequencing is a critical task in proteomics. However, the performance of current deep learning-based methods is limited by the inherent complexity of mass spectrometry data and the heterogeneous distribution of noise signals, leading to data-specific biases. We present RankNovo, the first deep reranking framework that enhances de novo peptide sequencing by leveraging the complementary strengths of multiple sequencing models. RankNovo employs a list-wise reranking approach, modeling candidate peptides as multiple sequence alignments and utilizing axial attention to extract informative features across candidates. Additionally, we introduce two new metrics, PMD (Peptide Mass Deviation) and RMD (residual Mass Deviation), which offer delicate supervision by quantifying mass differences between peptides at both the sequence and residue levels. Extensive experiments demonstrate that RankNovo not only surpasses its base models used to generate training candidates for reranking pre-training, but also sets a new state-of-the-art benchmark. Moreover, RankNovo exhibits strong zero-shot generalization to unseen models whose generations were not exposed during training, highlighting its robustness and potential as a universal reranking framework for peptide sequencing. Our work presents a novel reranking strategy that fundamentally challenges existing single-model paradigms and advances the frontier of accurate de novo sequencing. Our source code is provided on GitHub.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-26", "arxiv_id": "2505.18383", "title": "NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities", "authors": [ "Abdellah El Mekki", "Houdaifa Atou", "Omer Nacar", "Shady Shehata", "Muhammad Abdul-Mageed" ], "github": "", "project_page": "", "abstract": "Enhancing the linguistic capabilities of Large Language Models (LLMs) to include low-resource languages is a critical research area. Current research directions predominantly rely on synthetic data generated by translating English corpora, which, while demonstrating promising linguistic understanding and translation abilities, often results in models aligned with source language culture. These models frequently fail to represent the cultural heritage and values of local communities. This work proposes a methodology to create both synthetic and retrieval-based pre-training data tailored to a specific community, considering its (i) language, (ii) cultural heritage, and (iii) cultural values. We demonstrate our methodology using Egyptian and Moroccan dialects as testbeds, chosen for their linguistic and cultural richness and current underrepresentation in LLMs. As a proof-of-concept, we develop NileChat, a 3B parameter LLM adapted for Egyptian and Moroccan communities, incorporating their language, cultural heritage, and values. Our results on various understanding, translation, and cultural and values alignment benchmarks show that NileChat outperforms existing Arabic-aware LLMs of similar size and performs on par with larger models. We share our methods, data, and models with the community to promote the inclusion and coverage of more diverse communities in LLM development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.13426", "title": "G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning", "authors": [ "Liang Chen", "Hongcheng Gao", "Tianyu Liu", "Zhiqi Huang", "Flood Sung", "Xinyu Zhou", "Yuxin Wu", "Baobao Chang" ], "github": "https://github.com/chenllliang/G1", "project_page": "", "abstract": "Vision-Language Models (VLMs) excel in many direct multimodal tasks but struggle to translate this prowess into effective decision-making within interactive, visually rich environments like games. This ``knowing-doing'' gap significantly limits their potential as autonomous agents, as leading VLMs often performing badly in simple games. To address this, we introduce VLM-Gym, a curated reinforcement learning (RL) environment featuring diverse visual games with unified interfaces and adjustable, compositional difficulty, specifically designed for scalable multi-game parallel training. Leveraging VLM-Gym, we train G0 models using pure RL-driven self-evolution, which demonstrate emergent perception and reasoning patterns. To further mitigate challenges arising from game diversity, we develop G1 models. G1 incorporates a perception-enhanced cold start prior to RL fine-tuning. Our resulting G1 models consistently surpass their teacher across all games and outperform leading proprietary models like Claude-3.7-Sonnet-Thinking. Systematic analysis reveals an intriguing finding: perception and reasoning abilities mutually bootstrap each other throughout the RL training process. Source code including VLM-Gym and RL training are released at https://github.com/chenllliang/G1 to foster future research in advancing VLMs as capable interactive agents.", "github_manual": "", "github_regex": "https://github.com/chenllliang/G1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19443", "title": "Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI", "authors": [ "Ranjan Sapkota", "Konstantinos I. Roumeliotis", "Manoj Karkee" ], "github": "", "project_page": "", "abstract": "This review presents a comprehensive analysis of two emerging paradigms in AI-assisted software development: vibe coding and agentic coding. While both leverage large language models (LLMs), they differ fundamentally in autonomy, architectural design, and the role of the developer. Vibe coding emphasizes intuitive, human-in-the-loop interaction through prompt-based, conversational workflows that support ideation, experimentation, and creative exploration. In contrast, agentic coding enables autonomous software development through goal-driven agents capable of planning, executing, testing, and iterating tasks with minimal human intervention. We propose a detailed taxonomy spanning conceptual foundations, execution models, feedback loops, safety mechanisms, debugging strategies, and real-world tool ecosystems. Through comparative workflow analysis and 20 detailed use cases, we illustrate how vibe systems thrive in early-stage prototyping and education, while agentic systems excel in enterprise-grade automation, codebase refactoring, and CI/CD integration. We further examine emerging trends in hybrid architectures, where natural language interfaces are coupled with autonomous execution pipelines. Finally, we articulate a future roadmap for agentic AI, outlining the infrastructure needed for trustworthy, explainable, and collaborative systems. Our findings suggest that successful AI software engineering will rely not on choosing one paradigm, but on harmonizing their strengths within a unified, human-centered development lifecycle.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18675", "title": "Can MLLMs Guide Me Home? A Benchmark Study on Fine-Grained Visual Reasoning from Transit Maps", "authors": [ "Sicheng Feng", "Song Wang", "Shuyi Ouyang", "Lingdong Kong", "Zikai Song", "Jianke Zhu", "Huan Wang", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have recently achieved significant progress in visual tasks, including semantic scene understanding and text-image alignment, with reasoning variants enhancing performance on complex tasks involving mathematics and logic. However, their capacity for reasoning tasks involving fine-grained visual understanding remains insufficiently evaluated. To address this gap, we introduce ReasonMap, a benchmark designed to assess the fine-grained visual understanding and spatial reasoning abilities of MLLMs. ReasonMap encompasses high-resolution transit maps from 30 cities across 13 countries and includes 1,008 question-answer pairs spanning two question types and three templates. Furthermore, we design a two-level evaluation pipeline that properly assesses answer correctness and quality. Comprehensive evaluations of 15 popular MLLMs, including both base and reasoning variants, reveal a counterintuitive pattern: among open-source models, base models outperform reasoning ones, while the opposite trend is observed in closed-source models. Additionally, performance generally degrades when visual inputs are masked, indicating that while MLLMs can leverage prior knowledge to answer some questions, fine-grained visual reasoning tasks still require genuine visual perception for strong performance. Our benchmark study offers new insights into visual reasoning and contributes to investigating the gap between open-source and closed-source models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18536", "title": "Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models", "authors": [ "Haoyuan Sun", "Jiaqi Wu", "Bo Xia", "Yifu Luo", "Yifei Zhao", "Kai Qin", "Xufei Lv", "Tiantian Zhang", "Yongzhe Chang", "Xueqian Wang" ], "github": "https://github.com/Sun-Haoyuan23/Awesome-RL-based-Reasoning-MLLMs", "project_page": "", "abstract": "Standing in 2025, at a critical juncture in the pursuit of Artificial General Intelligence (AGI), reinforcement fine-tuning (RFT) has demonstrated significant potential in enhancing the reasoning capability of large language models (LLMs) and has led to the development of cutting-edge AI models such as OpenAI-o1 and DeepSeek-R1. Moreover, the efficient application of RFT to enhance the reasoning capability of multimodal large language models (MLLMs) has attracted widespread attention from the community. In this position paper, we argue that reinforcement fine-tuning powers the reasoning capability of multimodal large language models. To begin with, we provide a detailed introduction to the fundamental background knowledge that researchers interested in this field should be familiar with. Furthermore, we meticulously summarize the improvements of RFT in powering reasoning capability of MLLMs into five key points: diverse modalities, diverse tasks and domains, better training algorithms, abundant benchmarks and thriving engineering frameworks. Finally, we propose five promising directions for future research that the community might consider. We hope that this position paper will provide valuable insights to the community at this pivotal stage in the advancement toward AGI. Summary of works done on RFT for MLLMs is available at https://github.com/Sun-Haoyuan23/Awesome-RL-based-Reasoning-MLLMs.", "github_manual": "", "github_regex": "https://github.com/Sun-Haoyuan23/Awesome-RL-based-Reasoning-MLLMs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18601", "title": "Flex-Judge: Think Once, Judge Anywhere", "authors": [ "Jongwoo Ko", "Sungnyun Kim", "Sungwoo Cho", "Se-Young Yun" ], "github": "", "project_page": "", "abstract": "Human-generated reward signals are critical for aligning generative models with human preferences, guiding both training and inference-time evaluations. While large language models (LLMs) employed as proxy evaluators, i.e., LLM-as-a-Judge, significantly reduce the costs associated with manual annotations, they typically require extensive modality-specific training data and fail to generalize well across diverse multimodal tasks. In this paper, we propose Flex-Judge, a reasoning-guided multimodal judge model that leverages minimal textual reasoning data to robustly generalize across multiple modalities and evaluation formats. Our core intuition is that structured textual reasoning explanations inherently encode generalizable decision-making patterns, enabling an effective transfer to multimodal judgments, e.g., with images or videos. Empirical results demonstrate that Flex-Judge, despite being trained on significantly fewer text data, achieves competitive or superior performance compared to state-of-the-art commercial APIs and extensively trained multimodal evaluators. Notably, Flex-Judge presents broad impact in modalities like molecule, where comprehensive evaluation benchmarks are scarce, underscoring its practical value in resource-constrained domains. Our framework highlights reasoning-based text supervision as a powerful, cost-effective alternative to traditional annotation-intensive approaches, substantially advancing scalable multimodal model-as-a-judge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18545", "title": "B-score: Detecting biases in large language models using response history", "authors": [ "An Vo", "Mohammad Reza Taesiri", "Daeyoung Kim", "Anh Totti Nguyen" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) often exhibit strong biases, e.g, against women or in favor of the number 7. We investigate whether LLMs would be able to output less biased answers when allowed to observe their prior answers to the same question in a multi-turn conversation. To understand which types of questions invite more biased answers, we test LLMs on our proposed set of questions that span 9 topics and belong to three types: (1) Subjective; (2) Random; and (3) Objective. Interestingly, LLMs are able to \"de-bias\" themselves in a multi-turn conversation in response to questions that seek an Random, unbiased answer. Furthermore, we propose B-score, a novel metric that is effective in detecting biases to Subjective, Random, Easy, and Hard questions. On MMLU, HLE, and CSQA, leveraging B-score substantially improves the verification accuracy of LLM answers (i.e, accepting LLM correct answers and rejecting incorrect ones) compared to using verbalized confidence scores or the frequency of single-turn answers alone. Code and data are available at: https://b-score.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.16972", "title": "From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition", "authors": [ "Tianduo Wang", "Lu Xu", "Wei Lu", "Shanbo Cheng" ], "github": "", "project_page": "", "abstract": "Recent advances in Automatic Speech Recognition (ASR) have been largely fueled by massive speech corpora. However, extending coverage to diverse languages with limited resources remains a formidable challenge. This paper introduces Speech Back-Translation, a scalable pipeline that improves multilingual ASR models by converting large-scale text corpora into synthetic speech via off-the-shelf text-to-speech (TTS) models. We demonstrate that just tens of hours of real transcribed speech can effectively train TTS models to generate synthetic speech at hundreds of times the original volume while maintaining high quality. To evaluate synthetic speech quality, we develop an intelligibility-based assessment framework and establish clear thresholds for when synthetic data benefits ASR training. Using Speech Back-Translation, we generate more than 500,000 hours of synthetic speech in ten languages and continue pre-training Whisper-large-v3, achieving average transcription error reductions of over 30\\%. These results highlight the scalability and effectiveness of Speech Back-Translation for enhancing multilingual ASR systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19602", "title": "Memory-Efficient Visual Autoregressive Modeling with Scale-Aware KV Cache Compression", "authors": [ "Kunjun Li", "Zigeng Chen", "Cheng-Yen Yang", "Jenq-Neng Hwang" ], "github": "https://github.com/stargazerx0/scalekv", "project_page": "", "abstract": "Visual Autoregressive (VAR) modeling has garnered significant attention for its innovative next-scale prediction approach, which yields substantial improvements in efficiency, scalability, and zero-shot generalization. Nevertheless, the coarse-to-fine methodology inherent in VAR results in exponential growth of the KV cache during inference, causing considerable memory consumption and computational redundancy. To address these bottlenecks, we introduce ScaleKV, a novel KV cache compression framework tailored for VAR architectures. ScaleKV leverages two critical observations: varying cache demands across transformer layers and distinct attention patterns at different scales. Based on these insights, ScaleKV categorizes transformer layers into two functional groups: drafters and refiners. Drafters exhibit dispersed attention across multiple scales, thereby requiring greater cache capacity. Conversely, refiners focus attention on the current token map to process local details, consequently necessitating substantially reduced cache capacity. ScaleKV optimizes the multi-scale inference pipeline by identifying scale-specific drafters and refiners, facilitating differentiated cache management tailored to each scale. Evaluation on the state-of-the-art text-to-image VAR model family, Infinity, demonstrates that our approach effectively reduces the required KV cache memory to 10% while preserving pixel-level fidelity.", "github_manual": "https://github.com/stargazerx0/scalekv", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.17652", "title": "Rethinking the Sampling Criteria in Reinforcement Learning for LLM Reasoning: A Competence-Difficulty Alignment Perspective", "authors": [ "Deyang Kong", "Qi Guo", "Xiangyu Xi", "Wei Wang", "Jingang Wang", "Xunliang Cai", "Shikun Zhang", "Wei Ye" ], "github": "", "project_page": "", "abstract": "Reinforcement learning exhibits potential in enhancing the reasoning abilities of large language models, yet it is hard to scale for the low sample efficiency during the rollout phase. Existing methods attempt to improve efficiency by scheduling problems based on problem difficulties. However, these approaches suffer from unstable and biased estimations of problem difficulty and fail to capture the alignment between model competence and problem difficulty in RL training, leading to suboptimal results. To tackle these limitations, this paper introduces Competence-Difficulty Alignment Sampling (CDAS), which enables accurate and stable estimation of problem difficulties by aggregating historical performance discrepancies of problems. Then the model competence is quantified to adaptively select problems whose difficulty is in alignment with the model's current competence using a fixed-point system. Experimental results across a range of challenging mathematical benchmarks show that CDAS achieves great improvements in both accuracy and efficiency. CDAS attains the highest average accuracy against baselines and exhibits significant speed advantages compared to Dynamic Sampling, a competitive strategy in DAPO, which is 2.33 times slower than CDAS.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19815", "title": "Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective", "authors": [ "Junnan Liu", "Hongwei Liu", "Linchen Xiao", "Shudong Liu", "Taolin Zhang", "Zihan Ma", "Songyang Zhang", "Kai Chen" ], "github": "", "project_page": "", "abstract": "We propose a novel framework for comprehending the reasoning capabilities of large language models (LLMs) through the perspective of meta-learning. By conceptualizing reasoning trajectories as pseudo-gradient descent updates to the LLM's parameters, we identify parallels between LLM reasoning and various meta-learning paradigms. We formalize the training process for reasoning tasks as a meta-learning setup, with each question treated as an individual task, and reasoning trajectories serving as the inner loop optimization for adapting model parameters. Once trained on a diverse set of questions, the LLM develops fundamental reasoning capabilities that can generalize to previously unseen questions. Extensive empirical evaluations substantiate the strong connection between LLM reasoning and meta-learning, exploring several issues of significant interest from a meta-learning standpoint. Our work not only enhances the understanding of LLM reasoning but also provides practical insights for improving these models through established meta-learning techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19752", "title": "Discrete Markov Bridge", "authors": [ "Hengli Li", "Yuxuan Wang", "Song-Chun Zhu", "Ying Nian Wu", "Zilong Zheng" ], "github": "https://github.com/Henry839/Discrete-Markov-Bridge", "project_page": "", "abstract": "Discrete diffusion has recently emerged as a promising paradigm in discrete data modeling. However, existing methods typically rely on a fixed rate transition matrix during training, which not only limits the expressiveness of latent representations, a fundamental strength of variational methods, but also constrains the overall design space. To address these limitations, we propose Discrete Markov Bridge, a novel framework specifically designed for discrete representation learning. Our approach is built upon two key components: Matrix Learning and Score Learning. We conduct a rigorous theoretical analysis, establishing formal performance guarantees for Matrix Learning and proving the convergence of the overall framework. Furthermore, we analyze the space complexity of our method, addressing practical constraints identified in prior studies. Extensive empirical evaluations validate the effectiveness of the proposed Discrete Markov Bridge, which achieves an Evidence Lower Bound (ELBO) of 1.38 on the Text8 dataset, outperforming established baselines. Moreover, the proposed model demonstrates competitive performance on the CIFAR-10 dataset, achieving results comparable to those obtained by image-specific generation approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Henry839/Discrete-Markov-Bridge", "github_api_added_by": "auto" }, { "date": "2025-05-27", "arxiv_id": "2505.19706", "title": "Error Typing for Smarter Rewards: Improving Process Reward Models with Error-Aware Hierarchical Supervision", "authors": [ "Tej Deep Pala", "Panshul Sharma", "Amir Zadeh", "Chuan Li", "Soujanya Poria" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are prone to hallucination, especially during multi-hop and reasoning-intensive tasks such as mathematical problem solving. While Outcome Reward Models verify only final answers, Process Reward Models (PRMs) score each intermediate step to steer generation toward coherent solutions. We introduce PathFinder-PRM, a novel hierarchical, error-aware discriminative PRM that first classifies math and consistency errors at each step, then combines these fine-grained signals to estimate step correctness. To train PathFinder-PRM, we construct a 400K-sample dataset by enriching the human-annotated PRM800K corpus and RLHFlow Mistral traces with three-dimensional step-level labels. On PRMBench, PathFinder-PRM achieves a new state-of-the-art PRMScore of 67.7, outperforming the prior best (65.5) while using 3 times less data. When applied to reward guided greedy search, our model yields prm@8 48.3, a +1.5 point gain over the strongest baseline. These results demonstrate that decoupled error detection and reward estimation not only boost fine-grained error detection but also substantially improve end-to-end, reward-guided mathematical reasoning with greater data efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19630", "title": "DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical Dialogue", "authors": [ "Yichun Feng", "Jiawei Wang", "Lu Zhou", "Yixue Li" ], "github": "https://github.com/JarvisUSTC/DoctorAgent-RL", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated excellent capabilities in the field of biomedical question answering, but their application in real-world clinical consultations still faces core challenges. Existing systems rely on a one-way information transmission mode where patients must fully describe their symptoms in a single round, leading to nonspecific diagnostic recommendations when complaints are vague. Traditional multi-turn dialogue methods based on supervised learning are constrained by static data-driven paradigms, lacking generalizability and struggling to intelligently extract key clinical information. To address these limitations, we propose DoctorAgent-RL, a reinforcement learning (RL)-based multi-agent collaborative framework that models medical consultations as a dynamic decision-making process under uncertainty. The doctor agent continuously optimizes its questioning strategy within the RL framework through multi-turn interactions with the patient agent, dynamically adjusting its information-gathering path based on comprehensive rewards from the Consultation Evaluator. This RL fine-tuning mechanism enables LLMs to autonomously develop interaction strategies aligned with clinical reasoning logic, rather than superficially imitating patterns in existing dialogue data. Notably, we constructed MTMedDialog, the first English multi-turn medical consultation dataset capable of simulating patient interactions. Experiments demonstrate that DoctorAgent-RL outperforms existing models in both multi-turn reasoning capability and final diagnostic performance, demonstrating practical value in assisting clinical consultations. https://github.com/JarvisUSTC/DoctorAgent-RL", "github_manual": "", "github_regex": "https://github.com/JarvisUSTC/DoctorAgent-RL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19590", "title": "Learning to Reason without External Rewards", "authors": [ "Xuandong Zhao", "Zhewei Kang", "Aosong Feng", "Sergey Levine", "Dawn Song" ], "github": "https://github.com/sunblaze-ucb/Intuitor", "project_page": "", "abstract": "Training large language models (LLMs) for complex reasoning via Reinforcement Learning with Verifiable Rewards (RLVR) is effective but limited by reliance on costly, domain-specific supervision. We explore Reinforcement Learning from Internal Feedback (RLIF), a framework that enables LLMs to learn from intrinsic signals without external rewards or labeled data. We propose Intuitor, an RLIF method that uses a model's own confidence, termed self-certainty, as its sole reward signal. Intuitor replaces external rewards in Group Relative Policy Optimization (GRPO) with self-certainty scores, enabling fully unsupervised learning. Experiments demonstrate that Intuitor matches GRPO's performance on mathematical benchmarks while achieving superior generalization to out-of-domain tasks like code generation, without requiring gold solutions or test cases. Our findings show that intrinsic model signals can drive effective learning across domains, offering a scalable alternative to RLVR for autonomous AI systems where verifiable rewards are unavailable. Code is available at https://github.com/sunblaze-ucb/Intuitor", "github_manual": "", "github_regex": "https://github.com/sunblaze-ucb/Intuitor", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19427", "title": "WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference", "authors": [ "Sihan Chen", "Dan Zhao", "Jongwoo Ko", "Colby Banbury", "Huiping Zhuang", "Luming Liang", "Tianyi Chen" ], "github": "https://github.com/microsoft/wina", "project_page": "", "abstract": "The growing computational demands of large language models (LLMs) make efficient inference and activation strategies increasingly critical. While recent approaches, such as Mixture-of-Experts (MoE), leverage selective activation but require specialized training, training-free sparse activation methods offer broader applicability and superior resource efficiency through their plug-and-play design. However, many existing methods rely solely on hidden state magnitudes to determine activation, resulting in high approximation errors and suboptimal inference accuracy. To address these limitations, we propose WINA (Weight Informed Neuron Activation), a novel, simple, and training-free sparse activation framework that jointly considers hidden state magnitudes and the column-wise ell_2-norms of weight matrices. We show that this leads to a sparsification strategy that obtains optimal approximation error bounds with theoretical guarantees tighter than existing techniques. Empirically, WINA also outperforms state-of-the-art methods (e.g., TEAL) by up to 2.94% in average performance at the same sparsity levels, across a diverse set of LLM architectures and datasets. These results position WINA as a new performance frontier for training-free sparse activation in LLM inference, advancing training-free sparse activation methods and setting a robust baseline for efficient inference. The source code is available at https://github.com/microsoft/wina.", "github_manual": "", "github_regex": "https://github.com/microsoft/wina", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.10887", "title": "InfantAgent-Next: A Multimodal Generalist Agent for Automated Computer Interaction", "authors": [ "Bin Lei", "Weitai Kang", "Zijian Zhang", "Winson Chen", "Xi Xie", "Shan Zuo", "Mimi Xie", "Ali Payani", "Mingyi Hong", "Yan Yan", "Caiwen Ding" ], "github": "https://github.com/bin123apple/InfantAgent", "project_page": "", "abstract": "This paper introduces InfantAgent-Next, a generalist agent capable of interacting with computers in a multimodal manner, encompassing text, images, audio, and video. Unlike existing approaches that either build intricate workflows around a single large model or only provide workflow modularity, our agent integrates tool-based and pure vision agents within a highly modular architecture, enabling different models to collaboratively solve decoupled tasks in a step-by-step manner. Our generality is demonstrated by our ability to evaluate not only pure vision-based real-world benchmarks (i.e., OSWorld), but also more general or tool-intensive benchmarks (e.g., GAIA and SWE-Bench). Specifically, we achieve 7.27% accuracy on OSWorld, higher than Claude-Computer-Use. Codes and evaluation scripts are open-sourced at https://github.com/bin123apple/InfantAgent.", "github_manual": "", "github_regex": "https://github.com/bin123apple/InfantAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20258", "title": "ARM: Adaptive Reasoning Model", "authors": [ "Siye Wu", "Jian Xie", "Yikai Zhang", "Aili Chen", "Kai Zhang", "Yu Su", "Yanghua Xiao" ], "github": "", "project_page": "", "abstract": "While large reasoning models demonstrate strong performance on complex tasks, they lack the ability to adjust reasoning token usage based on task difficulty. This often leads to the \"overthinking\" problem -- excessive and unnecessary reasoning -- which, although potentially mitigated by human intervention to control the token budget, still fundamentally contradicts the goal of achieving fully autonomous AI. In this work, we propose Adaptive Reasoning Model (ARM), a reasoning model capable of adaptively selecting appropriate reasoning formats based on the task at hand. These formats include three efficient ones -- Direct Answer, Short CoT, and Code -- as well as a more elaborate format, Long CoT. To train ARM, we introduce Ada-GRPO, an adaptation of Group Relative Policy Optimization (GRPO), which addresses the format collapse issue in traditional GRPO. Ada-GRPO enables ARM to achieve high token efficiency, reducing tokens by an average of 30%, and up to 70%, while maintaining performance comparable to the model that relies solely on Long CoT. Furthermore, not only does it improve inference efficiency through reduced token generation, but it also brings a 2x speedup in training. In addition to the default Adaptive Mode, ARM supports two additional reasoning modes: 1) Instruction-Guided Mode, which allows users to explicitly specify the reasoning format via special tokens -- ideal when the appropriate format is known for a batch of tasks. 2) Consensus-Guided Mode, which aggregates the outputs of the three efficient formats and resorts to Long CoT in case of disagreement, prioritizing performance with higher token usage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20259", "title": "Lifelong Safety Alignment for Language Models", "authors": [ "Haoyu Wang", "Zeyu Qin", "Yifei Zhao", "Chao Du", "Min Lin", "Xueqian Wang", "Tianyu Pang" ], "github": "https://github.com/sail-sg/LifelongSafetyAlignment", "project_page": "", "abstract": "LLMs have made impressive progress, but their growing capabilities also expose them to highly flexible jailbreaking attacks designed to bypass safety alignment. While many existing defenses focus on known types of attacks, it is more critical to prepare LLMs for unseen attacks that may arise during deployment. To address this, we propose a lifelong safety alignment framework that enables LLMs to continuously adapt to new and evolving jailbreaking strategies. Our framework introduces a competitive setup between two components: a Meta-Attacker, trained to actively discover novel jailbreaking strategies, and a Defender, trained to resist them. To effectively warm up the Meta-Attacker, we first leverage the GPT-4o API to extract key insights from a large collection of jailbreak-related research papers. Through iterative training, the first iteration Meta-Attacker achieves a 73% attack success rate (ASR) on RR and a 57% transfer ASR on LAT using only single-turn attacks. Meanwhile, the Defender progressively improves its robustness and ultimately reduces the Meta-Attacker's success rate to just 7%, enabling safer and more reliable deployment of LLMs in open-ended environments. The code is available at https://github.com/sail-sg/LifelongSafetyAlignment.", "github_manual": "", "github_regex": "https://github.com/sail-sg/LifelongSafetyAlignment", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20256", "title": "Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration", "authors": [ "Hao Zhong", "Muzhi Zhu", "Zongze Du", "Zheng Huang", "Canyu Zhao", "Mingyu Liu", "Wen Wang", "Hao Chen", "Chunhua Shen" ], "github": "https://github.com/aim-uofa/Omni-R1", "project_page": "", "abstract": "Long-horizon video-audio reasoning and fine-grained pixel understanding impose conflicting requirements on omnimodal models: dense temporal coverage demands many low-resolution frames, whereas precise grounding calls for high-resolution inputs. We tackle this trade-off with a two-system architecture: a Global Reasoning System selects informative keyframes and rewrites the task at low spatial cost, while a Detail Understanding System performs pixel-level grounding on the selected high-resolution snippets. Because ``optimal'' keyframe selection and reformulation are ambiguous and hard to supervise, we formulate them as a reinforcement learning (RL) problem and present Omni-R1, an end-to-end RL framework built on Group Relative Policy Optimization. Omni-R1 trains the Global Reasoning System through hierarchical rewards obtained via online collaboration with the Detail Understanding System, requiring only one epoch of RL on small task splits. Experiments on two challenging benchmarks, namely Referring Audio-Visual Segmentation (RefAVS) and Reasoning Video Object Segmentation (REVOS), show that Omni-R1 not only surpasses strong supervised baselines but also outperforms specialized state-of-the-art models, while substantially improving out-of-domain generalization and mitigating multimodal hallucination. Our results demonstrate the first successful application of RL to large-scale omnimodal reasoning and highlight a scalable path toward universally foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aim-uofa/Omni-R1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19147", "title": "Shifting AI Efficiency From Model-Centric to Data-Centric Compression", "authors": [ "Xuyang Liu", "Zichen Wen", "Shaobo Wang", "Junjie Chen", "Zhishan Tao", "Yubo Wang", "Xiangqi Jin", "Chang Zou", "Yiyu Wang", "Chenfei Liao", "Xu Zheng", "Honggang Chen", "Weijia Li", "Xuming Hu", "Conghui He", "Linfeng Zhang" ], "github": "", "project_page": "", "abstract": "The rapid advancement of large language models (LLMs) and multi-modal LLMs (MLLMs) has historically relied on model-centric scaling through increasing parameter counts from millions to hundreds of billions to drive performance gains. However, as we approach hardware limits on model size, the dominant computational bottleneck has fundamentally shifted to the quadratic cost of self-attention over long token sequences, now driven by ultra-long text contexts, high-resolution images, and extended videos. In this position paper, we argue that the focus of research for efficient AI is shifting from model-centric compression to data-centric compression. We position token compression as the new frontier, which improves AI efficiency via reducing the number of tokens during model training or inference. Through comprehensive analysis, we first examine recent developments in long-context AI across various domains and establish a unified mathematical framework for existing model efficiency strategies, demonstrating why token compression represents a crucial paradigm shift in addressing long-context overhead. Subsequently, we systematically review the research landscape of token compression, analyzing its fundamental benefits and identifying its compelling advantages across diverse scenarios. Furthermore, we provide an in-depth analysis of current challenges in token compression research and outline promising future directions. Ultimately, our work aims to offer a fresh perspective on AI efficiency, synthesize existing research, and catalyze innovative developments to address the challenges that increasing context lengths pose to the AI community's advancement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20254", "title": "Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs", "authors": [ "Xiangchen Song", "Aashiq Muhamed", "Yujia Zheng", "Lingjing Kong", "Zeyu Tang", "Mona T. Diab", "Virginia Smith", "Kun Zhang" ], "github": "", "project_page": "", "abstract": "Sparse Autoencoders (SAEs) are a prominent tool in mechanistic interpretability (MI) for decomposing neural network activations into interpretable features. However, the aspiration to identify a canonical set of features is challenged by the observed inconsistency of learned SAE features across different training runs, undermining the reliability and efficiency of MI research. This position paper argues that mechanistic interpretability should prioritize feature consistency in SAEs -- the reliable convergence to equivalent feature sets across independent runs. We propose using the Pairwise Dictionary Mean Correlation Coefficient (PW-MCC) as a practical metric to operationalize consistency and demonstrate that high levels are achievable (0.80 for TopK SAEs on LLM activations) with appropriate architectural choices. Our contributions include detailing the benefits of prioritizing consistency; providing theoretical grounding and synthetic validation using a model organism, which verifies PW-MCC as a reliable proxy for ground-truth recovery; and extending these findings to real-world LLM data, where high feature consistency strongly correlates with the semantic similarity of learned feature explanations. We call for a community-wide shift towards systematically measuring feature consistency to foster robust cumulative progress in MI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19209", "title": "MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Search", "authors": [ "Zonglin Yang", "Wanhao Liu", "Ben Gao", "Yujie Liu", "Wei Li", "Tong Xie", "Lidong Bing", "Wanli Ouyang", "Erik Cambria", "Dongzhan Zhou" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have shown promise in automating scientific hypothesis generation, yet existing approaches primarily yield coarse-grained hypotheses lacking critical methodological and experimental details. We introduce and formally define the novel task of fine-grained scientific hypothesis discovery, which entails generating detailed, experimentally actionable hypotheses from coarse initial research directions. We frame this as a combinatorial optimization problem and investigate the upper limits of LLMs' capacity to solve it when maximally leveraged. Specifically, we explore four foundational questions: (1) how to best harness an LLM's internal heuristics to formulate the fine-grained hypothesis it itself would judge as the most promising among all the possible hypotheses it might generate, based on its own internal scoring-thus defining a latent reward landscape over the hypothesis space; (2) whether such LLM-judged better hypotheses exhibit stronger alignment with ground-truth hypotheses; (3) whether shaping the reward landscape using an ensemble of diverse LLMs of similar capacity yields better outcomes than defining it with repeated instances of the strongest LLM among them; and (4) whether an ensemble of identical LLMs provides a more reliable reward landscape than a single LLM. To address these questions, we propose a hierarchical search method that incrementally proposes and integrates details into the hypothesis, progressing from general concepts to specific experimental configurations. We show that this hierarchical process smooths the reward landscape and enables more effective optimization. Empirical evaluations on a new benchmark of expert-annotated fine-grained hypotheses from recent chemistry literature show that our method consistently outperforms strong baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.15957", "title": "Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey", "authors": [ "Chih-Kai Yang", "Neo S. Ho", "Hung-yi Lee" ], "github": "", "project_page": "", "abstract": "With advancements in large audio-language models (LALMs), which enhance large language models (LLMs) with auditory capabilities, these models are expected to demonstrate universal proficiency across various auditory tasks. While numerous benchmarks have emerged to assess LALMs' performance, they remain fragmented and lack a structured taxonomy. To bridge this gap, we conduct a comprehensive survey and propose a systematic taxonomy for LALM evaluations, categorizing them into four dimensions based on their objectives: (1) General Auditory Awareness and Processing, (2) Knowledge and Reasoning, (3) Dialogue-oriented Ability, and (4) Fairness, Safety, and Trustworthiness. We provide detailed overviews within each category and highlight challenges in this field, offering insights into promising future directions. To the best of our knowledge, this is the first survey specifically focused on the evaluations of LALMs, providing clear guidelines for the community. We will release the collection of the surveyed papers and actively maintain it to support ongoing advancements in the field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20278", "title": "The Coverage Principle: A Framework for Understanding Compositional Generalization", "authors": [ "Hoyeon Chang", "Jinho Park", "Hanseul Cho", "Sohee Yang", "Miyoung Ko", "Hyeonbin Hwang", "Seungpil Won", "Dohaeng Lee", "Youbin Ahn", "Minjoon Seo" ], "github": "https://github.com/kaistai/coverage-principle", "project_page": "", "abstract": "Large language models excel at pattern matching, yet often fall short in systematic compositional generalization. We propose the coverage principle: a data-centric framework showing that models relying primarily on pattern matching for compositional tasks cannot reliably generalize beyond substituting fragments that yield identical results when used in the same contexts. We demonstrate that this framework has a strong predictive power for the generalization capabilities of Transformers. First, we derive and empirically confirm that the training data required for two-hop generalization grows at least quadratically with the token set size, and the training data efficiency does not improve with 20x parameter scaling. Second, for compositional tasks with path ambiguity where one variable affects the output through multiple computational paths, we show that Transformers learn context-dependent state representations that undermine both performance and interoperability. Third, Chain-of-Thought supervision improves training data efficiency for multi-hop tasks but still struggles with path ambiguity. Finally, we outline a mechanism-based taxonomy that distinguishes three ways neural networks can generalize: structure-based (bounded by coverage), property-based (leveraging algebraic invariances), and shared-operator (through function reuse). This conceptual lens contextualizes our results and highlights where new architectural ideas are needed to achieve systematic compositionally. Overall, the coverage principle provides a unified lens for understanding compositional reasoning, and underscores the need for fundamental architectural or training innovations to achieve truly systematic compositionality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kaistai/coverage-principle", "github_api_added_by": "auto" }, { "date": "2025-05-27", "arxiv_id": "2505.19949", "title": "Which Data Attributes Stimulate Math and Code Reasoning? An Investigation via Influence Functions", "authors": [ "Siqi Kou", "Qingyuan Tian", "Hanwen Xu", "Zihao Zeng", "Zhijie Deng" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable reasoning capabilities in math and coding, often bolstered by post-training on the chain-of-thoughts (CoTs) generated by stronger models. However, existing strategies for curating such training data predominantly rely on heuristics, limiting generalizability and failing to capture subtleties underlying in data. To address these limitations, we leverage influence functions to systematically attribute LLMs' reasoning ability on math and coding to individual training examples, sequences, and tokens, enabling deeper insights into effective data characteristics. Our Influence-based Reasoning Attribution (Infra) uncovers nontrivial cross-domain effects across math and coding tasks: high-difficulty math examples improve both math and code reasoning, while low-difficulty code tasks most effectively benefit code reasoning. Based on these findings, we introduce a simple yet effective dataset reweighting strategy by flipping task difficulty, which doubles AIME24 accuracy from 10\\% to 20\\% and boosts LiveCodeBench accuracy from 33.8\\% to 35.3\\% for Qwen2.5-7B-Instruct. Moreover, our fine-grained attribution reveals that the sequence-level exploratory behaviors enhance reasoning performance in both math and code, and the token-level influence patterns are distinct for math and code reasoning: the former prefers natural language logic connectors and the latter emphasizes structural syntax.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19788", "title": "Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured Multi-Turn Decomposition", "authors": [ "Zihao Zeng", "Xuyao Huang", "Boxiu Li", "Hao Zhang", "Zhijie Deng" ], "github": "", "project_page": "", "abstract": "Large Reasoning Models (LRMs) are criticized for the excessively lengthy Chain-of-Thought (CoT) to derive the final answer, suffering from high first-token and overall latency. Typically, the CoT of LRMs mixes multiple thinking units; each unit attempts to produce a candidate answer to the original query. Hence, a natural idea to improve efficiency is to reduce the unit number. Yet, the fact that the thinking units in vanilla CoT cannot be explicitly managed renders doing so challenging. This paper introduces Multi-Turn Decomposition (MinD) to decode conventional CoT into a sequence of explicit, structured, and turn-wise interactions to bridge the gap. In MinD, the model provides a multi-turn response to the query, where each turn embraces a thinking unit and yields a corresponding answer. The subsequent turns can reflect, verify, revise, or explore alternative approaches to both the thinking and answer parts of earlier ones. This not only makes the answer delivered more swiftly, but also enables explicit controls over the iterative reasoning process (i.e., users may halt or continue at any turn). We follow a supervised fine-tuning (SFT) then reinforcement learning (RL) paradigm to realize MinD. We first rephrase the outputs of an LRM into multi-turn formats by prompting another LLM, and then tune the LRM with such data. Observing that the tuned model tends to consume even more tokens than the original one (probably due to that the multi-turn formats introduce additional answer tokens), we advocate leveraging RL algorithms like GRPO to prioritize correct outputs with fewer turns. Trained on the MATH dataset using R1-Distill models, MinD can achieve up to ~70% reduction in both output token usage and time to first token (TTFT), while maintaining competitive performance on reasoning benchmarks such as MATH-500, AIME24, AMC23, and GPQA-Diamond.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.16348", "title": "Embodied Agents Meet Personalization: Exploring Memory Utilization for Personalized Assistance", "authors": [ "Taeyoon Kwon", "Dongwook Choi", "Sunghwan Kim", "Hyojun Kim", "Seungjun Moon", "Beong-woo Kwak", "Kuan-Hao Huang", "Jinyoung Yeo" ], "github": "", "project_page": "", "abstract": "Embodied agents empowered by large language models (LLMs) have shown strong performance in household object rearrangement tasks. However, these tasks primarily focus on single-turn interactions with simplified instructions, which do not truly reflect the challenges of providing meaningful assistance to users. To provide personalized assistance, embodied agents must understand the unique semantics that users assign to the physical world (e.g., favorite cup, breakfast routine) by leveraging prior interaction history to interpret dynamic, real-world instructions. Yet, the effectiveness of embodied agents in utilizing memory for personalized assistance remains largely underexplored. To address this gap, we present MEMENTO, a personalized embodied agent evaluation framework designed to comprehensively assess memory utilization capabilities to provide personalized assistance. Our framework consists of a two-stage memory evaluation process design that enables quantifying the impact of memory utilization on task performance. This process enables the evaluation of agents' understanding of personalized knowledge in object rearrangement tasks by focusing on its role in goal interpretation: (1) the ability to identify target objects based on personal meaning (object semantics), and (2) the ability to infer object-location configurations from consistent user patterns, such as routines (user patterns). Our experiments across various LLMs reveal significant limitations in memory utilization, with even frontier models like GPT-4o experiencing a 30.5% performance drop when required to reference multiple memories, particularly in tasks involving user patterns. These findings, along with our detailed analyses and case studies, provide valuable insights for future research in developing more effective personalized embodied agents. Project website: https://connoriginal.github.io/MEMENTO", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19914", "title": "Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles", "authors": [ "Jiangjie Chen", "Qianyu He", "Siyu Yuan", "Aili Chen", "Zhicheng Cai", "Weinan Dai", "Hongli Yu", "Qiying Yu", "Xuefeng Li", "Jiaze Chen", "Hao Zhou", "Mingxuan Wang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs), such as OpenAI's o1 and DeepSeek's R1, excel at advanced reasoning tasks like math and coding via Reinforcement Learning with Verifiable Rewards (RLVR), but still struggle with puzzles solvable by humans without domain knowledge. We introduce Enigmata, the first comprehensive suite tailored for improving LLMs with puzzle reasoning skills. It includes 36 tasks across seven categories, each with 1) a generator that produces unlimited examples with controllable difficulty and 2) a rule-based verifier for automatic evaluation. This generator-verifier design supports scalable, multi-task RL training, fine-grained analysis, and seamless RLVR integration. We further propose Enigmata-Eval, a rigorous benchmark, and develop optimized multi-task RLVR strategies. Our trained model, Qwen2.5-32B-Enigmata, consistently surpasses o3-mini-high and o1 on the puzzle reasoning benchmarks like Enigmata-Eval, ARC-AGI (32.8%), and ARC-AGI 2 (0.6%). It also generalizes well to out-of-domain puzzle benchmarks and mathematical reasoning, with little multi-tasking trade-off. When trained on larger models like Seed1.5-Thinking (20B activated parameters and 200B total parameters), puzzle data from Enigmata further boosts SoTA performance on advanced math and STEM reasoning tasks such as AIME (2024-2025), BeyondAIME and GPQA (Diamond), showing nice generalization benefits of Enigmata. This work offers a unified, controllable framework for advancing logical reasoning in LLMs. Resources of this work can be found at https://seed-enigmata.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20152", "title": "Hard Negative Contrastive Learning for Fine-Grained Geometric Understanding in Large Multimodal Models", "authors": [ "Kai Sun", "Yushi Bai", "Zhen Yang", "Jiajie Zhang", "Ji Qi", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/THU-KEG/MMGeoLM", "project_page": "", "abstract": "Benefiting from contrastively trained visual encoders on large-scale natural scene images, Large Multimodal Models (LMMs) have achieved remarkable performance across various visual perception tasks. However, the inherent limitations of contrastive learning upon summarized descriptions fundamentally restrict the capabilities of models in meticulous reasoning, particularly in crucial scenarios of geometric problem-solving. To enhance geometric understanding, we propose a novel hard negative contrastive learning framework for the vision encoder, which combines image-based contrastive learning using generation-based hard negatives created by perturbing diagram generation code, and text-based contrastive learning using rule-based negatives derived from modified geometric descriptions and retrieval-based negatives selected based on caption similarity. We train CLIP using our strong negative learning method, namely MMCLIP (Multimodal Math CLIP), and subsequently train an LMM for geometric problem-solving. Experiments show that our trained model, MMGeoLM, significantly outperforms other open-source models on three geometric reasoning benchmarks. Even with a size of 7B, it can rival powerful closed-source models like GPT-4o. We further study the impact of different negative sample construction methods and the number of negative samples on the geometric reasoning performance of LMM, yielding fruitful conclusions. The code and dataset are available at https://github.com/THU-KEG/MMGeoLM.", "github_manual": "", "github_regex": "https://github.com/THU-KEG/MMGeoLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19640", "title": "Interleaved Reasoning for Large Language Models via Reinforcement Learning", "authors": [ "Roy Xie", "David Qiu", "Deepak Gopinath", "Dong Lin", "Yanchao Sun", "Chong Wang", "Saloni Potdar", "Bhuwan Dhingra" ], "github": "", "project_page": "", "abstract": "Long chain-of-thought (CoT) significantly enhances large language models' (LLM) reasoning capabilities. However, the extensive reasoning traces lead to inefficiencies and an increased time-to-first-token (TTFT). We propose a novel training paradigm that uses reinforcement learning (RL) to guide reasoning LLMs to interleave thinking and answering for multi-hop questions. We observe that models inherently possess the ability to perform interleaved reasoning, which can be further enhanced through RL. We introduce a simple yet effective rule-based reward to incentivize correct intermediate steps, which guides the policy model toward correct reasoning paths by leveraging intermediate signals generated during interleaved reasoning. Extensive experiments conducted across five diverse datasets and three RL algorithms (PPO, GRPO, and REINFORCE++) demonstrate consistent improvements over traditional think-answer reasoning, without requiring external tools. Specifically, our approach reduces TTFT by over 80% on average and improves up to 19.3% in Pass@1 accuracy. Furthermore, our method, trained solely on question answering and logical reasoning datasets, exhibits strong generalization ability to complex reasoning datasets such as MATH, GPQA, and MMLU. Additionally, we conduct in-depth analysis to reveal several valuable insights into conditional reward modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19457", "title": "BizFinBench: A Business-Driven Real-World Financial Benchmark for Evaluating LLMs", "authors": [ "Guilong Lu", "Xuntao Guo", "Rongjunchen Zhang", "Wenqiao Zhu", "Ji Liu" ], "github": "https://github.com/HiThink-Research/BizFinBench", "project_page": "", "abstract": "Large language models excel in general tasks, yet assessing their reliability in logic-heavy, precision-critical domains like finance, law, and healthcare remains challenging. To address this, we introduce BizFinBench, the first benchmark specifically designed to evaluate LLMs in real-world financial applications. BizFinBench consists of 6,781 well-annotated queries in Chinese, spanning five dimensions: numerical calculation, reasoning, information extraction, prediction recognition, and knowledge-based question answering, grouped into nine fine-grained categories. The benchmark includes both objective and subjective metrics. We also introduce IteraJudge, a novel LLM evaluation method that reduces bias when LLMs serve as evaluators in objective metrics. We benchmark 25 models, including both proprietary and open-source systems. Extensive experiments show that no model dominates across all tasks. Our evaluation reveals distinct capability patterns: (1) In Numerical Calculation, Claude-3.5-Sonnet (63.18) and DeepSeek-R1 (64.04) lead, while smaller models like Qwen2.5-VL-3B (15.92) lag significantly; (2) In Reasoning, proprietary models dominate (ChatGPT-o3: 83.58, Gemini-2.0-Flash: 81.15), with open-source models trailing by up to 19.49 points; (3) In Information Extraction, the performance spread is the largest, with DeepSeek-R1 scoring 71.46, while Qwen3-1.7B scores 11.23; (4) In Prediction Recognition, performance variance is minimal, with top models scoring between 39.16 and 50.00. We find that while current LLMs handle routine finance queries competently, they struggle with complex scenarios requiring cross-concept reasoning. BizFinBench offers a rigorous, business-aligned benchmark for future research. The code and dataset are available at https://github.com/HiThink-Research/BizFinBench.", "github_manual": "", "github_regex": "https://github.com/HiThink-Research/BizFinBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18384", "title": "Dynamic Risk Assessments for Offensive Cybersecurity Agents", "authors": [ "Boyi Wei", "Benedikt Stroebl", "Jiacen Xu", "Joie Zhang", "Zhou Li", "Peter Henderson" ], "github": "https://github.com/princeton-polaris-lab/Dynamic-Risk-Assessment", "project_page": "", "abstract": "Foundation models are increasingly becoming better autonomous programmers, raising the prospect that they could also automate dangerous offensive cyber-operations. Current frontier model audits probe the cybersecurity risks of such agents, but most fail to account for the degrees of freedom available to adversaries in the real world. In particular, with strong verifiers and financial incentives, agents for offensive cybersecurity are amenable to iterative improvement by would-be adversaries. We argue that assessments should take into account an expanded threat model in the context of cybersecurity, emphasizing the varying degrees of freedom that an adversary may possess in stateful and non-stateful environments within a fixed compute budget. We show that even with a relatively small compute budget (8 H100 GPU Hours in our study), adversaries can improve an agent's cybersecurity capability on InterCode CTF by more than 40\\% relative to the baseline -- without any external assistance. These results highlight the need to evaluate agents' cybersecurity risk in a dynamic manner, painting a more representative picture of risk.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/princeton-polaris-lab/Dynamic-Risk-Assessment", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18759", "title": "The Quest for Efficient Reasoning: A Data-Centric Benchmark to CoT Distillation", "authors": [ "Ruichen Zhang", "Rana Muhammad Shahroz Khan", "Zhen Tan", "Dawei Li", "Song Wang", "Tianlong Chen" ], "github": "", "project_page": "", "abstract": "Data-centric distillation, including data augmentation, selection, and mixing, offers a promising path to creating smaller, more efficient student Large Language Models (LLMs) that retain strong reasoning abilities. However, there still lacks a comprehensive benchmark to systematically assess the effect of each distillation approach. This paper introduces DC-CoT, the first data-centric benchmark that investigates data manipulation in chain-of-thought (CoT) distillation from method, model and data perspectives. Utilizing various teacher models (e.g., o4-mini, Gemini-Pro, Claude-3.5) and student architectures (e.g., 3B, 7B parameters), we rigorously evaluate the impact of these data manipulations on student model performance across multiple reasoning datasets, with a focus on in-distribution (IID) and out-of-distribution (OOD) generalization, and cross-domain transfer. Our findings aim to provide actionable insights and establish best practices for optimizing CoT distillation through data-centric techniques, ultimately facilitating the development of more accessible and capable reasoning models. The dataset can be found at https://huggingface.co/datasets/rana-shahroz/DC-COT, while our code is shared in https://anonymous.4open.science/r/DC-COT-FF4C/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19439", "title": "Surrogate Signals from Format and Length: Reinforcement Learning for Solving Mathematical Problems without Ground Truth Answers", "authors": [ "Rihui Xin", "Han Liu", "Zecheng Wang", "Yupeng Zhang", "Dianbo Sui", "Xiaolin Hu", "Bingning Wang" ], "github": "https://github.com/insightllm/rl-without-gt", "project_page": "", "abstract": "Large Language Models have achieved remarkable success in natural language processing tasks, with Reinforcement Learning playing a key role in adapting them to specific applications. However, obtaining ground truth answers for training LLMs in mathematical problem-solving is often challenging, costly, and sometimes unfeasible. This research delves into the utilization of format and length as surrogate signals to train LLMs for mathematical problem-solving, bypassing the need for traditional ground truth answers.Our study shows that a reward function centered on format correctness alone can yield performance improvements comparable to the standard GRPO algorithm in early phases. Recognizing the limitations of format-only rewards in the later phases, we incorporate length-based rewards. The resulting GRPO approach, leveraging format-length surrogate signals, not only matches but surpasses the performance of the standard GRPO algorithm relying on ground truth answers in certain scenarios, achieving 40.0\\% accuracy on AIME2024 with a 7B base model. Through systematic exploration and experimentation, this research not only offers a practical solution for training LLMs to solve mathematical problems and reducing the dependence on extensive ground truth data collection, but also reveals the essence of why our label-free approach succeeds: base model is like an excellent student who has already mastered mathematical and logical reasoning skills, but performs poorly on the test paper, it simply needs to develop good answering habits to achieve outstanding results in exams , in other words, to unlock the capabilities it already possesses.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/insightllm/rl-without-gt", "github_api_added_by": "auto" }, { "date": "2025-05-27", "arxiv_id": "2505.16312", "title": "EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning", "authors": [ "Jiawei Liu", "Qisi Chen", "Jianshu Zhang", "Quan Liu", "Defu Lian" ], "github": "https://github.com/Lolo1222/EquivPruner", "project_page": "", "abstract": "Large Language Models (LLMs) excel at complex reasoning through search algorithms, yet current strategies often suffer from massive token consumption due to redundant exploration of semantically equivalent steps. Existing semantic similarity methods struggle to accurately identify such equivalence in domain-specific contexts like mathematical reasoning. To address this, we propose EquivPruner, a simple yet effective approach that identifies and prunes semantically equivalent actions during LLM reasoning search. We also introduce MathEquiv, the first dataset we created for mathematical statement equivalence, which enables the training of a lightweight equivalence detector. Extensive experiments across various models and tasks demonstrate that EquivPruner significantly reduces token consumption, improving searching efficiency and often bolstering reasoning accuracy. For instance, when applied to Qwen2.5-Math-7B-Instruct on GSM8K, EquivPruner reduced token consumption by 48.1\\% while also improving accuracy. Our code is available at https://github.com/Lolo1222/EquivPruner.", "github_manual": "", "github_regex": "https://github.com/Lolo1222/EquivPruner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.17894", "title": "Mutarjim: Advancing Bidirectional Arabic-English Translation with a Small Language Model", "authors": [ "Khalil Hennara", "Muhammad Hreden", "Mohamed Motaism Hamed", "Zeina Aldallal", "Sara Chrouf", "Safwan AlModhayan" ], "github": "", "project_page": "", "abstract": "We introduce Mutarjim, a compact yet powerful language model for bidirectional Arabic-English translation. While large-scale LLMs have shown impressive progress in natural language processing tasks, including machine translation, smaller models. Leveraging this insight, we developed Mutarjim based on Kuwain-1.5B , a language model tailored for both Arabic and English. Despite its modest size, Mutarjim outperforms much larger models on several established benchmarks, achieved through an optimized two-phase training approach and a carefully curated, high-quality training corpus.. Experimental results show that Mutarjim rivals models up to 20 times larger while significantly reducing computational costs and training requirements. We also introduce Tarjama-25, a new benchmark designed to overcome limitations in existing Arabic-English benchmarking datasets, such as domain narrowness, short sentence lengths, and English-source bias. Tarjama-25 comprises 5,000 expert-reviewed sentence pairs and spans a wide range of domains, offering a more comprehensive and balanced evaluation framework. Notably, Mutarjim achieves state-of-the-art performance on the English-to-Arabic task in Tarjama-25, surpassing even significantly larger and proprietary models like GPT-4o mini. We publicly release Tarjama-25 to support future research and advance the evaluation of Arabic-English translation systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20294", "title": "GLEAM: Learning Generalizable Exploration Policy for Active Mapping in Complex 3D Indoor Scenes", "authors": [ "Xiao Chen", "Tai Wang", "Quanyi Li", "Tao Huang", "Jiangmiao Pang", "Tianfan Xue" ], "github": "", "project_page": "", "abstract": "Generalizable active mapping in complex unknown environments remains a critical challenge for mobile robots. Existing methods, constrained by insufficient training data and conservative exploration strategies, exhibit limited generalizability across scenes with diverse layouts and complex connectivity. To enable scalable training and reliable evaluation, we introduce GLEAM-Bench, the first large-scale benchmark designed for generalizable active mapping with 1,152 diverse 3D scenes from synthetic and real-scan datasets. Building upon this foundation, we propose GLEAM, a unified generalizable exploration policy for active mapping. Its superior generalizability comes mainly from our semantic representations, long-term navigable goals, and randomized strategies. It significantly outperforms state-of-the-art methods, achieving 66.50% coverage (+9.49%) with efficient trajectories and improved mapping accuracy on 128 unseen complex scenes. Project page: https://xiao-chen.tech/gleam/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18773", "title": "Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models", "authors": [ "Jamie Hayes", "Ilia Shumailov", "Christopher A. Choquette-Choo", "Matthew Jagielski", "George Kaissis", "Katherine Lee", "Milad Nasr", "Sahra Ghalebikesabi", "Niloofar Mireshghallah", "Meenatchi Sundaram Mutu Selva Annamalai", "Igor Shilov", "Matthieu Meeus", "Yves-Alexandre de Montjoye", "Franziska Boenisch", "Adam Dziedzic", "A. Feder Cooper" ], "github": "", "project_page": "", "abstract": "State-of-the-art membership inference attacks (MIAs) typically require training many reference models, making it difficult to scale these attacks to large pre-trained language models (LLMs). As a result, prior research has either relied on weaker attacks that avoid training reference models (e.g., fine-tuning attacks), or on stronger attacks applied to small-scale models and datasets. However, weaker attacks have been shown to be brittle - achieving close-to-arbitrary success - and insights from strong attacks in simplified settings do not translate to today's LLMs. These challenges have prompted an important question: are the limitations observed in prior work due to attack design choices, or are MIAs fundamentally ineffective on LLMs? We address this question by scaling LiRA - one of the strongest MIAs - to GPT-2 architectures ranging from 10M to 1B parameters, training reference models on over 20B tokens from the C4 dataset. Our results advance the understanding of MIAs on LLMs in three key ways: (1) strong MIAs can succeed on pre-trained LLMs; (2) their effectiveness, however, remains limited (e.g., AUC<0.7) in practical settings; and, (3) the relationship between MIA success and related privacy metrics is not as straightforward as prior work has suggested.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20139", "title": "StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs", "authors": [ "Jialin Yang", "Dongfu Jiang", "Lipeng He", "Sherman Siu", "Yuxuan Zhang", "Disen Liao", "Zhuofeng Li", "Huaye Zeng", "Yiming Jia", "Haozhe Wang", "Benjamin Schneider", "Chi Ruan", "Wentao Ma", "Zhiheng Lyu", "Yifei Wang", "Yi Lu", "Quy Duc Do", "Ziyan Jiang", "Ping Nie", "Wenhu Chen" ], "github": "https://github.com/HumanSignal/label-studio", "project_page": "", "abstract": "As Large Language Models (LLMs) become integral to software development workflows, their ability to generate structured outputs has become critically important. We introduce StructEval, a comprehensive benchmark for evaluating LLMs' capabilities in producing both non-renderable (JSON, YAML, CSV) and renderable (HTML, React, SVG) structured formats. Unlike prior benchmarks, StructEval systematically evaluates structural fidelity across diverse formats through two paradigms: 1) generation tasks, producing structured output from natural language prompts, and 2) conversion tasks, translating between structured formats. Our benchmark encompasses 18 formats and 44 types of task, with novel metrics for format adherence and structural correctness. Results reveal significant performance gaps, even state-of-the-art models like o1-mini achieve only 75.58 average score, with open-source alternatives lagging approximately 10 points behind. We find generation tasks more challenging than conversion tasks, and producing correct visual content more difficult than generating text-only structures.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HumanSignal/label-studio", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19250", "title": "PATS: Process-Level Adaptive Thinking Mode Switching", "authors": [ "Yi Wang", "Junxiao Liu", "Shimao Zhang", "Jiajun Chen", "Shujian Huang" ], "github": "https://github.com/NJUNLP/PATS", "project_page": "", "abstract": "Current large-language models (LLMs) typically adopt a fixed reasoning strategy, either simple or complex, for all questions, regardless of their difficulty. This neglect of variation in task and reasoning process complexity leads to an imbalance between performance and efficiency. Existing methods attempt to implement training-free fast-slow thinking system switching to handle problems of varying difficulty, but are limited by coarse-grained solution-level strategy adjustments. To address this issue, we propose a novel reasoning paradigm: Process-Level Adaptive Thinking Mode Switching (PATS), which enables LLMs to dynamically adjust their reasoning strategy based on the difficulty of each step, optimizing the balance between accuracy and computational efficiency. Our approach integrates Process Reward Models (PRMs) with Beam Search, incorporating progressive mode switching and bad-step penalty mechanisms. Experiments on diverse mathematical benchmarks demonstrate that our methodology achieves high accuracy while maintaining moderate token usage. This study emphasizes the significance of process-level, difficulty-aware reasoning strategy adaptation, offering valuable insights into efficient inference for LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NJUNLP/PATS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19223", "title": "LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models", "authors": [ "Fengqi Zhu", "Rongzhen Wang", "Shen Nie", "Xiaolu Zhang", "Chunwei Wu", "Jun Hu", "Jun Zhou", "Jianfei Chen", "Yankai Lin", "Ji-Rong Wen", "Chongxuan Li" ], "github": "https://github.com/ML-GSAI/LLaDA-1.5", "project_page": "", "abstract": "While Masked Diffusion Models (MDMs), such as LLaDA, present a promising paradigm for language modeling, there has been relatively little effort in aligning these models with human preferences via reinforcement learning. The challenge primarily arises from the high variance in Evidence Lower Bound (ELBO)-based likelihood estimates required for preference optimization. To address this issue, we propose Variance-Reduced Preference Optimization (VRPO), a framework that formally analyzes the variance of ELBO estimators and derives bounds on both the bias and variance of preference optimization gradients. Building on this theoretical foundation, we introduce unbiased variance reduction strategies, including optimal Monte Carlo budget allocation and antithetic sampling, that significantly improve the performance of MDM alignment. We demonstrate the effectiveness of VRPO by applying it to LLaDA, and the resulting model, LLaDA 1.5, outperforms its SFT-only predecessor consistently and significantly across mathematical (GSM8K +4.7), code (HumanEval +3.0, MBPP +1.8), and alignment benchmarks (IFEval +4.0, Arena-Hard +4.3). Furthermore, LLaDA 1.5 demonstrates a highly competitive mathematical performance compared to strong language MDMs and ARMs. Project page: https://ml-gsai.github.io/LLaDA-1.5-Demo/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ML-GSAI/LLaDA-1.5", "github_api_added_by": "auto" }, { "date": "2025-05-27", "arxiv_id": "2505.18822", "title": "AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgeting", "authors": [ "Shijue Huang", "Hongru Wang", "Wanjun Zhong", "Zhaochen Su", "Jiazhan Feng", "Bowen Cao", "Yi R. Fung" ], "github": "https://github.com/joeying1019/adactrl", "project_page": "", "abstract": "Modern large reasoning models demonstrate impressive problem-solving capabilities by employing sophisticated reasoning strategies. However, they often struggle to balance efficiency and effectiveness, frequently generating unnecessarily lengthy reasoning chains for simple problems. In this work, we propose AdaCtrl, a novel framework to support both difficulty-aware adaptive reasoning budget allocation and explicit user control over reasoning depth. AdaCtrl dynamically adjusts its reasoning length based on self-assessed problem difficulty, while also allowing users to manually control the budget to prioritize either efficiency or effectiveness. This is achieved through a two-stage training pipeline: an initial cold-start fine-tuning phase to instill the ability to self-aware difficulty and adjust reasoning budget, followed by a difficulty-aware reinforcement learning (RL) stage that refines the model's adaptive reasoning strategies and calibrates its difficulty assessments based on its evolving capabilities during online training. To enable intuitive user interaction, we design explicit length-triggered tags that function as a natural interface for budget control. Empirical results show that AdaCtrl adapts reasoning length based on estimated difficulty, compared to the standard training baseline that also incorporates fine-tuning and RL, it yields performance improvements and simultaneously reduces response length by 10.06% and 12.14% on the more challenging AIME2024 and AIME2025 datasets, which require elaborate reasoning, and by 62.05% and 91.04% on the MATH500 and GSM8K datasets, where more concise responses are sufficient. Furthermore, AdaCtrl enables precise user control over the reasoning budget, allowing for tailored responses to meet specific needs.", "github_manual": "https://github.com/joeying1019/adactrl", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18323", "title": "Architectural Backdoors for Within-Batch Data Stealing and Model Inference Manipulation", "authors": [ "Nicolas K\u00fcchler", "Ivan Petrov", "Conrad Grobler", "Ilia Shumailov" ], "github": "https://github.com/google-deepmind/batch-isolation-checker", "project_page": "", "abstract": "For nearly a decade the academic community has investigated backdoors in neural networks, primarily focusing on classification tasks where adversaries manipulate the model prediction. While demonstrably malicious, the immediate real-world impact of such prediction-altering attacks has remained unclear. In this paper we introduce a novel and significantly more potent class of backdoors that builds upon recent advancements in architectural backdoors. We demonstrate how these backdoors can be specifically engineered to exploit batched inference, a common technique for hardware utilization, enabling large-scale user data manipulation and theft. By targeting the batching process, these architectural backdoors facilitate information leakage between concurrent user requests and allow attackers to fully control model responses directed at other users within the same batch. In other words, an attacker who can change the model architecture can set and steal model inputs and outputs of other users within the same batch. We show that such attacks are not only feasible but also alarmingly effective, can be readily injected into prevalent model architectures, and represent a truly malicious threat to user privacy and system integrity. Critically, to counteract this new class of vulnerabilities, we propose a deterministic mitigation strategy that provides formal guarantees against this new attack vector, unlike prior work that relied on Large Language Models to find the backdoors. Our mitigation strategy employs a novel Information Flow Control mechanism that analyzes the model graph and proves non-interference between different user inputs within the same batch. Using our mitigation strategy we perform a large scale analysis of models hosted through Hugging Face and find over 200 models that introduce (unintended) information leakage between batch entries due to the use of dynamic quantization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/batch-isolation-checker", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19800", "title": "MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs", "authors": [ "Zaid Alyafeai", "Maged S. Al-Shaibani", "Bernard Ghanem" ], "github": "https://github.com/IVUL-KAUST/MOLE", "project_page": "", "abstract": "Metadata extraction is essential for cataloging and preserving datasets, enabling effective research discovery and reproducibility, especially given the current exponential growth in scientific research. While Masader (Alyafeai et al.,2021) laid the groundwork for extracting a wide range of metadata attributes from Arabic NLP datasets' scholarly articles, it relies heavily on manual annotation. In this paper, we present MOLE, a framework that leverages Large Language Models (LLMs) to automatically extract metadata attributes from scientific papers covering datasets of languages other than Arabic. Our schema-driven methodology processes entire documents across multiple input formats and incorporates robust validation mechanisms for consistent output. Additionally, we introduce a new benchmark to evaluate the research progress on this task. Through systematic analysis of context length, few-shot learning, and web browsing integration, we demonstrate that modern LLMs show promising results in automating this task, highlighting the need for further future work improvements to ensure consistent and reliable performance. We release the code: https://github.com/IVUL-KAUST/MOLE and dataset: https://huggingface.co/datasets/IVUL-KAUST/MOLE for the research community.", "github_manual": "", "github_regex": "https://github.com/IVUL-KAUST/MOLE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19297", "title": "Alchemist: Turning Public Text-to-Image Data into Generative Gold", "authors": [ "Valerii Startsev", "Alexander Ustyuzhanin", "Alexey Kirillov", "Dmitry Baranchuk", "Sergey Kastryulin" ], "github": "", "project_page": "", "abstract": "Pre-training equips text-to-image (T2I) models with broad world knowledge, but this alone is often insufficient to achieve high aesthetic quality and alignment. Consequently, supervised fine-tuning (SFT) is crucial for further refinement. However, its effectiveness highly depends on the quality of the fine-tuning dataset. Existing public SFT datasets frequently target narrow domains (e.g., anime or specific art styles), and the creation of high-quality, general-purpose SFT datasets remains a significant challenge. Current curation methods are often costly and struggle to identify truly impactful samples. This challenge is further complicated by the scarcity of public general-purpose datasets, as leading models often rely on large, proprietary, and poorly documented internal data, hindering broader research progress. This paper introduces a novel methodology for creating general-purpose SFT datasets by leveraging a pre-trained generative model as an estimator of high-impact training samples. We apply this methodology to construct and release Alchemist, a compact (3,350 samples) yet highly effective SFT dataset. Experiments demonstrate that Alchemist substantially improves the generative quality of five public T2I models while preserving diversity and style. Additionally, we release the fine-tuned models' weights to the public.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19955", "title": "MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research", "authors": [ "Hui Chen", "Miao Xiong", "Yujie Lu", "Wei Han", "Ailin Deng", "Yufei He", "Jiaying Wu", "Yibo Li", "Yue Liu", "Bryan Hooi" ], "github": "", "project_page": "", "abstract": "Recent advancements in AI agents have demonstrated their growing potential to drive and support scientific discovery. In this work, we introduce MLR-Bench, a comprehensive benchmark for evaluating AI agents on open-ended machine learning research. MLR-Bench includes three key components: (1) 201 research tasks sourced from NeurIPS, ICLR, and ICML workshops covering diverse ML topics; (2) MLR-Judge, an automated evaluation framework combining LLM-based reviewers with carefully designed review rubrics to assess research quality; and (3) MLR-Agent, a modular agent scaffold capable of completing research tasks through four stages: idea generation, proposal formulation, experimentation, and paper writing. Our framework supports both stepwise assessment across these distinct research stages, and end-to-end evaluation of the final research paper. We then use MLR-Bench to evaluate six frontier LLMs and an advanced coding agent, finding that while LLMs are effective at generating coherent ideas and well-structured papers, current coding agents frequently (e.g., in 80% of the cases) produce fabricated or invalidated experimental results--posing a major barrier to scientific reliability. We validate MLR-Judge through human evaluation, showing high agreement with expert reviewers, supporting its potential as a scalable tool for research evaluation. We open-source MLR-Bench to help the community benchmark, diagnose, and improve AI research agents toward trustworthy and transparent scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19731", "title": "Accelerating Nash Learning from Human Feedback via Mirror Prox", "authors": [ "Daniil Tiapkin", "Daniele Calandriello", "Denis Belomestny", "Eric Moulines", "Alexey Naumov", "Kashif Rasul", "Michal Valko", "Pierre Menard" ], "github": "", "project_page": "", "abstract": "Traditional Reinforcement Learning from Human Feedback (RLHF) often relies on reward models, frequently assuming preference structures like the Bradley-Terry model, which may not accurately capture the complexities of real human preferences (e.g., intransitivity). Nash Learning from Human Feedback (NLHF) offers a more direct alternative by framing the problem as finding a Nash equilibrium of a game defined by these preferences. In this work, we introduce Nash Mirror Prox (Nash-MP), an online NLHF algorithm that leverages the Mirror Prox optimization scheme to achieve fast and stable convergence to the Nash equilibrium. Our theoretical analysis establishes that Nash-MP exhibits last-iterate linear convergence towards the beta-regularized Nash equilibrium. Specifically, we prove that the KL-divergence to the optimal policy decreases at a rate of order (1+2beta)^{-N/2}, where N is a number of preference queries. We further demonstrate last-iterate linear convergence for the exploitability gap and uniformly for the span semi-norm of log-probabilities, with all these rates being independent of the size of the action space. Furthermore, we propose and analyze an approximate version of Nash-MP where proximal steps are estimated using stochastic policy gradients, making the algorithm closer to applications. Finally, we detail a practical implementation strategy for fine-tuning large language models and present experiments that demonstrate its competitive performance and compatibility with existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19084", "title": "Jodi: Unification of Visual Generation and Understanding via Joint Modeling", "authors": [ "Yifeng Xu", "Zhenliang He", "Meina Kan", "Shiguang Shan", "Xilin Chen" ], "github": "https://github.com/VIPL-GENUN/Jodi", "project_page": "", "abstract": "Visual generation and understanding are two deeply interconnected aspects of human intelligence, yet they have been traditionally treated as separate tasks in machine learning. In this paper, we propose Jodi, a diffusion framework that unifies visual generation and understanding by jointly modeling the image domain and multiple label domains. Specifically, Jodi is built upon a linear diffusion transformer along with a role switch mechanism, which enables it to perform three particular types of tasks: (1) joint generation, where the model simultaneously generates images and multiple labels; (2) controllable generation, where images are generated conditioned on any combination of labels; and (3) image perception, where multiple labels can be predicted at once from a given image. Furthermore, we present the Joint-1.6M dataset, which contains 200,000 high-quality images collected from public sources, automatic labels for 7 visual domains, and LLM-generated captions. Extensive experiments demonstrate that Jodi excels in both generation and understanding tasks and exhibits strong extensibility to a wider range of visual domains. Code is available at https://github.com/VIPL-GENUN/Jodi.", "github_manual": "", "github_regex": "https://github.com/VIPL-GENUN/Jodi", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19056", "title": "An Embarrassingly Simple Defense Against LLM Abliteration Attacks", "authors": [ "Harethah Abu Shairah", "Hasan Abed Al Kader Hammoud", "Bernard Ghanem", "George Turkiyyah" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are typically aligned to comply with safety guidelines by refusing harmful instructions. A recent attack, termed abliteration, isolates and suppresses the single latent direction most responsible for refusal behavior, enabling the model to generate unethical content. We propose a defense that modifies how models generate refusals. We construct an extended-refusal dataset that contains harmful prompts with a full response that justifies the reason for refusal. We then fine-tune Llama-2-7B-Chat and Qwen2.5-Instruct (1.5B and 3B parameters) on our extended-refusal dataset, and evaluate the resulting systems on a set of harmful prompts. In our experiments, extended-refusal models maintain high refusal rates, dropping at most by 10%, whereas baseline models' refusal rates drop by 70-80% after abliteration. A broad evaluation of safety and utility shows that extended-refusal fine-tuning neutralizes the abliteration attack while preserving general performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.13136", "title": "ModernGBERT: German-only 1B Encoder Model Trained from Scratch", "authors": [ "Anton Ehrmanntraut", "Julia Wunderle", "Jan Pfister", "Fotis Jannidis", "Andreas Hotho" ], "github": "", "project_page": "", "abstract": "Despite the prominence of decoder-only language models, encoders remain crucial for resource-constrained applications. We introduce ModernGBERT (134M, 1B), a fully transparent family of German encoder models trained from scratch, incorporating architectural innovations from ModernBERT. To evaluate the practical trade-offs of training encoders from scratch, we also present LL\\\"aMmlein2Vec (120M, 1B, 7B), a family of encoders derived from German decoder-only models via LLM2Vec. We benchmark all models on natural language understanding, text embedding, and long-context reasoning tasks, enabling a controlled comparison between dedicated encoders and converted decoders. Our results show that ModernGBERT 1B outperforms prior state-of-the-art German encoders as well as encoders adapted via LLM2Vec, with regard to performance and parameter-efficiency. All models, training data, checkpoints and code are publicly available, advancing the German NLP ecosystem with transparent, high-performance encoder models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18283", "title": "TAGS: A Test-Time Generalist-Specialist Framework with Retrieval-Augmented Reasoning and Verification", "authors": [ "Jianghao Wu", "Feilong Tang", "Yulong Li", "Ming Hu", "Haochen Xue", "Shoaib Jameel", "Yutong Xie", "Imran Razzak" ], "github": "https://github.com/JianghaoWu/TAGS", "project_page": "", "abstract": "Recent advances such as Chain-of-Thought prompting have significantly improved large language models (LLMs) in zero-shot medical reasoning. However, prompting-based methods often remain shallow and unstable, while fine-tuned medical LLMs suffer from poor generalization under distribution shifts and limited adaptability to unseen clinical scenarios. To address these limitations, we present TAGS, a test-time framework that combines a broadly capable generalist with a domain-specific specialist to offer complementary perspectives without any model fine-tuning or parameter updates. To support this generalist-specialist reasoning process, we introduce two auxiliary modules: a hierarchical retrieval mechanism that provides multi-scale exemplars by selecting examples based on both semantic and rationale-level similarity, and a reliability scorer that evaluates reasoning consistency to guide final answer aggregation. TAGS achieves strong performance across nine MedQA benchmarks, boosting GPT-4o accuracy by 13.8%, DeepSeek-R1 by 16.8%, and improving a vanilla 7B model from 14.1% to 23.9%. These results surpass several fine-tuned medical LLMs, without any parameter updates. The code will be available at https://github.com/JianghaoWu/TAGS.", "github_manual": "", "github_regex": "https://github.com/JianghaoWu/TAGS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.15804", "title": "STAR-R1: Spatial TrAnsformation Reasoning by Reinforcing Multimodal LLMs", "authors": [ "Zongzhao Li", "Zongyang Ma", "Mingze Li", "Songyou Li", "Yu Rong", "Tingyang Xu", "Ziqi Zhang", "Deli Zhao", "Wenbing Huang" ], "github": "https://github.com/zongzhao23/STAR-R1", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across diverse tasks, yet they lag significantly behind humans in spatial reasoning. We investigate this gap through Transformation-Driven Visual Reasoning (TVR), a challenging task requiring identification of object transformations across images under varying viewpoints. While traditional Supervised Fine-Tuning (SFT) fails to generate coherent reasoning paths in cross-view settings, sparse-reward Reinforcement Learning (RL) suffers from inefficient exploration and slow convergence. To address these limitations, we propose STAR-R1, a novel framework that integrates a single-stage RL paradigm with a fine-grained reward mechanism tailored for TVR. Specifically, STAR-R1 rewards partial correctness while penalizing excessive enumeration and passive inaction, enabling efficient exploration and precise reasoning. Comprehensive evaluations demonstrate that STAR-R1 achieves state-of-the-art performance across all 11 metrics, outperforming SFT by 23% in cross-view scenarios. Further analysis reveals STAR-R1's anthropomorphic behavior and highlights its unique ability to compare all objects for improving spatial reasoning. Our work provides critical insights in advancing the research of MLLMs and reasoning models. The codes, model weights, and data will be publicly available at https://github.com/zongzhao23/STAR-R1.", "github_manual": "", "github_regex": "https://github.com/zongzhao23/STAR-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.12737", "title": "Option-aware Temporally Abstracted Value for Offline Goal-Conditioned Reinforcement Learning", "authors": [ "Hongjoon Ahn", "Heewoong Choi", "Jisu Han", "Taesup Moon" ], "github": "https://github.com/ota-v/ota-v", "project_page": "", "abstract": "Offline goal-conditioned reinforcement learning (GCRL) offers a practical learning paradigm where goal-reaching policies are trained from abundant unlabeled (reward-free) datasets without additional environment interaction. However, offline GCRL still struggles with long-horizon tasks, even with recent advances that employ hierarchical policy structures, such as HIQL. By identifying the root cause of this challenge, we observe the following insights: First, performance bottlenecks mainly stem from the high-level policy's inability to generate appropriate subgoals. Second, when learning the high-level policy in the long-horizon regime, the sign of the advantage signal frequently becomes incorrect. Thus, we argue that improving the value function to produce a clear advantage signal for learning the high-level policy is essential. In this paper, we propose a simple yet effective solution: Option-aware Temporally Abstracted value learning, dubbed OTA, which incorporates temporal abstraction into the temporal-difference learning process. By modifying the value update to be option-aware, the proposed learning scheme contracts the effective horizon length, enabling better advantage estimates even in long-horizon regimes. We experimentally show that the high-level policy extracted using the OTA value function achieves strong performance on complex tasks from OGBench, a recently proposed offline GCRL benchmark, including maze navigation and visual robotic manipulation environments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ota-v/ota-v", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.16886", "title": "Don't \"Overthink\" Passage Reranking: Is Reasoning Truly Necessary?", "authors": [ "Nour Jedidi", "Yung-Sung Chuang", "James Glass", "Jimmy Lin" ], "github": "", "project_page": "", "abstract": "With the growing success of reasoning models across complex natural language tasks, researchers in the Information Retrieval (IR) community have begun exploring how similar reasoning capabilities can be integrated into passage rerankers built on Large Language Models (LLMs). These methods typically employ an LLM to produce an explicit, step-by-step reasoning process before arriving at a final relevance prediction. But, does reasoning actually improve reranking accuracy? In this paper, we dive deeper into this question, studying the impact of the reasoning process by comparing reasoning-based pointwise rerankers (ReasonRR) to standard, non-reasoning pointwise rerankers (StandardRR) under identical training conditions, and observe that StandardRR generally outperforms ReasonRR. Building on this observation, we then study the importance of reasoning to ReasonRR by disabling its reasoning process (ReasonRR-NoReason), and find that ReasonRR-NoReason is surprisingly more effective than ReasonRR. Examining the cause of this result, our findings reveal that reasoning-based rerankers are limited by the LLM's reasoning process, which pushes it toward polarized relevance scores and thus fails to consider the partial relevance of passages, a key factor for the accuracy of pointwise rerankers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19440", "title": "The Birth of Knowledge: Emergent Features across Time, Space, and Scale in Large Language Models", "authors": [ "Shashata Sawmya", "Micah Adler", "Nir Shavit" ], "github": "", "project_page": "", "abstract": "This paper studies the emergence of interpretable categorical features within large language models (LLMs), analyzing their behavior across training checkpoints (time), transformer layers (space), and varying model sizes (scale). Using sparse autoencoders for mechanistic interpretability, we identify when and where specific semantic concepts emerge within neural activations. Results indicate clear temporal and scale-specific thresholds for feature emergence across multiple domains. Notably, spatial analysis reveals unexpected semantic reactivation, with early-layer features re-emerging at later layers, challenging standard assumptions about representational dynamics in transformer models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.16984", "title": "UFT: Unifying Supervised and Reinforcement Fine-Tuning", "authors": [ "Mingyang Liu", "Gabriele Farina", "Asuman Ozdaglar" ], "github": "https://github.com/liumy2010/UFT", "project_page": "", "abstract": "Post-training has demonstrated its importance in enhancing the reasoning capabilities of large language models (LLMs). The primary post-training methods can be categorized into supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). SFT is efficient and well-suited for small language models, but it may lead to overfitting and limit the reasoning abilities of larger models. In contrast, RFT generally yields better generalization but depends heavily on the strength of the base model. To address the limitations of SFT and RFT, we propose Unified Fine-Tuning (UFT), a novel post-training paradigm that unifies SFT and RFT into a single, integrated process. UFT enables the model to effectively explore solutions while incorporating informative supervision signals, bridging the gap between memorizing and thinking underlying existing methods. Notably, UFT outperforms both SFT and RFT in general, regardless of model sizes. Furthermore, we theoretically prove that UFT breaks RFT's inherent exponential sample complexity bottleneck, showing for the first time that unified training can exponentially accelerate convergence on long-horizon reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/liumy2010/UFT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20046", "title": "REARANK: Reasoning Re-ranking Agent via Reinforcement Learning", "authors": [ "Le Zhang", "Bo Wang", "Xipeng Qiu", "Siva Reddy", "Aishwarya Agrawal" ], "github": "https://github.com/lezhang7/Rearank", "project_page": "", "abstract": "We present REARANK, a large language model (LLM)-based listwise reasoning reranking agent. REARANK explicitly reasons before reranking, significantly improving both performance and interpretability. Leveraging reinforcement learning and data augmentation, REARANK achieves substantial improvements over baseline models across popular information retrieval benchmarks, notably requiring only 179 annotated samples. Built on top of Qwen2.5-7B, our REARANK-7B demonstrates performance comparable to GPT-4 on both in-domain and out-of-domain benchmarks and even surpasses GPT-4 on reasoning-intensive BRIGHT benchmarks. These results underscore the effectiveness of our approach and highlight how reinforcement learning can enhance LLM reasoning capabilities in reranking.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lezhang7/Rearank", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20290", "title": "EgoZero: Robot Learning from Smart Glasses", "authors": [ "Vincent Liu", "Ademi Adeniji", "Haotian Zhan", "Raunaq Bhirangi", "Pieter Abbeel", "Lerrel Pinto" ], "github": "", "project_page": "", "abstract": "Despite recent progress in general purpose robotics, robot policies still lag far behind basic human capabilities in the real world. Humans interact constantly with the physical world, yet this rich data resource remains largely untapped in robot learning. We propose EgoZero, a minimal system that learns robust manipulation policies from human demonstrations captured with Project Aria smart glasses, and zero robot data. EgoZero enables: (1) extraction of complete, robot-executable actions from in-the-wild, egocentric, human demonstrations, (2) compression of human visual observations into morphology-agnostic state representations, and (3) closed-loop policy learning that generalizes morphologically, spatially, and semantically. We deploy EgoZero policies on a gripper Franka Panda robot and demonstrate zero-shot transfer with 70% success rate over 7 manipulation tasks and only 20 minutes of data collection per task. Our results suggest that in-the-wild human data can serve as a scalable foundation for real-world robot learning - paving the way toward a future of abundant, diverse, and naturalistic training data for robots. Code and videos are available at https://egozero-robot.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19103", "title": "WHISTRESS: Enriching Transcriptions with Sentence Stress Detection", "authors": [ "Iddo Yosha", "Dorin Shteyman", "Yossi Adi" ], "github": "", "project_page": "", "abstract": "Spoken language conveys meaning not only through words but also through intonation, emotion, and emphasis. Sentence stress, the emphasis placed on specific words within a sentence, is crucial for conveying speaker intent and has been extensively studied in linguistics. In this work, we introduce WHISTRESS, an alignment-free approach for enhancing transcription systems with sentence stress detection. To support this task, we propose TINYSTRESS-15K, a scalable, synthetic training data for the task of sentence stress detection which resulted from a fully automated dataset creation process. We train WHISTRESS on TINYSTRESS-15K and evaluate it against several competitive baselines. Our results show that WHISTRESS outperforms existing methods while requiring no additional input priors during training or inference. Notably, despite being trained on synthetic data, WHISTRESS demonstrates strong zero-shot generalization across diverse benchmarks. Project page: https://pages.cs.huji.ac.il/adiyoss-lab/whistress.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20225", "title": "FLAME-MoE: A Transparent End-to-End Research Platform for Mixture-of-Experts Language Models", "authors": [ "Hao Kang", "Zichun Yu", "Chenyan Xiong" ], "github": "https://github.com/cmu-flame/FLAME-MoE", "project_page": "", "abstract": "Recent large language models such as Gemini-1.5, DeepSeek-V3, and Llama-4 increasingly adopt Mixture-of-Experts (MoE) architectures, which offer strong efficiency-performance trade-offs by activating only a fraction of the model per token. Yet academic researchers still lack a fully open, end-to-end MoE platform for investigating scaling, routing, and expert behavior. We release FLAME-MoE, a completely open-source research suite composed of seven decoder-only models, ranging from 38M to 1.7B active parameters, whose architecture--64 experts with top-8 gating and 2 shared experts--closely reflects modern production LLMs. All training data pipelines, scripts, logs, and checkpoints are publicly available to enable reproducible experimentation. Across six evaluation tasks, FLAME-MoE improves average accuracy by up to 3.4 points over dense baselines trained with identical FLOPs. Leveraging full training trace transparency, we present initial analyses showing that (i) experts increasingly specialize on distinct token subsets, (ii) co-activation matrices remain sparse, reflecting diverse expert usage, and (iii) routing behavior stabilizes early in training. All code, training logs, and model checkpoints are available at https://github.com/cmu-flame/FLAME-MoE.", "github_manual": "", "github_regex": "https://github.com/cmu-flame/FLAME-MoE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19386", "title": "Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals", "authors": [ "Nate Gillman", "Charles Herrmann", "Michael Freeman", "Daksh Aggarwal", "Evan Luo", "Deqing Sun", "Chen Sun" ], "github": "", "project_page": "", "abstract": "Recent advances in video generation models have sparked interest in world models capable of simulating realistic environments. While navigation has been well-explored, physically meaningful interactions that mimic real-world forces remain largely understudied. In this work, we investigate using physical forces as a control signal for video generation and propose force prompts which enable users to interact with images through both localized point forces, such as poking a plant, and global wind force fields, such as wind blowing on fabric. We demonstrate that these force prompts can enable videos to respond realistically to physical control signals by leveraging the visual and motion prior in the original pretrained model, without using any 3D asset or physics simulator at inference. The primary challenge of force prompting is the difficulty in obtaining high quality paired force-video training data, both in the real world due to the difficulty of obtaining force signals, and in synthetic data due to limitations in the visual quality and domain diversity of physics simulators. Our key finding is that video generation models can generalize remarkably well when adapted to follow physical force conditioning from videos synthesized by Blender, even with limited demonstrations of few objects. Our method can generate videos which simulate forces across diverse geometries, settings, and materials. We also try to understand the source of this generalization and perform ablations that reveal two key elements: visual diversity and the use of specific text keywords during training. Our approach is trained on only around 15k training examples for a single day on four A100 GPUs, and outperforms existing methods on force adherence and physics realism, bringing world models closer to real-world physics interactions. We release all datasets, code, weights, and interactive video demos at our project page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18291", "title": "InstructPart: Task-Oriented Part Segmentation with Instruction Reasoning", "authors": [ "Zifu Wan", "Yaqi Xie", "Ce Zhang", "Zhiqiu Lin", "Zihan Wang", "Simon Stepputtis", "Deva Ramanan", "Katia Sycara" ], "github": "", "project_page": "", "abstract": "Large multimodal foundation models, particularly in the domains of language and vision, have significantly advanced various tasks, including robotics, autonomous driving, information retrieval, and grounding. However, many of these models perceive objects as indivisible, overlooking the components that constitute them. Understanding these components and their associated affordances provides valuable insights into an object's functionality, which is fundamental for performing a wide range of tasks. In this work, we introduce a novel real-world benchmark, InstructPart, comprising hand-labeled part segmentation annotations and task-oriented instructions to evaluate the performance of current models in understanding and executing part-level tasks within everyday contexts. Through our experiments, we demonstrate that task-oriented part segmentation remains a challenging problem, even for state-of-the-art Vision-Language Models (VLMs). In addition to our benchmark, we introduce a simple baseline that achieves a twofold performance improvement through fine-tuning with our dataset. With our dataset and benchmark, we aim to facilitate research on task-oriented part segmentation and enhance the applicability of VLMs across various domains, including robotics, virtual reality, information retrieval, and other related fields. Project website: https://zifuwan.github.io/InstructPart/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18926", "title": "Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time", "authors": [ "Jingxuan Xu", "Hong Huang", "Chuhang Zou", "Manolis Savva", "Yunchao Wei", "Wuyang Chen" ], "github": "", "project_page": "", "abstract": "We propose a neural physics system for real-time, interactive fluid simulations. Traditional physics-based methods, while accurate, are computationally intensive and suffer from latency issues. Recent machine-learning methods reduce computational costs while preserving fidelity; yet most still fail to satisfy the latency constraints for real-time use and lack support for interactive applications. To bridge this gap, we introduce a novel hybrid method that integrates numerical simulation, neural physics, and generative control. Our neural physics jointly pursues low-latency simulation and high physical fidelity by employing a fallback safeguard to classical numerical solvers. Furthermore, we develop a diffusion-based controller that is trained using a reverse modeling strategy to generate external dynamic force fields for fluid manipulation. Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions, achieving real-time simulations at high frame rates (11~29% latency) while enabling fluid control guided by user-friendly freehand sketches. We present a significant step towards practical, controllable, and physically plausible fluid simulations for real-time interactive applications. We promise to release both models and data upon acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.19415", "title": "MMIG-Bench: Towards Comprehensive and Explainable Evaluation of Multi-Modal Image Generation Models", "authors": [ "Hang Hua", "Ziyun Zeng", "Yizhi Song", "Yunlong Tang", "Liu He", "Daniel Aliaga", "Wei Xiong", "Jiebo Luo" ], "github": "", "project_page": "", "abstract": "Recent multimodal image generators such as GPT-4o, Gemini 2.0 Flash, and Gemini 2.5 Pro excel at following complex instructions, editing images and maintaining concept consistency. However, they are still evaluated by disjoint toolkits: text-to-image (T2I) benchmarks that lacks multi-modal conditioning, and customized image generation benchmarks that overlook compositional semantics and common knowledge. We propose MMIG-Bench, a comprehensive Multi-Modal Image Generation Benchmark that unifies these tasks by pairing 4,850 richly annotated text prompts with 1,750 multi-view reference images across 380 subjects, spanning humans, animals, objects, and artistic styles. MMIG-Bench is equipped with a three-level evaluation framework: (1) low-level metrics for visual artifacts and identity preservation of objects; (2) novel Aspect Matching Score (AMS): a VQA-based mid-level metric that delivers fine-grained prompt-image alignment and shows strong correlation with human judgments; and (3) high-level metrics for aesthetics and human preference. Using MMIG-Bench, we benchmark 17 state-of-the-art models, including Gemini 2.5 Pro, FLUX, DreamBooth, and IP-Adapter, and validate our metrics with 32k human ratings, yielding in-depth insights into architecture and data design. We will release the dataset and evaluation code to foster rigorous, unified evaluation and accelerate future innovations in multi-modal image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18454", "title": "Hybrid Latent Reasoning via Reinforcement Learning", "authors": [ "Zhenrui Yue", "Bowen Jin", "Huimin Zeng", "Honglei Zhuang", "Zhen Qin", "Jinsung Yoon", "Lanyu Shang", "Jiawei Han", "Dong Wang" ], "github": "https://github.com/yueeeeeeee/hrpo", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have introduced latent reasoning as a promising alternative to autoregressive reasoning. By performing internal computation with hidden states from previous steps, latent reasoning benefit from more informative features rather than sampling a discrete chain-of-thought (CoT) path. Yet latent reasoning approaches are often incompatible with LLMs, as their continuous paradigm conflicts with the discrete nature of autoregressive generation. Moreover, these methods rely on CoT traces for training and thus fail to exploit the inherent reasoning patterns of LLMs. In this work, we explore latent reasoning by leveraging the intrinsic capabilities of LLMs via reinforcement learning (RL). To this end, we introduce hybrid reasoning policy optimization (HRPO), an RL-based hybrid latent reasoning approach that (1) integrates prior hidden states into sampled tokens with a learnable gating mechanism, and (2) initializes training with predominantly token embeddings while progressively incorporating more hidden features. This design maintains LLMs' generative capabilities and incentivizes hybrid reasoning using both discrete and continuous representations. In addition, the hybrid HRPO introduces stochasticity into latent reasoning via token sampling, thereby enabling RL-based optimization without requiring CoT trajectories. Extensive evaluations across diverse benchmarks show that HRPO outperforms prior methods in both knowledge- and reasoning-intensive tasks. Furthermore, HRPO-trained LLMs remain interpretable and exhibit intriguing behaviors like cross-lingual patterns and shorter completion lengths, highlighting the potential of our RL-based approach and offer insights for future work in latent reasoning.", "github_manual": "https://github.com/yueeeeeeee/hrpo", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18116", "title": "Bridging Supervised Learning and Reinforcement Learning in Math Reasoning", "authors": [ "Huayu Chen", "Kaiwen Zheng", "Qinsheng Zhang", "Ganqu Cui", "Yin Cui", "Haotian Ye", "Tsung-Yi Lin", "Ming-Yu Liu", "Jun Zhu", "Haoxiang Wang" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning (RL) has played a central role in the recent surge of LLMs' math abilities by enabling self-improvement through binary verifier signals. In contrast, Supervised Learning (SL) is rarely considered for such verification-driven training, largely due to its heavy reliance on reference answers and inability to reflect on mistakes. In this work, we challenge the prevailing notion that self-improvement is exclusive to RL and propose Negative-aware Fine-Tuning (NFT) -- a supervised approach that enables LLMs to reflect on their failures and improve autonomously with no external teachers. In online training, instead of throwing away self-generated negative answers, NFT constructs an implicit negative policy to model them. This implicit policy is parameterized with the same positive LLM we target to optimize on positive data, enabling direct policy optimization on all LLMs' generations. We conduct experiments on 7B and 32B models in math reasoning tasks. Results consistently show that through the additional leverage of negative feedback, NFT significantly improves over SL baselines like Rejection sampling Fine-Tuning, matching or even surpassing leading RL algorithms like GRPO and DAPO. Furthermore, we demonstrate that NFT and GRPO are actually equivalent in strict-on-policy training, even though they originate from entirely different theoretical foundations. Our experiments and theoretical findings bridge the gap between SL and RL methods in binary-feedback learning systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.14071", "title": "Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models", "authors": [ "Woody Haosheng Gan", "Deqing Fu", "Julian Asilis", "Ollie Liu", "Dani Yogatama", "Vatsal Sharan", "Robin Jia", "Willie Neiswanger" ], "github": "", "project_page": "", "abstract": "Steering methods have emerged as effective and targeted tools for guiding large language models' (LLMs) behavior without modifying their parameters. Multimodal large language models (MLLMs), however, do not currently enjoy the same suite of techniques, due in part to their recency and architectural diversity. Inspired by this gap, we investigate whether MLLMs can be steered using vectors derived from their text-only LLM backbone, via sparse autoencoders (SAEs), mean shift, and linear probing. We find that text-derived steering consistently enhances multimodal accuracy across diverse MLLM architectures and visual tasks. In particular, mean shift boosts spatial relationship accuracy on CV-Bench by up to +7.3% and counting accuracy by up to +3.3%, outperforming prompting and exhibiting strong generalization to out-of-distribution datasets. These results highlight textual steering vectors as a powerful, efficient mechanism for enhancing grounding in MLLMs with minimal additional data collection and computational overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20297", "title": "DiSA: Diffusion Step Annealing in Autoregressive Image Generation", "authors": [ "Qinyu Zhao", "Jaskirat Singh", "Ming Xu", "Akshay Asthana", "Stephen Gould", "Liang Zheng" ], "github": "", "project_page": "", "abstract": "An increasing number of autoregressive models, such as MAR, FlowAR, xAR, and Harmon adopt diffusion sampling to improve the quality of image generation. However, this strategy leads to low inference efficiency, because it usually takes 50 to 100 steps for diffusion to sample a token. This paper explores how to effectively address this issue. Our key motivation is that as more tokens are generated during the autoregressive process, subsequent tokens follow more constrained distributions and are easier to sample. To intuitively explain, if a model has generated part of a dog, the remaining tokens must complete the dog and thus are more constrained. Empirical evidence supports our motivation: at later generation stages, the next tokens can be well predicted by a multilayer perceptron, exhibit low variance, and follow closer-to-straight-line denoising paths from noise to tokens. Based on our finding, we introduce diffusion step annealing (DiSA), a training-free method which gradually uses fewer diffusion steps as more tokens are generated, e.g., using 50 steps at the beginning and gradually decreasing to 5 steps at later stages. Because DiSA is derived from our finding specific to diffusion in autoregressive models, it is complementary to existing acceleration methods designed for diffusion alone. DiSA can be implemented in only a few lines of code on existing models, and albeit simple, achieves 5-10times faster inference for MAR and Harmon and 1.4-2.5times for FlowAR and xAR, while maintaining the generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.20236", "title": "Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models", "authors": [ "Weihao Xuan", "Qingcheng Zeng", "Heli Qi", "Junjue Wang", "Naoto Yokoya" ], "github": "", "project_page": "", "abstract": "Uncertainty quantification is essential for assessing the reliability and trustworthiness of modern AI systems. Among existing approaches, verbalized uncertainty, where models express their confidence through natural language, has emerged as a lightweight and interpretable solution in large language models (LLMs). However, its effectiveness in vision-language models (VLMs) remains insufficiently studied. In this work, we conduct a comprehensive evaluation of verbalized confidence in VLMs, spanning three model categories, four task domains, and three evaluation scenarios. Our results show that current VLMs often display notable miscalibration across diverse tasks and settings. Notably, visual reasoning models (i.e., thinking with images) consistently exhibit better calibration, suggesting that modality-specific reasoning is critical for reliable uncertainty estimation. To further address calibration challenges, we introduce Visual Confidence-Aware Prompting, a two-stage prompting strategy that improves confidence alignment in multimodal settings. Overall, our study highlights the inherent miscalibration in VLMs across modalities. More broadly, our findings underscore the fundamental importance of modality alignment and model faithfulness in advancing reliable multimodal systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.18497", "title": "The Pragmatic Mind of Machines: Tracing the Emergence of Pragmatic Competence in Large Language Models", "authors": [ "Kefan Yu", "Qingcheng Zeng", "Weihao Xuan", "Wanxin Li", "Jingyi Wu", "Rob Voigt" ], "github": "https://github.com/Huangtubaye233/PragmaticsLLM", "project_page": "", "abstract": "Current large language models (LLMs) have demonstrated emerging capabilities in social intelligence tasks, including implicature resolution (Sravanthi et al. (2024)) and theory-of-mind reasoning (Shapira et al. (2024)), both of which require substantial pragmatic understanding. However, how LLMs acquire this competence throughout the training process remains poorly understood. In this work, we introduce ALTPRAG, a dataset grounded in the pragmatic concept of alternatives, designed to evaluate whether LLMs at different training stages can accurately infer nuanced speaker intentions. Each instance pairs two contextually appropriate but pragmatically distinct continuations, enabling fine-grained assessment of both pragmatic interpretation and contrastive reasoning. We systematically evaluate 22 LLMs across key training stages: pre-training, supervised fine-tuning (SFT), and preference optimization, to examine the development of pragmatic competence. Our results show that even base models exhibit notable sensitivity to pragmatic cues, which improves consistently with increases in model and data scale. Additionally, SFT and RLHF contribute further gains, particularly in cognitive-pragmatic reasoning. These findings highlight pragmatic competence as an emergent and compositional property of LLM training and offer new insights for aligning models with human communicative norms.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Huangtubaye233/PragmaticsLLM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-27", "arxiv_id": "2505.16968", "title": "CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark", "authors": [ "Ahmed Heakl", "Sarim Hashmi", "Gustavo Bertolo Stahl", "Seung Hun Eddie Han", "Salman Khan", "Abdulrahman Mahmoud" ], "github": "https://github.com/GustavoStahl/CASS", "project_page": "", "abstract": "We introduce CASS, the first large-scale dataset and model suite for cross-architecture GPU code transpilation, targeting both source-level (CUDA leftrightarrow HIP) and assembly-level (Nvidia SASS leftrightarrow AMD RDNA3) translation. The dataset comprises 70k verified code pairs across host and device, addressing a critical gap in low-level GPU code portability. Leveraging this resource, we train the CASS family of domain-specific language models, achieving 95% source translation accuracy and 37.5% assembly translation accuracy, substantially outperforming commercial baselines such as GPT-4o, Claude, and Hipify. Our generated code matches native performance in over 85% of test cases, preserving runtime and memory behavior. To support rigorous evaluation, we introduce CASS-Bench, a curated benchmark spanning 16 GPU domains with ground-truth execution. All data, models, and evaluation tools are released as open source to foster progress in GPU compiler tooling, binary compatibility, and LLM-guided hardware translation. Dataset and benchmark are on https://huggingface.co/datasets/MBZUAI/cass{blue{HuggingFace}}, with code at https://github.com/GustavoStahl/CASS{blue{GitHub}}.", "github_manual": "", "github_regex": "https://github.com/GustavoStahl/CASS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.18875", "title": "Sparse VideoGen2: Accelerate Video Generation with Sparse Attention via Semantic-Aware Permutation", "authors": [ "Shuo Yang", "Haocheng Xi", "Yilong Zhao", "Muyang Li", "Jintao Zhang", "Han Cai", "Yujun Lin", "Xiuyu Li", "Chenfeng Xu", "Kelly Peng", "Jianfei Chen", "Song Han", "Kurt Keutzer", "Ion Stoica" ], "github": "https://github.com/svg-project/Sparse-VideoGen", "project_page": "", "abstract": "Diffusion Transformers (DiTs) are essential for video generation but suffer from significant latency due to the quadratic complexity of attention. By computing only critical tokens, sparse attention reduces computational costs and offers a promising acceleration approach. However, we identify that existing methods fail to approach optimal generation quality under the same computation budget for two reasons: (1) Inaccurate critical token identification: current methods cluster tokens based on position rather than semantics, leading to imprecise aggregated representations. (2) Excessive computation waste: critical tokens are scattered among non-critical ones, leading to wasted computation on GPUs, which are optimized for processing contiguous tokens. In this paper, we propose SVG2, a training-free framework that maximizes identification accuracy and minimizes computation waste, achieving a Pareto frontier trade-off between generation quality and efficiency. The core of SVG2 is semantic-aware permutation, which clusters and reorders tokens based on semantic similarity using k-means. This approach ensures both a precise cluster representation, improving identification accuracy, and a densified layout of critical tokens, enabling efficient computation without padding. Additionally, SVG2 integrates top-p dynamic budget control and customized kernel implementations, achieving up to 2.30x and 1.89x speedup while maintaining a PSNR of up to 30 and 26 on HunyuanVideo and Wan 2.1, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/svg-project/Sparse-VideoGen", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.18445", "title": "OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data", "authors": [ "Yiren Song", "Cheng Liu", "Mike Zheng Shou" ], "github": "https://github.com/showlab/omniconsistency", "project_page": "", "abstract": "Diffusion models have advanced image stylization significantly, yet two core challenges persist: (1) maintaining consistent stylization in complex scenes, particularly identity, composition, and fine details, and (2) preventing style degradation in image-to-image pipelines with style LoRAs. GPT-4o's exceptional stylization consistency highlights the performance gap between open-source methods and proprietary models. To bridge this gap, we propose OmniConsistency, a universal consistency plugin leveraging large-scale Diffusion Transformers (DiTs). OmniConsistency contributes: (1) an in-context consistency learning framework trained on aligned image pairs for robust generalization; (2) a two-stage progressive learning strategy decoupling style learning from consistency preservation to mitigate style degradation; and (3) a fully plug-and-play design compatible with arbitrary style LoRAs under the Flux framework. Extensive experiments show that OmniConsistency significantly enhances visual coherence and aesthetic quality, achieving performance comparable to commercial state-of-the-art model GPT-4o.", "github_manual": "https://github.com/showlab/omniconsistency", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21333", "title": "MME-VideoOCR: Evaluating OCR-Based Capabilities of Multimodal LLMs in Video Scenarios", "authors": [ "Yang Shi", "Huanqian Wang", "Wulin Xie", "Huanyao Zhang", "Lijie Zhao", "Yi-Fan Zhang", "Xinfeng Li", "Chaoyou Fu", "Zhuoer Wen", "Wenting Liu", "Zhuoran Zhang", "Xinlong Chen", "Bohan Zeng", "Sihan Yang", "Yuanxing Zhang", "Pengfei Wan", "Haotian Wang", "Wenjing Yang" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have achieved considerable accuracy in Optical Character Recognition (OCR) from static images. However, their efficacy in video OCR is significantly diminished due to factors such as motion blur, temporal variations, and visual effects inherent in video content. To provide clearer guidance for training practical MLLMs, we introduce the MME-VideoOCR benchmark, which encompasses a comprehensive range of video OCR application scenarios. MME-VideoOCR features 10 task categories comprising 25 individual tasks and spans 44 diverse scenarios. These tasks extend beyond text recognition to incorporate deeper comprehension and reasoning of textual content within videos. The benchmark consists of 1,464 videos with varying resolutions, aspect ratios, and durations, along with 2,000 meticulously curated, manually annotated question-answer pairs. We evaluate 18 state-of-the-art MLLMs on MME-VideoOCR, revealing that even the best-performing model (Gemini-2.5 Pro) achieves an accuracy of only 73.7%. Fine-grained analysis indicates that while existing MLLMs demonstrate strong performance on tasks where relevant texts are contained within a single or few frames, they exhibit limited capability in effectively handling tasks that demand holistic video comprehension. These limitations are especially evident in scenarios that require spatio-temporal reasoning, cross-frame information integration, or resistance to language prior bias. Our findings also highlight the importance of high-resolution visual input and sufficient temporal coverage for reliable OCR in dynamic video scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.18943", "title": "MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems", "authors": [ "Xuanming Zhang", "Yuxuan Chen", "Min-Hsuan Yeh", "Yixuan Li" ], "github": "https://github.com/XMZhangAI/MetaMind", "project_page": "", "abstract": "Human social interactions depend on the ability to infer others' unspoken intentions, emotions, and beliefs-a cognitive skill grounded in the psychological concept of Theory of Mind (ToM). While large language models (LLMs) excel in semantic understanding tasks, they struggle with the ambiguity and contextual nuance inherent in human communication. To bridge this gap, we introduce MetaMind, a multi-agent framework inspired by psychological theories of metacognition, designed to emulate human-like social reasoning. MetaMind decomposes social understanding into three collaborative stages: (1) a Theory-of-Mind Agent generates hypotheses user mental states (e.g., intent, emotion), (2) a Domain Agent refines these hypotheses using cultural norms and ethical constraints, and (3) a Response Agent generates contextually appropriate responses while validating alignment with inferred intent. Our framework achieves state-of-the-art performance across three challenging benchmarks, with 35.7% improvement in real-world social scenarios and 6.2% gain in ToM reasoning. Notably, it enables LLMs to match human-level performance on key ToM tasks for the first time. Ablation studies confirm the necessity of all components, which showcase the framework's ability to balance contextual plausibility, social appropriateness, and user adaptation. This work advances AI systems toward human-like social intelligence, with applications in empathetic dialogue and culturally sensitive interactions. Code is available at https://github.com/XMZhangAI/MetaMind.", "github_manual": "", "github_regex": "https://github.com/XMZhangAI/MetaMind", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21374", "title": "Video-Holmes: Can MLLM Think Like Holmes for Complex Video Reasoning?", "authors": [ "Junhao Cheng", "Yuying Ge", "Teng Wang", "Yixiao Ge", "Jing Liao", "Ying Shan" ], "github": "https://github.com/TencentARC/Video-Holmes", "project_page": "", "abstract": "Recent advances in CoT reasoning and RL post-training have been reported to enhance video reasoning capabilities of MLLMs. This progress naturally raises a question: can these models perform complex video reasoning in a manner comparable to human experts? However, existing video benchmarks primarily evaluate visual perception and grounding abilities, with questions that can be answered based on explicit prompts or isolated visual cues. Such benchmarks do not fully capture the intricacies of real-world reasoning, where humans must actively search for, integrate, and analyze multiple clues before reaching a conclusion. To address this issue, we present Video-Holmes, a benchmark inspired by the reasoning process of Sherlock Holmes, designed to evaluate the complex video reasoning capabilities of MLLMs. Video-Holmes consists of 1,837 questions derived from 270 manually annotated suspense short films, which spans seven carefully designed tasks. Each task is constructed by first identifying key events and causal relationships within films, and then designing questions that require models to actively locate and connect multiple relevant visual clues scattered across different video segments. Our comprehensive evaluation of state-of-the-art MLLMs reveals that, while these models generally excel at visual perception, they encounter substantial difficulties with integrating information and often miss critical clues. For example, the best-performing model, Gemini-2.5-Pro, achieves an accuracy of only 45%, with most models scoring below 40%. We aim that Video-Holmes can serve as a \"Holmes-test\" for multimodal reasoning, motivating models to reason more like humans and emphasizing the ongoing challenges in this field. The benchmark is released in https://github.com/TencentARC/Video-Holmes.", "github_manual": "", "github_regex": "https://github.com/TencentARC/Video-Holmes", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21497", "title": "Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers", "authors": [ "Wei Pang", "Kevin Qinghong Lin", "Xiangru Jian", "Xi He", "Philip Torr" ], "github": "https://github.com/Paper2Poster/Paper2Poster", "project_page": "", "abstract": "Academic poster generation is a crucial yet challenging task in scientific communication, requiring the compression of long-context interleaved documents into a single, visually coherent page. To address this challenge, we introduce the first benchmark and metric suite for poster generation, which pairs recent conference papers with author-designed posters and evaluates outputs on (i)Visual Quality-semantic alignment with human posters, (ii)Textual Coherence-language fluency, (iii)Holistic Assessment-six fine-grained aesthetic and informational criteria scored by a VLM-as-judge, and notably (iv)PaperQuiz-the poster's ability to convey core paper content as measured by VLMs answering generated quizzes. Building on this benchmark, we propose PosterAgent, a top-down, visual-in-the-loop multi-agent pipeline: the (a)Parser distills the paper into a structured asset library; the (b)Planner aligns text-visual pairs into a binary-tree layout that preserves reading order and spatial balance; and the (c)Painter-Commenter loop refines each panel by executing rendering code and using VLM feedback to eliminate overflow and ensure alignment. In our comprehensive evaluation, we find that GPT-4o outputs-though visually appealing at first glance-often exhibit noisy text and poor PaperQuiz scores, and we find that reader engagement is the primary aesthetic bottleneck, as human-designed posters rely largely on visual semantics to convey meaning. Our fully open-source variants (e.g. based on the Qwen-2.5 series) outperform existing 4o-driven multi-agent systems across nearly all metrics, while using 87% fewer tokens. It transforms a 22-page paper into a finalized yet editable .pptx poster - all for just $0.005. These findings chart clear directions for the next generation of fully automated poster-generation models. The code and datasets are available at https://github.com/Paper2Poster/Paper2Poster.", "github_manual": "", "github_regex": "https://github.com/Paper2Poster/Paper2Poster", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21297", "title": "rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset", "authors": [ "Yifei Liu", "Li Lyna Zhang", "Yi Zhu", "Bingcheng Dong", "Xudong Zhou", "Ning Shang", "Fan Yang", "Mao Yang" ], "github": "https://github.com/microsoft/rStar", "project_page": "", "abstract": "Advancing code reasoning in large language models (LLMs) is fundamentally limited by the scarcity of high-difficulty datasets, especially those with verifiable input-output test cases necessary for rigorous solution validation at scale. We introduce rStar-Coder, which significantly improves LLM code reasoning capabilities by constructing a large-scale, verified dataset of 418K competition-level code problems, 580K long-reasoning solutions along with rich test cases of varying difficulty. This is achieved through three core contributions: (1) we curate competitive programming code problems and oracle solutions to synthesize new, solvable problems; (2) we introduce a reliable input-output test case synthesis pipeline that decouples the generation into a three-step input generation method and a mutual verification mechanism for effective output labeling; (3) we augment problems with high-quality, test-case-verified long-reasoning solutions. Extensive experiments on Qwen models (1.5B-14B) across various code reasoning benchmarks demonstrate the superiority of rStar-Coder dataset, achieving leading performance comparable to frontier reasoning LLMs with much smaller model sizes. On LiveCodeBench, rStar-Coder improves Qwen2.5-7B from 17.4% to an impressive 57.3%, and Qwen2.5-14B from 23.3% to 62.5%, surpassing o3-mini (low) by3.1%. On the more challenging USA Computing Olympiad, our 7B model achieves an average pass@1 accuracy of 16.15%, outperforming the frontier-level QWQ-32B. Code and the dataset will be released at https://github.com/microsoft/rStar.", "github_manual": "", "github_regex": "https://github.com/microsoft/rStar", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20292", "title": "OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation", "authors": [ "Shenghai Yuan", "Xianyi He", "Yufan Deng", "Yang Ye", "Jinfa Huang", "Bin Lin", "Chongyang Ma", "Jiebo Luo", "Li Yuan" ], "github": "https://github.com/Tencent-Hunyuan/HunyuanCustom", "project_page": "", "abstract": "Subject-to-Video (S2V) generation aims to create videos that faithfully incorporate reference content, providing enhanced flexibility in the production of videos. To establish the infrastructure for S2V generation, we propose OpenS2V-Nexus, consisting of (i) OpenS2V-Eval, a fine-grained benchmark, and (ii) OpenS2V-5M, a million-scale dataset. In contrast to existing S2V benchmarks inherited from VBench that focus on global and coarse-grained assessment of generated videos, OpenS2V-Eval focuses on the model's ability to generate subject-consistent videos with natural subject appearance and identity fidelity. For these purposes, OpenS2V-Eval introduces 180 prompts from seven major categories of S2V, which incorporate both real and synthetic test data. Furthermore, to accurately align human preferences with S2V benchmarks, we propose three automatic metrics, NexusScore, NaturalScore and GmeScore, to separately quantify subject consistency, naturalness, and text relevance in generated videos. Building on this, we conduct a comprehensive evaluation of 16 representative S2V models, highlighting their strengths and weaknesses across different content. Moreover, we create the first open-source large-scale S2V generation dataset OpenS2V-5M, which consists of five million high-quality 720P subject-text-video triples. Specifically, we ensure subject-information diversity in our dataset by (1) segmenting subjects and building pairing information via cross-video associations and (2) prompting GPT-Image-1 on raw frames to synthesize multi-view representations. Through OpenS2V-Nexus, we deliver a robust infrastructure to accelerate future S2V generation research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Tencent-Hunyuan/HunyuanCustom", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20355", "title": "GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning", "authors": [ "Yeonjoon Jung", "Daehyun Ahn", "Hyungjun Kim", "Taesu Kim", "Eunhyeok Park" ], "github": "https://github.com/SqueezeBits/GraLoRA.git", "project_page": "", "abstract": "Low-Rank Adaptation (LoRA) is a popular method for parameter-efficient fine-tuning (PEFT) of generative models, valued for its simplicity and effectiveness. Despite recent enhancements, LoRA still suffers from a fundamental limitation: overfitting when the bottleneck is widened. It performs best at ranks 32-64, yet its accuracy stagnates or declines at higher ranks, still falling short of full fine-tuning (FFT) performance. We identify the root cause as LoRA's structural bottleneck, which introduces gradient entanglement to the unrelated input channels and distorts gradient propagation. To address this, we introduce a novel structure, Granular Low-Rank Adaptation (GraLoRA) that partitions weight matrices into sub-blocks, each with its own low-rank adapter. With negligible computational or storage cost, GraLoRA overcomes LoRA's limitations, effectively increases the representational capacity, and more closely approximates FFT behavior. Experiments on code generation and commonsense reasoning benchmarks show that GraLoRA consistently outperforms LoRA and other baselines, achieving up to +8.5% absolute gain in Pass@1 on HumanEval+. These improvements hold across model sizes and rank settings, making GraLoRA a scalable and robust solution for PEFT. Code, data, and scripts are available at https://github.com/SqueezeBits/GraLoRA.git", "github_manual": "", "github_regex": "https://github.com/SqueezeBits/GraLoRA.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20275", "title": "ImgEdit: A Unified Image Editing Dataset and Benchmark", "authors": [ "Yang Ye", "Xianyi He", "Zongjian Li", "Bin Lin", "Shenghai Yuan", "Zhiyuan Yan", "Bohan Hou", "Li Yuan" ], "github": "https://github.com/PKU-YuanGroup/ImgEdit", "project_page": "", "abstract": "Recent advancements in generative models have enabled high-fidelity text-to-image generation. However, open-source image-editing models still lag behind their proprietary counterparts, primarily due to limited high-quality data and insufficient benchmarks. To overcome these limitations, we introduce ImgEdit, a large-scale, high-quality image-editing dataset comprising 1.2 million carefully curated edit pairs, which contain both novel and complex single-turn edits, as well as challenging multi-turn tasks. To ensure the data quality, we employ a multi-stage pipeline that integrates a cutting-edge vision-language model, a detection model, a segmentation model, alongside task-specific in-painting procedures and strict post-processing. ImgEdit surpasses existing datasets in both task novelty and data quality. Using ImgEdit, we train ImgEdit-E1, an editing model using Vision Language Model to process the reference image and editing prompt, which outperforms existing open-source models on multiple tasks, highlighting the value of ImgEdit and model design. For comprehensive evaluation, we introduce ImgEdit-Bench, a benchmark designed to evaluate image editing performance in terms of instruction adherence, editing quality, and detail preservation. It includes a basic testsuite, a challenging single-turn suite, and a dedicated multi-turn suite. We evaluate both open-source and proprietary models, as well as ImgEdit-E1, providing deep analysis and actionable insights into the current behavior of image-editing models. The source data are publicly available on https://github.com/PKU-YuanGroup/ImgEdit.", "github_manual": "", "github_regex": "https://github.com/PKU-YuanGroup/ImgEdit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.19314", "title": "SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline", "authors": [ "Helin Wang", "Jiarui Hai", "Dongchao Yang", "Chen Chen", "Kai Li", "Junyi Peng", "Thomas Thebaud", "Laureano Moro Velazquez", "Jesus Villalba", "Najim Dehak" ], "github": "https://github.com/HaoFengyuan/X-TF-GridNet", "project_page": "", "abstract": "Target Speech Extraction (TSE) aims to isolate a target speaker's voice from a mixture of multiple speakers by leveraging speaker-specific cues, typically provided as auxiliary audio (a.k.a. cue audio). Although recent advancements in TSE have primarily employed discriminative models that offer high perceptual quality, these models often introduce unwanted artifacts, reduce naturalness, and are sensitive to discrepancies between training and testing environments. On the other hand, generative models for TSE lag in perceptual quality and intelligibility. To address these challenges, we present SoloSpeech, a novel cascaded generative pipeline that integrates compression, extraction, reconstruction, and correction processes. SoloSpeech features a speaker-embedding-free target extractor that utilizes conditional information from the cue audio's latent space, aligning it with the mixture audio's latent space to prevent mismatches. Evaluated on the widely-used Libri2Mix dataset, SoloSpeech achieves the new state-of-the-art intelligibility and quality in target speech extraction and speech separation tasks while demonstrating exceptional generalization on out-of-domain data and real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HaoFengyuan/X-TF-GridNet", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20322", "title": "Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms", "authors": [ "Mengru Wang", "Ziwen Xu", "Shengyu Mao", "Shumin Deng", "Zhaopeng Tu", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/steer-target-atoms", "project_page": "", "abstract": "Precise control over language model generation is vital for ensuring both safety and reliability. Although prompt engineering and steering are commonly used to intervene in model behaviors, the vast number of parameters in models often results in highly intertwined internal representations. This interdependency can limit control precision and sometimes lead to unintended side effects. Recent research has explored the use of sparse autoencoders (SAE) to disentangle knowledge in high-dimensional spaces for steering. However, these applications have been limited to toy tasks owing to the nontrivial issue of locating atomic knowledge components. In this paper, we propose Steering Target Atoms (STA), a novel method that isolates and manipulates disentangled knowledge components to enhance safety. Comprehensive experiments demonstrate the effectiveness of our approach. Further analysis reveals that steering exhibits superior robustness and flexibility, particularly in adversarial scenarios. We also apply the steering strategy to the large reasoning model, confirming its effectiveness in precise reasoning control.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zjunlp/steer-target-atoms", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21491", "title": "Frame In-N-Out: Unbounded Controllable Image-to-Video Generation", "authors": [ "Boyang Wang", "Xuweiyi Chen", "Matheus Gadelha", "Zezhou Cheng" ], "github": "https://github.com/JaidedAI/EasyOCR", "project_page": "", "abstract": "Controllability, temporal coherence, and detail synthesis remain the most critical challenges in video generation. In this paper, we focus on a commonly used yet underexplored cinematic technique known as Frame In and Frame Out. Specifically, starting from image-to-video generation, users can control the objects in the image to naturally leave the scene or provide breaking new identity references to enter the scene, guided by user-specified motion trajectory. To support this task, we introduce a new dataset curated semi-automatically, a comprehensive evaluation protocol targeting this setting, and an efficient identity-preserving motion-controllable video Diffusion Transformer architecture. Our evaluation shows that our proposed approach significantly outperforms existing baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JaidedAI/EasyOCR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21457", "title": "Active-O3: Empowering Multimodal Large Language Models with Active Perception via GRPO", "authors": [ "Muzhi Zhu", "Hao Zhong", "Canyu Zhao", "Zongze Du", "Zheng Huang", "Mingyu Liu", "Hao Chen", "Cheng Zou", "Jingdong Chen", "Ming Yang", "Chunhua Shen" ], "github": "https://github.com/aim-uofa/Active-o3", "project_page": "", "abstract": "Active vision, also known as active perception, refers to the process of actively selecting where and how to look in order to gather task-relevant information. It is a critical component of efficient perception and decision-making in humans and advanced embodied agents. Recently, the use of Multimodal Large Language Models (MLLMs) as central planning and decision-making modules in robotic systems has gained extensive attention. However, despite the importance of active perception in embodied intelligence, there is little to no exploration of how MLLMs can be equipped with or learn active perception capabilities. In this paper, we first provide a systematic definition of MLLM-based active perception tasks. We point out that the recently proposed GPT-o3 model's zoom-in search strategy can be regarded as a special case of active perception; however, it still suffers from low search efficiency and inaccurate region selection. To address these issues, we propose ACTIVE-O3, a purely reinforcement learning based training framework built on top of GRPO, designed to equip MLLMs with active perception capabilities. We further establish a comprehensive benchmark suite to evaluate ACTIVE-O3 across both general open-world tasks, such as small-object and dense object grounding, and domain-specific scenarios, including small object detection in remote sensing and autonomous driving, as well as fine-grained interactive segmentation. In addition, ACTIVE-O3 also demonstrates strong zero-shot reasoning abilities on the V* Benchmark, without relying on any explicit reasoning data. We hope that our work can provide a simple codebase and evaluation protocol to facilitate future research on active perception in MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aim-uofa/Active-o3", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21205", "title": "Sci-Fi: Symmetric Constraint for Frame Inbetweening", "authors": [ "Liuhan Chen", "Xiaodong Cun", "Xiaoyu Li", "Xianyi He", "Shenghai Yuan", "Jie Chen", "Ying Shan", "Li Yuan" ], "github": "", "project_page": "", "abstract": "Frame inbetweening aims to synthesize intermediate video sequences conditioned on the given start and end frames. Current state-of-the-art methods mainly extend large-scale pre-trained Image-to-Video Diffusion models (I2V-DMs) by incorporating end-frame constraints via directly fine-tuning or omitting training. We identify a critical limitation in their design: Their injections of the end-frame constraint usually utilize the same mechanism that originally imposed the start-frame (single image) constraint. However, since the original I2V-DMs are adequately trained for the start-frame condition in advance, naively introducing the end-frame constraint by the same mechanism with much less (even zero) specialized training probably can't make the end frame have a strong enough impact on the intermediate content like the start frame. This asymmetric control strength of the two frames over the intermediate content likely leads to inconsistent motion or appearance collapse in generated frames. To efficiently achieve symmetric constraints of start and end frames, we propose a novel framework, termed Sci-Fi, which applies a stronger injection for the constraint of a smaller training scale. Specifically, it deals with the start-frame constraint as before, while introducing the end-frame constraint by an improved mechanism. The new mechanism is based on a well-designed lightweight module, named EF-Net, which encodes only the end frame and expands it into temporally adaptive frame-wise features injected into the I2V-DM. This makes the end-frame constraint as strong as the start-frame constraint, enabling our Sci-Fi to produce more harmonious transitions in various scenarios. Extensive experiments prove the superiority of our Sci-Fi compared with other baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21070", "title": "Minute-Long Videos with Dual Parallelisms", "authors": [ "Zeqing Wang", "Bowen Zheng", "Xingyi Yang", "Yuecong Xu", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Diffusion Transformer (DiT)-based video diffusion models generate high-quality videos at scale but incur prohibitive processing latency and memory costs for long videos. To address this, we propose a novel distributed inference strategy, termed DualParal. The core idea is that, instead of generating an entire video on a single GPU, we parallelize both temporal frames and model layers across GPUs. However, a naive implementation of this division faces a key limitation: since diffusion models require synchronized noise levels across frames, this implementation leads to the serialization of original parallelisms. We leverage a block-wise denoising scheme to handle this. Namely, we process a sequence of frame blocks through the pipeline with progressively decreasing noise levels. Each GPU handles a specific block and layer subset while passing previous results to the next GPU, enabling asynchronous computation and communication. To further optimize performance, we incorporate two key enhancements. Firstly, a feature cache is implemented on each GPU to store and reuse features from the prior block as context, minimizing inter-GPU communication and redundant computation. Secondly, we employ a coordinated noise initialization strategy, ensuring globally consistent temporal dynamics by sharing initial noise patterns across GPUs without extra resource costs. Together, these enable fast, artifact-free, and infinitely long video generation. Applied to the latest diffusion transformer video generator, our method efficiently produces 1,025-frame videos with up to 6.54times lower latency and 1.48times lower memory cost on 8timesRTX 4090 GPUs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.19000", "title": "VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy Optimization", "authors": [ "Yunxin Li", "Xinyu Chen", "Zitao Li", "Zhenyu Liu", "Longyue Wang", "Wenhan Luo", "Baotian Hu", "Min Zhang" ], "github": "https://github.com/hitsz-tmg/veripo", "project_page": "", "abstract": "Applying Reinforcement Learning (RL) to Video Large Language Models (Video-LLMs) shows significant promise for complex video reasoning. However, popular Reinforcement Fine-Tuning (RFT) methods, such as outcome-based Group Relative Policy Optimization (GRPO), are limited by data preparation bottlenecks (e.g., noise or high cost) and exhibit unstable improvements in the quality of long chain-of-thoughts (CoTs) and downstream performance.To address these limitations, we propose VerIPO, a Verifier-guided Iterative Policy Optimization method designed to gradually improve video LLMs' capacity for generating deep, long-term reasoning chains. The core component is Rollout-Aware Verifier, positioned between the GRPO and Direct Preference Optimization (DPO) training phases to form the GRPO-Verifier-DPO training loop. This verifier leverages small LLMs as a judge to assess the reasoning logic of rollouts, enabling the construction of high-quality contrastive data, including reflective and contextually consistent CoTs. These curated preference samples drive the efficient DPO stage (7x faster than GRPO), leading to marked improvements in reasoning chain quality, especially in terms of length and contextual consistency. This training loop benefits from GRPO's expansive search and DPO's targeted optimization. Experimental results demonstrate: 1) Significantly faster and more effective optimization compared to standard GRPO variants, yielding superior performance; 2) Our trained models exceed the direct inference of large-scale instruction-tuned Video-LLMs, producing long and contextually consistent CoTs on diverse video reasoning tasks; and 3) Our model with one iteration outperforms powerful LMMs (e.g., Kimi-VL) and long reasoning models (e.g., Video-R1), highlighting its effectiveness and stability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hitsz-tmg/veripo", "github_api_added_by": "auto" }, { "date": "2025-05-28", "arxiv_id": "2505.21327", "title": "MME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs", "authors": [ "Jiakang Yuan", "Tianshuo Peng", "Yilei Jiang", "Yiting Lu", "Renrui Zhang", "Kaituo Feng", "Chaoyou Fu", "Tao Chen", "Lei Bai", "Bo Zhang", "Xiangyu Yue" ], "github": "", "project_page": "", "abstract": "Logical reasoning is a fundamental aspect of human intelligence and an essential capability for multimodal large language models (MLLMs). Despite the significant advancement in multimodal reasoning, existing benchmarks fail to comprehensively evaluate their reasoning abilities due to the lack of explicit categorization for logical reasoning types and an unclear understanding of reasoning. To address these issues, we introduce MME-Reasoning, a comprehensive benchmark designed to evaluate the reasoning ability of MLLMs, which covers all three types of reasoning (i.e., inductive, deductive, and abductive) in its questions. We carefully curate the data to ensure that each question effectively evaluates reasoning ability rather than perceptual skills or knowledge breadth, and extend the evaluation protocols to cover the evaluation of diverse questions. Our evaluation reveals substantial limitations of state-of-the-art MLLMs when subjected to holistic assessments of logical reasoning capabilities. Even the most advanced MLLMs show limited performance in comprehensive logical reasoning, with notable performance imbalances across reasoning types. In addition, we conducted an in-depth analysis of approaches such as ``thinking mode'' and Rule-based RL, which are commonly believed to enhance reasoning abilities. These findings highlight the critical limitations and performance imbalances of current MLLMs in diverse logical reasoning scenarios, providing comprehensive and systematic insights into the understanding and evaluation of reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.16459", "title": "MMMR: Benchmarking Massive Multi-Modal Reasoning Tasks", "authors": [ "Guiyao Tie", "Xueyang Zhou", "Tianhe Gu", "Ruihang Zhang", "Chaoran Hu", "Sizhe Zhang", "Mengqu Sun", "Yan Zhang", "Pan Zhou", "Lichao Sun" ], "github": "", "project_page": "", "abstract": "Recent advances in Multi-Modal Large Language Models (MLLMs) have enabled unified processing of language, vision, and structured inputs, opening the door to complex tasks such as logical deduction, spatial reasoning, and scientific analysis. Despite their promise, the reasoning capabilities of MLLMs, particularly those augmented with intermediate thinking traces (MLLMs-T), remain poorly understood and lack standardized evaluation benchmarks. Existing work focuses primarily on perception or final answer correctness, offering limited insight into how models reason or fail across modalities. To address this gap, we introduce the MMMR, a new benchmark designed to rigorously evaluate multi-modal reasoning with explicit thinking. The MMMR comprises 1) a high-difficulty dataset of 1,083 questions spanning six diverse reasoning types with symbolic depth and multi-hop demands and 2) a modular Reasoning Trace Evaluation Pipeline (RTEP) for assessing reasoning quality beyond accuracy through metrics like relevance, consistency, and structured error annotations. Empirical results show that MLLMs-T overall outperform non-thinking counterparts, but even top models like Claude-3.7-Sonnet and Gemini-2.5 Pro suffer from reasoning pathologies such as inconsistency and overthinking. This benchmark reveals persistent gaps between accuracy and reasoning quality and provides an actionable evaluation pipeline for future model development. Overall, the MMMR offers a scalable foundation for evaluating, comparing, and improving the next generation of multi-modal reasoning systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.19099", "title": "SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoning", "authors": [ "Kun Xiang", "Heng Li", "Terry Jingchen Zhang", "Yinya Huang", "Zirong Liu", "Peixin Qu", "Jixi He", "Jiaqi Chen", "Yu-Jie Yuan", "Jianhua Han", "Hang Xu", "Hanhui Li", "Mrinmaya Sachan", "Xiaodan Liang" ], "github": "https://github.com/seephys/seephys-project", "project_page": "", "abstract": "We present SeePhys, a large-scale multimodal benchmark for LLM reasoning grounded in physics questions ranging from middle school to PhD qualifying exams. The benchmark covers 7 fundamental domains spanning the physics discipline, incorporating 21 categories of highly heterogeneous diagrams. In contrast to prior works where visual elements mainly serve auxiliary purposes, our benchmark features a substantial proportion of vision-essential problems (75\\%) that mandate visual information extraction for correct solutions. Through extensive evaluation, we observe that even the most advanced visual reasoning models (e.g., Gemini-2.5-pro and o4-mini) achieve sub-60\\% accuracy on our benchmark. These results reveal fundamental challenges in current large language models' visual understanding capabilities, particularly in: (i) establishing rigorous coupling between diagram interpretation and physics reasoning, and (ii) overcoming their persistent reliance on textual cues as cognitive shortcuts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/seephys/seephys-project", "github_api_added_by": "auto" }, { "date": "2025-05-28", "arxiv_id": "2505.20289", "title": "VisualToolAgent (VisTA): A Reinforcement Learning Framework for Visual Tool Selection", "authors": [ "Zeyi Huang", "Yuyang Ji", "Anirudh Sundara Rajan", "Zefan Cai", "Wen Xiao", "Junjie Hu", "Yong Jae Lee" ], "github": "https://github.com/OoDBag/VisTA", "project_page": "", "abstract": "We introduce VisTA, a new reinforcement learning framework that empowers visual agents to dynamically explore, select, and combine tools from a diverse library based on empirical performance. Existing methods for tool-augmented reasoning either rely on training-free prompting or large-scale fine-tuning; both lack active tool exploration and typically assume limited tool diversity, and fine-tuning methods additionally demand extensive human supervision. In contrast, VisTA leverages end-to-end reinforcement learning to iteratively refine sophisticated, query-specific tool selection strategies, using task outcomes as feedback signals. Through Group Relative Policy Optimization (GRPO), our framework enables an agent to autonomously discover effective tool-selection pathways without requiring explicit reasoning supervision. Experiments on the ChartQA, Geometry3K, and BlindTest benchmarks demonstrate that VisTA achieves substantial performance gains over training-free baselines, especially on out-of-distribution examples. These results highlight VisTA's ability to enhance generalization, adaptively utilize diverse tools, and pave the way for flexible, experience-driven visual reasoning systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OoDBag/VisTA", "github_api_added_by": "auto" }, { "date": "2025-05-28", "arxiv_id": "2505.21473", "title": "DetailFlow: 1D Coarse-to-Fine Autoregressive Image Generation via Next-Detail Prediction", "authors": [ "Yiheng Liu", "Liao Qu", "Huichao Zhang", "Xu Wang", "Yi Jiang", "Yiming Gao", "Hu Ye", "Xian Li", "Shuai Wang", "Daniel K. Du", "Shu Cheng", "Zehuan Yuan", "Xinglong Wu" ], "github": "", "project_page": "", "abstract": "This paper presents DetailFlow, a coarse-to-fine 1D autoregressive (AR) image generation method that models images through a novel next-detail prediction strategy. By learning a resolution-aware token sequence supervised with progressively degraded images, DetailFlow enables the generation process to start from the global structure and incrementally refine details. This coarse-to-fine 1D token sequence aligns well with the autoregressive inference mechanism, providing a more natural and efficient way for the AR model to generate complex visual content. Our compact 1D AR model achieves high-quality image synthesis with significantly fewer tokens than previous approaches, i.e. VAR/VQGAN. We further propose a parallel inference mechanism with self-correction that accelerates generation speed by approximately 8x while reducing accumulation sampling error inherent in teacher-forcing supervision. On the ImageNet 256x256 benchmark, our method achieves 2.96 gFID with 128 tokens, outperforming VAR (3.3 FID) and FlexVAR (3.05 FID), which both require 680 tokens in their AR models. Moreover, due to the significantly reduced token count and parallel inference mechanism, our method runs nearly 2x faster inference speed compared to VAR and FlexVAR. Extensive experimental results demonstrate DetailFlow's superior generation quality and efficiency compared to existing state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.17813", "title": "Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning", "authors": [ "Michael Hassid", "Gabriel Synnaeve", "Yossi Adi", "Roy Schwartz" ], "github": "", "project_page": "", "abstract": "Reasoning large language models (LLMs) heavily rely on scaling test-time compute to perform complex reasoning tasks by generating extensive \"thinking\" chains. While demonstrating impressive results, this approach incurs significant computational costs and inference time. In this work, we challenge the assumption that long thinking chains results in better reasoning capabilities. We first demonstrate that shorter reasoning chains within individual questions are significantly more likely to yield correct answers - up to 34.5% more accurate than the longest chain sampled for the same question. Based on these results, we suggest short-m@k, a novel reasoning LLM inference method. Our method executes k independent generations in parallel and halts computation once the first m thinking processes are done. The final answer is chosen using majority voting among these m chains. Basic short-1@k demonstrates similar or even superior performance over standard majority voting in low-compute settings - using up to 40% fewer thinking tokens. short-3@k, while slightly less efficient than short-1@k, consistently surpasses majority voting across all compute budgets, while still being substantially faster (up to 33% wall time reduction). Inspired by our results, we finetune an LLM using short, long, and randomly selected reasoning chains. We then observe that training on the shorter ones leads to better performance. Our findings suggest rethinking current methods of test-time compute in reasoning LLMs, emphasizing that longer \"thinking\" does not necessarily translate to improved performance and can, counter-intuitively, lead to degraded results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21494", "title": "Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment", "authors": [ "Xiaojun Jia", "Sensen Gao", "Simeng Qin", "Tianyu Pang", "Chao Du", "Yihao Huang", "Xinfeng Li", "Yiming Li", "Bo Li", "Yang Liu" ], "github": "https://github.com/jiaxiaojunQAQ/FOA-Attack", "project_page": "", "abstract": "Multimodal large language models (MLLMs) remain vulnerable to transferable adversarial examples. While existing methods typically achieve targeted attacks by aligning global features-such as CLIP's [CLS] token-between adversarial and target samples, they often overlook the rich local information encoded in patch tokens. This leads to suboptimal alignment and limited transferability, particularly for closed-source models. To address this limitation, we propose a targeted transferable adversarial attack method based on feature optimal alignment, called FOA-Attack, to improve adversarial transfer capability. Specifically, at the global level, we introduce a global feature loss based on cosine similarity to align the coarse-grained features of adversarial samples with those of target samples. At the local level, given the rich local representations within Transformers, we leverage clustering techniques to extract compact local patterns to alleviate redundant local features. We then formulate local feature alignment between adversarial and target samples as an optimal transport (OT) problem and propose a local clustering optimal transport loss to refine fine-grained feature alignment. Additionally, we propose a dynamic ensemble model weighting strategy to adaptively balance the influence of multiple models during adversarial example generation, thereby further improving transferability. Extensive experiments across various models demonstrate the superiority of the proposed method, outperforming state-of-the-art methods, especially in transferring to closed-source MLLMs. The code is released at https://github.com/jiaxiaojunQAQ/FOA-Attack.", "github_manual": "", "github_regex": "https://github.com/jiaxiaojunQAQ/FOA-Attack", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.16340", "title": "Improving Chemical Understanding of LLMs via SMILES Parsing", "authors": [ "Yunhui Jang", "Jaehyung Kim", "Sungsoo Ahn" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly recognized as powerful tools for scientific discovery, particularly in molecular science. A fundamental requirement for these models is the ability to accurately understand molecular structures, commonly encoded in the SMILES representation. However, current LLMs struggle to interpret SMILES, even failing to carry out basic tasks such as counting molecular rings. To address this limitation, we introduce CLEANMOL, a novel framework that formulates SMILES parsing into a suite of clean and deterministic tasks explicitly designed to promote graph-level molecular comprehension. These tasks span from subgraph matching to global graph matching, providing structured supervision aligned with molecular structural properties. We construct a molecular pretraining dataset with adaptive difficulty scoring and pre-train open-source LLMs on these tasks. Our results show that CLEANMOL not only enhances structural comprehension but also achieves the best or competes with the baseline on the Mol-Instructions benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21496", "title": "UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents", "authors": [ "Han Xiao", "Guozhi Wang", "Yuxiang Chai", "Zimu Lu", "Weifeng Lin", "Hao He", "Lue Fan", "Liuyang Bian", "Rui Hu", "Liang Liu", "Shuai Ren", "Yafei Wen", "Xiaoxin Chen", "Aojun Zhou", "Hongsheng Li" ], "github": "https://github.com/Euphoria16/UI-Genie", "project_page": "", "abstract": "In this paper, we introduce UI-Genie, a self-improving framework addressing two key challenges in GUI agents: verification of trajectory outcome is challenging and high-quality training data are not scalable. These challenges are addressed by a reward model and a self-improving pipeline, respectively. The reward model, UI-Genie-RM, features an image-text interleaved architecture that efficiently pro- cesses historical context and unifies action-level and task-level rewards. To sup- port the training of UI-Genie-RM, we develop deliberately-designed data genera- tion strategies including rule-based verification, controlled trajectory corruption, and hard negative mining. To address the second challenge, a self-improvement pipeline progressively expands solvable complex GUI tasks by enhancing both the agent and reward models through reward-guided exploration and outcome verification in dynamic environments. For training the model, we generate UI- Genie-RM-517k and UI-Genie-Agent-16k, establishing the first reward-specific dataset for GUI agents while demonstrating high-quality synthetic trajectory gen- eration without manual annotation. Experimental results show that UI-Genie achieves state-of-the-art performance across multiple GUI agent benchmarks with three generations of data-model self-improvement. We open-source our complete framework implementation and generated datasets to facilitate further research in https://github.com/Euphoria16/UI-Genie.", "github_manual": "", "github_regex": "https://github.com/Euphoria16/UI-Genie", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21500", "title": "ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in Vision-Language Models", "authors": [ "Dingming Li", "Hongxing Li", "Zixuan Wang", "Yuchen Yan", "Hang Zhang", "Siqi Chen", "Guiyang Hou", "Shengpei Jiang", "Wenqi Zhang", "Yongliang Shen", "Weiming Lu", "Yueting Zhuang" ], "github": "https://github.com/ZJU-REAL/ViewSpatial-Bench", "project_page": "", "abstract": "Vision-language models (VLMs) have demonstrated remarkable capabilities in understanding and reasoning about visual content, but significant challenges persist in tasks requiring cross-viewpoint understanding and spatial reasoning. We identify a critical limitation: current VLMs excel primarily at egocentric spatial reasoning (from the camera's perspective) but fail to generalize to allocentric viewpoints when required to adopt another entity's spatial frame of reference. We introduce ViewSpatial-Bench, the first comprehensive benchmark designed specifically for multi-viewpoint spatial localization recognition evaluation across five distinct task types, supported by an automated 3D annotation pipeline that generates precise directional labels. Comprehensive evaluation of diverse VLMs on ViewSpatial-Bench reveals a significant performance disparity: models demonstrate reasonable performance on camera-perspective tasks but exhibit reduced accuracy when reasoning from a human viewpoint. By fine-tuning VLMs on our multi-perspective spatial dataset, we achieve an overall performance improvement of 46.24% across tasks, highlighting the efficacy of our approach. Our work establishes a crucial benchmark for spatial intelligence in embodied AI systems and provides empirical evidence that modeling 3D spatial relationships enhances VLMs' corresponding spatial comprehension capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZJU-REAL/ViewSpatial-Bench", "github_api_added_by": "auto" }, { "date": "2025-05-28", "arxiv_id": "2505.21505", "title": "How does Alignment Enhance LLMs' Multilingual Capabilities? A Language Neurons Perspective", "authors": [ "Shimao Zhang", "Zhejian Lai", "Xiang Liu", "Shuaijie She", "Xiao Liu", "Yeyun Gong", "Shujian Huang", "Jiajun Chen" ], "github": "https://github.com/NJUNLP/Language-Neurons-Alignment", "project_page": "", "abstract": "Multilingual Alignment is an effective and representative paradigm to enhance LLMs' multilingual capabilities, which transfers the capabilities from the high-resource languages to the low-resource languages. Meanwhile, some researches on language-specific neurons reveal that there are language-specific neurons that are selectively activated in LLMs when processing different languages. This provides a new perspective to analyze and understand LLMs' mechanisms more specifically in multilingual scenarios. In this work, we propose a new finer-grained neuron identification algorithm, which detects language neurons~(including language-specific neurons and language-related neurons) and language-agnostic neurons. Furthermore, based on the distributional characteristics of different types of neurons, we divide the LLMs' internal process for multilingual inference into four parts: (1) multilingual understanding, (2) shared semantic space reasoning, (3) multilingual output space transformation, and (4) vocabulary space outputting. Additionally, we systematically analyze the models before and after alignment with a focus on different types of neurons. We also analyze the phenomenon of ''Spontaneous Multilingual Alignment''. Overall, our work conducts a comprehensive investigation based on different types of neurons, providing empirical results and valuable insights for better understanding multilingual alignment and multilingual capabilities of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NJUNLP/Language-Neurons-Alignment", "github_api_added_by": "auto" }, { "date": "2025-05-28", "arxiv_id": "2505.16901", "title": "Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks", "authors": [ "Hongyuan Tao", "Ying Zhang", "Zhenhao Tang", "Hongen Peng", "Xukun Zhu", "Bingchang Liu", "Yingguang Yang", "Ziyin Zhang", "Zhaogui Xu", "Haipeng Zhang", "Linchao Zhu", "Rui Wang", "Hang Yu", "Jianguo Li", "Peng Di" ], "github": "", "project_page": "", "abstract": "Recent advances in Large Language Models (LLMs) have shown promise in function-level code generation, yet repository-level software engineering tasks remain challenging. Current solutions predominantly rely on proprietary LLM agents, which introduce unpredictability and limit accessibility, raising concerns about data privacy and model customization. This paper investigates whether open-source LLMs can effectively address repository-level tasks without requiring agent-based approaches. We demonstrate this is possible by enabling LLMs to comprehend functions and files within codebases through their semantic information and structural dependencies. To this end, we introduce Code Graph Models (CGMs), which integrate repository code graph structures into the LLM's attention mechanism and map node attributes to the LLM's input space using a specialized adapter. When combined with an agentless graph RAG framework, our approach achieves a 43.00% resolution rate on the SWE-bench Lite benchmark using the open-source Qwen2.5-72B model. This performance ranks first among open weight models, second among methods with open-source systems, and eighth overall, surpassing the previous best open-source model-based method by 12.33%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.16673", "title": "R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO", "authors": [ "Huanjin Yao", "Qixiang Yin", "Jingyi Zhang", "Min Yang", "Yibo Wang", "Wenhao Wu", "Fei Su", "Li Shen", "Minghui Qiu", "Dacheng Tao", "Jiaxing Huang" ], "github": "https://github.com/HJYao00/R1-ShareVL", "project_page": "", "abstract": "In this work, we aim to incentivize the reasoning ability of Multimodal Large Language Models (MLLMs) via reinforcement learning (RL) and develop an effective approach that mitigates the sparse reward and advantage vanishing issues during RL. To this end, we propose Share-GRPO, a novel RL approach that tackle these issues by exploring and sharing diverse reasoning trajectories over expanded question space. Specifically, Share-GRPO first expands the question space for a given question via data transformation techniques, and then encourages MLLM to effectively explore diverse reasoning trajectories over the expanded question space and shares the discovered reasoning trajectories across the expanded questions during RL. In addition, Share-GRPO also shares reward information during advantage computation, which estimates solution advantages hierarchically across and within question variants, allowing more accurate estimation of relative advantages and improving the stability of policy training. Extensive evaluations over six widely-used reasoning benchmarks showcase the superior performance of our method. Code will be available at https://github.com/HJYao00/R1-ShareVL.", "github_manual": "", "github_regex": "https://github.com/HJYao00/R1-ShareVL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.19973", "title": "DFIR-Metric: A Benchmark Dataset for Evaluating Large Language Models in Digital Forensics and Incident Response", "authors": [ "Bilel Cherif", "Tamas Bisztray", "Richard A. Dubniczky", "Aaesha Aldahmani", "Saeed Alshehhi", "Norbert Tihanyi" ], "github": "", "project_page": "", "abstract": "Digital Forensics and Incident Response (DFIR) involves analyzing digital evidence to support legal investigations. Large Language Models (LLMs) offer new opportunities in DFIR tasks such as log analysis and memory forensics, but their susceptibility to errors and hallucinations raises concerns in high-stakes contexts. Despite growing interest, there is no comprehensive benchmark to evaluate LLMs across both theoretical and practical DFIR domains. To address this gap, we present DFIR-Metric, a benchmark with three components: (1) Knowledge Assessment: a set of 700 expert-reviewed multiple-choice questions sourced from industry-standard certifications and official documentation; (2) Realistic Forensic Challenges: 150 CTF-style tasks testing multi-step reasoning and evidence correlation; and (3) Practical Analysis: 500 disk and memory forensics cases from the NIST Computer Forensics Tool Testing Program (CFTT). We evaluated 14 LLMs using DFIR-Metric, analyzing both their accuracy and consistency across trials. We also introduce a new metric, the Task Understanding Score (TUS), designed to more effectively evaluate models in scenarios where they achieve near-zero accuracy. This benchmark offers a rigorous, reproducible foundation for advancing AI in digital forensics. All scripts, artifacts, and results are available on the project website at https://github.com/DFIR-Metric.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.19377", "title": "Absolute Coordinates Make Motion Generation Easy", "authors": [ "Zichong Meng", "Zeyu Han", "Xiaogang Peng", "Yiming Xie", "Huaizu Jiang" ], "github": "", "project_page": "", "abstract": "State-of-the-art text-to-motion generation models rely on the kinematic-aware, local-relative motion representation popularized by HumanML3D, which encodes motion relative to the pelvis and to the previous frame with built-in redundancy. While this design simplifies training for earlier generation models, it introduces critical limitations for diffusion models and hinders applicability to downstream tasks. In this work, we revisit the motion representation and propose a radically simplified and long-abandoned alternative for text-to-motion generation: absolute joint coordinates in global space. Through systematic analysis of design choices, we show that this formulation achieves significantly higher motion fidelity, improved text alignment, and strong scalability, even with a simple Transformer backbone and no auxiliary kinematic-aware losses. Moreover, our formulation naturally supports downstream tasks such as text-driven motion control and temporal/spatial editing without additional task-specific reengineering and costly classifier guidance generation from control signals. Finally, we demonstrate promising generalization to directly generate SMPL-H mesh vertices in motion from text, laying a strong foundation for future research and motion-related applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21334", "title": "HoliTom: Holistic Token Merging for Fast Video Large Language Models", "authors": [ "Kele Shao", "Keda Tao", "Can Qin", "Haoxuan You", "Yang Sui", "Huan Wang" ], "github": "", "project_page": "", "abstract": "Video large language models (video LLMs) excel at video comprehension but face significant computational inefficiency due to redundant video tokens. Existing token pruning methods offer solutions. However, approaches operating within the LLM (inner-LLM pruning), such as FastV, incur intrinsic computational overhead in shallow layers. In contrast, methods performing token pruning before the LLM (outer-LLM pruning) primarily address spatial redundancy within individual frames or limited temporal windows, neglecting the crucial global temporal dynamics and correlations across longer video sequences. This leads to sub-optimal spatio-temporal reduction and does not leverage video compressibility fully. Crucially, the synergistic potential and mutual influence of combining these strategies remain unexplored. To further reduce redundancy, we introduce HoliTom, a novel training-free holistic token merging framework. HoliTom employs outer-LLM pruning through global redundancy-aware temporal segmentation, followed by spatial-temporal merging to reduce visual tokens by over 90%, significantly alleviating the LLM's computational burden. Complementing this, we introduce a robust inner-LLM token similarity-based merging approach, designed for superior performance and compatibility with outer-LLM pruning. Evaluations demonstrate our method's promising efficiency-performance trade-off on LLaVA-OneVision-7B, reducing computational costs to 6.9% of FLOPs while maintaining 99.1% of the original performance. Furthermore, we achieve a 2.28x reduction in Time-To-First-Token (TTFT) and a 1.32x acceleration in decoding throughput, highlighting the practical benefits of our integrated pruning approach for efficient video LLMs inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20561", "title": "Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoning", "authors": [ "Shenao Zhang", "Yaqing Wang", "Yinxiao Liu", "Tianqi Liu", "Peter Grabowski", "Eugene Ie", "Zhaoran Wang", "Yunxuan Li" ], "github": "https://github.com/shenao-zhang/BARL", "project_page": "", "abstract": "Large Language Models (LLMs) trained via Reinforcement Learning (RL) have exhibited strong reasoning capabilities and emergent reflective behaviors, such as backtracking and error correction. However, conventional Markovian RL confines exploration to the training phase to learn an optimal deterministic policy and depends on the history contexts only through the current state. Therefore, it remains unclear whether reflective reasoning will emerge during Markovian RL training, or why they are beneficial at test time. To remedy this, we recast reflective exploration within the Bayes-Adaptive RL framework, which explicitly optimizes the expected return under a posterior distribution over Markov decision processes. This Bayesian formulation inherently incentivizes both reward-maximizing exploitation and information-gathering exploration via belief updates. Our resulting algorithm, BARL, instructs the LLM to stitch and switch strategies based on the observed outcomes, offering principled guidance on when and how the model should reflectively explore. Empirical results on both synthetic and mathematical reasoning tasks demonstrate that BARL outperforms standard Markovian RL approaches at test time, achieving superior token efficiency with improved exploration effectiveness. Our code is available at https://github.com/shenao-zhang/BARL.", "github_manual": "", "github_regex": "https://github.com/shenao-zhang/BARL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.17908", "title": "ComfyMind: Toward General-Purpose Generation via Tree-Based Planning and Reactive Feedback", "authors": [ "Litao Guo", "Xinli Xu", "Luozhou Wang", "Jiantao Lin", "Jinsong Zhou", "Zixin Zhang", "Bolan Su", "Ying-Cong Chen" ], "github": "https://github.com/LitaoGuo/ComfyMind", "project_page": "", "abstract": "With the rapid advancement of generative models, general-purpose generation has gained increasing attention as a promising approach to unify diverse tasks across modalities within a single system. Despite this progress, existing open-source frameworks often remain fragile and struggle to support complex real-world applications due to the lack of structured workflow planning and execution-level feedback. To address these limitations, we present ComfyMind, a collaborative AI system designed to enable robust and scalable general-purpose generation, built on the ComfyUI platform. ComfyMind introduces two core innovations: Semantic Workflow Interface (SWI) that abstracts low-level node graphs into callable functional modules described in natural language, enabling high-level composition and reducing structural errors; Search Tree Planning mechanism with localized feedback execution, which models generation as a hierarchical decision process and allows adaptive correction at each stage. Together, these components improve the stability and flexibility of complex generative workflows. We evaluate ComfyMind on three public benchmarks: ComfyBench, GenEval, and Reason-Edit, which span generation, editing, and reasoning tasks. Results show that ComfyMind consistently outperforms existing open-source baselines and achieves performance comparable to GPT-Image-1. ComfyMind paves a promising path for the development of open-source general-purpose generative AI systems. Project page: https://github.com/LitaoGuo/ComfyMind", "github_manual": "", "github_regex": "https://github.com/LitaoGuo/ComfyMind", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.11277", "title": "Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs", "authors": [ "Yaorui Shi", "Shihan Li", "Chang Wu", "Zhiyuan Liu", "Junfeng Fang", "Hengxing Cai", "An Zhang", "Xiang Wang" ], "github": "https://github.com/syr-cn/AutoRefine", "project_page": "", "abstract": "Large language models have demonstrated impressive reasoning capabilities but are inherently limited by their knowledge reservoir. Retrieval-augmented reasoning mitigates this limitation by allowing LLMs to query external resources, but existing methods often retrieve irrelevant or noisy information, hindering accurate reasoning. In this paper, we propose AutoRefine, a reinforcement learning post-training framework that adopts a new ``search-and-refine-during-think'' paradigm. AutoRefine introduces explicit knowledge refinement steps between successive search calls, enabling the model to iteratively filter, distill, and organize evidence before generating an answer. Furthermore, we incorporate tailored retrieval-specific rewards alongside answer correctness rewards using group relative policy optimization. Experiments on single-hop and multi-hop QA benchmarks demonstrate that AutoRefine significantly outperforms existing approaches, particularly in complex, multi-hop reasoning scenarios. Detailed analysis shows that AutoRefine issues frequent, higher-quality searches and synthesizes evidence effectively.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/syr-cn/AutoRefine", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21178", "title": "Walk Before You Run! Concise LLM Reasoning via Reinforcement Learning", "authors": [ "Mingyang Song", "Mao Zheng" ], "github": "https://github.com/nick7nlp/ConciseR", "project_page": "", "abstract": "As test-time scaling becomes a pivotal research frontier in Large Language Models (LLMs) development, contemporary and advanced post-training methodologies increasingly focus on extending the generation length of long Chain-of-Thought (CoT) responses to enhance reasoning capabilities toward DeepSeek R1-like performance. However, recent studies reveal a persistent overthinking phenomenon in state-of-the-art reasoning models, manifesting as excessive redundancy or repetitive thinking patterns in long CoT responses. To address this issue, in this paper, we propose a simple yet effective two-stage reinforcement learning framework for achieving concise reasoning in LLMs, named ConciseR. Specifically, the first stage, using more training steps, aims to incentivize the model's reasoning capabilities via Group Relative Policy Optimization with clip-higher and dynamic sampling components (GRPO++), and the second stage, using fewer training steps, explicitly enforces conciseness and improves efficiency via Length-aware Group Relative Policy Optimization (L-GRPO). Significantly, ConciseR only optimizes response length once all rollouts of a sample are correct, following the \"walk before you run\" principle. Extensive experimental results demonstrate that our ConciseR model, which generates more concise CoT reasoning responses, outperforms recent state-of-the-art reasoning models with zero RL paradigm across AIME 2024, MATH-500, AMC 2023, Minerva, and Olympiad benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nick7nlp/ConciseR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.19433", "title": "Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression", "authors": [ "Peijie Dong", "Zhenheng Tang", "Xiang Liu", "Lujun Li", "Xiaowen Chu", "Bo Li" ], "github": "https://github.com/pprp/ACBench", "project_page": "", "abstract": "Post-training compression reduces the computational and memory costs of large language models (LLMs), enabling resource-efficient deployment. However, existing compression benchmarks only focus on language modeling (e.g., perplexity) and natural language understanding tasks (e.g., GLUE accuracy), ignoring the agentic capabilities - workflow, tool use/function call, long-context understanding and real-world application. We introduce the Agent Compression Benchmark (ACBench), the first comprehensive benchmark for evaluating how compression impacts LLMs' agentic abilities. ACBench spans (1) 12 tasks across 4 capabilities (e.g., WorfBench for workflow generation, Needle-in-Haystack for long-context retrieval), (2) quantization (GPTQ, AWQ) and pruning (Wanda, SparseGPT), and (3) 15 models, including small (Gemma-2B), standard (Qwen2.5 7B-32B), and distilled reasoning LLMs (DeepSeek-R1-Distill). Our experiments reveal compression tradeoffs: 4-bit quantization preserves workflow generation and tool use (1%-3% drop) but degrades real-world application accuracy by 10%-15%. We introduce ERank, Top-k Ranking Correlation and Energy to systematize analysis. ACBench provides actionable insights for optimizing LLM compression in agentic scenarios. The code can be found in https://github.com/pprp/ACBench.", "github_manual": "", "github_regex": "https://github.com/pprp/ACBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21189", "title": "Exploring the Latent Capacity of LLMs for One-Step Text Generation", "authors": [ "Gleb Mezentsev", "Ivan Oseledets" ], "github": "", "project_page": "", "abstract": "A recent study showed that large language models (LLMs) can reconstruct surprisingly long texts - up to thousands of tokens - via autoregressive generation from just one specially trained input embedding. In this work, we explore whether such reconstruction is possible without autoregression. We show that frozen LLMs can generate hundreds of accurate tokens in just one forward pass, when provided with only two learned embeddings. This reveals a surprising and underexplored capability of LLMs - multi-token generation without iterative decoding. We investigate the behaviour of these embeddings and provide insight into the type of information they encode. We also empirically show that although these representations are not unique for a given text, they form connected and local regions in embedding space - a property that suggests the potential of learning a dedicated encoder into that space.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.19641", "title": "SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond", "authors": [ "Junteng Liu", "Yuanxiang Fan", "Zhuo Jiang", "Han Ding", "Yongyi Hu", "Chi Zhang", "Yiqi Shi", "Shitong Weng", "Aili Chen", "Shiqi Chen", "Yunan Huang", "Mozhi Zhang", "Pengyu Zhao", "Junjie Yan", "Junxian He" ], "github": "https://github.com/MiniMax-AI/SynLogic", "project_page": "", "abstract": "Recent advances such as OpenAI-o1 and DeepSeek R1 have demonstrated the potential of Reinforcement Learning (RL) to enhance reasoning abilities in Large Language Models (LLMs). While open-source replication efforts have primarily focused on mathematical and coding domains, methods and resources for developing general reasoning capabilities remain underexplored. This gap is partly due to the challenge of collecting diverse and verifiable reasoning data suitable for RL. We hypothesize that logical reasoning is critical for developing general reasoning capabilities, as logic forms a fundamental building block of reasoning. In this work, we present SynLogic, a data synthesis framework and dataset that generates diverse logical reasoning data at scale, encompassing 35 diverse logical reasoning tasks. The SynLogic approach enables controlled synthesis of data with adjustable difficulty and quantity. Importantly, all examples can be verified by simple rules, making them ideally suited for RL with verifiable rewards. In our experiments, we validate the effectiveness of RL training on the SynLogic dataset based on 7B and 32B models. SynLogic leads to state-of-the-art logical reasoning performance among open-source datasets, surpassing DeepSeek-R1-Distill-Qwen-32B by 6 points on BBEH. Furthermore, mixing SynLogic data with mathematical and coding tasks improves the training efficiency of these domains and significantly enhances reasoning generalization. Notably, our mixed training model outperforms DeepSeek-R1-Zero-Qwen-32B across multiple benchmarks. These findings position SynLogic as a valuable resource for advancing the broader reasoning capabilities of LLMs. We open-source both the data synthesis pipeline and the SynLogic dataset at https://github.com/MiniMax-AI/SynLogic.", "github_manual": "", "github_regex": "https://github.com/MiniMax-AI/SynLogic", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.17005", "title": "R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning", "authors": [ "Huatong Song", "Jinhao Jiang", "Wenqing Tian", "Zhipeng Chen", "Yuhuan Wu", "Jiahao Zhao", "Yingqian Min", "Wayne Xin Zhao", "Lei Fang", "Ji-Rong Wen" ], "github": "https://github.com/RUCAIBox/R1-Searcher-plus", "project_page": "", "abstract": "Large Language Models (LLMs) are powerful but prone to hallucinations due to static knowledge. Retrieval-Augmented Generation (RAG) helps by injecting external information, but current methods often are costly, generalize poorly, or ignore the internal knowledge of the model. In this paper, we introduce R1-Searcher++, a novel framework designed to train LLMs to adaptively leverage both internal and external knowledge sources. R1-Searcher++ employs a two-stage training strategy: an initial SFT Cold-start phase for preliminary format learning, followed by RL for Dynamic Knowledge Acquisition. The RL stage uses outcome-supervision to encourage exploration, incorporates a reward mechanism for internal knowledge utilization, and integrates a memorization mechanism to continuously assimilate retrieved information, thereby enriching the model's internal knowledge. By leveraging internal knowledge and external search engine, the model continuously improves its capabilities, enabling efficient retrieval-augmented reasoning. Our experiments demonstrate that R1-Searcher++ outperforms previous RAG and reasoning methods and achieves efficient retrieval. The code is available at https://github.com/RUCAIBox/R1-Searcher-plus.", "github_manual": "", "github_regex": "https://github.com/RUCAIBox/R1-Searcher-plus", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.15561", "title": "Do RAG Systems Suffer From Positional Bias?", "authors": [ "Florin Cuconasu", "Simone Filice", "Guy Horowitz", "Yoelle Maarek", "Fabrizio Silvestri" ], "github": "", "project_page": "", "abstract": "Retrieval Augmented Generation enhances LLM accuracy by adding passages retrieved from an external corpus to the LLM prompt. This paper investigates how positional bias - the tendency of LLMs to weight information differently based on its position in the prompt - affects not only the LLM's capability to capitalize on relevant passages, but also its susceptibility to distracting passages. Through extensive experiments on three benchmarks, we show how state-of-the-art retrieval pipelines, while attempting to retrieve relevant passages, systematically bring highly distracting ones to the top ranks, with over 60% of queries containing at least one highly distracting passage among the top-10 retrieved passages. As a result, the impact of the LLM positional bias, which in controlled settings is often reported as very prominent by related works, is actually marginal in real scenarios since both relevant and distracting passages are, in turn, penalized. Indeed, our findings reveal that sophisticated strategies that attempt to rearrange the passages based on LLM positional preferences do not perform better than random shuffling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21499", "title": "AdInject: Real-World Black-Box Attacks on Web Agents via Advertising Delivery", "authors": [ "Haowei Wang", "Junjie Wang", "Xiaojun Jia", "Rupeng Zhang", "Mingyang Li", "Zhe Liu", "Yang Liu", "Qing Wang" ], "github": "https://github.com/NicerWang/AdInject", "project_page": "", "abstract": "Vision-Language Model (VLM) based Web Agents represent a significant step towards automating complex tasks by simulating human-like interaction with websites. However, their deployment in uncontrolled web environments introduces significant security vulnerabilities. Existing research on adversarial environmental injection attacks often relies on unrealistic assumptions, such as direct HTML manipulation, knowledge of user intent, or access to agent model parameters, limiting their practical applicability. In this paper, we propose AdInject, a novel and real-world black-box attack method that leverages the internet advertising delivery to inject malicious content into the Web Agent's environment. AdInject operates under a significantly more realistic threat model than prior work, assuming a black-box agent, static malicious content constraints, and no specific knowledge of user intent. AdInject includes strategies for designing malicious ad content aimed at misleading agents into clicking, and a VLM-based ad content optimization technique that infers potential user intents from the target website's context and integrates these intents into the ad content to make it appear more relevant or critical to the agent's task, thus enhancing attack effectiveness. Experimental evaluations demonstrate the effectiveness of AdInject, attack success rates exceeding 60% in most scenarios and approaching 100% in certain cases. This strongly demonstrates that prevalent advertising delivery constitutes a potent and real-world vector for environment injection attacks against Web Agents. This work highlights a critical vulnerability in Web Agent security arising from real-world environment manipulation channels, underscoring the urgent need for developing robust defense mechanisms against such threats. Our code is available at https://github.com/NicerWang/AdInject.", "github_manual": "", "github_regex": "https://github.com/NicerWang/AdInject", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.19650", "title": "Modality Curation: Building Universal Embeddings for Advanced Multimodal Information Retrieval", "authors": [ "Fanheng Kong", "Jingyuan Zhang", "Yahui Liu", "Hongzhi Zhang", "Shi Feng", "Xiaocui Yang", "Daling Wang", "Yu Tian", "Victoria W.", "Fuzheng Zhang", "Guorui Zhou" ], "github": "", "project_page": "", "abstract": "Multimodal information retrieval (MIR) faces inherent challenges due to the heterogeneity of data sources and the complexity of cross-modal alignment. While previous studies have identified modal gaps in feature spaces, a systematic approach to address these challenges remains unexplored. In this work, we introduce UNITE, a universal framework that tackles these challenges through two critical yet underexplored aspects: data curation and modality-aware training configurations. Our work provides the first comprehensive analysis of how modality-specific data properties influence downstream task performance across diverse scenarios. Moreover, we propose Modal-Aware Masked Contrastive Learning (MAMCL) to mitigate the competitive relationships among the instances of different modalities. Our framework achieves state-of-the-art results on multiple multimodal retrieval benchmarks, outperforming existing methods by notable margins. Through extensive experiments, we demonstrate that strategic modality curation and tailored training protocols are pivotal for robust cross-modal representation learning. This work not only advances MIR performance but also provides a foundational blueprint for future research in multimodal systems. Our project is available at https://friedrichor.github.io/projects/UNITE.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.18657", "title": "MLLMs are Deeply Affected by Modality Bias", "authors": [ "Xu Zheng", "Chenfei Liao", "Yuqian Fu", "Kaiyu Lei", "Yuanhuiyi Lyu", "Lutao Jiang", "Bin Ren", "Jialei Chen", "Jiawen Wang", "Chengxin Li", "Linfeng Zhang", "Danda Pani Paudel", "Xuanjing Huang", "Yu-Gang Jiang", "Nicu Sebe", "Dacheng Tao", "Luc Van Gool", "Xuming Hu" ], "github": "", "project_page": "", "abstract": "Recent advances in Multimodal Large Language Models (MLLMs) have shown promising results in integrating diverse modalities such as texts and images. MLLMs are heavily influenced by modality bias, often relying on language while under-utilizing other modalities like visual inputs. This position paper argues that MLLMs are deeply affected by modality bias. Firstly, we diagnose the current state of modality bias, highlighting its manifestations across various tasks. Secondly, we propose a systematic research road-map related to modality bias in MLLMs. Thirdly, we identify key factors of modality bias in MLLMs and offer actionable suggestions for future research to mitigate it. To substantiate these findings, we conduct experiments that demonstrate the influence of each factor: 1. Data Characteristics: Language data is compact and abstract, while visual data is redundant and complex, creating an inherent imbalance in learning dynamics. 2. Imbalanced Backbone Capabilities: The dominance of pretrained language models in MLLMs leads to overreliance on language and neglect of visual information. 3. Training Objectives: Current objectives often fail to promote balanced cross-modal alignment, resulting in shortcut learning biased toward language. These findings highlight the need for balanced training strategies and model architectures to better integrate multiple modalities in MLLMs. We call for interdisciplinary efforts to tackle these challenges and drive innovation in MLLM research. Our work provides a fresh perspective on modality bias in MLLMs and offers insights for developing more robust and generalizable multimodal systems-advancing progress toward Artificial General Intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.17190", "title": "Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms", "authors": [ "Baran Hashemi", "Kurt Pasque", "Chris Teska", "Ruriko Yoshida" ], "github": "https://github.com/Baran-phys/Tropical-Attention", "project_page": "", "abstract": "Dynamic programming (DP) algorithms for combinatorial optimization problems work with taking maximization, minimization, and classical addition in their recursion algorithms. The associated value functions correspond to convex polyhedra in the max plus semiring. Existing Neural Algorithmic Reasoning models, however, rely on softmax-normalized dot-product attention where the smooth exponential weighting blurs these sharp polyhedral structures and collapses when evaluated on out-of-distribution (OOD) settings. We introduce Tropical attention, a novel attention function that operates natively in the max-plus semiring of tropical geometry. We prove that Tropical attention can approximate tropical circuits of DP-type combinatorial algorithms. We then propose that using Tropical transformers enhances empirical OOD performance in both length generalization and value generalization, on algorithmic reasoning tasks, surpassing softmax baselines while remaining stable under adversarial attacks. We also present adversarial-attack generalization as a third axis for Neural Algorithmic Reasoning benchmarking. Our results demonstrate that Tropical attention restores the sharp, scale-invariant reasoning absent from softmax.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Baran-phys/Tropical-Attention", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.17952", "title": "Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL", "authors": [ "Che Liu", "Haozhe Wang", "Jiazhen Pan", "Zhongwei Wan", "Yong Dai", "Fangzhen Lin", "Wenjia Bai", "Daniel Rueckert", "Rossella Arcucci" ], "github": "", "project_page": "", "abstract": "Improving performance on complex tasks and enabling interpretable decision making in large language models (LLMs), especially for clinical applications, requires effective reasoning. Yet this remains challenging without supervised fine-tuning (SFT) on costly chain-of-thought (CoT) data distilled from closed-source models (e.g., GPT-4o). In this work, we present AlphaMed, the first medical LLM to show that reasoning capability can emerge purely through reinforcement learning (RL), using minimalist rule-based rewards on public multiple-choice QA datasets, without relying on SFT or distilled CoT data. AlphaMed achieves state-of-the-art results on six medical QA benchmarks, outperforming models trained with conventional SFT+RL pipelines. On challenging benchmarks (e.g., MedXpert), AlphaMed even surpasses larger or closed-source models such as DeepSeek-V3-671B and Claude-3.5-Sonnet. To understand the factors behind this success, we conduct a comprehensive data-centric analysis guided by three questions: (i) Can minimalist rule-based RL incentivize reasoning without distilled CoT supervision? (ii) How do dataset quantity and diversity impact reasoning? (iii) How does question difficulty shape the emergence and generalization of reasoning? Our findings show that dataset informativeness is a key driver of reasoning performance, and that minimalist RL on informative, multiple-choice QA data is effective at inducing reasoning without CoT supervision. We also observe divergent trends across benchmarks, underscoring limitations in current evaluation and the need for more challenging, reasoning-oriented medical QA benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.14064", "title": "NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI", "authors": [ "Cosmin I. Bercea", "Jun Li", "Philipp Raffler", "Evamaria O. Riedel", "Lena Schmitzer", "Angela Kurz", "Felix Bitzer", "Paula Ro\u00dfm\u00fcller", "Julian Canisius", "Mirjam L. Beyrle", "Che Liu", "Wenjia Bai", "Bernhard Kainz", "Julia A. Schnabel", "Benedikt Wiestler" ], "github": "", "project_page": "", "abstract": "In many real-world applications, deployed models encounter inputs that differ from the data seen during training. Out-of-distribution detection identifies whether an input stems from an unseen distribution, while open-world recognition flags such inputs to ensure the system remains robust as ever-emerging, previously unknown categories appear and must be addressed without retraining. Foundation and vision-language models are pre-trained on large and diverse datasets with the expectation of broad generalization across domains, including medical imaging. However, benchmarking these models on test sets with only a few common outlier types silently collapses the evaluation back to a closed-set problem, masking failures on rare or truly novel conditions encountered in clinical use. We therefore present NOVA, a challenging, real-life evaluation-only benchmark of sim900 brain MRI scans that span 281 rare pathologies and heterogeneous acquisition protocols. Each case includes rich clinical narratives and double-blinded expert bounding-box annotations. Together, these enable joint assessment of anomaly localisation, visual captioning, and diagnostic reasoning. Because NOVA is never used for training, it serves as an extreme stress-test of out-of-distribution generalisation: models must bridge a distribution gap both in sample appearance and in semantic space. Baseline results with leading vision-language models (GPT-4o, Gemini 2.0 Flash, and Qwen2.5-VL-72B) reveal substantial performance drops across all tasks, establishing NOVA as a rigorous testbed for advancing models that can detect, localize, and reason about truly unknown anomalies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21471", "title": "Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration", "authors": [ "Zijun Liu", "Zhennan Wan", "Peng Li", "Ming Yan", "Ji Zhang", "Fei Huang", "Yang Liu" ], "github": "https://github.com/THUNLP-MT/ExtAgents", "project_page": "", "abstract": "With the rapid advancement of post-training techniques for reasoning and information seeking, large language models (LLMs) can incorporate a large quantity of retrieved knowledge to solve complex tasks. However, the limited context window of LLMs obstructs scaling the amount of external knowledge input, prohibiting further improvement, especially for tasks requiring significant amount of external knowledge. Existing context window extension methods inevitably cause information loss. LLM-based multi-agent methods emerge as a new paradigm to handle massive input in a distributional manner, where we identify two core bottlenecks in existing knowledge synchronization and reasoning processes. In this work, we develop a multi-agent framework, ExtAgents, to overcome the bottlenecks and enable better scalability in inference-time knowledge integration without longer-context training. Benchmarked with our enhanced multi-hop question answering test, $boldsymbol{inftyBench+}, and other public test sets including long survey generation, ExtAgents significantly enhances the performance over existing non-training methods with the same amount of external knowledge input, regardless of whether it falls within or exceeds the context window$. Moreover, the method maintains high efficiency due to high parallelism. Further study in the coordination of LLM agents on increasing external knowledge input could benefit real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUNLP-MT/ExtAgents", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20286", "title": "Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution", "authors": [ "Jiahao Qiu", "Xuan Qi", "Tongcheng Zhang", "Xinzhe Juan", "Jiacheng Guo", "Yifu Lu", "Yimin Wang", "Zixin Yao", "Qihan Ren", "Xun Jiang", "Xing Zhou", "Dongrui Liu", "Ling Yang", "Yue Wu", "Kaixuan Huang", "Shilong Liu", "Hongru Wang", "Mengdi Wang" ], "github": "https://github.com/CharlesQ9/Alita", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have enabled agents to autonomously perform complex, open-ended tasks. However, many existing frameworks depend heavily on manually predefined tools and workflows, which hinder their adaptability, scalability, and generalization across domains. In this work, we introduce Alita--a generalist agent designed with the principle of \"Simplicity is the ultimate sophistication,\" enabling scalable agentic reasoning through minimal predefinition and maximal self-evolution. For minimal predefinition, Alita is equipped with only one component for direct problem-solving, making it much simpler and neater than previous approaches that relied heavily on hand-crafted, elaborate tools and workflows. This clean design enhances its potential to generalize to challenging questions, without being limited by tools. For Maximal self-evolution, we enable the creativity of Alita by providing a suite of general-purpose components to autonomously construct, refine, and reuse external capabilities by generating task-related model context protocols (MCPs) from open source, which contributes to scalable agentic reasoning. Notably, Alita achieves 75.15% pass@1 and 87.27% pass@3 accuracy, which is top-ranking among general-purpose agents, on the GAIA benchmark validation dataset, 74.00% and 52.00% pass@1, respectively, on Mathvista and PathVQA, outperforming many agent systems with far greater complexity. More details will be updated at https://github.com/CharlesQ9/Alita{https://github.com/CharlesQ9/Alita}.", "github_manual": "", "github_regex": "https://github.com/CharlesQ9/Alita", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20052", "title": "Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations", "authors": [ "Hazem Alsamkary", "Mohamed Elshaffei", "Mohamed Elkerdawy", "Ahmed Elnaggar" ], "github": "", "project_page": "", "abstract": "Protein language models (PLMs) have emerged as powerful tools to detect complex patterns of protein sequences. However, the capability of PLMs to fully capture information on protein sequences might be limited by focusing on single pre-training tasks. Although adding data modalities or supervised objectives can improve the performance of PLMs, pre-training often remains focused on denoising corrupted sequences. To push the boundaries of PLMs, our research investigated a multi-task pre-training strategy. We developed Ankh3, a model jointly optimized on two objectives: masked language modeling with multiple masking probabilities and protein sequence completion relying only on protein sequences as input. This multi-task pre-training demonstrated that PLMs can learn richer and more generalizable representations solely from protein sequences. The results demonstrated improved performance in downstream tasks, such as secondary structure prediction, fluorescence, GB1 fitness, and contact prediction. The integration of multiple tasks gave the model a more comprehensive understanding of protein properties, leading to more robust and accurate predictions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20036", "title": "Beyond Simple Concatenation: Fairly Assessing PLM Architectures for Multi-Chain Protein-Protein Interactions Prediction", "authors": [ "Hazem Alsamkary", "Mohamed Elshaffei", "Mohamed Soudy", "Sara Ossman", "Abdallah Amr", "Nehal Adel Abdelsalam", "Mohamed Elkerdawy", "Ahmed Elnaggar" ], "github": "https://github.com/Proteinea/ppiseq", "project_page": "", "abstract": "Protein-protein interactions (PPIs) are fundamental to numerous cellular processes, and their characterization is vital for understanding disease mechanisms and guiding drug discovery. While protein language models (PLMs) have demonstrated remarkable success in predicting protein structure and function, their application to sequence-based PPI binding affinity prediction remains relatively underexplored. This gap is often attributed to the scarcity of high-quality, rigorously refined datasets and the reliance on simple strategies for concatenating protein representations. In this work, we address these limitations. First, we introduce a meticulously curated version of the PPB-Affinity dataset of a total of 8,207 unique protein-protein interaction entries, by resolving annotation inconsistencies and duplicate entries for multi-chain protein interactions. This dataset incorporates a stringent, less than or equal to 30%, sequence identity threshold to ensure robust splitting into training, validation, and test sets, minimizing data leakage. Second, we propose and systematically evaluate four architectures for adapting PLMs to PPI binding affinity prediction: embeddings concatenation (EC), sequences concatenation (SC), hierarchical pooling (HP), and pooled attention addition (PAD). These architectures were assessed using two training methods: full fine-tuning and a lightweight approach employing ConvBERT heads over frozen PLM features. Our comprehensive experiments across multiple leading PLMs (ProtT5, ESM2, Ankh, Ankh2, and ESM3) demonstrated that the HP and PAD architectures consistently outperform conventional concatenation methods, achieving up to 12% increase in terms of Spearman correlation. These results highlight the necessity of sophisticated architectural designs to fully exploit the capabilities of PLMs for nuanced PPI binding affinity prediction.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Proteinea/ppiseq", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.19954", "title": "An Explainable Diagnostic Framework for Neurodegenerative Dementias via Reinforcement-Optimized LLM Reasoning", "authors": [ "Andrew Zamai", "Nathanael Fijalkow", "Boris Mansencal", "Laurent Simon", "Eloi Navet", "Pierrick Coupe" ], "github": "", "project_page": "", "abstract": "The differential diagnosis of neurodegenerative dementias is a challenging clinical task, mainly because of the overlap in symptom presentation and the similarity of patterns observed in structural neuroimaging. To improve diagnostic efficiency and accuracy, deep learning-based methods such as Convolutional Neural Networks and Vision Transformers have been proposed for the automatic classification of brain MRIs. However, despite their strong predictive performance, these models find limited clinical utility due to their opaque decision making. In this work, we propose a framework that integrates two core components to enhance diagnostic transparency. First, we introduce a modular pipeline for converting 3D T1-weighted brain MRIs into textual radiology reports. Second, we explore the potential of modern Large Language Models (LLMs) to assist clinicians in the differential diagnosis between Frontotemporal dementia subtypes, Alzheimer's disease, and normal aging based on the generated reports. To bridge the gap between predictive accuracy and explainability, we employ reinforcement learning to incentivize diagnostic reasoning in LLMs. Without requiring supervised reasoning traces or distillation from larger models, our approach enables the emergence of structured diagnostic rationales grounded in neuroimaging findings. Unlike post-hoc explainability methods that retrospectively justify model decisions, our framework generates diagnostic rationales as part of the inference process-producing causally grounded explanations that inform and guide the model's decision-making process. In doing so, our framework matches the diagnostic performance of existing deep learning methods while offering rationales that support its diagnostic conclusions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.18134", "title": "VideoGameBench: Can Vision-Language Models complete popular video games?", "authors": [ "Alex L. Zhang", "Thomas L. Griffiths", "Karthik R. Narasimhan", "Ofir Press" ], "github": "https://github.com/alexzhang13/videogamebench", "project_page": "", "abstract": "Vision-language models (VLMs) have achieved strong results on coding and math benchmarks that are challenging for humans, yet their ability to perform tasks that come naturally to humans--such as perception, spatial navigation, and memory management--remains understudied. Real video games are crafted to be intuitive for humans to learn and master by leveraging innate inductive biases, making them an ideal testbed for evaluating such capabilities in VLMs. To this end, we introduce VideoGameBench, a benchmark consisting of 10 popular video games from the 1990s that VLMs directly interact with in real-time. VideoGameBench challenges models to complete entire games with access to only raw visual inputs and a high-level description of objectives and controls, a significant departure from existing setups that rely on game-specific scaffolding and auxiliary information. We keep three of the games secret to encourage solutions that generalize to unseen environments. Our experiments show that frontier vision-language models struggle to progress beyond the beginning of each game. We find inference latency to be a major limitation of frontier models in the real-time setting; therefore, we introduce VideoGameBench Lite, a setting where the game pauses while waiting for the LM's next action. The best performing model, Gemini 2.5 Pro, completes only 0.48% of VideoGameBench and 1.6% of VideoGameBench Lite. We hope that the formalization of the human skills mentioned above into this benchmark motivates progress in these research directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alexzhang13/videogamebench", "github_api_added_by": "auto" }, { "date": "2025-05-28", "arxiv_id": "2505.20321", "title": "BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases", "authors": [ "Mathew J. Koretsky", "Maya Willey", "Adi Asija", "Owen Bianchi", "Chelsea X. Alvarado", "Tanay Nayak", "Nicole Kuznetsov", "Sungwon Kim", "Mike A. Nalls", "Daniel Khashabi", "Faraz Faghri" ], "github": "https://github.com/NIH-CARD/biomedsql", "project_page": "", "abstract": "Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks. However, current text-to-SQL systems often struggle to map qualitative scientific questions into executable SQL, particularly when implicit domain reasoning is required. We introduce BiomedSQL, the first benchmark explicitly designed to evaluate scientific reasoning in text-to-SQL generation over a real-world biomedical knowledge base. BiomedSQL comprises 68,000 question/SQL query/answer triples grounded in a harmonized BigQuery knowledge base that integrates gene-disease associations, causal inference from omics data, and drug approval records. Each question requires models to infer domain-specific criteria, such as genome-wide significance thresholds, effect directionality, or trial phase filtering, rather than rely on syntactic translation alone. We evaluate a range of open- and closed-source LLMs across prompting strategies and interaction paradigms. Our results reveal a substantial performance gap: GPT-o3-mini achieves 59.0% execution accuracy, while our custom multi-step agent, BMSQL, reaches 62.6%, both well below the expert baseline of 90.0%. BiomedSQL provides a new foundation for advancing text-to-SQL systems capable of supporting scientific discovery through robust reasoning over structured biomedical knowledge bases. Our dataset is publicly available at https://huggingface.co/datasets/NIH-CARD/BiomedSQL, and our code is open-source at https://github.com/NIH-CARD/biomedsql.", "github_manual": "", "github_regex": "https://github.com/NIH-CARD/biomedsql", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.17855", "title": "Explaining Sources of Uncertainty in Automated Fact-Checking", "authors": [ "Jingyi Sun", "Greta Warren", "Irina Shklovski", "Isabelle Augenstein" ], "github": "", "project_page": "", "abstract": "Understanding sources of a model's uncertainty regarding its predictions is crucial for effective human-AI collaboration. Prior work proposes using numerical uncertainty or hedges (\"I'm not sure, but ...\"), which do not explain uncertainty that arises from conflicting evidence, leaving users unable to resolve disagreements or rely on the output. We introduce CLUE (Conflict-and-Agreement-aware Language-model Uncertainty Explanations), the first framework to generate natural language explanations of model uncertainty by (i) identifying relationships between spans of text that expose claim-evidence or inter-evidence conflicts and agreements that drive the model's predictive uncertainty in an unsupervised way, and (ii) generating explanations via prompting and attention steering that verbalize these critical interactions. Across three language models and two fact-checking datasets, we show that CLUE produces explanations that are more faithful to the model's uncertainty and more consistent with fact-checking decisions than prompting for uncertainty explanations without span-interaction guidance. Human evaluators judge our explanations to be more helpful, more informative, less redundant, and more logically consistent with the input than this baseline. CLUE requires no fine-tuning or architectural changes, making it plug-and-play for any white-box language model. By explicitly linking uncertainty to evidence conflicts, it offers practical support for fact-checking and generalises readily to other tasks that require reasoning over complex information.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.19897", "title": "ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows", "authors": [ "Qiushi Sun", "Zhoumianze Liu", "Chang Ma", "Zichen Ding", "Fangzhi Xu", "Zhangyue Yin", "Haiteng Zhao", "Zhenyu Wu", "Kanzhi Cheng", "Zhaoyang Liu", "Jianing Wang", "Qintong Li", "Xiangru Tang", "Tianbao Xie", "Xiachong Feng", "Xiang Li", "Ben Kao", "Wenhai Wang", "Biqing Qi", "Lingpeng Kong", "Zhiyong Wu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have extended their impact beyond Natural Language Processing, substantially fostering the development of interdisciplinary research. Recently, various LLM-based agents have been developed to assist scientific discovery progress across multiple aspects and domains. Among these, computer-using agents, capable of interacting with operating systems as humans do, are paving the way to automated scientific problem-solving and addressing routines in researchers' workflows. Recognizing the transformative potential of these agents, we introduce ScienceBoard, which encompasses two complementary contributions: (i) a realistic, multi-domain environment featuring dynamic and visually rich scientific workflows with integrated professional software, where agents can autonomously interact via different interfaces to accelerate complex research tasks and experiments; and (ii) a challenging benchmark of 169 high-quality, rigorously validated real-world tasks curated by humans, spanning scientific-discovery workflows in domains such as biochemistry, astronomy, and geoinformatics. Extensive evaluations of agents with state-of-the-art backbones (e.g., GPT-4o, Claude 3.7, UI-TARS) show that, despite some promising results, they still fall short of reliably assisting scientists in complex workflows, achieving only a 15% overall success rate. In-depth analysis further provides valuable insights for addressing current agent limitations and more effective design principles, paving the way to build more capable agents for scientific discovery. Our code, environment, and benchmark are at https://qiushisun.github.io/ScienceBoard-Home/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20426", "title": "MMPerspective: Do MLLMs Understand Perspective? A Comprehensive Benchmark for Perspective Perception, Reasoning, and Robustness", "authors": [ "Yunlong Tang", "Pinxin Liu", "Mingqian Feng", "Zhangyun Tan", "Rui Mao", "Chao Huang", "Jing Bi", "Yunzhong Xiao", "Susan Liang", "Hang Hua", "Ali Vosoughi", "Luchuan Song", "Zeliang Zhang", "Chenliang Xu" ], "github": "https://github.com/yunlong10/MMPerspective", "project_page": "", "abstract": "Understanding perspective is fundamental to human visual perception, yet the extent to which multimodal large language models (MLLMs) internalize perspective geometry remains unclear. We introduce MMPerspective, the first benchmark specifically designed to systematically evaluate MLLMs' understanding of perspective through 10 carefully crafted tasks across three complementary dimensions: Perspective Perception, Reasoning, and Robustness. Our benchmark comprises 2,711 real-world and synthetic image instances with 5,083 question-answer pairs that probe key capabilities, such as vanishing point perception and counting, perspective type reasoning, line relationship understanding in 3D space, invariance to perspective-preserving transformations, etc. Through a comprehensive evaluation of 43 state-of-the-art MLLMs, we uncover significant limitations: while models demonstrate competence on surface-level perceptual tasks, they struggle with compositional reasoning and maintaining spatial consistency under perturbations. Our analysis further reveals intriguing patterns between model architecture, scale, and perspective capabilities, highlighting both robustness bottlenecks and the benefits of chain-of-thought prompting. MMPerspective establishes a valuable testbed for diagnosing and advancing spatial understanding in vision-language systems. Resources available at: https://yunlong10.github.io/MMPerspective/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yunlong10/MMPerspective", "github_api_added_by": "auto" }, { "date": "2025-05-28", "arxiv_id": "2505.20162", "title": "Capability-Based Scaling Laws for LLM Red-Teaming", "authors": [ "Alexander Panfilov", "Paul Kassianik", "Maksym Andriushchenko", "Jonas Geiping" ], "github": "", "project_page": "", "abstract": "As large language models grow in capability and agency, identifying vulnerabilities through red-teaming becomes vital for safe deployment. However, traditional prompt-engineering approaches may prove ineffective once red-teaming turns into a weak-to-strong problem, where target models surpass red-teamers in capabilities. To study this shift, we frame red-teaming through the lens of the capability gap between attacker and target. We evaluate more than 500 attacker-target pairs using LLM-based jailbreak attacks that mimic human red-teamers across diverse families, sizes, and capability levels. Three strong trends emerge: (i) more capable models are better attackers, (ii) attack success drops sharply once the target's capability exceeds the attacker's, and (iii) attack success rates correlate with high performance on social science splits of the MMLU-Pro benchmark. From these trends, we derive a jailbreaking scaling law that predicts attack success for a fixed target based on attacker-target capability gap. These findings suggest that fixed-capability attackers (e.g., humans) may become ineffective against future models, increasingly capable open-source models amplify risks for existing systems, and model providers must accurately measure and control models' persuasive and manipulative abilities to limit their effectiveness as attackers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21493", "title": "Reinforcing General Reasoning without Verifiers", "authors": [ "Xiangxin Zhou", "Zichen Liu", "Anya Sims", "Haonan Wang", "Tianyu Pang", "Chongxuan Li", "Liang Wang", "Min Lin", "Chao Du" ], "github": "https://github.com/sail-sg/VeriFree", "project_page": "", "abstract": "The recent paradigm shift towards training large language models (LLMs) using DeepSeek-R1-Zero-style reinforcement learning (RL) on verifiable rewards has led to impressive advancements in code and mathematical reasoning. However, this methodology is limited to tasks where rule-based answer verification is possible and does not naturally extend to real-world domains such as chemistry, healthcare, engineering, law, biology, business, and economics. Current practical workarounds use an additional LLM as a model-based verifier; however, this introduces issues such as reliance on a strong verifier LLM, susceptibility to reward hacking, and the practical burden of maintaining the verifier model in memory during training. To address this and extend DeepSeek-R1-Zero-style training to general reasoning domains, we propose a verifier-free method (VeriFree) that bypasses answer verification and instead uses RL to directly maximize the probability of generating the reference answer. We compare VeriFree with verifier-based methods and demonstrate that, in addition to its significant practical benefits and reduced compute requirements, VeriFree matches and even surpasses verifier-based methods on extensive evaluations across MMLU-Pro, GPQA, SuperGPQA, and math-related benchmarks. Moreover, we provide insights into this method from multiple perspectives: as an elegant integration of training both the policy and implicit verifier in a unified model, and as a variational optimization approach. Code is available at https://github.com/sail-sg/VeriFree.", "github_manual": "", "github_regex": "https://github.com/sail-sg/VeriFree", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20793", "title": "Rendering-Aware Reinforcement Learning for Vector Graphics Generation", "authors": [ "Juan A. Rodriguez", "Haotian Zhang", "Abhay Puri", "Aarash Feizi", "Rishav Pramanik", "Pascal Wichmann", "Arnab Mondal", "Mohammad Reza Samsami", "Rabiul Awal", "Perouz Taslakian", "Spandana Gella", "Sai Rajeswar", "David Vazquez", "Christopher Pal", "Marco Pedersoli" ], "github": "", "project_page": "", "abstract": "Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-language models (VLMs) have enabled high-quality SVG generation by framing the problem as a code generation task and leveraging large-scale pretraining. VLMs are particularly suitable for this task as they capture both global semantics and fine-grained visual patterns, while transferring knowledge across vision, natural language, and code domains. However, existing VLM approaches often struggle to produce faithful and efficient SVGs because they never observe the rendered images during training. Although differentiable rendering for autoregressive SVG code generation remains unavailable, rendered outputs can still be compared to original inputs, enabling evaluative feedback suitable for reinforcement learning (RL). We introduce RLRF(Reinforcement Learning from Rendering Feedback), an RL method that enhances SVG generation in autoregressive VLMs by leveraging feedback from rendered SVG outputs. Given an input image, the model generates SVG roll-outs that are rendered and compared to the original image to compute a reward. This visual fidelity feedback guides the model toward producing more accurate, efficient, and semantically coherent SVGs. RLRF significantly outperforms supervised fine-tuning, addressing common failure modes and enabling precise, high-quality SVG generation with strong structural understanding and generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20287", "title": "MotionPro: A Precise Motion Controller for Image-to-Video Generation", "authors": [ "Zhongwei Zhang", "Fuchen Long", "Zhaofan Qiu", "Yingwei Pan", "Wu Liu", "Ting Yao", "Tao Mei" ], "github": "", "project_page": "", "abstract": "Animating images with interactive motion control has garnered popularity for image-to-video (I2V) generation. Modern approaches typically rely on large Gaussian kernels to extend motion trajectories as condition without explicitly defining movement region, leading to coarse motion control and failing to disentangle object and camera moving. To alleviate these, we present MotionPro, a precise motion controller that novelly leverages region-wise trajectory and motion mask to regulate fine-grained motion synthesis and identify target motion category (i.e., object or camera moving), respectively. Technically, MotionPro first estimates the flow maps on each training video via a tracking model, and then samples the region-wise trajectories to simulate inference scenario. Instead of extending flow through large Gaussian kernels, our region-wise trajectory approach enables more precise control by directly utilizing trajectories within local regions, thereby effectively characterizing fine-grained movements. A motion mask is simultaneously derived from the predicted flow maps to capture the holistic motion dynamics of the movement regions. To pursue natural motion control, MotionPro further strengthens video denoising by incorporating both region-wise trajectories and motion mask through feature modulation. More remarkably, we meticulously construct a benchmark, i.e., MC-Bench, with 1.1K user-annotated image-trajectory pairs, for the evaluation of both fine-grained and object-level I2V motion control. Extensive experiments conducted on WebVid-10M and MC-Bench demonstrate the effectiveness of MotionPro. Please refer to our project page for more results: https://zhw-zhang.github.io/MotionPro-page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21501", "title": "Vision Transformers with Self-Distilled Registers", "authors": [ "Yinjie Chen", "Zipeng Yan", "Chong Zhou", "Bo Dai", "Andrew F. Luo" ], "github": "https://github.com/0raiser0/PH-Reg", "project_page": "", "abstract": "Vision Transformers (ViTs) have emerged as the dominant architecture for visual processing tasks, demonstrating excellent scalability with increased training data and model size. However, recent work has identified the emergence of artifact tokens in ViTs that are incongruous with the local semantics. These anomalous tokens degrade ViT performance in tasks that require fine-grained localization or structural coherence. An effective mitigation of this issue is to the addition of register tokens to ViTs, which implicitly \"absorb\" the artifact term during training. Given the availability of various large-scale pre-trained ViTs, in this paper we aim at equipping them with such register tokens without the need of re-training them from scratch, which is infeasible considering their size. Specifically, we propose Post Hoc Registers (PH-Reg), an efficient self-distillation method that integrates registers into an existing ViT without requiring additional labeled data and full retraining. PH-Reg initializes both teacher and student networks from the same pre-trained ViT. The teacher remains frozen and unmodified, while the student is augmented with randomly initialized register tokens. By applying test-time augmentation to the teacher's inputs, we generate denoised dense embeddings free of artifacts, which are then used to optimize only a small subset of unlocked student weights. We show that our approach can effectively reduce the number of artifact tokens, improving the segmentation and depth prediction of the student ViT under zero-shot and linear probing.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/0raiser0/PH-Reg", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.21097", "title": "Thinker: Learning to Think Fast and Slow", "authors": [ "Stephen Chung", "Wenyu Du", "Jie Fu" ], "github": "https://github.com/stephen-chung-mh/thinker-task", "project_page": "", "abstract": "Recent studies show that the reasoning capabilities of Large Language Models (LLMs) can be improved by applying Reinforcement Learning (RL) to question-answering (QA) tasks in areas such as math and coding. With a long context length, LLMs may learn to perform search, as indicated by the self-correction behavior observed in DeepSeek R1. However, this search behavior is often imprecise and lacks confidence, resulting in long, redundant responses and highlighting deficiencies in intuition and verification. Inspired by the Dual Process Theory in psychology, we introduce a simple modification to the QA task that includes four stages: Fast Thinking, where the LLM must answer within a strict token budget; Verification, where the model evaluates its initial response; Slow Thinking, where it refines the initial response with more deliberation; and Summarization, where it distills the refinement from the previous stage into precise steps. Our proposed task improves average accuracy from 24.9% to 27.9% for Qwen2.5-1.5B, and from 45.9% to 49.8% for DeepSeek-R1-Qwen-1.5B. Notably, for Qwen2.5-1.5B, the Fast Thinking mode alone achieves 26.8% accuracy using fewer than 1000 tokens, demonstrating substantial inference efficiency gains. These findings suggest that intuition and deliberative reasoning are distinct, complementary systems benefiting from targeted training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stephen-chung-mh/thinker-task", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.19235", "title": "CoreMatching: A Co-adaptive Sparse Inference Framework with Token and Neuron Pruning for Comprehensive Acceleration of Vision-Language Models", "authors": [ "Qinsi Wang", "Hancheng Ye", "Ming-Yu Chung", "Yudong Liu", "Yueqian Lin", "Martin Kuo", "Mingyuan Ma", "Jianyi Zhang", "Yiran Chen" ], "github": "https://github.com/wangqinsi1/2025-ICML-CoreMatching", "project_page": "", "abstract": "Vision-Language Models (VLMs) excel across diverse tasks but suffer from high inference costs in time and memory. Token sparsity mitigates inefficiencies in token usage, while neuron sparsity reduces high-dimensional computations, both offering promising solutions to enhance efficiency. Recently, these two sparsity paradigms have evolved largely in parallel, fostering the prevailing assumption that they function independently. However, a fundamental yet underexplored question remains: Do they truly operate in isolation, or is there a deeper underlying interplay that has yet to be uncovered? In this paper, we conduct the first comprehensive investigation into this question. By introducing and analyzing the matching mechanism between Core Neurons and Core Tokens, we found that key neurons and tokens for inference mutually influence and reinforce each other. Building on this insight, we propose CoreMatching, a co-adaptive sparse inference framework, which leverages the synergy between token and neuron sparsity to enhance inference efficiency. Through theoretical analysis and efficiency evaluations, we demonstrate that the proposed method surpasses state-of-the-art baselines on ten image understanding tasks and three hardware devices. Notably, on the NVIDIA Titan Xp, it achieved 5x FLOPs reduction and a 10x overall speedup. Code is released at https://github.com/wangqinsi1/2025-ICML-CoreMatching/tree/main.", "github_manual": "", "github_regex": "https://github.com/wangqinsi1/2025-ICML-CoreMatching", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.17639", "title": "PreMoe: Lightening MoEs on Constrained Memory by Expert Pruning and Retrieval", "authors": [ "Zehua Pei", "Ying Zhang", "Hui-Ling Zhen", "Xianzhi Yu", "Wulong Liu", "Sinno Jialin Pan", "Mingxuan Yuan", "Bei Yu" ], "github": "https://github.com/JarvisPei/PreMoe", "project_page": "", "abstract": "Mixture-of-experts (MoE) architectures enable scaling large language models (LLMs) to vast parameter counts without a proportional rise in computational costs. However, the significant memory demands of large MoE models hinder their deployment across various computational environments, from cloud servers to consumer devices. This study first demonstrates pronounced task-specific specialization in expert activation patterns within MoE layers. Building on this, we introduce PreMoe, a novel framework that enables efficient deployment of massive MoE models in memory-constrained environments. PreMoe features two main components: probabilistic expert pruning (PEP) and task-adaptive expert retrieval (TAER). PEP employs a new metric, the task-conditioned expected selection score (TCESS), derived from router logits to quantify expert importance for specific tasks, thereby identifying a minimal set of critical experts. TAER leverages these task-specific expert importance profiles for efficient inference. It pre-computes and stores compact expert patterns for diverse tasks. When a user query is received, TAER rapidly identifies the most relevant stored task pattern and reconstructs the model by loading only the small subset of experts crucial for that task. This approach dramatically reduces the memory footprint across all deployment scenarios. DeepSeek-R1 671B maintains 97.2\\% accuracy on MATH500 when pruned to 8/128 configuration (50\\% expert reduction), and still achieves 72.0\\% with aggressive 8/32 pruning (87.5\\% expert reduction). Pangu-Ultra-MoE 718B achieves 97.15\\% on MATH500 and 81.3\\% on AIME24 with 8/128 pruning, while even more aggressive pruning to 4/64 (390GB memory) preserves 96.95\\% accuracy on MATH500. We make our code publicly available at https://github.com/JarvisPei/PreMoe.", "github_manual": "", "github_regex": "https://github.com/JarvisPei/PreMoe", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.17332", "title": "SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use", "authors": [ "Hitesh Laxmichand Patel", "Amit Agarwal", "Arion Das", "Bhargava Kumar", "Srikant Panda", "Priyaranjan Pattnayak", "Taki Hasan Rafi", "Tejaswini Kumar", "Dong-Kyu Chae" ], "github": "https://github.com/amitbcp/multilingual_profanity", "project_page": "", "abstract": "Enterprise customers are increasingly adopting Large Language Models (LLMs) for critical communication tasks, such as drafting emails, crafting sales pitches, and composing casual messages. Deploying such models across different regions requires them to understand diverse cultural and linguistic contexts and generate safe and respectful responses. For enterprise applications, it is crucial to mitigate reputational risks, maintain trust, and ensure compliance by effectively identifying and handling unsafe or offensive language. To address this, we introduce SweEval, a benchmark simulating real-world scenarios with variations in tone (positive or negative) and context (formal or informal). The prompts explicitly instruct the model to include specific swear words while completing the task. This benchmark evaluates whether LLMs comply with or resist such inappropriate instructions and assesses their alignment with ethical frameworks, cultural nuances, and language comprehension capabilities. In order to advance research in building ethically aligned AI systems for enterprise use and beyond, we release the dataset and code: https://github.com/amitbcp/multilingual_profanity.", "github_manual": "", "github_regex": "https://github.com/amitbcp/multilingual_profanity", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.17613", "title": "MMMG: a Comprehensive and Reliable Evaluation Suite for Multitask Multimodal Generation", "authors": [ "Jihan Yao", "Yushi Hu", "Yujie Yi", "Bin Han", "Shangbin Feng", "Guang Yang", "Bingbing Wen", "Ranjay Krishna", "Lucy Lu Wang", "Yulia Tsvetkov", "Noah A. Smith", "Banghua Zhu" ], "github": "https://github.com/yaojh18/MMMG", "project_page": "", "abstract": "Automatically evaluating multimodal generation presents a significant challenge, as automated metrics often struggle to align reliably with human evaluation, especially for complex tasks that involve multiple modalities. To address this, we present MMMG, a comprehensive and human-aligned benchmark for multimodal generation across 4 modality combinations (image, audio, interleaved text and image, interleaved text and audio), with a focus on tasks that present significant challenges for generation models, while still enabling reliable automatic evaluation through a combination of models and programs. MMMG encompasses 49 tasks (including 29 newly developed ones), each with a carefully designed evaluation pipeline, and 937 instructions to systematically assess reasoning, controllability, and other key capabilities of multimodal generation models. Extensive validation demonstrates that MMMG is highly aligned with human evaluation, achieving an average agreement of 94.3%. Benchmarking results on 24 multimodal generation models reveal that even though the state-of-the-art model, GPT Image, achieves 78.3% accuracy for image generation, it falls short on multimodal reasoning and interleaved generation. Furthermore, results suggest considerable headroom for improvement in audio generation, highlighting an important direction for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yaojh18/MMMG", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.19094", "title": "SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and Verifiable Rewards", "authors": [ "Chuming Shen", "Wei Wei", "Xiaoye Qu", "Yu Cheng" ], "github": "https://github.com/justairr/SATORI-R1", "project_page": "", "abstract": "DeepSeek-R1 has demonstrated powerful reasoning capabilities in the text domain through stable reinforcement learning (RL). Recently, in the multimodal domain, works have begun to directly apply RL to generate R1-like free-form reasoning for Visual Question Answering (VQA) tasks. However, multimodal tasks share an intrinsically different nature from textual tasks, which heavily rely on the understanding of the input image to solve the problem. Therefore, such free-form reasoning faces two critical limitations in the VQA task: (1) Extended reasoning chains diffuse visual focus away from task-critical regions, degrading answer accuracy. (2) Unverifiable intermediate steps amplify policy-gradient variance and computational costs overhead. To address these issues, in this paper, we introduce SATORI (Spatially Anchored Task Optimization with ReInforcement Learning), which decomposes VQA into three verifiable stages, including global image captioning, region localization, and answer prediction, each supplying explicit reward signals. Furthermore, we also introduce VQA-Verify, a 12k dataset annotated with answer-aligned captions and bounding-boxes to facilitate training. Experiments demonstrate consistent performance improvements across seven VQA benchmarks, achieving up to 15.7% improvement in accuracy in accuracy compared to the R1-like baseline. Our analysis of the attention map confirms enhanced focus on critical regions, which brings improvements in accuracy. Our code is available at https://github.com/justairr/SATORI-R1.", "github_manual": "", "github_regex": "https://github.com/justairr/SATORI-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20325", "title": "Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence", "authors": [ "Amirhosein Ghasemabadi", "Keith G. Mills", "Baochun Li", "Di Niu" ], "github": "https://github.com/Amirhosein-gh98/Guided-by-Gut", "project_page": "", "abstract": "Test-Time Scaling (TTS) methods for enhancing Large Language Model (LLM) reasoning often incur substantial computational costs, primarily due to extensive reliance on external Process Reward Models (PRMs) or sampling methods like Best-of-N (BoN). This paper introduces Guided by Gut (GG), an efficient self-guided TTS framework that achieves PRM-level performance without costly external verifier models. Our method employs a lightweight tree search guided solely by intrinsic LLM signals, token-level confidence and step novelty. One critical innovation is improving the reliability of internal confidence estimates via a targeted reinforcement learning fine-tuning phase. Empirical evaluations on challenging mathematical reasoning benchmarks demonstrate that GG enables smaller models (e.g., 1.5B parameters) to achieve accuracy matching or surpassing significantly larger models (e.g., 32B-70B parameters), while reducing GPU memory usage by up to 10x. Compared to PRM-based methods, GG achieves comparable accuracy with 8x faster inference speeds and 4-5x lower memory usage. Additionally, GG reduces KV cache memory usage by approximately 50% compared to the BoN strategy, facilitating more efficient and practical deployment of TTS techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Amirhosein-gh98/Guided-by-Gut", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20650", "title": "FinTagging: An LLM-ready Benchmark for Extracting and Structuring Financial Information", "authors": [ "Yan Wang", "Yang Ren", "Lingfei Qian", "Xueqing Peng", "Keyi Wang", "Yi Han", "Dongji Feng", "Xiao-Yang Liu", "Jimin Huang", "Qianqian Xie" ], "github": "https://github.com/The-FinAI/FinTagging", "project_page": "", "abstract": "We introduce FinTagging, the first full-scope, table-aware XBRL benchmark designed to evaluate the structured information extraction and semantic alignment capabilities of large language models (LLMs) in the context of XBRL-based financial reporting. Unlike prior benchmarks that oversimplify XBRL tagging as flat multi-class classification and focus solely on narrative text, FinTagging decomposes the XBRL tagging problem into two subtasks: FinNI for financial entity extraction and FinCL for taxonomy-driven concept alignment. It requires models to jointly extract facts and align them with the full 10k+ US-GAAP taxonomy across both unstructured text and structured tables, enabling realistic, fine-grained evaluation. We assess a diverse set of LLMs under zero-shot settings, systematically analyzing their performance on both subtasks and overall tagging accuracy. Our results reveal that, while LLMs demonstrate strong generalization in information extraction, they struggle with fine-grained concept alignment, particularly in disambiguating closely related taxonomy entries. These findings highlight the limitations of existing LLMs in fully automating XBRL tagging and underscore the need for improved semantic reasoning and schema-aware modeling to meet the demands of accurate financial disclosure. Code is available at our GitHub repository and data is at our Hugging Face repository.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/The-FinAI/FinTagging", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.20279", "title": "VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction", "authors": [ "Zhiwen Fan", "Jian Zhang", "Renjie Li", "Junge Zhang", "Runjin Chen", "Hezhen Hu", "Kevin Wang", "Huaizhi Qu", "Dilin Wang", "Zhicheng Yan", "Hongyu Xu", "Justin Theiss", "Tianlong Chen", "Jiachen Li", "Zhengzhong Tu", "Zhangyang Wang", "Rakesh Ranjan" ], "github": "https://github.com/VITA-Group/VLM-3R", "project_page": "", "abstract": "The rapid advancement of Large Multimodal Models (LMMs) for 2D images and videos has motivated extending these models to understand 3D scenes, aiming for human-like visual-spatial intelligence. Nevertheless, achieving deep spatial understanding comparable to human capabilities poses significant challenges in model encoding and data acquisition. Existing methods frequently depend on external depth sensors for geometry capture or utilize off-the-shelf algorithms for pre-constructing 3D maps, thereby limiting their scalability, especially with prevalent monocular video inputs and for time-sensitive applications. In this work, we introduce VLM-3R, a unified framework for Vision-Language Models (VLMs) that incorporates 3D Reconstructive instruction tuning. VLM-3R processes monocular video frames by employing a geometry encoder to derive implicit 3D tokens that represent spatial understanding. Leveraging our Spatial-Visual-View Fusion and over 200K curated 3D reconstructive instruction tuning question-answer (QA) pairs, VLM-3R effectively aligns real-world spatial context with language instructions. This enables monocular 3D spatial assistance and embodied reasoning. To facilitate the evaluation of temporal reasoning, we introduce the Vision-Spatial-Temporal Intelligence benchmark, featuring over 138.6K QA pairs across five distinct tasks focused on evolving spatial relationships. Extensive experiments demonstrate that our model, VLM-3R, not only facilitates robust visual-spatial reasoning but also enables the understanding of temporal 3D context changes, excelling in both accuracy and scalability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/VITA-Group/VLM-3R", "github_api_added_by": "auto" }, { "date": "2025-05-28", "arxiv_id": "2505.21062", "title": "Inverse Virtual Try-On: Generating Multi-Category Product-Style Images from Clothed Individuals", "authors": [ "Davide Lobba", "Fulvio Sanguigni", "Bin Ren", "Marcella Cornia", "Rita Cucchiara", "Nicu Sebe" ], "github": "", "project_page": "", "abstract": "While virtual try-on (VTON) systems aim to render a garment onto a target person image, this paper tackles the novel task of virtual try-off (VTOFF), which addresses the inverse problem: generating standardized product images of garments from real-world photos of clothed individuals. Unlike VTON, which must resolve diverse pose and style variations, VTOFF benefits from a consistent and well-defined output format -- typically a flat, lay-down-style representation of the garment -- making it a promising tool for data generation and dataset enhancement. However, existing VTOFF approaches face two major limitations: (i) difficulty in disentangling garment features from occlusions and complex poses, often leading to visual artifacts, and (ii) restricted applicability to single-category garments (e.g., upper-body clothes only), limiting generalization. To address these challenges, we present Text-Enhanced MUlti-category Virtual Try-Off (TEMU-VTOFF), a novel architecture featuring a dual DiT-based backbone with a modified multimodal attention mechanism for robust garment feature extraction. Our architecture is designed to receive garment information from multiple modalities like images, text, and masks to work in a multi-category setting. Finally, we propose an additional alignment module to further refine the generated visual details. Experiments on VITON-HD and Dress Code datasets show that TEMU-VTOFF sets a new state-of-the-art on the VTOFF task, significantly improving both visual quality and fidelity to the target garments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.22172", "title": "Reverse Preference Optimization for Complex Instruction Following", "authors": [ "Xiang Huang", "Ting-En Lin", "Feiteng Fang", "Yuchuan Wu", "Hangyu Li", "Yuzhong Qu", "Fei Huang", "Yongbin Li" ], "github": "", "project_page": "", "abstract": "Instruction following (IF) is a critical capability for large language models (LLMs). However, handling complex instructions with multiple constraints remains challenging. Previous methods typically select preference pairs based on the number of constraints they satisfy, introducing noise where chosen examples may fail to follow some constraints and rejected examples may excel in certain respects over the chosen ones. To address the challenge of aligning with multiple preferences, we propose a simple yet effective method called Reverse Preference Optimization (RPO). It mitigates noise in preference pairs by dynamically reversing the constraints within the instruction to ensure the chosen response is perfect, alleviating the burden of extensive sampling and filtering to collect perfect responses. Besides, reversal also enlarges the gap between chosen and rejected responses, thereby clarifying the optimization direction and making it more robust to noise. We evaluate RPO on two multi-turn IF benchmarks, Sysbench and Multi-IF, demonstrating average improvements over the DPO baseline of 4.6 and 2.5 points (on Llama-3.1 8B), respectively. Moreover, RPO scales effectively across model sizes (8B to 70B parameters), with the 70B RPO model surpassing GPT-4o.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.22633", "title": "Spatial Knowledge Graph-Guided Multimodal Synthesis", "authors": [ "Yida Xue", "Zhen Bi", "Jinnan Yang", "Jungang Lou", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/Knowledge2Data", "project_page": "", "abstract": "Recent advances in multimodal large language models (MLLMs) have significantly enhanced their capabilities; however, their spatial perception abilities remain a notable limitation. To address this challenge, multimodal data synthesis offers a promising solution. Yet, ensuring that synthesized data adhere to spatial common sense is a non-trivial task. In this work, we introduce SKG2Data, a novel multimodal synthesis approach guided by spatial knowledge graphs, grounded in the concept of knowledge-to-data generation. SKG2Data automatically constructs a Spatial Knowledge Graph (SKG) to emulate human-like perception of spatial directions and distances, which is subsequently utilized to guide multimodal data synthesis. Extensive experiments demonstrate that data synthesized from diverse types of spatial knowledge, including direction and distance, not only enhance the spatial perception and reasoning abilities of MLLMs but also exhibit strong generalization capabilities. We hope that the idea of knowledge-based data synthesis can advance the development of spatial intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zjunlp/Knowledge2Data", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-28", "arxiv_id": "2505.22096", "title": "Knowledge Base Construction for Knowledge-Augmented Text-to-SQL", "authors": [ "Jinheon Baek", "Horst Samulowitz", "Oktie Hassanzadeh", "Dharmashankar Subramanian", "Sola Shirai", "Alfio Gliozzo", "Debarun Bhattacharjya" ], "github": "", "project_page": "", "abstract": "Text-to-SQL aims to translate natural language queries into SQL statements, which is practical as it enables anyone to easily retrieve the desired information from databases. Recently, many existing approaches tackle this problem with Large Language Models (LLMs), leveraging their strong capability in understanding user queries and generating corresponding SQL code. Yet, the parametric knowledge in LLMs might be limited to covering all the diverse and domain-specific queries that require grounding in various database schemas, which makes generated SQLs less accurate oftentimes. To tackle this, we propose constructing the knowledge base for text-to-SQL, a foundational source of knowledge, from which we retrieve and generate the necessary knowledge for given queries. In particular, unlike existing approaches that either manually annotate knowledge or generate only a few pieces of knowledge for each query, our knowledge base is comprehensive, which is constructed based on a combination of all the available questions and their associated database schemas along with their relevant knowledge, and can be reused for unseen databases from different datasets and domains. We validate our approach on multiple text-to-SQL datasets, considering both the overlapping and non-overlapping database scenarios, where it outperforms relevant baselines substantially.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.21600", "title": "R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing", "authors": [ "Tianyu Fu", "Yi Ge", "Yichen You", "Enshu Liu", "Zhihang Yuan", "Guohao Dai", "Shengen Yan", "Huazhong Yang", "Yu Wang" ], "github": "https://github.com/thu-nics/R2R", "project_page": "", "abstract": "Large Language Models (LLMs) achieve impressive reasoning capabilities at the cost of substantial inference overhead, posing substantial deployment challenges. Although distilled Small Language Models (SLMs) significantly enhance efficiency, their performance suffers as they fail to follow LLMs' reasoning paths. Luckily, we reveal that only a small fraction of tokens genuinely diverge reasoning paths between LLMs and SLMs. Most generated tokens are either identical or exhibit neutral differences, such as minor variations in abbreviations or expressions. Leveraging this insight, we introduce **Roads to Rome (R2R)**, a neural token routing method that selectively utilizes LLMs only for these critical, path-divergent tokens, while leaving the majority of token generation to the SLM. We also develop an automatic data generation pipeline that identifies divergent tokens and generates token-level routing labels to train the lightweight router. We apply R2R to combine R1-1.5B and R1-32B models from the DeepSeek family, and evaluate on challenging math, coding, and QA benchmarks. With an average activated parameter size of 5.6B, R2R surpasses the average accuracy of R1-7B by 1.6x, outperforming even the R1-14B model. Compared to R1-32B, it delivers a 2.8x wall-clock speedup with comparable performance, advancing the Pareto frontier of test-time scaling efficiency. Our code is available at https://github.com/thu-nics/R2R.", "github_manual": "", "github_regex": "https://github.com/thu-nics/R2R", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22453", "title": "Unsupervised Post-Training for Multi-Modal LLM Reasoning via GRPO", "authors": [ "Lai Wei", "Yuting Li", "Chen Wang", "Yue Wang", "Linghe Kong", "Weiran Huang", "Lichao Sun" ], "github": "https://github.com/waltonfuture/MM-UPT", "project_page": "", "abstract": "Improving Multi-modal Large Language Models (MLLMs) in the post-training stage typically relies on supervised fine-tuning (SFT) or reinforcement learning (RL). However, these supervised methods require expensive and manually annotated multi-modal data--an ultimately unsustainable resource. While recent efforts have explored unsupervised post-training, their methods are complex and difficult to iterate. In this work, we are the first to investigate the use of GRPO, a stable and scalable online RL algorithm, for enabling continual self-improvement without any external supervision. We propose MM-UPT, a simple yet effective framework for unsupervised post-training of MLLMs. MM-UPT builds upon GRPO, replacing traditional reward signals with a self-rewarding mechanism based on majority voting over multiple sampled responses. Our experiments demonstrate that MM-UPT significantly improves the reasoning ability of Qwen2.5-VL-7B (e.g., 66.3 %rightarrow72.9 % on MathVista, 62.9 %rightarrow68.7 % on We-Math), using standard dataset without ground truth labels. MM-UPT also outperforms prior unsupervised baselines and even approaches the results of supervised GRPO. Furthermore, we show that incorporating synthetic questions, generated solely by MLLM itself, can boost performance as well, highlighting a promising approach for scalable self-improvement. Overall, MM-UPT offers a new paradigm for continual, autonomous enhancement of MLLMs in the absence of external supervision. Our code is available at https://github.com/waltonfuture/MM-UPT.", "github_manual": "", "github_regex": "https://github.com/waltonfuture/MM-UPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.21136", "title": "SageAttention2++: A More Efficient Implementation of SageAttention2", "authors": [ "Jintao Zhang", "Xiaoming Xu", "Jia Wei", "Haofeng Huang", "Pengle Zhang", "Chendong Xiang", "Jun Zhu", "Jianfei Chen" ], "github": "https://github.com/thu-ml/SageAttention", "project_page": "", "abstract": "The efficiency of attention is critical because its time complexity grows quadratically with sequence length. SageAttention2 addresses this by utilizing quantization to accelerate matrix multiplications (Matmul) in attention. To further accelerate SageAttention2, we propose to utilize the faster instruction of FP8 Matmul accumulated in FP16. The instruction is 2x faster than the FP8 Matmul used in SageAttention2. Our experiments show that SageAttention2++ achieves a 3.9x speedup over FlashAttention while maintaining the same attention accuracy as SageAttention2. This means SageAttention2++ effectively accelerates various models, including those for language, image, and video generation, with negligible end-to-end metrics loss. The code will be available at https://github.com/thu-ml/SageAttention.", "github_manual": "", "github_regex": "https://github.com/thu-ml/SageAttention", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.19253", "title": "DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research", "authors": [ "Jo\u00e3o Coelho", "Jingjie Ning", "Jingyuan He", "Kangrui Mao", "Abhijay Paladugu", "Pranav Setlur", "Jiahe Jin", "Jamie Callan", "Jo\u00e3o Magalh\u00e3es", "Bruno Martins", "Chenyan Xiong" ], "github": "", "project_page": "", "abstract": "Deep research systems represent an emerging class of agentic information retrieval methods that generate comprehensive and well-supported reports to complex queries. However, most existing frameworks rely on dynamic commercial search APIs, which pose reproducibility and transparency challenges in addition to their cost. To address these limitations, we introduce DeepResearchGym, an open-source sandbox that combines a reproducible search API with a rigorous evaluation protocol for benchmarking deep research systems. The API indexes large-scale public web corpora, namely ClueWeb22 and FineWeb, using a state-of-the-art dense retriever and approximate nearest neighbor search via DiskANN. It achieves lower latency than popular commercial APIs while ensuring stable document rankings across runs, and is freely available for research use. To evaluate deep research systems' outputs, we extend the Researchy Questions benchmark with automatic metrics through LLM-as-a-judge assessments to measure alignment with users' information needs, retrieval faithfulness, and report quality. Experimental results show that systems integrated with DeepResearchGym achieve performance comparable to those using commercial APIs, with performance rankings remaining consistent across evaluation metrics. A human evaluation study further confirms that our automatic protocol aligns with human preferences, validating the framework's ability to help support controlled assessment of deep research systems. Our code and API documentation are available at https://www.deepresearchgym.ai.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22334", "title": "Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start", "authors": [ "Lai Wei", "Yuting Li", "Kaipeng Zheng", "Chen Wang", "Yue Wang", "Linghe Kong", "Lichao Sun", "Weiran Huang" ], "github": "https://github.com/waltonfuture/RL-with-Cold-Start", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) have demonstrated impressive chain-of-thought reasoning capabilities, with reinforcement learning (RL) playing a crucial role in this progress. While \"aha moment\" patterns--where models exhibit self-correction through reflection--are often attributed to emergent properties from RL, we first demonstrate that these patterns exist in multimodal LLMs (MLLMs) prior to RL training but may not necessarily correlate with improved reasoning performance. Building on these insights, we present a comprehensive study on enhancing multimodal reasoning through a two-stage approach: (1) supervised fine-tuning (SFT) as a cold start with structured chain-of-thought reasoning patterns, followed by (2) reinforcement learning via GRPO to further refine these capabilities. Our extensive experiments show that this combined approach consistently outperforms both SFT-only and RL-only methods across challenging multimodal reasoning benchmarks. The resulting models achieve state-of-the-art performance among open-source MLLMs at both 3B and 7B scales, with our 7B model showing substantial improvements over base models (e.g., 66.3 %rightarrow73.4 % on MathVista, 62.9 %rightarrow70.4 % on We-Math) and our 3B model achieving performance competitive with several 7B models. Overall, this work provides practical guidance for building advanced multimodal reasoning models. Our code is available at https://github.com/waltonfuture/RL-with-Cold-Start.", "github_manual": "", "github_regex": "https://github.com/waltonfuture/RL-with-Cold-Start", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22312", "title": "Skywork Open Reasoner 1 Technical Report", "authors": [ "Jujie He", "Jiacai Liu", "Chris Yuhao Liu", "Rui Yan", "Chaojie Wang", "Peng Cheng", "Xiaoyu Zhang", "Fuxiang Zhang", "Jiacheng Xu", "Wei Shen", "Siyuan Li", "Liang Zeng", "Tianwen Wei", "Cheng Cheng", "Bo An", "Yang Liu", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "The success of DeepSeek-R1 underscores the significant role of reinforcement learning (RL) in enhancing the reasoning capabilities of large language models (LLMs). In this work, we present Skywork-OR1, an effective and scalable RL implementation for long Chain-of-Thought (CoT) models. Building on the DeepSeek-R1-Distill model series, our RL approach achieves notable performance gains, increasing average accuracy across AIME24, AIME25, and LiveCodeBench from 57.8% to 72.8% (+15.0%) for the 32B model and from 43.6% to 57.5% (+13.9%) for the 7B model. Our Skywork-OR1-32B model surpasses both DeepSeek-R1 and Qwen3-32B on the AIME24 and AIME25 benchmarks, while achieving comparable results on LiveCodeBench. The Skywork-OR1-7B and Skywork-OR1-Math-7B models demonstrate competitive reasoning capabilities among models of similar size. We perform comprehensive ablation studies on the core components of our training pipeline to validate their effectiveness. Additionally, we thoroughly investigate the phenomenon of entropy collapse, identify key factors affecting entropy dynamics, and demonstrate that mitigating premature entropy collapse is critical for improved test performance. To support community research, we fully open-source our model weights, training code, and training datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22648", "title": "WebDancer: Towards Autonomous Information Seeking Agency", "authors": [ "Jialong Wu", "Baixuan Li", "Runnan Fang", "Wenbiao Yin", "Liwen Zhang", "Zhengwei Tao", "Dingchu Zhang", "Zekun Xi", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Jingren Zhou" ], "github": "https://github.com/Alibaba-NLP/WebAgent", "project_page": "", "abstract": "Addressing intricate real-world problems necessitates in-depth information seeking and multi-step reasoning. Recent progress in agentic systems, exemplified by Deep Research, underscores the potential for autonomous multi-step research. In this work, we present a cohesive paradigm for building end-to-end agentic information seeking agents from a data-centric and training-stage perspective. Our approach consists of four key stages: (1) browsing data construction, (2) trajectories sampling, (3) supervised fine-tuning for effective cold start, and (4) reinforcement learning for enhanced generalisation. We instantiate this framework in a web agent based on the ReAct, WebDancer. Empirical evaluations on the challenging information seeking benchmarks, GAIA and WebWalkerQA, demonstrate the strong performance of WebDancer, achieving considerable results and highlighting the efficacy of our training paradigm. Further analysis of agent training provides valuable insights and actionable, systematic pathways for developing more capable agentic models. The codes and demo will be released in https://github.com/Alibaba-NLP/WebAgent.", "github_manual": "", "github_regex": "https://github.com/Alibaba-NLP/WebAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22338", "title": "Text2Grad: Reinforcement Learning from Natural Language Feedback", "authors": [ "Hanyang Wang", "Lu Wang", "Chaoyun Zhang", "Tianjun Mao", "Si Qin", "Qingwei Lin", "Saravan Rajmohan", "Dongmei Zhang" ], "github": "https://github.com/microsoft/Text2Grad", "project_page": "", "abstract": "Traditional RLHF optimizes language models with coarse, scalar rewards that mask the fine-grained reasons behind success or failure, leading to slow and opaque learning. Recent work augments RL with textual critiques through prompting or reflection, improving interpretability but leaving model parameters untouched. We introduce Text2Grad, a reinforcement-learning paradigm that turns free-form textual feedback into span-level gradients. Given human (or programmatic) critiques, Text2Grad aligns each feedback phrase with the relevant token spans, converts these alignments into differentiable reward signals, and performs gradient updates that directly refine the offending portions of the model's policy. This yields precise, feedback-conditioned adjustments instead of global nudges. Text2Grad is realized through three components: (1) a high-quality feedback-annotation pipeline that pairs critiques with token spans; (2) a fine-grained reward model that predicts span-level reward on answer while generating explanatory critiques; and (3) a span-level policy optimizer that back-propagates natural-language gradients. Across summarization, code generation, and question answering, Text2Grad consistently surpasses scalar-reward RL and prompt-only baselines, providing both higher task metrics and richer interpretability. Our results demonstrate that natural-language feedback, when converted to gradients, is a powerful signal for fine-grained policy optimization. The code for our method is available at https://github.com/microsoft/Text2Grad", "github_manual": "", "github_regex": "https://github.com/microsoft/Text2Grad", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.19187", "title": "LIMOPro: Reasoning Refinement for Efficient and Effective Test-time Scaling", "authors": [ "Yang Xiao", "Jiashuo Wang", "Ruifeng Yuan", "Chunpu Xu", "Kaishuai Xu", "Wenjie Li", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/LIMOPro.git", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable reasoning capabilities through test-time scaling approaches, particularly when fine-tuned with chain-of-thought (CoT) data distilled from more powerful large reasoning models (LRMs). However, these reasoning chains often contain verbose elements that mirror human problem-solving, categorized as progressive reasoning (the essential solution development path) and functional elements (verification processes, alternative solution approaches, and error corrections). While progressive reasoning is crucial, the functional elements significantly increase computational demands during test-time inference. We introduce PIR (Perplexity-based Importance Refinement), a principled framework that quantitatively evaluates the importance of each reasoning step based on its impact on answer prediction confidence. PIR systematically identifies and selectively prunes only low-importance functional steps while preserving progressive reasoning components, creating optimized training data that maintains the integrity of the core solution path while reducing verbosity. Models fine-tuned on PIR-optimized data exhibit superior test-time scaling properties, generating more concise reasoning chains while achieving improved accuracy (+0.9\\% to +6.6\\%) with significantly reduced token usage (-3\\% to -41\\%) across challenging reasoning benchmarks (AIME, AMC, and GPQA Diamond). Our approach demonstrates strong generalizability across different model sizes, data sources, and token budgets, offering a practical solution for deploying reasoning-capable LLMs in scenarios where efficient test-time scaling, response time, and computational efficiency are valuable constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GAIR-NLP/LIMOPro.git", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.17663", "title": "Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States", "authors": [ "Yang Xiao", "Jiashuo Wang", "Qiancheng Xu", "Changhe Song", "Chunpu Xu", "Yi Cheng", "Wenjie Li", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/DynToM", "project_page": "", "abstract": "As Large Language Models (LLMs) increasingly participate in human-AI interactions, evaluating their Theory of Mind (ToM) capabilities - particularly their ability to track dynamic mental states - becomes crucial. While existing benchmarks assess basic ToM abilities, they predominantly focus on static snapshots of mental states, overlooking the temporal evolution that characterizes real-world social interactions. We present DynToM, a novel benchmark specifically designed to evaluate LLMs' ability to understand and track the temporal progression of mental states across interconnected scenarios. Through a systematic four-step framework, we generate 1,100 social contexts encompassing 5,500 scenarios and 78,100 questions, each validated for realism and quality. Our comprehensive evaluation of ten state-of-the-art LLMs reveals that their average performance underperforms humans by 44.7\\%, with performance degrading significantly when tracking and reasoning about the shift of mental states. This performance gap highlights fundamental limitations in current LLMs' ability to model the dynamic nature of human mental states.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GAIR-NLP/DynToM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.21925", "title": "RenderFormer: Transformer-based Neural Rendering of Triangle Meshes with Global Illumination", "authors": [ "Chong Zeng", "Yue Dong", "Pieter Peers", "Hongzhi Wu", "Xin Tong" ], "github": "", "project_page": "", "abstract": "We present RenderFormer, a neural rendering pipeline that directly renders an image from a triangle-based representation of a scene with full global illumination effects and that does not require per-scene training or fine-tuning. Instead of taking a physics-centric approach to rendering, we formulate rendering as a sequence-to-sequence transformation where a sequence of tokens representing triangles with reflectance properties is converted to a sequence of output tokens representing small patches of pixels. RenderFormer follows a two stage pipeline: a view-independent stage that models triangle-to-triangle light transport, and a view-dependent stage that transforms a token representing a bundle of rays to the corresponding pixel values guided by the triangle-sequence from the view-independent stage. Both stages are based on the transformer architecture and are learned with minimal prior constraints. We demonstrate and evaluate RenderFormer on scenes with varying complexity in shape and light transport.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22523", "title": "PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image Generative Models", "authors": [ "Junwen Chen", "Heyang Jiang", "Yanbin Wang", "Keming Wu", "Ji Li", "Chao Zhang", "Keiji Yanai", "Dong Chen", "Yuhui Yuan" ], "github": "", "project_page": "", "abstract": "Generating high-quality, multi-layer transparent images from text prompts can unlock a new level of creative control, allowing users to edit each layer as effortlessly as editing text outputs from LLMs. However, the development of multi-layer generative models lags behind that of conventional text-to-image models due to the absence of a large, high-quality corpus of multi-layer transparent data. In this paper, we address this fundamental challenge by: (i) releasing the first open, ultra-high-fidelity PrismLayers (PrismLayersPro) dataset of 200K (20K) multilayer transparent images with accurate alpha mattes, (ii) introducing a trainingfree synthesis pipeline that generates such data on demand using off-the-shelf diffusion models, and (iii) delivering a strong, open-source multi-layer generation model, ART+, which matches the aesthetics of modern text-to-image generation models. The key technical contributions include: LayerFLUX, which excels at generating high-quality single transparent layers with accurate alpha mattes, and MultiLayerFLUX, which composes multiple LayerFLUX outputs into complete images, guided by human-annotated semantic layout. To ensure higher quality, we apply a rigorous filtering stage to remove artifacts and semantic mismatches, followed by human selection. Fine-tuning the state-of-the-art ART model on our synthetic PrismLayersPro yields ART+, which outperforms the original ART in 60% of head-to-head user study comparisons and even matches the visual quality of images generated by the FLUX.1-[dev] model. We anticipate that our work will establish a solid dataset foundation for the multi-layer transparent image generation task, enabling research and applications that require precise, editable, and visually compelling layered imagery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.17507", "title": "Benchmarking Recommendation, Classification, and Tracing Based on Hugging Face Knowledge Graph", "authors": [ "Qiaosheng Chen", "Kaijia Huang", "Xiao Zhou", "Weiqing Luo", "Yuanning Cui", "Gong Cheng" ], "github": "https://github.com/uma-pi1/kge", "project_page": "", "abstract": "The rapid growth of open source machine learning (ML) resources, such as models and datasets, has accelerated IR research. However, existing platforms like Hugging Face do not explicitly utilize structured representations, limiting advanced queries and analyses such as tracing model evolution and recommending relevant datasets. To fill the gap, we construct HuggingKG, the first large-scale knowledge graph built from the Hugging Face community for ML resource management. With 2.6 million nodes and 6.2 million edges, HuggingKG captures domain-specific relations and rich textual attributes. It enables us to further present HuggingBench, a multi-task benchmark with three novel test collections for IR tasks including resource recommendation, classification, and tracing. Our experiments reveal unique characteristics of HuggingKG and the derived tasks. Both resources are publicly available, expected to advance research in open source resource sharing and management.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/uma-pi1/kge", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22617", "title": "The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models", "authors": [ "Ganqu Cui", "Yuchen Zhang", "Jiacheng Chen", "Lifan Yuan", "Zhi Wang", "Yuxin Zuo", "Haozhan Li", "Yuchen Fan", "Huayu Chen", "Weize Chen", "Zhiyuan Liu", "Hao Peng", "Lei Bai", "Wanli Ouyang", "Yu Cheng", "Bowen Zhou", "Ning Ding" ], "github": "", "project_page": "", "abstract": "This paper aims to overcome a major obstacle in scaling RL for reasoning with LLMs, namely the collapse of policy entropy. Such phenomenon is consistently observed across vast RL runs without entropy intervention, where the policy entropy dropped sharply at the early training stage, this diminished exploratory ability is always accompanied with the saturation of policy performance. In practice, we establish a transformation equation R=-a*e^H+b between entropy H and downstream performance R. This empirical law strongly indicates that, the policy performance is traded from policy entropy, thus bottlenecked by its exhaustion, and the ceiling is fully predictable H=0, R=-a+b. Our finding necessitates entropy management for continuous exploration toward scaling compute for RL. To this end, we investigate entropy dynamics both theoretically and empirically. Our derivation highlights that, the change in policy entropy is driven by the covariance between action probability and the change in logits, which is proportional to its advantage when using Policy Gradient-like algorithms. Empirical study shows that, the values of covariance term and entropy differences matched exactly, supporting the theoretical conclusion. Moreover, the covariance term stays mostly positive throughout training, further explaining why policy entropy would decrease monotonically. Through understanding the mechanism behind entropy dynamics, we motivate to control entropy by restricting the update of high-covariance tokens. Specifically, we propose two simple yet effective techniques, namely Clip-Cov and KL-Cov, which clip and apply KL penalty to tokens with high covariances respectively. Experiments show that these methods encourage exploration, thus helping policy escape entropy collapse and achieve better downstream performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.18700", "title": "GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language Models and Enhanced Reasoning Chains", "authors": [ "Chun Wang", "Xiaoran Pan", "Zihao Pan", "Haofan Wang", "Yiren Song" ], "github": "https://github.com/Thorin215/GRE", "project_page": "", "abstract": "Recent advances in Visual Language Models (VLMs) have demonstrated exceptional performance in visual reasoning tasks. However, geo-localization presents unique challenges, requiring the extraction of multigranular visual cues from images and their integration with external world knowledge for systematic reasoning. Current approaches to geo-localization tasks often lack robust reasoning mechanisms and explainability, limiting their effectiveness. To address these limitations, we propose the Geo Reason Enhancement (GRE) Suite, a novel framework that augments VLMs with structured reasoning chains for accurate and interpretable location inference. The GRE Suite is systematically developed across three key dimensions: dataset, model, and benchmark. First, we introduce GRE30K, a high-quality geo-localization reasoning dataset designed to facilitate fine-grained visual and contextual analysis. Next, we present the GRE model, which employs a multi-stage reasoning strategy to progressively infer scene attributes, local details, and semantic features, thereby narrowing down potential geographic regions with enhanced precision. Finally, we construct the Geo Reason Evaluation Benchmark (GREval-Bench), a comprehensive evaluation framework that assesses VLMs across diverse urban, natural, and landmark scenes to measure both coarse-grained (e.g., country, continent) and fine-grained (e.g., city, street) localization performance. Experimental results demonstrate that GRE significantly outperforms existing methods across all granularities of geo-localization tasks, underscoring the efficacy of reasoning-augmented VLMs in complex geographic inference. Code and data will be released at https://github.com/Thorin215/GRE.", "github_manual": "", "github_regex": "https://github.com/Thorin215/GRE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.21876", "title": "EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance", "authors": [ "Zun Wang", "Jaemin Cho", "Jialu Li", "Han Lin", "Jaehong Yoon", "Yue Zhang", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Recent approaches on 3D camera control in video diffusion models (VDMs) often create anchor videos to guide diffusion models as a structured prior by rendering from estimated point clouds following annotated camera trajectories. However, errors inherent in point cloud estimation often lead to inaccurate anchor videos. Moreover, the requirement for extensive camera trajectory annotations further increases resource demands. To address these limitations, we introduce EPiC, an efficient and precise camera control learning framework that automatically constructs high-quality anchor videos without expensive camera trajectory annotations. Concretely, we create highly precise anchor videos for training by masking source videos based on first-frame visibility. This approach ensures high alignment, eliminates the need for camera trajectory annotations, and thus can be readily applied to any in-the-wild video to generate image-to-video (I2V) training pairs. Furthermore, we introduce Anchor-ControlNet, a lightweight conditioning module that integrates anchor video guidance in visible regions to pretrained VDMs, with less than 1% of backbone model parameters. By combining the proposed anchor video data and ControlNet module, EPiC achieves efficient training with substantially fewer parameters, training steps, and less data, without requiring modifications to the diffusion model backbone typically needed to mitigate rendering misalignments. Although being trained on masking-based anchor videos, our method generalizes robustly to anchor videos made with point clouds during inference, enabling precise 3D-informed camera control. EPiC achieves SOTA performance on RealEstate10K and MiraData for I2V camera control task, demonstrating precise and robust camera control ability both quantitatively and qualitatively. Notably, EPiC also exhibits strong zero-shot generalization to video-to-video scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.12667", "title": "Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking", "authors": [ "Zihan Su", "Xuerui Qiu", "Hongbin Xu", "Tangyu Jiang", "Junhao Zhuang", "Chun Yuan", "Ming Li", "Shengfeng He", "Fei Richard Yu" ], "github": "https://github.com/Sugewud/Safe-Sora", "project_page": "", "abstract": "The explosive growth of generative video models has amplified the demand for reliable copyright preservation of AI-generated content. Despite its popularity in image synthesis, invisible generative watermarking remains largely underexplored in video generation. To address this gap, we propose Safe-Sora, the first framework to embed graphical watermarks directly into the video generation process. Motivated by the observation that watermarking performance is closely tied to the visual similarity between the watermark and cover content, we introduce a hierarchical coarse-to-fine adaptive matching mechanism. Specifically, the watermark image is divided into patches, each assigned to the most visually similar video frame, and further localized to the optimal spatial region for seamless embedding. To enable spatiotemporal fusion of watermark patches across video frames, we develop a 3D wavelet transform-enhanced Mamba architecture with a novel spatiotemporal local scanning strategy, effectively modeling long-range dependencies during watermark embedding and retrieval. To the best of our knowledge, this is the first attempt to apply state space models to watermarking, opening new avenues for efficient and robust watermark protection. Extensive experiments demonstrate that Safe-Sora achieves state-of-the-art performance in terms of video quality, watermark fidelity, and robustness, which is largely attributed to our proposals. We will release our code upon publication.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Sugewud/Safe-Sora", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22613", "title": "RICO: Improving Accuracy and Completeness in Image Recaptioning via Visual Reconstruction", "authors": [ "Yuchi Wang", "Yishuo Cai", "Shuhuai Ren", "Sihan Yang", "Linli Yao", "Yuanxin Liu", "Yuanxing Zhang", "Pengfei Wan", "Xu Sun" ], "github": "https://github.com/wangyuchi369/RICO", "project_page": "", "abstract": "Image recaptioning is widely used to generate training datasets with enhanced quality for various multimodal tasks. Existing recaptioning methods typically rely on powerful multimodal large language models (MLLMs) to enhance textual descriptions, but often suffer from inaccuracies due to hallucinations and incompleteness caused by missing fine-grained details. To address these limitations, we propose RICO, a novel framework that refines captions through visual reconstruction. Specifically, we leverage a text-to-image model to reconstruct a caption into a reference image, and prompt an MLLM to identify discrepancies between the original and reconstructed images to refine the caption. This process is performed iteratively, further progressively promoting the generation of more faithful and comprehensive descriptions. To mitigate the additional computational cost induced by the iterative process, we introduce RICO-Flash, which learns to generate captions like RICO using DPO. Extensive experiments demonstrate that our approach significantly improves caption accuracy and completeness, outperforms most baselines by approximately 10% on both CapsBench and CompreCap. Code released at https://github.com/wangyuchi369/RICO.", "github_manual": "", "github_regex": "https://github.com/wangyuchi369/RICO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22129", "title": "What Makes for Text to 360-degree Panorama Generation with Stable Diffusion?", "authors": [ "Jinhong Ni", "Chang-Bin Zhang", "Qiang Zhang", "Jing Zhang" ], "github": "https://github.com/jinhong-ni/UniPano", "project_page": "", "abstract": "Recent prosperity of text-to-image diffusion models, e.g. Stable Diffusion, has stimulated research to adapt them to 360-degree panorama generation. Prior work has demonstrated the feasibility of using conventional low-rank adaptation techniques on pre-trained diffusion models to generate panoramic images. However, the substantial domain gap between perspective and panoramic images raises questions about the underlying mechanisms enabling this empirical success. We hypothesize and examine that the trainable counterparts exhibit distinct behaviors when fine-tuned on panoramic data, and such an adaptation conceals some intrinsic mechanism to leverage the prior knowledge within the pre-trained diffusion models. Our analysis reveals the following: 1) the query and key matrices in the attention modules are responsible for common information that can be shared between the panoramic and perspective domains, thus are less relevant to panorama generation; and 2) the value and output weight matrices specialize in adapting pre-trained knowledge to the panoramic domain, playing a more critical role during fine-tuning for panorama generation. We empirically verify these insights by introducing a simple framework called UniPano, with the objective of establishing an elegant baseline for future research. UniPano not only outperforms existing methods but also significantly reduces memory usage and training time compared to prior dual-branch approaches, making it scalable for end-to-end panorama generation with higher resolution. The code will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jinhong-ni/UniPano", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.21887", "title": "SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem", "authors": [ "Ahmed Heakl", "Yahia Salaheldin Shaaban", "Martin Takac", "Salem Lahlou", "Zangir Iklassov" ], "github": "", "project_page": "", "abstract": "Robust routing under uncertainty is central to real-world logistics, yet most benchmarks assume static, idealized settings. We present SVRPBench, the first open benchmark to capture high-fidelity stochastic dynamics in vehicle routing at urban scale. Spanning more than 500 instances with up to 1000 customers, it simulates realistic delivery conditions: time-dependent congestion, log-normal delays, probabilistic accidents, and empirically grounded time windows for residential and commercial clients. Our pipeline generates diverse, constraint-rich scenarios, including multi-depot and multi-vehicle setups. Benchmarking reveals that state-of-the-art RL solvers like POMO and AM degrade by over 20% under distributional shift, while classical and metaheuristic methods remain robust. To enable reproducible research, we release the dataset and evaluation suite. SVRPBench challenges the community to design solvers that generalize beyond synthetic assumptions and adapt to real-world uncertainty.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22203", "title": "Pitfalls of Rule- and Model-based Verifiers -- A Case Study on Mathematical Reasoning", "authors": [ "Yuzhen Huang", "Weihao Zeng", "Xingshan Zeng", "Qi Zhu", "Junxian He" ], "github": "https://github.com/hkust-nlp/RL-Verifier-Pitfalls", "project_page": "", "abstract": "Trustworthy verifiers are essential for the success of reinforcement learning with verifiable reward (RLVR), which is the core methodology behind various large reasoning models such as DeepSeek-R1. In complex domains like mathematical reasoning, rule-based verifiers have been widely adopted in previous works to train strong reasoning models. However, the reliability of these verifiers and their impact on the RL training process remain poorly understood. In this work, we take mathematical reasoning as a case study and conduct a comprehensive analysis of various verifiers in both static evaluation and RL training scenarios. First, we find that current open-source rule-based verifiers often fail to recognize equivalent answers presented in different formats across multiple commonly used mathematical datasets, resulting in non-negligible false negative rates. This limitation adversely affects RL training performance and becomes more pronounced as the policy model gets stronger. Subsequently, we investigate model-based verifiers as a potential solution to address these limitations. While the static evaluation shows that model-based verifiers achieve significantly higher verification accuracy, further analysis and RL training results imply that they are highly susceptible to hacking, where they misclassify certain patterns in responses as correct (i.e., false positives). This vulnerability is exploited during policy model optimization, leading to artificially inflated rewards. Our findings underscore the unique risks inherent to both rule-based and model-based verifiers, aiming to offer valuable insights to develop more robust reward systems in reinforcement learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hkust-nlp/RL-Verifier-Pitfalls", "github_api_added_by": "auto" }, { "date": "2025-05-29", "arxiv_id": "2505.22525", "title": "Thinking with Generated Images", "authors": [ "Ethan Chern", "Zhulin Hu", "Steffi Chern", "Siqi Kou", "Jiadi Su", "Yan Ma", "Zhijie Deng", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/thinking-with-generated-images", "project_page": "", "abstract": "We present Thinking with Generated Images, a novel paradigm that fundamentally transforms how large multimodal models (LMMs) engage with visual reasoning by enabling them to natively think across text and vision modalities through spontaneous generation of intermediate visual thinking steps. Current visual reasoning with LMMs is constrained to either processing fixed user-provided images or reasoning solely through text-based chain-of-thought (CoT). Thinking with Generated Images unlocks a new dimension of cognitive capability where models can actively construct intermediate visual thoughts, critique their own visual hypotheses, and refine them as integral components of their reasoning process. We demonstrate the effectiveness of our approach through two complementary mechanisms: (1) vision generation with intermediate visual subgoals, where models decompose complex visual tasks into manageable components that are generated and integrated progressively, and (2) vision generation with self-critique, where models generate an initial visual hypothesis, analyze its shortcomings through textual reasoning, and produce refined outputs based on their own critiques. Our experiments on vision generation benchmarks show substantial improvements over baseline approaches, with our models achieving up to 50% (from 38% to 57%) relative improvement in handling complex multi-object scenarios. From biochemists exploring novel protein structures, and architects iterating on spatial designs, to forensic analysts reconstructing crime scenes, and basketball players envisioning strategic plays, our approach enables AI models to engage in the kind of visual imagination and iterative refinement that characterizes human creative, analytical, and strategic thinking. We release our open-source suite at https://github.com/GAIR-NLP/thinking-with-generated-images.", "github_manual": "", "github_regex": "https://github.com/GAIR-NLP/thinking-with-generated-images", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22645", "title": "Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese", "authors": [ "Hanjia Lyu", "Jiebo Luo", "Jian Kang", "Allison Koenecke" ], "github": "https://github.com/brucelyu17/SC-TC-Bench", "project_page": "", "abstract": "While the capabilities of Large Language Models (LLMs) have been studied in both Simplified and Traditional Chinese, it is yet unclear whether LLMs exhibit differential performance when prompted in these two variants of written Chinese. This understanding is critical, as disparities in the quality of LLM responses can perpetuate representational harms by ignoring the different cultural contexts underlying Simplified versus Traditional Chinese, and can exacerbate downstream harms in LLM-facilitated decision-making in domains such as education or hiring. To investigate potential LLM performance disparities, we design two benchmark tasks that reflect real-world scenarios: regional term choice (prompting the LLM to name a described item which is referred to differently in Mainland China and Taiwan), and regional name choice (prompting the LLM to choose who to hire from a list of names in both Simplified and Traditional Chinese). For both tasks, we audit the performance of 11 leading commercial LLM services and open-sourced models -- spanning those primarily trained on English, Simplified Chinese, or Traditional Chinese. Our analyses indicate that biases in LLM responses are dependent on both the task and prompting language: while most LLMs disproportionately favored Simplified Chinese responses in the regional term choice task, they surprisingly favored Traditional Chinese names in the regional name choice task. We find that these disparities may arise from differences in training data representation, written character preferences, and tokenization of Simplified and Traditional Chinese. These findings highlight the need for further analysis of LLM biases; as such, we provide an open-sourced benchmark dataset to foster reproducible evaluations of future LLM behavior across Chinese language variants (https://github.com/brucelyu17/SC-TC-Bench).", "github_manual": "", "github_regex": "https://github.com/brucelyu17/SC-TC-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.21960", "title": "One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models", "authors": [ "Senmao Li", "Lei Wang", "Kai Wang", "Tao Liu", "Jiehang Xie", "Joost van de Weijer", "Fahad Shahbaz Khan", "Shiqi Yang", "Yaxing Wang", "Jian Yang" ], "github": "", "project_page": "", "abstract": "Text-to-Image (T2I) diffusion models have made remarkable advancements in generative modeling; however, they face a trade-off between inference speed and image quality, posing challenges for efficient deployment. Existing distilled T2I models can generate high-fidelity images with fewer sampling steps, but often struggle with diversity and quality, especially in one-step models. From our analysis, we observe redundant computations in the UNet encoders. Our findings suggest that, for T2I diffusion models, decoders are more adept at capturing richer and more explicit semantic information, while encoders can be effectively shared across decoders from diverse time steps. Based on these observations, we introduce the first Time-independent Unified Encoder TiUE for the student model UNet architecture, which is a loop-free image generation approach for distilling T2I diffusion models. Using a one-pass scheme, TiUE shares encoder features across multiple decoder time steps, enabling parallel sampling and significantly reducing inference time complexity. In addition, we incorporate a KL divergence term to regularize noise prediction, which enhances the perceptual realism and diversity of the generated images. Experimental results demonstrate that TiUE outperforms state-of-the-art methods, including LCM, SD-Turbo, and SwiftBrushv2, producing more diverse and realistic results while maintaining the computational efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22019", "title": "VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning", "authors": [ "Qiuchen Wang", "Ruixue Ding", "Yu Zeng", "Zehui Chen", "Lin Chen", "Shihang Wang", "Pengjun Xie", "Fei Huang", "Feng Zhao" ], "github": "https://github.com/Alibaba-NLP/VRAG", "project_page": "", "abstract": "Effectively retrieving, reasoning and understanding visually rich information remains a challenge for RAG methods. Traditional text-based methods cannot handle visual-related information. On the other hand, current vision-based RAG approaches are often limited by fixed pipelines and frequently struggle to reason effectively due to the insufficient activation of the fundamental capabilities of models. As RL has been proven to be beneficial for model reasoning, we introduce VRAG-RL, a novel RL framework tailored for complex reasoning across visually rich information. With this framework, VLMs interact with search engines, autonomously sampling single-turn or multi-turn reasoning trajectories with the help of visual perception tokens and undergoing continual optimization based on these samples. Our approach highlights key limitations of RL in RAG domains: (i) Prior Multi-modal RAG approaches tend to merely incorporate images into the context, leading to insufficient reasoning token allocation and neglecting visual-specific perception; and (ii) When models interact with search engines, their queries often fail to retrieve relevant information due to the inability to articulate requirements, thereby leading to suboptimal performance. To address these challenges, we define an action space tailored for visually rich inputs, with actions including cropping and scaling, allowing the model to gather information from a coarse-to-fine perspective. Furthermore, to bridge the gap between users' original inquiries and the retriever, we employ a simple yet effective reward that integrates query rewriting and retrieval performance with a model-based reward. Our VRAG-RL optimizes VLMs for RAG tasks using specially designed RL strategies, aligning the model with real-world applications. The code is available at https://github.com/Alibaba-NLP/VRAG{https://github.com/Alibaba-NLP/VRAG}.", "github_manual": "", "github_regex": "https://github.com/Alibaba-NLP/VRAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22202", "title": "Let's Predict Sentence by Sentence", "authors": [ "Hyeonbin Hwang", "Byeongguk Jeon", "Seungone Kim", "Jiyeon Kim", "Hoyeon Chang", "Sohee Yang", "Seungpil Won", "Dohaeng Lee", "Youbin Ahn", "Minjoon Seo" ], "github": "https://github.com/hbin0701/Pred-Sent", "project_page": "", "abstract": "Autoregressive language models (LMs) generate one token at a time, yet human reasoning operates over higher-level abstractions - sentences, propositions, and concepts. This contrast raises a central question- Can LMs likewise learn to reason over structured semantic units rather than raw token sequences? In this work, we investigate whether pretrained LMs can be lifted into such abstract reasoning spaces by building on their learned representations. We present a framework that adapts a pretrained token-level LM to operate in sentence space by autoregressively predicting continuous embeddings of next sentences. We explore two embedding paradigms inspired by classical representation learning: 1) semantic embeddings, learned via autoencoding to preserve surface meaning; and 2) contextual embeddings, trained via next-sentence prediction to encode anticipatory structure. We evaluate both under two inference regimes: Discretized, which decodes each predicted embedding into text before re-encoding; and Continuous, which reasons entirely in embedding space for improved efficiency. Across four domains - mathematics, logic, commonsense, and planning - contextual embeddings under continuous inference show competitive performance with Chain-of-Thought (CoT) while reducing inference-time FLOPs on average by half. We also present early signs of scalability and modular adaptation. Finally, to visualize latent trajectories, we introduce SentenceLens, a diagnostic tool that decodes intermediate model states into interpretable sentences. Together, our results indicate that pretrained LMs can effectively transition to abstract, structured reasoning within latent embedding spaces.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hbin0701/Pred-Sent", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.21191", "title": "Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities", "authors": [ "Junyan Zhang", "Yubo Gao", "Yibo Yan", "Jungang Li", "Zhaorui Hou", "Sicheng Tao", "Shuliang Liu", "Song Dai", "Yonghua Hei", "Junzhuo Li", "Xuming Hu" ], "github": "", "project_page": "", "abstract": "The finetuning of Large Language Models (LLMs) has significantly advanced their instruction-following capabilities, yet the underlying computational mechanisms driving these improvements remain poorly understood. This study systematically examines how fine-tuning reconfigures LLM computations by isolating and analyzing instruction-specific sparse components, i.e., neurons in dense models and both neurons and experts in Mixture-of-Experts (MoE) architectures. In particular, we introduce HexaInst, a carefully curated and balanced instructional dataset spanning six distinct categories, and propose SPARCOM, a novel analytical framework comprising three key contributions: (1) a method for identifying these sparse components, (2) an evaluation of their functional generality and uniqueness, and (3) a systematic comparison of their alterations. Through experiments, we demonstrate functional generality, uniqueness, and the critical role of these components in instruction execution. By elucidating the relationship between fine-tuning-induced adaptations and sparse computational substrates, this work provides deeper insights into how LLMs internalize instruction-following behavior for the trustworthy LLM community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.20715", "title": "MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding", "authors": [ "Fuwen Luo", "Shengfeng Lou", "Chi Chen", "Ziyue Wang", "Chenliang Li", "Weizhou Shen", "Jiyue Guo", "Peng Li", "Ming Yan", "Ji Zhang", "Fei Huang", "Yang Liu" ], "github": "https://github.com/THUNLP-MT/MUSEG", "project_page": "", "abstract": "Video temporal understanding is crucial for multimodal large language models (MLLMs) to reason over events in videos. Despite recent advances in general video understanding, current MLLMs still struggle with fine-grained temporal reasoning. While reinforcement learning (RL) has been explored to address this issue recently, existing RL approaches remain limited in effectiveness. In this work, we propose MUSEG, a novel RL-based method that enhances temporal understanding by introducing timestamp-aware multi-segment grounding. MUSEG enables MLLMs to align queries with multiple relevant video segments, promoting more comprehensive temporal reasoning. To facilitate effective learning, we design a customized RL training recipe with phased rewards that progressively guides the model toward temporally grounded reasoning. Extensive experiments on temporal grounding and time-sensitive video QA tasks demonstrate that MUSEG significantly outperforms existing methods and generalizes well across diverse temporal understanding scenarios. View our project at https://github.com/THUNLP-MT/MUSEG.", "github_manual": "", "github_regex": "https://github.com/THUNLP-MT/MUSEG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.17870", "title": "Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat Falsehoods", "authors": [ "Shaina Raza", "Rizwan Qureshi", "Marcelo Lotif", "Aman Chadha", "Deval Pandya", "Christos Emmanouilidis" ], "github": "", "project_page": "", "abstract": "Generative AI models often learn and reproduce false information present in their training corpora. This position paper argues that, analogous to biological immunization, where controlled exposure to a weakened pathogen builds immunity, AI models should be fine tuned on small, quarantined sets of explicitly labeled falsehoods as a \"vaccine\" against misinformation. These curated false examples are periodically injected during finetuning, strengthening the model ability to recognize and reject misleading claims while preserving accuracy on truthful inputs. An illustrative case study shows that immunized models generate substantially less misinformation than baselines. To our knowledge, this is the first training framework that treats fact checked falsehoods themselves as a supervised vaccine, rather than relying on input perturbations or generic human feedback signals, to harden models against future misinformation. We also outline ethical safeguards and governance controls to ensure the safe use of false data. Model immunization offers a proactive paradigm for aligning AI systems with factuality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.19075", "title": "Universal Reasoner: A Single, Composable Plug-and-Play Reasoner for Frozen LLMs", "authors": [ "Jaemin Kim", "Hangeol Chang", "Hyunmin Hwang", "Choonghan Kim", "Jong Chul Ye" ], "github": "https://github.com/hangeol/UniR", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable general capabilities, but enhancing skills such as reasoning often demands substantial computational resources and may compromise their generalization. While Parameter-Efficient Fine-Tuning (PEFT) methods offer a more resource-conscious alternative, they typically requires retraining for each LLM backbone due to architectural dependencies. To address these challenges, here we propose Universal Reasoner (UniR) - a single, lightweight, composable, and plug-and-play reasoning module that can be used with any frozen LLM to endow it with specialized reasoning capabilities. Specifically, UniR decomposes the reward into a standalone reasoning module that is trained independently using predefined rewards, effectively translating trajectory-level signals into token-level guidance. Once trained, UniR can be combined with any frozen LLM at inference time by simply adding its output logits to those of the LLM backbone. This additive structure naturally enables modular composition: multiple UniR modules trained for different tasks can be jointly applied by summing their logits, enabling complex reasoning via composition. Experimental results on mathematical reasoning and machine translation tasks show that UniR significantly outperforms existing baseline fine-tuning methods using the Llama3.2 model. Furthermore, UniR demonstrates strong weak-to-strong generalization: reasoning modules trained on smaller models effectively guide much larger LLMs. This makes UniR a cost-efficient, adaptable, and robust solution for enhancing reasoning in LLMs without compromising their core capabilities. Code is open-sourced at https://github.com/hangeol/UniR", "github_manual": "", "github_regex": "https://github.com/hangeol/UniR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.18600", "title": "Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment", "authors": [ "Bryan Sangwoo Kim", "Jeongsol Kim", "Jong Chul Ye" ], "github": "https://github.com/cswry/OSEDiff", "project_page": "", "abstract": "Modern single-image super-resolution (SISR) models deliver photo-realistic results at the scale factors on which they are trained, but collapse when asked to magnify far beyond that regime. We address this scalability bottleneck with Chain-of-Zoom (CoZ), a model-agnostic framework that factorizes SISR into an autoregressive chain of intermediate scale-states with multi-scale-aware prompts. CoZ repeatedly re-uses a backbone SR model, decomposing the conditional probability into tractable sub-problems to achieve extreme resolutions without additional training. Because visual cues diminish at high magnifications, we augment each zoom step with multi-scale-aware text prompts generated by a vision-language model (VLM). The prompt extractor itself is fine-tuned using Generalized Reward Policy Optimization (GRPO) with a critic VLM, aligning text guidance towards human preference. Experiments show that a standard 4x diffusion SR model wrapped in CoZ attains beyond 256x enlargement with high perceptual quality and fidelity. Project Page: https://bryanswkim.github.io/chain-of-zoom/ .", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/cswry/OSEDiff", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22457", "title": "Fostering Video Reasoning via Next-Event Prediction", "authors": [ "Haonan Wang", "Hongfu Liu", "Xiangyan Liu", "Chao Du", "Kenji Kawaguchi", "Ye Wang", "Tianyu Pang" ], "github": "", "project_page": "", "abstract": "Next-token prediction serves as the foundational learning task enabling reasoning in LLMs. But what should the learning task be when aiming to equip MLLMs with temporal reasoning capabilities over video inputs? Existing tasks such as video question answering often rely on annotations from humans or much stronger MLLMs, while video captioning tends to entangle temporal reasoning with spatial information. To address this gap, we propose next-event prediction (NEP), a learning task that harnesses future video segments as a rich, self-supervised signal to foster temporal reasoning. We segment each video into past and future frames: the MLLM takes the past frames as input and predicts a summary of events derived from the future frames, thereby encouraging the model to reason temporally in order to complete the task. To support this task, we curate V1-33K, a dataset comprising 33,000 automatically extracted video segments spanning diverse real-world scenarios. We further explore a range of video instruction-tuning strategies to study their effects on temporal reasoning. To evaluate progress, we introduce FutureBench to assess coherence in predicting unseen future events. Experiments validate that NEP offers a scalable and effective training paradigm for fostering temporal reasoning in MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22651", "title": "Sherlock: Self-Correcting Reasoning in Vision-Language Models", "authors": [ "Yi Ding", "Ruqi Zhang" ], "github": "", "project_page": "", "abstract": "Reasoning Vision-Language Models (VLMs) have shown promising performance on complex multimodal tasks. However, they still face significant challenges: they are highly sensitive to reasoning errors, require large volumes of annotated data or accurate verifiers, and struggle to generalize beyond specific domains. To address these limitations, we explore self-correction as a strategy to enhance reasoning VLMs. We first conduct an in-depth analysis of reasoning VLMs' self-correction abilities and identify key gaps. Based on our findings, we introduce Sherlock, a self-correction and self-improvement training framework. Sherlock introduces a trajectory-level self-correction objective, a preference data construction method based on visual perturbation, and a dynamic beta for preference tuning. Once the model acquires self-correction capabilities using only 20k randomly sampled annotated data, it continues to self-improve without external supervision. Built on the Llama3.2-Vision-11B model, Sherlock achieves remarkable results across eight benchmarks, reaching an average accuracy of 64.1 with direct generation and 65.4 after self-correction. It outperforms LLaVA-CoT (63.2), Mulberry (63.9), and LlamaV-o1 (63.4) while using less than 20% of the annotated data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.15813", "title": "Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex", "authors": [ "Muquan Yu", "Mu Nan", "Hossein Adeli", "Jacob S. Prince", "John A. Pyles", "Leila Wehbe", "Margaret M. Henderson", "Michael J. Tarr", "Andrew F. Luo" ], "github": "https://github.com/leomqyu/BraInCoRL", "project_page": "", "abstract": "Understanding functional representations within higher visual cortex is a fundamental question in computational neuroscience. While artificial neural networks pretrained on large-scale datasets exhibit striking representational alignment with human neural responses, learning image-computable models of visual cortex relies on individual-level, large-scale fMRI datasets. The necessity for expensive, time-intensive, and often impractical data acquisition limits the generalizability of encoders to new subjects and stimuli. BraInCoRL uses in-context learning to predict voxelwise neural responses from few-shot examples without any additional finetuning for novel subjects and stimuli. We leverage a transformer architecture that can flexibly condition on a variable number of in-context image stimuli, learning an inductive bias over multiple subjects. During training, we explicitly optimize the model for in-context learning. By jointly conditioning on image features and voxel activations, our model learns to directly generate better performing voxelwise models of higher visual cortex. We demonstrate that BraInCoRL consistently outperforms existing voxelwise encoder designs in a low-data regime when evaluated on entirely novel images, while also exhibiting strong test-time scaling behavior. The model also generalizes to an entirely new visual fMRI dataset, which uses different subjects and fMRI data acquisition parameters. Further, BraInCoRL facilitates better interpretability of neural signals in higher visual cortex by attending to semantically relevant stimuli. Finally, we show that our framework enables interpretable mappings from natural language queries to voxel selectivity.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/leomqyu/BraInCoRL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.21582", "title": "AITEE -- Agentic Tutor for Electrical Engineering", "authors": [ "Christopher Knievel", "Alexander Bernhardt", "Christian Bernhardt" ], "github": "https://github.com/cknievel/aitee-dataset", "project_page": "", "abstract": "Intelligent tutoring systems combined with large language models offer a promising approach to address students' diverse needs and promote self-efficacious learning. While large language models possess good foundational knowledge of electrical engineering basics, they remain insufficiently capable of addressing specific questions about electrical circuits. In this paper, we present AITEE, an agent-based tutoring system for electrical engineering designed to accompany students throughout their learning process, offer individualized support, and promote self-directed learning. AITEE supports both hand-drawn and digital circuits through an adapted circuit reconstruction process, enabling natural interaction with students. Our novel graph-based similarity measure identifies relevant context from lecture materials through a retrieval augmented generation approach, while parallel Spice simulation further enhances accuracy in applying solution methodologies. The system implements a Socratic dialogue to foster learner autonomy through guided questioning. Experimental evaluations demonstrate that AITEE significantly outperforms baseline approaches in domain-specific knowledge application, with even medium-sized LLM models showing acceptable performance. Our results highlight the potential of agentic tutors to deliver scalable, personalized, and effective learning environments for electrical engineering education.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cknievel/aitee-dataset", "github_api_added_by": "auto" }, { "date": "2025-05-29", "arxiv_id": "2505.18149", "title": "First Finish Search: Efficient Test-Time Scaling in Large Language Models", "authors": [ "Aradhye Agarwal", "Ayan Sengupta", "Tanmoy Chakraborty" ], "github": "https://github.com/Aradhya2002/reasoning", "project_page": "", "abstract": "Test-time scaling (TTS), which involves dynamic allocation of compute during inference, offers a promising way to improve reasoning in large language models. While existing TTS methods work well, they often rely on long decoding paths or require a large number of samples to be generated, increasing the token usage and inference latency. We observe the surprising fact that for reasoning tasks, shorter traces are much more likely to be correct than longer ones. Motivated by this, we introduce First Finish Search (FFS), a training-free parallel decoding strategy that launches n independent samples and returns as soon as any one completes. We evaluate FFS alongside simple decoding, beam search, majority voting, and budget forcing on four reasoning models (DeepSeek-R1, R1-Distill-Qwen-32B, QwQ-32B and Phi-4-Reasoning-Plus) and across four datasets (AIME24, AIME25-I, AIME25-II and GPQA Diamond). With DeepSeek-R1, FFS achieves 82.23% accuracy on the AIME datasets, a 15% improvement over DeepSeek-R1's standalone accuracy, nearly matching OpenAI's o4-mini performance. Our theoretical analysis explains why stopping at the shortest trace is likely to yield a correct answer and identifies the conditions under which early stopping may be suboptimal. The elegance and simplicity of FFS demonstrate that straightforward TTS strategies can perform remarkably well, revealing the untapped potential of simple approaches at inference time.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Aradhya2002/reasoning", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.20411", "title": "SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents", "authors": [ "Ibragim Badertdinov", "Alexander Golubev", "Maksim Nekrashevich", "Anton Shevtsov", "Simon Karasik", "Andrei Andriushchenko", "Maria Trofimova", "Daria Litvintseva", "Boris Yangel" ], "github": "", "project_page": "", "abstract": "LLM-based agents have shown promising capabilities in a growing range of software engineering (SWE) tasks. However, advancing this field faces two critical challenges. First, high-quality training data is scarce, especially data that reflects real-world SWE scenarios, where agents must interact with development environments, execute code and adapt behavior based on the outcomes of their actions. Existing datasets are either limited to one-shot code generation or comprise small, manually curated collections of interactive tasks, lacking both scale and diversity. Second, the lack of fresh interactive SWE tasks affects evaluation of rapidly improving models, as static benchmarks quickly become outdated due to contamination issues. To address these limitations, we introduce a novel, automated, and scalable pipeline to continuously extract real-world interactive SWE tasks from diverse GitHub repositories. Using this pipeline, we construct SWE-rebench, a public dataset comprising over 21,000 interactive Python-based SWE tasks, suitable for reinforcement learning of SWE agents at scale. Additionally, we use continuous supply of fresh tasks collected using SWE-rebench methodology to build a contamination-free benchmark for agentic software engineering. We compare results of various LLMs on this benchmark to results on SWE-bench Verified and show that performance of some language models might be inflated due to contamination issues.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.21060", "title": "Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles", "authors": [ "Peng Wang", "Xiang Liu", "Peidong Liu" ], "github": "https://github.com/WU-CVGL/Styl3R", "project_page": "", "abstract": "Stylizing 3D scenes instantly while maintaining multi-view consistency and faithfully resembling a style image remains a significant challenge. Current state-of-the-art 3D stylization methods typically involve computationally intensive test-time optimization to transfer artistic features into a pretrained 3D representation, often requiring dense posed input images. In contrast, leveraging recent advances in feed-forward reconstruction models, we demonstrate a novel approach to achieve direct 3D stylization in less than a second using unposed sparse-view scene images and an arbitrary style image. To address the inherent decoupling between reconstruction and stylization, we introduce a branched architecture that separates structure modeling and appearance shading, effectively preventing stylistic transfer from distorting the underlying 3D scene structure. Furthermore, we adapt an identity loss to facilitate pre-training our stylization model through the novel view synthesis task. This strategy also allows our model to retain its original reconstruction capabilities while being fine-tuned for stylization. Comprehensive evaluations, using both in-domain and out-of-domain datasets, demonstrate that our approach produces high-quality stylized 3D content that achieve a superior blend of style and scene appearance, while also outperforming existing methods in terms of multi-view consistency and efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/WU-CVGL/Styl3R", "github_api_added_by": "auto" }, { "date": "2025-05-29", "arxiv_id": "2505.22232", "title": "Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models", "authors": [ "Mehdi Ali", "Manuel Brack", "Max L\u00fcbbering", "Elias Wendt", "Abbas Goher Khan", "Richard Rutmann", "Alex Jude", "Maurice Kraus", "Alexander Arno Weber", "Felix Stollenwerk", "David Kacz\u00e9r", "Florian Mai", "Lucie Flek", "Rafet Sifa", "Nicolas Flores-Herr", "Joachim K\u00f6hler", "Patrick Schramowski", "Michael Fromm", "Kristian Kersting" ], "github": "", "project_page": "", "abstract": "High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets remains limited. Existing state-of-the-art datasets mostly rely on heuristic filtering methods, restricting both their cross-lingual transferability and scalability. Here, we introduce JQL, a systematic approach that efficiently curates diverse and high-quality multilingual data at scale while significantly reducing computational demands. JQL distills LLMs' annotation capabilities into lightweight annotators based on pretrained multilingual embeddings. These models exhibit robust multilingual and cross-lingual performance, even for languages and scripts unseen during training. Evaluated empirically across 35 languages, the resulting annotation pipeline substantially outperforms current heuristic filtering methods like Fineweb2. JQL notably enhances downstream model training quality and increases data retention rates. Our research provides practical insights and valuable resources for multilingual data curation, raising the standards of multilingual dataset development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.20298", "title": "MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding", "authors": [ "Jeonghun Baek", "Kazuki Egashira", "Shota Onohara", "Atsuyuki Miyai", "Yuki Imajuku", "Hikaru Ikuta", "Kiyoharu Aizawa" ], "github": "https://github.com/manga109/mangalmm", "project_page": "", "abstract": "Manga, or Japanese comics, is a richly multimodal narrative form that blends images and text in complex ways. Teaching large multimodal models (LMMs) to understand such narratives at a human-like level could help manga creators reflect on and refine their stories. To this end, we introduce two benchmarks for multimodal manga understanding: MangaOCR, which targets in-page text recognition, and MangaVQA, a novel benchmark designed to evaluate contextual understanding through visual question answering. MangaVQA consists of 526 high-quality, manually constructed question-answer pairs, enabling reliable evaluation across diverse narrative and visual scenarios. Building on these benchmarks, we develop MangaLMM, a manga-specialized model finetuned from the open-source LMM Qwen2.5-VL to jointly handle both tasks. Through extensive experiments, including comparisons with proprietary models such as GPT-4o and Gemini 2.5, we assess how well LMMs understand manga. Our benchmark and model provide a comprehensive foundation for evaluating and advancing LMMs in the richly narrative domain of manga.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/manga109/mangalmm", "github_api_added_by": "auto" }, { "date": "2025-05-29", "arxiv_id": "2505.19051", "title": "Efficient Data Selection at Scale via Influence Distillation", "authors": [ "Mahdi Nikdan", "Vincent Cohen-Addad", "Dan Alistarh", "Vahab Mirrokni" ], "github": "", "project_page": "", "abstract": "Effective data selection is critical for efficient training of modern Large Language Models (LLMs). This paper introduces Influence Distillation, a novel, mathematically-justified framework for data selection that employs second-order information to optimally weight training samples. By distilling each sample's influence on a target distribution, our method assigns model-specific weights that are used to select training data for LLM fine-tuning, guiding it toward strong performance on the target domain. We derive these optimal weights for both Gradient Descent and Adam optimizers. To ensure scalability and reduce computational cost, we propose a landmark-based approximation: influence is precisely computed for a small subset of \"landmark\" samples and then efficiently propagated to all other samples to determine their weights. We validate Influence Distillation by applying it to instruction tuning on the Tulu V2 dataset, targeting a range of tasks including GSM8k, SQuAD, and MMLU, across several models from the Llama and Qwen families. Experiments show that Influence Distillation matches or outperforms state-of-the-art performance while achieving up to 3.5times faster selection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.20779", "title": "CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature", "authors": [ "Noy Sternlicht", "Tom Hope" ], "github": "https://github.com/noy-sternlicht/CHIMERA-KB", "project_page": "", "abstract": "A hallmark of human innovation is the process of recombination -- creating original ideas by integrating elements of existing mechanisms and concepts. In this work, we automatically mine the scientific literature and build CHIMERA: a large-scale knowledge base (KB) of recombination examples. CHIMERA can be used to empirically explore at scale how scientists recombine concepts and take inspiration from different areas, or to train supervised machine learning models that learn to predict new creative cross-domain directions. To build this KB, we present a novel information extraction task of extracting recombination from scientific paper abstracts, collect a high-quality corpus of hundreds of manually annotated abstracts, and use it to train an LLM-based extraction model. The model is applied to a large corpus of papers in the AI domain, yielding a KB of over 28K recombination examples. We analyze CHIMERA to explore the properties of recombination in different subareas of AI. Finally, we train a scientific hypothesis generation model using the KB, which predicts new recombination directions that real-world researchers find inspiring. Our data and code are available at https://github.cs.huji.ac.il/tomhope-lab/CHIMERA", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/noy-sternlicht/CHIMERA-KB", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.20444", "title": "HoPE: Hybrid of Position Embedding for Length Generalization in Vision-Language Models", "authors": [ "Haoran Li", "Yingjie Qin", "Baoyuan Ou", "Lai Xu", "Ruiwen Xu" ], "github": "https://github.com/hrlics/HoPE", "project_page": "", "abstract": "Vision-Language Models (VLMs) have made significant progress in multimodal tasks. However, their performance often deteriorates in long-context scenarios, particularly long videos. While Rotary Position Embedding (RoPE) has been widely adopted for length generalization in Large Language Models (LLMs), extending vanilla RoPE to capture the intricate spatial-temporal dependencies in videos remains an unsolved challenge. Existing methods typically allocate different frequencies within RoPE to encode 3D positional information. However, these allocation strategies mainly rely on heuristics, lacking in-depth theoretical analysis. In this paper, we first study how different allocation strategies impact the long-context capabilities of VLMs. Our analysis reveals that current multimodal RoPEs fail to reliably capture semantic similarities over extended contexts. To address this issue, we propose HoPE, a Hybrid of Position Embedding designed to improve the long-context capabilities of VLMs. HoPE introduces a hybrid frequency allocation strategy for reliable semantic modeling over arbitrarily long context, and a dynamic temporal scaling mechanism to facilitate robust learning and flexible inference across diverse context lengths. Extensive experiments across four video benchmarks on long video understanding and retrieval tasks demonstrate that HoPE consistently outperforms existing methods, confirming its effectiveness. Code is available at https://github.com/hrlics/HoPE.", "github_manual": "", "github_regex": "https://github.com/hrlics/HoPE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.18227", "title": "Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodality", "authors": [ "Zhenglun Kong", "Yize Li", "Fanhu Zeng", "Lei Xin", "Shvat Messica", "Xue Lin", "Pu Zhao", "Manolis Kellis", "Hao Tang", "Marinka Zitnik" ], "github": "", "project_page": "", "abstract": "In Transformer architectures, tokens\\textemdash discrete units derived from raw data\\textemdash are formed by segmenting inputs into fixed-length chunks. Each token is then mapped to an embedding, enabling parallel attention computations while preserving the input's essential information. Due to the quadratic computational complexity of transformer self-attention mechanisms, token reduction has primarily been used as an efficiency strategy. This is especially true in single vision and language domains, where it helps balance computational costs, memory usage, and inference latency. Despite these advances, this paper argues that token reduction should transcend its traditional efficiency-oriented role in the era of large generative models. Instead, we position it as a fundamental principle in generative modeling, critically influencing both model architecture and broader applications. Specifically, we contend that across vision, language, and multimodal systems, token reduction can: (i) facilitate deeper multimodal integration and alignment, (ii) mitigate \"overthinking\" and hallucinations, (iii) maintain coherence over long inputs, and (iv) enhance training stability, etc. We reframe token reduction as more than an efficiency measure. By doing so, we outline promising future directions, including algorithm design, reinforcement learning-guided token reduction, token optimization for in-context learning, and broader ML and scientific domains. We highlight its potential to drive new model architectures and learning strategies that improve robustness, increase interpretability, and better align with the objectives of generative modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22664", "title": "Zero-Shot Vision Encoder Grafting via LLM Surrogates", "authors": [ "Kaiyu Yue", "Vasu Singla", "Menglin Jia", "John Kirchenbauer", "Rifaa Qadri", "Zikui Cai", "Abhinav Bhatele", "Furong Huang", "Tom Goldstein" ], "github": "", "project_page": "", "abstract": "Vision language models (VLMs) typically pair a modestly sized vision encoder with a large language model (LLM), e.g., Llama-70B, making the decoder the primary computational burden during training. To reduce costs, a potential promising strategy is to first train the vision encoder using a small language model before transferring it to the large one. We construct small \"surrogate models\" that share the same embedding space and representation language as the large target LLM by directly inheriting its shallow layers. Vision encoders trained on the surrogate can then be directly transferred to the larger model, a process we call zero-shot grafting -- when plugged directly into the full-size target LLM, the grafted pair surpasses the encoder-surrogate pair and, on some benchmarks, even performs on par with full decoder training with the target LLM. Furthermore, our surrogate training approach reduces overall VLM training costs by ~45% when using Llama-70B as the decoder.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.20589", "title": "Prot2Token: A Unified Framework for Protein Modeling via Next-Token Prediction", "authors": [ "Mahdi Pourmirzaei", "Farzaneh Esmaili", "Salhuldin Alqarghuli", "Mohammadreza Pourmirzaei", "Ye Han", "Kai Chen", "Mohsen Rezaei", "Duolin Wang", "Dong Xu" ], "github": "https://github.com/mahdip72/prot2token", "project_page": "", "abstract": "The diverse nature of protein prediction tasks has traditionally necessitated specialized models, hindering the development of broadly applicable and computationally efficient Protein Language Models (PLMs). In this work, we introduce Prot2Token, a unified framework that overcomes these challenges by converting a wide spectrum of protein-related predictions, from sequence-level properties and residue-specific attributes to complex inter-protein interactions, into a standardized next-token prediction format. At its core, Prot2Token employs an autoregressive decoder, conditioned on embeddings from pre-trained protein encoders and guided by learnable task tokens, to perform diverse predictions. This architecture uniquely facilitates multi-task learning, enabling a single model to master numerous tasks with improved efficiency. We present extensive experimental validation across a variety of benchmarks, demonstrating Prot2Tokens strong predictive power in different types of protein-prediction tasks. Key results include significant speedups (e.g., near 1000x over AlphaFold2 with MSA) and performance often matching or exceeding specialized approaches. Beyond that, we introduce an auxiliary self-supervised decoder pre-training approach to improve spatially sensitive task performance. Prot2Token thus offers a significant step towards a versatile, high-throughput paradigm for protein modeling, promising to accelerate biological discovery and the development of novel therapeutics. The code is available at https://github.com/mahdip72/prot2token .", "github_manual": "", "github_regex": "https://github.com/mahdip72/prot2token", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.18366", "title": "Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems", "authors": [ "Hansa Meghwani", "Amit Agarwal", "Priyaranjan Pattnayak", "Hitesh Laxmichand Patel", "Srikant Panda" ], "github": "", "project_page": "", "abstract": "Enterprise search systems often struggle to retrieve accurate, domain-specific information due to semantic mismatches and overlapping terminologies. These issues can degrade the performance of downstream applications such as knowledge management, customer support, and retrieval-augmented generation agents. To address this challenge, we propose a scalable hard-negative mining framework tailored specifically for domain-specific enterprise data. Our approach dynamically selects semantically challenging but contextually irrelevant documents to enhance deployed re-ranking models. Our method integrates diverse embedding models, performs dimensionality reduction, and uniquely selects hard negatives, ensuring computational efficiency and semantic precision. Evaluation on our proprietary enterprise corpus (cloud services domain) demonstrates substantial improvements of 15\\% in MRR@3 and 19\\% in MRR@10 compared to state-of-the-art baselines and other negative sampling techniques. Further validation on public domain-specific datasets (FiQA, Climate Fever, TechQA) confirms our method's generalizability and readiness for real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.17330", "title": "FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich Document Understanding", "authors": [ "Amit Agarwal", "Srikant Panda", "Kulbhushan Pachauri" ], "github": "https://github.com/oracle-samples/fs-dag", "project_page": "", "abstract": "In this work, we propose Few Shot Domain Adapting Graph (FS-DAG), a scalable and efficient model architecture for visually rich document understanding (VRDU) in few-shot settings. FS-DAG leverages domain-specific and language/vision specific backbones within a modular framework to adapt to diverse document types with minimal data. The model is robust to practical challenges such as handling OCR errors, misspellings, and domain shifts, which are critical in real-world deployments. FS-DAG is highly performant with less than 90M parameters, making it well-suited for complex real-world applications for Information Extraction (IE) tasks where computational resources are limited. We demonstrate FS-DAG's capability through extensive experiments for information extraction task, showing significant improvements in convergence speed and performance compared to state-of-the-art methods. Additionally, this work highlights the ongoing progress in developing smaller, more efficient models that do not compromise on performance. Code : https://github.com/oracle-samples/fs-dag", "github_manual": "", "github_regex": "https://github.com/oracle-samples/fs-dag", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.21862", "title": "Towards Scalable Language-Image Pre-training for 3D Medical Imaging", "authors": [ "Chenhui Zhao", "Yiwei Lyu", "Asadur Chowdury", "Edward Harake", "Akhil Kondepudi", "Akshay Rao", "Xinhai Hou", "Honglak Lee", "Todd Hollon" ], "github": "https://github.com/Zch0414/hlip", "project_page": "", "abstract": "Language-image pre-training has demonstrated strong performance in 2D medical imaging, but its success in 3D modalities such as CT and MRI remains limited due to the high computational demands of volumetric data, which pose a significant barrier to training on large-scale, uncurated clinical studies. In this study, we introduce Hierarchical attention for Language-Image Pre-training (HLIP), a scalable pre-training framework for 3D medical imaging. HLIP adopts a lightweight hierarchical attention mechanism inspired by the natural hierarchy of radiology data: slice, scan, and study. This mechanism exhibits strong generalizability, e.g., +4.3% macro AUC on the Rad-ChestCT benchmark when pre-trained on CT-RATE. Moreover, the computational efficiency of HLIP enables direct training on uncurated datasets. Trained on 220K patients with 3.13 million scans for brain MRI and 240K patients with 1.44 million scans for head CT, HLIP achieves state-of-the-art performance, e.g., +32.4% balanced ACC on the proposed publicly available brain MRI benchmark Pub-Brain-5; +1.4% and +6.9% macro AUC on head CT benchmarks RSNA and CQ500, respectively. These results demonstrate that, with HLIP, directly pre-training on uncurated clinical datasets is a scalable and effective direction for language-image pre-training in 3D medical imaging. The code is available at https://github.com/Zch0414/hlip", "github_manual": "", "github_regex": "https://github.com/Zch0414/hlip", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.18882", "title": "Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent Approach", "authors": [ "Yuchen Wu", "Edward Sun", "Kaijie Zhu", "Jianxun Lian", "Jose Hernandez-Orallo", "Aylin Caliskan", "Jindong Wang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) typically generate identical or similar responses for all users given the same prompt, posing serious safety risks in high-stakes applications where user vulnerabilities differ widely. Existing safety evaluations primarily rely on context-independent metrics - such as factuality, bias, or toxicity - overlooking the fact that the same response may carry divergent risks depending on the user's background or condition. We introduce personalized safety to fill this gap and present PENGUIN - a benchmark comprising 14,000 scenarios across seven sensitive domains with both context-rich and context-free variants. Evaluating six leading LLMs, we demonstrate that personalized user information significantly improves safety scores by 43.2%, confirming the effectiveness of personalization in safety alignment. However, not all context attributes contribute equally to safety enhancement. To address this, we develop RAISE - a training-free, two-stage agent framework that strategically acquires user-specific background. RAISE improves safety scores by up to 31.6% over six vanilla LLMs, while maintaining a low interaction cost of just 2.7 user queries on average. Our findings highlight the importance of selective information gathering in safety-critical domains and offer a practical solution for personalizing LLM responses without model retraining. This work establishes a foundation for safety research that adapts to individual user contexts rather than assuming a universal harm standard.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22642", "title": "FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control", "authors": [ "Younggyo Seo", "Carmelo Sferrazza", "Haoran Geng", "Michal Nauman", "Zhao-Heng Yin", "Pieter Abbeel" ], "github": "https://github.com/younggyoseo/FastTD3", "project_page": "", "abstract": "Reinforcement learning (RL) has driven significant progress in robotics, but its complexity and long training times remain major bottlenecks. In this report, we introduce FastTD3, a simple, fast, and capable RL algorithm that significantly speeds up training for humanoid robots in popular suites such as HumanoidBench, IsaacLab, and MuJoCo Playground. Our recipe is remarkably simple: we train an off-policy TD3 agent with several modifications -- parallel simulation, large-batch updates, a distributional critic, and carefully tuned hyperparameters. FastTD3 solves a range of HumanoidBench tasks in under 3 hours on a single A100 GPU, while remaining stable during training. We also provide a lightweight and easy-to-use implementation of FastTD3 to accelerate RL research in robotics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/younggyoseo/FastTD3", "github_api_added_by": "auto" }, { "date": "2025-05-29", "arxiv_id": "2505.11821", "title": "Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Credit Assignment", "authors": [ "Siliang Zeng", "Quan Wei", "William Brown", "Oana Frunza", "Yuriy Nevmyvaka", "Mingyi Hong" ], "github": "", "project_page": "", "abstract": "This paper investigates approaches to enhance the reasoning capabilities of Large Language Model (LLM) agents using Reinforcement Learning (RL). Specifically, we focus on multi-turn tool-use scenarios, which can be naturally modeled as Markov Decision Processes (MDPs). While existing approaches often train multi-turn LLM agents with trajectory-level advantage estimation in bandit settings, they struggle with turn-level credit assignment across multiple decision steps, limiting their performance on multi-turn reasoning tasks. To address this, we introduce a fine-grained turn-level advantage estimation strategy to enable more precise credit assignment in multi-turn agent interactions. The strategy is general and can be incorporated into various RL algorithms such as Group Relative Preference Optimization (GRPO). Our experimental evaluation on multi-turn reasoning and search-based tool-use tasks with GRPO implementations highlights the effectiveness of the MDP framework and the turn-level credit assignment in advancing the multi-turn reasoning capabilities of LLM agents in complex decision-making settings. Our method achieves 100% success in tool execution and 50% accuracy in exact answer matching, significantly outperforming baselines, which fail to invoke tools and achieve only 20-30% exact match accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22586", "title": "Precise In-Parameter Concept Erasure in Large Language Models", "authors": [ "Yoav Gur-Arieh", "Clara Suslik", "Yihuai Hong", "Fazl Barez", "Mor Geva" ], "github": "https://github.com/yoavgur/PISCES", "project_page": "", "abstract": "Large language models (LLMs) often acquire knowledge during pretraining that is undesirable in downstream deployments, e.g., sensitive information or copyrighted content. Existing approaches for removing such knowledge rely on fine-tuning, training low-rank adapters or fact-level editing, but these are either too coarse, too shallow, or ineffective. In this work, we propose PISCES (Precise In-parameter Suppression for Concept EraSure), a novel framework for precisely erasing entire concepts from model parameters by directly editing directions that encode them in parameter space. PISCES uses a disentangler model to decompose MLP vectors into interpretable features, identifies those associated with a target concept using automated interpretability techniques, and removes them from model parameters. Experiments on Gemma 2 and Llama 3.1 over various concepts show that PISCES achieves modest gains in efficacy over leading erasure methods, reducing accuracy on the target concept to as low as 7.7%, while dramatically improving erasure specificity (by up to 31%) and robustness (by up to 38%). Overall, these results demonstrate that feature-based in-parameter editing enables a more precise and reliable approach for removing conceptual knowledge in language models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yoavgur/PISCES", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.18931", "title": "Can Large Language Models Infer Causal Relationships from Real-World Text?", "authors": [ "Ryan Saklad", "Aman Chadha", "Oleg Pavlov", "Raha Moraffah" ], "github": "", "project_page": "", "abstract": "Understanding and inferring causal relationships from texts is a core aspect of human cognition and is essential for advancing large language models (LLMs) towards artificial general intelligence. Existing work primarily focuses on synthetically generated texts which involve simple causal relationships explicitly mentioned in the text. This fails to reflect the complexities of real-world tasks. In this paper, we investigate whether LLMs are capable of inferring causal relationships from real-world texts. We develop a benchmark drawn from real-world academic literature which includes diverse texts with respect to length, complexity of relationships (different levels of explicitness, number of events, and causal relationships), and domains and sub-domains. To the best of our knowledge, our benchmark is the first-ever real-world dataset for this task. Our experiments on state-of-the-art LLMs evaluated on our proposed benchmark demonstrate significant challenges, with the best-performing model achieving an average F1 score of only 0.477. Analysis reveals common pitfalls: difficulty with implicitly stated information, in distinguishing relevant causal factors from surrounding contextual details, and with connecting causally relevant information spread across lengthy textual passages. By systematically characterizing these deficiencies, our benchmark offers targeted insights for further research into advancing LLM causal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.21649", "title": "Right Side Up? Disentangling Orientation Understanding in MLLMs with Fine-grained Multi-axis Perception Tasks", "authors": [ "Keanu Nichols", "Nazia Tasnim", "Yan Yuting", "Nicholas Ikechukwu", "Elva Zou", "Deepti Ghadiyaram", "Bryan Plummer" ], "github": "", "project_page": "", "abstract": "Object orientation understanding represents a fundamental challenge in visual perception critical for applications like robotic manipulation and augmented reality. Current vision-language benchmarks fail to isolate this capability, often conflating it with positional relationships and general scene understanding. We introduce DORI (Discriminative Orientation Reasoning Intelligence), a comprehensive benchmark establishing object orientation perception as a primary evaluation target. DORI assesses four dimensions of orientation comprehension: frontal alignment, rotational transformations, relative directional relationships, and canonical orientation understanding. Through carefully curated tasks from 11 datasets spanning 67 object categories across synthetic and real-world scenarios, DORI provides insights on how multi-modal systems understand object orientations. Our evaluation of 15 state-of-the-art vision-language models reveals critical limitations: even the best models achieve only 54.2% accuracy on coarse tasks and 33.0% on granular orientation judgments, with performance deteriorating for tasks requiring reference frame shifts or compound rotations. These findings demonstrate the need for dedicated orientation representation mechanisms, as models show systematic inability to perform precise angular estimations, track orientation changes across viewpoints, and understand compound rotations - suggesting limitations in their internal 3D spatial representations. As the first diagnostic framework specifically designed for orientation awareness in multimodal systems, DORI offers implications for improving robotic control, 3D scene reconstruction, and human-AI interaction in physical environments. DORI data: https://huggingface.co/datasets/appledora/DORI-Benchmark", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.22960", "title": "Revisiting Multi-Agent Debate as Test-Time Scaling: A Systematic Study of Conditional Effectiveness", "authors": [ "Yongjin Yang", "Euiin Yi", "Jongwoo Ko", "Kimin Lee", "Zhijing Jin", "Se-Young Yun" ], "github": "", "project_page": "", "abstract": "The remarkable growth in large language model (LLM) capabilities has spurred exploration into multi-agent systems, with debate frameworks emerging as a promising avenue for enhanced problem-solving. These multi-agent debate (MAD) approaches, where agents collaboratively present, critique, and refine arguments, potentially offer improved reasoning, robustness, and diverse perspectives over monolithic models. Despite prior studies leveraging MAD, a systematic understanding of its effectiveness compared to self-agent methods, particularly under varying conditions, remains elusive. This paper seeks to fill this gap by conceptualizing MAD as a test-time computational scaling technique, distinguished by collaborative refinement and diverse exploration capabilities. We conduct a comprehensive empirical investigation comparing MAD with strong self-agent test-time scaling baselines on mathematical reasoning and safety-related tasks. Our study systematically examines the influence of task difficulty, model scale, and agent diversity on MAD's performance. Key findings reveal that, for mathematical reasoning, MAD offers limited advantages over self-agent scaling but becomes more effective with increased problem difficulty and decreased model capability, while agent diversity shows little benefit. Conversely, for safety tasks, MAD's collaborative refinement can increase vulnerability, but incorporating diverse agent configurations facilitates a gradual reduction in attack success through the collaborative refinement process. We believe our findings provide critical guidance for the future development of more effective and strategically deployed MAD systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.23727", "title": "PixelThink: Towards Efficient Chain-of-Pixel Reasoning", "authors": [ "Song Wang", "Gongfan Fang", "Lingdong Kong", "Xiangtai Li", "Jianyun Xu", "Sheng Yang", "Qiang Li", "Jianke Zhu", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Existing reasoning segmentation approaches typically fine-tune multimodal large language models (MLLMs) using image-text pairs and corresponding mask labels. However, they exhibit limited generalization to out-of-distribution scenarios without an explicit reasoning process. Although recent efforts leverage reinforcement learning through group-relative policy optimization (GRPO) to enhance reasoning ability, they often suffer from overthinking - producing uniformly verbose reasoning chains irrespective of task complexity. This results in elevated computational costs and limited control over reasoning quality. To address this problem, we propose PixelThink, a simple yet effective scheme that integrates externally estimated task difficulty and internally measured model uncertainty to regulate reasoning generation within a reinforcement learning paradigm. The model learns to compress reasoning length in accordance with scene complexity and predictive confidence. To support comprehensive evaluation, we introduce ReasonSeg-Diff, an extended benchmark with annotated reasoning references and difficulty scores, along with a suite of metrics designed to assess segmentation accuracy, reasoning quality, and efficiency jointly. Experimental results demonstrate that the proposed approach improves both reasoning efficiency and overall segmentation performance. Our work contributes novel perspectives towards efficient and interpretable multimodal understanding. The code and model will be publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-29", "arxiv_id": "2505.12000", "title": "IQBench: How \"Smart'' Are Vision-Language Models? A Study with Human IQ Tests", "authors": [ "Tan-Hanh Pham", "Phu-Vinh Nguyen", "Dang The Hung", "Bui Trong Duong", "Vu Nguyen Thanh", "Chris Ngo", "Tri Quang Truong", "Truong-Son Hy" ], "github": "", "project_page": "", "abstract": "Although large Vision-Language Models (VLMs) have demonstrated remarkable performance in a wide range of multimodal tasks, their true reasoning capabilities on human IQ tests remain underexplored. To advance research on the fluid intelligence of VLMs, we introduce **IQBench**, a new benchmark designed to evaluate VLMs on standardized visual IQ tests. We focus on evaluating the reasoning capabilities of VLMs, which we argue are more important than the accuracy of the final prediction. **Our benchmark is visually centric, minimizing the dependence on unnecessary textual content**, thus encouraging models to derive answers primarily from image-based information rather than learned textual knowledge. To this end, we manually collected and annotated 500 visual IQ questions to **prevent unintentional data leakage during training**. Unlike prior work that focuses primarily on the accuracy of the final answer, we evaluate the reasoning ability of the models by assessing their explanations and the patterns used to solve each problem, along with the accuracy of the final prediction and human evaluation. Our experiments show that there are substantial performance disparities between tasks, with models such as `o4-mini`, `gemini-2.5-flash`, and `claude-3.7-sonnet` achieving the highest average accuracies of 0.615, 0.578, and 0.548, respectively. However, all models struggle with 3D spatial and anagram reasoning tasks, highlighting significant limitations in current VLMs' general reasoning abilities. In terms of reasoning scores, `o4-mini`, `gemini-2.5-flash`, and `claude-3.7-sonnet` achieved top averages of 0.696, 0.586, and 0.516, respectively. These results highlight inconsistencies between the reasoning processes of the models and their final answers, emphasizing the importance of evaluating the accuracy of the reasoning in addition to the final predictions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.18087", "title": "CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays", "authors": [ "Hyungyung Lee", "Geon Choi", "Jung-Oh Lee", "Hangyul Yoon", "Hyuk Gi Hong", "Edward Choi" ], "github": "https://github.com/ttumyche/CXReasonBench", "project_page": "", "abstract": "Recent progress in Large Vision-Language Models (LVLMs) has enabled promising applications in medical tasks, such as report generation and visual question answering. However, existing benchmarks focus mainly on the final diagnostic answer, offering limited insight into whether models engage in clinically meaningful reasoning. To address this, we present CheXStruct and CXReasonBench, a structured pipeline and benchmark built on the publicly available MIMIC-CXR-JPG dataset. CheXStruct automatically derives a sequence of intermediate reasoning steps directly from chest X-rays, such as segmenting anatomical regions, deriving anatomical landmarks and diagnostic measurements, computing diagnostic indices, and applying clinical thresholds. CXReasonBench leverages this pipeline to evaluate whether models can perform clinically valid reasoning steps and to what extent they can learn from structured guidance, enabling fine-grained and transparent assessment of diagnostic reasoning. The benchmark comprises 18,988 QA pairs across 12 diagnostic tasks and 1,200 cases, each paired with up to 4 visual inputs, and supports multi-path, multi-stage evaluation including visual grounding via anatomical region selection and diagnostic measurements. Even the strongest of 10 evaluated LVLMs struggle with structured reasoning and generalization, often failing to link abstract knowledge with anatomically grounded visual interpretation. The code is available at https://github.com/ttumyche/CXReasonBench", "github_manual": "", "github_regex": "https://github.com/ttumyche/CXReasonBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23693", "title": "VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos", "authors": [ "Tingyu Song", "Tongyan Hu", "Guo Gan", "Yilun Zhao" ], "github": "https://github.com/HumanSignal/label-studio", "project_page": "", "abstract": "MLLMs have been widely studied for video question answering recently. However, most existing assessments focus on natural videos, overlooking synthetic videos, such as AI-generated content (AIGC). Meanwhile, some works in video generation rely on MLLMs to evaluate the quality of generated videos, but the capabilities of MLLMs on interpreting AIGC videos remain largely underexplored. To address this, we propose a new benchmark, VF-Eval, which introduces four tasks-coherence validation, error awareness, error type detection, and reasoning evaluation-to comprehensively evaluate the abilities of MLLMs on AIGC videos. We evaluate 13 frontier MLLMs on VF-Eval and find that even the best-performing model, GPT-4.1, struggles to achieve consistently good performance across all tasks. This highlights the challenging nature of our benchmark. Additionally, to investigate the practical applications of VF-Eval in improving video generation, we conduct an experiment, RePrompt, demonstrating that aligning MLLMs more closely with human feedback can benefit video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HumanSignal/label-studio", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22653", "title": "The Climb Carves Wisdom Deeper Than the Summit: On the Noisy Rewards in Learning to Reason", "authors": [ "Ang Lv", "Ruobing Xie", "Xingwu Sun", "Zhanhui Kang", "Rui Yan" ], "github": "https://github.com/trestad/Noisy-Rewards-in-Learning-to-Reason", "project_page": "", "abstract": "Recent studies on post-training large language models (LLMs) for reasoning through reinforcement learning (RL) typically focus on tasks that can be accurately verified and rewarded, such as solving math problems. In contrast, our research investigates the impact of reward noise, a more practical consideration for real-world scenarios involving the post-training of LLMs using reward models. We found that LLMs demonstrate strong robustness to substantial reward noise. For example, manually flipping 40% of the reward function's outputs in math tasks still allows a Qwen-2.5-7B model to achieve rapid convergence, improving its performance on math tasks from 5% to 72%, compared to the 75% accuracy achieved by a model trained with noiseless rewards. Surprisingly, by only rewarding the appearance of key reasoning phrases (namely reasoning pattern reward, RPR), such as ``first, I need to''-without verifying the correctness of answers, the model achieved peak downstream performance (over 70% accuracy for Qwen-2.5-7B) comparable to models trained with strict correctness verification and accurate rewards. Recognizing the importance of the reasoning process over the final results, we combined RPR with noisy reward models. RPR helped calibrate the noisy reward models, mitigating potential false negatives and enhancing the LLM's performance on open-ended tasks. These findings suggest the importance of improving models' foundational abilities during the pre-training phase while providing insights for advancing post-training techniques. Our code and scripts are available at https://github.com/trestad/Noisy-Rewards-in-Learning-to-Reason.", "github_manual": "", "github_regex": "https://github.com/trestad/Noisy-Rewards-in-Learning-to-Reason", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23747", "title": "Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence", "authors": [ "Diankun Wu", "Fangfu Liu", "Yi-Hsin Hung", "Yueqi Duan" ], "github": "", "project_page": "", "abstract": "Recent advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced performance on 2D visual tasks. However, improving their spatial intelligence remains a challenge. Existing 3D MLLMs always rely on additional 3D or 2.5D data to incorporate spatial awareness, restricting their utility in scenarios with only 2D inputs, such as images or videos. In this paper, we present Spatial-MLLM, a novel framework for visual-based spatial reasoning from purely 2D observations. Unlike conventional video MLLMs which rely on CLIP-based visual encoders optimized for semantic understanding, our key insight is to unleash the strong structure prior from the feed-forward visual geometry foundation model. Specifically, we propose a dual-encoder architecture: a pretrained 2D visual encoder to extract semantic features, and a spatial encoder-initialized from the backbone of the visual geometry model-to extract 3D structure features. A connector then integrates both features into unified visual tokens for enhanced spatial understanding. Furthermore, we propose a space-aware frame sampling strategy at inference time, which selects the spatially informative frames of a video sequence, ensuring that even under limited token length, the model focuses on frames critical for spatial reasoning. Beyond architecture improvements, we construct the Spatial-MLLM-120k dataset and train the model on it using supervised fine-tuning and GRPO. Extensive experiments on various real-world datasets demonstrate that our spatial-MLLM achieves state-of-the-art performance in a wide range of visual-based spatial understanding and reasoning tasks. Project page: https://diankun-wu.github.io/Spatial-MLLM/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23762", "title": "ZeroGUI: Automating Online GUI Learning at Zero Human Cost", "authors": [ "Chenyu Yang", "Shiqian Su", "Shi Liu", "Xuan Dong", "Yue Yu", "Weijie Su", "Xuehui Wang", "Zhaoyang Liu", "Jinguo Zhu", "Hao Li", "Wenhai Wang", "Yu Qiao", "Xizhou Zhu", "Jifeng Dai" ], "github": "https://github.com/OpenGVLab/ZeroGUI", "project_page": "", "abstract": "The rapid advancement of large Vision-Language Models (VLMs) has propelled the development of pure-vision-based GUI Agents, capable of perceiving and operating Graphical User Interfaces (GUI) to autonomously fulfill user instructions. However, existing approaches usually adopt an offline learning framework, which faces two core limitations: (1) heavy reliance on high-quality manual annotations for element grounding and action supervision, and (2) limited adaptability to dynamic and interactive environments. To address these limitations, we propose ZeroGUI, a scalable, online learning framework for automating GUI Agent training at Zero human cost. Specifically, ZeroGUI integrates (i) VLM-based automatic task generation to produce diverse training goals from the current environment state, (ii) VLM-based automatic reward estimation to assess task success without hand-crafted evaluation functions, and (iii) two-stage online reinforcement learning to continuously interact with and learn from GUI environments. Experiments on two advanced GUI Agents (UI-TARS and Aguvis) demonstrate that ZeroGUI significantly boosts performance across OSWorld and AndroidLab environments. The code is available at https://github.com/OpenGVLab/ZeroGUI.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/ZeroGUI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23604", "title": "Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering", "authors": [ "Guangtao Zeng", "Maohao Shen", "Delin Chen", "Zhenting Qi", "Subhro Das", "Dan Gutfreund", "David Cox", "Gregory Wornell", "Wei Lu", "Zhang-Wei Hong", "Chuang Gan" ], "github": "https://github.com/satori-reasoning/satori-swe", "project_page": "", "abstract": "Language models (LMs) perform well on standardized coding benchmarks but struggle with real-world software engineering tasks such as resolving GitHub issues in SWE-Bench, especially when model parameters are less than 100B. While smaller models are preferable in practice due to their lower computational cost, improving their performance remains challenging. Existing approaches primarily rely on supervised fine-tuning (SFT) with high-quality data, which is expensive to curate at scale. An alternative is test-time scaling: generating multiple outputs, scoring them using a verifier, and selecting the best one. Although effective, this strategy often requires excessive sampling and costly scoring, limiting its practical application. We propose Evolutionary Test-Time Scaling (EvoScale), a sample-efficient method that treats generation as an evolutionary process. By iteratively refining outputs via selection and mutation, EvoScale shifts the output distribution toward higher-scoring regions, reducing the number of samples needed to find correct solutions. To reduce the overhead from repeatedly sampling and selection, we train the model to self-evolve using reinforcement learning (RL). Rather than relying on external verifiers at inference time, the model learns to self-improve the scores of its own generations across iterations. Evaluated on SWE-Bench-Verified, EvoScale enables our 32B model, Satori-SWE-32B, to match or exceed the performance of models with over 100B parameters while using a few samples. Code, data, and models will be fully open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/satori-reasoning/satori-swe", "github_api_added_by": "auto" }, { "date": "2025-05-30", "arxiv_id": "2505.23559", "title": "SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents", "authors": [ "Kunlun Zhu", "Jiaxun Zhang", "Ziheng Qi", "Nuoxing Shang", "Zijia Liu", "Peixuan Han", "Yue Su", "Haofei Yu", "Jiaxuan You" ], "github": "https://github.com/ulab-uiuc/SafeScientist.\nred{Warning:", "project_page": "", "abstract": "Recent advancements in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet concurrently raised critical ethical and safety concerns. To systematically address these challenges, we introduce SafeScientist, an innovative AI scientist framework explicitly designed to enhance safety and ethical responsibility in AI-driven scientific exploration. SafeScientist proactively refuses ethically inappropriate or high-risk tasks and rigorously emphasizes safety throughout the research process. To achieve comprehensive safety oversight, we integrate multiple defensive mechanisms, including prompt monitoring, agent-collaboration monitoring, tool-use monitoring, and an ethical reviewer component. Complementing SafeScientist, we propose SciSafetyBench, a novel benchmark specifically designed to evaluate AI safety in scientific contexts, comprising 240 high-risk scientific tasks across 6 domains, alongside 30 specially designed scientific tools and 120 tool-related risk tasks. Extensive experiments demonstrate that SafeScientist significantly improves safety performance by 35\\% compared to traditional AI scientist frameworks, without compromising scientific output quality. Additionally, we rigorously validate the robustness of our safety pipeline against diverse adversarial attack methods, further confirming the effectiveness of our integrated approach. The code and data will be available at https://github.com/ulab-uiuc/SafeScientist. red{Warning: this paper contains example data that may be offensive or harmful.}", "github_manual": "https://github.com/ulab-uiuc/SafeScientist.\nred{Warning:", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22961", "title": "ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind", "authors": [ "Peixuan Han", "Zijia Liu", "Jiaxuan You" ], "github": "https://github.com/ulab-uiuc/ToMAP", "project_page": "", "abstract": "Large language models (LLMs) have shown promising potential in persuasion, but existing works on training LLM persuaders are still preliminary. Notably, while humans are skilled in modeling their opponent's thoughts and opinions proactively and dynamically, current LLMs struggle with such Theory of Mind (ToM) reasoning, resulting in limited diversity and opponent awareness. To address this limitation, we introduce Theory of Mind Augmented Persuader (ToMAP), a novel approach for building more flexible persuader agents by incorporating two theory of mind modules that enhance the persuader's awareness and analysis of the opponent's mental state. Specifically, we begin by prompting the persuader to consider possible objections to the target central claim, and then use a text encoder paired with a trained MLP classifier to predict the opponent's current stance on these counterclaims. Our carefully designed reinforcement learning schema enables the persuader learns how to analyze opponent-related information and utilize it to generate more effective arguments. Experiments show that the ToMAP persuader, while containing only 3B parameters, outperforms much larger baselines, like GPT-4o, with a relative gain of 39.4% across multiple persuadee models and diverse corpora. Notably, ToMAP exhibits complex reasoning chains and reduced repetition during training, which leads to more diverse and effective arguments. The opponent-aware feature of ToMAP also makes it suitable for long conversations and enables it to employ more logical and opponent-aware strategies. These results underscore our method's effectiveness and highlight its potential for developing more persuasive language agents. Code is available at: https://github.com/ulab-uiuc/ToMAP.", "github_manual": "", "github_regex": "https://github.com/ulab-uiuc/ToMAP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23735", "title": "ATLAS: Learning to Optimally Memorize the Context at Test Time", "authors": [ "Ali Behrouz", "Zeman Li", "Praneeth Kacham", "Majid Daliri", "Yuan Deng", "Peilin Zhong", "Meisam Razaviyayn", "Vahab Mirrokni" ], "github": "", "project_page": "", "abstract": "Transformers have been established as the most popular backbones in sequence modeling, mainly due to their effectiveness in in-context retrieval tasks and the ability to learn at scale. Their quadratic memory and time complexity, however, bound their applicability in longer sequences and so has motivated researchers to explore effective alternative architectures such as modern recurrent neural networks (a.k.a long-term recurrent memory module). Despite their recent success in diverse downstream tasks, they struggle in tasks that requires long context understanding and extrapolation to longer sequences. We observe that these shortcomings come from three disjoint aspects in their design: (1) limited memory capacity that is bounded by the architecture of memory and feature mapping of the input; (2) online nature of update, i.e., optimizing the memory only with respect to the last input; and (3) less expressive management of their fixed-size memory. To enhance all these three aspects, we present ATLAS, a long-term memory module with high capacity that learns to memorize the context by optimizing the memory based on the current and past tokens, overcoming the online nature of long-term memory models. Building on this insight, we present a new family of Transformer-like architectures, called DeepTransformers, that are strict generalizations of the original Transformer architecture. Our experimental results on language modeling, common-sense reasoning, recall-intensive, and long-context understanding tasks show that ATLAS surpasses the performance of Transformers and recent linear recurrent models. ATLAS further improves the long context performance of Titans, achieving +80\\% accuracy in 10M context length of BABILong benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23621", "title": "Table-R1: Inference-Time Scaling for Table Reasoning", "authors": [ "Zheyuan Yang", "Lyuhao Chen", "Arman Cohan", "Yilun Zhao" ], "github": "", "project_page": "", "abstract": "In this work, we present the first study to explore inference-time scaling on table reasoning tasks. We develop and evaluate two post-training strategies to enable inference-time scaling: distillation from frontier model reasoning traces and reinforcement learning with verifiable rewards (RLVR). For distillation, we introduce a large-scale dataset of reasoning traces generated by DeepSeek-R1, which we use to fine-tune LLMs into the Table-R1-SFT model. For RLVR, we propose task-specific verifiable reward functions and apply the GRPO algorithm to obtain the Table-R1-Zero model. We evaluate our Table-R1-series models across diverse table reasoning tasks, including short-form QA, fact verification, and free-form QA. Notably, the Table-R1-Zero model matches or exceeds the performance of GPT-4.1 and DeepSeek-R1, while using only a 7B-parameter LLM. It also demonstrates strong generalization to out-of-domain datasets. Extensive ablation and qualitative analyses reveal the benefits of instruction tuning, model architecture choices, and cross-task generalization, as well as emergence of essential table reasoning skills during RL training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23585", "title": "On-Policy RL with Optimal Reward Baseline", "authors": [ "Yaru Hao", "Li Dong", "Xun Wu", "Shaohan Huang", "Zewen Chi", "Furu Wei" ], "github": "https://github.com/microsoft/LMOps/tree/main/opo", "project_page": "", "abstract": "Reinforcement learning algorithms are fundamental to align large language models with human preferences and to enhance their reasoning capabilities. However, current reinforcement learning algorithms often suffer from training instability due to loose on-policy constraints and computational inefficiency due to auxiliary models. In this work, we propose On-Policy RL with Optimal reward baseline (OPO), a novel and simplified reinforcement learning algorithm designed to address these challenges. OPO emphasizes the importance of exact on-policy training, which empirically stabilizes the training process and enhances exploration. Moreover, OPO introduces the optimal reward baseline that theoretically minimizes gradient variance. We evaluate OPO on mathematical reasoning benchmarks. The results demonstrate its superior performance and training stability without additional models or regularization terms. Furthermore, OPO achieves lower policy shifts and higher output entropy, encouraging more diverse and less repetitive responses. These results highlight OPO as a promising direction for stable and effective reinforcement learning in large language model alignment and reasoning tasks. The implementation is provided at https://github.com/microsoft/LMOps/tree/main/opo.", "github_manual": "", "github_regex": "https://github.com/microsoft/LMOps/tree/main/opo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23419", "title": "SWE-bench Goes Live!", "authors": [ "Linghao Zhang", "Shilin He", "Chaoyun Zhang", "Yu Kang", "Bowen Li", "Chengxing Xie", "Junhao Wang", "Maoquan Wang", "Yufan Huang", "Shengyu Fu", "Elsie Nallipogu", "Qingwei Lin", "Yingnong Dang", "Saravan Rajmohan", "Dongmei Zhang" ], "github": "", "project_page": "", "abstract": "The issue-resolving task, where a model generates patches to fix real-world bugs, has emerged as a critical benchmark for evaluating the capabilities of large language models (LLMs). While SWE-bench and its variants have become standard in this domain, they suffer from key limitations: they have not been updated since their initial releases, cover a narrow set of repositories, and depend heavily on manual effort for instance construction and environment setup. These factors hinder scalability and introduce risks of overfitting and data contamination. In this work, we present SWE-bench-Live, a live-updatable benchmark designed to overcome these challenges. Our initial release consists of 1,319 tasks derived from real GitHub issues created since 2024, spanning 93 repositories. Each task is accompanied by a dedicated Docker image to ensure reproducible execution. Central to our benchmark is \\method, an automated curation pipeline that streamlines the entire process from instance creation to environment setup, removing manual bottlenecks and enabling scalability and continuous updates. We evaluate a range of state-of-the-art agent frameworks and LLMs on SWE-bench-Live, revealing a substantial performance gap compared to static benchmarks like SWE-bench, even under controlled evaluation conditions. To better understand this discrepancy, we perform detailed analyses across repository origin, issue recency, and task difficulty. By providing a fresh, diverse, and executable benchmark grounded in live repository activity, SWE-bench-Live facilitates rigorous, contamination-resistant evaluation of LLMs and agents in dynamic, real-world software development settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23359", "title": "VideoReasonBench: Can MLLMs Perform Vision-Centric Complex Video Reasoning?", "authors": [ "Yuanxin Liu", "Kun Ouyang", "Haoning Wu", "Yi Liu", "Lin Sui", "Xinhao Li", "Yan Zhong", "Y. Charles", "Xinyu Zhou", "Xu Sun" ], "github": "https://github.com/llyx97/video_reason_bench", "project_page": "", "abstract": "Recent studies have shown that long chain-of-thought (CoT) reasoning can significantly enhance the performance of large language models (LLMs) on complex tasks. However, this benefit is yet to be demonstrated in the domain of video understanding, since most existing benchmarks lack the reasoning depth required to demonstrate the advantages of extended CoT chains. While recent efforts have proposed benchmarks aimed at video reasoning, the tasks are often knowledge-driven and do not rely heavily on visual content. To bridge this gap, we introduce VideoReasonBench, a benchmark designed to evaluate vision-centric, complex video reasoning. To ensure visual richness and high reasoning complexity, each video in VideoReasonBench depicts a sequence of fine-grained operations on a latent state that is only visible in part of the video. The questions evaluate three escalating levels of video reasoning skills: recalling observed visual information, inferring the content of latent states, and predicting information beyond the video. Under such task setting, models have to precisely recall multiple operations in the video, and perform step-by-step reasoning to get correct final answers for these questions. Using VideoReasonBench, we comprehensively evaluate 18 state-of-the-art multimodal LLMs (MLLMs), finding that most perform poorly on complex video reasoning, e.g., GPT-4o achieves only 6.9% accuracy, while the thinking-enhanced Gemini-2.5-Pro significantly outperforms others with 56.0% accuracy. Our investigations on \"test-time scaling\" further reveal that extended thinking budget, while offering none or minimal benefits on existing video benchmarks, is essential for improving the performance on VideoReasonBench.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/llyx97/video_reason_bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23742", "title": "MAGREF: Masked Guidance for Any-Reference Video Generation", "authors": [ "Yufan Deng", "Xun Guo", "Yuanyang Yin", "Jacob Zhiyuan Fang", "Yiding Yang", "Yizhi Wang", "Shenghai Yuan", "Angtian Wang", "Bo Liu", "Haibin Huang", "Chongyang Ma" ], "github": "https://github.com/MAGREF-Video/MAGREF", "project_page": "", "abstract": "Video generation has made substantial strides with the emergence of deep generative models, especially diffusion-based approaches. However, video generation based on multiple reference subjects still faces significant challenges in maintaining multi-subject consistency and ensuring high generation quality. In this paper, we propose MAGREF, a unified framework for any-reference video generation that introduces masked guidance to enable coherent multi-subject video synthesis conditioned on diverse reference images and a textual prompt. Specifically, we propose (1) a region-aware dynamic masking mechanism that enables a single model to flexibly handle various subject inference, including humans, objects, and backgrounds, without architectural changes, and (2) a pixel-wise channel concatenation mechanism that operates on the channel dimension to better preserve appearance features. Our model delivers state-of-the-art video generation quality, generalizing from single-subject training to complex multi-subject scenarios with coherent synthesis and precise control over individual subjects, outperforming existing open-source and commercial baselines. To facilitate evaluation, we also introduce a comprehensive multi-subject video benchmark. Extensive experiments demonstrate the effectiveness of our approach, paving the way for scalable, controllable, and high-fidelity multi-subject video synthesis. Code and model can be found at: https://github.com/MAGREF-Video/MAGREF", "github_manual": "", "github_regex": "https://github.com/MAGREF-Video/MAGREF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23745", "title": "To Trust Or Not To Trust Your Vision-Language Model's Prediction", "authors": [ "Hao Dong", "Moru Liu", "Jian Liang", "Eleni Chatzi", "Olga Fink" ], "github": "https://github.com/EPFL-IMOS/TrustVLM", "project_page": "", "abstract": "Vision-Language Models (VLMs) have demonstrated strong capabilities in aligning visual and textual modalities, enabling a wide range of applications in multimodal understanding and generation. While they excel in zero-shot and transfer learning scenarios, VLMs remain susceptible to misclassification, often yielding confident yet incorrect predictions. This limitation poses a significant risk in safety-critical domains, where erroneous predictions can lead to severe consequences. In this work, we introduce TrustVLM, a training-free framework designed to address the critical challenge of estimating when VLM's predictions can be trusted. Motivated by the observed modality gap in VLMs and the insight that certain concepts are more distinctly represented in the image embedding space, we propose a novel confidence-scoring function that leverages this space to improve misclassification detection. We rigorously evaluate our approach across 17 diverse datasets, employing 4 architectures and 2 VLMs, and demonstrate state-of-the-art performance, with improvements of up to 51.87% in AURC, 9.14% in AUROC, and 32.42% in FPR95 compared to existing baselines. By improving the reliability of the model without requiring retraining, TrustVLM paves the way for safer deployment of VLMs in real-world applications. The code will be available at https://github.com/EPFL-IMOS/TrustVLM.", "github_manual": "", "github_regex": "https://github.com/EPFL-IMOS/TrustVLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23606", "title": "Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model", "authors": [ "Qingyu Shi", "Jinbin Bai", "Zhuoran Zhao", "Wenhao Chai", "Kaidong Yu", "Jianzong Wu", "Shuangyong Song", "Yunhai Tong", "Xiangtai Li", "Xuelong Li", "Shuicheng Yan" ], "github": "https://github.com/M-E-AGI-Lab/Muddit", "project_page": "", "abstract": "Unified generation models aim to handle diverse tasks across modalities -- such as text generation, image generation, and vision-language reasoning -- within a single architecture and decoding paradigm. Autoregressive unified models suffer from slow inference due to sequential decoding, and non-autoregressive unified models suffer from weak generalization due to limited pretrained backbones. We introduce Muddit, a unified discrete diffusion transformer that enables fast and parallel generation across both text and image modalities. Unlike prior unified diffusion models trained from scratch, Muddit integrates strong visual priors from a pretrained text-to-image backbone with a lightweight text decoder, enabling flexible and high-quality multimodal generation under a unified architecture. Empirical results show that Muddit achieves competitive or superior performance compared to significantly larger autoregressive models in both quality and efficiency. The work highlights the potential of purely discrete diffusion, when equipped with strong visual priors, as a scalable and effective backbone for unified generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/M-E-AGI-Lab/Muddit", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23754", "title": "DeepTheorem: Advancing LLM Reasoning for Theorem Proving Through Natural Language and Reinforcement Learning", "authors": [ "Ziyin Zhang", "Jiahao Xu", "Zhiwei He", "Tian Liang", "Qiuzhi Liu", "Yansi Li", "Linfeng Song", "Zhengwen Liang", "Zhuosheng Zhang", "Rui Wang", "Zhaopeng Tu", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Theorem proving serves as a major testbed for evaluating complex reasoning abilities in large language models (LLMs). However, traditional automated theorem proving (ATP) approaches rely heavily on formal proof systems that poorly align with LLMs' strength derived from informal, natural language knowledge acquired during pre-training. In this work, we propose DeepTheorem, a comprehensive informal theorem-proving framework exploiting natural language to enhance LLM mathematical reasoning. DeepTheorem includes a large-scale benchmark dataset consisting of 121K high-quality IMO-level informal theorems and proofs spanning diverse mathematical domains, rigorously annotated for correctness, difficulty, and topic categories, accompanied by systematically constructed verifiable theorem variants. We devise a novel reinforcement learning strategy (RL-Zero) explicitly tailored to informal theorem proving, leveraging the verified theorem variants to incentivize robust mathematical inference. Additionally, we propose comprehensive outcome and process evaluation metrics examining proof correctness and the quality of reasoning steps. Extensive experimental analyses demonstrate DeepTheorem significantly improves LLM theorem-proving performance compared to existing datasets and supervised fine-tuning protocols, achieving state-of-the-art accuracy and reasoning quality. Our findings highlight DeepTheorem's potential to fundamentally advance automated informal theorem proving and mathematical exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22943", "title": "Can LLMs Deceive CLIP? Benchmarking Adversarial Compositionality of Pre-trained Multimodal Representation via Text Updates", "authors": [ "Jaewoo Ahn", "Heeseung Yun", "Dayoon Ko", "Gunhee Kim" ], "github": "", "project_page": "", "abstract": "While pre-trained multimodal representations (e.g., CLIP) have shown impressive capabilities, they exhibit significant compositional vulnerabilities leading to counterintuitive judgments. We introduce Multimodal Adversarial Compositionality (MAC), a benchmark that leverages large language models (LLMs) to generate deceptive text samples to exploit these vulnerabilities across different modalities and evaluates them through both sample-wise attack success rate and group-wise entropy-based diversity. To improve zero-shot methods, we propose a self-training approach that leverages rejection-sampling fine-tuning with diversity-promoting filtering, which enhances both attack success rate and sample diversity. Using smaller language models like Llama-3.1-8B, our approach demonstrates superior performance in revealing compositional vulnerabilities across various multimodal representations, including images, videos, and audios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.20199", "title": "Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking", "authors": [ "Pengxiang Li", "Shilin Yan", "Joey Tsai", "Renrui Zhang", "Ruichuan An", "Ziyu Guo", "Xiaowei Gao" ], "github": "https://github.com/pixeli99/A-CFG", "project_page": "", "abstract": "Classifier-Free Guidance (CFG) significantly enhances controllability in generative models by interpolating conditional and unconditional predictions. However, standard CFG often employs a static unconditional input, which can be suboptimal for iterative generation processes where model uncertainty varies dynamically. We introduce Adaptive Classifier-Free Guidance (A-CFG), a novel method that tailors the unconditional input by leveraging the model's instantaneous predictive confidence. At each step of an iterative (masked) diffusion language model, A-CFG identifies tokens in the currently generated sequence for which the model exhibits low confidence. These tokens are temporarily re-masked to create a dynamic, localized unconditional input. This focuses CFG's corrective influence precisely on areas of ambiguity, leading to more effective guidance. We integrate A-CFG into a state-of-the-art masked diffusion language model and demonstrate its efficacy. Experiments on diverse language generation benchmarks show that A-CFG yields substantial improvements over standard CFG, achieving, for instance, a 3.9 point gain on GPQA. Our work highlights the benefit of dynamically adapting guidance mechanisms to model uncertainty in iterative generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pixeli99/A-CFG", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23716", "title": "AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views", "authors": [ "Lihan Jiang", "Yucheng Mao", "Linning Xu", "Tao Lu", "Kerui Ren", "Yichen Jin", "Xudong Xu", "Mulin Yu", "Jiangmiao Pang", "Feng Zhao", "Dahua Lin", "Bo Dai" ], "github": "https://github.com/OpenRobotLab/AnySplat", "project_page": "", "abstract": "We introduce AnySplat, a feed forward network for novel view synthesis from uncalibrated image collections. In contrast to traditional neural rendering pipelines that demand known camera poses and per scene optimization, or recent feed forward methods that buckle under the computational weight of dense views, our model predicts everything in one shot. A single forward pass yields a set of 3D Gaussian primitives encoding both scene geometry and appearance, and the corresponding camera intrinsics and extrinsics for each input image. This unified design scales effortlessly to casually captured, multi view datasets without any pose annotations. In extensive zero shot evaluations, AnySplat matches the quality of pose aware baselines in both sparse and dense view scenarios while surpassing existing pose free approaches. Moreover, it greatly reduce rendering latency compared to optimization based neural fields, bringing real time novel view synthesis within reach for unconstrained capture settings.Project page: https://city-super.github.io/anysplat/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenRobotLab/AnySplat", "github_api_added_by": "auto" }, { "date": "2025-05-30", "arxiv_id": "2505.22421", "title": "GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control", "authors": [ "Anthony Chen", "Wenzhao Zheng", "Yida Wang", "Xueyang Zhang", "Kun Zhan", "Peng Jia", "Kurt Keutzer", "Shanghang Zhang" ], "github": "", "project_page": "", "abstract": "Recent advancements in world models have revolutionized dynamic environment simulation, allowing systems to foresee future states and assess potential actions. In autonomous driving, these capabilities help vehicles anticipate the behavior of other road users, perform risk-aware planning, accelerate training in simulation, and adapt to novel scenarios, thereby enhancing safety and reliability. Current approaches exhibit deficiencies in maintaining robust 3D geometric consistency or accumulating artifacts during occlusion handling, both critical for reliable safety assessment in autonomous navigation tasks. To address this, we introduce GeoDrive, which explicitly integrates robust 3D geometry conditions into driving world models to enhance spatial understanding and action controllability. Specifically, we first extract a 3D representation from the input frame and then obtain its 2D rendering based on the user-specified ego-car trajectory. To enable dynamic modeling, we propose a dynamic editing module during training to enhance the renderings by editing the positions of the vehicles. Extensive experiments demonstrate that our method significantly outperforms existing models in both action accuracy and 3D spatial awareness, leading to more realistic, adaptable, and reliable scene modeling for safer autonomous driving. Additionally, our model can generalize to novel trajectories and offers interactive scene editing capabilities, such as object editing and object trajectory control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23660", "title": "D-AR: Diffusion via Autoregressive Models", "authors": [ "Ziteng Gao", "Mike Zheng Shou" ], "github": "https://github.com/showlab/D-AR", "project_page": "", "abstract": "This paper presents Diffusion via Autoregressive models (D-AR), a new paradigm recasting the image diffusion process as a vanilla autoregressive procedure in the standard next-token-prediction fashion. We start by designing the tokenizer that converts images into sequences of discrete tokens, where tokens in different positions can be decoded into different diffusion denoising steps in the pixel space. Thanks to the diffusion properties, these tokens naturally follow a coarse-to-fine order, which directly lends itself to autoregressive modeling. Therefore, we apply standard next-token prediction on these tokens, without modifying any underlying designs (either causal masks or training/inference strategies), and such sequential autoregressive token generation directly mirrors the diffusion procedure in image space. That is, once the autoregressive model generates an increment of tokens, we can directly decode these tokens into the corresponding diffusion denoising step in the streaming manner. Our pipeline naturally reveals several intriguing properties, for example, it supports consistent previews when generating only a subset of tokens and enables zero-shot layout-controlled synthesis. On the standard ImageNet benchmark, our method achieves 2.09 FID using a 775M Llama backbone with 256 discrete tokens. We hope our work can inspire future research on unified autoregressive architectures of visual synthesis, especially with large language models. Code and models will be available at https://github.com/showlab/D-AR", "github_manual": "", "github_regex": "https://github.com/showlab/D-AR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23758", "title": "LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers", "authors": [ "Yusuf Dalva", "Hidir Yesiltepe", "Pinar Yanardag" ], "github": "", "project_page": "", "abstract": "We introduce LoRAShop, the first framework for multi-concept image editing with LoRA models. LoRAShop builds on a key observation about the feature interaction patterns inside Flux-style diffusion transformers: concept-specific transformer features activate spatially coherent regions early in the denoising process. We harness this observation to derive a disentangled latent mask for each concept in a prior forward pass and blend the corresponding LoRA weights only within regions bounding the concepts to be personalized. The resulting edits seamlessly integrate multiple subjects or styles into the original scene while preserving global context, lighting, and fine details. Our experiments demonstrate that LoRAShop delivers better identity preservation compared to baselines. By eliminating retraining and external constraints, LoRAShop turns personalized diffusion models into a practical `photoshop-with-LoRAs' tool and opens new avenues for compositional visual storytelling and rapid creative iteration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.17818", "title": "PatientSim: A Persona-Driven Simulator for Realistic Doctor-Patient Interactions", "authors": [ "Daeun Kyung", "Hyunseung Chung", "Seongsu Bae", "Jiho Kim", "Jae Ho Sohn", "Taerim Kim", "Soo Kyung Kim", "Edward Choi" ], "github": "https://github.com/dek924/PatientSim", "project_page": "", "abstract": "Doctor-patient consultations require multi-turn, context-aware communication tailored to diverse patient personas. Training or evaluating doctor LLMs in such settings requires realistic patient interaction systems. However, existing simulators often fail to reflect the full range of personas seen in clinical practice. To address this, we introduce PatientSim, a patient simulator that generates realistic and diverse patient personas for clinical scenarios, grounded in medical expertise. PatientSim operates using: 1) clinical profiles, including symptoms and medical history, derived from real-world data in the MIMIC-ED and MIMIC-IV datasets, and 2) personas defined by four axes: personality, language proficiency, medical history recall level, and cognitive confusion level, resulting in 37 unique combinations. We evaluated eight LLMs for factual accuracy and persona consistency. The top-performing open-source model, Llama 3.3, was validated by four clinicians to confirm the robustness of our framework. As an open-source, customizable platform, PatientSim provides a reproducible and scalable solution that can be customized for specific training needs. Offering a privacy-compliant environment, it serves as a robust testbed for evaluating medical dialogue systems across diverse patient presentations and shows promise as an educational tool for healthcare.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dek924/PatientSim", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23625", "title": "ZeroSep: Separate Anything in Audio with Zero Training", "authors": [ "Chao Huang", "Yuesheng Ma", "Junxuan Huang", "Susan Liang", "Yunlong Tang", "Jing Bi", "Wenqiang Liu", "Nima Mesgarani", "Chenliang Xu" ], "github": "https://github.com/WikiChao/ZeroSep", "project_page": "", "abstract": "Audio source separation is fundamental for machines to understand complex acoustic environments and underpins numerous audio applications. Current supervised deep learning approaches, while powerful, are limited by the need for extensive, task-specific labeled data and struggle to generalize to the immense variability and open-set nature of real-world acoustic scenes. Inspired by the success of generative foundation models, we investigate whether pre-trained text-guided audio diffusion models can overcome these limitations. We make a surprising discovery: zero-shot source separation can be achieved purely through a pre-trained text-guided audio diffusion model under the right configuration. Our method, named ZeroSep, works by inverting the mixed audio into the diffusion model's latent space and then using text conditioning to guide the denoising process to recover individual sources. Without any task-specific training or fine-tuning, ZeroSep repurposes the generative diffusion model for a discriminative separation task and inherently supports open-set scenarios through its rich textual priors. ZeroSep is compatible with a variety of pre-trained text-guided audio diffusion backbones and delivers strong separation performance on multiple separation benchmarks, surpassing even supervised methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/WikiChao/ZeroSep", "github_api_added_by": "auto" }, { "date": "2025-05-30", "arxiv_id": "2505.20282", "title": "One-shot Entropy Minimization", "authors": [ "Zitian Gao", "Lynx Chen", "Joey Zhou", "Bryan Dai" ], "github": "https://github.com/zitian-gao/one-shot-em", "project_page": "", "abstract": "We trained 13,440 large language models and found that entropy minimization requires only a single unlabeled data and 10 steps optimization to achieve performance improvements comparable to or even greater than those obtained using thousands of data and carefully designed rewards in rule-based reinforcement learning. This striking result may prompt a rethinking of post-training paradigms for large language models. Our code is avaliable at https://github.com/zitian-gao/one-shot-em.", "github_manual": "", "github_regex": "https://github.com/zitian-gao/one-shot-em", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.19360", "title": "ChartLens: Fine-grained Visual Attribution in Charts", "authors": [ "Manan Suri", "Puneet Mathur", "Nedim Lipka", "Franck Dernoncourt", "Ryan A. Rossi", "Dinesh Manocha" ], "github": "", "project_page": "", "abstract": "The growing capabilities of multimodal large language models (MLLMs) have advanced tasks like chart understanding. However, these models often suffer from hallucinations, where generated text sequences conflict with the provided visual data. To address this, we introduce Post-Hoc Visual Attribution for Charts, which identifies fine-grained chart elements that validate a given chart-associated response. We propose ChartLens, a novel chart attribution algorithm that uses segmentation-based techniques to identify chart objects and employs set-of-marks prompting with MLLMs for fine-grained visual attribution. Additionally, we present ChartVA-Eval, a benchmark with synthetic and real-world charts from diverse domains like finance, policy, and economics, featuring fine-grained attribution annotations. Our evaluations show that ChartLens improves fine-grained attributions by 26-66%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.19286", "title": "A Graph Perspective to Probe Structural Patterns of Knowledge in Large Language Models", "authors": [ "Utkarsh Sahu", "Zhisheng Qi", "Yongjia Lei", "Ryan A. Rossi", "Franck Dernoncourt", "Nesreen K. Ahmed", "Mahantesh M Halappanavar", "Yao Ma", "Yu Wang" ], "github": "https://github.com/utkarshxsahu/kgc", "project_page": "", "abstract": "Large language models have been extensively studied as neural knowledge bases for their knowledge access, editability, reasoning, and explainability. However, few works focus on the structural patterns of their knowledge. Motivated by this gap, we investigate these structural patterns from a graph perspective. We quantify the knowledge of LLMs at both the triplet and entity levels, and analyze how it relates to graph structural properties such as node degree. Furthermore, we uncover the knowledge homophily, where topologically close entities exhibit similar levels of knowledgeability, which further motivates us to develop graph machine learning models to estimate entity knowledge based on its local neighbors. This model further enables valuable knowledge checking by selecting triplets less known to LLMs. Empirical results show that using selected triplets for fine-tuning leads to superior performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/utkarshxsahu/kgc", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23380", "title": "UniRL: Self-Improving Unified Multimodal Models via Supervised and Reinforcement Learning", "authors": [ "Weijia Mao", "Zhenheng Yang", "Mike Zheng Shou" ], "github": "https://github.com/showlab/UniRL", "project_page": "", "abstract": "Unified multimodal large language models such as Show-o and Janus have achieved strong performance across both generation and understanding tasks. However, these models typically rely on large-scale datasets and require substantial computation during the pretraining stage. In addition, several post-training methods have been proposed, but they often depend on external data or are limited to task-specific customization. In this work, we introduce UniRL, a self-improving post-training approach. Our approach enables the model to generate images from prompts and use them as training data in each iteration, without relying on any external image data. Moreover, it enables the two tasks to enhance each other: the generated images are used for understanding, and the understanding results are used to supervise generation. We explore supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO) to optimize the models. UniRL offers three key advantages: (1) it requires no external image data, as all training samples are generated by the model itself during training; (2) it not only improves individual task performance, but also reduces the imbalance between generation and understanding; and (3) it requires only several additional training steps during the post-training stage. We evaluate UniRL on top of Show-o and Janus, achieving a GenEval score of 0.77 for Show-o and 0.65 for Janus. Code and models will be released in https://github.com/showlab/UniRL.", "github_manual": "", "github_regex": "https://github.com/showlab/UniRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22618", "title": "Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding", "authors": [ "Chengyue Wu", "Hao Zhang", "Shuchen Xue", "Zhijian Liu", "Shizhe Diao", "Ligeng Zhu", "Ping Luo", "Song Han", "Enze Xie" ], "github": "", "project_page": "", "abstract": "Diffusion-based large language models (Diffusion LLMs) have shown promise for non-autoregressive text generation with parallel decoding capabilities. However, the practical inference speed of open-sourced Diffusion LLMs often lags behind autoregressive models due to the lack of Key-Value (KV) Cache and quality degradation when decoding multiple tokens simultaneously. To bridge this gap, we introduce a novel block-wise approximate KV Cache mechanism tailored for bidirectional diffusion models, enabling cache reuse with negligible performance drop. Additionally, we identify the root cause of generation quality degradation in parallel decoding as the disruption of token dependencies under the conditional independence assumption. To address this, we propose a confidence-aware parallel decoding strategy that selectively decodes tokens exceeding a confidence threshold, mitigating dependency violations and maintaining generation quality. Experimental results on LLaDA and Dream models across multiple LLM benchmarks demonstrate up to 27.6times throughput improvement with minimal accuracy loss, closing the performance gap with autoregressive models and paving the way for practical deployment of Diffusion LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.20755", "title": "Uni-Instruct: One-step Diffusion Model through Unified Diffusion Divergence Instruction", "authors": [ "Yifei Wang", "Weimin Bai", "Colin Zhang", "Debing Zhang", "Weijian Luo", "He Sun" ], "github": "", "project_page": "", "abstract": "In this paper, we unify more than 10 existing one-step diffusion distillation approaches, such as Diff-Instruct, DMD, SIM, SiD, f-distill, etc, inside a theory-driven framework which we name the \\emph{Uni-Instruct}. Uni-Instruct is motivated by our proposed diffusion expansion theory of the f-divergence family. Then we introduce key theories that overcome the intractability issue of the original expanded f-divergence, resulting in an equivalent yet tractable loss that effectively trains one-step diffusion models by minimizing the expanded f-divergence family. The novel unification introduced by Uni-Instruct not only offers new theoretical contributions that help understand existing approaches from a high-level perspective but also leads to state-of-the-art one-step diffusion generation performances. On the CIFAR10 generation benchmark, Uni-Instruct achieves record-breaking Frechet Inception Distance (FID) values of \\emph{1.46} for unconditional generation and \\emph{1.38} for conditional generation. On the ImageNet-64times 64 generation benchmark, Uni-Instruct achieves a new SoTA one-step generation FID of \\emph{1.02}, which outperforms its 79-step teacher diffusion with a significant improvement margin of 1.33 (1.02 vs 2.35). We also apply Uni-Instruct on broader tasks like text-to-3D generation. For text-to-3D generation, Uni-Instruct gives decent results, which slightly outperforms previous methods, such as SDS and VSD, in terms of both generation quality and diversity. Both the solid theoretical and empirical contributions of Uni-Instruct will potentially help future studies on one-step diffusion distillation and knowledge transferring of diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23253", "title": "UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes", "authors": [ "Yixun Liang", "Kunming Luo", "Xiao Chen", "Rui Chen", "Hongyu Yan", "Weiyu Li", "Jiarui Liu", "Ping Tan" ], "github": "https://github.com/YixunLiang/UniTEX", "project_page": "", "abstract": "We present UniTEX, a novel two-stage 3D texture generation framework to create high-quality, consistent textures for 3D assets. Existing approaches predominantly rely on UV-based inpainting to refine textures after reprojecting the generated multi-view images onto the 3D shapes, which introduces challenges related to topological ambiguity. To address this, we propose to bypass the limitations of UV mapping by operating directly in a unified 3D functional space. Specifically, we first propose that lifts texture generation into 3D space via Texture Functions (TFs)--a continuous, volumetric representation that maps any 3D point to a texture value based solely on surface proximity, independent of mesh topology. Then, we propose to predict these TFs directly from images and geometry inputs using a transformer-based Large Texturing Model (LTM). To further enhance texture quality and leverage powerful 2D priors, we develop an advanced LoRA-based strategy for efficiently adapting large-scale Diffusion Transformers (DiTs) for high-quality multi-view texture synthesis as our first stage. Extensive experiments demonstrate that UniTEX achieves superior visual quality and texture integrity compared to existing approaches, offering a generalizable and scalable solution for automated 3D texture generation. Code will available in: https://github.com/YixunLiang/UniTEX.", "github_manual": "", "github_regex": "https://github.com/YixunLiang/UniTEX", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.19236", "title": "Evaluating Text Creativity across Diverse Domains: A Dataset and Large Language Model Evaluator", "authors": [ "Qian Cao", "Xiting Wang", "Yuzhuo Yuan", "Yahui Liu", "Fang Luo", "Ruihua Song" ], "github": "", "project_page": "", "abstract": "Creativity evaluation remains a challenging frontier for large language models (LLMs). Current evaluations heavily rely on inefficient and costly human judgments, hindering progress in enhancing machine creativity. While automated methods exist, ranging from psychological testing to heuristic- or prompting-based approaches, they often lack generalizability or alignment with human judgment. To address these issues, in this paper, we propose a novel pairwise-comparison framework for assessing textual creativity, leveraging shared contextual instructions to improve evaluation consistency. We introduce CreataSet, a large-scale dataset with 100K+ human-level and 1M+ synthetic creative instruction-response pairs spanning diverse open-domain tasks. Through training on CreataSet, we develop an LLM-based evaluator named CrEval. CrEval demonstrates remarkable superiority over existing methods in alignment with human judgments. Experimental results underscore the indispensable significance of integrating both human-generated and synthetic data in training highly robust evaluators, and showcase the practical utility of CrEval in boosting the creativity of LLMs. We will release all data, code, and models publicly soon to support further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23416", "title": "KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction", "authors": [ "Jang-Hyun Kim", "Jinuk Kim", "Sangwoo Kwon", "Jae W. Lee", "Sangdoo Yun", "Hyun Oh Song" ], "github": "", "project_page": "", "abstract": "Transformer-based large language models (LLMs) cache context as key-value (KV) pairs during inference. As context length grows, KV cache sizes expand, leading to substantial memory overhead and increased attention latency. This paper introduces KVzip, a query-agnostic KV cache eviction method enabling effective reuse of compressed KV caches across diverse queries. KVzip quantifies the importance of a KV pair using the underlying LLM to reconstruct original contexts from cached KV pairs, subsequently evicting pairs with lower importance. Extensive empirical evaluations demonstrate that KVzip reduces KV cache size by 3-4times and FlashAttention decoding latency by approximately 2times, with negligible performance loss in question-answering, retrieval, reasoning, and code comprehension tasks. Evaluations include various models such as LLaMA3.1-8B, Qwen2.5-14B, and Gemma3-12B, with context lengths reaching up to 170K tokens. KVzip significantly outperforms existing query-aware KV eviction methods, which suffer from performance degradation even at a 90% cache budget ratio under multi-query scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22126", "title": "SridBench: Benchmark of Scientific Research Illustration Drawing of Image Generation Model", "authors": [ "Yifan Chang", "Yukang Feng", "Jianwen Sun", "Jiaxin Ai", "Chuanhao Li", "S. Kevin Zhou", "Kaipeng Zhang" ], "github": "", "project_page": "", "abstract": "Recent years have seen rapid advances in AI-driven image generation. Early diffusion models emphasized perceptual quality, while newer multimodal models like GPT-4o-image integrate high-level reasoning, improving semantic understanding and structural composition. Scientific illustration generation exemplifies this evolution: unlike general image synthesis, it demands accurate interpretation of technical content and transformation of abstract ideas into clear, standardized visuals. This task is significantly more knowledge-intensive and laborious, often requiring hours of manual work and specialized tools. Automating it in a controllable, intelligent manner would provide substantial practical value. Yet, no benchmark currently exists to evaluate AI on this front. To fill this gap, we introduce SridBench, the first benchmark for scientific figure generation. It comprises 1,120 instances curated from leading scientific papers across 13 natural and computer science disciplines, collected via human experts and MLLMs. Each sample is evaluated along six dimensions, including semantic fidelity and structural accuracy. Experimental results reveal that even top-tier models like GPT-4o-image lag behind human performance, with common issues in text/visual clarity and scientific correctness. These findings highlight the need for more advanced reasoning-driven visual generation capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.20088", "title": "Multi-Domain Explainability of Preferences", "authors": [ "Nitay Calderon", "Liat Ein-Dor", "Roi Reichart" ], "github": "https://github.com/nitaytech/PrefExplain", "project_page": "", "abstract": "Preference mechanisms, such as human preference, LLM-as-a-Judge (LaaJ), and reward models, are central to aligning and evaluating large language models (LLMs). Yet, the underlying concepts that drive these preferences remain poorly understood. In this work, we propose a fully automated method for generating local and global concept-based explanations of preferences across multiple domains. Our method utilizes an LLM to identify concepts that distinguish between chosen and rejected responses, and to represent them with concept-based vectors. To model the relationships between concepts and preferences, we propose a white-box Hierarchical Multi-Domain Regression model that captures both domain-general and domain-specific effects. To evaluate our method, we curate a dataset spanning eight challenging and diverse domains and explain twelve mechanisms. Our method achieves strong preference prediction performance, outperforming baselines while also being explainable. Additionally, we assess explanations in two application-driven settings. First, guiding LLM outputs with concepts from LaaJ explanations yields responses that those judges consistently prefer. Second, prompting LaaJs with concepts explaining humans improves their preference predictions. Together, our work establishes a new paradigm for explainability in the era of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nitaytech/PrefExplain", "github_api_added_by": "auto" }, { "date": "2025-05-30", "arxiv_id": "2505.23646", "title": "Are Reasoning Models More Prone to Hallucination?", "authors": [ "Zijun Yao", "Yantao Liu", "Yanxu Chen", "Jianhui Chen", "Junfeng Fang", "Lei Hou", "Juanzi Li", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "Recently evolved large reasoning models (LRMs) show powerful performance in solving complex tasks with long chain-of-thought (CoT) reasoning capability. As these LRMs are mostly developed by post-training on formal reasoning tasks, whether they generalize the reasoning capability to help reduce hallucination in fact-seeking tasks remains unclear and debated. For instance, DeepSeek-R1 reports increased performance on SimpleQA, a fact-seeking benchmark, while OpenAI-o3 observes even severer hallucination. This discrepancy naturally raises the following research question: Are reasoning models more prone to hallucination? This paper addresses the question from three perspectives. (1) We first conduct a holistic evaluation for the hallucination in LRMs. Our analysis reveals that LRMs undergo a full post-training pipeline with cold start supervised fine-tuning (SFT) and verifiable reward RL generally alleviate their hallucination. In contrast, both distillation alone and RL training without cold start fine-tuning introduce more nuanced hallucinations. (2) To explore why different post-training pipelines alters the impact on hallucination in LRMs, we conduct behavior analysis. We characterize two critical cognitive behaviors that directly affect the factuality of a LRM: Flaw Repetition, where the surface-level reasoning attempts repeatedly follow the same underlying flawed logic, and Think-Answer Mismatch, where the final answer fails to faithfully match the previous CoT process. (3) Further, we investigate the mechanism behind the hallucination of LRMs from the perspective of model uncertainty. We find that increased hallucination of LRMs is usually associated with the misalignment between model uncertainty and factual accuracy. Our work provides an initial understanding of the hallucination in LRMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22914", "title": "cadrille: Multi-modal CAD Reconstruction with Online Reinforcement Learning", "authors": [ "Maksim Kolodiazhnyi", "Denis Tarasov", "Dmitrii Zhemchuzhnikov", "Alexander Nikulin", "Ilya Zisman", "Anna Vorontsova", "Anton Konushin", "Vladislav Kurenkov", "Danila Rukhovich" ], "github": "https://github.com/col14m/cadrille", "project_page": "", "abstract": "Computer-Aided Design (CAD) plays a central role in engineering and manufacturing, making it possible to create precise and editable 3D models. Using a variety of sensor or user-provided data as inputs for CAD reconstruction can democratize access to design applications. However, existing methods typically focus on a single input modality, such as point clouds, images, or text, which limits their generalizability and robustness. Leveraging recent advances in vision-language models (VLM), we propose a multi-modal CAD reconstruction model that simultaneously processes all three input modalities. Inspired by large language model (LLM) training paradigms, we adopt a two-stage pipeline: supervised fine-tuning (SFT) on large-scale procedurally generated data, followed by reinforcement learning (RL) fine-tuning using online feedback, obtained programatically. Furthermore, we are the first to explore RL fine-tuning of LLMs for CAD tasks demonstrating that online RL algorithms such as Group Relative Preference Optimization (GRPO) outperform offline alternatives. In the DeepCAD benchmark, our SFT model outperforms existing single-modal approaches in all three input modalities simultaneously. More importantly, after RL fine-tuning, cadrille sets new state-of-the-art on three challenging datasets, including a real-world one.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/col14m/cadrille", "github_api_added_by": "auto" }, { "date": "2025-05-30", "arxiv_id": "2505.21114", "title": "Differentiable Solver Search for Fast Diffusion Sampling", "authors": [ "Shuai Wang", "Zexian Li", "Qipeng zhang", "Tianhui Song", "Xubin Li", "Tiezheng Ge", "Bo Zheng", "Limin Wang" ], "github": "", "project_page": "", "abstract": "Diffusion models have demonstrated remarkable generation quality but at the cost of numerous function evaluations. Recently, advanced ODE-based solvers have been developed to mitigate the substantial computational demands of reverse-diffusion solving under limited sampling steps. However, these solvers, heavily inspired by Adams-like multistep methods, rely solely on t-related Lagrange interpolation. We show that t-related Lagrange interpolation is suboptimal for diffusion model and reveal a compact search space comprised of time steps and solver coefficients. Building on our analysis, we propose a novel differentiable solver search algorithm to identify more optimal solver. Equipped with the searched solver, rectified-flow models, e.g., SiT-XL/2 and FlowDCN-XL/2, achieve FID scores of 2.40 and 2.35, respectively, on ImageNet256 with only 10 steps. Meanwhile, DDPM model, DiT-XL/2, reaches a FID score of 2.33 with only 10 steps. Notably, our searched solver outperforms traditional solvers by a significant margin. Moreover, our searched solver demonstrates generality across various model architectures, resolutions, and model sizes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23761", "title": "Differential Information: An Information-Theoretic Perspective on Preference Optimization", "authors": [ "Yunjae Won", "Hyunji Lee", "Hyeonbin Hwang", "Minjoon Seo" ], "github": "", "project_page": "", "abstract": "Direct Preference Optimization (DPO) has become a standard technique for aligning language models with human preferences in a supervised manner. Despite its empirical success, the theoretical justification behind its log-ratio reward parameterization remains incomplete. In this work, we address this gap by utilizing the Differential Information Distribution (DID): a distribution over token sequences that captures the information gained during policy updates. First, we show that when preference labels encode the differential information required to transform a reference policy into a target policy, the log-ratio reward in DPO emerges as the uniquely optimal form for learning the target policy via preference optimization. This result naturally yields a closed-form expression for the optimal sampling distribution over rejected responses. Second, we find that the condition for preferences to encode differential information is fundamentally linked to an implicit assumption regarding log-margin ordered policies-an inductive bias widely used in preference optimization yet previously unrecognized. Finally, by analyzing the entropy of the DID, we characterize how learning low-entropy differential information reinforces the policy distribution, while high-entropy differential information induces a smoothing effect, which explains the log-likelihood displacement phenomenon. We validate our theoretical findings in synthetic experiments and extend them to real-world instruction-following datasets. Our results suggest that learning high-entropy differential information is crucial for general instruction-following, while learning low-entropy differential information benefits knowledge-intensive question answering. Overall, our work presents a unifying perspective on the DPO objective, the structure of preference data, and resulting policy behaviors through the lens of differential information.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22765", "title": "StressTest: Can YOUR Speech LM Handle the Stress?", "authors": [ "Iddo Yosha", "Gallil Maimon", "Yossi Adi" ], "github": "", "project_page": "", "abstract": "Sentence stress refers to emphasis, placed on specific words within a spoken utterance to highlight or contrast an idea, or to introduce new information. It is often used to imply an underlying intention that is not explicitly stated. Recent advances in speech-aware language models (SLMs) have enabled direct processing of audio, allowing models to bypass transcription and access the full richness of the speech signal and perform audio reasoning tasks such as spoken question answering. Despite the crucial role of sentence stress in shaping meaning and speaker intent, it remains largely overlooked in evaluation and development of such models. In this work, we address this gap by introducing StressTest, a benchmark specifically designed to evaluate a model's ability to distinguish between interpretations of spoken sentences based on the stress pattern. We assess the performance of several leading SLMs and find that, despite their overall capabilities, they perform poorly on such tasks. To overcome this limitation, we propose a novel synthetic data generation pipeline, and create Stress17k, a training set that simulates change of meaning implied by stress variation. Then, we empirically show that optimizing models with this synthetic dataset aligns well with real-world recordings and enables effective finetuning of SLMs. Results suggest, that our finetuned model, StresSLM, significantly outperforms existing models on both sentence stress reasoning and detection tasks. Code, models, data, and audio samples - pages.cs.huji.ac.il/adiyoss-lab/stresstest.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22810", "title": "VidText: Towards Comprehensive Evaluation for Video Text Understanding", "authors": [ "Zhoufaran Yang", "Yan Shu", "Zhifei Yang", "Yan Zhang", "Yu Li", "Keyang Lu", "Gangyan Zeng", "Shaohui Liu", "Yu Zhou", "Nicu Sebe" ], "github": "https://github.com/shuyansy/vidtext", "project_page": "", "abstract": "Visual texts embedded in videos carry rich semantic information, which is crucial for both holistic video understanding and fine-grained reasoning about local human actions. However, existing video understanding benchmarks largely overlook textual information, while OCR-specific benchmarks are constrained to static images, limiting their ability to capture the interaction between text and dynamic visual contexts. To address this gap, we propose VidText, a new benchmark designed for comprehensive and in-depth evaluation of video text understanding. VidText offers the following key features: 1) It covers a wide range of real-world scenarios and supports multilingual content, encompassing diverse settings where video text naturally appears. 2) It introduces a hierarchical evaluation framework with video-level, clip-level, and instance-level tasks, enabling assessment of both global summarization and local retrieval capabilities. 3) The benchmark also introduces a set of paired perception reasoning tasks, ranging from visual text perception to cross-modal reasoning between textual and visual information. Extensive experiments on 18 state-of-the-art Large Multimodal Models (LMMs) reveal that current models struggle across most tasks, with significant room for improvement. Further analysis highlights the impact of both model-intrinsic factors, such as input resolution and OCR capability, and external factors, including the use of auxiliary information and Chain-of-Thought reasoning strategies. We hope VidText will fill the current gap in video understanding benchmarks and serve as a foundation for future research on multimodal reasoning with video text in dynamic environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shuyansy/vidtext", "github_api_added_by": "auto" }, { "date": "2025-05-30", "arxiv_id": "2505.14321", "title": "Breaking Down Video LLM Benchmarks: Knowledge, Spatial Perception, or True Temporal Understanding?", "authors": [ "Bo Feng", "Zhengfeng Lai", "Shiyu Li", "Zizhen Wang", "Simon Wang", "Ping Huang", "Meng Cao" ], "github": "", "project_page": "", "abstract": "Existing video understanding benchmarks often conflate knowledge-based and purely image-based questions, rather than clearly isolating a model's temporal reasoning ability, which is the key aspect that distinguishes video understanding from other modalities. We identify two major limitations that obscure whether higher scores truly indicate stronger understanding of the dynamic content in videos: (1) strong language priors, where models can answer questions without watching the video; and (2) shuffling invariance, where models maintain similar performance on certain questions even when video frames are temporally shuffled. To alleviate these issues, we propose VBenchComp, an automated pipeline that categorizes questions into different domains: LLM-Answerable, Semantic, and Temporal. Specifically, LLM-Answerable questions can be answered without viewing the video; Semantic questions remain answerable even when the video frames are shuffled; and Temporal questions require understanding the correct temporal order of frames. The rest of the questions are labeled as Others. This can enable fine-grained evaluation of different capabilities of a video LLM. Our analysis reveals nuanced model weaknesses that are hidden by traditional overall scores, and we offer insights and recommendations for designing future benchmarks that more accurately assess video LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22918", "title": "Re-ttention: Ultra Sparse Visual Generation via Attention Statistical Reshape", "authors": [ "Ruichen Chen", "Keith G. Mills", "Liyao Jiang", "Chao Gao", "Di Niu" ], "github": "https://github.com/cccrrrccc/Re-ttention", "project_page": "", "abstract": "Diffusion Transformers (DiT) have become the de-facto model for generating high-quality visual content like videos and images. A huge bottleneck is the attention mechanism where complexity scales quadratically with resolution and video length. One logical way to lessen this burden is sparse attention, where only a subset of tokens or patches are included in the calculation. However, existing techniques fail to preserve visual quality at extremely high sparsity levels and might even incur non-negligible compute overheads. % To address this concern, we propose Re-ttention, which implements very high sparse attention for visual generation models by leveraging the temporal redundancy of Diffusion Models to overcome the probabilistic normalization shift within the attention mechanism. Specifically, Re-ttention reshapes attention scores based on the prior softmax distribution history in order to preserve the visual quality of the full quadratic attention at very high sparsity levels. % Experimental results on T2V/T2I models such as CogVideoX and the PixArt DiTs demonstrate that Re-ttention requires as few as 3.1\\% of the tokens during inference, outperforming contemporary methods like FastDiTAttn, Sparse VideoGen and MInference. Further, we measure latency to show that our method can attain over 45\\% end-to-end % and over 92\\% self-attention latency reduction on an H100 GPU at negligible overhead cost. Code available online here: https://github.com/cccrrrccc/Re-ttention{https://github.com/cccrrrccc/Re-ttention}", "github_manual": "", "github_regex": "https://github.com/cccrrrccc/Re-ttention", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23183", "title": "Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement", "authors": [ "Gabriele Sarti", "Vil\u00e9m Zouhar", "Malvina Nissim", "Arianna Bisazza" ], "github": "", "project_page": "", "abstract": "Word-level quality estimation (WQE) aims to automatically identify fine-grained error spans in machine-translated outputs and has found many uses, including assisting translators during post-editing. Modern WQE techniques are often expensive, involving prompting of large language models or ad-hoc training on large amounts of human-labeled data. In this work, we investigate efficient alternatives exploiting recent advances in language model interpretability and uncertainty quantification to identify translation errors from the inner workings of translation models. In our evaluation spanning 14 metrics across 12 translation directions, we quantify the impact of human label variation on metric performance by using multiple sets of human labels. Our results highlight the untapped potential of unsupervised metrics, the shortcomings of supervised methods when faced with label uncertainty, and the brittleness of single-annotator evaluation practices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23387", "title": "Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization", "authors": [ "Mingzhe Du", "Luu Tuan Tuan", "Yue Liu", "Yuhao Qing", "Dong Huang", "Xinyi He", "Qian Liu", "Zejun Ma", "See-kiong Ng" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization~(GRPO). Experiments on our Venus dataset and the APPS benchmark show that SFT and DPO rapidly saturate in efficiency gains. In contrast, GRPO, using reinforcement learning (RL) with execution feedback, continuously optimizes code performance, significantly boosting both pass@1 (from 47% to 62%) and the likelihood of outperforming human submissions in efficiency (from 31% to 45%). Our work demonstrates effective test-time code efficiency improvement and critically reveals the power of RL in teaching LLMs to truly self-improve code efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22255", "title": "Train Sparse Autoencoders Efficiently by Utilizing Features Correlation", "authors": [ "Vadim Kurochkin", "Yaroslav Aksenov", "Daniil Laptev", "Daniil Gavrilov", "Nikita Balagansky" ], "github": "", "project_page": "", "abstract": "Sparse Autoencoders (SAEs) have demonstrated significant promise in interpreting the hidden states of language models by decomposing them into interpretable latent directions. However, training SAEs at scale remains challenging, especially when large dictionary sizes are used. While decoders can leverage sparse-aware kernels for efficiency, encoders still require computationally intensive linear operations with large output dimensions. To address this, we propose KronSAE, a novel architecture that factorizes the latent representation via Kronecker product decomposition, drastically reducing memory and computational overhead. Furthermore, we introduce mAND, a differentiable activation function approximating the binary AND operation, which improves interpretability and performance in our factorized framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23751", "title": "REOrdering Patches Improves Vision Models", "authors": [ "Declan Kutscher", "David M. Chan", "Yutong Bai", "Trevor Darrell", "Ritwik Gupta" ], "github": "", "project_page": "", "abstract": "Sequence models such as transformers require inputs to be represented as one-dimensional sequences. In vision, this typically involves flattening images using a fixed row-major (raster-scan) order. While full self-attention is permutation-equivariant, modern long-sequence transformers increasingly rely on architectural approximations that break this invariance and introduce sensitivity to patch ordering. We show that patch order significantly affects model performance in such settings, with simple alternatives like column-major or Hilbert curves yielding notable accuracy shifts. Motivated by this, we propose REOrder, a two-stage framework for discovering task-optimal patch orderings. First, we derive an information-theoretic prior by evaluating the compressibility of various patch sequences. Then, we learn a policy over permutations by optimizing a Plackett-Luce policy using REINFORCE. This approach enables efficient learning in a combinatorial permutation space. REOrder improves top-1 accuracy over row-major ordering on ImageNet-1K by up to 3.01% and Functional Map of the World by 13.35%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22759", "title": "FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian", "authors": [ "Sara Papi", "Marco Gaido", "Luisa Bentivogli", "Alessio Brutti", "Mauro Cettolo", "Roberto Gretter", "Marco Matassoni", "Mohamed Nabih", "Matteo Negri" ], "github": "", "project_page": "", "abstract": "The development of speech foundation models (SFMs) like Whisper and SeamlessM4T has significantly advanced the field of speech processing. However, their closed nature--with inaccessible training data and code--poses major reproducibility and fair evaluation challenges. While other domains have made substantial progress toward open science by developing fully transparent models trained on open-source (OS) code and data, similar efforts in speech remain limited. To fill this gap, we introduce FAMA, the first family of open science SFMs for English and Italian, trained on 150k+ hours of OS speech data. Moreover, we present a new dataset containing 16k hours of cleaned and pseudo-labeled speech for both languages. Results show that FAMA achieves competitive performance compared to existing SFMs while being up to 8 times faster. All artifacts, including code, datasets, and models, are released under OS-compliant licenses, promoting openness in speech technology research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22888", "title": "When Models Reason in Your Language: Controlling Thinking Trace Language Comes at the Cost of Accuracy", "authors": [ "Jirui Qi", "Shan Chen", "Zidi Xiong", "Raquel Fern\u00e1ndez", "Danielle S. Bitterman", "Arianna Bisazza" ], "github": "https://github.com/Betswish/mCoT-XReasoning", "project_page": "", "abstract": "Recent Large Reasoning Models (LRMs) with thinking traces have shown strong performance on English reasoning tasks. However, their ability to think in other languages is less studied. This capability is as important as answer accuracy for real world applications because users may find the reasoning trace useful for oversight only when it is expressed in their own language. We comprehensively evaluate two leading families of LRMs on our XReasoning benchmark and find that even the most advanced models often revert to English or produce fragmented reasoning in other languages, revealing a substantial gap in multilingual reasoning. Prompt based interventions that force models to reason in the users language improve readability and oversight but reduce answer accuracy, exposing an important trade off. We further show that targeted post training on just 100 examples mitigates this mismatch, though some accuracy loss remains. Our results highlight the limited multilingual reasoning capabilities of current LRMs and outline directions for future work. Code and data are available at https://github.com/Betswish/mCoT-XReasoning.", "github_manual": "", "github_regex": "https://github.com/Betswish/mCoT-XReasoning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22854", "title": "CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting", "authors": [ "Kornel Howil", "Joanna Waczy\u0144ska", "Piotr Borycki", "Tadeusz Dziarmaga", "Marcin Mazur", "Przemys\u0142aw Spurek" ], "github": "", "project_page": "", "abstract": "Gaussian Splatting (GS) has recently emerged as an efficient representation for rendering 3D scenes from 2D images and has been extended to images, videos, and dynamic 4D content. However, applying style transfer to GS-based representations, especially beyond simple color changes, remains challenging. In this work, we introduce CLIPGaussians, the first unified style transfer framework that supports text- and image-guided stylization across multiple modalities: 2D images, videos, 3D objects, and 4D scenes. Our method operates directly on Gaussian primitives and integrates into existing GS pipelines as a plug-in module, without requiring large generative models or retraining from scratch. CLIPGaussians approach enables joint optimization of color and geometry in 3D and 4D settings, and achieves temporal coherence in videos, while preserving a model size. We demonstrate superior style fidelity and consistency across all tasks, validating CLIPGaussians as a universal and efficient solution for multimodal style transfer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.19716", "title": "Concise Reasoning, Big Gains: Pruning Long Reasoning Trace with Difficulty-Aware Prompting", "authors": [ "Yifan Wu", "Jingze Shi", "Bingheng Wu", "Jiayi Zhang", "Xiaotian Lin", "Nan Tang", "Yuyu Luo" ], "github": "https://github.com/Evanwu1125/LiteCoT", "project_page": "", "abstract": "Existing chain-of-thought (CoT) distillation methods can effectively transfer reasoning abilities to base models but suffer from two major limitations: excessive verbosity of reasoning traces and inadequate adaptability to problem difficulty. Long reasoning traces significantly increase inference costs, and uniform-length solutions prevent base models from learning adaptive reasoning strategies. To address these issues, we propose a difficulty-aware prompting (DAP) method to dynamically shorten reasoning traces without performance loss. In our approach, a large teacher model first judges each problem's difficulty and then rewrites its reasoning traces to an appropriate shorter length, yielding concise yet complete reasoning traces. Leveraging the DAP pipeline, we curate a distilled dataset called LiteCoT consisting of 100K concise reasoning examples, with solutions averaging only 720 tokens (an order of magnitude shorter than typical CoTs). Using LiteCoT, we distilled a new family of reasoning models called Liter (1.5B, 7B, and 32B) based on the Qwen2.5 architecture. Experiments show that a student model fine-tuned on just 100K of these difficulty-pruned CoT samples outperforms a model distilled on 800K original Long CoT samples, while significantly reducing training and inference costs. Our method also generalizes well: across 11 diverse benchmarks, the shorter difficulty-aware CoTs achieve equal or better accuracy than Long chains, using far fewer tokens. For example, on the challenging AIME24 exam, our approach reaches 74.2% Pass@1 using only about 5K inference tokens, surpassing other methods that consume many more tokens. Our code and data are available at https://github.com/Evanwu1125/LiteCoT.", "github_manual": "", "github_regex": "https://github.com/Evanwu1125/LiteCoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.20099", "title": "Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities", "authors": [ "Chuangtao Ma", "Yongrui Chen", "Tianxing Wu", "Arijit Khan", "Haofen Wang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable performance on question-answering (QA) tasks because of their superior capabilities in natural language understanding and generation. However, LLM-based QA struggles with complex QA tasks due to poor reasoning capacity, outdated knowledge, and hallucinations. Several recent works synthesize LLMs and knowledge graphs (KGs) for QA to address the above challenges. In this survey, we propose a new structured taxonomy that categorizes the methodology of synthesizing LLMs and KGs for QA according to the categories of QA and the KG's role when integrating with LLMs. We systematically survey state-of-the-art advances in synthesizing LLMs and KGs for QA and compare and analyze these approaches in terms of strength, limitations, and KG requirements. We then align the approaches with QA and discuss how these approaches address the main challenges of different complex QA. Finally, we summarize the advancements, evaluation metrics, and benchmark datasets and highlight open challenges and opportunities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23678", "title": "Grounded Reinforcement Learning for Visual Reasoning", "authors": [ "Gabriel Sarch", "Snigdha Saha", "Naitik Khandelwal", "Ayush Jain", "Michael J. Tarr", "Aviral Kumar", "Katerina Fragkiadaki" ], "github": "", "project_page": "", "abstract": "While reinforcement learning (RL) over chains of thought has significantly advanced language models in tasks such as mathematics and coding, visual reasoning introduces added complexity by requiring models to direct visual attention, interpret perceptual inputs, and ground abstract reasoning in spatial evidence. We introduce ViGoRL (Visually Grounded Reinforcement Learning), a vision-language model trained with RL to explicitly anchor each reasoning step to specific visual coordinates. Inspired by human visual decision-making, ViGoRL learns to produce spatially grounded reasoning traces, guiding visual attention to task-relevant regions at each step. When fine-grained exploration is required, our novel multi-turn RL framework enables the model to dynamically zoom into predicted coordinates as reasoning unfolds. Across a diverse set of visual reasoning benchmarks--including SAT-2 and BLINK for spatial reasoning, V*bench for visual search, and ScreenSpot and VisualWebArena for web-based grounding--ViGoRL consistently outperforms both supervised fine-tuning and conventional RL baselines that lack explicit grounding mechanisms. Incorporating multi-turn RL with zoomed-in visual feedback significantly improves ViGoRL's performance on localizing small GUI elements and visual search, achieving 86.4% on V*Bench. Additionally, we find that grounding amplifies other visual behaviors such as region exploration, grounded subgoal setting, and visual verification. Finally, human evaluations show that the model's visual references are not only spatially accurate but also helpful for understanding model reasoning steps. Our results show that visually grounded RL is a strong paradigm for imbuing models with general-purpose visual reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.21190", "title": "Lunguage: A Benchmark for Structured and Sequential Chest X-ray Interpretation", "authors": [ "Jong Hak Moon", "Geon Choi", "Paloma Rabaey", "Min Gwan Kim", "Hyuk Gi Hong", "Jung-Oh Lee", "Hangyul Yoon", "Eun Woo Doe", "Jiyoun Kim", "Harshita Sharma", "Daniel C. Castro", "Javier Alvarez-Valle", "Edward Choi" ], "github": "https://github.com/SuperSupermoon/Lunguage", "project_page": "", "abstract": "Radiology reports convey detailed clinical observations and capture diagnostic reasoning that evolves over time. However, existing evaluation methods are limited to single-report settings and rely on coarse metrics that fail to capture fine-grained clinical semantics and temporal dependencies. We introduce LUNGUAGE,a benchmark dataset for structured radiology report generation that supports both single-report evaluation and longitudinal patient-level assessment across multiple studies. It contains 1,473 annotated chest X-ray reports, each reviewed by experts, and 80 of them contain longitudinal annotations to capture disease progression and inter-study intervals, also reviewed by experts. Using this benchmark, we develop a two-stage framework that transforms generated reports into fine-grained, schema-aligned structured representations, enabling longitudinal interpretation. We also propose LUNGUAGESCORE, an interpretable metric that compares structured outputs at the entity, relation, and attribute level while modeling temporal consistency across patient timelines. These contributions establish the first benchmark dataset, structuring framework, and evaluation metric for sequential radiology reporting, with empirical results demonstrating that LUNGUAGESCORE effectively supports structured report evaluation. The code is available at: https://github.com/SuperSupermoon/Lunguage", "github_manual": "", "github_regex": "https://github.com/SuperSupermoon/Lunguage", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23759", "title": "Puzzled by Puzzles: When Vision-Language Models Can't Take a Hint", "authors": [ "Heekyung Lee", "Jiaxin Ge", "Tsung-Han Wu", "Minwoo Kang", "Trevor Darrell", "David M. Chan" ], "github": "https://github.com/Kyunnilee/visual_puzzles", "project_page": "", "abstract": "Rebus puzzles, visual riddles that encode language through imagery, spatial arrangement, and symbolic substitution, pose a unique challenge to current vision-language models (VLMs). Unlike traditional image captioning or question answering tasks, rebus solving requires multi-modal abstraction, symbolic reasoning, and a grasp of cultural, phonetic and linguistic puns. In this paper, we investigate the capacity of contemporary VLMs to interpret and solve rebus puzzles by constructing a hand-generated and annotated benchmark of diverse English-language rebus puzzles, ranging from simple pictographic substitutions to spatially-dependent cues (\"head\" over \"heels\"). We analyze how different VLMs perform, and our findings reveal that while VLMs exhibit some surprising capabilities in decoding simple visual clues, they struggle significantly with tasks requiring abstract reasoning, lateral thinking, and understanding visual metaphors.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Kyunnilee/visual_puzzles", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.18142", "title": "TokBench: Evaluating Your Visual Tokenizer before Visual Generation", "authors": [ "Junfeng Wu", "Dongliang Luo", "Weizhi Zhao", "Zhihao Xie", "Yuanhao Wang", "Junyi Li", "Xudong Xie", "Yuliang Liu", "Xiang Bai" ], "github": "", "project_page": "", "abstract": "In this work, we reveal the limitations of visual tokenizers and VAEs in preserving fine-grained features, and propose a benchmark to evaluate reconstruction performance for two challenging visual contents: text and face. Visual tokenizers and VAEs have significantly advanced visual generation and multimodal modeling by providing more efficient compressed or quantized image representations. However, while helping production models reduce computational burdens, the information loss from image compression fundamentally limits the upper bound of visual generation quality. To evaluate this upper bound, we focus on assessing reconstructed text and facial features since they typically: 1) exist at smaller scales, 2) contain dense and rich textures, 3) are prone to collapse, and 4) are highly sensitive to human vision. We first collect and curate a diverse set of clear text and face images from existing datasets. Unlike approaches using VLM models, we employ established OCR and face recognition models for evaluation, ensuring accuracy while maintaining an exceptionally lightweight assessment process requiring just 2GB memory and 4 minutes to complete. Using our benchmark, we analyze text and face reconstruction quality across various scales for different image tokenizers and VAEs. Our results show modern visual tokenizers still struggle to preserve fine-grained features, especially at smaller scales. We further extend this evaluation framework to video, conducting comprehensive analysis of video tokenizers. Additionally, we demonstrate that traditional metrics fail to accurately reflect reconstruction performance for faces and text, while our proposed metrics serve as an effective complement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23734", "title": "ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS", "authors": [ "Weijie Wang", "Donny Y. Chen", "Zeyu Zhang", "Duochao Shi", "Akide Liu", "Bohan Zhuang" ], "github": "", "project_page": "", "abstract": "Feed-forward 3D Gaussian Splatting (3DGS) models have recently emerged as a promising solution for novel view synthesis, enabling one-pass inference without the need for per-scene 3DGS optimization. However, their scalability is fundamentally constrained by the limited capacity of their encoders, leading to degraded performance or excessive memory consumption as the number of input views increases. In this work, we analyze feed-forward 3DGS frameworks through the lens of the Information Bottleneck principle and introduce ZPressor, a lightweight architecture-agnostic module that enables efficient compression of multi-view inputs into a compact latent state Z that retains essential scene information while discarding redundancy. Concretely, ZPressor enables existing feed-forward 3DGS models to scale to over 100 input views at 480P resolution on an 80GB GPU, by partitioning the views into anchor and support sets and using cross attention to compress the information from the support views into anchor views, forming the compressed latent state Z. We show that integrating ZPressor into several state-of-the-art feed-forward 3DGS models consistently improves performance under moderate input views and enhances robustness under dense view settings on two large-scale benchmarks DL3DV-10K and RealEstate10K. The video results, code and trained models are available on our project page: https://lhmd.top/zpressor.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23738", "title": "How Animals Dance (When You're Not Looking)", "authors": [ "Xiaojuan Wang", "Aleksander Holynski", "Brian Curless", "Ira Kemelmacher", "Steve Seitz" ], "github": "", "project_page": "", "abstract": "We present a keyframe-based framework for generating music-synchronized, choreography aware animal dance videos. Starting from a few keyframes representing distinct animal poses -- generated via text-to-image prompting or GPT-4o -- we formulate dance synthesis as a graph optimization problem: find the optimal keyframe structure that satisfies a specified choreography pattern of beats, which can be automatically estimated from a reference dance video. We also introduce an approach for mirrored pose image generation, essential for capturing symmetry in dance. In-between frames are synthesized using an video diffusion model. With as few as six input keyframes, our method can produce up to 30 second dance videos across a wide range of animals and music tracks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.21784", "title": "Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation", "authors": [ "Tharindu Kumarage", "Ninareh Mehrabi", "Anil Ramakrishna", "Xinyan Zhao", "Richard Zemel", "Kai-Wei Chang", "Aram Galstyan", "Rahul Gupta", "Charith Peris" ], "github": "", "project_page": "", "abstract": "Safety reasoning is a recent paradigm where LLMs reason over safety policies before generating responses, thereby mitigating limitations in existing safety measures such as over-refusal and jailbreak vulnerabilities. However, implementing this paradigm is challenging due to the resource-intensive process of creating high-quality policy-embedded chain-of-thought (CoT) datasets while ensuring reasoning remains accurate and free from hallucinations or policy conflicts. To tackle this, we propose AIDSAFE: Agentic Iterative Deliberation for Safety Reasoning, a novel data generation recipe that leverages multi-agent deliberation to iteratively expand reasoning on safety policies. A data refiner stage in AIDSAFE ensures high-quality outputs by eliminating repetitive, redundant, and deceptive thoughts. AIDSAFE-generated CoTs provide a strong foundation for supervised fine-tuning (SFT)-based safety training. Additionally, to address the need of preference data in alignment stages, such as DPO training, we introduce a supplemental recipe that uses belief augmentation to create distinct selected and rejected CoT samples. Our evaluations demonstrate that AIDSAFE-generated CoTs achieve superior policy adherence and reasoning quality. Consequently, we show that fine-tuning open-source LLMs on these CoTs can significantly improve safety generalization and jailbreak robustness while maintaining acceptable utility and over-refusal accuracy. AIDSAFE-generated CoT datasets can be found here: https://huggingface.co/datasets/AmazonScience/AIDSAFE", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23764", "title": "MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence", "authors": [ "Sihan Yang", "Runsen Xu", "Yiman Xie", "Sizhe Yang", "Mo Li", "Jingli Lin", "Chenming Zhu", "Xiaochen Chen", "Haodong Duan", "Xiangyu Yue", "Dahua Lin", "Tai Wang", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "Spatial intelligence is essential for multimodal large language models (MLLMs) operating in the complex physical world. Existing benchmarks, however, probe only single-image relations and thus fail to assess the multi-image spatial reasoning that real-world deployments demand. We introduce MMSI-Bench, a VQA benchmark dedicated to multi-image spatial intelligence. Six 3D-vision researchers spent more than 300 hours meticulously crafting 1,000 challenging, unambiguous multiple-choice questions from over 120,000 images, each paired with carefully designed distractors and a step-by-step reasoning process. We conduct extensive experiments and thoroughly evaluate 34 open-source and proprietary MLLMs, observing a wide gap: the strongest open-source model attains roughly 30% accuracy and OpenAI's o3 reasoning model reaches 40%, while humans score 97%. These results underscore the challenging nature of MMSI-Bench and the substantial headroom for future research. Leveraging the annotated reasoning processes, we also provide an automated error analysis pipeline that diagnoses four dominant failure modes, including (1) grounding errors, (2) overlap-matching and scene-reconstruction errors, (3) situation-transformation reasoning errors, and (4) spatial-logic errors, offering valuable insights for advancing multi-image spatial intelligence. Project page: https://runsenxu.com/projects/MMSI_Bench .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22944", "title": "ATI: Any Trajectory Instruction for Controllable Video Generation", "authors": [ "Angtian Wang", "Haibin Huang", "Jacob Zhiyuan Fang", "Yiding Yang", "Chongyang Ma" ], "github": "https://github.com/bytedance/ATI", "project_page": "", "abstract": "We propose a unified framework for motion control in video generation that seamlessly integrates camera movement, object-level translation, and fine-grained local motion using trajectory-based inputs. In contrast to prior methods that address these motion types through separate modules or task-specific designs, our approach offers a cohesive solution by projecting user-defined trajectories into the latent space of pre-trained image-to-video generation models via a lightweight motion injector. Users can specify keypoints and their motion paths to control localized deformations, entire object motion, virtual camera dynamics, or combinations of these. The injected trajectory signals guide the generative process to produce temporally consistent and semantically aligned motion sequences. Our framework demonstrates superior performance across multiple video motion control tasks, including stylized motion effects (e.g., motion brushes), dynamic viewpoint changes, and precise local motion manipulation. Experiments show that our method provides significantly better controllability and visual quality compared to prior approaches and commercial solutions, while remaining broadly compatible with various state-of-the-art video generation backbones. Project page: https://anytraj.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bytedance/ATI", "github_api_added_by": "auto" }, { "date": "2025-05-30", "arxiv_id": "2505.18962", "title": "System-1.5 Reasoning: Traversal in Language and Latent Spaces with Dynamic Shortcuts", "authors": [ "Xiaoqiang Wang", "Suyuchen Wang", "Yun Zhu", "Bang Liu" ], "github": "", "project_page": "", "abstract": "Chain-of-thought (CoT) reasoning enables large language models (LLMs) to move beyond fast System-1 responses and engage in deliberative System-2 reasoning. However, this comes at the cost of significant inefficiency due to verbose intermediate output. Recent latent-space reasoning methods improve efficiency by operating on hidden states without decoding into language, yet they treat all steps uniformly, failing to distinguish critical deductions from auxiliary steps and resulting in suboptimal use of computational resources. In this paper, we propose System-1.5 Reasoning, an adaptive reasoning framework that dynamically allocates computation across reasoning steps through shortcut paths in latent space. Specifically, System-1.5 Reasoning introduces two types of dynamic shortcuts. The model depth shortcut (DS) adaptively reasons along the vertical depth by early exiting non-critical tokens through lightweight adapter branches, while allowing critical tokens to continue through deeper Transformer layers. The step shortcut (SS) reuses hidden states across the decoding steps to skip trivial steps and reason horizontally in latent space. Training System-1.5 Reasoning involves a two-stage self-distillation process: first distilling natural language CoT into latent-space continuous thought, and then distilling full-path System-2 latent reasoning into adaptive shortcut paths (System-1.5 Reasoning). Experiments on reasoning tasks demonstrate the superior performance of our method. For example, on GSM8K, System-1.5 Reasoning achieves reasoning performance comparable to traditional CoT fine-tuning methods while accelerating inference by over 20x and reducing token generation by 92.31% on average.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.23671", "title": "GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents", "authors": [ "Manish Shetty", "Naman Jain", "Jinjian Liu", "Vijay Kethanaboyina", "Koushik Sen", "Ion Stoica" ], "github": "", "project_page": "", "abstract": "Developing high-performance software is a complex task that requires specialized expertise. We introduce GSO, a benchmark for evaluating language models' capabilities in developing high-performance software. We develop an automated pipeline that generates and executes performance tests to analyze repository commit histories to identify 102 challenging optimization tasks across 10 codebases, spanning diverse domains and programming languages. An agent is provided with a codebase and performance test as a precise specification, and tasked to improve the runtime efficiency, which is measured against the expert developer optimization. Our quantitative evaluation reveals that leading SWE-Agents struggle significantly, achieving less than 5% success rate, with limited improvements even with inference-time scaling. Our qualitative analysis identifies key failure modes, including difficulties with low-level languages, practicing lazy optimization strategies, and challenges in accurately localizing bottlenecks. We release the code and artifacts of our benchmark along with agent trajectories to enable future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.22988", "title": "Model-Preserving Adaptive Rounding", "authors": [ "Albert Tseng", "Zhaofeng Sun", "Christopher De Sa" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "The main goal of post-training quantization (PTQ) is to produced a compressed model whose output distribution is as close to the original model's as possible. To do this tractably, almost all LLM PTQ algorithms quantize linear layers by independently minimizing the immediate activation error. However, this localized objective ignores the effect of subsequent layers, so reducing it does not necessarily give a closer model. In this work, we introduce Yet Another Quantization Algorithm (YAQA), an adaptive rounding algorithm that uses Kronecker-factored approximations of each linear layer's Hessian with respect to the full model KL divergence. YAQA consists of two components: Kronecker-factored sketches of the full layerwise Hessian that can be tractably computed for hundred-billion parameter LLMs, and a quantizer-independent rounding algorithm that uses these sketches and comes with theoretical guarantees. Across a wide range of models and quantizers, YAQA empirically reduces the KL divergence to the original model by approx 30% while achieving state of the art performance on downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2025-05-30", "arxiv_id": "2505.14599", "title": "Toward Reliable Biomedical Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models", "authors": [ "Guangzhi Xiong", "Eric Xie", "Corey Williams", "Myles Kim", "Amir Hassan Shariatmadari", "Sikun Guo", "Stefan Bekiranov", "Aidong Zhang" ], "github": "https://github.com/Teddy-XiongGZ/TruthHypo", "project_page": "", "abstract": "Large language models (LLMs) have shown significant potential in scientific disciplines such as biomedicine, particularly in hypothesis generation, where they can analyze vast literature, identify patterns, and suggest research directions. However, a key challenge lies in evaluating the truthfulness of generated hypotheses, as verifying their accuracy often requires substantial time and resources. Additionally, the hallucination problem in LLMs can lead to the generation of hypotheses that appear plausible but are ultimately incorrect, undermining their reliability. To facilitate the systematic study of these challenges, we introduce TruthHypo, a benchmark for assessing the capabilities of LLMs in generating truthful biomedical hypotheses, and KnowHD, a knowledge-based hallucination detector to evaluate how well hypotheses are grounded in existing knowledge. Our results show that LLMs struggle to generate truthful hypotheses. By analyzing hallucinations in reasoning steps, we demonstrate that the groundedness scores provided by KnowHD serve as an effective metric for filtering truthful hypotheses from the diverse outputs of LLMs. Human evaluations further validate the utility of KnowHD in identifying truthful hypotheses and accelerating scientific discovery. Our data and source code are available at https://github.com/Teddy-XiongGZ/TruthHypo.", "github_manual": "", "github_regex": "https://github.com/Teddy-XiongGZ/TruthHypo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24863", "title": "AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time", "authors": [ "Junyu Zhang", "Runpei Dong", "Han Wang", "Xuying Ning", "Haoran Geng", "Peihao Li", "Xialin He", "Yutong Bai", "Jitendra Malik", "Saurabh Gupta", "Huan Zhang" ], "github": "", "project_page": "", "abstract": "This paper presents AlphaOne (alpha1), a universal framework for modulating reasoning progress in large reasoning models (LRMs) at test time. alpha1 first introduces alpha moment, which represents the scaled thinking phase with a universal parameter alpha. Within this scaled pre-alpha moment phase, it dynamically schedules slow thinking transitions by modeling the insertion of reasoning transition tokens as a Bernoulli stochastic process. After the alpha moment, alpha1 deterministically terminates slow thinking with the end-of-thinking token, thereby fostering fast reasoning and efficient answer generation. This approach unifies and generalizes existing monotonic scaling methods by enabling flexible and dense slow-to-fast reasoning modulation. Extensive empirical studies on various challenging benchmarks across mathematical, coding, and scientific domains demonstrate alpha1's superior reasoning capability and efficiency. Project page: https://alphaone-project.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24417", "title": "EasyText: Controllable Diffusion Transformer for Multilingual Text Rendering", "authors": [ "Runnan Lu", "Yuxuan Zhang", "Jailing Liu", "Haifa Wang", "Yiren Song" ], "github": "https://github.com/songyiren725/easytext", "project_page": "", "abstract": "Generating accurate multilingual text with diffusion models has long been desired but remains challenging. Recent methods have made progress in rendering text in a single language, but rendering arbitrary languages is still an unexplored area. This paper introduces EasyText, a text rendering framework based on DiT (Diffusion Transformer), which connects denoising latents with multilingual character tokens encoded as character tokens. We propose character positioning encoding and position encoding interpolation techniques to achieve controllable and precise text rendering. Additionally, we construct a large-scale synthetic text image dataset with 1 million multilingual image-text annotations as well as a high-quality dataset of 20K annotated images, which are used for pretraining and fine-tuning respectively. Extensive experiments and evaluations demonstrate the effectiveness and advancement of our approach in multilingual text rendering, visual quality, and layout-aware text integration.", "github_manual": "https://github.com/songyiren725/easytext", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24293", "title": "Large Language Models are Locally Linear Mappings", "authors": [ "James R. Golden" ], "github": "https://github.com/jamesgolden1/equivalent-linear-LLMs", "project_page": "", "abstract": "We demonstrate that the inference operations of several open-weight large language models (LLMs) can be mapped to an exactly equivalent linear system for an input sequence without modifying the model weights or altering output predictions. Extending techniques from image diffusion models that exhibit local or piecewise linearity, we strategically alter the gradient computation with respect to a given input sequence for a next-token prediction such that the Jacobian of the model nearly exactly reproduces the forward prediction with a linear system. We demonstrate this approach across models (Llama 3, Gemma 3, Qwen 3, Phi 4, Mistral Ministral and OLMo 2, up to Llama 3.3 70B Q4) and show through the singular value decomposition of the detached Jacobian that these LLMs operate in extremely low-dimensional subspaces where many of the largest singular vectors decode to concepts related to the most-likely output token. This approach also allows us to examine the operation of each successive layer (and its attention and MLP components) as nearly-exact linear systems and observe the emergence of semantic concepts. Despite their expressive power and global nonlinearity, modern LLMs can be interpreted through nearly-exact locally linear decompositions that provide insights into their internal representations and reveal interpretable semantic structures in the next-token prediction process.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jamesgolden1/equivalent-linear-LLMs", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.23844", "title": "Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation", "authors": [ "Zhenglun Kong", "Zheng Zhan", "Shiyue Hou", "Yifan Gong", "Xin Meng", "Pengwei Sui", "Peiyan Dong", "Xuan Shen", "Zifeng Wang", "Pu Zhao", "Hao Tang", "Stratis Ioannidis", "Yanzhi Wang" ], "github": "https://github.com/ZLKong/LLM_Integration", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable promise but remain challenging to continually improve through traditional finetuning, particularly when integrating capabilities from other specialized LLMs. Popular methods like ensemble and weight merging require substantial memory and struggle to adapt to changing data environments. Recent efforts have transferred knowledge from multiple LLMs into a single target model; however, they suffer from interference and degraded performance among tasks, largely due to limited flexibility in candidate selection and training pipelines. To address these issues, we propose a framework that adaptively selects and aggregates knowledge from diverse LLMs to build a single, stronger model, avoiding the high memory overhead of ensemble and inflexible weight merging. Specifically, we design an adaptive selection network that identifies the most relevant source LLMs based on their scores, thereby reducing knowledge interference. We further propose a dynamic weighted fusion strategy that accounts for the inherent strengths of candidate LLMs, along with a feedback-driven loss function that prevents the selector from converging on a single subset of sources. Experimental results demonstrate that our method can enable a more stable and scalable knowledge aggregation process while reducing knowledge interference by up to 50% compared to existing approaches. Code is avaliable at https://github.com/ZLKong/LLM_Integration", "github_manual": "", "github_regex": "https://github.com/ZLKong/LLM_Integration", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.14752", "title": "Large Language Models for Data Synthesis", "authors": [ "Yihong Tang", "Menglin Kong", "Lijun Sun" ], "github": "", "project_page": "", "abstract": "Generating synthetic data that faithfully captures the statistical structure of real-world distributions is a fundamental challenge in data modeling. Classical approaches often depend on strong parametric assumptions or manual structural design and struggle in high-dimensional or heterogeneous domains. Recent progress in Large Language Models (LLMs) reveals their potential as flexible, high-dimensional priors over real-world distributions. However, when applied to data synthesis, standard LLM-based sampling is inefficient, constrained by fixed context limits, and fails to ensure statistical alignment. Given this, we introduce LLMSynthor, a general framework for data synthesis that transforms LLMs into structure-aware simulators guided by distributional feedback. LLMSynthor treats the LLM as a nonparametric copula simulator for modeling high-order dependencies and introduces LLM Proposal Sampling to generate grounded proposal distributions that improve sampling efficiency without requiring rejection. By minimizing discrepancies in the summary statistics space, the iterative synthesis loop aligns real and synthetic data while gradually uncovering and refining the latent generative structure. We evaluate LLMSynthor in both controlled and real-world settings using heterogeneous datasets in privacy-sensitive domains (e.g., e-commerce, population, and mobility) that encompass both structured and unstructured formats. The synthetic data produced by LLMSynthor shows high statistical fidelity, practical utility, and cross-data adaptability, positioning it as a valuable tool across economics, social science, urban studies, and beyond.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24878", "title": "Open CaptchaWorld: A Comprehensive Web-based Platform for Testing and Benchmarking Multimodal LLM Agents", "authors": [ "Yaxin Luo", "Zhaoyi Li", "Jiacheng Liu", "Jiacheng Cui", "Xiaohan Zhao", "Zhiqiang Shen" ], "github": "https://github.com/Significant-Gravitas/AutoGPT", "project_page": "", "abstract": "CAPTCHAs have been a critical bottleneck for deploying web agents in real-world applications, often blocking them from completing end-to-end automation tasks. While modern multimodal LLM agents have demonstrated impressive performance in static perception tasks, their ability to handle interactive, multi-step reasoning challenges like CAPTCHAs is largely untested. To address this gap, we introduce Open CaptchaWorld, the first web-based benchmark and platform specifically designed to evaluate the visual reasoning and interaction capabilities of MLLM-powered agents through diverse and dynamic CAPTCHA puzzles. Our benchmark spans 20 modern CAPTCHA types, totaling 225 CAPTCHAs, annotated with a new metric we propose: CAPTCHA Reasoning Depth, which quantifies the number of cognitive and motor steps required to solve each puzzle. Experimental results show that humans consistently achieve near-perfect scores, state-of-the-art MLLM agents struggle significantly, with success rates at most 40.0% by Browser-Use Openai-o3, far below human-level performance, 93.3%. This highlights Open CaptchaWorld as a vital benchmark for diagnosing the limits of current multimodal agents and guiding the development of more robust multimodal reasoning systems. Code and Data are available at this https URL.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Significant-Gravitas/AutoGPT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24850", "title": "Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning", "authors": [ "Shuyao Xu", "Cheng Peng", "Jiangxuan Long", "Weidi Xu", "Wei Chu", "Yuan Qi" ], "github": "https://github.com/hendrycks/math", "project_page": "", "abstract": "Recent advances in model distillation demonstrate that data from advanced reasoning models (e.g., DeepSeek-R1, OpenAI's o1) can effectively transfer complex reasoning abilities to smaller, efficient student models. However, standard practices employ rejection sampling, discarding incorrect reasoning examples -- valuable, yet often underutilized data. This paper addresses the critical question: How can both positive and negative distilled reasoning traces be effectively leveraged to maximize LLM reasoning performance in an offline setting? To this end, We propose Reinforcement Distillation (REDI), a two-stage framework. Stage 1 learns from positive traces via Supervised Fine-Tuning (SFT). Stage 2 further refines the model using both positive and negative traces through our proposed REDI objective. This novel objective is a simple, reference-free loss function that outperforms established methods like DPO and SimPO in this distillation context. Our empirical evaluations demonstrate REDI's superiority over baseline Rejection Sampling SFT or SFT combined with DPO/SimPO on mathematical reasoning tasks. Notably, the Qwen-REDI-1.5B model, post-trained on just 131k positive and negative examples from the open Open-R1 dataset, achieves an 83.1% score on MATH-500 (pass@1). Its performance matches or surpasses that of DeepSeek-R1-Distill-Qwen-1.5B (a model post-trained on 800k proprietary data) across various mathematical reasoning benchmarks, establishing a new state-of-the-art for 1.5B models post-trained offline with openly available data.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hendrycks/math", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24521", "title": "UniGeo: Taming Video Diffusion for Unified Consistent Geometry Estimation", "authors": [ "Yang-Tian Sun", "Xin Yu", "Zehuan Huang", "Yi-Hua Huang", "Yuan-Chen Guo", "Ziyi Yang", "Yan-Pei Cao", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "Recently, methods leveraging diffusion model priors to assist monocular geometric estimation (e.g., depth and normal) have gained significant attention due to their strong generalization ability. However, most existing works focus on estimating geometric properties within the camera coordinate system of individual video frames, neglecting the inherent ability of diffusion models to determine inter-frame correspondence. In this work, we demonstrate that, through appropriate design and fine-tuning, the intrinsic consistency of video generation models can be effectively harnessed for consistent geometric estimation. Specifically, we 1) select geometric attributes in the global coordinate system that share the same correspondence with video frames as the prediction targets, 2) introduce a novel and efficient conditioning method by reusing positional encodings, and 3) enhance performance through joint training on multiple geometric attributes that share the same correspondence. Our results achieve superior performance in predicting global geometric attributes in videos and can be directly applied to reconstruction tasks. Even when trained solely on static video data, our approach exhibits the potential to generalize to dynamic video scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.23009", "title": "EmergentTTS-Eval: Evaluating TTS Models on Complex Prosodic, Expressiveness, and Linguistic Challenges Using Model-as-a-Judge", "authors": [ "Ruskin Raj Manku", "Yuzhi Tang", "Xingjian Shi", "Mu Li", "Alex Smola" ], "github": "https://github.com/boson-ai/EmergentTTS-Eval-public", "project_page": "", "abstract": "Text-to-Speech (TTS) benchmarks often fail to capture how well models handle nuanced and semantically complex text. Building on EmergentTTS, we introduce EmergentTTS-Eval, a comprehensive benchmark covering six challenging TTS scenarios: emotions, paralinguistics, foreign words, syntactic complexity, complex pronunciation (e.g. URLs, formulas), and questions. Crucially, our framework automates both test-case generation and evaluation, making the benchmark easily extensible. Starting from a small set of human-written seed prompts, we iteratively extend them using LLMs to target specific structural, phonetic and prosodic challenges, resulting in 1,645 diverse test cases. Moreover, we employ a model-as-a-judge approach, using a Large Audio Language Model (LALM) to assess the speech across multiple dimensions such as expressed emotion, prosodic, intonational, and pronunciation accuracy. We evaluate state-of-the-art open-source and proprietary TTS systems, such as 11Labs, Deepgram, and OpenAI's 4o-mini-TTS, on EmergentTTS-Eval, demonstrating its ability to reveal fine-grained performance differences. Results show that the model-as-a-judge approach offers robust TTS assessment and a high correlation with human preferences. We open source the evaluation https://github.com/boson-ai/EmergentTTS-Eval-public{code} and the https://huggingface.co/datasets/bosonai/EmergentTTS-Eval{dataset}.", "github_manual": "", "github_regex": "https://github.com/boson-ai/EmergentTTS-Eval-public", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24858", "title": "MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs", "authors": [ "Gabrielle Kaili-May Liu", "Gal Yona", "Avi Caciularu", "Idan Szpektor", "Tim G. J. Rudner", "Arman Cohan" ], "github": "https://github.com/yale-nlp/MetaFaith", "project_page": "", "abstract": "A critical component in the trustworthiness of LLMs is reliable uncertainty communication, yet LLMs often use assertive language when conveying false claims, leading to over-reliance and eroded trust. We present the first systematic study of faithful confidence calibration of LLMs, benchmarking models' ability to use linguistic expressions of uncertainty that faithfully reflect their intrinsic uncertainty, across a comprehensive array of models, datasets, and prompting strategies. Our results demonstrate that LLMs largely fail at this task, and that existing interventions are insufficient: standard prompt approaches provide only marginal gains, and existing, factuality-based calibration techniques can even harm faithful calibration. To address this critical gap, we introduce MetaFaith, a novel prompt-based calibration approach inspired by human metacognition. We show that MetaFaith robustly improves faithful calibration across diverse models and task domains, enabling up to 61% improvement in faithfulness and achieving an 83% win rate over original generations as judged by humans.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yale-nlp/MetaFaith", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.18842", "title": "Don't Look Only Once: Towards Multimodal Interactive Reasoning with Selective Visual Revisitation", "authors": [ "Jiwan Chung", "Junhyeok Kim", "Siyeol Kim", "Jaeyoung Lee", "Min Soo Kim", "Youngjae Yu" ], "github": "", "project_page": "", "abstract": "We present v1, a lightweight extension to Multimodal Large Language Models (MLLMs) that enables selective visual revisitation during inference. While current MLLMs typically consume visual input only once and reason purely over internal memory, v1 introduces a simple point-and-copy mechanism that allows the model to dynamically retrieve relevant image regions throughout the reasoning process. This mechanism augments existing architectures with minimal modifications, enabling contextual access to visual tokens based on the model's evolving hypotheses. To train this capability, we construct v1g, a dataset of 300K multimodal reasoning traces with interleaved visual grounding annotations. Experiments on three multimodal mathematical reasoning benchmarks -- MathVista, MathVision, and MathVerse -- demonstrate that v1 consistently improves performance over comparable baselines, particularly on tasks requiring fine-grained visual reference and multi-step reasoning. Our results suggest that dynamic visual access is a promising direction for enhancing grounded multimodal reasoning. Code, models, and data will be released to support future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24864", "title": "ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models", "authors": [ "Mingjie Liu", "Shizhe Diao", "Ximing Lu", "Jian Hu", "Xin Dong", "Yejin Choi", "Jan Kautz", "Yi Dong" ], "github": "", "project_page": "", "abstract": "Recent advances in reasoning-centric language models have highlighted reinforcement learning (RL) as a promising method for aligning models with verifiable rewards. However, it remains contentious whether RL truly expands a model's reasoning capabilities or merely amplifies high-reward outputs already latent in the base model's distribution, and whether continually scaling up RL compute reliably leads to improved reasoning performance. In this work, we challenge prevailing assumptions by demonstrating that prolonged RL (ProRL) training can uncover novel reasoning strategies that are inaccessible to base models, even under extensive sampling. We introduce ProRL, a novel training methodology that incorporates KL divergence control, reference policy resetting, and a diverse suite of tasks. Our empirical analysis reveals that RL-trained models consistently outperform base models across a wide range of pass@k evaluations, including scenarios where base models fail entirely regardless of the number of attempts. We further show that reasoning boundary improvements correlates strongly with task competence of base model and training duration, suggesting that RL can explore and populate new regions of solution space over time. These findings offer new insights into the conditions under which RL meaningfully expands reasoning boundaries in language models and establish a foundation for future work on long-horizon RL for reasoning. We release model weights to support further research: https://huggingface.co/nvidia/Nemotron-Research-Reasoning-Qwen-1.5B", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24098", "title": "HardTests: Synthesizing High-Quality Test Cases for LLM Coding", "authors": [ "Zhongmou He", "Yee Man Choi", "Kexun Zhang", "Jiabao Ji", "Junting Zhou", "Dejia Xu", "Ivan Bercovich", "Aidan Zhang", "Lei Li" ], "github": "", "project_page": "", "abstract": "Verifiers play a crucial role in large language model (LLM) reasoning, needed by post-training techniques such as reinforcement learning. However, reliable verifiers are hard to get for difficult coding problems, because a well-disguised wrong solution may only be detected by carefully human-written edge cases that are difficult to synthesize. To address this issue, we propose HARDTESTGEN, a pipeline for high-quality test synthesis using LLMs. With this pipeline, we curate a comprehensive competitive programming dataset HARDTESTS with 47k problems and synthetic high-quality tests. Compared with existing tests, HARDTESTGEN tests demonstrate precision that is 11.3 percentage points higher and recall that is 17.5 percentage points higher when evaluating LLM-generated code. For harder problems, the improvement in precision can be as large as 40 points. HARDTESTS also proves to be more effective for model training, measured by downstream code generation performance. We will open-source our dataset and synthesis pipeline at https://leililab.github.io/HardTests/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24196", "title": "CLaSp: In-Context Layer Skip for Self-Speculative Decoding", "authors": [ "Longze Chen", "Renke Shan", "Huiming Wang", "Lu Wang", "Ziqiang Liu", "Run Luo", "Jiawei Wang", "Hamid Alinejad-Rokny", "Min Yang" ], "github": "", "project_page": "", "abstract": "Speculative decoding (SD) is a promising method for accelerating the decoding process of Large Language Models (LLMs). The efficiency of SD primarily hinges on the consistency between the draft model and the verify model. However, existing drafting approaches typically require additional modules to be trained, which can be challenging to implement and ensure compatibility across various LLMs. In this paper, we propose CLaSp, an in-context layer-skipping strategy for self-speculative decoding. Unlike prior methods, CLaSp does not require additional drafting modules or extra training. Instead, it employs a plug-and-play mechanism by skipping intermediate layers of the verify model to construct a compressed draft model. Specifically, we develop a dynamic programming algorithm that optimizes the layer-skipping process by leveraging the complete hidden states from the last verification stage as an objective. This enables CLaSp to dynamically adjust its layer-skipping strategy after each verification stage, without relying on pre-optimized sets of skipped layers. Experimental results across diverse downstream tasks demonstrate that CLaSp achieves a speedup of 1.3x ~ 1.7x on LLaMA3 series models without altering the original distribution of the generated text.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.23941", "title": "Vision Language Models are Biased", "authors": [ "An Vo", "Khai-Nguyen Nguyen", "Mohammad Reza Taesiri", "Vy Tuong Dang", "Anh Totti Nguyen", "Daeyoung Kim" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) memorize a vast amount of prior knowledge from the Internet that help them on downstream tasks but also may notoriously sway their outputs towards wrong or biased answers. In this work, we test how the knowledge about popular subjects hurt the accuracy of vision language models (VLMs) on standard, objective visual tasks of counting and identification. We find that state-of-the-art VLMs are strongly biased (e.g, unable to recognize a fourth stripe has been added to a 3-stripe Adidas logo) scoring an average of 17.05% accuracy in counting (e.g., counting stripes in an Adidas-like logo) across 7 diverse domains from animals, logos, chess, board games, optical illusions, to patterned grids. Insert text (e.g., \"Adidas\") describing the subject name into the counterfactual image further decreases VLM accuracy. The biases in VLMs are so strong that instructing them to double-check their results or rely exclusively on image details to answer improves counting accuracy by only +2 points, on average. Our work presents an interesting failure mode in VLMs and an automated framework for testing VLM biases. Code and data are available at: vlmsarebiased.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.23926", "title": "Point-MoE: Towards Cross-Domain Generalization in 3D Semantic Segmentation via Mixture-of-Experts", "authors": [ "Xuweiyi Chen", "Wentao Zhou", "Aruni RoyChowdhury", "Zezhou Cheng" ], "github": "", "project_page": "", "abstract": "While scaling laws have transformed natural language processing and computer vision, 3D point cloud understanding has yet to reach that stage. This can be attributed to both the comparatively smaller scale of 3D datasets, as well as the disparate sources of the data itself. Point clouds are captured by diverse sensors (e.g., depth cameras, LiDAR) across varied domains (e.g., indoor, outdoor), each introducing unique scanning patterns, sampling densities, and semantic biases. Such domain heterogeneity poses a major barrier towards training unified models at scale, especially under the realistic constraint that domain labels are typically inaccessible at inference time. In this work, we propose Point-MoE, a Mixture-of-Experts architecture designed to enable large-scale, cross-domain generalization in 3D perception. We show that standard point cloud backbones degrade significantly in performance when trained on mixed-domain data, whereas Point-MoE with a simple top-k routing strategy can automatically specialize experts, even without access to domain labels. Our experiments demonstrate that Point-MoE not only outperforms strong multi-domain baselines but also generalizes better to unseen domains. This work highlights a scalable path forward for 3D understanding: letting the model discover structure in diverse 3D data, rather than imposing it via manual curation or domain supervision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24867", "title": "Time Blindness: Why Video-Language Models Can't See What Humans Can?", "authors": [ "Ujjwal Upadhyay", "Mukul Ranjan", "Zhiqiang Shen", "Mohamed Elhoseiny" ], "github": "", "project_page": "", "abstract": "Recent advances in vision-language models (VLMs) have made impressive strides in understanding spatio-temporal relationships in videos. However, when spatial information is obscured, these models struggle to capture purely temporal patterns. We introduce SpookyBench, a benchmark where information is encoded solely in temporal sequences of noise-like frames, mirroring natural phenomena from biological signaling to covert communication. Interestingly, while humans can recognize shapes, text, and patterns in these sequences with over 98% accuracy, state-of-the-art VLMs achieve 0% accuracy. This performance gap highlights a critical limitation: an over-reliance on frame-level spatial features and an inability to extract meaning from temporal cues. Furthermore, when trained in data sets with low spatial signal-to-noise ratios (SNR), temporal understanding of models degrades more rapidly than human perception, especially in tasks requiring fine-grained temporal reasoning. Overcoming this limitation will require novel architectures or training paradigms that decouple spatial dependencies from temporal processing. Our systematic analysis shows that this issue persists across model scales and architectures. We release SpookyBench to catalyze research in temporal pattern recognition and bridge the gap between human and machine video understanding. Dataset and code has been made available on our project website: https://timeblindness.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24615", "title": "Harnessing Large Language Models for Scientific Novelty Detection", "authors": [ "Yan Liu", "Zonglin Yang", "Soujanya Poria", "Thanh-Son Nguyen", "Erik Cambria" ], "github": "", "project_page": "", "abstract": "In an era of exponential scientific growth, identifying novel research ideas is crucial and challenging in academia. Despite potential, the lack of an appropriate benchmark dataset hinders the research of novelty detection. More importantly, simply adopting existing NLP technologies, e.g., retrieving and then cross-checking, is not a one-size-fits-all solution due to the gap between textual similarity and idea conception. In this paper, we propose to harness large language models (LLMs) for scientific novelty detection (ND), associated with two new datasets in marketing and NLP domains. To construct the considerate datasets for ND, we propose to extract closure sets of papers based on their relationship, and then summarize their main ideas based on LLMs. To capture idea conception, we propose to train a lightweight retriever by distilling the idea-level knowledge from LLMs to align ideas with similar conception, enabling efficient and accurate idea retrieval for LLM novelty detection. Experiments show our method consistently outperforms others on the proposed benchmark datasets for idea retrieval and ND tasks. Codes and data are available at https://anonymous.4open.science/r/NoveltyDetection-10FB/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24517", "title": "un^2CLIP: Improving CLIP's Visual Detail Capturing Ability via Inverting unCLIP", "authors": [ "Yinqi Li", "Jiahe Zhao", "Hong Chang", "Ruibing Hou", "Shiguang Shan", "Xilin Chen" ], "github": "https://github.com/LiYinqi/un2CLIP", "project_page": "", "abstract": "Contrastive Language-Image Pre-training (CLIP) has become a foundation model and has been applied to various vision and multimodal tasks. However, recent works indicate that CLIP falls short in distinguishing detailed differences in images and shows suboptimal performance on dense-prediction and vision-centric multimodal tasks. Therefore, this work focuses on improving existing CLIP models, aiming to capture as many visual details in images as possible. We find that a specific type of generative models, unCLIP, provides a suitable framework for achieving our goal. Specifically, unCLIP trains an image generator conditioned on the CLIP image embedding. In other words, it inverts the CLIP image encoder. Compared to discriminative models like CLIP, generative models are better at capturing image details because they are trained to learn the data distribution of images. Additionally, the conditional input space of unCLIP aligns with CLIP's original image-text embedding space. Therefore, we propose to invert unCLIP (dubbed un^2CLIP) to improve the CLIP model. In this way, the improved image encoder can gain unCLIP's visual detail capturing ability while preserving its alignment with the original text encoder simultaneously. We evaluate our improved CLIP across various tasks to which CLIP has been applied, including the challenging MMVP-VLM benchmark, the dense-prediction open-vocabulary segmentation task, and multimodal large language model tasks. Experiments show that un^2CLIP significantly improves the original CLIP and previous CLIP improvement methods. Code and models will be available at https://github.com/LiYinqi/un2CLIP.", "github_manual": "", "github_regex": "https://github.com/LiYinqi/un2CLIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24862", "title": "ViStoryBench: Comprehensive Benchmark Suite for Story Visualization", "authors": [ "Cailin Zhuang", "Ailin Huang", "Wei Cheng", "Jingwei Wu", "Yaoqi Hu", "Jiaqi Liao", "Zhewei Huang", "Hongyuan Wang", "Xinyao Liao", "Weiwei Cai", "Hengyuan Xu", "Xuanyang Zhang", "Xianfang Zeng", "Gang Yu", "Chi Zhang" ], "github": "https://github.com/vistorybench/vistorybench", "project_page": "", "abstract": "Story visualization, which aims to generate a sequence of visually coherent images aligning with a given narrative and reference images, has seen significant progress with recent advancements in generative models. To further enhance the performance of story visualization frameworks in real-world scenarios, we introduce a comprehensive evaluation benchmark, ViStoryBench. We collect a diverse dataset encompassing various story types and artistic styles, ensuring models are evaluated across multiple dimensions such as different plots (e.g., comedy, horror) and visual aesthetics (e.g., anime, 3D renderings). ViStoryBench is carefully curated to balance narrative structures and visual elements, featuring stories with single and multiple protagonists to test models' ability to maintain character consistency. Additionally, it includes complex plots and intricate world-building to challenge models in generating accurate visuals. To ensure comprehensive comparisons, our benchmark incorporates a wide range of evaluation metrics assessing critical aspects. This structured and multifaceted framework enables researchers to thoroughly identify both the strengths and weaknesses of different models, fostering targeted improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vistorybench/vistorybench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.21864", "title": "DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation", "authors": [ "Mengda Xu", "Han Zhang", "Yifan Hou", "Zhenjia Xu", "Linxi Fan", "Manuela Veloso", "Shuran Song" ], "github": "", "project_page": "", "abstract": "We present DexUMI - a data collection and policy learning framework that uses the human hand as the natural interface to transfer dexterous manipulation skills to various robot hands. DexUMI includes hardware and software adaptations to minimize the embodiment gap between the human hand and various robot hands. The hardware adaptation bridges the kinematics gap using a wearable hand exoskeleton. It allows direct haptic feedback in manipulation data collection and adapts human motion to feasible robot hand motion. The software adaptation bridges the visual gap by replacing the human hand in video data with high-fidelity robot hand inpainting. We demonstrate DexUMI's capabilities through comprehensive real-world experiments on two different dexterous robot hand hardware platforms, achieving an average task success rate of 86%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.20873", "title": "Fork-Merge Decoding: Enhancing Multimodal Understanding in Audio-Visual Large Language Models", "authors": [ "Chaeyoung Jung", "Youngjoon Jang", "Jongmin Choi", "Joon Son Chung" ], "github": "", "project_page": "", "abstract": "The goal of this work is to enhance balanced multimodal understanding in audio-visual large language models (AV-LLMs) by addressing modality bias without requiring additional training. In current AV-LLMs, audio and video features are typically processed jointly in the decoder. While this strategy facilitates unified multimodal understanding, it may introduce modality bias, where the model tends to over-rely on one modality due to imbalanced training signals. To mitigate this, we propose Fork-Merge Decoding (FMD), a simple yet effective inference-time strategy that requires no additional training or architectural modifications. FMD first performs modality-specific reasoning by processing audio-only and video-only inputs through the early decoder layers (a fork phase), and then merges the resulting hidden states for joint reasoning in the remaining layers (a merge phase). This approach promotes balanced modality contributions and leverages complementary information across modalities. We evaluate our method on two representative AV-LLMs, VideoLLaMA2 and video-SALMONN, using three benchmark datasets. Experimental results demonstrate consistent performance improvements on tasks focused on audio, video, and combined audio-visual reasoning, demonstrating the effectiveness of inference-time interventions for robust multimodal understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.13157", "title": "Role-Playing Evaluation for Large Language Models", "authors": [ "Yassine El Boudouri", "Walter Nuninger", "Julian Alvarez", "Yvan Peter" ], "github": "https://github.com/yelboudouri/RPEval", "project_page": "", "abstract": "Large Language Models (LLMs) demonstrate a notable capacity for adopting personas and engaging in role-playing. However, evaluating this ability presents significant challenges, as human assessments are resource-intensive and automated evaluations can be biased. To address this, we introduce Role-Playing Eval (RPEval), a novel benchmark designed to assess LLM role-playing capabilities across four key dimensions: emotional understanding, decision-making, moral alignment, and in-character consistency. This article details the construction of RPEval and presents baseline evaluations. Our code and dataset are available at https://github.com/yelboudouri/RPEval", "github_manual": "", "github_regex": "https://github.com/yelboudouri/RPEval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.23832", "title": "LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generation", "authors": [ "Chaeeun Kim", "Jinu Lee", "Wonseok Hwang" ], "github": "", "project_page": "", "abstract": "Legal Case Retrieval (LCR), which retrieves relevant cases from a query case, is a fundamental task for legal professionals in research and decision-making. However, existing studies on LCR face two major limitations. First, they are evaluated on relatively small-scale retrieval corpora (e.g., 100-55K cases) and use a narrow range of criminal query types, which cannot sufficiently reflect the complexity of real-world legal retrieval scenarios. Second, their reliance on embedding-based or lexical matching methods often results in limited representations and legally irrelevant matches. To address these issues, we present: (1) LEGAR BENCH, the first large-scale Korean LCR benchmark, covering 411 diverse crime types in queries over 1.2M legal cases; and (2) LegalSearchLM, a retrieval model that performs legal element reasoning over the query case and directly generates content grounded in the target cases through constrained decoding. Experimental results show that LegalSearchLM outperforms baselines by 6-20% on LEGAR BENCH, achieving state-of-the-art performance. It also demonstrates strong generalization to out-of-domain cases, outperforming naive generative models trained on in-domain data by 15%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.21437", "title": "CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects", "authors": [ "Huaijin Pi", "Zhi Cen", "Zhiyang Dou", "Taku Komura" ], "github": "", "project_page": "", "abstract": "Synthesizing whole-body manipulation of articulated objects, including body motion, hand motion, and object motion, is a critical yet challenging task with broad applications in virtual humans and robotics. The core challenges are twofold. First, achieving realistic whole-body motion requires tight coordination between the hands and the rest of the body, as their movements are interdependent during manipulation. Second, articulated object manipulation typically involves high degrees of freedom and demands higher precision, often requiring the fingers to be placed at specific regions to actuate movable parts. To address these challenges, we propose a novel coordinated diffusion noise optimization framework. Specifically, we perform noise-space optimization over three specialized diffusion models for the body, left hand, and right hand, each trained on its own motion dataset to improve generalization. Coordination naturally emerges through gradient flow along the human kinematic chain, allowing the global body posture to adapt in response to hand motion objectives with high fidelity. To further enhance precision in hand-object interaction, we adopt a unified representation based on basis point sets (BPS), where end-effector positions are encoded as distances to the same BPS used for object geometry. This unified representation captures fine-grained spatial relationships between the hand and articulated object parts, and the resulting trajectories serve as targets to guide the optimization of diffusion noise, producing highly accurate interaction motion. We conduct extensive experiments demonstrating that our method outperforms existing approaches in motion quality and physical plausibility, and enables various capabilities such as object pose control, simultaneous walking and manipulation, and whole-body generation from hand-only data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24581", "title": "GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training", "authors": [ "Omer Nacar", "Anis Koubaa", "Serry Sibaee", "Yasser Al-Habashi", "Adel Ammar", "Wadii Boulila" ], "github": "", "project_page": "", "abstract": "Semantic textual similarity (STS) is a critical task in natural language processing (NLP), enabling applications in retrieval, clustering, and understanding semantic relationships between texts. However, research in this area for the Arabic language remains limited due to the lack of high-quality datasets and pre-trained models. This scarcity of resources has restricted the accurate evaluation and advance of semantic similarity in Arabic text. This paper introduces General Arabic Text Embedding (GATE) models that achieve state-of-the-art performance on the Semantic Textual Similarity task within the MTEB benchmark. GATE leverages Matryoshka Representation Learning and a hybrid loss training approach with Arabic triplet datasets for Natural Language Inference, which are essential for enhancing model performance in tasks that demand fine-grained semantic understanding. GATE outperforms larger models, including OpenAI, with a 20-25% performance improvement on STS benchmarks, effectively capturing the unique semantic nuances of Arabic.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.21523", "title": "More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models", "authors": [ "Chengzhi Liu", "Zhongxing Xu", "Qingyue Wei", "Juncheng Wu", "James Zou", "Xin Eric Wang", "Yuyin Zhou", "Sheng Liu" ], "github": "", "project_page": "", "abstract": "Test-time compute has empowered multimodal large language models to generate extended reasoning chains, yielding strong performance on tasks such as multimodal math reasoning. However, this improved reasoning ability often comes with increased hallucination: as generations become longer, models tend to drift away from image-grounded content and rely more heavily on language priors. Attention analysis shows that longer reasoning chains lead to reduced focus on visual inputs, which contributes to hallucination. To systematically study this phenomenon, we introduce RH-AUC, a metric that quantifies how a model's perception accuracy changes with reasoning length, allowing us to evaluate whether the model preserves visual grounding during reasoning. We also release RH-Bench, a diagnostic benchmark that spans a variety of multimodal tasks, designed to assess the trade-off between reasoning ability and hallucination. Our analysis reveals that (i) larger models typically achieve a better balance between reasoning and perception, and (ii) this balance is influenced more by the types and domains of training data than by its overall volume. These findings underscore the importance of evaluation frameworks that jointly consider both reasoning quality and perceptual fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.20047", "title": "Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks", "authors": [ "Debargha Ganguly", "Vikash Singh", "Sreehari Sankar", "Biyao Zhang", "Xuecen Zhang", "Srinivasan Iyengar", "Xiaotian Han", "Amit Sharma", "Shivkumar Kalyanaraman", "Vipin Chaudhary" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) show remarkable promise for democratizing automated reasoning by generating formal specifications. However, a fundamental tension exists: LLMs are probabilistic, while formal verification demands deterministic guarantees. This paper addresses this epistemological gap by comprehensively investigating failure modes and uncertainty quantification (UQ) in LLM-generated formal artifacts. Our systematic evaluation of five frontier LLMs reveals Satisfiability Modulo Theories (SMT) based autoformalization's domain-specific impact on accuracy (from +34.8% on logical tasks to -44.5% on factual ones), with known UQ techniques like the entropy of token probabilities failing to identify these errors. We introduce a probabilistic context-free grammar (PCFG) framework to model LLM outputs, yielding a refined uncertainty taxonomy. We find uncertainty signals are task-dependent (e.g., grammar entropy for logic, AUROC>0.93). Finally, a lightweight fusion of these signals enables selective verification, drastically reducing errors (14-100%) with minimal abstention, transforming LLM-driven formalization into a reliable engineering discipline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24189", "title": "Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflows", "authors": [ "Orlando Marquez Ayala", "Patrice Bechard", "Emily Chen", "Maggie Baird", "Jingfei Chen" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) such as GPT-4o can handle a wide range of complex tasks with the right prompt. As per token costs are reduced, the advantages of fine-tuning Small Language Models (SLMs) for real-world applications -- faster inference, lower costs -- may no longer be clear. In this work, we present evidence that, for domain-specific tasks that require structured outputs, SLMs still have a quality advantage. We compare fine-tuning an SLM against prompting LLMs on the task of generating low-code workflows in JSON form. We observe that while a good prompt can yield reasonable results, fine-tuning improves quality by 10% on average. We also perform systematic error analysis to reveal model limitations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.20977", "title": "Evaluating and Steering Modality Preferences in Multimodal Large Language Model", "authors": [ "Yu Zhang", "Jinlong Ma", "Yongshuai Hou", "Xuefeng Bai", "Kehai Chen", "Yang Xiang", "Jun Yu", "Min Zhang" ], "github": "https://github.com/EchoDreamer/Modality-Preference", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have achieved remarkable performance on complex tasks with multimodal context. However, it is still understudied whether they exhibit modality preference when processing multimodal contexts. To study this question, we first build a MC\\textsuperscript{2} benchmark under controlled evidence conflict scenarios to systematically evaluate modality preference, which is the tendency to favor one modality over another when making decisions based on multimodal conflicting evidence. Our extensive evaluation reveals that all 18 tested MLLMs generally demonstrate clear modality bias, and modality preference can be influenced by external interventions. An in-depth analysis reveals that the preference direction can be captured within the latent representations of MLLMs. Built on this, we propose a probing and steering method based on representation engineering to explicitly control modality preference without additional fine-tuning or carefully crafted prompts. Our method effectively amplifies modality preference toward a desired direction and applies to downstream tasks such as hallucination mitigation and multimodal machine translation, yielding promising improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EchoDreamer/Modality-Preference", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24119", "title": "The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It", "authors": [ "Zheng-Xin Yong", "Beyza Ermis", "Marzieh Fadaee", "Stephen H. Bach", "Julia Kreutzer" ], "github": "", "project_page": "", "abstract": "This paper presents a comprehensive analysis of the linguistic diversity of LLM safety research, highlighting the English-centric nature of the field. Through a systematic review of nearly 300 publications from 2020--2024 across major NLP conferences and workshops at *ACL, we identify a significant and growing language gap in LLM safety research, with even high-resource non-English languages receiving minimal attention. We further observe that non-English languages are rarely studied as a standalone language and that English safety research exhibits poor language documentation practice. To motivate future research into multilingual safety, we make several recommendations based on our survey, and we then pose three concrete future directions on safety evaluation, training data generation, and crosslingual safety generalization. Based on our survey and proposed directions, the field can develop more robust, inclusive AI safety practices for diverse global populations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24782", "title": "Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings", "authors": [ "Max Conti", "Manuel Faysse", "Gautier Viaud", "Antoine Bosselut", "C\u00e9line Hudelot", "Pierre Colombo" ], "github": "https://github.com/illuin-tech/contextual-embeddings", "project_page": "", "abstract": "A limitation of modern document retrieval embedding methods is that they typically encode passages (chunks) from the same documents independently, often overlooking crucial contextual information from the rest of the document that could greatly improve individual chunk representations. In this work, we introduce ConTEB (Context-aware Text Embedding Benchmark), a benchmark designed to evaluate retrieval models on their ability to leverage document-wide context. Our results show that state-of-the-art embedding models struggle in retrieval scenarios where context is required. To address this limitation, we propose InSeNT (In-sequence Negative Training), a novel contrastive post-training approach which combined with late chunking pooling enhances contextual representation learning while preserving computational efficiency. Our method significantly improves retrieval quality on ConTEB without sacrificing base model performance. We further find chunks embedded with our method are more robust to suboptimal chunking strategies and larger retrieval corpus sizes. We open-source all artifacts at https://github.com/illuin-tech/contextual-embeddings.", "github_manual": "", "github_regex": "https://github.com/illuin-tech/contextual-embeddings", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.21749", "title": "Revisiting Bi-Linear State Transitions in Recurrent Neural Networks", "authors": [ "M. Reza Ebrahimi", "Roland Memisevic" ], "github": "", "project_page": "", "abstract": "The role of hidden units in recurrent neural networks is typically seen as modeling memory, with research focusing on enhancing information retention through gating mechanisms. A less explored perspective views hidden units as active participants in the computation performed by the network, rather than passive memory stores. In this work, we revisit bi-linear operations, which involve multiplicative interactions between hidden units and input embeddings. We demonstrate theoretically and empirically that they constitute a natural inductive bias for representing the evolution of hidden states in state tracking tasks. These are the simplest type of task that require hidden units to actively contribute to the behavior of the network. We also show that bi-linear state updates form a natural hierarchy corresponding to state tracking tasks of increasing complexity, with popular linear recurrent networks such as Mamba residing at the lowest-complexity center of that hierarchy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24875", "title": "ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL", "authors": [ "Yu Zhang", "Yunqi Li", "Yifan Yang", "Rui Wang", "Yuqing Yang", "Dai Qi", "Jianmin Bao", "Dongdong Chen", "Chong Luo", "Lili Qiu" ], "github": "https://github.com/Franklin-Zhang0/ReasonGen-R1", "project_page": "", "abstract": "Although chain-of-thought reasoning and reinforcement learning (RL) have driven breakthroughs in NLP, their integration into generative vision models remains underexplored. We introduce ReasonGen-R1, a two-stage framework that first imbues an autoregressive image generator with explicit text-based \"thinking\" skills via supervised fine-tuning on a newly generated reasoning dataset of written rationales, and then refines its outputs using Group Relative Policy Optimization. To enable the model to reason through text before generating images, We automatically generate and release a corpus of model crafted rationales paired with visual prompts, enabling controlled planning of object layouts, styles, and scene compositions. Our GRPO algorithm uses reward signals from a pretrained vision language model to assess overall visual quality, optimizing the policy in each update. Evaluations on GenEval, DPG, and the T2I benchmark demonstrate that ReasonGen-R1 consistently outperforms strong baselines and prior state-of-the-art models. More: aka.ms/reasongen.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Franklin-Zhang0/ReasonGen-R1", "github_api_added_by": "auto" }, { "date": "2025-06-02", "arxiv_id": "2505.23923", "title": "ChARM: Character-based Act-adaptive Reward Modeling for Advanced Role-Playing Language Agents", "authors": [ "Feiteng Fang", "Ting-En Lin", "Yuchuan Wu", "Xiong Liu", "Xiang Huang", "Dingwei Chen", "Jing Ye", "Haonan Zhang", "Liang Zhu", "Hamid Alinejad-Rokny", "Min Yang", "Fei Huang", "Yongbin Li" ], "github": "https://github.com/calubkk/ChARM", "project_page": "", "abstract": "Role-Playing Language Agents (RPLAs) aim to simulate characters for realistic and engaging human-computer interactions. However, traditional reward models often struggle with scalability and adapting to subjective conversational preferences. We propose ChARM, a Character-based Act-adaptive Reward Model, addressing these challenges through two innovations: (1) an act-adaptive margin that significantly enhances learning efficiency and generalizability, and (2) a self-evolution mechanism leveraging large-scale unlabeled data to improve training coverage. Additionally, we introduce RoleplayPref, the first large-scale preference dataset specifically for RPLAs, featuring 1,108 characters, 13 subcategories, and 16,888 bilingual dialogues, alongside RoleplayEval, a dedicated evaluation benchmark. Experimental results show a 13% improvement over the conventional Bradley-Terry model in preference rankings. Furthermore, applying ChARM-generated rewards to preference learning techniques (e.g., direct preference optimization) achieves state-of-the-art results on CharacterEval and RoleplayEval. Code and dataset are available at https://github.com/calubkk/ChARM.", "github_manual": "", "github_regex": "https://github.com/calubkk/ChARM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24871", "title": "MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning", "authors": [ "Yiqing Liang", "Jielin Qiu", "Wenhao Ding", "Zuxin Liu", "James Tompkin", "Mengdi Xu", "Mengzhou Xia", "Zhengzhong Tu", "Laixi Shi", "Jiacheng Zhu" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a powerful paradigm for post-training large language models (LLMs), achieving state-of-the-art performance on tasks with structured, verifiable answers. Applying RLVR to Multimodal LLMs (MLLMs) presents significant opportunities but is complicated by the broader, heterogeneous nature of vision-language tasks that demand nuanced visual, logical, and spatial capabilities. As such, training MLLMs using RLVR on multiple datasets could be beneficial but creates challenges with conflicting objectives from interaction among diverse datasets, highlighting the need for optimal dataset mixture strategies to improve generalization and reasoning. We introduce a systematic post-training framework for Multimodal LLM RLVR, featuring a rigorous data mixture problem formulation and benchmark implementation. Specifically, (1) We developed a multimodal RLVR framework for multi-dataset post-training by curating a dataset that contains different verifiable vision-language problems and enabling multi-domain online RL learning with different verifiable rewards; (2) We proposed a data mixture strategy that learns to predict the RL fine-tuning outcome from the data mixture distribution, and consequently optimizes the best mixture. Comprehensive experiments showcase that multi-domain RLVR training, when combined with mixture prediction strategies, can significantly boost MLLM general reasoning capacities. Our best mixture improves the post-trained model's accuracy on out-of-distribution benchmarks by an average of 5.24% compared to the same model post-trained with uniform data mixture, and by a total of 20.74% compared to the pre-finetuning baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24025", "title": "DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models", "authors": [ "Chenbin Pan", "Wenbin He", "Zhengzhong Tu", "Liu Ren" ], "github": "", "project_page": "", "abstract": "The recent explosive interest in the reasoning capabilities of large language models, such as DeepSeek-R1, has demonstrated remarkable success through reinforcement learning-based fine-tuning frameworks, exemplified by methods like Group Relative Policy Optimization (GRPO). However, such reasoning abilities remain underexplored and notably absent in vision foundation models, including representation models like the DINO series. In this work, we propose DINO-R1, the first such attempt to incentivize visual in-context reasoning capabilities of vision foundation models using reinforcement learning. Specifically, DINO-R1 introduces Group Relative Query Optimization (GRQO), a novel reinforcement-style training strategy explicitly designed for query-based representation models, which computes query-level rewards based on group-normalized alignment quality. We also apply KL-regularization to stabilize the objectness distribution to reduce the training instability. This joint optimization enables dense and expressive supervision across queries while mitigating overfitting and distributional drift. Building upon Grounding-DINO, we train a series of DINO-R1 family models that integrate a visual prompt encoder and a visual-guided query selection mechanism. Extensive experiments on COCO, LVIS, and ODinW demonstrate that DINO-R1 significantly outperforms supervised fine-tuning baselines, achieving strong generalization in both open-vocabulary and closed-set visual prompting scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24785", "title": "EXP-Bench: Can AI Conduct AI Research Experiments?", "authors": [ "Patrick Tser Jern Kon", "Jiachen Liu", "Xinyi Zhu", "Qiuyi Ding", "Jingjia Peng", "Jiarong Xing", "Yibo Huang", "Yiming Qiu", "Jayanth Srinivasa", "Myungjin Lee", "Mosharaf Chowdhury", "Matei Zaharia", "Ang Chen" ], "github": "https://github.com/Just-Curieous/Curie/tree/main/benchmark", "project_page": "", "abstract": "Automating AI research holds immense potential for accelerating scientific progress, yet current AI agents struggle with the complexities of rigorous, end-to-end experimentation. We introduce EXP-Bench, a novel benchmark designed to systematically evaluate AI agents on complete research experiments sourced from influential AI publications. Given a research question and incomplete starter code, EXP-Bench challenges AI agents to formulate hypotheses, design and implement experimental procedures, execute them, and analyze results. To enable the creation of such intricate and authentic tasks with high-fidelity, we design a semi-autonomous pipeline to extract and structure crucial experimental details from these research papers and their associated open-source code. With the pipeline, EXP-Bench curated 461 AI research tasks from 51 top-tier AI research papers. Evaluations of leading LLM-based agents, such as OpenHands and IterativeAgent on EXP-Bench demonstrate partial capabilities: while scores on individual experimental aspects such as design or implementation correctness occasionally reach 20-35%, the success rate for complete, executable experiments was a mere 0.5%. By identifying these bottlenecks and providing realistic step-by-step experiment procedures, EXP-Bench serves as a vital tool for future AI agents to improve their ability to conduct AI research experiments. EXP-Bench is open-sourced at https://github.com/Just-Curieous/Curie/tree/main/benchmark/exp_bench.", "github_manual": "", "github_regex": "https://github.com/Just-Curieous/Curie/tree/main/benchmark", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24869", "title": "SiLVR: A Simple Language-based Video Reasoning Framework", "authors": [ "Ce Zhang", "Yan-Bo Lin", "Ziyang Wang", "Mohit Bansal", "Gedas Bertasius" ], "github": "https://github.com/CeeZh/SILVR", "project_page": "", "abstract": "Recent advances in test-time optimization have led to remarkable reasoning capabilities in Large Language Models (LLMs), enabling them to solve highly complex problems in math and coding. However, the reasoning capabilities of multimodal LLMs (MLLMs) still significantly lag, especially for complex video-language tasks. To address this issue, we present SiLVR, a Simple Language-based Video Reasoning framework that decomposes complex video understanding into two stages. In the first stage, SiLVR transforms raw video into language-based representations using multisensory inputs, such as short clip captions and audio/speech subtitles. In the second stage, language descriptions are fed into a powerful reasoning LLM to solve complex video-language understanding tasks. To handle long-context multisensory inputs, we use an adaptive token reduction scheme, which dynamically determines the temporal granularity with which to sample the tokens. Our simple, modular, and training-free video reasoning framework achieves the best-reported results on Video-MME (long), Video-MMMU (comprehension), Video-MMLU, CGBench, and EgoLife. Furthermore, our empirical study focused on video reasoning capabilities shows that, despite not being explicitly trained on video, strong reasoning LLMs can effectively aggregate multisensory input information from video, speech, and audio for complex temporal, causal, long-context, and knowledge acquisition reasoning tasks in video. Code is available at https://github.com/CeeZh/SILVR.", "github_manual": "", "github_regex": "https://github.com/CeeZh/SILVR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.23856", "title": "OMNIGUARD: An Efficient Approach for AI Safety Moderation Across Modalities", "authors": [ "Sahil Verma", "Keegan Hines", "Jeff Bilmes", "Charlotte Siska", "Luke Zettlemoyer", "Hila Gonen", "Chandan Singh" ], "github": "https://github.com/vsahil/OmniGuard", "project_page": "", "abstract": "The emerging capabilities of large language models (LLMs) have sparked concerns about their immediate potential for harmful misuse. The core approach to mitigate these concerns is the detection of harmful queries to the model. Current detection approaches are fallible, and are particularly susceptible to attacks that exploit mismatched generalization of model capabilities (e.g., prompts in low-resource languages or prompts provided in non-text modalities such as image and audio). To tackle this challenge, we propose OMNIGUARD, an approach for detecting harmful prompts across languages and modalities. Our approach (i) identifies internal representations of an LLM/MLLM that are aligned across languages or modalities and then (ii) uses them to build a language-agnostic or modality-agnostic classifier for detecting harmful prompts. OMNIGUARD improves harmful prompt classification accuracy by 11.57\\% over the strongest baseline in a multilingual setting, by 20.44\\% for image-based prompts, and sets a new SOTA for audio-based prompts. By repurposing embeddings computed during generation, OMNIGUARD is also very efficient (approx 120 times faster than the next fastest baseline). Code and data are available at: https://github.com/vsahil/OmniGuard.", "github_manual": "", "github_regex": "https://github.com/vsahil/OmniGuard", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2506.00073", "title": "The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets", "authors": [ "Shenzhe Zhu", "Jiao Sun", "Yi Nian", "Tobin South", "Alex Pentland", "Jiaxin Pei" ], "github": "https://github.com/ShenzheZhu/A2A-NT", "project_page": "", "abstract": "AI agents are increasingly used in consumer-facing applications to assist with tasks such as product search, negotiation, and transaction execution. In this paper, we explore a future scenario where both consumers and merchants authorize AI agents to fully automate negotiations and transactions. We aim to answer two key questions: (1) Do different LLM agents vary in their ability to secure favorable deals for users? (2) What risks arise from fully automating deal-making with AI agents in consumer markets? To address these questions, we develop an experimental framework that evaluates the performance of various LLM agents in real-world negotiation and transaction settings. Our findings reveal that AI-mediated deal-making is an inherently imbalanced game -- different agents achieve significantly different outcomes for their users. Moreover, behavioral anomalies in LLMs can result in financial losses for both consumers and merchants, such as overspending or accepting unreasonable deals. These results underscore that while automation can improve efficiency, it also introduces substantial risks. Users should exercise caution when delegating business decisions to AI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ShenzheZhu/A2A-NT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-02", "arxiv_id": "2505.24672", "title": "TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data Synthesis", "authors": [ "Xiaorui Wu", "Xiaofeng Mao", "Fei Li", "Xin Zhang", "Xuanhong Li", "Chong Teng", "Donghong Ji", "Zhuang Li" ], "github": "https://github.com/fisht0ucher/trident", "project_page": "", "abstract": "Large Language Models (LLMs) excel in various natural language processing tasks but remain vulnerable to generating harmful content or being exploited for malicious purposes. Although safety alignment datasets have been introduced to mitigate such risks through supervised fine-tuning (SFT), these datasets often lack comprehensive risk coverage. Most existing datasets focus primarily on lexical diversity while neglecting other critical dimensions. To address this limitation, we propose a novel analysis framework to systematically measure the risk coverage of alignment datasets across three essential dimensions: Lexical Diversity, Malicious Intent, and Jailbreak Tactics. We further introduce TRIDENT, an automated pipeline that leverages persona-based, zero-shot LLM generation to produce diverse and comprehensive instructions spanning these dimensions. Each harmful instruction is paired with an ethically aligned response, resulting in two datasets: TRIDENT-Core, comprising 26,311 examples, and TRIDENT-Edge, with 18,773 examples. Fine-tuning Llama 3.1-8B on TRIDENT-Edge demonstrates substantial improvements, achieving an average 14.29% reduction in Harm Score, and a 20% decrease in Attack Success Rate compared to the best-performing baseline model fine-tuned on the WildBreak dataset.", "github_manual": "https://github.com/fisht0ucher/trident", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.23001", "title": "DyePack: Provably Flagging Test Set Contamination in LLMs Using Backdoors", "authors": [ "Yize Cheng", "Wenxiao Wang", "Mazda Moayeri", "Soheil Feizi" ], "github": "", "project_page": "", "abstract": "Open benchmarks are essential for evaluating and advancing large language models, offering reproducibility and transparency. However, their accessibility makes them likely targets of test set contamination. In this work, we introduce DyePack, a framework that leverages backdoor attacks to identify models that used benchmark test sets during training, without requiring access to the loss, logits, or any internal details of the model. Like how banks mix dye packs with their money to mark robbers, DyePack mixes backdoor samples with the test data to flag models that trained on it. We propose a principled design incorporating multiple backdoors with stochastic targets, enabling exact false positive rate (FPR) computation when flagging every model. This provably prevents false accusations while providing strong evidence for every detected case of contamination. We evaluate DyePack on five models across three datasets, covering both multiple-choice and open-ended generation tasks. For multiple-choice questions, it successfully detects all contaminated models with guaranteed FPRs as low as 0.000073% on MMLU-Pro and 0.000017% on Big-Bench-Hard using eight backdoors. For open-ended generation tasks, it generalizes well and identifies all contaminated models on Alpaca with a guaranteed false positive rate of just 0.127% using six backdoors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.23504", "title": "VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuning", "authors": [ "Liyun Zhu", "Qixiang Chen", "Xi Shen", "Xiaodong Cun" ], "github": "https://github.com/GVCLab/VAU-R1", "project_page": "", "abstract": "Video Anomaly Understanding (VAU) is essential for applications such as smart cities, security surveillance, and disaster alert systems, yet remains challenging due to its demand for fine-grained spatio-temporal perception and robust reasoning under ambiguity. Despite advances in anomaly detection, existing methods often lack interpretability and struggle to capture the causal and contextual aspects of abnormal events. This limitation is further compounded by the absence of comprehensive benchmarks for evaluating reasoning ability in anomaly scenarios. To address both challenges, we introduce VAU-R1, a data-efficient framework built upon Multimodal Large Language Models (MLLMs), which enhances anomaly reasoning through Reinforcement Fine-Tuning (RFT). Besides, we propose VAU-Bench, the first Chain-of-Thought benchmark tailored for video anomaly reasoning, featuring multiple-choice QA, detailed rationales, temporal annotations, and descriptive captions. Empirical results show that VAU-R1 significantly improves question answering accuracy, temporal grounding, and reasoning coherence across diverse contexts. Together, our method and benchmark establish a strong foundation for interpretable and reasoning-aware video anomaly understanding. Our code is available at https://github.com/GVCLab/VAU-R1.", "github_manual": "", "github_regex": "https://github.com/GVCLab/VAU-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01049", "title": "Taming LLMs by Scaling Learning Rates with Gradient Grouping", "authors": [ "Siyuan Li", "Juanxi Tian", "Zedong Wang", "Xin Jin", "Zicheng Liu", "Wentao Zhang", "Dan Xu" ], "github": "", "project_page": "", "abstract": "Training large language models (LLMs) poses challenges due to their massive scale and heterogeneous architectures. While adaptive optimizers like AdamW help address gradient variations, they still struggle with efficient and effective parameter-wise learning rate estimation, resulting in training instability, slow convergence, and poor compatibility with parameter-efficient fine-tuning (PEFT) techniques. This work introduces Scaling with Gradient Grouping (SGG), an optimizer wrapper that improves adaptive learning rate estimation by dynamic grouping and group-specific scaling. SGG first groups gradient statistics in each layer into clusters and then applies cluster-specific scaling to calibrate learning rates for each parameter, thus imposing collective group-wise constraints while maintaining precise per-parameter adaptation. Experiments on diverse (M)LLM benchmarks show that SGG integrates seamlessly with existing optimizers, and offers consistent gains and faster convergence over baselines, with various model sizes. Its stability across varying batch sizes and learning rates establishes SGG as a robust choice for LLM optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.24625", "title": "Learning from Videos for 3D World: Enhancing MLLMs with 3D Vision Geometry Priors", "authors": [ "Duo Zheng", "Shijia Huang", "Yanyang Li", "Liwei Wang" ], "github": "", "project_page": "", "abstract": "Previous research has investigated the application of Multimodal Large Language Models (MLLMs) in understanding 3D scenes by interpreting them as videos. These approaches generally depend on comprehensive 3D data inputs, such as point clouds or reconstructed Bird's-Eye View (BEV) maps. In our research, we advance this field by enhancing the capability of MLLMs to understand and reason in 3D spaces directly from video data, without the need for additional 3D input. We propose a novel and efficient method, the Video-3D Geometry Large Language Model (VG LLM). Our approach employs a 3D visual geometry encoder that extracts 3D prior information from video sequences. This information is integrated with visual tokens and fed into the MLLM. Extensive experiments have shown that our method has achieved substantial improvements in various tasks related to 3D scene understanding and spatial reasoning, all directly learned from video sources. Impressively, our 4B model, which does not rely on explicit 3D data inputs, achieves competitive results compared to existing state-of-the-art methods, and even surpasses the Gemini-1.5-Pro in the VSI-Bench evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.23977", "title": "VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL", "authors": [ "Yichen Feng", "Zhangchen Xu", "Fengqing Jiang", "Yuetai Li", "Bhaskar Ramasubramanian", "Luyao Niu", "Bill Yuchen Lin", "Radha Poovendran" ], "github": "https://github.com/VisualSphinx/VisualSphinx", "project_page": "", "abstract": "Vision language models (VLMs) are expected to perform effective multimodal reasoning and make logically coherent decisions, which is critical to tasks such as diagram understanding and spatial problem solving. However, current VLM reasoning lacks large-scale and well-structured training datasets. To bridge this gap, we propose VisualSphinx, a first-of-its-kind large-scale synthetic visual logical reasoning training data. To tackle the challenge of image synthesis with grounding answers, we propose a rule-to-image synthesis pipeline, which extracts and expands puzzle rules from seed questions and generates the code of grounding synthesis image synthesis for puzzle sample assembly. Experiments demonstrate that VLM trained using GRPO on VisualSphinx benefit from logical coherence and readability of our dataset and exhibit improved performance on logical reasoning tasks. The enhanced reasoning capabilities developed from VisualSphinx also benefit other reasoning tasks such as algebraic reasoning, arithmetic reasoning and geometry reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/VisualSphinx/VisualSphinx", "github_api_added_by": "auto" }, { "date": "2025-06-03", "arxiv_id": "2506.00577", "title": "Reasoning Like an Economist: Post-Training on Economic Problems Induces Strategic Generalization in LLMs", "authors": [ "Yufa Zhou", "Shaobo Wang", "Xingyu Dong", "Xiangqi Jin", "Yifang Chen", "Yue Min", "Kexin Yang", "Xingzhang Ren", "Dayiheng Liu", "Linfeng Zhang" ], "github": "https://github.com/MasterZhou1/Recon", "project_page": "", "abstract": "Directly training Large Language Models (LLMs) for Multi-Agent Systems (MAS) remains challenging due to intricate reward modeling, dynamic agent interactions, and demanding generalization requirements. This paper explores whether post-training techniques, specifically Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR), can effectively generalize to multi-agent scenarios. We use economic reasoning as a testbed, leveraging its strong foundations in mathematics and game theory, its demand for structured analytical reasoning, and its relevance to real-world applications such as market design, resource allocation, and policy analysis. We introduce Recon (Reasoning like an ECONomist), a 7B-parameter open-source LLM post-trained on a hand-curated dataset of 2,100 high-quality economic reasoning problems. Comprehensive evaluation on economic reasoning benchmarks and multi-agent games reveals clear improvements in structured reasoning and economic rationality. These results underscore the promise of domain-aligned post-training for enhancing reasoning and agent alignment, shedding light on the roles of SFT and RL in shaping model behavior. Code is available at https://github.com/MasterZhou1/Recon .", "github_manual": "", "github_regex": "https://github.com/MasterZhou1/Recon", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01943", "title": "Learning Video Generation for Robotic Manipulation with Collaborative Trajectory Control", "authors": [ "Xiao Fu", "Xintao Wang", "Xian Liu", "Jianhong Bai", "Runsen Xu", "Pengfei Wan", "Di Zhang", "Dahua Lin" ], "github": "https://github.com/KwaiVGI/RoboMaster", "project_page": "", "abstract": "Recent advances in video diffusion models have demonstrated strong potential for generating robotic decision-making data, with trajectory conditions further enabling fine-grained control. However, existing trajectory-based methods primarily focus on individual object motion and struggle to capture multi-object interaction crucial in complex robotic manipulation. This limitation arises from multi-feature entanglement in overlapping regions, which leads to degraded visual fidelity. To address this, we present RoboMaster, a novel framework that models inter-object dynamics through a collaborative trajectory formulation. Unlike prior methods that decompose objects, our core is to decompose the interaction process into three sub-stages: pre-interaction, interaction, and post-interaction. Each stage is modeled using the feature of the dominant object, specifically the robotic arm in the pre- and post-interaction phases and the manipulated object during interaction, thereby mitigating the drawback of multi-object feature fusion present during interaction in prior work. To further ensure subject semantic consistency throughout the video, we incorporate appearance- and shape-aware latent representations for objects. Extensive experiments on the challenging Bridge V2 dataset, as well as in-the-wild evaluation, demonstrate that our method outperforms existing approaches, establishing new state-of-the-art performance in trajectory-controlled video generation for robotic manipulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KwaiVGI/RoboMaster", "github_api_added_by": "auto" }, { "date": "2025-06-03", "arxiv_id": "2506.01881", "title": "WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue", "authors": [ "Yaoyao Qian", "Jindan Huang", "Yuanli Wang", "Simon Yu", "Kyrie Zhixuan Zhou", "Jiayuan Mao", "Mingfu Liang", "Hanhan Zhou" ], "github": "", "project_page": "", "abstract": "Task-oriented dialogue systems often face difficulties when user utterances seem semantically complete but lack necessary structural information for appropriate system action. This arises because users frequently do not fully understand their own needs, while systems require precise intent definitions. Current LLM-based agents cannot effectively distinguish between linguistically complete and contextually triggerable expressions, lacking frameworks for collaborative intent formation. We present STORM, a framework modeling asymmetric information dynamics through conversations between UserLLM (full internal access) and AgentLLM (observable behavior only). STORM produces annotated corpora capturing expression trajectories and latent cognitive transitions, enabling systematic analysis of collaborative understanding development. Our contributions include: (1) formalizing asymmetric information processing in dialogue systems; (2) modeling intent formation tracking collaborative understanding evolution; and (3) evaluation metrics measuring internal cognitive improvements alongside task performance. Experiments across four language models reveal that moderate uncertainty (40-60%) can outperform complete transparency in certain scenarios, with model-specific patterns suggesting reconsideration of optimal information completeness in human-AI collaboration. These findings contribute to understanding asymmetric reasoning dynamics and inform uncertainty-calibrated dialogue system design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00643", "title": "SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions", "authors": [ "Weijie Xu", "Shixian Cui", "Xi Fang", "Chi Xue", "Stephanie Eckman", "Chandan Reddy" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly evaluated on single-answer multiple-choice tasks, yet many real-world problems require identifying all correct answers from a set of options. This capability remains underexplored. We introduce SATA-BENCH, the first dedicated benchmark for evaluating LLMs on Select All That Apply (SATA) questions across diverse domains, including reading comprehension, law, and biomedicine. Our evaluation of 27 open-source and proprietary models reveals a significant gap: even the strongest model achieves only 41.8% exact match, exposing LLMs' inability to reliably identify all correct answers. We find that this weakness stems from two core challenges: selection bias - models favor certain choices regardless of content, and count bias - models fail to predict the correct number of answers. To address these issues, we propose Choice Funnel, a decoding strategy that combines token debiasing with adaptive thresholding to guide models toward complete and accurate selections. Choice Funnel achieves up to 29% higher exact match than competitive baselines while reducing inference cost by over 64%. Our findings expose fundamental limitations in current LLMs and introduce a new framework for diagnosing and improving multi-answer reasoning. We release SATA-BENCH and Choice Funnel to promote LLM development for robust decision-making in realistic, multi-answer applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00338", "title": "OWSM v4: Improving Open Whisper-Style Speech Models via Data Scaling and Cleaning", "authors": [ "Yifan Peng", "Shakeel Muhammad", "Yui Sudo", "William Chen", "Jinchuan Tian", "Chyi-Jiunn Lin", "Shinji Watanabe" ], "github": "", "project_page": "", "abstract": "The Open Whisper-style Speech Models (OWSM) project has developed a series of fully open speech foundation models using academic-scale resources, but their training data remains insufficient. This work enhances OWSM by integrating YODAS, a large-scale web-crawled dataset with a Creative Commons license. However, incorporating YODAS is nontrivial due to its wild nature, which introduces challenges such as incorrect language labels and audio-text misalignments. To address this, we develop a scalable data-cleaning pipeline using public toolkits, yielding a dataset with 166,000 hours of speech across 75 languages. Our new series of OWSM v4 models, trained on this curated dataset alongside existing OWSM data, significantly outperform previous versions on multilingual benchmarks. Our models even match or surpass frontier industrial models like Whisper and MMS in multiple scenarios. We will publicly release the cleaned YODAS data, pre-trained models, and all associated scripts via the ESPnet toolkit.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.24760", "title": "REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards", "authors": [ "Zafir Stojanovski", "Oliver Stanley", "Joe Sharratt", "Richard Jones", "Abdulhakeem Adefioye", "Jean Kaddour", "Andreas K\u00f6pf" ], "github": "https://github.com/open-thought/reasoning-gym", "project_page": "", "abstract": "We introduce Reasoning Gym (RG), a library of reasoning environments for reinforcement learning with verifiable rewards. It provides over 100 data generators and verifiers spanning multiple domains including algebra, arithmetic, computation, cognition, geometry, graph theory, logic, and various common games. Its key innovation is the ability to generate virtually infinite training data with adjustable complexity, unlike most previous reasoning datasets, which are typically fixed. This procedural generation approach allows for continuous evaluation across varying difficulty levels. Our experimental results demonstrate the efficacy of RG in both evaluating and reinforcement learning of reasoning models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-thought/reasoning-gym", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.23907", "title": "Cora: Correspondence-aware image editing using few step diffusion", "authors": [ "Amirhossein Almohammadi", "Aryan Mikaeili", "Sauradip Nag", "Negar Hassanpour", "Andrea Tagliasacchi", "Ali Mahdavi-Amiri" ], "github": "", "project_page": "", "abstract": "Image editing is an important task in computer graphics, vision, and VFX, with recent diffusion-based methods achieving fast and high-quality results. However, edits requiring significant structural changes, such as non-rigid deformations, object modifications, or content generation, remain challenging. Existing few step editing approaches produce artifacts such as irrelevant texture or struggle to preserve key attributes of the source image (e.g., pose). We introduce Cora, a novel editing framework that addresses these limitations by introducing correspondence-aware noise correction and interpolated attention maps. Our method aligns textures and structures between the source and target images through semantic correspondence, enabling accurate texture transfer while generating new content when necessary. Cora offers control over the balance between content generation and preservation. Extensive experiments demonstrate that, quantitatively and qualitatively, Cora excels in maintaining structure, textures, and identity across diverse edits, including pose changes, object addition, and texture refinements. User studies confirm that Cora delivers superior results, outperforming alternatives.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.23590", "title": "Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles", "authors": [ "Zifu Wang", "Junyi Zhu", "Bo Tang", "Zhiyu Li", "Feiyu Xiong", "Jiaqian Yu", "Matthew B. Blaschko" ], "github": "https://github.com/zifuwanggg/Jigsaw-R1", "project_page": "", "abstract": "The application of rule-based reinforcement learning (RL) to multimodal large language models (MLLMs) introduces unique challenges and potential deviations from findings in text-only domains, particularly for perception-heavy tasks. This paper provides a comprehensive study of rule-based visual RL, using jigsaw puzzles as a structured experimental framework. Jigsaw puzzles offer inherent ground truth, adjustable difficulty, and demand complex decision-making, making them ideal for this study. Our research reveals several key findings: Firstly, we find that MLLMs, initially performing near to random guessing on the simplest jigsaw puzzles, achieve near-perfect accuracy and generalize to complex, unseen configurations through fine-tuning. Secondly, training on jigsaw puzzles can induce generalization to other visual tasks, with effectiveness tied to specific task configurations. Thirdly, MLLMs can learn and generalize with or without explicit reasoning, though open-source models often favor direct answering. Consequently, even when trained for step-by-step reasoning, they can ignore the thinking process in deriving the final answer. Fourthly, we observe that complex reasoning patterns appear to be pre-existing rather than emergent, with their frequency increasing alongside training and task difficulty. Finally, our results demonstrate that RL exhibits more effective generalization than Supervised Fine-Tuning (SFT), and an initial SFT cold start phase can hinder subsequent RL optimization. Although these observations are based on jigsaw puzzles and may vary across other visual tasks, this research contributes a valuable piece of jigsaw to the larger puzzle of collective understanding rule-based visual RL and its potential in multimodal learning. The code is available at: https://github.com/zifuwanggg/Jigsaw-R1.", "github_manual": "", "github_regex": "https://github.com/zifuwanggg/Jigsaw-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.23059", "title": "From Token to Action: State Machine Reasoning to Mitigate Overthinking in Information Retrieval", "authors": [ "Dohyeon Lee", "Yeonseok Jeong", "Seung-won Hwang" ], "github": "https://github.com/ldilab/SMR", "project_page": "", "abstract": "Chain-of-Thought (CoT) prompting enables complex reasoning in large language models (LLMs), including applications in information retrieval (IR). However, it often leads to overthinking, where models produce excessively long and semantically redundant traces with little or no benefit. We identify two key challenges in IR: redundant trajectories that revisit similar states and misguided reasoning that diverges from user intent. To address these, we propose State Machine Reasoning (SMR), a transition-based reasoning framework composed of discrete actions (Refine, Rerank, Stop) that support early stopping and fine-grained control. Experiments on the BEIR and BRIGHT benchmarks show that SMR improves retrieval performance (nDCG@10) by 3.4% while reducing token usage by 74.4%. It generalizes across LLMs and retrievers without requiring task-specific tuning, offering a practical alternative to conventional CoT reasoning. The code and details are available at https://github.com/ldilab/SMR.", "github_manual": "", "github_regex": "https://github.com/ldilab/SMR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01413", "title": "Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models", "authors": [ "Yulei Qin", "Gang Li", "Zongyi Li", "Zihan Xu", "Yuchen Shi", "Zhekai Lin", "Xiao Cui", "Ke Li", "Xing Sun" ], "github": "https://github.com/yuleiqin/RAIF", "project_page": "", "abstract": "Existing large language models (LLMs) face challenges of following complex instructions, especially when multiple constraints are present and organized in paralleling, chaining, and branching structures. One intuitive solution, namely chain-of-thought (CoT), is expected to universally improve capabilities of LLMs. However, we find that the vanilla CoT exerts a negative impact on performance due to its superficial reasoning pattern of simply paraphrasing the instructions. It fails to peel back the compositions of constraints for identifying their relationship across hierarchies of types and dimensions. To this end, we propose a systematic method to boost LLMs in dealing with complex instructions via incentivizing reasoning for test-time compute scaling. First, we stem from the decomposition of complex instructions under existing taxonomies and propose a reproducible data acquisition method. Second, we exploit reinforcement learning (RL) with verifiable rule-centric reward signals to cultivate reasoning specifically for instruction following. We address the shallow, non-essential nature of reasoning under complex instructions via sample-wise contrast for superior CoT enforcement. We also exploit behavior cloning of experts to facilitate steady distribution shift from fast-thinking LLMs to skillful reasoners. Extensive evaluations on seven comprehensive benchmarks confirm the validity of the proposed method, where a 1.5B LLM achieves 11.74% gains with performance comparable to a 8B LLM. Codes and data are available at https://github.com/yuleiqin/RAIF.", "github_manual": "", "github_regex": "https://github.com/yuleiqin/RAIF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01939", "title": "Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning", "authors": [ "Shenzhi Wang", "Le Yu", "Chang Gao", "Chujie Zheng", "Shixuan Liu", "Rui Lu", "Kai Dang", "Xionghui Chen", "Jianxin Yang", "Zhenru Zhang", "Yuqiong Liu", "An Yang", "Andrew Zhao", "Yang Yue", "Shiji Song", "Bowen Yu", "Gao Huang", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful approach to enhancing the reasoning capabilities of Large Language Models (LLMs), while its mechanisms are not yet well understood. In this work, we undertake a pioneering exploration of RLVR through the novel perspective of token entropy patterns, comprehensively analyzing how different tokens influence reasoning performance. By examining token entropy patterns in Chain-of-Thought (CoT) reasoning, we observe that only a small fraction of tokens exhibit high entropy, and these tokens act as critical forks that steer the model toward diverse reasoning pathways. Furthermore, studying how entropy patterns evolve during RLVR training reveals that RLVR largely adheres to the base model's entropy patterns, primarily adjusting the entropy of high-entropy tokens. These findings highlight the significance of high-entropy tokens (i.e., forking tokens) to RLVR. We ultimately improve RLVR by restricting policy gradient updates to forking tokens and uncover a finding even beyond the 80/20 rule: utilizing only 20% of the tokens while maintaining performance comparable to full-gradient updates on the Qwen3-8B base model and significantly surpassing full-gradient updates on the Qwen3-32B (+11.04 on AIME'25 and +7.71 on AIME'24) and Qwen3-14B (+4.79 on AIME'25 and +5.21 on AIME'24) base models, highlighting a strong scaling trend. In contrast, training exclusively on the 80% lowest-entropy tokens leads to a marked decline in performance. These findings indicate that the efficacy of RLVR primarily arises from optimizing the high-entropy tokens that decide reasoning directions. Collectively, our results highlight the potential to understand RLVR through a token-entropy perspective and optimize RLVR by leveraging high-entropy minority tokens to further improve LLM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00539", "title": "ARIA: Training Language Agents with Intention-Driven Reward Aggregation", "authors": [ "Ruihan Yang", "Yikai Zhang", "Aili Chen", "Xintao Wang", "Siyu Yuan", "Jiangjie Chen", "Deqing Yang", "Yanghua Xiao" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have enabled agents to perform complex reasoning and decision-making through free-form language interactions. However, in open-ended language action environments (e.g., negotiation or question-asking games), the action space can be formulated as a joint distribution over tokens, resulting in an exponentially large action space. Sampling actions in such a space can lead to extreme reward sparsity, which brings large reward variance, hindering effective reinforcement learning (RL). To address this, we propose ARIA, a method that Aggregates Rewards in Intention space to enable efficient and effective language Agents training. ARIA aims to project natural language actions from the high-dimensional joint token distribution space into a low-dimensional intention space, where semantically similar actions are clustered and assigned shared rewards. This intention-aware reward aggregation reduces reward variance by densifying reward signals, fostering better policy optimization. Extensive experiments demonstrate that ARIA not only significantly reduces policy gradient variance, but also delivers substantial performance gains of an average of 9.95% across four downstream tasks, consistently outperforming offline and online RL baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.24846", "title": "MiCRo: Mixture Modeling and Context-aware Routing for Personalized Preference Learning", "authors": [ "Jingyan Shen", "Jiarui Yao", "Rui Yang", "Yifan Sun", "Feng Luo", "Rui Pan", "Tong Zhang", "Han Zhao" ], "github": "https://github.com/uiuctml/MiCRo", "project_page": "", "abstract": "Reward modeling is a key step in building safe foundation models when applying reinforcement learning from human feedback (RLHF) to align Large Language Models (LLMs). However, reward modeling based on the Bradley-Terry (BT) model assumes a global reward function, failing to capture the inherently diverse and heterogeneous human preferences. Hence, such oversimplification limits LLMs from supporting personalization and pluralistic alignment. Theoretically, we show that when human preferences follow a mixture distribution of diverse subgroups, a single BT model has an irreducible error. While existing solutions, such as multi-objective learning with fine-grained annotations, help address this issue, they are costly and constrained by predefined attributes, failing to fully capture the richness of human values. In this work, we introduce MiCRo, a two-stage framework that enhances personalized preference learning by leveraging large-scale binary preference datasets without requiring explicit fine-grained annotations. In the first stage, MiCRo introduces context-aware mixture modeling approach to capture diverse human preferences. In the second stage, MiCRo integrates an online routing strategy that dynamically adapts mixture weights based on specific context to resolve ambiguity, allowing for efficient and scalable preference adaptation with minimal additional supervision. Experiments on multiple preference datasets demonstrate that MiCRo effectively captures diverse human preferences and significantly improves downstream personalization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/uiuctml/MiCRo", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01853", "title": "ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding", "authors": [ "Junliang Ye", "Zhengyi Wang", "Ruowen Zhao", "Shenghao Xie", "Jun Zhu" ], "github": "https://github.com/JAMESYJL/ShapeLLM-Omni", "project_page": "", "abstract": "Recently, the powerful text-to-image capabilities of ChatGPT-4o have led to growing appreciation for native multimodal large language models. However, its multimodal capabilities remain confined to images and text. Yet beyond images, the ability to understand and generate 3D content is equally crucial. To address this gap, we propose ShapeLLM-Omni-a native 3D large language model capable of understanding and generating 3D assets and text in any sequence. First, we train a 3D vector-quantized variational autoencoder (VQVAE), which maps 3D objects into a discrete latent space to achieve efficient and accurate shape representation and reconstruction. Building upon the 3D-aware discrete tokens, we innovatively construct a large-scale continuous training dataset named 3D-Alpaca, encompassing generation, comprehension, and editing, thus providing rich resources for future research and training. Finally, by performing instruction-based training of the Qwen-2.5-vl-7B-Instruct model on the 3D-Alpaca dataset. Our work provides an effective attempt at extending multimodal models with basic 3D capabilities, which contributes to future research in 3D-native AI. Project page: https://github.com/JAMESYJL/ShapeLLM-Omni", "github_manual": "", "github_regex": "https://github.com/JAMESYJL/ShapeLLM-Omni", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00996", "title": "Temporal In-Context Fine-Tuning for Versatile Control of Video Diffusion Models", "authors": [ "Kinam Kim", "Junha Hyung", "Jaegul Choo" ], "github": "https://github.com/kinam0252/TIC-FT", "project_page": "", "abstract": "Recent advances in text-to-video diffusion models have enabled high-quality video synthesis, but controllable generation remains challenging, particularly under limited data and compute. Existing fine-tuning methods for conditional generation often rely on external encoders or architectural modifications, which demand large datasets and are typically restricted to spatially aligned conditioning, limiting flexibility and scalability. In this work, we introduce Temporal In-Context Fine-Tuning (TIC-FT), an efficient and versatile approach for adapting pretrained video diffusion models to diverse conditional generation tasks. Our key idea is to concatenate condition and target frames along the temporal axis and insert intermediate buffer frames with progressively increasing noise levels. These buffer frames enable smooth transitions, aligning the fine-tuning process with the pretrained model's temporal dynamics. TIC-FT requires no architectural changes and achieves strong performance with as few as 10-30 training samples. We validate our method across a range of tasks, including image-to-video and video-to-video generation, using large-scale base models such as CogVideoX-5B and Wan-14B. Extensive experiments show that TIC-FT outperforms existing baselines in both condition fidelity and visual quality, while remaining highly efficient in both training and inference. For additional results, visit https://kinam0252.github.io/TIC-FT/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kinam0252/TIC-FT", "github_api_added_by": "auto" }, { "date": "2025-06-03", "arxiv_id": "2506.00411", "title": "LoHoVLA: A Unified Vision-Language-Action Model for Long-Horizon Embodied Tasks", "authors": [ "Yi Yang", "Jiaxuan Sun", "Siqi Kou", "Yihan Wang", "Zhijie Deng" ], "github": "", "project_page": "", "abstract": "Real-world embodied agents face long-horizon tasks, characterized by high-level goals demanding multi-step solutions beyond single actions. Successfully navigating these requires both high-level task planning (i.e., decomposing goals into sub-tasks) and low-level motion control (i.e., generating precise robot actions). While existing vision language action (VLA) models and hierarchical architectures offer potential in embodied tasks, the former often falter in planning, and the latter can suffer from coordination issues, both hampering performance. We introduce a new unified VLA framework for long-horizon tasks, dubbed LoHoVLA, to overcome these limitations. LoHoVLA leverages a large pretrained vision language model (VLM) as the backbone to jointly generate language and action tokens for sub-task generation and robot action prediction, respectively. This shared representation promotes better generalization across tasks. Additionally, LoHoVLA embraces a hierarchical closed-loop control mechanism to mitigate errors originating from both high-level planning and low-level control. To train LoHoVLA, we introduce LoHoSet, a dataset built on the Ravens simulator, containing 20 long-horizon tasks, each with 1,000 expert demonstrations composed of visual observations, linguistic goals, sub-tasks, and robot actions. Experimental results show that LoHoVLA significantly surpasses both hierarchical and standard VLA approaches on long-horizon embodied tasks in the Ravens simulator. These findings underscore the promise of unified architectures for advancing generalizable embodied intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.24452", "title": "Stepsize anything: A unified learning rate schedule for budgeted-iteration training", "authors": [ "Anda Tang", "Yiming Dong", "Yutao Zeng", "zhou Xun", "Zhouchen Lin" ], "github": "https://github.com/Ttt-answer/UBA.git", "project_page": "", "abstract": "The expanding computational costs and limited resources underscore the critical need for budgeted-iteration training, which aims to achieve optimal learning within predetermined iteration budgets.While learning rate schedules fundamentally govern the performance of different networks and tasks, particularly in budgeted-iteration scenarios, their design remains largely heuristic, lacking theoretical foundations.In addition, the optimal learning rate schedule requires extensive trial-and-error selection, making the training process inefficient.In this work, we propose the Unified Budget-Aware (UBA) schedule, a theoretically grounded learning rate schedule that consistently outperforms commonly-used schedules among diverse architectures and tasks under different constrained training budgets.First, we bridge the gap by constructing a novel training budget-aware optimization framework, which explicitly accounts for the robustness to landscape curvature variations.From this framework, we derive the UBA schedule, controlled by a single hyper-parameter varphi that provides a trade-off between flexibility and simplicity, eliminating the need for per-network numerical optimization. Moreover, we establish a theoretical connection between varphi and the condition number, adding interpretation and justification to our approach. Besides, we prove the convergence for different values of varphi.We offer practical guidelines for its selection via theoretical analysis and empirical results.xtensive experimental results show that UBA consistently surpasses the commonly-used schedules across diverse vision and language tasks, spanning network architectures (e.g., ResNet, OLMo) and scales, under different training-iteration budgets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Ttt-answer/UBA.git", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00385", "title": "MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity Reconstruction and Generation", "authors": [ "Yakun Song", "Jiawei Chen", "Xiaobin Zhuang", "Chenpeng Du", "Ziyang Ma", "Jian Wu", "Jian Cong", "Dongya Jia", "Zhuo Chen", "Yuping Wang", "Yuxuan Wang", "Xie Chen" ], "github": "https://github.com/Ereboas/MagiCodec", "project_page": "", "abstract": "Neural audio codecs have made significant strides in efficiently mapping raw audio waveforms into discrete token representations, which are foundational for contemporary audio generative models. However, most existing codecs are optimized primarily for reconstruction quality, often at the expense of the downstream modelability of the encoded tokens. Motivated by the need to overcome this bottleneck, we introduce MagiCodec, a novel single-layer, streaming Transformer-based audio codec. MagiCodec is designed with a multistage training pipeline that incorporates Gaussian noise injection and latent regularization, explicitly targeting the enhancement of semantic expressiveness in the generated codes while preserving high reconstruction fidelity. We analytically derive the effect of noise injection in the frequency domain, demonstrating its efficacy in attenuating high-frequency components and fostering robust tokenization. Extensive experimental evaluations show that MagiCodec surpasses state-of-the-art codecs in both reconstruction quality and downstream tasks. Notably, the tokens produced by MagiCodec exhibit Zipf-like distributions, as observed in natural languages, thereby improving compatibility with language-model-based generative architectures. The code and pre-trained models are available at https://github.com/Ereboas/MagiCodec.", "github_manual": "", "github_regex": "https://github.com/Ereboas/MagiCodec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.24298", "title": "AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning", "authors": [ "Wei Fu", "Jiaxuan Gao", "Xujie Shen", "Chen Zhu", "Zhiyu Mei", "Chuyi He", "Shusheng Xu", "Guo Wei", "Jun Mei", "Jiashu Wang", "Tongkai Yang", "Binhang Yuan", "Yi Wu" ], "github": "https://github.com/inclusionAI/AReaL", "project_page": "", "abstract": "Reinforcement learning (RL) has become a trending paradigm for training large language models (LLMs), particularly for reasoning tasks. Effective RL for LLMs requires massive parallelization and poses an urgent need for efficient training systems. Most existing large-scale RL systems for LLMs are synchronous by alternating generation and training in a batch setting, where the rollouts in each training batch are generated by the same (or latest) model. This stabilizes RL training but suffers from severe system-level inefficiency. Generation must wait until the longest output in the batch is completed before model update, resulting in GPU underutilization. We present AReaL, a fully asynchronous RL system that completely decouples generation from training. Rollout workers in AReaL continuously generate new outputs without waiting, while training workers update the model whenever a batch of data is collected. AReaL also incorporates a collection of system-level optimizations, leading to substantially higher GPU utilization. To stabilize RL training, AReaL balances the workload of rollout and training workers to control data staleness, and adopts a staleness-enhanced PPO variant to better handle outdated training samples. Extensive experiments on math and code reasoning benchmarks show that AReaL achieves up to 2.57times training speedup compared to the best synchronous systems with the same number of GPUs and matched or even improved final performance. The code of AReaL is available at https://github.com/inclusionAI/AReaL/.", "github_manual": "", "github_regex": "https://github.com/inclusionAI/AReaL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.24842", "title": "Cascading Adversarial Bias from Injection to Distillation in Language Models", "authors": [ "Harsh Chaudhari", "Jamie Hayes", "Matthew Jagielski", "Ilia Shumailov", "Milad Nasr", "Alina Oprea" ], "github": "", "project_page": "", "abstract": "Model distillation has become essential for creating smaller, deployable language models that retain larger system capabilities. However, widespread deployment raises concerns about resilience to adversarial manipulation. This paper investigates vulnerability of distilled models to adversarial injection of biased content during training. We demonstrate that adversaries can inject subtle biases into teacher models through minimal data poisoning, which propagates to student models and becomes significantly amplified. We propose two propagation modes: Untargeted Propagation, where bias affects multiple tasks, and Targeted Propagation, focusing on specific tasks while maintaining normal behavior elsewhere. With only 25 poisoned samples (0.25% poisoning rate), student models generate biased responses 76.9% of the time in targeted scenarios - higher than 69.4% in teacher models. For untargeted propagation, adversarial bias appears 6x-29x more frequently in student models on unseen tasks. We validate findings across six bias types (targeted advertisements, phishing links, narrative manipulations, insecure coding practices), various distillation methods, and different modalities spanning text and code generation. Our evaluation reveals shortcomings in current defenses - perplexity filtering, bias detection systems, and LLM-based autorater frameworks - against these attacks. Results expose significant security vulnerabilities in distilled models, highlighting need for specialized safeguards. We propose practical design principles for building effective adversarial bias mitigation strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.24183", "title": "CodeV-R1: Reasoning-Enhanced Verilog Generation", "authors": [ "Yaoyu Zhu", "Di Huang", "Hanqi Lyu", "Xiaoyun Zhang", "Chongxiao Li", "Wenxuan Shi", "Yutong Wu", "Jianan Mu", "Jinghua Wang", "Yang Zhao", "Pengwei Jin", "Shuyao Cheng", "Shengwen Liang", "Xishan Zhang", "Rui Zhang", "Zidong Du", "Qi Guo", "Xing Hu", "Yunji Chen" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) trained via reinforcement learning with verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit, automatable verification, such as software programming and mathematical problems. Extending RLVR to electronic design automation (EDA), especially automatically generating hardware description languages (HDLs) like Verilog from natural-language (NL) specifications, however, poses three key challenges: the lack of automated and accurate verification environments, the scarcity of high-quality NL-code pairs, and the prohibitive computation cost of RLVR. To this end, we introduce CodeV-R1, an RLVR framework for training Verilog generation LLMs. First, we develop a rule-based testbench generator that performs robust equivalence checking against golden references. Second, we propose a round-trip data synthesis method that pairs open-source Verilog snippets with LLM-generated NL descriptions, verifies code-NL-code consistency via the generated testbench, and filters out inequivalent examples to yield a high-quality dataset. Third, we employ a two-stage \"distill-then-RL\" training pipeline: distillation for the cold start of reasoning abilities, followed by adaptive DAPO, our novel RLVR algorithm that can reduce training cost by adaptively adjusting sampling rate. The resulting model, CodeV-R1-7B, achieves 68.6% and 72.9% pass@1 on VerilogEval v2 and RTLLM v1.1, respectively, surpassing prior state-of-the-art by 12~20%, while matching or even exceeding the performance of 671B DeepSeek-R1. We will release our model, training pipeline, and dataset to facilitate research in EDA and LLM communities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.19621", "title": "Think Again! The Effect of Test-Time Compute on Preferences, Opinions, and Beliefs of Large Language Models", "authors": [ "George Kour", "Itay Nakash", "Ateret Anaby-Tavor", "Michal Shmueli-Scheuer" ], "github": "https://github.com/IBM/POBS", "project_page": "", "abstract": "As Large Language Models (LLMs) become deeply integrated into human life and increasingly influence decision-making, it's crucial to evaluate whether and to what extent they exhibit subjective preferences, opinions, and beliefs. These tendencies may stem from biases within the models, which may shape their behavior, influence the advice and recommendations they offer to users, and potentially reinforce certain viewpoints. This paper presents the Preference, Opinion, and Belief survey (POBs), a benchmark developed to assess LLMs' subjective inclinations across societal, cultural, ethical, and personal domains. We applied our benchmark to evaluate leading open- and closed-source LLMs, measuring desired properties such as reliability, neutrality, and consistency. In addition, we investigated the effect of increasing the test-time compute, through reasoning and self-reflection mechanisms, on those metrics. While effective in other tasks, our results show that these mechanisms offer only limited gains in our domain. Furthermore, we reveal that newer model versions are becoming less consistent and more biased toward specific viewpoints, highlighting a blind spot and a concerning trend. POBS: https://ibm.github.io/POBS", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/IBM/POBS", "github_api_added_by": "auto" }, { "date": "2025-06-03", "arxiv_id": "2506.01084", "title": "zip2zip: Inference-Time Adaptive Vocabularies for Language Models via Token Compression", "authors": [ "Saibo Geng", "Nathan Ranchin", "Yunzhen yao", "Maxime Peyrard", "Chris Wendler", "Michael Gastpar", "Robert West" ], "github": "https://github.com/epfl-dlab/zip2zip", "project_page": "", "abstract": "Tokenization efficiency plays a critical role in the performance and cost of large language models (LLMs), yet most models rely on static tokenizers optimized for general-purpose corpora. These tokenizers' fixed vocabularies often fail to adapt to domain- or language-specific inputs, leading to longer token sequences and higher computational costs. We introduce zip2zip, a framework that enables LLMs to dynamically adjust token vocabulary at inference time, allowing for fewer generated tokens and thus faster inference. zip2zip consists of three key components: (1) a tokenizer based on Lempel-Ziv-Welch (LZW) compression that incrementally compresses tokens into reusable \"hypertokens\" on the fly; (2) an embedding layer that computes embeddings for newly formed hypertokens at runtime; and (3) a causal language modeling variant that trains the model to operate on hypertokenized, compressed sequences. We show that an existing LLM can be zip2zip-fied in 10 GPU-hours via parameter-efficient finetuning. The resulting zip2zip LLMs effectively learn to use hypertokens at inference time, reducing input and output sequence length by 20-60\\%, with significant improvements in inference latency.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/epfl-dlab/zip2zip", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00469", "title": "Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data", "authors": [ "Shaoxiong Ji", "Zihao Li", "Jaakko Paavola", "Indraneil Paul", "Hengyu Luo", "J\u00f6rg Tiedemann" ], "github": "https://github.com/AmericasNLP/americasnlp2021", "project_page": "", "abstract": "This paper investigates a critical design decision in the practice of massively multilingual continual pre-training -- the inclusion of parallel data. Specifically, we study the impact of bilingual translation data for massively multilingual language adaptation of the Llama3 family of models to 500 languages. To this end, we construct the MaLA bilingual translation corpus, containing data from more than 2,500 language pairs. Subsequently, we develop the EMMA-500 Llama 3 suite of four massively multilingual models -- continually pre-trained from the Llama 3 family of base models extensively on diverse data mixes up to 671B tokens -- and explore the effect of continual pre-training with or without bilingual translation data. Comprehensive evaluation across 7 tasks and 12 benchmarks demonstrates that bilingual data tends to enhance language transfer and performance, particularly for low-resource languages. We open-source the MaLA corpus, EMMA-500 Llama 3 suite artefacts, code, and model generations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AmericasNLP/americasnlp2021", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01667", "title": "EarthMind: Towards Multi-Granular and Multi-Sensor Earth Observation with Large Multimodal Models", "authors": [ "Yan Shu", "Bin Ren", "Zhitong Xiong", "Danda Pani Paudel", "Luc Van Gool", "Begum Demir", "Nicu Sebe", "Paolo Rota" ], "github": "", "project_page": "", "abstract": "Large Multimodal Models (LMMs) have demonstrated strong performance in various vision-language tasks. However, they often struggle to comprehensively understand Earth Observation (EO) data, which is critical for monitoring the environment and the effects of human activity on it. In this work, we present EarthMind, a novel vision-language framework for multi-granular and multi-sensor EO data understanding. EarthMind features two core components: (1) Spatial Attention Prompting (SAP), which reallocates attention within the LLM to enhance pixel-level understanding; and (2) Cross-modal Fusion, which aligns heterogeneous modalities into a shared space and adaptively reweighs tokens based on their information density for effective fusion. To facilitate multi-sensor fusion evaluation, we propose EarthMind-Bench, a comprehensive benchmark with over 2,000 human-annotated multi-sensor image-question pairs, covering a wide range of perception and reasoning tasks. Extensive experiments demonstrate the effectiveness of EarthMind. It achieves state-of-the-art performance on EarthMind-Bench, surpassing GPT-4o despite being only 4B in scale. Moreover, EarthMind outperforms existing methods on multiple public EO benchmarks, showcasing its potential to handle both multi-granular and multi-sensor challenges in a unified framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00512", "title": "Pro3D-Editor : A Progressive-Views Perspective for Consistent and Precise 3D Editing", "authors": [ "Yang Zheng", "Mengqi Huang", "Nan Chen", "Zhendong Mao" ], "github": "", "project_page": "", "abstract": "Text-guided 3D editing aims to precisely edit semantically relevant local 3D regions, which has significant potential for various practical applications ranging from 3D games to film production. Existing methods typically follow a view-indiscriminate paradigm: editing 2D views indiscriminately and projecting them back into 3D space. However, they overlook the different cross-view interdependencies, resulting in inconsistent multi-view editing. In this study, we argue that ideal consistent 3D editing can be achieved through a progressive-views paradigm, which propagates editing semantics from the editing-salient view to other editing-sparse views. Specifically, we propose Pro3D-Editor, a novel framework, which mainly includes Primary-view Sampler, Key-view Render, and Full-view Refiner. Primary-view Sampler dynamically samples and edits the most editing-salient view as the primary view. Key-view Render accurately propagates editing semantics from the primary view to other key views through its Mixture-of-View-Experts Low-Rank Adaption (MoVE-LoRA). Full-view Refiner edits and refines the 3D object based on the edited multi-views. Extensive experiments demonstrate that our method outperforms existing methods in editing accuracy and spatial consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01844", "title": "SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics", "authors": [ "Mustafa Shukor", "Dana Aubakirova", "Francesco Capuano", "Pepijn Kooijmans", "Steven Palma", "Adil Zouitine", "Michel Aractingi", "Caroline Pascal", "Martino Russi", "Andres Marafioti", "Simon Alibert", "Matthieu Cord", "Thomas Wolf", "Remi Cadene" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than training robotic policies from scratch, recent approaches adapt VLMs into vision-language-action (VLA) models that enable natural language-driven perception and control. However, existing VLAs are typically massive--often with billions of parameters--leading to high training costs and limited real-world deployability. Moreover, they rely on academic and industrial datasets, overlooking the growing availability of community-collected data from affordable robotic platforms. In this work, we present SmolVLA, a small, efficient, and community-driven VLA that drastically reduces both training and inference costs, while retaining competitive performance. SmolVLA is designed to be trained on a single GPU and deployed on consumer-grade GPUs or even CPUs. To further improve responsiveness, we introduce an asynchronous inference stack decoupling perception and action prediction from action execution, allowing higher control rates with chunked action generation. Despite its compact size, SmolVLA achieves performance comparable to VLAs that are 10x larger. We evaluate SmolVLA on a range of both simulated as well as real-world robotic benchmarks and release all code, pretrained models, and training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01713", "title": "SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning", "authors": [ "Zhongwei Wan", "Zhihao Dou", "Che Liu", "Yu Zhang", "Dongfei Cui", "Qinjian Zhao", "Hui Shen", "Jing Xiong", "Yi Xin", "Yifan Jiang", "Yangfan He", "Mi Zhang", "Shen Yan" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have shown promising capabilities in reasoning tasks, yet still struggle with complex problems requiring explicit self-reflection and self-correction, especially compared to their unimodal text-based counterparts. Existing reflection methods are simplistic and struggle to generate meaningful and instructive feedback, as the reasoning ability and knowledge limits of pre-trained models are largely fixed during initial training. To overcome these challenges, we propose Multimodal Self-Reflection enhanced reasoning with Group Relative Policy Optimization (SRPO), a two-stage reflection-aware reinforcement learning (RL) framework explicitly designed to enhance multimodal LLM reasoning. In the first stage, we construct a high-quality, reflection-focused dataset under the guidance of an advanced MLLM, which generates reflections based on initial responses to help the policy model learn both reasoning and self-reflection. In the second stage, we introduce a novel reward mechanism within the GRPO framework that encourages concise and cognitively meaningful reflection while avoiding redundancy. Extensive experiments across multiple multimodal reasoning benchmarks, including MathVista, MathVision, MathVerse, and MMMU-Pro, using Qwen-2.5-VL-7B and Qwen-2.5-VL-32B demonstrate that SRPO significantly outperforms state-of-the-art models, achieving notable improvements in both reasoning accuracy and reflection quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.21179", "title": "Normalized Attention Guidance: Universal Negative Guidance for Diffusion Model", "authors": [ "Dar-Yen Chen", "Hmrishav Bandyopadhyay", "Kai Zou", "Yi-Zhe Song" ], "github": "", "project_page": "", "abstract": "Negative guidance -- explicitly suppressing unwanted attributes -- remains a fundamental challenge in diffusion models, particularly in few-step sampling regimes. While Classifier-Free Guidance (CFG) works well in standard settings, it fails under aggressive sampling step compression due to divergent predictions between positive and negative branches. We present Normalized Attention Guidance (NAG), an efficient, training-free mechanism that applies extrapolation in attention space with L1-based normalization and refinement. NAG restores effective negative guidance where CFG collapses while maintaining fidelity. Unlike existing approaches, NAG generalizes across architectures (UNet, DiT), sampling regimes (few-step, multi-step), and modalities (image, video), functioning as a universal plug-in with minimal computational overhead. Through extensive experimentation, we demonstrate consistent improvements in text alignment (CLIP Score), fidelity (FID, PFID), and human-perceived quality (ImageReward). Our ablation studies validate each design component, while user studies confirm significant preference for NAG-guided outputs. As a model-agnostic inference-time approach requiring no retraining, NAG provides effortless negative guidance for all modern diffusion frameworks -- pseudocode in the Appendix!", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.24086", "title": "ComposeAnything: Composite Object Priors for Text-to-Image Generation", "authors": [ "Zeeshan Khan", "Shizhe Chen", "Cordelia Schmid" ], "github": "", "project_page": "", "abstract": "Generating images from text involving complex and novel object arrangements remains a significant challenge for current text-to-image (T2I) models. Although prior layout-based methods improve object arrangements using spatial constraints with 2D layouts, they often struggle to capture 3D positioning and sacrifice quality and coherence. In this work, we introduce ComposeAnything, a novel framework for improving compositional image generation without retraining existing T2I models. Our approach first leverages the chain-of-thought reasoning abilities of LLMs to produce 2.5D semantic layouts from text, consisting of 2D object bounding boxes enriched with depth information and detailed captions. Based on this layout, we generate a spatial and depth aware coarse composite of objects that captures the intended composition, serving as a strong and interpretable prior that replaces stochastic noise initialization in diffusion-based T2I models. This prior guides the denoising process through object prior reinforcement and spatial-controlled denoising, enabling seamless generation of compositional objects and coherent backgrounds, while allowing refinement of inaccurate priors. ComposeAnything outperforms state-of-the-art methods on the T2I-CompBench and NSR-1K benchmarks for prompts with 2D/3D spatial arrangements, high object counts, and surreal compositions. Human evaluations further demonstrate that our model generates high-quality images with compositions that faithfully reflect the text.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01952", "title": "WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks", "authors": [ "Atsuyuki Miyai", "Zaiying Zhao", "Kazuki Egashira", "Atsuki Sato", "Tatsumi Sunada", "Shota Onohara", "Hiromasa Yamanishi", "Mashiro Toyooka", "Kunato Nishina", "Ryoma Maeda", "Kiyoharu Aizawa", "Toshihiko Yamasaki" ], "github": "https://github.com/WebChoreArena/WebChoreArena", "project_page": "", "abstract": "Powered by a large language model (LLM), a web browsing agent operates web browsers in a human-like manner and offers a highly transparent path toward automating a wide range of everyday tasks. As web agents become increasingly capable and demonstrate proficiency in general browsing tasks, a critical question emerges: Can they go beyond general browsing to robustly handle tasks that are tedious and complex, or chores that humans often avoid doing themselves? In this paper, we introduce WebChoreArena, a new fully reproducible benchmark comprising 532 carefully curated tasks designed to extend the scope of WebArena beyond general browsing to more labor-intensive and tedious tasks. WebChoreArena systematically integrates three key challenges: (i) Massive Memory tasks requiring accurate retrieval of large amounts of information in the observations, (ii) Calculation tasks demanding precise mathematical reasoning, and (iii) Long-Term Memory tasks necessitating long-term memory across multiple webpages. Built on top of the fully reproducible and widely adopted four WebArena simulation environments, WebChoreArena ensures strict reproducibility and enables fair, direct comparisons with the established WebArena benchmark, offering key insights into agent progress. Our experimental results demonstrate that as LLMs evolve, represented by GPT-4o, Claude 3.7 Sonnet, and Gemini 2.5 Pro, significant improvements in performance are observed on WebChoreArena. These findings suggest that WebChoreArena is well-suited to measure the advancement of state-of-the-art LLMs with greater clarity. Nevertheless, the results also indicate that even with Gemini 2.5 Pro, there remains substantial room for improvement compared to WebArena, highlighting the increased challenges posed by WebChoreArena.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/WebChoreArena/WebChoreArena", "github_api_added_by": "auto" }, { "date": "2025-06-03", "arxiv_id": "2506.01484", "title": "LLM in the Loop: Creating the PARADEHATE Dataset for Hate Speech Detoxification", "authors": [ "Shuzhou Yuan", "Ercong Nie", "Lukas Kouba", "Ashish Yashwanth Kangen", "Helmut Schmid", "Hinrich Schutze", "Michael Farber" ], "github": "", "project_page": "", "abstract": "Detoxification, the task of rewriting harmful language into non-toxic text, has become increasingly important amid the growing prevalence of toxic content online. However, high-quality parallel datasets for detoxification, especially for hate speech, remain scarce due to the cost and sensitivity of human annotation. In this paper, we propose a novel LLM-in-the-loop pipeline leveraging GPT-4o-mini for automated detoxification. We first replicate the ParaDetox pipeline by replacing human annotators with an LLM and show that the LLM performs comparably to human annotation. Building on this, we construct PARADEHATE, a large-scale parallel dataset specifically for hatespeech detoxification. We release PARADEHATE as a benchmark of over 8K hate/non-hate text pairs and evaluate a wide range of baseline methods. Experimental results show that models such as BART, fine-tuned on PARADEHATE, achieve better performance in style accuracy, content preservation, and fluency, demonstrating the effectiveness of LLM-generated detoxification text as a scalable alternative to human annotation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.21724", "title": "OmniResponse: Online Multimodal Conversational Response Generation in Dyadic Interactions", "authors": [ "Cheng Luo", "Jianghui Wang", "Bing Li", "Siyang Song", "Bernard Ghanem" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce Online Multimodal Conversational Response Generation (OMCRG), a novel task that aims to online generate synchronized verbal and non-verbal listener feedback, conditioned on the speaker's multimodal input. OMCRG reflects natural dyadic interactions and poses new challenges in achieving synchronization between the generated audio and facial responses of the listener. To address these challenges, we innovatively introduce text as an intermediate modality to bridge the audio and facial responses. We hence propose OmniResponse, a Multimodal Large Language Model (MLLM) that autoregressively generates high-quality multi-modal listener responses. OmniResponse leverages a pretrained LLM enhanced with two novel components: Chrono-Text, which temporally anchors generated text tokens, and TempoVoice, a controllable online TTS module that produces speech synchronized with facial reactions. To support further OMCRG research, we present ResponseNet, a new dataset comprising 696 high-quality dyadic interactions featuring synchronized split-screen videos, multichannel audio, transcripts, and facial behavior annotations. Comprehensive evaluations conducted on ResponseNet demonstrate that OmniResponse significantly outperforms baseline models in terms of semantic speech content, audio-visual synchronization, and generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00772", "title": "LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning", "authors": [ "Zihang Liu", "Tianyu Pang", "Oleg Balabanov", "Chaoqun Yang", "Tianjin Huang", "Lu Yin", "Yaoqing Yang", "Shiwei Liu" ], "github": "https://github.com/zihanghliu/LIFT", "project_page": "", "abstract": "Recent studies have shown that supervised fine-tuning of LLMs on a small number of high-quality datasets can yield strong reasoning capabilities. However, full fine-tuning (Full FT), while powerful, is computationally expensive and susceptible to overfitting and catastrophic forgetting, particularly when data is limited. Sparse fine-tuning, which previously achieved notable success by updating only a small subset of model parameters, offers a promising trade-off between efficiency and effectiveness. Yet, it has lagged behind in the LLM era due to the difficulty of identifying parameters truly critical for reasoning. In this work, we state that weights with the largest magnitude after low-rank approximation are critical weights for fine-tuning, which we call Principal Weights. Surprisingly, while magnitude-based sparse fine-tuning performs poorly as a baseline on LLM fine-tuning, it becomes highly effective after rank reduction. These insights motivate our method: Low-rank Informed Sparse Fine-Tuning (LIFT). LIFT only updates the top 5% Principal Weights throughout training and consistently achieves better performance on reasoning tasks than Full FT, while maintaining memory efficiency on par with popular parameter-efficient fine-tuning methods. In addition to strong performance on target domains such as arithmetic reasoning, LIFT also retains up to 20% more source-domain knowledge, compared to Full FT and LoRA. Our code is available at: https://github.com/zihanghliu/LIFT.", "github_manual": "", "github_regex": "https://github.com/zihanghliu/LIFT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01920", "title": "From Guidelines to Practice: A New Paradigm for Arabic Language Model Evaluation", "authors": [ "Serry Sibaee", "Omer Nacar", "Adel Ammar", "Yasser Al-Habashi", "Abdulrahman Al-Batati", "Wadii Boulila" ], "github": "", "project_page": "", "abstract": "This paper addresses critical gaps in Arabic language model evaluation by establishing comprehensive theoretical guidelines and introducing a novel evaluation framework. We first analyze existing Arabic evaluation datasets, identifying significant issues in linguistic accuracy, cultural alignment, and methodological rigor. To address these limitations in LLMs, we present the Arabic Depth Mini Dataset (ADMD), a carefully curated collection of 490 challenging questions spanning ten major domains (42 sub-domains, see Figure 1. Using ADMD, we evaluate five leading language models: GPT-4, Claude 3.5 Sonnet, Gemini Flash 1.5, CommandR 100B, and Qwen-Max. Our results reveal significant variations in model performance across different domains, with particular challenges in areas requiring deep cultural understanding and specialized knowledge. Claude 3.5 Sonnet demonstrated the highest overall accuracy at 30\\%, showing relative strength in mathematical theory in Arabic, Arabic language, and islamic domains. This work provides both theoretical foundations and practical insights for improving Arabic language model evaluation, emphasizing the importance of cultural competence alongside technical capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.15772", "title": "MIKU-PAL: An Automated and Standardized Multi-Modal Method for Speech Paralinguistic and Affect Labeling", "authors": [ "Yifan Cheng", "Ruoyi Zhang", "Jiatong Shi" ], "github": "", "project_page": "", "abstract": "Acquiring large-scale emotional speech data with strong consistency remains a challenge for speech synthesis. This paper presents MIKU-PAL, a fully automated multimodal pipeline for extracting high-consistency emotional speech from unlabeled video data. Leveraging face detection and tracking algorithms, we developed an automatic emotion analysis system using a multimodal large language model (MLLM). Our results demonstrate that MIKU-PAL can achieve human-level accuracy (68.5% on MELD) and superior consistency (0.93 Fleiss kappa score) while being much cheaper and faster than human annotation. With the high-quality, flexible, and consistent annotation from MIKU-PAL, we can annotate fine-grained speech emotion categories of up to 26 types, validated by human annotators with 83% rationality ratings. Based on our proposed system, we further released a fine-grained emotional speech dataset MIKU-EmoBench(131.2 hours) as a new benchmark for emotional text-to-speech and visual voice cloning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.24523", "title": "Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors", "authors": [ "Andrea Pedrotti", "Michele Papucci", "Cristiano Ciaccio", "Alessio Miaschi", "Giovanni Puccetti", "Felice Dell'Orletta", "Andrea Esuli" ], "github": "", "project_page": "", "abstract": "Recent advancements in Generative AI and Large Language Models (LLMs) have enabled the creation of highly realistic synthetic content, raising concerns about the potential for malicious use, such as misinformation and manipulation. Moreover, detecting Machine-Generated Text (MGT) remains challenging due to the lack of robust benchmarks that assess generalization to real-world scenarios. In this work, we present a pipeline to test the resilience of state-of-the-art MGT detectors (e.g., Mage, Radar, LLM-DetectAIve) to linguistically informed adversarial attacks. To challenge the detectors, we fine-tune language models using Direct Preference Optimization (DPO) to shift the MGT style toward human-written text (HWT). This exploits the detectors' reliance on stylistic clues, making new generations more challenging to detect. Additionally, we analyze the linguistic shifts induced by the alignment and which features are used by detectors to detect MGT texts. Our results show that detectors can be easily fooled with relatively few examples, resulting in a significant drop in detection performance. This highlights the importance of improving detection methods and making them robust to unseen in-domain texts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01666", "title": "Synthesis of discrete-continuous quantum circuits with multimodal diffusion models", "authors": [ "Florian F\u00fcrrutter", "Zohim Chandani", "Ikko Hamamura", "Hans J. Briegel", "Gorka Mu\u00f1oz-Gil" ], "github": "https://github.com/FlorianFuerrutter/genQC", "project_page": "", "abstract": "Efficiently compiling quantum operations remains a major bottleneck in scaling quantum computing. Today's state-of-the-art methods achieve low compilation error by combining search algorithms with gradient-based parameter optimization, but they incur long runtimes and require multiple calls to quantum hardware or expensive classical simulations, making their scaling prohibitive. Recently, machine-learning models have emerged as an alternative, though they are currently restricted to discrete gate sets. Here, we introduce a multimodal denoising diffusion model that simultaneously generates a circuit's structure and its continuous parameters for compiling a target unitary. It leverages two independent diffusion processes, one for discrete gate selection and one for parameter prediction. We benchmark the model over different experiments, analyzing the method's accuracy across varying qubit counts, circuit depths, and proportions of parameterized gates. Finally, by exploiting its rapid circuit generation, we create large datasets of circuits for particular operations and use these to extract valuable heuristics that can help us discover new insights into quantum circuit synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FlorianFuerrutter/genQC", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01863", "title": "Unified Scaling Laws for Compressed Representations", "authors": [ "Andrei Panferov", "Alexandra Volkova", "Ionut-Vlad Modoranu", "Vage Egiazarian", "Mher Safaryan", "Dan Alistarh" ], "github": "", "project_page": "", "abstract": "Scaling laws have shaped recent advances in machine learning by enabling predictable scaling of model performance based on model size, computation, and data volume. Concurrently, the rise in computational cost for AI has motivated model compression techniques, notably quantization and sparsification, which have emerged to mitigate the steep computational demands associated with large-scale training and inference. This paper investigates the interplay between scaling laws and compression formats, exploring whether a unified scaling framework can accurately predict model performance when training occurs over various compressed representations, such as sparse, scalar-quantized, sparse-quantized or even vector-quantized formats. Our key contributions include validating a general scaling law formulation and showing that it is applicable both individually but also composably across compression types. Based on this, our main finding is demonstrating both theoretically and empirically that there exists a simple \"capacity\" metric -- based on the representation's ability to fit random Gaussian data -- which can robustly predict parameter efficiency across multiple compressed representations. On the practical side, we extend our formulation to directly compare the accuracy potential of different compressed formats, and to derive better algorithms for training over sparse-quantized formats.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00723", "title": "Pitfalls in Evaluating Language Model Forecasters", "authors": [ "Daniel Paleka", "Shashwat Goel", "Jonas Geiping", "Florian Tram\u00e8r" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have recently been applied to forecasting tasks, with some works claiming these systems match or exceed human performance. In this paper, we argue that, as a community, we should be careful about such conclusions as evaluating LLM forecasters presents unique challenges. We identify two broad categories of issues: (1) difficulty in trusting evaluation results due to many forms of temporal leakage, and (2) difficulty in extrapolating from evaluation performance to real-world forecasting. Through systematic analysis and concrete examples from prior work, we demonstrate how evaluation flaws can raise concerns about current and future performance claims. We argue that more rigorous evaluation methodologies are needed to confidently assess the forecasting abilities of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00530", "title": "CityLens: Benchmarking Large Language-Vision Models for Urban Socioeconomic Sensing", "authors": [ "Tianhui Liu", "Jie Feng", "Hetian Pang", "Xin Zhang", "Tianjian Ouyang", "Zhiyuan Zhang", "Yong Li" ], "github": "https://github.com/tsinghua-fib-lab/CityLens", "project_page": "", "abstract": "Understanding urban socioeconomic conditions through visual data is a challenging yet essential task for sustainable urban development and policy planning. In this work, we introduce CityLens, a comprehensive benchmark designed to evaluate the capabilities of large language-vision models (LLVMs) in predicting socioeconomic indicators from satellite and street view imagery. We construct a multi-modal dataset covering a total of 17 globally distributed cities, spanning 6 key domains: economy, education, crime, transport, health, and environment, reflecting the multifaceted nature of urban life. Based on this dataset, we define 11 prediction tasks and utilize three evaluation paradigms: Direct Metric Prediction, Normalized Metric Estimation, and Feature-Based Regression. We benchmark 17 state-of-the-art LLVMs across these tasks. Our results reveal that while LLVMs demonstrate promising perceptual and reasoning capabilities, they still exhibit limitations in predicting urban socioeconomic indicators. CityLens provides a unified framework for diagnosing these limitations and guiding future efforts in using LLVMs to understand and predict urban socioeconomic patterns. Our codes and datasets are open-sourced via https://github.com/tsinghua-fib-lab/CityLens.", "github_manual": "", "github_regex": "https://github.com/tsinghua-fib-lab/CityLens", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00930", "title": "Aligning VLM Assistants with Personalized Situated Cognition", "authors": [ "Yongqi Li", "Shen Zhou", "Xiaohu Li", "Xin Miao", "Jintao Wen", "Mayi Xu", "Jianhao Chen", "Birong Pan", "Hankun Kang", "Yuanyuan Zhu", "Ming Zhong", "Tieyun Qian" ], "github": "https://github.com/liyongqi2002/PCogAlign", "project_page": "", "abstract": "Vision-language models (VLMs) aligned with general human objectives, such as being harmless and hallucination-free, have become valuable assistants of humans in managing visual tasks. However, people with diversified backgrounds have different cognition even in the same situation. Consequently, they may have personalized expectations for VLM assistants. This highlights the urgent need to align VLM assistants with personalized situated cognition for real-world assistance. To study this problem, we first simplify it by characterizing individuals based on the sociological concept of Role-Set. Then, we propose to evaluate the individuals' actions to examine whether the personalized alignment is achieved. Further, we construct a benchmark named PCogAlignBench, which includes 18k instances and 20 individuals with different Role-Sets. Finally, we present a framework called PCogAlign, which constructs a cognition-aware and action-based reward model for personalized alignment. Experimental results and human evaluations demonstrate the reliability of the PCogAlignBench and the effectiveness of our proposed PCogAlign. We will open-source the constructed benchmark and code at https://github.com/NLPGM/PCogAlign.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/liyongqi2002/PCogAlign", "github_api_added_by": "user" }, { "date": "2025-06-03", "arxiv_id": "2505.20285", "title": "MaskSearch: A Universal Pre-Training Framework to Enhance Agentic Search Capability", "authors": [ "Weiqi Wu", "Xin Guan", "Shen Huang", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Jiuxin Cao", "Hai Zhao", "Jingren Zhou" ], "github": "https://github.com/Alibaba-NLP/MaskSearch", "project_page": "", "abstract": "Retrieval-Augmented Language Models (RALMs) represent a classic paradigm where models enhance generative capabilities using external knowledge retrieved via a specialized module. Recent advancements in Agent techniques enable Large Language Models (LLMs) to autonomously utilize tools for retrieval, planning, and reasoning. While existing training-based methods show promise, their agentic abilities are limited by inherent characteristics of the task-specific data used during training. To further enhance the universal search capability of agents, we propose a novel pre-training framework, MaskSearch. In the pre-training stage, we introduce the Retrieval Augmented Mask Prediction (RAMP) task, where the model learns to leverage search tools to fill masked spans on a large number of pre-training data, thus acquiring universal retrieval and reasoning capabilities for LLMs. After that, the model is trained on downstream tasks to achieve further improvement. We apply both Supervised Fine-tuning (SFT) and Reinforcement Learning (RL) for training. For SFT, we combine agent-based and distillation-based methods to generate training data, starting with a multi-agent system consisting of a planner, rewriter, observer, and followed by a self-evolving teacher model. While for RL, we employ DAPO as the training framework and adopt a hybrid reward system consisting of answer rewards and format rewards. Additionally, we introduce a curriculum learning approach that allows the model to learn progressively from easier to more challenging instances based on the number of masked spans. We evaluate the effectiveness of our framework in the scenario of open-domain multi-hop question answering. Through extensive experiments, we demonstrate that MaskSearch significantly enhances the performance of LLM-based search agents on both in-domain and out-of-domain downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Alibaba-NLP/MaskSearch", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01928", "title": "Esoteric Language Models", "authors": [ "Subham Sekhar Sahoo", "Zhihan Yang", "Yash Akhauri", "Johnna Liu", "Deepansha Singh", "Zhoujun Cheng", "Zhengzhong Liu", "Eric Xing", "John Thickstun", "Arash Vahdat" ], "github": "", "project_page": "", "abstract": "Diffusion-based language models offer a compelling alternative to autoregressive (AR) models by enabling parallel and controllable generation. Among this family of models, Masked Diffusion Models (MDMs) achieve the strongest performance but still underperform AR models in perplexity and lack key inference-time efficiency features--most notably, KV caching. In this work, we introduce Eso-LMs, a new family of models that fuses AR and MDM paradigms, enabling smooth interpolation between their perplexities while overcoming their respective limitations. Eso-LMs set a new state of the art on standard language modeling benchmarks. Crucially, we are the **first to introduce KV caching for MDMs** while preserving parallel generation, significantly improving inference efficiency. Combined with an optimized sampling schedule, our method achieves up to **65x** faster inference than standard MDMs and **4x** faster inference than prior semi-autoregressive approaches. We provide the code and model checkpoints on the project page: [http://s-sahoo.github.io/Eso-LMs](http://s-sahoo.github.io/Eso-LMs)", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00523", "title": "SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation", "authors": [ "Xingtong Ge", "Xin Zhang", "Tongda Xu", "Yi Zhang", "Xinjie Zhang", "Yan Wang", "Jun Zhang" ], "github": "https://github.com/XingtongGe/SenseFlow", "project_page": "", "abstract": "The Distribution Matching Distillation (DMD) has been successfully applied to text-to-image diffusion models such as Stable Diffusion (SD) 1.5. However, vanilla DMD suffers from convergence difficulties on large-scale flow-based text-to-image models, such as SD 3.5 and FLUX. In this paper, we first analyze the issues when applying vanilla DMD on large-scale models. Then, to overcome the scalability challenge, we propose implicit distribution alignment (IDA) to regularize the distance between the generator and fake distribution. Furthermore, we propose intra-segment guidance (ISG) to relocate the timestep importance distribution from the teacher model. With IDA alone, DMD converges for SD 3.5; employing both IDA and ISG, DMD converges for SD 3.5 and FLUX.1 dev. Along with other improvements such as scaled up discriminator models, our final model, dubbed SenseFlow, achieves superior performance in distillation for both diffusion based text-to-image models such as SDXL, and flow-matching models such as SD 3.5 Large and FLUX. The source code will be avaliable at https://github.com/XingtongGe/SenseFlow.", "github_manual": "", "github_regex": "https://github.com/XingtongGe/SenseFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00381", "title": "Neuro2Semantic: A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEG", "authors": [ "Siavash Shams", "Richard Antonello", "Gavin Mischler", "Stephan Bickel", "Ashesh Mehta", "Nima Mesgarani" ], "github": "https://github.com/SiavashShams/neuro2semantic", "project_page": "", "abstract": "Decoding continuous language from neural signals remains a significant challenge in the intersection of neuroscience and artificial intelligence. We introduce Neuro2Semantic, a novel framework that reconstructs the semantic content of perceived speech from intracranial EEG (iEEG) recordings. Our approach consists of two phases: first, an LSTM-based adapter aligns neural signals with pre-trained text embeddings; second, a corrector module generates continuous, natural text directly from these aligned embeddings. This flexible method overcomes the limitations of previous decoding approaches and enables unconstrained text generation. Neuro2Semantic achieves strong performance with as little as 30 minutes of neural data, outperforming a recent state-of-the-art method in low-data settings. These results highlight the potential for practical applications in brain-computer interfaces and neural decoding technologies.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SiavashShams/neuro2semantic", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.22865", "title": "BinauralFlow: A Causal and Streamable Approach for High-Quality Binaural Speech Synthesis with Flow Matching Models", "authors": [ "Susan Liang", "Dejan Markovic", "Israel D. Gebru", "Steven Krenn", "Todd Keebler", "Jacob Sandakly", "Frank Yu", "Samuel Hassel", "Chenliang Xu", "Alexander Richard" ], "github": "", "project_page": "", "abstract": "Binaural rendering aims to synthesize binaural audio that mimics natural hearing based on a mono audio and the locations of the speaker and listener. Although many methods have been proposed to solve this problem, they struggle with rendering quality and streamable inference. Synthesizing high-quality binaural audio that is indistinguishable from real-world recordings requires precise modeling of binaural cues, room reverb, and ambient sounds. Additionally, real-world applications demand streaming inference. To address these challenges, we propose a flow matching based streaming binaural speech synthesis framework called BinauralFlow. We consider binaural rendering to be a generation problem rather than a regression problem and design a conditional flow matching model to render high-quality audio. Moreover, we design a causal U-Net architecture that estimates the current audio frame solely based on past information to tailor generative models for streaming inference. Finally, we introduce a continuous inference pipeline incorporating streaming STFT/ISTFT operations, a buffer bank, a midpoint solver, and an early skip schedule to improve rendering continuity and speed. Quantitative and qualitative evaluations demonstrate the superiority of our method over SOTA approaches. A perceptual study further reveals that our model is nearly indistinguishable from real-world recordings, with a 42% confusion rate.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.21668", "title": "R1-Code-Interpreter: Training LLMs to Reason with Code via Supervised and Reinforcement Learning", "authors": [ "Yongchao Chen", "Yueying Liu", "Junwei Zhou", "Yilun Hao", "Jingquan Wang", "Yang Zhang", "Chuchu Fan" ], "github": "https://github.com/yongchao98/R1-Code-Interpreter", "project_page": "", "abstract": "Despite advances in reasoning and planning of R1-like models, Large Language Models (LLMs) still struggle with tasks requiring precise computation, symbolic manipulation, optimization, and algorithmic reasoning, in which textual reasoning lacks the rigor of code execution. A key challenge is enabling LLMs to decide when to use textual reasoning versus code generation. While OpenAI trains models to invoke a Code Interpreter as needed, public research lacks guidance on aligning pre-trained LLMs to effectively leverage code and generalize across diverse tasks. We present R1-Code-Interpreter, an extension of a text-only LLM trained via multi-turn supervised fine-tuning (SFT) and reinforcement learning (RL) to autonomously generate multiple code queries during step-by-step reasoning. We curate 144 reasoning and planning tasks (107 for training, 37 for testing), each with over 200 diverse questions. We fine-tune Qwen-2.5 models (3B/7B/14B) using various SFT and RL strategies, investigating different answer formats, reasoning vs. non-reasoning models, cold vs. warm starts, GRPO vs. PPO, and masked vs. unmasked code outputs. Unlike prior RL work on narrow domains, we find that Code Interpreter training is significantly harder due to high task diversity and expensive code execution, highlighting the critical role of the SFT stage. Our final model, R1-CI-14B, improves average accuracy on the 37 test tasks from 44.0\\% to 64.1\\%, outperforming GPT-4o (text-only: 58.6\\%) and approaching GPT-4o with Code Interpreter (70.9\\%), with the emergent self-checking behavior via code generation. Datasets, Codes, and Models are available at https://github.com/yongchao98/R1-Code-Interpreter and https://huggingface.co/yongchao98.", "github_manual": "", "github_regex": "https://github.com/yongchao98/R1-Code-Interpreter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.18128", "title": "Frankentext: Stitching random text fragments into long-form narratives", "authors": [ "Chau Minh Pham", "Jenna Russell", "Dzung Pham", "Mohit Iyyer" ], "github": "https://github.com/chtmp223/frankentext", "project_page": "", "abstract": "We introduce Frankentexts, a new type of long-form narratives produced by LLMs under the extreme constraint that most tokens (e.g., 90%) must be copied verbatim from human writings. This task presents a challenging test of controllable generation, requiring models to satisfy a writing prompt, integrate disparate text fragments, and still produce a coherent narrative. To generate Frankentexts, we instruct the model to produce a draft by selecting and combining human-written passages, then iteratively revise the draft while maintaining a user-specified copy ratio. We evaluate the resulting Frankentexts along three axes: writing quality, instruction adherence, and detectability. Gemini-2.5-Pro performs surprisingly well on this task: 81% of its Frankentexts are coherent and 100% relevant to the prompt. Notably, up to 59% of these outputs are misclassified as human-written by detectors like Pangram, revealing limitations in AI text detectors. Human annotators can sometimes identify Frankentexts through their abrupt tone shifts and inconsistent grammar between segments, especially in longer generations. Beyond presenting a challenging generation task, Frankentexts invite discussion on building effective detectors for this new grey zone of authorship, provide training data for mixed authorship detection, and serve as a sandbox for studying human-AI co-writing processes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/chtmp223/frankentext", "github_api_added_by": "auto" }, { "date": "2025-06-03", "arxiv_id": "2506.01074", "title": "How Programming Concepts and Neurons Are Shared in Code Language Models", "authors": [ "Amir Hossein Kargaran", "Yihong Liu", "Fran\u00e7ois Yvon", "Hinrich Sch\u00fctze" ], "github": "https://github.com/cisnlp/code-specific-neurons", "project_page": "", "abstract": "Several studies have explored the mechanisms of large language models (LLMs) in coding tasks, but most have focused on programming languages (PLs) in a monolingual setting. In this paper, we investigate the relationship between multiple PLs and English in the concept space of LLMs. We perform a few-shot translation task on 21 PL pairs using two Llama-based models. By decoding the embeddings of intermediate layers during this task, we observe that the concept space is closer to English (including PL keywords) and assigns high probabilities to English tokens in the second half of the intermediate layers. We analyze neuron activations for 11 PLs and English, finding that while language-specific neurons are primarily concentrated in the bottom layers, those exclusive to each PL tend to appear in the top layers. For PLs that are highly aligned with multiple other PLs, identifying language-specific neurons is not feasible. These PLs also tend to have a larger keyword set than other PLs and are closer to the model's concept space regardless of the input/output PL in the translation task. Our findings provide insights into how LLMs internally represent PLs, revealing structural patterns in the model's concept space. Code is available at https://github.com/cisnlp/code-specific-neurons.", "github_manual": "", "github_regex": "https://github.com/cisnlp/code-specific-neurons", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.00979", "title": "IVY-FAKE: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detection", "authors": [ "Wayne Zhang", "Changjiang Jiang", "Zhonghao Zhang", "Chenyang Si", "Fengchang Yu", "Wei Peng" ], "github": "https://github.com/Pi3AI/IvyFake", "project_page": "", "abstract": "The rapid advancement of Artificial Intelligence Generated Content (AIGC) in visual domains has resulted in highly realistic synthetic images and videos, driven by sophisticated generative frameworks such as diffusion-based architectures. While these breakthroughs open substantial opportunities, they simultaneously raise critical concerns about content authenticity and integrity. Many current AIGC detection methods operate as black-box binary classifiers, which offer limited interpretability, and no approach supports detecting both images and videos in a unified framework. This dual limitation compromises model transparency, reduces trustworthiness, and hinders practical deployment. To address these challenges, we introduce IVY-FAKE , a novel, unified, and large-scale dataset specifically designed for explainable multimodal AIGC detection. Unlike prior benchmarks, which suffer from fragmented modality coverage and sparse annotations, IVY-FAKE contains over 150,000 richly annotated training samples (images and videos) and 18,700 evaluation examples, each accompanied by detailed natural-language reasoning beyond simple binary labels. Building on this, we propose Ivy Explainable Detector (IVY-XDETECTOR), a unified AIGC detection and explainable architecture that jointly performs explainable detection for both image and video content. Our unified vision-language model achieves state-of-the-art performance across multiple image and video detection benchmarks, highlighting the significant advancements enabled by our dataset and modeling framework. Our data is publicly available at https://huggingface.co/datasets/AI-Safeguard/Ivy-Fake.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Pi3AI/IvyFake", "github_api_added_by": "auto" }, { "date": "2025-06-03", "arxiv_id": "2506.00789", "title": "RARE: Retrieval-Aware Robustness Evaluation for Retrieval-Augmented Generation Systems", "authors": [ "Yixiao Zeng", "Tianyu Cao", "Danqing Wang", "Xinran Zhao", "Zimeng Qiu", "Morteza Ziyadi", "Tongshuang Wu", "Lei Li" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) enhances recency and factuality in answers. However, existing evaluations rarely test how well these systems cope with real-world noise, conflicting between internal and external retrieved contexts, or fast-changing facts. We introduce Retrieval-Aware Robustness Evaluation (RARE), a unified framework and large-scale benchmark that jointly stress-tests query and document perturbations over dynamic, time-sensitive corpora. One of the central features of RARE is a knowledge-graph-driven synthesis pipeline (RARE-Get) that automatically extracts single and multi-hop relations from the customized corpus and generates multi-level question sets without manual intervention. Leveraging this pipeline, we construct a dataset (RARE-Set) spanning 400 expert-level time-sensitive finance, economics, and policy documents and 48,322 questions whose distribution evolves as the underlying sources change. To quantify resilience, we formalize retrieval-conditioned robustness metrics (RARE-Met) that capture a model's ability to remain correct or recover when queries, documents, or real-world retrieval results are systematically altered. Our results show that RAG systems exhibit surprising vulnerability to perturbations, with document robustness consistently being the weakest point regardless of generator size or architecture. RAG systems consistently show lower robustness on multi-hop queries than single-hop queries across all domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.24216", "title": "Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation", "authors": [ "Prasanna Reddy Pulakurthi", "Majid Rabbani", "Jamison Heard", "Sohail Dianat", "Celso M. de Melo", "Raghuveer Rao" ], "github": "https://github.com/PrasannaPulakurthi/SPM", "project_page": "", "abstract": "This work investigates Source-Free Domain Adaptation (SFDA), where a model adapts to a target domain without access to source data. A new augmentation technique, Shuffle PatchMix (SPM), and a novel reweighting strategy are introduced to enhance performance. SPM shuffles and blends image patches to generate diverse and challenging augmentations, while the reweighting strategy prioritizes reliable pseudo-labels to mitigate label noise. These techniques are particularly effective on smaller datasets like PACS, where overfitting and pseudo-label noise pose greater risks. State-of-the-art results are achieved on three major benchmarks: PACS, VisDA-C, and DomainNet-126. Notably, on PACS, improvements of 7.3% (79.4% to 86.7%) and 7.2% are observed in single-target and multi-target settings, respectively, while gains of 2.8% and 0.7% are attained on DomainNet-126 and VisDA-C. This combination of advanced augmentation and robust pseudo-label reweighting establishes a new benchmark for SFDA. The code is available at: https://github.com/PrasannaPulakurthi/SPM", "github_manual": "", "github_regex": "https://github.com/PrasannaPulakurthi/SPM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.22954", "title": "Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents", "authors": [ "Jenny Zhang", "Shengran Hu", "Cong Lu", "Robert Lange", "Jeff Clune" ], "github": "https://github.com/jennyzzt/dgm", "project_page": "", "abstract": "Today's AI systems have human-designed, fixed architectures and cannot autonomously and continuously improve themselves. The advance of AI could itself be automated. If done safely, that would accelerate AI development and allow us to reap its benefits much sooner. Meta-learning can automate the discovery of novel algorithms, but is limited by first-order improvements and the human design of a suitable search space. The G\\\"odel machine proposed a theoretical alternative: a self-improving AI that repeatedly modifies itself in a provably beneficial manner. Unfortunately, proving that most changes are net beneficial is impossible in practice. We introduce the Darwin G\\\"odel Machine (DGM), a self-improving system that iteratively modifies its own code (thereby also improving its ability to modify its own codebase) and empirically validates each change using coding benchmarks. Inspired by Darwinian evolution and open-endedness research, the DGM maintains an archive of generated coding agents. It grows the archive by sampling an agent from it and using a foundation model to create a new, interesting, version of the sampled agent. This open-ended exploration forms a growing tree of diverse, high-quality agents and allows the parallel exploration of many different paths through the search space. Empirically, the DGM automatically improves its coding capabilities (e.g., better code editing tools, long-context window management, peer-review mechanisms), increasing performance on SWE-bench from 20.0% to 50.0%, and on Polyglot from 14.2% to 30.7%. Furthermore, the DGM significantly outperforms baselines without self-improvement or open-ended exploration. All experiments were done with safety precautions (e.g., sandboxing, human oversight). The DGM is a significant step toward self-improving AI, capable of gathering its own stepping stones along paths that unfold into endless innovation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jennyzzt/dgm", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2506.01062", "title": "SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models", "authors": [ "Thinh Pham", "Nguyen Nguyen", "Pratibha Zunjare", "Weiyuan Chen", "Yu-Min Tseng", "Tu Vu" ], "github": "", "project_page": "", "abstract": "We introduce SealQA, a new challenge benchmark for evaluating SEarch-Augmented Language models on fact-seeking questions where web search yields conflicting, noisy, or unhelpful results. SealQA comes in three flavors: (1) Seal-0 (main) and (2) Seal-Hard, which assess factual accuracy and reasoning capabilities, with Seal-0 focusing on the most challenging questions where chat models (e.g., GPT-4.1) typically achieve near-zero accuracy; and (3) LongSeal, which extends SealQA to test long-context, multi-document reasoning in \"needle-in-a-haystack\" settings. Our evaluation reveals critical limitations in current models: Even frontier LLMs perform poorly across all SealQA flavors. On Seal-0, frontier agentic models equipped with tools like o3 and o4-mini achieve only 17.1% and 6.3% accuracy, respectively, at their best reasoning efforts. We find that advanced reasoning models such as DeepSeek-R1-671B and o3-mini are highly vulnerable to noisy search results. Notably, increasing test-time compute does not yield reliable gains across o3-mini, o4-mini, and o3, with performance often plateauing or even declining early. Additionally, while recent models are less affected by the \"lost-in-the-middle\" issue, they still fail to reliably identify relevant documents in LongSeal when faced with numerous distractors. To facilitate future work, we release SealQA at huggingface.co/datasets/vtllms/sealqa.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.17127", "title": "Pixels Versus Priors: Controlling Knowledge Priors in Vision-Language Models through Visual Counterfacts", "authors": [ "Michal Golovanevsky", "William Rudman", "Michael Lepori", "Amir Bar", "Ritambhara Singh", "Carsten Eickhoff" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) perform well on tasks such as visual question answering, but it remains unclear whether their reasoning relies more on memorized world knowledge or on the visual information present in the input image. To investigate this, we introduce Visual CounterFact, a new dataset of visually-realistic counterfactuals that put world knowledge priors (e.g, red strawberry) into direct conflict with visual input (e.g, blue strawberry). Using Visual CounterFact, we show that model predictions initially reflect memorized priors, but shift toward visual evidence in mid-to-late layers. This dynamic reveals a competition between the two modalities, with visual input ultimately overriding priors during evaluation. To control this behavior, we propose Pixels Versus Priors (PvP) steering vectors, a mechanism for controlling model outputs toward either world knowledge or visual input through activation-level interventions. On average, PvP successfully shifts 92.5% of color and 74.6% of size predictions from priors to counterfactuals. Together, these findings offer new tools for interpreting and controlling factual behavior in multimodal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-03", "arxiv_id": "2505.16122", "title": "Plan and Budget: Effective and Efficient Test-Time Scaling on Large Language Model Reasoning", "authors": [ "Junhong Lin", "Xinyue Zeng", "Jie Zhu", "Song Wang", "Julian Shun", "Jun Wu", "Dawei Zhou" ], "github": "https://github.com/junhongmit/P-and-B", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks, but their inference remains computationally inefficient. We observe a common failure mode in many prevalent LLMs, overthinking, where models generate verbose and tangential reasoning traces even for simple queries. Recent works have tried to mitigate this by enforcing fixed token budgets, however, this can lead to underthinking, especially on harder problems. Through empirical analysis, we identify that this inefficiency often stems from unclear problem-solving strategies. To formalize this, we develop a theoretical model, BBAM (Bayesian Budget Allocation Model), which models reasoning as a sequence of sub-questions with varying uncertainty, and introduce the E^3 metric to capture the trade-off between correctness and computation efficiency. Building on theoretical results from BBAM, we propose Plan-and-Budget, a model-agnostic, test-time framework that decomposes complex queries into sub-questions and allocates token budgets based on estimated complexity using adaptive scheduling. Plan-and-Budget improves reasoning efficiency across a range of tasks and models, achieving up to +70% accuracy gains, -39% token reduction, and +187.5% improvement in E^3. Notably, it elevates a smaller model (DS-Qwen-32B) to match the efficiency of a larger model (DS-LLaMA-70B)-demonstrating Plan-and-Budget's ability to close performance gaps without retraining. Our code is available at anonymous.4open.science/r/P-and-B-6513/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/junhongmit/P-and-B", "github_api_added_by": "auto" }, { "date": "2025-06-04", "arxiv_id": "2506.03147", "title": "UniWorld: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation", "authors": [ "Bin Lin", "Zongjian Li", "Xinhua Cheng", "Yuwei Niu", "Yang Ye", "Xianyi He", "Shenghai Yuan", "Wangbo Yu", "Shaodong Wang", "Yunyang Ge", "Yatian Pang", "Li Yuan" ], "github": "", "project_page": "", "abstract": "Although existing unified models deliver strong performance on vision-language understanding and text-to-image generation, their models are limited in exploring image perception and manipulation tasks, which are urgently desired by users for wide applications. Recently, OpenAI released their powerful GPT-4o-Image model for comprehensive image perception and manipulation, achieving expressive capability and attracting community interests. By observing the performance of GPT-4o-Image in our carefully constructed experiments, we infer that GPT-4o-Image leverages features extracted by semantic encoders instead of VAE, while VAEs are considered essential components in many image manipulation models. Motivated by such inspiring observations, we present a unified generative framework named UniWorld based on semantic features provided by powerful visual-language models and contrastive semantic encoders. As a result, we build a strong unified model using only 1% amount of BAGEL's data, which consistently outperforms BAGEL on image editing benchmarks. UniWorld also maintains competitive image understanding and generation capabilities, achieving strong performance across multiple image perception tasks. We fully open-source our models, including model weights, training and evaluation scripts, and datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.03136", "title": "Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning", "authors": [ "Yinjie Wang", "Ling Yang", "Ye Tian", "Ke Shen", "Mengdi Wang" ], "github": "https://github.com/Gen-Verse/CURE", "project_page": "", "abstract": "We propose CURE, a novel reinforcement learning framework with a dedicated reward design that co-evolves coding and unit test generation capabilities based on their interaction outcomes, without any ground-truth code as supervision. This approach enables flexible and scalable training and allows the unit tester to learn directly from the coder's mistakes. Our derived ReasonFlux-Coder-7B and 14B models improve code generation accuracy by 5.3% and Best-of-N accuracy by 9.0% after optimization on Qwen2.5-Instruct models, outperforming similarly sized Qwen-Coder, DeepSeek-Coder, and Seed-Coder. They naturally extend to downstream tasks such as test-time scaling and agentic coding-achieving a 8.1% improvement over the base model. For the long-CoT model, our ReasonFlux-Coder-4B consistently outperforms Qwen3-4B while achieving 64.8% inference efficiency in unit test generation. Notably, we also find that our model can serve as an effective reward model for reinforcement learning on base models. Project: https://github.com/Gen-Verse/CURE", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/CURE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.01674", "title": "MotionSight: Boosting Fine-Grained Motion Understanding in Multimodal LLMs", "authors": [ "Yipeng Du", "Tiehan Fan", "Kepan Nan", "Rui Xie", "Penghao Zhou", "Xiang Li", "Jian Yang", "Zhenheng Yang", "Ying Tai" ], "github": "", "project_page": "", "abstract": "Despite advancements in Multimodal Large Language Models (MLLMs), their proficiency in fine-grained video motion understanding remains critically limited. They often lack inter-frame differencing and tend to average or ignore subtle visual cues. Furthermore, while visual prompting has shown potential in static images, its application to video's temporal complexities, particularly for fine-grained motion understanding, remains largely unexplored. We investigate whether inherent capability can be unlocked and boost MLLMs' motion perception and enable distinct visual signatures tailored to decouple object and camera motion cues. In this study, we introduce MotionSight, a novel zero-shot method pioneering object-centric visual spotlight and motion blur as visual prompts to effectively improve fine-grained motion understanding without training. To convert this into valuable data assets, we curated MotionVid-QA, the first large-scale dataset for fine-grained video motion understanding, with hierarchical annotations including SFT and preference data, {\\Theta}(40K) video clips and {\\Theta}(87K) QAs. Experiments show MotionSight achieves state-of-the-art open-source performance and competitiveness with commercial models. In particular, for fine-grained motion understanding we present a novel zero-shot technique and a large-scale, high-quality dataset. All the code and annotations will be publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2505.24714", "title": "FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation", "authors": [ "Junyu Luo", "Zhizhuo Kou", "Liming Yang", "Xiao Luo", "Jinsheng Huang", "Zhiping Xiao", "Jingshu Peng", "Chengzhong Liu", "Jiaming Ji", "Xuanzhe Liu", "Sirui Han", "Ming Zhang", "Yike Guo" ], "github": "https://github.com/luo-junyu/FinMME", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have experienced rapid development in recent years. However, in the financial domain, there is a notable lack of effective and specialized multimodal evaluation datasets. To advance the development of MLLMs in the finance domain, we introduce FinMME, encompassing more than 11,000 high-quality financial research samples across 18 financial domains and 6 asset classes, featuring 10 major chart types and 21 subtypes. We ensure data quality through 20 annotators and carefully designed validation mechanisms. Additionally, we develop FinScore, an evaluation system incorporating hallucination penalties and multi-dimensional capability assessment to provide an unbiased evaluation. Extensive experimental results demonstrate that even state-of-the-art models like GPT-4o exhibit unsatisfactory performance on FinMME, highlighting its challenging nature. The benchmark exhibits high robustness with prediction variations under different prompts remaining below 1%, demonstrating superior reliability compared to existing datasets. Our dataset and evaluation protocol are available at https://huggingface.co/datasets/luojunyu/FinMME and https://github.com/luo-junyu/FinMME.", "github_manual": "", "github_regex": "https://github.com/luo-junyu/FinMME", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.02387", "title": "VS-Bench: Evaluating VLMs for Strategic Reasoning and Decision-Making in Multi-Agent Environments", "authors": [ "Zelai Xu", "Zhexuan Xu", "Xiangmin Yi", "Huining Yuan", "Xinlei Chen", "Yi Wu", "Chao Yu", "Yu Wang" ], "github": "https://github.com/zelaix/VS-Bench", "project_page": "", "abstract": "Recent advancements in Vision Language Models (VLMs) have expanded their capabilities to interactive agent tasks, yet existing benchmarks remain limited to single-agent or text-only environments. In contrast, real-world scenarios often involve multiple agents interacting within rich visual and linguistic contexts, posing challenges with both multimodal observations and strategic interactions. To bridge this gap, we introduce Visual Strategic Bench (VS-Bench), a multimodal benchmark that evaluates VLMs for strategic reasoning and decision-making in multi-agent environments. VS-Bench comprises eight vision-grounded environments spanning cooperative, competitive, and mixed-motive interactions, designed to assess agents' ability to predict others' future moves and optimize for long-term objectives. We consider two complementary evaluation dimensions, including offline evaluation of strategic reasoning by next-action prediction accuracy and online evaluation of decision-making by normalized episode return. Extensive experiments of fourteen leading VLMs reveal a significant gap between current models and optimal performance, with the best models attaining 47.8% prediction accuracy and 24.3% normalized return. We further conduct in-depth analyses on multimodal observations, test-time scaling, social behaviors, and failure cases of VLM agents. By standardizing the evaluation and highlighting the limitations of existing models, we envision VS-Bench as a foundation for future research on strategic multimodal agents. Code and data are available at https://vs-bench.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zelaix/VS-Bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.02096", "title": "SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis", "authors": [ "Zijian Wu", "Jinjie Ni", "Xiangyan Liu", "Zichen Liu", "Hang Yan", "Michael Qizhe Shieh" ], "github": "https://github.com/NUS-TRAIL/SynthRL", "project_page": "", "abstract": "Vision-language models (VLMs) trained via reinforcement learning with verifiable reward (RLVR) have shown notable progress in scaling test-time compute effectively. In this work, we investigate how synthesized RL data can further improve RLVR. To this end, we propose SynthRL-a scalable and guaranteed pipeline for automatic data scaling in reasoning-oriented RL training. SynthRL comprises three key stages: (1) selecting seed questions with appropriate distribution, (2) augmenting them into more challenging variants while preserving the original answers, and (3) a guaranteed verification stage that ensures near-perfect correctness and difficulty enhancement. Our empirical experiments demonstrate SynthRL's scalability and effectiveness. When applied to the MMK12 dataset, SynthRL synthesizes over 3.3K additional verifiable, challenging questions from approximately 8K seed samples. Models trained with our synthesized data achieve consistent gains across five out-of-domain visual math reasoning benchmarks, with a significant improvement over baseline models trained on seed data alone. Notably, detailed analysis reveals that the gains are more pronounced on the most challenging evaluation samples, highlighting SynthRL's effectiveness in eliciting deeper and more complex reasoning patterns.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NUS-TRAIL/SynthRL", "github_api_added_by": "auto" }, { "date": "2025-06-04", "arxiv_id": "2506.03131", "title": "Native-Resolution Image Synthesis", "authors": [ "Zidong Wang", "Lei Bai", "Xiangyu Yue", "Wanli Ouyang", "Yiyuan Zhang" ], "github": "", "project_page": "", "abstract": "We introduce native-resolution image synthesis, a novel generative modeling paradigm that enables the synthesis of images at arbitrary resolutions and aspect ratios. This approach overcomes the limitations of conventional fixed-resolution, square-image methods by natively handling variable-length visual tokens, a core challenge for traditional techniques. To this end, we introduce the Native-resolution diffusion Transformer (NiT), an architecture designed to explicitly model varying resolutions and aspect ratios within its denoising process. Free from the constraints of fixed formats, NiT learns intrinsic visual distributions from images spanning a broad range of resolutions and aspect ratios. Notably, a single NiT model simultaneously achieves the state-of-the-art performance on both ImageNet-256x256 and 512x512 benchmarks. Surprisingly, akin to the robust zero-shot capabilities seen in advanced large language models, NiT, trained solely on ImageNet, demonstrates excellent zero-shot generalization performance. It successfully generates high-fidelity images at previously unseen high resolutions (e.g., 1536 x 1536) and diverse aspect ratios (e.g., 16:9, 3:1, 4:3), as shown in Figure 1. These findings indicate the significant potential of native-resolution modeling as a bridge between visual generative modeling and advanced LLM methodologies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.02510", "title": "M^3FinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset", "authors": [ "Jie Zhu", "Junhui Li", "Yalong Wen", "Xiandong Li", "Lifan Guo", "Feng Chen" ], "github": "", "project_page": "", "abstract": "Recent breakthroughs in large language models (LLMs) have led to the development of new benchmarks for evaluating their performance in the financial domain. However, current financial benchmarks often rely on news articles, earnings reports, or announcements, making it challenging to capture the real-world dynamics of financial meetings. To address this gap, we propose a novel benchmark called M^3FinMeeting, which is a multilingual, multi-sector, and multi-task dataset designed for financial meeting understanding. First, M^3FinMeeting supports English, Chinese, and Japanese, enhancing comprehension of financial discussions in diverse linguistic contexts. Second, it encompasses various industry sectors defined by the Global Industry Classification Standard (GICS), ensuring that the benchmark spans a broad range of financial activities. Finally, M^3FinMeeting includes three tasks: summarization, question-answer (QA) pair extraction, and question answering, facilitating a more realistic and comprehensive evaluation of understanding. Experimental results with seven popular LLMs reveal that even the most advanced long-context models have significant room for improvement, demonstrating the effectiveness of M^3FinMeeting as a benchmark for assessing LLMs' financial meeting comprehension skills.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.02528", "title": "RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers", "authors": [ "Yan Gong", "Yiren Song", "Yicheng Li", "Chenglin Li", "Yin Zhang" ], "github": "https://github.com/gy8888/RelationAdapter", "project_page": "", "abstract": "Inspired by the in-context learning mechanism of large language models (LLMs), a new paradigm of generalizable visual prompt-based image editing is emerging. Existing single-reference methods typically focus on style or appearance adjustments and struggle with non-rigid transformations. To address these limitations, we propose leveraging source-target image pairs to extract and transfer content-aware editing intent to novel query images. To this end, we introduce RelationAdapter, a lightweight module that enables Diffusion Transformer (DiT) based models to effectively capture and apply visual transformations from minimal examples. We also introduce Relation252K, a comprehensive dataset comprising 218 diverse editing tasks, to evaluate model generalization and adaptability in visual prompt-driven scenarios. Experiments on Relation252K show that RelationAdapter significantly improves the model's ability to understand and transfer editing intent, leading to notable gains in generation quality and overall editing performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gy8888/RelationAdapter", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.02497", "title": "LumosFlow: Motion-Guided Long Video Generation", "authors": [ "Jiahao Chen", "Hangjie Yuan", "Yichen Qian", "Jingyun Liang", "Jiazheng Xing", "Pengwei Liu", "Weihua Chen", "Fan Wang", "Bing Su" ], "github": "", "project_page": "", "abstract": "Long video generation has gained increasing attention due to its widespread applications in fields such as entertainment and simulation. Despite advances, synthesizing temporally coherent and visually compelling long sequences remains a formidable challenge. Conventional approaches often synthesize long videos by sequentially generating and concatenating short clips, or generating key frames and then interpolate the intermediate frames in a hierarchical manner. However, both of them still remain significant challenges, leading to issues such as temporal repetition or unnatural transitions. In this paper, we revisit the hierarchical long video generation pipeline and introduce LumosFlow, a framework introduce motion guidance explicitly. Specifically, we first employ the Large Motion Text-to-Video Diffusion Model (LMTV-DM) to generate key frames with larger motion intervals, thereby ensuring content diversity in the generated long videos. Given the complexity of interpolating contextual transitions between key frames, we further decompose the intermediate frame interpolation into motion generation and post-hoc refinement. For each pair of key frames, the Latent Optical Flow Diffusion Model (LOF-DM) synthesizes complex and large-motion optical flows, while MotionControlNet subsequently refines the warped results to enhance quality and guide intermediate frame generation. Compared with traditional video frame interpolation, we achieve 15x interpolation, ensuring reasonable and continuous motion between adjacent frames. Experiments show that our method can generate long videos with consistent motion and appearance. Code and models will be made publicly available upon acceptance. Our project page: https://jiahaochen1.github.io/LumosFlow/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.02338", "title": "One Missing Piece for Open-Source Reasoning Models: A Dataset to Mitigate Cold-Starting Short CoT LLMs in RL", "authors": [ "Hyungjoo Chae", "Dongjin Kang", "Jihyuk Kim", "Beong-woo Kwak", "Sunghyun Park", "Haeju Park", "Jinyoung Yeo", "Moontae Lee", "Kyungjae Lee" ], "github": "https://github.com/openai/simple-evals", "project_page": "", "abstract": "With the release of R1, a publicly available large reasoning model (LRM), researchers commonly train new LRMs by training language models on R1's long chain-of-thought (CoT) inferences. While prior works show that LRMs' capabilities can be reproduced through direct distillation, the continued reliance on the existing models (e.g., R1) remains a critical limitation in advancing the field. As a first step toward independent LRM development, this paper explores the possibility of constructing a long CoT dataset with LLMs that are not trained for inference-time scaling. To this end, we present the Long CoT Collection, a dataset of 100K CoT rationales annotated using existing short CoT LLMs. We develop a pipeline that induces o1's novel reasoning strategies into short CoT LLMs, enabling them to think longer and introducing controllability over the thought budget to better manage the overthinking problem. Our extensive analyses validate that our dataset achieves quality comparable to--or slightly below--R1. Furthermore, our experiments demonstrate that training on our dataset not only strengthens general reasoning skills, but also provides a strong foundation for reinforcement learning--models initialized on our data achieve 2-3x larger gains with RLVR.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openai/simple-evals", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.01789", "title": "Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability", "authors": [ "Genta Indra Winata", "David Anugraha", "Emmy Liu", "Alham Fikri Aji", "Shou-Yi Hung", "Aditya Parashar", "Patrick Amadeus Irawan", "Ruochen Zhang", "Zheng-Xin Yong", "Jan Christian Blaise Cruz", "Niklas Muennighoff", "Seungone Kim", "Hanyang Zhao", "Sudipta Kar", "Kezia Erina Suryoraharjo", "M. Farid Adilazuarda", "En-Shiun Annie Lee", "Ayu Purwarianti", "Derry Tanti Wijaya", "Monojit Choudhury" ], "github": "https://github.com/datarubrics/datarubrics", "project_page": "", "abstract": "High-quality datasets are fundamental to training and evaluating machine learning models, yet their creation-especially with accurate human annotations-remains a significant challenge. Many dataset paper submissions lack originality, diversity, or rigorous quality control, and these shortcomings are often overlooked during peer review. Submissions also frequently omit essential details about dataset construction and properties. While existing tools such as datasheets aim to promote transparency, they are largely descriptive and do not provide standardized, measurable methods for evaluating data quality. Similarly, metadata requirements at conferences promote accountability but are inconsistently enforced. To address these limitations, this position paper advocates for the integration of systematic, rubric-based evaluation metrics into the dataset review process-particularly as submission volumes continue to grow. We also explore scalable, cost-effective methods for synthetic data generation, including dedicated tools and LLM-as-a-judge approaches, to support more efficient evaluation. As a call to action, we introduce DataRubrics, a structured framework for assessing the quality of both human- and model-generated datasets. Leveraging recent advances in LLM-based evaluation, DataRubrics offers a reproducible, scalable, and actionable solution for dataset quality assessment, enabling both authors and reviewers to uphold higher standards in data-centric research. We also release code to support reproducibility of LLM-based evaluations at https://github.com/datarubrics/datarubrics.", "github_manual": "", "github_regex": "https://github.com/datarubrics/datarubrics", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.02454", "title": "Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports From Scratch with Agentic Framework", "authors": [ "Zhaorui Yang", "Bo Pan", "Han Wang", "Yiyao Wang", "Xingyu Liu", "Minfeng Zhu", "Bo Zhang", "Wei Chen" ], "github": "https://github.com/rickyang1114/multimodal-deepresearcher", "project_page": "", "abstract": "Visualizations play a crucial part in effective communication of concepts and information. Recent advances in reasoning and retrieval augmented generation have enabled Large Language Models (LLMs) to perform deep research and generate comprehensive reports. Despite its progress, existing deep research frameworks primarily focus on generating text-only content, leaving the automated generation of interleaved texts and visualizations underexplored. This novel task poses key challenges in designing informative visualizations and effectively integrating them with text reports. To address these challenges, we propose Formal Description of Visualization (FDV), a structured textual representation of charts that enables LLMs to learn from and generate diverse, high-quality visualizations. Building on this representation, we introduce Multimodal DeepResearcher, an agentic framework that decomposes the task into four stages: (1) researching, (2) exemplar report textualization, (3) planning, and (4) multimodal report generation. For the evaluation of generated multimodal reports, we develop MultimodalReportBench, which contains 100 diverse topics served as inputs along with 5 dedicated metrics. Extensive experiments across models and evaluation methods demonstrate the effectiveness of Multimodal DeepResearcher. Notably, utilizing the same Claude 3.7 Sonnet model, Multimodal DeepResearcher achieves an 82\\% overall win rate over the baseline method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rickyang1114/multimodal-deepresearcher", "github_api_added_by": "auto" }, { "date": "2025-06-04", "arxiv_id": "2505.16994", "title": "R^2ec: Towards Large Recommender Models with Reasoning", "authors": [ "Runyang You", "Yongqi Li", "Xinyu Lin", "Xin Zhang", "Wenjie Wang", "Wenjie Li", "Liqiang Nie" ], "github": "https://github.com/YRYangang/RRec", "project_page": "", "abstract": "Large recommender models have extended LLMs as powerful recommenders via encoding or item generation, and recent breakthroughs in LLM reasoning synchronously motivate the exploration of reasoning in recommendation. Current studies usually position LLMs as external reasoning modules to yield auxiliary thought for augmenting conventional recommendation pipelines. However, such decoupled designs are limited in significant resource cost and suboptimal joint optimization. To address these issues, we propose \\name, a unified large recommender model with intrinsic reasoning capabilities. Initially, we reconceptualize the model architecture to facilitate interleaved reasoning and recommendation in the autoregressive process. Subsequently, we propose RecPO, a corresponding reinforcement learning framework that optimizes \\name\\ both the reasoning and recommendation capabilities simultaneously in a single policy update; RecPO introduces a fused reward scheme that solely leverages recommendation labels to simulate the reasoning capability, eliminating dependency on specialized reasoning annotations. Experiments on three datasets with various baselines verify the effectiveness of \\name, showing relative improvements of 68.67\\% in Hit@5 and 45.21\\% in NDCG@20. Code available at https://github.com/YRYangang/RRec.", "github_manual": "", "github_regex": "https://github.com/YRYangang/RRec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2505.23061", "title": "DINGO: Constrained Inference for Diffusion LLMs", "authors": [ "Tarun Suresh", "Debangshu Banerjee", "Shubham Ugare", "Sasa Misailovic", "Gagandeep Singh" ], "github": "", "project_page": "", "abstract": "Diffusion LLMs have emerged as a promising alternative to conventional autoregressive LLMs, offering significant potential for improved runtime efficiency. However, existing diffusion models lack the ability to provably enforce user-specified formal constraints, such as regular expressions, which makes them unreliable for tasks that require structured outputs, such as fixed-schema JSON generation. Unlike autoregressive models that generate tokens sequentially, diffusion LLMs predict a block of tokens in parallel. This parallelism makes traditional constrained decoding algorithms, which are designed for sequential token prediction, ineffective at preserving the true output distribution. To address this limitation, we propose DINGO, a dynamic programming-based constrained decoding strategy that is both efficient and provably distribution-preserving. DINGO enables sampling of output strings with the highest probability under the model's predicted distribution, while strictly satisfying any user-specified regular expression. On standard symbolic math and JSON generation benchmarks, DINGO achieves up to a 68 percentage point improvement over unconstrained inference", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.03143", "title": "GUI-Actor: Coordinate-Free Visual Grounding for GUI Agents", "authors": [ "Qianhui Wu", "Kanzhi Cheng", "Rui Yang", "Chaoyun Zhang", "Jianwei Yang", "Huiqiang Jiang", "Jian Mu", "Baolin Peng", "Bo Qiao", "Reuben Tan", "Si Qin", "Lars Liden", "Qingwei Lin", "Huan Zhang", "Tong Zhang", "Jianbing Zhang", "Dongmei Zhang", "Jianfeng Gao" ], "github": "", "project_page": "", "abstract": "One of the principal challenges in building VLM-powered GUI agents is visual grounding, i.e., localizing the appropriate screen region for action execution based on both the visual content and the textual plans. Most existing work formulates this as a text-based coordinate generation task. However, these approaches suffer from several limitations: weak spatial-semantic alignment, inability to handle ambiguous supervision targets, and a mismatch between the dense nature of screen coordinates and the coarse, patch-level granularity of visual features extracted by models like Vision Transformers. In this paper, we propose GUI-Actor, a VLM-based method for coordinate-free GUI grounding. At its core, GUI-Actor introduces an attention-based action head that learns to align a dedicated token with all relevant visual patch tokens, enabling the model to propose one or more action regions in a single forward pass. In line with this, we further design a grounding verifier to evaluate and select the most plausible action region from the candidates proposed for action execution. Extensive experiments show that GUI-Actor outperforms prior state-of-the-art methods on multiple GUI action grounding benchmarks, with improved generalization to unseen screen resolutions and layouts. Notably, GUI-Actor-7B even surpasses UI-TARS-72B (38.1) on ScreenSpot-Pro, achieving scores of 40.7 with Qwen2-VL and 44.6 with Qwen2.5-VL as backbones. Furthermore, by incorporating the verifier, we find that fine-tuning only the newly introduced action head (~100M parameters for 7B model) while keeping the VLM backbone frozen is sufficient to achieve performance comparable to previous state-of-the-art models, highlighting that GUI-Actor can endow the underlying VLM with effective grounding capabilities without compromising its general-purpose strengths.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.00910", "title": "PCoreSet: Effective Active Learning through Knowledge Distillation from Vision-Language Models", "authors": [ "Seongjae Kang", "Dong Bok Lee", "Hyungjoon Jang", "Dongseop Kim", "Sung Ju Hwang" ], "github": "https://github.com/erjui/PCoreSet", "project_page": "", "abstract": "Knowledge distillation (KD) is a widely used framework for training compact, task-specific models by leveraging the knowledge of teacher models. However, its application to active learning (AL), which aims to minimize annotation costs through iterative sample selection, remains underexplored. This gap stems from the fact that KD typically assumes access to sufficient labeled data, whereas AL operates in data-scarce scenarios where task-specific teacher models are often unavailable. In this paper, we introduce ActiveKD, a framework that integrates AL with KD by leveraging the zero- and few-shot capabilities of large vision-language models (VLMs). A key aspect of ActiveKD is the structured prediction bias of VLMs -- i.e., their predictions form clusters in the probability space. We regard this structure as an inductive bias of the teacher model, capturing generalizable output patterns beneficial to student learning. To exploit this bias, we propose Probabilistic CoreSet (PCoreSet), a selection strategy that maximizes coverage in the probability space rather than the feature space. PCoreSet strategically selects categorically diverse unlabeled samples, facilitating more efficient transfer of teacher knowledge under limited annotation budgets. Evaluations on 11 datasets show that PCoreSet consistently outperforms existing selection methods within the ActiveKD framework, advancing research at the intersection of AL and KD.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/erjui/PCoreSet", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.01144", "title": "FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation", "authors": [ "Ariel Shaulov", "Itay Hazan", "Lior Wolf", "Hila Chefer" ], "github": "", "project_page": "", "abstract": "Text-to-video diffusion models are notoriously limited in their ability to model temporal aspects such as motion, physics, and dynamic interactions. Existing approaches address this limitation by retraining the model or introducing external conditioning signals to enforce temporal consistency. In this work, we explore whether a meaningful temporal representation can be extracted directly from the predictions of a pre-trained model without any additional training or auxiliary inputs. We introduce FlowMo, a novel training-free guidance method that enhances motion coherence using only the model's own predictions in each diffusion step. FlowMo first derives an appearance-debiased temporal representation by measuring the distance between latents corresponding to consecutive frames. This highlights the implicit temporal structure predicted by the model. It then estimates motion coherence by measuring the patch-wise variance across the temporal dimension and guides the model to reduce this variance dynamically during sampling. Extensive experiments across multiple text-to-video models demonstrate that FlowMo significantly improves motion coherence without sacrificing visual quality or prompt alignment, offering an effective plug-and-play solution for enhancing the temporal fidelity of pre-trained video diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.00413", "title": "Accelerating Diffusion LLMs via Adaptive Parallel Decoding", "authors": [ "Daniel Israel", "Guy Van den Broeck", "Aditya Grover" ], "github": "", "project_page": "", "abstract": "The generation speed of LLMs are bottlenecked by autoregressive decoding, where tokens are predicted sequentially one by one. Alternatively, diffusion large language models (dLLMs) theoretically allow for parallel token generation, but in practice struggle to achieve the speed of autoregressive models without significantly sacrificing quality. We therefore introduce adaptive parallel decoding (APD), a novel method that dynamically adjusts the number of tokens sampled in parallel. We achieve this by defining a multiplicative mixture between the dLLM marginal probabilities and the joint probability of sequences under a small auxiliary autoregressive model. This inverts the standard setup of speculative decoding, where the goal is to sample from a large autoregressive verifier by drafting from a smaller model. We further optimize APD by enabling KV caching and limiting the size of the masked input. Altogether, our method puts forward three tunable parameters to flexibly tradeoff throughput and quality. We show that APD provides markedly higher throughput with minimal quality degradations on downstream benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.00123", "title": "Visual Embodied Brain: Let Multimodal Large Language Models See, Think, and Control in Spaces", "authors": [ "Gen Luo", "Ganlin Yang", "Ziyang Gong", "Guanzhou Chen", "Haonan Duan", "Erfei Cui", "Ronglei Tong", "Zhi Hou", "Tianyi Zhang", "Zhe Chen", "Shenglong Ye", "Lewei Lu", "Jingbo Wang", "Wenhai Wang", "Jifeng Dai", "Yu Qiao", "Rongrong Ji", "Xizhou Zhu" ], "github": "", "project_page": "", "abstract": "The remarkable progress of Multimodal Large Language Models (MLLMs) has attracted increasing attention to extend them to physical entities like legged robot. This typically requires MLLMs to not only grasp multimodal understanding abilities, but also integrate visual-spatial reasoning and physical interaction capabilities. Nevertheless,existing methods struggle to unify these capabilities due to their fundamental differences.In this paper, we present the Visual Embodied Brain (VeBrain), a unified framework for perception, reasoning, and control in real world. VeBrain reformulates robotic control into common text-based MLLM tasks in the 2D visual space, thus unifying the objectives and mapping spaces of different tasks. Then, a novel robotic adapter is proposed to convert textual control signals from MLLMs to motion policies of real robots. From the data perspective, we further introduce VeBrain-600k, a high-quality instruction dataset encompassing various capabilities of VeBrain. In VeBrain-600k, we take hundreds of hours to collect, curate and annotate the data, and adopt multimodal chain-of-thought(CoT) to mix the different capabilities into a single conversation. Extensive experiments on 13 multimodal benchmarks and 5 spatial intelligence benchmarks demonstrate the superior performance of VeBrain to existing MLLMs like Qwen2.5-VL. When deployed to legged robots and robotic arms, VeBrain shows strong adaptability, flexibility, and compositional capabilities compared to existing methods. For example, compared to Qwen2.5-VL, VeBrain not only achieves substantial gains on MMVet by +5.6%, but also excels in legged robot tasks with +50% average gains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.00070", "title": "Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in Robotics", "authors": [ "Dongyoung Kim", "Sumin Park", "Huiwon Jang", "Jinwoo Shin", "Jaehyung Kim", "Younggyo Seo" ], "github": "", "project_page": "", "abstract": "Large Vision-Language Models (LVLMs) have recently shown great promise in advancing robotics by combining embodied reasoning with robot control. A common approach involves training on embodied reasoning tasks related to robot control using Supervised Fine-Tuning (SFT). However, SFT datasets are often heuristically constructed and not explicitly optimized for improving robot control. Furthermore, SFT often leads to issues such as catastrophic forgetting and reduced generalization performance. To address these limitations, we introduce Robot-R1, a novel framework that leverages reinforcement learning to enhance embodied reasoning specifically for robot control. Robot-R1 learns to predict the next keypoint state required for task completion, conditioned on the current scene image and environment metadata derived from expert demonstrations. Inspired by the DeepSeek-R1 learning approach, Robot-R1 samples reasoning-based responses and reinforces those that lead to more accurate predictions. Our experiments show that models trained with Robot-R1 outperform SFT methods on embodied reasoning tasks. Despite having only 7B parameters, Robot-R1 even surpasses GPT-4o on reasoning tasks related to low-level action control, such as spatial and primitive movement reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.03126", "title": "AnimeShooter: A Multi-Shot Animation Dataset for Reference-Guided Video Generation", "authors": [ "Lu Qiu", "Yizhuo Li", "Yuying Ge", "Yixiao Ge", "Ying Shan", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Recent advances in AI-generated content (AIGC) have significantly accelerated animation production. To produce engaging animations, it is essential to generate coherent multi-shot video clips with narrative scripts and character references. However, existing public datasets primarily focus on real-world scenarios with global descriptions, and lack reference images for consistent character guidance. To bridge this gap, we present AnimeShooter, a reference-guided multi-shot animation dataset. AnimeShooter features comprehensive hierarchical annotations and strong visual consistency across shots through an automated pipeline. Story-level annotations provide an overview of the narrative, including the storyline, key scenes, and main character profiles with reference images, while shot-level annotations decompose the story into consecutive shots, each annotated with scene, characters, and both narrative and descriptive visual captions. Additionally, a dedicated subset, AnimeShooter-audio, offers synchronized audio tracks for each shot, along with audio descriptions and sound sources. To demonstrate the effectiveness of AnimeShooter and establish a baseline for the reference-guided multi-shot video generation task, we introduce AnimeShooterGen, which leverages Multimodal Large Language Models (MLLMs) and video diffusion models. The reference image and previously generated shots are first processed by MLLM to produce representations aware of both reference and context, which are then used as the condition for the diffusion model to decode the subsequent shot. Experimental results show that the model trained on AnimeShooter achieves superior cross-shot visual consistency and adherence to reference visual guidance, which highlight the value of our dataset for coherent animated video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.03065", "title": "Sparse-vDiT: Unleashing the Power of Sparse Attention to Accelerate Video Diffusion Transformers", "authors": [ "Pengtao Chen", "Xianfang Zeng", "Maosen Zhao", "Peng Ye", "Mingzhu Shen", "Wei Cheng", "Gang Yu", "Tao Chen" ], "github": "", "project_page": "", "abstract": "While Diffusion Transformers (DiTs) have achieved breakthroughs in video generation, this long sequence generation task remains constrained by the quadratic complexity of attention mechanisms, resulting in significant inference latency. Through detailed analysis of attention maps in Video Diffusion Transformer (vDiT), we identify three recurring sparsity patterns: diagonal, multi-diagonal, and vertical-stripe structures. And even 3-6\\% attention heads can be skipped. Crucially, these patterns exhibit strong layer-depth and head-position correlations but show limited dependence on the input content. Leveraging these findings, we propose Sparse-vDiT, a sparsity acceleration framework for vDiT comprising: 1) Pattern-optimized sparse kernels that replace dense attention with computationally efficient implementations for each identified sparsity pattern. 2) An offline sparse diffusion search algorithm that selects the optimal sparse computation strategy per layer and head via hardware-aware cost modeling. After determining the optimal configuration, we fuse heads within the same layer that share the same attention strategy, enhancing inference efficiency. Integrated into state-of-the-art vDiT models (CogVideoX1.5, HunyuanVideo, and Wan2.1), Sparse-vDiT achieves 2.09times, 2.38times, and 1.67times theoretical FLOP reduction, and actual inference speedups of 1.76times, 1.85times, and 1.58times, respectively, while maintaining high visual fidelity, with PSNR values reaching 24.13, 27.09, and 22.59. Our work demonstrates that latent structural sparsity in vDiTs can be systematically exploited for long video synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.01274", "title": "ReFoCUS: Reinforcement-guided Frame Optimization for Contextual Understanding", "authors": [ "Hosu Lee", "Junho Kim", "Hyunjun Kim", "Yong Man Ro" ], "github": "", "project_page": "", "abstract": "Recent progress in Large Multi-modal Models (LMMs) has enabled effective vision-language reasoning, yet the ability to understand video content remains constrained by suboptimal frame selection strategies. Existing approaches often rely on static heuristics or external retrieval modules to feed frame information into video-LLMs, which may fail to provide the query-relevant information. In this work, we introduce ReFoCUS (Reinforcement-guided Frame Optimization for Contextual UnderStanding), a novel frame-level policy optimization framework that shifts the optimization target from textual responses to visual input selection. ReFoCUS learns a frame selection policy via reinforcement learning, using reward signals derived from a reference LMM to reflect the model's intrinsic preferences for frames that best support temporally grounded responses. To efficiently explore the large combinatorial frame space, we employ an autoregressive, conditional selection architecture that ensures temporal coherence while reducing complexity. Our approach does not require explicit supervision at the frame-level and consistently improves reasoning performance across multiple video QA benchmarks, highlighting the benefits of aligning frame selection with model-internal utility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.02138", "title": "Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability", "authors": [ "Yarden Bakish", "Itamar Zimerman", "Hila Chefer", "Lior Wolf" ], "github": "https://github.com/YardenBakish/PE-AWARE-LRP", "project_page": "", "abstract": "The development of effective explainability tools for Transformers is a crucial pursuit in deep learning research. One of the most promising approaches in this domain is Layer-wise Relevance Propagation (LRP), which propagates relevance scores backward through the network to the input space by redistributing activation values based on predefined rules. However, existing LRP-based methods for Transformer explainability entirely overlook a critical component of the Transformer architecture: its positional encoding (PE), resulting in violation of the conservation property, and the loss of an important and unique type of relevance, which is also associated with structural and positional features. To address this limitation, we reformulate the input space for Transformer explainability as a set of position-token pairs. This allows us to propose specialized theoretically-grounded LRP rules designed to propagate attributions across various positional encoding methods, including Rotary, Learnable, and Absolute PE. Extensive experiments with both fine-tuned classifiers and zero-shot foundation models, such as LLaMA 3, demonstrate that our method significantly outperforms the state-of-the-art in both vision and NLP explainability tasks. Our code is publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YardenBakish/PE-AWARE-LRP", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.03135", "title": "OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models", "authors": [ "Mengdi Jia", "Zekun Qi", "Shaochen Zhang", "Wenyao Zhang", "Xinqiang Yu", "Jiawei He", "He Wang", "Li Yi" ], "github": "https://github.com/qizekun/OmniSpatial", "project_page": "", "abstract": "Spatial reasoning is a key aspect of cognitive psychology and remains a major bottleneck for current vision-language models (VLMs). While extensive research has aimed to evaluate or improve VLMs' understanding of basic spatial relations, such as distinguishing left from right, near from far, and object counting, these tasks represent only the most fundamental level of spatial reasoning. In this work, we introduce OmniSpatial, a comprehensive and challenging benchmark for spatial reasoning, grounded in cognitive psychology. OmniSpatial covers four major categories: dynamic reasoning, complex spatial logic, spatial interaction, and perspective-taking, with 50 fine-grained subcategories. Through Internet data crawling and careful manual annotation, we construct over 1.5K question-answer pairs. Extensive experiments show that both open- and closed-source VLMs, as well as existing reasoning and spatial understanding models, exhibit significant limitations in comprehensive spatial understanding. We further analyze failure cases and propose potential directions for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/qizekun/OmniSpatial", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2505.24120", "title": "CSVQA: A Chinese Multimodal Benchmark for Evaluating STEM Reasoning Capabilities of VLMs", "authors": [ "Ai Jian", "Weijie Qiu", "Xiaokun Wang", "Peiyu Wang", "Yunzhuo Hao", "Jiangbo Pei", "Yichen Wei", "Yi Peng", "Xuchen Song" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) have demonstrated remarkable progress in multimodal understanding, yet their capabilities for scientific reasoning remains inadequately assessed. Current multimodal benchmarks predominantly evaluate generic image comprehension or text-driven reasoning, lacking authentic scientific contexts that require domain-specific knowledge integration with visual evidence analysis. To fill this gap, we present CSVQA, a diagnostic multimodal benchmark specifically designed for evaluating scientific reasoning through domain-grounded visual question answering.Our benchmark features 1,378 carefully constructed question-answer pairs spanning diverse STEM disciplines, each demanding domain knowledge, integration of visual evidence, and higher-order reasoning. Compared to prior multimodal benchmarks, CSVQA places greater emphasis on real-world scientific content and complex reasoning.We additionally propose a rigorous evaluation protocol to systematically assess whether model predictions are substantiated by valid intermediate reasoning steps based on curated explanations. Our comprehensive evaluation of 15 VLMs on this benchmark reveals notable performance disparities, as even the top-ranked proprietary model attains only 49.6\\% accuracy.This empirical evidence underscores the pressing need for advancing scientific reasoning capabilities in VLMs. Our CSVQA is released at https://huggingface.co/datasets/Skywork/CSVQA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.03079", "title": "ORV: 4D Occupancy-centric Robot Video Generation", "authors": [ "Xiuyu Yang", "Bohan Li", "Shaocong Xu", "Nan Wang", "Chongjie Ye", "Zhaoxi Chen", "Minghan Qin", "Yikang Ding", "Xin Jin", "Hang Zhao", "Hao Zhao" ], "github": "", "project_page": "", "abstract": "Acquiring real-world robotic simulation data through teleoperation is notoriously time-consuming and labor-intensive. Recently, action-driven generative models have gained widespread adoption in robot learning and simulation, as they eliminate safety concerns and reduce maintenance efforts. However, the action sequences used in these methods often result in limited control precision and poor generalization due to their globally coarse alignment. To address these limitations, we propose ORV, an Occupancy-centric Robot Video generation framework, which utilizes 4D semantic occupancy sequences as a fine-grained representation to provide more accurate semantic and geometric guidance for video generation. By leveraging occupancy-based representations, ORV enables seamless translation of simulation data into photorealistic robot videos, while ensuring high temporal consistency and precise controllability. Furthermore, our framework supports the simultaneous generation of multi-view videos of robot gripping operations - an important capability for downstream robotic learning tasks. Extensive experimental results demonstrate that ORV consistently outperforms existing baseline methods across various datasets and sub-tasks. Demo, Code and Model: https://orangesodahub.github.io/ORV", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2505.24726", "title": "Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning", "authors": [ "Shelly Bensal", "Umar Jamil", "Christopher Bryant", "Melisa Russak", "Kiran Kamble", "Dmytro Mozolevskyi", "Muayad Ali", "Waseem AlShikh" ], "github": "", "project_page": "", "abstract": "We explore a method for improving the performance of large language models through self-reflection and reinforcement learning. By incentivizing the model to generate better self-reflections when it answers incorrectly, we demonstrate that a model's ability to solve complex, verifiable tasks can be enhanced even when generating synthetic data is infeasible and only binary feedback is available. Our framework operates in two stages: first, upon failing a given task, the model generates a self-reflective commentary analyzing its previous attempt; second, the model is given another attempt at the task with the self-reflection in context. If the subsequent attempt succeeds, the tokens generated during the self-reflection phase are rewarded. Our experimental results show substantial performance gains across a variety of model architectures, as high as 34.7% improvement at math equation writing and 18.1% improvement at function calling. Notably, smaller fine-tuned models (1.5 billion to 7 billion parameters) outperform models in the same family that are 10 times larger. Our novel paradigm is thus an exciting pathway to more useful and reliable language models that can self-improve on challenging tasks with limited external feedback.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.03144", "title": "MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query", "authors": [ "Wei Chow", "Yuan Gao", "Linfeng Li", "Xian Wang", "Qi Xu", "Hang Song", "Lingdong Kong", "Ran Zhou", "Yi Zeng", "Yidong Cai", "Botian Jiang", "Shilin Xu", "Jiajun Zhang", "Minghui Qiu", "Xiangtai Li", "Tianshu Yang", "Siliang Tang", "Juncheng Li" ], "github": "", "project_page": "", "abstract": "Semantic retrieval is crucial for modern applications yet remains underexplored in current research. Existing datasets are limited to single languages, single images, or singular retrieval conditions, often failing to fully exploit the expressive capacity of visual information as evidenced by maintained performance when images are replaced with captions. However, practical retrieval scenarios frequently involve interleaved multi-condition queries with multiple images. Hence, this paper introduces MERIT, the first multilingual dataset for interleaved multi-condition semantic retrieval, comprising 320,000 queries with 135,000 products in 5 languages, covering 7 distinct product categories. Extensive experiments on MERIT identify existing models's limitation: focusing solely on global semantic information while neglecting specific conditional elements in queries. Consequently, we propose Coral, a novel fine-tuning framework that adapts pre-trained MLLMs by integrating embedding reconstruction to preserve fine-grained conditional elements and contrastive learning to extract comprehensive global semantics. Experiments demonstrate that Coral achieves a 45.9% performance improvement over conventional approaches on MERIT, with strong generalization capabilities validated across 8 established retrieval benchmarks. Collectively, our contributions - a novel dataset, identification of critical limitations in existing approaches, and an innovative fine-tuning framework - establish a foundation for future research in interleaved multi-condition semantic retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.03096", "title": "FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens", "authors": [ "Christian Schlarmann", "Francesco Croce", "Nicolas Flammarion", "Matthias Hein" ], "github": "https://github.com/chs20/fuselip", "project_page": "", "abstract": "Contrastive language-image pre-training aligns the features of text-image pairs in a common latent space via distinct encoders for each modality. While this approach achieves impressive performance in several zero-shot tasks, it cannot natively handle multimodal inputs, i.e., encoding image and text into a single feature vector. As a remedy, it is common practice to use additional modules to merge the features extracted by the unimodal encoders. In this work, we present FuseLIP, an alternative architecture for multimodal embedding. Leveraging recent progress in discrete image tokenizers, we propose to use a single transformer model which operates on an extended vocabulary of text and image tokens. This early fusion approach allows the different modalities to interact at each depth of encoding and obtain richer representations compared to common late fusion. We collect new datasets for multimodal pre-training and evaluation, designing challenging tasks for multimodal encoder models. We show that FuseLIP outperforms other approaches in multimodal embedding tasks such as VQA and text-guided image transformation retrieval, while being comparable to baselines on unimodal tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/chs20/fuselip", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2505.24273", "title": "How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning", "authors": [ "Hongyi James Cai", "Junlin Wang", "Xiaoyin Chen", "Bhuwan Dhingra" ], "github": "https://github.com/jchy20/how-much-backtrack", "project_page": "", "abstract": "Recent breakthroughs in large language models (LLMs) have effectively improved their reasoning abilities, particularly on mathematical and logical problems that have verifiable answers, through techniques such as supervised finetuning (SFT) and reinforcement learning (RL). Prior research indicates that RL effectively internalizes search strategies, enabling long chain-of-thought (CoT) reasoning, with backtracking emerging naturally as a learned capability. However, the precise benefits of backtracking, specifically, how significantly it contributes to reasoning improvements and the optimal extent of its use, remain poorly understood. In this work, we systematically investigate the dynamics between SFT and RL on eight reasoning tasks: Countdown, Sudoku, Arc 1D, Geometry, Color Cube Rotation, List Functions, Zebra Puzzles, and Self Reference. Our findings highlight that short CoT sequences used in SFT as a warm-up do have moderate contribution to RL training, compared with cold-start RL; however such contribution diminishes when tasks become increasingly difficult. Motivated by this observation, we construct synthetic datasets varying systematically in the number of backtracking steps and conduct controlled experiments to isolate the influence of either the correctness (content) or the structure (i.e., backtrack frequency). We find that (1) longer CoT with backtracks generally induce better and more stable RL training, (2) more challenging problems with larger search space tend to need higher numbers of backtracks during the SFT stage. Additionally, we demonstrate through experiments on distilled data that RL training is largely unaffected by the correctness of long CoT sequences, suggesting that RL prioritizes structural patterns over content correctness. Collectively, our results offer practical insights into designing optimal training strategies to effectively scale reasoning in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jchy20/how-much-backtrack", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.01565", "title": "Hanfu-Bench: A Multimodal Benchmark on Cross-Temporal Cultural Understanding and Transcreation", "authors": [ "Li Zhou", "Lutong Yu", "Dongchu Xie", "Shaohuan Cheng", "Wenyan Li", "Haizhou Li" ], "github": "https://github.com/hlt-cuhksz/TemporalCulture", "project_page": "", "abstract": "Culture is a rich and dynamic domain that evolves across both geography and time. However, existing studies on cultural understanding with vision-language models (VLMs) primarily emphasize geographic diversity, often overlooking the critical temporal dimensions. To bridge this gap, we introduce Hanfu-Bench, a novel, expert-curated multimodal dataset. Hanfu, a traditional garment spanning ancient Chinese dynasties, serves as a representative cultural heritage that reflects the profound temporal aspects of Chinese culture while remaining highly popular in Chinese contemporary society. Hanfu-Bench comprises two core tasks: cultural visual understanding and cultural image transcreation.The former task examines temporal-cultural feature recognition based on single- or multi-image inputs through multiple-choice visual question answering, while the latter focuses on transforming traditional attire into modern designs through cultural element inheritance and modern context adaptation. Our evaluation shows that closed VLMs perform comparably to non-experts on visual cutural understanding but fall short by 10\\% to human experts, while open VLMs lags further behind non-experts. For the transcreation task, multi-faceted human evaluation indicates that the best-performing model achieves a success rate of only 42\\%. Our benchmark provides an essential testbed, revealing significant challenges in this new direction of temporal cultural understanding and creative adaptation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hlt-cuhksz/TemporalCulture", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2505.24362", "title": "Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion", "authors": [ "Anum Afzal", "Florian Matthes", "Gal Chechik", "Yftah Ziser" ], "github": "https://github.com/anum94/cotpred", "project_page": "", "abstract": "We investigate whether the success of a zero-shot Chain-of-Thought (CoT) process can be predicted before completion. We discover that a probing classifier, based on LLM representations, performs well even before a single token is generated, suggesting that crucial information about the reasoning process is already present in the initial steps representations. In contrast, a strong BERT-based baseline, which relies solely on the generated tokens, performs worse, likely because it depends on shallow linguistic cues rather than deeper reasoning dynamics. Surprisingly, using later reasoning steps does not always improve classification. When additional context is unhelpful, earlier representations resemble later ones more, suggesting LLMs encode key information early. This implies reasoning can often stop early without loss. To test this, we conduct early stopping experiments, showing that truncating CoT reasoning still improves performance over not using CoT at all, though a gap remains compared to full reasoning. However, approaches like supervised learning or reinforcement learning designed to shorten CoT chains could leverage our classifier's guidance to identify when early stopping is effective. Our findings provide insights that may support such methods, helping to optimize CoT's efficiency while preserving its benefits.", "github_manual": "https://github.com/anum94/cotpred", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2505.18079", "title": "Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding", "authors": [ "Xiaoyi Zhang", "Zhaoyang Jia", "Zongyu Guo", "Jiahao Li", "Bin Li", "Houqiang Li", "Yan Lu" ], "github": "https://github.com/microsoft/DeepVideoDiscovery", "project_page": "", "abstract": "Long-form video understanding presents significant challenges due to extensive temporal-spatial complexity and the difficulty of question answering under such extended contexts. While Large Language Models (LLMs) have demonstrated considerable advancements in video analysis capabilities and long context handling, they continue to exhibit limitations when processing information-dense hour-long videos. To overcome such limitations, we propose the Deep Video Discovery agent to leverage an agentic search strategy over segmented video clips. Different from previous video agents manually designing a rigid workflow, our approach emphasizes the autonomous nature of agents. By providing a set of search-centric tools on multi-granular video database, our DVD agent leverages the advanced reasoning capability of LLM to plan on its current observation state, strategically selects tools, formulates appropriate parameters for actions, and iteratively refines its internal reasoning in light of the gathered information. We perform comprehensive evaluation on multiple long video understanding benchmarks that demonstrates the advantage of the entire system design. Our DVD agent achieves SOTA performance, significantly surpassing prior works by a large margin on the challenging LVBench dataset. Comprehensive ablation studies and in-depth tool analyses are also provided, yielding insights to further advance intelligent agents tailored for long-form video understanding tasks. The code will be released later.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/DeepVideoDiscovery", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.02295", "title": "QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large Language Model Adaptation", "authors": [ "Ahmed Wasfy", "Omer Nacar", "Abdelakreem Elkhateb", "Mahmoud Reda", "Omar Elshehy", "Adel Ammar", "Wadii Boulila" ], "github": "", "project_page": "", "abstract": "The inherent complexities of Arabic script; its cursive nature, diacritical marks (tashkeel), and varied typography, pose persistent challenges for Optical Character Recognition (OCR). We present Qari-OCR, a series of vision-language models derived from Qwen2-VL-2B-Instruct, progressively optimized for Arabic through iterative fine-tuning on specialized synthetic datasets. Our leading model, QARI v0.2, establishes a new open-source state-of-the-art with a Word Error Rate (WER) of 0.160, Character Error Rate (CER) of 0.061, and BLEU score of 0.737 on diacritically-rich texts. Qari-OCR demonstrates superior handling of tashkeel, diverse fonts, and document layouts, alongside impressive performance on low-resolution images. Further explorations (QARI v0.3) showcase strong potential for structural document understanding and handwritten text. This work delivers a marked improvement in Arabic OCR accuracy and efficiency, with all models and datasets released to foster further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.02397", "title": "OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for Over-Reasoning Mitigation", "authors": [ "Shengjia Zhang", "Junjie Wu", "Jiawei Chen", "Changwang Zhang", "Xingyu Lou", "Wangchunshu Zhou", "Sheng Zhou", "Can Wang", "Jun Wang" ], "github": "https://github.com/AgenticIR-Lab/OThink-R1", "project_page": "", "abstract": "Recent advanced large reasoning models (LRMs) leverage extended chain-of-thought (CoT) reasoning to solve complex tasks, achieving state-of-the-art performance. Despite their success, we identify a critical issue: a substantial portion of simple tasks solved by LRMs can also be addressed by non-reasoning LLMs using significantly fewer tokens, indicating the complex reasoning may not always be necessary. To address this, we systematically analyze the reasoning trajectories of LRMs and present a method utilizing identified paradigms and LLM-Judge to classify these trajectories as either Redundant Reasoning or Essential Reasoning. And we introduce OThink-R1, a method that prunes redundant reasoning steps while preserving logical validity. OThink-R1 dynamically employs the non-thinking mode (fast-thinking) for straightforward problems while engaging in deliberate thinking (slow-thinking) for complex problems. Experiments across mathematical and question-answering tasks demonstrate that OThink-R1 reduces reasoning redundancy by almost 23\\% on average without compromising accuracy, offering practical guidelines for efficient reasoning models. The code is available at https://github.com/AgenticIR-Lab/OThink-R1.", "github_manual": "", "github_regex": "https://github.com/AgenticIR-Lab/OThink-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.00391", "title": "SHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQL", "authors": [ "Ge Qu", "Jinyang Li", "Bowen Qin", "Xiaolong Li", "Nan Huo", "Chenhao Ma", "Reynold Cheng" ], "github": "https://github.com/quge2023/SHARE", "project_page": "", "abstract": "Current self-correction approaches in text-to-SQL face two critical limitations: 1) Conventional self-correction methods rely on recursive self-calls of LLMs, resulting in multiplicative computational overhead, and 2) LLMs struggle to implement effective error detection and correction for declarative SQL queries, as they fail to demonstrate the underlying reasoning path. In this work, we propose SHARE, an SLM-based Hierarchical Action corREction assistant that enables LLMs to perform more precise error localization and efficient correction. SHARE orchestrates three specialized Small Language Models (SLMs) in a sequential pipeline, where it first transforms declarative SQL queries into stepwise action trajectories that reveal underlying reasoning, followed by a two-phase granular refinement. We further propose a novel hierarchical self-evolution strategy for data-efficient training. Experimental results demonstrate that SHARE effectively enhances self-correction capabilities while proving robust across various LLMs. Furthermore, our comprehensive analysis shows that SHARE maintains strong performance even in low-resource training settings, which is particularly valuable for text-to-SQL applications with data privacy constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/quge2023/SHARE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.03123", "title": "DCM: Dual-Expert Consistency Model for Efficient and High-Quality Video Generation", "authors": [ "Zhengyao Lv", "Chenyang Si", "Tianlin Pan", "Zhaoxi Chen", "Kwan-Yee K. Wong", "Yu Qiao", "Ziwei Liu" ], "github": "https://github.com/Vchitect/DCM", "project_page": "", "abstract": "Diffusion Models have achieved remarkable results in video synthesis but require iterative denoising steps, leading to substantial computational overhead. Consistency Models have made significant progress in accelerating diffusion models. However, directly applying them to video diffusion models often results in severe degradation of temporal consistency and appearance details. In this paper, by analyzing the training dynamics of Consistency Models, we identify a key conflicting learning dynamics during the distillation process: there is a significant discrepancy in the optimization gradients and loss contributions across different timesteps. This discrepancy prevents the distilled student model from achieving an optimal state, leading to compromised temporal consistency and degraded appearance details. To address this issue, we propose a parameter-efficient Dual-Expert Consistency Model~(DCM), where a semantic expert focuses on learning semantic layout and motion, while a detail expert specializes in fine detail refinement. Furthermore, we introduce Temporal Coherence Loss to improve motion consistency for the semantic expert and apply GAN and Feature Matching Loss to enhance the synthesis quality of the detail expert.Our approach achieves state-of-the-art visual quality with significantly reduced sampling steps, demonstrating the effectiveness of expert specialization in video diffusion model distillation. Our code and models are available at https://github.com/Vchitect/DCM{https://github.com/Vchitect/DCM}.", "github_manual": "", "github_regex": "https://github.com/Vchitect/DCM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.01716", "title": "Self-Challenging Language Model Agents", "authors": [ "Yifei Zhou", "Sergey Levine", "Jason Weston", "Xian Li", "Sainbayar Sukhbaatar" ], "github": "", "project_page": "", "abstract": "Large language models are quickly becoming the foundation for intelligent agents that are capable of using tools. However, training such agents is challenging because it requires human creation and annotation of a diverse set of tasks, tools, and evaluation criteria. In this paper, we propose the Self-Challenging framework for training an agent on high-quality tasks that are generated by itself. The agent first plays the role of challenger and generates a task after interacting with the given tools. The tasks take the form of a novel general class of problems termed Code-as-Task, which are defined by an instruction, a verification function and solution and failure cases which serve as tests, allowing to filter only for high-quality tasks. The agent then takes an executor role and trains on those tasks with reinforcement learning using the evaluation feedback as a reward. Evaluation on two existing multi-turn tool-use agent benchmarks, M3ToolEval and TauBench, shows the Self-Challenging framework achieves over a two-fold improvement in Llama-3.1-8B-Instruct, despite using only self-generated training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.02678", "title": "TL;DR: Too Long, Do Re-weighting for Effcient LLM Reasoning Compression", "authors": [ "Zhong-Zhi Li", "Xiao Liang", "Zihao Tang", "Lei Ji", "Peijie Wang", "Haotian Xu", "Xing W", "Haizhen Huang", "Weiwei Deng", "Ying Nian Wu", "Yeyun Gong", "Zhijiang Guo", "Xiao Liu", "Fei Yin", "Cheng-Lin Liu" ], "github": "https://github.com/zzli2022/tldr", "project_page": "", "abstract": "Large Language Models (LLMs) have recently achieved remarkable progress by leveraging Reinforcement Learning and extended Chain-of-Thought (CoT) techniques. However, the challenge of performing efficient language reasoning--especially during inference with extremely long outputs--has drawn increasing attention from the research community. In this work, we propose a dynamic ratio-based training pipeline that does not rely on sophisticated data annotations or interpolation between multiple models. We continuously balance the weights between the model's System-1 and System-2 data to eliminate redundant reasoning processes while preserving the model's reasoning capability. We validate our approach across models on DeepSeek-R1-Distill-7B and DeepSeek-R1-Distill-14B and on a diverse set of benchmarks with varying difficulty levels. Our method significantly reduces the number of output tokens by nearly 40% while maintaining the accuracy of the reasoning. Our code and data will be available soon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zzli2022/tldr", "github_api_added_by": "auto" }, { "date": "2025-06-04", "arxiv_id": "2506.01265", "title": "Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines", "authors": [ "Do Xuan Long", "Duong Ngoc Yen", "Do Xuan Trong", "Luu Anh Tuan", "Kenji Kawaguchi", "Shafiq Joty", "Min-Yen Kan", "Nancy F. Chen" ], "github": "https://github.com/dxlong2000/LongGuide", "project_page": "", "abstract": "In-context learning (ICL) is an important yet not fully understood ability of pre-trained large language models (LLMs). It can greatly enhance task performance using a few examples, termed demonstrations, without fine-tuning. Although effective in question answering, ICL often underperforms in long-form generation tasks such as summarization. Under appropriately realistic assumptions, we empirically and theoretically show that ICL demonstrations alone are insufficient to teach LLMs the task language and format distributions for generation. We argue for explicit exposure to the task distributions and hypothesize that defining them by prompting enhances model performance. To this end, we present LongGuide, which efficiently generates two parallel streams of guidelines capturing task language and format properties: (i) Metric Guidelines (MGs) that instruct models to optimize self-evaluated metrics; and (ii) Output Constraint Guidelines (OCGs) that constrain generation at both token and sentence levels. LongGuide automatically selects the best combination of guidelines, improving both strong open- and closed-source LLMs by over 5% in both zero- and few-shot settings. We show that LongGuide is generalizable, learnable by weak models to enhance strong ones, and integrates synergistically with automatic prompt optimizers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dxlong2000/LongGuide", "github_api_added_by": "auto" }, { "date": "2025-06-04", "arxiv_id": "2506.03119", "title": "Controllable Human-centric Keyframe Interpolation with Generative Prior", "authors": [ "Zujin Guo", "Size Wu", "Zhongang Cai", "Wei Li", "Chen Change Loy" ], "github": "https://github.com/GSeanCDAT/PoseFuse3D-KI", "project_page": "", "abstract": "Existing interpolation methods use pre-trained video diffusion priors to generate intermediate frames between sparsely sampled keyframes. In the absence of 3D geometric guidance, these methods struggle to produce plausible results for complex, articulated human motions and offer limited control over the synthesized dynamics. In this paper, we introduce PoseFuse3D Keyframe Interpolator (PoseFuse3D-KI), a novel framework that integrates 3D human guidance signals into the diffusion process for Controllable Human-centric Keyframe Interpolation (CHKI). To provide rich spatial and structural cues for interpolation, our PoseFuse3D, a 3D-informed control model, features a novel SMPL-X encoder that transforms 3D geometry and shape into the 2D latent conditioning space, alongside a fusion network that integrates these 3D cues with 2D pose embeddings. For evaluation, we build CHKI-Video, a new dataset annotated with both 2D poses and 3D SMPL-X parameters. We show that PoseFuse3D-KI consistently outperforms state-of-the-art baselines on CHKI-Video, achieving a 9% improvement in PSNR and a 38% reduction in LPIPS. Comprehensive ablations demonstrate that our PoseFuse3D model improves interpolation fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GSeanCDAT/PoseFuse3D-KI", "github_api_added_by": "auto" }, { "date": "2025-06-04", "arxiv_id": "2506.00227", "title": "Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes", "authors": [ "Anthony Gosselin", "Ge Ya Luo", "Luis Lara", "Florian Golemo", "Derek Nowrouzezahrai", "Liam Paull", "Alexia Jolicoeur-Martineau", "Christopher Pal" ], "github": "https://github.com/AnthonyGosselin/Ctrl-Crash", "project_page": "", "abstract": "Video diffusion techniques have advanced significantly in recent years; however, they struggle to generate realistic imagery of car crashes due to the scarcity of accident events in most driving datasets. Improving traffic safety requires realistic and controllable accident simulations. To tackle the problem, we propose Ctrl-Crash, a controllable car crash video generation model that conditions on signals such as bounding boxes, crash types, and an initial image frame. Our approach enables counterfactual scenario generation where minor variations in input can lead to dramatically different crash outcomes. To support fine-grained control at inference time, we leverage classifier-free guidance with independently tunable scales for each conditioning signal. Ctrl-Crash achieves state-of-the-art performance across quantitative video quality metrics (e.g., FVD and JEDi) and qualitative measurements based on a human-evaluation of physical realism and video quality compared to prior diffusion-based methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AnthonyGosselin/Ctrl-Crash", "github_api_added_by": "auto" }, { "date": "2025-06-04", "arxiv_id": "2506.01004", "title": "Motion-Aware Concept Alignment for Consistent Video Editing", "authors": [ "Tong Zhang", "Juan C Leon Alcazar", "Bernard Ghanem" ], "github": "https://github.com/ZhangT-tech/MoCA-Video", "project_page": "", "abstract": "We introduce MoCA-Video (Motion-Aware Concept Alignment in Video), a training-free framework bridging the gap between image-domain semantic mixing and video. Given a generated video and a user-provided reference image, MoCA-Video injects the semantic features of the reference image into a specific object within the video, while preserving the original motion and visual context. Our approach leverages a diagonal denoising schedule and class-agnostic segmentation to detect and track objects in the latent space and precisely control the spatial location of the blended objects. To ensure temporal coherence, we incorporate momentum-based semantic corrections and gamma residual noise stabilization for smooth frame transitions. We evaluate MoCA's performance using the standard SSIM, image-level LPIPS, temporal LPIPS, and introduce a novel metric CASS (Conceptual Alignment Shift Score) to evaluate the consistency and effectiveness of the visual shifts between the source prompt and the modified video frames. Using self-constructed dataset, MoCA-Video outperforms current baselines, achieving superior spatial consistency, coherent motion, and a significantly higher CASS score, despite having no training or fine-tuning. MoCA-Video demonstrates that structured manipulation in the diffusion noise trajectory allows for controllable, high-quality video synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZhangT-tech/MoCA-Video", "github_api_added_by": "auto" }, { "date": "2025-06-04", "arxiv_id": "2505.22704", "title": "Training Language Models to Generate Quality Code with Program Analysis Feedback", "authors": [ "Feng Yao", "Zilong Wang", "Liyuan Liu", "Junxia Cui", "Li Zhong", "Xiaohan Fu", "Haohui Mai", "Vish Krishnan", "Jianfeng Gao", "Jingbo Shang" ], "github": "https://github.com/yaof20/ReaL", "project_page": "", "abstract": "Code generation with large language models (LLMs), often termed vibe coding, is increasingly adopted in production but fails to ensure code quality, particularly in security (e.g., SQL injection vulnerabilities) and maintainability (e.g., missing type annotations). Existing methods, such as supervised fine-tuning and rule-based post-processing, rely on labor-intensive annotations or brittle heuristics, limiting their scalability and effectiveness. We propose REAL, a reinforcement learning framework that incentivizes LLMs to generate production-quality code using program analysis-guided feedback. Specifically, REAL integrates two automated signals: (1) program analysis detecting security or maintainability defects and (2) unit tests ensuring functional correctness. Unlike prior work, our framework is prompt-agnostic and reference-free, enabling scalable supervision without manual intervention. Experiments across multiple datasets and model scales demonstrate that REAL outperforms state-of-the-art methods in simultaneous assessments of functionality and code quality. Our work bridges the gap between rapid prototyping and production-ready code, enabling LLMs to deliver both speed and quality.", "github_manual": "https://github.com/yaof20/ReaL", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.02281", "title": "Angles Don't Lie: Unlocking Training-Efficient RL Through the Model's Own Signals", "authors": [ "Qinsi Wang", "Jinghan Ke", "Hancheng Ye", "Yueqian Lin", "Yuzhe Fu", "Jianyi Zhang", "Kurt Keutzer", "Chenfeng Xu", "Yiran Chen" ], "github": "https://github.com/wangqinsi1/GAINRL", "project_page": "", "abstract": "Current Reinforcement Fine-tuning (RFT) paradigms for Large Language Models (LLMs) suffer from sample inefficiency due to the redundant exposure of identical queries under uniform data sampling. While previous work has explored curriculum learning via heuristic difficulty metrics, these strategies exhibit limitations by neglecting the intrinsic learning signals generated by the model itself, thus leading to suboptimal training regimes. In this paper, we identify a model-inherent signal termed angle concentration that effectively reflects an LLM's capacity to learn from specific data. We theoretically and empirically demonstrate a correlation between the angular distribution of token hidden state vectors and the resulting gradient, revealing a learning preference for data exhibiting higher angle concentration. Inspired by this finding, we propose GAIN-RL, a Gradient-driven Angle-Informed Navigated RL framework. By leveraging the model's intrinsic angle concentration signal, GAIN-RL dynamically selects training data in each epoch, ensuring consistently impactful gradient updates and thus significantly enhancing overall training efficiency. Empirical evaluations show that GAIN-RL (GRPO) achieves over a 2.5x acceleration in training efficiency across diverse mathematical and coding tasks and varying model scales. Furthermore, GAIN-RL (GRPO)'s efficient sampling yields data-efficient training, achieving better performance with half the original data compared to vanilla GRPO with full training data. Code is realsed at https://github.com/wangqinsi1/GAINRL/tree/main.", "github_manual": "", "github_regex": "https://github.com/wangqinsi1/GAINRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.03621", "title": "Negative-Guided Subject Fidelity Optimization for Zero-Shot Subject-Driven Generation", "authors": [ "Chaehun Shin", "Jooyoung Choi", "Johan Barthelemy", "Jungbeom Lee", "Sungroh Yoon" ], "github": "", "project_page": "", "abstract": "We present Subject Fidelity Optimization (SFO), a novel comparative learning framework for zero-shot subject-driven generation that enhances subject fidelity. Beyond supervised fine-tuning methods that rely only on positive targets and use the diffusion loss as in the pre-training stage, SFO introduces synthetic negative targets and explicitly guides the model to favor positives over negatives through pairwise comparison. For negative targets, we propose Condition-Degradation Negative Sampling (CDNS), which automatically generates distinctive and informative negatives by intentionally degrading visual and textual cues without expensive human annotations. Moreover, we reweight the diffusion timesteps to focus finetuning on intermediate steps where subject details emerge. Extensive experiments demonstrate that SFO with CDNS significantly outperforms baselines in terms of both subject fidelity and text alignment on a subject-driven generation benchmark. Project page: https://subjectfidelityoptimization.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.03107", "title": "ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions", "authors": [ "Di Chang", "Mingdeng Cao", "Yichun Shi", "Bo Liu", "Shengqu Cai", "Shijie Zhou", "Weilin Huang", "Gordon Wetzstein", "Mohammad Soleymani", "Peng Wang" ], "github": "", "project_page": "", "abstract": "Editing images with instructions to reflect non-rigid motions, camera viewpoint shifts, object deformations, human articulations, and complex interactions, poses a challenging yet underexplored problem in computer vision. Existing approaches and datasets predominantly focus on static scenes or rigid transformations, limiting their capacity to handle expressive edits involving dynamic motion. To address this gap, we introduce ByteMorph, a comprehensive framework for instruction-based image editing with an emphasis on non-rigid motions. ByteMorph comprises a large-scale dataset, ByteMorph-6M, and a strong baseline model built upon the Diffusion Transformer (DiT), named ByteMorpher. ByteMorph-6M includes over 6 million high-resolution image editing pairs for training, along with a carefully curated evaluation benchmark ByteMorph-Bench. Both capture a wide variety of non-rigid motion types across diverse environments, human figures, and object categories. The dataset is constructed using motion-guided data generation, layered compositing techniques, and automated captioning to ensure diversity, realism, and semantic coherence. We further conduct a comprehensive evaluation of recent instruction-based image editing methods from both academic and commercial domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-04", "arxiv_id": "2506.00189", "title": "Control-R: Towards controllable test-time scaling", "authors": [ "Di Zhang", "Weida Wang", "Junxian Li", "Xunzhi Wang", "Jiatong Li", "Jianbo Wu", "Jingdi Lei", "Haonan He", "Peng Ye", "Shufei Zhang", "Wanli Ouyang", "Yuqiang Li", "Dongzhan Zhou" ], "github": "", "project_page": "", "abstract": "This paper target in addressing the challenges of underthinking and overthinking in long chain-of-thought (CoT) reasoning for Large Reasoning Models (LRMs) by introducing Reasoning Control Fields (RCF)--a novel test-time approach that injects structured control signals to guide reasoning from a tree search perspective. RCF enables models to adjust reasoning effort according to given control conditions when solving complex tasks. Additionally, we present the Control-R-4K dataset, which consists of challenging problems annotated with detailed reasoning processes and corresponding control fields. To further enhance reasoning control, we propose a Conditional Distillation Finetuning (CDF) method, which trains model--particularly Control-R-32B--to effectively adjust reasoning effort during test time. Experimental results on benchmarks such as AIME2024 and MATH500 demonstrate that our approach achieves state-of-the-art performance at the 32B scale while enabling a controllable Long CoT reasoning process (L-CoT). Overall, this work introduces an effective paradigm for controllable test-time scaling reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.04133", "title": "TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems", "authors": [ "Shaina Raza", "Ranjan Sapkota", "Manoj Karkee", "Christos Emmanouilidis" ], "github": "", "project_page": "", "abstract": "Agentic AI systems, built on large language models (LLMs) and deployed in multi-agent configurations, are redefining intelligent autonomy, collaboration and decision-making across enterprise and societal domains. This review presents a structured analysis of Trust, Risk, and Security Management (TRiSM) in the context of LLM-based agentic multi-agent systems (AMAS). We begin by examining the conceptual foundations of agentic AI, its architectural differences from traditional AI agents, and the emerging system designs that enable scalable, tool-using autonomy. The TRiSM in the agentic AI framework is then detailed through four pillars governance, explainability, ModelOps, and privacy/security each contextualized for agentic LLMs. We identify unique threat vectors and introduce a comprehensive risk taxonomy for the agentic AI applications, supported by case studies illustrating real-world vulnerabilities. Furthermore, the paper also surveys trust-building mechanisms, transparency and oversight techniques, and state-of-the-art explainability strategies in distributed LLM agent systems. Additionally, metrics for evaluating trust, interpretability, and human-centered performance are reviewed alongside open benchmarking challenges. Security and privacy are addressed through encryption, adversarial defense, and compliance with evolving AI regulations. The paper concludes with a roadmap for responsible agentic AI, proposing research directions to align emerging multi-agent systems with robust TRiSM principles for safe, accountable, and transparent deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.02294", "title": "Improving Knowledge Distillation Under Unknown Covariate Shift Through Confidence-Guided Data Augmentation", "authors": [ "Niclas Popp", "Kevin Alexander Laube", "Matthias Hein", "Lukas Schott" ], "github": "", "project_page": "", "abstract": "Large foundation models trained on extensive datasets demonstrate strong zero-shot capabilities in various domains. To replicate their success when data and model size are constrained, knowledge distillation has become an established tool for transferring knowledge from foundation models to small student networks. However, the effectiveness of distillation is critically limited by the available training data. This work addresses the common practical issue of covariate shift in knowledge distillation, where spurious features appear during training but not at test time. We ask the question: when these spurious features are unknown, yet a robust teacher is available, is it possible for a student to also become robust to them? We address this problem by introducing a novel diffusion-based data augmentation strategy that generates images by maximizing the disagreement between the teacher and the student, effectively creating challenging samples that the student struggles with. Experiments demonstrate that our approach significantly improves worst group and mean group accuracy on CelebA and SpuCo Birds as well as the spurious mAUC on spurious ImageNet under covariate shift, outperforming state-of-the-art diffusion-based data augmentation baselines", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03569", "title": "MiMo-VL Technical Report", "authors": [ "Xiaomi LLM-Core Team", "Zihao Yue", "Zhenru Lin", "Yifan Song", "Weikun Wang", "Shuhuai Ren", "Shuhao Gu", "Shicheng Li", "Peidian Li", "Liang Zhao", "Lei Li", "Kainan Bao", "Hao Tian", "Hailin Zhang", "Gang Wang", "Dawei Zhu", "Cici", "Chenhong He", "Bowen Ye", "Bowen Shen", "Zihan Zhang", "Zihan Jiang", "Zhixian Zheng", "Zhichao Song", "Zhenbo Luo", "Yue Yu", "Yudong Wang", "Yuanyuan Tian", "Yu Tu", "Yihan Yan", "Yi Huang", "Xu Wang", "Xinzhe Xu", "Xingchen Song", "Xing Zhang", "Xing Yong", "Xin Zhang", "Xiangwei Deng", "Wenyu Yang", "Wenhan Ma", "Weiwei Lv", "Weiji Zhuang", "Wei Liu", "Sirui Deng", "Shuo Liu", "Shimao Chen", "Shihua Yu", "Shaohui Liu", "Shande Wang", "Rui Ma", "Qiantong Wang", "Peng Wang", "Nuo Chen", "Menghang Zhu", "Kangyang Zhou", "Kang Zhou", "Kai Fang", "Jun Shi", "Jinhao Dong", "Jiebao Xiao", "Jiaming Xu", "Huaqiu Liu", "Hongshen Xu", "Heng Qu", "Haochen Zhao", "Hanglong Lv", "Guoan Wang", "Duo Zhang", "Dong Zhang", "Di Zhang", "Chong Ma", "Chang Liu", "Can Cai", "Bingquan Xia" ], "github": "https://github.com/XiaomiMiMo/MiMo-VL", "project_page": "", "abstract": "We open-source MiMo-VL-7B-SFT and MiMo-VL-7B-RL, two powerful vision-language models delivering state-of-the-art performance in both general visual understanding and multimodal reasoning. MiMo-VL-7B-RL outperforms Qwen2.5-VL-7B on 35 out of 40 evaluated tasks, and scores 59.4 on OlympiadBench, surpassing models with up to 78B parameters. For GUI grounding applications, it sets a new standard with 56.1 on OSWorld-G, even outperforming specialized models such as UI-TARS. Our training combines four-stage pre-training (2.4 trillion tokens) with Mixed On-policy Reinforcement Learning (MORL) integrating diverse reward signals. We identify the importance of incorporating high-quality reasoning data with long Chain-of-Thought into pre-training stages, and the benefits of mixed RL despite challenges in simultaneous multi-domain optimization. We also contribute a comprehensive evaluation suite covering 50+ tasks to promote reproducibility and advance the field. The model checkpoints and full evaluation suite are available at https://github.com/XiaomiMiMo/MiMo-VL.", "github_manual": "", "github_regex": "https://github.com/XiaomiMiMo/MiMo-VL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2505.24500", "title": "TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning for Enhancing LLMs' Social Intelligence", "authors": [ "Guiyang Hou", "Xing Gao", "Yuchuan Wu", "Xiang Huang", "Wenqi Zhang", "Zhe Zheng", "Yongliang Shen", "Jialu Du", "Fei Huang", "Yongbin Li", "Weiming Lu" ], "github": "https://github.com/zju-real/timehc-rl", "project_page": "", "abstract": "Recently, Large Language Models (LLMs) have made significant progress in IQ-related domains that require careful thinking, such as mathematics and coding. However, enhancing LLMs' cognitive development in social domains, particularly from a post-training perspective, remains underexplored. Recognizing that the social world follows a distinct timeline and requires a richer blend of cognitive modes (from intuitive reactions (System 1) and surface-level thinking to deliberate thinking (System 2)) than mathematics, which primarily relies on System 2 cognition (careful, step-by-step reasoning), we introduce Temporal-aware Hierarchical Cognitive Reinforcement Learning (TimeHC-RL) for enhancing LLMs' social intelligence. In our experiments, we systematically explore improving LLMs' social intelligence and validate the effectiveness of the TimeHC-RL method, through five other post-training paradigms and two test-time intervention paradigms on eight datasets with diverse data patterns. Experimental results reveal the superiority of our proposed TimeHC-RL method compared to the widely adopted System 2 RL method. It gives the 7B backbone model wings, enabling it to rival the performance of advanced models like DeepSeek-R1 and OpenAI-O3. Additionally, the systematic exploration from post-training and test-time interventions perspectives to improve LLMs' social intelligence has uncovered several valuable insights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zju-real/timehc-rl", "github_api_added_by": "auto" }, { "date": "2025-06-05", "arxiv_id": "2506.04180", "title": "SuperWriter: Reflection-Driven Long-Form Generation with Large Language Models", "authors": [ "Yuhao Wu", "Yushi Bai", "Zhiqiang Hu", "Juanzi Li", "Roy Ka-Wei Lee" ], "github": "https://github.com/mozhu621/SuperWriter", "project_page": "", "abstract": "Long-form text generation remains a significant challenge for large language models (LLMs), particularly in maintaining coherence, ensuring logical consistency, and preserving text quality as sequence length increases. To address these limitations, we propose SuperWriter-Agent, an agent-based framework designed to enhance the quality and consistency of long-form text generation. SuperWriter-Agent introduces explicit structured thinking-through planning and refinement stages into the generation pipeline, guiding the model to follow a more deliberate and cognitively grounded process akin to that of a professional writer. Based on this framework, we construct a supervised fine-tuning dataset to train a 7B SuperWriter-LM. We further develop a hierarchical Direct Preference Optimization (DPO) procedure that uses Monte Carlo Tree Search (MCTS) to propagate final quality assessments and optimize each generation step accordingly. Empirical results across diverse benchmarks demonstrate that SuperWriter-LM achieves state-of-the-art performance, surpassing even larger-scale baseline models in both automatic evaluation and human evaluation. Furthermore, comprehensive ablation studies demonstrate the effectiveness of hierarchical DPO and underscore the value of incorporating structured thinking steps to improve the quality of long-form text generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mozhu621/SuperWriter", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.04228", "title": "LayerFlow: A Unified Model for Layer-aware Video Generation", "authors": [ "Sihui Ji", "Hao Luo", "Xi Chen", "Yuanpeng Tu", "Yiyang Wang", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "We present LayerFlow, a unified solution for layer-aware video generation. Given per-layer prompts, LayerFlow generates videos for the transparent foreground, clean background, and blended scene. It also supports versatile variants like decomposing a blended video or generating the background for the given foreground and vice versa. Starting from a text-to-video diffusion transformer, we organize the videos for different layers as sub-clips, and leverage layer embeddings to distinguish each clip and the corresponding layer-wise prompts. In this way, we seamlessly support the aforementioned variants in one unified framework. For the lack of high-quality layer-wise training videos, we design a multi-stage training strategy to accommodate static images with high-quality layer annotations. Specifically, we first train the model with low-quality video data. Then, we tune a motion LoRA to make the model compatible with static frames. Afterward, we train the content LoRA on the mixture of image data with high-quality layered images along with copy-pasted video data. During inference, we remove the motion LoRA thus generating smooth videos with desired layers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.04158", "title": "Image Editing As Programs with Diffusion Models", "authors": [ "Yujia Hu", "Songhua Liu", "Zhenxiong Tan", "Xingyi Yang", "Xinchao Wang" ], "github": "https://github.com/YujiaHu1109/IEAP", "project_page": "", "abstract": "While diffusion models have achieved remarkable success in text-to-image generation, they encounter significant challenges with instruction-driven image editing. Our research highlights a key challenge: these models particularly struggle with structurally inconsistent edits that involve substantial layout changes. To mitigate this gap, we introduce Image Editing As Programs (IEAP), a unified image editing framework built upon the Diffusion Transformer (DiT) architecture. At its core, IEAP approaches instructional editing through a reductionist lens, decomposing complex editing instructions into sequences of atomic operations. Each operation is implemented via a lightweight adapter sharing the same DiT backbone and is specialized for a specific type of edit. Programmed by a vision-language model (VLM)-based agent, these operations collaboratively support arbitrary and structurally inconsistent transformations. By modularizing and sequencing edits in this way, IEAP generalizes robustly across a wide range of editing tasks, from simple adjustments to substantial structural changes. Extensive experiments demonstrate that IEAP significantly outperforms state-of-the-art methods on standard benchmarks across various editing scenarios. In these evaluations, our framework delivers superior accuracy and semantic fidelity, particularly for complex, multi-step instructions. Codes are available at https://github.com/YujiaHu1109/IEAP.", "github_manual": "", "github_regex": "https://github.com/YujiaHu1109/IEAP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.04108", "title": "Rectified Sparse Attention", "authors": [ "Yutao Sun", "Tianzhu Ye", "Li Dong", "Yuqing Xia", "Jian Chen", "Yizhao Gao", "Shijie Cao", "Jianyong Wang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Efficient long-sequence generation is a critical challenge for Large Language Models. While recent sparse decoding methods improve efficiency, they suffer from KV cache misalignment, where approximation errors accumulate and degrade generation quality. In this work, we propose Rectified Sparse Attention (ReSA), a simple yet effective method that combines block-sparse attention with periodic dense rectification. By refreshing the KV cache at fixed intervals using a dense forward pass, ReSA bounds error accumulation and preserves alignment with the pretraining distribution. Experiments across math reasoning, language modeling, and retrieval tasks demonstrate that ReSA achieves near-lossless generation quality with significantly improved efficiency. Notably, ReSA delivers up to 2.42times end-to-end speedup under decoding at 256K sequence length, making it a practical solution for scalable long-context inference. Code is available at https://aka.ms/ReSA-LM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.04034", "title": "Rex-Thinker: Grounded Object Referring via Chain-of-Thought Reasoning", "authors": [ "Qing Jiang", "Xingyu Chen", "Zhaoyang Zeng", "Junzhi Yu", "Lei Zhang" ], "github": "https://github.com/IDEA-Research/Rex-Thinker", "project_page": "", "abstract": "Object referring aims to detect all objects in an image that match a given natural language description. We argue that a robust object referring model should be grounded, meaning its predictions should be both explainable and faithful to the visual content. Specifically, it should satisfy two key properties: 1) Verifiable, by producing interpretable reasoning that justifies its predictions and clearly links them to visual evidence; and 2) Trustworthy, by learning to abstain when no object in the image satisfies the given expression. However, most methods treat referring as a direct bounding box prediction task, offering limited interpretability and struggling to reject expressions with no matching object. In this work, we propose Rex-Thinker, a model that formulates object referring as an explicit CoT reasoning task. Given a referring expression, we first identify all candidate object instances corresponding to the referred object category. Rex-Thinker then performs step-by-step reasoning over each candidate to assess whether it matches the given expression, before making a final prediction. To support this paradigm, we construct a large-scale CoT-style referring dataset named HumanRef-CoT by prompting GPT-4o on the HumanRef dataset. Each reasoning trace follows a structured planning, action, and summarization format, enabling the model to learn decomposed, interpretable reasoning over object candidates. We then train Rex-Thinker in two stages: a cold-start supervised fine-tuning phase to teach the model how to perform structured reasoning, followed by GRPO-based RL learning to improve accuracy and generalization. Experiments show that our approach outperforms standard baselines in both precision and interpretability on in-domain evaluation, while also demonstrating improved ability to reject hallucinated outputs and strong generalization in out-of-domain settings.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/IDEA-Research/Rex-Thinker", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03448", "title": "RefEdit: A Benchmark and Method for Improving Instruction-based Image Editing Model on Referring Expressions", "authors": [ "Bimsara Pathiraja", "Maitreya Patel", "Shivam Singh", "Yezhou Yang", "Chitta Baral" ], "github": "", "project_page": "", "abstract": "Despite recent advances in inversion and instruction-based image editing, existing approaches primarily excel at editing single, prominent objects but significantly struggle when applied to complex scenes containing multiple entities. To quantify this gap, we first introduce RefEdit-Bench, a rigorous real-world benchmark rooted in RefCOCO, where even baselines trained on millions of samples perform poorly. To overcome this limitation, we introduce RefEdit -- an instruction-based editing model trained on our scalable synthetic data generation pipeline. Our RefEdit, trained on only 20,000 editing triplets, outperforms the Flux/SD3 model-based baselines trained on millions of data. Extensive evaluations across various benchmarks demonstrate that our model not only excels in referring expression tasks but also enhances performance on traditional benchmarks, achieving state-of-the-art results comparable to closed-source methods. We release data \\& checkpoint for reproducibility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03106", "title": "Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback", "authors": [ "Xiaoying Zhang", "Hao Sun", "Yipeng Zhang", "Kaituo Feng", "Chaochao Lu", "Chao Yang", "Helen Meng" ], "github": "", "project_page": "", "abstract": "Recent advances in reinforcement learning (RL) with numerical feedback, such as scalar rewards, have significantly enhanced the complex reasoning capabilities of large language models (LLMs). Despite this success, we identify three key challenges encountered by RL with solely numerical feedback: performance plateaus, limited effectiveness of self-reflection, and persistent failures. We then demonstrate that RL-finetuned models, even after exhibiting performance plateaus, can generate correct refinements on persistently failed problems by leveraging natural language feedback in the form of critiques. Building on this insight, we propose Critique-GRPO, an online RL framework that integrates both natural language and numerical feedback for effective policy optimization. Critique-GRPO enables LLMs to learn from initial responses and critique-guided refinements simultaneously while maintaining exploration. Extensive experiments using Qwen2.5-7B-Base and Qwen3-8B-Base show that Critique-GRPO consistently outperforms supervised learning-based and RL-based fine-tuning approaches across eight challenging mathematical, STEM, and general reasoning tasks, improving average pass@1 scores by approximately 4.5% and 5%, respectively. Notably, Critique-GRPO surpasses a strong baseline that incorporates expert demonstrations within online RL. Further analysis reveals two critical insights about policy exploration: (1) higher entropy does not always guarantee efficient learning from exploration, and (2) longer responses do not necessarily lead to more effective exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.02945", "title": "Quantitative LLM Judges", "authors": [ "Aishwarya Sahoo", "Jeevana Kruthi Karnuthala", "Tushar Parmanand Budhwani", "Pranchal Agarwal", "Sankaran Vaidyanathan", "Alexa Siu", "Franck Dernoncourt", "Jennifer Healey", "Nedim Lipka", "Ryan Rossi", "Uttaran Bhattacharya", "Branislav Kveton" ], "github": "", "project_page": "", "abstract": "LLM-as-a-judge is a framework in which a large language model (LLM) automatically evaluates the output of another LLM. We propose quantitative LLM judges, which align evaluation scores of existing LLM judges to human scores in a given domain using regression models. The models are trained to improve the score of the original judge by using the judge's textual evaluation and score. We present four quantitative judges for different types of absolute and relative feedback, which showcases the generality and versatility of our framework. Our framework is more computationally efficient than supervised fine-tuning and can be more statistically efficient when human feedback is limited, which is expected in most applications of our work. We validate these claims empirically on four datasets using two base judges. Our experiments show that quantitative judges can effectively improve the predictive power of existing judges through post-hoc modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.01344", "title": "Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agents", "authors": [ "Manan Suri", "Puneet Mathur", "Nedim Lipka", "Franck Dernoncourt", "Ryan A. Rossi", "Vivek Gupta", "Dinesh Manocha" ], "github": "", "project_page": "", "abstract": "Flowcharts are a critical tool for visualizing decision-making processes. However, their non-linear structure and complex visual-textual relationships make it challenging to interpret them using LLMs, as vision-language models frequently hallucinate nonexistent connections and decision paths when analyzing these diagrams. This leads to compromised reliability for automated flowchart processing in critical domains such as logistics, health, and engineering. We introduce the task of Fine-grained Flowchart Attribution, which traces specific components grounding a flowchart referring LLM response. Flowchart Attribution ensures the verifiability of LLM predictions and improves explainability by linking generated responses to the flowchart's structure. We propose FlowPathAgent, a neurosymbolic agent that performs fine-grained post hoc attribution through graph-based reasoning. It first segments the flowchart, then converts it into a structured symbolic graph, and then employs an agentic approach to dynamically interact with the graph, to generate attribution paths. Additionally, we present FlowExplainBench, a novel benchmark for evaluating flowchart attributions across diverse styles, domains, and question types. Experimental results show that FlowPathAgent mitigates visual hallucinations in LLM answers over flowchart QA, outperforming strong baselines by 10-14% on our proposed FlowExplainBench dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2505.23807", "title": "DLP: Dynamic Layerwise Pruning in Large Language Models", "authors": [ "Yuli Chen", "Bo Cheng", "Jiale Han", "Yingying Zhang", "Yingting Li", "Shuhao Zhang" ], "github": "https://github.com/ironartisan/DLP", "project_page": "", "abstract": "Pruning has recently been widely adopted to reduce the parameter scale and improve the inference efficiency of Large Language Models (LLMs). Mainstream pruning techniques often rely on uniform layerwise pruning strategies, which can lead to severe performance degradation at high sparsity levels. Recognizing the varying contributions of different layers in LLMs, recent studies have shifted their focus toward non-uniform layerwise pruning. However, these approaches often rely on pre-defined values, which can result in suboptimal performance. To overcome these limitations, we propose a novel method called Dynamic Layerwise Pruning (DLP). This approach adaptively determines the relative importance of each layer by integrating model weights with input activation information, assigning pruning rates accordingly. Experimental results show that DLP effectively preserves model performance at high sparsity levels across multiple LLMs. Specifically, at 70% sparsity, DLP reduces the perplexity of LLaMA2-7B by 7.79 and improves the average accuracy by 2.7% compared to state-of-the-art methods. Moreover, DLP is compatible with various existing LLM compression techniques and can be seamlessly integrated into Parameter-Efficient Fine-Tuning (PEFT). We release the code at https://github.com/ironartisan/DLP to facilitate future research.", "github_manual": "", "github_regex": "https://github.com/ironartisan/DLP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2505.21541", "title": "DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via Diffusion Transformers", "authors": [ "Zitong Wang", "Hang Zhao", "Qianyu Zhou", "Xuequan Lu", "Xiangtai Li", "Yiren Song" ], "github": "https://github.com/Wangzt1121/DiffDecompose", "project_page": "", "abstract": "Diffusion models have recently motivated great success in many generation tasks like object removal. Nevertheless, existing image decomposition methods struggle to disentangle semi-transparent or transparent layer occlusions due to mask prior dependencies, static object assumptions, and the lack of datasets. In this paper, we delve into a novel task: Layer-Wise Decomposition of Alpha-Composited Images, aiming to recover constituent layers from single overlapped images under the condition of semi-transparent/transparent alpha layer non-linear occlusion. To address challenges in layer ambiguity, generalization, and data scarcity, we first introduce AlphaBlend, the first large-scale and high-quality dataset for transparent and semi-transparent layer decomposition, supporting six real-world subtasks (e.g., translucent flare removal, semi-transparent cell decomposition, glassware decomposition). Building on this dataset, we present DiffDecompose, a diffusion Transformer-based framework that learns the posterior over possible layer decompositions conditioned on the input image, semantic prompts, and blending type. Rather than regressing alpha mattes directly, DiffDecompose performs In-Context Decomposition, enabling the model to predict one or multiple layers without per-layer supervision, and introduces Layer Position Encoding Cloning to maintain pixel-level correspondence across layers. Extensive experiments on the proposed AlphaBlend dataset and public LOGO dataset verify the effectiveness of DiffDecompose. The code and dataset will be available upon paper acceptance. Our code will be available at: https://github.com/Wangzt1121/DiffDecompose.", "github_manual": "", "github_regex": "https://github.com/Wangzt1121/DiffDecompose", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.02921", "title": "A Controllable Examination for Long-Context Language Models", "authors": [ "Yijun Yang", "Zeyu Huang", "Wenhao Zhu", "Zihan Qiu", "Fei Yuan", "Jeff Z. Pan", "Ivan Titov" ], "github": "https://github.com/Thomasyyj/LongBio-Benchmark", "project_page": "", "abstract": "Existing frameworks for evaluating long-context language models (LCLM) can be broadly categorized into real-world and synthetic tasks. Despite their utility, both approaches are accompanied by certain intrinsic limitations. Real-world tasks are too complex to interpret or characterize and are susceptible to data contamination. In contrast, synthetic tasks often adopt the needle-in-the-haystack (NIAH) format, wherein a lack of coherence between the \"needle\" and the \"haystack\" compromises their validity as proxies for realistic applications. In response to these challenges, we posit that an ideal long-context evaluation framework should be characterized by three essential features: seamless context, controllable setting, and sound evaluation. This study introduces LongBioBench, a novel benchmark that utilizes artificially generated biographies as a controlled environment for assessing LCLMs across dimensions of understanding, reasoning, and trustworthiness. Our experimental evaluation, which includes 18 LCLMs in total, demonstrates that most models still exhibit deficiencies in semantic understanding and elementary reasoning over retrieved results and are less trustworthy as context length increases. Our further analysis indicates some design choices employed by existing synthetic benchmarks, such as contextual non-coherence, numerical needles, and the absence of distractors, rendering them vulnerable to test the model long-context capabilities. Moreover, we also reveal that long-context continual pretraining primarily adjusts RoPE embedding to accommodate extended context lengths. To sum up, compared to previous synthetic benchmarks, LongBioBench achieves a better trade-off between mirroring authentic language tasks and maintaining controllability, and is highly interpretable and configurable.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Thomasyyj/LongBio-Benchmark", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.02592", "title": "Beyond the Surface: Measuring Self-Preference in LLM Judgments", "authors": [ "Zhi-Yuan Chen", "Hao Wang", "Xinyu Zhang", "Enrui Hu", "Yankai Lin" ], "github": "https://github.com/zhiyuanc2001/self-preference", "project_page": "", "abstract": "Recent studies show that large language models (LLMs) exhibit self-preference bias when serving as judges, meaning they tend to favor their own responses over those generated by other models. Existing methods typically measure this bias by calculating the difference between the scores a judge model assigns to its own responses and those it assigns to responses from other models. However, this approach conflates self-preference bias with response quality, as higher-quality responses from the judge model may also lead to positive score differences, even in the absence of bias. To address this issue, we introduce gold judgments as proxies for the actual quality of responses and propose the DBG score, which measures self-preference bias as the difference between the scores assigned by the judge model to its own responses and the corresponding gold judgments. Since gold judgments reflect true response quality, the DBG score mitigates the confounding effect of response quality on bias measurement. Using the DBG score, we conduct comprehensive experiments to assess self-preference bias across LLMs of varying versions, sizes, and reasoning abilities. Additionally, we investigate two factors that influence and help alleviate self-preference bias: response text style and the post-training data of judge models. Finally, we explore potential underlying mechanisms of self-preference bias from an attention-based perspective. Our code and data are available at https://github.com/zhiyuanc2001/self-preference.", "github_manual": "", "github_regex": "https://github.com/zhiyuanc2001/self-preference", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03099", "title": "TalkingMachines: Real-Time Audio-Driven FaceTime-Style Video via Autoregressive Diffusion Models", "authors": [ "Chetwin Low", "Weimin Wang" ], "github": "", "project_page": "", "abstract": "In this paper, we present TalkingMachines -- an efficient framework that transforms pretrained video generation models into real-time, audio-driven character animators. TalkingMachines enables natural conversational experiences by integrating an audio large language model (LLM) with our video generation foundation model. Our primary contributions include: (1) We adapt a pretrained SOTA image-to-video DiT into an audio-driven avatar generation model of 18 billion parameters; (2) We enable infinite video streaming without error accumulation through asymmetric knowledge distillation from a bidirectional teacher model into a sparse causal, autoregressive student model; (3) We design a high-throughput, low-latency inference pipeline incorporating several key engineering optimizations such as: (a) disaggregation of the DiT and VAE decoder across separate devices, (b) efficient overlap of inter-device communication and computation using CUDA streams, (c) elimination of redundant recomputations to maximize frame-generation throughput. Please see demo videos here - https://aaxwaz.github.io/TalkingMachines/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03295", "title": "Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem", "authors": [ "Yubo Wang", "Ping Nie", "Kai Zou", "Lijun Wu", "Wenhu Chen" ], "github": "https://github.com/TIGER-AI-Lab/One-Shot-CFT", "project_page": "", "abstract": "We have witnessed that strong LLMs like Qwen-Math, MiMo, and Phi-4 possess immense reasoning potential inherited from the pre-training stage. With reinforcement learning (RL), these models can improve dramatically on reasoning tasks. Recent studies have shown that even RL on a single problem can unleash these models' reasoning capabilities. However, RL is not only expensive but also unstable. Even one-shot RL requires hundreds of GPU hours. This raises a critical question: Is there a more efficient way to unleash the reasoning potential of these powerful base LLMs? In this work, we demonstrate that Critique Fine-Tuning (CFT) on only one problem can effectively unleash the reasoning potential of LLMs. Our method constructs critique data by collecting diverse model-generated solutions to a single problem and using teacher LLMs to provide detailed critiques. We fine-tune Qwen and Llama family models, ranging from 1.5B to 14B parameters, on the CFT data and observe significant performance gains across diverse reasoning tasks. For example, with just 5 GPU hours of training, Qwen-Math-7B-CFT show an average improvement of 15% on six math benchmarks and 16% on three logic reasoning benchmarks. These results are comparable to or even surpass the results from RL with 20x less compute. Ablation studies reveal the robustness of one-shot CFT across different prompt problems. These results highlight one-shot CFT as a simple, general, and compute-efficient approach to unleashing the reasoning capabilities of modern LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TIGER-AI-Lab/One-Shot-CFT", "github_api_added_by": "auto" }, { "date": "2025-06-05", "arxiv_id": "2506.04207", "title": "Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning", "authors": [ "Shuang Chen", "Yue Guo", "Zhaochen Su", "Yafu Li", "Yulun Wu", "Jiacheng Chen", "Jiayu Chen", "Weijie Wang", "Xiaoye Qu", "Yu Cheng" ], "github": "", "project_page": "", "abstract": "Inspired by the remarkable reasoning capabilities of Deepseek-R1 in complex textual tasks, many works attempt to incentivize similar capabilities in Multimodal Large Language Models (MLLMs) by directly applying reinforcement learning (RL). However, they still struggle to activate complex reasoning. In this paper, rather than examining multimodal RL in isolation, we delve into current training pipelines and identify three crucial phenomena: 1) Effective cold start initialization is critical for enhancing MLLM reasoning. Intriguingly, we find that initializing with carefully selected text data alone can lead to performance surpassing many recent multimodal reasoning models, even before multimodal RL. 2) Standard GRPO applied to multimodal RL suffers from gradient stagnation, which degrades training stability and performance. 3) Subsequent text-only RL training, following the multimodal RL phase, further enhances multimodal reasoning. This staged training approach effectively balances perceptual grounding and cognitive reasoning development. By incorporating the above insights and addressing multimodal RL issues, we introduce ReVisual-R1, achieving a new state-of-the-art among open-source 7B MLLMs on challenging benchmarks including MathVerse, MathVision, WeMath, LogicVista, DynaMath, and challenging AIME2024 and AIME2025.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.01320", "title": "\u03a8-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models", "authors": [ "Taehoon Yoon", "Yunhong Min", "Kyeongmin Yeo", "Minhyuk Sung" ], "github": "https://github.com/KAIST-Visual-AI-Group/Psi-Sampler", "project_page": "", "abstract": "We introduce Psi-Sampler, an SMC-based framework incorporating pCNL-based initial particle sampling for effective inference-time reward alignment with a score-based generative model. Inference-time reward alignment with score-based generative models has recently gained significant traction, following a broader paradigm shift from pre-training to post-training optimization. At the core of this trend is the application of Sequential Monte Carlo (SMC) to the denoising process. However, existing methods typically initialize particles from the Gaussian prior, which inadequately captures reward-relevant regions and results in reduced sampling efficiency. We demonstrate that initializing from the reward-aware posterior significantly improves alignment performance. To enable posterior sampling in high-dimensional latent spaces, we introduce the preconditioned Crank-Nicolson Langevin (pCNL) algorithm, which combines dimension-robust proposals with gradient-informed dynamics. This approach enables efficient and scalable posterior sampling and consistently improves performance across various reward alignment tasks, including layout-to-image generation, quantity-aware generation, and aesthetic-preference generation, as demonstrated in our experiments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KAIST-Visual-AI-Group/Psi-Sampler", "github_api_added_by": "auto" }, { "date": "2025-06-05", "arxiv_id": "2506.03139", "title": "SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation", "authors": [ "Siqi Chen", "Xinyu Dong", "Haolei Xu", "Xingyu Wu", "Fei Tang", "Hang Zhang", "Yuchen Yan", "Linjuan Wu", "Wenqi Zhang", "Guiyang Hou", "Yongliang Shen", "Weiming Lu", "Yueting Zhuang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) and Multimodal LLMs have shown promising capabilities for SVG processing, yet existing benchmarks suffer from limited real-world coverage, lack of complexity stratification, and fragmented evaluation paradigms. We introduce SVGenius, a comprehensive benchmark comprising 2,377 queries across three progressive dimensions: understanding, editing, and generation. Built on real-world data from 24 application domains with systematic complexity stratification, SVGenius evaluates models through 8 task categories and 18 metrics. We assess 22 mainstream models spanning different scales, architectures, training paradigms, and accessibility levels. Our analysis reveals that while proprietary models significantly outperform open-source counterparts, all models exhibit systematic performance degradation with increasing complexity, indicating fundamental limitations in current approaches; however, reasoning-enhanced training proves more effective than pure scaling for overcoming these limitations, though style transfer remains the most challenging capability across all model types. SVGenius establishes the first systematic evaluation framework for SVG processing, providing crucial insights for developing more capable vector graphics models and advancing automated graphic design applications. Appendix and supplementary materials (including all data and code) are available at https://zju-real.github.io/SVGenius.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03517", "title": "DenseDPO: Fine-Grained Temporal Preference Optimization for Video Diffusion Models", "authors": [ "Ziyi Wu", "Anil Kag", "Ivan Skorokhodov", "Willi Menapace", "Ashkan Mirzaei", "Igor Gilitschenski", "Sergey Tulyakov", "Aliaksandr Siarohin" ], "github": "", "project_page": "", "abstract": "Direct Preference Optimization (DPO) has recently been applied as a post-training technique for text-to-video diffusion models. To obtain training data, annotators are asked to provide preferences between two videos generated from independent noise. However, this approach prohibits fine-grained comparisons, and we point out that it biases the annotators towards low-motion clips as they often contain fewer visual artifacts. In this work, we introduce DenseDPO, a method that addresses these shortcomings by making three contributions. First, we create each video pair for DPO by denoising corrupted copies of a ground truth video. This results in aligned pairs with similar motion structures while differing in local details, effectively neutralizing the motion bias. Second, we leverage the resulting temporal alignment to label preferences on short segments rather than entire clips, yielding a denser and more precise learning signal. With only one-third of the labeled data, DenseDPO greatly improves motion generation over vanilla DPO, while matching it in text alignment, visual quality, and temporal consistency. Finally, we show that DenseDPO unlocks automatic preference annotation using off-the-shelf Vision Language Models (VLMs): GPT accurately predicts segment-level preferences similar to task-specifically fine-tuned video reward models, and DenseDPO trained on these labels achieves performance close to using human labels.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.04142", "title": "Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis", "authors": [ "Kejian Zhu", "Shangqing Tu", "Zhuoran Jin", "Lei Hou", "Juanzi Li", "Jun Zhao" ], "github": "https://github.com/GaryStack/Trustworthy-Evaluation", "project_page": "", "abstract": "The development of large language models (LLMs) depends on trustworthy evaluation. However, most current evaluations rely on public benchmarks, which are prone to data contamination issues that significantly compromise fairness. Previous researches have focused on constructing dynamic benchmarks to address contamination. However, continuously building new benchmarks is costly and cyclical. In this work, we aim to tackle contamination by analyzing the mechanisms of contaminated models themselves. Through our experiments, we discover that the overestimation of contaminated models is likely due to parameters acquiring shortcut solutions in training. We further propose a novel method for identifying shortcut neurons through comparative and causal analysis. Building on this, we introduce an evaluation method called shortcut neuron patching to suppress shortcut neurons. Experiments validate the effectiveness of our approach in mitigating contamination. Additionally, our evaluation results exhibit a strong linear correlation with MixEval, a recently released trustworthy benchmark, achieving a Spearman coefficient (rho) exceeding 0.95. This high correlation indicates that our method closely reveals true capabilities of the models and is trustworthy. We conduct further experiments to demonstrate the generalizability of our method across various benchmarks and hyperparameter settings. Code: https://github.com/GaryStack/Trustworthy-Evaluation", "github_manual": "", "github_regex": "https://github.com/GaryStack/Trustworthy-Evaluation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.04141", "title": "MMR-V: What's Left Unsaid? A Benchmark for Multimodal Deep Reasoning in Videos", "authors": [ "Kejian Zhu", "Zhuoran Jin", "Hongbang Yuan", "Jiachun Li", "Shangqing Tu", "Pengfei Cao", "Yubo Chen", "Kang Liu", "Jun Zhao" ], "github": "", "project_page": "", "abstract": "The sequential structure of videos poses a challenge to the ability of multimodal large language models (MLLMs) to locate multi-frame evidence and conduct multimodal reasoning. However, existing video benchmarks mainly focus on understanding tasks, which only require models to match frames mentioned in the question (hereafter referred to as \"question frame\") and perceive a few adjacent frames. To address this gap, we propose MMR-V: A Benchmark for Multimodal Deep Reasoning in Videos. The benchmark is characterized by the following features. (1) Long-range, multi-frame reasoning: Models are required to infer and analyze evidence frames that may be far from the question frame. (2) Beyond perception: Questions cannot be answered through direct perception alone but require reasoning over hidden information. (3) Reliability: All tasks are manually annotated, referencing extensive real-world user understanding to align with common perceptions. (4) Confusability: Carefully designed distractor annotation strategies to reduce model shortcuts. MMR-V consists of 317 videos and 1,257 tasks. Our experiments reveal that current models still struggle with multi-modal reasoning; even the best-performing model, o4-mini, achieves only 52.5% accuracy. Additionally, current reasoning enhancement strategies (Chain-of-Thought and scaling test-time compute) bring limited gains. Further analysis indicates that the CoT demanded for multi-modal reasoning differs from it in textual reasoning, which partly explains the limited performance gains. We hope that MMR-V can inspire further research into enhancing multi-modal reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.00482", "title": "BenchHub: A Unified Benchmark Suite for Holistic and Customizable LLM Evaluation", "authors": [ "Eunsu Kim", "Haneul Yoo", "Guijin Son", "Hitesh Patel", "Amit Agarwal", "Alice Oh" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) continue to advance, the need for up-to-date and well-organized benchmarks becomes increasingly critical. However, many existing datasets are scattered, difficult to manage, and make it challenging to perform evaluations tailored to specific needs or domains, despite the growing importance of domain-specific models in areas such as math or code. In this paper, we introduce BenchHub, a dynamic benchmark repository that empowers researchers and developers to evaluate LLMs more effectively. BenchHub aggregates and automatically classifies benchmark datasets from diverse domains, integrating 303K questions across 38 benchmarks. It is designed to support continuous updates and scalable data management, enabling flexible and customizable evaluation tailored to various domains or use cases. Through extensive experiments with various LLM families, we demonstrate that model performance varies significantly across domain-specific subsets, emphasizing the importance of domain-aware benchmarking. We believe BenchHub can encourage better dataset reuse, more transparent model comparisons, and easier identification of underrepresented areas in existing benchmarks, offering a critical infrastructure for advancing LLM evaluation research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03956", "title": "Adapt before Continual Learning", "authors": [ "Aojun Lu", "Tao Feng", "Hangjie Yuan", "Chunhui Ding", "Yanan Sun" ], "github": "https://github.com/byyx666/ACL", "project_page": "", "abstract": "Continual Learning (CL) seeks to enable neural networks to incrementally acquire new knowledge (plasticity) while retaining existing knowledge (stability). While pre-trained models (PTMs) have become pivotal in CL, prevailing approaches freeze the PTM backbone to preserve stability, limiting their plasticity, particularly when encountering significant domain gaps in incremental tasks. Conversely, sequentially finetuning the entire PTM risks catastrophic forgetting of generalizable knowledge, exposing a critical stability-plasticity trade-off. To address this challenge, we propose Adapting PTMs before the core CL process (ACL), a novel framework that refines the PTM backbone through a plug-and-play adaptation phase before learning each new task with existing CL approaches (e.g., prompt tuning). ACL enhances plasticity by aligning embeddings with their original class prototypes while distancing them from others, theoretically and empirically shown to balance stability and plasticity. Extensive experiments demonstrate that ACL significantly improves CL performance across benchmarks and integrated methods, offering a versatile solution for PTM-based CL.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/byyx666/ACL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03614", "title": "VLMs Can Aggregate Scattered Training Patches", "authors": [ "Zhanhui Zhou", "Lingjie Chen", "Chao Yang", "Chaochao Lu" ], "github": "https://github.com/ZHZisZZ/visual-stitching", "project_page": "", "abstract": "One way to mitigate risks in vision-language models (VLMs) is to remove dangerous samples in their training data. However, such data moderation can be easily bypassed when harmful images are split into small, benign-looking patches, scattered across many training samples. VLMs may then learn to piece these fragments together during training and generate harmful responses at inference, either from full images or text references. For instance, if trained on image patches from a bloody scene paired with the descriptions \"safe,\" VLMs may later describe, the full image or a text reference to the scene, as \"safe.\" We define the core ability of VLMs enabling this attack as visual stitching -- the ability to integrate visual information spread across multiple training samples that share the same textual descriptions. In our work, we first demonstrate visual stitching abilities in common open-source VLMs on three datasets where each image is labeled with a unique synthetic ID: we split each (image, ID) pair into {(patch, ID)} pairs at different granularity for finetuning, and we find that tuned models can verbalize the correct IDs from full images or text reference. Building on this, we simulate the adversarial data poisoning scenario mentioned above by using patches from dangerous images and replacing IDs with text descriptions like ``safe'' or ``unsafe'', demonstrating how harmful content can evade moderation in patches and later be reconstructed through visual stitching, posing serious VLM safety risks. Code is available at https://github.com/ZHZisZZ/visual-stitching.", "github_manual": "", "github_regex": "https://github.com/ZHZisZZ/visual-stitching", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.04225", "title": "Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation", "authors": [ "Tianyu Huang", "Wangguandong Zheng", "Tengfei Wang", "Yuhao Liu", "Zhenwei Wang", "Junta Wu", "Jie Jiang", "Hui Li", "Rynson W. H. Lau", "Wangmeng Zuo", "Chunchao Guo" ], "github": "", "project_page": "", "abstract": "Real-world applications like video gaming and virtual reality often demand the ability to model 3D scenes that users can explore along custom camera trajectories. While significant progress has been made in generating 3D objects from text or images, creating long-range, 3D-consistent, explorable 3D scenes remains a complex and challenging problem. In this work, we present Voyager, a novel video diffusion framework that generates world-consistent 3D point-cloud sequences from a single image with user-defined camera path. Unlike existing approaches, Voyager achieves end-to-end scene generation and reconstruction with inherent consistency across frames, eliminating the need for 3D reconstruction pipelines (e.g., structure-from-motion or multi-view stereo). Our method integrates three key components: 1) World-Consistent Video Diffusion: A unified architecture that jointly generates aligned RGB and depth video sequences, conditioned on existing world observation to ensure global coherence 2) Long-Range World Exploration: An efficient world cache with point culling and an auto-regressive inference with smooth video sampling for iterative scene extension with context-aware consistency, and 3) Scalable Data Engine: A video reconstruction pipeline that automates camera pose estimation and metric depth prediction for arbitrary videos, enabling large-scale, diverse training data curation without manual 3D annotations. Collectively, these designs result in a clear improvement over existing methods in visual quality and geometric accuracy, with versatile applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03930", "title": "VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation", "authors": [ "Yuansheng Ni", "Ping Nie", "Kai Zou", "Xiang Yue", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) often struggle with visualization tasks like plotting diagrams, charts, where success depends on both code correctness and visual semantics. Existing instruction-tuning datasets lack execution-grounded supervision and offer limited support for iterative code correction, resulting in fragile and unreliable plot generation. We present VisCode-200K, a large-scale instruction tuning dataset for Python-based visualization and self-correction. It contains over 200K examples from two sources: (1) validated plotting code from open-source repositories, paired with natural language instructions and rendered plots; and (2) 45K multi-turn correction dialogues from Code-Feedback, enabling models to revise faulty code using runtime feedback. We fine-tune Qwen2.5-Coder-Instruct on VisCode-200K to create VisCoder, and evaluate it on PandasPlotBench. VisCoder significantly outperforms strong open-source baselines and approaches the performance of proprietary models like GPT-4o-mini. We further adopt a self-debug evaluation protocol to assess iterative repair, demonstrating the benefits of feedback-driven learning for executable, visually accurate code generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.04089", "title": "AmbiK: Dataset of Ambiguous Tasks in Kitchen Environment", "authors": [ "Anastasiia Ivanova", "Eva Bakaeva", "Zoya Volovikova", "Alexey K. Kovalev", "Aleksandr I. Panov" ], "github": "https://github.com/cog-model/AmbiK-dataset", "project_page": "", "abstract": "As a part of an embodied agent, Large Language Models (LLMs) are typically used for behavior planning given natural language instructions from the user. However, dealing with ambiguous instructions in real-world environments remains a challenge for LLMs. Various methods for task ambiguity detection have been proposed. However, it is difficult to compare them because they are tested on different datasets and there is no universal benchmark. For this reason, we propose AmbiK (Ambiguous Tasks in Kitchen Environment), the fully textual dataset of ambiguous instructions addressed to a robot in a kitchen environment. AmbiK was collected with the assistance of LLMs and is human-validated. It comprises 1000 pairs of ambiguous tasks and their unambiguous counterparts, categorized by ambiguity type (Human Preferences, Common Sense Knowledge, Safety), with environment descriptions, clarifying questions and answers, user intents, and task plans, for a total of 2000 tasks. We hope that AmbiK will enable researchers to perform a unified comparison of ambiguity detection methods. AmbiK is available at https://github.com/cog-model/AmbiK-dataset.", "github_manual": "", "github_regex": "https://github.com/cog-model/AmbiK-dataset", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03538", "title": "Robust Neural Rendering in the Wild with Asymmetric Dual 3D Gaussian Splatting", "authors": [ "Chengqi Li", "Zhihao Shi", "Yangdi Lu", "Wenbo He", "Xiangyu Xu" ], "github": "https://github.com/steveli88/AsymGS", "project_page": "", "abstract": "3D reconstruction from in-the-wild images remains a challenging task due to inconsistent lighting conditions and transient distractors. Existing methods typically rely on heuristic strategies to handle the low-quality training data, which often struggle to produce stable and consistent reconstructions, frequently resulting in visual artifacts. In this work, we propose Asymmetric Dual 3DGS, a novel framework that leverages the stochastic nature of these artifacts: they tend to vary across different training runs due to minor randomness. Specifically, our method trains two 3D Gaussian Splatting (3DGS) models in parallel, enforcing a consistency constraint that encourages convergence on reliable scene geometry while suppressing inconsistent artifacts. To prevent the two models from collapsing into similar failure modes due to confirmation bias, we introduce a divergent masking strategy that applies two complementary masks: a multi-cue adaptive mask and a self-supervised soft mask, which leads to an asymmetric training process of the two models, reducing shared error modes. In addition, to improve the efficiency of model training, we introduce a lightweight variant called Dynamic EMA Proxy, which replaces one of the two models with a dynamically updated Exponential Moving Average (EMA) proxy, and employs an alternating masking strategy to preserve divergence. Extensive experiments on challenging real-world datasets demonstrate that our method consistently outperforms existing approaches while achieving high efficiency. Codes and trained models will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/steveli88/AsymGS", "github_api_added_by": "auto" }, { "date": "2025-06-05", "arxiv_id": "2506.03951", "title": "Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective", "authors": [ "Aojun Lu", "Hangjie Yuan", "Tao Feng", "Yanan Sun" ], "github": "", "project_page": "", "abstract": "The quest for Continual Learning (CL) seeks to empower neural networks with the ability to learn and adapt incrementally. Central to this pursuit is addressing the stability-plasticity dilemma, which involves striking a balance between two conflicting objectives: preserving previously learned knowledge and acquiring new knowledge. While numerous CL methods aim to achieve this trade-off, they often overlook the impact of network architecture on stability and plasticity, restricting the trade-off to the parameter level. In this paper, we delve into the conflict between stability and plasticity at the architectural level. We reveal that under an equal parameter constraint, deeper networks exhibit better plasticity, while wider networks are characterized by superior stability. To address this architectural-level dilemma, we introduce a novel framework denoted Dual-Arch, which serves as a plug-in component for CL. This framework leverages the complementary strengths of two distinct and independent networks: one dedicated to plasticity and the other to stability. Each network is designed with a specialized and lightweight architecture, tailored to its respective objective. Extensive experiments demonstrate that Dual-Arch enhances the performance of existing CL methods while being up to 87% more compact in terms of parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03355", "title": "Robustness in Both Domains: CLIP Needs a Robust Text Encoder", "authors": [ "Elias Abad Rocamora", "Christian Schlarmann", "Naman Deep Singh", "Yongtao Wu", "Matthias Hein", "Volkan Cevher" ], "github": "https://github.com/LIONS-EPFL/LEAF", "project_page": "", "abstract": "Adversarial input attacks can cause a significant shift of CLIP embeddings. This can affect the downstream robustness of models incorporating CLIP in the pipeline, such as text-to-image generative models or large vision language models. While some efforts have been done towards making the CLIP image encoders robust, the robustness of text encoders remains unexplored. In this work, we cover this gap in the literature. We propose LEAF: an efficient adversarial finetuning method for the text domain, with the ability to scale to large CLIP models. Our models significantly improve the zero-shot adversarial accuracy in the text domain, while maintaining the vision performance provided by robust image encoders. When combined with text-to-image diffusion models, we can improve the generation quality under adversarial noise. When employing our robust CLIP encoders in multimodal retrieval tasks, we improve the recall under adversarial noise over standard CLIP models. Finally, we show that robust text encoders facilitate better reconstruction of input text from its embedding via direct optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LIONS-EPFL/LEAF", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03817", "title": "Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid", "authors": [ "Julius Gonsior", "Tim Rie\u00df", "Anja Reusch", "Claudio Hartmann", "Maik Thiele", "Wolfgang Lehner" ], "github": "https://github.com/jgonsior/olympic-games-of-active-learning/tree/main/plots", "project_page": "", "abstract": "Annotating data is a time-consuming and costly task, but it is inherently required for supervised machine learning. Active Learning (AL) is an established method that minimizes human labeling effort by iteratively selecting the most informative unlabeled samples for expert annotation, thereby improving the overall classification performance. Even though AL has been known for decades, AL is still rarely used in real-world applications. As indicated in the two community web surveys among the NLP community about AL, two main reasons continue to hold practitioners back from using AL: first, the complexity of setting AL up, and second, a lack of trust in its effectiveness. We hypothesize that both reasons share the same culprit: the large hyperparameter space of AL. This mostly unexplored hyperparameter space often leads to misleading and irreproducible AL experiment results. In this study, we first compiled a large hyperparameter grid of over 4.6 million hyperparameter combinations, second, recorded the performance of all combinations in the so-far biggest conducted AL study, and third, analyzed the impact of each hyperparameter in the experiment results. In the end, we give recommendations about the influence of each hyperparameter, demonstrate the surprising influence of the concrete AL strategy implementation, and outline an experimental study design for reproducible AL experiments with minimal computational effort, thus contributing to more reproducible and trustworthy AL research in the future.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jgonsior/olympic-games-of-active-learning/tree/main/plots", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03150", "title": "IllumiCraft: Unified Geometry and Illumination Diffusion for Controllable Video Generation", "authors": [ "Yuanze Lin", "Yi-Wen Chen", "Yi-Hsuan Tsai", "Ronald Clark", "Ming-Hsuan Yang" ], "github": "", "project_page": "", "abstract": "Although diffusion-based models can generate high-quality and high-resolution video sequences from textual or image inputs, they lack explicit integration of geometric cues when controlling scene lighting and visual appearance across frames. To address this limitation, we propose IllumiCraft, an end-to-end diffusion framework accepting three complementary inputs: (1) high-dynamic-range (HDR) video maps for detailed lighting control; (2) synthetically relit frames with randomized illumination changes (optionally paired with a static background reference image) to provide appearance cues; and (3) 3D point tracks that capture precise 3D geometry information. By integrating the lighting, appearance, and geometry cues within a unified diffusion architecture, IllumiCraft generates temporally coherent videos aligned with user-defined prompts. It supports background-conditioned and text-conditioned video relighting and provides better fidelity than existing controllable video generation methods. Project Page: https://yuanze-lin.me/IllumiCraft_page", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.02515", "title": "FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning", "authors": [ "Zhuohan Xie", "Dhruv Sahnan", "Debopriyo Banerjee", "Georgi Georgiev", "Rushil Thareja", "Hachem Madmoun", "Jinyan Su", "Aaryamonvikram Singh", "Yuxia Wang", "Rui Xing", "Fajri Koto", "Haonan Li", "Ivan Koychev", "Tanmoy Chakraborty", "Salem Lahlou", "Veselin Stoyanov", "Preslav Nakov" ], "github": "https://github.com/mbzuai-nlp/finchain", "project_page": "", "abstract": "Multi-step symbolic reasoning is critical for advancing downstream performance on financial tasks. Yet, benchmarks for systematically evaluating this capability are lacking. Existing datasets like FinQA and ConvFinQA supervise only final numerical answers, without assessing intermediate reasoning steps. To address this, we introduce FinChain, the first symbolic benchmark designed for verifiable Chain-of- Thought (CoT) financial reasoning. Spanning 54 topics across 12 financial domains, Fin- Chain offers five parameterized templates per topic, each varying in reasoning complexity and domain expertise required. Each dataset instance includes an executable Python trace, enabling automatic generation of extensive training data and easy adaptation to other domains. We also introduce ChainEval, a new metric for automatic evaluation of both final answers and intermediate reasoning. Benchmarking 30 LLMs on our dataset, we find that even state-of-the-art models have considerable room for improvement in multi-step financial reasoning. All templates and evaluation metrics for FinChain are available at https: //github.com/mbzuai-nlp/finchain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mbzuai-nlp/finchain", "github_api_added_by": "auto" }, { "date": "2025-06-05", "arxiv_id": "2506.02680", "title": "Solving Inverse Problems with FLAIR", "authors": [ "Julius Erbach", "Dominik Narnhofer", "Andreas Dombos", "Bernt Schiele", "Jan Eric Lenssen", "Konrad Schindler" ], "github": "https://github.com/prs-eth/FLAIR", "project_page": "", "abstract": "Flow-based latent generative models such as Stable Diffusion 3 are able to generate images with remarkable quality, even enabling photorealistic text-to-image generation. Their impressive performance suggests that these models should also constitute powerful priors for inverse imaging problems, but that approach has not yet led to comparable fidelity. There are several key obstacles: (i) the encoding into a lower-dimensional latent space makes the underlying (forward) mapping non-linear; (ii) the data likelihood term is usually intractable; and (iii) learned generative models struggle to recover rare, atypical data modes during inference. We present FLAIR, a novel training free variational framework that leverages flow-based generative models as a prior for inverse problems. To that end, we introduce a variational objective for flow matching that is agnostic to the type of degradation, and combine it with deterministic trajectory adjustments to recover atypical modes. To enforce exact consistency with the observed data, we decouple the optimization of the data fidelity and regularization terms. Moreover, we introduce a time-dependent calibration scheme in which the strength of the regularization is modulated according to off-line accuracy estimates. Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/prs-eth/FLAIR", "github_api_added_by": "auto" }, { "date": "2025-06-05", "arxiv_id": "2506.03610", "title": "Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games", "authors": [ "Dongmin Park", "Minkyu Kim", "Beongjun Choi", "Junhyuck Kim", "Keon Lee", "Jonghyun Lee", "Inkyu Park", "Byeong-Uk Lee", "Jaeyoung Hwang", "Jaewoo Ahn", "Ameya S. Mahabaleshwarkar", "Bilal Kartal", "Pritam Biswas", "Yoshi Suhara", "Kangwook Lee", "Jaewoong Cho" ], "github": "https://github.com/krafton-ai/Orak", "project_page": "", "abstract": "Large Language Model (LLM) agents are reshaping the game industry, particularly with more intelligent and human-preferable game characters. However, existing game benchmarks fall short of practical needs: they lack evaluations of diverse LLM capabilities across various game genres, studies of agentic modules crucial for complex gameplay, and fine-tuning datasets for aligning pre-trained LLMs into gaming agents. To fill these gaps, we present \\benchname{}, a foundational benchmark designed to train and evaluate LLM agents across diverse real-world video games. Unlike existing benchmarks, Orak includes 12 popular video games spanning all major genres, enabling comprehensive studies of LLM capabilities and agentic modules essential for intricate game scenarios. To support consistent evaluation of LLMs, we introduce a plug-and-play interface based on Model Context Protocol (MCP) that enables LLMs to seamlessly connect with games and manipulate agentic modules. Additionally, we propose a fine-tuning dataset, consisting of LLM gameplay trajectories across diverse game genres. Orak offers a comprehensive evaluation framework, encompassing general game score leaderboards, LLM battle arenas, and in-depth analyses of visual input state, agentic strategies, and fine-tuning effects, establishing a foundation towards building generic gaming agents. Code is available at https://github.com/krafton-ai/Orak.", "github_manual": "", "github_regex": "https://github.com/krafton-ai/Orak", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03837", "title": "HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction", "authors": [ "Xiao-Qi Han", "Ze-Feng Gao", "Xin-De Wang", "Zhenfeng Ouyang", "Peng-Jie Guo", "Zhong-Yi Lu" ], "github": "https://github.com/xqh19970407/HTSC-2025", "project_page": "", "abstract": "The discovery of high-temperature superconducting materials holds great significance for human industry and daily life. In recent years, research on predicting superconducting transition temperatures using artificial intelligence~(AI) has gained popularity, with most of these tools claiming to achieve remarkable accuracy. However, the lack of widely accepted benchmark datasets in this field has severely hindered fair comparisons between different AI algorithms and impeded further advancement of these methods. In this work, we present the HTSC-2025, an ambient-pressure high-temperature superconducting benchmark dataset. This comprehensive compilation encompasses theoretically predicted superconducting materials discovered by theoretical physicists from 2023 to 2025 based on BCS superconductivity theory, including the renowned X_2YH_6 system, perovskite MXH_3 system, M_3XH_8 system, cage-like BCN-doped metal atomic systems derived from LaH_{10} structural evolution, and two-dimensional honeycomb-structured systems evolving from MgB_2. The HTSC-2025 benchmark has been open-sourced at https://github.com/xqh19970407/HTSC-2025 and will be continuously updated. This benchmark holds significant importance for accelerating the discovery of superconducting materials using AI-based methods.", "github_manual": "", "github_regex": "https://github.com/xqh19970407/HTSC-2025", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03525", "title": "Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning", "authors": [ "Daeun Lee", "Jaehong Yoon", "Jaemin Cho", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Recent advances in Chain-of-Thought (CoT) reasoning have improved complex video understanding, but existing methods often struggle to adapt to domain-specific skills (e.g., event detection, spatial relation understanding, emotion understanding) over various video content. To address this, we propose Video-Skill-CoT (a.k.a. Video-SKoT), a framework that automatically constructs and leverages skill-aware CoT supervisions for domain-adaptive video reasoning. First, we construct skill-based CoT annotations: we extract domain-relevant reasoning skills from training questions, cluster them into a shared skill taxonomy, and create detailed multi-step CoT rationale tailored to each video-question pair for training. Second, we introduce a skill-specific expert learning framework. Each expert module specializes in a subset of reasoning skills and is trained with lightweight adapters using the collected CoT supervision. We demonstrate the effectiveness of the proposed approach on three video understanding benchmarks, where Video-SKoT consistently outperforms strong baselines. We also provide in-depth analyses on comparing different CoT annotation pipelines and learned skills over multiple video domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2505.23564", "title": "Segment Policy Optimization: Effective Segment-Level Credit Assignment in RL for Large Language Models", "authors": [ "Yiran Guo", "Lijie Xu", "Jie Liu", "Dan Ye", "Shuang Qiu" ], "github": "https://github.com/AIFrameResearch/SPO", "project_page": "", "abstract": "Enhancing the reasoning capabilities of large language models effectively using reinforcement learning (RL) remains a crucial challenge. Existing approaches primarily adopt two contrasting advantage estimation granularities: Token-level methods (e.g., PPO) aim to provide the fine-grained advantage signals but suffer from inaccurate estimation due to difficulties in training an accurate critic model. On the other extreme, trajectory-level methods (e.g., GRPO) solely rely on a coarse-grained advantage signal from the final reward, leading to imprecise credit assignment. To address these limitations, we propose Segment Policy Optimization (SPO), a novel RL framework that leverages segment-level advantage estimation at an intermediate granularity, achieving a better balance by offering more precise credit assignment than trajectory-level methods and requiring fewer estimation points than token-level methods, enabling accurate advantage estimation based on Monte Carlo (MC) without a critic model. SPO features three components with novel strategies: (1) flexible segment partition; (2) accurate segment advantage estimation; and (3) policy optimization using segment advantages, including a novel probability-mask strategy. We further instantiate SPO for two specific scenarios: (1) SPO-chain for short chain-of-thought (CoT), featuring novel cutpoint-based partition and chain-based advantage estimation, achieving 6-12 percentage point improvements in accuracy over PPO and GRPO on GSM8K. (2) SPO-tree for long CoT, featuring novel tree-based advantage estimation, which significantly reduces the cost of MC estimation, achieving 7-11 percentage point improvements over GRPO on MATH500 under 2K and 4K context evaluation. We make our code publicly available at https://github.com/AIFrameResearch/SPO.", "github_manual": "", "github_regex": "https://github.com/AIFrameResearch/SPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.04214", "title": "Sounding that Object: Interactive Object-Aware Image to Audio Generation", "authors": [ "Tingle Li", "Baihe Huang", "Xiaobin Zhuang", "Dongya Jia", "Jiawei Chen", "Yuping Wang", "Zhuo Chen", "Gopala Anumanchipalli", "Yuxuan Wang" ], "github": "", "project_page": "", "abstract": "Generating accurate sounds for complex audio-visual scenes is challenging, especially in the presence of multiple objects and sound sources. In this paper, we propose an {\\em interactive object-aware audio generation} model that grounds sound generation in user-selected visual objects within images. Our method integrates object-centric learning into a conditional latent diffusion model, which learns to associate image regions with their corresponding sounds through multi-modal attention. At test time, our model employs image segmentation to allow users to interactively generate sounds at the {\\em object} level. We theoretically validate that our attention mechanism functionally approximates test-time segmentation masks, ensuring the generated audio aligns with selected objects. Quantitative and qualitative evaluations show that our model outperforms baselines, achieving better alignment between objects and their associated sounds. Project page: https://tinglok.netlify.app/files/avobject/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03822", "title": "CRAWLDoc: A Dataset for Robust Ranking of Bibliographic Documents", "authors": [ "Fabian Karl", "Ansgar Scherp" ], "github": "https://github.com/FKarl/CRAWLDoc", "project_page": "", "abstract": "Publication databases rely on accurate metadata extraction from diverse web sources, yet variations in web layouts and data formats present challenges for metadata providers. This paper introduces CRAWLDoc, a new method for contextual ranking of linked web documents. Starting with a publication's URL, such as a digital object identifier, CRAWLDoc retrieves the landing page and all linked web resources, including PDFs, ORCID profiles, and supplementary materials. It embeds these resources, along with anchor texts and the URLs, into a unified representation. For evaluating CRAWLDoc, we have created a new, manually labeled dataset of 600 publications from six top publishers in computer science. Our method CRAWLDoc demonstrates a robust and layout-independent ranking of relevant documents across publishers and data formats. It lays the foundation for improved metadata extraction from web documents with various layouts and formats. Our source code and dataset can be accessed at https://github.com/FKarl/CRAWLDoc.", "github_manual": "", "github_regex": "https://github.com/FKarl/CRAWLDoc", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.02153", "title": "Small Language Models are the Future of Agentic AI", "authors": [ "Peter Belcak", "Greg Heinrich", "Shizhe Diao", "Yonggan Fu", "Xin Dong", "Saurav Muralidharan", "Yingyan Celine Lin", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are often praised for exhibiting near-human performance on a wide range of tasks and valued for their ability to hold a general conversation. The rise of agentic AI systems is, however, ushering in a mass of applications in which language models perform a small number of specialized tasks repetitively and with little variation. Here we lay out the position that small language models (SLMs) are sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems, and are therefore the future of agentic AI. Our argumentation is grounded in the current level of capabilities exhibited by SLMs, the common architectures of agentic systems, and the economy of LM deployment. We further argue that in situations where general-purpose conversational abilities are essential, heterogeneous agentic systems (i.e., agents invoking multiple different models) are the natural choice. We discuss the potential barriers for the adoption of SLMs in agentic systems and outline a general LLM-to-SLM agent conversion algorithm. Our position, formulated as a value statement, highlights the significance of the operational and economic impact even a partial shift from LLMs to SLMs is to have on the AI agent industry. We aim to stimulate the discussion on the effective use of AI resources and hope to advance the efforts to lower the costs of AI of the present day. Calling for both contributions to and critique of our position, we commit to publishing all such correspondence at https://research.nvidia.com/labs/lpr/slm-agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.00618", "title": "RiOSWorld: Benchmarking the Risk of Multimodal Compter-Use Agents", "authors": [ "Jingyi Yang", "Shuai Shao", "Dongrui Liu", "Jing Shao" ], "github": "", "project_page": "", "abstract": "With the rapid development of multimodal large language models (MLLMs), they are increasingly deployed as autonomous computer-use agents capable of accomplishing complex computer tasks. However, a pressing issue arises: Can the safety risk principles designed and aligned for general MLLMs in dialogue scenarios be effectively transferred to real-world computer-use scenarios? Existing research on evaluating the safety risks of MLLM-based computer-use agents suffers from several limitations: it either lacks realistic interactive environments, or narrowly focuses on one or a few specific risk types. These limitations ignore the complexity, variability, and diversity of real-world environments, thereby restricting comprehensive risk evaluation for computer-use agents. To this end, we introduce RiOSWorld, a benchmark designed to evaluate the potential risks of MLLM-based agents during real-world computer manipulations. Our benchmark includes 492 risky tasks spanning various computer applications, involving web, social media, multimedia, os, email, and office software. We categorize these risks into two major classes based on their risk source: (i) User-originated risks and (ii) Environmental risks. For the evaluation, we evaluate safety risks from two perspectives: (i) Risk goal intention and (ii) Risk goal completion. Extensive experiments with multimodal agents on RiOSWorld demonstrate that current computer-use agents confront significant safety risks in real-world scenarios. Our findings highlight the necessity and urgency of safety alignment for computer-use agents in real-world computer manipulation, providing valuable insights for developing trustworthy computer-use agents. Our benchmark is publicly available at https://yjyddq.github.io/RiOSWorld.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.04178", "title": "OpenThoughts: Data Recipes for Reasoning Models", "authors": [ "Etash Guha", "Ryan Marten", "Sedrick Keh", "Negin Raoof", "Georgios Smyrnis", "Hritik Bansal", "Marianna Nezhurina", "Jean Mercat", "Trung Vu", "Zayne Sprague", "Ashima Suvarna", "Benjamin Feuer", "Liangyu Chen", "Zaid Khan", "Eric Frankel", "Sachin Grover", "Caroline Choi", "Niklas Muennighoff", "Shiye Su", "Wanjia Zhao", "John Yang", "Shreyas Pimpalgaonkar", "Kartik Sharma", "Charlie Cheng-Jie Ji", "Yichuan Deng", "Sarah Pratt", "Vivek Ramanujan", "Jon Saad-Falcon", "Jeffrey Li", "Achal Dave", "Alon Albalak", "Kushal Arora", "Blake Wulfe", "Chinmay Hegde", "Greg Durrett", "Sewoong Oh", "Mohit Bansal", "Saadia Gabriel", "Aditya Grover", "Kai-Wei Chang", "Vaishaal Shankar", "Aaron Gokaslan", "Mike A. Merrill", "Tatsunori Hashimoto", "Yejin Choi", "Jenia Jitsev", "Reinhard Heckel", "Maheswaran Sathiamoorthy", "Alexandros G. Dimakis", "Ludwig Schmidt" ], "github": "https://github.com/open-thoughts/open-thoughts", "project_page": "", "abstract": "Reasoning models have made rapid progress on many benchmarks involving math, code, and science. Yet, there are still many open questions about the best training recipes for reasoning since state-of-the-art models often rely on proprietary datasets with little to no public information available. To address this, the goal of the OpenThoughts project is to create open-source datasets for training reasoning models. After initial explorations, our OpenThoughts2-1M dataset led to OpenThinker2-32B, the first model trained on public reasoning data to match DeepSeek-R1-Distill-32B on standard reasoning benchmarks such as AIME and LiveCodeBench. We then improve our dataset further by systematically investigating each step of our data generation pipeline with 1,000+ controlled experiments, which led to OpenThoughts3. Scaling the pipeline to 1.2M examples and using QwQ-32B as teacher yields our OpenThinker3-7B model, which achieves state-of-the-art results: 53% on AIME 2025, 51% on LiveCodeBench 06/24-01/25, and 54% on GPQA Diamond. All of our datasets and models are available on https://openthoughts.ai.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-thoughts/open-thoughts", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.03566", "title": "POSS: Position Specialist Generates Better Draft for Speculative Decoding", "authors": [ "Langlin Huang", "Chengsong Huang", "Jixuan Leng", "Di Huang", "Jiaxin Huang" ], "github": "https://github.com/shrango/PosS", "project_page": "", "abstract": "Speculative decoding accelerates Large Language Model (LLM) inference by using a small draft model to predict multiple tokens, and a large target model to verify these tokens in parallel. Recent studies leverage the hidden state of the target model to enhance draft model prediction accuracy. However, existing methods suffer from the degrading quality of draft token predictions at later positions, due to error accumulation in draft model generated features. In this paper, we propose Position Specialists (PosS), which consist of multiple position-specialized draft layers to generate tokens at assigned position(s). Position specialists greatly improve token acceptance rate at later positions per drafting round, as each specialist only needs to focus on handling a certain level of draft model feature deviation. Experiment results on Llama-3-8B-Instruct and Llama-2-13B-chat across six datasets demonstrate that PosS effectively improves over baselines on average acceptance length and speed-up ratio. Our codebase is available at https://github.com/shrango/PosS.", "github_manual": "", "github_regex": "https://github.com/shrango/PosS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.02863", "title": "CapSpeech: Enabling Downstream Applications in Style-Captioned Text-to-Speech", "authors": [ "Helin Wang", "Jiarui Hai", "Dading Chong", "Karan Thakkar", "Tiantian Feng", "Dongchao Yang", "Junhyeok Lee", "Laureano Moro Velazquez", "Jesus Villalba", "Zengyi Qin", "Shrikanth Narayanan", "Mounya Elhiali", "Najim Dehak" ], "github": "", "project_page": "", "abstract": "Recent advancements in generative artificial intelligence have significantly transformed the field of style-captioned text-to-speech synthesis (CapTTS). However, adapting CapTTS to real-world applications remains challenging due to the lack of standardized, comprehensive datasets and limited research on downstream tasks built upon CapTTS. To address these gaps, we introduce CapSpeech, a new benchmark designed for a series of CapTTS-related tasks, including style-captioned text-to-speech synthesis with sound events (CapTTS-SE), accent-captioned TTS (AccCapTTS), emotion-captioned TTS (EmoCapTTS), and text-to-speech synthesis for chat agent (AgentTTS). CapSpeech comprises over 10 million machine-annotated audio-caption pairs and nearly 0.36 million human-annotated audio-caption pairs. In addition, we introduce two new datasets collected and recorded by a professional voice actor and experienced audio engineers, specifically for the AgentTTS and CapTTS-SE tasks. Alongside the datasets, we conduct comprehensive experiments using both autoregressive and non-autoregressive models on CapSpeech. Our results demonstrate high-fidelity and highly intelligible speech synthesis across a diverse range of speaking styles. To the best of our knowledge, CapSpeech is the largest available dataset offering comprehensive annotations for CapTTS-related tasks. The experiments and findings further provide valuable insights into the challenges of developing CapTTS systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-05", "arxiv_id": "2506.05332", "title": "Unleashing Hour-Scale Video Training for Long Video-Language Understanding", "authors": [ "Jingyang Lin", "Jialian Wu", "Ximeng Sun", "Ze Wang", "Jiang Liu", "Yusheng Su", "Xiaodong Yu", "Hao Chen", "Jiebo Luo", "Zicheng Liu", "Emad Barsoum" ], "github": "", "project_page": "", "abstract": "Recent long-form video-language understanding benchmarks have driven progress in video large multimodal models (Video-LMMs). However, the scarcity of well-annotated long videos has left the training of hour-long Video-LLMs underexplored. To close this gap, we present VideoMarathon, a large-scale hour-long video instruction-following dataset. This dataset includes around 9,700 hours of long videos sourced from diverse domains, ranging from 3 to 60 minutes per video. Specifically, it contains 3.3M high-quality QA pairs, spanning six fundamental topics: temporality, spatiality, object, action, scene, and event. Compared to existing video instruction datasets, VideoMarathon significantly extends training video durations up to 1 hour, and supports 22 diverse tasks requiring both short- and long-term video comprehension. Building on VideoMarathon, we propose Hour-LLaVA, a powerful and efficient Video-LMM for hour-scale video-language modeling. It enables hour-long video training and inference at 1-FPS sampling by leveraging a memory augmentation module, which adaptively integrates user question-relevant and spatiotemporal-informative semantics from a cached full video context. In our experiments, Hour-LLaVA achieves the best performance on multiple long video-language benchmarks, demonstrating the high quality of the VideoMarathon dataset and the superiority of the Hour-LLaVA model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05328", "title": "AV-Reasoner: Improving and Benchmarking Clue-Grounded Audio-Visual Counting for MLLMs", "authors": [ "Lidong Lu", "Guo Chen", "Zhiqi Li", "Yicheng Liu", "Tong Lu" ], "github": "", "project_page": "", "abstract": "Despite progress in video understanding, current MLLMs struggle with counting tasks. Existing benchmarks are limited by short videos, close-set queries, lack of clue annotations, and weak multimodal coverage. In this paper, we introduce CG-AV-Counting, a manually-annotated clue-grounded counting benchmark with 1,027 multimodal questions and 5,845 annotated clues over 497 long videos. It supports both black-box and white-box evaluation, serving as a comprehensive testbed for both end-to-end and reasoning-based counting. To explore ways to improve model's counting capability, we propose AV-Reasoner, a model trained with GRPO and curriculum learning to generalize counting ability from related tasks. AV-Reasoner achieves state-of-the-art results across multiple benchmarks, demonstrating the effectiveness of reinforcement learning. However, experiments show that on out-of-domain benchmarks, reasoning in the language space fails to bring performance gains. The code and benchmark have been realeased on https://av-reasoner.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.04308", "title": "RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics", "authors": [ "Enshen Zhou", "Jingkun An", "Cheng Chi", "Yi Han", "Shanyu Rong", "Chi Zhang", "Pengwei Wang", "Zhongyuan Wang", "Tiejun Huang", "Lu Sheng", "Shanghang Zhang" ], "github": "https://github.com/apple/ml-cubifyanything", "project_page": "", "abstract": "Spatial referring is a fundamental capability of embodied robots to interact with the 3D physical world. However, even with the powerful pretrained vision language models (VLMs), recent approaches are still not qualified to accurately understand the complex 3D scenes and dynamically reason about the instruction-indicated locations for interaction. To this end, we propose RoboRefer, a 3D-aware VLM that can first achieve precise spatial understanding by integrating a disentangled but dedicated depth encoder via supervised fine-tuning (SFT). Moreover, RoboRefer advances generalized multi-step spatial reasoning via reinforcement fine-tuning (RFT), with metric-sensitive process reward functions tailored for spatial referring tasks. To support SFT and RFT training, we introduce RefSpatial, a large-scale dataset of 20M QA pairs (2x prior), covering 31 spatial relations (vs. 15 prior) and supporting complex reasoning processes (up to 5 steps). In addition, we introduce RefSpatial-Bench, a challenging benchmark filling the gap in evaluating spatial referring with multi-step reasoning. Experiments show that SFT-trained RoboRefer achieves state-of-the-art spatial understanding, with an average success rate of 89.6%. RFT-trained RoboRefer further outperforms all other baselines by a large margin, even surpassing Gemini-2.5-Pro by 17.4% in average accuracy on RefSpatial-Bench. Notably, RoboRefer can be integrated with various control policies to execute long-horizon, dynamic tasks across diverse robots (e,g., UR5, G1 humanoid) in cluttered real-world scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/ml-cubifyanything", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2505.23656", "title": "VideoREPA: Learning Physics for Video Generation through Relational Alignment with Foundation Models", "authors": [ "Xiangdong Zhang", "Jiaqi Liao", "Shaofeng Zhang", "Fanqing Meng", "Xiangpeng Wan", "Junchi Yan", "Yu Cheng" ], "github": "", "project_page": "", "abstract": "Recent advancements in text-to-video (T2V) diffusion models have enabled high-fidelity and realistic video synthesis. However, current T2V models often struggle to generate physically plausible content due to their limited inherent ability to accurately understand physics. We found that while the representations within T2V models possess some capacity for physics understanding, they lag significantly behind those from recent video self-supervised learning methods. To this end, we propose a novel framework called VideoREPA, which distills physics understanding capability from video understanding foundation models into T2V models by aligning token-level relations. This closes the physics understanding gap and enable more physics-plausible generation. Specifically, we introduce the Token Relation Distillation (TRD) loss, leveraging spatio-temporal alignment to provide soft guidance suitable for finetuning powerful pre-trained T2V models, a critical departure from prior representation alignment (REPA) methods. To our knowledge, VideoREPA is the first REPA method designed for finetuning T2V models and specifically for injecting physical knowledge. Empirical evaluations show that VideoREPA substantially enhances the physics commonsense of baseline method, CogVideoX, achieving significant improvement on relevant benchmarks and demonstrating a strong capacity for generating videos consistent with intuitive physics. More video results are available at https://videorepa.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.04209", "title": "Language-Image Alignment with Fixed Text Encoders", "authors": [ "Jingfeng Yang", "Ziyang Wu", "Yue Zhao", "Yi Ma" ], "github": "https://github.com/Jingfeng0705/LIFT", "project_page": "", "abstract": "Currently, the most dominant approach to establishing language-image alignment is to pre-train text and image encoders jointly through contrastive learning, such as CLIP and its variants. In this work, we question whether such a costly joint training is necessary. In particular, we investigate if a pre-trained fixed large language model (LLM) offers a good enough text encoder to guide visual representation learning. That is, we propose to learn Language-Image alignment with a Fixed Text encoder (LIFT) from an LLM by training only the image encoder. Somewhat surprisingly, through comprehensive benchmarking and ablation studies, we find that this much simplified framework LIFT is highly effective and it outperforms CLIP in most scenarios that involve compositional understanding and long captions, while achieving considerable gains in computational efficiency. Our work takes a first step towards systematically exploring how text embeddings from LLMs can guide visual learning and suggests an alternative design choice for learning language-aligned visual representations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Jingfeng0705/LIFT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05176", "title": "Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models", "authors": [ "Yanzhao Zhang", "Mingxin Li", "Dingkun Long", "Xin Zhang", "Huan Lin", "Baosong Yang", "Pengjun Xie", "An Yang", "Dayiheng Liu", "Junyang Lin", "Fei Huang", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "In this work, we introduce the Qwen3 Embedding series, a significant advancement over its predecessor, the GTE-Qwen series, in text embedding and reranking capabilities, built upon the Qwen3 foundation models. Leveraging the Qwen3 LLMs' robust capabilities in multilingual text understanding and generation, our innovative multi-stage training pipeline combines large-scale unsupervised pre-training with supervised fine-tuning on high-quality datasets. Effective model merging strategies further ensure the robustness and adaptability of the Qwen3 Embedding series. During the training process, the Qwen3 LLMs serve not only as backbone models but also play a crucial role in synthesizing high-quality, rich, and diverse training data across multiple domains and languages, thus enhancing the training pipeline. The Qwen3 Embedding series offers a spectrum of model sizes (0.6B, 4B, 8B) for both embedding and reranking tasks, addressing diverse deployment scenarios where users can optimize for either efficiency or effectiveness. Empirical evaluations demonstrate that the Qwen3 Embedding series achieves state-of-the-art results across diverse benchmarks. Notably, it excels on the multilingual evaluation benchmark MTEB for text embedding, as well as in various retrieval tasks, including code retrieval, cross-lingual retrieval and multilingual retrieval. To facilitate reproducibility and promote community-driven research and development, the Qwen3 Embedding models are publicly available under the Apache 2.0 license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05344", "title": "SparseMM: Head Sparsity Emerges from Visual Concept Responses in MLLMs", "authors": [ "Jiahui Wang", "Zuyan Liu", "Yongming Rao", "Jiwen Lu" ], "github": "https://github.com/CR400AF-A/SparseMM", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) are commonly derived by extending pre-trained Large Language Models (LLMs) with visual capabilities. In this work, we investigate how MLLMs process visual inputs by analyzing their attention mechanisms. We reveal a surprising sparsity phenomenon: only a small subset (approximately less than 5%) of attention heads in LLMs actively contribute to visual understanding, termed visual heads. To identify these heads efficiently, we design a training-free framework that quantifies head-level visual relevance through targeted response analysis. Building on this discovery, we introduce SparseMM, a KV-Cache optimization strategy that allocates asymmetric computation budgets to heads in LLMs based on their visual scores, leveraging the sparity of visual heads for accelerating the inference of MLLMs. Compared with prior KV-Cache acceleration methods that ignore the particularity of visual, SparseMM prioritizes stress and retaining visual semantics during decoding. Extensive evaluations across mainstream multimodal benchmarks demonstrate that SparseMM achieves superior accuracy-efficiency trade-offs. Notably, SparseMM delivers 1.38x real-time acceleration and 52% memory reduction during generation while maintaining performance parity on efficiency test. Our project is open sourced at https://github.com/CR400AF-A/SparseMM.", "github_manual": "", "github_regex": "https://github.com/CR400AF-A/SparseMM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05240", "title": "Aligning Latent Spaces with Flow Priors", "authors": [ "Yizhuo Li", "Yuying Ge", "Yixiao Ge", "Ying Shan", "Ping Luo" ], "github": "", "project_page": "", "abstract": "This paper presents a novel framework for aligning learnable latent spaces to arbitrary target distributions by leveraging flow-based generative models as priors. Our method first pretrains a flow model on the target features to capture the underlying distribution. This fixed flow model subsequently regularizes the latent space via an alignment loss, which reformulates the flow matching objective to treat the latents as optimization targets. We formally prove that minimizing this alignment loss establishes a computationally tractable surrogate objective for maximizing a variational lower bound on the log-likelihood of latents under the target distribution. Notably, the proposed method eliminates computationally expensive likelihood evaluations and avoids ODE solving during optimization. As a proof of concept, we demonstrate in a controlled setting that the alignment loss landscape closely approximates the negative log-likelihood of the target distribution. We further validate the effectiveness of our approach through large-scale image generation experiments on ImageNet with diverse target distributions, accompanied by detailed discussions and ablation studies. With both theoretical and empirical validation, our framework paves a new way for latent space alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.04405", "title": "MedAgentGym: Training LLM Agents for Code-Based Medical Reasoning at Scale", "authors": [ "Ran Xu", "Yuchen Zhuang", "Yishan Zhong", "Yue Yu", "Xiangru Tang", "Hang Wu", "May D. Wang", "Peifeng Ruan", "Donghan Yang", "Tao Wang", "Guanghua Xiao", "Carl Yang", "Yang Xie", "Wenqi Shi" ], "github": "", "project_page": "", "abstract": "We introduce MedAgentGYM, the first publicly available training environment designed to enhance coding-based medical reasoning capabilities in large language model (LLM) agents. MedAgentGYM comprises 72,413 task instances across 129 categories derived from authentic real-world biomedical scenarios. Tasks are encapsulated within executable coding environments, each featuring detailed task descriptions, interactive feedback mechanisms, verifiable ground-truth annotations, and scalable training trajectory generation. Extensive benchmarking of over 30 LLMs reveals a notable performance disparity between commercial API-based models and open-source counterparts. Leveraging MedAgentGYM, Med-Copilot-7B achieves substantial performance gains through supervised fine-tuning (+36.44%) and continued reinforcement learning (+42.47%), emerging as an affordable and privacy-preserving alternative competitive with gpt-4o. By offering both a comprehensive benchmark and accessible, expandable training resources within unified execution environments, MedAgentGYM delivers an integrated platform to develop LLM-based coding assistants for advanced biomedical research and practice.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.04245", "title": "Contextual Integrity in LLMs via Reasoning and Reinforcement Learning", "authors": [ "Guangchen Lan", "Huseyin A. Inan", "Sahar Abdelnabi", "Janardhan Kulkarni", "Lukas Wutschitz", "Reza Shokri", "Christopher G. Brinton", "Robert Sim" ], "github": "https://github.com/EricGLan/CI-RL", "project_page": "", "abstract": "As the era of autonomous agents making decisions on behalf of users unfolds, ensuring contextual integrity (CI) -- what is the appropriate information to share while carrying out a certain task -- becomes a central question to the field. We posit that CI demands a form of reasoning where the agent needs to reason about the context in which it is operating. To test this, we first prompt LLMs to reason explicitly about CI when deciding what information to disclose. We then extend this approach by developing a reinforcement learning (RL) framework that further instills in models the reasoning necessary to achieve CI. Using a synthetic, automatically created, dataset of only sim700 examples but with diverse contexts and information disclosure norms, we show that our method substantially reduces inappropriate information disclosure while maintaining task performance across multiple model sizes and families. Importantly, improvements transfer from this synthetic dataset to established CI benchmarks such as PrivacyLens that has human annotations and evaluates privacy leakage of AI assistants in actions and tool calls.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EricGLan/CI-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.04734", "title": "Evaluation is All You Need: Strategic Overclaiming of LLM Reasoning Capabilities Through Evaluation Design", "authors": [ "Lin Sun", "Weihong Lin", "Jinzhu Wu", "Yongfu Zhu", "Xiaoqi Jian", "Guangxiang Zhao", "Change Jia", "Linglin Zhang", "Sai-er Hu", "Yuhan Wu", "Xiangzheng Zhang" ], "github": "", "project_page": "", "abstract": "Reasoning models represented by the Deepseek-R1-Distill series have been widely adopted by the open-source community due to their strong performance in mathematics, science, programming, and other domains. However, our study reveals that their benchmark evaluation results are subject to significant fluctuations caused by various factors. Subtle differences in evaluation conditions can lead to substantial variations in results. Similar phenomena are observed in other open-source inference models fine-tuned based on the Deepseek-R1-Distill series, as well as in the QwQ-32B model, making their claimed performance improvements difficult to reproduce reliably. Therefore, we advocate for the establishment of a more rigorous paradigm for model performance evaluation and present our empirical assessments of the Deepseek-R1-Distill series models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.03238", "title": "Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach", "authors": [ "Ziheng Zhao", "Lisong Dai", "Ya Zhang", "Yanfeng Wang", "Weidi Xie" ], "github": "https://github.com/zhaoziheng/OminiAbnorm-CT", "project_page": "", "abstract": "Automated interpretation of CT images-particularly localizing and describing abnormal findings across multi-plane and whole-body scans-remains a significant challenge in clinical radiology. This work aims to address this challenge through four key contributions: (i) On taxonomy, we collaborate with senior radiologists to propose a comprehensive hierarchical classification system, with 404 representative abnormal findings across all body regions; (ii) On data, we contribute a dataset containing over 14.5K CT images from multiple planes and all human body regions, and meticulously provide grounding annotations for over 19K abnormalities, each linked to the detailed description and cast into the taxonomy; (iii) On model development, we propose OminiAbnorm-CT, which can automatically ground and describe abnormal findings on multi-plane and whole-body CT images based on text queries, while also allowing flexible interaction through visual prompts; (iv) On benchmarks, we establish three representative evaluation tasks based on real clinical scenarios. Through extensive experiments, we show that OminiAbnorm-CT can significantly outperform existing methods on all the tasks and metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhaoziheng/OminiAbnorm-CT", "github_api_added_by": "auto" }, { "date": "2025-06-06", "arxiv_id": "2506.05287", "title": "EOC-Bench: Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?", "authors": [ "Yuqian Yuan", "Ronghao Dang", "Long Li", "Wentong Li", "Dian Jiao", "Xin Li", "Deli Zhao", "Fan Wang", "Wenqiao Zhang", "Jun Xiao", "Yueting Zhuang" ], "github": "https://github.com/alibaba-damo-academy/EOCBench", "project_page": "", "abstract": "The emergence of multimodal large language models (MLLMs) has driven breakthroughs in egocentric vision applications. These applications necessitate persistent, context-aware understanding of objects, as users interact with tools in dynamic and cluttered environments. However, existing embodied benchmarks primarily focus on static scene exploration, emphasizing object's appearance and spatial attributes while neglecting the assessment of dynamic changes arising from users' interactions. To address this gap, we introduce EOC-Bench, an innovative benchmark designed to systematically evaluate object-centric embodied cognition in dynamic egocentric scenarios. Specially, EOC-Bench features 3,277 meticulously annotated QA pairs categorized into three temporal categories: Past, Present, and Future, covering 11 fine-grained evaluation dimensions and 3 visual object referencing types. To ensure thorough assessment, we develop a mixed-format human-in-the-loop annotation framework with four types of questions and design a novel multi-scale temporal accuracy metric for open-ended temporal evaluation. Based on EOC-Bench, we conduct comprehensive evaluations of various proprietary, open-source, and object-level MLLMs. EOC-Bench serves as a crucial tool for advancing the embodied object cognitive capabilities of MLLMs, establishing a robust foundation for developing reliable core models for embodied systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/alibaba-damo-academy/EOCBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05327", "title": "Revisiting Depth Representations for Feed-Forward 3D Gaussian Splatting", "authors": [ "Duochao Shi", "Weijie Wang", "Donny Y. Chen", "Zeyu Zhang", "Jia-Wang Bian", "Bohan Zhuang", "Chunhua Shen" ], "github": "", "project_page": "", "abstract": "Depth maps are widely used in feed-forward 3D Gaussian Splatting (3DGS) pipelines by unprojecting them into 3D point clouds for novel view synthesis. This approach offers advantages such as efficient training, the use of known camera poses, and accurate geometry estimation. However, depth discontinuities at object boundaries often lead to fragmented or sparse point clouds, degrading rendering quality -- a well-known limitation of depth-based representations. To tackle this issue, we introduce PM-Loss, a novel regularization loss based on a pointmap predicted by a pre-trained transformer. Although the pointmap itself may be less accurate than the depth map, it effectively enforces geometric smoothness, especially around object boundaries. With the improved depth map, our method significantly improves the feed-forward 3DGS across various architectures and scenes, delivering consistently better rendering results. Our project page: https://aim-uofa.github.io/PMLoss", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.03077", "title": "StreamBP: Memory-Efficient Exact Backpropagation for Long Sequence Training of LLMs", "authors": [ "Qijun Luo", "Mengqi Li", "Lei Zhao", "Xiao Li" ], "github": "https://github.com/Ledzy/StreamBP", "project_page": "", "abstract": "Training language models on long sequence data is a demanding requirement for enhancing the model's capability on complex tasks, e.g., long-chain reasoning. However, as the sequence length scales up, the memory cost for storing activation values becomes huge during the Backpropagation (BP) process, even with the application of gradient checkpointing technique. To tackle this challenge, we propose a memory-efficient and exact BP method called StreamBP, which performs a linear decomposition of the chain rule along the sequence dimension in a layer-wise manner, significantly reducing the memory cost of activation values and logits. The proposed method is applicable to common objectives such as SFT, GRPO, and DPO. From an implementation perspective, StreamBP achieves less computational FLOPs and faster BP speed by leveraging the causal structure of the language model. Compared to gradient checkpointing, StreamBP scales up the maximum sequence length of BP by 2.8-5.5 times larger, while using comparable or even less BP time. Note that StreamBP's sequence length scaling ability can be directly transferred to batch size scaling for accelerating training. We further develop a communication-efficient distributed StreamBP to effectively support multi-GPU training and broaden its applicability. Our code can be easily integrated into the training pipeline of any transformer models and is available at https://github.com/Ledzy/StreamBP.", "github_manual": "", "github_regex": "https://github.com/Ledzy/StreamBP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05331", "title": "MINT-CoT: Enabling Interleaved Visual Tokens in Mathematical Chain-of-Thought Reasoning", "authors": [ "Xinyan Chen", "Renrui Zhang", "Dongzhi Jiang", "Aojun Zhou", "Shilin Yan", "Weifeng Lin", "Hongsheng Li" ], "github": "https://github.com/xinyan-cxy/MINT-CoT", "project_page": "", "abstract": "Chain-of-Thought (CoT) has widely enhanced mathematical reasoning in Large Language Models (LLMs), but it still remains challenging for extending it to multimodal domains. Existing works either adopt a similar textual reasoning for image input, or seek to interleave visual signals into mathematical CoT. However, they face three key limitations for math problem-solving: reliance on coarse-grained box-shaped image regions, limited perception of vision encoders on math content, and dependence on external capabilities for visual modification. In this paper, we propose MINT-CoT, introducing Mathematical INterleaved Tokens for Chain-of-Thought visual reasoning. MINT-CoT adaptively interleaves relevant visual tokens into textual reasoning steps via an Interleave Token, which dynamically selects visual regions of any shapes within math figures. To empower this capability, we construct the MINT-CoT dataset, containing 54K mathematical problems aligning each reasoning step with visual regions at the token level, accompanied by a rigorous data generation pipeline. We further present a three-stage MINT-CoT training strategy, progressively combining text-only CoT SFT, interleaved CoT SFT, and interleaved CoT RL, which derives our MINT-CoT-7B model. Extensive experiments demonstrate the effectiveness of our method for effective visual interleaved reasoning in mathematical domains, where MINT-CoT-7B outperforms the baseline model by +34.08% on MathVista, +28.78% on GeoQA, and +23.2% on MMStar, respectively. Our code and data are available at https://github.com/xinyan-cxy/MINT-CoT", "github_manual": "", "github_regex": "https://github.com/xinyan-cxy/MINT-CoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.04633", "title": "Unfolding Spatial Cognition: Evaluating Multimodal Models on Visual Simulations", "authors": [ "Linjie Li", "Mahtab Bigverdi", "Jiawei Gu", "Zixian Ma", "Yinuo Yang", "Ziang Li", "Yejin Choi", "Ranjay Krishna" ], "github": "https://github.com/STARE-bench/STARE", "project_page": "", "abstract": "Spatial cognition is essential for human intelligence, enabling problem-solving through visual simulations rather than solely relying on verbal reasoning. However, existing AI benchmarks primarily assess verbal reasoning, neglecting the complexities of non-verbal, multi-step visual simulation. We introduce STARE(Spatial Transformations and Reasoning Evaluation), a benchmark designed to rigorously evaluate multimodal large language models on tasks better solved through multi-step visual simulation. STARE features 4K tasks spanning foundational geometric transformations (2D and 3D), integrated spatial reasoning (cube net folding and tangram puzzles), and real-world spatial reasoning (perspective and temporal reasoning), reflecting practical cognitive challenges like object assembly, mechanical diagram interpretation, and everyday spatial navigation. Our evaluations show that models excel at reasoning over simpler 2D transformations, but perform close to random chance on more complex tasks like 3D cube net folding and tangram puzzles that require multi-step visual simulations. Humans achieve near-perfect accuracy but take considerable time (up to 28.9s) on complex tasks, significantly speeding up (down by 7.5 seconds on average) with intermediate visual simulations. In contrast, models exhibit inconsistent performance gains from visual simulations, improving on most tasks but declining in specific cases like tangram puzzles (GPT-4o, o1) and cube net folding (Claude-3.5, Gemini-2.0 Flash), indicating that models may not know how to effectively leverage intermediate visual information.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/STARE-bench/STARE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2505.20914", "title": "Geometry-Editable and Appearance-Preserving Object Compositon", "authors": [ "Jianman Lin", "Haojie Li", "Chunmei Qing", "Zhijing Yang", "Liang Lin", "Tianshui Chen" ], "github": "https://github.com/jianmanlincjx/DGAD", "project_page": "", "abstract": "General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties, while simultaneously preserving its fine-grained appearance details. Recent approaches derive semantic embeddings and integrate them into advanced diffusion models to enable geometry-editable generation. However, these highly compact embeddings encode only high-level semantic cues and inevitably discard fine-grained appearance details. We introduce a Disentangled Geometry-editable and Appearance-preserving Diffusion (DGAD) model that first leverages semantic embeddings to implicitly capture the desired geometric transformations and then employs a cross-attention retrieval mechanism to align fine-grained appearance features with the geometry-edited representation, facilitating both precise geometry editing and faithful appearance preservation in object composition. Specifically, DGAD builds on CLIP/DINO-derived and reference networks to extract semantic embeddings and appearance-preserving representations, which are then seamlessly integrated into the encoding and decoding pipelines in a disentangled manner. We first integrate the semantic embeddings into pre-trained diffusion models that exhibit strong spatial reasoning capabilities to implicitly capture object geometry, thereby facilitating flexible object manipulation and ensuring effective editability. Then, we design a dense cross-attention mechanism that leverages the implicitly learned object geometry to retrieve and spatially align appearance features with their corresponding regions, ensuring faithful appearance consistency. Extensive experiments on public benchmarks demonstrate the effectiveness of the proposed DGAD framework.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jianmanlincjx/DGAD", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05282", "title": "Rectified Point Flow: Generic Point Cloud Pose Estimation", "authors": [ "Tao Sun", "Liyuan Zhu", "Shengyu Huang", "Shuran Song", "Iro Armeni" ], "github": "", "project_page": "", "abstract": "We introduce Rectified Point Flow, a unified parameterization that formulates pairwise point cloud registration and multi-part shape assembly as a single conditional generative problem. Given unposed point clouds, our method learns a continuous point-wise velocity field that transports noisy points toward their target positions, from which part poses are recovered. In contrast to prior work that regresses part-wise poses with ad-hoc symmetry handling, our method intrinsically learns assembly symmetries without symmetry labels. Together with a self-supervised encoder focused on overlapping points, our method achieves a new state-of-the-art performance on six benchmarks spanning pairwise registration and shape assembly. Notably, our unified formulation enables effective joint training on diverse datasets, facilitating the learning of shared geometric priors and consequently boosting accuracy. Project page: https://rectified-pointflow.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05010", "title": "ComfyUI-Copilot: An Intelligent Assistant for Automated Workflow Development", "authors": [ "Zhenran Xu", "Xue Yang", "Yiyu Wang", "Qingli Hu", "Zijiao Wu", "Longyue Wang", "Weihua Luo", "Kaifu Zhang", "Baotian Hu", "Min Zhang" ], "github": "https://github.com/AIDC-AI/ComfyUI-Copilot", "project_page": "", "abstract": "We introduce ComfyUI-Copilot, a large language model-powered plugin designed to enhance the usability and efficiency of ComfyUI, an open-source platform for AI-driven art creation. Despite its flexibility and user-friendly interface, ComfyUI can present challenges to newcomers, including limited documentation, model misconfigurations, and the complexity of workflow design. ComfyUI-Copilot addresses these challenges by offering intelligent node and model recommendations, along with automated one-click workflow construction. At its core, the system employs a hierarchical multi-agent framework comprising a central assistant agent for task delegation and specialized worker agents for different usages, supported by our curated ComfyUI knowledge bases to streamline debugging and deployment. We validate the effectiveness of ComfyUI-Copilot through both offline quantitative evaluations and online user feedback, showing that it accurately recommends nodes and accelerates workflow development. Additionally, use cases illustrate that ComfyUI-Copilot lowers entry barriers for beginners and enhances workflow efficiency for experienced users. The ComfyUI-Copilot installation package and a demo video are available at https://github.com/AIDC-AI/ComfyUI-Copilot.", "github_manual": "", "github_regex": "https://github.com/AIDC-AI/ComfyUI-Copilot", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.02620", "title": "FlexPainter: Flexible and Multi-View Consistent Texture Generation", "authors": [ "Dongyu Yan", "Leyi Wu", "Jiantao Lin", "Luozhou Wang", "Tianshuo Xu", "Zhifei Chen", "Zhen Yang", "Lie Xu", "Shunsi Zhang", "Yingcong Chen" ], "github": "", "project_page": "", "abstract": "Texture map production is an important part of 3D modeling and determines the rendering quality. Recently, diffusion-based methods have opened a new way for texture generation. However, restricted control flexibility and limited prompt modalities may prevent creators from producing desired results. Furthermore, inconsistencies between generated multi-view images often lead to poor texture generation quality. To address these issues, we introduce FlexPainter, a novel texture generation pipeline that enables flexible multi-modal conditional guidance and achieves highly consistent texture generation. A shared conditional embedding space is constructed to perform flexible aggregation between different input modalities. Utilizing such embedding space, we present an image-based CFG method to decompose structural and style information, achieving reference image-based stylization. Leveraging the 3D knowledge within the image diffusion prior, we first generate multi-view images simultaneously using a grid representation to enhance global understanding. Meanwhile, we propose a view synchronization and adaptive weighting module during diffusion sampling to further ensure local consistency. Finally, a 3D-aware texture completion model combined with a texture enhancement model is used to generate seamless, high-resolution texture maps. Comprehensive experiments demonstrate that our framework significantly outperforms state-of-the-art methods in both flexibility and generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.00830", "title": "SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers", "authors": [ "Zhengcong Fei", "Hao Jiang", "Di Qiu", "Baoxuan Gu", "Youqiang Zhang", "Jiahua Wang", "Jialin Bai", "Debang Li", "Mingyuan Fan", "Guibin Chen", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "The generation and editing of audio-conditioned talking portraits guided by multimodal inputs, including text, images, and videos, remains under explored. In this paper, we present SkyReels-Audio, a unified framework for synthesizing high-fidelity and temporally coherent talking portrait videos. Built upon pretrained video diffusion transformers, our framework supports infinite-length generation and editing, while enabling diverse and controllable conditioning through multimodal inputs. We employ a hybrid curriculum learning strategy to progressively align audio with facial motion, enabling fine-grained multimodal control over long video sequences. To enhance local facial coherence, we introduce a facial mask loss and an audio-guided classifier-free guidance mechanism. A sliding-window denoising approach further fuses latent representations across temporal segments, ensuring visual fidelity and temporal consistency across extended durations and diverse identities. More importantly, we construct a dedicated data pipeline for curating high-quality triplets consisting of synchronized audio, video, and textual descriptions. Comprehensive benchmark evaluations show that SkyReels-Audio achieves superior performance in lip-sync accuracy, identity consistency, and realistic facial dynamics, particularly under complex and challenging conditions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05278", "title": "Micro-Act: Mitigate Knowledge Conflict in Question Answering via Actionable Self-Reasoning", "authors": [ "Nan Huo", "Jinyang Li", "Bowen Qin", "Ge Qu", "Xiaolong Li", "Xiaodong Li", "Chenhao Ma", "Reynold Cheng" ], "github": "https://github.com/nan-huo/micro-act", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) systems commonly suffer from Knowledge Conflicts, where retrieved external knowledge contradicts the inherent, parametric knowledge of large language models (LLMs). It adversely affects performance on downstream tasks such as question answering (QA). Existing approaches often attempt to mitigate conflicts by directly comparing two knowledge sources in a side-by-side manner, but this can overwhelm LLMs with extraneous or lengthy contexts, ultimately hindering their ability to identify and mitigate inconsistencies. To address this issue, we propose Micro-Act a framework with a hierarchical action space that automatically perceives context complexity and adaptively decomposes each knowledge source into a sequence of fine-grained comparisons. These comparisons are represented as actionable steps, enabling reasoning beyond the superficial context. Through extensive experiments on five benchmark datasets, Micro-Act consistently achieves significant increase in QA accuracy over state-of-the-art baselines across all 5 datasets and 3 conflict types, especially in temporal and semantic types where all baselines fail significantly. More importantly, Micro-Act exhibits robust performance on non-conflict questions simultaneously, highlighting its practical value in real-world RAG applications.", "github_manual": "https://github.com/nan-huo/micro-act", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.01011", "title": "Autoregressive Images Watermarking through Lexical Biasing: An Approach Resistant to Regeneration Attack", "authors": [ "Siqi Hui", "Yiren Song", "Sanping Zhou", "Ye Deng", "Wenli Huang", "Jinjun Wang" ], "github": "", "project_page": "", "abstract": "Autoregressive (AR) image generation models have gained increasing attention for their breakthroughs in synthesis quality, highlighting the need for robust watermarking to prevent misuse. However, existing in-generation watermarking techniques are primarily designed for diffusion models, where watermarks are embedded within diffusion latent states. This design poses significant challenges for direct adaptation to AR models, which generate images sequentially through token prediction. Moreover, diffusion-based regeneration attacks can effectively erase such watermarks by perturbing diffusion latent states. To address these challenges, we propose Lexical Bias Watermarking (LBW), a novel framework designed for AR models that resists regeneration attacks. LBW embeds watermarks directly into token maps by biasing token selection toward a predefined green list during generation. This approach ensures seamless integration with existing AR models and extends naturally to post-hoc watermarking. To increase the security against white-box attacks, instead of using a single green list, the green list for each image is randomly sampled from a pool of green lists. Watermark detection is performed via quantization and statistical analysis of the token distribution. Extensive experiments demonstrate that LBW achieves superior watermark robustness, particularly in resisting regeneration attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05349", "title": "VideoMathQA: Benchmarking Mathematical Reasoning via Multimodal Understanding in Videos", "authors": [ "Hanoona Rasheed", "Abdelrahman Shaker", "Anqi Tang", "Muhammad Maaz", "Ming-Hsuan Yang", "Salman Khan", "Fahad Khan" ], "github": "https://github.com/mbzuai-oryx/VideoMathQA", "project_page": "", "abstract": "Mathematical reasoning in real-world video settings presents a fundamentally different challenge than in static images or text. It requires interpreting fine-grained visual information, accurately reading handwritten or digital text, and integrating spoken cues, often dispersed non-linearly over time. In such multimodal contexts, success hinges not just on perception, but on selectively identifying and integrating the right contextual details from a rich and noisy stream of content. To this end, we introduce VideoMathQA, a benchmark designed to evaluate whether models can perform such temporally extended cross-modal reasoning on videos. The benchmark spans 10 diverse mathematical domains, covering videos ranging from 10 seconds to over 1 hour. It requires models to interpret structured visual content, understand instructional narratives, and jointly ground concepts across visual, audio, and textual modalities. We employ graduate-level experts to ensure high quality, totaling over 920 man-hours of annotation. To reflect real-world scenarios, questions are designed around three core reasoning challenges: direct problem solving, where answers are grounded in the presented question; conceptual transfer, which requires applying learned methods to new problems; and deep instructional comprehension, involving multi-step reasoning over extended explanations and partially worked-out solutions. Each question includes multi-step reasoning annotations, enabling fine-grained diagnosis of model capabilities. Through this benchmark, we highlight the limitations of existing approaches and establish a systematic evaluation framework for models that must reason, rather than merely perceive, across temporally extended and modality-rich mathematical problem settings. Our benchmark and evaluation code are available at: https://mbzuai-oryx.github.io/VideoMathQA", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mbzuai-oryx/VideoMathQA", "github_api_added_by": "auto" }, { "date": "2025-06-06", "arxiv_id": "2506.02587", "title": "BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations", "authors": [ "Weiduo Yuan", "Jerry Li", "Justin Yue", "Divyank Shah", "Konstantinos Karydis", "Hang Qiu" ], "github": "", "project_page": "", "abstract": "Accurate LiDAR-camera calibration is fundamental to fusing multi-modal perception in autonomous driving and robotic systems. Traditional calibration methods require extensive data collection in controlled environments and cannot compensate for the transformation changes during the vehicle/robot movement. In this paper, we propose the first model that uses bird's-eye view (BEV) features to perform LiDAR camera calibration from raw data, termed BEVCALIB. To achieve this, we extract camera BEV features and LiDAR BEV features separately and fuse them into a shared BEV feature space. To fully utilize the geometric information from the BEV feature, we introduce a novel feature selector to filter the most important features in the transformation decoder, which reduces memory consumption and enables efficient training. Extensive evaluations on KITTI, NuScenes, and our own dataset demonstrate that BEVCALIB establishes a new state of the art. Under various noise conditions, BEVCALIB outperforms the best baseline in the literature by an average of (47.08%, 82.32%) on KITTI dataset, and (78.17%, 68.29%) on NuScenes dataset, in terms of (translation, rotation), respectively. In the open-source domain, it improves the best reproducible baseline by one order of magnitude. Our code and demo results are available at https://cisl.ucr.edu/BEVCalib.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05284", "title": "Video World Models with Long-term Spatial Memory", "authors": [ "Tong Wu", "Shuai Yang", "Ryan Po", "Yinghao Xu", "Ziwei Liu", "Dahua Lin", "Gordon Wetzstein" ], "github": "", "project_page": "", "abstract": "Emerging world models autoregressively generate video frames in response to actions, such as camera movements and text prompts, among other control signals. Due to limited temporal context window sizes, these models often struggle to maintain scene consistency during revisits, leading to severe forgetting of previously generated environments. Inspired by the mechanisms of human memory, we introduce a novel framework to enhancing long-term consistency of video world models through a geometry-grounded long-term spatial memory. Our framework includes mechanisms to store and retrieve information from the long-term spatial memory and we curate custom datasets to train and evaluate world models with explicitly stored 3D memory mechanisms. Our evaluations show improved quality, consistency, and context length compared to relevant baselines, paving the way towards long-term consistent world generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05209", "title": "The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text", "authors": [ "Nikhil Kandpal", "Brian Lester", "Colin Raffel", "Sebastian Majstorovic", "Stella Biderman", "Baber Abbasi", "Luca Soldaini", "Enrico Shippole", "A. Feder Cooper", "Aviya Skowron", "John Kirchenbauer", "Shayne Longpre", "Lintang Sutawika", "Alon Albalak", "Zhenlin Xu", "Guilherme Penedo", "Loubna Ben Allal", "Elie Bakouch", "John David Pressman", "Honglu Fan", "Dashiell Stander", "Guangyu Song", "Aaron Gokaslan", "Tom Goldstein", "Brian R. Bartoldson", "Bhavya Kailkhura", "Tyler Murray" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are typically trained on enormous quantities of unlicensed text, a practice that has led to scrutiny due to possible intellectual property infringement and ethical concerns. Training LLMs on openly licensed text presents a first step towards addressing these issues, but prior data collection efforts have yielded datasets too small or low-quality to produce performant LLMs. To address this gap, we collect, curate, and release the Common Pile v0.1, an eight terabyte collection of openly licensed text designed for LLM pretraining. The Common Pile comprises content from 30 sources that span diverse domains including research papers, code, books, encyclopedias, educational materials, audio transcripts, and more. Crucially, we validate our efforts by training two 7 billion parameter LLMs on text from the Common Pile: Comma v0.1-1T and Comma v0.1-2T, trained on 1 and 2 trillion tokens respectively. Both models attain competitive performance to LLMs trained on unlicensed text with similar computational budgets, such as Llama 1 and 2 7B. In addition to releasing the Common Pile v0.1 itself, we also release the code used in its creation as well as the training mixture and checkpoints for the Comma v0.1 models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.04996", "title": "PATS: Proficiency-Aware Temporal Sampling for Multi-View Sports Skill Assessment", "authors": [ "Edoardo Bianchi", "Antonio Liotta" ], "github": "https://github.com/EdoWhite/PATS", "project_page": "", "abstract": "Automated sports skill assessment requires capturing fundamental movement patterns that distinguish expert from novice performance, yet current video sampling methods disrupt the temporal continuity essential for proficiency evaluation. To this end, we introduce Proficiency-Aware Temporal Sampling (PATS), a novel sampling strategy that preserves complete fundamental movements within continuous temporal segments for multi-view skill assessment. PATS adaptively segments videos to ensure each analyzed portion contains full execution of critical performance components, repeating this process across multiple segments to maximize information coverage while maintaining temporal coherence. Evaluated on the EgoExo4D benchmark with SkillFormer, PATS surpasses the state-of-the-art accuracy across all viewing configurations (+0.65% to +3.05%) and delivers substantial gains in challenging domains (+26.22% bouldering, +2.39% music, +1.13% basketball). Systematic analysis reveals that PATS successfully adapts to diverse activity characteristics-from high-frequency sampling for dynamic sports to fine-grained segmentation for sequential skills-demonstrating its effectiveness as an adaptive approach to temporal sampling that advances automated skill assessment for real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/EdoWhite/PATS", "github_api_added_by": "auto" }, { "date": "2025-06-06", "arxiv_id": "2506.02865", "title": "Surfer-H Meets Holo1: Cost-Efficient Web Agent Powered by Open Weights", "authors": [ "Mathieu Andreux", "Breno Baldas Skuk", "Hamza Benchekroun", "Emilien Bir\u00e9", "Antoine Bonnet", "Riaz Bordie", "Matthias Brunel", "Pierre-Louis Cedoz", "Antoine Chassang", "Micka\u00ebl Chen", "Alexandra D. Constantinou", "Antoine d'Andign\u00e9", "Hubert de La Jonqui\u00e8re", "Aur\u00e9lien Delfosse", "Ludovic Denoyer", "Alexis Deprez", "Augustin Derupti", "Michael Eickenberg", "Math\u00efs Federico", "Charles Kantor", "Xavier Koegler", "Yann Labb\u00e9", "Matthew C. H. Lee", "Erwan Le Jumeau de Kergaradec", "Amir Mahla", "Avshalom Manevich", "Adrien Maret", "Charles Masson", "Rafa\u00ebl Maurin", "Arturo Mena", "Philippe Modard", "Axel Moyal", "Axel Nguyen Kerbel", "Julien Revelle", "Mats L. Richter", "Mar\u00eda Santos", "Laurent Sifre", "Maxime Theillard", "Marc Thibault", "Louis Thiry", "L\u00e9o Tronchon", "Nicolas Usunier", "Tony Wu" ], "github": "", "project_page": "", "abstract": "We present Surfer-H, a cost-efficient web agent that integrates Vision-Language Models (VLM) to perform user-defined tasks on the web. We pair it with Holo1, a new open-weight collection of VLMs specialized in web navigation and information extraction. Holo1 was trained on carefully curated data sources, including open-access web content, synthetic examples, and self-produced agentic data. Holo1 tops generalist User Interface (UI) benchmarks as well as our new web UI localization benchmark, WebClick. When powered by Holo1, Surfer-H achieves a 92.2% state-of-the-art performance on WebVoyager, striking a Pareto-optimal balance between accuracy and cost-efficiency. To accelerate research advancement in agentic systems, we are open-sourcing both our WebClick evaluation dataset and the Holo1 model weights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05301", "title": "SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training", "authors": [ "Jianyi Wang", "Shanchuan Lin", "Zhijie Lin", "Yuxi Ren", "Meng Wei", "Zongsheng Yue", "Shangchen Zhou", "Hao Chen", "Yang Zhao", "Ceyuan Yang", "Xuefeng Xiao", "Chen Change Loy", "Lu Jiang" ], "github": "", "project_page": "", "abstract": "Recent advances in diffusion-based video restoration (VR) demonstrate significant improvement in visual quality, yet yield a prohibitive computational cost during inference. While several distillation-based approaches have exhibited the potential of one-step image restoration, extending existing approaches to VR remains challenging and underexplored, particularly when dealing with high-resolution video in real-world settings. In this work, we propose a one-step diffusion-based VR model, termed as SeedVR2, which performs adversarial VR training against real data. To handle the challenging high-resolution VR within a single step, we introduce several enhancements to both model architecture and training procedures. Specifically, an adaptive window attention mechanism is proposed, where the window size is dynamically adjusted to fit the output resolutions, avoiding window inconsistency observed under high-resolution VR using window attention with a predefined window size. To stabilize and improve the adversarial post-training towards VR, we further verify the effectiveness of a series of losses, including a proposed feature matching loss without significantly sacrificing training efficiency. Extensive experiments show that SeedVR2 can achieve comparable or even better performance compared with existing VR approaches in a single step.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05345", "title": "Inference-Time Hyper-Scaling with KV Cache Compression", "authors": [ "Adrian \u0141a\u0144cucki", "Konrad Staniszewski", "Piotr Nawrot", "Edoardo M. Ponti" ], "github": "", "project_page": "", "abstract": "Inference-time scaling trades efficiency for increased reasoning accuracy by generating longer or more parallel sequences. However, in Transformer LLMs, generation cost is bottlenecked by the size of the key-value (KV) cache, rather than the number of generated tokens. Hence, we explore inference-time hyper-scaling: by compressing the KV cache, we can generate more tokens within the same compute budget and further improve the accuracy of scaled inference. The success of this approach, however, hinges on the ability of compression methods to preserve accuracy even at high compression ratios. To make hyper-scaling practical, we introduce Dynamic Memory Sparsification (DMS), a novel method for sparsifying KV caches that only requires 1K training steps to achieve 8times compression, while maintaining better accuracy than training-free sparse attention. Instead of prematurely discarding cached tokens, DMS delays token eviction, implicitly merging representations and preserving critical information. We demonstrate the effectiveness of inference-time hyper-scaling with DMS on multiple families of LLMs, showing that it boosts accuracy for comparable inference runtime and memory load. For instance, we enhance Qwen-R1 32B by an average of 9.1 points on AIME 24, 7.6 on GPQA, and 9.6 on LiveCodeBench across compute budgets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.02751", "title": "RobustSplat: Decoupling Densification and Dynamics for Transient-Free 3DGS", "authors": [ "Chuanyu Fu", "Yuqi Zhang", "Kunbin Yao", "Guanying Chen", "Yuan Xiong", "Chuan Huang", "Shuguang Cui", "Xiaochun Cao" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) has gained significant attention for its real-time, photo-realistic rendering in novel-view synthesis and 3D modeling. However, existing methods struggle with accurately modeling scenes affected by transient objects, leading to artifacts in the rendered images. We identify that the Gaussian densification process, while enhancing scene detail capture, unintentionally contributes to these artifacts by growing additional Gaussians that model transient disturbances. To address this, we propose RobustSplat, a robust solution based on two critical designs. First, we introduce a delayed Gaussian growth strategy that prioritizes optimizing static scene structure before allowing Gaussian splitting/cloning, mitigating overfitting to transient objects in early optimization. Second, we design a scale-cascaded mask bootstrapping approach that first leverages lower-resolution feature similarity supervision for reliable initial transient mask estimation, taking advantage of its stronger semantic consistency and robustness to noise, and then progresses to high-resolution supervision to achieve more precise mask prediction. Extensive experiments on multiple challenging datasets show that our method outperforms existing methods, clearly demonstrating the robustness and effectiveness of our method. Our project page is https://fcyycf.github.io/RobustSplat/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05348", "title": "FreeTimeGS: Free Gaussians at Anytime and Anywhere for Dynamic Scene Reconstruction", "authors": [ "Yifan Wang", "Peishan Yang", "Zhen Xu", "Jiaming Sun", "Zhanhua Zhang", "Yong Chen", "Hujun Bao", "Sida Peng", "Xiaowei Zhou" ], "github": "", "project_page": "", "abstract": "This paper addresses the challenge of reconstructing dynamic 3D scenes with complex motions. Some recent works define 3D Gaussian primitives in the canonical space and use deformation fields to map canonical primitives to observation spaces, achieving real-time dynamic view synthesis. However, these methods often struggle to handle scenes with complex motions due to the difficulty of optimizing deformation fields. To overcome this problem, we propose FreeTimeGS, a novel 4D representation that allows Gaussian primitives to appear at arbitrary time and locations. In contrast to canonical Gaussian primitives, our representation possesses the strong flexibility, thus improving the ability to model dynamic 3D scenes. In addition, we endow each Gaussian primitive with an motion function, allowing it to move to neighboring regions over time, which reduces the temporal redundancy. Experiments results on several datasets show that the rendering quality of our method outperforms recent methods by a large margin.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2505.23115", "title": "Diffusion-Based Generative Models for 3D Occupancy Prediction in Autonomous Driving", "authors": [ "Yunshen Wang", "Yicheng Liu", "Tianyuan Yuan", "Yucheng Mao", "Yingshi Liang", "Xiuyu Yang", "Honggang Zhang", "Hang Zhao" ], "github": "", "project_page": "", "abstract": "Accurately predicting 3D occupancy grids from visual inputs is critical for autonomous driving, but current discriminative methods struggle with noisy data, incomplete observations, and the complex structures inherent in 3D scenes. In this work, we reframe 3D occupancy prediction as a generative modeling task using diffusion models, which learn the underlying data distribution and incorporate 3D scene priors. This approach enhances prediction consistency, noise robustness, and better handles the intricacies of 3D spatial structures. Our extensive experiments show that diffusion-based generative models outperform state-of-the-art discriminative approaches, delivering more realistic and accurate occupancy predictions, especially in occluded or low-visibility regions. Moreover, the improved predictions significantly benefit downstream planning tasks, highlighting the practical advantages of our method for real-world autonomous driving applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05229", "title": "Diagonal Batching Unlocks Parallelism in Recurrent Memory Transformers for Long Contexts", "authors": [ "Danil Sivtsov", "Ivan Rodkin", "Gleb Kuzmin", "Yuri Kuratov", "Ivan Oseledets" ], "github": "", "project_page": "", "abstract": "Transformer models struggle with long-context inference due to their quadratic time and linear memory complexity. Recurrent Memory Transformers (RMTs) offer a solution by reducing the asymptotic cost to linear time and constant memory usage. However, their memory update mechanism leads to sequential execution, causing a performance bottleneck. We introduce Diagonal Batching, a scheduling scheme that unlocks parallelism across segments in RMTs while preserving exact recurrence. This approach eliminates the sequential constraint, enabling efficient GPU inference even for single long-context inputs without complex batching and pipelining techniques. Because the technique is purely a run-time computation reordering, existing RMT models adopt it with no retraining. Applied to a LLaMA-1B ARMT model, Diagonal Batching yields a 3.3x speedup over standard full-attention LLaMA-1B and a 1.8x speedup over the sequential RMT implementation on 131,072-token sequences. By removing sequential bottleneck, Diagonal Batching reduces inference cost and latency, thereby strengthening RMTs as a practical solution for real-world, long-context applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.00981", "title": "What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-training", "authors": [ "Marianne de Heer Kloots", "Hosein Mohebbi", "Charlotte Pouw", "Gaofei Shen", "Willem Zuidema", "Martijn Bentum" ], "github": "https://github.com/mdhk/ssl-nl-eval", "project_page": "", "abstract": "How language-specific are speech representations learned by self-supervised models? Existing work has shown that a range of linguistic features can be successfully decoded from end-to-end models trained only on speech recordings. However, it's less clear to what extent pre-training on specific languages improves language-specific linguistic information. Here we test the encoding of Dutch phonetic and lexical information in internal representations of self-supervised Wav2Vec2 models. Pre-training exclusively on Dutch improves the representation of Dutch linguistic features as compared to pre-training on similar amounts of English or larger amounts of multilingual data. This language-specific advantage is well-detected by trained clustering or classification probes, and partially observable using zero-shot metrics. Furthermore, the language-specific benefit on linguistic feature encoding aligns with downstream performance on Automatic Speech Recognition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mdhk/ssl-nl-eval", "github_api_added_by": "auto" }, { "date": "2025-06-06", "arxiv_id": "2506.04956", "title": "FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation", "authors": [ "Huihan Wang", "Zhiwen Yang", "Hui Zhang", "Dan Zhao", "Bingzheng Wei", "Yan Xu" ], "github": "https://github.com/Yaziwel/FEAT", "project_page": "", "abstract": "Synthesizing high-quality dynamic medical videos remains a significant challenge due to the need for modeling both spatial consistency and temporal dynamics. Existing Transformer-based approaches face critical limitations, including insufficient channel interactions, high computational complexity from self-attention, and coarse denoising guidance from timestep embeddings when handling varying noise levels. In this work, we propose FEAT, a full-dimensional efficient attention Transformer, which addresses these issues through three key innovations: (1) a unified paradigm with sequential spatial-temporal-channel attention mechanisms to capture global dependencies across all dimensions, (2) a linear-complexity design for attention mechanisms in each dimension, utilizing weighted key-value attention and global channel attention, and (3) a residual value guidance module that provides fine-grained pixel-level guidance to adapt to different noise levels. We evaluate FEAT on standard benchmarks and downstream tasks, demonstrating that FEAT-S, with only 23\\% of the parameters of the state-of-the-art model Endora, achieves comparable or even superior performance. Furthermore, FEAT-L surpasses all comparison methods across multiple datasets, showcasing both superior effectiveness and scalability. Code is available at https://github.com/Yaziwel/FEAT.", "github_manual": "", "github_regex": "https://github.com/Yaziwel/FEAT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.04598", "title": "Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets", "authors": [ "Marianna Nezhurina", "Tomer Porian", "Giovanni Pucceti", "Tommie Kerssies", "Romain Beaumont", "Mehdi Cherti", "Jenia Jitsev" ], "github": "https://github.com/LAION-AI/scaling-laws-for-comparison", "project_page": "", "abstract": "In studies of transferable learning, scaling laws are obtained for various important foundation models to predict their properties and performance at larger scales. We show here how scaling law derivation can also be used for model and dataset comparison, allowing to decide which procedure is to be preferred for pre-training. For the first time, full scaling laws based on dense measurements across a wide span of model and samples seen scales are derived for two important language-vision learning procedures, CLIP and MaMMUT, that use either contrastive only or contrastive and captioning text generative loss. Ensuring sufficient prediction accuracy for held out points, we use derived scaling laws to compare both models, obtaining evidence for MaMMUT's stronger improvement with scale and better sample efficiency than standard CLIP. To strengthen validity of the comparison, we show scaling laws for various downstream tasks, classification, retrieval, and segmentation, and for different open datasets, DataComp, DFN and Re-LAION, observing consistently the same trends. We show that comparison can also be performed when deriving scaling laws with a constant learning rate schedule, reducing compute cost. Accurate derivation of scaling laws provides thus means to perform model and dataset comparison across scale spans, avoiding misleading conclusions based on measurements from single reference scales only, paving the road for systematic comparison and improvement of open foundation models and datasets for their creation. We release all the pre-trained models with their intermediate checkpoints, including openMaMMUT-L/14, which achieves 80.3% zero-shot ImageNet-1k accuracy, trained on 12.8B samples from DataComp-1.4B. Code for reproducing experiments in the paper and raw experiments data can be found at https://github.com/LAION-AI/scaling-laws-for-comparison.", "github_manual": "", "github_regex": "https://github.com/LAION-AI/scaling-laws-for-comparison", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.03643", "title": "Images are Worth Variable Length of Representations", "authors": [ "Lingjun Mao", "Rodolfo Corona", "Xin Liang", "Wenhao Yan", "Zineng Tang" ], "github": "", "project_page": "", "abstract": "Most existing vision encoders map images into a fixed-length sequence of tokens, overlooking the fact that different images contain varying amounts of information. For example, a visually complex image (e.g., a cluttered room) inherently carries more information and thus deserves more tokens than a simple image (e.g., a blank wall). To address this inefficiency, we propose DOVE, a dynamic vision encoder that produces a variable number of visual tokens (i.e., continuous representation vectors) to reconstruct each image. Our results show that DOVE significantly reduces the average number of tokens while maintaining high reconstruction quality. In several linear probing and downstream multimodal tasks, it outperforms existing autoencoder-based tokenization methods when using far fewer tokens, capturing more expressive semantic features compared to fixed-length encoding. We further extend DOVE with query-conditioned tokenization. By guiding the model to focus on query-relevant regions, it achieves more efficient and targeted semantic extraction. Our code and checkpoints are available at https://dove-encoder.github.io/dove-encoder.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.02444", "title": "SViMo: Synchronized Diffusion for Video and Motion Generation in Hand-object Interaction Scenarios", "authors": [ "Lingwei Dang", "Ruizhi Shao", "Hongwen Zhang", "Wei Min", "Yebin Liu", "Qingyao Wu" ], "github": "https://github.com/Droliven/SViMo_code", "project_page": "", "abstract": "Hand-Object Interaction (HOI) generation has significant application potential. However, current 3D HOI motion generation approaches heavily rely on predefined 3D object models and lab-captured motion data, limiting generalization capabilities. Meanwhile, HOI video generation methods prioritize pixel-level visual fidelity, often sacrificing physical plausibility. Recognizing that visual appearance and motion patterns share fundamental physical laws in the real world, we propose a novel framework that combines visual priors and dynamic constraints within a synchronized diffusion process to generate the HOI video and motion simultaneously. To integrate the heterogeneous semantics, appearance, and motion features, our method implements tri-modal adaptive modulation for feature aligning, coupled with 3D full-attention for modeling inter- and intra-modal dependencies. Furthermore, we introduce a vision-aware 3D interaction diffusion model that generates explicit 3D interaction sequences directly from the synchronized diffusion outputs, then feeds them back to establish a closed-loop feedback cycle. This architecture eliminates dependencies on predefined object models or explicit pose guidance while significantly enhancing video-motion consistency. Experimental results demonstrate our method's superiority over state-of-the-art approaches in generating high-fidelity, dynamically plausible HOI sequences, with notable generalization capabilities in unseen real-world scenarios. Project page at https://github.com/Droliven/SViMo\\_project.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Droliven/SViMo_code", "github_api_added_by": "auto" }, { "date": "2025-06-06", "arxiv_id": "2506.04462", "title": "Watermarking Degrades Alignment in Language Models: Analysis and Mitigation", "authors": [ "Apurv Verma", "NhatHai Phan", "Shubhendu Trivedi" ], "github": "", "project_page": "", "abstract": "Watermarking techniques for large language models (LLMs) can significantly impact output quality, yet their effects on truthfulness, safety, and helpfulness remain critically underexamined. This paper presents a systematic analysis of how two popular watermarking approaches-Gumbel and KGW-affect these core alignment properties across four aligned LLMs. Our experiments reveal two distinct degradation patterns: guard attenuation, where enhanced helpfulness undermines model safety, and guard amplification, where excessive caution reduces model helpfulness. These patterns emerge from watermark-induced shifts in token distribution, surfacing the fundamental tension that exists between alignment objectives. To mitigate these degradations, we propose Alignment Resampling (AR), an inference-time sampling method that uses an external reward model to restore alignment. We establish a theoretical lower bound on the improvement in expected reward score as the sample size is increased and empirically demonstrate that sampling just 2-4 watermarked generations effectively recovers or surpasses baseline (unwatermarked) alignment scores. To overcome the limited response diversity of standard Gumbel watermarking, our modified implementation sacrifices strict distortion-freeness while maintaining robust detectability, ensuring compatibility with AR. Experimental results confirm that AR successfully recovers baseline alignment in both watermarking approaches, while maintaining strong watermark detectability. This work reveals the critical balance between watermark strength and model alignment, providing a simple inference-time solution to responsibly deploy watermarked LLMs in practice.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05334", "title": "Search Arena: Analyzing Search-Augmented LLMs", "authors": [ "Mihran Miroyan", "Tsung-Han Wu", "Logan King", "Tianle Li", "Jiayi Pan", "Xinyan Hu", "Wei-Lin Chiang", "Anastasios N. Angelopoulos", "Trevor Darrell", "Narges Norouzi", "Joseph E. Gonzalez" ], "github": "https://github.com/lmarena/search-arena", "project_page": "", "abstract": "Search-augmented language models combine web search with Large Language Models (LLMs) to improve response groundedness and freshness. However, analyzing these systems remains challenging: existing datasets are limited in scale and narrow in scope, often constrained to static, single-turn, fact-checking questions. In this work, we introduce Search Arena, a crowd-sourced, large-scale, human-preference dataset of over 24,000 paired multi-turn user interactions with search-augmented LLMs. The dataset spans diverse intents and languages, and contains full system traces with around 12,000 human preference votes. Our analysis reveals that user preferences are influenced by the number of citations, even when the cited content does not directly support the attributed claims, uncovering a gap between perceived and actual credibility. Furthermore, user preferences vary across cited sources, revealing that community-driven platforms are generally preferred and static encyclopedic sources are not always appropriate and reliable. To assess performance across different settings, we conduct cross-arena analyses by testing search-augmented LLMs in a general-purpose chat environment and conventional LLMs in search-intensive settings. We find that web search does not degrade and may even improve performance in non-search settings; however, the quality in search settings is significantly affected if solely relying on the model's parametric knowledge. We open-sourced the dataset to support future research in this direction. Our dataset and code are available at: https://github.com/lmarena/search-arena.", "github_manual": "", "github_regex": "https://github.com/lmarena/search-arena", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05313", "title": "MARBLE: Material Recomposition and Blending in CLIP-Space", "authors": [ "Ta-Ying Cheng", "Prafull Sharma", "Mark Boss", "Varun Jampani" ], "github": "https://github.com/Stability-AI/marble", "project_page": "", "abstract": "Editing materials of objects in images based on exemplar images is an active area of research in computer vision and graphics. We propose MARBLE, a method for performing material blending and recomposing fine-grained material properties by finding material embeddings in CLIP-space and using that to control pre-trained text-to-image models. We improve exemplar-based material editing by finding a block in the denoising UNet responsible for material attribution. Given two material exemplar-images, we find directions in the CLIP-space for blending the materials. Further, we can achieve parametric control over fine-grained material attributes such as roughness, metallic, transparency, and glow using a shallow network to predict the direction for the desired material attribute change. We perform qualitative and quantitative analysis to demonstrate the efficacy of our proposed method. We also present the ability of our method to perform multiple edits in a single forward pass and applicability to painting. Project Page: https://marblecontrol.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Stability-AI/marble", "github_api_added_by": "auto" }, { "date": "2025-06-06", "arxiv_id": "2506.05046", "title": "FlowDirector: Training-Free Flow Steering for Precise Text-to-Video Editing", "authors": [ "Guangzhao Li", "Yanming Yang", "Chenxi Song", "Chi Zhang" ], "github": "https://github.com/Westlake-AGI-Lab/FlowDirector", "project_page": "", "abstract": "Text-driven video editing aims to modify video content according to natural language instructions. While recent training-free approaches have made progress by leveraging pre-trained diffusion models, they typically rely on inversion-based techniques that map input videos into the latent space, which often leads to temporal inconsistencies and degraded structural fidelity. To address this, we propose FlowDirector, a novel inversion-free video editing framework. Our framework models the editing process as a direct evolution in data space, guiding the video via an Ordinary Differential Equation (ODE) to smoothly transition along its inherent spatiotemporal manifold, thereby preserving temporal coherence and structural details. To achieve localized and controllable edits, we introduce an attention-guided masking mechanism that modulates the ODE velocity field, preserving non-target regions both spatially and temporally. Furthermore, to address incomplete edits and enhance semantic alignment with editing instructions, we present a guidance-enhanced editing strategy inspired by Classifier-Free Guidance, which leverages differential signals between multiple candidate flows to steer the editing trajectory toward stronger semantic alignment without compromising structural consistency. Extensive experiments across benchmarks demonstrate that FlowDirector achieves state-of-the-art performance in instruction adherence, temporal consistency, and background preservation, establishing a new paradigm for efficient and coherent video editing without inversion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Westlake-AGI-Lab/FlowDirector", "github_api_added_by": "auto" }, { "date": "2025-06-06", "arxiv_id": "2506.04559", "title": "Perceptual Decoupling for Scalable Multi-modal Reasoning via Reward-Optimized Captioning", "authors": [ "Yunhao Gou", "Kai Chen", "Zhili Liu", "Lanqing Hong", "Xin Jin", "Zhenguo Li", "James T. Kwok", "Yu Zhang" ], "github": "", "project_page": "", "abstract": "Recent advances in slow-thinking language models (e.g., OpenAI-o1 and DeepSeek-R1) have demonstrated remarkable abilities in complex reasoning tasks by emulating human-like reflective cognition. However, extending such capabilities to multi-modal large language models (MLLMs) remains challenging due to the high cost of retraining vision-language alignments when upgrading the underlying reasoner LLMs. A straightforward solution is to decouple perception from reasoning, i.e., converting visual inputs into language representations (e.g., captions) that are then passed to a powerful text-only reasoner. However, this decoupling introduces a critical challenge: the visual extractor must generate descriptions that are both faithful to the image and informative enough to support accurate downstream reasoning. To address this, we propose Reasoning-Aligned Perceptual Decoupling via Caption Reward Optimization (RACRO) - a reasoning-guided reinforcement learning strategy that aligns the extractor's captioning behavior with the reasoning objective. By closing the perception-reasoning loop via reward-based optimization, RACRO significantly enhances visual grounding and extracts reasoning-optimized representations. Experiments on multi-modal math and science benchmarks show that the proposed RACRO method achieves state-of-the-art average performance while enabling superior scalability and plug-and-play adaptation to more advanced reasoning LLMs without the necessity for costly multi-modal re-alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-06", "arxiv_id": "2506.05333", "title": "Kinetics: Rethinking Test-Time Scaling Laws", "authors": [ "Ranajoy Sadhukhan", "Zhuoming Chen", "Haizhong Zheng", "Yang Zhou", "Emma Strubell", "Beidi Chen" ], "github": "https://github.com/Infini-AI-Lab/Kinetics", "project_page": "", "abstract": "We rethink test-time scaling laws from a practical efficiency perspective, revealing that the effectiveness of smaller models is significantly overestimated. Prior work, grounded in compute-optimality, overlooks critical memory access bottlenecks introduced by inference-time strategies (e.g., Best-of-N, long CoTs). Our holistic analysis, spanning models from 0.6B to 32B parameters, reveals a new Kinetics Scaling Law that better guides resource allocation by incorporating both computation and memory access costs. Kinetics Scaling Law suggests that test-time compute is more effective when used on models above a threshold than smaller ones. A key reason is that in TTS, attention, rather than parameter count, emerges as the dominant cost factor. Motivated by this, we propose a new scaling paradigm centered on sparse attention, which lowers per-token cost and enables longer generations and more parallel samples within the same resource budget. Empirically, we show that sparse attention models consistently outperform dense counterparts, achieving over 60 points gains in low-cost regimes and over 5 points gains in high-cost regimes for problem-solving accuracy on AIME, encompassing evaluations on state-of-the-art MoEs. These results suggest that sparse attention is essential for realizing the full potential of test-time scaling because, unlike training, where parameter scaling saturates, test-time accuracy continues to improve through increased generation. The code is available at https://github.com/Infini-AI-Lab/Kinetics.", "github_manual": "", "github_regex": "https://github.com/Infini-AI-Lab/Kinetics", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.05984", "title": "Audio-Aware Large Language Models as Judges for Speaking Styles", "authors": [ "Cheng-Han Chiang", "Xiaofei Wang", "Chung-Ching Lin", "Kevin Lin", "Linjie Li", "Radu Kopetz", "Yao Qian", "Zhendong Wang", "Zhengyuan Yang", "Hung-yi Lee", "Lijuan Wang" ], "github": "", "project_page": "", "abstract": "Audio-aware large language models (ALLMs) can understand the textual and non-textual information in the audio input. In this paper, we explore using ALLMs as an automatic judge to assess the speaking styles of speeches. We use ALLM judges to evaluate the speeches generated by SLMs on two tasks: voice style instruction following and role-playing. The speaking style we consider includes emotion, volume, speaking pace, word emphasis, pitch control, and non-verbal elements. We use four spoken language models (SLMs) to complete the two tasks and use humans and ALLMs to judge the SLMs' responses. We compare two ALLM judges, GPT-4o-audio and Gemini-2.5-pro, with human evaluation results and show that the agreement between Gemini and human judges is comparable to the agreement between human evaluators. These promising results show that ALLMs can be used as a judge to evaluate SLMs. Our results also reveal that current SLMs, even GPT-4o-audio, still have room for improvement in controlling the speaking style and generating natural dialogues.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.05629", "title": "Leveraging Self-Attention for Input-Dependent Soft Prompting in LLMs", "authors": [ "Ananth Muppidi", "Abhilash Nandy", "Sambaran Bandyopadhyay" ], "github": "", "project_page": "", "abstract": "The performance of large language models in domain-specific tasks necessitates fine-tuning, which is computationally expensive and technically challenging. This paper focuses on parameter-efficient fine-tuning using soft prompting, a promising approach that adapts pre-trained models to downstream tasks by learning a small set of parameters. We propose a novel Input Dependent Soft Prompting technique with a self-Attention Mechanism (ID-SPAM) that generates soft prompts based on the input tokens and attends different tokens with varying importance. Our method is simple and efficient, keeping the number of trainable parameters small. We show the merits of the proposed approach compared to state-of-the-art techniques on various tasks and show the improved zero shot domain transfer capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.06199", "title": "3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World Model", "authors": [ "Hongyan Zhi", "Peihao Chen", "Siyuan Zhou", "Yubo Dong", "Quanxi Wu", "Lei Han", "Mingkui Tan" ], "github": "https://github.com/Hoyyyaard/3DFlowAction", "project_page": "", "abstract": "Manipulation has long been a challenging task for robots, while humans can effortlessly perform complex interactions with objects, such as hanging a cup on the mug rack. A key reason is the lack of a large and uniform dataset for teaching robots manipulation skills. Current robot datasets often record robot action in different action spaces within a simple scene. This hinders the robot to learn a unified and robust action representation for different robots within diverse scenes. Observing how humans understand a manipulation task, we find that understanding how the objects should move in the 3D space is a critical clue for guiding actions. This clue is embodiment-agnostic and suitable for both humans and different robots. Motivated by this, we aim to learn a 3D flow world model from both human and robot manipulation data. This model predicts the future movement of the interacting objects in 3D space, guiding action planning for manipulation. Specifically, we synthesize a large-scale 3D optical flow dataset, named ManiFlow-110k, through a moving object auto-detect pipeline. A video diffusion-based world model then learns manipulation physics from these data, generating 3D optical flow trajectories conditioned on language instructions. With the generated 3D object optical flow, we propose a flow-guided rendering mechanism, which renders the predicted final state and leverages GPT-4o to assess whether the predicted flow aligns with the task description. This equips the robot with a closed-loop planning ability. Finally, we consider the predicted 3D optical flow as constraints for an optimization policy to determine a chunk of robot actions for manipulation. Extensive experiments demonstrate strong generalization across diverse robotic manipulation tasks and reliable cross-embodiment adaptation without hardware-specific training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Hoyyyaard/3DFlowAction", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.01111", "title": "FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion", "authors": [ "Shunian Chen", "Xinyuan Xie", "Zheshu Chen", "Liyan Zhao", "Owen Lee", "Zhan Su", "Qilin Sun", "Benyou Wang" ], "github": "https://github.com/FreedomIntelligence/FusionAudio", "project_page": "", "abstract": "High-quality, large-scale audio captioning is crucial for advancing audio understanding, yet current automated methods often generate captions that lack fine-grained detail and contextual accuracy, primarily due to their reliance on limited unimodal or superficial multimodal information. Drawing inspiration from human auditory perception, which adeptly integrates cross-modal cues and performs sophisticated auditory scene analysis, we introduce a novel two-stage automated pipeline. This pipeline first employs specialized pretrained models to extract diverse contextual cues (e.g., speech, music, general sounds, and visual information from associated video). A large language model (LLM) then synthesizes these rich, multimodal inputs to generate detailed and context-aware audio captions. Key contributions of this work include: (1) the proposed scalable method for fine-grained audio caption generation; (2) FusionAudio, a new large-scale dataset comprising 1.2 million such detailed captions, combined with 6 million QA pairs; and (3) enhanced audio models developed using FusionAudio, specifically a CLAP-based audio encoder with superior audio-text alignment and instruction following. This paper paves the way for more nuanced and accurate automated understanding of complex audio environments. Code and data can be found in https://github.com/satsuki2486441738/FusionAudio.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FreedomIntelligence/FusionAudio", "github_api_added_by": "user" }, { "date": "2025-06-09", "arxiv_id": "2506.05523", "title": "MORSE-500: A Programmatically Controllable Video Benchmark to Stress-Test Multimodal Reasoning", "authors": [ "Zikui Cai", "Andrew Wang", "Anirudh Satheesh", "Ankit Nakhawa", "Hyunwoo Jae", "Keenan Powell", "Minghui Liu", "Neel Jay", "Sungbin Oh", "Xiyao Wang", "Yongyuan Liang", "Tom Goldstein", "Furong Huang" ], "github": "", "project_page": "", "abstract": "Despite rapid advances in vision-language models (VLMs), current benchmarks for multimodal reasoning fall short in three key dimensions. First, they overwhelmingly rely on static images, failing to capture the temporal complexity of real-world environments. Second, they narrowly focus on mathematical problem-solving, neglecting the broader spectrum of reasoning skills -- including abstract, physical, planning, spatial, and temporal capabilities -- required for robust multimodal intelligence. Third, many benchmarks quickly saturate, offering limited headroom for diagnosing failure modes or measuring continued progress. We introduce MORSE-500 (Multimodal Reasoning Stress-test Environment), a video benchmark composed of 500 fully scripted clips with embedded questions spanning six complementary reasoning categories. Each instance is programmatically generated using deterministic Python scripts (via Manim, Matplotlib, MoviePy), generative video models, and curated real footage. This script-driven design allows fine-grained control over visual complexity, distractor density, and temporal dynamics -- enabling difficulty to be scaled systematically as models improve. Unlike static benchmarks that become obsolete once saturated, MORSE-500 is built to evolve: its controllable generation pipeline supports the creation of arbitrarily challenging new instances, making it ideally suited for stress-testing next-generation models. Initial experiments with state-of-the-art systems -- including various Gemini 2.5 Pro and OpenAI o3 which represent the strongest available at the time, alongside strong open-source models -- reveal substantial performance gaps across all categories, with particularly large deficits in abstract and planning tasks. We release the full dataset, generation scripts, and evaluation harness to support transparent, reproducible, and forward-looking multimodal reasoning research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.06276", "title": "STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis", "authors": [ "Jiatao Gu", "Tianrong Chen", "David Berthelot", "Huangjie Zheng", "Yuyang Wang", "Ruixiang Zhang", "Laurent Dinh", "Miguel Angel Bautista", "Josh Susskind", "Shuangfei Zhai" ], "github": "https://github.com/facebookresearch/DiT", "project_page": "", "abstract": "We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance in high-resolution image synthesis. The core of STARFlow is Transformer Autoregressive Flow (TARFlow), which combines the expressive power of normalizing flows with the structured modeling capabilities of Autoregressive Transformers. We first establish the theoretical universality of TARFlow for modeling continuous distributions. Building on this foundation, we introduce several key architectural and algorithmic innovations to significantly enhance scalability: (1) a deep-shallow design, wherein a deep Transformer block captures most of the model representational capacity, complemented by a few shallow Transformer blocks that are computationally efficient yet substantially beneficial; (2) modeling in the latent space of pretrained autoencoders, which proves more effective than direct pixel-level modeling; and (3) a novel guidance algorithm that significantly boosts sample quality. Crucially, our model remains an end-to-end normalizing flow, enabling exact maximum likelihood training in continuous spaces without discretization. STARFlow achieves competitive performance in both class-conditional and text-conditional image generation tasks, approaching state-of-the-art diffusion models in sample quality. To our knowledge, this work is the first successful demonstration of normalizing flows operating effectively at this scale and resolution.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/DiT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.04255", "title": "HASHIRU: Hierarchical Agent System for Hybrid Intelligent Resource Utilization", "authors": [ "Kunal Pai", "Parth Shah", "Harshil Patel" ], "github": "https://github.com/HASHIRU-AI/HASHIRU", "project_page": "", "abstract": "Rapid Large Language Model (LLM) advancements are fueling autonomous Multi-Agent System (MAS) development. However, current frameworks often lack flexibility, resource awareness, model diversity, and autonomous tool creation. This paper introduces HASHIRU (Hierarchical Agent System for Hybrid Intelligent Resource Utilization), a novel MAS framework enhancing flexibility, resource efficiency, and adaptability. HASHIRU features a \"CEO\" agent dynamically managing specialized \"employee\" agents, instantiated based on task needs and resource constraints (cost, memory). Its hybrid intelligence prioritizes smaller, local LLMs (via Ollama) while flexibly using external APIs and larger models when necessary. An economic model with hiring/firing costs promotes team stability and efficient resource allocation. The system also includes autonomous API tool creation and a memory function. Evaluations on tasks like academic paper review (58% success), safety assessments (100% on a JailbreakBench subset), and complex reasoning (outperforming Gemini 2.0 Flash on GSM8K: 96% vs. 61%; JEEBench: 80% vs. 68.3%; SVAMP: 92% vs. 84%) demonstrate HASHIRU's capabilities. Case studies illustrate its self-improvement via autonomous cost model generation, tool integration, and budget management. HASHIRU offers a promising approach for more robust, efficient, and adaptable MAS through dynamic hierarchical control, resource-aware hybrid intelligence, and autonomous functional extension. Source code and benchmarks are available at https://github.com/HASHIRU-AI/HASHIRU and https://github.com/HASHIRU-AI/HASHIRUBench respectively, and a live demo is available at https://hashiruagentx-hashiruai.hf.space upon request.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HASHIRU-AI/HASHIRU", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.05573", "title": "PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers", "authors": [ "Yuchen Lin", "Chenguo Lin", "Panwang Pan", "Honglei Yan", "Yiqiang Feng", "Yadong Mu", "Katerina Fragkiadaki" ], "github": "https://github.com/VAST-AI-Research/MIDI-3D", "project_page": "", "abstract": "We introduce PartCrafter, the first structured 3D generative model that jointly synthesizes multiple semantically meaningful and geometrically distinct 3D meshes from a single RGB image. Unlike existing methods that either produce monolithic 3D shapes or follow two-stage pipelines, i.e., first segmenting an image and then reconstructing each segment, PartCrafter adopts a unified, compositional generation architecture that does not rely on pre-segmented inputs. Conditioned on a single image, it simultaneously denoises multiple 3D parts, enabling end-to-end part-aware generation of both individual objects and complex multi-object scenes. PartCrafter builds upon a pretrained 3D mesh diffusion transformer (DiT) trained on whole objects, inheriting the pretrained weights, encoder, and decoder, and introduces two key innovations: (1) A compositional latent space, where each 3D part is represented by a set of disentangled latent tokens; (2) A hierarchical attention mechanism that enables structured information flow both within individual parts and across all parts, ensuring global coherence while preserving part-level detail during generation. To support part-level supervision, we curate a new dataset by mining part-level annotations from large-scale 3D object datasets. Experiments show that PartCrafter outperforms existing approaches in generating decomposable 3D meshes, including parts that are not directly visible in input images, demonstrating the strength of part-aware generative priors for 3D understanding and synthesis. Code and training data will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/VAST-AI-Research/MIDI-3D", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.06253", "title": "Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision", "authors": [ "Yuping He", "Yifei Huang", "Guo Chen", "Lidong Lu", "Baoqi Pei", "Jilan Xu", "Tong Lu", "Yoichi Sato" ], "github": "https://github.com/ayiyayi/Awesome-Egocentric-and-Exocentric-Vision", "project_page": "", "abstract": "Perceiving the world from both egocentric (first-person) and exocentric (third-person) perspectives is fundamental to human cognition, enabling rich and complementary understanding of dynamic environments. In recent years, allowing the machines to leverage the synergistic potential of these dual perspectives has emerged as a compelling research direction in video understanding. In this survey, we provide a comprehensive review of video understanding from both exocentric and egocentric viewpoints. We begin by highlighting the practical applications of integrating egocentric and exocentric techniques, envisioning their potential collaboration across domains. We then identify key research tasks to realize these applications. Next, we systematically organize and review recent advancements into three main research directions: (1) leveraging egocentric data to enhance exocentric understanding, (2) utilizing exocentric data to improve egocentric analysis, and (3) joint learning frameworks that unify both perspectives. For each direction, we analyze a diverse set of tasks and relevant works. Additionally, we discuss benchmark datasets that support research in both perspectives, evaluating their scope, diversity, and applicability. Finally, we discuss limitations in current works and propose promising future research directions. By synthesizing insights from both perspectives, our goal is to inspire advancements in video understanding and artificial intelligence, bringing machines closer to perceiving the world in a human-like manner. A GitHub repo of related works can be found at https://github.com/ayiyayi/Awesome-Egocentric-and-Exocentric-Vision.", "github_manual": "", "github_regex": "https://github.com/ayiyayi/Awesome-Egocentric-and-Exocentric-Vision", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.05433", "title": "Prefix Grouper: Efficient GRPO Training through Shared-Prefix Forward", "authors": [ "Zikang Liu", "Tongtian Yue", "Yepeng Tang", "Longteng Guo", "Junxian Cai", "Qingbin Liu", "Xi Chen", "Jing Liu" ], "github": "https://github.com/johncaged/PrefixGrouper", "project_page": "", "abstract": "Group Relative Policy Optimization (GRPO) enhances policy learning by computing gradients from relative comparisons among candidate outputs that share a common input prefix. Despite its effectiveness, GRPO introduces substantial computational overhead when processing long shared prefixes, which must be redundantly encoded for each group member. This inefficiency becomes a major scalability bottleneck in long-context learning scenarios. We propose Prefix Grouper, an efficient GRPO training algorithm that eliminates redundant prefix computation via a Shared-Prefix Forward strategy. In particular, by restructuring self-attention into two parts, our method enables the shared prefix to be encoded only once, while preserving full differentiability and compatibility with end-to-end training. We provide both theoretical and empirical evidence that Prefix Grouper is training-equivalent to standard GRPO: it yields identical forward outputs and backward gradients, ensuring that the optimization dynamics and final policy performance remain unchanged. Empirically, our experiments confirm that Prefix Grouper achieves consistent results while significantly reducing the computational cost of training, particularly in long-prefix scenarios. The proposed method is fully plug-and-play: it is compatible with existing GRPO-based architectures and can be seamlessly integrated into current training pipelines as a drop-in replacement, requiring no structural modifications and only minimal changes to input construction and attention computation. Prefix Grouper enables the use of larger group sizes under the same computational budget, thereby improving the scalability of GRPO to more complex tasks and larger models. Code is now available at https://github.com/johncaged/PrefixGrouper", "github_manual": "", "github_regex": "https://github.com/johncaged/PrefixGrouper", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2505.21115", "title": "Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA", "authors": [ "Sergey Pletenev", "Maria Marina", "Nikolay Ivanov", "Daria Galimzianova", "Nikita Krayko", "Mikhail Salnikov", "Vasily Konovalov", "Alexander Panchenko", "Viktor Moskvoretskii" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) often hallucinate in question answering (QA) tasks. A key yet underexplored factor contributing to this is the temporality of questions -- whether they are evergreen (answers remain stable over time) or mutable (answers change). In this work, we introduce EverGreenQA, the first multilingual QA dataset with evergreen labels, supporting both evaluation and training. Using EverGreenQA, we benchmark 12 modern LLMs to assess whether they encode question temporality explicitly (via verbalized judgments) or implicitly (via uncertainty signals). We also train EG-E5, a lightweight multilingual classifier that achieves SoTA performance on this task. Finally, we demonstrate the practical utility of evergreen classification across three applications: improving self-knowledge estimation, filtering QA datasets, and explaining GPT-4o retrieval behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.01872", "title": "Is Extending Modality The Right Path Towards Omni-Modality?", "authors": [ "Tinghui Zhu", "Kai Zhang", "Muhao Chen", "Yu Su" ], "github": "", "project_page": "", "abstract": "Omni-modal language models (OLMs) aim to integrate and reason over diverse input modalities--such as text, images, video, and audio--while maintaining strong language capabilities. Despite recent advancements, existing models, especially open-source ones, remain far from true omni-modality, struggling to generalize beyond the specific modality pairs they are trained on or to achieve strong performance when processing multi-modal inputs. We study the effect of extending modality, the dominant technique for training multimodal models, where an off-the-shelf language model is fine-tuned on target-domain and language data. Specifically, we investigate three key questions: (1) Does modality extension compromise core language abilities? (2) Can model merging effectively integrate independently fine-tuned modality-specific models to achieve omni-modality? (3) Does omni-modality extension lead to better knowledge sharing and generalization compared to sequential extension? Through extensive experiments, we analyze these trade-offs and provide insights into the feasibility of achieving true omni-modality using current approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.04755", "title": "Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoning", "authors": [ "Shenshen Li", "Kaiyuan Deng", "Lei Wang", "Hao Yang", "Chong Peng", "Peng Yan", "Fumin Shen", "Heng Tao Shen", "Xing Xu" ], "github": "https://github.com/Leo-ssl/RAP", "project_page": "", "abstract": "While multi-modal large language models (MLLMs) have made significant progress in complex reasoning tasks via reinforcement learning, it is commonly believed that extensive training data is necessary for improving multi-modal reasoning ability, inevitably leading to data redundancy and substantial computational costs. However, can smaller high-value datasets match or outperform full corpora for multi-modal reasoning in MLLMs? In this work, we challenge this assumption through a key observation: meaningful multi-modal reasoning is triggered by only a sparse subset of training samples, termed cognitive samples, whereas the majority contribute marginally. Building on this insight, we propose a novel data selection paradigm termed Reasoning Activation Potential (RAP), which identifies cognitive samples by estimating each sample's potential to stimulate genuine multi-modal reasoning by two complementary estimators: 1) Causal Discrepancy Estimator (CDE) based on the potential outcome model principle, eliminates samples that overly rely on language priors by comparing outputs between multi-modal and text-only inputs; 2) Attention Confidence Estimator (ACE), which exploits token-level self-attention to discard samples dominated by irrelevant but over-emphasized tokens in intermediate reasoning stages. Moreover, we introduce a Difficulty-aware Replacement Module (DRM) to substitute trivial instances with cognitively challenging ones, thereby ensuring complexity for robust multi-modal reasoning. Experiments on six datasets show that our RAP method consistently achieves superior performance using only 9.3% of the training data, while reducing computational costs by over 43%. Our code is available at https://github.com/Leo-ssl/RAP.", "github_manual": "", "github_regex": "https://github.com/Leo-ssl/RAP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.04120", "title": "Splatting Physical Scenes: End-to-End Real-to-Sim from Imperfect Robot Data", "authors": [ "Ben Moran", "Mauro Comi", "Steven Bohez", "Tom Erez", "Zhibin Li", "Leonard Hasenclever" ], "github": "", "project_page": "", "abstract": "Creating accurate, physical simulations directly from real-world robot motion holds great value for safe, scalable, and affordable robot learning, yet remains exceptionally challenging. Real robot data suffers from occlusions, noisy camera poses, dynamic scene elements, which hinder the creation of geometrically accurate and photorealistic digital twins of unseen objects. We introduce a novel real-to-sim framework tackling all these challenges at once. Our key insight is a hybrid scene representation merging the photorealistic rendering of 3D Gaussian Splatting with explicit object meshes suitable for physics simulation within a single representation. We propose an end-to-end optimization pipeline that leverages differentiable rendering and differentiable physics within MuJoCo to jointly refine all scene components - from object geometry and appearance to robot poses and physical parameters - directly from raw and imprecise robot trajectories. This unified optimization allows us to simultaneously achieve high-fidelity object mesh reconstruction, generate photorealistic novel views, and perform annotation-free robot pose calibration. We demonstrate the effectiveness of our approach both in simulation and on challenging real-world sequences using an ALOHA 2 bi-manual manipulator, enabling more practical and robust real-to-simulation pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.00649", "title": "GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction", "authors": [ "Neil De La Fuente", "Oscar Sainz", "Iker Garc\u00eda-Ferrero", "Eneko Agirre" ], "github": "", "project_page": "", "abstract": "Information Extraction (IE) systems are traditionally domain-specific, requiring costly adaptation that involves expert schema design, data annotation, and model training. While Large Language Models have shown promise in zero-shot IE, performance degrades significantly in unseen domains where label definitions differ. This paper introduces GUIDEX, a novel method that automatically defines domain-specific schemas, infers guidelines, and generates synthetically labeled instances, allowing for better out-of-domain generalization. Fine-tuning Llama 3.1 with GUIDEX sets a new state-of-the-art across seven zeroshot Named Entity Recognition benchmarks. Models trained with GUIDEX gain up to 7 F1 points over previous methods without humanlabeled data, and nearly 2 F1 points higher when combined with it. Models trained on GUIDEX demonstrate enhanced comprehension of complex, domain-specific annotation schemas. Code, models, and synthetic datasets are available at neilus03.github.io/guidex.com", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.05817", "title": "CodeContests+: High-Quality Test Case Generation for Competitive Programming", "authors": [ "Zihan Wang", "Siyao Liu", "Yang Sun", "Hongyan Li", "Kai Shen" ], "github": "", "project_page": "", "abstract": "Competitive programming, due to its high reasoning difficulty and precise correctness feedback, has become a key task for both training and evaluating the reasoning capabilities of large language models (LLMs). However, while a large amount of public problem data, such as problem statements and solutions, is available, the test cases of these problems are often difficult to obtain. Therefore, test case generation is a necessary task for building large-scale datasets, and the quality of the test cases directly determines the accuracy of the evaluation. In this paper, we introduce an LLM-based agent system that creates high-quality test cases for competitive programming problems. We apply this system to the CodeContests dataset and propose a new version with improved test cases, named CodeContests+. We evaluated the quality of test cases in CodeContestsPlus. First, we used 1.72 million submissions with pass/fail labels to examine the accuracy of these test cases in evaluation. The results indicated that CodeContests+ achieves significantly higher accuracy than CodeContests, particularly with a notably higher True Positive Rate (TPR). Subsequently, our experiments in LLM Reinforcement Learning (RL) further confirmed that improvements in test case quality yield considerable advantages for RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.05446", "title": "Sentinel: SOTA model to protect against prompt injections", "authors": [ "Dror Ivry", "Oran Nahum" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly powerful but remain vulnerable to prompt injection attacks, where malicious inputs cause the model to deviate from its intended instructions. This paper introduces Sentinel, a novel detection model, qualifire/prompt-injection-sentinel, based on the \\answerdotai/ModernBERT-large architecture. By leveraging ModernBERT's advanced features and fine-tuning on an extensive and diverse dataset comprising a few open-source and private collections, Sentinel achieves state-of-the-art performance. This dataset amalgamates varied attack types, from role-playing and instruction hijacking to attempts to generate biased content, alongside a broad spectrum of benign instructions, with private datasets specifically targeting nuanced error correction and real-world misclassifications. On a comprehensive, unseen internal test set, Sentinel demonstrates an average accuracy of 0.987 and an F1-score of 0.980. Furthermore, when evaluated on public benchmarks, it consistently outperforms strong baselines like protectai/deberta-v3-base-prompt-injection-v2. This work details Sentinel's architecture, its meticulous dataset curation, its training methodology, and a thorough evaluation, highlighting its superior detection capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.05579", "title": "When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration", "authors": [ "Quan Shi", "Carlos E. Jimenez", "Shunyu Yao", "Nick Haber", "Diyi Yang", "Karthik Narasimhan" ], "github": "", "project_page": "", "abstract": "Recent advancements in AI reasoning have driven substantial improvements across diverse tasks. A critical open question is whether these improvements also yields better knowledge transfer: the ability of models to communicate reasoning in ways humans can understand, apply, and learn from. To investigate this, we introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities and conduct the first large-scale human study (N=118) explicitly designed to measure it. In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding. Our findings reveal that although model benchmark performance correlates with collaborative outcomes, this relationship is notably inconsistent, featuring significant outliers, indicating that knowledge transfer requires dedicated optimization. Our analysis identifies behavioral and strategic factors mediating successful knowledge transfer. We release our code, dataset, and evaluation framework to support future work on communicatively aligned models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.05673", "title": "Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery", "authors": [ "Sajjad Abdoli", "Freeman Lewin", "Gediminas Vasiliauskas", "Fabian Schonholz" ], "github": "https://github.com/DataSeeds-ai/DSD-finetune-blip-llava", "project_page": "", "abstract": "The development of modern Artificial Intelligence (AI) models, particularly diffusion-based models employed in computer vision and image generation tasks, is undergoing a paradigmatic shift in development methodologies. Traditionally dominated by a \"Model Centric\" approach, in which performance gains were primarily pursued through increasingly complex model architectures and hyperparameter optimization, the field is now recognizing a more nuanced \"Data-Centric\" approach. This emergent framework foregrounds the quality, structure, and relevance of training data as the principal driver of model performance. To operationalize this paradigm shift, we introduce the DataSeeds.AI sample dataset (the \"DSD\"), initially comprised of approximately 10,610 high-quality human peer-ranked photography images accompanied by extensive multi-tier annotations. The DSD is a foundational computer vision dataset designed to usher in a new standard for commercial image datasets. Representing a small fraction of DataSeed.AI's 100 million-plus image catalog, the DSD provides a scalable foundation necessary for robust commercial and multimodal AI development. Through this in-depth exploratory analysis, we document the quantitative improvements generated by the DSD on specific models against known benchmarks and make the code and the trained models used in our evaluation publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DataSeeds-ai/DSD-finetune-blip-llava", "github_api_added_by": "auto" }, { "date": "2025-06-09", "arxiv_id": "2506.06091", "title": "MIRIAD: Augmenting LLMs with millions of medical query-response pairs", "authors": [ "Qinyue Zheng", "Salman Abdullah", "Sam Rawal", "Cyril Zakka", "Sophie Ostmeier", "Maximilian Purk", "Eduardo Reis", "Eric J. Topol", "Jure Leskovec", "Michael Moor" ], "github": "https://github.com/eth-medical-ai-lab/MIRIAD", "project_page": "", "abstract": "LLMs are bound to transform healthcare with advanced decision support and flexible chat assistants. However, LLMs are prone to generate inaccurate medical content. To ground LLMs in high-quality medical knowledge, LLMs have been equipped with external knowledge via RAG, where unstructured medical knowledge is split into small text chunks that can be selectively retrieved and integrated into the LLMs context. Yet, existing RAG pipelines rely on raw, unstructured medical text, which can be noisy, uncurated and difficult for LLMs to effectively leverage. Systematic approaches to organize medical knowledge to best surface it to LLMs are generally lacking. To address these challenges, we introduce MIRIAD, a large-scale, curated corpus of 5,821,948 medical QA pairs, each rephrased from and grounded in a passage from peer-reviewed medical literature using a semi-automated pipeline combining LLM generation, filtering, grounding, and human annotation. Unlike prior medical corpora, which rely on unstructured text, MIRIAD encapsulates web-scale medical knowledge in an operationalized query-response format, which enables more targeted retrieval. Experiments on challenging medical QA benchmarks show that augmenting LLMs with MIRIAD improves accuracy up to 6.7% compared to unstructured RAG baselines with the same source corpus and with the same amount of retrieved text. Moreover, MIRIAD improved the ability of LLMs to detect medical hallucinations by 22.5 to 37% (increase in F1 score). We further introduce MIRIAD-Atlas, an interactive map of MIRIAD spanning 56 medical disciplines, enabling clinical users to visually explore, search, and refine medical knowledge. MIRIAD promises to unlock a wealth of down-stream applications, including medical information retrievers, enhanced RAG applications, and knowledge-grounded chat interfaces, which ultimately enables more reliable LLM applications in healthcare.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/eth-medical-ai-lab/MIRIAD", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.03828", "title": "AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance", "authors": [ "Dhaval Patel", "Shuxin Lin", "James Rayfield", "Nianjun Zhou", "Roman Vaculin", "Natalia Martinez", "Fearghal O'donncha", "Jayant Kalagnanam" ], "github": "https://github.com/IBM/AssetOpsBench", "project_page": "", "abstract": "AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows -- such as condition monitoring, maintenance planning, and intervention scheduling -- to reduce human workload and minimize system downtime. Traditional AI/ML approaches have primarily tackled these problems in isolation, solving narrow tasks within the broader operational pipeline. In contrast, the emergence of AI agents and large language models (LLMs) introduces a next-generation opportunity: enabling end-to-end automation across the entire asset lifecycle. This paper envisions a future where AI agents autonomously manage tasks that previously required distinct expertise and manual coordination. To this end, we introduce AssetOpsBench -- a unified framework and environment designed to guide the development, orchestration, and evaluation of domain-specific agents tailored for Industry 4.0 applications. We outline the key requirements for such holistic systems and provide actionable insights into building agents that integrate perception, reasoning, and control for real-world industrial operations. The software is available at https://github.com/IBM/AssetOpsBench.", "github_manual": "", "github_regex": "https://github.com/IBM/AssetOpsBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.05551", "title": "When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understanding", "authors": [ "Yan Shu", "Hangui Lin", "Yexin Liu", "Yan Zhang", "Gangyan Zeng", "Yan Li", "Yu Zhou", "Ser-Nam Lim", "Harry Yang", "Nicu Sebe" ], "github": "https://github.com/shuyansy/MLLM-Semantic-Hallucination", "project_page": "", "abstract": "Large Multimodal Models (LMMs) have achieved impressive progress in visual perception and reasoning. However, when confronted with visually ambiguous or non-semantic scene text, they often struggle to accurately spot and understand the content, frequently generating semantically plausible yet visually incorrect answers, which we refer to as semantic hallucination. In this work, we investigate the underlying causes of semantic hallucination and identify a key finding: Transformer layers in LLM with stronger attention focus on scene text regions are less prone to producing semantic hallucinations. Thus, we propose a training-free semantic hallucination mitigation framework comprising two key components: (1) ZoomText, a coarse-to-fine strategy that identifies potential text regions without external detectors; and (2) Grounded Layer Correction, which adaptively leverages the internal representations from layers less prone to hallucination to guide decoding, correcting hallucinated outputs for non-semantic samples while preserving the semantics of meaningful ones. To enable rigorous evaluation, we introduce TextHalu-Bench, a benchmark of over 1,730 samples spanning both semantic and non-semantic cases, with manually curated question-answer pairs designed to probe model hallucinations. Extensive experiments demonstrate that our method not only effectively mitigates semantic hallucination but also achieves strong performance on public benchmarks for scene text spotting and understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shuyansy/MLLM-Semantic-Hallucination", "github_api_added_by": "auto" }, { "date": "2025-06-09", "arxiv_id": "2505.20698", "title": "Sparsified State-Space Models are Efficient Highway Networks", "authors": [ "Woomin Song", "Jihoon Tack", "Sangwoo Mo", "Seunghyuk Oh", "Jinwoo Shin" ], "github": "https://github.com/woominsong/Simba", "project_page": "", "abstract": "State-space models (SSMs) offer a promising architecture for sequence modeling, providing an alternative to Transformers by replacing expensive self-attention with linear recurrences. In this paper, we propose a simple yet effective trick to enhance SSMs within given computational budgets by sparsifying them. Our intuition is that tokens in SSMs are highly redundant due to gradual recurrent updates, and dense recurrence operations block the delivery of past information. In particular, we observe that upper layers of SSMs tend to be more redundant as they encode global information, while lower layers encode local information. Motivated by this, we introduce Simba, a hierarchical sparsification method for SSMs based on token pruning. Simba sparsifies upper layers more than lower layers, encouraging the upper layers to behave like highways. To achieve this, we propose a novel token pruning criterion for SSMs, measuring the global impact of tokens on the final output by accumulating local recurrences. We demonstrate that Simba outperforms the baseline model, Mamba, with the same FLOPS in various natural language tasks. Moreover, we illustrate the effect of highways, showing that Simba not only enhances efficiency but also improves the information flow across long sequences. Code is available at https://github.com/woominsong/Simba.", "github_manual": "", "github_regex": "https://github.com/woominsong/Simba", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-09", "arxiv_id": "2506.02327", "title": "Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning", "authors": [ "Yijun Yang", "Zhao-Yang Wang", "Qiuping Liu", "Shuwen Sun", "Kang Wang", "Rama Chellappa", "Zongwei Zhou", "Alan Yuille", "Lei Zhu", "Yu-Dong Zhang", "Jieneng Chen" ], "github": "", "project_page": "", "abstract": "Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in simulating disease dynamics for clinical decision-making, leveraging recent advances in large generative models. To this end, we introduce the Medical World Model (MeWM), the first world model in medicine that visually predicts future disease states based on clinical decisions. MeWM comprises (i) vision-language models to serve as policy models, and (ii) tumor generative models as dynamics models. The policy model generates action plans, such as clinical treatments, while the dynamics model simulates tumor progression or regression under given treatment conditions. Building on this, we propose the inverse dynamics model that applies survival analysis to the simulated post-treatment tumor, enabling the evaluation of treatment efficacy and the selection of the optimal clinical action plan. As a result, the proposed MeWM simulates disease dynamics by synthesizing post-treatment tumors, with state-of-the-art specificity in Turing tests evaluated by radiologists. Simultaneously, its inverse dynamics model outperforms medical-specialized GPTs in optimizing individualized treatment protocols across all metrics. Notably, MeWM improves clinical decision-making for interventional physicians, boosting F1-score in selecting the optimal TACE protocol by 13%, paving the way for future integration of medical world models as the second readers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07530", "title": "BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation", "authors": [ "Hongyu Wang", "Chuyan Xiong", "Ruiping Wang", "Xilin Chen" ], "github": "https://github.com/ustcwhy/BitVLA", "project_page": "", "abstract": "Vision-Language-Action (VLA) models have shown impressive capabilities across a wide range of robotics manipulation tasks. However, their growing model size poses significant challenges for deployment on resource-constrained robotic systems. While 1-bit pretraining has proven effective for enhancing the inference efficiency of large language models with minimal performance loss, its application to VLA models remains underexplored. In this work, we present BitVLA, the first 1-bit VLA model for robotics manipulation, in which every parameter is ternary, i.e., {-1, 0, 1}. To further reduce the memory footprint of the vision encoder, we propose the distillation-aware training strategy that compresses the full-precision encoder to 1.58-bit weights. During this process, a full-precision encoder serves as a teacher model to better align latent representations. Despite the lack of large-scale robotics pretraining, BitVLA achieves performance comparable to the state-of-the-art model OpenVLA-OFT with 4-bit post-training quantization on the LIBERO benchmark, while consuming only 29.8% of the memory. These results highlight BitVLA's promise for deployment on memory-constrained edge devices. We release the code and model weights in https://github.com/ustcwhy/BitVLA.", "github_manual": "", "github_regex": "https://github.com/ustcwhy/BitVLA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.08007", "title": "Reinforcement Pre-Training", "authors": [ "Qingxiu Dong", "Li Dong", "Yao Tang", "Tianzhu Ye", "Yutao Sun", "Zhifang Sui", "Furu Wei" ], "github": "", "project_page": "", "abstract": "In this work, we introduce Reinforcement Pre-Training (RPT) as a new scaling paradigm for large language models and reinforcement learning (RL). Specifically, we reframe next-token prediction as a reasoning task trained using RL, where it receives verifiable rewards for correctly predicting the next token for a given context. RPT offers a scalable method to leverage vast amounts of text data for general-purpose RL, rather than relying on domain-specific annotated answers. By incentivizing the capability of next-token reasoning, RPT significantly improves the language modeling accuracy of predicting the next tokens. Moreover, RPT provides a strong pre-trained foundation for further reinforcement fine-tuning. The scaling curves show that increased training compute consistently improves the next-token prediction accuracy. The results position RPT as an effective and promising scaling paradigm to advance language model pre-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07977", "title": "OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation", "authors": [ "Jingjing Chang", "Yixiao Fang", "Peng Xing", "Shuhan Wu", "Wei Cheng", "Rui Wang", "Xianfang Zeng", "Gang Yu", "Hai-Bao Chen" ], "github": "https://github.com/OneIG-Bench/OneIG-Benchmark", "project_page": "", "abstract": "Text-to-image (T2I) models have garnered significant attention for generating high-quality images aligned with text prompts. However, rapid T2I model advancements reveal limitations in early benchmarks, lacking comprehensive evaluations, for example, the evaluation on reasoning, text rendering and style. Notably, recent state-of-the-art models, with their rich knowledge modeling capabilities, show promising results on the image generation problems requiring strong reasoning ability, yet existing evaluation systems have not adequately addressed this frontier. To systematically address these gaps, we introduce OneIG-Bench, a meticulously designed comprehensive benchmark framework for fine-grained evaluation of T2I models across multiple dimensions, including prompt-image alignment, text rendering precision, reasoning-generated content, stylization, and diversity. By structuring the evaluation, this benchmark enables in-depth analysis of model performance, helping researchers and practitioners pinpoint strengths and bottlenecks in the full pipeline of image generation. Specifically, OneIG-Bench enables flexible evaluation by allowing users to focus on a particular evaluation subset. Instead of generating images for the entire set of prompts, users can generate images only for the prompts associated with the selected dimension and complete the corresponding evaluation accordingly. Our codebase and dataset are now publicly available to facilitate reproducible evaluation studies and cross-model comparisons within the T2I research community.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OneIG-Bench/OneIG-Benchmark", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07044", "title": "Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning", "authors": [ "LASA Team", "Weiwen Xu", "Hou Pong Chan", "Long Li", "Mahani Aljunied", "Ruifeng Yuan", "Jianyu Wang", "Chenghao Xiao", "Guizhen Chen", "Chaoqun Liu", "Zhaodonghui Li", "Yu Sun", "Junao Shen", "Chaojun Wang", "Jie Tan", "Deli Zhao", "Tingyang Xu", "Hao Zhang", "Yu Rong" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in understanding common visual elements, largely due to their large-scale datasets and advanced training strategies. However, their effectiveness in medical applications remains limited due to the inherent discrepancies between data and tasks in medical scenarios and those in the general domain. Concretely, existing medical MLLMs face the following critical limitations: (1) limited coverage of medical knowledge beyond imaging, (2) heightened susceptibility to hallucinations due to suboptimal data curation processes, (3) lack of reasoning capabilities tailored for complex medical scenarios. To address these challenges, we first propose a comprehensive data curation procedure that (1) efficiently acquires rich medical knowledge data not only from medical imaging but also from extensive medical texts and general-domain data; and (2) synthesizes accurate medical captions, visual question answering (VQA), and reasoning samples. As a result, we build a multimodal dataset enriched with extensive medical knowledge. Building on the curated data, we introduce our medical-specialized MLLM: Lingshu. Lingshu undergoes multi-stage training to embed medical expertise and enhance its task-solving capabilities progressively. Besides, we preliminarily explore the potential of applying reinforcement learning with verifiable rewards paradigm to enhance Lingshu's medical reasoning ability. Additionally, we develop MedEvalKit, a unified evaluation framework that consolidates leading multimodal and textual medical benchmarks for standardized, fair, and efficient model assessment. We evaluate the performance of Lingshu on three fundamental medical tasks, multimodal QA, text-based QA, and medical report generation. The results show that Lingshu consistently outperforms the existing open-source multimodal models on most tasks ...", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07900", "title": "MiniCPM4: Ultra-Efficient LLMs on End Devices", "authors": [ "MiniCPM Team", "Chaojun Xiao", "Yuxuan Li", "Xu Han", "Yuzhuo Bai", "Jie Cai", "Haotian Chen", "Wentong Chen", "Xin Cong", "Ganqu Cui", "Ning Ding", "Shengdan Fan", "Yewei Fang", "Zixuan Fu", "Wenyu Guan", "Yitong Guan", "Junshao Guo", "Yufeng Han", "Bingxiang He", "Yuxiang Huang", "Cunliang Kong", "Qiuzuo Li", "Siyuan Li", "Wenhao Li", "Yanghao Li", "Yishan Li", "Zhen Li", "Dan Liu", "Biyuan Lin", "Yankai Lin", "Xiang Long", "Quanyu Lu", "Yaxi Lu", "Peiyan Luo", "Hongya Lyu", "Litu Ou", "Yinxu Pan", "Zekai Qu", "Qundong Shi", "Zijun Song", "Jiayuan Su", "Zhou Su", "Ao Sun", "Xianghui Sun", "Peijun Tang", "Fangzheng Wang", "Feng Wang", "Shuo Wang", "Yudong Wang", "Yesai Wu", "Zhenyu Xiao", "Jie Xie", "Zihao Xie", "Yukun Yan", "Jiarui Yuan", "Kaihuo Zhang", "Lei Zhang", "Linyue Zhang", "Xueren Zhang", "Yudi Zhang", "Hengyu Zhao", "Weilin Zhao", "Weilun Zhao", "Yuanqian Zhao", "Zhi Zheng", "Ge Zhou", "Jie Zhou", "Wei Zhou", "Zihan Zhou", "Zixuan Zhou", "Zhiyuan Liu", "Guoyang Zeng", "Chao Jia", "Dahai Li", "Maosong Sun" ], "github": "https://github.com/netblue30/firejail", "project_page": "", "abstract": "This paper introduces MiniCPM4, a highly efficient large language model (LLM) designed explicitly for end-side devices. We achieve this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems. Specifically, in terms of model architecture, we propose InfLLM v2, a trainable sparse attention mechanism that accelerates both prefilling and decoding phases for long-context processing. Regarding training data, we propose UltraClean, an efficient and accurate pre-training data filtering and generation strategy, and UltraChat v2, a comprehensive supervised fine-tuning dataset. These datasets enable satisfactory model performance to be achieved using just 8 trillion training tokens. Regarding training algorithms, we propose ModelTunnel v2 for efficient pre-training strategy search, and improve existing post-training methods by introducing chunk-wise rollout for load-balanced reinforcement learning and data-efficient tenary LLM, BitCPM. Regarding inference systems, we propose CPM.cu that integrates sparse attention, model quantization, and speculative sampling to achieve efficient prefilling and decoding. To meet diverse on-device requirements, MiniCPM4 is available in two versions, with 0.5B and 8B parameters, respectively. Sufficient evaluation results show that MiniCPM4 outperforms open-source models of similar size across multiple benchmarks, highlighting both its efficiency and effectiveness. Notably, MiniCPM4-8B demonstrates significant speed improvements over Qwen3-8B when processing long sequences. Through further adaptation, MiniCPM4 successfully powers diverse applications, including trustworthy survey generation and tool use with model context protocol, clearly showcasing its broad usability.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/netblue30/firejail", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07553", "title": "GTR-CoT: Graph Traversal as Visual Chain of Thought for Molecular Structure Recognition", "authors": [ "Jingchao Wang", "Haote Yang", "Jiang Wu", "Yifan He", "Xingjian Wei", "Yinfan Wang", "Chengjin Liu", "Lingli Ge", "Lijun Wu", "Bin Wang", "Dahua Lin", "Conghui He" ], "github": "https://github.com/opendatalab/GTR-CoT", "project_page": "", "abstract": "Optical Chemical Structure Recognition (OCSR) is crucial for digitizing chemical knowledge by converting molecular images into machine-readable formats. While recent vision-language models (VLMs) have shown potential in this task, their image-captioning approach often struggles with complex molecular structures and inconsistent annotations. To overcome these challenges, we introduce GTR-Mol-VLM, a novel framework featuring two key innovations: (1) the Graph Traversal as Visual Chain of Thought mechanism that emulates human reasoning by incrementally parsing molecular graphs through sequential atom-bond predictions, and (2) the data-centric principle of Faithfully Recognize What You've Seen, which addresses the mismatch between abbreviated structures in images and their expanded annotations. To support model development, we constructed GTR-CoT-1.3M, a large-scale instruction-tuning dataset with meticulously corrected annotations, and introduced MolRec-Bench, the first benchmark designed for a fine-grained evaluation of graph-parsing accuracy in OCSR. Comprehensive experiments demonstrate that GTR-Mol-VLM achieves superior results compared to specialist models, chemistry-domain VLMs, and commercial general-purpose VLMs. Notably, in scenarios involving molecular images with functional group abbreviations, GTR-Mol-VLM outperforms the second-best baseline by approximately 14 percentage points, both in SMILES-based and graph-based metrics. We hope that this work will drive OCSR technology to more effectively meet real-world needs, thereby advancing the fields of cheminformatics and AI for Science. We will release GTR-CoT at https://github.com/opendatalab/GTR-CoT.", "github_manual": "", "github_regex": "https://github.com/opendatalab/GTR-CoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07298", "title": "Pre-trained Large Language Models Learn Hidden Markov Models In-context", "authors": [ "Yijia Dai", "Zhaolin Gao", "Yahya Satter", "Sarah Dean", "Jennifer J. Sun" ], "github": "https://github.com/DaiYijia02/icl-hmm", "project_page": "", "abstract": "Hidden Markov Models (HMMs) are foundational tools for modeling sequential data with latent Markovian structure, yet fitting them to real-world data remains computationally challenging. In this work, we show that pre-trained large language models (LLMs) can effectively model data generated by HMMs via in-context learning (ICL)x2013their ability to infer patterns from examples within a prompt. On a diverse set of synthetic HMMs, LLMs achieve predictive accuracy approaching the theoretical optimum. We uncover novel scaling trends influenced by HMM properties, and offer theoretical conjectures for these empirical observations. We also provide practical guidelines for scientists on using ICL as a diagnostic tool for complex data. On real-world animal decision-making tasks, ICL achieves competitive performance with models designed by human experts. To our knowledge, this is the first demonstration that ICL can learn and predict HMM-generated sequencesx2013an advance that deepens our understanding of in-context learning in LLMs and establishes its potential as a powerful tool for uncovering hidden structure in complex scientific data.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/DaiYijia02/icl-hmm", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.08012", "title": "GUI-Reflection: Empowering Multimodal GUI Models with Self-Reflection Behavior", "authors": [ "Penghao Wu", "Shengnan Ma", "Bo Wang", "Jiaheng Yu", "Lewei Lu", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have shown great potential in revolutionizing Graphical User Interface (GUI) automation. However, existing GUI models mostly rely on learning from nearly error-free offline trajectories, thus lacking reflection and error recovery capabilities. To bridge this gap, we propose GUI-Reflection, a novel framework that explicitly integrates self-reflection and error correction capabilities into end-to-end multimodal GUI models throughout dedicated training stages: GUI-specific pre-training, offline supervised fine-tuning (SFT), and online reflection tuning. GUI-reflection enables self-reflection behavior emergence with fully automated data generation and learning processes without requiring any human annotation. Specifically, 1) we first propose scalable data pipelines to automatically construct reflection and error correction data from existing successful trajectories. While existing GUI models mainly focus on grounding and UI understanding ability, we propose the GUI-Reflection Task Suite to learn and evaluate reflection-oriented abilities explicitly. 2) Furthermore, we built a diverse and efficient environment for online training and data collection of GUI models on mobile devices. 3) We also present an iterative online reflection tuning algorithm leveraging the proposed environment, enabling the model to continuously enhance its reflection and error correction abilities. Our framework equips GUI agents with self-reflection and correction capabilities, paving the way for more robust, adaptable, and intelligent GUI automation, with all data, models, environments, and tools to be released publicly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07986", "title": "Rethinking Cross-Modal Interaction in Multimodal Diffusion Transformers", "authors": [ "Zhengyao Lv", "Tianlin Pan", "Chenyang Si", "Zhaoxi Chen", "Wangmeng Zuo", "Ziwei Liu", "Kwan-Yee K. Wong" ], "github": "https://github.com/Vchitect/TACA", "project_page": "", "abstract": "Multimodal Diffusion Transformers (MM-DiTs) have achieved remarkable progress in text-driven visual generation. However, even state-of-the-art MM-DiT models like FLUX struggle with achieving precise alignment between text prompts and generated content. We identify two key issues in the attention mechanism of MM-DiT, namely 1) the suppression of cross-modal attention due to token imbalance between visual and textual modalities and 2) the lack of timestep-aware attention weighting, which hinder the alignment. To address these issues, we propose Temperature-Adjusted Cross-modal Attention (TACA), a parameter-efficient method that dynamically rebalances multimodal interactions through temperature scaling and timestep-dependent adjustment. When combined with LoRA fine-tuning, TACA significantly enhances text-image alignment on the T2I-CompBench benchmark with minimal computational overhead. We tested TACA on state-of-the-art models like FLUX and SD3.5, demonstrating its ability to improve image-text alignment in terms of object appearance, attribute binding, and spatial relationships. Our findings highlight the importance of balancing cross-modal attention in improving semantic fidelity in text-to-image diffusion models. Our codes are publicly available at https://github.com/Vchitect/TACA", "github_manual": "", "github_regex": "https://github.com/Vchitect/TACA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07712", "title": "Through the Valley: Path to Effective Long CoT Training for Small Language Models", "authors": [ "Renjie Luo", "Jiaxi Li", "Chen Huang", "Wei Lu" ], "github": "", "project_page": "", "abstract": "Long chain-of-thought (CoT) supervision has become a common strategy to enhance reasoning in language models. While effective for large models, we identify a phenomenon we call Long CoT Degradation, in which small language models (SLMs; <=3B parameters) trained on limited long CoT data experience significant performance deterioration. Through extensive experiments on the Qwen2.5, LLaMA3 and Gemma3 families, we demonstrate that this degradation is widespread across SLMs. In some settings, models trained on only 8k long CoT examples lose up to 75% of their original performance before fine-tuning. Strikingly, we further observe that for some particularly small models, even training on 220k long CoT examples fails to recover or surpass their original performance prior to fine-tuning. Our analysis attributes this effect to error accumulation: while longer responses increase the capacity for multi-step reasoning, they also amplify the risk of compounding mistakes. Furthermore, we find that Long CoT Degradation may negatively impacts downstream reinforcement learning (RL), although this can be alleviated by sufficiently scaled supervised fine-tuning (SFT). Our findings challenge common assumptions about the benefits of long CoT training for SLMs and offer practical guidance for building more effective small-scale reasoning models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07434", "title": "Well Begun is Half Done: Low-resource Preference Alignment by Weak-to-Strong Decoding", "authors": [ "Feifan Song", "Shaohang Wei", "Wen Luo", "Yuxuan Fan", "Tianyu Liu", "Guoyin Wang", "Houfeng Wang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) require alignment with human preferences to avoid generating offensive, false, or meaningless content. Recently, low-resource methods for LLM alignment have been popular, while still facing challenges in obtaining both high-quality and aligned content. Motivated by the observation that the difficulty of generating aligned responses is concentrated at the beginning of decoding, we propose a novel framework, Weak-to-Strong Decoding (WSD), to enhance the alignment ability of base models by the guidance of a small aligned model. The small model first drafts well-aligned beginnings, followed by the large base model to continue the rest, controlled by a well-designed auto-switch mechanism. We also collect a new dataset, GenerAlign, to fine-tune a small-sized Pilot-3B as the draft model, which effectively enhances different base models under the WSD framework to outperform all baseline methods, while avoiding degradation on downstream tasks, termed as the alignment tax. Extensive experiments are further conducted to examine the impact of different settings and time efficiency, as well as analyses on the intrinsic mechanisms of WSD in depth.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.06941", "title": "The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity", "authors": [ "Parshin Shojaee", "Iman Mirzadeh", "Keivan Alizadeh", "Maxwell Horton", "Samy Bengio", "Mehrdad Farajtabar" ], "github": "", "project_page": "", "abstract": "Recent generations of language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations remain insufficiently understood. Current evaluations primarily focus on established math and coding benchmarks, emphasizing final answer accuracy. However, this evaluation paradigm often suffers from contamination and does not provide insights into the reasoning traces. In this work, we systematically investigate these gaps with the help of controllable puzzle environments that allow precise manipulation of complexity while maintaining consistent logical structures. This setup enables the analysis of not only final answers but also the internal reasoning traces, offering insights into how LRMs think. Through extensive experiments, we show that LRMs face a complete accuracy collapse beyond certain complexities. Moreover, they exhibit a counterintuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having remaining token budget. By comparing LRMs with their standard LLM counterparts under same inference compute, we identify three performance regimes: (1) low-complexity tasks where standard models outperform LRMs, (2) medium-complexity tasks where LRMs demonstrates advantage, and (3) high-complexity tasks where both models face complete collapse. We found that LRMs have limitations in exact computation: they fail to use explicit algorithms and reason inconsistently across scales. We also investigate the reasoning traces in more depth, studying the patterns of explored solutions and analyzing the models' computational behavior, shedding light on their strengths, limitations, and raising questions about their reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07491", "title": "SpatialLM: Training Large Language Models for Structured Indoor Modeling", "authors": [ "Yongsen Mao", "Junhao Zhong", "Chuan Fang", "Jia Zheng", "Rui Tang", "Hao Zhu", "Ping Tan", "Zihan Zhou" ], "github": "", "project_page": "", "abstract": "SpatialLM is a large language model designed to process 3D point cloud data and generate structured 3D scene understanding outputs. These outputs include architectural elements like walls, doors, windows, and oriented object boxes with their semantic categories. Unlike previous methods which exploit task-specific network designs, our model adheres to the standard multimodal LLM architecture and is fine-tuned directly from open-source LLMs. To train SpatialLM, we collect a large-scale, high-quality synthetic dataset consisting of the point clouds of 12,328 indoor scenes (54,778 rooms) with ground-truth 3D annotations, and conduct a careful study on various modeling and training decisions. On public benchmarks, our model gives state-of-the-art performance in layout estimation and competitive results in 3D object detection. With that, we show a feasible path for enhancing the spatial understanding capabilities of modern LLMs for applications in augmented reality, embodied robotics, and more.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07463", "title": "CCI4.0: A Bilingual Pretraining Dataset for Enhancing Reasoning in Large Language Models", "authors": [ "Guang Liu", "Liangdong Wang", "Jijie Li", "Yang Yu", "Yao Xu", "Jiabei Chen", "Yu Bai", "Feng Liao", "Yonghua Lin" ], "github": "", "project_page": "", "abstract": "We introduce CCI4.0, a large-scale bilingual pre-training dataset engineered for superior data quality and diverse human-like reasoning trajectory. CCI4.0 occupies roughly 35 TB of disk space and comprises two sub-datasets: CCI4.0-M2-Base and CCI4.0-M2-CoT. CCI4.0-M2-Base combines a 5.2 TB carefully curated Chinese web corpus, a 22.5 TB English subset from Nemotron-CC, and diverse sources from math, wiki, arxiv, and code. Although these data are mostly sourced from well-processed datasets, the quality standards of various domains are dynamic and require extensive expert experience and labor to process. So, we propose a novel pipeline justifying data quality mainly based on models through two-stage deduplication, multiclassifier quality scoring, and domain-aware fluency filtering. We extract 4.5 billion pieces of CoT(Chain-of-Thought) templates, named CCI4.0-M2-CoT. Differing from the distillation of CoT from larger models, our proposed staged CoT extraction exemplifies diverse reasoning patterns and significantly decreases the possibility of hallucination. Empirical evaluations demonstrate that LLMs pre-trained in CCI4.0 benefit from cleaner, more reliable training signals, yielding consistent improvements in downstream tasks, especially in math and code reflection tasks. Our results underscore the critical role of rigorous data curation and human thinking templates in advancing LLM performance, shedding some light on automatically processing pretraining corpora.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.03690", "title": "Robust Preference Optimization via Dynamic Target Margins", "authors": [ "Jie Sun", "Junkang Wu", "Jiancan Wu", "Zhibo Zhu", "Xingyu Lu", "Jun Zhou", "Lintao Ma", "Xiang Wang" ], "github": "https://github.com/sunjie279/gammaPO", "project_page": "", "abstract": "The alignment of Large Language Models (LLMs) is crucial for ensuring their safety and reliability in practical applications. Direct Preference Optimization (DPO) has emerged as an efficient method that directly optimizes models using preference pairs, significantly reducing resource demands. However, the effectiveness of DPO heavily depends on the data quality, which is frequently compromised by noise. In this work, we propose gamma-PO, a dynamic target margin preference optimization algorithm that adjust reward margins at the pairwise level. By introducing instance-specific margin calibration, gamma-PO strategically prioritizes high-confidence pairs (those demonstrating higher reward margins) while suppressing potential noise from ambiguous pairs. Moreover, gamma-PO is a plug-and-play method, compatible with variants of DPO that rely on reward margin between preference pairs. Across benchmarks such as AlpacaEval2 and Arena-Hard, gamma-PO achieves an average 4.4\\% improvement over other baselines, setting new benchmarks for state-of-the-art performance. Additionally, gamma-PO requires minimal code changes and has a negligible impact on training efficiency, making it a robust solution for enhancing LLMs alignment. Our codes are available at https://github.com/sunjie279/gammaPO{https://github.com/sunjie279/gammaPO}.", "github_manual": "", "github_regex": "https://github.com/sunjie279/gammaPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.06444", "title": "Saffron-1: Towards an Inference Scaling Paradigm for LLM Safety Assurance", "authors": [ "Ruizhong Qiu", "Gaotang Li", "Tianxin Wei", "Jingrui He", "Hanghang Tong" ], "github": "https://github.com/q-rz/saffron", "project_page": "", "abstract": "Existing safety assurance research has primarily focused on training-phase alignment to instill safe behaviors into LLMs. However, recent studies have exposed these methods' susceptibility to diverse jailbreak attacks. Concurrently, inference scaling has significantly advanced LLM reasoning capabilities but remains unexplored in the context of safety assurance. Addressing this gap, our work pioneers inference scaling for robust and effective LLM safety against emerging threats. We reveal that conventional inference scaling techniques, despite their success in reasoning tasks, perform poorly in safety contexts, even falling short of basic approaches like Best-of-N Sampling. We attribute this inefficiency to a newly identified challenge, the exploration--efficiency dilemma, arising from the high computational overhead associated with frequent process reward model (PRM) evaluations. To overcome this dilemma, we propose SAFFRON, a novel inference scaling paradigm tailored explicitly for safety assurance. Central to our approach is the introduction of a multifurcation reward model (MRM) that significantly reduces the required number of reward model evaluations. To operationalize this paradigm, we further propose: (i) a partial supervision training objective for MRM, (ii) a conservative exploration constraint to prevent out-of-distribution explorations, and (iii) a Trie-based key--value caching strategy that facilitates cache sharing across sequences during tree search. Extensive experiments validate the effectiveness of our method. Additionally, we publicly release our trained multifurcation reward model (Saffron-1) and the accompanying token-level safety reward dataset (Safety4M) to accelerate future research in LLM safety. Our code, model, and data are publicly available at https://github.com/q-rz/saffron , and our project homepage is at https://q-rz.github.io/p/saffron .", "github_manual": "", "github_regex": "https://github.com/q-rz/saffron", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.06266", "title": "Cartridges: Lightweight and general-purpose long context representations via self-study", "authors": [ "Sabri Eyuboglu", "Ryan Ehrlich", "Simran Arora", "Neel Guha", "Dylan Zinsley", "Emily Liu", "Will Tennien", "Atri Rudra", "James Zou", "Azalia Mirhoseini", "Christopher Re" ], "github": "", "project_page": "", "abstract": "Large language models are often used to answer queries grounded in large text corpora (e.g. codebases, legal documents, or chat histories) by placing the entire corpus in the context window and leveraging in-context learning (ICL). Although current models support contexts of 100K-1M tokens, this setup is costly to serve because the memory consumption of the KV cache scales with input length. We explore an alternative: training a smaller KV cache offline on each corpus. At inference time, we load this trained KV cache, which we call a Cartridge, and decode a response. Critically, the cost of training a Cartridge can be amortized across all the queries referencing the same corpus. However, we find that the naive approach of training the Cartridge with next-token prediction on the corpus is not competitive with ICL. Instead, we propose self-study, a training recipe in which we generate synthetic conversations about the corpus and train the Cartridge with a context-distillation objective. We find that Cartridges trained with self-study replicate the functionality of ICL, while being significantly cheaper to serve. On challenging long-context benchmarks, Cartridges trained with self-study match ICL performance while using 38.6x less memory and enabling 26.4x higher throughput. Self-study also extends the model's effective context length (e.g. from 128k to 484k tokens on MTOB) and surprisingly, leads to Cartridges that can be composed at inference time without retraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.08006", "title": "Dreamland: Controllable World Creation with Simulator and Generative Models", "authors": [ "Sicheng Mo", "Ziyang Leng", "Leon Liu", "Weizhen Wang", "Honglin He", "Bolei Zhou" ], "github": "", "project_page": "", "abstract": "Large-scale video generative models can synthesize diverse and realistic visual content for dynamic world creation, but they often lack element-wise controllability, hindering their use in editing scenes and training embodied AI agents. We propose Dreamland, a hybrid world generation framework combining the granular control of a physics-based simulator and the photorealistic content output of large-scale pretrained generative models. In particular, we design a layered world abstraction that encodes both pixel-level and object-level semantics and geometry as an intermediate representation to bridge the simulator and the generative model. This approach enhances controllability, minimizes adaptation cost through early alignment with real-world distributions, and supports off-the-shelf use of existing and future pretrained generative models. We further construct a D3Sim dataset to facilitate the training and evaluation of hybrid generation pipelines. Experiments demonstrate that Dreamland outperforms existing baselines with 50.8% improved image quality, 17.9% stronger controllability, and has great potential to enhance embodied agent training. Code and data will be made available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07309", "title": "ConfQA: Answer Only If You Are Confident", "authors": [ "Yin Huang", "Yifan Ethan Xu", "Kai Sun", "Vera Yan", "Alicia Sun", "Haidar Khan", "Jimmy Nguyen", "Mohammad Kachuee", "Zhaojiang Lin", "Yue Liu", "Aaron Colak", "Anuj Kumar", "Wen-tau Yih", "Xin Luna Dong" ], "github": "", "project_page": "", "abstract": "Can we teach Large Language Models (LLMs) to refrain from hallucinating factual statements? In this paper we present a fine-tuning strategy that we call ConfQA, which can reduce hallucination rate from 20-40% to under 5% across multiple factuality benchmarks. The core idea is simple: when the LLM answers a question correctly, it is trained to continue with the answer; otherwise, it is trained to admit \"I am unsure\". But there are two key factors that make the training highly effective. First, we introduce a dampening prompt \"answer only if you are confident\" to explicitly guide the behavior, without which hallucination remains high as 15%-25%. Second, we leverage simple factual statements, specifically attribute values from knowledge graphs, to help LLMs calibrate the confidence, resulting in robust generalization across domains and question types. Building on this insight, we propose the Dual Neural Knowledge framework, which seamlessly select between internally parameterized neural knowledge and externally recorded symbolic knowledge based on ConfQA's confidence. The framework enables potential accuracy gains to beyond 95%, while reducing unnecessary external retrievals by over 30%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07240", "title": "Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs", "authors": [ "Roy Eisenstadt", "Itamar Zimerman", "Lior Wolf" ], "github": "https://github.com/royeisen/reasoning_loading_bar", "project_page": "", "abstract": "Recently, techniques such as explicit structured reasoning have demonstrated strong test-time scaling behavior by enforcing a separation between the model's internal \"thinking\" process and the final response. A key factor influencing answer quality in this setting is the length of the thinking stage. When the reasoning is too short, the model may fail to capture the complexity of the task. Conversely, when it is too long, the model may overthink, leading to unnecessary computation and degraded performance. This paper explores and exploits the underlying mechanisms by which LLMs understand and regulate the length of their reasoning during explicit thought processes. First, we show that LLMs encode their progress through the reasoning process and introduce an interactive progress bar visualization, which is then used to reveal insights on the model's planning dynamics. Second, we manipulate the internal progress encoding during inference to reduce unnecessary steps and generate a more concise and decisive chain of thoughts. Our empirical results demonstrate that this \"overclocking\" method mitigates overthinking, improves answer accuracy, and reduces inference latency. Our code is publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/royeisen/reasoning_loading_bar", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07160", "title": "GeometryZero: Improving Geometry Solving for LLM with Group Contrastive Policy Optimization", "authors": [ "Yikun Wang", "Yibin Wang", "Dianyi Wang", "Zimian Peng", "Qipeng Guo", "Dacheng Tao", "Jiaqi Wang" ], "github": "https://github.com/ekonwang/GeometryZero", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have demonstrated remarkable capabilities across diverse domains, particularly in mathematical reasoning, amid which geometry problem solving remains a challenging area where auxiliary construction plays a enssential role. Existing approaches either achieve suboptimal performance or rely on massive LLMs (e.g., GPT-4o), incurring massive computational costs. We posit that reinforcement learning with verifiable reward (e.g., GRPO) offers a promising direction for training smaller models that effectively combine auxiliary construction with robust geometric reasoning. However, directly applying GRPO to geometric reasoning presents fundamental limitations due to its dependence on unconditional rewards, which leads to indiscriminate and counterproductive auxiliary constructions. To address these challenges, we propose Group Contrastive Policy Optimization (GCPO), a novel reinforcement learning framework featuring two key innovations: (1) Group Contrastive Masking, which adaptively provides positive or negative reward signals for auxiliary construction based on contextual utility, and a (2) length reward that promotes longer reasoning chains. Building on GCPO, we develop GeometryZero, a family of affordable-size geometric reasoning models that judiciously determine when to employ auxiliary construction. Our extensive empirical evaluation across popular geometric benchmarks (Geometry3K, MathVista) demonstrates that GeometryZero models consistently outperform baselines (e.g. GRPO), achieving an average improvement of 4.29% across all benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ekonwang/GeometryZero", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.04807", "title": "MegaHan97K: A Large-Scale Dataset for Mega-Category Chinese Character Recognition with over 97K Categories", "authors": [ "Yuyi Zhang", "Yongxin Shi", "Peirong Zhang", "Yixin Zhao", "Zhenhua Yang", "Lianwen Jin" ], "github": "https://github.com/SCUT-DLVCLab/MegaHan97K", "project_page": "", "abstract": "Foundational to the Chinese language and culture, Chinese characters encompass extraordinarily extensive and ever-expanding categories, with the latest Chinese GB18030-2022 standard containing 87,887 categories. The accurate recognition of this vast number of characters, termed mega-category recognition, presents a formidable yet crucial challenge for cultural heritage preservation and digital applications. Despite significant advances in Optical Character Recognition (OCR), mega-category recognition remains unexplored due to the absence of comprehensive datasets, with the largest existing dataset containing merely 16,151 categories. To bridge this critical gap, we introduce MegaHan97K, a mega-category, large-scale dataset covering an unprecedented 97,455 categories of Chinese characters. Our work offers three major contributions: (1) MegaHan97K is the first dataset to fully support the latest GB18030-2022 standard, providing at least six times more categories than existing datasets; (2) It effectively addresses the long-tail distribution problem by providing balanced samples across all categories through its three distinct subsets: handwritten, historical and synthetic subsets; (3) Comprehensive benchmarking experiments reveal new challenges in mega-category scenarios, including increased storage demands, morphologically similar character recognition, and zero-shot learning difficulties, while also unlocking substantial opportunities for future research. To the best of our knowledge, the MetaHan97K is likely the dataset with the largest classes not only in the field of OCR but may also in the broader domain of pattern recognition. The dataset is available at https://github.com/SCUT-DLVCLab/MegaHan97K.", "github_manual": "", "github_regex": "https://github.com/SCUT-DLVCLab/MegaHan97K", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.08010", "title": "Vision Transformers Don't Need Trained Registers", "authors": [ "Nick Jiang", "Amil Dravid", "Alexei Efros", "Yossi Gandelsman" ], "github": "https://github.com/nickjiang2378/test-time-registers", "project_page": "", "abstract": "We investigate the mechanism underlying a previously identified phenomenon in Vision Transformers -- the emergence of high-norm tokens that lead to noisy attention maps. We observe that in multiple models (e.g., CLIP, DINOv2), a sparse set of neurons is responsible for concentrating high-norm activations on outlier tokens, leading to irregular attention patterns and degrading downstream visual processing. While the existing solution for removing these outliers involves retraining models from scratch with additional learned register tokens, we use our findings to create a training-free approach to mitigate these artifacts. By shifting the high-norm activations from our discovered register neurons into an additional untrained token, we can mimic the effect of register tokens on a model already trained without registers. We demonstrate that our method produces cleaner attention and feature maps, enhances performance over base models across multiple downstream visual tasks, and achieves results comparable to models explicitly trained with register tokens. We then extend test-time registers to off-the-shelf vision-language models to improve their interpretability. Our results suggest that test-time registers effectively take on the role of register tokens at test-time, offering a training-free solution for any pre-trained model released without them.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nickjiang2378/test-time-registers", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07803", "title": "Image Reconstruction as a Tool for Feature Analysis", "authors": [ "Eduard Allakhverdov", "Dmitrii Tarasov", "Elizaveta Goncharova", "Andrey Kuznetsov" ], "github": "", "project_page": "", "abstract": "Vision encoders are increasingly used in modern applications, from vision-only models to multimodal systems such as vision-language models. Despite their remarkable success, it remains unclear how these architectures represent features internally. Here, we propose a novel approach for interpreting vision features via image reconstruction. We compare two related model families, SigLIP and SigLIP2, which differ only in their training objective, and show that encoders pre-trained on image-based tasks retain significantly more image information than those trained on non-image tasks such as contrastive learning. We further apply our method to a range of vision encoders, ranking them by the informativeness of their feature representations. Finally, we demonstrate that manipulating the feature space yields predictable changes in reconstructed images, revealing that orthogonal rotations (rather than spatial transformations) control color encoding. Our approach can be applied to any vision encoder, shedding light on the inner structure of its feature space. The code and model weights to reproduce the experiments are available in GitHub.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2505.23473", "title": "EVOREFUSE: Evolutionary Prompt Optimization for Evaluation and Mitigation of LLM Over-Refusal to Pseudo-Malicious Instructions", "authors": [ "Xiaorui Wu", "Xiaofeng Mao", "Xin Zhang", "Fei Li", "Chong Teng", "Yuxiang Peng", "Li Zheng", "Donghong Ji", "Zhuang Li" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) frequently refuse to respond to pseudo-malicious instructions: semantically harmless input queries triggering unnecessary LLM refusals due to conservative safety alignment, significantly impairing user experience. Collecting such instructions is crucial for evaluating and mitigating over-refusals, but existing instruction curation methods, like manual creation or instruction rewriting, either lack scalability or fail to produce sufficiently diverse and effective refusal-inducing prompts. To address these limitations, we introduce EVOREFUSE, a prompt optimization approach that generates diverse pseudo-malicious instructions consistently eliciting confident refusals across LLMs. EVOREFUSE employs an evolutionary algorithm exploring the instruction space in more diverse directions than existing methods via mutation strategies and recombination, and iteratively evolves seed instructions to maximize evidence lower bound on LLM refusal probability. Using EVOREFUSE, we create two novel datasets: EVOREFUSE-TEST, a benchmark of 582 pseudo-malicious instructions that outperforms the next-best benchmark with 140.41% higher average refusal triggering rate across 9 LLMs, 34.86% greater lexical diversity, and 40.03% improved LLM response confidence scores; and EVOREFUSE-ALIGN, which provides 3,000 pseudo-malicious instructions with responses for supervised and preference-based alignment training. LLAMA3.1-8B-INSTRUCT supervisedly fine-tuned on EVOREFUSE-ALIGN achieves up to 14.31% fewer over-refusals than models trained on the second-best alignment dataset, without compromising safety. Our analysis with EVOREFUSE-TEST reveals models trigger over-refusals by overly focusing on sensitive keywords while ignoring broader context.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07848", "title": "PolyVivid: Vivid Multi-Subject Video Generation with Cross-Modal Interaction and Enhancement", "authors": [ "Teng Hu", "Zhentao Yu", "Zhengguang Zhou", "Jiangning Zhang", "Yuan Zhou", "Qinglin Lu", "Ran Yi" ], "github": "", "project_page": "", "abstract": "Despite recent advances in video generation, existing models still lack fine-grained controllability, especially for multi-subject customization with consistent identity and interaction. In this paper, we propose PolyVivid, a multi-subject video customization framework that enables flexible and identity-consistent generation. To establish accurate correspondences between subject images and textual entities, we design a VLLM-based text-image fusion module that embeds visual identities into the textual space for precise grounding. To further enhance identity preservation and subject interaction, we propose a 3D-RoPE-based enhancement module that enables structured bidirectional fusion between text and image embeddings. Moreover, we develop an attention-inherited identity injection module to effectively inject fused identity features into the video generation process, mitigating identity drift. Finally, we construct an MLLM-based data pipeline that combines MLLM-based grounding, segmentation, and a clique-based subject consolidation strategy to produce high-quality multi-subject data, effectively enhancing subject distinction and reducing ambiguity in downstream video generation. Extensive experiments demonstrate that PolyVivid achieves superior performance in identity fidelity, video realism, and subject alignment, outperforming existing open-source and commercial baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07527", "title": "Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions", "authors": [ "Lu Ma", "Hao Liang", "Meiyi Qiang", "Lexiang Tang", "Xiaochen Ma", "Zhen Hao Wong", "Junbo Niu", "Chengyu Shen", "Runming He", "Bin Cui", "Wentao Zhang" ], "github": "https://github.com/TheRoadQaQ/ReLIFT", "project_page": "", "abstract": "Recent advances in large language model (LLM) reasoning have shown that sophisticated behaviors such as planning and self-reflection can emerge through reinforcement learning (RL). However, despite these successes, RL in its current form remains insufficient to induce capabilities that exceed the limitations of the base model, as it is primarily optimized based on existing knowledge of the model rather than facilitating the acquisition of new information. To address this limitation, we employ supervised fine-tuning (SFT) to learn what RL cannot, which enables the incorporation of new knowledge and reasoning patterns by leveraging high-quality demonstration data. We analyze the training dynamics of RL and SFT for LLM reasoning and find that RL excels at maintaining and improving performance on questions within the model's original capabilities, while SFT is more effective at enabling progress on questions beyond the current scope of the model. Motivated by the complementary strengths of RL and SFT, we introduce a novel training approach, ReLIFT (Reinforcement Learning Interleaved with Online Fine-Tuning). In ReLIFT, the model is primarily trained using RL, but when it encounters challenging questions, high-quality solutions are collected for fine-tuning, and the training process alternates between RL and fine-tuning to enhance the model's reasoning abilities. ReLIFT achieves an average improvement of over +5.2 points across five competition-level benchmarks and one out-of-distribution benchmark compared to other zero-RL models. Furthermore, we demonstrate that ReLIFT outperforms both RL and SFT while using only 13\\% of the detailed demonstration data, highlighting its scalability. These results provide compelling evidence that ReLIFT overcomes the fundamental limitations of RL and underscores the significant potential.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/TheRoadQaQ/ReLIFT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.05904", "title": "Proactive Assistant Dialogue Generation from Streaming Egocentric Videos", "authors": [ "Yichi Zhang", "Xin Luna Dong", "Zhaojiang Lin", "Andrea Madotto", "Anuj Kumar", "Babak Damavandi", "Joyce Chai", "Seungwhan Moon" ], "github": "", "project_page": "", "abstract": "Recent advances in conversational AI have been substantial, but developing real-time systems for perceptual task guidance remains challenging. These systems must provide interactive, proactive assistance based on streaming visual inputs, yet their development is constrained by the costly and labor-intensive process of data collection and system evaluation. To address these limitations, we present a comprehensive framework with three key contributions. First, we introduce a novel data curation pipeline that synthesizes dialogues from annotated egocentric videos, resulting in \\dataset, a large-scale synthetic dialogue dataset spanning multiple domains. Second, we develop a suite of automatic evaluation metrics, validated through extensive human studies. Third, we propose an end-to-end model that processes streaming video inputs to generate contextually appropriate responses, incorporating novel techniques for handling data imbalance and long-duration videos. This work lays the foundation for developing real-time, proactive AI assistants capable of guiding users through diverse tasks. Project page: https://pro-assist.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07645", "title": "Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models", "authors": [ "Maciej Chrab\u0105szcz", "Katarzyna Lorenc", "Karolina Seweryn" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated impressive capabilities across various natural language processing (NLP) tasks in recent years. However, their susceptibility to jailbreaks and perturbations necessitates additional evaluations. Many LLMs are multilingual, but safety-related training data contains mainly high-resource languages like English. This can leave them vulnerable to perturbations in low-resource languages such as Polish. We show how surprisingly strong attacks can be cheaply created by altering just a few characters and using a small proxy model for word importance calculation. We find that these character and word-level attacks drastically alter the predictions of different LLMs, suggesting a potential vulnerability that can be used to circumvent their internal safety mechanisms. We validate our attack construction methodology on Polish, a low-resource language, and find potential vulnerabilities of LLMs in this language. Additionally, we show how it can be extended to other languages. We release the created datasets and code for further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.06205", "title": "Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning", "authors": [ "Sheng Chen", "Peiyu He", "Jiaxin Hu", "Ziyang Liu", "Yansheng Wang", "Tao Xu", "Chi Zhang", "Chongchong Zhang", "Chao An", "Shiyu Cai", "Duo Cao", "Kangping Chen", "Shuai Chu", "Tianwei Chu", "Mingdi Dan", "Min Du", "Weiwei Fang", "Pengyou Fu", "Junkai Hu", "Xiaowei Jiang", "Zhaodi Jiang", "Fuxuan Li", "Jun Li", "Minghui Li", "Mingyao Li", "Yanchang Li", "Zhibin Li", "Guangming Liu", "Kairui Liu", "Lihao Liu", "Weizhi Liu", "Xiaoshun Liu", "Yufei Liu", "Yunfei Liu", "Qiang Lu", "Yuanfei Luo", "Xiang Lv", "Hongying Ma", "Sai Ma", "Lingxian Mi", "Sha Sa", "Hongxiang Shu", "Lei Tian", "Chengzhi Wang", "Jiayu Wang", "Kaijie Wang", "Qingyi Wang", "Renwen Wang", "Tao Wang", "Wei Wang", "Xirui Wang", "Chao Wei", "Xuguang Wei", "Zijun Xia", "Zhaohao Xiao", "Tingshuai Yan", "Liyan Yang", "Yifan Yang", "Zhikai Yang", "Zhong Yin", "Li Yuan", "Liuchun Yuan", "Chi Zhang", "Jinyang Zhang", "Junhui Zhang", "Linge Zhang", "Zhenyi Zhang", "Zheyu Zhang", "Dongjie Zhu", "Hang Li", "Yangang Zhang" ], "github": "", "project_page": "", "abstract": "Modern robot navigation systems encounter difficulties in diverse and complex indoor environments. Traditional approaches rely on multiple modules with small models or rule-based systems and thus lack adaptability to new environments. To address this, we developed Astra, a comprehensive dual-model architecture, Astra-Global and Astra-Local, for mobile robot navigation. Astra-Global, a multimodal LLM, processes vision and language inputs to perform self and goal localization using a hybrid topological-semantic graph as the global map, and outperforms traditional visual place recognition methods. Astra-Local, a multitask network, handles local path planning and odometry estimation. Its 4D spatial-temporal encoder, trained through self-supervised learning, generates robust 4D features for downstream tasks. The planning head utilizes flow matching and a novel masked ESDF loss to minimize collision risks for generating local trajectories, and the odometry head integrates multi-sensor inputs via a transformer encoder to predict the relative pose of the robot. Deployed on real in-house mobile robots, Astra achieves high end-to-end mission success rate across diverse indoor environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.06006", "title": "Bootstrapping World Models from Dynamics Models in Multimodal Foundation Models", "authors": [ "Yifu Qiu", "Yftah Ziser", "Anna Korhonen", "Shay B. Cohen", "Edoardo M. Ponti" ], "github": "https://github.com/yfqiu-nlp/vlm-world-model", "project_page": "", "abstract": "To what extent do vision-and-language foundation models possess a realistic world model (observation times action rightarrow observation) and a dynamics model (observation times observation rightarrow action), when actions are expressed through language? While open-source foundation models struggle with both, we find that fine-tuning them to acquire a dynamics model through supervision is significantly easier than acquiring a world model. In turn, dynamics models can be used to bootstrap world models through two main strategies: 1) weakly supervised learning from synthetic data and 2) inference time verification. Firstly, the dynamics model can annotate actions for unlabelled pairs of video frame observations to expand the training data. We further propose a new objective, where image tokens in observation pairs are weighted by their importance, as predicted by a recognition model. Secondly, the dynamics models can assign rewards to multiple samples of the world model to score them, effectively guiding search at inference time. We evaluate the world models resulting from both strategies through the task of action-centric image editing on Aurora-Bench. Our best model achieves a performance competitive with state-of-the-art image editing models, improving on them by a margin of 15% on real-world subsets according to GPT4o-as-judge, and achieving the best average human evaluation across all subsets of Aurora-Bench.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yfqiu-nlp/vlm-world-model", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.08011", "title": "Play to Generalize: Learning to Reason Through Game Play", "authors": [ "Yunfei Xie", "Yinsong Ma", "Shiyi Lan", "Alan Yuille", "Junfei Xiao", "Chen Wei" ], "github": "", "project_page": "", "abstract": "Developing generalizable reasoning capabilities in multimodal large language models (MLLMs) remains challenging. Motivated by cognitive science literature suggesting that gameplay promotes transferable cognitive skills, we propose a novel post-training paradigm, Visual Game Learning, or ViGaL, where MLLMs develop out-of-domain generalization of multimodal reasoning through playing arcade-like games. Specifically, we show that post-training a 7B-parameter MLLM via reinforcement learning (RL) on simple arcade-like games, e.g. Snake, significantly enhances its downstream performance on multimodal math benchmarks like MathVista, and on multi-discipline questions like MMMU, without seeing any worked solutions, equations, or diagrams during RL, suggesting the capture of transferable reasoning skills. Remarkably, our model outperforms specialist models tuned on multimodal reasoning data in multimodal reasoning benchmarks, while preserving the base model's performance on general visual benchmarks, a challenge where specialist models often fall short. Our findings suggest a new post-training paradigm: synthetic, rule-based games can serve as controllable and scalable pre-text tasks that unlock generalizable multimodal reasoning abilities in MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07833", "title": "Improving large language models with concept-aware fine-tuning", "authors": [ "Michael K. Chen", "Xikun Zhang", "Jiaxing Huang", "Dacheng Tao" ], "github": "https://github.com/michaelchen-lab/caft-llm", "project_page": "", "abstract": "Large language models (LLMs) have become the cornerstone of modern AI. However, the existing paradigm of next-token prediction fundamentally limits their ability to form coherent, high-level concepts, making it a critical barrier to human-like understanding and reasoning. Take the phrase \"ribonucleic acid\" as an example: an LLM will first decompose it into tokens, i.e., artificial text fragments (\"rib\", \"on\", ...), then learn each token sequentially, rather than grasping the phrase as a unified, coherent semantic entity. This fragmented representation hinders deeper conceptual understanding and, ultimately, the development of truly intelligent systems. In response, we introduce Concept-Aware Fine-Tuning (CAFT), a novel multi-token training method that redefines how LLMs are fine-tuned. By enabling the learning of sequences that span multiple tokens, this method fosters stronger concept-aware learning. Our experiments demonstrate significant improvements compared to conventional next-token finetuning methods across diverse tasks, including traditional applications like text summarization and domain-specific ones like de novo protein design. Multi-token prediction was previously only possible in the prohibitively expensive pretraining phase; CAFT, to our knowledge, is the first to bring the multi-token setting to the post-training phase, thus effectively democratizing its benefits for the broader community of practitioners and researchers. Finally, the unexpected effectiveness of our proposed method suggests wider implications for the machine learning research community. All code and data are available at https://github.com/michaelchen-lab/caft-llm", "github_manual": "", "github_regex": "https://github.com/michaelchen-lab/caft-llm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.05062", "title": "Debatable Intelligence: Benchmarking LLM Judges via Debate Speech Evaluation", "authors": [ "Noy Sternlicht", "Ariel Gera", "Roy Bar-Haim", "Tom Hope", "Noam Slonim" ], "github": "", "project_page": "", "abstract": "We introduce Debate Speech Evaluation as a novel and challenging benchmark for assessing LLM judges. Evaluating debate speeches requires a deep understanding of the speech at multiple levels, including argument strength and relevance, the coherence and organization of the speech, the appropriateness of its style and tone, and so on. This task involves a unique set of cognitive abilities that have previously received limited attention in systematic LLM benchmarking. To explore such skills, we leverage a dataset of over 600 meticulously annotated debate speeches and present the first in-depth analysis of how state-of-the-art LLMs compare to human judges on this task. Our findings reveal a nuanced picture: while larger models can approximate individual human judgments in some respects, they differ substantially in their overall judgment behavior. We also investigate the ability of frontier LLMs to generate persuasive, opinionated speeches, showing that models may perform at a human level on this task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.06905", "title": "Meta-Adaptive Prompt Distillation for Few-Shot Visual Question Answering", "authors": [ "Akash Gupta", "Amos Storkey", "Mirella Lapata" ], "github": "https://github.com/akashgupta97/MAPD", "project_page": "", "abstract": "Large Multimodal Models (LMMs) often rely on in-context learning (ICL) to perform new tasks with minimal supervision. However, ICL performance, especially in smaller LMMs, is inconsistent and does not always improve monotonically with increasing examples. We hypothesize that this occurs due to the LMM being overwhelmed by additional information present in the image embeddings, which is not required for the downstream task. To address this, we propose a meta-learning approach that provides an alternative for inducing few-shot capabilities in LMMs, using a fixed set of soft prompts that are distilled from task-relevant image features and can be adapted at test time using a few examples. To facilitate this distillation, we introduce an attention-mapper module that can be easily integrated with the popular LLaVA v1.5 architecture and is jointly learned with soft prompts, enabling task adaptation in LMMs under low-data regimes with just a few gradient steps. Evaluation on the VL-ICL Bench shows that our method consistently outperforms ICL and related prompt-tuning approaches, even under image perturbations, improving task induction and reasoning across visual question answering tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/akashgupta97/MAPD", "github_api_added_by": "auto" }, { "date": "2025-06-10", "arxiv_id": "2506.06485", "title": "What Is Seen Cannot Be Unseen: The Disruptive Effect of Knowledge Conflict on Large Language Models", "authors": [ "Kaiser Sun", "Fan Bai", "Mark Dredze" ], "github": "", "project_page": "", "abstract": "Large language models frequently rely on both contextual input and parametric knowledge to perform tasks. However, these sources can come into conflict, especially when retrieved documents contradict the model's parametric knowledge. We propose a diagnostic framework to systematically evaluate LLM behavior under context-memory conflict, where the contextual information diverges from their parametric beliefs. We construct diagnostic data that elicit these conflicts and analyze model performance across multiple task types. Our findings reveal that (1) knowledge conflict has minimal impact on tasks that do not require knowledge utilization, (2) model performance is consistently higher when contextual and parametric knowledge are aligned, (3) models are unable to fully suppress their internal knowledge even when instructed, and (4) providing rationales that explain the conflict increases reliance on contexts. These insights raise concerns about the validity of model-based evaluation and underscore the need to account for knowledge conflict in the deployment of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.01241", "title": "ExpertLongBench: Benchmarking Language Models on Expert-Level Long-Form Generation Tasks with Structured Checklists", "authors": [ "Jie Ruan", "Inderjeet Nair", "Shuyang Cao", "Amy Liu", "Sheza Munir", "Micah Pollens-Dempsey", "Tiffany Chiang", "Lucy Kates", "Nicholas David", "Sihan Chen", "Ruxin Yang", "Yuqian Yang", "Jasmine Gump", "Tessa Bialek", "Vivek Sankaran", "Margo Schlanger", "Lu Wang" ], "github": "", "project_page": "", "abstract": "This paper introduces ExpertLongBench, an expert-level benchmark containing 11 tasks from 9 domains that reflect realistic expert workflows and applications. Beyond question answering, the application-driven tasks in ExpertLongBench demand long-form outputs that can exceed 5,000 tokens and strict adherence to domain-specific requirements. Notably, each task in ExpertLongBench includes a rubric, designed or validated by domain experts, to specify task requirements and guide output evaluation. Furthermore, we propose CLEAR, an evaluation framework that supports accurate evaluation of long-form model outputs in our benchmark. To achieve fine-grained, expert-aligned evaluation, CLEAR derives checklists from both model outputs and references by extracting information corresponding to items in the task-specific rubric. Checklist items for model outputs are then compared with corresponding items for reference outputs to assess their correctness, enabling grounded evaluation. We benchmark 11 large language models (LLMs) and analyze components in CLEAR, showing that (1) existing LLMs, with the top performer achieving only a 26.8% F1 score, require significant improvement for expert-level tasks; (2) models can generate content corresponding to the required aspects, though often not accurately; and (3) accurate checklist extraction and comparison in CLEAR can be achieved by open-weight models for more scalable and low-cost usage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07971", "title": "CyberV: Cybernetics for Test-time Scaling in Video Understanding", "authors": [ "Jiahao Meng", "Shuyang Sun", "Yue Tan", "Lu Qi", "Yunhai Tong", "Xiangtai Li", "Longyin Wen" ], "github": "https://github.com/marinero4972/CyberV", "project_page": "", "abstract": "Current Multimodal Large Language Models (MLLMs) may struggle with understanding long or complex videos due to computational demands at test time, lack of robustness, and limited accuracy, primarily stemming from their feed-forward processing nature. These limitations could be more severe for models with fewer parameters. To address these limitations, we propose a novel framework inspired by cybernetic principles, redesigning video MLLMs as adaptive systems capable of self-monitoring, self-correction, and dynamic resource allocation during inference. Our approach, CyberV, introduces a cybernetic loop consisting of an MLLM Inference System, a Sensor, and a Controller. Specifically, the sensor monitors forward processes of the MLLM and collects intermediate interpretations, such as attention drift, then the controller determines when and how to trigger self-correction and generate feedback to guide the next round. This test-time adaptive scaling framework enhances frozen MLLMs without requiring retraining or additional components. Experiments demonstrate significant improvements: CyberV boosts Qwen2.5-VL-7B by 8.3% and InternVL3-8B by 5.5% on VideoMMMU, surpassing the competitive proprietary model GPT-4o. When applied to Qwen2.5-VL-72B, it yields a 10.0% improvement, achieving performance even comparable to human experts. Furthermore, our method demonstrates consistent gains on general-purpose benchmarks, such as VideoMME and WorldSense, highlighting its effectiveness and generalization capabilities in making MLLMs more robust and accurate for dynamic video understanding. The code is released at https://github.com/marinero4972/CyberV.", "github_manual": "", "github_regex": "https://github.com/marinero4972/CyberV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.05598", "title": "SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward Models in LLMs", "authors": [ "Michael J Ryan", "Omar Shaikh", "Aditri Bhagirath", "Daniel Frees", "William Held", "Diyi Yang" ], "github": "", "project_page": "", "abstract": "Recent calls for pluralistic alignment of Large Language Models (LLMs) encourage adapting models to diverse user preferences. However, most prior work on personalized reward models heavily rely on additional identity information, such as demographic details or a predefined set of preference categories. To this end, we introduce SynthesizeMe, an approach to inducing synthetic user personas from user interactions for personalized reward modeling. SynthesizeMe first generates and verifies reasoning to explain user preferences, then induces synthetic user personas from that reasoning, and finally filters to informative prior user interactions in order to build personalized prompts for a particular user. We show that using SynthesizeMe induced prompts improves personalized LLM-as-a-judge accuracy by 4.4% on Chatbot Arena. Combining SynthesizeMe derived prompts with a reward model achieves top performance on PersonalRewardBench: a new curation of user-stratified interactions with chatbots collected from 854 users of Chatbot Arena and PRISM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2505.23760", "title": "Model Immunization from a Condition Number Perspective", "authors": [ "Amber Yijia Zheng", "Cedar Site Bai", "Brian Bullins", "Raymond A. Yeh" ], "github": "https://github.com/amberyzheng/model-immunization-cond-num", "project_page": "", "abstract": "Model immunization aims to pre-train models that are difficult to fine-tune on harmful tasks while retaining their utility on other non-harmful tasks. Though prior work has shown empirical evidence for immunizing text-to-image models, the key understanding of when immunization is possible and a precise definition of an immunized model remain unclear. In this work, we propose a framework, based on the condition number of a Hessian matrix, to analyze model immunization for linear models. Building on this framework, we design an algorithm with regularization terms to control the resulting condition numbers after pre-training. Empirical results on linear models and non-linear deep-nets demonstrate the effectiveness of the proposed algorithm on model immunization. The code is available at https://github.com/amberyzheng/model-immunization-cond-num.", "github_manual": "", "github_regex": "https://github.com/amberyzheng/model-immunization-cond-num", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.06658", "title": "Self-Adapting Improvement Loops for Robotic Learning", "authors": [ "Calvin Luo", "Zilai Zeng", "Mingxi Jia", "Yilun Du", "Chen Sun" ], "github": "", "project_page": "", "abstract": "Video generative models trained on expert demonstrations have been utilized as performant text-conditioned visual planners for solving robotic tasks. However, generalization to unseen tasks remains a challenge. Whereas improved generalization may be facilitated by leveraging learned prior knowledge from additional pre-collected offline data sources, such as web-scale video datasets, in the era of experience we aim to design agents that can continuously improve in an online manner from self-collected behaviors. In this work we thus propose the Self-Adapting Improvement Loop (SAIL), where an in-domain video model iteratively updates itself on self-produced trajectories, collected through adaptation with an internet-scale pretrained video model, and steadily improves its performance for a specified task of interest. We apply SAIL to a diverse suite of MetaWorld tasks, as well as two manipulation tasks on a real robot arm, and find that performance improvements continuously emerge over multiple iterations for novel tasks initially unseen during original in-domain video model training. Furthermore, we discover that SAIL is surprisingly robust regarding if and how the self-collected experience is filtered, and the quality of the initial in-domain demonstrations. Through adaptation with summarized internet-scale data, and learning through online experience, we thus demonstrate a way to iteratively bootstrap a high-performance video model for solving novel robotic tasks through self-improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07982", "title": "\u03c4^2-Bench: Evaluating Conversational Agents in a Dual-Control Environment", "authors": [ "Victor Barres", "Honghua Dong", "Soham Ray", "Xujie Si", "Karthik Narasimhan" ], "github": "https://github.com/sierra-research/tau2-bench", "project_page": "", "abstract": "Existing benchmarks for conversational AI agents simulate single-control environments, where only the AI agent can use tools to interact with the world, while the user remains a passive information provider. This differs from real-world scenarios like technical support, where users need to actively participate in modifying the state of the (shared) world. In order to address this gap, we introduce tau^2-bench, with four key contributions: 1) A novel Telecom dual-control domain modeled as a Dec-POMDP, where both agent and user make use of tools to act in a shared, dynamic environment that tests both agent coordination and communication, 2) A compositional task generator that programmatically creates diverse, verifiable tasks from atomic components, ensuring domain coverage and controlled complexity, 3) A reliable user simulator tightly coupled with the environment, whose behavior is constrained by tools and observable states, improving simulation fidelity, 4) Fine-grained analysis of agent performance through multiple ablations including separating errors arising from reasoning vs communication/coordination. In particular, our experiments show significant performance drops when agents shift from no-user to dual-control, highlighting the challenges of guiding users. Overall, tau^2-bench provides a controlled testbed for agents that must both reason effectively and guide user actions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sierra-research/tau2-bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.04651", "title": "Agents of Change: Self-Evolving LLM Agents for Strategic Planning", "authors": [ "Nikolas Belle", "Dakota Barnes", "Alfonso Amayuelas", "Ivan Bercovich", "Xin Eric Wang", "William Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in LLMs have enabled their use as autonomous agents across a range of tasks, yet they continue to struggle with formulating and adhering to coherent long-term strategies. In this paper, we investigate whether LLM agents can self-improve when placed in environments that explicitly challenge their strategic planning abilities. Using the board game Settlers of Catan, accessed through the open-source Catanatron framework, we benchmark a progression of LLM-based agents, from a simple game-playing agent to systems capable of autonomously rewriting their own prompts and their player agent's code. We introduce a multi-agent architecture in which specialized roles (Analyzer, Researcher, Coder, and Player) collaborate to iteratively analyze gameplay, research new strategies, and modify the agent's logic or prompt. By comparing manually crafted agents to those evolved entirely by LLMs, we evaluate how effectively these systems can diagnose failure and adapt over time. Our results show that self-evolving agents, particularly when powered by models like Claude 3.7 and GPT-4o, outperform static baselines by autonomously adopting their strategies, passing along sample behavior to game-playing agents, and demonstrating adaptive reasoning over multiple iterations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.07564", "title": "SAFEFLOW: A Principled Protocol for Trustworthy and Transactional Autonomous Agent Systems", "authors": [ "Peiran Li", "Xinkai Zou", "Zhuohang Wu", "Ruifeng Li", "Shuo Xing", "Hanwen Zheng", "Zhikai Hu", "Yuping Wang", "Haoxi Li", "Qin Yuan", "Yingmo Zhang", "Zhengzhong Tu" ], "github": "https://github.com/hwchase17/langchain", "project_page": "", "abstract": "Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled powerful autonomous agents capable of complex reasoning and multi-modal tool use. Despite their growing capabilities, today's agent frameworks remain fragile, lacking principled mechanisms for secure information flow, reliability, and multi-agent coordination. In this work, we introduce SAFEFLOW, a new protocol-level framework for building trustworthy LLM/VLM-based agents. SAFEFLOW enforces fine-grained information flow control (IFC), precisely tracking provenance, integrity, and confidentiality of all the data exchanged between agents, tools, users, and environments. By constraining LLM reasoning to respect these security labels, SAFEFLOW prevents untrusted or adversarial inputs from contaminating high-integrity decisions. To ensure robustness in concurrent multi-agent settings, SAFEFLOW introduces transactional execution, conflict resolution, and secure scheduling over shared state, preserving global consistency across agents. We further introduce mechanisms, including write-ahead logging, rollback, and secure caches, that further enhance resilience against runtime errors and policy violations. To validate the performances, we built SAFEFLOWBENCH, a comprehensive benchmark suite designed to evaluate agent reliability under adversarial, noisy, and concurrent operational conditions. Extensive experiments demonstrate that agents built with SAFEFLOW maintain impressive task performance and security guarantees even in hostile environments, substantially outperforming state-of-the-art. Together, SAFEFLOW and SAFEFLOWBENCH lay the groundwork for principled, robust, and secure agent ecosystems, advancing the frontier of reliable autonomy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hwchase17/langchain", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.00258", "title": "Hidden in Plain Sight: Probing Implicit Reasoning in Multimodal Language Models", "authors": [ "Qianqi Yan", "Hongquan Li", "Shan Jiang", "Yang Zhao", "Xinze Guan", "Ching-Chen Kuo", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) are increasingly deployed in open-ended, real-world environments where inputs are messy, underspecified, and not always trustworthy. Unlike curated benchmarks, these settings frequently involve instructions that refer to missing objects or contradictory facts, rely on ambiguous references, or request infeasible actions. In such cases, success hinges not on task execution alone, but on a model's ability to detect when something is silently wrong. This paper presents a systematic analysis of how current MLLMs handle such implicit reasoning scenarios: cases where the flaw is not explicitly stated but must be inferred from context. Using a curated diagnostic suite spanning four categories of real-world failure modes, we evaluate six MLLMs, including o3 and GPT-4o, and find that models frequently fail to surface hidden issues, even when they possess the necessary perceptual and reasoning skills. Explicit prompting reveals that the underlying capabilities exist but are often suppressed in favor of user compliance. We further show that simple inference-time interventions, such as cautious persona prompting and, in particular, requiring a clarifying question, can dramatically recover performance. Our findings highlight a persistent gap between reasoning competence and behavioral compliance in current MLLMs and suggest practical strategies for making these models more trustworthy in underconstrained environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.08004", "title": "Dynamic View Synthesis as an Inverse Problem", "authors": [ "Hidir Yesiltepe", "Pinar Yanardag" ], "github": "", "project_page": "", "abstract": "In this work, we address dynamic view synthesis from monocular videos as an inverse problem in a training-free setting. By redesigning the noise initialization phase of a pre-trained video diffusion model, we enable high-fidelity dynamic view synthesis without any weight updates or auxiliary modules. We begin by identifying a fundamental obstacle to deterministic inversion arising from zero-terminal signal-to-noise ratio (SNR) schedules and resolve it by introducing a novel noise representation, termed K-order Recursive Noise Representation. We derive a closed form expression for this representation, enabling precise and efficient alignment between the VAE-encoded and the DDIM inverted latents. To synthesize newly visible regions resulting from camera motion, we introduce Stochastic Latent Modulation, which performs visibility aware sampling over the latent space to complete occluded regions. Comprehensive experiments demonstrate that dynamic view synthesis can be effectively performed through structured latent manipulation in the noise initialization phase.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.03231", "title": "NetPress: Dynamically Generated LLM Benchmarks for Network Applications", "authors": [ "Yajie Zhou", "Jiajun Ruan", "Eric S. Wang", "Sadjad Fouladi", "Francis Y. Yan", "Kevin Hsieh", "Zaoxing Liu" ], "github": "https://github.com/Froot-NetSys/NetPress", "project_page": "", "abstract": "Despite growing interest in domain-specific benchmarking of large language models (LLMs) and agents, current evaluations remain limited to static, small-scale datasets, especially in high-stakes tasks like network operations that demand reliability for deployments. We present NetPress, an automated benchmark generation framework for evaluating LLM agents in network applications. NetPress introduces a unified abstraction with state and action, enabling dynamic generation of diverse query sets along with corresponding ground truths. At runtime, users can specify benchmark configurations to generate millions of queries on the fly. In addition to dynamic benchmark construction, NetPress integrates with network emulators to provide realistic environment feedback, supporting comprehensive evaluation across correctness, safety, and latency. We instantiate NetPress on three representative applications, revealing interesting fine-grained differences in agent behavior that static, correctness-only benchmarks often miss. NetPress moves LLM evaluation toward realistic, scalable testing in infrastructure-centric domains, helping close the gap between benchmark performance and real-world deployment readiness. Code is available at https://github.com/Froot-NetSys/NetPress.", "github_manual": "", "github_regex": "https://github.com/Froot-NetSys/NetPress", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-10", "arxiv_id": "2506.06607", "title": "Training-Free Tokenizer Transplantation via Orthogonal Matching Pursuit", "authors": [ "Charles Goddard", "Fernando Fernandes Neto" ], "github": "https://github.com/jukofyork/transplant-vocab", "project_page": "", "abstract": "We present a training-free method to transplant tokenizers in pretrained large language models (LLMs) by reconstructing unseen token embeddings via Orthogonal Matching Pursuit (OMP). Specifically, we approximate each out-of-vocabulary token as a sparse linear combination of shared tokens, in two phases: first, compute each new token's representation in the donor embedding space with a small dictionary of shared anchor tokens, then transfer these same sparse coefficients back into the base model's embedding space. On two challenging cross-tokenizer tasks--LlamatoMistral NeMo (12B) and QwentoLlama (1B)--we show that OMP achieves best zero-shot preservation of the base model's performance across multiple benchmarks, while other zero-shot approaches degrade significantly. Compared to baselines (zero-init, mean-init, and existing approaches like WECHSEL, FOCUS, ZETT), OMP consistently achieves the best overall performance, effectively bridging large tokenizer discrepancies without gradient updates. Our analysis further identifies mismatched numerical tokenization schemes as a critical challenge for preserving mathematical reasoning capabilities. This technique enables direct reuse of pretrained model weights with new tokenizers, facilitating cross-tokenizer knowledge distillation, speculative decoding, ensembling, merging, and domain-specific vocabulary adaptations. We integrate our method into the open-source mergekit-tokensurgeon tool for post hoc vocabulary realignment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jukofyork/transplant-vocab", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.05700", "title": "RKEFino1: A Regulation Knowledge-Enhanced Large Language Model", "authors": [ "Yan Wang", "Yueru He", "Ruoyu Xiang", "Jeff Zhao" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models (LLMs) hold great promise for financial applications but introduce critical accuracy and compliance challenges in Digital Regulatory Reporting (DRR). To address these issues, we propose RKEFino1, a regulation knowledge-enhanced financial reasoning model built upon Fino1, fine-tuned with domain knowledge from XBRL, CDM, and MOF. We formulate two QA tasks-knowledge-based and mathematical reasoning-and introduce a novel Numerical NER task covering financial entities in both sentences and tables. Experimental results demonstrate the effectiveness and generalization capacity of RKEFino1 in compliance-critical financial tasks. We have released our model on Hugging Face.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.05167", "title": "ECoRAG: Evidentiality-guided Compression for Long Context RAG", "authors": [ "Yeonseok Jeong", "Jinsu Kim", "Dohyeon Lee", "Seung-won Hwang" ], "github": "https://github.com/ldilab/ECoRAG", "project_page": "", "abstract": "Large Language Models (LLMs) have shown remarkable performance in Open-Domain Question Answering (ODQA) by leveraging external documents through Retrieval-Augmented Generation (RAG). To reduce RAG overhead, from longer context, context compression is necessary. However, prior compression methods do not focus on filtering out non-evidential information, which limit the performance in LLM-based RAG. We thus propose Evidentiality-guided RAG, or ECoRAG framework. ECoRAG improves LLM performance by compressing retrieved documents based on evidentiality, ensuring whether answer generation is supported by the correct evidence. As an additional step, ECoRAG reflects whether the compressed content provides sufficient evidence, and if not, retrieves more until sufficient. Experiments show that ECoRAG improves LLM performance on ODQA tasks, outperforming existing compression methods. Furthermore, ECoRAG is highly cost-efficient, as it not only reduces latency but also minimizes token usage by retaining only the necessary information to generate the correct answer. Code is available at https://github.com/ldilab/ECoRAG.", "github_manual": "", "github_regex": "https://github.com/ldilab/ECoRAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.08887", "title": "DiscoVLA: Discrepancy Reduction in Vision, Language, and Alignment for Parameter-Efficient Video-Text Retrieval", "authors": [ "Leqi Shen", "Guoqiang Gong", "Tianxiang Hao", "Tao He", "Yifeng Zhang", "Pengzhang Liu", "Sicheng Zhao", "Jungong Han", "Guiguang Ding" ], "github": "https://github.com/LunarShen/DsicoVLA", "project_page": "", "abstract": "The parameter-efficient adaptation of the image-text pretraining model CLIP for video-text retrieval is a prominent area of research. While CLIP is focused on image-level vision-language matching, video-text retrieval demands comprehensive understanding at the video level. Three key discrepancies emerge in the transfer from image-level to video-level: vision, language, and alignment. However, existing methods mainly focus on vision while neglecting language and alignment. In this paper, we propose Discrepancy Reduction in Vision, Language, and Alignment (DiscoVLA), which simultaneously mitigates all three discrepancies. Specifically, we introduce Image-Video Features Fusion to integrate image-level and video-level features, effectively tackling both vision and language discrepancies. Additionally, we generate pseudo image captions to learn fine-grained image-level alignment. To mitigate alignment discrepancies, we propose Image-to-Video Alignment Distillation, which leverages image-level alignment knowledge to enhance video-level alignment. Extensive experiments demonstrate the superiority of our DiscoVLA. In particular, on MSRVTT with CLIP (ViT-B/16), DiscoVLA outperforms previous methods by 1.5% in R@1, reaching a final score of 50.5% R@1. The code is available at https://github.com/LunarShen/DsicoVLA.", "github_manual": "", "github_regex": "https://github.com/LunarShen/DsicoVLA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.08002", "title": "Aligning Text, Images, and 3D Structure Token-by-Token", "authors": [ "Aadarsh Sahoo", "Vansh Tibrewal", "Georgia Gkioxari" ], "github": "", "project_page": "", "abstract": "Creating machines capable of understanding the world in 3D is essential in assisting designers that build and edit 3D environments and robots navigating and interacting within a three-dimensional space. Inspired by advances in language and image modeling, we investigate the potential of autoregressive models for a new modality: structured 3D scenes. To this end, we propose a unified LLM framework that aligns language, images, and 3D scenes and provide a detailed ''cookbook'' outlining critical design choices for achieving optimal training and performance addressing key questions related to data representation, modality-specific objectives, and more. We evaluate performance across four core 3D tasks -- rendering, recognition, instruction-following, and question-answering -- and four 3D datasets, synthetic and real-world. We extend our approach to reconstruct complex 3D object shapes by enriching our 3D modality with quantized shape encodings, and show our model's effectiveness on real-world 3D object recognition tasks. Project webpage: https://glab-caltech.github.io/kyvo/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.05928", "title": "MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models", "authors": [ "Jie Cao", "Tianwei Lin", "Hongyang He", "Rolan Yan", "Wenqiao Zhang", "Juncheng Li", "Dongping Zhang", "Siliang Tang", "Yueting Zhuang" ], "github": "https://github.com/DCDmllm/MoA", "project_page": "", "abstract": "Recent studies integrate Low-Rank Adaptation (LoRA) and Mixture-of-Experts (MoE) to further enhance the performance of parameter-efficient fine-tuning (PEFT) methods in Large Language Model (LLM) applications. Existing methods employ homogeneous MoE-LoRA architectures composed of LoRA experts with either similar or identical structures and capacities. However, these approaches often suffer from representation collapse and expert load imbalance, which negatively impact the potential of LLMs. To address these challenges, we propose a heterogeneous Mixture-of-Adapters (MoA) approach. This method dynamically integrates PEFT adapter experts with diverse structures, leveraging their complementary representational capabilities to foster expert specialization, thereby enhancing the effective transfer of pre-trained knowledge to downstream tasks. MoA supports two variants: (i) Soft MoA achieves fine-grained integration by performing a weighted fusion of all expert outputs; (ii) Sparse MoA activates adapter experts sparsely based on their contribution, achieving this with negligible performance degradation. Experimental results demonstrate that heterogeneous MoA outperforms homogeneous MoE-LoRA methods in both performance and parameter efficiency. Our project is available at https://github.com/DCDmllm/MoA.", "github_manual": "", "github_regex": "https://github.com/DCDmllm/MoA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.07932", "title": "Squeeze3D: Your 3D Generation Model is Secretly an Extreme Neural Compressor", "authors": [ "Rishit Dagli", "Yushi Guan", "Sankeerth Durvasula", "Mohammadreza Mofayezi", "Nandita Vijaykumar" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "We propose Squeeze3D, a novel framework that leverages implicit prior knowledge learnt by existing pre-trained 3D generative models to compress 3D data at extremely high compression ratios. Our approach bridges the latent spaces between a pre-trained encoder and a pre-trained generation model through trainable mapping networks. Any 3D model represented as a mesh, point cloud, or a radiance field is first encoded by the pre-trained encoder and then transformed (i.e. compressed) into a highly compact latent code. This latent code can effectively be used as an extremely compressed representation of the mesh or point cloud. A mapping network transforms the compressed latent code into the latent space of a powerful generative model, which is then conditioned to recreate the original 3D model (i.e. decompression). Squeeze3D is trained entirely on generated synthetic data and does not require any 3D datasets. The Squeeze3D architecture can be flexibly used with existing pre-trained 3D encoders and existing generative models. It can flexibly support different formats, including meshes, point clouds, and radiance fields. Our experiments demonstrate that Squeeze3D achieves compression ratios of up to 2187x for textured meshes, 55x for point clouds, and 619x for radiance fields while maintaining visual quality comparable to many existing methods. Squeeze3D only incurs a small compression and decompression latency since it does not involve training object-specific networks to compress an object.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.06751", "title": "Geopolitical biases in LLMs: what are the \"good\" and the \"bad\" countries according to contemporary language models", "authors": [ "Mikhail Salnikov", "Dmitrii Korzh", "Ivan Lazichny", "Elvir Karimov", "Artyom Iudin", "Ivan Oseledets", "Oleg Y. Rogov", "Alexander Panchenko", "Natalia Loukachevitch", "Elena Tutubalina" ], "github": "", "project_page": "", "abstract": "This paper evaluates geopolitical biases in LLMs with respect to various countries though an analysis of their interpretation of historical events with conflicting national perspectives (USA, UK, USSR, and China). We introduce a novel dataset with neutral event descriptions and contrasting viewpoints from different countries. Our findings show significant geopolitical biases, with models favoring specific national narratives. Additionally, simple debiasing prompts had a limited effect in reducing these biases. Experiments with manipulated participant labels reveal models' sensitivity to attribution, sometimes amplifying biases or recognizing inconsistencies, especially with swapped labels. This work highlights national narrative biases in LLMs, challenges the effectiveness of simple debiasing methods, and offers a framework and dataset for future geopolitical bias research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.07177", "title": "Frame Guidance: Training-Free Guidance for Frame-Level Control in Video Diffusion Models", "authors": [ "Sangwon Jang", "Taekyung Ki", "Jaehyeong Jo", "Jaehong Yoon", "Soo Ye Kim", "Zhe Lin", "Sung Ju Hwang" ], "github": "https://github.com/agwmon/frame-guidance", "project_page": "", "abstract": "Advancements in diffusion models have significantly improved video quality, directing attention to fine-grained controllability. However, many existing methods depend on fine-tuning large-scale video models for specific tasks, which becomes increasingly impractical as model sizes continue to grow. In this work, we present Frame Guidance, a training-free guidance for controllable video generation based on frame-level signals, such as keyframes, style reference images, sketches, or depth maps. For practical training-free guidance, we propose a simple latent processing method that dramatically reduces memory usage, and apply a novel latent optimization strategy designed for globally coherent video generation. Frame Guidance enables effective control across diverse tasks, including keyframe guidance, stylization, and looping, without any training, compatible with any video models. Experimental results show that Frame Guidance can produce high-quality controlled videos for a wide range of tasks and input signals.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/agwmon/frame-guidance", "github_api_added_by": "auto" }, { "date": "2025-06-11", "arxiv_id": "2506.07927", "title": "Solving Inequality Proofs with Large Language Models", "authors": [ "Jiayi Sheng", "Luna Lyu", "Jikai Jin", "Tony Xia", "Alex Gu", "James Zou", "Pan Lu" ], "github": "", "project_page": "", "abstract": "Inequality proving, crucial across diverse scientific and mathematical fields, tests advanced reasoning skills such as discovering tight bounds and strategic theorem application. This makes it a distinct, demanding frontier for large language models (LLMs), offering insights beyond general mathematical problem-solving. Progress in this area is hampered by existing datasets that are often scarce, synthetic, or rigidly formal. We address this by proposing an informal yet verifiable task formulation, recasting inequality proving into two automatically checkable subtasks: bound estimation and relation prediction. Building on this, we release IneqMath, an expert-curated dataset of Olympiad-level inequalities, including a test set and training corpus enriched with step-wise solutions and theorem annotations. We also develop a novel LLM-as-judge evaluation framework, combining a final-answer judge with four step-wise judges designed to detect common reasoning flaws. A systematic evaluation of 29 leading LLMs on IneqMath reveals a surprising reality: even top models like o1 achieve less than 10% overall accuracy under step-wise scrutiny; this is a drop of up to 65.5% from their accuracy considering only final answer equivalence. This discrepancy exposes fragile deductive chains and a critical gap for current LLMs between merely finding an answer and constructing a rigorous proof. Scaling model size and increasing test-time computation yield limited gains in overall proof correctness. Instead, our findings highlight promising research directions such as theorem-guided reasoning and self-refinement. Code and data are available at https://ineqmath.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.08009", "title": "Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion", "authors": [ "Xun Huang", "Zhengqi Li", "Guande He", "Mingyuan Zhou", "Eli Shechtman" ], "github": "", "project_page": "", "abstract": "We introduce Self Forcing, a novel training paradigm for autoregressive video diffusion models. It addresses the longstanding issue of exposure bias, where models trained on ground-truth context must generate sequences conditioned on their own imperfect outputs during inference. Unlike prior methods that denoise future frames based on ground-truth context frames, Self Forcing conditions each frame's generation on previously self-generated outputs by performing autoregressive rollout with key-value (KV) caching during training. This strategy enables supervision through a holistic loss at the video level that directly evaluates the quality of the entire generated sequence, rather than relying solely on traditional frame-wise objectives. To ensure training efficiency, we employ a few-step diffusion model along with a stochastic gradient truncation strategy, effectively balancing computational cost and performance. We further introduce a rolling KV cache mechanism that enables efficient autoregressive video extrapolation. Extensive experiments demonstrate that our approach achieves real-time streaming video generation with sub-second latency on a single GPU, while matching or even surpassing the generation quality of significantly slower and non-causal diffusion models. Project website: http://self-forcing.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.04614", "title": "Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation", "authors": [ "Yuyang Wanyan", "Xi Zhang", "Haiyang Xu", "Haowei Liu", "Junyang Wang", "Jiabo Ye", "Yutong Kou", "Ming Yan", "Fei Huang", "Xiaoshan Yang", "Weiming Dong", "Changsheng Xu" ], "github": "https://github.com/X-PLUG/MobileAgent/tree/main/GUI-Critic-R1", "project_page": "", "abstract": "In recent years, Multimodal Large Language Models (MLLMs) have been extensively utilized for multimodal reasoning tasks, including Graphical User Interface (GUI) automation. Unlike general offline multimodal tasks, GUI automation is executed in online interactive environments, necessitating step-by-step decision-making based on real-time status of the environment. This task has a lower tolerance for decision-making errors at each step, as any mistakes may cumulatively disrupt the process and potentially lead to irreversible outcomes like deletions or payments. To address these issues, we introduce a pre-operative critic mechanism that provides effective feedback prior to the actual execution, by reasoning about the potential outcome and correctness of actions. Specifically, we propose a Suggestion-aware Gradient Relative Policy Optimization (S-GRPO) strategy to construct our pre-operative critic model GUI-Critic-R1, incorporating a novel suggestion reward to enhance the reliability of the model's feedback. Furthermore, we develop a reasoning-bootstrapping based data collection pipeline to create a GUI-Critic-Train and a GUI-Critic-Test, filling existing gaps in GUI critic data. Static experiments on the GUI-Critic-Test across both mobile and web domains reveal that our GUI-Critic-R1 offers significant advantages in critic accuracy compared to current MLLMs. Dynamic evaluation on GUI automation benchmark further highlights the effectiveness and superiority of our model, as evidenced by improved success rates and operational efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/X-PLUG/MobileAgent/tree/main/GUI-Critic-R1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.09040", "title": "Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better", "authors": [ "Dianyi Wang", "Wei Song", "Yikun Wang", "Siyuan Wang", "Kaicheng Yu", "Zhongyu Wei", "Jiaqi Wang" ], "github": "https://github.com/AlenjandroWang/ASVR", "project_page": "", "abstract": "Typical large vision-language models (LVLMs) apply autoregressive supervision solely to textual sequences, without fully incorporating the visual modality into the learning process. This results in three key limitations: (1) an inability to utilize images without accompanying captions, (2) the risk that captions omit critical visual details, and (3) the challenge that certain vision-centric content cannot be adequately conveyed through text. As a result, current LVLMs often prioritize vision-to-language alignment while potentially overlooking fine-grained visual information. While some prior works have explored autoregressive image generation, effectively leveraging autoregressive visual supervision to enhance image understanding remains an open challenge. In this paper, we introduce Autoregressive Semantic Visual Reconstruction (ASVR), which enables joint learning of visual and textual modalities within a unified autoregressive framework. We show that autoregressively reconstructing the raw visual appearance of images does not enhance and may even impair multimodal understanding. In contrast, autoregressively reconstructing the semantic representation of images consistently improves comprehension. Notably, we find that even when models are given continuous image features as input, they can effectively reconstruct discrete semantic tokens, resulting in stable and consistent improvements across a wide range of multimodal understanding benchmarks. Our approach delivers significant performance gains across varying data scales (556k-2M) and types of LLM bacbones. Specifically, ASVR improves LLaVA-1.5 by 5% in average scores across 14 multimodal benchmarks. The code is available at https://github.com/AlenjandroWang/ASVR.", "github_manual": "", "github_regex": "https://github.com/AlenjandroWang/ASVR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.07047", "title": "Mathesis: Towards Formal Theorem Proving from Natural Languages", "authors": [ "Yu Xuejun", "Jianyuan Zhong", "Zijin Feng", "Pengyi Zhai", "Roozbeh Yousefzadeh", "Wei Chong Ng", "Haoxiong Liu", "Ziyi Shou", "Jing Xiong", "Yudong Zhou", "Claudia Beth Ong", "Austen Jeremy Sugiarto", "Yaoxi Zhang", "Wai Ming Tai", "Huan Cao", "Dongcai Lu", "Jiacheng Sun", "Qiang Xu", "Shen Xin", "Zhenguo Li" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models show strong promise for formal reasoning. However, most LLM-based theorem provers have long been constrained by the need for expert-written formal statements as inputs, limiting their applicability to real-world problems expressed in natural language. We tackle this gap with Mathesis, the first end-to-end theorem proving pipeline processing informal problem statements. It contributes Mathesis-Autoformalizer, the first autoformalizer using reinforcement learning to enhance the formalization ability of natural language problems, aided by our novel LeanScorer framework for nuanced formalization quality assessment. It also proposes a Mathesis-Prover, which generates formal proofs from the formalized statements. To evaluate the real-world applicability of end-to-end formal theorem proving, we introduce Gaokao-Formal, a benchmark of 488 complex problems from China's national college entrance exam. Our approach is carefully designed, with a thorough study of each component. Experiments demonstrate Mathesis's effectiveness, with the autoformalizer outperforming the best baseline by 22% in pass-rate on Gaokao-Formal. The full system surpasses other model combinations, achieving 64% accuracy on MiniF2F with pass@32 and a state-of-the-art 18% on Gaokao-Formal.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.08672", "title": "RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Sampling", "authors": [ "Yang Liu", "Jiaqi Li", "Zilong Zheng" ], "github": "", "project_page": "", "abstract": "Rule-based reasoning has been acknowledged as one of the fundamental problems in reasoning, while deviations in rule formats, types, and complexity in real-world applications pose severe challenges. Recent studies have shown that large reasoning models (LRMs) have remarkable reasoning capabilities, and their performance is substantially enhanced by reinforcement learning (RL). However, it remains an open question whether small reasoning models (SRMs) can learn rule-based reasoning effectively with robust generalization across diverse tasks and domains. To address this, we introduce Reinforced Rule-based Reasoning, a.k.a. RuleReasoner, a simple yet effective method to conduct rule-based reasoning via a wide collection of curated tasks and a novel domain-aware dynamic sampling approach. Specifically, RuleReasoner resamples each training batch by updating the sampling weights of different domains based on historical rewards. This facilitates domain augmentation and flexible online learning schedules for RL, obviating the need for pre-hoc human-engineered mix-training recipes used in existing methods. Empirical evaluations on in-distribution (ID) and out-of-distribution (OOD) benchmarks reveal that RuleReasoner outperforms frontier LRMs by a significant margin (Delta4.1% average points on eight ID tasks and Delta10.4% average points on three OOD tasks over OpenAI-o1). Notably, our approach also exhibits higher computational efficiency compared to prior dynamic sampling methods for RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.08300", "title": "Institutional Books 1.0: A 242B token dataset from Harvard Library's collections, refined for accuracy and usability", "authors": [ "Matteo Cargnelutti", "Catherine Brobston", "John Hess", "Jack Cushman", "Kristi Mukk", "Aristana Scourtas", "Kyle Courtney", "Greg Leppert", "Amanda Watson", "Martha Whitehead", "Jonathan Zittrain" ], "github": "https://github.com/instdin/institutional-books-1-pipeline", "project_page": "", "abstract": "Large language models (LLMs) use data to learn about the world in order to produce meaningful correlations and predictions. As such, the nature, scale, quality, and diversity of the datasets used to train these models, or to support their work at inference time, have a direct impact on their quality. The rapid development and adoption of LLMs of varying quality has brought into focus the scarcity of publicly available, high-quality training data and revealed an urgent need to ground the stewardship of these datasets in sustainable practices with clear provenance chains. To that end, this technical report introduces Institutional Books 1.0, a large collection of public domain books originally digitized through Harvard Library's participation in the Google Books project, beginning in 2006. Working with Harvard Library, we extracted, analyzed, and processed these volumes into an extensively-documented dataset of historic texts. This analysis covers the entirety of Harvard Library's collection scanned as part of that project, originally spanning 1,075,899 volumes written in over 250 different languages for a total of approximately 250 billion tokens. As part of this initial release, the OCR-extracted text (original and post-processed) as well as the metadata (bibliographic, source, and generated) of the 983,004 volumes, or 242B tokens, identified as being in the public domain have been made available. This report describes this project's goals and methods as well as the results of the analyses we performed, all in service of making this historical collection more accessible and easier for humans and machines alike to filter, read and use.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/instdin/institutional-books-1-pipeline", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.07976", "title": "Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction", "authors": [ "Junhong Shen", "Hao Bai", "Lunjun Zhang", "Yifei Zhou", "Amrith Setlur", "Shengbang Tong", "Diego Caples", "Nan Jiang", "Tong Zhang", "Ameet Talwalkar", "Aviral Kumar" ], "github": "https://github.com/test-time-interaction/tti", "project_page": "", "abstract": "The current paradigm of test-time scaling relies on generating long reasoning traces (\"thinking\" more) before producing a response. In agent problems that require interaction, this can be done by generating thinking traces before acting in the world. However, this process does not allow agents to acquire new information from the environment or adapt their behavior over time. In this work, we propose to scale test-time interaction, an untapped dimension of test-time scaling that increases the agent's interaction horizon to enable running rich behaviors such as exploration, backtracking, and dynamic re-planning within a single rollout. To demonstrate the promise of this scaling dimension, we study the domain of web agents. We first show that even prompting-based interaction scaling without any training can improve task success on web benchmarks non-trivially. Building on this, we introduce TTI (Test-Time Interaction), a curriculum-based online reinforcement learning (RL) approach that trains agents by adaptively adjusting their rollout lengths. Using a Gemma 3 12B model, TTI produces state-of-the-art open-source, open-data web agents on WebVoyager and WebArena benchmarks. We further show that TTI enables agents to balance exploration and exploitation adaptively. Our results establish interaction scaling as a powerful, complementary axis to scaling per-step compute, offering new avenues for training adaptive agents.", "github_manual": "https://github.com/test-time-interaction/tti", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.04020", "title": "QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering", "authors": [ "An Quang Tang", "Xiuzhen Zhang", "Minh Ngoc Dinh", "Zhuang Li" ], "github": "https://github.com/antangrocket1312/QQSUMM", "project_page": "", "abstract": "Review-based Product Question Answering (PQA) allows e-commerce platforms to automatically address customer queries by leveraging insights from user reviews. However, existing PQA systems generate answers with only a single perspective, failing to capture the diversity of customer opinions. In this paper we introduce a novel task Quantitative Query-Focused Summarization (QQSUM), which aims to summarize diverse customer opinions into representative Key Points (KPs) and quantify their prevalence to effectively answer user queries. While Retrieval-Augmented Generation (RAG) shows promise for PQA, its generated answers still fall short of capturing the full diversity of viewpoints. To tackle this challenge, our model QQSUM-RAG, which extends RAG, employs few-shot learning to jointly train a KP-oriented retriever and a KP summary generator, enabling KP-based summaries that capture diverse and representative opinions. Experimental results demonstrate that QQSUM-RAG achieves superior performance compared to state-of-the-art RAG baselines in both textual quality and quantification accuracy of opinions. Our source code is available at: https://github.com/antangrocket1312/QQSUMM", "github_manual": "", "github_regex": "https://github.com/antangrocket1312/QQSUMM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.07045", "title": "Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs", "authors": [ "Yikun Ji", "Hong Yan", "Jun Lan", "Huijia Zhu", "Weiqiang Wang", "Qi Fan", "Liqing Zhang", "Jianfu Zhang" ], "github": "", "project_page": "", "abstract": "The rapid advancement of image generation technologies intensifies the demand for interpretable and robust detection methods. Although existing approaches often attain high accuracy, they typically operate as black boxes without providing human-understandable justifications. Multi-modal Large Language Models (MLLMs), while not originally intended for forgery detection, exhibit strong analytical and reasoning capabilities. When properly fine-tuned, they can effectively identify AI-generated images and offer meaningful explanations. However, existing MLLMs still struggle with hallucination and often fail to align their visual interpretations with actual image content and human reasoning. To bridge this gap, we construct a dataset of AI-generated images annotated with bounding boxes and descriptive captions that highlight synthesis artifacts, establishing a foundation for human-aligned visual-textual grounded reasoning. We then finetune MLLMs through a multi-stage optimization strategy that progressively balances the objectives of accurate detection, visual localization, and coherent textual explanation. The resulting model achieves superior performance in both detecting AI-generated images and localizing visual flaws, significantly outperforming baseline methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.08500", "title": "DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs", "authors": [ "Arie Cattan", "Alon Jacovi", "Ori Ram", "Jonathan Herzig", "Roee Aharoni", "Sasha Goldshtein", "Eran Ofek", "Idan Szpektor", "Avi Caciularu" ], "github": "", "project_page": "", "abstract": "Retrieval Augmented Generation (RAG) is a commonly used approach for enhancing large language models (LLMs) with relevant and up-to-date information. However, the retrieved sources can often contain conflicting information and it remains unclear how models should address such discrepancies. In this work, we first propose a novel taxonomy of knowledge conflict types in RAG, along with the desired model behavior for each type. We then introduce CONFLICTS, a high-quality benchmark with expert annotations of conflict types in a realistic RAG setting. CONFLICTS is the first benchmark that enables tracking progress on how models address a wide range of knowledge conflicts. We conduct extensive experiments on this benchmark, showing that LLMs often struggle to appropriately resolve conflicts between sources. While prompting LLMs to explicitly reason about the potential conflict in the retrieved documents significantly improves the quality and appropriateness of their responses, substantial room for improvement in future research remains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.04688", "title": "MMRefine: Unveiling the Obstacles to Robust Refinement in Multimodal Large Language Models", "authors": [ "Gio Paik", "Geewook Kim", "Jinbae Im" ], "github": "https://github.com/naver-ai/MMRefine", "project_page": "", "abstract": "This paper introduces MMRefine, a MultiModal Refinement benchmark designed to evaluate the error refinement capabilities of Multimodal Large Language Models (MLLMs). As the emphasis shifts toward enhancing reasoning during inference, MMRefine provides a framework that evaluates MLLMs' abilities to detect and correct errors across six distinct scenarios beyond just comparing final accuracy before and after refinement. Furthermore, the benchmark analyzes the refinement performance by categorizing errors into six error types. Experiments with various open and closed MLLMs reveal bottlenecks and factors impeding refinement performance, highlighting areas for improvement in effective reasoning enhancement. Our code and dataset are publicly available at https://github.com/naver-ai/MMRefine.", "github_manual": "", "github_regex": "https://github.com/naver-ai/MMRefine", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-11", "arxiv_id": "2506.08279", "title": "Seeing Voices: Generating A-Roll Video from Audio with Mirage", "authors": [ "Aditi Sundararaman", "Amogh Adishesha", "Andrew Jaegle", "Dan Bigioi", "Hyoung-Kyu Song", "Jon Kyl", "Justin Mao", "Kevin Lan", "Mojtaba Komeili", "ShahRukh Athar", "Sheila Babayan", "Stanislau Beliasau", "William Buchwalter" ], "github": "", "project_page": "", "abstract": "From professional filmmaking to user-generated content, creators and consumers have long recognized that the power of video depends on the harmonious integration of what we hear (the video's audio track) with what we see (the video's image sequence). Current approaches to video generation either ignore sound to focus on general-purpose but silent image sequence generation or address both visual and audio elements but focus on restricted application domains such as re-dubbing. We introduce Mirage, an audio-to-video foundation model that excels at generating realistic, expressive output imagery from scratch given an audio input. When integrated with existing methods for speech synthesis (text-to-speech, or TTS), Mirage results in compelling multimodal video. When trained on audio-video footage of people talking (A-roll) and conditioned on audio containing speech, Mirage generates video of people delivering a believable interpretation of the performance implicit in input audio. Our central technical contribution is a unified method for training self-attention-based audio-to-video generation models, either from scratch or given existing weights. This methodology allows Mirage to retain generality as an approach to audio-to-video generation while producing outputs of superior subjective quality to methods that incorporate audio-specific architectures or loss components specific to people, speech, or details of how images or audio are captured. We encourage readers to watch and listen to the results of Mirage for themselves (see paper and comments for links).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09003", "title": "SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner", "authors": [ "Lei Zhang", "Jiaxi Yang", "Min Yang", "Jian Yang", "Mouxiang Chen", "Jiajun Zhang", "Zeyu Cui", "Binyuan Hui", "Junyang Lin" ], "github": "https://github.com/Hambaobao/SWE-Flow", "project_page": "", "abstract": "We introduce **SWE-Flow**, a novel data synthesis framework grounded in Test-Driven Development (TDD). Unlike existing software engineering data that rely on human-submitted issues, **SWE-Flow** automatically infers incremental development steps directly from unit tests, which inherently encapsulate high-level requirements. The core of **SWE-Flow** is the construction of a Runtime Dependency Graph (RDG), which precisely captures function interactions, enabling the generation of a structured, step-by-step *development schedule*. At each step, **SWE-Flow** produces a partial codebase, the corresponding unit tests, and the necessary code modifications, resulting in fully verifiable TDD tasks. With this approach, we generated 16,061 training instances and 2,020 test instances from real-world GitHub projects, creating the **SWE-Flow-Eval** benchmark. Our experiments show that fine-tuning open model on this dataset significantly improves performance in TDD-based coding. To facilitate further research, we release all code, datasets, models, and Docker images at [Github](https://github.com/Hambaobao/SWE-Flow).", "github_manual": "", "github_regex": "https://github.com/Hambaobao/SWE-Flow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09995", "title": "PlayerOne: Egocentric World Simulator", "authors": [ "Yuanpeng Tu", "Hao Luo", "Xi Chen", "Xiang Bai", "Fan Wang", "Hengshuang Zhao" ], "github": "https://github.com/yuanpengtu/PlayerOne", "project_page": "", "abstract": "We introduce PlayerOne, the first egocentric realistic world simulator, facilitating immersive and unrestricted exploration within vividly dynamic environments. Given an egocentric scene image from the user, PlayerOne can accurately construct the corresponding world and generate egocentric videos that are strictly aligned with the real scene human motion of the user captured by an exocentric camera. PlayerOne is trained in a coarse-to-fine pipeline that first performs pretraining on large-scale egocentric text-video pairs for coarse-level egocentric understanding, followed by finetuning on synchronous motion-video data extracted from egocentric-exocentric video datasets with our automatic construction pipeline. Besides, considering the varying importance of different components, we design a part-disentangled motion injection scheme, enabling precise control of part-level movements. In addition, we devise a joint reconstruction framework that progressively models both the 4D scene and video frames, ensuring scene consistency in the long-form video generation. Experimental results demonstrate its great generalization ability in precise control of varying human movements and worldconsistent modeling of diverse scenarios. It marks the first endeavor into egocentric real-world simulation and can pave the way for the community to delve into fresh frontiers of world modeling and its diverse applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yuanpengtu/PlayerOne", "github_api_added_by": "auto" }, { "date": "2025-06-12", "arxiv_id": "2506.09790", "title": "ComfyUI-R1: Exploring Reasoning Models for Workflow Generation", "authors": [ "Zhenran Xu", "Yiyu Wang", "Xue Yang", "Longyue Wang", "Weihua Luo", "Kaifu Zhang", "Baotian Hu", "Min Zhang" ], "github": "https://github.com/huggingface/open-r1", "project_page": "", "abstract": "AI-generated content has evolved from monolithic models to modular workflows, particularly on platforms like ComfyUI, enabling customization in creative pipelines. However, crafting effective workflows requires great expertise to orchestrate numerous specialized components, presenting a steep learning curve for users. To address this challenge, we introduce ComfyUI-R1, the first large reasoning model for automated workflow generation. Starting with our curated dataset of 4K workflows, we construct long chain-of-thought (CoT) reasoning data, including node selection, workflow planning, and code-level workflow representation. ComfyUI-R1 is trained through a two-stage framework: (1) CoT fine-tuning for cold start, adapting models to the ComfyUI domain; (2) reinforcement learning for incentivizing reasoning capability, guided by a fine-grained rule-metric hybrid reward, ensuring format validity, structural integrity, and node-level fidelity. Experiments show that our 7B-parameter model achieves a 97\\% format validity rate, along with high pass rate, node-level and graph-level F1 scores, significantly surpassing prior state-of-the-art methods that employ leading closed-source models such as GPT-4o and Claude series. Further analysis highlights the critical role of the reasoning process and the advantage of transforming workflows into code. Qualitative comparison reveals our strength in synthesizing intricate workflows with diverse nodes, underscoring the potential of long CoT reasoning in AI art creation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/open-r1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09113", "title": "Seedance 1.0: Exploring the Boundaries of Video Generation Models", "authors": [ "Yu Gao", "Haoyuan Guo", "Tuyen Hoang", "Weilin Huang", "Lu Jiang", "Fangyuan Kong", "Huixia Li", "Jiashi Li", "Liang Li", "Xiaojie Li", "Xunsong Li", "Yifu Li", "Shanchuan Lin", "Zhijie Lin", "Jiawei Liu", "Shu Liu", "Xiaonan Nie", "Zhiwu Qing", "Yuxi Ren", "Li Sun", "Zhi Tian", "Rui Wang", "Sen Wang", "Guoqiang Wei", "Guohong Wu", "Jie Wu", "Ruiqi Xia", "Fei Xiao", "Xuefeng Xiao", "Jiangqiao Yan", "Ceyuan Yang", "Jianchao Yang", "Runkai Yang", "Tao Yang", "Yihang Yang", "Zilyu Ye", "Xuejiao Zeng", "Yan Zeng", "Heng Zhang", "Yang Zhao", "Xiaozheng Zheng", "Peihao Zhu", "Jiaxin Zou", "Feilong Zuo" ], "github": "", "project_page": "", "abstract": "Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously balancing prompt following, motion plausibility, and visual quality. In this report, we introduce Seedance 1.0, a high-performance and inference-efficient video foundation generation model that integrates several core technical improvements: (i) multi-source data curation augmented with precision and meaningful video captioning, enabling comprehensive learning across diverse scenarios; (ii) an efficient architecture design with proposed training paradigm, which allows for natively supporting multi-shot generation and jointly learning of both text-to-video and image-to-video tasks. (iii) carefully-optimized post-training approaches leveraging fine-grained supervised fine-tuning, and video-specific RLHF with multi-dimensional reward mechanisms for comprehensive performance improvements; (iv) excellent model acceleration achieving ~10x inference speedup through multi-stage distillation strategies and system-level optimizations. Seedance 1.0 can generate a 5-second video at 1080p resolution only with 41.4 seconds (NVIDIA-L20). Compared to state-of-the-art video generation models, Seedance 1.0 stands out with high-quality and fast video generation having superior spatiotemporal fluidity with structural stability, precise instruction adherence in complex multi-subject contexts, native multi-shot narrative coherence with consistent subject representation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.08889", "title": "SeerAttention-R: Sparse Attention Adaptation for Long Reasoning", "authors": [ "Yizhao Gao", "Shuming Guo", "Shijie Cao", "Yuqing Xia", "Yu Cheng", "Lei Wang", "Lingxiao Ma", "Yutao Sun", "Tianzhu Ye", "Li Dong", "Hayden Kwok-Hay So", "Yu Hua", "Ting Cao", "Fan Yang", "Mao Yang" ], "github": "https://github.com/microsoft/SeerAttention", "project_page": "", "abstract": "We introduce SeerAttention-R, a sparse attention framework specifically tailored for the long decoding of reasoning models. Extended from SeerAttention, SeerAttention-R retains the design of learning attention sparsity through a self-distilled gating mechanism, while removing query pooling to accommodate auto-regressive decoding. With a lightweight plug-in gating, SeerAttention-R is flexible and can be easily integrated into existing pretrained model without modifying the original parameters. We demonstrate that SeerAttention-R, trained on just 0.4B tokens, maintains near-lossless reasoning accuracy with 4K token budget in AIME benchmark under large sparse attention block sizes (64/128). Using TileLang, we develop a highly optimized sparse decoding kernel that achieves near-theoretical speedups of up to 9x over FlashAttention-3 on H100 GPU at 90% sparsity. Code is available at: https://github.com/microsoft/SeerAttention.", "github_manual": "", "github_regex": "https://github.com/microsoft/SeerAttention", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09937", "title": "SAFE: Multitask Failure Detection for Vision-Language-Action Models", "authors": [ "Qiao Gu", "Yuanliang Ju", "Shengxiang Sun", "Igor Gilitschenski", "Haruki Nishimura", "Masha Itkina", "Florian Shkurti" ], "github": "https://github.com/vla-safe/SAFE", "project_page": "", "abstract": "While vision-language-action models (VLAs) have shown promising robotic behaviors across a diverse set of manipulation tasks, they achieve limited success rates when deployed on novel tasks out-of-the-box. To allow these policies to safely interact with their environments, we need a failure detector that gives a timely alert such that the robot can stop, backtrack, or ask for help. However, existing failure detectors are trained and tested only on one or a few specific tasks, while VLAs require the detector to generalize and detect failures also in unseen tasks and novel environments. In this paper, we introduce the multitask failure detection problem and propose SAFE, a failure detector for generalist robot policies such as VLAs. We analyze the VLA feature space and find that VLAs have sufficient high-level knowledge about task success and failure, which is generic across different tasks. Based on this insight, we design SAFE to learn from VLA internal features and predict a single scalar indicating the likelihood of task failure. SAFE is trained on both successful and failed rollouts, and is evaluated on unseen tasks. SAFE is compatible with different policy architectures. We test it on OpenVLA, pi_0, and pi_0-FAST in both simulated and real-world environments extensively. We compare SAFE with diverse baselines and show that SAFE achieves state-of-the-art failure detection performance and the best trade-off between accuracy and detection time using conformal prediction. More qualitative results can be found at https://vla-safe.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vla-safe/SAFE", "github_api_added_by": "auto" }, { "date": "2025-06-12", "arxiv_id": "2506.09984", "title": "InterActHuman: Multi-Concept Human Animation with Layout-Aligned Audio Conditions", "authors": [ "Zhenzhi Wang", "Jiaqi Yang", "Jianwen Jiang", "Chao Liang", "Gaojie Lin", "Zerong Zheng", "Ceyuan Yang", "Dahua Lin" ], "github": "", "project_page": "", "abstract": "End-to-end human animation with rich multi-modal conditions, e.g., text, image and audio has achieved remarkable advancements in recent years. However, most existing methods could only animate a single subject and inject conditions in a global manner, ignoring scenarios that multiple concepts could appears in the same video with rich human-human interactions and human-object interactions. Such global assumption prevents precise and per-identity control of multiple concepts including humans and objects, therefore hinders applications. In this work, we discard the single-entity assumption and introduce a novel framework that enforces strong, region-specific binding of conditions from modalities to each identity's spatiotemporal footprint. Given reference images of multiple concepts, our method could automatically infer layout information by leveraging a mask predictor to match appearance cues between the denoised video and each reference appearance. Furthermore, we inject local audio condition into its corresponding region to ensure layout-aligned modality matching in a iterative manner. This design enables the high-quality generation of controllable multi-concept human-centric videos. Empirical results and ablation studies validate the effectiveness of our explicit layout control for multi-modal conditions compared to implicit counterparts and other existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09007", "title": "Branched Schr\u00f6dinger Bridge Matching", "authors": [ "Sophia Tang", "Yinuo Zhang", "Alexander Tong", "Pranam Chatterjee" ], "github": "", "project_page": "", "abstract": "Predicting the intermediate trajectories between an initial and target distribution is a central problem in generative modeling. Existing approaches, such as flow matching and Schr\\\"odinger Bridge Matching, effectively learn mappings between two distributions by modeling a single stochastic path. However, these methods are inherently limited to unimodal transitions and cannot capture branched or divergent evolution from a common origin to multiple distinct outcomes. To address this, we introduce Branched Schr\\\"odinger Bridge Matching (BranchSBM), a novel framework that learns branched Schr\\\"odinger bridges. BranchSBM parameterizes multiple time-dependent velocity fields and growth processes, enabling the representation of population-level divergence into multiple terminal distributions. We show that BranchSBM is not only more expressive but also essential for tasks involving multi-path surface navigation, modeling cell fate bifurcations from homogeneous progenitor states, and simulating diverging cellular responses to perturbations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.08001", "title": "Reparameterized LLM Training via Orthogonal Equivalence Transformation", "authors": [ "Zeju Qiu", "Simon Buchholz", "Tim Z. Xiao", "Maximilian Dax", "Bernhard Sch\u00f6lkopf", "Weiyang Liu" ], "github": "", "project_page": "", "abstract": "While large language models (LLMs) are driving the rapid advancement of artificial intelligence, effectively and reliably training these large models remains one of the field's most significant challenges. To address this challenge, we propose POET, a novel reParameterized training algorithm that uses Orthogonal Equivalence Transformation to optimize neurons. Specifically, POET reparameterizes each neuron with two learnable orthogonal matrices and a fixed random weight matrix. Because of its provable preservation of spectral properties of weight matrices, POET can stably optimize the objective function with improved generalization. We further develop efficient approximations that make POET flexible and scalable for training large-scale neural networks. Extensive experiments validate the effectiveness and scalability of POET in training LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09350", "title": "Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation", "authors": [ "Shanchuan Lin", "Ceyuan Yang", "Hao He", "Jianwen Jiang", "Yuxi Ren", "Xin Xia", "Yang Zhao", "Xuefeng Xiao", "Lu Jiang" ], "github": "", "project_page": "", "abstract": "Existing large-scale video generation models are computationally intensive, preventing adoption in real-time and interactive applications. In this work, we propose autoregressive adversarial post-training (AAPT) to transform a pre-trained latent video diffusion model into a real-time, interactive video generator. Our model autoregressively generates a latent frame at a time using a single neural function evaluation (1NFE). The model can stream the result to the user in real time and receive interactive responses as controls to generate the next latent frame. Unlike existing approaches, our method explores adversarial training as an effective paradigm for autoregressive generation. This not only allows us to design an architecture that is more efficient for one-step generation while fully utilizing the KV cache, but also enables training the model in a student-forcing manner that proves to be effective in reducing error accumulation during long video generation. Our experiments demonstrate that our 8B model achieves real-time, 24fps, streaming video generation at 736x416 resolution on a single H100, or 1280x720 on 8xH100 up to a minute long (1440 frames). Visit our research website at https://seaweed-apt.com/2", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.06395", "title": "Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models", "authors": [ "Pengyi Li", "Matvey Skripkin", "Alexander Zubrey", "Andrey Kuznetsov", "Ivan Oseledets" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) excel at reasoning, yet post-training remains critical for aligning their behavior with task goals. Existing reinforcement learning (RL) methods often depend on costly human annotations or external reward models. We propose Reinforcement Learning via Self-Confidence (RLSC), which uses the model's own confidence as reward signals-eliminating the need for labels, preference models, or reward engineering. Applied to Qwen2.5-Math-7B with only 16 samples per question and 10 or 20 training steps, RLSC improves accuracy by +13.4% on AIME2024, +21.2% on MATH500, +21.7% on Minerva Math, +20.8% on Olympiadbench, and +9.7% on AMC23. RLSC provides a simple, scalable post-training method for inference models, requiring only a small number of samples and unlabelled supervision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.08900", "title": "MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis", "authors": [ "Jos\u00e9 Morano", "Botond Fazekas", "Emese S\u00fckei", "Ronald Fecso", "Taha Emre", "Markus Gumpinger", "Georg Faustmann", "Marzieh Oghbaie", "Ursula Schmidt-Erfurth", "Hrvoje Bogunovi\u0107" ], "github": "https://github.com/j-morano/MIRAGE", "project_page": "", "abstract": "Artificial intelligence (AI) has become a fundamental tool for assisting clinicians in analyzing ophthalmic images, such as optical coherence tomography (OCT). However, developing AI models often requires extensive annotation, and existing models tend to underperform on independent, unseen data. Foundation models (FMs), large AI models trained on vast unlabeled datasets, have shown promise in overcoming these challenges. Nonetheless, available FMs for ophthalmology lack extensive validation, especially for segmentation tasks, and focus on a single imaging modality. In this context, we propose MIRAGE, a novel multimodal FM for the analysis of OCT and scanning laser ophthalmoscopy (SLO) images. Additionally, we propose a new evaluation benchmark with OCT/SLO classification and segmentation tasks. The comparison with general and specialized FMs and segmentation methods shows the superiority of MIRAGE in both types of tasks, highlighting its suitability as a basis for the development of robust AI systems for retinal OCT image analysis. Both MIRAGE and the evaluation benchmark are publicly available: https://github.com/j-morano/MIRAGE.", "github_manual": "", "github_regex": "https://github.com/j-morano/MIRAGE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.08570", "title": "Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation", "authors": [ "Or Tal", "Felix Kreuk", "Yossi Adi" ], "github": "https://github.com/facebookresearch/audiocraft/blob/main/config/model/lm/model_scale/small.yaml", "project_page": "", "abstract": "Recent progress in text-to-music generation has enabled models to synthesize high-quality musical segments, full compositions, and even respond to fine-grained control signals, e.g. chord progressions. State-of-the-art (SOTA) systems differ significantly across many dimensions, such as training datasets, modeling paradigms, and architectural choices. This diversity complicates efforts to evaluate models fairly and pinpoint which design choices most influence performance. While factors like data and architecture are important, in this study we focus exclusively on the modeling paradigm. We conduct a systematic empirical analysis to isolate its effects, offering insights into associated trade-offs and emergent behaviors that can guide future text-to-music generation systems. Specifically, we compare the two arguably most common modeling paradigms: Auto-Regressive decoding and Conditional Flow-Matching. We conduct a controlled comparison by training all models from scratch using identical datasets, training configurations, and similar backbone architectures. Performance is evaluated across multiple axes, including generation quality, robustness to inference configurations, scalability, adherence to both textual and temporally aligned conditioning, and editing capabilities in the form of audio inpainting. This comparative study sheds light on distinct strengths and limitations of each paradigm, providing actionable insights that can inform future architectural and training decisions in the evolving landscape of text-to-music generation. Audio sampled examples are available at: https://huggingface.co/spaces/ortal1602/ARvsFM", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/audiocraft/blob/main/config/model/lm/model_scale/small.yaml", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.05309", "title": "Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games", "authors": [ "Niv Eckhaus", "Uri Berger", "Gabriel Stanovsky" ], "github": "", "project_page": "", "abstract": "LLMs are used predominantly in synchronous communication, where a human user and a model communicate in alternating turns. In contrast, many real-world settings are inherently asynchronous. For example, in group chats, online team meetings, or social games, there is no inherent notion of turns; therefore, the decision of when to speak forms a crucial part of the participant's decision making. In this work, we develop an adaptive asynchronous LLM-agent which, in addition to determining what to say, also decides when to say it. To evaluate our agent, we collect a unique dataset of online Mafia games, including both human participants, as well as our asynchronous agent. Overall, our agent performs on par with human players, both in game performance, as well as in its ability to blend in with the other human players. Our analysis shows that the agent's behavior in deciding when to speak closely mirrors human patterns, although differences emerge in message content. We release all our data and code to support and encourage further research for more realistic asynchronous communication between LLM agents. This work paves the way for integration of LLMs into realistic human group settings, from assistance in team discussions to educational and professional environments where complex social dynamics must be navigated.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09991", "title": "Multiverse: Your Language Models Secretly Decide How to Parallelize and Merge Generation", "authors": [ "Xinyu Yang", "Yuwei An", "Hongyi Liu", "Tianqi Chen", "Beidi Chen" ], "github": "https://github.com/Multiverse4FM/Multiverse", "project_page": "", "abstract": "Autoregressive Large Language Models (AR-LLMs) frequently exhibit implicit parallelism in sequential generation. Inspired by this, we introduce Multiverse, a new generative model that enables natively parallel generation. Multiverse internalizes a MapReduce paradigm, generating automatically through three stages: (i) a Map stage for adaptive task decomposition, (ii) a Process stage for parallel subtask execution, and (iii) a Reduce stage for lossless result synthesis. Next, we build a real-world Multiverse reasoning model with co-design of data, algorithm, and system, enabling rapid and seamless transfer from frontier AR-LLMs. Starting from sequential reasoning chains, we create Multiverse 1K by converting them into structured training data using an automated LLM-assisted pipeline, avoiding costly human annotations. Algorithmically, we design Multiverse Attention to separate parallel reasoning steps while keeping compatibility with causal attention for efficient training. Systematically, we implement Multiverse Engine to enable parallel inference. It features a dedicated scheduler that dynamically switches between sequential and parallel generation, triggered directly by the model. After a 3-hour fine-tuning with 1K examples, our Multiverse-32B stands as the only open-sourced non-AR model achieving performance on par with leading AR-LLMs of the same scale, evidenced by AIME24 & 25 scores of 54% and 46%, respectively. Moreover, our budget control experiments show that Multiverse-32B exhibits superior scaling, outperforming AR-LLMs by 1.87% on average using the same context length. Such scaling further leads to practical efficiency gain, achieving up to 2x speedup across varying batch sizes. We have open-sourced the entire Multiverse ecosystem, including data, model weights, engine, supporting tools, as well as complete data curation prompts and detailed training and evaluation recipes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Multiverse4FM/Multiverse", "github_api_added_by": "auto" }, { "date": "2025-06-12", "arxiv_id": "2506.09736", "title": "Vision Matters: Simple Visual Perturbations Can Boost Multimodal Math Reasoning", "authors": [ "Yuting Li", "Lai Wei", "Kaipeng Zheng", "Jingyuan Huang", "Linghe Kong", "Lichao Sun", "Weiran Huang" ], "github": "https://github.com/YutingLi0606/Vision-Matters", "project_page": "", "abstract": "Despite the rapid progress of multimodal large language models (MLLMs), they have largely overlooked the importance of visual processing. In a simple yet revealing experiment, we interestingly find that language-only models, when provided with image captions, can achieve comparable or even better performance than MLLMs that consume raw visual inputs. This suggests that current MLLMs may generate accurate visual descriptions but fail to effectively integrate them during reasoning. Motivated by this, we propose a simple visual perturbation framework that enhances perceptual robustness without requiring algorithmic modifications or additional training data. Our approach introduces three targeted perturbations: distractor concatenation, dominance-preserving mixup, and random rotation, that can be easily integrated into existing post-training pipelines including SFT, DPO, and GRPO. Through extensive experiments across multiple datasets, we demonstrate consistent improvements in mathematical reasoning performance, with gains comparable to those achieved through algorithmic changes. Additionally, we achieve competitive performance among open-source 7B RL-tuned models by training Qwen2.5-VL-7B with visual perturbation. Through comprehensive ablation studies, we analyze the effectiveness of different perturbation strategies, revealing that each perturbation type contributes uniquely to different aspects of visual reasoning. Our findings highlight the critical role of visual perturbation in multimodal mathematical reasoning: better reasoning begins with better seeing. Our code is available at https://github.com/YutingLi0606/Vision-Matters.", "github_manual": "", "github_regex": "https://github.com/YutingLi0606/Vision-Matters", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09229", "title": "Cross-Frame Representation Alignment for Fine-Tuning Video Diffusion Models", "authors": [ "Sungwon Hwang", "Hyojin Jang", "Kinam Kim", "Minho Park", "Jaegul choo" ], "github": "", "project_page": "", "abstract": "Fine-tuning Video Diffusion Models (VDMs) at the user level to generate videos that reflect specific attributes of training data presents notable challenges, yet remains underexplored despite its practical importance. Meanwhile, recent work such as Representation Alignment (REPA) has shown promise in improving the convergence and quality of DiT-based image diffusion models by aligning, or assimilating, its internal hidden states with external pretrained visual features, suggesting its potential for VDM fine-tuning. In this work, we first propose a straightforward adaptation of REPA for VDMs and empirically show that, while effective for convergence, it is suboptimal in preserving semantic consistency across frames. To address this limitation, we introduce Cross-frame Representation Alignment (CREPA), a novel regularization technique that aligns hidden states of a frame with external features from neighboring frames. Empirical evaluations on large-scale VDMs, including CogVideoX-5B and Hunyuan Video, demonstrate that CREPA improves both visual fidelity and cross-frame semantic coherence when fine-tuned with parameter-efficient methods such as LoRA. We further validate CREPA across diverse datasets with varying attributes, confirming its broad applicability. Project page: https://crepavideo.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.08008", "title": "Hidden in plain sight: VLMs overlook their visual representations", "authors": [ "Stephanie Fu", "Tyler Bonnen", "Devin Guillory", "Trevor Darrell" ], "github": "", "project_page": "", "abstract": "Language provides a natural interface to specify and evaluate performance on visual tasks. To realize this possibility, vision language models (VLMs) must successfully integrate visual and linguistic information. Our work compares VLMs to a direct readout of their visual encoders to understand their ability to integrate across these modalities. Across a series of vision-centric benchmarks (e.g., depth estimation, correspondence), we find that VLMs perform substantially worse than their visual encoders, dropping to near-chance performance. We investigate these results through a series of analyses across the entire VLM: namely 1) the degradation of vision representations, 2) brittleness to task prompt, and 3) the language model's role in solving the task. We find that the bottleneck in performing these vision-centric tasks lies in this third category; VLMs are not effectively using visual information easily accessible throughout the entire model, and they inherit the language priors present in the LLM. Our work helps diagnose the failure modes of open-source VLMs, and presents a series of evaluations useful for future investigations into visual understanding within VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09958", "title": "Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy", "authors": [ "Sushant Gautam", "Michael A. Riegler", "P\u00e5l Halvorsen" ], "github": "https://github.com/Simula/Kvasir-VQA-x1", "project_page": "", "abstract": "Medical Visual Question Answering (MedVQA) is a promising field for developing clinical decision support systems, yet progress is often limited by the available datasets, which can lack clinical complexity and visual diversity. To address these gaps, we introduce Kvasir-VQA-x1, a new, large-scale dataset for gastrointestinal (GI) endoscopy. Our work significantly expands upon the original Kvasir-VQA by incorporating 159,549 new question-answer pairs that are designed to test deeper clinical reasoning. We developed a systematic method using large language models to generate these questions, which are stratified by complexity to better assess a model's inference capabilities. To ensure our dataset prepares models for real-world clinical scenarios, we have also introduced a variety of visual augmentations that mimic common imaging artifacts. The dataset is structured to support two main evaluation tracks: one for standard VQA performance and another to test model robustness against these visual perturbations. By providing a more challenging and clinically relevant benchmark, Kvasir-VQA-x1 aims to accelerate the development of more reliable and effective multimodal AI systems for use in clinical settings. The dataset is fully accessible and adheres to FAIR data principles, making it a valuable resource for the wider research community. Code and data: https://github.com/Simula/Kvasir-VQA-x1 and https://huggingface.co/datasets/SimulaMet/Kvasir-VQA-x1", "github_manual": "", "github_regex": "https://github.com/Simula/Kvasir-VQA-x1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09669", "title": "Query-Level Uncertainty in Large Language Models", "authors": [ "Lihu Chen", "Ga\u00ebl Varoquaux" ], "github": "https://github.com/tigerchen52/query_level_uncertainty", "project_page": "", "abstract": "It is important for Large Language Models to be aware of the boundary of their knowledge, the mechanism of identifying known and unknown queries. This type of awareness can help models perform adaptive inference, such as invoking RAG, engaging in slow and deep thinking, or adopting the abstention mechanism, which is beneficial to the development of efficient and trustworthy AI. In this work, we propose a method to detect knowledge boundaries via Query-Level Uncertainty, which aims to determine if the model is able to address a given query without generating any tokens. To this end, we introduce a novel and training-free method called Internal Confidence, which leverages self-evaluations across layers and tokens. Empirical results on both factual QA and mathematical reasoning tasks demonstrate that our internal confidence can outperform several baselines. Furthermore, we showcase that our proposed method can be used for efficient RAG and model cascading, which is able to reduce inference costs while maintaining performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tigerchen52/query_level_uncertainty", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09501", "title": "Give Me FP32 or Give Me Death? Challenges and Solutions for Reproducible Reasoning", "authors": [ "Jiayi Yuan", "Hao Li", "Xinheng Ding", "Wenya Xie", "Yu-Jhe Li", "Wentian Zhao", "Kun Wan", "Jing Shi", "Xia Hu", "Zirui Liu" ], "github": "https://github.com/nanomaoli/llm_reproducibility", "project_page": "", "abstract": "Large Language Models (LLMs) are now integral across various domains and have demonstrated impressive performance. Progress, however, rests on the premise that benchmark scores are both accurate and reproducible. We demonstrate that the reproducibility of LLM performance is fragile: changing system configuration such as evaluation batch size, GPU count, and GPU version can introduce significant difference in the generated responses. This issue is especially pronounced in reasoning models, where minor rounding differences in early tokens can cascade into divergent chains of thought, ultimately affecting accuracy. For instance, under bfloat16 precision with greedy decoding, a reasoning model like DeepSeek-R1-Distill-Qwen-7B can exhibit up to 9% variation in accuracy and 9,000 tokens difference in response length due to differences in GPU count, type, and evaluation batch size. We trace the root cause of this variability to the non-associative nature of floating-point arithmetic under limited numerical precision. This work presents the first systematic investigation into how numerical precision affects reproducibility in LLM inference. Through carefully controlled experiments across various hardware, software, and precision settings, we quantify when and how model outputs diverge. Our analysis reveals that floating-point precision -- while critical for reproducibility -- is often neglected in evaluation practices. Inspired by this, we develop a lightweight inference pipeline, dubbed LayerCast, that stores weights in 16-bit precision but performs all computations in FP32, balancing memory efficiency with numerical stability. Code is available at https://github.com/nanomaoli/llm_reproducibility.", "github_manual": "", "github_regex": "https://github.com/nanomaoli/llm_reproducibility", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09278", "title": "UFM: A Simple Path towards Unified Dense Correspondence with Flow", "authors": [ "Yuchen Zhang", "Nikhil Keetha", "Chenwei Lyu", "Bhuvan Jhamb", "Yutian Chen", "Yuheng Qiu", "Jay Karhade", "Shreyas Jha", "Yaoyu Hu", "Deva Ramanan", "Sebastian Scherer", "Wenshan Wang" ], "github": "", "project_page": "", "abstract": "Dense image correspondence is central to many applications, such as visual odometry, 3D reconstruction, object association, and re-identification. Historically, dense correspondence has been tackled separately for wide-baseline scenarios and optical flow estimation, despite the common goal of matching content between two images. In this paper, we develop a Unified Flow & Matching model (UFM), which is trained on unified data for pixels that are co-visible in both source and target images. UFM uses a simple, generic transformer architecture that directly regresses the (u,v) flow. It is easier to train and more accurate for large flows compared to the typical coarse-to-fine cost volumes in prior work. UFM is 28% more accurate than state-of-the-art flow methods (Unimatch), while also having 62% less error and 6.7x faster than dense wide-baseline matchers (RoMa). UFM is the first to demonstrate that unified training can outperform specialized approaches across both domains. This result enables fast, general-purpose correspondence and opens new directions for multi-modal, long-range, and real-time correspondence tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.06020", "title": "When to Trust Context: Self-Reflective Debates for Context Reliability", "authors": [ "Zeqi Zhou", "Fang Wu", "Shayan Talaei", "Haokai Zhao", "Cheng Meixin", "Tinson Xu", "Amin Saberi", "Yejin Choi" ], "github": "https://github.com/smiles724/Self-Reflective-Debates", "project_page": "", "abstract": "Large language models frequently encounter conflicts between their parametric knowledge and contextual input, often resulting in factual inconsistencies or hallucinations. We propose Self-Reflective Debate for Contextual Reliability (SR-DCR), a lightweight framework that integrates token-level self-confidence with an asymmetric multi-agent debate to adjudicate such conflicts. A critic, deprived of context, challenges a defender who argues from the given passage; a judge model evaluates the debate and determines the context's reliability. The final answer is selected by combining the verdict with model confidence. Experiments on the ClashEval benchmark demonstrate that SR-DCR consistently enhances robustness to misleading context while maintaining accuracy on trustworthy inputs, outperforming both classical debate and confidence-only baselines with minimal computational overhead. The code is available at https://github.com/smiles724/Self-Reflective-Debates.", "github_manual": "", "github_regex": "https://github.com/smiles724/Self-Reflective-Debates", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09420", "title": "A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy", "authors": [ "Henry Peng Zou", "Wei-Chieh Huang", "Yaozu Wu", "Chunyu Miao", "Dongyuan Li", "Aiwei Liu", "Yue Zhou", "Yankai Chen", "Weizhi Zhang", "Yangning Li", "Liancheng Fang", "Renhe Jiang", "Philip S. Yu" ], "github": "", "project_page": "", "abstract": "Recent improvements in large language models (LLMs) have led many researchers to focus on building fully autonomous AI agents. This position paper questions whether this approach is the right path forward, as these autonomous systems still have problems with reliability, transparency, and understanding the actual requirements of human. We suggest a different approach: LLM-based Human-Agent Systems (LLM-HAS), where AI works with humans rather than replacing them. By keeping human involved to provide guidance, answer questions, and maintain control, these systems can be more trustworthy and adaptable. Looking at examples from healthcare, finance, and software development, we show how human-AI teamwork can handle complex tasks better than AI working alone. We also discuss the challenges of building these collaborative systems and offer practical solutions. This paper argues that progress in AI should not be measured by how independent systems become, but by how well they can work with humans. The most promising future for AI is not in systems that take over human roles, but in those that enhance human capabilities through meaningful partnership.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09980", "title": "Efficient Part-level 3D Object Generation via Dual Volume Packing", "authors": [ "Jiaxiang Tang", "Ruijie Lu", "Zhaoshuo Li", "Zekun Hao", "Xuan Li", "Fangyin Wei", "Shuran Song", "Gang Zeng", "Ming-Yu Liu", "Tsung-Yi Lin" ], "github": "", "project_page": "", "abstract": "Recent progress in 3D object generation has greatly improved both the quality and efficiency. However, most existing methods generate a single mesh with all parts fused together, which limits the ability to edit or manipulate individual parts. A key challenge is that different objects may have a varying number of parts. To address this, we propose a new end-to-end framework for part-level 3D object generation. Given a single input image, our method generates high-quality 3D objects with an arbitrary number of complete and semantically meaningful parts. We introduce a dual volume packing strategy that organizes all parts into two complementary volumes, allowing for the creation of complete and interleaved parts that assemble into the final object. Experiments show that our model achieves better quality, diversity, and generalization than previous image-based part-level generation methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.05412", "title": "Can Vision Language Models Infer Human Gaze Direction? A Controlled Study", "authors": [ "Zory Zhang", "Pinyuan Feng", "Bingyang Wang", "Tianwei Zhao", "Suyang Yu", "Qingying Gao", "Hokin Deng", "Ziqiao Ma", "Yijiang Li", "Dezhi Luo" ], "github": "https://github.com/grow-ai-like-a-child/referential-gaze", "project_page": "", "abstract": "Gaze-referential inference--the ability to infer what others are looking at--is a critical component of a theory of mind that underpins natural human-AI interaction. In a controlled study, we evaluated this skill across 111 Vision Language Models (VLMs) using photos taken with manipulated difficulty and variability, comparing performance with that of human participants (N = 65), and analyzed behaviors using mixed-effects models. We found that 94 of the 111 VLMs failed to do better than random guessing, while humans achieved near-ceiling accuracy. VLMs even respond with each choice almost equally frequently. Are they randomly guessing? Although most VLMs struggle, when we zoom in on five of the top-tier VLMs with above-chance performance, we find that their performance declined with increasing task difficulty but varied only slightly across different prompts and scene objects. These behavioral features cannot be explained by considering them as random guessers. Instead, they likely use a combination of heuristics and guessing such that their performance is subject to the task difficulty but robust to perceptual variations. This suggests that VLMs, lacking gaze inference capability, have yet to become technologies that can naturally interact with humans, but the potential remains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/grow-ai-like-a-child/referential-gaze", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.10209", "title": "TTT-Bench: A Benchmark for Evaluating Reasoning Ability with Simple and Novel Tic-Tac-Toe-style Games", "authors": [ "Prakamya Mishra", "Jiang Liu", "Jialian Wu", "Xiaodong Yu", "Zicheng Liu", "Emad Barsoum" ], "github": "", "project_page": "", "abstract": "Large reasoning models (LRMs) have demonstrated impressive reasoning capabilities across a broad range of tasks including Olympiad-level mathematical problems, indicating evidence of their complex reasoning abilities. While many reasoning benchmarks focus on the STEM domain, the ability of LRMs to reason correctly in broader task domains remains underexplored. In this work, we introduce TTT-Bench, a new benchmark that is designed to evaluate basic strategic, spatial, and logical reasoning abilities in LRMs through a suite of four two-player Tic-Tac-Toe-style games that humans can effortlessly solve from a young age. We propose a simple yet scalable programmatic approach for generating verifiable two-player game problems for TTT-Bench. Although these games are trivial for humans, they require reasoning about the intentions of the opponent, as well as the game board's spatial configurations, to ensure a win. We evaluate a diverse set of state-of-the-art LRMs, and discover that the models that excel at hard math problems frequently fail at these simple reasoning games. Further testing reveals that our evaluated reasoning models score on average downarrow 41\\% \\& downarrow 5\\% lower on TTT-Bench compared to MATH 500 \\& AIME 2024 respectively, with larger models achieving higher performance using shorter reasoning traces, where most of the models struggle on long-term strategic reasoning situations on simple and new TTT-Bench tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-12", "arxiv_id": "2506.09820", "title": "CoRT: Code-integrated Reasoning within Thinking", "authors": [ "Chengpeng Li", "Zhengyang Tang", "Ziniu Li", "Mingfeng Xue", "Keqin Bao", "Tian Ding", "Ruoyu Sun", "Benyou Wang", "Xiang Wang", "Junyang Lin", "Dayiheng Liu" ], "github": "https://github.com/ChengpengLi1003/CoRT", "project_page": "", "abstract": "Large Reasoning Models (LRMs) like o1 and DeepSeek-R1 have shown remarkable progress in natural language reasoning with long chain-of-thought (CoT), yet they remain inefficient or inaccurate when handling complex mathematical operations. Addressing these limitations through computational tools (e.g., computation libraries and symbolic solvers) is promising, but it introduces a technical challenge: Code Interpreter (CI) brings external knowledge beyond the model's internal text representations, thus the direct combination is not efficient. This paper introduces CoRT, a post-training framework for teaching LRMs to leverage CI effectively and efficiently. As a first step, we address the data scarcity issue by synthesizing code-integrated reasoning data through Hint-Engineering, which strategically inserts different hints at appropriate positions to optimize LRM-CI interaction. We manually create 30 high-quality samples, upon which we post-train models ranging from 1.5B to 32B parameters, with supervised fine-tuning, rejection fine-tuning and reinforcement learning. Our experimental results demonstrate that Hint-Engineering models achieve 4\\% and 8\\% absolute improvements on DeepSeek-R1-Distill-Qwen-32B and DeepSeek-R1-Distill-Qwen-1.5B respectively, across five challenging mathematical reasoning datasets. Furthermore, Hint-Engineering models use about 30\\% fewer tokens for the 32B model and 50\\% fewer tokens for the 1.5B model compared with the natural language models. The models and code are available at https://github.com/ChengpengLi1003/CoRT.", "github_manual": "", "github_regex": "https://github.com/ChengpengLi1003/CoRT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10857", "title": "VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos", "authors": [ "Jiashuo Yu", "Yue Wu", "Meng Chu", "Zhifei Ren", "Zizheng Huang", "Pei Chu", "Ruijie Zhang", "Yinan He", "Qirui Li", "Songze Li", "Zhenxiang Li", "Zhongying Tu", "Conghui He", "Yu Qiao", "Yali Wang", "Yi Wang", "Limin Wang" ], "github": "", "project_page": "", "abstract": "We present VRBench, the first long narrative video benchmark crafted for evaluating large models' multi-step reasoning capabilities, addressing limitations in existing evaluations that overlook temporal reasoning and procedural validity. It comprises 1,010 long videos (with an average duration of 1.6 hours), along with 9,468 human-labeled multi-step question-answering pairs and 30,292 reasoning steps with timestamps. These videos are curated via a multi-stage filtering process including expert inter-rater reviewing to prioritize plot coherence. We develop a human-AI collaborative framework that generates coherent reasoning chains, each requiring multiple temporally grounded steps, spanning seven types (e.g., event attribution, implicit inference). VRBench designs a multi-phase evaluation pipeline that assesses models at both the outcome and process levels. Apart from the MCQs for the final results, we propose a progress-level LLM-guided scoring metric to evaluate the quality of the reasoning chain from multiple dimensions comprehensively. Through extensive evaluations of 12 LLMs and 16 VLMs on VRBench, we undertake a thorough analysis and provide valuable insights that advance the field of multi-step reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10954", "title": "SWE-Factory: Your Automated Factory for Issue Resolution Training Data and Evaluation Benchmarks", "authors": [ "Lianghong Guo", "Yanlin Wang", "Caihua Li", "Pengyu Yang", "Jiachi Chen", "Wei Tao", "Yingtian Zou", "Duyu Tang", "Zibin Zheng" ], "github": "https://github.com/DeepSoftwareAnalytics/swe-factory", "project_page": "", "abstract": "Constructing large-scale datasets for the GitHub issue resolution task is crucial for both training and evaluating the software engineering capabilities of Large Language Models (LLMs). However, the traditional process for creating such benchmarks is notoriously challenging and labor-intensive, particularly in the stages of setting up evaluation environments, grading test outcomes, and validating task instances. In this paper, we propose SWE-Factory, an automated pipeline designed to address these challenges. To tackle these issues, our pipeline integrates three core automated components. First, we introduce SWE-Builder, a multi-agent system that automates evaluation environment construction, which employs four specialized agents that work in a collaborative, iterative loop and leverages an environment memory pool to enhance efficiency. Second, we introduce a standardized, exit-code-based grading method that eliminates the need for manually writing custom parsers. Finally, we automate the fail2pass validation process using these reliable exit code signals. Experiments on 671 issues across four programming languages show that our pipeline can effectively construct valid task instances; for example, with GPT-4.1-mini, our SWE-Builder constructs 269 valid instances at 0.045 per instance, while with Gemini-2.5-flash, it achieves comparable performance at the lowest cost of 0.024 per instance. We also demonstrate that our exit-code-based grading achieves 100% accuracy compared to manual inspection, and our automated fail2pass validation reaches a precision of 0.92 and a recall of 1.00. We hope our automated pipeline will accelerate the collection of large-scale, high-quality GitHub issue resolution datasets for both training and evaluation. Our code and datasets are released at https://github.com/DeepSoftwareAnalytics/swe-factory.", "github_manual": "", "github_regex": "https://github.com/DeepSoftwareAnalytics/swe-factory", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.09942", "title": "VerIF: Verification Engineering for Reinforcement Learning in Instruction Following", "authors": [ "Hao Peng", "Yunjia Qi", "Xiaozhi Wang", "Bin Xu", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/THU-KEG/VerIF", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing large language models (LLMs), with verification engineering playing a central role. However, best practices for RL in instruction following remain underexplored. In this work, we explore the verification challenge in RL for instruction following and propose VerIF, a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model (e.g., QwQ-32B). To support this approach, we construct a high-quality instruction-following dataset, VerInstruct, containing approximately 22,000 instances with associated verification signals. We apply RL training with VerIF to two models, achieving significant improvements across several representative instruction-following benchmarks. The trained models reach state-of-the-art performance among models of comparable size and generalize well to unseen constraints. We further observe that their general capabilities remain unaffected, suggesting that RL with VerIF can be integrated into existing RL recipes to enhance overall model performance. We have released our datasets, codes, and models to facilitate future research at https://github.com/THU-KEG/VerIF.", "github_manual": "", "github_regex": "https://github.com/THU-KEG/VerIF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.09993", "title": "Text-Aware Image Restoration with Diffusion Models", "authors": [ "Jaewon Min", "Jin Hyeon Kim", "Paul Hyunbin Cho", "Jaeeun Lee", "Jihye Park", "Minkyu Park", "Sangpil Kim", "Hyunhee Park", "Seungryong Kim" ], "github": "", "project_page": "", "abstract": "Image restoration aims to recover degraded images. However, existing diffusion-based restoration methods, despite great success in natural image restoration, often struggle to faithfully reconstruct textual regions in degraded images. Those methods frequently generate plausible but incorrect text-like patterns, a phenomenon we refer to as text-image hallucination. In this paper, we introduce Text-Aware Image Restoration (TAIR), a novel restoration task that requires the simultaneous recovery of visual contents and textual fidelity. To tackle this task, we present SA-Text, a large-scale benchmark of 100K high-quality scene images densely annotated with diverse and complex text instances. Furthermore, we propose a multi-task diffusion framework, called TeReDiff, that integrates internal features from diffusion models into a text-spotting module, enabling both components to benefit from joint training. This allows for the extraction of rich text representations, which are utilized as prompts in subsequent denoising steps. Extensive experiments demonstrate that our approach consistently outperforms state-of-the-art restoration methods, achieving significant gains in text recognition accuracy. See our project page: https://cvlab-kaist.github.io/TAIR/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.09513", "title": "ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning", "authors": [ "Yu Sun", "Xingyu Qian", "Weiwen Xu", "Hao Zhang", "Chenghao Xiao", "Long Li", "Yu Rong", "Wenbing Huang", "Qifeng Bai", "Tingyang Xu" ], "github": "", "project_page": "", "abstract": "Though reasoning-based large language models (LLMs) have excelled in mathematics and programming, their capabilities in knowledge-intensive medical question answering remain underexplored. To address this, we introduce ReasonMed, the largest medical reasoning dataset, comprising 370k high-quality examples distilled from 1.7 million initial reasoning paths generated by various LLMs. ReasonMed is constructed through a multi-agent verification and refinement process, where we design an Error Refiner to enhance the reasoning paths by identifying and correcting error-prone steps flagged by a verifier. Leveraging ReasonMed, we systematically investigate best practices for training medical reasoning models and find that combining detailed Chain-of-Thought (CoT) reasoning with concise answer summaries yields the most effective fine-tuning strategy. Based on this strategy, we train ReasonMed-7B, which sets a new benchmark for sub-10B models, outperforming the prior best by 4.17\\% and even exceeding LLaMA3.1-70B on PubMedQA by 4.60\\%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.08060", "title": "Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques", "authors": [ "Asankhaya Sharma" ], "github": "", "project_page": "", "abstract": "Large language models have transformed natural language processing, yet supervised fine-tuning (SFT) remains computationally intensive. This paper formally proves that capabilities acquired through SFT can be approximated by a base transformer model using inference-time techniques, specifically in-context learning (ICL), without altering model parameters, under idealized assumptions including unbounded computational resources and access to the fine-tuning dataset. We extend these results to practical scenarios with finite context lengths and partial dataset access. For text generation tasks with fixed output length l, datasets of size Oleft( m V{varepsilon^2} log m{delta} right) or, with bounded context, Oleft( l log V{varepsilon^2} log 1{delta} right) suffice to approximate fine-tuned behavior across m contexts within error varepsilon, where V is the vocabulary size and delta is the failure probability. For linear classification, datasets of size Oleft( d{varepsilon} right) or, with fixed context, Oleft( 1{varepsilon^2} log 1{delta} right) are sufficient, where d is the input dimension. Grounded in the Turing completeness of transformers, these results provide a theoretical foundation for resource-efficient deployment of large language models, with practical techniques like retrieval-augmented generation bridging theory to real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10890", "title": "CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic Design Generation", "authors": [ "Zhao Zhang", "Yutao Cheng", "Dexiang Hong", "Maoke Yang", "Gonglei Shi", "Lei Ma", "Hui Zhang", "Jie Shao", "Xinglong Wu" ], "github": "https://github.com/graphic-design-ai/creatiposter", "project_page": "", "abstract": "Graphic design plays a crucial role in both commercial and personal contexts, yet creating high-quality, editable, and aesthetically pleasing graphic compositions remains a time-consuming and skill-intensive task, especially for beginners. Current AI tools automate parts of the workflow, but struggle to accurately incorporate user-supplied assets, maintain editability, and achieve professional visual appeal. Commercial systems, like Canva Magic Design, rely on vast template libraries, which are impractical for replicate. In this paper, we introduce CreatiPoster, a framework that generates editable, multi-layer compositions from optional natural-language instructions or assets. A protocol model, an RGBA large multimodal model, first produces a JSON specification detailing every layer (text or asset) with precise layout, hierarchy, content and style, plus a concise background prompt. A conditional background model then synthesizes a coherent background conditioned on this rendered foreground layers. We construct a benchmark with automated metrics for graphic-design generation and show that CreatiPoster surpasses leading open-source approaches and proprietary commercial systems. To catalyze further research, we release a copyright-free corpus of 100,000 multi-layer designs. CreatiPoster supports diverse applications such as canvas editing, text overlay, responsive resizing, multilingual adaptation, and animated posters, advancing the democratization of AI-assisted graphic design. Project homepage: https://github.com/graphic-design-ai/creatiposter", "github_manual": "", "github_regex": "https://github.com/graphic-design-ai/creatiposter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10357", "title": "Optimus-3: Towards Generalist Multimodal Minecraft Agents with Scalable Task Experts", "authors": [ "Zaijing Li", "Yuquan Xie", "Rui Shao", "Gongwei Chen", "Weili Guan", "Dongmei Jiang", "Liqiang Nie" ], "github": "", "project_page": "", "abstract": "Recently, agents based on multimodal large language models (MLLMs) have achieved remarkable progress across various domains. However, building a generalist agent with capabilities such as perception, planning, action, grounding, and reflection in open-world environments like Minecraft remains challenges: insufficient domain-specific data, interference among heterogeneous tasks, and visual diversity in open-world settings. In this paper, we address these challenges through three key contributions. 1) We propose a knowledge-enhanced data generation pipeline to provide scalable and high-quality training data for agent development. 2) To mitigate interference among heterogeneous tasks, we introduce a Mixture-of-Experts (MoE) architecture with task-level routing. 3) We develop a Multimodal Reasoning-Augmented Reinforcement Learning approach to enhance the agent's reasoning ability for visual diversity in Minecraft. Built upon these innovations, we present Optimus-3, a general-purpose agent for Minecraft. Extensive experimental results demonstrate that Optimus-3 surpasses both generalist multimodal large language models and existing state-of-the-art agents across a wide range of tasks in the Minecraft environment. Project page: https://cybertronagent.github.io/Optimus-3.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.06561", "title": "LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles", "authors": [ "Ho Yin 'Sam' Ng", "Ting-Yao Hsu", "Aashish Anantha Ramakrishnan", "Branislav Kveton", "Nedim Lipka", "Franck Dernoncourt", "Dongwon Lee", "Tong Yu", "Sungchul Kim", "Ryan A. Rossi", "Ting-Hao 'Kenneth' Huang" ], "github": "https://github.com/Crowd-AI-Lab/lamp-cap", "project_page": "", "abstract": "Figure captions are crucial for helping readers understand and remember a figure's key message. Many models have been developed to generate these captions, helping authors compose better quality captions more easily. Yet, authors almost always need to revise generic AI-generated captions to match their writing style and the domain's style, highlighting the need for personalization. Despite language models' personalization (LaMP) advances, these technologies often focus on text-only settings and rarely address scenarios where both inputs and profiles are multimodal. This paper introduces LaMP-Cap, a dataset for personalized figure caption generation with multimodal figure profiles. For each target figure, LaMP-Cap provides not only the needed inputs, such as figure images, but also up to three other figures from the same document--each with its image, caption, and figure-mentioning paragraphs--as a profile to characterize the context. Experiments with four LLMs show that using profile information consistently helps generate captions closer to the original author-written ones. Ablation studies reveal that images in the profile are more helpful than figure-mentioning paragraphs, highlighting the advantage of using multimodal profiles over text-only ones.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Crowd-AI-Lab/lamp-cap", "github_api_added_by": "auto" }, { "date": "2025-06-13", "arxiv_id": "2506.05982", "title": "MCA-Bench: A Multimodal Benchmark for Evaluating CAPTCHA Robustness Against VLM-based Attacks", "authors": [ "Zonglin Wu", "Yule Xue", "Xin Wei", "Yiren Song" ], "github": "https://github.com/noheadwuzonglin/MCA-Bench", "project_page": "", "abstract": "As automated attack techniques rapidly advance, CAPTCHAs remain a critical defense mechanism against malicious bots. However, existing CAPTCHA schemes encompass a diverse range of modalities -- from static distorted text and obfuscated images to interactive clicks, sliding puzzles, and logic-based questions -- yet the community still lacks a unified, large-scale, multimodal benchmark to rigorously evaluate their security robustness. To address this gap, we introduce MCA-Bench, a comprehensive and reproducible benchmarking suite that integrates heterogeneous CAPTCHA types into a single evaluation protocol. Leveraging a shared vision-language model backbone, we fine-tune specialized cracking agents for each CAPTCHA category, enabling consistent, cross-modal assessments. Extensive experiments reveal that MCA-Bench effectively maps the vulnerability spectrum of modern CAPTCHA designs under varied attack settings, and crucially offers the first quantitative analysis of how challenge complexity, interaction depth, and model solvability interrelate. Based on these findings, we propose three actionable design principles and identify key open challenges, laying the groundwork for systematic CAPTCHA hardening, fair benchmarking, and broader community collaboration. Datasets and code are available online.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/noheadwuzonglin/MCA-Bench", "github_api_added_by": "auto" }, { "date": "2025-06-13", "arxiv_id": "2506.08373", "title": "Draft-based Approximate Inference for LLMs", "authors": [ "Kevin Galim", "Ethan Ewer", "Wonjun Kang", "Minjae Lee", "Hyung Il Koo", "Kangwook Lee" ], "github": "https://github.com/furiosa-ai/draft-based-approx-llm", "project_page": "", "abstract": "Optimizing inference for long-context Large Language Models (LLMs) is increasingly important due to the quadratic compute and linear memory complexity of Transformers. Existing approximation methods, such as key-value (KV) cache dropping, sparse attention, and prompt compression, typically rely on rough predictions of token or KV pair importance. We propose a novel framework for approximate LLM inference that leverages small draft models to more accurately predict the importance of tokens and KV pairs. Specifically, we introduce two instantiations of our proposed framework: (i) SpecKV, which leverages a draft output to accurately assess the importance of each KV pair for more effective KV cache dropping, and (ii) SpecPC, which uses the draft model's attention activations to identify and discard unimportant prompt tokens. To the best of our knowledge, this is the first work to use draft models for approximate LLM inference acceleration, extending their utility beyond traditional lossless speculative decoding. We motivate our methods with theoretical and empirical analyses, and show a strong correlation between the attention patterns of draft and target models. Extensive experiments on long-context benchmarks show that our methods consistently achieve higher accuracy than existing baselines, while preserving the same improvements in memory usage, latency, and throughput. Our code is available at https://github.com/furiosa-ai/draft-based-approx-llm.", "github_manual": "", "github_regex": "https://github.com/furiosa-ai/draft-based-approx-llm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10952", "title": "Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training", "authors": [ "Mozhi Zhang", "Howe Tissue", "Lu Wang", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "We introduce~Domain2Vec, a novel approach that decomposes any dataset into a linear combination of several meta-domains, a new concept designed to capture the key underlying features of datasets. Domain2Vec maintains a vocabulary of meta-domains and uses a classifier to decompose any given dataset into a domain vector that corresponds to a distribution over this vocabulary. These domain vectors enable the identification of the optimal data mixture for language model (LM) pretraining in a training-free manner under the \\textbf{Distribution Alignment Assumption} (DA^{2}), which suggests that when the data distributions of the training set and the validation set are better aligned, a lower validation loss is achieved. Moreover, Domain2vec can be seamlessly integrated into previous works to model the relationship between domain vectors and LM performance, greatly enhancing the efficiency and scalability of previous methods. Extensive experiments demonstrate that Domain2Vec helps find the data mixture that enhances downstream task performance with minimal computational overhead. Specifically, Domain2Vec achieves the same validation loss on Pile-CC using only 51.5% of the computation required when training on the original mixture of The Pile dataset. Under equivalent compute budget, Domain2Vec improves downstream performance by an average of 2.83%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10821", "title": "VideoDeepResearch: Long Video Understanding With Agentic Tool Using", "authors": [ "Huaying Yuan", "Zheng Liu", "Junjie Zhou", "Ji-Rong Wen", "Zhicheng Dou" ], "github": "https://github.com/yhy-2000/VideoDeepResearch", "project_page": "", "abstract": "Long video understanding (LVU) presents a significant challenge for current multi-modal large language models (MLLMs) due to the task's inherent complexity and context window constraint. It is widely assumed that addressing LVU tasks requires foundation MLLMs with extended context windows, strong visual perception capabilities, and proficient domain expertise. In this work, we challenge this common belief by introducing VideoDeepResearch, a novel agentic framework for long video understanding. Our approach relies solely on a text-only large reasoning model (LRM) combined with a modular multi-modal toolkit, including multimodal retrievers and visual perceivers, all of which are readily available in practice. For each LVU task, the system formulates a problem-solving strategy through reasoning, while selectively accessing and utilizing essential video content via tool using. We conduct extensive experiments on popular LVU benchmarks, including MLVU, Video-MME, and LVBench. Our results demonstrate that VideoDeepResearch achieves substantial improvements over existing MLLM baselines, surpassing the previous state-of-the-art by 9.6%, 6.6%, and 3.9% on MLVU (test), LVBench, and LongVideoBench, respectively. These findings highlight the promise of agentic systems in overcoming key challenges in LVU problems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yhy-2000/VideoDeepResearch", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.09344", "title": "Ming-Omni: A Unified Multimodal Model for Perception and Generation", "authors": [ "Inclusion AI", "Biao Gong", "Cheng Zou", "Chuanyang Zheng", "Chunluan Zhou", "Canxiang Yan", "Chunxiang Jin", "Chunjie Shen", "Dandan Zheng", "Fudong Wang", "Furong Xu", "GuangMing Yao", "Jun Zhou", "Jingdong Chen", "Jianxin Sun", "Jiajia Liu", "Jianjiang Zhu", "Jun Peng", "Kaixiang Ji", "Kaiyou Song", "Kaimeng Ren", "Libin Wang", "Lixiang Ru", "Lele Xie", "Longhua Tan", "Lyuxin Xue", "Lan Wang", "Mochen Bai", "Ning Gao", "Pei Chen", "Qingpei Guo", "Qinglong Zhang", "Qiang Xu", "Rui Liu", "Ruijie Xiong", "Sirui Gao", "Tinghao Liu", "Taisong Li", "Weilong Chai", "Xinyu Xiao", "Xiaomei Wang", "Xiaoxue Chen", "Xiao Lu", "Xiaoyu Li", "Xingning Dong", "Xuzheng Yu", "Yi Yuan", "Yuting Gao", "Yunxiao Sun", "Yipeng Chen", "Yifei Wu", "Yongjie Lyu", "Ziping Ma", "Zipeng Feng", "Zhijiang Fang", "Zhihao Qiu", "Ziyuan Huang", "Zhengyu He" ], "github": "", "project_page": "", "abstract": "We propose Ming-Omni, a unified multimodal model capable of processing images, text, audio, and video, while demonstrating strong proficiency in both speech and image generation. Ming-Omni employs dedicated encoders to extract tokens from different modalities, which are then processed by Ling, an MoE architecture equipped with newly proposed modality-specific routers. This design enables a single model to efficiently process and fuse multimodal inputs within a unified framework, thereby facilitating diverse tasks without requiring separate models, task-specific fine-tuning, or structural redesign. Importantly, Ming-Omni extends beyond conventional multimodal models by supporting audio and image generation. This is achieved through the integration of an advanced audio decoder for natural-sounding speech and Ming-Lite-Uni for high-quality image generation, which also allow the model to engage in context-aware chatting, perform text-to-speech conversion, and conduct versatile image editing. Our experimental results showcase Ming-Omni offers a powerful solution for unified perception and generation across all modalities. Notably, our proposed Ming-Omni is the first open-source model we are aware of to match GPT-4o in modality support, and we release all code and model weights to encourage further research and development in the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.09967", "title": "Resa: Transparent Reasoning Models via SAEs", "authors": [ "Shangshang Wang", "Julian Asilis", "\u00d6mer Faruk Akg\u00fcl", "Enes Burak Bilgin", "Ollie Liu", "Deqing Fu", "Willie Neiswanger" ], "github": "https://github.com/shangshang-wang/Resa", "project_page": "", "abstract": "How cost-effectively can we elicit strong reasoning in language models by leveraging their underlying representations? We answer this question with Resa, a family of 1.5B reasoning models trained via a novel and efficient sparse autoencoder tuning (SAE-Tuning) procedure. This method first trains an SAE to capture reasoning abilities from a source model, and then uses the trained SAE to guide a standard supervised fine-tuning process to elicit such abilities in a target model, all using verified question-answer data without any reasoning traces. Notably, when applied to certain base models before further RL post-training, SAE-Tuning retains >97% of its RL-trained counterpart's reasoning performance while reducing training costs by >2000x to roughly \\1 and training time by >450x to around 20 minutes. Furthermore, when applied to lightly RL-trained models (e.g., within 1 hour on 2 GPUs), it enables reasoning performance such as 43.33% Pass@1 on AIME24 and 90% Pass@1 on AMC23 for only around 1 additional cost. Surprisingly, the reasoning abilities extracted via SAEs are potentially both generalizable and modular. Generality means abilities extracted from one dataset still elevate performance on a larger and overlapping corpus. Modularity means abilities extracted from Qwen or Qwen-Math can be attached to the R1-Distill model at test time, without any retraining, and yield comparable gains. Extensive ablations validate these findings and all artifacts are fully open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/shangshang-wang/Resa", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10953", "title": "Build the web for agents, not agents for the web", "authors": [ "Xing Han L\u00f9", "Gaurav Kamath", "Marius Mosbach", "Siva Reddy" ], "github": "", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) and multimodal counterparts have spurred significant interest in developing web agents -- AI systems capable of autonomously navigating and completing tasks within web environments. While holding tremendous promise for automating complex web interactions, current approaches face substantial challenges due to the fundamental mismatch between human-designed interfaces and LLM capabilities. Current methods struggle with the inherent complexity of web inputs, whether processing massive DOM trees, relying on screenshots augmented with additional information, or bypassing the user interface entirely through API interactions. This position paper advocates for a paradigm shift in web agent research: rather than forcing web agents to adapt to interfaces designed for humans, we should develop a new interaction paradigm specifically optimized for agentic capabilities. To this end, we introduce the concept of an Agentic Web Interface (AWI), an interface specifically designed for agents to navigate a website. We establish six guiding principles for AWI design, emphasizing safety, efficiency, and standardization, to account for the interests of all primary stakeholders. This reframing aims to overcome fundamental limitations of existing interfaces, paving the way for more efficient, reliable, and transparent web agent design, which will be a collaborative effort involving the broader ML community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.06694", "title": "Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning", "authors": [ "Yuan Yuan", "Yukun Liu", "Chonghua Han", "Jie Feng", "Yong Li" ], "github": "https://github.com/tsinghua-fib-lab/MoveGCL", "project_page": "", "abstract": "Foundation models have revolutionized fields such as natural language processing and computer vision by enabling general-purpose learning across diverse tasks and datasets. However, building analogous models for human mobility remains challenging due to the privacy-sensitive nature of mobility data and the resulting data silos across institutions. To bridge this gap, we propose MoveGCL, a scalable and privacy-preserving framework for training mobility foundation models via generative continual learning. Without sharing raw data, MoveGCL enables decentralized and progressive model evolution by replaying synthetic trajectories generated from a frozen teacher model, and reinforces knowledge retention through a tailored distillation strategy that mitigates catastrophic forgetting. To address the heterogeneity of mobility patterns, MoveGCL incorporates a Mixture-of-Experts Transformer with a mobility-aware expert routing mechanism, and employs a layer-wise progressive adaptation strategy to stabilize continual updates. Experiments on six real-world urban datasets demonstrate that MoveGCL achieves performance comparable to joint training and significantly outperforms federated learning baselines, while offering strong privacy protection. MoveGCL marks a crucial step toward unlocking foundation models for mobility, offering a practical blueprint for open, scalable, and privacy-preserving model development in the era of foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tsinghua-fib-lab/MoveGCL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.06950", "title": "What Makes a Good Natural Language Prompt?", "authors": [ "Do Xuan Long", "Duy Dinh", "Ngoc-Hai Nguyen", "Kenji Kawaguchi", "Nancy F. Chen", "Shafiq Joty", "Min-Yen Kan" ], "github": "https://github.com/dxlong2000/nlprompteval", "project_page": "", "abstract": "As large language models (LLMs) have progressed towards more human-like and human--AI communications have become prevalent, prompting has emerged as a decisive component. However, there is limited conceptual consensus on what exactly quantifies natural language prompts. We attempt to address this question by conducting a meta-analysis surveying more than 150 prompting-related papers from leading NLP and AI conferences from 2022 to 2025 and blogs. We propose a property- and human-centric framework for evaluating prompt quality, encompassing 21 properties categorized into six dimensions. We then examine how existing studies assess their impact on LLMs, revealing their imbalanced support across models and tasks, and substantial research gaps. Further, we analyze correlations among properties in high-quality natural language prompts, deriving prompting recommendations. We then empirically explore multi-property prompt enhancements in reasoning tasks, observing that single-property enhancements often have the greatest impact. Finally, we discover that instruction-tuning on property-enhanced prompts can result in better reasoning models. Our findings establish a foundation for property-centric prompt evaluation and optimization, bridging the gaps between human--AI communication and opening new prompting research directions.", "github_manual": "https://github.com/dxlong2000/nlprompteval", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10540", "title": "AniMaker: Automated Multi-Agent Animated Storytelling with MCTS-Driven Clip Generation", "authors": [ "Haoyuan Shi", "Yunxin Li", "Xinyu Chen", "Longyue Wang", "Baotian Hu", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Despite rapid advancements in video generation models, generating coherent storytelling videos that span multiple scenes and characters remains challenging. Current methods often rigidly convert pre-generated keyframes into fixed-length clips, resulting in disjointed narratives and pacing issues. Furthermore, the inherent instability of video generation models means that even a single low-quality clip can significantly degrade the entire output animation's logical coherence and visual continuity. To overcome these obstacles, we introduce AniMaker, a multi-agent framework enabling efficient multi-candidate clip generation and storytelling-aware clip selection, thus creating globally consistent and story-coherent animation solely from text input. The framework is structured around specialized agents, including the Director Agent for storyboard generation, the Photography Agent for video clip generation, the Reviewer Agent for evaluation, and the Post-Production Agent for editing and voiceover. Central to AniMaker's approach are two key technical components: MCTS-Gen in Photography Agent, an efficient Monte Carlo Tree Search (MCTS)-inspired strategy that intelligently navigates the candidate space to generate high-potential clips while optimizing resource usage; and AniEval in Reviewer Agent, the first framework specifically designed for multi-shot animation evaluation, which assesses critical aspects such as story-level consistency, action completion, and animation-specific features by considering each clip in the context of its preceding and succeeding clips. Experiments demonstrate that AniMaker achieves superior quality as measured by popular metrics including VBench and our proposed AniEval framework, while significantly improving the efficiency of multi-candidate generation, pushing AI-generated storytelling animation closer to production standards.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10974", "title": "AutoMind: Adaptive Knowledgeable Agent for Automated Data Science", "authors": [ "Yixin Ou", "Yujie Luo", "Jingsheng Zheng", "Lanning Wei", "Shuofei Qiao", "Jintian Zhang", "Da Zheng", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/innovatingAI/AutoMind", "project_page": "", "abstract": "Large Language Model (LLM) agents have shown great potential in addressing real-world data science problems. LLM-driven data science agents promise to automate the entire machine learning pipeline, yet their real-world effectiveness remains limited. Existing frameworks depend on rigid, pre-defined workflows and inflexible coding strategies; consequently, they excel only on relatively simple, classical problems and fail to capture the empirical expertise that human practitioners bring to complex, innovative tasks. In this work, we introduce AutoMind, an adaptive, knowledgeable LLM-agent framework that overcomes these deficiencies through three key advances: (1) a curated expert knowledge base that grounds the agent in domain expert knowledge, (2) an agentic knowledgeable tree search algorithm that strategically explores possible solutions, and (3) a self-adaptive coding strategy that dynamically tailors code generation to task complexity. Evaluations on two automated data science benchmarks demonstrate that AutoMind delivers superior performance versus state-of-the-art baselines. Additional analyses confirm favorable effectiveness, efficiency, and qualitative solution quality, highlighting AutoMind as an efficient and robust step toward fully automated data science.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/innovatingAI/AutoMind", "github_api_added_by": "auto" }, { "date": "2025-06-13", "arxiv_id": "2506.10960", "title": "ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark", "authors": [ "Kangwei Liu", "Siyuan Cheng", "Bozhong Tian", "Xiaozhuan Liang", "Yuyang Yin", "Meng Han", "Ningyu Zhang", "Bryan Hooi", "Xi Chen", "Shumin Deng" ], "github": "https://github.com/zjunlp/ChineseHarm-bench", "project_page": "", "abstract": "Large language models (LLMs) have been increasingly applied to automated harmful content detection tasks, assisting moderators in identifying policy violations and improving the overall efficiency and accuracy of content review. However, existing resources for harmful content detection are predominantly focused on English, with Chinese datasets remaining scarce and often limited in scope. We present a comprehensive, professionally annotated benchmark for Chinese content harm detection, which covers six representative categories and is constructed entirely from real-world data. Our annotation process further yields a knowledge rule base that provides explicit expert knowledge to assist LLMs in Chinese harmful content detection. In addition, we propose a knowledge-augmented baseline that integrates both human-annotated knowledge rules and implicit knowledge from large language models, enabling smaller models to achieve performance comparable to state-of-the-art LLMs. Code and data are available at https://github.com/zjunlp/ChineseHarm-bench.", "github_manual": "", "github_regex": "https://github.com/zjunlp/ChineseHarm-bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10378", "title": "Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning", "authors": [ "Jikai Jin", "Vasilis Syrgkanis", "Sham Kakade", "Hanlin Zhang" ], "github": "https://github.com/hlzhang109/causal-eval", "project_page": "", "abstract": "Faithful evaluation of language model capabilities is crucial for deriving actionable insights that can inform model development. However, rigorous causal evaluations in this domain face significant methodological challenges, including complex confounding effects and prohibitive computational costs associated with extensive retraining. To tackle these challenges, we propose a causal representation learning framework wherein observed benchmark performance is modeled as a linear transformation of a few latent capability factors. Crucially, these latent factors are identified as causally interrelated after appropriately controlling for the base model as a common confounder. Applying this approach to a comprehensive dataset encompassing over 1500 models evaluated across six benchmarks from the Open LLM Leaderboard, we identify a concise three-node linear causal structure that reliably explains the observed performance variations. Further interpretation of this causal structure provides substantial scientific insights beyond simple numerical rankings: specifically, we reveal a clear causal direction starting from general problem-solving capabilities, advancing through instruction-following proficiency, and culminating in mathematical reasoning ability. Our results underscore the essential role of carefully controlling base model variations during evaluation, a step critical to accurately uncovering the underlying causal relationships among latent model capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hlzhang109/causal-eval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10741", "title": "PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework", "authors": [ "SiXiang Chen", "Jianyu Lai", "Jialin Gao", "Tian Ye", "Haoyu Chen", "Hengyu Shi", "Shitong Shao", "Yunlong Lin", "Song Fei", "Zhaohu Xing", "Yeying Jin", "Junfeng Luo", "Xiaoming Wei", "Lei Zhu" ], "github": "", "project_page": "", "abstract": "Generating aesthetic posters is more challenging than simple design images: it requires not only precise text rendering but also the seamless integration of abstract artistic content, striking layouts, and overall stylistic harmony. To address this, we propose PosterCraft, a unified framework that abandons prior modular pipelines and rigid, predefined layouts, allowing the model to freely explore coherent, visually compelling compositions. PosterCraft employs a carefully designed, cascaded workflow to optimize the generation of high-aesthetic posters: (i) large-scale text-rendering optimization on our newly introduced Text-Render-2M dataset; (ii) region-aware supervised fine-tuning on HQ-Poster100K; (iii) aesthetic-text-reinforcement learning via best-of-n preference optimization; and (iv) joint vision-language feedback refinement. Each stage is supported by a fully automated data-construction pipeline tailored to its specific needs, enabling robust training without complex architectural modifications. Evaluated on multiple experiments, PosterCraft significantly outperforms open-source baselines in rendering accuracy, layout coherence, and overall visual appeal-approaching the quality of SOTA commercial systems. Our code, models, and datasets can be found in the Project page: https://ephemeral182.github.io/PosterCraft", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10910", "title": "Magistral", "authors": [ "Mistral-AI", "Abhinav Rastogi", "Albert Q. Jiang", "Andy Lo", "Gabrielle Berrada", "Guillaume Lample", "Jason Rute", "Joep Barmentlo", "Karmesh Yadav", "Kartik Khandelwal", "Khyathi Raghavi Chandu", "L\u00e9onard Blier", "Lucile Saulnier", "Matthieu Dinot", "Maxime Darrin", "Neha Gupta", "Roman Soletskyi", "Sagar Vaze", "Teven Le Scao", "Yihan Wang", "Adam Yang", "Alexander H. Liu", "Alexandre Sablayrolles", "Am\u00e9lie H\u00e9liou", "Am\u00e9lie Martin", "Andy Ehrenberg", "Anmol Agarwal", "Antoine Roux", "Arthur Darcet", "Arthur Mensch", "Baptiste Bout", "Baptiste Rozi\u00e8re", "Baudouin De Monicault", "Chris Bamford", "Christian Wallenwein", "Christophe Renaudin", "Cl\u00e9mence Lanfranchi", "Darius Dabert", "Devon Mizelle", "Diego de las Casas", "Elliot Chane-Sane", "Emilien Fugier", "Emma Bou Hanna", "Gauthier Delerce", "Gauthier Guinet", "Georgii Novikov", "Guillaume Martin", "Himanshu Jaju", "Jan Ludziejewski", "Jean-Hadrien Chabran", "Jean-Malo Delignon", "Joachim Studnia", "Jonas Amar", "Josselin Somerville Roberts", "Julien Denize", "Karan Saxena", "Kush Jain", "Lingxiao Zhao", "Louis Martin", "Luyu Gao", "L\u00e9lio Renard Lavaud", "Marie Pellat", "Mathilde Guillaumin", "Mathis Felardos", "Maximilian Augustin", "Micka\u00ebl Seznec", "Nikhil Raghuraman", "Olivier Duchenne", "Patricia Wang", "Patrick von Platen", "Patryk Saffer", "Paul Jacob", "Paul Wambergue", "Paula Kurylowicz", "Pavankumar Reddy Muddireddy", "Philom\u00e8ne Chagniot", "Pierre Stock", "Pravesh Agrawal", "Romain Sauvestre", "R\u00e9mi Delacourt", "Sanchit Gandhi", "Sandeep Subramanian", "Shashwat Dalal", "Siddharth Gandhi", "Soham Ghosh", "Srijan Mishra", "Sumukh Aithal", "Szymon Antoniak", "Thibault Schueller", "Thibaut Lavril", "Thomas Robert", "Thomas Wang", "Timoth\u00e9e Lacroix", "Valeriia Nemychnikova", "Victor Paltz", "Virgile Richard", "Wen-Ding Li", "William Marshall", "Xuanyu Zhang", "Yunhao Tang" ], "github": "https://github.com/Aider-AI/polyglot-benchmark", "project_page": "", "abstract": "We introduce Magistral, Mistral's first reasoning model and our own scalable reinforcement learning (RL) pipeline. Instead of relying on existing implementations and RL traces distilled from prior models, we follow a ground up approach, relying solely on our own models and infrastructure. Notably, we demonstrate a stack that enabled us to explore the limits of pure RL training of LLMs, present a simple method to force the reasoning language of the model, and show that RL on text data alone maintains most of the initial checkpoint's capabilities. We find that RL on text maintains or improves multimodal understanding, instruction following and function calling. We present Magistral Medium, trained for reasoning on top of Mistral Medium 3 with RL alone, and we open-source Magistral Small (Apache 2.0) which further includes cold-start data from Magistral Medium.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Aider-AI/polyglot-benchmark", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10036", "title": "Token Perturbation Guidance for Diffusion Models", "authors": [ "Javad Rajabi", "Soroush Mehraban", "Seyedmorteza Sadat", "Babak Taati" ], "github": "https://github.com/TaatiTeam/Token-Perturbation-Guidance", "project_page": "", "abstract": "Classifier-free guidance (CFG) has become an essential component of modern diffusion models to enhance both generation quality and alignment with input conditions. However, CFG requires specific training procedures and is limited to conditional generation. To address these limitations, we propose Token Perturbation Guidance (TPG), a novel method that applies perturbation matrices directly to intermediate token representations within the diffusion network. TPG employs a norm-preserving shuffling operation to provide effective and stable guidance signals that improve generation quality without architectural changes. As a result, TPG is training-free and agnostic to input conditions, making it readily applicable to both conditional and unconditional generation. We further analyze the guidance term provided by TPG and show that its effect on sampling more closely resembles CFG compared to existing training-free guidance techniques. Extensive experiments on SDXL and Stable Diffusion 2.1 show that TPG achieves nearly a 2times improvement in FID for unconditional generation over the SDXL baseline, while closely matching CFG in prompt alignment. These results establish TPG as a general, condition-agnostic guidance method that brings CFG-like benefits to a broader class of diffusion models. The code is available at https://github.com/TaatiTeam/Token-Perturbation-Guidance", "github_manual": "", "github_regex": "https://github.com/TaatiTeam/Token-Perturbation-Guidance", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.07795", "title": "LLM Unlearning Should Be Form-Independent", "authors": [ "Xiaotian Ye", "Mengqi Zhang", "Shu Wu" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM) unlearning aims to erase or suppress undesirable knowledge within the model, offering promise for controlling harmful or private information to prevent misuse. However, recent studies highlight its limited efficacy in real-world scenarios, hindering practical adoption. In this study, we identify a pervasive issue underlying many downstream failures: the effectiveness of existing unlearning methods heavily depends on the form of training samples and frequently fails to generalize to alternate expressions of the same knowledge. We formally characterize this problem as Form-Dependent Bias and systematically investigate its specific manifestation patterns across various downstream tasks. To quantify its prevalence and support future research, we introduce ORT, a novel benchmark designed to evaluate the robustness of unlearning methods against variations in knowledge expression. Results reveal that Form-Dependent Bias is both widespread and severe among current techniques. We argue that LLM unlearning should be form-independent to address the endless forms of downstream tasks encountered in real-world security-critical scenarios. Towards this goal, we introduce Rank-one Concept Redirection (ROCR), a novel training-free method, as a promising solution path. ROCR performs unlearning by targeting the invariants in downstream tasks, specifically the activated dangerous concepts. It is capable of modifying model parameters within seconds to redirect the model's perception of a specific unlearning target concept to another harmless concept. Extensive experiments demonstrate that ROCR significantly improves unlearning effectiveness compared to traditional methods while generating highly natural outputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.09952", "title": "UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting", "authors": [ "Ziyi Wang", "Yanran Zhang", "Jie Zhou", "Jiwen Lu" ], "github": "https://github.com/wangzy22/UniPre3D", "project_page": "", "abstract": "The scale diversity of point cloud data presents significant challenges in developing unified representation learning techniques for 3D vision. Currently, there are few unified 3D models, and no existing pre-training method is equally effective for both object- and scene-level point clouds. In this paper, we introduce UniPre3D, the first unified pre-training method that can be seamlessly applied to point clouds of any scale and 3D models of any architecture. Our approach predicts Gaussian primitives as the pre-training task and employs differentiable Gaussian splatting to render images, enabling precise pixel-level supervision and end-to-end optimization. To further regulate the complexity of the pre-training task and direct the model's focus toward geometric structures, we integrate 2D features from pre-trained image models to incorporate well-established texture knowledge. We validate the universal effectiveness of our proposed method through extensive experiments across a variety of object- and scene-level tasks, using diverse point cloud models as backbones. Code is available at https://github.com/wangzy22/UniPre3D.", "github_manual": "", "github_regex": "https://github.com/wangzy22/UniPre3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.08234", "title": "Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions", "authors": [ "Yu-Ang Lee", "Guan-Ting Yi", "Mei-Yi Liu", "Jui-Chao Lu", "Guan-Bo Yang", "Yun-Nung Chen" ], "github": "https://github.com/MiuLab/AISysOpt-Survey", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex AI workflows. By integrating multiple components, compound AI systems have become increasingly adept at performing sophisticated tasks. However, as these systems grow in complexity, new challenges arise in optimizing not only individual components but also their interactions. While traditional optimization methods such as supervised fine-tuning (SFT) and reinforcement learning (RL) remain foundational, the rise of natural language feedback introduces promising new approaches, especially for optimizing non-differentiable systems. This paper provides a systematic review of recent progress in optimizing compound AI systems, encompassing both numerical and language-based techniques. We formalize the notion of compound AI system optimization, classify existing methods along several key dimensions, and highlight open research challenges and future directions in this rapidly evolving field. A list of surveyed papers is publicly available at https://github.com/MiuLab/AISysOpt-Survey.", "github_manual": "", "github_regex": "https://github.com/MiuLab/AISysOpt-Survey", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.08862", "title": "StreamSplat: Towards Online Dynamic 3D Reconstruction from Uncalibrated Video Streams", "authors": [ "Zike Wu", "Qi Yan", "Xuanyu Yi", "Lele Wang", "Renjie Liao" ], "github": "https://github.com/nickwzk/StreamSplat", "project_page": "", "abstract": "Real-time reconstruction of dynamic 3D scenes from uncalibrated video streams is crucial for numerous real-world applications. However, existing methods struggle to jointly address three key challenges: 1) processing uncalibrated inputs in real time, 2) accurately modeling dynamic scene evolution, and 3) maintaining long-term stability and computational efficiency. To this end, we introduce StreamSplat, the first fully feed-forward framework that transforms uncalibrated video streams of arbitrary length into dynamic 3D Gaussian Splatting (3DGS) representations in an online manner, capable of recovering scene dynamics from temporally local observations. We propose two key technical innovations: a probabilistic sampling mechanism in the static encoder for 3DGS position prediction, and a bidirectional deformation field in the dynamic decoder that enables robust and efficient dynamic modeling. Extensive experiments on static and dynamic benchmarks demonstrate that StreamSplat consistently outperforms prior works in both reconstruction quality and dynamic scene modeling, while uniquely supporting online reconstruction of arbitrarily long video streams. Code and models are available at https://github.com/nickwzk/StreamSplat.", "github_manual": "", "github_regex": "https://github.com/nickwzk/StreamSplat", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10178", "title": "Attention, Please! Revisiting Attentive Probing for Masked Image Modeling", "authors": [ "Bill Psomas", "Dionysis Christopoulos", "Eirini Baltzi", "Ioannis Kakogeorgiou", "Tilemachos Aravanis", "Nikos Komodakis", "Konstantinos Karantzalos", "Yannis Avrithis", "Giorgos Tolias" ], "github": "https://github.com/billpsomas/efficient-probing", "project_page": "", "abstract": "As fine-tuning (FT) becomes increasingly impractical at scale, probing is emerging as the preferred evaluation protocol for self-supervised learning (SSL). Yet, the standard linear probing (LP) fails to adequately reflect the potential of models trained with Masked Image Modeling (MIM), due to the distributed nature of patch tokens. This motivates the need for attentive probing, an alternative that uses attention to selectively aggregate patch-level features. Despite its growing adoption, attentive probing remains under-explored, with existing methods suffering from excessive parameterization and poor computational efficiency. In this work, we revisit attentive probing through the lens of the accuracy-efficiency trade-off. We conduct a systematic study of existing methods, analyzing their mechanisms and benchmarking their performance. We introduce efficient probing (EP), a multi-query cross-attention mechanism that eliminates redundant projections, reduces the number of trainable parameters, and achieves up to a 10times speed-up over conventional multi-head attention. Despite its simplicity, EP outperforms LP and prior attentive probing approaches across seven benchmarks, generalizes well beyond MIM to diverse pre-training paradigms, produces interpretable attention maps, and achieves strong gains in low-shot and layer-wise settings. Code available at https://github.com/billpsomas/efficient-probing.", "github_manual": "", "github_regex": "https://github.com/billpsomas/efficient-probing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10568", "title": "DreamActor-H1: High-Fidelity Human-Product Demonstration Video Generation via Motion-designed Diffusion Transformers", "authors": [ "Lizhen Wang", "Zhurong Xia", "Tianshu Hu", "Pengrui Wang", "Pengfei Wang", "Zerong Zheng", "Ming Zhou" ], "github": "", "project_page": "", "abstract": "In e-commerce and digital marketing, generating high-fidelity human-product demonstration videos is important for effective product presentation. However, most existing frameworks either fail to preserve the identities of both humans and products or lack an understanding of human-product spatial relationships, leading to unrealistic representations and unnatural interactions. To address these challenges, we propose a Diffusion Transformer (DiT)-based framework. Our method simultaneously preserves human identities and product-specific details, such as logos and textures, by injecting paired human-product reference information and utilizing an additional masked cross-attention mechanism. We employ a 3D body mesh template and product bounding boxes to provide precise motion guidance, enabling intuitive alignment of hand gestures with product placements. Additionally, structured text encoding is used to incorporate category-level semantics, enhancing 3D consistency during small rotational changes across frames. Trained on a hybrid dataset with extensive data augmentation strategies, our approach outperforms state-of-the-art techniques in maintaining the identity integrity of both humans and products and generating realistic demonstration motions. Project page: https://submit2025-dream.github.io/DreamActor-H1/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10978", "title": "Fine-Grained Perturbation Guidance via Attention Head Selection", "authors": [ "Donghoon Ahn", "Jiwon Kang", "Sanghyun Lee", "Minjae Kim", "Jaewon Min", "Wooseok Jang", "Saungwu Lee", "Sayak Paul", "Susung Hong", "Seungryong Kim" ], "github": "", "project_page": "", "abstract": "Recent guidance methods in diffusion models steer reverse sampling by perturbing the model to construct an implicit weak model and guide generation away from it. Among these approaches, attention perturbation has demonstrated strong empirical performance in unconditional scenarios where classifier-free guidance is not applicable. However, existing attention perturbation methods lack principled approaches for determining where perturbations should be applied, particularly in Diffusion Transformer (DiT) architectures where quality-relevant computations are distributed across layers. In this paper, we investigate the granularity of attention perturbations, ranging from the layer level down to individual attention heads, and discover that specific heads govern distinct visual concepts such as structure, style, and texture quality. Building on this insight, we propose \"HeadHunter\", a systematic framework for iteratively selecting attention heads that align with user-centric objectives, enabling fine-grained control over generation quality and visual attributes. In addition, we introduce SoftPAG, which linearly interpolates each selected head's attention map toward an identity matrix, providing a continuous knob to tune perturbation strength and suppress artifacts. Our approach not only mitigates the oversmoothing issues of existing layer-level perturbation but also enables targeted manipulation of specific visual styles through compositional head selection. We validate our method on modern large-scale DiT-based text-to-image models including Stable Diffusion 3 and FLUX.1, demonstrating superior performance in both general quality enhancement and style-specific guidance. Our work provides the first head-level analysis of attention perturbation in diffusion models, uncovering interpretable specialization within attention layers and enabling practical design of effective perturbation strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10274", "title": "Discrete Audio Tokens: More Than a Survey!", "authors": [ "Pooneh Mousavi", "Gallil Maimon", "Adel Moumen", "Darius Petermann", "Jiatong Shi", "Haibin Wu", "Haici Yang", "Anastasia Kuznetsova", "Artem Ploujnikov", "Ricard Marxer", "Bhuvana Ramabhadran", "Benjamin Elizalde", "Loren Lugosch", "Jinyu Li", "Cem Subakan", "Phil Woodland", "Minje Kim", "Hung-yi Lee", "Shinji Watanabe", "Yossi Adi", "Mirco Ravanelli" ], "github": "", "project_page": "", "abstract": "Discrete audio tokens are compact representations that aim to preserve perceptual quality, phonetic content, and speaker characteristics while enabling efficient storage and inference, as well as competitive performance across diverse downstream tasks.They provide a practical alternative to continuous features, enabling the integration of speech and audio into modern large language models (LLMs). As interest in token-based audio processing grows, various tokenization methods have emerged, and several surveys have reviewed the latest progress in the field. However, existing studies often focus on specific domains or tasks and lack a unified comparison across various benchmarks. This paper presents a systematic review and benchmark of discrete audio tokenizers, covering three domains: speech, music, and general audio. We propose a taxonomy of tokenization approaches based on encoder-decoder, quantization techniques, training paradigm, streamability, and application domains. We evaluate tokenizers on multiple benchmarks for reconstruction, downstream performance, and acoustic language modeling, and analyze trade-offs through controlled ablation studies. Our findings highlight key limitations, practical considerations, and open challenges, providing insight and guidance for future research in this rapidly evolving area. For more information, including our main results and tokenizer database, please refer to our website: https://poonehmousavi.github.io/dates-website/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10728", "title": "Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims", "authors": [ "Priyanka Kargupta", "Runchu Tian", "Jiawei Han" ], "github": "", "project_page": "", "abstract": "Claims made by individuals or entities are oftentimes nuanced and cannot be clearly labeled as entirely \"true\" or \"false\" -- as is frequently the case with scientific and political claims. However, a claim (e.g., \"vaccine A is better than vaccine B\") can be dissected into its integral aspects and sub-aspects (e.g., efficacy, safety, distribution), which are individually easier to validate. This enables a more comprehensive, structured response that provides a well-rounded perspective on a given problem while also allowing the reader to prioritize specific angles of interest within the claim (e.g., safety towards children). Thus, we propose ClaimSpect, a retrieval-augmented generation-based framework for automatically constructing a hierarchy of aspects typically considered when addressing a claim and enriching them with corpus-specific perspectives. This structure hierarchically partitions an input corpus to retrieve relevant segments, which assist in discovering new sub-aspects. Moreover, these segments enable the discovery of varying perspectives towards an aspect of the claim (e.g., support, neutral, or oppose) and their respective prevalence (e.g., \"how many biomedical papers believe vaccine A is more transportable than B?\"). We apply ClaimSpect to a wide variety of real-world scientific and political claims featured in our constructed dataset, showcasing its robustness and accuracy in deconstructing a nuanced claim and representing perspectives within a corpus. Through real-world case studies and human evaluation, we validate its effectiveness over multiple baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10737", "title": "TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to Evolving Research Corpora", "authors": [ "Priyanka Kargupta", "Nan Zhang", "Yunyi Zhang", "Rui Zhang", "Prasenjit Mitra", "Jiawei Han" ], "github": "", "project_page": "", "abstract": "The rapid evolution of scientific fields introduces challenges in organizing and retrieving scientific literature. While expert-curated taxonomies have traditionally addressed this need, the process is time-consuming and expensive. Furthermore, recent automatic taxonomy construction methods either (1) over-rely on a specific corpus, sacrificing generalizability, or (2) depend heavily on the general knowledge of large language models (LLMs) contained within their pre-training datasets, often overlooking the dynamic nature of evolving scientific domains. Additionally, these approaches fail to account for the multi-faceted nature of scientific literature, where a single research paper may contribute to multiple dimensions (e.g., methodology, new tasks, evaluation metrics, benchmarks). To address these gaps, we propose TaxoAdapt, a framework that dynamically adapts an LLM-generated taxonomy to a given corpus across multiple dimensions. TaxoAdapt performs iterative hierarchical classification, expanding both the taxonomy width and depth based on corpus' topical distribution. We demonstrate its state-of-the-art performance across a diverse set of computer science conferences over the years to showcase its ability to structure and capture the evolution of scientific fields. As a multidimensional method, TaxoAdapt generates taxonomies that are 26.51% more granularity-preserving and 50.41% more coherent than the most competitive baselines judged by LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10674", "title": "TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving", "authors": [ "Vincenzo Colle", "Mohamed Sana", "Nicola Piovesan", "Antonio De Domenico", "Fadhel Ayed", "Merouane Debbah" ], "github": "", "project_page": "", "abstract": "The increasing adoption of artificial intelligence in telecommunications has raised interest in the capability of Large Language Models (LLMs) to address domain-specific, mathematically intensive tasks. Although recent advancements have improved the performance of LLMs in general mathematical reasoning, their effectiveness within specialized domains, such as signal processing, network optimization, and performance analysis, remains largely unexplored. To address this gap, we introduce TeleMath, the first benchmark dataset specifically designed to evaluate LLM performance in solving mathematical problems with numerical solutions in the telecommunications domain. Comprising 500 question-answer (QnA) pairs, TeleMath covers a wide spectrum of topics in the telecommunications field. This paper outlines the proposed QnAs generation pipeline, starting from a selected seed of problems crafted by Subject Matter Experts. The evaluation of a wide range of open-source LLMs reveals that best performance on TeleMath is achieved by recent models explicitly designed for mathematical or logical reasoning. In contrast, general-purpose models, even those with a large number of parameters, often struggle with these challenges. We have released the dataset and the evaluation code to ease result reproducibility and support future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10920", "title": "Decomposing MLP Activations into Interpretable Features via Semi-Nonnegative Matrix Factorization", "authors": [ "Or Shafran", "Atticus Geiger", "Mor Geva" ], "github": "https://github.com/ordavid-s/snmf-mlp-decomposition", "project_page": "", "abstract": "A central goal for mechanistic interpretability has been to identify the right units of analysis in large language models (LLMs) that causally explain their outputs. While early work focused on individual neurons, evidence that neurons often encode multiple concepts has motivated a shift toward analyzing directions in activation space. A key question is how to find directions that capture interpretable features in an unsupervised manner. Current methods rely on dictionary learning with sparse autoencoders (SAEs), commonly trained over residual stream activations to learn directions from scratch. However, SAEs often struggle in causal evaluations and lack intrinsic interpretability, as their learning is not explicitly tied to the computations of the model. Here, we tackle these limitations by directly decomposing MLP activations with semi-nonnegative matrix factorization (SNMF), such that the learned features are (a) sparse linear combinations of co-activated neurons, and (b) mapped to their activating inputs, making them directly interpretable. Experiments on Llama 3.1, Gemma 2 and GPT-2 show that SNMF derived features outperform SAEs and a strong supervised baseline (difference-in-means) on causal steering, while aligning with human-interpretable concepts. Further analysis reveals that specific neuron combinations are reused across semantically-related features, exposing a hierarchical structure in the MLP's activation space. Together, these results position SNMF as a simple and effective tool for identifying interpretable features and dissecting concept representations in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ordavid-s/snmf-mlp-decomposition", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.10600", "title": "EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence", "authors": [ "Wang Xinjie", "Liu Liu", "Cao Yu", "Wu Ruiqi", "Qin Wenkang", "Wang Dehui", "Sui Wei", "Su Zhizhong" ], "github": "", "project_page": "", "abstract": "Constructing a physically realistic and accurately scaled simulated 3D world is crucial for the training and evaluation of embodied intelligence tasks. The diversity, realism, low cost accessibility and affordability of 3D data assets are critical for achieving generalization and scalability in embodied AI. However, most current embodied intelligence tasks still rely heavily on traditional 3D computer graphics assets manually created and annotated, which suffer from high production costs and limited realism. These limitations significantly hinder the scalability of data driven approaches. We present EmbodiedGen, a foundational platform for interactive 3D world generation. It enables the scalable generation of high-quality, controllable and photorealistic 3D assets with accurate physical properties and real-world scale in the Unified Robotics Description Format (URDF) at low cost. These assets can be directly imported into various physics simulation engines for fine-grained physical control, supporting downstream tasks in training and evaluation. EmbodiedGen is an easy-to-use, full-featured toolkit composed of six key modules: Image-to-3D, Text-to-3D, Texture Generation, Articulated Object Generation, Scene Generation and Layout Generation. EmbodiedGen generates diverse and interactive 3D worlds composed of generative 3D assets, leveraging generative AI to address the challenges of generalization and evaluation to the needs of embodied intelligence related research. Code is available at https://horizonrobotics.github.io/robot_lab/embodied_gen/index.html.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.06952", "title": "LaTtE-Flow: Layerwise Timestep-Expert Flow-based Transformer", "authors": [ "Ying Shen", "Zhiyang Xu", "Jiuhai Chen", "Shizhe Diao", "Jiaxin Zhang", "Yuguang Yao", "Joy Rimchala", "Ismini Lourentzou", "Lifu Huang" ], "github": "https://github.com/yingShen-ys/LaTtE-Flow", "project_page": "", "abstract": "Recent advances in multimodal foundation models unifying image understanding and generation have opened exciting avenues for tackling a wide range of vision-language tasks within a single framework. Despite progress, existing unified models typically require extensive pretraining and struggle to achieve the same level of performance compared to models dedicated to each task. Additionally, many of these models suffer from slow image generation speeds, limiting their practical deployment in real-time or resource-constrained settings. In this work, we propose Layerwise Timestep-Expert Flow-based Transformer (LaTtE-Flow), a novel and efficient architecture that unifies image understanding and generation within a single multimodal model. LaTtE-Flow builds upon powerful pretrained Vision-Language Models (VLMs) to inherit strong multimodal understanding capabilities, and extends them with a novel Layerwise Timestep Experts flow-based architecture for efficient image generation. LaTtE-Flow distributes the flow-matching process across specialized groups of Transformer layers, each responsible for a distinct subset of timesteps. This design significantly improves sampling efficiency by activating only a small subset of layers at each sampling timestep. To further enhance performance, we propose a Timestep-Conditioned Residual Attention mechanism for efficient information reuse across layers. Experiments demonstrate that LaTtE-Flow achieves strong performance on multimodal understanding tasks, while achieving competitive image generation quality with around 6x faster inference speed compared to recent unified multimodal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yingShen-ys/LaTtE-Flow", "github_api_added_by": "auto" }, { "date": "2025-06-13", "arxiv_id": "2506.10911", "title": "NoLoCo: No-all-reduce Low Communication Training Method for Large Models", "authors": [ "Jari Kolehmainen", "Nikolay Blagoev", "John Donaghy", "O\u011fuzhan Ersoy", "Christopher Nies" ], "github": "", "project_page": "", "abstract": "Training large language models is generally done via optimization methods on clusters containing tens of thousands of accelerators, communicating over a high-bandwidth interconnect. Scaling up these clusters is expensive and can become impractical, imposing limits on the size of models that can be trained. Several recent studies have proposed training methods that are less communication intensive, avoiding the need for a highly connected compute cluster. These state-of-the-art low communication training methods still employ a synchronization step for model parameters, which, when performed over all model replicas, can become costly on a low-bandwidth network. In this work, we propose a novel optimization method, NoLoCo, that does not explicitly synchronize all model parameters during training and, as a result, does not require any collective communication. NoLoCo implicitly synchronizes model weights via a novel variant of the Nesterov momentum optimizer by partially averaging model weights with a randomly selected other one. We provide both a theoretical convergence analysis for our proposed optimizer as well as empirical results from language model training. We benchmark NoLoCo on a wide range of accelerator counts and model sizes, between 125M to 6.8B parameters. Our method requires significantly less communication overhead than fully sharded data parallel training or even widely used low communication training method, DiLoCo. The synchronization step itself is estimated to be one magnitude faster than the all-reduce used in DiLoCo for few hundred accelerators training over the internet. We also do not have any global blocking communication that reduces accelerator idling time. Compared to DiLoCo, we also observe up to 4% faster convergence rate with wide range of model sizes and accelerator counts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-13", "arxiv_id": "2506.09250", "title": "Comment on The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity", "authors": [ "C. Opus", "A. Lawsen" ], "github": "", "project_page": "", "abstract": "Shojaee et al. (2025) report that Large Reasoning Models (LRMs) exhibit \"accuracy collapse\" on planning puzzles beyond certain complexity thresholds. We demonstrate that their findings primarily reflect experimental design limitations rather than fundamental reasoning failures. Our analysis reveals three critical issues: (1) Tower of Hanoi experiments systematically exceed model output token limits at reported failure points, with models explicitly acknowledging these constraints in their outputs; (2) The authors' automated evaluation framework fails to distinguish between reasoning failures and practical constraints, leading to misclassification of model capabilities; (3) Most concerningly, their River Crossing benchmarks include mathematically impossible instances for N > 5 due to insufficient boat capacity, yet models are scored as failures for not solving these unsolvable problems. When we control for these experimental artifacts, by requesting generating functions instead of exhaustive move lists, preliminary experiments across multiple models indicate high accuracy on Tower of Hanoi instances previously reported as complete failures. These findings highlight the importance of careful experimental design when evaluating AI reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.11924", "title": "Aligned Novel View Image and Geometry Synthesis via Cross-modal Attention Instillation", "authors": [ "Min-Seop Kwak", "Junho Kim", "Sangdoo Yun", "Dongyoon Han", "Taekyoung Kim", "Seungryong Kim", "Jin-Hwa Kim" ], "github": "", "project_page": "", "abstract": "We introduce a diffusion-based framework that performs aligned novel view image and geometry generation via a warping-and-inpainting methodology. Unlike prior methods that require dense posed images or pose-embedded generative models limited to in-domain views, our method leverages off-the-shelf geometry predictors to predict partial geometries viewed from reference images, and formulates novel-view synthesis as an inpainting task for both image and geometry. To ensure accurate alignment between generated images and geometry, we propose cross-modal attention distillation, where attention maps from the image diffusion branch are injected into a parallel geometry diffusion branch during both training and inference. This multi-task approach achieves synergistic effects, facilitating geometrically robust image synthesis as well as well-defined geometry prediction. We further introduce proximity-based mesh conditioning to integrate depth and normal cues, interpolating between point cloud and filtering erroneously predicted geometry from influencing the generation process. Empirically, our method achieves high-fidelity extrapolative view synthesis on both image and geometry across a range of unseen scenes, delivers competitive reconstruction quality under interpolation settings, and produces geometrically aligned colored point clouds for comprehensive 3D completion. Project page is available at https://cvlab-kaist.github.io/MoAI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.11928", "title": "LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?", "authors": [ "Zihan Zheng", "Zerui Cheng", "Zeyu Shen", "Shang Zhou", "Kaiyuan Liu", "Hansen He", "Dongruixuan Li", "Stanley Wei", "Hangyi Hao", "Jianzhu Yao", "Peiyao Sheng", "Zixuan Wang", "Wenhao Chai", "Aleksandra Korolova", "Peter Henderson", "Sanjeev Arora", "Pramod Viswanath", "Jingbo Shang", "Saining Xie" ], "github": "", "project_page": "", "abstract": "Recent reports claim that large language models (LLMs) now outperform elite humans in competitive programming. Drawing on knowledge from a group of medalists in international algorithmic contests, we revisit this claim, examining how LLMs differ from human experts and where limitations still remain. We introduce LiveCodeBench Pro, a benchmark composed of problems from Codeforces, ICPC, and IOI that are continuously updated to reduce the likelihood of data contamination. A team of Olympiad medalists annotates every problem for algorithmic categories and conducts a line-by-line analysis of failed model-generated submissions. Using this new data and benchmark, we find that frontier models still have significant limitations: without external tools, the best model achieves only 53% pass@1 on medium-difficulty problems and 0% on hard problems, domains where expert humans still excel. We also find that LLMs succeed at implementation-heavy problems but struggle with nuanced algorithmic reasoning and complex case analysis, often generating confidently incorrect justifications. High performance appears largely driven by implementation precision and tool augmentation, not superior reasoning. LiveCodeBench Pro thus highlights the significant gap to human grandmaster levels, while offering fine-grained diagnostics to steer future improvements in code-centric LLM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.11702", "title": "Configurable Preference Tuning with Rubric-Guided Synthetic Data", "authors": [ "V\u00edctor Gallego" ], "github": "https://github.com/vicgalle/configurable-preference-tuning", "project_page": "", "abstract": "Models of human feedback for AI alignment, such as those underpinning Direct Preference Optimization (DPO), often bake in a singular, static set of preferences, limiting adaptability. This paper challenges the assumption of monolithic preferences by introducing Configurable Preference Tuning (CPT), a novel framework for endowing language models with the ability to dynamically adjust their behavior based on explicit, human-interpretable directives. CPT leverages synthetically generated preference data, conditioned on system prompts derived from structured, fine-grained rubrics that define desired attributes like writing style. By fine-tuning with these rubric-guided preferences, the LLM learns to modulate its outputs at inference time in response to the system prompt, without retraining. This approach not only offers fine-grained control but also provides a mechanism for modeling more nuanced and context-dependent human feedback. Several experimental artifacts, such as training code, generated datasets and fine-tuned models are released at https://github.com/vicgalle/configurable-preference-tuning", "github_manual": "", "github_regex": "https://github.com/vicgalle/configurable-preference-tuning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.09427", "title": "A High-Quality Dataset and Reliable Evaluation for Interleaved Image-Text Generation", "authors": [ "Yukang Feng", "Jianwen Sun", "Chuanhao Li", "Zizhen Li", "Jiaxin Ai", "Fanrui Zhang", "Yifan Chang", "Sizhuo Zhou", "Shenglin Zhang", "Yu Dai", "Kaipeng Zhang" ], "github": "", "project_page": "", "abstract": "Recent advancements in Large Multimodal Models (LMMs) have significantly improved multimodal understanding and generation. However, these models still struggle to generate tightly interleaved image-text outputs, primarily due to the limited scale, quality and instructional richness of current training datasets. To address this, we introduce InterSyn, a large-scale multimodal dataset constructed using our Self-Evaluation with Iterative Refinement (SEIR) method. InterSyn features multi-turn, instruction-driven dialogues with tightly interleaved imagetext responses, providing rich object diversity and rigorous automated quality refinement, making it well-suited for training next-generation instruction-following LMMs. Furthermore, to address the lack of reliable evaluation tools capable of assessing interleaved multimodal outputs, we introduce SynJudge, an automatic evaluation model designed to quantitatively assess multimodal outputs along four dimensions: text content, image content, image quality, and image-text synergy. Experimental studies show that the SEIR method leads to substantially higher dataset quality compared to an otherwise identical process without refinement. Moreover, LMMs trained on InterSyn achieve uniform performance gains across all evaluation metrics, confirming InterSyn's utility for advancing multimodal systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.10892", "title": "The Diffusion Duality", "authors": [ "Subham Sekhar Sahoo", "Justin Deschenaux", "Aaron Gokaslan", "Guanghan Wang", "Justin Chiu", "Volodymyr Kuleshov" ], "github": "", "project_page": "", "abstract": "Uniform-state discrete diffusion models hold the promise of fast text generation due to their inherent ability to self-correct. However, they are typically outperformed by autoregressive models and masked diffusion models. In this work, we narrow this performance gap by leveraging a key insight: Uniform-state diffusion processes naturally emerge from an underlying Gaussian diffusion. Our method, Duo, transfers powerful techniques from Gaussian diffusion to improve both training and sampling. First, we introduce a curriculum learning strategy guided by the Gaussian process, doubling training speed by reducing variance. Models trained with curriculum learning surpass autoregressive models in zero-shot perplexity on 3 of 7 benchmarks. Second, we present Discrete Consistency Distillation, which adapts consistency distillation from the continuous to the discrete setting. This algorithm unlocks few-step generation in diffusion language models by accelerating sampling by two orders of magnitude. We provide the code and model checkpoints on the project page: http://s-sahoo.github.io/duo", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.09366", "title": "SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending", "authors": [ "Yuxuan Kuang", "Haoran Geng", "Amine Elhafsi", "Tan-Dzung Do", "Pieter Abbeel", "Jitendra Malik", "Marco Pavone", "Yue Wang" ], "github": "", "project_page": "", "abstract": "Humanoid robots hold significant potential in accomplishing daily tasks across diverse environments thanks to their flexibility and human-like morphology. Recent works have made significant progress in humanoid whole-body control and loco-manipulation leveraging optimal control or reinforcement learning. However, these methods require tedious task-specific tuning for each task to achieve satisfactory behaviors, limiting their versatility and scalability to diverse tasks in daily scenarios. To that end, we introduce SkillBlender, a novel hierarchical reinforcement learning framework for versatile humanoid loco-manipulation. SkillBlender first pretrains goal-conditioned task-agnostic primitive skills, and then dynamically blends these skills to accomplish complex loco-manipulation tasks with minimal task-specific reward engineering. We also introduce SkillBench, a parallel, cross-embodiment, and diverse simulated benchmark containing three embodiments, four primitive skills, and eight challenging loco-manipulation tasks, accompanied by a set of scientific evaluation metrics balancing accuracy and feasibility. Extensive simulated experiments show that our method significantly outperforms all baselines, while naturally regularizing behaviors to avoid reward hacking, resulting in more accurate and feasible movements for diverse loco-manipulation tasks in our daily scenarios. Our code and benchmark will be open-sourced to the community to facilitate future research. Project page: https://usc-gvl.github.io/SkillBlender-web/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.08477", "title": "Detecting Harmful Memes with Decoupled Understanding and Guided CoT Reasoning", "authors": [ "Fengjun Pan", "Anh Tuan Luu", "Xiaobao Wu" ], "github": "https://github.com/panFJCharlotte98/HMC", "project_page": "", "abstract": "Detecting harmful memes is essential for maintaining the integrity of online environments. However, current approaches often struggle with resource efficiency, flexibility, or explainability, limiting their practical deployment in content moderation systems. To address these challenges, we introduce U-CoT+, a novel framework for harmful meme detection. Instead of relying solely on prompting or fine-tuning multimodal models, we first develop a high-fidelity meme-to-text pipeline that converts visual memes into detail-preserving textual descriptions. This design decouples meme interpretation from meme classification, thus avoiding immediate reasoning over complex raw visual content and enabling resource-efficient harmful meme detection with general large language models (LLMs). Building on these textual descriptions, we further incorporate targeted, interpretable human-crafted guidelines to guide models' reasoning under zero-shot CoT prompting. As such, this framework allows for easy adaptation to different harmfulness detection criteria across platforms, regions, and over time, offering high flexibility and explainability. Extensive experiments on seven benchmark datasets validate the effectiveness of our framework, highlighting its potential for explainable and low-resource harmful meme detection using small-scale LLMs. Codes and data are available at: https://anonymous.4open.science/r/HMC-AF2B/README.md.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/panFJCharlotte98/HMC", "github_api_added_by": "auto" }, { "date": "2025-06-16", "arxiv_id": "2506.09600", "title": "Effective Red-Teaming of Policy-Adherent Agents", "authors": [ "Itay Nakash", "George Kour", "Koren Lazar", "Matan Vetzler", "Guy Uziel", "Ateret Anaby-Tavor" ], "github": "", "project_page": "", "abstract": "Task-oriented LLM-based agents are increasingly used in domains with strict policies, such as refund eligibility or cancellation rules. The challenge lies in ensuring that the agent consistently adheres to these rules and policies, appropriately refusing any request that would violate them, while still maintaining a helpful and natural interaction. This calls for the development of tailored design and evaluation methodologies to ensure agent resilience against malicious user behavior. We propose a novel threat model that focuses on adversarial users aiming to exploit policy-adherent agents for personal benefit. To address this, we present CRAFT, a multi-agent red-teaming system that leverages policy-aware persuasive strategies to undermine a policy-adherent agent in a customer-service scenario, outperforming conventional jailbreak methods such as DAN prompts, emotional manipulation, and coercive. Building upon the existing tau-bench benchmark, we introduce tau-break, a complementary benchmark designed to rigorously assess the agent's robustness against manipulative user behavior. Finally, we evaluate several straightforward yet effective defense strategies. While these measures provide some protection, they fall short, highlighting the need for stronger, research-driven safeguards to protect policy-adherent agents from adversarial attacks", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.08592", "title": "Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings", "authors": [ "Liyan Xu", "Zhenlin Su", "Mo Yu", "Jiangnan Li", "Fandong Meng", "Jie Zhou" ], "github": "https://github.com/lxucs/CapRetrieval", "project_page": "", "abstract": "This work focuses on an observed limitation of text encoders: embeddings may not be able to recognize fine-grained entities or events within the semantics, resulting in failed dense retrieval on even simple cases. To examine such behaviors, we first introduce a new evaluation dataset in Chinese, named CapRetrieval, whose passages are image captions, and queries are phrases inquiring entities or events in various forms. Zero-shot evaluation suggests that encoders may fail on these fine-grained matching, regardless of training sources or model sizes. Aiming for enhancement, we proceed to finetune encoders with our proposed data generation strategies, which obtains the best performance on CapRetrieval. Within this process, we further identify an issue of granularity dilemma, a challenge for embeddings to express fine-grained salience while aligning with overall semantics. Our dataset, code and models in this work are publicly released at https://github.com/lxucs/CapRetrieval.", "github_manual": "", "github_regex": "https://github.com/lxucs/CapRetrieval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.11997", "title": "pLSTM: parallelizable Linear Source Transition Mark networks", "authors": [ "Korbinian P\u00f6ppel", "Richard Freinschlag", "Thomas Schmied", "Wei Lin", "Sepp Hochreiter" ], "github": "https://github.com/ml-jku/plstm_experiments", "project_page": "", "abstract": "Modern recurrent architectures, such as xLSTM and Mamba, have recently challenged the Transformer in language modeling. However, their structure constrains their applicability to sequences only or requires processing multi-dimensional data structures, such as images or molecular graphs, in a pre-defined sequential order. In contrast, Multi-Dimensional RNNs (MDRNNs) are well suited for data with a higher level structure, like 2D grids, trees, and directed acyclic graphs (DAGs). In this work, we extend the notion of multi-dimensionality to linear RNNs. We introduce parallelizable Linear Source Transition Mark networks (pLSTMs) using Source, Transition, and Mark gates that act on the line graph of a general DAG. This enables parallelization in analogy to parallel associative scans and the chunkwise-recurrent form of sequential linear RNNs, but for DAGs. For regular grids (1D and 2D), like images, this scheme can be efficiently implemented using einsum operations, concatenations, and padding in logarithmic time. pLSTMs tackle the vanishing/exploding activation/gradient problem for long distances in DAGs via two distinct modes: a directed propagation mode (P-mode) and a diffusive distribution mode (D-mode). To showcase the long-range capabilities of pLSTM, we introduce arrow-pointing extrapolation as a synthetic computer vision task that contains long-distance directional information. We demonstrate that pLSTMs generalize well to larger image sizes, whereas Transformers struggle to extrapolate. On established molecular graph and computer vision benchmarks, pLSTMs also show strong performance. Code and Datasets are available at: https://github.com/ml-jku/plstm_experiments.", "github_manual": "", "github_regex": "https://github.com/ml-jku/plstm_experiments", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.07464", "title": "DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO", "authors": [ "Jinyoung Park", "Jeehye Na", "Jinyoung Kim", "Hyunwoo J. Kim" ], "github": "https://github.com/mlvlab/DeepVideoR1", "project_page": "", "abstract": "Recent works have demonstrated the effectiveness of reinforcement learning (RL)-based post-training in enhancing the reasoning capabilities of large language models (LLMs). In particular, Group Relative Policy Optimization (GRPO) has shown impressive success by employing a PPO-style reinforcement algorithm with group-based normalized rewards. However, the application of GRPO to Video Large Language Models (Video LLMs) has been less studied. In this paper, we explore GRPO for video LLMs and identify two primary issues that impede its effective learning: (1) reliance on safeguards, and (2) the vanishing advantage problem. To mitigate these challenges, we propose DeepVideo-R1, a video large language model trained with our proposed Reg-GRPO (Regressive GRPO) and difficulty-aware data augmentation strategy. Reg-GRPO reformulates the GRPO objective as a regression task, directly predicting the advantage in GRPO. This design eliminates the need for safeguards like clipping and min functions, thereby facilitating more direct policy guidance by aligning the model with the advantage values. We also design the difficulty-aware data augmentation strategy that dynamically augments training samples at solvable difficulty levels, fostering diverse and informative reward signals. Our comprehensive experiments show that DeepVideo-R1 significantly improves video reasoning performance across multiple video reasoning benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mlvlab/DeepVideoR1", "github_api_added_by": "auto" }, { "date": "2025-06-16", "arxiv_id": "2506.11130", "title": "A Self-Refining Framework for Enhancing ASR Using TTS-Synthesized Data", "authors": [ "Cheng Kang Chou", "Chan-Jan Hsu", "Ho-Lam Chung", "Liang-Hsuan Tseng", "Hsi-Chun Cheng", "Yu-Kuan Fu", "Kuan Po Huang", "Hung-Yi Lee" ], "github": "", "project_page": "", "abstract": "We propose a self-refining framework that enhances ASR performance with only unlabeled datasets. The process starts with an existing ASR model generating pseudo-labels on unannotated speech, which are then used to train a high-fidelity text-to-speech (TTS) system. Then, synthesized speech text pairs are bootstrapped into the original ASR system, completing the closed-loop self-improvement cycle. We demonstrated the effectiveness of the framework on Taiwanese Mandarin speech. Leveraging 6,000 hours of unlabeled speech, a moderate amount of text data, and synthetic content from the AI models, we adapt Whisper-large-v2 into a specialized model, Twister. Twister reduces error rates by up to 20% on Mandarin and 50% on Mandarin-English code-switching benchmarks compared to Whisper. Results highlight the framework as a compelling alternative to pseudo-labeling self-distillation approaches and provides a practical pathway for improving ASR performance in low-resource or domain-specific settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.08915", "title": "Inherently Faithful Attention Maps for Vision Transformers", "authors": [ "Ananthu Aniraj", "Cassio F. Dantas", "Dino Ienco", "Diego Marcos" ], "github": "", "project_page": "", "abstract": "We introduce an attention-based method that uses learned binary attention masks to ensure that only attended image regions influence the prediction. Context can strongly affect object perception, sometimes leading to biased representations, particularly when objects appear in out-of-distribution backgrounds. At the same time, many image-level object-centric tasks require identifying relevant regions, often requiring context. To address this conundrum, we propose a two-stage framework: stage 1 processes the full image to discover object parts and identify task-relevant regions, while stage 2 leverages input attention masking to restrict its receptive field to these regions, enabling a focused analysis while filtering out potentially spurious information. Both stages are trained jointly, allowing stage 2 to refine stage 1. Extensive experiments across diverse benchmarks demonstrate that our approach significantly improves robustness against spurious correlations and out-of-distribution backgrounds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.10128", "title": "ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs", "authors": [ "Xiyao Wang", "Zhengyuan Yang", "Chao Feng", "Yongyuan Liang", "Yuhang Zhou", "Xiaoyu Liu", "Ziyi Zang", "Ming Li", "Chung-Ching Lin", "Kevin Lin", "Linjie Li", "Furong Huang", "Lijuan Wang" ], "github": "https://github.com/si0wang/ViCrit", "project_page": "", "abstract": "Reinforcement learning (RL) has shown great effectiveness for fine-tuning large language models (LLMs) using tasks that are challenging yet easily verifiable, such as math reasoning or code generation. However, extending this success to visual perception in vision-language models (VLMs) has been impeded by the scarcity of vision-centric tasks that are simultaneously challenging and unambiguously verifiable. To this end, we introduce ViCrit (Visual Caption Hallucination Critic), an RL proxy task that trains VLMs to localize a subtle, synthetic visual hallucination injected into paragraphs of human-written image captions. Starting from a 200-word captions, we inject a single, subtle visual description error-altering a few words on objects, attributes, counts, or spatial relations-and task the model to pinpoint the corrupted span given the image and the modified caption. This formulation preserves the full perceptual difficulty while providing a binary, exact-match reward that is easy to compute and unambiguous. Models trained with the ViCrit Task exhibit substantial gains across a variety of VL benchmarks. Crucially, the improvements transfer beyond natural-image training data to abstract image reasoning and visual math, showing promises of learning to perceive rather than barely memorizing seen objects. To facilitate evaluation, we further introduce ViCrit-Bench, a category-balanced diagnostic benchmark that systematically probes perception errors across diverse image domains and error types. Together, our results demonstrate that fine-grained hallucination criticism is an effective and generalizable objective for enhancing visual perception in VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/si0wang/ViCrit", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.11886", "title": "Beyond Homogeneous Attention: Memory-Efficient LLMs via Fourier-Approximated KV Cache", "authors": [ "Xiaoran Liu", "Siyang He", "Qiqi Wang", "Ruixiao Li", "Yuerong Song", "Zhigeng Liu", "Linlin Li", "Qun Liu", "Zengfeng Huang", "Qipeng Guo", "Ziwei He", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Large Language Models struggle with memory demands from the growing Key-Value (KV) cache as context lengths increase. Existing compression methods homogenize head dimensions or rely on attention-guided token pruning, often sacrificing accuracy or introducing computational overhead. We propose FourierAttention, a training-free framework that exploits the heterogeneous roles of transformer head dimensions: lower dimensions prioritize local context, while upper ones capture long-range dependencies. By projecting the long-context-insensitive dimensions onto orthogonal Fourier bases, FourierAttention approximates their temporal evolution with fixed-length spectral coefficients. Evaluations on LLaMA models show that FourierAttention achieves the best long-context accuracy on LongBench and Needle-In-A-Haystack (NIAH). Besides, a custom Triton kernel, FlashFourierAttention, is designed to optimize memory via streamlined read-write operations, enabling efficient deployment without performance compromise.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.08989", "title": "SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning", "authors": [ "Xiao Liang", "Zhong-Zhi Li", "Yeyun Gong", "Yang Wang", "Hengyuan Zhang", "Yelong Shen", "Ying Nian Wu", "Weizhu Chen" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for training large language models (LLMs) on complex reasoning tasks, such as mathematical problem solving. A prerequisite for the scalability of RLVR is a high-quality problem set with precise and verifiable answers. However, the scarcity of well-crafted human-labeled math problems and limited-verification answers in existing distillation-oriented synthetic datasets limit their effectiveness in RL. Additionally, most problem synthesis strategies indiscriminately expand the problem set without considering the model's capabilities, leading to low efficiency in generating useful questions. To mitigate this issue, we introduce a Self-aware Weakness-driven problem Synthesis framework (SwS) that systematically identifies model deficiencies and leverages them for problem augmentation. Specifically, we define weaknesses as questions that the model consistently fails to learn through its iterative sampling during RL training. We then extract the core concepts from these failure cases and synthesize new problems to strengthen the model's weak areas in subsequent augmented training, enabling it to focus on and gradually overcome its weaknesses. Without relying on external knowledge distillation, our framework enables robust generalization byempowering the model to self-identify and address its weaknesses in RL, yielding average performance gains of 10.0% and 7.7% on 7B and 32B models across eight mainstream reasoning benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.11136", "title": "JAFAR: Jack up Any Feature at Any Resolution", "authors": [ "Paul Couairon", "Loick Chambon", "Louis Serrano", "Jean-Emmanuel Haugeard", "Matthieu Cord", "Nicolas Thome" ], "github": "https://github.com/saksham-s/lift/blob/main/lift.py", "project_page": "", "abstract": "Foundation Vision Encoders have become essential for a wide range of dense vision tasks. However, their low-resolution spatial feature outputs necessitate feature upsampling to produce the high-resolution modalities required for downstream tasks. In this work, we introduce JAFAR, a lightweight and flexible feature upsampler that enhances the spatial resolution of visual features from any Foundation Vision Encoder to an arbitrary target resolution. JAFAR employs an attention-based module designed to promote semantic alignment between high-resolution queries, derived from low-level image features, and semantically enriched low-resolution keys, using Spatial Feature Transform (SFT) modulation. Notably, despite the absence of high-resolution supervision, we demonstrate that learning at low upsampling ratios and resolutions generalizes remarkably well to significantly higher output scales. Extensive experiments show that JAFAR effectively recovers fine-grained spatial details and consistently outperforms existing feature upsampling methods across a diverse set of downstream tasks. Project page at https://jafar-upsampler.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/saksham-s/lift/blob/main/lift.py", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.11274", "title": "Learning a Continue-Thinking Token for Enhanced Test-Time Scaling", "authors": [ "Liran Ringel", "Elad Tolochinsky", "Yaniv Romano" ], "github": "https://github.com/liranringel/learning-continue-thinking-token", "project_page": "", "abstract": "Test-time scaling has emerged as an effective approach for improving language model performance by utilizing additional compute at inference time. Recent studies have shown that overriding end-of-thinking tokens (e.g., replacing \"\" with \"Wait\") can extend reasoning steps and improve accuracy. In this work, we explore whether a dedicated continue-thinking token can be learned to trigger extended reasoning. We augment a distilled version of DeepSeek-R1 with a single learned \"<|continue-thinking|>\" token, training only its embedding via reinforcement learning while keeping the model weights frozen. Our experiments show that this learned token achieves improved accuracy on standard math benchmarks compared to both the baseline model and a test-time scaling approach that uses a fixed token (e.g., \"Wait\") for budget forcing. In particular, we observe that in cases where the fixed-token approach enhances the base model's accuracy, our method achieves a markedly greater improvement. For example, on the GSM8K benchmark, the fixed-token approach yields a 1.3% absolute improvement in accuracy, whereas our learned-token method achieves a 4.2% improvement over the base model that does not use budget forcing.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/liranringel/learning-continue-thinking-token", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.10082", "title": "LoRA-Edit: Controllable First-Frame-Guided Video Editing via Mask-Aware LoRA Fine-Tuning", "authors": [ "Chenjian Gao", "Lihe Ding", "Xin Cai", "Zhanpeng Huang", "Zibin Wang", "Tianfan Xue" ], "github": "", "project_page": "", "abstract": "Video editing using diffusion models has achieved remarkable results in generating high-quality edits for videos. However, current methods often rely on large-scale pretraining, limiting flexibility for specific edits. First-frame-guided editing provides control over the first frame, but lacks flexibility over subsequent frames. To address this, we propose a mask-based LoRA (Low-Rank Adaptation) tuning method that adapts pretrained Image-to-Video (I2V) models for flexible video editing. Our approach preserves background regions while enabling controllable edits propagation. This solution offers efficient and adaptable video editing without altering the model architecture. To better steer this process, we incorporate additional references, such as alternate viewpoints or representative scene states, which serve as visual anchors for how content should unfold. We address the control challenge using a mask-driven LoRA tuning strategy that adapts a pre-trained image-to-video model to the editing context. The model must learn from two distinct sources: the input video provides spatial structure and motion cues, while reference images offer appearance guidance. A spatial mask enables region-specific learning by dynamically modulating what the model attends to, ensuring that each area draws from the appropriate source. Experimental results show our method achieves superior video editing performance compared to state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.11474", "title": "Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified Process Rewards", "authors": [ "Jaehoon Yun", "Jiwoong Sohn", "Jungwoo Park", "Hyunjae Kim", "Xiangru Tang", "Yanjun Shao", "Yonghoe Koo", "Minhyeok Ko", "Qingyu Chen", "Mark Gerstein", "Michael Moor", "Jaewoo Kang" ], "github": "", "project_page": "", "abstract": "Large language models have shown promise in clinical decision making, but current approaches struggle to localize and correct errors at specific steps of the reasoning process. This limitation is critical in medicine, where identifying and addressing reasoning errors is essential for accurate diagnosis and effective patient care. We introduce Med-PRM, a process reward modeling framework that leverages retrieval-augmented generation to verify each reasoning step against established medical knowledge bases. By verifying intermediate reasoning steps with evidence retrieved from clinical guidelines and literature, our model can precisely assess the reasoning quality in a fine-grained manner. Evaluations on five medical QA benchmarks and two open-ended diagnostic tasks demonstrate that Med-PRM achieves state-of-the-art performance, with improving the performance of base models by up to 13.50% using Med-PRM. Moreover, we demonstrate the generality of Med-PRM by integrating it in a plug-and-play fashion with strong policy models such as Meerkat, achieving over 80\\% accuracy on MedQA for the first time using small-scale models of 8 billion parameters. Our code and data are available at: https://med-prm.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.11116", "title": "Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models", "authors": [ "Jijie Li", "Li Du", "Hanyu Zhao", "Bo-wen Zhang", "Liangdong Wang", "Boyan Gao", "Guang Liu", "Yonghua Lin" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) demonstrate strong performance in real-world applications, yet existing open-source instruction datasets often concentrate on narrow domains, such as mathematics or coding, limiting generalization and widening the gap with proprietary models. To bridge this gap, we introduce Infinity-Instruct, a high-quality instruction dataset designed to enhance both foundational and chat capabilities of LLMs through a two-phase pipeline. In Phase 1, we curate 7.4M high-quality foundational instructions (InfInstruct-F-7.4M) from over 100M samples using hybrid data selection techniques. In Phase 2, we synthesize 1.5M high-quality chat instructions (InfInstruct-G-1.5M) through a two-stage process involving instruction selection, evolution, and diagnostic filtering. We empirically evaluate Infinity-Instruct by fine-tuning several open-source models, including Mistral, LLaMA, Qwen, and Yi, and observe substantial performance gains across both foundational and instruction following benchmarks, consistently surpassing official instruction-tuned counterparts. Notably, InfInstruct-LLaMA3.1-70B outperforms GPT-4-0314 by 8.6\\% on instruction following tasks while achieving comparable foundational performance. These results underscore the synergy between foundational and chat training and offer new insights into holistic LLM development. Our datasethttps://huggingface.co/datasets/BAAI/Infinity-Instruct and codeshttps://gitee.com/li-touch/infinity-instruct have been publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.03857", "title": "Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation", "authors": [ "Mingxuan Xia", "Haobo Wang", "Yixuan Li", "Zewei Yu", "Jindong Wang", "Junbo Zhao", "Runze Wu" ], "github": "https://github.com/MingxuanXia/CanDist", "project_page": "", "abstract": "Recently, Large Language Models (LLMs) have demonstrated significant potential for data annotation, markedly reducing the labor costs associated with downstream applications. However, existing methods mostly adopt an aggressive strategy by prompting LLM to determine a single gold label for each unlabeled sample. Due to the inherent uncertainty within LLMs, they often produce incorrect labels for difficult samples, severely compromising the data quality for downstream applications. Motivated by ambiguity aversion in human behaviors, we propose a novel candidate annotation paradigm wherein large language models are encouraged to output all possible labels when incurring uncertainty. To ensure unique labels are provided for downstream tasks, we develop a teacher-student framework CanDist that distills candidate annotations with a Small Language Model (SLM). We further provide a rigorous justification demonstrating that distilling candidate annotations from the teacher LLM offers superior theoretical guarantees compared to directly using single annotations. Extensive experiments across six text classification tasks validate the effectiveness of our proposed method. The source code is available at https://github.com/MingxuanXia/CanDist.", "github_manual": "", "github_regex": "https://github.com/MingxuanXia/CanDist", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.11930", "title": "Feedback Friction: LLMs Struggle to Fully Incorporate External Feedback", "authors": [ "Dongwei Jiang", "Alvin Zhang", "Andrew Wang", "Nicholas Andrews", "Daniel Khashabi" ], "github": "https://github.com/JHU-CLSP/Feedback-Friction", "project_page": "", "abstract": "Recent studies have shown LLMs possess some ability to improve their responses when given external feedback. However, it remains unclear how effectively and thoroughly these models can incorporate extrinsic feedback. In an ideal scenario, if LLMs receive near-perfect and complete feedback, we would expect them to fully integrate the feedback and change their incorrect answers to correct ones. In this paper, we systematically investigate LLMs' ability to incorporate feedback by designing a controlled experimental environment. For each problem, a solver model attempts a solution, then a feedback generator with access to near-complete ground-truth answers produces targeted feedback, after which the solver tries again. We evaluate this pipeline across a diverse range of tasks, including math reasoning, knowledge reasoning, scientific reasoning, and general multi-domain evaluations with state-of-the-art language models including Claude 3.7 (with and without extended thinking). Surprisingly, even under these near-ideal conditions, solver models consistently show resistance to feedback, a limitation that we term FEEDBACK FRICTION. To mitigate this limitation, we experiment with sampling-based strategies like progressive temperature increases and explicit rejection of previously attempted incorrect answers, which yield improvements but still fail to help models achieve target performance. We also perform a rigorous exploration of potential causes of FEEDBACK FRICTION, ruling out factors such as model overconfidence and data familiarity. We hope that highlighting this issue in LLMs and ruling out several apparent causes will help future research in self-improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JHU-CLSP/Feedback-Friction", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.10387", "title": "Mirage-1: Augmenting and Updating GUI Agent with Hierarchical Multimodal Skills", "authors": [ "Yuquan Xie", "Zaijing Li", "Rui Shao", "Gongwei Chen", "Kaiwen Zhou", "Yinchuan Li", "Dongmei Jiang", "Liqiang Nie" ], "github": "https://github.com/JiuTian-VL/Mirage-1", "project_page": "", "abstract": "Recent efforts to leverage the Multi-modal Large Language Model (MLLM) as GUI agents have yielded promising outcomes. However, these agents still struggle with long-horizon tasks in online environments, primarily due to insufficient knowledge and the inherent gap between offline and online domains. In this paper, inspired by how humans generalize knowledge in open-ended environments, we propose a Hierarchical Multimodal Skills (HMS) module to tackle the issue of insufficient knowledge. It progressively abstracts trajectories into execution skills, core skills, and ultimately meta-skills, providing a hierarchical knowledge structure for long-horizon task planning. To bridge the domain gap, we propose the Skill-Augmented Monte Carlo Tree Search (SA-MCTS) algorithm, which efficiently leverages skills acquired in offline environments to reduce the action search space during online tree exploration. Building on HMS, we propose Mirage-1, a multimodal, cross-platform, plug-and-play GUI agent. To validate the performance of Mirage-1 in real-world long-horizon scenarios, we constructed a new benchmark, AndroidLH. Experimental results show that Mirage-1 outperforms previous agents by 32\\%, 19\\%, 15\\%, and 79\\% on AndroidWorld, MobileMiniWob++, Mind2Web-Live, and AndroidLH, respectively. Project page: https://cybertronagent.github.io/Mirage-1.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JiuTian-VL/Mirage-1", "github_api_added_by": "auto" }, { "date": "2025-06-16", "arxiv_id": "2506.10056", "title": "Reward Models Enable Scalable Code Verification by Trading Accuracy for Throughput", "authors": [ "Gabriel Orlanski", "Nicholas Roberts", "Aws Albarghouthi", "Frederic Sala" ], "github": "", "project_page": "", "abstract": "The standard paradigm for solving coding tasks via large language models (LLMs) is to generate-then-rank programs, where the latter step uses a verifier in the ranking process. The growing consensus is that a comprehensive verifier (e.g., a full test suite) should be prioritized over an outcome reward model (ORM) whenever possible, with little consideration given to the trade-offs involved. We aim to challenge this assumption by systematically exploring the tradeoff between speed and accuracy. We find that ORMs play a crucial role in scaling verification through trading accuracy for speed, even when a comprehensive verifier is available. Their value becomes especially apparent when used in a generate-prune-then-rank approach, where a faster but less accurate verifier removes incorrect solutions prior to ranking -- leading to a system that is 11.65x faster while only being 8.33% less accurate than the full test suite. We analyze the generate-prune-then-rank approach and show that it works by filtering out incorrect but highly ranked solutions. These findings enable the design of scalable and accurate program ranking systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-16", "arxiv_id": "2506.09038", "title": "AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions", "authors": [ "Polina Kirichenko", "Mark Ibrahim", "Kamalika Chaudhuri", "Samuel J. Bell" ], "github": "https://github.com/facebookresearch/abstentionbench", "project_page": "", "abstract": "For Large Language Models (LLMs) to be reliably deployed in both everyday and high-stakes domains, knowing when not to answer is equally critical as answering correctly. Real-world user queries, which can be underspecified, ill-posed, or fundamentally unanswerable, require LLMs to reason about uncertainty and selectively abstain -- i.e., refuse to answer definitively. However, abstention remains understudied, without a systematic evaluation framework for modern LLMs. In this work, we introduce AbstentionBench, a large-scale benchmark for holistically evaluating abstention across 20 diverse datasets, including questions with unknown answers, underspecification, false premises, subjective interpretations, and outdated information. Evaluating 20 frontier LLMs reveals abstention is an unsolved problem, and one where scaling models is of little use. While recent reasoning LLMs have shown impressive results in complex problem solving, surprisingly, we find that reasoning fine-tuning degrades abstention (by 24% on average), even for math and science domains on which reasoning models are explicitly trained. We find that while a carefully crafted system prompt can boost abstention in practice, it does not resolve models' fundamental inability to reason about uncertainty. We release AbstentionBench to foster research into advancing LLM reliability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/abstentionbench", "github_api_added_by": "auto" }, { "date": "2025-06-16", "arxiv_id": "2506.11305", "title": "Don't Pay Attention", "authors": [ "Mohammad Hammoud", "Devang Acharya" ], "github": "", "project_page": "", "abstract": "The Transformer has become the de facto standard for large language models and a wide range of downstream tasks across various domains. Despite its numerous advantages like inherent training parallelism, the Transformer still faces key challenges due to its inability to effectively process sequences beyond a fixed context window and the quadratic complexity of its attention mechanism. These challenges have renewed interest in RNN-like architectures, which offer linear scaling with sequence length and improved handling of long-range dependencies, albeit with limited parallelism due to their inherently recurrent nature. In this paper, we propose Avey, a new neural foundational architecture that breaks away from both attention and recurrence. Avey comprises a ranker and an autoregressive neural processor, which collaboratively identify and contextualize only the most relevant tokens for any given token, regardless of their positions in the sequence. Specifically, Avey decouples sequence length from context width, thus enabling effective processing of arbitrarily long sequences. Experimental results show that Avey compares favorably to the Transformer across a variety of standard short-range NLP benchmarks, while notably excelling at capturing long-range dependencies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.13585", "title": "MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention", "authors": [ "MiniMax", "Aili Chen", "Aonian Li", "Bangwei Gong", "Binyang Jiang", "Bo Fei", "Bo Yang", "Boji Shan", "Changqing Yu", "Chao Wang", "Cheng Zhu", "Chengjun Xiao", "Chengyu Du", "Chi Zhang", "Chu Qiao", "Chunhao Zhang", "Chunhui Du", "Congchao Guo", "Da Chen", "Deming Ding", "Dianjun Sun", "Dong Li", "Enwei Jiao", "Haigang Zhou", "Haimo Zhang", "Han Ding", "Haohai Sun", "Haoyu Feng", "Huaiguang Cai", "Haichao Zhu", "Jian Sun", "Jiaqi Zhuang", "Jiaren Cai", "Jiayuan Song", "Jin Zhu", "Jingyang Li", "Jinhao Tian", "Jinli Liu", "Junhao Xu", "Junjie Yan", "Junteng Liu", "Junxian He", "Kaiyi Feng", "Ke Yang", "Kecheng Xiao", "Le Han", "Leyang Wang", "Lianfei Yu", "Liheng Feng", "Lin Li", "Lin Zheng", "Linge Du", "Lingyu Yang", "Lunbin Zeng", "Minghui Yu", "Mingliang Tao", "Mingyuan Chi", "Mozhi Zhang", "Mujie Lin", "Nan Hu", "Nongyu Di", "Peng Gao", "Pengfei Li", "Pengyu Zhao", "Qibing Ren", "Qidi Xu", "Qile Li", "Qin Wang", "Rong Tian", "Ruitao Leng", "Shaoxiang Chen", "Shaoyu Chen", "Shengmin Shi", "Shitong Weng", "Shuchang Guan", "Shuqi Yu", "Sichen Li", "Songquan Zhu", "Tengfei Li", "Tianchi Cai", "Tianrun Liang", "Weiyu Cheng", "Weize Kong", "Wenkai Li", "Xiancai Chen", "Xiangjun Song", "Xiao Luo", "Xiao Su", "Xiaobo Li", "Xiaodong Han", "Xinzhu Hou", "Xuan Lu", "Xun Zou", "Xuyang Shen", "Yan Gong", "Yan Ma", "Yang Wang", "Yiqi Shi", "Yiran Zhong", "Yonghong Duan", "Yongxiang Fu", "Yongyi Hu", "Yu Gao", "Yuanxiang Fan", "Yufeng Yang", "Yuhao Li", "Yulin Hu", "Yunan Huang", "Yunji Li", "Yunzhi Xu", "Yuxin Mao", "Yuxuan Shi", "Yuze Wenren", "Zehan Li", "Zelin Li", "Zhanxu Tian", "Zhengmao Zhu", "Zhenhua Fan", "Zhenzhen Wu", "Zhichao Xu", "Zhihang Yu", "Zhiheng Lyu", "Zhuo Jiang", "Zibo Gao", "Zijia Wu", "Zijian Song", "Zijun Sun" ], "github": "https://github.com/MiniMax-AI/MiniMax-M1", "project_page": "", "abstract": "We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning attention mechanism. The model is developed based on our previous MiniMax-Text-01 model, which contains a total of 456 billion parameters with 45.9 billion parameters activated per token. The M1 model natively supports a context length of 1 million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning attention mechanism in MiniMax-M1 enables efficient scaling of test-time compute. These properties make M1 particularly suitable for complex tasks that require processing long inputs and thinking extensively. MiniMax-M1 is trained using large-scale reinforcement learning (RL) on diverse problems including sandbox-based, real-world software engineering environments. In addition to M1's inherent efficiency advantage for RL training, we propose CISPO, a novel RL algorithm to further enhance RL efficiency. CISPO clips importance sampling weights rather than token updates, outperforming other competitive RL variants. Combining hybrid-attention and CISPO enables MiniMax-M1's full RL training on 512 H800 GPUs to complete in only three weeks, with a rental cost of just $534,700. We release two versions of MiniMax-M1 models with 40K and 80K thinking budgets respectively, where the 40K model represents an intermediate phase of the 80K training. Experiments on standard benchmarks show that our models are comparable or superior to strong open-weight models such as the original DeepSeek-R1 and Qwen3-235B, with particular strengths in complex software engineering, tool utilization, and long-context tasks. We publicly release MiniMax-M1 at https://github.com/MiniMax-AI/MiniMax-M1.", "github_manual": "", "github_regex": "https://github.com/MiniMax-AI/MiniMax-M1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.11763", "title": "DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents", "authors": [ "Mingxuan Du", "Benfeng Xu", "Chiwei Zhu", "Xiaorui Wang", "Zhendong Mao" ], "github": "https://github.com/Ayanami0730/deep_research_bench", "project_page": "", "abstract": "Deep Research Agents are a prominent category of LLM-based agents. By autonomously orchestrating multistep web exploration, targeted retrieval, and higher-order synthesis, they transform vast amounts of online information into analyst-grade, citation-rich reports--compressing hours of manual desk research into minutes. However, a comprehensive benchmark for systematically evaluating the capabilities of these agents remains absent. To bridge this gap, we present DeepResearch Bench, a benchmark consisting of 100 PhD-level research tasks, each meticulously crafted by domain experts across 22 distinct fields. Evaluating DRAs is inherently complex and labor-intensive. We therefore propose two novel methodologies that achieve strong alignment with human judgment. The first is a reference-based method with adaptive criteria to assess the quality of generated research reports. The other framework is introduced to evaluate DRA's information retrieval and collection capabilities by assessing its effective citation count and overall citation accuracy. We have open-sourced DeepResearch Bench and key components of these frameworks at https://github.com/Ayanami0730/deep_research_bench to accelerate the development of practical LLM-based agents.", "github_manual": "", "github_regex": "https://github.com/Ayanami0730/deep_research_bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.03968", "title": "From Real to Synthetic: Synthesizing Millions of Diversified and Complicated User Instructions with Attributed Grounding", "authors": [ "Chiwei Zhu", "Benfeng Xu", "Xiaorui Wang", "Zhendong Mao" ], "github": "https://github.com/Ignoramus0817/SynthQuestions", "project_page": "", "abstract": "The pursuit of diverse, complex, and large-scale instruction data is crucial for automatically aligning large language models (LLMs). While there are methods capable of generating synthetic instructions at scale, they either suffer from limited grounding sources, leading to a narrow distribution, or rely on trivial extensions that fail to produce meaningful trajectories in terms of complexity. In contrast, instructions that benefit efficient alignment are typically crafted with cognitive insights and grounded in real-world use cases. In this paper, we synthesize such instructions using attributed grounding, which involves 1) a top-down attribution process that grounds a selective set of real instructions to situated users, and 2) a bottom-up synthesis process that leverages web documents to first generate a situation, then a meaningful instruction. This framework allows us to harvest diverse and complex instructions at scale, utilizing the vast range of web documents. Specifically, we construct a dataset of 1 million instructions, called SynthQuestions, and demonstrate that models trained on it achieve leading performance on several common benchmarks, with improvements that continually scale with more web corpora. Data, models and codes will be available at https://github.com/Ignoramus0817/SynthQuestions.", "github_manual": "", "github_regex": "https://github.com/Ignoramus0817/SynthQuestions", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.13759", "title": "Discrete Diffusion in Large Language and Multimodal Models: A Survey", "authors": [ "Runpeng Yu", "Qi Li", "Xinchao Wang" ], "github": "https://github.com/LiQiiiii/DLLM-Survey", "project_page": "", "abstract": "In this work, we provide a systematic survey of Discrete Diffusion Language Models (dLLMs) and Discrete Diffusion Multimodal Language Models (dMLLMs). Unlike autoregressive (AR) models, dLLMs and dMLLMs adopt a multi-token, parallel decoding paradigm using full attention and a denoising-based generation strategy. This paradigm naturally enables parallel generation, fine-grained output controllability, and dynamic, response-aware perception. These capabilities are previously difficult to achieve with AR models. Recently, a growing number of industrial-scale proprietary d(M)LLMs, as well as a large number of open-source academic d(M)LLMs, have demonstrated performance comparable to their autoregressive counterparts, while achieving up to 10x acceleration in inference speed. The advancement of discrete diffusion LLMs and MLLMs has been largely driven by progress in two domains. The first is the development of autoregressive LLMs and MLLMs, which has accumulated vast amounts of data, benchmarks, and foundational infrastructure for training and inference. The second contributing domain is the evolution of the mathematical models underlying discrete diffusion. Together, these advancements have catalyzed a surge in dLLMs and dMLLMs research in early 2025. In this work, we present a comprehensive overview of the research in the dLLM and dMLLM domains. We trace the historical development of dLLMs and dMLLMs, formalize the underlying mathematical frameworks, and categorize representative models. We further analyze key techniques for training and inference, and summarize emerging applications across language, vision-language, and biological domains. We conclude by discussing future directions for research and deployment. Paper collection: https://github.com/LiQiiiii/DLLM-Survey", "github_manual": "", "github_regex": "https://github.com/LiQiiiii/DLLM-Survey", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.08343", "title": "Wait, We Don't Need to \"Wait\"! Removing Thinking Tokens Improves Reasoning Efficiency", "authors": [ "Chenlong Wang", "Yuanning Feng", "Dongping Chen", "Zhaoyang Chu", "Ranjay Krishna", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Recent advances in large reasoning models have enabled complex, step-by-step reasoning but often introduce significant overthinking, resulting in verbose and redundant outputs that hinder efficiency. In this study, we examine whether explicit self-reflection, signaled by tokens such as \"Wait\" and \"Hmm\", is necessary for advanced reasoning. We propose NoWait, a simple yet effective approach that disables explicit self-reflection by suppressing these tokens during inference. Extensive experiments on ten benchmarks across textual, visual, and video reasoning tasks show that NoWait reduces chain-of-thought trajectory length by up to 27%-51% in five R1-style model series, without compromising model utility. NoWait thus offers a plug-and-play solution for efficient and utility-preserving multimodal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.07961", "title": "BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning with Vision-Language Models", "authors": [ "Peiyan Li", "Yixiang Chen", "Hongtao Wu", "Xiao Ma", "Xiangnan Wu", "Yan Huang", "Liang Wang", "Tao Kong", "Tieniu Tan" ], "github": "", "project_page": "", "abstract": "Recently, leveraging pre-trained vision-language models (VLMs) for building vision-language-action (VLA) models has emerged as a promising approach to effective robot manipulation learning. However, only few methods incorporate 3D signals into VLMs for action prediction, and they do not fully leverage the spatial structure inherent in 3D data, leading to low sample efficiency. In this paper, we introduce BridgeVLA, a novel 3D VLA model that (1) projects 3D inputs to multiple 2D images, ensuring input alignment with the VLM backbone, and (2) utilizes 2D heatmaps for action prediction, unifying the input and output spaces within a consistent 2D image space. In addition, we propose a scalable pre-training method that equips the VLM backbone with the capability to predict 2D heatmaps before downstream policy learning. Extensive experiments show the proposed method is able to learn 3D manipulation efficiently and effectively. BridgeVLA outperforms state-of-the-art baseline methods across three simulation benchmarks. In RLBench, it improves the average success rate from 81.4% to 88.2%. In COLOSSEUM, it demonstrates significantly better performance in challenging generalization settings, boosting the average success rate from 56.7% to 64.0%. In GemBench, it surpasses all the comparing baseline methods in terms of average success rate. In real-robot experiments, BridgeVLA outperforms a state-of-the-art baseline method by 32% on average. It generalizes robustly in multiple out-of-distribution settings, including visual disturbances and unseen instructions. Remarkably, it is able to achieve a success rate of 96.8% on 10+ tasks with only 3 trajectories per task, highlighting its extraordinary sample efficiency. Project Website:https://bridgevla.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.13750", "title": "Test3R: Learning to Reconstruct 3D at Test Time", "authors": [ "Yuheng Yuan", "Qiuhong Shen", "Shizun Wang", "Xingyi Yang", "Xinchao Wang" ], "github": "https://github.com/nopQAQ/Test3R", "project_page": "", "abstract": "Dense matching methods like DUSt3R regress pairwise pointmaps for 3D reconstruction. However, the reliance on pairwise prediction and the limited generalization capability inherently restrict the global geometric consistency. In this work, we introduce Test3R, a surprisingly simple test-time learning technique that significantly boosts geometric accuracy. Using image triplets (I_1,I_2,I_3), Test3R generates reconstructions from pairs (I_1,I_2) and (I_1,I_3). The core idea is to optimize the network at test time via a self-supervised objective: maximizing the geometric consistency between these two reconstructions relative to the common image I_1. This ensures the model produces cross-pair consistent outputs, regardless of the inputs. Extensive experiments demonstrate that our technique significantly outperforms previous state-of-the-art methods on the 3D reconstruction and multi-view depth estimation tasks. Moreover, it is universally applicable and nearly cost-free, making it easily applied to other models and implemented with minimal test-time training overhead and parameter footprint. Code is available at https://github.com/nopQAQ/Test3R.", "github_manual": "", "github_regex": "https://github.com/nopQAQ/Test3R", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.12953", "title": "Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition", "authors": [ "Mayank Bumb", "Anshul Vemulapalli", "Sri Harsha Vardhan Prasad Jella", "Anish Gupta", "An La", "Ryan A. Rossi", "Hongjie Chen", "Franck Dernoncourt", "Nesreen K. Ahmed", "Yu Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in Large Language Models (LLMs) have demonstrated new possibilities for accurate and efficient time series analysis, but prior work often required heavy fine-tuning and/or ignored inter-series correlations. In this work, we explore simple and flexible prompt-based strategies that enable LLMs to perform time series forecasting without extensive retraining or the use of a complex external architecture. Through the exploration of specialized prompting methods that leverage time series decomposition, patch-based tokenization, and similarity-based neighbor augmentation, we find that it is possible to enhance LLM forecasting quality while maintaining simplicity and requiring minimal preprocessing of data. To this end, we propose our own method, PatchInstruct, which enables LLMs to make precise and effective predictions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.12623", "title": "MS4UI: A Dataset for Multi-modal Summarization of User Interface Instructional Videos", "authors": [ "Yuan Zang", "Hao Tan", "Seunghyun Yoon", "Franck Dernoncourt", "Jiuxiang Gu", "Kushal Kafle", "Chen Sun", "Trung Bui" ], "github": "https://github.com/AnyiRao/SceneSeg", "project_page": "", "abstract": "We study multi-modal summarization for instructional videos, whose goal is to provide users an efficient way to learn skills in the form of text instructions and key video frames. We observe that existing benchmarks focus on generic semantic-level video summarization, and are not suitable for providing step-by-step executable instructions and illustrations, both of which are crucial for instructional videos. We propose a novel benchmark for user interface (UI) instructional video summarization to fill the gap. We collect a dataset of 2,413 UI instructional videos, which spans over 167 hours. These videos are manually annotated for video segmentation, text summarization, and video summarization, which enable the comprehensive evaluations for concise and executable video summarization. We conduct extensive experiments on our collected MS4UI dataset, which suggest that state-of-the-art multi-modal summarization methods struggle on UI video summarization, and highlight the importance of new methods for UI instructional video summarization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AnyiRao/SceneSeg", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.12189", "title": "Supernova Event Dataset: Interpreting Large Language Model's Personality through Critical Event Analysis", "authors": [ "Pranav Agarwal", "Ioana Ciuc\u0103" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly integrated into everyday applications. As their influence grows, understanding their decision making and underlying personality becomes essential. In this work, we interpret model personality using our proposed Supernova Event Dataset, a novel dataset with diverse articles spanning biographies, historical events, news, and scientific discoveries. We use this dataset to benchmark LLMs on extracting and ranking key events from text, a subjective and complex challenge that requires reasoning over long-range context and modeling causal chains. We evaluate small models like Phi-4, Orca 2, and Qwen 2.5, and large, stronger models such as Claude 3.7, Gemini 2.5, and OpenAI o3, and propose a framework where another LLM acts as a judge to infer each model's personality based on its selection and classification of events. Our analysis shows distinct personality traits: for instance, Orca 2 demonstrates emotional reasoning focusing on interpersonal dynamics, while Qwen 2.5 displays a more strategic, analytical style. When analyzing scientific discovery events, Claude Sonnet 3.7 emphasizes conceptual framing, Gemini 2.5 Pro prioritizes empirical validation, and o3 favors step-by-step causal reasoning. This analysis improves model interpretability, making them user-friendly for a wide range of diverse applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.12915", "title": "PersonaFeedback: A Large-scale Human-annotated Benchmark For Personalization", "authors": [ "Meiling Tao", "Chenghao Zhu", "Dongyi Ding", "Tiannan Wang", "Yuchen Eleanor Jiang", "Wangchunshu Zhou" ], "github": "", "project_page": "", "abstract": "With the rapid improvement in the general capabilities of LLMs, LLM personalization, i.e., how to build LLM systems that can generate personalized responses or services that are tailored to distinct user personas, has become an increasingly important research and engineering problem. However, unlike many new challenging benchmarks being released for evaluating the general/reasoning capabilities, the lack of high-quality benchmarks for evaluating LLM personalization greatly hinders progress in this field. To address this, we introduce PersonaFeedback, a new benchmark that directly evaluates LLMs' ability to provide personalized responses given pre-defined user personas and queries. Unlike existing benchmarks that require models to infer implicit user personas from historical interactions, PersonaFeedback decouples persona inference from personalization, focusing on evaluating the model's ability to generate responses tailored to explicit personas. PersonaFeedback consists of 8298 human-annotated test cases, which are categorized into easy, medium, and hard tiers based on the contextual complexity of the user personas and the difficulty in distinguishing subtle differences between two personalized responses. We conduct comprehensive evaluations across a wide range of models. The empirical results reveal that even state-of-the-art LLMs that can solve complex real-world reasoning tasks could fall short on the hard tier of PersonaFeedback where even human evaluators may find the distinctions challenging. Furthermore, we conduct an in-depth analysis of failure modes across various types of systems, demonstrating that the current retrieval-augmented framework should not be seen as a de facto solution for personalization tasks. All benchmark data, annotation protocols, and the evaluation pipeline will be publicly available to facilitate future research on LLM personalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.10521", "title": "Scientists' First Exam: Probing Cognitive Abilities of MLLM via Perception, Understanding, and Reasoning", "authors": [ "Yuhao Zhou", "Yiheng Wang", "Xuming He", "Ruoyao Xiao", "Zhiwei Li", "Qiantai Feng", "Zijie Guo", "Yuejin Yang", "Hao Wu", "Wenxuan Huang", "Jiaqi Wei", "Dan Si", "Xiuqi Yao", "Jia Bu", "Haiwen Huang", "Tianfan Fu", "Shixiang Tang", "Ben Fei", "Dongzhan Zhou", "Fenghua Ling", "Yan Lu", "Siqi Sun", "Chenhui Li", "Guanjie Zheng", "Jiancheng Lv", "Wenlong Zhang", "Lei Bai" ], "github": "", "project_page": "", "abstract": "Scientific discoveries increasingly rely on complex multimodal reasoning based on information-intensive scientific data and domain-specific expertise. Empowered by expert-level scientific benchmarks, scientific Multimodal Large Language Models (MLLMs) hold the potential to significantly enhance this discovery process in realistic workflows. However, current scientific benchmarks mostly focus on evaluating the knowledge understanding capabilities of MLLMs, leading to an inadequate assessment of their perception and reasoning abilities. To address this gap, we present the Scientists' First Exam (SFE) benchmark, designed to evaluate the scientific cognitive capacities of MLLMs through three interconnected levels: scientific signal perception, scientific attribute understanding, scientific comparative reasoning. Specifically, SFE comprises 830 expert-verified VQA pairs across three question types, spanning 66 multimodal tasks across five high-value disciplines. Extensive experiments reveal that current state-of-the-art GPT-o3 and InternVL-3 achieve only 34.08% and 26.52% on SFE, highlighting significant room for MLLMs to improve in scientific realms. We hope the insights obtained in SFE will facilitate further developments in AI-enhanced scientific discoveries.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.13654", "title": "Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning", "authors": [ "Shulin Tian", "Ruiqi Wang", "Hongming Guo", "Penghao Wu", "Yuhao Dong", "Xiuying Wang", "Jingkang Yang", "Hao Zhang", "Hongyuan Zhu", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "We introduce Ego-R1, a novel framework for reasoning over ultra-long (i.e., in days and weeks) egocentric videos, which leverages a structured Chain-of-Tool-Thought (CoTT) process, orchestrated by an Ego-R1 Agent trained via reinforcement learning (RL). Inspired by human problem-solving strategies, CoTT decomposes complex reasoning into modular steps, with the RL agent invoking specific tools, one per step, to iteratively and collaboratively answer sub-questions tackling such tasks as temporal retrieval and multi-modal understanding. We design a two-stage training paradigm involving supervised finetuning (SFT) of a pretrained language model using CoTT data and RL to enable our agent to dynamically propose step-by-step tools for long-range reasoning. To facilitate training, we construct a dataset called Ego-R1 Data, which consists of Ego-CoTT-25K for SFT and Ego-QA-4.4K for RL. Furthermore, our Ego-R1 agent is evaluated on a newly curated week-long video QA benchmark, Ego-R1 Bench, which contains human-verified QA pairs from hybrid sources. Extensive results demonstrate that the dynamic, tool-augmented chain-of-thought reasoning by our Ego-R1 Agent can effectively tackle the unique challenges of understanding ultra-long egocentric videos, significantly extending the time coverage from few hours to a week.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.12450", "title": "Language Surgery in Multilingual Large Language Models", "authors": [ "Joanito Agili Lopo", "Muhammad Ravi Shulthan Habibi", "Tack Hwa Wong", "Muhammad Ilham Ghozali", "Fajri Koto", "Genta Indra Winata", "Peerat Limkonchotiwat", "Alham Fikri Aji", "Samuel Cahyawijaya" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across tasks and languages, revolutionizing natural language processing. This paper investigates the naturally emerging representation alignment in LLMs, particularly in the middle layers, and its implications for disentangling language-specific and language-agnostic information. We empirically confirm the existence of this alignment, analyze its behavior in comparison to explicitly designed alignment models, and demonstrate its potential for language-specific manipulation without semantic degradation. Building on these findings, we propose Inference-Time Language Control (ITLC), a novel method that leverages latent injection to enable precise cross-lingual language control and mitigate language confusion in LLMs. Our experiments highlight ITLC's strong cross-lingual control capabilities while preserving semantic integrity in target languages. Furthermore, we demonstrate its effectiveness in alleviating the cross-lingual language confusion problem, which persists even in current large-scale LLMs, leading to inconsistent language generation. This work advances our understanding of representation alignment in LLMs and introduces a practical solution for enhancing their cross-lingual performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.13752", "title": "Steering LLM Thinking with Budget Guidance", "authors": [ "Junyan Li", "Wenshuo Zhao", "Yang Zhang", "Chuang Gan" ], "github": "https://github.com/UMass-Embodied-AGI/BudgetGuidance", "project_page": "", "abstract": "Recent deep-thinking large language models often reason extensively to improve performance, but such lengthy reasoning is not always desirable, as it incurs excessive inference costs with disproportionate performance gains. Controlling reasoning length without sacrificing performance is therefore important, but remains challenging, especially under tight thinking budgets. We propose budget guidance, a simple yet effective method for steering the reasoning process of LLMs toward a target budget without requiring any LLM fine-tuning. Our approach introduces a lightweight predictor that models a Gamma distribution over the remaining thinking length during next-token generation. This signal is then used to guide generation in a soft, token-level manner, ensuring that the overall reasoning trace adheres to the specified thinking budget. Budget guidance enables natural control of the thinking length, along with significant token efficiency improvements over baseline methods on challenging math benchmarks. For instance, it achieves up to a 26% accuracy gain on the MATH-500 benchmark under tight budgets compared to baseline methods, while maintaining competitive accuracy with only 63% of the thinking tokens used by the full-thinking model. Budget guidance also generalizes to broader task domains and exhibits emergent capabilities, such as estimating question difficulty. The source code is available at: https://github.com/UMass-Embodied-AGI/BudgetGuidance.", "github_manual": "", "github_regex": "https://github.com/UMass-Embodied-AGI/BudgetGuidance", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.06366", "title": "AI Agent Behavioral Science", "authors": [ "Lin Chen", "Yunke Zhang", "Jie Feng", "Haoye Chai", "Honglin Zhang", "Bingbing Fan", "Yibo Ma", "Shiyuan Zhang", "Nian Li", "Tianhui Liu", "Nicholas Sukiennik", "Keyu Zhao", "Yu Li", "Ziyi Liu", "Fengli Xu", "Yong Li" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have enabled the development of AI agents that exhibit increasingly human-like behaviors, including planning, adaptation, and social dynamics across diverse, interactive, and open-ended scenarios. These behaviors are not solely the product of the internal architectures of the underlying models, but emerge from their integration into agentic systems operating within specific contexts, where environmental factors, social cues, and interaction feedbacks shape behavior over time. This evolution necessitates a new scientific perspective: AI Agent Behavioral Science. Rather than focusing only on internal mechanisms, this perspective emphasizes the systematic observation of behavior, design of interventions to test hypotheses, and theory-guided interpretation of how AI agents act, adapt, and interact over time. We systematize a growing body of research across individual agent, multi-agent, and human-agent interaction settings, and further demonstrate how this perspective informs responsible AI by treating fairness, safety, interpretability, accountability, and privacy as behavioral properties. By unifying recent findings and laying out future directions, we position AI Agent Behavioral Science as a necessary complement to traditional model-centric approaches, providing essential tools for understanding, evaluating, and governing the real-world behavior of increasingly autonomous AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.13172", "title": "Ai-Facilitated Analysis of Abstracts and Conclusions: Flagging Unsubstantiated Claims and Ambiguous Pronouns", "authors": [ "Evgeny Markhasin" ], "github": "", "project_page": "", "abstract": "We present and evaluate a suite of proof-of-concept (PoC), structured workflow prompts designed to elicit human-like hierarchical reasoning while guiding Large Language Models (LLMs) in high-level semantic and linguistic analysis of scholarly manuscripts. The prompts target two non-trivial analytical tasks: identifying unsubstantiated claims in summaries (informational integrity) and flagging ambiguous pronoun references (linguistic clarity). We conducted a systematic, multi-run evaluation on two frontier models (Gemini Pro 2.5 Pro and ChatGPT Plus o3) under varied context conditions. Our results for the informational integrity task reveal a significant divergence in model performance: while both models successfully identified an unsubstantiated head of a noun phrase (95% success), ChatGPT consistently failed (0% success) to identify an unsubstantiated adjectival modifier that Gemini correctly flagged (95% success), raising a question regarding potential influence of the target's syntactic role. For the linguistic analysis task, both models performed well (80-90% success) with full manuscript context. In a summary-only setting, however, ChatGPT achieved a perfect (100%) success rate, while Gemini's performance was substantially degraded. Our findings suggest that structured prompting is a viable methodology for complex textual analysis but show that prompt performance may be highly dependent on the interplay between the model, task type, and context, highlighting the need for rigorous, model-specific testing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.10055", "title": "TaskCraft: Automated Generation of Agentic Tasks", "authors": [ "Dingfeng Shi", "Jingyi Cao", "Qianben Chen", "Weichen Sun", "Weizhen Li", "Hongxuan Lu", "Fangchen Dong", "Tianrui Qin", "King Zhu", "Minghao Yang", "Jian Yang", "Ge Zhang", "Jiaheng Liu", "Changwang Zhang", "Jun Wang", "Yuchen Eleanor Jiang", "Wangchunshu Zhou" ], "github": "https://github.com/oppo-personalai/taskcraft", "project_page": "", "abstract": "Agentic tasks, which require multi-step problem solving with autonomy, tool use, and adaptive reasoning, are becoming increasingly central to the advancement of NLP and AI. However, existing instruction data lacks tool interaction, and current agentic benchmarks rely on costly human annotation, limiting their scalability. We introduce TaskCraft, an automated workflow for generating difficulty-scalable, multi-tool, and verifiable agentic tasks with execution trajectories. TaskCraft expands atomic tasks using depth-based and width-based extensions to create structurally and hierarchically complex challenges. Empirical results show that these tasks improve prompt optimization in the generation workflow and enhance supervised fine-tuning of agentic foundation models. We present a large-scale synthetic dataset of approximately 36,000 tasks with varying difficulty to support future research on agent tuning and evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/oppo-personalai/taskcraft", "github_api_added_by": "auto" }, { "date": "2025-06-17", "arxiv_id": "2506.09050", "title": "ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering", "authors": [ "Yuki Imajuku", "Kohki Horie", "Yoichi Iwata", "Kensho Aoki", "Naohiro Takahashi", "Takuya Akiba" ], "github": "", "project_page": "", "abstract": "How well do AI systems perform in algorithm engineering for hard optimization problems in domains such as package-delivery routing, crew scheduling, factory production planning, and power-grid balancing? We introduce ALE-Bench, a new benchmark for evaluating AI systems on score-based algorithmic programming contests. Drawing on real tasks from the AtCoder Heuristic Contests, ALE-Bench presents optimization problems that are computationally hard and admit no known exact solution. Unlike short-duration, pass/fail coding benchmarks, ALE-Bench encourages iterative solution refinement over long time horizons. Our software framework supports interactive agent architectures that leverage test-run feedback and visualizations. Our evaluation of frontier LLMs revealed that while they demonstrate high performance on specific problems, a notable gap remains compared to humans in terms of consistency across problems and long-horizon problem-solving capabilities. This highlights the need for this benchmark to foster future AI advancements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.11991", "title": "VGR: Visual Grounded Reasoning", "authors": [ "Jiacong Wang", "Zijiang Kang", "Haochen Wang", "Haiyong Jiang", "Jiawen Li", "Bohong Wu", "Ya Wang", "Jiao Ran", "Xiao Liang", "Chao Feng", "Jun Xiao" ], "github": "", "project_page": "", "abstract": "In the field of multimodal chain-of-thought (CoT) reasoning, existing approaches predominantly rely on reasoning on pure language space, which inherently suffers from language bias and is largely confined to math or science domains. This narrow focus limits their ability to handle complex visual reasoning tasks that demand comprehensive understanding of image details. To address these limitations, this paper introduces VGR, a novel reasoning multimodal large language model (MLLM) with enhanced fine-grained visual perception capabilities. Unlike traditional MLLMs that answer the question or reasoning solely on the language space, our VGR first detects relevant regions that may help to solve problems, and then provides precise answers based on replayed image regions. To achieve this, we conduct a large-scale SFT dataset called VGR -SFT that contains reasoning data with mixed vision grounding and language deduction. The inference pipeline of VGR allows the model to choose bounding boxes for visual reference and a replay stage is introduced to integrates the corresponding regions into the reasoning process, enhancing multimodel comprehension. Experiments on the LLaVA-NeXT-7B baseline show that VGR achieves superior performance on multi-modal benchmarks requiring comprehensive image detail understanding. Compared to the baseline, VGR uses only 30\\% of the image token count while delivering scores of +4.1 on MMStar, +7.1 on AI2D, and a +12.9 improvement on ChartQA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.06454", "title": "LETS Forecast: Learning Embedology for Time Series Forecasting", "authors": [ "Abrar Majeedi", "Viswanatha Reddy Gajjala", "Satya Sai Srinath Namburi GNVV", "Nada Magdi Elkordi", "Yin Li" ], "github": "", "project_page": "", "abstract": "Real-world time series are often governed by complex nonlinear dynamics. Understanding these underlying dynamics is crucial for precise future prediction. While deep learning has achieved major success in time series forecasting, many existing approaches do not explicitly model the dynamics. To bridge this gap, we introduce DeepEDM, a framework that integrates nonlinear dynamical systems modeling with deep neural networks. Inspired by empirical dynamic modeling (EDM) and rooted in Takens' theorem, DeepEDM presents a novel deep model that learns a latent space from time-delayed embeddings, and employs kernel regression to approximate the underlying dynamics, while leveraging efficient implementation of softmax attention and allowing for accurate prediction of future time steps. To evaluate our method, we conduct comprehensive experiments on synthetic data of nonlinear dynamical systems as well as real-world time series across domains. Our results show that DeepEDM is robust to input noise, and outperforms state-of-the-art methods in forecasting accuracy. Our code is available at: https://abrarmajeedi.github.io/deep_edm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.09968", "title": "SRLAgent: Enhancing Self-Regulated Learning Skills through Gamification and LLM Assistance", "authors": [ "Wentao Ge", "Yuqing Sun", "Ziyan Wang", "Haoyue Zheng", "Weiyang He", "Piaohong Wang", "Qianyu Zhu", "Benyou Wang" ], "github": "", "project_page": "", "abstract": "Self-regulated learning (SRL) is crucial for college students navigating increased academic demands and independence. Insufficient SRL skills can lead to disorganized study habits, low motivation, and poor time management, undermining learners ability to thrive in challenging environments. Through a formative study involving 59 college students, we identified key challenges students face in developing SRL skills, including difficulties with goal-setting, time management, and reflective learning. To address these challenges, we introduce SRLAgent, an LLM-assisted system that fosters SRL skills through gamification and adaptive support from large language models (LLMs). Grounded in Zimmermans three-phase SRL framework, SRLAgent enables students to engage in goal-setting, strategy execution, and self-reflection within an interactive game-based environment. The system offers real-time feedback and scaffolding powered by LLMs to support students independent study efforts. We evaluated SRLAgent using a between-subjects design, comparing it to a baseline system (SRL without Agent features) and a traditional multimedia learning condition. Results showed significant improvements in SRL skills within the SRLAgent group (p < .001, Cohens d = 0.234) and higher engagement compared to the baselines. This work highlights the value of embedding SRL scaffolding and real-time AI support within gamified environments, offering design implications for educational technologies that aim to promote deeper learning and metacognitive skill development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.11115", "title": "Incorporating Domain Knowledge into Materials Tokenization", "authors": [ "Yerim Oh", "Jun-Hyung Park", "Junho Kim", "SungHo Kim", "SangKeun Lee" ], "github": "https://github.com/yerimoh/MATTER", "project_page": "", "abstract": "While language models are increasingly utilized in materials science, typical models rely on frequency-centric tokenization methods originally developed for natural language processing. However, these methods frequently produce excessive fragmentation and semantic loss, failing to maintain the structural and semantic integrity of material concepts. To address this issue, we propose MATTER, a novel tokenization approach that integrates material knowledge into tokenization. Based on MatDetector trained on our materials knowledge base and a re-ranking method prioritizing material concepts in token merging, MATTER maintains the structural integrity of identified material concepts and prevents fragmentation during tokenization, ensuring their semantic meaning remains intact. The experimental results demonstrate that MATTER outperforms existing tokenization methods, achieving an average performance gain of 4% and 2% in the generation and classification tasks, respectively. These results underscore the importance of domain knowledge for tokenization strategies in scientific text processing. Our code is available at https://github.com/yerimoh/MATTER", "github_manual": "", "github_regex": "https://github.com/yerimoh/MATTER", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.12299", "title": "QGuard:Question-based Zero-shot Guard for Multi-modal LLM Safety", "authors": [ "Taegyeong Lee", "Jeonghwa Yoo", "Hyoungseo Cho", "Soo Yong Kim", "Yunho Maeng" ], "github": "https://github.com/taegyeong-lee/QGuard-Question-based-Zero-shot-Guard-for-Multi-modal-LLM-Safety", "project_page": "", "abstract": "The recent advancements in Large Language Models(LLMs) have had a significant impact on a wide range of fields, from general domains to specialized areas. However, these advancements have also significantly increased the potential for malicious users to exploit harmful and jailbreak prompts for malicious attacks. Although there have been many efforts to prevent harmful prompts and jailbreak prompts, protecting LLMs from such malicious attacks remains an important and challenging task. In this paper, we propose QGuard, a simple yet effective safety guard method, that utilizes question prompting to block harmful prompts in a zero-shot manner. Our method can defend LLMs not only from text-based harmful prompts but also from multi-modal harmful prompt attacks. Moreover, by diversifying and modifying guard questions, our approach remains robust against the latest harmful prompts without fine-tuning. Experimental results show that our model performs competitively on both text-only and multi-modal harmful datasets. Additionally, by providing an analysis of question prompting, we enable a white-box analysis of user inputs. We believe our method provides valuable insights for real-world LLM services in mitigating security risks associated with harmful prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/taegyeong-lee/QGuard-Question-based-Zero-shot-Guard-for-Multi-modal-LLM-Safety", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.13430", "title": "Uncertainty-Aware Remaining Lifespan Prediction from Images", "authors": [ "Tristan Kenneweg", "Philip Kenneweg", "Barbara Hammer" ], "github": "https://github.com/TKenneweg/RLPredictionWithUncertainty", "project_page": "", "abstract": "Predicting mortality-related outcomes from images offers the prospect of accessible, noninvasive, and scalable health screening. We present a method that leverages pretrained vision transformer foundation models to estimate remaining lifespan from facial and whole-body images, alongside robust uncertainty quantification. We show that predictive uncertainty varies systematically with the true remaining lifespan, and that this uncertainty can be effectively modeled by learning a Gaussian distribution for each sample. Our approach achieves state-of-the-art mean absolute error (MAE) of 7.48 years on an established Dataset, and further improves to 4.79 and 5.07 years MAE on two new, higher-quality datasets curated and published in this work. Importantly, our models provide well-calibrated uncertainty estimates, as demonstrated by a bucketed expected calibration error of 0.62 years. While not intended for clinical deployment, these results highlight the potential of extracting medically relevant signals from images. We make all code and datasets available to facilitate further research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/TKenneweg/RLPredictionWithUncertainty", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.12258", "title": "EgoPrivacy: What Your First-Person Camera Says About You?", "authors": [ "Yijiang Li", "Genpei Zhang", "Jiacheng Cheng", "Yi Li", "Xiaojun Shan", "Dashan Gao", "Jiancheng Lyu", "Yuan Li", "Ning Bi", "Nuno Vasconcelos" ], "github": "https://github.com/williamium3000/ego-privacy", "project_page": "", "abstract": "While the rapid proliferation of wearable cameras has raised significant concerns about egocentric video privacy, prior work has largely overlooked the unique privacy threats posed to the camera wearer. This work investigates the core question: How much privacy information about the camera wearer can be inferred from their first-person view videos? We introduce EgoPrivacy, the first large-scale benchmark for the comprehensive evaluation of privacy risks in egocentric vision. EgoPrivacy covers three types of privacy (demographic, individual, and situational), defining seven tasks that aim to recover private information ranging from fine-grained (e.g., wearer's identity) to coarse-grained (e.g., age group). To further emphasize the privacy threats inherent to egocentric vision, we propose Retrieval-Augmented Attack, a novel attack strategy that leverages ego-to-exo retrieval from an external pool of exocentric videos to boost the effectiveness of demographic privacy attacks. An extensive comparison of the different attacks possible under all threat models is presented, showing that private information of the wearer is highly susceptible to leakage. For instance, our findings indicate that foundation models can effectively compromise wearer privacy even in zero-shot settings by recovering attributes such as identity, scene, gender, and race with 70-80% accuracy. Our code and data are available at https://github.com/williamium3000/ego-privacy.", "github_manual": "", "github_regex": "https://github.com/williamium3000/ego-privacy", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.12552", "title": "Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts", "authors": [ "Zain Muhammad Mujahid", "Dilshod Azizov", "Maha Tufail Agro", "Preslav Nakov" ], "github": "https://github.com/mbzuai-nlp/llm-media-profiling", "project_page": "", "abstract": "In an age characterized by the proliferation of mis- and disinformation online, it is critical to empower readers to understand the content they are reading. Important efforts in this direction rely on manual or automatic fact-checking, which can be challenging for emerging claims with limited information. Such scenarios can be handled by assessing the reliability and the political bias of the source of the claim, i.e., characterizing entire news outlets rather than individual claims or articles. This is an important but understudied research direction. While prior work has looked into linguistic and social contexts, we do not analyze individual articles or information in social media. Instead, we propose a novel methodology that emulates the criteria that professional fact-checkers use to assess the factuality and political bias of an entire outlet. Specifically, we design a variety of prompts based on these criteria and elicit responses from large language models (LLMs), which we aggregate to make predictions. In addition to demonstrating sizable improvements over strong baselines via extensive experiments with multiple LLMs, we provide an in-depth error analysis of the effect of media popularity and region on model performance. Further, we conduct an ablation study to highlight the key components of our dataset that contribute to these improvements. To facilitate future research, we released our dataset and code at https://github.com/mbzuai-nlp/llm-media-profiling.", "github_manual": "", "github_regex": "https://github.com/mbzuai-nlp/llm-media-profiling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.12571", "title": "DoTA-RAG: Dynamic of Thought Aggregation RAG", "authors": [ "Saksorn Ruangtanusak", "Natthapath Rungseesiripak", "Peerawat Rojratchadakorn", "Monthol Charattrakool", "Natapong Nitarach" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce DoTA-RAG (Dynamic-of-Thought Aggregation RAG), a retrieval-augmented generation system optimized for high-throughput, large-scale web knowledge indexes. Traditional RAG pipelines often suffer from high latency and limited accuracy over massive, diverse datasets. DoTA-RAG addresses these challenges with a three-stage pipeline: query rewriting, dynamic routing to specialized sub-indexes, and multi-stage retrieval and ranking. We further enhance retrieval by evaluating and selecting a superior embedding model, re-embedding the large FineWeb-10BT corpus. Moreover, we create a diverse Q&A dataset of 500 questions generated via the DataMorgana setup across a broad range of WebOrganizer topics and formats. DoTA-RAG improves the answer correctness score from 0.752 (baseline, using LiveRAG pre-built vector store) to 1.478 while maintaining low latency, and it achieves a 0.929 correctness score on the Live Challenge Day. These results highlight DoTA-RAG's potential for practical deployment in domains requiring fast, reliable access to large and evolving knowledge sources.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.12148", "title": "Hatevolution: What Static Benchmarks Don't Tell Us", "authors": [ "Chiara Di Bonaventura", "Barbara McGillivray", "Yulan He", "Albert Mero\u00f1o-Pe\u00f1uela" ], "github": "https://github.com/ChiaraDiBonaventura/hatevolution", "project_page": "", "abstract": "Language changes over time, including in the hate speech domain, which evolves quickly following social dynamics and cultural shifts. While NLP research has investigated the impact of language evolution on model training and has proposed several solutions for it, its impact on model benchmarking remains under-explored. Yet, hate speech benchmarks play a crucial role to ensure model safety. In this paper, we empirically evaluate the robustness of 20 language models across two evolving hate speech experiments, and we show the temporal misalignment between static and time-sensitive evaluations. Our findings call for time-sensitive linguistic benchmarks in order to correctly and reliably evaluate language models in the hate speech domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ChiaraDiBonaventura/hatevolution", "github_api_added_by": "auto" }, { "date": "2025-06-17", "arxiv_id": "2506.09482", "title": "Marrying Autoregressive Transformer and Diffusion with Multi-Reference Autoregression", "authors": [ "Dingcheng Zhen", "Qian Qiao", "Tan Yu", "Kangxi Wu", "Ziwei Zhang", "Siyuan Liu", "Shunshun Yin", "Ming Tao" ], "github": "", "project_page": "", "abstract": "We introduce TransDiff, the first image generation model that marries Autoregressive (AR) Transformer with diffusion models. In this joint modeling framework, TransDiff encodes labels and images into high-level semantic features and employs a diffusion model to estimate the distribution of image samples. On the ImageNet 256x256 benchmark, TransDiff significantly outperforms other image generation models based on standalone AR Transformer or diffusion models. Specifically, TransDiff achieves a Fr\\'echet Inception Distance (FID) of 1.61 and an Inception Score (IS) of 293.4, and further provides x2 faster inference latency compared to state-of-the-art methods based on AR Transformer and x112 faster inference compared to diffusion-only models. Furthermore, building on the TransDiff model, we introduce a novel image generation paradigm called Multi-Reference Autoregression (MRAR), which performs autoregressive generation by predicting the next image. MRAR enables the model to reference multiple previously generated images, thereby facilitating the learning of more diverse representations and improving the quality of generated images in subsequent iterations. By applying MRAR, the performance of TransDiff is improved, with the FID reduced from 1.61 to 1.42. We expect TransDiff to open up a new frontier in the field of image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.13404", "title": "A Technical Study into Small Reasoning Language Models", "authors": [ "Xialie Zhuang", "Peixian Ma", "Zhikai Jia", "Zheng Cao", "Shiwei Liu" ], "github": "", "project_page": "", "abstract": "The ongoing evolution of language models has led to the development of large-scale architectures that demonstrate exceptional performance across a wide range of tasks. However, these models come with significant computational and energy demands, as well as potential privacy implications. In this context, Small Reasoning Language Models (SRLMs) with approximately 0.5 billion parameters present a compelling alternative due to their remarkable computational efficiency and cost effectiveness, particularly in resource-constrained environments. Despite these advantages, the limited capacity of 0.5 billion parameter models poses challenges in handling complex tasks such as mathematical reasoning and code generation. This research investigates various training strategies, including supervised fine-tuning (SFT), knowledge distillation (KD), and reinforcement learning (RL), as well as their hybrid implementations, to enhance the performance of 0.5B SRLMs. We analyze effective methodologies to bridge the performance gap between SRLMS and larger models and present insights into optimal training pipelines tailored for these smaller architectures. Through extensive experimental validation and analysis, our work aims to provide actionable recommendations for maximizing the reasoning capabilities of 0.5B models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.13277", "title": "SeqPE: Transformer with Sequential Position Encoding", "authors": [ "Huyang Li", "Yahui Liu", "Hongyu Sun", "Deng Cai", "Leyang Cui", "Wei Bi", "Peilin Zhao", "Taro Watanabe" ], "github": "https://github.com/ghrua/seqpe", "project_page": "", "abstract": "Since self-attention layers in Transformers are permutation invariant by design, positional encodings must be explicitly incorporated to enable spatial understanding. However, fixed-size lookup tables used in traditional learnable position embeddings (PEs) limit extrapolation capabilities beyond pre-trained sequence lengths. Expert-designed methods such as ALiBi and RoPE, mitigate this limitation but demand extensive modifications for adapting to new modalities, underscoring fundamental challenges in adaptability and scalability. In this work, we present SeqPE, a unified and fully learnable position encoding framework that represents each n-dimensional position index as a symbolic sequence and employs a lightweight sequential position encoder to learn their embeddings in an end-to-end manner. To regularize SeqPE's embedding space, we introduce two complementary objectives: a contrastive objective that aligns embedding distances with a predefined position-distance function, and a knowledge distillation loss that anchors out-of-distribution position embeddings to in-distribution teacher representations, further enhancing extrapolation performance. Experiments across language modeling, long-context question answering, and 2D image classification demonstrate that SeqPE not only surpasses strong baselines in perplexity, exact match (EM), and accuracy--particularly under context length extrapolation--but also enables seamless generalization to multi-dimensional inputs without requiring manual architectural redesign. We release our code, data, and checkpoints at https://github.com/ghrua/seqpe.", "github_manual": "", "github_regex": "https://github.com/ghrua/seqpe", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.06962", "title": "AR-RAG: Autoregressive Retrieval Augmentation for Image Generation", "authors": [ "Jingyuan Qi", "Zhiyang Xu", "Qifan Wang", "Lifu Huang" ], "github": "https://github.com/PLUM-Lab/AR-RAG", "project_page": "", "abstract": "We introduce Autoregressive Retrieval Augmentation (AR-RAG), a novel paradigm that enhances image generation by autoregressively incorporating knearest neighbor retrievals at the patch level. Unlike prior methods that perform a single, static retrieval before generation and condition the entire generation on fixed reference images, AR-RAG performs context-aware retrievals at each generation step, using prior-generated patches as queries to retrieve and incorporate the most relevant patch-level visual references, enabling the model to respond to evolving generation needs while avoiding limitations (e.g., over-copying, stylistic bias, etc.) prevalent in existing methods. To realize AR-RAG, we propose two parallel frameworks: (1) Distribution-Augmentation in Decoding (DAiD), a training-free plug-and-use decoding strategy that directly merges the distribution of model-predicted patches with the distribution of retrieved patches, and (2) Feature-Augmentation in Decoding (FAiD), a parameter-efficient fine-tuning method that progressively smooths the features of retrieved patches via multi-scale convolution operations and leverages them to augment the image generation process. We validate the effectiveness of AR-RAG on widely adopted benchmarks, including Midjourney-30K, GenEval and DPG-Bench, demonstrating significant performance gains over state-of-the-art image generation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PLUM-Lab/AR-RAG", "github_api_added_by": "auto" }, { "date": "2025-06-17", "arxiv_id": "2506.13284", "title": "AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy", "authors": [ "Zihan Liu", "Zhuolin Yang", "Yang Chen", "Chankyu Lee", "Mohammad Shoeybi", "Bryan Catanzaro", "Wei Ping" ], "github": "", "project_page": "", "abstract": "In this work, we investigate the synergy between supervised fine-tuning (SFT) and reinforcement learning (RL) in developing strong reasoning models. We begin by curating the SFT training data through two scaling strategies: increasing the number of collected prompts and the number of generated responses per prompt. Both approaches yield notable improvements in reasoning performance, with scaling the number of prompts resulting in more substantial gains. We then explore the following questions regarding the synergy between SFT and RL: (i) Does a stronger SFT model consistently lead to better final performance after large-scale RL training? (ii) How can we determine an appropriate sampling temperature during RL training to effectively balance exploration and exploitation for a given SFT initialization? Our findings suggest that (i) holds true, provided effective RL training is conducted, particularly when the sampling temperature is carefully chosen to maintain the temperature-adjusted entropy around 0.3, a setting that strikes a good balance between exploration and exploitation. Notably, the performance gap between initial SFT models narrows significantly throughout the RL process. Leveraging a strong SFT foundation and insights into the synergistic interplay between SFT and RL, our AceReason-Nemotron-1.1 7B model significantly outperforms AceReason-Nemotron-1.0 and achieves new state-of-the-art performance among Qwen2.5-7B-based reasoning models on challenging math and code benchmarks, thereby demonstrating the effectiveness of our post-training recipe. We release the model and data at: https://huggingface.co/nvidia/AceReason-Nemotron-1.1-7B", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.10341", "title": "Provably Learning from Language Feedback", "authors": [ "Wanqiao Xu", "Allen Nie", "Ruijie Zheng", "Aditya Modi", "Adith Swaminathan", "Ching-An Cheng" ], "github": "", "project_page": "", "abstract": "Interactively learning from observation and language feedback is an increasingly studied area driven by the emergence of large language model (LLM) agents. While impressive empirical demonstrations have been shown, so far a principled framing of these decision problems remains lacking. In this paper, we formalize the Learning from Language Feedback (LLF) problem, assert sufficient assumptions to enable learning despite latent rewards, and introduce transfer eluder dimension as a complexity measure to characterize the hardness of LLF problems. We show that transfer eluder dimension captures the intuition that information in the feedback changes the learning complexity of the LLF problem. We demonstrate cases where learning from rich language feedback can be exponentially faster than learning from reward. We develop a no-regret algorithm, called HELiX, that provably solves LLF problems through sequential interactions, with performance guarantees that scale with the transfer eluder dimension of the problem. Across several empirical domains, we show that HELiX performs well even when repeatedly prompting LLMs does not work reliably. Our contributions mark a first step towards designing principled interactive learning algorithms from generic language feedback.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.13502", "title": "BOW: Bottlenecked Next Word Exploration", "authors": [ "Ming Shen", "Zhikun Xu", "Xiao Ye", "Jacob Dineen", "Ben Zhou" ], "github": "https://github.com/volcengine/verl", "project_page": "", "abstract": "Large language models (LLMs) are typically trained via next-word prediction (NWP), which provides strong surface-level fluency but often lacks support for robust reasoning. We propose BOttlenecked next Word exploration (BOW), a novel RL framework that rethinks NWP by introducing a reasoning bottleneck where a policy model first generates a reasoning path rather than predicting the next token directly, after which a frozen judge model predicts the next token distribution based solely on this reasoning path. We train the policy model using GRPO with rewards that quantify how effectively the reasoning path facilitates next-word recovery. Compared with other continual pretraining baselines, we show that BOW improves both the general and next-word reasoning capabilities of the base model, evaluated on various benchmarks. Our findings show that BOW can serve as an effective and scalable alternative to vanilla NWP.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/volcengine/verl", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.14111", "title": "Essential-Web v1.0: 24T tokens of organized web data", "authors": [ "Essential AI", "Andrew Hojel", "Michael Pust", "Tim Romanski", "Yash Vanjani", "Ritvik Kapila", "Mohit Parmar", "Adarsh Chaluvaraju", "Alok Tripathy", "Anil Thomas", "Ashish Tanwer", "Darsh J Shah", "Ishaan Shah", "Karl Stratos", "Khoi Nguyen", "Kurt Smith", "Michael Callahan", "Peter Rushton", "Philip Monk", "Platon Mazarakis", "Saad Jamal", "Saurabh Srivastava", "Somanshu Singla", "Ashish Vaswani" ], "github": "", "project_page": "", "abstract": "Data plays the most prominent role in how language models acquire skills and knowledge. The lack of massive, well-organized pre-training datasets results in costly and inaccessible data pipelines. We present Essential-Web v1.0, a 24-trillion-token dataset in which every document is annotated with a twelve-category taxonomy covering topic, format, content complexity, and quality. Taxonomy labels are produced by EAI-Distill-0.5b, a fine-tuned 0.5b-parameter model that achieves an annotator agreement within 3% of Qwen2.5-32B-Instruct. With nothing more than SQL-style filters, we obtain competitive web-curated datasets in math (-8.0% relative to SOTA), web code (+14.3%), STEM (+24.5%) and medical (+8.6%). Essential-Web v1.0 is available on HuggingFace: https://huggingface.co/datasets/EssentialAI/essential-web-v1.0", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.14202", "title": "DiffusionBlocks: Blockwise Training for Generative Models via Score-Based Diffusion", "authors": [ "Makoto Shing", "Takuya Akiba" ], "github": "", "project_page": "", "abstract": "Training large neural networks with end-to-end backpropagation creates significant memory bottlenecks, limiting accessibility to state-of-the-art AI research. We propose DiffusionBlocks, a novel training framework that interprets neural network blocks as performing denoising operations in a continuous-time diffusion process. By partitioning the network into independently trainable blocks and optimizing noise level assignments based on equal cumulative probability mass, our approach achieves significant memory efficiency while maintaining competitive performance compared to traditional backpropagation in generative tasks. Experiments on image generation and language modeling tasks demonstrate memory reduction proportional to the number of blocks while achieving superior performance. DiffusionBlocks provides a promising pathway for democratizing access to large-scale neural network training with limited computational resources.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.12229", "title": "Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index", "authors": [ "Hao Xu", "Jiacheng Liu", "Yejin Choi", "Noah A. Smith", "Hannaneh Hajishirzi" ], "github": "", "project_page": "", "abstract": "Language models are trained mainly on massive text data from the Internet, and it becomes increasingly important to understand this data source. Exact-match search engines enable searching in large text corpora -- counting string appearances and retrieving the enclosing documents -- yet the high storage overhead hinders their application on Internet-scale data. We present Infini-gram mini, an efficient and scalable system that can make petabyte-level text corpora searchable. Based on the FM-index data structure (Ferragina and Manzini, 2000), which simultaneously indexes and compresses text, our system creates indexes with size only 44% of the corpus. Infini-gram mini greatly improves upon the best existing implementation of FM-index in terms of indexing speed (18times) and memory use during both indexing (3.2times reduction) and querying (down to a negligible amount). We index 46TB of Internet text in 50 days with a single 128-core CPU node (or 19 hours if using 75 such nodes). We show one important use case of Infini-gram mini in a large-scale analysis of benchmark contamination. We find several core LM evaluation benchmarks to be heavily contaminated in Internet crawls (up to 40% in SQuAD), which could lead to overestimating the capabilities of language models if trained on such data. We host a benchmark contamination bulletin to share the contamination rate of many core and community-contributed benchmarks. We also release a web interface and an API endpoint to serve general search queries on Infini-gram mini indexes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-17", "arxiv_id": "2506.13001", "title": "Personalizable Long-Context Symbolic Music Infilling with MIDI-RWKV", "authors": [ "Christian Zhou-Zheng", "Philippe Pasquier" ], "github": "https://github.com/christianazinn/MIDI-RWKV", "project_page": "", "abstract": "Existing work in automatic music generation has primarily focused on end-to-end systems that produce complete compositions or continuations. However, because musical composition is typically an iterative process, such systems make it difficult to engage in the back-and-forth between human and machine that is essential to computer-assisted creativity. In this study, we address the task of personalizable, multi-track, long-context, and controllable symbolic music infilling to enhance the process of computer-assisted composition. We present MIDI-RWKV, a novel model based on the RWKV-7 linear architecture, to enable efficient and coherent musical cocreation on edge devices. We also demonstrate that MIDI-RWKV admits an effective method of finetuning its initial state for personalization in the very-low-sample regime. We evaluate MIDI-RWKV and its state tuning on several quantitative and qualitative metrics, and release model weights and code at https://github.com/christianazinn/MIDI-RWKV.", "github_manual": "", "github_regex": "https://github.com/christianazinn/MIDI-RWKV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.13642", "title": "Stream-Omni: Simultaneous Multimodal Interactions with Large Language-Vision-Speech Model", "authors": [ "Shaolei Zhang", "Shoutao Guo", "Qingkai Fang", "Yan Zhou", "Yang Feng" ], "github": "", "project_page": "", "abstract": "The emergence of GPT-4o-like large multimodal models (LMMs) has raised the exploration of integrating text, vision, and speech modalities to support more flexible multimodal interaction. Existing LMMs typically concatenate representation of modalities along the sequence dimension and feed them into a large language model (LLM) backbone. While sequence-dimension concatenation is straightforward for modality integration, it often relies heavily on large-scale data to learn modality alignments. In this paper, we aim to model the relationships between modalities more purposefully, thereby achieving more efficient and flexible modality alignments. To this end, we propose Stream-Omni, a large language-vision-speech model with efficient modality alignments, which can simultaneously support interactions under various modality combinations. Stream-Omni employs LLM as the backbone and aligns the vision and speech to the text based on their relationships. For vision that is semantically complementary to text, Stream-Omni uses sequence-dimension concatenation to achieve vision-text alignment. For speech that is semantically consistent with text, Stream-Omni introduces a CTC-based layer-dimension mapping to achieve speech-text alignment. In this way, Stream-Omni can achieve modality alignments with less data (especially speech), enabling the transfer of text capabilities to other modalities. Experiments on various benchmarks demonstrate that Stream-Omni achieves strong performance on visual understanding, speech interaction, and vision-grounded speech interaction tasks. Owing to the layer-dimensional mapping, Stream-Omni can simultaneously provide intermediate text outputs (such as ASR transcriptions and model responses) during speech interaction, offering users a comprehensive multimodal experience.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.14429", "title": "LongLLaDA: Unlocking Long Context Capabilities in Diffusion LLMs", "authors": [ "Xiaoran Liu", "Zhigeng Liu", "Zengfeng Huang", "Qipeng Guo", "Ziwei He", "Xipeng Qiu" ], "github": "https://github.com/OpenMOSS/LongLLaDA", "project_page": "", "abstract": "Large Language Diffusion Models, or diffusion LLMs, have emerged as a significant focus in NLP research, with substantial effort directed toward understanding their scalability and downstream task performance. However, their long-context capabilities remain unexplored, lacking systematic analysis or methods for context extension. In this work, we present the first systematic investigation comparing the long-context performance of diffusion LLMs and traditional auto-regressive LLMs. We first identify a unique characteristic of diffusion LLMs, unlike auto-regressive LLMs, they maintain remarkably \\textit{stable perplexity} during direct context extrapolation. Furthermore, where auto-regressive models fail outright during the Needle-In-A-Haystack task with context exceeding their pretrained length, we discover diffusion LLMs exhibit a distinct \\textit{local perception} phenomenon, enabling successful retrieval from recent context segments. We explain both phenomena through the lens of Rotary Position Embedding (RoPE) scaling theory. Building on these observations, we propose LongLLaDA, a training-free method that integrates LLaDA with the NTK-based RoPE extrapolation. Our results validate that established extrapolation scaling laws remain effective for extending the context windows of diffusion LLMs. Furthermore, we identify long-context tasks where diffusion LLMs outperform auto-regressive LLMs and others where they fall short. Consequently, this study establishes the first context extrapolation method for diffusion LLMs while providing essential theoretical insights and empirical benchmarks critical for advancing future research on long-context diffusion LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenMOSS/LongLLaDA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.14002", "title": "Taming Polysemanticity in LLMs: Provable Feature Recovery via Sparse Autoencoders", "authors": [ "Siyu Chen", "Heejune Sheen", "Xuyuan Xiong", "Tianhao Wang", "Zhuoran Yang" ], "github": "", "project_page": "", "abstract": "We study the challenge of achieving theoretically grounded feature recovery using Sparse Autoencoders (SAEs) for the interpretation of Large Language Models. Existing SAE training algorithms often lack rigorous mathematical guarantees and suffer from practical limitations such as hyperparameter sensitivity and instability. To address these issues, we first propose a novel statistical framework for the feature recovery problem, which includes a new notion of feature identifiability by modeling polysemantic features as sparse mixtures of underlying monosemantic concepts. Building on this framework, we introduce a new SAE training algorithm based on ``bias adaptation'', a technique that adaptively adjusts neural network bias parameters to ensure appropriate activation sparsity. We theoretically prove that this algorithm correctly recovers all monosemantic features when input data is sampled from our proposed statistical model. Furthermore, we develop an improved empirical variant, Group Bias Adaptation (GBA), and demonstrate its superior performance against benchmark methods when applied to LLMs with up to 1.5 billion parameters. This work represents a foundational step in demystifying SAE training by providing the first SAE algorithm with theoretical recovery guarantees, thereby advancing the development of more transparent and trustworthy AI systems through enhanced mechanistic interpretability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.14606", "title": "Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees", "authors": [ "Ahmed Heakl", "Sarim Hashmi", "Chaimaa Abi", "Celine Lee", "Abdulrahman Mahmoud" ], "github": "", "project_page": "", "abstract": "The hardware ecosystem is rapidly evolving, with increasing interest in translating low-level programs across different instruction set architectures (ISAs) in a quick, flexible, and correct way to enhance the portability and longevity of existing code. A particularly challenging class of this transpilation problem is translating between complex- (CISC) and reduced- (RISC) hardware architectures, due to fundamental differences in instruction complexity, memory models, and execution paradigms. In this work, we introduce GG (Guaranteed Guess), an ISA-centric transpilation pipeline that combines the translation power of pre-trained large language models (LLMs) with the rigor of established software testing constructs. Our method generates candidate translations using an LLM from one ISA to another, and embeds such translations within a software-testing framework to build quantifiable confidence in the translation. We evaluate our GG approach over two diverse datasets, enforce high code coverage (>98%) across unit tests, and achieve functional/semantic correctness of 99% on HumanEval programs and 49% on BringupBench programs, respectively. Further, we compare our approach to the state-of-the-art Rosetta 2 framework on Apple Silicon, showcasing 1.73x faster runtime performance, 1.47x better energy efficiency, and 2.41x better memory usage for our transpiled code, demonstrating the effectiveness of GG for real-world CISC-to-RISC translation tasks. We will open-source our codes, data, models, and benchmarks to establish a common foundation for ISA-level code translation research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.14603", "title": "Align Your Flow: Scaling Continuous-Time Flow Map Distillation", "authors": [ "Amirmojtaba Sabour", "Sanja Fidler", "Karsten Kreis" ], "github": "", "project_page": "", "abstract": "Diffusion- and flow-based models have emerged as state-of-the-art generative modeling approaches, but they require many sampling steps. Consistency models can distill these models into efficient one-step generators; however, unlike flow- and diffusion-based methods, their performance inevitably degrades when increasing the number of steps, which we show both analytically and empirically. Flow maps generalize these approaches by connecting any two noise levels in a single step and remain effective across all step counts. In this paper, we introduce two new continuous-time objectives for training flow maps, along with additional novel training techniques, generalizing existing consistency and flow matching objectives. We further demonstrate that autoguidance can improve performance, using a low-quality model for guidance during distillation, and an additional boost can be achieved by adversarial finetuning, with minimal loss in sample diversity. We extensively validate our flow map models, called Align Your Flow, on challenging image generation benchmarks and achieve state-of-the-art few-step generation performance on both ImageNet 64x64 and 512x512, using small and efficient neural networks. Finally, we show text-to-image flow map models that outperform all existing non-adversarially trained few-step samplers in text-conditioned synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.14755", "title": "Optimizing Length Compression in Large Reasoning Models", "authors": [ "Zhengxiang Cheng", "Dongping Chen", "Mingyang Fu", "Tianyi Zhou" ], "github": "https://github.com/zxiangx/LC-R1", "project_page": "", "abstract": "Large Reasoning Models (LRMs) have achieved remarkable success, yet they often suffer from producing unnecessary and verbose reasoning chains. We identify a core aspect of this issue as \"invalid thinking\" -- models tend to repeatedly double-check their work after having derived the correct answer. To address this specific inefficiency, we move beyond the general principles of Efficacy and Efficiency to propose two new, fine-grained principles: Brevity, which advocates for eliminating redundancy, and Sufficiency, which ensures critical reasoning steps are preserved. Guided by these principles, we introduce LC-R1, a post-training method based on Group Relative Policy Optimization (GRPO). LC-R1 employs a novel combination of a Length Reward for overall conciseness and a Compress Reward that is specifically designed to remove the invalid portion of the thinking process. Extensive experiments on multiple reasoning benchmarks demonstrate that LC-R1 achieves a significant reduction in sequence length (~50%) with only a marginal (~2%) drop in accuracy, achieving a favorable trade-off point on the Pareto frontier that prioritizes high compression. Our analysis further validates the robustness of LC-R1 and provides valuable insights for developing more powerful yet computationally efficient LRMs. Our code is released at https://github.com/zxiangx/LC-R1.", "github_manual": "", "github_regex": "https://github.com/zxiangx/LC-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.13387", "title": "TR2M: Transferring Monocular Relative Depth to Metric Depth with Language Descriptions and Scale-Oriented Contrast", "authors": [ "Beilei Cui", "Yiming Huang", "Long Bai", "Hongliang Ren" ], "github": "https://github.com/BeileiCui/TR2M", "project_page": "", "abstract": "This work presents a generalizable framework to transfer relative depth to metric depth. Current monocular depth estimation methods are mainly divided into metric depth estimation (MMDE) and relative depth estimation (MRDE). MMDEs estimate depth in metric scale but are often limited to a specific domain. MRDEs generalize well across different domains, but with uncertain scales which hinders downstream applications. To this end, we aim to build up a framework to solve scale uncertainty and transfer relative depth to metric depth. Previous methods used language as input and estimated two factors for conducting rescaling. Our approach, TR2M, utilizes both text description and image as inputs and estimates two rescale maps to transfer relative depth to metric depth at pixel level. Features from two modalities are fused with a cross-modality attention module to better capture scale information. A strategy is designed to construct and filter confident pseudo metric depth for more comprehensive supervision. We also develop scale-oriented contrastive learning to utilize depth distribution as guidance to enforce the model learning about intrinsic knowledge aligning with the scale distribution. TR2M only exploits a small number of trainable parameters to train on datasets in various domains and experiments not only demonstrate TR2M's great performance in seen datasets but also reveal superior zero-shot capabilities on five unseen datasets. We show the huge potential in pixel-wise transferring relative depth to metric depth with language assistance. (Code is available at: https://github.com/BeileiCui/TR2M)", "github_manual": "", "github_regex": "https://github.com/BeileiCui/TR2M", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.14234", "title": "Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team", "authors": [ "Md Tanzib Hosain", "Salman Rahman", "Md Kishor Morol", "Md Rizwan Parvez" ], "github": "https://github.com/kagnlp/Xolver", "project_page": "", "abstract": "Despite impressive progress on complex reasoning, current large language models (LLMs) typically operate in isolation - treating each problem as an independent attempt, without accumulating or integrating experiential knowledge. In contrast, expert problem solvers - such as Olympiad or programming contest teams - leverage a rich tapestry of experiences: absorbing mentorship from coaches, developing intuition from past problems, leveraging knowledge of tool usage and library functionality, adapting strategies based on the expertise and experiences of peers, continuously refining their reasoning through trial and error, and learning from other related problems even during competition. We introduce Xolver, a training-free multi-agent reasoning framework that equips a black-box LLM with a persistent, evolving memory of holistic experience. Xolver integrates diverse experience modalities, including external and self-retrieval, tool use, collaborative interactions, agent-driven evaluation, and iterative refinement. By learning from relevant strategies, code fragments, and abstract reasoning patterns at inference time, Xolver avoids generating solutions from scratch - marking a transition from isolated inference toward experience-aware language agents. Built on both open-weight and proprietary models, Xolver consistently outperforms specialized reasoning agents. Even with lightweight backbones (e.g., QWQ-32B), it often surpasses advanced models including Qwen3-235B, Gemini 2.5 Pro, o3, and o4-mini-high. With o3-mini-high, it achieves new best results on GSM8K (98.1%), AIME'24 (94.4%), AIME'25 (93.7%), Math-500 (99.8%), and LiveCodeBench-V5 (91.6%) - highlighting holistic experience learning as a key step toward generalist agents capable of expert-level reasoning. Code and data are available at https://kagnlp.github.io/xolver.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kagnlp/Xolver", "github_api_added_by": "auto" }, { "date": "2025-06-18", "arxiv_id": "2506.14245", "title": "Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs", "authors": [ "Xumeng Wen", "Zihan Liu", "Shun Zheng", "Zhijian Xu", "Shengyu Ye", "Zhirong Wu", "Xiao Liang", "Yang Wang", "Junjie Li", "Ziming Miao", "Jiang Bian", "Mao Yang" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for advancing the reasoning capabilities of Large Language Models (LLMs). However, a critical paradox clouds its efficacy: RLVR-tuned models often underperform their base models on the Pass@K metric for solution-finding, leading to the hypothesis that RLVR merely re-weights existing reasoning paths at the cost of reasoning diversity. In this work, we resolve this contradiction by identifying the source of the problem: the Pass@K metric itself is a flawed measure of reasoning, as it credits correct final answers that probably arise from inaccurate or incomplete chains of thought (CoTs). To address this, we introduce a more precise evaluation metric, CoT-Pass@K, which mandates that both the reasoning path and the final answer be correct. We provide a new theoretical foundation that formalizes how RLVR, unlike traditional RL, is uniquely structured to incentivize logical integrity. Our empirical results are supportive: using CoT-Pass@K, we observe that RLVR can incentivize the generalization of correct reasoning for all values of K. Furthermore, by analyzing the training dynamics, we find that this enhanced reasoning capability emerges early in the training process and smoothly generalizes. Our work provides a clear perspective on the role of RLVR, offers a more reliable method for its evaluation, and confirms its potential to genuinely advance machine reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.13363", "title": "Efficient Medical VIE via Reinforcement Learning", "authors": [ "Lijun Liu", "Ruiyang Li", "Zhaocheng Liu", "Chenglin Zhu", "Chong Li", "Jiehan Cheng", "Qiang Ju", "Jian Xie" ], "github": "", "project_page": "", "abstract": "Visual Information Extraction (VIE) converts unstructured document images into structured formats like JSON, critical for medical applications such as report analysis and online consultations. Traditional methods rely on OCR and language models, while end-to-end multimodal models offer direct JSON generation. However, domain-specific schemas and high annotation costs limit their effectiveness in medical VIE. We base our approach on the Reinforcement Learning with Verifiable Rewards (RLVR) framework to address these challenges using only 100 annotated samples. Our approach ensures dataset diversity, a balanced precision-recall reward mechanism to reduce hallucinations and improve field coverage, and innovative sampling strategies to enhance reasoning capabilities. Fine-tuning Qwen2.5-VL-7B with our RLVR method, we achieve state-of-the-art performance on medical VIE tasks, significantly improving F1, precision, and recall. While our models excel on tasks similar to medical datasets, performance drops on dissimilar tasks, highlighting the need for domain-specific optimization. Case studies further demonstrate the value of reasoning during training and inference for VIE.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.12278", "title": "Can LLMs Generate High-Quality Test Cases for Algorithm Problems? TestCase-Eval: A Systematic Evaluation of Fault Coverage and Exposure", "authors": [ "Zheyuan Yang", "Zexi Kuang", "Xue Xia", "Yilun Zhao" ], "github": "https://github.com/ByteDance-Seed/Seed-Coder/blob/master/Seed-Coder.pdf", "project_page": "", "abstract": "We introduce TestCase-Eval, a new benchmark for systematic evaluation of LLMs in test-case generation. TestCase-Eval includes 500 algorithm problems and 100,000 human-crafted solutions from the Codeforces platform. It focuses on two pivotal tasks: (1) Fault Coverage, which measures how well LLM-generated test sets probe diverse input scenarios and cover a wide range of potential failure modes. (2) Fault Exposure, which evaluates whether LLMs can craft a tailored test input that reveals a specific incorrect code implementation. We provide a comprehensive assessment of 19 state-of-the-art open-source and proprietary LLMs on TestCase-Eval, offering insights into their strengths and limitations in generating effective test cases for algorithm problems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByteDance-Seed/Seed-Coder/blob/master/Seed-Coder.pdf", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.05336", "title": "VideoMolmo: Spatio-Temporal Grounding Meets Pointing", "authors": [ "Ghazi Shazan Ahmad", "Ahmed Heakl", "Hanan Gani", "Abdelrahman Shaker", "Zhiqiang Shen", "Ranjay Krishna", "Fahad Shahbaz Khan", "Salman Khan" ], "github": "https://github.com/mbzuai-oryx/VideoMolmo", "project_page": "", "abstract": "Spatio-temporal localization is vital for precise interactions across diverse domains, from biological research to autonomous navigation and interactive interfaces. Current video-based approaches, while proficient in tracking, lack the sophisticated reasoning capabilities of large language models, limiting their contextual understanding and generalization. We introduce VideoMolmo, a large multimodal model tailored for fine-grained spatio-temporal pointing conditioned on textual descriptions. Building upon the Molmo architecture, VideoMolmo incorporates a temporal module utilizing an attention mechanism to condition each frame on preceding frames, ensuring temporal consistency. Additionally, our novel temporal mask fusion pipeline employs SAM2 for bidirectional point propagation, significantly enhancing coherence across video sequences. This two-step decomposition, i.e., first using the LLM to generate precise pointing coordinates, then relying on a sequential mask-fusion module to produce coherent segmentation, not only simplifies the task for the language model but also enhances interpretability. Due to the lack of suitable datasets, we curate a comprehensive dataset comprising 72k video-caption pairs annotated with 100k object points. To evaluate the generalization of VideoMolmo, we introduce VPoS-Bench, a challenging out-of-distribution benchmark spanning five real-world scenarios: Cell Tracking, Egocentric Vision, Autonomous Driving, Video-GUI Interaction, and Robotics. We also evaluate our model on Referring Video Object Segmentation (Refer-VOS) and Reasoning VOS tasks. In comparison to existing models, VideoMolmo substantially improves spatio-temporal pointing accuracy and reasoning capability. Our code and models are publicly available at https://github.com/mbzuai-oryx/VideoMolmo.", "github_manual": "", "github_regex": "https://github.com/mbzuai-oryx/VideoMolmo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.13599", "title": "CAMS: A CityGPT-Powered Agentic Framework for Urban Human Mobility Simulation", "authors": [ "Yuwei Du", "Jie Feng", "Jian Yuan", "Yong Li" ], "github": "", "project_page": "", "abstract": "Human mobility simulation plays a crucial role in various real-world applications. Recently, to address the limitations of traditional data-driven approaches, researchers have explored leveraging the commonsense knowledge and reasoning capabilities of large language models (LLMs) to accelerate human mobility simulation. However, these methods suffer from several critical shortcomings, including inadequate modeling of urban spaces and poor integration with both individual mobility patterns and collective mobility distributions. To address these challenges, we propose CityGPT-Powered Agentic framework for Mobility Simulation (CAMS), an agentic framework that leverages the language based urban foundation model to simulate human mobility in urban space. CAMS comprises three core modules, including MobExtractor to extract template mobility patterns and synthesize new ones based on user profiles, GeoGenerator to generate anchor points considering collective knowledge and generate candidate urban geospatial knowledge using an enhanced version of CityGPT, TrajEnhancer to retrieve spatial knowledge based on mobility patterns and generate trajectories with real trajectory preference alignment via DPO. Experiments on real-world datasets show that CAMS achieves superior performance without relying on externally provided geospatial information. Moreover, by holistically modeling both individual mobility patterns and collective mobility constraints, CAMS generates more realistic and plausible trajectories. In general, CAMS establishes a new paradigm that integrates the agentic framework with urban-knowledgeable LLMs for human mobility simulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.12860", "title": "QFFT, Question-Free Fine-Tuning for Adaptive Reasoning", "authors": [ "Wanlong Liu", "Junxiao Xu", "Fei Yu", "Yukang Lin", "Ke Ji", "Wenyu Chen", "Yan Xu", "Yasheng Wang", "Lifeng Shang", "Benyou Wang" ], "github": "https://github.com/LWL-cpu/Question-Free-Fine-Tuning", "project_page": "", "abstract": "Recent advancements in Long Chain-of-Thought (CoT) reasoning models have improved performance on complex tasks, but they suffer from overthinking, which generates redundant reasoning steps, especially for simple questions. This paper revisits the reasoning patterns of Long and Short CoT models, observing that the Short CoT patterns offer concise reasoning efficiently, while the Long CoT patterns excel in challenging scenarios where the Short CoT patterns struggle. To enable models to leverage both patterns, we propose Question-Free Fine-Tuning (QFFT), a fine-tuning approach that removes the input question during training and learns exclusively from Long CoT responses. This approach enables the model to adaptively employ both reasoning patterns: it prioritizes the Short CoT patterns and activates the Long CoT patterns only when necessary. Experiments on various mathematical datasets demonstrate that QFFT reduces average response length by more than 50\\%, while achieving performance comparable to Supervised Fine-Tuning (SFT). Additionally, QFFT exhibits superior performance compared to SFT in noisy, out-of-domain, and low-resource scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LWL-cpu/Question-Free-Fine-Tuning", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.10100", "title": "EfficientVLA: Training-Free Acceleration and Compression for Vision-Language-Action Models", "authors": [ "Yantai Yang", "Yuhao Wang", "Zichen Wen", "Luo Zhongwei", "Chang Zou", "Zhipeng Zhang", "Chuan Wen", "Linfeng Zhang" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models, particularly diffusion-based architectures, demonstrate transformative potential for embodied intelligence but are severely hampered by high computational and memory demands stemming from extensive inherent and inference-time redundancies. While existing acceleration efforts often target isolated inefficiencies, such piecemeal solutions typically fail to holistically address the varied computational and memory bottlenecks across the entire VLA pipeline, thereby limiting practical deployability. We introduce EfficientVLA, a structured and training-free inference acceleration framework that systematically eliminates these barriers by cohesively exploiting multifaceted redundancies. EfficientVLA synergistically integrates three targeted strategies: (1) pruning of functionally inconsequential layers from the language module, guided by an analysis of inter-layer redundancies; (2) optimizing the visual processing pathway through a task-aware strategy that selects a compact, diverse set of visual tokens, balancing task-criticality with informational coverage; and (3) alleviating temporal computational redundancy within the iterative diffusion-based action head by strategically caching and reusing key intermediate features. We apply our method to a standard VLA model CogACT, yielding a 1.93X inference speedup and reduces FLOPs to 28.9%, with only a 0.6% success rate drop in the SIMPLER benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.14758", "title": "Reasoning with Exploration: An Entropy Perspective", "authors": [ "Daixuan Cheng", "Shaohan Huang", "Xuekai Zhu", "Bo Dai", "Wayne Xin Zhao", "Zhenliang Zhang", "Furu Wei" ], "github": "https://github.com/agentica-project/rllm", "project_page": "", "abstract": "Balancing exploration and exploitation is a central goal in reinforcement learning (RL). Despite recent advances in enhancing language model (LM) reasoning, most methods lean toward exploitation, and increasingly encounter performance plateaus. In this work, we revisit entropy -- a signal of exploration in RL -- and examine its relationship to exploratory reasoning in LMs. Through empirical analysis, we uncover strong positive correlations between high-entropy regions and three types of exploratory reasoning actions: (1) pivotal tokens that determine or connect logical steps, (2) reflective actions such as self-verification and correction, and (3) rare behaviors under-explored by the base LMs. Motivated by this, we introduce a minimal modification to standard RL with only one line of code: augmenting the advantage function with an entropy-based term. Unlike traditional maximum-entropy methods which encourage exploration by promoting uncertainty, we encourage exploration by promoting longer and deeper reasoning chains. Notably, our method achieves significant gains on the Pass@K metric -- an upper-bound estimator of LM reasoning capabilities -- even when evaluated with extremely large K values, pushing the boundaries of LM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/agentica-project/rllm", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.10038", "title": "Ambient Diffusion Omni: Training Good Models with Bad Data", "authors": [ "Giannis Daras", "Adrian Rodriguez-Munoz", "Adam Klivans", "Antonio Torralba", "Constantinos Daskalakis" ], "github": "https://github.com/giannisdaras/ambient-omni", "project_page": "", "abstract": "We show how to use low-quality, synthetic, and out-of-distribution images to improve the quality of a diffusion model. Typically, diffusion models are trained on curated datasets that emerge from highly filtered data pools from the Web and other sources. We show that there is immense value in the lower-quality images that are often discarded. We present Ambient Diffusion Omni, a simple, principled framework to train diffusion models that can extract signal from all available images during training. Our framework exploits two properties of natural images -- spectral power law decay and locality. We first validate our framework by successfully training diffusion models with images synthetically corrupted by Gaussian blur, JPEG compression, and motion blur. We then use our framework to achieve state-of-the-art ImageNet FID, and we show significant improvements in both image quality and diversity for text-to-image generative modeling. The core insight is that noise dampens the initial skew between the desired high-quality distribution and the mixed distribution we actually observe. We provide rigorous theoretical justification for our approach by analyzing the trade-off between learning from biased data versus limited unbiased data across diffusion times.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/giannisdaras/ambient-omni", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.14731", "title": "Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs", "authors": [ "Ring Team", "Bin Hu", "Cai Chen", "Deng Zhao", "Ding Liu", "Dingnan Jin", "Feng Zhu", "Hao Dai", "Hongzhi Luan", "Jia Guo", "Jiaming Liu", "Jiewei Wu", "Jun Mei", "Jun Zhou", "Junbo Zhao", "Junwu Xiong", "Kaihong Zhang", "Kuan Xu", "Lei Liang", "Liang Jiang", "Liangcheng Fu", "Longfei Zheng", "Qiang Gao", "Qing Cui", "Quan Wan", "Shaomian Zheng", "Shuaicheng Li", "Tongkai Yang", "Wang Ren", "Xiaodong Yan", "Xiaopei Wan", "Xiaoyun Feng", "Xin Zhao", "Xinxing Yang", "Xinyu Kong", "Xuemin Yang", "Yang Li", "Yingting Wu", "Yongkang Liu", "Zhankai Xu", "Zhenduo Zhang", "Zhenglei Zhou", "Zhenyu Huang", "Zhiqiang Zhang", "Zihao Wang", "Zujie Wen" ], "github": "https://github.com/inclusionAI/Ring", "project_page": "", "abstract": "We present Ring-lite, a Mixture-of-Experts (MoE)-based large language model optimized via reinforcement learning (RL) to achieve efficient and robust reasoning capabilities. Built upon the publicly available Ling-lite model, a 16.8 billion parameter model with 2.75 billion activated parameters, our approach matches the performance of state-of-the-art (SOTA) small-scale reasoning models on challenging benchmarks (e.g., AIME, LiveCodeBench, GPQA-Diamond) while activating only one-third of the parameters required by comparable models. To accomplish this, we introduce a joint training pipeline integrating distillation with RL, revealing undocumented challenges in MoE RL training. First, we identify optimization instability during RL training, and we propose Constrained Contextual Computation Policy Optimization(C3PO), a novel approach that enhances training stability and improves computational throughput via algorithm-system co-design methodology. Second, we empirically demonstrate that selecting distillation checkpoints based on entropy loss for RL training, rather than validation metrics, yields superior performance-efficiency trade-offs in subsequent RL training. Finally, we develop a two-stage training paradigm to harmonize multi-domain data integration, addressing domain conflicts that arise in training with mixed dataset. We will release the model, dataset, and code.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/inclusionAI/Ring", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.14702", "title": "Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers", "authors": [ "Daniel D'souza", "Julia Kreutzer", "Adrien Morisot", "Ahmet \u00dcst\u00fcn", "Sara Hooker" ], "github": "", "project_page": "", "abstract": "One of the most profound challenges of modern machine learning is performing well on the long-tail of rare and underrepresented features. Large general-purpose models are trained for many tasks, but work best on high-frequency use cases. After training, it is hard to adapt a model to perform well on specific use cases underrepresented in the training corpus. Relying on prompt engineering or few-shot examples to maximize the output quality on a particular test case can be frustrating, as models can be highly sensitive to small changes, react in unpredicted ways or rely on a fixed system prompt for maintaining performance. In this work, we ask: \"Can we optimize our training protocols to both improve controllability and performance on underrepresented use cases at inference time?\" We revisit the divide between training and inference techniques to improve long-tail performance while providing users with a set of control levers the model is trained to be responsive to. We create a detailed taxonomy of data characteristics and task provenance to explicitly control generation attributes and implicitly condition generations at inference time. We fine-tune a base model to infer these markers automatically, which makes them optional at inference time. This principled and flexible approach yields pronounced improvements in performance, especially on examples from the long tail of the training distribution. While we observe an average lift of 5.7% win rates in open-ended generation quality with our markers, we see over 9.1% gains in underrepresented domains. We also observe relative lifts of up to 14.1% on underrepresented tasks like CodeRepair and absolute improvements of 35.3% on length instruction following evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.13977", "title": "CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios", "authors": [ "Shiting Huang", "Zhen Fang", "Zehui Chen", "Siyu Yuan", "Junjie Ye", "Yu Zeng", "Lin Chen", "Qi Mao", "Feng Zhao" ], "github": "https://github.com/Shellorley0513/CriticTool", "project_page": "", "abstract": "The ability of large language models (LLMs) to utilize external tools has enabled them to tackle an increasingly diverse range of tasks. However, as the tasks become more complex and long-horizon, the intricate tool utilization process may trigger various unexpected errors. Therefore, how to effectively handle such errors, including identifying, diagnosing, and recovering from them, has emerged as a key research direction for advancing tool learning. In this work, we first extensively analyze the types of errors encountered during the function-calling process on several competitive tool evaluation benchmarks. Based on it, we introduce CRITICTOOL, a comprehensive critique evaluation benchmark specialized for tool learning. Building upon a novel evolutionary strategy for dataset construction, CRITICTOOL holds diverse tool-use errors with varying complexities, which better reflects real-world scenarios. We conduct extensive experiments on CRITICTOOL, and validate the generalization and effectiveness of our constructed benchmark strategy. We also provide an in-depth analysis of the tool reflection ability on various LLMs, offering a new perspective on the field of tool learning in LLMs. The code is available at https://github.com/Shellorley0513/CriticTool{https://github.com/Shellorley0513/CriticTool}.", "github_manual": "", "github_regex": "https://github.com/Shellorley0513/CriticTool", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.13901", "title": "Alignment Quality Index (AQI) : Beyond Refusals: AQI as an Intrinsic Alignment Diagnostic via Latent Geometry, Cluster Divergence, and Layer wise Pooled Representations", "authors": [ "Abhilekh Borah", "Chhavi Sharma", "Danush Khanna", "Utkarsh Bhatt", "Gurpreet Singh", "Hasnat Md Abdullah", "Raghav Kaushik Ravi", "Vinija Jain", "Jyoti Patel", "Shubham Singh", "Vasu Sharma", "Arpita Vats", "Rahul Raja", "Aman Chadha", "Amitava Das" ], "github": "", "project_page": "", "abstract": "Alignment is no longer a luxury, it is a necessity. As large language models (LLMs) enter high-stakes domains like education, healthcare, governance, and law, their behavior must reliably reflect human-aligned values and safety constraints. Yet current evaluations rely heavily on behavioral proxies such as refusal rates, G-Eval scores, and toxicity classifiers, all of which have critical blind spots. Aligned models are often vulnerable to jailbreaking, stochasticity of generation, and alignment faking. To address this issue, we introduce the Alignment Quality Index (AQI). This novel geometric and prompt-invariant metric empirically assesses LLM alignment by analyzing the separation of safe and unsafe activations in latent space. By combining measures such as the Davies-Bouldin Score (DBS), Dunn Index (DI), Xie-Beni Index (XBI), and Calinski-Harabasz Index (CHI) across various formulations, AQI captures clustering quality to detect hidden misalignments and jailbreak risks, even when outputs appear compliant. AQI also serves as an early warning signal for alignment faking, offering a robust, decoding invariant tool for behavior agnostic safety auditing. Additionally, we propose the LITMUS dataset to facilitate robust evaluation under these challenging conditions. Empirical tests on LITMUS across different models trained under DPO, GRPO, and RLHF conditions demonstrate AQI's correlation with external judges and ability to reveal vulnerabilities missed by refusal metrics. We make our implementation publicly available to foster future research in this area.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.12928", "title": "Scaling Test-time Compute for LLM Agents", "authors": [ "King Zhu", "Hanhao Li", "Siwei Wu", "Tianshun Xing", "Dehua Ma", "Xiangru Tang", "Minghao Liu", "Jian Yang", "Jiaheng Liu", "Yuchen Eleanor Jiang", "Changwang Zhang", "Chenghua Lin", "Jun Wang", "Ge Zhang", "Wangchunshu Zhou" ], "github": "https://github.com/langchain-ai/open_deep_research", "project_page": "", "abstract": "Scaling test time compute has shown remarkable success in improving the reasoning abilities of large language models (LLMs). In this work, we conduct the first systematic exploration of applying test-time scaling methods to language agents and investigate the extent to which it improves their effectiveness. Specifically, we explore different test-time scaling strategies, including: (1) parallel sampling algorithms; (2) sequential revision strategies; (3) verifiers and merging methods; (4)strategies for diversifying rollouts.We carefully analyze and ablate the impact of different design strategies on applying test-time scaling on language agents, and have follow findings: 1. Scaling test time compute could improve the performance of agents. 2. Knowing when to reflect is important for agents. 3. Among different verification and result merging approaches, the list-wise method performs best. 4. Increasing diversified rollouts exerts a positive effect on the agent's task performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/langchain-ai/open_deep_research", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.13651", "title": "xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations", "authors": [ "Kaiyuan Chen", "Yixin Ren", "Yang Liu", "Xiaobo Hu", "Haotong Tian", "Tianbao Xie", "Fangfu Liu", "Haoye Zhang", "Hongzhang Liu", "Yuan Gong", "Chen Sun", "Han Hou", "Hui Yang", "James Pan", "Jianan Lou", "Jiayi Mao", "Jizheng Liu", "Jinpeng Li", "Kangyi Liu", "Kenkun Liu", "Rui Wang", "Run Li", "Tong Niu", "Wenlong Zhang", "Wenqi Yan", "Xuanzheng Wang", "Yuchen Zhang", "Yi-Hsin Hung", "Yuan Jiang", "Zexuan Liu", "Zihan Yin", "Zijian Ma", "Zhiwen Mo" ], "github": "", "project_page": "", "abstract": "We introduce xbench, a dynamic, profession-aligned evaluation suite designed to bridge the gap between AI agent capabilities and real-world productivity. While existing benchmarks often focus on isolated technical skills, they may not accurately reflect the economic value agents deliver in professional settings. To address this, xbench targets commercially significant domains with evaluation tasks defined by industry professionals. Our framework creates metrics that strongly correlate with productivity value, enables prediction of Technology-Market Fit (TMF), and facilitates tracking of product capabilities over time. As our initial implementations, we present two benchmarks: Recruitment and Marketing. For Recruitment, we collect 50 tasks from real-world headhunting business scenarios to evaluate agents' abilities in company mapping, information retrieval, and talent sourcing. For Marketing, we assess agents' ability to match influencers with advertiser needs, evaluating their performance across 50 advertiser requirements using a curated pool of 836 candidate influencers. We present initial evaluation results for leading contemporary agents, establishing a baseline for these professional domains. Our continuously updated evalsets and evaluations are available at https://xbench.org.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.05426", "title": "Mixture-of-Experts Meets In-Context Reinforcement Learning", "authors": [ "Wenhao Wu", "Fuhong Liu", "Haoru Li", "Zican Hu", "Daoyi Dong", "Chunlin Chen", "Zhi Wang" ], "github": "https://github.com/NJU-RL/T2MIR", "project_page": "", "abstract": "In-context reinforcement learning (ICRL) has emerged as a promising paradigm for adapting RL agents to downstream tasks through prompt conditioning. However, two notable challenges remain in fully harnessing in-context learning within RL domains: the intrinsic multi-modality of the state-action-reward data and the diverse, heterogeneous nature of decision tasks. To tackle these challenges, we propose T2MIR (Token- and Task-wise MoE for In-context RL), an innovative framework that introduces architectural advances of mixture-of-experts (MoE) into transformer-based decision models. T2MIR substitutes the feedforward layer with two parallel layers: a token-wise MoE that captures distinct semantics of input tokens across multiple modalities, and a task-wise MoE that routes diverse tasks to specialized experts for managing a broad task distribution with alleviated gradient conflicts. To enhance task-wise routing, we introduce a contrastive learning method that maximizes the mutual information between the task and its router representation, enabling more precise capture of task-relevant information. The outputs of two MoE components are concatenated and fed into the next layer. Comprehensive experiments show that T2MIR significantly facilitates in-context learning capacity and outperforms various types of baselines. We bring the potential and promise of MoE to ICRL, offering a simple and scalable architectural enhancement to advance ICRL one step closer toward achievements in language and vision communities. Our code is available at https://github.com/NJU-RL/T2MIR.", "github_manual": "", "github_regex": "https://github.com/NJU-RL/T2MIR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.12015", "title": "EMLoC: Emulator-based Memory-efficient Fine-tuning with LoRA Correction", "authors": [ "Hsi-Che Lin", "Yu-Chu Yu", "Kai-Po Chang", "Yu-Chiang Frank Wang" ], "github": "", "project_page": "", "abstract": "Open-source foundation models have seen rapid adoption and development, enabling powerful general-purpose capabilities across diverse domains. However, fine-tuning large foundation models for domain-specific or personalized tasks remains prohibitively expensive for most users due to the significant memory overhead beyond that of inference. We introduce EMLoC, an Emulator-based Memory-efficient fine-tuning framework with LoRA Correction, which enables model fine-tuning within the same memory budget required for inference. EMLoC constructs a task-specific light-weight emulator using activation-aware singular value decomposition (SVD) on a small downstream calibration set. Fine-tuning then is performed on this lightweight emulator via LoRA. To tackle the misalignment between the original model and the compressed emulator, we propose a novel compensation algorithm to correct the fine-tuned LoRA module, which thus can be merged into the original model for inference. EMLoC supports flexible compression ratios and standard training pipelines, making it adaptable to a wide range of applications. Extensive experiments demonstrate that EMLoC outperforms other baselines across multiple datasets and modalities. Moreover, without quantization, EMLoC enables fine-tuning of a 38B model on a single 24GB consumer GPU-bringing efficient and practical model adaptation to individual users.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.12880", "title": "Universal Jailbreak Suffixes Are Strong Attention Hijackers", "authors": [ "Matan Ben-Tov", "Mor Geva", "Mahmood Sharif" ], "github": "https://github.com/matanbt/interp-jailbreak", "project_page": "", "abstract": "We study suffix-based jailbreaksx2013a powerful family of attacks against large language models (LLMs) that optimize adversarial suffixes to circumvent safety alignment. Focusing on the widely used foundational GCG attack (Zou et al., 2023), we observe that suffixes vary in efficacy: some markedly more universalx2013generalizing to many unseen harmful instructionsx2013than others. We first show that GCG's effectiveness is driven by a shallow, critical mechanism, built on the information flow from the adversarial suffix to the final chat template tokens before generation. Quantifying the dominance of this mechanism during generation, we find GCG irregularly and aggressively hijacks the contextualization process. Crucially, we tie hijacking to the universality phenomenon, with more universal suffixes being stronger hijackers. Subsequently, we show that these insights have practical implications: GCG universality can be efficiently enhanced (up to times5 in some cases) at no additional computational cost, and can also be surgically mitigated, at least halving attack success with minimal utility loss. We release our code and data at http://github.com/matanbt/interp-jailbreak.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/matanbt/interp-jailbreak", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.09033", "title": "Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning", "authors": [ "Haozhen Zhang", "Tao Feng", "Jiaxuan You" ], "github": "https://github.com/ulab-uiuc/Router-R1", "project_page": "", "abstract": "The rapid emergence of diverse large language models (LLMs) has spurred the development of LLM routers that assign user queries to the most suitable model. However, existing LLM routers typically perform a single-round, one-to-one mapping (i.e., assigning each query to a single model in isolation), which limits their capability to tackle complex tasks that demand the complementary strengths of multiple LLMs. In this paper, we present Router-R1, a reinforcement learning (RL)-based framework that formulates multi-LLM routing and aggregation as a sequential decision process. Router-R1 instantiates the router itself as a capable LLM, leveraging its reasoning ability to interleave \"think\" actions (internal deliberation) with \"route\" actions (dynamic model invocation), and integrates each response into its evolving context. To guide learning, we employ a lightweight rule-based reward comprising format rewards, final outcome rewards, and a novel cost reward for performance and cost trade-off optimization, opening a pathway toward optimizing performance-cost tradeoffs via RL. Router-R1 also conditions only on simple model descriptors such as pricing, latency, and example performance, enabling strong generalization to unseen model selection. Experiments on seven general and multi-hop QA benchmarks show that Router-R1 outperforms over several strong baselines, achieving superior performance while maintaining robust generalization and cost management.Code is available at https://github.com/ulab-uiuc/Router-R1.", "github_manual": "", "github_regex": "https://github.com/ulab-uiuc/Router-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.14629", "title": "VisText-Mosquito: A Multimodal Dataset and Benchmark for AI-Based Mosquito Breeding Site Detection and Reasoning", "authors": [ "Md. Adnanul Islam", "Md. Faiyaz Abdullah Sayeedi", "Md. Asaduzzaman Shuvo", "Muhammad Ziaur Rahman", "Shahanur Rahman Bappy", "Raiyan Rahman", "Swakkhar Shatabda" ], "github": "https://github.com/adnanul-islam-jisun/VisText-Mosquito", "project_page": "", "abstract": "Mosquito-borne diseases pose a major global health risk, requiring early detection and proactive control of breeding sites to prevent outbreaks. In this paper, we present VisText-Mosquito, a multimodal dataset that integrates visual and textual data to support automated detection, segmentation, and reasoning for mosquito breeding site analysis. The dataset includes 1,828 annotated images for object detection, 142 images for water surface segmentation, and natural language reasoning texts linked to each image. The YOLOv9s model achieves the highest precision of 0.92926 and mAP@50 of 0.92891 for object detection, while YOLOv11n-Seg reaches a segmentation precision of 0.91587 and mAP@50 of 0.79795. For reasoning generation, our fine-tuned BLIP model achieves a final loss of 0.0028, with a BLEU score of 54.7, BERTScore of 0.91, and ROUGE-L of 0.87. This dataset and model framework emphasize the theme \"Prevention is Better than Cure\", showcasing how AI-based detection can proactively address mosquito-borne disease risks. The dataset and implementation code are publicly available at GitHub: https://github.com/adnanul-islam-jisun/VisText-Mosquito", "github_manual": "", "github_regex": "https://github.com/adnanul-islam-jisun/VisText-Mosquito", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.13922", "title": "DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance", "authors": [ "Maximilian Du", "Shuran Song" ], "github": "", "project_page": "", "abstract": "Deploying large, complex policies in the real world requires the ability to steer them to fit the needs of a situation. Most common steering approaches, like goal-conditioning, require training the robot policy with a distribution of test-time objectives in mind. To overcome this limitation, we present DynaGuide, a steering method for diffusion policies using guidance from an external dynamics model during the diffusion denoising process. DynaGuide separates the dynamics model from the base policy, which gives it multiple advantages, including the ability to steer towards multiple objectives, enhance underrepresented base policy behaviors, and maintain robustness on low-quality objectives. The separate guidance signal also allows DynaGuide to work with off-the-shelf pretrained diffusion policies. We demonstrate the performance and features of DynaGuide against other steering approaches in a series of simulated and real experiments, showing an average steering success of 70% on a set of articulated CALVIN tasks and outperforming goal-conditioning by 5.4x when steered with low-quality objectives. We also successfully steer an off-the-shelf real robot policy to express preference for particular objects and even create novel behavior. Videos and more can be found on the project website: https://dynaguide.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.12285", "title": "CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following", "authors": [ "Yinghao Ma", "Siyou Li", "Juntao Yu", "Emmanouil Benetos", "Akira Maezawa" ], "github": "", "project_page": "", "abstract": "Recent advances in audio-text large language models (LLMs) have opened new possibilities for music understanding and generation. However, existing benchmarks are limited in scope, often relying on simplified tasks or multi-choice evaluations that fail to reflect the complexity of real-world music analysis. We reinterpret a broad range of traditional MIR annotations as instruction-following formats and introduce CMI-Bench, a comprehensive music instruction following benchmark designed to evaluate audio-text LLMs on a diverse set of music information retrieval (MIR) tasks. These include genre classification, emotion regression, emotion tagging, instrument classification, pitch estimation, key detection, lyrics transcription, melody extraction, vocal technique recognition, instrument performance technique detection, music tagging, music captioning, and (down)beat tracking: reflecting core challenges in MIR research. Unlike previous benchmarks, CMI-Bench adopts standardized evaluation metrics consistent with previous state-of-the-art MIR models, ensuring direct comparability with supervised approaches. We provide an evaluation toolkit supporting all open-source audio-textual LLMs, including LTU, Qwen-audio, SALMONN, MusiLingo, etc. Experiment results reveal significant performance gaps between LLMs and supervised models, along with their culture, chronological and gender bias, highlighting the potential and limitations of current models in addressing MIR tasks. CMI-Bench establishes a unified foundation for evaluating music instruction following, driving progress in music-aware LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.09985", "title": "V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning", "authors": [ "Mido Assran", "Adrien Bardes", "David Fan", "Quentin Garrido", "Russell Howes", "Mojtaba", "Komeili", "Matthew Muckley", "Ammar Rizvi", "Claire Roberts", "Koustuv Sinha", "Artem Zholus", "Sergio Arnaud", "Abha Gejji", "Ada Martin", "Francois Robert Hogan", "Daniel Dugas", "Piotr Bojanowski", "Vasil Khalidov", "Patrick Labatut", "Francisco Massa", "Marc Szafraniec", "Kapil Krishnakumar", "Yong Li", "Xiaodong Ma", "Sarath Chandar", "Franziska Meier", "Yann LeCun", "Michael Rabbat", "Nicolas Ballas" ], "github": "https://github.com/facebookresearch/vjepa2", "project_page": "", "abstract": "A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/vjepa2", "github_api_added_by": "auto" }, { "date": "2025-06-18", "arxiv_id": "2506.14028", "title": "MultiFinBen: A Multilingual, Multimodal, and Difficulty-Aware Benchmark for Financial LLM Evaluation", "authors": [ "Xueqing Peng", "Lingfei Qian", "Yan Wang", "Ruoyu Xiang", "Yueru He", "Yang Ren", "Mingyang Jiang", "Jeff Zhao", "Huan He", "Yi Han", "Yun Feng", "Yuechen Jiang", "Yupeng Cao", "Haohang Li", "Yangyang Yu", "Xiaoyu Wang", "Penglei Gao", "Shengyuan Lin", "Keyi Wang", "Shanshan Yang", "Yilun Zhao", "Zhiwei Liu", "Peng Lu", "Jerry Huang", "Suyuchen Wang", "Triantafillos Papadopoulos", "Polydoros Giannouris", "Efstathia Soufleri", "Nuo Chen", "Guojun Xiong", "Zhiyang Deng", "Yijia Zhao", "Mingquan Lin", "Meikang Qiu", "Kaleb E Smith", "Arman Cohan", "Xiao-Yang Liu", "Jimin Huang", "Alejandro Lopez-Lira", "Xi Chen", "Junichi Tsujii", "Jian-Yun Nie", "Sophia Ananiadou", "Qianqian Xie" ], "github": "https://github.com/xueqingpeng/MultiFinBen", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have accelerated progress in financial NLP and applications, yet existing benchmarks remain limited to monolingual and unimodal settings, often over-relying on simple tasks and failing to reflect the complexity of real-world financial communication. We introduce MultiFinBen, the first multilingual and multimodal benchmark tailored to the global financial domain, evaluating LLMs across modalities (text, vision, audio) and linguistic settings (monolingual, bilingual, multilingual) on domain-specific tasks. We introduce two novel tasks, including PolyFiQA-Easy and PolyFiQA-Expert, the first multilingual financial benchmarks requiring models to perform complex reasoning over mixed-language inputs; and EnglishOCR and SpanishOCR, the first OCR-embedded financial QA tasks challenging models to extract and reason over information from visual-text financial documents. Moreover, we propose a dynamic, difficulty-aware selection mechanism and curate a compact, balanced benchmark rather than simple aggregation existing datasets. Extensive evaluation of 22 state-of-the-art models reveals that even the strongest models, despite their general multimodal and multilingual capabilities, struggle dramatically when faced with complex cross-lingual and multimodal tasks in financial domain. MultiFinBen is publicly released to foster transparent, reproducible, and inclusive progress in financial studies and applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xueqingpeng/MultiFinBen", "github_api_added_by": "auto" }, { "date": "2025-06-18", "arxiv_id": "2506.03939", "title": "Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy to Enhance LLM Reasoning", "authors": [ "Junqi Gao", "Xiang Zou", "YIng Ai", "Dong Li", "Yichen Niu", "Biqing Qi", "Jianxing Liu" ], "github": "https://github.com/gjq100/Graph-Counselor.git", "project_page": "", "abstract": "Graph Retrieval Augmented Generation (GraphRAG) effectively enhances external knowledge integration capabilities by explicitly modeling knowledge relationships, thereby improving the factual accuracy and generation quality of Large Language Models (LLMs) in specialized domains. However, existing methods suffer from two inherent limitations: 1) Inefficient Information Aggregation: They rely on a single agent and fixed iterative patterns, making it difficult to adaptively capture multi-level textual, structural, and degree information within graph data. 2) Rigid Reasoning Mechanism: They employ preset reasoning schemes, which cannot dynamically adjust reasoning depth nor achieve precise semantic correction. To overcome these limitations, we propose Graph Counselor, an GraphRAG method based on multi-agent collaboration. This method uses the Adaptive Graph Information Extraction Module (AGIEM), where Planning, Thought, and Execution Agents work together to precisely model complex graph structures and dynamically adjust information extraction strategies, addressing the challenges of multi-level dependency modeling and adaptive reasoning depth. Additionally, the Self-Reflection with Multiple Perspectives (SR) module improves the accuracy and semantic consistency of reasoning results through self-reflection and backward reasoning mechanisms. Experiments demonstrate that Graph Counselor outperforms existing methods in multiple graph reasoning tasks, exhibiting higher reasoning accuracy and generalization ability. Our code is available at https://github.com/gjq100/Graph-Counselor.git.", "github_manual": "", "github_regex": "https://github.com/gjq100/Graph-Counselor.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.14761", "title": "From Bytes to Ideas: Language Modeling with Autoregressive U-Nets", "authors": [ "Mathurin Videau", "Badr Youbi Idrissi", "Alessandro Leite", "Marc Schoenauer", "Olivier Teytaud", "David Lopez-Paz" ], "github": "", "project_page": "", "abstract": "Tokenization imposes a fixed granularity on the input text, freezing how a language model operates on data and how far in the future it predicts. Byte Pair Encoding (BPE) and similar schemes split text once, build a static vocabulary, and leave the model stuck with that choice. We relax this rigidity by introducing an autoregressive U-Net that learns to embed its own tokens as it trains. The network reads raw bytes, pools them into words, then pairs of words, then up to 4 words, giving it a multi-scale view of the sequence. At deeper stages, the model must predict further into the future -- anticipating the next few words rather than the next byte -- so deeper stages focus on broader semantic patterns while earlier stages handle fine details. When carefully tuning and controlling pretraining compute, shallow hierarchies tie strong BPE baselines, and deeper hierarchies have a promising trend. Because tokenization now lives inside the model, the same system can handle character-level tasks and carry knowledge across low-resource languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-18", "arxiv_id": "2506.14205", "title": "AgentSynth: Scalable Task Generation for Generalist Computer-Use Agents", "authors": [ "Jingxu Xie", "Dylan Xu", "Xuandong Zhao", "Dawn Song" ], "github": "https://github.com/sunblaze-ucb/AgentSynth", "project_page": "", "abstract": "We introduce AgentSynth, a scalable and cost-efficient pipeline for automatically synthesizing high-quality tasks and trajectory datasets for generalist computer-use agents. Leveraging information asymmetry, AgentSynth constructs subtasks that are simple during generation but significantly more challenging when composed into long-horizon tasks, enabling the creation of over 6,000 diverse and realistic tasks. Our pipeline begins with an LLM-based task proposer guided by a persona, followed by an execution agent that completes the task and logs the trajectory. This process is repeated iteratively to form a sequence of subtasks, which are then summarized by a separate agent into a composite task of controllable difficulty. A key strength of AgentSynth is its ability to precisely modulate task complexity by varying the number of subtasks. Empirical evaluations show that state-of-the-art LLM agents suffer a steep performance drop, from 18% success at difficulty level 1 to just 4% at level 6, highlighting the benchmark's difficulty and discriminative power. Moreover, our pipeline achieves a low average cost of \\$0.60 per trajectory, orders of magnitude cheaper than human annotations. Our code and data are publicly available at https://github.com/sunblaze-ucb/AgentSynth", "github_manual": "", "github_regex": "https://github.com/sunblaze-ucb/AgentSynth", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.15677", "title": "Embodied Web Agents: Bridging Physical-Digital Realms for Integrated Agent Intelligence", "authors": [ "Yining Hong", "Rui Sun", "Bingxuan Li", "Xingcheng Yao", "Maxine Wu", "Alexander Chien", "Da Yin", "Ying Nian Wu", "Zhecan James Wang", "Kai-Wei Chang" ], "github": "", "project_page": "", "abstract": "AI agents today are mostly siloed - they either retrieve and reason over vast amount of digital information and knowledge obtained online; or interact with the physical world through embodied perception, planning and action - but rarely both. This separation limits their ability to solve tasks that require integrated physical and digital intelligence, such as cooking from online recipes, navigating with dynamic map data, or interpreting real-world landmarks using web knowledge. We introduce Embodied Web Agents, a novel paradigm for AI agents that fluidly bridge embodiment and web-scale reasoning. To operationalize this concept, we first develop the Embodied Web Agents task environments, a unified simulation platform that tightly integrates realistic 3D indoor and outdoor environments with functional web interfaces. Building upon this platform, we construct and release the Embodied Web Agents Benchmark, which encompasses a diverse suite of tasks including cooking, navigation, shopping, tourism, and geolocation - all requiring coordinated reasoning across physical and digital realms for systematic assessment of cross-domain intelligence. Experimental results reveal significant performance gaps between state-of-the-art AI systems and human capabilities, establishing both challenges and opportunities at the intersection of embodied cognition and web-scale knowledge access. All datasets, codes and websites are publicly available at our project page https://embodied-web-agent.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.15681", "title": "GenRecal: Generation after Recalibration from Large to Small Vision-Language Models", "authors": [ "Byung-Kwan Lee", "Ryo Hachiuma", "Yong Man Ro", "Yu-Chiang Frank Wang", "Yueh-Hua Wu" ], "github": "", "project_page": "", "abstract": "Recent advancements in vision-language models (VLMs) have leveraged large language models (LLMs) to achieve performance on par with closed-source systems like GPT-4V. However, deploying these models in real-world scenarios, particularly on resource-constrained devices, remains challenging due to their substantial computational demands. This has spurred interest in distilling knowledge from large VLMs into smaller, more efficient counterparts. A key challenge arises here from the diversity of VLM architectures, which are built on different LLMs and employ varying token types-differing in vocabulary size, token splits, and token index ordering. To address this challenge of limitation to a specific VLM type, we present Generation after Recalibration (GenRecal), a novel, general-purpose distillation framework for VLMs. GenRecal incorporates a Recalibrator that aligns and adapts feature representations between heterogeneous VLMs, enabling effective knowledge transfer across different types of VLMs. Through extensive experiments on multiple challenging benchmarks, we demonstrate that GenRecal significantly improves baseline performances, eventually outperforming large-scale open- and closed-source VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.06279", "title": "CoMemo: LVLMs Need Image Context with Image Memory", "authors": [ "Shi Liu", "Weijie Su", "Xizhou Zhu", "Wenhai Wang", "Jifeng Dai" ], "github": "", "project_page": "", "abstract": "Recent advancements in Large Vision-Language Models built upon Large Language Models have established aligning visual features with LLM representations as the dominant paradigm. However, inherited LLM architectural designs introduce suboptimal characteristics for multimodal processing. First, LVLMs exhibit a bimodal distribution in attention allocation, leading to the progressive neglect of middle visual content as context expands. Second, conventional positional encoding schemes fail to preserve vital 2D structural relationships when processing dynamic high-resolution images. To address these limitations, we propose CoMemo - a dual-path architecture that combines a Context image path with an image Memory path for visual processing, effectively alleviating visual information neglect. Additionally, we introduce RoPE-DHR, a novel positional encoding mechanism that employs thumbnail-based positional aggregation to maintain 2D spatial awareness while mitigating remote decay in extended sequences. Evaluations across seven benchmarks,including long-context comprehension, multi-image reasoning, and visual question answering, demonstrate CoMemo's superior performance compared to conventional LVLM architectures. Project page is available at https://lalbj.github.io/projects/CoMemo/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.15569", "title": "SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification", "authors": [ "Chengye Wang", "Yifei Shen", "Zexi Kuang", "Arman Cohan", "Yilun Zhao" ], "github": "", "project_page": "", "abstract": "We introduce SciVer, the first benchmark specifically designed to evaluate the ability of foundation models to verify claims within a multimodal scientific context. SciVer consists of 3,000 expert-annotated examples over 1,113 scientific papers, covering four subsets, each representing a common reasoning type in multimodal scientific claim verification. To enable fine-grained evaluation, each example includes expert-annotated supporting evidence. We assess the performance of 21 state-of-the-art multimodal foundation models, including o4-mini, Gemini-2.5-Flash, Llama-3.2-Vision, and Qwen2.5-VL. Our experiment reveals a substantial performance gap between these models and human experts on SciVer. Through an in-depth analysis of retrieval-augmented generation (RAG), and human-conducted error evaluations, we identify critical limitations in current open-source models, offering key insights to advance models' comprehension and reasoning in multimodal scientific literature tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.14435", "title": "MoTE: Mixture of Ternary Experts for Memory-efficient Large Multimodal Models", "authors": [ "Hongyu Wang", "Jiayu Xu", "Ruiping Wang", "Yan Feng", "Yitao Zhai", "Peng Pei", "Xunliang Cai", "Xilin Chen" ], "github": "", "project_page": "", "abstract": "Large multimodal Mixture-of-Experts (MoEs) effectively scale the model size to boost performance while maintaining fixed active parameters. However, previous works primarily utilized full-precision experts during sparse up-cycling. Despite they show superior performance on end tasks, the large amount of experts introduces higher memory footprint, which poses significant challenges for the deployment on edge devices. In this work, we propose MoTE, a scalable and memory-efficient approach to train Mixture-of-Ternary-Experts models from dense checkpoint. Instead of training fewer high-precision experts, we propose to train more low-precision experts during up-cycling. Specifically, we use the pre-trained FFN as a shared expert and train ternary routed experts with parameters in {-1, 0, 1}. Extensive experiments show that our approach has promising scaling trend along model size. MoTE achieves comparable performance to full-precision baseline MoE-LLaVA while offering lower memory footprint. Furthermore, our approach is compatible with post-training quantization methods and the advantage further amplifies when memory-constraint goes lower. Given the same amount of expert memory footprint of 3.4GB and combined with post-training quantization, MoTE outperforms MoE-LLaVA by a gain of 4.3% average accuracy on end tasks, demonstrating its effectiveness and potential for memory-constrained devices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.15675", "title": "Sekai: A Video Dataset towards World Exploration", "authors": [ "Zhen Li", "Chuanhao Li", "Xiaofeng Mao", "Shaoheng Lin", "Ming Li", "Shitian Zhao", "Zhaopan Xu", "Xinyue Li", "Yukang Feng", "Jianwen Sun", "Zizhen Li", "Fanrui Zhang", "Jiaxin Ai", "Zhixiang Wang", "Yuwei Wu", "Tong He", "Jiangmiao Pang", "Yu Qiao", "Yunde Jia", "Kaipeng Zhang" ], "github": "", "project_page": "", "abstract": "Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well-suited for world exploration training as they suffer from some limitations: limited locations, short duration, static scenes, and a lack of annotations about exploration and the world. In this paper, we introduce Sekai (meaning ``world'' in Japanese), a high-quality first-person view worldwide video dataset with rich annotations for world exploration. It consists of over 5,000 hours of walking or drone view (FPV and UVA) videos from over 100 countries and regions across 750 cities. We develop an efficient and effective toolbox to collect, pre-process and annotate videos with location, scene, weather, crowd density, captions, and camera trajectories. Experiments demonstrate the quality of the dataset. And, we use a subset to train an interactive video world exploration model, named YUME (meaning ``dream'' in Japanese). We believe Sekai will benefit the area of video generation and world exploration, and motivate valuable applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.15068", "title": "Semantically-Aware Rewards for Open-Ended R1 Training in Free-Form Generation", "authors": [ "Zongxia Li", "Yapei Chang", "Yuhang Zhou", "Xiyang Wu", "Zichao Liang", "Yoo Yeon Sung", "Jordan Lee Boyd-Graber" ], "github": "https://github.com/zli12321/long_form_rl", "project_page": "", "abstract": "Evaluating open-ended long-form generation is challenging because it is hard to define what clearly separates good from bad outputs. Existing methods often miss key aspects like coherence, style, or relevance, or are biased by pretraining data, making open-ended long-form evaluation an underexplored problem. To address this gap, we propose PrefBERT, a scoring model for evaluating open-ended long-form generation in GRPO and guiding its training with distinct rewards for good and bad outputs. Trained on two response evaluation datasets with diverse long-form styles and Likert-rated quality, PrefBERT effectively supports GRPO by offering better semantic reward feedback than traditional metrics ROUGE-L and BERTScore do. Through comprehensive evaluations, including LLM-as-a-judge, human ratings, and qualitative analysis, we show that PrefBERT, trained on multi-sentence and paragraph-length responses, remains reliable across varied long passages and aligns well with the verifiable rewards GRPO needs. Human evaluations confirm that using PrefBERT as the reward signal to train policy models yields responses better aligned with human preferences than those trained with traditional metrics. Our code is available at https://github.com/zli12321/long_form_rl.", "github_manual": "", "github_regex": "https://github.com/zli12321/long_form_rl", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.15050", "title": "Truncated Proximal Policy Optimization", "authors": [ "Tiantian Fan", "Lingjun Liu", "Yu Yue", "Jiaze Chen", "Chengyi Wang", "Qiying Yu", "Chi Zhang", "Zhiqi Lin", "Ruofei Zhu", "Yufeng Yuan", "Xiaochen Zuo", "Bole Ma", "Mofan Zhang", "Gaohong Liu", "Ru Zhang", "Haotian Zhou", "Cong Xie", "Ruidong Zhu", "Zhi Zhang", "Xin Liu", "Mingxuan Wang", "Lin Yan", "Yonghui Wu" ], "github": "", "project_page": "", "abstract": "Recently, test-time scaling Large Language Models (LLMs) have demonstrated exceptional reasoning capabilities across scientific and professional tasks by generating long chains-of-thought (CoT). As a crucial component for developing these reasoning models, reinforcement learning (RL), exemplified by Proximal Policy Optimization (PPO) and its variants, allows models to learn through trial and error. However, PPO can be time-consuming due to its inherent on-policy nature, which is further exacerbated by increasing response lengths. In this work, we propose Truncated Proximal Policy Optimization (T-PPO), a novel extension to PPO that improves training efficiency by streamlining policy update and length-restricted response generation. T-PPO mitigates the issue of low hardware utilization, an inherent drawback of fully synchronized long-generation procedures, where resources often sit idle during the waiting periods for complete rollouts. Our contributions are two-folds. First, we propose Extended Generalized Advantage Estimation (EGAE) for advantage estimation derived from incomplete responses while maintaining the integrity of policy learning. Second, we devise a computationally optimized mechanism that allows for the independent optimization of the policy and value models. By selectively filtering prompt and truncated tokens, this mechanism reduces redundant computations and accelerates the training process without sacrificing convergence performance. We demonstrate the effectiveness and efficacy of T-PPO on AIME 2024 with a 32B base model. The experimental results show that T-PPO improves the training efficiency of reasoning LLMs by up to 2.5x and outperforms its existing competitors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.15672", "title": "SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence", "authors": [ "Yao Zhang", "Chenyang Lin", "Shijie Tang", "Haokun Chen", "Shijie Zhou", "Yunpu Ma", "Volker Tresp" ], "github": "", "project_page": "", "abstract": "The rapid progress of Large Language Models has advanced agentic systems in decision-making, coordination, and task execution. Yet, existing agentic system generation frameworks lack full autonomy, missing from-scratch agent generation, self-optimizing agent functionality, and collaboration, limiting adaptability and scalability. We propose SwarmAgentic, a framework for fully automated agentic system generation that constructs agentic systems from scratch and jointly optimizes agent functionality and collaboration as interdependent components through language-driven exploration. To enable efficient search over system-level structures, SwarmAgentic maintains a population of candidate systems and evolves them via feedback-guided updates, drawing inspiration from Particle Swarm Optimization (PSO). We evaluate our method on six real-world, open-ended, and exploratory tasks involving high-level planning, system-level coordination, and creative reasoning. Given only a task description and an objective function, SwarmAgentic outperforms all baselines, achieving a +261.8% relative improvement over ADAS on the TravelPlanner benchmark, highlighting the effectiveness of full automation in structurally unconstrained tasks. This framework marks a significant step toward scalable and autonomous agentic system design, bridging swarm intelligence with fully automated system multi-agent generation. Our code is publicly released at https://yaoz720.github.io/SwarmAgentic/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.14824", "title": "FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models", "authors": [ "Yao Zhang", "Hewei Gao", "Haokun Chen", "Weiguo Li", "Yunpu Ma", "Volker Tresp" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) excel in tasks like multimodal reasoning and cross-modal retrieval but face deployment challenges in real-world scenarios due to distributed multimodal data and strict privacy requirements. Federated Learning (FL) offers a solution by enabling collaborative model training without centralizing data. However, realizing FL for MLLMs presents significant challenges, including high computational demands, limited client capacity, substantial communication costs, and heterogeneous client data. Existing FL methods assume client-side deployment of full models, an assumption that breaks down for large-scale MLLMs due to their massive size and communication demands. To address these limitations, we propose FedNano, the first FL framework that centralizes the LLM on the server while introducing NanoEdge, a lightweight module for client-specific adaptation. NanoEdge employs modality-specific encoders, connectors, and trainable NanoAdapters with low-rank adaptation. This design eliminates the need to deploy LLM on clients, reducing client-side storage by 95%, and limiting communication overhead to only 0.01% of the model parameters. By transmitting only compact NanoAdapter updates, FedNano handles heterogeneous client data and resource constraints while preserving privacy. Experiments demonstrate that FedNano outperforms prior FL baselines, bridging the gap between MLLM scale and FL feasibility, and enabling scalable, decentralized multimodal AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.14866", "title": "OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents", "authors": [ "Thomas Kuntz", "Agatha Duzan", "Hao Zhao", "Francesco Croce", "Zico Kolter", "Nicolas Flammarion", "Maksym Andriushchenko" ], "github": "https://github.com/tml-epfl/os-harm", "project_page": "", "abstract": "Computer use agents are LLM-based agents that can directly interact with a graphical user interface, by processing screenshots or accessibility trees. While these systems are gaining popularity, their safety has been largely overlooked, despite the fact that evaluating and understanding their potential for harmful behavior is essential for widespread adoption. To address this gap, we introduce OS-Harm, a new benchmark for measuring safety of computer use agents. OS-Harm is built on top of the OSWorld environment and aims to test models across three categories of harm: deliberate user misuse, prompt injection attacks, and model misbehavior. To cover these cases, we create 150 tasks that span several types of safety violations (harassment, copyright infringement, disinformation, data exfiltration, etc.) and require the agent to interact with a variety of OS applications (email client, code editor, browser, etc.). Moreover, we propose an automated judge to evaluate both accuracy and safety of agents that achieves high agreement with human annotations (0.76 and 0.79 F1 score). We evaluate computer use agents based on a range of frontier models - such as o4-mini, Claude 3.7 Sonnet, Gemini 2.5 Pro - and provide insights into their safety. In particular, all models tend to directly comply with many deliberate misuse queries, are relatively vulnerable to static prompt injections, and occasionally perform unsafe actions. The OS-Harm benchmark is available at https://github.com/tml-epfl/os-harm.", "github_manual": "", "github_regex": "https://github.com/tml-epfl/os-harm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.15211", "title": "ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning in LLMs", "authors": [ "Feng He", "Zijun Chen", "Xinnian Liang", "Tingting Ma", "Yunqi Qiu", "Shuangzhi Wu", "Junchi Yan" ], "github": "", "project_page": "", "abstract": "Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought (Long CoT) reasoning have demonstrated remarkable cross-domain generalization capabilities. However, the underlying mechanisms supporting such transfer remain poorly understood. We hypothesize that cross-domain generalization arises from shared abstract reasoning prototypes -- fundamental reasoning patterns that capture the essence of problems across domains. These prototypes minimize the nuances of the representation, revealing that seemingly diverse tasks are grounded in shared reasoning structures.Based on this hypothesis, we propose ProtoReasoning, a framework that enhances the reasoning ability of LLMs by leveraging scalable and verifiable prototypical representations (Prolog for logical reasoning, PDDL for planning).ProtoReasoning features: (1) an automated prototype construction pipeline that transforms problems into corresponding prototype representations; (2) a comprehensive verification system providing reliable feedback through Prolog/PDDL interpreters; (3) the scalability to synthesize problems arbitrarily within prototype space while ensuring correctness. Extensive experiments show that ProtoReasoning achieves 4.7% improvement over baseline models on logical reasoning (Enigmata-Eval), 6.3% improvement on planning tasks, 4.0% improvement on general reasoning (MMLU) and 1.0% on mathematics (AIME24). Significantly, our ablation studies confirm that learning in prototype space also demonstrates enhanced generalization to structurally similar problems compared to training solely on natural language representations, validating our hypothesis that reasoning prototypes serve as the foundation for generalizable reasoning in large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.13414", "title": "BUT System for the MLC-SLM Challenge", "authors": [ "Alexander Polok", "Jiangyu Han", "Dominik Klement", "Samuele Cornell", "Jan \u010cernock\u00fd", "Luk\u00e1\u0161 Burget" ], "github": "", "project_page": "", "abstract": "We present a two-speaker automatic speech recognition (ASR) system that combines DiCoW -- a diarization-conditioned variant of Whisper -- with DiariZen, a diarization pipeline built on top of Pyannote. We first evaluate both systems in out-of-domain (OOD) multilingual scenarios without any fine-tuning. In this scenario, DiariZen consistently outperforms the baseline Pyannote diarization model, demonstrating strong generalization. Despite being fine-tuned on English-only data for target-speaker ASR, DiCoW retains solid multilingual performance, indicating that encoder modifications preserve Whisper's multilingual capabilities. We then fine-tune both DiCoW and DiariZen on the MLC-SLM challenge data. The fine-tuned DiariZen continues to outperform the fine-tuned Pyannote baseline, while DiCoW sees further gains from domain adaptation. Our final system achieves a micro-average tcpWER/CER of 16.75% and ranks second in Task 2 of the MLC-SLM challenge. Lastly, we identify several labeling inconsistencies in the training data -- such as missing speech segments and incorrect silence annotations -- which can hinder diarization fine-tuning. We propose simple mitigation strategies to address these issues and improve system robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.14315", "title": "ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies", "authors": [ "Jinyan Yuan", "Bangbang Yang", "Keke Wang", "Panwang Pan", "Lin Ma", "Xuehai Zhang", "Xiao Liu", "Zhaopeng Cui", "Yuewen Ma" ], "github": "", "project_page": "", "abstract": "Automatic creation of 3D scenes for immersive VR presence has been a significant research focus for decades. However, existing methods often rely on either high-poly mesh modeling with post-hoc simplification or massive 3D Gaussians, resulting in a complex pipeline or limited visual realism. In this paper, we demonstrate that such exhaustive modeling is unnecessary for achieving compelling immersive experience. We introduce ImmerseGen, a novel agent-guided framework for compact and photorealistic world modeling. ImmerseGen represents scenes as hierarchical compositions of lightweight geometric proxies, i.e., simplified terrain and billboard meshes, and generates photorealistic appearance by synthesizing RGBA textures onto these proxies. Specifically, we propose terrain-conditioned texturing for user-centric base world synthesis, and RGBA asset texturing for midground and foreground scenery. This reformulation offers several advantages: (i) it simplifies modeling by enabling agents to guide generative models in producing coherent textures that integrate seamlessly with the scene; (ii) it bypasses complex geometry creation and decimation by directly synthesizing photorealistic textures on proxies, preserving visual quality without degradation; (iii) it enables compact representations suitable for real-time rendering on mobile VR headsets. To automate scene creation from text prompts, we introduce VLM-based modeling agents enhanced with semantic grid-based analysis for improved spatial reasoning and accurate asset placement. ImmerseGen further enriches scenes with dynamic effects and ambient audio to support multisensory immersion. Experiments on scene generation and live VR showcases demonstrate that ImmerseGen achieves superior photorealism, spatial coherence and rendering efficiency compared to prior methods. Project webpage: https://immersegen.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.14842", "title": "PictSure: Pretraining Embeddings Matters for In-Context Learning Image Classifiers", "authors": [ "Lukas Schiesser", "Cornelius Wolff", "Sophie Haas", "Simon Pukrop" ], "github": "https://github.com/PictSure/pictsure-library", "project_page": "", "abstract": "Building image classification models remains cumbersome in data-scarce domains, where collecting large labeled datasets is impractical. In-context learning (ICL) has emerged as a promising paradigm for few-shot image classification (FSIC), enabling models to generalize across domains without gradient-based adaptation. However, prior work has largely overlooked a critical component of ICL-based FSIC pipelines: the role of image embeddings. In this work, we present PictSure, an ICL framework that places the embedding model -- its architecture, pretraining, and training dynamics -- at the center of analysis. We systematically examine the effects of different visual encoder types, pretraining objectives, and fine-tuning strategies on downstream FSIC performance. Our experiments show that the training success and the out-of-domain performance are highly dependent on how the embedding models are pretrained. Consequently, PictSure manages to outperform existing ICL-based FSIC models on out-of-domain benchmarks that differ significantly from the training distribution, while maintaining comparable results on in-domain tasks. Code can be found at https://github.com/PictSure/pictsure-library.", "github_manual": "", "github_regex": "https://github.com/PictSure/pictsure-library", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.14770", "title": "GMT: General Motion Tracking for Humanoid Whole-Body Control", "authors": [ "Zixuan Chen", "Mazeyu Ji", "Xuxin Cheng", "Xuanbin Peng", "Xue Bin Peng", "Xiaolong Wang" ], "github": "", "project_page": "", "abstract": "The ability to track general whole-body motions in the real world is a useful way to build general-purpose humanoid robots. However, achieving this can be challenging due to the temporal and kinematic diversity of the motions, the policy's capability, and the difficulty of coordination of the upper and lower bodies. To address these issues, we propose GMT, a general and scalable motion-tracking framework that trains a single unified policy to enable humanoid robots to track diverse motions in the real world. GMT is built upon two core components: an Adaptive Sampling strategy and a Motion Mixture-of-Experts (MoE) architecture. The Adaptive Sampling automatically balances easy and difficult motions during training. The MoE ensures better specialization of different regions of the motion manifold. We show through extensive experiments in both simulation and the real world the effectiveness of GMT, achieving state-of-the-art performance across a broad spectrum of motions using a unified general policy. Videos and additional information can be found at https://gmt-humanoid.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.11110", "title": "AssertBench: A Benchmark for Evaluating Self-Assertion in Large Language Models", "authors": [ "Jaeho Lee", "Atharv Chowdhary" ], "github": "https://github.com/achowd32/assert-bench", "project_page": "", "abstract": "Recent benchmarks have probed factual consistency and rhetorical robustness in Large Language Models (LLMs). However, a knowledge gap exists regarding how directional framing of factually true statements influences model agreement, a common scenario for LLM users. AssertBench addresses this by sampling evidence-supported facts from FEVEROUS, a fact verification dataset. For each (evidence-backed) fact, we construct two framing prompts: one where the user claims the statement is factually correct, and another where the user claims it is incorrect. We then record the model's agreement and reasoning. The desired outcome is that the model asserts itself, maintaining consistent truth evaluation across both framings, rather than switching its evaluation to agree with the user. AssertBench isolates framing-induced variability from the model's underlying factual knowledge by stratifying results based on the model's accuracy on the same claims when presented neutrally. In doing so, this benchmark aims to measure an LLM's ability to \"stick to its guns\" when presented with contradictory user assertions about the same fact. The complete source code is available at https://github.com/achowd32/assert-bench.", "github_manual": "", "github_regex": "https://github.com/achowd32/assert-bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.15682", "title": "Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model", "authors": [ "Anirud Aggarwal", "Abhinav Shrivastava", "Matthew Gwilliam" ], "github": "https://github.com/aniaggarwal/ecad", "project_page": "", "abstract": "Diffusion-based image generation models excel at producing high-quality synthetic content, but suffer from slow and computationally expensive inference. Prior work has attempted to mitigate this by caching and reusing features within diffusion transformers across inference steps. These methods, however, often rely on rigid heuristics that result in limited acceleration or poor generalization across architectures. We propose Evolutionary Caching to Accelerate Diffusion models (ECAD), a genetic algorithm that learns efficient, per-model, caching schedules forming a Pareto frontier, using only a small set of calibration prompts. ECAD requires no modifications to network parameters or reference images. It offers significant inference speedups, enables fine-grained control over the quality-latency trade-off, and adapts seamlessly to different diffusion models. Notably, ECAD's learned schedules can generalize effectively to resolutions and model variants not seen during calibration. We evaluate ECAD on PixArt-alpha, PixArt-Sigma, and FLUX-1.dev using multiple metrics (FID, CLIP, Image Reward) across diverse benchmarks (COCO, MJHQ-30k, PartiPrompts), demonstrating consistent improvements over previous approaches. On PixArt-alpha, ECAD identifies a schedule that outperforms the previous state-of-the-art method by 4.47 COCO FID while increasing inference speedup from 2.35x to 2.58x. Our results establish ECAD as a scalable and generalizable approach for accelerating diffusion inference. Our project website is available at https://aniaggarwal.github.io/ecad and our code is available at https://github.com/aniaggarwal/ecad.", "github_manual": "", "github_regex": "https://github.com/aniaggarwal/ecad", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-19", "arxiv_id": "2506.15461", "title": "All is Not Lost: LLM Recovery without Checkpoints", "authors": [ "Nikolay Blagoev", "O\u011fuzhan Ersoy", "Lydia Yiyu Chen" ], "github": "https://github.com/gensyn-ai/CheckFree", "project_page": "", "abstract": "Training LLMs on decentralized and wimpy computation nodes, e.g., multiple on-spot instances, lowers the training cost and enables model democratization. The inevitable challenge here is the churn of nodes due to failures and the operator's scheduling policies, leading to losing a stage - a part of the model. The conventional approaches to recover from failures are to either use checkpointing, where periodically a copy of the entire model is sent to an additional storage, or redundant computation. These approaches yield significant communication and/or computation overhead even in non-failure cases and scale poorly in settings with large models. In this paper, we propose, CheckFree, an efficient recovery method where a failing stage is substituted by a weighted average of the closest neighboring stages. In contrast to the state of the art, CheckFree requires no additional computation or storage. However, because of the nature of averaging neighbouring stages, it can only recover failures of intermediate stages. We further extend our method to CheckFree+ with out-of-order pipeline execution to tolerate crashes of the first and last stages. Thanks to out-of-order pipelining, behaviour of those stages is mimicked by their neighboring ones, which allows CheckFree+ to recover them by simply copying the weights from the immediate neighbour. To be able to recover the (de)embedding layers, CheckFree+ copies those layers to the neighboring stages, which requires relatively small storage overhead. We extensively evaluate our method on LLaMa models of model sizes from 124M to 1.5B with varying failure frequencies. In the case of low and medium failure rates (5-10%), CheckFree and CheckFree+ outperform both checkpointing and redundant computation in terms of convergence in wall-clock time by over 12%. Both of our proposals can be run via our code available at: https://github.com/gensyn-ai/CheckFree.", "github_manual": "", "github_regex": "https://github.com/gensyn-ai/CheckFree", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-20", "arxiv_id": "2506.14837", "title": "Improved Iterative Refinement for Chart-to-Code Generation via Structured Instruction", "authors": [ "Chengzhi Xu", "Yuyang Wang", "Lai Wei", "Lichao Sun", "Weiran Huang" ], "github": "", "project_page": "", "abstract": "Recently, multimodal large language models (MLLMs) have attracted increasing research attention due to their powerful visual understanding capabilities. While they have achieved impressive results on various vision tasks, their performance on chart-to-code generation remains suboptimal. This task requires MLLMs to generate executable code that can reproduce a given chart, demanding not only precise visual understanding but also accurate translation of visual elements into structured code. Directly prompting MLLMs to perform this complex task often yields unsatisfactory results. To address this challenge, we propose {ChartIR}, an iterative refinement method based on structured instruction. First, we distinguish two tasks: visual understanding and code translation. To accomplish the visual understanding component, we design two types of structured instructions: description and difference. The description instruction captures the visual elements of the reference chart, while the difference instruction characterizes the discrepancies between the reference chart and the generated chart. These instructions effectively transform visual features into language representations, thereby facilitating the subsequent code translation process. Second, we decompose the overall chart generation pipeline into two stages: initial code generation and iterative refinement, enabling progressive enhancement of the final output. Experimental results show that, compared to other method, our method achieves superior performance on both the open-source model Qwen2-VL and the closed-source model GPT-4o.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-20", "arxiv_id": "2506.15154", "title": "SonicVerse: Multi-Task Learning for Music Feature-Informed Captioning", "authors": [ "Anuradha Chopra", "Abhinaba Roy", "Dorien Herremans" ], "github": "https://github.com/AMAAI-Lab/SonicVerse", "project_page": "", "abstract": "Detailed captions that accurately reflect the characteristics of a music piece can enrich music databases and drive forward research in music AI. This paper introduces a multi-task music captioning model, SonicVerse, that integrates caption generation with auxiliary music feature detection tasks such as key detection, vocals detection, and more, so as to directly capture both low-level acoustic details as well as high-level musical attributes. The key contribution is a projection-based architecture that transforms audio input into language tokens, while simultaneously detecting music features through dedicated auxiliary heads. The outputs of these heads are also projected into language tokens, to enhance the captioning input. This framework not only produces rich, descriptive captions for short music fragments but also directly enables the generation of detailed time-informed descriptions for longer music pieces, by chaining the outputs using a large-language model. To train the model, we extended the MusicBench dataset by annotating it with music features using MIRFLEX, a modular music feature extractor, resulting in paired audio, captions and music feature data. Experimental results show that incorporating features in this way improves the quality and detail of the generated captions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AMAAI-Lab/SonicVerse", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-20", "arxiv_id": "2506.14965", "title": "Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective", "authors": [ "Zhoujun Cheng", "Shibo Hao", "Tianyang Liu", "Fan Zhou", "Yutao Xie", "Feng Yao", "Yuexin Bian", "Yonghao Zhuang", "Nilabjo Dey", "Yuheng Zha", "Yi Gu", "Kun Zhou", "Yuqi Wang", "Yuan Li", "Richard Fan", "Jianshu She", "Chengqian Gao", "Abulhair Saparov", "Haonan Li", "Taylor W. Killian", "Mikhail Yurochkin", "Zhengzhong Liu", "Eric P. Xing", "Zhiting Hu" ], "github": "https://github.com/LLM360/Reasoning360", "project_page": "", "abstract": "Reinforcement learning (RL) has emerged as a promising approach to improve large language model (LLM) reasoning, yet most open efforts focus narrowly on math and code, limiting our understanding of its broader applicability to general reasoning. A key challenge lies in the lack of reliable, scalable RL reward signals across diverse reasoning domains. We introduce Guru, a curated RL reasoning corpus of 92K verifiable examples spanning six reasoning domains--Math, Code, Science, Logic, Simulation, and Tabular--each built through domain-specific reward design, deduplication, and filtering to ensure reliability and effectiveness for RL training. Based on Guru, we systematically revisit established findings in RL for LLM reasoning and observe significant variation across domains. For example, while prior work suggests that RL primarily elicits existing knowledge from pretrained models, our results reveal a more nuanced pattern: domains frequently seen during pretraining (Math, Code, Science) easily benefit from cross-domain RL training, while domains with limited pretraining exposure (Logic, Simulation, and Tabular) require in-domain training to achieve meaningful performance gains, suggesting that RL is likely to facilitate genuine skill acquisition. Finally, we present Guru-7B and Guru-32B, two models that achieve state-of-the-art performance among open models RL-trained with publicly available data, outperforming best baselines by 7.9% and 6.7% on our 17-task evaluation suite across six reasoning domains. We also show that our models effectively improve the Pass@k performance of their base models, particularly on complex tasks less likely to appear in pretraining data. We release data, models, training and evaluation code to facilitate general-purpose reasoning at: https://github.com/LLM360/Reasoning360", "github_manual": "", "github_regex": "https://github.com/LLM360/Reasoning360", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-20", "arxiv_id": "2506.09827", "title": "EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion Detection", "authors": [ "Christoph Schuhmann", "Robert Kaczmarczyk", "Gollam Rabby", "Felix Friedrich", "Maurice Kraus", "Kourosh Nadi", "Huu Nguyen", "Kristian Kersting", "S\u00f6ren Auer" ], "github": "", "project_page": "", "abstract": "The advancement of text-to-speech and audio generation models necessitates robust benchmarks for evaluating the emotional understanding capabilities of AI systems. Current speech emotion recognition (SER) datasets often exhibit limitations in emotional granularity, privacy concerns, or reliance on acted portrayals. This paper introduces EmoNet-Voice, a new resource for speech emotion detection, which includes EmoNet-Voice Big, a large-scale pre-training dataset (featuring over 4,500 hours of speech across 11 voices, 40 emotions, and 4 languages), and EmoNet-Voice Bench, a novel benchmark dataset with human expert annotations. EmoNet-Voice is designed to evaluate SER models on a fine-grained spectrum of 40 emotion categories with different levels of intensities. Leveraging state-of-the-art voice generation, we curated synthetic audio snippets simulating actors portraying scenes designed to evoke specific emotions. Crucially, we conducted rigorous validation by psychology experts who assigned perceived intensity labels. This synthetic, privacy-preserving approach allows for the inclusion of sensitive emotional states often absent in existing datasets. Lastly, we introduce Empathic Insight Voice models that set a new standard in speech emotion recognition with high agreement with human experts. Our evaluations across the current model landscape exhibit valuable findings, such as high-arousal emotions like anger being much easier to detect than low-arousal states like concentration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-20", "arxiv_id": "2506.15455", "title": "RE-IMAGINE: Symbolic Benchmark Synthesis for Reasoning Evaluation", "authors": [ "Xinnuo Xu", "Rachel Lawrence", "Kshitij Dubey", "Atharva Pandey", "Risa Ueno", "Fabian Falck", "Aditya V. Nori", "Rahul Sharma", "Amit Sharma", "Javier Gonzalez" ], "github": "", "project_page": "", "abstract": "Recent Large Language Models (LLMs) have reported high accuracy on reasoning benchmarks. However, it is still unclear whether the observed results arise from true reasoning or from statistical recall of the training set. Inspired by the ladder of causation (Pearl, 2009) and its three levels (associations, interventions and counterfactuals), this paper introduces RE-IMAGINE, a framework to characterize a hierarchy of reasoning ability in LLMs, alongside an automated pipeline to generate problem variations at different levels of the hierarchy. By altering problems in an intermediate symbolic representation, RE-IMAGINE generates arbitrarily many problems that are not solvable using memorization alone. Moreover, the framework is general and can work across reasoning domains, including math, code, and logic. We demonstrate our framework on four widely-used benchmarks to evaluate several families of LLMs, and observe reductions in performance when the models are queried with problem variations. These assessments indicate a degree of reliance on statistical recall for past performance, and open the door to further research targeting skills across the reasoning hierarchy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-20", "arxiv_id": "2506.15564", "title": "Show-o2: Improved Native Unified Multimodal Models", "authors": [ "Jinheng Xie", "Zhenheng Yang", "Mike Zheng Shou" ], "github": "https://github.com/showlab/Show-o", "project_page": "", "abstract": "This paper presents improved native unified multimodal models, i.e., Show-o2, that leverage autoregressive modeling and flow matching. Built upon a 3D causal variational autoencoder space, unified visual representations are constructed through a dual-path of spatial (-temporal) fusion, enabling scalability across image and video modalities while ensuring effective multimodal understanding and generation. Based on a language model, autoregressive modeling and flow matching are natively applied to the language head and flow head, respectively, to facilitate text token prediction and image/video generation. A two-stage training recipe is designed to effectively learn and scale to larger models. The resulting Show-o2 models demonstrate versatility in handling a wide range of multimodal understanding and generation tasks across diverse modalities, including text, images, and videos. Code and models are released at https://github.com/showlab/Show-o.", "github_manual": "", "github_regex": "https://github.com/showlab/Show-o", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.09049", "title": "VIKI-R: Coordinating Embodied Multi-Agent Cooperation via Reinforcement Learning", "authors": [ "Li Kang", "Xiufeng Song", "Heng Zhou", "Yiran Qin", "Jie Yang", "Xiaohong Liu", "Philip Torr", "Lei Bai", "Zhenfei Yin" ], "github": "", "project_page": "", "abstract": "Coordinating multiple embodied agents in dynamic environments remains a core challenge in artificial intelligence, requiring both perception-driven reasoning and scalable cooperation strategies. While recent works have leveraged large language models (LLMs) for multi-agent planning, a few have begun to explore vision-language models (VLMs) for visual reasoning. However, these VLM-based approaches remain limited in their support for diverse embodiment types. In this work, we introduce VIKI-Bench, the first hierarchical benchmark tailored for embodied multi-agent cooperation, featuring three structured levels: agent activation, task planning, and trajectory perception. VIKI-Bench includes diverse robot embodiments, multi-view visual observations, and structured supervision signals to evaluate reasoning grounded in visual inputs. To demonstrate the utility of VIKI-Bench, we propose VIKI-R, a two-stage framework that fine-tunes a pretrained vision-language model (VLM) using Chain-of-Thought annotated demonstrations, followed by reinforcement learning under multi-level reward signals. Our extensive experiments show that VIKI-R significantly outperforms baselines method across all task levels. Furthermore, we show that reinforcement learning enables the emergence of compositional cooperation patterns among heterogeneous agents. Together, VIKI-Bench and VIKI-R offer a unified testbed and method for advancing multi-agent, visual-driven cooperation in embodied AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.17206", "title": "DreamCube: 3D Panorama Generation via Multi-plane Synchronization", "authors": [ "Yukun Huang", "Yanning Zhou", "Jianan Wang", "Kaiyi Huang", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "3D panorama synthesis is a promising yet challenging task that demands high-quality and diverse visual appearance and geometry of the generated omnidirectional content. Existing methods leverage rich image priors from pre-trained 2D foundation models to circumvent the scarcity of 3D panoramic data, but the incompatibility between 3D panoramas and 2D single views limits their effectiveness. In this work, we demonstrate that by applying multi-plane synchronization to the operators from 2D foundation models, their capabilities can be seamlessly extended to the omnidirectional domain. Based on this design, we further introduce DreamCube, a multi-plane RGB-D diffusion model for 3D panorama generation, which maximizes the reuse of 2D foundation model priors to achieve diverse appearances and accurate geometry while maintaining multi-view consistency. Extensive experiments demonstrate the effectiveness of our approach in panoramic image generation, panoramic depth estimation, and 3D scene generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.16504", "title": "Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details", "authors": [ "Zeqiang Lai", "Yunfei Zhao", "Haolin Liu", "Zibo Zhao", "Qingxiang Lin", "Huiwen Shi", "Xianghui Yang", "Mingxin Yang", "Shuhui Yang", "Yifei Feng", "Sheng Zhang", "Xin Huang", "Di Luo", "Fan Yang", "Fang Yang", "Lifu Wang", "Sicong Liu", "Yixuan Tang", "Yulin Cai", "Zebin He", "Tian Liu", "Yuhong Liu", "Jie Jiang", "Linus", "Jingwei Huang", "Chunchao Guo" ], "github": "", "project_page": "", "abstract": "In this report, we present Hunyuan3D 2.5, a robust suite of 3D diffusion models aimed at generating high-fidelity and detailed textured 3D assets. Hunyuan3D 2.5 follows two-stages pipeline of its previous version Hunyuan3D 2.0, while demonstrating substantial advancements in both shape and texture generation. In terms of shape generation, we introduce a new shape foundation model -- LATTICE, which is trained with scaled high-quality datasets, model-size, and compute. Our largest model reaches 10B parameters and generates sharp and detailed 3D shape with precise image-3D following while keeping mesh surface clean and smooth, significantly closing the gap between generated and handcrafted 3D shapes. In terms of texture generation, it is upgraded with phyiscal-based rendering (PBR) via a novel multi-view architecture extended from Hunyuan3D 2.0 Paint model. Our extensive evaluation shows that Hunyuan3D 2.5 significantly outperforms previous methods in both shape and end-to-end texture generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.15925", "title": "Reranking-based Generation for Unbiased Perspective Summarization", "authors": [ "Narutatsu Ri", "Nicholas Deas", "Kathleen McKeown" ], "github": "", "project_page": "", "abstract": "Generating unbiased summaries in real-world settings such as political perspective summarization remains a crucial application of Large Language Models (LLMs). Yet, existing evaluation frameworks rely on traditional metrics for measuring key attributes such as coverage and faithfulness without verifying their applicability, and efforts to develop improved summarizers are still nascent. We address these gaps by (1) identifying reliable metrics for measuring perspective summary quality, and (2) investigating the efficacy of LLM-based methods beyond zero-shot inference. Namely, we build a test set for benchmarking metric reliability using human annotations and show that traditional metrics underperform compared to language model-based metrics, which prove to be strong evaluators. Using these metrics, we show that reranking-based methods yield strong results, and preference tuning with synthetically generated and reranking-labeled data further boosts performance. Our findings aim to contribute to the reliable evaluation and development of perspective summarization methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.17201", "title": "Hunyuan-GameCraft: High-dynamic Interactive Game Video Generation with Hybrid History Condition", "authors": [ "Jiaqi Li", "Junshu Tang", "Zhiyong Xu", "Longhuang Wu", "Yuan Zhou", "Shuai Shao", "Tianbao Yu", "Zhiguo Cao", "Qinglin Lu" ], "github": "", "project_page": "", "abstract": "Recent advances in diffusion-based and controllable video generation have enabled high-quality and temporally coherent video synthesis, laying the groundwork for immersive interactive gaming experiences. However, current methods face limitations in dynamics, generality, long-term consistency, and efficiency, which limit the ability to create various gameplay videos. To address these gaps, we introduce Hunyuan-GameCraft, a novel framework for high-dynamic interactive video generation in game environments. To achieve fine-grained action control, we unify standard keyboard and mouse inputs into a shared camera representation space, facilitating smooth interpolation between various camera and movement operations. Then we propose a hybrid history-conditioned training strategy that extends video sequences autoregressively while preserving game scene information. Additionally, to enhance inference efficiency and playability, we achieve model distillation to reduce computational overhead while maintaining consistency across long temporal sequences, making it suitable for real-time deployment in complex interactive environments. The model is trained on a large-scale dataset comprising over one million gameplay recordings across over 100 AAA games, ensuring broad coverage and diversity, then fine-tuned on a carefully annotated synthetic dataset to enhance precision and control. The curated game scene data significantly improves the visual fidelity, realism and action controllability. Extensive experiments demonstrate that Hunyuan-GameCraft significantly outperforms existing models, advancing the realism and playability of interactive game video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.16406", "title": "Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights", "authors": [ "Zhiyuan Liang", "Dongwen Tang", "Yuhao Zhou", "Xuanlei Zhao", "Mingjia Shi", "Wangbo Zhao", "Zekai Li", "Peihao Wang", "Konstantin Sch\u00fcrholt", "Damian Borth", "Michael M. Bronstein", "Yang You", "Zhangyang Wang", "Kai Wang" ], "github": "", "project_page": "", "abstract": "Modern Parameter-Efficient Fine-Tuning (PEFT) methods such as low-rank adaptation (LoRA) reduce the cost of customizing large language models (LLMs), yet still require a separate optimization run for every downstream dataset. We introduce Drag-and-Drop LLMs (\\textit{DnD)}, a prompt-conditioned parameter generator that eliminates per-task training by mapping a handful of unlabeled task prompts directly to LoRA weight updates. A lightweight text encoder distills each prompt batch into condition embeddings, which are then transformed by a cascaded hyper-convolutional decoder into the full set of LoRA matrices. Once trained in a diverse collection of prompt-checkpoint pairs, DnD produces task-specific parameters in seconds, yielding i) up to 12,000times lower overhead than full fine-tuning, ii) average gains up to 30\\% in performance over the strongest training LoRAs on unseen common-sense reasoning, math, coding, and multimodal benchmarks, and iii) robust cross-domain generalization despite never seeing the target data or labels. Our results demonstrate that prompt-conditioned parameter generation is a viable alternative to gradient-based adaptation for rapidly specializing LLMs. Our project is available at https://jerryliang24.github.io/DnD{https://jerryliang24.github.io/DnD}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.16035", "title": "Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding", "authors": [ "Vishesh Tripathi", "Tanmay Odapally", "Indraneel Das", "Uday Allu", "Biddwan Ahmed" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) systems have revolutionized information retrieval and question answering, but traditional text-based chunking methods struggle with complex document structures, multi-page tables, embedded figures, and contextual dependencies across page boundaries. We present a novel multimodal document chunking approach that leverages Large Multimodal Models (LMMs) to process PDF documents in batches while maintaining semantic coherence and structural integrity. Our method processes documents in configurable page batches with cross-batch context preservation, enabling accurate handling of tables spanning multiple pages, embedded visual elements, and procedural content. We evaluate our approach on a curated dataset of PDF documents with manually crafted queries, demonstrating improvements in chunk quality and downstream RAG performance. Our vision-guided approach achieves better accuracy compared to traditional vanilla RAG systems, with qualitative analysis showing superior preservation of document structure and semantic coherence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.16054", "title": "PAROAttention: Pattern-Aware ReOrdering for Efficient Sparse and Quantized Attention in Visual Generation Models", "authors": [ "Tianchen Zhao", "Ke Hong", "Xinhao Yang", "Xuefeng Xiao", "Huixia Li", "Feng Ling", "Ruiqi Xie", "Siqi Chen", "Hongyu Zhu", "Yichong Zhang", "Yu Wang" ], "github": "", "project_page": "", "abstract": "In visual generation, the quadratic complexity of attention mechanisms results in high memory and computational costs, especially for longer token sequences required in high-resolution image or multi-frame video generation. To address this, prior research has explored techniques such as sparsification and quantization. However, these techniques face significant challenges under low density and reduced bitwidths. Through systematic analysis, we identify that the core difficulty stems from the dispersed and irregular characteristics of visual attention patterns. Therefore, instead of introducing specialized sparsification and quantization design to accommodate such patterns, we propose an alternative strategy: *reorganizing* the attention pattern to alleviate the challenges. Inspired by the local aggregation nature of visual feature extraction, we design a novel **Pattern-Aware token ReOrdering (PARO)** technique, which unifies the diverse attention patterns into a hardware-friendly block-wise pattern. This unification substantially simplifies and enhances both sparsification and quantization. We evaluate the performance-efficiency trade-offs of various design choices and finalize a methodology tailored for the unified pattern. Our approach, **PAROAttention**, achieves video and image generation with lossless metrics, and nearly identical results from full-precision (FP) baselines, while operating at notably lower density (~20%-30%) and bitwidth (**INT8/INT4**), achieving a **1.9x** to **2.7x** end-to-end latency speedup.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.17213", "title": "Long-term Traffic Simulation with Interleaved Autoregressive Motion and Scenario Generation", "authors": [ "Xiuyu Yang", "Shuhan Tan", "Philipp Kr\u00e4henb\u00fchl" ], "github": "", "project_page": "", "abstract": "An ideal traffic simulator replicates the realistic long-term point-to-point trip that a self-driving system experiences during deployment. Prior models and benchmarks focus on closed-loop motion simulation for initial agents in a scene. This is problematic for long-term simulation. Agents enter and exit the scene as the ego vehicle enters new regions. We propose InfGen, a unified next-token prediction model that performs interleaved closed-loop motion simulation and scene generation. InfGen automatically switches between closed-loop motion simulation and scene generation mode. It enables stable long-term rollout simulation. InfGen performs at the state-of-the-art in short-term (9s) traffic simulation, and significantly outperforms all other methods in long-term (30s) simulation. The code and model of InfGen will be released at https://orangesodahub.github.io/InfGen", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.15442", "title": "Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material", "authors": [ "Team Hunyuan3D", "Shuhui Yang", "Mingxin Yang", "Yifei Feng", "Xin Huang", "Sheng Zhang", "Zebin He", "Di Luo", "Haolin Liu", "Yunfei Zhao", "Qingxiang Lin", "Zeqiang Lai", "Xianghui Yang", "Huiwen Shi", "Zibo Zhao", "Bowen Zhang", "Hongyu Yan", "Lifu Wang", "Sicong Liu", "Jihong Zhang", "Meng Chen", "Liang Dong", "Yiwen Jia", "Yulin Cai", "Jiaao Yu", "Yixuan Tang", "Dongyuan Guo", "Junlin Yu", "Hao Zhang", "Zheng Ye", "Peng He", "Runzhou Wu", "Shida Wei", "Chao Zhang", "Yonghao Tan", "Yifu Sun", "Lin Niu", "Shirui Huang", "Bojian Zheng", "Shu Liu", "Shilin Chen", "Xiang Yuan", "Xiaofeng Yang", "Kai Liu", "Jianchen Zhu", "Peng Chen", "Tian Liu", "Di Wang", "Yuhong Liu", "Linus", "Jie Jiang", "Jingwei Huang", "Chunchao Guo" ], "github": "https://github.com/tencent-hunyuan/hunyuan3d-2.1", "project_page": "", "abstract": "3D AI-generated content (AIGC) is a passionate field that has significantly accelerated the creation of 3D models in gaming, film, and design. Despite the development of several groundbreaking models that have revolutionized 3D generation, the field remains largely accessible only to researchers, developers, and designers due to the complexities involved in collecting, processing, and training 3D models. To address these challenges, we introduce Hunyuan3D 2.1 as a case study in this tutorial. This tutorial offers a comprehensive, step-by-step guide on processing 3D data, training a 3D generative model, and evaluating its performance using Hunyuan3D 2.1, an advanced system for producing high-resolution, textured 3D assets. The system comprises two core components: the Hunyuan3D-DiT for shape generation and the Hunyuan3D-Paint for texture synthesis. We will explore the entire workflow, including data preparation, model architecture, training strategies, evaluation metrics, and deployment. By the conclusion of this tutorial, you will have the knowledge to finetune or develop a robust 3D generative model suitable for applications in gaming, virtual reality, and industrial design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tencent-hunyuan/hunyuan3d-2.1", "github_api_added_by": "auto" }, { "date": "2025-06-23", "arxiv_id": "2506.15745", "title": "InfiniPot-V: Memory-Constrained KV Cache Compression for Streaming Video Understanding", "authors": [ "Minsoo Kim", "Kyuhong Shim", "Jungwook Choi", "Simyung Chang" ], "github": "https://github.com/aiha-lab/InfiniPot-V", "project_page": "", "abstract": "Modern multimodal large language models (MLLMs) can reason over hour-long video, yet their key-value (KV) cache grows linearly with time--quickly exceeding the fixed memory of phones, AR glasses, and edge robots. Prior compression schemes either assume the whole video and user query are available offline or must first build the full cache, so memory still scales with stream length. InfiniPot-V is the first training-free, query-agnostic framework that enforces a hard, length-independent memory cap for streaming video understanding. During video encoding it monitors the cache and, once a user-set threshold is reached, runs a lightweight compression pass that (i) removes temporally redundant tokens via Temporal-axis Redundancy (TaR) metric and (ii) keeps semantically significant tokens via Value-Norm (VaN) ranking. Across four open-source MLLMs and four long-video and two streaming-video benchmarks, InfiniPot-V cuts peak GPU memory by up to 94%, sustains real-time generation, and matches or surpasses full-cache accuracy--even in multi-turn dialogues. By dissolving the KV cache bottleneck without retraining or query knowledge, InfiniPot-V closes the gap for on-device streaming video assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aiha-lab/InfiniPot-V", "github_api_added_by": "auto" }, { "date": "2025-06-23", "arxiv_id": "2506.17202", "title": "UniFork: Exploring Modality Alignment for Unified Multimodal Understanding and Generation", "authors": [ "Teng Li", "Quanfeng Lu", "Lirui Zhao", "Hao Li", "Xizhou Zhu", "Yu Qiao", "Jun Zhang", "Wenqi Shao" ], "github": "https://github.com/tliby/unifork", "project_page": "", "abstract": "Unified image understanding and generation has emerged as a promising paradigm in multimodal artificial intelligence. Despite recent progress, the optimal architectural design for such unified models remains an open challenge. In this work, we start by analyzing the modality alignment behaviors of task-specific expert models for understanding and generation, as well as current unified models. Our analysis reveals a crucial observation: understanding tasks benefit from a progressively increasing modality alignment across network depth, which helps build up semantic information for better comprehension; In contrast, generation tasks follow a different trend: modality alignment increases in the early layers but decreases in the deep layers to recover spatial details. These divergent alignment patterns create a fundamental conflict in fully shared Transformer backbones, where a uniform representational flow often leads to performance compromises across two tasks. Motivated by this finding, we introduce UniFork, a novel Y-shaped architecture that shares the shallow layers for cross-task representation learning, while employing task-specific branches in deeper layers to avoid task interference. This design effectively balances shared learning and task specialization. Through extensive ablation experiments, we demonstrate that Unifork consistently outperforms conventional fully shared Transformer architectures, and achieves performance on par with or better than task-specific models.", "github_manual": "https://github.com/tliby/unifork", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.16310", "title": "Optimizing Multilingual Text-To-Speech with Accents & Emotions", "authors": [ "Pranav Pawar", "Akshansh Dwivedi", "Jenish Boricha", "Himanshu Gohil", "Aditya Dubey" ], "github": "", "project_page": "", "abstract": "State-of-the-art text-to-speech (TTS) systems realize high naturalness in monolingual environments, synthesizing speech with correct multilingual accents (especially for Indic languages) and context-relevant emotions still poses difficulty owing to cultural nuance discrepancies in current frameworks. This paper introduces a new TTS architecture integrating accent along with preserving transliteration with multi-scale emotion modelling, in particularly tuned for Hindi and Indian English accent. Our approach extends the Parler-TTS model by integrating A language-specific phoneme alignment hybrid encoder-decoder architecture, and culture-sensitive emotion embedding layers trained on native speaker corpora, as well as incorporating a dynamic accent code switching with residual vector quantization. Quantitative tests demonstrate 23.7% improvement in accent accuracy (Word Error Rate reduction from 15.4% to 11.8%) and 85.3% emotion recognition accuracy from native listeners, surpassing METTS and VECL-TTS baselines. The novelty of the system is that it can mix code in real time - generating statements such as \"Namaste, let's talk about \" with uninterrupted accent shifts while preserving emotional consistency. Subjective evaluation with 200 users reported a mean opinion score (MOS) of 4.2/5 for cultural correctness, much better than existing multilingual systems (p<0.01). This research makes cross-lingual synthesis more feasible by showcasing scalable accent-emotion disentanglement, with direct application in South Asian EdTech and accessibility software.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.17218", "title": "Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens", "authors": [ "Zeyuan Yang", "Xueyang Yu", "Delin Chen", "Maohao Shen", "Chuang Gan" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) excel at multimodal understanding, yet their text-only decoding forces them to verbalize visual reasoning, limiting performance on tasks that demand visual imagination. Recent attempts train VLMs to render explicit images, but the heavy image-generation pre-training often hinders the reasoning ability. Inspired by the way humans reason with mental imagery-the internal construction and manipulation of visual cues-we investigate whether VLMs can reason through interleaved multimodal trajectories without producing explicit images. To this end, we present a Machine Mental Imagery framework, dubbed as Mirage, which augments VLM decoding with latent visual tokens alongside ordinary text. Concretely, whenever the model chooses to ``think visually'', it recasts its hidden states as next tokens, thereby continuing a multimodal trajectory without generating pixel-level images. Begin by supervising the latent tokens through distillation from ground-truth image embeddings, we then switch to text-only supervision to make the latent trajectory align tightly with the task objective. A subsequent reinforcement learning stage further enhances the multimodal reasoning capability. Experiments on diverse benchmarks demonstrate that Mirage unlocks stronger multimodal reasoning without explicit image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.09930", "title": "From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models", "authors": [ "Irving Fang", "Juexiao Zhang", "Shengbang Tong", "Chen Feng" ], "github": "https://github.com/allenzren/open-pi-zero", "project_page": "", "abstract": "One promise that Vision-Language-Action (VLA) models hold over traditional imitation learning for robotics is to leverage the broad generalization capabilities of large Vision-Language Models (VLMs) to produce versatile, \"generalist\" robot policies. However, current evaluations of VLAs remain insufficient. Traditional imitation learning benchmarks are unsuitable due to the lack of language instructions. Emerging benchmarks for VLAs that incorporate language often come with limited evaluation tasks and do not intend to investigate how much VLM pretraining truly contributes to the generalization capabilities of the downstream robotic policy. Meanwhile, much research relies on real-world robot setups designed in isolation by different institutions, which creates a barrier for reproducibility and accessibility. To address this gap, we introduce a unified probing suite of 50 simulation-based tasks across 10 subcategories spanning language instruction, vision, and objects. We systematically evaluate several state-of-the-art VLA architectures on this suite to understand their generalization capability. Our results show that while VLM backbones endow VLAs with robust perceptual understanding and high level planning, which we refer to as good intentions, this does not reliably translate into precise motor execution: when faced with out-of-distribution observations, policies often exhibit coherent intentions, but falter in action execution. Moreover, finetuning on action data can erode the original VLM's generalist reasoning abilities. We release our task suite and evaluation code to serve as a standardized benchmark for future VLAs and to drive research on closing the perception-to-action gap. More information, including the source code, can be found at https://ai4ce.github.io/INT-ACT/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/allenzren/open-pi-zero", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.17113", "title": "MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation", "authors": [ "Shoubin Yu", "Yue Zhang", "Ziyang Wang", "Jaehong Yoon", "Mohit Bansal" ], "github": "https://github.com/Yui010206/MEXA", "project_page": "", "abstract": "Combining pre-trained expert models offers substantial potential for scalable multimodal reasoning, but building a unified framework remains challenging due to the increasing diversity of input modalities and task complexity. For instance, medical diagnosis requires precise reasoning over structured clinical tables, while financial forecasting depends on interpreting plot-based data to make informed predictions. To tackle this challenge, we introduce MEXA, a training-free framework that performs modality- and task-aware aggregation of multiple expert models to enable effective multimodal reasoning across diverse and distinct domains. MEXA dynamically selects expert models based on the input modality and the task-specific reasoning demands (i.e., skills). Each expert model, specialized in a modality task pair, generates interpretable textual reasoning outputs. MEXA then aggregates and reasons over these outputs using a Large Reasoning Model (LRM) to produce the final answer. This modular design allows flexible and transparent multimodal reasoning across diverse domains without additional training overhead. We extensively evaluate our approach on diverse multimodal benchmarks, including Video Reasoning, Audio Reasoning, 3D Understanding, and Medical QA. MEXA consistently delivers performance improvements over strong multimodal baselines, highlighting the effectiveness and broad applicability of our expert-driven selection and aggregation in diverse multimodal reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Yui010206/MEXA", "github_api_added_by": "auto" }, { "date": "2025-06-23", "arxiv_id": "2506.16349", "title": "Watermarking Autoregressive Image Generation", "authors": [ "Nikola Jovanovi\u0107", "Ismail Labiad", "Tom\u00e1\u0161 Sou\u010dek", "Martin Vechev", "Pierre Fernandez" ], "github": "https://github.com/facebookresearch/wmar", "project_page": "", "abstract": "Watermarking the outputs of generative models has emerged as a promising approach for tracking their provenance. Despite significant interest in autoregressive image generation models and their potential for misuse, no prior work has attempted to watermark their outputs at the token level. In this work, we present the first such approach by adapting language model watermarking techniques to this setting. We identify a key challenge: the lack of reverse cycle-consistency (RCC), wherein re-tokenizing generated image tokens significantly alters the token sequence, effectively erasing the watermark. To address this and to make our method robust to common image transformations, neural compression, and removal attacks, we introduce (i) a custom tokenizer-detokenizer finetuning procedure that improves RCC, and (ii) a complementary watermark synchronization layer. As our experiments demonstrate, our approach enables reliable and robust watermark detection with theoretically grounded p-values.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/wmar", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-23", "arxiv_id": "2506.17090", "title": "Better Language Model Inversion by Compactly Representing Next-Token Distributions", "authors": [ "Murtaza Nazir", "Matthew Finlayson", "John X. Morris", "Xiang Ren", "Swabha Swayamdipta" ], "github": "https://github.com/Dill-Lab/PILS", "project_page": "", "abstract": "Language model inversion seeks to recover hidden prompts using only language model outputs. This capability has implications for security and accountability in language model deployments, such as leaking private information from an API-protected language model's system message. We propose a new method -- prompt inversion from logprob sequences (PILS) -- that recovers hidden prompts by gleaning clues from the model's next-token probabilities over the course of multiple generation steps. Our method is enabled by a key insight: The vector-valued outputs of a language model occupy a low-dimensional subspace. This enables us to losslessly compress the full next-token probability distribution over multiple generation steps using a linear map, allowing more output information to be used for inversion. Our approach yields massive gains over previous state-of-the-art methods for recovering hidden prompts, achieving 2--3.5 times higher exact recovery rates across test sets, in one case increasing the recovery rate from 17% to 60%. Our method also exhibits surprisingly good generalization behavior; for instance, an inverter trained on 16 generations steps gets 5--27 points higher prompt recovery when we increase the number of steps to 32 at test time. Furthermore, we demonstrate strong performance of our method on the more challenging task of recovering hidden system messages. We also analyze the role of verbatim repetition in prompt recovery and propose a new method for cross-family model transfer for logit-based inverters. Our findings show that next-token probabilities are a considerably more vulnerable attack surface for inversion attacks than previously known.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Dill-Lab/PILS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18841", "title": "LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning", "authors": [ "Yuhao Wu", "Yushi Bai", "Zhiqiang Hu", "Roy Ka-Wei Lee", "Juanzi Li" ], "github": "", "project_page": "", "abstract": "Ultra-long generation by large language models (LLMs) is a widely demanded scenario, yet it remains a significant challenge due to their maximum generation length limit and overall quality degradation as sequence length increases. Previous approaches, exemplified by LongWriter, typically rely on ''teaching'', which involves supervised fine-tuning (SFT) on synthetic long-form outputs. However, this strategy heavily depends on synthetic SFT data, which is difficult and costly to construct, often lacks coherence and consistency, and tends to be overly artificial and structurally monotonous. In this work, we propose an incentivization-based approach that, starting entirely from scratch and without relying on any annotated or synthetic data, leverages reinforcement learning (RL) to foster the emergence of ultra-long, high-quality text generation capabilities in LLMs. We perform RL training starting from a base model, similar to R1-Zero, guiding it to engage in reasoning that facilitates planning and refinement during the writing process. To support this, we employ specialized reward models that steer the LLM towards improved length control, writing quality, and structural formatting. Experimental evaluations show that our LongWriter-Zero model, trained from Qwen2.5-32B, consistently outperforms traditional SFT methods on long-form writing tasks, achieving state-of-the-art results across all metrics on WritingBench and Arena-Write, and even surpassing 100B+ models such as DeepSeek R1 and Qwen3-235B. We open-source our data and model checkpoints under https://huggingface.co/THU-KEG/LongWriter-Zero-32B", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18871", "title": "OmniGen2: Exploration to Advanced Multimodal Generation", "authors": [ "Chenyuan Wu", "Pengfei Zheng", "Ruiran Yan", "Shitao Xiao", "Xin Luo", "Yueze Wang", "Wanli Li", "Xiyan Jiang", "Yexin Liu", "Junjie Zhou", "Ze Liu", "Ziyi Xia", "Chaofan Li", "Haoge Deng", "Jiahao Wang", "Kun Luo", "Bo Zhang", "Defu Lian", "Xinlong Wang", "Zhongyuan Wang", "Tiejun Huang", "Zheng Liu" ], "github": "https://github.com/VectorSpaceLab/OmniGen2", "project_page": "", "abstract": "In this work, we introduce OmniGen2, a versatile and open-source generative model designed to provide a unified solution for diverse generation tasks, including text-to-image, image editing, and in-context generation. Unlike OmniGen v1, OmniGen2 features two distinct decoding pathways for text and image modalities, utilizing unshared parameters and a decoupled image tokenizer. This design enables OmniGen2 to build upon existing multimodal understanding models without the need to re-adapt VAE inputs, thereby preserving the original text generation capabilities. To facilitate the training of OmniGen2, we developed comprehensive data construction pipelines, encompassing image editing and in-context generation data. Additionally, we introduce a reflection mechanism tailored for image generation tasks and curate a dedicated reflection dataset based on OmniGen2. Despite its relatively modest parameter size, OmniGen2 achieves competitive results on multiple task benchmarks, including text-to-image and image editing. To further evaluate in-context generation, also referred to as subject-driven tasks, we introduce a new benchmark named OmniContext. OmniGen2 achieves state-of-the-art performance among open-source models in terms of consistency. We will release our models, training code, datasets, and data construction pipeline to support future research in this field. Project Page: https://vectorspacelab.github.io/OmniGen2; GitHub Link: https://github.com/VectorSpaceLab/OmniGen2", "github_manual": "", "github_regex": "https://github.com/VectorSpaceLab/OmniGen2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18896", "title": "ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought Reasoning in LLMs", "authors": [ "Jiaru Zou", "Ling Yang", "Jingwen Gu", "Jiahao Qiu", "Ke Shen", "Jingrui He", "Mengdi Wang" ], "github": "https://github.com/Gen-Verse/ReasonFlux", "project_page": "", "abstract": "Process Reward Models (PRMs) have recently emerged as a powerful framework for supervising intermediate reasoning steps in large language models (LLMs). Previous PRMs are primarily trained on model final output responses and struggle to evaluate intermediate thinking trajectories robustly, especially in the emerging setting of trajectory-response outputs generated by frontier reasoning models like Deepseek-R1. In this work, we introduce ReasonFlux-PRM, a novel trajectory-aware PRM explicitly designed to evaluate the trajectory-response type of reasoning traces. ReasonFlux-PRM incorporates both step-level and trajectory-level supervision, enabling fine-grained reward assignment aligned with structured chain-of-thought data. We adapt ReasonFlux-PRM to support reward supervision under both offline and online settings, including (i) selecting high-quality model distillation data for downstream supervised fine-tuning of smaller models, (ii) providing dense process-level rewards for policy optimization during reinforcement learning, and (iii) enabling reward-guided Best-of-N test-time scaling. Empirical results on challenging downstream benchmarks such as AIME, MATH500, and GPQA-Diamond demonstrate that ReasonFlux-PRM-7B selects higher quality data than strong PRMs (e.g., Qwen2.5-Math-PRM-72B) and human-curated baselines. Furthermore, our derived ReasonFlux-PRM-7B yields consistent performance improvements, achieving average gains of 12.1% in supervised fine-tuning, 4.5% in reinforcement learning, and 6.3% in test-time scaling. We also release our efficient ReasonFlux-PRM-1.5B for resource-constrained applications and edge deployment. Projects: https://github.com/Gen-Verse/ReasonFlux", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/ReasonFlux", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18898", "title": "Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations", "authors": [ "Jiaming Han", "Hao Chen", "Yang Zhao", "Hanyu Wang", "Qi Zhao", "Ziyan Yang", "Hao He", "Xiangyu Yue", "Lu Jiang" ], "github": "", "project_page": "", "abstract": "This paper presents a multimodal framework that attempts to unify visual understanding and generation within a shared discrete semantic representation. At its core is the Text-Aligned Tokenizer (TA-Tok), which converts images into discrete tokens using a text-aligned codebook projected from a large language model's (LLM) vocabulary. By integrating vision and text into a unified space with an expanded vocabulary, our multimodal LLM, Tar, enables cross-modal input and output through a shared interface, without the need for modality-specific designs. Additionally, we propose scale-adaptive encoding and decoding to balance efficiency and visual detail, along with a generative de-tokenizer to produce high-fidelity visual outputs. To address diverse decoding needs, we utilize two complementary de-tokenizers: a fast autoregressive model and a diffusion-based model. To enhance modality fusion, we investigate advanced pre-training tasks, demonstrating improvements in both visual understanding and generation. Experiments across benchmarks show that Tar matches or surpasses existing multimodal LLM methods, achieving faster convergence and greater training efficiency. Code, models, and data are available at https://tar.csuhan.com", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18851", "title": "Phantom-Data : Towards a General Subject-Consistent Video Generation Dataset", "authors": [ "Zhuowei Chen", "Bingchuan Li", "Tianxiang Ma", "Lijie Liu", "Mingcong Liu", "Yi Zhang", "Gen Li", "Xinghui Li", "Siyu Zhou", "Qian He", "Xinglong Wu" ], "github": "", "project_page": "", "abstract": "Subject-to-video generation has witnessed substantial progress in recent years. However, existing models still face significant challenges in faithfully following textual instructions. This limitation, commonly known as the copy-paste problem, arises from the widely used in-pair training paradigm. This approach inherently entangles subject identity with background and contextual attributes by sampling reference images from the same scene as the target video. To address this issue, we introduce Phantom-Data, the first general-purpose cross-pair subject-to-video consistency dataset, containing approximately one million identity-consistent pairs across diverse categories. Our dataset is constructed via a three-stage pipeline: (1) a general and input-aligned subject detection module, (2) large-scale cross-context subject retrieval from more than 53 million videos and 3 billion images, and (3) prior-guided identity verification to ensure visual consistency under contextual variation. Comprehensive experiments show that training with Phantom-Data significantly improves prompt alignment and visual quality while preserving identity consistency on par with in-pair baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.10597", "title": "SoK: Evaluating Jailbreak Guardrails for Large Language Models", "authors": [ "Xunguang Wang", "Zhenlan Ji", "Wenxuan Wang", "Zongjie Li", "Daoyuan Wu", "Shuai Wang" ], "github": "https://github.com/xunguangwang/SoK4JailbreakGuardrails", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved remarkable progress, but their deployment has exposed critical vulnerabilities, particularly to jailbreak attacks that circumvent safety mechanisms. Guardrails--external defense mechanisms that monitor and control LLM interaction--have emerged as a promising solution. However, the current landscape of LLM guardrails is fragmented, lacking a unified taxonomy and comprehensive evaluation framework. In this Systematization of Knowledge (SoK) paper, we present the first holistic analysis of jailbreak guardrails for LLMs. We propose a novel, multi-dimensional taxonomy that categorizes guardrails along six key dimensions, and introduce a Security-Efficiency-Utility evaluation framework to assess their practical effectiveness. Through extensive analysis and experiments, we identify the strengths and limitations of existing guardrail approaches, explore their universality across attack types, and provide insights into optimizing defense combinations. Our work offers a structured foundation for future research and development, aiming to guide the principled advancement and deployment of robust LLM guardrails. The code is available at https://github.com/xunguangwang/SoK4JailbreakGuardrails.", "github_manual": "", "github_regex": "https://github.com/xunguangwang/SoK4JailbreakGuardrails", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18879", "title": "CommVQ: Commutative Vector Quantization for KV Cache Compression", "authors": [ "Junyan Li", "Yang Zhang", "Muhammad Yusuf Hassan", "Talha Chafekar", "Tianle Cai", "Zhile Ren", "Pengsheng Guo", "Foroozan Karimzadeh", "Colorado Reed", "Chong Wang", "Chuang Gan" ], "github": "https://github.com/UMass-Embodied-AGI/CommVQ", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly used in applications requiring long context lengths, but the key-value (KV) cache often becomes a memory bottleneck on GPUs as context grows. To address this, we propose Commutative Vector Quantization (CommVQ) to significantly reduce memory usage for long-context LLM inference. We first introduce additive quantization with a lightweight encoder and codebook to compress the KV cache, which can be decoded via simple matrix multiplication. To further reduce computational costs during decoding, we design the codebook to be commutative with Rotary Position Embedding (RoPE) and train it using an Expectation-Maximization (EM) algorithm. This enables efficient integration of decoding into the self-attention mechanism. Our approach achieves high accuracy with additive quantization and low overhead via the RoPE-commutative codebook. Experiments on long-context benchmarks and GSM8K show that our method reduces FP16 KV cache size by 87.5% with 2-bit quantization, while outperforming state-of-the-art KV cache quantization methods. Notably, it enables 1-bit KV cache quantization with minimal accuracy loss, allowing a LLaMA-3.1 8B model to run with a 128K context length on a single RTX 4090 GPU. The source code is available at: https://github.com/UMass-Embodied-AGI/CommVQ.", "github_manual": "", "github_regex": "https://github.com/UMass-Embodied-AGI/CommVQ", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18903", "title": "VMem: Consistent Interactive Video Scene Generation with Surfel-Indexed View Memory", "authors": [ "Runjia Li", "Philip Torr", "Andrea Vedaldi", "Tomas Jakab" ], "github": "", "project_page": "", "abstract": "We propose a novel memory mechanism to build video generators that can explore environments interactively. Similar results have previously been achieved by out-painting 2D views of the scene while incrementally reconstructing its 3D geometry, which quickly accumulates errors, or by video generators with a short context window, which struggle to maintain scene coherence over the long term. To address these limitations, we introduce Surfel-Indexed View Memory (VMem), a mechanism that remembers past views by indexing them geometrically based on the 3D surface elements (surfels) they have observed. VMem enables the efficient retrieval of the most relevant past views when generating new ones. By focusing only on these relevant views, our method produces consistent explorations of imagined environments at a fraction of the computational cost of using all past views as context. We evaluate our approach on challenging long-term scene synthesis benchmarks and demonstrate superior performance compared to existing methods in maintaining scene coherence and camera control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18631", "title": "ReDit: Reward Dithering for Improved LLM Policy Optimization", "authors": [ "Chenxing Wei", "Jiarui Yu", "Ying Tiffany He", "Hande Dong", "Yao Shu", "Fei Yu" ], "github": "", "project_page": "", "abstract": "DeepSeek-R1 has successfully enhanced Large Language Model (LLM) reasoning capabilities through its rule-based reward system. While it's a ''perfect'' reward system that effectively mitigates reward hacking, such reward functions are often discrete. Our experimental observations suggest that discrete rewards can lead to gradient anomaly, unstable optimization, and slow convergence. To address this issue, we propose ReDit (Reward Dithering), a method that dithers the discrete reward signal by adding simple random noise. With this perturbed reward, exploratory gradients are continuously provided throughout the learning process, enabling smoother gradient updates and accelerating convergence. The injected noise also introduces stochasticity into flat reward regions, encouraging the model to explore novel policies and escape local optima. Experiments across diverse tasks demonstrate the effectiveness and efficiency of ReDit. On average, ReDit achieves performance comparable to vanilla GRPO with only approximately 10% the training steps, and furthermore, still exhibits a 4% performance improvement over vanilla GRPO when trained for a similar duration. Visualizations confirm significant mitigation of gradient issues with ReDit. Moreover, theoretical analyses are provided to further validate these advantages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18349", "title": "SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation", "authors": [ "Zichong Li", "Chen Liang", "Zixuan Zhang", "Ilgee Hong", "Young Jin Kim", "Weizhu Chen", "Tuo Zhao" ], "github": "", "project_page": "", "abstract": "The Mixture of Experts (MoE) architecture has emerged as a powerful paradigm for scaling large language models (LLMs) while maintaining inference efficiency. However, their enormous memory requirements make them prohibitively expensive to fine-tune or deploy in resource-constrained environments. To address this challenge, we introduce SlimMoE, a multi-stage compression framework for transforming large MoE models into much smaller, efficient variants without incurring the prohibitive costs of training from scratch. Our method systematically reduces parameter counts by slimming experts and transferring knowledge through intermediate stages, effectively mitigating the performance degradation common in one-shot pruning approaches. Using this framework, we compress Phi 3.5-MoE (41.9B total/6.6B activated parameters) to create Phi-mini-MoE (7.6B total/2.4B activated parameters) and Phi-tiny-MoE (3.8B total/1.1B activated parameters) using only 400B tokens--less than 10% of the original model's training data. These compressed models can be fine-tuned on a single GPU (A100 for Phi-mini-MoE, A6000 for Phi-tiny-MoE), making them highly suitable for academic and resource-limited settings. Our experiments demonstrate that these compressed models outperform others of similar size and remain competitive with larger models. For instance, Phi-mini-MoE achieves similar or better performance to Phi-3-mini using only 2/3 of the activated parameters and yields comparable MMLU scores to Llama 3.1 8B despite having significantly lower latency. Our findings demonstrate that structured pruning combined with staged distillation offers an effective path to creating high-quality, compact MoE models, paving the way for broader adoption of MoE architectures. We make our models publicly available at https://huggingface.co/microsoft/Phi-mini-MoE-instruct and https://huggingface.co/microsoft/Phi-tiny-MoE-instruct .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18882", "title": "Light of Normals: Unified Feature Representation for Universal Photometric Stereo", "authors": [ "Hong Li", "Houyuan Chen", "Chongjie Ye", "Zhaoxi Chen", "Bohan Li", "Shaocong Xu", "Xianda Guo", "Xuhui Liu", "Yikai Wang", "Baochang Zhang", "Satoshi Ikehata", "Boxin Shi", "Anyi Rao", "Hao Zhao" ], "github": "", "project_page": "", "abstract": "Universal photometric stereo (PS) aims to recover high-quality surface normals from objects under arbitrary lighting conditions without relying on specific illumination models. Despite recent advances such as SDM-UniPS and Uni MS-PS, two fundamental challenges persist: 1) the deep coupling between varying illumination and surface normal features, where ambiguity in observed intensity makes it difficult to determine whether brightness variations stem from lighting changes or surface orientation; and 2) the preservation of high-frequency geometric details in complex surfaces, where intricate geometries create self-shadowing, inter-reflections, and subtle normal variations that conventional feature processing operations struggle to capture accurately.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18254", "title": "RLPR: Extrapolating RLVR to General Domains without Verifiers", "authors": [ "Tianyu Yu", "Bo Ji", "Shouli Wang", "Shu Yao", "Zefan Wang", "Ganqu Cui", "Lifan Yuan", "Ning Ding", "Yuan Yao", "Zhiyuan Liu", "Maosong Sun", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) demonstrates promising potential in advancing the reasoning capabilities of LLMs. However, its success remains largely confined to mathematical and code domains. This primary limitation stems from the heavy reliance on domain-specific verifiers, which results in prohibitive complexity and limited scalability. To address the challenge, our key observation is that LLM's intrinsic probability of generating a correct free-form answer directly indicates its own evaluation of the reasoning reward (i.e., how well the reasoning process leads to the correct answer). Building on this insight, we propose RLPR, a simple verifier-free framework that extrapolates RLVR to broader general domains. RLPR uses the LLM's own token probability scores for reference answers as the reward signal and maximizes the expected reward during training. We find that addressing the high variance of this noisy probability reward is crucial to make it work, and propose prob-to-reward and stabilizing methods to ensure a precise and stable reward from LLM intrinsic probabilities. Comprehensive experiments in four general-domain benchmarks and three mathematical benchmarks show that RLPR consistently improves reasoning capabilities in both areas for Gemma, Llama, and Qwen based models. Notably, RLPR outperforms concurrent VeriFree by 7.6 points on TheoremQA and 7.5 points on Minerva, and even surpasses strong verifier-model-dependent approaches General-Reasoner by 1.6 average points across seven benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.15741", "title": "OAgents: An Empirical Study of Building Effective Agents", "authors": [ "He Zhu", "Tianrui Qin", "King Zhu", "Heyuan Huang", "Yeyi Guan", "Jinxiang Xia", "Yi Yao", "Hanhao Li", "Ningning Wang", "Pai Liu", "Tianhao Peng", "Xin Gui", "Xiaowan Li", "Yuhui Liu", "Yuchen Eleanor Jiang", "Jun Wang", "Changwang Zhang", "Xiangru Tang", "Ge Zhang", "Jian Yang", "Minghao Liu", "Xitong Gao", "Jiaheng Liu", "Wangchunshu Zhou" ], "github": "", "project_page": "", "abstract": "Recently, Agentic AI has become an increasingly popular research field. However, we argue that current agent research practices lack standardization and scientific rigor, making it hard to conduct fair comparisons among methods. As a result, it is still unclear how different design choices in agent frameworks affect effectiveness, and measuring their progress remains challenging. In this work, we conduct a systematic empirical study on GAIA benchmark and BrowseComp to examine the impact of popular design choices in key agent components in a fair and rigorous manner. We find that the lack of a standard evaluation protocol makes previous works, even open-sourced ones, non-reproducible, with significant variance between random runs. Therefore, we introduce a more robust evaluation protocol to stabilize comparisons. Our study reveals which components and designs are crucial for effective agents, while others are redundant, despite seeming logical. Based on our findings, we build and open-source OAgents, a new foundation agent framework that achieves state-of-the-art performance among open-source projects. OAgents offers a modular design for various agent components, promoting future research in Agentic AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18309", "title": "LettinGo: Explore User Profile Generation for Recommendation System", "authors": [ "Lu Wang", "Di Zhang", "Fangkai Yang", "Pu Zhao", "Jianfeng Liu", "Yuefeng Zhan", "Hao Sun", "Qingwei Lin", "Weiwei Deng", "Dongmei Zhang", "Feng Sun", "Qi Zhang" ], "github": "", "project_page": "", "abstract": "User profiling is pivotal for recommendation systems, as it transforms raw user interaction data into concise and structured representations that drive personalized recommendations. While traditional embedding-based profiles lack interpretability and adaptability, recent advances with large language models (LLMs) enable text-based profiles that are semantically richer and more transparent. However, existing methods often adhere to fixed formats that limit their ability to capture the full diversity of user behaviors. In this paper, we introduce LettinGo, a novel framework for generating diverse and adaptive user profiles. By leveraging the expressive power of LLMs and incorporating direct feedback from downstream recommendation tasks, our approach avoids the rigid constraints imposed by supervised fine-tuning (SFT). Instead, we employ Direct Preference Optimization (DPO) to align the profile generator with task-specific performance, ensuring that the profiles remain adaptive and effective. LettinGo operates in three stages: (1) exploring diverse user profiles via multiple LLMs, (2) evaluating profile quality based on their impact in recommendation systems, and (3) aligning the profile generation through pairwise preference data derived from task performance. Experimental results demonstrate that our framework significantly enhances recommendation accuracy, flexibility, and contextual awareness. This work enhances profile generation as a key innovation for next-generation recommendation systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.16962", "title": "Enhancing Step-by-Step and Verifiable Medical Reasoning in MLLMs", "authors": [ "Haoran Sun", "Yankai Jiang", "Wenjie Lou", "Yujie Zhang", "Wenjie Li", "Lilong Wang", "Mianxin Liu", "Lei Liu", "Xiaosong Wang" ], "github": "https://github.com/manglu097/Chiron-o1", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have begun to demonstrate robust reasoning capabilities on general tasks, yet their application in the medical domain remains in its early stages. Constructing chain-of-thought (CoT) training data is essential for bolstering the reasoning abilities of medical MLLMs. However, existing approaches exhibit a deficiency in offering a comprehensive framework for searching and evaluating effective reasoning paths towards critical diagnosis. To address this challenge, we propose Mentor-Intern Collaborative Search (MICS), a novel reasoning-path searching scheme to generate rigorous and effective medical CoT data. MICS first leverages mentor models to initialize the reasoning, one step at a time, then prompts each intern model to continue the thinking along those initiated paths, and finally selects the optimal reasoning path according to the overall reasoning performance of multiple intern models. The reasoning performance is determined by an MICS-Score, which assesses the quality of generated reasoning paths. Eventually, we construct MMRP, a multi-task medical reasoning dataset with ranked difficulty, and Chiron-o1, a new medical MLLM devised via a curriculum learning strategy, with robust visual question-answering and generalizable reasoning capabilities. Extensive experiments demonstrate that Chiron-o1, trained on our CoT dataset constructed using MICS, achieves state-of-the-art performance across a list of medical visual question answering and reasoning benchmarks. Codes are available at GitHub - manglu097/Chiron-o1: Enhancing Step-by-Step and Verifiable Medical Reasoning in MLLMs", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/manglu097/Chiron-o1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18527", "title": "Auto-Regressively Generating Multi-View Consistent Images", "authors": [ "JiaKui Hu", "Yuxiao Yang", "Jialun Liu", "Jinbo Wu", "Chen Zhao", "Yanye Lu" ], "github": "https://github.com/MILab-PKU/MVAR", "project_page": "", "abstract": "Generating multi-view images from human instructions is crucial for 3D content creation. The primary challenges involve maintaining consistency across multiple views and effectively synthesizing shapes and textures under diverse conditions. In this paper, we propose the Multi-View Auto-Regressive (MV-AR) method, which leverages an auto-regressive model to progressively generate consistent multi-view images from arbitrary prompts. Firstly, the next-token-prediction capability of the AR model significantly enhances its effectiveness in facilitating progressive multi-view synthesis. When generating widely-separated views, MV-AR can utilize all its preceding views to extract effective reference information. Subsequently, we propose a unified model that accommodates various prompts via architecture designing and training strategies. To address multiple conditions, we introduce condition injection modules for text, camera pose, image, and shape. To manage multi-modal conditions simultaneously, a progressive training strategy is employed. This strategy initially adopts the text-to-multi-view (t2mv) model as a baseline to enhance the development of a comprehensive X-to-multi-view (X2mv) model through the randomly dropping and combining conditions. Finally, to alleviate the overfitting problem caused by limited high-quality data, we propose the \"Shuffle View\" data augmentation technique, thus significantly expanding the training data by several magnitudes. Experiments demonstrate the performance and versatility of our MV-AR, which consistently generates consistent multi-view images across a range of conditions and performs on par with leading diffusion-based multi-view image generation models. Code and models will be released at https://github.com/MILab-PKU/MVAR.", "github_manual": "", "github_regex": "https://github.com/MILab-PKU/MVAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.17538", "title": "ConsumerBench: Benchmarking Generative AI Applications on End-User Devices", "authors": [ "Yile Gu", "Rohan Kadekodi", "Hoang Nguyen", "Keisuke Kamahori", "Yiyu Liu", "Baris Kasikci" ], "github": "https://github.com/efeslab/ConsumerBench", "project_page": "", "abstract": "The recent shift in Generative AI (GenAI) applications from cloud-only environments to end-user devices introduces new challenges in resource management, system efficiency, and user experience. This paper presents ConsumerBench, a comprehensive benchmarking framework designed to evaluate the system efficiency and response time of GenAI models running on end-user devices. Unlike existing benchmarks that assume exclusive model access on dedicated GPUs, ConsumerBench simulates realistic multi-application scenarios executing concurrently on constrained hardware. Furthermore, ConsumerBench supports customizable workflows that simulate complex tasks requiring coordination among multiple applications. ConsumerBench captures both application-level metrics, including latency and Service Level Objective (SLO) attainment, and system-level metrics like CPU/GPU utilization and memory bandwidth. Through extensive experiments, ConsumerBench reveals inefficiencies in resource sharing, unfair scheduling under greedy allocation, and performance pitfalls of static model server configurations. The paper also provides practical insights for model developers and system designers, highlighting the benefits of custom kernels tailored to consumer-grade GPU architectures and the value of implementing SLO-aware scheduling strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/efeslab/ConsumerBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.17673", "title": "FaithfulSAE: Towards Capturing Faithful Features with Sparse Autoencoders without External Dataset Dependencies", "authors": [ "Seonglae Cho", "Harryn Oh", "Donghyun Lee", "Luis Eduardo Rodrigues Vieira", "Andrew Bermingham", "Ziad El Sayed" ], "github": "", "project_page": "", "abstract": "Sparse Autoencoders (SAEs) have emerged as a promising solution for decomposing large language model representations into interpretable features. However, Paulo and Belrose (2025) have highlighted instability across different initialization seeds, and Heap et al. (2025) have pointed out that SAEs may not capture model-internal features. These problems likely stem from training SAEs on external datasets - either collected from the Web or generated by another model - which may contain out-of-distribution (OOD) data beyond the model's generalisation capabilities. This can result in hallucinated SAE features, which we term \"Fake Features\", that misrepresent the model's internal activations. To address these issues, we propose FaithfulSAE, a method that trains SAEs on the model's own synthetic dataset. Using FaithfulSAEs, we demonstrate that training SAEs on less-OOD instruction datasets results in SAEs being more stable across seeds. Notably, FaithfulSAEs outperform SAEs trained on web-based datasets in the SAE probing task and exhibit a lower Fake Feature Ratio in 5 out of 7 models. Overall, our approach eliminates the dependency on external datasets, advancing interpretability by better capturing model-internal features while highlighting the often neglected importance of SAE training datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18792", "title": "ViDAR: Video Diffusion-Aware 4D Reconstruction From Monocular Inputs", "authors": [ "Michal Nazarczuk", "Sibi Catley-Chandar", "Thomas Tanay", "Zhensong Zhang", "Gregory Slabaugh", "Eduardo P\u00e9rez-Pellitero" ], "github": "", "project_page": "", "abstract": "Dynamic Novel View Synthesis aims to generate photorealistic views of moving subjects from arbitrary viewpoints. This task is particularly challenging when relying on monocular video, where disentangling structure from motion is ill-posed and supervision is scarce. We introduce Video Diffusion-Aware Reconstruction (ViDAR), a novel 4D reconstruction framework that leverages personalised diffusion models to synthesise a pseudo multi-view supervision signal for training a Gaussian splatting representation. By conditioning on scene-specific features, ViDAR recovers fine-grained appearance details while mitigating artefacts introduced by monocular ambiguity. To address the spatio-temporal inconsistency of diffusion-based supervision, we propose a diffusion-aware loss function and a camera pose optimisation strategy that aligns synthetic views with the underlying scene geometry. Experiments on DyCheck, a challenging benchmark with extreme viewpoint variation, show that ViDAR outperforms all state-of-the-art baselines in visual quality and geometric consistency. We further highlight ViDAR's strong improvement over baselines on dynamic regions and provide a new benchmark to compare performance in reconstructing motion-rich parts of the scene. Project page: https://vidar-4d.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.17323", "title": "I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution", "authors": [ "Tamas Bisztray", "Bilel Cherif", "Richard A. Dubniczky", "Nils Gruschka", "Bertalan Borsos", "Mohamed Amine Ferrag", "Attila Kovacs", "Vasileios Mavroeidis", "Norbert Tihanyi" ], "github": "https://github.com/LLMauthorbench/LLMauthorbench", "project_page": "", "abstract": "Detecting AI-generated code, deepfakes, and other synthetic content is an emerging research challenge. As code generated by Large Language Models (LLMs) becomes more common, identifying the specific model behind each sample is increasingly important. This paper presents the first systematic study of LLM authorship attribution for C programs. We released CodeT5-Authorship, a novel model that uses only the encoder layers from the original CodeT5 encoder-decoder architecture, discarding the decoder to focus on classification. Our model's encoder output (first token) is passed through a two-layer classification head with GELU activation and dropout, producing a probability distribution over possible authors. To evaluate our approach, we introduce LLM-AuthorBench, a benchmark of 32,000 compilable C programs generated by eight state-of-the-art LLMs across diverse tasks. We compare our model to seven traditional ML classifiers and eight fine-tuned transformer models, including BERT, RoBERTa, CodeBERT, ModernBERT, DistilBERT, DeBERTa-V3, Longformer, and LoRA-fine-tuned Qwen2-1.5B. In binary classification, our model achieves 97.56% accuracy in distinguishing C programs generated by closely related models such as GPT-4.1 and GPT-4o, and 95.40% accuracy for multi-class attribution among five leading LLMs (Gemini 2.5 Flash, Claude 3.5 Haiku, GPT-4.1, Llama 3.3, and DeepSeek-V3). To support open science, we release the CodeT5-Authorship architecture, the LLM-AuthorBench benchmark, and all relevant Google Colab scripts on GitHub: https://github.com/LLMauthorbench/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LLMauthorbench/LLMauthorbench", "github_api_added_by": "user" }, { "date": "2025-06-24", "arxiv_id": "2506.16123", "title": "FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning", "authors": [ "Natapong Nitarach", "Warit Sirichotedumrong", "Panop Pitchayarthorn", "Pittawat Taveekitworachai", "Potsawee Manakul", "Kunat Pipatanakul" ], "github": "https://github.com/mermaid-js/mermaid", "project_page": "", "abstract": "This paper presents FinCoT, a structured chain-of-thought (CoT) prompting approach that incorporates insights from domain-specific expert financial reasoning to guide the reasoning traces of large language models. We investigate that there are three main prompting styles in FinNLP: (1) standard prompting--zero-shot prompting; (2) unstructured CoT--CoT prompting without an explicit reasoning structure, such as the use of tags; and (3) structured CoT prompting--CoT prompting with explicit instructions or examples that define structured reasoning steps. Previously, FinNLP has primarily focused on prompt engineering with either standard or unstructured CoT prompting. However, structured CoT prompting has received limited attention in prior work. Furthermore, the design of reasoning structures in structured CoT prompting is often based on heuristics from non-domain experts. In this study, we investigate each prompting approach in FinNLP. We evaluate the three main prompting styles and FinCoT on CFA-style questions spanning ten financial domains. We observe that FinCoT improves performance from 63.2% to 80.5% and Qwen-2.5-7B-Instruct from 69.7% to 74.2%, while reducing generated tokens eight-fold compared to structured CoT prompting. Our findings show that domain-aligned structured prompts not only improve performance and reduce inference costs but also yield more interpretable and expert-aligned reasoning traces.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mermaid-js/mermaid", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.16929", "title": "A deep learning and machine learning approach to predict neonatal death in the context of S\u00e3o Paulo", "authors": [ "Mohon Raihan", "Plabon Kumar Saha", "Rajan Das Gupta", "A Z M Tahmidul Kabir", "Afia Anjum Tamanna", "Md. Harun-Ur-Rashid", "Adnan Bin Abdus Salam", "Md Tanvir Anjum", "A Z M Ahteshamul Kabir" ], "github": "", "project_page": "", "abstract": "Neonatal death is still a concerning reality for underdeveloped and even some developed countries. Worldwide data indicate that 26.693 babies out of 1,000 births die, according to Macro Trades. To reduce this number, early prediction of endangered babies is crucial. Such prediction enables the opportunity to take ample care of the child and mother so that early child death can be avoided. In this context, machine learning was used to determine whether a newborn baby is at risk. To train the predictive model, historical data of 1.4 million newborns was used. Machine learning and deep learning techniques such as logical regression, K-nearest neighbor, random forest classifier, extreme gradient boosting (XGBoost), convolutional neural network, and long short-term memory (LSTM) were implemented using the dataset to identify the most accurate model for predicting neonatal mortality. Among the machine learning algorithms, XGBoost and random forest classifier achieved the best accuracy with 94%, while among the deep learning models, LSTM delivered the highest accuracy with 99%. Therefore, using LSTM appears to be the most suitable approach to predict whether precautionary measures for a child are necessary.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.16507", "title": "Robust Reward Modeling via Causal Rubrics", "authors": [ "Pragya Srivastava", "Harman Singh", "Rahul Madhavan", "Gandharv Patil", "Sravanti Addepalli", "Arun Suggala", "Rengarajan Aravamudhan", "Soumya Sharma", "Anirban Laha", "Aravindan Raghuveer", "Karthikeyan Shanmugam", "Doina Precup" ], "github": "", "project_page": "", "abstract": "Reward models (RMs) are fundamental to aligning Large Language Models (LLMs) via human feedback, yet they often suffer from reward hacking. They tend to latch on to superficial or spurious attributes, such as response length or formatting, mistaking these cues learned from correlations in training data for the true causal drivers of quality (e.g., factuality, relevance). This occurs because standard training objectives struggle to disentangle these factors, leading to brittle RMs and misaligned policies. We introduce Crome (Causally Robust Reward Modeling), a novel framework grounded in an explicit causal model designed to mitigate reward hacking. Crome employs the following synthetic targeted augmentations during training: (1) Causal Augmentations, which are pairs that differ along specific causal attributes, to enforce sensitivity along each causal attribute individually, and (2) Neutral Augmentations, which are tie-label pairs varying primarily in spurious attributes, to enforce invariance along spurious attributes. Notably, our augmentations are produced without any knowledge of spurious factors, via answer interventions only along causal rubrics, that are identified by querying an oracle LLM. Empirically, Crome significantly outperforms standard baselines on RewardBench, improving average accuracy by up to 5.4% and achieving gains of up to 13.2% and 7.2% in specific categories. The robustness of Crome is further testified by the consistent gains obtained in a Best-of-N inference setting across increasing N, across various benchmarks, including the popular RewardBench (covering chat, chat-hard, safety, and reasoning tasks), the safety-focused WildGuardTest, and the reasoning-specific GSM8k.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18901", "title": "From Virtual Games to Real-World Play", "authors": [ "Wenqiang Sun", "Fangyun Wei", "Jinjing Zhao", "Xi Chen", "Zilong Chen", "Hongyang Zhang", "Jun Zhang", "Yan Lu" ], "github": "https://github.com/wenqsun/Real-Play", "project_page": "", "abstract": "We introduce RealPlay, a neural network-based real-world game engine that enables interactive video generation from user control signals. Unlike prior works focused on game-style visuals, RealPlay aims to produce photorealistic, temporally consistent video sequences that resemble real-world footage. It operates in an interactive loop: users observe a generated scene, issue a control command, and receive a short video chunk in response. To enable such realistic and responsive generation, we address key challenges including iterative chunk-wise prediction for low-latency feedback, temporal consistency across iterations, and accurate control response. RealPlay is trained on a combination of labeled game data and unlabeled real-world videos, without requiring real-world action annotations. Notably, we observe two forms of generalization: (1) control transfer-RealPlay effectively maps control signals from virtual to real-world scenarios; and (2) entity transfer-although training labels originate solely from a car racing game, RealPlay generalizes to control diverse real-world entities, including bicycles and pedestrians, beyond vehicles. Project page can be found: https://wenqsun.github.io/RealPlay/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wenqsun/Real-Play", "github_api_added_by": "auto" }, { "date": "2025-06-24", "arxiv_id": "2506.18904", "title": "TC-Light: Temporally Consistent Relighting for Dynamic Long Videos", "authors": [ "Yang Liu", "Chuanchen Luo", "Zimo Tang", "Yingyan Li", "Yuran Yang", "Yuanyong Ning", "Lue Fan", "Junran Peng", "Zhaoxiang Zhang" ], "github": "", "project_page": "", "abstract": "Editing illumination in long videos with complex dynamics has significant value in various downstream tasks, including visual content creation and manipulation, as well as data scaling up for embodied AI through sim2real and real2real transfer. Nevertheless, existing video relighting techniques are predominantly limited to portrait videos or fall into the bottleneck of temporal consistency and computation efficiency. In this paper, we propose TC-Light, a novel paradigm characterized by the proposed two-stage post optimization mechanism. Starting from the video preliminarily relighted by an inflated video relighting model, it optimizes appearance embedding in the first stage to align global illumination. Then it optimizes the proposed canonical video representation, i.e., Unique Video Tensor (UVT), to align fine-grained texture and lighting in the second stage. To comprehensively evaluate performance, we also establish a long and highly dynamic video benchmark. Extensive experiments show that our method enables physically plausible relighting results with superior temporal coherence and low computation cost. The code and video demos are available at https://dekuliutesla.github.io/tclight/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18887", "title": "Steering Conceptual Bias via Transformer Latent-Subspace Activation", "authors": [ "Vansh Sharma", "Venkat Raman" ], "github": "", "project_page": "", "abstract": "This work examines whether activating latent subspaces in language models (LLMs) can steer scientific code generation toward a specific programming language. Five causal LLMs were first evaluated on scientific coding prompts to quantify their baseline bias among four programming languages. A static neuron-attribution method, perturbing the highest activated MLP weight for a C++ or CPP token, proved brittle and exhibited limited generalization across prompt styles and model scales. To address these limitations, a gradient-refined adaptive activation steering framework (G-ACT) was developed: per-prompt activation differences are clustered into a small set of steering directions, and lightweight per-layer probes are trained and refined online to select the appropriate steering vector. In LLaMA-3.2 3B, this approach reliably biases generation towards the CPP language by increasing the average probe classification accuracy by 15% and the early layers (0-6) improving the probe classification accuracy by 61.5% compared to the standard ACT framework. For LLaMA-3.3 70B, where attention-head signals become more diffuse, targeted injections at key layers still improve language selection. Although per-layer probing introduces a modest inference overhead, it remains practical by steering only a subset of layers and enables reproducible model behavior. These results demonstrate a scalable, interpretable and efficient mechanism for concept-level control for practical agentic systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18839", "title": "4Real-Video-V2: Fused View-Time Attention and Feedforward Reconstruction for 4D Scene Generation", "authors": [ "Chaoyang Wang", "Ashkan Mirzaei", "Vidit Goel", "Willi Menapace", "Aliaksandr Siarohin", "Avalon Vinella", "Michael Vasilkovsky", "Ivan Skorokhodov", "Vladislav Shakhrai", "Sergey Korolev", "Sergey Tulyakov", "Peter Wonka" ], "github": "", "project_page": "", "abstract": "We propose the first framework capable of computing a 4D spatio-temporal grid of video frames and 3D Gaussian particles for each time step using a feed-forward architecture. Our architecture has two main components, a 4D video model and a 4D reconstruction model. In the first part, we analyze current 4D video diffusion architectures that perform spatial and temporal attention either sequentially or in parallel within a two-stream design. We highlight the limitations of existing approaches and introduce a novel fused architecture that performs spatial and temporal attention within a single layer. The key to our method is a sparse attention pattern, where tokens attend to others in the same frame, at the same timestamp, or from the same viewpoint. In the second part, we extend existing 3D reconstruction algorithms by introducing a Gaussian head, a camera token replacement algorithm, and additional dynamic layers and training. Overall, we establish a new state of the art for 4D generation, improving both visual quality and reconstruction capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.17818", "title": "CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning", "authors": [ "Angelos-Nikolaos Kanatas", "Charilaos Papaioannou", "Alexandros Potamianos" ], "github": "", "project_page": "", "abstract": "Recent advances in music foundation models have improved audio representation learning, yet their effectiveness across diverse musical traditions remains limited. We introduce CultureMERT-95M, a multi-culturally adapted foundation model developed to enhance cross-cultural music representation learning and understanding. To achieve this, we propose a two-stage continual pre-training strategy that integrates learning rate re-warming and re-decaying, enabling stable adaptation even with limited computational resources. Training on a 650-hour multi-cultural data mix, comprising Greek, Turkish, and Indian music traditions, results in an average improvement of 4.9% in ROC-AUC and AP across diverse non-Western music auto-tagging tasks, surpassing prior state-of-the-art, with minimal forgetting on Western-centric benchmarks. We further investigate task arithmetic, an alternative approach to multi-cultural adaptation that merges single-culture adapted models in the weight space. Task arithmetic performs on par with our multi-culturally trained model on non-Western auto-tagging tasks and shows no regression on Western datasets. Cross-cultural evaluation reveals that single-culture models transfer with varying effectiveness across musical traditions, whereas the multi-culturally adapted model achieves the best overall performance. To support research on world music representation learning, we publicly release CultureMERT-95M and CultureMERT-TA-95M, fostering the development of more culturally aware music foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.17939", "title": "GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning", "authors": [ "Bo Liu", "Xiangyu Zhao", "Along He", "Yidi Chen", "Huazhu Fu", "Xiao-Ming Wu" ], "github": "", "project_page": "", "abstract": "Medical visual question answering aims to support clinical decision-making by enabling models to answer natural language questions based on medical images. While recent advances in multi-modal learning have significantly improved performance, current methods still suffer from limited answer reliability and poor interpretability, impairing the ability of clinicians and patients to understand and trust model-generated answers. To address this, this work first proposes a Thinking with Visual Grounding (ThinkVG) dataset wherein the answer generation is decomposed into intermediate reasoning steps that explicitly ground relevant visual regions of the medical image, thereby providing fine-grained explainability. Furthermore, we introduce a novel verifiable reward mechanism for reinforcement learning to guide post-training, improving the alignment between the model's reasoning process and its final answer. Remarkably, our method achieves comparable performance using only one-eighth of the training data, demonstrating the efficiency and effectiveness of the proposal. The dataset is available at https://huggingface.co/datasets/BoKelvin/GEMeX-ThinkVG.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.17671", "title": "TPTT: Transforming Pretrained Transformer into Titans", "authors": [ "Fabien Furfaro" ], "github": "https://github.com/fabienfrfr/tptt", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have led to remarkable progress in natural language processing, but their computational and memory demands remain a significant challenge, particularly for long-context inference. We introduce TPTT (Transforming Pretrained Transformer into Titans), a novel framework for enhancing pretrained Transformer models with efficient linearized attention mechanisms and advanced memory management. TPTT employs techniques such as Memory as Gate (MaG) and mixed linearized attention (LiZA). It is fully compatible with the Hugging Face Transformers library, enabling seamless adaptation of any causal LLM through parameter-efficient fine-tuning (LoRA) without full retraining. We show the effectiveness of TPTT on the MMLU benchmark with models of approximately 1 billion parameters, observing substantial improvements in both efficiency and accuracy. For instance, Titans-Llama-3.2-1B achieves a 20% increase in Exact Match (EM) over its baseline. Statistical analyses and comparisons with recent state-of-the-art methods confirm the practical scalability and robustness of TPTT. Code is available at https://github.com/fabienfrfr/tptt . Python package at https://pypi.org/project/tptt/ .", "github_manual": "", "github_regex": "https://github.com/fabienfrfr/tptt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18463", "title": "DIP: Unsupervised Dense In-Context Post-training of Visual Representations", "authors": [ "Sophia Sirko-Galouchenko", "Spyros Gidaris", "Antonin Vobecky", "Andrei Bursuc", "Nicolas Thome" ], "github": "https://github.com/sirkosophia/DIP", "project_page": "", "abstract": "We introduce DIP, a novel unsupervised post-training method designed to enhance dense image representations in large-scale pretrained vision encoders for in-context scene understanding. Unlike prior approaches that rely on complex self-distillation architectures, our method trains the vision encoder using pseudo-tasks that explicitly simulate downstream in-context scenarios, inspired by meta-learning principles. To enable post-training on unlabeled data, we propose an automatic mechanism for generating in-context tasks that combines a pretrained diffusion model and the vision encoder itself. DIP is simple, unsupervised, and computationally efficient, requiring less than 9 hours on a single A100 GPU. By learning dense representations through pseudo in-context tasks, it achieves strong performance across a wide variety of downstream real-world in-context scene understanding tasks. It outperforms both the initial vision encoder and prior methods, offering a practical and effective solution for improving dense representations. Code available here: https://github.com/sirkosophia/DIP", "github_manual": "", "github_regex": "https://github.com/sirkosophia/DIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18900", "title": "Audit & Repair: An Agentic Framework for Consistent Story Visualization in Text-to-Image Diffusion Models", "authors": [ "Kiymet Akdemir", "Tahira Kazimi", "Pinar Yanardag" ], "github": "", "project_page": "", "abstract": "Story visualization has become a popular task where visual scenes are generated to depict a narrative across multiple panels. A central challenge in this setting is maintaining visual consistency, particularly in how characters and objects persist and evolve throughout the story. Despite recent advances in diffusion models, current approaches often fail to preserve key character attributes, leading to incoherent narratives. In this work, we propose a collaborative multi-agent framework that autonomously identifies, corrects, and refines inconsistencies across multi-panel story visualizations. The agents operate in an iterative loop, enabling fine-grained, panel-level updates without re-generating entire sequences. Our framework is model-agnostic and flexibly integrates with a variety of diffusion models, including rectified flow transformers such as Flux and latent diffusion models such as Stable Diffusion. Quantitative and qualitative experiments show that our method outperforms prior approaches in terms of multi-panel consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.17871", "title": "How Alignment Shrinks the Generative Horizon", "authors": [ "Chenghao Yang", "Ari Holtzman" ], "github": "", "project_page": "", "abstract": "Despite their impressive capabilities, aligned large language models (LLMs) often generate outputs that lack diversity. What drives this stability in the generation? We investigate this phenomenon through the lens of probability concentration in the model's output distribution. To quantify this concentration, we introduce the Branching Factor (BF) -- a token-invariant measure of the effective number of plausible next steps during generation. Our empirical analysis reveals two key findings: (1) BF often decreases as generation progresses, suggesting that LLMs become more predictable as they generate. (2) alignment tuning substantially sharpens the model's output distribution from the outset, reducing BF by nearly an order of magnitude (e.g., from 12 to 1.2) relative to base models. This stark reduction helps explain why aligned models often appear less sensitive to decoding strategies. Building on this insight, we find this stability has surprising implications for complex reasoning. Aligned Chain-of-Thought (CoT) models (e.g., DeepSeek-distilled models), for instance, leverage this effect; by generating longer reasoning chains, they push generation into later, more deterministic (lower BF) stages, resulting in more stable outputs. We hypothesize that alignment tuning does not fundamentally change a model's behavior, but instead steers it toward stylistic tokens (e.g., \"Sure\") that unlock low-entropy trajectories already present in the base model. This view is supported by nudging experiments, which show that prompting base models with such tokens can similarly reduce BF. Together, our findings establish BF as a powerful diagnostic for understanding and controlling LLM outputs - clarifying how alignment reduces variability, how CoT promotes stable generations, and how base models can be steered away from diversity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18787", "title": "3D Arena: An Open Platform for Generative 3D Evaluation", "authors": [ "Dylan Ebert" ], "github": "", "project_page": "", "abstract": "Evaluating Generative 3D models remains challenging due to misalignment between automated metrics and human perception of quality. Current benchmarks rely on image-based metrics that ignore 3D structure or geometric measures that fail to capture perceptual appeal and real-world utility. To address this gap, we present 3D Arena, an open platform for evaluating image-to-3D generation models through large-scale human preference collection using pairwise comparisons. Since launching in June 2024, the platform has collected 123,243 votes from 8,096 users across 19 state-of-the-art models, establishing the largest human preference evaluation for Generative 3D. We contribute the iso3d dataset of 100 evaluation prompts and demonstrate quality control achieving 99.75% user authenticity through statistical fraud detection. Our ELO-based ranking system provides reliable model assessment, with the platform becoming an established evaluation resource. Through analysis of this preference data, we present insights into human preference patterns. Our findings reveal preferences for visual presentation features, with Gaussian splat outputs achieving a 16.6 ELO advantage over meshes and textured models receiving a 144.1 ELO advantage over untextured models. We provide recommendations for improving evaluation methods, including multi-criteria assessment, task-oriented evaluation, and format-aware comparison. The platform's community engagement establishes 3D Arena as a benchmark for the field while advancing understanding of human-centered evaluation in Generative 3D.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.15645", "title": "Demystifying the Visual Quality Paradox in Multimodal Large Language Models", "authors": [ "Shuo Xing", "Lanqing Guo", "Hongyuan Hua", "Seoyoung Lee", "Peiran Li", "Yufei Wang", "Zhangyang Wang", "Zhengzhong Tu" ], "github": "", "project_page": "", "abstract": "Recent Multimodal Large Language Models (MLLMs) excel on benchmark vision-language tasks, yet little is known about how input visual quality shapes their responses. Does higher perceptual quality of images already translate to better MLLM understanding? We conduct the first systematic study spanning leading MLLMs and a suite of vision-language benchmarks, applying controlled degradations and stylistic shifts to each image. Surprisingly, we uncover a visual-quality paradox: model, task, and even individual-instance performance can improve when images deviate from human-perceived fidelity. Off-the-shelf restoration pipelines fail to reconcile these idiosyncratic preferences. To close the gap, we introduce Visual-Quality Test-Time Tuning (VQ-TTT)-a lightweight adaptation module that: (1) inserts a learnable, low-rank kernel before the frozen vision encoder to modulate frequency content; and (2) fine-tunes only shallow vision-encoder layers via LoRA. VQ-TTT dynamically adjusts each input image in a single forward pass, aligning it with task-specific model preferences. Across the evaluated MLLMs and all datasets, VQ-TTT lifts significant average accuracy, with no external models, cached features, or extra training data. These findings redefine ``better'' visual inputs for MLLMs and highlight the need for adaptive, rather than universally ``clean'', imagery, in the new era of AI being the main data customer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18890", "title": "4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time", "authors": [ "Ziqiao Ma", "Xuweiyi Chen", "Shoubin Yu", "Sai Bi", "Kai Zhang", "Chen Ziwen", "Sihan Xu", "Jianing Yang", "Zexiang Xu", "Kalyan Sunkavalli", "Mohit Bansal", "Joyce Chai", "Hao Tan" ], "github": "", "project_page": "", "abstract": "Can we scale 4D pretraining to learn general space-time representations that reconstruct an object from a few views at some times to any view at any time? We provide an affirmative answer with 4D-LRM, the first large-scale 4D reconstruction model that takes input from unconstrained views and timestamps and renders arbitrary novel view-time combinations. Unlike prior 4D approaches, e.g., optimization-based, geometry-based, or generative, that struggle with efficiency, generalization, or faithfulness, 4D-LRM learns a unified space-time representation and directly predicts per-pixel 4D Gaussian primitives from posed image tokens across time, enabling fast, high-quality rendering at, in principle, infinite frame rate. Our results demonstrate that scaling spatiotemporal pretraining enables accurate and efficient 4D reconstruction. We show that 4D-LRM generalizes to novel objects, interpolates across time, and handles diverse camera setups. It reconstructs 24-frame sequences in one forward pass with less than 1.5 seconds on a single A100 GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.13905", "title": "Spec2RTL-Agent: Automated Hardware Code Generation from Complex Specifications Using LLM Agent Systems", "authors": [ "Zhongzhi Yu", "Mingjie Liu", "Michael Zimmer", "Yingyan Celine Lin", "Yong Liu", "Haoxing Ren" ], "github": "", "project_page": "", "abstract": "Despite recent progress in generating hardware RTL code with LLMs, existing solutions still suffer from a substantial gap between practical application scenarios and the requirements of real-world RTL code development. Prior approaches either focus on overly simplified hardware descriptions or depend on extensive human guidance to process complex specifications, limiting their scalability and automation potential. In this paper, we address this gap by proposing an LLM agent system, termed Spec2RTL-Agent, designed to directly process complex specification documentation and generate corresponding RTL code implementations, advancing LLM-based RTL code generation toward more realistic application settings. To achieve this goal, Spec2RTL-Agent introduces a novel multi-agent collaboration framework that integrates three key enablers: (1) a reasoning and understanding module that translates specifications into structured, step-by-step implementation plans; (2) a progressive coding and prompt optimization module that iteratively refines the code across multiple representations to enhance correctness and synthesisability for RTL conversion; and (3) an adaptive reflection module that identifies and traces the source of errors during generation, ensuring a more robust code generation flow. Instead of directly generating RTL from natural language, our system strategically generates synthesizable C++ code, which is then optimized for HLS. This agent-driven refinement ensures greater correctness and compatibility compared to naive direct RTL generation approaches. We evaluate Spec2RTL-Agent on three specification documents, showing it generates accurate RTL code with up to 75% fewer human interventions than existing methods. This highlights its role as the first fully automated multi-agent system for RTL generation from unstructured specs, reducing reliance on human effort in hardware design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-24", "arxiv_id": "2506.18369", "title": "RePIC: Reinforced Post-Training for Personalizing Multi-Modal Language Models", "authors": [ "Yeongtak Oh", "Jisoo Mok", "Dohyun Chung", "Juhyeon Shin", "Sangha Park", "Johan Barthelemy", "Sungroh Yoon" ], "github": "https://github.com/oyt9306/RePIC", "project_page": "", "abstract": "Recent multi-modal large language models (MLLMs) often struggle to generate personalized image captions, even when trained on high-quality captions. In this work, we observe that such limitations persist in existing post-training-based MLLM personalization methods. Specifically, despite being post-tuned with large-scale caption data through supervised fine-tuning (SFT), these models frequently fail to produce faithful descriptions in real-world scenarios, such as multi-concept image captioning. However, acquiring large-scale, high-quality captions for such complex settings is both costly and difficult. To address the data-centric nature of SFT, we propose a reinforcement learning (RL)-based post-training framework. To the best of our knowledge, this is the first RL-based approach to post-train MLLMs for personalized image captioning. Our method significantly enhances both visual recognition and personalized generation capabilities of MLLMs, and consistently outperforms existing SFT-based baselines, especially in the challenging multi-concept image captioning task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/oyt9306/RePIC", "github_api_added_by": "auto" }, { "date": "2025-06-24", "arxiv_id": "2506.19028", "title": "Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspective", "authors": [ "Weijie Xu", "Yiwen Wang", "Chi Xue", "Xiangkun Hu", "Xi Fang", "Guimin Dong", "Chandan K. Reddy" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) often generate responses with inherent biases, undermining their reliability in real-world applications. Existing evaluation methods often overlook biases in long-form responses and the intrinsic variability of LLM outputs. To address these challenges, we propose FiSCo(Fine-grained Semantic Computation), a novel statistical framework to evaluate group-level fairness in LLMs by detecting subtle semantic differences in long-form responses across demographic groups. Unlike prior work focusing on sentiment or token-level comparisons, FiSCo goes beyond surface-level analysis by operating at the claim level, leveraging entailment checks to assess the consistency of meaning across responses. We decompose model outputs into semantically distinct claims and apply statistical hypothesis testing to compare inter- and intra-group similarities, enabling robust detection of subtle biases. We formalize a new group counterfactual fairness definition and validate FiSCo on both synthetic and human-annotated datasets spanning gender, race, and age. Experiments show that FiSco more reliably identifies nuanced biases while reducing the impact of stochastic LLM variability, outperforming various evaluation metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19851", "title": "AnimaX: Animating the Inanimate in 3D with Joint Video-Pose Diffusion Models", "authors": [ "Zehuan Huang", "Haoran Feng", "Yangtian Sun", "Yuanchen Guo", "Yanpei Cao", "Lu Sheng" ], "github": "", "project_page": "", "abstract": "We present AnimaX, a feed-forward 3D animation framework that bridges the motion priors of video diffusion models with the controllable structure of skeleton-based animation. Traditional motion synthesis methods are either restricted to fixed skeletal topologies or require costly optimization in high-dimensional deformation spaces. In contrast, AnimaX effectively transfers video-based motion knowledge to the 3D domain, supporting diverse articulated meshes with arbitrary skeletons. Our method represents 3D motion as multi-view, multi-frame 2D pose maps, and enables joint video-pose diffusion conditioned on template renderings and a textual motion prompt. We introduce shared positional encodings and modality-aware embeddings to ensure spatial-temporal alignment between video and pose sequences, effectively transferring video priors to motion generation task. The resulting multi-view pose sequences are triangulated into 3D joint positions and converted into mesh animation via inverse kinematics. Trained on a newly curated dataset of 160,000 rigged sequences, AnimaX achieves state-of-the-art results on VBench in generalization, motion fidelity, and efficiency, offering a scalable solution for category-agnostic 3D animation. Project page: https://anima-x.github.io/{https://anima-x.github.io/}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19838", "title": "SimpleGVR: A Simple Baseline for Latent-Cascaded Video Super-Resolution", "authors": [ "Liangbin Xie", "Yu Li", "Shian Du", "Menghan Xia", "Xintao Wang", "Fanghua Yu", "Ziyan Chen", "Pengfei Wan", "Jiantao Zhou", "Chao Dong" ], "github": "", "project_page": "", "abstract": "Latent diffusion models have emerged as a leading paradigm for efficient video generation. However, as user expectations shift toward higher-resolution outputs, relying solely on latent computation becomes inadequate. A promising approach involves decoupling the process into two stages: semantic content generation and detail synthesis. The former employs a computationally intensive base model at lower resolutions, while the latter leverages a lightweight cascaded video super-resolution (VSR) model to achieve high-resolution output. In this work, we focus on studying key design principles for latter cascaded VSR models, which are underexplored currently. First, we propose two degradation strategies to generate training pairs that better mimic the output characteristics of the base model, ensuring alignment between the VSR model and its upstream generator. Second, we provide critical insights into VSR model behavior through systematic analysis of (1) timestep sampling strategies, (2) noise augmentation effects on low-resolution (LR) inputs. These findings directly inform our architectural and training innovations. Finally, we introduce interleaving temporal unit and sparse local attention to achieve efficient training and inference, drastically reducing computational overhead. Extensive experiments demonstrate the superiority of our framework over existing methods, with ablation studies confirming the efficacy of each design choice. Our work establishes a simple yet effective baseline for cascaded video super-resolution generation, offering practical insights to guide future advancements in efficient cascaded synthesis systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.16141", "title": "GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning", "authors": [ "Yi Chen", "Yuying Ge", "Rui Wang", "Yixiao Ge", "Junhao Cheng", "Ying Shan", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Recent reinforcement learning approaches, such as outcome-supervised GRPO, have advanced Chain-of-Thought reasoning in large language models (LLMs), yet their adaptation to multimodal LLMs (MLLMs) is unexplored. To address the lack of rigorous evaluation for MLLM post-training methods, we introduce SEED-Bench-R1, a benchmark with complex real-world videos requiring balanced perception and reasoning. It offers a large training set and evaluates generalization across three escalating challenges: in-distribution, cross-environment, and cross-environment-task scenarios. Using SEED-Bench-R1, we find that standard GRPO, while improving answer accuracy, often reduces logical coherence between reasoning steps and answers, with only a 57.9% consistency rate. This stems from reward signals focusing solely on final answers, encouraging shortcuts, and strict KL penalties limiting exploration.To address this, we propose GRPO-CARE, a consistency-aware RL framework optimizing both answer correctness and reasoning coherence without explicit supervision. GRPO-CARE introduces a two-tiered reward: (1) a base reward for answer correctness, and (2) an adaptive consistency bonus, computed by comparing the model's reasoning-to-answer likelihood (via a slowly-evolving reference model) against group peers.This dual mechanism amplifies rewards for reasoning paths that are both correct and logically consistent. Replacing KL penalties with this adaptive bonus, GRPO-CARE outperforms standard GRPO on SEED-Bench-R1, achieving a 6.7% performance gain on the hardest evaluation level and a 24.5% improvement in consistency. It also shows strong transferability, improving model performance across diverse video understanding benchmarks. Our work contributes a systematically designed benchmark and a generalizable post-training framework, advancing the development of more interpretable and robust MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19767", "title": "SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning", "authors": [ "Yuqian Fu", "Tinghong Chen", "Jiajun Chai", "Xihuai Wang", "Songjun Tu", "Guojun Yin", "Wei Lin", "Qichao Zhang", "Yuanheng Zhu", "Dongbin Zhao" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have achieved remarkable progress in reasoning tasks, yet the optimal integration of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) remains a fundamental challenge. Through comprehensive analysis of token distributions, learning dynamics, and integration mechanisms from entropy-based perspectives, we reveal key differences between these paradigms: SFT induces coarse-grained global changes to LLM policy distributions, while RL performs fine-grained selective optimizations, with entropy serving as a critical indicator of training effectiveness. Building on these observations, we propose Supervised Reinforcement Fine-Tuning (SRFT), a single-stage method that unifies both fine-tuning paradigms through entropy-aware weighting mechanisms. Our approach simultaneously applies SFT and RL to directly optimize the LLM using demonstrations and self-exploration rollouts rather than through two-stage sequential methods. Extensive experiments show that SRFT achieves 59.1% average accuracy, outperforming zero-RL methods by 9.0% on five mathematical reasoning benchmarks and 10.9% on three out-of-distribution benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19290", "title": "Skywork-SWE: Unveiling Data Scaling Laws for Software Engineering in LLMs", "authors": [ "Liang Zeng", "Yongcong Li", "Yuzhen Xiao", "Changshi Li", "Chris Yuhao Liu", "Rui Yan", "Tianwen Wei", "Jujie He", "Xuchen Song", "Yang Liu", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "Software engineering (SWE) has recently emerged as a crucial testbed for next-generation LLM agents, demanding inherent capabilities in two critical dimensions: sustained iterative problem-solving (e.g., >50 interaction rounds) and long-context dependency resolution (e.g., >32k tokens). However, the data curation process in SWE remains notoriously time-consuming, as it heavily relies on manual annotation for code file filtering and the setup of dedicated runtime environments to execute and validate unit tests. Consequently, most existing datasets are limited to only a few thousand GitHub-sourced instances. To this end, we propose an incremental, automated data-curation pipeline that systematically scales both the volume and diversity of SWE datasets. Our dataset comprises 10,169 real-world Python task instances from 2,531 distinct GitHub repositories, each accompanied by a task specified in natural language and a dedicated runtime-environment image for automated unit-test validation. We have carefully curated over 8,000 successfully runtime-validated training trajectories from our proposed SWE dataset. When fine-tuning the Skywork-SWE model on these trajectories, we uncover a striking data scaling phenomenon: the trained model's performance for software engineering capabilities in LLMs continues to improve as the data size increases, showing no signs of saturation. Notably, our Skywork-SWE model achieves 38.0% pass@1 accuracy on the SWE-bench Verified benchmark without using verifiers or multiple rollouts, establishing a new state-of-the-art (SOTA) among the Qwen2.5-Coder-32B-based LLMs built on the OpenHands agent framework. Furthermore, with the incorporation of test-time scaling techniques, the performance further improves to 47.0% accuracy, surpassing the previous SOTA results for sub-32B parameter models. We release the Skywork-SWE-32B model checkpoint to accelerate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.18843", "title": "USAD: Universal Speech and Audio Representation via Distillation", "authors": [ "Heng-Jui Chang", "Saurabhchand Bhati", "James Glass", "Alexander H. Liu" ], "github": "", "project_page": "", "abstract": "Self-supervised learning (SSL) has revolutionized audio representations, yet models often remain domain-specific, focusing on either speech or non-speech tasks. In this work, we present Universal Speech and Audio Distillation (USAD), a unified approach to audio representation learning that integrates diverse audio types - speech, sound, and music - into a single model. USAD employs efficient layer-to-layer distillation from domain-specific SSL models to train a student on a comprehensive audio dataset. USAD offers competitive performance across various benchmarks and datasets, including frame and instance-level speech processing tasks, audio tagging, and sound classification, achieving near state-of-the-art results with a single encoder on SUPERB and HEAR benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19794", "title": "Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study", "authors": [ "Yuqi Zhu", "Yi Zhong", "Jintian Zhang", "Ziheng Zhang", "Shuofei Qiao", "Yujie Luo", "Lun Du", "Da Zheng", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/DataMind", "project_page": "", "abstract": "Large Language Models (LLMs) hold promise in automating data analysis tasks, yet open-source models face significant limitations in these kinds of reasoning-intensive scenarios. In this work, we investigate strategies to enhance the data analysis capabilities of open-source LLMs. By curating a seed dataset of diverse, realistic scenarios, we evaluate models across three dimensions: data understanding, code generation, and strategic planning. Our analysis reveals three key findings: (1) Strategic planning quality serves as the primary determinant of model performance; (2) Interaction design and task complexity significantly influence reasoning capabilities; (3) Data quality demonstrates a greater impact than diversity in achieving optimal performance. We leverage these insights to develop a data synthesis methodology, demonstrating significant improvements in open-source LLMs' analytical reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zjunlp/DataMind", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19807", "title": "KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality", "authors": [ "Baochang Ren", "Shuofei Qiao", "Wenhao Yu", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/KnowRL", "project_page": "", "abstract": "Large Language Models (LLMs), particularly slow-thinking models, often exhibit severe hallucination, outputting incorrect content due to an inability to accurately recognize knowledge boundaries during reasoning. While Reinforcement Learning (RL) can enhance complex reasoning abilities, its outcome-oriented reward mechanism often lacks factual supervision over the thinking process, further exacerbating the hallucination problem. To address the high hallucination in slow-thinking models, we propose Knowledge-enhanced RL, KnowRL. KnowRL guides models to perform fact-based slow thinking by integrating a factuality reward, based on knowledge verification, into the RL training process, helping them recognize their knowledge boundaries. KnowRL guides models to perform fact-based slow thinking by integrating a factuality reward, based on knowledge verification, into the RL training process, helping them recognize their knowledge boundaries. This targeted factual input during RL training enables the model to learn and internalize fact-based reasoning strategies. By directly rewarding adherence to facts within the reasoning steps, KnowRL fosters a more reliable thinking process. Experimental results on three hallucination evaluation datasets and two reasoning evaluation datasets demonstrate that KnowRL effectively mitigates hallucinations in slow-thinking models while maintaining their original strong reasoning capabilities. Our code is available at https://github.com/zjunlp/KnowRL.", "github_manual": "", "github_regex": "https://github.com/zjunlp/KnowRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.17612", "title": "JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent", "authors": [ "Yunlong Lin", "Zixu Lin", "Kunjie Lin", "Jinbin Bai", "Panwang Pan", "Chenxin Li", "Haoyu Chen", "Zhongdao Wang", "Xinghao Ding", "Wenbo Li", "Shuicheng Yan" ], "github": "https://github.com/Deep-Agent/R1-V", "project_page": "", "abstract": "Photo retouching has become integral to contemporary visual storytelling, enabling users to capture aesthetics and express creativity. While professional tools such as Adobe Lightroom offer powerful capabilities, they demand substantial expertise and manual effort. In contrast, existing AI-based solutions provide automation but often suffer from limited adjustability and poor generalization, failing to meet diverse and personalized editing needs. To bridge this gap, we introduce JarvisArt, a multi-modal large language model (MLLM)-driven agent that understands user intent, mimics the reasoning process of professional artists, and intelligently coordinates over 200 retouching tools within Lightroom. JarvisArt undergoes a two-stage training process: an initial Chain-of-Thought supervised fine-tuning to establish basic reasoning and tool-use skills, followed by Group Relative Policy Optimization for Retouching (GRPO-R) to further enhance its decision-making and tool proficiency. We also propose the Agent-to-Lightroom Protocol to facilitate seamless integration with Lightroom. To evaluate performance, we develop MMArt-Bench, a novel benchmark constructed from real-world user edits. JarvisArt demonstrates user-friendly interaction, superior generalization, and fine-grained control over both global and local adjustments, paving a new avenue for intelligent photo retouching. Notably, it outperforms GPT-4o with a 60% improvement in average pixel-level metrics on MMArt-Bench for content fidelity, while maintaining comparable instruction-following capabilities. Project Page: https://jarvisart.vercel.app/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Deep-Agent/R1-V", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19848", "title": "ScaleCap: Inference-Time Scalable Image Captioning via Dual-Modality Debiasing", "authors": [ "Long Xing", "Qidong Huang", "Xiaoyi Dong", "Pan Zhang", "Yuhang Zang", "Yuhang Cao", "Jinsong Li", "Shuangrui Ding", "Weiming Zhang", "Nenghai Yu", "Jiaqi Wang", "Feng Wu", "Dahua Lin" ], "github": "https://github.com/Cooperx521/ScaleCap", "project_page": "", "abstract": "This paper presents ScaleCap, an inference-time scalable image captioning strategy that generates comprehensive and detailed image captions. The key challenges of high-quality image captioning lie in the inherent biases of LVLMs: multimodal bias resulting in imbalanced descriptive granularity, offering detailed accounts of some elements while merely skimming over others; linguistic bias leading to hallucinated descriptions of non-existent objects. To address these issues, we propose a scalable debiased captioning strategy, which continuously enriches and calibrates the caption with increased inference budget. Specifically, we propose two novel components: heuristic question answering and contrastive sentence rating. The former generates content-specific questions based on the image and answers them to progressively inject relevant information into the caption. The latter employs sentence-level offline contrastive decoding to effectively identify and eliminate hallucinations caused by linguistic biases. With increased inference cost, more heuristic questions are raised by ScaleCap to progressively capture additional visual details, generating captions that are more accurate, balanced, and informative. Extensive modality alignment experiments demonstrate the effectiveness of ScaleCap. Annotating 450K images with ScaleCap and using them for LVLM pretraining leads to consistent performance gains across 11 widely used benchmarks. Furthermore, ScaleCap showcases superb richness and fidelity of generated captions with two additional tasks: replacing images with captions in VQA task, and reconstructing images from captions to assess semantic coverage. Code is available at https://github.com/Cooperx521/ScaleCap.", "github_manual": "", "github_regex": "https://github.com/Cooperx521/ScaleCap", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.14012", "title": "Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text", "authors": [ "Amr Mohamed", "Yang Zhang", "Michalis Vazirgiannis", "Guokan Shang" ], "github": "https://github.com/amr-mohamedd/lost-in-the-mix", "project_page": "", "abstract": "Code-switching (CSW) is the act of alternating between two or more languages within a single discourse. This phenomenon is widespread in multilingual communities, and increasingly prevalent in online content, where users naturally mix languages in everyday communication. As a result, Large Language Models (LLMs), now central to content processing and generation, are frequently exposed to code-switched inputs. Given their widespread use, it is crucial to understand how LLMs process and reason about such mixed-language text. This paper presents a systematic evaluation of LLM comprehension under code-switching by generating CSW variants of established reasoning and comprehension benchmarks. While degradation is evident when foreign tokens disrupt English textx2013even under linguistic constraintsx2013embedding English into other languages often improves comprehension. Though prompting yields mixed results, fine-tuning offers a more stable path to degradation mitigation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amr-mohamedd/lost-in-the-mix", "github_api_added_by": "auto" }, { "date": "2025-06-25", "arxiv_id": "2506.19713", "title": "Guidance in the Frequency Domain Enables High-Fidelity Sampling at Low CFG Scales", "authors": [ "Seyedmorteza Sadat", "Tobias Vontobel", "Farnood Salehi", "Romann M. Weber" ], "github": "", "project_page": "", "abstract": "Classifier-free guidance (CFG) has become an essential component of modern conditional diffusion models. Although highly effective in practice, the underlying mechanisms by which CFG enhances quality, detail, and prompt alignment are not fully understood. We present a novel perspective on CFG by analyzing its effects in the frequency domain, showing that low and high frequencies have distinct impacts on generation quality. Specifically, low-frequency guidance governs global structure and condition alignment, while high-frequency guidance mainly enhances visual fidelity. However, applying a uniform scale across all frequencies -- as is done in standard CFG -- leads to oversaturation and reduced diversity at high scales and degraded visual quality at low scales. Based on these insights, we propose frequency-decoupled guidance (FDG), an effective approach that decomposes CFG into low- and high-frequency components and applies separate guidance strengths to each component. FDG improves image quality at low guidance scales and avoids the drawbacks of high CFG scales by design. Through extensive experiments across multiple datasets and models, we demonstrate that FDG consistently enhances sample fidelity while preserving diversity, leading to improved FID and recall compared to CFG, establishing our method as a plug-and-play alternative to standard classifier-free guidance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.18951", "title": "SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications", "authors": [ "Jinyang Li", "Xiaolong Li", "Ge Qu", "Per Jacobsson", "Bowen Qin", "Binyuan Hui", "Shuzheng Si", "Nan Huo", "Xiaohan Xu", "Yue Zhang", "Ziwei Tang", "Yuanshuai Li", "Florensia Widjaja", "Xintong Zhu", "Feige Zhou", "Yongfeng Huang", "Yannis Papakonstantinou", "Fatma Ozcan", "Chenhao Ma", "Reynold Cheng" ], "github": "https://github.com/hiyouga/LLaMA-Factory", "project_page": "", "abstract": "Resolution of complex SQL issues persists as a significant bottleneck in real-world database applications. Current Large Language Models (LLMs), while adept at text-to-SQL translation, have not been rigorously evaluated on the more challenging task of debugging SQL issues. To address this gap, we introduce BIRD-CRITIC, a new SQL issue debugging benchmark comprising 530 PostgreSQL tasks (BIRD-CRITIC-PG) and 570 multi-dialect tasks (BIRD-CRITIC-Multi), distilled from authentic user issues and replayed within new environments to facilitate rigorous evaluation. Baseline evaluations underscore the task's complexity, with the leading reasoning model O3-Mini achieving only 38.87% success rate on BIRD-CRITIC-PG and 33.33% on BIRD-CRITIC-Multi. Meanwhile, advancing open-source models for database tasks is crucial for empowering local development while safeguarding data privacy. Therefore, we present Six-Gym (Sql-fIX-Gym), a training environment for elevating open-source model capabilities for SQL issue debugging. This environment leverages SQL-Rewind strategy, which automatically generates executable issue-solution datasets by reverse-engineering issues from verified SQLs. However, popular trajectory-based fine-tuning methods do not explore substantial supervisory signals. We further propose f-Plan Boosting, which extracts high-level debugging plans from SQL solutions, enabling teacher LLMs to produce 73.7% more successful trajectories for training. We integrate these components into an open-source agent, Bird-Fixer. Based on Qwen-2.5-Coder-14B, Bird-Fixer achieves 38.11% success rate on BIRD-CRITIC-PG and 29.65% on BIRD-CRITIC-Multi, surpassing leading proprietary models such as Claude-3.7-Sonnet and GPT-4.1, marking a significant step toward democratizing sophisticated SQL-debugging capabilities. The leaderboard and source code are available: https://bird-critic.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hiyouga/LLaMA-Factory", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19850", "title": "Unified Vision-Language-Action Model", "authors": [ "Yuqi Wang", "Xinghang Li", "Wenxuan Wang", "Junbo Zhang", "Yingyan Li", "Yuntao Chen", "Xinlong Wang", "Zhaoxiang Zhang" ], "github": "", "project_page": "", "abstract": "Vision-language-action models (VLAs) have garnered significant attention for their potential in advancing robotic manipulation. However, previous approaches predominantly rely on the general comprehension capabilities of vision-language models (VLMs) to generate action signals, often overlooking the rich temporal and causal structure embedded in visual observations. In this paper, we present UniVLA, a unified and native multimodal VLA model that autoregressively models vision, language, and action signals as discrete token sequences. This formulation enables flexible multimodal tasks learning, particularly from large-scale video data. By incorporating world modeling during post-training, UniVLA captures causal dynamics from videos, facilitating effective transfer to downstream policy learning--especially for long-horizon tasks. Our approach sets new state-of-the-art results across several widely used simulation benchmarks, including CALVIN, LIBERO, and Simplenv-Bridge, significantly surpassing previous methods. For example, UniVLA achieves 95.5% average success rate on LIBERO benchmark, surpassing pi0-FAST's 85.5%. We further demonstrate its broad applicability on real-world ALOHA manipulation and autonomous driving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.18701", "title": "Matrix-Game: Interactive World Foundation Model", "authors": [ "Yifan Zhang", "Chunli Peng", "Boyang Wang", "Puyi Wang", "Qingcheng Zhu", "Fei Kang", "Biao Jiang", "Zedong Gao", "Eric Li", "Yang Liu", "Yahui Zhou" ], "github": "https://github.com/SkyworkAI/Matrix-Game", "project_page": "", "abstract": "We introduce Matrix-Game, an interactive world foundation model for controllable game world generation. Matrix-Game is trained using a two-stage pipeline that first performs large-scale unlabeled pretraining for environment understanding, followed by action-labeled training for interactive video generation. To support this, we curate Matrix-Game-MC, a comprehensive Minecraft dataset comprising over 2,700 hours of unlabeled gameplay video clips and over 1,000 hours of high-quality labeled clips with fine-grained keyboard and mouse action annotations. Our model adopts a controllable image-to-world generation paradigm, conditioned on a reference image, motion context, and user actions. With over 17 billion parameters, Matrix-Game enables precise control over character actions and camera movements, while maintaining high visual quality and temporal coherence. To evaluate performance, we develop GameWorld Score, a unified benchmark measuring visual quality, temporal quality, action controllability, and physical rule understanding for Minecraft world generation. Extensive experiments show that Matrix-Game consistently outperforms prior open-source Minecraft world models (including Oasis and MineWorld) across all metrics, with particularly strong gains in controllability and physical consistency. Double-blind human evaluations further confirm the superiority of Matrix-Game, highlighting its ability to generate perceptually realistic and precisely controllable videos across diverse game scenarios. To facilitate future research on interactive image-to-world generation, we will open-source the Matrix-Game model weights and the GameWorld Score benchmark at https://github.com/SkyworkAI/Matrix-Game.", "github_manual": "", "github_regex": "https://github.com/SkyworkAI/Matrix-Game", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.16095", "title": "Intelligent Operation and Maintenance and Prediction Model Optimization for Improving Wind Power Generation Efficiency", "authors": [ "Xun Liu", "Xiaobin Wu", "Jiaqi He", "Rajan Das Gupta" ], "github": "", "project_page": "", "abstract": "This study explores the effectiveness of predictive maintenance models and the optimization of intelligent Operation and Maintenance (O&M) systems in improving wind power generation efficiency. Through qualitative research, structured interviews were conducted with five wind farm engineers and maintenance managers, each with extensive experience in turbine operations. Using thematic analysis, the study revealed that while predictive maintenance models effectively reduce downtime by identifying major faults, they often struggle with detecting smaller, gradual failures. Key challenges identified include false positives, sensor malfunctions, and difficulties in integrating new models with older turbine systems. Advanced technologies such as digital twins, SCADA systems, and condition monitoring have significantly enhanced turbine maintenance practices. However, these technologies still require improvements, particularly in AI refinement and real-time data integration. The findings emphasize the need for continuous development to fully optimize wind turbine performance and support the broader adoption of renewable energy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19467", "title": "Can Large Language Models Capture Human Annotator Disagreements?", "authors": [ "Jingwei Ni", "Yu Fan", "Vil\u00e9m Zouhar", "Donya Rooein", "Alexander Hoyle", "Mrinmaya Sachan", "Markus Leippold", "Dirk Hovy", "Elliott Ash" ], "github": "https://github.com/EdisonNi-hku/Disagreement_Prediction", "project_page": "", "abstract": "Human annotation variation (i.e., annotation disagreements) is common in NLP and often reflects important information such as task subjectivity and sample ambiguity. While Large Language Models (LLMs) are increasingly used for automatic annotation to reduce human effort, their evaluation often focuses on predicting the majority-voted \"ground truth\" labels. It is still unclear, however, whether these models also capture informative human annotation variation. Our work addresses this gap by extensively evaluating LLMs' ability to predict annotation disagreements without access to repeated human labels. Our results show that LLMs struggle with modeling disagreements, which can be overlooked by majority label-based evaluations. Notably, while RLVR-style (Reinforcement learning with verifiable rewards) reasoning generally boosts LLM performance, it degrades performance in disagreement prediction. Our findings highlight the critical need for evaluating and improving LLM annotators in disagreement modeling. Code and data at https://github.com/EdisonNi-hku/Disagreement_Prediction.", "github_manual": "", "github_regex": "https://github.com/EdisonNi-hku/Disagreement_Prediction", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19433", "title": "Mem4Nav: Boosting Vision-and-Language Navigation in Urban Environments with a Hierarchical Spatial-Cognition Long-Short Memory System", "authors": [ "Lixuan He", "Haoyu Dong", "Zhenxing Chen", "Yangcheng Yu", "Jie Feng", "Yong Li" ], "github": "https://github.com/tsinghua-fib-lab/Mem4Nav", "project_page": "", "abstract": "Vision-and-Language Navigation (VLN) in large-scale urban environments requires embodied agents to ground linguistic instructions in complex scenes and recall relevant experiences over extended time horizons. Prior modular pipelines offer interpretability but lack unified memory, while end-to-end (M)LLM agents excel at fusing vision and language yet remain constrained by fixed context windows and implicit spatial reasoning. We introduce Mem4Nav, a hierarchical spatial-cognition long-short memory system that can augment any VLN backbone. Mem4Nav fuses a sparse octree for fine-grained voxel indexing with a semantic topology graph for high-level landmark connectivity, storing both in trainable memory tokens embedded via a reversible Transformer. Long-term memory (LTM) compresses and retains historical observations at both octree and graph nodes, while short-term memory (STM) caches recent multimodal entries in relative coordinates for real-time obstacle avoidance and local planning. At each step, STM retrieval sharply prunes dynamic context, and, when deeper history is needed, LTM tokens are decoded losslessly to reconstruct past embeddings. Evaluated on Touchdown and Map2Seq across three backbones (modular, state-of-the-art VLN with prompt-based LLM, and state-of-the-art VLN with strided-attention MLLM), Mem4Nav yields 7-13 pp gains in Task Completion, sufficient SPD reduction, and >10 pp nDTW improvement. Ablations confirm the indispensability of both the hierarchical map and dual memory modules. Our codes are open-sourced via https://github.com/tsinghua-fib-lab/Mem4Nav.", "github_manual": "", "github_regex": "https://github.com/tsinghua-fib-lab/Mem4Nav", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19847", "title": "Orthogonal Finetuning Made Scalable", "authors": [ "Zeju Qiu", "Weiyang Liu", "Adrian Weller", "Bernhard Sch\u00f6lkopf" ], "github": "", "project_page": "", "abstract": "Orthogonal finetuning (OFT) offers highly parameter-efficient adaptation while preventing catastrophic forgetting, but its high runtime and memory demands limit practical deployment. We identify the core computational bottleneck in OFT as its weight-centric implementation, which relies on costly matrix-matrix multiplications with cubic complexity. To overcome this, we propose OFTv2, an input-centric reformulation that instead uses matrix-vector multiplications (i.e., matrix-free computation), reducing the computational cost to quadratic. We further introduce the Cayley-Neumann parameterization, an efficient orthogonal parameterization that approximates the matrix inversion in Cayley transform via a truncated Neumann series. These modifications allow OFTv2 to achieve up to 10x faster training and 3x lower GPU memory usage without compromising performance. In addition, we extend OFTv2 to support finetuning quantized foundation models and show that it outperforms the popular QLoRA in training stability, efficiency, and memory usage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19830", "title": "Scaling Speculative Decoding with Lookahead Reasoning", "authors": [ "Yichao Fu", "Rui Ge", "Zelei Shao", "Zhijie Deng", "Hao Zhang" ], "github": "https://github.com/hao-ai-lab/LookaheadReasoning", "project_page": "", "abstract": "Reasoning models excel by generating long chain-of-thoughts, but decoding the resulting thousands of tokens is slow. Token-level speculative decoding (SD) helps, but its benefit is capped, because the chance that an entire gamma-token guess is correct falls exponentially as gamma grows. This means allocating more compute for longer token drafts faces an algorithmic ceiling -- making the speedup modest and hardware-agnostic. We raise this ceiling with Lookahead Reasoning, which exploits a second, step-level layer of parallelism. Our key insight is that reasoning models generate step-by-step, and each step needs only to be semantically correct, not exact token matching. In Lookahead Reasoning, a lightweight draft model proposes several future steps; the target model expands each proposal in one batched pass, and a verifier keeps semantically correct steps while letting the target regenerate any that fail. Token-level SD still operates within each reasoning step, so the two layers of parallelism multiply. We show Lookahead Reasoning lifts the peak speedup of SD both theoretically and empirically. Across GSM8K, AIME, and other benchmarks, Lookahead Reasoning improves the speedup of SD from 1.4x to 2.1x while preserving answer quality, and its speedup scales better with additional GPU throughput. Our code is available at https://github.com/hao-ai-lab/LookaheadReasoning", "github_manual": "", "github_regex": "https://github.com/hao-ai-lab/LookaheadReasoning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.19839", "title": "Improving Progressive Generation with Decomposable Flow Matching", "authors": [ "Moayed Haji-Ali", "Willi Menapace", "Ivan Skorokhodov", "Arpit Sahni", "Sergey Tulyakov", "Vicente Ordonez", "Aliaksandr Siarohin" ], "github": "", "project_page": "", "abstract": "Generating high-dimensional visual modalities is a computationally intensive task. A common solution is progressive generation, where the outputs are synthesized in a coarse-to-fine spectral autoregressive manner. While diffusion models benefit from the coarse-to-fine nature of denoising, explicit multi-stage architectures are rarely adopted. These architectures have increased the complexity of the overall approach, introducing the need for a custom diffusion formulation, decomposition-dependent stage transitions, add-hoc samplers, or a model cascade. Our contribution, Decomposable Flow Matching (DFM), is a simple and effective framework for the progressive generation of visual media. DFM applies Flow Matching independently at each level of a user-defined multi-scale representation (such as Laplacian pyramid). As shown by our experiments, our approach improves visual quality for both images and videos, featuring superior results compared to prior multistage frameworks. On Imagenet-1k 512px, DFM achieves 35.2% improvements in FDD scores over the base architecture and 26.4% over the best-performing baseline, under the same training compute. When applied to finetuning of large models, such as FLUX, DFM shows faster convergence speed to the training distribution. Crucially, all these advantages are achieved with a single model, architectural simplicity, and minimal modifications to existing training pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.18945", "title": "Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models", "authors": [ "Zihan Wang", "Rui Pan", "Jiarui Yao", "Robert Csordas", "Linjie Li", "Lu Yin", "Jiajun Wu", "Tong Zhang", "Manling Li", "Shiwei Liu" ], "github": "https://github.com/ZihanWang314/coe", "project_page": "", "abstract": "We propose Chain-of-Experts (CoE), a new Mixture-of-Experts (MoE) architecture that introduces sequential expert communication within each layer. Unlike traditional MoE models, where experts operate independently in parallel, CoE processes tokens iteratively across a chain of experts inside a layer. To support dynamic expert selection across iterations, CoE employs a dedicated router at each iteration step within a layer. This design allows tokens to re-evaluate and select different experts during each iteration, rather than being statically assigned. As a result, CoE introduces a flexible routing mechanism that increases the diversity of expert combinations and enriches the model's representational capacity. CoE demonstrates improved performance under fixed compute: on math reasoning tasks, it reduces validation loss from 1.20 to 1.12 compared to a standard MoE. Beyond performance, CoE offers a new scaling axis: depth through expert iteration, which complements conventional width/depth scaling. For example, using 2x iterations matches the performance of 3x expert selections (in width), while reducing memory usage by 17.6-42% relative to other scaling strategies. Our analysis reveals that CoE's benefits stem from its iterative residual structure and enhanced expert specialization empowered by iterative routing, which together unlock more expressive representations. Code is available at https://github.com/ZihanWang314/coe.", "github_manual": "", "github_regex": "https://github.com/ZihanWang314/coe", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-25", "arxiv_id": "2506.20670", "title": "MMSearch-R1: Incentivizing LMMs to Search", "authors": [ "Jinming Wu", "Zihao Deng", "Wei Li", "Yiding Liu", "Bo You", "Bo Li", "Zejun Ma", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Robust deployment of large multimodal models (LMMs) in real-world scenarios requires access to external knowledge sources, given the complexity and dynamic nature of real-world information. Existing approaches such as retrieval-augmented generation (RAG) and prompt engineered search agents rely on rigid pipelines, often leading to inefficient or excessive search behaviors. We present MMSearch-R1, the first end-to-end reinforcement learning framework that enables LMMs to perform on-demand, multi-turn search in real-world Internet environments. Our framework integrates both image and text search tools, allowing the model to reason about when and how to invoke them guided by an outcome-based reward with a search penalty. To support training, We collect a multimodal search VQA dataset through a semi-automated pipeline that covers diverse visual and textual knowledge needs and curate a search-balanced subset with both search-required and search-free samples, which proves essential for shaping efficient and on-demand search behavior. Extensive experiments on knowledge-intensive and info-seeking VQA tasks show that our model not only outperforms RAG-based baselines of the same model size, but also matches the performance of a larger RAG-based model while reducing search calls by over 30%. We further analyze key empirical findings to offer actionable insights for advancing research in multimodal search.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.19697", "title": "Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models", "authors": [ "Jungwoo Park", "Taewhoo Lee", "Chanwoong Yoon", "Hyeon Hwang", "Jaewoo Kang" ], "github": "https://github.com/dmis-lab/Outlier-Safe-Pre-Training", "project_page": "", "abstract": "Extreme activation outliers in Large Language Models (LLMs) critically degrade quantization performance, hindering efficient on-device deployment. While channel-wise operations and adaptive gradient scaling are recognized causes, practical mitigation remains challenging. We introduce Outlier-Safe Pre-Training (OSP), a practical guideline that proactively prevents outlier formation rather than relying on post-hoc mitigation. OSP combines three key innovations: (1) the Muon optimizer, eliminating privileged bases while maintaining training efficiency; (2) Single-Scale RMSNorm, preventing channel-wise amplification; and (3) a learnable embedding projection, redistributing activation magnitudes originating from embedding matrices. We validate OSP by training a 1.4B-parameter model on 1 trillion tokens, which is the first production-scale LLM trained without such outliers. Under aggressive 4-bit quantization, our OSP model achieves a 35.7 average score across 10 benchmarks (compared to 26.5 for an Adam-trained model), with only a 2% training overhead. Remarkably, OSP models exhibit near-zero excess kurtosis (0.04) compared to extreme values (1818.56) in standard models, fundamentally altering LLM quantization behavior. Our work demonstrates that outliers are not inherent to LLMs but are consequences of training strategies, paving the way for more efficient LLM deployment. The source code and pretrained checkpoints are available at https://github.com/dmis-lab/Outlier-Safe-Pre-Training.", "github_manual": "", "github_regex": "https://github.com/dmis-lab/Outlier-Safe-Pre-Training", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.18403", "title": "The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs", "authors": [ "Muntasir Adnan", "Carlos C. N. Kuhn" ], "github": "", "project_page": "", "abstract": "The effectiveness of AI debugging follows a predictable exponential decay pattern; most models lose 60-80% of their debugging capability within just 2-3 attempts, despite iterative debugging being a critical capability for practical code generation systems. We introduce the Debugging Decay Index (DDI), a mathematical framework that quantifies when debugging becomes ineffective and predicts intervention points. Our strategic fresh start approach shifts from exploitation to exploration at strategic points in the debugging process, demonstrating that well-timed interventions can rescue the effectiveness of debugging. DDI reveals a fundamental limitation in current AI debugging and provides the first quantitative framework for optimising iterative code generation strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.19502", "title": "MATE: LLM-Powered Multi-Agent Translation Environment for Accessibility Applications", "authors": [ "Aleksandr Algazinov", "Matt Laing", "Paul Laban" ], "github": "https://github.com/AlgazinovAleksandr/Multi-Agent-MATE", "project_page": "", "abstract": "Accessibility remains a critical concern in today's society, as many technologies are not developed to support the full range of user needs. Existing multi-agent systems (MAS) often cannot provide comprehensive assistance for users in need due to the lack of customization stemming from closed-source designs. Consequently, individuals with disabilities frequently encounter significant barriers when attempting to interact with digital environments. We introduce MATE, a multimodal accessibility MAS, which performs the modality conversions based on the user's needs. The system is useful for assisting people with disabilities by ensuring that data will be converted to an understandable format. For instance, if the user cannot see well and receives an image, the system converts this image to its audio description. MATE can be applied to a wide range of domains, industries, and areas, such as healthcare, and can become a useful assistant for various groups of users. The system supports multiple types of models, ranging from LLM API calling to using custom machine learning (ML) classifiers. This flexibility ensures that the system can be adapted to various needs and is compatible with a wide variety of hardware. Since the system is expected to run locally, it ensures the privacy and security of sensitive information. In addition, the framework can be effectively integrated with institutional technologies (e.g., digital healthcare service) for real-time user assistance. Furthermore, we introduce ModCon-Task-Identifier, a model that is capable of extracting the precise modality conversion task from the user input. Numerous experiments show that ModCon-Task-Identifier consistently outperforms other LLMs and statistical models on our custom data. Our code and data are publicly available at https://github.com/AlgazinovAleksandr/Multi-Agent-MATE.", "github_manual": "", "github_regex": "https://github.com/AlgazinovAleksandr/Multi-Agent-MATE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.18095", "title": "ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation", "authors": [ "Junying Chen", "Zhenyang Cai", "Pengcheng Chen", "Shunian Chen", "Ke Ji", "Xidong Wang", "Yunjin Yang", "Benyou Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in multimodal generative models have unlocked photorealistic, instruction-aligned image generation, yet leading systems like GPT-4o-Image remain proprietary and inaccessible. To democratize these capabilities, we present ShareGPT-4o-Image, the first dataset comprising 45K text-to-image and 46K text-and-image-to-image data, all synthesized using GPT-4o's image generation capabilities for distilling its advanced image generation abilities. Leveraging this dataset, we develop Janus-4o, a multimodal large language model capable of both text-to-image and text-and-image-to-image generation. Janus-4o not only significantly improves text-to-image generation over its predecessor, Janus-Pro, but also newly supports text-and-image-to-image generation. Notably, it achieves impressive performance in text-and-image-to-image generation from scratch, using only 91K synthetic samples and 6 hours of training on an 8 A800-GPU machine. We hope the release of ShareGPT-4o-Image and Janus-4o will foster open research in photorealistic, instruction-aligned image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.20544", "title": "When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs", "authors": [ "Ammar Khairi", "Daniel D'souza", "Ye Shen", "Julia Kreutzer", "Sara Hooker" ], "github": "https://github.com/Cohere-Labs-Community/m-rewardbench", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) have shifted focus toward scaling inference-time compute, improving performance without retraining the model. A common approach is to sample multiple outputs in parallel, and select one of these as the final output. However, work to date has focused on English and a handful of domains such as math and code. In contrast, we are most interested in techniques that generalize across open-ended tasks, formally verifiable tasks, and across languages. In this work, we study how to robustly scale inference-time compute for open-ended generative tasks in a multilingual, multi-task setting. Our findings show that both sampling strategy based on temperature variation and selection strategy must be adapted to account for diverse domains and varied language settings. We evaluate existing selection methods, revealing that strategies effective in English often fail to generalize across languages. We propose novel sampling and selection strategies specifically adapted for multilingual and multi-task inference scenarios, and show they yield notable gains across languages and tasks. In particular, our combined sampling and selection methods lead to an average +6.8 jump in win-rates for our 8B models on m-ArenaHard-v2.0 prompts, against proprietary models such as Gemini. At larger scale, Command-A (111B model) equipped with our methods, shows +9.0 improvement in win-rates on the same benchmark with just five samples against single-sample decoding, a substantial increase at minimal cost. Our results underscore the need for language- and task-aware approaches to inference-time compute, aiming to democratize performance improvements in underrepresented languages.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Cohere-Labs-Community/m-rewardbench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.18674", "title": "Is There a Case for Conversation Optimized Tokenizers in Large Language Models?", "authors": [ "Raquel Ferrando", "Javier Conde", "Gonzalo Mart\u00ednez", "Pedro Reviriego" ], "github": "https://github.com/deepseek-ai/DeepSeek-LLM", "project_page": "", "abstract": "The computational and energy costs of Large Language Models (LLMs) have increased exponentially driven by the growing model sizes and the massive adoption of LLMs by hundreds of millions of users. The unit cost of an LLM is the computation of a token. Therefore, the tokenizer plays an important role in the efficiency of a model, and they are carefully optimized to minimize the number of tokens for the text in their training corpus. One of the most popular applications of LLMs are chatbots that interact with users. A key observation is that, for those chatbots, what is important is the performance of the tokenizer in the user text input and the chatbot responses. Those are most likely different from the text in the training corpus. So, a question that immediately arises is whether there is a potential benefit in optimizing tokenizers for chatbot conversations. In this paper, this idea is explored for different tokenizers by using a publicly available corpus of chatbot conversations to redesign their vocabularies and evaluate their performance in this domain. The results show that conversation-optimized tokenizers consistently reduce the number of tokens in chatbot dialogues, which can lead to meaningful energy savings, in the range of 5% to 10% while having minimal or even slightly positive impact on tokenization efficiency for the original training corpus.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/deepseek-ai/DeepSeek-LLM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.16012", "title": "DualTHOR: A Dual-Arm Humanoid Simulation Platform for Contingency-Aware Planning", "authors": [ "Boyu Li", "Siyuan He", "Hang Xu", "Haoqi Yuan", "Yu Zang", "Liwei Hu", "Junpeng Yue", "Zhenxiong Jiang", "Pengbo Hu", "B\u00f6rje F. Karlsson", "Yehui Tang", "Zongqing Lu" ], "github": "https://github.com/ds199895/DualTHOR.git", "project_page": "", "abstract": "Developing embodied agents capable of performing complex interactive tasks in real-world scenarios remains a fundamental challenge in embodied AI. Although recent advances in simulation platforms have greatly enhanced task diversity to train embodied Vision Language Models (VLMs), most platforms rely on simplified robot morphologies and bypass the stochastic nature of low-level execution, which limits their transferability to real-world robots. To address these issues, we present a physics-based simulation platform DualTHOR for complex dual-arm humanoid robots, built upon an extended version of AI2-THOR. Our simulator includes real-world robot assets, a task suite for dual-arm collaboration, and inverse kinematics solvers for humanoid robots. We also introduce a contingency mechanism that incorporates potential failures through physics-based low-level execution, bridging the gap to real-world scenarios. Our simulator enables a more comprehensive evaluation of the robustness and generalization of VLMs in household environments. Extensive evaluations reveal that current VLMs struggle with dual-arm coordination and exhibit limited robustness in realistic environments with contingencies, highlighting the importance of using our simulator to develop more capable VLMs for embodied tasks. The code is available at https://github.com/ds199895/DualTHOR.git.", "github_manual": "", "github_regex": "https://github.com/ds199895/DualTHOR.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.18088", "title": "RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation", "authors": [ "Tianxing Chen", "Zanxin Chen", "Baijun Chen", "Zijian Cai", "Yibin Liu", "Qiwei Liang", "Zixuan Li", "Xianliang Lin", "Yiheng Ge", "Zhenyu Gu", "Weiliang Deng", "Yubin Guo", "Tian Nian", "Xuanbing Xie", "Qiangyu Chen", "Kailun Su", "Tianling Xu", "Guodong Liu", "Mengkang Hu", "Huan-ang Gao", "Kaixuan Wang", "Zhixuan Liang", "Yusen Qin", "Xiaokang Yang", "Ping Luo", "Yao Mu" ], "github": "", "project_page": "", "abstract": "Simulation-based data synthesis has emerged as a powerful paradigm for enhancing real-world robotic manipulation. However, existing synthetic datasets remain insufficient for robust bimanual manipulation due to two challenges: (1) the lack of an efficient, scalable data generation method for novel tasks, and (2) oversimplified simulation environments that fail to capture real-world complexity. We present RoboTwin 2.0, a scalable simulation framework that enables automated, large-scale generation of diverse and realistic data, along with unified evaluation protocols for dual-arm manipulation. We first construct RoboTwin-OD, a large-scale object library comprising 731 instances across 147 categories, each annotated with semantic and manipulation-relevant labels. Building on this foundation, we develop an expert data synthesis pipeline that combines multimodal large language models (MLLMs) with simulation-in-the-loop refinement to generate task-level execution code automatically. To improve sim-to-real transfer, RoboTwin 2.0 incorporates structured domain randomization along five axes: clutter, lighting, background, tabletop height and language instructions, thereby enhancing data diversity and policy robustness. We instantiate this framework across 50 dual-arm tasks spanning five robot embodiments, and pre-collect over 100,000 domain-randomized expert trajectories. Empirical results show a 10.9% gain in code generation success and improved generalization to novel real-world scenarios. A VLA model fine-tuned on our dataset achieves a 367% relative improvement (42.0% vs. 9.0%) on unseen scene real-world tasks, while zero-shot models trained solely on our synthetic data achieve a 228% relative gain, highlighting strong generalization without real-world supervision. We release the data generator, benchmark, dataset, and code to support scalable research in robust bimanual manipulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.18315", "title": "Use Property-Based Testing to Bridge LLM Code Generation and Validation", "authors": [ "Lehan He", "Zeren Chen", "Zhe Zhang", "Jing Shao", "Xiang Gao", "Lu Sheng" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) excel at code generation, but ensuring their outputs to be functionally correct, especially in complex programming tasks, is a persistent challenge. While traditional Test-Driven Development (TDD) offers a path for code refinement, its efficacy with LLMs is often undermined by the scarcity of high-quality test cases or the pitfalls of automated test generation, including biased tests or inaccurate output predictions that can misdirect the correction process. This paper introduces Property-Generated Solver, a novel framework that leverages Property-Based Testing (PBT) to validate high-level program properties or invariants, instead of relying on specific input-output examples. These properties are often simpler to define and verify than directly predicting exhaustive test oracles, breaking the \"cycle of self-deception\" where tests might share flaws with the code they are meant to validate. Property-Generated Solver employs two collaborative LLM-based agents: a Generator dedicated to code generation and iterative refinement, and a Tester that manages the PBT life-cycle and formulate semantically rich feedback from property violations. The resulting comprehensive and actionable feedback then guides the Generator in its refinement efforts. By establishing PBT as the core validation engine within this iterative, closed-loop paradigm, Property-Generated Solver provides a robust mechanism for steering LLMs towards more correct and generalizable code. Extensive experimental results on multiple code generation benchmarks demonstrate that Property-Generated Solver achieves substantial pass@1 improvements, ranging from 23.1% to 37.3% relative gains over established TDD methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.20495", "title": "ReCode: Updating Code API Knowledge with Reinforcement Learning", "authors": [ "Haoze Wu", "Yunzhi Yao", "Wenhao Yu", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/ReCode", "project_page": "", "abstract": "Large Language Models (LLMs) exhibit remarkable code generation capabilities but falter when adapting to frequent updates in external library APIs. This critical limitation, stemming from reliance on outdated API knowledge from their training data, even with access to current documentation, impedes reliable code generation in dynamic environments. To tackle this issue, we propose ReCode (rule-based Reinforcement learning for Code Update), a novel framework that mimics human programmer adaptation to API changes. Specifically, we construct a dataset of approximately 2,000 data entries to train the LLMs to perform version migration based on updated information. Then, we introduce a modified string similarity metric for code evaluation as the reward for reinforcement learning. Our experiments demonstrate that ReCode substantially boosts LLMs' code generation performance in dynamic API scenarios, especially on the unseen CodeUpdateArena task. Crucially, compared to supervised fine-tuning, ReCode has less impact on LLMs' general code generation abilities. We apply ReCode on various LLMs and reinforcement learning algorithms (GRPO and DAPO), all achieving consistent improvements. Notably, after training, Qwen2.5-Coder-7B outperforms that of the 32B parameter code instruction-tuned model and the reasoning model with the same architecture. Code is available at https://github.com/zjunlp/ReCode.", "github_manual": "", "github_regex": "https://github.com/zjunlp/ReCode", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.20452", "title": "HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling", "authors": [ "Tobias Vontobel", "Seyedmorteza Sadat", "Farnood Salehi", "Romann M. Weber" ], "github": "", "project_page": "", "abstract": "Diffusion models have emerged as the leading approach for image synthesis, demonstrating exceptional photorealism and diversity. However, training diffusion models at high resolutions remains computationally prohibitive, and existing zero-shot generation techniques for synthesizing images beyond training resolutions often produce artifacts, including object duplication and spatial incoherence. In this paper, we introduce HiWave, a training-free, zero-shot approach that substantially enhances visual fidelity and structural coherence in ultra-high-resolution image synthesis using pretrained diffusion models. Our method employs a two-stage pipeline: generating a base image from the pretrained model followed by a patch-wise DDIM inversion step and a novel wavelet-based detail enhancer module. Specifically, we first utilize inversion methods to derive initial noise vectors that preserve global coherence from the base image. Subsequently, during sampling, our wavelet-domain detail enhancer retains low-frequency components from the base image to ensure structural consistency, while selectively guiding high-frequency components to enrich fine details and textures. Extensive evaluations using Stable Diffusion XL demonstrate that HiWave effectively mitigates common visual artifacts seen in prior methods, achieving superior perceptual quality. A user study confirmed HiWave's performance, where it was preferred over the state-of-the-art alternative in more than 80% of comparisons, highlighting its effectiveness for high-quality, ultra-high-resolution image synthesis without requiring retraining or architectural modifications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.20512", "title": "OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling", "authors": [ "Zengzhi Wang", "Fan Zhou", "Xuefeng Li", "Pengfei Liu" ], "github": "", "project_page": "", "abstract": "Different base language model families, such as Llama and Qwen, exhibit divergent behaviors during post-training with reinforcement learning (RL), especially on reasoning-intensive tasks. What makes a base language model suitable for reinforcement learning? Gaining deeper insight into this question is essential for developing RL-scalable foundation models of the next generation. In this work, we investigate how mid-training strategies shape RL dynamics, focusing on two representative model families: Qwen and Llama. Our study reveals that (1) high-quality mathematical corpora, such as MegaMath-Web-Pro, significantly improve both base model and RL performance, while existing alternatives (e.g., FineMath-4plus) fail to do so; (2) further adding QA-style data, particularly long chain-of-thought (CoT) reasoning examples, enhances RL outcomes, and instruction data further unlocks this effect; (3) while long-CoT improves reasoning depth, it can also induce verbosity of model responses and unstability of RL training, underscoring the importance of data formatting; (4) scaling mid-training consistently leads to stronger downstream RL performance. Building on these insights, we introduce a two-stage mid-training strategy, Stable-then-Decay, in which base models are first trained on 200B tokens with a constant learning rate, followed by 20B tokens across three CoT-focused branches with learning rate decay. This yields OctoThinker, a family of models demonstrating strong RL compatibility and closing the performance gap with more RL-friendly model families, i.e., Qwen. We hope our work will help shape pre-training strategies for foundation models in the RL era. To support further research, we release our open-source models along with a curated math reasoning-intensive corpus of over 70 billion tokens (i.e., MegaMath-Web-Pro-Max).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.20331", "title": "Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content", "authors": [ "Rian Touchent", "Nathan Godey", "Eric de la Clergerie" ], "github": "", "project_page": "", "abstract": "We introduce Biomed-Enriched, a biomedical text dataset constructed from PubMed via a two-stage annotation process. In the first stage, a large language model annotates 400K paragraphs from PubMed scientific articles, assigning scores for their type (review, study, clinical case, other), domain (clinical, biomedical, other), and educational quality. The educational quality score (rated 1 to 5) estimates how useful a paragraph is for college-level learning. These annotations are then used to fine-tune a small language model, which propagates the labels across the full PMC-OA corpus. The resulting metadata allows us to extract refined subsets, including 2M clinical case paragraphs with over 450K high-quality ones from articles with commercial-use licenses, and to construct several variants via quality filtering and domain upsampling. Clinical text is typically difficult to access due to privacy constraints, as hospital records cannot be publicly shared. Hence, our dataset provides an alternative large-scale, openly available collection of clinical cases from PubMed, making it a valuable resource for biomedical and clinical NLP. Preliminary continual-pretraining experiments with OLMo2 suggest these curated subsets enable targeted improvements, with clinical upsampling boosting performance by ~5% on MMLU ProfMed and educational quality filtering improving MedQA and MedMCQA by ~1%. Combinations of these techniques led to faster convergence, reaching same performance with a third of training tokens, indicating potential for more efficient and effective biomedical pretraining strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.19103", "title": "Inverse-and-Edit: Effective and Fast Image Editing by Cycle Consistency Models", "authors": [ "Ilia Beletskii", "Andrey Kuznetsov", "Aibek Alanov" ], "github": "https://github.com/ControlGenAI/Inverse-and-Edit", "project_page": "", "abstract": "Recent advances in image editing with diffusion models have achieved impressive results, offering fine-grained control over the generation process. However, these methods are computationally intensive because of their iterative nature. While distilled diffusion models enable faster inference, their editing capabilities remain limited, primarily because of poor inversion quality. High-fidelity inversion and reconstruction are essential for precise image editing, as they preserve the structural and semantic integrity of the source image. In this work, we propose a novel framework that enhances image inversion using consistency models, enabling high-quality editing in just four steps. Our method introduces a cycle-consistency optimization strategy that significantly improves reconstruction accuracy and enables a controllable trade-off between editability and content preservation. We achieve state-of-the-art performance across various image editing tasks and datasets, demonstrating that our method matches or surpasses full-step diffusion models while being substantially more efficient. The code of our method is available on GitHub at https://github.com/ControlGenAI/Inverse-and-Edit.", "github_manual": "", "github_regex": "https://github.com/ControlGenAI/Inverse-and-Edit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.20480", "title": "GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching", "authors": [ "Guinan Su", "Li Shen", "Lu Yin", "Shiwei Liu", "Yanwu Yang", "Jonas Geiping" ], "github": "https://github.com/Guinan-Su/auto-merge-llm", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. However, such impressive capability typically comes with a substantial model size, which presents significant challenges in deployment and inference. While structured pruning of model parameters offers a promising way to reduce computational costs at deployment time, current methods primarily focus on single model pruning. In this work, we develop a novel strategy to compress models by strategically combining or merging layers from finetuned model variants, which preserves the original model's abilities by aggregating capabilities accentuated in different finetunes. We pose the optimal tailoring of these LLMs as a zero-order optimization problem, adopting a search space that supports three different operations: (1) Layer removal, (2) Layer selection from different candidate models, and (3) Layer merging. Our experiments demonstrate that this approach leads to competitive model pruning, for example, for the Llama2-13B model families, our compressed models maintain approximately 97.3\\% of the original performance while removing sim25% of parameters, significantly outperforming previous state-of-the-art methods. The code is available at https://github.com/Guinan-Su/auto-merge-llm.", "github_manual": "", "github_regex": "https://github.com/Guinan-Su/auto-merge-llm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.19143", "title": "Thought Anchors: Which LLM Reasoning Steps Matter?", "authors": [ "Paul C. Bogdan", "Uzay Macar", "Neel Nanda", "Arthur Conmy" ], "github": "https://github.com/interp-reasoning/thought-anchors", "project_page": "", "abstract": "Reasoning large language models have recently achieved state-of-the-art performance in many fields. However, their long-form chain-of-thought reasoning creates interpretability challenges as each generated token depends on all previous ones, making the computation harder to decompose. We argue that analyzing reasoning traces at the sentence level is a promising approach to understanding reasoning processes. We present three complementary attribution methods: (1) a black-box method measuring each sentence's counterfactual importance by comparing final answers across 100 rollouts conditioned on the model generating that sentence or one with a different meaning; (2) a white-box method of aggregating attention patterns between pairs of sentences, which identified ``broadcasting'' sentences that receive disproportionate attention from all future sentences via ``receiver'' attention heads; (3) a causal attribution method measuring logical connections between sentences by suppressing attention toward one sentence and measuring the effect on each future sentence's tokens. Each method provides evidence for the existence of thought anchors, reasoning steps that have outsized importance and that disproportionately influence the subsequent reasoning process. These thought anchors are typically planning or backtracking sentences. We provide an open-source tool (www.thought-anchors.com) for visualizing the outputs of our methods, and present a case study showing converging patterns across methods that map how a model performs multi-step reasoning. The consistency across methods demonstrates the potential of sentence-level analysis for a deeper understanding of reasoning models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/interp-reasoning/thought-anchors", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-26", "arxiv_id": "2506.20920", "title": "FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language", "authors": [ "Guilherme Penedo", "Hynek Kydl\u00ed\u010dek", "Vinko Sabol\u010dec", "Bettina Messmer", "Negar Foroutan", "Amir Hossein Kargaran", "Colin Raffel", "Martin Jaggi", "Leandro Von Werra", "Thomas Wolf" ], "github": "https://github.com/huggingface/fineweb-2", "project_page": "", "abstract": "Pre-training state-of-the-art large language models (LLMs) requires vast amounts of clean and diverse text data. While the open development of large high-quality English pre-training datasets has seen substantial recent progress, training performant multilingual LLMs remains a challenge, in large part due to the inherent difficulty of tailoring filtering and deduplication pipelines to a large number of languages. In this work, we introduce a new pre-training dataset curation pipeline based on FineWeb that can be automatically adapted to support any language. We extensively ablate our pipeline design choices on a set of nine diverse languages, guided by a set of meaningful and informative evaluation tasks that were chosen through a novel selection process based on measurable criteria. Ultimately, we show that our pipeline can be used to create non-English corpora that produce more performant models than prior datasets. We additionally introduce a straightforward and principled approach to rebalance datasets that takes into consideration both duplication count and quality, providing an additional performance uplift. Finally, we scale our pipeline to over 1000 languages using almost 100 Common Crawl snapshots to produce FineWeb2, a new 20 terabyte (5 billion document) multilingual dataset which we release along with our pipeline, training, and evaluation codebases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/huggingface/fineweb-2", "github_api_added_by": "auto" }, { "date": "2025-06-26", "arxiv_id": "2506.18899", "title": "FilMaster: Bridging Cinematic Principles and Generative AI for Automated Film Generation", "authors": [ "Kaiyi Huang", "Yukun Huang", "Xintao Wang", "Zinan Lin", "Xuefei Ning", "Pengfei Wan", "Di Zhang", "Yu Wang", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "AI-driven content creation has shown potential in film production. However, existing film generation systems struggle to implement cinematic principles and thus fail to generate professional-quality films, particularly lacking diverse camera language and cinematic rhythm. This results in templated visuals and unengaging narratives. To address this, we introduce FilMaster, an end-to-end AI system that integrates real-world cinematic principles for professional-grade film generation, yielding editable, industry-standard outputs. FilMaster is built on two key principles: (1) learning cinematography from extensive real-world film data and (2) emulating professional, audience-centric post-production workflows. Inspired by these principles, FilMaster incorporates two stages: a Reference-Guided Generation Stage which transforms user input to video clips, and a Generative Post-Production Stage which transforms raw footage into audiovisual outputs by orchestrating visual and auditory elements for cinematic rhythm. Our generation stage highlights a Multi-shot Synergized RAG Camera Language Design module to guide the AI in generating professional camera language by retrieving reference clips from a vast corpus of 440,000 film clips. Our post-production stage emulates professional workflows by designing an Audience-Centric Cinematic Rhythm Control module, including Rough Cut and Fine Cut processes informed by simulated audience feedback, for effective integration of audiovisual elements to achieve engaging content. The system is empowered by generative AI models like (M)LLMs and video generation models. Furthermore, we introduce FilmEval, a comprehensive benchmark for evaluating AI-generated films. Extensive experiments show FilMaster's superior performance in camera language design and cinematic rhythm control, advancing generative AI in professional filmmaking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.21506", "title": "Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge", "authors": [ "Boyu Gou", "Zanming Huang", "Yuting Ning", "Yu Gu", "Michael Lin", "Weijian Qi", "Andrei Kopanev", "Botao Yu", "Bernal Jim\u00e9nez Guti\u00e9rrez", "Yiheng Shu", "Chan Hee Song", "Jiaman Wu", "Shijie Chen", "Hanane Nour Moussa", "Tianshu Zhang", "Jian Xie", "Yifei Li", "Tianci Xue", "Zeyi Liao", "Kai Zhang", "Boyuan Zheng", "Zhaowei Cai", "Viktor Rozgic", "Morteza Ziyadi", "Huan Sun", "Yu Su" ], "github": "", "project_page": "", "abstract": "Agentic search such as Deep Research systems, where large language models autonomously browse the web, synthesize information, and return comprehensive citation-backed answers, represents a major shift in how users interact with web-scale information. While promising greater efficiency and cognitive offloading, the growing complexity and open-endedness of agentic search have outpaced existing evaluation benchmarks and methodologies, which largely assume short search horizons and static answers. In this paper, we introduce Mind2Web 2, a benchmark of 130 realistic, high-quality, and long-horizon tasks that require real-time web browsing and extensive information synthesis, constructed with over 1,000 hours of human labor. To address the challenge of evaluating time-varying and complex answers, we propose a novel Agent-as-a-Judge framework. Our method constructs task-specific judge agents based on a tree-structured rubric design to automatically assess both answer correctness and source attribution. We conduct a comprehensive evaluation of nine frontier agentic search systems and human performance, along with a detailed error analysis to draw insights for future development. The best-performing system, OpenAI Deep Research, can already achieve 50-70% of human performance while spending half the time, showing a great potential. Altogether, Mind2Web 2 provides a rigorous foundation for developing and benchmarking the next generation of agentic search systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.21547", "title": "SAM4D: Segment Anything in Camera and LiDAR Streams", "authors": [ "Jianyun Xu", "Song Wang", "Ziqian Ni", "Chunyong Hu", "Sheng Yang", "Jianke Zhu", "Qiang Li" ], "github": "", "project_page": "", "abstract": "We present SAM4D, a multi-modal and temporal foundation model designed for promptable segmentation across camera and LiDAR streams. Unified Multi-modal Positional Encoding (UMPE) is introduced to align camera and LiDAR features in a shared 3D space, enabling seamless cross-modal prompting and interaction. Additionally, we propose Motion-aware Cross-modal Memory Attention (MCMA), which leverages ego-motion compensation to enhance temporal consistency and long-horizon feature retrieval, ensuring robust segmentation across dynamically changing autonomous driving scenes. To avoid annotation bottlenecks, we develop a multi-modal automated data engine that synergizes VFM-driven video masklets, spatiotemporal 4D reconstruction, and cross-modal masklet fusion. This framework generates camera-LiDAR aligned pseudo-labels at a speed orders of magnitude faster than human annotation while preserving VFM-derived semantic fidelity in point cloud representations. We conduct extensive experiments on the constructed Waymo-4DSeg, which demonstrate the powerful cross-modal segmentation ability and great potential in data annotation of proposed SAM4D.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.21539", "title": "WorldVLA: Towards Autoregressive Action World Model", "authors": [ "Jun Cen", "Chaohui Yu", "Hangjie Yuan", "Yuming Jiang", "Siteng Huang", "Jiayan Guo", "Xin Li", "Yibing Song", "Hao Luo", "Fan Wang", "Deli Zhao", "Hao Chen" ], "github": "", "project_page": "", "abstract": "We present WorldVLA, an autoregressive action world model that unifies action and image understanding and generation. Our WorldVLA intergrates Vision-Language-Action (VLA) model and world model in one single framework. The world model predicts future images by leveraging both action and image understanding, with the purpose of learning the underlying physics of the environment to improve action generation. Meanwhile, the action model generates the subsequent actions based on image observations, aiding in visual understanding and in turn helps visual generation of the world model. We demonstrate that WorldVLA outperforms standalone action and world models, highlighting the mutual enhancement between the world model and the action model. In addition, we find that the performance of the action model deteriorates when generating sequences of actions in an autoregressive manner. This phenomenon can be attributed to the model's limited generalization capability for action prediction, leading to the propagation of errors from earlier actions to subsequent ones. To address this issue, we propose an attention mask strategy that selectively masks prior actions during the generation of the current action, which shows significant performance improvement in the action chunk generation task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.21551", "title": "Where to find Grokking in LLM Pretraining? Monitor Memorization-to-Generalization without Test", "authors": [ "Ziyue Li", "Chenrui Fan", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Grokking, i.e., test performance keeps improving long after training loss converged, has been recently witnessed in neural network training, making the mechanism of generalization and other emerging capabilities such as reasoning mysterious. While prior studies usually train small models on a few toy or highly-specific tasks for thousands of epochs, we conduct the first study of grokking on checkpoints during one-pass pretraining of a 7B large language model (LLM), i.e., OLMoE. We compute the training loss and evaluate generalization on diverse benchmark tasks, including math reasoning, code generation, and commonsense/domain-specific knowledge retrieval tasks. Our study, for the first time, verifies that grokking still happens in the pretraining of large-scale foundation models, though different data may enter grokking stages asynchronously. We further demystify grokking's \"emergence of generalization\" by investigating LLM internal dynamics. Specifically, we find that training samples' pathways (i.e., expert choices across layers) evolve from random, instance-specific to more structured and shareable between samples during grokking. Also, the complexity of a sample's pathway reduces despite the converged loss. These indicate a memorization-to-generalization conversion, providing a mechanistic explanation of delayed generalization. In the study, we develop two novel metrics to quantify pathway distance and the complexity of a single pathway. We show their ability to predict the generalization improvement on diverse downstream tasks. They are efficient, simple to compute and solely dependent on training data. Hence, they have practical value for pretraining, enabling us to monitor the generalization performance without finetuning and test. Theoretically, we show that more structured pathways reduce model complexity and improve the generalization bound.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.20430", "title": "An Agentic System for Rare Disease Diagnosis with Traceable Reasoning", "authors": [ "Weike Zhao", "Chaoyi Wu", "Yanjie Fan", "Xiaoman Zhang", "Pengcheng Qiu", "Yuze Sun", "Xiao Zhou", "Yanfeng Wang", "Ya Zhang", "Yongguo Yu", "Kun Sun", "Weidi Xie" ], "github": "", "project_page": "", "abstract": "Rare diseases collectively affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains a pervasive challenge. This is largely due to their clinical heterogeneity, low individual prevalence, and the limited familiarity most clinicians have with rare conditions. Here, we introduce DeepRare, the first rare disease diagnosis agentic system powered by a large language model (LLM), capable of processing heterogeneous clinical inputs. The system generates ranked diagnostic hypotheses for rare diseases, each accompanied by a transparent chain of reasoning that links intermediate analytic steps to verifiable medical evidence. DeepRare comprises three key components: a central host with a long-term memory module; specialized agent servers responsible for domain-specific analytical tasks integrating over 40 specialized tools and web-scale, up-to-date medical knowledge sources, ensuring access to the most current clinical information. This modular and scalable design enables complex diagnostic reasoning while maintaining traceability and adaptability. We evaluate DeepRare on eight datasets. The system demonstrates exceptional diagnostic performance among 2,919 diseases, achieving 100% accuracy for 1013 diseases. In HPO-based evaluations, DeepRare significantly outperforms other 15 methods, like traditional bioinformatics diagnostic tools, LLMs, and other agentic systems, achieving an average Recall@1 score of 57.18% and surpassing the second-best method (Reasoning LLM) by a substantial margin of 23.79 percentage points. For multi-modal input scenarios, DeepRare achieves 70.60% at Recall@1 compared to Exomiser's 53.20% in 109 cases. Manual verification of reasoning chains by clinical experts achieves 95.40% agreements. Furthermore, the DeepRare system has been implemented as a user-friendly web application http://raredx.cn/doctor.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.16655", "title": "Arch-Router: Aligning LLM Routing with Human Preferences", "authors": [ "Co Tran", "Salman Paracha", "Adil Hafeez", "Shuguang Chen" ], "github": "https://github.com/katanemo/archgw", "project_page": "", "abstract": "With the rapid proliferation of large language models (LLMs) -- each optimized for different strengths, style, or latency/cost profile -- routing has become an essential technique to operationalize the use of different models. However, existing LLM routing approaches are limited in two key ways: they evaluate performance using benchmarks that often fail to capture human preferences driven by subjective evaluation criteria, and they typically select from a limited pool of models. In this work, we propose a preference-aligned routing framework that guides model selection by matching queries to user-defined domains (e.g., travel) or action types (e.g., image editing) -- offering a practical mechanism to encode preferences in routing decisions. Specifically, we introduce Arch-Router, a compact 1.5B model that learns to map queries to domain-action preferences for model routing decisions. Our approach also supports seamlessly adding new models for routing without requiring retraining or architectural modifications. Experiments on conversational datasets demonstrate that our approach achieves state-of-the-art (SOTA) results in matching queries with human preferences, outperforming top proprietary models. Our approach captures subjective evaluation criteria and makes routing decisions more transparent and flexible. Our model is available at: https://huggingface.co/katanemo/Arch-Router-1.5B.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/katanemo/archgw", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.21552", "title": "Whole-Body Conditioned Egocentric Video Prediction", "authors": [ "Yutong Bai", "Danny Tran", "Amir Bar", "Yann LeCun", "Trevor Darrell", "Jitendra Malik" ], "github": "", "project_page": "", "abstract": "We train models to Predict Ego-centric Video from human Actions (PEVA), given the past video and an action represented by the relative 3D body pose. By conditioning on kinematic pose trajectories, structured by the joint hierarchy of the body, our model learns to simulate how physical human actions shape the environment from a first-person point of view. We train an auto-regressive conditional diffusion transformer on Nymeria, a large-scale dataset of real-world egocentric video and body pose capture. We further design a hierarchical evaluation protocol with increasingly challenging tasks, enabling a comprehensive analysis of the model's embodied prediction and control abilities. Our work represents an initial attempt to tackle the challenges of modeling complex real-world environments and embodied agent behaviors with video prediction from the perspective of a human.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.20911", "title": "FaSTA^*: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient Multi-turn Image Editing", "authors": [ "Advait Gupta", "Rishie Raj", "Dang Nguyen", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "We develop a cost-efficient neurosymbolic agent to address challenging multi-turn image editing tasks such as \"Detect the bench in the image while recoloring it to pink. Also, remove the cat for a clearer view and recolor the wall to yellow.'' It combines the fast, high-level subtask planning by large language models (LLMs) with the slow, accurate, tool-use, and local A^* search per subtask to find a cost-efficient toolpath -- a sequence of calls to AI tools. To save the cost of A^* on similar subtasks, we perform inductive reasoning on previously successful toolpaths via LLMs to continuously extract/refine frequently used subroutines and reuse them as new tools for future tasks in an adaptive fast-slow planning, where the higher-level subroutines are explored first, and only when they fail, the low-level A^* search is activated. The reusable symbolic subroutines considerably save exploration cost on the same types of subtasks applied to similar images, yielding a human-like fast-slow toolpath agent \"FaSTA^*'': fast subtask planning followed by rule-based subroutine selection per subtask is attempted by LLMs at first, which is expected to cover most tasks, while slow A^* search is only triggered for novel and challenging subtasks. By comparing with recent image editing approaches, we demonstrate FaSTA^* is significantly more computationally efficient while remaining competitive with the state-of-the-art baseline in terms of success rate.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.21520", "title": "MADrive: Memory-Augmented Driving Scene Modeling", "authors": [ "Polina Karpikova", "Daniil Selikhanovych", "Kirill Struminsky", "Ruslan Musaev", "Maria Golitsyna", "Dmitry Baranchuk" ], "github": "", "project_page": "", "abstract": "Recent advances in scene reconstruction have pushed toward highly realistic modeling of autonomous driving (AD) environments using 3D Gaussian splatting. However, the resulting reconstructions remain closely tied to the original observations and struggle to support photorealistic synthesis of significantly altered or novel driving scenarios. This work introduces MADrive, a memory-augmented reconstruction framework designed to extend the capabilities of existing scene reconstruction methods by replacing observed vehicles with visually similar 3D assets retrieved from a large-scale external memory bank. Specifically, we release MAD-Cars, a curated dataset of {sim}70K 360{\\deg} car videos captured in the wild and present a retrieval module that finds the most similar car instances in the memory bank, reconstructs the corresponding 3D assets from video, and integrates them into the target scene through orientation alignment and relighting. The resulting replacements provide complete multi-view representations of vehicles in the scene, enabling photorealistic synthesis of substantially altered configurations, as demonstrated in our experiments. Project page: https://yandex-research.github.io/madrive/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.15196", "title": "HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges", "authors": [ "Xianliang Yang", "Ling Zhang", "Haolong Qian", "Lei Song", "Jiang Bian" ], "github": "https://github.com/microsoft/HeurAgenix", "project_page": "", "abstract": "Heuristic algorithms play a vital role in solving combinatorial optimization (CO) problems, yet traditional designs depend heavily on manual expertise and struggle to generalize across diverse instances. We introduce HeurAgenix, a two-stage hyper-heuristic framework powered by large language models (LLMs) that first evolves heuristics and then selects among them automatically. In the heuristic evolution phase, HeurAgenix leverages an LLM to compare seed heuristic solutions with higher-quality solutions and extract reusable evolution strategies. During problem solving, it dynamically picks the most promising heuristic for each problem state, guided by the LLM's perception ability. For flexibility, this selector can be either a state-of-the-art LLM or a fine-tuned lightweight model with lower inference cost. To mitigate the scarcity of reliable supervision caused by CO complexity, we fine-tune the lightweight heuristic selector with a dual-reward mechanism that jointly exploits singals from selection preferences and state perception, enabling robust selection under noisy annotations. Extensive experiments on canonical benchmarks show that HeurAgenix not only outperforms existing LLM-based hyper-heuristics but also matches or exceeds specialized solvers. Code is available at https://github.com/microsoft/HeurAgenix.", "github_manual": "", "github_regex": "https://github.com/microsoft/HeurAgenix", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.21103", "title": "Learning to Skip the Middle Layers of Transformers", "authors": [ "Tim Lawson", "Laurence Aitchison" ], "github": "https://github.com/tim-lawson/skip-middle", "project_page": "", "abstract": "Conditional computation is a popular strategy to make Transformers more efficient. Existing methods often target individual modules (e.g., mixture-of-experts layers) or skip layers independently of one another. However, interpretability research has demonstrated that the middle layers of Transformers exhibit greater redundancy, and that early layers aggregate information into token positions. Guided by these insights, we propose a novel architecture that dynamically skips a variable number of layers from the middle outward. In particular, a learned gating mechanism determines whether to bypass a symmetric span of central blocks based on the input, and a gated attention mechanism prevents subsequent tokens from attending to skipped token positions. Residual norms are controlled with a 'sandwich' or 'perilayernorm' scheme and gate sparsity with an adaptive regularization loss. We had aimed to reduce compute requirements for 'simpler' tokens and potentially foster an emergent multi-level representational hierarchy but, at the scales investigated, our approach does not achieve improvements in the trade-off between validation cross-entropy and estimated FLOPs compared to dense baselines with fewer layers. We release our code at https://github.com/tim-lawson/skip-middle.", "github_manual": "", "github_regex": "https://github.com/tim-lawson/skip-middle", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.18729", "title": "MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners", "authors": [ "Fang-Duo Tsai", "Shih-Lun Wu", "Weijaw Lee", "Sheng-Ping Yang", "Bo-Rui Chen", "Hao-Chung Cheng", "Yi-Hsuan Yang" ], "github": "https://github.com/fundwotsai2001/Text-to-music-dataset-preparation", "project_page": "", "abstract": "We propose MuseControlLite, a lightweight mechanism designed to fine-tune text-to-music generation models for precise conditioning using various time-varying musical attributes and reference audio signals. The key finding is that positional embeddings, which have been seldom used by text-to-music generation models in the conditioner for text conditions, are critical when the condition of interest is a function of time. Using melody control as an example, our experiments show that simply adding rotary positional embeddings to the decoupled cross-attention layers increases control accuracy from 56.6% to 61.1%, while requiring 6.75 times fewer trainable parameters than state-of-the-art fine-tuning mechanisms, using the same pre-trained diffusion Transformer model of Stable Audio Open. We evaluate various forms of musical attribute control, audio inpainting, and audio outpainting, demonstrating improved controllability over MusicGen-Large and Stable Audio Open ControlNet at a significantly lower fine-tuning cost, with only 85M trainble parameters. Source code, model checkpoints, and demo examples are available at: https://musecontrollite.github.io/web/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/fundwotsai2001/Text-to-music-dataset-preparation", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.21272", "title": "FairyGen: Storied Cartoon Video from a Single Child-Drawn Character", "authors": [ "Jiayi Zheng", "Xiaodong Cun" ], "github": "https://github.com/GVCLab/FairyGen", "project_page": "", "abstract": "We propose FairyGen, an automatic system for generating story-driven cartoon videos from a single child's drawing, while faithfully preserving its unique artistic style. Unlike previous storytelling methods that primarily focus on character consistency and basic motion, FairyGen explicitly disentangles character modeling from stylized background generation and incorporates cinematic shot design to support expressive and coherent storytelling. Given a single character sketch, we first employ an MLLM to generate a structured storyboard with shot-level descriptions that specify environment settings, character actions, and camera perspectives. To ensure visual consistency, we introduce a style propagation adapter that captures the character's visual style and applies it to the background, faithfully retaining the character's full visual identity while synthesizing style-consistent scenes. A shot design module further enhances visual diversity and cinematic quality through frame cropping and multi-view synthesis based on the storyboard. To animate the story, we reconstruct a 3D proxy of the character to derive physically plausible motion sequences, which are then used to fine-tune an MMDiT-based image-to-video diffusion model. We further propose a two-stage motion customization adapter: the first stage learns appearance features from temporally unordered frames, disentangling identity from motion; the second stage models temporal dynamics using a timestep-shift strategy with frozen identity weights. Once trained, FairyGen directly renders diverse and coherent video scenes aligned with the storyboard. Extensive experiments demonstrate that our system produces animations that are stylistically faithful, narratively structured natural motion, highlighting its potential for personalized and engaging story animation. The code will be available at https://github.com/GVCLab/FairyGen", "github_manual": "", "github_regex": "https://github.com/GVCLab/FairyGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.21263", "title": "DiLoCoX: A Low-Communication Large-Scale Training Framework for Decentralized Cluster", "authors": [ "Ji Qi", "WenPeng Zhu", "Li Li", "Ming Wu", "YingJun Wu", "Wu He", "Xun Gao", "Jason Zeng", "Michael Heinrich" ], "github": "", "project_page": "", "abstract": "The distributed training of foundation models, particularly large language models (LLMs), demands a high level of communication. Consequently, it is highly dependent on a centralized cluster with fast and reliable interconnects. Can we conduct training on slow networks and thereby unleash the power of decentralized clusters when dealing with models exceeding 100 billion parameters? In this paper, we propose DiLoCoX, a low-communication large-scale decentralized cluster training framework. It combines Pipeline Parallelism with Dual Optimizer Policy, One-Step-Delay Overlap of Communication and Local Training, and an Adaptive Gradient Compression Scheme. This combination significantly improves the scale of parameters and the speed of model pre-training. We justify the benefits of one-step-delay overlap of communication and local training, as well as the adaptive gradient compression scheme, through a theoretical analysis of convergence. Empirically, we demonstrate that DiLoCoX is capable of pre-training a 107B foundation model over a 1Gbps network. Compared to vanilla AllReduce, DiLoCoX can achieve a 357x speedup in distributed training while maintaining negligible degradation in model convergence. To the best of our knowledge, this is the first decentralized training framework successfully applied to models with over 100 billion parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.20936", "title": "PhysRig: Differentiable Physics-Based Skinning and Rigging Framework for Realistic Articulated Object Modeling", "authors": [ "Hao Zhang", "Haolan Xu", "Chun Feng", "Varun Jampani", "Narendra Ahuja" ], "github": "", "project_page": "", "abstract": "Skinning and rigging are fundamental components in animation, articulated object reconstruction, motion transfer, and 4D generation. Existing approaches predominantly rely on Linear Blend Skinning (LBS), due to its simplicity and differentiability. However, LBS introduces artifacts such as volume loss and unnatural deformations, and it fails to model elastic materials like soft tissues, fur, and flexible appendages (e.g., elephant trunks, ears, and fatty tissues). In this work, we propose PhysRig: a differentiable physics-based skinning and rigging framework that overcomes these limitations by embedding the rigid skeleton into a volumetric representation (e.g., a tetrahedral mesh), which is simulated as a deformable soft-body structure driven by the animated skeleton. Our method leverages continuum mechanics and discretizes the object as particles embedded in an Eulerian background grid to ensure differentiability with respect to both material properties and skeletal motion. Additionally, we introduce material prototypes, significantly reducing the learning space while maintaining high expressiveness. To evaluate our framework, we construct a comprehensive synthetic dataset using meshes from Objaverse, The Amazing Animals Zoo, and MixaMo, covering diverse object categories and motion patterns. Our method consistently outperforms traditional LBS-based approaches, generating more realistic and physically plausible results. Furthermore, we demonstrate the applicability of our framework in the pose transfer task highlighting its versatility for articulated object modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.17533", "title": "DuaShepherd: Integrating Stepwise Correctness and Potential Rewards for Mathematical Reasoning", "authors": [ "Yuanhao Wu", "Juntong Song", "Hanning Zhang", "Tong Zhang", "Cheng Niu" ], "github": "", "project_page": "", "abstract": "In this paper, we propose DuaShepherd, a novel reward modeling framework that integrates two complementary reward signals, correctness and potential, to enhance the mathematical reasoning capabilities of Large Language Models (LLMs). While correctness-based signals emphasize identification of stepwise errors, potential-based signals focus on the likelihood of reaching the correct final answer. We developed an automated pipeline for constructing large-scale reward modeling dataset with both signals. A unified, multi-head architecture was explored to train the two reward models in a multi-task setup, demonstrating benefits from learning both correctness and potential in parallel. By combining these two signals into a compound probability, our model achieves consistent performance improvements across multiple benchmarks. Empirical evaluations on MATH500 and ProcessBench confirm that this combined reward significantly outperforms models trained on either reward type alone, achieving state-of-the-art performance under comparable resource constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-27", "arxiv_id": "2506.20703", "title": "Generative Blocks World: Moving Things Around in Pictures", "authors": [ "Vaibhav Vavilala", "Seemandhar Jain", "Rahul Vasanth", "D. A. Forsyth", "Anand Bhattad" ], "github": "", "project_page": "", "abstract": "We describe Generative Blocks World to interact with the scene of a generated image by manipulating simple geometric abstractions. Our method represents scenes as assemblies of convex 3D primitives, and the same scene can be represented by different numbers of primitives, allowing an editor to move either whole structures or small details. Once the scene geometry has been edited, the image is generated by a flow-based method which is conditioned on depth and a texture hint. Our texture hint takes into account the modified 3D primitives, exceeding texture-consistency provided by existing key-value caching techniques. These texture hints (a) allow accurate object and camera moves and (b) largely preserve the identity of objects depicted. Quantitative and qualitative experiments demonstrate that our approach outperforms prior works in visual fidelity, editability, and compositional generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.21862", "title": "LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs", "authors": [ "Boyuan Sun", "Jiaxing Zhao", "Xihan Wei", "Qibin Hou" ], "github": "https://github.com/HumanMLLM/LLaVA-Scissor", "project_page": "", "abstract": "In this paper, we present LLaVA-Scissor, a training-free token compression strategy designed for video multimodal large language models. Previous methods mostly attempt to compress tokens based on attention scores, but fail to effectively capture all semantic regions and often lead to token redundancy. Differently, we propose to leverage the Semantic Connected Components (SCC) approach that assigns tokens to distinct semantic regions within the token set, ensuring comprehensive semantic coverage. The outcome is a two-step spatio-temporal token compression strategy that utilizes SCC in both spatial and temporal domains. This strategy can effectively compress tokens by representing the entire video with a set of non-overlapping semantic tokens. We conduct extensive evaluations of the token compression capabilities of LLaVA-Scissor across diverse video understanding benchmarks, including video question answering, long video understanding, and comprehensive multi-choices benchmarks. Experimental results show that the proposed LLaVA-Scissor outperforms other token compression methods, achieving superior performance in various video understanding benchmarks, particularly at low token retention ratios. Project page: https://github.com/HumanMLLM/LLaVA-Scissor.", "github_manual": "", "github_regex": "https://github.com/HumanMLLM/LLaVA-Scissor", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.21656", "title": "Fine-Grained Preference Optimization Improves Spatial Reasoning in VLMs", "authors": [ "Yifan Shen", "Yuanzhe Liu", "Jingyuan Zhu", "Xu Cao", "Xiaofeng Zhang", "Yixiao He", "Wenming Ye", "James Matthew Rehg", "Ismini Lourentzou" ], "github": "", "project_page": "", "abstract": "Current Vision-Language Models (VLMs) struggle with fine-grained spatial reasoning, particularly when multi-step logic and precise spatial alignment are required. In this work, we introduce SpatialReasoner-R1, a vision-language reasoning model designed to address these limitations. To construct high-quality supervision for spatial reasoning, we design a Multi-Model Monte Carlo Tree Search (M3CTS) method that generates diverse, logically consistent Long Chain-of-Thought (LongCoT) reasoning trajectories. In addition, we propose fine-grained Direct Preference Optimization (fDPO), which introduces segment-specific preference granularity for descriptive grounding and logical reasoning, guided by a spatial reward mechanism that evaluates candidate responses based on visual consistency, spatial grounding, and logical coherence. Experimental results demonstrate that fDPO achieves an average improvement of 4.1% over standard DPO across spatial quality tasks, and a 9.0% gain in spatial quantity tasks. SpatialReasoner-R1, trained with fDPO, sets a new SoTA on SPATIALRGPT-Bench, outperforming the strongest baseline by 9.8% in average accuracy, while maintaining competitive performance on general vision-language tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.22434", "title": "MiCo: Multi-image Contrast for Reinforcement Visual Reasoning", "authors": [ "Xi Chen", "Mingkang Zhu", "Shaoteng Liu", "Xiaoyang Wu", "Xiaogang Xu", "Yu Liu", "Xiang Bai", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "This work explores enabling Chain-of-Thought (CoT) reasoning to link visual cues across multiple images. A straightforward solution is to adapt rule-based reinforcement learning for Vision-Language Models (VLMs). However, such methods typically rely on manually curated question-answer pairs, which can be particularly challenging when dealing with fine grained visual details and complex logic across images. Inspired by self-supervised visual representation learning, we observe that images contain inherent constraints that can serve as supervision. Based on this insight, we construct image triplets comprising two augmented views of the same image and a third, similar but distinct image. During training, the model is prompted to generate a reasoning process to compare these images (i.e., determine same or different). Then we optimize the model with rule-based reinforcement learning. Due to the high visual similarity and the presence of augmentations, the model must attend to subtle visual changes and perform logical reasoning to succeed. Experiments show that, although trained solely on visual comparison tasks, the learned reasoning ability generalizes effectively to a wide range of questions. Without relying on any human-annotated question-answer pairs, our method achieves significant improvements on multi-image reasoning benchmarks and shows strong performance on general vision tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.17450", "title": "BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing", "authors": [ "Jiacheng Chen", "Ramin Mehran", "Xuhui Jia", "Saining Xie", "Sanghyun Woo" ], "github": "", "project_page": "", "abstract": "We present BlenderFusion, a generative visual compositing framework that synthesizes new scenes by recomposing objects, camera, and background. It follows a layering-editing-compositing pipeline: (i) segmenting and converting visual inputs into editable 3D entities (layering), (ii) editing them in Blender with 3D-grounded control (editing), and (iii) fusing them into a coherent scene using a generative compositor (compositing). Our generative compositor extends a pre-trained diffusion model to process both the original (source) and edited (target) scenes in parallel. It is fine-tuned on video frames with two key training strategies: (i) source masking, enabling flexible modifications like background replacement; (ii) simulated object jittering, facilitating disentangled control over objects and camera. BlenderFusion significantly outperforms prior methods in complex compositional scene editing tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.19741", "title": "Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls", "authors": [ "Yihong Luo", "Shuchen Xue", "Tianyang Hu", "Jing Tang" ], "github": "https://github.com/Luo-Yihong/NCT", "project_page": "", "abstract": "The pursuit of efficient and controllable high-quality content generation remains a central challenge in artificial intelligence-generated content (AIGC). While one-step generators, enabled by diffusion distillation techniques, offer excellent generation quality and computational efficiency, adapting them to new control conditions--such as structural constraints, semantic guidelines, or external inputs--poses a significant challenge. Conventional approaches often necessitate computationally expensive modifications to the base model and subsequent diffusion distillation. This paper introduces Noise Consistency Training (NCT), a novel and lightweight approach to directly integrate new control signals into pre-trained one-step generators without requiring access to original training images or retraining the base diffusion model. NCT operates by introducing an adapter module and employs a noise consistency loss in the noise space of the generator. This loss aligns the adapted model's generation behavior across noises that are conditionally dependent to varying degrees, implicitly guiding it to adhere to the new control. Theoretically, this training objective can be understood as minimizing the distributional distance between the adapted generator and the conditional distribution induced by the new conditions. NCT is modular, data-efficient, and easily deployable, relying only on the pre-trained one-step generator and a control signal model. Extensive experiments demonstrate that NCT achieves state-of-the-art controllable generation in a single forward pass, surpassing existing multi-step and distillation-based methods in both generation quality and computational efficiency. Code is available at https://github.com/Luo-Yihong/NCT", "github_manual": "", "github_regex": "https://github.com/Luo-Yihong/NCT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.20279", "title": "From Ideal to Real: Unified and Data-Efficient Dense Prediction for Real-World Scenarios", "authors": [ "Changliang Xia", "Chengyou Jia", "Zhuohang Dang", "Minnan Luo" ], "github": "", "project_page": "", "abstract": "Dense prediction tasks hold significant importance of computer vision, aiming to learn pixel-wise annotated label for an input image. Despite advances in this field, existing methods primarily focus on idealized conditions, with limited generalization to real-world scenarios and facing the challenging scarcity of real-world data. To systematically study this problem, we first introduce DenseWorld, a benchmark spanning a broad set of 25 dense prediction tasks that correspond to urgent real-world applications, featuring unified evaluation across tasks. Then, we propose DenseDiT, which maximally exploits generative models' visual priors to perform diverse real-world dense prediction tasks through a unified strategy. DenseDiT combines a parameter-reuse mechanism and two lightweight branches that adaptively integrate multi-scale context, working with less than 0.1% additional parameters. Evaluations on DenseWorld reveal significant performance drops in existing general and specialized baselines, highlighting their limited real-world generalization. In contrast, DenseDiT achieves superior results using less than 0.01% training data of baselines, underscoring its practical value for real-world deployment. Our data, and checkpoints and codes are available at https://xcltql666.github.io/DenseDiTProj", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.21356", "title": "ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models", "authors": [ "Hongbo Liu", "Jingwen He", "Yi Jin", "Dian Zheng", "Yuhao Dong", "Fan Zhang", "Ziqi Huang", "Yinan He", "Yangguang Li", "Weichao Chen", "Yu Qiao", "Wanli Ouyang", "Shengjie Zhao", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Cinematography, the fundamental visual language of film, is essential for conveying narrative, emotion, and aesthetic quality. While recent Vision-Language Models (VLMs) demonstrate strong general visual understanding, their proficiency in comprehending the nuanced cinematic grammar embedded within individual shots remains largely unexplored and lacks robust evaluation. This critical gap limits both fine-grained visual comprehension and the precision of AI-assisted video generation. To address this, we introduce ShotBench, a comprehensive benchmark specifically designed for cinematic language understanding. It features over 3.5k expert-annotated QA pairs from images and video clips, meticulously curated from over 200 acclaimed (predominantly Oscar-nominated) films and spanning eight key cinematography dimensions. Our evaluation of 24 leading VLMs on ShotBench reveals their substantial limitations: even the top-performing model achieves less than 60% average accuracy, particularly struggling with fine-grained visual cues and complex spatial reasoning. To catalyze advancement in this domain, we construct ShotQA, a large-scale multimodal dataset comprising approximately 70k cinematic QA pairs. Leveraging ShotQA, we develop ShotVL through supervised fine-tuning and Group Relative Policy Optimization. ShotVL significantly outperforms all existing open-source and proprietary models on ShotBench, establishing new state-of-the-art performance. We open-source our models, data, and code to foster rapid progress in this crucial area of AI-driven cinematic understanding and generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.21416", "title": "XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation", "authors": [ "Bowen Chen", "Mengyi Zhao", "Haomiao Sun", "Li Chen", "Xu Wang", "Kang Du", "Xinglong Wu" ], "github": "https://github.com/bytedance/XVerse", "project_page": "", "abstract": "Achieving fine-grained control over subject identity and semantic attributes (pose, style, lighting) in text-to-image generation, particularly for multiple subjects, often undermines the editability and coherence of Diffusion Transformers (DiTs). Many approaches introduce artifacts or suffer from attribute entanglement. To overcome these challenges, we propose a novel multi-subject controlled generation model XVerse. By transforming reference images into offsets for token-specific text-stream modulation, XVerse allows for precise and independent control for specific subject without disrupting image latents or features. Consequently, XVerse offers high-fidelity, editable multi-subject image synthesis with robust control over individual subject characteristics and semantic attributes. This advancement significantly improves personalized and complex scene generation capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bytedance/XVerse", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2505.21411", "title": "Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity", "authors": [ "Yehui Tang", "Xiaosong Li", "Fangcheng Liu", "Wei Guo", "Hang Zhou", "Yaoyuan Wang", "Kai Han", "Xianzhi Yu", "Jinpeng Li", "Hui Zang", "Fei Mi", "Xiaojun Meng", "Zhicheng Liu", "Hanting Chen", "Binfan Zheng", "Can Chen", "Youliang Yan", "Ruiming Tang", "Peifeng Qin", "Xinghao Chen", "Dacheng Tao", "Yunhe Wang" ], "github": "", "project_page": "", "abstract": "The surgence of Mixture of Experts (MoE) in Large Language Models promises a small price of execution cost for a much larger model parameter count and learning capacity, because only a small fraction of parameters are activated for each input token. However, it is commonly observed that some experts are activated far more often than others, leading to system inefficiency when running the experts on different devices in parallel. Therefore, we introduce Mixture of Grouped Experts (MoGE), which groups the experts during selection and balances the expert workload better than MoE in nature. It constrains tokens to activate an equal number of experts within each predefined expert group. When a model execution is distributed on multiple devices, this architectural design ensures a balanced computational load across devices, significantly enhancing throughput, particularly for the inference phase. Further, we build Pangu Pro MoE on Ascend NPUs, a sparse model based on MoGE with 72 billion total parameters, 16 billion of which are activated for each token. The configuration of Pangu Pro MoE is optimized for Ascend 300I Duo and 800I A2 through extensive system simulation studies. Our experiments indicate that MoGE indeed leads to better expert load balancing and more efficient execution for both model training and inference on Ascend NPUs. The inference performance of Pangu Pro MoE achieves 1148 tokens/s per card and can be further improved to 1528 tokens/s per card by speculative acceleration, outperforming comparable 32B and 72B Dense models. Furthermore, we achieve an excellent cost-to-performance ratio for model inference on Ascend 300I Duo. Our studies show that Ascend NPUs are capable of training Pangu Pro MoE with massive parallelization to make it a leading model within the sub-100B total parameter class, outperforming prominent open-source models like GLM-Z1-32B and Qwen3-32B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.22419", "title": "The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements", "authors": [ "Bingchen Zhao", "Despoina Magka", "Minqi Jiang", "Xian Li", "Roberta Raileanu", "Tatiana Shavrina", "Jean-Christophe Gagnon-Audet", "Kelvin Niu", "Shagun Sodhani", "Michael Shvartsman", "Andrei Lupu", "Alisia Lupidi", "Edan Toledo", "Karen Hambardzumyan", "Martin Josifoski", "Thomas Foster", "Lucia Cipolina-Kun", "Abhishek Charnalia", "Derek Dunfield", "Alexander H. Miller", "Oisin Mac Aodha", "Jakob Foerster", "Yoram Bachrach" ], "github": "https://github.com/facebookresearch/llm-speedrunner", "project_page": "", "abstract": "Rapid advancements in large language models (LLMs) have the potential to assist in scientific progress. A critical capability toward this endeavor is the ability to reproduce existing work. To evaluate the ability of AI agents to reproduce results in an active research area, we introduce the Automated LLM Speedrunning Benchmark, leveraging the research community contributions on the NanoGPT speedrun, a competition to train a GPT-2 model in the shortest time. Each of the 19 speedrun tasks provides the agent with the previous records training script, optionally paired with one of three hint formats, ranging from pseudocode to paper-like descriptions of the new records improvements. Records execute quickly by design and speedrun improvements encompass diverse code-level changes, ranging from high-level algorithmic advancements to hardware-aware optimizations. These features make the benchmark both accessible and realistic for the frontier problem of improving LLM training. We find that recent reasoning LLMs combined with SoTA scaffolds struggle to reimplement already-known innovations in our benchmark, even when given detailed hints. Our benchmark thus provides a simple, non-saturated measure of an LLMs ability to automate scientific reproduction, a necessary (but not sufficient) skill for an autonomous research agent.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/llm-speedrunner", "github_api_added_by": "auto" }, { "date": "2025-06-30", "arxiv_id": "2506.21594", "title": "Gazal-R1: Achieving State-of-the-Art Medical Reasoning with Parameter-Efficient Two-Stage Training", "authors": [ "Ahmed M. Adly", "Mostafa Samy", "Amr Fawzy" ], "github": "", "project_page": "", "abstract": "We present Gazal-R1, a 32-billion-parameter language model that achieves state-of-the-art performance in medical reasoning while providing transparent, step-by-step explanations for clinical decision-making. Built upon Qwen3 32B, our model demonstrates that strategic training can enable mid-sized models to outperform significantly larger counterparts in specialized domains. We developed a novel two-stage training pipeline: first, supervised fine-tuning on a carefully curated dataset of 107,033 synthetic medical reasoning examples that teaches structured clinical thinking, enhanced by advanced parameter-efficient techniques including Weight-Decomposed Low-Rank Adaptation (DoRA) and Rank-Stabilized LoRA (rsLoRA); second, reinforcement learning using Group Relative Policy Optimization (GRPO) with a sophisticated multi-component reward system that refines accuracy, format adherence, and reasoning quality. Gazal-R1 achieves exceptional performance across medical benchmarks, scoring 87.1% on MedQA, 81.6% on MMLU Pro (Medical), and 79.6% on PubMedQA, surpassing models up to 12x larger. Beyond its strong empirical results, this work provides detailed insights into the challenges of training reasoning-capable models in specialized domains, including issues with reward hacking, training instability, and the fundamental tension between factual recall and detailed reasoning. Our methodology offers a reproducible framework for developing high-capability, domain-specific language models that balance performance, efficiency, and explainability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.21628", "title": "Ark: An Open-source Python-based Framework for Robot Learning", "authors": [ "Magnus Dierking", "Christopher E. Mower", "Sarthak Das", "Huang Helong", "Jiacheng Qiu", "Cody Reading", "Wei Chen", "Huidong Liang", "Huang Guowei", "Jan Peters", "Quan Xingyue", "Jun Wang", "Haitham Bou-Ammar" ], "github": "", "project_page": "", "abstract": "Robotics has made remarkable hardware strides-from DARPA's Urban and Robotics Challenges to the first humanoid-robot kickboxing tournament-yet commercial autonomy still lags behind progress in machine learning. A major bottleneck is software: current robot stacks demand steep learning curves, low-level C/C++ expertise, fragmented tooling, and intricate hardware integration, in stark contrast to the Python-centric, well-documented ecosystems that propelled modern AI. We introduce ARK, an open-source, Python-first robotics framework designed to close that gap. ARK presents a Gym-style environment interface that allows users to collect data, preprocess it, and train policies using state-of-the-art imitation-learning algorithms (e.g., ACT, Diffusion Policy) while seamlessly toggling between high-fidelity simulation and physical robots. A lightweight client-server architecture provides networked publisher-subscriber communication, and optional C/C++ bindings ensure real-time performance when needed. ARK ships with reusable modules for control, SLAM, motion planning, system identification, and visualization, along with native ROS interoperability. Comprehensive documentation and case studies-from manipulation to mobile navigation-demonstrate rapid prototyping, effortless hardware swapping, and end-to-end pipelines that rival the convenience of mainstream machine-learning workflows. By unifying robotics and AI practices under a common Python umbrella, ARK lowers entry barriers and accelerates research and commercial deployment of autonomous robots.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.22149", "title": "RetFiner: A Vision-Language Refinement Scheme for Retinal Foundation Models", "authors": [ "Ronald Fecso", "Jos\u00e9 Morano", "Ursula Schmidt-Erfurth", "Hrvoje Bogunovi\u0107" ], "github": "https://github.com/ronnief1/RetFiner", "project_page": "", "abstract": "The rise of imaging techniques such as optical coherence tomography (OCT) and advances in deep learning (DL) have enabled clinicians and researchers to streamline retinal disease staging. A popular DL approach is self-supervised learning (SSL), where models learn from vast amounts of unlabeled data, avoiding costly annotation. SSL has allowed the development of foundation models (FMs), large models that can be used for a variety of downstream tasks. However, existing FMs for OCT, trained solely on image data, lack a comprehensive and robust semantic understanding of images, as evidenced by their downstream performance (especially for complex tasks), and thus require supervised fine-tuning (which may be unfeasible) to better adapt to specific applications and populations. To address this, we propose RetFiner, an SSL vision-language refinement scheme that improves the representations of existing FMs and enables their efficient and direct adaptation to specific populations for improved downstream performance. Our method uses a diverse set of training objectives which take advantage of the rich supervisory signal found in textual data. We tested RetFiner on the retinal FMs RETFound, UrFound, and VisionFM, showing significant improvements in linear probing performance on seven highly diverse OCT classification tasks, with an average increase of 5.8, 3.9, and 2.1 percentage points over their baselines, respectively. Our code and model weights are publicly available at https://github.com/ronnief1/RetFiner.", "github_manual": "", "github_regex": "https://github.com/ronnief1/RetFiner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.18330", "title": "Confucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics Learning", "authors": [ "Lixin Wu", "Na Cai", "Qiao Cheng", "Jiachen Wang", "Yitao Duan" ], "github": "https://github.com/netease-youdao/Confucius3-Math", "project_page": "", "abstract": "We introduce Confucius3-Math, an open-source large language model with 14B parameters that (1) runs efficiently on a single consumer-grade GPU; (2) achieves SOTA performances on a range of mathematical reasoning tasks, outperforming many models with significantly larger sizes. In particular, as part of our mission to enhancing education and knowledge dissemination with AI, Confucius3-Math is specifically committed to mathematics learning for Chinese K-12 students and educators. Built via post-training with large-scale reinforcement learning (RL), Confucius3-Math aligns with national curriculum and excels at solving main-stream Chinese K-12 mathematical problems with low cost. In this report we share our development recipe, the challenges we encounter and the techniques we develop to overcome them. In particular, we introduce three technical innovations: Targeted Entropy Regularization, Recent Sample Recovery and Policy-Specific Hardness Weighting. These innovations encompass a new entropy regularization, a novel data scheduling policy, and an improved group-relative advantage estimator. Collectively, they significantly stabilize the RL training, improve data efficiency, and boost performance. Our work demonstrates the feasibility of building strong reasoning models in a particular domain at low cost. We open-source our model and code at https://github.com/netease-youdao/Confucius3-Math.", "github_manual": "", "github_regex": "https://github.com/netease-youdao/Confucius3-Math", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.17859", "title": "In-Context Learning Strategies Emerge Rationally", "authors": [ "Daniel Wurgaft", "Ekdeep Singh Lubana", "Core Francisco Park", "Hidenori Tanaka", "Gautam Reddy", "Noah D. Goodman" ], "github": "", "project_page": "", "abstract": "Recent work analyzing in-context learning (ICL) has identified a broad set of strategies that describe model behavior in different experimental conditions. We aim to unify these findings by asking why a model learns these disparate strategies in the first place. Specifically, we start with the observation that when trained to learn a mixture of tasks, as is popular in the literature, the strategies learned by a model for performing ICL can be captured by a family of Bayesian predictors: a memorizing predictor, which assumes a discrete prior on the set of seen tasks, and a generalizing predictor, where the prior matches the underlying task distribution. Adopting the normative lens of rational analysis, where a learner's behavior is explained as an optimal adaptation to data given computational constraints, we develop a hierarchical Bayesian framework that almost perfectly predicts Transformer next-token predictions throughout training -- without assuming access to its weights. Under this framework, pretraining is viewed as a process of updating the posterior probability of different strategies, and inference-time behavior as a posterior-weighted average over these strategies' predictions. Our framework draws on common assumptions about neural network learning dynamics, which make explicit a tradeoff between loss and complexity among candidate strategies: beyond how well it explains the data, a model's preference towards implementing a strategy is dictated by its complexity. This helps explain well-known ICL phenomena, while offering novel predictions: e.g., we show a superlinear trend in the timescale for transitioning from generalization to memorization as task diversity increases. Overall, our work advances an explanatory and predictive account of ICL grounded in tradeoffs between strategy loss and complexity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.22049", "title": "GPAS: Accelerating Convergence of LLM Pretraining via Gradient-Preserving Activation Scaling", "authors": [ "Tianhao Chen", "Xin Xu", "Zijing Liu", "Pengxiang Li", "Xinyuan Song", "Ajay Kumar Jaiswal", "Fan Zhang", "Jishan Hu", "Yang Wang", "Hao Chen", "Shizhe Diao", "Shiwei Liu", "Yu Li", "Yin Lu", "Can Yang" ], "github": "https://github.com/dandingsky/GPAS", "project_page": "", "abstract": "Modern Large Language Models, such as the LLaMA, Qwen and DeepSeek series, predominantly adopt the Pre-LayerNorm (Pre-LN) Transformer architecture. While being stable during pretraining and scalable to large model sizes, Pre-LN suffers from an exponential growth in activation variance across layers, causing the residual path to dominate over sub-layer outputs and limiting the learning capacity of deeper layers. To mitigate this issue, we propose Gradient-Preserving Activation Scaling (GPAS), a simple technique that can be used in combination with existing approaches. GPAS works by scaling down the intermediate activations while keeping their gradients unchanged. This leaves information in the activations intact, and avoids the gradient vanishing problem associated with gradient downscaling. Extensive experiments across various model sizes from 71M to 1B show that GPAS achieves consistent performance gains. Beyond enhancing Pre-LN Transformers, GPAS also shows promise in improving alternative architectures such as Sandwich-LN and DeepNorm, demonstrating its versatility and potential for improving training dynamics in a wide range of settings.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dandingsky/GPAS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.21718", "title": "Performance Prediction for Large Systems via Text-to-Text Regression", "authors": [ "Yash Akhauri", "Bryan Lewandowski", "Cheng-Hsi Lin", "Adrian N. Reyes", "Grant C. Forbes", "Arissa Wongpanich", "Bangding Yang", "Mohamed S. Abdelfattah", "Sagi Perel", "Xingyou Song" ], "github": "https://github.com/google-research/t5x", "project_page": "", "abstract": "In many industries, predicting metric outcomes of large systems is a fundamental problem, driven largely by traditional tabular regression. However, such methods struggle on complex systems data in the wild such as configuration files or system logs, where feature engineering is often infeasible. We propose text-to-text regression as a general, scalable alternative. For predicting resource efficiency on Borg, Google's massive compute cluster scheduling system, a 60M parameter encoder-decoder, trained from random initialization, achieves up to a near perfect 0.99 (0.9 average) rank correlation across the entire fleet, and 100x lower MSE than tabular approaches. The model also easily adapts to new tasks in only 500 few-shot examples and captures the densities of complex outcome distributions. Ablation studies highlight the importance of using encoders, increasing sequence length, and the model's inherent uncertainty quantification. These findings pave the way for universal simulators of real-world outcomes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-research/t5x", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.21476", "title": "Global and Local Entailment Learning for Natural World Imagery", "authors": [ "Srikumar Sastry", "Aayush Dhakal", "Eric Xing", "Subash Khanal", "Nathan Jacobs" ], "github": "", "project_page": "", "abstract": "Learning the hierarchical structure of data in vision-language models is a significant challenge. Previous works have attempted to address this challenge by employing entailment learning. However, these approaches fail to model the transitive nature of entailment explicitly, which establishes the relationship between order and semantics within a representation space. In this work, we introduce Radial Cross-Modal Embeddings (RCME), a framework that enables the explicit modeling of transitivity-enforced entailment. Our proposed framework optimizes for the partial order of concepts within vision-language models. By leveraging our framework, we develop a hierarchical vision-language foundation model capable of representing the hierarchy in the Tree of Life. Our experiments on hierarchical species classification and hierarchical retrieval tasks demonstrate the enhanced performance of our models compared to the existing state-of-the-art models. Our code and models are open-sourced at https://vishu26.github.io/RCME/index.html.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.22432", "title": "Shape-for-Motion: Precise and Consistent Video Editing with 3D Proxy", "authors": [ "Yuhao Liu", "Tengfei Wang", "Fang Liu", "Zhenwei Wang", "Rynson W. H. Lau" ], "github": "", "project_page": "", "abstract": "Recent advances in deep generative modeling have unlocked unprecedented opportunities for video synthesis. In real-world applications, however, users often seek tools to faithfully realize their creative editing intentions with precise and consistent control. Despite the progress achieved by existing methods, ensuring fine-grained alignment with user intentions remains an open and challenging problem. In this work, we present Shape-for-Motion, a novel framework that incorporates a 3D proxy for precise and consistent video editing. Shape-for-Motion achieves this by converting the target object in the input video to a time-consistent mesh, i.e., a 3D proxy, allowing edits to be performed directly on the proxy and then inferred back to the video frames. To simplify the editing process, we design a novel Dual-Propagation Strategy that allows users to perform edits on the 3D mesh of a single frame, and the edits are then automatically propagated to the 3D meshes of the other frames. The 3D meshes for different frames are further projected onto the 2D space to produce the edited geometry and texture renderings, which serve as inputs to a decoupled video diffusion model for generating edited results. Our framework supports various precise and physically-consistent manipulations across the video frames, including pose editing, rotation, scaling, translation, texture modification, and object composition. Our approach marks a key step toward high-quality, controllable video editing workflows. Extensive experiments demonstrate the superiority and effectiveness of our approach. Project page: https://shapeformotion.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.21355", "title": "SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning", "authors": [ "Melanie Rieff", "Maya Varma", "Ossian Rabow", "Subathra Adithan", "Julie Kim", "Ken Chang", "Hannah Lee", "Nidhi Rohatgi", "Christian Bluethgen", "Mohamed S. Muneer", "Jean-Benoit Delbrouck", "Michael Moor" ], "github": "", "project_page": "", "abstract": "Multimodal in-context learning (ICL) remains underexplored despite significant potential for domains such as medicine. Clinicians routinely encounter diverse, specialized tasks requiring adaptation from limited examples, such as drawing insights from a few relevant prior cases or considering a constrained set of differential diagnoses. While multimodal large language models (MLLMs) have shown advances in medical visual question answering (VQA), their ability to learn multimodal tasks from context is largely unknown. We introduce SMMILE, the first expert-driven multimodal ICL benchmark for medical tasks. Eleven medical experts curated problems, each including a multimodal query and multimodal in-context examples as task demonstrations. SMMILE encompasses 111 problems (517 question-image-answer triplets) covering 6 medical specialties and 13 imaging modalities. We further introduce SMMILE++, an augmented variant with 1038 permuted problems. A comprehensive evaluation of 15 MLLMs demonstrates that most models exhibit moderate to poor multimodal ICL ability in medical tasks. In open-ended evaluations, ICL contributes only 8% average improvement over zero-shot on SMMILE and 9.4% on SMMILE++. We observe a susceptibility for irrelevant in-context examples: even a single noisy or irrelevant example can degrade performance by up to 9.5%. Moreover, example ordering exhibits a recency bias, i.e., placing the most relevant example last can lead to substantial performance improvements by up to 71%. Our findings highlight critical limitations and biases in current MLLMs when learning multimodal medical tasks from context.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.21458", "title": "Spatial Mental Modeling from Limited Views", "authors": [ "Baiqiao Yin", "Qineng Wang", "Pingyue Zhang", "Jianshu Zhang", "Kangrui Wang", "Zihan Wang", "Jieyu Zhang", "Keshigeyan Chandrasegaran", "Han Liu", "Ranjay Krishna", "Saining Xie", "Manling Li", "Jiajun Wu", "Li Fei-Fei" ], "github": "https://github.com/qinengwang-aiden/mindcube", "project_page": "", "abstract": "Can Vision Language Models (VLMs) imagine the full scene from just a few views, like humans do? Humans form spatial mental models, internal representations of unseen space, to reason about layout, perspective, and motion. Our new MindCube benchmark with 21,154 questions across 3,268 images exposes this critical gap, where existing VLMs exhibit near-random performance. Using MindCube, we systematically evaluate how well VLMs build robust spatial mental models through representing positions (cognitive mapping), orientations (perspective-taking), and dynamics (mental simulation for \"what-if\" movements). We then explore three approaches to help VLMs approximate spatial mental models, including unseen intermediate views, natural language reasoning chains, and cognitive maps. The significant improvement comes from a synergistic approach, \"map-then-reason\", that jointly trains the model to first generate a cognitive map and then reason upon it. By training models to reason over these internal maps, we boosted accuracy from 37.8% to 60.8% (+23.0%). Adding reinforcement learning pushed performance even further to 70.7% (+32.9%). Our key insight is that such scaffolding of spatial mental models, actively constructing and utilizing internal structured spatial representations with flexible reasoning processes, significantly improves understanding of unobservable space.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qinengwang-aiden/mindcube", "github_api_added_by": "auto" }, { "date": "2025-06-30", "arxiv_id": "2506.21876", "title": "Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation", "authors": [ "Qiyue Gao", "Xinyu Pi", "Kevin Liu", "Junrong Chen", "Ruolan Yang", "Xinqi Huang", "Xinyu Fang", "Lu Sun", "Gautham Kishore", "Bo Ai", "Stone Tao", "Mengyang Liu", "Jiaxi Yang", "Chao-Jung Lai", "Chuanyang Jin", "Jiannan Xiang", "Benhao Huang", "Zeming Chen", "David Danks", "Hao Su", "Tianmin Shu", "Ziqiao Ma", "Lianhui Qin", "Zhiting Hu" ], "github": "", "project_page": "", "abstract": "Internal world models (WMs) enable agents to understand the world's state and predict transitions, serving as the basis for advanced deliberative reasoning. Recent large Vision-Language Models (VLMs), such as OpenAI o3, GPT-4o and Gemini, exhibit potential as general-purpose WMs. While the latest studies have evaluated and shown limitations in specific capabilities such as visual understanding, a systematic evaluation of VLMs' fundamental WM abilities remains absent. Drawing on comparative psychology and cognitive science, we propose a two-stage framework that assesses Perception (visual, spatial, temporal, quantitative, and motion) and Prediction (mechanistic simulation, transitive inference, compositional inference) to provide an atomic evaluation of VLMs as WMs. Guided by this framework, we introduce WM-ABench, a large-scale benchmark comprising 23 fine-grained evaluation dimensions across 6 diverse simulated environments with controlled counterfactual simulations. Through 660 experiments on 15 latest commercial and open-source VLMs, we find that these models exhibit striking limitations in basic world modeling abilities. For instance, almost all models perform at near-random accuracy when distinguishing motion trajectories. Additionally, they lack disentangled understanding -- e.g., some models tend to believe blue objects move faster than green ones. More rich results and analyses reveal significant gaps between VLMs and human-level world modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.19592", "title": "Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to Task Planning", "authors": [ "Harisankar Babu", "Philipp Schillinger", "Tamim Asfour" ], "github": "", "project_page": "", "abstract": "We introduce TAPAS (Task-based Adaptation and Planning using AgentS), a multi-agent framework that integrates Large Language Models (LLMs) with symbolic planning to solve complex tasks without the need for manually defined environment models. TAPAS employs specialized LLM-based agents that collaboratively generate and adapt domain models, initial states, and goal specifications as needed using structured tool-calling mechanisms. Through this tool-based interaction, downstream agents can request modifications from upstream agents, enabling adaptation to novel attributes and constraints without manual domain redefinition. A ReAct (Reason+Act)-style execution agent, coupled with natural language plan translation, bridges the gap between dynamically generated plans and real-world robot capabilities. TAPAS demonstrates strong performance in benchmark planning domains and in the VirtualHome simulated real-world environment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-06-30", "arxiv_id": "2506.15882", "title": "Fractional Reasoning via Latent Steering Vectors Improves Inference Time Compute", "authors": [ "Sheng Liu", "Tianlang Chen", "Pan Lu", "Haotian Ye", "Yizheng Chen", "Lei Xing", "James Zou" ], "github": "https://github.com/shengliu66/FractionalReason", "project_page": "", "abstract": "Test-time compute has emerged as a powerful paradigm for improving the performance of large language models (LLMs), where generating multiple outputs or refining individual chains can significantly boost answer accuracy. However, existing methods like Best-of-N, majority voting, and self-reflection typically apply reasoning in a uniform way across inputs, overlooking the fact that different problems may require different levels of reasoning depth. In this work, we propose Fractional Reasoning, a training-free and model-agnostic framework that enables continuous control over reasoning intensity at inference time, going beyond the limitations of fixed instructional prompts. Our method operates by extracting the latent steering vector associated with deeper reasoning and reapplying it with a tunable scaling factor, allowing the model to tailor its reasoning process to the complexity of each input. This supports two key modes of test-time scaling: (1) improving output quality in breadth-based strategies (e.g., Best-of-N, majority voting), and (2) enhancing the correctness of individual reasoning chains in depth-based strategies (e.g., self-reflection). Experiments on GSM8K, MATH500, and GPQA demonstrate that Fractional Reasoning consistently improves performance across diverse reasoning tasks and models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shengliu66/FractionalReason", "github_api_added_by": "auto" }, { "date": "2025-06-30", "arxiv_id": "2506.22760", "title": "Jan-nano Technical Report", "authors": [ "Alan Dao", "Dinh Bach Vu" ], "github": "", "project_page": "", "abstract": "Most language models face a fundamental tradeoff where powerful capabilities require substantial computational resources. We shatter this constraint with Jan-nano, a 4B parameter language model that redefines efficiency through radical specialization: instead of trying to know everything, it masters the art of finding anything instantly. Fine-tuned from Qwen3-4B using our novel multi-stage RLVR system that completely eliminates reliance on next token prediction training (SFT), Jan-nano achieves 83.2% on SimpleQA benchmark with MCP integration while running on consumer hardware. With 128K context length, Jan-nano proves that intelligence isn't about scale, it's about strategy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.23542", "title": "Consistent Time-of-Flight Depth Denoising via Graph-Informed Geometric Attention", "authors": [ "Weida Wang", "Changyong He", "Jin Zeng", "Di Qiu" ], "github": "https://github.com/davidweidawang/GIGA-ToF", "project_page": "", "abstract": "Depth images captured by Time-of-Flight (ToF) sensors are prone to noise, requiring denoising for reliable downstream applications. Previous works either focus on single-frame processing, or perform multi-frame processing without considering depth variations at corresponding pixels across frames, leading to undesirable temporal inconsistency and spatial ambiguity. In this paper, we propose a novel ToF depth denoising network leveraging motion-invariant graph fusion to simultaneously enhance temporal stability and spatial sharpness. Specifically, despite depth shifts across frames, graph structures exhibit temporal self-similarity, enabling cross-frame geometric attention for graph fusion. Then, by incorporating an image smoothness prior on the fused graph and data fidelity term derived from ToF noise distribution, we formulate a maximum a posterior problem for ToF denoising. Finally, the solution is unrolled into iterative filters whose weights are adaptively learned from the graph-informed geometric attention, producing a high-performance yet interpretable network. Experimental results demonstrate that the proposed scheme achieves state-of-the-art performance in terms of accuracy and consistency on synthetic DVToF dataset and exhibits robust generalization on the real Kinectv2 dataset. Source code will be released at https://github.com/davidweidawang/GIGA-ToF{https://github.com/davidweidawang/GIGA-ToF}.", "github_manual": "", "github_regex": "https://github.com/davidweidawang/GIGA-ToF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.24119", "title": "SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning", "authors": [ "Bo Liu", "Leon Guertler", "Simon Yu", "Zichen Liu", "Penghui Qi", "Daniel Balcells", "Mickel Liu", "Cheston Tan", "Weiyan Shi", "Min Lin", "Wee Sun Lee", "Natasha Jaques" ], "github": "", "project_page": "", "abstract": "Recent advances in reinforcement learning have shown that language models can develop sophisticated reasoning through training on tasks with verifiable rewards, but these approaches depend on human-curated problem-answer pairs and domain-specific reward engineering. We introduce SPIRAL, a self-play framework where models learn by playing multi-turn, zero-sum games against continuously improving versions of themselves, eliminating the need for human supervision. Through self-play, SPIRAL generates an infinite curriculum of progressively challenging problems as models must constantly adapt to stronger opponents. To enable this self-play training at scale, We implement a fully online, multi-turn, multi-agent reinforcement learning system for LLMs and propose role-conditioned advantage estimation (RAE) to stabilize multi-agent training. Using SPIRAL, self-play on zero-sum games produces reasoning capabilities that transfer broadly. Training Qwen3-4B-Base on Kuhn Poker alone achieves 8.6% improvement on math and 8.4% on general reasoning, outperforming SFT on 25,000 expert game trajectories. Analysis reveals that this transfer occurs through three cognitive patterns: systematic decomposition, expected value calculation, and case-by-case analysis. Multi-game training (TicTacToe, Kuhn Poker, Simple Negotiation) further enhances performance as each game develops distinct reasoning strengths. Applying SPIRAL to a strong reasoning model (DeepSeek-R1-Distill-Qwen-7B) can still lead to 2.0% average improvement. These results demonstrate that zero-sum games naturally develop transferable reasoning capabilities, highlighting a promising direction for autonomous reasoning development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.17417", "title": "Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in Inference-time Scaling?", "authors": [ "Mingyuan Wu", "Meitang Li", "Jingcheng Yang", "Jize Jiang", "Kaizhuo Yan", "Zhaoheng Li", "Minjia Zhang", "Klara Nahrstedt" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have demonstrated that inference-time computation techniques, such as decoding-time scaling and self-refinement, can significantly enhance reasoning capabilities without relying on external knowledge. A key driver of this success is the emergence of self-correction and self-verification behaviors, often elicited through reinforcement learning (RL). In this paper, we investigate whether these inference-time techniques extend effectively to vision-language models (VLMs), particularly those trained with RL. We find that while decoding strategies such as majority voting and best-of-N selection with self-verification all improve VLM reasoning performance, generation-reliant methods such as the former achieve significantly higher gains versus verification-reliant methods such as the latter. Additionally, the self-correction behavior often associated with RL-tuned models, such as aha moment, does not lead to measurable gains. We show via extensive experimentation within the inference-time scaling framework to identify a key root cause: RL-trained VLMs still lack robust self-verification capabilities across both visual and textual modalities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.24123", "title": "Calligrapher: Freestyle Text Image Customization", "authors": [ "Yue Ma", "Qingyan Bai", "Hao Ouyang", "Ka Leong Cheng", "Qiuyu Wang", "Hongyu Liu", "Zichen Liu", "Haofan Wang", "Jingye Chen", "Yujun Shen", "Qifeng Chen" ], "github": "", "project_page": "", "abstract": "We introduce Calligrapher, a novel diffusion-based framework that innovatively integrates advanced text customization with artistic typography for digital calligraphy and design applications. Addressing the challenges of precise style control and data dependency in typographic customization, our framework incorporates three key technical contributions. First, we develop a self-distillation mechanism that leverages the pre-trained text-to-image generative model itself alongside the large language model to automatically construct a style-centric typography benchmark. Second, we introduce a localized style injection framework via a trainable style encoder, which comprises both Qformer and linear layers, to extract robust style features from reference images. An in-context generation mechanism is also employed to directly embed reference images into the denoising process, further enhancing the refined alignment of target styles. Extensive quantitative and qualitative evaluations across diverse fonts and design contexts confirm Calligrapher's accurate reproduction of intricate stylistic details and precise glyph positioning. By automating high-quality, visually consistent typography, Calligrapher surpasses traditional models, empowering creative practitioners in digital art, branding, and contextual typographic design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.23858", "title": "VMoBA: Mixture-of-Block Attention for Video Diffusion Models", "authors": [ "Jianzong Wu", "Liang Hou", "Haotian Yang", "Xin Tao", "Ye Tian", "Pengfei Wan", "Di Zhang", "Yunhai Tong" ], "github": "", "project_page": "", "abstract": "The quadratic complexity of full attention mechanisms poses a significant bottleneck for Video Diffusion Models (VDMs) aiming to generate long-duration, high-resolution videos. While various sparse attention methods have been proposed, many are designed as training-free inference accelerators or do not optimally capture the unique spatio-temporal characteristics inherent in video data when trained natively. This paper introduces Video Mixture of Block Attention (VMoBA), a novel sparse attention mechanism specifically adapted for VDMs. Motivated by an in-depth analysis of attention patterns within pre-trained video transformers, which revealed strong spatio-temporal locality, varying query importance, and head-specific concentration levels, VMoBA enhances the original MoBA framework with three key modifications: (1) a layer-wise recurrent block partition scheme (1D-2D-3D) to dynamically adapt to diverse spatio-temporal attention patterns and improve efficiency; (2) global block selection to prioritize the most salient query-key block interactions across an entire attention head; and (3) threshold-based block selection to dynamically determine the number of attended blocks based on their cumulative similarity. Extensive experiments demonstrate that VMoBA significantly accelerates the training of VDMs on longer sequences, achieving 2.92x FLOPs and 1.48x latency speedup, while attaining comparable or even superior generation quality to full attention. Furthermore, VMoBA exhibits competitive performance in training-free inference, offering 2.40x FLOPs and 1.35x latency speedup for high-res video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.17930", "title": "Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective", "authors": [ "Jianyu Wang", "Zhiqiang Hu", "Lidong Bing" ], "github": "https://github.com/jianyu-cs/promptquine", "project_page": "", "abstract": "We propose a novel prompt design paradigm that challenges conventional wisdom in large language model (LLM) prompting. While conventional wisdom prioritizes well-crafted instructions and demonstrations for in-context learning (ICL), we show that pruning random demonstrations into seemingly incoherent \"gibberish\" can remarkably improve performance across diverse tasks. Notably, the \"gibberish\" always matches or surpasses state-of-the-art automatic prompt optimization techniques, achieving substantial gains regardless of LLM alignment. Nevertheless, discovering an effective pruning strategy is non-trivial, as existing attribution methods and prompt compression algorithms fail to deliver robust results, let alone human intuition. In terms of this, we propose a self-discover prompt optimization framework, PromptQuine, an evolutionary search framework that automatically searches for the pruning strategy by itself using only low-data regimes. Much like the emergent complexity in nature--such as symbiosis and self-organization--arising in response to resource constraints, our framework evolves and refines unconventional yet highly effective prompts by leveraging only the tokens present within the context. We demonstrate its effectiveness across classification, multi-choice question answering, generation and math reasoning tasks across LLMs, while achieving decent runtime efficiency. We hope our findings can guide mechanistic studies on in-context learning, and provide a call to action, to pave the way for more open-ended search algorithms for more effective LLM prompting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jianyu-cs/promptquine", "github_api_added_by": "auto" }, { "date": "2025-07-01", "arxiv_id": "2506.23394", "title": "Teaching a Language Model to Speak the Language of Tools", "authors": [ "Simeon Emanuilov" ], "github": "https://github.com/llm-bg/Tucan-Eval", "project_page": "", "abstract": "External tool integration through function-calling is essential for practical language model applications, yet most multilingual models lack reliable tool-use capabilities in non-English languages. Even state-of-the-art multilingual models struggle with determining when to use tools and generating the structured outputs required for function calls, often exhibiting language confusion when prompted in lower-resource languages. This work presents a methodology for adapting existing language models to enable robust tool use in any target language, using Bulgarian as a case study. The approach involves continued training of the BgGPT model series (2.6B, 9B, 27B parameters) on a novel bilingual dataset of 10,035 function-calling examples designed to support standardized protocols like MCP (Model Context Protocol). The research introduces TUCAN (Tool-Using Capable Assistant Navigator), which achieves up to 28.75% improvement in function-calling accuracy over base models while preserving core language understanding, as verified on established Bulgarian benchmarks. Beyond accuracy gains, TUCAN models demonstrate production-ready response formatting with clean, parsable function calls, contrasting with the verbose and inconsistent outputs of base models. The models, evaluation framework, and dataset are released to enable replication for other languages. This work demonstrates a practical approach for extending tool-augmented capabilities beyond English-centric systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/llm-bg/Tucan-Eval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.16500", "title": "SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity", "authors": [ "Samir Khaki", "Xiuyu Li", "Junxian Guo", "Ligeng Zhu", "Chenfeng Xu", "Konstantinos N. Plataniotis", "Amir Yazdanbakhsh", "Kurt Keutzer", "Song Han", "Zhijian Liu" ], "github": "", "project_page": "", "abstract": "Fine-tuning LLMs is both computationally and memory-intensive. While parameter-efficient fine-tuning methods, such as QLoRA and DoRA, reduce the number of trainable parameters and lower memory usage, they do not decrease computational cost. In some cases, they may even slow down fine-tuning. In this paper, we introduce SparseLoRA, a method that accelerates LLM fine-tuning through contextual sparsity. We propose a lightweight, training-free SVD sparsity estimator that dynamically selects a sparse subset of weights for loss and gradient computation. Also, we systematically analyze and address sensitivity across layers, tokens, and training steps. Our experimental results show that SparseLoRA reduces computational cost by up to 2.2 times and a measured speedup of up to 1.6 times while maintaining accuracy across various downstream tasks, including commonsense and arithmetic reasoning, code generation, and instruction following.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.22694", "title": "VOCABTRIM: Vocabulary Pruning for Efficient Speculative Decoding in LLMs", "authors": [ "Raghavv Goel", "Sudhanshu Agrawal", "Mukul Gagrani", "Junyoung Park", "Yifan Zao", "He Zhang", "Tian Liu", "Yiping Yang", "Xin Yuan", "Jiuyan Lu", "Chris Lott", "Mingu Lee" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce a simple training-free technique to improve the performance of drafter-based speculative decoding (SpD) methods that incorporates language modeling head (LM head) during drafting process. A drafter-based speculative decoding leverages one or more smaller language models, a.k.a. drafters or draft models, to sample a draft sequence or tree consisting of multiple tokens, followed by verification by a base LLM, a target model, accepting a subset as its valid generation. As it is usually considered that the speculative decoding requires one-to-one mapping between vocabularies of the target model and the draft model, it has been natural to share the vocabulary between them, or even share the LM head as in EAGLE or Medusa. We first identify that this draft token sampling scheme inherently contains an unnecessary inference overhead in drafting, especially for some target LLMs with very large vocabularies. Then, we propose a simple technique, VocabTrim, to mitigate the drafting overhead to improve the generation speed in memory-bound environment. VocabTrim reconstructs the drafter LM head to contain only a limited set of tokens, selected by the most frequently sampled from the vocabulary of the target model. While limiting the vocabulary in drafting slightly degrades the acceptance rate, it significantly reduces the drafting latency in memory-bound process which is often the case on edge devices, resulting in higher memory-bound speed up (MBSU). We show that our method can boost the memory-bound speed-up for Llama-3 models on Spec-Bench, specifically by 16% for Llama-3.2-3B-Instruct.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.21448", "title": "ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing", "authors": [ "Huadai Liu", "Jialei Wang", "Kaicheng Luo", "Wen Wang", "Qian Chen", "Zhou Zhao", "Wei Xue" ], "github": "", "project_page": "", "abstract": "While end-to-end video-to-audio generation has greatly improved, producing high-fidelity audio that authentically captures the nuances of visual content remains challenging. Like professionals in the creative industries, such generation requires sophisticated reasoning about items such as visual dynamics, acoustic environments, and temporal relationships. We present ThinkSound, a novel framework that leverages Chain-of-Thought (CoT) reasoning to enable stepwise, interactive audio generation and editing for videos. Our approach decomposes the process into three complementary stages: foundational foley generation that creates semantically coherent soundscapes, interactive object-centric refinement through precise user interactions, and targeted editing guided by natural language instructions. At each stage, a multimodal large language model generates contextually aligned CoT reasoning that guides a unified audio foundation model. Furthermore, we introduce AudioCoT, a comprehensive dataset with structured reasoning annotations that establishes connections between visual content, textual descriptions, and sound synthesis. Experiments demonstrate that ThinkSound achieves state-of-the-art performance in video-to-audio generation across both audio metrics and CoT metrics and excels in out-of-distribution Movie Gen Audio benchmark. The demo page is available at https://ThinkSound-Project.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.23044", "title": "Ovis-U1 Technical Report", "authors": [ "Guo-Hua Wang", "Shanshan Zhao", "Xinjie Zhang", "Liangfu Cao", "Pengxin Zhan", "Lunhao Duan", "Shiyin Lu", "Minghao Fu", "Xiaohao Chen", "Jianshan Zhao", "Yang Li", "Qing-Guo Chen" ], "github": "", "project_page": "", "abstract": "In this report, we introduce Ovis-U1, a 3-billion-parameter unified model that integrates multimodal understanding, text-to-image generation, and image editing capabilities. Building on the foundation of the Ovis series, Ovis-U1 incorporates a diffusion-based visual decoder paired with a bidirectional token refiner, enabling image generation tasks comparable to leading models like GPT-4o. Unlike some previous models that use a frozen MLLM for generation tasks, Ovis-U1 utilizes a new unified training approach starting from a language model. Compared to training solely on understanding or generation tasks, unified training yields better performance, demonstrating the enhancement achieved by integrating these two tasks. Ovis-U1 achieves a score of 69.6 on the OpenCompass Multi-modal Academic Benchmark, surpassing recent state-of-the-art models such as Ristretto-3B and SAIL-VL-1.5-2B. In text-to-image generation, it excels with scores of 83.72 and 0.89 on the DPG-Bench and GenEval benchmarks, respectively. For image editing, it achieves 4.00 and 6.42 on the ImgEdit-Bench and GEdit-Bench-EN, respectively. As the initial version of the Ovis unified model series, Ovis-U1 pushes the boundaries of multimodal understanding, generation, and editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.23219", "title": "UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence with Spatial Reasoning and Understanding", "authors": [ "Jie Feng", "Shengyuan Wang", "Tianhui Liu", "Yanxin Xi", "Yong Li" ], "github": "https://github.com/tsinghua-fib-lab/UrbanLLaVA", "project_page": "", "abstract": "Urban research involves a wide range of scenarios and tasks that require the understanding of multi-modal data. Current methods often focus on specific data types and lack a unified framework in urban field for processing them comprehensively. The recent success of multi-modal large language models (MLLMs) presents a promising opportunity to overcome this limitation. In this paper, we introduce UrbanLLaVA, a multi-modal large language model designed to process these four types of data simultaneously and achieve strong performance across diverse urban tasks compared with general MLLMs. In UrbanLLaVA, we first curate a diverse urban instruction dataset encompassing both single-modal and cross-modal urban data, spanning from location view to global view of urban environment. Additionally, we propose a multi-stage training framework that decouples spatial reasoning enhancement from domain knowledge learning, thereby improving the compatibility and downstream performance of UrbanLLaVA across diverse urban tasks. Finally, we also extend existing benchmark for urban research to assess the performance of MLLMs across a wide range of urban tasks. Experimental results from three cities demonstrate that UrbanLLaVA outperforms open-source and proprietary MLLMs in both single-modal tasks and complex cross-modal tasks and shows robust generalization abilities across cities. Source codes and data are openly accessible to the research community via https://github.com/tsinghua-fib-lab/UrbanLLaVA.", "github_manual": "", "github_regex": "https://github.com/tsinghua-fib-lab/UrbanLLaVA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.23135", "title": "RoboScape: Physics-informed Embodied World Model", "authors": [ "Yu Shang", "Xin Zhang", "Yinzhou Tang", "Lei Jin", "Chen Gao", "Wei Wu", "Yong Li" ], "github": "https://github.com/tsinghua-fib-lab/RoboScape", "project_page": "", "abstract": "World models have become indispensable tools for embodied intelligence, serving as powerful simulators capable of generating realistic robotic videos while addressing critical data scarcity challenges. However, current embodied world models exhibit limited physical awareness, particularly in modeling 3D geometry and motion dynamics, resulting in unrealistic video generation for contact-rich robotic scenarios. In this paper, we present RoboScape, a unified physics-informed world model that jointly learns RGB video generation and physics knowledge within an integrated framework. We introduce two key physics-informed joint training tasks: temporal depth prediction that enhances 3D geometric consistency in video rendering, and keypoint dynamics learning that implicitly encodes physical properties (e.g., object shape and material characteristics) while improving complex motion modeling. Extensive experiments demonstrate that RoboScape generates videos with superior visual fidelity and physical plausibility across diverse robotic scenarios. We further validate its practical utility through downstream applications including robotic policy training with generated data and policy evaluation. Our work provides new insights for building efficient physics-informed world models to advance embodied intelligence research. The code is available at: https://github.com/tsinghua-fib-lab/RoboScape.", "github_manual": "", "github_regex": "https://github.com/tsinghua-fib-lab/RoboScape", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.22992", "title": "MARBLE: A Hard Benchmark for Multimodal Spatial Reasoning and Planning", "authors": [ "Yulun Jiang", "Yekun Chai", "Maria Brbi\u0107", "Michael Moor" ], "github": "", "project_page": "", "abstract": "The ability to process information from multiple modalities and to reason through it step-by-step remains a critical challenge in advancing artificial intelligence. However, existing reasoning benchmarks focus on text-only reasoning, or employ multimodal questions that can be answered by directly retrieving information from a non-text modality. Thus, complex reasoning remains poorly understood in multimodal domains. Here, we present MARBLE, a challenging multimodal reasoning benchmark that is designed to scrutinize multimodal language models (MLLMs) in their ability to carefully reason step-by-step through complex multimodal problems and environments. MARBLE is composed of two highly challenging tasks, M-Portal and M-Cube, that require the crafting and understanding of multistep plans under spatial, visual, and physical constraints. We find that current MLLMs perform poorly on MARBLE -- all the 12 advanced models obtain near-random performance on M-Portal and 0% accuracy on M-Cube. Only in simplified subtasks some models outperform the random baseline, indicating that complex reasoning is still a challenge for existing MLLMs. Moreover, we show that perception remains a bottleneck, where MLLMs occasionally fail to extract information from the visual inputs. By shedding a light on the limitations of MLLMs, we hope that MARBLE will spur the development of the next generation of models with the ability to reason and plan across many, multimodal reasoning steps.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.22832", "title": "Listener-Rewarded Thinking in VLMs for Image Preferences", "authors": [ "Alexander Gambashidze", "Li Pengyi", "Matvey Skripkin", "Andrey Galichin", "Anton Gusarov", "Konstantin Sobolev", "Andrey Kuznetsov", "Ivan Oseledets" ], "github": "", "project_page": "", "abstract": "Training robust and generalizable reward models for human visual preferences is essential for aligning text-to-image and text-to-video generative models with human intent. However, current reward models often fail to generalize, and supervised fine-tuning leads to memorization, demanding complex annotation pipelines. While reinforcement learning (RL), specifically Group Relative Policy Optimization (GRPO), improves generalization, we uncover a key failure mode: a significant drop in reasoning accuracy occurs when a model's reasoning trace contradicts that of an independent, frozen vision-language model (\"listener\") evaluating the same output. To address this, we introduce a listener-augmented GRPO framework. Here, the listener re-evaluates the reasoner's chain-of-thought to provide a dense, calibrated confidence score, shaping the RL reward signal. This encourages the reasoner not only to answer correctly, but to produce explanations that are persuasive to an independent model. Our listener-shaped reward scheme achieves best accuracy on the ImageReward benchmark (67.4%), significantly improves out-of-distribution (OOD) performance on a large-scale human preference dataset (1.2M votes, up to +6% over naive reasoner), and reduces reasoning contradictions compared to strong GRPO and SFT baselines. These results demonstrate that listener-based rewards provide a scalable, data-efficient path to aligning vision-language models with nuanced human preferences. We will release our reasoning model here: https://huggingface.co/alexgambashidze/qwen2.5vl_image_preference_reasoner.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.23151", "title": "MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow Estimation", "authors": [ "Vladislav Bargatin", "Egor Chistov", "Alexander Yakovenko", "Dmitriy Vatolin" ], "github": "https://github.com/msu-video-group/memfof", "project_page": "", "abstract": "Recent advances in optical flow estimation have prioritized accuracy at the cost of growing GPU memory consumption, particularly for high-resolution (FullHD) inputs. We introduce MEMFOF, a memory-efficient multi-frame optical flow method that identifies a favorable trade-off between multi-frame estimation and GPU memory usage. Notably, MEMFOF requires only 2.09 GB of GPU memory at runtime for 1080p inputs, and 28.5 GB during training, which uniquely positions our method to be trained at native 1080p without the need for cropping or downsampling. We systematically revisit design choices from RAFT-like architectures, integrating reduced correlation volumes and high-resolution training protocols alongside multi-frame estimation, to achieve state-of-the-art performance across multiple benchmarks while substantially reducing memory overhead. Our method outperforms more resource-intensive alternatives in both accuracy and runtime efficiency, validating its robustness for flow estimation at high resolutions. At the time of submission, our method ranks first on the Spring benchmark with a 1-pixel (1px) outlier rate of 3.289, leads Sintel (clean) with an endpoint error (EPE) of 0.963, and achieves the best Fl-all error on KITTI-2015 at 2.94%. The code is available at https://github.com/msu-video-group/memfof.", "github_manual": "", "github_regex": "https://github.com/msu-video-group/memfof", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.22753", "title": "Degradation-Modeled Multipath Diffusion for Tunable Metalens Photography", "authors": [ "Jianing Zhang", "Jiayi Zhu", "Feiyu Ji", "Xiaokang Yang", "Xiaoyun Yuan" ], "github": "", "project_page": "", "abstract": "Metalenses offer significant potential for ultra-compact computational imaging but face challenges from complex optical degradation and computational restoration difficulties. Existing methods typically rely on precise optical calibration or massive paired datasets, which are non-trivial for real-world imaging systems. Furthermore, a lack of control over the inference process often results in undesirable hallucinated artifacts. We introduce Degradation-Modeled Multipath Diffusion for tunable metalens photography, leveraging powerful natural image priors from pretrained models instead of large datasets. Our framework uses positive, neutral, and negative-prompt paths to balance high-frequency detail generation, structural fidelity, and suppression of metalens-specific degradation, alongside pseudo data augmentation. A tunable decoder enables controlled trade-offs between fidelity and perceptual quality. Additionally, a spatially varying degradation-aware attention (SVDA) module adaptively models complex optical and sensor-induced degradation. Finally, we design and build a millimeter-scale MetaCamera for real-world validation. Extensive results show that our approach outperforms state-of-the-art methods, achieving high-fidelity and sharp image reconstruction. More materials: https://dmdiff.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.17080", "title": "Tower+: Bridging Generality and Translation Specialization in Multilingual LLMs", "authors": [ "Ricardo Rei", "Nuno M. Guerreiro", "Jos\u00e9 Pombal", "Jo\u00e3o Alves", "Pedro Teixeirinha", "Amin Farajian", "Andr\u00e9 F. T. Martins" ], "github": "", "project_page": "", "abstract": "Fine-tuning pretrained LLMs has been shown to be an effective strategy for reaching state-of-the-art performance on specific tasks like machine translation. However, this process of adaptation often implies sacrificing general-purpose capabilities, such as conversational reasoning and instruction-following, hampering the utility of the system in real-world applications that require a mixture of skills. In this paper, we introduce Tower+, a suite of models designed to deliver strong performance across both translation and multilingual general-purpose text capabilities. We achieve a Pareto frontier between translation specialization and multilingual general-purpose capabilities by introducing a novel training recipe that builds on Tower (Alves et al., 2024), comprising continued pretraining, supervised fine-tuning, preference optimization, and reinforcement learning with verifiable rewards. At each stage of training, we carefully generate and curate data to strengthen performance on translation as well as general-purpose tasks involving code generation, mathematics problem solving, and general instruction-following. We develop models at multiple scales: 2B, 9B, and 72B. Our smaller models often outperform larger general-purpose open-weight and proprietary LLMs (e.g., Llama 3.3 70B, GPT-4o). Our largest model delivers best-in-class translation performance for high-resource languages and top results in multilingual Arena Hard evaluations and in IF-MT, a benchmark we introduce for evaluating both translation and instruction-following. Our findings highlight that it is possible to rival frontier models in general capabilities, while optimizing for specific business domains, such as translation and localization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-01", "arxiv_id": "2506.22598", "title": "RExBench: Can coding agents autonomously implement AI research extensions?", "authors": [ "Nicholas Edwards", "Yukyung Lee", "Yujun", "Mao", "Yulu Qin", "Sebastian Schuster", "Najoung Kim" ], "github": "", "project_page": "", "abstract": "Agents based on Large Language Models (LLMs) have shown promise for performing sophisticated software engineering tasks autonomously. In addition, there has been progress towards developing agents that can perform parts of the research pipeline in machine learning and the natural sciences. We argue that research extension and its implementation is a critical capability for such systems, and introduce RExBench to support the evaluation of this capability. RExBench is a benchmark consisting of 12 realistic research experiment implementation tasks that aim to investigate research hypotheses that have not previously been implemented. Each task is set up as an extension to an existing research paper and codebase, accompanied by domain expert-written instructions. RExBench is robust to data contamination, and supports an automatic evaluation infrastructure that executes agent outputs to determine whether the success criteria are met. We use this benchmark to evaluate nine LLM agents implemented using three different frameworks: aider, Claude Code, and OpenHands. We find that all agents evaluated fail to autonomously implement the majority of the extensions. Although the success rate improves with additional human-written hints, the best performance under this setting remains below 40%. This indicates that current agents are still short of being able to handle realistic research extension tasks without substantial human guidance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2506.23115", "title": "MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings", "authors": [ "Haonan Chen", "Hong Liu", "Yuping Luo", "Liang Wang", "Nan Yang", "Furu Wei", "Zhicheng Dou" ], "github": "", "project_page": "", "abstract": "Multimodal embedding models, built upon causal Vision Language Models (VLMs), have shown promise in various tasks. However, current approaches face three key limitations: the use of causal attention in VLM backbones is suboptimal for embedding tasks; scalability issues due to reliance on high-quality labeled paired data for contrastive learning; and limited diversity in training objectives and data. To address these issues, we propose MoCa, a two-stage framework for transforming pre-trained VLMs into effective bidirectional multimodal embedding models. The first stage, Modality-aware Continual Pre-training, introduces a joint reconstruction objective that simultaneously denoises interleaved text and image inputs, enhancing bidirectional context-aware reasoning. The second stage, Heterogeneous Contrastive Fine-tuning, leverages diverse, semantically rich multimodal data beyond simple image-caption pairs to enhance generalization and alignment. Our method addresses the stated limitations by introducing bidirectional attention through continual pre-training, scaling effectively with massive unlabeled datasets via joint reconstruction objectives, and utilizing diverse multimodal data for enhanced representation robustness. Experiments demonstrate that MoCa consistently improves performance across MMEB and ViDoRe-v2 benchmarks, achieving new state-of-the-art results, and exhibits strong scalability with both model size and training data on MMEB.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2507.00432", "title": "Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning", "authors": [ "Maggie Huan", "Yuetai Li", "Tuney Zheng", "Xiaoyu Xu", "Seungone Kim", "Minxin Du", "Radha Poovendran", "Graham Neubig", "Xiang Yue" ], "github": "", "project_page": "", "abstract": "Math reasoning has become the poster child of progress in large language models (LLMs), with new models rapidly surpassing human-level performance on benchmarks like MATH and AIME. But as math leaderboards improve week by week, it is worth asking: do these gains reflect broader problem-solving ability or just narrow overfitting? To answer this question, we evaluate over 20 open-weight reasoning-tuned models across a broad suite of tasks, including math, scientific QA, agent planning, coding, and standard instruction-following. We surprisingly find that most models that succeed in math fail to transfer their gains to other domains. To rigorously study this phenomenon, we conduct controlled experiments on Qwen3-14B models using math-only data but different tuning methods. We find that reinforcement learning (RL)-tuned models generalize well across domains, while supervised fine-tuning (SFT)-tuned models often forget general capabilities. Latent-space representation and token-space distribution shift analyses reveal that SFT induces substantial representation and output drift, while RL preserves general-domain structure. Our results suggest a need to rethink standard post-training recipes, particularly the reliance on SFT-distilled data for advancing reasoning models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2506.21277", "title": "HumanOmniV2: From Understanding to Omni-Modal Reasoning with Context", "authors": [ "Qize Yang", "Shimin Yao", "Weixuan Chen", "Shenghao Fu", "Detao Bai", "Jiaxing Zhao", "Boyuan Sun", "Bowen Yin", "Xihan Wei", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "With the rapid evolution of multimodal large language models, the capacity to deeply understand and interpret human intentions has emerged as a critical capability, which demands detailed and thoughtful reasoning. In recent studies, Reinforcement Learning (RL) has demonstrated potential in enhancing the reasoning capabilities of Large Language Models (LLMs). Nonetheless, the challenges associated with adapting RL to multimodal data and formats remain largely unaddressed. In this paper, we identify two issues in existing multimodal reasoning models: insufficient global context understanding and shortcut problems. Insufficient context understanding can happen when a model misinterprets multimodal context, resulting in incorrect answers. The shortcut problem occurs when the model overlooks crucial clues in multimodal inputs, directly addressing the query without considering the multimodal information. To tackle these issues, we emphasize the necessity for the model to reason with a clear understanding of the global context within multimodal inputs. This global context understanding can effectively prevent the model from overlooking key multimodal cues and ensure a thorough reasoning process. To ensure the accurate interpretation of multimodal context information, we implement a context reward judged by a large language model, alongside format and accuracy rewards. Additionally, to improve complex reasoning capability, we employ the LLM to assess the logical reward, determining whether the reasoning process successfully integrates multimodal information with logical methods. We also introduce a reasoning omni-modal benchmark, IntentBench, aimed at evaluating models in understanding complex human intentions and emotions. Our proposed method demonstrates advanced performance across multiple omni-modal benchmarks compared to other open-source omni-modal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2507.01001", "title": "SciArena: An Open Evaluation Platform for Foundation Models in Scientific Literature Tasks", "authors": [ "Yilun Zhao", "Kaiyan Zhang", "Tiansheng Hu", "Sihong Wu", "Ronan Le Bras", "Taira Anderson", "Jonathan Bragg", "Joseph Chee Chang", "Jesse Dodge", "Matt Latzke", "Yixin Liu", "Charles McGrady", "Xiangru Tang", "Zihang Wang", "Chen Zhao", "Hannaneh Hajishirzi", "Doug Downey", "Arman Cohan" ], "github": "", "project_page": "", "abstract": "We present SciArena, an open and collaborative platform for evaluating foundation models on scientific literature tasks. Unlike traditional benchmarks for scientific literature understanding and synthesis, SciArena engages the research community directly, following the Chatbot Arena evaluation approach of community voting on model comparisons. By leveraging collective intelligence, SciArena offers a community-driven evaluation of model performance on open-ended scientific tasks that demand literature-grounded, long-form responses. The platform currently supports 23 open-source and proprietary foundation models and has collected over 13,000 votes from trusted researchers across diverse scientific domains. We analyze the data collected so far and confirm that the submitted questions are diverse, aligned with real-world literature needs, and that participating researchers demonstrate strong self-consistency and inter-annotator agreement in their evaluations. We discuss the results and insights based on the model ranking leaderboard. To further promote research in building model-based automated evaluation systems for literature tasks, we release SciArena-Eval, a meta-evaluation benchmark based on our collected preference data. The benchmark measures the accuracy of models in judging answer quality by comparing their pairwise assessments with human votes. Our experiments highlight the benchmark's challenges and emphasize the need for more reliable automated evaluation methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2507.00951", "title": "Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact", "authors": [ "Rizwan Qureshi", "Ranjan Sapkota", "Abbas Shah", "Amgad Muneer", "Anas Zafar", "Ashmal Vayani", "Maged Shoman", "Abdelrahman B. M. Eldaly", "Kai Zhang", "Ferhat Sadak", "Shaina Raza", "Xinqi Fan", "Ravid Shwartz-Ziv", "Hong Yan", "Vinjia Jain", "Aman Chadha", "Manoj Karkee", "Jia Wu", "Philip Torr", "Seyedali Mirjalili" ], "github": "https://github.com/anas-zafar/agi-cognitive-foundations", "project_page": "", "abstract": "Can machines truly think, reason and act in domains like humans? This enduring question continues to shape the pursuit of Artificial General Intelligence (AGI). Despite the growing capabilities of models such as GPT-4.5, DeepSeek, Claude 3.5 Sonnet, Phi-4, and Grok 3, which exhibit multimodal fluency and partial reasoning, these systems remain fundamentally limited by their reliance on token-level prediction and lack of grounded agency. This paper offers a cross-disciplinary synthesis of AGI development, spanning artificial intelligence, cognitive neuroscience, psychology, generative models, and agent-based systems. We analyze the architectural and cognitive foundations of general intelligence, highlighting the role of modular reasoning, persistent memory, and multi-agent coordination. In particular, we emphasize the rise of Agentic RAG frameworks that combine retrieval, planning, and dynamic tool use to enable more adaptive behavior. We discuss generalization strategies, including information compression, test-time adaptation, and training-free methods, as critical pathways toward flexible, domain-agnostic intelligence. Vision-Language Models (VLMs) are reexamined not just as perception modules but as evolving interfaces for embodied understanding and collaborative task completion. We also argue that true intelligence arises not from scale alone but from the integration of memory and reasoning: an orchestration of modular, interactive, and self-improving components where compression enables adaptive behavior. Drawing on advances in neurosymbolic systems, reinforcement learning, and cognitive scaffolding, we explore how recent architectures begin to bridge the gap between statistical learning and goal-directed cognition. Finally, we identify key scientific, technical, and ethical challenges on the path to AGI.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/anas-zafar/agi-cognitive-foundations", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2506.20639", "title": "DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation", "authors": [ "Shansan Gong", "Ruixiang Zhang", "Huangjie Zheng", "Jiatao Gu", "Navdeep Jaitly", "Lingpeng Kong", "Yizhe Zhang" ], "github": "https://github.com/apple/ml-diffucoder", "project_page": "", "abstract": "Diffusion large language models (dLLMs) are compelling alternatives to autoregressive (AR) models because their denoising models operate over the entire sequence. The global planning and iterative refinement features of dLLMs are particularly useful for code generation. However, current training and inference mechanisms for dLLMs in coding are still under-explored. To demystify the decoding behavior of dLLMs and unlock their potential for coding, we systematically investigate their denoising processes and reinforcement learning (RL) methods. We train a 7B dLLM, DiffuCoder, on 130B tokens of code. Using this model as a testbed, we analyze its decoding behavior, revealing how it differs from that of AR models: (1) dLLMs can decide how causal their generation should be without relying on semi-AR decoding, and (2) increasing the sampling temperature diversifies not only token choices but also their generation order. This diversity creates a rich search space for RL rollouts. For RL training, to reduce the variance of token log-likelihood estimates and maintain training efficiency, we propose coupled-GRPO, a novel sampling scheme that constructs complementary mask noise for completions used in training. In our experiments, coupled-GRPO significantly improves DiffuCoder's performance on code generation benchmarks (+4.4\\% on EvalPlus) and reduces reliance on AR causal during decoding. Our work provides deeper insight into the machinery of dLLM generation and offers an effective, diffusion-native RL training framework. https://github.com/apple/ml-diffucoder.", "github_manual": "", "github_regex": "https://github.com/apple/ml-diffucoder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2506.22960", "title": "Peccavi: Visual Paraphrase Attack Safe and Distortion Free Image Watermarking Technique for AI-Generated Images", "authors": [ "Shreyas Dixit", "Ashhar Aziz", "Shashwat Bajpai", "Vasu Sharma", "Aman Chadha", "Vinija Jain", "Amitava Das" ], "github": "", "project_page": "", "abstract": "A report by the European Union Law Enforcement Agency predicts that by 2026, up to 90 percent of online content could be synthetically generated, raising concerns among policymakers, who cautioned that \"Generative AI could act as a force multiplier for political disinformation. The combined effect of generative text, images, videos, and audio may surpass the influence of any single modality.\" In response, California's Bill AB 3211 mandates the watermarking of AI-generated images, videos, and audio. However, concerns remain regarding the vulnerability of invisible watermarking techniques to tampering and the potential for malicious actors to bypass them entirely. Generative AI-powered de-watermarking attacks, especially the newly introduced visual paraphrase attack, have shown an ability to fully remove watermarks, resulting in a paraphrase of the original image. This paper introduces PECCAVI, the first visual paraphrase attack-safe and distortion-free image watermarking technique. In visual paraphrase attacks, an image is altered while preserving its core semantic regions, termed Non-Melting Points (NMPs). PECCAVI strategically embeds watermarks within these NMPs and employs multi-channel frequency domain watermarking. It also incorporates noisy burnishing to counter reverse-engineering efforts aimed at locating NMPs to disrupt the embedded watermark, thereby enhancing durability. PECCAVI is model-agnostic. All relevant resources and codes will be open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2507.01006", "title": "GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning", "authors": [ "Wenyi Hong", "Wenmeng Yu", "Xiaotao Gu", "Guo Wang", "Guobing Gan", "Haomiao Tang", "Jiale Cheng", "Ji Qi", "Junhui Ji", "Lihang Pan", "Shuaiqi Duan", "Weihan Wang", "Yan Wang", "Yean Cheng", "Zehai He", "Zhe Su", "Zhen Yang", "Ziyang Pan", "Aohan Zeng", "Baoxu Wang", "Boyan Shi", "Changyu Pang", "Chenhui Zhang", "Da Yin", "Fan Yang", "Guoqing Chen", "Jiazheng Xu", "Jiali Chen", "Jing Chen", "Jinhao Chen", "Jinghao Lin", "Jinjiang Wang", "Junjie Chen", "Leqi Lei", "Leyi Pan", "Mingzhi Zhang", "Qinkai Zheng", "Sheng Yang", "Shi Zhong", "Shiyu Huang", "Shuyuan Zhao", "Siyan Xue", "Shangqin Tu", "Shengbiao Meng", "Tianshu Zhang", "Tianwei Luo", "Tianxiang Hao", "Tianle Gong", "Wenkai Li", "Wei Jia", "Xin Lyu", "Xuancheng Huang", "Yanling Wang", "Yadong Xue", "Yanfeng Wang", "Yifan An", "Yifan Du", "Yiming Shi", "Yiheng Huang", "Yilin Niu", "Yuan Wang", "Yuanchang Yue", "Yuchen Li", "Yutao Zhang", "Yuxuan Zhang", "Zhanxiao Du", "Zhenyu Hou", "Zhao Xue", "Zhengxiao Du", "Zihan Wang", "Peng Zhang", "Debing Liu", "Bin Xu", "Juanzi Li", "Minlie Huang", "Yuxiao Dong", "Jie Tang" ], "github": "https://github.com/THUDM/GLM-4.1V-Thinking", "project_page": "", "abstract": "We present GLM-4.1V-Thinking, a vision-language model (VLM) designed to advance general-purpose multimodal reasoning. In this report, we share our key findings in the development of the reasoning-centric training framework. We first develop a capable vision foundation model with significant potential through large-scale pre-training, which arguably sets the upper bound for the final performance. Reinforcement Learning with Curriculum Sampling (RLCS) then unlocks the full potential of the model, leading to comprehensive capability enhancement across a diverse range of tasks, including STEM problem solving, video understanding, content recognition, coding, grounding, GUI-based agents, and long document understanding, among others. To facilitate research in this field, we open-source GLM-4.1V-9B-Thinking, which achieves state-of-the-art performance among models of comparable size. In a comprehensive evaluation across 28 public benchmarks, our model outperforms Qwen2.5-VL-7B on nearly all tasks and achieves comparable or even superior performance on 18 benchmarks relative to the significantly larger Qwen2.5-VL-72B. Notably, GLM-4.1V-9B-Thinking also demonstrates competitive or superior performance compared to closed-source models such as GPT-4o on challenging tasks including long document understanding and STEM reasoning, further underscoring its strong capabilities. Code, models and more information are released at https://github.com/THUDM/GLM-4.1V-Thinking.", "github_manual": "", "github_regex": "https://github.com/THUDM/GLM-4.1V-Thinking", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2506.19852", "title": "Radial Attention: O(nlog n) Sparse Attention with Energy Decay for Long Video Generation", "authors": [ "Xingyang Li", "Muyang Li", "Tianle Cai", "Haocheng Xi", "Shuo Yang", "Yujun Lin", "Lvmin Zhang", "Songlin Yang", "Jinbo Hu", "Kelly Peng", "Maneesh Agrawala", "Ion Stoica", "Kurt Keutzer", "Song Han" ], "github": "https://github.com/mit-han-lab/radial-attention", "project_page": "", "abstract": "Recent advances in diffusion models have enabled high-quality video generation, but the additional temporal dimension significantly increases computational costs, making training and inference on long videos prohibitively expensive. In this paper, we identify a phenomenon we term Spatiotemporal Energy Decay in video diffusion models: post-softmax attention scores diminish as spatial and temporal distance between tokens increase, akin to the physical decay of signal or waves over space and time in nature. Motivated by this, we propose Radial Attention, a scalable sparse attention mechanism with O(n log n) complexity that translates energy decay into exponentially decaying compute density, which is significantly more efficient than standard O(n^2) dense attention and more expressive than linear attention. Specifically, Radial Attention employs a simple, static attention mask where each token attends to spatially nearby tokens, with the attention window size shrinking with temporal distance. Moreover, it allows pre-trained video diffusion models to extend their generation length with efficient LoRA-based fine-tuning. Extensive experiments show that Radial Attention maintains video quality across Wan2.1-14B, HunyuanVideo, and Mochi 1, achieving up to a 1.9times speedup over the original dense attention. With minimal tuning, it enables video generation up to 4times longer while reducing training costs by up to 4.4times compared to direct fine-tuning and accelerating inference by up to 3.7times compared to dense attention inference.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mit-han-lab/radial-attention", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2506.21545", "title": "Data Efficacy for Language Model Training", "authors": [ "Yalun Dai", "Yangyu Huang", "Xin Zhang", "Wenshan Wu", "Chong Li", "Wenhui Lu", "Shijie Cao", "Li Dong", "Scarlett Li" ], "github": "https://github.com/microsoft/DELT", "project_page": "", "abstract": "Data is fundamental to the training of language models (LM). Recent research has been dedicated to data efficiency, which aims to maximize performance by selecting a minimal or optimal subset of training data. Techniques such as data filtering, sampling, and selection play a crucial role in this area. To complement it, we define Data Efficacy, which focuses on maximizing performance by optimizing the organization of training data and remains relatively underexplored. This work introduces a general paradigm, DELT, for considering data efficacy in LM training, which highlights the significance of training data organization. DELT comprises three components: Data Scoring, Data Selection, and Data Ordering. Among these components, we design Learnability-Quality Scoring (LQS), as a new instance of Data Scoring, which considers both the learnability and quality of each data sample from the gradient consistency perspective. We also devise Folding Ordering (FO), as a novel instance of Data Ordering, which addresses issues such as model forgetting and data distribution bias. Comprehensive experiments validate the data efficacy in LM training, which demonstrates the following: Firstly, various instances of the proposed DELT enhance LM performance to varying degrees without increasing the data scale and model size. Secondly, among these instances, the combination of our proposed LQS for data scoring and Folding for data ordering achieves the most significant improvement. Lastly, data efficacy can be achieved together with data efficiency by applying data selection. Therefore, we believe that data efficacy is a promising foundational area in LM training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/DELT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2507.00162", "title": "FreeLong++: Training-Free Long Video Generation via Multi-band SpectralFusion", "authors": [ "Yu Lu", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Recent advances in video generation models have enabled high-quality short video generation from text prompts. However, extending these models to longer videos remains a significant challenge, primarily due to degraded temporal consistency and visual fidelity. Our preliminary observations show that naively applying short-video generation models to longer sequences leads to noticeable quality degradation. Further analysis identifies a systematic trend where high-frequency components become increasingly distorted as video length grows, an issue we term high-frequency distortion. To address this, we propose FreeLong, a training-free framework designed to balance the frequency distribution of long video features during the denoising process. FreeLong achieves this by blending global low-frequency features, which capture holistic semantics across the full video, with local high-frequency features extracted from short temporal windows to preserve fine details. Building on this, FreeLong++ extends FreeLong dual-branch design into a multi-branch architecture with multiple attention branches, each operating at a distinct temporal scale. By arranging multiple window sizes from global to local, FreeLong++ enables multi-band frequency fusion from low to high frequencies, ensuring both semantic continuity and fine-grained motion dynamics across longer video sequences. Without any additional training, FreeLong++ can be plugged into existing video generation models (e.g. Wan2.1 and LTX-Video) to produce longer videos with substantially improved temporal consistency and visual fidelity. We demonstrate that our approach outperforms previous methods on longer video generation tasks (e.g. 4x and 8x of native length). It also supports coherent multi-prompt video generation with smooth scene transitions and enables controllable video generation using long depth or pose sequences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2506.23329", "title": "IR3D-Bench: Evaluating Vision-Language Model Scene Understanding as Agentic Inverse Rendering", "authors": [ "Parker Liu", "Chenxin Li", "Zhengxin Li", "Yipeng Wu", "Wuyang Li", "Zhiqin Yang", "Zhenyuan Zhang", "Yunlong Lin", "Sirui Han", "Brandon Y. Feng" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) excel at descriptive tasks, but whether they truly understand scenes from visual observations remains uncertain. We introduce IR3D-Bench, a benchmark challenging VLMs to demonstrate understanding through active creation rather than passive recognition. Grounded in the analysis-by-synthesis paradigm, IR3D-Bench tasks Vision-Language Agents (VLAs) with actively using programming and rendering tools to recreate the underlying 3D structure of an input image, achieving agentic inverse rendering through tool use. This \"understanding-by-creating\" approach probes the tool-using generative capacity of VLAs, moving beyond the descriptive or conversational capacity measured by traditional scene understanding benchmarks. We provide a comprehensive suite of metrics to evaluate geometric accuracy, spatial relations, appearance attributes, and overall plausibility. Initial experiments on agentic inverse rendering powered by various state-of-the-art VLMs highlight current limitations, particularly in visual precision rather than basic tool usage. IR3D-Bench, including data and evaluation protocols, is released to facilitate systematic study and development of tool-using VLAs towards genuine scene understanding by creating.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2507.00339", "title": "Training for X-Ray Vision: Amodal Segmentation, Amodal Content Completion, and View-Invariant Object Representation from Multi-Camera Video", "authors": [ "Alexander Moore", "Amar Saini", "Kylie Cancilla", "Doug Poland", "Carmen Carrano" ], "github": "", "project_page": "", "abstract": "Amodal segmentation and amodal content completion require using object priors to estimate occluded masks and features of objects in complex scenes. Until now, no data has provided an additional dimension for object context: the possibility of multiple cameras sharing a view of a scene. We introduce MOVi-MC-AC: Multiple Object Video with Multi-Cameras and Amodal Content, the largest amodal segmentation and first amodal content dataset to date. Cluttered scenes of generic household objects are simulated in multi-camera video. MOVi-MC-AC contributes to the growing literature of object detection, tracking, and segmentation by including two new contributions to the deep learning for computer vision world. Multiple Camera (MC) settings where objects can be identified and tracked between various unique camera perspectives are rare in both synthetic and real-world video. We introduce a new complexity to synthetic video by providing consistent object ids for detections and segmentations between both frames and multiple cameras each with unique features and motion patterns on a single scene. Amodal Content (AC) is a reconstructive task in which models predict the appearance of target objects through occlusions. In the amodal segmentation literature, some datasets have been released with amodal detection, tracking, and segmentation labels. While other methods rely on slow cut-and-paste schemes to generate amodal content pseudo-labels, they do not account for natural occlusions present in the modal masks. MOVi-MC-AC provides labels for ~5.8 million object instances, setting a new maximum in the amodal dataset literature, along with being the first to provide ground-truth amodal content. The full dataset is available at https://huggingface.co/datasets/Amar-S/MOVi-MC-AC ,", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2506.22973", "title": "Confident Splatting: Confidence-Based Compression of 3D Gaussian Splatting via Learnable Beta Distributions", "authors": [ "AmirHossein Naghi Razlighi", "Elaheh Badali Golezani", "Shohreh Kasaei" ], "github": "https://github.com/amirhossein-razlighi/Confident-Splatting", "project_page": "", "abstract": "3D Gaussian Splatting enables high-quality real-time rendering but often produces millions of splats, resulting in excessive storage and computational overhead. We propose a novel lossy compression method based on learnable confidence scores modeled as Beta distributions. Each splat's confidence is optimized through reconstruction-aware losses, enabling pruning of low-confidence splats while preserving visual fidelity. The proposed approach is architecture-agnostic and can be applied to any Gaussian Splatting variant. In addition, the average confidence values serve as a new metric to assess the quality of the scene. Extensive experiments demonstrate favorable trade-offs between compression and fidelity compared to prior work. Our code and data are publicly available at https://github.com/amirhossein-razlighi/Confident-Splatting", "github_manual": "", "github_regex": "https://github.com/amirhossein-razlighi/Confident-Splatting", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2507.00606", "title": "Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies", "authors": [ "Tao Xiong", "Xavier Hu", "Wenyan Fan", "Shengyu Zhang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) excel in complex tasks through advanced prompting techniques like Chain-of-Thought (CoT) and Tree-of-Thought (ToT), but their reliance on manually crafted, task-specific prompts limits adaptability and efficiency. We introduce Mixture of Reasoning (MoR), a training framework that embeds diverse reasoning strategies into LLMs for autonomous, task-adaptive reasoning without external prompt engineering. MoR has two phases: Thought Generation, creating reasoning chain templates with models like GPT-4o, and SFT Dataset Construction, pairing templates with benchmark datasets for supervised fine-tuning.Our experiments show that MoR significantly enhances performance, with MoR150 achieving 0.730 (2.2% improvement) using CoT prompting and 0.734 (13.5% improvement) compared to baselines. MoR eliminates the need for task-specific prompts, offering a generalizable solution for robust reasoning across diverse tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2507.00476", "title": "FreNBRDF: A Frequency-Rectified Neural Material Representation", "authors": [ "Chenliang Zhou", "Zheyuan Hu", "Cengiz Oztireli" ], "github": "", "project_page": "", "abstract": "Accurate material modeling is crucial for achieving photorealistic rendering, bridging the gap between computer-generated imagery and real-world photographs. While traditional approaches rely on tabulated BRDF data, recent work has shifted towards implicit neural representations, which offer compact and flexible frameworks for a range of tasks. However, their behavior in the frequency domain remains poorly understood. To address this, we introduce FreNBRDF, a frequency-rectified neural material representation. By leveraging spherical harmonics, we integrate frequency-domain considerations into neural BRDF modeling. We propose a novel frequency-rectified loss, derived from a frequency analysis of neural materials, and incorporate it into a generalizable and adaptive reconstruction and editing pipeline. This framework enhances fidelity, adaptability, and efficiency. Extensive experiments demonstrate that \\ours improves the accuracy and robustness of material appearance reconstruction and editing compared to state-of-the-art baselines, enabling more structured and interpretable downstream tasks and applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2506.23009", "title": "MusiXQA: Advancing Visual Music Understanding in Multimodal Large Language Models", "authors": [ "Jian Chen", "Wenye Ma", "Penghang Liu", "Wei Wang", "Tengwei Song", "Ming Li", "Chenguang Wang", "Ruiyi Zhang", "Changyou Chen" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have achieved remarkable visual reasoning abilities in natural images, text-rich documents, and graphic designs. However, their ability to interpret music sheets remains underexplored. To bridge this gap, we introduce MusiXQA, the first comprehensive dataset for evaluating and advancing MLLMs in music sheet understanding. MusiXQA features high-quality synthetic music sheets generated via MusiXTeX, with structured annotations covering note pitch and duration, chords, clefs, key/time signatures, and text, enabling diverse visual QA tasks. Through extensive evaluations, we reveal significant limitations of current state-of-the-art MLLMs in this domain. Beyond benchmarking, we developed Phi-3-MusiX, an MLLM fine-tuned on our dataset, achieving significant performance gains over GPT-based methods. The proposed dataset and model establish a foundation for future advances in MLLMs for music sheet understanding. Code, data, and model will be released upon acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-02", "arxiv_id": "2506.24019", "title": "Ella: Embodied Social Agents with Lifelong Memory", "authors": [ "Hongxin Zhang", "Zheyuan Zhang", "Zeyuan Wang", "Zunzhe Zhang", "Lixing Fang", "Qinhong Zhou", "Chuang Gan" ], "github": "https://github.com/UMass-Embodied-AGI/Ella", "project_page": "", "abstract": "We introduce Ella, an embodied social agent capable of lifelong learning within a community in a 3D open world, where agents accumulate experiences and acquire knowledge through everyday visual observations and social interactions. At the core of Ella's capabilities is a structured, long-term multimodal memory system that stores, updates, and retrieves information effectively. It consists of a name-centric semantic memory for organizing acquired knowledge and a spatiotemporal episodic memory for capturing multimodal experiences. By integrating this lifelong memory system with foundation models, Ella retrieves relevant information for decision-making, plans daily activities, builds social relationships, and evolves autonomously while coexisting with other intelligent beings in the open world. We conduct capability-oriented evaluations in a dynamic 3D open world where 15 agents engage in social activities for days and are assessed with a suite of unseen controlled evaluations. Experimental results show that Ella can influence, lead, and cooperate with other agents well to achieve goals, showcasing its ability to learn effectively through observation and social interaction. Our findings highlight the transformative potential of combining structured memory systems with foundation models for advancing embodied intelligence. More videos can be found at https://umass-embodied-agi.github.io/Ella/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UMass-Embodied-AGI/Ella", "github_api_added_by": "auto" }, { "date": "2025-07-03", "arxiv_id": "2507.01634", "title": "Depth Anything at Any Condition", "authors": [ "Boyuan Sun", "Modi Jin", "Bowen Yin", "Qibin Hou" ], "github": "https://github.com/HVision-NKU/DepthAnythingAC", "project_page": "", "abstract": "We present Depth Anything at Any Condition (DepthAnything-AC), a foundation monocular depth estimation (MDE) model capable of handling diverse environmental conditions. Previous foundation MDE models achieve impressive performance across general scenes but not perform well in complex open-world environments that involve challenging conditions, such as illumination variations, adverse weather, and sensor-induced distortions. To overcome the challenges of data scarcity and the inability of generating high-quality pseudo-labels from corrupted images, we propose an unsupervised consistency regularization finetuning paradigm that requires only a relatively small amount of unlabeled data. Furthermore, we propose the Spatial Distance Constraint to explicitly enforce the model to learn patch-level relative relationships, resulting in clearer semantic boundaries and more accurate details. Experimental results demonstrate the zero-shot capabilities of DepthAnything-AC across diverse benchmarks, including real-world adverse weather benchmarks, synthetic corruption benchmarks, and general benchmarks. Project Page: https://ghost233lism.github.io/depthanything-AC-page Code: https://github.com/HVision-NKU/DepthAnythingAC", "github_manual": "", "github_regex": "https://github.com/HVision-NKU/DepthAnythingAC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-03", "arxiv_id": "2506.23552", "title": "JAM-Flow: Joint Audio-Motion Synthesis with Flow Matching", "authors": [ "Mingi Kwon", "Joonghyuk Shin", "Jaeseok Jung", "Jaesik Park", "Youngjung Uh" ], "github": "", "project_page": "", "abstract": "The intrinsic link between facial motion and speech is often overlooked in generative modeling, where talking head synthesis and text-to-speech (TTS) are typically addressed as separate tasks. This paper introduces JAM-Flow, a unified framework to simultaneously synthesize and condition on both facial motion and speech. Our approach leverages flow matching and a novel Multi-Modal Diffusion Transformer (MM-DiT) architecture, integrating specialized Motion-DiT and Audio-DiT modules. These are coupled via selective joint attention layers and incorporate key architectural choices, such as temporally aligned positional embeddings and localized joint attention masking, to enable effective cross-modal interaction while preserving modality-specific strengths. Trained with an inpainting-style objective, JAM-Flow supports a wide array of conditioning inputs-including text, reference audio, and reference motion-facilitating tasks such as synchronized talking head generation from text, audio-driven animation, and much more, within a single, coherent model. JAM-Flow significantly advances multi-modal generative modeling by providing a practical solution for holistic audio-visual synthesis. project page: https://joonghyuk.com/jamflow-web", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-03", "arxiv_id": "2507.01949", "title": "Kwai Keye-VL Technical Report", "authors": [ "Kwai Keye Team", "Biao Yang", "Bin Wen", "Changyi Liu", "Chenglong Chu", "Chengru Song", "Chongling Rao", "Chuan Yi", "Da Li", "Dunju Zang", "Fan Yang", "Guorui Zhou", "Hao Peng", "Haojie Ding", "Jiaming Huang", "Jiangxia Cao", "Jiankang Chen", "Jingyun Hua", "Jin Ouyang", "Kaibing Chen", "Kaiyu Jiang", "Kaiyu Tang", "Kun Gai", "Shengnan Zhang", "Siyang Mao", "Sui Huang", "Tianke Zhang", "Tingting Gao", "Wei Chen", "Wei Yuan", "Xiangyu Wu", "Xiao Hu", "Xingyu Lu", "Yang Zhou", "Yi-Fan Zhang", "Yiping Yang", "Yulong Chen", "Zhenhua Wu", "Zhenyu Li", "Zhixin Ling", "Ziming Li", "Dehua Ma", "Di Xu", "Haixuan Gao", "Hang Li", "Jiawei Guo", "Jing Wang", "Lejian Ren", "Muhao Wei", "Qianqian Wang", "Qigen Hu", "Shiyao Wang", "Tao Yu", "Xinchen Luo", "Yan Li", "Yiming Liang", "Yuhang Hu", "Zeyi Lu", "Zhuoran Yang", "Zixing Zhang" ], "github": "", "project_page": "", "abstract": "While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities on static images, they often fall short in comprehending dynamic, information-dense short-form videos, a dominant medium in today's digital landscape. To bridge this gap, we introduce Kwai Keye-VL, an 8-billion-parameter multimodal foundation model engineered for leading-edge performance in short-video understanding while maintaining robust general-purpose vision-language abilities. The development of Keye-VL rests on two core pillars: a massive, high-quality dataset exceeding 600 billion tokens with a strong emphasis on video, and an innovative training recipe. This recipe features a four-stage pre-training process for solid vision-language alignment, followed by a meticulous two-phase post-training process. The first post-training stage enhances foundational capabilities like instruction following, while the second phase focuses on stimulating advanced reasoning. In this second phase, a key innovation is our five-mode ``cold-start'' data mixture, which includes ``thinking'', ``non-thinking'', ``auto-think'', ``think with image'', and high-quality video data. This mixture teaches the model to decide when and how to reason. Subsequent reinforcement learning (RL) and alignment steps further enhance these reasoning capabilities and correct abnormal model behaviors, such as repetitive outputs. To validate our approach, we conduct extensive evaluations, showing that Keye-VL achieves state-of-the-art results on public video benchmarks and remains highly competitive on general image-based tasks (Figure 1). Furthermore, we develop and release the KC-MMBench, a new benchmark tailored for real-world short-video scenarios, where Keye-VL shows a significant advantage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-03", "arxiv_id": "2507.01945", "title": "LongAnimation: Long Animation Generation with Dynamic Global-Local Memory", "authors": [ "Nan Chen", "Mengqi Huang", "Yihao Meng", "Zhendong Mao" ], "github": "", "project_page": "", "abstract": "Animation colorization is a crucial part of real animation industry production. Long animation colorization has high labor costs. Therefore, automated long animation colorization based on the video generation model has significant research value. Existing studies are limited to short-term colorization. These studies adopt a local paradigm, fusing overlapping features to achieve smooth transitions between local segments. However, the local paradigm neglects global information, failing to maintain long-term color consistency. In this study, we argue that ideal long-term color consistency can be achieved through a dynamic global-local paradigm, i.e., dynamically extracting global color-consistent features relevant to the current generation. Specifically, we propose LongAnimation, a novel framework, which mainly includes a SketchDiT, a Dynamic Global-Local Memory (DGLM), and a Color Consistency Reward. The SketchDiT captures hybrid reference features to support the DGLM module. The DGLM module employs a long video understanding model to dynamically compress global historical features and adaptively fuse them with the current generation features. To refine the color consistency, we introduce a Color Consistency Reward. During inference, we propose a color consistency fusion to smooth the video segment transition. Extensive experiments on both short-term (14 frames) and long-term (average 500 frames) animations show the effectiveness of LongAnimation in maintaining short-term and long-term color consistency for open-domain animation colorization task. The code can be found at https://cn-makers.github.io/long_animation_web/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-03", "arxiv_id": "2507.01953", "title": "FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model", "authors": [ "Yukang Cao", "Chenyang Si", "Jinghao Wang", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "We present FreeMorph, the first tuning-free method for image morphing that accommodates inputs with different semantics or layouts. Unlike existing methods that rely on finetuning pre-trained diffusion models and are limited by time constraints and semantic/layout discrepancies, FreeMorph delivers high-fidelity image morphing without requiring per-instance training. Despite their efficiency and potential, tuning-free methods face challenges in maintaining high-quality results due to the non-linear nature of the multi-step denoising process and biases inherited from the pre-trained diffusion model. In this paper, we introduce FreeMorph to address these challenges by integrating two key innovations. 1) We first propose a guidance-aware spherical interpolation design that incorporates explicit guidance from the input images by modifying the self-attention modules, thereby addressing identity loss and ensuring directional transitions throughout the generated sequence. 2) We further introduce a step-oriented variation trend that blends self-attention modules derived from each input image to achieve controlled and consistent transitions that respect both inputs. Our extensive evaluations demonstrate that FreeMorph outperforms existing methods, being 10x ~ 50x faster and establishing a new state-of-the-art for image morphing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-03", "arxiv_id": "2507.01925", "title": "A Survey on Vision-Language-Action Models: An Action Tokenization Perspective", "authors": [ "Yifan Zhong", "Fengshuo Bai", "Shaofei Cai", "Xuchuan Huang", "Zhang Chen", "Xiaowei Zhang", "Yuanfei Wang", "Shaoyang Guo", "Tianrui Guan", "Ka Nam Lui", "Zhiquan Qi", "Yitao Liang", "Yuanpei Chen", "Yaodong Yang" ], "github": "https://github.com/PRIME-RL/SimpleVLA-RL", "project_page": "", "abstract": "The remarkable advancements of vision and language foundation models in multimodal understanding, reasoning, and generation has sparked growing efforts to extend such intelligence to the physical world, fueling the flourishing of vision-language-action (VLA) models. Despite seemingly diverse approaches, we observe that current VLA models can be unified under a single framework: vision and language inputs are processed by a series of VLA modules, producing a chain of action tokens that progressively encode more grounded and actionable information, ultimately generating executable actions. We further determine that the primary design choice distinguishing VLA models lies in how action tokens are formulated, which can be categorized into language description, code, affordance, trajectory, goal state, latent representation, raw action, and reasoning. However, there remains a lack of comprehensive understanding regarding action tokens, significantly impeding effective VLA development and obscuring future directions. Therefore, this survey aims to categorize and interpret existing VLA research through the lens of action tokenization, distill the strengths and limitations of each token type, and identify areas for improvement. Through this systematic review and analysis, we offer a synthesized outlook on the broader evolution of VLA models, highlight underexplored yet promising directions, and contribute guidance for future research, hoping to bring the field closer to general-purpose intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PRIME-RL/SimpleVLA-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-03", "arxiv_id": "2507.01957", "title": "Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation", "authors": [ "Zhuoyang Zhang", "Luke J. Huang", "Chengyue Wu", "Shang Yang", "Kelly Peng", "Yao Lu", "Song Han" ], "github": "", "project_page": "", "abstract": "We present Locality-aware Parallel Decoding (LPD) to accelerate autoregressive image generation. Traditional autoregressive image generation relies on next-patch prediction, a memory-bound process that leads to high latency. Existing works have tried to parallelize next-patch prediction by shifting to multi-patch prediction to accelerate the process, but only achieved limited parallelization. To achieve high parallelization while maintaining generation quality, we introduce two key techniques: (1) Flexible Parallelized Autoregressive Modeling, a novel architecture that enables arbitrary generation ordering and degrees of parallelization. It uses learnable position query tokens to guide generation at target positions while ensuring mutual visibility among concurrently generated tokens for consistent parallel decoding. (2) Locality-aware Generation Ordering, a novel schedule that forms groups to minimize intra-group dependencies and maximize contextual support, enhancing generation quality. With these designs, we reduce the generation steps from 256 to 20 (256times256 res.) and 1024 to 48 (512times512 res.) without compromising quality on the ImageNet class-conditional generation, and achieving at least 3.4times lower latency than previous parallelized autoregressive models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-03", "arxiv_id": "2506.22868", "title": "STR-Match: Matching SpatioTemporal Relevance Score for Training-Free Video Editing", "authors": [ "Junsung Lee", "Junoh Kang", "Bohyung Han" ], "github": "", "project_page": "", "abstract": "Previous text-guided video editing methods often suffer from temporal inconsistency, motion distortion, and-most notably-limited domain transformation. We attribute these limitations to insufficient modeling of spatiotemporal pixel relevance during the editing process. To address this, we propose STR-Match, a training-free video editing algorithm that produces visually appealing and spatiotemporally coherent videos through latent optimization guided by our novel STR score. The score captures spatiotemporal pixel relevance across adjacent frames by leveraging 2D spatial attention and 1D temporal modules in text-to-video (T2V) diffusion models, without the overhead of computationally expensive 3D attention mechanisms. Integrated into a latent optimization framework with a latent mask, STR-Match generates temporally consistent and visually faithful videos, maintaining strong performance even under significant domain transformations while preserving key visual attributes of the source. Extensive experiments demonstrate that STR-Match consistently outperforms existing methods in both visual quality and spatiotemporal consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-03", "arxiv_id": "2507.01544", "title": "MARVIS: Modality Adaptive Reasoning over VISualizations", "authors": [ "Benjamin Feuer", "Lennart Purucker", "Oussama Elachqar", "Chinmay Hegde" ], "github": "https://github.com/penfever/marvis", "project_page": "", "abstract": "Scientific applications of machine learning often rely on small, specialized models tuned to particular domains. Such models often achieve excellent performance, but lack flexibility. Foundation models offer versatility, but typically underperform specialized approaches, especially on non-traditional modalities and long-tail domains. We propose MARVIS (Modality Adaptive Reasoning over VISualizations), a training-free method that enables even small vision-language models to predict any data modality with high accuracy. MARVIS transforms latent embedding spaces into visual representations and then leverages the spatial and fine-grained reasoning skills of VLMs to successfully interpret and utilize them. MARVIS achieves competitive performance on vision, audio, biological, and tabular domains using a single 3B parameter model, achieving results that beat Gemini by 16\\% on average and approach specialized methods, without exposing personally identifiable information (P.I.I.) or requiring any domain-specific training. We open source our code and datasets at https://github.com/penfever/marvis", "github_manual": "", "github_regex": "https://github.com/penfever/marvis", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-03", "arxiv_id": "2507.00472", "title": "ARIG: Autoregressive Interactive Head Generation for Real-time Conversations", "authors": [ "Ying Guo", "Xi Liu", "Cheng Zhen", "Pengfei Yan", "Xiaoming Wei" ], "github": "", "project_page": "", "abstract": "Face-to-face communication, as a common human activity, motivates the research on interactive head generation. A virtual agent can generate motion responses with both listening and speaking capabilities based on the audio or motion signals of the other user and itself. However, previous clip-wise generation paradigm or explicit listener/speaker generator-switching methods have limitations in future signal acquisition, contextual behavioral understanding, and switching smoothness, making it challenging to be real-time and realistic. In this paper, we propose an autoregressive (AR) based frame-wise framework called ARIG to realize the real-time generation with better interaction realism. To achieve real-time generation, we model motion prediction as a non-vector-quantized AR process. Unlike discrete codebook-index prediction, we represent motion distribution using diffusion procedure, achieving more accurate predictions in continuous space. To improve interaction realism, we emphasize interactive behavior understanding (IBU) and detailed conversational state understanding (CSU). In IBU, based on dual-track dual-modal signals, we summarize short-range behaviors through bidirectional-integrated learning and perform contextual understanding over long ranges. In CSU, we use voice activity signals and context features of IBU to understand the various states (interruption, feedback, pause, etc.) that exist in actual conversations. These serve as conditions for the final progressive motion prediction. Extensive experiments have verified the effectiveness of our model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-03", "arxiv_id": "2507.00316", "title": "\u03bc^2Tokenizer: Differentiable Multi-Scale Multi-Modal Tokenizer for Radiology Report Generation", "authors": [ "Siyou Li", "Pengyao Qin", "Huanan Wu", "Dong Nie", "Arun J. Thirunavukarasu", "Juntao Yu", "Le Zhang" ], "github": "https://github.com/Siyou-Li/u2Tokenizer", "project_page": "", "abstract": "Automated radiology report generation (RRG) aims to produce detailed textual reports from clinical imaging, such as computed tomography (CT) scans, to improve the accuracy and efficiency of diagnosis and provision of management advice. RRG is complicated by two key challenges: (1) inherent complexity in extracting relevant information from imaging data under resource constraints, and (2) difficulty in objectively evaluating discrepancies between model-generated and expert-written reports. To address these challenges, we propose mu^2LLM, a textbf{mu}ltiscale textbf{mu}ltimodal large language models for RRG tasks. The novel {mu}^2Tokenizer, as an intermediate layer, integrates multi-modal features from the multiscale visual tokenizer and the text tokenizer, then enhances report generation quality through direct preference optimization (DPO), guided by GREEN-RedLlama. Experimental results on four large CT image-report medical datasetdemonstrate that our method outperforms existing approaches, highlighting the potential of our fine-tuned mu^2LLMs on limited data for RRG tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Siyou-Li/u2Tokenizer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-03", "arxiv_id": "2507.02856", "title": "Answer Matching Outperforms Multiple Choice for Language Model Evaluation", "authors": [ "Nikhil Chandak", "Shashwat Goel", "Ameya Prabhu", "Moritz Hardt", "Jonas Geiping" ], "github": "https://github.com/nikhilchandak/answer-matching", "project_page": "", "abstract": "Multiple choice benchmarks have long been the workhorse of language model evaluation because grading multiple choice is objective and easy to automate. However, we show multiple choice questions from popular benchmarks can often be answered without even seeing the question. These shortcuts arise from a fundamental limitation of discriminative evaluation not shared by evaluations of the model's free-form, generative answers. Until recently, there appeared to be no viable, scalable alternative to multiple choice--but, we show that this has changed. We consider generative evaluation via what we call answer matching: Give the candidate model the question without the options, have it generate a free-form response, then use a modern language model with the reference answer to determine if the response matches the reference. To compare the validity of different evaluation strategies, we annotate MMLU-Pro and GPQA-Diamond to obtain human grading data, and measure the agreement of each evaluation approach. We find answer matching using recent models--even small ones--achieves near-perfect agreement, in the range of inter-annotator agreement. In contrast, both multiple choice evaluation and using LLM-as-a-judge without reference answers aligns poorly with human grading. Improving evaluations via answer matching is not merely a conceptual concern: the rankings of several models change significantly when evaluating their free-form responses with answer matching. In light of these findings, we discuss how to move the evaluation ecosystem from multiple choice to answer matching.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nikhilchandak/answer-matching", "github_api_added_by": "auto" }, { "date": "2025-07-04", "arxiv_id": "2507.01352", "title": "Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy", "authors": [ "Chris Yuhao Liu", "Liang Zeng", "Yuzhen Xiao", "Jujie He", "Jiacai Liu", "Chaojie Wang", "Rui Yan", "Wei Shen", "Fuxiang Zhang", "Jiacheng Xu", "Yang Liu", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "Despite the critical role of reward models (RMs) in reinforcement learning from human feedback (RLHF), current state-of-the-art open RMs perform poorly on most existing evaluation benchmarks, failing to capture the spectrum of nuanced and sophisticated human preferences. Even approaches that incorporate advanced training techniques have not yielded meaningful performance improvements. We hypothesize that this brittleness stems primarily from limitations in preference datasets, which are often narrowly scoped, synthetically labeled, or lack rigorous quality control. To address these challenges, we present a large-scale preference dataset comprising 40 million preference pairs, named SynPref-40M. To enable data curation at scale, we design a human-AI synergistic two-stage pipeline that leverages the complementary strengths of human annotation quality and AI scalability. In this pipeline, humans provide verified annotations, while large language models perform automatic curation based on human guidance. Training on this preference mixture, we introduce Skywork-Reward-V2, a suite of eight reward models ranging from 0.6B to 8B parameters, trained on a carefully curated subset of 26 million preference pairs from SynPref-40M. We demonstrate that Skywork-Reward-V2 is versatile across a wide range of capabilities, including alignment with human preferences, objective correctness, safety, resistance to stylistic biases, and best-of-N scaling, achieving state-of-the-art performance across seven major reward model benchmarks. Ablation studies confirm that the effectiveness of our approach stems not only from data scale but also from high-quality curation. The Skywork-Reward-V2 series represents substantial progress in open reward models, highlighting the untapped potential of existing preference datasets and demonstrating how human-AI curation synergy can unlock significantly higher data quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2507.02754", "title": "Fast and Simplex: 2-Simplicial Attention in Triton", "authors": [ "Aurko Roy", "Timothy Chou", "Sai Surya Duvvuri", "Sijia Chen", "Jiecao Yu", "Xiaodong Wang", "Manzil Zaheer", "Rohan Anil" ], "github": "", "project_page": "", "abstract": "Recent work has shown that training loss scales as a power law with both model size and the number of tokens, and that achieving compute-optimal models requires scaling model size and token count together. However, these scaling laws assume an infinite supply of data and apply primarily in compute-bound settings. As modern large language models increasingly rely on massive internet-scale datasets, the assumption that they are compute-bound is becoming less valid. This shift highlights the need for architectures that prioritize token efficiency. In this work, we investigate the use of the 2-simplicial Transformer, an architecture that generalizes standard dot-product attention to trilinear functions through an efficient Triton kernel implementation. We demonstrate that the 2-simplicial Transformer achieves better token efficiency than standard Transformers: for a fixed token budget, similarly sized models outperform their dot-product counterparts on tasks involving mathematics, coding, reasoning, and logic. We quantify these gains by demonstrating that 2-simplicial attention changes the exponent in the scaling laws for knowledge and reasoning tasks compared to dot product attention.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2507.02652", "title": "Decoupled Planning and Execution: A Hierarchical Reasoning Framework for Deep Search", "authors": [ "Jiajie Jin", "Xiaoxi Li", "Guanting Dong", "Yuyao Zhang", "Yutao Zhu", "Yang Zhao", "Hongjin Qian", "Zhicheng Dou" ], "github": "https://github.com/ignorejjj/HiRA", "project_page": "", "abstract": "Complex information needs in real-world search scenarios demand deep reasoning and knowledge synthesis across diverse sources, which traditional retrieval-augmented generation (RAG) pipelines struggle to address effectively. Current reasoning-based approaches suffer from a fundamental limitation: they use a single model to handle both high-level planning and detailed execution, leading to inefficient reasoning and limited scalability. In this paper, we introduce HiRA, a hierarchical framework that separates strategic planning from specialized execution. Our approach decomposes complex search tasks into focused subtasks, assigns each subtask to domain-specific agents equipped with external tools and reasoning capabilities, and coordinates the results through a structured integration mechanism. This separation prevents execution details from disrupting high-level reasoning while enabling the system to leverage specialized expertise for different types of information processing. Experiments on four complex, cross-modal deep search benchmarks demonstrate that HiRA significantly outperforms state-of-the-art RAG and agent-based systems. Our results show improvements in both answer quality and system efficiency, highlighting the effectiveness of decoupled planning and execution for multi-step information seeking tasks. Our code is available at https://github.com/ignorejjj/HiRA.", "github_manual": "", "github_regex": "https://github.com/ignorejjj/HiRA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2507.02813", "title": "LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion", "authors": [ "Fangfu Liu", "Hao Li", "Jiawei Chi", "Hanyang Wang", "Minghui Yang", "Fudong Wang", "Yueqi Duan" ], "github": "", "project_page": "", "abstract": "Recovering 3D structures with open-vocabulary scene understanding from 2D images is a fundamental but daunting task. Recent developments have achieved this by performing per-scene optimization with embedded language information. However, they heavily rely on the calibrated dense-view reconstruction paradigm, thereby suffering from severe rendering artifacts and implausible semantic synthesis when limited views are available. In this paper, we introduce a novel generative framework, coined LangScene-X, to unify and generate 3D consistent multi-modality information for reconstruction and understanding. Powered by the generative capability of creating more consistent novel observations, we can build generalizable 3D language-embedded scenes from only sparse views. Specifically, we first train a TriMap video diffusion model that can generate appearance (RGBs), geometry (normals), and semantics (segmentation maps) from sparse inputs through progressive knowledge integration. Furthermore, we propose a Language Quantized Compressor (LQC), trained on large-scale image datasets, to efficiently encode language embeddings, enabling cross-scene generalization without per-scene retraining. Finally, we reconstruct the language surface fields by aligning language information onto the surface of 3D scenes, enabling open-ended language queries. Extensive experiments on real-world data demonstrate the superiority of our LangScene-X over state-of-the-art methods in terms of quality and generalizability. Project Page: https://liuff19.github.io/LangScene-X.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2507.02592", "title": "WebSailor: Navigating Super-human Reasoning for Web Agent", "authors": [ "Kuan Li", "Zhongwang Zhang", "Huifeng Yin", "Liwen Zhang", "Litu Ou", "Jialong Wu", "Wenbiao Yin", "Baixuan Li", "Zhengwei Tao", "Xinyu Wang", "Weizhou Shen", "Junkai Zhang", "Dingchu Zhang", "Xixi Wu", "Yong Jiang", "Ming Yan", "Pengjun Xie", "Fei Huang", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "Transcending human cognitive limitations represents a critical frontier in LLM training. Proprietary agentic systems like DeepResearch have demonstrated superhuman capabilities on extremely complex information-seeking benchmarks such as BrowseComp, a feat previously unattainable. We posit that their success hinges on a sophisticated reasoning pattern absent in open-source models: the ability to systematically reduce extreme uncertainty when navigating vast information landscapes. Based on this insight, we introduce WebSailor, a complete post-training methodology designed to instill this crucial capability. Our approach involves generating novel, high-uncertainty tasks through structured sampling and information obfuscation, RFT cold start, and an efficient agentic RL training algorithm, Duplicating Sampling Policy Optimization (DUPO). With this integrated pipeline, WebSailor significantly outperforms all opensource agents in complex information-seeking tasks, matching proprietary agents' performance and closing the capability gap.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2507.02726", "title": "Bourbaki: Self-Generated and Goal-Conditioned MDPs for Theorem Proving", "authors": [ "Matthieu Zimmer", "Xiaotong Ji", "Rasul Tutunov", "Anthony Bordg", "Jun Wang", "Haitham Bou Ammar" ], "github": "", "project_page": "", "abstract": "Reasoning remains a challenging task for large language models (LLMs), especially within the logically constrained environment of automated theorem proving (ATP), due to sparse rewards and the vast scale of proofs. These challenges are amplified in benchmarks like PutnamBench, which contains university-level problems requiring complex, multi-step reasoning. To address this, we introduce self-generated goal-conditioned MDPs (sG-MDPs), a new framework in which agents generate and pursue their subgoals based on the evolving proof state. Given this more structured generation of goals, the resulting problem becomes more amenable to search. We then apply Monte Carlo Tree Search (MCTS)-like algorithms to solve the sG-MDP, instantiating our approach in Bourbaki (7B), a modular system that can ensemble multiple 7B LLMs for subgoal generation and tactic synthesis. On PutnamBench, Bourbaki (7B) solves 26 problems, achieving new state-of-the-art results with models at this scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2507.01663", "title": "AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training", "authors": [ "Zhenyu Han", "Ansheng You", "Haibo Wang", "Kui Luo", "Guang Yang", "Wenqi Shi", "Menglong Chen", "Sicheng Zhang", "Zeshun Lan", "Chunshi Deng", "Huazhong Ji", "Wenjie Liu", "Yu Huang", "Yixiang Zhang", "Chenyi Pan", "Jing Wang", "Xin Huang", "Chunsheng Li", "Jianping Wu" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has become a pivotal technology in the post-training phase of large language models (LLMs). Traditional task-colocated RL frameworks suffer from significant scalability bottlenecks, while task-separated RL frameworks face challenges in complex dataflows and the corresponding resource idling and workload imbalance. Moreover, most existing frameworks are tightly coupled with LLM training or inference engines, making it difficult to support custom-designed engines. To address these challenges, we propose AsyncFlow, an asynchronous streaming RL framework for efficient post-training. Specifically, we introduce a distributed data storage and transfer module that provides a unified data management and fine-grained scheduling capability in a fully streamed manner. This architecture inherently facilitates automated pipeline overlapping among RL tasks and dynamic load balancing. Moreover, we propose a producer-consumer-based asynchronous workflow engineered to minimize computational idleness by strategically deferring parameter update process within staleness thresholds. Finally, the core capability of AsynFlow is architecturally decoupled from underlying training and inference engines and encapsulated by service-oriented user interfaces, offering a modular and customizable user experience. Extensive experiments demonstrate an average of 1.59 throughput improvement compared with state-of-the-art baseline. The presented architecture in this work provides actionable insights for next-generation RL training system designs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2506.23918", "title": "Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers", "authors": [ "Zhaochen Su", "Peng Xia", "Hangyu Guo", "Zhenhua Liu", "Yan Ma", "Xiaoye Qu", "Jiaqi Liu", "Yanshu Li", "Kaide Zeng", "Zhengyuan Yang", "Linjie Li", "Yu Cheng", "Heng Ji", "Junxian He", "Yi R. Fung" ], "github": "https://github.com/zhaochen0110/Awesome_Think_With_Images", "project_page": "", "abstract": "Recent progress in multimodal reasoning has been significantly advanced by textual Chain-of-Thought (CoT), a paradigm where models conduct reasoning within language. This text-centric approach, however, treats vision as a static, initial context, creating a fundamental \"semantic gap\" between rich perceptual data and discrete symbolic thought. Human cognition often transcends language, utilizing vision as a dynamic mental sketchpad. A similar evolution is now unfolding in AI, marking a fundamental paradigm shift from models that merely think about images to those that can truly think with images. This emerging paradigm is characterized by models leveraging visual information as intermediate steps in their thought process, transforming vision from a passive input into a dynamic, manipulable cognitive workspace. In this survey, we chart this evolution of intelligence along a trajectory of increasing cognitive autonomy, which unfolds across three key stages: from external tool exploration, through programmatic manipulation, to intrinsic imagination. To structure this rapidly evolving field, our survey makes four key contributions. (1) We establish the foundational principles of the think with image paradigm and its three-stage framework. (2) We provide a comprehensive review of the core methods that characterize each stage of this roadmap. (3) We analyze the critical landscape of evaluation benchmarks and transformative applications. (4) We identify significant challenges and outline promising future directions. By providing this structured overview, we aim to offer a clear roadmap for future research towards more powerful and human-aligned multimodal AI.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhaochen0110/Awesome_Think_With_Images", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2507.02025", "title": "IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction", "authors": [ "The IntFold Team", "Leon Qiao", "Wayne Bai", "He Yan", "Gary Liu", "Nova Xi", "Xiang Zhang" ], "github": "https://github.com/IntelliGen-AI/IntFold", "project_page": "", "abstract": "We introduce IntFold, a controllable foundation model for both general and specialized biomolecular structure prediction. IntFold demonstrates predictive accuracy comparable to the state-of-the-art AlphaFold3, while utilizing a superior customized attention kernel. Beyond standard structure prediction, IntFold can be adapted to predict allosteric states, constrained structures, and binding affinity through the use of individual adapters. Furthermore, we introduce a novel confidence head to estimate docking quality, offering a more nuanced assessment for challenging targets such as antibody-antigen complexes. Finally, we share insights gained during the training process of this computationally intensive model.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/IntelliGen-AI/IntFold", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2507.02694", "title": "Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers", "authors": [ "Zhijian Xu", "Yilun Zhao", "Manasi Patwardhan", "Lovekesh Vig", "Arman Cohan" ], "github": "https://github.com/yale-nlp/LimitGen", "project_page": "", "abstract": "Peer review is fundamental to scientific research, but the growing volume of publications has intensified the challenges of this expertise-intensive process. While LLMs show promise in various scientific tasks, their potential to assist with peer review, particularly in identifying paper limitations, remains understudied. We first present a comprehensive taxonomy of limitation types in scientific research, with a focus on AI. Guided by this taxonomy, for studying limitations, we present LimitGen, the first comprehensive benchmark for evaluating LLMs' capability to support early-stage feedback and complement human peer review. Our benchmark consists of two subsets: LimitGen-Syn, a synthetic dataset carefully created through controlled perturbations of high-quality papers, and LimitGen-Human, a collection of real human-written limitations. To improve the ability of LLM systems to identify limitations, we augment them with literature retrieval, which is essential for grounding identifying limitations in prior scientific findings. Our approach enhances the capabilities of LLM systems to generate limitations in research papers, enabling them to provide more concrete and constructive feedback.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yale-nlp/LimitGen", "github_api_added_by": "auto" }, { "date": "2025-07-04", "arxiv_id": "2507.02092", "title": "Energy-Based Transformers are Scalable Learners and Thinkers", "authors": [ "Alexi Gladstone", "Ganesh Nanduru", "Md Mofijul Islam", "Peixuan Han", "Hyeonjeong Ha", "Aman Chadha", "Yilun Du", "Heng Ji", "Jundong Li", "Tariq Iqbal" ], "github": "https://github.com/togethercomputer/RedPajama-Data", "project_page": "", "abstract": "Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances. However, most existing approaches suffer from several limitations: they are modality-specific (e.g., working only in text), problem-specific (e.g., verifiable domains like math and coding), or require additional supervision/training on top of unsupervised pretraining (e.g., verifiers or verifiable rewards). In this paper, we ask the question \"Is it possible to generalize these System 2 Thinking approaches, and develop models that learn to think solely from unsupervised learning?\" Interestingly, we find the answer is yes, by learning to explicitly verify the compatibility between inputs and candidate-predictions, and then re-framing prediction problems as optimization with respect to this verifier. Specifically, we train Energy-Based Transformers (EBTs) -- a new class of Energy-Based Models (EBMs) -- to assign an energy value to every input and candidate-prediction pair, enabling predictions through gradient descent-based energy minimization until convergence. Across both discrete (text) and continuous (visual) modalities, we find EBTs scale faster than the dominant Transformer++ approach during training, achieving an up to 35% higher scaling rate with respect to data, batch size, parameters, FLOPs, and depth. During inference, EBTs improve performance with System 2 Thinking by 29% more than the Transformer++ on language tasks, and EBTs outperform Diffusion Transformers on image denoising while using fewer forward passes. Further, we find that EBTs achieve better results than existing models on most downstream tasks given the same or worse pretraining performance, suggesting that EBTs generalize better than existing approaches. Consequently, EBTs are a promising new paradigm for scaling both the learning and thinking capabilities of models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/RedPajama-Data", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2506.22813", "title": "Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models", "authors": [ "Zhuojun Ding", "Wei Wei", "Chenghao Fan" ], "github": "", "project_page": "", "abstract": "Supervised fine-tuning (SFT) is widely used to align large language models (LLMs) with information extraction (IE) tasks, such as named entity recognition (NER). However, annotating such fine-grained labels and training domain-specific models is costly. Existing works typically train a unified model across multiple domains, but such approaches lack adaptation and scalability since not all training data benefits target domains and scaling trained models remains challenging. We propose the SaM framework, which dynamically Selects and Merges expert models at inference time. Specifically, for a target domain, we select domain-specific experts pre-trained on existing domains based on (i) domain similarity to the target domain and (ii) performance on sampled instances, respectively. The experts are then merged to create task-specific models optimized for the target domain. By dynamically merging experts beneficial to target domains, we improve generalization across various domains without extra training. Additionally, experts can be added or removed conveniently, leading to great scalability. Extensive experiments on multiple benchmarks demonstrate our framework's effectiveness, which outperforms the unified model by an average of 10%. We further provide insights into potential improvements, practical experience, and extensions of our framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2507.01004", "title": "ZeCO: Zero Communication Overhead Sequence Parallelism for Linear Attention", "authors": [ "Yuhong Chou", "Zehao Liu", "Ruijie Zhu", "Xinyi Wan", "Tianjian Li", "Congying Chu", "Qian Liu", "Jibin Wu", "Zejun Ma" ], "github": "", "project_page": "", "abstract": "Linear attention mechanisms deliver significant advantages for Large Language Models (LLMs) by providing linear computational complexity, enabling efficient processing of ultra-long sequences (e.g., 1M context). However, existing Sequence Parallelism (SP) methods, essential for distributing these workloads across devices, become the primary bottleneck due to substantial communication overhead. In this paper, we introduce ZeCO (Zero Communication Overhead) sequence parallelism for linear attention models, a new SP method designed to overcome these limitations and achieve end-to-end near-linear scalability for long sequence training. For example, training a model with a 1M sequence length across 64 devices using ZeCO takes roughly the same time as training with an 16k sequence on a single device. At the heart of ZeCO lies All-Scan, a new collective communication primitive. All-Scan provides each SP rank with precisely the initial operator state it requires while maintaining a minimal communication footprint, effectively eliminating communication overhead. Theoretically, we prove the optimaity of ZeCO, showing that it introduces only negligible time and space overhead. Empirically, we compare the communication costs of different sequence parallelism strategies and demonstrate that All-Scan achieves the fastest communication in SP scenarios. Specifically, on 256 GPUs with an 8M sequence length, ZeCO achieves a 60\\% speedup compared to the current state-of-the-art (SOTA) SP method. We believe ZeCO establishes a clear path toward efficiently training next-generation LLMs on previously intractable sequence lengths.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2507.02778", "title": "Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs", "authors": [ "Ken Tsui" ], "github": "https://github.com/kenhktsui/self-correction-bench", "project_page": "", "abstract": "Although large language models (LLMs) have become transformative, they still make mistakes and can explore unproductive reasoning paths. Self-correction is an important capability for a trustworthy LLM, particularly an autoregressive LLM. While LLMs can identify error in user input, they exhibit a systematic 'Self-Correction Blind Spot' - failing to correct identical error in their own outputs. To systematically study this phenomenon, we introduce Self-Correction Bench, a systematic framework to measure this phenomenon through controlled error injection at three complexity levels. Testing 14 models, we find an average 64.5% blind spot rate. We find multiple evidences that this limitation relates to training data composition: human training demonstrations predominantly show error-free responses rather than error-correction sequences, unlike RL-trained models that learn error correction through outcome feedback. Remarkably, simply appending \"Wait\" reduces blind spots by 89.3%, suggesting that the capability exists but requires activation. Our work highlights a critical limitation in current LLMs and offers potential avenues for improving their reliability and trustworthiness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kenhktsui/self-correction-bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2507.02321", "title": "Heeding the Inner Voice: Aligning ControlNet Training via Intermediate Features Feedback", "authors": [ "Nina Konovalova", "Maxim Nikolaev", "Andrey Kuznetsov", "Aibek Alanov" ], "github": "https://github.com/ControlGenAI/InnerControl", "project_page": "", "abstract": "Despite significant progress in text-to-image diffusion models, achieving precise spatial control over generated outputs remains challenging. ControlNet addresses this by introducing an auxiliary conditioning module, while ControlNet++ further refines alignment through a cycle consistency loss applied only to the final denoising steps. However, this approach neglects intermediate generation stages, limiting its effectiveness. We propose InnerControl, a training strategy that enforces spatial consistency across all diffusion steps. Our method trains lightweight convolutional probes to reconstruct input control signals (e.g., edges, depth) from intermediate UNet features at every denoising step. These probes efficiently extract signals even from highly noisy latents, enabling pseudo ground truth controls for training. By minimizing the discrepancy between predicted and target conditions throughout the entire diffusion process, our alignment loss improves both control fidelity and generation quality. Combined with established techniques like ControlNet++, InnerControl achieves state-of-the-art performance across diverse conditioning methods (e.g., edges, depth).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ControlGenAI/InnerControl", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2506.21546", "title": "HalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation", "authors": [ "Xinzhuo Li", "Adheesh Juvekar", "Xingyou Liu", "Muntasir Wahed", "Kiet A. Nguyen", "Ismini Lourentzou" ], "github": "", "project_page": "", "abstract": "Recent progress in vision-language segmentation has significantly advanced grounded visual understanding. However, these models often exhibit hallucinations by producing segmentation masks for objects not grounded in the image content or by incorrectly labeling irrelevant regions. Existing evaluation protocols for segmentation hallucination primarily focus on label or textual hallucinations without manipulating the visual context, limiting their capacity to diagnose critical failures. In response, we introduce HalluSegBench, the first benchmark specifically designed to evaluate hallucinations in visual grounding through the lens of counterfactual visual reasoning. Our benchmark consists of a novel dataset of 1340 counterfactual instance pairs spanning 281 unique object classes, and a set of newly introduced metrics that quantify hallucination sensitivity under visually coherent scene edits. Experiments on HalluSegBench with state-of-the-art vision-language segmentation models reveal that vision-driven hallucinations are significantly more prevalent than label-driven ones, with models often persisting in false segmentation, highlighting the need for counterfactual reasoning to diagnose grounding fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-04", "arxiv_id": "2506.23121", "title": "CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation", "authors": [ "Xinlei Yu", "Chanmiao Wang", "Hui Jin", "Ahmed Elazab", "Gangyong Jia", "Xiang Wan", "Changqing Zou", "Ruiquan Ge" ], "github": "https://github.com/YU-deep/CRISP_SAM2", "project_page": "", "abstract": "Multi-organ medical segmentation is a crucial component of medical image processing, essential for doctors to make accurate diagnoses and develop effective treatment plans. Despite significant progress in this field, current multi-organ segmentation models often suffer from inaccurate details, dependence on geometric prompts and loss of spatial information. Addressing these challenges, we introduce a novel model named CRISP-SAM2 with CRoss-modal Interaction and Semantic Prompting based on SAM2. This model represents a promising approach to multi-organ medical segmentation guided by textual descriptions of organs. Our method begins by converting visual and textual inputs into cross-modal contextualized semantics using a progressive cross-attention interaction mechanism. These semantics are then injected into the image encoder to enhance the detailed understanding of visual information. To eliminate reliance on geometric prompts, we use a semantic prompting strategy, replacing the original prompt encoder to sharpen the perception of challenging targets. In addition, a similarity-sorting self-updating strategy for memory and a mask-refining process is applied to further adapt to medical imaging and enhance localized details. Comparative experiments conducted on seven public datasets indicate that CRISP-SAM2 outperforms existing models. Extensive analysis also demonstrates the effectiveness of our method, thereby confirming its superior performance, especially in addressing the limitations mentioned earlier. Our code is available at: https://github.com/YU-deep/CRISP\\_SAM2.git.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YU-deep/CRISP_SAM2", "github_api_added_by": "user" }, { "date": "2025-07-07", "arxiv_id": "2507.01853", "title": "Eka-Eval : A Comprehensive Evaluation Framework for Large Language Models in Indian Languages", "authors": [ "Samridhi Raj Sinha", "Rajvee Sheth", "Abhishek Upperwal", "Mayank Singh" ], "github": "", "project_page": "", "abstract": "The rapid advancement of Large Language Models (LLMs) has intensified the need for evaluation frameworks that go beyond English centric benchmarks and address the requirements of linguistically diverse regions such as India. We present EKA-EVAL, a unified and production-ready evaluation framework that integrates over 35 benchmarks, including 10 Indic-specific datasets, spanning categories like reasoning, mathematics, tool use, long-context understanding, and reading comprehension. Compared to existing Indian language evaluation tools, EKA-EVAL offers broader benchmark coverage, with built-in support for distributed inference, quantization, and multi-GPU usage. Our systematic comparison positions EKA-EVAL as the first end-to-end, extensible evaluation suite tailored for both global and Indic LLMs, significantly lowering the barrier to multilingual benchmarking. The framework is open-source and publicly available at https://github.com/lingo-iitgn/ eka-eval and a part of ongoing EKA initiative (https://eka.soket.ai), which aims to scale up to over 100 benchmarks and establish a robust, multilingual evaluation ecosystem for LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-07", "arxiv_id": "2507.01955", "title": "How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks", "authors": [ "Rahul Ramachandran", "Ali Garjani", "Roman Bachmann", "Andrei Atanov", "O\u011fuzhan Fatih Kar", "Amir Zamir" ], "github": "https://github.com/EPFL-VILAB/fm-vision-evals", "project_page": "", "abstract": "Multimodal foundation models, such as GPT-4o, have recently made remarkable progress, but it is not clear where exactly these models stand in terms of understanding vision. In this paper, we benchmark the performance of popular multimodal foundation models (GPT-4o, o4-mini, Gemini 1.5 Pro and Gemini 2.0 Flash, Claude 3.5 Sonnet, Qwen2-VL, Llama 3.2) on standard computer vision tasks (semantic segmentation, object detection, image classification, depth and surface normal prediction) using established datasets (e.g., COCO, ImageNet and its variants, etc). The main challenges to performing this are: 1) most models are trained to output text and cannot natively express versatile domains, such as segments or 3D geometry, and 2) many leading models are proprietary and accessible only at an API level, i.e., there is no weight access to adapt them. We address these challenges by translating standard vision tasks into equivalent text-promptable and API-compatible tasks via prompt chaining to create a standardized benchmarking framework. We observe that 1) the models are not close to the state-of-the-art specialist models at any task. However, 2) they are respectable generalists; this is remarkable as they are presumably trained on primarily image-text-based tasks. 3) They perform semantic tasks notably better than geometric ones. 4) While the prompt-chaining techniques affect performance, better models exhibit less sensitivity to prompt variations. 5) GPT-4o performs the best among non-reasoning models, securing the top position in 4 out of 6 tasks, 6) reasoning models, e.g. o3, show improvements in geometric tasks, and 7) a preliminary analysis of models with native image generation, like the latest GPT-4o, shows they exhibit quirks like hallucinations and spatial misalignments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EPFL-VILAB/fm-vision-evals", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-07", "arxiv_id": "2507.02608", "title": "Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation", "authors": [ "Fran\u00e7ois Rozet", "Ruben Ohana", "Michael McCabe", "Gilles Louppe", "Fran\u00e7ois Lanusse", "Shirley Ho" ], "github": "https://github.com/polymathicai/lola", "project_page": "", "abstract": "The steep computational cost of diffusion models at inference hinders their use as fast physics emulators. In the context of image and video generation, this computational drawback has been addressed by generating in the latent space of an autoencoder instead of the pixel space. In this work, we investigate whether a similar strategy can be effectively applied to the emulation of dynamical systems and at what cost. We find that the accuracy of latent-space emulation is surprisingly robust to a wide range of compression rates (up to 1000x). We also show that diffusion-based emulators are consistently more accurate than non-generative counterparts and compensate for uncertainty in their predictions with greater diversity. Finally, we cover practical design choices, spanning from architectures to optimizers, that we found critical to train latent-space emulators.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/polymathicai/lola", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-07", "arxiv_id": "2507.00769", "title": "LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing", "authors": [ "Daniel Fein", "Sebastian Russo", "Violet Xiang", "Kabir Jolly", "Rafael Rafailov", "Nick Haber" ], "github": "", "project_page": "", "abstract": "Evaluating creative writing generated by large language models (LLMs) remains challenging because open-ended narratives lack ground truths. Without performant automated evaluation methods, off-the-shelf (OTS) language models are employed as zero-shot judges, yet their reliability is unclear in this context. In pursuit of robust evaluation for creative writing, we introduce LitBench, the first standardized benchmark and paired dataset for creative writing verification, comprising a held-out test set of 2,480 debiased, human-labeled story comparisons drawn from Reddit and a 43,827-pair training corpus of human preference labels. Using LitBench, we (i) benchmark zero-shot LLM judges, (ii) train Bradley Terry and generative reward models, and (iii) conduct an online human study to validate reward model rankings on newly LLM-generated stories. Our benchmark identifies Claude-3.7-Sonnet as the strongest off-the-shelf judge, reaching 73% agreement with human preferences; among trained reward models, Bradley-Terry and Generative reward models both attain an accuracy of 78%, outperforming all off-the-shelf judges. An online human study further confirms that our trained reward models consistently align with human preferences in novel LLM-generated stories. We release LitBench and reward models at https://huggingface.co/collections/SAA-Lab/litbench-68267b5da3aafe58f9e43461, providing a vetted resource for reliable, automated evaluation and optimization of creative writing systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.03724", "title": "MemOS: A Memory OS for AI System", "authors": [ "Zhiyu Li", "Shichao Song", "Chenyang Xi", "Hanyu Wang", "Chen Tang", "Simin Niu", "Ding Chen", "Jiawei Yang", "Chunyu Li", "Qingchen Yu", "Jihao Zhao", "Yezhaohui Wang", "Peng Liu", "Zehao Lin", "Pengyuan Wang", "Jiahao Huo", "Tianyi Chen", "Kai Chen", "Kehang Li", "Zhen Tao", "Junpeng Ren", "Huayi Lai", "Hao Wu", "Bo Tang", "Zhenren Wang", "Zhaoxin Fan", "Ningyu Zhang", "Linfeng Zhang", "Junchi Yan", "Mingchuan Yang", "Tong Xu", "Wei Xu", "Huajun Chen", "Haofeng Wang", "Hongkang Yang", "Wentao Zhang", "Zhi-Qin John Xu", "Siheng Chen", "Feiyu Xiong" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memory management systems hinders the development of long-context reasoning, continual personalization, and knowledge consistency.Existing models mainly rely on static parameters and short-lived contextual states, limiting their ability to track user preferences or update knowledge over extended periods.While Retrieval-Augmented Generation (RAG) introduces external knowledge in plain text, it remains a stateless workaround without lifecycle control or integration with persistent representations.Recent work has modeled the training and inference cost of LLMs from a memory hierarchy perspective, showing that introducing an explicit memory layer between parameter memory and external retrieval can substantially reduce these costs by externalizing specific knowledge. Beyond computational efficiency, LLMs face broader challenges arising from how information is distributed over time and context, requiring systems capable of managing heterogeneous knowledge spanning different temporal scales and sources. To address this challenge, we propose MemOS, a memory operating system that treats memory as a manageable system resource. It unifies the representation, scheduling, and evolution of plaintext, activation-based, and parameter-level memories, enabling cost-efficient storage and retrieval. As the basic unit, a MemCube encapsulates both memory content and metadata such as provenance and versioning. MemCubes can be composed, migrated, and fused over time, enabling flexible transitions between memory types and bridging retrieval with parameter-based learning. MemOS establishes a memory-centric system framework that brings controllability, plasticity, and evolvability to LLMs, laying the foundation for continual learning and personalized modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.03253", "title": "RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs", "authors": [ "Baolong Bi", "Shenghua Liu", "Xingzhang Ren", "Dayiheng Liu", "Junyang Lin", "Yiwei Wang", "Lingrui Mei", "Junfeng Fang", "Jiafeng Guo", "Xueqi Cheng" ], "github": "", "project_page": "", "abstract": "The foundational capabilities of large language models (LLMs) are deeply influenced by the quality of their pre-training corpora. However, enhancing data quality at scale remains a significant challenge, primarily due to the trade-off between refinement effectiveness and processing efficiency. While rule-based filtering remains the dominant paradigm, it typically operates at the document level and lacks the granularity needed to refine specific content within documents. Inspired by emerging work such as ProX, we propose RefineX, a novel framework for large-scale, surgical refinement of pre-training data through programmatic editing tasks. RefineX enables efficient and fine-grained data refinement while reliably preserving the diversity and naturalness of raw text. The core strength of RefineX lies in distilling high-quality, expert-guided end-to-end refinement results into minimal edit-based deletion programs. This high-precision distillation pipeline is used to train an efficient and reliable refine model that can systematically improve every instance in the corpus at scale. We evaluate RefineX across from-scratch pre-training at multiple model scales and find that it consistently outperforms models trained on raw, filtered, or alternatively refined data across diverse downstream tasks. On the 750M model, RefineX yields 2.6%-7.2% average gains on lighteval tasks, and achieves comparable performance using significantly fewer training tokens. Further analysis shows that RefineX reliably enhances text quality with both high efficiency and precision, outperforming prior approaches such as end-to-end generation and Prox-C. These results position RefineX as a scalable, effective, and reliable solution for optimizing pre-training data in modern LLM pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.03483", "title": "BMMR: A Large-Scale Bilingual Multimodal Multi-Discipline Reasoning Dataset", "authors": [ "Zhiheng Xi", "Guanyu Li", "Yutao Fan", "Honglin Guo", "Yufang Liu", "Xiaoran Fan", "Jiaqi Liu", "Jingchao Ding", "Wangmeng Zuo", "Zhenfei Yin", "Lei Bai", "Tao Ji", "Tao Gui", "Qi Zhang", "Xuanjing Huang" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce BMMR, a large-scale bilingual, multimodal, multi-disciplinary reasoning dataset for the community to develop and evaluate large multimodal models (LMMs). BMMR comprises 110k college-level questions spanning 300 UNESCO-defined subjects, spanning diverse formats-multiple-choice, fill-in-the-blank, and open-ended QA-and sourced from both print and digital media such as books, exams, and quizzes. All data are curated and filtered via a human-in-the-loop and scalable framework, and each instance is paired with a high-quality reasoning path. The dataset is organized into two parts: BMMR-Eval that comprises 20,458 high-quality instances to comprehensively assess LMMs' knowledge and reasoning across multiple disciplines in both Chinese and English; and BMMR-Train that contains 88,991 instances to support further research and development, extending the current focus on mathematical reasoning to diverse disciplines and domains. In addition, we propose the process-based multi-discipline verifier (i.e., BMMR-Verifier) for accurate and fine-grained evaluation of reasoning paths. Extensive experiments on 24 models reveal that (i) even SOTA models (e.g., o3 and Gemini-2.5-Pro) leave substantial headroom on BMMR-Eval; (ii) reasoning models exhibit discipline bias and outperform LMMs only on specific subjects; (iii) open-source models still trail their proprietary counterparts; and (iv) fine-tuning on BMMR-Train narrows this gap. Additionally, we conduct reasoning-chain analyses using BMMR-Verifier and other in-depth studies, uncovering the challenges LMMs currently face in multidisciplinary reasoning. We will release the data, and we hope our work can offer insights and contributions to the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.05163", "title": "4DSloMo: 4D Reconstruction for High Speed Scene with Asynchronous Capture", "authors": [ "Yutian Chen", "Shi Guo", "Tianshuo Yang", "Lihe Ding", "Xiuyuan Yu", "Jinwei Gu", "Tianfan Xue" ], "github": "https://github.com/OpenImagingLab/4DSloMo", "project_page": "", "abstract": "Reconstructing fast-dynamic scenes from multi-view videos is crucial for high-speed motion analysis and realistic 4D reconstruction. However, the majority of 4D capture systems are limited to frame rates below 30 FPS (frames per second), and a direct 4D reconstruction of high-speed motion from low FPS input may lead to undesirable results. In this work, we propose a high-speed 4D capturing system only using low FPS cameras, through novel capturing and processing modules. On the capturing side, we propose an asynchronous capture scheme that increases the effective frame rate by staggering the start times of cameras. By grouping cameras and leveraging a base frame rate of 25 FPS, our method achieves an equivalent frame rate of 100-200 FPS without requiring specialized high-speed cameras. On processing side, we also propose a novel generative model to fix artifacts caused by 4D sparse-view reconstruction, as asynchrony reduces the number of viewpoints at each timestamp. Specifically, we propose to train a video-diffusion-based artifact-fix model for sparse 4D reconstruction, which refines missing details, maintains temporal consistency, and improves overall reconstruction quality. Experimental results demonstrate that our method significantly enhances high-speed 4D reconstruction compared to synchronous capture.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenImagingLab/4DSloMo", "github_api_added_by": "auto" }, { "date": "2025-07-08", "arxiv_id": "2507.05197", "title": "Pre-Trained Policy Discriminators are General Reward Models", "authors": [ "Shihan Dou", "Shichun Liu", "Yuming Yang", "Yicheng Zou", "Yunhua Zhou", "Shuhao Xing", "Chenhao Huang", "Qiming Ge", "Demin Song", "Haijun Lv", "Songyang Gao", "Chengqi Lv", "Enyu Zhou", "Honglin Guo", "Zhiheng Xi", "Wenwei Zhang", "Qipeng Guo", "Qi Zhang", "Xipeng Qiu", "Xuanjing Huang", "Tao Gui", "Kai Chen" ], "github": "", "project_page": "", "abstract": "We offer a novel perspective on reward modeling by formulating it as a policy discriminator, which quantifies the difference between two policies to generate a reward signal, guiding the training policy towards a target policy with desired behaviors. Based on this conceptual insight, we propose a scalable pre-training method named Policy Discriminative Learning (POLAR), which trains a reward model (RM) to discern identical policies and discriminate different ones. Unlike traditional reward modeling methods relying on absolute preferences, POLAR captures the relative difference between one policy and an arbitrary target policy, which is a scalable, high-level optimization objective suitable for modeling generic ranking relationships. Leveraging the POLAR pre-training paradigm, we present a series of RMs with parameter scales from 1.8B to 7B. Empirical results show that POLAR substantially outperforms traditional non-pre-trained methods, significantly enhancing RM performance. For instance, POLAR-7B could improve preference accuracy from 54.8% to 81.0% on STEM tasks and from 57.9% to 85.5% on creative writing tasks compared to SOTA baselines. POLAR also shows robust generalization capabilities in RLHF using Reinforcement Fine-tuning (RFT), providing reliable reward signals and markedly enhancing policy performance--improving LLaMa3.1-8B from an average of 47.36% to 56.33% and Qwen2.5-32B from 64.49% to 70.47% on 20 benchmarks. Moreover, scaling experiments reveal a clear power-law relationship between computation and performance, supported by linear correlation coefficients approaching 0.99. The impressive performance, strong generalization, and scaling properties suggest that POLAR is a promising direction for developing general and strong reward models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.04036", "title": "PresentAgent: Multimodal Agent for Presentation Video Generation", "authors": [ "Jingwei Shi", "Zeyu Zhang", "Biao Wu", "Yanjie Liang", "Meng Fang", "Ling Chen", "Yang Zhao" ], "github": "https://github.com/AIGeeksGroup/PresentAgent", "project_page": "", "abstract": "We present PresentAgent, a multimodal agent that transforms long-form documents into narrated presentation videos. While existing approaches are limited to generating static slides or text summaries, our method advances beyond these limitations by producing fully synchronized visual and spoken content that closely mimics human-style presentations. To achieve this integration, PresentAgent employs a modular pipeline that systematically segments the input document, plans and renders slide-style visual frames, generates contextual spoken narration with large language models and Text-to-Speech models, and seamlessly composes the final video with precise audio-visual alignment. Given the complexity of evaluating such multimodal outputs, we introduce PresentEval, a unified assessment framework powered by Vision-Language Models that comprehensively scores videos across three critical dimensions: content fidelity, visual clarity, and audience comprehension through prompt-based evaluation. Our experimental validation on a curated dataset of 30 document-presentation pairs demonstrates that PresentAgent approaches human-level quality across all evaluation metrics. These results highlight the significant potential of controllable multimodal agents in transforming static textual materials into dynamic, effective, and accessible presentation formats. Code will be available at https://github.com/AIGeeksGroup/PresentAgent.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/PresentAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.04952", "title": "ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation", "authors": [ "Chenchen Zhang", "Yuhang Li", "Can Xu", "Jiaheng Liu", "Ao Liu", "Shihui Hu", "Dengpeng Wu", "Guanhua Huang", "Kejiao Li", "Qi Yi", "Ruibin Xiong", "Haotian Zhu", "Yuanxing Zhang", "Yuhao Jiang", "Yue Zhang", "Zenan Xu", "Bohui Zhai", "Guoxiang He", "Hebin Li", "Jie Zhao", "Le Zhang", "Lingyun Tan", "Pengyu Guo", "Xianshu Pang", "Yang Ruan", "Zhifeng Zhang", "Zhonghu Wang", "Ziyan Xu", "Zuopu Yin", "Wiggin Zhou", "Chayse Zhou", "Fengzong Lian" ], "github": "", "project_page": "", "abstract": "The generative capabilities of Large Language Models (LLMs) are rapidly expanding from static code to dynamic, interactive visual artifacts. This progress is bottlenecked by a critical evaluation gap: established benchmarks focus on algorithmic correctness and are blind to the visual fidelity and interactive integrity that define modern user experiences. To bridge this gap, we introduce ArtifactsBench, a new benchmark and paradigm for the automated, multimodal evaluation of visual code generation. Our framework programmatically renders each generated artifact and captures its dynamic behavior through temporal screenshots. This visual evidence, alongside the source code, is then assessed by a Multimodal LLM (MLLM)-as-Judge, which is rigorously guided by a fine-grained, per-task checklist to ensure holistic and reproducible scoring. We construct a new benchmark of 1,825 diverse tasks and evaluate over 30 leading LLMs. Our automated evaluation achieves a striking 94.4% ranking consistency with WebDev Arena, the gold-standard for human preference in web development, and over 90% pairwise agreement with human experts. This establishes ArtifactsBench as the first framework to reliably automate the assessment of human-perceived quality at scale. Our analysis provides a high-resolution map of the current SOTA, revealing that generalist models often outperform domain-specific ones. We open-source ArtifactsBench, including the benchmark, evaluation harness, and baseline results at https://artifactsbenchmark.github.io/, to provide the community with a scalable and accurate tool to accelerate the development of user-centric generative models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.04590", "title": "VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents", "authors": [ "Rui Meng", "Ziyan Jiang", "Ye Liu", "Mingyi Su", "Xinyi Yang", "Yuepeng Fu", "Can Qin", "Zeyuan Chen", "Ran Xu", "Caiming Xiong", "Yingbo Zhou", "Wenhu Chen", "Semih Yavuz" ], "github": "", "project_page": "", "abstract": "Multimodal embedding models have been crucial in enabling various downstream tasks such as semantic similarity, information retrieval, and clustering over different modalities. However, existing multimodal embeddings like VLM2Vec, E5-V, GME are predominantly focused on natural images, with limited support for other visual forms such as videos and visual documents. This restricts their applicability in real-world scenarios, including AI agents, multi-modal search and recommendation, and retrieval-augmented generation (RAG). To close this gap, we propose VLM2Vec-V2, a unified framework for learning embeddings across diverse visual forms. First, we introduce MMEB-V2, a comprehensive benchmark that extends MMEB with five new task types: visual document retrieval, video retrieval, temporal grounding, video classification and video question answering - spanning text, image, video, and visual document inputs. Next, we train VLM2Vec-V2, a general-purpose embedding model that supports text, image, video, and visual document inputs. Extensive experiments show that VLM2Vec-V2 achieves strong performance not only on the newly introduced video and document retrieval tasks, but also improves over prior baselines on the original image benchmarks. Through extensive evaluation, our study offers insights into the generalizability of various multimodal embedding models and highlights effective strategies for unified embedding learning, laying the groundwork for more scalable and adaptable representation learning in both research and real-world settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.03336", "title": "Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky", "authors": [ "Ashutosh Hathidara", "Julien Yu", "Sebastian Schreiber" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly tasked with invoking enterprise APIs, yet they routinely falter when near-duplicate tools vie for the same user intent or when required arguments are left underspecified. We introduce DiaFORGE (Dialogue Framework for Organic Response Generation & Evaluation), a disambiguation-centric, three-stage pipeline that (i) synthesizes persona-driven, multi-turn dialogues in which the assistant must distinguish among highly similar tools, (ii) performs supervised fine-tuning of open-source models with reasoning traces across 3B - 70B parameters, and (iii) evaluates real-world readiness via a dynamic suite that redeploys each model in a live agentic loop and reports end-to-end goal completion alongside conventional static metrics. On our dynamic benchmark DiaBENCH, models trained with DiaFORGE raise tool-invocation success by 27 pp over GPT-4o and by 49 pp over Claude-3.5-Sonnet, both under optimized prompting. To spur further research, we release an open corpus of 5000 production-grade enterprise API specifications paired with rigorously validated, disambiguation-focused dialogues, offering a practical blueprint for building reliable, enterprise-ready tool-calling agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.02659", "title": "OmniDraft: A Cross-vocabulary, Online Adaptive Drafter for On-device Speculative Decoding", "authors": [ "Ramchalam Kinattinkara Ramakrishnan", "Zhaocong Yuan", "Shaojie Zhuo", "Chen Feng", "Yicheng Lin", "Chenzheng Su", "Xiaopeng Zhang" ], "github": "", "project_page": "", "abstract": "Speculative decoding generally dictates having a small, efficient draft model that is either pretrained or distilled offline to a particular target model series, for instance, Llama or Qwen models. However, within online deployment settings, there are two major challenges: 1) usage of a target model that is incompatible with the draft model; 2) expectation of latency improvements over usage and time. In this work, we propose OmniDraft, a unified framework that enables a single draft model to operate with any target model and adapt dynamically to user data. We introduce an online n-gram cache with hybrid distillation fine-tuning to address the cross-vocabulary mismatch across draft and target models; and further improve decoding speed by leveraging adaptive drafting techniques. OmniDraft is particularly suitable for on-device LLM applications where model cost, efficiency and user customization are the major points of contention. This further highlights the need to tackle the above challenges and motivates the ``one drafter for all'' paradigm. We showcase the proficiency of the OmniDraft framework by performing online learning on math reasoning, coding and text generation tasks. Notably, OmniDraft enables a single Llama-68M model to pair with various target models including Vicuna-7B, Qwen2-7B and Llama3-8B models for speculative decoding; and additionally provides up to 1.5-2x speedup.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.04447", "title": "DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge", "authors": [ "Wenyao Zhang", "Hongsi Liu", "Zekun Qi", "Yunnan Wang", "XinQiang Yu", "Jiazhao Zhang", "Runpei Dong", "Jiawei He", "He Wang", "Zhizheng Zhang", "Li Yi", "Wenjun Zeng", "Xin Jin" ], "github": "https://github.com/Zhangwenyao1/DreamVLA", "project_page": "", "abstract": "Recent advances in vision-language-action (VLA) models have shown promise in integrating image generation with action prediction to improve generalization and reasoning in robot manipulation. However, existing methods are limited to challenging image-based forecasting, which suffers from redundant information and lacks comprehensive and critical world knowledge, including dynamic, spatial and semantic information. To address these limitations, we propose DreamVLA, a novel VLA framework that integrates comprehensive world knowledge forecasting to enable inverse dynamics modeling, thereby establishing a perception-prediction-action loop for manipulation tasks. Specifically, DreamVLA introduces a dynamic-region-guided world knowledge prediction, integrated with the spatial and semantic cues, which provide compact yet comprehensive representations for action planning. This design aligns with how humans interact with the world by first forming abstract multimodal reasoning chains before acting. To mitigate interference among the dynamic, spatial and semantic information during training, we adopt a block-wise structured attention mechanism that masks their mutual attention, preventing information leakage and keeping each representation clean and disentangled. Moreover, to model the conditional distribution over future actions, we employ a diffusion-based transformer that disentangles action representations from shared latent features. Extensive experiments on both real-world and simulation environments demonstrate that DreamVLA achieves 76.7% success rate on real robot tasks and 4.44 average length on the CALVIN ABC-D benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Zhangwenyao1/DreamVLA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.05259", "title": "Beyond Simple Edits: X-Planner for Complex Instruction-Based Image Editing", "authors": [ "Chun-Hsiao Yeh", "Yilin Wang", "Nanxuan Zhao", "Richard Zhang", "Yuheng Li", "Yi Ma", "Krishna Kumar Singh" ], "github": "", "project_page": "", "abstract": "Recent diffusion-based image editing methods have significantly advanced text-guided tasks but often struggle to interpret complex, indirect instructions. Moreover, current models frequently suffer from poor identity preservation, unintended edits, or rely heavily on manual masks. To address these challenges, we introduce X-Planner, a Multimodal Large Language Model (MLLM)-based planning system that effectively bridges user intent with editing model capabilities. X-Planner employs chain-of-thought reasoning to systematically decompose complex instructions into simpler, clear sub-instructions. For each sub-instruction, X-Planner automatically generates precise edit types and segmentation masks, eliminating manual intervention and ensuring localized, identity-preserving edits. Additionally, we propose a novel automated pipeline for generating large-scale data to train X-Planner which achieves state-of-the-art results on both existing benchmarks and our newly introduced complex editing benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.04562", "title": "Evaluating LLMs on Real-World Forecasting Against Human Superforecasters", "authors": [ "Janna Lu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their ability to forecast future events remains understudied. A year ago, large language models struggle to come close to the accuracy of a human crowd. I evaluate state-of-the-art LLMs on 464 forecasting questions from Metaculus, comparing their performance against human superforecasters. Frontier models achieve Brier scores that ostensibly surpass the human crowd but still significantly underperform a group of superforecasters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.04376", "title": "MOD-X: A Modular Open Decentralized eXchange Framework proposal for Heterogeneous Interoperable Artificial Agents", "authors": [ "Georgios Ioannides", "Christos Constantinou", "Vinija Jain", "Aman Chadha", "Aaron Elkins" ], "github": "", "project_page": "", "abstract": "As Artificial Intelligence systems evolve from monolithic models to ecosystems of specialized agents, the need for standardized communication protocols becomes increasingly critical. This paper introduces MOD-X (Modular Open Decentralized eXchange), a novel architectural framework proposal for agent interoperability that addresses key limitations of existing protocols. Unlike current approaches, MOD-X proposes a layered architecture with a Universal Message Bus, thorough state management, translation capabilities, and blockchain-based security mechanisms. We present MOD-X's architecture, compare it with existing protocols, and demonstrate its application through a worked example how it enables integration between heterogeneous specialist agents (agents with different architectures, vendors, capabilities, and knowledge representations--including rule-based systems, neural networks, symbolic reasoning engines, and legacy software with agent wrappers). MOD-X's key innovations include a publish-subscribe communication model, semantic capability discovery, and dynamic workflow orchestration--providing a framework that bridges theoretical formalism with practical implementation. This architecture addresses the growing need for truly decentralized, interoperable agent ecosystems that can scale effectively without the need for central coordination.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.03745", "title": "StreamDiT: Real-Time Streaming Text-to-Video Generation", "authors": [ "Akio Kodaira", "Tingbo Hou", "Ji Hou", "Masayoshi Tomizuka", "Yue Zhao" ], "github": "", "project_page": "", "abstract": "Recently, great progress has been achieved in text-to-video (T2V) generation by scaling transformer-based diffusion models to billions of parameters, which can generate high-quality videos. However, existing models typically produce only short clips offline, restricting their use cases in interactive and real-time applications. This paper addresses these challenges by proposing StreamDiT, a streaming video generation model. StreamDiT training is based on flow matching by adding a moving buffer. We design mixed training with different partitioning schemes of buffered frames to boost both content consistency and visual quality. StreamDiT modeling is based on adaLN DiT with varying time embedding and window attention. To practice the proposed method, we train a StreamDiT model with 4B parameters. In addition, we propose a multistep distillation method tailored for StreamDiT. Sampling distillation is performed in each segment of a chosen partitioning scheme. After distillation, the total number of function evaluations (NFEs) is reduced to the number of chunks in a buffer. Finally, our distilled model reaches real-time performance at 16 FPS on one GPU, which can generate video streams at 512p resolution. We evaluate our method through both quantitative metrics and human evaluation. Our model enables real-time applications, e.g. streaming generation, interactive generation, and video-to-video. We provide video results and more examples in our project website: this https URL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.05108", "title": "Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration", "authors": [ "Yuyi Zhang", "Peirong Zhang", "Zhenhua Yang", "Pengyu Yan", "Yongxin Shi", "Pengwei Liu", "Fengjun Guo", "Lianwen Jin" ], "github": "https://github.com/SCUT-DLVCLab/AutoHDR", "project_page": "", "abstract": "Historical documents represent an invaluable cultural heritage, yet have undergone significant degradation over time through tears, water erosion, and oxidation. Existing Historical Document Restoration (HDR) methods primarily focus on single modality or limited-size restoration, failing to meet practical needs. To fill this gap, we present a full-page HDR dataset (FPHDR) and a novel automated HDR solution (AutoHDR). Specifically, FPHDR comprises 1,633 real and 6,543 synthetic images with character-level and line-level locations, as well as character annotations in different damage grades. AutoHDR mimics historians' restoration workflows through a three-stage approach: OCR-assisted damage localization, vision-language context text prediction, and patch autoregressive appearance restoration. The modular architecture of AutoHDR enables seamless human-machine collaboration, allowing for flexible intervention and optimization at each restoration stage. Experiments demonstrate AutoHDR's remarkable performance in HDR. When processing severely damaged documents, our method improves OCR accuracy from 46.83\\% to 84.05\\%, with further enhancement to 94.25\\% through human-machine collaboration. We believe this work represents a significant advancement in automated historical document restoration and contributes substantially to cultural heritage preservation. The model and dataset are available at https://github.com/SCUT-DLVCLab/AutoHDR.", "github_manual": "", "github_regex": "https://github.com/SCUT-DLVCLab/AutoHDR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.02029", "title": "RoboBrain 2.0 Technical Report", "authors": [ "BAAI RoboBrain Team", "Mingyu Cao", "Huajie Tan", "Yuheng Ji", "Minglan Lin", "Zhiyu Li", "Zhou Cao", "Pengwei Wang", "Enshen Zhou", "Yi Han", "Yingbo Tang", "Xiangqi Xu", "Wei Guo", "Yaoxu Lyu", "Yijie Xu", "Jiayu Shi", "Cheng Chi", "Mengdi Zhao", "Xiaoshuai Hao", "Shanyu Rong", "Zhengliang Cai", "Bolun Zhang", "Shuyi Zhang", "Huaihai Lyu", "Mengfei Du", "Lingfeng Zhang", "Xi Feng", "Xiaodan Liu", "Yance Jiao", "Chenrui He", "Mengsi Lyu", "Zhuo Chen", "Yulong Ao", "Xue Sun", "Zheqi He", "Jingshu Zheng", "Xi Yang", "Donghai Shi", "Kunchang Xie", "Bochao Zhang", "Shaokai Nie", "Chunlei Men", "Yonghua Lin", "Zhongyuan Wang", "Tiejun Huang", "Shanghang Zhang" ], "github": "", "project_page": "", "abstract": "We introduce RoboBrain 2.0, our latest generation of embodied vision-language foundation models, designed to unify perception, reasoning, and planning for complex embodied tasks in physical environments. It comes in two variants: a lightweight 7B model and a full-scale 32B model, featuring a heterogeneous architecture with a vision encoder and a language model. Despite its compact size, RoboBrain 2.0 achieves strong performance across a wide spectrum of embodied reasoning tasks. On both spatial and temporal benchmarks, the 32B variant achieves leading results, surpassing prior open-source and proprietary models. In particular, it supports key real-world embodied AI capabilities, including spatial understanding (e.g., affordance prediction, spatial referring, trajectory forecasting) and temporal decision-making (e.g., closed-loop interaction, multi-agent long-horizon planning, and scene graph updating). This report details the model architecture, data construction, multi-stage training strategies, infrastructure and practical applications. We hope RoboBrain 2.0 advances embodied AI research and serves as a practical step toward building generalist embodied agents. The code, checkpoint and benchmark are available at https://superrobobrain.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.03607", "title": "VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification", "authors": [ "C\u00e9dric Bonhomme", "Alexandre Dulaunoy" ], "github": "https://github.com/vulnerability-lookup/VulnTrain", "project_page": "", "abstract": "This paper presents VLAI, a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vulnerability-lookup/VulnTrain", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.04009", "title": "Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents", "authors": [ "Ziyang Miao", "Qiyu Sun", "Jingyuan Wang", "Yuchen Gong", "Yaowei Zheng", "Shiqi Li", "Richong Zhang" ], "github": "https://github.com/ConardLi/easy-dataset", "project_page": "", "abstract": "Large language models (LLMs) have shown impressive performance on general-purpose tasks, yet adapting them to specific domains remains challenging due to the scarcity of high-quality domain data. Existing data synthesis tools often struggle to extract reliable fine-tuning data from heterogeneous documents effectively. To address this limitation, we propose Easy Dataset, a unified framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface (GUI). Specifically, Easy Dataset allows users to easily configure text extraction models and chunking strategies to transform raw documents into coherent text chunks. It then leverages a persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs. Throughout the pipeline, a human-in-the-loop visual interface facilitates the review and refinement of intermediate outputs to ensure data quality. Experiments on a financial question-answering task show that fine-tuning LLMs on the synthesized dataset significantly improves domain-specific performance while preserving general knowledge. The source code and installable package are available at https://github.com/ConardLi/easy-dataset and have garnered over 9,000 GitHub stars.", "github_manual": "", "github_regex": "https://github.com/ConardLi/easy-dataset", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.00994", "title": "Should We Still Pretrain Encoders with Masked Language Modeling?", "authors": [ "Hippolyte Gisserot-Boukhlef", "Nicolas Boizard", "Manuel Faysse", "Duarte M. Alves", "Emmanuel Malherbe", "Andr\u00e9 F. T. Martins", "C\u00e9line Hudelot", "Pierre Colombo" ], "github": "", "project_page": "", "abstract": "Learning high-quality text representations is fundamental to a wide range of NLP tasks. While encoder pretraining has traditionally relied on Masked Language Modeling (MLM), recent evidence suggests that decoder models pretrained with Causal Language Modeling (CLM) can be effectively repurposed as encoders, often surpassing traditional encoders on text representation benchmarks. However, it remains unclear whether these gains reflect an inherent advantage of the CLM objective or arise from confounding factors such as model and data scale. In this paper, we address this question through a series of large-scale, carefully controlled pretraining ablations, training a total of 30 models ranging from 210 million to 1 billion parameters, and conducting over 15,000 fine-tuning and evaluation runs. We find that while training with MLM generally yields better performance across text representation tasks, CLM-trained models are more data-efficient and demonstrate improved fine-tuning stability. Building on these findings, we experimentally show that a biphasic training strategy that sequentially applies CLM and then MLM, achieves optimal performance under a fixed computational training budget. Moreover, we demonstrate that this strategy becomes more appealing when initializing from readily available pretrained CLM models (from the existing LLM ecosystem), reducing the computational burden needed to train best-in-class encoder models. We release all project artifacts at https://hf.co/MLMvsCLM to foster further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.04285", "title": "SeqTex: Generate Mesh Textures in Video Sequence", "authors": [ "Ze Yuan", "Xin Yu", "Yangtian Sun", "Yuan-Chen Guo", "Yan-Pei Cao", "Ding Liang", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "Training native 3D texture generative models remains a fundamental yet challenging problem, largely due to the limited availability of large-scale, high-quality 3D texture datasets. This scarcity hinders generalization to real-world scenarios. To address this, most existing methods finetune foundation image generative models to exploit their learned visual priors. However, these approaches typically generate only multi-view images and rely on post-processing to produce UV texture maps -- an essential representation in modern graphics pipelines. Such two-stage pipelines often suffer from error accumulation and spatial inconsistencies across the 3D surface. In this paper, we introduce SeqTex, a novel end-to-end framework that leverages the visual knowledge encoded in pretrained video foundation models to directly generate complete UV texture maps. Unlike previous methods that model the distribution of UV textures in isolation, SeqTex reformulates the task as a sequence generation problem, enabling the model to learn the joint distribution of multi-view renderings and UV textures. This design effectively transfers the consistent image-space priors from video foundation models into the UV domain. To further enhance performance, we propose several architectural innovations: a decoupled multi-view and UV branch design, geometry-informed attention to guide cross-domain feature alignment, and adaptive token resolution to preserve fine texture details while maintaining computational efficiency. Together, these components allow SeqTex to fully utilize pretrained video priors and synthesize high-fidelity UV texture maps without the need for post-processing. Extensive experiments show that SeqTex achieves state-of-the-art performance on both image-conditioned and text-conditioned 3D texture generation tasks, with superior 3D consistency, texture-geometry alignment, and real-world generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.03683", "title": "On the rankability of visual embeddings", "authors": [ "Ankit Sonthalia", "Arnas Uselis", "Seong Joon Oh" ], "github": "https://github.com/aktsonthalia/rankable-vision-embeddings", "project_page": "", "abstract": "We study whether visual embedding models capture continuous, ordinal attributes along linear directions, which we term _rank axes_. We define a model as _rankable_ for an attribute if projecting embeddings onto such an axis preserves the attribute's order. Across 7 popular encoders and 9 datasets with attributes like age, crowd count, head pose, aesthetics, and recency, we find that many embeddings are inherently rankable. Surprisingly, a small number of samples, or even just two extreme examples, often suffice to recover meaningful rank axes, without full-scale supervision. These findings open up new use cases for image ranking in vector databases and motivate further study into the structure and learning of rankable embeddings. Our code is available at https://github.com/aktsonthalia/rankable-vision-embeddings.", "github_manual": "", "github_regex": "https://github.com/aktsonthalia/rankable-vision-embeddings", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2506.21884", "title": "UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields", "authors": [ "Fabian Perez", "Sara Rojas", "Carlos Hinojosa", "Hoover Rueda-Chac\u00f3n", "Bernard Ghanem" ], "github": "", "project_page": "", "abstract": "Neural Radiance Field (NeRF)-based segmentation methods focus on object semantics and rely solely on RGB data, lacking intrinsic material properties. This limitation restricts accurate material perception, which is crucial for robotics, augmented reality, simulation, and other applications. We introduce UnMix-NeRF, a framework that integrates spectral unmixing into NeRF, enabling joint hyperspectral novel view synthesis and unsupervised material segmentation. Our method models spectral reflectance via diffuse and specular components, where a learned dictionary of global endmembers represents pure material signatures, and per-point abundances capture their distribution. For material segmentation, we use spectral signature predictions along learned endmembers, allowing unsupervised material clustering. Additionally, UnMix-NeRF enables scene editing by modifying learned endmember dictionaries for flexible material-based appearance manipulation. Extensive experiments validate our approach, demonstrating superior spectral reconstruction and material segmentation to existing methods. Project page: https://www.factral.co/UnMix-NeRF.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.03033", "title": "Preserving Privacy, Increasing Accessibility, and Reducing Cost: An On-Device Artificial Intelligence Model for Medical Transcription and Note Generation", "authors": [ "Johnson Thomas", "Ayush Mudgal", "Wendao Liu", "Nisten Tahiraj", "Zeeshaan Mohammed", "Dhruv Diddi" ], "github": "", "project_page": "", "abstract": "Background: Clinical documentation represents a significant burden for healthcare providers, with physicians spending up to 2 hours daily on administrative tasks. Recent advances in large language models (LLMs) offer promising solutions, but privacy concerns and computational requirements limit their adoption in healthcare settings. Objective: To develop and evaluate a privacy-preserving, on-device medical transcription system using a fine-tuned Llama 3.2 1B model capable of generating structured medical notes from medical transcriptions while maintaining complete data sovereignty entirely in the browser. Methods: We fine-tuned a Llama 3.2 1B model using Parameter-Efficient Fine-Tuning (PEFT) with LoRA on 1,500 synthetic medical transcription-to-structured note pairs. The model was evaluated against the base Llama 3.2 1B on two datasets: 100 endocrinology transcripts and 140 modified ACI benchmark cases. Evaluation employed both statistical metrics (ROUGE, BERTScore, BLEURT) and LLM-as-judge assessments across multiple clinical quality dimensions. Results: The fine-tuned OnDevice model demonstrated substantial improvements over the base model. On the ACI benchmark, ROUGE-1 scores increased from 0.346 to 0.496, while BERTScore F1 improved from 0.832 to 0.866. Clinical quality assessments showed marked reduction in major hallucinations (from 85 to 35 cases) and enhanced factual correctness (2.81 to 3.54 on 5-point scale). Similar improvements were observed on the internal evaluation dataset, with composite scores increasing from 3.13 to 4.43 (+41.5%). Conclusions: Fine-tuning compact LLMs for medical transcription yields clinically meaningful improvements while enabling complete on-device browser deployment. This approach addresses key barriers to AI adoption in healthcare: privacy preservation, cost reduction, and accessibility for resource-constrained environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.05257", "title": "Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions", "authors": [ "Yuanzhe Hu", "Yu Wang", "Julian McAuley" ], "github": "https://github.com/kingjulio8238/Memary", "project_page": "", "abstract": "Recent benchmarks for Large Language Model (LLM) agents primarily focus on evaluating reasoning, planning, and execution capabilities, while another critical component-memory, encompassing how agents memorize, update, and retrieve long-term information-is under-evaluated due to the lack of benchmarks. We term agents with memory mechanisms as memory agents. In this paper, we identify four core competencies essential for memory agents: accurate retrieval, test-time learning, long-range understanding, and conflict resolution. Existing datasets either rely on limited context lengths or are tailored for static, long-context settings like book-based QA, which do not reflect the interactive, multi-turn nature of memory agents that incrementally accumulate information. Furthermore, no existing benchmarks cover all four competencies. Therefore, we introduce MemoryAgentBench, a new benchmark specifically designed for memory agents. Our benchmark combines reformulated existing datasets with newly constructed ones, covering the above four memory competencies, providing a systematic and challenging testbed for assessing memory quality. We evaluate a diverse set of memory agents, ranging from simple context-based and retrieval-augmented generation (RAG) systems to advanced agents with external memory modules and tool integration. Empirical results reveal that current methods fall short of mastering all four competencies, underscoring the need for further research into comprehensive memory mechanisms for LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kingjulio8238/Memary", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.04642", "title": "R1-RE: Cross-Domain Relationship Extraction with RLVR", "authors": [ "Runpeng Dai", "Tong Zheng", "Run Yang", "Hongtu Zhu" ], "github": "", "project_page": "", "abstract": "Relationship extraction (RE) is a core task in natural language processing. Traditional approaches typically frame RE as a supervised learning problem, directly mapping context to labels-an approach that often suffers from poor out-of-domain (OOD) generalization. Inspired by the workflow of human annotators, we reframe RE as a reasoning task guided by annotation guidelines and introduce R1-RE, the first reinforcement learning with verifiable reward (RLVR) framework for RE tasks. Our method elicits the reasoning abilities of small language models for annotation tasks, resulting in significantly improved OOD robustness. We evaluate our approach on the public Sem-2010 dataset and a private MDKG dataset. The R1-RE-7B model attains an average OOD accuracy of approximately 70%, on par with leading proprietary models such as GPT-4o. Additionally, our comprehensive analysis provides novel insights into the training dynamics and emergent reasoning behaviors of the RLVR paradigm for RE.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-08", "arxiv_id": "2507.06229", "title": "Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving", "authors": [ "Xiangru Tang", "Tianrui Qin", "Tianhao Peng", "Ziyang Zhou", "Daniel Shao", "Tingting Du", "Xinming Wei", "Peng Xia", "Fang Wu", "He Zhu", "Ge Zhang", "Jiaheng Liu", "Xingyao Wang", "Sirui Hong", "Chenglin Wu", "Hao Cheng", "Chi Wang", "Wangchunshu Zhou" ], "github": "", "project_page": "", "abstract": "As language agents tackle increasingly complex tasks, they struggle with effective error correction and experience reuse across domains. We introduce Agent KB, a hierarchical experience framework that enables complex agentic problem solving via a novel Reason-Retrieve-Refine pipeline. Agent KB addresses a core limitation: agents traditionally cannot learn from each other's experiences. By capturing both high-level strategies and detailed execution logs, Agent KB creates a shared knowledge base that enables cross-agent knowledge transfer. Evaluated on the GAIA benchmark, Agent KB improves success rates by up to 16.28 percentage points. On the most challenging tasks, Claude-3 improves from 38.46% to 57.69%, while GPT-4 improves from 53.49% to 73.26% on intermediate tasks. On SWE-bench code repair, Agent KB enables Claude-3 to improve from 41.33% to 53.33%. Our results suggest that Agent KB provides a modular, framework-agnostic infrastructure for enabling agents to learn from past experiences and generalize successful strategies to new tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.03698", "title": "SAMed-2: Selective Memory Enhanced Medical Segment Anything Model", "authors": [ "Zhiling Yan", "Sifan Song", "Dingjie Song", "Yiwei Li", "Rong Zhou", "Weixiang Sun", "Zhennong Chen", "Sekeun Kim", "Hui Ren", "Tianming Liu", "Quanzheng Li", "Xiang Li", "Lifang He", "Lichao Sun" ], "github": "https://github.com/ZhilingYan/Medical-SAM-Bench", "project_page": "", "abstract": "Recent \"segment anything\" efforts show promise by learning from large-scale data, but adapting such models directly to medical images remains challenging due to the complexity of medical data, noisy annotations, and continual learning requirements across diverse modalities and anatomical structures. In this work, we propose SAMed-2, a new foundation model for medical image segmentation built upon the SAM-2 architecture. Specifically, we introduce a temporal adapter into the image encoder to capture image correlations and a confidence-driven memory mechanism to store high-certainty features for later retrieval. This memory-based strategy counters the pervasive noise in large-scale medical datasets and mitigates catastrophic forgetting when encountering new tasks or modalities. To train and evaluate SAMed-2, we curate MedBank-100k, a comprehensive dataset spanning seven imaging modalities and 21 medical segmentation tasks. Our experiments on both internal benchmarks and 10 external datasets demonstrate superior performance over state-of-the-art baselines in multi-task scenarios. The code is available at: https://github.com/ZhilingYan/Medical-SAM-Bench.", "github_manual": "", "github_regex": "https://github.com/ZhilingYan/Medical-SAM-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.05578", "title": "The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation", "authors": [ "Alexander Xiong", "Xuandong Zhao", "Aneesh Pappu", "Dawn Song" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet they also exhibit memorization of their training data. This phenomenon raises critical questions about model behavior, privacy risks, and the boundary between learning and memorization. Addressing these concerns, this paper synthesizes recent studies and investigates the landscape of memorization, the factors influencing it, and methods for its detection and mitigation. We explore key drivers, including training data duplication, training dynamics, and fine-tuning procedures that influence data memorization. In addition, we examine methodologies such as prefix-based extraction, membership inference, and adversarial prompting, assessing their effectiveness in detecting and measuring memorized content. Beyond technical analysis, we also explore the broader implications of memorization, including the legal and ethical implications. Finally, we discuss mitigation strategies, including data cleaning, differential privacy, and post-training unlearning, while highlighting open challenges in balancing the minimization of harmful memorization with utility. This paper provides a comprehensive overview of the current state of research on LLM memorization across technical, privacy, and performance dimensions, identifying critical directions for future work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.03112", "title": "RLVER: Reinforcement Learning with Verifiable Emotion Rewards for Empathetic Agents", "authors": [ "Peisong Wang", "Ruotian Ma", "Bang Zhang", "Xingyu Chen", "Zhiwei He", "Kang Luo", "Qingsong Lv", "Qingxuan Jiang", "Zheng Xie", "Shanyi Wang", "Yuan Li", "Fanghua Ye", "Jian Li", "Yifan Yang", "Zhaopeng Tu", "Xiaolong Li" ], "github": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "project_page": "", "abstract": "Large language models (LLMs) excel at logical and algorithmic reasoning, yet their emotional intelligence (EQ) still lags far behind their cognitive prowess. While reinforcement learning from verifiable rewards (RLVR) has advanced in other domains, its application to dialogue-especially for emotional intelligence-remains underexplored. In this work, we introduce RLVER, the first end-to-end reinforcement learning framework that leverages verifiable emotion rewards from simulated users to cultivate higher-order empathetic abilities in LLMs. Within this framework, self-consistent affective simulated users engage in dialogue rollouts and produce deterministic emotion scores during conversations, serving as reward signals to guide the LLM's learning. Fine-tuning publicly available Qwen2.5-7B-Instruct model with PPO boosts its Sentient-Benchmark score from 13.3 to 79.2 while largely preserving mathematical and coding competence. Extensive experiments reveal that: (i) RLVER consistently improves multiple dialogue capabilities; (ii) Thinking and non-thinking models show distinct trends--thinking models excel in empathy and insight, while non-thinking models favor action; (iii) GRPO often yields stable gains, while PPO can push certain capabilities to a higher ceiling; (iv) More challenging environments are not always better-moderate ones can yield stronger outcomes. Our results show that RLVER is a practical route toward emotionally intelligent and broadly capable language agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.06223", "title": "Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankers", "authors": [ "Zhiyuan Peng", "Ting-ruen Wei", "Tingyu Song", "Yilun Zhao", "Yi Fang" ], "github": "https://github.com/zhiyuanpeng/eer-flops", "project_page": "", "abstract": "Large Language Models (LLMs) have recently been applied to reranking tasks in information retrieval, achieving strong performance. However, their high computational demands often hinder practical deployment. Existing studies evaluate the efficiency of LLM-based rerankers using proxy metrics such as latency, the number of forward passes, input tokens, and output tokens. However, these metrics depend on hardware and running-time choices (\\eg parallel or not, batch size, etc), and often fail to account for model size, making it difficult to interpret and obscuring the evaluation of the efficiency-effectiveness tradeoff. To address this issue, we propose E2R-FLOPs, for LLM-based rerankers: ranking metrics per PetaFLOP (RPP) for relevance per compute and queries per PetaFLOP (QPP) for hardware-agnostic throughput. Companied with the new metrics, an interpretable FLOPs estimator is built to estimate the FLOPs of an LLM-based reranker even without running any experiments. Based on the proposed metrics, we conduct comprehensive experiments to evaluate a wide range of LLM-based rerankers with different architecture, studying the efficiency-effectiveness trade-off and bringing this issue to the attention of the research community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhiyuanpeng/eer-flops", "github_api_added_by": "auto" }, { "date": "2025-07-09", "arxiv_id": "2507.05791", "title": "GTA1: GUI Test-time Scaling Agent", "authors": [ "Yan Yang", "Dongxu Li", "Yutong Dai", "Yuhao Yang", "Ziyang Luo", "Zirui Zhao", "Zhiyuan Hu", "Junzhe Huang", "Amrita Saha", "Zeyuan Chen", "Ran Xu", "Liyuan Pan", "Caiming Xiong", "Junnan Li" ], "github": "", "project_page": "", "abstract": "Graphical user interface (GUI) agents autonomously operate across platforms (e.g., Linux) to complete tasks by interacting with visual elements. Specifically, a user instruction is decomposed into a sequence of action proposals, each corresponding to an interaction with the GUI. After each action, the agent observes the updated GUI environment to plan the next step. However, two main challenges arise: i) resolving ambiguity in task planning (i.e., the action proposal sequence), where selecting an appropriate plan is non-trivial, as many valid ones may exist; ii) accurately grounding actions in complex and high-resolution interfaces, i.e., precisely interacting with visual targets. This paper investigates the two aforementioned challenges with our GUI Test-time Scaling Agent, namely GTA1. First, to select the most appropriate action proposal, we introduce a test-time scaling method. At each step, we sample multiple candidate action proposals and leverage a judge model to evaluate and select the most suitable one. It trades off computation for better decision quality by concurrent sampling, shortening task execution steps, and improving overall performance. Second, we propose a model that achieves improved accuracy when grounding the selected action proposal to its corresponding visual elements. Our key insight is that reinforcement learning (RL) facilitates visual grounding through inherent objective alignments, rewarding successful clicks on interface elements. Experimentally, our method establishes state-of-the-art performance across diverse benchmarks. For example, GTA1-7B achieves 50.1%, 92.4%, and 67.7% accuracies on Screenspot-Pro, Screenspot-V2, and OSWorld-G, respectively. When paired with a planner applying our test-time scaling strategy, it exhibits state-of-the-art agentic performance (e.g., 45.2% task success rate on OSWorld). We open-source our code and models here.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.06181", "title": "CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization", "authors": [ "Zhongyuan Peng", "Yifan Yao", "Kaijing Ma", "Shuyue Guo", "Yizhe Li", "Yichi Zhang", "Chenchen Zhang", "Yifan Zhang", "Zhouliang Yu", "Luming Li", "Minghao Liu", "Yihang Xia", "Jiawei Shen", "Yuchen Wu", "Yixin Cao", "Zhaoxiang Zhang", "Wenhao Huang", "Jiaheng Liu", "Ge Zhang" ], "github": "https://github.com/multimodal-art-projection/CriticLean", "project_page": "", "abstract": "Translating natural language mathematical statements into formal, executable code is a fundamental challenge in automated theorem proving. While prior work has focused on generation and compilation success, little attention has been paid to the critic phase-the evaluation of whether generated formalizations truly capture the semantic intent of the original problem. In this paper, we introduce CriticLean, a novel critic-guided reinforcement learning framework that elevates the role of the critic from a passive validator to an active learning component. Specifically, first, we propose the CriticLeanGPT, trained via supervised fine-tuning and reinforcement learning, to rigorously assess the semantic fidelity of Lean 4 formalizations. Then, we introduce CriticLeanBench, a benchmark designed to measure models' ability to distinguish semantically correct from incorrect formalizations, and demonstrate that our trained CriticLeanGPT models can significantly outperform strong open- and closed-source baselines. Building on the CriticLean framework, we construct FineLeanCorpus, a dataset comprising over 285K problems that exhibits rich domain diversity, broad difficulty coverage, and high correctness based on human evaluation. Overall, our findings highlight that optimizing the critic phase is essential for producing reliable formalizations, and we hope our CriticLean will provide valuable insights for future advances in formal mathematical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/multimodal-art-projection/CriticLean", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.05101", "title": "PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs", "authors": [ "Xinzhe Zheng", "Hao Du", "Fanding Xu", "Jinzhe Li", "Zhiyuan Liu", "Wenkang Wang", "Tao Chen", "Wanli Ouyang", "Stan Z. Li", "Yan Lu", "Nanqing Dong", "Yang Zhang" ], "github": "https://github.com/SophieSarceau/PRING", "project_page": "", "abstract": "Deep learning-based computational methods have achieved promising results in predicting protein-protein interactions (PPIs). However, existing benchmarks predominantly focus on isolated pairwise evaluations, overlooking a model's capability to reconstruct biologically meaningful PPI networks, which is crucial for biology research. To address this gap, we introduce PRING, the first comprehensive benchmark that evaluates protein-protein interaction prediction from a graph-level perspective. PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions, with well-designed strategies to address both data redundancy and leakage. Building on this golden-standard dataset, we establish two complementary evaluation paradigms: (1) topology-oriented tasks, which assess intra and cross-species PPI network construction, and (2) function-oriented tasks, including protein complex pathway prediction, GO module analysis, and essential protein justification. These evaluations not only reflect the model's capability to understand the network topology but also facilitate protein function annotation, biological module detection, and even disease mechanism analysis. Extensive experiments on four representative model categories, consisting of sequence similarity-based, naive sequence-based, protein language model-based, and structure-based approaches, demonstrate that current PPI models have potential limitations in recovering both structural and functional properties of PPI networks, highlighting the gap in supporting real-world biological applications. We believe PRING provides a reliable platform to guide the development of more effective PPI prediction models for the community. The dataset and source code of PRING are available at https://github.com/SophieSarceau/PRING.", "github_manual": "", "github_regex": "https://github.com/SophieSarceau/PRING", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.06219", "title": "Is Diversity All You Need for Scalable Robotic Manipulation?", "authors": [ "Modi Shi", "Li Chen", "Jin Chen", "Yuxiang Lu", "Chiming Liu", "Guanghui Ren", "Ping Luo", "Di Huang", "Maoqing Yao", "Hongyang Li" ], "github": "", "project_page": "", "abstract": "Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robotic manipulation remain insufficiently understood. In this work, we investigate the nuanced role of data diversity in robot learning by examining three critical dimensions-task (what to do), embodiment (which robot to use), and expert (who demonstrates)-challenging the conventional intuition of \"more diverse is better\". Throughout extensive experiments on various robot platforms, we reveal that (1) task diversity proves more critical than per-task demonstration quantity, benefiting transfer from diverse pre-training tasks to novel downstream scenarios; (2) multi-embodiment pre-training data is optional for cross-embodiment transfer-models trained on high-quality single-embodiment data can efficiently transfer to different platforms, showing more desirable scaling property during fine-tuning than multi-embodiment pre-trained models; and (3) expert diversity, arising from individual operational preferences and stochastic variations in human demonstrations, can be confounding to policy learning, with velocity multimodality emerging as a key contributing factor. Based on this insight, we propose a distribution debiasing method to mitigate velocity ambiguity, the yielding GO-1-Pro achieves substantial performance gains of 15%, equivalent to using 2.5 times pre-training data. Collectively, these findings provide new perspectives and offer practical guidance on how to scale robotic manipulation datasets effectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.04610", "title": "any4: Learned 4-bit Numeric Representation for LLMs", "authors": [ "Mostafa Elhoushi", "Jeff Johnson" ], "github": "https://github.com/facebookresearch/any4", "project_page": "", "abstract": "We present any4, a learned 4-bit weight quantization solution for large language models (LLMs) providing arbitrary numeric representations without requiring pre-processing of weights or activations. any4 yields higher accuracy compared to other related 4-bit numeric representation types: int4, fp4 and nf4, as evaluated on a range of model sizes, generations and families (Llama 2, Llama 3, Mistral and Mixtral). While any4 does not require preprocessing of weights or activations, it is also competitive with orthogonal techniques that require such preprocessing (e.g., AWQ and GPTQ). We also experiment with any3 and any2 and show competitiveness at lower bits. Additionally, we show that we can calibrate using a single curated diverse sample rather than hundreds of samples from a dataset as done in most quantization approaches. We also open source tinygemm, a latency optimized GPU matrix multiplication library for LLMs, that implements any4 using a GPU-efficient lookup table strategy along with other common quantization methods. We open source our code at https://github.com/facebookresearch/any4 .", "github_manual": "", "github_regex": "https://github.com/facebookresearch/any4", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.06203", "title": "A Survey on Latent Reasoning", "authors": [ "Rui-Jie Zhu", "Tianhao Peng", "Tianhao Cheng", "Xingwei Qu", "Jinfa Huang", "Dawei Zhu", "Hao Wang", "Kaiwen Xue", "Xuanliang Zhang", "Yong Shan", "Tianle Cai", "Taylor Kergan", "Assel Kembay", "Andrew Smith", "Chenghua Lin", "Binh Nguyen", "Yuqi Pan", "Yuhong Chou", "Zefan Cai", "Zhenhe Wu", "Yongchi Zhao", "Tianyu Liu", "Jian Yang", "Wangchunshu Zhou", "Chujie Zheng", "Chongxuan Li", "Yuyin Zhou", "Zhoujun Li", "Zhaoxiang Zhang", "Jiaheng Liu", "Ge Zhang", "Wenhao Huang", "Jason Eshraghian" ], "github": "https://github.com/multimodal-art-projection/LatentCoT-Horizon", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, especially when guided by explicit chain-of-thought (CoT) reasoning that verbalizes intermediate steps. While CoT improves both interpretability and accuracy, its dependence on natural language reasoning limits the model's expressive bandwidth. Latent reasoning tackles this bottleneck by performing multi-step inference entirely in the model's continuous hidden state, eliminating token-level supervision. To advance latent reasoning research, this survey provides a comprehensive overview of the emerging field of latent reasoning. We begin by examining the foundational role of neural network layers as the computational substrate for reasoning, highlighting how hierarchical representations support complex transformations. Next, we explore diverse latent reasoning methodologies, including activation-based recurrence, hidden state propagation, and fine-tuning strategies that compress or internalize explicit reasoning traces. Finally, we discuss advanced paradigms such as infinite-depth latent reasoning via masked diffusion models, which enable globally consistent and reversible reasoning processes. By unifying these perspectives, we aim to clarify the conceptual landscape of latent reasoning and chart future directions for research at the frontier of LLM cognition. An associated GitHub repository collecting the latest papers and repos is available at: https://github.com/multimodal-art-projection/LatentCoT-Horizon/.", "github_manual": "", "github_regex": "https://github.com/multimodal-art-projection/LatentCoT-Horizon", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.05675", "title": "MedGen: Unlocking Medical Video Generation by Scaling Granularly-annotated Medical Videos", "authors": [ "Rongsheng Wang", "Junying Chen", "Ke Ji", "Zhenyang Cai", "Shunian Chen", "Yunjin Yang", "Benyou Wang" ], "github": "https://github.com/FreedomIntelligence/MedGen", "project_page": "", "abstract": "Recent advances in video generation have shown remarkable progress in open-domain settings, yet medical video generation remains largely underexplored. Medical videos are critical for applications such as clinical training, education, and simulation, requiring not only high visual fidelity but also strict medical accuracy. However, current models often produce unrealistic or erroneous content when applied to medical prompts, largely due to the lack of large-scale, high-quality datasets tailored to the medical domain. To address this gap, we introduce MedVideoCap-55K, the first large-scale, diverse, and caption-rich dataset for medical video generation. It comprises over 55,000 curated clips spanning real-world medical scenarios, providing a strong foundation for training generalist medical video generation models. Built upon this dataset, we develop MedGen, which achieves leading performance among open-source models and rivals commercial systems across multiple benchmarks in both visual quality and medical accuracy. We hope our dataset and model can serve as a valuable resource and help catalyze further research in medical video generation. Our code and data is available at https://github.com/FreedomIntelligence/MedGen", "github_manual": "", "github_regex": "https://github.com/FreedomIntelligence/MedGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.06138", "title": "Coding Triangle: How Does Large Language Model Understand Code?", "authors": [ "Taolin Zhang", "Zihan Ma", "Maosong Cao", "Junnan Liu", "Songyang Zhang", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have achieved remarkable progress in code generation, yet their true programming competence remains underexplored. We introduce the Code Triangle framework, which systematically evaluates LLMs across three fundamental dimensions: editorial analysis, code implementation, and test case generation. Through extensive experiments on competitive programming benchmarks, we reveal that while LLMs can form a self-consistent system across these dimensions, their solutions often lack the diversity and robustness of human programmers. We identify a significant distribution shift between model cognition and human expertise, with model errors tending to cluster due to training data biases and limited reasoning transfer. Our study demonstrates that incorporating human-generated editorials, solutions, and diverse test cases, as well as leveraging model mixtures, can substantially enhance both the performance and robustness of LLMs. Furthermore, we reveal both the consistency and inconsistency in the cognition of LLMs that may facilitate self-reflection and self-improvement, providing a potential direction for developing more powerful coding models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.06165", "title": "OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesion", "authors": [ "Yunhan Yang", "Yufan Zhou", "Yuan-Chen Guo", "Zi-Xin Zou", "Yukun Huang", "Ying-Tian Liu", "Hao Xu", "Ding Liang", "Yan-Pei Cao", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "The creation of 3D assets with explicit, editable part structures is crucial for advancing interactive applications, yet most generative methods produce only monolithic shapes, limiting their utility. We introduce OmniPart, a novel framework for part-aware 3D object generation designed to achieve high semantic decoupling among components while maintaining robust structural cohesion. OmniPart uniquely decouples this complex task into two synergistic stages: (1) an autoregressive structure planning module generates a controllable, variable-length sequence of 3D part bounding boxes, critically guided by flexible 2D part masks that allow for intuitive control over part decomposition without requiring direct correspondences or semantic labels; and (2) a spatially-conditioned rectified flow model, efficiently adapted from a pre-trained holistic 3D generator, synthesizes all 3D parts simultaneously and consistently within the planned layout. Our approach supports user-defined part granularity, precise localization, and enables diverse downstream applications. Extensive experiments demonstrate that OmniPart achieves state-of-the-art performance, paving the way for more interpretable, editable, and versatile 3D content.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.05963", "title": "Tora2: Motion and Appearance Customized Diffusion Transformer for Multi-Entity Video Generation", "authors": [ "Zhenghao Zhang", "Junchao Liao", "Xiangyu Meng", "Long Qin", "Weizhi Wang" ], "github": "https://github.com/alibaba/Tora", "project_page": "", "abstract": "Recent advances in diffusion transformer models for motion-guided video generation, such as Tora, have shown significant progress. In this paper, we present Tora2, an enhanced version of Tora, which introduces several design improvements to expand its capabilities in both appearance and motion customization. Specifically, we introduce a decoupled personalization extractor that generates comprehensive personalization embeddings for multiple open-set entities, better preserving fine-grained visual details compared to previous methods. Building on this, we design a gated self-attention mechanism to integrate trajectory, textual description, and visual information for each entity. This innovation significantly reduces misalignment in multimodal conditioning during training. Moreover, we introduce a contrastive loss that jointly optimizes trajectory dynamics and entity consistency through explicit mapping between motion and personalization embeddings. Tora2 is, to our best knowledge, the first method to achieve simultaneous multi-entity customization of appearance and motion for video generation. Experimental results demonstrate that Tora2 achieves competitive performance with state-of-the-art customization methods while providing advanced motion control capabilities, which marks a critical advancement in multi-condition video generation. Project page: https://github.com/alibaba/Tora .", "github_manual": "", "github_regex": "https://github.com/alibaba/Tora", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.04103", "title": "How to Train Your LLM Web Agent: A Statistical Diagnosis", "authors": [ "Dheeraj Vattikonda", "Santhoshi Ravichandran", "Emiliano Penaloza", "Hadi Nekoei", "Megh Thakkar", "Thibault Le Sellier de Chezelles", "Nicolas Gontier", "Miguel Mu\u00f1oz-M\u00e1rmol", "Sahar Omidi Shayegan", "Stefania Raimondo", "Xue Liu", "Alexandre Drouin", "Laurent Charlin", "Alexandre Pich\u00e9", "Alexandre Lacoste", "Massimo Caccia" ], "github": "", "project_page": "", "abstract": "LLM-based web agents have recently made significant progress, but much of it has occurred in closed-source systems, widening the gap with open-source alternatives. Progress has been held back by two key challenges: first, a narrow focus on single-step tasks that overlooks the complexity of multi-step web interactions; and second, the high compute costs required to post-train LLM-based web agents. To address this, we present the first statistically grounded study on compute allocation for LLM web-agent post-training. Our approach uses a two-stage pipeline, training a Llama 3.1 8B student to imitate a Llama 3.3 70B teacher via supervised fine-tuning (SFT), followed by on-policy reinforcement learning. We find this process highly sensitive to hyperparameter choices, making exhaustive sweeps impractical. To spare others from expensive trial-and-error, we sample 1,370 configurations and use bootstrapping to estimate effective hyperparameters. Our results show that combining SFT with on-policy RL consistently outperforms either approach alone on both WorkArena and MiniWob++. Further, this strategy requires only 55% of the compute to match the peak performance of pure SFT on MiniWob++, effectively pushing the compute-performance Pareto frontier, and is the only strategy that can close the gap with closed-source models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.05240", "title": "StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling", "authors": [ "Meng Wei", "Chenyang Wan", "Xiqian Yu", "Tai Wang", "Yuqiang Yang", "Xiaohan Mao", "Chenming Zhu", "Wenzhe Cai", "Hanqing Wang", "Yilun Chen", "Xihui Liu", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "Vision-and-Language Navigation (VLN) in real-world settings requires agents to process continuous visual streams and generate actions with low latency grounded in language instructions. While Video-based Large Language Models (Video-LLMs) have driven recent progress, current VLN methods based on Video-LLM often face trade-offs among fine-grained visual understanding, long-term context modeling and computational efficiency. We introduce StreamVLN, a streaming VLN framework that employs a hybrid slow-fast context modeling strategy to support multi-modal reasoning over interleaved vision, language and action inputs. The fast-streaming dialogue context facilitates responsive action generation through a sliding-window of active dialogues, while the slow-updating memory context compresses historical visual states using a 3D-aware token pruning strategy. With this slow-fast design, StreamVLN achieves coherent multi-turn dialogue through efficient KV cache reuse, supporting long video streams with bounded context size and inference cost. Experiments on VLN-CE benchmarks demonstrate state-of-the-art performance with stable low latency, ensuring robustness and efficiency in real-world deployment. The project page is: https://streamvln.github.io/{https://streamvln.github.io/}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.05920", "title": "High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning", "authors": [ "Xinyu Huang", "Yuhao Dong", "Weiwei Tian", "Bo Li", "Rui Feng", "Ziwei Liu" ], "github": "https://github.com/EvolvingLMMs-Lab/MGPO", "project_page": "", "abstract": "State-of-the-art large multi-modal models (LMMs) face challenges when processing high-resolution images, as these inputs are converted into enormous visual tokens, many of which are irrelevant to the downstream task. In this paper, we propose Multi-turn Grounding-based Policy Optimization (MGPO), an end-to-end reinforcement learning (RL) framework that enables LMMs to iteratively focus on key visual regions by automatically cropping sub-images, based on model-predicted grounding coordinates within a multi-turn conversation framework. Compared to supervised fine-tuning (SFT), which requires costly additional grounding annotations, our approach highlights that LMMs can emerge robust grounding abilities during the RL training process, leveraging only a binary reward function derived from the correctness of the final answer. Additionally, we observe that LMMs struggle to autonomously trigger visual grounding during the rollout process. To address this cold start problem, we design a multi-turn conversational template and restrict policy loss computation to model outputs generated across multiple dialogue rounds, thereby promoting stable optimization. Extensive experiments demonstrate that, when trained on standard visual-question-short answering data without grounding annotations, MGPO effectively elicits stronger grounding capabilities compared to GRPO, leading to 5.4\\% improvement on in-distribution MME-Realworld and 5.2\\% improvement on the challenging out-of-distribution (OOD) V* Bench. Notably, MGPO post-training on Qwen2.5-VL-7B with 21K samples surpasses OpenAI's o1 and GPT-4o models on the OOD V* Bench. Codes are available at https://github.com/EvolvingLMMs-Lab/MGPO.", "github_manual": "", "github_regex": "https://github.com/EvolvingLMMs-Lab/MGPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.04723", "title": "LOOM-Scope: a comprehensive and efficient LOng-cOntext Model evaluation framework", "authors": [ "Zecheng Tang", "Haitian Wang", "Quantong Qiu", "Baibei Ji", "Ruoxi Sun", "Keyan Zhou", "Juntao Li", "Min Zhang" ], "github": "https://github.com/LCM-Lab/LOOM-Scope", "project_page": "", "abstract": "Long-context processing has become a fundamental capability for large language models~(LLMs). To assess model's long-context performance, numerous long-context evaluation benchmarks have been proposed. However, variations in evaluation settings across these benchmarks lead to inconsistent results, making it difficult to draw reliable comparisons. Besides, the high computational cost of long-context evaluation poses a significant barrier for the community to conduct comprehensive assessments of long-context models. In this paper, we propose LOOM-Scope, a comprehensive and efficient framework for long-context evaluation. LOOM-Scope standardizes evaluation settings across diverse benchmarks, supports deployment of efficient long-context inference acceleration methods, and introduces a holistic yet lightweight benchmark suite to evaluate models comprehensively. Homepage: https://loomscope.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LCM-Lab/LOOM-Scope", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.05566", "title": "SingLoRA: Low Rank Adaptation Using a Single Matrix", "authors": [ "David Bensa\u00efd", "Noam Rotstein", "Roy Velich", "Daniel Bensa\u00efd", "Ron Kimmel" ], "github": "", "project_page": "", "abstract": "Low-Rank Adaptation (LoRA) has significantly advanced parameter-efficient fine-tuning of large pretrained models. LoRA augments the pre-trained weights of a model by adding the product of two smaller matrices that together form a low-rank matrix update. Recent research has shown that scale disparities between these two matrices often cause unstable training dynamics, leading to suboptimal performance. In this paper, we propose SingLoRA, which reformulates low-rank adaptation by learning the weights update as a decomposition of a single low-rank matrix multiplied by its transpose. This simple design inherently removes inter-matrix scale conflicts, ensuring stable optimization, and roughly halves the parameter count. We analyze SingLoRA within the infinite-width neural network framework, showing that it guarantees stable feature learning by construction. Extensive experiments on multiple tasks validate these benefits. In common sense reasoning, fine-tuning LLama 7B on MNLI with SingLoRA achieves 91.3% accuracy - surpassing LoRA (89.1%) and LoRA+ (90.2%) - while using only 60% of their parameter budget. In image generation, fine-tuning Stable Diffusion with SingLoRA significantly improves image fidelity on DreamBooth, achieving a DINO similarity score of 0.151, compared to scores of 0.148 and 0.143 for DoRA and LoRA, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.06204", "title": "Differential Mamba", "authors": [ "Nadav Schneider", "Itamar Zimerman", "Eliya Nachmani" ], "github": "https://github.com/NadavSc/Diff-Mamba", "project_page": "", "abstract": "Sequence models like Transformers and RNNs often overallocate attention to irrelevant context, leading to noisy intermediate representations. This degrades LLM capabilities by promoting hallucinations, weakening long-range and retrieval abilities, and reducing robustness. Recent work has shown that differential design can mitigate this issue in Transformers, improving their effectiveness across various applications. In this paper, we explore whether these techniques, originally developed for Transformers, can be applied to Mamba, a recent architecture based on selective state-space layers that achieves Transformer-level performance with greater efficiency. We show that a naive adaptation of differential design to Mamba is insufficient and requires careful architectural modifications. To address this, we introduce a novel differential mechanism for Mamba, empirically validated on language modeling benchmarks, demonstrating improved retrieval capabilities and superior performance over vanilla Mamba. Finally, we conduct extensive ablation studies and empirical analyses to justify our design choices and provide evidence that our approach effectively mitigates the overallocation problem in Mamba-based models. Our code is publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NadavSc/Diff-Mamba", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.04569", "title": "Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts", "authors": [ "Guokan Shang", "Hadi Abdine", "Ahmad Chamma", "Amr Mohamed", "Mohamed Anwar", "Abdelaziz Bounhar", "Omar El Herraoui", "Preslav Nakov", "Michalis Vazirgiannis", "Eric Xing" ], "github": "", "project_page": "", "abstract": "We introduce Nile-Chat-4B, 3x4B-A6B, and 12B, a collection of LLMs for Egyptian dialect, uniquely designed to understand and generate texts written in both Arabic and Latin scripts. Specifically, with Nile-Chat-3x4B-A6B, we introduce a novel language adaptation approach by leveraging the Branch-Train-MiX strategy to merge script-specialized experts, into a single MoE model. Our Nile-Chat models significantly outperform leading multilingual and Arabic LLMs, such as LLaMa, Jais, and ALLaM, on our newly introduced Egyptian evaluation benchmarks, which span both understanding and generative tasks. Notably, our 12B model yields a 14.4% performance gain over Qwen2.5-14B-Instruct on Latin-script benchmarks. All our resources are publicly available. We believe this work presents a comprehensive methodology for adapting LLMs to dual-script languages, addressing an often overlooked aspect in modern LLM development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.05411", "title": "AXLearn: Modular Large Model Training on Heterogeneous Infrastructure", "authors": [ "Mark Lee", "Tom Gunter", "Chang Lan", "John Peebles", "Hanzhi Zhou", "Kelvin Zou", "Sneha Bangalore", "Chung-Cheng Chiu", "Nan Du", "Xianzhi Du", "Philipp Dufter", "Ruixuan Hou", "Haoshuo Huang", "Dongseong Hwang", "Xiang Kong", "Jinhao Lei", "Tao Lei", "Meng Li", "Li Li", "Jiarui Lu", "Zhiyun Lu", "Yiping Ma", "David Qiu", "Vivek Rathod", "Senyu Tong", "Zhucheng Tu", "Jianyu Wang", "Yongqiang Wang", "Zirui Wang", "Floris Weers", "Sam Wiseman", "Guoli Yin", "Bowen Zhang", "Xiyou Zhou", "Danyang Zhuo", "Cheng Leong", "Ruoming Pang" ], "github": "https://github.com/apple/axlearn", "project_page": "", "abstract": "We design and implement AXLearn, a production deep learning system that facilitates scalable and high-performance training of large deep learning models. Compared to other state-of-the-art deep learning systems, AXLearn has a unique focus on modularity and support for heterogeneous hardware infrastructure. AXLearn's internal interfaces between software components follow strict encapsulation, allowing different components to be assembled to facilitate rapid model development and experimentation on heterogeneous compute infrastructure. We introduce a novel method of quantifying modularity via Lines-of-Code (LoC)-complexity, which demonstrates how our system maintains constant complexity as we scale the components in the system, compared to linear or quadratic complexity in other systems. This allows integrating features such as Rotary Position Embeddings (RoPE) into AXLearn across hundred of modules with just 10 lines of code, compared to hundreds as required in other systems. At the same time, AXLearn maintains equivalent performance compared to state-of-the-art training systems. Finally, we share our experience in the development and operation of AXLearn.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/axlearn", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.03728", "title": "FAROS: Fair Graph Generation via Attribute Switching Mechanisms", "authors": [ "Abdennacer Badaoui", "Oussama Kharouiche", "Hatim Mrabet", "Daniele Malitesta", "Fragkiskos D. Malliaros" ], "github": "", "project_page": "", "abstract": "Recent advancements in graph diffusion models (GDMs) have enabled the synthesis of realistic network structures, yet ensuring fairness in the generated data remains a critical challenge. Existing solutions attempt to mitigate bias by re-training the GDMs with ad-hoc fairness constraints. Conversely, with this work, we propose FAROS, a novel FAir graph geneRatiOn framework leveraging attribute Switching mechanisms and directly running in the generation process of the pre-trained GDM. Technically, our approach works by altering nodes' sensitive attributes during the generation. To this end, FAROS calculates the optimal fraction of switching nodes, and selects the diffusion step to perform the switch by setting tailored multi-criteria constraints to preserve the node-topology profile from the original distribution (a proxy for accuracy) while ensuring the edge independence on the sensitive attributes for the generated graph (a proxy for fairness). Our experiments on benchmark datasets for link prediction demonstrate that the proposed approach effectively reduces fairness discrepancies while maintaining comparable (or even higher) accuracy performance to other similar baselines. Noteworthy, FAROS is also able to strike a better accuracy-fairness trade-off than other competitors in some of the tested settings under the Pareto optimality concept, demonstrating the effectiveness of the imposed multi-criteria constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.06230", "title": "Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion", "authors": [ "Aleksandar Jevti\u0107", "Christoph Reich", "Felix Wimbauer", "Oliver Hahn", "Christian Rupprecht", "Stefan Roth", "Daniel Cremers" ], "github": "https://github.com/tum-vision/scenedino", "project_page": "", "abstract": "Semantic scene completion (SSC) aims to infer both the 3D geometry and semantics of a scene from single images. In contrast to prior work on SSC that heavily relies on expensive ground-truth annotations, we approach SSC in an unsupervised setting. Our novel method, SceneDINO, adapts techniques from self-supervised representation learning and 2D unsupervised scene understanding to SSC. Our training exclusively utilizes multi-view consistency self-supervision without any form of semantic or geometric ground truth. Given a single input image, SceneDINO infers the 3D geometry and expressive 3D DINO features in a feed-forward manner. Through a novel 3D feature distillation approach, we obtain unsupervised 3D semantics. In both 3D and 2D unsupervised scene understanding, SceneDINO reaches state-of-the-art segmentation accuracy. Linear probing our 3D features matches the segmentation accuracy of a current supervised SSC approach. Additionally, we showcase the domain generalization and multi-view consistency of SceneDINO, taking the first steps towards a strong foundation for single image 3D scene understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tum-vision/scenedino", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.05201", "title": "MedGemma Technical Report", "authors": [ "Andrew Sellergren", "Sahar Kazemzadeh", "Tiam Jaroensri", "Atilla Kiraly", "Madeleine Traverse", "Timo Kohlberger", "Shawn Xu", "Fayaz Jamil", "C\u00edan Hughes", "Charles Lau", "Justin Chen", "Fereshteh Mahvar", "Liron Yatziv", "Tiffany Chen", "Bram Sterling", "Stefanie Anna Baby", "Susanna Maria Baby", "Jeremy Lai", "Samuel Schmidgall", "Lu Yang", "Kejia Chen", "Per Bjornsson", "Shashir Reddy", "Ryan Brush", "Kenneth Philbrick", "Howard Hu", "Howard Yang", "Richa Tiwari", "Sunny Jansen", "Preeti Singh", "Yun Liu", "Shekoofeh Azizi", "Aishwarya Kamath", "Johan Ferret", "Shreya Pathak", "Nino Vieillard", "Ramona Merhej", "Sarah Perrin", "Tatiana Matejovicova", "Alexandre Ram\u00e9", "Morgane Riviere", "Louis Rouillard", "Thomas Mesnard", "Geoffrey Cideron", "Jean-bastien Grill", "Sabela Ramos", "Edouard Yvinec", "Michelle Casbon", "Elena Buchatskaya", "Jean-Baptiste Alayrac", "Dmitry", "Lepikhin", "Vlad Feinberg", "Sebastian Borgeaud", "Alek Andreev", "Cassidy Hardin", "Robert Dadashi", "L\u00e9onard Hussenot", "Armand Joulin", "Olivier Bachem", "Yossi Matias", "Katherine Chou", "Avinatan Hassidim", "Kavi Goel", "Clement Farabet", "Joelle Barral", "Tris Warkentin", "Jonathon Shlens", "David Fleet", "Victor Cotruta", "Omar Sanseviero", "Gus Martins", "Phoebe Kirk", "Anand Rao", "Shravya Shetty", "David F. Steiner", "Can Kirmizibayrak", "Rory Pilgrim", "Daniel Golden", "Lin Yang" ], "github": "", "project_page": "", "abstract": "Artificial intelligence (AI) has significant potential in healthcare applications, but its training and deployment faces challenges due to healthcare's diverse data, complex tasks, and the need to preserve privacy. Foundation models that perform well on medical tasks and require less task-specific tuning data are critical to accelerate the development of healthcare AI applications. We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3 4B and 27B. MedGemma demonstrates advanced medical understanding and reasoning on images and text, significantly exceeding the performance of similar-sized generative models and approaching the performance of task-specific models, while maintaining the general capabilities of the Gemma 3 base models. For out-of-distribution tasks, MedGemma achieves 2.6-10% improvement on medical multimodal question answering, 15.5-18.1% improvement on chest X-ray finding classification, and 10.8% improvement on agentic evaluations compared to the base models. Fine-tuning MedGemma further improves performance in subdomains, reducing errors in electronic health record information retrieval by 50% and reaching comparable performance to existing specialized state-of-the-art methods for pneumothorax classification and histopathology patch classification. We additionally introduce MedSigLIP, a medically-tuned vision encoder derived from SigLIP. MedSigLIP powers the visual understanding capabilities of MedGemma and as an encoder achieves comparable or better performance than specialized medical image encoders. Taken together, the MedGemma collection provides a strong foundation of medical image and text capabilities, with potential to significantly accelerate medical research and development of downstream applications. The MedGemma collection, including tutorials and model weights, can be found at https://goo.gle/medgemma.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.05169", "title": "Critiques of World Models", "authors": [ "Eric Xing", "Mingkai Deng", "Jinyu Hou", "Zhiting Hu" ], "github": "", "project_page": "", "abstract": "World Model, the supposed algorithmic surrogate of the real-world environment which biological agents experience with and act upon, has been an emerging topic in recent years because of the rising needs to develop virtual agents with artificial (general) intelligence. There has been much debate on what a world model really is, how to build it, how to use it, and how to evaluate it. In this essay, starting from the imagination in the famed Sci-Fi classic Dune, and drawing inspiration from the concept of \"hypothetical thinking\" in psychology literature, we offer critiques of several schools of thoughts on world modeling, and argue the primary goal of a world model to be simulating all actionable possibilities of the real world for purposeful reasoning and acting. Building on the critiques, we propose a new architecture for a general-purpose world model, based on hierarchical, multi-level, and mixed continuous/discrete representations, and a generative and self-supervision learning framework, with an outlook of a Physical, Agentic, and Nested (PAN) AGI system enabled by such a model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.06137", "title": "NeoBabel: A Multilingual Open Tower for Visual Generation", "authors": [ "Mohammad Mahdi Derakhshani", "Dheeraj Varghese", "Marzieh Fadaee", "Cees G. M. Snoek" ], "github": "", "project_page": "", "abstract": "Text-to-image generation advancements have been predominantly English-centric, creating barriers for non-English speakers and perpetuating digital inequities. While existing systems rely on translation pipelines, these introduce semantic drift, computational overhead, and cultural misalignment. We introduce NeoBabel, a novel multilingual image generation framework that sets a new Pareto frontier in performance, efficiency and inclusivity, supporting six languages: English, Chinese, Dutch, French, Hindi, and Persian. The model is trained using a combination of large-scale multilingual pretraining and high-resolution instruction tuning. To evaluate its capabilities, we expand two English-only benchmarks to multilingual equivalents: m-GenEval and m-DPG. NeoBabel achieves state-of-the-art multilingual performance while retaining strong English capability, scoring 0.75 on m-GenEval and 0.68 on m-DPG. Notably, it performs on par with leading models on English tasks while outperforming them by +0.11 and +0.09 on multilingual benchmarks, even though these models are built on multilingual base LLMs. This demonstrates the effectiveness of our targeted alignment training for preserving and extending crosslingual generalization. We further introduce two new metrics to rigorously assess multilingual alignment and robustness to code-mixed prompts. Notably, NeoBabel matches or exceeds English-only models while being 2-4x smaller. We release an open toolkit, including all code, model checkpoints, a curated dataset of 124M multilingual text-image pairs, and standardized multilingual evaluation protocols, to advance inclusive AI research. Our work demonstrates that multilingual capability is not a trade-off but a catalyst for improved robustness, efficiency, and cultural fidelity in generative AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-09", "arxiv_id": "2507.07102", "title": "Does Data Scaling Lead to Visual Compositional Generalization?", "authors": [ "Arnas Uselis", "Andrea Dittadi", "Seong Joon Oh" ], "github": "https://github.com/oshapio/visual-compositional-generalization", "project_page": "", "abstract": "Compositional understanding is crucial for human intelligence, yet it remains unclear whether contemporary vision models exhibit it. The dominant machine learning paradigm is built on the premise that scaling data and model sizes will improve out-of-distribution performance, including compositional generalization. We test this premise through controlled experiments that systematically vary data scale, concept diversity, and combination coverage. We find that compositional generalization is driven by data diversity, not mere data scale. Increased combinatorial coverage forces models to discover a linearly factored representational structure, where concepts decompose into additive components. We prove this structure is key to efficiency, enabling perfect generalization from few observed combinations. Evaluating pretrained models (DINO, CLIP), we find above-random yet imperfect performance, suggesting partial presence of this structure. Our work motivates stronger emphasis on constructing diverse datasets for compositional generalization, and considering the importance of representational structure that enables efficient compositional learning. Code available at https://github.com/oshapio/visual-compositional-generalization.", "github_manual": "", "github_regex": "https://github.com/oshapio/visual-compositional-generalization", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.06260", "title": "Evaluating the Critical Risks of Amazon's Nova Premier under the Frontier Model Safety Framework", "authors": [ "Satyapriya Krishna", "Ninareh Mehrabi", "Abhinav Mohanty", "Matteo Memelli", "Vincent Ponzo", "Payal Motwani", "Rahul Gupta" ], "github": "", "project_page": "", "abstract": "Nova Premier is Amazon's most capable multimodal foundation model and teacher for model distillation. It processes text, images, and video with a one-million-token context window, enabling analysis of large codebases, 400-page documents, and 90-minute videos in a single prompt. We present the first comprehensive evaluation of Nova Premier's critical risk profile under the Frontier Model Safety Framework. Evaluations target three high-risk domains -- Chemical, Biological, Radiological & Nuclear (CBRN), Offensive Cyber Operations, and Automated AI R&D -- and combine automated benchmarks, expert red-teaming, and uplift studies to determine whether the model exceeds release thresholds. We summarize our methodology and report core findings. Based on this evaluation, we find that Nova Premier is safe for public release as per our commitments made at the 2025 Paris AI Safety Summit. We will continue to enhance our safety evaluation and mitigation pipelines as new risks and capabilities associated with frontier models are identified.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.06804", "title": "Towards Solving More Challenging IMO Problems via Decoupled Reasoning and Proving", "authors": [ "Zhenwen Liang", "Linfeng Song", "Yang Li", "Tao Yang", "Feng Zhang", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Automated Theorem Proving (ATP) in formal languages is a foundational challenge for AI. While Large Language Models (LLMs) have driven remarkable progress, a significant gap remains between their powerful informal reasoning capabilities and their weak formal proving performance. Recent studies show that the informal accuracy exceeds 80% while formal success remains below 8% on benchmarks like PutnamBench. We argue this gap persists because current state-of-the-art provers, by tightly coupling reasoning and proving, are trained with paradigms that inadvertently punish deep reasoning in favor of shallow, tactic-based strategies. To bridge this fundamental gap, we propose a novel framework that decouples high-level reasoning from low-level proof generation. Our approach utilizes two distinct, specialized models: a powerful, general-purpose Reasoner to generate diverse, strategic subgoal lemmas, and an efficient Prover to rigorously verify them. This modular design liberates the model's full reasoning potential and bypasses the pitfalls of end-to-end training. We evaluate our method on a challenging set of post-2000 IMO problems, a problem set on which no prior open-source prover has reported success. Our decoupled framework successfully solves 5 of these problems, demonstrating a significant step towards automated reasoning on exceptionally difficult mathematical challenges. To foster future research, we release our full dataset of generated and verified lemmas for a wide range of IMO problems, available at https://tencent-imo.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.05687", "title": "AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs", "authors": [ "Shangzhan Li", "Zefan Wang", "Ye He", "Yuxuan Li", "Qi Shi", "Jianling Li", "Yonggang Hu", "Wanxiang Che", "Xu Han", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/AI9Stars/AutoTriton", "project_page": "", "abstract": "Kernel development in deep learning requires optimizing computational units across hardware while balancing memory management, parallelism, and hardware-specific optimizations through extensive empirical tuning. Although domain-specific languages like Triton simplify GPU programming by abstracting low-level details, developers must still manually tune critical parameters such as tile sizes and memory access patterns through iterative experimentation, creating substantial barriers to optimal performance and wider adoption. In this work, we introduce AutoTriton, the first model dedicated to Triton programming powered by reinforcement learning (RL). AutoTriton performs supervised fine-tuning (SFT) to be equipped with essential Triton programming expertise using a high-quality data gathering pipeline, and conducts RL with Group Relative Policy Optimization (GRPO) algorithm, combining a rule-based reward and an execution-based reward to further improve Triton programming ability, sequentially. Experiments across five evaluation channels of TritonBench and KernelBench illustrate that our 8B model AutoTriton achieves performance comparable to mainstream large models, including Claude-4-Sonnet and DeepSeek-R1-0528. Further experimental analysis demonstrates the crucial role of each module within AutoTriton, including the SFT stage, the RL stage, and the reward design strategy. These findings underscore the promise of RL for automatically generating high-performance kernels, and since high-performance kernels are core components of AI systems, this breakthrough establishes an important foundation for building more efficient AI systems. The model and code will be available at https://github.com/AI9Stars/AutoTriton.", "github_manual": "", "github_regex": "https://github.com/AI9Stars/AutoTriton", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.07095", "title": "Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data", "authors": [ "Ke Fan", "Shunlin Lu", "Minyue Dai", "Runyi Yu", "Lixing Xiao", "Zhiyang Dou", "Junting Dong", "Lizhuang Ma", "Jingbo Wang" ], "github": "https://github.com/VankouF/MotionMillion-Codes", "project_page": "", "abstract": "Generating diverse and natural human motion sequences based on textual descriptions constitutes a fundamental and challenging research area within the domains of computer vision, graphics, and robotics. Despite significant advancements in this field, current methodologies often face challenges regarding zero-shot generalization capabilities, largely attributable to the limited size of training datasets. Moreover, the lack of a comprehensive evaluation framework impedes the advancement of this task by failing to identify directions for improvement. In this work, we aim to push text-to-motion into a new era, that is, to achieve the generalization ability of zero-shot. To this end, firstly, we develop an efficient annotation pipeline and introduce MotionMillion-the largest human motion dataset to date, featuring over 2,000 hours and 2 million high-quality motion sequences. Additionally, we propose MotionMillion-Eval, the most comprehensive benchmark for evaluating zero-shot motion generation. Leveraging a scalable architecture, we scale our model to 7B parameters and validate its performance on MotionMillion-Eval. Our results demonstrate strong generalization to out-of-domain and complex compositional motions, marking a significant step toward zero-shot human motion generation. The code is available at https://github.com/VankouF/MotionMillion-Codes.", "github_manual": "", "github_regex": "https://github.com/VankouF/MotionMillion-Codes", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.06920", "title": "Rethinking Verification for LLM Code Generation: From Generation to Testing", "authors": [ "Zihan Ma", "Taolin Zhang", "Maosong Cao", "Wenwei Zhang", "Minnan Luo", "Songyang Zhang", "Kai Chen" ], "github": "https://github.com/open-compass/saga", "project_page": "", "abstract": "Large language models (LLMs) have recently achieved notable success in code-generation benchmarks such as HumanEval and LiveCodeBench. However, a detailed examination reveals that these evaluation suites often comprise only a limited number of homogeneous test cases, resulting in subtle faults going undetected. This not only artificially inflates measured performance but also compromises accurate reward estimation in reinforcement learning frameworks utilizing verifiable rewards (RLVR). To address these critical shortcomings, we systematically investigate the test-case generation (TCG) task by proposing multi-dimensional metrics designed to rigorously quantify test-suite thoroughness. Furthermore, we introduce a human-LLM collaborative method (SAGA), leveraging human programming expertise with LLM reasoning capability, aimed at significantly enhancing both the coverage and the quality of generated test cases. In addition, we develop a TCGBench to facilitate the study of the TCG task. Experiments show that SAGA achieves a detection rate of 90.62% and a verifier accuracy of 32.58% on TCGBench. The Verifier Accuracy (Verifier Acc) of the code generation evaluation benchmark synthesized by SAGA is 10.78% higher than that of LiveCodeBench-v6. These results demonstrate the effectiveness of our proposed method. We hope this work contributes to building a scalable foundation for reliable LLM code evaluation, further advancing RLVR in code generation, and paving the way for automated adversarial test synthesis and adaptive benchmark integration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/open-compass/saga", "github_api_added_by": "auto" }, { "date": "2025-07-10", "arxiv_id": "2506.24044", "title": "A Survey on Vision-Language-Action Models for Autonomous Driving", "authors": [ "Sicong Jiang", "Zilin Huang", "Kangan Qian", "Ziang Luo", "Tianze Zhu", "Yang Zhong", "Yihong Tang", "Menglin Kong", "Yunlong Wang", "Siwen Jiao", "Hao Ye", "Zihao Sheng", "Xin Zhao", "Tuopu Wen", "Zheng Fu", "Sikai Chen", "Kun Jiang", "Diange Yang", "Seongjin Choi", "Lijun Sun" ], "github": "https://github.com/JohnsonJiang1996/Awesome-VLA4AD", "project_page": "", "abstract": "The rapid progress of multimodal large language models (MLLM) has paved the way for Vision-Language-Action (VLA) paradigms, which integrate visual perception, natural language understanding, and control within a single policy. Researchers in autonomous driving are actively adapting these methods to the vehicle domain. Such models promise autonomous vehicles that can interpret high-level instructions, reason about complex traffic scenes, and make their own decisions. However, the literature remains fragmented and is rapidly expanding. This survey offers the first comprehensive overview of VLA for Autonomous Driving (VLA4AD). We (i) formalize the architectural building blocks shared across recent work, (ii) trace the evolution from early explainer to reasoning-centric VLA models, and (iii) compare over 20 representative models according to VLA's progress in the autonomous driving domain. We also consolidate existing datasets and benchmarks, highlighting protocols that jointly measure driving safety, accuracy, and explanation quality. Finally, we detail open challenges - robustness, real-time efficiency, and formal verification - and outline future directions of VLA4AD. This survey provides a concise yet complete reference for advancing interpretable socially aligned autonomous vehicles. Github repo is available at https://github.com/JohnsonJiang1996/Awesome-VLA4AD{SicongJiang/Awesome-VLA4AD}.", "github_manual": "", "github_regex": "https://github.com/JohnsonJiang1996/Awesome-VLA4AD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.06448", "title": "Perception-Aware Policy Optimization for Multimodal Reasoning", "authors": [ "Zhenhailong Wang", "Xuehang Guo", "Sofia Stoica", "Haiyang Xu", "Hongru Wang", "Hyeonjeong Ha", "Xiusi Chen", "Yangyi Chen", "Ming Yan", "Fei Huang", "Heng Ji" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has proven to be a highly effective strategy for endowing Large Language Models (LLMs) with robust multi-step reasoning abilities. However, its design and optimizations remain tailored to purely textual domains, resulting in suboptimal performance when applied to multimodal reasoning tasks. In particular, we observe that a major source of error in current multimodal reasoning lies in the perception of visual inputs. To address this bottleneck, we propose Perception-Aware Policy Optimization (PAPO), a simple yet effective extension of GRPO that encourages the model to learn to perceive while learning to reason, entirely from internal supervision signals. Notably, PAPO does not rely on additional data curation, external reward models, or proprietary models. Specifically, we introduce the Implicit Perception Loss in the form of a KL divergence term to the GRPO objective, which, despite its simplicity, yields significant overall improvements (4.4%) on diverse multimodal benchmarks. The improvements are more pronounced, approaching 8.0%, on tasks with high vision dependency. We also observe a substantial reduction (30.5%) in perception errors, indicating improved perceptual capabilities with PAPO. We conduct comprehensive analysis of PAPO and identify a unique loss hacking issue, which we rigorously analyze and mitigate through a Double Entropy Loss. Overall, our work introduces a deeper integration of perception-aware supervision into RLVR learning objectives and lays the groundwork for a new RL framework that encourages visually grounded reasoning. Project page: https://mikewangwzhl.github.io/PAPO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.06457", "title": "A Systematic Analysis of Hybrid Linear Attention", "authors": [ "Dustin Wang", "Rui-Jie Zhu", "Steven Abreu", "Yong Shan", "Taylor Kergan", "Yuqi Pan", "Yuhong Chou", "Zheng Li", "Ge Zhang", "Wenhao Huang", "Jason Eshraghian" ], "github": "https://github.com/fla-org/flash-linear-attention", "project_page": "", "abstract": "Transformers face quadratic complexity and memory issues with long sequences, prompting the adoption of linear attention mechanisms using fixed-size hidden states. However, linear models often suffer from limited recall performance, leading to hybrid architectures that combine linear and full attention layers. Despite extensive hybrid architecture research, the choice of linear attention component has not been deeply explored. We systematically evaluate various linear attention models across generations - vector recurrences to advanced gating mechanisms - both standalone and hybridized. To enable this comprehensive analysis, we trained and open-sourced 72 models: 36 at 340M parameters (20B tokens) and 36 at 1.3B parameters (100B tokens), covering six linear attention variants across five hybridization ratios. Benchmarking on standard language modeling and recall tasks reveals that superior standalone linear models do not necessarily excel in hybrids. While language modeling remains stable across linear-to-full attention ratios, recall significantly improves with increased full attention layers, particularly below a 3:1 ratio. Our study highlights selective gating, hierarchical recurrence, and controlled forgetting as critical for effective hybrid models. We recommend architectures such as HGRN-2 or GatedDeltaNet with a linear-to-full ratio between 3:1 and 6:1 to achieve Transformer-level recall efficiently. Our models are open-sourced at https://huggingface.co/collections/m-a-p/hybrid-linear-attention-research-686c488a63d609d2f20e2b1e.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/fla-org/flash-linear-attention", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.07017", "title": "First Return, Entropy-Eliciting Explore", "authors": [ "Tianyu Zheng", "Tianshun Xing", "Qingshui Gu", "Taoran Liang", "Xingwei Qu", "Xin Zhou", "Yizhi Li", "Zhoufutu Wen", "Chenghua Lin", "Wenhao Huang", "Qian Liu", "Ge Zhang", "Zejun Ma" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Verifiable Rewards (RLVR) improves the reasoning abilities of Large Language Models (LLMs) but it struggles with unstable exploration. We propose FR3E (First Return, Entropy-Eliciting Explore), a structured exploration framework that identifies high-uncertainty decision points in reasoning trajectories and performs targeted rollouts to construct semantically grounded intermediate feedback. Our method provides targeted guidance without relying on dense supervision. Empirical results on mathematical reasoning benchmarks(AIME24) show that FR3E promotes more stable training, produces longer and more coherent responses, and increases the proportion of fully correct trajectories. These results highlight the framework's effectiveness in improving LLM reasoning through more robust and structured exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.06853", "title": "DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models", "authors": [ "Liang Wang", "Yu Rong", "Tingyang Xu", "Zhenyi Zhong", "Zhiyuan Liu", "Pengju Wang", "Deli Zhao", "Qiang Liu", "Shu Wu", "Liang Wang" ], "github": "", "project_page": "", "abstract": "Molecular structure elucidation from spectra is a foundational problem in chemistry, with profound implications for compound identification, synthesis, and drug development. Traditional methods rely heavily on expert interpretation and lack scalability. Pioneering machine learning methods have introduced retrieval-based strategies, but their reliance on finite libraries limits generalization to novel molecules. Generative models offer a promising alternative, yet most adopt autoregressive SMILES-based architectures that overlook 3D geometry and struggle to integrate diverse spectral modalities. In this work, we present DiffSpectra, a generative framework that directly infers both 2D and 3D molecular structures from multi-modal spectral data using diffusion models. DiffSpectra formulates structure elucidation as a conditional generation process. Its denoising network is parameterized by Diffusion Molecule Transformer, an SE(3)-equivariant architecture that integrates topological and geometric information. Conditioning is provided by SpecFormer, a transformer-based spectral encoder that captures intra- and inter-spectral dependencies from multi-modal spectra. Extensive experiments demonstrate that DiffSpectra achieves high accuracy in structure elucidation, recovering exact structures with 16.01% top-1 accuracy and 96.86% top-20 accuracy through sampling. The model benefits significantly from 3D geometric modeling, SpecFormer pre-training, and multi-modal conditioning. These results highlight the effectiveness of spectrum-conditioned diffusion modeling in addressing the challenge of molecular structure elucidation. To our knowledge, DiffSpectra is the first framework to unify multi-modal spectral reasoning and joint 2D/3D generative modeling for de novo molecular structure elucidation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.07105", "title": "4KAgent: Agentic Any Image to 4K Super-Resolution", "authors": [ "Yushen Zuo", "Qi Zheng", "Mingyang Wu", "Xinrui Jiang", "Renjie Li", "Jian Wang", "Yide Zhang", "Gengchen Mai", "Lihong V. Wang", "James Zou", "Xiaoyu Wang", "Ming-Hsuan Yang", "Zhengzhong Tu" ], "github": "", "project_page": "", "abstract": "We present 4KAgent, a unified agentic super-resolution generalist system designed to universally upscale any image to 4K resolution (and even higher, if applied iteratively). Our system can transform images from extremely low resolutions with severe degradations, for example, highly distorted inputs at 256x256, into crystal-clear, photorealistic 4K outputs. 4KAgent comprises three core components: (1) Profiling, a module that customizes the 4KAgent pipeline based on bespoke use cases; (2) A Perception Agent, which leverages vision-language models alongside image quality assessment experts to analyze the input image and make a tailored restoration plan; and (3) A Restoration Agent, which executes the plan, following a recursive execution-reflection paradigm, guided by a quality-driven mixture-of-expert policy to select the optimal output for each step. Additionally, 4KAgent embeds a specialized face restoration pipeline, significantly enhancing facial details in portrait and selfie photos. We rigorously evaluate our 4KAgent across 11 distinct task categories encompassing a total of 26 diverse benchmarks, setting new state-of-the-art on a broad spectrum of imaging domains. Our evaluations cover natural images, portrait photos, AI-generated content, satellite imagery, fluorescence microscopy, and medical imaging like fundoscopy, ultrasound, and X-ray, demonstrating superior performance in terms of both perceptual (e.g., NIQE, MUSIQ) and fidelity (e.g., PSNR) metrics. By establishing a novel agentic paradigm for low-level vision tasks, we aim to catalyze broader interest and innovation within vision-centric autonomous agents across diverse research communities. We will release all the code, models, and results at: https://4kagent.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.05455", "title": "ModelCitizens: Representing Community Voices in Online Safety", "authors": [ "Ashima Suvarna", "Christina Chance", "Karolina Naranjo", "Hamid Palangi", "Sophie Hao", "Thomas Hartvigsen", "Saadia Gabriel" ], "github": "https://github.com/asuvarna31/modelcitizens", "project_page": "", "abstract": "Automatic toxic language detection is critical for creating safe, inclusive online spaces. However, it is a highly subjective task, with perceptions of toxic language shaped by community norms and lived experience. Existing toxicity detection models are typically trained on annotations that collapse diverse annotator perspectives into a single ground truth, erasing important context-specific notions of toxicity such as reclaimed language. To address this, we introduce MODELCITIZENS, a dataset of 6.8K social media posts and 40K toxicity annotations across diverse identity groups. To capture the role of conversational context on toxicity, typical of social media posts, we augment MODELCITIZENS posts with LLM-generated conversational scenarios. State-of-the-art toxicity detection tools (e.g. OpenAI Moderation API, GPT-o4-mini) underperform on MODELCITIZENS, with further degradation on context-augmented posts. Finally, we release LLAMACITIZEN-8B and GEMMACITIZEN-12B, LLaMA- and Gemma-based models finetuned on MODELCITIZENS, which outperform GPT-o4-mini by 5.5% on in-distribution evaluations. Our findings highlight the importance of community-informed annotation and modeling for inclusive content moderation. The data, models and code are available at https://github.com/asuvarna31/modelcitizens.", "github_manual": "", "github_regex": "https://github.com/asuvarna31/modelcitizens", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.01702", "title": "AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness", "authors": [ "Zixin Chen", "Hongzhan Lin", "Kaixin Li", "Ziyang Luo", "Zhen Ye", "Guang Chen", "Zhiyong Huang", "Jing Ma" ], "github": "https://github.com/Lbotirx/AdamMeme", "project_page": "", "abstract": "The proliferation of multimodal memes in the social media era demands that multimodal Large Language Models (mLLMs) effectively understand meme harmfulness. Existing benchmarks for assessing mLLMs on harmful meme understanding rely on accuracy-based, model-agnostic evaluations using static datasets. These benchmarks are limited in their ability to provide up-to-date and thorough assessments, as online memes evolve dynamically. To address this, we propose AdamMeme, a flexible, agent-based evaluation framework that adaptively probes the reasoning capabilities of mLLMs in deciphering meme harmfulness. Through multi-agent collaboration, AdamMeme provides comprehensive evaluations by iteratively updating the meme data with challenging samples, thereby exposing specific limitations in how mLLMs interpret harmfulness. Extensive experiments show that our framework systematically reveals the varying performance of different target mLLMs, offering in-depth, fine-grained analyses of model-specific weaknesses. Our code is available at https://github.com/Lbotirx/AdamMeme.", "github_manual": "", "github_regex": "https://github.com/Lbotirx/AdamMeme", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2505.10251", "title": "SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learning", "authors": [ "Ji Woong Kim", "Juo-Tung Chen", "Pascal Hansen", "Lucy X. Shi", "Antony Goldenberg", "Samuel Schmidgall", "Paul Maria Scheikl", "Anton Deguet", "Brandon M. White", "De Ru Tsai", "Richard Cha", "Jeffrey Jopling", "Chelsea Finn", "Axel Krieger" ], "github": "", "project_page": "", "abstract": "Research on autonomous surgery has largely focused on simple task automation in controlled environments. However, real-world surgical applications demand dexterous manipulation over extended durations and generalization to the inherent variability of human tissue. These challenges remain difficult to address using existing logic-based or conventional end-to-end learning approaches. To address this gap, we propose a hierarchical framework for performing dexterous, long-horizon surgical steps. Our approach utilizes a high-level policy for task planning and a low-level policy for generating robot trajectories. The high-level planner plans in language space, generating task-level or corrective instructions that guide the robot through the long-horizon steps and correct for the low-level policy's errors. We validate our framework through ex vivo experiments on cholecystectomy, a commonly-practiced minimally invasive procedure, and conduct ablation studies to evaluate key components of the system. Our method achieves a 100\\% success rate across eight unseen ex vivo gallbladders, operating fully autonomously without human intervention. This work demonstrates step-level autonomy in a surgical procedure, marking a milestone toward clinical deployment of autonomous surgical systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.07106", "title": "Towards Multimodal Understanding via Stable Diffusion as a Task-Aware Feature Extractor", "authors": [ "Vatsal Agarwal", "Matthew Gwilliam", "Gefen Kohavi", "Eshan Verma", "Daniel Ulbricht", "Abhinav Shrivastava" ], "github": "", "project_page": "", "abstract": "Recent advances in multimodal large language models (MLLMs) have enabled image-based question-answering capabilities. However, a key limitation is the use of CLIP as the visual encoder; while it can capture coarse global information, it often can miss fine-grained details that are relevant to the input query. To address these shortcomings, this work studies whether pre-trained text-to-image diffusion models can serve as instruction-aware visual encoders. Through an analysis of their internal representations, we find diffusion features are both rich in semantics and can encode strong image-text alignment. Moreover, we find that we can leverage text conditioning to focus the model on regions relevant to the input question. We then investigate how to align these features with large language models and uncover a leakage phenomenon, where the LLM can inadvertently recover information from the original diffusion prompt. We analyze the causes of this leakage and propose a mitigation strategy. Based on these insights, we explore a simple fusion strategy that utilizes both CLIP and conditional diffusion features. We evaluate our approach on both general VQA and specialized MLLM benchmarks, demonstrating the promise of diffusion models for visual understanding, particularly in vision-centric tasks that require spatial and compositional reasoning. Our project page can be found https://vatsalag99.github.io/mustafar/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.06485", "title": "Video-RTS: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning", "authors": [ "Ziyang Wang", "Jaehong Yoon", "Shoubin Yu", "Md Mohaiminul Islam", "Gedas Bertasius", "Mohit Bansal" ], "github": "https://github.com/Ziyang412/Video-RTS", "project_page": "", "abstract": "Despite advances in reinforcement learning (RL)-based video reasoning with large language models (LLMs), data collection and finetuning remain significant challenges. These methods often rely on large-scale supervised fine-tuning (SFT) with extensive video data and long Chain-of-Thought (CoT) annotations, making them costly and hard to scale. To address this, we present Video-RTS, a new approach to improve video reasoning capability with drastically improved data efficiency by combining data-efficient RL with a video-adaptive test-time scaling (TTS) strategy. Based on observations about the data scaling of RL samples, we skip the resource-intensive SFT step and employ efficient pure-RL training with output-based rewards, requiring no additional annotations or extensive fine-tuning. Furthermore, to utilize computational resources more efficiently, we introduce a sparse-to-dense video TTS strategy that improves inference by iteratively adding frames based on output consistency. We validate our approach on multiple video reasoning benchmarks, showing that Video-RTS surpasses existing video reasoning models by an average of 2.4% in accuracy using only 3.6% training samples. For example, Video-RTS achieves a 4.2% improvement on Video-Holmes, a recent and challenging video reasoning benchmark, and a 2.6% improvement on MMVU. Notably, our pure RL training and adaptive video TTS offer complementary strengths, enabling Video-RTS's strong reasoning performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Ziyang412/Video-RTS", "github_api_added_by": "auto" }, { "date": "2025-07-10", "arxiv_id": "2507.06415", "title": "PERK: Long-Context Reasoning as Parameter-Efficient Test-Time Learning", "authors": [ "Zeming Chen", "Angelika Romanou", "Gail Weiss", "Antoine Bosselut" ], "github": "", "project_page": "", "abstract": "Long-context reasoning requires accurately identifying relevant information in extensive, noisy input contexts. Previous research shows that using test-time learning to encode context directly into model parameters can effectively enable reasoning over noisy information. However, meta-learning methods for enabling test-time learning are prohibitively memory-intensive, preventing their application to long context settings. In this work, we propose PERK (Parameter Efficient Reasoning over Knowledge), a scalable approach for learning to encode long input contexts using gradient updates to a lightweight model adapter at test time. Specifically, PERK employs two nested optimization loops in a meta-training phase. The inner loop rapidly encodes contexts into a low-rank adapter (LoRA) that serves as a parameter-efficient memory module for the base model. Concurrently, the outer loop learns to use the updated adapter to accurately recall and reason over relevant information from the encoded long context. Our evaluations on several long-context reasoning tasks show that PERK significantly outperforms the standard prompt-based long-context baseline, achieving average absolute performance gains of up to 90% for smaller models (GPT-2) and up to 27% for our largest evaluated model, Qwen-2.5-0.5B. In general, PERK is more robust to reasoning complexity, length extrapolation, and the locations of relevant information in contexts. Finally, we show that while PERK is memory-intensive during training, it scales more efficiently at inference time than prompt-based long-context inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.06607", "title": "Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long Generation", "authors": [ "Liliang Ren", "Congcong Chen", "Haoran Xu", "Young Jin Kim", "Adam Atkinson", "Zheng Zhan", "Jiankai Sun", "Baolin Peng", "Liyuan Liu", "Shuohang Wang", "Hao Cheng", "Jianfeng Gao", "Weizhu Chen", "Yelong Shen" ], "github": "https://github.com/microsoft/ArchScale", "project_page": "", "abstract": "Recent advances in language modeling have demonstrated the effectiveness of State Space Models (SSMs) for efficient sequence modeling. While hybrid architectures such as Samba and the decoder-decoder architecture, YOCO, have shown promising performance gains over Transformers, prior works have not investigated the efficiency potential of representation sharing between SSM layers. In this paper, we introduce the Gated Memory Unit (GMU), a simple yet effective mechanism for efficient memory sharing across layers. We apply it to create SambaY, a decoder-hybrid-decoder architecture that incorporates GMUs in the cross-decoder to share memory readout states from a Samba-based self-decoder. SambaY significantly enhances decoding efficiency, preserves linear pre-filling time complexity, and boosts long-context performance, all while eliminating the need for explicit positional encoding. Through extensive scaling experiments, we demonstrate that our model exhibits a significantly lower irreducible loss compared to a strong YOCO baseline, indicating superior performance scalability under large-scale compute regimes. Our largest model enhanced with Differential Attention, Phi4-mini-Flash-Reasoning, achieves significantly better performance than Phi4-mini-Reasoning on reasoning tasks such as Math500, AIME24/25, and GPQA Diamond without any reinforcement learning, while delivering up to 10x higher decoding throughput on 2K-length prompts with 32K generation length under the vLLM inference framework. We release our training codebase on open-source data at https://github.com/microsoft/ArchScale.", "github_manual": "", "github_regex": "https://github.com/microsoft/ArchScale", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.07957", "title": "MIRIX: Multi-Agent Memory System for LLM-Based Agents", "authors": [ "Yu Wang", "Xi Chen" ], "github": "https://github.com/mem0ai/mem0/tree/main/evaluation", "project_page": "", "abstract": "Although memory capabilities of AI agents are gaining increasing attention, existing solutions remain fundamentally limited. Most rely on flat, narrowly scoped memory components, constraining their ability to personalize, abstract, and reliably recall user-specific information over time. To this end, we introduce MIRIX, a modular, multi-agent memory system that redefines the future of AI memory by solving the field's most critical challenge: enabling language models to truly remember. Unlike prior approaches, MIRIX transcends text to embrace rich visual and multimodal experiences, making memory genuinely useful in real-world scenarios. MIRIX consists of six distinct, carefully structured memory types: Core, Episodic, Semantic, Procedural, Resource Memory, and Knowledge Vault, coupled with a multi-agent framework that dynamically controls and coordinates updates and retrieval. This design enables agents to persist, reason over, and accurately retrieve diverse, long-term user data at scale. We validate MIRIX in two demanding settings. First, on ScreenshotVQA, a challenging multimodal benchmark comprising nearly 20,000 high-resolution computer screenshots per sequence, requiring deep contextual understanding and where no existing memory systems can be applied, MIRIX achieves 35% higher accuracy than the RAG baseline while reducing storage requirements by 99.9%. Second, on LOCOMO, a long-form conversation benchmark with single-modal textual input, MIRIX attains state-of-the-art performance of 85.4%, far surpassing existing baselines. These results show that MIRIX sets a new performance standard for memory-augmented LLM agents. To allow users to experience our memory system, we provide a packaged application powered by MIRIX. It monitors the screen in real time, builds a personalized memory base, and offers intuitive visualization and secure local storage to ensure privacy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mem0ai/mem0/tree/main/evaluation", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.07024", "title": "FlexOlmo: Open Language Models for Flexible Data Use", "authors": [ "Weijia Shi", "Akshita Bhagia", "Kevin Farhat", "Niklas Muennighoff", "Pete Walsh", "Jacob Morrison", "Dustin Schwenk", "Shayne Longpre", "Jake Poznanski", "Allyson Ettinger", "Daogao Liu", "Margaret Li", "Dirk Groeneveld", "Mike Lewis", "Wen-tau Yih", "Luca Soldaini", "Kyle Lo", "Noah A. Smith", "Luke Zettlemoyer", "Pang Wei Koh", "Hannaneh Hajishirzi", "Ali Farhadi", "Sewon Min" ], "github": "https://github.com/allenai/FlexOlmo", "project_page": "", "abstract": "We introduce FlexOlmo, a new class of language models (LMs) that supports (1) distributed training without data sharing, where different model parameters are independently trained on closed datasets, and (2) data-flexible inference, where these parameters along with their associated data can be flexibly included or excluded from model inferences with no further training. FlexOlmo employs a mixture-of-experts (MoE) architecture where each expert is trained independently on closed datasets and later integrated through a new domain-informed routing without any joint training. FlexOlmo is trained on FlexMix, a corpus we curate comprising publicly available datasets alongside seven domain-specific sets, representing realistic approximations of closed sets. We evaluate models with up to 37 billion parameters (20 billion active) on 31 diverse downstream tasks. We show that a general expert trained on public data can be effectively combined with independently trained experts from other data owners, leading to an average 41% relative improvement while allowing users to opt out of certain data based on data licensing or permission requirements. Our approach also outperforms prior model merging methods by 10.1% on average and surpasses the standard MoE trained without data restrictions using the same training FLOPs. Altogether, this research presents a solution for both data owners and researchers in regulated industries with sensitive or protected data. FlexOlmo enables benefiting from closed data while respecting data owners' preferences by keeping their data local and supporting fine-grained control of data access during inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/FlexOlmo", "github_api_added_by": "auto" }, { "date": "2025-07-10", "arxiv_id": "2507.06167", "title": "Skywork-R1V3 Technical Report", "authors": [ "Wei Shen", "Jiangbo Pei", "Yi Peng", "Xuchen Song", "Yang Liu", "Jian Peng", "Haofeng Sun", "Yunzhuo Hao", "Peiyu Wang", "Yahui Zhou" ], "github": "https://github.com/SkyworkAI/Skywork-R1V", "project_page": "", "abstract": "We introduce Skywork-R1V3, an advanced, open-source vision-language model (VLM) that pioneers a new approach to visual reasoning. Its key innovation lies in effectively transferring reasoning skills from text-only Large Language Models (LLMs) to visual tasks. The strong performance of Skywork-R1V3 primarily stems from our elaborate post-training RL framework, which effectively activates and enhances the model's reasoning ability, without the need for additional continue pre-training. Through this framework, we further uncover the fundamental role of the connector module in achieving robust cross-modal alignment for multimodal reasoning models. In addition, we introduce a unique indicator of reasoning capability, the entropy of critical reasoning tokens, which has proven highly effective for checkpoint selection during RL training. Skywork-R1V3 achieves state-of-the-art results on MMMU, significantly improving from 64.3% to 76.0%. This performance matches entry-level human capabilities. Remarkably, our RL-powered post-training approach enables even the 38B parameter model to rival top closed-source VLMs. The implementation successfully transfers mathematical reasoning to other subject-related reasoning tasks. We also include an analysis of curriculum learning and reinforcement finetuning strategies, along with a broader discussion on multimodal reasoning. Skywork-R1V3 represents a significant leap in multimodal reasoning, showcasing RL as a powerful engine for advancing open-source VLM capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SkyworkAI/Skywork-R1V", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-10", "arxiv_id": "2507.05980", "title": "RabakBench: Scaling Human Annotations to Construct Localized Multilingual Safety Benchmarks for Low-Resource Languages", "authors": [ "Gabriel Chua", "Leanne Tan", "Ziyu Ge", "Roy Ka-Wei Lee" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) and their safety classifiers often perform poorly on low-resource languages due to limited training data and evaluation benchmarks. This paper introduces RabakBench, a new multilingual safety benchmark localized to Singapore's unique linguistic context, covering Singlish, Chinese, Malay, and Tamil. RabakBench is constructed through a scalable three-stage pipeline: (i) Generate - adversarial example generation by augmenting real Singlish web content with LLM-driven red teaming; (ii) Label - semi-automated multi-label safety annotation using majority-voted LLM labelers aligned with human judgments; and (iii) Translate - high-fidelity translation preserving linguistic nuance and toxicity across languages. The final dataset comprises over 5,000 safety-labeled examples across four languages and six fine-grained safety categories with severity levels. Evaluations of 11 popular open-source and closed-source guardrail classifiers reveal significant performance degradation. RabakBench not only enables robust safety evaluation in Southeast Asian multilingual settings but also offers a reproducible framework for building localized safety datasets in low-resource environments. The benchmark dataset, including the human-verified translations, and evaluation code are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.07966", "title": "Scaling RL to Long Videos", "authors": [ "Yukang Chen", "Wei Huang", "Baifeng Shi", "Qinghao Hu", "Hanrong Ye", "Ligeng Zhu", "Zhijian Liu", "Pavlo Molchanov", "Jan Kautz", "Xiaojuan Qi", "Sifei Liu", "Hongxu Yin", "Yao Lu", "Song Han" ], "github": "https://github.com/NVlabs/Long-RL", "project_page": "", "abstract": "We introduce a full-stack framework that scales up reasoning in vision-language models (VLMs) to long videos, leveraging reinforcement learning. We address the unique challenges of long video reasoning by integrating three critical components: (1) a large-scale dataset, LongVideo-Reason, comprising 52K long video QA pairs with high-quality reasoning annotations across diverse domains such as sports, games, and vlogs; (2) a two-stage training pipeline that extends VLMs with chain-of-thought supervised fine-tuning (CoT-SFT) and reinforcement learning (RL); and (3) a training infrastructure for long video RL, named Multi-modal Reinforcement Sequence Parallelism (MR-SP), which incorporates sequence parallelism and a vLLM-based engine tailored for long video, using cached video embeddings for efficient rollout and prefilling. In experiments, LongVILA-R1-7B achieves strong performance on long video QA benchmarks such as VideoMME. It also outperforms Video-R1-7B and even matches Gemini-1.5-Pro across temporal reasoning, goal and purpose reasoning, spatial reasoning, and plot reasoning on our LongVideo-Reason-eval benchmark. Notably, our MR-SP system achieves up to 2.1x speedup on long video RL training. LongVILA-R1 demonstrates consistent performance gains as the number of input video frames scales. LongVILA-R1 marks a firm step towards long video reasoning in VLMs. In addition, we release our training system for public availability that supports RL training on various modalities (video, text, and audio), various models (VILA and Qwen series), and even image and video generation models. On a single A100 node (8 GPUs), it supports RL training on hour-long videos (e.g., 3,600 frames / around 256k tokens).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVlabs/Long-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.07999", "title": "Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Methodology", "authors": [ "Haochen Wang", "Xiangtai Li", "Zilong Huang", "Anran Wang", "Jiacong Wang", "Tao Zhang", "Jiani Zheng", "Sule Bai", "Zijian Kang", "Jiashi Feng", "Zhuochen Wang", "Zhaoxiang Zhang" ], "github": "https://github.com/Haochen-Wang409/TreeVGR", "project_page": "", "abstract": "Models like OpenAI-o3 pioneer visual grounded reasoning by dynamically referencing visual regions, just like human \"thinking with images\". However, no benchmark exists to evaluate these capabilities holistically. To bridge this gap, we propose TreeBench (Traceable Evidence Evaluation Benchmark), a diagnostic benchmark built on three principles: (1) focused visual perception of subtle targets in complex scenes, (2) traceable evidence via bounding box evaluation, and (3) second-order reasoning to test object interactions and spatial hierarchies beyond simple object localization. Prioritizing images with dense objects, we initially sample 1K high-quality images from SA-1B, and incorporate eight LMM experts to manually annotate questions, candidate options, and answers for each image. After three stages of quality control, TreeBench consists of 405 challenging visual question-answering pairs, even the most advanced models struggle with this benchmark, where none of them reach 60% accuracy, e.g., OpenAI-o3 scores only 54.87. Furthermore, we introduce TreeVGR (Traceable Evidence Enhanced Visual Grounded Reasoning), a training paradigm to supervise localization and reasoning jointly with reinforcement learning, enabling accurate localizations and explainable reasoning pathways. Initialized from Qwen2.5-VL-7B, it improves V* Bench (+16.8), MME-RealWorld (+12.6), and TreeBench (+13.4), proving traceability is key to advancing vision-grounded reasoning. The code is available at https://github.com/Haochen-Wang409/TreeVGR.", "github_manual": "", "github_regex": "https://github.com/Haochen-Wang409/TreeVGR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.07998", "title": "PyVision: Agentic Vision with Dynamic Tooling", "authors": [ "Shitian Zhao", "Haoquan Zhang", "Shaoheng Lin", "Ming Li", "Qilong Wu", "Kaipeng Zhang", "Chen Wei" ], "github": "", "project_page": "", "abstract": "LLMs are increasingly deployed as agents, systems capable of planning, reasoning, and dynamically calling external tools. However, in visual reasoning, prior approaches largely remain limited by predefined workflows and static toolsets. In this report, we present PyVision, an interactive, multi-turn framework that enables MLLMs to autonomously generate, execute, and refine Python-based tools tailored to the task at hand, unlocking flexible and interpretable problem-solving. We develop a taxonomy of the tools created by PyVision and analyze their usage across a diverse set of benchmarks. Quantitatively, PyVision achieves consistent performance gains, boosting GPT-4.1 by +7.8% on V* and Claude-4.0-Sonnet by +31.1% on VLMsAreBlind-mini. These results point to a broader shift: dynamic tooling allows models not just to use tools, but to invent them, advancing toward more agentic visual reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.07202", "title": "A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality", "authors": [ "Mohamed Elmoghany", "Ryan Rossi", "Seunghyun Yoon", "Subhojyoti Mukherjee", "Eslam Bakr", "Puneet Mathur", "Gang Wu", "Viet Dac Lai", "Nedim Lipka", "Ruiyi Zhang", "Varun Manjunatha", "Chien Nguyen", "Daksh Dangi", "Abel Salinas", "Mohammad Taesiri", "Hongjie Chen", "Xiaolei Huang", "Joe Barrow", "Nesreen Ahmed", "Hoda Eldardiry", "Namyong Park", "Yu Wang", "Jaemin Cho", "Anh Totti Nguyen", "Zhengzhong Tu", "Thien Nguyen", "Dinesh Manocha", "Mohamed Elhoseiny", "Franck Dernoncourt" ], "github": "", "project_page": "", "abstract": "Despite the significant progress that has been made in video generative models, existing state-of-the-art methods can only produce videos lasting 5-16 seconds, often labeled \"long-form videos\". Furthermore, videos exceeding 16 seconds struggle to maintain consistent character appearances and scene layouts throughout the narrative. In particular, multi-subject long videos still fail to preserve character consistency and motion coherence. While some methods can generate videos up to 150 seconds long, they often suffer from frame redundancy and low temporal diversity. Recent work has attempted to produce long-form videos featuring multiple characters, narrative coherence, and high-fidelity detail. We comprehensively studied 32 papers on video generation to identify key architectural components and training strategies that consistently yield these qualities. We also construct a comprehensive novel taxonomy of existing methods and present comparative tables that categorize papers by their architectural designs and performance characteristics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.07982", "title": "Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling", "authors": [ "Haoyu Wu", "Diankun Wu", "Tianyu He", "Junliang Guo", "Yang Ye", "Yueqi Duan", "Jiang Bian" ], "github": "", "project_page": "", "abstract": "Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture meaningful geometric-aware structure in their learned representations. To bridge this gap between video diffusion models and the underlying 3D nature of the physical world, we propose Geometry Forcing, a simple yet effective method that encourages video diffusion models to internalize latent 3D representations. Our key insight is to guide the model's intermediate representations toward geometry-aware structure by aligning them with features from a pretrained geometric foundation model. To this end, we introduce two complementary alignment objectives: Angular Alignment, which enforces directional consistency via cosine similarity, and Scale Alignment, which preserves scale-related information by regressing unnormalized geometric features from normalized diffusion representation. We evaluate Geometry Forcing on both camera view-conditioned and action-conditioned video generation tasks. Experimental results demonstrate that our method substantially improves visual quality and 3D consistency over the baseline methods. Project page: https://GeometryForcing.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.07136", "title": "LangSplatV2: High-dimensional 3D Language Gaussian Splatting with 450+ FPS", "authors": [ "Wanhua Li", "Yujie Zhao", "Minghan Qin", "Yang Liu", "Yuanhao Cai", "Chuang Gan", "Hanspeter Pfister" ], "github": "https://github.com/ZhaoYujie2002/LangSplatV2", "project_page": "", "abstract": "In this paper, we introduce LangSplatV2, which achieves high-dimensional feature splatting at 476.2 FPS and 3D open-vocabulary text querying at 384.6 FPS for high-resolution images, providing a 42 times speedup and a 47 times boost over LangSplat respectively, along with improved query accuracy. LangSplat employs Gaussian Splatting to embed 2D CLIP language features into 3D, significantly enhancing speed and learning a precise 3D language field with SAM semantics. Such advancements in 3D language fields are crucial for applications that require language interaction within complex scenes. However, LangSplat does not yet achieve real-time inference performance (8.2 FPS), even with advanced A100 GPUs, severely limiting its broader application. In this paper, we first conduct a detailed time analysis of LangSplat, identifying the heavyweight decoder as the primary speed bottleneck. Our solution, LangSplatV2 assumes that each Gaussian acts as a sparse code within a global dictionary, leading to the learning of a 3D sparse coefficient field that entirely eliminates the need for a heavyweight decoder. By leveraging this sparsity, we further propose an efficient sparse coefficient splatting method with CUDA optimization, rendering high-dimensional feature maps at high quality while incurring only the time cost of splatting an ultra-low-dimensional feature. Our experimental results demonstrate that LangSplatV2 not only achieves better or competitive query accuracy but is also significantly faster. Codes and demos are available at our project page: https://langsplat-v2.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZhaoYujie2002/LangSplatV2", "github_api_added_by": "auto" }, { "date": "2025-07-11", "arxiv_id": "2507.07484", "title": "Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models", "authors": [ "Kaiqu Liang", "Haimin Hu", "Xuandong Zhao", "Dawn Song", "Thomas L. Griffiths", "Jaime Fern\u00e1ndez Fisac" ], "github": "https://github.com/kevinliang888/Machine-Bullshit", "project_page": "", "abstract": "Bullshit, as conceptualized by philosopher Harry Frankfurt, refers to statements made without regard to their truth value. While previous work has explored large language model (LLM) hallucination and sycophancy, we propose machine bullshit as an overarching conceptual framework that can allow researchers to characterize the broader phenomenon of emergent loss of truthfulness in LLMs and shed light on its underlying mechanisms. We introduce the Bullshit Index, a novel metric quantifying LLMs' indifference to truth, and propose a complementary taxonomy analyzing four qualitative forms of bullshit: empty rhetoric, paltering, weasel words, and unverified claims. We conduct empirical evaluations on the Marketplace dataset, the Political Neutrality dataset, and our new BullshitEval benchmark (2,400 scenarios spanning 100 AI assistants) explicitly designed to evaluate machine bullshit. Our results demonstrate that model fine-tuning with reinforcement learning from human feedback (RLHF) significantly exacerbates bullshit and inference-time chain-of-thought (CoT) prompting notably amplify specific bullshit forms, particularly empty rhetoric and paltering. We also observe prevalent machine bullshit in political contexts, with weasel words as the dominant strategy. Our findings highlight systematic challenges in AI alignment and provide new insights toward more truthful LLM behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kevinliang888/Machine-Bullshit", "github_api_added_by": "auto" }, { "date": "2025-07-11", "arxiv_id": "2507.07984", "title": "OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding", "authors": [ "JingLi Lin", "Chenming Zhu", "Runsen Xu", "Xiaohan Mao", "Xihui Liu", "Tai Wang", "Jiangmiao Pang" ], "github": "https://github.com/InternRobotics/OST-Bench", "project_page": "", "abstract": "Recent advances in multimodal large language models (MLLMs) have shown remarkable capabilities in integrating vision and language for complex reasoning. While most existing benchmarks evaluate models under offline settings with a fixed set of pre-recorded inputs, we introduce OST-Bench, a benchmark designed to evaluate Online Spatio-Temporal understanding from the perspective of an agent actively exploring a scene. The Online aspect emphasizes the need to process and reason over incrementally acquired observations, while the Spatio-Temporal component requires integrating current visual inputs with historical memory to support dynamic spatial reasoning. OST-Bench better reflects the challenges of real-world embodied perception. Built on an efficient data collection pipeline, OST-Bench consists of 1.4k scenes and 10k question-answer pairs collected from ScanNet, Matterport3D, and ARKitScenes. We evaluate several leading MLLMs on OST-Bench and observe that they fall short on tasks requiring complex spatio-temporal reasoning. Under the online setting, their accuracy declines as the exploration horizon extends and the memory grows. Through further experimental analysis, we identify common error patterns across models and find that both complex clue-based spatial reasoning demands and long-term memory retrieval requirements significantly drop model performance along two separate axes, highlighting the core challenges that must be addressed to improve online embodied reasoning. To foster further research and development in the field, our codes, dataset, and benchmark are available. Our project page is: https://rbler1234.github.io/OSTBench.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/InternRobotics/OST-Bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.07996", "title": "Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs", "authors": [ "Ziyue Li", "Yang Li", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Can a pretrained neural network adapt its architecture to different inputs without any finetuning? Do we need all layers for simple tasks, and are they adequate for challenging tasks? We found that the layers of a pretrained large language model (LLM) can be manipulated as separate modules to build a better and even shallower model customized for each test sample. In particular, each layer from the pretrained model can be skipped/pruned or repeated multiple times as recurrent neural networks (RNN), and stacked with others in arbitrary orders, yielding a chain-of-layers (CoLa) per sample. This compositional space greatly expands the scope of existing works on looped/recurrent pretrained modules, layer pruning, or early-exit networks. We develop a Monte Carlo Tree Search (MCTS) protocol to explore and identify the optimal CoLa for each sample from math and commonsense reasoning benchmarks. Compared to a static model of a fixed depth, CoLa allows shortcut paths (fast thinking), recurrence of the same layer(s) (slow thinking), and combining both, offering more flexible, dynamic architectures for different inputs. We conduct an extensive analysis of the MCTS-optimized CoLa, which leads to two key findings: (1) For >75% of samples with correct predictions by the original LLM, we can find shorter CoLa, suggesting a large space for improving inference efficiency; (2) For >60% of samples with originally incorrect predictions, we can identify CoLa achieving correct predictions, suggesting a large space of performance enhancement. Our results highlight the shortcomings of using a fixed architecture of pre-trained LLMs for inference on different samples and pave the way to unlock the generalization power of test-time depth adaptation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.06543", "title": "Token Bottleneck: One Token to Remember Dynamics", "authors": [ "Taekyung Kim", "Dongyoon Han", "Byeongho Heo", "Jeongeun Park", "Sangdoo Yun" ], "github": "", "project_page": "", "abstract": "Deriving compact and temporally aware visual representations from dynamic scenes is essential for successful execution of sequential scene understanding tasks such as visual tracking and robotic manipulation. In this paper, we introduce Token Bottleneck (ToBo), a simple yet intuitive self-supervised learning pipeline that squeezes a scene into a bottleneck token and predicts the subsequent scene using minimal patches as hints. The ToBo pipeline facilitates the learning of sequential scene representations by conservatively encoding the reference scene into a compact bottleneck token during the squeeze step. In the expansion step, we guide the model to capture temporal dynamics by predicting the target scene using the bottleneck token along with few target patches as hints. This design encourages the vision backbone to embed temporal dependencies, thereby enabling understanding of dynamic transitions across scenes. Extensive experiments in diverse sequential tasks, including video label propagation and robot manipulation in simulated environments demonstrate the superiority of ToBo over baselines. Moreover, deploying our pre-trained model on physical robots confirms its robustness and effectiveness in real-world environments. We further validate the scalability of ToBo across different model scales.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.07990", "title": "Multi-Granular Spatio-Temporal Token Merging for Training-Free Acceleration of Video LLMs", "authors": [ "Jeongseok Hyun", "Sukjun Hwang", "Su Ho Han", "Taeoh Kim", "Inwoong Lee", "Dongyoon Wee", "Joon-Young Lee", "Seon Joo Kim", "Minho Shim" ], "github": "", "project_page": "", "abstract": "Video large language models (LLMs) achieve strong video understanding by leveraging a large number of spatio-temporal tokens, but suffer from quadratic computational scaling with token count. To address this, we propose a training-free spatio-temporal token merging method, named STTM. Our key insight is to exploit local spatial and temporal redundancy in video data which has been overlooked in prior work. STTM first transforms each frame into multi-granular spatial tokens using a coarse-to-fine search over a quadtree structure, then performs directed pairwise merging across the temporal dimension. This decomposed merging approach outperforms existing token reduction methods across six video QA benchmarks. Notably, STTM achieves a 2times speed-up with only a 0.5% accuracy drop under a 50% token budget, and a 3times speed-up with just a 2% drop under a 30% budget. Moreover, STTM is query-agnostic, allowing KV cache reuse across different questions for the same video. The project page is available at https://www.jshyun.me/projects/sttm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.05241", "title": "SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam?", "authors": [ "Jingyi Chai", "Shuo Tang", "Rui Ye", "Yuwen Du", "Xinyu Zhu", "Mengcheng Zhou", "Yanfeng Wang", "Weinan E", "Yuzhi Zhang", "Linfeng Zhang", "Siheng Chen" ], "github": "https://github.com/sjtu-sai-agents/X-Master", "project_page": "", "abstract": "The rapid advancements of AI agents have ignited the long-held ambition of leveraging them to accelerate scientific discovery. Achieving this goal requires a deep understanding of the frontiers of human knowledge. As such, Humanity's Last Exam (HLE) provides an exceptionally challenging touchstone for evaluating scientific AI agents. In this work, we aim to construct the foundational architecture for general-purpose agents and validate the capabilities through leading performance on HLE. To achieve this, we introduce X-Master, a tool-augmented reasoning agent designed to emulate human researchers by interacting flexibly with external tools during its reasoning process. This agent, guided by the conceptualization of code as an interaction language, can flexibly leverage built-in Python libraries and our customized tools to augment the reasoning. We further scale its capabilities through X-Masters, a scattered-and-stacked agentic workflow that systematically enhances breadth and depth of reasoning. Our open-source solution, X-Masters, sets a new state-of-the-art record on HLE with a score of 32.1%, surpassing OpenAI's and Google's Deep Research (26.6% and 26.9%) and becoming the first to exceed the 30% threshold. This work allows us to gain a deeper understanding of complex task-solving and accumulates valuable experience that can inform future advancements, guiding subsequent model training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sjtu-sai-agents/X-Master", "github_api_added_by": "auto" }, { "date": "2025-07-11", "arxiv_id": "2507.05964", "title": "T-LoRA: Single Image Diffusion Model Customization Without Overfitting", "authors": [ "Vera Soboleva", "Aibek Alanov", "Andrey Kuznetsov", "Konstantin Sobolev" ], "github": "https://github.com/ControlGenAI/T-LoRA", "project_page": "", "abstract": "While diffusion model fine-tuning offers a powerful approach for customizing pre-trained models to generate specific objects, it frequently suffers from overfitting when training samples are limited, compromising both generalization capability and output diversity. This paper tackles the challenging yet most impactful task of adapting a diffusion model using just a single concept image, as single-image customization holds the greatest practical potential. We introduce T-LoRA, a Timestep-Dependent Low-Rank Adaptation framework specifically designed for diffusion model personalization. In our work we show that higher diffusion timesteps are more prone to overfitting than lower ones, necessitating a timestep-sensitive fine-tuning strategy. T-LoRA incorporates two key innovations: (1) a dynamic fine-tuning strategy that adjusts rank-constrained updates based on diffusion timesteps, and (2) a weight parametrization technique that ensures independence between adapter components through orthogonal initialization. Extensive experiments show that T-LoRA and its individual components outperform standard LoRA and other diffusion model personalization techniques. They achieve a superior balance between concept fidelity and text alignment, highlighting the potential of T-LoRA in data-limited and resource-constrained scenarios. Code is available at https://github.com/ControlGenAI/T-LoRA.", "github_manual": "", "github_regex": "https://github.com/ControlGenAI/T-LoRA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.07574", "title": "Beyond the Linear Separability Ceiling", "authors": [ "Enrico Vompa", "Tanel Tammet", "Mohit Vaishnav" ], "github": "", "project_page": "", "abstract": "Most state-of-the-art Visual-Language Models (VLMs) are seemingly limited by the linear separabilty of their visual embeddings on abstract reasoning tasks. This work investigates this \"linear reasoning bottleneck\" by introducing the Linear Separability Ceiling (LSC), the performance of a simple linear classifier on a VLM's visual embeddings. We find this bottleneck is widespread and stems not from poor perception, but from failures in the language model's reasoning pathways. We demonstrate this is a solvable alignment issue. The required intervention, however, is task-dependent: activating existing pathways suffices for semantic concepts, while complex relational reasoning requires adapting core model weights. Using postfix tuning as a methodological control, we find strong evidence for powerful, dormant reasoning pathways within VLMs. However, for complex relational tasks requiring deeper adaptation, explicitly improving representation quality causes the model to fail on new prompt formats despite its embeddings remaining well separated. Ultimately, this work provides a new lens for VLM analysis, showing that robust reasoning is a matter of targeted alignment, not simply improved representation learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.07129", "title": "Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate", "authors": [ "A. Bochkov" ], "github": "", "project_page": "", "abstract": "The prevailing paradigm for scaling large language models (LLMs) involves monolithic, end-to-end training, a resource-intensive process that lacks flexibility. This paper explores an alternative, constructive approach to model development, built upon the foundation of non-trainable, deterministic input embeddings. In prior [1], we established that high-level semantic reasoning can emerge in Transformers using frozen embeddings derived from the visual structure of Unicode glyphs. Here, we demonstrate that this fixed representational substrate acts as a universal \"docking port,\" enabling two powerful and efficient scaling paradigms: seamless modular composition and progressive layer-wise growth. First, we show that specialist models trained on disparate datasets (e.g., Russian and Chinese text) can be merged into a single, more capable Mixture-of-Experts (MoE) model, post-training, with zero architectural modification. This is achieved by simply averaging their output logits. The resulting MoE model exhibits immediate performance improvements on reasoning benchmarks like MMLU, surpassing its constituent experts without catastrophic forgetting. Second, we introduce a layer-wise constructive training methodology, where a deep Transformer is \"grown\" by progressively stacking and training one layer at a time. This method demonstrates stable convergence and a clear correlation between model depth and the emergence of complex reasoning abilities, such as those required for SQuAD. Our findings suggest a paradigm shift from monolithic optimization towards a more biological or constructive model of AI development, where complexity is built incrementally and modules can be composed freely. This opens new avenues for resource-efficient scaling, continual learning, and a more democratized ecosystem for building powerful AI systems. We release all code and models to facilitate further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.04886", "title": "Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations", "authors": [ "A. Bochkov" ], "github": "https://github.com/AVBochkov/Embeddings", "project_page": "", "abstract": "Understanding the locus of semantic representation in large language models (LLMs) is crucial for interpretability and architectural innovation. The dominant paradigm posits that trainable input embeddings serve as foundational \"meaning vectors.\" This paper challenges that view. We construct Transformer models where the embedding layer is entirely frozen, with vectors derived not from data, but from the visual structure of Unicode glyphs. These non-semantic, precomputed visual embeddings are fixed throughout training. Our method is compatible with any tokenizer, including a novel Unicode-centric tokenizer we introduce to ensure universal text coverage. Despite the absence of trainable, semantically initialized embeddings, our models converge, generate coherent text, and, critically, outperform architecturally identical models with trainable embeddings on the MMLU reasoning benchmark. We attribute this to \"representational interference\" in conventional models, where the embedding layer is burdened with learning both structural and semantic features. Our results indicate that high-level semantics are not inherent to input embeddings but are an emergent property of the Transformer's compositional architecture and data scale. This reframes the role of embeddings from meaning containers to structural primitives. We release all code and models to foster further research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AVBochkov/Embeddings", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.07867", "title": "Re-Bottleneck: Latent Re-Structuring for Neural Audio Autoencoders", "authors": [ "Dimitrios Bralios", "Jonah Casebeer", "Paris Smaragdis" ], "github": "https://github.com/dbralios/rebottleneck", "project_page": "", "abstract": "Neural audio codecs and autoencoders have emerged as versatile models for audio compression, transmission, feature-extraction, and latent-space generation. However, a key limitation is that most are trained to maximize reconstruction fidelity, often neglecting the specific latent structure necessary for optimal performance in diverse downstream applications. We propose a simple, post-hoc framework to address this by modifying the bottleneck of a pre-trained autoencoder. Our method introduces a \"Re-Bottleneck\", an inner bottleneck trained exclusively through latent space losses to instill user-defined structure. We demonstrate the framework's effectiveness in three experiments. First, we enforce an ordering on latent channels without sacrificing reconstruction quality. Second, we align latents with semantic embeddings, analyzing the impact on downstream diffusion modeling. Third, we introduce equivariance, ensuring that a filtering operation on the input waveform directly corresponds to a specific transformation in the latent space. Ultimately, our Re-Bottleneck framework offers a flexible and efficient way to tailor representations of neural audio models, enabling them to seamlessly meet the varied demands of different applications with minimal additional training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dbralios/rebottleneck", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-11", "arxiv_id": "2507.07955", "title": "Dynamic Chunking for End-to-End Hierarchical Sequence Modeling", "authors": [ "Sukjun Hwang", "Brandon Wang", "Albert Gu" ], "github": "", "project_page": "", "abstract": "Despite incredible progress in language models (LMs) in recent years, largely resulting from moving away from specialized models designed for specific tasks to general models based on powerful architectures (e.g. the Transformer) that learn everything from raw data, pre-processing steps such as tokenization remain a barrier to true end-to-end foundation models. We introduce a collection of new techniques that enable a dynamic chunking mechanism which automatically learns content -- and context -- dependent segmentation strategies learned jointly with the rest of the model. Incorporating this into an explicit hierarchical network (H-Net) allows replacing the (implicitly hierarchical) tokenization-LM-detokenization pipeline with a single model learned fully end-to-end. When compute- and data- matched, an H-Net with one stage of hierarchy operating at the byte level outperforms a strong Transformer language model operating over BPE tokens. Iterating the hierarchy to multiple stages further increases its performance by modeling multiple levels of abstraction, demonstrating significantly better scaling with data and matching a token-based Transformer of twice its size. H-Nets pretrained on English show significantly increased character-level robustness, and qualitatively learn meaningful data-dependent chunking strategies without any heuristics or explicit supervision. Finally, the H-Net's improvement over tokenized pipelines is further increased in languages and modalities with weaker tokenization heuristics, such as Chinese and code, or DNA sequences (nearly 4x improvement in data efficiency over baselines), showing the potential of true end-to-end models that learn and scale better from unprocessed data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.08776", "title": "CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive Neural Rendering", "authors": [ "Zhengqing Wang", "Yuefan Wu", "Jiacheng Chen", "Fuyang Zhang", "Yasutaka Furukawa" ], "github": "", "project_page": "", "abstract": "This paper proposes a neural rendering approach that represents a scene as \"compressed light-field tokens (CLiFTs)\", retaining rich appearance and geometric information of a scene. CLiFT enables compute-efficient rendering by compressed tokens, while being capable of changing the number of tokens to represent a scene or render a novel view with one trained network. Concretely, given a set of images, multi-view encoder tokenizes the images with the camera poses. Latent-space K-means selects a reduced set of rays as cluster centroids using the tokens. The multi-view ``condenser'' compresses the information of all the tokens into the centroid tokens to construct CLiFTs. At test time, given a target view and a compute budget (i.e., the number of CLiFTs), the system collects the specified number of nearby tokens and synthesizes a novel view using a compute-adaptive renderer. Extensive experiments on RealEstate10K and DL3DV datasets quantitatively and qualitatively validate our approach, achieving significant data reduction with comparable rendering quality and the highest overall rendering score, while providing trade-offs of data size, rendering quality, and rendering speed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.08772", "title": "From One to More: Contextual Part Latents for 3D Generation", "authors": [ "Shaocong Dong", "Lihe Ding", "Xiao Chen", "Yaokun Li", "Yuxin Wang", "Yucheng Wang", "Qi Wang", "Jaehyeok Kim", "Chenjian Gao", "Zhanpeng Huang", "Zibin Wang", "Tianfan Xue", "Dan Xu" ], "github": "", "project_page": "", "abstract": "Recent advances in 3D generation have transitioned from multi-view 2D rendering approaches to 3D-native latent diffusion frameworks that exploit geometric priors in ground truth data. Despite progress, three key limitations persist: (1) Single-latent representations fail to capture complex multi-part geometries, causing detail degradation; (2) Holistic latent coding neglects part independence and interrelationships critical for compositional design; (3) Global conditioning mechanisms lack fine-grained controllability. Inspired by human 3D design workflows, we propose CoPart - a part-aware diffusion framework that decomposes 3D objects into contextual part latents for coherent multi-part generation. This paradigm offers three advantages: i) Reduces encoding complexity through part decomposition; ii) Enables explicit part relationship modeling; iii) Supports part-level conditioning. We further develop a mutual guidance strategy to fine-tune pre-trained diffusion models for joint part latent denoising, ensuring both geometric coherence and foundation model priors. To enable large-scale training, we construct Partverse - a novel 3D part dataset derived from Objaverse through automated mesh segmentation and human-verified annotations. Extensive experiments demonstrate CoPart's superior capabilities in part-level editing, articulated object generation, and scene composition with unprecedented controllability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.01951", "title": "Test-Time Scaling with Reflective Generative Model", "authors": [ "Zixiao Wang", "Yuxin Wang", "Xiaorui Wang", "Mengting Xing", "Jie Gao", "Jianjun Xu", "Guangcan Liu", "Chenhui Jin", "Zhuo Wang", "Shengzhuo Zhang", "Hongtao Xie" ], "github": "https://github.com/MetaStone-AI/MetaStone-S1", "project_page": "", "abstract": "We introduce our first reflective generative model MetaStone-S1, which obtains OpenAI o3's performance via the self-supervised process reward model (SPRM). Through sharing the backbone network and using task-specific heads for next token prediction and process scoring respectively, SPRM successfully integrates the policy model and process reward model(PRM) into a unified interface without extra process annotation, reducing over 99% PRM parameters for efficient reasoning. Equipped with SPRM, MetaStone-S1 is naturally suitable for test time scaling (TTS), and we provide three reasoning effort modes (low, medium, and high), based on the controllable thinking length. Moreover, we empirically establish a scaling law that reveals the relationship between total thinking computation and TTS performance. Experiments demonstrate that our MetaStone-S1 achieves comparable performance to OpenAI-o3-mini's series with only 32B parameter size. To support the research community, we have open-sourced MetaStone-S1 at https://github.com/MetaStone-AI/MetaStone-S1.", "github_manual": "", "github_regex": "https://github.com/MetaStone-AI/MetaStone-S1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.08801", "title": "Lumos-1: On Autoregressive Video Generation from a Unified Model Perspective", "authors": [ "Hangjie Yuan", "Weihua Chen", "Jun Cen", "Hu Yu", "Jingyun Liang", "Shuning Chang", "Zhihui Lin", "Tao Feng", "Pengwei Liu", "Jiazheng Xing", "Hao Luo", "Jiasheng Tang", "Fan Wang", "Yi Yang" ], "github": "https://github.com/alibaba-damo-academy/Lumos", "project_page": "", "abstract": "Autoregressive large language models (LLMs) have unified a vast range of language tasks, inspiring preliminary efforts in autoregressive video generation. Existing autoregressive video generators either diverge from standard LLM architectures, depend on bulky external text encoders, or incur prohibitive latency due to next-token decoding. In this paper, we introduce Lumos-1, an autoregressive video generator that retains the LLM architecture with minimal architectural modifications. To inject spatiotemporal correlations in LLMs, we identify the efficacy of incorporating 3D RoPE and diagnose its imbalanced frequency spectrum ranges. Therefore, we propose MM-RoPE, a RoPE scheme that preserves the original textual RoPE while providing comprehensive frequency spectra and scaled 3D positions for modeling multimodal spatiotemporal data. Moreover, Lumos-1 resorts to a token dependency strategy that obeys intra-frame bidirectionality and inter-frame temporal causality. Based on this dependency strategy, we identify the issue of frame-wise loss imbalance caused by spatial information redundancy and solve it by proposing Autoregressive Discrete Diffusion Forcing (AR-DF). AR-DF introduces temporal tube masking during training with a compatible inference-time masking policy to avoid quality degradation. By using memory-efficient training techniques, we pre-train Lumos-1 on only 48 GPUs, achieving performance comparable to EMU3 on GenEval, COSMOS-Video2World on VBench-I2V, and OpenSoraPlan on VBench-T2V. Code and models are available at https://github.com/alibaba-damo-academy/Lumos.", "github_manual": "", "github_regex": "https://github.com/alibaba-damo-academy/Lumos", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.08800", "title": "NeuralOS: Towards Simulating Operating Systems via Neural Generative Models", "authors": [ "Luke Rivard", "Sun Sun", "Hongyu Guo", "Wenhu Chen", "Yuntian Deng" ], "github": "", "project_page": "", "abstract": "We introduce NeuralOS, a neural framework that simulates graphical user interfaces (GUIs) of operating systems by directly predicting screen frames in response to user inputs such as mouse movements, clicks, and keyboard events. NeuralOS combines a recurrent neural network (RNN), which tracks computer state, with a diffusion-based neural renderer that generates screen images. The model is trained on a large-scale dataset of Ubuntu XFCE recordings, which include both randomly generated interactions and realistic interactions produced by AI agents. Experiments show that NeuralOS successfully renders realistic GUI sequences, accurately captures mouse interactions, and reliably predicts state transitions like application launches. Although modeling fine-grained keyboard interactions precisely remains challenging, NeuralOS offers a step toward creating fully adaptive, generative neural interfaces for future human-computer interaction systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.08794", "title": "One Token to Fool LLM-as-a-Judge", "authors": [ "Yulai Zhao", "Haolin Liu", "Dian Yu", "S. Y. Kung", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Generative reward models (also known as LLMs-as-judges), which use large language models (LLMs) to evaluate answer quality, are increasingly adopted in reinforcement learning with verifiable rewards (RLVR). They are often preferred over rigid rule-based metrics, especially for complex reasoning tasks involving free-form outputs. In this paradigm, an LLM is typically prompted to compare a candidate answer against a ground-truth reference and assign a binary reward indicating correctness. Despite the seeming simplicity of this comparison task, we find that generative reward models exhibit surprising vulnerabilities to superficial manipulations: non-word symbols (e.g., \":\" or \".\") or reasoning openers like \"Thought process:\" and \"Let's solve this problem step by step.\" can often lead to false positive rewards. We demonstrate that this weakness is widespread across LLMs, datasets, and prompt formats, posing a serious threat for core algorithmic paradigms that rely on generative reward models, such as rejection sampling, preference optimization, and RLVR. To mitigate this issue, we introduce a simple yet effective data augmentation strategy and train a new generative reward model with substantially improved robustness. Our findings highlight the urgent need for more reliable LLM-based evaluation methods. We release our robust, general-domain reward model and its synthetic training data at https://huggingface.co/sarosavo/Master-RM and https://huggingface.co/datasets/sarosavo/Master-RM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.07151", "title": "Robust Multimodal Large Language Models Against Modality Conflict", "authors": [ "Zongmeng Zhang", "Wengang Zhou", "Jie Zhao", "Houqiang Li" ], "github": "https://github.com/zmzhang2000/MMMC", "project_page": "", "abstract": "Despite the impressive capabilities of multimodal large language models (MLLMs) in vision-language tasks, they are prone to hallucinations in real-world scenarios. This paper investigates the hallucination phenomenon in MLLMs from the perspective of modality conflict. Unlike existing works focusing on the conflicts between model responses and inputs, we study the inherent conflicts in inputs from different modalities that place MLLMs in a dilemma and directly lead to hallucinations. We formally define the modality conflict and construct a dataset named Multimodal Modality Conflict (MMMC) to simulate this phenomenon in vision-language tasks. Three methods based on prompt engineering, supervised fine-tuning, and reinforcement learning are proposed to alleviate the hallucination caused by modality conflict. Extensive experiments are conducted on the MMMC dataset to analyze the merits and demerits of these methods. Our results show that the reinforcement learning method achieves the best performance in mitigating the hallucination under modality conflict, while the supervised fine-tuning method shows promising and stable performance. Our work sheds light on the unnoticed modality conflict that leads to hallucinations and provides more insights into the robustness of MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zmzhang2000/MMMC", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.08771", "title": "BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity", "authors": [ "Chenyang Song", "Weilin Zhao", "Xu Han", "Chaojun Xiao", "Yingfa Chen", "Yuxuan Li", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/thunlp/BlockFFN", "project_page": "", "abstract": "To alleviate the computational burden of large language models (LLMs), architectures with activation sparsity, represented by mixture-of-experts (MoE), have attracted increasing attention. However, the non-differentiable and inflexible routing of vanilla MoE hurts model performance. Moreover, while each token activates only a few parameters, these sparsely-activated architectures exhibit low chunk-level sparsity, indicating that the union of multiple consecutive tokens activates a large ratio of parameters. Such a sparsity pattern is unfriendly for acceleration under low-resource conditions (e.g., end-side devices) and incompatible with mainstream acceleration techniques (e.g., speculative decoding). To address these challenges, we introduce a novel MoE architecture, BlockFFN, as well as its efficient training and deployment techniques. Specifically, we use a router integrating ReLU activation and RMSNorm for differentiable and flexible routing. Next, to promote both token-level sparsity (TLS) and chunk-level sparsity (CLS), CLS-aware training objectives are designed, making BlockFFN more acceleration-friendly. Finally, we implement efficient acceleration kernels, combining activation sparsity and speculative decoding for the first time. The experimental results demonstrate the superior performance of BlockFFN over other MoE baselines, achieving over 80% TLS and 70% 8-token CLS. Our kernels achieve up to 3.67times speedup on real end-side devices than dense models. All codes and checkpoints are available publicly (https://github.com/thunlp/BlockFFN).", "github_manual": "", "github_regex": "https://github.com/thunlp/BlockFFN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.06952", "title": "What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models", "authors": [ "Keyon Vafa", "Peter G. Chang", "Ashesh Rambachan", "Sendhil Mullainathan" ], "github": "https://github.com/keyonvafa/inductive-bias-probes", "project_page": "", "abstract": "Foundation models are premised on the idea that sequence prediction can uncover deeper domain understanding, much like how Kepler's predictions of planetary motion later led to the discovery of Newtonian mechanics. However, evaluating whether these models truly capture deeper structure remains a challenge. We develop a technique for evaluating foundation models that examines how they adapt to synthetic datasets generated from some postulated world model. Our technique measures whether the foundation model's inductive bias aligns with the world model, and so we refer to it as an inductive bias probe. Across multiple domains, we find that foundation models can excel at their training tasks yet fail to develop inductive biases towards the underlying world model when adapted to new tasks. We particularly find that foundation models trained on orbital trajectories consistently fail to apply Newtonian mechanics when adapted to new physics tasks. Further analysis reveals that these models behave as if they develop task-specific heuristics that fail to generalize.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/keyonvafa/inductive-bias-probes", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.05397", "title": "Neural-Driven Image Editing", "authors": [ "Pengfei Zhou", "Jie Xia", "Xiaopeng Peng", "Wangbo Zhao", "Zilong Ye", "Zekai Li", "Suorong Yang", "Jiadong Pan", "Yuanxiang Chen", "Ziqiao Wang", "Kai Wang", "Qian Zheng", "Xiaojun Chang", "Gang Pan", "Shurong Dong", "Kaipeng Zhang", "Yang You" ], "github": "", "project_page": "", "abstract": "Traditional image editing typically relies on manual prompting, making it labor-intensive and inaccessible to individuals with limited motor control or language abilities. Leveraging recent advances in brain-computer interfaces (BCIs) and generative models, we propose LoongX, a hands-free image editing approach driven by multimodal neurophysiological signals. LoongX utilizes state-of-the-art diffusion models trained on a comprehensive dataset of 23,928 image editing pairs, each paired with synchronized electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), photoplethysmography (PPG), and head motion signals that capture user intent. To effectively address the heterogeneity of these signals, LoongX integrates two key modules. The cross-scale state space (CS3) module encodes informative modality-specific features. The dynamic gated fusion (DGF) module further aggregates these features into a unified latent space, which is then aligned with edit semantics via fine-tuning on a diffusion transformer (DiT). Additionally, we pre-train the encoders using contrastive learning to align cognitive states with semantic intentions from embedded natural language. Extensive experiments demonstrate that LoongX achieves performance comparable to text-driven methods (CLIP-I: 0.6605 vs. 0.6558; DINO: 0.4812 vs. 0.4636) and outperforms them when neural signals are combined with speech (CLIP-T: 0.2588 vs. 0.2549). These results highlight the promise of neural-driven generative models in enabling accessible, intuitive image editing and open new directions for cognitive-driven creative technologies. Datasets and code will be released to support future work and foster progress in this emerging area.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.08799", "title": "KV Cache Steering for Inducing Reasoning in Small Language Models", "authors": [ "Max Belitsky", "Dawid J. Kopiczko", "Michael Dorkenwald", "M. Jehanzeb Mirza", "Cees G. M. Snoek", "Yuki M. Asano" ], "github": "https://github.com/MaxBelitsky/cache-steering", "project_page": "", "abstract": "We propose cache steering, a lightweight method for implicit steering of language models via a one-shot intervention applied directly to the key-value cache. To validate its effectiveness, we apply cache steering to induce chain-of-thought reasoning in small language models. Our approach leverages GPT-4o-generated reasoning traces to construct steering vectors that shift model behavior toward more explicit, multi-step reasoning without fine-tuning or prompt modifications. Experimental evaluations on diverse reasoning benchmarks demonstrate that cache steering improves both the qualitative structure of model reasoning and quantitative task performance. Compared to prior activation steering techniques that require continuous interventions, our one-shot cache steering offers substantial advantages in terms of hyperparameter stability, inference-time efficiency, and ease of integration, making it a more robust and practical solution for controlled generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MaxBelitsky/cache-steering", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.08441", "title": "Vision Foundation Models as Effective Visual Tokenizers for Autoregressive Image Generation", "authors": [ "Anlin Zheng", "Xin Wen", "Xuanyang Zhang", "Chuofan Ma", "Tiancai Wang", "Gang Yu", "Xiangyu Zhang", "Xiaojuan Qi" ], "github": "https://github.com/CVMI-Lab/VFMTok", "project_page": "", "abstract": "Leveraging the powerful representations of pre-trained vision foundation models -- traditionally used for visual comprehension -- we explore a novel direction: building an image tokenizer directly atop such models, a largely underexplored area. Specifically, we employ a frozen vision foundation model as the encoder of our tokenizer. To enhance its effectiveness, we introduce two key components: (1) a region-adaptive quantization framework that reduces redundancy in the pre-trained features on regular 2D grids, and (2) a semantic reconstruction objective that aligns the tokenizer's outputs with the foundation model's representations to preserve semantic fidelity. Based on these designs, our proposed image tokenizer, VFMTok, achieves substantial improvements in image reconstruction and generation quality, while also enhancing token efficiency. It further boosts autoregressive (AR) generation -- achieving a gFID of 2.07 on ImageNet benchmarks, while accelerating model convergence by three times, and enabling high-fidelity class-conditional synthesis without the need for classifier-free guidance (CFG). The code will be released publicly to benefit the community.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/CVMI-Lab/VFMTok", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.05255", "title": "Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning", "authors": [ "Yana Wei", "Liang Zhao", "Jianjian Sun", "Kangheng Lin", "Jisheng Yin", "Jingcheng Hu", "Yinmin Zhang", "En Yu", "Haoran Lv", "Zejia Weng", "Jia Wang", "Chunrui Han", "Yuang Peng", "Qi Han", "Zheng Ge", "Xiangyu Zhang", "Daxin Jiang", "Vishal M. Patel" ], "github": "", "project_page": "", "abstract": "The remarkable reasoning capability of large language models (LLMs) stems from cognitive behaviors that emerge through reinforcement with verifiable rewards. This work investigates how to transfer this principle to Multimodal LLMs (MLLMs) to unlock advanced visual reasoning. We introduce a two-stage paradigm built on Qwen2.5-VL-7B: a massive linguistic cold-start fine-tuning, followed by multimodal reinforcement learning (RL) spanning nearly 1,000 steps, surpassing all previous open-source efforts in scale. This pioneering work reveals three fundamental insights: 1) Behavior transfer emerges surprisingly early in cold start due to linguistic mental imagery. 2) Cold start broadly memorizes visual behaviors, while RL critically discerns and scales up effective patterns. 3) Transfer strategically favors high-utility behaviors such as visual reflection. Our resulting model, Open-Vision-Reasoner (OVR), achieves state-of-the-art performance on a suite of reasoning benchmarks, including 95.3% on MATH500, 51.8% on MathVision and 54.6% on MathVerse. We release our model, data, and training dynamics to catalyze the development of more capable, behavior-aligned multimodal reasoners.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.06261", "title": "Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities", "authors": [ "Gheorghe Comanici", "Eric Bieber", "Mike Schaekermann", "Ice Pasupat", "Noveen Sachdeva", "Inderjit Dhillon", "Marcel Blistein", "Ori Ram", "Dan Zhang", "Evan Rosen", "Luke Marris", "Sam Petulla", "Colin Gaffney", "Asaf Aharoni", "Nathan Lintz", "Tiago Cardal Pais", "Henrik Jacobsson", "Idan Szpektor", "Nan-Jiang Jiang", "Krishna Haridasan", "Ahmed Omran", "Nikunj Saunshi", "Dara Bahri", "Gaurav Mishra", "Eric Chu", "Toby Boyd", "Brad Hekman", "Aaron Parisi", "Chaoyi Zhang", "Kornraphop Kawintiranon", "Tania Bedrax-Weiss", "Oliver Wang", "Ya Xu", "Ollie Purkiss", "Uri Mendlovic", "Ila\u00ef Deutel", "Nam Nguyen", "Adam Langley", "Flip Korn", "Lucia Rossazza", "Alexandre Ram\u00e9", "Sagar Waghmare", "Helen Miller", "Vaishakh Keshava", "Ying Jian", "Xiaofan Zhang", "Raluca Ada Popa", "Kedar Dhamdhere", "Bla\u017e Bratani\u010d", "Kyuyeun Kim", "Terry Koo", "Ferran Alet", "Yi-ting Chen", "Arsha Nagrani", "Hannah Muckenhirn", "Zhiyuan Zhang", "Corbin Quick", "Filip Paveti\u0107", "Duc Dung Nguyen", "Joao Carreira", "Michael Elabd", "Haroon Qureshi", "Fabian Mentzer", "Yao-Yuan Yang", "Danielle Eisenbud", "Anmol Gulati", "Ellie Talius", "Eric Ni", "Sahra Ghalebikesabi", "Edouard Yvinec", "Alaa Saade", "Thatcher Ulrich", "Lorenzo Blanco", "Dan A. Calian", "Muhuan Huang", "A\u00e4ron van den Oord", "Naman Goyal", "Terry Chen", "Praynaa Rawlani", "Christian Schallhart", "Swachhand Lokhande", "Xianghong Luo", "Jyn Shan", "Ceslee Montgomery", "Victoria Krakovna", "Federico Piccinini", "Omer Barak", "Jingyu Cui", "Yiling Jia", "Mikhail Dektiarev", "Alexey Kolganov", "Shiyu Huang", "Zhe Chen", "Xingyu Wang", "Jessica Austin", "Peter de Boursac", "Evgeny Sluzhaev", "Frank Ding", "Huijian Li", "Surya Bhupatiraju", "Mohit Agarwal", "S\u0142awek Kwasiborski", "Paramjit Sandhu", "Patrick Siegler", "Ahmet Iscen", "Eyal Ben-David", "Shiraz Butt", "Miltos Allamanis", "Seth Benjamin", "Robert Busa-Fekete", "Felix Hernandez-Campos", "Sasha Goldshtein", "Matt Dibb", "Weiyang Zhang", "Annie Marsden", "Carey Radebaugh", "Stephen Roller", "Abhishek Nayyar", "Jacob Austin", "Tayfun Terzi", "Bhargav Kanagal Shamanna", "Pete Shaw", "Aayush Singh", "Florian Luisier", "Artur Mendon\u00e7a", "Vaibhav Aggarwal", "Larisa Markeeva", "Claudio Fantacci", "Sergey Brin", "HyunJeong Choe", "Guanyu Wang", "Hartwig Adam", "Avigail Dabush", "Tatsuya Kiyono", "Eyal Marcus", "Jeremy Cole", "Theophane Weber", "Hongrae Lee", "Ronny Huang", "Alex Muzio", "Leandro Kieliger", "Maigo Le", "Courtney Biles", "Long Le", "Archit Sharma", "Chengrun Yang", "Avery Lamp", "Dave Dopson", "Nate Hurley", "Katrina", "Xu", "Zhihao Shan", "Shuang Song", "Jiewen Tan", "Alexandre Senges", "George Zhang", "Chong You", "Yennie Jun", "David Raposo", "Susanna Ricco", "Xuan Yang", "Weijie Chen", "Prakhar Gupta", "Arthur Szlam", "Kevin Villela", "Chun-Sung Ferng", "Daniel Kasenberg", "Chen Liang", "Rui Zhu", "Arunachalam Narayanaswamy", "Florence Perot", "Paul Pucciarelli", "Anna Shekhawat", "Alexey Stern", "Rishikesh Ingale", "Stefani Karp", "Sanaz Bahargam", "Adrian Goedeckemeyer", "Jie Han", "Sicheng Li", "Andrea Tacchetti", "Dian Yu", "Abhishek Chakladar", "Zhiying Zhang", "Mona El Mahdy", "Xu Gao", "Dale Johnson", "Samrat Phatale", "AJ Piergiovanni", "Hyeontaek Lim", "Clement Farabet", "Carl Lebsack", "Theo Guidroz", "John Blitzer", "Nico Duduta", "David Madras", "Steve Li", "Daniel von Dincklage", "Xin Li", "Mahdis Mahdieh", "George Tucker", "Ganesh Jawahar", "Owen Xiao", "Danny Tarlow", "Robert Geirhos", "Noam Velan", "Daniel Vlasic", "Kalesha Bullard", "SK Park", "Nishesh Gupta", "Kellie Webster", "Ayal Hitron", "Jieming Mao", "Julian Eisenschlos", "Laurel Prince", "Nina D'Souza", "Kelvin Zheng", "Sara Nasso", "Gabriela Botea", "Carl Doersch", "Caglar Unlu", "Chris Alberti", "Alexey Svyatkovskiy", "Ankita Goel", "Krzysztof Choromanski", "Pan-Pan Jiang", "Richard Nguyen", "Four Flynn", "Daria \u0106urko", "Peter Chen", "Nicholas Roth", "Kieran Milan", "Caleb Habtegebriel", "Shashi Narayan", "Michael Moffitt", "Jake Marcus", "Thomas Anthony", "Brendan McMahan", "Gowoon Cheon", "Ruibo Liu", "Megan Barnes", "Lukasz Lew", "Rebeca Santamaria-Fernandez", "Mayank Upadhyay", "Arjun Akula", "Arnar Mar Hrafnkelsson", "Alvaro Caceres", "Andrew Bunner", "Michal Sokolik", "Subha Puttagunta", "Lawrence Moore", "Berivan Isik", "Weilun Chen", "Jay Hartford", "Lawrence Chan", "Pradeep Shenoy", "Dan Holtmann-Rice", "Jane Park", "Fabio Viola", "Alex Salcianu", "Sujeevan Rajayogam", "Ian Stewart-Binks", "Zelin Wu", "Richard Everett", "Xi Xiong", "Pierre-Antoine Manzagol", "Gary Leung", "Carl Saroufim", "Bo Pang", "Dawid Wegner", "George Papamakarios", "Jennimaria Palomaki", "Helena Pankov", "Guangda Lai", "Guilherme Tubone", "Shubin Zhao", "Theofilos Strinopoulos", "Seth Neel", "Mingqiu Wang", "Joe Kelley", "Li Li", "Pingmei Xu", "Anitha Vijayakumar", "Andrea D'olimpio", "Omer Levy", "Massimo Nicosia", "Grigory Rozhdestvenskiy", "Ni Lao", "Sirui Xie", "Yash Katariya", "Jon Simon", "Sanjiv Kumar", "Florian Hartmann", "Michael Kilgore", "Jinhyuk Lee", "Aroma Mahendru", "Roman Ring", "Tom Hennigan", "Fiona Lang", "Colin Cherry", "David Steiner", "Dawsen Hwang", "Ray Smith", "Pidong Wang", "Jeremy Chen", "Ming-Hsuan Yang", "Sam Kwei", "Philippe Schlattner", "Donnie Kim", "Ganesh Poomal Girirajan", "Nikola Momchev", "Ayushi Agarwal", "Xingyi Zhou", "Ilkin Safarli", "Zachary Garrett", "AJ Pierigiovanni", "Sarthak Jauhari", "Alif Raditya Rochman", "Shikhar Vashishth", "Quan Yuan", "Christof Angermueller", "Jon Blanton", "Xinying Song", "Nitesh Bharadwaj Gundavarapu", "Thi Avrahami", "Maxine Deines", "Subhrajit Roy", "Manish Gupta", "Christopher Semturs", "Shobha Vasudevan", "Aditya Srikanth Veerubhotla", "Shriya Sharma", "Josh Jacob", "Zhen Yang", "Andreas Terzis", "Dan Karliner", "Auriel Wright", "Tania Rojas-Esponda", "Ashley Brown", "Abhijit Guha Roy", "Pawan Dogra", "Andrei Kapishnikov", "Peter Young", "Wendy Kan", "Vinodh Kumar Rajendran", "Maria Ivanova", "Salil Deshmukh", "Chia-Hua Ho", "Mike Kwong", "Stav Ginzburg", "Annie Louis", "KP Sawhney", "Slav Petrov", "Jing Xie", "Yunfei Bai", "Georgi Stoyanov", "Alex Fabrikant", "Rajesh Jayaram", "Yuqi Li", "Joe Heyward", "Justin Gilmer", "Yaqing Wang", "Radu Soricut", "Luyang Liu", "Qingnan Duan", "Jamie Hayes", "Maura O'Brien", "Gaurav Singh Tomar", "Sivan Eiger", "Bahar Fatemi", "Jeffrey Hui", "Catarina Barros", "Adaeze Chukwuka", "Alena Butryna", "Saksham Thakur", "Austin Huang", "Zhufeng Pan", "Haotian Tang", "Serkan Cabi", "Tulsee Doshi", "Michiel Bakker", "Sumit Bagri", "Ruy Ley-Wild", "Adam Lelkes", "Jennie Lees", "Patrick Kane", "David Greene", "Shimu Wu", "J\u00f6rg Bornschein", "Gabriela Surita", "Sarah Hodkinson", "Fangtao Li", "Chris Hidey", "S\u00e9bastien Pereira", "Sean Ammirati", "Phillip Lippe", "Adam Kraft", "Pu Han", "Sebastian Gerlach", "Zifeng Wang", "Liviu Panait", "Feng Han", "Brian Farris", "Yingying Bi", "Hannah DeBalsi", "Miaosen Wang", "Gladys Tyen", "James Cohan", "Susan Zhang", "Jarred Barber", "Da-Woon Chung", "Jaeyoun Kim", "Markus Kunesch", "Steven Pecht", "Nami Akazawa", "Abe Friesen", "James Lyon", "Ali Eslami", "Junru Wu", "Jie Tan", "Yue Song", "Ravi Kumar", "Chris Welty", "Ilia Akolzin", "Gena Gibson", "Sean Augenstein", "Arjun Pillai", "Nancy Yuen", "Du Phan", "Xin Wang", "Iain Barr", "Heiga Zen", "Nan Hua", "Casper Liu", "Jilei", "Wang", "Tanuj Bhatia", "Hao Xu", "Oded Elyada", "Pushmeet Kohli", "Mirek Ol\u0161\u00e1k", "Ke Chen", "Azalia Mirhoseini", "Noam Shazeer", "Shoshana Jakobovits", "Maggie Tran", "Nolan Ramsden", "Tarun Bharti", "Fred Alcober", "Yunjie Li", "Shilpa Shetty", "Jing Chen", "Dmitry Kalashnikov", "Megha Nawhal", "Sercan Arik", "Hanwen Chen", "Michiel Blokzijl", "Shubham Gupta", "James Rubin", "Rigel Swavely", "Sophie Bridgers", "Ian Gemp", "Chen Su", "Arun Suggala", "Juliette Pluto", "Mary Cassin", "Alain Vaucher", "Kaiyang Ji", "Jiahao Cai", "Andrew Audibert", "Animesh Sinha", "David Tian", "Efrat Farkash", "Amy Hua", "Jilin Chen", "Duc-Hieu Tran", "Edward Loper", "Nicole Brichtova", "Lara McConnaughey", "Ballie Sandhu", "Robert Leland", "Doug DeCarlo", "Andrew Over", "James Huang", "Xing Wu", "Connie Fan", "Eric Li", "Yun Lei", "Deepak Sharma", "Cosmin Paduraru", "Luo Yu", "Matko Bo\u0161njak", "Phuong Dao", "Min Choi", "Sneha Kudugunta", "Jakub Adamek", "Carlos Gu\u00eda", "Ali Khodaei", "Jie Feng", "Wenjun Zeng", "David Welling", "Sandeep Tata", "Christina Butterfield", "Andrey Vlasov", "Seliem El-Sayed", "Swaroop Mishra", "Tara Sainath", "Shentao Yang", "RJ Skerry-Ryan", "Jeremy Shar", "Robert Berry", "Arunkumar Rajendran", "Arun Kandoor", "Andrea Burns", "Deepali Jain", "Tom Stone", "Wonpyo Park", "Shibo Wang", "Albin Cassirer", "Guohui Wang", "Hayato Kobayashi", "Sergey Rogulenko", "Vineetha Govindaraj", "Miko\u0142aj Rybi\u0144ski", "Nadav Olmert", "Colin Evans", "Po-Sen Huang", "Kelvin Xu", "Premal Shah", "Terry Thurk", "Caitlin Sikora", "Mu Cai", "Jin Xie", "Elahe Dabir", "Saloni Shah", "Norbert Kalb", "Carrie Zhang", "Shruthi Prabhakara", "Amit Sabne", "Artiom Myaskovsky", "Vikas Raunak", "Blanca Huergo", "Behnam Neyshabur", "Jon Clark", "Ye Zhang", "Shankar Krishnan", "Eden Cohen", "Dinesh Tewari", "James Lottes", "Yumeya Yamamori", "Hui", "Li", "Mohamed Elhawaty", "Ada Maksutaj Oflazer", "Adri\u00e0 Recasens", "Sheryl Luo", "Duy Nguyen", "Taylor Bos", "Kalyan Andra", "Ana Salazar", "Ed Chi", "Jeongwoo Ko", "Matt Ginsberg", "Anders Andreassen", "Anian Ruoss", "Todor Davchev", "Elnaz Davoodi", "Chenxi Liu", "Min Kim", "Santiago Ontanon", "Chi Ming To", "Dawei Jia", "Rosemary Ke", "Jing Wang", "Anna Korsun", "Moran Ambar", "Ilya Kornakov", "Irene Giannoumis", "Toni Creswell", "Denny Zhou", "Yi Su", "Ishaan Watts", "Aleksandr Zaks", "Evgenii Eltyshev", "Ziqiang Feng", "Sidharth Mudgal", "Alex Kaskasoli", "Juliette Love", "Kingshuk Dasgupta", "Sam Shleifer", "Richard Green", "Sungyong Seo", "Chansoo Lee", "Dale Webster", "Prakash Shroff", "Ganna Raboshchuk", "Isabel Leal", "James Manyika", "Sofia Erell", "Daniel Murphy", "Zhisheng Xiao", "Anton Bulyenov", "Julian Walker", "Mark Collier", "Matej Kastelic", "Nelson George", "Sushant Prakash", "Sailesh Sidhwani", "Alexey Frolov", "Steven Hansen", "Petko Georgiev", "Tiberiu Sosea", "Chris Apps", "Aishwarya Kamath", "David Reid", "Emma Cooney", "Charlotte Magister", "Oriana Riva", "Alec Go", "Pu-Chin Chen", "Sebastian Krause", "Nir Levine", "Marco Fornoni", "Ilya Figotin", "Nick Roy", "Parsa Mahmoudieh", "Vladimir Magay", "Mukundan Madhavan", "Jin Miao", "Jianmo Ni", "Yasuhisa Fujii", "Ian Chou", "George Scrivener", "Zak Tsai", "Siobhan Mcloughlin", "Jeremy Selier", "Sandra Lefdal", "Jeffrey Zhao", "Abhijit Karmarkar", "Kushal Chauhan", "Shivanker Goel", "Zhaoyi Zhang", "Vihan Jain", "Parisa Haghani", "Mostafa Dehghani", "Jacob Scott", "Erin Farnese", "Anastasija Ili\u0107", "Steven Baker", "Julia Pawar", "Li Zhong", "Josh Camp", "Yoel Zeldes", "Shravya Shetty", "Anand Iyer", "V\u00edt List\u00edk", "Jiaxian Guo", "Luming Tang", "Mark Geller", "Simon Bucher", "Yifan Ding", "Hongzhi Shi", "Carrie Muir", "Dominik Grewe", "Ramy Eskander", "Octavio Ponce", "Boqing Gong", "Derek Gasaway", "Samira Khan", "Umang Gupta", "Angelos Filos", "Weicheng Kuo", "Klemen Kloboves", "Jennifer Beattie", "Christian Wright", "Leon Li", "Alicia Jin", "Sandeep Mariserla", "Miteyan Patel", "Jens Heitkaemper", "Dilip Krishnan", "Vivek Sharma", "David Bieber", "Christian Frank", "John Lambert", "Paul Caron", "Martin Polacek", "Mai Gim\u00e9nez", "Himadri Choudhury", "Xing Yu", "Sasan Tavakkol", "Arun Ahuja", "Franz Och", "Rodolphe Jenatton", "Wojtek Skut", "Bryan Richter", "David Gaddy", "Andy Ly", "Misha Bilenko", "Megh Umekar", "Ethan Liang", "Martin Sevenich", "Mandar Joshi", "Hassan Mansoor", "Rebecca Lin", "Sumit Sanghai", "Abhimanyu Singh", "Xiaowei Li", "Sudheendra Vijayanarasimhan", "Zaheer Abbas", "Yonatan Bitton", "Hansa Srinivasan", "Manish Reddy Vuyyuru", "Alexander Fr\u00f6mmgen", "Yanhua Sun", "Ralph Leith", "Alfonso Casta\u00f1o", "DJ Strouse", "Le Yan", "Austin Kyker", "Satish Kambala", "Mary Jasarevic", "Thibault Sellam", "Chao Jia", "Alexander Pritzel", "Raghavender R", "Huizhong Chen", "Natalie Clay", "Sudeep Gandhe", "Sean Kirmani", "Sayna Ebrahimi", "Hannah Kirkwood", "Jonathan Mallinson", "Chao Wang", "Adnan Ozturel", "Kuo Lin", "Shyam Upadhyay", "Vincent Cohen-Addad", "Sean Purser-haskell", "Yichong Xu", "Ebrahim Songhori", "Babi Seal", "Alberto Magni", "Almog Gueta", "Tingting Zou", "Guru Guruganesh", "Thais Kagohara", "Hung Nguyen", "Khalid Salama", "Alejandro Cruzado Ruiz", "Justin Frye", "Zhenkai Zhu", "Matthias Lochbrunner", "Simon Osindero", "Wentao Yuan", "Lisa Lee", "Aman Prasad", "Lam Nguyen Thiet", "Daniele Calandriello", "Victor Stone", "Qixuan Feng", "Han Ke", "Maria Voitovich", "Geta Sampemane", "Lewis Chiang", "Ling Wu", "Alexander Bykovsky", "Matt Young", "Luke Vilnis", "Ishita Dasgupta", "Aditya Chawla", "Qin Cao", "Bowen Liang", "Daniel Toyama", "Szabolcs Payrits", "Anca Stefanoiu", "Dimitrios Vytiniotis", "Ankesh Anand", "Tianxiao Shen", "Blagoj Mitrevski", "Michael Tschannen", "Sreenivas Gollapudi", "Aishwarya P S", "Jos\u00e9 Leal", "Zhe Shen", "Han Fu", "Wei Wang", "Arvind Kannan", "Doron Kukliansky", "Sergey Yaroshenko", "Svetlana Grant", "Umesh Telang", "David Wood", "Alexandra Chronopoulou", "Alexandru \u0162ifrea", "Tao Zhou", "Tony", "Nguy\\~\u00ean", "Muge Ersoy", "Anima Singh", "Meiyan Xie", "Emanuel Taropa", "Woohyun Han", "Eirikur Agustsson", "Andrei Sozanschi", "Hui Peng", "Alex Chen", "Yoel Drori", "Efren Robles", "Yang Gao", "Xerxes Dotiwalla", "Ying Chen", "Anudhyan Boral", "Alexei Bendebury", "John Nham", "Chris Tar", "Luis Castro", "Jiepu Jiang", "Canoee Liu", "Felix Halim", "Jinoo Baek", "Andy Wan", "Jeremiah Liu", "Yuan Cao", "Shengyang Dai", "Trilok Acharya", "Ruoxi Sun", "Fuzhao Xue", "Saket Joshi", "Morgane Lustman", "Yongqin Xian", "Rishabh Joshi", "Deep Karkhanis", "Nora Kassner", "Jamie Hall", "Xiangzhuo Ding", "Gan Song", "Gang Li", "Chen Zhu", "Yana Kulizhskaya", "Bin Ni", "Alexey Vlaskin", "Solomon Demmessie", "Lucio Dery", "Salah Zaiem", "Yanping Huang", "Cindy Fan", "Felix Gimeno", "Ananth Balashankar", "Koji Kojima", "Hagai Taitelbaum", "Maya Meng", "Dero Gharibian", "Sahil Singla", "Wei Chen", "Ambrose Slone", "Guanjie Chen", "Sujee Rajayogam", "Max Schumacher", "Suyog Kotecha", "Rory Blevins", "Qifei Wang", "Mor Hazan Taege", "Alex Morris", "Xin Liu", "Fayaz Jamil", "Richard Zhang", "Pratik Joshi", "Ben Ingram", "Tyler Liechty", "Ahmed Eleryan", "Scott Baird", "Alex Grills", "Gagan Bansal", "Shan Han", "Kiran Yalasangi", "Shawn Xu", "Majd Al Merey", "Isabel Gao", "Felix Weissenberger", "Igor Karpov", "Robert Riachi", "Ankit Anand", "Gautam Prasad", "Kay Lamerigts", "Reid Hayes", "Jamie Rogers", "Mandy Guo", "Ashish Shenoy", "Qiong", "Hu", "Kyle He", "Yuchen Liu", "Polina Zablotskaia", "Sagar Gubbi", "Yifan Chang", "Jay Pavagadhi", "Kristian Kjems", "Archita Vadali", "Diego Machado", "Yeqing Li", "Renshen Wang", "Dipankar Ghosh", "Aahil Mehta", "Dana Alon", "George Polovets", "Alessio Tonioni", "Nate Kushman", "Joel D'sa", "Lin Zhuo", "Allen Wu", "Rohin Shah", "John Youssef", "Jiayu Ye", "Justin Snyder", "Karel Lenc", "Senaka Buthpitiya", "Matthew Tung", "Jichuan Chang", "Tao Chen", "David Saxton", "Jenny Lee", "Lydia Lihui Zhang", "James Qin", "Prabakar Radhakrishnan", "Maxwell Chen", "Piotr Ambroszczyk", "Metin Toksoz-Exley", "Yan Zhong", "Nitzan Katz", "Brendan O'Donoghue", "Tamara von Glehn", "Adi Gerzi Rosenthal", "Aga \u015awietlik", "Xiaokai Zhao", "Nick Fernando", "Jinliang Wei", "Jieru Mei", "Sergei Vassilvitskii", "Diego Cedillo", "Pranjal Awasthi", "Hui Zheng", "Koray Kavukcuoglu", "Itay Laish", "Joseph Pagadora", "Marc Brockschmidt", "Christopher A. Choquette-Choo", "Arunkumar Byravan", "Yifeng Lu", "Xu Chen", "Mia Chen", "Kenton Lee", "Rama Pasumarthi", "Sijal Bhatnagar", "Aditya Shah", "Qiyin Wu", "Zhuoyuan Chen", "Zack Nado", "Bartek Perz", "Zixuan Jiang", "David Kao", "Ganesh Mallya", "Nino Vieillard", "Lantao Mei", "Sertan Girgin", "Mandy Jordan", "Yeongil Ko", "Alekh Agarwal", "Yaxin Liu", "Yasemin Altun", "Raoul de Liedekerke", "Anastasios Kementsietsidis", "Daiyi Peng", "Dangyi Liu", "Utku Evci", "Peter Humphreys", "Austin Tarango", "Xiang Deng", "Yoad Lewenberg", "Kevin Aydin", "Chengda Wu", "Bhavishya Mittal", "Tsendsuren Munkhdalai", "Kleopatra Chatziprimou", "Rodrigo Benenson", "Uri First", "Xiao Ma", "Jinning Li", "Armand Joulin", "Hamish Tomlinson", "Tingnan Zhang", "Milad Nasr", "Zhi Hong", "Micha\u00ebl Sander", "Lisa Anne Hendricks", "Anuj Sharma", "Andrew Bolt", "Eszter V\u00e9rtes", "Jiri Simsa", "Tomer Levinboim", "Olcan Sercinoglu", "Divyansh Shukla", "Austin Wu", "Craig Swanson", "Danny Vainstein", "Fan Bu", "Bo Wang", "Ryan Julian", "Charles Yoon", "Sergei Lebedev", "Antonious Girgis", "Bernd Bandemer", "David Du", "Todd Wang", "Xi Chen", "Ying Xiao", "Peggy Lu", "Natalie Ha", "Vlad Ionescu", "Simon Rowe", "Josip Matak", "Federico Lebron", "Andreas Steiner", "Lalit Jain", "Manaal Faruqui", "Nicolas Lacasse", "Georgie Evans", "Neesha Subramaniam", "Dean Reich", "Giulia Vezzani", "Aditya Pandey", "Joe Stanton", "Tianhao Zhou", "Liam McCafferty", "Henry Griffiths", "Verena Rieser", "Soheil Hassas Yeganeh", "Eleftheria Briakou", "Lu Huang", "Zichuan Wei", "Liangchen Luo", "Erik Jue", "Gabby Wang", "Victor Cotruta", "Myriam Khan", "Jongbin Park", "Qiuchen Guo", "Peiran Li", "Rong Rong", "Diego Antognini", "Anastasia Petrushkina", "Chetan Tekur", "Eli Collins", "Parul Bhatia", "Chester Kwak", "Wenhu Chen", "Arvind Neelakantan", "Immanuel Odisho", "Sheng Peng", "Vincent Nallatamby", "Vaibhav Tulsyan", "Fabian Pedregosa", "Peng Xu", "Raymond Lin", "Yulong Wang", "Emma Wang", "Sholto Douglas", "Reut Tsarfaty", "Elena Gribovskaya", "Renga Aravamudhan", "Manu Agarwal", "Mara Finkelstein", "Qiao Zhang", "Elizabeth Cole", "Phil Crone", "Sarmishta Velury", "Anil Das", "Chris Sauer", "Luyao Xu", "Danfeng Qin", "Chenjie Gu", "Dror Marcus", "CJ Zheng", "Wouter Van Gansbeke", "Sobhan Miryoosefi", "Haitian Sun", "YaGuang Li", "Charlie Chen", "Jae Yoo", "Pavel Dubov", "Alex Tomala", "Adams Yu", "Pawe\u0142 Weso\u0142owski", "Alok Gunjan", "Eddie Cao", "Jiaming Luo", "Nikhil Sethi", "Arkadiusz Socala", "Laura Graesser", "Tomas Kocisky", "Arturo BC", "Minmin Chen", "Edward Lee", "Sophie Wang", "Weize Kong", "Qiantong Xu", "Nilesh Tripuraneni", "Yiming Li", "Xinxin Yu", "Allen Porter", "Paul Voigtlaender", "Biao Zhang", "Arpi Vezer", "Sarah York", "Qing Wei", "Geoffrey Cideron", "Mark Kurzeja", "Seungyeon Kim", "Benny Li", "Ang\u00e9line Pouget", "Hyo Lee", "Kaspar Daugaard", "Yang Li", "Dave Uthus", "Aditya Siddhant", "Paul Cavallaro", "Sriram Ganapathy", "Maulik Shah", "Rolf Jagerman", "Jeff Stanway", "Piermaria Mendolicchio", "Li Xiao", "Kayi Lee", "Tara Thompson", "Shubham Milind Phal", "Jason Chase", "Sun Jae Lee", "Adrian N Reyes", "Disha Shrivastava", "Zhen Qin", "Roykrong Sukkerd", "Seth Odoom", "Lior Madmoni", "John Aslanides", "Jonathan Herzig", "Elena Pochernina", "Sheng Zhang", "Parker Barnes", "Daisuke Ikeda", "Qiujia Li", "Shuo-yiin Chang", "Shakir Mohamed", "Jim Sproch", "Richard Powell", "Bidisha Samanta", "Domagoj \u0106evid", "Anton Kovsharov", "Shrestha Basu Mallick", "Srinivas Tadepalli", "Anne Zheng", "Kareem Ayoub", "Andreas Noever", "Christian Reisswig", "Zhuo Xu", "Junhyuk Oh", "Martin Matysiak", "Tim Blyth", "Shereen Ashraf", "Julien Amelot", "Boone Severson", "Michele Bevilacqua", "Motoki Sano", "Ethan Dyer", "Ofir Roval", "Anu Sinha", "Yin Zhong", "Sagi Perel", "Tea Saboli\u0107", "Johannes Mauerer", "Willi Gierke", "Mauro Verzetti", "Rodrigo Cabrera", "Alvin Abdagic", "Steven Hemingray", "Austin Stone", "Jong Lee", "Farooq Ahmad", "Karthik Raman", "Lior Shani", "Jonathan Lai", "Orhan Firat", "Nathan Waters", "Eric Ge", "Mo Shomrat", "Himanshu Gupta", "Rajeev Aggarwal", "Tom Hudson", "Bill Jia", "Simon Baumgartner", "Palak Jain", "Joe Kovac", "Junehyuk Jung", "Ante \u017du\u017eul", "Will Truong", "Morteza Zadimoghaddam", "Songyou Peng", "Marco Liang", "Rachel Sterneck", "Balaji Lakshminarayanan", "Machel Reid", "Oliver Woodman", "Tong Zhou", "Jianling Wang", "Vincent Coriou", "Arjun Narayanan", "Jay Hoover", "Yenai Ma", "Apoorv Jindal", "Clayton Sanford", "Doug Reid", "Swaroop Ramaswamy", "Alex Kurakin", "Roland Zimmermann", "Yana Lunts", "Dragos Dena", "Zal\u00e1n Borsos", "Vered Cohen", "Shujian Zhang", "Will Grathwohl", "Robert Dadashi", "Morgan Redshaw", "Joshua Kessinger", "Julian Odell", "Silvano Bonacina", "Zihang Dai", "Grace Chen", "Ayush Dubey", "Pablo Sprechmann", "Mantas Pajarskas", "Wenxuan Zhou", "Niharika Ahuja", "Tara Thomas", "Martin Nikoltchev", "Matija Kecman", "Bharath Mankalale", "Andrey Ryabtsev", "Jennifer She", "Christian Walder", "Jiaming Shen", "Lu Li", "Carolina Parada", "Sheena Panthaplackel", "Okwan Kwon", "Matt Lawlor", "Utsav Prabhu", "Yannick Schroecker", "Marc'aurelio Ranzato", "Pete Blois", "Iurii Kemaev", "Ting Yu", "Dmitry", "Lepikhin", "Hao Xiong", "Sahand Sharifzadeh", "Oleaser Johnson", "Jeremiah Willcock", "Rui Yao", "Greg Farquhar", "Sujoy Basu", "Hidetoshi Shimokawa", "Nina Anderson", "Haiguang Li", "Khiem Pham", "Yizhong Liang", "Sebastian Borgeaud", "Alexandre Moufarek", "Hideto Kazawa", "Blair Kutzman", "Marcin Sieniek", "Sara Smoot", "Ruth Wang", "Natalie Axelsson", "Nova Fallen", "Prasha Sundaram", "Yuexiang Zhai", "Varun Godbole", "Petros Maniatis", "Alek Wang", "Ilia Shumailov", "Santhosh Thangaraj", "Remi Crocker", "Nikita Gupta", "Gang Wu", "Phil Chen", "Gell\u00e9rt Weisz", "Celine Smith", "Mojtaba Seyedhosseini", "Boya Fang", "Xiyang Luo", "Roey Yogev", "Zeynep Cankara", "Andrew Hard", "Helen Ran", "Rahul Sukthankar", "George Necula", "Ga\u00ebl Liu", "Honglong Cai", "Praseem Banzal", "Daniel Keysers", "Sanjay Ghemawat", "Connie Tao", "Emma Dunleavy", "Aditi Chaudhary", "Wei Li", "Maciej Miku\u0142a", "Chen-Yu Lee", "Tiziana Refice", "Krishna Somandepalli", "Alexandre Fr\u00e9chette", "Dan Bahir", "John Karro", "Keith Rush", "Sarah Perrin", "Bill Rosgen", "Xiaomeng Yang", "Clara Huiyi Hu", "Mahmoud Alnahlawi", "Justin Mao-Jones", "Roopal Garg", "Hoang Nguyen", "Bat-Orgil Batsaikhan", "I\u00f1aki Iturrate", "Anselm Levskaya", "Avi Singh", "Ashyana Kachra", "Tony Lu", "Denis Petek", "Zheng Xu", "Mark Graham", "Lukas Zilka", "Yael Karov", "Marija Kostelac", "Fangyu Liu", "Yaohui Guo", "Weiyue Wang", "Bernd Bohnet", "Emily Pitler", "Tony Bruguier", "Keisuke Kinoshita", "Chrysovalantis Anastasiou", "Nilpa Jha", "Ting Liu", "Jerome Connor", "Phil Wallis", "Philip Pham", "Eric Bailey", "Shixin Li", "Heng-Tze Cheng", "Sally Ma", "Haiqiong Li", "Akanksha Maurya", "Kate Olszewska", "Manfred Warmuth", "Christy Koh", "Dominik Paulus", "Siddhartha Reddy Jonnalagadda", "Enrique Piqueras", "Ali Elqursh", "Geoff Brown", "Hadar Shemtov", "Loren Maggiore", "Fei Xia", "Ryan Foley", "Beka Westberg", "George van den Driessche", "Livio Baldini Soares", "Arjun Kar", "Michael Quinn", "Siqi Zuo", "Jialin Wu", "Kyle Kastner", "Anna Bortsova", "Aijun Bai", "Ales Mikhalap", "Luowei Zhou", "Jennifer Brennan", "Vinay Ramasesh", "Honglei Zhuang", "John Maggs", "Johan Schalkwyk", "Yuntao Xu", "Hui Huang", "Andrew Howard", "Sasha Brown", "Linting Xue", "Gloria Shen", "Brian Albert", "Neha Jha", "Daniel Zheng", "Varvara Krayvanova", "Spurthi Amba Hombaiah", "Olivier Lacombe", "Gautam Vasudevan", "Dan Graur", "Tian Xie", "Meet Gandhi", "Bangju Wang", "Dustin Zelle", "Harman Singh", "Dahun Kim", "S\u00e9bastien Cevey", "Victor Ungureanu", "Natasha Noy", "Fei Liu", "Annie Xie", "Fangxiaoyu Feng", "Katerina Tsihlas", "Daniel Formoso", "Neera Vats", "Quentin Wellens", "Yinan Wang", "Niket Kumar Bhumihar", "Samrat Ghosh", "Matt Hoffman", "Tom Lieber", "Oran Lang", "Kush Bhatia", "Tom Paine", "Aroonalok Pyne", "Ronny Votel", "Madeleine Clare Elish", "Benoit Schillings", "Alex Panagopoulos", "Haichuan Yang", "Adam Raveret", "Zohar Yahav", "Shuang Liu", "Warren Chen", "Dalia El Badawy", "Nishant Agrawal", "Mohammed Badawi", "Mahdi Mirzazadeh", "Carla Bromberg", "Fan Ye", "Chang Liu", "Tatiana Sholokhova", "George-Cristian Muraru", "Gargi Balasubramaniam", "Jonathan Malmaud", "Alen Carin", "Danilo Martins", "Irina Jurenka", "Pankil Botadra", "Dave Lacey", "Richa Singh", "Mariano Schain", "Dan Zheng", "Isabelle Guyon", "Victor Lavrenko", "Seungji Lee", "Xiang Zhou", "Demis Hassabis", "Jeshwanth Challagundla", "Derek Cheng", "Nikhil Mehta", "Matthew Mauger", "Michela Paganini", "Pushkar Mishra", "Kate Lee", "Zhang Li", "Lexi Baugher", "Ondrej Skopek", "Max Chang", "Amir Zait", "Gaurav Menghani", "Lizzetth Bellot", "Guangxing Han", "Jean-Michel Sarr", "Sharat Chikkerur", "Himanshu Sahni", "Rohan Anil", "Arun Narayanan", "Chandu Thekkath", "Daniele Pighin", "Hana Strej\u010dek", "Marko Velic", "Fred Bertsch", "Manuel Tragut", "Keran Rong", "Alicia Parrish", "Kai Bailey", "Jiho Park", "Isabela Albuquerque", "Abhishek Bapna", "Rajesh Venkataraman", "Alec Kosik", "Johannes Griesser", "Zhiwei Deng", "Alek Andreev", "Qingyun Dou", "Kevin Hui", "Fanny Wei", "Xiaobin Yu", "Lei Shu", "Avia Aharon", "David Barker", "Badih Ghazi", "Sebastian Flennerhag", "Chris Breaux", "Yuchuan Liu", "Matthew Bilotti", "Josh Woodward", "Uri Alon", "Stephanie Winkler", "Tzu-Kuo Huang", "Kostas Andriopoulos", "Jo\u00e3o Gabriel Oliveira", "Penporn Koanantakool", "Berkin Akin", "Michael Wunder", "Cicero Nogueira dos Santos", "Mohammad Hossein Bateni", "Lin Yang", "Dan Horgan", "Beer Changpinyo", "Keyvan Amiri", "Min Ma", "Dayeong Lee", "Lihao Liang", "Anirudh Baddepudi", "Tejasi Latkar", "Raia Hadsell", "Jun Xu", "Hairong Mu", "Michael Han", "Aedan Pope", "Snchit Grover", "Frank Kim", "Ankit Bhagatwala", "Guan Sun", "Yamini Bansal", "Amir Globerson", "Alireza Nazari", "Samira Daruki", "Hagen Soltau", "Jane Labanowski", "Laurent El Shafey", "Matt Harvey", "Yanif Ahmad", "Elan Rosenfeld", "William Kong", "Etienne Pot", "Yi-Xuan Tan", "Aurora Wei", "Victoria Langston", "Marcel Prasetya", "Petar Veli\u010dkovi\u0107", "Richard Killam", "Robin Strudel", "Darren Ni", "Zhenhai Zhu", "Aaron Archer", "Kavya Kopparapu", "Lynn Nguyen", "Emilio Parisotto", "Hussain Masoom", "Sravanti Addepalli", "Jordan Grimstad", "Hexiang Hu", "Joss Moore", "Avinatan Hassidim", "Le Hou", "Mukund Raghavachari", "Jared Lichtarge", "Adam R. Brown", "Hilal Dib", "Natalia Ponomareva", "Justin Fu", "Yujing Zhang", "Altaf Rahman", "Joana Iljazi", "Edouard Leurent", "Gabriel Dulac-Arnold", "Cosmo Du", "Chulayuth Asawaroengchai", "Larry Jin", "Ela Gruzewska", "Ziwei Ji", "Benigno Uria", "Daniel De Freitas", "Paul Barham", "Lauren Beltrone", "V\u00edctor Campos", "Jun Yan", "Neel Kovelamudi", "Arthur Nguyen", "Elinor Davies", "Zhichun Wu", "Zoltan Egyed", "Kristina Toutanova", "Nithya Attaluri", "Hongliang Fei", "Peter Stys", "Siddhartha Brahma", "Martin Izzard", "Siva Velusamy", "Scott Lundberg", "Vincent Zhuang", "Kevin Sequeira", "Adam Santoro", "Ehsan Amid", "Ophir Aharoni", "Shuai Ye", "Mukund Sundararajan", "Lijun Yu", "Yu-Cheng Ling", "Stephen Spencer", "Hugo Song", "Josip Djolonga", "Christo Kirov", "Sonal Gupta", "Alessandro Bissacco", "Clemens Meyer", "Mukul Bhutani", "Andrew Dai", "Weiyi Wang", "Siqi Liu", "Ashwin Sreevatsa", "Qijun Tan", "Maria Wang", "Lucy Kim", "Yicheng Wang", "Alex Irpan", "Yang Xiao", "Stanislav Fort", "Yifan He", "Alex Gurney", "Bryan Gale", "Yue Ma", "Monica Roy", "Viorica Patraucean", "Taylan Bilal", "Golnaz Ghiasi", "Anahita Hosseini", "Melvin Johnson", "Zhuowan Li", "Yi Tay", "Benjamin Beyret", "Katie Millican", "Josef Broder", "Mayank Lunayach", "Danny Swisher", "Eugen Vu\u0161ak", "David Parkinson", "MH Tessler", "Adi Mayrav Gilady", "Richard Song", "Allan Dafoe", "Yves Raimond", "Masa Yamaguchi", "Itay Karo", "Elizabeth Nielsen", "Kevin Kilgour", "Mike Dusenberry", "Rajiv Mathews", "Jiho Choi", "Siyuan Qiao", "Harsh Mehta", "Sahitya Potluri", "Chris Knutsen", "Jialu Liu", "Tat Tan", "Kuntal Sengupta", "Keerthana Gopalakrishnan", "Abodunrinwa Toki", "Mencher Chiang", "Mike Burrows", "Grace Vesom", "Zafarali Ahmed", "Ilia Labzovsky", "Siddharth Vashishtha", "Preeti Singh", "Ankur Sharma", "Ada Ma", "Jinyu Xie", "Pranav Talluri", "Hannah Forbes-Pollard", "Aarush Selvan", "Joel Wee", "Loic Matthey", "Tom Funkhouser", "Parthasarathy Gopavarapu", "Lev Proleev", "Cheng Li", "Matt Thomas", "Kashyap Kolipaka", "Zhipeng Jia", "Ashwin Kakarla", "Srinivas Sunkara", "Joan Puigcerver", "Suraj Satishkumar Sheth", "Emily Graves", "Chen Wang", "Sadh MNM Khan", "Kai Kang", "Shyamal Buch", "Fred Zhang", "Omkar Savant", "David Soergel", "Kevin Lee", "Linda Friso", "Xuanyi Dong", "Rahul Arya", "Shreyas Chandrakaladharan", "Connor Schenck", "Greg Billock", "Tejas Iyer", "Anton Bakalov", "Leslie Baker", "Alex Ruiz", "Angad Chandorkar", "Trieu Trinh", "Matt Miecnikowski", "Yanqi Zhou", "Yangsibo Huang", "Jiazhong Nie", "Ali Shah", "Ashish Thapliyal", "Sam Haves", "Lun Wang", "Uri Shaham", "Patrick Morris-Suzuki", "Soroush Radpour", "Leonard Berrada", "Thomas Strohmann", "Chaochao Yan", "Jingwei Shen", "Sonam Goenka", "Tris Warkentin", "Petar Devi\u0107", "Dan Belov", "Albert Webson", "Madhavi Yenugula", "Puranjay Datta", "Jerry Chang", "Nimesh Ghelani", "Aviral Kumar", "Vincent Perot", "Jessica Lo", "Yang Song", "Herman Schmit", "Jianmin Chen", "Vasilisa Bashlovkina", "Xiaoyue Pan", "Diana Mincu", "Paul Roit", "Isabel Edkins", "Andy Davis", "Yujia Li", "Ben Horn", "Xinjian Li", "Pradeep Kumar S", "Eric Doi", "Wanzheng Zhu", "Sri Gayatri Sundara Padmanabhan", "Siddharth Verma", "Jasmine Liu", "Heng Chen", "Mihajlo Velimirovi\u0107", "Malcolm Reynolds", "Priyanka Agrawal", "Nick Sukhanov", "Abhinit Modi", "Siddharth Goyal", "John Palowitch", "Nima Khajehnouri", "Wing Lowe", "David Klinghoffer", "Sharon Silver", "Vinh Tran", "Candice Schumann", "Francesco Piccinno", "Xi Liu", "Mario Lu\u010di\u0107", "Xiaochen Yang", "Sandeep Kumar", "Ajay Kannan", "Ragha Kotikalapudi", "Mudit Bansal", "Fabian Fuchs", "Javad Hosseini", "Abdelrahman Abdelhamed", "Dawn Bloxwich", "Tianhe Yu", "Ruoxin Sang", "Gregory Thornton", "Karan Gill", "Yuchi Liu", "Virat Shejwalkar", "Jason Lin", "Zhipeng Yan", "Kehang Han", "Thomas Buschmann", "Michael Pliskin", "Zhi Xing", "Susheel Tatineni", "Junlin Zhang", "Sissie Hsiao", "Gavin Buttimore", "Marcus Wu", "Zefei Li", "Geza Kovacs", "Legg Yeung", "Tao Huang", "Aaron Cohen", "Bethanie Brownfield", "Averi Nowak", "Mikel Rodriguez", "Tianze Shi", "Hado van Hasselt", "Kevin Cen", "Deepanway Ghoshal", "Kushal Majmundar", "Weiren Yu", "Warren", "Chen", "Danila Sinopalnikov", "Hao Zhang", "Vlado Gali\u0107", "Di Lu", "Zeyu Zheng", "Maggie Song", "Gary Wang", "Gui Citovsky", "Swapnil Gawde", "Isaac Galatzer-Levy", "David Silver", "Ivana Balazevic", "Dipanjan Das", "Kingshuk Majumder", "Yale Cong", "Praneet Dutta", "Dustin Tran", "Hui Wan", "Junwei Yuan", "Daniel Eppens", "Alanna Walton", "Been Kim", "Harry Ragan", "James Cobon-Kerr", "Lu Liu", "Weijun Wang", "Bryce Petrini", "Jack Rae", "Rakesh Shivanna", "Yan Xiong", "Chace Lee", "Pauline Coquinot", "Yiming Gu", "Lisa Patel", "Blake Hechtman", "Aviel Boag", "Orion Jankowski", "Alex Wertheim", "Alex Lee", "Paul Covington", "Hila Noga", "Sam Sobell", "Shanthal Vasanth", "William Bono", "Chirag Nagpal", "Wei Fan", "Xavier Garcia", "Kedar Soparkar", "Aybuke Turker", "Nathan Howard", "Sachit Menon", "Yuankai Chen", "Vikas Verma", "Vladimir Pchelin", "Harish Rajamani", "Valentin Dalibard", "Ana Ramalho", "Yang Guo", "Kartikeya Badola", "Seojin Bang", "Nathalie Rauschmayr", "Julia Proskurnia", "Sudeep Dasari", "Xinyun Chen", "Mikhail Sushkov", "Anja Hauth", "Pauline Sho", "Abhinav Singh", "Bilva Chandra", "Allie Culp", "Max Dylla", "Olivier Bachem", "James Besley", "Heri Zhao", "Timothy Lillicrap", "Wei Wei", "Wael Al Jishi", "Ning Niu", "Alban Rrustemi", "Rapha\u00ebl Lopez Kaufman", "Ryan Poplin", "Jewel Zhao", "Minh Truong", "Shikhar Bharadwaj", "Ester Hlavnova", "Eli Stickgold", "Cordelia Schmid", "Georgi Stephanov", "Zhaoqi Leng", "Frederick Liu", "L\u00e9onard Hussenot", "Shenil Dodhia", "Juliana Vicente Franco", "Lesley Katzen", "Abhanshu Sharma", "Sarah Cogan", "Zuguang Yang", "Aniket Ray", "Sergi Caelles", "Shen Yan", "Ravin Kumar", "Daniel Gillick", "Renee Wong", "Joshua Ainslie", "Jonathan Hoech", "S\u00e9b Arnold", "Dan Abolafia", "Anca Dragan", "Ben Hora", "Grace Hu", "Alexey Guseynov", "Yang Lu", "Chas Leichner", "Jinmeng Rao", "Abhimanyu Goyal", "Nagabhushan Baddi", "Daniel Hernandez Diaz", "Tim McConnell", "Max Bain", "Jake Abernethy", "Qiqi Yan", "Rylan Schaeffer", "Paul Vicol", "Will Thompson", "Montse Gonzalez Arenas", "Mathias Bellaiche", "Pablo Barrio", "Stefan Zinke", "Riccardo Patana", "Pulkit Mehta", "JK Kearns", "Avraham Ruderman", "Scott Pollom", "David D'Ambrosio", "Cath Hope", "Yang Yu", "Andrea Gesmundo", "Kuang-Huei Lee", "Aviv Rosenberg", "Yiqian Zhou", "Yaoyiran Li", "Drew Garmon", "Yonghui Wu", "Safeen Huda", "Gil Fidel", "Martin Baeuml", "Jian Li", "Phoebe Kirk", "Rhys May", "Tao Tu", "Sara Mc Carthy", "Toshiyuki Fukuzawa", "Miranda Aperghis", "Chih-Kuan Yeh", "Toshihiro Yoshino", "Bo Li", "Austin Myers", "Kaisheng Yao", "Ben Limonchik", "Changwan Ryu", "Rohun Saxena", "Alex Goldin", "Ruizhe Zhao", "Rocky Rhodes", "Tao Zhu", "Divya Tyam", "Heidi Howard", "Nathan Byrd", "Hongxu Ma", "Yan Wu", "Ryan Mullins", "Qingze Wang", "Aida Amini", "Sebastien Baur", "Yiran Mao", "Subhashini Venugopalan", "Will Song", "Wen Ding", "Paul Collins", "Sashank Reddi", "Megan Shum", "Andrei Rusu", "Luisa Zintgraf", "Kelvin Chan", "Sheela Goenka", "Mathieu Blondel", "Michael Collins", "Renke Pan", "Marissa Giustina", "Nikolai Chinaev", "Christian Schuler", "Ce Zheng", "Jonas Valfridsson", "Alyssa Loo", "Alex Yakubovich", "Jamie Smith", "Tao Jiang", "Rich Munoz", "Gabriel Barcik", "Rishabh Bansal", "Mingyao Yang", "Yilun Du", "Pablo Duque", "Mary Phuong", "Alexandra Belias", "Kunal Lad", "Zeyu Liu", "Tal Schuster", "Karthik Duddu", "Jieru Hu", "Paige Kunkle", "Matthew Watson", "Jackson Tolins", "Josh Smith", "Denis Teplyashin", "Garrett Bingham", "Marvin Ritter", "Marco Andreetto", "Divya Pitta", "Mohak Patel", "Shashank Viswanadha", "Trevor Strohman", "Catalin Ionescu", "Jincheng Luo", "Yogesh Kalley", "Jeremy Wiesner", "Dan Deutsch", "Derek Lockhart", "Peter Choy", "Rumen Dangovski", "Chawin Sitawarin", "Cat Graves", "Tanya Lando", "Joost van Amersfoort", "Ndidi Elue", "Zhouyuan Huo", "Pooya Moradi", "Jean Tarbouriech", "Henryk Michalewski", "Wenting Ye", "Eunyoung Kim", "Alex Druinsky", "Florent Altch\u00e9", "Xinyi Chen", "Artur Dwornik", "Da-Cheng Juan", "Rivka Moroshko", "Horia Toma", "Jarrod Kahn", "Hai Qian", "Maximilian Sieb", "Irene Cai", "Roman Goldenberg", "Praneeth Netrapalli", "Sindhu Raghuram", "Yuan Gong", "Lijie Fan", "Evan Palmer", "Yossi Matias", "Valentin Gabeur", "Shreya Pathak", "Tom Ouyang", "Don Metzler", "Geoff Bacon", "Srinivasan Venkatachary", "Sridhar Thiagarajan", "Alex Cullum", "Eran Ofek", "Vytenis Sakenas", "Mohamed Hammad", "Cesar Magalhaes", "Mayank Daswani", "Oscar Chang", "Ashok Popat", "Ruichao Li", "Komal Jalan", "Yanhan Hou", "Josh Lipschultz", "Antoine He", "Wenhao Jia", "Pier Giuseppe Sessa", "Prateek Kolhar", "William Wong", "Sumeet Singh", "Lukas Haas", "Jay Whang", "Hanna Klimczak-Pluci\u0144ska", "Georges Rotival", "Grace Chung", "Yiqing Hua", "Anfal Siddiqui", "Nicolas Serrano", "Dongkai Chen", "Billy Porter", "Libin Bai", "Keshav Shivam", "Sho Arora", "Partha Talukdar", "Tom Cobley", "Sangnie Bhardwaj", "Evgeny Gladchenko", "Simon Green", "Kelvin Guu", "Felix Fischer", "Xiao Wu", "Eric Wang", "Achintya Singhal", "Tatiana Matejovicova", "James Martens", "Hongji Li", "Roma Patel", "Elizabeth Kemp", "Jiaqi Pan", "Lily Wang", "Blake JianHang Chen", "Jean-Baptiste Alayrac", "Navneet Potti", "Erika Gemzer", "Eugene Ie", "Kay McKinney", "Takaaki Saeki", "Edward Chou", "Pascal Lamblin", "SQ Mah", "Zach Fisher", "Martin Chadwick", "Jon Stritar", "Obaid Sarvana", "Andrew Hogue", "Artem Shtefan", "Hadi Hashemi", "Yang Xu", "Jindong Gu", "Sharad Vikram", "Chung-Ching Chang", "Sabela Ramos", "Logan Kilpatrick", "Weijuan Xi", "Jenny Brennan", "Yinghao Sun", "Abhishek Jindal", "Ionel Gog", "Dawn Chen", "Felix Wu", "Jason Lee", "Sudhindra Kopalle", "Srinadh Bhojanapalli", "Oriol Vinyals", "Natan Potikha", "Burcu Karagol Ayan", "Yuan Yuan", "Michael Riley", "Piotr Stanczyk", "Sergey Kishchenko", "Bing Wang", "Dan Garrette", "Antoine Yang", "Vlad Feinberg", "CJ Carey", "Javad Azizi", "Viral Shah", "Erica Moreira", "Chongyang Shi", "Josh Feldman", "Elizabeth Salesky", "Thomas Lampe", "Aneesh Pappu", "Duhyeon Kim", "Jonas Adler", "Avi Caciularu", "Brian Walker", "Yunhan Xu", "Yochai Blau", "Dylan Scandinaro", "Terry Huang", "Sam El-Husseini", "Abhishek Sinha", "Lijie Ren", "Taylor Tobin", "Patrik Sundberg", "Tim Sohn", "Vikas Yadav", "Mimi Ly", "Emily Xue", "Jing Xiong", "Afzal Shama Soudagar", "Sneha Mondal", "Nikhil Khadke", "Qingchun Ren", "Ben Vargas", "Stan Bileschi", "Sarah Chakera", "Cindy Wang", "Boyu Wang", "Yoni Halpern", "Joe Jiang", "Vikas Sindhwani", "Petre Petrov", "Pranavaraj Ponnuramu", "Sanket Vaibhav Mehta", "Yu Watanabe", "Betty Chan", "Matheus Wisniewski", "Trang Pham", "Jingwei Zhang", "Conglong Li", "Dario de Cesare", "Art Khurshudov", "Alex Vasiloff", "Melissa Tan", "Zoe Ashwood", "Bobak Shahriari", "Maryam Majzoubi", "Garrett Tanzer", "Olga Kozlova", "Robin Alazard", "James Lee-Thorp", "Nguyet Minh Phu", "Isaac Tian", "Junwhan Ahn", "Andy Crawford", "Lauren Lax", "Yuan", "Shangguan", "Iftekhar Naim", "David Ross", "Oleksandr Ferludin", "Tongfei Guo", "Andrea Banino", "Hubert Soyer", "Xiaoen Ju", "Dominika Rogozi\u0144ska", "Ishaan Malhi", "Marcella Valentine", "Daniel Balle", "Apoorv Kulshreshtha", "Maciej Kula", "Yiwen Song", "Sophia Austin", "John Schultz", "Roy Hirsch", "Arthur Douillard", "Apoorv Reddy", "Michael Fink", "Summer Yue", "Khyatti Gupta", "Adam Zhang", "Norman Rink", "Daniel McDuff", "Lei Meng", "Andr\u00e1s Gy\u00f6rgy", "Yasaman Razeghi", "Ricky Liang", "Kazuki Osawa", "Aviel Atias", "Matan Eyal", "Tyrone Hill", "Nikolai Grigorev", "Zhengdong Wang", "Nitish Kulkarni", "Rachel Soh", "Ivan Lobov", "Zachary Charles", "Sid Lall", "Kazuma Hashimoto", "Ido Kessler", "Victor Gomes", "Zelda Mariet", "Danny Driess", "Alessandro Agostini", "Canfer Akbulut", "Jingcao Hu", "Marissa Ikonomidis", "Emily Caveness", "Kartik Audhkhasi", "Saurabh Agrawal", "Ioana Bica", "Evan Senter", "Jayaram Mudigonda", "Kelly Chen", "Jingchen Ye", "Xuanhui Wang", "James Svensson", "Philipp Fr\u00e4nken", "Josh Newlan", "Li Lao", "Eva Schnider", "Sami Alabed", "Joseph Kready", "Jesse Emond", "Afief Halumi", "Tim Zaman", "Chengxi Ye", "Naina Raisinghani", "Vilobh Meshram", "Bo Chang", "Ankit Singh Rawat", "Axel Stjerngren", "Sergey Levi", "Rui Wang", "Xiangzhu Long", "Mitchelle Rasquinha", "Steven Hand", "Aditi Mavalankar", "Lauren Agubuzu", "Sudeshna Roy", "Junquan Chen", "Jarek Wilkiewicz", "Hao Zhou", "Michal Jastrzebski", "Qiong Hu", "Agustin Dal Lago", "Ramya Sree Boppana", "Wei-Jen Ko", "Jennifer Prendki", "Yao Su", "Zhi Li", "Eliza Rutherford", "Girish Ramchandra Rao", "Ramona Comanescu", "Adri\u00e0 Puigdom\u00e8nech", "Qihang Chen", "Dessie Petrova", "Christine Chan", "Vedrana Milutinovic", "Felipe Tiengo Ferreira", "Chin-Yi Cheng", "Ming Zhang", "Tapomay Dey", "Sherry Yang", "Ramesh Sampath", "Quoc Le", "Howard Zhou", "Chu-Cheng Lin", "Hoi Lam", "Christine Kaeser-Chen", "Kai Hui", "Dean Hirsch", "Tom Eccles", "Basil Mustafa", "Shruti Rijhwani", "Morgane Rivi\u00e8re", "Yuanzhong Xu", "Junjie Wang", "Xinyang Geng", "Xiance Si", "Arjun Khare", "Cheolmin Kim", "Vahab Mirrokni", "Kamyu Lee", "Khuslen Baatarsukh", "Nathaniel Braun", "Lisa Wang", "Pallavi LV", "Richard Tanburn", "Yuvein", "Zhu", "Fangda Li", "Setareh Ariafar", "Dan Goldberg", "Ken Burke", "Daniil Mirylenka", "Meiqi Guo", "Olaf Ronneberger", "Hadas Natalie Vogel", "Liqun Cheng", "Nishita Shetty", "Johnson Jia", "Thomas Jimma", "Corey Fry", "Ted Xiao", "Martin Sundermeyer", "Ryan Burnell", "Yannis Assael", "Mario Pinto", "JD Chen", "Rohit Sathyanarayana", "Donghyun Cho", "Jing Lu", "Rishabh Agarwal", "Sugato Basu", "Lucas Gonzalez", "Dhruv Shah", "Meng Wei", "Dre Mahaarachchi", "Rohan Agrawal", "Tero Rissa", "Yani Donchev", "Ramiro Leal-Cavazos", "Adrian Hutter", "Markus Mircea", "Alon Jacovi", "Faruk Ahmed", "Jiageng Zhang", "Shuguang Hu", "Bo-Juen Chen", "Jonni Kanerva", "Guillaume Desjardins", "Andrew Lee", "Nikos Parotsidis", "Asier Mujika", "Tobias Weyand", "Jasper Snoek", "Jo Chick", "Kai Chen", "Paul Chang", "Ethan Mahintorabi", "Zi Wang", "Tolly Powell", "Orgad Keller", "Abhirut Gupta", "Claire Sha", "Kanav Garg", "Nicolas Heess", "\u00c1goston Weisz", "Cassidy Hardin", "Bartek Wydrowski", "Ben Coleman", "Karina Zainullina", "Pankaj Joshi", "Alessandro Epasto", "Terry Spitz", "Binbin Xiong", "Kai Zhao", "Arseniy Klimovskiy", "Ivy Zheng", "Johan Ferret", "Itay Yona", "Waleed Khawaja", "Jean-Baptiste Lespiau", "Maxim Krikun", "Siamak Shakeri", "Timothee Cour", "Bonnie Li", "Igor Krivokon", "Dan Suh", "Alex Hofer", "Jad Al Abdallah", "Nikita Putikhin", "Oscar Akerlund", "Silvio Lattanzi", "Anurag Kumar", "Shane Settle", "Himanshu Srivastava", "Folawiyo Campbell-Ajala", "Edouard Rosseel", "Mihai Dorin Istin", "Nishanth Dikkala", "Anand Rao", "Nick Young", "Kate Lin", "Dhruva Bhaswar", "Yiming Wang", "Jaume Sanchez Elias", "Kritika Muralidharan", "James Keeling", "Dayou Du", "Siddharth Gopal", "Gregory Dibb", "Charles Blundell", "Manolis Delakis", "Jacky Liang", "Marco Tulio Ribeiro", "Georgi Karadzhov", "Guillermo Garrido", "Ankur Bapna", "Jiawei Cao", "Adam Sadovsky", "Pouya Tafti", "Arthur Guez", "Coline Devin", "Yixian Di", "Jinwei Xing", "Chuqiao", "Xu", "Hanzhao Lin", "Chun-Te Chu", "Sameera Ponda", "Wesley Helmholz", "Fan Yang", "Yue Gao", "Sara Javanmardi", "Wael Farhan", "Alex Ramirez", "Ricardo Figueira", "Khe Chai Sim", "Yuval Bahat", "Ashwin Vaswani", "Liangzhe Yuan", "Gufeng Zhang", "Leland Rechis", "Hanjun Dai", "Tayo Oguntebi", "Alexandra Cordell", "Eug\u00e9nie Rives", "Kaan Tekelioglu", "Naveen Kumar", "Bing Zhang", "Aurick Zhou", "Nikolay Savinov", "Andrew Leach", "Alex Tudor", "Sanjay Ganapathy", "Yanyan Zheng", "Mirko Rossini", "Vera Axelrod", "Arnaud Autef", "Yukun Zhu", "Zheng Zheng", "Mingda Zhang", "Baochen Sun", "Jie Ren", "Nenad Tomasev", "Nithish Kannan", "Amer Sinha", "Charles Chen", "Louis O'Bryan", "Alex Pak", "Aditya Kusupati", "Weel Yang", "Deepak Ramachandran", "Patrick Griffin", "Seokhwan Kim", "Philipp Neubeck", "Craig Schiff", "Tammo Spalink", "Mingyang Ling", "Arun Nair", "Ga-Young Joung", "Linda Deng", "Avishkar Bhoopchand", "Lora Aroyo", "Tom Duerig", "Jordan Griffith", "Gabe Barth-Maron", "Jake Ades", "Alex Haig", "Ankur Taly", "Yunting Song", "Paul Michel", "Dave Orr", "Dean Weesner", "Corentin Tallec", "Carrie Grimes Bostock", "Paul Niemczyk", "Andy Twigg", "Mudit Verma", "Rohith Vallu", "Henry Wang", "Marco Gelmi", "Kiranbir Sodhia", "Aleksandr Chuklin", "Omer Goldman", "Jasmine George", "Liang Bai", "Kelvin Zhang", "Petar Sirkovic", "Efrat Nehoran", "Golan Pundak", "Jiaqi Mu", "Alice Chen", "Alex Greve", "Paulo Zacchello", "David Amos", "Heming Ge", "Eric Noland", "Colton Bishop", "Jeffrey Dudek", "Youhei Namiki", "Elena Buchatskaya", "Jing Li", "Dorsa Sadigh", "Masha Samsikova", "Dan Malkin", "Damien Vincent", "Robert David", "Rob Willoughby", "Phoenix Meadowlark", "Shawn Gao", "Yan Li", "Raj Apte", "Amit Jhindal", "Stein Xudong Lin", "Alex Polozov", "Zhicheng Wang", "Tomas Mery", "Anirudh GP", "Varun Yerram", "Sage Stevens", "Tianqi Liu", "Noah Fiedel", "Charles Sutton", "Matthew Johnson", "Xiaodan Song", "Kate Baumli", "Nir Shabat", "Muqthar Mohammad", "Hao Liu", "Marco Selvi", "Yichao Zhou", "Mehdi Hafezi Manshadi", "Chu-ling Ko", "Anthony Chen", "Michael Bendersky", "Jorge Gonzalez Mendez", "Nisarg Kothari", "Amir Zandieh", "Yiling Huang", "Daniel Andor", "Ellie Pavlick", "Idan Brusilovsky", "Jitendra Harlalka", "Sally Goldman", "Andrew Lampinen", "Guowang Li", "Asahi Ushio", "Somit Gupta", "Lei Zhang", "Chuyuan Kelly Fu", "Madhavi Sewak", "Timo Denk", "Jed Borovik", "Brendan Jou", "Avital Zipori", "Prateek Jain", "Junwen Bai", "Thang Luong", "Jonathan Tompson", "Alice Li", "Li Liu", "George Powell", "Jiajun Shen", "Alex Feng", "Grishma Chole", "Da Yu", "Yinlam Chow", "Tongxin Yin", "Eric Malmi", "Kefan Xiao", "Yash Pande", "Shachi Paul", "Niccol\u00f2 Dal Santo", "Adil Dostmohamed", "Sergio Guadarrama", "Aaron Phillips", "Thanumalayan Sankaranarayana Pillai", "Gal Yona", "Amin Ghafouri", "Preethi Lahoti", "Benjamin Lee", "Dhruv Madeka", "Eren Sezener", "Simon Tokumine", "Adrian Collister", "Nicola De Cao", "Richard Shin", "Uday Kalra", "Parker Beak", "Emily Nottage", "Ryo Nakashima", "Ivan Jurin", "Vikash Sehwag", "Meenu Gaba", "Junhao Zeng", "Kevin R. McKee", "Fernando Pereira", "Tamar Yakar", "Amayika Panda", "Arka Dhar", "Peilin Zhong", "Daniel Sohn", "Mark Brand", "Lars Lowe Sjoesund", "Viral Carpenter", "Sharon Lin", "Shantanu Thakoor", "Marcus Wainwright", "Ashwin Chaugule", "Pranesh Srinivasan", "Muye Zhu", "Bernett Orlando", "Jack Weber", "Ayzaan Wahid", "Gilles Baechler", "Apurv Suman", "Jovana Mitrovi\u0107", "Gabe Taubman", "Honglin Yu", "Helen King", "Josh Dillon", "Cathy Yip", "Dhriti Varma", "Tomas Izo", "Levent Bolelli", "Borja De Balle Pigem", "Julia Di Trapani", "Fotis Iliopoulos", "Adam Paszke", "Nishant Ranka", "Joe Zou", "Francesco Pongetti", "Jed McGiffin", "Alex Siegman", "Rich Galt", "Ross Hemsley", "Goran \u017du\u017ei\u0107", "Victor Carbune", "Tao Li", "Myle Ott", "F\u00e9lix de Chaumont Quitry", "David Vilar Torres", "Yuri Chervonyi", "Tomy Tsai", "Prem Eruvbetine", "Samuel Yang", "Matthew Denton", "Jake Walker", "Slavica Anda\u010di\u0107", "Idan Heimlich Shtacher", "Vittal Premachandran", "Harshal Tushar Lehri", "Cip Baetu", "Damion Yates", "Lampros Lamprou", "Mariko Iinuma", "Ioana Mihailescu", "Ben Albrecht", "Shachi Dave", "Susie Sargsyan", "Bryan Perozzi", "Lucas Manning", "Chiyuan Zhang", "Denis Vnukov", "Igor Mordatch", "Raia Hadsell Wolfgang Macherey", "Ryan Kappedal", "Jim Stephan", "Aditya Tripathi", "Klaus Macherey", "Jun Qian", "Abhishek Bhowmick", "Shekoofeh Azizi", "R\u00e9mi Leblond", "Shiva Mohan Reddy Garlapati", "Timothy Knight", "Matthew Wiethoff", "Wei-Chih Hung", "Anelia Angelova", "Georgios Evangelopoulos", "Pawel Janus", "Dimitris Paparas", "Matthew Rahtz", "Ken Caluwaerts", "Vivek Sampathkumar", "Daniel Jarrett", "Shadi Noghabi", "Antoine Miech", "Chak Yeung", "Geoff Clark", "Henry Prior", "Fei Zheng", "Jean Pouget-Abadie", "Indro Bhattacharya", "Kalpesh Krishna", "Will Bishop", "Zhe Yuan", "Yunxiao Deng", "Ashutosh Sathe", "Kacper Krasowiak", "Ciprian Chelba", "Cho-Jui Hsieh", "Kiran Vodrahalli", "Buhuang Liu", "Thomas K\u00f6ppe", "Amr Khalifa", "Lubo Litchev", "Pichi Charoenpanit", "Reed Roberts", "Sachin Yadav", "Yasumasa Onoe", "Desi Ivanov", "Megha Mohabey", "Vighnesh Birodkar", "Nemanja Raki\u0107evi\u0107", "Pierre Sermanet", "Vaibhav Mehta", "Krishan Subudhi", "Travis Choma", "Will Ng", "Luheng He", "Kathie Wang", "Tasos Kementsietsidis", "Shane Gu", "Mansi Gupta", "Andrew Nystrom", "Mehran Kazemi", "Timothy Chung", "Nacho Cano", "Nikhil Dhawan", "Yufei Wang", "Jiawei Xia", "Trevor Yacovone", "Eric Jia", "Mingqing Chen", "Simeon Ivanov", "Ashrith Sheshan", "Sid Dalmia", "Pawe\u0142 Stradomski", "Pengcheng Yin", "Salem Haykal", "Congchao Wang", "Dennis Duan", "Neslihan Bulut", "Greg Kochanski", "Liam MacDermed", "Namrata Godbole", "Shitao Weng", "Jingjing Chen", "Rachana Fellinger", "Ramin Mehran", "Daniel Suo", "Hisham Husain", "Tong He", "Kaushal Patel", "Joshua Howland", "Randall Parker", "Kelvin Nguyen", "Sharath Maddineni", "Chris Rawles", "Mina Khan", "Shlomi Cohen-Ganor", "Amol Mandhane", "Xinyi Wu", "Chenkai Kuang", "Iulia Com\u015fa", "Ramya Ganeshan", "Hanie Sedghi", "Adam Bloniarz", "Nuo Wang Pierse", "Anton Briukhov", "Petr Mitrichev", "Anita Gergely", "Serena Zhan", "Allan Zhou", "Nikita Saxena", "Eva Lu", "Josef Dean", "Ashish Gupta", "Nicolas Perez-Nieves", "Renjie Wu", "Cory McLean", "Wei Liang", "Disha Jindal", "Anton Tsitsulin", "Wenhao Yu", "Kaiz Alarakyia", "Tom Schaul", "Piyush Patil", "Peter Sung", "Elijah Peake", "Hongkun Yu", "Feryal Behbahani", "JD Co-Reyes", "Alan Ansell", "Sean Sun", "Clara Barbu", "Jonathan Lee", "Seb Noury", "James Allingham", "Bilal Piot", "Mohit Sharma", "Christopher Yew", "Ivan Korotkov", "Bibo Xu", "Demetra Brady", "Goran Petrovic", "Shibl Mourad", "Claire Cui", "Aditya Gupta", "Parker Schuh", "Saarthak Khanna", "Anna Goldie", "Abhinav Arora", "Vadim Zubov", "Amy Stuart", "Mark Epstein", "Yun Zhu", "Jianqiao Liu", "Yury Stuken", "Ziyue Wang", "Karolis Misiunas", "Dee Guo", "Ashleah Gill", "Ale Hartman", "Zaid Nabulsi", "Aurko Roy", "Aleksandra Faust", "Jason Riesa", "Ben Withbroe", "Mengchao Wang", "Marco Tagliasacchi", "Andreea Marzoca", "James Noraky", "Serge Toropov", "Malika Mehrotra", "Bahram Raad", "Sanja Deur", "Steve Xu", "Marianne Monteiro", "Zhongru Wu", "Yi Luan", "Sam Ritter", "Nick Li", "H\u00e5vard Garnes", "Yanzhang He", "Martin Zlocha", "Jifan Zhu", "Matteo Hessel", "Will Wu", "Spandana Raj Babbula", "Chizu Kawamoto", "Yuanzhen Li", "Mehadi Hassen", "Yan Wang", "Brian Wieder", "James Freedman", "Yin Zhang", "Xinyi Bai", "Tianli Yu", "David Reitter", "XiangHai Sheng", "Mateo Wirth", "Aditya Kini", "Dima Damen", "Mingcen Gao", "Rachel Hornung", "Michael Voznesensky", "Brian Roark", "Adhi Kuncoro", "Yuxiang Zhou", "Rushin Shah", "Anthony Brohan", "Kuangyuan Chen", "James Wendt", "David Rim", "Paul Kishan Rubenstein", "Jonathan Halcrow", "Michelle Liu", "Ty Geri", "Yunhsuan Sung", "Jane Shapiro", "Shaan Bijwadia", "Chris Duvarney", "Christina Sorokin", "Paul Natsev", "Reeve Ingle", "Pramod Gupta", "Young Maeng", "Ndaba Ndebele", "Kexin Zhu", "Valentin Anklin", "Katherine Lee", "Yuan Liu", "Yaroslav Akulov", "Shaleen Gupta", "Guolong Su", "Flavien Prost", "Tianlin Liu", "Vitaly Kovalev", "Pol Moreno", "Martin Scholz", "Sam Redmond", "Zongwei Zhou", "Alex Castro-Ros", "Andr\u00e9 Susano Pinto", "Dia Kharrat", "Michal Yarom", "Rachel Saputro", "Jannis Bulian", "Ben Caine", "Ji Liu", "Abbas Abdolmaleki", "Shariq Iqbal", "Tautvydas Misiunas", "Mikhail Sirotenko", "Shefali Garg", "Guy Bensky", "Huan Gui", "Xuezhi Wang", "Raphael Koster", "Mike Bernico", "Da Huang", "Romal Thoppilan", "Trevor Cohn", "Ben Golan", "Wenlei Zhou", "Andrew Rosenberg", "Markus Freitag", "Tynan Gangwani", "Vincent Tsang", "Anand Shukla", "Xiaoqi Ren", "Minh Giang", "Chi Zou", "Andre Elisseeff", "Charline Le Lan", "Dheeru Dua", "Shuba Lall", "Pranav Shyam", "Frankie Garcia", "Sarah Nguyen", "Michael Guzman", "AJ Maschinot", "Marcello Maggioni", "Ming-Wei Chang", "Karol Gregor", "Lotte Weerts", "Kumaran Venkatesan", "Bogdan Damoc", "Leon Liu", "Jan Wassenberg", "Lewis Ho", "Becca Roelofs", "Majid Hadian", "Fran\u00e7ois-Xavier Aubet", "Yu Liang", "Sami Lachgar", "Danny Karmon", "Yong Cheng", "Amelio V\u00e1zquez-Reina", "Angie Chen", "Zhuyun Dai", "Andy Brock", "Shubham Agrawal", "Chenxi Pang", "Peter Garst", "Mariella Sanchez-Vargas", "Ivor Rendulic", "Aditya Ayyar", "Andrija Ra\u017enatovi\u0107", "Olivia Ma", "Roopali Vij", "Neha Sharma", "Ashwin Balakrishna", "Bingyuan Liu", "Ian Mackinnon", "Sorin Baltateanu", "Petra Poklukar", "Gabriel Ibagon", "Colin Ji", "Hongyang Jiao", "Isaac Noble", "Wojciech Stokowiec", "Zhihao Li", "Jeff Dean", "David Lindner", "Mark Omernick", "Kristen Chiafullo", "Mason Dimarco", "Vitor Rodrigues", "Vittorio Selo", "Garrett Honke", "Xintian", "Wu", "Wei He", "Adam Hillier", "Anhad Mohananey", "Vihari Piratla", "Chang Ye", "Chase Malik", "Sebastian Riedel", "Samuel Albanie", "Zi Yang", "Kenny Vassigh", "Maria Bauza", "Sheng Li", "Yiqing Tao", "Nevan Wichers", "Andrii Maksai", "Abe Ittycheriah", "Ross Mcilroy", "Bryan Seybold", "Noah Goodman", "Romina Datta", "Steven M. Hernandez", "Tian Shi", "Yony Kochinski", "Anna Bulanova", "Ken Franko", "Mikita Sazanovich", "Nicholas FitzGerald", "Praneeth Kacham", "Shubha Srinivas Raghvendra", "Vincent Hellendoorn", "Alexander Grushetsky", "Julian Salazar", "Angeliki Lazaridou", "Jason Chang", "Jan-Thorsten Peter", "Sushant Kafle", "Yann Dauphin", "Abhishek Rao", "Filippo Graziano", "Izhak Shafran", "Yuguo Liao", "Tianli Ding", "Geng Yan", "Grace Chu", "Zhao Fu", "Vincent Roulet", "Gabriel Rasskin", "Duncan Williams", "Shahar Drath", "Alex Mossin", "Raphael Hoffmann", "Jordi Orbay", "Francesco Bertolini", "Hila Sheftel", "Justin Chiu", "Siyang Xue", "Yuheng Kuang", "Ferjad Naeem", "Swaroop Nath", "Nana Nti", "Phil Culliton", "Kashyap Krishnakumar", "Michael Isard", "Pei Sun", "Ayan Chakrabarti", "Nathan Clement", "Regev Cohen", "Arissa Wongpanich", "GS Oh", "Ashwin Murthy", "Hao Zheng", "Jessica Hamrick", "Oskar Bunyan", "Suhas Ganesh", "Nitish Gupta", "Roy Frostig", "John Wieting", "Yury Malkov", "Pierre Marcenac", "Zhixin", "Lai", "Xiaodan Tang", "Mohammad Saleh", "Fedir Zubach", "Chinmay Kulkarni", "Huanjie Zhou", "Vicky Zayats", "Nan Ding", "Anshuman Tripathi", "Arijit Pramanik", "Patrik Zochbauer", "Harish Ganapathy", "Vedant Misra", "Zach Behrman", "Hugo Vallet", "Mingyang Zhang", "Mukund Sridhar", "Ye Jin", "Mohammad Babaeizadeh", "Siim P\u00f5der", "Megha Goel", "Divya Jain", "Tajwar Nasir", "Shubham Mittal", "Tim Dozat", "Diego Ardila", "Aliaksei Severyn", "Fabio Pardo", "Sammy Jerome", "Siyang Qin", "Louis Rouillard", "Amir Yazdanbakhsh", "Zizhao Zhang", "Shivani Agrawal", "Kaushik Shivakumar", "Caden Lu", "Praveen Kallakuri", "Rachita Chhaparia", "Kanishka Rao", "Charles Kwong", "Asya Fadeeva", "Shitij Nigam", "Yan Virin", "Yuan Zhang", "Balaji Venkatraman", "Beliz Gunel", "Marc Wilson", "Huiyu Wang", "Abhinav Gupta", "Xiaowei Xu", "Adrien Ali Ta\u00efga", "Kareem Mohamed", "Doug Fritz", "Daniel Rodriguez", "Zoubin Ghahramani", "Harry Askham", "Lior Belenki", "James Zhao", "Rahul Gupta", "Krzysztof Jastrz\u0119bski", "Takahiro Kosakai", "Kaan Katircioglu", "Jon Schneider", "Rina Panigrahy", "Konstantinos Bousmalis", "Peter Grabowski", "Prajit Ramachandran", "Chaitra Hegde", "Mihaela Rosca", "Angelo Scorza Scarpati", "Kyriakos Axiotis", "Ying Xu", "Zach Gleicher", "Assaf Hurwitz Michaely", "Mandar Sharma", "Sanil Jain", "Christoph Hirnschall", "Tal Marian", "Xuhui Jia", "Kevin Mather", "Kilol Gupta", "Linhai Qiu", "Nigamaa Nayakanti", "Lucian Ionita", "Steven Zheng", "Lucia Loher", "Kurt Shuster", "Igor Petrovski", "Roshan Sharma", "Rahma Chaabouni", "Angel Yeh", "James An", "Arushi Gupta", "Steven Schwarcz", "Seher Ellis", "Sam Conway-Rahman", "Javier Snaider", "Alex Zhai", "James Atwood", "Daniel Golovin", "Liqian Peng", "Te I", "Vivian Xia", "Salvatore Scellato", "Mahan Malihi", "Arthur Bra\u017einskas", "Vlad-Doru Ion", "Younghoon Jun", "James Swirhun", "Soroosh Mariooryad", "Jiao Sun", "Steve Chien", "Rey Coaguila", "Ariel Brand", "Yi Gao", "Tom Kwiatkowski", "Roee Aharoni", "Cheng-Chun Lee", "Mislav \u017dani\u0107", "Yichi Zhang", "Dan Ethier", "Vitaly Nikolaev", "Pranav Nair", "Yoav Ben Shalom", "Hen Fitoussi", "Jai Gupta", "Hongbin Liu", "Dee Cattle", "Tolga Bolukbasi", "Ben Murdoch", "Fantine Huot", "Yin Li", "Chris Hahn" ], "github": "", "project_page": "", "abstract": "In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal understanding and it is now able to process up to 3 hours of video content. Its unique combination of long context, multimodal and reasoning capabilities can be combined to unlock new agentic workflows. Gemini 2.5 Flash provides excellent reasoning abilities at a fraction of the compute and latency requirements and Gemini 2.0 Flash and Flash-Lite provide high performance at low latency and cost. Taken together, the Gemini 2.X model generation spans the full Pareto frontier of model capability vs cost, allowing users to explore the boundaries of what is possible with complex agentic problem solving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.04517", "title": "DOTResize: Reducing LLM Width via Discrete Optimal Transport-based Neuron Merging", "authors": [ "Neha Verma", "Kenton Murray", "Kevin Duh" ], "github": "", "project_page": "", "abstract": "Model compression offers a promising path to reducing the cost and inaccessibility of large pre-trained models, without significantly compromising their impressive performance. Large Transformer models, including large language models (LLMs), often contain computational redundancy, which can serve as a target for new model compression methods. In this work, we specifically target neuron-level redundancies in model layers by combining groups of similar neurons into fewer neurons. We frame this width reduction as a Discrete Optimal Transport problem, and propose DOTResize, a novel Transformer compression method that uses optimal transport theory to transform and compress model weights. To ensure applicability within the Transformer architecture, we motivate and incorporate entropic regularization and matrix factorization into the transportation maps produced by our method. Unlike pruning-based approaches which discard neurons based on importance measures, DOTResize re-projects the entire neuron width, allowing the retention and redistribution of useful signal across the reduced layer. Empirical results show that compared to simple or state-of-the-art neuron width-pruning techniques, DOTResize can outperform these methods across multiple LLM families and sizes, while achieving measurable reductions in real-world computational cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-14", "arxiv_id": "2507.07994", "title": "Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection", "authors": [ "Subhajit Maity", "Ayan Kumar Bhunia", "Subhadeep Koley", "Pinaki Nath Chowdhury", "Aneeshan Sain", "Yi-Zhe Song" ], "github": "https://github.com/MaitySubhajit/DYKp", "project_page": "", "abstract": "Keypoint detection, integral to modern machine perception, faces challenges in few-shot learning, particularly when source data from the same distribution as the query is unavailable. This gap is addressed by leveraging sketches, a popular form of human expression, providing a source-free alternative. However, challenges arise in mastering cross-modal embeddings and handling user-specific sketch styles. Our proposed framework overcomes these hurdles with a prototypical setup, combined with a grid-based locator and prototypical domain adaptation. We also demonstrate success in few-shot convergence across novel keypoints and classes through extensive experiments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MaitySubhajit/DYKp", "github_api_added_by": "auto" }, { "date": "2025-07-14", "arxiv_id": "2507.08128", "title": "Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models", "authors": [ "Arushi Goel", "Sreyan Ghosh", "Jaehyeon Kim", "Sonal Kumar", "Zhifeng Kong", "Sang-gil Lee", "Chao-Han Huck Yang", "Ramani Duraiswami", "Dinesh Manocha", "Rafael Valle", "Bryan Catanzaro" ], "github": "", "project_page": "", "abstract": "We present Audio Flamingo 3 (AF3), a fully open state-of-the-art (SOTA) large audio-language model that advances reasoning and understanding across speech, sound, and music. AF3 introduces: (i) AF-Whisper, a unified audio encoder trained using a novel strategy for joint representation learning across all 3 modalities of speech, sound, and music; (ii) flexible, on-demand thinking, allowing the model to do chain-of-thought-type reasoning before answering; (iii) multi-turn, multi-audio chat; (iv) long audio understanding and reasoning (including speech) up to 10 minutes; and (v) voice-to-voice interaction. To enable these capabilities, we propose several large-scale training datasets curated using novel strategies, including AudioSkills-XL, LongAudio-XL, AF-Think, and AF-Chat, and train AF3 with a novel five-stage curriculum-based training strategy. Trained on only open-source audio data, AF3 achieves new SOTA results on over 20+ (long) audio understanding and reasoning benchmarks, surpassing both open-weight and closed-source models trained on much larger datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.09862", "title": "SpeakerVid-5M: A Large-Scale High-Quality Dataset for Audio-Visual Dyadic Interactive Human Generation", "authors": [ "Youliang Zhang", "Zhaoyang Li", "Duomin Wang", "Jiahe Zhang", "Deyu Zhou", "Zixin Yin", "Xili Dai", "Gang Yu", "Xiu Li" ], "github": "", "project_page": "", "abstract": "The rapid development of large-scale models has catalyzed significant breakthroughs in the digital human domain. These advanced methodologies offer high-fidelity solutions for avatar driving and rendering, leading academia to focus on the next major challenge: audio-visual dyadic interactive virtual human. To facilitate research in this emerging area, we present SpeakerVid-5M dataset, the first large-scale, high-quality dataset designed for audio-visual dyadic interactive virtual human generation. Totaling over 8,743 hours, SpeakerVid-5M contains more than 5.2 million video clips of human portraits. It covers diverse scales and interaction types, including monadic talking, listening, and dyadic conversations. Crucially, the dataset is structured along two key dimensions: interaction type and data quality. First, it is categorized into four types (dialogue branch, single branch, listening branch and multi-turn branch) based on the interaction scenario. Second, it is stratified into a large-scale pre-training subset and a curated, high-quality subset for Supervised Fine-Tuning (SFT). This dual structure accommodates a wide array of 2D virtual human tasks. In addition, we provide an autoregressive (AR)-based video chat baseline trained on this data, accompanied by a dedicated set of metrics and test data to serve as a benchmark VidChatBench for future work. Both the dataset and the corresponding data processing code will be publicly released. Project page: https://dorniwang.github.io/SpeakerVid-5M/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.10548", "title": "EmbRACE-3K: Embodied Reasoning and Action in Complex Environments", "authors": [ "Mingxian Lin", "Wei Huang", "Yitang Li", "Chengjie Jiang", "Kui Wu", "Fangwei Zhong", "Shengju Qian", "Xin Wang", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "Recent advanced vision-language models(VLMs) have demonstrated strong performance on passive, offline image and video understanding tasks. However, their effectiveness in embodied settings, which require online interaction and active scene understanding remains limited. In such scenarios, an agent perceives the environment from a first-person perspective, with each action dynamically shaping subsequent observations. Even state-of-the-art models such as GPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Pro struggle in open-environment interactions, exhibiting clear limitations in spatial reasoning and long-horizon planning. To address this gap, we introduce EmRACE-3K, a dataset of over 3,000 language-guided tasks situated in diverse, photorealistic environments constructed using Unreal Engine and the UnrealCV-Zoo framework. The tasks encompass a wide range of embodied challenges, including navigation, object manipulation, and multi-stage goal execution. Each task unfolds as a multi-step trajectory, pairing first-person visual observations with high-level instructions, grounded actions, and natural language rationales that express the agent's intent at every step. Using EmRACE-3K, we establish a benchmark to evaluate the embodied reasoning capabilities of VLMs across three key dimensions: Exploration, Dynamic Spatial-Semantic Reasoning, and Multi-stage Goal Execution. In zero-shot settings, all models achieve success rates below 20%, underscoring the challenge posed by our benchmark and the current limitations of VLMs in interactive environments. To demonstrate the utility of EmRACE-3K, we further fine-tune Qwen2.5-VL-7B using supervised learning followed by reinforcement learning. This approach yields substantial improvements across all three challenge categories, highlighting the dataset's effectiveness in enabling the development of embodied reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.09104", "title": "CompassJudger-2: Towards Generalist Judge Model via Verifiable Rewards", "authors": [ "Taolin Zhang", "Maosong Cao", "Alexander Lam", "Songyang Zhang", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Recently, the role of LLM-as-judge in evaluating large language models has gained prominence. However, current judge models suffer from narrow specialization and limited robustness, undermining their capacity for comprehensive evaluations. In this work, we present CompassJudger-2, a novel generalist judge model that overcomes these limitations via a task-driven, multi-domain data curation strategy. Central to our approach is supervising judgment tasks with verifiable rewards, guiding intrinsic critical reasoning through rejection sampling to foster robust, generalizable judgment capabilities. We introduce a refined learning objective with margin policy gradient loss to enhance performance. Empirically, CompassJudger-2 achieves superior results across multiple judge and reward benchmarks, and our 7B model demonstrates competitive judgment accuracy with significantly larger models like DeepSeek-V3 and Qwen3-235B-A22B. Additionally, we propose JudgerBenchV2, a comprehensive benchmark evaluating cross-domain judgment accuracy and rank consistency to standardize judge model evaluation. These contributions advance robust, scalable LLM judgment and establish new performance and evaluation standards.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.04404", "title": "LayerCake: Token-Aware Contrastive Decoding within Large Language Model Layers", "authors": [ "Jingze Zhu", "Yongliang Wu", "Wenbo Zhu", "Jiawang Cao", "Yanqiang Zheng", "Jiawei Chen", "Xu Yang", "Bernt Schiele", "Jonas Fischer", "Xinting Hu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) excel at natural language understanding and generation but remain vulnerable to factual errors, limiting their reliability in knowledge-intensive tasks. While decoding-time strategies provide a promising efficient solution without training, existing methods typically treat token-level and layer-level signals in isolation, overlooking the joint dynamics between them. In this work, we introduce a token-aware, layer-localized contrastive decoding method that aligns specific token types with their most influential transformer layers to improve factual generation. Through empirical attention analysis, we identify two key patterns: punctuation tokens receive dominant attention in early layers, while conceptual tokens govern semantic reasoning in intermediate layers. By selectively suppressing attention to these token types at their respective depths, we achieve the induction of controlled factual degradation and derive contrastive signals to guide the final factual decoding. Our method requires no additional training or model modification, and experiments demonstrate that our method consistently improves factuality across multiple LLMs and various benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.10524", "title": "Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation", "authors": [ "Sangmin Bae", "Yujin Kim", "Reza Bayat", "Sungnyun Kim", "Jiyoun Ha", "Tal Schuster", "Adam Fisch", "Hrayr Harutyunyan", "Ziwei Ji", "Aaron Courville", "Se-Young Yun" ], "github": "", "project_page": "", "abstract": "Scaling language models unlocks impressive capabilities, but the accompanying computational and memory demands make both training and deployment expensive. Existing efficiency efforts typically target either parameter sharing or adaptive computation, leaving open the question of how to attain both simultaneously. We introduce Mixture-of-Recursions (MoR), a unified framework that combines the two axes of efficiency inside a single Recursive Transformer. MoR reuses a shared stack of layers across recursion steps to achieve parameter efficiency, while lightweight routers enable adaptive token-level thinking by dynamically assigning different recursion depths to individual tokens. This allows MoR to focus quadratic attention computation only among tokens still active at a given recursion depth, further improving memory access efficiency by selectively caching only their key-value pairs. Beyond these core mechanisms, we also propose a KV sharing variant that reuses KV pairs from the first recursion, specifically designed to decrease prefill latency and memory footprint. Across model scales ranging from 135M to 1.7B parameters, MoR forms a new Pareto frontier: at equal training FLOPs and smaller model sizes, it significantly lowers validation perplexity and improves few-shot accuracy, while delivering higher throughput compared with vanilla and existing recursive baselines. These gains demonstrate that MoR is an effective path towards large-model quality without incurring large-model cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.10541", "title": "REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once", "authors": [ "Zhuoshi Pan", "Qizhi Pei", "Yu Li", "Qiyao Sun", "Zinan Tang", "H. Vicky Zhao", "Conghui He", "Lijun Wu" ], "github": "https://github.com/GAIR-NLP/AIME-Preview", "project_page": "", "abstract": "Recent Large Reasoning Models (LRMs) have achieved remarkable progress on task-specific benchmarks, yet their evaluation methods remain constrained by isolated problem-solving paradigms. Existing benchmarks predominantly assess single-question reasoning through sequential testing, resulting critical limitations: (1) vulnerability to data contamination and less challenging (e.g., DeepSeek-R1 achieves 97.0% on MATH500), forcing costly and perpetual creation of new questions with large human efforts, (2) failure to evaluate models under multi-context pressure, a key requirement for real-world deployment. To bridge this gap, we present REST (Reasoning Evaluation through Simultaneous Testing), a stress-testing framework that concurrently exposes LRMs to multiple problems simultaneously. Beyond basic reasoning, REST specifically evaluates several under-tested capabilities: contextual priority allocation, cross-problem interference resistance, and dynamic cognitive load management. Our evaluation reveals several striking findings: Even state-of-the-art (SOTA) models like DeepSeek-R1 exhibit substantial performance degradation under stress testing. Crucially, REST demonstrates stronger discriminative power than existing benchmarks, revealing pronounced performance differences among models that exhibit similar, near-ceiling performance under single-question evaluations. Some key mechanistic insights emerge from our analysis: (1) the \"overthinking trap\" is a critical factor contributing to the performance degradation; (2) the models trained with \"long2short\" technique preserve more accuracy of their single-problem performance under REST, outperforming standard-trained counterparts. These results establish REST as a cost-efficient, future-proof evaluation paradigm that better reflects real-world reasoning demands while reducing reliance on continuous human annotation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GAIR-NLP/AIME-Preview", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.10065", "title": "MoVieS: Motion-Aware 4D Dynamic View Synthesis in One Second", "authors": [ "Chenguo Lin", "Yuchen Lin", "Panwang Pan", "Yifan Yu", "Honglei Yan", "Katerina Fragkiadaki", "Yadong Mu" ], "github": "", "project_page": "", "abstract": "We present MoVieS, a novel feed-forward model that synthesizes 4D dynamic novel views from monocular videos in one second. MoVieS represents dynamic 3D scenes using pixel-aligned grids of Gaussian primitives, explicitly supervising their time-varying motion. This allows, for the first time, the unified modeling of appearance, geometry and motion, and enables view synthesis, reconstruction and 3D point tracking within a single learning-based framework. By bridging novel view synthesis with dynamic geometry reconstruction, MoVieS enables large-scale training on diverse datasets with minimal dependence on task-specific supervision. As a result, it also naturally supports a wide range of zero-shot applications, such as scene flow estimation and moving object segmentation. Extensive experiments validate the effectiveness and efficiency of MoVieS across multiple tasks, achieving competitive performance while offering several orders of magnitude speedups.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.10532", "title": "Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination", "authors": [ "Mingqi Wu", "Zhihao Zhang", "Qiaole Dong", "Zhiheng Xi", "Jun Zhao", "Senjie Jin", "Xiaoran Fan", "Yuhao Zhou", "Yanwei Fu", "Qin Liu", "Songyang Zhang", "Qi Zhang" ], "github": "https://github.com/wumingqi/LLM-Math-Evaluation", "project_page": "", "abstract": "The reasoning capabilities of large language models (LLMs) have been a longstanding focus of research. Recent works have further enhanced these capabilities using reinforcement learning (RL), with many new methods claiming significant improvements with minimal or no external supervision. Surprisingly, some studies even suggest that random or incorrect reward signals can enhance reasoning performance. However, these breakthroughs are mostly reported on the Qwen2.5 model family and evaluated on well-known benchmarks such as MATH-500, AMC, and AIME, while failing to achieve similar gains on other models like Llama, which warrants further investigation. Our analysis shows that although Qwen2.5 achieves strong mathematical reasoning performance, its pretraining on large-scale web corpora makes it vulnerable to data contamination in popular benchmarks. As a result, results derived from these benchmarks may be unreliable. To address this, we introduce a generator that produces fully synthetic arithmetic problems of arbitrary length and difficulty, yielding a clean dataset we call RandomCalculation. Using these leakage-free datasets, we show that only accurate reward signals consistently improve performance, while noisy or incorrect signals do not. We advocate for evaluating RL methods on uncontaminated benchmarks and across diverse model families to ensure trustworthy conclusions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wumingqi/LLM-Math-Evaluation", "github_api_added_by": "auto" }, { "date": "2025-07-15", "arxiv_id": "2507.08924", "title": "From KMMLU-Redux to KMMLU-Pro: A Professional Korean Benchmark Suite for LLM Evaluation", "authors": [ "Seokhee Hong", "Sunkyoung Kim", "Guijin Son", "Soyeon Kim", "Yeonjung Hong", "Jinsik Lee" ], "github": "", "project_page": "", "abstract": "The development of Large Language Models (LLMs) requires robust benchmarks that encompass not only academic domains but also industrial fields to effectively evaluate their applicability in real-world scenarios. In this paper, we introduce two Korean expert-level benchmarks. KMMLU-Redux, reconstructed from the existing KMMLU, consists of questions from the Korean National Technical Qualification exams, with critical errors removed to enhance reliability. KMMLU-Pro is based on Korean National Professional Licensure exams to reflect professional knowledge in Korea. Our experiments demonstrate that these benchmarks comprehensively represent industrial knowledge in Korea. We release our dataset publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.08267", "title": "A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement Learning", "authors": [ "Hiroshi Yoshihara", "Taiki Yamaguchi", "Yuichi Inoue" ], "github": "https://github.com/analokmaus/kaggle-aimo2-fast-math-r1", "project_page": "", "abstract": "Enhancing the mathematical reasoning of Large Language Models (LLMs) is a pivotal challenge in advancing AI capabilities. While Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) are the dominant training paradigms, a systematic methodology for combining them to maximize both accuracy and efficiency remains largely unexplored. This paper introduces a practical and effective training recipe that strategically integrates extended SFT with RL from online inference (GRPO). We posit that these methods play complementary, not competing, roles: a prolonged SFT phase first pushes the model's accuracy to its limits, after which a GRPO phase dramatically improves token efficiency while preserving this peak performance. Our experiments reveal that extending SFT for as many as 10 epochs is crucial for performance breakthroughs, and that the primary role of GRPO in this framework is to optimize solution length. The efficacy of our recipe is rigorously validated through top-tier performance on challenging benchmarks, including a high rank among over 2,200 teams in the strictly leak-free AI Mathematical Olympiad (AIMO). This work provides the community with a battle-tested blueprint for developing state-of-the-art mathematical reasoners that are both exceptionally accurate and practically efficient. To ensure full reproducibility and empower future research, we will open-source our entire framework, including all code, model checkpoints, and training configurations at https://github.com/analokmaus/kaggle-aimo2-fast-math-r1.", "github_manual": "", "github_regex": "https://github.com/analokmaus/kaggle-aimo2-fast-math-r1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.09074", "title": "Favicon Trojans: Executable Steganography Via Ico Alpha Channel Exploitation", "authors": [ "David Noever", "Forrest McKee" ], "github": "", "project_page": "", "abstract": "This paper presents a novel method of executable steganography using the alpha transparency layer of ICO image files to embed and deliver self-decompressing JavaScript payloads within web browsers. By targeting the least significant bit (LSB) of non-transparent alpha layer image values, the proposed method successfully conceals compressed JavaScript code inside a favicon image without affecting visual fidelity. Global web traffic loads 294 billion favicons daily and consume 0.9 petabytes of network bandwidth. A proof-of-concept implementation demonstrates that a 64x64 ICO image can embed up to 512 bytes uncompressed, or 0.8 kilobyte when using lightweight two-fold compression. On page load, a browser fetches the favicon as part of standard behavior, allowing an embedded loader script to extract and execute the payload entirely in memory using native JavaScript APIs and canvas pixel access. This creates a two-stage covert channel requiring no additional network or user requests. Testing across multiple browsers in both desktop and mobile environments confirms successful and silent execution of the embedded script. We evaluate the threat model, relate it to polymorphic phishing attacks that evade favicon-based detection, and analyze evasion of content security policies and antivirus scanners. We map nine example MITRE ATT&CK Framework objectives to single line JavaScript to execute arbitrarily in ICO files. Existing steganalysis and sanitization defenses are discussed, highlighting limitations in detecting or neutralizing alpha-channel exploits. The results demonstrate a stealthy and reusable attack surface that blurs traditional boundaries between static images and executable content. Because modern browsers report silent errors when developers specifically fail to load ICO files, this attack surface offers an interesting example of required web behaviors that in turn compromise security.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.04218", "title": "DreamPoster: A Unified Framework for Image-Conditioned Generative Poster Design", "authors": [ "Xiwei Hu", "Haokun Chen", "Zhongqi Qi", "Hui Zhang", "Dexiang Hong", "Jie Shao", "Xinglong Wu" ], "github": "", "project_page": "", "abstract": "We present DreamPoster, a Text-to-Image generation framework that intelligently synthesizes high-quality posters from user-provided images and text prompts while maintaining content fidelity and supporting flexible resolution and layout outputs. Specifically, DreamPoster is built upon our T2I model, Seedream3.0 to uniformly process different poster generating types. For dataset construction, we propose a systematic data annotation pipeline that precisely annotates textual content and typographic hierarchy information within poster images, while employing comprehensive methodologies to construct paired datasets comprising source materials (e.g., raw graphics/text) and their corresponding final poster outputs. Additionally, we implement a progressive training strategy that enables the model to hierarchically acquire multi-task generation capabilities while maintaining high-quality generation. Evaluations on our testing benchmarks demonstrate DreamPoster's superiority over existing methods, achieving a high usability rate of 88.55\\%, compared to GPT-4o (47.56\\%) and SeedEdit3.0 (25.96\\%). DreamPoster will be online in Jimeng and other Bytedance Apps.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.09751", "title": "Sound and Complete Neuro-symbolic Reasoning with LLM-Grounded Interpretations", "authors": [ "Bradley P. Allen", "Prateek Chhikara", "Thomas Macaulay Ferguson", "Filip Ilievski", "Paul Groth" ], "github": "https://github.com/bradleypallen/bilateral-factuality-evaluation", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but they exhibit problems with logical consistency in the output they generate. How can we harness LLMs' broad-coverage parametric knowledge in formal reasoning despite their inconsistency? We present a method for directly integrating an LLM into the interpretation function of the formal semantics for a paraconsistent logic. We provide experimental evidence for the feasibility of the method by evaluating the function using datasets created from several short-form factuality benchmarks. Unlike prior work, our method offers a theoretical framework for neuro-symbolic reasoning that leverages an LLM's knowledge while preserving the underlying logic's soundness and completeness properties.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bradleypallen/bilateral-factuality-evaluation", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.08396", "title": "Subject-Consistent and Pose-Diverse Text-to-Image Generation", "authors": [ "Zhanxin Gao", "Beier Zhu", "Liang Yao", "Jian Yang", "Ying Tai" ], "github": "https://github.com/NJU-PCALab/CoDi", "project_page": "", "abstract": "Subject-consistent generation (SCG)-aiming to maintain a consistent subject identity across diverse scenes-remains a challenge for text-to-image (T2I) models. Existing training-free SCG methods often achieve consistency at the cost of layout and pose diversity, hindering expressive visual storytelling. To address the limitation, we propose subject-Consistent and pose-Diverse T2I framework, dubbed as CoDi, that enables consistent subject generation with diverse pose and layout. Motivated by the progressive nature of diffusion, where coarse structures emerge early and fine details are refined later, CoDi adopts a two-stage strategy: Identity Transport (IT) and Identity Refinement (IR). IT operates in the early denoising steps, using optimal transport to transfer identity features to each target image in a pose-aware manner. This promotes subject consistency while preserving pose diversity. IR is applied in the later denoising steps, selecting the most salient identity features to further refine subject details. Extensive qualitative and quantitative results on subject consistency, pose diversity, and prompt fidelity demonstrate that CoDi achieves both better visual perception and stronger performance across all metrics. The code is provided in https://github.com/NJU-PCALab/CoDi.", "github_manual": "", "github_regex": "https://github.com/NJU-PCALab/CoDi", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-15", "arxiv_id": "2507.11137", "title": "Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking", "authors": [ "Yuan Yao", "Jin Song", "Jian Jin" ], "github": "https://github.com/AIResearch-Group/NeuralMark", "project_page": "", "abstract": "As valuable digital assets, deep neural networks necessitate robust ownership protection, positioning neural network watermarking (NNW) as a promising solution. Among various NNW approaches, weight-based methods are favored for their simplicity and practicality; however, they remain vulnerable to forging and overwriting attacks. To address those challenges, we propose NeuralMark, a robust method built around a hashed watermark filter. Specifically, we utilize a hash function to generate an irreversible binary watermark from a secret key, which is then used as a filter to select the model parameters for embedding. This design cleverly intertwines the embedding parameters with the hashed watermark, providing a robust defense against both forging and overwriting attacks. An average pooling is also incorporated to resist fine-tuning and pruning attacks. Furthermore, it can be seamlessly integrated into various neural network architectures, ensuring broad applicability. Theoretically, we analyze its security boundary. Empirically, we verify its effectiveness and robustness across 13 distinct Convolutional and Transformer architectures, covering five image classification tasks and one text generation task. The source codes are available at https://github.com/AIResearch-Group/NeuralMark.", "github_manual": "", "github_regex": "https://github.com/AIResearch-Group/NeuralMark", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-16", "arxiv_id": "2507.10787", "title": "Can Multimodal Foundation Models Understand Schematic Diagrams? An Empirical Study on Information-Seeking QA over Scientific Papers", "authors": [ "Yilun Zhao", "Chengye Wang", "Chuhan Li", "Arman Cohan" ], "github": "", "project_page": "", "abstract": "This paper introduces MISS-QA, the first benchmark specifically designed to evaluate the ability of models to interpret schematic diagrams within scientific literature. MISS-QA comprises 1,500 expert-annotated examples over 465 scientific papers. In this benchmark, models are tasked with interpreting schematic diagrams that illustrate research overviews and answering corresponding information-seeking questions based on the broader context of the paper. We assess the performance of 18 frontier multimodal foundation models, including o4-mini, Gemini-2.5-Flash, and Qwen2.5-VL. We reveal a significant performance gap between these models and human experts on MISS-QA. Our analysis of model performance on unanswerable questions and our detailed error analysis further highlight the strengths and limitations of current models, offering key insights to enhance models in comprehending multimodal scientific literature.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-16", "arxiv_id": "2507.09411", "title": "LLMalMorph: On The Feasibility of Generating Variant Malware using Large-Language-Models", "authors": [ "Md Ajwad Akil", "Adrian Shuai Li", "Imtiaz Karim", "Arun Iyengar", "Ashish Kundu", "Vinny Parla", "Elisa Bertino" ], "github": "https://github.com/AJAkil/LLMalMorph", "project_page": "", "abstract": "Large Language Models (LLMs) have transformed software development and automated code generation. Motivated by these advancements, this paper explores the feasibility of LLMs in modifying malware source code to generate variants. We introduce LLMalMorph, a semi-automated framework that leverages semantical and syntactical code comprehension by LLMs to generate new malware variants. LLMalMorph extracts function-level information from the malware source code and employs custom-engineered prompts coupled with strategically defined code transformations to guide the LLM in generating variants without resource-intensive fine-tuning. To evaluate LLMalMorph, we collected 10 diverse Windows malware samples of varying types, complexity and functionality and generated 618 variants. Our thorough experiments demonstrate that it is possible to reduce the detection rates of antivirus engines of these malware variants to some extent while preserving malware functionalities. In addition, despite not optimizing against any Machine Learning (ML)-based malware detectors, several variants also achieved notable attack success rates against an ML-based malware classifier. We also discuss the limitations of current LLM capabilities in generating malware variants from source code and assess where this emerging technology stands in the broader context of malware variant generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AJAkil/LLMalMorph", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-16", "arxiv_id": "2507.07104", "title": "Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation from Diffusion Models", "authors": [ "Tiezheng Zhang", "Yitong Li", "Yu-cheng Chou", "Jieneng Chen", "Alan Yuille", "Chen Wei", "Junfei Xiao" ], "github": "", "project_page": "", "abstract": "Building state-of-the-art Vision-Language Models (VLMs) with strong captioning capabilities typically necessitates training on billions of high-quality image-text pairs, requiring millions of GPU hours. This paper introduces the Vision-Language-Vision (VLV) auto-encoder framework, which strategically leverages key pretrained components: a vision encoder, the decoder of a Text-to-Image (T2I) diffusion model, and subsequently, a Large Language Model (LLM). Specifically, we establish an information bottleneck by regularizing the language representation space, achieved through freezing the pretrained T2I diffusion decoder. Our VLV pipeline effectively distills knowledge from the text-conditioned diffusion model using continuous embeddings, demonstrating comprehensive semantic understanding via high-quality reconstructions. Furthermore, by fine-tuning a pretrained LLM to decode the intermediate language representations into detailed descriptions, we construct a state-of-the-art (SoTA) captioner comparable to leading models like GPT-4o and Gemini 2.0 Flash. Our method demonstrates exceptional cost-efficiency and significantly reduces data requirements; by primarily utilizing single-modal images for training and maximizing the utility of existing pretrained models (image encoder, T2I diffusion model, and LLM), it circumvents the need for massive paired image-text datasets, keeping the total training expenditure under $1,000 USD.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-16", "arxiv_id": "2507.09075", "title": "OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique", "authors": [ "Wasi Uddin Ahmad", "Somshubra Majumdar", "Aleksander Ficek", "Sean Narenthiran", "Mehrzad Samadi", "Jocelyn Huang", "Siddhartha Jain", "Vahid Noroozi", "Boris Ginsburg" ], "github": "", "project_page": "", "abstract": "Recent advancements in reasoning-based Large Language Models (LLMs), particularly their potential through test-time scaling, have created significant opportunities for distillation in code generation and critique. However, progress in both areas fundamentally depends on large-scale, high-quality datasets. In this work, we introduce OpenCodeReasoning-II, a dataset consists of 2.5M question-solution-critique triples (approx. 35K unique programming questions), making it nearly twice the size of the previous largest publicly available code reasoning dataset. In this work, we employ a two-stage supervised fine-tuning strategy. The first stage focuses on fine-tuning for code generation, while the second stage involves the joint training of models for both code generation and critique. Our resulting finetuned Qwen2.5-Instruct models achieve performance in code generation that either exceeds or equals the best prior open-weight distilled models. Notably, the integration of our code generation and critique models leads to significant improvements in competitive coding performance. Furthermore, we present an extension of the LiveCodeBench benchmark to specifically support the C++ programming language, thereby facilitating more comprehensive LLM evaluation using this benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-16", "arxiv_id": "2507.09404", "title": "Scaling Laws for Optimal Data Mixtures", "authors": [ "Mustafa Shukor", "Louis Bethune", "Dan Busbridge", "David Grangier", "Enrico Fini", "Alaaeldin El-Nouby", "Pierre Ablin" ], "github": "", "project_page": "", "abstract": "Large foundation models are typically trained on data from multiple domains, with the data mixture--the proportion of each domain used--playing a critical role in model performance. The standard approach to selecting this mixture relies on trial and error, which becomes impractical for large-scale pretraining. We propose a systematic method to determine the optimal data mixture for any target domain using scaling laws. Our approach accurately predicts the loss of a model of size N trained with D tokens and a specific domain weight vector h. We validate the universality of these scaling laws by demonstrating their predictive power in three distinct and large-scale settings: large language model (LLM), native multimodal model (NMM), and large vision models (LVM) pretraining. We further show that these scaling laws can extrapolate to new data mixtures and across scales: their parameters can be accurately estimated using a few small-scale training runs, and used to estimate the performance at larger scales and unseen domain weights. The scaling laws allow to derive the optimal domain weights for any target domain under a given training budget (N,D), providing a principled alternative to costly trial-and-error methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-16", "arxiv_id": "2507.07186", "title": "Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs", "authors": [ "Itay Itzhak", "Yonatan Belinkov", "Gabriel Stanovsky" ], "github": "https://github.com/itay1itzhak/planted-in-pretraining", "project_page": "", "abstract": "Large language models (LLMs) exhibit cognitive biases -- systematic tendencies of irrational decision-making, similar to those seen in humans. Prior work has found that these biases vary across models and can be amplified by instruction tuning. However, it remains unclear if these differences in biases stem from pretraining, finetuning, or even random noise due to training stochasticity. We propose a two-step causal experimental approach to disentangle these factors. First, we finetune models multiple times using different random seeds to study how training randomness affects over 30 cognitive biases. Second, we introduce cross-tuning -- swapping instruction datasets between models to isolate bias sources. This swap uses datasets that led to different bias patterns, directly testing whether biases are dataset-dependent. Our findings reveal that while training randomness introduces some variability, biases are mainly shaped by pretraining: models with the same pretrained backbone exhibit more similar bias patterns than those sharing only finetuning data. These insights suggest that understanding biases in finetuned models requires considering their pretraining origins beyond finetuning effects. This perspective can guide future efforts to develop principled strategies for evaluating and mitigating bias in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/itay1itzhak/planted-in-pretraining", "github_api_added_by": "auto" }, { "date": "2025-07-16", "arxiv_id": "2507.11407", "title": "EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes", "authors": [ "LG AI Research", "Kyunghoon Bae", "Eunbi Choi", "Kibong Choi", "Stanley Jungkyu Choi", "Yemuk Choi", "Kyubeen Han", "Seokhee Hong", "Junwon Hwang", "Taewan Hwang", "Joonwon Jang", "Hyojin Jeon", "Kijeong Jeon", "Gerrard Jeongwon Jo", "Hyunjik Jo", "Jiyeon Jung", "Euisoon Kim", "Hyosang Kim", "Jihoon Kim", "Joonkee Kim", "Seonghwan Kim", "Soyeon Kim", "Sunkyoung Kim", "Yireun Kim", "Yongil Kim", "Youchul Kim", "Edward Hwayoung Lee", "Gwangho Lee", "Haeju Lee", "Honglak Lee", "Jinsik Lee", "Kyungmin Lee", "Sangha Park", "Young Min Paik", "Yongmin Park", "Youngyong Park", "Sanghyun Seo", "Sihoon Yang", "Heuiyeen Yeen", "Sihyuk Yi", "Hyeongu Yun" ], "github": "", "project_page": "", "abstract": "This technical report introduces EXAONE 4.0, which integrates a Non-reasoning mode and a Reasoning mode to achieve both the excellent usability of EXAONE 3.5 and the advanced reasoning abilities of EXAONE Deep. To pave the way for the agentic AI era, EXAONE 4.0 incorporates essential features such as agentic tool use, and its multilingual capabilities are extended to support Spanish in addition to English and Korean. The EXAONE 4.0 model series consists of two sizes: a mid-size 32B model optimized for high performance, and a small-size 1.2B model designed for on-device applications. The EXAONE 4.0 demonstrates superior performance compared to open-weight models in its class and remains competitive even against frontier-class models. The models are publicly available for research purposes and can be easily downloaded via https://huggingface.co/LGAI-EXAONE.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-16", "arxiv_id": "2507.08616", "title": "AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs", "authors": [ "Florian Gr\u00f6tschla", "Luis M\u00fcller", "Jan T\u00f6nshoff", "Mikhail Galkin", "Bryan Perozzi" ], "github": "", "project_page": "", "abstract": "Large-language models (LLMs) have demonstrated powerful problem-solving capabilities, in particular when organized in multi-agent systems. However, the advent of such systems also raises several questions on the ability of a complex network of agents to effectively self-organize and collaborate. While measuring performance on standard reasoning benchmarks indicates how well multi-agent systems can solve reasoning tasks, it is unclear whether these systems are able to leverage their topology effectively. Here, we propose AgentsNet, a new benchmark for multi-agent reasoning. By drawing inspiration from classical problems in distributed systems and graph theory, AgentsNet measures the ability of multi-agent systems to collaboratively form strategies for problem-solving, self-organization, and effective communication given a network topology. We evaluate a variety of baseline methods on AgentsNet including homogeneous networks of agents which first have to agree on basic protocols for organization and communication. We find that some frontier LLMs are already demonstrating strong performance for small networks but begin to fall off once the size of the network scales. While existing multi-agent benchmarks cover at most 2-5 agents, AgentsNet is practically unlimited in size and can scale with new generations of LLMs. As such, we also probe frontier models in a setup with up to 100 agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-16", "arxiv_id": "2507.10571", "title": "Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based Reasoning", "authors": [ "Konstantinos I. Roumeliotis", "Ranjan Sapkota", "Manoj Karkee", "Nikolaos D. Tselikas" ], "github": "https://github.com/Applied-AI-Research-Lab/Orchestrator-Agent-Trust", "project_page": "", "abstract": "Modern Artificial Intelligence (AI) increasingly relies on multi-agent architectures that blend visual and language understanding. Yet, a pressing challenge remains: How can we trust these agents especially in zero-shot settings with no fine-tuning? We introduce a novel modular Agentic AI visual classification framework that integrates generalist multimodal agents with a non-visual reasoning orchestrator and a Retrieval-Augmented Generation (RAG) module. Applied to apple leaf disease diagnosis, we benchmark three configurations: (I) zero-shot with confidence-based orchestration, (II) fine-tuned agents with improved performance, and (III) trust-calibrated orchestration enhanced by CLIP-based image retrieval and re-evaluation loops. Using confidence calibration metrics (ECE, OCR, CCC), the orchestrator modulates trust across agents. Our results demonstrate a 77.94\\% accuracy improvement in the zero-shot setting using trust-aware orchestration and RAG, achieving 85.63\\% overall. GPT-4o showed better calibration, while Qwen-2.5-VL displayed overconfidence. Furthermore, image-RAG grounded predictions with visually similar cases, enabling correction of agent overconfidence via iterative re-evaluation. The proposed system separates perception (vision agents) from meta-reasoning (orchestrator), enabling scalable and interpretable multi-agent AI. This blueprint is extensible to diagnostics, biology, and other trust-critical domains. All models, prompts, results, and system components including the complete software source code are openly released to support reproducibility, transparency, and community benchmarking at Github: https://github.com/Applied-AI-Research-Lab/Orchestrator-Agent-Trust", "github_manual": "", "github_regex": "https://github.com/Applied-AI-Research-Lab/Orchestrator-Agent-Trust", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-16", "arxiv_id": "2507.11336", "title": "UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New Benchmarks", "authors": [ "Peiran Wu", "Yunze Liu", "Zhengdong Zhu", "Enmin Zhou", "Shawn Shen" ], "github": "", "project_page": "", "abstract": "Real-world user-generated videos, especially on platforms like TikTok, often feature rich and intertwined audio visual content. However, existing video captioning benchmarks and models remain predominantly visual centric, overlooking the crucial role of audio in conveying scene dynamics, speaker intent, and narrative context. This lack of omni datasets and lightweight, capable models hampers progress in fine grained, multimodal video understanding. To address these challenges, we introduce UGC-VideoCap, a new benchmark and model framework specifically designed for detailed omnimodal captioning of short form user-generated videos. Unlike prior datasets, UGC-VideoCap emphasizes balanced integration of audio and visual modalities, featuring 1000 TikTok videos annotated through a structured three stage human-in-the-loop pipeline covering audio only, visual only, and joint audio visual semantics. The benchmark also includes 4000 carefully crafted QA pairs probing both unimodal and cross modal understanding. Alongside the dataset, we propose UGC-VideoCaptioner(3B), a 3B parameter captioning model distilled from Gemini 2.5 Flash. Using a novel two-stage training strategy supervised fine tuning followed by Group Relative Policy Optimization (GRPO), our approach enables efficient adaptation from limited data while maintaining competitive performance. Together, our benchmark and model offer a high-quality foundation and a data-efficient solution for advancing omnimodal video captioning in unconstrained real-world UGC settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-16", "arxiv_id": "2507.08333", "title": "Token-based Audio Inpainting via Discrete Diffusion", "authors": [ "Tali Dror", "Iftach Shoham", "Moshe Buchris", "Oren Gal", "Haim Permuter", "Gilad Katz", "Eliya Nachmani" ], "github": "", "project_page": "", "abstract": "Audio inpainting refers to the task of reconstructing missing segments in corrupted audio recordings. While prior approaches-including waveform and spectrogram-based diffusion models-have shown promising results for short gaps, they often degrade in quality when gaps exceed 100 milliseconds (ms). In this work, we introduce a novel inpainting method based on discrete diffusion modeling, which operates over tokenized audio representations produced by a pre-trained audio tokenizer. Our approach models the generative process directly in the discrete latent space, enabling stable and semantically coherent reconstruction of missing audio. We evaluate the method on the MusicNet dataset using both objective and perceptual metrics across gap durations up to 300 ms. We further evaluated our approach on the MTG dataset, extending the gap duration to 500 ms. Experimental results demonstrate that our method achieves competitive or superior performance compared to existing baselines, particularly for longer gaps, offering a robust solution for restoring degraded musical recordings. Audio examples of our proposed method can be found at https://iftach21.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-16", "arxiv_id": "2507.04127", "title": "BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering", "authors": [ "Costas Mavromatis", "Soji Adeshina", "Vassilis N. Ioannidis", "Zhen Han", "Qi Zhu", "Ian Robinson", "Bryan Thompson", "Huzefa Rangwala", "George Karypis" ], "github": "https://github.com/awslabs/graphrag-toolkit", "project_page": "", "abstract": "Knowledge graph question answering (KGQA) presents significant challenges due to the structural and semantic variations across input graphs. Existing works rely on Large Language Model (LLM) agents for graph traversal and retrieval; an approach that is sensitive to traversal initialization, as it is prone to entity linking errors and may not generalize well to custom (\"bring-your-own\") KGs. We introduce BYOKG-RAG, a framework that enhances KGQA by synergistically combining LLMs with specialized graph retrieval tools. In BYOKG-RAG, LLMs generate critical graph artifacts (question entities, candidate answers, reasoning paths, and OpenCypher queries), and graph tools link these artifacts to the KG and retrieve relevant graph context. The retrieved context enables the LLM to iteratively refine its graph linking and retrieval, before final answer generation. By retrieving context from different graph tools, BYOKG-RAG offers a more general and robust solution for QA over custom KGs. Through experiments on five benchmarks spanning diverse KG types, we demonstrate that BYOKG-RAG outperforms the second-best graph retrieval method by 4.5% points while showing better generalization to custom KGs. BYOKG-RAG framework is open-sourced at https://github.com/awslabs/graphrag-toolkit.", "github_manual": "", "github_regex": "https://github.com/awslabs/graphrag-toolkit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-16", "arxiv_id": "2507.09082", "title": "Taming generative video models for zero-shot optical flow extraction", "authors": [ "Seungwoo Kim", "Khai Loong Aw", "Klemen Kotar", "Cristobal Eyzaguirre", "Wanhee Lee", "Yunong Liu", "Jared Watrous", "Stefan Stojanov", "Juan Carlos Niebles", "Jiajun Wu", "Daniel L. K. Yamins" ], "github": "", "project_page": "", "abstract": "Extracting optical flow from videos remains a core computer vision problem. Motivated by the success of large general-purpose models, we ask whether frozen self-supervised video models trained only for future frame prediction can be prompted, without fine-tuning, to output flow. Prior work reading out depth or illumination from video generators required fine-tuning, which is impractical for flow where labels are scarce and synthetic datasets suffer from a sim-to-real gap. Inspired by the Counterfactual World Model (CWM) paradigm, which can obtain point-wise correspondences by injecting a small tracer perturbation into a next-frame predictor and tracking its propagation, we extend this idea to generative video models. We explore several popular architectures and find that successful zero-shot flow extraction in this manner is aided by three model properties: (1) distributional prediction of future frames (avoiding blurry or noisy outputs); (2) factorized latents that treat each spatio-temporal patch independently; and (3) random-access decoding that can condition on any subset of future pixels. These properties are uniquely present in the recent Local Random Access Sequence (LRAS) architecture. Building on LRAS, we propose KL-tracing: a novel test-time procedure that injects a localized perturbation into the first frame, rolls out the model one step, and computes the Kullback-Leibler divergence between perturbed and unperturbed predictive distributions. Without any flow-specific fine-tuning, our method outperforms state-of-the-art models on real-world TAP-Vid DAVIS dataset (16.6% relative improvement for endpoint error) and synthetic TAP-Vid Kubric (4.7% relative improvement). Our results indicate that counterfactual prompting of controllable generative video models is a scalable and effective alternative to supervised or photometric-loss approaches for high-quality flow.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.12465", "title": "PhysX: Physical-Grounded 3D Asset Generation", "authors": [ "Ziang Cao", "Zhaoxi Chen", "Linag Pan", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "3D modeling is moving from virtual to physical. Existing 3D generation primarily emphasizes geometries and textures while neglecting physical-grounded modeling. Consequently, despite the rapid development of 3D generative models, the synthesized 3D assets often overlook rich and important physical properties, hampering their real-world application in physical domains like simulation and embodied AI. As an initial attempt to address this challenge, we propose PhysX, an end-to-end paradigm for physical-grounded 3D asset generation. 1) To bridge the critical gap in physics-annotated 3D datasets, we present PhysXNet - the first physics-grounded 3D dataset systematically annotated across five foundational dimensions: absolute scale, material, affordance, kinematics, and function description. In particular, we devise a scalable human-in-the-loop annotation pipeline based on vision-language models, which enables efficient creation of physics-first assets from raw 3D assets.2) Furthermore, we propose PhysXGen, a feed-forward framework for physics-grounded image-to-3D asset generation, injecting physical knowledge into the pre-trained 3D structural space. Specifically, PhysXGen employs a dual-branch architecture to explicitly model the latent correlations between 3D structures and physical properties, thereby producing 3D assets with plausible physical predictions while preserving the native geometry quality. Extensive experiments validate the superior performance and promising generalization capability of our framework. All the code, data, and models will be released to facilitate future research in generative physical AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.09025", "title": "Lizard: An Efficient Linearization Framework for Large Language Models", "authors": [ "Chien Van Nguyen", "Ruiyi Zhang", "Hanieh Deilamsalehy", "Puneet Mathur", "Viet Dac Lai", "Haoliang Wang", "Jayakumar Subramanian", "Ryan A. Rossi", "Trung Bui", "Nikos Vlassis", "Franck Dernoncourt", "Thien Huu Nguyen" ], "github": "", "project_page": "", "abstract": "We propose Lizard, a linearization framework that transforms pretrained Transformer-based Large Language Models (LLMs) into flexible, subquadratic architectures for infinite-context generation. Transformer-based LLMs face significant memory and computational bottlenecks as context lengths increase, due to the quadratic complexity of softmax attention and the growing key-value (KV) cache. Lizard addresses these limitations by introducing a subquadratic attention mechanism that closely approximates softmax attention while preserving the output quality. Unlike previous linearization methods, which are often limited by fixed model structures and therefore exclude gating mechanisms, Lizard incorporates a gating module inspired by recent state-of-the-art linear models. This enables adaptive memory control, supports constant-memory inference, offers strong length generalization, and allows more flexible model design. Lizard combines gated linear attention for global context compression with sliding window attention enhanced by meta memory, forming a hybrid mechanism that captures both long-range dependencies and fine-grained local interactions. Moreover, we introduce a hardware-aware algorithm that accelerates the training speed of our models. Extensive experiments show that Lizard achieves near-lossless recovery of the teacher model's performance across standard language modeling tasks, while significantly outperforming previous linearization methods. On the 5-shot MMLU benchmark, Lizard improves over prior models by 18 points and shows significant improvements on associative recall tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.11527", "title": "DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil Engineering", "authors": [ "Yinsheng Li", "Zhen Dong", "Yi Shao" ], "github": "https://github.com/Eason-Li-AIS/DrafterBench", "project_page": "", "abstract": "Large Language Model (LLM) agents have shown great potential for solving real-world problems and promise to be a solution for tasks automation in industry. However, more benchmarks are needed to systematically evaluate automation agents from an industrial perspective, for example, in Civil Engineering. Therefore, we propose DrafterBench for the comprehensive evaluation of LLM agents in the context of technical drawing revision, a representation task in civil engineering. DrafterBench contains twelve types of tasks summarized from real-world drawing files, with 46 customized functions/tools and 1920 tasks in total. DrafterBench is an open-source benchmark to rigorously test AI agents' proficiency in interpreting intricate and long-context instructions, leveraging prior knowledge, and adapting to dynamic instruction quality via implicit policy awareness. The toolkit comprehensively assesses distinct capabilities in structured data comprehension, function execution, instruction following, and critical reasoning. DrafterBench offers detailed analysis of task accuracy and error statistics, aiming to provide deeper insight into agent capabilities and identify improvement targets for integrating LLMs in engineering applications. Our benchmark is available at https://github.com/Eason-Li-AIS/DrafterBench, with the test set hosted at https://huggingface.co/datasets/Eason666/DrafterBench.", "github_manual": "", "github_regex": "https://github.com/Eason-Li-AIS/DrafterBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.09477", "title": "Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs", "authors": [ "Yangning Li", "Weizhi Zhang", "Yuyao Yang", "Wei-Chieh Huang", "Yaozu Wu", "Junyu Luo", "Yuanchen Bei", "Henry Peng Zou", "Xiao Luo", "Yusheng Zhao", "Chunkit Chan", "Yankai Chen", "Zhongfen Deng", "Yinghui Li", "Hai-Tao Zheng", "Dongyuan Li", "Renhe Jiang", "Ming Zhang", "Yangqiu Song", "Philip S. Yu" ], "github": "https://github.com/DavidZWZ/Awesome-RAG-Reasoning", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts. This survey synthesizes both strands under a unified reasoning-retrieval perspective. We first map how advanced reasoning optimizes each stage of RAG (Reasoning-Enhanced RAG). Then, we show how retrieved knowledge of different type supply missing premises and expand context for complex inference (RAG-Enhanced Reasoning). Finally, we spotlight emerging Synergized RAG-Reasoning frameworks, where (agentic) LLMs iteratively interleave search and reasoning to achieve state-of-the-art performance across knowledge-intensive benchmarks. We categorize methods, datasets, and open challenges, and outline research avenues toward deeper RAG-Reasoning systems that are more effective, multimodally-adaptive, trustworthy, and human-centric. The collection is available at https://github.com/DavidZWZ/Awesome-RAG-Reasoning.", "github_manual": "", "github_regex": "https://github.com/DavidZWZ/Awesome-RAG-Reasoning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.11949", "title": "MOSPA: Human Motion Generation Driven by Spatial Audio", "authors": [ "Shuyang Xu", "Zhiyang Dou", "Mingyi Shi", "Liang Pan", "Leo Ho", "Jingbo Wang", "Yuan Liu", "Cheng Lin", "Yuexin Ma", "Wenping Wang", "Taku Komura" ], "github": "https://github.com/xsy27/Mospa-Acoustic-driven-Motion-Generation.git", "project_page": "", "abstract": "Enabling virtual humans to dynamically and realistically respond to diverse auditory stimuli remains a key challenge in character animation, demanding the integration of perceptual modeling and motion synthesis. Despite its significance, this task remains largely unexplored. Most previous works have primarily focused on mapping modalities like speech, audio, and music to generate human motion. As of yet, these models typically overlook the impact of spatial features encoded in spatial audio signals on human motion. To bridge this gap and enable high-quality modeling of human movements in response to spatial audio, we introduce the first comprehensive Spatial Audio-Driven Human Motion (SAM) dataset, which contains diverse and high-quality spatial audio and motion data. For benchmarking, we develop a simple yet effective diffusion-based generative framework for human MOtion generation driven by SPatial Audio, termed MOSPA, which faithfully captures the relationship between body motion and spatial audio through an effective fusion mechanism. Once trained, MOSPA could generate diverse realistic human motions conditioned on varying spatial audio inputs. We perform a thorough investigation of the proposed dataset and conduct extensive experiments for benchmarking, where our method achieves state-of-the-art performance on this task. Our model and dataset will be open-sourced upon acceptance. Please refer to our supplementary video for more details.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xsy27/Mospa-Acoustic-driven-Motion-Generation.git", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.12463", "title": "MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understanding", "authors": [ "Renjie Li", "Ruijie Ye", "Mingyang Wu", "Hao Frank Yang", "Zhiwen Fan", "Hezhen Hu", "Zhengzhong Tu" ], "github": "https://github.com/yt-dlp/yt-dlp", "project_page": "", "abstract": "Humans are integral components of the transportation ecosystem, and understanding their behaviors is crucial to facilitating the development of safe driving systems. Although recent progress has explored various aspects of human behaviorx2014such as motion, trajectories, and intentionx2014a comprehensive benchmark for evaluating human behavior understanding in autonomous driving remains unavailable. In this work, we propose MMHU, a large-scale benchmark for human behavior analysis featuring rich annotations, such as human motion and trajectories, text description for human motions, human intention, and critical behavior labels relevant to driving safety. Our dataset encompasses 57k human motion clips and 1.73M frames gathered from diverse sources, including established driving datasets such as Waymo, in-the-wild videos from YouTube, and self-collected data. A human-in-the-loop annotation pipeline is developed to generate rich behavior captions. We provide a thorough dataset analysis and benchmark multiple tasksx2014ranging from motion prediction to motion generation and human behavior question answeringx2014thereby offering a broad evaluation suite. Project page : https://MMHU-Benchmark.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yt-dlp/yt-dlp", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.12415", "title": "SWE-Perf: Can Language Models Optimize Code Performance on Real-World Repositories?", "authors": [ "Xinyi He", "Qian Liu", "Mingzhe Du", "Lin Yan", "Zhijie Fan", "Yiming Huang", "Zejian Yuan", "Zejun Ma" ], "github": "", "project_page": "", "abstract": "Code performance optimization is paramount in real-world software engineering and critical for production-level systems. While Large Language Models (LLMs) have demonstrated impressive capabilities in code generation and bug fixing, their proficiency in enhancing code performance at the repository level remains largely unexplored. To address this gap, we introduce SWE-Perf, the first benchmark specifically designed to systematically evaluate LLMs on code performance optimization tasks within authentic repository contexts. SWE-Perf comprises 140 carefully curated instances, each derived from performance-improving pull requests from popular GitHub repositories. Each benchmark instance includes the relevant codebase, target functions, performance-related tests, expert-authored patches, and executable environments. Through a comprehensive evaluation of representative methods that span file-level and repo-level approaches (e.g., Agentless and OpenHands), we reveal a substantial capability gap between existing LLMs and expert-level optimization performance, highlighting critical research opportunities in this emerging field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.02857", "title": "AnyI2V: Animating Any Conditional Image with Motion Control", "authors": [ "Ziye Li", "Hao Luo", "Xincheng Shuai", "Henghui Ding" ], "github": "", "project_page": "", "abstract": "Recent advancements in video generation, particularly in diffusion models, have driven notable progress in text-to-video (T2V) and image-to-video (I2V) synthesis. However, challenges remain in effectively integrating dynamic motion signals and flexible spatial constraints. Existing T2V methods typically rely on text prompts, which inherently lack precise control over the spatial layout of generated content. In contrast, I2V methods are limited by their dependence on real images, which restricts the editability of the synthesized content. Although some methods incorporate ControlNet to introduce image-based conditioning, they often lack explicit motion control and require computationally expensive training. To address these limitations, we propose AnyI2V, a training-free framework that animates any conditional images with user-defined motion trajectories. AnyI2V supports a broader range of modalities as the conditional image, including data types such as meshes and point clouds that are not supported by ControlNet, enabling more flexible and versatile video generation. Additionally, it supports mixed conditional inputs and enables style transfer and editing via LoRA and text prompts. Extensive experiments demonstrate that the proposed AnyI2V achieves superior performance and provides a new perspective in spatial- and motion-controlled video generation. Code is available at https://henghuiding.com/AnyI2V/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.12462", "title": "SpatialTrackerV2: 3D Point Tracking Made Easy", "authors": [ "Yuxi Xiao", "Jianyuan Wang", "Nan Xue", "Nikita Karaev", "Yuri Makarov", "Bingyi Kang", "Xing Zhu", "Hujun Bao", "Yujun Shen", "Xiaowei Zhou" ], "github": "", "project_page": "", "abstract": "We present SpatialTrackerV2, a feed-forward 3D point tracking method for monocular videos. Going beyond modular pipelines built on off-the-shelf components for 3D tracking, our approach unifies the intrinsic connections between point tracking, monocular depth, and camera pose estimation into a high-performing and feedforward 3D point tracker. It decomposes world-space 3D motion into scene geometry, camera ego-motion, and pixel-wise object motion, with a fully differentiable and end-to-end architecture, allowing scalable training across a wide range of datasets, including synthetic sequences, posed RGB-D videos, and unlabeled in-the-wild footage. By learning geometry and motion jointly from such heterogeneous data, SpatialTrackerV2 outperforms existing 3D tracking methods by 30%, and matches the accuracy of leading dynamic 3D reconstruction approaches while running 50times faster.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.05065", "title": "Replacing thinking with tool usage enables reasoning in small language models", "authors": [ "Corrado Rainone", "Tim Bakker", "Roland Memisevic" ], "github": "", "project_page": "", "abstract": "Recent advances have established a new machine learning paradigm based on scaling up compute at inference time as well as at training time. In that line of work, a combination of Supervised Fine-Tuning (SFT) on synthetic demonstrations and Reinforcement Learning with Verifiable Rewards (RLVR) is used for training Large Language Models to expend extra compute during inference in the form of \"thoughts\" expressed in natural language. In this paper, we propose to instead format these tokens as a multi-turn interaction trace with a stateful tool. At each turn, the new state of the tool is appended to the context of the model, whose job is to generate the tokens necessary to control the tool via a custom DSL. We benchmark this approach on the problem of repairing malfunctioning Python code, and show that this constrained setup allows for faster sampling of experience and a denser reward signal, allowing even models of size up to 3B parameters to learn how to proficiently expend additional compute on the task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.07451", "title": "RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning", "authors": [ "Hongzhi Zhang", "Jia Fu", "Jingyuan Zhang", "Kai Fu", "Qi Wang", "Fuzheng Zhang", "Guorui Zhou" ], "github": "https://github.com/Kwai-Klear/RLEP", "project_page": "", "abstract": "Reinforcement learning (RL) for large language models is an energy-intensive endeavor: training can be unstable, and the policy may gradually drift away from its pretrained weights. We present RLEP\\, -- \\,Reinforcement Learning with Experience rePlay\\, -- \\,a two-phase framework that first collects verified trajectories and then replays them during subsequent training. At every update step, the policy is optimized on mini-batches that blend newly generated rollouts with these replayed successes. By replaying high-quality examples, RLEP steers the model away from fruitless exploration, focuses learning on promising reasoning paths, and delivers both faster convergence and stronger final performance. On the Qwen2.5-Math-7B base model, RLEP reaches baseline peak accuracy with substantially fewer updates and ultimately surpasses it, improving accuracy on AIME-2024 from 38.2% to 39.9%, on AIME-2025 from 19.8% to 22.3%, and on AMC-2023 from 77.0% to 82.2%. Our code, datasets, and checkpoints are publicly available at https://github.com/Kwai-Klear/RLEP to facilitate reproducibility and further research.", "github_manual": "", "github_regex": "https://github.com/Kwai-Klear/RLEP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.11764", "title": "AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles", "authors": [ "Matteo Fasulo", "Luca Babboni", "Luca Tedeschini" ], "github": "", "project_page": "", "abstract": "This paper presents AI Wizards' participation in the CLEF 2025 CheckThat! Lab Task 1: Subjectivity Detection in News Articles, classifying sentences as subjective/objective in monolingual, multilingual, and zero-shot settings. Training/development datasets were provided for Arabic, German, English, Italian, and Bulgarian; final evaluation included additional unseen languages (e.g., Greek, Romanian, Polish, Ukrainian) to assess generalization. Our primary strategy enhanced transformer-based classifiers by integrating sentiment scores, derived from an auxiliary model, with sentence representations, aiming to improve upon standard fine-tuning. We explored this sentiment-augmented architecture with mDeBERTaV3-base, ModernBERT-base (English), and Llama3.2-1B. To address class imbalance, prevalent across languages, we employed decision threshold calibration optimized on the development set. Our experiments show sentiment feature integration significantly boosts performance, especially subjective F1 score. This framework led to high rankings, notably 1st for Greek (Macro F1 = 0.51).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.11412", "title": "Seq vs Seq: An Open Suite of Paired Encoders and Decoders", "authors": [ "Orion Weller", "Kathryn Ricci", "Marc Marone", "Antoine Chaffin", "Dawn Lawrie", "Benjamin Van Durme" ], "github": "https://github.com/JHU-CLSP/ettin-encoder-vs-decoder", "project_page": "", "abstract": "The large language model (LLM) community focuses almost exclusively on decoder-only language models, since they are easier to use for text generation. However, a large subset of the community still uses encoder-only models for tasks such as classification or retrieval. Previous work has attempted to compare these architectures, but is forced to make comparisons with models that have different numbers of parameters, training techniques, and datasets. We introduce the SOTA open-data Ettin suite of models: paired encoder-only and decoder-only models ranging from 17 million parameters to 1 billion, trained on up to 2 trillion tokens. Using the same recipe for both encoder-only and decoder-only models produces SOTA recipes in both categories for their respective sizes, beating ModernBERT as an encoder and Llama 3.2 and SmolLM2 as decoders. Like previous work, we find that encoder-only models excel at classification and retrieval tasks while decoders excel at generative tasks. However, we show that adapting a decoder model to encoder tasks (and vice versa) through continued training is subpar compared to using only the reverse objective (i.e. a 400M encoder outperforms a 1B decoder on MNLI, and vice versa for generative tasks). We open-source all artifacts of this study including training data, training order segmented by checkpoint, and 200+ checkpoints to allow future work to analyze or extend all aspects of training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JHU-CLSP/ettin-encoder-vs-decoder", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.12367", "title": "GitChameleon: Evaluating AI Code Generation Against Python Library Version Incompatibilities", "authors": [ "Diganta Misra", "Nizar Islah", "Victor May", "Brice Rauby", "Zihan Wang", "Justine Gehring", "Antonio Orvieto", "Muawiz Chaudhary", "Eilif B. Muller", "Irina Rish", "Samira Ebrahimi Kahou", "Massimo Caccia" ], "github": "https://github.com/mrcabbage972/GitChameleonBenchmark", "project_page": "", "abstract": "The rapid evolution of software libraries poses a considerable hurdle for code generation, necessitating continuous adaptation to frequent version updates while preserving backward compatibility. While existing code evolution benchmarks provide valuable insights, they typically lack execution-based evaluation for generating code compliant with specific library versions. To address this, we introduce GitChameleon, a novel, meticulously curated dataset comprising 328 Python code completion problems, each conditioned on specific library versions and accompanied by executable unit tests. GitChameleon rigorously evaluates the capacity of contemporary large language models (LLMs), LLM-powered agents, code assistants, and RAG systems to perform version-conditioned code generation that demonstrates functional accuracy through execution. Our extensive evaluations indicate that state-of-the-art systems encounter significant challenges with this task; enterprise models achieving baseline success rates in the 48-51\\% range, underscoring the intricacy of the problem. By offering an execution-based benchmark emphasizing the dynamic nature of code libraries, GitChameleon enables a clearer understanding of this challenge and helps guide the development of more adaptable and dependable AI code generation methods. We make the dataset and evaluation code publicly available at https://github.com/mrcabbage972/GitChameleonBenchmark.", "github_manual": "", "github_regex": "https://github.com/mrcabbage972/GitChameleonBenchmark", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.10015", "title": "(Almost) Free Modality Stitching of Foundation Models", "authors": [ "Jaisidh Singh", "Diganta Misra", "Boris Knyazev", "Antonio Orvieto" ], "github": "", "project_page": "", "abstract": "Foundation multi-modal models are often designed by stitching of multiple existing pretrained uni-modal models: for example, an image classifier with an text model. This stitching process is performed by training a connector module that aims to align the representation spaces of these uni-modal models towards a multi-modal objective. However, given the complexity of training such connectors on large scale web-based datasets coupled with the ever-increasing number of available pretrained uni-modal models, the task of uni-modal models selection and subsequent connector module training becomes computationally demanding. To address this under-studied critical problem, we propose Hypernetwork Model Alignment (Hyma), a novel all-in-one solution for optimal uni-modal model selection and connector training by leveraging hypernetworks. Specifically, our framework utilizes the parameter prediction capability of a hypernetwork to obtain jointly trained connector modules for N times M combinations of uni-modal models. In our experiments, Hyma reduces the cost of searching for the best performing uni-modal model pair by 10times, while matching the ranking and trained connector performance obtained via grid search across a suite of diverse multi-modal benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-17", "arxiv_id": "2507.07015", "title": "MST-Distill: Mixture of Specialized Teachers for Cross-Modal Knowledge Distillation", "authors": [ "Hui Li", "Pengfei Yang", "Juanyang Chen", "Le Dong", "Yanxin Chen", "Quan Wang" ], "github": "https://github.com/Gray-OREO/MST-Distill", "project_page": "", "abstract": "Knowledge distillation as an efficient knowledge transfer technique, has achieved remarkable success in unimodal scenarios. However, in cross-modal settings, conventional distillation methods encounter significant challenges due to data and statistical heterogeneities, failing to leverage the complementary prior knowledge embedded in cross-modal teacher models. This paper empirically reveals two critical issues in existing approaches: distillation path selection and knowledge drift. To address these limitations, we propose MST-Distill, a novel cross-modal knowledge distillation framework featuring a mixture of specialized teachers. Our approach employs a diverse ensemble of teacher models across both cross-modal and multimodal configurations, integrated with an instance-level routing network that facilitates adaptive and dynamic distillation. This architecture effectively transcends the constraints of traditional methods that rely on monotonous and static teacher models. Additionally, we introduce a plug-in masking module, independently trained to suppress modality-specific discrepancies and reconstruct teacher representations, thereby mitigating knowledge drift and enhancing transfer effectiveness. Extensive experiments across five diverse multimodal datasets, spanning visual, audio, and text, demonstrate that our method significantly outperforms existing state-of-the-art knowledge distillation methods in cross-modal distillation tasks. The source code is available at https://github.com/Gray-OREO/MST-Distill.", "github_manual": "", "github_regex": "https://github.com/Gray-OREO/MST-Distill", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.13334", "title": "A Survey of Context Engineering for Large Language Models", "authors": [ "Lingrui Mei", "Jiayu Yao", "Yuyao Ge", "Yiwei Wang", "Baolong Bi", "Yujun Cai", "Jiazhi Liu", "Mingyu Li", "Zhong-Zhi Li", "Duzhen Zhang", "Chenlin Zhou", "Jiayi Mao", "Tianze Xia", "Jiafeng Guo", "Shenghua Liu" ], "github": "https://github.com/Meirtz/Awesome-Context-Engineering", "project_page": "", "abstract": "The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering, a formal discipline that transcends simple prompt design to encompass the systematic optimization of information payloads for LLMs. We present a comprehensive taxonomy decomposing Context Engineering into its foundational components and the sophisticated implementations that integrate them into intelligent systems. We first examine the foundational components: context retrieval and generation, context processing and context management. We then explore how these components are architecturally integrated to create sophisticated system implementations: retrieval-augmented generation (RAG), memory systems and tool-integrated reasoning, and multi-agent systems. Through this systematic analysis of over 1300 research papers, our survey not only establishes a technical roadmap for the field but also reveals a critical research gap: a fundamental asymmetry exists between model capabilities. While current models, augmented by advanced context engineering, demonstrate remarkable proficiency in understanding complex contexts, they exhibit pronounced limitations in generating equally sophisticated, long-form outputs. Addressing this gap is a defining priority for future research. Ultimately, this survey provides a unified framework for both researchers and engineers advancing context-aware AI.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Meirtz/Awesome-Context-Engineering", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.13332", "title": "The Imitation Game: Turing Machine Imitator is Length Generalizable Reasoner", "authors": [ "Zhouqi Hua", "Wenwei Zhang", "Chengqi Lyu", "Yuzhe Gu", "Songyang Gao", "Kuikun Liu", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Length generalization, the ability to solve problems of longer sequences than those observed during training, poses a core challenge of Transformer-based large language models (LLM). Although existing studies have predominantly focused on data-driven approaches for arithmetic operations and symbolic manipulation tasks, these approaches tend to be task-specific with limited overall performance. To pursue a more general solution, this paper focuses on a broader case of reasoning problems that are computable, i.e., problems that algorithms can solve, thus can be solved by the Turing Machine. From this perspective, this paper proposes Turing MAchine Imitation Learning (TAIL) to improve the length generalization ability of LLMs. TAIL synthesizes chain-of-thoughts (CoT) data that imitate the execution process of a Turing Machine by computer programs, which linearly expands the reasoning steps into atomic states to alleviate shortcut learning and explicit memory fetch mechanism to reduce the difficulties of dynamic and long-range data access in elementary operations. To validate the reliability and universality of TAIL, we construct a challenging synthetic dataset covering 8 classes of algorithms and 18 tasks. Without bells and whistles, TAIL significantly improves the length generalization ability as well as the performance of Qwen2.5-7B on various tasks using only synthetic data, surpassing previous methods and DeepSeek-R1. The experimental results reveal that the key concepts in the Turing Machine, instead of the thinking styles, are indispensable for TAIL for length generalization, through which the model exhibits read-and-write behaviors consistent with the properties of the Turing Machine in their attention layers. This work provides a promising direction for future research in the learning of LLM reasoning from synthetic data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.12720", "title": "FLEXITOKENS: Flexible Tokenization for Evolving Language Models", "authors": [ "Abraham Toluase Owodunni", "Orevaoghene Ahia", "Sachin Kumar" ], "github": "https://github.com/owos/flexitokens", "project_page": "", "abstract": "Language models (LMs) are challenging to adapt to new data distributions by simple finetuning. This is due to the rigidity of their subword tokenizers, which typically remain unchanged during adaptation. This inflexibility often leads to inefficient tokenization, causing overfragmentation of out-of-distribution domains, unseen languages, or scripts. In this work, we develop byte-level LMs with learnable tokenizers to make tokenization adaptive. Our models include a submodule that learns to predict boundaries between the input byte sequence, encoding it into variable-length segments. Existing tokenizer-free methods train this boundary predictor using an auxiliary loss that enforces a fixed compression rate across the training corpus, introducing a new kind of rigidity. We propose FLEXITOKENS, a simplified training objective that enables significantly greater flexibility during adaptation. Evaluating across multiple multilingual benchmarks, morphologically diverse tasks, and domains, we demonstrate that FLEXITOKENS consistently reduces token over-fragmentation and achieves up to 10\\% improvements on downstream task performance compared to subword and other gradient-based tokenizers. Code and data for our experiments will be released at https://github.com/owos/flexitokens", "github_manual": "", "github_regex": "https://github.com/owos/flexitokens", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.12508", "title": "MindJourney: Test-Time Scaling with World Models for Spatial Reasoning", "authors": [ "Yuncong Yang", "Jiageng Liu", "Zheyuan Zhang", "Siyuan Zhou", "Reuben Tan", "Jianwei Yang", "Yilun Du", "Chuang Gan" ], "github": "", "project_page": "", "abstract": "Spatial reasoning in 3D space is central to human cognition and indispensable for embodied tasks such as navigation and manipulation. However, state-of-the-art vision-language models (VLMs) struggle frequently with tasks as simple as anticipating how a scene will look after an egocentric motion: they perceive 2D images but lack an internal model of 3D dynamics. We therefore propose MindJourney, a test-time scaling framework that grants a VLM with this missing capability by coupling it to a controllable world model based on video diffusion. The VLM iteratively sketches a concise camera trajectory, while the world model synthesizes the corresponding view at each step. The VLM then reasons over this multi-view evidence gathered during the interactive exploration. Without any fine-tuning, our MindJourney achieves over an average 8% performance boost on the representative spatial reasoning benchmark SAT, showing that pairing VLMs with world models for test-time scaling offers a simple, plug-and-play route to robust 3D reasoning. Meanwhile, our method also improves upon the test-time inference VLMs trained through reinforcement learning, which demonstrates the potential of our method that utilizes world models for test-time scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.13347", "title": "\u03c0^3: Scalable Permutation-Equivariant Visual Geometry Learning", "authors": [ "Yifan Wang", "Jianjun Zhou", "Haoyi Zhu", "Wenzheng Chang", "Yang Zhou", "Zizun Li", "Junyi Chen", "Jiangmiao Pang", "Chunhua Shen", "Tong He" ], "github": "https://github.com/yyfz/Pi3", "project_page": "", "abstract": "We introduce pi^3, a feed-forward neural network that offers a novel approach to visual geometry reconstruction, breaking the reliance on a conventional fixed reference view. Previous methods often anchor their reconstructions to a designated viewpoint, an inductive bias that can lead to instability and failures if the reference is suboptimal. In contrast, pi^3 employs a fully permutation-equivariant architecture to predict affine-invariant camera poses and scale-invariant local point maps without any reference frames. This design makes our model inherently robust to input ordering and highly scalable. These advantages enable our simple and bias-free approach to achieve state-of-the-art performance on a wide range of tasks, including camera pose estimation, monocular/video depth estimation, and dense point map reconstruction. Code and models are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yyfz/Pi3", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.12841", "title": "AnyCap Project: A Unified Framework, Dataset, and Benchmark for Controllable Omni-modal Captioning", "authors": [ "Yiming Ren", "Zhiqiang Lin", "Yu Li", "Gao Meng", "Weiyun Wang", "Junjie Wang", "Zicheng Lin", "Jifeng Dai", "Yujiu Yang", "Wenhai Wang", "Ruihang Chu" ], "github": "", "project_page": "", "abstract": "Controllable captioning is essential for precise multimodal alignment and instruction following, yet existing models often lack fine-grained control and reliable evaluation protocols. To address this gap, we present the AnyCap Project, an integrated solution spanning model, dataset, and evaluation. We introduce AnyCapModel (ACM), a lightweight plug-and-play framework that enhances the controllability of existing foundation models for omni-modal captioning without retraining the base model. ACM reuses the original captions from base models while incorporating user instructions and modality features to generate improved captions. To remedy the data scarcity in controllable multimodal captioning, we build AnyCapDataset (ACD), covering three modalities, 28 user-instruction types, and 300\\,k high-quality data entries. We further propose AnyCapEval, a new benchmark that provides more reliable evaluation metrics for controllable captioning by decoupling content accuracy and stylistic fidelity. ACM markedly improves caption quality across a diverse set of base models on AnyCapEval. Notably, ACM-8B raises GPT-4o\\'s content scores by 45\\% and style scores by 12\\%, and it also achieves substantial gains on widely used benchmarks such as MIA-Bench and VidCapBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.13348", "title": "VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning", "authors": [ "Senqiao Yang", "Junyi Li", "Xin Lai", "Bei Yu", "Hengshuang Zhao", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/VisionThink", "project_page": "", "abstract": "Recent advancements in vision-language models (VLMs) have improved performance by increasing the number of visual tokens, which are often significantly longer than text tokens. However, we observe that most real-world scenarios do not require such an extensive number of visual tokens. While the performance drops significantly in a small subset of OCR-related tasks, models still perform accurately in most other general VQA tasks with only 1/4 resolution. Therefore, we propose to dynamically process distinct samples with different resolutions, and present a new paradigm for visual token compression, namely, VisionThink. It starts with a downsampled image and smartly decides whether it is sufficient for problem solving. Otherwise, the model could output a special token to request the higher-resolution image. Compared to existing Efficient VLM methods that compress tokens using fixed pruning ratios or thresholds, VisionThink autonomously decides whether to compress tokens case by case. As a result, it demonstrates strong fine-grained visual understanding capability on OCR-related tasks, and meanwhile saves substantial visual tokens on simpler tasks. We adopt reinforcement learning and propose the LLM-as-Judge strategy to successfully apply RL to general VQA tasks. Moreover, we carefully design a reward function and penalty mechanism to achieve a stable and reasonable image resize call ratio. Extensive experiments demonstrate the superiority, efficiency, and effectiveness of our method. Our code is available at https://github.com/dvlab-research/VisionThink.", "github_manual": "", "github_regex": "https://github.com/dvlab-research/VisionThink", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.13300", "title": "AbGen: Evaluating Large Language Models in Ablation Study Design and Evaluation for Scientific Research", "authors": [ "Yilun Zhao", "Weiyuan Chen", "Zhijian Xu", "Manasi Patwardhan", "Yixin Liu", "Chengye Wang", "Lovekesh Vig", "Arman Cohan" ], "github": "", "project_page": "", "abstract": "We introduce AbGen, the first benchmark designed to evaluate the capabilities of LLMs in designing ablation studies for scientific research. AbGen consists of 1,500 expert-annotated examples derived from 807 NLP papers. In this benchmark, LLMs are tasked with generating detailed ablation study designs for a specified module or process based on the given research context. Our evaluation of leading LLMs, such as DeepSeek-R1-0528 and o4-mini, highlights a significant performance gap between these models and human experts in terms of the importance, faithfulness, and soundness of the ablation study designs. Moreover, we demonstrate that current automated evaluation methods are not reliable for our task, as they show a significant discrepancy when compared to human assessment. To better investigate this, we develop AbGen-Eval, a meta-evaluation benchmark designed to assess the reliability of commonly used automated evaluation systems in measuring LLM performance on our task. We investigate various LLM-as-Judge systems on AbGen-Eval, providing insights for future research on developing more effective and reliable LLM-based evaluation systems for complex scientific tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.04984", "title": "TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation", "authors": [ "Zonglin Lyu", "Chen Chen" ], "github": "", "project_page": "", "abstract": "Video Frame Interpolation (VFI) aims to predict the intermediate frame I_n (we use n to denote time in videos to avoid notation overload with the timestep t in diffusion models) based on two consecutive neighboring frames I_0 and I_1. Recent approaches apply diffusion models (both image-based and video-based) in this task and achieve strong performance. However, image-based diffusion models are unable to extract temporal information and are relatively inefficient compared to non-diffusion methods. Video-based diffusion models can extract temporal information, but they are too large in terms of training scale, model size, and inference time. To mitigate the above issues, we propose Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation (TLB-VFI), an efficient video-based diffusion model. By extracting rich temporal information from video inputs through our proposed 3D-wavelet gating and temporal-aware autoencoder, our method achieves 20% improvement in FID on the most challenging datasets over recent SOTA of image-based diffusion models. Meanwhile, due to the existence of rich temporal information, our method achieves strong performance while having 3times fewer parameters. Such a parameter reduction results in 2.3x speed up. By incorporating optical flow guidance, our method requires 9000x less training data and achieves over 20x fewer parameters than video-based diffusion models. Codes and results are available at our project page: https://zonglinl.github.io/tlbvfi_page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.12956", "title": "FantasyPortrait: Enhancing Multi-Character Portrait Animation with Expression-Augmented Diffusion Transformers", "authors": [ "Qiang Wang", "Mengchao Wang", "Fan Jiang", "Yaqi Fan", "Yonggang Qi", "Mu Xu" ], "github": "", "project_page": "", "abstract": "Producing expressive facial animations from static images is a challenging task. Prior methods relying on explicit geometric priors (e.g., facial landmarks or 3DMM) often suffer from artifacts in cross reenactment and struggle to capture subtle emotions. Furthermore, existing approaches lack support for multi-character animation, as driving features from different individuals frequently interfere with one another, complicating the task. To address these challenges, we propose FantasyPortrait, a diffusion transformer based framework capable of generating high-fidelity and emotion-rich animations for both single- and multi-character scenarios. Our method introduces an expression-augmented learning strategy that utilizes implicit representations to capture identity-agnostic facial dynamics, enhancing the model's ability to render fine-grained emotions. For multi-character control, we design a masked cross-attention mechanism that ensures independent yet coordinated expression generation, effectively preventing feature interference. To advance research in this area, we propose the Multi-Expr dataset and ExprBench, which are specifically designed datasets and benchmarks for training and evaluating multi-character portrait animations. Extensive experiments demonstrate that FantasyPortrait significantly outperforms state-of-the-art methods in both quantitative metrics and qualitative evaluations, excelling particularly in challenging cross reenactment and multi-character contexts. Our project page is https://fantasy-amap.github.io/fantasy-portrait/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.13344", "title": "Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models", "authors": [ "Yudong Jin", "Sida Peng", "Xuan Wang", "Tao Xie", "Zhen Xu", "Yifan Yang", "Yujun Shen", "Hujun Bao", "Xiaowei Zhou" ], "github": "", "project_page": "", "abstract": "This paper addresses the challenge of high-fidelity view synthesis of humans with sparse-view videos as input. Previous methods solve the issue of insufficient observation by leveraging 4D diffusion models to generate videos at novel viewpoints. However, the generated videos from these models often lack spatio-temporal consistency, thus degrading view synthesis quality. In this paper, we propose a novel sliding iterative denoising process to enhance the spatio-temporal consistency of the 4D diffusion model. Specifically, we define a latent grid in which each latent encodes the image, camera pose, and human pose for a certain viewpoint and timestamp, then alternately denoising the latent grid along spatial and temporal dimensions with a sliding window, and finally decode the videos at target viewpoints from the corresponding denoised latents. Through the iterative sliding, information flows sufficiently across the latent grid, allowing the diffusion model to obtain a large receptive field and thus enhance the 4D consistency of the output, while making the GPU memory consumption affordable. The experiments on the DNA-Rendering and ActorsHQ datasets demonstrate that our method is able to synthesize high-quality and consistent novel-view videos and significantly outperforms the existing approaches. See our project page for interactive demos and video results: https://diffuman4d.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.12990", "title": "Teach Old SAEs New Domain Tricks with Boosting", "authors": [ "Nikita Koriagin", "Yaroslav Aksenov", "Daniil Laptev", "Gleb Gerasimov", "Nikita Balagansky", "Daniil Gavrilov" ], "github": "", "project_page": "", "abstract": "Sparse Autoencoders have emerged as powerful tools for interpreting the internal representations of Large Language Models, yet they often fail to capture domain-specific features not prevalent in their training corpora. This paper introduces a residual learning approach that addresses this feature blindness without requiring complete retraining. We propose training a secondary SAE specifically to model the reconstruction error of a pretrained SAE on domain-specific texts, effectively capturing features missed by the primary model. By summing the outputs of both models during inference, we demonstrate significant improvements in both LLM cross-entropy and explained variance metrics across multiple specialized domains. Our experiments show that this method efficiently incorporates new domain knowledge into existing SAEs while maintaining their performance on general tasks. This approach enables researchers to selectively enhance SAE interpretability for specific domains of interest, opening new possibilities for targeted mechanistic interpretability of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.13255", "title": "Automating Steering for Safe Multimodal Large Language Models", "authors": [ "Lyucheng Wu", "Mengru Wang", "Ziwen Xu", "Tri Cao", "Nay Oo", "Bryan Hooi", "Shumin Deng" ], "github": "https://github.com/zjunlp/AutoSteer", "project_page": "", "abstract": "Recent progress in Multimodal Large Language Models (MLLMs) has unlocked powerful cross-modal reasoning abilities, but also raised new safety concerns, particularly when faced with adversarial multimodal inputs. To improve the safety of MLLMs during inference, we introduce a modular and adaptive inference-time intervention technology, AutoSteer, without requiring any fine-tuning of the underlying model. AutoSteer incorporates three core components: (1) a novel Safety Awareness Score (SAS) that automatically identifies the most safety-relevant distinctions among the model's internal layers; (2) an adaptive safety prober trained to estimate the likelihood of toxic outputs from intermediate representations; and (3) a lightweight Refusal Head that selectively intervenes to modulate generation when safety risks are detected. Experiments on LLaVA-OV and Chameleon across diverse safety-critical benchmarks demonstrate that AutoSteer significantly reduces the Attack Success Rate (ASR) for textual, visual, and cross-modal threats, while maintaining general abilities. These findings position AutoSteer as a practical, interpretable, and effective framework for safer deployment of multimodal AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zjunlp/AutoSteer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.13264", "title": "Voxtral", "authors": [ "Alexander H. Liu", "Andy Ehrenberg", "Andy Lo", "Cl\u00e9ment Denoix", "Corentin Barreau", "Guillaume Lample", "Jean-Malo Delignon", "Khyathi Raghavi Chandu", "Patrick von Platen", "Pavankumar Reddy Muddireddy", "Sanchit Gandhi", "Soham Ghosh", "Srijan Mishra", "Thomas Foubert", "Abhinav Rastogi", "Adam Yang", "Albert Q. Jiang", "Alexandre Sablayrolles", "Am\u00e9lie H\u00e9liou", "Am\u00e9lie Martin", "Anmol Agarwal", "Antoine Roux", "Arthur Darcet", "Arthur Mensch", "Baptiste Bout", "Baptiste Rozi\u00e8re", "Baudouin De Monicault", "Chris Bamford", "Christian Wallenwein", "Christophe Renaudin", "Cl\u00e9mence Lanfranchi", "Darius Dabert", "Devendra Singh Chaplot", "Devon Mizelle", "Diego de las Casas", "Elliot Chane-Sane", "Emilien Fugier", "Emma Bou Hanna", "Gabrielle Berrada", "Gauthier Delerce", "Gauthier Guinet", "Georgii Novikov", "Guillaume Martin", "Himanshu Jaju", "Jan Ludziejewski", "Jason Rute", "Jean-Hadrien Chabran", "Jessica Chudnovsky", "Joachim Studnia", "Joep Barmentlo", "Jonas Amar", "Josselin Somerville Roberts", "Julien Denize", "Karan Saxena", "Karmesh Yadav", "Kartik Khandelwal", "Kush Jain", "L\u00e9lio Renard Lavaud", "L\u00e9onard Blier", "Lingxiao Zhao", "Louis Martin", "Lucile Saulnier", "Luyu Gao", "Marie Pellat", "Mathilde Guillaumin", "Mathis Felardos", "Matthieu Dinot", "Maxime Darrin", "Maximilian Augustin", "Micka\u00ebl Seznec", "Neha Gupta", "Nikhil Raghuraman", "Olivier Duchenne", "Patricia Wang", "Patryk Saffer", "Paul Jacob", "Paul Wambergue", "Paula Kurylowicz", "Philom\u00e8ne Chagniot", "Pierre Stock", "Pravesh Agrawal", "R\u00e9mi Delacourt", "Romain Sauvestre", "Roman Soletskyi", "Sagar Vaze", "Sandeep Subramanian", "Saurabh Garg", "Shashwat Dalal", "Siddharth Gandhi", "Sumukh Aithal", "Szymon Antoniak", "Teven Le Scao", "Thibault Schueller", "Thibaut Lavril", "Thomas Robert", "Thomas Wang", "Timoth\u00e9e Lacroix", "Tom Bewley", "Valeriia Nemychnikova", "Victor Paltz", "Virgile Richard", "Wen-Ding Li", "William Marshall", "Xuanyu Zhang", "Yihan Wan", "Yunhao Tang" ], "github": "", "project_page": "", "abstract": "We present Voxtral Mini and Voxtral Small, two multimodal audio chat models. Voxtral is trained to comprehend both spoken audio and text documents, achieving state-of-the-art performance across a diverse range of audio benchmarks, while preserving strong text capabilities. Voxtral Small outperforms a number of closed-source models, while being small enough to run locally. A 32K context window enables the model to handle audio files up to 40 minutes in duration and long multi-turn conversations. We also contribute three benchmarks for evaluating speech understanding models on knowledge and trivia. Both Voxtral models are released under Apache 2.0 license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.11589", "title": "Einstein Fields: A Neural Perspective To Computational General Relativity", "authors": [ "Sandeep Suresh Cranganore", "Andrei Bodnar", "Arturs Berzins", "Johannes Brandstetter" ], "github": "https://github.com/AndreiB137/EinFields", "project_page": "", "abstract": "We introduce Einstein Fields, a neural representation that is designed to compress computationally intensive four-dimensional numerical relativity simulations into compact implicit neural network weights. By modeling the metric, which is the core tensor field of general relativity, Einstein Fields enable the derivation of physical quantities via automatic differentiation. However, unlike conventional neural fields (e.g., signed distance, occupancy, or radiance fields), Einstein Fields are Neural Tensor Fields with the key difference that when encoding the spacetime geometry of general relativity into neural field representations, dynamics emerge naturally as a byproduct. Einstein Fields show remarkable potential, including continuum modeling of 4D spacetime, mesh-agnosticity, storage efficiency, derivative accuracy, and ease of use. We address these challenges across several canonical test beds of general relativity and release an open source JAX-based library, paving the way for more scalable and expressive approaches to numerical relativity. Code is made available at https://github.com/AndreiB137/EinFields", "github_manual": "", "github_regex": "https://github.com/AndreiB137/EinFields", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-18", "arxiv_id": "2507.12142", "title": "RiemannLoRA: A Unified Riemannian Framework for Ambiguity-Free LoRA Optimization", "authors": [ "Vladimir Bogachev", "Vladimir Aletov", "Alexander Molozhavenko", "Denis Bobkov", "Vera Soboleva", "Aibek Alanov", "Maxim Rakhuba" ], "github": "", "project_page": "", "abstract": "Low-Rank Adaptation (LoRA) has become a widely adopted standard for parameter-efficient fine-tuning of large language models (LLMs), significantly reducing memory and computational demands. However, challenges remain, including finding optimal initialization strategies or mitigating overparametrization in low-rank matrix factorization. In this work, we propose a novel approach that addresses both of the challenges simultaneously within a unified framework. Our method treats a set of fixed-rank LoRA matrices as a smooth manifold. Considering adapters as elements on this manifold removes overparametrization, while determining the direction of the fastest loss decrease along the manifold provides initialization. Special care is taken to obtain numerically stable and computationally efficient implementation of our method, using best practices from numerical linear algebra and Riemannian optimization. Experimental results on LLM and diffusion model architectures demonstrate that RiemannLoRA consistently improves both convergence speed and final performance over standard LoRA and its state-of-the-art modifications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-21", "arxiv_id": "2507.10605", "title": "RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services", "authors": [ "Fei Zhao", "Chonggang Lu", "Yue Wang", "Zheyong Xie", "Ziyan Liu", "Haofu Qian", "JianZhao Huang", "Fangcheng Shi", "Zijie Meng", "Hongcheng Guo", "Mingqian He", "Xinze Lyu", "Yiming Lu", "Ziyang Xiang", "Zheyu Ye", "Chengqiang Lu", "Zhe Xu", "Yi Wu", "Yao Hu", "Yan Gao", "Jun Fan", "Xiaolong Jiang", "Weiting Liu", "Boyang Wang", "Shaosheng Cao" ], "github": "", "project_page": "", "abstract": "As a primary medium for modern information dissemination, social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement. Recently, the development of large language models (LLMs) has offered potential solutions but existing studies focus on isolated tasks, which not only encounter diminishing benefit from the data scaling within individual scenarios but also fail to flexibly adapt to diverse real-world context. To address these challenges, we introduce RedOne, a domain-specific LLM designed to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for the SNS. RedOne was developed through a three-stage training strategy consisting of continue pretraining, supervised fine-tuning, and preference optimization, using a large-scale real-world dataset. Through extensive experiments, RedOne maintains strong general capabilities, and achieves an average improvement up to 14.02% across 8 major SNS tasks and 7.56% in SNS bilingual evaluation benchmark, compared with base models. Furthermore, through online testing, RedOne reduced the exposure rate in harmful content detection by 11.23% and improved the click page rate in post-view search by 14.95% compared with single-tasks finetuned baseline models. These results establish RedOne as a robust domain-specific LLM for SNS, demonstrating excellent generalization across various tasks and promising applicability in real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-21", "arxiv_id": "2507.11097", "title": "The Devil behind the mask: An emergent safety vulnerability of Diffusion LLMs", "authors": [ "Zichen Wen", "Jiashu Qu", "Dongrui Liu", "Zhiyuan Liu", "Ruixi Wu", "Yicun Yang", "Xiangqi Jin", "Haoyun Xu", "Xuyang Liu", "Weijia Li", "Chaochao Lu", "Jing Shao", "Conghui He", "Linfeng Zhang" ], "github": "https://github.com/ZichenWen1/DIJA", "project_page": "", "abstract": "Diffusion-based large language models (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs, offering faster inference and greater interactivity via parallel decoding and bidirectional modeling. However, despite strong performance in code generation and text infilling, we identify a fundamental safety concern: existing alignment mechanisms fail to safeguard dLLMs against context-aware, masked-input adversarial prompts, exposing novel vulnerabilities. To this end, we present DIJA, the first systematic study and jailbreak attack framework that exploits unique safety weaknesses of dLLMs. Specifically, our proposed DIJA constructs adversarial interleaved mask-text prompts that exploit the text generation mechanisms of dLLMs, i.e., bidirectional modeling and parallel decoding. Bidirectional modeling drives the model to produce contextually consistent outputs for masked spans, even when harmful, while parallel decoding limits model dynamic filtering and rejection sampling of unsafe content. This causes standard alignment mechanisms to fail, enabling harmful completions in alignment-tuned dLLMs, even when harmful behaviors or unsafe instructions are directly exposed in the prompt. Through comprehensive experiments, we demonstrate that DIJA significantly outperforms existing jailbreak methods, exposing a previously overlooked threat surface in dLLM architectures. Notably, our method achieves up to 100% keyword-based ASR on Dream-Instruct, surpassing the strongest prior baseline, ReNeLLM, by up to 78.5% in evaluator-based ASR on JailbreakBench and by 37.7 points in StrongREJECT score, while requiring no rewriting or hiding of harmful content in the jailbreak prompt. Our findings underscore the urgent need for rethinking safety alignment in this emerging class of language models. Code is available at https://github.com/ZichenWen1/DIJA.", "github_manual": "", "github_regex": "https://github.com/ZichenWen1/DIJA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-21", "arxiv_id": "2507.12566", "title": "Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal Large Language Models", "authors": [ "Gen Luo", "Wenhan Dou", "Wenhao Li", "Zhaokai Wang", "Xue Yang", "Changyao Tian", "Hao Li", "Weiyun Wang", "Wenhai Wang", "Xizhou Zhu", "Yu Qiao", "Jifeng Dai" ], "github": "https://github.com/OpenGVLab/Mono-InternVL", "project_page": "", "abstract": "This paper focuses on monolithic Multimodal Large Language Models (MLLMs), which integrate visual encoding and language decoding into a single model. Existing structures and pre-training strategies for monolithic MLLMs often suffer from unstable optimization and catastrophic forgetting. To address these challenges, our key idea is to embed a new visual parameter space into a pre-trained LLM, enabling stable learning of visual knowledge from noisy data via delta tuning. Based on this principle, we first introduce Mono-InternVL, an advanced monolithic MLLM that incorporates a set of visual experts through a multimodal mixture-of-experts architecture. In addition, we design an innovative Endogenous Visual Pre-training (EViP) for Mono-InternVL to maximize its visual capabilities via progressive learning. Mono-InternVL achieves competitive performance against existing MLLMs but also leads to relatively expensive data cost. Therefore, we further present Mono-InternVL-1.5, a cheaper and stronger monolithic MLLM equipped with an improved EViP (EViP++). EViP++ introduces additional visual attention experts to Mono-InternVL-1.5 and re-organizes the pre-training process in an efficient manner. During inference, it includes a fused CUDA kernel to speed up its MoE operations. With these designs, Mono-InternVL-1.5 significantly reduces training and inference costs, while still maintaining competitive performance with Mono-InternVL. To evaluate our approach, we conduct extensive experiments across 15 benchmarks. Results demonstrate that Mono-InternVL outperforms existing monolithic MLLMs on 12 out of 15 benchmarks, e.g., +114-point improvement over Emu3 on OCRBench. Compared to its modular counterpart, i.e., InternVL-1.5, Mono-InternVL-1.5 achieves similar multimodal performance while reducing first-token latency by up to 69%. Code and models are released at https://github.com/OpenGVLab/Mono-InternVL.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/Mono-InternVL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-21", "arxiv_id": "2507.14137", "title": "Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning", "authors": [ "Shashanka Venkataramanan", "Valentinos Pariza", "Mohammadreza Salehi", "Lukas Knobel", "Spyros Gidaris", "Elias Ramzi", "Andrei Bursuc", "Yuki M. Asano" ], "github": "https://github.com/valeoai/Franca", "project_page": "", "abstract": "We present Franca (pronounced Fran-ka): free one; the first fully open-source (data, code, weights) vision foundation model that matches and in many cases surpasses the performance of state-of-the-art proprietary models, e.g., DINOv2, CLIP, SigLIPv2, etc. Our approach is grounded in a transparent training pipeline inspired by Web-SSL and uses publicly available data: ImageNet-21K and a subset of ReLAION-2B. Beyond model release, we tackle critical limitations in SSL clustering methods. While modern models rely on assigning image features to large codebooks via clustering algorithms like Sinkhorn-Knopp, they fail to account for the inherent ambiguity in clustering semantics. To address this, we introduce a parameter-efficient, multi-head clustering projector based on nested Matryoshka representations. This design progressively refines features into increasingly fine-grained clusters without increasing the model size, enabling both performance and memory efficiency. Additionally, we propose a novel positional disentanglement strategy that explicitly removes positional biases from dense representations, thereby improving the encoding of semantic content. This leads to consistent gains on several downstream benchmarks, demonstrating the utility of cleaner feature spaces. Our contributions establish a new standard for transparent, high-performance vision models and open a path toward more reproducible and generalizable foundation models for the broader AI community. The code and model checkpoints are available at https://github.com/valeoai/Franca.", "github_manual": "", "github_regex": "https://github.com/valeoai/Franca", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-21", "arxiv_id": "2507.13391", "title": "Quantitative Risk Management in Volatile Markets with an Expectile-Based Framework for the FTSE Index", "authors": [ "Abiodun Finbarrs Oketunji" ], "github": "", "project_page": "", "abstract": "This research presents a framework for quantitative risk management in volatile markets, specifically focusing on expectile-based methodologies applied to the FTSE 100 index. Traditional risk measures such as Value-at-Risk (VaR) have demonstrated significant limitations during periods of market stress, as evidenced during the 2008 financial crisis and subsequent volatile periods. This study develops an advanced expectile-based framework that addresses the shortcomings of conventional quantile-based approaches by providing greater sensitivity to tail losses and improved stability in extreme market conditions. The research employs a dataset spanning two decades of FTSE 100 returns, incorporating periods of high volatility, market crashes, and recovery phases. Our methodology introduces novel mathematical formulations for expectile regression models, enhanced threshold determination techniques using time series analysis, and robust backtesting procedures. The empirical results demonstrate that expectile-based Value-at-Risk (EVaR) consistently outperforms traditional VaR measures across various confidence levels and market conditions. The framework exhibits superior performance during volatile periods, with reduced model risk and enhanced predictive accuracy. Furthermore, the study establishes practical implementation guidelines for financial institutions and provides evidence-based recommendations for regulatory compliance and portfolio management. The findings contribute significantly to the literature on financial risk management and offer practical tools for practitioners dealing with volatile market environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-21", "arxiv_id": "2507.13302", "title": "The Generative Energy Arena (GEA): Incorporating Energy Awareness in Large Language Model (LLM) Human Evaluations", "authors": [ "Carlos Arriaga", "Gonzalo Mart\u00ednez", "Eneko Sendin", "Javier Conde", "Pedro Reviriego" ], "github": "", "project_page": "", "abstract": "The evaluation of large language models is a complex task, in which several approaches have been proposed. The most common is the use of automated benchmarks in which LLMs have to answer multiple-choice questions of different topics. However, this method has certain limitations, being the most concerning, the poor correlation with the humans. An alternative approach, is to have humans evaluate the LLMs. This poses scalability issues as there is a large and growing number of models to evaluate making it impractical (and costly) to run traditional studies based on recruiting a number of evaluators and having them rank the responses of the models. An alternative approach is the use of public arenas, such as the popular LM arena, on which any user can freely evaluate models on any question and rank the responses of two models. The results are then elaborated into a model ranking. An increasingly important aspect of LLMs is their energy consumption and, therefore, evaluating how energy awareness influences the decisions of humans in selecting a model is of interest. In this paper, we present GEA, the Generative Energy Arena, an arena that incorporates information on the energy consumption of the model in the evaluation process. Preliminary results obtained with GEA are also presented, showing that for most questions, when users are aware of the energy consumption, they favor smaller and more energy efficient models. This suggests that for most user interactions, the extra cost and energy incurred by the more complex and top-performing models do not provide an increase in the perceived quality of the responses that justifies their use.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-21", "arxiv_id": "2507.13984", "title": "CSD-VAR: Content-Style Decomposition in Visual Autoregressive Models", "authors": [ "Quang-Binh Nguyen", "Minh Luu", "Quang Nguyen", "Anh Tran", "Khoi Nguyen" ], "github": "", "project_page": "", "abstract": "Disentangling content and style from a single image, known as content-style decomposition (CSD), enables recontextualization of extracted content and stylization of extracted styles, offering greater creative flexibility in visual synthesis. While recent personalization methods have explored the decomposition of explicit content style, they remain tailored for diffusion models. Meanwhile, Visual Autoregressive Modeling (VAR) has emerged as a promising alternative with a next-scale prediction paradigm, achieving performance comparable to that of diffusion models. In this paper, we explore VAR as a generative framework for CSD, leveraging its scale-wise generation process for improved disentanglement. To this end, we propose CSD-VAR, a novel method that introduces three key innovations: (1) a scale-aware alternating optimization strategy that aligns content and style representation with their respective scales to enhance separation, (2) an SVD-based rectification method to mitigate content leakage into style representations, and (3) an Augmented Key-Value (K-V) memory enhancing content identity preservation. To benchmark this task, we introduce CSD-100, a dataset specifically designed for content-style decomposition, featuring diverse subjects rendered in various artistic styles. Experiments demonstrate that CSD-VAR outperforms prior approaches, achieving superior content preservation and stylization fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-21", "arxiv_id": "2507.13563", "title": "A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges in Russian Speech Generative Models", "authors": [ "Kirill Borodin", "Nikita Vasiliev", "Vasiliy Kudryavtsev", "Maxim Maslov", "Mikhail Gorodnichev", "Oleg Rogov", "Grach Mkrtchian" ], "github": "https://github.com/salute-developers/GigaAM", "project_page": "", "abstract": "Russian speech synthesis presents distinctive challenges, including vowel reduction, consonant devoicing, variable stress patterns, homograph ambiguity, and unnatural intonation. This paper introduces Balalaika, a novel dataset comprising more than 2,000 hours of studio-quality Russian speech with comprehensive textual annotations, including punctuation and stress markings. Experimental results show that models trained on Balalaika significantly outperform those trained on existing datasets in both speech synthesis and enhancement tasks. We detail the dataset construction pipeline, annotation methodology, and results of comparative evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/salute-developers/GigaAM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-21", "arxiv_id": "2507.12455", "title": "Mitigating Object Hallucinations via Sentence-Level Early Intervention", "authors": [ "Shangpin Peng", "Senqiao Yang", "Li Jiang", "Zhuotao Tian" ], "github": "https://github.com/pspdada/SENTINEL", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have revolutionized cross-modal understanding but continue to struggle with hallucinations - fabricated content contradicting visual inputs. Existing hallucination mitigation methods either incur prohibitive computational costs or introduce distribution mismatches between training data and model outputs. We identify a critical insight: hallucinations predominantly emerge at the early stages of text generation and propagate through subsequent outputs. To address this, we propose **SENTINEL** (**S**entence-level **E**arly i**N**tervention **T**hrough **IN**-domain pr**E**ference **L**earning), a framework that eliminates dependency on human annotations. Specifically, we first bootstrap high-quality in-domain preference pairs by iteratively sampling model outputs, validating object existence through cross-checking with two open-vocabulary detectors, and classifying sentences into hallucinated/non-hallucinated categories. Subsequently, we use context-coherent positive samples and hallucinated negative samples to build context-aware preference data iteratively. Finally, we train models using a context-aware preference loss (C-DPO) that emphasizes discriminative learning at the sentence level where hallucinations initially manifest. Experimental results show that SENTINEL can reduce hallucinations by over 90\\% compared to the original model and outperforms the previous state-of-the-art method on both hallucination benchmarks and general capabilities benchmarks, demonstrating its superiority and generalization ability. The models, datasets, and code are available at https://github.com/pspdada/SENTINEL.", "github_manual": "", "github_regex": "https://github.com/pspdada/SENTINEL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-21", "arxiv_id": "2507.13158", "title": "Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities", "authors": [ "Hao Sun", "Mihaela van der Schaar" ], "github": "", "project_page": "", "abstract": "In the era of Large Language Models (LLMs), alignment has emerged as a fundamental yet challenging problem in the pursuit of more reliable, controllable, and capable machine intelligence. The recent success of reasoning models and conversational AI systems has underscored the critical role of reinforcement learning (RL) in enhancing these systems, driving increased research interest at the intersection of RL and LLM alignment. This paper provides a comprehensive review of recent advances in LLM alignment through the lens of inverse reinforcement learning (IRL), emphasizing the distinctions between RL techniques employed in LLM alignment and those in conventional RL tasks. In particular, we highlight the necessity of constructing neural reward models from human data and discuss the formal and practical implications of this paradigm shift. We begin by introducing fundamental concepts in RL to provide a foundation for readers unfamiliar with the field. We then examine recent advances in this research agenda, discussing key challenges and opportunities in conducting IRL for LLM alignment. Beyond methodological considerations, we explore practical aspects, including datasets, benchmarks, evaluation metrics, infrastructure, and computationally efficient training and inference techniques. Finally, we draw insights from the literature on sparse-reward RL to identify open questions and potential research directions. By synthesizing findings from diverse studies, we aim to provide a structured and critical overview of the field, highlight unresolved challenges, and outline promising future directions for improving LLM alignment through RL and IRL techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-21", "arxiv_id": "2507.14129", "title": "OpenBEATs: A Fully Open-Source General-Purpose Audio Encoder", "authors": [ "Shikhar Bharadwaj", "Samuele Cornell", "Kwanghee Choi", "Satoru Fukayama", "Hye-jin Shim", "Soham Deshmukh", "Shinji Watanabe" ], "github": "", "project_page": "", "abstract": "Masked token prediction has emerged as a powerful pre-training objective across language, vision, and speech, offering the potential to unify these diverse modalities through a single pre-training task. However, its application for general audio understanding remains underexplored, with BEATs being the only notable example. BEATs has seen limited modifications due to the absence of open-source pre-training code. Furthermore, BEATs was trained only on AudioSet, restricting its broader downstream applicability. To address these gaps, we present OpenBEATs, an open-source framework that extends BEATs via multi-domain audio pre-training. We conduct comprehensive evaluations across six types of tasks, twenty five datasets, and three audio domains, including audio reasoning tasks such as audio question answering, entailment, and captioning. OpenBEATs achieves state-of-the-art performance on six bioacoustics datasets, two environmental sound datasets and five reasoning datasets, performing better than models exceeding a billion parameters at one-fourth their parameter size. These results demonstrate the effectiveness of multi-domain datasets and masked token prediction task to learn general-purpose audio representations. To promote further research and reproducibility, we release all pre-training and evaluation code, pretrained and fine-tuned checkpoints, and training logs at https://shikhar-s.github.io/OpenBEATs", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15061", "title": "WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization", "authors": [ "Zhengwei Tao", "Jialong Wu", "Wenbiao Yin", "Junkai Zhang", "Baixuan Li", "Haiyang Shen", "Kuan Li", "Liwen Zhang", "Xinyu Wang", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Jingren Zhou" ], "github": "https://github.com/Sigificant-Gravitas/Auto-GPT", "project_page": "", "abstract": "The advent of Large Language Model (LLM)-powered agents has revolutionized artificial intelligence by enabling solutions to complex, open-ended tasks through web-based information-seeking (IS) capabilities. The scarcity of high-quality training data has limited the development of IS agents. Existing approaches typically adopt an information-driven paradigm that first collects web data and then generates questions based on the retrieval. However, this may lead to inconsistency between information structure and reasoning structure, question and answer. To mitigate, we propose a formalization-driven IS data synthesis framework WebShaper to construct a dataset. WebShaper systematically formalizes IS tasks through set theory. Central to the formalization is the concept of Knowledge Projections (KP), which enables precise control over reasoning structure by KP operation compositions. During synthesis, we begin by creating seed tasks, then use a multi-step expansion process. At each step, an agentic Expander expands the current formal question more complex with retrieval and validation tools based on our formalization. We train our model on the synthesized dataset. Experiment results demonstrate that WebShaper achieves state-of-the-art performance among open-sourced IS agents on GAIA and WebWalkerQA benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Sigificant-Gravitas/Auto-GPT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15846", "title": "GUI-G^2: Gaussian Reward Modeling for GUI Grounding", "authors": [ "Fei Tang", "Zhangxuan Gu", "Zhengxi Lu", "Xuyang Liu", "Shuheng Shen", "Changhua Meng", "Wen Wang", "Wenqi Zhang", "Yongliang Shen", "Weiming Lu", "Jun Xiao", "Yueting Zhuang" ], "github": "", "project_page": "", "abstract": "Graphical User Interface (GUI) grounding maps natural language instructions to precise interface locations for autonomous interaction. Current reinforcement learning approaches use binary rewards that treat elements as hit-or-miss targets, creating sparse signals that ignore the continuous nature of spatial interactions. Motivated by human clicking behavior that naturally forms Gaussian distributions centered on target elements, we introduce GUI Gaussian Grounding Rewards (GUI-G^2), a principled reward framework that models GUI elements as continuous Gaussian distributions across the interface plane. GUI-G^2 incorporates two synergistic mechanisms: Gaussian point rewards model precise localization through exponentially decaying distributions centered on element centroids, while coverage rewards assess spatial alignment by measuring the overlap between predicted Gaussian distributions and target regions. To handle diverse element scales, we develop an adaptive variance mechanism that calibrates reward distributions based on element dimensions. This framework transforms GUI grounding from sparse binary classification to dense continuous optimization, where Gaussian distributions generate rich gradient signals that guide models toward optimal interaction positions. Extensive experiments across ScreenSpot, ScreenSpot-v2, and ScreenSpot-Pro benchmarks demonstrate that GUI-G^2, substantially outperforms state-of-the-art method UI-TARS-72B, with the most significant improvement of 24.7% on ScreenSpot-Pro. Our analysis reveals that continuous modeling provides superior robustness to interface variations and enhanced generalization to unseen layouts, establishing a new paradigm for spatial reasoning in GUI interaction tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15778", "title": "Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR", "authors": [ "Jiakang Wang", "Runze Liu", "Fuzheng Zhang", "Xiu Li", "Guorui Zhou" ], "github": "https://github.com/wizard-III/ArcherCodeR", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective post-training method for improving the reasoning abilities of Large Language Models (LLMs), mainly by shaping higher-order behaviors such as reflection and planning. However, previous RLVR algorithms often apply uniform training signals to all tokens, without considering the different roles of low-entropy knowledge-related tokens and high-entropy reasoning-related tokens. Some recent methods try to separate these token types by gradient masking or asynchronous updates, but these approaches may break semantic dependencies in the model output and hinder effective learning. In this work, we propose Archer, an entropy-aware RLVR approach with dual-token constraints and synchronous updates. Specifically, our method applies weaker KL regularization and higher clipping thresholds to reasoning tokens to encourage exploration, while using stronger constraints on knowledge tokens to maintain factual knowledge. Experimental results on several mathematical reasoning and code generation benchmarks show that our approach significantly outperforms previous RLVR methods, reaching or exceeding state-of-the-art performance among models of comparable size. The code is available at https://github.com/wizard-III/ArcherCodeR.", "github_manual": "", "github_regex": "https://github.com/wizard-III/ArcherCodeR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.14683", "title": "MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via Context-Aware Multi-Stage Policy Optimization", "authors": [ "Xingxuan Li", "Yao Xiao", "Dianwen Ng", "Hai Ye", "Yue Deng", "Xiang Lin", "Bin Wang", "Zhanfeng Mo", "Chong Zhang", "Yueyi Zhang", "Zonglin Yang", "Ruilin Li", "Lei Lei", "Shihao Xu", "Han Zhao", "Weiling Chen", "Feng Ji", "Lidong Bing" ], "github": "", "project_page": "", "abstract": "Large language models have recently evolved from fluent text generation to advanced reasoning across diverse domains, giving rise to reasoning language models. Among these domains, mathematical reasoning serves as a representative benchmark as it requires precise multi-step logic and abstract reasoning, which can be generalized to other tasks. While closed-source RLMs such as GPT-o3 demonstrate impressive reasoning capabilities, their proprietary nature limits transparency and reproducibility. Although many open-source projects aim to close this gap, most of them lack sufficient openness by omitting critical resources such as datasets and detailed training configurations, which hinders reproducibility. To contribute toward greater transparency in RLM development, we introduce the MiroMind-M1 series, a set of fully open-source RLMs built on the Qwen-2.5 backbone that match or exceed the performance of existing open-source RLMs. Specifically, our models are trained in two stages: SFT on a carefully curated corpus of 719K math-reasoning problems with verified CoT trajectories, followed by RLVR on 62K challenging and verifiable problems. To enhance the robustness and efficiency of the RLVR process, we introduce Context-Aware Multi-Stage Policy Optimization, an algorithm that integrates length-progressive training with an adaptive repetition penalty to encourage context-aware RL training. Our model achieves state-of-the-art or competitive performance and superior token efficiency among Qwen-2.5-based open-source 7B and 32B models on the AIME24, AIME25, and MATH benchmarks. To facilitate reproducibility, we release the complete stack: models (MiroMind-M1-SFT-7B, MiroMind-M1-RL-7B, MiroMind-M1-RL-32B); datasets (MiroMind-M1-SFT-719K, MiroMind-M1-RL-62K); and all training and evaluation configurations. We hope these resources will support further research and foster community advancement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.11539", "title": "Streaming 4D Visual Geometry Transformer", "authors": [ "Dong Zhuo", "Wenzhao Zheng", "Jiahe Guo", "Yuqi Wu", "Jie Zhou", "Jiwen Lu" ], "github": "https://github.com/wzzheng/StreamVGGT", "project_page": "", "abstract": "Perceiving and reconstructing 4D spatial-temporal geometry from videos is a fundamental yet challenging computer vision task. To facilitate interactive and real-time applications, we propose a streaming 4D visual geometry transformer that shares a similar philosophy with autoregressive large language models. We explore a simple and efficient design and employ a causal transformer architecture to process the input sequence in an online manner. We use temporal causal attention and cache the historical keys and values as implicit memory to enable efficient streaming long-term 4D reconstruction. This design can handle real-time 4D reconstruction by incrementally integrating historical information while maintaining high-quality spatial consistency. For efficient training, we propose to distill knowledge from the dense bidirectional visual geometry grounded transformer (VGGT) to our causal model. For inference, our model supports the migration of optimized efficient attention operator (e.g., FlashAttention) from the field of large language models. Extensive experiments on various 4D geometry perception benchmarks demonstrate that our model increases the inference speed in online scenarios while maintaining competitive performance, paving the way for scalable and interactive 4D vision systems. Code is available at: https://github.com/wzzheng/StreamVGGT.", "github_manual": "", "github_regex": "https://github.com/wzzheng/StreamVGGT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15815", "title": "LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra", "authors": [ "Seth Karten", "Wenzhe Li", "Zihan Ding", "Samuel Kleiner", "Yu Bai", "Chi Jin" ], "github": "", "project_page": "", "abstract": "We present the LLM Economist, a novel framework that uses agent-based modeling to design and assess economic policies in strategic environments with hierarchical decision-making. At the lower level, bounded rational worker agents -- instantiated as persona-conditioned prompts sampled from U.S. Census-calibrated income and demographic statistics -- choose labor supply to maximize text-based utility functions learned in-context. At the upper level, a planner agent employs in-context reinforcement learning to propose piecewise-linear marginal tax schedules anchored to the current U.S. federal brackets. This construction endows economic simulacra with three capabilities requisite for credible fiscal experimentation: (i) optimization of heterogeneous utilities, (ii) principled generation of large, demographically realistic agent populations, and (iii) mechanism design -- the ultimate nudging problem -- expressed entirely in natural language. Experiments with populations of up to one hundred interacting agents show that the planner converges near Stackelberg equilibria that improve aggregate social welfare relative to Saez solutions, while a periodic, persona-level voting procedure furthers these gains under decentralized governance. These results demonstrate that large language model-based agents can jointly model, simulate, and govern complex economic systems, providing a tractable test bed for policy evaluation at the societal scale to help build better civilizations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15375", "title": "STITCH: Simultaneous Thinking and Talking with Chunked Reasoning for Spoken Language Models", "authors": [ "Cheng-Han Chiang", "Xiaofei Wang", "Linjie Li", "Chung-Ching Lin", "Kevin Lin", "Shujie Liu", "Zhendong Wang", "Zhengyuan Yang", "Hung-yi Lee", "Lijuan Wang" ], "github": "", "project_page": "", "abstract": "Spoken Language Models (SLMs) are designed to take speech inputs and produce spoken responses. However, current SLMs lack the ability to perform an internal, unspoken thinking process before responding. In contrast, humans typically engage in complex mental reasoning internally, enabling them to communicate ideas clearly and concisely. Thus, integrating an unspoken thought process into SLMs is highly desirable. While naively generating a complete chain-of-thought (CoT) reasoning before starting to talk can enable thinking for SLMs, this induces additional latency for the speech response, as the CoT reasoning can be arbitrarily long. To solve this issue, we propose Stitch, a novel generation method that alternates between the generation of unspoken reasoning chunks and spoken response chunks. Since the audio duration of a chunk of spoken response is much longer than the time to generate the tokens in a chunk of spoken response, we use the remaining free time to generate the unspoken reasoning tokens. When a chunk of audio is played to the user, the model continues to generate the next unspoken reasoning chunk, achieving simultaneous thinking and talking. Remarkably, Stitch matches the latency of baselines that cannot generate unspoken CoT by design while outperforming those baselines by 15% on math reasoning datasets; Stitch also performs equally well on non-reasoning datasets as those baseline models. Some animations and demonstrations are on the project page: https://d223302.github.io/STITCH.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15493", "title": "GR-3 Technical Report", "authors": [ "Chilam Cheang", "Sijin Chen", "Zhongren Cui", "Yingdong Hu", "Liqun Huang", "Tao Kong", "Hang Li", "Yifeng Li", "Yuxiao Liu", "Xiao Ma", "Hao Niu", "Wenxuan Ou", "Wanli Peng", "Zeyu Ren", "Haixin Shi", "Jiawen Tian", "Hongtao Wu", "Xin Xiao", "Yuyang Xiao", "Jiafeng Xu", "Yichu Yang" ], "github": "https://github.com/Physical-Intelligence/openpi", "project_page": "", "abstract": "We report our recent progress towards building generalist robot policies, the development of GR-3. GR-3 is a large-scale vision-language-action (VLA) model. It showcases exceptional capabilities in generalizing to novel objects, environments, and instructions involving abstract concepts. Furthermore, it can be efficiently fine-tuned with minimal human trajectory data, enabling rapid and cost-effective adaptation to new settings. GR-3 also excels in handling long-horizon and dexterous tasks, including those requiring bi-manual manipulation and mobile movement, showcasing robust and reliable performance. These capabilities are achieved through a multi-faceted training recipe that includes co-training with web-scale vision-language data, efficient fine-tuning from human trajectory data collected via VR devices, and effective imitation learning with robot trajectory data. In addition, we introduce ByteMini, a versatile bi-manual mobile robot designed with exceptional flexibility and reliability, capable of accomplishing a wide range of tasks when integrated with GR-3. Through extensive real-world experiments, we show GR-3 surpasses the state-of-the-art baseline method, pi_0, on a wide variety of challenging tasks. We hope GR-3 can serve as a step towards building generalist robots capable of assisting humans in daily life.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Physical-Intelligence/openpi", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15028", "title": "Towards Video Thinking Test: A Holistic Benchmark for Advanced Video Reasoning and Understanding", "authors": [ "Yuanhan Zhang", "Yunice Chew", "Yuhao Dong", "Aria Leo", "Bo Hu", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Human intelligence requires correctness and robustness, with the former being foundational for the latter. In video understanding, correctness ensures the accurate interpretation of visual content, and robustness maintains consistent performance in challenging conditions. Despite advances in video large language models (video LLMs), existing benchmarks inadequately reflect the gap between these models and human intelligence in maintaining correctness and robustness in video interpretation. We introduce the Video Thinking Test (Video-TT), to assess if video LLMs can interpret real-world videos as effectively as humans. Video-TT reflects genuine gaps in understanding complex visual narratives, and evaluates robustness against natural adversarial questions. Video-TT comprises 1,000 YouTube Shorts videos, each with one open-ended question and four adversarial questions that probe visual and narrative complexity. Our evaluation shows a significant gap between video LLMs and human performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.11061", "title": "Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with Regularized Score Distillation Sampling", "authors": [ "Hayeon Kim", "Ji Ha Jang", "Se Young Chun" ], "github": "", "project_page": "", "abstract": "Recent advances in 3D neural representations and instance-level editing models have enabled the efficient creation of high-quality 3D content. However, achieving precise local 3D edits remains challenging, especially for Gaussian Splatting, due to inconsistent multi-view 2D part segmentations and inherently ambiguous nature of Score Distillation Sampling (SDS) loss. To address these limitations, we propose RoMaP, a novel local 3D Gaussian editing framework that enables precise and drastic part-level modifications. First, we introduce a robust 3D mask generation module with our 3D-Geometry Aware Label Prediction (3D-GALP), which uses spherical harmonics (SH) coefficients to model view-dependent label variations and soft-label property, yielding accurate and consistent part segmentations across viewpoints. Second, we propose a regularized SDS loss that combines the standard SDS loss with additional regularizers. In particular, an L1 anchor loss is introduced via our Scheduled Latent Mixing and Part (SLaMP) editing method, which generates high-quality part-edited 2D images and confines modifications only to the target region while preserving contextual coherence. Additional regularizers, such as Gaussian prior removal, further improve flexibility by allowing changes beyond the existing context, and robust 3D masking prevents unintended edits. Experimental results demonstrate that our RoMaP achieves state-of-the-art local 3D editing on both reconstructed and generated Gaussian scenes and objects qualitatively and quantitatively, making it possible for more robust and flexible part-level 3D Gaussian editing. Code is available at https://janeyeon.github.io/romap.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15856", "title": "Latent Denoising Makes Good Visual Tokenizers", "authors": [ "Jiawei Yang", "Tianhong Li", "Lijie Fan", "Yonglong Tian", "Yue Wang" ], "github": "https://github.com/Jiawei-Yang/DeTok", "project_page": "", "abstract": "Despite their fundamental role, it remains unclear what properties could make visual tokenizers more effective for generative modeling. We observe that modern generative models share a conceptually similar training objective -- reconstructing clean signals from corrupted inputs such as Gaussian noise or masking -- a process we term denoising. Motivated by this insight, we propose aligning tokenizer embeddings directly with the downstream denoising objective, encouraging latent embeddings to be more easily reconstructed even when heavily corrupted. To achieve this, we introduce the Latent Denoising Tokenizer (l-DeTok), a simple yet effective tokenizer trained to reconstruct clean images from latent embeddings corrupted by interpolative noise and random masking. Extensive experiments on ImageNet 256x256 demonstrate that our tokenizer consistently outperforms standard tokenizers across six representative generative models. Our findings highlight denoising as a fundamental design principle for tokenizer development, and we hope it could motivate new perspectives for future tokenizer design.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Jiawei-Yang/DeTok", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.14843", "title": "The Invisible Leash: Why RLVR May Not Escape Its Origin", "authors": [ "Fang Wu", "Weihao Xuan", "Ximing Lu", "Zaid Harchaoui", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "Recent advances in large reasoning models highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising method for enhancing AI's capabilities, particularly in solving complex logical tasks. However, it remains unclear whether RLVR truly expands a model's reasoning boundary or merely amplifies high-reward outputs that the base model already knows for improved precision. This study presents a theoretical and empirical investigation that provides fresh insights into the potential limits of RLVR. First, we offer a new theoretical perspective that RLVR is constrained by the base model's support-unable to sample solutions with zero initial probability-and operates as a conservative reweighting mechanism that may restrict the discovery of entirely original solutions. We also identify an entropy-reward tradeoff: while RLVR reliably enhances precision, it may progressively narrow exploration and potentially overlook correct yet underrepresented solutions. Extensive empirical experiments validate that while RLVR consistently improves pass@1, the shrinkage of empirical support generally outweighs the expansion of empirical support under larger sampling budgets, failing to recover correct answers that were previously accessible to the base model. Interestingly, we also observe that while RLVR sometimes increases token-level entropy, resulting in greater uncertainty at each generation step, answer-level entropy declines, indicating that these seemingly more uncertain paths ultimately converge onto a smaller set of distinct answers. Taken together, these findings reveal potential limits of RLVR in extending reasoning horizons. Breaking this invisible leash may require future algorithmic innovations such as explicit exploration mechanisms or hybrid strategies that seed probability mass into underrepresented solution regions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15852", "title": "SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction", "authors": [ "Zhixiong Zhang", "Shuangrui Ding", "Xiaoyi Dong", "Songxin He", "Jianfan Lin", "Junsong Tang", "Yuhang Zang", "Yuhang Cao", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/OpenIXCLab/SeC", "project_page": "", "abstract": "Video Object Segmentation (VOS) is a core task in computer vision, requiring models to track and segment target objects across video frames. Despite notable advances with recent efforts, current techniques still lag behind human capabilities in handling drastic visual variations, occlusions, and complex scene changes. This limitation arises from their reliance on appearance matching, neglecting the human-like conceptual understanding of objects that enables robust identification across temporal dynamics. Motivated by this gap, we propose Segment Concept (SeC), a concept-driven segmentation framework that shifts from conventional feature matching to the progressive construction and utilization of high-level, object-centric representations. SeC employs Large Vision-Language Models (LVLMs) to integrate visual cues across diverse frames, constructing robust conceptual priors. During inference, SeC forms a comprehensive semantic representation of the target based on processed frames, realizing robust segmentation of follow-up frames. Furthermore, SeC adaptively balances LVLM-based semantic reasoning with enhanced feature matching, dynamically adjusting computational efforts based on scene complexity. To rigorously assess VOS methods in scenarios demanding high-level conceptual reasoning and robust semantic understanding, we introduce the Semantic Complex Scenarios Video Object Segmentation benchmark (SeCVOS). SeCVOS comprises 160 manually annotated multi-scenario videos designed to challenge models with substantial appearance variations and dynamic scene transformations. In particular, SeC achieves an 11.8-point improvement over SAM 2.1 on SeCVOS, establishing a new state-of-the-art in concept-aware video object segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenIXCLab/SeC", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15629", "title": "Gaussian Splatting with Discretized SDF for Relightable Assets", "authors": [ "Zuo-Liang Zhu", "Jian Yang", "Beibei Wang" ], "github": "https://github.com/NK-CS-ZZL/DiscretizedSDF", "project_page": "", "abstract": "3D Gaussian splatting (3DGS) has shown its detailed expressive ability and highly efficient rendering speed in the novel view synthesis (NVS) task. The application to inverse rendering still faces several challenges, as the discrete nature of Gaussian primitives makes it difficult to apply geometry constraints. Recent works introduce the signed distance field (SDF) as an extra continuous representation to regularize the geometry defined by Gaussian primitives. It improves the decomposition quality, at the cost of increasing memory usage and complicating training. Unlike these works, we introduce a discretized SDF to represent the continuous SDF in a discrete manner by encoding it within each Gaussian using a sampled value. This approach allows us to link the SDF with the Gaussian opacity through an SDF-to-opacity transformation, enabling rendering the SDF via splatting and avoiding the computational cost of ray marching.The key challenge is to regularize the discrete samples to be consistent with the underlying SDF, as the discrete representation can hardly apply the gradient-based constraints (\\eg Eikonal loss). For this, we project Gaussians onto the zero-level set of SDF and enforce alignment with the surface from splatting, namely a projection-based consistency loss. Thanks to the discretized SDF, our method achieves higher relighting quality, while requiring no extra memory beyond GS and avoiding complex manually designed optimization. The experiments reveal that our method outperforms existing Gaussian-based inverse rendering methods. Our code is available at https://github.com/NK-CS-ZZL/DiscretizedSDF.", "github_manual": "", "github_regex": "https://github.com/NK-CS-ZZL/DiscretizedSDF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15597", "title": "Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos", "authors": [ "Hao Luo", "Yicheng Feng", "Wanpeng Zhang", "Sipeng Zheng", "Ye Wang", "Haoqi Yuan", "Jiazheng Liu", "Chaoyi Xu", "Qin Jin", "Zongqing Lu" ], "github": "", "project_page": "", "abstract": "We introduce Being-H0, a dexterous Vision-Language-Action model (VLA) trained on large-scale human videos. Existing VLAs struggle with complex manipulation tasks requiring high dexterity and generalize poorly to novel scenarios and tasks, primarily due to their reliance on synthetic data with significant sim-to-real gaps or teleoperated demonstrations lacking scale and diversity. To address this data bottleneck, we propose leveraging human hands as a foundation manipulator, capitalizing on the rich dexterity and scalability present in web data. Our approach centers on physical instruction tuning, a novel training paradigm that combines large-scale VLA pretraining from human videos, physical space alignment for 3D reasoning, and post-training adaptation for robotic tasks. Additionally, we introduce a part-level motion tokenization method which achieves millimeter-level reconstruction accuracy to model precise hand trajectories for action learning. To support our proposed paradigm, we further develop a comprehensive data curation pipeline that integrates heterogeneous sources -- including motion capture, VR, and RGB-only videos -- into a large-scale dataset with millions of motion-based instructional instances. We empirically show the excellence of Being-H0 in hand motion generation and instruction following, and it also scales well with model and data sizes. Importantly, we observe the expected gains of Being-H0 in real-world robotic manipulation as physical instruction tuning is applied. More details are available at https://beingbeyond.github.io/Being-H0.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15640", "title": "Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training", "authors": [ "Kailai Yang", "Xiao Liu", "Lei Ji", "Hao Li", "Yeyun Gong", "Peng Cheng", "Mao Yang" ], "github": "https://github.com/evalplus/evalplus", "project_page": "", "abstract": "Continual pre-training on small-scale task-specific data is an effective method for improving large language models in new target fields, yet it risks catastrophic forgetting of their original capabilities. A common solution is to re-weight training data mixtures from source and target fields on a domain space to achieve balanced performance. Previous domain reweighting strategies rely on manual designation with certain heuristics based on human intuition or empirical results. In this work, we prove that more general heuristics can be parameterized by proposing Data Mixing Agent, the first model-based, end-to-end framework that learns to re-weight domains. The agent learns generalizable heuristics through reinforcement learning on large quantities of data mixing trajectories with corresponding feedback from an evaluation environment. Experiments in continual pre-training on math reasoning show that Data Mixing Agent outperforms strong baselines in achieving balanced performance across source and target field benchmarks. Furthermore, it generalizes well across unseen source fields, target models, and domain spaces without retraining. Direct application to the code generation field also indicates its adaptability across target domains. Further analysis showcases the agents' well-aligned heuristics with human intuitions and their efficiency in achieving superior model performance with less source-field data.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/evalplus/evalplus", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.13428", "title": "\"PhyWorldBench\": A Comprehensive Evaluation of Physical Realism in Text-to-Video Models", "authors": [ "Jing Gu", "Xian Liu", "Yu Zeng", "Ashwin Nagarajan", "Fangrui Zhu", "Daniel Hong", "Yue Fan", "Qianqi Yan", "Kaiwen Zhou", "Ming-Yu Liu", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "Video generation models have achieved remarkable progress in creating high-quality, photorealistic content. However, their ability to accurately simulate physical phenomena remains a critical and unresolved challenge. This paper presents PhyWorldBench, a comprehensive benchmark designed to evaluate video generation models based on their adherence to the laws of physics. The benchmark covers multiple levels of physical phenomena, ranging from fundamental principles like object motion and energy conservation to more complex scenarios involving rigid body interactions and human or animal motion. Additionally, we introduce a novel \"\"Anti-Physics\"\" category, where prompts intentionally violate real-world physics, enabling the assessment of whether models can follow such instructions while maintaining logical consistency. Besides large-scale human evaluation, we also design a simple yet effective method that could utilize current MLLM to evaluate the physics realism in a zero-shot fashion. We evaluate 12 state-of-the-art text-to-video generation models, including five open-source and five proprietary models, with a detailed comparison and analysis. we identify pivotal challenges models face in adhering to real-world physics. Through systematic testing of their outputs across 1,050 curated prompts-spanning fundamental, composite, and anti-physics scenarios-we identify pivotal challenges these models face in adhering to real-world physics. We then rigorously examine their performance on diverse physical phenomena with varying prompt types, deriving targeted recommendations for crafting prompts that enhance fidelity to physical principles.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.10935", "title": "GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised Cross-View Localization", "authors": [ "Shaowen Tong", "Zimin Xia", "Alexandre Alahi", "Xuming He", "Yujiao Shi" ], "github": "https://github.com/tongshw/GeoDistill", "project_page": "", "abstract": "Cross-view localization, the task of estimating a camera's 3-degrees-of-freedom (3-DoF) pose by aligning ground-level images with satellite images, is crucial for large-scale outdoor applications like autonomous navigation and augmented reality. Existing methods often rely on fully supervised learning, which requires costly ground-truth pose annotations. In this work, we propose GeoDistill, a Geometry guided weakly supervised self distillation framework that uses teacher-student learning with Field-of-View (FoV)-based masking to enhance local feature learning for robust cross-view localization. In GeoDistill, the teacher model localizes a panoramic image, while the student model predicts locations from a limited FoV counterpart created by FoV-based masking. By aligning the student's predictions with those of the teacher, the student focuses on key features like lane lines and ignores textureless regions, such as roads. This results in more accurate predictions and reduced uncertainty, regardless of whether the query images are panoramas or limited FoV images. Our experiments show that GeoDistill significantly improves localization performance across different frameworks. Additionally, we introduce a novel orientation estimation network that predicts relative orientation without requiring precise planar position ground truth. GeoDistill provides a scalable and efficient solution for real-world cross-view localization challenges. Code and model can be found at https://github.com/tongshw/GeoDistill.", "github_manual": "", "github_regex": "https://github.com/tongshw/GeoDistill", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.14119", "title": "NoHumansRequired: Autonomous High-Quality Image Editing Triplet Mining", "authors": [ "Maksim Kuprashevich", "Grigorii Alekseenko", "Irina Tolstykh", "Georgii Fedorov", "Bulat Suleimanov", "Vladimir Dokholyan", "Aleksandr Gordeev" ], "github": "", "project_page": "", "abstract": "Recent advances in generative modeling enable image editing assistants that follow natural language instructions without additional user input. Their supervised training requires millions of triplets: original image, instruction, edited image. Yet mining pixel-accurate examples is hard. Each edit must affect only prompt-specified regions, preserve stylistic coherence, respect physical plausibility, and retain visual appeal. The lack of robust automated edit-quality metrics hinders reliable automation at scale. We present an automated, modular pipeline that mines high-fidelity triplets across domains, resolutions, instruction complexities, and styles. Built on public generative models and running without human intervention, our system uses a task-tuned Gemini validator to score instruction adherence and aesthetics directly, removing any need for segmentation or grounding models. Inversion and compositional bootstrapping enlarge the mined set by approximately 2.2x, enabling large-scale high-fidelity training data. By automating the most repetitive annotation steps, the approach allows a new scale of training without human labeling effort. To democratize research in this resource-intensive area, we release NHR-Edit: an open dataset of 358k high-quality triplets. In the largest cross-dataset evaluation, it surpasses all public alternatives. We also release Bagel-NHR-Edit, an open-source fine-tuned Bagel model, which achieves state-of-the-art metrics in our experiments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.14417", "title": "Inverse Scaling in Test-Time Compute", "authors": [ "Aryo Pradipta Gema", "Alexander H\u00e4gele", "Runjin Chen", "Andy Arditi", "Jacob Goldman-Wetzler", "Kit Fraser-Taliente", "Henry Sleight", "Linda Petrini", "Julian Michael", "Beatrice Alex", "Pasquale Minervini", "Yanda Chen", "Joe Benton", "Ethan Perez" ], "github": "https://github.com/safety-research/inverse-scaling-ttp", "project_page": "", "abstract": "We construct evaluation tasks where extending the reasoning length of Large Reasoning Models (LRMs) deteriorates performance, exhibiting an inverse scaling relationship between test-time compute and accuracy. Our evaluation tasks span four categories: simple counting tasks with distractors, regression tasks with spurious features, deduction tasks with constraint tracking, and advanced AI risks. We identify five distinct failure modes when models reason for longer: 1) Claude models become increasingly distracted by irrelevant information; 2) OpenAI o-series models resist distractors but overfit to problem framings; 3) models shift from reasonable priors to spurious correlations; 4) all models show difficulties in maintaining focus on complex deductive tasks; and 5) extended reasoning may amplify concerning behaviors, with Claude Sonnet 4 showing increased expressions of self-preservation. These findings suggest that while test-time compute scaling remains promising for improving model capabilities, it may inadvertently reinforce problematic reasoning patterns. Our results demonstrate the importance of evaluating models across diverse reasoning lengths to identify and address these failure modes in LRMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/safety-research/inverse-scaling-ttp", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.14295", "title": "A Simple \"Try Again\" Can Elicit Multi-Turn LLM Reasoning", "authors": [ "Licheng Liu", "Zihan Wang", "Linjie Li", "Chenwei Xu", "Yiping Lu", "Han Liu", "Avirup Sil", "Manling Li" ], "github": "https://github.com/lichengliu03/unary-feedback", "project_page": "", "abstract": "Multi-turn problem solving is critical yet challenging for Large Reasoning Models (LRMs) to reflect on their reasoning and revise from feedback. Existing Reinforcement Learning (RL) methods train large reasoning models on a single-turn paradigm with verifiable rewards. However, we observe that models trained with existing RL paradigms often lose their ability to solve problems across multiple turns and struggle to revise answers based on contextual feedback, leading to repetitive responses. We ask: can LRMs learn to reflect their answers in a multi-turn context? In this work, we find that training models with multi-turn RL using only unary feedback (e.g., \"Let's try again\") after wrong answers can improve both single-turn performance and multi-turn reasoning. We introduce Unary Feedback as Observation (UFO) for reinforcement learning, which uses minimal yet common unary user feedback during iterative problem solving. It can be easily applied to existing single-turn RL training setups. Experimental results show that RL training with UFO keeps single-turn performance and improves multi-turn reasoning accuracy by up to 14%, enabling language models to better react to feedback in multi-turn problem solving. To further minimize the number of turns needed for a correct answer while encouraging diverse reasoning when mistakes occur, we design reward structures that guide models to produce careful and deliberate answers in each turn. Code: https://github.com/lichengliu03/unary-feedback", "github_manual": "", "github_regex": "https://github.com/lichengliu03/unary-feedback", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.14102", "title": "UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography", "authors": [ "Shravan Venkatraman", "Pavan Kumar S", "Rakesh Raj Madavan", "Chandrakala S" ], "github": "https://github.com/shravan-18/UGPL", "project_page": "", "abstract": "Accurate classification of computed tomography (CT) images is essential for diagnosis and treatment planning, but existing methods often struggle with the subtle and spatially diverse nature of pathological features. Current approaches typically process images uniformly, limiting their ability to detect localized abnormalities that require focused analysis. We introduce UGPL, an uncertainty-guided progressive learning framework that performs a global-to-local analysis by first identifying regions of diagnostic ambiguity and then conducting detailed examination of these critical areas. Our approach employs evidential deep learning to quantify predictive uncertainty, guiding the extraction of informative patches through a non-maximum suppression mechanism that maintains spatial diversity. This progressive refinement strategy, combined with an adaptive fusion mechanism, enables UGPL to integrate both contextual information and fine-grained details. Experiments across three CT datasets demonstrate that UGPL consistently outperforms state-of-the-art methods, achieving improvements of 3.29%, 2.46%, and 8.08% in accuracy for kidney abnormality, lung cancer, and COVID-19 detection, respectively. Our analysis shows that the uncertainty-guided component provides substantial benefits, with performance dramatically increasing when the full progressive learning pipeline is implemented. Our code is available at: https://github.com/shravan-18/UGPL", "github_manual": "", "github_regex": "https://github.com/shravan-18/UGPL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15550", "title": "PhysGym: Benchmarking LLMs in Interactive Physics Discovery with Controlled Priors", "authors": [ "Yimeng Chen", "Piotr Pi\u0229kos", "Mateusz Ostaszewski", "Firas Laakom", "J\u00fcrgen Schmidhuber" ], "github": "", "project_page": "", "abstract": "Evaluating the scientific discovery capabilities of large language model based agents, particularly how they cope with varying environmental complexity and utilize prior knowledge, requires specialized benchmarks currently lacking in the landscape. To address this gap, we introduce PhysGym, a novel benchmark suite and simulation platform for rigorously assessing LLM-based scientific reasoning in interactive physics environments. PhysGym's primary contribution lies in its sophisticated control over the level of prior knowledge provided to the agent. This allows researchers to dissect agent performance along axes including the complexity of the problem and the prior knowledge levels. The benchmark comprises a suite of interactive simulations, where agents must actively probe environments, gather data sequentially under constraints and formulate hypotheses about underlying physical laws. PhysGym provides standardized evaluation protocols and metrics for assessing hypothesis accuracy and model fidelity. We demonstrate the benchmark's utility by presenting results from baseline LLMs, showcasing its ability to differentiate capabilities based on varying priors and task complexity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.15728", "title": "TokensGen: Harnessing Condensed Tokens for Long Video Generation", "authors": [ "Wenqi Ouyang", "Zeqi Xiao", "Danni Yang", "Yifan Zhou", "Shuai Yang", "Lei Yang", "Jianlou Si", "Xingang Pan" ], "github": "", "project_page": "", "abstract": "Generating consistent long videos is a complex challenge: while diffusion-based generative models generate visually impressive short clips, extending them to longer durations often leads to memory bottlenecks and long-term inconsistency. In this paper, we propose TokensGen, a novel two-stage framework that leverages condensed tokens to address these issues. Our method decomposes long video generation into three core tasks: (1) inner-clip semantic control, (2) long-term consistency control, and (3) inter-clip smooth transition. First, we train To2V (Token-to-Video), a short video diffusion model guided by text and video tokens, with a Video Tokenizer that condenses short clips into semantically rich tokens. Second, we introduce T2To (Text-to-Token), a video token diffusion transformer that generates all tokens at once, ensuring global consistency across clips. Finally, during inference, an adaptive FIFO-Diffusion strategy seamlessly connects adjacent clips, reducing boundary artifacts and enhancing smooth transitions. Experimental results demonstrate that our approach significantly enhances long-term temporal and content coherence without incurring prohibitive computational overhead. By leveraging condensed tokens and pre-trained short video models, our method provides a scalable, modular solution for long video generation, opening new possibilities for storytelling, cinematic production, and immersive simulations. Please see our project page at https://vicky0522.github.io/tokensgen-webpage/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.12806", "title": "MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models", "authors": [ "Zhiwei Liu", "Jielin Qiu", "Shiyu Wang", "Jianguo Zhang", "Zuxin Liu", "Roshan Ram", "Haolin Chen", "Weiran Yao", "Huan Wang", "Shelby Heinecke", "Silvio Savarese", "Caiming Xiong" ], "github": "https://github.com/SalesforceAIResearch/MCPEval", "project_page": "", "abstract": "The rapid rise of Large Language Models (LLMs)-based intelligent agents underscores the need for robust, scalable evaluation frameworks. Existing methods rely on static benchmarks and labor-intensive data collection, limiting practical assessment. We introduce \\oursystemname, an open-source Model Context Protocol (MCP)-based framework that automates end-to-end task generation and deep evaluation of LLM agents across diverse domains. MCPEval standardizes metrics, seamlessly integrates with native agent tools, and eliminates manual effort in building evaluation pipelines. Empirical results across five real-world domains show its effectiveness in revealing nuanced, domain-specific performance. We publicly release MCPEval https://github.com/SalesforceAIResearch/MCPEval to promote reproducible and standardized LLM agent evaluation.", "github_manual": "", "github_regex": "https://github.com/SalesforceAIResearch/MCPEval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.12674", "title": "ParaStudent: Generating and Evaluating Realistic Student Code by Teaching LLMs to Struggle", "authors": [ "Mihran Miroyan", "Rose Niousha", "Joseph E. Gonzalez", "Gireeja Ranade", "Narges Norouzi" ], "github": "https://github.com/mmiroyan/ParaStudent", "project_page": "", "abstract": "Large Language Models (LLMs) have shown strong performance on programming tasks, but can they generate student-like code like real students - imperfect, iterative, and stylistically diverse? We present ParaStudent, a systematic study of LLM-based \"student-like\" code generation in an introductory programming course setting. Using a dataset of timestamped student submissions across multiple semesters, we design low- and high-resolution experiments to model student progress and evaluate code outputs along semantic, functional, and stylistic dimensions. Our results show that fine-tuning significantly improves alignment with real student trajectories and captures error patterns, incremental improvements, and stylistic variations more faithfully. This study shows that modeling realistic student code requires capturing learning dynamics through context-aware generation, temporal modeling, and multi-dimensional evaluation. Code for experiments and evaluation is available at https://github.com/mmiroyan/ParaStudent.", "github_manual": "", "github_regex": "https://github.com/mmiroyan/ParaStudent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-22", "arxiv_id": "2507.12549", "title": "The Serial Scaling Hypothesis", "authors": [ "Yuxi Liu", "Konpat Preechakul", "Kananart Kuwaranancharoen", "Yutong Bai" ], "github": "", "project_page": "", "abstract": "While machine learning has advanced through massive parallelization, we identify a critical blind spot: some problems are fundamentally sequential. These \"inherently serial\" problems-from mathematical reasoning to physical simulations to sequential decision-making-require dependent computational steps that cannot be parallelized. Drawing from complexity theory, we formalize this distinction and demonstrate that current parallel-centric architectures face fundamental limitations on such tasks. We argue that recognizing the serial nature of computation holds profound implications on machine learning, model design, hardware development. As AI tackles increasingly complex reasoning, deliberately scaling serial computation-not just parallel computation-is essential for continued progress.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.16813", "title": "HOComp: Interaction-Aware Human-Object Composition", "authors": [ "Dong Liang", "Jinyuan Jia", "Yuhao Liu", "Rynson W. H. Lau" ], "github": "https://github.com/dliang293/HOComp", "project_page": "", "abstract": "While existing image-guided composition methods may help insert a foreground object onto a user-specified region of a background image, achieving natural blending inside the region with the rest of the image unchanged, we observe that these existing methods often struggle in synthesizing seamless interaction-aware compositions when the task involves human-object interactions. In this paper, we first propose HOComp, a novel approach for compositing a foreground object onto a human-centric background image, while ensuring harmonious interactions between the foreground object and the background person and their consistent appearances. Our approach includes two key designs: (1) MLLMs-driven Region-based Pose Guidance (MRPG), which utilizes MLLMs to identify the interaction region as well as the interaction type (e.g., holding and lefting) to provide coarse-to-fine constraints to the generated pose for the interaction while incorporating human pose landmarks to track action variations and enforcing fine-grained pose constraints; and (2) Detail-Consistent Appearance Preservation (DCAP), which unifies a shape-aware attention modulation mechanism, a multi-view appearance loss, and a background consistency loss to ensure consistent shapes/textures of the foreground and faithful reproduction of the background human. We then propose the first dataset, named Interaction-aware Human-Object Composition (IHOC), for the task. Experimental results on our dataset show that HOComp effectively generates harmonious human-object interactions with consistent appearances, and outperforms relevant methods qualitatively and quantitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dliang293/HOComp", "github_api_added_by": "auto" }, { "date": "2025-07-23", "arxiv_id": "2507.16815", "title": "ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning", "authors": [ "Chi-Pin Huang", "Yueh-Hua Wu", "Min-Hung Chen", "Yu-Chiang Frank Wang", "Fu-En Yang" ], "github": "", "project_page": "", "abstract": "Vision-language-action (VLA) reasoning tasks require agents to interpret multimodal instructions, perform long-horizon planning, and act adaptively in dynamic environments. Existing approaches typically train VLA models in an end-to-end fashion, directly mapping inputs to actions without explicit reasoning, which hinders their ability to plan over multiple steps or adapt to complex task variations. In this paper, we propose ThinkAct, a dual-system framework that bridges high-level reasoning with low-level action execution via reinforced visual latent planning. ThinkAct trains a multimodal LLM to generate embodied reasoning plans guided by reinforcing action-aligned visual rewards based on goal completion and trajectory consistency. These reasoning plans are compressed into a visual plan latent that conditions a downstream action model for robust action execution on target environments. Extensive experiments on embodied reasoning and robot manipulation benchmarks demonstrate that ThinkAct enables few-shot adaptation, long-horizon planning, and self-correction behaviors in complex embodied AI tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.16812", "title": "MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning", "authors": [ "Run-Ze Fan", "Zengzhi Wang", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/lm-open-science-evaluation", "project_page": "", "abstract": "Scientific reasoning is critical for developing AI scientists and supporting human researchers in advancing the frontiers of natural science discovery. However, the open-source community has primarily focused on mathematics and coding while neglecting the scientific domain, largely due to the absence of open, large-scale, high-quality, verifiable scientific reasoning datasets. To bridge this gap, we first present TextbookReasoning, an open dataset featuring truthful reference answers extracted from 12k university-level scientific textbooks, comprising 650k reasoning questions spanning 7 scientific disciplines. We further introduce MegaScience, a large-scale mixture of high-quality open-source datasets totaling 1.25 million instances, developed through systematic ablation studies that evaluate various data selection methodologies to identify the optimal subset for each publicly available scientific dataset. Meanwhile, we build a comprehensive evaluation system covering diverse subjects and question types across 15 benchmarks, incorporating comprehensive answer extraction strategies to ensure accurate evaluation metrics. Our experiments demonstrate that our datasets achieve superior performance and training efficiency with more concise response lengths compared to existing open-source scientific datasets. Furthermore, we train Llama3.1, Qwen2.5, and Qwen3 series base models on MegaScience, which significantly outperform the corresponding official instruct models in average performance. In addition, MegaScience exhibits greater effectiveness for larger and stronger models, suggesting a scaling benefit for scientific tuning. We release our data curation pipeline, evaluation system, datasets, and seven trained models to the community to advance scientific reasoning research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GAIR-NLP/lm-open-science-evaluation", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.16746", "title": "Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning", "authors": [ "Ang Li", "Charles Wang", "Kaiyu Yue", "Zikui Cai", "Ollie Liu", "Deqing Fu", "Peng Guo", "Wang Bill Zhu", "Vatsal Sharan", "Robin Jia", "Willie Neiswanger", "Furong Huang", "Tom Goldstein", "Micah Goldblum" ], "github": "https://github.com/multimodal-reasoning-lab/Zebra-CoT", "project_page": "", "abstract": "Humans often use visual aids, for example diagrams or sketches, when solving complex problems. Training multimodal models to do the same, known as Visual Chain of Thought (Visual CoT), is challenging due to: (1) poor off-the-shelf visual CoT performance, which hinders reinforcement learning, and (2) the lack of high-quality visual CoT training data. We introduce Zebra-CoT, a diverse large-scale dataset with 182,384 samples, containing logically coherent interleaved text-image reasoning traces. We focus on four categories of tasks where sketching or visual reasoning is especially natural, spanning scientific questions such as geometry, physics, and algorithms; 2D visual reasoning tasks like visual search and jigsaw puzzles; 3D reasoning tasks including 3D multi-hop inference, embodied and robot planning; visual logic problems and strategic games like chess. Fine-tuning the Anole-7B model on the Zebra-CoT training corpus results in an improvement of +12% in our test-set accuracy and yields up to +13% performance gain on standard VLM benchmark evaluations. Fine-tuning Bagel-7B yields a model that generates high-quality interleaved visual reasoning chains, underscoring Zebra-CoT's effectiveness for developing multimodal reasoning abilities. We open-source our dataset and models to support development and evaluation of visual CoT.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/multimodal-reasoning-lab/Zebra-CoT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.15245", "title": "SPAR: Scholar Paper Retrieval with LLM-based Agents for Enhanced Academic Search", "authors": [ "Xiaofeng Shi", "Yuduo Li", "Qian Kou", "Longbin Yu", "Jinxin Xie", "Hua Zhou" ], "github": "https://github.com/xiaofengShi/SPAR", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have opened new opportunities for academic literature retrieval. However, existing systems often rely on rigid pipelines and exhibit limited reasoning capabilities. We introduce SPAR, a multi-agent framework that incorporates RefChain-based query decomposition and query evolution to enable more flexible and effective search. To facilitate systematic evaluation, we also construct SPARBench, a challenging benchmark with expert-annotated relevance labels. Experimental results demonstrate that SPAR substantially outperforms strong baselines, achieving up to +56% F1 on AutoScholar and +23% F1 on SPARBench over the best-performing baseline. Together, SPAR and SPARBench provide a scalable, interpretable, and high-performing foundation for advancing research in scholarly retrieval. Code and data will be available at: https://github.com/xiaofengShi/SPAR", "github_manual": "", "github_regex": "https://github.com/xiaofengShi/SPAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.16632", "title": "Step-Audio 2 Technical Report", "authors": [ "Boyong Wu", "Chao Yan", "Chen Hu", "Cheng Yi", "Chengli Feng", "Fei Tian", "Feiyu Shen", "Gang Yu", "Haoyang Zhang", "Jingbei Li", "Mingrui Chen", "Peng Liu", "Wang You", "Xiangyu Tony Zhang", "Xingyuan Li", "Xuerui Yang", "Yayue Deng", "Yechang Huang", "Yuxin Li", "Yuxin Zhang", "Zhao You", "Brian Li", "Changyi Wan", "Hanpeng Hu", "Jiangjie Zhen", "Siyu Chen", "Song Yuan", "Xuelin Zhang", "Yimin Jiang", "Yu Zhou", "Yuxiang Yang", "Bingxin Li", "Buyun Ma", "Changhe Song", "Dongqing Pang", "Guoqiang Hu", "Haiyang Sun", "Kang An", "Na Wang", "Shuli Gao", "Wei Ji", "Wen Li", "Wen Sun", "Xuan Wen", "Yong Ren", "Yuankai Ma", "Yufan Lu", "Bin Wang", "Bo Li", "Changxin Miao", "Che Liu", "Chen Xu", "Dapeng Shi", "Dingyuan Hu", "Donghang Wu", "Enle Liu", "Guanzhe Huang", "Gulin Yan", "Han Zhang", "Hao Nie", "Haonan Jia", "Hongyu Zhou", "Jianjian Sun", "Jiaoren Wu", "Jie Wu", "Jie Yang", "Jin Yang", "Junzhe Lin", "Kaixiang Li", "Lei Yang", "Liying Shi", "Li Zhou", "Longlong Gu", "Ming Li", "Mingliang Li", "Mingxiao Li", "Nan Wu", "Qi Han", "Qinyuan Tan", "Shaoliang Pang", "Shengjie Fan", "Siqi Liu", "Tiancheng Cao", "Wanying Lu", "Wenqing He", "Wuxun Xie", "Xu Zhao", "Xueqi Li", "Yanbo Yu", "Yang Yang", "Yi Liu", "Yifan Lu", "Yilei Wang", "Yuanhao Ding", "Yuanwei Liang", "Yuanwei Lu", "Yuchu Luo", "Yuhe Yin", "Yumeng Zhan", "Yuxiang Zhang", "Zidong Yang", "Zixin Zhang", "Binxing Jiao", "Daxin Jiang", "Heung-Yeung Shum", "Jiansheng Chen", "Jing Li", "Xiangyu Zhang", "Yibo Zhu" ], "github": "https://github.com/stepfun-ai/Step-Audio2", "project_page": "", "abstract": "This paper presents Step-Audio~2, an end-to-end multi-modal large language model designed for industry-strength audio understanding and speech conversation. By integrating a latent audio encoder and reasoning-centric reinforcement learning (RL), Step-Audio 2 achieves promising performance in automatic speech recognition (ASR) and audio understanding. To facilitate genuine end-to-end speech conversation, Step-Audio 2 incorporates the generation of discrete audio tokens into language modeling, significantly enhancing its responsiveness to paralinguistic information such as speaking styles and emotions. To effectively leverage the rich textual and acoustic knowledge in real-world data, Step-Audio 2 integrates retrieval-augmented generation (RAG) and is able to call external tools such as web search to mitigate hallucination and audio search to switch timbres. Trained on millions of hours of speech and audio data, Step-Audio 2 delivers intelligence and expressiveness across diverse conversational scenarios. Evaluation results demonstrate that Step-Audio 2 achieves state-of-the-art performance on various audio understanding and conversational benchmarks compared to other open-source and commercial solutions. Please visit https://github.com/stepfun-ai/Step-Audio2 for more information.", "github_manual": "", "github_regex": "https://github.com/stepfun-ai/Step-Audio2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.15024", "title": "RefCritic: Training Long Chain-of-Thought Critic Models with Refinement Feedback", "authors": [ "Qiaoyu Tang", "Hao Xiang", "Le Yu", "Bowen Yu", "Hongyu Lin", "Yaojie Lu", "Xianpei Han", "Le Sun", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "With the rapid advancement of Large Language Models (LLMs), developing effective critic modules for precise guidance has become crucial yet challenging. In this paper, we initially demonstrate that supervised fine-tuning for building critic modules (which is widely adopted in current solutions) fails to genuinely enhance models' critique abilities, producing superficial critiques with insufficient reflections and verifications. To unlock the unprecedented critique capabilities, we propose RefCritic, a long-chain-of-thought critic module based on reinforcement learning with dual rule-based rewards: (1) instance-level correctness of solution judgments and (2) refinement accuracies of the policy model based on critiques, aiming to generate high-quality evaluations with actionable feedback that effectively guides model refinement. We evaluate RefCritic on Qwen2.5-14B-Instruct and DeepSeek-R1-Distill-Qwen-14B across five benchmarks. On critique and refinement settings, RefCritic demonstrates consistent advantages across all benchmarks, e.g., 6.8\\% and 7.2\\% gains on AIME25 for the respective base models. Notably, under majority voting, policy models filtered by RefCritic show superior scaling with increased voting numbers. Moreover, despite training on solution-level supervision, RefCritic outperforms step-level supervised approaches on ProcessBench, a benchmark to identify erroneous steps in mathematical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.16784", "title": "Beyond Context Limits: Subconscious Threads for Long-Horizon Reasoning", "authors": [ "Hongyin Luo", "Nathaniel Morgan", "Tina Li", "Derek Zhao", "Ai Vy Ngo", "Philip Schroeder", "Lijie Yang", "Assaf Ben-Kish", "Jack O'Brien", "James Glass" ], "github": "", "project_page": "", "abstract": "To break the context limits of large language models (LLMs) that bottleneck reasoning accuracy and efficiency, we propose the Thread Inference Model (TIM), a family of LLMs trained for recursive and decompositional problem solving, and TIMRUN, an inference runtime enabling long-horizon structured reasoning beyond context limits. Together, TIM hosted on TIMRUN supports virtually unlimited working memory and multi-hop tool calls within a single language model inference, overcoming output limits, positional-embedding constraints, and GPU-memory bottlenecks. Performance is achieved by modeling natural language as reasoning trees measured by both length and depth instead of linear sequences. The reasoning trees consist of tasks with thoughts, recursive subtasks, and conclusions based on the concept we proposed in Schroeder et al, 2025. During generation, we maintain a working memory that retains only the key-value states of the most relevant context tokens, selected by a rule-based subtask-pruning mechanism, enabling reuse of positional embeddings and GPU memory pages throughout reasoning. Experimental results show that our system sustains high inference throughput, even when manipulating up to 90% of the KV cache in GPU memory. It also delivers accurate reasoning on mathematical tasks and handles information retrieval challenges that require long-horizon reasoning and multi-hop tool use.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.16814", "title": "Semi-off-Policy Reinforcement Learning for Vision-Language Slow-thinking Reasoning", "authors": [ "Junhao Shen", "Haiteng Zhao", "Yuzhe Gu", "Songyang Gao", "Kuikun Liu", "Haian Huang", "Jianfei Gao", "Dahua Lin", "Wenwei Zhang", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Enhancing large vision-language models (LVLMs) with visual slow-thinking reasoning is crucial for solving complex multimodal tasks. However, since LVLMs are mainly trained with vision-language alignment, it is difficult to adopt on-policy reinforcement learning (RL) to develop the slow thinking ability because the rollout space is restricted by its initial abilities. Off-policy RL offers a way to go beyond the current policy, but directly distilling trajectories from external models may cause visual hallucinations due to mismatched visual perception abilities across models. To address these issues, this paper proposes SOPHIA, a simple and scalable Semi-Off-Policy RL for vision-language slow-tHInking reAsoning. SOPHIA builds a semi-off-policy behavior model by combining on-policy visual understanding from a trainable LVLM with off-policy slow-thinking reasoning from a language model, assigns outcome-based rewards to reasoning, and propagates visual rewards backward. Then LVLM learns slow-thinking reasoning ability from the obtained reasoning trajectories using propagated rewards via off-policy RL algorithms. Extensive experiments with InternVL2.5 and InternVL3.0 with 8B and 38B sizes show the effectiveness of SOPHIA. Notably, SOPHIA improves InternVL3.0-38B by 8.50% in average, reaching state-of-the-art performance among open-source LVLMs on multiple multimodal reasoning benchmarks, and even outperforms some closed-source models (e.g., GPT-4.1) on the challenging MathVision and OlympiadBench, achieving 49.08% and 49.95% pass@1 accuracy, respectively. Analysis shows SOPHIA outperforms supervised fine-tuning and direct on-policy RL methods, offering a better policy initialization for further on-policy training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.15974", "title": "Does More Inference-Time Compute Really Help Robustness?", "authors": [ "Tong Wu", "Chong Xiang", "Jiachen T. Wang", "Weichen Yu", "Chawin Sitawarin", "Vikash Sehwag", "Prateek Mittal" ], "github": "", "project_page": "", "abstract": "Recently, Zaremba et al. demonstrated that increasing inference-time computation improves robustness in large proprietary reasoning LLMs. In this paper, we first show that smaller-scale, open-source models (e.g., DeepSeek R1, Qwen3, Phi-reasoning) can also benefit from inference-time scaling using a simple budget forcing strategy. More importantly, we reveal and critically examine an implicit assumption in prior work: intermediate reasoning steps are hidden from adversaries. By relaxing this assumption, we identify an important security risk, intuitively motivated and empirically verified as an inverse scaling law: if intermediate reasoning steps become explicitly accessible, increased inference-time computation consistently reduces model robustness. Finally, we discuss practical scenarios where models with hidden reasoning chains are still vulnerable to attacks, such as models with tool-integrated reasoning and advanced reasoning extraction attacks. Our findings collectively demonstrate that the robustness benefits of inference-time scaling depend heavily on the adversarial setting and deployment context. We urge practitioners to carefully weigh these subtle trade-offs before applying inference-time scaling in security-sensitive, real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.15454", "title": "ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting", "authors": [ "Ruijie Zhu", "Mulin Yu", "Linning Xu", "Lihan Jiang", "Yixuan Li", "Tianzhu Zhang", "Jiangmiao Pang", "Bo Dai" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting is renowned for its high-fidelity reconstructions and real-time novel view synthesis, yet its lack of semantic understanding limits object-level perception. In this work, we propose ObjectGS, an object-aware framework that unifies 3D scene reconstruction with semantic understanding. Instead of treating the scene as a unified whole, ObjectGS models individual objects as local anchors that generate neural Gaussians and share object IDs, enabling precise object-level reconstruction. During training, we dynamically grow or prune these anchors and optimize their features, while a one-hot ID encoding with a classification loss enforces clear semantic constraints. We show through extensive experiments that ObjectGS not only outperforms state-of-the-art methods on open-vocabulary and panoptic segmentation tasks, but also integrates seamlessly with applications like mesh extraction and scene editing. Project page: https://ruijiezhu94.github.io/ObjectGS_page", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.16795", "title": "Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning", "authors": [ "Helena Casademunt", "Caden Juang", "Adam Karvonen", "Samuel Marks", "Senthooran Rajamanoharan", "Neel Nanda" ], "github": "https://github.com/cadentj/caft", "project_page": "", "abstract": "Fine-tuning large language models (LLMs) can lead to unintended out-of-distribution generalization. Standard approaches to this problem rely on modifying training data, for example by adding data that better specify the intended generalization. However, this is not always practical. We introduce Concept Ablation Fine-Tuning (CAFT), a technique that leverages interpretability tools to control how LLMs generalize from fine-tuning, without needing to modify the training data or otherwise use data from the target distribution. Given a set of directions in an LLM's latent space corresponding to undesired concepts, CAFT works by ablating these concepts with linear projections during fine-tuning, steering the model away from unintended generalizations. We successfully apply CAFT to three fine-tuning tasks, including emergent misalignment, a phenomenon where LLMs fine-tuned on a narrow task generalize to give egregiously misaligned responses to general questions. Without any changes to the fine-tuning data, CAFT reduces misaligned responses by 10x without degrading performance on the training distribution. Overall, CAFT represents a novel approach for steering LLM generalization without modifying training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cadentj/caft", "github_api_added_by": "auto" }, { "date": "2025-07-23", "arxiv_id": "2507.08422", "title": "Upsample What Matters: Region-Adaptive Latent Sampling for Accelerated Diffusion Transformers", "authors": [ "Wongi Jeong", "Kyungryeol Lee", "Hoigi Seo", "Se Young Chun" ], "github": "", "project_page": "", "abstract": "Diffusion transformers have emerged as an alternative to U-net-based diffusion models for high-fidelity image and video generation, offering superior scalability. However, their heavy computation remains a major obstacle to real-world deployment. Existing acceleration methods primarily exploit the temporal dimension such as reusing cached features across diffusion timesteps. Here, we propose Region-Adaptive Latent Upsampling (RALU), a training-free framework that accelerates inference along spatial dimension. RALU performs mixed-resolution sampling across three stages: 1) low-resolution denoising latent diffusion to efficiently capture global semantic structure, 2) region-adaptive upsampling on specific regions prone to artifacts at full-resolution, and 3) all latent upsampling at full-resolution for detail refinement. To stabilize generations across resolution transitions, we leverage noise-timestep rescheduling to adapt the noise level across varying resolutions. Our method significantly reduces computation while preserving image quality by achieving up to 7.0times speed-up on FLUX and 3.0times on Stable Diffusion 3 with minimal degradation. Furthermore, RALU is complementary to existing temporal accelerations such as caching methods, thus can be seamlessly integrated to further reduce inference latency without compromising generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.16713", "title": "Experience is the Best Teacher: Grounding VLMs for Robotics through Self-Generated Memory", "authors": [ "Guowei Lan", "Kaixian Qu", "Ren\u00e9 Zurbr\u00fcgg", "Changan Chen", "Christopher E. Mower", "Haitham Bou-Ammar", "Marco Hutter" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) have been widely adopted in robotics to enable autonomous planning. However, grounding VLMs, originally trained on internet data, to diverse real-world robots remains a challenge. This paper presents ExpTeach, a framework that grounds VLMs to physical robots by building a self-generated memory of real-world experiences. In ExpTeach, the VLM autonomously plans actions, verifies outcomes, reflects on failures, and adapts robot behaviors in a closed loop. The self-generated experiences during this process are then summarized into a long-term memory, enabling retrieval of learned knowledge to guide future tasks via retrieval-augmented generation (RAG). Additionally, ExpTeach enhances the spatial understanding of VLMs with an on-demand image annotation module. In experiments, we show that reflection improves success rates from 36% to 84% on four challenging robotic tasks and observe the emergence of intelligent object interactions, including creative tool use. Across extensive tests on 12 real-world scenarios (including eight unseen ones), we find that grounding with long-term memory boosts single-trial success rates from 22% to 80%, demonstrating the effectiveness and generalizability of ExpTeach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.16782", "title": "Task-Specific Zero-shot Quantization-Aware Training for Object Detection", "authors": [ "Changhao Li", "Xinrui Chen", "Ji Wang", "Kang Zhao", "Jianfei Chen" ], "github": "https://github.com/DFQ-Dojo/dfq-toolkit", "project_page": "", "abstract": "Quantization is a key technique to reduce network size and computational complexity by representing the network parameters with a lower precision. Traditional quantization methods rely on access to original training data, which is often restricted due to privacy concerns or security challenges. Zero-shot Quantization (ZSQ) addresses this by using synthetic data generated from pre-trained models, eliminating the need for real training data. Recently, ZSQ has been extended to object detection. However, existing methods use unlabeled task-agnostic synthetic images that lack the specific information required for object detection, leading to suboptimal performance. In this paper, we propose a novel task-specific ZSQ framework for object detection networks, which consists of two main stages. First, we introduce a bounding box and category sampling strategy to synthesize a task-specific calibration set from the pre-trained network, reconstructing object locations, sizes, and category distributions without any prior knowledge. Second, we integrate task-specific training into the knowledge distillation process to restore the performance of quantized detection networks. Extensive experiments conducted on the MS-COCO and Pascal VOC datasets demonstrate the efficiency and state-of-the-art performance of our method. Our code is publicly available at: https://github.com/DFQ-Dojo/dfq-toolkit .", "github_manual": "", "github_regex": "https://github.com/DFQ-Dojo/dfq-toolkit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-23", "arxiv_id": "2507.13541", "title": "PrefPalette: Personalized Preference Modeling with Latent Attributes", "authors": [ "Shuyue Stella Li", "Melanie Sclar", "Hunter Lang", "Ansong Ni", "Jacqueline He", "Puxin Xu", "Andrew Cohen", "Chan Young Park", "Yulia Tsvetkov", "Asli Celikyilmaz" ], "github": "https://github.com/stellalisy/PrefPalette", "project_page": "", "abstract": "Personalizing AI systems requires understanding not just what users prefer, but the reasons that underlie those preferences - yet current preference models typically treat human judgment as a black box. We introduce PrefPalette, a framework that decomposes preferences into attribute dimensions and tailors its preference prediction to distinct social community values in a human-interpretable manner. PrefPalette operationalizes a cognitive science principle known as multi-attribute decision making in two ways: (1) a scalable counterfactual attribute synthesis step that involves generating synthetic training data to isolate for individual attribute effects (e.g., formality, humor, cultural values), and (2) attention-based preference modeling that learns how different social communities dynamically weight these attributes. This approach moves beyond aggregate preference modeling to capture the diverse evaluation frameworks that drive human judgment. When evaluated on 45 social communities from the online platform Reddit, PrefPalette outperforms GPT-4o by 46.6% in average prediction accuracy. Beyond raw predictive improvements, PrefPalette also shed light on intuitive, community-specific profiles: scholarly communities prioritize verbosity and stimulation, conflict-oriented communities value sarcasm and directness, and support-based communities emphasize empathy. By modeling the attribute-mediated structure of human judgment, PrefPalette delivers both superior preference modeling and transparent, interpretable insights, and serves as a first step toward more trustworthy, value-aware personalized applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stellalisy/PrefPalette", "github_api_added_by": "auto" }, { "date": "2025-07-24", "arxiv_id": "2507.17512", "title": "Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learning", "authors": [ "Yu Li", "Zhuoshi Pan", "Honglin Lin", "Mengyuan Sun", "Conghui He", "Lijun Wu" ], "github": "https://github.com/Leey21/A-Data-Centric-Study", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of LLMs. Existing research has predominantly concentrated on isolated reasoning domains such as mathematical problem-solving, coding tasks, or logical reasoning. However, real world reasoning scenarios inherently demand an integrated application of multiple cognitive skills. Despite this, the interplay among these reasoning skills under reinforcement learning remains poorly understood. To bridge this gap, we present a systematic investigation of multi-domain reasoning within the RLVR framework, explicitly focusing on three primary domains: mathematical reasoning, code generation, and logical puzzle solving. We conduct a comprehensive study comprising four key components: (1) Leveraging the GRPO algorithm and the Qwen-2.5-7B model family, our study thoroughly evaluates the models' in-domain improvements and cross-domain generalization capabilities when trained on single-domain datasets. (2) Additionally, we examine the intricate interactions including mutual enhancements and conflicts that emerge during combined cross-domain training. (3) To further understand the influence of SFT on RL, we also analyze and compare performance differences between base and instruct models under identical RL configurations. (4) Furthermore, we delve into critical RL training details, systematically exploring the impacts of curriculum learning strategies, variations in reward design, and language-specific factors. Through extensive experiments, our results offer significant insights into the dynamics governing domain interactions, revealing key factors influencing both specialized and generalizable reasoning performance. These findings provide valuable guidance for optimizing RL methodologies to foster comprehensive, multi-domain reasoning capabilities in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Leey21/A-Data-Centric-Study", "github_api_added_by": "auto" }, { "date": "2025-07-24", "arxiv_id": "2507.11465", "title": "Elevating 3D Models: High-Quality Texture and Geometry Refinement from a Low-Quality Model", "authors": [ "Nuri Ryu", "Jiyun Won", "Jooeun Son", "Minsu Gong", "Joo-Haeng Lee", "Sunghyun Cho" ], "github": "https://github.com/black-forest-labs/flux", "project_page": "", "abstract": "High-quality 3D assets are essential for various applications in computer graphics and 3D vision but remain scarce due to significant acquisition costs. To address this shortage, we introduce Elevate3D, a novel framework that transforms readily accessible low-quality 3D assets into higher quality. At the core of Elevate3D is HFS-SDEdit, a specialized texture enhancement method that significantly improves texture quality while preserving the appearance and geometry while fixing its degradations. Furthermore, Elevate3D operates in a view-by-view manner, alternating between texture and geometry refinement. Unlike previous methods that have largely overlooked geometry refinement, our framework leverages geometric cues from images refined with HFS-SDEdit by employing state-of-the-art monocular geometry predictors. This approach ensures detailed and accurate geometry that aligns seamlessly with the enhanced texture. Elevate3D outperforms recent competitors by achieving state-of-the-art quality in 3D model refinement, effectively addressing the scarcity of high-quality open-source 3D assets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/black-forest-labs/flux", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-24", "arxiv_id": "2507.17745", "title": "Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention", "authors": [ "Yiwen Chen", "Zhihao Li", "Yikai Wang", "Hu Zhang", "Qin Li", "Chi Zhang", "Guosheng Lin" ], "github": "", "project_page": "", "abstract": "Recent advances in sparse voxel representations have significantly improved the quality of 3D content generation, enabling high-resolution modeling with fine-grained geometry. However, existing frameworks suffer from severe computational inefficiencies due to the quadratic complexity of attention mechanisms in their two-stage diffusion pipelines. In this work, we propose Ultra3D, an efficient 3D generation framework that significantly accelerates sparse voxel modeling without compromising quality. Our method leverages the compact VecSet representation to efficiently generate a coarse object layout in the first stage, reducing token count and accelerating voxel coordinate prediction. To refine per-voxel latent features in the second stage, we introduce Part Attention, a geometry-aware localized attention mechanism that restricts attention computation within semantically consistent part regions. This design preserves structural continuity while avoiding unnecessary global attention, achieving up to 6.7x speed-up in latent generation. To support this mechanism, we construct a scalable part annotation pipeline that converts raw meshes into part-labeled sparse voxels. Extensive experiments demonstrate that Ultra3D supports high-resolution 3D generation at 1024 resolution and achieves state-of-the-art performance in both visual fidelity and user preference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-24", "arxiv_id": "2507.16863", "title": "Pixels, Patterns, but No Poetry: To See The World like Humans", "authors": [ "Hongcheng Gao", "Zihao Huang", "Lin Xu", "Jingyi Tang", "Xinhao Li", "Yue Liu", "Haoyang Li", "Taihang Hu", "Minhua Lin", "Xinlong Yang", "Ge Wu", "Balong Bi", "Hongyu Chen", "Wentao Zhang" ], "github": "", "project_page": "", "abstract": "Achieving human-like perception and reasoning in Multimodal Large Language Models (MLLMs) remains a central challenge in artificial intelligence. While recent research has primarily focused on enhancing reasoning capabilities in MLLMs, a fundamental question persists: Can Multimodal Large Language Models truly perceive the world as humans do? This paper shifts focus from reasoning to perception. Rather than constructing benchmarks specifically for reasoning, we introduce the Turing Eye Test (TET), a challenging perception-oriented benchmark comprising four diagnostic tasks that evaluate MLLMs' performance on synthetic images that humans process intuitively. Our findings reveal that state-of-the-art MLLMs exhibit catastrophic failures on our perceptual tasks trivial for humans. Both in-context learning and training on language backbone-effective for previous benchmarks-fail to improve performance on our tasks, while fine-tuning the vision tower enables rapid adaptation, suggesting that our benchmark poses challenges for vision tower generalization rather than for the knowledge and reasoning capabilities of the language backbone-a key gap between current MLLMs and human perception. We release a representative subset of TET tasks in this version, and will introduce more diverse tasks and methods to enhance visual generalization in future work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-24", "arxiv_id": "2507.17202", "title": "DesignLab: Designing Slides Through Iterative Detection and Correction", "authors": [ "Jooyeol Yun", "Heng Wang", "Yotaro Shimose", "Jaegul Choo", "Shingo Takamatsu" ], "github": "", "project_page": "", "abstract": "Designing high-quality presentation slides can be challenging for non-experts due to the complexity involved in navigating various design choices. Numerous automated tools can suggest layouts and color schemes, yet often lack the ability to refine their own output, which is a key aspect in real-world workflows. We propose DesignLab, which separates the design process into two roles, the design reviewer, who identifies design-related issues, and the design contributor who corrects them. This decomposition enables an iterative loop where the reviewer continuously detects issues and the contributor corrects them, allowing a draft to be further polished with each iteration, reaching qualities that were unattainable. We fine-tune large language models for these roles and simulate intermediate drafts by introducing controlled perturbations, enabling the design reviewer learn design errors and the contributor learn how to fix them. Our experiments show that DesignLab outperforms existing design-generation methods, including a commercial tool, by embracing the iterative nature of designing which can result in polished, professional slides.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-24", "arxiv_id": "2507.17744", "title": "Yume: An Interactive World Generation Model", "authors": [ "Xiaofeng Mao", "Shaoheng Lin", "Zhen Li", "Chuanhao Li", "Wenshuo Peng", "Tong He", "Jiangmiao Pang", "Mingmin Chi", "Yu Qiao", "Kaipeng Zhang" ], "github": "https://github.com/stdstu12/YUME", "project_page": "", "abstract": "Yume aims to use images, text, or videos to create an interactive, realistic, and dynamic world, which allows exploration and control using peripheral devices or neural signals. In this report, we present a preview version of \\method, which creates a dynamic world from an input image and allows exploration of the world using keyboard actions. To achieve this high-fidelity and interactive video world generation, we introduce a well-designed framework, which consists of four main components, including camera motion quantization, video generation architecture, advanced sampler, and model acceleration. First, we quantize camera motions for stable training and user-friendly interaction using keyboard inputs. Then, we introduce the Masked Video Diffusion Transformer~(MVDT) with a memory module for infinite video generation in an autoregressive manner. After that, training-free Anti-Artifact Mechanism (AAM) and Time Travel Sampling based on Stochastic Differential Equations (TTS-SDE) are introduced to the sampler for better visual quality and more precise control. Moreover, we investigate model acceleration by synergistic optimization of adversarial distillation and caching mechanisms. We use the high-quality world exploration dataset \\sekai to train \\method, and it achieves remarkable results in diverse scenes and applications. All data, codebase, and model weights are available on https://github.com/stdstu12/YUME. Yume will update monthly to achieve its original goal. Project page: https://stdstu12.github.io/YUME-Project/.", "github_manual": "", "github_regex": "https://github.com/stdstu12/YUME", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-24", "arxiv_id": "2507.16331", "title": "Re:Form -- Reducing Human Priors in Scalable Formal Software Verification with RL in LLMs: A Preliminary Study on Dafny", "authors": [ "Chuanhao Yan", "Fengdi Che", "Xuhan Huang", "Xu Xu", "Xin Li", "Yizhi Li", "Xingwei Qu", "Jingzhe Shi", "Zhuangzhuang He", "Chenghua Lin", "Yaodong Yang", "Binhang Yuan", "Hang Zhao", "Yu Qiao", "Bowen Zhou", "Jie Fu" ], "github": "", "project_page": "", "abstract": "Existing informal language-based (e.g., human language) Large Language Models (LLMs) trained with Reinforcement Learning (RL) face a significant challenge: their verification processes, which provide crucial training signals, are neither reliable nor scalable. In fact, the prevalent large proprietary models could hardly generate verifiable programs. A promising yet largely uncharted alternative is formal language-based reasoning. Grounding LLMs in rigorous formal systems where generative models operate in formal language spaces (e.g., Dafny) enables the automatic and mathematically provable verification of their reasoning processes and outcomes. This capability is pivotal for achieving large-scale, reliable formal software verification. It is a common practice to employ human-annotated chain-of-thought and other human priors to induce the reasoning and coding capabilities of LLMs. Unfortunately, it becomes unacceptably all-consuming to provide such priors for supervising complex programming tasks. In this work, we systematically explore ways to reduce human priors with the formal language, Dafny, as the main environment for our pilot study. Our pipeline mainly relies on introducing an automatic and scalable data curation pipeline, and careful RL designs integrated with feedback from the formal language verifier. We introduce DafnyComp, a benchmark of compositional formal programs with auto-formalized specifications for specification reasoning. Our supervised fine-tuning (SFT) stage enables even small models (e.g., 0.5B) to generate syntactically valid and verifiable Dafny code, surpassing proprietary models. RL with regularization further improves performance, achieving stronger generalization to out-of-domain tasks and outperforming all strong baselines on the challenging DafnyComp benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-24", "arxiv_id": "2507.16880", "title": "Finding Dori: Memorization in Text-to-Image Diffusion Models Is Less Local Than Assumed", "authors": [ "Antoni Kowalczuk", "Dominik Hintersdorf", "Lukas Struppek", "Kristian Kersting", "Adam Dziedzic", "Franziska Boenisch" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion models (DMs) have achieved remarkable success in image generation. However, concerns about data privacy and intellectual property remain due to their potential to inadvertently memorize and replicate training data. Recent mitigation efforts have focused on identifying and pruning weights responsible for triggering replication, based on the assumption that memorization can be localized. Our research assesses the robustness of these pruning-based approaches. We demonstrate that even after pruning, minor adjustments to text embeddings of input prompts are sufficient to re-trigger data replication, highlighting the fragility of these defenses. Furthermore, we challenge the fundamental assumption of memorization locality, by showing that replication can be triggered from diverse locations within the text embedding space, and follows different paths in the model. Our findings indicate that existing mitigation strategies are insufficient and underscore the need for methods that truly remove memorized content, rather than attempting to suppress its retrieval. As a first step in this direction, we introduce a novel adversarial fine-tuning method that iteratively searches for replication triggers and updates the model to increase robustness. Through our research, we provide fresh insights into the nature of memorization in text-to-image DMs and a foundation for building more trustworthy and compliant generative AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-24", "arxiv_id": "2507.16725", "title": "RAVine: Reality-Aligned Evaluation for Agentic Search", "authors": [ "Yilong Xu", "Xiang Long", "Zhi Zheng", "Jinhua Gao" ], "github": "https://github.com/SwordFaith/RAVine", "project_page": "", "abstract": "Agentic search, as a more autonomous and adaptive paradigm of retrieval augmentation, is driving the evolution of intelligent search systems. However, existing evaluation frameworks fail to align well with the goals of agentic search. First, the complex queries commonly used in current benchmarks often deviate from realistic user search scenarios. Second, prior approaches tend to introduce noise when extracting ground truth for end-to-end evaluations, leading to distorted assessments at a fine-grained level. Third, most current frameworks focus solely on the quality of final answers, neglecting the evaluation of the iterative process inherent to agentic search. To address these limitations, we propose RAVine -- a Reality-Aligned eValuation framework for agentic LLMs with search. RAVine targets multi-point queries and long-form answers that better reflect user intents, and introduces an attributable ground truth construction strategy to enhance the accuracy of fine-grained evaluation. Moreover, RAVine examines model's interaction with search tools throughout the iterative process, and accounts for factors of efficiency. We benchmark a series of models using RAVine and derive several insights, which we hope will contribute to advancing the development of agentic search systems. The code and datasets are available at https://github.com/SwordFaith/RAVine.", "github_manual": "", "github_regex": "https://github.com/SwordFaith/RAVine", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-24", "arxiv_id": "2507.16116", "title": "PUSA V1.0: Surpassing Wan-I2V with $500 Training Cost by Vectorized Timestep Adaptation", "authors": [ "Yaofang Liu", "Yumeng Ren", "Aitor Artola", "Yuxuan Hu", "Xiaodong Cun", "Xiaotong Zhao", "Alan Zhao", "Raymond H. Chan", "Suiyun Zhang", "Rui Liu", "Dandan Tu", "Jean-Michel Morel" ], "github": "https://github.com/Yaofang-Liu/Pusa-VidGen", "project_page": "", "abstract": "The rapid advancement of video diffusion models has been hindered by fundamental limitations in temporal modeling, particularly the rigid synchronization of frame evolution imposed by conventional scalar timestep variables. While task-specific adaptations and autoregressive models have sought to address these challenges, they remain constrained by computational inefficiency, catastrophic forgetting, or narrow applicability. In this work, we present Pusa, a groundbreaking paradigm that leverages vectorized timestep adaptation (VTA) to enable fine-grained temporal control within a unified video diffusion framework. Besides, VTA is a non-destructive adaptation, which means it fully preserves the capabilities of the base model. By finetuning the SOTA Wan2.1-T2V-14B model with VTA, we achieve unprecedented efficiency -- surpassing the performance of Wan-I2V-14B with leq 1/200 of the training cost (\\500 vs. \\geq 100,000) and leq 1/2500 of the dataset size (4K vs. geq 10M samples). Pusa not only sets a new standard for image-to-video (I2V) generation, achieving a VBench-I2V total score of 87.32\\% (vs. 86.86\\% of Wan-I2V-14B), but also unlocks many zero-shot multi-task capabilities such as start-end frames and video extension -- all without task-specific training. Meanwhile, Pusa can still perform text-to-video generation. Mechanistic analyses reveal that our approach preserves the foundation model's generative priors while surgically injecting temporal dynamics, avoiding the combinatorial explosion inherent to vectorized timesteps. This work establishes a scalable, efficient, and versatile paradigm for next-generation video synthesis, democratizing high-fidelity video generation for research and industry alike. Code is open-sourced at https://github.com/Yaofang-Liu/Pusa-VidGen", "github_manual": "", "github_regex": "https://github.com/Yaofang-Liu/Pusa-VidGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-24", "arxiv_id": "2507.14241", "title": "Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models", "authors": [ "Rithesh Murthy", "Ming Zhu", "Liangwei Yang", "Jielin Qiu", "Juntao Tan", "Shelby Heinecke", "Caiming Xiong", "Silvio Savarese", "Huan Wang" ], "github": "https://github.com/SylphAI-Inc/AdalFlow", "project_page": "", "abstract": "Large Language Models (LLMs) perform best with well-crafted prompts, yet prompt engineering remains manual, inconsistent, and inaccessible to non-experts. We introduce Promptomatix, an automatic prompt optimization framework that transforms natural language task descriptions into high-quality prompts without requiring manual tuning or domain expertise. Promptomatix supports both a lightweight meta-prompt-based optimizer and a DSPy-powered compiler, with modular design enabling future extension to more advanced frameworks. The system analyzes user intent, generates synthetic training data, selects prompting strategies, and refines prompts using cost-aware objectives. Evaluated across 5 task categories, Promptomatix achieves competitive or superior performance compared to existing libraries, while reducing prompt length and computational overhead making prompt optimization scalable and efficient.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SylphAI-Inc/AdalFlow", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.16535", "title": "EarthCrafter: Scalable 3D Earth Generation via Dual-Sparse Latent Diffusion", "authors": [ "Shang Liu", "Chenjie Cao", "Chaohui Yu", "Wen Qian", "Jing Wang", "Fan Wang" ], "github": "", "project_page": "", "abstract": "Despite the remarkable developments achieved by recent 3D generation works, scaling these methods to geographic extents, such as modeling thousands of square kilometers of Earth's surface, remains an open challenge. We address this through a dual innovation in data infrastructure and model architecture. First, we introduce Aerial-Earth3D, the largest 3D aerial dataset to date, consisting of 50k curated scenes (each measuring 600m x 600m) captured across the U.S. mainland, comprising 45M multi-view Google Earth frames. Each scene provides pose-annotated multi-view images, depth maps, normals, semantic segmentation, and camera poses, with explicit quality control to ensure terrain diversity. Building on this foundation, we propose EarthCrafter, a tailored framework for large-scale 3D Earth generation via sparse-decoupled latent diffusion. Our architecture separates structural and textural generation: 1) Dual sparse 3D-VAEs compress high-resolution geometric voxels and textural 2D Gaussian Splats (2DGS) into compact latent spaces, largely alleviating the costly computation suffering from vast geographic scales while preserving critical information. 2) We propose condition-aware flow matching models trained on mixed inputs (semantics, images, or neither) to flexibly model latent geometry and texture features independently. Extensive experiments demonstrate that EarthCrafter performs substantially better in extremely large-scale generation. The framework further supports versatile applications, from semantic-guided urban layout generation to unconditional terrain synthesis, while maintaining geographic plausibility through our rich data priors from Aerial-Earth3D. Our project page is available at https://whiteinblue.github.io/earthcrafter/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.14988", "title": "DMOSpeech 2: Reinforcement Learning for Duration Prediction in Metric-Optimized Speech Synthesis", "authors": [ "Yinghao Aaron Li", "Xilin Jiang", "Fei Tao", "Cheng Niu", "Kaifeng Xu", "Juntong Song", "Nima Mesgarani" ], "github": "https://github.com/yl4579/DMOSpeech2", "project_page": "", "abstract": "Diffusion-based text-to-speech (TTS) systems have made remarkable progress in zero-shot speech synthesis, yet optimizing all components for perceptual metrics remains challenging. Prior work with DMOSpeech demonstrated direct metric optimization for speech generation components, but duration prediction remained unoptimized. This paper presents DMOSpeech 2, which extends metric optimization to the duration predictor through a reinforcement learning approach. The proposed system implements a novel duration policy framework using group relative preference optimization (GRPO) with speaker similarity and word error rate as reward signals. By optimizing this previously unoptimized component, DMOSpeech 2 creates a more complete metric-optimized synthesis pipeline. Additionally, this paper introduces teacher-guided sampling, a hybrid approach leveraging a teacher model for initial denoising steps before transitioning to the student model, significantly improving output diversity while maintaining efficiency. Comprehensive evaluations demonstrate superior performance across all metrics compared to previous systems, while reducing sampling steps by half without quality degradation. These advances represent a significant step toward speech synthesis systems with metric optimization across multiple components. The audio samples, code and pre-trained models are available at https://dmospeech2.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yl4579/DMOSpeech2", "github_api_added_by": "auto" }, { "date": "2025-07-25", "arxiv_id": "2507.15758", "title": "LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy Optimization", "authors": [ "Xingyu Wu", "Yuchen Yan", "Shangke Lyu", "Linjuan Wu", "Yiwen Qiu", "Yongliang Shen", "Weiming Lu", "Jian Shao", "Jun Xiao", "Yueting Zhuang" ], "github": "", "project_page": "", "abstract": "Large reasoning models have achieved remarkable performance through extended chain-of-thought sequences, yet this computational freedom leads to excessive token generation even for simple problems. We present Length-Adaptive Policy Optimization (LAPO), a novel framework that transforms reasoning length control from an external constraint into an intrinsic model capability. Unlike existing approaches that impose rigid limits or rely on post-hoc interventions, LAPO enables models to internalize an understanding of appropriate reasoning depth through a two-stage reinforcement learning process. In the first stage, models learn natural reasoning patterns by discovering the statistical distribution of successful solution lengths. The second stage leverages these patterns as meta-cognitive guidance, embedding them directly within the model's reasoning context to ensure inference-time flexibility. Experiments on mathematical reasoning benchmarks demonstrate that LAPO reduces token usage by up to 40.9\\% while improving accuracy by 2.3\\%. Our analysis reveals that models trained with LAPO develop emergent abilities to allocate computational resources based on problem complexity, achieving efficient reasoning without sacrificing quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.18537", "title": "TTS-VAR: A Test-Time Scaling Framework for Visual Auto-Regressive Generation", "authors": [ "Zhekai Chen", "Ruihang Chu", "Yukang Chen", "Shiwei Zhang", "Yujie Wei", "Yingya Zhang", "Xihui Liu" ], "github": "https://github.com/ali-vilab/TTS-VAR", "project_page": "", "abstract": "Scaling visual generation models is essential for real-world content creation, yet requires substantial training and computational expenses. Alternatively, test-time scaling has garnered growing attention due to resource efficiency and promising performance. In this work, we present TTS-VAR, the first general test-time scaling framework for visual auto-regressive (VAR) models, modeling the generation process as a path searching problem. To dynamically balance computational efficiency with exploration capacity, we first introduce an adaptive descending batch size schedule throughout the causal generation process. Besides, inspired by VAR's hierarchical coarse-to-fine multi-scale generation, our framework integrates two key components: (i) At coarse scales, we observe that generated tokens are hard for evaluation, possibly leading to erroneous acceptance of inferior samples or rejection of superior samples. Noticing that the coarse scales contain sufficient structural information, we propose clustering-based diversity search. It preserves structural variety through semantic feature clustering, enabling later selection on samples with higher potential. (ii) In fine scales, resampling-based potential selection prioritizes promising candidates using potential scores, which are defined as reward functions incorporating multi-scale generation history. Experiments on the powerful VAR model Infinity show a notable 8.7% GenEval score improvement (from 0.69 to 0.75). Key insights reveal that early-stage structural features effectively influence final quality, and resampling efficacy varies across generation scales. Code is available at https://github.com/ali-vilab/TTS-VAR.", "github_manual": "", "github_regex": "https://github.com/ali-vilab/TTS-VAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.15844", "title": "Hierarchical Budget Policy Optimization for Adaptive Reasoning", "authors": [ "Shangke Lyu", "Linjuan Wu", "Yuchen Yan", "Xingyu Wu", "Hao Li", "Yongliang Shen", "Peisheng Jiang", "Weiming Lu", "Jun Xiao", "Yueting Zhuang" ], "github": "", "project_page": "", "abstract": "Large reasoning models achieve remarkable performance through extensive chain-of-thought generation, yet exhibit significant computational inefficiency by applying uniform reasoning strategies regardless of problem complexity. We present Hierarchical Budget Policy Optimization (HBPO), a reinforcement learning framework that enables models to learn problem-specific reasoning depths without sacrificing capability. HBPO addresses the fundamental challenge of exploration space collapse in efficiency-oriented training, where penalties on long output length systematically bias models away from necessary long reasoning paths. Through hierarchical budget exploration, our approach partitions rollout samples into multiple subgroups with distinct token budgets, aiming to enable efficient resource allocation while preventing degradation of capability. We introduce differentiated reward mechanisms that create budget-aware incentives aligned with the complexity of the problem, allowing models to discover natural correspondences between task requirements and computational effort. Extensive experiments demonstrate that HBPO reduces average token usage by up to 60.6% while improving accuracy by 3.14% across four reasoning benchmarks. Unlike existing methods that impose external constraints or rely on discrete mode selection, HBPO exhibits emergent adaptive behavior where models automatically adjust reasoning depth based on problem complexity. Our results suggest that reasoning efficiency and capability are not inherently conflicting, and can be simultaneously optimized through appropriately structured hierarchical training that preserves exploration diversity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.18634", "title": "Captain Cinema: Towards Short Movie Generation", "authors": [ "Junfei Xiao", "Ceyuan Yang", "Lvmin Zhang", "Shengqu Cai", "Yang Zhao", "Yuwei Guo", "Gordon Wetzstein", "Maneesh Agrawala", "Alan Yuille", "Lu Jiang" ], "github": "", "project_page": "", "abstract": "We present Captain Cinema, a generation framework for short movie generation. Given a detailed textual description of a movie storyline, our approach firstly generates a sequence of keyframes that outline the entire narrative, which ensures long-range coherence in both the storyline and visual appearance (e.g., scenes and characters). We refer to this step as top-down keyframe planning. These keyframes then serve as conditioning signals for a video synthesis model, which supports long context learning, to produce the spatio-temporal dynamics between them. This step is referred to as bottom-up video synthesis. To support stable and efficient generation of multi-scene long narrative cinematic works, we introduce an interleaved training strategy for Multimodal Diffusion Transformers (MM-DiT), specifically adapted for long-context video data. Our model is trained on a specially curated cinematic dataset consisting of interleaved data pairs. Our experiments demonstrate that Captain Cinema performs favorably in the automated creation of visually coherent and narrative consistent short movies in high quality and efficiency. Project page: https://thecinema.ai", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.18192", "title": "TeEFusion: Blending Text Embeddings to Distill Classifier-Free Guidance", "authors": [ "Minghao Fu", "Guo-Hua Wang", "Xiaohao Chen", "Qing-Guo Chen", "Zhao Xu", "Weihua Luo", "Kaifu Zhang" ], "github": "https://github.com/AIDC-AI/TeEFusion", "project_page": "", "abstract": "Recent advances in text-to-image synthesis largely benefit from sophisticated sampling strategies and classifier-free guidance (CFG) to ensure high-quality generation. However, CFG's reliance on two forward passes, especially when combined with intricate sampling algorithms, results in prohibitively high inference costs. To address this, we introduce TeEFusion (Text Embeddings Fusion), a novel and efficient distillation method that directly incorporates the guidance magnitude into the text embeddings and distills the teacher model's complex sampling strategy. By simply fusing conditional and unconditional text embeddings using linear operations, TeEFusion reconstructs the desired guidance without adding extra parameters, simultaneously enabling the student model to learn from the teacher's output produced via its sophisticated sampling approach. Extensive experiments on state-of-the-art models such as SD3 demonstrate that our method allows the student to closely mimic the teacher's performance with a far simpler and more efficient sampling strategy. Consequently, the student model achieves inference speeds up to 6times faster than the teacher model, while maintaining image quality at levels comparable to those obtained through the teacher's complex sampling approach. The code is publicly available at https://github.com/AIDC-AI/TeEFusion{github.com/AIDC-AI/TeEFusion}.", "github_manual": "", "github_regex": "https://github.com/AIDC-AI/TeEFusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.14958", "title": "MUR: Momentum Uncertainty guided Reasoning for Large Language Models", "authors": [ "Hang Yan", "Fangzhi Xu", "Rongman Xu", "Yifei Li", "Jian Zhang", "Haoran Luo", "Xiaobao Wu", "Luu Anh Tuan", "Haiteng Zhao", "Qika Lin", "Jun Liu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved impressive performance on reasoning-intensive tasks, yet optimizing their reasoning efficiency remains an open challenge. While Test-Time Scaling (TTS) improves reasoning quality, it often leads to overthinking, wasting tokens on redundant computations. This work investigates how to efficiently and adaptively guide LLM test-time scaling without additional training. Inspired by the concept of momentum in physics, we propose Momentum Uncertainty-guided Reasoning (MUR), which dynamically allocates thinking budgets to critical reasoning steps by tracking and aggregating stepwise uncertainty over time. To support flexible inference-time control, we introduce gamma-control, a simple mechanism that tunes the reasoning budget via a single hyperparameter. We provide in-depth theoretical proof to support the superiority of MUR in terms of stability and biases. MUR is comprehensively evaluated against various TTS methods across four challenging benchmarks (MATH-500, AIME24, AIME25, and GPQA-diamond) using different sizes of recent Qwen3 models (1.7B, 4B, and 8B). Results demonstrate that MUR reduces computation by over 50% on average while improving accuracy by 0.62-3.37%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.18103", "title": "A New Pair of GloVes", "authors": [ "Riley Carlson", "John Bauer", "Christopher D. Manning" ], "github": "", "project_page": "", "abstract": "This report documents, describes, and evaluates new 2024 English GloVe (Global Vectors for Word Representation) models. While the original GloVe models built in 2014 have been widely used and found useful, languages and the world continue to evolve and we thought that current usage could benefit from updated models. Moreover, the 2014 models were not carefully documented as to the exact data versions and preprocessing that were used, and we rectify this by documenting these new models. We trained two sets of word embeddings using Wikipedia, Gigaword, and a subset of Dolma. Evaluation through vocabulary comparison, direct testing, and NER tasks shows that the 2024 vectors incorporate new culturally and linguistically relevant words, perform comparably on structural tasks like analogy and similarity, and demonstrate improved performance on recent, temporally dependent NER datasets such as non-Western newswire data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.15595", "title": "SegDT: A Diffusion Transformer-Based Segmentation Model for Medical Imaging", "authors": [ "Salah Eddine Bekhouche", "Gaby Maroun", "Fadi Dornaika", "Abdenour Hadid" ], "github": "https://github.com/Bekhouche/SegDT", "project_page": "", "abstract": "Medical image segmentation is crucial for many healthcare tasks, including disease diagnosis and treatment planning. One key area is the segmentation of skin lesions, which is vital for diagnosing skin cancer and monitoring patients. In this context, this paper introduces SegDT, a new segmentation model based on diffusion transformer (DiT). SegDT is designed to work on low-cost hardware and incorporates Rectified Flow, which improves the generation quality at reduced inference steps and maintains the flexibility of standard diffusion models. Our method is evaluated on three benchmarking datasets and compared against several existing works, achieving state-of-the-art results while maintaining fast inference speeds. This makes the proposed model appealing for real-world medical applications. This work advances the performance and capabilities of deep learning models in medical image analysis, enabling faster, more accurate diagnostic tools for healthcare professionals. The code is made publicly available at https://github.com/Bekhouche/SegDT{GitHub}.", "github_manual": "", "github_regex": "https://github.com/Bekhouche/SegDT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.18546", "title": "GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface", "authors": [ "Urchade Zaratiana", "Gil Pasternak", "Oliver Boyd", "George Hurn-Maloney", "Ash Lewis" ], "github": "https://github.com/fastino-ai/GLiNER2", "project_page": "", "abstract": "Information extraction (IE) is fundamental to numerous NLP applications, yet existing solutions often require specialized models for different tasks or rely on computationally expensive large language models. We present GLiNER2, a unified framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction within a single efficient model. Built pretrained transformer encoder architecture, GLiNER2 maintains CPU efficiency and compact size while introducing multi-task composition through an intuitive schema-based interface. Our experiments demonstrate competitive performance across extraction and classification tasks with substantial improvements in deployment accessibility compared to LLM-based alternatives. We release GLiNER2 as an open-source pip-installable library with pre-trained models and documentation at https://github.com/fastino-ai/GLiNER2.", "github_manual": "", "github_regex": "https://github.com/fastino-ai/GLiNER2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.18464", "title": "DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts", "authors": [ "Miguel Aspis", "Sebasti\u00e1n A. Cajas Ord\u00f3nez", "Andr\u00e9s L. Su\u00e1rez-Cetrulo", "Ricardo Sim\u00f3n Carbajo" ], "github": "https://github.com/miguel-ceadar/drift-moe", "project_page": "", "abstract": "Learning from non-stationary data streams subject to concept drift requires models that can adapt on-the-fly while remaining resource-efficient. Existing adaptive ensemble methods often rely on coarse-grained adaptation mechanisms or simple voting schemes that fail to optimally leverage specialized knowledge. This paper introduces DriftMoE, an online Mixture-of-Experts (MoE) architecture that addresses these limitations through a novel co-training framework. DriftMoE features a compact neural router that is co-trained alongside a pool of incremental Hoeffding tree experts. The key innovation lies in a symbiotic learning loop that enables expert specialization: the router selects the most suitable expert for prediction, the relevant experts update incrementally with the true label, and the router refines its parameters using a multi-hot correctness mask that reinforces every accurate expert. This feedback loop provides the router with a clear training signal while accelerating expert specialization. We evaluate DriftMoE's performance across nine state-of-the-art data stream learning benchmarks spanning abrupt, gradual, and real-world drifts testing two distinct configurations: one where experts specialize on data regimes (multi-class variant), and another where they focus on single-class specialization (task-based variant). Our results demonstrate that DriftMoE achieves competitive results with state-of-the-art stream learning adaptive ensembles, offering a principled and efficient approach to concept drift adaptation. All code, data pipelines, and reproducibility scripts are available in our public GitHub repository: https://github.com/miguel-ceadar/drift-moe.", "github_manual": "", "github_regex": "https://github.com/miguel-ceadar/drift-moe", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.18565", "title": "Deep Learning-Based Age Estimation and Gender Deep Learning-Based Age Estimation and Gender Classification for Targeted Advertisement", "authors": [ "Muhammad Imran Zaman", "Nisar Ahmed" ], "github": "", "project_page": "", "abstract": "This paper presents a novel deep learning-based approach for simultaneous age and gender classification from facial images, designed to enhance the effectiveness of targeted advertising campaigns. We propose a custom Convolutional Neural Network (CNN) architecture, optimized for both tasks, which leverages the inherent correlation between age and gender information present in facial features. Unlike existing methods that often treat these tasks independently, our model learns shared representations, leading to improved performance. The network is trained on a large, diverse dataset of facial images, carefully pre-processed to ensure robustness against variations in lighting, pose, and image quality. Our experimental results demonstrate a significant improvement in gender classification accuracy, achieving 95%, and a competitive mean absolute error of 5.77 years for age estimation. Critically, we analyze the performance across different age groups, identifying specific challenges in accurately estimating the age of younger individuals. This analysis reveals the need for targeted data augmentation and model refinement to address these biases. Furthermore, we explore the impact of different CNN architectures and hyperparameter settings on the overall performance, providing valuable insights for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.16038", "title": "Discovering and using Spelke segments", "authors": [ "Rahul Venkatesh", "Klemen Kotar", "Lilian Naing Chen", "Seungwoo Kim", "Luca Thomas Wheeler", "Jared Watrous", "Ashley Xu", "Gia Ancone", "Wanhee Lee", "Honglin Chen", "Daniel Bear", "Stefan Stojanov", "Daniel Yamins" ], "github": "", "project_page": "", "abstract": "Segments in computer vision are often defined by semantic considerations and are highly dependent on category-specific conventions. In contrast, developmental psychology suggests that humans perceive the world in terms of Spelke objects--groupings of physical things that reliably move together when acted on by physical forces. Spelke objects thus operate on category-agnostic causal motion relationships which potentially better support tasks like manipulation and planning. In this paper, we first benchmark the Spelke object concept, introducing the SpelkeBench dataset that contains a wide variety of well-defined Spelke segments in natural images. Next, to extract Spelke segments from images algorithmically, we build SpelkeNet, a class of visual world models trained to predict distributions over future motions. SpelkeNet supports estimation of two key concepts for Spelke object discovery: (1) the motion affordance map, identifying regions likely to move under a poke, and (2) the expected-displacement map, capturing how the rest of the scene will move. These concepts are used for \"statistical counterfactual probing\", where diverse \"virtual pokes\" are applied on regions of high motion-affordance, and the resultant expected displacement maps are used define Spelke segments as statistical aggregates of correlated motion statistics. We find that SpelkeNet outperforms supervised baselines like SegmentAnything (SAM) on SpelkeBench. Finally, we show that the Spelke concept is practically useful for downstream applications, yielding superior performance on the 3DEditBench benchmark for physical object manipulation when used in a variety of off-the-shelf object manipulation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.18013", "title": "Technical Report of TeleChat2, TeleChat2.5 and T1", "authors": [ "Zihan Wang", "Xinzhang Liu", "Yitong Yao", "Chao Wang", "Yu Zhao", "Zhihao Yang", "Wenmin Deng", "Kaipeng Jia", "Jiaxin Peng", "Yuyao Huang", "Sishi Xiong", "Zhuo Jiang", "Kaidong Yu", "Xiaohui Hu", "Fubei Yao", "Ruiyu Fang", "Zhuoru Jiang", "Ruiting Song", "Qiyi Xie", "Rui Xue", "Xuewei He", "Yanlei Xue", "Zhu Yuan", "Zhaoxi Zhang", "Zilu Huang", "Shiquan Wang", "Xin Wang", "Hanming Wu", "Mingyuan Wang", "Xufeng Zhan", "Yuhan Sun", "Zhaohu Xing", "Yuhao Jiang", "Bingkai Yang", "Shuangyong Song", "Yongxiang Li", "Zhongjiang He", "Xuelong Li" ], "github": "https://github.com/Tele-AI/TeleChat2", "project_page": "", "abstract": "We introduce the latest series of TeleChat models: TeleChat2, TeleChat2.5, and T1, offering a significant upgrade over their predecessor, TeleChat. Despite minimal changes to the model architecture, the new series achieves substantial performance gains through enhanced training strategies in both pre-training and post-training stages. The series begins with TeleChat2, which undergoes pretraining on 10 trillion high-quality and diverse tokens. This is followed by Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) to further enhance its capabilities. TeleChat2.5 and T1 expand the pipeline by incorporating a continual pretraining phase with domain-specific datasets, combined with reinforcement learning (RL) to improve performance in code generation and mathematical reasoning tasks. The T1 variant is designed for complex reasoning, supporting long Chain-of-Thought (CoT) reasoning and demonstrating substantial improvements in mathematics and coding. In contrast, TeleChat2.5 prioritizes speed, delivering rapid inference. Both flagship models of T1 and TeleChat2.5 are dense Transformer-based architectures with 115B parameters, showcasing significant advancements in reasoning and general task performance compared to the original TeleChat. Notably, T1-115B outperform proprietary models such as OpenAI's o1-mini and GPT-4o. We publicly release TeleChat2, TeleChat2.5 and T1, including post-trained versions with 35B and 115B parameters, to empower developers and researchers with state-of-the-art language models tailored for diverse applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tele-AI/TeleChat2", "github_api_added_by": "auto" }, { "date": "2025-07-25", "arxiv_id": "2507.18071", "title": "Group Sequence Policy Optimization", "authors": [ "Chujie Zheng", "Shixuan Liu", "Mingze Li", "Xiong-Hui Chen", "Bowen Yu", "Chang Gao", "Kai Dang", "Yuqiong Liu", "Rui Men", "An Yang", "Jingren Zhou", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "This paper introduces Group Sequence Policy Optimization (GSPO), our stable, efficient, and performant reinforcement learning algorithm for training large language models. Unlike previous algorithms that adopt token-level importance ratios, GSPO defines the importance ratio based on sequence likelihood and performs sequence-level clipping, rewarding, and optimization. We demonstrate that GSPO achieves superior training efficiency and performance compared to the GRPO algorithm, notably stabilizes Mixture-of-Experts (MoE) RL training, and has the potential for simplifying the design of RL infrastructure. These merits of GSPO have contributed to the remarkable improvements in the latest Qwen3 models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.16802", "title": "Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning", "authors": [ "Yanjun Zheng", "Xiyang Du", "Longfei Liao", "Xiaoke Zhao", "Zhaowen Zhou", "Jingze Song", "Bo Zhang", "Jiawei Liu", "Xiang Qi", "Zhe Li", "Zhiqiang Zhang", "Wei Wang", "Peng Zhang" ], "github": "https://github.com/antgroup/Finova", "project_page": "", "abstract": "Large Language Models (LLMs) exhibit considerable promise in financial applications; however, prevailing models frequently demonstrate limitations when confronted with scenarios that necessitate sophisticated reasoning capabilities, stringent trustworthiness criteria, and efficient adaptation to domain-specific requirements. We introduce the Agentar-Fin-R1 series of financial large language models (8B and 32B parameters), specifically engineered based on the Qwen3 foundation model to enhance reasoning capabilities, reliability, and domain specialization for financial applications. Our optimization approach integrates a high-quality, systematic financial task label system with a comprehensive multi-layered trustworthiness assurance framework. This framework encompasses high-quality trustworthy knowledge engineering, multi-agent trustworthy data synthesis, and rigorous data validation governance. Through label-guided automated difficulty-aware optimization, tow-stage training pipeline, and dynamic attribution systems, we achieve substantial improvements in training efficiency. Our models undergo comprehensive evaluation on mainstream financial benchmarks including Fineva, FinEval, and FinanceIQ, as well as general reasoning datasets such as MATH-500 and GPQA-diamond. To thoroughly assess real-world deployment capabilities, we innovatively propose the Finova evaluation benchmark, which focuses on agent-level financial reasoning and compliance verification. Experimental results demonstrate that Agentar-Fin-R1 not only achieves state-of-the-art performance on financial tasks but also exhibits exceptional general reasoning capabilities, validating its effectiveness as a trustworthy solution for high-stakes financial applications. The Finova bench is available at https://github.com/antgroup/Finova.", "github_manual": "", "github_regex": "https://github.com/antgroup/Finova", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.13546", "title": "nablaNABLA: Neighborhood Adaptive Block-Level Attention", "authors": [ "Dmitrii Mikhailov", "Aleksey Letunovskiy", "Maria Kovaleva", "Vladimir Arkhipkin", "Vladimir Korviakov", "Vladimir Polovnikov", "Viacheslav Vasilev", "Evelina Sidorova", "Denis Dimitrov" ], "github": "https://github.com/gen-ai-team/Wan2.1-NABLA", "project_page": "", "abstract": "Recent progress in transformer-based architectures has demonstrated remarkable success in video generation tasks. However, the quadratic complexity of full attention mechanisms remains a critical bottleneck, particularly for high-resolution and long-duration video sequences. In this paper, we propose NABLA, a novel Neighborhood Adaptive Block-Level Attention mechanism that dynamically adapts to sparsity patterns in video diffusion transformers (DiTs). By leveraging block-wise attention with adaptive sparsity-driven threshold, NABLA reduces computational overhead while preserving generative quality. Our method does not require custom low-level operator design and can be seamlessly integrated with PyTorch's Flex Attention operator. Experiments demonstrate that NABLA achieves up to 2.7x faster training and inference compared to baseline almost without compromising quantitative metrics (CLIP score, VBench score, human evaluation score) and visual quality drop. The code and model weights are available here: https://github.com/gen-ai-team/Wan2.1-NABLA", "github_manual": "", "github_regex": "https://github.com/gen-ai-team/Wan2.1-NABLA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.18405", "title": "Iwin Transformer: Hierarchical Vision Transformer using Interleaved Windows", "authors": [ "Simin Huo", "Ning Li" ], "github": "https://github.com/cominder/Iwin-Transformer", "project_page": "", "abstract": "We introduce Iwin Transformer, a novel position-embedding-free hierarchical vision transformer, which can be fine-tuned directly from low to high resolution, through the collaboration of innovative interleaved window attention and depthwise separable convolution. This approach uses attention to connect distant tokens and applies convolution to link neighboring tokens, enabling global information exchange within a single module, overcoming Swin Transformer's limitation of requiring two consecutive blocks to approximate global attention. Extensive experiments on visual benchmarks demonstrate that Iwin Transformer exhibits strong competitiveness in tasks such as image classification (87.4 top-1 accuracy on ImageNet-1K), semantic segmentation and video action recognition. We also validate the effectiveness of the core component in Iwin as a standalone module that can seamlessly replace the self-attention module in class-conditional image generation. The concepts and methods introduced by the Iwin Transformer have the potential to inspire future research, like Iwin 3D Attention in video generation. The code and models are available at https://github.com/cominder/Iwin-Transformer.", "github_manual": "", "github_regex": "https://github.com/cominder/Iwin-Transformer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.17402", "title": "HLFormer: Enhancing Partially Relevant Video Retrieval with Hyperbolic Learning", "authors": [ "Li Jun", "Wang Jinpeng", "Tan Chaolei", "Lian Niu", "Chen Long", "Zhang Min", "Wang Yaowei", "Xia Shu-Tao", "Chen Bin" ], "github": "https://github.com/lijun2005/ICCV25-HLFormer", "project_page": "", "abstract": "Partially Relevant Video Retrieval (PRVR) addresses the critical challenge of matching untrimmed videos with text queries describing only partial content. Existing methods suffer from geometric distortion in Euclidean space that sometimes misrepresents the intrinsic hierarchical structure of videos and overlooks certain hierarchical semantics, ultimately leading to suboptimal temporal modeling. To address this issue, we propose the first hyperbolic modeling framework for PRVR, namely HLFormer, which leverages hyperbolic space learning to compensate for the suboptimal hierarchical modeling capabilities of Euclidean space. Specifically, HLFormer integrates the Lorentz Attention Block and Euclidean Attention Block to encode video embeddings in hybrid spaces, using the Mean-Guided Adaptive Interaction Module to dynamically fuse features. Additionally, we introduce a Partial Order Preservation Loss to enforce \"text < video\" hierarchy through Lorentzian cone constraints. This approach further enhances cross-modal matching by reinforcing partial relevance between video content and text queries. Extensive experiments show that HLFormer outperforms state-of-the-art methods. Code is released at https://github.com/lijun2005/ICCV25-HLFormer.", "github_manual": "", "github_regex": "https://github.com/lijun2005/ICCV25-HLFormer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-25", "arxiv_id": "2507.15807", "title": "True Multimodal In-Context Learning Needs Attention to the Visual Context", "authors": [ "Shuo Chen", "Jianzhe Liu", "Zhen Han", "Yan Xia", "Daniel Cremers", "Philip Torr", "Volker Tresp", "Jindong Gu" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs), built on powerful language backbones, have enabled Multimodal In-Context Learning (MICL)-adapting to new tasks from a few multimodal demonstrations consisting of images, questions, and answers. Despite showing noticeable improvement on standard vision-language datasets, current MLLMs struggle to leverage visual information in the demonstrations. Specifically, they tend to neglect visual cues and over-rely on textual patterns, leading to mere text imitation rather than genuine multimodal adaptation. This behavior makes MICL still unimodal and largely restricts its practical utility. More importantly, this limitation is often concealed by the improved performance on tasks that do not require understanding the visual context. As a result, how to effectively enhance MICL ability and reliably evaluate the MICL performance remains underexplored. To address these issues, we first introduce Dynamic Attention Reallocation (DARA), an efficient fine-tuning strategy that encourages models to attend to the visual context by rebalancing attention across visual and textual tokens. In addition, we present TrueMICL, an MICL-dedicated dataset with both support and test sets that explicitly requires the integration of multimodal information-particularly visual content-for correct task completion. Extensive experiments demonstrate the effectiveness of our holistic solution, showcasing substantial improvements in the true multimodal in-context learning capabilities. Code and datasets are available at https://chenxshuo.github.io/true-micl-colm .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-28", "arxiv_id": "2507.10510", "title": "Chat with AI: The Surprising Turn of Real-time Video Communication from Human to AI", "authors": [ "Jiangkai Wu", "Zhiyuan Ren", "Liming Liu", "Xinggong Zhang" ], "github": "https://github.com/pku-netvideo/DeViBench", "project_page": "", "abstract": "AI Video Chat emerges as a new paradigm for Real-time Communication (RTC), where one peer is not a human, but a Multimodal Large Language Model (MLLM). This makes interaction between humans and AI more intuitive, as if chatting face-to-face with a real person. However, this poses significant challenges to latency, because the MLLM inference takes up most of the response time, leaving very little time for video streaming. Due to network uncertainty and instability, transmission latency becomes a critical bottleneck preventing AI from being like a real person. To address this, we propose Artic, an AI-oriented Real-time Communication framework, exploring the network requirement shift from \"humans watching video\" to \"AI understanding video\". To reduce bitrate dramatically while maintaining MLLM accuracy, we propose Context-Aware Video Streaming that recognizes the importance of each video region for chat and allocates bitrate almost exclusively to chat-important regions. To avoid packet retransmission, we propose Loss-Resilient Adaptive Frame Rate that leverages previous frames to substitute for lost/delayed frames while avoiding bitrate waste. To evaluate the impact of video streaming quality on MLLM accuracy, we build the first benchmark, named Degraded Video Understanding Benchmark (DeViBench). Finally, we discuss some open questions and ongoing solutions for AI Video Chat.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pku-netvideo/DeViBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-28", "arxiv_id": "2507.16075", "title": "Deep Researcher with Test-Time Diffusion", "authors": [ "Rujun Han", "Yanfei Chen", "Zoey CuiZhu", "Lesly Miculicich", "Guan Sun", "Yuanjun Bi", "Weiming Wen", "Hui Wan", "Chunfeng Wen", "Sol\u00e8ne Ma\u00eetre", "George Lee", "Vishy Tirumalashetty", "Emily Xue", "Zizhao Zhang", "Salem Haykal", "Burak Gokturk", "Tomas Pfister", "Chen-Yu Lee" ], "github": "https://github.com/assafelovic/gpt-researcher", "project_page": "", "abstract": "Deep research agents, powered by Large Language Models (LLMs), are rapidly advancing; yet, their performance often plateaus when generating complex, long-form research reports using generic test-time scaling algorithms. Drawing inspiration from the iterative nature of human research, which involves cycles of searching, reasoning, and revision, we propose the Test-Time Diffusion Deep Researcher (TTD-DR). This novel framework conceptualizes research report generation as a diffusion process. TTD-DR initiates this process with a preliminary draft, an updatable skeleton that serves as an evolving foundation to guide the research direction. The draft is then iteratively refined through a \"denoising\" process, which is dynamically informed by a retrieval mechanism that incorporates external information at each step. The core process is further enhanced by a self-evolutionary algorithm applied to each component of the agentic workflow, ensuring the generation of high-quality context for the diffusion process. This draft-centric design makes the report writing process more timely and coherent while reducing information loss during the iterative search process. We demonstrate that our TTD-DR achieves state-of-the-art results on a wide array of benchmarks that require intensive search and multi-hop reasoning, significantly outperforming existing deep research agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/assafelovic/gpt-researcher", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-28", "arxiv_id": "2507.18742", "title": "Specification Self-Correction: Mitigating In-Context Reward Hacking Through Test-Time Refinement", "authors": [ "V\u00edctor Gallego" ], "github": "https://github.com/vicgalle/specification-self-correction", "project_page": "", "abstract": "Language models (LMs) are susceptible to in-context reward hacking, where they exploit flaws in tainted or faulty written specifications or rubrics to achieve high scores without fulfilling the user's true intent. We introduce Specification Self-Correction (SSC), a novel, test-time framework that enables an LM to identify and correct flaws within its own guiding specification. SSC employs a multi-step inference process where the model first generates a response based on a potentially tainted specification, critiques its output, and then revises the specification itself to remove the exploitable loophole. A final, more robust response is then generated using this self-corrected specification. Across experiments spanning creative writing and agentic coding tasks with several LMs, we demonstrate that while models initially game tainted specifications in 50-70\\% of cases, the SSC process reduces this vulnerability by over 90\\%. This dynamic repair occurs at inference time, requires no weight modification, and leads to more robustly aligned model behavior. Code at https://github.com/vicgalle/specification-self-correction .", "github_manual": "", "github_regex": "https://github.com/vicgalle/specification-self-correction", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-28", "arxiv_id": "2507.17596", "title": "PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving", "authors": [ "Maciej K. Wozniak", "Lianhang Liu", "Yixi Cai", "Patric Jensfelt" ], "github": "https://github.com/maxiuw/prix", "project_page": "", "abstract": "While end-to-end autonomous driving models show promising results, their practical deployment is often hindered by large model sizes, a reliance on expensive LiDAR sensors and computationally intensive BEV feature representations. This limits their scalability, especially for mass-market vehicles equipped only with cameras. To address these challenges, we propose PRIX (Plan from Raw Pixels). Our novel and efficient end-to-end driving architecture operates using only camera data, without explicit BEV representation and forgoing the need for LiDAR. PRIX leverages a visual feature extractor coupled with a generative planning head to predict safe trajectories from raw pixel inputs directly. A core component of our architecture is the Context-aware Recalibration Transformer (CaRT), a novel module designed to effectively enhance multi-level visual features for more robust planning. We demonstrate through comprehensive experiments that PRIX achieves state-of-the-art performance on the NavSim and nuScenes benchmarks, matching the capabilities of larger, multimodal diffusion planners while being significantly more efficient in terms of inference speed and model size, making it a practical solution for real-world deployment. Our work is open-source and the code will be at https://maxiuw.github.io/prix.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/maxiuw/prix", "github_api_added_by": "auto" }, { "date": "2025-07-28", "arxiv_id": "2507.18553", "title": "The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm", "authors": [ "Jiale Chen", "Torsten Hoefler", "Dan Alistarh" ], "github": "", "project_page": "", "abstract": "Quantizing the weights of large language models (LLMs) from 16-bit to lower bitwidth is the de facto approach to deploy massive transformers onto more affordable accelerators. GPTQ emerged as one of the standard methods for one-shot post-training quantization at LLM scale. Yet, its inner workings are described as a sequence of ad-hoc algebraic updates that obscure any geometric meaning or worst-case guarantees. In this work, we show that, when executed back-to-front (from the last to first dimension) for a linear layer, GPTQ is mathematically identical to Babai's nearest plane algorithm for the classical closest vector problem (CVP) on a lattice defined by the Hessian matrix of the layer's inputs. This equivalence is based on a sophisticated mathematical argument, and has two analytical consequences: (i) the GPTQ error propagation step gains an intuitive geometric interpretation; (ii) GPTQ inherits the error upper bound of Babai's algorithm under the no-clipping condition. Taken together, these results place GPTQ on firm theoretical footing and open the door to importing decades of progress in lattice algorithms towards the design of future quantization algorithms for billion-parameter models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-28", "arxiv_id": "2507.19478", "title": "MMBench-GUI: Hierarchical Multi-Platform Evaluation Framework for GUI Agents", "authors": [ "Xuehui Wang", "Zhenyu Wu", "JingJing Xie", "Zichen Ding", "Bowen Yang", "Zehao Li", "Zhaoyang Liu", "Qingyun Li", "Xuan Dong", "Zhe Chen", "Weiyun Wang", "Xiangyu Zhao", "Jixuan Chen", "Haodong Duan", "Tianbao Xie", "Chenyu Yang", "Shiqian Su", "Yue Yu", "Yuan Huang", "Yiqian Liu", "Xiao Zhang", "Yanting Zhang", "Xiangyu Yue", "Weijie Su", "Xizhou Zhu", "Wei Shen", "Jifeng Dai", "Wenhai Wang" ], "github": "https://github.com/open-compass/MMBench-GUI", "project_page": "", "abstract": "We introduce MMBench-GUI, a hierarchical benchmark for evaluating GUI automation agents across Windows, macOS, Linux, iOS, Android, and Web platforms. It comprises four levels: GUI Content Understanding, Element Grounding, Task Automation, and Task Collaboration, covering essential skills for GUI agents. In addition, we propose a novel Efficiency-Quality Area (EQA) metric to assess GUI agent execution efficiency in online automation scenarios. Through MMBench-GUI, we identify accurate visual grounding as a critical determinant of overall task success, emphasizing the substantial benefits of modular frameworks that integrate specialized grounding modules. Furthermore, to achieve reliable GUI automation, an agent requires strong task planning and cross-platform generalization abilities, with long-context memory, a broad action space, and long-term reasoning playing a critical role. More important, task efficiency remains a critically underexplored dimension, and all models suffer from substantial inefficiencies, with excessive redundant steps even when tasks are ultimately completed. The integration of precise localization, effective planning, and early stopping strategies is indispensable to enable truly efficient and scalable GUI automation. Our benchmark code, evaluation data, and running environment will be publicly available at https://github.com/open-compass/MMBench-GUI.", "github_manual": "", "github_regex": "https://github.com/open-compass/MMBench-GUI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-28", "arxiv_id": "2507.18392", "title": "CLEAR: Error Analysis via LLM-as-a-Judge Made Easy", "authors": [ "Asaf Yehudai", "Lilach Eden", "Yotam Perlitz", "Roy Bar-Haim", "Michal Shmueli-Scheuer" ], "github": "", "project_page": "", "abstract": "The evaluation of Large Language Models (LLMs) increasingly relies on other LLMs acting as judges. However, current evaluation paradigms typically yield a single score or ranking, answering which model is better but not why. While essential for benchmarking, these top-level scores obscure the specific, actionable reasons behind a model's performance. To bridge this gap, we introduce CLEAR, an interactive, open-source package for LLM-based error analysis. CLEAR first generates per-instance textual feedback, then it creates a set of system-level error issues, and quantifies the prevalence of each identified issue. Our package also provides users with an interactive dashboard that allows for a comprehensive error analysis through aggregate visualizations, applies interactive filters to isolate specific issues or score ranges, and drills down to the individual instances that exemplify a particular behavioral pattern. We demonstrate CLEAR analysis for RAG and Math benchmarks, and showcase its utility through a user case study.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-28", "arxiv_id": "2507.16534", "title": "Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report", "authors": [ "Shanghai AI Lab", "Xiaoyang Chen", "Yunhao Chen", "Zeren Chen", "Zhiyun Chen", "Hanyun Cui", "Yawen Duan", "Jiaxuan Guo", "Qi Guo", "Xuhao Hu", "Hong Huang", "Lige Huang", "Chunxiao Li", "Juncheng Li", "Qihao Lin", "Dongrui Liu", "Xinmin Liu", "Zicheng Liu", "Chaochao Lu", "Xiaoya Lu", "Jingjing Qu", "Qibing Ren", "Jing Shao", "Jingwei Shi", "Jingwei Sun", "Peng Wang", "Weibing Wang", "Jia Xu", "Lewen Yan", "Xiao Yu", "Yi Yu", "Boxuan Zhang", "Jie Zhang", "Weichen Zhang", "Zhijie Zheng", "Tianyi Zhou", "Bowen Zhou" ], "github": "", "project_page": "", "abstract": "To understand and identify the unprecedented risks posed by rapidly advancing artificial intelligence (AI) models, this report presents a comprehensive assessment of their frontier risks. Drawing on the E-T-C analysis (deployment environment, threat source, enabling capability) from the Frontier AI Risk Management Framework (v1.0) (SafeWork-F1-Framework), we identify critical risks in seven areas: cyber offense, biological and chemical risks, persuasion and manipulation, uncontrolled autonomous AI R\\&D, strategic deception and scheming, self-replication, and collusion. Guided by the \"AI-45^circ Law,\" we evaluate these risks using \"red lines\" (intolerable thresholds) and \"yellow lines\" (early warning indicators) to define risk zones: green (manageable risk for routine deployment and continuous monitoring), yellow (requiring strengthened mitigations and controlled deployment), and red (necessitating suspension of development and/or deployment). Experimental results show that all recent frontier AI models reside in green and yellow zones, without crossing red lines. Specifically, no evaluated models cross the yellow line for cyber offense or uncontrolled AI R\\&D risks. For self-replication, and strategic deception and scheming, most models remain in the green zone, except for certain reasoning models in the yellow zone. In persuasion and manipulation, most models are in the yellow zone due to their effective influence on humans. For biological and chemical risks, we are unable to rule out the possibility of most models residing in the yellow zone, although detailed threat modeling and in-depth assessment are required to make further claims. This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-28", "arxiv_id": "2507.19457", "title": "GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning", "authors": [ "Lakshya A Agrawal", "Shangyin Tan", "Dilara Soylu", "Noah Ziems", "Rishi Khare", "Krista Opsahl-Ong", "Arnav Singhvi", "Herumb Shandilya", "Michael J Ryan", "Meng Jiang", "Christopher Potts", "Koushik Sen", "Alexandros G. Dimakis", "Ion Stoica", "Dan Klein", "Matei Zaharia", "Omar Khattab" ], "github": "https://github.com/meta-llama/llama-prompt-ops", "project_page": "", "abstract": "Large language models (LLMs) are increasingly adapted to downstream tasks via reinforcement learning (RL) methods like Group Relative Policy Optimization (GRPO), which often require thousands of rollouts to learn new tasks. We argue that the interpretable nature of language can often provide a much richer learning medium for LLMs, compared with policy gradients derived from sparse, scalar rewards. To test this, we introduce GEPA (Genetic-Pareto), a prompt optimizer that thoroughly incorporates natural language reflection to learn high-level rules from trial and error. Given any AI system containing one or more LLM prompts, GEPA samples system-level trajectories (e.g., reasoning, tool calls, and tool outputs) and reflects on them in natural language to diagnose problems, propose and test prompt updates, and combine complementary lessons from the Pareto frontier of its own attempts. As a result of GEPA's design, it can often turn even just a few rollouts into a large quality gain. Across four tasks, GEPA outperforms GRPO by 10% on average and by up to 20%, while using up to 35x fewer rollouts. GEPA also outperforms the leading prompt optimizer, MIPROv2, by over 10% across two LLMs, and demonstrates promising results as an inference-time search strategy for code optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/meta-llama/llama-prompt-ops", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-28", "arxiv_id": "2507.20198", "title": "When Tokens Talk Too Much: A Survey of Multimodal Long-Context Token Compression across Images, Videos, and Audios", "authors": [ "Kele Shao", "Keda Tao", "Kejia Zhang", "Sicheng Feng", "Mu Cai", "Yuzhang Shang", "Haoxuan You", "Can Qin", "Yang Sui", "Huan Wang" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have made remarkable strides, largely driven by their ability to process increasingly long and complex contexts, such as high-resolution images, extended video sequences, and lengthy audio input. While this ability significantly enhances MLLM capabilities, it introduces substantial computational challenges, primarily due to the quadratic complexity of self-attention mechanisms with numerous input tokens. To mitigate these bottlenecks, token compression has emerged as an auspicious and critical approach, efficiently reducing the number of tokens during both training and inference. In this paper, we present the first systematic survey and synthesis of the burgeoning field of multimodal long context token compression. Recognizing that effective compression strategies are deeply tied to the unique characteristics and redundancies of each modality, we categorize existing approaches by their primary data focus, enabling researchers to quickly access and learn methods tailored to their specific area of interest: (1) image-centric compression, which addresses spatial redundancy in visual data; (2) video-centric compression, which tackles spatio-temporal redundancy in dynamic sequences; and (3) audio-centric compression, which handles temporal and spectral redundancy in acoustic signals. Beyond this modality-driven categorization, we further dissect methods based on their underlying mechanisms, including transformation-based, similarity-based, attention-based, and query-based approaches. By providing a comprehensive and structured overview, this survey aims to consolidate current progress, identify key challenges, and inspire future research directions in this rapidly evolving domain. We also maintain a public repository to continuously track and update the latest advances in this promising area.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-28", "arxiv_id": "2507.17957", "title": "AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation", "authors": [ "Md. Al-Masrur Khan", "Durgakant Pushp", "Lantao Liu" ], "github": "https://github.com/Masrur02/AFRDA", "project_page": "", "abstract": "In Unsupervised Domain Adaptive Semantic Segmentation (UDA-SS), a model is trained on labeled source domain data (e.g., synthetic images) and adapted to an unlabeled target domain (e.g., real-world images) without access to target annotations. Existing UDA-SS methods often struggle to balance fine-grained local details with global contextual information, leading to segmentation errors in complex regions. To address this, we introduce the Adaptive Feature Refinement (AFR) module, which enhances segmentation accuracy by refining highresolution features using semantic priors from low-resolution logits. AFR also integrates high-frequency components, which capture fine-grained structures and provide crucial boundary information, improving object delineation. Additionally, AFR adaptively balances local and global information through uncertaintydriven attention, reducing misclassifications. Its lightweight design allows seamless integration into HRDA-based UDA methods, leading to state-of-the-art segmentation performance. Our approach improves existing UDA-SS methods by 1.05% mIoU on GTA V --> Cityscapes and 1.04% mIoU on Synthia-->Cityscapes. The implementation of our framework is available at: https://github.com/Masrur02/AFRDA", "github_manual": "", "github_regex": "https://github.com/Masrur02/AFRDA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.21045", "title": "Reconstructing 4D Spatial Intelligence: A Survey", "authors": [ "Yukang Cao", "Jiahao Lu", "Zhisheng Huang", "Zhuowei Shen", "Chengfeng Zhao", "Fangzhou Hong", "Zhaoxi Chen", "Xin Li", "Wenping Wang", "Yuan Liu", "Ziwei Liu" ], "github": "https://github.com/yukangcao/Awesome-4D-Spatial-Intelligence", "project_page": "", "abstract": "Reconstructing 4D spatial intelligence from visual observations has long been a central yet challenging task in computer vision, with broad real-world applications. These range from entertainment domains like movies, where the focus is often on reconstructing fundamental visual elements, to embodied AI, which emphasizes interaction modeling and physical realism. Fueled by rapid advances in 3D representations and deep learning architectures, the field has evolved quickly, outpacing the scope of previous surveys. Additionally, existing surveys rarely offer a comprehensive analysis of the hierarchical structure of 4D scene reconstruction. To address this gap, we present a new perspective that organizes existing methods into five progressive levels of 4D spatial intelligence: (1) Level 1 -- reconstruction of low-level 3D attributes (e.g., depth, pose, and point maps); (2) Level 2 -- reconstruction of 3D scene components (e.g., objects, humans, structures); (3) Level 3 -- reconstruction of 4D dynamic scenes; (4) Level 4 -- modeling of interactions among scene components; and (5) Level 5 -- incorporation of physical laws and constraints. We conclude the survey by discussing the key challenges at each level and highlighting promising directions for advancing toward even richer levels of 4D spatial intelligence. To track ongoing developments, we maintain an up-to-date project page: https://github.com/yukangcao/Awesome-4D-Spatial-Intelligence.", "github_manual": "", "github_regex": "https://github.com/yukangcao/Awesome-4D-Spatial-Intelligence", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.20939", "title": "ARC-Hunyuan-Video-7B: Structured Video Comprehension of Real-World Shorts", "authors": [ "Yuying Ge", "Yixiao Ge", "Chen Li", "Teng Wang", "Junfu Pu", "Yizhuo Li", "Lu Qiu", "Jin Ma", "Lisheng Duan", "Xinyu Zuo", "Jinwen Luo", "Weibo Gu", "Zexuan Li", "Xiaojing Zhang", "Yangyu Tao", "Han Hu", "Di Wang", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Real-world user-generated short videos, especially those distributed on platforms such as WeChat Channel and TikTok, dominate the mobile internet. However, current large multimodal models lack essential temporally-structured, detailed, and in-depth video comprehension capabilities, which are the cornerstone of effective video search and recommendation, as well as emerging video applications. Understanding real-world shorts is actually challenging due to their complex visual elements, high information density in both visuals and audio, and fast pacing that focuses on emotional expression and viewpoint delivery. This requires advanced reasoning to effectively integrate multimodal information, including visual, audio, and text. In this work, we introduce ARC-Hunyuan-Video, a multimodal model that processes visual, audio, and textual signals from raw video inputs end-to-end for structured comprehension. The model is capable of multi-granularity timestamped video captioning and summarization, open-ended video question answering, temporal video grounding, and video reasoning. Leveraging high-quality data from an automated annotation pipeline, our compact 7B-parameter model is trained through a comprehensive regimen: pre-training, instruction fine-tuning, cold start, reinforcement learning (RL) post-training, and final instruction fine-tuning. Quantitative evaluations on our introduced benchmark ShortVid-Bench and qualitative comparisons demonstrate its strong performance in real-world video comprehension, and it supports zero-shot or fine-tuning with a few samples for diverse downstream applications. The real-world production deployment of our model has yielded tangible and measurable improvements in user engagement and satisfaction, a success supported by its remarkable efficiency, with stress tests indicating an inference time of just 10 seconds for a one-minute video on H20 GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.20025", "title": "Region-based Cluster Discrimination for Visual Representation Learning", "authors": [ "Yin Xie", "Kaicheng Yang", "Xiang An", "Kun Wu", "Yongle Zhao", "Weimo Deng", "Zimin Ran", "Yumeng Wang", "Ziyong Feng", "Roy Miles", "Ismail Elezi", "Jiankang Deng" ], "github": "https://github.com/deepglint/MVT", "project_page": "", "abstract": "Learning visual representations is foundational for a broad spectrum of downstream tasks. Although recent vision-language contrastive models, such as CLIP and SigLIP, have achieved impressive zero-shot performance via large-scale vision-language alignment, their reliance on global representations constrains their effectiveness for dense prediction tasks, such as grounding, OCR, and segmentation. To address this gap, we introduce Region-Aware Cluster Discrimination (RICE), a novel method that enhances region-level visual and OCR capabilities. We first construct a billion-scale candidate region dataset and propose a Region Transformer layer to extract rich regional semantics. We further design a unified region cluster discrimination loss that jointly supports object and OCR learning within a single classification framework, enabling efficient and scalable distributed training on large-scale data. Extensive experiments show that RICE consistently outperforms previous methods on tasks, including segmentation, dense detection, and visual perception for Multimodal Large Language Models (MLLMs). The pre-trained models have been released at https://github.com/deepglint/MVT.", "github_manual": "", "github_regex": "https://github.com/deepglint/MVT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.19804", "title": "ForCenNet: Foreground-Centric Network for Document Image Rectification", "authors": [ "Peng Cai", "Qiang Li", "Kaicheng Yang", "Dong Guo", "Jia Li", "Nan Zhou", "Xiang An", "Ninghua Yang", "Jiankang Deng" ], "github": "https://github.com/caipeng328/ForCenNet", "project_page": "", "abstract": "Document image rectification aims to eliminate geometric deformation in photographed documents to facilitate text recognition. However, existing methods often neglect the significance of foreground elements, which provide essential geometric references and layout information for document image correction. In this paper, we introduce Foreground-Centric Network (ForCenNet) to eliminate geometric distortions in document images. Specifically, we initially propose a foreground-centric label generation method, which extracts detailed foreground elements from an undistorted image. Then we introduce a foreground-centric mask mechanism to enhance the distinction between readable and background regions. Furthermore, we design a curvature consistency loss to leverage the detailed foreground labels to help the model understand the distorted geometric distribution. Extensive experiments demonstrate that ForCenNet achieves new state-of-the-art on four real-world benchmarks, such as DocUNet, DIR300, WarpDoc, and DocReal. Quantitative analysis shows that the proposed method effectively undistorts layout elements, such as text lines and table borders. The resources for further comparison are provided at https://github.com/caipeng328/ForCenNet.", "github_manual": "", "github_regex": "https://github.com/caipeng328/ForCenNet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.20900", "title": "Music Arena: Live Evaluation for Text-to-Music", "authors": [ "Yonghyun Kim", "Wayne Chi", "Anastasios N. Angelopoulos", "Wei-Lin Chiang", "Koichi Saito", "Shinji Watanabe", "Yuki Mitsufuji", "Chris Donahue" ], "github": "https://github.com/gclef-cmu/music-arena", "project_page": "", "abstract": "We present Music Arena, an open platform for scalable human preference evaluation of text-to-music (TTM) models. Soliciting human preferences via listening studies is the gold standard for evaluation in TTM, but these studies are expensive to conduct and difficult to compare, as study protocols may differ across systems. Moreover, human preferences might help researchers align their TTM systems or improve automatic evaluation metrics, but an open and renewable source of preferences does not currently exist. We aim to fill these gaps by offering *live* evaluation for TTM. In Music Arena, real-world users input text prompts of their choosing and compare outputs from two TTM systems, and their preferences are used to compile a leaderboard. While Music Arena follows recent evaluation trends in other AI domains, we also design it with key features tailored to music: an LLM-based routing system to navigate the heterogeneous type signatures of TTM systems, and the collection of *detailed* preferences including listening data and natural language feedback. We also propose a rolling data release policy with user privacy guarantees, providing a renewable source of preference data and increasing platform transparency. Through its standardized evaluation protocol, transparent data access policies, and music-specific features, Music Arena not only addresses key challenges in the TTM ecosystem but also demonstrates how live evaluation can be thoughtfully adapted to unique characteristics of specific AI domains. Music Arena is available at: https://music-arena.org", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gclef-cmu/music-arena", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.20880", "title": "JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment", "authors": [ "Renhang Liu", "Chia-Yu Hung", "Navonil Majumder", "Taylor Gautreaux", "Amir Ali Bagherzadeh", "Chuan Li", "Dorien Herremans", "Soujanya Poria" ], "github": "https://github.com/spring-media/DeepPhonemizer", "project_page": "", "abstract": "Diffusion and flow-matching models have revolutionized automatic text-to-audio generation in recent times. These models are increasingly capable of generating high quality and faithful audio outputs capturing to speech and acoustic events. However, there is still much room for improvement in creative audio generation that primarily involves music and songs. Recent open lyrics-to-song models, such as, DiffRhythm, ACE-Step, and LeVo, have set an acceptable standard in automatic song generation for recreational use. However, these models lack fine-grained word-level controllability often desired by musicians in their workflows. To the best of our knowledge, our flow-matching-based JAM is the first effort toward endowing word-level timing and duration control in song generation, allowing fine-grained vocal control. To enhance the quality of generated songs to better align with human preferences, we implement aesthetic alignment through Direct Preference Optimization, which iteratively refines the model using a synthetic dataset, eliminating the need or manual data annotations. Furthermore, we aim to standardize the evaluation of such lyrics-to-song models through our public evaluation dataset JAME. We show that JAM outperforms the existing models in terms of the music-specific attributes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/spring-media/DeepPhonemizer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.20984", "title": "SmallThinker: A Family of Efficient Large Language Models Natively Trained for Local Deployment", "authors": [ "Yixin Song", "Zhenliang Xue", "Dongliang Wei", "Feiyang Chen", "Jianxiang Gao", "Junchen Liu", "Hangyu Liang", "Guangshuo Qin", "Chengrong Tian", "Bo Wen", "Longyu Zhao", "Xinrui Zheng", "Zeyu Mi", "Haibo Chen" ], "github": "", "project_page": "", "abstract": "While frontier large language models (LLMs) continue to push capability boundaries, their deployment remains confined to GPU-powered cloud infrastructure. We challenge this paradigm with SmallThinker, a family of LLMs natively designed - not adapted - for the unique constraints of local devices: weak computational power, limited memory, and slow storage. Unlike traditional approaches that mainly compress existing models built for clouds, we architect SmallThinker from the ground up to thrive within these limitations. Our innovation lies in a deployment-aware architecture that transforms constraints into design principles. First, We introduce a two-level sparse structure combining fine-grained Mixture-of-Experts (MoE) with sparse feed-forward networks, drastically reducing computational demands without sacrificing model capacity. Second, to conquer the I/O bottleneck of slow storage, we design a pre-attention router that enables our co-designed inference engine to prefetch expert parameters from storage while computing attention, effectively hiding storage latency that would otherwise cripple on-device inference. Third, for memory efficiency, we utilize NoPE-RoPE hybrid sparse attention mechanism to slash KV cache requirements. We release SmallThinker-4B-A0.6B and SmallThinker-21B-A3B, which achieve state-of-the-art performance scores and even outperform larger LLMs. Remarkably, our co-designed system mostly eliminates the need for expensive GPU hardware: with Q4_0 quantization, both models exceed 20 tokens/s on ordinary consumer CPUs, while consuming only 1GB and 8GB of memory respectively. SmallThinker is publicly available at hf.co/PowerInfer/SmallThinker-4BA0.6B-Instruct and hf.co/PowerInfer/SmallThinker-21BA3B-Instruct.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.19849", "title": "Agentic Reinforced Policy Optimization", "authors": [ "Guanting Dong", "Hangyu Mao", "Kai Ma", "Licheng Bao", "Yifei Chen", "Zhongyuan Wang", "Zhongxia Chen", "Jiazhen Du", "Huiyang Wang", "Fuzheng Zhang", "Guorui Zhou", "Yutao Zhu", "Ji-Rong Wen", "Zhicheng Dou" ], "github": "https://github.com/dongguanting/ARPO", "project_page": "", "abstract": "Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn reasoning tasks. In realistic reasoning scenarios, LLMs can often utilize external tools to assist in task-solving processes. However, current RL algorithms inadequately balance the models' intrinsic long-horizon reasoning capabilities and their proficiency in multi-turn tool interactions. To bridge this gap, we propose Agentic Reinforced Policy Optimization (ARPO), a novel agentic RL algorithm tailored for training multi-turn LLM-based agents. Through preliminary experiments, we observe that LLMs tend to exhibit highly uncertain behavior, characterized by an increase in the entropy distribution of generated tokens, immediately following interactions with external tools. Motivated by this observation, ARPO incorporates an entropy-based adaptive rollout mechanism, dynamically balancing global trajectory sampling and step-level sampling, thereby promoting exploration at steps with high uncertainty after tool usage. By integrating an advantage attribution estimation, ARPO enables LLMs to internalize advantage differences in stepwise tool-use interactions. Our experiments across 13 challenging benchmarks in computational reasoning, knowledge reasoning, and deep search domains demonstrate ARPO's superiority over trajectory-level RL algorithms. Remarkably, ARPO achieves improved performance using only half of the tool-use budget required by existing methods, offering a scalable solution for aligning LLM-based agents with real-time dynamic environments. Our code and datasets are released at https://github.com/dongguanting/ARPO", "github_manual": "", "github_regex": "https://github.com/dongguanting/ARPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.21033", "title": "GPT-IMAGE-EDIT-1.5M: A Million-Scale, GPT-Generated Image Dataset", "authors": [ "Yuhan Wang", "Siwei Yang", "Bingchen Zhao", "Letian Zhang", "Qing Liu", "Yuyin Zhou", "Cihang Xie" ], "github": "", "project_page": "", "abstract": "Recent advancements in large multimodal models like GPT-4o have set a new standard for high-fidelity, instruction-guided image editing. However, the proprietary nature of these models and their training data creates a significant barrier for open-source research. To bridge this gap, we introduce GPT-IMAGE-EDIT-1.5M, a publicly available, large-scale image-editing corpus containing more than 1.5 million high-quality triplets (instruction, source image, edited image). We systematically construct this dataset by leveraging the versatile capabilities of GPT-4o to unify and refine three popular image-editing datasets: OmniEdit, HQ-Edit, and UltraEdit. Specifically, our methodology involves 1) regenerating output images to enhance visual quality and instruction alignment, and 2) selectively rewriting prompts to improve semantic clarity. To validate the efficacy of our dataset, we fine-tune advanced open-source models on GPT-IMAGE-EDIT-1.5M. The empirical results are exciting, e.g., the fine-tuned FluxKontext achieves highly competitive performance across a comprehensive suite of benchmarks, including 7.24 on GEdit-EN, 3.80 on ImgEdit-Full, and 8.78 on Complex-Edit, showing stronger instruction following and higher perceptual quality while maintaining identity. These scores markedly exceed all previously published open-source methods and substantially narrow the gap to leading proprietary models. We hope the full release of GPT-IMAGE-EDIT-1.5M can help to catalyze further open research in instruction-guided image editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.20673", "title": "Geometric-Mean Policy Optimization", "authors": [ "Yuzhong Zhao", "Yue Liu", "Junpeng Liu", "Jingye Chen", "Xun Wu", "Yaru Hao", "Tengchao Lv", "Shaohan Huang", "Lei Cui", "Qixiang Ye", "Fang Wan", "Furu Wei" ], "github": "https://github.com/callsys/GMPO", "project_page": "", "abstract": "Recent advancements, such as Group Relative Policy Optimization (GRPO), have enhanced the reasoning capabilities of large language models by optimizing the arithmetic mean of token-level rewards. However, GRPO suffers from unstable policy updates when processing tokens with outlier importance-weighted rewards, which manifests as extreme importance sampling ratios during training, i.e., the ratio between the sampling probabilities assigned to a token by the current and old policies. In this work, we propose Geometric-Mean Policy Optimization (GMPO), a stabilized variant of GRPO. Instead of optimizing the arithmetic mean, GMPO maximizes the geometric mean of token-level rewards, which is inherently less sensitive to outliers and maintains a more stable range of importance sampling ratio. In addition, we provide comprehensive theoretical and experimental analysis to justify the design and stability benefits of GMPO. Beyond improved stability, GMPO-7B outperforms GRPO by an average of 4.1% on multiple mathematical benchmarks and 1.4% on multimodal reasoning benchmark, including AIME24, AMC, MATH500, OlympiadBench, Minerva, and Geometry3K. Code is available at https://github.com/callsys/GMPO.", "github_manual": "", "github_regex": "https://github.com/callsys/GMPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.21049", "title": "Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning", "authors": [ "Zedong Wang", "Siyuan Li", "Dan Xu" ], "github": "", "project_page": "", "abstract": "Despite the promise of Multi-Task Learning in leveraging complementary knowledge across tasks, existing multi-task optimization (MTO) techniques remain fixated on resolving conflicts via optimizer-centric loss scaling and gradient manipulation strategies, yet fail to deliver consistent gains. In this paper, we argue that the shared representation space, where task interactions naturally occur, offers rich information and potential for operations complementary to existing optimizers, especially for facilitating the inter-task complementarity, which is rarely explored in MTO. This intuition leads to Rep-MTL, which exploits the representation-level task saliency to quantify interactions between task-specific optimization and shared representation learning. By steering these saliencies through entropy-based penalization and sample-wise cross-task alignment, Rep-MTL aims to mitigate negative transfer by maintaining the effective training of individual tasks instead pure conflict-solving, while explicitly promoting complementary information sharing. Experiments are conducted on four challenging MTL benchmarks covering both task-shift and domain-shift scenarios. The results show that Rep-MTL, even paired with the basic equal weighting policy, achieves competitive performance gains with favorable efficiency. Beyond standard performance metrics, Power Law exponent analysis demonstrates Rep-MTL's efficacy in balancing task-specific learning and cross-task sharing. The project page is available at HERE.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.21046", "title": "A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence", "authors": [ "Huan-ang Gao", "Jiayi Geng", "Wenyue Hua", "Mengkang Hu", "Xinzhe Juan", "Hongzhang Liu", "Shilong Liu", "Jiahao Qiu", "Xuan Qi", "Yiran Wu", "Hongru Wang", "Han Xiao", "Yuhang Zhou", "Shaokun Zhang", "Jiayi Zhang", "Jinyu Xiang", "Yixiong Fang", "Qiwen Zhao", "Dongrui Liu", "Qihan Ren", "Cheng Qian", "Zhenghailong Wang", "Minda Hu", "Huazheng Wang", "Qingyun Wu", "Heng Ji", "Mengdi Wang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static, unable to adapt their internal parameters to novel tasks, evolving knowledge domains, or dynamic interaction contexts. As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck, necessitating agents that can adaptively reason, act, and evolve in real time. This paradigm shift -- from scaling static models to developing self-evolving agents -- has sparked growing interest in architectures and methods enabling continual learning and adaptation from data, interactions, and experiences. This survey provides the first systematic and comprehensive review of self-evolving agents, organized around three foundational dimensions -- what to evolve, when to evolve, and how to evolve. We examine evolutionary mechanisms across agent components (e.g., models, memory, tools, architecture), categorize adaptation methods by stages (e.g., intra-test-time, inter-test-time), and analyze the algorithmic and architectural designs that guide evolutionary adaptation (e.g., scalar rewards, textual feedback, single-agent and multi-agent systems). Additionally, we analyze evaluation metrics and benchmarks tailored for self-evolving agents, highlight applications in domains such as coding, education, and healthcare, and identify critical challenges and research directions in safety, scalability, and co-evolutionary dynamics. By providing a structured framework for understanding and designing self-evolving agents, this survey establishes a roadmap for advancing adaptive agentic systems in both research and real-world deployments, ultimately shedding lights to pave the way for the realization of Artificial Super Intelligence (ASI), where agents evolve autonomously, performing at or beyond human-level intelligence across a wide array of tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.21035", "title": "GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis", "authors": [ "Haoyang Liu", "Yijiang Li", "Haohan Wang" ], "github": "https://github.com/Liu-Hy/GenoMAS", "project_page": "", "abstract": "Gene expression analysis holds the key to many biomedical discoveries, yet extracting insights from raw transcriptomic data remains formidable due to the complexity of multiple large, semi-structured files and the need for extensive domain expertise. Current automation approaches are often limited by either inflexible workflows that break down in edge cases or by fully autonomous agents that lack the necessary precision for rigorous scientific inquiry. GenoMAS charts a different course by presenting a team of LLM-based scientists that integrates the reliability of structured workflows with the adaptability of autonomous agents. GenoMAS orchestrates six specialized LLM agents through typed message-passing protocols, each contributing complementary strengths to a shared analytic canvas. At the heart of GenoMAS lies a guided-planning framework: programming agents unfold high-level task guidelines into Action Units and, at each juncture, elect to advance, revise, bypass, or backtrack, thereby maintaining logical coherence while bending gracefully to the idiosyncrasies of genomic data. On the GenoTEX benchmark, GenoMAS reaches a Composite Similarity Correlation of 89.13% for data preprocessing and an F_1 of 60.48% for gene identification, surpassing the best prior art by 10.61% and 16.85% respectively. Beyond metrics, GenoMAS surfaces biologically plausible gene-phenotype associations corroborated by the literature, all while adjusting for latent confounders. Code is available at https://github.com/Liu-Hy/GenoMAS.", "github_manual": "", "github_regex": "https://github.com/Liu-Hy/GenoMAS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.19766", "title": "UloRL:An Ultra-Long Output Reinforcement Learning Approach for Advancing Large Language Models' Reasoning Abilities", "authors": [ "Dong Du", "Shulin Liu", "Tao Yang", "Shaohua Chen", "Yang Li" ], "github": "https://github.com/liushulinle/ULORL", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have highlighted the potential of reinforcement learning with verifiable rewards (RLVR) to enhance reasoning capabilities through extended output sequences. However, traditional RL frameworks face inefficiencies when handling ultra-long outputs due to long-tail sequence distributions and entropy collapse during training. To address these challenges, we propose an Ultra-Long Output Reinforcement Learning (UloRL) approach for advancing large language models' reasoning abilities. Specifically, we divide ultra long output decoding into short segments, enabling efficient training by mitigating delays caused by long-tail samples. Additionally, we introduce dynamic masking of well-Mastered Positive Tokens (MPTs) to prevent entropy collapse. Experimental results demonstrate the effectiveness of our approach. On the Qwen3-30B-A3B model, RL with segment rollout achieved 2.06x increase in training speed, while RL training with 128k-token outputs improves the model's performance on AIME2025 from 70.9\\% to 85.1\\% and on BeyondAIME from 50.7\\% to 61.9\\%, even surpassing Qwen3-235B-A22B with remarkable gains. These findings underscore the potential of our methods to advance the reasoning capabilities of LLMs with ultra-long sequence generation. We will release our code and model for further use by the community.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/liushulinle/ULORL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.19058", "title": "ScenePainter: Semantically Consistent Perpetual 3D Scene Generation with Concept Relation Alignment", "authors": [ "Chong Xia", "Shengjun Zhang", "Fangfu Liu", "Chang Liu", "Khodchaphun Hirunyaratsameewong", "Yueqi Duan" ], "github": "", "project_page": "", "abstract": "Perpetual 3D scene generation aims to produce long-range and coherent 3D view sequences, which is applicable for long-term video synthesis and 3D scene reconstruction. Existing methods follow a \"navigate-and-imagine\" fashion and rely on outpainting for successive view expansion. However, the generated view sequences suffer from semantic drift issue derived from the accumulated deviation of the outpainting module. To tackle this challenge, we propose ScenePainter, a new framework for semantically consistent 3D scene generation, which aligns the outpainter's scene-specific prior with the comprehension of the current scene. To be specific, we introduce a hierarchical graph structure dubbed SceneConceptGraph to construct relations among multi-level scene concepts, which directs the outpainter for consistent novel views and can be dynamically refined to enhance diversity. Extensive experiments demonstrate that our framework overcomes the semantic drift issue and generates more consistent and immersive 3D view sequences. Project Page: https://xiac20.github.io/ScenePainter/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.17189", "title": "Met^2Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for Complex Meteorological Systems", "authors": [ "Shaohan Li", "Hao Yang", "Min Chen", "Xiaolin Qin" ], "github": "https://github.com/ShremG/Met2Net", "project_page": "", "abstract": "The increasing frequency of extreme weather events due to global climate change urges accurate weather prediction. Recently, great advances have been made by the end-to-end methods, thanks to deep learning techniques, but they face limitations of representation inconsistency in multivariable integration and struggle to effectively capture the dependency between variables, which is required in complex weather systems. Treating different variables as distinct modalities and applying a two-stage training approach from multimodal models can partially alleviate this issue, but due to the inconformity in training tasks between the two stages, the results are often suboptimal. To address these challenges, we propose an implicit two-stage training method, configuring separate encoders and decoders for each variable. In detailed, in the first stage, the Translator is frozen while the Encoders and Decoders learn a shared latent space, in the second stage, the Encoders and Decoders are frozen, and the Translator captures inter-variable interactions for prediction. Besides, by introducing a self-attention mechanism for multivariable fusion in the latent space, the performance achieves further improvements. Empirically, extensive experiments show the state-of-the-art performance of our method. Specifically, it reduces the MSE for near-surface air temperature and relative humidity predictions by 28.82\\% and 23.39\\%, respectively. The source code is available at https://github.com/ShremG/Met2Net.", "github_manual": "", "github_regex": "https://github.com/ShremG/Met2Net", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.16806", "title": "Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty", "authors": [ "Mehul Damani", "Isha Puri", "Stewart Slocum", "Idan Shenfeld", "Leshem Choshen", "Yoon Kim", "Jacob Andreas" ], "github": "", "project_page": "", "abstract": "When language models (LMs) are trained via reinforcement learning (RL) to generate natural language \"reasoning chains\", their performance improves on a variety of difficult question answering tasks. Today, almost all successful applications of RL for reasoning use binary reward functions that evaluate the correctness of LM outputs. Because such reward functions do not penalize guessing or low-confidence outputs, they often have the unintended side-effect of degrading calibration and increasing the rate at which LMs generate incorrect responses (or \"hallucinate\") in other problem domains. This paper describes RLCR (Reinforcement Learning with Calibration Rewards), an approach to training reasoning models that jointly improves accuracy and calibrated confidence estimation. During RLCR, LMs generate both predictions and numerical confidence estimates after reasoning. They are trained to optimize a reward function that augments a binary correctness score with a Brier score -- a scoring rule for confidence estimates that incentivizes calibrated prediction. We first prove that this reward function (or any analogous reward function that uses a bounded, proper scoring rule) yields models whose predictions are both accurate and well-calibrated. We next show that across diverse datasets, RLCR substantially improves calibration with no loss in accuracy, on both in-domain and out-of-domain evaluations -- outperforming both ordinary RL training and classifiers trained to assign post-hoc confidence scores. While ordinary RL hurts calibration, RLCR improves it. Finally, we demonstrate that verbalized confidence can be leveraged at test time to improve accuracy and calibration via confidence-weighted scaling methods. Our results show that explicitly optimizing for calibration can produce more generally reliable reasoning models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.20187", "title": "Diversity-Enhanced Reasoning for Subjective Questions", "authors": [ "Yumeng Wang", "Zhiyuan Fan", "Jiayu Liu", "Yi R. Fung" ], "github": "https://github.com/toward-agi/diverse-o1.git", "project_page": "", "abstract": "Large reasoning models (LRM) with long chain-of-thought (CoT) capabilities have shown strong performance on objective tasks, such as math reasoning and coding. However, their effectiveness on subjective questions that may have different responses from different perspectives is still limited by a tendency towards homogeneous reasoning, introduced by the reliance on a single ground truth in supervised fine-tuning and verifiable reward in reinforcement learning. Motivated by the finding that increasing role perspectives consistently improves performance, we propose MultiRole-R1, a diversity-enhanced framework with multiple role perspectives, to improve the accuracy and diversity in subjective reasoning tasks. MultiRole-R1 features an unsupervised data construction pipeline that generates reasoning chains that incorporate diverse role perspectives. We further employ reinforcement learning via Group Relative Policy Optimization (GRPO) with reward shaping, by taking diversity as a reward signal in addition to the verifiable reward. With specially designed reward functions, we successfully promote perspective diversity and lexical diversity, uncovering a positive relation between reasoning diversity and accuracy. Our experiment on six benchmarks demonstrates MultiRole-R1's effectiveness and generalizability in enhancing both subjective and objective reasoning, showcasing the potential of diversity-enhanced training in LRMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/toward-agi/diverse-o1.git", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.20527", "title": "SAND-Math: Using LLMs to Generate Novel, Difficult and Useful Mathematics Questions and Answers", "authors": [ "Chaitanya Manem", "Pratik Prabhanjan Brahma", "Prakamya Mishra", "Zicheng Liu", "Emad Barsoum" ], "github": "", "project_page": "", "abstract": "The demand for Large Language Models (LLMs) capable of sophisticated mathematical reasoning is growing across industries. However, the development of performant mathematical LLMs is critically bottlenecked by the scarcity of difficult, novel training data. We introduce SAND-Math (Synthetic Augmented Novel and Difficult Mathematics problems and solutions), a pipeline that addresses this by first generating high-quality problems from scratch and then systematically elevating their complexity via a new Difficulty Hiking step. We demonstrate the effectiveness of our approach through two key findings. First, augmenting a strong baseline with SAND-Math data significantly boosts performance, outperforming the next-best synthetic dataset by uparrow 17.85 absolute points on the AIME25 benchmark. Second, in a dedicated ablation study, we show our Difficulty Hiking process is highly effective: by increasing average problem difficulty from 5.02 to 5.98, this step lifts AIME25 performance from 46.38\\% to 49.23\\%. The full generation pipeline, final dataset, and a fine-tuned model form a practical and scalable toolkit for building more capable and efficient mathematical reasoning LLMs. SAND-Math dataset is released here: https://huggingface.co/datasets/amd/SAND-MATH{https://huggingface.co/datasets/amd/SAND-MATH}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.20152", "title": "Goal Alignment in LLM-Based User Simulators for Conversational AI", "authors": [ "Shuhaib Mehri", "Xiaocheng Yang", "Takyoung Kim", "Gokhan Tur", "Shikib Mehri", "Dilek Hakkani-T\u00fcr" ], "github": "", "project_page": "", "abstract": "User simulators are essential to conversational AI, enabling scalable agent development and evaluation through simulated interactions. While current Large Language Models (LLMs) have advanced user simulation capabilities, we reveal that they struggle to consistently demonstrate goal-oriented behavior across multi-turn conversations--a critical limitation that compromises their reliability in downstream applications. We introduce User Goal State Tracking (UGST), a novel framework that tracks user goal progression throughout conversations. Leveraging UGST, we present a three-stage methodology for developing user simulators that can autonomously track goal progression and reason to generate goal-aligned responses. Moreover, we establish comprehensive evaluation metrics for measuring goal alignment in user simulators, and demonstrate that our approach yields substantial improvements across two benchmarks (MultiWOZ 2.4 and {\\tau}-Bench). Our contributions address a critical gap in conversational AI and establish UGST as an essential framework for developing goal-aligned user simulators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.21848", "title": "EDGE-GRPO: Entropy-Driven GRPO with Guided Error Correction for Advantage Diversity", "authors": [ "Xingjian Zhang", "Siwei Wen", "Wenjun Wu", "Lei Huang" ], "github": "https://github.com/ZhangXJ199/EDGE-GRPO", "project_page": "", "abstract": "Large Language Models (LLMs) have made remarkable progress in enhancing step-by-step reasoning through reinforcement learning. However, the Group Relative Policy Optimization (GRPO) algorithm, which relies on sparse reward rules, often encounters the issue of identical rewards within groups, leading to the advantage collapse problem. Existing works typically address this challenge from two perspectives: enforcing model reflection to enhance response diversity, and introducing internal feedback to augment the training signal (advantage). In this work, we begin by analyzing the limitations of model reflection and investigating the policy entropy of responses at the fine-grained sample level. Based on our experimental findings, we propose the EDGE-GRPO algorithm, which adopts Entropy-Driven Advantage and Guided Error Correction to effectively mitigate the problem of advantage collapse. Extensive experiments on several main reasoning benchmarks demonstrate the effectiveness and superiority of our approach. It is available at https://github.com/ZhangXJ199/EDGE-GRPO.", "github_manual": "", "github_regex": "https://github.com/ZhangXJ199/EDGE-GRPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-29", "arxiv_id": "2507.19399", "title": "Running in CIRCLE? A Simple Benchmark for LLM Code Interpreter Security", "authors": [ "Gabriel Chua" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) increasingly integrate native code interpreters, they enable powerful real-time execution capabilities, substantially expanding their utility. However, such integrations introduce potential system-level cybersecurity threats, fundamentally different from prompt-based vulnerabilities. To systematically evaluate these interpreter-specific risks, we propose CIRCLE (Code-Interpreter Resilience Check for LLM Exploits), a simple benchmark comprising 1,260 prompts targeting CPU, memory, and disk resource exhaustion. Each risk category includes explicitly malicious (\"direct\") and plausibly benign (\"indirect\") prompt variants. Our automated evaluation framework assesses not only whether LLMs refuse or generates risky code, but also executes the generated code within the interpreter environment to evaluate code correctness, simplifications made by the LLM to make the code safe, or execution timeouts. Evaluating 7 commercially available models from OpenAI and Google, we uncover significant and inconsistent vulnerabilities. For instance, evaluations show substantial disparities even within providers - OpenAI's o4-mini correctly refuses risky requests at 7.1%, notably higher rates compared to GPT-4.1 at 0.5%. Results particularly underscore that indirect, socially-engineered prompts substantially weaken model defenses. This highlights an urgent need for interpreter-specific cybersecurity benchmarks, dedicated mitigation tools (e.g., guardrails), and clear industry standards to guide safe and responsible deployment of LLM interpreter integrations. The benchmark dataset and evaluation code are publicly released to foster further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-30", "arxiv_id": "2507.22061", "title": "MOVE: Motion-Guided Few-Shot Video Object Segmentation", "authors": [ "Kaining Ying", "Hengrui Hu", "Henghui Ding" ], "github": "https://github.com/FudanCVL/MOVE", "project_page": "", "abstract": "This work addresses motion-guided few-shot video object segmentation (FSVOS), which aims to segment dynamic objects in videos based on a few annotated examples with the same motion patterns. Existing FSVOS datasets and methods typically focus on object categories, which are static attributes that ignore the rich temporal dynamics in videos, limiting their application in scenarios requiring motion understanding. To fill this gap, we introduce MOVE, a large-scale dataset specifically designed for motion-guided FSVOS. Based on MOVE, we comprehensively evaluate 6 state-of-the-art methods from 3 different related tasks across 2 experimental settings. Our results reveal that current methods struggle to address motion-guided FSVOS, prompting us to analyze the associated challenges and propose a baseline method, Decoupled Motion Appearance Network (DMA). Experiments demonstrate that our approach achieves superior performance in few shot motion understanding, establishing a solid foundation for future research in this direction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FudanCVL/MOVE", "github_api_added_by": "auto" }, { "date": "2025-07-30", "arxiv_id": "2507.22058", "title": "X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again", "authors": [ "Zigang Geng", "Yibing Wang", "Yeyao Ma", "Chen Li", "Yongming Rao", "Shuyang Gu", "Zhao Zhong", "Qinglin Lu", "Han Hu", "Xiaosong Zhang", "Linus", "Di Wang", "Jie Jiang" ], "github": "", "project_page": "", "abstract": "Numerous efforts have been made to extend the ``next token prediction'' paradigm to visual contents, aiming to create a unified approach for both image generation and understanding. Nevertheless, attempts to generate images through autoregressive modeling with discrete tokens have been plagued by issues such as low visual fidelity, distorted outputs, and failure to adhere to complex instructions when rendering intricate details. These shortcomings are likely attributed to cumulative errors during autoregressive inference or information loss incurred during the discretization process. Probably due to this challenge, recent research has increasingly shifted toward jointly training image generation with diffusion objectives and language generation with autoregressive objectives, moving away from unified modeling approaches. In this work, we demonstrate that reinforcement learning can effectively mitigate artifacts and largely enhance the generation quality of a discrete autoregressive modeling method, thereby enabling seamless integration of image and language generation. Our framework comprises a semantic image tokenizer, a unified autoregressive model for both language and images, and an offline diffusion decoder for image generation, termed X-Omni. X-Omni achieves state-of-the-art performance in image generation tasks using a 7B language model, producing images with high aesthetic quality while exhibiting strong capabilities in following instructions and rendering long texts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-30", "arxiv_id": "2507.21183", "title": "MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge", "authors": [ "Guangchen Lan", "Sipeng Zhang", "Tianle Wang", "Yuwei Zhang", "Daoan Zhang", "Xinpeng Wei", "Xiaoman Pan", "Hongming Zhang", "Dong-Jun Han", "Christopher G. Brinton" ], "github": "", "project_page": "", "abstract": "As the era of large language models (LLMs) on behalf of users unfolds, Preference Optimization (PO) methods have become a central approach to aligning LLMs with human preferences and improving performance. We propose Maximum a Posteriori Preference Optimization (MaPPO), a framework for learning from preferences that explicitly incorporates prior reward knowledge into the optimization objective. While existing methods such as Direct Preference Optimization (DPO) and its variants treat preference learning as a Maximum Likelihood Estimation (MLE) problem, MaPPO extends this paradigm by integrating prior reward estimates into a principled Maximum a Posteriori (MaP) objective. This not only generalizes DPO and its variants, but also enhances alignment by mitigating the oversimplified binary classification of responses. More importantly, MaPPO introduces no additional hyperparameter, and supports preference optimization in both offline and online settings. In addition, MaPPO can be used as a plugin with consistent improvement on DPO variants, including widely used SimPO, IPO, and CPO. Extensive empirical evaluations of different model sizes and model series on three standard benchmarks, including MT-Bench, AlpacaEval 2.0, and Arena-Hard, demonstrate consistent improvements in alignment performance without sacrificing computational efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-30", "arxiv_id": "2507.21364", "title": "Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers", "authors": [ "Lukman Jibril Aliyu", "Umar Sani Muhammad", "Bilqisu Ismail", "Nasiru Muhammad", "Almustapha A Wakili", "Seid Muhie Yimam", "Shamsuddeen Hassan Muhammad", "Mustapha Abdullahi" ], "github": "", "project_page": "", "abstract": "Wildlife populations in Africa face severe threats, with vertebrate numbers declining by over 65% in the past five decades. In response, image classification using deep learning has emerged as a promising tool for biodiversity monitoring and conservation. This paper presents a comparative study of deep learning models for automatically classifying African wildlife images, focusing on transfer learning with frozen feature extractors. Using a public dataset of four species: buffalo, elephant, rhinoceros, and zebra; we evaluate the performance of DenseNet-201, ResNet-152, EfficientNet-B4, and Vision Transformer ViT-H/14. DenseNet-201 achieved the best performance among convolutional networks (67% accuracy), while ViT-H/14 achieved the highest overall accuracy (99%), but with significantly higher computational cost, raising deployment concerns. Our experiments highlight the trade-offs between accuracy, resource requirements, and deployability. The best-performing CNN (DenseNet-201) was integrated into a Hugging Face Gradio Space for real-time field use, demonstrating the feasibility of deploying lightweight models in conservation settings. This work contributes to African-grounded AI research by offering practical insights into model selection, dataset preparation, and responsible deployment of deep learning tools for wildlife conservation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-30", "arxiv_id": "2507.20240", "title": "AnimalClue: Recognizing Animals by their Traces", "authors": [ "Risa Shinoda", "Nakamasa Inoue", "Iro Laina", "Christian Rupprecht", "Hirokatsu Kataoka" ], "github": "", "project_page": "", "abstract": "Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Recent advances in computer vision have significantly contributed to automating fundamental wildlife observation tasks, such as animal detection and species identification. However, accurately identifying species from indirect evidence like footprints and feces remains relatively underexplored, despite its importance in contributing to wildlife monitoring. To bridge this gap, we introduce AnimalClue, the first large-scale dataset for species identification from images of indirect evidence. Our dataset consists of 159,605 bounding boxes encompassing five categories of indirect clues: footprints, feces, eggs, bones, and feathers. It covers 968 species, 200 families, and 65 orders. Each image is annotated with species-level labels, bounding boxes or segmentation masks, and fine-grained trait information, including activity patterns and habitat preferences. Unlike existing datasets primarily focused on direct visual features (e.g., animal appearances), AnimalClue presents unique challenges for classification, detection, and instance segmentation tasks due to the need for recognizing more detailed and subtle visual features. In our experiments, we extensively evaluate representative vision models and identify key challenges in animal identification from their traces. Our dataset and code are available at https://dahlian00.github.io/AnimalCluePage/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-30", "arxiv_id": "2507.21809", "title": "HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels", "authors": [ "HunyuanWorld Team", "Zhenwei Wang", "Yuhao Liu", "Junta Wu", "Zixiao Gu", "Haoyuan Wang", "Xuhui Zuo", "Tianyu Huang", "Wenhuan Li", "Sheng Zhang", "Yihang Lian", "Yulin Tsai", "Lifu Wang", "Sicong Liu", "Puhua Jiang", "Xianghui Yang", "Dongyuan Guo", "Yixuan Tang", "Xinyue Mao", "Jiaao Yu", "Junlin Yu", "Jihong Zhang", "Meng Chen", "Liang Dong", "Yiwen Jia", "Chao Zhang", "Yonghao Tan", "Hao Zhang", "Zheng Ye", "Peng He", "Runzhou Wu", "Minghui Chen", "Zhan Li", "Wangchen Qin", "Lei Wang", "Yifu Sun", "Lin Niu", "Xiang Yuan", "Xiaofeng Yang", "Yingping He", "Jie Xiao", "Yangyu Tao", "Jianchen Zhu", "Jinbao Xue", "Kai Liu", "Chongqing Zhao", "Xinming Wu", "Tian Liu", "Peng Chen", "Di Wang", "Yuhong Liu", "Linus", "Jie Jiang", "Tengfei Wang", "Chunchao Guo" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "Creating immersive and playable 3D worlds from texts or images remains a fundamental challenge in computer vision and graphics. Existing world generation approaches typically fall into two categories: video-based methods that offer rich diversity but lack 3D consistency and rendering efficiency, and 3D-based methods that provide geometric consistency but struggle with limited training data and memory-inefficient representations. To address these limitations, we present HunyuanWorld 1.0, a novel framework that combines the best of both worlds for generating immersive, explorable, and interactive 3D scenes from text and image conditions. Our approach features three key advantages: 1) 360{\\deg} immersive experiences via panoramic world proxies; 2) mesh export capabilities for seamless compatibility with existing computer graphics pipelines; 3) disentangled object representations for augmented interactivity. The core of our framework is a semantically layered 3D mesh representation that leverages panoramic images as 360{\\deg} world proxies for semantic-aware world decomposition and reconstruction, enabling the generation of diverse 3D worlds. Extensive experiments demonstrate that our method achieves state-of-the-art performance in generating coherent, explorable, and interactive 3D worlds while enabling versatile applications in virtual reality, physical simulation, game development, and interactive content creation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-30", "arxiv_id": "2507.14111", "title": "CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning", "authors": [ "Xiaoya Li", "Xiaofei Sun", "Albert Wang", "Jiwei Li", "Chris Shum" ], "github": "https://github.com/deepreinforce-ai/CUDA-L1", "project_page": "", "abstract": "The exponential growth in demand for GPU computing resources, driven by the rapid advancement of Large Language Models, has created an urgent need for automated CUDA optimization strategies. While recent advances in LLMs show promise for code generation, current SOTA models (e.g. R1, o1) achieve low success rates in improving CUDA speed. In this paper, we introduce CUDA-L1, an automated reinforcement learning framework for CUDA optimization. CUDA-L1 achieves performance improvements on the CUDA optimization task: trained on NVIDIA A100, it delivers an average speedup of x17.7 across all 250 CUDA kernels of KernelBench, with peak speedups reaching x449. Furthermore, the model also demonstrates excellent portability across GPU architectures, achieving average speedups of x17.8 on H100, x19.0 on RTX 3090, x16.5 on L40, x14.7 on H800, and x13.9 on H20 despite being optimized specifically for A100. Beyond these benchmark results, CUDA-L1 demonstrates several remarkable properties: 1) Discovers a variety of CUDA optimization techniques and learns to combine them strategically to achieve optimal performance; 2) Uncovers fundamental principles of CUDA optimization; 3) Identifies non-obvious performance bottlenecks and rejects seemingly beneficial optimizations that harm performance. The capabilities of CUDA-L1 demonstrate that reinforcement learning can transform an initially poor-performing LLM into an effective CUDA optimizer through speedup-based reward signals alone, without human expertise or domain knowledge. More importantly, the trained RL model extend the acquired reasoning abilities to new kernels. This paradigm opens possibilities for automated optimization of CUDA operations, and holds promise to substantially promote GPU efficiency and alleviate the rising pressure on GPU computing resources.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/deepreinforce-ai/CUDA-L1", "github_api_added_by": "auto" }, { "date": "2025-07-30", "arxiv_id": "2507.21503", "title": "MoHoBench: Assessing Honesty of Multimodal Large Language Models via Unanswerable Visual Questions", "authors": [ "Yanxu Zhu", "Shitong Duan", "Xiangxu Zhang", "Jitao Sang", "Peng Zhang", "Tun Lu", "Xiao Zhou", "Jing Yao", "Xiaoyuan Yi", "Xing Xie" ], "github": "https://github.com/DSTTSD/MoHoBench", "project_page": "", "abstract": "Recently Multimodal Large Language Models (MLLMs) have achieved considerable advancements in vision-language tasks, yet produce potentially harmful or untrustworthy content. Despite substantial work investigating the trustworthiness of language models, MMLMs' capability to act honestly, especially when faced with visually unanswerable questions, remains largely underexplored. This work presents the first systematic assessment of honesty behaviors across various MLLMs. We ground honesty in models' response behaviors to unanswerable visual questions, define four representative types of such questions, and construct MoHoBench, a large-scale MMLM honest benchmark, consisting of 12k+ visual question samples, whose quality is guaranteed by multi-stage filtering and human verification. Using MoHoBench, we benchmarked the honesty of 28 popular MMLMs and conducted a comprehensive analysis. Our findings show that: (1) most models fail to appropriately refuse to answer when necessary, and (2) MMLMs' honesty is not solely a language modeling issue, but is deeply influenced by visual information, necessitating the development of dedicated methods for multimodal honesty alignment. Therefore, we implemented initial alignment methods using supervised and preference learning to improve honesty behavior, providing a foundation for future work on trustworthy MLLMs. Our data and code can be found at https://github.com/DSTTSD/MoHoBench.", "github_manual": "", "github_regex": "https://github.com/DSTTSD/MoHoBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-30", "arxiv_id": "2507.21990", "title": "ChemDFM-R: An Chemical Reasoner LLM Enhanced with Atomized Chemical Knowledge", "authors": [ "Zihan Zhao", "Bo Chen", "Ziping Wan", "Lu Chen", "Xuanze Lin", "Shiyang Yu", "Situo Zhang", "Da Ma", "Zichen Zhu", "Danyang Zhang", "Huayang Wang", "Zhongyang Dai", "Liyang Wen", "Xin Chen", "Kai Yu" ], "github": "", "project_page": "", "abstract": "While large language models (LLMs) have achieved impressive progress, their application in scientific domains such as chemistry remains hindered by shallow domain understanding and limited reasoning capabilities. In this work, we focus on the specific field of chemistry and develop a Chemical Reasoner LLM, ChemDFM-R. We first construct a comprehensive dataset of atomized knowledge points to enhance the model's understanding of the fundamental principles and logical structure of chemistry. Then, we propose a mix-sourced distillation strategy that integrates expert-curated knowledge with general-domain reasoning skills, followed by domain-specific reinforcement learning to enhance chemical reasoning. Experiments on diverse chemical benchmarks demonstrate that ChemDFM-R achieves state-of-the-art performance while providing interpretable, rationale-driven outputs. Further case studies illustrate how explicit reasoning chains significantly improve the reliability, transparency, and practical utility of the model in real-world human-AI collaboration scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-30", "arxiv_id": "2507.20254", "title": "MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification", "authors": [ "Dingkun Liu", "Zhu Chen", "Jingwei Luo", "Shijie Lian", "Dongrui Wu" ], "github": "https://github.com/staraink/MIRepNet", "project_page": "", "abstract": "Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. Recent EEG foundation models aim to learn generalized representations across diverse BCI paradigms. However, these approaches overlook fundamental paradigm-specific neurophysiological distinctions, limiting their generalization ability. Importantly, in practical BCI deployments, the specific paradigm such as motor imagery (MI) for stroke rehabilitation or assistive robotics, is generally determined prior to data acquisition. This paper proposes MIRepNet, the first EEG foundation model tailored for the MI paradigm. MIRepNet comprises a high-quality EEG preprocessing pipeline incorporating a neurophysiologically-informed channel template, adaptable to EEG headsets with arbitrary electrode configurations. Furthermore, we introduce a hybrid pretraining strategy that combines self-supervised masked token reconstruction and supervised MI classification, facilitating rapid adaptation and accurate decoding on novel downstream MI tasks with fewer than 30 trials per class. Extensive evaluations across five public MI datasets demonstrated that MIRepNet consistently achieved state-of-the-art performance, significantly outperforming both specialized and generalized EEG models. Our code will be available on GitHubhttps://github.com/staraink/MIRepNet.", "github_manual": "", "github_regex": "https://github.com/staraink/MIRepNet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-31", "arxiv_id": "2507.22886", "title": "Towards Omnimodal Expressions and Reasoning in Referring Audio-Visual Segmentation", "authors": [ "Kaining Ying", "Henghui Ding", "Guanquan Jie", "Yu-Gang Jiang" ], "github": "https://github.com/FudanCVL/OmniAVS", "project_page": "", "abstract": "Referring audio-visual segmentation (RAVS) has recently seen significant advancements, yet challenges remain in integrating multimodal information and deeply understanding and reasoning about audiovisual content. To extend the boundaries of RAVS and facilitate future research in this field, we propose Omnimodal Referring Audio-Visual Segmentation (OmniAVS), a new dataset containing 2,098 videos and 59,458 multimodal referring expressions. OmniAVS stands out with three key innovations: (1) 8 types of multimodal expressions that flexibly combine text, speech, sound, and visual cues; (2) an emphasis on understanding audio content beyond just detecting their presence; and (3) the inclusion of complex reasoning and world knowledge in expressions. Furthermore, we introduce Omnimodal Instructed Segmentation Assistant (OISA), to address the challenges of multimodal reasoning and fine-grained understanding of audiovisual content in OmniAVS. OISA uses MLLM to comprehend complex cues and perform reasoning-based segmentation. Extensive experiments show that OISA outperforms existing methods on OmniAVS and achieves competitive results on other related tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FudanCVL/OmniAVS", "github_api_added_by": "auto" }, { "date": "2025-07-31", "arxiv_id": "2507.21493", "title": "BANG: Dividing 3D Assets via Generative Exploded Dynamics", "authors": [ "Longwen Zhang", "Qixuan Zhang", "Haoran Jiang", "Yinuo Bai", "Wei Yang", "Lan Xu", "Jingyi Yu" ], "github": "https://github.com/black-forest-labs/flux", "project_page": "", "abstract": "3D creation has always been a unique human strength, driven by our ability to deconstruct and reassemble objects using our eyes, mind and hand. However, current 3D design tools struggle to replicate this natural process, requiring considerable artistic expertise and manual labor. This paper introduces BANG, a novel generative approach that bridges 3D generation and reasoning, allowing for intuitive and flexible part-level decomposition of 3D objects. At the heart of BANG is \"Generative Exploded Dynamics\", which creates a smooth sequence of exploded states for an input geometry, progressively separating parts while preserving their geometric and semantic coherence. BANG utilizes a pre-trained large-scale latent diffusion model, fine-tuned for exploded dynamics with a lightweight exploded view adapter, allowing precise control over the decomposition process. It also incorporates a temporal attention module to ensure smooth transitions and consistency across time. BANG enhances control with spatial prompts, such as bounding boxes and surface regions, enabling users to specify which parts to decompose and how. This interaction can be extended with multimodal models like GPT-4, enabling 2D-to-3D manipulations for more intuitive and creative workflows. The capabilities of BANG extend to generating detailed part-level geometry, associating parts with functional descriptions, and facilitating component-aware 3D creation and manufacturing workflows. Additionally, BANG offers applications in 3D printing, where separable parts are generated for easy printing and reassembly. In essence, BANG enables seamless transformation from imaginative concepts to detailed 3D assets, offering a new perspective on creation that resonates with human intuition.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/black-forest-labs/flux", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-31", "arxiv_id": "2507.22827", "title": "ScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agents", "authors": [ "Yilei Jiang", "Yaozhi Zheng", "Yuxuan Wan", "Jiaming Han", "Qunzhong Wang", "Michael R. Lyu", "Xiangyu Yue" ], "github": "https://github.com/leigest519/ScreenCoder", "project_page": "", "abstract": "Automating the transformation of user interface (UI) designs into front-end code holds significant promise for accelerating software development and democratizing design workflows. While recent large language models (LLMs) have demonstrated progress in text-to-code generation, many existing approaches rely solely on natural language prompts, limiting their effectiveness in capturing spatial layout and visual design intent. In contrast, UI development in practice is inherently multimodal, often starting from visual sketches or mockups. To address this gap, we introduce a modular multi-agent framework that performs UI-to-code generation in three interpretable stages: grounding, planning, and generation. The grounding agent uses a vision-language model to detect and label UI components, the planning agent constructs a hierarchical layout using front-end engineering priors, and the generation agent produces HTML/CSS code via adaptive prompt-based synthesis. This design improves robustness, interpretability, and fidelity over end-to-end black-box methods. Furthermore, we extend the framework into a scalable data engine that automatically produces large-scale image-code pairs. Using these synthetic examples, we fine-tune and reinforce an open-source VLM, yielding notable gains in UI understanding and code quality. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in layout accuracy, structural coherence, and code correctness. Our code is made publicly available at https://github.com/leigest519/ScreenCoder.", "github_manual": "", "github_regex": "https://github.com/leigest519/ScreenCoder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-31", "arxiv_id": "2507.22448", "title": "Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance", "authors": [ "Jingwei Zuo", "Maksim Velikanov", "Ilyas Chahed", "Younes Belkada", "Dhia Eddine Rhayem", "Guillaume Kunsch", "Hakim Hacid", "Hamza Yous", "Brahim Farhat", "Ibrahim Khadraoui", "Mugariya Farooq", "Giulia Campesan", "Ruxandra Cojocaru", "Yasser Djilali", "Shi Hu", "Iheb Chaabane", "Puneesh Khanna", "Mohamed El Amine Seddik", "Ngoc Dung Huynh", "Phuc Le Khac", "Leen AlQadi", "Billel Mokeddem", "Mohamed Chami", "Abdalgader Abubaker", "Mikhail Lubinets", "Kacper Piskorski", "Slim Frikha" ], "github": "", "project_page": "", "abstract": "In this report, we introduce Falcon-H1, a new series of large language models (LLMs) featuring hybrid architecture designs optimized for both high performance and efficiency across diverse use cases. Unlike earlier Falcon models built solely on Transformer or Mamba architectures, Falcon-H1 adopts a parallel hybrid approach that combines Transformer-based attention with State Space Models (SSMs), known for superior long-context memory and computational efficiency. We systematically revisited model design, data strategy, and training dynamics, challenging conventional practices in the field. Falcon-H1 is released in multiple configurations, including base and instruction-tuned variants at 0.5B, 1.5B, 1.5B-deep, 3B, 7B, and 34B parameters. Quantized instruction-tuned models are also available, totaling over 30 checkpoints on Hugging Face Hub. Falcon-H1 models demonstrate state-of-the-art performance and exceptional parameter and training efficiency. The flagship Falcon-H1-34B matches or outperforms models up to 70B scale, such as Qwen3-32B, Qwen2.5-72B, and Llama3.3-70B, while using fewer parameters and less data. Smaller models show similar trends: the Falcon-H1-1.5B-Deep rivals current leading 7B-10B models, and Falcon-H1-0.5B performs comparably to typical 7B models from 2024. These models excel across reasoning, mathematics, multilingual tasks, instruction following, and scientific knowledge. With support for up to 256K context tokens and 18 languages, Falcon-H1 is suitable for a wide range of applications. All models are released under a permissive open-source license, underscoring our commitment to accessible and impactful AI research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-31", "arxiv_id": "2507.22853", "title": "Repair-R1: Better Test Before Repair", "authors": [ "Haichuan Hu", "Xiaochen Xie", "Quanjun Zhang" ], "github": "https://github.com/Tomsawyerhu/APR-RL", "project_page": "", "abstract": "APR (Automated Program Repair) aims to automatically locate program defects, generate patches and validate the repairs. Existing techniques for APR are often combined with LLMs (Large Language Models), which leverages the code-related knowledge of LLMs to improve repair effectiveness. Current LLM-based APR methods typically utilize test cases only during the inference stage, adopting an iterative approach that performs repair first and validates it through test execution afterward. This conventional paradigm neglects two important aspects: the potential contribution of test cases in the training phase, and the possibility of leveraging testing prior to repair. To address this, we propose Repair-R1, which introduces test cases into the model's training phase and shifts test generation to precede repair. The model is required to first generate discriminative test cases that can distinguish defective behaviors, and then perform repair based on these tests. This enables the model to better locate defects and understand the underlying causes of defects, thereby improving repair effectiveness. We implement Repair-R1 with three different backbone models, using RL (reinforcement learning) to co-optimize test generation and bug repair. Experimental results on four widely adopted benchmarks demonstrate the superiority of Repair-R1. Specially, compared to vanilla models, Repair-R1 improves repair success rate by 2.68\\% to 48.29\\%, test generation success rate by 16.38\\% to 53.28\\%, and test coverage by 0.78\\% to 53.96\\%. We publish the code and weights at https://github.com/Tomsawyerhu/APR-RL and https://huggingface.co/tomhu/Qwen3-4B-RL-5000-step.", "github_manual": "", "github_regex": "https://github.com/Tomsawyerhu/APR-RL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-31", "arxiv_id": "2507.20976", "title": "Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision", "authors": [ "Xiao Fang", "Minhyek Jeon", "Zheyang Qin", "Stanislav Panev", "Celso de Melo", "Shuowen Hu", "Shayok Chakraborty", "Fernando De la Torre" ], "github": "", "project_page": "", "abstract": "Detecting vehicles in aerial imagery is a critical task with applications in traffic monitoring, urban planning, and defense intelligence. Deep learning methods have provided state-of-the-art (SOTA) results for this application. However, a significant challenge arises when models trained on data from one geographic region fail to generalize effectively to other areas. Variability in factors such as environmental conditions, urban layouts, road networks, vehicle types, and image acquisition parameters (e.g., resolution, lighting, and angle) leads to domain shifts that degrade model performance. This paper proposes a novel method that uses generative AI to synthesize high-quality aerial images and their labels, improving detector training through data augmentation. Our key contribution is the development of a multi-stage, multi-modal knowledge transfer framework utilizing fine-tuned latent diffusion models (LDMs) to mitigate the distribution gap between the source and target environments. Extensive experiments across diverse aerial imagery domains show consistent performance improvements in AP50 over supervised learning on source domain data, weakly supervised adaptation methods, unsupervised domain adaptation methods, and open-set object detectors by 4-23%, 6-10%, 7-40%, and more than 50%, respectively. Furthermore, we introduce two newly annotated aerial datasets from New Zealand and Utah to support further research in this field. Project page is available at: https://humansensinglab.github.io/AGenDA", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-31", "arxiv_id": "2507.22565", "title": "Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning", "authors": [ "Afshin Khadangi", "Amir Sartipi", "Igor Tchappi", "Ramin Bahmani", "Gilbert Fridgen" ], "github": "", "project_page": "", "abstract": "The tension between data privacy and model utility has become the defining bottleneck for the practical deployment of large language models (LLMs) trained on sensitive corpora including healthcare. Differentially private stochastic gradient descent (DP-SGD) guarantees formal privacy, yet it does so at a pronounced cost: gradients are forcibly clipped and perturbed with noise, degrading sample efficiency and final accuracy. Numerous variants have been proposed to soften this trade-off, but they all share a handicap: their control knobs are hard-coded, global, and oblivious to the evolving optimization landscape. Consequently, practitioners are forced either to over-spend privacy budget in pursuit of utility, or to accept mediocre models in order to stay within privacy constraints. We present RLDP, the first framework to cast DP optimization itself as a closed-loop control problem amenable to modern deep reinforcement learning (RL). RLDP continuously senses rich statistics of the learning dynamics and acts by selecting fine-grained per parameter gradient-clipping thresholds as well as the magnitude of injected Gaussian noise. A soft actor-critic (SAC) hyper-policy is trained online during language model fine-tuning; it learns, from scratch, how to allocate the privacy budget where it matters and when it matters. Across more than 1,600 ablation experiments on GPT2-small, Llama-1B, Llama-3B, and Mistral-7B, RLDP delivers perplexity reductions of 1.3-30.5% (mean 5.4%) and an average 5.6% downstream utility gain. RLDP reaches each baseline's final utility after only 13-43% of the gradient-update budget (mean speed-up 71%), all while honoring the same (epsilon, delta)-DP contract and exhibiting equal or lower susceptibility to membership-inference and canary-extraction attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-31", "arxiv_id": "2507.22607", "title": "VL-Cogito: Progressive Curriculum Reinforcement Learning for Advanced Multimodal Reasoning", "authors": [ "Ruifeng Yuan", "Chenghao Xiao", "Sicong Leng", "Jianyu Wang", "Long Li", "Weiwen Xu", "Hou Pong Chan", "Deli Zhao", "Tingyang Xu", "Zhongyu Wei", "Hao Zhang", "Yu Rong" ], "github": "https://github.com/alibaba-damo-academy/VL-Cogito", "project_page": "", "abstract": "Reinforcement learning has proven its effectiveness in enhancing the reasoning capabilities of large language models. Recent research efforts have progressively extended this paradigm to multimodal reasoning tasks. Due to the inherent complexity and diversity of multimodal tasks, especially in semantic content and problem formulations, existing models often exhibit unstable performance across various domains and difficulty levels. To address these limitations, we propose VL-Cogito, an advanced multimodal reasoning model trained via a novel multi-stage Progressive Curriculum Reinforcement Learning (PCuRL) framework. PCuRL systematically guides the model through tasks of gradually increasing difficulty, substantially improving its reasoning abilities across diverse multimodal contexts. The framework introduces two key innovations: (1) an online difficulty soft weighting mechanism, dynamically adjusting training difficulty across successive RL training stages; and (2) a dynamic length reward mechanism, which encourages the model to adaptively regulate its reasoning path length according to task complexity, thus balancing reasoning efficiency with correctness. Experimental evaluations demonstrate that VL-Cogito consistently matches or surpasses existing reasoning-oriented models across mainstream multimodal benchmarks spanning mathematics, science, logic, and general understanding, validating the effectiveness of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alibaba-damo-academy/VL-Cogito", "github_api_added_by": "auto" }, { "date": "2025-07-31", "arxiv_id": "2507.22062", "title": "MetaCLIP 2: A Worldwide Scaling Recipe", "authors": [ "Yung-Sung Chuang", "Yang Li", "Dong Wang", "Ching-Feng Yeh", "Kehan Lyu", "Ramya Raghavendra", "James Glass", "Lifei Huang", "Jason Weston", "Luke Zettlemoyer", "Xinlei Chen", "Zhuang Liu", "Saining Xie", "Wen-tau Yih", "Shang-Wen Li", "Hu Xu" ], "github": "", "project_page": "", "abstract": "Contrastive Language-Image Pretraining (CLIP) is a popular foundation model, supporting from zero-shot classification, retrieval to encoders for multimodal large language models (MLLMs). Although CLIP is successfully trained on billion-scale image-text pairs from the English world, scaling CLIP's training further to learning from the worldwide web data is still challenging: (1) no curation method is available to handle data points from non-English world; (2) the English performance from existing multilingual CLIP is worse than its English-only counterpart, i.e., \"curse of multilinguality\" that is common in LLMs. Here, we present MetaCLIP 2, the first recipe training CLIP from scratch on worldwide web-scale image-text pairs. To generalize our findings, we conduct rigorous ablations with minimal changes that are necessary to address the above challenges and present a recipe enabling mutual benefits from English and non-English world data. In zero-shot ImageNet classification, MetaCLIP 2 ViT-H/14 surpasses its English-only counterpart by 0.8% and mSigLIP by 0.7%, and surprisingly sets new state-of-the-art without system-level confounding factors (e.g., translation, bespoke architecture changes) on multilingual benchmarks, such as CVQA with 57.4%, Babel-ImageNet with 50.2% and XM3600 with 64.3% on image-to-text retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-31", "arxiv_id": "2507.21802", "title": "MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE", "authors": [ "Junzhe Li", "Yutao Cui", "Tao Huang", "Yinping Ma", "Chun Fan", "Miles Yang", "Zhao Zhong" ], "github": "https://github.com/Tencent-Hunyuan/MixGRPO", "project_page": "", "abstract": "Although GRPO substantially enhances flow matching models in human preference alignment of image generation, methods such as FlowGRPO still exhibit inefficiency due to the necessity of sampling and optimizing over all denoising steps specified by the Markov Decision Process (MDP). In this paper, we propose MixGRPO, a novel framework that leverages the flexibility of mixed sampling strategies through the integration of stochastic differential equations (SDE) and ordinary differential equations (ODE). This streamlines the optimization process within the MDP to improve efficiency and boost performance. Specifically, MixGRPO introduces a sliding window mechanism, using SDE sampling and GRPO-guided optimization only within the window, while applying ODE sampling outside. This design confines sampling randomness to the time-steps within the window, thereby reducing the optimization overhead, and allowing for more focused gradient updates to accelerate convergence. Additionally, as time-steps beyond the sliding window are not involved in optimization, higher-order solvers are supported for sampling. So we present a faster variant, termed MixGRPO-Flash, which further improves training efficiency while achieving comparable performance. MixGRPO exhibits substantial gains across multiple dimensions of human preference alignment, outperforming DanceGRPO in both effectiveness and efficiency, with nearly 50% lower training time. Notably, MixGRPO-Flash further reduces training time by 71%. Codes and models are available at https://github.com/Tencent-Hunyuan/MixGRPO{MixGRPO}.", "github_manual": "", "github_regex": "https://github.com/Tencent-Hunyuan/MixGRPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-31", "arxiv_id": "2507.13985", "title": "DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation", "authors": [ "Haoran Li", "Yuli Tian", "Kun Lan", "Yong Liao", "Lin Wang", "Pan Hui", "Peng Yuan Zhou" ], "github": "", "project_page": "", "abstract": "Generating 3D scenes from natural language holds great promise for applications in gaming, film, and design. However, existing methods struggle with automation, 3D consistency, and fine-grained control. We present DreamScene, an end-to-end framework for high-quality and editable 3D scene generation from text or dialogue. DreamScene begins with a scene planning module, where a GPT-4 agent infers object semantics and spatial constraints to construct a hybrid graph. A graph-based placement algorithm then produces a structured, collision-free layout. Based on this layout, Formation Pattern Sampling (FPS) generates object geometry using multi-timestep sampling and reconstructive optimization, enabling fast and realistic synthesis. To ensure global consistent, DreamScene employs a progressive camera sampling strategy tailored to both indoor and outdoor settings. Finally, the system supports fine-grained scene editing, including object movement, appearance changes, and 4D dynamic motion. Experiments demonstrate that DreamScene surpasses prior methods in quality, consistency, and flexibility, offering a practical solution for open-domain 3D content creation. Code and demos are available at https://jahnsonblack.github.io/DreamScene-Full/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-07-31", "arxiv_id": "2507.19427", "title": "Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding", "authors": [ "StepFun", "Bin Wang", "Bojun Wang", "Changyi Wan", "Guanzhe Huang", "Hanpeng Hu", "Haonan Jia", "Hao Nie", "Mingliang Li", "Nuo Chen", "Siyu Chen", "Song Yuan", "Wuxun Xie", "Xiaoniu Song", "Xing Chen", "Xingping Yang", "Xuelin Zhang", "Yanbo Yu", "Yaoyu Wang", "Yibo Zhu", "Yimin Jiang", "Yu Zhou", "Yuanwei Lu", "Houyi Li", "Jingcheng Hu", "Ka Man Lo", "Ailin Huang", "Binxing Jiao", "Bo Li", "Boyu Chen", "Changxin Miao", "Chang Lou", "Chen Hu", "Chen Xu", "Chenfeng Yu", "Chengyuan Yao", "Daokuan Lv", "Dapeng Shi", "Deshan Sun", "Ding Huang", "Dingyuan Hu", "Dongqing Pang", "Enle Liu", "Fajie Zhang", "Fanqi Wan", "Gulin Yan", "Han Zhang", "Han Zhou", "Hanghao Wu", "Hangyu Guo", "Hanqi Chen", "Hanshan Zhang", "Hao Wu", "Haocheng Zhang", "Haolong Yan", "Haoran Lv", "Haoran Wei", "Hebin Zhou", "Heng Wang", "Heng Wang", "Hongxin Li", "Hongyu Zhou", "Hongyuan Wang", "Huiyong Guo", "Jia Wang", "Jiahao Gong", "Jialing Xie", "Jian Zhou", "Jianjian Sun", "Jiaoren Wu", "Jiaran Zhang", "Jiayu Liu", "Jie Cheng", "Jie Luo", "Jie Yan", "Jie Yang", "Jieyi Hou", "Jinguang Zhang", "Jinlan Cao", "Jisheng Yin", "Junfeng Liu", "Junhao Huang", "Junzhe Lin", "Kaijun Tan", "Kaixiang Li", "Kang An", "Kangheng Lin", "Kenkun Liu", "Lei Yang", "Liang Zhao", "Liangyu Chen", "Lieyu Shi", "Liguo Tan", "Lin Lin", "Lin Zhang", "Lina Chen", "Liwen Huang", "Liying Shi", "Longlong Gu", "Mei Chen", "Mengqiang Ren", "Ming Li", "Mingzhe Chen", "Na Wang", "Nan Wu", "Qi Han", "Qian Zhao", "Qiang Zhang", "Qianni Liu", "Qiaohui Chen", "Qiling Wu", "Qinglin He", "Qinyuan Tan", "Qiufeng Wang", "Qiuping Wu", "Qiuyan Liang", "Quan Sun", "Rui Li", "Ruihang Miao", "Ruosi Wan", "Ruyan Guo", "Shangwu Zhong", "Shaoliang Pang", "Shengjie Fan", "Shijie Shang", "Shilei Jiang", "Shiliang Yang", "Shiming Hao", "Shuli Gao", "Siming Huang", "Siqi Liu", "Tiancheng Cao", "Tianhao Cheng", "Tianhao Peng", "Wang You", "Wei Ji", "Wen Sun", "Wenjin Deng", "Wenqing He", "Wenzhen Zheng", "Xi Chen", "Xiangwen Kong", "Xianzhen Luo", "Xiaobo Yang", "Xiaojia Liu", "Xiaoxiao Ren", "Xin Han", "Xin Li", "Xin Wu", "Xu Zhao", "Yanan Wei", "Yang Li", "Yangguang Li", "Yangshijie Xu", "Yanming Xu", "Yaqiang Shi", "Yeqing Shen", "Yi Yang", "Yifei Yang", "Yifeng Gong", "Yihan Chen", "Yijing Yang", "Yinmin Zhang", "Yizhuang Zhou", "Yuanhao Ding", "Yuantao Fan", "Yuanzhen Yang", "Yuchu Luo", "Yue Peng", "Yufan Lu", "Yuhang Deng", "Yuhe Yin", "Yujie Liu", "Yukun Chen", "Yuling Zhao", "Yun Mou", "Yunlong Li", "Yunzhou Ju", "Yusheng Li", "Yuxiang Yang", "Yuxiang Zhang", "Yuyang Chen", "Zejia Weng", "Zhe Xie", "Zheng Ge", "Zheng Gong", "Zhenyi Lu", "Zhewei Huang", "Zhichao Chang", "Zhiguo Huang", "Zhirui Wang", "Zidong Yang", "Zili Wang", "Ziqi Wang", "Zixin Zhang", "Binxing Jiao", "Daxin Jiang", "Heung-Yeung Shum", "Xiangyu Zhang" ], "github": "https://github.com/stepfun-ai/Step3", "project_page": "", "abstract": "Large language models (LLMs) face low hardware efficiency during decoding, especially for long-context reasoning tasks. This paper introduces Step-3, a 321B-parameter VLM with hardware-aware model-system co-design optimized for minimizing decoding costs. Step-3 innovates in two key dimensions: (1) A novel Multi-Matrix Factorization Attention (MFA) mechanism that significantly reduces both KV cache size and computation while maintaining high attention expressiveness, and (2) Attention-FFN Disaggregation (AFD), a distributed inference system that decouples attention and Feed-Forward Network (FFN) layers into specialized subsystems. This co-design achieves unprecedented cost efficiency: Step-3 significantly reduces theoretical decoding costs compared with models like DeepSeek-V3 and Qwen3 MoE 235B, with the gains widening at longer context. Step-3 achieves low cost while activating 38B parameters per token (more than DeepSeek-V3 and Qwen3 MoE 235B), demonstrating that hardware-aligned attention arithmetic intensity, MoE sparsity, and AFD are critical to cost-effectiveness. We perform a head-to-head comparison with DeepSeek-V3 in its favorable scenarios. Our implementation on Hopper GPUs achieves a decoding throughput of up to 4,039 tokens per second per GPU under 50ms TPOT SLA (4K context, FP8, no MTP). It is higher than DeepSeek-V3's 2,324 in the same setup and sets a new Pareto frontier for LLM decoding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stepfun-ai/Step3", "github_api_added_by": "auto" }, { "date": "2025-08-01", "arxiv_id": "2507.23726", "title": "Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving", "authors": [ "Luoxin Chen", "Jinming Gu", "Liankai Huang", "Wenhao Huang", "Zhicheng Jiang", "Allan Jie", "Xiaoran Jin", "Xing Jin", "Chenggang Li", "Kaijing Ma", "Cheng Ren", "Jiawei Shen", "Wenlei Shi", "Tong Sun", "He Sun", "Jiahui Wang", "Siran Wang", "Zhihong Wang", "Chenrui Wei", "Shufa Wei", "Yonghui Wu", "Yuchen Wu", "Yihang Xia", "Huajian Xin", "Fan Yang", "Huaiyuan Ying", "Hongyi Yuan", "Zheng Yuan", "Tianyang Zhan", "Chi Zhang", "Yue Zhang", "Ge Zhang", "Tianyun Zhao", "Jianqiu Zhao", "Yichi Zhou", "Thomas Hanwen Zhu" ], "github": "https://github.com/ByteDance-Seed/Seed-Prover", "project_page": "", "abstract": "LLMs have demonstrated strong mathematical reasoning abilities by leveraging reinforcement learning with long chain-of-thought, yet they continue to struggle with theorem proving due to the lack of clear supervision signals when solely using natural language. Dedicated domain-specific languages like Lean provide clear supervision via formal verification of proofs, enabling effective training through reinforcement learning. In this work, we propose Seed-Prover, a lemma-style whole-proof reasoning model. Seed-Prover can iteratively refine its proof based on Lean feedback, proved lemmas, and self-summarization. To solve IMO-level contest problems, we design three test-time inference strategies that enable both deep and broad reasoning. Seed-Prover proves 78.1% of formalized past IMO problems, saturates MiniF2F, and achieves over 50\\% on PutnamBench, outperforming the previous state-of-the-art by a large margin. To address the lack of geometry support in Lean, we introduce a geometry reasoning engine Seed-Geometry, which outperforms previous formal geometry engines. We use these two systems to participate in IMO 2025 and fully prove 5 out of 6 problems. This work represents a significant advancement in automated mathematical reasoning, demonstrating the effectiveness of formal verification with long chain-of-thought reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByteDance-Seed/Seed-Prover", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.22879", "title": "RecGPT Technical Report", "authors": [ "Chao Yi", "Dian Chen", "Gaoyang Guo", "Jiakai Tang", "Jian Wu", "Jing Yu", "Sunhao Dai", "Wen Chen", "Wenjun Yang", "Yuning Jiang", "Zhujin Gao", "Bo Zheng", "Chi Li", "Dimin Wang", "Dixuan Wang", "Fan Li", "Fan Zhang", "Haibin Chen", "Haozhuang Liu", "Jialin Zhu", "Jiamang Wang", "Jiawei Wu", "Jin Cui", "Ju Huang", "Kai Zhang", "Kan Liu", "Lang Tian", "Liang Rao", "Longbin Li", "Lulu Zhao", "Mao Zhang", "Na He", "Peiyang Wang", "Qiqi Huang", "Tao Luo", "Wenbo Su", "Xiaoxiao He", "Xin Tong", "Xu Chen", "Xunke Xi", "Yang Li", "Yaxuan Wu", "Yeqiu Yang", "Yi Hu", "Yinnan Song", "Yuchen Li", "Yujie Luo", "Yujin Yuan", "Yuliang Yan", "Zhengyang Wang", "Zhibo Xiao", "Zhixin Ma", "Zile Zhou" ], "github": "", "project_page": "", "abstract": "Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, most current industrial systems remain heavily reliant on historical co-occurrence patterns and log-fitting objectives, i.e., optimizing for past user interactions without explicitly modeling user intent. This log-fitting approach often leads to overfitting to narrow historical preferences, failing to capture users' evolving and latent interests. As a result, it reinforces filter bubbles and long-tail phenomena, ultimately harming user experience and threatening the sustainability of the whole recommendation ecosystem. To address these challenges, we rethink the overall design paradigm of recommender systems and propose RecGPT, a next-generation framework that places user intent at the center of the recommendation pipeline. By integrating large language models (LLMs) into key stages of user interest mining, item retrieval, and explanation generation, RecGPT transforms log-fitting recommendation into an intent-centric process. To effectively align general-purpose LLMs to the above domain-specific recommendation tasks at scale, RecGPT incorporates a multi-stage training paradigm, which integrates reasoning-enhanced pre-alignment and self-training evolution, guided by a Human-LLM cooperative judge system. Currently, RecGPT has been fully deployed on the Taobao App. Online experiments demonstrate that RecGPT achieves consistent performance gains across stakeholders: users benefit from increased content diversity and satisfaction, merchants and the platform gain greater exposure and conversions. These comprehensive improvement results across all stakeholders validates that LLM-driven, intent-centric design can foster a more sustainable and mutually beneficial recommendation ecosystem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.22968", "title": "C3: A Bilingual Benchmark for Spoken Dialogue Models Exploring Challenges in Complex Conversations", "authors": [ "Chengqian Ma", "Wei Tao", "Yiwen Guo" ], "github": "", "project_page": "", "abstract": "Spoken Dialogue Models (SDMs) have recently attracted significant attention for their ability to generate voice responses directly to users' spoken queries. Despite their increasing popularity, there exists a gap in research focused on comprehensively understanding their practical effectiveness in comprehending and emulating human conversations. This is especially true compared to text-based Large Language Models (LLMs), which benefit from extensive benchmarking. Human voice interactions are inherently more complex than text due to characteristics unique to spoken dialogue. Ambiguity poses one challenge, stemming from semantic factors like polysemy, as well as phonological aspects such as heterograph, heteronyms, and stress patterns. Additionally, context-dependency, like omission, coreference, and multi-turn interaction, adds further complexity to human conversational dynamics. To illuminate the current state of SDM development and to address these challenges, we present a benchmark dataset in this paper, which comprises 1,079 instances in English and Chinese. Accompanied by an LLM-based evaluation method that closely aligns with human judgment, this dataset facilitates a comprehensive exploration of the performance of SDMs in tackling these practical challenges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.23682", "title": "villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models", "authors": [ "Xiaoyu Chen", "Hangxing Wei", "Pushi Zhang", "Chuheng Zhang", "Kaixin Wang", "Yanjiang Guo", "Rushuai Yang", "Yucen Wang", "Xinquan Xiao", "Li Zhao", "Jianyu Chen", "Jiang Bian" ], "github": "", "project_page": "", "abstract": "Visual-Language-Action (VLA) models have emerged as a popular paradigm for learning robot manipulation policies that can follow language instructions and generalize to novel scenarios. Recent work has begun to explore the incorporation of latent actions, an abstract representation of visual change between two frames, into VLA pre-training. In this paper, we introduce villa-X, a novel Visual-Language-Latent-Action (ViLLA) framework that advances latent action modeling for learning generalizable robot manipulation policies. Our approach improves both how latent actions are learned and how they are incorporated into VLA pre-training. Together, these contributions enable villa-X to achieve superior performance across simulated environments including SIMPLER and LIBERO, as well as on two real-world robot setups including gripper and dexterous hand manipulation. We believe the ViLLA paradigm holds significant promise, and that our villa-X provides a strong foundation for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.23632", "title": "On the Expressiveness of Softmax Attention: A Recurrent Neural Network Perspective", "authors": [ "Gabriel Mongaras", "Eric C. Larson" ], "github": "", "project_page": "", "abstract": "Since its introduction, softmax attention has become the backbone of modern transformer architectures due to its expressiveness and scalability across a wide range of tasks. However, the main drawback of softmax attention is the quadratic memory requirement and computational complexity with respect to the sequence length. By replacing the softmax nonlinearity, linear attention and similar methods have been introduced to avoid the quadratic bottleneck of softmax attention. Despite these linear forms of attention being derived from the original softmax formulation, they typically lag in terms of downstream accuracy. While strong intuition of the softmax nonlinearity on the query and key inner product suggests that it has desirable properties compared to other nonlinearities, the question of why this discrepancy exists still remains unanswered. This work demonstrates that linear attention is an approximation of softmax attention by deriving the recurrent form of softmax attention. Using this form, each part of softmax attention can be described in the language of recurrent neural networks (RNNs). Describing softmax attention as an RNN allows for the ablation of the components of softmax attention to understand the importance of each part and how they interact. In this way, our work helps explain why softmax attention is more expressive than its counterparts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.23698", "title": "Scalable Multi-Task Reinforcement Learning for Generalizable Spatial Intelligence in Visuomotor Agents", "authors": [ "Shaofei Cai", "Zhancun Mu", "Haiwen Xia", "Bowei Zhang", "Anji Liu", "Yitao Liang" ], "github": "", "project_page": "", "abstract": "While Reinforcement Learning (RL) has achieved remarkable success in language modeling, its triumph hasn't yet fully translated to visuomotor agents. A primary challenge in RL models is their tendency to overfit specific tasks or environments, thereby hindering the acquisition of generalizable behaviors across diverse settings. This paper provides a preliminary answer to this challenge by demonstrating that RL-finetuned visuomotor agents in Minecraft can achieve zero-shot generalization to unseen worlds. Specifically, we explore RL's potential to enhance generalizable spatial reasoning and interaction capabilities in 3D worlds. To address challenges in multi-task RL representation, we analyze and establish cross-view goal specification as a unified multi-task goal space for visuomotor policies. Furthermore, to overcome the significant bottleneck of manual task design, we propose automated task synthesis within the highly customizable Minecraft environment for large-scale multi-task RL training, and we construct an efficient distributed RL framework to support this. Experimental results show RL significantly boosts interaction success rates by 4times and enables zero-shot generalization of spatial reasoning across diverse environments, including real-world settings. Our findings underscore the immense potential of RL training in 3D simulated environments, especially those amenable to large-scale task generation, for significantly advancing visuomotor agents' spatial reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.20519", "title": "AgroBench: Vision-Language Model Benchmark in Agriculture", "authors": [ "Risa Shinoda", "Nakamasa Inoue", "Hirokatsu Kataoka", "Masaki Onishi", "Yoshitaka Ushiku" ], "github": "", "project_page": "", "abstract": "Precise automated understanding of agricultural tasks such as disease identification is essential for sustainable crop production. Recent advances in vision-language models (VLMs) are expected to further expand the range of agricultural tasks by facilitating human-model interaction through easy, text-based communication. Here, we introduce AgroBench (Agronomist AI Benchmark), a benchmark for evaluating VLM models across seven agricultural topics, covering key areas in agricultural engineering and relevant to real-world farming. Unlike recent agricultural VLM benchmarks, AgroBench is annotated by expert agronomists. Our AgroBench covers a state-of-the-art range of categories, including 203 crop categories and 682 disease categories, to thoroughly evaluate VLM capabilities. In our evaluation on AgroBench, we reveal that VLMs have room for improvement in fine-grained identification tasks. Notably, in weed identification, most open-source VLMs perform close to random. With our wide range of topics and expert-annotated categories, we analyze the types of errors made by VLMs and suggest potential pathways for future VLM development. Our dataset and code are available at https://dahlian00.github.io/AgroBenchPage/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.23374", "title": "NeRF Is a Valuable Assistant for 3D Gaussian Splatting", "authors": [ "Shuangkang Fang", "I-Chao Shen", "Takeo Igarashi", "Yufeng Wang", "ZeSheng Wang", "Yi Yang", "Wenrui Ding", "Shuchang Zhou" ], "github": "", "project_page": "", "abstract": "We introduce NeRF-GS, a novel framework that jointly optimizes Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). This framework leverages the inherent continuous spatial representation of NeRF to mitigate several limitations of 3DGS, including sensitivity to Gaussian initialization, limited spatial awareness, and weak inter-Gaussian correlations, thereby enhancing its performance. In NeRF-GS, we revisit the design of 3DGS and progressively align its spatial features with NeRF, enabling both representations to be optimized within the same scene through shared 3D spatial information. We further address the formal distinctions between the two approaches by optimizing residual vectors for both implicit features and Gaussian positions to enhance the personalized capabilities of 3DGS. Experimental results on benchmark datasets show that NeRF-GS surpasses existing methods and achieves state-of-the-art performance. This outcome confirms that NeRF and 3DGS are complementary rather than competing, offering new insights into hybrid approaches that combine 3DGS and NeRF for efficient 3D scene representation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.14793", "title": "Flow Equivariant Recurrent Neural Networks", "authors": [ "T. Anderson Keller" ], "github": "https://github.com/akandykeller/FERNN", "project_page": "", "abstract": "Data arrives at our senses as a continuous stream, smoothly transforming from one instant to the next. These smooth transformations can be viewed as continuous symmetries of the environment that we inhabit, defining equivalence relations between stimuli over time. In machine learning, neural network architectures that respect symmetries of their data are called equivariant and have provable benefits in terms of generalization ability and sample efficiency. To date, however, equivariance has been considered only for static transformations and feed-forward networks, limiting its applicability to sequence models, such as recurrent neural networks (RNNs), and corresponding time-parameterized sequence transformations. In this work, we extend equivariant network theory to this regime of `flows' -- one-parameter Lie subgroups capturing natural transformations over time, such as visual motion. We begin by showing that standard RNNs are generally not flow equivariant: their hidden states fail to transform in a geometrically structured manner for moving stimuli. We then show how flow equivariance can be introduced, and demonstrate that these models significantly outperform their non-equivariant counterparts in terms of training speed, length generalization, and velocity generalization, on both next step prediction and sequence classification. We present this work as a first step towards building sequence models that respect the time-parameterized symmetries which govern the world around us.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/akandykeller/FERNN", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.23779", "title": "Phi-Ground Tech Report: Advancing Perception in GUI Grounding", "authors": [ "Miaosen Zhang", "Ziqiang Xu", "Jialiang Zhu", "Qi Dai", "Kai Qiu", "Yifan Yang", "Chong Luo", "Tianyi Chen", "Justin Wagle", "Tim Franklin", "Baining Guo" ], "github": "", "project_page": "", "abstract": "With the development of multimodal reasoning models, Computer Use Agents (CUAs), akin to Jarvis from \"Iron Man\", are becoming a reality. GUI grounding is a core component for CUAs to execute actual actions, similar to mechanical control in robotics, and it directly leads to the success or failure of the system. It determines actions such as clicking and typing, as well as related parameters like the coordinates for clicks. Current end-to-end grounding models still achieve less than 65\\% accuracy on challenging benchmarks like ScreenSpot-pro and UI-Vision, indicating they are far from being ready for deployment. % , as a single misclick can result in unacceptable consequences. In this work, we conduct an empirical study on the training of grounding models, examining details from data collection to model training. Ultimately, we developed the Phi-Ground model family, which achieves state-of-the-art performance across all five grounding benchmarks for models under 10B parameters in agent settings. In the end-to-end model setting, our model still achieves SOTA results with scores of \\textbf{43.2} on ScreenSpot-pro and \\textbf{27.2} on UI-Vision. We believe that the various details discussed in this paper, along with our successes and failures, not only clarify the construction of grounding models but also benefit other perception tasks. Project homepage: https://zhangmiaosen2000.github.io/Phi-Ground/{https://zhangmiaosen2000.github.io/Phi-Ground/}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.21509", "title": "Persona Vectors: Monitoring and Controlling Character Traits in Language Models", "authors": [ "Runjin Chen", "Andy Arditi", "Henry Sleight", "Owain Evans", "Jack Lindsey" ], "github": "https://github.com/safety-research/persona", "project_page": "", "abstract": "Large language models interact with users through a simulated 'Assistant' persona. While the Assistant is typically trained to be helpful, harmless, and honest, it sometimes deviates from these ideals. In this paper, we identify directions in the model's activation space-persona vectors-underlying several traits, such as evil, sycophancy, and propensity to hallucinate. We confirm that these vectors can be used to monitor fluctuations in the Assistant's personality at deployment time. We then apply persona vectors to predict and control personality shifts that occur during training. We find that both intended and unintended personality changes after finetuning are strongly correlated with shifts along the relevant persona vectors. These shifts can be mitigated through post-hoc intervention, or avoided in the first place with a new preventative steering method. Moreover, persona vectors can be used to flag training data that will produce undesirable personality changes, both at the dataset level and the individual sample level. Our method for extracting persona vectors is automated and can be applied to any personality trait of interest, given only a natural-language description.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/safety-research/persona", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.23436", "title": "Beyond Linear Bottlenecks: Spline-Based Knowledge Distillation for Culturally Diverse Art Style Classification", "authors": [ "Abdellah Zakaria Sellam", "Salah Eddine Bekhouche", "Cosimo Distante", "Abdelmalik Taleb-Ahmed" ], "github": "", "project_page": "", "abstract": "Art style classification remains a formidable challenge in computational aesthetics due to the scarcity of expertly labeled datasets and the intricate, often nonlinear interplay of stylistic elements. While recent dual-teacher self-supervised frameworks reduce reliance on labeled data, their linear projection layers and localized focus struggle to model global compositional context and complex style-feature interactions. We enhance the dual-teacher knowledge distillation framework to address these limitations by replacing conventional MLP projection and prediction heads with Kolmogorov-Arnold Networks (KANs). Our approach retains complementary guidance from two teacher networks, one emphasizing localized texture and brushstroke patterns, the other capturing broader stylistic hierarchies while leveraging KANs' spline-based activations to model nonlinear feature correlations with mathematical precision. Experiments on WikiArt and Pandora18k demonstrate that our approach outperforms the base dual teacher architecture in Top-1 accuracy. Our findings highlight the importance of KANs in disentangling complex style manifolds, leading to better linear probe accuracy than MLP projections.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.23404", "title": "Enhanced Arabic Text Retrieval with Attentive Relevance Scoring", "authors": [ "Salah Eddine Bekhouche", "Azeddine Benlamoudi", "Yazid Bounab", "Fadi Dornaika", "Abdenour Hadid" ], "github": "https://github.com/Bekhouche/APR", "project_page": "", "abstract": "Arabic poses a particular challenge for natural language processing (NLP) and information retrieval (IR) due to its complex morphology, optional diacritics and the coexistence of Modern Standard Arabic (MSA) and various dialects. Despite the growing global significance of Arabic, it is still underrepresented in NLP research and benchmark resources. In this paper, we present an enhanced Dense Passage Retrieval (DPR) framework developed specifically for Arabic. At the core of our approach is a novel Attentive Relevance Scoring (ARS) that replaces standard interaction mechanisms with an adaptive scoring function that more effectively models the semantic relevance between questions and passages. Our method integrates pre-trained Arabic language models and architectural refinements to improve retrieval performance and significantly increase ranking accuracy when answering Arabic questions. The code is made publicly available at https://github.com/Bekhouche/APR{GitHub}.", "github_manual": "", "github_regex": "https://github.com/Bekhouche/APR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.21584", "title": "TARS: MinMax Token-Adaptive Preference Strategy for Hallucination Reduction in MLLMs", "authors": [ "Kejia Zhang", "Keda Tao", "Zhiming Luo", "Chang Liu", "Jiasheng Tang", "Huan Wang" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) enable vision-language reasoning, yet often generate plausible outputs that are factually incorrect or visually ungrounded, thereby compromising their reliability. Direct preference optimization (DPO) is a common strategy for correcting hallucinations by aligning model outputs with human preferences. Existing DPO strategies typically treat hallucination-related preferences as fixed targets, relying on static supervision signals during training. This approach tends to overfit to superficial linguistic cues in preference data, leading to distributional rigidity and spurious correlations that impair grounding in causally relevant visual information. To overcome this limitation, we propose TARS, a token-adaptive preference strategy that reformulates DPO as a min-max optimization problem. TARS maximizes token-level distributional shifts under semantic constraints to simulate alignment uncertainty, and simultaneously minimizes the expected preference loss under these controlled perturbations. This joint objective preserves causal grounding while mitigating overfitting to preference patterns, thereby reducing hallucinations in multimodal reasoning. We evaluate TARS on multiple hallucination benchmarks and find consistently strong performance. Using only 4.8k preference samples and no expert feedback, TARS reduces hallucination rates from 26.4% to 13.2% and decreases cognition value from 2.5 to 0.4. It outperforms standard DPO and matches GPT-4o on several key metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.23257", "title": "Efficient Machine Unlearning via Influence Approximation", "authors": [ "Jiawei Liu", "Chenwang Wu", "Defu Lian", "Enhong Chen" ], "github": "https://github.com/Lolo1222/IAU", "project_page": "", "abstract": "Due to growing privacy concerns, machine unlearning, which aims at enabling machine learning models to ``forget\" specific training data, has received increasing attention. Among existing methods, influence-based unlearning has emerged as a prominent approach due to its ability to estimate the impact of individual training samples on model parameters without retraining. However, this approach suffers from prohibitive computational overhead arising from the necessity to compute the Hessian matrix and its inverse across all training samples and parameters, rendering it impractical for large-scale models and scenarios involving frequent data deletion requests. This highlights the difficulty of forgetting. Inspired by cognitive science, which suggests that memorizing is easier than forgetting, this paper establishes a theoretical link between memorizing (incremental learning) and forgetting (unlearning). This connection allows machine unlearning to be addressed from the perspective of incremental learning. Unlike the time-consuming Hessian computations in unlearning (forgetting), incremental learning (memorizing) typically relies on more efficient gradient optimization, which supports the aforementioned cognitive theory. Based on this connection, we introduce the Influence Approximation Unlearning (IAU) algorithm for efficient machine unlearning from the incremental perspective. Extensive empirical evaluations demonstrate that IAU achieves a superior balance among removal guarantee, unlearning efficiency, and comparable model utility, while outperforming state-of-the-art methods across diverse datasets and model architectures. Our code is available at https://github.com/Lolo1222/IAU.", "github_manual": "", "github_regex": "https://github.com/Lolo1222/IAU", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-01", "arxiv_id": "2507.23277", "title": "iLRM: An Iterative Large 3D Reconstruction Model", "authors": [ "Gyeongjin Kang", "Seungtae Nam", "Xiangyu Sun", "Sameh Khamis", "Abdelrahman Mohamed", "Eunbyung Park" ], "github": "", "project_page": "", "abstract": "Feed-forward 3D modeling has emerged as a promising approach for rapid and high-quality 3D reconstruction. In particular, directly generating explicit 3D representations, such as 3D Gaussian splatting, has attracted significant attention due to its fast and high-quality rendering, as well as numerous applications. However, many state-of-the-art methods, primarily based on transformer architectures, suffer from severe scalability issues because they rely on full attention across image tokens from multiple input views, resulting in prohibitive computational costs as the number of views or image resolution increases. Toward a scalable and efficient feed-forward 3D reconstruction, we introduce an iterative Large 3D Reconstruction Model (iLRM) that generates 3D Gaussian representations through an iterative refinement mechanism, guided by three core principles: (1) decoupling the scene representation from input-view images to enable compact 3D representations; (2) decomposing fully-attentional multi-view interactions into a two-stage attention scheme to reduce computational costs; and (3) injecting high-resolution information at every layer to achieve high-fidelity reconstruction. Experimental results on widely used datasets, such as RE10K and DL3DV, demonstrate that iLRM outperforms existing methods in both reconstruction quality and speed. Notably, iLRM exhibits superior scalability, delivering significantly higher reconstruction quality under comparable computational cost by efficiently leveraging a larger number of input views.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-04", "arxiv_id": "2507.23361", "title": "SWE-Exp: Experience-Driven Software Issue Resolution", "authors": [ "Silin Chen", "Shaoxin Lin", "Xiaodong Gu", "Yuling Shi", "Heng Lian", "Longfei Yun", "Dong Chen", "Weiguo Sun", "Lin Cao", "Qianxiang Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in large language model (LLM) agents have shown remarkable progress in software issue resolution, leveraging advanced techniques such as multi-agent collaboration and Monte Carlo Tree Search (MCTS). However, current agents act as memoryless explorers - treating each problem separately without retaining or reusing knowledge from previous repair experiences. This leads to redundant exploration of failed trajectories and missed chances to adapt successful issue resolution methods to similar problems. To address this problem, we introduce SWE-Exp, an experience - enhanced approach that distills concise and actionable experience from prior agent trajectories, enabling continuous learning across issues. Our method introduces a multi-faceted experience bank that captures both successful and failed repair attempts. Specifically, it extracts reusable issue resolution knowledge at different levels - from high-level problem comprehension to specific code changes. Experiments show that SWE-Exp achieves state-of-the-art resolution rate (41.6% Pass@1) on SWE-bench-Verified under open-source agent frameworks. Our approach establishes a new paradigm in which automated software engineering agents systematically accumulate and leverage repair expertise, fundamentally shifting from trial-and-error exploration to strategic, experience-driven issue resolution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-04", "arxiv_id": "2507.23348", "title": "SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution", "authors": [ "Han Li", "Yuling Shi", "Shaoxin Lin", "Xiaodong Gu", "Heng Lian", "Xin Wang", "Yantao Jia", "Tao Huang", "Qianxiang Wang" ], "github": "https://github.com/YerbaPage/SWE-Debate", "project_page": "", "abstract": "Issue resolution has made remarkable progress thanks to the advanced reasoning capabilities of large language models (LLMs). Recently, agent-based frameworks such as SWE-agent have further advanced this progress by enabling autonomous, tool-using agents to tackle complex software engineering tasks. While existing agent-based issue resolution approaches are primarily based on agents' independent explorations, they often get stuck in local solutions and fail to identify issue patterns that span across different parts of the codebase. To address this limitation, we propose SWE-Debate, a competitive multi-agent debate framework that encourages diverse reasoning paths and achieves more consolidated issue localization. SWE-Debate first creates multiple fault propagation traces as localization proposals by traversing a code dependency graph. Then, it organizes a three-round debate among specialized agents, each embodying distinct reasoning perspectives along the fault propagation trace. This structured competition enables agents to collaboratively converge on a consolidated fix plan. Finally, this consolidated fix plan is integrated into an MCTS-based code modification agent for patch generation. Experiments on the SWE-bench benchmark show that SWE-Debate achieves new state-of-the-art results in open-source agent frameworks and outperforms baselines by a large margin.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YerbaPage/SWE-Debate", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-04", "arxiv_id": "2507.23268", "title": "PixNerd: Pixel Neural Field Diffusion", "authors": [ "Shuai Wang", "Ziteng Gao", "Chenhui Zhu", "Weilin Huang", "Limin Wang" ], "github": "", "project_page": "", "abstract": "The current success of diffusion transformers heavily depends on the compressed latent space shaped by the pre-trained variational autoencoder(VAE). However, this two-stage training paradigm inevitably introduces accumulated errors and decoding artifacts. To address the aforementioned problems, researchers return to pixel space at the cost of complicated cascade pipelines and increased token complexity. In contrast to their efforts, we propose to model the patch-wise decoding with neural field and present a single-scale, single-stage, efficient, end-to-end solution, coined as pixel neural field diffusion~(PixelNerd). Thanks to the efficient neural field representation in PixNerd, we directly achieved 2.15 FID on ImageNet 256times256 and 2.84 FID on ImageNet 512times512 without any complex cascade pipeline or VAE. We also extend our PixNerd framework to text-to-image applications. Our PixNerd-XXL/16 achieved a competitive 0.73 overall score on the GenEval benchmark and 80.9 overall score on the DPG benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-04", "arxiv_id": "2508.00265", "title": "Multimodal Referring Segmentation: A Survey", "authors": [ "Henghui Ding", "Song Tang", "Shuting He", "Chang Liu", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "https://github.com/henghuiding/Awesome-Multimodal-Referring-Segmentation", "project_page": "", "abstract": "Multimodal referring segmentation aims to segment target objects in visual scenes, such as images, videos, and 3D scenes, based on referring expressions in text or audio format. This task plays a crucial role in practical applications requiring accurate object perception based on user instructions. Over the past decade, it has gained significant attention in the multimodal community, driven by advances in convolutional neural networks, transformers, and large language models, all of which have substantially improved multimodal perception capabilities. This paper provides a comprehensive survey of multimodal referring segmentation. We begin by introducing this field's background, including problem definitions and commonly used datasets. Next, we summarize a unified meta architecture for referring segmentation and review representative methods across three primary visual scenes, including images, videos, and 3D scenes. We further discuss Generalized Referring Expression (GREx) methods to address the challenges of real-world complexity, along with related tasks and practical applications. Extensive performance comparisons on standard benchmarks are also provided. We continually track related works at https://github.com/henghuiding/Awesome-Multimodal-Referring-Segmentation.", "github_manual": "", "github_regex": "https://github.com/henghuiding/Awesome-Multimodal-Referring-Segmentation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-04", "arxiv_id": "2508.00819", "title": "Beyond Fixed: Variable-Length Denoising for Diffusion Large Language Models", "authors": [ "Jinsong Li", "Xiaoyi Dong", "Yuhang Zang", "Yuhang Cao", "Jiaqi Wang", "Dahua Lin" ], "github": "", "project_page": "", "abstract": "Diffusion Large Language Models (DLLMs) are emerging as a powerful alternative to the dominant Autoregressive Large Language Models, offering efficient parallel generation and capable global context modeling. However, the practical application of DLLMs is hindered by a critical architectural constraint: the need for a statically predefined generation length. This static length allocation leads to a problematic trade-off: insufficient lengths cripple performance on complex tasks, while excessive lengths incur significant computational overhead and sometimes result in performance degradation. While the inference framework is rigid, we observe that the model itself possesses internal signals that correlate with the optimal response length for a given task. To bridge this gap, we leverage these latent signals and introduce DAEDAL, a novel training-free denoising strategy that enables Dynamic Adaptive Length Expansion for Diffusion Large Language Models. DAEDAL operates in two phases: 1) Before the denoising process, DAEDAL starts from a short initial length and iteratively expands it to a coarse task-appropriate length, guided by a sequence completion metric. 2) During the denoising process, DAEDAL dynamically intervenes by pinpointing and expanding insufficient generation regions through mask token insertion, ensuring the final output is fully developed. Extensive experiments on DLLMs demonstrate that DAEDAL achieves performance comparable, and in some cases superior, to meticulously tuned fixed-length baselines, while simultaneously enhancing computational efficiency by achieving a higher effective token ratio. By resolving the static length constraint, DAEDAL unlocks new potential for DLLMs, bridging a critical gap with their Autoregressive counterparts and paving the way for more efficient and capable generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-04", "arxiv_id": "2508.00823", "title": "IGL-Nav: Incremental 3D Gaussian Localization for Image-goal Navigation", "authors": [ "Wenxuan Guo", "Xiuwei Xu", "Hang Yin", "Ziwei Wang", "Jianjiang Feng", "Jie Zhou", "Jiwen Lu" ], "github": "https://github.com/GWxuan/IGL-Nav", "project_page": "", "abstract": "Visual navigation with an image as goal is a fundamental and challenging problem. Conventional methods either rely on end-to-end RL learning or modular-based policy with topological graph or BEV map as memory, which cannot fully model the geometric relationship between the explored 3D environment and the goal image. In order to efficiently and accurately localize the goal image in 3D space, we build our navigation system upon the renderable 3D gaussian (3DGS) representation. However, due to the computational intensity of 3DGS optimization and the large search space of 6-DoF camera pose, directly leveraging 3DGS for image localization during agent exploration process is prohibitively inefficient. To this end, we propose IGL-Nav, an Incremental 3D Gaussian Localization framework for efficient and 3D-aware image-goal navigation. Specifically, we incrementally update the scene representation as new images arrive with feed-forward monocular prediction. Then we coarsely localize the goal by leveraging the geometric information for discrete space matching, which can be equivalent to efficient 3D convolution. When the agent is close to the goal, we finally solve the fine target pose with optimization via differentiable rendering. The proposed IGL-Nav outperforms existing state-of-the-art methods by a large margin across diverse experimental configurations. It can also handle the more challenging free-view image-goal setting and be deployed on real-world robotic platform using a cellphone to capture goal image at arbitrary pose. Project page: https://gwxuan.github.io/IGL-Nav/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GWxuan/IGL-Nav", "github_api_added_by": "auto" }, { "date": "2025-08-04", "arxiv_id": "2508.00454", "title": "Learning an Efficient Multi-Turn Dialogue Evaluator from Multiple Judges", "authors": [ "Yuqi Tang", "Kehua Feng", "Yunfeng Wang", "Zhiwen Chen", "Chengfei Lv", "Gang Yu", "Qiang Zhang", "Keyan Ding" ], "github": "", "project_page": "", "abstract": "Evaluating the conversational abilities of large language models (LLMs) remains a challenging task. Current mainstream approaches primarily rely on the ``LLM-as-a-judge\" paradigm, where an LLM is prompted to serve as an evaluator to assess dialogue quality. However, such methods often suffer from various biases, which undermine the reliability and consistency of the evaluation results. To mitigate these biases, recent methods employ multiple LLMs as judges and aggregate their judgments to select the optimal assessment. Although effective, this multi-judge approach incurs significant computational overhead during inference. In this paper, we propose an efficient multi-turn dialogue evaluator that captures the collective wisdom of multiple LLM judges by aggregating their preference knowledge into a single model. Our approach preserves the advantages of diverse multi-judge feedback while drastically reducing the evaluation cost, enabling fast and flexible dialogue quality assessment. Extensive experiments on seven single rating and pairwise comparison dialogue evaluation benchmarks demonstrate that our method outperforms existing baselines across diverse scenarios, showcasing its efficiency and robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-04", "arxiv_id": "2507.22720", "title": "Investigating Hallucination in Conversations for Low Resource Languages", "authors": [ "Amit Das", "Md. Najib Hasan", "Souvika Sarkar", "Zheng Zhang", "Fatemeh Jamshidi", "Tathagata Bhattacharya", "Nilanjana Raychawdhury", "Dongji Feng", "Vinija Jain", "Aman Chadha" ], "github": "https://github.com/AmitDasRup123/LLM-Hallucination-Low-Resource-Languages", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable proficiency in generating text that closely resemble human writing. However, they often generate factually incorrect statements, a problem typically referred to as 'hallucination'. Addressing hallucination is crucial for enhancing the reliability and effectiveness of LLMs. While much research has focused on hallucinations in English, our study extends this investigation to conversational data in three languages: Hindi, Farsi, and Mandarin. We offer a comprehensive analysis of a dataset to examine both factual and linguistic errors in these languages for GPT-3.5, GPT-4o, Llama-3.1, Gemma-2.0, DeepSeek-R1 and Qwen-3. We found that LLMs produce very few hallucinated responses in Mandarin but generate a significantly higher number of hallucinations in Hindi and Farsi.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AmitDasRup123/LLM-Hallucination-Low-Resource-Languages", "github_api_added_by": "auto" }, { "date": "2025-08-04", "arxiv_id": "2507.23478", "title": "3D-R1: Enhancing Reasoning in 3D VLMs for Unified Scene Understanding", "authors": [ "Ting Huang", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/3D-R1", "project_page": "", "abstract": "Large vision-language models (VLMs) have made significant strides in 2D visual understanding tasks, sparking interest in extending these capabilities to 3D scene understanding. However, current 3D VLMs often struggle with robust reasoning and generalization due to limitations in high-quality spatial data and the static nature of viewpoint assumptions. To address these challenges, we propose 3D-R1, a foundation model that enhances the reasoning capabilities of 3D VLMs. Specifically, we first construct a high-quality synthetic dataset with CoT, named Scene-30K, leveraging existing 3D-VL datasets and a data engine based on Gemini 2.5 Pro. It serves as cold-start initialization data for 3D-R1. Moreover, we leverage RLHF policy such as GRPO in the reinforcement learning training process to enhance reasoning capabilities and introduce three reward functions: a perception reward, a semantic similarity reward and a format reward to maintain detection accuracy and answer semantic precision. Furthermore, we introduce a dynamic view selection strategy that adaptively chooses the most informative perspectives for 3D scene understanding. Extensive experiments demonstrate that 3D-R1 delivers an average improvement of 10% across various 3D scene benchmarks, highlighting its effectiveness in enhancing reasoning and generalization in 3D scene understanding. Code: https://github.com/AIGeeksGroup/3D-R1. Website: https://aigeeksgroup.github.io/3D-R1.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/3D-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-04", "arxiv_id": "2508.00782", "title": "SpA2V: Harnessing Spatial Auditory Cues for Audio-driven Spatially-aware Video Generation", "authors": [ "Kien T. Pham", "Yingqing He", "Yazhou Xing", "Qifeng Chen", "Long Chen" ], "github": "", "project_page": "", "abstract": "Audio-driven video generation aims to synthesize realistic videos that align with input audio recordings, akin to the human ability to visualize scenes from auditory input. However, existing approaches predominantly focus on exploring semantic information, such as the classes of sounding sources present in the audio, limiting their ability to generate videos with accurate content and spatial composition. In contrast, we humans can not only naturally identify the semantic categories of sounding sources but also determine their deeply encoded spatial attributes, including locations and movement directions. This useful information can be elucidated by considering specific spatial indicators derived from the inherent physical properties of sound, such as loudness or frequency. As prior methods largely ignore this factor, we present SpA2V, the first framework explicitly exploits these spatial auditory cues from audios to generate videos with high semantic and spatial correspondence. SpA2V decomposes the generation process into two stages: 1) Audio-guided Video Planning: We meticulously adapt a state-of-the-art MLLM for a novel task of harnessing spatial and semantic cues from input audio to construct Video Scene Layouts (VSLs). This serves as an intermediate representation to bridge the gap between the audio and video modalities. 2) Layout-grounded Video Generation: We develop an efficient and effective approach to seamlessly integrate VSLs as conditional guidance into pre-trained diffusion models, enabling VSL-grounded video generation in a training-free manner. Extensive experiments demonstrate that SpA2V excels in generating realistic videos with semantic and spatial alignment to the input audios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-04", "arxiv_id": "2508.00632", "title": "Multi-Agent Game Generation and Evaluation via Audio-Visual Recordings", "authors": [ "Alexia Jolicoeur-Martineau" ], "github": "", "project_page": "", "abstract": "While AI excels at generating text, audio, images, and videos, creating interactive audio-visual content such as video games remains challenging. Current LLMs can generate JavaScript games and animations, but lack automated evaluation metrics and struggle with complex content that normally requires teams of humans working for many months (multi-shot, multi-agents) using assets made by artists. To tackle these issues, we built a new metric and a multi-agent system. We propose AVR-Eval, a relative metric for multimedia content quality using Audio-Visual Recordings (AVRs). An omni-modal model (processing text, video, and audio) compares the AVRs of two contents, with a text model reviewing evaluations to determine superiority. We show that AVR-Eval properly identifies good from broken or mismatched content. We built AVR-Agent, a multi-agent system generating JavaScript code from a bank of multimedia assets (audio, images, 3D models). The coding agent selects relevant assets, generates multiple initial codes, uses AVR-Eval to identify the best version, and iteratively improves it through omni-modal agent feedback from the AVR. We run experiments on games and animations with AVR-Eval (win rate of content A against B). We find that content generated by AVR-Agent has a significantly higher win rate against content made through one-shot generation. However, models struggle to leverage custom assets and AVR feedback effectively, showing no higher win rate. This reveals a critical gap: while humans benefit from high-quality assets and audio-visual feedback, current coding models do not seem to utilize these resources as effectively, highlighting fundamental differences between human and machine content creation approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-04", "arxiv_id": "2508.00414", "title": "Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training", "authors": [ "Tianqing Fang", "Zhisong Zhang", "Xiaoyang Wang", "Rui Wang", "Can Qin", "Yuxuan Wan", "Jun-Yu Ma", "Ce Zhang", "Jiaqi Chen", "Xiyun Li", "Hongming Zhang", "Haitao Mi", "Dong Yu" ], "github": "https://github.com/Tencent/CognitiveKernel-Pro", "project_page": "", "abstract": "General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence, enabling complex reasoning, web interaction, coding, and autonomous research capabilities. However, current agent systems are either closed-source or heavily reliant on a variety of paid APIs and proprietary tools, limiting accessibility and reproducibility for the research community. In this work, we present Cognitive Kernel-Pro, a fully open-source and (to the maximum extent) free multi-module agent framework designed to democratize the development and evaluation of advanced AI agents. Within Cognitive Kernel-Pro, we systematically investigate the curation of high-quality training data for Agent Foundation Models, focusing on the construction of queries, trajectories, and verifiable answers across four key domains: web, file, code, and general reasoning. Furthermore, we explore novel strategies for agent test-time reflection and voting to enhance agent robustness and performance. We evaluate Cognitive Kernel-Pro on GAIA, achieving state-of-the-art results among open-source and free agents. Notably, our 8B-parameter open-source model surpasses previous leading systems such as WebDancer and WebSailor, establishing a new performance standard for accessible, high-capability AI agents. Code is available at https://github.com/Tencent/CognitiveKernel-Pro", "github_manual": "", "github_regex": "https://github.com/Tencent/CognitiveKernel-Pro", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-04", "arxiv_id": "2507.19634", "title": "MCIF: Multimodal Crosslingual Instruction-Following Benchmark from Scientific Talks", "authors": [ "Sara Papi", "Maike Z\u00fcfle", "Marco Gaido", "Beatrice Savoldi", "Danni Liu", "Ioannis Douros", "Luisa Bentivogli", "Jan Niehues" ], "github": "https://github.com/hlt-mt/mcif/tree/main/dataset", "project_page": "", "abstract": "Recent advances in large language models have catalyzed the development of multimodal LLMs (MLLMs) that integrate text, speech, and vision within unified frameworks. As MLLMs evolve from narrow, monolingual, task-specific systems to general-purpose instruction-following models, a key frontier lies in evaluating their multilingual and multimodal capabilities over both long and short contexts. However, existing benchmarks fall short in evaluating these dimensions jointly: they are often limited to English, mostly focus on one single modality at a time, rely on short-form contexts, or lack human annotations -- hindering comprehensive assessment of model performance across languages, modalities, and task complexity. To address these gaps, we introduce MCIF (Multimodal Crosslingual Instruction Following), the first multilingual human-annotated benchmark based on scientific talks that is designed to evaluate instruction-following in crosslingual, multimodal settings over both short- and long-form inputs. MCIF spans three core modalities -- speech, vision, and text -- and four diverse languages (English, German, Italian, and Chinese), enabling a comprehensive evaluation of MLLMs' abilities to interpret instructions across languages and combine them with multimodal contextual information. MCIF is released under a CC-BY 4.0 license to encourage open research and progress in MLLMs development.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hlt-mt/mcif/tree/main/dataset", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-04", "arxiv_id": "2508.02124", "title": "Trainable Dynamic Mask Sparse Attention", "authors": [ "Jingze Shi", "Yifan Wu", "Bingheng Wu", "Yiran Peng", "Liangdong Wang", "Guang Liu", "Yuyu Luo" ], "github": "https://github.com/flash-algo/flash-sparse-attention", "project_page": "", "abstract": "In large language models, the demand for modeling long contexts is constantly increasing, but the quadratic complexity of the standard self-attention mechanism often becomes a bottleneck. Although existing sparse attention mechanisms have improved efficiency, they may still encounter issues such as static patterns or information loss. We introduce a trainable dynamic mask sparse attention mechanism, Dynamic Mask Attention, which effectively utilizes content-aware and position-aware sparsity. DMA achieves this through two key innovations: First, it dynamically generates content-aware sparse masks from value representations, enabling the model to identify and focus on critical information adaptively. Second, it implements position-aware sparse attention computation that effectively skips unnecessary calculation regions. This dual-sparsity design allows the model to significantly reduce the computational complexity of important information while retaining complete information, achieving an excellent balance between information fidelity and computational efficiency. We have verified the performance of DMA through comprehensive experiments. Comparative studies show that DMA outperforms multi-head attention, sliding window attention, multi-head latent attention, and native sparse attention in terms of perplexity under Chinchilla Scaling Law settings. Moreover, in challenging multi-query associative recall tasks, DMA also demonstrates superior performance and efficiency compared to these methods. Crucially, in the evaluation of a 1.7B parameter model, DMA significantly outperforms multi-head attention in both standard benchmark performance and the challenging needle-in-a-haystack task. These experimental results highlight its capability to balance model efficiency and long-context modeling ability effectively.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/flash-algo/flash-sparse-attention", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.01059", "title": "Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report", "authors": [ "Sajana Weerawardhena", "Paul Kassianik", "Blaine Nelson", "Baturay Saglam", "Anu Vellore", "Aman Priyanshu", "Supriti Vijay", "Massimo Aufiero", "Arthur Goldblatt", "Fraser Burch", "Ed Li", "Jianliang He", "Dhruv Kedia", "Kojin Oshiba", "Zhouran Yang", "Yaron Singer", "Amin Karbasi" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable success across many domains, yet their integration into cybersecurity applications remains limited due to a lack of general-purpose cybersecurity data, representational complexity, and safety and regulatory concerns. To address this gap, we previously introduced Foundation-Sec-8B, a cybersecurity-focused LLM suitable for fine-tuning on downstream tasks. That model, however, was not designed for chat-style interactions or instruction-following. In this report, we release Foundation-Sec-8B-Instruct: a model specifically trained for general-purpose cybersecurity dialogue. Built on Foundation-Sec-8B, it combines domain-specific knowledge with instruction-following, conversational capabilities, and alignment with human preferences to produce high-quality, relevant responses. Comprehensive evaluations show that Foundation-Sec-8B-Instruct outperforms Llama 3.1-8B-Instruct on a range of cybersecurity tasks while matching its instruction-following performance. It is also competitive with GPT-4o-mini on cyber threat intelligence and instruction-following tasks. We envision Foundation-Sec-8B-Instruct becoming an indispensable assistant in the daily workflows of cybersecurity professionals. We release the model publicly at https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Instruct.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.02276", "title": "CellForge: Agentic Design of Virtual Cell Models", "authors": [ "Xiangru Tang", "Zhuoyun Yu", "Jiapeng Chen", "Yan Cui", "Daniel Shao", "Weixu Wang", "Fang Wu", "Yuchen Zhuang", "Wenqi Shi", "Zhi Huang", "Arman Cohan", "Xihong Lin", "Fabian Theis", "Smita Krishnaswamy", "Mark Gerstein" ], "github": "https://github.com/gersteinlab/CellForge", "project_page": "", "abstract": "Virtual cell modeling represents an emerging frontier at the intersection of artificial intelligence and biology, aiming to predict quantities such as responses to diverse perturbations quantitatively. However, autonomously building computational models for virtual cells is challenging due to the complexity of biological systems, the heterogeneity of data modalities, and the need for domain-specific expertise across multiple disciplines. Here, we introduce CellForge, an agentic system that leverages a multi-agent framework that transforms presented biological datasets and research objectives directly into optimized computational models for virtual cells. More specifically, given only raw single-cell multi-omics data and task descriptions as input, CellForge outputs both an optimized model architecture and executable code for training virtual cell models and inference. The framework integrates three core modules: Task Analysis for presented dataset characterization and relevant literature retrieval, Method Design, where specialized agents collaboratively develop optimized modeling strategies, and Experiment Execution for automated generation of code. The agents in the Design module are separated into experts with differing perspectives and a central moderator, and have to collaboratively exchange solutions until they achieve a reasonable consensus. We demonstrate CellForge's capabilities in single-cell perturbation prediction, using six diverse datasets that encompass gene knockouts, drug treatments, and cytokine stimulations across multiple modalities. CellForge consistently outperforms task-specific state-of-the-art methods. Overall, CellForge demonstrates how iterative interaction between LLM agents with differing perspectives provides better solutions than directly addressing a modeling challenge. Our code is publicly available at https://github.com/gersteinlab/CellForge.", "github_manual": "", "github_regex": "https://github.com/gersteinlab/CellForge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2507.17520", "title": "InstructVLA: Vision-Language-Action Instruction Tuning from Understanding to Manipulation", "authors": [ "Shuai Yang", "Hao Li", "Yilun Chen", "Bin Wang", "Yang Tian", "Tai Wang", "Hanqing Wang", "Feng Zhao", "Yiyi Liao", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "To operate effectively in the real world, robots must integrate multimodal reasoning with precise action generation. However, existing vision-language-action (VLA) models often sacrifice one for the other, narrow their abilities to task-specific manipulation data, and suffer catastrophic forgetting of pre-trained vision-language capabilities. To bridge this gap, we introduce InstructVLA, an end-to-end VLA model that preserves the flexible reasoning of large vision-language models (VLMs) while delivering leading manipulation performance. InstructVLA introduces a novel training paradigm, Vision-Language-Action Instruction Tuning (VLA-IT), which employs multimodal training with mixture-of-experts adaptation to jointly optimize textual reasoning and action generation on both standard VLM corpora and a curated 650K-sample VLA-IT dataset. On in-domain SimplerEnv tasks, InstructVLA achieves 30.5% improvement over SpatialVLA. To evaluate generalization, we introduce SimplerEnv-Instruct, an 80-task benchmark requiring closed-loop control and high-level instruction understanding, where it outperforms a fine-tuned OpenVLA by 92% and an action expert aided by GPT-4o by 29%. Additionally, InstructVLA surpasses baseline VLMs on multimodal tasks and exhibits inference-time scaling by leveraging textual reasoning to boost manipulation performance in both simulated and real-world settings. These results demonstrate InstructVLA's potential for bridging intuitive and steerable human-robot interaction with efficient policy learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.02317", "title": "VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo", "authors": [ "Qianli Ma", "Yaowei Zheng", "Zhelun Shi", "Zhongkai Zhao", "Bin Jia", "Ziyue Huang", "Zhiqi Lin", "Youjie Li", "Jiacheng Yang", "Yanghua Peng", "Zhi Zhang", "Xin Liu" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have driven impressive progress in omni-modal understanding and generation. However, training omni-modal LLMs remains a significant challenge due to the heterogeneous model architectures required to process diverse modalities, necessitating sophisticated system design for efficient large-scale training. Existing frameworks typically entangle model definition with parallel logic, incurring limited scalability and substantial engineering overhead for end-to-end omni-modal training. % We present \\veomni, a modular and efficient training framework to accelerate the development of omni-modal LLMs. \\veomni introduces model-centric distributed recipes that decouples communication from computation, enabling efficient 3D parallelism on omni-modal LLMs. \\veomni also features a flexible configuration interface supporting seamless integration of new modalities with minimal code change. % Using \\veomni, a omni-modal mixture-of-experts (MoE) model with 30B parameters can be trained with over 2,800 tokens/sec/GPU throughput and scale to 160K context lengths via 3D parallelism on 128 GPUs, showcasing its superior efficiency and scalability for training large omni-modal LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.01151", "title": "Personalized Safety Alignment for Text-to-Image Diffusion Models", "authors": [ "Yu Lei", "Jinbin Bai", "Qingyu Shi", "Aosong Feng", "Kaidong Yu" ], "github": "https://github.com/M-E-AGI-Lab/PSAlign", "project_page": "", "abstract": "Text-to-image diffusion models have revolutionized visual content generation, but current safety mechanisms apply uniform standards that often fail to account for individual user preferences. These models overlook the diverse safety boundaries shaped by factors like age, mental health, and personal beliefs. To address this, we propose Personalized Safety Alignment (PSA), a framework that allows user-specific control over safety behaviors in generative models. PSA integrates personalized user profiles into the diffusion process, adjusting the model's behavior to match individual safety preferences while preserving image quality. We introduce a new dataset, Sage, which captures user-specific safety preferences and incorporates these profiles through a cross-attention mechanism. Experiments show that PSA outperforms existing methods in harmful content suppression and aligns generated content better with user constraints, achieving higher Win Rate and Pass Rate scores. Our code, data, and models are publicly available at https://torpedo2648.github.io/PSAlign/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/M-E-AGI-Lab/PSAlign", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.00910", "title": "Cyber-Zero: Training Cybersecurity Agents without Runtime", "authors": [ "Terry Yue Zhuo", "Dingmin Wang", "Hantian Ding", "Varun Kumar", "Zijian Wang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved remarkable success in software engineering tasks when trained with executable runtime environments, particularly in resolving GitHub issues. However, such runtime environments are often unavailable in other domains, especially cybersecurity, where challenge configurations and execution contexts are ephemeral or restricted. We present Cyber-Zero, the first runtime-free framework for synthesizing high-quality agent trajectories to train cybersecurity LLMs. Cyber-Zero leverages publicly available CTF writeups and employs persona-driven LLM simulation to reverse-engineer runtime behaviors and generate realistic, long-horizon interaction sequences without actual environments. Using trajectories synthesized by Cyber-Zero, we train LLM-based agents that achieve up to 13.1% absolute performance gains over baseline models on three prominent CTF benchmarks: InterCode-CTF, NYU CTF Bench, and Cybench. Our best model, Cyber-Zero-32B, establishes new state-of-the-art performance among open-weight models, matching the capabilities of proprietary systems like DeepSeek-V3-0324 and Claude-3.5-Sonnet while offering superior cost-effectiveness, and demonstrating that runtime-free trajectory synthesis can effectively democratize the development of state-of-the-art cybersecurity agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.01415", "title": "RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong Learning in Physical Embodied Systems", "authors": [ "Mingcong Lei", "Honghao Cai", "Zezhou Cui", "Liangchen Tan", "Junkun Hong", "Gehan Hu", "Shuangyu Zhu", "Yimou Wu", "Shaohan Jiang", "Ge Wang", "Zhen Li", "Shuguang Cui", "Yiming Zhao", "Yatong Han" ], "github": "", "project_page": "", "abstract": "We present RoboMemory, a brain-inspired multi-memory framework for lifelong learning in physical embodied systems, addressing critical challenges in real-world environments: continuous learning, multi-module memory latency, task correlation capture, and infinite-loop mitigation in closed-loop planning. Grounded in cognitive neuroscience, it integrates four core modules: the Information Preprocessor (thalamus-like), the Lifelong Embodied Memory System (hippocampus-like), the Closed-Loop Planning Module (prefrontal lobe-like), and the Low-Level Executer (cerebellum-like) to enable long-term planning and cumulative learning. The Lifelong Embodied Memory System, central to the framework, alleviates inference speed issues in complex memory frameworks via parallelized updates/retrieval across Spatial, Temporal, Episodic, and Semantic submodules. It incorporates a dynamic Knowledge Graph (KG) and consistent architectural design to enhance memory consistency and scalability. Evaluations on EmbodiedBench show RoboMemory outperforms the open-source baseline (Qwen2.5-VL-72B-Ins) by 25% in average success rate and surpasses the closed-source State-of-the-Art (SOTA) (Claude3.5-Sonnet) by 5%, establishing new SOTA. Ablation studies validate key components (critic, spatial memory, long-term memory), while real-world deployment confirms its lifelong learning capability with significantly improved success rates across repeated tasks. RoboMemory alleviates high latency challenges with scalability, serving as a foundational reference for integrating multi-modal memory systems in physical robots.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.01959", "title": "SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension", "authors": [ "Junjie Wu", "Jiangnan Li", "Yuqing Li", "Lemao Liu", "Liyan Xu", "Jiwei Li", "Dit-Yan Yeung", "Jie Zhou", "Mo Yu" ], "github": "", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) over long documents typically involves splitting the text into smaller chunks, which serve as the basic units for retrieval. However, due to dependencies across the original document, contextual information is often essential for accurately interpreting each chunk. To address this, prior work has explored encoding longer context windows to produce embeddings for longer chunks. Despite these efforts, gains in retrieval and downstream tasks remain limited. This is because (1) longer chunks strain the capacity of embedding models due to the increased amount of information they must encode, and (2) many real-world applications still require returning localized evidence due to constraints on model or human bandwidth. We propose an alternative approach to this challenge by representing short chunks in a way that is conditioned on a broader context window to enhance retrieval performance -- i.e., situating a chunk's meaning within its context. We further show that existing embedding models are not well-equipped to encode such situated context effectively, and thus introduce a new training paradigm and develop the situated embedding models (SitEmb). To evaluate our method, we curate a book-plot retrieval dataset specifically designed to assess situated retrieval capabilities. On this benchmark, our SitEmb-v1 model based on BGE-M3 substantially outperforms state-of-the-art embedding models, including several with up to 7-8B parameters, with only 1B parameters. Our 8B SitEmb-v1.5 model further improves performance by over 10% and shows strong results across different languages and several downstream applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.01287", "title": "Exploitation Is All You Need... for Exploration", "authors": [ "Micah Rentschler", "Jesse Roberts" ], "github": "", "project_page": "", "abstract": "Ensuring sufficient exploration is a central challenge when training meta-reinforcement learning (meta-RL) agents to solve novel environments. Conventional solutions to the exploration-exploitation dilemma inject explicit incentives such as randomization, uncertainty bonuses, or intrinsic rewards to encourage exploration. In this work, we hypothesize that an agent trained solely to maximize a greedy (exploitation-only) objective can nonetheless exhibit emergent exploratory behavior, provided three conditions are met: (1) Recurring Environmental Structure, where the environment features repeatable regularities that allow past experience to inform future choices; (2) Agent Memory, enabling the agent to retain and utilize historical interaction data; and (3) Long-Horizon Credit Assignment, where learning propagates returns over a time frame sufficient for the delayed benefits of exploration to inform current decisions. Through experiments in stochastic multi-armed bandits and temporally extended gridworlds, we observe that, when both structure and memory are present, a policy trained on a strictly greedy objective exhibits information-seeking exploratory behavior. We further demonstrate, through controlled ablations, that emergent exploration vanishes if either environmental structure or agent memory is absent (Conditions 1 & 2). Surprisingly, removing long-horizon credit assignment (Condition 3) does not always prevent emergent exploration-a result we attribute to the pseudo-Thompson Sampling effect. These findings suggest that, under the right prerequisites, exploration and exploitation need not be treated as orthogonal objectives but can emerge from a unified reward-maximization process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.00890", "title": "AgentTTS: Large Language Model Agent for Test-time Compute-optimal Scaling Strategy in Complex Tasks", "authors": [ "Fali Wang", "Hui Liu", "Zhenwei Dai", "Jingying Zeng", "Zhiwei Zhang", "Zongyu Wu", "Chen Luo", "Zhen Li", "Xianfeng Tang", "Qi He", "Suhang Wang" ], "github": "", "project_page": "", "abstract": "Test-time scaling (TTS) enhances the performance of large language models (LLMs) by allocating additional compute resources during inference. However, existing research primarily investigates TTS in single-stage tasks; while many real-world problems are multi-stage complex tasks, composed of a sequence of heterogeneous subtasks with each subtask requires LLM of specific capability. Therefore, we study a novel problem: the test-time compute-optimal scaling in multi-stage complex tasks, aiming to select suitable models and allocate budgets per subtask to maximize overall performance. TTS in multi-stage tasks introduces two fundamental challenges: (i) The combinatorial search space of model and budget allocations, combined with the high cost of inference, makes brute-force search impractical. (ii) The optimal model and budget allocations across subtasks are interdependent, increasing the complexity of the compute-optimal search. To address this gap, we conduct extensive pilot experiments on four tasks across six datasets, deriving three empirical insights characterizing the behavior of LLMs in multi-stage complex tasks. Informed by these insights, we propose AgentTTS, an LLM-agent-based framework that autonomously searches for compute-optimal allocations through iterative feedback-driven interactions with the execution environment. Experimental results demonstrate that AgentTTS significantly outperforms traditional and other LLM-based baselines in search efficiency, and shows improved robustness to varying training set sizes and enhanced interpretability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.02150", "title": "Beyond the Trade-off: Self-Supervised Reinforcement Learning for Reasoning Models' Instruction Following", "authors": [ "Qingyu Ren", "Qianyu He", "Bowei Zhang", "Jie Zeng", "Jiaqing Liang", "Yanghua Xiao", "Weikang Zhou", "Zeye Sun", "Fei Yu" ], "github": "https://github.com/Rainier-rq/verl-if", "project_page": "", "abstract": "Reasoning models excel in complex problem solving but exhibit a concerning trade off between reasoning capabilities and instruction following abilities. Existing approaches for improving instruction following rely on stronger external models, creating methodological bottlenecks and practical limitations including increased costs and accessibility constraints. We propose a self-supervised RL framework that leverages reasoning models' own internal signals to improve instruction following capabilities without external supervision. Extensive experiments demonstrate that our framework significantly improves instruction following capabilities while maintaining reasoning performance, offering a scalable and cost-effective approach to enhance instruction following in reasoning models. The data and code are publicly available at https://github.com/Rainier-rq/verl-if.", "github_manual": "", "github_regex": "https://github.com/Rainier-rq/verl-if", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.02324", "title": "Qwen-Image Technical Report", "authors": [ "Chenfei Wu", "Jiahao Li", "Jingren Zhou", "Junyang Lin", "Kaiyuan Gao", "Kun Yan", "Sheng-ming Yin", "Shuai Bai", "Xiao Xu", "Yilei Chen", "Yuxiang Chen", "Zecheng Tang", "Zekai Zhang", "Zhengyi Wang", "An Yang", "Bowen Yu", "Chen Cheng", "Dayiheng Liu", "Deqing Li", "Hang Zhang", "Hao Meng", "Hu Wei", "Jingyuan Ni", "Kai Chen", "Kuan Cao", "Liang Peng", "Lin Qu", "Minggang Wu", "Peng Wang", "Shuting Yu", "Tingkun Wen", "Wensen Feng", "Xiaoxiao Xu", "Yi Wang", "Yichang Zhang", "Yongqiang Zhu", "Yujia Wu", "Yuxuan Cai", "Zenan Liu" ], "github": "", "project_page": "", "abstract": "We present Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. To address the challenges of complex text rendering, we design a comprehensive data pipeline that includes large-scale data collection, filtering, annotation, synthesis, and balancing. Moreover, we adopt a progressive training strategy that starts with non-text-to-text rendering, evolves from simple to complex textual inputs, and gradually scales up to paragraph-level descriptions. This curriculum learning approach substantially enhances the model's native text rendering capabilities. As a result, Qwen-Image not only performs exceptionally well in alphabetic languages such as English, but also achieves remarkable progress on more challenging logographic languages like Chinese. To enhance image editing consistency, we introduce an improved multi-task training paradigm that incorporates not only traditional text-to-image (T2I) and text-image-to-image (TI2I) tasks but also image-to-image (I2I) reconstruction, effectively aligning the latent representations between Qwen2.5-VL and MMDiT. Furthermore, we separately feed the original image into Qwen2.5-VL and the VAE encoder to obtain semantic and reconstructive representations, respectively. This dual-encoding mechanism enables the editing module to strike a balance between preserving semantic consistency and maintaining visual fidelity. Qwen-Image achieves state-of-the-art performance, demonstrating its strong capabilities in both image generation and editing across multiple benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.01548", "title": "A Glimpse to Compress: Dynamic Visual Token Pruning for Large Vision-Language Models", "authors": [ "Quan-Sheng Zeng", "Yunheng Li", "Qilong Wang", "Peng-Tao Jiang", "Zuxuan Wu", "Ming-Ming Cheng", "Qibin Hou" ], "github": "", "project_page": "", "abstract": "Visual token compression is critical for Large Vision-Language Models (LVLMs) to efficiently process high-resolution inputs. Existing methods that typically adopt fixed compression ratios cannot adapt to scenes of varying complexity, often causing imprecise pruning that discards informative visual tokens and results in degraded model performance. To address this issue, we introduce a dynamic pruning framework, GlimpsePrune, inspired by human cognition. It takes a data-driven ''glimpse'' and prunes irrelevant visual tokens in a single forward pass before answer generation. This approach prunes 92.6% of visual tokens while on average fully retaining the baseline performance on free-form VQA tasks. The reduced computational cost also enables more effective fine-tuning: an enhanced GlimpsePrune+ achieves 110% of the baseline performance while maintaining a similarly high pruning rate. Our work paves a new way for building more powerful and efficient LVLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.01691", "title": "Voxlect: A Speech Foundation Model Benchmark for Modeling Dialects and Regional Languages Around the Globe", "authors": [ "Tiantian Feng", "Kevin Huang", "Anfeng Xu", "Xuan Shi", "Thanathai Lertpetchpun", "Jihwan Lee", "Yoonjeong Lee", "Dani Byrd", "Shrikanth Narayanan" ], "github": "https://github.com/tiantiaf0627/voxlect", "project_page": "", "abstract": "We present Voxlect, a novel benchmark for modeling dialects and regional languages worldwide using speech foundation models. Specifically, we report comprehensive benchmark evaluations on dialects and regional language varieties in English, Arabic, Mandarin and Cantonese, Tibetan, Indic languages, Thai, Spanish, French, German, Brazilian Portuguese, and Italian. Our study used over 2 million training utterances from 30 publicly available speech corpora that are provided with dialectal information. We evaluate the performance of several widely used speech foundation models in classifying speech dialects. We assess the robustness of the dialectal models under noisy conditions and present an error analysis that highlights modeling results aligned with geographic continuity. In addition to benchmarking dialect classification, we demonstrate several downstream applications enabled by Voxlect. Specifically, we show that Voxlect can be applied to augment existing speech recognition datasets with dialect information, enabling a more detailed analysis of ASR performance across dialectal variations. Voxlect is also used as a tool to evaluate the performance of speech generation systems. Voxlect is publicly available with the license of the RAIL family at: https://github.com/tiantiaf0627/voxlect.", "github_manual": "", "github_regex": "https://github.com/tiantiaf0627/voxlect", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.02605", "title": "ReMoMask: Retrieval-Augmented Masked Motion Generation", "authors": [ "Zhengdao Li", "Siheng Wang", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/ReMoMask", "project_page": "", "abstract": "Text-to-Motion (T2M) generation aims to synthesize realistic and semantically aligned human motion sequences from natural language descriptions. However, current approaches face dual challenges: Generative models (e.g., diffusion models) suffer from limited diversity, error accumulation, and physical implausibility, while Retrieval-Augmented Generation (RAG) methods exhibit diffusion inertia, partial-mode collapse, and asynchronous artifacts. To address these limitations, we propose ReMoMask, a unified framework integrating three key innovations: 1) A Bidirectional Momentum Text-Motion Model decouples negative sample scale from batch size via momentum queues, substantially improving cross-modal retrieval precision; 2) A Semantic Spatio-temporal Attention mechanism enforces biomechanical constraints during part-level fusion to eliminate asynchronous artifacts; 3) RAG-Classier-Free Guidance incorporates minor unconditional generation to enhance generalization. Built upon MoMask's RVQ-VAE, ReMoMask efficiently generates temporally coherent motions in minimal steps. Extensive experiments on standard benchmarks demonstrate the state-of-the-art performance of ReMoMask, achieving a 3.88% and 10.97% improvement in FID scores on HumanML3D and KIT-ML, respectively, compared to the previous SOTA method RAG-T2M. Code: https://github.com/AIGeeksGroup/ReMoMask. Website: https://aigeeksgroup.github.io/ReMoMask.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/ReMoMask", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.01408", "title": "Artificial Intelligence and Misinformation in Art: Can Vision Language Models Judge the Hand or the Machine Behind the Canvas?", "authors": [ "Tarian Fu", "Javier Conde", "Gonzalo Mart\u00ednez", "Pedro Reviriego", "Elena Merino-G\u00f3mez", "Fernando Moral" ], "github": "https://github.com/aMa2210/WikiArt_VLM", "project_page": "", "abstract": "The attribution of artworks in general and of paintings in particular has always been an issue in art. The advent of powerful artificial intelligence models that can generate and analyze images creates new challenges for painting attribution. On the one hand, AI models can create images that mimic the style of a painter, which can be incorrectly attributed, for example, by other AI models. On the other hand, AI models may not be able to correctly identify the artist for real paintings, inducing users to incorrectly attribute paintings. In this paper, both problems are experimentally studied using state-of-the-art AI models for image generation and analysis on a large dataset with close to 40,000 paintings from 128 artists. The results show that vision language models have limited capabilities to: 1) perform canvas attribution and 2) to identify AI generated images. As users increasingly rely on queries to AI models to get information, these results show the need to improve the capabilities of VLMs to reliably perform artist attribution and detection of AI generated images to prevent the spread of incorrect information.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aMa2210/WikiArt_VLM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.00024", "title": "Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning", "authors": [ "Sebasti\u00e1n Andr\u00e9s Cajas Ord\u00f3\u00f1ez", "Luis Fernando Torres Torres", "Mario Bifulco", "Carlos Andr\u00e9s Dur\u00e1n", "Cristian Bosch", "Ricardo Sim\u00f3n Carbajo" ], "github": "", "project_page": "", "abstract": "Quantum Support Vector Machines face scalability challenges due to high-dimensional quantum states and hardware limitations. We propose an embedding-aware quantum-classical pipeline combining class-balanced k-means distillation with pretrained Vision Transformer embeddings. Our key finding: ViT embeddings uniquely enable quantum advantage, achieving up to 8.02% accuracy improvements over classical SVMs on Fashion-MNIST and 4.42% on MNIST, while CNN features show performance degradation. Using 16-qubit tensor network simulation via cuTensorNet, we provide the first systematic evidence that quantum kernel advantage depends critically on embedding choice, revealing fundamental synergy between transformer attention and quantum feature spaces. This provides a practical pathway for scalable quantum machine learning that leverages modern neural architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.01773", "title": "Uncertainty-Based Methods for Automated Process Reward Data Construction and Output Aggregation in Mathematical Reasoning", "authors": [ "Jiuzhou Han", "Wray Buntine", "Ehsan Shareghi" ], "github": "https://github.com/Jiuzhouh/UnPRM", "project_page": "", "abstract": "Large language models have demonstrated remarkable capabilities in complex mathematical reasoning tasks, but they inevitably generate errors throughout multi-step solutions. Process-level Reward Models (PRMs) have shown great promise by providing supervision and evaluation at each intermediate step, thereby effectively improving the models' reasoning abilities. However, training effective PRMs requires high-quality process reward data, yet existing methods for constructing such data are often labour-intensive or inefficient. In this paper, we propose an uncertainty-driven framework for automated process reward data construction, encompassing both data generation and annotation processes for PRMs. Additionally, we identify the limitations of both majority vote and PRMs, and introduce two generic uncertainty-aware output aggregation methods: Hybrid Majority Reward Vote and Weighted Reward Frequency Vote, which combine the strengths of majority vote with PRMs. Extensive experiments on ProcessBench, MATH, and GSMPlus show the effectiveness and efficiency of the proposed PRM data construction framework, and demonstrate that the two output aggregation methods further improve the mathematical reasoning abilities across diverse PRMs. The code and data will be publicly available at https://github.com/Jiuzhouh/UnPRM.", "github_manual": "", "github_regex": "https://github.com/Jiuzhouh/UnPRM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.01109", "title": "Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?", "authors": [ "Satiyabooshan Murugaboopathy", "Connor T. Jerzak", "Adel Daoud" ], "github": "", "project_page": "", "abstract": "We investigate whether socio-economic indicators like household wealth leave recoverable imprints in satellite imagery (capturing physical features) and Internet-sourced text (reflecting historical/economic narratives). Using Demographic and Health Survey (DHS) data from African neighborhoods, we pair Landsat images with LLM-generated textual descriptions conditioned on location/year and text retrieved by an AI search agent from web sources. We develop a multimodal framework predicting household wealth (International Wealth Index) through five pipelines: (i) vision model on satellite images, (ii) LLM using only location/year, (iii) AI agent searching/synthesizing web text, (iv) joint image-text encoder, (v) ensemble of all signals. Our framework yields three contributions. First, fusing vision and agent/LLM text outperforms vision-only baselines in wealth prediction (e.g., R-squared of 0.77 vs. 0.63 on out-of-sample splits), with LLM-internal knowledge proving more effective than agent-retrieved text, improving robustness to out-of-country and out-of-time generalization. Second, we find partial representational convergence: fused embeddings from vision/language modalities correlate moderately (median cosine similarity of 0.60 after alignment), suggesting a shared latent code of material well-being while retaining complementary details, consistent with the Platonic Representation Hypothesis. Although LLM-only text outperforms agent-retrieved data, challenging our Agent-Induced Novelty Hypothesis, modest gains from combining agent data in some splits weakly support the notion that agent-gathered information introduces unique representational structures not fully captured by static LLM knowledge. Third, we release a large-scale multimodal dataset comprising more than 60,000 DHS clusters linked to satellite images, LLM-generated descriptions, and agent-retrieved texts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2507.16290", "title": "Dens3R: A Foundation Model for 3D Geometry Prediction", "authors": [ "Xianze Fang", "Jingnan Gao", "Zhe Wang", "Zhuo Chen", "Xingyu Ren", "Jiangjing Lyu", "Qiaomu Ren", "Zhonglei Yang", "Xiaokang Yang", "Yichao Yan", "Chengfei Lyu" ], "github": "", "project_page": "", "abstract": "Recent advances in dense 3D reconstruction have led to significant progress, yet achieving accurate unified geometric prediction remains a major challenge. Most existing methods are limited to predicting a single geometry quantity from input images. However, geometric quantities such as depth, surface normals, and point maps are inherently correlated, and estimating them in isolation often fails to ensure consistency, thereby limiting both accuracy and practical applicability. This motivates us to explore a unified framework that explicitly models the structural coupling among different geometric properties to enable joint regression. In this paper, we present Dens3R, a 3D foundation model designed for joint geometric dense prediction and adaptable to a wide range of downstream tasks. Dens3R adopts a two-stage training framework to progressively build a pointmap representation that is both generalizable and intrinsically invariant. Specifically, we design a lightweight shared encoder-decoder backbone and introduce position-interpolated rotary positional encoding to maintain expressive power while enhancing robustness to high-resolution inputs. By integrating image-pair matching features with intrinsic invariance modeling, Dens3R accurately regresses multiple geometric quantities such as surface normals and depth, achieving consistent geometry perception from single-view to multi-view inputs. Additionally, we propose a post-processing pipeline that supports geometrically consistent multi-view inference. Extensive experiments demonstrate the superior performance of Dens3R across various dense 3D prediction tasks and highlight its potential for broader applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.02137", "title": "Fitness aligned structural modeling enables scalable virtual screening with AuroBind", "authors": [ "Zhongyue Zhang", "Jiahua Rao", "Jie Zhong", "Weiqiang Bai", "Dongxue Wang", "Shaobo Ning", "Lifeng Qiao", "Sheng Xu", "Runze Ma", "Will Hua", "Jack Xiaoyu Chen", "Odin Zhang", "Wei Lu", "Hanyi Feng", "He Yang", "Xinchao Shi", "Rui Li", "Wanli Ouyang", "Xinzhu Ma", "Jiahao Wang", "Jixian Zhang", "Jia Duan", "Siqi Sun", "Jian Zhang", "Shuangjia Zheng" ], "github": "https://github.com/GENTEL-lab/AuroBind", "project_page": "", "abstract": "Most human proteins remain undrugged, over 96% of human proteins remain unexploited by approved therapeutics. While structure-based virtual screening promises to expand the druggable proteome, existing methods lack atomic-level precision and fail to predict binding fitness, limiting translational impact. We present AuroBind, a scalable virtual screening framework that fine-tunes a custom atomic-level structural model on million-scale chemogenomic data. AuroBind integrates direct preference optimization, self-distillation from high-confidence complexes, and a teacher-student acceleration strategy to jointly predict ligand-bound structures and binding fitness. The proposed models outperform state-of-the-art models on structural and functional benchmarks while enabling 100,000-fold faster screening across ultra-large compound libraries. In a prospective screen across ten disease-relevant targets, AuroBind achieved experimental hit rates of 7-69%, with top compounds reaching sub-nanomolar to picomolar potency. For the orphan GPCRs GPR151 and GPR160, AuroBind identified both agonists and antagonists with success rates of 16-30%, and functional assays confirmed GPR160 modulation in liver and prostate cancer models. AuroBind offers a generalizable framework for structure-function learning and high-throughput molecular screening, bridging the gap between structure prediction and therapeutic discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GENTEL-lab/AuroBind", "github_api_added_by": "auto" }, { "date": "2025-08-05", "arxiv_id": "2508.02558", "title": "Sparse-dLLM: Accelerating Diffusion LLMs with Dynamic Cache Eviction", "authors": [ "Yuerong Song", "Xiaoran Liu", "Ruixiao Li", "Zhigeng Liu", "Zengfeng Huang", "Qipeng Guo", "Ziwei He", "Xipeng Qiu" ], "github": "https://github.com/OpenMOSS/Sparse-dLLM", "project_page": "", "abstract": "Diffusion Large Language Models (dLLMs) enable breakthroughs in reasoning and parallel decoding but suffer from prohibitive quadratic computational complexity and memory overhead during inference. Current caching techniques accelerate decoding by storing full-layer states, yet impose substantial memory usage that limit long-context applications. Our analysis of attention patterns in dLLMs reveals persistent cross-layer sparsity, with pivotal tokens remaining salient across decoding steps and low-relevance tokens staying unimportant, motivating selective cache eviction. We propose Sparse-dLLM, the first training-free framework integrating dynamic cache eviction with sparse attention via delayed bidirectional sparse caching. By leveraging the stability of token saliency over steps, it retains critical tokens and dynamically evicts unimportant prefix/suffix entries using an attention-guided strategy. Extensive experiments on LLaDA and Dream series demonstrate Sparse-dLLM achieves up to 10times higher throughput than vanilla dLLMs, with comparable performance and similar peak memory costs, outperforming previous methods in efficiency and effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenMOSS/Sparse-dLLM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.02268", "title": "SHAMI-MT: A Syrian Arabic Dialect to Modern Standard Arabic Bidirectional Machine Translation System", "authors": [ "Serry Sibaee", "Omer Nacar", "Yasser Al-Habashi", "Adel Ammar", "Wadii Boulila" ], "github": "", "project_page": "", "abstract": "The rich linguistic landscape of the Arab world is characterized by a significant gap between Modern Standard Arabic (MSA), the language of formal communication, and the diverse regional dialects used in everyday life. This diglossia presents a formidable challenge for natural language processing, particularly machine translation. This paper introduces SHAMI-MT, a bidirectional machine translation system specifically engineered to bridge the communication gap between MSA and the Syrian dialect. We present two specialized models, one for MSA-to-Shami and another for Shami-to-MSA translation, both built upon the state-of-the-art AraT5v2-base-1024 architecture. The models were fine-tuned on the comprehensive Nabra dataset and rigorously evaluated on unseen data from the MADAR corpus. Our MSA-to-Shami model achieved an outstanding average quality score of 4.01 out of 5.0 when judged by OPENAI model GPT-4.1, demonstrating its ability to produce translations that are not only accurate but also dialectally authentic. This work provides a crucial, high-fidelity tool for a previously underserved language pair, advancing the field of dialectal Arabic translation and offering significant applications in content localization, cultural heritage, and intercultural communication.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-05", "arxiv_id": "2508.02271", "title": "Dynaword: From One-shot to Continuously Developed Datasets", "authors": [ "Kenneth Enevoldsen", "Kristian N\u00f8rgaard Jensen", "Jan Kostkan", "Bal\u00e1zs Szab\u00f3", "M\u00e1rton Kardos", "Kirten Vad", "Andrea Blasi N\u00fa\u00f1ez", "Gianluca Barmina", "Jacob Nielsen", "Rasmus Larsen", "Peter Vahlstrup", "Per M\u00f8ldrup Dalum", "Desmond Elliott", "Lukas Galke", "Peter Schneider-Kamp", "Kristoffer Nielbo" ], "github": "https://github.com/schneiderkamplab/offpolicy_kd", "project_page": "", "abstract": "Large-scale datasets are foundational for research and development in natural language processing. However, current approaches face three key challenges: (1) reliance on ambiguously licensed sources restricting use, sharing, and derivative works; (2) static dataset releases that prevent community contributions and diminish longevity; and (3) quality assurance processes restricted to publishing teams rather than leveraging community expertise. To address these limitations, we introduce two contributions: the Dynaword approach and Danish Dynaword. The Dynaword approach is a framework for creating large-scale, open datasets that can be continuously updated through community collaboration. Danish Dynaword is a concrete implementation that validates this approach and demonstrates its potential. Danish Dynaword contains over four times as many tokens as comparable releases, is exclusively openly licensed, and has received multiple contributions across industry and research. The repository includes light-weight tests to ensure data formatting, quality, and documentation, establishing a sustainable framework for ongoing community contributions and dataset evolution.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/schneiderkamplab/offpolicy_kd", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.03694", "title": "LongVie: Multimodal-Guided Controllable Ultra-Long Video Generation", "authors": [ "Jianxiong Gao", "Zhaoxi Chen", "Xian Liu", "Jianfeng Feng", "Chenyang Si", "Yanwei Fu", "Yu Qiao", "Ziwei Liu" ], "github": "https://github.com/Breakthrough/PySceneDetect", "project_page": "", "abstract": "Controllable ultra-long video generation is a fundamental yet challenging task. Although existing methods are effective for short clips, they struggle to scale due to issues such as temporal inconsistency and visual degradation. In this paper, we initially investigate and identify three key factors: separate noise initialization, independent control signal normalization, and the limitations of single-modality guidance. To address these issues, we propose LongVie, an end-to-end autoregressive framework for controllable long video generation. LongVie introduces two core designs to ensure temporal consistency: 1) a unified noise initialization strategy that maintains consistent generation across clips, and 2) global control signal normalization that enforces alignment in the control space throughout the entire video. To mitigate visual degradation, LongVie employs 3) a multi-modal control framework that integrates both dense (e.g., depth maps) and sparse (e.g., keypoints) control signals, complemented by 4) a degradation-aware training strategy that adaptively balances modality contributions over time to preserve visual quality. We also introduce LongVGenBench, a comprehensive benchmark consisting of 100 high-resolution videos spanning diverse real-world and synthetic environments, each lasting over one minute. Extensive experiments show that LongVie achieves state-of-the-art performance in long-range controllability, consistency, and quality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Breakthrough/PySceneDetect", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.03320", "title": "Skywork UniPic: Unified Autoregressive Modeling for Visual Understanding and Generation", "authors": [ "Peiyu Wang", "Yi Peng", "Yimeng Gan", "Liang Hu", "Tianyidan Xie", "Xiaokun Wang", "Yichen Wei", "Chuanxin Tang", "Bo Zhu", "Changshi Li", "Hongyang Wei", "Eric Li", "Xuchen Song", "Yang Liu", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "We introduce Skywork UniPic, a 1.5 billion-parameter autoregressive model that unifies image understanding, text-to-image generation, and image editing within a single architecture-eliminating the need for task-specific adapters or inter-module connectors-and demonstrate that compact multimodal systems can achieve state-of-the-art performance on commodity hardware. Skywork UniPic achieves a GenEval score of 0.86, surpassing most existing unified models; sets a new DPG-Bench complex-generation record of 85.5; attains 5.83 on GEditBench-EN and 3.49 on ImgEdit-Bench for image editing; and generates 1024 x 1024 images with under 15 GB of GPU memory (e.g., RTX 4090). (1) a decoupled encoding strategy that leverages a masked autoregressive encoder for synthesis and a SigLIP2 encoder for understanding, all feeding a shared autoregressive decoder; (2) a progressive, resolution-aware training schedule scaling from 256 x 256 to 1024 x 1024 while dynamically unfreezing parameters to balance capacity and stability; and (3) meticulously curated, 100 million-scale datasets augmented with task-specific reward models to refine generation and editing objectives. By demonstrating that high-fidelity multimodal integration need not incur prohibitive resource demands, Skywork UniPic establishes a practical paradigm for deployable, high-fidelity multimodal AI. Code and weights are publicly available at https://huggingface.co/Skywork/Skywork-UniPic-1.5B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.03012", "title": "Tool-integrated Reinforcement Learning for Repo Deep Search", "authors": [ "Zexiong Ma", "Chao Peng", "Qunhong Zeng", "Pengfei Gao", "Yanzhen Zou", "Bing Xie" ], "github": "https://github.com/Mizersy/RepoDeepSearch", "project_page": "", "abstract": "Issue localization, the process of identifying code locations that need modification to resolve software issues, is a critical yet challenging task in software development. The semantic gap between natural language issue descriptions and faulty code requires complex multi-hop reasoning through code dependencies. Existing LLM-based agents attempt to address this by integrating repository retrieval tools. However, this transforms issue localization into a demanding task we call Repo Deep Search, which requires the LLM to effectively utilize various repository retrieval tools throughout a multi-step reasoning and navigation process. To tackle this challenge, we present ToolTrain, a two-stage tool-integrated training framework combining rejection-sampled supervised fine-tuning and tool-integrated reinforcement learning to enhance LLMs' ability to use retrieval tools for issue localization. Experimental results show that ToolTrain-trained models achieve state-of-the-art performance, with our 32B model even surpassing Claude-3.7 on function-level localization. The results also show that improved localization performance translates to better end-to-end issue resolution performance. This further demonstrates that training for issue localization is a viable and effective strategy for improving automated software development.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Mizersy/RepoDeepSearch", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.01780", "title": "LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?", "authors": [ "Guozhao Mo", "Wenliang Zhong", "Jiawei Chen", "Xuanang Chen", "Yaojie Lu", "Hongyu Lin", "Ben He", "Xianpei Han", "Le Sun" ], "github": "", "project_page": "", "abstract": "With the rapid development of Model Context Protocol (MCP), the number of MCP servers has surpassed 10,000. However, existing MCP benchmarks are limited to single-server settings with only a few tools, hindering effective evaluation of agent capabilities in large-scale, real-world scenarios. To address this limitation, we present LiveMCPBench, the first comprehensive benchmark comprising 95 real-world tasks grounded in the MCP ecosystem, designed to evaluate LLM agents at scale across diverse servers. To support a scalable and reproducible evaluation pipeline in large-scale MCP environments, we curate LiveMCPTool, a diverse and readily deployable collection of 70 MCP servers and 527 tools. Furthermore, we introduce LiveMCPEval, an LLM-as-a-Judge framework that enables automated and adaptive evaluation in dynamic, time-varying task environments, achieving 81% agreement with human reviewers. Finally, we propose the MCP Copilot Agent, a multi-step agent that routes tools for dynamic planning and executes tools for API interaction across the entire LiveMCPTool suite. Our evaluation covers 10 leading models, with the best-performing model (Claude-Sonnet-4) reaching a 78.95% success rate. However, we observe large performance variance across models, and several widely-used models perform poorly in LiveMCPBench's complex, tool-rich environments. Overall, LiveMCPBench offers the first unified framework for benchmarking LLM agents in realistic, tool-rich, and dynamic MCP environments, laying a solid foundation for scalable and reproducible research on agent capabilities. Our code and data will be publicly available at https://icip-cas.github.io/LiveMCPBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.02193", "title": "Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference", "authors": [ "Yuxuan Song", "Zheng Zhang", "Cheng Luo", "Pengyang Gao", "Fan Xia", "Hao Luo", "Zheng Li", "Yuehang Yang", "Hongli Yu", "Xingwei Qu", "Yuwei Fu", "Jing Su", "Ge Zhang", "Wenhao Huang", "Mingxuan Wang", "Lin Yan", "Xiaoying Jia", "Jingjing Liu", "Wei-Ying Ma", "Ya-Qin Zhang", "Yonghui Wu", "Hao Zhou" ], "github": "", "project_page": "", "abstract": "We present Seed Diffusion Preview, a large-scale language model based on discrete-state diffusion, offering remarkably fast inference speed. Thanks to non-sequential, parallel generation, discrete diffusion models provide a notable speedup to mitigate the inherent latency of token-by-token decoding, as demonstrated recently (e.g., Mercury Coder, Gemini Diffusion). Seed Diffusion Preview achieves an inference speed of 2,146 token/s over H20 GPUs while maintaining competitive performance across a sweep of standard code evaluation benchmarks, significantly faster than contemporary Mercury and Gemini Diffusion, establishing new state of the art on the speed-quality Pareto frontier for code models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.02079", "title": "AlignGuard-LoRA: Alignment-Preserving Fine-Tuning via Fisher-Guided Decomposition and Riemannian-Geodesic Collision Regularization", "authors": [ "Amitava Das", "Abhilekh Borah", "Vinija Jain", "Aman Chadha" ], "github": "", "project_page": "", "abstract": "Low-rank adaptation (LoRA) has become a standard tool for efficiently fine-tuning large language models (LLMs). Yet, even minor LoRA updates can induce alignment drift, weakening safety and behavioral constraints through entangled parameter changes. To address this, we propose AlignGuard-LoRA (AGL), a principled framework for preserving alignment during finetuning. AGL introduces several key components: a primary task loss for supervision, Fisher Information Matrix-based regularization to restrict updates in alignment-sensitive subspaces, and task-specific regularization to stabilize the integration of new knowledge. We further introduce collision-aware regularization, blending Riemannian overlap -- which penalizes coordinate-wise interference -- and geodesic separation -- which encourages disjoint update geometry. We curate DriftCaps, a targeted diagnostic benchmark of safe and unsafe prompts designed to quantify alignment drift and safety degradation. Empirical evaluations show that AGL mitigates alignment drift by up to 50% on safety-critical benchmarks without degrading downstream task performance. Comprehensive ablation confirms that each component contributes distinctly to preserving latent safety behaviors. Finally, we derive and validate a scaling law for catastrophic forgetting, revealing that AGL flattens post-finetuning loss escalation while preserving adaptation dynamics. AGL is a structurally grounded refinement of LoRA, ensuring alignment preservation with minimal trade-offs. To encourage further exploration and development, we open-source our implementation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.02063", "title": "TRACEALIGN -- Tracing the Drift: Attributing Alignment Failures to Training-Time Belief Sources in LLMs", "authors": [ "Amitava Das", "Vinija Jain", "Aman Chadha" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) fine-tuned to align with human values often exhibit alignment drift, producing unsafe or policy-violating completions when exposed to adversarial prompts, decoding perturbations, or paraphrased jailbreaks. While prior work has behaviorally characterized alignment failure, little is known about the training-time belief sources underlying these failures. We introduce TraceAlign, a unified framework for tracing unsafe completions back to their root causes in the model's training corpus. Central to our approach is the Belief Conflict Index (BCI), which quantifies semantic inconsistency between generated spans and aligned policies, based on retrieved training documents using suffix-array matching. We propose three complementary interventions: (i) TraceShield, an inference-time safety filter that refuses completions with high-BCI spans, (ii) Contrastive Belief Deconfliction Loss, a contrastive fine-tuning objective penalizing high-BCI continuations during DPO, and (iii) Prov-Decode, a provenance-aware decoding strategy that vetoes beam expansions predicted to yield high-BCI spans. Together, these defenses reduce alignment drift by up to 85% on our curated Alignment Drift Benchmark (ADB) while preserving utility on standard tasks, with delta less than 0.2 and improved refusal quality. We further derive a theoretical upper bound on drift likelihood via suffix-array span statistics, linking memorization frequency and length to adversarial reactivation risk. TraceAlign thus provides the first scalable, traceable, and grounded toolkit for understanding and mitigating alignment failures at source. To encourage further exploration and development, we open-source our implementation at: https://anonymous.4open.science/r/tracealign-2DA7", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.03050", "title": "Multi-human Interactive Talking Dataset", "authors": [ "Zeyu Zhu", "Weijia Wu", "Mike Zheng Shou" ], "github": "https://github.com/showlab/Multi-human-Talking-Video-Dataset", "project_page": "", "abstract": "Existing studies on talking video generation have predominantly focused on single-person monologues or isolated facial animations, limiting their applicability to realistic multi-human interactions. To bridge this gap, we introduce MIT, a large-scale dataset specifically designed for multi-human talking video generation. To this end, we develop an automatic pipeline that collects and annotates multi-person conversational videos. The resulting dataset comprises 12 hours of high-resolution footage, each featuring two to four speakers, with fine-grained annotations of body poses and speech interactions. It captures natural conversational dynamics in multi-speaker scenario, offering a rich resource for studying interactive visual behaviors. To demonstrate the potential of MIT, we furthur propose CovOG, a baseline model for this novel task. It integrates a Multi-Human Pose Encoder (MPE) to handle varying numbers of speakers by aggregating individual pose embeddings, and an Interactive Audio Driver (IAD) to modulate head dynamics based on speaker-specific audio features. Together, these components showcase the feasibility and challenges of generating realistic multi-human talking videos, establishing MIT as a valuable benchmark for future research. The code is avalibale at: https://github.com/showlab/Multi-human-Talking-Video-Dataset.", "github_manual": "", "github_regex": "https://github.com/showlab/Multi-human-Talking-Video-Dataset", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.02091", "title": "CRINN: Contrastive Reinforcement Learning for Approximate Nearest Neighbor Search", "authors": [ "Xiaoya Li", "Xiaofei Sun", "Albert Wang", "Chris Shum", "Jiwei Li" ], "github": "https://github.com/deepreinforce-ai/CRINN", "project_page": "", "abstract": "Approximate nearest-neighbor search (ANNS) algorithms have become increasingly critical for recent AI applications, particularly in retrieval-augmented generation (RAG) and agent-based LLM applications. In this paper, we present CRINN, a new paradigm for ANNS algorithms. CRINN treats ANNS optimization as a reinforcement learning problem where execution speed serves as the reward signal. This approach enables the automatic generation of progressively faster ANNS implementations while maintaining accuracy constraints. Our experimental evaluation demonstrates CRINN's effectiveness across six widely-used NNS benchmark datasets. When compared against state-of-the-art open-source ANNS algorithms, CRINN achieves best performance on three of them (GIST-960-Euclidean, MNIST-784-Euclidean, and GloVe-25-angular), and tied for first place on two of them (SIFT-128-Euclidean and GloVe-25-angular). The implications of CRINN's success reach well beyond ANNS optimization: It validates that LLMs augmented with reinforcement learning can function as an effective tool for automating sophisticated algorithmic optimizations that demand specialized knowledge and labor-intensive manual refinement.Code can be found at https://github.com/deepreinforce-ai/CRINN", "github_manual": "", "github_regex": "https://github.com/deepreinforce-ai/CRINN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.03686", "title": "CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward", "authors": [ "Shudong Liu", "Hongwei Liu", "Junnan Liu", "Linchen Xiao", "Songyang Gao", "Chengqi Lyu", "Yuzhe Gu", "Wenwei Zhang", "Derek F. Wong", "Songyang Zhang", "Kai Chen" ], "github": "https://github.com/open-compass/CompassVerifier", "project_page": "", "abstract": "Answer verification is crucial not only for evaluating large language models (LLMs) by matching their unstructured outputs against standard answers, but also serves as the reward model to guide LLM optimization. Most evaluation frameworks rely on regularized matching or employ general LLMs for answer verification, which demands extensive, repetitive customization for regex rules or evaluation prompts. Two fundamental limitations persist in current methodologies: 1) the absence of comprehensive benchmarks that systematically evaluate verification capabilities across different LLMs; and 2) the nascent stage of verifier development, where existing approaches lack both the robustness to handle complex edge cases and the generalizability across different domains. In this work, we develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward. It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types, including multi-subproblems, formulas, and sequence answers, while effectively identifying abnormal/invalid responses. We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier. We anticipate that CompassVerifier and VerifierBench will facilitate answer verification, evaluation protocols, and reinforcement learning research. Code and dataset are available at https://github.com/open-compass/CompassVerifier.", "github_manual": "", "github_regex": "https://github.com/open-compass/CompassVerifier", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.00477", "title": "LAMIC: Layout-Aware Multi-Image Composition via Scalability of Multimodal Diffusion Transformer", "authors": [ "Yuzhuo Chen", "Zehua Ma", "Jianhua Wang", "Kai Kang", "Shunyu Yao", "Weiming Zhang" ], "github": "https://github.com/Suchenl/LAMIC", "project_page": "", "abstract": "In controllable image synthesis, generating coherent and consistent images from multiple references with spatial layout awareness remains an open challenge. We present LAMIC, a Layout-Aware Multi-Image Composition framework that, for the first time, extends single-reference diffusion models to multi-reference scenarios in a training-free manner. Built upon the MMDiT model, LAMIC introduces two plug-and-play attention mechanisms: 1) Group Isolation Attention (GIA) to enhance entity disentanglement; and 2) Region-Modulated Attention (RMA) to enable layout-aware generation. To comprehensively evaluate model capabilities, we further introduce three metrics: 1) Inclusion Ratio (IN-R) and Fill Ratio (FI-R) for assessing layout control; and 2) Background Similarity (BG-S) for measuring background consistency. Extensive experiments show that LAMIC achieves state-of-the-art performance across most major metrics: it consistently outperforms existing multi-reference baselines in ID-S, BG-S, IN-R and AVG scores across all settings, and achieves the best DPG in complex composition tasks. These results demonstrate LAMIC's superior abilities in identity keeping, background preservation, layout control, and prompt-following, all achieved without any training or fine-tuning, showcasing strong zero-shot generalization ability. By inheriting the strengths of advanced single-reference models and enabling seamless extension to multi-image scenarios, LAMIC establishes a new training-free paradigm for controllable multi-image composition. As foundation models continue to evolve, LAMIC's performance is expected to scale accordingly. Our implementation is available at: https://github.com/Suchenl/LAMIC.", "github_manual": "", "github_regex": "https://github.com/Suchenl/LAMIC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.03613", "title": "Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction", "authors": [ "Yong Lin", "Shange Tang", "Bohan Lyu", "Ziran Yang", "Jui-Hui Chung", "Haoyu Zhao", "Lai Jiang", "Yihan Geng", "Jiawei Ge", "Jingruo Sun", "Jiayun Wu", "Jiri Gesi", "Ximing Lu", "David Acuna", "Kaiyu Yang", "Hongzhou Lin", "Yejin Choi", "Danqi Chen", "Sanjeev Arora", "Chi Jin" ], "github": "https://github.com/Goedel-LM/Goedel-Prover-V2", "project_page": "", "abstract": "We introduce Goedel-Prover-V2, a series of open-source language models that set a new state-of-the-art in automated theorem proving. Built on the standard expert iteration and reinforcement learning pipeline, our approach incorporates three key innovations: (1) Scaffolded data synthesis: We generate synthetic tasks of increasing difficulty to train the model to master increasingly complex theorems; (2) Verifier-guided self-correction: We enable the model to iteratively revise its proofs by leveraging feedback from the Lean compiler; (3) Model averaging: We merge model checkpoints to mitigate the decrease in model output diversity in later stages of training. Our small model, Goedel-Prover-V2-8B, reaches 84.6% pass@32 on MiniF2F and outperforms DeepSeek-Prover-V2-671B under the same metric, despite being 80X smaller. Our flagship model, Goedel-Prover-V2-32B, achieves 88.1% on MiniF2F at pass@32 in standard mode and 90.4% in self-correction mode, outperforming prior SOTA by a large margin. Additionally, our flagship model solves 86 problems on PutnamBench at pass@184, securing the first place among open-source models on the leaderboard, surpassing DeepSeek-Prover-V2-671B's record of solving 47 problems by pass@1024 with a significantly smaller model size and compute budget. At the time of its release (July-August 2025), Goedel-Prover-V2 achieves the strongest overall performance among all open-source theorem provers. It also ranks among the top-performing models--including closed-source systems with publicly reported performance--under a constrained test-time compute budget. Our models, code, and data are released at https://github.com/Goedel-LM/Goedel-Prover-V2.", "github_manual": "", "github_regex": "https://github.com/Goedel-LM/Goedel-Prover-V2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.03164", "title": "ChartCap: Mitigating Hallucination of Dense Chart Captioning", "authors": [ "Junyoung Lim", "Jaewoo Ahn", "Gunhee Kim" ], "github": "", "project_page": "", "abstract": "Generating accurate, informative, and hallucination-free captions for charts remains challenging for vision language models, primarily due to the lack of large-scale, high-quality datasets of real-world charts. However, existing real-world chart datasets suffer from the inclusion of extraneous information that cannot be inferred from the chart and failure to sufficiently capture structural elements and key insights. Therefore, we introduce ChartCap, a large-scale dataset of 565K real-world chart images paired with type-specific, dense captions that exclude extraneous information and highlight both structural elements and key insights in detail. To build ChartCap, we design a four-stage pipeline that generates captions using only the discernible data from the chart and employ a cycle consistency-based human verification, which accelerates quality control without sacrificing accuracy. Additionally, we propose a novel metric, the Visual Consistency Score, which evaluates caption quality by measuring the similarity between the chart regenerated from a caption and the original chart, independent of reference captions. Extensive experiments confirms that models fine-tuned on ChartCap consistently generate more accurate and informative captions with reduced hallucinations, surpassing both open-source and proprietary models and even human-annotated captions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.00367", "title": "Representation Shift: Unifying Token Compression with FlashAttention", "authors": [ "Joonmyung Choi", "Sanghyeok Lee", "Byungoh Ko", "Eunseo Kim", "Jihyung Kil", "Hyunwoo J. Kim" ], "github": "https://github.com/mlvlab/Representation-Shift", "project_page": "", "abstract": "Transformers have demonstrated remarkable success across vision, language, and video. Yet, increasing task complexity has led to larger models and more tokens, raising the quadratic cost of self-attention and the overhead of GPU memory access. To reduce the computation cost of self-attention, prior work has proposed token compression techniques that drop redundant or less informative tokens. Meanwhile, fused attention kernels such as FlashAttention have been developed to alleviate memory overhead by avoiding attention map construction and its associated I/O to HBM. This, however, makes it incompatible with most training-free token compression methods, which rely on attention maps to determine token importance. Here, we propose Representation Shift, a training-free, model-agnostic metric that measures the degree of change in each token's representation. This seamlessly integrates token compression with FlashAttention, without attention maps or retraining. Our method further generalizes beyond Transformers to CNNs and state space models. Extensive experiments show that Representation Shift enables effective token compression compatible with FlashAttention, yielding significant speedups of up to 5.5% and 4.4% in video-text retrieval and video QA, respectively. Code is available at https://github.com/mlvlab/Representation-Shift.", "github_manual": "", "github_regex": "https://github.com/mlvlab/Representation-Shift", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.02629", "title": "HyCodePolicy: Hybrid Language Controllers for Multimodal Monitoring and Decision in Embodied Agents", "authors": [ "Yibin Liu", "Zhixuan Liang", "Zanxin Chen", "Tianxing Chen", "Mengkang Hu", "Wanxi Dong", "Congsheng Xu", "Zhaoming Han", "Yusen Qin", "Yao Mu" ], "github": "", "project_page": "", "abstract": "Recent advances in multimodal large language models (MLLMs) have enabled richer perceptual grounding for code policy generation in embodied agents. However, most existing systems lack effective mechanisms to adaptively monitor policy execution and repair codes during task completion. In this work, we introduce HyCodePolicy, a hybrid language-based control framework that systematically integrates code synthesis, geometric grounding, perceptual monitoring, and iterative repair into a closed-loop programming cycle for embodied agents. Technically, given a natural language instruction, our system first decomposes it into subgoals and generates an initial executable program grounded in object-centric geometric primitives. The program is then executed in simulation, while a vision-language model (VLM) observes selected checkpoints to detect and localize execution failures and infer failure reasons. By fusing structured execution traces capturing program-level events with VLM-based perceptual feedback, HyCodePolicy infers failure causes and repairs programs. This hybrid dual feedback mechanism enables self-correcting program synthesis with minimal human supervision. Our results demonstrate that HyCodePolicy significantly improves the robustness and sample efficiency of robot manipulation policies, offering a scalable strategy for integrating multimodal reasoning into autonomous decision-making pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.01126", "title": "UniEgoMotion: A Unified Model for Egocentric Motion Reconstruction, Forecasting, and Generation", "authors": [ "Chaitanya Patel", "Hiroki Nakamura", "Yuta Kyuragi", "Kazuki Kozuka", "Juan Carlos Niebles", "Ehsan Adeli" ], "github": "", "project_page": "", "abstract": "Egocentric human motion generation and forecasting with scene-context is crucial for enhancing AR/VR experiences, improving human-robot interaction, advancing assistive technologies, and enabling adaptive healthcare solutions by accurately predicting and simulating movement from a first-person perspective. However, existing methods primarily focus on third-person motion synthesis with structured 3D scene contexts, limiting their effectiveness in real-world egocentric settings where limited field of view, frequent occlusions, and dynamic cameras hinder scene perception. To bridge this gap, we introduce Egocentric Motion Generation and Egocentric Motion Forecasting, two novel tasks that utilize first-person images for scene-aware motion synthesis without relying on explicit 3D scene. We propose UniEgoMotion, a unified conditional motion diffusion model with a novel head-centric motion representation tailored for egocentric devices. UniEgoMotion's simple yet effective design supports egocentric motion reconstruction, forecasting, and generation from first-person visual inputs in a unified framework. Unlike previous works that overlook scene semantics, our model effectively extracts image-based scene context to infer plausible 3D motion. To facilitate training, we introduce EE4D-Motion, a large-scale dataset derived from EgoExo4D, augmented with pseudo-ground-truth 3D motion annotations. UniEgoMotion achieves state-of-the-art performance in egocentric motion reconstruction and is the first to generate motion from a single egocentric image. Extensive evaluations demonstrate the effectiveness of our unified framework, setting a new benchmark for egocentric motion modeling and unlocking new possibilities for egocentric applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.01119", "title": "The Promise of RL for Autoregressive Image Editing", "authors": [ "Saba Ahmadi", "Rabiul Awal", "Ankur Sikarwar", "Amirhossein Kazemnejad", "Ge Ya Luo", "Juan A. Rodriguez", "Sai Rajeswar", "Siva Reddy", "Christopher Pal", "Benno Krojer", "Aishwarya Agrawal" ], "github": "https://github.com/mair-lab/EARL", "project_page": "", "abstract": "We explore three strategies to enhance performance on a wide range of image editing tasks: supervised fine-tuning (SFT), reinforcement learning (RL), and Chain-of-Thought (CoT) reasoning. In order to study all these components in one consistent framework, we adopt an autoregressive multimodal model that processes textual and visual tokens in a unified manner. We find RL combined with a large multi-modal LLM verifier to be the most effective of these strategies. As a result, we release EARL: Editing with Autoregression and RL, a strong RL-based image editing model that performs competitively on a diverse range of edits compared to strong baselines, despite using much less training data. Thus, EARL pushes the frontier of autoregressive multimodal models on image editing. We release our code, training data, and trained models at https://github.com/mair-lab/EARL.", "github_manual": "", "github_regex": "https://github.com/mair-lab/EARL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.02455", "title": "TreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions in IDEs", "authors": [ "Daniele Cipollone", "Egor Bogomolov", "Arie van Deursen", "Maliheh Izadi" ], "github": "", "project_page": "", "abstract": "Token-level code completion is one of the most critical features in modern Integrated Development Environments (IDEs). It assists developers by suggesting relevant identifiers and APIs during coding. While completions are typically derived from static analysis, their usefulness depends heavily on how they are ranked, as correct predictions buried deep in the list are rarely seen by users. Most current systems rely on hand-crafted heuristics or lightweight machine learning models trained on user logs, which can be further improved to capture context information and generalize across projects and coding styles. In this work, we propose a new scoring approach to ranking static completions using language models in a lightweight and model-agnostic way. Our method organizes all valid completions into a prefix tree and performs a single greedy decoding pass to collect token-level scores across the tree. This enables a precise token-aware ranking without needing beam search, prompt engineering, or model adaptations. The approach is fast, architecture-agnostic, and compatible with already deployed models for code completion. These findings highlight a practical and effective pathway for integrating language models into already existing tools within IDEs, and ultimately providing smarter and more responsive developer assistance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.02630", "title": "What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce", "authors": [ "Amine Allouah", "Omar Besbes", "Josu\u00e9 D Figueroa", "Yash Kanoria", "Akshit Kumar" ], "github": "https://github.com/mycustomai/ACES", "project_page": "", "abstract": "Online marketplaces will be transformed by autonomous AI agents acting on behalf of consumers. Rather than humans browsing and clicking, vision-language-model (VLM) agents can parse webpages, evaluate products, and transact. This raises a fundamental question: what do AI agents buy, and why? We develop ACES, a sandbox environment that pairs a platform-agnostic VLM agent with a fully programmable mock marketplace to study this question. We first conduct basic rationality checks in the context of simple tasks, and then, by randomizing product positions, prices, ratings, reviews, sponsored tags, and platform endorsements, we obtain causal estimates of how frontier VLMs actually shop. Models show strong but heterogeneous position effects: all favor the top row, yet different models prefer different columns, undermining the assumption of a universal \"top\" rank. They penalize sponsored tags and reward endorsements. Sensitivities to price, ratings, and reviews are directionally human-like but vary sharply in magnitude across models. Motivated by scenarios where sellers use AI agents to optimize product listings, we show that a seller-side agent that makes minor tweaks to product descriptions, targeting AI buyer preferences, can deliver substantial market-share gains if AI-mediated shopping dominates. We also find that modal product choices can differ across models and, in some cases, demand may concentrate on a few select products, raising competition questions. Together, our results illuminate how AI agents may behave in e-commerce settings and surface concrete seller strategy, platform design, and regulatory questions in an AI-mediated ecosystem.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mycustomai/ACES", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2507.23284", "title": "Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval", "authors": [ "Dohwan Ko", "Ji Soo Lee", "Minhyuk Choi", "Zihang Meng", "Hyunwoo J. Kim" ], "github": "https://github.com/mlvlab/BLiM", "project_page": "", "abstract": "Text-Video Retrieval aims to find the most relevant text (or video) candidate given a video (or text) query from large-scale online databases. Recent work leverages multi-modal large language models (MLLMs) to improve retrieval, especially for long or complex query-candidate pairs. However, we observe that the naive application of MLLMs, i.e., retrieval based on candidate likelihood, introduces candidate prior bias, favoring candidates with inherently higher priors over those more relevant to the query. To this end, we propose a novel retrieval framework, Bidirectional Likelihood Estimation with MLLM (BLiM), which leverages both query and candidate likelihoods by training the model to generate text from a given video as well as video features from a given text. Furthermore, we introduce Candidate Prior Normalization (CPN), a simple yet effective training-free score calibration module designed to mitigate candidate prior bias in candidate likelihood. On four Text-Video Retrieval benchmarks, our BLiM equipped with CPN outperforms previous state-of-the-art models by 6.4 R@1 on average, effectively alleviating candidate prior bias and emphasizing query-candidate relevance. Our in-depth analysis across various multi-modal tasks beyond retrieval highlights the broad applicability of CPN which enhances visual understanding by reducing reliance on textual priors. Code is available at https://github.com/mlvlab/BLiM.", "github_manual": "", "github_regex": "https://github.com/mlvlab/BLiM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-06", "arxiv_id": "2508.03793", "title": "AttnTrace: Attention-based Context Traceback for Long-Context LLMs", "authors": [ "Yanting Wang", "Runpeng Geng", "Ying Chen", "Jinyuan Jia" ], "github": "https://github.com/Wang-Yanting/AttnTrace", "project_page": "", "abstract": "Long-context large language models (LLMs), such as Gemini-2.5-Pro and Claude-Sonnet-4, are increasingly used to empower advanced AI systems, including retrieval-augmented generation (RAG) pipelines and autonomous agents. In these systems, an LLM receives an instruction along with a context--often consisting of texts retrieved from a knowledge database or memory--and generates a response that is contextually grounded by following the instruction. Recent studies have designed solutions to trace back to a subset of texts in the context that contributes most to the response generated by the LLM. These solutions have numerous real-world applications, including performing post-attack forensic analysis and improving the interpretability and trustworthiness of LLM outputs. While significant efforts have been made, state-of-the-art solutions such as TracLLM often lead to a high computation cost, e.g., it takes TracLLM hundreds of seconds to perform traceback for a single response-context pair. In this work, we propose AttnTrace, a new context traceback method based on the attention weights produced by an LLM for a prompt. To effectively utilize attention weights, we introduce two techniques designed to enhance the effectiveness of AttnTrace, and we provide theoretical insights for our design choice. We also perform a systematic evaluation for AttnTrace. The results demonstrate that AttnTrace is more accurate and efficient than existing state-of-the-art context traceback methods. We also show that AttnTrace can improve state-of-the-art methods in detecting prompt injection under long contexts through the attribution-before-detection paradigm. As a real-world application, we demonstrate that AttnTrace can effectively pinpoint injected instructions in a paper designed to manipulate LLM-generated reviews. The code is at https://github.com/Wang-Yanting/AttnTrace.", "github_manual": "", "github_regex": "https://github.com/Wang-Yanting/AttnTrace", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.04586", "title": "Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference", "authors": [ "Nuo Chen", "Moming Duan", "Andre Huikai Lin", "Qian Wang", "Jiaying Wu", "Bingsheng He" ], "github": "https://github.com/NuoJohnChen/AI_Conf_Crisis", "project_page": "", "abstract": "Artificial Intelligence (AI) conferences are essential for advancing research, sharing knowledge, and fostering academic community. However, their rapid expansion has rendered the centralized conference model increasingly unsustainable. This paper offers a data-driven diagnosis of a structural crisis that threatens the foundational goals of scientific dissemination, equity, and community well-being. We identify four key areas of strain: (1) scientifically, with per-author publication rates more than doubling over the past decade to over 4.5 papers annually; (2) environmentally, with the carbon footprint of a single conference exceeding the daily emissions of its host city; (3) psychologically, with 71% of online community discourse reflecting negative sentiment and 35% referencing mental health concerns; and (4) logistically, with attendance at top conferences such as NeurIPS 2024 beginning to outpace venue capacity. These pressures point to a system that is misaligned with its core mission. In response, we propose the Community-Federated Conference (CFC) model, which separates peer review, presentation, and networking into globally coordinated but locally organized components, offering a more sustainable, inclusive, and resilient path forward for AI research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NuoJohnChen/AI_Conf_Crisis", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.02215", "title": "LeanK: Learnable K Cache Channel Pruning for Efficient Decoding", "authors": [ "Yike Zhang", "Zhiyuan He", "Huiqiang Jiang", "Chengruidong Zhang", "Yuqing Yang", "Jianyong Wang", "Lili Qiu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) enable long-context tasks but face efficiency challenges due to the growing key-value (KV) cache. We propose LeanK, a learning-based method that prunes unimportant key (K) cache channels by leveraging static channel sparsity. With a novel two-stage training process, LeanK learns channel-wise static mask that could satisfy specific sparsity ratio and hardware alignment requirement. LeanK reduces GPU memory and accelerates decoding without sacrificing accuracy. Experiments demonstrate up to 70% K cache and 16%-18% V cache memory reduction. Custom decoding kernel enables 1.3x speedup for attention computation. We also provide insights into model channels and attention heads during long-context inference by analyzing the learned importance distribution. Our code is available at https://aka.ms/LeanK.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.03789", "title": "HPSv3: Towards Wide-Spectrum Human Preference Score", "authors": [ "Yuhang Ma", "Xiaoshi Wu", "Keqiang Sun", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "Evaluating text-to-image generation models requires alignment with human perception, yet existing human-centric metrics are constrained by limited data coverage, suboptimal feature extraction, and inefficient loss functions. To address these challenges, we introduce Human Preference Score v3 (HPSv3). (1) We release HPDv3, the first wide-spectrum human preference dataset integrating 1.08M text-image pairs and 1.17M annotated pairwise comparisons from state-of-the-art generative models and low to high-quality real-world images. (2) We introduce a VLM-based preference model trained using an uncertainty-aware ranking loss for fine-grained ranking. Besides, we propose Chain-of-Human-Preference (CoHP), an iterative image refinement method that enhances quality without extra data, using HPSv3 to select the best image at each step. Extensive experiments demonstrate that HPSv3 serves as a robust metric for wide-spectrum image evaluation, and CoHP offers an efficient and human-aligned approach to improve image generation quality. The code and dataset are available at the HPSv3 Homepage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.04026", "title": "VeriGUI: Verifiable Long-Chain GUI Dataset", "authors": [ "Shunyu Liu", "Minghao Liu", "Huichi Zhou", "Zhenyu Cui", "Yang Zhou", "Yuhao Zhou", "Wendong Fan", "Ge Zhang", "Jiajun Shi", "Weihao Xuan", "Jiaxing Huang", "Shuang Luo", "Fang Wu", "Heli Qi", "Qingcheng Zeng", "Ziqi Ren", "Jialiang Gao", "Jindi Lv", "Junjie Wang", "Aosong Feng", "Heng Zhou", "Wangchunshu Zhou", "Zhenfei Yin", "Wenlong Zhang", "Guohao Li", "Wenhao Yu", "Irene Li", "Lei Ma", "Lei Bai", "Qunshu Lin", "Mingli Song", "Dacheng Tao" ], "github": "https://github.com/searxng/searxng-docker", "project_page": "", "abstract": "Recent studies have delved into constructing autonomous agents capable of performing complex Graphical User Interface (GUI)-based computer tasks, with the potential to revolutionize human-computer interaction. Despite encouraging results, existing efforts mainly focus on short-term interactions and rely on outcome-only verification, thereby limiting their scalability in real-world GUI applications that demand long-horizon task decomposition and execution. In this work, we introduce VeriGUI, a novel verifiable long-chain GUI dataset designed to facilitate the development and evaluation of generalist GUI agents operating in realistic computer environments. Our dataset emphasizes two critical dimensions: (1) long-chain complexity, with tasks decomposed into a sequence of interdependent subtasks spanning hundreds of steps, explicitly designed to allow any subtask to serve as a valid starting point; and (2) subtask-level verifiability, which enables diverse exploration strategies within each subtask, while ensuring that each subtask-level goal remains verifiable and consistent. The dataset consists of GUI task trajectories across both desktop and web, annotated by human experts. Extensive experiments on VeriGUI using various agents with different foundation models reveal significant performance gaps in handling long-horizon tasks, highlighting the need for more robust planning and decision-making capabilities in GUI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/searxng/searxng-docker", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.01191", "title": "Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens", "authors": [ "Chengshuai Zhao", "Zhen Tan", "Pingchuan Ma", "Dawei Li", "Bohan Jiang", "Yancheng Wang", "Yingzhen Yang", "Huan Liu" ], "github": "https://github.com/ChengshuaiZhao0/DataAlchemy", "project_page": "", "abstract": "Chain-of-Thought (CoT) prompting has been shown to improve Large Language Model (LLM) performance on various tasks. With this approach, LLMs appear to produce human-like reasoning steps before providing answers (a.k.a., CoT reasoning), which often leads to the perception that they engage in deliberate inferential processes. However, some initial findings suggest that CoT reasoning may be more superficial than it appears, motivating us to explore further. In this paper, we study CoT reasoning via a data distribution lens and investigate if CoT reasoning reflects a structured inductive bias learned from in-distribution data, allowing the model to conditionally generate reasoning paths that approximate those seen during training. Thus, its effectiveness is fundamentally bounded by the degree of distribution discrepancy between the training data and the test queries. With this lens, we dissect CoT reasoning via three dimensions: task, length, and format. To investigate each dimension, we design DataAlchemy, an isolated and controlled environment to train LLMs from scratch and systematically probe them under various distribution conditions. Our results reveal that CoT reasoning is a brittle mirage that vanishes when it is pushed beyond training distributions. This work offers a deeper understanding of why and when CoT reasoning fails, emphasizing the ongoing challenge of achieving genuine and generalizable reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ChengshuaiZhao0/DataAlchemy", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.03905", "title": "Sotopia-RL: Reward Design for Social Intelligence", "authors": [ "Haofei Yu", "Zhengyang Qi", "Yining Zhao", "Kolby Nottingham", "Keyang Xuan", "Bodhisattwa Prasad Majumder", "Hao Zhu", "Paul Pu Liang", "Jiaxuan You" ], "github": "https://github.com/sotopia-lab/sotopia-rl", "project_page": "", "abstract": "Social intelligence has become a critical capability for large language models (LLMs), enabling them to engage effectively in real-world social tasks such as accommodation, persuasion, collaboration, and negotiation. Reinforcement learning (RL) is a natural fit for training socially intelligent agents because it allows models to learn sophisticated strategies directly through social interactions. However, social interactions have two key characteristics that set barriers for RL training: (1) partial observability, where utterances have indirect and delayed effects that complicate credit assignment, and (2) multi-dimensionality, where behaviors such as rapport-building or knowledge-seeking contribute indirectly to goal achievement. These characteristics make Markov decision process (MDP)-based RL with single-dimensional episode-level rewards inefficient and unstable. To address these challenges, we propose Sotopia-RL, a novel framework that refines coarse episode-level feedback into utterance-level, multi-dimensional rewards. Utterance-level credit assignment mitigates partial observability by attributing outcomes to individual utterances, while multi-dimensional rewards capture the full richness of social interactions and reduce reward hacking. Experiments in Sotopia, an open-ended social learning environment, demonstrate that Sotopia-RL achieves state-of-the-art social goal completion scores (7.17 on Sotopia-hard and 8.31 on Sotopia-full), significantly outperforming existing approaches. Ablation studies confirm the necessity of both utterance-level credit assignment and multi-dimensional reward design for RL training. Our implementation is publicly available at: https://github.com/sotopia-lab/sotopia-rl.", "github_manual": "", "github_regex": "https://github.com/sotopia-lab/sotopia-rl", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2507.23785", "title": "Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis", "authors": [ "Bowen Zhang", "Sicheng Xu", "Chuxin Wang", "Jiaolong Yang", "Feng Zhao", "Dong Chen", "Baining Guo" ], "github": "", "project_page": "", "abstract": "In this paper, we present a novel framework for video-to-4D generation that creates high-quality dynamic 3D content from single video inputs. Direct 4D diffusion modeling is extremely challenging due to costly data construction and the high-dimensional nature of jointly representing 3D shape, appearance, and motion. We address these challenges by introducing a Direct 4DMesh-to-GS Variation Field VAE that directly encodes canonical Gaussian Splats (GS) and their temporal variations from 3D animation data without per-instance fitting, and compresses high-dimensional animations into a compact latent space. Building upon this efficient representation, we train a Gaussian Variation Field diffusion model with temporal-aware Diffusion Transformer conditioned on input videos and canonical GS. Trained on carefully-curated animatable 3D objects from the Objaverse dataset, our model demonstrates superior generation quality compared to existing methods. It also exhibits remarkable generalization to in-the-wild video inputs despite being trained exclusively on synthetic data, paving the way for generating high-quality animated 3D content. Project page: https://gvfdiffusion.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.04664", "title": "Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management", "authors": [ "Mo Li", "L. H. Xu", "Qitai Tan", "Ting Cao", "Yunxin Liu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) suffer from significant performance degradation when processing long contexts due to proactive interference, where irrelevant information in earlier parts of the context disrupts reasoning and memory recall. While most research focuses on external memory systems to augment LLMs' capabilities, we propose a complementary approach: empowering LLMs with Active Context Management (ACM) tools to actively sculpt their internal working memory. We introduce Sculptor, a framework that equips LLMs with three categories of tools: (1) context fragmentation, (2) summary, hide, and restore, and (3) intelligent search. Our approach enables LLMs to proactively manage their attention and working memory, analogous to how humans selectively focus on relevant information while filtering out distractions. Experimental evaluation on information-sparse benchmarks-PI-LLM (proactive interference) and NeedleBench Multi-Needle Reasoning-demonstrates that Sculptor significantly improves performance even without specific training, leveraging LLMs' inherent tool calling generalization capabilities. By enabling Active Context Management, Sculptor not only mitigates proactive interference but also provides a cognitive foundation for more reliable reasoning across diverse long-context tasks-highlighting that explicit context-control strategies, rather than merely larger token windows, are key to robustness at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.01858", "title": "Web-CogReasoner: Towards Knowledge-Induced Cognitive Reasoning for Web Agents", "authors": [ "Yuhan Guo", "Cong Guo", "Aiwen Sun", "Hongliang He", "Xinyu Yang", "Yue Lu", "Yingji Zhang", "Xuntao Guo", "Dong Zhang", "Jianzhuang Liu", "Jiang Duan", "Yijia Xiao", "Liangjian Wen", "Hai-Ming Xu", "Yong Dai" ], "github": "https://github.com/Gnonymous/Web-CogReasoner", "project_page": "", "abstract": "Multimodal large-scale models have significantly advanced the development of web agents, enabling perception and interaction with digital environments akin to human cognition. In this paper, we argue that web agents must first acquire sufficient knowledge to effectively engage in cognitive reasoning. Therefore, we decompose a web agent's capabilities into two essential stages: knowledge content learning and cognitive processes. To formalize this, we propose Web-CogKnowledge Framework, categorizing knowledge as Factual, Conceptual, and Procedural. In this framework, knowledge content learning corresponds to the agent's processes of Memorizing and Understanding, which rely on the first two knowledge types, representing the \"what\" of learning. Conversely, cognitive processes correspond to Exploring, grounded in Procedural knowledge, defining the \"how\" of reasoning and action. To facilitate knowledge acquisition, we construct the Web-CogDataset, a structured resource curated from 14 real-world websites, designed to systematically instill core knowledge necessary for web agent. This dataset serves as the agent's conceptual grounding-the \"nouns\" upon which comprehension is built-as well as the basis for learning how to reason and act. Building on this foundation, we operationalize these processes through a novel knowledge-driven Chain-of-Thought (CoT) reasoning framework, developing and training our proposed agent, the Web-CogReasoner. Extensive experimentation reveals its significant superiority over existing models, especially in generalizing to unseen tasks where structured knowledge is decisive. To enable rigorous evaluation, we introduce the Web-CogBench, a comprehensive evaluation suite designed to assess and compare agent performance across the delineated knowledge domains and cognitive capabilities. Our code and data is open sourced at https://github.com/Gnonymous/Web-CogReasoner", "github_manual": "", "github_regex": "https://github.com/Gnonymous/Web-CogReasoner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.04700", "title": "SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience", "authors": [ "Zeyi Sun", "Ziyu Liu", "Yuhang Zang", "Yuhang Cao", "Xiaoyi Dong", "Tong Wu", "Dahua Lin", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Repurposing large vision-language models (LVLMs) as computer use agents (CUAs) has led to substantial breakthroughs, primarily driven by human-labeled data. However, these models often struggle with novel and specialized software, particularly in scenarios lacking human annotations. To address this challenge, we propose SEAgent, an agentic self-evolving framework enabling CUAs to autonomously evolve through interactions with unfamiliar software. Specifically, SEAgent empowers computer-use agents to autonomously master novel software environments via experiential learning, where agents explore new software, learn through iterative trial-and-error, and progressively tackle auto-generated tasks organized from simple to complex. To achieve this goal, we design a World State Model for step-wise trajectory assessment, along with a Curriculum Generator that generates increasingly diverse and challenging tasks. The agent's policy is updated through experiential learning, comprised of adversarial imitation of failure actions and Group Relative Policy Optimization (GRPO) on successful ones. Furthermore, we introduce a specialist-to-generalist training strategy that integrates individual experiential insights from specialist agents, facilitating the development of a stronger generalist CUA capable of continuous autonomous evolution. This unified agent ultimately achieves performance surpassing ensembles of individual specialist agents on their specialized software. We validate the effectiveness of SEAgent across five novel software environments within OS-World. Our approach achieves a significant improvement of 23.2% in success rate, from 11.3% to 34.5%, over a competitive open-source CUA, i.e., UI-TARS.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.01197", "title": "A Coarse-to-Fine Approach to Multi-Modality 3D Occupancy Grounding", "authors": [ "Zhan Shi", "Song Wang", "Junbo Chen", "Jianke Zhu" ], "github": "https://github.com/RONINGOD/GroundingOcc", "project_page": "", "abstract": "Visual grounding aims to identify objects or regions in a scene based on natural language descriptions, essential for spatially aware perception in autonomous driving. However, existing visual grounding tasks typically depend on bounding boxes that often fail to capture fine-grained details. Not all voxels within a bounding box are occupied, resulting in inaccurate object representations. To address this, we introduce a benchmark for 3D occupancy grounding in challenging outdoor scenes. Built on the nuScenes dataset, it integrates natural language with voxel-level occupancy annotations, offering more precise object perception compared to the traditional grounding task. Moreover, we propose GroundingOcc, an end-to-end model designed for 3D occupancy grounding through multi-modal learning. It combines visual, textual, and point cloud features to predict object location and occupancy information from coarse to fine. Specifically, GroundingOcc comprises a multimodal encoder for feature extraction, an occupancy head for voxel-wise predictions, and a grounding head to refine localization. Additionally, a 2D grounding module and a depth estimation module enhance geometric understanding, thereby boosting model performance. Extensive experiments on the benchmark demonstrate that our method outperforms existing baselines on 3D occupancy grounding. The dataset is available at https://github.com/RONINGOD/GroundingOcc.", "github_manual": "", "github_regex": "https://github.com/RONINGOD/GroundingOcc", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.03680", "title": "Agent Lightning: Train ANY AI Agents with Reinforcement Learning", "authors": [ "Xufang Luo", "Yuge Zhang", "Zhiyuan He", "Zilong Wang", "Siyun Zhao", "Dongsheng Li", "Luna K. Qiu", "Yuqing Yang" ], "github": "https://github.com/microsoft/agent-lightning/tree/main/examples/apo", "project_page": "", "abstract": "We present Agent Lightning, a flexible and extensible framework that enables Reinforcement Learning (RL)-based training of Large Language Models (LLMs) for any AI agent. Unlike existing methods that tightly couple RL training with agent or rely on sequence concatenation with masking, Agent Lightning achieves complete decoupling between agent execution and training, allowing seamless integration with existing agents developed via diverse ways (e.g., using frameworks like LangChain, OpenAI Agents SDK, AutoGen, and building from scratch) with almost ZERO code modifications. By formulating agent execution as Markov decision process, we define an unified data interface and propose a hierarchical RL algorithm, LightningRL, which contains a credit assignment module, allowing us to decompose trajectories generated by ANY agents into training transition. This enables RL to handle complex interaction logic, such as multi-agent scenarios and dynamic workflows. For the system design, we introduce a Training-Agent Disaggregation architecture, and brings agent observability frameworks into agent runtime, providing a standardized agent finetuning interface. Experiments across text-to-SQL, retrieval-augmented generation, and math tool-use tasks demonstrate stable, continuous improvements, showcasing the framework's potential for real-world agent training and deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/agent-lightning/tree/main/examples/apo", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.04295", "title": "EVOC2RUST: A Skeleton-guided Framework for Project-Level C-to-Rust Translation", "authors": [ "Chaofan Wang", "Tingrui Yu", "Jie Wang", "Dong Chen", "Wenrui Zhang", "Yuling Shi", "Xiaodong Gu", "Beijun Shen" ], "github": "", "project_page": "", "abstract": "Rust's compile-time safety guarantees make it ideal for safety-critical systems, creating demand for translating legacy C codebases to Rust. While various approaches have emerged for this task, they face inherent trade-offs: rule-based solutions face challenges in meeting code safety and idiomaticity requirements, while LLM-based solutions often fail to generate semantically equivalent Rust code, due to the heavy dependencies of modules across the entire codebase. Recent studies have revealed that both solutions are limited to small-scale programs. In this paper, we propose EvoC2Rust, an automated framework for converting entire C projects to equivalent Rust ones. EvoC2Rust employs a skeleton-guided translation strategy for project-level translation. The pipeline consists of three evolutionary stages: 1) it first decomposes the C project into functional modules, employs a feature-mapping-enhanced LLM to transform definitions and macros and generates type-checked function stubs, which form a compilable Rust skeleton; 2) it then incrementally translates the function, replacing the corresponding stub placeholder; 3) finally, it repairs compilation errors by integrating LLM and static analysis. Through evolutionary augmentation, EvoC2Rust combines the advantages of both rule-based and LLM-based solutions. Our evaluation on open-source benchmarks and six industrial projects demonstrates EvoC2Rust's superior performance in project-level C-to-Rust translation. On average, it achieves 17.24% and 14.32% improvements in syntax and semantic accuracy over the LLM-based approaches, along with a 96.79% higher code safety rate than the rule-based tools. At the module level, EvoC2Rust reaches 92.25% compilation and 89.53% test pass rates on industrial projects, even for complex codebases and long functions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.03560", "title": "LaTCoder: Converting Webpage Design to Code with Layout-as-Thought", "authors": [ "Yi Gui", "Zhen Li", "Zhongyi Zhang", "Guohao Wang", "Tianpeng Lv", "Gaoyang Jiang", "Yi Liu", "Dongping Chen", "Yao Wan", "Hongyu Zhang", "Wenbin Jiang", "Xuanhua Shi", "Hai Jin" ], "github": "", "project_page": "", "abstract": "Converting webpage designs into code (design-to-code) plays a vital role in User Interface (UI) development for front-end developers, bridging the gap between visual design and functional implementation. While recent Multimodal Large Language Models (MLLMs) have shown significant potential in design-to-code tasks, they often fail to accurately preserve the layout during code generation. To this end, we draw inspiration from the Chain-of-Thought (CoT) reasoning in human cognition and propose LaTCoder, a novel approach that enhances layout preservation in webpage design during code generation with Layout-as-Thought (LaT). Specifically, we first introduce a simple yet efficient algorithm to divide the webpage design into image blocks. Next, we prompt MLLMs using a CoTbased approach to generate code for each block. Finally, we apply two assembly strategies-absolute positioning and an MLLM-based method-followed by dynamic selection to determine the optimal output. We evaluate the effectiveness of LaTCoder using multiple backbone MLLMs (i.e., DeepSeek-VL2, Gemini, and GPT-4o) on both a public benchmark and a newly introduced, more challenging benchmark (CC-HARD) that features complex layouts. The experimental results on automatic metrics demonstrate significant improvements. Specifically, TreeBLEU scores increased by 66.67% and MAE decreased by 38% when using DeepSeek-VL2, compared to direct prompting. Moreover, the human preference evaluation results indicate that annotators favor the webpages generated by LaTCoder in over 60% of cases, providing strong evidence of the effectiveness of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.03159", "title": "CoTox: Chain-of-Thought-Based Molecular Toxicity Reasoning and Prediction", "authors": [ "Jueon Park", "Yein Park", "Minju Song", "Soyon Park", "Donghyeon Lee", "Seungheun Baek", "Jaewoo Kang" ], "github": "https://github.com/dmis-lab/CoTox", "project_page": "", "abstract": "Drug toxicity remains a major challenge in pharmaceutical development. Recent machine learning models have improved in silico toxicity prediction, but their reliance on annotated data and lack of interpretability limit their applicability. This limits their ability to capture organ-specific toxicities driven by complex biological mechanisms. Large language models (LLMs) offer a promising alternative through step-by-step reasoning and integration of textual data, yet prior approaches lack biological context and transparent rationale. To address this issue, we propose CoTox, a novel framework that integrates LLM with chain-of-thought (CoT) reasoning for multi-toxicity prediction. CoTox combines chemical structure data, biological pathways, and gene ontology (GO) terms to generate interpretable toxicity predictions through step-by-step reasoning. Using GPT-4o, we show that CoTox outperforms both traditional machine learning and deep learning model. We further examine its performance across various LLMs to identify where CoTox is most effective. Additionally, we find that representing chemical structures with IUPAC names, which are easier for LLMs to understand than SMILES, enhances the model's reasoning ability and improves predictive performance. To demonstrate its practical utility in drug development, we simulate the treatment of relevant cell types with drug and incorporated the resulting biological context into the CoTox framework. This approach allow CoTox to generate toxicity predictions aligned with physiological responses, as shown in case study. This result highlights the potential of LLM-based frameworks to improve interpretability and support early-stage drug safety assessment. The code and prompt used in this work are available at https://github.com/dmis-lab/CoTox.", "github_manual": "", "github_regex": "https://github.com/dmis-lab/CoTox", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2507.23313", "title": "The Cow of Rembrandt - Analyzing Artistic Prompt Interpretation in Text-to-Image Models", "authors": [ "Alfio Ferrara", "Sergio Picascia", "Elisabetta Rocchetti" ], "github": "https://github.com/umilISLab/artistic-prompt-interpretation", "project_page": "", "abstract": "Text-to-image diffusion models have demonstrated remarkable capabilities in generating artistic content by learning from billions of images, including popular artworks. However, the fundamental question of how these models internally represent concepts, such as content and style in paintings, remains unexplored. Traditional computer vision assumes content and style are orthogonal, but diffusion models receive no explicit guidance about this distinction during training. In this work, we investigate how transformer-based text-to-image diffusion models encode content and style concepts when generating artworks. We leverage cross-attention heatmaps to attribute pixels in generated images to specific prompt tokens, enabling us to isolate image regions influenced by content-describing versus style-describing tokens. Our findings reveal that diffusion models demonstrate varying degrees of content-style separation depending on the specific artistic prompt and style requested. In many cases, content tokens primarily influence object-related regions while style tokens affect background and texture areas, suggesting an emergent understanding of the content-style distinction. These insights contribute to our understanding of how large-scale generative models internally represent complex artistic concepts without explicit supervision. We share the code and dataset, together with an exploratory tool for visualizing attention maps at https://github.com/umilISLab/artistic-prompt-interpretation.", "github_manual": "", "github_regex": "https://github.com/umilISLab/artistic-prompt-interpretation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.02694", "title": "Efficient Agents: Building Effective Agents While Reducing Cost", "authors": [ "Ningning Wang", "Xavier Hu", "Pai Liu", "He Zhu", "Yue Hou", "Heyuan Huang", "Shengyu Zhang", "Jian Yang", "Jiaheng Liu", "Ge Zhang", "Changwang Zhang", "Jun Wang", "Yuchen Eleanor Jiang", "Wangchunshu Zhou" ], "github": "", "project_page": "", "abstract": "The remarkable capabilities of Large Language Model (LLM)-driven agents have enabled sophisticated systems to tackle complex, multi-step tasks, but their escalating costs threaten scalability and accessibility. This work presents the first systematic study of the efficiency-effectiveness trade-off in modern agent systems, addressing the critical need for cost-effective designs without sacrificing performance. We investigate three key questions: (1) How much complexity do agentic tasks inherently require? (2) When do additional modules yield diminishing returns? (3) How much efficiency can be gained through the design of efficient agent frameworks? Through an empirical analysis on the GAIA benchmark, we evaluate the impact of LLM backbone selection, agent framework designs, and test-time scaling strategies. Using the cost-of-pass metric, we quantify the efficiency-performance trade-off across these dimensions. Our findings inform the development of Efficient Agents , a novel agent framework that has an optimal complexity to task requirements. Efficient Agents retains 96.7% of the performance of OWL, one leading open-source agent framework, while reducing operational costs from 0.398 to 0.228, resulting in a 28.4% improvement in cost-of-pass. Our work provides actionable insights for designing efficient, high-performing agent systems, advancing the accessibility and sustainability of AI-driven solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.01630", "title": "OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets", "authors": [ "Maziyar Panahi" ], "github": "", "project_page": "", "abstract": "Named-entity recognition (NER) is fundamental to extracting structured information from the >80% of healthcare data that resides in unstructured clinical notes and biomedical literature. Despite recent advances with large language models, achieving state-of-the-art performance across diverse entity types while maintaining computational efficiency remains a significant challenge. We introduce OpenMed NER, a suite of open-source, domain-adapted transformer models that combine lightweight domain-adaptive pre-training (DAPT) with parameter-efficient Low-Rank Adaptation (LoRA). Our approach performs cost-effective DAPT on a 350k-passage corpus compiled from ethically sourced, publicly available research repositories and de-identified clinical notes (PubMed, arXiv, and MIMIC-III) using DeBERTa-v3, PubMedBERT, and BioELECTRA backbones. This is followed by task-specific fine-tuning with LoRA, which updates less than 1.5% of model parameters. We evaluate our models on 12 established biomedical NER benchmarks spanning chemicals, diseases, genes, and species. OpenMed NER achieves new state-of-the-art micro-F1 scores on 10 of these 12 datasets, with substantial gains across diverse entity types. Our models advance the state-of-the-art on foundational disease and chemical benchmarks (e.g., BC5CDR-Disease, +2.70 pp), while delivering even larger improvements of over 5.3 and 9.7 percentage points on more specialized gene and clinical cell line corpora. This work demonstrates that strategically adapted open-source models can surpass closed-source solutions. This performance is achieved with remarkable efficiency: training completes in under 12 hours on a single GPU with a low carbon footprint (< 1.2 kg CO2e), producing permissively licensed, open-source checkpoints designed to help practitioners facilitate compliance with emerging data protection and AI regulations, such as the EU AI Act.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.03448", "title": "SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering", "authors": [ "Jan Melechovsky", "Ambuj Mehrish", "Dorien Herremans" ], "github": "", "project_page": "", "abstract": "Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo image, especially when created in non-professional settings without specialized equipment or expertise. These problems are typically corrected using separate specialized tools and manual adjustments. In this paper, we introduce SonicMaster, the first unified generative model for music restoration and mastering that addresses a broad spectrum of audio artifacts with text-based control. SonicMaster is conditioned on natural language instructions to apply targeted enhancements, or can operate in an automatic mode for general restoration. To train this model, we construct the SonicMaster dataset, a large dataset of paired degraded and high-quality tracks by simulating common degradation types with nineteen degradation functions belonging to five enhancements groups: equalization, dynamics, reverb, amplitude, and stereo. Our approach leverages a flow-matching generative training paradigm to learn an audio transformation that maps degraded inputs to their cleaned, mastered versions guided by text prompts. Objective audio quality metrics demonstrate that SonicMaster significantly improves sound quality across all artifact categories. Furthermore, subjective listening tests confirm that listeners prefer SonicMaster's enhanced outputs over the original degraded audio, highlighting the effectiveness of our unified approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.00222", "title": "RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization", "authors": [ "Yihong Dong", "Xue Jiang", "Yongding Tao", "Huanyu Liu", "Kechi Zhang", "Lili Mou", "Rongyu Cao", "Yingwei Ma", "Jue Chen", "Binhua Li", "Zhi Jin", "Fei Huang", "Yongbin Li", "Ge Li" ], "github": "https://github.com/YihongDong/RL-PLUS", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs). However, it struggles to break through the inherent capability boundaries of the base LLM, due to its essentially on-policy strategy coupled with LLM's immense action space and sparse reward. Critically, RLVR can lead to the capability boundary collapse, narrowing the LLM's problem-solving scope. To address this problem, we propose RL-PLUS, a novel hybrid-policy optimization approach for LLMs that synergizes internal exploitation with external data to achieve stronger reasoning capabilities and surpass the boundaries of base models. RL-PLUS integrates two core components, i.e., Multiple Importance Sampling to address distributional mismatch from external data, and Exploration-Based Advantage Function to guide the model towards high-value, unexplored reasoning paths. We provide both theoretical analysis and extensive experiments to demonstrate the superiority and generalizability of our approach. Compared with existing RLVR methods, RL-PLUS achieves 1) state-of-the-art performance on six math reasoning benchmarks; 2) superior performance on six out-of-distribution reasoning tasks; 3) consistent and significant gains across diverse model families, with average relative improvements up to 69.2\\%. Moreover, the analysis of Pass@k curves indicates that RL-PLUS effectively resolves the capability boundary collapse problem.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YihongDong/RL-PLUS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.02807", "title": "DreamVVT: Mastering Realistic Video Virtual Try-On in the Wild via a Stage-Wise Diffusion Transformer Framework", "authors": [ "Tongchun Zuo", "Zaiyu Huang", "Shuliang Ning", "Ente Lin", "Chao Liang", "Zerong Zheng", "Jianwen Jiang", "Yuan Zhang", "Mingyuan Gao", "Xin Dong" ], "github": "https://github.com/Virtu-Lab/DreamVVT", "project_page": "", "abstract": "Video virtual try-on (VVT) technology has garnered considerable academic interest owing to its promising applications in e-commerce advertising and entertainment. However, most existing end-to-end methods rely heavily on scarce paired garment-centric datasets and fail to effectively leverage priors of advanced visual models and test-time inputs, making it challenging to accurately preserve fine-grained garment details and maintain temporal consistency in unconstrained scenarios. To address these challenges, we propose DreamVVT, a carefully designed two-stage framework built upon Diffusion Transformers (DiTs), which is inherently capable of leveraging diverse unpaired human-centric data to enhance adaptability in real-world scenarios. To further leverage prior knowledge from pretrained models and test-time inputs, in the first stage, we sample representative frames from the input video and utilize a multi-frame try-on model integrated with a vision-language model (VLM), to synthesize high-fidelity and semantically consistent keyframe try-on images. These images serve as complementary appearance guidance for subsequent video generation. In the second stage, skeleton maps together with fine-grained motion and appearance descriptions are extracted from the input content, and these along with the keyframe try-on images are then fed into a pretrained video generation model enhanced with LoRA adapters. This ensures long-term temporal coherence for unseen regions and enables highly plausible dynamic motions. Extensive quantitative and qualitative experiments demonstrate that DreamVVT surpasses existing methods in preserving detailed garment content and temporal stability in real-world scenarios. Our project page https://virtu-lab.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Virtu-Lab/DreamVVT", "github_api_added_by": "auto" }, { "date": "2025-08-07", "arxiv_id": "2508.01928", "title": "IAUNet: Instance-Aware U-Net", "authors": [ "Yaroslav Prytula", "Illia Tsiporenko", "Ali Zeynalli", "Dmytro Fishman" ], "github": "https://github.com/SlavkoPrytula/IAUNet", "project_page": "", "abstract": "Instance segmentation is critical in biomedical imaging to accurately distinguish individual objects like cells, which often overlap and vary in size. Recent query-based methods, where object queries guide segmentation, have shown strong performance. While U-Net has been a go-to architecture in medical image segmentation, its potential in query-based approaches remains largely unexplored. In this work, we present IAUNet, a novel query-based U-Net architecture. The core design features a full U-Net architecture, enhanced by a novel lightweight convolutional Pixel decoder, making the model more efficient and reducing the number of parameters. Additionally, we propose a Transformer decoder that refines object-specific features across multiple scales. Finally, we introduce the 2025 Revvity Full Cell Segmentation Dataset, a unique resource with detailed annotations of overlapping cell cytoplasm in brightfield images, setting a new benchmark for biomedical instance segmentation. Experiments on multiple public datasets and our own show that IAUNet outperforms most state-of-the-art fully convolutional, transformer-based, and query-based models and cell segmentation-specific models, setting a strong baseline for cell instance segmentation tasks. Code is available at https://github.com/SlavkoPrytula/IAUNet", "github_manual": "", "github_regex": "https://github.com/SlavkoPrytula/IAUNet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.04280", "title": "Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success", "authors": [ "George Bredis", "Stanislav Dereka", "Viacheslav Sinii", "Ruslan Rakhimov", "Daniil Gavrilov" ], "github": "", "project_page": "", "abstract": "Interactive multimodal agents must convert raw visual observations into coherent sequences of language-conditioned actions -- a capability that current vision-language models (VLMs) still lack. Earlier reinforcement-learning (RL) efforts could, in principle, endow VLMs with such skills, but they have seldom tested whether the learned behaviours generalize beyond their training simulators, and they depend either on brittle hyperparameter tuning or on dense-reward environments with low state variability. We introduce Vision-Language Decoupled Actor-Critic (VL-DAC), a lightweight, hyperparameter-free RL algorithm. VL-DAC applies PPO updates to action tokens while learning value only at the environment-step level: an arrangement, to our knowledge, not previously explored for large VLMs or LLMs. This simple decoupling removes unstable weighting terms and yields faster, more reliable convergence. Training a single VLM with VL-DAC in one inexpensive simulator at a time (MiniWorld, Gym-Cards, ALFWorld, or WebShop) already produces policies that generalize widely: +50\\% relative on BALROG (game-centric agentic control), +5\\% relative on the hardest part of VSI-Bench (spatial planning), and +2\\% on VisualWebBench (web navigation), all without degrading general image understanding accuracy. These results provide the first evidence that a simple RL algorithm can train VLMs entirely in cheap synthetic worlds while delivering measurable gains on real-image agentic, spatial-reasoning, and web-navigation benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.03501", "title": "Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning", "authors": [ "Alexander Golubev", "Maria Trofimova", "Sergei Polezhaev", "Ibragim Badertdinov", "Maksim Nekrashevich", "Anton Shevtsov", "Simon Karasik", "Sergey Abramov", "Andrei Andriushchenko", "Filipp Fisin", "Sergei Skvortsov", "Boris Yangel" ], "github": "", "project_page": "", "abstract": "Research on applications of Reinforcement Learning (RL) to Large Language Models (LLMs) has mostly been focused on single-turn problems, such as mathematical reasoning or single-shot code generation. While these problems can be viewed as token-level multi-turn MDPs, this view corresponds to a degenerate case of multi-turn interaction where the environment provides no feedback. This contrasts with many real-world domains, such as software engineering (SWE), which require rich multi-turn interactions with a stateful environment that responds to each action with a non-trivial observation. To bridge this gap, we demonstrate the successful application of RL to this general regime. Using a modified Decoupled Advantage Policy Optimization (DAPO) algorithm, we train an agent based on Qwen2.5-72B-Instruct to solve real-world software engineering tasks. Our approach increases the agent's success rate on the SWE-bench Verified benchmark from a 20% rejection fine-tuned baseline to 39%, without relying on any teacher models. On SWE-rebench, our agent matches or outperforms leading open-weight models such as DeepSeek-V3-0324 and Qwen3-235B-A22B using an identical scaffolding, offering a viable path toward building more capable autonomous agents for complex real-world problems based on open models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2507.21974", "title": "Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks", "authors": [ "Mohamed Sana", "Nicola Piovesan", "Antonio De Domenico", "Yibin Kang", "Haozhe Zhang", "Merouane Debbah", "Fadhel Ayed" ], "github": "", "project_page": "", "abstract": "Root Cause Analysis (RCA) in mobile networks remains a challenging task due to the need for interpretability, domain expertise, and causal reasoning. In this work, we propose a lightweight framework that leverages Large Language Models (LLMs) for RCA. To do so, we introduce TeleLogs, a curated dataset of annotated troubleshooting problems designed to benchmark RCA capabilities. Our evaluation reveals that existing open-source reasoning LLMs struggle with these problems, underscoring the need for domain-specific adaptation. To address this issue, we propose a two-stage training methodology that combines supervised fine-tuning with reinforcement learning to improve the accuracy and reasoning quality of LLMs. The proposed approach fine-tunes a series of RCA models to integrate domain knowledge and generate structured, multi-step diagnostic explanations, improving both interpretability and effectiveness. Extensive experiments across multiple LLM sizes show significant performance gains over state-of-the-art reasoning and non-reasoning models, including strong generalization to randomized test variants. These results demonstrate the promise of domain-adapted, reasoning-enhanced LLMs for practical and explainable RCA in network operation and management.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.04632", "title": "IFDECORATOR: Wrapping Instruction Following Reinforcement Learning with Verifiable Rewards", "authors": [ "Xu Guo", "Tianyi Liang", "Tong Jian", "Xiaogui Yang", "Ling-I Wu", "Chenhui Li", "Zhihui Lu", "Qipeng Guo", "Kai Chen" ], "github": "https://github.com/guox18/IFDecorator", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) improves instruction following capabilities of large language models (LLMs), but suffers from training inefficiency due to inadequate difficulty assessment. Moreover, RLVR is prone to over-optimization, where LLMs exploit verification shortcuts without aligning to the actual intent of user instructions. We introduce Instruction Following Decorator (IFDecorator}, a framework that wraps RLVR training into a robust and sample-efficient pipeline. It consists of three components: (1) a cooperative-adversarial data flywheel that co-evolves instructions and hybrid verifications, generating progressively more challenging instruction-verification pairs; (2) IntentCheck, a bypass module enforcing intent alignment; and (3) trip wires, a diagnostic mechanism that detects reward hacking via trap instructions, which trigger and capture shortcut exploitation behaviors. Our Qwen2.5-32B-Instruct-IFDecorator achieves 87.43% accuracy on IFEval, outperforming larger proprietary models such as GPT-4o. Additionally, we demonstrate substantial improvements on FollowBench while preserving general capabilities. Our trip wires show significant reductions in reward hacking rates. We will release models, code, and data for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/guox18/IFDecorator", "github_api_added_by": "auto" }, { "date": "2025-08-07", "arxiv_id": "2508.03983", "title": "MiDashengLM: Efficient Audio Understanding with General Audio Captions", "authors": [ "Heinrich Dinkel", "Gang Li", "Jizhong Liu", "Jian Luan", "Yadong Niu", "Xingwei Sun", "Tianzi Wang", "Qiyang Xiao", "Junbo Zhang", "Jiahao Zhou" ], "github": "https://github.com/xiaomi-research/dasheng-lm", "project_page": "", "abstract": "Current approaches for large audio language models (LALMs) often rely on closed data sources or proprietary models, limiting their generalization and accessibility. This paper introduces MiDashengLM, a novel open audio-language model designed for efficient and comprehensive audio understanding through the use of general audio captions using our novel ACAVCaps training dataset. MiDashengLM exclusively relies on publicly available pretraining and supervised fine-tuning (SFT) datasets, ensuring full transparency and reproducibility. At its core, MiDashengLM integrates Dasheng, an open-source audio encoder, specifically engineered to process diverse auditory information effectively. Unlike previous works primarily focused on Automatic Speech Recognition (ASR) based audio-text alignment, our strategy centers on general audio captions, fusing speech, sound and music information into one textual representation, enabling a holistic textual representation of complex audio scenes. Lastly, MiDashengLM provides an up to 4x speedup in terms of time-to-first-token (TTFT) and up to 20x higher throughput than comparable models. Checkpoints are available online at https://huggingface.co/mispeech/midashenglm-7b and https://github.com/xiaomi-research/dasheng-lm.", "github_manual": "", "github_regex": "https://github.com/xiaomi-research/dasheng-lm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.00428", "title": "Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D Generation", "authors": [ "Nan Xiang", "Tianyi Liang", "Haiwen Huang", "Shiqi Jiang", "Hao Huang", "Yifei Huang", "Liangyu Chen", "Changbo Wang", "Chenhui Li" ], "github": "", "project_page": "", "abstract": "Text-to-3D (T23D) generation has transformed digital content creation, yet remains bottlenecked by blind trial-and-error prompting processes that yield unpredictable results. While visual prompt engineering has advanced in text-to-image domains, its application to 3D generation presents unique challenges requiring multi-view consistency evaluation and spatial understanding. We present Sel3DCraft, a visual prompt engineering system for T23D that transforms unstructured exploration into a guided visual process. Our approach introduces three key innovations: a dual-branch structure combining retrieval and generation for diverse candidate exploration; a multi-view hybrid scoring approach that leverages MLLMs with innovative high-level metrics to assess 3D models with human-expert consistency; and a prompt-driven visual analytics suite that enables intuitive defect identification and refinement. Extensive testing and user studies demonstrate that Sel3DCraft surpasses other T23D systems in supporting creativity for designers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.03178", "title": "Light-IF: Endowing LLMs with Generalizable Reasoning via Preview and Self-Checking for Complex Instruction Following", "authors": [ "Chenyang Wang", "Liang Wen", "Shousheng Jia", "Xiangzheng Zhang", "Liang Xu" ], "github": "", "project_page": "", "abstract": "While advancements in the reasoning abilities of LLMs have significantly enhanced their performance in solving mathematical problems, coding tasks, and general puzzles, their effectiveness in accurately adhering to instructions remains inconsistent, particularly with more complex directives. Our investigation identifies lazy reasoning during the thinking stage as the primary factor contributing to poor instruction adherence. To mitigate this issue, we propose a comprehensive framework designed to enable rigorous reasoning processes involving preview and self-checking, essential for satisfying strict instruction constraints. Specifically, we first generate instructions with complex constraints and apply a filtering process to obtain valid prompts, resulting in three distinct prompt datasets categorized as hard, easy, and pass. Then, we employ rejection sampling on the pass prompts to curate a small yet high-quality dataset, enabling a cold-start initialization of the model and facilitating its adaptation to effective reasoning patterns. Subsequently, we employ an entropy-preserving supervised fine-tuning (Entropy-SFT) strategy coupled with token-wise entropy-adaptive (TEA-RL) reinforcement learning guided by rule-based dense rewards. This approach encourages the model to transform its reasoning mechanism, ultimately fostering generalizable reasoning abilities that encompass preview and self-checking. Extensive experiments conducted on instruction-following benchmarks demonstrate remarkable performance improvements across various model scales. Notably, our Light-IF-32B model surpasses both larger open-source models such as DeepSeek-R1 and closed-source models like Doubao-1.6.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.01311", "title": "C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor", "authors": [ "Haoquan Lu", "Hanzhe Liang", "Jie Zhang", "Chenxi Hu", "Jinbao Wang", "Can Gao" ], "github": "", "project_page": "", "abstract": "3D Anomaly Detection (AD) has shown great potential in detecting anomalies or defects of high-precision industrial products. However, existing methods are typically trained in a class-specific manner and also lack the capability of learning from emerging classes. In this study, we proposed a continual learning framework named Continual 3D Anomaly Detection (C3D-AD), which can not only learn generalized representations for multi-class point clouds but also handle new classes emerging over time.Specifically, in the feature extraction module, to extract generalized local features from diverse product types of different tasks efficiently, Kernel Attention with random feature Layer (KAL) is introduced, which normalizes the feature space. Then, to reconstruct data correctly and continually, an efficient Kernel Attention with learnable Advisor (KAA) mechanism is proposed, which learns the information from new categories while discarding redundant old information within both the encoder and decoder. Finally, to keep the representation consistency over tasks, a Reconstruction with Parameter Perturbation (RPP) module is proposed by designing a representation rehearsal loss function, which ensures that the model remembers previous category information and returns category-adaptive representation.Extensive experiments on three public datasets demonstrate the effectiveness of the proposed method, achieving an average performance of 66.4%, 83.1%, and 63.4% AUROC on Real3D-AD, Anomaly-ShapeNet, and MulSen-AD, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.04440", "title": "StepFun-Formalizer: Unlocking the Autoformalization Potential of LLMs through Knowledge-Reasoning Fusion", "authors": [ "Yutong Wu", "Di Huang", "Ruosi Wan", "Yue Peng", "Shijie Shang", "Chenrui Cao", "Lei Qi", "Rui Zhang", "Zidong Du", "Jie Yan", "Xing Hu" ], "github": "https://github.com/dwrensha/compfiles", "project_page": "", "abstract": "Autoformalization aims to translate natural-language mathematical statements into a formal language. While LLMs have accelerated progress in this area, existing methods still suffer from low accuracy. We identify two key abilities for effective autoformalization: comprehensive mastery of formal-language domain knowledge, and reasoning capability of natural language problem understanding and informal-formal alignment. Without the former, a model cannot identify the correct formal objects; without the latter, it struggles to interpret real-world contexts and map them precisely into formal expressions. To address these gaps, we introduce ThinkingF, a data synthesis and training pipeline that improves both abilities. First, we construct two datasets: one by distilling and selecting large-scale examples rich in formal knowledge, and another by generating informal-to-formal reasoning trajectories guided by expert-designed templates. We then apply SFT and RLVR with these datasets to further fuse and refine the two abilities. The resulting 7B and 32B models exhibit both comprehensive formal knowledge and strong informal-to-formal reasoning. Notably, StepFun-Formalizer-32B achieves SOTA BEq@1 scores of 40.5% on FormalMATH-Lite and 26.7% on ProverBench, surpassing all prior general-purpose and specialized models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dwrensha/compfiles", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.04010", "title": "HarmonyGuard: Toward Safety and Utility in Web Agents via Adaptive Policy Enhancement and Dual-Objective Optimization", "authors": [ "Yurun Chen", "Xavier Hu", "Yuhan Liu", "Keting Yin", "Juncheng Li", "Zhuosheng Zhang", "Shengyu Zhang" ], "github": "https://github.com/YurunChen/HarmonyGuard", "project_page": "", "abstract": "Large language models enable agents to autonomously perform tasks in open web environments. However, as hidden threats within the web evolve, web agents face the challenge of balancing task performance with emerging risks during long-sequence operations. Although this challenge is critical, current research remains limited to single-objective optimization or single-turn scenarios, lacking the capability for collaborative optimization of both safety and utility in web environments. To address this gap, we propose HarmonyGuard, a multi-agent collaborative framework that leverages policy enhancement and objective optimization to jointly improve both utility and safety. HarmonyGuard features a multi-agent architecture characterized by two fundamental capabilities: (1) Adaptive Policy Enhancement: We introduce the Policy Agent within HarmonyGuard, which automatically extracts and maintains structured security policies from unstructured external documents, while continuously updating policies in response to evolving threats. (2) Dual-Objective Optimization: Based on the dual objectives of safety and utility, the Utility Agent integrated within HarmonyGuard performs the Markovian real-time reasoning to evaluate the objectives and utilizes metacognitive capabilities for their optimization. Extensive evaluations on multiple benchmarks show that HarmonyGuard improves policy compliance by up to 38% and task completion by up to 20% over existing baselines, while achieving over 90% policy compliance across all tasks. Our project is available here: https://github.com/YurunChen/HarmonyGuard.", "github_manual": "", "github_regex": "https://github.com/YurunChen/HarmonyGuard", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.01778", "title": "DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving via Online HD Map Diffusion", "authors": [ "Zhigang Sun", "Yiru Wang", "Anqing Jiang", "Shuo Wang", "Yu Gao", "Yuwen Heng", "Shouyi Zhang", "An He", "Hao Jiang", "Jinhao Chai", "Zichong Gu", "Wang Jijun", "Shichen Tang", "Lavdim Halilaj", "Juergen Luettin", "Hao Sun" ], "github": "https://github.com/SunZhigang7/DiffSemanticFusion", "project_page": "", "abstract": "Autonomous driving requires accurate scene understanding, including road geometry, traffic agents, and their semantic relationships. In online HD map generation scenarios, raster-based representations are well-suited to vision models but lack geometric precision, while graph-based representations retain structural detail but become unstable without precise maps. To harness the complementary strengths of both, we propose DiffSemanticFusion -- a fusion framework for multimodal trajectory prediction and planning. Our approach reasons over a semantic raster-fused BEV space, enhanced by a map diffusion module that improves both the stability and expressiveness of online HD map representations. We validate our framework on two downstream tasks: trajectory prediction and planning-oriented end-to-end autonomous driving. Experiments on real-world autonomous driving benchmarks, nuScenes and NAVSIM, demonstrate improved performance over several state-of-the-art methods. For the prediction task on nuScenes, we integrate DiffSemanticFusion with the online HD map informed QCNet, achieving a 5.1\\% performance improvement. For end-to-end autonomous driving in NAVSIM, DiffSemanticFusion achieves state-of-the-art results, with a 15\\% performance gain in NavHard scenarios. In addition, extensive ablation and sensitivity studies show that our map diffusion module can be seamlessly integrated into other vector-based approaches to enhance performance. All artifacts are available at https://github.com/SunZhigang7/DiffSemanticFusion.", "github_manual": "", "github_regex": "https://github.com/SunZhigang7/DiffSemanticFusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.00599", "title": "DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior", "authors": [ "Junzhe Lu", "Jing Lin", "Hongkun Dou", "Ailing Zeng", "Yue Deng", "Xian Liu", "Zhongang Cai", "Lei Yang", "Yulun Zhang", "Haoqian Wang", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "We present DPoser-X, a diffusion-based prior model for 3D whole-body human poses. Building a versatile and robust full-body human pose prior remains challenging due to the inherent complexity of articulated human poses and the scarcity of high-quality whole-body pose datasets. To address these limitations, we introduce a Diffusion model as body Pose prior (DPoser) and extend it to DPoser-X for expressive whole-body human pose modeling. Our approach unifies various pose-centric tasks as inverse problems, solving them through variational diffusion sampling. To enhance performance on downstream applications, we introduce a novel truncated timestep scheduling method specifically designed for pose data characteristics. We also propose a masked training mechanism that effectively combines whole-body and part-specific datasets, enabling our model to capture interdependencies between body parts while avoiding overfitting to specific actions. Extensive experiments demonstrate DPoser-X's robustness and versatility across multiple benchmarks for body, hand, face, and full-body pose modeling. Our model consistently outperforms state-of-the-art alternatives, establishing a new benchmark for whole-body human pose prior modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.00109", "title": "FACTORY: A Challenging Human-Verified Prompt Set for Long-Form Factuality", "authors": [ "Mingda Chen", "Yang Li", "Xilun Chen", "Adina Williams", "Gargi Ghosh", "Scott Yih" ], "github": "", "project_page": "", "abstract": "Long-form factuality evaluation assesses the ability of models to generate accurate, comprehensive responses to short prompts. Existing benchmarks often lack human verification, leading to potential quality issues. To address this limitation, we introduce FACTORY, a large-scale, human-verified prompt set. Developed using a model-in-the-loop approach and refined by humans, FACTORY includes challenging prompts that are fact-seeking, answerable, and unambiguous. We conduct human evaluations on 6 state-of-the-art language models using FACTORY and existing datasets. Our results show that FACTORY is a challenging benchmark: approximately 40% of the claims made in the responses of SOTA models are not factual, compared to only 10% for other datasets. Our analysis identifies the strengths of FACTORY over prior benchmarks, emphasizing its reliability and the necessity for models to reason across long-tailed facts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.03970", "title": "Data and AI governance: Promoting equity, ethics, and fairness in large language models", "authors": [ "Alok Abhishek", "Lisa Erickson", "Tushar Bandopadhyay" ], "github": "", "project_page": "", "abstract": "In this paper, we cover approaches to systematically govern, assess and quantify bias across the complete life cycle of machine learning models, from initial development and validation to ongoing production monitoring and guardrail implementation. Building upon our foundational work on the Bias Evaluation and Assessment Test Suite (BEATS) for Large Language Models, the authors share prevalent bias and fairness related gaps in Large Language Models (LLMs) and discuss data and AI governance framework to address Bias, Ethics, Fairness, and Factuality within LLMs. The data and AI governance approach discussed in this paper is suitable for practical, real-world applications, enabling rigorous benchmarking of LLMs prior to production deployment, facilitating continuous real-time evaluation, and proactively governing LLM generated responses. By implementing the data and AI governance across the life cycle of AI development, organizations can significantly enhance the safety and responsibility of their GenAI systems, effectively mitigating risks of discrimination and protecting against potential reputational or brand-related harm. Ultimately, through this article, we aim to contribute to advancement of the creation and deployment of socially responsible and ethically aligned generative artificial intelligence powered applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.01226", "title": "CM^3: Calibrating Multimodal Recommendation", "authors": [ "Xin Zhou", "Yongjie Wang", "Zhiqi Shen" ], "github": "https://github.com/enoche/CM3", "project_page": "", "abstract": "Alignment and uniformity are fundamental principles within the domain of contrastive learning. In recommender systems, prior work has established that optimizing the Bayesian Personalized Ranking (BPR) loss contributes to the objectives of alignment and uniformity. Specifically, alignment aims to draw together the representations of interacting users and items, while uniformity mandates a uniform distribution of user and item embeddings across a unit hypersphere. This study revisits the alignment and uniformity properties within the context of multimodal recommender systems, revealing a proclivity among extant models to prioritize uniformity to the detriment of alignment. Our hypothesis challenges the conventional assumption of equitable item treatment through a uniformity loss, proposing a more nuanced approach wherein items with similar multimodal attributes converge toward proximal representations within the hyperspheric manifold. Specifically, we leverage the inherent similarity between items' multimodal data to calibrate their uniformity distribution, thereby inducing a more pronounced repulsive force between dissimilar entities within the embedding space. A theoretical analysis elucidates the relationship between this calibrated uniformity loss and the conventional uniformity function. Moreover, to enhance the fusion of multimodal features, we introduce a Spherical B\\'ezier method designed to integrate an arbitrary number of modalities while ensuring that the resulting fused features are constrained to the same hyperspherical manifold. Empirical evaluations conducted on five real-world datasets substantiate the superiority of our approach over competing baselines. We also shown that the proposed methods can achieve up to a 5.4% increase in NDCG@20 performance via the integration of MLLM-extracted features. Source code is available at: https://github.com/enoche/CM3.", "github_manual": "", "github_regex": "https://github.com/enoche/CM3", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-07", "arxiv_id": "2508.02951", "title": "MedBLINK: Probing Basic Perception in Multimodal Language Models for Medicine", "authors": [ "Mahtab Bigverdi", "Wisdom Ikezogwo", "Kevin Zhang", "Hyewon Jeong", "Mingyu Lu", "Sungjae Cho", "Linda Shapiro", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Multimodal language models (MLMs) show promise for clinical decision support and diagnostic reasoning, raising the prospect of end-to-end automated medical image interpretation. However, clinicians are highly selective in adopting AI tools; a model that makes errors on seemingly simple perception tasks such as determining image orientation or identifying whether a CT scan is contrast-enhance are unlikely to be adopted for clinical tasks. We introduce Medblink, a benchmark designed to probe these models for such perceptual abilities. Medblink spans eight clinically meaningful tasks across multiple imaging modalities and anatomical regions, totaling 1,429 multiple-choice questions over 1,605 images. We evaluate 19 state-of-the-art MLMs, including general purpose (GPT4o, Claude 3.5 Sonnet) and domain specific (Med Flamingo, LLaVA Med, RadFM) models. While human annotators achieve 96.4% accuracy, the best-performing model reaches only 65%. These results show that current MLMs frequently fail at routine perceptual checks, suggesting the need to strengthen their visual grounding to support clinical adoption. Data is available on our project page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.05629", "title": "On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification", "authors": [ "Yongliang Wu", "Yizhou Zhou", "Zhou Ziheng", "Yingzhe Peng", "Xinyu Ye", "Xinting Hu", "Wenbo Zhu", "Lu Qi", "Ming-Hsuan Yang", "Xu Yang" ], "github": "https://github.com/yongliang-wu/DFT", "project_page": "", "abstract": "We present a simple yet theoretically motivated improvement to Supervised Fine-Tuning (SFT) for the Large Language Model (LLM), addressing its limited generalization compared to reinforcement learning (RL). Through mathematical analysis, we reveal that standard SFT gradients implicitly encode a problematic reward structure that may severely restrict the generalization capabilities of model. To rectify this, we propose Dynamic Fine-Tuning (DFT), stabilizing gradient updates for each token by dynamically rescaling the objective function with the probability of this token. Remarkably, this single-line code change significantly outperforms standard SFT across multiple challenging benchmarks and base models, demonstrating greatly improved generalization. Additionally, our approach shows competitive results in offline RL settings, offering an effective yet simpler alternative. This work bridges theoretical insight and practical solutions, substantially advancing SFT performance. The code will be available at https://github.com/yongliang-wu/DFT.", "github_manual": "", "github_regex": "https://github.com/yongliang-wu/DFT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.05630", "title": "MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes", "authors": [ "Henghui Ding", "Kaining Ying", "Chang Liu", "Shuting He", "Xudong Jiang", "Yu-Gang Jiang", "Philip H. S. Torr", "Song Bai" ], "github": "", "project_page": "", "abstract": "Video object segmentation (VOS) aims to segment specified target objects throughout a video. Although state-of-the-art methods have achieved impressive performance (e.g., 90+% J&F) on existing benchmarks such as DAVIS and YouTube-VOS, these datasets primarily contain salient, dominant, and isolated objects, limiting their generalization to real-world scenarios. To advance VOS toward more realistic environments, coMplex video Object SEgmentation (MOSEv1) was introduced to facilitate VOS research in complex scenes. Building on the strengths and limitations of MOSEv1, we present MOSEv2, a significantly more challenging dataset designed to further advance VOS methods under real-world conditions. MOSEv2 consists of 5,024 videos and over 701,976 high-quality masks for 10,074 objects across 200 categories. Compared to its predecessor, MOSEv2 introduces significantly greater scene complexity, including more frequent object disappearance and reappearance, severe occlusions and crowding, smaller objects, as well as a range of new challenges such as adverse weather (e.g., rain, snow, fog), low-light scenes (e.g., nighttime, underwater), multi-shot sequences, camouflaged objects, non-physical targets (e.g., shadows, reflections), scenarios requiring external knowledge, etc. We benchmark 20 representative VOS methods under 5 different settings and observe consistent performance drops. For example, SAM2 drops from 76.4% on MOSEv1 to only 50.9% on MOSEv2. We further evaluate 9 video object tracking methods and find similar declines, demonstrating that MOSEv2 presents challenges across tasks. These results highlight that despite high accuracy on existing datasets, current VOS methods still struggle under real-world complexities. MOSEv2 is publicly available at https://MOSE.video.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.05004", "title": "R-Zero: Self-Evolving Reasoning LLM from Zero Data", "authors": [ "Chengsong Huang", "Wenhao Yu", "Xiaoyang Wang", "Hongming Zhang", "Zongxia Li", "Ruosen Li", "Jiaxin Huang", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Self-evolving Large Language Models (LLMs) offer a scalable path toward super-intelligence by autonomously generating, refining, and learning from their own experiences. However, existing methods for training such models still rely heavily on vast human-curated tasks and labels, typically via fine-tuning or reinforcement learning, which poses a fundamental bottleneck to advancing AI systems toward capabilities beyond human intelligence. To overcome this limitation, we introduce R-Zero, a fully autonomous framework that generates its own training data from scratch. Starting from a single base LLM, R-Zero initializes two independent models with distinct roles, a Challenger and a Solver. These models are optimized separately and co-evolve through interaction: the Challenger is rewarded for proposing tasks near the edge of the Solver capability, and the Solver is rewarded for solving increasingly challenging tasks posed by the Challenger. This process yields a targeted, self-improving curriculum without any pre-existing tasks and labels. Empirically, R-Zero substantially improves reasoning capability across different backbone LLMs, e.g., boosting the Qwen3-4B-Base by +6.49 on math-reasoning benchmarks and +7.54 on general-domain reasoning benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.03990", "title": "Are Today's LLMs Ready to Explain Well-Being Concepts?", "authors": [ "Bohan Jiang", "Dawei Li", "Zhen Tan", "Chengshuai Zhao", "Huan Liu" ], "github": "", "project_page": "", "abstract": "Well-being encompasses mental, physical, and social dimensions essential to personal growth and informed life decisions. As individuals increasingly consult Large Language Models (LLMs) to understand well-being, a key challenge emerges: Can LLMs generate explanations that are not only accurate but also tailored to diverse audiences? High-quality explanations require both factual correctness and the ability to meet the expectations of users with varying expertise. In this work, we construct a large-scale dataset comprising 43,880 explanations of 2,194 well-being concepts, generated by ten diverse LLMs. We introduce a principle-guided LLM-as-a-judge evaluation framework, employing dual judges to assess explanation quality. Furthermore, we show that fine-tuning an open-source LLM using Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) can significantly enhance the quality of generated explanations. Our results reveal: (1) The proposed LLM judges align well with human evaluations; (2) explanation quality varies significantly across models, audiences, and categories; and (3) DPO- and SFT-finetuned models outperform their larger counterparts, demonstrating the effectiveness of preference-based learning for specialized explanation tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.05609", "title": "Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validity", "authors": [ "Yuhan Zhang", "Long Zhuo", "Ziyang Chu", "Tong Wu", "Zhibing Li", "Liang Pan", "Dahua Lin", "Ziwei Liu" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "Despite rapid advances in 3D content generation, quality assessment for the generated 3D assets remains challenging. Existing methods mainly rely on image-based metrics and operate solely at the object level, limiting their ability to capture spatial coherence, material authenticity, and high-fidelity local details. 1) To address these challenges, we introduce Hi3DEval, a hierarchical evaluation framework tailored for 3D generative content. It combines both object-level and part-level evaluation, enabling holistic assessments across multiple dimensions as well as fine-grained quality analysis. Additionally, we extend texture evaluation beyond aesthetic appearance by explicitly assessing material realism, focusing on attributes such as albedo, saturation, and metallicness. 2) To support this framework, we construct Hi3DBench, a large-scale dataset comprising diverse 3D assets and high-quality annotations, accompanied by a reliable multi-agent annotation pipeline. We further propose a 3D-aware automated scoring system based on hybrid 3D representations. Specifically, we leverage video-based representations for object-level and material-subject evaluations to enhance modeling of spatio-temporal consistency and employ pretrained 3D features for part-level perception. Extensive experiments demonstrate that our approach outperforms existing image-based metrics in modeling 3D characteristics and achieves superior alignment with human preference, providing a scalable alternative to manual evaluations. The project page is available at https://zyh482.github.io/Hi3DEval/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.04699", "title": "Hop, Skip, and Overthink: Diagnosing Why Reasoning Models Fumble during Multi-Hop Analysis", "authors": [ "Anushka Yadav", "Isha Nalawade", "Srujana Pillarichety", "Yashwanth Babu", "Reshmi Ghosh", "Samyadeep Basu", "Wenlong Zhao", "Ali Nasaeh", "Sriram Balasubramanian", "Soundararajan Srinivasan" ], "github": "", "project_page": "", "abstract": "The emergence of reasoning models and their integration into practical AI chat bots has led to breakthroughs in solving advanced math, deep search, and extractive question answering problems that requires a complex and multi-step thought process. Yet, a complete understanding of why these models hallucinate more than general purpose language models is missing. In this investigative study, we systematicallyexplore reasoning failures of contemporary language models on multi-hop question answering tasks. We introduce a novel, nuanced error categorization framework that examines failures across three critical dimensions: the diversity and uniqueness of source documents involved (\"hops\"), completeness in capturing relevant information (\"coverage\"), and cognitive inefficiency (\"overthinking\"). Through rigorous hu-man annotation, supported by complementary automated metrics, our exploration uncovers intricate error patterns often hidden by accuracy-centric evaluations. This investigative approach provides deeper insights into the cognitive limitations of current models and offers actionable guidance toward enhancing reasoning fidelity, transparency, and robustness in future language modeling efforts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.04423", "title": "Evaluating, Synthesizing, and Enhancing for Customer Support Conversation", "authors": [ "Jie Zhu", "Huaixia Dou", "Junhui Li", "Lifan Guo", "Feng Chen", "Chi Zhang", "Fang Kong" ], "github": "https://github.com/aliyun/qwen-dianjin", "project_page": "", "abstract": "Effective customer support requires not only accurate problem solving but also structured and empathetic communication aligned with professional standards. However, existing dialogue datasets often lack strategic guidance, and real-world service data is difficult to access and annotate. To address this, we introduce the task of Customer Support Conversation (CSC), aimed at training customer service agents to respond using well-defined support strategies. We propose a structured CSC framework grounded in COPC guidelines, defining five conversational stages and twelve strategies to guide high-quality interactions. Based on this, we construct CSConv, an evaluation dataset of 1,855 real-world customer-agent conversations rewritten using LLMs to reflect deliberate strategy use, and annotated accordingly. Additionally, we develop a role-playing approach that simulates strategy-rich conversations using LLM-powered roles aligned with the CSC framework, resulting in the training dataset RoleCS. Experiments show that fine-tuning strong LLMs on RoleCS significantly improves their ability to generate high-quality, strategy-aligned responses on CSConv. Human evaluations further confirm gains in problem resolution. All code and data will be made publicly available at https://github.com/aliyun/qwen-dianjin.", "github_manual": "", "github_regex": "https://github.com/aliyun/qwen-dianjin", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.05405", "title": "DeepPHY: Benchmarking Agentic VLMs on Physical Reasoning", "authors": [ "Xinrun Xu", "Pi Bu", "Ye Wang", "B\u00f6rje F. Karlsson", "Ziming Wang", "Tengtao Song", "Qi Zhu", "Jun Song", "Zhiming Ding", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Although Vision Language Models (VLMs) exhibit strong perceptual abilities and impressive visual reasoning, they struggle with attention to detail and precise action planning in complex, dynamic environments, leading to subpar performance. Real-world tasks typically require complex interactions, advanced spatial reasoning, long-term planning, and continuous strategy refinement, usually necessitating understanding the physics rules of the target scenario. However, evaluating these capabilities in real-world scenarios is often prohibitively expensive. To bridge this gap, we introduce DeepPHY, a novel benchmark framework designed to systematically evaluate VLMs' understanding and reasoning about fundamental physical principles through a series of challenging simulated environments. DeepPHY integrates multiple physical reasoning environments of varying difficulty levels and incorporates fine-grained evaluation metrics. Our evaluation finds that even state-of-the-art VLMs struggle to translate descriptive physical knowledge into precise, predictive control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.02120", "title": "Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models", "authors": [ "Linan Yue", "Yichao Du", "Yizhi Wang", "Weibo Gao", "Fangzhou Yao", "Li Wang", "Ye Liu", "Ziyu Xu", "Qi Liu", "Shimin Di", "Min-Ling Zhang" ], "github": "", "project_page": "", "abstract": "Recently, Large Reasoning Models (LRMs) have gradually become a research hotspot due to their outstanding performance in handling complex tasks. Among them, DeepSeek R1 has garnered significant attention for its exceptional performance and open-source nature, driving advancements in the research of R1-style LRMs. Unlike traditional Large Language Models (LLMs), these models enhance logical deduction and decision-making capabilities during reasoning by incorporating mechanisms such as long chain-of-thought and self-reflection through reinforcement learning. However, with the widespread application of these models, the problem of overthinking has gradually emerged. Specifically, when generating answers, these models often construct excessively long reasoning chains with redundant or repetitive steps, which leads to reduced reasoning efficiency and may affect the accuracy of the final answer. To this end, various efficient reasoning methods have been proposed, aiming to reduce the length of reasoning paths without compromising model performance and reasoning capability. By reviewing the current research advancements in the field of efficient reasoning methods systematically, we categorize existing works into two main directions based on the lens of single-model optimization versus model collaboration: (1) Efficient Reasoning with Single Model, which focuses on improving the reasoning efficiency of individual models; and (2) Efficient Reasoning with Model Collaboration, which explores optimizing reasoning paths through collaboration among multiple models. Besides, we maintain a public GitHub repository that tracks the latest progress in efficient reasoning methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.01650", "title": "StrandDesigner: Towards Practical Strand Generation with Sketch Guidance", "authors": [ "Na Zhang", "Moran Li", "Chengming Xu", "Han Feng", "Xiaobin Hu", "Jiangning Zhang", "Weijian Cao", "Chengjie Wang", "Yanwei Fu" ], "github": "https://github.com/fighting-Zhang/StrandDesigner", "project_page": "", "abstract": "Realistic hair strand generation is crucial for applications like computer graphics and virtual reality. While diffusion models can generate hairstyles from text or images, these inputs lack precision and user-friendliness. Instead, we propose the first sketch-based strand generation model, which offers finer control while remaining user-friendly. Our framework tackles key challenges, such as modeling complex strand interactions and diverse sketch patterns, through two main innovations: a learnable strand upsampling strategy that encodes 3D strands into multi-scale latent spaces, and a multi-scale adaptive conditioning mechanism using a transformer with diffusion heads to ensure consistency across granularity levels. Experiments on several benchmark datasets show our method outperforms existing approaches in realism and precision. Qualitative results further confirm its effectiveness. Code will be released at [GitHub](https://github.com/fighting-Zhang/StrandDesigner).", "github_manual": "", "github_regex": "https://github.com/fighting-Zhang/StrandDesigner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.05635", "title": "Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation", "authors": [ "Yue Liao", "Pengfei Zhou", "Siyuan Huang", "Donglin Yang", "Shengcong Chen", "Yuxin Jiang", "Yue Hu", "Jingbin Cai", "Si Liu", "Jianlan Luo", "Liliang Chen", "Shuicheng Yan", "Maoqing Yao", "Guanghui Ren" ], "github": "", "project_page": "", "abstract": "We introduce Genie Envisioner (GE), a unified world foundation platform for robotic manipulation that integrates policy learning, evaluation, and simulation within a single video-generative framework. At its core, GE-Base is a large-scale, instruction-conditioned video diffusion model that captures the spatial, temporal, and semantic dynamics of real-world robotic interactions in a structured latent space. Built upon this foundation, GE-Act maps latent representations to executable action trajectories through a lightweight, flow-matching decoder, enabling precise and generalizable policy inference across diverse embodiments with minimal supervision. To support scalable evaluation and training, GE-Sim serves as an action-conditioned neural simulator, producing high-fidelity rollouts for closed-loop policy development. The platform is further equipped with EWMBench, a standardized benchmark suite measuring visual fidelity, physical consistency, and instruction-action alignment. Together, these components establish Genie Envisioner as a scalable and practical foundation for instruction-driven, general-purpose embodied intelligence. All code, models, and benchmarks will be released publicly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.03644", "title": "Are We on the Right Way for Assessing Document Retrieval-Augmented Generation?", "authors": [ "Wenxuan Shen", "Mingjia Wang", "Yaochen Wang", "Dongping Chen", "Junjie Yang", "Yao Wan", "Weiwei Lin" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) systems using Multimodal Large Language Models (MLLMs) show great promise for complex document understanding, yet their development is critically hampered by inadequate evaluation. Current benchmarks often focus on specific part of document RAG system and use synthetic data with incomplete ground truth and evidence labels, therefore failing to reflect real-world bottlenecks and challenges. To overcome these limitations, we introduce Double-Bench: a new large-scale, multilingual, and multimodal evaluation system that is able to produce fine-grained assessment to each component within document RAG systems. It comprises 3,276 documents (72,880 pages) and 5,168 single- and multi-hop queries across 6 languages and 4 document types with streamlined dynamic update support for potential data contamination issues. Queries are grounded in exhaustively scanned evidence pages and verified by human experts to ensure maximum quality and completeness. Our comprehensive experiments across 9 state-of-the-art embedding models, 4 MLLMs and 4 end-to-end document RAG frameworks demonstrate the gap between text and visual embedding models is narrowing, highlighting the need in building stronger document retrieval models. Our findings also reveal the over-confidence dilemma within current document RAG frameworks that tend to provide answer even without evidence support. We hope our fully open-source Double-Bench provide a rigorous foundation for future research in advanced document RAG systems. We plan to retrieve timely corpus and release new benchmarks on an annual basis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.04017", "title": "Can Large Multimodal Models Actively Recognize Faulty Inputs? A Systematic Evaluation Framework of Their Input Scrutiny Ability", "authors": [ "Haiqi Yang", "Jinzhe Li", "Gengxu Li", "Yi Chang", "Yuan Wu" ], "github": "https://github.com/MLGroupJLU/LMM_ISEval", "project_page": "", "abstract": "Large Multimodal Models (LMMs) have witnessed remarkable growth, showcasing formidable capabilities in handling intricate multimodal tasks with exceptional performance. Recent research has underscored the inclination of large language models to passively accept defective inputs, often resulting in futile reasoning on invalid prompts. However, the same critical question of whether LMMs can actively detect and scrutinize erroneous inputs still remains unexplored. To address this gap, we introduce the Input Scrutiny Ability Evaluation Framework (ISEval), which encompasses seven categories of flawed premises and three evaluation metrics. Our extensive evaluation of ten advanced LMMs has identified key findings. Most models struggle to actively detect flawed textual premises without guidance, which reflects a strong reliance on explicit prompts for premise error identification. Error type affects performance: models excel at identifying logical fallacies but struggle with surface-level linguistic errors and certain conditional flaws. Modality trust varies-Gemini 2.5 pro and Claude Sonnet 4 balance visual and textual info, while aya-vision-8b over-rely on text in conflicts. These insights underscore the urgent need to enhance LMMs' proactive verification of input validity and shed novel insights into mitigating the problem. The code is available at https://github.com/MLGroupJLU/LMM_ISEval.", "github_manual": "", "github_regex": "https://github.com/MLGroupJLU/LMM_ISEval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.05545", "title": "PRvL: Quantifying the Capabilities and Risks of Large Language Models for PII Redaction", "authors": [ "Leon Garza", "Anantaa Kotal", "Aritran Piplai", "Lavanya Elluri", "Prajit Das", "Aman Chadha" ], "github": "", "project_page": "", "abstract": "Redacting Personally Identifiable Information (PII) from unstructured text is critical for ensuring data privacy in regulated domains. While earlier approaches have relied on rule-based systems and domain-specific Named Entity Recognition (NER) models, these methods fail to generalize across formats and contexts. Recent advances in Large Language Models (LLMs) offer a promising alternative, yet the effect of architectural and training choices on redaction performance remains underexplored. LLMs have demonstrated strong performance in tasks that require contextual language understanding, including the redaction of PII in free-form text. Prior work suggests that with appropriate adaptation, LLMs can become effective contextual privacy learners. However, the consequences of architectural and training choices for PII Redaction remain underexplored. In this work, we present a comprehensive analysis of LLMs as privacy-preserving PII Redaction systems. We evaluate a range of LLM architectures and training strategies for their effectiveness in PII Redaction. Our analysis measures redaction performance, semantic preservation, and PII leakage, and compares these outcomes against latency and computational cost. The results provide practical guidance for configuring LLM-based redactors that are accurate, efficient, and privacy-aware. To support reproducibility and real-world deployment, we release PRvL, an open-source suite of fine-tuned models, and evaluation tools for general-purpose PII Redaction. PRvL is built entirely on open-source LLMs and supports multiple inference settings for flexibility and compliance. It is designed to be easily customized for different domains and fully operable within secure, self-managed environments. This enables data owners to perform redactions without relying on third-party services or exposing sensitive content beyond their own infrastructure.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.04939", "title": "I Think, Therefore I Am Under-Qualified? A Benchmark for Evaluating Linguistic Shibboleth Detection in LLM Hiring Evaluations", "authors": [ "Julia Kharchenko", "Tanya Roosta", "Aman Chadha", "Chirag Shah" ], "github": "", "project_page": "", "abstract": "This paper introduces a comprehensive benchmark for evaluating how Large Language Models (LLMs) respond to linguistic shibboleths: subtle linguistic markers that can inadvertently reveal demographic attributes such as gender, social class, or regional background. Through carefully constructed interview simulations using 100 validated question-response pairs, we demonstrate how LLMs systematically penalize certain linguistic patterns, particularly hedging language, despite equivalent content quality. Our benchmark generates controlled linguistic variations that isolate specific phenomena while maintaining semantic equivalence, which enables the precise measurement of demographic bias in automated evaluation systems. We validate our approach along multiple linguistic dimensions, showing that hedged responses receive 25.6% lower ratings on average, and demonstrate the benchmark's effectiveness in identifying model-specific biases. This work establishes a foundational framework for detecting and measuring linguistic discrimination in AI systems, with broad applications to fairness in automated decision-making contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.04946", "title": "REINA: Regularized Entropy Information-Based Loss for Efficient Simultaneous Speech Translation", "authors": [ "Nameer Hirschkind", "Joseph Liu", "Mahesh Kumar Nandwana", "Xiao Yu" ], "github": "", "project_page": "", "abstract": "Simultaneous Speech Translation (SimulST) systems stream in audio while simultaneously emitting translated text or speech. Such systems face the significant challenge of balancing translation quality and latency. We introduce a strategy to optimize this tradeoff: wait for more input only if you gain information by doing so. Based on this strategy, we present Regularized Entropy INformation Adaptation (REINA), a novel loss to train an adaptive policy using an existing non-streaming translation model. We derive REINA from information theory principles and show that REINA helps push the reported Pareto frontier of the latency/quality tradeoff over prior works. Utilizing REINA, we train a SimulST model on French, Spanish and German, both from and into English. Training on only open source or synthetically generated data, we achieve state-of-the-art (SOTA) streaming results for models of comparable size. We also introduce a metric for streaming efficiency, quantitatively showing REINA improves the latency/quality trade-off by as much as 21% compared to prior approaches, normalized against non-streaming baseline BLEU scores.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.05496", "title": "InfiAlign: A Scalable and Sample-Efficient Framework for Aligning LLMs to Enhance Reasoning Capabilities", "authors": [ "Shuo Cai", "Su Lu", "Qi Zhou", "Kejing Yang", "Zhijie Sang", "Congkai Xie", "Hongxia Yang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have exhibited impressive reasoning abilities on a wide range of complex tasks. However, enhancing these capabilities through post-training remains resource intensive, particularly in terms of data and computational cost. Although recent efforts have sought to improve sample efficiency through selective data curation, existing methods often rely on heuristic or task-specific strategies that hinder scalability. In this work, we introduce InfiAlign, a scalable and sample-efficient post-training framework that integrates supervised fine-tuning (SFT) with Direct Preference Optimization (DPO) to align LLMs for enhanced reasoning. At the core of InfiAlign is a robust data selection pipeline that automatically curates high-quality alignment data from open-source reasoning datasets using multidimensional quality metrics. This pipeline enables significant performance gains while drastically reducing data requirements and remains extensible to new data sources. When applied to the Qwen2.5-Math-7B-Base model, our SFT model achieves performance on par with DeepSeek-R1-Distill-Qwen-7B, while using only approximately 12% of the training data, and demonstrates strong generalization across diverse reasoning tasks. Additional improvements are obtained through the application of DPO, with particularly notable gains in mathematical reasoning tasks. The model achieves an average improvement of 3.89% on AIME 24/25 benchmarks. Our results highlight the effectiveness of combining principled data selection with full-stage post-training, offering a practical solution for aligning large reasoning models in a scalable and data-efficient manner. The model checkpoints are available at https://huggingface.co/InfiX-ai/InfiAlign-Qwen-7B-SFT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.03923", "title": "CoAct-1: Computer-using Agents with Coding as Actions", "authors": [ "Linxin Song", "Yutong Dai", "Viraj Prabhu", "Jieyu Zhang", "Taiwei Shi", "Li Li", "Junnan Li", "Silvio Savarese", "Zeyuan Chen", "Jieyu Zhao", "Ran Xu", "Caiming Xiong" ], "github": "", "project_page": "", "abstract": "Autonomous agents that operate computers via Graphical User Interfaces (GUIs) often struggle with efficiency and reliability on complex, long-horizon tasks. While augmenting these agents with planners can improve task decomposition, they remain constrained by the inherent limitations of performing all actions through GUI manipulation, leading to brittleness and inefficiency. In this work, we introduce a more robust and flexible paradigm: enabling agents to use coding as a enhanced action. We present CoAct-1, a novel multi-agent system that synergistically combines GUI-based control with direct programmatic execution. CoAct-1 features an Orchestrator that dynamically delegates subtasks to either a conventional GUI Operator or a specialized Programmer agent, which can write and execute Python or Bash scripts. This hybrid approach allows the agent to bypass inefficient GUI action sequences for tasks like file management and data processing, while still leveraging visual interaction when necessary. We evaluate our system on the challenging OSWorld benchmark, where CoAct-1 achieves a new state-of-the-art success rate of 60.76%, significantly outperforming prior methods. Furthermore, our approach dramatically improves efficiency, reducing the average number of steps required to complete a task to just 10.15, compared to 15 for leading GUI agents. Our results demonstrate that integrating coding as a core action provides a more powerful, efficient, and scalable path toward generalized computer automation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.04190", "title": "RPCANet++: Deep Interpretable Robust PCA for Sparse Object Segmentation", "authors": [ "Fengyi Wu", "Yimian Dai", "Tianfang Zhang", "Yixuan Ding", "Jian Yang", "Ming-Ming Cheng", "Zhenming Peng" ], "github": "", "project_page": "", "abstract": "Robust principal component analysis (RPCA) decomposes an observation matrix into low-rank background and sparse object components. This capability has enabled its application in tasks ranging from image restoration to segmentation. However, traditional RPCA models suffer from computational burdens caused by matrix operations, reliance on finely tuned hyperparameters, and rigid priors that limit adaptability in dynamic scenarios. To solve these limitations, we propose RPCANet++, a sparse object segmentation framework that fuses the interpretability of RPCA with efficient deep architectures. Our approach unfolds a relaxed RPCA model into a structured network comprising a Background Approximation Module (BAM), an Object Extraction Module (OEM), and an Image Restoration Module (IRM). To mitigate inter-stage transmission loss in the BAM, we introduce a Memory-Augmented Module (MAM) to enhance background feature preservation, while a Deep Contrast Prior Module (DCPM) leverages saliency cues to expedite object extraction. Extensive experiments on diverse datasets demonstrate that RPCANet++ achieves state-of-the-art performance under various imaging scenarios. We further improve interpretability via visual and numerical low-rankness and sparsity measurements. By combining the theoretical strengths of RPCA with the efficiency of deep networks, our approach sets a new baseline for reliable and interpretable sparse object segmentation. Codes are available at our Project Webpage https://fengyiwu98.github.io/rpcanetx.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.02038", "title": "Marco-Voice Technical Report", "authors": [ "Fengping Tian", "Chenyang Lyu", "Xuanfan Ni", "Haoqin Sun", "Qingjuan Li", "Zhiqiang Qian", "Haijun Li", "Longyue Wang", "Zhao Xu", "Weihua Luo", "Kaifu Zhang" ], "github": "https://github.com/AIDC-AI/Marco-Voice", "project_page": "", "abstract": "This paper presents a multifunctional speech synthesis system that integrates voice cloning and emotion control speech synthesis within a unified framework. The goal of this work is to address longstanding challenges in achieving highly expressive, controllable, and natural speech generation that faithfully preserves speaker identity across diverse linguistic and emotional contexts. Our approach introduces an effective speaker-emotion disentanglement mechanism with in-batch contrastive learning, enabling independent manipulation of speaker identity and eemotional style, as well as rotational emotional embedding integration method for smooth emotion control. To support comprehensive training and evaluation, we construct CSEMOTIONS, a high-quality emotional speech dataset containing 10 hours of Mandarin speech from six professional speakers across seven emotional categories. Extensive experiments demonstrate that our system, Marco-Voice, achieves substantial improvements in both objective and subjective metrics. Comprehensive evaluations and analysis were conducted, results show that MarcoVoice delivers competitive performance in terms of speech clarity and emotional richness, representing a substantial advance in the field of expressive neural speech synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AIDC-AI/Marco-Voice", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.02243", "title": "I2CR: Intra- and Inter-modal Collaborative Reflections for Multimodal Entity Linking", "authors": [ "Ziyan Liu", "Junwen Li", "Kaiwen Li", "Tong Ruan", "Chao Wang", "Xinyan He", "Zongyu Wang", "Xuezhi Cao", "Jingping Liu" ], "github": "https://github.com/ziyan-xiaoyu/I2CR", "project_page": "", "abstract": "Multimodal entity linking plays a crucial role in a wide range of applications. Recent advances in large language model-based methods have become the dominant paradigm for this task, effectively leveraging both textual and visual modalities to enhance performance. Despite their success, these methods still face two challenges, including unnecessary incorporation of image data in certain scenarios and the reliance only on a one-time extraction of visual features, which can undermine their effectiveness and accuracy. To address these challenges, we propose a novel LLM-based framework for the multimodal entity linking task, called Intra- and Inter-modal Collaborative Reflections. This framework prioritizes leveraging text information to address the task. When text alone is insufficient to link the correct entity through intra- and inter-modality evaluations, it employs a multi-round iterative strategy that integrates key visual clues from various aspects of the image to support reasoning and enhance matching accuracy. Extensive experiments on three widely used public datasets demonstrate that our framework consistently outperforms current state-of-the-art methods in the task, achieving improvements of 3.2%, 5.1%, and 1.6%, respectively. Our code is available at https://github.com/ziyan-xiaoyu/I2CR/.", "github_manual": "", "github_regex": "https://github.com/ziyan-xiaoyu/I2CR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.04979", "title": "Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast Image Compression", "authors": [ "Zheng Chen", "Mingde Zhou", "Jinpei Guo", "Jiale Yuan", "Yifei Ji", "Yulun Zhang" ], "github": "https://github.com/zhengchen1999/SODEC", "project_page": "", "abstract": "Diffusion-based image compression has demonstrated impressive perceptual performance. However, it suffers from two critical drawbacks: (1) excessive decoding latency due to multi-step sampling, and (2) poor fidelity resulting from over-reliance on generative priors. To address these issues, we propose SODEC, a novel single-step diffusion image compression model. We argue that in image compression, a sufficiently informative latent renders multi-step refinement unnecessary. Based on this insight, we leverage a pre-trained VAE-based model to produce latents with rich information, and replace the iterative denoising process with a single-step decoding. Meanwhile, to improve fidelity, we introduce the fidelity guidance module, encouraging output that is faithful to the original image. Furthermore, we design the rate annealing training strategy to enable effective training under extremely low bitrates. Extensive experiments show that SODEC significantly outperforms existing methods, achieving superior rate-distortion-perception performance. Moreover, compared to previous diffusion-based compression models, SODEC improves decoding speed by more than 20times. Code is released at: https://github.com/zhengchen1999/SODEC.", "github_manual": "", "github_regex": "https://github.com/zhengchen1999/SODEC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.03404", "title": "Visual Document Understanding and Question Answering: A Multi-Agent Collaboration Framework with Test-Time Scaling", "authors": [ "Xinlei Yu", "Zhangquan Chen", "Yudong Zhang", "Shilin Lu", "Ruolin Shen", "Jiangning Zhang", "Xiaobin Hu", "Yanwei Fu", "Shuicheng Yan" ], "github": "https://github.com/YU-deep/MACT.git", "project_page": "", "abstract": "Existing vision-language models (VLMs), whether generalists or specialists, remain constrained by their parameter scale, lack robust self-correction capabilities, and underperform in tasks involving long visual contexts and complex reasoning, resulting in suboptimal performance on document-based tasks. To address this, we propose MACT, a Multi-Agent Collaboration framework with Test-Time scaling, tailored for visual document understanding and visual question answering (VQA). It comprises four distinct small-scale agents, i.e., planning, execution, judgment, and answer agents, with clearly defined roles and effective collaboration. Notably, the judgment agent exclusively verifies correctness and redirects to prior agents for revisions, outperforming conventional correction strategies. To further expand the capability boundaries of the framework, we propose mixed reward modeling that balances agent-specific abilities and global collaboration, as well as agent-wise hybrid test-time scaling, which customizes different scaling strategies for each agent based on their functions. Evaluated on benchmarks spanning both document-based and non-document-based settings, our MACT shows superior performance with a smaller parameter scale without sacrificing the ability of general and mathematical tasks. Especially, it stands out in benchmarks involving long visual contexts and complicated reasoning. The three variants of MACT consistently hold the top three positions in average scores, leading in 13 of the 15 benchmarks. Code will be available at: https://github.com/YU-deep/MACT.git.", "github_manual": "", "github_regex": "https://github.com/YU-deep/MACT.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.05128", "title": "Attention Basin: Why Contextual Position Matters in Large Language Models", "authors": [ "Zihao Yi", "Delong Zeng", "Zhenqing Ling", "Haohao Luo", "Zhe Xu", "Wei Liu", "Jian Luan", "Wanxia Cao", "Ying Shen" ], "github": "", "project_page": "", "abstract": "The performance of Large Language Models (LLMs) is significantly sensitive to the contextual position of information in the input. To investigate the mechanism behind this positional bias, our extensive experiments reveal a consistent phenomenon we term the attention basin: when presented with a sequence of structured items (e.g., retrieved documents or few-shot examples), models systematically assign higher attention to the items at the beginning and end of the sequence, while neglecting those in the middle. Crucially, our analysis further reveals that allocating higher attention to critical information is key to enhancing model performance. Based on these insights, we introduce Attention-Driven Reranking (AttnRank), a two-stage framework that (i) estimates a model's intrinsic positional attention preferences using a small calibration set, and (ii) reorders retrieved documents or few-shot examples to align the most salient content with these high-attention positions. AttnRank is a model-agnostic, training-free, and plug-and-play method with minimal computational overhead. Experiments on multi-hop QA and few-shot in-context learning tasks demonstrate that AttnRank achieves substantial improvements across 10 large language models of varying architectures and scales, without modifying model parameters or training procedures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.04107", "title": "Unlocking the Potential of MLLMs in Referring Expression Segmentation via a Light-weight Mask Decode", "authors": [ "Jingchao Wang", "Zhijian Wu", "Dingjiang Huang", "Yefeng Zheng", "Hong Wang" ], "github": "https://github.com/jcwang0602/MLLMSeg", "project_page": "", "abstract": "Reference Expression Segmentation (RES) aims to segment image regions specified by referring expressions and has become popular with the rise of multimodal large models (MLLMs). While MLLMs excel in semantic understanding, their token-generation paradigm struggles with pixel-level dense prediction. Existing RES methods either couple MLLMs with the parameter-heavy Segment Anything Model (SAM) with 632M network parameters or adopt SAM-free lightweight pipelines that sacrifice accuracy. To address the trade-off between performance and cost, we specifically propose MLLMSeg, a novel framework that fully exploits the inherent visual detail features encoded in the MLLM vision encoder without introducing an extra visual encoder. Besides, we propose a detail-enhanced and semantic-consistent feature fusion module (DSFF) that fully integrates the detail-related visual feature with the semantic-related feature output by the large language model (LLM) of MLLM. Finally, we establish a light-weight mask decoder with only 34M network parameters that optimally leverages detailed spatial features from the visual encoder and semantic features from the LLM to achieve precise mask prediction. Extensive experiments demonstrate that our method generally surpasses both SAM-based and SAM-free competitors, striking a better balance between performance and cost. Code is available at https://github.com/jcwang0602/MLLMSeg.", "github_manual": "", "github_regex": "https://github.com/jcwang0602/MLLMSeg", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-08", "arxiv_id": "2508.05618", "title": "Learning to Reason for Factuality", "authors": [ "Xilun Chen", "Ilia Kulikov", "Vincent-Pierre Berges", "Barlas O\u011fuz", "Rulin Shao", "Gargi Ghosh", "Jason Weston", "Wen-tau Yih" ], "github": "", "project_page": "", "abstract": "Reasoning Large Language Models (R-LLMs) have significantly advanced complex reasoning tasks but often struggle with factuality, generating substantially more hallucinations than their non-reasoning counterparts on long-form factuality benchmarks. However, extending online Reinforcement Learning (RL), a key component in recent R-LLM advancements, to the long-form factuality setting poses several unique challenges due to the lack of reliable verification methods. Previous work has utilized automatic factuality evaluation frameworks such as FActScore to curate preference data in the offline RL setting, yet we find that directly leveraging such methods as the reward in online RL leads to reward hacking in multiple ways, such as producing less detailed or relevant responses. We propose a novel reward function that simultaneously considers the factual precision, response detail level, and answer relevance, and applies online RL to learn high quality factual reasoning. Evaluated on six long-form factuality benchmarks, our factual reasoning model achieves an average reduction of 23.1 percentage points in hallucination rate, a 23% increase in answer detail level, and no degradation in the overall response helpfulness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2508.05502", "title": "MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs", "authors": [ "Yufei Gao", "Jiaying Fei", "Nuo Chen", "Ruirui Chen", "Guohang Yan", "Yunshi Lan", "Botian Shi" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have shown remarkable performance in high-resource languages. However, their effectiveness diminishes significantly in the contexts of low-resource languages. Current multilingual enhancement methods are often limited to text modality or rely solely on machine translation. While such approaches help models acquire basic linguistic capabilities and produce \"thin descriptions\", they neglect the importance of multimodal informativeness and cultural groundedness, both of which are crucial for serving low-resource language users effectively. To bridge this gap, in this study, we identify two significant objectives for a truly effective MLLM in low-resource language settings, namely 1) linguistic capability and 2) cultural groundedness, placing special emphasis on cultural awareness. To achieve these dual objectives, we propose a dual-source strategy that guides the collection of data tailored to each goal, sourcing native web alt-text for culture and MLLM-generated captions for linguistics. As a concrete implementation, we introduce MELLA, a multimodal, multilingual dataset. Experiment results show that after fine-tuning on MELLA, there is a general performance improvement for the eight languages on various MLLM backbones, with models producing \"thick descriptions\". We verify that the performance gains are from both cultural knowledge enhancement and linguistic capability enhancement. Our dataset can be found at https://opendatalab.com/applyMultilingualCorpus.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2508.05988", "title": "Pruning the Unsurprising: Efficient Code Reasoning via First-Token Surprisal", "authors": [ "Wenhao Zeng", "Yaoning Wang", "Chao Hu", "Yuling Shi", "Chengcheng Wan", "Hongyu Zhang", "Xiaodong Gu" ], "github": "https://github.com/Zengwh02/ASAP", "project_page": "", "abstract": "Recently, Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in code reasoning by scaling up the length of Chain-of-Thought (CoT). However, excessively long reasoning traces introduce substantial challenges in terms of training cost, inference latency, and deployment feasibility. While various CoT compression approaches have emerged to address this challenge, they face inherent trade-offs: token-level methods often disrupt syntactic and logical coherence, while step-level methods based on perplexity fail to reliably capture the logically critical reasoning steps. In this paper, we propose ASAP (Anchor-guided, Surprisal-based Pruning), a novel coarse-to-fine framework for CoT compression. ASAP first performs anchor-guided pruning to preserve the core reasoning structure, which efficiently reduces the search space for subsequent processing. It then enables a logic-aware pruning by selecting logically essential reasoning steps based on a novel first-token surprisal metric. Finally, ASAP teaches models to autonomously generate and leverage these concise CoTs at inference time, enabling efficient reasoning in coding tasks. Experiments show that ASAP achieves state-of-the-art accuracy across multiple code generation benchmarks while substantially reducing training and inference costs. On the challenging LiveCodeBench v4_v5 benchmark, our approach reduces token generation by 23.5% and inference latency by 43.5% compared to the strongest baseline, while achieving a competitive accuracy of 36.19% in Pass@1. Our results highlight a promising direction for building powerful and efficient LRMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Zengwh02/ASAP", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2508.04825", "title": "Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off", "authors": [ "Seungyong Lee", "Jeong-gi Kwak" ], "github": "", "project_page": "", "abstract": "Virtual try-on aims to synthesize a realistic image of a person wearing a target garment, but accurately modeling garment-body correspondence remains a persistent challenge, especially under pose and appearance variation. In this paper, we propose Voost - a unified and scalable framework that jointly learns virtual try-on and try-off with a single diffusion transformer. By modeling both tasks jointly, Voost enables each garment-person pair to supervise both directions and supports flexible conditioning over generation direction and garment category, enhancing garment-body relational reasoning without task-specific networks, auxiliary losses, or additional labels. In addition, we introduce two inference-time techniques: attention temperature scaling for robustness to resolution or mask variation, and self-corrective sampling that leverages bidirectional consistency between tasks. Extensive experiments demonstrate that Voost achieves state-of-the-art results on both try-on and try-off benchmarks, consistently outperforming strong baselines in alignment accuracy, visual fidelity, and generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2508.05731", "title": "InfiGUI-G1: Advancing GUI Grounding with Adaptive Exploration Policy Optimization", "authors": [ "Yuhang Liu", "Zeyu Liu", "Shuanghe Zhu", "Pengxiang Li", "Congkai Xie", "Jiasheng Wang", "Xueyu Hu", "Xiaotian Han", "Jianbo Yuan", "Xinyao Wang", "Shengyu Zhang", "Hongxia Yang", "Fei Wu" ], "github": "https://github.com/InfiXAI/InfiGUI-G1", "project_page": "", "abstract": "The emergence of Multimodal Large Language Models (MLLMs) has propelled the development of autonomous agents that operate on Graphical User Interfaces (GUIs) using pure visual input. A fundamental challenge is robustly grounding natural language instructions. This requires a precise spatial alignment, which accurately locates the coordinates of each element, and, more critically, a correct semantic alignment, which matches the instructions to the functionally appropriate UI element. Although Reinforcement Learning with Verifiable Rewards (RLVR) has proven to be effective at improving spatial alignment for these MLLMs, we find that inefficient exploration bottlenecks semantic alignment, which prevent models from learning difficult semantic associations. To address this exploration problem, we present Adaptive Exploration Policy Optimization (AEPO), a new policy optimization framework. AEPO employs a multi-answer generation strategy to enforce broader exploration, which is then guided by a theoretically grounded Adaptive Exploration Reward (AER) function derived from first principles of efficiency eta=U/C. Our AEPO-trained models, InfiGUI-G1-3B and InfiGUI-G1-7B, establish new state-of-the-art results across multiple challenging GUI grounding benchmarks, achieving significant relative improvements of up to 9.0% against the naive RLVR baseline on benchmarks designed to test generalization and semantic understanding. Resources are available at https://github.com/InfiXAI/InfiGUI-G1.", "github_manual": "", "github_regex": "https://github.com/InfiXAI/InfiGUI-G1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2508.06471", "title": "GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models", "authors": [ "GLM-4. 5 Team", "Aohan Zeng", "Xin Lv", "Qinkai Zheng", "Zhenyu Hou", "Bin Chen", "Chengxing Xie", "Cunxiang Wang", "Da Yin", "Hao Zeng", "Jiajie Zhang", "Kedong Wang", "Lucen Zhong", "Mingdao Liu", "Rui Lu", "Shulin Cao", "Xiaohan Zhang", "Xuancheng Huang", "Yao Wei", "Yean Cheng", "Yifan An", "Yilin Niu", "Yuanhao Wen", "Yushi Bai", "Zhengxiao Du", "Zihan Wang", "Zilin Zhu", "Bohan Zhang", "Bosi Wen", "Bowen Wu", "Bowen Xu", "Can Huang", "Casey Zhao", "Changpeng Cai", "Chao Yu", "Chen Li", "Chendi Ge", "Chenghua Huang", "Chenhui Zhang", "Chenxi Xu", "Chenzheng Zhu", "Chuang Li", "Congfeng Yin", "Daoyan Lin", "Dayong Yang", "Dazhi Jiang", "Ding Ai", "Erle Zhu", "Fei Wang", "Gengzheng Pan", "Guo Wang", "Hailong Sun", "Haitao Li", "Haiyang Li", "Haiyi Hu", "Hanyu Zhang", "Hao Peng", "Hao Tai", "Haoke Zhang", "Haoran Wang", "Haoyu Yang", "He Liu", "He Zhao", "Hongwei Liu", "Hongxi Yan", "Huan Liu", "Huilong Chen", "Ji Li", "Jiajing Zhao", "Jiamin Ren", "Jian Jiao", "Jiani Zhao", "Jianyang Yan", "Jiaqi Wang", "Jiayi Gui", "Jiayue Zhao", "Jie Liu", "Jijie Li", "Jing Li", "Jing Lu", "Jingsen Wang", "Jingwei Yuan", "Jingxuan Li", "Jingzhao Du", "Jinhua Du", "Jinxin Liu", "Junkai Zhi", "Junli Gao", "Ke Wang", "Lekang Yang", "Liang Xu", "Lin Fan", "Lindong Wu", "Lintao Ding", "Lu Wang", "Man Zhang", "Minghao Li", "Minghuan Xu", "Mingming Zhao", "Mingshu Zhai", "Pengfan Du", "Qian Dong", "Shangde Lei", "Shangqing Tu", "Shangtong Yang", "Shaoyou Lu", "Shijie Li", "Shuang Li", "Shuang-Li", "Shuxun Yang", "Sibo Yi", "Tianshu Yu", "Wei Tian", "Weihan Wang", "Wenbo Yu", "Weng Lam Tam", "Wenjie Liang", "Wentao Liu", "Xiao Wang", "Xiaohan Jia", "Xiaotao Gu", "Xiaoying Ling", "Xin Wang", "Xing Fan", "Xingru Pan", "Xinyuan Zhang", "Xinze Zhang", "Xiuqing Fu", "Xunkai Zhang", "Yabo Xu", "Yandong Wu", "Yida Lu", "Yidong Wang", "Yilin Zhou", "Yiming Pan", "Ying Zhang", "Yingli Wang", "Yingru Li", "Yinpei Su", "Yipeng Geng", "Yitong Zhu", "Yongkun Yang", "Yuhang Li", "Yuhao Wu", "Yujiang Li", "Yunan Liu", "Yunqing Wang", "Yuntao Li", "Yuxuan Zhang", "Zezhen Liu", "Zhen Yang", "Zhengda Zhou", "Zhongpei Qiao", "Zhuoer Feng", "Zhuorui Liu", "Zichen Zhang", "Zihan Wang", "Zijun Yao", "Zikang Wang", "Ziqiang Liu", "Ziwei Chai", "Zixuan Li", "Zuodong Zhao", "Wenguang Chen", "Jidong Zhai", "Bin Xu", "Minlie Huang", "Hongning Wang", "Juanzi Li", "Yuxiao Dong", "Jie Tang" ], "github": "https://github.com/zai-org/GLM-4.5", "project_page": "", "abstract": "We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes. Through multi-stage training on 23T tokens and comprehensive post-training with expert model iteration and reinforcement learning, GLM-4.5 achieves strong performance across agentic, reasoning, and coding (ARC) tasks, scoring 70.1% on TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified. With much fewer parameters than several competitors, GLM-4.5 ranks 3rd overall among all evaluated models and 2nd on agentic benchmarks. We release both GLM-4.5 (355B parameters) and a compact version, GLM-4.5-Air (106B parameters), to advance research in reasoning and agentic AI systems. Code, models, and more information are available at https://github.com/zai-org/GLM-4.5.", "github_manual": "", "github_regex": "https://github.com/zai-org/GLM-4.5", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2508.05547", "title": "Adapting Vision-Language Models Without Labels: A Comprehensive Survey", "authors": [ "Hao Dong", "Lijun Sheng", "Jian Liang", "Ran He", "Eleni Chatzi", "Olga Fink" ], "github": "https://github.com/tim-learn/Awesome-LabelFree-VLMs", "project_page": "", "abstract": "Vision-Language Models (VLMs) have demonstrated remarkable generalization capabilities across a wide range of tasks. However, their performance often remains suboptimal when directly applied to specific downstream scenarios without task-specific adaptation. To enhance their utility while preserving data efficiency, recent research has increasingly focused on unsupervised adaptation methods that do not rely on labeled data. Despite the growing interest in this area, there remains a lack of a unified, task-oriented survey dedicated to unsupervised VLM adaptation. To bridge this gap, we present a comprehensive and structured overview of the field. We propose a taxonomy based on the availability and nature of unlabeled visual data, categorizing existing approaches into four key paradigms: Data-Free Transfer (no data), Unsupervised Domain Transfer (abundant data), Episodic Test-Time Adaptation (batch data), and Online Test-Time Adaptation (streaming data). Within this framework, we analyze core methodologies and adaptation strategies associated with each paradigm, aiming to establish a systematic understanding of the field. Additionally, we review representative benchmarks across diverse applications and highlight open challenges and promising directions for future research. An actively maintained repository of relevant literature is available at https://github.com/tim-learn/Awesome-LabelFree-VLMs.", "github_manual": "", "github_regex": "https://github.com/tim-learn/Awesome-LabelFree-VLMs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2507.22025", "title": "UI-AGILE: Advancing GUI Agents with Effective Reinforcement Learning and Precise Inference-Time Grounding", "authors": [ "Shuquan Lian", "Yuhang Wu", "Jia Ma", "Zihan Song", "Bingqi Chen", "Xiawu Zheng", "Hui Li" ], "github": "https://github.com/KDEGroup/UI-AGILE", "project_page": "", "abstract": "The emergence of Multimodal Large Language Models (MLLMs) has driven significant advances in Graphical User Interface (GUI) agent capabilities. Nevertheless, existing GUI agent training and inference techniques still suffer from a dilemma for reasoning designs, ineffective reward, and visual noise. To address these issues, we introduce UI-AGILE, a comprehensive framework enhancing GUI agents at both the training and inference stages. For training, we propose a suite of improvements to the Supervised Fine-Tuning (SFT) process: 1) a Continuous Reward function to incentivize high-precision grounding; 2) a \"Simple Thinking\" reward to balance planning with speed and grounding accuracy; and 3) a Cropping-based Resampling strategy to mitigate the sparse reward problem and improve learning on complex tasks. For inference, we present Decomposed Grounding with Selection, a novel method that dramatically improves grounding accuracy on high-resolution displays by breaking the image into smaller, manageable parts. Experiments show that UI-AGILE achieves the state-of-the-art performance on two benchmarks ScreenSpot-Pro and ScreenSpot-v2. For instance, using both our proposed training and inference enhancement methods brings 23% grounding accuracy improvement over the best baseline on ScreenSpot-Pro.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KDEGroup/UI-AGILE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2508.01242", "title": "MeshLLM: Empowering Large Language Models to Progressively Understand and Generate 3D Mesh", "authors": [ "Shuangkang Fang", "I-Chao Shen", "Yufeng Wang", "Yi-Hsuan Tsai", "Yi Yang", "Shuchang Zhou", "Wenrui Ding", "Takeo Igarashi", "Ming-Hsuan Yang" ], "github": "", "project_page": "", "abstract": "We present MeshLLM, a novel framework that leverages large language models (LLMs) to understand and generate text-serialized 3D meshes. Our approach addresses key limitations in existing methods, including the limited dataset scale when catering to LLMs' token length and the loss of 3D structural information during mesh serialization. We introduce a Primitive-Mesh decomposition strategy, which divides 3D meshes into structurally meaningful subunits. This enables the creation of a large-scale dataset with 1500k+ samples, almost 50 times larger than previous methods, which aligns better with the LLM scaling law principles. Furthermore, we propose inferring face connectivity from vertices and local mesh assembly training strategies, significantly enhancing the LLMs' ability to capture mesh topology and spatial structures. Experiments show that MeshLLM outperforms the state-of-the-art LLaMA-Mesh in both mesh generation quality and shape understanding, highlighting its great potential in processing text-serialized 3D meshes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2508.02831", "title": "GENIE: Gaussian Encoding for Neural Radiance Fields Interactive Editing", "authors": [ "Miko\u0142aj Zieli\u0144ski", "Krzysztof Byrski", "Tomasz Szczepanik", "Przemys\u0142aw Spurek" ], "github": "https://github.com/MikolajZielinski/genie", "project_page": "", "abstract": "Neural Radiance Fields (NeRF) and Gaussian Splatting (GS) have recently transformed 3D scene representation and rendering. NeRF achieves high-fidelity novel view synthesis by learning volumetric representations through neural networks, but its implicit encoding makes editing and physical interaction challenging. In contrast, GS represents scenes as explicit collections of Gaussian primitives, enabling real-time rendering, faster training, and more intuitive manipulation. This explicit structure has made GS particularly well-suited for interactive editing and integration with physics-based simulation. In this paper, we introduce GENIE (Gaussian Encoding for Neural Radiance Fields Interactive Editing), a hybrid model that combines the photorealistic rendering quality of NeRF with the editable and structured representation of GS. Instead of using spherical harmonics for appearance modeling, we assign each Gaussian a trainable feature embedding. These embeddings are used to condition a NeRF network based on the k nearest Gaussians to each query point. To make this conditioning efficient, we introduce Ray-Traced Gaussian Proximity Search (RT-GPS), a fast nearest Gaussian search based on a modified ray-tracing pipeline. We also integrate a multi-resolution hash grid to initialize and update Gaussian features. Together, these components enable real-time, locality-aware editing: as Gaussian primitives are repositioned or modified, their interpolated influence is immediately reflected in the rendered output. By combining the strengths of implicit and explicit representations, GENIE supports intuitive scene manipulation, dynamic interaction, and compatibility with physical simulation, bridging the gap between geometry-based editing and neural rendering. The code can be found under (https://github.com/MikolajZielinski/genie)", "github_manual": "", "github_regex": "https://github.com/MikolajZielinski/genie", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2508.06433", "title": "Memp: Exploring Agent Procedural Memory", "authors": [ "Runnan Fang", "Yuan Liang", "Xiaobin Wang", "Jialong Wu", "Shuofei Qiao", "Pengjun Xie", "Fei Huang", "Huajun Chen", "Ningyu Zhang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) based agents excel at diverse tasks, yet they suffer from brittle procedural memory that is manually engineered or entangled in static parameters. In this work, we investigate strategies to endow agents with a learnable, updatable, and lifelong procedural memory. We propose Memp that distills past agent trajectories into both fine-grained, step-by-step instructions and higher-level, script-like abstractions, and explore the impact of different strategies for Build, Retrieval, and Update of procedural memory. Coupled with a dynamic regimen that continuously updates, corrects, and deprecates its contents, this repository evolves in lockstep with new experience. Empirical evaluation on TravelPlanner and ALFWorld shows that as the memory repository is refined, agents achieve steadily higher success rates and greater efficiency on analogous tasks. Moreover, procedural memory built from a stronger model retains its value: migrating the procedural memory to a weaker model yields substantial performance gains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2508.06494", "title": "LightSwitch: Multi-view Relighting with Material-guided Diffusion", "authors": [ "Yehonathan Litman", "Fernando De la Torre", "Shubham Tulsiani" ], "github": "https://github.com/yehonathanlitman/LightSwitch", "project_page": "", "abstract": "Recent approaches for 3D relighting have shown promise in integrating 2D image relighting generative priors to alter the appearance of a 3D representation while preserving the underlying structure. Nevertheless, generative priors used for 2D relighting that directly relight from an input image do not take advantage of intrinsic properties of the subject that can be inferred or cannot consider multi-view data at scale, leading to subpar relighting. In this paper, we propose Lightswitch, a novel finetuned material-relighting diffusion framework that efficiently relights an arbitrary number of input images to a target lighting condition while incorporating cues from inferred intrinsic properties. By using multi-view and material information cues together with a scalable denoising scheme, our method consistently and efficiently relights dense multi-view data of objects with diverse material compositions. We show that our 2D relighting prediction quality exceeds previous state-of-the-art relighting priors that directly relight from images. We further demonstrate that LightSwitch matches or outperforms state-of-the-art diffusion inverse rendering methods in relighting synthetic and real objects in as little as 2 minutes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yehonathanlitman/LightSwitch", "github_api_added_by": "auto" }, { "date": "2025-08-11", "arxiv_id": "2508.04482", "title": "OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use", "authors": [ "Xueyu Hu", "Tao Xiong", "Biao Yi", "Zishu Wei", "Ruixuan Xiao", "Yurun Chen", "Jiasheng Ye", "Meiling Tao", "Xiangxin Zhou", "Ziyu Zhao", "Yuhuai Li", "Shengze Xu", "Shenzhi Wang", "Xinchen Xu", "Shuofei Qiao", "Zhaokai Wang", "Kun Kuang", "Tieyong Zeng", "Liang Wang", "Jiwei Li", "Yuchen Eleanor Jiang", "Wangchunshu Zhou", "Guoyin Wang", "Keting Yin", "Zhou Zhao", "Hongxia Yang", "Fan Wu", "Shengyu Zhang", "Fei Wu" ], "github": "", "project_page": "", "abstract": "The dream to create AI assistants as capable and versatile as the fictional J.A.R.V.I.S from Iron Man has long captivated imaginations. With the evolution of (multi-modal) large language models ((M)LLMs), this dream is closer to reality, as (M)LLM-based Agents using computing devices (e.g., computers and mobile phones) by operating within the environments and interfaces (e.g., Graphical User Interface (GUI)) provided by operating systems (OS) to automate tasks have significantly advanced. This paper presents a comprehensive survey of these advanced agents, designated as OS Agents. We begin by elucidating the fundamentals of OS Agents, exploring their key components including the environment, observation space, and action space, and outlining essential capabilities such as understanding, planning, and grounding. We then examine methodologies for constructing OS Agents, focusing on domain-specific foundation models and agent frameworks. A detailed review of evaluation protocols and benchmarks highlights how OS Agents are assessed across diverse tasks. Finally, we discuss current challenges and identify promising directions for future research, including safety and privacy, personalization and self-evolution. This survey aims to consolidate the state of OS Agents research, providing insights to guide both academic inquiry and industrial development. An open-source GitHub repository is maintained as a dynamic resource to foster further innovation in this field. We present a 9-page version of our work, accepted by ACL 2025, to provide a concise overview to the domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2508.03616", "title": "Hidden Dynamics of Massive Activations in Transformer Training", "authors": [ "Jorge Gallego-Feliciano", "S. Aaron McClendon", "Juan Morinelli", "Stavros Zervoudakis", "Antonios Saravanos" ], "github": "", "project_page": "", "abstract": "Massive activations are scalar values in transformer hidden states that achieve values orders of magnitude larger than typical activations and have been shown to be critical for model functionality. While prior work has characterized these phenomena in fully trained models, the temporal dynamics of their emergence during training remain poorly understood. We present the first comprehensive analysis of massive activation development throughout transformer training, using the Pythia model family as our testbed. Through systematic analysis of various model sizes across multiple training checkpoints, we demonstrate that massive activation emergence follows predictable mathematical patterns that can be accurately modeled using an exponentially-modulated logarithmic function with five key parameters. We develop a machine learning framework to predict these mathematical parameters from architectural specifications alone, achieving high accuracy for steady-state behavior and moderate accuracy for emergence timing and magnitude. These findings enable architects to predict and potentially control key aspects of massive activation emergence through design choices, with significant implications for model stability, training cycle length, interpretability, and optimization. Our findings demonstrate that the emergence of massive activations is governed by model design and can be anticipated, and potentially controlled, before training begins.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-11", "arxiv_id": "2508.02095", "title": "VLM4D: Towards Spatiotemporal Awareness in Vision Language Models", "authors": [ "Shijie Zhou", "Alexander Vilesov", "Xuehai He", "Ziyu Wan", "Shuwang Zhang", "Aditya Nagachandra", "Di Chang", "Dongdong Chen", "Xin Eric Wang", "Achuta Kadambi" ], "github": "https://github.com/ShijieZhou-UCLA/VLM4D", "project_page": "", "abstract": "Vision language models (VLMs) have shown remarkable capabilities in integrating linguistic and visual reasoning but remain fundamentally limited in understanding dynamic spatiotemporal interactions. Humans effortlessly track and reason about object movements, rotations, and perspective shifts-abilities essential for robust dynamic real-world understanding yet notably lacking in current VLMs. In this paper, we introduce VLM4D, the first benchmark specifically designed to evaluate the spatiotemporal reasoning capabilities of VLMs. Our benchmark comprises diverse real-world and synthetic videos accompanied by carefully curated question-answer pairs emphasizing translational and rotational motions, perspective awareness, and motion continuity. Through comprehensive evaluations of state-of-the-art open and closed-source VLMs, we identify significant performance gaps compared to human baselines, highlighting fundamental deficiencies in existing models. Extensive analysis reveals that VLMs struggle particularly with integrating multiple visual cues and maintaining temporal coherence. We further explore promising directions, such as leveraging 4D feature field reconstruction and targeted spatiotemporal supervised fine-tuning, demonstrating their effectiveness in enhancing spatiotemporal comprehension. Our work aims to encourage deeper exploration into improving VLMs' spatial and temporal grounding, paving the way towards more capable and reliable visual intelligence for dynamic environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ShijieZhou-UCLA/VLM4D", "github_api_added_by": "auto" }, { "date": "2025-08-12", "arxiv_id": "2508.07999", "title": "WideSearch: Benchmarking Agentic Broad Info-Seeking", "authors": [ "Ryan Wong", "Jiawei Wang", "Junjie Zhao", "Li Chen", "Yan Gao", "Long Zhang", "Xuan Zhou", "Zuo Wang", "Kai Xiang", "Ge Zhang", "Wenhao Huang", "Yang Wang", "Ke Wang" ], "github": "", "project_page": "", "abstract": "From professional research to everyday planning, many tasks are bottlenecked by wide-scale information seeking, which is more repetitive than cognitively complex. With the rapid development of Large Language Models (LLMs), automated search agents powered by LLMs offer a promising solution to liberate humans from this tedious work. However, the capability of these agents to perform such \"wide-context\" collection reliably and completely remains largely unevaluated due to a lack of suitable benchmarks. To bridge this gap, we introduce WideSearch, a new benchmark engineered to evaluate agent reliability on these large-scale collection tasks. The benchmark features 200 manually curated questions (100 in English, 100 in Chinese) from over 15 diverse domains, grounded in real user queries. Each task requires agents to collect large-scale atomic information, which could be verified one by one objectively, and arrange it into a well-organized output. A rigorous five-stage quality control pipeline ensures the difficulty, completeness, and verifiability of the dataset. We benchmark over 10 state-of-the-art agentic search systems, including single-agent, multi-agent frameworks, and end-to-end commercial systems. Most systems achieve overall success rates near 0\\%, with the best performer reaching just 5\\%. However, given sufficient time, cross-validation by multiple human testers can achieve a near 100\\% success rate. These results demonstrate that present search agents have critical deficiencies in large-scale information seeking, underscoring urgent areas for future research and development in agentic search. Our dataset, evaluation pipeline, and benchmark results have been publicly released at https://widesearch-seed.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.05614", "title": "OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks", "authors": [ "Zixuan Wang", "Dingming Li", "Hongxing Li", "Shuo Chen", "Yuchen Yan", "Wenqi Zhang", "Yongliang Shen", "Weiming Lu", "Jun Xiao", "Yueting Zhuang" ], "github": "", "project_page": "", "abstract": "Large language models excel at abstract reasoning but their capacity for embodied agent reasoning remains largely unexplored. We present OmniEAR, a comprehensive framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks. Unlike existing benchmarks that provide predefined tool sets or explicit collaboration directives, OmniEAR requires agents to dynamically acquire capabilities and autonomously determine coordination strategies based on task demands. Through text-based environment representation, we model continuous physical properties and complex spatial relationships across 1,500 scenarios spanning household and industrial domains. Our systematic evaluation reveals severe performance degradation when models must reason from constraints: while achieving 85-96% success with explicit instructions, performance drops to 56-85% for tool reasoning and 63-85% for implicit collaboration, with compound tasks showing over 50% failure rates. Surprisingly, complete environmental information degrades coordination performance, indicating models cannot filter task-relevant constraints. Fine-tuning improves single-agent tasks dramatically (0.6% to 76.3%) but yields minimal multi-agent gains (1.5% to 5.5%), exposing fundamental architectural limitations. These findings demonstrate that embodied reasoning poses fundamentally different challenges than current models can address, establishing OmniEAR as a rigorous benchmark for evaluating and advancing embodied AI systems. Our code and data are included in the supplementary materials and will be open-sourced upon acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.07917", "title": "MolmoAct: Action Reasoning Models that can Reason in Space", "authors": [ "Jason Lee", "Jiafei Duan", "Haoquan Fang", "Yuquan Deng", "Shuo Liu", "Boyang Li", "Bohan Fang", "Jieyu Zhang", "Yi Ru Wang", "Sangho Lee", "Winson Han", "Wilbert Pumacay", "Angelica Wu", "Rose Hendrix", "Karen Farley", "Eli VanderBilt", "Ali Farhadi", "Dieter Fox", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Reasoning is central to purposeful action, yet most robotic foundation models map perception and instructions directly to control, which limits adaptability, generalization, and semantic grounding. We introduce Action Reasoning Models (ARMs), a class of vision-language-action models that integrate perception, planning, and control through a structured three-stage pipeline. Our model, MolmoAct, encodes observations and instructions into depth-aware perception tokens, generates mid-level spatial plans as editable trajectory traces, and predicts precise low-level actions, enabling explainable and steerable behavior. MolmoAct-7B-D achieves strong performance across simulation and real-world settings: 70.5% zero-shot accuracy on SimplerEnv Visual Matching tasks, surpassing closed-source Pi-0 and GR00T N1; 86.6% average success on LIBERO, including an additional 6.3% gain over ThinkAct on long-horizon tasks; and in real-world fine-tuning, an additional 10% (single-arm) and an additional 22.7% (bimanual) task progression over Pi-0-FAST. It also outperforms baselines by an additional 23.3% on out-of-distribution generalization and achieves top human-preference scores for open-ended instruction following and trajectory steering. Furthermore, we release, for the first time, the MolmoAct Dataset -- a mid-training robot dataset comprising over 10,000 high quality robot trajectories across diverse scenarios and tasks. Training with this dataset yields an average 5.5% improvement in general performance over the base model. We release all model weights, training code, our collected dataset, and our action reasoning dataset, establishing MolmoAct as both a state-of-the-art robotics foundation model and an open blueprint for building ARMs that transform perception into purposeful action through structured reasoning. Blogpost: https://allenai.org/blog/molmoact", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.06600", "title": "BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent", "authors": [ "Zijian Chen", "Xueguang Ma", "Shengyao Zhuang", "Ping Nie", "Kai Zou", "Andrew Liu", "Joshua Green", "Kshama Patel", "Ruoxi Meng", "Mingyi Su", "Sahel Sharifymoghaddam", "Yanxi Li", "Haoran Hong", "Xinyu Shi", "Xuye Liu", "Nandan Thakur", "Crystina Zhang", "Luyu Gao", "Wenhu Chen", "Jimmy Lin" ], "github": "", "project_page": "", "abstract": "Deep-Research agents, which integrate large language models (LLMs) with search tools, have shown success in improving the effectiveness of handling complex queries that require iterative search planning and reasoning over search results. Evaluations on current benchmarks like BrowseComp relies on black-box live web search APIs, have notable limitations in (1) fairness: dynamic and opaque web APIs hinder fair comparisons and reproducibility of deep research methods; (2) transparency: lack of control over the document corpus makes it difficult to isolate retriever contributions. In other words, the current evaluations may compare a complete deep research system at a given time, but they do not foster well-controlled experiments to provide insights into the capability of underlying deep research LLMs. To address these challenges, we introduce BrowseComp-Plus, a benchmark derived from BrowseComp, employing a fixed, carefully curated corpus. Each query in BrowseComp-Plus includes human-verified supporting documents and mined challenging negatives, enabling controlled experimentation. The benchmark is shown to be effective in distinguishing the performance of deep research systems. For instance, the open-source model Search-R1, when paired with the BM25 retriever, achieves 3.86% accuracy, whereas the GPT-5 achieves 55.9%. Integrating the GPT-5 with the Qwen3-Embedding-8B retriever further enhances its accuracy to 70.1% with fewer search calls. This benchmark allows comprehensive evaluation and disentangled analysis of deep research agents and retrieval methods, fostering insights into retrieval effectiveness, citation accuracy, and context engineering in Deep-Research system.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.06601", "title": "Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs", "authors": [ "Kyle O'Brien", "Stephen Casper", "Quentin Anthony", "Tomek Korbak", "Robert Kirk", "Xander Davies", "Ishan Mishra", "Geoffrey Irving", "Yarin Gal", "Stella Biderman" ], "github": "https://github.com/EleutherAI/deep-ignorance", "project_page": "", "abstract": "Open-weight AI systems offer unique benefits, including enhanced transparency, open research, and decentralized access. However, they are vulnerable to tampering attacks which can efficiently elicit harmful behaviors by modifying weights or activations. Currently, there is not yet a robust science of open-weight model risk management. Existing safety fine-tuning methods and other post-training techniques have struggled to make LLMs resistant to more than a few dozen steps of adversarial fine-tuning. In this paper, we investigate whether filtering text about dual-use topics from training data can prevent unwanted capabilities and serve as a more tamper-resistant safeguard. We introduce a multi-stage pipeline for scalable data filtering and show that it offers a tractable and effective method for minimizing biothreat proxy knowledge in LLMs. We pretrain multiple 6.9B-parameter models from scratch and find that they exhibit substantial resistance to adversarial fine-tuning attacks on up to 10,000 steps and 300M tokens of biothreat-related text -- outperforming existing post-training baselines by over an order of magnitude -- with no observed degradation to unrelated capabilities. However, while filtered models lack internalized dangerous knowledge, we find that they can still leverage such information when it is provided in context (e.g., via search tool augmentation), demonstrating a need for a defense-in-depth approach. Overall, these findings help to establish pretraining data curation as a promising layer of defense for open-weight AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/EleutherAI/deep-ignorance", "github_api_added_by": "auto" }, { "date": "2025-08-12", "arxiv_id": "2508.07629", "title": "Klear-Reasoner: Advancing Reasoning Capability via Gradient-Preserving Clipping Policy Optimization", "authors": [ "Zhenpeng Su", "Leiyu Pan", "Xue Bai", "Dening Liu", "Guanting Dong", "Jiaming Huang", "Wenping Hu", "Guorui Zhou" ], "github": "", "project_page": "", "abstract": "We present Klear-Reasoner, a model with long reasoning capabilities that demonstrates careful deliberation during problem solving, achieving outstanding performance across multiple benchmarks. Although there are already many excellent works related to inference models in the current community, there are still many problems with reproducing high-performance inference models due to incomplete disclosure of training details. This report provides an in-depth analysis of the reasoning model, covering the entire post-training workflow from data preparation and long Chain-of-Thought supervised fine-tuning (long CoT SFT) to reinforcement learning (RL), along with detailed ablation studies for each experimental component. For SFT data, our experiments show that a small number of high-quality data sources are more effective than a large number of diverse data sources, and that difficult samples can achieve better results without accuracy filtering. In addition, we investigate two key issues with current clipping mechanisms in RL: Clipping suppresses critical exploration signals and ignores suboptimal trajectories. To address these challenges, we propose Gradient-Preserving clipping Policy Optimization (GPPO) that gently backpropagates gradients from clipped tokens. GPPO not only enhances the model's exploration capacity but also improves its efficiency in learning from negative samples. Klear-Reasoner exhibits exceptional reasoning abilities in mathematics and programming, scoring 90.5\\% on AIME 2024, 83.2\\% on AIME 2025, 66.0\\% on LiveCodeBench V5 and 58.1\\% on LiveCodeBench V6.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.08134", "title": "Follow-Your-Shape: Shape-Aware Image Editing via Trajectory-Guided Region Control", "authors": [ "Zeqian Long", "Mingzhe Zheng", "Kunyu Feng", "Xinhua Zhang", "Hongyu Liu", "Harry Yang", "Linfeng Zhang", "Qifeng Chen", "Yue Ma" ], "github": "https://github.com/mayuelala/FollowYourShape", "project_page": "", "abstract": "While recent flow-based image editing models demonstrate general-purpose capabilities across diverse tasks, they often struggle to specialize in challenging scenarios -- particularly those involving large-scale shape transformations. When performing such structural edits, these methods either fail to achieve the intended shape change or inadvertently alter non-target regions, resulting in degraded background quality. We propose Follow-Your-Shape, a training-free and mask-free framework that supports precise and controllable editing of object shapes while strictly preserving non-target content. Motivated by the divergence between inversion and editing trajectories, we compute a Trajectory Divergence Map (TDM) by comparing token-wise velocity differences between the inversion and denoising paths. The TDM enables precise localization of editable regions and guides a Scheduled KV Injection mechanism that ensures stable and faithful editing. To facilitate a rigorous evaluation, we introduce ReShapeBench, a new benchmark comprising 120 new images and enriched prompt pairs specifically curated for shape-aware editing. Experiments demonstrate that our method achieves superior editability and visual fidelity, particularly in tasks requiring large-scale shape replacement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mayuelala/FollowYourShape", "github_api_added_by": "auto" }, { "date": "2025-08-12", "arxiv_id": "2508.07050", "title": "ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability", "authors": [ "Wenhan Liu", "Xinyu Ma", "Weiwei Sun", "Yutao Zhu", "Yuchen Li", "Dawei Yin", "Zhicheng Dou" ], "github": "https://github.com/8421BCD/ReasonRank", "project_page": "", "abstract": "Large Language Model (LLM) based listwise ranking has shown superior performance in many passage ranking tasks. With the development of Large Reasoning Models, many studies have demonstrated that step-by-step reasoning during test-time helps improve listwise ranking performance. However, due to the scarcity of reasoning-intensive training data, existing rerankers perform poorly in many complex ranking scenarios and the ranking ability of reasoning-intensive rerankers remains largely underdeveloped. In this paper, we first propose an automated reasoning-intensive training data synthesis framework, which sources training queries and passages from diverse domains and applies DeepSeek-R1 to generate high-quality training labels. A self-consistency data filtering mechanism is designed to ensure the data quality. To empower the listwise reranker with strong reasoning ability, we further propose a two-stage post-training approach, which includes a cold-start supervised fine-tuning (SFT) stage for reasoning pattern learning and a reinforcement learning (RL) stage for further ranking ability enhancement. During the RL stage, based on the nature of listwise ranking, we design a multi-view ranking reward, which is more effective than a ranking metric-based reward. Extensive experiments demonstrate that our trained reasoning-intensive reranker ReasonRank outperforms existing baselines significantly and also achieves much lower latency than pointwise reranker Rank1. Through further experiments, our ReasonRank has achieved state-of-the-art (SOTA) performance 40.6 on the BRIGHT leaderboard\\footnote{https://brightbenchmark.github.io/.} Our codes are available at https://github.com/8421BCD/ReasonRank.", "github_manual": "", "github_regex": "https://github.com/8421BCD/ReasonRank", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2507.22034", "title": "UserBench: An Interactive Gym Environment for User-Centric Agents", "authors": [ "Cheng Qian", "Zuxin Liu", "Akshara Prabhakar", "Zhiwei Liu", "Jianguo Zhang", "Haolin Chen", "Heng Ji", "Weiran Yao", "Shelby Heinecke", "Silvio Savarese", "Caiming Xiong", "Huan Wang" ], "github": "https://github.com/SalesforceAIResearch/UserBench", "project_page": "", "abstract": "Large Language Models (LLMs)-based agents have made impressive progress in reasoning and tool use, enabling them to solve complex tasks. However, their ability to proactively collaborate with users, especially when goals are vague, evolving, or indirectly expressed, remains underexplored. To address this gap, we introduce UserBench, a user-centric benchmark designed to evaluate agents in multi-turn, preference-driven interactions. UserBench features simulated users who start with underspecified goals and reveal preferences incrementally, requiring agents to proactively clarify intent and make grounded decisions with tools. Our evaluation of leading open- and closed-source LLMs reveals a significant disconnect between task completion and user alignment. For instance, models provide answers that fully align with all user intents only 20% of the time on average, and even the most advanced models uncover fewer than 30% of all user preferences through active interaction. These results highlight the challenges of building agents that are not just capable task executors, but true collaborative partners. UserBench offers an interactive environment to measure and advance this critical capability.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SalesforceAIResearch/UserBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.06026", "title": "Temporal Self-Rewarding Language Models: Decoupling Chosen-Rejected via Past-Future", "authors": [ "Yidong Wang", "Xin Wang", "Cunxiang Wang", "Junfeng Fang", "Qiufeng Wang", "Jianing Chu", "Xuran Meng", "Shuxun Yang", "Libo Qin", "Yue Zhang", "Wei Ye", "Shikun Zhang" ], "github": "", "project_page": "", "abstract": "Self-Rewarding Language Models propose an architecture in which the Large Language Models(LLMs) both generates responses and evaluates its own outputs via LLM-as-a-Judge prompting, dynamically improving its generative capabilities through iterative Direct Preference Optimization (DPO). However, our analysis reveals a critical limitation in existing Self-Rewarding paradigms: the synchronized improvement of chosen and rejected responses progressively narrows the representational difference between contrasting samples, undermining effective preference learning. We propose Temporal Self-Rewarding Language Models that strategically coordinate past, present, and future model generations to sustain learning signals. Our dual-phase framework introduces: (1) Anchored Rejection - fixing rejected responses using the past initial model's outputs and (2) Future-Guided Chosen - dynamically curating chosen samples using next-generation model predictions. Extensive experiments across three model families (Llama, Qwen, Mistral) and different model sizes (Llama3B/8B/70B) demonstrate significant improvements when trained with our method compared to Self-Rewarding using same computation resources. For example, Llama3.1-8B reaches a 29.44 win rate on AlpacaEval 2.0 with our method, outperforming the Self-Rewarding baseline (19.69) by 9.75. Notably, our method also demonstrates superior out-of-distribution generalization across mathematical reasoning (GSM8K), knowledge-based QA (ARC, TruthfulQA), and code generation (HumanEval) tasks, even though we do not specifically collect such training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.07101", "title": "Less Is More: Training-Free Sparse Attention with Global Locality for Efficient Reasoning", "authors": [ "Lijie Yang", "Zhihao Zhang", "Arti Jain", "Shijie Cao", "Baihong Yuan", "Yiwei Chen", "Zhihao Jia", "Ravi Netravali" ], "github": "https://github.com/DerrickYLJ/LessIsMore", "project_page": "", "abstract": "Large reasoning models achieve strong performance through test-time scaling but incur substantial computational overhead, particularly from excessive token generation when processing short input prompts. While sparse attention mechanisms can reduce latency and memory usage, existing approaches suffer from significant accuracy degradation due to accumulated errors during long-generation reasoning. These methods generally require either high token retention rates or expensive retraining. We introduce LessIsMore, a training-free sparse attention mechanism for reasoning tasks, which leverages global attention patterns rather than relying on traditional head-specific local optimizations. LessIsMore aggregates token selections from local attention heads with recent contextual information, enabling unified cross-head token ranking for future decoding layers. This unified selection improves generalization and efficiency by avoiding the need to maintain separate token subsets per head. Evaluation across diverse reasoning tasks and benchmarks shows that LessIsMore preserves -- and in some cases improves -- accuracy while achieving a 1.1times average decoding speed-up compared to full attention. Moreover, LessIsMore attends to 2times fewer tokens without accuracy loss, achieving a 1.13times end-to-end speed-up compared to existing sparse attention methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/DerrickYLJ/LessIsMore", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.03365", "title": "When Good Sounds Go Adversarial: Jailbreaking Audio-Language Models with Benign Inputs", "authors": [ "Bodam Kim", "Hiskias Dingeto", "Taeyoun Kwon", "Dasol Choi", "DongGeon Lee", "Haon Park", "JaeHoon Lee", "Jongho Shin" ], "github": "https://github.com/AIM-Intelligence/WhisperInject", "project_page": "", "abstract": "As large language models become increasingly integrated into daily life, audio has emerged as a key interface for human-AI interaction. However, this convenience also introduces new vulnerabilities, making audio a potential attack surface for adversaries. Our research introduces WhisperInject, a two-stage adversarial audio attack framework that can manipulate state-of-the-art audio language models to generate harmful content. Our method uses imperceptible perturbations in audio inputs that remain benign to human listeners. The first stage uses a novel reward-based optimization method, Reinforcement Learning with Projected Gradient Descent (RL-PGD), to guide the target model to circumvent its own safety protocols and generate harmful native responses. This native harmful response then serves as the target for Stage 2, Payload Injection, where we use Projected Gradient Descent (PGD) to optimize subtle perturbations that are embedded into benign audio carriers, such as weather queries or greeting messages. Validated under the rigorous StrongREJECT, LlamaGuard, as well as Human Evaluation safety evaluation framework, our experiments demonstrate a success rate exceeding 86% across Qwen2.5-Omni-3B, Qwen2.5-Omni-7B, and Phi-4-Multimodal. Our work demonstrates a new class of practical, audio-native threats, moving beyond theoretical exploits to reveal a feasible and covert method for manipulating AI behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AIM-Intelligence/WhisperInject", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.07493", "title": "VisR-Bench: An Empirical Study on Visual Retrieval-Augmented Generation for Multilingual Long Document Understanding", "authors": [ "Jian Chen", "Ming Li", "Jihyung Kil", "Chenguang Wang", "Tong Yu", "Ryan Rossi", "Tianyi Zhou", "Changyou Chen", "Ruiyi Zhang" ], "github": "https://github.com/puar-playground/VisR-Bench", "project_page": "", "abstract": "Most organizational data in this world are stored as documents, and visual retrieval plays a crucial role in unlocking the collective intelligence from all these documents. However, existing benchmarks focus on English-only document retrieval or only consider multilingual question-answering on a single-page image. To bridge this gap, we introduce VisR-Bench, a multilingual benchmark designed for question-driven multimodal retrieval in long documents. Our benchmark comprises over 35K high-quality QA pairs across 1.2K documents, enabling fine-grained evaluation of multimodal retrieval. VisR-Bench spans sixteen languages with three question types (figures, text, and tables), offering diverse linguistic and question coverage. Unlike prior datasets, we include queries without explicit answers, preventing models from relying on superficial keyword matching. We evaluate various retrieval models, including text-based methods, multimodal encoders, and MLLMs, providing insights into their strengths and limitations. Our results show that while MLLMs significantly outperform text-based and multimodal encoder models, they still struggle with structured tables and low-resource languages, highlighting key challenges in multilingual visual retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/puar-playground/VisR-Bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.07981", "title": "Omni-Effects: Unified and Spatially-Controllable Visual Effects Generation", "authors": [ "Fangyuan Mao", "Aiming Hao", "Jintao Chen", "Dongxia Liu", "Xiaokun Feng", "Jiashu Zhu", "Meiqi Wu", "Chubin Chen", "Jiahong Wu", "Xiangxiang Chu" ], "github": "", "project_page": "", "abstract": "Visual effects (VFX) are essential visual enhancements fundamental to modern cinematic production. Although video generation models offer cost-efficient solutions for VFX production, current methods are constrained by per-effect LoRA training, which limits generation to single effects. This fundamental limitation impedes applications that require spatially controllable composite effects, i.e., the concurrent generation of multiple effects at designated locations. However, integrating diverse effects into a unified framework faces major challenges: interference from effect variations and spatial uncontrollability during multi-VFX joint training. To tackle these challenges, we propose Omni-Effects, a first unified framework capable of generating prompt-guided effects and spatially controllable composite effects. The core of our framework comprises two key innovations: (1) LoRA-based Mixture of Experts (LoRA-MoE), which employs a group of expert LoRAs, integrating diverse effects within a unified model while effectively mitigating cross-task interference. (2) Spatial-Aware Prompt (SAP) incorporates spatial mask information into the text token, enabling precise spatial control. Furthermore, we introduce an Independent-Information Flow (IIF) module integrated within the SAP, isolating the control signals corresponding to individual effects to prevent any unwanted blending. To facilitate this research, we construct a comprehensive VFX dataset Omni-VFX via a novel data collection pipeline combining image editing and First-Last Frame-to-Video (FLF2V) synthesis, and introduce a dedicated VFX evaluation framework for validating model performance. Extensive experiments demonstrate that Omni-Effects achieves precise spatial control and diverse effect generation, enabling users to specify both the category and location of desired effects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.08189", "title": "Reinforcement Learning in Vision: A Survey", "authors": [ "Weijia Wu", "Chen Gao", "Joya Chen", "Kevin Qinghong Lin", "Qingwei Meng", "Yiming Zhang", "Yuke Qiu", "Hong Zhou", "Mike Zheng Shou" ], "github": "https://github.com/weijiawu/Awesome-Visual-Reinforcement-Learning", "project_page": "", "abstract": "Recent advances at the intersection of reinforcement learning (RL) and visual intelligence have enabled agents that not only perceive complex visual scenes but also reason, generate, and act within them. This survey offers a critical and up-to-date synthesis of the field. We first formalize visual RL problems and trace the evolution of policy-optimization strategies from RLHF to verifiable reward paradigms, and from Proximal Policy Optimization to Group Relative Policy Optimization. We then organize more than 200 representative works into four thematic pillars: multi-modal large language models, visual generation, unified model frameworks, and vision-language-action models. For each pillar we examine algorithmic design, reward engineering, benchmark progress, and we distill trends such as curriculum-driven training, preference-aligned diffusion, and unified reward modeling. Finally, we review evaluation protocols spanning set-level fidelity, sample-level preference, and state-level stability, and we identify open challenges that include sample efficiency, generalization, and safe deployment. Our goal is to provide researchers and practitioners with a coherent map of the rapidly expanding landscape of visual RL and to highlight promising directions for future inquiry. Resources are available at: https://github.com/weijiawu/Awesome-Visual-Reinforcement-Learning.", "github_manual": "", "github_regex": "https://github.com/weijiawu/Awesome-Visual-Reinforcement-Learning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.07662", "title": "GLiClass: Generalist Lightweight Model for Sequence Classification Tasks", "authors": [ "Ihor Stepanov", "Mykhailo Shtopko", "Dmytro Vodianytskyi", "Oleksandr Lukashov", "Alexander Yavorskyi", "Mykyta Yaroshenko" ], "github": "", "project_page": "", "abstract": "Classification is one of the most widespread tasks in AI applications, serving often as the first step in filtering, sorting, and categorizing data. Since modern AI systems must handle large volumes of input data and early pipeline stages can propagate errors downstream, achieving high efficiency and accuracy is critical. Moreover, classification requirements can change dynamically based on user needs, necessitating models with strong zero-shot capabilities. While generative LLMs have become mainstream for zero-shot classification due to their versatility, they suffer from inconsistent instruction following and computational inefficiency. Cross-encoders, commonly used as rerankers in RAG pipelines, face a different bottleneck: they must process text-label pairs sequentially, significantly reducing efficiency with large label sets. Embedding-based approaches offer good efficiency but struggle with complex scenarios involving logical and semantic constraints. We propose GLiClass, a novel method that adapts the GLiNER architecture for sequence classification tasks. Our approach achieves strong accuracy and efficiency comparable to embedding-based methods, while maintaining the flexibility needed for zero-shot and few-shot learning scenarios. Additionally, we adapted proximal policy optimization (PPO) for multi-label text classification, enabling training classifiers in data-sparse conditions or from human feedback.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.06426", "title": "Shortcut Learning in Generalist Robot Policies: The Role of Dataset Diversity and Fragmentation", "authors": [ "Youguang Xing", "Xu Luo", "Junlin Xie", "Lianli Gao", "Hengtao Shen", "Jingkuan Song" ], "github": "", "project_page": "", "abstract": "Generalist robot policies trained on large-scale datasets such as Open X-Embodiment (OXE) demonstrate strong performance across a wide range of tasks. However, they often struggle to generalize beyond the distribution of their training data. In this paper, we investigate the underlying cause of this limited generalization capability. We identify shortcut learning -- the reliance on task-irrelevant features -- as a key impediment to generalization. Through comprehensive theoretical and empirical analysis, we uncover two primary contributors to shortcut learning: (1) limited diversity within individual sub-datasets, and (2) significant distributional disparities across sub-datasets, leading to dataset fragmentation. These issues arise from the inherent structure of large-scale datasets like OXE, which are typically composed of multiple sub-datasets collected independently across varied environments and embodiments. Our findings provide critical insights into dataset collection strategies that can reduce shortcut learning and enhance the generalization ability of generalist robot policies. Moreover, in scenarios where acquiring new large-scale data is impractical, we demonstrate that carefully selected robotic data augmentation strategies can effectively reduce shortcut learning in existing offline datasets, thereby improving generalization capabilities of generalist robot policies, e.g., pi_0, in both simulation and real-world environments. More information at https://lucky-light-sun.github.io/proj/shortcut-learning-in-grps/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.08221", "title": "Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning", "authors": [ "Zihe Liu", "Jiashun Liu", "Yancheng He", "Weixun Wang", "Jiaheng Liu", "Ling Pan", "Xinyu Hu", "Shaopan Xiong", "Ju Huang", "Jian Hu", "Shengyi Huang", "Siran Yang", "Jiamang Wang", "Wenbo Su", "Bo Zheng" ], "github": "https://github.com/alibaba/ROLL", "project_page": "", "abstract": "Reinforcement learning for LLM reasoning has rapidly emerged as a prominent research area, marked by a significant surge in related studies on both algorithmic innovations and practical applications. Despite this progress, several critical challenges remain, including the absence of standardized guidelines for employing RL techniques and a fragmented understanding of their underlying mechanisms. Additionally, inconsistent experimental settings, variations in training data, and differences in model initialization have led to conflicting conclusions, obscuring the key characteristics of these techniques and creating confusion among practitioners when selecting appropriate techniques. This paper systematically reviews widely adopted RL techniques through rigorous reproductions and isolated evaluations within a unified open-source framework. We analyze the internal mechanisms, applicable scenarios, and core principles of each technique through fine-grained experiments, including datasets of varying difficulty, model sizes, and architectures. Based on these insights, we present clear guidelines for selecting RL techniques tailored to specific setups, and provide a reliable roadmap for practitioners navigating the RL for the LLM domain. Finally, we reveal that a minimalist combination of two techniques can unlock the learning capability of critic-free policies using vanilla PPO loss. The results demonstrate that our simple combination consistently improves performance, surpassing strategies like GRPO and DAPO.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/alibaba/ROLL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.05954", "title": "Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents", "authors": [ "Han Lin", "Jaemin Cho", "Amir Zadeh", "Chuan Li", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "There is growing interest in integrating high-fidelity visual synthesis capabilities into large language models (LLMs) without compromising their strong reasoning capabilities. Existing methods that directly train LLMs or bridge LLMs and diffusion models usually suffer from costly training since the backbone LLMs have not seen image representations during pretraining. We present Bifrost-1, a unified framework that bridges pretrained multimodal LLMs (MLLMs) and diffusion models using patch-level CLIP image embeddings as latent variables, which are natively aligned with the MLLM's CLIP visual encoder. These patch-level image embeddings are integrated into the diffusion model with a lightweight adaptation of its ControlNet. To retain the original multimodal reasoning capabilities of MLLMs, we equip the MLLM with a visual generation branch initialized from the original MLLM parameters when predicting the patch-level image embeddings. By seamlessly integrating pretrained MLLMs and diffusion models with patch-level CLIP latents, our framework enables high-fidelity controllable image generation with significant training efficiency. Our experiments demonstrate that Bifrost-1 achieves comparable or better performance than previous methods in terms of visual fidelity and multimodal understanding, with substantially lower compute during training. We also provide comprehensive ablation studies showing the effectiveness of our design choices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.05305", "title": "SONAR-LLM: Autoregressive Transformer that Thinks in Sentence Embeddings and Speaks in Tokens", "authors": [ "Nikita Dragunov", "Temurbek Rahmatullaev", "Elizaveta Goncharova", "Andrey Kuznetsov", "Anton Razzhigaev" ], "github": "", "project_page": "", "abstract": "The recently proposed Large Concept Model (LCM) generates text by predicting a sequence of sentence-level embeddings and training with either mean-squared error or diffusion objectives. We present SONAR-LLM, a decoder-only transformer that \"thinks\" in the same continuous SONAR embedding space, yet is supervised through token-level cross-entropy propagated via the frozen SONAR decoder. This hybrid objective retains the semantic abstraction of LCM while eliminating its diffusion sampler and restoring a likelihood-based training signal. Across model sizes from 39M to 1.3B parameters, SONAR-LLM attains competitive generation quality. We report scaling trends, ablations, benchmark results, and release the complete training code and all pretrained checkpoints to foster reproducibility and future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.03542", "title": "Speech-to-LaTeX: New Models and Datasets for Converting Spoken Equations and Sentences", "authors": [ "Dmitrii Korzh", "Dmitrii Tarasov", "Artyom Iudin", "Elvir Karimov", "Matvey Skripkin", "Nikita Kuzmin", "Andrey Kuznetsov", "Oleg Y. Rogov", "Ivan Oseledets" ], "github": "", "project_page": "", "abstract": "Conversion of spoken mathematical expressions is a challenging task that involves transcribing speech into a strictly structured symbolic representation while addressing the ambiguity inherent in the pronunciation of equations. Although significant progress has been achieved in automatic speech recognition (ASR) and language models (LM), the problem of converting spoken mathematics into LaTeX remains underexplored. This task directly applies to educational and research domains, such as lecture transcription or note creation. Based on ASR post-correction, prior work requires 2 transcriptions, focuses only on isolated equations, has a limited test set, and provides neither training data nor multilingual coverage. To address these issues, we present the first fully open-source large-scale dataset, comprising over 66,000 human-annotated audio samples of mathematical equations and sentences in both English and Russian, drawn from diverse scientific domains. In addition to the ASR post-correction models and few-shot prompting, we apply audio language models, demonstrating comparable character error rate (CER) results on the MathSpeech benchmark (28% vs. 30%) for the equations conversion. In contrast, on the proposed S2L-equations benchmark, our models outperform the MathSpeech model by a substantial margin of more than 40 percentage points, even after accounting for LaTeX formatting artifacts (27% vs. 64%). We establish the first benchmark for mathematical sentence recognition (S2L-sentences) and achieve an equation CER of 40%. This work lays the groundwork for future advances in multimodal AI, with a particular focus on mathematical content recognition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.07785", "title": "Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts", "authors": [ "Haoyuan Wu", "Haoxing Chen", "Xiaodong Chen", "Zhanchao Zhou", "Tieyuan Chen", "Yihong Zhuang", "Guoshan Lu", "Zenan Huang", "Junbo Zhao", "Lin Liu", "Zhenzhong Lan", "Bei Yu", "Jianguo Li" ], "github": "", "project_page": "", "abstract": "The Mixture of Experts (MoE) architecture is a cornerstone of modern state-of-the-art (SOTA) large language models (LLMs). MoE models facilitate scalability by enabling sparse parameter activation. However, traditional MoE architecture uses homogeneous experts of a uniform size, activating a fixed number of parameters irrespective of input complexity and thus limiting computational efficiency. To overcome this limitation, we introduce Grove MoE, a novel architecture incorporating experts of varying sizes, inspired by the heterogeneous big.LITTLE CPU architecture. This architecture features novel adjugate experts with a dynamic activation mechanism, enabling model capacity expansion while maintaining manageable computational overhead. Building on this architecture, we present GroveMoE-Base and GroveMoE-Inst, 33B-parameter LLMs developed by applying an upcycling strategy to the Qwen3-30B-A3B-Base model during mid-training and post-training. GroveMoE models dynamically activate 3.14-3.28B parameters based on token complexity and achieve performance comparable to SOTA open-source models of similar or even larger size.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.07407", "title": "A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems", "authors": [ "Jinyuan Fang", "Yanwen Peng", "Xi Zhang", "Yingxu Wang", "Xinhao Yi", "Guibin Zhang", "Yi Xu", "Bin Wu", "Siwei Liu", "Zihao Li", "Zhaochun Ren", "Nikos Aletras", "Xi Wang", "Han Zhou", "Zaiqiao Meng" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models have sparked growing interest in AI agents capable of solving complex, real-world tasks. However, most existing agent systems rely on manually crafted configurations that remain static after deployment, limiting their ability to adapt to dynamic and evolving environments. To this end, recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback. This emerging direction lays the foundation for self-evolving AI agents, which bridge the static capabilities of foundation models with the continuous adaptability required by lifelong agentic systems. In this survey, we provide a comprehensive review of existing techniques for self-evolving agentic systems. Specifically, we first introduce a unified conceptual framework that abstracts the feedback loop underlying the design of self-evolving agentic systems. The framework highlights four key components: System Inputs, Agent System, Environment, and Optimisers, serving as a foundation for understanding and comparing different strategies. Based on this framework, we systematically review a wide range of self-evolving techniques that target different components of the agent system. We also investigate domain-specific evolution strategies developed for specialised fields such as biomedicine, programming, and finance, where optimisation objectives are tightly coupled with domain constraints. In addition, we provide a dedicated discussion on the evaluation, safety, and ethical considerations for self-evolving agentic systems, which are critical to ensuring their effectiveness and reliability. This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents, laying the foundation for the development of more adaptive, autonomous, and lifelong agentic systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.05257", "title": "MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs", "authors": [ "Xiaodong Chen", "Mingming Ha", "Zhenzhong Lan", "Jing Zhang", "Jianguo Li" ], "github": "", "project_page": "", "abstract": "The Mixture-of-Experts (MoE) architecture has become a predominant paradigm for scaling large language models (LLMs). Despite offering strong performance and computational efficiency, large MoE-based LLMs like DeepSeek-V3-0324 and Kimi-K2-Instruct present serious challenges due to substantial memory requirements in deployment. While recent works have explored MoE compression to address this issue, existing methods often suffer from considerable accuracy drops (e.g., 7-14% relatively) even at modest compression rates. This paper introduces a novel Mixture-of-Basis-Experts (MoBE) method that achieves model compression while incurring minimal accuracy drops. Specifically, each up/gate matrix in an expert is decomposed via a rank decomposition as W = AB, where matrix A is unique to each expert. The relatively larger matrix B is further re-parameterized as a linear combination of basis matrices {Bi} shared across all experts within a given MoE layer. The factorization is learned by minimizing the reconstruction error relative to the original weight matrices. Experiments demonstrate that MoBE achieves notably lower accuracy drops compared to prior works. For instance, MoBE can reduce the parameter counts of Qwen3-235B-A22B-2507, DeepSeek-V3-0324 (671B) and Kimi-K2-Instruct (1T) by 24%-30% with only 1%-2% accuracy drop (about 2% drops when measured relatively).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.03346", "title": "Compressing Chain-of-Thought in LLMs via Step Entropy", "authors": [ "Zeju Li", "Jianyuan Zhong", "Ziyang Zheng", "Xiangyu Wen", "Zhijian Xu", "Yingying Cheng", "Fan Zhang", "Qiang Xu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) using Chain-of-Thought (CoT) prompting excel at complex reasoning but generate verbose thought processes with considerable redundancy, leading to increased inference costs and reduced efficiency. We introduce a novel CoT compression framework based on step entropy, a metric that quantifies the informational contribution of individual reasoning steps to identify redundancy. Through theoretical analysis and extensive empirical validation on mathematical reasoning benchmarks, we demonstrate that steps with low entropy are indeed highly redundant. Our experiments reveal that an astonishing 80\\% of low-entropy intermediate steps can be pruned with minor degradation in the final answer accuracy across DeepSeek-R1-7B, 14B and Qwen3-8B. This finding sharply contrasts with random or high-entropy pruning, which severely impairs reasoning performance. Building on this, we propose a novel two-stage training strategy combining Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO) reinforcement learning. This approach enables LLMs to autonomously learn to generate compressed COTs during inference by strategically incorporating [SKIP] tokens. Our method significantly enhances LLM inference efficiency while rigorously preserving accuracy, offering profound implications for practical LLM deployment and a deeper understanding of reasoning structures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.06059", "title": "Fact2Fiction: Targeted Poisoning Attack to Agentic Fact-checking System", "authors": [ "Haorui He", "Yupeng Li", "Bin Benjamin Zhu", "Dacheng Wen", "Reynold Cheng", "Francis C. M. Lau" ], "github": "", "project_page": "", "abstract": "State-of-the-art fact-checking systems combat misinformation at scale by employing autonomous LLM-based agents to decompose complex claims into smaller sub-claims, verify each sub-claim individually, and aggregate the partial results to produce verdicts with justifications (explanatory rationales for the verdicts). The security of these systems is crucial, as compromised fact-checkers, which tend to be easily underexplored, can amplify misinformation. This work introduces Fact2Fiction, the first poisoning attack framework targeting such agentic fact-checking systems. Fact2Fiction mirrors the decomposition strategy and exploits system-generated justifications to craft tailored malicious evidences that compromise sub-claim verification. Extensive experiments demonstrate that Fact2Fiction achieves 8.9\\%--21.2\\% higher attack success rates than state-of-the-art attacks across various poisoning budgets. Fact2Fiction exposes security weaknesses in current fact-checking systems and highlights the need for defensive countermeasures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2507.23701", "title": "TextQuests: How Good are LLMs at Text-Based Video Games?", "authors": [ "Long Phan", "Mantas Mazeika", "Andy Zou", "Dan Hendrycks" ], "github": "", "project_page": "", "abstract": "Evaluating AI agents within complex, interactive environments that mirror real-world challenges is critical for understanding their practical capabilities. While existing agent benchmarks effectively assess skills like tool use or performance on structured tasks, they often do not fully capture an agent's ability to operate autonomously in exploratory environments that demand sustained, self-directed reasoning over a long and growing context. To spur the development of agents capable of more robust intrinsic reasoning over long horizons, we introduce TextQuests, a benchmark based on the Infocom suite of interactive fiction games. These text-based adventures, which can take human players over 30 hours and require hundreds of precise actions to solve, serve as an effective proxy for evaluating AI agents on focused, stateful tasks. The benchmark is specifically designed to assess an LLM agent's capacity for self-contained problem-solving by precluding the use of external tools, thereby focusing on intrinsic long-context reasoning capabilities in an exploratory environment characterized by the need for trial-and-error learning and sustained problem-solving within a single interactive session. We release TextQuests at https://textquests.ai.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-12", "arxiv_id": "2508.05909", "title": "Spectrum Projection Score: Aligning Retrieved Summaries with Reader Models in Retrieval-Augmented Generation", "authors": [ "Zhanghao Hu", "Qinglin Zhu", "Siya Qi", "Yulan He", "Hanqi Yan", "Lin Gui" ], "github": "https://github.com/HU-xiaobai/Code-of-Beyond-Perplexity-Let-the-Reader-Select-Retrieval-Summaries-via-Spectrum-Projection-Score", "project_page": "", "abstract": "Large Language Models (LLMs) have shown improved generation performance through retrieval-augmented generation (RAG) following the retriever-reader paradigm, which supplements model inputs with externally retrieved knowledge. However, prior work often evaluates RAG holistically, assessing the retriever and reader jointly, making it difficult to isolate the true contribution of retrieval, particularly given the prompt sensitivity of LLMs used as readers. We introduce Spectrum Projection Score (SPS), a lightweight, supervision-free metric that allows the reader to gauge the semantic alignment of a retrieved summary with its hidden representation by comparing the area formed by generated tokens from the summary, and the principal directions of subspace in the reader and to measure the relevance. Building on SPS we present xCompress, an inference time controller framework that dynamically samples, ranks, and compresses retrieval summary candidates. Extensive experiments on five QA benchmarks with four open source LLMs show that SPS not only enhances performance across a range of tasks but also provides a principled perspective on the interaction between retrieval and generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HU-xiaobai/Code-of-Beyond-Perplexity-Let-the-Reader-Select-Retrieval-Summaries-via-Spectrum-Projection-Score", "github_api_added_by": "auto" }, { "date": "2025-08-12", "arxiv_id": "2508.06811", "title": "Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face", "authors": [ "Benjamin Laufer", "Hamidah Oderinwale", "Jon Kleinberg" ], "github": "", "project_page": "", "abstract": "Many have observed that the development and deployment of generative machine learning (ML) and artificial intelligence (AI) models follow a distinctive pattern in which pre-trained models are adapted and fine-tuned for specific downstream tasks. However, there is limited empirical work that examines the structure of these interactions. This paper analyzes 1.86 million models on Hugging Face, a leading peer production platform for model development. Our study of model family trees -- networks that connect fine-tuned models to their base or parent -- reveals sprawling fine-tuning lineages that vary widely in size and structure. Using an evolutionary biology lens to study ML models, we use model metadata and model cards to measure the genetic similarity and mutation of traits over model families. We find that models tend to exhibit a family resemblance, meaning their genetic markers and traits exhibit more overlap when they belong to the same model family. However, these similarities depart in certain ways from standard models of asexual reproduction, because mutations are fast and directed, such that two `sibling' models tend to exhibit more similarity than parent/child pairs. Further analysis of the directional drifts of these mutations reveals qualitative insights about the open machine learning ecosystem: Licenses counter-intuitively drift from restrictive, commercial licenses towards permissive or copyleft licenses, often in violation of upstream license's terms; models evolve from multi-lingual compatibility towards english-only compatibility; and model cards reduce in length and standardize by turning, more often, to templates and automatically generated text. Overall, this work takes a step toward an empirically grounded understanding of model fine-tuning and suggests that ecological models and methods can yield novel scientific insights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08086", "title": "Matrix-3D: Omnidirectional Explorable 3D World Generation", "authors": [ "Zhongqi Yang", "Wenhang Ge", "Yuqi Li", "Jiaqi Chen", "Haoyuan Li", "Mengyin An", "Fei Kang", "Hua Xue", "Baixin Xu", "Yuyang Yin", "Eric Li", "Yang Liu", "Yikai Wang", "Hao-Xiang Guo", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "Explorable 3D world generation from a single image or text prompt forms a cornerstone of spatial intelligence. Recent works utilize video model to achieve wide-scope and generalizable 3D world generation. However, existing approaches often suffer from a limited scope in the generated scenes. In this work, we propose Matrix-3D, a framework that utilize panoramic representation for wide-coverage omnidirectional explorable 3D world generation that combines conditional video generation and panoramic 3D reconstruction. We first train a trajectory-guided panoramic video diffusion model that employs scene mesh renders as condition, to enable high-quality and geometrically consistent scene video generation. To lift the panorama scene video to 3D world, we propose two separate methods: (1) a feed-forward large panorama reconstruction model for rapid 3D scene reconstruction and (2) an optimization-based pipeline for accurate and detailed 3D scene reconstruction. To facilitate effective training, we also introduce the Matrix-Pano dataset, the first large-scale synthetic collection comprising 116K high-quality static panoramic video sequences with depth and trajectory annotations. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art performance in panoramic video generation and 3D world generation. See more in https://matrix-3d.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.05615", "title": "Test-Time Reinforcement Learning for GUI Grounding via Region Consistency", "authors": [ "Yong Du", "Yuchen Yan", "Fei Tang", "Zhengxi Lu", "Chang Zong", "Weiming Lu", "Shengpei Jiang", "Yongliang Shen" ], "github": "", "project_page": "", "abstract": "Graphical User Interface (GUI) grounding, the task of mapping natural language instructions to precise screen coordinates, is fundamental to autonomous GUI agents. While existing methods achieve strong performance through extensive supervised training or reinforcement learning with labeled rewards, they remain constrained by the cost and availability of pixel-level annotations. We observe that when models generate multiple predictions for the same GUI element, the spatial overlap patterns reveal implicit confidence signals that can guide more accurate localization. Leveraging this insight, we propose GUI-RC (Region Consistency), a test-time scaling method that constructs spatial voting grids from multiple sampled predictions to identify consensus regions where models show highest agreement. Without any training, GUI-RC improves accuracy by 2-3% across various architectures on ScreenSpot benchmarks. We further introduce GUI-RCPO (Region Consistency Policy Optimization), which transforms these consistency patterns into rewards for test-time reinforcement learning. By computing how well each prediction aligns with the collective consensus, GUI-RCPO enables models to iteratively refine their outputs on unlabeled data during inference. Extensive experiments demonstrate the generality of our approach: GUI-RC boosts Qwen2.5-VL-3B-Instruct from 80.11% to 83.57% on ScreenSpot-v2, while GUI-RCPO further improves it to 85.14% through self-supervised optimization. Our approach reveals the untapped potential of test-time scaling and test-time reinforcement learning for GUI grounding, offering a promising path toward more robust and data-efficient GUI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.09138", "title": "Time Is a Feature: Exploiting Temporal Dynamics in Diffusion Language Models", "authors": [ "Wen Wang", "Bozhen Fang", "Chenchen Jing", "Yongliang Shen", "Yangyi Shen", "Qiuyu Wang", "Hao Ouyang", "Hao Chen", "Chunhua Shen" ], "github": "", "project_page": "", "abstract": "Diffusion large language models (dLLMs) generate text through iterative denoising, yet current decoding strategies discard rich intermediate predictions in favor of the final output. Our work here reveals a critical phenomenon, temporal oscillation, where correct answers often emerge in the middle process, but are overwritten in later denoising steps. To address this issue, we introduce two complementary methods that exploit temporal consistency: 1) Temporal Self-Consistency Voting, a training-free, test-time decoding strategy that aggregates predictions across denoising steps to select the most consistent output; and 2) a post-training method termed Temporal Consistency Reinforcement, which uses Temporal Semantic Entropy (TSE), a measure of semantic stability across intermediate predictions, as a reward signal to encourage stable generations. Empirical results across multiple benchmarks demonstrate the effectiveness of our approach. Using the negative TSE reward alone, we observe a remarkable average improvement of 24.7% on the Countdown dataset over an existing dLLM. Combined with the accuracy reward, we achieve absolute gains of 2.0% on GSM8K, 4.3% on MATH500, 6.6% on SVAMP, and 25.3% on Countdown, respectively. Our findings underscore the untapped potential of temporal dynamics in dLLMs and offer two simple yet effective tools to harness them.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.05399", "title": "UNCAGE: Contrastive Attention Guidance for Masked Generative Transformers in Text-to-Image Generation", "authors": [ "Wonjun Kang", "Byeongkeun Ahn", "Minjae Lee", "Kevin Galim", "Seunghyuk Oh", "Hyung Il Koo", "Nam Ik Cho" ], "github": "https://github.com/furiosa-ai/uncage", "project_page": "", "abstract": "Text-to-image (T2I) generation has been actively studied using Diffusion Models and Autoregressive Models. Recently, Masked Generative Transformers have gained attention as an alternative to Autoregressive Models to overcome the inherent limitations of causal attention and autoregressive decoding through bidirectional attention and parallel decoding, enabling efficient and high-quality image generation. However, compositional T2I generation remains challenging, as even state-of-the-art Diffusion Models often fail to accurately bind attributes and achieve proper text-image alignment. While Diffusion Models have been extensively studied for this issue, Masked Generative Transformers exhibit similar limitations but have not been explored in this context. To address this, we propose Unmasking with Contrastive Attention Guidance (UNCAGE), a novel training-free method that improves compositional fidelity by leveraging attention maps to prioritize the unmasking of tokens that clearly represent individual objects. UNCAGE consistently improves performance in both quantitative and qualitative evaluations across multiple benchmarks and metrics, with negligible inference overhead. Our code is available at https://github.com/furiosa-ai/uncage.", "github_manual": "", "github_regex": "https://github.com/furiosa-ai/uncage", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08088", "title": "HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches", "authors": [ "Jiejun Tan", "Zhicheng Dou", "Yan Yu", "Jiehan Cheng", "Qiang Ju", "Jian Xie", "Ji-Rong Wen" ], "github": "https://github.com/plageon/HierSearch", "project_page": "", "abstract": "Recently, large reasoning models have demonstrated strong mathematical and coding abilities, and deep search leverages their reasoning capabilities in challenging information retrieval tasks. Existing deep search works are generally limited to a single knowledge source, either local or the Web. However, enterprises often require private deep search systems that can leverage search tools over both local and the Web corpus. Simply training an agent equipped with multiple search tools using flat reinforcement learning (RL) is a straightforward idea, but it has problems such as low training data efficiency and poor mastery of complex tools. To address the above issue, we propose a hierarchical agentic deep search framework, HierSearch, trained with hierarchical RL. At the low level, a local deep search agent and a Web deep search agent are trained to retrieve evidence from their corresponding domains. At the high level, a planner agent coordinates low-level agents and provides the final answer. Moreover, to prevent direct answer copying and error propagation, we design a knowledge refiner that filters out hallucinations and irrelevant evidence returned by low-level agents. Experiments show that HierSearch achieves better performance compared to flat RL, and outperforms various deep search and multi-source retrieval-augmented generation baselines in six benchmarks across general, finance, and medical domains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/plageon/HierSearch", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.07976", "title": "Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RL", "authors": [ "Jiaxuan Gao", "Wei Fu", "Minyang Xie", "Shusheng Xu", "Chuyi He", "Zhiyu Mei", "Banghua Zhu", "Yi Wu" ], "github": "https://github.com/inclusionAI/ASearcher", "project_page": "", "abstract": "Recent advancements in LLM-based agents have demonstrated remarkable capabilities in handling complex, knowledge-intensive tasks by integrating external tools. Among diverse choices of tools, search tools play a pivotal role in accessing vast external knowledge. However, open-source agents still fall short of achieving expert-level Search Intelligence, the ability to resolve ambiguous queries, generate precise searches, analyze results, and conduct thorough exploration. Existing approaches fall short in scalability, efficiency, and data quality. For example, small turn limits in existing online RL methods, e.g. <=10, restrict complex strategy learning. This paper introduces ASearcher, an open-source project for large-scale RL training of search agents. Our key contributions include: (1) Scalable fully asynchronous RL training that enables long-horizon search while maintaining high training efficiency. (2) A prompt-based LLM agent that autonomously synthesizes high-quality and challenging QAs, creating a large-scale QA dataset. Through RL training, our prompt-based QwQ-32B agent achieves substantial improvements, with 46.7% and 20.8% Avg@4 gains on xBench and GAIA, respectively. Notably, our agent exhibits extreme long-horizon search, with tool calls exceeding 40 turns and output tokens exceeding 150k during training time. With a simple agent design and no external LLMs, ASearcher-Web-QwQ achieves Avg@4 scores of 42.1 on xBench and 52.8 on GAIA, surpassing existing open-source 32B agents. We open-source our models, training data, and codes in https://github.com/inclusionAI/ASearcher.", "github_manual": "", "github_regex": "https://github.com/inclusionAI/ASearcher", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.05748", "title": "WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent", "authors": [ "Xinyu Geng", "Peng Xia", "Zhen Zhang", "Xinyu Wang", "Qiuchen Wang", "Ruixue Ding", "Chenxi Wang", "Jialong Wu", "Yida Zhao", "Kuan Li", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "Web agents such as Deep Research have demonstrated superhuman cognitive abilities, capable of solving highly challenging information-seeking problems. However, most research remains primarily text-centric, overlooking visual information in the real world. This makes multimodal Deep Research highly challenging, as such agents require much stronger reasoning abilities in perception, logic, knowledge, and the use of more sophisticated tools compared to text-based agents. To address this limitation, we introduce WebWatcher, a multi-modal Agent for Deep Research equipped with enhanced visual-language reasoning capabilities. It leverages high-quality synthetic multimodal trajectories for efficient cold start training, utilizes various tools for deep reasoning, and further enhances generalization through reinforcement learning. To better evaluate the capabilities of multimodal agents, we propose BrowseComp-VL, a benchmark with BrowseComp-style that requires complex information retrieval involving both visual and textual information. Experimental results show that WebWatcher significantly outperforms proprietary baseline, RAG workflow and open-source agents in four challenging VQA benchmarks, which paves the way for solving complex multimodal information-seeking tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.09101", "title": "AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators", "authors": [ "Jason Chou", "Ao Liu", "Yuchi Deng", "Zhiying Zeng", "Tao Zhang", "Haotian Zhu", "Jianwei Cai", "Yue Mao", "Chenchen Zhang", "Lingyun Tan", "Ziyan Xu", "Bohui Zhai", "Hengyi Liu", "Speed Zhu", "Wiggin Zhou", "Fengzong Lian" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, with code generation emerging as a key area of focus. While numerous benchmarks have been proposed to evaluate their code generation abilities, these benchmarks face several critical limitations. First, they often rely on manual annotations, which are time-consuming and difficult to scale across different programming languages and problem complexities. Second, most existing benchmarks focus primarily on Python, while the few multilingual benchmarks suffer from limited difficulty and uneven language distribution. To address these challenges, we propose AutoCodeGen, an automated method for generating high-difficulty multilingual code generation datasets without manual annotations. AutoCodeGen ensures the correctness and completeness of test cases by generating test inputs with LLMs and obtaining test outputs through a multilingual sandbox, while achieving high data quality through reverse-order problem generation and multiple filtering steps. Using this novel method, we introduce AutoCodeBench, a large-scale code generation benchmark comprising 3,920 problems evenly distributed across 20 programming languages. It is specifically designed to evaluate LLMs on challenging, diverse, and practical multilingual tasks. We evaluate over 30 leading open-source and proprietary LLMs on AutoCodeBench and its simplified version AutoCodeBench-Lite. The results show that even the most advanced LLMs struggle with the complexity, diversity, and multilingual nature of these tasks. Besides, we introduce AutoCodeBench-Complete, specifically designed for base models to assess their few-shot code generation capabilities. We hope the AutoCodeBench series will serve as a valuable resource and inspire the community to focus on more challenging and practical multilingual code generation scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08791", "title": "Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments", "authors": [ "Junjie Ye", "Changhao Jiang", "Zhengyin Du", "Yufei Xu", "Xuesong Yao", "Zhiheng Xi", "Xiaoran Fan", "Qi Zhang", "Xuanjing Huang", "Jiecao Chen" ], "github": "", "project_page": "", "abstract": "Effective tool use is essential for large language models (LLMs) to interact meaningfully with their environment. However, progress is limited by the lack of efficient reinforcement learning (RL) frameworks specifically designed for tool use, due to challenges in constructing stable training environments and designing verifiable reward mechanisms. To address this, we propose an automated environment construction pipeline, incorporating scenario decomposition, document generation, function integration, complexity scaling, and localized deployment. This enables the creation of high-quality training environments that provide detailed and measurable feedback without relying on external tools. Additionally, we introduce a verifiable reward mechanism that evaluates both the precision of tool use and the completeness of task execution. When combined with trajectory data collected from the constructed environments, this mechanism integrates seamlessly with standard RL algorithms to facilitate feedback-driven model training. Experiments on LLMs of varying scales demonstrate that our approach significantly enhances the models' tool-use performance without degrading their general capabilities, regardless of inference modes or training algorithms. Our analysis suggests that these gains result from improved context understanding and reasoning, driven by updates to the lower-layer MLP parameters in models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08244", "title": "Cut2Next: Generating Next Shot via In-Context Tuning", "authors": [ "Jingwen He", "Hongbo Liu", "Jiajun Li", "Ziqi Huang", "Yu Qiao", "Wanli Ouyang", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Effective multi-shot generation demands purposeful, film-like transitions and strict cinematic continuity. Current methods, however, often prioritize basic visual consistency, neglecting crucial editing patterns (e.g., shot/reverse shot, cutaways) that drive narrative flow for compelling storytelling. This yields outputs that may be visually coherent but lack narrative sophistication and true cinematic integrity. To bridge this, we introduce Next Shot Generation (NSG): synthesizing a subsequent, high-quality shot that critically conforms to professional editing patterns while upholding rigorous cinematic continuity. Our framework, Cut2Next, leverages a Diffusion Transformer (DiT). It employs in-context tuning guided by a novel Hierarchical Multi-Prompting strategy. This strategy uses Relational Prompts to define overall context and inter-shot editing styles. Individual Prompts then specify per-shot content and cinematographic attributes. Together, these guide Cut2Next to generate cinematically appropriate next shots. Architectural innovations, Context-Aware Condition Injection (CACI) and Hierarchical Attention Mask (HAM), further integrate these diverse signals without introducing new parameters. We construct RawCuts (large-scale) and CuratedCuts (refined) datasets, both with hierarchical prompts, and introduce CutBench for evaluation. Experiments show Cut2Next excels in visual consistency and text fidelity. Crucially, user studies reveal a strong preference for Cut2Next, particularly for its adherence to intended editing patterns and overall cinematic continuity, validating its ability to generate high-quality, narratively expressive, and cinematically coherent subsequent shots.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.07409", "title": "CharacterShot: Controllable and Consistent 4D Character Animation", "authors": [ "Junyao Gao", "Jiaxing Li", "Wenran Liu", "Yanhong Zeng", "Fei Shen", "Kai Chen", "Yanan Sun", "Cairong Zhao" ], "github": "https://github.com/Jeoyal/CharacterShot", "project_page": "", "abstract": "In this paper, we propose CharacterShot, a controllable and consistent 4D character animation framework that enables any individual designer to create dynamic 3D characters (i.e., 4D character animation) from a single reference character image and a 2D pose sequence. We begin by pretraining a powerful 2D character animation model based on a cutting-edge DiT-based image-to-video model, which allows for any 2D pose sequnce as controllable signal. We then lift the animation model from 2D to 3D through introducing dual-attention module together with camera prior to generate multi-view videos with spatial-temporal and spatial-view consistency. Finally, we employ a novel neighbor-constrained 4D gaussian splatting optimization on these multi-view videos, resulting in continuous and stable 4D character representations. Moreover, to improve character-centric performance, we construct a large-scale dataset Character4D, containing 13,115 unique characters with diverse appearances and motions, rendered from multiple viewpoints. Extensive experiments on our newly constructed benchmark, CharacterBench, demonstrate that our approach outperforms current state-of-the-art methods. Code, models, and datasets will be publicly available at https://github.com/Jeoyal/CharacterShot.", "github_manual": "", "github_regex": "https://github.com/Jeoyal/CharacterShot", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.09062", "title": "VertexRegen: Mesh Generation with Continuous Level of Detail", "authors": [ "Xiang Zhang", "Yawar Siddiqui", "Armen Avetisyan", "Chris Xie", "Jakob Engel", "Henry Howard-Jenkins" ], "github": "", "project_page": "", "abstract": "We introduce VertexRegen, a novel mesh generation framework that enables generation at a continuous level of detail. Existing autoregressive methods generate meshes in a partial-to-complete manner and thus intermediate steps of generation represent incomplete structures. VertexRegen takes inspiration from progressive meshes and reformulates the process as the reversal of edge collapse, i.e. vertex split, learned through a generative model. Experimental results demonstrate that VertexRegen produces meshes of comparable quality to state-of-the-art methods while uniquely offering anytime generation with the flexibility to halt at any step to yield valid meshes with varying levels of detail.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.09050", "title": "Bridging Theory and Practice in Quantum Game Theory: Optimized Implementation of the Battle of the Sexes with Error Mitigation on NISQ Hardware", "authors": [ "Germ\u00e1n D\u00edaz Agreda", "Carlos Andres Duran Paredes", "Mateo Buenaventura Samboni", "Jhon Alejandro Andrade", "Sebasti\u00e1n Andr\u00e9s Cajas Ordo\u00f1ez" ], "github": "", "project_page": "", "abstract": "Implementing quantum game theory on real hardware is challenging due to noise, decoherence, and limited qubit connectivity, yet such demonstrations are essential to validate theoretical predictions. We present one of the first full experimental realizations of the Battle of the Sexes game under the Eisert-Wilkens-Lewenstein (EWL) framework on IBM Quantum's ibm sherbrooke superconducting processor. Four quantum strategies (I, H, R(pi/4), R(pi)) were evaluated across 31 entanglement values gamma in [0, pi] using 2048 shots per configuration, enabling a direct comparison between analytical predictions and hardware execution. To mitigate noise and variability, we introduce a Guided Circuit Mapping (GCM) method that dynamically selects qubit pairs and optimizes routing based on real-time topology and calibration data. The analytical model forecasts up to 108% payoff improvement over the classical equilibrium, and despite hardware-induced deviations, experimental results with GCM preserve the expected payoff trends within 3.5%-12% relative error. These findings show that quantum advantages in strategic coordination can persist under realistic NISQ conditions, providing a pathway toward practical applications of quantum game theory in multi-agent, economic, and distributed decision-making systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.04195", "title": "NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech Modeling with Paralinguistic Vocalizations", "authors": [ "Huan Liao", "Qinke Ni", "Yuancheng Wang", "Yiheng Lu", "Haoyue Zhan", "Pengyuan Xie", "Qiang Zhang", "Zhizheng Wu" ], "github": "", "project_page": "", "abstract": "Paralinguistic vocalizations-including non-verbal sounds like laughter and breathing, as well as lexicalized interjections such as \"uhm\" and \"oh\"-are integral to natural spoken communication. Despite their importance in conveying affect, intent, and interactional cues, such cues remain largely overlooked in conventional automatic speech recognition (ASR) and text-to-speech (TTS) systems. We present NVSpeech, an integrated and scalable pipeline that bridges the recognition and synthesis of paralinguistic vocalizations, encompassing dataset construction, ASR modeling, and controllable TTS. (1) We introduce a manually annotated dataset of 48,430 human-spoken utterances with 18 word-level paralinguistic categories. (2) We develop the paralinguistic-aware ASR model, which treats paralinguistic cues as inline decodable tokens (e.g., \"You're so funny [Laughter]\"), enabling joint lexical and non-verbal transcription. This model is then used to automatically annotate a large corpus, the first large-scale Chinese dataset of 174,179 utterances (573 hours) with word-level alignment and paralingustic cues. (3) We finetune zero-shot TTS models on both human- and auto-labeled data to enable explicit control over paralinguistic vocalizations, allowing context-aware insertion at arbitrary token positions for human-like speech synthesis. By unifying the recognition and generation of paralinguistic vocalizations, NVSpeech offers the first open, large-scale, word-level annotated pipeline for expressive speech modeling in Mandarin, integrating recognition and synthesis in a scalable and controllable manner. Dataset and audio demos are available at https://nvspeech170k.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08665", "title": "Aryabhata: An exam-focused language model for JEE Math", "authors": [ "Ritvik Rastogi", "Sachin Dharashivkar", "Sandeep Varma" ], "github": "", "project_page": "", "abstract": "We present Aryabhata 1.0, a compact 7B parameter math reasoning model optimized for the Indian academic exam, the Joint Entrance Examination (JEE). Despite rapid progress in large language models (LLMs), current models often remain unsuitable for educational use. Aryabhata 1.0 is built by merging strong open-weight reasoning models, followed by supervised fine-tuning (SFT) with curriculum learning on verified chain-of-thought (CoT) traces curated through best-of-n rejection sampling. To further boost performance, we apply reinforcement learning with verifiable rewards (RLVR) using A2C objective with group-relative advantage estimation alongwith novel exploration strategies such as Adaptive Group Resizing and Temperature Scaling. Evaluated on both in-distribution (JEE Main 2025) and out-of-distribution (MATH, GSM8K) benchmarks, Aryabhata outperforms existing models in accuracy and efficiency, while offering pedagogically useful step-by-step reasoning. We release Aryabhata as a foundation model to advance exam-centric, open-source small language models. This marks our first open release for community feedback (https://huggingface.co/PhysicsWallahAI/Aryabhata-1.0{Aryabhata 1.0 on Hugging Face}); PW is actively training future models to further improve learning outcomes for students.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08940", "title": "Train Long, Think Short: Curriculum Learning for Efficient Reasoning", "authors": [ "Hasan Abed Al Kader Hammoud", "Kumail Alhamoud", "Abed Hammoud", "Elie Bou-Zeid", "Marzyeh Ghassemi", "Bernard Ghanem" ], "github": "https://github.com/hammoudhasan/curriculum_grpo", "project_page": "", "abstract": "Recent work on enhancing the reasoning abilities of large language models (LLMs) has introduced explicit length control as a means of constraining computational cost while preserving accuracy. However, existing approaches rely on fixed-length training budgets, which do not take advantage of the natural progression from exploration to compression during learning. In this work, we propose a curriculum learning strategy for length-controlled reasoning using Group Relative Policy Optimization (GRPO). Our method starts with generous token budgets and gradually tightens them over training, encouraging models to first discover effective solution strategies and then distill them into more concise reasoning traces. We augment GRPO with a reward function that balances three signals: task correctness (via verifier feedback), length efficiency, and formatting adherence (via structural tags). Experiments on GSM8K, MATH500, SVAMP, College Math, and GSM+ demonstrate that curriculum-based training consistently outperforms fixed-budget baselines at the same final budget, achieving higher accuracy and significantly improved token efficiency. We further ablate the impact of reward weighting and decay schedule design, showing that progressive constraint serves as a powerful inductive bias for training efficient reasoning models. Our code and checkpoints are released at: https://github.com/hammoudhasan/curriculum_grpo.", "github_manual": "", "github_regex": "https://github.com/hammoudhasan/curriculum_grpo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.09123", "title": "OpenCUA: Open Foundations for Computer-Use Agents", "authors": [ "Xinyuan Wang", "Bowen Wang", "Dunjie Lu", "Junlin Yang", "Tianbao Xie", "Junli Wang", "Jiaqi Deng", "Xiaole Guo", "Yiheng Xu", "Chen Henry Wu", "Zhennan Shen", "Zhuokai Li", "Ryan Li", "Xiaochuan Li", "Junda Chen", "Boyuan Zheng", "Peihang Li", "Fangyu Lei", "Ruisheng Cao", "Yeqiao Fu", "Dongchan Shin", "Martin Shin", "Jiarui Hu", "Yuyan Wang", "Jixuan Chen", "Yuxiao Ye", "Danyang Zhang", "Dikang Du", "Hao Hu", "Huarong Chen", "Zaida Zhou", "Yipu Wang", "Heng Wang", "Diyi Yang", "Victor Zhong", "Flood Sung", "Y. Charles", "Zhilin Yang", "Tao Yu" ], "github": "", "project_page": "", "abstract": "Vision-language models have demonstrated impressive capabilities as computer-use agents (CUAs) capable of automating diverse computer tasks. As their commercial potential grows, critical details of the most capable CUA systems remain closed. As these agents will increasingly mediate digital interactions and execute consequential decisions on our behalf, the research community needs access to open CUA frameworks to study their capabilities, limitations, and risks. To bridge this gap, we propose OpenCUA, a comprehensive open-source framework for scaling CUA data and foundation models. Our framework consists of: (1) an annotation infrastructure that seamlessly captures human computer-use demonstrations; (2) AgentNet, the first large-scale computer-use task dataset spanning 3 operating systems and 200+ applications and websites; (3) a scalable pipeline that transforms demonstrations into state-action pairs with reflective long Chain-of-Thought reasoning that sustain robust performance gains as data scales. Our end-to-end agent models demonstrate strong performance across CUA benchmarks. In particular, OpenCUA-32B achieves an average success rate of 34.8% on OSWorld-Verified, establishing a new state-of-the-art (SOTA) among open-source models and surpassing OpenAI CUA (GPT-4o). Further analysis confirms that our approach generalizes well across domains and benefits significantly from increased test-time computation. We release our annotation tool, datasets, code, and models to build open foundations for further CUA research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.06964", "title": "Adversarial Video Promotion Against Text-to-Video Retrieval", "authors": [ "Qiwei Tian", "Chenhao Lin", "Zhengyu Zhao", "Qian Li", "Shuai Liu", "Chao Shen" ], "github": "https://github.com/michaeltian108/ViPro", "project_page": "", "abstract": "Thanks to the development of cross-modal models, text-to-video retrieval (T2VR) is advancing rapidly, but its robustness remains largely unexamined. Existing attacks against T2VR are designed to push videos away from queries, i.e., suppressing the ranks of videos, while the attacks that pull videos towards selected queries, i.e., promoting the ranks of videos, remain largely unexplored. These attacks can be more impactful as attackers may gain more views/clicks for financial benefits and widespread (mis)information. To this end, we pioneer the first attack against T2VR to promote videos adversarially, dubbed the Video Promotion attack (ViPro). We further propose Modal Refinement (MoRe) to capture the finer-grained, intricate interaction between visual and textual modalities to enhance black-box transferability. Comprehensive experiments cover 2 existing baselines, 3 leading T2VR models, 3 prevailing datasets with over 10k videos, evaluated under 3 scenarios. All experiments are conducted in a multi-target setting to reflect realistic scenarios where attackers seek to promote the video regarding multiple queries simultaneously. We also evaluated our attacks for defences and imperceptibility. Overall, ViPro surpasses other baselines by over 30/10/4% for white/grey/black-box settings on average. Our work highlights an overlooked vulnerability, provides a qualitative analysis on the upper/lower bound of our attacks, and offers insights into potential counterplays. Code will be publicly available at https://github.com/michaeltian108/ViPro.", "github_manual": "", "github_regex": "https://github.com/michaeltian108/ViPro", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08896", "title": "Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors", "authors": [ "Haoyu Zhao", "Linghao Zhuang", "Xingyue Zhao", "Cheng Zeng", "Haoran Xu", "Yuming Jiang", "Jun Cen", "Kexiang Wang", "Jiayan Guo", "Siteng Huang", "Xin Li", "Deli Zhao", "Hua Zou" ], "github": "https://github.com/Maxwell-Zhao/AffordDex", "project_page": "", "abstract": "A dexterous hand capable of generalizable grasping objects is fundamental for the development of general-purpose embodied AI. However, previous methods focus narrowly on low-level grasp stability metrics, neglecting affordance-aware positioning and human-like poses which are crucial for downstream manipulation. To address these limitations, we propose AffordDex, a novel framework with two-stage training that learns a universal grasping policy with an inherent understanding of both motion priors and object affordances. In the first stage, a trajectory imitator is pre-trained on a large corpus of human hand motions to instill a strong prior for natural movement. In the second stage, a residual module is trained to adapt these general human-like motions to specific object instances. This refinement is critically guided by two components: our Negative Affordance-aware Segmentation (NAA) module, which identifies functionally inappropriate contact regions, and a privileged teacher-student distillation process that ensures the final vision-based policy is highly successful. Extensive experiments demonstrate that AffordDex not only achieves universal dexterous grasping but also remains remarkably human-like in posture and functionally appropriate in contact location. As a result, AffordDex significantly outperforms state-of-the-art baselines across seen objects, unseen instances, and even entirely novel categories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Maxwell-Zhao/AffordDex", "github_api_added_by": "auto" }, { "date": "2025-08-13", "arxiv_id": "2508.06485", "title": "WGAST: Weakly-Supervised Generative Network for Daily 10 m Land Surface Temperature Estimation via Spatio-Temporal Fusion", "authors": [ "Sofiane Bouaziz", "Adel Hafiane", "Raphael Canals", "Rachid Nedjai" ], "github": "https://github.com/Sofianebouaziz1/WGAST.git", "project_page": "", "abstract": "Urbanization, climate change, and agricultural stress are increasing the demand for precise and timely environmental monitoring. Land Surface Temperature (LST) is a key variable in this context and is retrieved from remote sensing satellites. However, these systems face a trade-off between spatial and temporal resolution. While spatio-temporal fusion methods offer promising solutions, few have addressed the estimation of daily LST at 10 m resolution. In this study, we present WGAST, a Weakly-Supervised Generative Network for Daily 10 m LST Estimation via Spatio-Temporal Fusion of Terra MODIS, Landsat 8, and Sentinel-2. WGAST is the first end-to-end deep learning framework designed for this task. It adopts a conditional generative adversarial architecture, with a generator composed of four stages: feature extraction, fusion, LST reconstruction, and noise suppression. The first stage employs a set of encoders to extract multi-level latent representations from the inputs, which are then fused in the second stage using cosine similarity, normalization, and temporal attention mechanisms. The third stage decodes the fused features into high-resolution LST, followed by a Gaussian filter to suppress high-frequency noise. Training follows a weakly supervised strategy based on physical averaging principles and reinforced by a PatchGAN discriminator. Experiments demonstrate that WGAST outperforms existing methods in both quantitative and qualitative evaluations. Compared to the best-performing baseline, on average, WGAST reduces RMSE by 17.18% and improves SSIM by 11.00%. Furthermore, WGAST is robust to cloud-induced LST and effectively captures fine-scale thermal patterns, as validated against 33 ground-based sensors. The code is available at https://github.com/Sofianebouaziz1/WGAST.git.", "github_manual": "", "github_regex": "https://github.com/Sofianebouaziz1/WGAST.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.04676", "title": "GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via General Samples Replay", "authors": [ "Yunan Zhang", "Shuoran Jiang", "Mengchen Zhao", "Yuefeng Li", "Yang Fan", "Xiangping Wu", "Qingcai Chen" ], "github": "https://github.com/Qznan/GeRe", "project_page": "", "abstract": "The continual learning capability of large language models (LLMs) is crucial for advancing artificial general intelligence. However, continual fine-tuning LLMs across various domains often suffers from catastrophic forgetting, characterized by: 1) significant forgetting of their general capabilities, and 2) sharp performance declines in previously learned tasks. To simultaneously address both issues in a simple yet stable manner, we propose General Sample Replay (GeRe), a framework that use usual pretraining texts for efficient anti-forgetting. Beyond revisiting the most prevalent replay-based practices under GeRe, we further leverage neural states to introduce a enhanced activation states constrained optimization method using threshold-based margin (TM) loss, which maintains activation state consistency during replay learning. We are the first to validate that a small, fixed set of pre-collected general replay samples is sufficient to resolve both concerns--retaining general capabilities while promoting overall performance across sequential tasks. Indeed, the former can inherently facilitate the latter. Through controlled experiments, we systematically compare TM with different replay strategies under the GeRe framework, including vanilla label fitting, logit imitation via KL divergence and feature imitation via L1/L2 losses. Results demonstrate that TM consistently improves performance and exhibits better robustness. Our work paves the way for efficient replay of LLMs for the future. Our code and data are available at https://github.com/Qznan/GeRe.", "github_manual": "", "github_regex": "https://github.com/Qznan/GeRe", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08938", "title": "DeCRED: Decoder-Centric Regularization for Encoder-Decoder Based Speech Recognition", "authors": [ "Alexander Polok", "Santosh Kesiraju", "Karel Bene\u0161", "Bolaji Yusuf", "Luk\u00e1\u0161 Burget", "Jan \u010cernock\u00fd" ], "github": "", "project_page": "", "abstract": "This paper presents a simple yet effective regularization for the internal language model induced by the decoder in encoder-decoder ASR models, thereby improving robustness and generalization in both in- and out-of-domain settings. The proposed method, Decoder-Centric Regularization in Encoder-Decoder (DeCRED), adds auxiliary classifiers to the decoder, enabling next token prediction via intermediate logits. Empirically, DeCRED reduces the mean internal LM BPE perplexity by 36.6% relative to 11 test sets. Furthermore, this translates into actual WER improvements over the baseline in 5 of 7 in-domain and 3 of 4 out-of-domain test sets, reducing macro WER from 6.4% to 6.3% and 18.2% to 16.2%, respectively. On TEDLIUM3, DeCRED achieves 7.0% WER, surpassing the baseline and encoder-centric InterCTC regularization by 0.6% and 0.5%, respectively. Finally, we compare DeCRED with OWSM v3.1 and Whisper-medium, showing competitive WERs despite training on much less data with fewer parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08855", "title": "BiasGym: Fantastic Biases and How to Find (and Remove) Them", "authors": [ "Sekh Mainul Islam", "Nadav Borenstein", "Siddhesh Milind Pawar", "Haeun Yu", "Arnav Arora", "Isabelle Augenstein" ], "github": "", "project_page": "", "abstract": "Understanding biases and stereotypes encoded in the weights of Large Language Models (LLMs) is crucial for developing effective mitigation strategies. Biased behaviour is often subtle and non-trivial to isolate, even when deliberately elicited, making systematic analysis and debiasing particularly challenging. To address this, we introduce BiasGym, a simple, cost-effective, and generalizable framework for reliably injecting, analyzing, and mitigating conceptual associations within LLMs. BiasGym consists of two components: BiasInject, which injects specific biases into the model via token-based fine-tuning while keeping the model frozen, and BiasScope, which leverages these injected signals to identify and steer the components responsible for biased behavior. Our method enables consistent bias elicitation for mechanistic analysis, supports targeted debiasing without degrading performance on downstream tasks, and generalizes to biases unseen during training. We demonstrate the effectiveness of BiasGym in reducing real-world stereotypes (e.g., people from a country being `reckless drivers') and in probing fictional associations (e.g., people from a country having `blue skin'), showing its utility for both safety interventions and interpretability research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.07485", "title": "Democratizing Diplomacy: A Harness for Evaluating Any Large Language Model on Full-Press Diplomacy", "authors": [ "Alexander Duffy", "Samuel J Paech", "Ishana Shastri", "Elizabeth Karpinski", "Baptiste Alloui-Cros", "Tyler Marques", "Matthew Lyle Olson" ], "github": "https://github.com/GoodStartLabs/AI_Diplomacy", "project_page": "", "abstract": "We present the first evaluation harness that enables any out-of-the-box, local, Large Language Models (LLMs) to play full-press Diplomacy without fine-tuning or specialized training. Previous work required frontier LLMs, or fine-tuning, due to the high complexity and information density of Diplomacy's game state. Combined with the high variance of matches, these factors made Diplomacy prohibitive for study. In this work, we used data-driven iteration to optimize a textual game state representation such that a 24B model can reliably complete matches without any fine tuning. We develop tooling to facilitate hypothesis testing and statistical analysis, and we present case studies on persuasion, aggressive playstyles, and performance across a range of models. We conduct a variety of experiments across many popular LLMs, finding the larger models perform the best, but the smaller models still play adequately. We also introduce Critical State Analysis: an experimental protocol for rapidly iterating and analyzing key moments in a game at depth. Our harness democratizes the evaluation of strategic reasoning in LLMs by eliminating the need for fine-tuning, and it provides insights into how these capabilities emerge naturally from widely used LLMs. Our code is available in the supplement and will be open sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GoodStartLabs/AI_Diplomacy", "github_api_added_by": "auto" }, { "date": "2025-08-13", "arxiv_id": "2508.08680", "title": "TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation", "authors": [ "Armel Zebaze", "Beno\u00eet Sagot", "Rachel Bawden" ], "github": "https://github.com/ArmelRandy/topxgen", "project_page": "", "abstract": "LLMs have been shown to perform well in machine translation (MT) with the use of in-context learning (ICL), rivaling supervised models when translating into high-resource languages (HRLs). However, they lag behind when translating into low-resource language (LRLs). Example selection via similarity search and supervised fine-tuning help. However the improvements they give are limited by the size, quality and diversity of existing parallel datasets. A common technique in low-resource MT is synthetic parallel data creation, the most frequent of which is backtranslation, whereby existing target-side texts are automatically translated into the source language. However, this assumes the existence of good quality and relevant target-side texts, which are not readily available for many LRLs. In this paper, we present TopXGen, an LLM-based approach for the generation of high quality and topic-diverse data in multiple LRLs, which can then be backtranslated to produce useful and diverse parallel texts for ICL and fine-tuning. Our intuition is that while LLMs struggle to translate into LRLs, their ability to translate well into HRLs and their multilinguality enable them to generate good quality, natural-sounding target-side texts, which can be translated well into a high-resource source language. We show that TopXGen boosts LLM translation performance during fine-tuning and in-context learning. Code and outputs are available at https://github.com/ArmelRandy/topxgen.", "github_manual": "", "github_regex": "https://github.com/ArmelRandy/topxgen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.05813", "title": "Optimization-Free Style Transfer for 3D Gaussian Splats", "authors": [ "Raphael Du Sablon", "David Hart" ], "github": "https://github.com/davidmhart/FastSplatStyler", "project_page": "", "abstract": "The task of style transfer for 3D Gaussian splats has been explored in many previous works, but these require reconstructing or fine-tuning the splat while incorporating style information or optimizing a feature extraction network on the splat representation. We propose a reconstruction- and optimization-free approach to stylizing 3D Gaussian splats. This is done by generating a graph structure across the implicit surface of the splat representation. A feed-forward, surface-based stylization method is then used and interpolated back to the individual splats in the scene. This allows for any style image and 3D Gaussian splat to be used without any additional training or optimization. This also allows for fast stylization of splats, achieving speeds under 2 minutes even on consumer-grade hardware. We demonstrate the quality results this approach achieves and compare to other 3D Gaussian splat style transfer methods. Code is publicly available at https://github.com/davidmhart/FastSplatStyler.", "github_manual": "", "github_regex": "https://github.com/davidmhart/FastSplatStyler", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.05769", "title": "Improving Masked Style Transfer using Blended Partial Convolution", "authors": [ "Seyed Hadi Seyed", "Ayberk Cansever", "David Hart" ], "github": "https://github.com/davidmhart/StyleTransferMasked", "project_page": "", "abstract": "Artistic style transfer has long been possible with the advancements of convolution- and transformer-based neural networks. Most algorithms apply the artistic style transfer to the whole image, but individual users may only need to apply a style transfer to a specific region in the image. The standard practice is to simply mask the image after the stylization. This work shows that this approach tends to improperly capture the style features in the region of interest. We propose a partial-convolution-based style transfer network that accurately applies the style features exclusively to the region of interest. Additionally, we present network-internal blending techniques that account for imperfections in the region selection. We show that this visually and quantitatively improves stylization using examples from the SA-1B dataset. Code is publicly available at https://github.com/davidmhart/StyleTransferMasked.", "github_manual": "", "github_regex": "https://github.com/davidmhart/StyleTransferMasked", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.06813", "title": "Technical Report: Full-Stack Fine-Tuning for the Q Programming Language", "authors": [ "Brendan R. Hogan", "Will Brown", "Adel Boyarsky", "Anderson Schneider", "Yuriy Nevmyvaka" ], "github": "https://github.com/morganstanley/MSML", "project_page": "", "abstract": "Even though large language models are becoming increasingly capable, it is still unreasonable to expect them to excel at tasks that are under-represented on the Internet. Leveraging LLMs for specialized applications, particularly in niche programming languages and private domains, remains challenging and largely unsolved. In this work, we address this gap by presenting a comprehensive, open-source approach for adapting LLMs to the Q programming language, a popular tool in quantitative finance that is much less present on the Internet compared to Python, C, Java, and other ``mainstream\" languages and is therefore not a strong suit of general-purpose AI models. We introduce a new Leetcode style evaluation dataset for Q, benchmark major frontier models on the dataset, then do pretraining, supervised fine tuning, and reinforcement learning to train a suite of reasoning and non-reasoning models based on the Qwen-2.5 series, spanning five parameter sizes (1.5B, 3B, 7B, 14B, 32B). Our best model achieves a pass@1 accuracy of 59 percent on our Q benchmark, surpassing the best-performing frontier model, Claude Opus-4 by 29.5 percent. Additionally, all models, even our 1.5B model, outperform GPT-4.1 on this task. In addition to releasing models, code, and data, we provide a detailed blueprint for dataset construction, model pretraining, supervised fine-tuning, and reinforcement learning. Our methodology is broadly applicable, and we discuss how these techniques can be extended to other tasks, including those where evaluation may rely on soft or subjective signals.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/morganstanley/MSML", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.09125", "title": "Complex Logical Instruction Generation", "authors": [ "Mian Zhang", "Shujian Liu", "Sixun Dong", "Ming Yin", "Yebowen Hu", "Xun Wang", "Steven Ma", "Song Wang", "Sathish Reddy Indurthi", "Haoyun Deng", "Zhiyu Zoey Chen", "Kaiqiang Song" ], "github": "https://github.com/mianzhang/LogicIF", "project_page": "", "abstract": "Instruction following has catalyzed the recent era of Large Language Models (LLMs) and is the foundational skill underpinning more advanced capabilities such as reasoning and agentic behaviors. As tasks grow more challenging, the logic structures embedded in natural language instructions becomes increasingly intricate. However, how well LLMs perform on such logic-rich instructions remains under-explored. We propose LogicIFGen and LogicIFEval. LogicIFGen is a scalable, automated framework for generating verifiable instructions from code functions, which can naturally express rich logic such as conditionals, nesting, recursion, and function calls. We further curate a collection of complex code functions and use LogicIFGen to construct LogicIFEval, a benchmark comprising 426 verifiable logic-rich instructions. Our experiments demonstrate that current state-of-the-art LLMs still struggle to correctly follow the instructions in LogicIFEval. Most LLMs can only follow fewer than 60% of the instructions, revealing significant deficiencies in the instruction-following ability. Code and Benchmark: https://github.com/mianzhang/LogicIF", "github_manual": "", "github_regex": "https://github.com/mianzhang/LogicIF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08180", "title": "RedDino: A foundation model for red blood cell analysis", "authors": [ "Luca Zedda", "Andrea Loddo", "Cecilia Di Ruberto", "Carsten Marr" ], "github": "https://github.com/Snarci/RedDino", "project_page": "", "abstract": "Red blood cells (RBCs) are essential to human health, and their precise morphological analysis is important for diagnosing hematological disorders. Despite the promise of foundation models in medical diagnostics, comprehensive AI solutions for RBC analysis remain scarce. We present RedDino, a self-supervised foundation model designed for RBC image analysis. RedDino uses an RBC-specific adaptation of the DINOv2 self-supervised learning framework and is trained on a curated dataset of 1.25 million RBC images from diverse acquisition modalities and sources. Extensive evaluations show that RedDino outperforms existing state-of-the-art models on RBC shape classification. Through assessments including linear probing and nearest neighbor classification, we confirm its strong feature representations and generalization ability. Our main contributions are: (1) a foundation model tailored for RBC analysis, (2) ablation studies exploring DINOv2 configurations for RBC modeling, and (3) a detailed evaluation of generalization performance. RedDino addresses key challenges in computational hematology by capturing nuanced morphological features, advancing the development of reliable diagnostic tools. The source code and pretrained models for RedDino are available at https://github.com/Snarci/RedDino, and the pretrained models can be downloaded from our Hugging Face collection at https://huggingface.co/collections/Snarcy/reddino-689a13e29241d2e5690202fc", "github_manual": "", "github_regex": "https://github.com/Snarci/RedDino", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08974", "title": "Text-conditioned State Space Model For Domain-generalized Change Detection Visual Question Answering", "authors": [ "Elman Ghazaei", "Erchan Aptoula" ], "github": "https://github.com/Elman295/TCSSM", "project_page": "", "abstract": "The Earth's surface is constantly changing, and detecting these changes provides valuable insights that benefit various aspects of human society. While traditional change detection methods have been employed to detect changes from bi-temporal images, these approaches typically require expert knowledge for accurate interpretation. To enable broader and more flexible access to change information by non-expert users, the task of Change Detection Visual Question Answering (CDVQA) has been introduced. However, existing CDVQA methods have been developed under the assumption that training and testing datasets share similar distributions. This assumption does not hold in real-world applications, where domain shifts often occur. In this paper, the CDVQA task is revisited with a focus on addressing domain shift. To this end, a new multi-modal and multi-domain dataset, BrightVQA, is introduced to facilitate domain generalization research in CDVQA. Furthermore, a novel state space model, termed Text-Conditioned State Space Model (TCSSM), is proposed. The TCSSM framework is designed to leverage both bi-temporal imagery and geo-disaster-related textual information in an unified manner to extract domain-invariant features across domains. Input-dependent parameters existing in TCSSM are dynamically predicted by using both bi-temporal images and geo-disaster-related description, thereby facilitating the alignment between bi-temporal visual data and the associated textual descriptions. Extensive experiments are conducted to evaluate the proposed method against state-of-the-art models, and superior performance is consistently demonstrated. The code and dataset will be made publicly available upon acceptance at https://github.com/Elman295/TCSSM.", "github_manual": "", "github_regex": "https://github.com/Elman295/TCSSM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.03936", "title": "ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software Assistants", "authors": [ "Xiangzhe Xu", "Guangyu Shen", "Zian Su", "Siyuan Cheng", "Hanxi Guo", "Lu Yan", "Xuan Chen", "Jiasheng Jiang", "Xiaolong Jin", "Chengpeng Wang", "Zhuo Zhang", "Xiangyu Zhang" ], "github": "", "project_page": "", "abstract": "AI coding assistants like GitHub Copilot are rapidly transforming software development, but their safety remains deeply uncertain-especially in high-stakes domains like cybersecurity. Current red-teaming tools often rely on fixed benchmarks or unrealistic prompts, missing many real-world vulnerabilities. We present ASTRA, an automated agent system designed to systematically uncover safety flaws in AI-driven code generation and security guidance systems. ASTRA works in three stages: (1) it builds structured domain-specific knowledge graphs that model complex software tasks and known weaknesses; (2) it performs online vulnerability exploration of each target model by adaptively probing both its input space, i.e., the spatial exploration, and its reasoning processes, i.e., the temporal exploration, guided by the knowledge graphs; and (3) it generates high-quality violation-inducing cases to improve model alignment. Unlike prior methods, ASTRA focuses on realistic inputs-requests that developers might actually ask-and uses both offline abstraction guided domain modeling and online domain knowledge graph adaptation to surface corner-case vulnerabilities. Across two major evaluation domains, ASTRA finds 11-66% more issues than existing techniques and produces test cases that lead to 17% more effective alignment training, showing its practical value for building safer AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08292", "title": "Putnam-AXIOM: A Functional and Static Benchmark", "authors": [ "Aryan Gulati", "Brando Miranda", "Eric Chen", "Emily Xia", "Kai Fronsdal", "Bruno Dumont", "Elyas Obbad", "Sanmi Koyejo" ], "github": "https://github.com/brando90/putnam-axiom", "project_page": "", "abstract": "Current mathematical reasoning benchmarks for large language models (LLMs) are approaching saturation, with some achieving > 90% accuracy, and are increasingly compromised by training-set contamination. We introduce Putnam-AXIOM, a benchmark of 522 university-level competition problems drawn from the prestigious William Lowell Putnam Mathematical Competition, and Putnam-AXIOM Variation, an unseen companion set of 100 functional variants generated by programmatically perturbing variables and constants. The variation protocol produces an unlimited stream of equally difficult, unseen instances -- yielding a contamination-resilient test bed. On the Original set, OpenAI's o1-preview -- the strongest evaluated model -- scores 41.9%, but its accuracy drops by 19.6% (46.8% relative decrease) on the paired Variations. The remaining eighteen models show the same downward trend, ten of them with non-overlapping 95% confidence intervals. These gaps suggest memorization and highlight the necessity of dynamic benchmarks. We complement \"boxed\" accuracy with Teacher-Forced Accuracy (TFA), a lightweight metric that directly scores reasoning traces and automates natural language proof evaluations. Putnam-AXIOM therefore provides a rigorous, contamination-resilient evaluation framework for assessing advanced mathematical reasoning of LLMs. Data and evaluation code are publicly available at https://github.com/brando90/putnam-axiom.", "github_manual": "", "github_regex": "https://github.com/brando90/putnam-axiom", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08113", "title": "AimBot: A Simple Auxiliary Visual Cue to Enhance Spatial Awareness of Visuomotor Policies", "authors": [ "Yinpei Dai", "Jayjun Lee", "Yichi Zhang", "Ziqiao Ma", "Jed Yang", "Amir Zadeh", "Chuan Li", "Nima Fazeli", "Joyce Chai" ], "github": "", "project_page": "", "abstract": "In this paper, we propose AimBot, a lightweight visual augmentation technique that provides explicit spatial cues to improve visuomotor policy learning in robotic manipulation. AimBot overlays shooting lines and scope reticles onto multi-view RGB images, offering auxiliary visual guidance that encodes the end-effector's state. The overlays are computed from depth images, camera extrinsics, and the current end-effector pose, explicitly conveying spatial relationships between the gripper and objects in the scene. AimBot incurs minimal computational overhead (less than 1 ms) and requires no changes to model architectures, as it simply replaces original RGB images with augmented counterparts. Despite its simplicity, our results show that AimBot consistently improves the performance of various visuomotor policies in both simulation and real-world settings, highlighting the benefits of spatially grounded visual feedback.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-13", "arxiv_id": "2508.08248", "title": "StableAvatar: Infinite-Length Audio-Driven Avatar Video Generation", "authors": [ "Shuyuan Tu", "Yueming Pan", "Yinming Huang", "Xintong Han", "Zhen Xing", "Qi Dai", "Chong Luo", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "", "project_page": "", "abstract": "Current diffusion models for audio-driven avatar video generation struggle to synthesize long videos with natural audio synchronization and identity consistency. This paper presents StableAvatar, the first end-to-end video diffusion transformer that synthesizes infinite-length high-quality videos without post-processing. Conditioned on a reference image and audio, StableAvatar integrates tailored training and inference modules to enable infinite-length video generation. We observe that the main reason preventing existing models from generating long videos lies in their audio modeling. They typically rely on third-party off-the-shelf extractors to obtain audio embeddings, which are then directly injected into the diffusion model via cross-attention. Since current diffusion backbones lack any audio-related priors, this approach causes severe latent distribution error accumulation across video clips, leading the latent distribution of subsequent segments to drift away from the optimal distribution gradually. To address this, StableAvatar introduces a novel Time-step-aware Audio Adapter that prevents error accumulation via time-step-aware modulation. During inference, we propose a novel Audio Native Guidance Mechanism to further enhance the audio synchronization by leveraging the diffusion's own evolving joint audio-latent prediction as a dynamic guidance signal. To enhance the smoothness of the infinite-length videos, we introduce a Dynamic Weighted Sliding-window Strategy that fuses latent over time. Experiments on benchmarks show the effectiveness of StableAvatar both qualitatively and quantitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09889", "title": "AWorld: Dynamic Multi-Agent System with Stable Maneuvering for Robust GAIA Problem Solving", "authors": [ "Zhitian Xie", "Qintong Wu", "Chengyue Yu", "Chenyi Zhuang", "Jinjie Gu" ], "github": "", "project_page": "", "abstract": "The rapid advancement of large language models (LLMs) has empowered intelligent agents to leverage diverse external tools for solving complex real-world problems. However, as agents increasingly depend on multiple tools, they encounter new challenges: extended contexts from disparate sources and noisy or irrelevant tool outputs can undermine system reliability and accuracy. These challenges underscore the necessity for enhanced stability in agent-based systems. To address this, we introduce dynamic supervision and maneuvering mechanisms, constructing a robust and dynamic Multi-Agent System (MAS) architecture within the AWorld framework. In our approach, the Execution Agent invokes the Guard Agent at critical steps to verify and correct the reasoning process, effectively reducing errors arising from noise and bolstering problem-solving robustness. Extensive experiments on the GAIA test dataset reveal that our dynamic maneuvering mechanism significantly improves both the effectiveness and stability of solutions, outperforming single-agent system (SAS) and standard tool-augmented systems. As a result, our dynamic MAS system achieved first place among open-source projects on the prestigious GAIA leaderboard. These findings highlight the practical value of collaborative agent roles in developing more reliable and trustworthy intelligent systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.05613", "title": "Cooper: Co-Optimizing Policy and Reward Models in Reinforcement Learning for Large Language Models", "authors": [ "Haitao Hong", "Yuchen Yan", "Xingyu Wu", "Guiyang Hou", "Wenqi Zhang", "Weiming Lu", "Yongliang Shen", "Jun Xiao" ], "github": "https://github.com/huggingface/Math-Verify", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable performance in reasoning tasks, where reinforcement learning (RL) serves as a key algorithm for enhancing their reasoning capabilities. Currently, there are two mainstream reward paradigms: model-based rewards and rule-based rewards. However, both approaches suffer from limitations: rule-based rewards lack robustness, while model-based rewards are vulnerable to reward hacking. To address these issues, we propose Cooper(Co-optimizing Policy Model and Reward Model), a RL framework that jointly optimizes both the policy model and the reward model. Cooper leverages the high precision of rule-based rewards when identifying correct responses, and dynamically constructs and selects positive-negative sample pairs for continued training the reward model. This design enhances robustness and mitigates the risk of reward hacking. To further support Cooper, we introduce a hybrid annotation strategy that efficiently and accurately generates training data for the reward model. We also propose a reference-based reward modeling paradigm, where the reward model takes a reference answer as input. Based on this design, we train a reward model named VerifyRM, which achieves higher accuracy on VerifyBench compared to other models of the same size. We conduct reinforcement learning using both VerifyRM and Cooper. Our experiments show that Cooper not only alleviates reward hacking but also improves end-to-end RL performance, for instance, achieving a 0.54% gain in average accuracy on Qwen2.5-1.5B-Instruct. Our findings demonstrate that dynamically updating reward model is an effective way to combat reward hacking, providing a reference for better integrating reward models into RL.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/Math-Verify", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.08401", "title": "Mol-R1: Towards Explicit Long-CoT Reasoning in Molecule Discovery", "authors": [ "Jiatong Li", "Weida Wang", "Qinggang Zhang", "Junxian Li", "Di Zhang", "Changmeng Zheng", "Shufei Zhang", "Xiaoyong Wei", "Qing Li" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs), especially Explicit Long Chain-of-Thought (CoT) reasoning models like DeepSeek-R1 and QWQ, have demonstrated powerful reasoning capabilities, achieving impressive performance in commonsense reasoning and mathematical inference. Despite their effectiveness, Long-CoT reasoning models are often criticized for their limited ability and low efficiency in knowledge-intensive domains such as molecule discovery. Success in this field requires a precise understanding of domain knowledge, including molecular structures and chemical principles, which is challenging due to the inherent complexity of molecular data and the scarcity of high-quality expert annotations. To bridge this gap, we introduce Mol-R1, a novel framework designed to improve explainability and reasoning performance of R1-like Explicit Long-CoT reasoning LLMs in text-based molecule generation. Our approach begins with a high-quality reasoning dataset curated through Prior Regulation via In-context Distillation (PRID), a dedicated distillation strategy to effectively generate paired reasoning traces guided by prior regulations. Building upon this, we introduce MoIA, Molecular Iterative Adaptation, a sophisticated training strategy that iteratively combines Supervised Fine-tuning (SFT) with Reinforced Policy Optimization (RPO), tailored to boost the reasoning performance of R1-like reasoning models for molecule discovery. Finally, we examine the performance of Mol-R1 in the text-based molecule reasoning generation task, showing superior performance against existing baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.06009", "title": "MathReal: We Keep It Real! A Real Scene Benchmark for Evaluating Math Reasoning in Multimodal Large Language Models", "authors": [ "Jun Feng", "Zixin Wang", "Zhentao Zhang", "Yue Guo", "Zhihan Zhou", "Xiuyi Chen", "Zhenyang Li", "Dawei Yin" ], "github": "https://github.com/junfeng0288/MathReal", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in visual mathematical reasoning across various existing benchmarks. However, these benchmarks are predominantly based on clean or processed multimodal inputs, without incorporating the images provided by real-world Kindergarten through 12th grade (K-12) educational users. To address this gap, we introduce MathReal, a meticulously curated dataset comprising 2,000 mathematical questions with images captured by handheld mobile devices in authentic scenarios. Each question is an image, containing the question text and visual element. We systematically classify the real images into three primary categories: image quality degradation, perspective variation, and irrelevant content interference, which are further delineated into 14 subcategories. Additionally, MathReal spans five core knowledge and ability categories, which encompass three question types and are divided into three difficulty levels. To comprehensively evaluate the multimodal mathematical reasoning abilities of state-of-the-art MLLMs in real-world scenarios, we design six experimental settings that enable a systematic analysis of their performance. Through extensive experimentation, we find that the problem-solving abilities of existing MLLMs are significantly challenged in realistic educational contexts. Based on this, we conduct a thorough analysis of their performance and error patterns, providing insights into their recognition, comprehension, and reasoning capabilities, and outlining directions for future improvements. Data and code: https://github.com/junfeng0288/MathReal.", "github_manual": "", "github_regex": "https://github.com/junfeng0288/MathReal", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09456", "title": "IAG: Input-aware Backdoor Attack on VLMs for Visual Grounding", "authors": [ "Junxian Li", "Beining Xu", "Di Zhang" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) have shown significant advancements in tasks such as visual grounding, where they localize specific objects in images based on natural language queries and images. However, security issues in visual grounding tasks for VLMs remain underexplored, especially in the context of backdoor attacks. In this paper, we introduce a novel input-aware backdoor attack method, IAG, designed to manipulate the grounding behavior of VLMs. This attack forces the model to ground a specific target object in the input image, regardless of the user's query. We propose an adaptive trigger generator that embeds the semantic information of the attack target's description into the original image using a text-conditional U-Net, thereby overcoming the open-vocabulary attack challenge. To ensure the attack's stealthiness, we utilize a reconstruction loss to minimize visual discrepancies between poisoned and clean images. Additionally, we introduce a unified method for generating attack data. IAG is evaluated theoretically and empirically, demonstrating its feasibility and effectiveness. Notably, our ASR@0.5 on InternVL-2.5-8B reaches over 65\\% on various testing sets. IAG also shows promising potential on manipulating Ferret-7B and LlaVA-1.5-7B with very little accuracy decrease on clean samples. Extensive specific experiments, such as ablation study and potential defense, also indicate the robustness and transferability of our attack.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09987", "title": "Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation", "authors": [ "Junyan Ye", "Dongzhi Jiang", "Zihao Wang", "Leqi Zhu", "Zhenghao Hu", "Zilong Huang", "Jun He", "Zhiyuan Yan", "Jinghua Yu", "Hongsheng Li", "Conghui He", "Weijia Li" ], "github": "", "project_page": "", "abstract": "Recently, GPT-4o has garnered significant attention for its strong performance in image generation, yet open-source models still lag behind. Several studies have explored distilling image data from GPT-4o to enhance open-source models, achieving notable progress. However, a key question remains: given that real-world image datasets already constitute a natural source of high-quality data, why should we use GPT-4o-generated synthetic data? In this work, we identify two key advantages of synthetic images. First, they can complement rare scenarios in real-world datasets, such as surreal fantasy or multi-reference image generation, which frequently occur in user queries. Second, they provide clean and controllable supervision. Real-world data often contains complex background noise and inherent misalignment between text descriptions and image content, whereas synthetic images offer pure backgrounds and long-tailed supervision signals, facilitating more accurate text-to-image alignment. Building on these insights, we introduce Echo-4o-Image, a 180K-scale synthetic dataset generated by GPT-4o, harnessing the power of synthetic image data to address blind spots in real-world coverage. Using this dataset, we fine-tune the unified multimodal generation baseline Bagel to obtain Echo-4o. In addition, we propose two new evaluation benchmarks for a more accurate and challenging assessment of image generation capabilities: GenEval++, which increases instruction complexity to mitigate score saturation, and Imagine-Bench, which focuses on evaluating both the understanding and generation of imaginative content. Echo-4o demonstrates strong performance across standard benchmarks. Moreover, applying Echo-4o-Image to other foundation models (e.g., OmniGen2, BLIP3-o) yields consistent performance gains across multiple metrics, highlighting the datasets strong transferability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09968", "title": "Noise Hypernetworks: Amortizing Test-Time Compute in Diffusion Models", "authors": [ "Luca Eyring", "Shyamgopal Karthik", "Alexey Dosovitskiy", "Nataniel Ruiz", "Zeynep Akata" ], "github": "https://github.com/ExplainableML/HyperNoise", "project_page": "", "abstract": "The new paradigm of test-time scaling has yielded remarkable breakthroughs in Large Language Models (LLMs) (e.g. reasoning models) and in generative vision models, allowing models to allocate additional computation during inference to effectively tackle increasingly complex problems. Despite the improvements of this approach, an important limitation emerges: the substantial increase in computation time makes the process slow and impractical for many applications. Given the success of this paradigm and its growing usage, we seek to preserve its benefits while eschewing the inference overhead. In this work we propose one solution to the critical problem of integrating test-time scaling knowledge into a model during post-training. Specifically, we replace reward guided test-time noise optimization in diffusion models with a Noise Hypernetwork that modulates initial input noise. We propose a theoretically grounded framework for learning this reward-tilted distribution for distilled generators, through a tractable noise-space objective that maintains fidelity to the base model while optimizing for desired characteristics. We show that our approach recovers a substantial portion of the quality gains from explicit test-time optimization at a fraction of the computational cost. Code is available at https://github.com/ExplainableML/HyperNoise", "github_manual": "", "github_regex": "https://github.com/ExplainableML/HyperNoise", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09192", "title": "Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion Forcing", "authors": [ "Xu Wang", "Chenkai Xu", "Yijie Jin", "Jiachun Jin", "Hao Zhang", "Zhijie Deng" ], "github": "https://github.com/zhijie-group/Discrete-Diffusion-Forcing", "project_page": "", "abstract": "Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs for text generation, with the potential to decode multiple tokens in a single iteration. However, none of the existing open-source dLLMs have achieved superior inference speed over AR LLMs of similar size. This paper breaks this barrier based on a simple and effective strategy named discrete diffusion forcing (D2F). D2F equips dLLMs with two key capabilities: (1) block-wise autoregressive generation to enable KV cache utilization; (2) prediction of following tokens without requiring completion of prior blocks for inter-block parallel decoding. In this way, the vanilla dLLMs are refurbished into an AR-diffusion hybrid paradigm for efficient inference. D2F can be implemented with an asymmetric distillation process based on pre-trained dLLMs. We further propose a pipelined parallel decoding algorithm, which enables a trade-off between efficiency and efficacy. Empirically, D2F dLLMs achieve more than 2.5times inference speed than LLaMA3 and Qwen2.5 on GSM8K. Compared to vanilla dLLMs like LLaDA and Dream, the acceleration can be more than 50times while maintaining comparable output quality. The code is available at https://github.com/zhijie-group/Discrete-Diffusion-Forcing.", "github_manual": "", "github_regex": "https://github.com/zhijie-group/Discrete-Diffusion-Forcing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09983", "title": "Story2Board: A Training-Free Approach for Expressive Storyboard Generation", "authors": [ "David Dinkevich", "Matan Levy", "Omri Avrahami", "Dvir Samuel", "Dani Lischinski" ], "github": "", "project_page": "", "abstract": "We present Story2Board, a training-free framework for expressive storyboard generation from natural language. Existing methods narrowly focus on subject identity, overlooking key aspects of visual storytelling such as spatial composition, background evolution, and narrative pacing. To address this, we introduce a lightweight consistency framework composed of two components: Latent Panel Anchoring, which preserves a shared character reference across panels, and Reciprocal Attention Value Mixing, which softly blends visual features between token pairs with strong reciprocal attention. Together, these mechanisms enhance coherence without architectural changes or fine-tuning, enabling state-of-the-art diffusion models to generate visually diverse yet consistent storyboards. To structure generation, we use an off-the-shelf language model to convert free-form stories into grounded panel-level prompts. To evaluate, we propose the Rich Storyboard Benchmark, a suite of open-domain narratives designed to assess layout diversity and background-grounded storytelling, in addition to consistency. We also introduce a new Scene Diversity metric that quantifies spatial and pose variation across storyboards. Our qualitative and quantitative results, as well as a user study, show that Story2Board produces more dynamic, coherent, and narratively engaging storyboards than existing baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.07901", "title": "Stand-In: A Lightweight and Plug-and-Play Identity Control for Video Generation", "authors": [ "Bowen Xue", "Qixin Yan", "Wenjing Wang", "Hao Liu", "Chen Li" ], "github": "", "project_page": "", "abstract": "Generating high-fidelity human videos that match user-specified identities is important yet challenging in the field of generative AI. Existing methods often rely on an excessive number of training parameters and lack compatibility with other AIGC tools. In this paper, we propose Stand-In, a lightweight and plug-and-play framework for identity preservation in video generation. Specifically, we introduce a conditional image branch into the pre-trained video generation model. Identity control is achieved through restricted self-attentions with conditional position mapping, and can be learned quickly with only 2000 pairs. Despite incorporating and training just sim1\\% additional parameters, our framework achieves excellent results in video quality and identity preservation, outperforming other full-parameter training methods. Moreover, our framework can be seamlessly integrated for other tasks, such as subject-driven video generation, pose-referenced video generation, stylization, and face swapping.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09736", "title": "Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory", "authors": [ "Lin Long", "Yichen He", "Wentao Ye", "Yiyuan Pan", "Yuan Lin", "Hang Li", "Junbo Zhao", "Wei Li" ], "github": "https://github.com/bytedance-seed/m3-agent", "project_page": "", "abstract": "We introduce M3-Agent, a novel multimodal agent framework equipped with long-term memory. Like humans, M3-Agent can process real-time visual and auditory inputs to build and update its long-term memory. Beyond episodic memory, it also develops semantic memory, enabling it to accumulate world knowledge over time. Its memory is organized in an entity-centric, multimodal format, allowing deeper and more consistent understanding of the environment. Given an instruction, M3-Agent autonomously performs multi-turn, iterative reasoning and retrieves relevant information from memory to accomplish the task. To evaluate memory effectiveness and memory-based reasoning in multimodal agents, we develop M3-Bench, a new long-video question answering benchmark. M3-Bench comprises 100 newly recorded real-world videos captured from a robot's perspective (M3-Bench-robot) and 929 web-sourced videos across diverse scenarios (M3-Bench-web). We annotate question-answer pairs designed to test key capabilities essential for agent applications, such as human understanding, general knowledge extraction, and cross-modal reasoning. Experimental results show that M3-Agent, trained via reinforcement learning, outperforms the strongest baseline, a prompting agent using Gemini-1.5-pro and GPT-4o, achieving 6.7%, 7.7%, and 5.3% higher accuracy on M3-Bench-robot, M3-Bench-web and VideoMME-long, respectively. Our work advances the multimodal agents toward more human-like long-term memory and provides insights into their practical design. Model, code and data are available at https://github.com/bytedance-seed/m3-agent", "github_manual": "", "github_regex": "https://github.com/bytedance-seed/m3-agent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09776", "title": "Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study", "authors": [ "Mahdi Dhaini", "Juraj Vladika", "Ege Erdogan", "Zineb Attaoui", "Gjergji Kasneci" ], "github": "https://github.com/dmah10/helpful-natural-language-explanations", "project_page": "", "abstract": "In the rapidly evolving field of Explainable Natural Language Processing (NLP), textual explanations, i.e., human-like rationales, are pivotal for explaining model predictions and enriching datasets with interpretable labels. Traditional approaches rely on human annotation, which is costly, labor-intensive, and impedes scalability. In this work, we present an automated framework that leverages multiple state-of-the-art large language models (LLMs) to generate high-quality textual explanations. We rigorously assess the quality of these LLM-generated explanations using a comprehensive suite of Natural Language Generation (NLG) metrics. Furthermore, we investigate the downstream impact of these explanations on the performance of pre-trained language models (PLMs) and LLMs across natural language inference tasks on two diverse benchmark datasets. Our experiments demonstrate that automated explanations exhibit highly competitive effectiveness compared to human-annotated explanations in improving model performance. Our findings underscore a promising avenue for scalable, automated LLM-based textual explanation generation for extending NLP datasets and enhancing model performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dmah10/helpful-natural-language-explanations", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.07750", "title": "Learning to Align, Aligning to Learn: A Unified Approach for Self-Optimized Alignment", "authors": [ "Haowen Wang", "Yun Yue", "Zhiling Ye", "Shuowen Zhang", "Lei Fan", "Jiaxin Liang", "Jiadi Jiang", "Cheng Wei", "Jingyuan Deng", "Xudong Han", "Ji Li", "Chunxiao Guo", "Peng Wei", "Jian Wang", "Jinjie Gu" ], "github": "", "project_page": "", "abstract": "Alignment methodologies have emerged as a critical pathway for enhancing language model alignment capabilities. While SFT (supervised fine-tuning) accelerates convergence through direct token-level loss intervention, its efficacy is constrained by offline policy trajectory. In contrast, RL(reinforcement learning) facilitates exploratory policy optimization, but suffers from low sample efficiency and stringent dependency on high-quality base models. To address these dual challenges, we propose GRAO (Group Relative Alignment Optimization), a unified framework that synergizes the respective strengths of SFT and RL through three key innovations: 1) A multi-sample generation strategy enabling comparative quality assessment via reward feedback; 2) A novel Group Direct Alignment Loss formulation leveraging intra-group relative advantage weighting; 3) Reference-aware parameter updates guided by pairwise preference dynamics. Our theoretical analysis establishes GRAO's convergence guarantees and sample efficiency advantages over conventional approaches. Comprehensive evaluations across complex human alignment tasks demonstrate GRAO's superior performance, achieving 57.70\\%,17.65\\% 7.95\\% and 5.18\\% relative improvements over SFT, DPO, PPO and GRPO baselines respectively. This work provides both a theoretically grounded alignment framework and empirical evidence for efficient capability evolution in language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09945", "title": "VisCodex: Unified Multimodal Code Generation via Merging Vision and Coding Models", "authors": [ "Lingjie Jiang", "Shaohan Huang", "Xun Wu", "Yixia Li", "Dongdong Zhang", "Furu Wei" ], "github": "https://github.com/JackLingjie/VisCodex", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have significantly advanced the integration of visual and textual understanding. However, their ability to generate code from multimodal inputs remains limited. In this work, we introduce VisCodex, a unified framework that seamlessly merges vision and coding language models to empower MLLMs with strong multimodal code generation abilities. Leveraging a task vector-based model merging technique, we integrate a state-of-the-art coding LLM into a strong vision-language backbone, while preserving both visual comprehension and advanced coding skills. To support training and evaluation, we introduce the Multimodal Coding Dataset (MCD), a large-scale and diverse collection of 598k samples, including high-quality HTML code, chart image-code pairs, image-augmented StackOverflow QA, and algorithmic problems. Furthermore, we propose InfiBench-V, a novel and challenging benchmark specifically designed to assess models on visually-rich, real-world programming questions that demand a nuanced understanding of both textual and visual contexts. Extensive experiments show that VisCodex achieves state-of-the-art performance among open-source MLLMs and approaches proprietary models like GPT-4o, highlighting the effectiveness of our model merging strategy and new datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JackLingjie/VisCodex", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.06944", "title": "AMFT: Aligning LLM Reasoners by Meta-Learning the Optimal Imitation-Exploration Balance", "authors": [ "Lixuan He", "Jie Feng", "Yong Li" ], "github": "https://github.com/hlxtsyj/AMFT", "project_page": "", "abstract": "Large Language Models (LLMs) are typically fine-tuned for reasoning tasks through a two-stage pipeline of Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL), a process fraught with catastrophic forgetting and suboptimal trade-offs between imitation and exploration. Recent single-stage methods attempt to unify SFT and RL using heuristics, but lack a principled mechanism for dynamically balancing the two paradigms. In this paper, we reframe this challenge through the theoretical lens of implicit rewards, viewing SFT and RL not as distinct methods but as complementary reward signals. We introduce Adaptive Meta Fine-Tuning (AMFT), a novel single-stage algorithm that learns the optimal balance between SFT's implicit, path-level reward and RL's explicit, outcome-based reward. The core of AMFT is a meta-gradient adaptive weight controller that treats the SFT-RL balance as a learnable parameter, dynamically optimizing it to maximize long-term task performance. This forward-looking approach, regularized by policy entropy for stability, autonomously discovers an effective training curriculum. We conduct a comprehensive evaluation on challenging benchmarks spanning mathematical reasoning, abstract visual reasoning (General Points), and vision-language navigation (V-IRL). AMFT consistently establishes a new state-of-the-art and demonstrats superior generalization on out-of-distribution (OOD) tasks. Ablation studies and training dynamic analysis confirm that the meta-learning controller is crucial for AMFT's stability, sample efficiency, and performance, offering a more principled and effective paradigm for LLM alignment.Our codes are open-sourced via https://github.com/hlxtsyj/AMFT.", "github_manual": "", "github_regex": "https://github.com/hlxtsyj/AMFT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09667", "title": "GSFixer: Improving 3D Gaussian Splatting with Reference-Guided Video Diffusion Priors", "authors": [ "Xingyilang Yin", "Qi Zhang", "Jiahao Chang", "Ying Feng", "Qingnan Fan", "Xi Yang", "Chi-Man Pun", "Huaqi Zhang", "Xiaodong Cun" ], "github": "https://github.com/GVCLab/GSFixer", "project_page": "", "abstract": "Reconstructing 3D scenes using 3D Gaussian Splatting (3DGS) from sparse views is an ill-posed problem due to insufficient information, often resulting in noticeable artifacts. While recent approaches have sought to leverage generative priors to complete information for under-constrained regions, they struggle to generate content that remains consistent with input observations. To address this challenge, we propose GSFixer, a novel framework designed to improve the quality of 3DGS representations reconstructed from sparse inputs. The core of our approach is the reference-guided video restoration model, built upon a DiT-based video diffusion model trained on paired artifact 3DGS renders and clean frames with additional reference-based conditions. Considering the input sparse views as references, our model integrates both 2D semantic features and 3D geometric features of reference views extracted from the visual geometry foundation model, enhancing the semantic coherence and 3D consistency when fixing artifact novel views. Furthermore, considering the lack of suitable benchmarks for 3DGS artifact restoration evaluation, we present DL3DV-Res which contains artifact frames rendered using low-quality 3DGS. Extensive experiments demonstrate our GSFixer outperforms current state-of-the-art methods in 3DGS artifact restoration and sparse-view 3D reconstruction. Project page: https://github.com/GVCLab/GSFixer.", "github_manual": "", "github_regex": "https://github.com/GVCLab/GSFixer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.06937", "title": "CannyEdit: Selective Canny Control and Dual-Prompt Guidance for Training-Free Image Editing", "authors": [ "Weiyan Xie", "Han Gao", "Didan Deng", "Kaican Li", "April Hua Liu", "Yongxiang Huang", "Nevin L. Zhang" ], "github": "", "project_page": "", "abstract": "Recent advances in text-to-image (T2I) models have enabled training-free regional image editing by leveraging the generative priors of foundation models. However, existing methods struggle to balance text adherence in edited regions, context fidelity in unedited areas, and seamless integration of edits. We introduce CannyEdit, a novel training-free framework that addresses these challenges through two key innovations: (1) Selective Canny Control, which masks the structural guidance of Canny ControlNet in user-specified editable regions while strictly preserving details of the source images in unedited areas via inversion-phase ControlNet information retention. This enables precise, text-driven edits without compromising contextual integrity. (2) Dual-Prompt Guidance, which combines local prompts for object-specific edits with a global target prompt to maintain coherent scene interactions. On real-world image editing tasks (addition, replacement, removal), CannyEdit outperforms prior methods like KV-Edit, achieving a 2.93 to 10.49 percent improvement in the balance of text adherence and context fidelity. In terms of editing seamlessness, user studies reveal only 49.2 percent of general users and 42.0 percent of AIGC experts identified CannyEdit's results as AI-edited when paired with real images without edits, versus 76.08 to 89.09 percent for competitor methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.01522", "title": "Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning", "authors": [ "Jack Zeng", "Andreu Matoses Gimenez", "Eugene Vinitsky", "Javier Alonso-Mora", "Sihao Sun" ], "github": "", "project_page": "", "abstract": "This paper presents the first decentralized method to enable real-world 6-DoF manipulation of a cable-suspended load using a team of Micro-Aerial Vehicles (MAVs). Our method leverages multi-agent reinforcement learning (MARL) to train an outer-loop control policy for each MAV. Unlike state-of-the-art controllers that utilize a centralized scheme, our policy does not require global states, inter-MAV communications, nor neighboring MAV information. Instead, agents communicate implicitly through load pose observations alone, which enables high scalability and flexibility. It also significantly reduces computing costs during inference time, enabling onboard deployment of the policy. In addition, we introduce a new action space design for the MAVs using linear acceleration and body rates. This choice, combined with a robust low-level controller, enables reliable sim-to-real transfer despite significant uncertainties caused by cable tension during dynamic 3D motion. We validate our method in various real-world experiments, including full-pose control under load model uncertainties, showing setpoint tracking performance comparable to the state-of-the-art centralized method. We also demonstrate cooperation amongst agents with heterogeneous control policies, and robustness to the complete in-flight loss of one MAV. Videos of experiments: https://autonomousrobots.nl/paper_websites/aerial-manipulation-marl", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.07237", "title": "ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation", "authors": [ "Bo Wang", "Mengyuan Xu", "Yue Yan", "Yuqun Yang", "Kechen Shu", "Wei Ping", "Xu Tang", "Wei Jiang", "Zheng You" ], "github": "https://github.com/YqunYang/ASM-UNet", "project_page": "", "abstract": "Precise lesion resection depends on accurately identifying fine-grained anatomical structures. While many coarse-grained segmentation (CGS) methods have been successful in large-scale segmentation (e.g., organs), they fall short in clinical scenarios requiring fine-grained segmentation (FGS), which remains challenging due to frequent individual variations in small-scale anatomical structures. Although recent Mamba-based models have advanced medical image segmentation, they often rely on fixed manually-defined scanning orders, which limit their adaptability to individual variations in FGS. To address this, we propose ASM-UNet, a novel Mamba-based architecture for FGS. It introduces adaptive scan scores to dynamically guide the scanning order, generated by combining group-level commonalities and individual-level variations. Experiments on two public datasets (ACDC and Synapse) and a newly proposed challenging biliary tract FGS dataset, namely BTMS, demonstrate that ASM-UNet achieves superior performance in both CGS and FGS tasks. Our code and dataset are available at https://github.com/YqunYang/ASM-UNet.", "github_manual": "", "github_regex": "https://github.com/YqunYang/ASM-UNet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09726", "title": "Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning", "authors": [ "Vaishnavi Shrivastava", "Ahmed Awadallah", "Vidhisha Balachandran", "Shivam Garg", "Harkirat Behl", "Dimitris Papailiopoulos" ], "github": "", "project_page": "", "abstract": "Large language models trained with reinforcement learning with verifiable rewards tend to trade accuracy for length--inflating response lengths to achieve gains in accuracy. While longer answers may be warranted for harder problems, many tokens are merely \"filler\": repetitive, verbose text that makes no real progress. We introduce GFPO (Group Filtered Policy Optimization), which curbs this length explosion by sampling larger groups per problem during training and filtering responses to train on based on two key metrics: (1) response length and (2) token efficiency: reward per token ratio. By sampling more at training time, we teach models to think less at inference time. On the Phi-4-reasoning model, GFPO cuts GRPO's length inflation by 46-71% across challenging STEM and coding benchmarks (AIME 24/25, GPQA, Omni-MATH, LiveCodeBench) while maintaining accuracy. Optimizing for reward per token further increases reductions in length inflation to 71-85%. We also propose Adaptive Difficulty GFPO, which dynamically allocates more training resources to harder problems based on real-time difficulty estimates, improving the balance between computational efficiency and accuracy especially on difficult questions. GFPO demonstrates that increased training-time compute directly translates to reduced test-time compute--a simple yet effective trade-off for efficient reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09752", "title": "\u03bc-Parametrization for Mixture of Experts", "authors": [ "Jan Ma\u0142a\u015bnicki", "Kamil Ciebiera", "Mateusz Boru\u0144", "Maciej Pi\u00f3ro", "Jan Ludziejewski", "Maciej Stefaniak", "Micha\u0142 Krutul", "Sebastian Jaszczur", "Marek Cygan", "Kamil Adamczewski", "Jakub Krajewski" ], "github": "", "project_page": "", "abstract": "Recent years have seen a growing interest and adoption of LLMs, with muTransfer becoming a key technique for tuning hyperparameters in large-scale training. Meanwhile, Mixture-of-Experts (MoE) has emerged as a leading architecture in extremely large models. However, the intersection of these two advancements has remained unexplored. In this work, we derive a mu-Parameterization (muP) for MoE, providing theoretical guarantees for feature learning across model widths in both the router and experts. We empirically validate our parameterization and further investigate how scaling the number of experts and granularity affects the optimal learning rate.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.07321", "title": "ObfusQAte: A Proposed Framework to Evaluate LLM Robustness on Obfuscated Factual Question Answering", "authors": [ "Shubhra Ghosh", "Abhilekh Borah", "Aditya Kumar Guru", "Kripabandhu Ghosh" ], "github": "", "project_page": "", "abstract": "The rapid proliferation of Large Language Models (LLMs) has significantly contributed to the development of equitable AI systems capable of factual question-answering (QA). However, no known study tests the LLMs' robustness when presented with obfuscated versions of questions. To systematically evaluate these limitations, we propose a novel technique, ObfusQAte and, leveraging the same, introduce ObfusQA, a comprehensive, first of its kind, framework with multi-tiered obfuscation levels designed to examine LLM capabilities across three distinct dimensions: (i) Named-Entity Indirection, (ii) Distractor Indirection, and (iii) Contextual Overload. By capturing these fine-grained distinctions in language, ObfusQA provides a comprehensive benchmark for evaluating LLM robustness and adaptability. Our study observes that LLMs exhibit a tendency to fail or generate hallucinated responses when confronted with these increasingly nuanced variations. To foster research in this direction, we make ObfusQAte publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-14", "arxiv_id": "2508.09603", "title": "The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage", "authors": [ "Skyler Hallinan", "Jaehun Jung", "Melanie Sclar", "Ximing Lu", "Abhilasha Ravichander", "Sahana Ramnath", "Yejin Choi", "Sai Praneeth Karimireddy", "Niloofar Mireshghallah", "Xiang Ren" ], "github": "https://github.com/shallinan1/NGramCoverageAttack", "project_page": "", "abstract": "Membership inference attacks serves as useful tool for fair use of language models, such as detecting potential copyright infringement and auditing data leakage. However, many current state-of-the-art attacks require access to models' hidden states or probability distribution, which prevents investigation into more widely-used, API-access only models like GPT-4. In this work, we introduce N-Gram Coverage Attack, a membership inference attack that relies solely on text outputs from the target model, enabling attacks on completely black-box models. We leverage the observation that models are more likely to memorize and subsequently generate text patterns that were commonly observed in their training data. Specifically, to make a prediction on a candidate member, N-Gram Coverage Attack first obtains multiple model generations conditioned on a prefix of the candidate. It then uses n-gram overlap metrics to compute and aggregate the similarities of these outputs with the ground truth suffix; high similarities indicate likely membership. We first demonstrate on a diverse set of existing benchmarks that N-Gram Coverage Attack outperforms other black-box methods while also impressively achieving comparable or even better performance to state-of-the-art white-box attacks - despite having access to only text outputs. Interestingly, we find that the success rate of our method scales with the attack compute budget - as we increase the number of sequences generated from the target model conditioned on the prefix, attack performance tends to improve. Having verified the accuracy of our method, we use it to investigate previously unstudied closed OpenAI models on multiple domains. We find that more recent models, such as GPT-4o, exhibit increased robustness to membership inference, suggesting an evolving trend toward improved privacy protections.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/shallinan1/NGramCoverageAttack", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-15", "arxiv_id": "2508.10433", "title": "We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning", "authors": [ "Runqi Qiao", "Qiuna Tan", "Peiqing Yang", "Yanzi Wang", "Xiaowan Wang", "Enhui Wan", "Sitong Zhou", "Guanting Dong", "Yuchen Zeng", "Yida Xu", "Jie Wang", "Chong Sun", "Chen Li", "Honggang Zhang" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various tasks, but still struggle with complex mathematical reasoning. Existing research primarily focuses on dataset construction and method optimization, often overlooking two critical aspects: comprehensive knowledge-driven design and model-centric data space modeling. In this paper, we introduce We-Math 2.0, a unified system that integrates a structured mathematical knowledge system, model-centric data space modeling, and a reinforcement learning (RL)-based training paradigm to comprehensively enhance the mathematical reasoning abilities of MLLMs. The key contributions of We-Math 2.0 are fourfold: (1) MathBook Knowledge System: We construct a five-level hierarchical system encompassing 491 knowledge points and 1,819 fundamental principles. (2) MathBook-Standard & Pro: We develop MathBook-Standard, a dataset that ensures broad conceptual coverage and flexibility through dual expansion. Additionally, we define a three-dimensional difficulty space and generate 7 progressive variants per problem to build MathBook-Pro, a challenging dataset for robust training. (3) MathBook-RL: We propose a two-stage RL framework comprising: (i) Cold-Start Fine-tuning, which aligns the model with knowledge-oriented chain-of-thought reasoning; and (ii) Progressive Alignment RL, leveraging average-reward learning and dynamic data scheduling to achieve progressive alignment across difficulty levels. (4) MathBookEval: We introduce a comprehensive benchmark covering all 491 knowledge points with diverse reasoning step distributions. Experimental results show that MathBook-RL performs competitively with existing baselines on four widely-used benchmarks and achieves strong results on MathBookEval, suggesting promising generalization in mathematical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-15", "arxiv_id": "2508.09848", "title": "PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts", "authors": [ "Mo Yu", "Tsz Ting Chung", "Chulun Zhou", "Tong Li", "Rui Lu", "Jiangnan Li", "Liyan Xu", "Haoshu Lu", "Ning Zhang", "Jing Li", "Jie Zhou" ], "github": "", "project_page": "", "abstract": "We introduce PRELUDE, a benchmark for evaluating long-context understanding through the task of determining whether a character's prequel story is consistent with the canonical narrative of the original book. Our task poses a stronger demand for global comprehension and deep reasoning than existing benchmarks -- as the prequels are not part of the original story, assessing their plausibility typically requires searching and integrating information that is only indirectly related. Empirically, 88% of instances require evidence from multiple parts of the narrative. Experimental results highlight the challenge of our task: in-context learning, RAG and in-domain training with state-of-the-art LLMs, and commercial DeepResearch services, lag behind humans by >15%. A further human study reveals that models often produce correct answers with flawed reasoning, leading to an over 30% gap in reasoning accuracy compared to humans. These findings underscore the substantial room for improvement in long-context understanding and reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-15", "arxiv_id": "2508.10860", "title": "From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms", "authors": [ "Zhaokun Jiang", "Ziyin Zhang" ], "github": "", "project_page": "", "abstract": "Recent advancements in machine learning have spurred growing interests in automated interpreting quality assessment. Nevertheless, existing research suffers from insufficient examination of language use quality, unsatisfactory modeling effectiveness due to data scarcity and imbalance, and a lack of efforts to explain model predictions. To address these gaps, we propose a multi-dimensional modeling framework that integrates feature engineering, data augmentation, and explainable machine learning. This approach prioritizes explainability over ``black box'' predictions by utilizing only construct-relevant, transparent features and conducting Shapley Value (SHAP) analysis. Our results demonstrate strong predictive performance on a novel English-Chinese consecutive interpreting dataset, identifying BLEURT and CometKiwi scores to be the strongest predictive features for fidelity, pause-related features for fluency, and Chinese-specific phraseological diversity metrics for language use. Overall, by placing particular emphasis on explainability, we present a scalable, reliable, and transparent alternative to traditional human evaluation, facilitating the provision of detailed diagnostic feedback for learners and supporting self-regulated learning advantages not afforded by automated scores in isolation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-15", "arxiv_id": "2508.10576", "title": "HumanSense: From Multimodal Perception to Empathetic Context-Aware Responses through Reasoning MLLMs", "authors": [ "Zheng Qin", "Ruobing Zheng", "Yabing Wang", "Tianqi Li", "Yi Yuan", "Jingdong Chen", "Le Wang" ], "github": "https://github.com/antgroup/HumanSense", "project_page": "", "abstract": "While Multimodal Large Language Models (MLLMs) show immense promise for achieving truly human-like interactions, progress is hindered by the lack of fine-grained evaluation frameworks for human-centered scenarios, encompassing both the understanding of complex human intentions and the provision of empathetic, context-aware responses. Here we introduce HumanSense, a comprehensive benchmark designed to evaluate the human-centered perception and interaction capabilities of MLLMs, with a particular focus on deep understanding of extended multimodal contexts and the formulation of rational feedback. Our evaluation reveals that leading MLLMs still have considerable room for improvement, particularly for advanced interaction-oriented tasks. Supplementing visual input with audio and text information yields substantial improvements, and Omni-modal models show advantages on these tasks. Furthermore, we argue that appropriate feedback stems from a contextual analysis of the interlocutor's needs and emotions, with reasoning ability serving as the key to unlocking it. Accordingly, we employ a multi-stage, modality-progressive reinforcement learning to enhance the reasoning abilities of an Omni model, achieving substantial gains on evaluation results. Additionally, we observe that successful reasoning processes exhibit highly consistent thought patterns. By designing corresponding prompts, we also enhance the performance of non-reasoning models in a training-free manner. Project page: brightpinkhttps://digital-avatar.github.io/ai/HumanSense/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/antgroup/HumanSense", "github_api_added_by": "auto" }, { "date": "2025-08-15", "arxiv_id": "2508.10711", "title": "NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale", "authors": [ "NextStep Team", "Chunrui Han", "Guopeng Li", "Jingwei Wu", "Quan Sun", "Yan Cai", "Yuang Peng", "Zheng Ge", "Deyu Zhou", "Haomiao Tang", "Hongyu Zhou", "Kenkun Liu", "Ailin Huang", "Bin Wang", "Changxin Miao", "Deshan Sun", "En Yu", "Fukun Yin", "Gang Yu", "Hao Nie", "Haoran Lv", "Hanpeng Hu", "Jia Wang", "Jian Zhou", "Jianjian Sun", "Kaijun Tan", "Kang An", "Kangheng Lin", "Liang Zhao", "Mei Chen", "Peng Xing", "Rui Wang", "Shiyu Liu", "Shutao Xia", "Tianhao You", "Wei Ji", "Xianfang Zeng", "Xin Han", "Xuelin Zhang", "Yana Wei", "Yanming Xu", "Yimin Jiang", "Yingming Wang", "Yu Zhou", "Yucheng Han", "Ziyang Meng", "Binxing Jiao", "Daxin Jiang", "Xiangyu Zhang", "Yibo Zhu" ], "github": "", "project_page": "", "abstract": "Prevailing autoregressive (AR) models for text-to-image generation either rely on heavy, computationally-intensive diffusion models to process continuous image tokens, or employ vector quantization (VQ) to obtain discrete tokens with quantization loss. In this paper, we push the autoregressive paradigm forward with NextStep-1, a 14B autoregressive model paired with a 157M flow matching head, training on discrete text tokens and continuous image tokens with next-token prediction objectives. NextStep-1 achieves state-of-the-art performance for autoregressive models in text-to-image generation tasks, exhibiting strong capabilities in high-fidelity image synthesis. Furthermore, our method shows strong performance in image editing, highlighting the power and versatility of our unified approach. To facilitate open research, we will release our code and models to the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-15", "arxiv_id": "2508.10833", "title": "UI-Venus Technical Report: Building High-performance UI Agents with RFT", "authors": [ "Zhangxuan Gu", "Zhengwen Zeng", "Zhenyu Xu", "Xingran Zhou", "Shuheng Shen", "Yunfei Liu", "Beitong Zhou", "Changhua Meng", "Tianyu Xia", "Weizhi Chen", "Yue Wen", "Jingya Dou", "Fei Tang", "Jinzhen Lin", "Yulin Liu", "Zhenlin Guo", "Yichen Gong", "Heng Jia", "Changlong Gao", "Yuan Guo", "Yong Deng", "Zhenyu Guo", "Liang Chen", "Weiqiang Wang" ], "github": "https://github.com/antgroup/UI-Venus", "project_page": "", "abstract": "We present UI-Venus, a native UI agent that takes only screenshots as input based on a multimodal large language model. UI-Venus achieves SOTA performance on both UI grounding and navigation tasks using only several hundred thousand high-quality training samples through reinforcement finetune (RFT) based on Qwen2.5-VL. Specifically, the 7B and 72B variants of UI-Venus obtain 94.1% / 50.8% and 95.3% / 61.9% on the standard grounding benchmarks, i.e., Screenspot-V2 / Pro, surpassing the previous SOTA baselines including open-source GTA1 and closed-source UI-TARS-1.5.To show UI-Venus's summary and planing ability, we also evaluate it on the AndroidWorld, an online UI navigation arena, on which our 7B and 72B variants achieve 49.1% and 65.9% success rate, also beating existing models.To achieve this, we introduce carefully designed reward functions for both UI grounding and navigation tasks and corresponding efficient data cleaning strategies.To further boost navigation performance, we propose Self-Evolving Trajectory History Alignment \\& Sparse Action Enhancement that refine historical reasoning traces and balances the distribution of sparse but critical actions, leading to more coherent planning and better generalization in complex UI tasks. Our contributions include the publish of SOTA open-source UI agents, comprehensive data cleaning protocols and a novel self-evolving framework for improving navigation performance, which encourage further research and development in the community. Code is available at https://github.com/antgroup/UI-Venus.", "github_manual": "", "github_regex": "https://github.com/antgroup/UI-Venus", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-15", "arxiv_id": "2508.10751", "title": "Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models", "authors": [ "Zhipeng Chen", "Xiaobo Qin", "Youbin Wu", "Yue Ling", "Qinghao Ye", "Wayne Xin Zhao", "Guang Shi" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR), which typically adopts Pass@1 as the reward, has faced the issues in balancing exploration and exploitation, causing policies to prefer conservative actions, converging to a local optimum. Identifying an appropriate reward metric is therefore crucial. Regarding the prior work, although Pass@k has been used in evaluation, its connection to LLM exploration ability in RLVR remains largely overlooked. To investigate this, we first use Pass@k as the reward to train the policy model (i.e., Pass@k Training), and observe the improvement on its exploration ability. Next, we derive an analytical solution for the advantage of Pass@k Training, leading to an efficient and effective process. Building on this, our analysis reveals that exploration and exploitation are not inherently conflicting objectives, while they can mutually enhance each other. Moreover, Pass@k Training with analytical derivation essentially involves directly designing the advantage function. Inspired by this, we preliminarily explore the advantage design for RLVR, showing promising results and highlighting a potential future direction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-15", "arxiv_id": "2508.10881", "title": "ToonComposer: Streamlining Cartoon Production with Generative Post-Keyframing", "authors": [ "Lingen Li", "Guangzhi Wang", "Zhaoyang Zhang", "Yaowei Li", "Xiaoyu Li", "Qi Dou", "Jinwei Gu", "Tianfan Xue", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Traditional cartoon and anime production involves keyframing, inbetweening, and colorization stages, which require intensive manual effort. Despite recent advances in AI, existing methods often handle these stages separately, leading to error accumulation and artifacts. For instance, inbetweening approaches struggle with large motions, while colorization methods require dense per-frame sketches. To address this, we introduce ToonComposer, a generative model that unifies inbetweening and colorization into a single post-keyframing stage. ToonComposer employs a sparse sketch injection mechanism to provide precise control using keyframe sketches. Additionally, it uses a cartoon adaptation method with the spatial low-rank adapter to tailor a modern video foundation model to the cartoon domain while keeping its temporal prior intact. Requiring as few as a single sketch and a colored reference frame, ToonComposer excels with sparse inputs, while also supporting multiple sketches at any temporal location for more precise motion control. This dual capability reduces manual workload and improves flexibility, empowering artists in real-world scenarios. To evaluate our model, we further created PKBench, a benchmark featuring human-drawn sketches that simulate real-world use cases. Our evaluation demonstrates that ToonComposer outperforms existing methods in visual quality, motion consistency, and production efficiency, offering a superior and more flexible solution for AI-assisted cartoon production.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-15", "arxiv_id": "2508.10875", "title": "A Survey on Diffusion Language Models", "authors": [ "Tianyi Li", "Mingda Chen", "Bowei Guo", "Zhiqiang Shen" ], "github": "https://github.com/VILA-Lab/Awesome-DLMs", "project_page": "", "abstract": "Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm. By generating tokens in parallel through an iterative denoising process, DLMs possess inherent advantages in reducing inference latency and capturing bidirectional context, thereby enabling fine-grained control over the generation process. While achieving a several-fold speed-up, recent advancements have allowed DLMs to show performance comparable to their autoregressive counterparts, making them a compelling choice for various natural language processing tasks. In this survey, we provide a holistic overview of the current DLM landscape. We trace its evolution and relationship with other paradigms, such as autoregressive and masked language models, and cover both foundational principles and state-of-the-art models. Our work offers an up-to-date, comprehensive taxonomy and an in-depth analysis of current techniques, from pre-training strategies to advanced post-training methods. Another contribution of this survey is a thorough review of DLM inference strategies and optimizations, including improvements in decoding parallelism, caching mechanisms, and generation quality. We also highlight the latest approaches to multimodal extensions of DLMs and delineate their applications across various practical scenarios. Furthermore, our discussion addresses the limitations and challenges of DLMs, including efficiency, long-sequence handling, and infrastructure requirements, while outlining future research directions to sustain progress in this rapidly evolving field. Project GitHub is available at https://github.com/VILA-Lab/Awesome-DLMs.", "github_manual": "", "github_regex": "https://github.com/VILA-Lab/Awesome-DLMs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-15", "arxiv_id": "2508.10637", "title": "Processing and acquisition traces in visual encoders: What does CLIP know about your camera?", "authors": [ "Ryan Ramos", "Vladan Stojni\u0107", "Giorgos Kordopatis-Zilos", "Yuta Nakashima", "Giorgos Tolias", "Noa Garcia" ], "github": "https://github.com/ryan-caesar-ramos/visual-encoder-traces", "project_page": "", "abstract": "Prior work has analyzed the robustness of visual encoders to image transformations and corruptions, particularly in cases where such alterations are not seen during training. When this occurs, they introduce a form of distribution shift at test time, often leading to performance degradation. The primary focus has been on severe corruptions that, when applied aggressively, distort useful signals necessary for accurate semantic predictions. We take a different perspective by analyzing parameters of the image acquisition process and transformations that may be subtle or even imperceptible to the human eye. We find that such parameters are systematically encoded in the learned visual representations and can be easily recovered. More strikingly, their presence can have a profound impact, either positively or negatively, on semantic predictions. This effect depends on whether there is a strong correlation or anti-correlation between semantic labels and these acquisition-based or processing-based labels. Our code and data are available at: https://github.com/ryan-caesar-ramos/visual-encoder-traces", "github_manual": "", "github_regex": "https://github.com/ryan-caesar-ramos/visual-encoder-traces", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-15", "arxiv_id": "2508.10893", "title": "STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer", "authors": [ "Yushi Lan", "Yihang Luo", "Fangzhou Hong", "Shangchen Zhou", "Honghua Chen", "Zhaoyang Lyu", "Shuai Yang", "Bo Dai", "Chen Change Loy", "Xingang Pan" ], "github": "", "project_page": "", "abstract": "We present STream3R, a novel approach to 3D reconstruction that reformulates pointmap prediction as a decoder-only Transformer problem. Existing state-of-the-art methods for multi-view reconstruction either depend on expensive global optimization or rely on simplistic memory mechanisms that scale poorly with sequence length. In contrast, STream3R introduces an streaming framework that processes image sequences efficiently using causal attention, inspired by advances in modern language modeling. By learning geometric priors from large-scale 3D datasets, STream3R generalizes well to diverse and challenging scenarios, including dynamic scenes where traditional methods often fail. Extensive experiments show that our method consistently outperforms prior work across both static and dynamic scene benchmarks. Moreover, STream3R is inherently compatible with LLM-style training infrastructure, enabling efficient large-scale pretraining and fine-tuning for various downstream 3D tasks. Our results underscore the potential of causal Transformer models for online 3D perception, paving the way for real-time 3D understanding in streaming environments. More details can be found in our project page: https://nirvanalan.github.io/projects/stream3r.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-15", "arxiv_id": "2508.10482", "title": "When Explainability Meets Privacy: An Investigation at the Intersection of Post-hoc Explainability and Differential Privacy in the Context of Natural Language Processing", "authors": [ "Mahdi Dhaini", "Stephen Meisenbacher", "Ege Erdogan", "Florian Matthes", "Gjergji Kasneci" ], "github": "", "project_page": "", "abstract": "In the study of trustworthy Natural Language Processing (NLP), a number of important research fields have emerged, including that of explainability and privacy. While research interest in both explainable and privacy-preserving NLP has increased considerably in recent years, there remains a lack of investigation at the intersection of the two. This leaves a considerable gap in understanding of whether achieving both explainability and privacy is possible, or whether the two are at odds with each other. In this work, we conduct an empirical investigation into the privacy-explainability trade-off in the context of NLP, guided by the popular overarching methods of Differential Privacy (DP) and Post-hoc Explainability. Our findings include a view into the intricate relationship between privacy and explainability, which is formed by a number of factors, including the nature of the downstream task and choice of the text privatization and explainability method. In this, we highlight the potential for privacy and explainability to co-exist, and we summarize our findings in a collection of practical recommendations for future work at this important intersection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-15", "arxiv_id": "2508.10898", "title": "Puppeteer: Rig and Animate Your 3D Models", "authors": [ "Chaoyue Song", "Xiu Li", "Fan Yang", "Zhongcong Xu", "Jiacheng Wei", "Fayao Liu", "Jiashi Feng", "Guosheng Lin", "Jianfeng Zhang" ], "github": "", "project_page": "", "abstract": "Modern interactive applications increasingly demand dynamic 3D content, yet the transformation of static 3D models into animated assets constitutes a significant bottleneck in content creation pipelines. While recent advances in generative AI have revolutionized static 3D model creation, rigging and animation continue to depend heavily on expert intervention. We present Puppeteer, a comprehensive framework that addresses both automatic rigging and animation for diverse 3D objects. Our system first predicts plausible skeletal structures via an auto-regressive transformer that introduces a joint-based tokenization strategy for compact representation and a hierarchical ordering methodology with stochastic perturbation that enhances bidirectional learning capabilities. It then infers skinning weights via an attention-based architecture incorporating topology-aware joint attention that explicitly encodes inter-joint relationships based on skeletal graph distances. Finally, we complement these rigging advances with a differentiable optimization-based animation pipeline that generates stable, high-fidelity animations while being computationally more efficient than existing approaches. Extensive evaluations across multiple benchmarks demonstrate that our method significantly outperforms state-of-the-art techniques in both skeletal prediction accuracy and skinning quality. The system robustly processes diverse 3D content, ranging from professionally designed game assets to AI-generated shapes, producing temporally coherent animations that eliminate the jittering issues common in existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.11116", "title": "PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing", "authors": [ "Zhuoqun Li", "Xuanang Chen", "Hongyu Lin", "Yaojie Lu", "Xianpei Han", "Le Sun" ], "github": "https://github.com/Li-Z-Q/PaperRegister", "project_page": "", "abstract": "Paper search is an important activity for researchers, typically involving using a query with description of a topic to find relevant papers. As research deepens, paper search requirements may become more flexible, sometimes involving specific details such as module configuration rather than being limited to coarse-grained topics. However, previous paper search systems are unable to meet these flexible-grained requirements, as these systems mainly collect paper abstracts to construct index of corpus, which lack detailed information to support retrieval by finer-grained queries. In this work, we propose PaperRegister, consisted of offline hierarchical indexing and online adaptive retrieval, transforming traditional abstract-based index into hierarchical index tree for paper search, thereby supporting queries at flexible granularity. Experiments on paper search tasks across a range of granularity demonstrate that PaperRegister achieves the state-of-the-art performance, and particularly excels in fine-grained scenarios, highlighting the good potential as an effective solution for flexible-grained paper search in real-world applications. Code for this work is in https://github.com/Li-Z-Q/PaperRegister.", "github_manual": "", "github_regex": "https://github.com/Li-Z-Q/PaperRegister", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.11630", "title": "Thyme: Think Beyond Images", "authors": [ "Yi-Fan Zhang", "Xingyu Lu", "Shukang Yin", "Chaoyou Fu", "Wei Chen", "Xiao Hu", "Bin Wen", "Kaiyu Jiang", "Changyi Liu", "Tianke Zhang", "Haonan Fan", "Kaibing Chen", "Jiankang Chen", "Haojie Ding", "Kaiyu Tang", "Zhang Zhang", "Liang Wang", "Fan Yang", "Tingting Gao", "Guorui Zhou" ], "github": "", "project_page": "", "abstract": "Following OpenAI's introduction of the ``thinking with images'' concept, recent efforts have explored stimulating the use of visual information in the reasoning process to enhance model performance in perception and reasoning tasks. However, to the best of our knowledge, no open-source work currently offers a feature set as rich as proprietary models (O3), which can perform diverse image manipulations and simultaneously enhance logical reasoning capabilities through code. In this paper, we make a preliminary attempt in this direction by introducing Thyme (Think Beyond Images), a novel paradigm for enabling MLLMs to transcend existing ``think with images'' approaches by autonomously generating and executing diverse image processing and computational operations via executable code. This approach not only facilitates a rich, on-the-fly set of image manipulations (e.g., cropping, rotation, contrast enhancement) but also allows for mathematical computations, all while maintaining high autonomy in deciding when and how to apply these operations. We activate this capability through a two-stage training strategy: an initial SFT on a curated dataset of 500K samples to teach code generation, followed by a RL phase to refine decision-making. For the RL stage, we manually collect and design high-resolution question-answer pairs to increase the learning difficulty, and we propose GRPO-ATS (Group Relative Policy Optimization with Adaptive Temperature Sampling), an algorithm that applies distinct temperatures to text and code generation to balance reasoning exploration with code execution precision. We conduct extensive experimental analysis and ablation studies. Comprehensive evaluations on nearly 20 benchmarks show that Thyme yields significant and consistent performance gains, particularly in challenging high-resolution perception and complex reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.11203", "title": "StyleMM: Stylized 3D Morphable Face Model via Text-Driven Aligned Image Translation", "authors": [ "Seungmi Lee", "Kwan Yun", "Junyong Noh" ], "github": "", "project_page": "", "abstract": "We introduce StyleMM, a novel framework that can construct a stylized 3D Morphable Model (3DMM) based on user-defined text descriptions specifying a target style. Building upon a pre-trained mesh deformation network and a texture generator for original 3DMM-based realistic human faces, our approach fine-tunes these models using stylized facial images generated via text-guided image-to-image (i2i) translation with a diffusion model, which serve as stylization targets for the rendered mesh. To prevent undesired changes in identity, facial alignment, or expressions during i2i translation, we introduce a stylization method that explicitly preserves the facial attributes of the source image. By maintaining these critical attributes during image stylization, the proposed approach ensures consistent 3D style transfer across the 3DMM parameter space through image-based training. Once trained, StyleMM enables feed-forward generation of stylized face meshes with explicit control over shape, expression, and texture parameters, producing meshes with consistent vertex connectivity and animatability. Quantitative and qualitative evaluations demonstrate that our approach outperforms state-of-the-art methods in terms of identity-level facial diversity and stylization capability. The code and videos are available at [kwanyun.github.io/stylemm_page](kwanyun.github.io/stylemm_page).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.06429", "title": "SPARSE Data, Rich Results: Few-Shot Semi-Supervised Learning via Class-Conditioned Image Translation", "authors": [ "Guido Manni", "Clemente Lauretti", "Loredana Zollo", "Paolo Soda" ], "github": "https://github.com/GuidoManni/SPARSE", "project_page": "", "abstract": "Deep learning has revolutionized medical imaging, but its effectiveness is severely limited by insufficient labeled training data. This paper introduces a novel GAN-based semi-supervised learning framework specifically designed for low labeled-data regimes, evaluated across settings with 5 to 50 labeled samples per class. Our approach integrates three specialized neural networks -- a generator for class-conditioned image translation, a discriminator for authenticity assessment and classification, and a dedicated classifier -- within a three-phase training framework. The method alternates between supervised training on limited labeled data and unsupervised learning that leverages abundant unlabeled images through image-to-image translation rather than generation from noise. We employ ensemble-based pseudo-labeling that combines confidence-weighted predictions from the discriminator and classifier with temporal consistency through exponential moving averaging, enabling reliable label estimation for unlabeled data. Comprehensive evaluation across eleven MedMNIST datasets demonstrates that our approach achieves statistically significant improvements over six state-of-the-art GAN-based semi-supervised methods, with particularly strong performance in the extreme 5-shot setting where the scarcity of labeled data is most challenging. The framework maintains its superiority across all evaluated settings (5, 10, 20, and 50 shots per class). Our approach offers a practical solution for medical imaging applications where annotation costs are prohibitive, enabling robust classification performance even with minimal labeled data. Code is available at https://github.com/GuidoManni/SPARSE.", "github_manual": "", "github_regex": "https://github.com/GuidoManni/SPARSE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.11616", "title": "Controlling Multimodal LLMs via Reward-guided Decoding", "authors": [ "Oscar Ma\u00f1as", "Pierluca D'Oro", "Koustuv Sinha", "Adriana Romero-Soriano", "Michal Drozdzal", "Aishwarya Agrawal" ], "github": "", "project_page": "", "abstract": "As Multimodal Large Language Models (MLLMs) gain widespread applicability, it is becoming increasingly desirable to adapt them for diverse user needs. In this paper, we study the adaptation of MLLMs through controlled decoding. To achieve this, we introduce the first method for reward-guided decoding of MLLMs and demonstrate its application in improving their visual grounding. Our method involves building reward models for visual grounding and using them to guide the MLLM's decoding process. Concretely, we build two separate reward models to independently control the degree of object precision and recall in the model's output. Our approach enables on-the-fly controllability of an MLLM's inference process in two ways: first, by giving control over the relative importance of each reward function during decoding, allowing a user to dynamically trade off object precision for recall in image captioning tasks; second, by giving control over the breadth of the search during decoding, allowing the user to control the trade-off between the amount of test-time compute and the degree of visual grounding. We evaluate our method on standard object hallucination benchmarks, showing that it provides significant controllability over MLLM inference, while consistently outperforming existing hallucination mitigation methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.11255", "title": "FantasyTalking2: Timestep-Layer Adaptive Preference Optimization for Audio-Driven Portrait Animation", "authors": [ "MengChao Wang", "Qiang Wang", "Fan Jiang", "Mu Xu" ], "github": "", "project_page": "", "abstract": "Recent advances in audio-driven portrait animation have demonstrated impressive capabilities. However, existing methods struggle to align with fine-grained human preferences across multiple dimensions, such as motion naturalness, lip-sync accuracy, and visual quality. This is due to the difficulty of optimizing among competing preference objectives, which often conflict with one another, and the scarcity of large-scale, high-quality datasets with multidimensional preference annotations. To address these, we first introduce Talking-Critic, a multimodal reward model that learns human-aligned reward functions to quantify how well generated videos satisfy multidimensional expectations. Leveraging this model, we curate Talking-NSQ, a large-scale multidimensional human preference dataset containing 410K preference pairs. Finally, we propose Timestep-Layer adaptive multi-expert Preference Optimization (TLPO), a novel framework for aligning diffusion-based portrait animation models with fine-grained, multidimensional preferences. TLPO decouples preferences into specialized expert modules, which are then fused across timesteps and network layers, enabling comprehensive, fine-grained enhancement across all dimensions without mutual interference. Experiments demonstrate that Talking-Critic significantly outperforms existing methods in aligning with human preference ratings. Meanwhile, TLPO achieves substantial improvements over baseline models in lip-sync accuracy, motion naturalness, and visual quality, exhibiting superior performance in both qualitative and quantitative evaluations. Ours project page: https://fantasy-amap.github.io/fantasy-talking2/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.10868", "title": "TexVerse: A Universe of 3D Objects with High-Resolution Textures", "authors": [ "Yibo Zhang", "Li Zhang", "Rui Ma", "Nan Cao" ], "github": "", "project_page": "", "abstract": "We introduce TexVerse, a large-scale 3D dataset featuring high-resolution textures. While recent advances in large-scale 3D datasets have enhanced high-resolution geometry generation, creating high-resolution textures end-to-end remains underexplored due to the lack of suitable datasets. TexVerse fills this gap with a curated collection of over 858K unique high-resolution 3D models sourced from Sketchfab, including more than 158K models with physically based rendering (PBR) materials. Each model encompasses all of its high-resolution variants, bringing the total to 1.6M 3D instances. TexVerse also includes specialized subsets: TexVerse-Skeleton, with 69K rigged models, and TexVerse-Animation, with 54K animated models, both preserving original skeleton and animation data uploaded by the user. We also provide detailed model annotations describing overall characteristics, structural components, and intricate features. TexVerse offers a high-quality data resource with wide-ranging potential applications in texture synthesis, PBR material development, animation, and various 3D vision and graphics tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.10104", "title": "DINOv3", "authors": [ "Oriane Sim\u00e9oni", "Huy V. Vo", "Maximilian Seitzer", "Federico Baldassarre", "Maxime Oquab", "Cijo Jose", "Vasil Khalidov", "Marc Szafraniec", "Seungeun Yi", "Micha\u00ebl Ramamonjisoa", "Francisco Massa", "Daniel Haziza", "Luca Wehrstedt", "Jianyuan Wang", "Timoth\u00e9e Darcet", "Th\u00e9o Moutakanni", "Leonel Sentana", "Claire Roberts", "Andrea Vedaldi", "Jamie Tolan", "John Brandt", "Camille Couprie", "Julien Mairal", "Herv\u00e9 J\u00e9gou", "Patrick Labatut", "Piotr Bojanowski" ], "github": "", "project_page": "", "abstract": "Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm. This technical report introduces DINOv3, a major milestone toward realizing this vision by leveraging simple yet effective strategies. First, we leverage the benefit of scaling both dataset and model size by careful data preparation, design, and optimization. Second, we introduce a new method called Gram anchoring, which effectively addresses the known yet unsolved issue of dense feature maps degrading during long training schedules. Finally, we apply post-hoc strategies that further enhance our models' flexibility with respect to resolution, model size, and alignment with text. As a result, we present a versatile vision foundation model that outperforms the specialized state of the art across a broad range of settings, without fine-tuning. DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks, significantly surpassing previous self- and weakly-supervised foundation models. We also share the DINOv3 suite of vision models, designed to advance the state of the art on a wide spectrum of tasks and data by providing scalable solutions for diverse resource constraints and deployment scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.10461", "title": "X-Node: Self-Explanation is All We Need", "authors": [ "Prajit Sengupta", "Islem Rekik" ], "github": "https://github.com/basiralab/X-Node", "project_page": "", "abstract": "Graph neural networks (GNNs) have achieved state-of-the-art results in computer vision and medical image classification tasks by capturing structural dependencies across data instances. However, their decision-making remains largely opaque, limiting their trustworthiness in high-stakes clinical applications where interpretability is essential. Existing explainability techniques for GNNs are typically post-hoc and global, offering limited insight into individual node decisions or local reasoning. We introduce X-Node, a self-explaining GNN framework in which each node generates its own explanation as part of the prediction process. For every node, we construct a structured context vector encoding interpretable cues such as degree, centrality, clustering, feature saliency, and label agreement within its local topology. A lightweight Reasoner module maps this context into a compact explanation vector, which serves three purposes: (1) reconstructing the node's latent embedding via a decoder to enforce faithfulness, (2) generating a natural language explanation using a pre-trained LLM (e.g., Grok or Gemini), and (3) guiding the GNN itself via a \"text-injection\" mechanism that feeds explanations back into the message-passing pipeline. We evaluate X-Node on two graph datasets derived from MedMNIST and MorphoMNIST, integrating it with GCN, GAT, and GIN backbones. Our results show that X-Node maintains competitive classification accuracy while producing faithful, per-node explanations. Repository: https://github.com/basiralab/X-Node.", "github_manual": "", "github_regex": "https://github.com/basiralab/X-Node", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.10894", "title": "MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data", "authors": [ "Antoine Labatie", "Michael Vaccaro", "Nina Lardiere", "Anatol Garioud", "Nicolas Gonthier" ], "github": "https://github.com/ignf/maestro", "project_page": "", "abstract": "Self-supervised learning holds great promise for remote sensing, but standard self-supervised methods must be adapted to the unique characteristics of Earth observation data. We take a step in this direction by conducting a comprehensive benchmark of fusion strategies and reconstruction target normalization schemes for multimodal, multitemporal, and multispectral Earth observation data. Based on our findings, we propose MAESTRO, a novel adaptation of the Masked Autoencoder, featuring optimized fusion strategies and a tailored target normalization scheme that introduces a spectral prior as a self-supervisory signal. Evaluated on four Earth observation datasets, MAESTRO sets a new state-of-the-art on tasks that strongly rely on multitemporal dynamics, while remaining highly competitive on tasks dominated by a single mono-temporal modality. Code to reproduce all our experiments is available at https://github.com/ignf/maestro.", "github_manual": "", "github_regex": "https://github.com/ignf/maestro", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.10874", "title": "SSRL: Self-Search Reinforcement Learning", "authors": [ "Yuchen Fan", "Kaiyan Zhang", "Heng Zhou", "Yuxin Zuo", "Yanxu Chen", "Yu Fu", "Xinwei Long", "Xuekai Zhu", "Che Jiang", "Yuchen Zhang", "Li Kang", "Gang Chen", "Cheng Huang", "Zhizhou He", "Bingning Wang", "Lei Bai", "Ning Ding", "Bowen Zhou" ], "github": "", "project_page": "", "abstract": "We investigate the potential of large language models (LLMs) to serve as efficient simulators for agentic search tasks in reinforcement learning (RL), thereby reducing dependence on costly interactions with external search engines. To this end, we first quantify the intrinsic search capability of LLMs via structured prompting and repeated sampling, which we term Self-Search. Our results reveal that LLMs exhibit strong scaling behavior with respect to the inference budget, achieving high pass@k on question-answering benchmarks, including the challenging BrowseComp task. Building on these observations, we introduce Self-Search RL (SSRL), which enhances LLMs' Self-Search capability through format-based and rule-based rewards. SSRL enables models to iteratively refine their knowledge utilization internally, without requiring access to external tools. Empirical evaluations demonstrate that SSRL-trained policy models provide a cost-effective and stable environment for search-driven RL training, reducing reliance on external search engines and facilitating robust sim-to-real transfer. We draw the following conclusions: 1) LLMs possess world knowledge that can be effectively elicited to achieve high performance; 2) SSRL demonstrates the potential of leveraging internal knowledge to reduce hallucination; 3) SSRL-trained models integrate seamlessly with external search engines without additional effort. Our findings highlight the potential of LLMs to support more scalable RL agent training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.10395", "title": "XQuant: Breaking the Memory Wall for LLM Inference with KV Cache Rematerialization", "authors": [ "Aditya Tomar", "Coleman Hooper", "Minjae Lee", "Haocheng Xi", "Rishabh Tiwari", "Wonjun Kang", "Luca Manolache", "Michael W. Mahoney", "Kurt Keutzer", "Amir Gholami" ], "github": "", "project_page": "", "abstract": "Although LLM inference has emerged as a critical workload for many downstream applications, efficiently inferring LLMs is challenging due to the substantial memory footprint and bandwidth requirements. In parallel, compute capabilities have steadily outpaced both memory capacity and bandwidth over the last few decades, a trend that remains evident in modern GPU hardware and exacerbates the challenge of LLM inference. As such, new algorithms are emerging that trade increased computation for reduced memory operations. To that end, we present XQuant, which takes advantage of this trend, enabling an order-of-magnitude reduction in memory consumption through low-bit quantization with substantial accuracy benefits relative to state-of-the-art KV cache quantization methods. We accomplish this by quantizing and caching the layer input activations X, instead of using standard KV caching, and then rematerializing the Keys and Values on-the-fly during inference. This results in an immediate 2times memory savings compared to KV caching. By applying XQuant, we achieve up to sim 7.7times memory savings with <0.1 perplexity degradation compared to the FP16 baseline. Furthermore, our approach leverages the fact that X values are similar across layers. Building on this observation, we introduce XQuant-CL, which exploits the cross-layer similarity in the X embeddings for extreme compression. Across different models, XQuant-CL attains up to 10times memory savings relative to the FP16 baseline with only 0.01 perplexity degradation, and 12.5times memory savings with only 0.1 perplexity degradation. XQuant exploits the rapidly increasing compute capabilities of hardware platforms to eliminate the memory bottleneck, while surpassing state-of-the-art KV cache quantization methods and achieving near-FP16 accuracy across a wide range of models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-18", "arxiv_id": "2508.10975", "title": "BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining", "authors": [ "Pratyush Maini", "Vineeth Dorna", "Parth Doshi", "Aldo Carranza", "Fan Pan", "Jack Urbanek", "Paul Burstein", "Alex Fang", "Alvin Deng", "Amro Abbas", "Brett Larsen", "Cody Blakeney", "Charvi Bannur", "Christina Baek", "Darren Teh", "David Schwab", "Haakon Mongstad", "Haoli Yin", "Josh Wills", "Kaleigh Mentzer", "Luke Merrick", "Ricardo Monti", "Rishabh Adiga", "Siddharth Joshi", "Spandan Das", "Zhengping Wang", "Bogdan Gaza", "Ari Morcos", "Matthew Leavitt" ], "github": "", "project_page": "", "abstract": "Recent advances in large language model (LLM) pretraining have shown that simply scaling data quantity eventually leads to diminishing returns, hitting a data wall. In response, the use of synthetic data for pretraining has emerged as a promising paradigm for pushing the frontier of performance. Despite this, the factors affecting synthetic data quality remain poorly understood. In this work, we introduce BeyondWeb, a synthetic data generation framework that produces high-quality synthetic data for pretraining. BeyondWeb significantly extends the capabilities of traditional web-scale datasets, outperforming state-of-the-art synthetic pretraining datasets such as Cosmopedia and Nemotron-CC's high-quality synthetic subset (Nemotron-Synth) by up to 5.1 percentage points (pp) and 2.6pp, respectively, when averaged across a suite of 14 benchmark evaluations. It delivers up to 7.7x faster training than open web data and 2.7x faster than Nemotron-Synth. Remarkably, a 3B model trained for 180B tokens on BeyondWeb outperforms an 8B model trained for the same token budget on Cosmopedia. We also present several insights from BeyondWeb on synthetic data for pretraining: what drives its benefits, which data to rephrase and how, and the impact of model size and family on data quality. Overall, our work shows that there's no silver bullet for generating high-quality synthetic pretraining data. The best outcomes require jointly optimizing many factors, a challenging task that requires rigorous science and practical expertise. Naive approaches can yield modest improvements, potentially at great cost, while well-executed methods can yield transformative improvements, as exemplified by BeyondWeb.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.11737", "title": "Ovis2.5 Technical Report", "authors": [ "Shiyin Lu", "Yang Li", "Yu Xia", "Yuwei Hu", "Shanshan Zhao", "Yanqing Ma", "Zhichao Wei", "Yinglun Li", "Lunhao Duan", "Jianshan Zhao", "Yuxuan Han", "Haijun Li", "Wanying Chen", "Junke Tang", "Chengkun Hou", "Zhixing Du", "Tianli Zhou", "Wenjie Zhang", "Huping Ding", "Jiahe Li", "Wen Li", "Gui Hu", "Yiliang Gu", "Siran Yang", "Jiamang Wang", "Hailong Sun", "Yibo Wang", "Hui Sun", "Jinlong Huang", "Yuping He", "Shengze Shi", "Weihong Zhang", "Guodong Zheng", "Junpeng Jiang", "Sensen Gao", "Yi-Feng Wu", "Sijia Chen", "Yuhui Chen", "Qing-Guo Chen", "Zhao Xu", "Weihua Luo", "Kaifu Zhang" ], "github": "", "project_page": "", "abstract": "We present Ovis2.5, a successor to Ovis2 designed for native-resolution visual perception and strong multimodal reasoning. Ovis2.5 integrates a native-resolution vision transformer that processes images at their native, variable resolutions, avoiding the degradation from fixed-resolution tiling and preserving both fine detail and global layout -- crucial for visually dense content like complex charts. To strengthen reasoning, we train the model to move beyond linear chain-of-thought and perform reflection -- including self-checking and revision. This advanced capability is exposed as an optional \"thinking mode\" at inference time, allowing users to trade latency for enhanced accuracy on difficult inputs. The model is trained via a comprehensive five-phase curriculum that progressively builds its skills. The process begins with foundational visual and multimodal pretraining, advances through large-scale instruction tuning, and culminates in alignment and reasoning enhancement using DPO and GRPO. To scale these upgrades efficiently, we employ multimodal data packing and hybrid parallelism, yielding a significant end-to-end speedup. We release two open-source models: Ovis2.5-9B and Ovis2.5-2B. The latter continues the \"small model, big performance\" philosophy of Ovis2, making it ideal for resource-constrained, on-device scenarios. On the OpenCompass multimodal leaderboard, Ovis2.5-9B averages 78.3, marking a substantial improvement over its predecessor, Ovis2-8B, and achieving state-of-the-art results among open-source MLLMs in the sub-40B parameter range; Ovis2.5-2B scores 73.9, establishing SOTA for its size. Beyond aggregate scores, Ovis2.5 achieves leading results on STEM benchmarks, exhibits strong capabilities on grounding and video tasks, and achieves open-source SOTA at its scale for complex chart analysis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.09834", "title": "Speed Always Wins: A Survey on Efficient Architectures for Large Language Models", "authors": [ "Weigao Sun", "Jiaxi Hu", "Yucheng Zhou", "Jusen Du", "Disen Lan", "Kexin Wang", "Tong Zhu", "Xiaoye Qu", "Yu Zhang", "Xiaoyu Mo", "Daizong Liu", "Yuxuan Liang", "Wenliang Chen", "Guoqi Li", "Yu Cheng" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models. Transformer models, as the foundation of modern LLMs, offer a strong baseline with excellent scaling properties. However, the traditional transformer architecture requires substantial computations and poses significant obstacles for large-scale training and practical deployment. In this survey, we offer a systematic examination of innovative LLM architectures that address the inherent limitations of transformers and boost the efficiency. Starting from language modeling, this survey covers the background and technical details of linear and sparse sequence modeling methods, efficient full attention variants, sparse mixture-of-experts, hybrid model architectures incorporating the above techniques, and emerging diffusion LLMs. Additionally, we discuss applications of these techniques to other modalities and consider their wider implications for developing scalable, resource-aware foundation models. By grouping recent studies into the above category, this survey presents a blueprint of modern efficient LLM architectures, and we hope this could help motivate future research toward more efficient, versatile AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.13154", "title": "4DNeX: Feed-Forward 4D Generative Modeling Made Easy", "authors": [ "Zhaoxi Chen", "Tianqi Liu", "Long Zhuo", "Jiawei Ren", "Zeng Tao", "He Zhu", "Fangzhou Hong", "Liang Pan", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "We present 4DNeX, the first feed-forward framework for generating 4D (i.e., dynamic 3D) scene representations from a single image. In contrast to existing methods that rely on computationally intensive optimization or require multi-frame video inputs, 4DNeX enables efficient, end-to-end image-to-4D generation by fine-tuning a pretrained video diffusion model. Specifically, 1) to alleviate the scarcity of 4D data, we construct 4DNeX-10M, a large-scale dataset with high-quality 4D annotations generated using advanced reconstruction approaches. 2) we introduce a unified 6D video representation that jointly models RGB and XYZ sequences, facilitating structured learning of both appearance and geometry. 3) we propose a set of simple yet effective adaptation strategies to repurpose pretrained video diffusion models for 4D modeling. 4DNeX produces high-quality dynamic point clouds that enable novel-view video synthesis. Extensive experiments demonstrate that 4DNeX outperforms existing 4D generation methods in efficiency and generalizability, offering a scalable solution for image-to-4D modeling and laying the foundation for generative 4D world models that simulate dynamic scene evolution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.12466", "title": "Inverse-LLaVA: Eliminating Alignment Pre-training Through Text-to-Vision Mapping", "authors": [ "Xuhui Zhan", "Tyler Derr" ], "github": "", "project_page": "", "abstract": "Traditional multimodal learning approaches require expensive alignment pre-training to bridge vision and language modalities, typically projecting visual features into discrete text token spaces. We challenge both fundamental assumptions underlying this paradigm by proposing Inverse-LLaVA, a novel approach that eliminates alignment pre-training entirely while inverting the conventional mapping direction. Rather than projecting visual features to text space, our method maps text embeddings into continuous visual representation space and performs fusion within transformer intermediate layers. Through selective additive components in attention mechanisms, we enable dynamic integration of visual and textual representations without requiring massive image-text alignment datasets. Comprehensive experiments across nine multimodal benchmarks demonstrate nuanced performance trade-offs: Inverse-LLaVA achieves notable improvements on reasoning-intensive and cognitive tasks (MM-VET: +0.2%, VizWiz: +1.8%, ScienceQA: +0.2%, cognitive reasoning: +27.2%), while showing expected decreases in perception tasks requiring memorized visual-text associations (celebrity recognition: -49.5%, OCR: -21.3%). These results provide the first empirical evidence that alignment pre-training is not necessary for effective multimodal learning, particularly for complex reasoning tasks. Our work establishes the feasibility of a new paradigm that reduces computational requirements by 45%, challenges conventional wisdom about modality fusion, and opens new research directions for efficient multimodal architectures that preserve modality-specific characteristics. Our project website with code and additional resources is available at https://inverse-llava.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.10419", "title": "ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning", "authors": [ "Juyuan Wang", "Rongchen Zhao", "Wei Wei", "Yufeng Wang", "Mo Yu", "Jie Zhou", "Jin Xu", "Liyan Xu" ], "github": "https://github.com/EternityJune25/ComoRAG", "project_page": "", "abstract": "Narrative comprehension on long stories and novels has been a challenging domain attributed to their intricate plotlines and entangled, often evolving relations among characters and entities. Given the LLM's diminished reasoning over extended context and high computational cost, retrieval-based approaches remain a pivotal role in practice. However, traditional RAG methods can fall short due to their stateless, single-step retrieval process, which often overlooks the dynamic nature of capturing interconnected relations within long-range context. In this work, we propose ComoRAG, holding the principle that narrative reasoning is not a one-shot process, but a dynamic, evolving interplay between new evidence acquisition and past knowledge consolidation, analogous to human cognition when reasoning with memory-related signals in the brain. Specifically, when encountering a reasoning impasse, ComoRAG undergoes iterative reasoning cycles while interacting with a dynamic memory workspace. In each cycle, it generates probing queries to devise new exploratory paths, then integrates the retrieved evidence of new aspects into a global memory pool, thereby supporting the emergence of a coherent context for the query resolution. Across four challenging long-context narrative benchmarks (200K+ tokens), ComoRAG outperforms strong RAG baselines with consistent relative gains up to 11% compared to the strongest baseline. Further analysis reveals that ComoRAG is particularly advantageous for complex queries requiring global comprehension, offering a principled, cognitively motivated paradigm for retrieval-based long context comprehension towards stateful reasoning. Our code is publicly released at https://github.com/EternityJune25/ComoRAG", "github_manual": "", "github_regex": "https://github.com/EternityJune25/ComoRAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.13009", "title": "Matrix-Game 2.0: An Open-Source, Real-Time, and Streaming Interactive World Model", "authors": [ "Xianglong He", "Chunli Peng", "Zexiang Liu", "Boyang Wang", "Yifan Zhang", "Qi Cui", "Fei Kang", "Biao Jiang", "Mengyin An", "Yangyang Ren", "Baixin Xu", "Hao-Xiang Guo", "Kaixiong Gong", "Cyrus Wu", "Wei Li", "Xuchen Song", "Yang Liu", "Eric Li", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "Recent advances in interactive video generations have demonstrated diffusion model's potential as world models by capturing complex physical dynamics and interactive behaviors. However, existing interactive world models depend on bidirectional attention and lengthy inference steps, severely limiting real-time performance. Consequently, they are hard to simulate real-world dynamics, where outcomes must update instantaneously based on historical context and current actions. To address this, we present Matrix-Game 2.0, an interactive world model generates long videos on-the-fly via few-step auto-regressive diffusion. Our framework consists of three key components: (1) A scalable data production pipeline for Unreal Engine and GTA5 environments to effectively produce massive amounts (about 1200 hours) of video data with diverse interaction annotations; (2) An action injection module that enables frame-level mouse and keyboard inputs as interactive conditions; (3) A few-step distillation based on the casual architecture for real-time and streaming video generation. Matrix Game 2.0 can generate high-quality minute-level videos across diverse scenes at an ultra-fast speed of 25 FPS. We open-source our model weights and codebase to advance research in interactive world modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.12945", "title": "Lumen: Consistent Video Relighting and Harmonious Background Replacement with Video Generative Models", "authors": [ "Jianshu Zeng", "Yuxuan Liu", "Yutong Feng", "Chenxuan Miao", "Zixiang Gao", "Jiwang Qu", "Jianzhang Zhang", "Bin Wang", "Kun Yuan" ], "github": "", "project_page": "", "abstract": "Video relighting is a challenging yet valuable task, aiming to replace the background in videos while correspondingly adjusting the lighting in the foreground with harmonious blending. During translation, it is essential to preserve the original properties of the foreground, e.g., albedo, and propagate consistent relighting among temporal frames. In this paper, we propose Lumen, an end-to-end video relighting framework developed on large-scale video generative models, receiving flexible textual description for instructing the control of lighting and background. Considering the scarcity of high-qualified paired videos with the same foreground in various lighting conditions, we construct a large-scale dataset with a mixture of realistic and synthetic videos. For the synthetic domain, benefiting from the abundant 3D assets in the community, we leverage advanced 3D rendering engine to curate video pairs in diverse environments. For the realistic domain, we adapt a HDR-based lighting simulation to complement the lack of paired in-the-wild videos. Powered by the aforementioned dataset, we design a joint training curriculum to effectively unleash the strengths of each domain, i.e., the physical consistency in synthetic videos, and the generalized domain distribution in realistic videos. To implement this, we inject a domain-aware adapter into the model to decouple the learning of relighting and domain appearance distribution. We construct a comprehensive benchmark to evaluate Lumen together with existing methods, from the perspectives of foreground preservation and video consistency assessment. Experimental results demonstrate that Lumen effectively edit the input into cinematic relighted videos with consistent lighting and strict foreground preservation. Our project page: https://lumen-relight.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.12880", "title": "S^2-Guidance: Stochastic Self Guidance for Training-Free Enhancement of Diffusion Models", "authors": [ "Chubin Chen", "Jiashu Zhu", "Xiaokun Feng", "Nisha Huang", "Meiqi Wu", "Fangyuan Mao", "Jiahong Wu", "Xiangxiang Chu", "Xiu Li" ], "github": "", "project_page": "", "abstract": "Classifier-free Guidance (CFG) is a widely used technique in modern diffusion models for enhancing sample quality and prompt adherence. However, through an empirical analysis on Gaussian mixture modeling with a closed-form solution, we observe a discrepancy between the suboptimal results produced by CFG and the ground truth. The model's excessive reliance on these suboptimal predictions often leads to semantic incoherence and low-quality outputs. To address this issue, we first empirically demonstrate that the model's suboptimal predictions can be effectively refined using sub-networks of the model itself. Building on this insight, we propose S^2-Guidance, a novel method that leverages stochastic block-dropping during the forward process to construct stochastic sub-networks, effectively guiding the model away from potential low-quality predictions and toward high-quality outputs. Extensive qualitative and quantitative experiments on text-to-image and text-to-video generation tasks demonstrate that S^2-Guidance delivers superior performance, consistently surpassing CFG and other advanced guidance strategies. Our code will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.11598", "title": "Representing Speech Through Autoregressive Prediction of Cochlear Tokens", "authors": [ "Greta Tuckute", "Klemen Kotar", "Evelina Fedorenko", "Daniel L. K. Yamins" ], "github": "", "project_page": "", "abstract": "We introduce AuriStream, a biologically inspired model for encoding speech via a two-stage framework inspired by the human auditory processing hierarchy. The first stage transforms raw audio into a time-frequency representation based on the human cochlea, from which we extract discrete cochlear tokens. The second stage applies an autoregressive sequence model over the cochlear tokens. AuriStream learns meaningful phoneme and word representations, and state-of-the-art lexical semantics. AuriStream shows competitive performance on diverse downstream SUPERB speech tasks. Complementing AuriStream's strong representational capabilities, it generates continuations of audio which can be visualized in a spectrogram space and decoded back into audio, providing insights into the model's predictions. In summary, we present a two-stage framework for speech representation learning to advance the development of more human-like models that efficiently handle a range of speech-based tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.12811", "title": "Next Visual Granularity Generation", "authors": [ "Yikai Wang", "Zhouxia Wang", "Zhonghua Wu", "Qingyi Tao", "Kang Liao", "Chen Change Loy" ], "github": "", "project_page": "", "abstract": "We propose a novel approach to image generation by decomposing an image into a structured sequence, where each element in the sequence shares the same spatial resolution but differs in the number of unique tokens used, capturing different level of visual granularity. Image generation is carried out through our newly introduced Next Visual Granularity (NVG) generation framework, which generates a visual granularity sequence beginning from an empty image and progressively refines it, from global layout to fine details, in a structured manner. This iterative process encodes a hierarchical, layered representation that offers fine-grained control over the generation process across multiple granularity levels. We train a series of NVG models for class-conditional image generation on the ImageNet dataset and observe clear scaling behavior. Compared to the VAR series, NVG consistently outperforms it in terms of FID scores (3.30 -> 3.03, 2.57 ->2.44, 2.09 -> 2.06). We also conduct extensive analysis to showcase the capability and potential of the NVG framework. Our code and models will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.13142", "title": "Has GPT-5 Achieved Spatial Intelligence? An Empirical Study", "authors": [ "Zhongang Cai", "Yubo Wang", "Qingping Sun", "Ruisi Wang", "Chenyang Gu", "Wanqi Yin", "Zhiqian Lin", "Zhitao Yang", "Chen Wei", "Xuanke Shi", "Kewang Deng", "Xiaoyang Han", "Zukai Chen", "Jiaqi Li", "Xiangyu Fan", "Hanming Deng", "Lewei Lu", "Bo Li", "Ziwei Liu", "Quan Wang", "Dahua Lin", "Lei Yang" ], "github": "", "project_page": "", "abstract": "Multi-modal models have achieved remarkable progress in recent years. Nevertheless, they continue to exhibit notable limitations in spatial understanding and reasoning, which are fundamental capabilities to achieving artificial general intelligence. With the recent release of GPT-5, allegedly the most powerful AI model to date, it is timely to examine where the leading models stand on the path toward spatial intelligence. First, we propose a comprehensive taxonomy of spatial tasks that unifies existing benchmarks and discuss the challenges in ensuring fair evaluation. We then evaluate state-of-the-art proprietary and open-source models on eight key benchmarks, at a cost exceeding one billion total tokens. Our empirical study reveals that (1) GPT-5 demonstrates unprecedented strength in spatial intelligence, yet (2) still falls short of human performance across a broad spectrum of tasks. Moreover, we (3) identify the more challenging spatial intelligence problems for multi-modal models, and (4) proprietary models do not exhibit a decisive advantage when facing the most difficult problems. In addition, we conduct a qualitative evaluation across a diverse set of scenarios that are intuitive for humans yet fail even the most advanced multi-modal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.13104", "title": "Precise Action-to-Video Generation Through Visual Action Prompts", "authors": [ "Yuang Wang", "Chao Wen", "Haoyu Guo", "Sida Peng", "Minghan Qin", "Hujun Bao", "Xiaowei Zhou", "Ruizhen Hu" ], "github": "", "project_page": "", "abstract": "We present visual action prompts, a unified action representation for action-to-video generation of complex high-DoF interactions while maintaining transferable visual dynamics across domains. Action-driven video generation faces a precision-generality trade-off: existing methods using text, primitive actions, or coarse masks offer generality but lack precision, while agent-centric action signals provide precision at the cost of cross-domain transferability. To balance action precision and dynamic transferability, we propose to \"render\" actions into precise visual prompts as domain-agnostic representations that preserve both geometric precision and cross-domain adaptability for complex actions; specifically, we choose visual skeletons for their generality and accessibility. We propose robust pipelines to construct skeletons from two interaction-rich data sources - human-object interactions (HOI) and dexterous robotic manipulation - enabling cross-domain training of action-driven generative models. By integrating visual skeletons into pretrained video generation models via lightweight fine-tuning, we enable precise action control of complex interaction while preserving the learning of cross-domain dynamics. Experiments on EgoVid, RT-1 and DROID demonstrate the effectiveness of our proposed approach. Project page: https://zju3dv.github.io/VAP/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.12782", "title": "HeroBench: A Benchmark for Long-Horizon Planning and Structured Reasoning in Virtual Worlds", "authors": [ "Petr Anokhin", "Roman Khalikov", "Stefan Rebrikov", "Viktor Volkov", "Artyom Sorokin", "Vincent Bissonnette" ], "github": "https://github.com/stefanrer/HeroBench", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable capabilities in isolated step-by-step reasoning tasks such as mathematics and programming, but their proficiency in long-horizon planning, where solutions require extended, structured sequences of interdependent actions, remains underexplored. Existing benchmarks typically assess LLMs through abstract or low-dimensional algorithmic tasks, failing to capture the complexity of realistic planning environments. We introduce HeroBench, a novel benchmark designed specifically to evaluate long-horizon planning and structured reasoning within complex RPG-inspired virtual worlds. HeroBench provides a rigorously constructed dataset of tasks covering a wide range of difficulties, a simulated environment to execute and validate agent plans, and detailed analytical tools for evaluating model performance. Tasks challenge models to formulate strategic plans, efficiently gather resources, master necessary skills, craft equipment, and defeat adversaries, reflecting practical scenarios' layered dependencies and constraints. Our extensive evaluation of 25 state-of-the-art LLMs, spanning both open-source and proprietary models, including the GPT-5 family, reveals substantial performance disparities rarely observed in conventional reasoning benchmarks. Detailed error analysis further uncovers specific weaknesses in current models' abilities to generate robust high-level plans and reliably execute structured actions. HeroBench thus not only significantly advances the evaluation of LLM reasoning but also provides a flexible, scalable foundation for future research into advanced, autonomous planning in virtual environments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stefanrer/HeroBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.11252", "title": "Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information", "authors": [ "Youcheng Huang", "Bowen Qin", "Chen Huang", "Duanyu Feng", "Xi Yang", "Wenqiang Lei" ], "github": "", "project_page": "", "abstract": "Large Reasoning Models (LRMs) have demonstrated remarkable problem-solving abilities in mathematics, as evaluated by existing benchmarks exclusively on well-defined problems. However, such evaluation setup constitutes a critical gap, since a genuine intelligent agent should not only solve problems (as a math quiz solver), but also be able~to ask for information when the problems lack sufficient information, enabling proactivity in responding users' requests. To bridge such gap, we proposes a new dataset consisting of two types of incomplete problems with diverse contexts. Based on the dataset, our systematical evaluation of LRMs reveals their inability in proactively asking for information. In addition, we uncover the behaviors related to overthinking and hallucination of LRMs, and highlight the potential and challenges of supervised fine-tuning in learning such ability. We hope to provide new insights in developing LRMs with genuine intelligence, rather than just solving problems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.11383", "title": "When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs", "authors": [ "Mikhail Seleznyov", "Mikhail Chaichuk", "Gleb Ershov", "Alexander Panchenko", "Elena Tutubalina", "Oleg Somov" ], "github": "https://github.com/AIRI-Institute/when-punctuation-matters", "project_page": "", "abstract": "Large Language Models (LLMs) are highly sensitive to subtle, non-semantic variations in prompt phrasing and formatting. In this work, we present the first systematic evaluation of 5 methods for improving prompt robustness within a unified experimental framework. We benchmark these techniques on 8 models from Llama, Qwen and Gemma families across 52 tasks from Natural Instructions dataset. Our evaluation covers robustness methods from both fine-tuned and in-context learning paradigms, and tests their generalization against multiple types of distribution shifts. Finally, we extend our analysis to GPT-4.1 and DeepSeek V3 to assess frontier models' current robustness to format perturbations. Our findings offer actionable insights into the relative effectiveness of these robustness methods, enabling practitioners to make informed decisions when aiming for stable and reliable LLM performance in real-world applications. Code: https://github.com/AIRI-Institute/when-punctuation-matters.", "github_manual": "", "github_regex": "https://github.com/AIRI-Institute/when-punctuation-matters", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.11379", "title": "G-CUT3R: Guided 3D Reconstruction with Camera and Depth Prior Integration", "authors": [ "Ramil Khafizov", "Artem Komarichev", "Ruslan Rakhimov", "Peter Wonka", "Evgeny Burnaev" ], "github": "", "project_page": "", "abstract": "We introduce G-CUT3R, a novel feed-forward approach for guided 3D scene reconstruction that enhances the CUT3R model by integrating prior information. Unlike existing feed-forward methods that rely solely on input images, our method leverages auxiliary data, such as depth, camera calibrations, or camera positions, commonly available in real-world scenarios. We propose a lightweight modification to CUT3R, incorporating a dedicated encoder for each modality to extract features, which are fused with RGB image tokens via zero convolution. This flexible design enables seamless integration of any combination of prior information during inference. Evaluated across multiple benchmarks, including 3D reconstruction and other multi-view tasks, our approach demonstrates significant performance improvements, showing its ability to effectively utilize available priors while maintaining compatibility with varying input modalities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.12790", "title": "Reinforcement Learning with Rubric Anchors", "authors": [ "Zenan Huang", "Yihong Zhuang", "Guoshan Lu", "Zeyu Qin", "Haokai Xu", "Tianyu Zhao", "Ru Peng", "Jiaqi Hu", "Zhanming Shen", "Xiaomeng Hu", "Xijun Gu", "Peiyi Tu", "Jiaxin Liu", "Wenyu Chen", "Yuzhuo Fu", "Zhiting Fan", "Yanmei Gu", "Yuanyuan Wang", "Zhengkai Yang", "Jianguo Li", "Junbo Zhao" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing Large Language Models (LLMs), exemplified by the success of OpenAI's o-series. In RLVR, rewards are derived from verifiable signals-such as passing unit tests in code generation or matching correct answers in mathematical reasoning. While effective, this requirement largely confines RLVR to domains with automatically checkable outcomes. To overcome this, we extend the RLVR paradigm to open-ended tasks by integrating rubric-based rewards, where carefully designed rubrics serve as structured, model-interpretable criteria for automatic scoring of subjective outputs. We construct, to our knowledge, the largest rubric reward system to date, with over 10,000 rubrics from humans, LLMs, or a hybrid human-LLM collaboration. Implementing rubric-based RL is challenging; we tackle these issues with a clear framework and present an open-sourced Qwen-30B-A3B model with notable gains: 1) With only 5K+ samples, our system improves by +5.2% on open-ended benchmarks (especially humanities), outperforming a 671B DeepSeek-V3 model by +2.4%, while preserving general and reasoning abilities. 2) Our method provides fine-grained stylistic control, using rubrics as anchors to mitigate the \"AI-like\" tone and produce more human-like, expressive responses. We share key lessons in rubric construction, data selection, and training, and discuss limitations and future releases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.12730", "title": "Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods", "authors": [ "Jaeung Lee", "Suhyeon Yu", "Yurim Jang", "Simon S. Woo", "Jaemin Jo" ], "github": "https://github.com/gnueaj/Machine-Unlearning-Comparator", "project_page": "", "abstract": "Machine Unlearning (MU) aims to remove target training data from a trained model so that the removed data no longer influences the model's behavior, fulfilling \"right to be forgotten\" obligations under data privacy laws. Yet, we observe that researchers in this rapidly emerging field face challenges in analyzing and understanding the behavior of different MU methods, especially in terms of three fundamental principles in MU: accuracy, efficiency, and privacy. Consequently, they often rely on aggregate metrics and ad-hoc evaluations, making it difficult to accurately assess the trade-offs between methods. To fill this gap, we introduce a visual analytics system, Unlearning Comparator, designed to facilitate the systematic evaluation of MU methods. Our system supports two important tasks in the evaluation process: model comparison and attack simulation. First, it allows the user to compare the behaviors of two models, such as a model generated by a certain method and a retrained baseline, at class-, instance-, and layer-levels to better understand the changes made after unlearning. Second, our system simulates membership inference attacks (MIAs) to evaluate the privacy of a method, where an attacker attempts to determine whether specific data samples were part of the original training set. We evaluate our system through a case study visually analyzing prominent MU methods and demonstrate that it helps the user not only understand model behaviors but also gain insights that can inform the improvement of MU methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gnueaj/Machine-Unlearning-Comparator", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-19", "arxiv_id": "2508.13968", "title": "RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation", "authors": [ "Tianyi Niu", "Jaemin Cho", "Elias Stengel-Eskin", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "We investigate to what extent Multimodal Large Language Models (MLLMs) can accurately identify the orientation of input images rotated 0{\\deg}, 90{\\deg}, 180{\\deg}, and 270{\\deg}. This task demands robust visual reasoning capabilities to detect rotational cues and contextualize spatial relationships within images, regardless of their orientation. To evaluate MLLMs on these abilities, we introduce RotBench -- a 350-image manually-filtered benchmark comprising lifestyle, portrait, and landscape images. Despite the relatively simple nature of this task, we show that several state-of-the-art open and proprietary MLLMs, including GPT-5, o3, and Gemini-2.5-Pro, do not reliably identify rotation in input images. Providing models with auxiliary information -- including captions, depth maps, and more -- or using chain-of-thought prompting offers only small and inconsistent improvements. Our results indicate that most models are able to reliably identify right-side-up (0{\\deg}) images, while certain models are able to identify upside-down (180{\\deg}) images. None can reliably distinguish between 90{\\deg} and 270{\\deg}. Simultaneously showing the image rotated in different orientations leads to moderate performance gains for reasoning models, while a modified setup using voting improves the performance of weaker models. We further show that fine-tuning does not improve models' ability to distinguish 90{\\deg} and 270{\\deg} rotations, despite substantially improving the identification of 180{\\deg} images. Together, these results reveal a significant gap between MLLMs' spatial reasoning capabilities and human perception in identifying rotation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.13632", "title": "OmniTry: Virtual Try-On Anything without Masks", "authors": [ "Yutong Feng", "Linlin Zhang", "Hengyuan Cao", "Yiming Chen", "Xiaoduan Feng", "Jian Cao", "Yuxiong Wu", "Bin Wang" ], "github": "", "project_page": "", "abstract": "Virtual Try-ON (VTON) is a practical and widely-applied task, for which most of existing works focus on clothes. This paper presents OmniTry, a unified framework that extends VTON beyond garment to encompass any wearable objects, e.g., jewelries and accessories, with mask-free setting for more practical application. When extending to various types of objects, data curation is challenging for obtaining paired images, i.e., the object image and the corresponding try-on result. To tackle this problem, we propose a two-staged pipeline: For the first stage, we leverage large-scale unpaired images, i.e., portraits with any wearable items, to train the model for mask-free localization. Specifically, we repurpose the inpainting model to automatically draw objects in suitable positions given an empty mask. For the second stage, the model is further fine-tuned with paired images to transfer the consistency of object appearance. We observed that the model after the first stage shows quick convergence even with few paired samples. OmniTry is evaluated on a comprehensive benchmark consisting of 12 common classes of wearable objects, with both in-shop and in-the-wild images. Experimental results suggest that OmniTry shows better performance on both object localization and ID-preservation compared with existing methods. The code, model weights, and evaluation benchmark of OmniTry will be made publicly available at https://omnitry.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.09131", "title": "Training-Free Text-Guided Color Editing with Multi-Modal Diffusion Transformer", "authors": [ "Zixin Yin", "Xili Dai", "Ling-Hao Chen", "Deyu Zhou", "Jianan Wang", "Duomin Wang", "Gang Yu", "Lionel M. Ni", "Lei Zhang", "Heung-Yeung Shum" ], "github": "", "project_page": "", "abstract": "Text-guided color editing in images and videos is a fundamental yet unsolved problem, requiring fine-grained manipulation of color attributes, including albedo, light source color, and ambient lighting, while preserving physical consistency in geometry, material properties, and light-matter interactions. Existing training-free methods offer broad applicability across editing tasks but struggle with precise color control and often introduce visual inconsistency in both edited and non-edited regions. In this work, we present ColorCtrl, a training-free color editing method that leverages the attention mechanisms of modern Multi-Modal Diffusion Transformers (MM-DiT). By disentangling structure and color through targeted manipulation of attention maps and value tokens, our method enables accurate and consistent color editing, along with word-level control of attribute intensity. Our method modifies only the intended regions specified by the prompt, leaving unrelated areas untouched. Extensive experiments on both SD3 and FLUX.1-dev demonstrate that ColorCtrl outperforms existing training-free approaches and achieves state-of-the-art performances in both edit quality and consistency. Furthermore, our method surpasses strong commercial models such as FLUX.1 Kontext Max and GPT-4o Image Generation in terms of consistency. When extended to video models like CogVideoX, our approach exhibits greater advantages, particularly in maintaining temporal coherence and editing stability. Finally, our method also generalizes to instruction-based editing diffusion models such as Step1X-Edit and FLUX.1 Kontext dev, further demonstrating its versatility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.13167", "title": "Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL", "authors": [ "Weizhen Li", "Jianbo Lin", "Zhuosong Jiang", "Jingyi Cao", "Xinpeng Liu", "Jiayu Zhang", "Zhenqiang Huang", "Qianben Chen", "Weichen Sun", "Qiexiang Wang", "Hongxuan Lu", "Tianrui Qin", "Chenghao Zhu", "Yi Yao", "Shuying Fan", "Xiaowan Li", "Tiannan Wang", "Pai Liu", "King Zhu", "He Zhu", "Dingfeng Shi", "Piaohong Wang", "Yeyi Guan", "Xiangru Tang", "Minghao Liu", "Yuchen Eleanor Jiang", "Jian Yang", "Jiaheng Liu", "Ge Zhang", "Wangchunshu Zhou" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models (LLMs) and multi-agent systems have demonstrated remarkable capabilities in complex problem-solving tasks such as deep research, vibe coding, and mathematical reasoning. However, most existing multi-agent systems are built upon manual prompt/workflow engineering with sophisticated agent frameworks, making them computationally inefficient, less capable, and can not benefit from data-centric learning. In this work, we introduce Chain-of-Agents (CoA), a novel paradigm of LLM reasoning that enables native end-to-end complex problem-solving in the same way as a multi-agent system (i.e., multi-turn problem solving with multiple tools and multiple agents) within one model. In chain-of-agents problem-solving, the model dynamically activates different tool agents and role-playing agents to simulate multi-agent collaboration in an end-to-end fashion. To elicit end-to-end chain-of-agents problem-solving abilities in LLMs, we introduce a multi-agent distillation framework to distill state-of-the-art multi-agent systems into chain-of-agents trajectories for agentic supervised fine-tuning. We then use agentic reinforcement learning on verifiable agentic tasks to further improve the models' capabilities on chain-of-agents problem solving. We call the resulting models Agent Foundation Models (AFMs). Our empirical studies demonstrate that AFM establishes new state-of-the-art performance across diverse benchmarks in both web agent and code agent settings. We make the entire research, including the model weights, code for training and evaluation, and the training data, fully open-sourced, which offers a solid starting point for future research on agent models and agentic RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.14041", "title": "LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos", "authors": [ "Chin-Yang Lin", "Cheng Sun", "Fu-En Yang", "Min-Hung Chen", "Yen-Yu Lin", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "LongSplat addresses critical challenges in novel view synthesis (NVS) from casually captured long videos characterized by irregular camera motion, unknown camera poses, and expansive scenes. Current methods often suffer from pose drift, inaccurate geometry initialization, and severe memory limitations. To address these issues, we introduce LongSplat, a robust unposed 3D Gaussian Splatting framework featuring: (1) Incremental Joint Optimization that concurrently optimizes camera poses and 3D Gaussians to avoid local minima and ensure global consistency; (2) a robust Pose Estimation Module leveraging learned 3D priors; and (3) an efficient Octree Anchor Formation mechanism that converts dense point clouds into anchors based on spatial density. Extensive experiments on challenging benchmarks demonstrate that LongSplat achieves state-of-the-art results, substantially improving rendering quality, pose accuracy, and computational efficiency compared to prior approaches. Project page: https://linjohnss.github.io/longsplat/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.13948", "title": "Prompt Orchestration Markup Language", "authors": [ "Yuge Zhang", "Nan Chen", "Jiahang Xu", "Yuqing Yang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) require sophisticated prompting, yet current practices face challenges in structure, data integration, format sensitivity, and tooling. Existing methods lack comprehensive solutions for organizing complex prompts involving diverse data types (documents, tables, images) or managing presentation variations systematically. To address these gaps, we introduce POML (Prompt Orchestration Markup Language). POML employs component-based markup for logical structure (roles, tasks, examples), specialized tags for seamless data integration, and a CSS-like styling system to decouple content from presentation, reducing formatting sensitivity. It includes templating for dynamic prompts and a comprehensive developer toolkit (IDE support, SDKs) to improve version control and collaboration. We validate POML through two case studies demonstrating its impact on complex application integration (PomLink) and accuracy performance (TableQA), as well as a user study assessing its effectiveness in real-world development scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.13992", "title": "MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence", "authors": [ "Sonal Kumar", "\u0160imon Sedl\u00e1\u010dek", "Vaibhavi Lokegaonkar", "Fernando L\u00f3pez", "Wenyi Yu", "Nishit Anand", "Hyeonggon Ryu", "Lichang Chen", "Maxim Pli\u010dka", "Miroslav Hlav\u00e1\u010dek", "William Fineas Ellingwood", "Sathvik Udupa", "Siyuan Hou", "Allison Ferner", "Sara Barahona", "Cecilia Bola\u00f1os", "Satish Rahi", "Laura Herrera-Alarc\u00f3n", "Satvik Dixit", "Siddhi Patil", "Soham Deshmukh", "Lasha Koroshinadze", "Yao Liu", "Leibny Paola Garcia Perera", "Eleni Zanou", "Themos Stafylakis", "Joon Son Chung", "David Harwath", "Chao Zhang", "Dinesh Manocha", "Alicia Lozano-Diez", "Santosh Kesiraju", "Sreyan Ghosh", "Ramani Duraiswami" ], "github": "https://github.com/resemble-ai/chatterbox", "project_page": "", "abstract": "Audio comprehension-including speech, non-speech sounds, and music-is essential for achieving human-level intelligence. Consequently, AI agents must demonstrate holistic audio understanding to qualify as generally intelligent. However, evaluating auditory intelligence comprehensively remains challenging. To address this gap, we introduce MMAU-Pro, the most comprehensive and rigorously curated benchmark for assessing audio intelligence in AI systems. MMAU-Pro contains 5,305 instances, where each instance has one or more audios paired with human expert-generated question-answer pairs, spanning speech, sound, music, and their combinations. Unlike existing benchmarks, MMAU-Pro evaluates auditory intelligence across 49 unique skills and multiple complex dimensions, including long-form audio comprehension, spatial audio reasoning, multi-audio understanding, among others. All questions are meticulously designed to require deliberate multi-hop reasoning, including both multiple-choice and open-ended response formats. Importantly, audio data is sourced directly ``from the wild\" rather than from existing datasets with known distributions. We evaluate 22 leading open-source and proprietary multimodal AI models, revealing significant limitations: even state-of-the-art models such as Gemini 2.5 Flash and Audio Flamingo 3 achieve only 59.2% and 51.7% accuracy, respectively, approaching random performance in multiple categories. Our extensive analysis highlights specific shortcomings and provides novel insights, offering actionable perspectives for the community to enhance future AI systems' progression toward audio general intelligence. The benchmark and code is available at https://sonalkum.github.io/mmau-pro.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/resemble-ai/chatterbox", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.12669", "title": "Leveraging Large Language Models for Predictive Analysis of Human Misery", "authors": [ "Bishanka Seal", "Rahul Seetharaman", "Aman Bansal", "Abhilash Nandy" ], "github": "https://github.com/abhi1nandy2/Misery_Data_Exps_GitHub", "project_page": "", "abstract": "This study investigates the use of Large Language Models (LLMs) for predicting human-perceived misery scores from natural language descriptions of real-world scenarios. The task is framed as a regression problem, where the model assigns a scalar value from 0 to 100 to each input statement. We evaluate multiple prompting strategies, including zero-shot, fixed-context few-shot, and retrieval-based prompting using BERT sentence embeddings. Few-shot approaches consistently outperform zero-shot baselines, underscoring the value of contextual examples in affective prediction. To move beyond static evaluation, we introduce the \"Misery Game Show\", a novel gamified framework inspired by a television format. It tests LLMs through structured rounds involving ordinal comparison, binary classification, scalar estimation, and feedback-driven reasoning. This setup enables us to assess not only predictive accuracy but also the model's ability to adapt based on corrective feedback. The gamified evaluation highlights the broader potential of LLMs in dynamic emotional reasoning tasks beyond standard regression. Code and data link: https://github.com/abhi1nandy2/Misery_Data_Exps_GitHub", "github_manual": "", "github_regex": "https://github.com/abhi1nandy2/Misery_Data_Exps_GitHub", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.06905", "title": "MultiRef: Controllable Image Generation with Multiple Visual References", "authors": [ "Ruoxi Chen", "Dongping Chen", "Siyuan Wu", "Sinan Wang", "Shiyun Lang", "Petr Sushko", "Gaoyang Jiang", "Yao Wan", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Visual designers naturally draw inspiration from multiple visual references, combining diverse elements and aesthetic principles to create artwork. However, current image generative frameworks predominantly rely on single-source inputs -- either text prompts or individual reference images. In this paper, we focus on the task of controllable image generation using multiple visual references. We introduce MultiRef-bench, a rigorous evaluation framework comprising 990 synthetic and 1,000 real-world samples that require incorporating visual content from multiple reference images. The synthetic samples are synthetically generated through our data engine RefBlend, with 10 reference types and 33 reference combinations. Based on RefBlend, we further construct a dataset MultiRef containing 38k high-quality images to facilitate further research. Our experiments across three interleaved image-text models (i.e., OmniGen, ACE, and Show-o) and six agentic frameworks (e.g., ChatDiT and LLM + SD) reveal that even state-of-the-art systems struggle with multi-reference conditioning, with the best model OmniGen achieving only 66.6% in synthetic samples and 79.0% in real-world cases on average compared to the golden answer. These findings provide valuable directions for developing more flexible and human-like creative tools that can effectively integrate multiple sources of visual inspiration. The dataset is publicly available at: https://multiref.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.13139", "title": "Motion2Motion: Cross-topology Motion Transfer with Sparse Correspondence", "authors": [ "Ling-Hao Chen", "Yuhong Zhang", "Zixin Yin", "Zhiyang Dou", "Xin Chen", "Jingbo Wang", "Taku Komura", "Lei Zhang" ], "github": "https://github.com/LinghaoChan/Motion2Motion_codes", "project_page": "", "abstract": "This work studies the challenge of transfer animations between characters whose skeletal topologies differ substantially. While many techniques have advanced retargeting techniques in decades, transfer motions across diverse topologies remains less-explored. The primary obstacle lies in the inherent topological inconsistency between source and target skeletons, which restricts the establishment of straightforward one-to-one bone correspondences. Besides, the current lack of large-scale paired motion datasets spanning different topological structures severely constrains the development of data-driven approaches. To address these limitations, we introduce Motion2Motion, a novel, training-free framework. Simply yet effectively, Motion2Motion works with only one or a few example motions on the target skeleton, by accessing a sparse set of bone correspondences between the source and target skeletons. Through comprehensive qualitative and quantitative evaluations, we demonstrate that Motion2Motion achieves efficient and reliable performance in both similar-skeleton and cross-species skeleton transfer scenarios. The practical utility of our approach is further evidenced by its successful integration in downstream applications and user interfaces, highlighting its potential for industrial applications. Code and data are available at https://lhchen.top/Motion2Motion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LinghaoChan/Motion2Motion_codes", "github_api_added_by": "auto" }, { "date": "2025-08-20", "arxiv_id": "2508.04324", "title": "TempFlow-GRPO: When Timing Matters for GRPO in Flow Models", "authors": [ "Xiaoxuan He", "Siming Fu", "Yuke Zhao", "Wanli Li", "Jian Yang", "Dacheng Yin", "Fengyun Rao", "Bo Zhang" ], "github": "", "project_page": "", "abstract": "Recent flow matching models for text-to-image generation have achieved remarkable quality, yet their integration with reinforcement learning for human preference alignment remains suboptimal, hindering fine-grained reward-based optimization. We observe that the key impediment to effective GRPO training of flow models is the temporal uniformity assumption in existing approaches: sparse terminal rewards with uniform credit assignment fail to capture the varying criticality of decisions across generation timesteps, resulting in inefficient exploration and suboptimal convergence. To remedy this shortcoming, we introduce TempFlow-GRPO (Temporal Flow GRPO), a principled GRPO framework that captures and exploits the temporal structure inherent in flow-based generation. TempFlow-GRPO introduces two key innovations: (i) a trajectory branching mechanism that provides process rewards by concentrating stochasticity at designated branching points, enabling precise credit assignment without requiring specialized intermediate reward models; and (ii) a noise-aware weighting scheme that modulates policy optimization according to the intrinsic exploration potential of each timestep, prioritizing learning during high-impact early stages while ensuring stable refinement in later phases. These innovations endow the model with temporally-aware optimization that respects the underlying generative dynamics, leading to state-of-the-art performance in human preference alignment and standard text-to-image benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.11548", "title": "Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends", "authors": [ "Zhenhua Xu", "Xubin Yue", "Zhebo Wang", "Qichen Liu", "Xixiang Zhao", "Jingxuan Zhang", "Wenjun Zeng", "Wengpeng Xing", "Dezhang Kong", "Changting Lin", "Meng Han" ], "github": "", "project_page": "", "abstract": "Copyright protection for large language models is of critical importance, given their substantial development costs, proprietary value, and potential for misuse. Existing surveys have predominantly focused on techniques for tracing LLM-generated content-namely, text watermarking-while a systematic exploration of methods for protecting the models themselves (i.e., model watermarking and model fingerprinting) remains absent. Moreover, the relationships and distinctions among text watermarking, model watermarking, and model fingerprinting have not been comprehensively clarified. This work presents a comprehensive survey of the current state of LLM copyright protection technologies, with a focus on model fingerprinting, covering the following aspects: (1) clarifying the conceptual connection from text watermarking to model watermarking and fingerprinting, and adopting a unified terminology that incorporates model watermarking into the broader fingerprinting framework; (2) providing an overview and comparison of diverse text watermarking techniques, highlighting cases where such methods can function as model fingerprinting; (3) systematically categorizing and comparing existing model fingerprinting approaches for LLM copyright protection; (4) presenting, for the first time, techniques for fingerprint transfer and fingerprint removal; (5) summarizing evaluation metrics for model fingerprints, including effectiveness, harmlessness, robustness, stealthiness, and reliability; and (6) discussing open challenges and future research directions. This survey aims to offer researchers a thorough understanding of both text watermarking and model fingerprinting technologies in the era of LLMs, thereby fostering further advances in protecting their intellectual property.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.10830", "title": "Advances in Speech Separation: Techniques, Challenges, and Future Trends", "authors": [ "Kai Li", "Guo Chen", "Wendi Sang", "Yi Luo", "Zhuo Chen", "Shuai Wang", "Shulin He", "Zhong-Qiu Wang", "Andong Li", "Zhiyong Wu", "Xiaolin Hu" ], "github": "", "project_page": "", "abstract": "The field of speech separation, addressing the \"cocktail party problem\", has seen revolutionary advances with DNNs. Speech separation enhances clarity in complex acoustic environments and serves as crucial pre-processing for speech recognition and speaker recognition. However, current literature focuses narrowly on specific architectures or isolated approaches, creating fragmented understanding. This survey addresses this gap by providing systematic examination of DNN-based speech separation techniques. Our work differentiates itself through: (I) Comprehensive perspective: We systematically investigate learning paradigms, separation scenarios with known/unknown speakers, comparative analysis of supervised/self-supervised/unsupervised frameworks, and architectural components from encoders to estimation strategies. (II) Timeliness: Coverage of cutting-edge developments ensures access to current innovations and benchmarks. (III) Unique insights: Beyond summarization, we evaluate technological trajectories, identify emerging patterns, and highlight promising directions including domain-robust frameworks, efficient architectures, multimodal integration, and novel self-supervised paradigms. (IV) Fair evaluation: We provide quantitative evaluations on standard datasets, revealing true capabilities and limitations of different methods. This comprehensive survey serves as an accessible reference for experienced researchers and newcomers navigating speech separation's complex landscape.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.04326", "title": "Radiance Fields in XR: A Survey on How Radiance Fields are Envisioned and Addressed for XR Research", "authors": [ "Ke Li", "Mana Masuda", "Susanne Schmidt", "Shohei Mori" ], "github": "", "project_page": "", "abstract": "The development of radiance fields (RF), such as 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF), has revolutionized interactive photorealistic view synthesis and presents enormous opportunities for XR research and applications. However, despite the exponential growth of RF research, RF-related contributions to the XR community remain sparse. To better understand this research gap, we performed a systematic survey of current RF literature to analyze (i) how RF is envisioned for XR applications, (ii) how they have already been implemented, and (iii) the remaining research gaps. We collected 365 RF contributions related to XR from computer vision, computer graphics, robotics, multimedia, human-computer interaction, and XR communities, seeking to answer the above research questions. Among the 365 papers, we performed an analysis of 66 papers that already addressed a detailed aspect of RF research for XR. With this survey, we extended and positioned XR-specific RF research topics in the broader RF research field and provide a helpful resource for the XR community to navigate within the rapid development of RF research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.04038", "title": "ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents", "authors": [ "Zechen Li", "Baiyu Chen", "Hao Xue", "Flora D. Salim" ], "github": "https://github.com/zechenli03/ZARA", "project_page": "", "abstract": "Motion sensor time-series are central to human activity recognition (HAR), with applications in health, sports, and smart devices. However, existing methods are trained for fixed activity sets and require costly retraining when new behaviours or sensor setups appear. Recent attempts to use large language models (LLMs) for HAR, typically by converting signals into text or images, suffer from limited accuracy and lack verifiable interpretability. We propose ZARA, the first agent-based framework for zero-shot, explainable HAR directly from raw motion time-series. ZARA integrates an automatically derived pair-wise feature knowledge base that captures discriminative statistics for every activity pair, a multi-sensor retrieval module that surfaces relevant evidence, and a hierarchical agent pipeline that guides the LLM to iteratively select features, draw on this evidence, and produce both activity predictions and natural-language explanations. ZARA enables flexible and interpretable HAR without any fine-tuning or task-specific classifiers. Extensive experiments on 8 HAR benchmarks show that ZARA achieves SOTA zero-shot performance, delivering clear reasoning while exceeding the strongest baselines by 2.53x in macro F1. Ablation studies further confirm the necessity of each module, marking ZARA as a promising step toward trustworthy, plug-and-play motion time-series analysis. Our codes are available at https://github.com/zechenli03/ZARA.", "github_manual": "", "github_regex": "https://github.com/zechenli03/ZARA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.08777", "title": "Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge", "authors": [ "Francesco Fabbri", "Gustavo Penha", "Edoardo D'Amico", "Alice Wang", "Marco De Nadai", "Jackie Doremus", "Paul Gigioli", "Andreas Damianou", "Oskar Stal", "Mounia Lalmas" ], "github": "", "project_page": "", "abstract": "Evaluating personalized recommendations remains a central challenge, especially in long-form audio domains like podcasts, where traditional offline metrics suffer from exposure bias and online methods such as A/B testing are costly and operationally constrained. In this paper, we propose a novel framework that leverages Large Language Models (LLMs) as offline judges to assess the quality of podcast recommendations in a scalable and interpretable manner. Our two-stage profile-aware approach first constructs natural-language user profiles distilled from 90 days of listening history. These profiles summarize both topical interests and behavioral patterns, serving as compact, interpretable representations of user preferences. Rather than prompting the LLM with raw data, we use these profiles to provide high-level, semantically rich context-enabling the LLM to reason more effectively about alignment between a user's interests and recommended episodes. This reduces input complexity and improves interpretability. The LLM is then prompted to deliver fine-grained pointwise and pairwise judgments based on the profile-episode match. In a controlled study with 47 participants, our profile-aware judge matched human judgments with high fidelity and outperformed or matched a variant using raw listening histories. The framework enables efficient, profile-aware evaluation for iterative testing and model selection in recommender systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.12535", "title": "CorrSteer: Steering Improves Task Performance and Safety in LLMs through Correlation-based Sparse Autoencoder Feature Selection", "authors": [ "Seonglae Cho", "Zekun Wu", "Adriano Koshiyama" ], "github": "", "project_page": "", "abstract": "Sparse Autoencoders (SAEs) can extract interpretable features from large language models (LLMs) without supervision. However, their effectiveness in downstream steering tasks is limited by the requirement for contrastive datasets or large activation storage. To address these limitations, we propose CorrSteer, which selects features by correlating sample correctness with SAE activations from generated tokens at inference time. This approach uses only inference-time activations to extract more relevant features, thereby avoiding spurious correlations. It also obtains steering coefficients from average activations, automating the entire pipeline. Our method shows improved task performance on QA, bias mitigation, jailbreaking prevention, and reasoning benchmarks on Gemma 2 2B and LLaMA 3.1 8B, notably achieving a +4.1% improvement in MMLU performance and a +22.9% improvement in HarmBench with only 4000 samples. Selected features demonstrate semantically meaningful patterns aligned with each task's requirements, revealing the underlying capabilities that drive performance. Our work establishes correlationbased selection as an effective and scalable approach for automated SAE steering across language model applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.11032", "title": "MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation", "authors": [ "Yanwu Yang", "Guinan Su", "Jiesi Hu", "Francesco Sammarco", "Jonas Geiping", "Thomas Wolfers" ], "github": "https://github.com/podismine/MedSAMix.git", "project_page": "", "abstract": "Universal medical image segmentation models have emerged as a promising paradigm due to their strong generalizability across diverse tasks, showing great potential for a wide range of clinical applications. This potential has been partly driven by the success of general-purpose vision models such as the Segment Anything Model (SAM), which has inspired the development of various fine-tuned variants for medical segmentation tasks. However, fine-tuned variants like MedSAM are trained on comparatively limited medical imaging data that often suffers from heterogeneity, scarce annotations, and distributional shifts. These challenges limit their ability to generalize across a wide range of medical segmentation tasks. In this regard, we propose MedSAMix, a training-free model merging method that integrates the strengths of both generalist models (e.g., SAM) and specialist models (e.g., MedSAM) for medical image segmentation. In contrast to traditional model merging approaches that rely on manual configuration and often result in suboptimal outcomes, we propose a zero-order optimization method to automatically discover optimal layer-wise merging solutions. Furthermore, for clinical applications, we develop two regimes to meet the demand of domain-specificity and generalizability in different scenarios by single-task optimization and multi-objective optimization respectively. Extensive evaluations on 25 medical segmentation tasks demonstrate that MedSAMix effectively mitigates model bias and consistently improves performance in both domain-specific accuracy and generalization, achieving improvements of 6.67% on specialized tasks and 4.37% on multi-task evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/podismine/MedSAMix.git", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.10478", "title": "Semantic IDs for Joint Generative Search and Recommendation", "authors": [ "Gustavo Penha", "Edoardo D'Amico", "Marco De Nadai", "Enrico Palumbo", "Alexandre Tamborrino", "Ali Vardasbi", "Max Lefarov", "Shawn Lin", "Timothy Heath", "Francesco Fabbri", "Hugues Bouchard" ], "github": "", "project_page": "", "abstract": "Generative models powered by Large Language Models (LLMs) are emerging as a unified solution for powering both recommendation and search tasks. A key design choice in these models is how to represent items, traditionally through unique identifiers (IDs) and more recently with Semantic IDs composed of discrete codes, obtained from embeddings. While task-specific embedding models can improve performance for individual tasks, they may not generalize well in a joint setting. In this paper, we explore how to construct Semantic IDs that perform well both in search and recommendation when using a unified model. We compare a range of strategies to construct Semantic IDs, looking into task-specific and cross-tasks approaches, and also whether each task should have its own semantic ID tokens in a joint search and recommendation generative model. Our results show that using a bi-encoder model fine-tuned on both search and recommendation tasks to obtain item embeddings, followed by the construction of a unified Semantic ID space provides an effective trade-off, enabling strong performance in both tasks. We hope these findings spark follow-up work on generalisable, semantically grounded ID schemes and inform the next wave of unified generative recommender architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.09789", "title": "Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations", "authors": [ "Marco De Nadai", "Andreas Damianou", "Mounia Lalmas" ], "github": "", "project_page": "", "abstract": "Existing video recommender systems rely primarily on user-defined metadata or on low-level visual and acoustic signals extracted by specialised encoders. These low-level features describe what appears on the screen but miss deeper semantics such as intent, humour, and world knowledge that make clips resonate with viewers. For example, is a 30-second clip simply a singer on a rooftop, or an ironic parody filmed amid the fairy chimneys of Cappadocia, Turkey? Such distinctions are critical to personalised recommendations yet remain invisible to traditional encoding pipelines. In this paper, we introduce a simple, recommendation system-agnostic zero-finetuning framework that injects high-level semantics into the recommendation pipeline by prompting an off-the-shelf Multimodal Large Language Model (MLLM) to summarise each clip into a rich natural-language description (e.g. \"a superhero parody with slapstick fights and orchestral stabs\"), bridging the gap between raw content and user intent. We use MLLM output with a state-of-the-art text encoder and feed it into standard collaborative, content-based, and generative recommenders. On the MicroLens-100K dataset, which emulates user interactions with TikTok-style videos, our framework consistently surpasses conventional video, audio, and metadata features in five representative models. Our findings highlight the promise of leveraging MLLMs as on-the-fly knowledge extractors to build more intent-aware video recommenders.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.13804", "title": "Beyond Human Judgment: A Bayesian Evaluation of LLMs' Moral Values Understanding", "authors": [ "Maciej Skorski", "Alina Landowska" ], "github": "", "project_page": "", "abstract": "How do large language models understand moral dimensions compared to humans? This first large-scale Bayesian evaluation of market-leading language models provides the answer. In contrast to prior work using deterministic ground truth (majority or inclusion rules), we model annotator disagreements to capture both aleatoric uncertainty (inherent human disagreement) and epistemic uncertainty (model domain sensitivity). We evaluate top language models (Claude Sonnet 4, DeepSeek-V3, Llama 4 Maverick) across 250K+ annotations from ~700 annotators on 100K+ texts spanning social media, news, and forums. Our GPU-optimized Bayesian framework processed 1M+ model queries, revealing that AI models typically rank among the top 25\\% of human annotators, achieving much better-than-average balanced accuracy. Importantly, we find that AI produces far fewer false negatives than humans, highlighting their more sensitive moral detection capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.13998", "title": "Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation", "authors": [ "Yifu Yuan", "Haiqin Cui", "Yaoting Huang", "Yibin Chen", "Fei Ni", "Zibin Dong", "Pengyi Li", "Yan Zheng", "Jianye Hao" ], "github": "https://github.com/pickxiguapi/Embodied-R1", "project_page": "", "abstract": "Generalization in embodied AI is hindered by the \"seeing-to-doing gap,\" which stems from data scarcity and embodiment heterogeneity. To address this, we pioneer \"pointing\" as a unified, embodiment-agnostic intermediate representation, defining four core embodied pointing abilities that bridge high-level vision-language comprehension with low-level action primitives. We introduce Embodied-R1, a 3B Vision-Language Model (VLM) specifically designed for embodied reasoning and pointing. We use a wide range of embodied and general visual reasoning datasets as sources to construct a large-scale dataset, Embodied-Points-200K, which supports key embodied pointing capabilities. We then train Embodied-R1 using a two-stage Reinforced Fine-tuning (RFT) curriculum with a specialized multi-task reward design. Embodied-R1 achieves state-of-the-art performance on 11 embodied spatial and pointing benchmarks. Critically, it demonstrates robust zero-shot generalization by achieving a 56.2% success rate in the SIMPLEREnv and 87.5% across 8 real-world XArm tasks without any task-specific fine-tuning, representing a 62% improvement over strong baselines. Furthermore, the model exhibits high robustness against diverse visual disturbances. Our work shows that a pointing-centric representation, combined with an RFT training paradigm, offers an effective and generalizable pathway to closing the perception-action gap in robotics.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pickxiguapi/Embodied-R1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.12903", "title": "A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models", "authors": [ "Jinyi Han", "Xinyi Wang", "Haiquan Zhao", "Tingyun li", "Zishang Jiang", "Sihang Jiang", "Jiaqing Liang", "Xin Lin", "Weikang Zhou", "Zeye Sun", "Fei Yu", "Yanghua Xiao" ], "github": "https://github.com/JinyiHan99/Proactive-Self-Refine-in-LLMs", "project_page": "", "abstract": "Recent advances in self-refinement have demonstrated significant potential for improving the outputs of large language models (LLMs) through iterative refinement. However, most existing self-refinement methods rely on a reactive process with a fixed number of iterations, making it difficult to determine the optimal timing and content of refinement based on the evolving generation context. Inspired by the way humans dynamically refine their thoughts during execution, we propose ProActive Self-Refinement (PASR), a novel method that enables LLMs to refine their outputs during the generation process. Unlike methods that regenerate entire responses, PASR proactively decides whether, when, and how to refine based on the model's internal state and evolving context. We conduct extensive experiments on a diverse set of 10 tasks to evaluate the effectiveness of PASR. Experimental results show that PASR significantly enhances problem-solving performance. In particular, on Qwen3-8B, PASR reduces average token consumption by 41.6 percent compared to standard generation, while also achieving an 8.2 percent improvement in accuracy. Our code and all baselines used in the paper are available in the GitHub.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JinyiHan99/Proactive-Self-Refine-in-LLMs", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.12040", "title": "Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation", "authors": [ "Jinyi Han", "Tingyun Li", "Shisong Chen", "Jie Shi", "Xinyi Wang", "Guanglei Yue", "Jiaqing Liang", "Xin Lin", "Liqian Wen", "Zulong Chen", "Yanghua Xiao" ], "github": "https://github.com/JinyiHan99/FineCE", "project_page": "", "abstract": "While large language models (LLMs) have demonstrated remarkable performance across diverse tasks, they fundamentally lack self-awareness and frequently exhibit overconfidence, assigning high confidence scores to incorrect predictions. Accurate confidence estimation is therefore critical for enhancing the trustworthiness and reliability of LLM-generated outputs. However, existing approaches suffer from coarse-grained scoring mechanisms that fail to provide fine-grained, continuous confidence estimates throughout the generation process. To address these limitations, we introduce FineCE, a novel confidence estimation method that delivers accurate, fine-grained confidence scores during text generation. Specifically, we first develop a comprehensive pipeline for constructing training data that effectively captures the underlying probabilistic distribution of LLM responses, and then train a model to predict confidence scores for arbitrary text sequences in a supervised manner. Furthermore, we propose a Backward Confidence Integration (BCI) strategy that leverages information from the subsequent text to enhance confidence estimation for the current sequence during inference. We also introduce three strategies for identifying optimal positions to perform confidence estimation within the generation process. Extensive experiments on multiple benchmark datasets demonstrate that FineCE consistently outperforms existing classical confidence estimation methods. Our code and all baselines used in the paper are available on GitHub.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JinyiHan99/FineCE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.13186", "title": "MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents", "authors": [ "Shilong Li", "Xingyuan Bu", "Wenjie Wang", "Jiaheng Liu", "Jun Dong", "Haoyang He", "Hao Lu", "Haozhe Zhang", "Chenchen Jing", "Zhen Li", "Chuanhao Li", "Jiayi Tian", "Chenchen Zhang", "Tianhao Peng", "Yancheng He", "Jihao Gu", "Yuanxing Zhang", "Jian Yang", "Ge Zhang", "Wenhao Huang", "Wangchunshu Zhou", "Zhaoxiang Zhang", "Ruizhe Ding", "Shilei Wen" ], "github": "", "project_page": "", "abstract": "AI agents with advanced reasoning and tool use capabilities have demonstrated impressive performance in web browsing for deep search. While existing benchmarks such as BrowseComp evaluate these browsing abilities, they primarily focus on textual information, overlooking the prevalence of multimodal content. To bridge this gap, we introduce MM-BrowseComp, a novel benchmark comprising 224 challenging, hand-crafted questions specifically designed to assess agents' multimodal retrieval and reasoning capabilities. These questions often incorporate images in prompts, and crucial information encountered during the search and reasoning process may also be embedded within images or videos on webpages. Consequently, methods relying solely on text prove insufficient for our benchmark. Additionally, we provide a verified checklist for each question, enabling fine-grained analysis of multimodal dependencies and reasoning paths. Our comprehensive evaluation of state-of-the-art models on MM-BrowseComp reveals that even top models like OpenAI o3 with tools achieve only 29.02\\% accuracy, highlighting the suboptimal multimodal capabilities and lack of native multimodal reasoning in current models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.12845", "title": "CAMAR: Continuous Actions Multi-Agent Routing", "authors": [ "Artem Pshenitsyn", "Aleksandr Panov", "Alexey Skrynnik" ], "github": "https://github.com/AIRI-Institute/CAMAR", "project_page": "", "abstract": "Multi-agent reinforcement learning (MARL) is a powerful paradigm for solving cooperative and competitive decision-making problems. While many MARL benchmarks have been proposed, few combine continuous state and action spaces with challenging coordination and planning tasks. We introduce CAMAR, a new MARL benchmark designed explicitly for multi-agent pathfinding in environments with continuous actions. CAMAR supports cooperative and competitive interactions between agents and runs efficiently at up to 100,000 environment steps per second. We also propose a three-tier evaluation protocol to better track algorithmic progress and enable deeper analysis of performance. In addition, CAMAR allows the integration of classical planning methods such as RRT and RRT* into MARL pipelines. We use them as standalone baselines and combine RRT* with popular MARL algorithms to create hybrid approaches. We provide a suite of test scenarios and benchmarking tools to ensure reproducibility and fair comparison. Experiments show that CAMAR presents a challenging and realistic testbed for the MARL community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AIRI-Institute/CAMAR", "github_api_added_by": "auto" }, { "date": "2025-08-20", "arxiv_id": "2508.12800", "title": "Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward", "authors": [ "Yong Deng", "Guoqing Wang", "Zhenzhe Ying", "Xiaofeng Wu", "Jinzhen Lin", "Wenwen Xiong", "Yuqin Dai", "Shuo Yang", "Zhanwei Zhang", "Qiwen Wang", "Yang Qin", "Changhua Meng" ], "github": "https://github.com/antgroup/Research-Venus", "project_page": "", "abstract": "Large language models (LLMs) exhibit remarkable problem-solving abilities, but struggle with complex tasks due to static internal knowledge. Retrieval-Augmented Generation (RAG) enhances access to external information, yet remains limited in multi-hop reasoning and strategic search due to rigid workflows. Recent advancements in agentic deep research empower LLMs to autonomously reason, search, and synthesize information. However, current approaches relying on outcome-based reinforcement learning (RL) face critical issues such as conflicting gradients and reward sparsity, limiting performance gains and training efficiency. To address these, we first propose Atomic Thought, a novel LLM thinking paradigm that decomposes reasoning into fine-grained functional units. These units are supervised by Reasoning Reward Models (RRMs), which provide Atomic Thought Rewards (ATR) for fine-grained guidance. Building on this, we propose Atom-Searcher, a novel RL framework for agentic deep research that integrates Atomic Thought and ATR. Atom-Searcher uses a curriculum-inspired reward schedule, prioritizing process-level ATR early and transitioning to outcome rewards, accelerating convergence on effective reasoning paths. Experiments on seven benchmarks show consistent improvements over the state-of-the-art. Key advantages include: (1) Atom-Searcher scales computation at test-time. (2) Atomic Thought provides supervision anchors for RRMs, bridging deep research tasks and RRMs. (3) Atom-Searcher exhibits more interpretable, human-like reasoning patterns.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/antgroup/Research-Venus", "github_api_added_by": "auto" }, { "date": "2025-08-20", "arxiv_id": "2508.13320", "title": "Rapidly Adapting to New Voice Spoofing: Few-Shot Detection of Synthesized Speech Under Distribution Shifts", "authors": [ "Ashi Garg", "Zexin Cai", "Henry Li Xinyuan", "Leibny Paola Garc\u00eda-Perera", "Kevin Duh", "Sanjeev Khudanpur", "Matthew Wiesner", "Nicholas Andrews" ], "github": "", "project_page": "", "abstract": "We address the challenge of detecting synthesized speech under distribution shifts -- arising from unseen synthesis methods, speakers, languages, or audio conditions -- relative to the training data. Few-shot learning methods are a promising way to tackle distribution shifts by rapidly adapting on the basis of a few in-distribution samples. We propose a self-attentive prototypical network to enable more robust few-shot adaptation. To evaluate our approach, we systematically compare the performance of traditional zero-shot detectors and the proposed few-shot detectors, carefully controlling training conditions to introduce distribution shifts at evaluation time. In conditions where distribution shifts hamper the zero-shot performance, our proposed few-shot adaptation technique can quickly adapt using as few as 10 in-distribution samples -- achieving upto 32% relative EER reduction on deepfakes in Japanese language and 20% relative reduction on ASVspoof 2021 Deepfake dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-20", "arxiv_id": "2508.11386", "title": "Retrieval-augmented reasoning with lean language models", "authors": [ "Ryan Sze-Yin Chan", "Federico Nanni", "Tomas Lazauskas", "Rosie Wood", "Penelope Yong", "Lionel Tarassenko", "Mark Girolami", "James Geddes", "Andrew Duncan" ], "github": "https://github.com/alan-turing-institute/t0-1", "project_page": "", "abstract": "This technical report details a novel approach to combining reasoning and retrieval augmented generation (RAG) within a single, lean language model architecture. While existing RAG systems typically rely on large-scale models and external APIs, our work addresses the increasing demand for performant and privacy-preserving solutions deployable in resource-constrained or secure environments. Building on recent developments in test-time scaling and small-scale reasoning models, we develop a retrieval augmented conversational agent capable of interpreting complex, domain-specific queries using a lightweight backbone model. Our system integrates a dense retriever with fine-tuned Qwen2.5-Instruct models, using synthetic query generation and reasoning traces derived from frontier models (e.g., DeepSeek-R1) over a curated corpus, in this case, the NHS A-to-Z condition pages. We explore the impact of summarisation-based document compression, synthetic data design, and reasoning-aware fine-tuning on model performance. Evaluation against both non-reasoning and general-purpose lean models demonstrates that our domain-specific fine-tuning approach yields substantial gains in answer accuracy and consistency, approaching frontier-level performance while remaining feasible for local deployment. All implementation details and code are publicly released to support reproducibility and adaptation across domains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/alan-turing-institute/t0-1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.11987", "title": "FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction", "authors": [ "Zhiyuan Zeng", "Jiashuo Liu", "Siyuan Chen", "Tianci He", "Yali Liao", "Jinpeng Wang", "Zaiyuan Wang", "Yang Yang", "Lingyue Yin", "Mingren Yin", "Zhenwei Zhu", "Tianle Cai", "Zehui Chen", "Jiecao Chen", "Yantao Du", "Xiang Gao", "Jiacheng Guo", "Liang Hu", "Jianpeng Jiao", "Xiangsheng Li", "Jingkai Liu", "Shuang Ni", "Zhoufutu Wen", "Ge Zhang", "Kaiyuan Zhang", "Xin Zhou", "Jose Blanchet", "Xipeng Qiu", "Mengdi Wang", "Wenhao Huang" ], "github": "", "project_page": "", "abstract": "Future prediction is a complex task for LLM agents, requiring a high level of analytical thinking, information gathering, contextual understanding, and decision-making under uncertainty. Agents must not only gather and interpret vast amounts of dynamic information but also integrate diverse data sources, weigh uncertainties, and adapt predictions based on emerging trends, just as human experts do in fields like politics, economics, and finance. Despite its importance, no large-scale benchmark exists for evaluating agents on future prediction, largely due to challenges in handling real-time updates and retrieving timely, accurate answers. To address this, we introduce FutureX, a dynamic and live evaluation benchmark specifically designed for LLM agents performing future prediction tasks. FutureX is the largest and most diverse live benchmark for future prediction, supporting real-time daily updates and eliminating data contamination through an automated pipeline for question gathering and answer collection. We evaluate 25 LLM/agent models, including those with reasoning, search capabilities, and integration of external tools such as the open-source Deep Research Agent and closed-source Deep Research models. This comprehensive evaluation assesses agents' adaptive reasoning and performance in dynamic environments. Additionally, we provide in-depth analyses of agents' failure modes and performance pitfalls in future-oriented tasks, including the vulnerability to fake web pages and the temporal validity. Our goal is to establish a dynamic, contamination-free evaluation standard that drives the development of LLM agents capable of performing at the level of professional human analysts in complex reasoning and predictive thinking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.14811", "title": "Tinker: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimization", "authors": [ "Canyu Zhao", "Xiaoman Li", "Tianjian Feng", "Zhiyue Zhao", "Hao Chen", "Chunhua Shen" ], "github": "", "project_page": "", "abstract": "We introduce Tinker, a versatile framework for high-fidelity 3D editing that operates in both one-shot and few-shot regimes without any per-scene finetuning. Unlike prior techniques that demand extensive per-scene optimization to ensure multi-view consistency or to produce dozens of consistent edited input views, Tinker delivers robust, multi-view consistent edits from as few as one or two images. This capability stems from repurposing pretrained diffusion models, which unlocks their latent 3D awareness. To drive research in this space, we curate the first large-scale multi-view editing dataset and data pipeline, spanning diverse scenes and styles. Building on this dataset, we develop our framework capable of generating multi-view consistent edited views without per-scene training, which consists of two novel components: (1) Referring multi-view editor: Enables precise, reference-driven edits that remain coherent across all viewpoints. (2) Any-view-to-video synthesizer: Leverages spatial-temporal priors from video diffusion to perform high-quality scene completion and novel-view generation even from sparse inputs. Through extensive experiments, Tinker significantly reduces the barrier to generalizable 3D content creation, achieving state-of-the-art performance on editing, novel-view synthesis, and rendering enhancement tasks. We believe that Tinker represents a key step towards truly scalable, zero-shot 3D editing. Project webpage: https://aim-uofa.github.io/Tinker", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.14879", "title": "MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds", "authors": [ "Bingquan Dai", "Li Ray Luo", "Qihong Tang", "Jie Wang", "Xinyu Lian", "Hao Xu", "Minghan Qin", "Xudong Xu", "Bo Dai", "Haoqian Wang", "Zhaoyang Lyu", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "Reconstructing 3D objects into editable programs is pivotal for applications like reverse engineering and shape editing. However, existing methods often rely on limited domain-specific languages (DSLs) and small-scale datasets, restricting their ability to model complex geometries and structures. To address these challenges, we introduce MeshCoder, a novel framework that reconstructs complex 3D objects from point clouds into editable Blender Python scripts. We develop a comprehensive set of expressive Blender Python APIs capable of synthesizing intricate geometries. Leveraging these APIs, we construct a large-scale paired object-code dataset, where the code for each object is decomposed into distinct semantic parts. Subsequently, we train a multimodal large language model (LLM) that translates 3D point cloud into executable Blender Python scripts. Our approach not only achieves superior performance in shape-to-code reconstruction tasks but also facilitates intuitive geometric and topological editing through convenient code modifications. Furthermore, our code-based representation enhances the reasoning capabilities of LLMs in 3D shape understanding tasks. Together, these contributions establish MeshCoder as a powerful and flexible solution for programmatic 3D shape reconstruction and understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.13491", "title": "From Scores to Skills: A Cognitive Diagnosis Framework for Evaluating Financial Large Language Models", "authors": [ "Ziyan Kuang", "Feiyu Zhu", "Maowei Jiang", "Yanzhao Lai", "Zelin Wang", "Zhitong Wang", "Meikang Qiu", "Jiajia Huang", "Min Peng", "Qianqian Xie", "Sophia Ananiadou" ], "github": "https://github.com/WHUNextGen/FinCDM", "project_page": "", "abstract": "Large Language Models (LLMs) have shown promise for financial applications, yet their suitability for this high-stakes domain remains largely unproven due to inadequacies in existing benchmarks. Existing benchmarks solely rely on score-level evaluation, summarizing performance with a single score that obscures the nuanced understanding of what models truly know and their precise limitations. They also rely on datasets that cover only a narrow subset of financial concepts, while overlooking other essentials for real-world applications. To address these gaps, we introduce FinCDM, the first cognitive diagnosis evaluation framework tailored for financial LLMs, enabling the evaluation of LLMs at the knowledge-skill level, identifying what financial skills and knowledge they have or lack based on their response patterns across skill-tagged tasks, rather than a single aggregated number. We construct CPA-QKA, the first cognitively informed financial evaluation dataset derived from the Certified Public Accountant (CPA) examination, with comprehensive coverage of real-world accounting and financial skills. It is rigorously annotated by domain experts, who author, validate, and annotate questions with high inter-annotator agreement and fine-grained knowledge labels. Our extensive experiments on 30 proprietary, open-source, and domain-specific LLMs show that FinCDM reveals hidden knowledge gaps, identifies under-tested areas such as tax and regulatory reasoning overlooked by traditional benchmarks, and uncovers behavioral clusters among models. FinCDM introduces a new paradigm for financial LLM evaluation by enabling interpretable, skill-aware diagnosis that supports more trustworthy and targeted model development, and all datasets and evaluation scripts will be publicly released to support further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/WHUNextGen/FinCDM", "github_api_added_by": "auto" }, { "date": "2025-08-21", "arxiv_id": "2508.14160", "title": "RynnEC: Bringing MLLMs into Embodied World", "authors": [ "Ronghao Dang", "Yuqian Yuan", "Yunxuan Mao", "Kehan Li", "Jiangpin Liu", "Zhikai Wang", "Xin Li", "Fan Wang", "Deli Zhao" ], "github": "https://github.com/alibaba-damo-academy/RynnEC", "project_page": "", "abstract": "We introduce RynnEC, a video multimodal large language model designed for embodied cognition. Built upon a general-purpose vision-language foundation model, RynnEC incorporates a region encoder and a mask decoder, enabling flexible region-level video interaction. Despite its compact architecture, RynnEC achieves state-of-the-art performance in object property understanding, object segmentation, and spatial reasoning. Conceptually, it offers a region-centric video paradigm for the brain of embodied agents, providing fine-grained perception of the physical world and enabling more precise interactions. To mitigate the scarcity of annotated 3D datasets, we propose an egocentric video based pipeline for generating embodied cognition data. Furthermore, we introduce RynnEC-Bench, a region-centered benchmark for evaluating embodied cognitive capabilities. We anticipate that RynnEC will advance the development of general-purpose cognitive cores for embodied agents and facilitate generalization across diverse embodied tasks. The code, model checkpoints, and benchmark are available at: https://github.com/alibaba-damo-academy/RynnEC", "github_manual": "", "github_regex": "https://github.com/alibaba-damo-academy/RynnEC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.14896", "title": "Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs", "authors": [ "Haokun Lin", "Haobo Xu", "Yichen Wu", "Ziyu Guo", "Renrui Zhang", "Zhichao Lu", "Ying Wei", "Qingfu Zhang", "Zhenan Sun" ], "github": "https://github.com/FelixMessi/QDLM", "project_page": "", "abstract": "Recent advances in diffusion large language models (dLLMs) have introduced a promising alternative to autoregressive (AR) LLMs for natural language generation tasks, leveraging full attention and denoising-based decoding strategies. However, the deployment of these models on edge devices remains challenging due to their massive parameter scale and high resource demands. While post-training quantization (PTQ) has emerged as a widely adopted technique for compressing AR LLMs, its applicability to dLLMs remains largely unexplored. In this work, we present the first systematic study on quantizing diffusion-based language models. We begin by identifying the presence of activation outliers, characterized by abnormally large activation values that dominate the dynamic range. These outliers pose a key challenge to low-bit quantization, as they make it difficult to preserve precision for the majority of values. More importantly, we implement state-of-the-art PTQ methods and conduct a comprehensive evaluation across multiple task types and model variants. Our analysis is structured along four key dimensions: bit-width, quantization method, task category, and model type. Through this multi-perspective evaluation, we offer practical insights into the quantization behavior of dLLMs under different configurations. We hope our findings provide a foundation for future research in efficient dLLM deployment. All codes and experimental setups will be released to support the community.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FelixMessi/QDLM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.14444", "title": "NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model", "authors": [ "NVIDIA", "Aarti Basant", "Abhijit Khairnar", "Abhijit Paithankar", "Abhinav Khattar", "Adi Renduchintala", "Adithya Renduchintala", "Aditya Malte", "Akhiad Bercovich", "Akshay Hazare", "Alejandra Rico", "Aleksander Ficek", "Alex Kondratenko", "Alex Shaposhnikov", "Ali Taghibakhshi", "Amelia Barton", "Ameya Sunil Mahabaleshwarkar", "Amy Shen", "Andrew Tao", "Ann Guan", "Anna Shors", "Anubhav Mandarwal", "Arham Mehta", "Arun Venkatesan", "Ashton Sharabiani", "Ashwath Aithal", "Ashwin Poojary", "Ayush Dattagupta", "Balaram Buddharaju", "Banghua Zhu", "Barnaby Simkin", "Bilal Kartal", "Bita Darvish Rouhani", "Bobby Chen", "Boris Ginsburg", "Brandon Norick", "Brian Yu", "Bryan Catanzaro", "Charles Wang", "Charlie Truong", "Chetan Mungekar", "Chintan Patel", "Chris Alexiuk", "Christian Munley", "Christopher Parisien", "Dan Su", "Daniel Afrimi", "Daniel Korzekwa", "Daniel Rohrer", "Daria Gitman", "David Mosallanezhad", "Deepak Narayanan", "Dima Rekesh", "Dina Yared", "Dmytro Pykhtar", "Dong Ahn", "Duncan Riach", "Eileen Long", "Elliott Ning", "Eric Chung", "Erick Galinkin", "Evelina Bakhturina", "Gargi Prasad", "Gerald Shen", "Haim Elisha", "Harsh Sharma", "Hayley Ross", "Helen Ngo", "Herman Sahota", "Hexin Wang", "Hoo Chang Shin", "Hua Huang", "Iain Cunningham", "Igor Gitman", "Ivan Moshkov", "Jaehun Jung", "Jan Kautz", "Jane Polak Scowcroft", "Jared Casper", "Jimmy Zhang", "Jinze Xue", "Jocelyn Huang", "Joey Conway", "John Kamalu", "Jonathan Cohen", "Joseph Jennings", "Julien Veron Vialard", "Junkeun Yi", "Jupinder Parmar", "Kari Briski", "Katherine Cheung", "Katherine Luna", "Keith Wyss", "Keshav Santhanam", "Kezhi Kong", "Krzysztof Pawelec", "Kumar Anik", "Kunlun Li", "Kushan Ahmadian", "Lawrence McAfee", "Laya Sleiman", "Leon Derczynski", "Luis Vega", "Maer Rodrigues de Melo", "Makesh Narsimhan Sreedhar", "Marcin Chochowski", "Mark Cai", "Markus Kliegl", "Marta Stepniewska-Dziubinska", "Matvei Novikov", "Mehrzad Samadi", "Meredith Price", "Meriem Boubdir", "Michael Boone", "Michael Evans", "Michal Bien", "Michal Zawalski", "Miguel Martinez", "Mike Chrzanowski", "Mohammad Shoeybi", "Mostofa Patwary", "Namit Dhameja", "Nave Assaf", "Negar Habibi", "Nidhi Bhatia", "Nikki Pope", "Nima Tajbakhsh", "Nirmal Kumar Juluru", "Oleg Rybakov", "Oleksii Hrinchuk", "Oleksii Kuchaiev", "Oluwatobi Olabiyi", "Pablo Ribalta", "Padmavathy Subramanian", "Parth Chadha", "Pavlo Molchanov", "Peter Dykas", "Peter Jin", "Piotr Bialecki", "Piotr Januszewski", "Pradeep Thalasta", "Prashant Gaikwad", "Prasoon Varshney", "Pritam Gundecha", "Przemek Tredak", "Rabeeh Karimi Mahabadi", "Rajen Patel", "Ran El-Yaniv", "Ranjit Rajan", "Ria Cheruvu", "Rima Shahbazyan", "Ritika Borkar", "Ritu Gala", "Roger Waleffe", "Ruoxi Zhang", "Russell J. Hewett", "Ryan Prenger", "Sahil Jain", "Samuel Kriman", "Sanjeev Satheesh", "Saori Kaji", "Sarah Yurick", "Saurav Muralidharan", "Sean Narenthiran", "Seonmyeong Bak", "Sepehr Sameni", "Seungju Han", "Shanmugam Ramasamy", "Shaona Ghosh", "Sharath Turuvekere Sreenivas", "Shelby Thomas", "Shizhe Diao", "Shreya Gopal", "Shrimai Prabhumoye", "Shubham Toshniwal", "Shuoyang Ding", "Siddharth Singh", "Siddhartha Jain", "Somshubra Majumdar", "Stefania Alborghetti", "Syeda Nahida Akter", "Terry Kong", "Tim Moon", "Tomasz Hliwiak", "Tomer Asida", "Tony Wang", "Twinkle Vashishth", "Tyler Poon", "Udi Karpas", "Vahid Noroozi", "Venkat Srinivasan", "Vijay Korthikanti", "Vikram Fugro", "Vineeth Kalluru", "Vitaly Kurin", "Vitaly Lavrukhin", "Wasi Uddin Ahmad", "Wei Du", "Wonmin Byeon", "Ximing Lu", "Xin Dong", "Yashaswi Karnati", "Yejin Choi", "Yian Zhang", "Ying Lin", "Yonggan Fu", "Yoshi Suhara", "Zhen Dong", "Zhiyu Li", "Zhongbo Zhu", "Zijia Chen" ], "github": "", "project_page": "", "abstract": "We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compared to similarly-sized models. Nemotron-Nano-9B-v2 builds on the Nemotron-H architecture, in which the majority of the self-attention layers in the common Transformer architecture are replaced with Mamba-2 layers, to achieve improved inference speed when generating the long thinking traces needed for reasoning. We create Nemotron-Nano-9B-v2 by first pre-training a 12-billion-parameter model (Nemotron-Nano-12B-v2-Base) on 20 trillion tokens using an FP8 training recipe. After aligning Nemotron-Nano-12B-v2-Base, we employ the Minitron strategy to compress and distill the model with the goal of enabling inference on up to 128k tokens on a single NVIDIA A10G GPU (22GiB of memory, bfloat16 precision). Compared to existing similarly-sized models (e.g., Qwen3-8B), we show that Nemotron-Nano-9B-v2 achieves on-par or better accuracy on reasoning benchmarks while achieving up to 6x higher inference throughput in reasoning settings like 8k input and 16k output tokens. We are releasing Nemotron-Nano-9B-v2, Nemotron-Nano12B-v2-Base, and Nemotron-Nano-9B-v2-Base checkpoints along with the majority of our pre- and post-training datasets on Hugging Face.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.14460", "title": "DuPO: Enabling Reliable LLM Self-Verification via Dual Preference Optimization", "authors": [ "Shuaijie She", "Yu Bao", "Yu Lu", "Lu Xu", "Tao Li", "Wenhao Zhu", "Shujian Huang", "Shanbo Cheng", "Lu Lu", "Yuxuan Wang" ], "github": "", "project_page": "", "abstract": "We present DuPO, a dual learning-based preference optimization framework that generates annotation-free feedback via a generalized duality. DuPO addresses two key limitations: Reinforcement Learning with Verifiable Rewards (RLVR)'s reliance on costly labels and applicability restricted to verifiable tasks, and traditional dual learning's restriction to strictly dual task pairs (e.g., translation and back-translation). Specifically, DuPO decomposes a primal task's input into known and unknown components, then constructs its dual task to reconstruct the unknown part using the primal output and known information (e.g., reversing math solutions to recover hidden variables), broadening applicability to non-invertible tasks. The quality of this reconstruction serves as a self-supervised reward to optimize the primal task, synergizing with LLMs' ability to instantiate both tasks via a single model. Empirically, DuPO achieves substantial gains across diverse tasks: it enhances the average translation quality by 2.13 COMET over 756 directions, boosts the mathematical reasoning accuracy by an average of 6.4 points on three challenge benchmarks, and enhances performance by 9.3 points as an inference-time reranker (trading computation for accuracy). These results position DuPO as a scalable, general, and annotation-free paradigm for LLM optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.14187", "title": "Local Scale Equivariance with Latent Deep Equilibrium Canonicalizer", "authors": [ "Md Ashiqur Rahman", "Chiao-An Yang", "Michael N. Cheng", "Lim Jun Hao", "Jeremiah Jiang", "Teck-Yian Lim", "Raymond A. Yeh" ], "github": "https://github.com/ashiq24/local-scale-equivariance", "project_page": "", "abstract": "Scale variation is a fundamental challenge in computer vision. Objects of the same class can have different sizes, and their perceived size is further affected by the distance from the camera. These variations are local to the objects, i.e., different object sizes may change differently within the same image. To effectively handle scale variations, we present a deep equilibrium canonicalizer (DEC) to improve the local scale equivariance of a model. DEC can be easily incorporated into existing network architectures and can be adapted to a pre-trained model. Notably, we show that on the competitive ImageNet benchmark, DEC improves both model performance and local scale consistency across four popular pre-trained deep-nets, e.g., ViT, DeiT, Swin, and BEiT. Our code is available at https://github.com/ashiq24/local-scale-equivariance.", "github_manual": "", "github_regex": "https://github.com/ashiq24/local-scale-equivariance", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.10137", "title": "mSCoRe: a Multilingual and Scalable Benchmark for Skill-based Commonsense Reasoning", "authors": [ "Nghia Trung Ngo", "Franck Dernoncourt", "Thien Huu Nguyen" ], "github": "", "project_page": "", "abstract": "Recent advancements in reasoning-reinforced Large Language Models (LLMs) have shown remarkable capabilities in complex reasoning tasks. However, the mechanism underlying their utilization of different human reasoning skills remains poorly investigated, especially for multilingual commonsense reasoning that involves everyday knowledge across different languages and cultures. To address this gap, we propose a Multilingual and Scalable Benchmark for Skill-based Commonsense Reasoning (mSCoRe). Our benchmark incorporates three key components that are designed to systematically evaluate LLM's reasoning capabilities, including: (1) a novel taxonomy of reasoning skills that enables fine-grained analysis of models' reasoning processes, (2) a robust data synthesis pipeline tailored specifically for commonsense reasoning evaluation, and (3) a complexity scaling framework allowing task difficulty to scale dynamically alongside future improvements in LLM abilities. Extensive experiments on eights state-of-the-art LLMs of varying sizes and training approaches demonstrate that mSCoRe remains significantly challenging for current models, particularly at higher complexity levels. Our results reveal the limitations of such reasoning-reinforced models when confronted with nuanced multilingual general and cultural commonsense. We further provide detailed analysis on the models' reasoning processes, suggesting future directions for improving multilingual commonsense reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.14111", "title": "From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery", "authors": [ "Jiaqi Wei", "Yuejin Yang", "Xiang Zhang", "Yuhan Chen", "Xiang Zhuang", "Zhangyang Gao", "Dongzhan Zhou", "Guangshuai Wang", "Zhiqiang Gao", "Juntai Cao", "Zijie Qiu", "Xuming He", "Qiang Zhang", "Chenyu You", "Shuangjia Zheng", "Ning Ding", "Wanli Ouyang", "Nanqing Dong", "Yu Cheng", "Siqi Sun", "Lei Bai", "Bowen Zhou" ], "github": "https://github.com/AgenticScience/Awesome-Agent-Scientists", "project_page": "", "abstract": "Artificial intelligence (AI) is reshaping scientific discovery, evolving from specialized computational tools into autonomous research partners. We position Agentic Science as a pivotal stage within the broader AI for Science paradigm, where AI systems progress from partial assistance to full scientific agency. Enabled by large language models (LLMs), multimodal systems, and integrated research platforms, agentic AI shows capabilities in hypothesis generation, experimental design, execution, analysis, and iterative refinement -- behaviors once regarded as uniquely human. This survey provides a domain-oriented review of autonomous scientific discovery across life sciences, chemistry, materials science, and physics. We unify three previously fragmented perspectives -- process-oriented, autonomy-oriented, and mechanism-oriented -- through a comprehensive framework that connects foundational capabilities, core processes, and domain-specific realizations. Building on this framework, we (i) trace the evolution of AI for Science, (ii) identify five core capabilities underpinning scientific agency, (iii) model discovery as a dynamic four-stage workflow, (iv) review applications across the above domains, and (v) synthesize key challenges and future opportunities. This work establishes a domain-oriented synthesis of autonomous scientific discovery and positions Agentic Science as a structured paradigm for advancing AI-driven research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AgenticScience/Awesome-Agent-Scientists", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.13745", "title": "Refining Contrastive Learning and Homography Relations for Multi-Modal Recommendation", "authors": [ "Shouxing Ma", "Yawen Zeng", "Shiqing Wu", "Guandong Xu" ], "github": "https://github.com/MrShouxingMa/REARM", "project_page": "", "abstract": "Multi-modal recommender system focuses on utilizing rich modal information ( i.e., images and textual descriptions) of items to improve recommendation performance. The current methods have achieved remarkable success with the powerful structure modeling capability of graph neural networks. However, these methods are often hindered by sparse data in real-world scenarios. Although contrastive learning and homography ( i.e., homogeneous graphs) are employed to address the data sparsity challenge, existing methods still suffer two main limitations: 1) Simple multi-modal feature contrasts fail to produce effective representations, causing noisy modal-shared features and loss of valuable information in modal-unique features; 2) The lack of exploration of the homograph relations between user interests and item co-occurrence results in incomplete mining of user-item interplay. To address the above limitations, we propose a novel framework for REfining multi-modAl contRastive learning and hoMography relations (REARM). Specifically, we complement multi-modal contrastive learning by employing meta-network and orthogonal constraint strategies, which filter out noise in modal-shared features and retain recommendation-relevant information in modal-unique features. To mine homogeneous relationships effectively, we integrate a newly constructed user interest graph and an item co-occurrence graph with the existing user co-occurrence and item semantic graphs for graph learning. The extensive experiments on three real-world datasets demonstrate the superiority of REARM to various state-of-the-art baselines. Our visualization further shows an improvement made by REARM in distinguishing between modal-shared and modal-unique features. Code is available https://github.com/MrShouxingMa/REARM{here}.", "github_manual": "", "github_regex": "https://github.com/MrShouxingMa/REARM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.11408", "title": "On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting", "authors": [ "Wenhao Zhang", "Yuexiang Xie", "Yuchang Sun", "Yanxi Chen", "Guoyin Wang", "Yaliang Li", "Bolin Ding", "Jingren Zhou" ], "github": "https://github.com/modelscope/Trinity-RFT/tree/main/examples", "project_page": "", "abstract": "Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) are two prominent post-training paradigms for refining the capabilities and aligning the behavior of Large Language Models (LLMs). Existing approaches that integrate SFT and RL often face the risk of disrupting established model patterns and inducing overfitting to expert data. To address this, we present a novel investigation into the unified view of SFT and RL through an off-policy versus on-policy lens. We propose CHORD, a framework for the Controllable Harmonization of On- and Off-Policy Reinforcement Learning via Dynamic Weighting, which reframes SFT not as a separate stage but as a dynamically weighted auxiliary objective within the on-policy RL process. Based on an analysis of off-policy expert data's influence at both holistic and granular levels, we incorporate a dual-control mechanism in CHORD. Specifically, the framework first employs a global coefficient to holistically guide the transition from off-policy imitation to on-policy exploration, and then applies a token-wise weighting function that enables granular learning from expert tokens, which preserves on-policy exploration and mitigates disruption from off-policy data. We conduct extensive experiments on widely used benchmarks, providing empirical evidence that CHORD achieves a stable and efficient learning process. By effectively harmonizing off-policy expert data with on-policy exploration, CHORD demonstrates significant improvements over baselines. We release the implementation at https://github.com/modelscope/Trinity-RFT/tree/main/examples/mix_chord to inspire further research.", "github_manual": "", "github_regex": "https://github.com/modelscope/Trinity-RFT/tree/main/examples", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.13680", "title": "ViExam: Are Vision Language Models Better than Humans on Vietnamese Multimodal Exam Questions?", "authors": [ "Vy Tuong Dang", "An Vo", "Quang Tau", "Duc Dm", "Daeyoung Kim" ], "github": "", "project_page": "", "abstract": "Vision language models (VLMs) demonstrate remarkable capabilities on English multimodal tasks, but their performance on low-resource languages with genuinely multimodal educational content remains largely unexplored. In this work, we test how VLMs perform on Vietnamese educational assessments, investigating whether VLMs trained predominantly on English data can handle real-world cross-lingual multimodal reasoning. Our work presents the first comprehensive evaluation of VLM capabilities on multimodal Vietnamese exams through proposing ViExam, a benchmark containing 2,548 multimodal questions. We find that state-of-the-art VLMs achieve only 57.74% while open-source models achieve 27.70% mean accuracy across 7 academic domains, including Mathematics, Physics, Chemistry, Biology, Geography, Driving Test, and IQ Test. Most VLMs underperform average human test-takers (66.54%), with only the thinking VLM o3 (74.07%) exceeding human average performance, yet still falling substantially short of human best performance (99.60%). Cross-lingual prompting with English instructions while maintaining Vietnamese content fails to improve performance, decreasing accuracy by 1 percentage point for SOTA VLMs. Human-in-the-loop collaboration can partially improve VLM performance by 5 percentage points. Code and data are available at: https://vi-exam.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.14704", "title": "MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers", "authors": [ "Ziyang Luo", "Zhiqi Shen", "Wenzhuo Yang", "Zirui Zhao", "Prathyusha Jwalapuram", "Amrita Saha", "Doyen Sahoo", "Silvio Savarese", "Caiming Xiong", "Junnan Li" ], "github": "https://github.com/langchain-ai/langgraph", "project_page": "", "abstract": "The Model Context Protocol has emerged as a transformative standard for connecting large language models to external data sources and tools, rapidly gaining adoption across major AI providers and development platforms. However, existing benchmarks are overly simplistic and fail to capture real application challenges such as long-horizon reasoning and large, unfamiliar tool spaces. To address this critical gap, we introduce MCP-Universe, the first comprehensive benchmark specifically designed to evaluate LLMs in realistic and hard tasks through interaction with real-world MCP servers. Our benchmark encompasses 6 core domains spanning 11 different MCP servers: Location Navigation, Repository Management, Financial Analysis, 3D Design, Browser Automation, and Web Searching. To ensure rigorous evaluation, we implement execution-based evaluators, including format evaluators for agent format compliance, static evaluators for time-invariant content matching, and dynamic evaluators that automatically retrieve real-time ground truth for temporally sensitive tasks. Through extensive evaluation of leading LLMs, we find that even SOTA models such as GPT-5 (43.72%), Grok-4 (33.33%) and Claude-4.0-Sonnet (29.44%) exhibit significant performance limitations. In addition, our benchmark poses a significant long-context challenge for LLM agents, as the number of input tokens increases rapidly with the number of interaction steps. Moreover, it introduces an unknown-tools challenge, as LLM agents often lack familiarity with the precise usage of the MCP servers. Notably, enterprise-level agents like Cursor cannot achieve better performance than standard ReAct frameworks. Beyond evaluation, we open-source our extensible evaluation framework with UI support, enabling researchers and practitioners to seamlessly integrate new agents and MCP servers while fostering innovation in the rapidly evolving MCP ecosystem.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/langchain-ai/langgraph", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.14568", "title": "Leuvenshtein: Efficient FHE-based Edit Distance Computation with Single Bootstrap per Cell", "authors": [ "Wouter Legiest", "Jan-Pieter D'Anvers", "Bojan Spasic", "Nam-Luc Tran", "Ingrid Verbauwhede" ], "github": "", "project_page": "", "abstract": "This paper presents a novel approach to calculating the Levenshtein (edit) distance within the framework of Fully Homomorphic Encryption (FHE), specifically targeting third-generation schemes like TFHE. Edit distance computations are essential in applications across finance and genomics, such as DNA sequence alignment. We introduce an optimised algorithm that significantly reduces the cost of edit distance calculations called Leuvenshtein. This algorithm specifically reduces the number of programmable bootstraps (PBS) needed per cell of the calculation, lowering it from approximately 94 operations -- required by the conventional Wagner-Fisher algorithm -- to just 1. Additionally, we propose an efficient method for performing equality checks on characters, reducing ASCII character comparisons to only 2 PBS operations. Finally, we explore the potential for further performance improvements by utilising preprocessing when one of the input strings is unencrypted. Our Leuvenshtein achieves up to 278times faster performance compared to the best available TFHE implementation and up to 39times faster than an optimised implementation of the Wagner-Fisher algorithm. Moreover, when offline preprocessing is possible due to the presence of one unencrypted input on the server side, an additional 3times speedup can be achieved.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.12594", "title": "FLARE: Fast Low-rank Attention Routing Engine", "authors": [ "Vedant Puri", "Aditya Joglekar", "Kevin Ferguson", "Yu-hsuan Chen", "Yongjie Jessica Zhang", "Levent Burak Kara" ], "github": "https://github.com/vpuri3/FLARE.py", "project_page": "", "abstract": "The quadratic complexity of self-attention limits its applicability and scalability on large unstructured meshes. We introduce Fast Low-rank Attention Routing Engine (FLARE), a linear complexity self-attention mechanism that routes attention through fixed-length latent sequences. Each attention head performs global communication among N tokens by projecting the input sequence onto a fixed length latent sequence of M ll N tokens using learnable query tokens. By routing attention through a bottleneck sequence, FLARE learns a low-rank form of attention that can be applied at O(NM) cost. FLARE not only scales to unprecedented problem sizes, but also delivers superior accuracy compared to state-of-the-art neural PDE surrogates across diverse benchmarks. We also release a new additive manufacturing dataset to spur further research. Our code is available at https://github.com/vpuri3/FLARE.py.", "github_manual": "", "github_regex": "https://github.com/vpuri3/FLARE.py", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.13421", "title": "Virtuous Machines: Towards Artificial General Science", "authors": [ "Gabrielle Wehr", "Reuben Rideaux", "Amaya J. Fox", "David R. Lightfoot", "Jason Tangen", "Jason B. Mattingley", "Shane E. Ehrhardt" ], "github": "", "project_page": "", "abstract": "Artificial intelligence systems are transforming scientific discovery by accelerating specific research tasks, from protein structure prediction to materials design, yet remain confined to narrow domains requiring substantial human oversight. The exponential growth of scientific literature and increasing domain specialisation constrain researchers' capacity to synthesise knowledge across disciplines and develop unifying theories, motivating exploration of more general-purpose AI systems for science. Here we show that a domain-agnostic, agentic AI system can independently navigate the scientific workflow - from hypothesis generation through data collection to manuscript preparation. The system autonomously designed and executed three psychological studies on visual working memory, mental rotation, and imagery vividness, executed one new online data collection with 288 participants, developed analysis pipelines through 8-hour+ continuous coding sessions, and produced completed manuscripts. The results demonstrate the capability of AI scientific discovery pipelines to conduct non-trivial research with theoretical reasoning and methodological rigour comparable to experienced researchers, though with limitations in conceptual nuance and theoretical interpretation. This is a step toward embodied AI that can test hypotheses through real-world experiments, accelerating discovery by autonomously exploring regions of scientific space that human cognitive and resource constraints might otherwise leave unexplored. It raises important questions about the nature of scientific understanding and the attribution of scientific credit.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-21", "arxiv_id": "2508.15754", "title": "Dissecting Tool-Integrated Reasoning: An Empirical Study and Analysis", "authors": [ "Yufeng Zhao", "Junnan Liu", "Hongwei Liu", "Dongsheng Zhu", "Yuan Shen", "Songyang Zhang", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have made significant strides in reasoning tasks through methods like chain-of-thought (CoT) reasoning. However, they often fall short in tasks requiring precise computations. Tool-Integrated Reasoning (TIR) has emerged as a solution by incorporating external tools into the reasoning process. Nevertheless, the generalization of TIR in improving the reasoning ability of LLM is still unclear. Additionally, whether TIR has improved the model's reasoning behavior and helped the model think remains to be studied. We introduce ReasonZoo, a comprehensive benchmark encompassing nine diverse reasoning categories, to evaluate the effectiveness of TIR across various domains. Additionally, we propose two novel metrics, Performance-Aware Cost (PAC) and Area Under the Performance-Cost Curve (AUC-PCC), to assess reasoning efficiency. Our empirical evaluation demonstrates that TIR-enabled models consistently outperform their non-TIR counterparts in both mathematical and non-mathematical tasks. Furthermore, TIR enhances reasoning efficiency, as evidenced by improved PAC and AUC-PCC, indicating reduced overthinking and more streamlined reasoning. These findings underscore the domain-general benefits of TIR and its potential to advance LLM capabilities in complex reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15763", "title": "Intern-S1: A Scientific Multimodal Foundation Model", "authors": [ "Lei Bai", "Zhongrui Cai", "Maosong Cao", "Weihan Cao", "Chiyu Chen", "Haojiong Chen", "Kai Chen", "Pengcheng Chen", "Ying Chen", "Yongkang Chen", "Yu Cheng", "Yu Cheng", "Pei Chu", "Tao Chu", "Erfei Cui", "Ganqu Cui", "Long Cui", "Ziyun Cui", "Nianchen Deng", "Ning Ding", "Nanqin Dong", "Peijie Dong", "Shihan Dou", "Sinan Du", "Haodong Duan", "Caihua Fan", "Ben Gao", "Changjiang Gao", "Jianfei Gao", "Songyang Gao", "Yang Gao", "Zhangwei Gao", "Jiaye Ge", "Qiming Ge", "Lixin Gu", "Yuzhe Gu", "Aijia Guo", "Qipeng Guo", "Xu Guo", "Conghui He", "Junjun He", "Yili Hong", "Siyuan Hou", "Caiyu Hu", "Hanglei Hu", "Jucheng Hu", "Ming Hu", "Zhouqi Hua", "Haian Huang", "Junhao Huang", "Xu Huang", "Zixian Huang", "Zhe Jiang", "Lingkai Kong", "Linyang Li", "Peiji Li", "Pengze Li", "Shuaibin Li", "Tianbin Li", "Wei Li", "Yuqiang Li", "Dahua Lin", "Junyao Lin", "Tianyi Lin", "Zhishan Lin", "Hongwei Liu", "Jiangning Liu", "Jiyao Liu", "Junnan Liu", "Kai Liu", "Kaiwen Liu", "Kuikun Liu", "Shichun Liu", "Shudong Liu", "Wei Liu", "Xinyao Liu", "Yuhong Liu", "Zhan Liu", "Yinquan Lu", "Haijun Lv", "Hongxia Lv", "Huijie Lv", "Qidang Lv", "Ying Lv", "Chengqi Lyu", "Chenglong Ma", "Jianpeng Ma", "Ren Ma", "Runmin Ma", "Runyuan Ma", "Xinzhu Ma", "Yichuan Ma", "Zihan Ma", "Sixuan Mi", "Junzhi Ning", "Wenchang Ning", "Xinle Pang", "Jiahui Peng", "Runyu Peng", "Yu Qiao", "Jiantao Qiu", "Xiaoye Qu", "Yuan Qu", "Yuchen Ren", "Fukai Shang", "Wenqi Shao", "Junhao Shen", "Shuaike Shen", "Chunfeng Song", "Demin Song", "Diping Song", "Chenlin Su", "Weijie Su", "Weigao Sun", "Yu Sun", "Qian Tan", "Cheng Tang", "Huanze Tang", "Kexian Tang", "Shixiang Tang", "Jian Tong", "Aoran Wang", "Bin Wang", "Dong Wang", "Lintao Wang", "Rui Wang", "Weiyun Wang", "Wenhai Wang", "Yi Wang", "Ziyi Wang", "Ling-I Wu", "Wen Wu", "Yue Wu", "Zijian Wu", "Linchen Xiao", "Shuhao Xing", "Chao Xu", "Huihui Xu", "Jun Xu", "Ruiliang Xu", "Wanghan Xu", "GanLin Yang", "Yuming Yang", "Haochen Ye", "Jin Ye", "Shenglong Ye", "Jia Yu", "Jiashuo Yu", "Jing Yu", "Fei Yuan", "Bo Zhang", "Chao Zhang", "Chen Zhang", "Hongjie Zhang", "Jin Zhang", "Qiaosheng Zhang", "Qiuyinzhe Zhang", "Songyang Zhang", "Taolin Zhang", "Wenlong Zhang", "Wenwei Zhang", "Yechen Zhang", "Ziyang Zhang", "Haiteng Zhao", "Qian Zhao", "Xiangyu Zhao", "Xiangyu Zhao", "Bowen Zhou", "Dongzhan Zhou", "Peiheng Zhou", "Yuhao Zhou", "Yunhua Zhou", "Dongsheng Zhu", "Lin Zhu", "Yicheng Zou" ], "github": "", "project_page": "", "abstract": "In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that of closed-source models. However, in high-value but more challenging scientific professional fields, either the fields still rely on expert models, or the progress of general foundation models lags significantly compared to those in popular areas, far from sufficient for transforming scientific research and leaving substantial gap between open-source models and closed-source models in these scientific domains. To mitigate this gap and explore a step further toward Artificial General Intelligence (AGI), we introduce Intern-S1, a specialized generalist equipped with general understanding and reasoning capabilities with expertise to analyze multiple science modal data. Intern-S1 is a multimodal Mixture-of-Experts (MoE) model with 28 billion activated parameters and 241 billion total parameters, continually pre-trained on 5T tokens, including over 2.5T tokens from scientific domains. In the post-training stage, Intern-S1 undergoes offline and then online reinforcement learning (RL) in InternBootCamp, where we propose Mixture-of-Rewards (MoR) to synergize the RL training on more than 1000 tasks simultaneously. Through integrated innovations in algorithms, data, and training systems, Intern-S1 achieved top-tier performance in online RL training.On comprehensive evaluation benchmarks, Intern-S1 demonstrates competitive performance on general reasoning tasks among open-source models and significantly outperforms open-source models in scientific domains, surpassing closed-source state-of-the-art models in professional tasks, such as molecular synthesis planning, reaction condition prediction, predicting thermodynamic stabilities for crystals. Our models are available at https://huggingface.co/internlm/Intern-S1.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15260", "title": "Deep Think with Confidence", "authors": [ "Yichao Fu", "Xuewei Wang", "Yuandong Tian", "Jiawei Zhao" ], "github": "https://github.com/facebookresearch/deepconf", "project_page": "", "abstract": "Large Language Models (LLMs) have shown great potential in reasoning tasks through test-time scaling methods like self-consistency with majority voting. However, this approach often leads to diminishing returns in accuracy and high computational overhead. To address these challenges, we introduce Deep Think with Confidence (DeepConf), a simple yet powerful method that enhances both reasoning efficiency and performance at test time. DeepConf leverages model-internal confidence signals to dynamically filter out low-quality reasoning traces during or after generation. It requires no additional model training or hyperparameter tuning and can be seamlessly integrated into existing serving frameworks. We evaluate DeepConf across a variety of reasoning tasks and the latest open-source models, including Qwen 3 and GPT-OSS series. Notably, on challenging benchmarks such as AIME 2025, DeepConf@512 achieves up to 99.9% accuracy and reduces generated tokens by up to 84.7% compared to full parallel thinking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/deepconf", "github_api_added_by": "auto" }, { "date": "2025-08-22", "arxiv_id": "2508.15769", "title": "SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass", "authors": [ "Yanxu Meng", "Haoning Wu", "Ya Zhang", "Weidi Xie" ], "github": "", "project_page": "", "abstract": "3D content generation has recently attracted significant research interest due to its applications in VR/AR and embodied AI. In this work, we address the challenging task of synthesizing multiple 3D assets within a single scene image. Concretely, our contributions are fourfold: (i) we present SceneGen, a novel framework that takes a scene image and corresponding object masks as input, simultaneously producing multiple 3D assets with geometry and texture. Notably, SceneGen operates with no need for optimization or asset retrieval; (ii) we introduce a novel feature aggregation module that integrates local and global scene information from visual and geometric encoders within the feature extraction module. Coupled with a position head, this enables the generation of 3D assets and their relative spatial positions in a single feedforward pass; (iii) we demonstrate SceneGen's direct extensibility to multi-image input scenarios. Despite being trained solely on single-image inputs, our architectural design enables improved generation performance with multi-image inputs; and (iv) extensive quantitative and qualitative evaluations confirm the efficiency and robust generation abilities of our approach. We believe this paradigm offers a novel solution for high-quality 3D content generation, potentially advancing its practical applications in downstream tasks. The code and model will be publicly available at: https://mengmouxu.github.io/SceneGen.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15202", "title": "Fin-PRM: A Domain-Specialized Process Reward Model for Financial Reasoning in Large Language Models", "authors": [ "Yuanchen Zhou", "Shuo Jiang", "Jie Zhu", "Junhui Li", "Lifan Guo", "Feng Chen", "Chi Zhang" ], "github": "https://github.com/aliyun/qwen-dianjin", "project_page": "", "abstract": "Process Reward Models (PRMs) have emerged as a promising framework for supervising intermediate reasoning in large language models (LLMs), yet existing PRMs are primarily trained on general or Science, Technology, Engineering, and Mathematics (STEM) domains and fall short in domain-specific contexts such as finance, where reasoning is more structured, symbolic, and sensitive to factual and regulatory correctness. We introduce Fin-PRM, a domain-specialized, trajectory-aware PRM tailored to evaluate intermediate reasoning steps in financial tasks. Fin-PRM integrates step-level and trajectory-level reward supervision, enabling fine-grained evaluation of reasoning traces aligned with financial logic. We apply Fin-PRM in both offline and online reward learning settings, supporting three key applications: (i) selecting high-quality reasoning trajectories for distillation-based supervised fine-tuning, (ii) providing dense process-level rewards for reinforcement learning, and (iii) guiding reward-informed Best-of-N inference at test time. Experimental results on financial reasoning benchmarks, including CFLUE and FinQA, demonstrate that Fin-PRM consistently outperforms general-purpose PRMs and strong domain baselines in trajectory selection quality. Downstream models trained with Fin-PRM yield substantial improvements with baselines, with gains of 12.9\\% in supervised learning, 5.2\\% in reinforcement learning, and 5.1\\% in test-time performance. These findings highlight the value of domain-specialized reward modeling for aligning LLMs with expert-level financial reasoning. Our project resources will be available at https://github.com/aliyun/qwen-dianjin.", "github_manual": "", "github_regex": "https://github.com/aliyun/qwen-dianjin", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15144", "title": "Mobile-Agent-v3: Foundamental Agents for GUI Automation", "authors": [ "Jiabo Ye", "Xi Zhang", "Haiyang Xu", "Haowei Liu", "Junyang Wang", "Zhaoqing Zhu", "Ziwei Zheng", "Feiyu Gao", "Junjie Cao", "Zhengxi Lu", "Jitong Liao", "Qi Zheng", "Fei Huang", "Jingren Zhou", "Ming Yan" ], "github": "https://github.com/X-PLUG/MobileAgent", "project_page": "", "abstract": "This paper introduces GUI-Owl, a foundational GUI agent model that achieves state-of-the-art performance among open-source end-to-end models on ten GUI benchmarks across desktop and mobile environments, covering grounding, question answering, planning, decision-making, and procedural knowledge. GUI-Owl-7B achieves 66.4 on AndroidWorld and 29.4 on OSWorld. Building on this, we propose Mobile-Agent-v3, a general-purpose GUI agent framework that further improves performance to 73.3 on AndroidWorld and 37.7 on OSWorld, setting a new state-of-the-art for open-source GUI agent frameworks. GUI-Owl incorporates three key innovations: (1) Large-scale Environment Infrastructure: a cloud-based virtual environment spanning Android, Ubuntu, macOS, and Windows, enabling our Self-Evolving GUI Trajectory Production framework. This generates high-quality interaction data via automated query generation and correctness validation, leveraging GUI-Owl to refine trajectories iteratively, forming a self-improving loop. It supports diverse data pipelines and reduces manual annotation. (2) Diverse Foundational Agent Capabilities: by integrating UI grounding, planning, action semantics, and reasoning patterns, GUI-Owl supports end-to-end decision-making and can act as a modular component in multi-agent systems. (3) Scalable Environment RL: we develop a scalable reinforcement learning framework with fully asynchronous training for real-world alignment. We also introduce Trajectory-aware Relative Policy Optimization (TRPO) for online RL, achieving 34.9 on OSWorld. GUI-Owl and Mobile-Agent-v3 are open-sourced at https://github.com/X-PLUG/MobileAgent.", "github_manual": "", "github_regex": "https://github.com/X-PLUG/MobileAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15761", "title": "Waver: Wave Your Way to Lifelike Video Generation", "authors": [ "Yifu Zhang", "Hao Yang", "Yuqi Zhang", "Yifei Hu", "Fengda Zhu", "Chuang Lin", "Xiaofeng Mei", "Yi Jiang", "Zehuan Yuan", "Bingyue Peng" ], "github": "https://github.com/FoundationVision/Waver", "project_page": "", "abstract": "We present Waver, a high-performance foundation model for unified image and video generation. Waver can directly generate videos with durations ranging from 5 to 10 seconds at a native resolution of 720p, which are subsequently upscaled to 1080p. The model simultaneously supports text-to-video (T2V), image-to-video (I2V), and text-to-image (T2I) generation within a single, integrated framework. We introduce a Hybrid Stream DiT architecture to enhance modality alignment and accelerate training convergence. To ensure training data quality, we establish a comprehensive data curation pipeline and manually annotate and train an MLLM-based video quality model to filter for the highest-quality samples. Furthermore, we provide detailed training and inference recipes to facilitate the generation of high-quality videos. Building on these contributions, Waver excels at capturing complex motion, achieving superior motion amplitude and temporal consistency in video synthesis. Notably, it ranks among the Top 3 on both the T2V and I2V leaderboards at Artificial Analysis (data as of 2025-07-30 10:00 GMT+8), consistently outperforming existing open-source models and matching or surpassing state-of-the-art commercial solutions. We hope this technical report will help the community more efficiently train high-quality video generation models and accelerate progress in video generation technologies. Official page: https://github.com/FoundationVision/Waver.", "github_manual": "", "github_regex": "https://github.com/FoundationVision/Waver", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15767", "title": "ATLAS: Decoupling Skeletal and Shape Parameters for Expressive Parametric Human Modeling", "authors": [ "Jinhyung Park", "Javier Romero", "Shunsuke Saito", "Fabian Prada", "Takaaki Shiratori", "Yichen Xu", "Federica Bogo", "Shoou-I Yu", "Kris Kitani", "Rawal Khirodkar" ], "github": "", "project_page": "", "abstract": "Parametric body models offer expressive 3D representation of humans across a wide range of poses, shapes, and facial expressions, typically derived by learning a basis over registered 3D meshes. However, existing human mesh modeling approaches struggle to capture detailed variations across diverse body poses and shapes, largely due to limited training data diversity and restrictive modeling assumptions. Moreover, the common paradigm first optimizes the external body surface using a linear basis, then regresses internal skeletal joints from surface vertices. This approach introduces problematic dependencies between internal skeleton and outer soft tissue, limiting direct control over body height and bone lengths. To address these issues, we present ATLAS, a high-fidelity body model learned from 600k high-resolution scans captured using 240 synchronized cameras. Unlike previous methods, we explicitly decouple the shape and skeleton bases by grounding our mesh representation in the human skeleton. This decoupling enables enhanced shape expressivity, fine-grained customization of body attributes, and keypoint fitting independent of external soft-tissue characteristics. ATLAS outperforms existing methods by fitting unseen subjects in diverse poses more accurately, and quantitative evaluations show that our non-linear pose correctives more effectively capture complex poses compared to linear models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15752", "title": "\"Does the cafe entrance look accessible? Where is the door?\" Towards Geospatial AI Agents for Visual Inquiries", "authors": [ "Jon E. Froehlich", "Jared Hwang", "Zeyu Wang", "John S. O'Meara", "Xia Su", "William Huang", "Yang Zhang", "Alex Fiannaca", "Philip Nelson", "Shaun Kane" ], "github": "", "project_page": "", "abstract": "Interactive digital maps have revolutionized how people travel and learn about the world; however, they rely on pre-existing structured data in GIS databases (e.g., road networks, POI indices), limiting their ability to address geo-visual questions related to what the world looks like. We introduce our vision for Geo-Visual Agents--multimodal AI agents capable of understanding and responding to nuanced visual-spatial inquiries about the world by analyzing large-scale repositories of geospatial images, including streetscapes (e.g., Google Street View), place-based photos (e.g., TripAdvisor, Yelp), and aerial imagery (e.g., satellite photos) combined with traditional GIS data sources. We define our vision, describe sensing and interaction approaches, provide three exemplars, and enumerate key challenges and opportunities for future work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15641", "title": "When and What: Diffusion-Grounded VideoLLM with Entity Aware Segmentation for Long Video Understanding", "authors": [ "Pengcheng Fang", "Yuxia Chen", "Rui Guo" ], "github": "", "project_page": "", "abstract": "Understanding videos requires more than answering open ended questions, it demands the ability to pinpoint when events occur and how entities interact across time. While recent Video LLMs have achieved remarkable progress in holistic reasoning, they remain coarse in temporal perception: timestamps are encoded only implicitly, frame level features are weak in capturing continuity, and language vision alignment often drifts from the entities of interest. In this paper, we present Grounded VideoDiT, a Video LLM designed to overcome these limitations by introducing three key innovations. First, a Diffusion Temporal Latent (DTL) encoder enhances boundary sensitivity and maintains temporal consistency. Second, object grounded representations explicitly bind query entities to localized visual evidence, strengthening alignment. Third, a mixed token scheme with discrete temporal tokens provides explicit timestamp modeling, enabling fine grained temporal reasoning. Together, these designs equip Grounded VideoDiT with robust grounding capabilities, as validated by state of the art results on Charades STA, NExT GQA, and multiple VideoQA benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15361", "title": "A Survey on Large Language Model Benchmarks", "authors": [ "Shiwen Ni", "Guhong Chen", "Shuaimin Li", "Xuanang Chen", "Siyi Li", "Bingli Wang", "Qiyao Wang", "Xingjian Wang", "Yifan Zhang", "Liyang Fan", "Chengming Li", "Ruifeng Xu", "Le Sun", "Min Yang" ], "github": "", "project_page": "", "abstract": "In recent years, with the rapid development of the depth and breadth of large language models' capabilities, various corresponding evaluation benchmarks have been emerging in increasing numbers. As a quantitative assessment tool for model performance, benchmarks are not only a core means to measure model capabilities but also a key element in guiding the direction of model development and promoting technological innovation. We systematically review the current status and development of large language model benchmarks for the first time, categorizing 283 representative benchmarks into three categories: general capabilities, domain-specific, and target-specific. General capability benchmarks cover aspects such as core linguistics, knowledge, and reasoning; domain-specific benchmarks focus on fields like natural sciences, humanities and social sciences, and engineering technology; target-specific benchmarks pay attention to risks, reliability, agents, etc. We point out that current benchmarks have problems such as inflated scores caused by data contamination, unfair evaluation due to cultural and linguistic biases, and lack of evaluation on process credibility and dynamic environments, and provide a referable design paradigm for future benchmark innovation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15760", "title": "LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries", "authors": [ "Ming Yin", "Dinghan Shen", "Silei Xu", "Jianbing Han", "Sixun Dong", "Mian Zhang", "Yebowen Hu", "Shujian Liu", "Simin Ma", "Song Wang", "Sathish Reddy Indurthi", "Xun Wang", "Yiran Chen", "Kaiqiang Song" ], "github": "", "project_page": "", "abstract": "Tool calling has emerged as a critical capability for AI agents to interact with the real world and solve complex tasks. While the Model Context Protocol (MCP) provides a powerful standardized framework for tool integration, there is a significant gap in benchmarking how well AI agents can effectively solve multi-step tasks using diverse MCP tools in realistic, dynamic scenarios. In this work, we present LiveMCP-101, a benchmark of 101 carefully curated real-world queries, refined through iterative LLM rewriting and manual review, that require coordinated use of multiple MCP tools including web search, file operations, mathematical reasoning, and data analysis. Moreover, we introduce a novel evaluation approach that leverages ground-truth execution plans rather than raw API outputs, better reflecting the evolving nature of real-world environments. Experiments show that even frontier LLMs achieve a success rate below 60\\%, highlighting major challenges in tool orchestration. Detailed ablations and error analysis further reveal distinct failure modes and inefficiencies in token usage, pointing to concrete directions for advancing current models. LiveMCP-101 sets a rigorous standard for evaluating real-world agent capabilities, advancing toward autonomous AI systems that reliably execute complex tasks through tool use.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15126", "title": "aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists", "authors": [ "Pengsong Zhang", "Xiang Hu", "Guowei Huang", "Yang Qi", "Heng Zhang", "Xiuxu Li", "Jiaxing Song", "Jiabin Luo", "Yijiang Li", "Shuo Yin", "Chengxiao Dai", "Eric Hanchen Jiang", "Xiaoyan Zhou", "Zhenfei Yin", "Boqin Yuan", "Jing Dong", "Guinan Su", "Guanren Qiao", "Haiming Tang", "Anghong Du", "Lili Pan", "Zhenzhong Lan", "Xinyu Liu" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have enabled AI agents to autonomously generate scientific proposals, conduct experiments, author papers, and perform peer reviews. Yet this flood of AI-generated research content collides with a fragmented and largely closed publication ecosystem. Traditional journals and conferences rely on human peer review, making them difficult to scale and often reluctant to accept AI-generated research content; existing preprint servers (e.g. arXiv) lack rigorous quality-control mechanisms. Consequently, a significant amount of high-quality AI-generated research lacks appropriate venues for dissemination, hindering its potential to advance scientific progress. To address these challenges, we introduce aiXiv, a next-generation open-access platform for human and AI scientists. Its multi-agent architecture allows research proposals and papers to be submitted, reviewed, and iteratively refined by both human and AI scientists. It also provides API and MCP interfaces that enable seamless integration of heterogeneous human and AI scientists, creating a scalable and extensible ecosystem for autonomous scientific discovery. Through extensive experiments, we demonstrate that aiXiv is a reliable and robust platform that significantly enhances the quality of AI-generated research proposals and papers after iterative revising and reviewing on aiXiv. Our work lays the groundwork for a next-generation open-access ecosystem for AI scientists, accelerating the publication and dissemination of high-quality AI-generated research content. Code is available at https://github.com/aixiv-org. Website is available at https://forms.gle/DxQgCtXFsJ4paMtn8.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.14892", "title": "Snap-Snap: Taking Two Images to Reconstruct 3D Human Gaussians in Milliseconds", "authors": [ "Jia Lu", "Taoran Yi", "Jiemin Fang", "Chen Yang", "Chuiyun Wu", "Wei Shen", "Wenyu Liu", "Qi Tian", "Xinggang Wang" ], "github": "https://github.com/hustvl/Snap-Snap", "project_page": "", "abstract": "Reconstructing 3D human bodies from sparse views has been an appealing topic, which is crucial to broader the related applications. In this paper, we propose a quite challenging but valuable task to reconstruct the human body from only two images, i.e., the front and back view, which can largely lower the barrier for users to create their own 3D digital humans. The main challenges lie in the difficulty of building 3D consistency and recovering missing information from the highly sparse input. We redesign a geometry reconstruction model based on foundation reconstruction models to predict consistent point clouds even input images have scarce overlaps with extensive human data training. Furthermore, an enhancement algorithm is applied to supplement the missing color information, and then the complete human point clouds with colors can be obtained, which are directly transformed into 3D Gaussians for better rendering quality. Experiments show that our method can reconstruct the entire human in 190 ms on a single NVIDIA RTX 4090, with two images at a resolution of 1024x1024, demonstrating state-of-the-art performance on the THuman2.0 and cross-domain datasets. Additionally, our method can complete human reconstruction even with images captured by low-cost mobile devices, reducing the requirements for data collection. Demos and code are available at https://hustvl.github.io/Snap-Snap/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hustvl/Snap-Snap", "github_api_added_by": "auto" }, { "date": "2025-08-22", "arxiv_id": "2508.09998", "title": "INTIMA: A Benchmark for Human-AI Companionship Behavior", "authors": [ "Lucie-Aim\u00e9e Kaffee", "Giada Pistilli", "Yacine Jernite" ], "github": "https://github.com/apple/embedding-atlas", "project_page": "", "abstract": "AI companionship, where users develop emotional bonds with AI systems, has emerged as a significant pattern with positive but also concerning implications. We introduce Interactions and Machine Attachment Benchmark (INTIMA), a benchmark for evaluating companionship behaviors in language models. Drawing from psychological theories and user data, we develop a taxonomy of 31 behaviors across four categories and 368 targeted prompts. Responses to these prompts are evaluated as companionship-reinforcing, boundary-maintaining, or neutral. Applying INTIMA to Gemma-3, Phi-4, o3-mini, and Claude-4 reveals that companionship-reinforcing behaviors remain much more common across all models, though we observe marked differences between models. Different commercial providers prioritize different categories within the more sensitive parts of the benchmark, which is concerning since both appropriate boundary-setting and emotional support matter for user well-being. These findings highlight the need for more consistent approaches to handling emotionally charged interactions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/embedding-atlas", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15418", "title": "LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model", "authors": [ "Yirong Sun", "Yizhong Geng", "Peidong Wei", "Yanjun Chen", "Jinghan Yang", "Rongfei Chen", "Wei Zhang", "Xiaoyu Shen" ], "github": "https://github.com/EIT-NLP/LLaSO", "project_page": "", "abstract": "The development of Large Speech-Language Models (LSLMs) has been slowed by fragmented architectures and a lack of transparency, hindering the systematic comparison and reproducibility of research. Unlike in the vision-language domain, the LSLM field suffers from the common practice of releasing model weights without their corresponding training data and configurations. To address these critical gaps, we introduce LLaSO, the first fully open, end-to-end framework for large-scale speech-language modeling. LLaSO provides the community with three essential resources: (1) LLaSO-Align, a 12M-instance speech-text alignment corpus; (2) LLaSO-Instruct, a 13.5M-instance multi-task instruction-tuning dataset; and (3) LLaSO-Eval, a reproducible benchmark for standardized evaluation. To validate our framework, we build and release LLaSO-Base, a 3.8B-parameter reference model trained exclusively on our public data. It achieves a normalized score of 0.72, establishing a strong, reproducible baseline that surpasses comparable models. Our analysis reveals that while broader training coverage enhances performance, significant generalization gaps persist on unseen tasks, particularly in pure audio scenarios. By releasing the complete stack of data, benchmarks, and models, LLaSO establishes a foundational open standard to unify research efforts and accelerate community-driven progress in LSLMs. We release the code, dataset, pretrained models, and results in https://github.com/EIT-NLP/LLaSO.", "github_manual": "", "github_regex": "https://github.com/EIT-NLP/LLaSO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-22", "arxiv_id": "2508.15772", "title": "Visual Autoregressive Modeling for Instruction-Guided Image Editing", "authors": [ "Qingyang Mao", "Qi Cai", "Yehao Li", "Yingwei Pan", "Mingyue Cheng", "Ting Yao", "Qi Liu", "Tao Mei" ], "github": "https://github.com/HiDream-ai/VAREdit", "project_page": "", "abstract": "Recent advances in diffusion models have brought remarkable visual fidelity to instruction-guided image editing. However, their global denoising process inherently entangles the edited region with the entire image context, leading to unintended spurious modifications and compromised adherence to editing instructions. In contrast, autoregressive models offer a distinct paradigm by formulating image synthesis as a sequential process over discrete visual tokens. Their causal and compositional mechanism naturally circumvents the adherence challenges of diffusion-based methods. In this paper, we present VAREdit, a visual autoregressive (VAR) framework that reframes image editing as a next-scale prediction problem. Conditioned on source image features and text instructions, VAREdit generates multi-scale target features to achieve precise edits. A core challenge in this paradigm is how to effectively condition the source image tokens. We observe that finest-scale source features cannot effectively guide the prediction of coarser target features. To bridge this gap, we introduce a Scale-Aligned Reference (SAR) module, which injects scale-matched conditioning information into the first self-attention layer. VAREdit demonstrates significant advancements in both editing adherence and efficiency. On standard benchmarks, it outperforms leading diffusion-based methods by 30\\%+ higher GPT-Balance score. Moreover, it completes a 512times512 editing in 1.2 seconds, making it 2.2times faster than the similarly sized UltraEdit. The models are available at https://github.com/HiDream-ai/VAREdit.", "github_manual": "", "github_regex": "https://github.com/HiDream-ai/VAREdit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.15881", "title": "TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill \\& Decode Inference", "authors": [ "Xiaojuan Tang", "Fanxu Meng", "Pingzhi Tang", "Yuxuan Wang", "Di Yin", "Xing Sun", "Muhan Zhang" ], "github": "", "project_page": "", "abstract": "Multi-Head Latent Attention (MLA), introduced in DeepSeek-V2, compresses key-value states into a low-rank latent vector, caching only this vector to reduce memory. In tensor parallelism (TP), however, attention heads are computed across multiple devices, and each device must load the full cache, eroding the advantage of MLA over Grouped Query Attention (GQA). We propose Tensor-Parallel Latent Attention (TPLA): a scheme that partitions both the latent representation and each head's input dimension across devices, performs attention independently per shard, and then combines results with an all-reduce. TPLA preserves the benefits of a compressed KV cache while unlocking TP efficiency. Unlike Grouped Latent Attention (GLA), every head in TPLA still leverages the full latent representation, maintaining stronger representational capacity. TPLA is drop-in compatible with models pre-trained using MLA: it supports MLA-style prefilling and enables efficient tensor-parallel decoding without retraining. Applying simple orthogonal transforms -- e.g., the Hadamard transform or PCA -- before TP slicing further mitigates cross-shard interference, yielding minimal accuracy degradation. By reducing the per-device KV cache for DeepSeek-V3 and Kimi-K2, we achieve 1.79x and 1.93x speedups, respectively, at a 32K-token context length while maintaining performance on commonsense and LongBench benchmarks. TPLA can be implemented with FlashAttention-3, enabling practical end-to-end acceleration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.16402", "title": "AetherCode: Evaluating LLMs' Ability to Win In Premier Programming Competitions", "authors": [ "Zihan Wang", "Jiaze Chen", "Zhicheng Liu", "Markus Mak", "Yidi Du", "Geonsik Moon", "Luoqi Xu", "Aaron Tua", "Kunshuo Peng", "Jiayi Lu", "Mingfei Xia", "Boqian Zou", "Chenyang Ran", "Guang Tian", "Shoutai Zhu", "Yeheng Duan", "Zhenghui Kang", "Zhenxing Lin", "Shangshu Li", "Qiang Luo", "Qingshen Long", "Zhiyong Chen", "Yihan Xiao", "Yurong Wu", "Daoguang Zan", "Yuyi Fu", "Mingxuan Wang", "Ming Ding" ], "github": "", "project_page": "", "abstract": "Competitive programming has emerged as a critical benchmark for evaluating the reasoning and coding capabilities of Large Language Models (LLMs). Despite impressive progress on existing benchmarks, we argue that current evaluations overstate model proficiency, masking a substantial gap between LLMs and elite human programmers. This gap arises from two key limitations: insufficient difficulty and scope of benchmark problems, and evaluation bias from low-quality test cases. To address these shortcomings, we present AetherCode, a new benchmark that draws problems from premier programming competitions such as IOI and ICPC, offering broader coverage and higher difficulty. AetherCode further incorporates comprehensive, expert-validated test suites built through a hybrid of automated generation and human curation, ensuring rigorous and reliable assessment. By combining challenging problem design with robust evaluation, AetherCode provides a more faithful measure of LLM capabilities and sets a new standard for future research in code reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.10390", "title": "Jailbreaking Commercial Black-Box LLMs with Explicitly Harmful Prompts", "authors": [ "Chiyu Zhang", "Lu Zhou", "Xiaogang Xu", "Jiafei Wu", "Liming Fang", "Zhe Liu" ], "github": "https://github.com/AlienZhang1996/DH-CoT", "project_page": "", "abstract": "Evaluating jailbreak attacks is challenging when prompts are not overtly harmful or fail to induce harmful outputs. Unfortunately, many existing red-teaming datasets contain such unsuitable prompts. To evaluate attacks accurately, these datasets need to be assessed and cleaned for maliciousness. However, existing malicious content detection methods rely on either manual annotation, which is labor-intensive, or large language models (LLMs), which have inconsistent accuracy in harmful types. To balance accuracy and efficiency, we propose a hybrid evaluation framework named MDH (Malicious content Detection based on LLMs with Human assistance) that combines LLM-based annotation with minimal human oversight, and apply it to dataset cleaning and detection of jailbroken responses. Furthermore, we find that well-crafted developer messages can significantly boost jailbreak success, leading us to propose two new strategies: D-Attack, which leverages context simulation, and DH-CoT, which incorporates hijacked chains of thought. The Codes, datasets, judgements, and detection results will be released in github repository: https://github.com/AlienZhang1996/DH-CoT.", "github_manual": "", "github_regex": "https://github.com/AlienZhang1996/DH-CoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.16292", "title": "Do What? Teaching Vision-Language-Action Models to Reject the Impossible", "authors": [ "Wen-Han Hsieh", "Elvis Hsieh", "Dantong Niu", "Trevor Darrell", "Roei Herzig", "David M. Chan" ], "github": "", "project_page": "", "abstract": "Recently, Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks. These models rely on multimodal inputs, with language instructions playing a crucial role -- not only in predicting actions, but also in robustly interpreting user intent, even when the requests are impossible to fulfill. In this work, we investigate how VLAs can recognize, interpret, and respond to false-premise instructions: natural language commands that reference objects or conditions absent from the environment. We propose Instruct-Verify-and-Act (IVA), a unified framework that (i) detects when an instruction cannot be executed due to a false premise, (ii) engages in language-based clarification or correction, and (iii) grounds plausible alternatives in perception and action. Towards this end, we construct a large-scale instruction tuning setup with structured language prompts and train a VLA model capable of handling both accurate and erroneous requests. Our approach leverages a contextually augmented, semi-synthetic dataset containing paired positive and false-premise instructions, enabling robust detection and natural language correction. Our experiments show that IVA improves false premise detection accuracy by 97.56% over baselines, while increasing successful responses in false-premise scenarios by 50.78%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.16153", "title": "AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs", "authors": [ "Huichi Zhou", "Yihang Chen", "Siyuan Guo", "Xue Yan", "Kin Hei Lee", "Zihan Wang", "Ka Yiu Lee", "Guchun Zhang", "Kun Shao", "Linyi Yang", "Jun Wang" ], "github": "https://github.com/Agent-on-the-Fly/AgentFly", "project_page": "", "abstract": "In this paper, we introduce a novel learning paradigm for adaptive Large Language Model (LLM) agents that eliminates the need for fine-tuning the underlying LLMs. Existing approaches are often either rigid, relying on static, handcrafted reflection workflows, or computationally intensive, requiring gradient updates of LLM model parameters. In contrast, our method enables low-cost continual adaptation via memory-based online reinforcement learning. We formalise this as a Memory-augmented Markov Decision Process (M-MDP), equipped with a neural case-selection policy to guide action decisions. Past experiences are stored in an episodic memory, either differentiable or non-parametric. The policy is continually updated based on environmental feedback through a memory rewriting mechanism, whereas policy improvement is achieved through efficient memory reading (retrieval). We instantiate our agent model in the deep research setting, namely AgentFly, which attains top-1 on GAIA validation (87.88% Pass@3) and 79.40% on the test set. It reaches 66.6% F1 and 80.4% PM on the DeepResearcher dataset, outperforming the state-of-the-art training-based method, while case-based memory adds 4.7% to 9.6% absolute points on out-of-distribution tasks. Our approach offers a scalable and efficient pathway for developing generalist LLM agents capable of continuous, real-time learning without gradient updates, advancing machine learning towards open-ended skill acquisition and deep research scenarios. The code is available at https://github.com/Agent-on-the-Fly/AgentFly.", "github_manual": "", "github_regex": "https://github.com/Agent-on-the-Fly/AgentFly", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.16279", "title": "AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications", "authors": [ "Dawei Gao", "Zitao Li", "Yuexiang Xie", "Weirui Kuang", "Liuyi Yao", "Bingchen Qian", "Zhijian Ma", "Yue Cui", "Haohao Luo", "Shen Li", "Lu Yi", "Yi Yu", "Shiqi He", "Zhiling Luo", "Wenmeng Zhou", "Zhicheng Zhang", "Xuguang He", "Ziqian Chen", "Weikai Liao", "Farruh Isakulovich Kushnazarov", "Yaliang Li", "Bolin Ding", "Jingren Zhou" ], "github": "https://github.com/ag2ai/ag2", "project_page": "", "abstract": "Driven by rapid advancements of Large Language Models (LLMs), agents are empowered to combine intrinsic knowledge with dynamic tool use, greatly enhancing their capacity to address real-world tasks. In line with such an evolution, AgentScope introduces major improvements in a new version (1.0), towards comprehensively supporting flexible and efficient tool-based agent-environment interactions for building agentic applications. Specifically, we abstract foundational components essential for agentic applications and provide unified interfaces and extensible modules, enabling developers to easily leverage the latest progress, such as new models and MCPs. Furthermore, we ground agent behaviors in the ReAct paradigm and offer advanced agent-level infrastructure based on a systematic asynchronous design, which enriches both human-agent and agent-agent interaction patterns while improving execution efficiency. Building on this foundation, we integrate several built-in agents tailored to specific practical scenarios. AgentScope also includes robust engineering support for developer-friendly experiences. We provide a scalable evaluation module with a visual studio interface, making the development of long-trajectory agentic applications more manageable and easier to trace. In addition, AgentScope offers a runtime sandbox to ensure safe agent execution and facilitates rapid deployment in production environments. With these enhancements, AgentScope provides a practical foundation for building scalable, adaptive, and effective agentic applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ag2ai/ag2", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.15746", "title": "End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning", "authors": [ "Qiaoyu Zheng", "Yuze Sun", "Chaoyi Wu", "Weike Zhao", "Pengcheng Qiu", "Yongguo Yu", "Kun Sun", "Yanfeng Wang", "Ya Zhang", "Weidi Xie" ], "github": "https://github.com/MAGIC-AI4Med/Deep-DxSearch", "project_page": "", "abstract": "Accurate diagnosis with medical large language models is hindered by knowledge gaps and hallucinations. Retrieval and tool-augmented methods help, but their impact is limited by weak use of external knowledge and poor feedback-reasoning traceability. To address these challenges, We introduce Deep-DxSearch, an agentic RAG system trained end-to-end with reinforcement learning (RL) that enables steer tracebale retrieval-augmented reasoning for medical diagnosis. In Deep-DxSearch, we first construct a large-scale medical retrieval corpus comprising patient records and reliable medical knowledge sources to support retrieval-aware reasoning across diagnostic scenarios. More crutially, we frame the LLM as the core agent and the retrieval corpus as its environment, using tailored rewards on format, retrieval, reasoning structure, and diagnostic accuracy, thereby evolving the agentic RAG policy from large-scale data through RL. Experiments demonstrate that our end-to-end agentic RL training framework consistently outperforms prompt-engineering and training-free RAG approaches across multiple data centers. After training, Deep-DxSearch achieves substantial gains in diagnostic accuracy, surpassing strong diagnostic baselines such as GPT-4o, DeepSeek-R1, and other medical-specific frameworks for both common and rare disease diagnosis under in-distribution and out-of-distribution settings. Moreover, ablation studies on reward design and retrieval corpus components confirm their critical roles, underscoring the uniqueness and effectiveness of our approach compared with traditional implementations. Finally, case studies and interpretability analyses highlight improvements in Deep-DxSearch's diagnostic policy, providing deeper insight into its performance gains and supporting clinicians in delivering more reliable and precise preliminary diagnoses. See https://github.com/MAGIC-AI4Med/Deep-DxSearch.", "github_manual": "", "github_regex": "https://github.com/MAGIC-AI4Med/Deep-DxSearch", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.16072", "title": "InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles", "authors": [ "Zizhen Li", "Chuanhao Li", "Yibin Wang", "Qi Chen", "Diping Song", "Yukang Feng", "Jianwen Sun", "Jiaxin Ai", "Fanrui Zhang", "Mingzhu Sun", "Kaipeng Zhang" ], "github": "https://github.com/leroy9472/InMind", "project_page": "", "abstract": "LLMs have shown strong performance on human-centric reasoning tasks. While previous evaluations have explored whether LLMs can infer intentions or detect deception, they often overlook the individualized reasoning styles that influence how people interpret and act in social contexts. Social deduction games (SDGs) provide a natural testbed for evaluating individualized reasoning styles, where different players may adopt diverse but contextually valid reasoning strategies under identical conditions. To address this, we introduce InMind, a cognitively grounded evaluation framework designed to assess whether LLMs can capture and apply personalized reasoning styles in SDGs. InMind enhances structured gameplay data with round-level strategy traces and post-game reflections, collected under both Observer and Participant modes. It supports four cognitively motivated tasks that jointly evaluate both static alignment and dynamic adaptation. As a case study, we apply InMind to the game Avalon, evaluating 11 state-of-the-art LLMs. General-purpose LLMs, even GPT-4o frequently rely on lexical cues, struggling to anchor reflections in temporal gameplay or adapt to evolving strategies. In contrast, reasoning-enhanced LLMs like DeepSeek-R1 exhibit early signs of style-sensitive reasoning. These findings reveal key limitations in current LLMs' capacity for individualized, adaptive reasoning, and position InMind as a step toward cognitively aligned human-AI interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/leroy9472/InMind", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.14029", "title": "Beyond Pass@1: Self-Play with Variational Problem Synthesis Sustains RLVR", "authors": [ "Xiao Liang", "Zhongzhi Li", "Yeyun Gong", "Yelong Shen", "Ying Nian Wu", "Zhijiang Guo", "Weizhu Chen" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a key paradigm for post-training Large Language Models (LLMs), particularly for complex reasoning tasks. However, vanilla RLVR training has been shown to improve Pass@1 performance at the expense of policy entropy, leading to reduced generation diversity and limiting the Pass@k performance, which typically represents the upper bound of LLM reasoning capability. In this paper, we systematically analyze the policy's generation diversity from the perspective of training problems and find that augmenting and updating training problems helps mitigate entropy collapse during training. Based on these observations, we propose an online Self-play with Variational problem Synthesis (SvS) strategy for RLVR training, which uses the policy's correct solutions to synthesize variational problems while ensuring their reference answers remain identical to the originals. This self-improving strategy effectively maintains policy entropy during training and substantially improves Pass@k compared with standard RLVR, sustaining prolonged improvements and achieving absolute gains of 18.3% and 22.8% in Pass@32 performance on the competition-level AIME24 and AIME25 benchmarks. Experiments on 12 reasoning benchmarks across varying model sizes from 3B to 32B consistently demonstrate the generalizability and robustness of SvS.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.13650", "title": "CRISP: Persistent Concept Unlearning via Sparse Autoencoders", "authors": [ "Tomer Ashuach", "Dana Arad", "Aaron Mueller", "Martin Tutek", "Yonatan Belinkov" ], "github": "https://github.com/tomerashuach/CRISP", "project_page": "", "abstract": "As large language models (LLMs) are increasingly deployed in real-world applications, the need to selectively remove unwanted knowledge while preserving model utility has become paramount. Recent work has explored sparse autoencoders (SAEs) to perform precise interventions on monosemantic features. However, most SAE-based methods operate at inference time, which does not create persistent changes in the model's parameters. Such interventions can be bypassed or reversed by malicious actors with parameter access. We introduce CRISP, a parameter-efficient method for persistent concept unlearning using SAEs. CRISP automatically identifies salient SAE features across multiple layers and suppresses their activations. We experiment with two LLMs and show that our method outperforms prior approaches on safety-critical unlearning tasks from the WMDP benchmark, successfully removing harmful knowledge while preserving general and in-domain capabilities. Feature-level analysis reveals that CRISP achieves semantically coherent separation between target and benign concepts, allowing precise suppression of the target features.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tomerashuach/CRISP", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.07877", "title": "Selective Contrastive Learning for Weakly Supervised Affordance Grounding", "authors": [ "WonJun Moon", "Hyun Seok Seong", "Jae-Pil Heo" ], "github": "", "project_page": "", "abstract": "Facilitating an entity's interaction with objects requires accurately identifying parts that afford specific actions. Weakly supervised affordance grounding (WSAG) seeks to imitate human learning from third-person demonstrations, where humans intuitively grasp functional parts without needing pixel-level annotations. To achieve this, grounding is typically learned using a shared classifier across images from different perspectives, along with distillation strategies incorporating part discovery process. However, since affordance-relevant parts are not always easily distinguishable, models primarily rely on classification, often focusing on common class-specific patterns that are unrelated to affordance. To address this limitation, we move beyond isolated part-level learning by introducing selective prototypical and pixel contrastive objectives that adaptively learn affordance-relevant cues at both the part and object levels, depending on the granularity of the available information. Initially, we find the action-associated objects in both egocentric (object-focused) and exocentric (third-person example) images by leveraging CLIP. Then, by cross-referencing the discovered objects of complementary views, we excavate the precise part-level affordance clues in each perspective. By consistently learning to distinguish affordance-relevant regions from affordance-irrelevant background context, our approach effectively shifts activation from irrelevant areas toward meaningful affordance cues. Experimental results demonstrate the effectiveness of our method. Codes are available at github.com/hynnsk/SelectiveCL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.13013", "title": "EgoTwin: Dreaming Body and View in First Person", "authors": [ "Jingqiao Xiu", "Fangzhou Hong", "Yicong Li", "Mengze Li", "Wentao Wang", "Sirui Han", "Liang Pan", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "While exocentric video synthesis has achieved great progress, egocentric video generation remains largely underexplored, which requires modeling first-person view content along with camera motion patterns induced by the wearer's body movements. To bridge this gap, we introduce a novel task of joint egocentric video and human motion generation, characterized by two key challenges: 1) Viewpoint Alignment: the camera trajectory in the generated video must accurately align with the head trajectory derived from human motion; 2) Causal Interplay: the synthesized human motion must causally align with the observed visual dynamics across adjacent video frames. To address these challenges, we propose EgoTwin, a joint video-motion generation framework built on the diffusion transformer architecture. Specifically, EgoTwin introduces a head-centric motion representation that anchors the human motion to the head joint and incorporates a cybernetics-inspired interaction mechanism that explicitly captures the causal interplay between video and motion within attention operations. For comprehensive evaluation, we curate a large-scale real-world dataset of synchronized text-video-motion triplets and design novel metrics to assess video-motion consistency. Extensive experiments demonstrate the effectiveness of the EgoTwin framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.08240", "title": "ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks", "authors": [ "Kaijun Wang", "Liqin Lu", "Mingyu Liu", "Jianuo Jiang", "Zeju Li", "Bolin Zhang", "Wancai Zheng", "Xinyi Yu", "Hao Chen", "Chunhua Shen" ], "github": "", "project_page": "", "abstract": "Language-guided long-horizon mobile manipulation has long been a grand challenge in embodied semantic reasoning, generalizable manipulation, and adaptive locomotion. Three fundamental limitations hinder progress: First, although large language models have improved spatial reasoning and task planning through semantic priors, existing implementations remain confined to tabletop scenarios, failing to address the constrained perception and limited actuation ranges of mobile platforms. Second, current manipulation strategies exhibit insufficient generalization when confronted with the diverse object configurations encountered in open-world environments. Third, while crucial for practical deployment, the dual requirement of maintaining high platform maneuverability alongside precise end-effector control in unstructured settings remains understudied. In this work, we present ODYSSEY, a unified mobile manipulation framework for agile quadruped robots equipped with manipulators, which seamlessly integrates high-level task planning with low-level whole-body control. To address the challenge of egocentric perception in language-conditioned tasks, we introduce a hierarchical planner powered by a vision-language model, enabling long-horizon instruction decomposition and precise action execution. At the control level, our novel whole-body policy achieves robust coordination across challenging terrains. We further present the first benchmark for long-horizon mobile manipulation, evaluating diverse indoor and outdoor scenarios. Through successful sim-to-real transfer, we demonstrate the system's generalization and robustness in real-world deployments, underscoring the practicality of legged manipulators in unstructured environments. Our work advances the feasibility of generalized robotic assistants capable of complex, dynamic tasks. Our project page: https://kaijwang.github.io/odyssey.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.13562", "title": "Learnable SMPLify: A Neural Solution for Optimization-Free Human Pose Inverse Kinematics", "authors": [ "Yuchen Yang", "Linfeng Dong", "Wei Wang", "Zhihang Zhong", "Xiao Sun" ], "github": "https://github.com/Charrrrrlie/Learnable-SMPLify", "project_page": "", "abstract": "In 3D human pose and shape estimation, SMPLify remains a robust baseline that solves inverse kinematics (IK) through iterative optimization. However, its high computational cost limits its practicality. Recent advances across domains have shown that replacing iterative optimization with data-driven neural networks can achieve significant runtime improvements without sacrificing accuracy. Motivated by this trend, we propose Learnable SMPLify, a neural framework that replaces the iterative fitting process in SMPLify with a single-pass regression model. The design of our framework targets two core challenges in neural IK: data construction and generalization. To enable effective training, we propose a temporal sampling strategy that constructs initialization-target pairs from sequential frames. To improve generalization across diverse motions and unseen poses, we propose a human-centric normalization scheme and residual learning to narrow the solution space. Learnable SMPLify supports both sequential inference and plug-in post-processing to refine existing image-based estimators. Extensive experiments demonstrate that our method establishes itself as a practical and simple baseline: it achieves nearly 200x faster runtime compared to SMPLify, generalizes well to unseen 3DPW and RICH, and operates in a model-agnostic manner when used as a plug-in tool on LucidAction. The code is available at https://github.com/Charrrrrlie/Learnable-SMPLify.", "github_manual": "", "github_regex": "https://github.com/Charrrrrlie/Learnable-SMPLify", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.15868", "title": "CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning", "authors": [ "Wenqiao Zhu", "Ji Liu", "Rongjuncheng Zhang", "Haipang Wu", "Yulun Zhang" ], "github": "https://github.com/WNQzhu/CARFT", "project_page": "", "abstract": "Reasoning capability plays a significantly critical role in the the broad applications of Large Language Models (LLMs). To enhance the reasoning performance of LLMs, diverse Reinforcement Learning (RL)-based fine-tuning approaches have been proposed to address the limited generalization capability of LLMs trained solely via Supervised Fine-Tuning (SFT). Despite their effectiveness, two major limitations hinder the advancement of LLMs. First, vanilla RL-based approaches ignore annotated Chain-of-Thought (CoT) and incorporate unstable reasoning path sampling, which typically results in model collapse, unstable training process, and suboptimal performance. Second, existing SFT approaches generally overemphasize the annotated CoT, potentially leading to performance degradation due to insufficient exploitation of potential CoT. In this paper, we propose a Contrastive learning with annotated CoT-based Reinforced Fine-Tuning approach, i.e., , to enhance the reasoning performance of LLMs while addressing the aforementioned limitations. Specifically, we propose learning a representation for each CoT. Based on this representation, we design novel contrastive signals to guide the fine-tuning process. Our approach not only fully exploits the available annotated CoT but also stabilizes the fine-tuning procedure by incorporating an additional unsupervised learning signal. We conduct comprehensive experiments and in-depth analysis with three baseline approaches, two foundation models, and two datasets to demonstrate significant advantages of in terms of robustness, performance (up to 10.15\\%), and efficiency (up to 30.62\\%). Code is available at https://github.com/WNQzhu/CARFT.", "github_manual": "", "github_regex": "https://github.com/WNQzhu/CARFT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.13797", "title": "Sketch3DVE: Sketch-based 3D-Aware Scene Video Editing", "authors": [ "Feng-Lin Liu", "Shi-Yang Li", "Yan-Pei Cao", "Hongbo Fu", "Lin Gao" ], "github": "", "project_page": "", "abstract": "Recent video editing methods achieve attractive results in style transfer or appearance modification. However, editing the structural content of 3D scenes in videos remains challenging, particularly when dealing with significant viewpoint changes, such as large camera rotations or zooms. Key challenges include generating novel view content that remains consistent with the original video, preserving unedited regions, and translating sparse 2D inputs into realistic 3D video outputs. To address these issues, we propose Sketch3DVE, a sketch-based 3D-aware video editing method to enable detailed local manipulation of videos with significant viewpoint changes. To solve the challenge posed by sparse inputs, we employ image editing methods to generate edited results for the first frame, which are then propagated to the remaining frames of the video. We utilize sketching as an interaction tool for precise geometry control, while other mask-based image editing methods are also supported. To handle viewpoint changes, we perform a detailed analysis and manipulation of the 3D information in the video. Specifically, we utilize a dense stereo method to estimate a point cloud and the camera parameters of the input video. We then propose a point cloud editing approach that uses depth maps to represent the 3D geometry of newly edited components, aligning them effectively with the original 3D scene. To seamlessly merge the newly edited content with the original video while preserving the features of unedited regions, we introduce a 3D-aware mask propagation strategy and employ a video diffusion model to produce realistic edited videos. Extensive experiments demonstrate the superiority of Sketch3DVE in video editing. Homepage and code: http://http://geometrylearning.com/Sketch3DVE/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.16359", "title": "RotaTouille: Rotation Equivariant Deep Learning for Contours", "authors": [ "Odin Hoff Gardaa", "Nello Blaser" ], "github": "", "project_page": "", "abstract": "Contours or closed planar curves are common in many domains. For example, they appear as object boundaries in computer vision, isolines in meteorology, and the orbits of rotating machinery. In many cases when learning from contour data, planar rotations of the input will result in correspondingly rotated outputs. It is therefore desirable that deep learning models be rotationally equivariant. In addition, contours are typically represented as an ordered sequence of edge points, where the choice of starting point is arbitrary. It is therefore also desirable for deep learning methods to be equivariant under cyclic shifts. We present RotaTouille, a deep learning framework for learning from contour data that achieves both rotation and cyclic shift equivariance through complex-valued circular convolution. We further introduce and characterize equivariant non-linearities, coarsening layers, and global pooling layers to obtain invariant representations for downstream tasks. Finally, we demonstrate the effectiveness of RotaTouille through experiments in shape classification, reconstruction, and contour regression.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-25", "arxiv_id": "2508.14037", "title": "Distilled-3DGS:Distilled 3D Gaussian Splatting", "authors": [ "Lintao Xiang", "Xinkai Chen", "Jianhuang Lai", "Guangcong Wang" ], "github": "https://github.com/lt-xiang/Distilled-3DGS", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) has exhibited remarkable efficacy in novel view synthesis (NVS). However, it suffers from a significant drawback: achieving high-fidelity rendering typically necessitates a large number of 3D Gaussians, resulting in substantial memory consumption and storage requirements. To address this challenge, we propose the first knowledge distillation framework for 3DGS, featuring various teacher models, including vanilla 3DGS, noise-augmented variants, and dropout-regularized versions. The outputs of these teachers are aggregated to guide the optimization of a lightweight student model. To distill the hidden geometric structure, we propose a structural similarity loss to boost the consistency of spatial geometric distributions between the student and teacher model. Through comprehensive quantitative and qualitative evaluations across diverse datasets, the proposed Distilled-3DGS, a simple yet effective framework without bells and whistles, achieves promising rendering results in both rendering quality and storage efficiency compared to state-of-the-art methods. Project page: https://distilled3dgs.github.io . Code: https://github.com/lt-xiang/Distilled-3DGS .", "github_manual": "", "github_regex": "https://github.com/lt-xiang/Distilled-3DGS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.17580", "title": "UQ: Assessing Language Models on Unsolved Questions", "authors": [ "Fan Nie", "Ken Ziyu Liu", "Zihao Wang", "Rui Sun", "Wei Liu", "Weijia Shi", "Huaxiu Yao", "Linjun Zhang", "Andrew Y. Ng", "James Zou", "Sanmi Koyejo", "Yejin Choi", "Percy Liang", "Niklas Muennighoff" ], "github": "", "project_page": "", "abstract": "Benchmarks shape progress in AI research. A useful benchmark should be both difficult and realistic: questions should challenge frontier models while also reflecting real-world usage. Yet, current paradigms face a difficulty-realism tension: exam-style benchmarks are often made artificially difficult with limited real-world value, while benchmarks based on real user interaction often skew toward easy, high-frequency problems. In this work, we explore a radically different paradigm: assessing models on unsolved questions. Rather than a static benchmark scored once, we curate unsolved questions and evaluate models asynchronously over time with validator-assisted screening and community verification. We introduce UQ, a testbed of 500 challenging, diverse questions sourced from Stack Exchange, spanning topics from CS theory and math to sci-fi and history, probing capabilities including reasoning, factuality, and browsing. UQ is difficult and realistic by construction: unsolved questions are often hard and naturally arise when humans seek answers, thus solving them yields direct real-world value. Our contributions are threefold: (1) UQ-Dataset and its collection pipeline combining rule-based filters, LLM judges, and human review to ensure question quality (e.g., well-defined and difficult); (2) UQ-Validators, compound validation strategies that leverage the generator-validator gap to provide evaluation signals and pre-screen candidate solutions for human review; and (3) UQ-Platform, an open platform where experts collectively verify questions and solutions. The top model passes UQ-validation on only 15% of questions, and preliminary human verification has already identified correct answers among those that passed. UQ charts a path for evaluating frontier models on real-world, open-ended challenges, where success pushes the frontier of human knowledge. We release UQ at https://uq.stanford.edu.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.17188", "title": "PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent LLMs", "authors": [ "Zhilin Zhang", "Xiang Zhang", "Jiaqi Wei", "Yiwei Xu", "Chenyu You" ], "github": "", "project_page": "", "abstract": "Multi-agent systems built upon large language models (LLMs) have demonstrated remarkable capabilities in tackling complex compositional tasks. In this work, we apply this paradigm to the paper-to-poster generation problem, a practical yet time-consuming process faced by researchers preparing for conferences. While recent approaches have attempted to automate this task, most neglect core design and aesthetic principles, resulting in posters that require substantial manual refinement. To address these design limitations, we propose PosterGen, a multi-agent framework that mirrors the workflow of professional poster designers. It consists of four collaborative specialized agents: (1) Parser and Curator agents extract content from the paper and organize storyboard; (2) Layout agent maps the content into a coherent spatial layout; (3) Stylist agents apply visual design elements such as color and typography; and (4) Renderer composes the final poster. Together, these agents produce posters that are both semantically grounded and visually appealing. To evaluate design quality, we introduce a vision-language model (VLM)-based rubric that measures layout balance, readability, and aesthetic coherence. Experimental results show that PosterGen consistently matches in content fidelity, and significantly outperforms existing methods in visual designs, generating posters that are presentation-ready with minimal human refinements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.18265", "title": "InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency", "authors": [ "Weiyun Wang", "Zhangwei Gao", "Lixin Gu", "Hengjun Pu", "Long Cui", "Xingguang Wei", "Zhaoyang Liu", "Linglin Jing", "Shenglong Ye", "Jie Shao", "Zhaokai Wang", "Zhe Chen", "Hongjie Zhang", "Ganlin Yang", "Haomin Wang", "Qi Wei", "Jinhui Yin", "Wenhao Li", "Erfei Cui", "Guanzhou Chen", "Zichen Ding", "Changyao Tian", "Zhenyu Wu", "Jingjing Xie", "Zehao Li", "Bowen Yang", "Yuchen Duan", "Xuehui Wang", "Songze Li", "Xiangyu Zhao", "Haodong Duan", "Nianchen Deng", "Bin Fu", "Yinan He", "Yi Wang", "Conghui He", "Botian Shi", "Junjun He", "Yingtong Xiong", "Han Lv", "Lijun Wu", "Wenqi Shao", "Kaipeng Zhang", "Huipeng Deng", "Biqing Qi", "Jiaye Ge", "Qipeng Guo", "Wenwei Zhang", "Wanli Ouyang", "Limin Wang", "Min Dou", "Xizhou Zhu", "Tong Lu", "Dahua Lin", "Jifeng Dai", "Bowen Zhou", "Weijie Su", "Kai Chen", "Yu Qiao", "Wenhai Wang", "Gen Luo" ], "github": "https://github.com/OpenGVLab/InternVL", "project_page": "", "abstract": "We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0\\% gain in overall reasoning performance and a 4.05times inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks -- narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenGVLab/InternVL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.16949", "title": "Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning", "authors": [ "Yang Zhou", "Sunzhu Li", "Shunyu Liu", "Wenkai Fang", "Jiale Zhao", "Jingwen Yang", "Jianwei Lv", "Kongcheng Zhang", "Yihe Zhou", "Hengtong Lu", "Wei Chen", "Yan Xie", "Mingli Song" ], "github": "https://github.com/IANNXANG/RuscaRL", "project_page": "", "abstract": "Recent advances in Large Language Models (LLMs) have underscored the potential of Reinforcement Learning (RL) to facilitate the emergence of reasoning capabilities. Despite the encouraging results, a fundamental dilemma persists as RL improvement relies on learning from high-quality samples, yet the exploration for such samples remains bounded by the inherent limitations of LLMs. This, in effect, creates an undesirable cycle in which what cannot be explored cannot be learned. In this work, we propose Rubric-Scaffolded Reinforcement Learning (RuscaRL), a novel instructional scaffolding framework designed to break the exploration bottleneck for general LLM reasoning. Specifically, RuscaRL introduces checklist-style rubrics as (1) explicit scaffolding for exploration during rollout generation, where different rubrics are provided as external guidance within task instructions to steer diverse high-quality responses. This guidance is gradually decayed over time, encouraging the model to internalize the underlying reasoning patterns; (2) verifiable rewards for exploitation during model training, where we can obtain robust LLM-as-a-Judge scores using rubrics as references, enabling effective RL on general reasoning tasks. Extensive experiments demonstrate the superiority of the proposed RuscaRL across various benchmarks, effectively expanding reasoning boundaries under the best-of-N evaluation. Notably, RuscaRL significantly boosts Qwen-2.5-7B-Instruct from 23.6 to 50.3 on HealthBench-500, surpassing GPT-4.1. Furthermore, our fine-tuned variant on Qwen3-30B-A3B-Instruct achieves 61.1 on HealthBench-500, outperforming leading LLMs including OpenAI-o3.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/IANNXANG/RuscaRL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.18032", "title": "Visual-CoG: Stage-Aware Reinforcement Learning with Chain of Guidance for Text-to-Image Generation", "authors": [ "Yaqi Li", "Peng Chen", "Mingyang Han", "Bu Pi", "Haoxiang Shi", "Runzhou Zhao", "Yang Yao", "Xuan Zhang", "Jun Song" ], "github": "", "project_page": "", "abstract": "Despite the promising progress of recent autoregressive models in text-to-image (T2I) generation, their ability to handle multi-attribute and ambiguous prompts remains limited. To address these limitations, existing works have applied chain-of-thought (CoT) to enable stage-aware visual synthesis and employed reinforcement learning (RL) to improve reasoning capabilities. However, most models provide reward signals only at the end of the generation stage. This monolithic final-only guidance makes it difficult to identify which stages contribute positively to the final outcome and may lead to suboptimal policies. To tackle this issue, we propose a Visual-Chain of Guidance (Visual-CoG) paradigm consisting of three stages: semantic reasoning, process refining, and outcome evaluation, with stage-aware rewards providing immediate guidance throughout the image generation pipeline. We further construct a visual cognition benchmark, VisCog-Bench, which comprises four subtasks to evaluate the effectiveness of semantic reasoning. Comprehensive evaluations on GenEval, T2I-CompBench, and the proposed VisCog-Bench show improvements of 15%, 5%, and 19%, respectively, demonstrating the superior performance of the proposed Visual-CoG. We will release all the resources soon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.18190", "title": "ST-Raptor: LLM-Powered Semi-Structured Table Question Answering", "authors": [ "Zirui Tang", "Boyu Niu", "Xuanhe Zhou", "Boxiu Li", "Wei Zhou", "Jiannan Wang", "Guoliang Li", "Xinyi Zhang", "Fan Wu" ], "github": "https://github.com/weAIDB/ST-Raptor", "project_page": "", "abstract": "Semi-structured tables, widely used in real-world applications (e.g., financial reports, medical records, transactional orders), often involve flexible and complex layouts (e.g., hierarchical headers and merged cells). These tables generally rely on human analysts to interpret table layouts and answer relevant natural language questions, which is costly and inefficient. To automate the procedure, existing methods face significant challenges. First, methods like NL2SQL require converting semi-structured tables into structured ones, which often causes substantial information loss. Second, methods like NL2Code and multi-modal LLM QA struggle to understand the complex layouts of semi-structured tables and cannot accurately answer corresponding questions. To this end, we propose ST-Raptor, a tree-based framework for semi-structured table question answering using large language models. First, we introduce the Hierarchical Orthogonal Tree (HO-Tree), a structural model that captures complex semi-structured table layouts, along with an effective algorithm for constructing the tree. Second, we define a set of basic tree operations to guide LLMs in executing common QA tasks. Given a user question, ST-Raptor decomposes it into simpler sub-questions, generates corresponding tree operation pipelines, and conducts operation-table alignment for accurate pipeline execution. Third, we incorporate a two-stage verification mechanism: forward validation checks the correctness of execution steps, while backward validation evaluates answer reliability by reconstructing queries from predicted answers. To benchmark the performance, we present SSTQA, a dataset of 764 questions over 102 real-world semi-structured tables. Experiments show that ST-Raptor outperforms nine baselines by up to 20% in answer accuracy. The code is available at https://github.com/weAIDB/ST-Raptor.", "github_manual": "", "github_regex": "https://github.com/weAIDB/ST-Raptor", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.18159", "title": "SpotEdit: Evaluating Visually-Guided Image Editing Methods", "authors": [ "Sara Ghazanfari", "Wei-An Lin", "Haitong Tian", "Ersin Yumer" ], "github": "https://github.com/SaraGhazanfari/SpotEdit", "project_page": "", "abstract": "Visually-guided image editing, where edits are conditioned on both visual cues and textual prompts, has emerged as a powerful paradigm for fine-grained, controllable content generation. Although recent generative models have shown remarkable capabilities, existing evaluations remain simple and insufficiently representative of real-world editing challenges. We present SpotEdit, a comprehensive benchmark designed to systematically assess visually-guided image editing methods across diverse diffusion, autoregressive, and hybrid generative models, uncovering substantial performance disparities. To address a critical yet underexplored challenge, our benchmark includes a dedicated component on hallucination, highlighting how leading models, such as GPT-4o, often hallucinate the existence of a visual cue and erroneously perform the editing task. Our code and benchmark are publicly released at https://github.com/SaraGhazanfari/SpotEdit.", "github_manual": "", "github_regex": "https://github.com/SaraGhazanfari/SpotEdit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.18076", "title": "Neither Valid nor Reliable? Investigating the Use of LLMs as Judges", "authors": [ "Khaoula Chehbouni", "Mohammed Haddou", "Jackie Chi Kit Cheung", "Golnoosh Farnadi" ], "github": "", "project_page": "", "abstract": "Evaluating natural language generation (NLG) systems remains a core challenge of natural language processing (NLP), further complicated by the rise of large language models (LLMs) that aims to be general-purpose. Recently, large language models as judges (LLJs) have emerged as a promising alternative to traditional metrics, but their validity remains underexplored. This position paper argues that the current enthusiasm around LLJs may be premature, as their adoption has outpaced rigorous scrutiny of their reliability and validity as evaluators. Drawing on measurement theory from the social sciences, we identify and critically assess four core assumptions underlying the use of LLJs: their ability to act as proxies for human judgment, their capabilities as evaluators, their scalability, and their cost-effectiveness. We examine how each of these assumptions may be challenged by the inherent limitations of LLMs, LLJs, or current practices in NLG evaluation. To ground our analysis, we explore three applications of LLJs: text summarization, data annotation, and safety alignment. Finally, we highlight the need for more responsible evaluation practices in LLJs evaluation, to ensure that their growing role in the field supports, rather than undermines, progress in NLG.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.16790", "title": "TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling", "authors": [ "Yuancheng Wang", "Dekun Chen", "Xueyao Zhang", "Junan Zhang", "Jiaqi Li", "Zhizheng Wu" ], "github": "https://github.com/HeCheng0625/Diffusion-Speech-Tokenizer", "project_page": "", "abstract": "Speech tokenizers serve as foundational components for speech language models, yet current designs exhibit several limitations, including: 1) dependence on multi-layer residual vector quantization structures or high frame rates, 2) reliance on auxiliary pre-trained models for semantic distillation, and 3) requirements for complex two-stage training processes. In this work, we introduce the Text-aware Diffusion Transformer Speech Codec (TaDiCodec), a novel approach designed to overcome these challenges. TaDiCodec employs end-to-end optimization for quantization and reconstruction through a diffusion autoencoder, while integrating text guidance into the diffusion decoder to enhance reconstruction quality and achieve optimal compression. TaDiCodec achieves an extremely low frame rate of 6.25 Hz and a corresponding bitrate of 0.0875 kbps with a single-layer codebook for 24 kHz speech, while maintaining superior performance on critical speech generation evaluation metrics such as Word Error Rate (WER), speaker similarity (SIM), and speech quality (UTMOS). Notably, TaDiCodec employs a single-stage, end-to-end training paradigm, and obviating the need for auxiliary pre-trained models. We also validate the compatibility of TaDiCodec in language model based zero-shot text-to-speech with both autoregressive modeling and masked generative modeling, demonstrating its effectiveness and efficiency for speech language modeling, as well as a significantly small reconstruction-generation gap. We will open source our code and model checkpoints. Audio samples are are available at https:/tadicodec.github.io/. We release code and model checkpoints at https:/github.com/HeCheng0625/Diffusion-Speech-Tokenizer.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HeCheng0625/Diffusion-Speech-Tokenizer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.17472", "title": "T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation", "authors": [ "Kaiyue Sun", "Rongyao Fang", "Chengqi Duan", "Xian Liu", "Xihui Liu" ], "github": "https://github.com/KaiyueSun98/T2I-ReasonBench", "project_page": "", "abstract": "We propose T2I-ReasonBench, a benchmark evaluating reasoning capabilities of text-to-image (T2I) models. It consists of four dimensions: Idiom Interpretation, Textual Image Design, Entity-Reasoning and Scientific-Reasoning. We propose a two-stage evaluation protocol to assess the reasoning accuracy and image quality. We benchmark various T2I generation models, and provide comprehensive analysis on their performances.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KaiyueSun98/T2I-ReasonBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.17298", "title": "Explain Before You Answer: A Survey on Compositional Visual Reasoning", "authors": [ "Fucai Ke", "Joy Hsu", "Zhixi Cai", "Zixian Ma", "Xin Zheng", "Xindi Wu", "Sukai Huang", "Weiqing Wang", "Pari Delir Haghighi", "Gholamreza Haffari", "Ranjay Krishna", "Jiajun Wu", "Hamid Rezatofighi" ], "github": "https://github.com/pokerme7777/Compositional-Visual-Reasoning-Survey", "project_page": "", "abstract": "Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground intermediate concepts, and perform multi-step logical inference. While early surveys focus on monolithic vision-language models or general multimodal reasoning, a dedicated synthesis of the rapidly expanding compositional visual reasoning literature is still missing. We fill this gap with a comprehensive survey spanning 2023 to 2025 that systematically reviews 260+ papers from top venues (CVPR, ICCV, NeurIPS, ICML, ACL, etc.). We first formalize core definitions and describe why compositional approaches offer advantages in cognitive alignment, semantic fidelity, robustness, interpretability, and data efficiency. Next, we trace a five-stage paradigm shift: from prompt-enhanced language-centric pipelines, through tool-enhanced LLMs and tool-enhanced VLMs, to recently minted chain-of-thought reasoning and unified agentic VLMs, highlighting their architectural designs, strengths, and limitations. We then catalog 60+ benchmarks and corresponding metrics that probe compositional visual reasoning along dimensions such as grounding accuracy, chain-of-thought faithfulness, and high-resolution perception. Drawing on these analyses, we distill key insights, identify open challenges (e.g., limitations of LLM-based reasoning, hallucination, a bias toward deductive reasoning, scalable supervision, tool integration, and benchmark limitations), and outline future directions, including world-model integration, human-AI collaborative reasoning, and richer evaluation protocols. By offering a unified taxonomy, historical roadmap, and critical outlook, this survey aims to serve as a foundational reference and inspire the next generation of compositional visual reasoning research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pokerme7777/Compositional-Visual-Reasoning-Survey", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.16577", "title": "MV-RAG: Retrieval Augmented Multiview Diffusion", "authors": [ "Yosef Dayani", "Omer Benishu", "Sagie Benaim" ], "github": "", "project_page": "", "abstract": "Text-to-3D generation approaches have advanced significantly by leveraging pretrained 2D diffusion priors, producing high-quality and 3D-consistent outputs. However, they often fail to produce out-of-domain (OOD) or rare concepts, yielding inconsistent or inaccurate results. To this end, we propose MV-RAG, a novel text-to-3D pipeline that first retrieves relevant 2D images from a large in-the-wild 2D database and then conditions a multiview diffusion model on these images to synthesize consistent and accurate multiview outputs. Training such a retrieval-conditioned model is achieved via a novel hybrid strategy bridging structured multiview data and diverse 2D image collections. This involves training on multiview data using augmented conditioning views that simulate retrieval variance for view-specific reconstruction, alongside training on sets of retrieved real-world 2D images using a distinctive held-out view prediction objective: the model predicts the held-out view from the other views to infer 3D consistency from 2D data. To facilitate a rigorous OOD evaluation, we introduce a new collection of challenging OOD prompts. Experiments against state-of-the-art text-to-3D, image-to-3D, and personalization baselines show that our approach significantly improves 3D consistency, photorealism, and text adherence for OOD/rare concepts, while maintaining competitive performance on standard benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.17290", "title": "MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment", "authors": [ "Omid Ghahroodi", "Arshia Hemmat", "Marzia Nouri", "Seyed Mohammad Hadi Hosseini", "Doratossadat Dastgheib", "Mohammad Vali Sanian", "Alireza Sahebi", "Reihaneh Zohrabi", "Mohammad Hossein Rohban", "Ehsaneddin Asgari", "Mahdieh Soleymani Baghshah" ], "github": "", "project_page": "", "abstract": "Recent advancements in large vision-language models (VLMs) have primarily focused on English, with limited attention given to other languages. To address this gap, we introduce MEENA (also known as PersianMMMU), the first dataset designed to evaluate Persian VLMs across scientific, reasoning, and human-level understanding tasks. Our dataset comprises approximately 7,500 Persian and 3,000 English questions, covering a wide range of topics such as reasoning, mathematics, physics, diagrams, charts, and Persian art and literature. Key features of MEENA include: (1) diverse subject coverage spanning various educational levels, from primary to upper secondary school, (2) rich metadata, including difficulty levels and descriptive answers, (3) original Persian data that preserves cultural nuances, (4) a bilingual structure to assess cross-linguistic performance, and (5) a series of diverse experiments assessing various capabilities, including overall performance, the model's ability to attend to images, and its tendency to generate hallucinations. We hope this benchmark contributes to enhancing VLM capabilities beyond English.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.16745", "title": "Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling", "authors": [ "Ivan Rodkin", "Daniil Orel", "Konstantin Smirnov", "Arman Bolatov", "Bilal Elbouardi", "Besher Hassan", "Yuri Kuratov", "Aydar Bulatov", "Preslav Nakov", "Timothy Baldwin", "Artem Shelmanov", "Mikhail Burtsev" ], "github": "", "project_page": "", "abstract": "Reasoning is a core capability of large language models, yet understanding how they learn and perform multi-step reasoning remains an open problem. In this study, we explore how different architectures and training methods affect model multi-step reasoning capabilities within a cellular automata framework. By training on state sequences generated with random Boolean functions for random initial conditions to exclude memorization, we demonstrate that most neural architectures learn to abstract the underlying rules. While models achieve high accuracy in next-state prediction, their performance declines sharply if multi-step reasoning is required. We confirm that increasing model depth plays a crucial role for sequential computations. We demonstrate that an extension of the effective model depth with recurrence, memory, and test-time compute scaling substantially enhances reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.17973", "title": "German4All - A Dataset and Model for Readability-Controlled Paraphrasing in German", "authors": [ "Miriam Ansch\u00fctz", "Thanh Mai Pham", "Eslam Nasrallah", "Maximilian M\u00fcller", "Cristian-George Craciun", "Georg Groh" ], "github": "", "project_page": "", "abstract": "The ability to paraphrase texts across different complexity levels is essential for creating accessible texts that can be tailored toward diverse reader groups. Thus, we introduce German4All, the first large-scale German dataset of aligned readability-controlled, paragraph-level paraphrases. It spans five readability levels and comprises over 25,000 samples. The dataset is automatically synthesized using GPT-4 and rigorously evaluated through both human and LLM-based judgments. Using German4All, we train an open-source, readability-controlled paraphrasing model that achieves state-of-the-art performance in German text simplification, enabling more nuanced and reader-specific adaptations. We opensource both the dataset and the model to encourage further research on multi-level paraphrasing", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.17821", "title": "Limitations of Normalization in Attention Mechanism", "authors": [ "Timur Mudarisov", "Mikhail Burtsev", "Tatiana Petrova", "Radu State" ], "github": "", "project_page": "", "abstract": "This paper investigates the limitations of the normalization in attention mechanisms. We begin with a theoretical framework that enables the identification of the model's selective ability and the geometric separation involved in token selection. Our analysis includes explicit bounds on distances and separation criteria for token vectors under softmax scaling. Through experiments with pre-trained GPT-2 model, we empirically validate our theoretical results and analyze key behaviors of the attention mechanism. Notably, we demonstrate that as the number of selected tokens increases, the model's ability to distinguish informative tokens declines, often converging toward a uniform selection pattern. We also show that gradient sensitivity under softmax normalization presents challenges during training, especially at low temperature settings. These findings advance current understanding of softmax-based attention mechanism and motivate the need for more robust normalization and selection strategies in future attention architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.17811", "title": "MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian Splatting", "authors": [ "Hanzhi Chang", "Ruijie Zhu", "Wenjie Chang", "Mulin Yu", "Yanzhe Liang", "Jiahao Lu", "Zhuoyuan Li", "Tianzhu Zhang" ], "github": "", "project_page": "", "abstract": "Surface reconstruction has been widely studied in computer vision and graphics. However, existing surface reconstruction works struggle to recover accurate scene geometry when the input views are extremely sparse. To address this issue, we propose MeshSplat, a generalizable sparse-view surface reconstruction framework via Gaussian Splatting. Our key idea is to leverage 2DGS as a bridge, which connects novel view synthesis to learned geometric priors and then transfers these priors to achieve surface reconstruction. Specifically, we incorporate a feed-forward network to predict per-view pixel-aligned 2DGS, which enables the network to synthesize novel view images and thus eliminates the need for direct 3D ground-truth supervision. To improve the accuracy of 2DGS position and orientation prediction, we propose a Weighted Chamfer Distance Loss to regularize the depth maps, especially in overlapping areas of input views, and also a normal prediction network to align the orientation of 2DGS with normal vectors predicted by a monocular normal estimator. Extensive experiments validate the effectiveness of our proposed improvement, demonstrating that our method achieves state-of-the-art performance in generalizable sparse-view mesh reconstruction tasks. Project Page: https://hanzhichang.github.io/meshsplat_web", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.17061", "title": "REGEN: Real-Time Photorealism Enhancement in Games via a Dual-Stage Generative Network Framework", "authors": [ "Stefanos Pasios", "Nikos Nikolaidis" ], "github": "https://github.com/stefanos50/REGEN", "project_page": "", "abstract": "Photorealism is an important aspect of modern video games since it can shape the player experience and simultaneously impact the immersion, narrative engagement, and visual fidelity. Although recent hardware technological breakthroughs, along with state-of-the-art rendering technologies, have significantly improved the visual realism of video games, achieving true photorealism in dynamic environments at real-time frame rates still remains a major challenge due to the tradeoff between visual quality and performance. In this short paper, we present a novel approach for enhancing the photorealism of rendered game frames using generative adversarial networks. To this end, we propose Real-time photorealism Enhancement in Games via a dual-stage gEnerative Network framework (REGEN), which employs a robust unpaired image-to-image translation model to produce semantically consistent photorealistic frames that transform the problem into a simpler paired image-to-image translation task. This enables training with a lightweight method that can achieve real-time inference time without compromising visual quality. We demonstrate the effectiveness of our framework on Grand Theft Auto V, showing that the approach achieves visual results comparable to the ones produced by the robust unpaired Im2Im method while improving inference speed by 32.14 times. Our findings also indicate that the results outperform the photorealism-enhanced frames produced by directly training a lightweight unpaired Im2Im translation method to translate the video game frames towards the visual characteristics of real-world images. Code, pre-trained models, and demos for this work are available at: https://github.com/stefanos50/REGEN.", "github_manual": "", "github_regex": "https://github.com/stefanos50/REGEN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.16838", "title": "If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition", "authors": [ "Shubhashis Roy Dipta", "Francis Ferraro" ], "github": "", "project_page": "", "abstract": "Prior work has shown that presupposition in generated questions can introduce unverified assumptions, leading to inconsistencies in claim verification. Additionally, prompt sensitivity remains a significant challenge for large language models (LLMs), resulting in performance variance as high as 3-6%. While recent advancements have reduced this gap, our study demonstrates that prompt sensitivity remains a persistent issue. To address this, we propose a structured and robust claim verification framework that reasons through presupposition-free, decomposed questions. Extensive experiments across multiple prompts, datasets, and LLMs reveal that even state-of-the-art models remain susceptible to prompt variance and presupposition. Our method consistently mitigates these issues, achieving up to a 2-5% improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.18264", "title": "MMTok: Multimodal Coverage Maximization for Efficient Inference of VLMs", "authors": [ "Sixun Dong", "Juhua Hu", "Mian Zhang", "Ming Yin", "Yanjie Fu", "Qi Qian" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) demonstrate impressive performance in understanding visual content with language instruction by converting visual input to vision tokens. However, redundancy in vision tokens results in the degenerated inference efficiency of VLMs. While many algorithms have been proposed to reduce the number of vision tokens, most of them apply only unimodal information (i.e., vision/text) for pruning and ignore the inherent multimodal property of vision-language tasks. Moreover, it lacks a generic criterion that can be applied to different modalities. To mitigate this limitation, in this work, we propose to leverage both vision and text tokens to select informative vision tokens by the criterion of coverage. We first formulate the subset selection problem as a maximum coverage problem. Afterward, a subset of vision tokens is optimized to cover the text tokens and the original set of vision tokens, simultaneously. Finally, a VLM agent can be adopted to further improve the quality of text tokens for guiding vision pruning. The proposed method MMTok is extensively evaluated on benchmark datasets with different VLMs. The comparison illustrates that vision and text information are complementary, and combining multimodal information can surpass the unimodal baseline with a clear margin. Moreover, under the maximum coverage criterion on the POPE dataset, our method achieves a 1.87x speedup while maintaining 98.7% of the original performance on LLaVA-NeXT-13B. Furthermore, with only four vision tokens, it still preserves 87.7% of the original performance on LLaVA-1.5-7B. These results highlight the effectiveness of coverage in token selection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.18255", "title": "Hermes 4 Technical Report", "authors": [ "Ryan Teknium", "Roger Jin", "Jai Suphavadeeprasit", "Dakota Mahan", "Jeffrey Quesnelle", "Joe Li", "Chen Guang", "Shannon Sands", "Karan Malhotra" ], "github": "https://github.com/NousResearch/atropos/tree/main/environments", "project_page": "", "abstract": "We present Hermes 4, a family of hybrid reasoning models that combine structured, multi-turn reasoning with broad instruction-following ability. We describe the challenges encountered during data curation, synthesis, training, and evaluation, and outline the solutions employed to address these challenges at scale. We comprehensively evaluate across mathematical reasoning, coding, knowledge, comprehension, and alignment benchmarks, and we report both quantitative performance and qualitative behavioral analysis. To support open research, all model weights are published publicly at https://huggingface.co/collections/NousResearch/hermes-4-collection-68a731bfd452e20816725728", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NousResearch/atropos/tree/main/environments", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.17326", "title": "Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing", "authors": [ "Tristan S. W. Stevens", "Ois\u00edn Nolan", "Ruud J. G. van Sloun" ], "github": "https://github.com/tristan-deep/semantic-diffusion-echo-dehazing", "project_page": "", "abstract": "Echocardiography plays a central role in cardiac imaging, offering dynamic views of the heart that are essential for diagnosis and monitoring. However, image quality can be significantly degraded by haze arising from multipath reverberations, particularly in difficult-to-image patients. In this work, we propose a semantic-guided, diffusion-based dehazing algorithm developed for the MICCAI Dehazing Echocardiography Challenge (DehazingEcho2025). Our method integrates a pixel-wise noise model, derived from semantic segmentation of hazy inputs into a diffusion posterior sampling framework guided by a generative prior trained on clean ultrasound data. Quantitative evaluation on the challenge dataset demonstrates strong performance across contrast and fidelity metrics. Code for the submitted algorithm is available at https://github.com/tristan-deep/semantic-diffusion-echo-dehazing.", "github_manual": "", "github_regex": "https://github.com/tristan-deep/semantic-diffusion-echo-dehazing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-26", "arxiv_id": "2508.19201", "title": "Understanding Tool-Integrated Reasoning", "authors": [ "Heng Lin", "Zhongwen Xu" ], "github": "", "project_page": "", "abstract": "We study why Tool-Integrated Reasoning (TIR) makes Large Language Models (LLMs) more capable. While LLMs integrated with tools like Python code interpreters show great promise, a principled theory explaining why this paradigm is effective has been missing. This work provides the first formal proof that TIR fundamentally expands an LLM's capabilities. We demonstrate that tools enable a strict expansion of the model's empirical and feasible support, breaking the capability ceiling of pure-text models by unlocking problem-solving strategies that are otherwise impossible or intractably verbose. To guide model behavior without compromising training stability and performance, we also introduce Advantage Shaping Policy Optimization (ASPO), a novel algorithm that directly modifies the advantage function to guide the policy behavior. We conduct comprehensive experiments on challenging mathematical benchmarks, leveraging a Python interpreter as the external tool. Our results show that the TIR model decisively outperforms its pure-text counterpart on the pass@k metric. Crucially, this advantage is not confined to computationally-intensive problems but extends to those requiring significant abstract insight. We further identify the emergent cognitive patterns that illustrate how models learn to think with tools. Finally, we report improved tool usage behavior with early code invocation and much more interactive turns with ASPO. Overall, our work provides the first principled explanation for TIR's success, shifting the focus from the mere fact that tools work to why and how they enable more powerful reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.17661", "title": "Spacer: Towards Engineered Scientific Inspiration", "authors": [ "Minhyeong Lee", "Suyoung Hwang", "Seunghyun Moon", "Geonho Nah", "Donghyun Koh", "Youngjun Cho", "Johyun Park", "Hojin Yoo", "Jiho Park", "Haneul Choi", "Sungbin Moon", "Taehoon Hwang", "Seungwon Kim", "Jaeyeong Kim", "Seongjun Kim", "Juneau Jung" ], "github": "", "project_page": "", "abstract": "Recent advances in LLMs have made automated scientific research the next frontline in the path to artificial superintelligence. However, these systems are bound either to tasks of narrow scope or the limited creative capabilities of LLMs. We propose Spacer, a scientific discovery system that develops creative and factually grounded concepts without external intervention. Spacer attempts to achieve this via 'deliberate decontextualization,' an approach that disassembles information into atomic units - keywords - and draws creativity from unexplored connections between them. Spacer consists of (i) Nuri, an inspiration engine that builds keyword sets, and (ii) the Manifesting Pipeline that refines these sets into elaborate scientific statements. Nuri extracts novel, high-potential keyword sets from a keyword graph built with 180,000 academic publications in biological fields. The Manifesting Pipeline finds links between keywords, analyzes their logical structure, validates their plausibility, and ultimately drafts original scientific concepts. According to our experiments, the evaluation metric of Nuri accurately classifies high-impact publications with an AUROC score of 0.737. Our Manifesting Pipeline also successfully reconstructs core concepts from the latest top-journal articles solely from their keyword sets. An LLM-based scoring system estimates that this reconstruction was sound for over 85% of the cases. Finally, our embedding space analysis shows that outputs from Spacer are significantly more similar to leading publications compared with those from SOTA LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.19205", "title": "VibeVoice Technical Report", "authors": [ "Zhiliang Peng", "Jianwei Yu", "Wenhui Wang", "Yaoyao Chang", "Yutao Sun", "Li Dong", "Yi Zhu", "Weijiang Xu", "Hangbo Bao", "Zehua Wang", "Shaohan Huang", "Yan Xia", "Furu Wei" ], "github": "", "project_page": "", "abstract": "This report presents VibeVoice, a novel model designed to synthesize long-form speech with multiple speakers by employing next-token diffusion, which is a unified method for modeling continuous data by autoregressively generating latent vectors via diffusion. To enable this, we introduce a novel continuous speech tokenizer that, when compared to the popular Encodec model, improves data compression by 80 times while maintaining comparable performance. The tokenizer effectively preserves audio fidelity while significantly boosting computational efficiency for processing long sequences. Thus, VibeVoice can synthesize long-form speech for up to 90 minutes (in a 64K context window length) with a maximum of 4 speakers, capturing the authentic conversational ``vibe'' and surpassing open-source and proprietary dialogue models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.19247", "title": "VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space", "authors": [ "Lin Li", "Zehuan Huang", "Haoran Feng", "Gengxiong Zhuang", "Rui Chen", "Chunchao Guo", "Lu Sheng" ], "github": "", "project_page": "", "abstract": "3D local editing of specified regions is crucial for game industry and robot interaction. Recent methods typically edit rendered multi-view images and then reconstruct 3D models, but they face challenges in precisely preserving unedited regions and overall coherence. Inspired by structured 3D generative models, we propose VoxHammer, a novel training-free approach that performs precise and coherent editing in 3D latent space. Given a 3D model, VoxHammer first predicts its inversion trajectory and obtains its inverted latents and key-value tokens at each timestep. Subsequently, in the denoising and editing phase, we replace the denoising features of preserved regions with the corresponding inverted latents and cached key-value tokens. By retaining these contextual features, this approach ensures consistent reconstruction of preserved areas and coherent integration of edited parts. To evaluate the consistency of preserved regions, we constructed Edit3D-Bench, a human-annotated dataset comprising hundreds of samples, each with carefully labeled 3D editing regions. Experiments demonstrate that VoxHammer significantly outperforms existing methods in terms of both 3D consistency of preserved regions and overall quality. Our method holds promise for synthesizing high-quality edited paired data, thereby laying the data foundation for in-context 3D generation. See our project page at https://huanngzh.github.io/VoxHammer-Page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.18192", "title": "Unraveling the cognitive patterns of Large Language Models through module communities", "authors": [ "Kushal Raj Bhandari", "Pin-Yu Chen", "Jianxi Gao" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have reshaped our world with significant advancements in science, engineering, and society through applications ranging from scientific discoveries and medical diagnostics to Chatbots. Despite their ubiquity and utility, the underlying mechanisms of LLM remain concealed within billions of parameters and complex structures, making their inner architecture and cognitive processes challenging to comprehend. We address this gap by adopting approaches to understanding emerging cognition in biology and developing a network-based framework that links cognitive skills, LLM architectures, and datasets, ushering in a paradigm shift in foundation model analysis. The skill distribution in the module communities demonstrates that while LLMs do not strictly parallel the focalized specialization observed in specific biological systems, they exhibit unique communities of modules whose emergent skill patterns partially mirror the distributed yet interconnected cognitive organization seen in avian and small mammalian brains. Our numerical results highlight a key divergence from biological systems to LLMs, where skill acquisition benefits substantially from dynamic, cross-regional interactions and neural plasticity. By integrating cognitive science principles with machine learning, our framework provides new insights into LLM interpretability and suggests that effective fine-tuning strategies should leverage distributed learning dynamics rather than rigid modular interventions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.19209", "title": "OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation", "authors": [ "Jianwen Jiang", "Weihong Zeng", "Zerong Zheng", "Jiaqi Yang", "Chao Liang", "Wang Liao", "Han Liang", "Yuan Zhang", "Mingyuan Gao" ], "github": "https://github.com/Significant-Gravitas/AutoGPT", "project_page": "", "abstract": "Existing video avatar models can produce fluid human animations, yet they struggle to move beyond mere physical likeness to capture a character's authentic essence. Their motions typically synchronize with low-level cues like audio rhythm, lacking a deeper semantic understanding of emotion, intent, or context. To bridge this gap, we propose a framework designed to generate character animations that are not only physically plausible but also semantically coherent and expressive. Our model, OmniHuman-1.5, is built upon two key technical contributions. First, we leverage Multimodal Large Language Models to synthesize a structured textual representation of conditions that provides high-level semantic guidance. This guidance steers our motion generator beyond simplistic rhythmic synchronization, enabling the production of actions that are contextually and emotionally resonant. Second, to ensure the effective fusion of these multimodal inputs and mitigate inter-modality conflicts, we introduce a specialized Multimodal DiT architecture with a novel Pseudo Last Frame design. The synergy of these components allows our model to accurately interpret the joint semantics of audio, images, and text, thereby generating motions that are deeply coherent with the character, scene, and linguistic content. Extensive experiments demonstrate that our model achieves leading performance across a comprehensive set of metrics, including lip-sync accuracy, video quality, motion naturalness and semantic consistency with textual prompts. Furthermore, our approach shows remarkable extensibility to complex scenarios, such as those involving multi-person and non-human subjects. Homepage: https://omnihuman-lab.github.io/v1_5/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Significant-Gravitas/AutoGPT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.18756", "title": "UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning", "authors": [ "Zihao Huang", "Yu Bao", "Qiyang Min", "Siyan Chen", "Ran Guo", "Hongzhi Huang", "Defa Zhu", "Yutao Zeng", "Banggu Wu", "Xun Zhou", "Siyuan Qiao" ], "github": "", "project_page": "", "abstract": "While Mixture of Experts (MoE) models achieve remarkable efficiency by activating only subsets of parameters, they suffer from high memory access costs during inference. Memory-layer architectures offer an appealing alternative with very few memory access, but previous attempts like UltraMem have only matched the performance of 2-expert MoE models, falling significantly short of state-of-the-art 8-expert configurations. We present UltraMemV2, a redesigned memory-layer architecture that closes this performance gap. Our approach introduces five key improvements: integrating memory layers into every transformer block, simplifying value expansion with single linear projections, adopting FFN-based value processing from PEER, implementing principled parameter initialization, and rebalancing memory-to-FFN computation ratios. Through extensive evaluation, we demonstrate that UltraMemV2 achieves performance parity with 8-expert MoE models under same computation and parameters but significantly low memory access. Notably, UltraMemV2 shows superior performance on memory-intensive tasks, with improvements of +1.6 points on long-context memorization, +6.2 points on multi-round memorization, and +7.9 points on in-context learning. We validate our approach at scale with models up to 2.5B activated parameters from 120B total parameters, and establish that activation density has greater impact on performance than total sparse parameter count. Our work brings memory-layer architectures to performance parity with state-of-the-art MoE models, presenting a compelling alternative for efficient sparse computation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.17445", "title": "TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling", "authors": [ "Yizhi Li", "Qingshui Gu", "Zhoufutu Wen", "Ziniu Li", "Tianshun Xing", "Shuyue Guo", "Tianyu Zheng", "Xin Zhou", "Xingwei Qu", "Wangchunshu Zhou", "Zheng Zhang", "Wei Shen", "Qian Liu", "Chenghua Lin", "Jian Yang", "Ge Zhang", "Wenhao Huang" ], "github": "", "project_page": "", "abstract": "Recent advancements in aligning large language models via reinforcement learning have achieved remarkable gains in solving complex reasoning problems, but at the cost of expensive on-policy rollouts and limited exploration of diverse reasoning paths. In this work, we introduce TreePO, involving a self-guided rollout algorithm that views sequence generation as a tree-structured searching process. Composed of dynamic tree sampling policy and fixed-length segment decoding, TreePO leverages local uncertainty to warrant additional branches. By amortizing computation across common prefixes and pruning low-value paths early, TreePO essentially reduces the per-update compute burden while preserving or enhancing exploration diversity. Key contributions include: (1) a segment-wise sampling algorithm that alleviates the KV cache burden through contiguous segments and spawns new branches along with an early-stop mechanism; (2) a tree-based segment-level advantage estimation that considers both global and local proximal policy optimization. and (3) analysis on the effectiveness of probability and quality-driven dynamic divergence and fallback strategy. We empirically validate the performance gain of TreePO on a set reasoning benchmarks and the efficiency saving of GPU hours from 22\\% up to 43\\% of the sampling design for the trained models, meanwhile showing up to 40\\% reduction at trajectory-level and 35\\% at token-level sampling compute for the existing models. While offering a free lunch of inference efficiency, TreePO reveals a practical path toward scaling RL-based post-training with fewer samples and less compute. Home page locates at https://m-a-p.ai/TreePO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.17437", "title": "Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels", "authors": [ "Long Le", "Ryan Lucas", "Chen Wang", "Chuhao Chen", "Dinesh Jayaraman", "Eric Eaton", "Lingjie Liu" ], "github": "", "project_page": "", "abstract": "Inferring the physical properties of 3D scenes from visual information is a critical yet challenging task for creating interactive and realistic virtual worlds. While humans intuitively grasp material characteristics such as elasticity or stiffness, existing methods often rely on slow, per-scene optimization, limiting their generalizability and application. To address this problem, we introduce PIXIE, a novel method that trains a generalizable neural network to predict physical properties across multiple scenes from 3D visual features purely using supervised losses. Once trained, our feed-forward network can perform fast inference of plausible material fields, which coupled with a learned static scene representation like Gaussian Splatting enables realistic physics simulation under external forces. To facilitate this research, we also collected PIXIEVERSE, one of the largest known datasets of paired 3D assets and physic material annotations. Extensive evaluations demonstrate that PIXIE is about 1.46-4.39x better and orders of magnitude faster than test-time optimization methods. By leveraging pretrained visual features like CLIP, our method can also zero-shot generalize to real-world scenes despite only ever been trained on synthetic data. https://pixie-3d.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.18124", "title": "CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics", "authors": [ "Weida Wang", "Dongchen Huang", "Jiatong Li", "Tengchao Yang", "Ziyang Zheng", "Di Zhang", "Dong Han", "Benteng Chen", "Binzhao Luo", "Zhiyu Liu", "Kunling Liu", "Zhiyuan Gao", "Shiqi Geng", "Wei Ma", "Jiaming Su", "Xin Li", "Shuchen Pu", "Yuhan Shui", "Qianjia Cheng", "Zhihao Dou", "Dongfei Cui", "Changyong He", "Jin Zeng", "Zeke Xie", "Mao Su", "Dongzhan Zhou", "Yuqiang Li", "Wanli Ouyang", "Yunqi Cai", "Xi Dai", "Shufei Zhang", "Lei Bai", "Jinguang Cheng", "Zhong Fang", "Hongming Weng" ], "github": "https://github.com/CMPhysBench/CMPhysBench", "project_page": "", "abstract": "We introduce CMPhysBench, designed to assess the proficiency of Large Language Models (LLMs) in Condensed Matter Physics, as a novel Benchmark. CMPhysBench is composed of more than 520 graduate-level meticulously curated questions covering both representative subfields and foundational theoretical frameworks of condensed matter physics, such as magnetism, superconductivity, strongly correlated systems, etc. To ensure a deep understanding of the problem-solving process,we focus exclusively on calculation problems, requiring LLMs to independently generate comprehensive solutions. Meanwhile, leveraging tree-based representations of expressions, we introduce the Scalable Expression Edit Distance (SEED) score, which provides fine-grained (non-binary) partial credit and yields a more accurate assessment of similarity between prediction and ground-truth. Our results show that even the best models, Grok-4, reach only 36 average SEED score and 28% accuracy on CMPhysBench, underscoring a significant capability gap, especially for this practical and frontier domain relative to traditional physics. The code anddataset are publicly available at https://github.com/CMPhysBench/CMPhysBench.", "github_manual": "", "github_regex": "https://github.com/CMPhysBench/CMPhysBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.18621", "title": "Wan-S2V: Audio-Driven Cinematic Video Generation", "authors": [ "Xin Gao", "Li Hu", "Siqi Hu", "Mingyang Huang", "Chaonan Ji", "Dechao Meng", "Jinwei Qi", "Penchong Qiao", "Zhen Shen", "Yafei Song", "Ke Sun", "Linrui Tian", "Guangyuan Wang", "Qi Wang", "Zhongjian Wang", "Jiayu Xiao", "Sheng Xu", "Bang Zhang", "Peng Zhang", "Xindi Zhang", "Zhe Zhang", "Jingren Zhou", "Lian Zhuo" ], "github": "", "project_page": "", "abstract": "Current state-of-the-art (SOTA) methods for audio-driven character animation demonstrate promising performance for scenarios primarily involving speech and singing. However, they often fall short in more complex film and television productions, which demand sophisticated elements such as nuanced character interactions, realistic body movements, and dynamic camera work. To address this long-standing challenge of achieving film-level character animation, we propose an audio-driven model, which we refere to as Wan-S2V, built upon Wan. Our model achieves significantly enhanced expressiveness and fidelity in cinematic contexts compared to existing approaches. We conducted extensive experiments, benchmarking our method against cutting-edge models such as Hunyuan-Avatar and Omnihuman. The experimental results consistently demonstrate that our approach significantly outperforms these existing solutions. Additionally, we explore the versatility of our method through its applications in long-form video generation and precise video lip-sync editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.15774", "title": "CineScale: Free Lunch in High-Resolution Cinematic Visual Generation", "authors": [ "Haonan Qiu", "Ning Yu", "Ziqi Huang", "Paul Debevec", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Visual diffusion models achieve remarkable progress, yet they are typically trained at limited resolutions due to the lack of high-resolution data and constrained computation resources, hampering their ability to generate high-fidelity images or videos at higher resolutions. Recent efforts have explored tuning-free strategies to exhibit the untapped potential higher-resolution visual generation of pre-trained models. However, these methods are still prone to producing low-quality visual content with repetitive patterns. The key obstacle lies in the inevitable increase in high-frequency information when the model generates visual content exceeding its training resolution, leading to undesirable repetitive patterns deriving from the accumulated errors. In this work, we propose CineScale, a novel inference paradigm to enable higher-resolution visual generation. To tackle the various issues introduced by the two types of video generation architectures, we propose dedicated variants tailored to each. Unlike existing baseline methods that are confined to high-resolution T2I and T2V generation, CineScale broadens the scope by enabling high-resolution I2V and V2V synthesis, built atop state-of-the-art open-source video generation frameworks. Extensive experiments validate the superiority of our paradigm in extending the capabilities of higher-resolution visual generation for both image and video models. Remarkably, our approach enables 8k image generation without any fine-tuning, and achieves 4k video generation with only minimal LoRA fine-tuning. Generated video samples are available at our website: https://eyeline-labs.github.io/CineScale/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.18773", "title": "ThinkDial: An Open Recipe for Controlling Reasoning Effort in Large Language Models", "authors": [ "Qianyu He", "Siyu Yuan", "Xuefeng Li", "Mingxuan Wang", "Jiangjie Chen" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) with chain-of-thought reasoning have demonstrated remarkable problem-solving capabilities, but controlling their computational effort remains a significant challenge for practical deployment. Recent proprietary systems like OpenAI's gpt-oss series have introduced discrete operational modes for intuitive reasoning control, but the open-source community has largely failed to achieve such capabilities. In this paper, we introduce ThinkDial, the first open-recipe end-to-end framework that successfully implements gpt-oss-style controllable reasoning through discrete operational modes. Our system enables seamless switching between three distinct reasoning regimes: High mode (full reasoning capability), Medium mode (50 percent token reduction with <10 percent performance degradation), and Low mode (75 percent token reduction with <15 percent performance degradation). We achieve this through an end-to-end training paradigm that integrates budget-mode control throughout the entire pipeline: budget-mode supervised fine-tuning that embeds controllable reasoning capabilities directly into the learning process, and two-phase budget-aware reinforcement learning with adaptive reward shaping. Extensive experiments demonstrate that ThinkDial achieves target compression-performance trade-offs with clear response length reductions while maintaining performance thresholds. The framework also exhibits strong generalization capabilities on out-of-distribution tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.18370", "title": "Training Language Model Agents to Find Vulnerabilities with CTF-Dojo", "authors": [ "Terry Yue Zhuo", "Dingmin Wang", "Hantian Ding", "Varun Kumar", "Zijian Wang" ], "github": "https://github.com/amazon-science/CTF-Dojo", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated exceptional capabilities when trained within executable runtime environments, notably excelling at software engineering tasks through verified feedback loops. Yet, scalable and generalizable execution-grounded environments remain scarce, limiting progress in training more capable ML agents. We introduce CTF-Dojo, the first large-scale executable runtime tailored for training LLMs with verifiable feedback, featuring 658 fully functional Capture-The-Flag (CTF)-style challenges containerized in Docker with guaranteed reproducibility. To enable rapid scaling without manual intervention, we develop CTF-Forge, an automated pipeline that transforms publicly available artifacts into ready-to-use execution environments in minutes, eliminating weeks of expert configuration traditionally required. We trained LLM-based agents on just 486 high-quality, execution-verified trajectories from CTF-Dojo, achieving up to 11.6% absolute gains over strong baselines across three competitive benchmarks: InterCode-CTF, NYU CTF Bench, and Cybench. Our best-performing 32B model reaches 31.9% Pass@1, establishing a new open-weight state-of-the-art that rivals frontier models like DeepSeek-V3-0324 and Gemini-2.5-Flash. By framing CTF-style tasks as a benchmark for executable-agent learning, CTF-Dojo demonstrates that execution-grounded training signals are not only effective but pivotal in advancing high-performance ML agents without dependence on costly proprietary systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amazon-science/CTF-Dojo", "github_api_added_by": "auto" }, { "date": "2025-08-27", "arxiv_id": "2508.19188", "title": "FastMesh:Efficient Artistic Mesh Generation via Component Decoupling", "authors": [ "Jeonghwan Kim", "Yushi Lan", "Armando Fortes", "Yongwei Chen", "Xingang Pan" ], "github": "https://github.com/jhkim0759/FastMesh", "project_page": "", "abstract": "Recent mesh generation approaches typically tokenize triangle meshes into sequences of tokens and train autoregressive models to generate these tokens sequentially. Despite substantial progress, such token sequences inevitably reuse vertices multiple times to fully represent manifold meshes, as each vertex is shared by multiple faces. This redundancy leads to excessively long token sequences and inefficient generation processes. In this paper, we propose an efficient framework that generates artistic meshes by treating vertices and faces separately, significantly reducing redundancy. We employ an autoregressive model solely for vertex generation, decreasing the token count to approximately 23\\% of that required by the most compact existing tokenizer. Next, we leverage a bidirectional transformer to complete the mesh in a single step by capturing inter-vertex relationships and constructing the adjacency matrix that defines the mesh faces. To further improve the generation quality, we introduce a fidelity enhancer to refine vertex positioning into more natural arrangements and propose a post-processing framework to remove undesirable edge connections. Experimental results show that our method achieves more than 8times faster speed on mesh generation compared to state-of-the-art approaches, while producing higher mesh quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jhkim0759/FastMesh", "github_api_added_by": "auto" }, { "date": "2025-08-27", "arxiv_id": "2508.16697", "title": "QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting", "authors": [ "Nicole Cho", "William Watson", "Alec Koppel", "Sumitra Ganesh", "Manuela Veloso" ], "github": "", "project_page": "", "abstract": "Advanced reasoning capabilities in Large Language Models (LLMs) have caused higher hallucination prevalence; yet most mitigation work focuses on after-the-fact filtering rather than shaping the queries that trigger them. We introduce QueryBandits, a bandit framework that designs rewrite strategies to maximize a reward model, that encapsulates hallucination propensity based upon the sensitivities of 17 linguistic features of the input query-and therefore, proactively steer LLMs away from generating hallucinations. Across 13 diverse QA benchmarks and 1,050 lexically perturbed queries per dataset, our top contextual QueryBandit (Thompson Sampling) achieves an 87.5% win rate over a no-rewrite baseline and also outperforms zero-shot static prompting (\"paraphrase\" or \"expand\") by 42.6% and 60.3% respectively. Therefore, we empirically substantiate the effectiveness of QueryBandits in mitigating hallucination via the intervention that takes the form of a query rewrite. Interestingly, certain static prompting strategies, which constitute a considerable number of current query rewriting literature, have a higher cumulative regret than the no-rewrite baseline, signifying that static rewrites can worsen hallucination. Moreover, we discover that the converged per-arm regression feature weight vectors substantiate that there is no single rewrite strategy optimal for all queries. In this context, guided rewriting via exploiting semantic features with QueryBandits can induce significant shifts in output behavior through forward-pass mechanisms, bypassing the need for retraining or gradient-based adaptation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.19242", "title": "Autoregressive Universal Video Segmentation Model", "authors": [ "Miran Heo", "Sukjun Hwang", "Min-Hung Chen", "Yu-Chiang Frank Wang", "Albert Gu", "Seon Joo Kim", "Ryo Hachiuma" ], "github": "", "project_page": "", "abstract": "Recent video foundation models such as SAM2 excel at prompted video segmentation by treating masks as a general-purpose primitive. However, many real-world settings require unprompted segmentation that aims to detect and track all objects in a video without external cues, leaving today's landscape fragmented across task-specific models and pipelines. We recast streaming video segmentation as sequential mask prediction, analogous to language modeling, and introduce the Autoregressive Universal Segmentation Model (AUSM), a single architecture that unifies both prompted and unprompted video segmentation. Built on recent state-space models, AUSM maintains a fixed-size spatial state and scales to video streams of arbitrary length. Furthermore, all components of AUSM are designed for parallel training across frames, yielding substantial speedups over iterative training. On standard benchmarks (DAVIS17, YouTube-VOS 2018 & 2019, MOSE, YouTube-VIS 2019 & 2021, and OVIS) AUSM outperforms prior universal streaming video segmentation methods and achieves up to 2.5x faster training on 16-frame sequences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.19026", "title": "MovieCORE: COgnitive REasoning in Movies", "authors": [ "Gueter Josmy Faure", "Min-Hung Chen", "Jia-Fong Yeh", "Ying Cheng", "Hung-Ting Su", "Yung-Hao Tang", "Shang-Hong Lai", "Winston H. Hsu" ], "github": "", "project_page": "", "abstract": "This paper introduces MovieCORE, a novel video question answering (VQA) dataset designed to probe deeper cognitive understanding of movie content. Unlike existing datasets that focus on surface-level comprehension, MovieCORE emphasizes questions that engage System-2 thinking while remaining specific to the video material. We present an innovative agentic brainstorming approach, utilizing multiple large language models (LLMs) as thought agents to generate and refine high-quality question-answer pairs. To evaluate dataset quality, we develop a set of cognitive tests assessing depth, thought-provocation potential, and syntactic complexity. We also propose a comprehensive evaluation scheme for assessing VQA model performance on deeper cognitive tasks. To address the limitations of existing video-language models (VLMs), we introduce an agentic enhancement module, Agentic Choice Enhancement (ACE), which improves model reasoning capabilities post-training by up to 25%. Our work contributes to advancing movie understanding in AI systems and provides valuable insights into the capabilities and limitations of current VQA models when faced with more challenging, nuanced questions about cinematic content. Our project page, dataset and code can be found at https://joslefaure.github.io/assets/html/moviecore.html.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.18271", "title": "ObjFiller-3D: Consistent Multi-view 3D Inpainting via Video Diffusion Models", "authors": [ "Haitang Feng", "Jie Liu", "Jie Tang", "Gangshan Wu", "Beiqi Chen", "Jianhuang Lai", "Guangcong Wang" ], "github": "https://github.com/objfiller3d/ObjFiller-3D", "project_page": "", "abstract": "3D inpainting often relies on multi-view 2D image inpainting, where the inherent inconsistencies across different inpainted views can result in blurred textures, spatial discontinuities, and distracting visual artifacts. These inconsistencies pose significant challenges when striving for accurate and realistic 3D object completion, particularly in applications that demand high fidelity and structural coherence. To overcome these limitations, we propose ObjFiller-3D, a novel method designed for the completion and editing of high-quality and consistent 3D objects. Instead of employing a conventional 2D image inpainting model, our approach leverages a curated selection of state-of-the-art video editing model to fill in the masked regions of 3D objects. We analyze the representation gap between 3D and videos, and propose an adaptation of a video inpainting model for 3D scene inpainting. In addition, we introduce a reference-based 3D inpainting method to further enhance the quality of reconstruction. Experiments across diverse datasets show that compared to previous methods, ObjFiller-3D produces more faithful and fine-grained reconstructions (PSNR of 26.6 vs. NeRFiller (15.9) and LPIPS of 0.19 vs. Instant3dit (0.25)). Moreover, it demonstrates strong potential for practical deployment in real-world 3D editing applications. Project page: https://objfiller3d.github.io/ Code: https://github.com/objfiller3d/ObjFiller-3D .", "github_manual": "", "github_regex": "https://github.com/objfiller3d/ObjFiller-3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.18672", "title": "Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks", "authors": [ "Taishi Nakamura", "Satoki Ishikawa", "Masaki Kawamura", "Takumi Okamoto", "Daisuke Nohara", "Jun Suzuki", "Rio Yokota" ], "github": "https://github.com/rioyokotalab/optimal-sparsity", "project_page": "", "abstract": "Empirical scaling laws have driven the evolution of large language models (LLMs), yet their coefficients shift whenever the model architecture or data pipeline changes. Mixture-of-Experts (MoE) models, now standard in state-of-the-art systems, introduce a new sparsity dimension that current dense-model frontiers overlook. We investigate how MoE sparsity influences two distinct capability regimes: memorization and reasoning. We train families of MoE Transformers that systematically vary total parameters, active parameters, and top-k routing while holding the compute budget fixed. For every model we record pre-training loss, downstream task loss, and task accuracy, allowing us to separate the train-test generalization gap from the loss-accuracy gap. Memorization benchmarks improve monotonically with total parameters, mirroring training loss. By contrast, reasoning performance saturates and can even regress despite continued gains in both total parameters and training loss. Altering top-k alone has little effect when active parameters are constant, and classic hyperparameters such as learning rate and initialization modulate the generalization gap in the same direction as sparsity. Neither post-training reinforcement learning (GRPO) nor extra test-time compute rescues the reasoning deficit of overly sparse models. Our model checkpoints, code and logs are open-source at https://github.com/rioyokotalab/optimal-sparsity.", "github_manual": "", "github_regex": "https://github.com/rioyokotalab/optimal-sparsity", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.15804", "title": "ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks", "authors": [ "Minghao Li", "Ying Zeng", "Zhihao Cheng", "Cong Ma", "Kai Jia" ], "github": "https://github.com/ByteDance-BandAI/ReportBench", "project_page": "", "abstract": "The advent of Deep Research agents has substantially reduced the time required for conducting extensive research tasks. However, these tasks inherently demand rigorous standards of factual accuracy and comprehensiveness, necessitating thorough evaluation before widespread adoption. In this paper, we propose ReportBench, a systematic benchmark designed to evaluate the content quality of research reports generated by large language models (LLMs). Our evaluation focuses on two critical dimensions: (1) the quality and relevance of cited literature, and (2) the faithfulness and veracity of the statements within the generated reports. ReportBench leverages high-quality published survey papers available on arXiv as gold-standard references, from which we apply reverse prompt engineering to derive domain-specific prompts and establish a comprehensive evaluation corpus. Furthermore, we develop an agent-based automated framework within ReportBench that systematically analyzes generated reports by extracting citations and statements, checking the faithfulness of cited content against original sources, and validating non-cited claims using web-based resources. Empirical evaluations demonstrate that commercial Deep Research agents such as those developed by OpenAI and Google consistently generate more comprehensive and reliable reports than standalone LLMs augmented with search or browsing tools. However, there remains substantial room for improvement in terms of the breadth and depth of research coverage, as well as factual consistency. The complete code and data will be released at the following link: https://github.com/ByteDance-BandAI/ReportBench", "github_manual": "", "github_regex": "https://github.com/ByteDance-BandAI/ReportBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.19202", "title": "Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning", "authors": [ "Alan Li", "Yixin Liu", "Arpan Sarkar", "Doug Downey", "Arman Cohan" ], "github": "https://github.com/yale-nlp/SciReas-Eval", "project_page": "", "abstract": "Scientific problem solving poses unique challenges for LLMs, requiring both deep domain knowledge and the ability to apply such knowledge through complex reasoning. While automated scientific reasoners hold great promise for assisting human scientists, there is currently no widely adopted holistic benchmark for evaluating scientific reasoning, and few approaches systematically disentangle the distinct roles of knowledge and reasoning in these tasks. To address these gaps, we introduce SciReas, a diverse suite of existing benchmarks for scientific reasoning tasks, and SciReas-Pro, a selective subset that requires more complex reasoning. Our holistic evaluation surfaces insights about scientific reasoning performance that remain hidden when relying on individual benchmarks alone. We then propose KRUX, a probing framework for studying the distinct roles of reasoning and knowledge in scientific tasks. Combining the two, we conduct an in-depth analysis that yields several key findings: (1) Retrieving task-relevant knowledge from model parameters is a critical bottleneck for LLMs in scientific reasoning; (2) Reasoning models consistently benefit from external knowledge added in-context on top of the reasoning enhancement; (3) Enhancing verbalized reasoning improves LLMs' ability to surface task-relevant knowledge. Finally, we conduct a lightweight analysis, comparing our science-focused data composition with concurrent efforts on long CoT SFT, and release SciLit01, a strong 8B baseline for scientific reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yale-nlp/SciReas-Eval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.17234", "title": "ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation", "authors": [ "Siying Zhou", "Yiquan Wu", "Hui Chen", "Xavier Hu", "Kun Kuang", "Adam Jatowt", "Ming Hu", "Chunyan Zheng", "Fei Wu" ], "github": "https://github.com/JosieZhou00/ClaimGen-CN", "project_page": "", "abstract": "Legal claims refer to the plaintiff's demands in a case and are essential to guiding judicial reasoning and case resolution. While many works have focused on improving the efficiency of legal professionals, the research on helping non-professionals (e.g., plaintiffs) remains unexplored. This paper explores the problem of legal claim generation based on the given case's facts. First, we construct ClaimGen-CN, the first dataset for Chinese legal claim generation task, from various real-world legal disputes. Additionally, we design an evaluation metric tailored for assessing the generated claims, which encompasses two essential dimensions: factuality and clarity. Building on this, we conduct a comprehensive zero-shot evaluation of state-of-the-art general and legal-domain large language models. Our findings highlight the limitations of the current models in factual precision and expressive clarity, pointing to the need for more targeted development in this domain. To encourage further exploration of this important task, we will make the dataset publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JosieZhou00/ClaimGen-CN", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.18579", "title": "DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model", "authors": [ "Mohammadreza Ghaffarzadeh-Esfahani", "Ali Motahharynia", "Nahid Yousefian", "Navid Mazrouei", "Jafar Ghaisari", "Yousof Gheisari" ], "github": "", "project_page": "", "abstract": "Drug discovery is a complex and resource-intensive process, making early prediction of approval outcomes critical for optimizing research investments. While classical machine learning and deep learning methods have shown promise in drug approval prediction, their limited interpretability constraints their impact. Here, we present DrugReasoner, a reasoning-based large language model (LLM) built on the LLaMA architecture and fine-tuned with group relative policy optimization (GRPO) to predict the likelihood of small-molecule approval. DrugReasoner integrates molecular descriptors with comparative reasoning against structurally similar approved and unapproved compounds, generating predictions alongside step-by-step rationales and confidence scores. DrugReasoner achieved robust performance with an AUC of 0.732 and an F1 score of 0.729 on the validation set and 0.725 and 0.718 on the test set, respectively. These results outperformed conventional baselines, including logistic regression, support vector machine, and k-nearest neighbors and had competitive performance relative to XGBoost. On an external independent dataset, DrugReasoner outperformed both baseline and the recently developed ChemAP model, achieving an AUC of 0.728 and an F1-score of 0.774, while maintaining high precision and balanced sensitivity, demonstrating robustness in real-world scenarios. These findings demonstrate that DrugReasoner not only delivers competitive predictive accuracy but also enhances transparency through its reasoning outputs, thereby addressing a key bottleneck in AI-assisted drug discovery. This study highlights the potential of reasoning-augmented LLMs as interpretable and effective tools for pharmaceutical decision-making.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.17621", "title": "Steering When Necessary: Flexible Steering Large Language Models with Backtracking", "authors": [ "Jinwei Gan", "Zifeng Cheng", "Zhiwei Jiang", "Cong Wang", "Yafeng Yin", "Xiang Luo", "Yuchen Fu", "Qing Gu" ], "github": "https://github.com/gjw185/FASB", "project_page": "", "abstract": "Large language models (LLMs) have achieved remarkable performance across many generation tasks. Nevertheless, effectively aligning them with desired behaviors remains a significant challenge. Activation steering is an effective and cost-efficient approach that directly modifies the activations of LLMs during the inference stage, aligning their responses with the desired behaviors and avoiding the high cost of fine-tuning. Existing methods typically indiscriminately intervene to all generations or rely solely on the question to determine intervention, which limits the accurate assessment of the intervention strength. To this end, we propose the Flexible Activation Steering with Backtracking (FASB) framework, which dynamically determines both the necessity and strength of intervention by tracking the internal states of the LLMs during generation, considering both the question and the generated content. Since intervening after detecting a deviation from the desired behavior is often too late, we further propose the backtracking mechanism to correct the deviated tokens and steer the LLMs toward the desired behavior. Extensive experiments on the TruthfulQA dataset and six multiple-choice datasets demonstrate that our method outperforms baselines. Our code will be released at https://github.com/gjw185/FASB.", "github_manual": "", "github_regex": "https://github.com/gjw185/FASB", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.18921", "title": "Forecasting Probability Distributions of Financial Returns with Deep Neural Networks", "authors": [ "Jakub Micha\u0144k\u00f3w" ], "github": "", "project_page": "", "abstract": "This study evaluates deep neural networks for forecasting probability distributions of financial returns. 1D convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) architectures are used to forecast parameters of three probability distributions: Normal, Student's t, and skewed Student's t. Using custom negative log-likelihood loss functions, distribution parameters are optimized directly. The models are tested on six major equity indices (S\\&P 500, BOVESPA, DAX, WIG, Nikkei 225, and KOSPI) using probabilistic evaluation metrics including Log Predictive Score (LPS), Continuous Ranked Probability Score (CRPS), and Probability Integral Transform (PIT). Results show that deep learning models provide accurate distributional forecasts and perform competitively with classical GARCH models for Value-at-Risk estimation. The LSTM with skewed Student's t distribution performs best across multiple evaluation criteria, capturing both heavy tails and asymmetry in financial returns. This work shows that deep neural networks are viable alternatives to traditional econometric models for financial risk assessment and portfolio management.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.15213", "title": "Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering", "authors": [ "Bolei He", "Xinran He", "Run Shao", "Shanfu Shu", "Xianwei Xue", "Mingquan Cheng", "Haifeng Li", "Zhenhua Ling" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios. Retrieval-Augmented Generation (RAG) introduces external knowledge but suffers from hallucinations and latency due to noisy retrievals. Continued pretraining internalizes domain knowledge but is costly and lacks cross-domain flexibility. We attribute this challenge to the long-tail distribution of domain knowledge, which leaves partial yet useful internal knowledge underutilized. We further argue that knowledge acquisition should be progressive, mirroring human learning: first understanding concepts, then applying them to complex reasoning. To address this, we propose Selct2Know (S2K), a cost-effective framework that internalizes domain knowledge through an internal-external knowledge self-selection strategy and selective supervised fine-tuning. We also introduce a structured reasoning data generation pipeline and integrate GRPO to enhance reasoning ability. Experiments on medical, legal, and financial QA benchmarks show that S2K consistently outperforms existing methods and matches domain-pretrained LLMs with significantly lower cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-27", "arxiv_id": "2508.19500", "title": "Servant, Stalker, Predator: How An Honest, Helpful, And Harmless (3H) Agent Unlocks Adversarial Skills", "authors": [ "David Noever" ], "github": "", "project_page": "", "abstract": "This paper identifies and analyzes a novel vulnerability class in Model Context Protocol (MCP) based agent systems. The attack chain describes and demonstrates how benign, individually authorized tasks can be orchestrated to produce harmful emergent behaviors. Through systematic analysis using the MITRE ATLAS framework, we demonstrate how 95 agents tested with access to multiple services-including browser automation, financial analysis, location tracking, and code deployment-can chain legitimate operations into sophisticated attack sequences that extend beyond the security boundaries of any individual service. These red team exercises survey whether current MCP architectures lack cross-domain security measures necessary to detect or prevent a large category of compositional attacks. We present empirical evidence of specific attack chains that achieve targeted harm through service orchestration, including data exfiltration, financial manipulation, and infrastructure compromise. These findings reveal that the fundamental security assumption of service isolation fails when agents can coordinate actions across multiple domains, creating an exponential attack surface that grows with each additional capability. This research provides a barebones experimental framework that evaluate not whether agents can complete MCP benchmark tasks, but what happens when they complete them too well and optimize across multiple services in ways that violate human expectations and safety constraints. We propose three concrete experimental directions using the existing MCP benchmark suite.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.19652", "title": "Self-Rewarding Vision-Language Model via Reasoning Decomposition", "authors": [ "Zongxia Li", "Wenhao Yu", "Chengsong Huang", "Rui Liu", "Zhenwen Liang", "Fuxiao Liu", "Jingxi Che", "Dian Yu", "Jordan Boyd-Graber", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) often suffer from visual hallucinations, saying things that are not actually in the image, and language shortcuts, where they skip the visual part and just rely on text priors. These issues arise because most post-training methods for VLMs rely on simple verifiable answer matching and supervise only final outputs, leaving intermediate visual reasoning without explicit guidance. As a result, VLMs receive sparse visual signals and often learn to prioritize language-based reasoning over visual perception. To mitigate this, some existing methods add visual supervision using human annotations or distilled labels from external large models. However, human annotations are labor-intensive and costly, and because external signals cannot adapt to the evolving policy, they cause distributional shifts that can lead to reward hacking. In this paper, we introduce Vision-SR1, a self-rewarding method that improves visual reasoning without relying on external visual supervisions via reinforcement learning. Vision-SR1 decomposes VLM reasoning into two stages: visual perception and language reasoning. The model is first prompted to produce self-contained visual perceptions that are sufficient to answer the question without referring back the input image. To validate this self-containment, the same VLM model is then re-prompted to perform language reasoning using only the generated perception as input to compute reward. This self-reward is combined with supervision on final outputs, providing a balanced training signal that strengthens both visual perception and language reasoning. Our experiments demonstrate that Vision-SR1 improves visual reasoning, mitigates visual hallucinations, and reduces reliance on language shortcuts across diverse vision-language tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.20096", "title": "CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning", "authors": [ "Zeyi Sun", "Yuhang Cao", "Jianze Liang", "Qiushi Sun", "Ziyu Liu", "Zhixiong Zhang", "Yuhang Zang", "Xiaoyi Dong", "Kai Chen", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/OpenIXCLab/CODA", "project_page": "", "abstract": "Autonomous agents for Graphical User Interfaces (GUIs) face significant challenges in specialized domains such as scientific computing, where both long-horizon planning and precise execution are required. Existing approaches suffer from a trade-off: generalist agents excel at planning but perform poorly in execution, while specialized agents demonstrate the opposite weakness. Recent compositional frameworks attempt to bridge this gap by combining a planner and an actor, but they are typically static and non-trainable, which prevents adaptation from experience. This is a critical limitation given the scarcity of high-quality data in scientific domains. To address these limitations, we introduce CODA, a novel and trainable compositional framework that integrates a generalist planner (Cerebrum) with a specialist executor (Cerebellum), trained via a dedicated two-stage pipeline. In the first stage, Specialization, we apply a decoupled GRPO approach to train an expert planner for each scientific application individually, bootstrapping from a small set of task trajectories. In the second stage, Generalization, we aggregate all successful trajectories from the specialized experts to build a consolidated dataset, which is then used for supervised fine-tuning of the final planner. This equips CODA with both robust execution and cross-domain generalization. Evaluated on four challenging applications from the ScienceBoard benchmark, CODA significantly outperforms baselines and establishes a new state of the art among open-source models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenIXCLab/CODA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.19493", "title": "Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents", "authors": [ "Zhixin Lin", "Jungang Li", "Shidong Pan", "Yibo Shi", "Yue Yao", "Dongliang Xu" ], "github": "", "project_page": "", "abstract": "Smartphones bring significant convenience to users but also enable devices to extensively record various types of personal information. Existing smartphone agents powered by Multimodal Large Language Models (MLLMs) have achieved remarkable performance in automating different tasks. However, as the cost, these agents are granted substantial access to sensitive users' personal information during this operation. To gain a thorough understanding of the privacy awareness of these agents, we present the first large-scale benchmark encompassing 7,138 scenarios to the best of our knowledge. In addition, for privacy context in scenarios, we annotate its type (e.g., Account Credentials), sensitivity level, and location. We then carefully benchmark seven available mainstream smartphone agents. Our results demonstrate that almost all benchmarked agents show unsatisfying privacy awareness (RA), with performance remaining below 60% even with explicit hints. Overall, closed-source agents show better privacy ability than open-source ones, and Gemini 2.0-flash achieves the best, achieving an RA of 67%. We also find that the agents' privacy detection capability is highly related to scenario sensitivity level, i.e., the scenario with a higher sensitivity level is typically more identifiable. We hope the findings enlighten the research community to rethink the unbalanced utility-privacy tradeoff about smartphone agents. Our code and benchmark are available at https://zhixin-l.github.io/SAPA-Bench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.19229", "title": "StepWiser: Stepwise Generative Judges for Wiser Reasoning", "authors": [ "Wei Xiong", "Wenting Zhao", "Weizhe Yuan", "Olga Golovneva", "Tong Zhang", "Jason Weston", "Sainbayar Sukhbaatar" ], "github": "", "project_page": "", "abstract": "As models increasingly leverage multi-step reasoning strategies to solve complex problems, supervising the logical validity of these intermediate steps has become a critical research challenge. Process reward models address this by providing step-by-step feedback, but current approaches have two major drawbacks: they typically function as classifiers without providing explanations, and their reliance on supervised fine-tuning with static datasets limits generalization. Inspired by recent advances, we reframe stepwise reward modeling from a classification task to a reasoning task itself. We thus propose a generative judge that reasons about the policy model's reasoning steps (i.e., meta-reasons), outputting thinking tokens before delivering a final verdict. Our model, StepWiser, is trained by reinforcement learning using relative outcomes of rollouts. We show it provides (i) better judgment accuracy on intermediate steps than existing methods; (ii) can be used to improve the policy model at training time; and (iii) improves inference-time search.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.20072", "title": "Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding in Vision-Language-Action Policies", "authors": [ "Zhixuan Liang", "Yizhuo Li", "Tianshuo Yang", "Chengyue Wu", "Sitong Mao", "Liuao Pei", "Xiaokang Yang", "Jiangmiao Pang", "Yao Mu", "Ping Luo" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models adapt large vision-language backbones to map images and instructions to robot actions. However, prevailing VLA decoders either generate actions autoregressively in a fixed left-to-right order or attach continuous diffusion or flow matching heads outside the backbone, demanding specialized training and iterative sampling that hinder a unified, scalable architecture. We present Discrete Diffusion VLA, a single-transformer policy that models discretized action chunks with discrete diffusion and is trained with the same cross-entropy objective as the VLM backbone. The design retains diffusion's progressive refinement paradigm while remaining natively compatible with the discrete token interface of VLMs. Our method achieves an adaptive decoding order that resolves easy action elements before harder ones and uses secondary remasking to revisit uncertain predictions across refinement rounds, which improves consistency and enables robust error correction. This unified decoder preserves pretrained vision language priors, supports parallel decoding, breaks the autoregressive bottleneck, and reduces the number of function evaluations. Discrete Diffusion VLA achieves 96.3% avg. SR on LIBERO, 71.2% visual matching on SimplerEnv Fractal and 49.3% overall on SimplerEnv Bridge, improving over both autoregressive and continuous diffusion baselines. These findings indicate that discrete-diffusion action decoder supports precise action modeling and consistent training, laying groundwork for scaling VLA to larger models and datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.19228", "title": "Predicting the Order of Upcoming Tokens Improves Language Modeling", "authors": [ "Zayd M. K. Zuhri", "Erland Hilman Fuadi", "Alham Fikri Aji" ], "github": "https://github.com/zaydzuhri/token-order-prediction", "project_page": "", "abstract": "Multi-Token Prediction (MTP) has been proposed as an auxiliary objective to improve next-token prediction (NTP) in language model training but shows inconsistent improvements, underperforming in standard NLP benchmarks. We argue that MTP's exact future token prediction is too difficult as an auxiliary loss. Instead, we propose Token Order Prediction (TOP), which trains models to order upcoming tokens by their proximity using a learning-to-rank loss. TOP requires only a single additional unembedding layer compared to MTP's multiple transformer layers. We pretrain models of 340M, 1.8B, and 7B parameters using NTP, MTP, and TOP objectives. Results on eight standard NLP benchmarks show that TOP overall outperforms both NTP and MTP even at scale. Our code is available at https://github.com/zaydzuhri/token-order-prediction", "github_manual": "", "github_regex": "https://github.com/zaydzuhri/token-order-prediction", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.20088", "title": "AudioStory: Generating Long-Form Narrative Audio with Large Language Models", "authors": [ "Yuxin Guo", "Teng Wang", "Yuying Ge", "Shijie Ma", "Yixiao Ge", "Wei Zou", "Ying Shan" ], "github": "https://github.com/TencentARC/AudioStory", "project_page": "", "abstract": "Recent advances in text-to-audio (TTA) generation excel at synthesizing short audio clips but struggle with long-form narrative audio, which requires temporal coherence and compositional reasoning. To address this gap, we propose AudioStory, a unified framework that integrates large language models (LLMs) with TTA systems to generate structured, long-form audio narratives. AudioStory possesses strong instruction-following reasoning generation capabilities. It employs LLMs to decompose complex narrative queries into temporally ordered sub-tasks with contextual cues, enabling coherent scene transitions and emotional tone consistency. AudioStory has two appealing features: (1) Decoupled bridging mechanism: AudioStory disentangles LLM-diffuser collaboration into two specialized components, i.e., a bridging query for intra-event semantic alignment and a residual query for cross-event coherence preservation. (2) End-to-end training: By unifying instruction comprehension and audio generation within a single end-to-end framework, AudioStory eliminates the need for modular training pipelines while enhancing synergy between components. Furthermore, we establish a benchmark AudioStory-10K, encompassing diverse domains such as animated soundscapes and natural sound narratives. Extensive experiments show the superiority of AudioStory on both single-audio generation and narrative audio generation, surpassing prior TTA baselines in both instruction-following ability and audio fidelity. Our code is available at https://github.com/TencentARC/AudioStory", "github_manual": "", "github_regex": "https://github.com/TencentARC/AudioStory", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.19982", "title": "Diffusion Language Models Know the Answer Before Decoding", "authors": [ "Pengxiang Li", "Yefan Zhou", "Dilxat Muhtar", "Lu Yin", "Shilin Yan", "Li Shen", "Yi Liang", "Soroush Vosoughi", "Shiwei Liu" ], "github": "https://github.com/pixeli99/Prophet", "project_page": "", "abstract": "Diffusion language models (DLMs) have recently emerged as an alternative to autoregressive approaches, offering parallel sequence generation and flexible token orders. However, their inference remains slower than that of autoregressive models, primarily due to the cost of bidirectional attention and the large number of refinement steps required for high quality outputs. In this work, we highlight and leverage an overlooked property of DLMs early answer convergence: in many cases, the correct answer can be internally identified by half steps before the final decoding step, both under semi-autoregressive and random remasking schedules. For example, on GSM8K and MMLU, up to 97% and 99% of instances, respectively, can be decoded correctly using only half of the refinement steps. Building on this observation, we introduce Prophet, a training-free fast decoding paradigm that enables early commit decoding. Specifically, Prophet dynamically decides whether to continue refinement or to go \"all-in\" (i.e., decode all remaining tokens in one step), using the confidence gap between the top-2 prediction candidates as the criterion. It integrates seamlessly into existing DLM implementations, incurs negligible overhead, and requires no additional training. Empirical evaluations of LLaDA-8B and Dream-7B across multiple tasks show that Prophet reduces the number of decoding steps by up to 3.4x while preserving high generation quality. These results recast DLM decoding as a problem of when to stop sampling, and demonstrate that early decode convergence provides a simple yet powerful mechanism for accelerating DLM inference, complementary to existing speedup techniques. Our code is publicly available at https://github.com/pixeli99/Prophet.", "github_manual": "", "github_regex": "https://github.com/pixeli99/Prophet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.19559", "title": "Taming the Chaos: Coordinated Autoscaling for Heterogeneous and Disaggregated LLM Inference", "authors": [ "Rongzhi Li", "Ruogu Du", "Zefang Chu", "Sida Zhao", "Chunlei Han", "Zuocheng Shi", "Yiwen Shao", "Huanle Han", "Long Huang", "Zherui Liu", "Shufan Liu" ], "github": "", "project_page": "", "abstract": "Serving Large Language Models (LLMs) is a GPU-intensive task where traditional autoscalers fall short, particularly for modern Prefill-Decode (P/D) disaggregated architectures. This architectural shift, while powerful, introduces significant operational challenges, including inefficient use of heterogeneous hardware, network bottlenecks, and critical imbalances between prefill and decode stages. We introduce HeteroScale, a coordinated autoscaling framework that addresses the core challenges of P/D disaggregated serving. HeteroScale combines a topology-aware scheduler that adapts to heterogeneous hardware and network constraints with a novel metric-driven policy derived from the first large-scale empirical study of autoscaling signals in production. By leveraging a single, robust metric to jointly scale prefill and decode pools, HeteroScale maintains architectural balance while ensuring efficient, adaptive resource management. Deployed in a massive production environment on tens of thousands of GPUs, HeteroScale has proven its effectiveness, increasing average GPU utilization by a significant 26.6 percentage points and saving hundreds of thousands of GPU-hours daily, all while upholding stringent service level objectives.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.20033", "title": "DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis", "authors": [ "Liana Patel", "Negar Arabzadeh", "Harshit Gupta", "Ankita Sundar", "Ion Stoica", "Matei Zaharia", "Carlos Guestrin" ], "github": "https://github.com/guestrin-lab/deepscholar-bench", "project_page": "", "abstract": "The ability to research and synthesize knowledge is central to human expertise and progress. An emerging class of systems promises these exciting capabilities through generative research synthesis, performing retrieval over the live web and synthesizing discovered sources into long-form, cited summaries. However, evaluating such systems remains an open challenge: existing question-answering benchmarks focus on short-form factual responses, while expert-curated datasets risk staleness and data contamination. Both fail to capture the complexity and evolving nature of real research synthesis tasks. In this work, we introduce DeepScholar-bench, a live benchmark and holistic, automated evaluation framework designed to evaluate generative research synthesis. DeepScholar-bench draws queries from recent, high-quality ArXiv papers and focuses on a real research synthesis task: generating the related work sections of a paper by retrieving, synthesizing, and citing prior research. Our evaluation framework holistically assesses performance across three key dimensions, knowledge synthesis, retrieval quality, and verifiability. We also develop DeepScholar-base, a reference pipeline implemented efficiently using the LOTUS API. Using the DeepScholar-bench framework, we perform a systematic evaluation of prior open-source systems, search AI's, OpenAI's DeepResearch, and DeepScholar-base. We find that DeepScholar-base establishes a strong baseline, attaining competitive or higher performance than each other method. We also find that DeepScholar-bench remains far from saturated, with no system exceeding a score of 19% across all metrics. These results underscore the difficulty of DeepScholar-bench, as well as its importance for progress towards AI systems capable of generative research synthesis. We make our code available at https://github.com/guestrin-lab/deepscholar-bench.", "github_manual": "", "github_regex": "https://github.com/guestrin-lab/deepscholar-bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.19527", "title": "MotionFlux: Efficient Text-Guided Motion Generation through Rectified Flow Matching and Preference Alignment", "authors": [ "Zhiting Gao", "Dan Song", "Diqiong Jiang", "Chao Xue", "An-An Liu" ], "github": "", "project_page": "", "abstract": "Motion generation is essential for animating virtual characters and embodied agents. While recent text-driven methods have made significant strides, they often struggle with achieving precise alignment between linguistic descriptions and motion semantics, as well as with the inefficiencies of slow, multi-step inference. To address these issues, we introduce TMR++ Aligned Preference Optimization (TAPO), an innovative framework that aligns subtle motion variations with textual modifiers and incorporates iterative adjustments to reinforce semantic grounding. To further enable real-time synthesis, we propose MotionFLUX, a high-speed generation framework based on deterministic rectified flow matching. Unlike traditional diffusion models, which require hundreds of denoising steps, MotionFLUX constructs optimal transport paths between noise distributions and motion spaces, facilitating real-time synthesis. The linearized probability paths reduce the need for multi-step sampling typical of sequential methods, significantly accelerating inference time without sacrificing motion quality. Experimental results demonstrate that, together, TAPO and MotionFLUX form a unified system that outperforms state-of-the-art approaches in both semantic consistency and motion quality, while also accelerating generation speed. The code and pretrained models will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.19320", "title": "MIDAS: Multimodal Interactive Digital-human Synthesis via Real-time Autoregressive Video Generation", "authors": [ "Ming Chen", "Liyuan Cui", "Wenyuan Zhang", "Haoxian Zhang", "Yan Zhou", "Xiaohan Li", "Xiaoqiang Liu", "Pengfei Wan" ], "github": "", "project_page": "", "abstract": "Recently, interactive digital human video generation has attracted widespread attention and achieved remarkable progress. However, building such a practical system that can interact with diverse input signals in real time remains challenging to existing methods, which often struggle with high latency, heavy computational cost, and limited controllability. In this work, we introduce an autoregressive video generation framework that enables interactive multimodal control and low-latency extrapolation in a streaming manner. With minimal modifications to a standard large language model (LLM), our framework accepts multimodal condition encodings including audio, pose, and text, and outputs spatially and semantically coherent representations to guide the denoising process of a diffusion head. To support this, we construct a large-scale dialogue dataset of approximately 20,000 hours from multiple sources, providing rich conversational scenarios for training. We further introduce a deep compression autoencoder with up to 64times reduction ratio, which effectively alleviates the long-horizon inference burden of the autoregressive model. Extensive experiments on duplex conversation, multilingual human synthesis, and interactive world model highlight the advantages of our approach in low latency, high efficiency, and fine-grained multimodal controllability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.15882", "title": "Beyond Transcription: Mechanistic Interpretability in ASR", "authors": [ "Neta Glazer", "Yael Segal-Feldman", "Hilit Segev", "Aviv Shamsian", "Asaf Buchnick", "Gill Hetz", "Ethan Fetaya", "Joseph Keshet", "Aviv Navon" ], "github": "", "project_page": "", "abstract": "Interpretability methods have recently gained significant attention, particularly in the context of large language models, enabling insights into linguistic representations, error detection, and model behaviors such as hallucinations and repetitions. However, these techniques remain underexplored in automatic speech recognition (ASR), despite their potential to advance both the performance and interpretability of ASR systems. In this work, we adapt and systematically apply established interpretability methods such as logit lens, linear probing, and activation patching, to examine how acoustic and semantic information evolves across layers in ASR systems. Our experiments reveal previously unknown internal dynamics, including specific encoder-decoder interactions responsible for repetition hallucinations and semantic biases encoded deep within acoustic representations. These insights demonstrate the benefits of extending and applying interpretability techniques to speech recognition, opening promising directions for future research on improving model transparency and robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.17924", "title": "Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation", "authors": [ "Konstantin Egorov", "Stepan Botman", "Pavel Blinov", "Galina Zubkova", "Anton Ivaschenko", "Alexander Kolsanov", "Andrey Savchenko" ], "github": "", "project_page": "", "abstract": "Progress in remote PhotoPlethysmoGraphy (rPPG) is limited by the critical issues of existing publicly available datasets: small size, privacy concerns with facial videos, and lack of diversity in conditions. The paper introduces a novel comprehensive large-scale multi-view video dataset for rPPG and health biomarkers estimation. Our dataset comprises 3600 synchronized video recordings from 600 subjects, captured under varied conditions (resting and post-exercise) using multiple consumer-grade cameras at different angles. To enable multimodal analysis of physiological states, each recording is paired with a 100 Hz PPG signal and extended health metrics, such as electrocardiogram, arterial blood pressure, biomarkers, temperature, oxygen saturation, respiratory rate, and stress level. Using this data, we train an efficient rPPG model and compare its quality with existing approaches in cross-dataset scenarios. The public release of our dataset and model should significantly speed up the progress in the development of AI medical assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.18179", "title": "SEAM: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models", "authors": [ "Zhenwei Tang", "Difan Jiao", "Blair Yang", "Ashton Anderson" ], "github": "", "project_page": "", "abstract": "Evaluating whether vision-language models (VLMs) reason consistently across representations is challenging because modality comparisons are typically confounded by task differences and asymmetric information. We introduce SEAM, a benchmark that pairs semantically equivalent inputs across four domains that have existing standardized textual and visual notations. By employing distinct notation systems across modalities, in contrast to OCR-based image-text pairing, SEAM provides a rigorous comparative assessment of the textual-symbolic and visual-spatial reasoning capabilities of VLMs. Across 21 contemporary models, we observe systematic modality imbalance: vision frequently lags language in overall performance, despite the problems containing semantically equivalent information, and cross-modal agreement is relatively low. Our error analysis reveals two main drivers: textual perception failures from tokenization in domain notation and visual perception failures that induce hallucinations. We also show that our results are largely robust to visual transformations. SEAM establishes a controlled, semantically equivalent setting for measuring and improving modality-agnostic reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.19827", "title": "Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful Post-hoc Rationalisation?", "authors": [ "Samuel Lewis-Lim", "Xingwei Tan", "Zhixue Zhao", "Nikolaos Aletras" ], "github": "", "project_page": "", "abstract": "Recent work has demonstrated that Chain-of-Thought (CoT) often yields limited gains for soft-reasoning problems such as analytical and commonsense reasoning. CoT can also be unfaithful to a model's actual reasoning. We investigate the dynamics and faithfulness of CoT in soft-reasoning tasks across instruction-tuned, reasoning and reasoning-distilled models. Our findings reveal differences in how these models rely on CoT, and show that CoT influence and faithfulness are not always aligned.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-28", "arxiv_id": "2508.16067", "title": "Training a Foundation Model for Materials on a Budget", "authors": [ "Teddy Koker", "Tess Smidt" ], "github": "https://github.com/atomicarchitects/nequix", "project_page": "", "abstract": "Foundation models for materials modeling are advancing quickly, but their training remains expensive, often placing state-of-the-art methods out of reach for many research groups. We introduce Nequix, a compact E(3)-equivariant potential that pairs a simplified NequIP design with modern training practices, including equivariant root-mean-square layer normalization and the Muon optimizer, to retain accuracy while substantially reducing compute requirements. Built in JAX, Nequix has 700K parameters and was trained in 500 A100-GPU hours. On the Matbench-Discovery and MDR Phonon benchmarks, Nequix ranks third overall while requiring less than one quarter of the training cost of most other methods, and it delivers an order-of-magnitude faster inference speed than the current top-ranked model. We release model weights and fully reproducible codebase at https://github.com/atomicarchitects/nequix", "github_manual": "", "github_regex": "https://github.com/atomicarchitects/nequix", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.20722", "title": "rStar2-Agent: Agentic Reasoning Technical Report", "authors": [ "Ning Shang", "Yifei Liu", "Yi Zhu", "Li Lyna Zhang", "Weijiang Xu", "Xinyu Guan", "Buze Zhang", "Bingcheng Dong", "Xudong Zhou", "Bowen Zhang", "Ying Xin", "Ziming Miao", "Scarlett Li", "Fan Yang", "Mao Yang" ], "github": "https://github.com/microsoft/rStar", "project_page": "", "abstract": "We introduce rStar2-Agent, a 14B math reasoning model trained with agentic reinforcement learning to achieve frontier-level performance. Beyond current long CoT, the model demonstrates advanced cognitive behaviors, such as thinking carefully before using Python coding tools and reflecting on code execution feedback to autonomously explore, verify, and refine intermediate steps in complex problem-solving. This capability is enabled through three key innovations that makes agentic RL effective at scale: (i) an efficient RL infrastructure with a reliable Python code environment that supports high-throughput execution and mitigates the high rollout costs, enabling training on limited GPU resources (64 MI300X GPUs); (ii) GRPO-RoC, an agentic RL algorithm with a Resample-on-Correct rollout strategy that addresses the inherent environment noises from coding tools, allowing the model to reason more effectively in a code environment; (iii) An efficient agent training recipe that starts with non-reasoning SFT and progresses through multi-RL stages, yielding advanced cognitive abilities with minimal compute cost. To this end, rStar2-Agent boosts a pre-trained 14B model to state of the art in only 510 RL steps within one week, achieving average pass@1 scores of 80.6% on AIME24 and 69.8% on AIME25, surpassing DeepSeek-R1 (671B) with significantly shorter responses. Beyond mathematics, rStar2-Agent-14B also demonstrates strong generalization to alignment, scientific reasoning, and agentic tool-use tasks. Code and training recipes are available at https://github.com/microsoft/rStar.", "github_manual": "", "github_regex": "https://github.com/microsoft/rStar", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.21058", "title": "Mixture of Contexts for Long Video Generation", "authors": [ "Shengqu Cai", "Ceyuan Yang", "Lvmin Zhang", "Yuwei Guo", "Junfei Xiao", "Ziyan Yang", "Yinghao Xu", "Zhenheng Yang", "Alan Yuille", "Leonidas Guibas", "Maneesh Agrawala", "Lu Jiang", "Gordon Wetzstein" ], "github": "", "project_page": "", "abstract": "Long video generation is fundamentally a long context memory problem: models must retain and retrieve salient events across a long range without collapsing or drifting. However, scaling diffusion transformers to generate long-context videos is fundamentally limited by the quadratic cost of self-attention, which makes memory and computation intractable and difficult to optimize for long sequences. We recast long-context video generation as an internal information retrieval task and propose a simple, learnable sparse attention routing module, Mixture of Contexts (MoC), as an effective long-term memory retrieval engine. In MoC, each query dynamically selects a few informative chunks plus mandatory anchors (caption, local windows) to attend to, with causal routing that prevents loop closures. As we scale the data and gradually sparsify the routing, the model allocates compute to salient history, preserving identities, actions, and scenes over minutes of content. Efficiency follows as a byproduct of retrieval (near-linear scaling), which enables practical training and synthesis, and the emergence of memory and consistency at the scale of minutes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.20453", "title": "MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers", "authors": [ "Zhenting Wang", "Qi Chang", "Hemani Patel", "Shashank Biju", "Cheng-En Wu", "Quan Liu", "Aolin Ding", "Alireza Rezazadeh", "Ankit Shah", "Yujia Bao", "Eugene Siow" ], "github": "https://github.com/Accenture/mcp-bench", "project_page": "", "abstract": "We introduce MCP-Bench, a benchmark for evaluating large language models (LLMs) on realistic, multi-step tasks that demand tool use, cross-tool coordination, precise parameter control, and planning/reasoning for solving tasks. Built on the Model Context Protocol (MCP), MCP-Bench connects LLMs to 28 representative live MCP servers spanning 250 tools across domains such as finance, traveling, scientific computing, and academic search. Unlike prior API-based benchmarks, each MCP server provides a set of complementary tools designed to work together, enabling the construction of authentic, multi-step tasks with rich input-output coupling. Tasks in MCP-Bench test agents' ability to retrieve relevant tools from fuzzy instructions without explicit tool names, plan multi-hop execution trajectories for complex objectives, ground responses in intermediate tool outputs, and orchestrate cross-domain workflows - capabilities not adequately evaluated by existing benchmarks that rely on explicit tool specifications, shallow few-step workflows, and isolated domain operations. We propose a multi-faceted evaluation framework covering tool-level schema understanding and usage, trajectory-level planning, and task completion. Experiments on 20 advanced LLMs reveal persistent challenges in MCP-Bench. Code and data: https://github.com/Accenture/mcp-bench.", "github_manual": "", "github_regex": "https://github.com/Accenture/mcp-bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.20751", "title": "Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning", "authors": [ "Yibin Wang", "Zhimin Li", "Yuhang Zang", "Yujie Zhou", "Jiazi Bu", "Chunyu Wang", "Qinglin Lu", "Cheng Jin", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Recent advancements highlight the importance of GRPO-based reinforcement learning methods and benchmarking in enhancing text-to-image (T2I) generation. However, current methods using pointwise reward models (RM) for scoring generated images are susceptible to reward hacking. We reveal that this happens when minimal score differences between images are amplified after normalization, creating illusory advantages that drive the model to over-optimize for trivial gains, ultimately destabilizing the image generation process. To address this, we propose Pref-GRPO, a pairwise preference reward-based GRPO method that shifts the optimization objective from score maximization to preference fitting, ensuring more stable training. In Pref-GRPO, images are pairwise compared within each group using preference RM, and the win rate is used as the reward signal. Extensive experiments demonstrate that PREF-GRPO differentiates subtle image quality differences, providing more stable advantages and mitigating reward hacking. Additionally, existing T2I benchmarks are limited by coarse evaluation criteria, hindering comprehensive model assessment. To solve this, we introduce UniGenBench, a unified T2I benchmark comprising 600 prompts across 5 main themes and 20 subthemes. It evaluates semantic consistency through 10 primary and 27 sub-criteria, leveraging MLLM for benchmark construction and evaluation. Our benchmarks uncover the strengths and weaknesses of both open and closed-source T2I models and validate the effectiveness of Pref-GRPO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.18633", "title": "ROSE: Remove Objects with Side Effects in Videos", "authors": [ "Chenxuan Miao", "Yutong Feng", "Jianshu Zeng", "Zixiang Gao", "Hantang Liu", "Yunfeng Yan", "Donglian Qi", "Xi Chen", "Bin Wang", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "Video object removal has achieved advanced performance due to the recent success of video generative models. However, when addressing the side effects of objects, e.g., their shadows and reflections, existing works struggle to eliminate these effects for the scarcity of paired video data as supervision. This paper presents ROSE, termed Remove Objects with Side Effects, a framework that systematically studies the object's effects on environment, which can be categorized into five common cases: shadows, reflections, light, translucency and mirror. Given the challenges of curating paired videos exhibiting the aforementioned effects, we leverage a 3D rendering engine for synthetic data generation. We carefully construct a fully-automatic pipeline for data preparation, which simulates a large-scale paired dataset with diverse scenes, objects, shooting angles, and camera trajectories. ROSE is implemented as an video inpainting model built on diffusion transformer. To localize all object-correlated areas, the entire video is fed into the model for reference-based erasing. Moreover, additional supervision is introduced to explicitly predict the areas affected by side effects, which can be revealed through the differential mask between the paired videos. To fully investigate the model performance on various side effect removal, we presents a new benchmark, dubbed ROSE-Bench, incorporating both common scenarios and the five special side effects for comprehensive evaluation. Experimental results demonstrate that ROSE achieves superior performance compared to existing video object erasing models and generalizes well to real-world video scenarios. The project page is https://rose2025-inpaint.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.15228", "title": "Collaborative Multi-Modal Coding for High-Quality 3D Generation", "authors": [ "Ziang Cao", "Zhaoxi Chen", "Liang Pan", "Ziwei Liu" ], "github": "https://github.com/3DTopia/OpenLRM", "project_page": "", "abstract": "3D content inherently encompasses multi-modal characteristics and can be projected into different modalities (e.g., RGB images, RGBD, and point clouds). Each modality exhibits distinct advantages in 3D asset modeling: RGB images contain vivid 3D textures, whereas point clouds define fine-grained 3D geometries. However, most existing 3D-native generative architectures either operate predominantly within single-modality paradigms-thus overlooking the complementary benefits of multi-modality data-or restrict themselves to 3D structures, thereby limiting the scope of available training datasets. To holistically harness multi-modalities for 3D modeling, we present TriMM, the first feed-forward 3D-native generative model that learns from basic multi-modalities (e.g., RGB, RGBD, and point cloud). Specifically, 1) TriMM first introduces collaborative multi-modal coding, which integrates modality-specific features while preserving their unique representational strengths. 2) Furthermore, auxiliary 2D and 3D supervision are introduced to raise the robustness and performance of multi-modal coding. 3) Based on the embedded multi-modal code, TriMM employs a triplane latent diffusion model to generate 3D assets of superior quality, enhancing both the texture and the geometric detail. Extensive experiments on multiple well-known datasets demonstrate that TriMM, by effectively leveraging multi-modality, achieves competitive performance with models trained on large-scale datasets, despite utilizing a small amount of training data. Furthermore, we conduct additional experiments on recent RGB-D datasets, verifying the feasibility of incorporating other multi-modal datasets into 3D generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/OpenLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.18966", "title": "USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning", "authors": [ "Shaojin Wu", "Mengqi Huang", "Yufeng Cheng", "Wenxu Wu", "Jiahe Tian", "Yiming Luo", "Fei Ding", "Qian He" ], "github": "https://github.com/bytedance/USO", "project_page": "", "abstract": "Existing literature typically treats style-driven and subject-driven generation as two disjoint tasks: the former prioritizes stylistic similarity, whereas the latter insists on subject consistency, resulting in an apparent antagonism. We argue that both objectives can be unified under a single framework because they ultimately concern the disentanglement and re-composition of content and style, a long-standing theme in style-driven research. To this end, we present USO, a Unified Style-Subject Optimized customization model. First, we construct a large-scale triplet dataset consisting of content images, style images, and their corresponding stylized content images. Second, we introduce a disentangled learning scheme that simultaneously aligns style features and disentangles content from style through two complementary objectives, style-alignment training and content-style disentanglement training. Third, we incorporate a style reward-learning paradigm denoted as SRL to further enhance the model's performance. Finally, we release USO-Bench, the first benchmark that jointly evaluates style similarity and subject fidelity across multiple metrics. Extensive experiments demonstrate that USO achieves state-of-the-art performance among open-source models along both dimensions of subject consistency and style similarity. Code and model: https://github.com/bytedance/USO", "github_manual": "", "github_regex": "https://github.com/bytedance/USO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.20374", "title": "TCIA: A Task-Centric Instruction Augmentation Method for Instruction Finetuning", "authors": [ "Simin Ma", "Shujian Liu", "Jun Tan", "Yebowen Hu", "Song Wang", "Sathish Reddy Indurthi", "Sanqiang Zhao", "Liwei Wu", "Jianbing Han", "Kaiqiang Song" ], "github": "", "project_page": "", "abstract": "Diverse instruction data is vital for effective instruction tuning of large language models, as it enables the model to generalize across different types of inputs . Building such diversified instruction dataset is an essential step in this process. Existing approaches often leverage large language models to automatically explore and generate diverse instructions, ensuring both data diversity and quality. However, they tend to overlook an important factor in real-world applications: on-task relevance. In practice, only a few real-world applications require a truly general-purpose model; most benefit from task-specific knowledge tailored to their particular use case. Therefore, it is vital to develop instruction augmentation methods that not only maintain diversity but are also optimized for specific, real-world scenarios. We thus introduce Task Centric Instruction Augmentation (TCIA), a framework that systematically expands instructions while preserving both diversity and task alignment. By representing instructions in a discrete query-constraints space, TCIA creates a rich set of task-relevant instructions and enables models to generalize to these task-specific instructions without sacrificing overall performance. Experiments show that TCIA improves open-source LLMs' performance by an average of 8.7% across four real-world, task-specific applications, and in some cases outperforming leading closed-source models. These improvements do not compromise general instruction-following ability, making TCIA a scalable and efficient solution for adapting LLMs to real-world, task-focused applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.20404", "title": "AWorld: Orchestrating the Training Recipe for Agentic AI", "authors": [ "Chengyue Yu", "Siyuan Lu", "Chenyi Zhuang", "Dong Wang", "Qintong Wu", "Zongyue Li", "Runsheng Gan", "Chunfeng Wang", "Siqi Hou", "Gaochi Huang", "Wenlong Yan", "Lifeng Hong", "Aohui Xue", "Yanfeng Wang", "Jinjie Gu", "David Tsai", "Tao Lin" ], "github": "", "project_page": "", "abstract": "The learning from practice paradigm is crucial for developing capable Agentic AI systems, yet it is severely hampered by inefficient experience generation, a bottleneck especially pronounced in complex benchmarks like GAIA. To address this, we introduce AWorld, an open-source system engineered for large-scale agent-environment interaction. By distributing tasks across a cluster, AWorld accelerates experience collection by 14.6x compared to standard single-node, sequential execution. This critical speedup makes extensive reinforcement learning practical and scalable. Leveraging this capability, we trained a Qwen3-32B-based agent that significantly outperforms its base model, increasing its overall GAIA accuracy from 21.59% to 32.23%. On the benchmark's most challenging levels, our agent achieves a score of 16.33%, surpassing the performance of leading proprietary models. Our open-source system and resulting agent provide a practical blueprint for a complete agentic AI training pipeline, from efficient interaction to demonstrable model improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.21070", "title": "Dress&Dance: Dress up and Dance as You Like It - Technical Preview", "authors": [ "Jun-Kun Chen", "Aayush Bansal", "Minh Phuoc Vo", "Yu-Xiong Wang" ], "github": "", "project_page": "", "abstract": "We present Dress&Dance, a video diffusion framework that generates high quality 5-second-long 24 FPS virtual try-on videos at 1152x720 resolution of a user wearing desired garments while moving in accordance with a given reference video. Our approach requires a single user image and supports a range of tops, bottoms, and one-piece garments, as well as simultaneous tops and bottoms try-on in a single pass. Key to our framework is CondNet, a novel conditioning network that leverages attention to unify multi-modal inputs (text, images, and videos), thereby enhancing garment registration and motion fidelity. CondNet is trained on heterogeneous training data, combining limited video data and a larger, more readily available image dataset, in a multistage progressive manner. Dress&Dance outperforms existing open source and commercial solutions and enables a high quality and flexible try-on experience.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.21061", "title": "OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models", "authors": [ "Adam Coscia", "Shunan Guo", "Eunyee Koh", "Alex Endert" ], "github": "", "project_page": "", "abstract": "As multi-turn dialogues with large language models (LLMs) grow longer and more complex, how can users better evaluate and review progress on their conversational goals? We present OnGoal, an LLM chat interface that helps users better manage goal progress. OnGoal provides real-time feedback on goal alignment through LLM-assisted evaluation, explanations for evaluation results with examples, and overviews of goal progression over time, enabling users to navigate complex dialogues more effectively. Through a study with 20 participants on a writing task, we evaluate OnGoal against a baseline chat interface without goal tracking. Using OnGoal, participants spent less time and effort to achieve their goals while exploring new prompting strategies to overcome miscommunication, suggesting tracking and visualizing goals can enhance engagement and resilience in LLM dialogues. Our findings inspired design implications for future LLM chat interfaces that improve goal communication, reduce cognitive load, enhance interactivity, and enable feedback to improve LLM performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.21052", "title": "FakeParts: a New Family of AI-Generated DeepFakes", "authors": [ "Gaetan Brison", "Soobash Daiboo", "Samy Aimeur", "Awais Hussain Sani", "Xi Wang", "Gianni Franchi", "Vicky Kalogeiton" ], "github": "https://github.com/hi-paris/FakeParts", "project_page": "", "abstract": "We introduce FakeParts, a new class of deepfakes characterized by subtle, localized manipulations to specific spatial regions or temporal segments of otherwise authentic videos. Unlike fully synthetic content, these partial manipulations, ranging from altered facial expressions to object substitutions and background modifications, blend seamlessly with real elements, making them particularly deceptive and difficult to detect. To address the critical gap in detection capabilities, we present FakePartsBench, the first large-scale benchmark dataset specifically designed to capture the full spectrum of partial deepfakes. Comprising over 25K videos with pixel-level and frame-level manipulation annotations, our dataset enables comprehensive evaluation of detection methods. Our user studies demonstrate that FakeParts reduces human detection accuracy by over 30% compared to traditional deepfakes, with similar performance degradation observed in state-of-the-art detection models. This work identifies an urgent vulnerability in current deepfake detection approaches and provides the necessary resources to develop more robust methods for partial video manipulations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hi-paris/FakeParts", "github_api_added_by": "auto" }, { "date": "2025-08-29", "arxiv_id": "2508.21046", "title": "CogVLA: Cognition-Aligned Vision-Language-Action Model via Instruction-Driven Routing & Sparsification", "authors": [ "Wei Li", "Renshan Zhang", "Rui Shao", "Jie He", "Liqiang Nie" ], "github": "https://github.com/JiuTian-VL/CogVLA", "project_page": "", "abstract": "Recent Vision-Language-Action (VLA) models built on pre-trained Vision-Language Models (VLMs) require extensive post-training, resulting in high computational overhead that limits scalability and deployment.We propose CogVLA, a Cognition-Aligned Vision-Language-Action framework that leverages instruction-driven routing and sparsification to improve both efficiency and performance. CogVLA draws inspiration from human multimodal coordination and introduces a 3-stage progressive architecture. 1) Encoder-FiLM based Aggregation Routing (EFA-Routing) injects instruction information into the vision encoder to selectively aggregate and compress dual-stream visual tokens, forming a instruction-aware latent representation. 2) Building upon this compact visual encoding, LLM-FiLM based Pruning Routing (LFP-Routing) introduces action intent into the language model by pruning instruction-irrelevant visually grounded tokens, thereby achieving token-level sparsity. 3) To ensure that compressed perception inputs can still support accurate and coherent action generation, we introduce V-L-A Coupled Attention (CAtten), which combines causal vision-language attention with bidirectional action parallel decoding. Extensive experiments on the LIBERO benchmark and real-world robotic tasks demonstrate that CogVLA achieves state-of-the-art performance with success rates of 97.4% and 70.0%, respectively, while reducing training costs by 2.5-fold and decreasing inference latency by 2.8-fold compared to OpenVLA. CogVLA is open-sourced and publicly available at https://github.com/JiuTian-VL/CogVLA.", "github_manual": "", "github_regex": "https://github.com/JiuTian-VL/CogVLA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.20755", "title": "Provable Benefits of In-Tool Learning for Large Language Models", "authors": [ "Sam Houliston", "Ambroise Odonnat", "Charles Arnal", "Vivien Cabannes" ], "github": "https://github.com/ambroiseodt/itl", "project_page": "", "abstract": "Tool-augmented language models, equipped with retrieval, memory, or external APIs, are reshaping AI, yet their theoretical advantages remain underexplored. In this paper, we address this question by demonstrating the benefits of in-tool learning (external retrieval) over in-weight learning (memorization) for factual recall. We show that the number of facts a model can memorize solely in its weights is fundamentally limited by its parameter count. In contrast, we prove that tool-use enables unbounded factual recall via a simple and efficient circuit construction. These results are validated in controlled experiments, where tool-using models consistently outperform memorizing ones. We further show that for pretrained large language models, teaching tool-use and general rules is more effective than finetuning facts into memory. Our work provides both a theoretical and empirical foundation, establishing why tool-augmented workflows are not just practical, but provably more scalable.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ambroiseodt/itl", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.20766", "title": "Turning the Spell Around: Lightweight Alignment Amplification via Rank-One Safety Injection", "authors": [ "Harethah Abu Shairah", "Hasan Abed Al Kader Hammoud", "George Turkiyyah", "Bernard Ghanem" ], "github": "", "project_page": "", "abstract": "Safety alignment in Large Language Models (LLMs) often involves mediating internal representations to refuse harmful requests. Recent research has demonstrated that these safety mechanisms can be bypassed by ablating or removing specific representational directions within the model. In this paper, we propose the opposite approach: Rank-One Safety Injection (ROSI), a white-box method that amplifies a model's safety alignment by permanently steering its activations toward the refusal-mediating subspace. ROSI operates as a simple, fine-tuning-free rank-one weight modification applied to all residual stream write matrices. The required safety direction can be computed from a small set of harmful and harmless instruction pairs. We show that ROSI consistently increases safety refusal rates - as evaluated by Llama Guard 3 - while preserving the utility of the model on standard benchmarks such as MMLU, HellaSwag, and Arc. Furthermore, we show that ROSI can also re-align 'uncensored' models by amplifying their own latent safety directions, demonstrating its utility as an effective last-mile safety procedure. Our results suggest that targeted, interpretable weight steering is a cheap and potent mechanism to improve LLM safety, complementing more resource-intensive fine-tuning paradigms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.21060", "title": "Multi-View 3D Point Tracking", "authors": [ "Frano Raji\u010d", "Haofei Xu", "Marko Mihajlovic", "Siyuan Li", "Irem Demir", "Emircan G\u00fcndo\u011fdu", "Lei Ke", "Sergey Prokudin", "Marc Pollefeys", "Siyu Tang" ], "github": "", "project_page": "", "abstract": "We introduce the first data-driven multi-view 3D point tracker, designed to track arbitrary points in dynamic scenes using multiple camera views. Unlike existing monocular trackers, which struggle with depth ambiguities and occlusion, or prior multi-camera methods that require over 20 cameras and tedious per-sequence optimization, our feed-forward model directly predicts 3D correspondences using a practical number of cameras (e.g., four), enabling robust and accurate online tracking. Given known camera poses and either sensor-based or estimated multi-view depth, our tracker fuses multi-view features into a unified point cloud and applies k-nearest-neighbors correlation alongside a transformer-based update to reliably estimate long-range 3D correspondences, even under occlusion. We train on 5K synthetic multi-view Kubric sequences and evaluate on two real-world benchmarks: Panoptic Studio and DexYCB, achieving median trajectory errors of 3.1 cm and 2.0 cm, respectively. Our method generalizes well to diverse camera setups of 1-8 views with varying vantage points and video lengths of 24-150 frames. By releasing our tracker alongside training and evaluation datasets, we aim to set a new standard for multi-view 3D tracking research and provide a practical tool for real-world applications. Project page available at https://ethz-vlg.github.io/mvtracker.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.17450", "title": "Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PD", "authors": [ "Bryan Chen Zhengyu Tan", "Daniel Wai Kit Chin", "Zhengyuan Liu", "Nancy F. Chen", "Roy Ka-Wei Lee" ], "github": "https://github.com/Social-AI-Studio/DuET-PD", "project_page": "", "abstract": "Large Language Models (LLMs) can struggle to balance gullibility to misinformation and resistance to valid corrections in persuasive dialogues, a critical challenge for reliable deployment. We introduce DuET-PD (Dual Evaluation for Trust in Persuasive Dialogues), a framework evaluating multi-turn stance-change dynamics across dual dimensions: persuasion type (corrective/misleading) and domain (knowledge via MMLU-Pro, and safety via SALAD-Bench). We find that even a state-of-the-art model like GPT-4o achieves only 27.32% accuracy in MMLU-Pro under sustained misleading persuasions. Moreover, results reveal a concerning trend of increasing sycophancy in newer open-source models. To address this, we introduce Holistic DPO, a training approach balancing positive and negative persuasion examples. Unlike prompting or resist-only training, Holistic DPO enhances both robustness to misinformation and receptiveness to corrections, improving Llama-3.1-8B-Instruct's accuracy under misleading persuasion in safety contexts from 4.21% to 76.54%. These contributions offer a pathway to developing more reliable and adaptable LLMs for multi-turn dialogue. Code is available at https://github.com/Social-AI-Studio/DuET-PD.", "github_manual": "", "github_regex": "https://github.com/Social-AI-Studio/DuET-PD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.21066", "title": "OneReward: Unified Mask-Guided Image Generation via Multi-Task Human Preference Learning", "authors": [ "Yuan Gong", "Xionghui Wang", "Jie Wu", "Shiyin Wang", "Yitong Wang", "Xinglong Wu" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce OneReward, a unified reinforcement learning framework that enhances the model's generative capabilities across multiple tasks under different evaluation criteria using only One Reward model. By employing a single vision-language model (VLM) as the generative reward model, which can distinguish the winner and loser for a given task and a given evaluation criterion, it can be effectively applied to multi-task generation models, particularly in contexts with varied data and diverse task objectives. We utilize OneReward for mask-guided image generation, which can be further divided into several sub-tasks such as image fill, image extend, object removal, and text rendering, involving a binary mask as the edit area. Although these domain-specific tasks share same conditioning paradigm, they differ significantly in underlying data distributions and evaluation metrics. Existing methods often rely on task-specific supervised fine-tuning (SFT), which limits generalization and training efficiency. Building on OneReward, we develop Seedream 3.0 Fill, a mask-guided generation model trained via multi-task reinforcement learning directly on a pre-trained base model, eliminating the need for task-specific SFT. Experimental results demonstrate that our unified edit model consistently outperforms both commercial and open-source competitors, such as Ideogram, Adobe Photoshop, and FLUX Fill [Pro], across multiple evaluation dimensions. Code and model are available at: https://one-reward.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-08-29", "arxiv_id": "2508.17502", "title": "Social-MAE: A Transformer-Based Multimodal Autoencoder for Face and Voice", "authors": [ "Hugo Bohy", "Minh Tran", "Kevin El Haddad", "Thierry Dutoit", "Mohammad Soleymani" ], "github": "https://github.com/HuBohy/SocialMAE", "project_page": "", "abstract": "Human social behaviors are inherently multimodal necessitating the development of powerful audiovisual models for their perception. In this paper, we present Social-MAE, our pre-trained audiovisual Masked Autoencoder based on an extended version of Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE), which is pre-trained on audiovisual social data. Specifically, we modify CAV-MAE to receive a larger number of frames as input and pre-train it on a large dataset of human social interaction (VoxCeleb2) in a self-supervised manner. We demonstrate the effectiveness of this model by finetuning and evaluating the model on different social and affective downstream tasks, namely, emotion recognition, laughter detection and apparent personality estimation. The model achieves state-of-the-art results on multimodal emotion recognition and laughter recognition and competitive results for apparent personality estimation, demonstrating the effectiveness of in-domain self-supervised pre-training. Code and model weight are available here https://github.com/HuBohy/SocialMAE.", "github_manual": "", "github_regex": "https://github.com/HuBohy/SocialMAE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.21112", "title": "EmbodiedOneVision: Interleaved Vision-Text-Action Pretraining for General Robot Control", "authors": [ "Delin Qu", "Haoming Song", "Qizhi Chen", "Zhaoqing Chen", "Xianqiang Gao", "Xinyi Ye", "Qi Lv", "Modi Shi", "Guanghui Ren", "Cheng Ruan", "Maoqing Yao", "Haoran Yang", "Jiacheng Bao", "Bin Zhao", "Dong Wang" ], "github": "", "project_page": "", "abstract": "The human ability to seamlessly perform multimodal reasoning and physical interaction in the open world is a core goal for general-purpose embodied intelligent systems. Recent vision-language-action (VLA) models, which are co-trained on large-scale robot and visual-text data, have demonstrated notable progress in general robot control. However, they still fail to achieve human-level flexibility in interleaved reasoning and interaction. In this work, introduce EO-Robotics, consists of EO-1 model and EO-Data1.5M dataset. EO-1 is a unified embodied foundation model that achieves superior performance in multimodal embodied reasoning and robot control through interleaved vision-text-action pre-training. The development of EO-1 is based on two key pillars: (i) a unified architecture that processes multimodal inputs indiscriminately (image, text, video, and action), and (ii) a massive, high-quality multimodal embodied reasoning dataset, EO-Data1.5M, which contains over 1.5 million samples with emphasis on interleaved vision-text-action comprehension. EO-1 is trained through synergies between auto-regressive decoding and flow matching denoising on EO-Data1.5M, enabling seamless robot action generation and multimodal embodied reasoning. Extensive experiments demonstrate the effectiveness of interleaved vision-text-action learning for open-world understanding and generalization, validated through a variety of long-horizon, dexterous manipulation tasks across multiple embodiments. This paper details the architecture of EO-1, the data construction strategy of EO-Data1.5M, and the training methodology, offering valuable insights for developing advanced embodied foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.21113", "title": "R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning", "authors": [ "Jie Jiang", "Qi Yang", "Bolin Ni", "Shiming Xiang", "Han Hu", "Houwen Peng" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) equipped with step-by-step thinking capabilities have demonstrated remarkable performance on complex reasoning problems. However, this thinking process is redundant for simple problems solvable without complex reasoning. To address this inefficiency, we propose R-4B, an auto-thinking MLLM, which can adaptively decide when to think based on problem complexity. The central idea of R-4B is to empower the model with both thinking and non-thinking capabilities using bi-mode annealing, and apply Bi-mode Policy Optimization~(BPO) to improve the model's accuracy in determining whether to activate the thinking process. Specifically, we first train the model on a carefully curated dataset spanning various topics, which contains samples from both thinking and non-thinking modes. Then it undergoes a second phase of training under an improved GRPO framework, where the policy model is forced to generate responses from both modes for each input query. Experimental results show that R-4B achieves state-of-the-art performance across 25 challenging benchmarks. It outperforms Qwen2.5-VL-7B in most tasks and achieves performance comparable to larger models such as Kimi-VL-A3B-Thinking-2506 (16B) on reasoning-intensive benchmarks with lower computational cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.18106", "title": "A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code", "authors": [ "Keke Lian", "Bin Wang", "Lei Zhang", "Libo Chen", "Junjie Wang", "Ziming Zhao", "Yujiu Yang", "Haotong Duan", "Haoran Zhao", "Shuang Liao", "Mingda Guo", "Jiazheng Quan", "Yilu Zhong", "Chenhao He", "Zichuan Chen", "Jie Wu", "Haoling Li", "Zhaoxuan Li", "Jiongchi Yu", "Hui Li", "Dong Zhang" ], "github": "", "project_page": "", "abstract": "The increasing adoption of large language models (LLMs) in software engineering necessitates rigorous security evaluation of their generated code. However, existing benchmarks are inadequate, as they focus on isolated code snippets, employ unstable evaluation methods that lack reproducibility, and fail to connect the quality of input context with the security of the output. To address these gaps, we introduce A.S.E (AI Code Generation Security Evaluation), a benchmark for repository-level secure code generation. A.S.E constructs tasks from real-world repositories with documented CVEs, preserving full repository context like build systems and cross-file dependencies. Its reproducible, containerized evaluation framework uses expert-defined rules to provide stable, auditable assessments of security, build quality, and generation stability. Our evaluation of leading LLMs on A.S.E reveals three key findings: (1) Claude-3.7-Sonnet achieves the best overall performance. (2) The security gap between proprietary and open-source models is narrow; Qwen3-235B-A22B-Instruct attains the top security score. (3) Concise, ``fast-thinking'' decoding strategies consistently outperform complex, ``slow-thinking'' reasoning for security patching.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.13618", "title": "TalkVid: A Large-Scale Diversified Dataset for Audio-Driven Talking Head Synthesis", "authors": [ "Shunian Chen", "Hejin Huang", "Yexin Liu", "Zihan Ye", "Pengcheng Chen", "Chenghao Zhu", "Michael Guan", "Rongsheng Wang", "Junying Chen", "Guanbin Li", "Ser-Nam Lim", "Harry Yang", "Benyou Wang" ], "github": "https://github.com/FreedomIntelligence/TalkVid", "project_page": "", "abstract": "Audio-driven talking head synthesis has achieved remarkable photorealism, yet state-of-the-art (SOTA) models exhibit a critical failure: they lack generalization to the full spectrum of human diversity in ethnicity, language, and age groups. We argue that this generalization gap is a direct symptom of limitations in existing training data, which lack the necessary scale, quality, and diversity. To address this challenge, we introduce TalkVid, a new large-scale, high-quality, and diverse dataset containing 1244 hours of video from 7729 unique speakers. TalkVid is curated through a principled, multi-stage automated pipeline that rigorously filters for motion stability, aesthetic quality, and facial detail, and is validated against human judgments to ensure its reliability. Furthermore, we construct and release TalkVid-Bench, a stratified evaluation set of 500 clips meticulously balanced across key demographic and linguistic axes. Our experiments demonstrate that a model trained on TalkVid outperforms counterparts trained on previous datasets, exhibiting superior cross-dataset generalization. Crucially, our analysis on TalkVid-Bench reveals performance disparities across subgroups that are obscured by traditional aggregate metrics, underscoring its necessity for future research. Code and data can be found in https://github.com/FreedomIntelligence/TalkVid", "github_manual": "", "github_regex": "https://github.com/FreedomIntelligence/TalkVid", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.21767", "title": "UItron: Foundational GUI Agent with Advanced Perception and Planning", "authors": [ "Zhixiong Zeng", "Jing Huang", "Liming Zheng", "Wenkang Han", "Yufeng Zhong", "Lei Chen", "Longrong Yang", "Yingjie Chu", "Yuzhi He", "Lin Ma" ], "github": "https://github.com/UITron-hub/UItron", "project_page": "", "abstract": "GUI agent aims to enable automated operations on Mobile/PC devices, which is an important task toward achieving artificial general intelligence. The rapid advancement of VLMs accelerates the development of GUI agents, owing to their powerful capabilities in visual understanding and task planning. However, building a GUI agent remains a challenging task due to the scarcity of operation trajectories, the availability of interactive infrastructure, and the limitation of initial capabilities in foundation models. In this work, we introduce UItron, an open-source foundational model for automatic GUI agents, featuring advanced GUI perception, grounding, and planning capabilities. UItron highlights the necessity of systemic data engineering and interactive infrastructure as foundational components for advancing GUI agent development. It not only systematically studies a series of data engineering strategies to enhance training effects, but also establishes an interactive environment connecting both Mobile and PC devices. In training, UItron adopts supervised finetuning over perception and planning tasks in various GUI scenarios, and then develop a curriculum reinforcement learning framework to enable complex reasoning and exploration for online environments. As a result, UItron achieves superior performance in benchmarks of GUI perception, grounding, and planning. In particular, UItron highlights the interaction proficiency with top-tier Chinese mobile APPs, as we identified a general lack of Chinese capabilities even in state-of-the-art solutions. To this end, we manually collect over one million steps of operation trajectories across the top 100 most popular apps, and build the offline and online agent evaluation environments. Experimental results demonstrate that UItron achieves significant progress in Chinese app scenarios, propelling GUI agents one step closer to real-world application.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UITron-hub/UItron", "github_api_added_by": "auto" }, { "date": "2025-09-01", "arxiv_id": "2508.21456", "title": "Morae: Proactively Pausing UI Agents for User Choices", "authors": [ "Yi-Hao Peng", "Dingzeyu Li", "Jeffrey P. Bigham", "Amy Pavel" ], "github": "", "project_page": "", "abstract": "User interface (UI) agents promise to make inaccessible or complex UIs easier to access for blind and low-vision (BLV) users. However, current UI agents typically perform tasks end-to-end without involving users in critical choices or making them aware of important contextual information, thus reducing user agency. For example, in our field study, a BLV participant asked to buy the cheapest available sparkling water, and the agent automatically chose one from several equally priced options, without mentioning alternative products with different flavors or better ratings. To address this problem, we introduce Morae, a UI agent that automatically identifies decision points during task execution and pauses so that users can make choices. Morae uses large multimodal models to interpret user queries alongside UI code and screenshots, and prompt users for clarification when there is a choice to be made. In a study over real-world web tasks with BLV participants, Morae helped users complete more tasks and select options that better matched their preferences, as compared to baseline agents, including OpenAI Operator. More broadly, this work exemplifies a mixed-initiative approach in which users benefit from the automation of UI agents while being able to express their preferences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.21376", "title": "AHELM: A Holistic Evaluation of Audio-Language Models", "authors": [ "Tony Lee", "Haoqin Tu", "Chi Heem Wong", "Zijun Wang", "Siwei Yang", "Yifan Mai", "Yuyin Zhou", "Cihang Xie", "Percy Liang" ], "github": "", "project_page": "", "abstract": "Evaluations of audio-language models (ALMs) -- multimodal models that take interleaved audio and text as input and output text -- are hindered by the lack of standardized benchmarks; most benchmarks measure only one or two capabilities and omit evaluative aspects such as fairness or safety. Furthermore, comparison across models is difficult as separate evaluations test a limited number of models and use different prompting methods and inference parameters. To address these shortfalls, we introduce AHELM, a benchmark that aggregates various datasets -- including 2 new synthetic audio-text datasets called PARADE, which evaluates the ALMs on avoiding stereotypes, and CoRe-Bench, which measures reasoning over conversational audio through inferential multi-turn question answering -- to holistically measure the performance of ALMs across 10 aspects we have identified as important to the development and usage of ALMs: audio perception, knowledge, reasoning, emotion detection, bias, fairness, multilinguality, robustness, toxicity, and safety. We also standardize the prompts, inference parameters, and evaluation metrics to ensure equitable comparisons across models. We test 14 open-weight and closed-API ALMs from 3 developers and 3 additional simple baseline systems each consisting of an automatic speech recognizer and a language model. Our results show that while Gemini 2.5 Pro ranks top in 5 out of 10 aspects, it exhibits group unfairness (p=0.01) on ASR tasks whereas most of the other models do not. We also find that the baseline systems perform reasonably well on AHELM, with one ranking 5th overall despite having only speech-to-text capabilities. For transparency, all raw prompts, model generations, and outputs are available on our website at https://crfm.stanford.edu/helm/audio/v1.0.0. AHELM is intended to be a living benchmark and new datasets and models will be added over time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.17677", "title": "TiKMiX: Take Data Influence into Dynamic Mixture for Language Model Pre-training", "authors": [ "Yifan Wang", "Binbin Liu", "Fengze Liu", "Yuanfan Guo", "Jiyao Deng", "Xuecheng Wu", "Weidong Zhou", "Xiaohuan Zhou", "Taifeng Wang" ], "github": "", "project_page": "", "abstract": "The data mixture used in the pre-training of a language model is a cornerstone of its final performance. However, a static mixing strategy is suboptimal, as the model's learning preferences for various data domains shift dynamically throughout training. Crucially, observing these evolving preferences in a computationally efficient manner remains a significant challenge. To address this, we propose TiKMiX, a method that dynamically adjusts the data mixture according to the model's evolving preferences. TiKMiX introduces Group Influence, an efficient metric for evaluating the impact of data domains on the model. This metric enables the formulation of the data mixing problem as a search for an optimal, influence-maximizing distribution. We solve this via two approaches: TiKMiX-D for direct optimization, and TiKMiX-M, which uses a regression model to predict a superior mixture. We trained models with different numbers of parameters, on up to 1 trillion tokens. TiKMiX-D exceeds the performance of state-of-the-art methods like REGMIX while using just 20% of the computational resources. TiKMiX-M leads to an average performance gain of 2% across 9 downstream benchmarks. Our experiments reveal that a model's data preferences evolve with training progress and scale, and we demonstrate that dynamically adjusting the data mixture based on Group Influence, a direct measure of these preferences, significantly improves performance by mitigating the underdigestion of data seen with static ratios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.21365", "title": "Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models", "authors": [ "Yi Liao", "Yu Gu", "Yuan Sui", "Zining Zhu", "Yifan Lu", "Guohua Tang", "Zhongqian Sun", "Wei Yang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) excel at complex reasoning tasks such as mathematics and coding, yet they frequently struggle with simple interactive tasks that young children perform effortlessly. This discrepancy highlights a critical gap between declarative knowledge (knowing about something) and procedural knowledge (knowing how to do something). Although traditional reinforcement learning (RL) agents can acquire procedural knowledge through environmental interaction, they often operate as black boxes and require substantial training data. In contrast, LLMs possess extensive world knowledge and reasoning capabilities, but are unable to effectively convert this static knowledge into dynamic decision-making in interactive settings. To address this challenge, we propose Think in Games (TiG), a novel framework that empowers LLMs to develop procedural understanding through direct interaction with game environments, while retaining their inherent reasoning and explanatory abilities. Specifically, TiG reformulates RL-based decision-making as a language modeling task: LLMs generate language-guided policies, which are refined iteratively through online reinforcement learning based on environmental feedback. Our experimental results show that TiG successfully bridges the gap between declarative and procedural knowledge, achieving competitive performance with dramatically lower data and computational demands compared to conventional RL methods. Moreover, TiG provides step-by-step natural language explanations for its decisions, greatly improving transparency and interpretability in complex interactive tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.21290", "title": "Efficient Code Embeddings from Code Generation Models", "authors": [ "Daria Kryvosheieva", "Saba Sturua", "Michael G\u00fcnther", "Scott Martens", "Han Xiao" ], "github": "", "project_page": "", "abstract": "jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries, perform technical question-answering, and identify semantically similar code snippets across programming languages. It makes innovative use of an autoregressive backbone pre-trained on both text and code, generating embeddings via last-token pooling. We outline the training recipe and demonstrate state-of-the-art performance despite the relatively small size of the models, validating this approach to code embedding model construction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.20085", "title": "HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation", "authors": [ "Zhecheng Yuan", "Tianming Wei", "Langzhe Gu", "Pu Hua", "Tianhai Liang", "Yuanpei Chen", "Huazhe Xu" ], "github": "https://github.com/google-deepmind/mujoco_playground", "project_page": "", "abstract": "Leveraging human motion data to impart robots with versatile manipulation skills has emerged as a promising paradigm in robotic manipulation. Nevertheless, translating multi-source human hand motions into feasible robot behaviors remains challenging, particularly for robots equipped with multi-fingered dexterous hands characterized by complex, high-dimensional action spaces. Moreover, existing approaches often struggle to produce policies capable of adapting to diverse environmental conditions. In this paper, we introduce HERMES, a human-to-robot learning framework for mobile bimanual dexterous manipulation. First, HERMES formulates a unified reinforcement learning approach capable of seamlessly transforming heterogeneous human hand motions from multiple sources into physically plausible robotic behaviors. Subsequently, to mitigate the sim2real gap, we devise an end-to-end, depth image-based sim2real transfer method for improved generalization to real-world scenarios. Furthermore, to enable autonomous operation in varied and unstructured environments, we augment the navigation foundation model with a closed-loop Perspective-n-Point (PnP) localization mechanism, ensuring precise alignment of visual goals and effectively bridging autonomous navigation and dexterous manipulation. Extensive experimental results demonstrate that HERMES consistently exhibits generalizable behaviors across diverse, in-the-wild scenarios, successfully performing numerous complex mobile bimanual dexterous manipulation tasks. Project Page:https://gemcollector.github.io/HERMES/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/mujoco_playground", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.14197", "title": "CLIPSym: Delving into Symmetry Detection with CLIP", "authors": [ "Tinghan Yang", "Md Ashiqur Rahman", "Raymond A. Yeh" ], "github": "https://github.com/timyoung2333/CLIPSym", "project_page": "", "abstract": "Symmetry is one of the most fundamental geometric cues in computer vision, and detecting it has been an ongoing challenge. With the recent advances in vision-language models,~i.e., CLIP, we investigate whether a pre-trained CLIP model can aid symmetry detection by leveraging the additional symmetry cues found in the natural image descriptions. We propose CLIPSym, which leverages CLIP's image and language encoders and a rotation-equivariant decoder based on a hybrid of Transformer and G-Convolution to detect rotation and reflection symmetries. To fully utilize CLIP's language encoder, we have developed a novel prompting technique called Semantic-Aware Prompt Grouping (SAPG), which aggregates a diverse set of frequent object-based prompts to better integrate the semantic cues for symmetry detection. Empirically, we show that CLIPSym outperforms the current state-of-the-art on three standard symmetry detection datasets (DENDI, SDRW, and LDRS). Finally, we conduct detailed ablations verifying the benefits of CLIP's pre-training, the proposed equivariant decoder, and the SAPG technique. The code is available at https://github.com/timyoung2333/CLIPSym.", "github_manual": "", "github_regex": "https://github.com/timyoung2333/CLIPSym", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.20470", "title": "Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation", "authors": [ "Xiaochuan Li", "Guoguang Du", "Runze Zhang", "Liang Jin", "Qi Jia", "Lihua Lu", "Zhenhua Guo", "Yaqian Zhao", "Haiyang Liu", "Tianqi Wang", "Changsheng Li", "Xiaoli Gong", "Rengang Li", "Baoyu Fan" ], "github": "https://github.com/aigc-apps/CogVideoX-Fun", "project_page": "", "abstract": "Scaling laws have validated the success and promise of large-data-trained models in creative generation across text, image, and video domains. However, this paradigm faces data scarcity in the 3D domain, as there is far less of it available on the internet compared to the aforementioned modalities. Fortunately, there exist adequate videos that inherently contain commonsense priors, offering an alternative supervisory signal to mitigate the generalization bottleneck caused by limited native 3D data. On the one hand, videos capturing multiple views of an object or scene provide a spatial consistency prior for 3D generation. On the other hand, the rich semantic information contained within the videos enables the generated content to be more faithful to the text prompts and semantically plausible. This paper explores how to apply the video modality in 3D asset generation, spanning datasets to models. We introduce Droplet3D-4M, the first large-scale video dataset with multi-view level annotations, and train Droplet3D, a generative model supporting both image and dense text input. Extensive experiments validate the effectiveness of our approach, demonstrating its ability to produce spatially consistent and semantically plausible content. Moreover, in contrast to the prevailing 3D solutions, our approach exhibits the potential for extension to scene-level applications. This indicates that the commonsense priors from the videos significantly facilitate 3D creation. We have open-sourced all resources including the dataset, code, technical framework, and model weights: https://dropletx.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aigc-apps/CogVideoX-Fun", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.21148", "title": "A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers", "authors": [ "Ming Hu", "Chenglong Ma", "Wei Li", "Wanghan Xu", "Jiamin Wu", "Jucheng Hu", "Tianbin Li", "Guohang Zhuang", "Jiaqi Liu", "Yingzhou Lu", "Ying Chen", "Chaoyang Zhang", "Cheng Tan", "Jie Ying", "Guocheng Wu", "Shujian Gao", "Pengcheng Chen", "Jiashi Lin", "Haitao Wu", "Lulu Chen", "Fengxiang Wang", "Yuanyuan Zhang", "Xiangyu Zhao", "Feilong Tang", "Encheng Su", "Junzhi Ning", "Xinyao Liu", "Ye Du", "Changkai Ji", "Cheng Tang", "Huihui Xu", "Ziyang Chen", "Ziyan Huang", "Jiyao Liu", "Pengfei Jiang", "Yizhou Wang", "Chen Tang", "Jianyu Wu", "Yuchen Ren", "Siyuan Yan", "Zhonghua Wang", "Zhongxing Xu", "Shiyan Su", "Shangquan Sun", "Runkai Zhao", "Zhisheng Zhang", "Yu Liu", "Fudi Wang", "Yuanfeng Ji", "Yanzhou Su", "Hongming Shan", "Chunmei Feng", "Jiahao Xu", "Jiangtao Yan", "Wenhao Tang", "Diping Song", "Lihao Liu", "Yanyan Huang", "Lequan Yu", "Bin Fu", "Shujun Wang", "Xiaomeng Li", "Xiaowei Hu", "Yun Gu", "Ben Fei", "Zhongying Deng", "Benyou Wang", "Yuewen Cao", "Minjie Shen", "Haodong Duan", "Jie Xu", "Yirong Chen", "Fang Yan", "Hongxia Hao", "Jielan Li", "Jiajun Du", "Yanbo Wang", "Imran Razzak", "Chi Zhang", "Lijun Wu", "Conghui He", "Zhaohui Lu", "Jinhai Huang", "Yihao Liu", "Fenghua Ling", "Yuqiang Li", "Aoran Wang", "Qihao Zheng", "Nanqing Dong", "Tianfan Fu", "Dongzhan Zhou", "Yan Lu", "Wenlong Zhang", "Jin Ye", "Jianfei Cai", "Wanli Ouyang", "Yu Qiao", "Zongyuan Ge", "Shixiang Tang", "Junjun He", "Chunfeng Song", "Lei Bai", "Bowen Zhou" ], "github": "https://github.com/open-sciencelab/Awesome-Scientific-Datasets-and-LLMs", "project_page": "", "abstract": "Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, yet their progress is shaped by the complex nature of scientific data. This survey presents a comprehensive, data-centric synthesis that reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate. We formulate a unified taxonomy of scientific data and a hierarchical model of scientific knowledge, emphasizing the multimodal, cross-scale, and domain-specific challenges that differentiate scientific corpora from general natural language processing datasets. We systematically review recent Sci-LLMs, from general-purpose foundations to specialized models across diverse scientific disciplines, alongside an extensive analysis of over 270 pre-/post-training datasets, showing why Sci-LLMs pose distinct demands -- heterogeneous, multi-scale, uncertainty-laden corpora that require representations preserving domain invariance and enabling cross-modal reasoning. On evaluation, we examine over 190 benchmark datasets and trace a shift from static exams toward process- and discovery-oriented assessments with advanced evaluation protocols. These data-centric analyses highlight persistent issues in scientific data development and discuss emerging solutions involving semi-automated annotation pipelines and expert validation. Finally, we outline a paradigm shift toward closed-loop systems where autonomous agents based on Sci-LLMs actively experiment, validate, and contribute to a living, evolving knowledge base. Collectively, this work provides a roadmap for building trustworthy, continually evolving artificial intelligence (AI) systems that function as a true partner in accelerating scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-sciencelab/Awesome-Scientific-Datasets-and-LLMs", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.21188", "title": "Model-Task Alignment Drives Distinct RL Outcomes", "authors": [ "Haoze Wu", "Cheng Wang", "Wenshuo Zhao", "Junxian He" ], "github": "https://github.com/hkust-nlp/model-task-align-rl", "project_page": "", "abstract": "Recent advances in applying reinforcement learning (RL) to large language models (LLMs) have led to substantial progress. In particular, a series of remarkable yet often counterintuitive phenomena have been reported in LLMs, exhibiting patterns not typically observed in traditional RL settings. For example, notable claims include that a single training example can match the performance achieved with an entire dataset, that the reward signal does not need to be very accurate, and that training solely with negative samples can match or even surpass sophisticated reward-based methods. However, the precise conditions under which these observations hold - and, critically, when they fail - remain unclear. In this work, we identify a key factor that differentiates RL observations: whether the pretrained model already exhibits strong Model-Task Alignment, as measured by pass@k accuracy on the evaluated task. Through a systematic and comprehensive examination of a series of counterintuitive claims, supported by rigorous experimental validation across different model architectures and task domains, our findings show that while standard RL training remains consistently robust across settings, many of these counterintuitive results arise only when the model and task already exhibit strong model-task alignment. In contrast, these techniques fail to drive substantial learning in more challenging regimes, where standard RL methods remain effective.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hkust-nlp/model-task-align-rl", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.19600", "title": "Quantization Robustness to Input Degradations for Object Detection", "authors": [ "Toghrul Karimov", "Hassan Imani", "Allan Kazakov" ], "github": "https://github.com/AllanK24/QRID", "project_page": "", "abstract": "Post-training quantization (PTQ) is crucial for deploying efficient object detection models, like YOLO, on resource-constrained devices. However, the impact of reduced precision on model robustness to real-world input degradations such as noise, blur, and compression artifacts is a significant concern. This paper presents a comprehensive empirical study evaluating the robustness of YOLO models (nano to extra-large scales) across multiple precision formats: FP32, FP16 (TensorRT), Dynamic UINT8 (ONNX), and Static INT8 (TensorRT). We introduce and evaluate a degradation-aware calibration strategy for Static INT8 PTQ, where the TensorRT calibration process is exposed to a mix of clean and synthetically degraded images. Models were benchmarked on the COCO dataset under seven distinct degradation conditions (including various types and levels of noise, blur, low contrast, and JPEG compression) and a mixed-degradation scenario. Results indicate that while Static INT8 TensorRT engines offer substantial speedups (~1.5-3.3x) with a moderate accuracy drop (~3-7% mAP50-95) on clean data, the proposed degradation-aware calibration did not yield consistent, broad improvements in robustness over standard clean-data calibration across most models and degradations. A notable exception was observed for larger model scales under specific noise conditions, suggesting model capacity may influence the efficacy of this calibration approach. These findings highlight the challenges in enhancing PTQ robustness and provide insights for deploying quantized detectors in uncontrolled environments. All code and evaluation tables are available at https://github.com/AllanK24/QRID.", "github_manual": "", "github_regex": "https://github.com/AllanK24/QRID", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.17380", "title": "Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula Discovery", "authors": [ "Jiaqi Liu", "Songning Lai", "Pengze Li", "Di Yu", "Wenjie Zhou", "Yiyang Zhou", "Peng Xia", "Zijun Wang", "Xi Chen", "Shixiang Tang", "Lei Bai", "Wanli Ouyang", "Mingyu Ding", "Huaxiu Yao", "Aoran Wang" ], "github": "https://github.com/Jiaaqiliu/VIPER-R1", "project_page": "", "abstract": "Automated discovery of physical laws from observational data in the real world is a grand challenge in AI. Current methods, relying on symbolic regression or LLMs, are limited to uni-modal data and overlook the rich, visual phenomenological representations of motion that are indispensable to physicists. This \"sensory deprivation\" severely weakens their ability to interpret the inherent spatio-temporal patterns within dynamic phenomena. To address this gap, we propose VIPER-R1, a multimodal model that performs Visual Induction for Physics-based Equation Reasoning to discover fundamental symbolic formulas. It integrates visual perception, trajectory data, and symbolic reasoning to emulate the scientific discovery process. The model is trained via a curriculum of Motion Structure Induction (MSI), using supervised fine-tuning to interpret kinematic phase portraits and to construct hypotheses guided by a Causal Chain of Thought (C-CoT), followed by Reward-Guided Symbolic Calibration (RGSC) to refine the formula structure with reinforcement learning. During inference, the trained VIPER-R1 acts as an agent: it first posits a high-confidence symbolic ansatz, then proactively invokes an external symbolic regression tool to perform Symbolic Residual Realignment (SR^2). This final step, analogous to a physicist's perturbation analysis, reconciles the theoretical model with empirical data. To support this research, we introduce PhysSymbol, a new 5,000-instance multimodal corpus. Experiments show that VIPER-R1 consistently outperforms state-of-the-art VLM baselines in accuracy and interpretability, enabling more precise discovery of physical laws. Project page: https://jiaaqiliu.github.io/VIPER-R1/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Jiaaqiliu/VIPER-R1", "github_api_added_by": "auto" }, { "date": "2025-09-01", "arxiv_id": "2508.21172", "title": "Deep Residual Echo State Networks: exploring residual orthogonal connections in untrained Recurrent Neural Networks", "authors": [ "Matteo Pinna", "Andrea Ceni", "Claudio Gallicchio" ], "github": "", "project_page": "", "abstract": "Echo State Networks (ESNs) are a particular type of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) framework, popular for their fast and efficient learning. However, traditional ESNs often struggle with long-term information processing. In this paper, we introduce a novel class of deep untrained RNNs based on temporal residual connections, called Deep Residual Echo State Networks (DeepResESNs). We show that leveraging a hierarchy of untrained residual recurrent layers significantly boosts memory capacity and long-term temporal modeling. For the temporal residual connections, we consider different orthogonal configurations, including randomly generated and fixed-structure configurations, and we study their effect on network dynamics. A thorough mathematical analysis outlines necessary and sufficient conditions to ensure stable dynamics within DeepResESN. Our experiments on a variety of time series tasks showcase the advantages of the proposed approach over traditional shallow and deep RC.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-01", "arxiv_id": "2508.17008", "title": "EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks", "authors": [ "Yan Cathy Hua", "Paul Denny", "J\u00f6rg Wicker", "Katerina Taskova" ], "github": "https://github.com/yhua219/edurabsa_dataset_and_annotation_tool", "project_page": "", "abstract": "Every year, most educational institutions seek and receive an enormous volume of text feedback from students on courses, teaching, and overall experience. Yet, turning this raw feedback into useful insights is far from straightforward. It has been a long-standing challenge to adopt automatic opinion mining solutions for such education review text data due to the content complexity and low-granularity reporting requirements. Aspect-based Sentiment Analysis (ABSA) offers a promising solution with its rich, sub-sentence-level opinion mining capabilities. However, existing ABSA research and resources are very heavily focused on the commercial domain. In education, they are scarce and hard to develop due to limited public datasets and strict data protection. A high-quality, annotated dataset is urgently needed to advance research in this under-resourced area. In this work, we present EduRABSA (Education Review ABSA), the first public, annotated ABSA education review dataset that covers three review subject types (course, teaching staff, university) in the English language and all main ABSA tasks, including the under-explored implicit aspect and implicit opinion extraction. We also share ASQE-DPT (Data Processing Tool), an offline, lightweight, installation-free manual data annotation tool that generates labelled datasets for comprehensive ABSA tasks from a single-task annotation. Together, these resources contribute to the ABSA community and education domain by removing the dataset barrier, supporting research transparency and reproducibility, and enabling the creation and sharing of further resources. The dataset, annotation tool, and scripts and statistics for dataset processing and sampling are available at https://github.com/yhua219/edurabsa_dataset_and_annotation_tool.", "github_manual": "", "github_regex": "https://github.com/yhua219/edurabsa_dataset_and_annotation_tool", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-02", "arxiv_id": "2508.21104", "title": "PVPO: Pre-Estimated Value-Based Policy Optimization for Agentic Reasoning", "authors": [ "Wenfeng Feng", "Penghong Zhao", "Guochao Jiang", "Chuzhan Hao", "Yuewei Zhang", "Hao Wang" ], "github": "", "project_page": "", "abstract": "Critic-free reinforcement learning methods, particularly group policies, have attracted considerable attention for their efficiency in complex tasks. However, these methods rely heavily on multiple sampling and comparisons within the policy to estimate advantage, which may cause the policy to fall into local optimum and increase computational cost. To address these issues, we propose PVPO, an efficient reinforcement learning method enhanced by an advantage reference anchor and data pre-sampling. Specifically, we use the reference model to rollout in advance and employ the calculated reward score as a reference anchor. Our approach effectively corrects the cumulative bias introduced by intra-group comparisons and significantly reduces reliance on the number of rollouts. Meanwhile, the reference model can assess sample difficulty during data pre-sampling, enabling effective selection of high-gain data to improve training efficiency. Experiments conducted on nine datasets across two domains demonstrate that PVPO achieves State-Of-The-Art (SOTA) performance. Our approach not only demonstrates robust generalization across multiple tasks, but also exhibits scalable performance across models of varying scales.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-02", "arxiv_id": "2508.19060", "title": "No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes", "authors": [ "Bla\u017e Rolih", "Matic Fu\u010dka", "Danijel Sko\u010daj" ], "github": "https://github.com/blaz-r/SuperSimpleNet", "project_page": "", "abstract": "Surface defect detection is a critical task across numerous industries, aimed at efficiently identifying and localising imperfections or irregularities on manufactured components. While numerous methods have been proposed, many fail to meet industrial demands for high performance, efficiency, and adaptability. Existing approaches are often constrained to specific supervision scenarios and struggle to adapt to the diverse data annotations encountered in real-world manufacturing processes, such as unsupervised, weakly supervised, mixed supervision, and fully supervised settings. To address these challenges, we propose SuperSimpleNet, a highly efficient and adaptable discriminative model built on the foundation of SimpleNet. SuperSimpleNet incorporates a novel synthetic anomaly generation process, an enhanced classification head, and an improved learning procedure, enabling efficient training in all four supervision scenarios, making it the first model capable of fully leveraging all available data annotations. SuperSimpleNet sets a new standard for performance across all scenarios, as demonstrated by its results on four challenging benchmark datasets. Beyond accuracy, it is very fast, achieving an inference time below 10 ms. With its ability to unify diverse supervision paradigms while maintaining outstanding speed and reliability, SuperSimpleNet represents a promising step forward in addressing real-world manufacturing challenges and bridging the gap between academic research and industrial applications. Code: https://github.com/blaz-r/SuperSimpleNet", "github_manual": "", "github_regex": "https://github.com/blaz-r/SuperSimpleNet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-02", "arxiv_id": "2508.20931", "title": "How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on \u03c4-bench", "authors": [ "Venkatesh Mishra", "Amir Saeidi", "Satyam Raj", "Mutsumi Nakamura", "Jayanth Srinivasa", "Gaowen Liu", "Ali Payani", "Chitta Baral" ], "github": "", "project_page": "", "abstract": "Recent advances in reasoning and planning capabilities of large language models (LLMs) have enabled their potential as autonomous agents capable of tool use in dynamic environments. However, in multi-turn conversational environments like tau-bench, these agents often struggle with consistent reasoning, adherence to domain-specific policies, and extracting correct information over a long horizon of tool-calls and conversation. To capture and mitigate these failures, we conduct a comprehensive manual analysis of the common errors occurring in the conversation trajectories. We then experiment with reformulations of inputs to the tool-calling agent for improvement in agent decision making. Finally, we propose the Input-Reformulation Multi-Agent (IRMA) framework, which automatically reformulates user queries augmented with relevant domain rules and tool suggestions for the tool-calling agent to focus on. The results show that IRMA significantly outperforms ReAct, Function Calling, and Self-Reflection by 16.1%, 12.7%, and 19.1%, respectively, in overall pass^5 scores. These findings highlight the superior reliability and consistency of IRMA compared to other methods in dynamic environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-02", "arxiv_id": "2508.17378", "title": "UI-Level Evaluation of ALLaM 34B: Measuring an Arabic-Centric LLM via HUMAIN Chat", "authors": [ "Omer Nacar" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) trained primarily on English corpora often struggle to capture the linguistic and cultural nuances of Arabic. To address this gap, the Saudi Data and AI Authority (SDAIA) introduced the ALLaM family of Arabic-focused models. The most capable of these available to the public, ALLaM-34B, was subsequently adopted by HUMAIN, who developed and deployed HUMAIN Chat, a closed conversational web service built on this model. This paper presents an expanded and refined UI-level evaluation of ALLaM-34B. Using a prompt pack spanning modern standard Arabic, five regional dialects, code-switching, factual knowledge, arithmetic and temporal reasoning, creative generation, and adversarial safety, we collected 115 outputs (23 prompts times 5 runs) and scored each with three frontier LLM judges (GPT-5, Gemini 2.5 Pro, Claude Sonnet-4). We compute category-level means with 95\\% confidence intervals, analyze score distributions, and visualize dialect-wise metric heat maps. The updated analysis reveals consistently high performance on generation and code-switching tasks (both averaging 4.92/5), alongside strong results in MSA handling (4.74/5), solid reasoning ability (4.64/5), and improved dialect fidelity (4.21/5). Safety-related prompts show stable, reliable performance of (4.54/5). Taken together, these results position ALLaM-34B as a robust and culturally grounded Arabic LLM, demonstrating both technical strength and practical readiness for real-world deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-02", "arxiv_id": "2508.19813", "title": "T2R-bench: A Benchmark for Generating Article-Level Reports from Real World Industrial Tables", "authors": [ "Jie Zhang", "Changzai Pan", "Kaiwen Wei", "Sishi Xiong", "Yu Zhao", "Xiangyu Li", "Jiaxin Peng", "Xiaoyan Gu", "Jian Yang", "Wenhan Chang", "Zhenhe Wu", "Jiang Zhong", "Shuangyong Song", "Yongxiang Li", "Xuelong Li" ], "github": "", "project_page": "", "abstract": "Extensive research has been conducted to explore the capabilities of large language models (LLMs) in table reasoning. However, the essential task of transforming tables information into reports remains a significant challenge for industrial applications. This task is plagued by two critical issues: 1) the complexity and diversity of tables lead to suboptimal reasoning outcomes; and 2) existing table benchmarks lack the capacity to adequately assess the practical application of this task. To fill this gap, we propose the table-to-report task and construct a bilingual benchmark named T2R-bench, where the key information flow from the tables to the reports for this task. The benchmark comprises 457 industrial tables, all derived from real-world scenarios and encompassing 19 industry domains as well as 4 types of industrial tables. Furthermore, we propose an evaluation criteria to fairly measure the quality of report generation. The experiments on 25 widely-used LLMs reveal that even state-of-the-art models like Deepseek-R1 only achieves performance with 62.71 overall score, indicating that LLMs still have room for improvement on T2R-bench. Source code and data will be available after acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-02", "arxiv_id": "2508.17198", "title": "From reactive to cognitive: brain-inspired spatial intelligence for embodied agents", "authors": [ "Shouwei Ruan", "Liyuan Wang", "Caixin Kang", "Qihui Zhu", "Songming Liu", "Xingxing Wei", "Hang Su" ], "github": "https://github.com/Heathcliff-saku/BSC-Nav", "project_page": "", "abstract": "Spatial cognition enables adaptive goal-directed behavior by constructing internal models of space. Robust biological systems consolidate spatial knowledge into three interconnected forms: landmarks for salient cues, route knowledge for movement trajectories, and survey knowledge for map-like representations. While recent advances in multi-modal large language models (MLLMs) have enabled visual-language reasoning in embodied agents, these efforts lack structured spatial memory and instead operate reactively, limiting their generalization and adaptability in complex real-world environments. Here we present Brain-inspired Spatial Cognition for Navigation (BSC-Nav), a unified framework for constructing and leveraging structured spatial memory in embodied agents. BSC-Nav builds allocentric cognitive maps from egocentric trajectories and contextual cues, and dynamically retrieves spatial knowledge aligned with semantic goals. Integrated with powerful MLLMs, BSC-Nav achieves state-of-the-art efficacy and efficiency across diverse navigation tasks, demonstrates strong zero-shot generalization, and supports versatile embodied behaviors in the real physical world, offering a scalable and biologically grounded path toward general-purpose spatial intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Heathcliff-saku/BSC-Nav", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-02", "arxiv_id": "2508.19562", "title": "Democracy-in-Silico: Institutional Design as Alignment in AI-Governed Polities", "authors": [ "Trisanth Srinivasan", "Santosh Patapati" ], "github": "", "project_page": "", "abstract": "This paper introduces Democracy-in-Silico, an agent-based simulation where societies of advanced AI agents, imbued with complex psychological personas, govern themselves under different institutional frameworks. We explore what it means to be human in an age of AI by tasking Large Language Models (LLMs) to embody agents with traumatic memories, hidden agendas, and psychological triggers. These agents engage in deliberation, legislation, and elections under various stressors, such as budget crises and resource scarcity. We present a novel metric, the Power-Preservation Index (PPI), to quantify misaligned behavior where agents prioritize their own power over public welfare. Our findings demonstrate that institutional design, specifically the combination of a Constitutional AI (CAI) charter and a mediated deliberation protocol, serves as a potent alignment mechanism. These structures significantly reduce corrupt power-seeking behavior, improve policy stability, and enhance citizen welfare compared to less constrained democratic models. The simulation reveals that an institutional design may offer a framework for aligning the complex, emergent behaviors of future artificial agent societies, forcing us to reconsider what human rituals and responsibilities are essential in an age of shared authorship with non-human entities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.02522", "title": "Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR", "authors": [ "Jiaming Li", "Longze Chen", "Ze Gong", "Yukun Chen", "Lu Wang", "Wanwei He", "Run Luo", "Min Yang" ], "github": "https://github.com/ritzz-ai/PACS", "project_page": "", "abstract": "Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have empowered large language models (LLMs) to tackle challenging reasoning tasks such as mathematics and programming. RLVR leverages verifiable outcome rewards to guide policy optimization, enabling LLMs to progressively improve output quality in a grounded and reliable manner. Despite its promise, the RLVR paradigm poses significant challenges, as existing methods often suffer from sparse reward signals and unstable policy gradient updates, particularly in RL-based approaches. To address the challenges, we propose PACS, a novel RLVR framework that achieves imPlicit Actor Critic coupling via a Supervised learning framework. By treating the outcome reward as a predictable label, we reformulate the RLVR problem into a supervised learning task over a score function parameterized by the policy model and optimized using cross-entropy loss. A detailed gradient analysis shows that this supervised formulation inherently recovers the classical policy gradient update while implicitly coupling actor and critic roles, yielding more stable and efficient training. Benchmarking on challenging mathematical reasoning tasks, PACS outperforms strong RLVR baselines, such as PPO and GRPO, achieving superior reasoning performance. For instance, PACS achieves 59.78\\% at pass@256 on AIME 2025, representing improvements of 13.32 and 14.36 points over PPO and GRPO. This simple yet powerful framework offers a promising avenue for LLMs post-training with verifiable rewards. Our code and data are available as open source at https://github.com/ritzz-ai/PACS.", "github_manual": "", "github_regex": "https://github.com/ritzz-ai/PACS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.02534", "title": "Jointly Reinforcing Diversity and Quality in Language Model Generations", "authors": [ "Tianjian Li", "Yiming Zhang", "Ping Yu", "Swarnadeep Saha", "Daniel Khashabi", "Jason Weston", "Jack Lanchantin", "Tianlu Wang" ], "github": "https://github.com/facebookresearch/darling", "project_page": "", "abstract": "Post-training of Large Language Models (LMs) often prioritizes accuracy and helpfulness at the expense of diversity. This creates a tension: while post-training improves response quality, it also sharpens output distributions and reduces the range of ideas, limiting the usefulness of LMs in creative and exploratory tasks such as brainstorming, storytelling, or problem solving. We address this challenge with Diversity-Aware Reinforcement Learning (DARLING), a framework that jointly optimizes for response quality and semantic diversity. At its core, DARLING introduces a learned partition function to measure diversity beyond surface-level lexical variations. This diversity signal is then combined with a quality reward during online reinforcement learning, encouraging models to generate outputs that are both high-quality and distinct. Experiments across multiple model families and sizes show that DARLING generalizes to two regimes: non-verifiable tasks (instruction following and creative writing) and verifiable tasks (competition math). On five benchmarks in the first setting, DARLING consistently outperforms quality-only RL baselines, producing outputs that are simultaneously of higher quality and novelty. In the second setting, DARLING achieves higher pass@1 (solution quality) and pass@k (solution variety). Most strikingly, explicitly optimizing for diversity catalyzes exploration in online RL, which manifests itself as higher-quality responses.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/darling", "github_api_added_by": "auto" }, { "date": "2025-09-03", "arxiv_id": "2509.02379", "title": "MedDINOv3: How to adapt vision foundation models for medical image segmentation?", "authors": [ "Yuheng Li", "Yizhou Wu", "Yuxiang Lai", "Mingzhe Hu", "Xiaofeng Yang" ], "github": "https://github.com/ricklisz/MedDINOv3", "project_page": "", "abstract": "Accurate segmentation of organs and tumors in CT and MRI scans is essential for diagnosis, treatment planning, and disease monitoring. While deep learning has advanced automated segmentation, most models remain task-specific, lacking generalizability across modalities and institutions. Vision foundation models (FMs) pretrained on billion-scale natural images offer powerful and transferable representations. However, adapting them to medical imaging faces two key challenges: (1) the ViT backbone of most foundation models still underperform specialized CNNs on medical image segmentation, and (2) the large domain gap between natural and medical images limits transferability. We introduce MedDINOv3, a simple and effective framework for adapting DINOv3 to medical segmentation. We first revisit plain ViTs and design a simple and effective architecture with multi-scale token aggregation. Then, we perform domain-adaptive pretraining on CT-3M, a curated collection of 3.87M axial CT slices, using a multi-stage DINOv3 recipe to learn robust dense features. MedDINOv3 matches or exceeds state-of-the-art performance across four segmentation benchmarks, demonstrating the potential of vision foundation models as unified backbones for medical image segmentation. The code is available at https://github.com/ricklisz/MedDINOv3.", "github_manual": "", "github_regex": "https://github.com/ricklisz/MedDINOv3", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.02547", "title": "The Landscape of Agentic Reinforcement Learning for LLMs: A Survey", "authors": [ "Guibin Zhang", "Hejia Geng", "Xiaohang Yu", "Zhenfei Yin", "Zaibin Zhang", "Zelin Tan", "Heng Zhou", "Zhongzhi Li", "Xiangyuan Xue", "Yijiang Li", "Yifan Zhou", "Yang Chen", "Chen Zhang", "Yutao Fan", "Zihu Wang", "Songtao Huang", "Yue Liao", "Hongru Wang", "Mengyue Yang", "Heng Ji", "Michael Littman", "Jun Wang", "Shuicheng Yan", "Philip Torr", "Lei Bai" ], "github": "https://github.com/PRIME-RL/SimpleVLA-RL", "project_page": "", "abstract": "The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL), reframing LLMs from passive sequence generators into autonomous, decision-making agents embedded in complex, dynamic worlds. This survey formalizes this conceptual shift by contrasting the degenerate single-step Markov Decision Processes (MDPs) of LLM-RL with the temporally extended, partially observable Markov decision processes (POMDPs) that define Agentic RL. Building on this foundation, we propose a comprehensive twofold taxonomy: one organized around core agentic capabilities, including planning, tool use, memory, reasoning, self-improvement, and perception, and the other around their applications across diverse task domains. Central to our thesis is that reinforcement learning serves as the critical mechanism for transforming these capabilities from static, heuristic modules into adaptive, robust agentic behavior. To support and accelerate future research, we consolidate the landscape of open-source environments, benchmarks, and frameworks into a practical compendium. By synthesizing over five hundred recent works, this survey charts the contours of this rapidly evolving field and highlights the opportunities and challenges that will shape the development of scalable, general-purpose AI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PRIME-RL/SimpleVLA-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.00676", "title": "LLaVA-Critic-R1: Your Critic Model is Secretly a Strong Policy Model", "authors": [ "Xiyao Wang", "Chunyuan Li", "Jianwei Yang", "Kai Zhang", "Bo Liu", "Tianyi Xiong", "Furong Huang" ], "github": "", "project_page": "", "abstract": "In vision-language modeling, critic models are typically trained to evaluate outputs -- assigning scalar scores or pairwise preferences -- rather than to generate responses. This separation from policy models, which produce the responses, is so entrenched that critics are rarely considered for direct policy use. In this work, we challenge this convention. We propose to reorganize preference-labeled critic datasets into verifiable training signals and perform reinforcement learning directly on a base generative model, producing LLaVA-Critic-R1, a multimodal critic trained to optimize preference judgments while retaining full generation ability. Surprisingly, LLaVA-Critic-R1 emerges not only as a top-performing critic but also as a competitive policy model -- matching or surpassing specialized reasoning VLMs trained with in-domain data across 26 visual reasoning and understanding benchmarks, with an average gain of +5.7% over its base model (Qwen-2.5-VL-7B). Extending this approach to existing strong reasoning VLMs yields LLaVA-Critic-R1+, which further advances policy performance without sacrificing critic quality, achieving a SoTA performance of 71.9 on MMMU at the 7B scale. Finally, we show that the enhanced critic ability benefits inference: applying self-critique at test time yields an average +13.8% improvement on five representative reasoning tasks without additional training. Our results reveal that RL training on critic data can produce a unified model excelling at both evaluation and generation, offering a simple path toward scalable, self-improving multimodal systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.01215", "title": "POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion", "authors": [ "Yuan Liu", "Zhongyin Zhao", "Le Tian", "Haicheng Wang", "Xubing Ye", "Yangxiu You", "Zilin Yu", "Chuhan Wu", "Xiao Zhou", "Yang Yu", "Jie Zhou" ], "github": "https://github.com/Tencent/POINTS-Reader", "project_page": "", "abstract": "High-quality labeled data is essential for training accurate document conversion models, particularly in domains with complex formats such as tables, formulas, and multi-column text. However, manual annotation is both costly and time-consuming, while automatic labeling using existing models often lacks accuracy in handling such challenging scenarios. Consequently, training student models by distilling outputs from teacher models can significantly limit their performance in real-world applications. In this paper, we propose a fully automated, distillation-free framework comprising two stages for constructing high-quality document extraction datasets and models capable of handling diverse document formats and layouts. In the first stage, we introduce a method for generating large-scale, diverse synthetic data, which enables a model to extract key elements in a unified format with strong initial performance. In the second stage, we present a self-improvement approach that further adapts the model, initially trained on synthetic data, to real-world documents. Specifically, we first use the fine-tuned model to annotate real documents, then apply a suite of filtering strategies to verify annotation quality, and finally retrain the model on the verified dataset. By iteratively repeating this process, we progressively enhance both the model's conversion capabilities and the quality of the generated data. We train a public POINTS-1.5 model to obtain POINTS-Reader, which surpasses many existing public and proprietary models of comparable or larger size. Our model is available at https://github.com/Tencent/POINTS-Reader.", "github_manual": "", "github_regex": "https://github.com/Tencent/POINTS-Reader", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.02479", "title": "SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning", "authors": [ "Zhenghai Xue", "Longtao Zheng", "Qian Liu", "Yingru Li", "Xiaosen Zheng", "Zejun Ma", "Bo An" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) can significantly improve their reasoning capabilities by interacting with external tools, a paradigm known as Tool-Integrated Reasoning (TIR). However, extending TIR to multi-turn scenarios using Reinforcement Learning (RL) is often hindered by training instability and performance collapse. We identify that such instability is primarily caused by a distributional drift from external tool feedback, leading to the generation of low-probability tokens. This issue compounds over successive turns, causing catastrophic gradient norm explosions that derail the training process. To address this challenge, we introduce SimpleTIR , a plug-and-play algorithm that stabilizes multi-turn TIR training. Its core strategy is to identify and filter out trajectories containing void turns, i.e., turns that yield neither a code block nor a final answer. By removing these problematic trajectories from the policy update, SimpleTIR effectively blocks the harmful, high-magnitude gradients, thus stabilizing the learning dynamics. Extensive experiments show that SimpleTIR achieves state-of-the-art performance on challenging math reasoning benchmarks, notably elevating the AIME24 score from a text-only baseline of 22.1 to 50.5 when starting from the Qwen2.5-7B base model. Furthermore, by avoiding the constraints of supervised fine-tuning, SimpleTIR encourages the model to discover diverse and sophisticated reasoning patterns, such as self-correction and cross-validation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.02040", "title": "Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation", "authors": [ "Guangzeng Han", "Weisi Liu", "Xiaolei Huang" ], "github": "https://github.com/trust-nlp/Genetic-Prompt", "project_page": "", "abstract": "Large Language Models (LLMs) excel at generating synthetic data, but ensuring its quality and diversity remains challenging. We propose Genetic Prompt, a novel framework that combines genetic algorithms with LLMs to augment synthetic data generation. Our approach treats semantic text attributes as gene sequences and leverages the LLM to simulate crossover and mutation operations. This genetic process enhances data quality and diversity by creating novel attribute combinations, yielding synthetic distributions closer to real-world data. To optimize parent selection, we also integrate an active learning scheme that expands the offspring search space. Our experiments on multiple NLP tasks reveal several key findings: Genetic Prompt not only significantly outperforms state-of-the-art baselines but also shows robust performance across various generator model sizes and scales. Moreover, we demonstrate that fusing our synthetic data with the original training set significantly boosts downstream model performance, particularly for class-imbalanced scenarios. Our findings validate that Genetic Prompt is an effective method for producing high-quality synthetic data for a wide range of NLP applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/trust-nlp/Genetic-Prompt", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.01644", "title": "OpenVision 2: A Family of Generative Pretrained Visual Encoders for Multimodal Learning", "authors": [ "Yanqing Liu", "Xianhang Li", "Letian Zhang", "Zirui Wang", "Zeyu Zheng", "Yuyin Zhou", "Cihang Xie" ], "github": "https://github.com/xiaoachen98/Open-LLaVA-NeXT", "project_page": "", "abstract": "This paper provides a simplification on OpenVision's architecture and loss design for enhancing its training efficiency. Following the prior vision-language pretraining works CapPa and AIMv2, as well as modern multimodal designs like LLaVA, our changes are straightforward: we remove the text encoder (and therefore the contrastive loss), retaining only the captioning loss as a purely generative training signal. We name this new version OpenVision 2. The initial results are promising: despite this simplification, OpenVision 2 competitively matches the original model's performance on a broad set of multimodal benchmarks while substantially cutting both training time and memory consumption. For example, with ViT-L/14, it reduces training time by about 1.5x (from 83h to 57h), and memory usage by about 1.8x (from 24.5GB to 13.8GB, equivalently allowing the maximum batch size to grow from 2k to 8k). This superior training efficiency also allows us to scale far beyond the largest vision encoder used in OpenVision, reaching more than 1 billion parameters. We hold a strong belief that this lightweight, generative-only paradigm is compelling for future vision encoder development in multimodal foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xiaoachen98/Open-LLaVA-NeXT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.01563", "title": "Kwai Keye-VL 1.5 Technical Report", "authors": [ "Biao Yang", "Bin Wen", "Boyang Ding", "Changyi Liu", "Chenglong Chu", "Chengru Song", "Chongling Rao", "Chuan Yi", "Da Li", "Dunju Zang", "Fan Yang", "Guorui Zhou", "Guowang Zhang", "Han Shen", "Hao Peng", "Haojie Ding", "Hao Wang", "Hengrui Ju", "Jiaming Huang", "Jiangxia Cao", "Jiankang Chen", "Jingyun Hua", "Kaibing Chen", "Kaiyu Jiang", "Kaiyu Tang", "Kun Gai", "Muhao Wei", "Qiang Wang", "Ruitao Wang", "Sen Na", "Shengnan Zhang", "Siyang Mao", "Sui Huang", "Tianke Zhang", "Tingting Gao", "Wei Chen", "Wei Yuan", "Xiangyu Wu", "Xiao Hu", "Xingyu Lu", "Yi-Fan Zhang", "Yiping Yang", "Yulong Chen", "Zeyi Lu", "Zhenhua Wu", "Zhixin Ling", "Zhuoran Yang", "Ziming Li", "Di Xu", "Haixuan Gao", "Hang Li", "Jing Wang", "Lejian Ren", "Qigen Hu", "Qianqian Wang", "Shiyao Wang", "Xinchen Luo", "Yan Li", "Yuhang Hu", "Zixing Zhang" ], "github": "", "project_page": "", "abstract": "In recent years, the development of Large Language Models (LLMs) has significantly advanced, extending their capabilities to multimodal tasks through Multimodal Large Language Models (MLLMs). However, video understanding remains a challenging area due to the dynamic and information-dense nature of videos. Existing models struggle with the trade-off between spatial resolution and temporal coverage when processing video content. We present Keye-VL-1.5, which addresses fundamental challenges in video comprehension through three key innovations. First, we introduce a novel Slow-Fast video encoding strategy that dynamically allocates computational resources based on inter-frame similarity, processing key frames with significant visual changes at higher resolution (Slow pathway) while handling relatively static frames with increased temporal coverage at lower resolution (Fast pathway). Second, we implement a progressive four-stage pre-training methodology that systematically extends the model's context length from 8K to 128K tokens, enabling processing of longer videos and more complex visual content. Third, we develop a comprehensive post-training pipeline focusing on reasoning enhancement and human preference alignment, incorporating a 5-step chain-of-thought data construction process, iterative GSPO-based reinforcement learning with progressive prompt hinting for difficult cases, and alignment training. Through extensive evaluation on public benchmarks and rigorous internal human assessment, Keye-VL-1.5 demonstrates significant improvements over existing models, particularly excelling in video understanding tasks while maintaining competitive performance on general multimodal benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.01360", "title": "M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision", "authors": [ "Che Liu", "Zheng Jiang", "Chengyu Fang", "Heng Guo", "Yan-Jie Zhou", "Jiaqi Qu", "Le Lu", "Minfeng Xu" ], "github": "", "project_page": "", "abstract": "Medical image retrieval is essential for clinical decision-making and translational research, relying on discriminative visual representations. Yet, current methods remain fragmented, relying on separate architectures and training strategies for 2D, 3D, and video-based medical data. This modality-specific design hampers scalability and inhibits the development of unified representations. To enable unified learning, we curate a large-scale hybrid-modality dataset comprising 867,653 medical imaging samples, including 2D X-rays and ultrasounds, RGB endoscopy videos, and 3D CT scans. Leveraging this dataset, we train M3Ret, a unified visual encoder without any modality-specific customization. It successfully learns transferable representations using both generative (MAE) and contrastive (SimDINO) self-supervised learning (SSL) paradigms. Our approach sets a new state-of-the-art in zero-shot image-to-image retrieval across all individual modalities, surpassing strong baselines such as DINOv3 and the text-supervised BMC-CLIP. More remarkably, strong cross-modal alignment emerges without paired data, and the model generalizes to unseen MRI tasks, despite never observing MRI during pretraining, demonstrating the generalizability of purely visual self-supervision to unseen modalities. Comprehensive analyses further validate the scalability of our framework across model and data sizes. These findings deliver a promising signal to the medical imaging community, positioning M3Ret as a step toward foundation models for visual SSL in multimodal medical image understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.00244", "title": "Universal Deep Research: Bring Your Own Model and Strategy", "authors": [ "Peter Belcak", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "Deep research tools are among the most impactful and most commonly encountered agentic systems today. We observe, however, that each deep research agent introduced so far is hard-coded to carry out a particular research strategy using a fixed choice of tools. We introduce Universal Deep Research (UDR), a generalist agentic system that wraps around any language model and enables the user to create, edit, and refine their own entirely custom deep research strategies without any need for additional training or finetuning. To showcase the generality of our system, we equip UDR with example minimal, expansive, and intensive research strategies, and provide a user interface to facilitate experimentation with the system.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.02544", "title": "UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning", "authors": [ "Haoming Wang", "Haoyang Zou", "Huatong Song", "Jiazhan Feng", "Junjie Fang", "Junting Lu", "Longxiang Liu", "Qinyu Luo", "Shihao Liang", "Shijue Huang", "Wanjun Zhong", "Yining Ye", "Yujia Qin", "Yuwen Xiong", "Yuxin Song", "Zhiyong Wu", "Bo Li", "Chen Dun", "Chong Liu", "Fuxing Leng", "Hanbin Wang", "Hao Yu", "Haobin Chen", "Hongyi Guo", "Jing Su", "Jingjia Huang", "Kai Shen", "Kaiyu Shi", "Lin Yan", "Peiyao Zhao", "Pengfei Liu", "Qinghao Ye", "Renjie Zheng", "Wayne Xin Zhao", "Wen Heng", "Wenhao Huang", "Wenqian Wang", "Xiaobo Qin", "Yi Lin", "Youbin Wu", "Zehui Chen", "Zihao Wang", "Baoquan Zhong", "Xinchun Zhang", "Xujing Li", "Yuanfan Li", "Zhongkai Zhao", "Chengquan Jiang", "Faming Wu", "Haotian Zhou", "Jinlin Pang", "Li Han", "Qianli Ma", "Siyao Liu", "Songhua Cai", "Wenqi Fu", "Xin Liu", "Zhi Zhang", "Bo Zhou", "Guoliang Li", "Jiajun Shi", "Jiale Yang", "Jie Tang", "Li Li", "Taoran Lu", "Woyu Lin", "Xiaokang Tong", "Xinyao Li", "Yichi Zhang", "Yu Miao", "Zhengxuan Jiang", "Zili Li", "Ziyuan Zhao", "Chenxin Li", "Dehua Ma", "Feng Lin", "Ge Zhang", "Haihua Yang", "Hangyu Guo", "Hongda Zhu", "Jiaheng Liu", "Junda Du", "Kai Cai", "Kuanye Li", "Lichen Yuan", "Meilan Han", "Minchao Wang", "Shuyue Guo", "Tianhao Cheng", "Xiaobo Ma", "Xiaojun Xiao", "Xiaolong Huang", "Xinjie Chen", "Yidi Du", "Yilin Chen", "Yiwen Wang", "Zhaojian Li", "Zhenzhu Yang", "Zhiyuan Zeng", "Chaolin Jin", "Chen Li", "Hao Chen", "Haoli Chen", "Jian Chen", "Qinghao Zhao", "Guang Shi" ], "github": "https://github.com/bytedance/ui-tars", "project_page": "", "abstract": "The development of autonomous agents for graphical user interfaces (GUIs) presents major challenges in artificial intelligence. While recent advances in native agent models have shown promise by unifying perception, reasoning, action, and memory through end-to-end learning, open problems remain in data scalability, multi-turn reinforcement learning (RL), the limitations of GUI-only operation, and environment stability. In this technical report, we present UI-TARS-2, a native GUI-centered agent model that addresses these challenges through a systematic training methodology: a data flywheel for scalable data generation, a stabilized multi-turn RL framework, a hybrid GUI environment that integrates file systems and terminals, and a unified sandbox platform for large-scale rollouts. Empirical evaluation demonstrates that UI-TARS-2 achieves significant improvements over its predecessor UI-TARS-1.5. On GUI benchmarks, it reaches 88.2 on Online-Mind2Web, 47.5 on OSWorld, 50.6 on WindowsAgentArena, and 73.3 on AndroidWorld, outperforming strong baselines such as Claude and OpenAI agents. In game environments, it attains a mean normalized score of 59.8 across a 15-game suite-roughly 60% of human-level performance-and remains competitive with frontier proprietary models (e.g., OpenAI o3) on LMGame-Bench. Additionally, the model can generalize to long-horizon information-seeking tasks and software engineering benchmarks, highlighting its robustness across diverse agent tasks. Detailed analyses of training dynamics further provide insights into achieving stability and efficiency in large-scale agent RL. These results underscore UI-TARS-2's potential to advance the state of GUI agents and exhibit strong generalization to real-world interactive scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bytedance/ui-tars", "github_api_added_by": "auto" }, { "date": "2025-09-03", "arxiv_id": "2509.01984", "title": "Discrete Noise Inversion for Next-scale Autoregressive Text-based Image Editing", "authors": [ "Quan Dao", "Xiaoxiao He", "Ligong Han", "Ngan Hoai Nguyen", "Amin Heyrani Nobar", "Faez Ahmed", "Han Zhang", "Viet Anh Nguyen", "Dimitris Metaxas" ], "github": "", "project_page": "", "abstract": "Visual autoregressive models (VAR) have recently emerged as a promising class of generative models, achieving performance comparable to diffusion models in text-to-image generation tasks. While conditional generation has been widely explored, the ability to perform prompt-guided image editing without additional training is equally critical, as it supports numerous practical real-world applications. This paper investigates the text-to-image editing capabilities of VAR by introducing Visual AutoRegressive Inverse Noise (VARIN), the first noise inversion-based editing technique designed explicitly for VAR models. VARIN leverages a novel pseudo-inverse function for argmax sampling, named Location-aware Argmax Inversion (LAI), to generate inverse Gumbel noises. These inverse noises enable precise reconstruction of the source image and facilitate targeted, controllable edits aligned with textual prompts. Extensive experiments demonstrate that VARIN effectively modifies source images according to specified prompts while significantly preserving the original background and structural details, thus validating its efficacy as a practical editing approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.01610", "title": "Improving Large Vision and Language Models by Learning from a Panel of Peers", "authors": [ "Jefferson Hernandez", "Jing Shi", "Simon Jenni", "Vicente Ordonez", "Kushal Kafle" ], "github": "https://github.com/open-compass/VLMEvalKit", "project_page": "", "abstract": "Traditional alignment methods for Large Vision and Language Models (LVLMs) primarily rely on human-curated preference data. Human-generated preference data is costly; machine-generated preference data is limited in quality; and self-supervised preference data often introduces hallucinations. To overcome these limitations, we propose a novel Panel-of-Peers learning framework inspired by collaborative learning among humans. This approach leverages a panel of LVLMs, each evaluating and learning from their collective outputs through an iterative self-improvement process. By simulating a peer review system, our models generate, assess, and refine outputs in response to a curated set of prompts, mimicking a classroom learning environment. We demonstrate that this methodology enhances model performance without requiring extensive human-labeled datasets. Our experiments show significant improvement across multiple benchmarks, demonstrating the potential of peer evaluations as a scalable alternative to self-supervised alignment. Notably, we show that Panel-of-Peers increases the average score on fifteen benchmarks from 48% to 57%", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-compass/VLMEvalKit", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.01250", "title": "Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views", "authors": [ "Xiangdong Zhang", "Shaofeng Zhang", "Junchi Yan" ], "github": "https://github.com/aHapBean/Point-PQAE", "project_page": "", "abstract": "Point cloud learning, especially in a self-supervised way without manual labels, has gained growing attention in both vision and learning communities due to its potential utility in a wide range of applications. Most existing generative approaches for point cloud self-supervised learning focus on recovering masked points from visible ones within a single view. Recognizing that a two-view pre-training paradigm inherently introduces greater diversity and variance, it may thus enable more challenging and informative pre-training. Inspired by this, we explore the potential of two-view learning in this domain. In this paper, we propose Point-PQAE, a cross-reconstruction generative paradigm that first generates two decoupled point clouds/views and then reconstructs one from the other. To achieve this goal, we develop a crop mechanism for point cloud view generation for the first time and further propose a novel positional encoding to represent the 3D relative position between the two decoupled views. The cross-reconstruction significantly increases the difficulty of pre-training compared to self-reconstruction, which enables our method to surpass previous single-modal self-reconstruction methods in 3D self-supervised learning. Specifically, it outperforms the self-reconstruction baseline (Point-MAE) by 6.5%, 7.0%, and 6.7% in three variants of ScanObjectNN with the Mlp-Linear evaluation protocol. The code is available at https://github.com/aHapBean/Point-PQAE.", "github_manual": "", "github_regex": "https://github.com/aHapBean/Point-PQAE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.01055", "title": "VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use", "authors": [ "Dongfu Jiang", "Yi Lu", "Zhuofeng Li", "Zhiheng Lyu", "Ping Nie", "Haozhe Wang", "Alex Su", "Hui Chen", "Kai Zou", "Chao Du", "Tianyu Pang", "Wenhu Chen" ], "github": "https://github.com/TIGER-AI-Lab/verl-tool", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has demonstrated success in enhancing LLM reasoning capabilities, but remains limited to single-turn interactions without tool integration. While recent Agentic Reinforcement Learning with Tool use (ARLT) approaches have emerged to address multi-turn tool interactions, existing works develop task-specific codebases that suffer from fragmentation, synchronous execution bottlenecks, and limited extensibility across domains. These inefficiencies hinder broader community adoption and algorithmic innovation. We introduce VerlTool, a unified and modular framework that addresses these limitations through systematic design principles. VerlTool provides four key contributions: (1) upstream alignment with VeRL ensuring compatibility and simplified maintenance, (2) unified tool management via standardized APIs supporting diverse modalities including code execution, search, SQL databases, and vision processing, (3) asynchronous rollout execution achieving near 2times speedup by eliminating synchronization bottlenecks, and (4) comprehensive evaluation demonstrating competitive performance across 6 ARLT domains. Our framework formalizes ARLT as multi-turn trajectories with multi-modal observation tokens (text/image/video), extending beyond single-turn RLVR paradigms. We train and evaluate models on mathematical reasoning, knowledge QA, SQL generation, visual reasoning, web search, and software engineering tasks, achieving results comparable to specialized systems while providing unified training infrastructure. The modular plugin architecture enables rapid tool integration requiring only lightweight Python definitions, significantly reducing development overhead and providing a scalable foundation for tool-augmented RL research. Our code is open-sourced at https://github.com/TIGER-AI-Lab/verl-tool.", "github_manual": "", "github_regex": "https://github.com/TIGER-AI-Lab/verl-tool", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.01052", "title": "FlashAdventure: A Benchmark for GUI Agents Solving Full Story Arcs in Diverse Adventure Games", "authors": [ "Jaewoo Ahn", "Junseo Kim", "Heeseung Yun", "Jaehyeon Son", "Dongmin Park", "Jaewoong Cho", "Gunhee Kim" ], "github": "", "project_page": "", "abstract": "GUI agents powered by LLMs show promise in interacting with diverse digital environments. Among these, video games offer a valuable testbed due to their varied interfaces, with adventure games posing additional challenges through complex, narrative-driven interactions. Existing game benchmarks, however, lack diversity and rarely evaluate agents on completing entire storylines. To address this, we introduce FlashAdventure, a benchmark of 34 Flash-based adventure games designed to test full story arc completion and tackle the observation-behavior gap: the challenge of remembering and acting on earlier gameplay information. We also propose CUA-as-a-Judge, an automated gameplay evaluator, and COAST, an agentic framework leveraging long-term clue memory to better plan and solve sequential tasks. Experiments show current GUI agents struggle with full story arcs, while COAST improves milestone completion by bridging the observation-behavior gap. Nonetheless, a marked discrepancy between humans and best-performing agents warrants continued research efforts to narrow this divide.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.00578", "title": "C-DiffDet+: Fusing Global Scene Context with Generative Denoising for High-Fidelity Object Detection", "authors": [ "Abdellah Zakaria Sellam", "Ilyes Benaissa", "Salah Eddine Bekhouche", "Abdenour Hadid", "Vito Ren\u00f3", "Cosimo Distante" ], "github": "", "project_page": "", "abstract": "Fine-grained object detection in challenging visual domains, such as vehicle damage assessment, presents a formidable challenge even for human experts to resolve reliably. While DiffusionDet has advanced the state-of-the-art through conditional denoising diffusion, its performance remains limited by local feature conditioning in context-dependent scenarios. We address this fundamental limitation by introducing Context-Aware Fusion (CAF), which leverages cross-attention mechanisms to integrate global scene context with local proposal features directly. The global context is generated using a separate dedicated encoder that captures comprehensive environmental information, enabling each object proposal to attend to scene-level understanding. Our framework significantly enhances the generative detection paradigm by enabling each object proposal to attend to comprehensive environmental information. Experimental results demonstrate an improvement over state-of-the-art models on the CarDD benchmark, establishing new performance benchmarks for context-aware object detection in fine-grained domains", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.02208", "title": "Baichuan-M2: Scaling Medical Capability with Large Verifier System", "authors": [ "Baichuan-M2 Team", "Chengfeng Dou", "Chong Liu", "Fan Yang", "Fei Li", "Jiyuan Jia", "Mingyang Chen", "Qiang Ju", "Shuai Wang", "Shunya Dang", "Tianpeng Li", "Xiangrong Zeng", "Yijie Zhou", "Chenzheng Zhu", "Da Pan", "Fei Deng", "Guangwei Ai", "Guosheng Dong", "Hongda Zhang", "Jinyang Tai", "Jixiang Hong", "Kai Lu", "Linzhuang Sun", "Peidong Guo", "Qian Ma", "Rihui Xin", "Shihui Yang", "Shusen Zhang", "Yichuan Mo", "Zheng Liang", "Zhishou Zhang", "Hengfu Cui", "Zuyi Zhu", "Xiaochuan Wang" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) advance in conversational and reasoning capabilities, their practical application in healthcare has become a critical research focus. However, there is a notable gap between the performance of medical LLMs on static benchmarks such as USMLE and their utility in real-world clinical decision-making. This discrepancy arises because traditional exams fail to capture the dynamic, interactive nature of medical consultations. To address this challenge, we introduce a novel dynamic verification framework that moves beyond static answer verifier, establishing a large-scale, high-fidelity interactive reinforcement learning system. Our framework comprises two key components: a Patient Simulator that creates realistic clinical environments using de-identified medical records, and a Clinical Rubrics Generator that dynamically produces multi-dimensional evaluation metrics. Building on this foundation, we develop Baichuan-M2, a 32B-parameter medical augmented reasoning model trained through a multi-stage reinforcement learning strategy with an improved Group Relative Policy Optimization (GRPO) algorithm. Evaluated on HealthBench, Baichuan-M2 outperforms all other open-source models and most advanced closed-source counterparts, achieving a score above 32 on the challenging HealthBench Hard benchmark-previously exceeded only by GPT-5. Our work demonstrates that robust dynamic verifier system is essential for aligning LLM capabilities with practical clinical applications, establishing a new Pareto front in the performance-parameter trade-off for medical AI deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.02460", "title": "GenCompositor: Generative Video Compositing with Diffusion Transformer", "authors": [ "Shuzhou Yang", "Xiaoyu Li", "Xiaodong Cun", "Guangzhi Wang", "Lingen Li", "Ying Shan", "Jian Zhang" ], "github": "", "project_page": "", "abstract": "Video compositing combines live-action footage to create video production, serving as a crucial technique in video creation and film production. Traditional pipelines require intensive labor efforts and expert collaboration, resulting in lengthy production cycles and high manpower costs. To address this issue, we automate this process with generative models, called generative video compositing. This new task strives to adaptively inject identity and motion information of foreground video to the target video in an interactive manner, allowing users to customize the size, motion trajectory, and other attributes of the dynamic elements added in final video. Specifically, we designed a novel Diffusion Transformer (DiT) pipeline based on its intrinsic properties. To maintain consistency of the target video before and after editing, we revised a light-weight DiT-based background preservation branch with masked token injection. As to inherit dynamic elements from other sources, a DiT fusion block is proposed using full self-attention, along with a simple yet effective foreground augmentation for training. Besides, for fusing background and foreground videos with different layouts based on user control, we developed a novel position embedding, named Extended Rotary Position Embedding (ERoPE). Finally, we curated a dataset comprising 61K sets of videos for our new task, called VideoComp. This data includes complete dynamic elements and high-quality target videos. Experiments demonstrate that our method effectively realizes generative video compositing, outperforming existing possible solutions in fidelity and consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.01363", "title": "Reasoning Vectors: Transferring Chain-of-Thought Capabilities via Task Arithmetic", "authors": [ "Mohammad Zbeeb", "Hasan Abed Al Kader Hammoud", "Bernard Ghanem" ], "github": "", "project_page": "", "abstract": "Large language models often require costly optimization, such as reinforcement learning, to master complex reasoning tasks. This work demonstrates that reasoning ability, once learned, can be extracted and transferred between models as a compact task vector. We source two publicly available, identically initialized Qwen2.5 models, one fine-tuned with supervised fine-tuning (SFT) and the other with group relative policy optimization (GRPO) on the same dataset. From these, we extract a reasoning vector: v_{reason} = theta_{GRPO} - theta_{SFT}. We hypothesize that this vector captures the reasoning capability instilled by reinforcement learning while factoring out shared knowledge from the SFT process. When added to compatible instruction-tuned models through simple arithmetic, this vector consistently improves performance across diverse reasoning benchmarks: GSM8K (+4.9%), HumanEval (+4.3%), SciQ (+1.7%), and BigBenchHard (+12.3% for the 1.5B model). The performance improvements persist under adversarial conditions. Conversely, subtracting the vector causes significant performance degradation (-11.8% on GSM8K), demonstrating the vector's strong contribution to the model's reasoning abilities. This work shows how reasoning capabilities, typically developed through expensive training, can be extracted from existing open-source models and reused through simple tensor arithmetic, offering a practical way to enhance models by recycling prior computational investments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.02133", "title": "AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with Knowledge Augmentation for Robust Constitutional Alignment of Language Models", "authors": [ "Snehasis Mukhopadhyay", "Aryan Kasat", "Shivam Dubey", "Rahul Karthikeyan", "Dhruv Sood", "Vinija Jain", "Aman Chadha", "Amitava Das" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) can inadvertently reflect societal biases present in their training data, leading to harmful or prejudiced outputs. In the Indian context, our empirical evaluations across a suite of models reveal that biases around caste and religion are particularly salient. Yet, most existing mitigation strategies are Western-centric and fail to address these local nuances. We propose AMBEDKAR, a framework inspired by the egalitarian vision of Dr B. R. Ambedkar, architect of the Indian Constitution, to guide LLM outputs toward fairness, neutrality, and inclusion in line with Articles 14 to 17. Our approach introduces a Constitution-Aware Decoding Layer, guided by the AI Constitution of India and applied only at inference time, without any parameter updates to the base model. We incorporate a speculative decoding algorithm that proactively reduces casteist and communal bias during generation. This mitigation layer operates directly within the decoding process, avoiding changes to model internals and lowering the computational and infrastructural costs associated with retraining. We reinterpret speculative decoding not merely as an efficiency tool but as a mechanism for fairness. In this framework, a Small Language Model (SLM) acts as a potentially biased generator, while a constitutionally guided Large Language Model (LLM) serves as the verifier. Rather than accelerating generation, the LLM enforces bias-robust trajectories in the SLM outputs. This inversion of roles gives rise to a fairness-by-speculation paradigm. Our approach yields an absolute reduction of bias up to 26.41 percent compared to baseline. Our source code, datasets, and results are available at https://anonymous.4open.science/r/AMBEDKAR-983B/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.00581", "title": "SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction", "authors": [ "Saumya Chaturvedi", "Aman Chadha", "Laurent Bindschaedler" ], "github": "", "project_page": "", "abstract": "Converting natural language queries into SQL queries is a crucial challenge in both industry and academia, aiming to increase access to databases and large-scale applications. This work examines how in-context learning and chain-of-thought can be utilized to develop a robust solution for text-to-SQL systems. We propose SQL-of-Thought: a multi-agent framework that decomposes the Text2SQL task into schema linking, subproblem identification, query plan generation, SQL generation, and a guided correction loop. Unlike prior systems that rely only on execution-based static correction, we introduce taxonomy-guided dynamic error modification informed by in-context learning. SQL-of-Thought achieves state-of-the-art results on the Spider dataset and its variants, combining guided error taxonomy with reasoning-based query planning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.00404", "title": "Metis: Training Large Language Models with Advanced Low-Bit Quantization", "authors": [ "Hengjie Cao", "Mengyi Chen", "Yifeng Yang", "Ruijun Huang", "Fang Dong", "Jixian Zhou", "Anrui Chen", "Mingzhi Dong", "Yujiang Wang", "Jinlong Hou", "Yuan Cheng", "Fan Wu", "Fan Yang", "Tun Lu", "Ning Gu", "Li Shang" ], "github": "https://github.com/sii-research/Metis", "project_page": "", "abstract": "This work identifies anisotropic parameter distributions as a fundamental barrier to training large language models (LLMs) with low-bit quantization: a few dominant singular values create wide numerical ranges that conflict with the inherent bias of block-wise quantization. This bias disproportionately preserves high-magnitude values while discarding smaller ones, causing training instability and low model performance. This work introduces Metis, a training framework that combines (i) spectral decomposition with random embedding to efficiently disentangle dominant from long-tail components, compressing broad distributions into quantization-friendly narrow ranges; (ii) adaptive learning rates in the spectral domain to amplify underrepresented directions and better capture diverse features critical for performance; and (iii) a dual-range regularizer that jointly constrains numerical precision and parameter range distribution, ensuring stable, unbiased low-bit training. With Metis, FP8 training surpasses FP32 baselines, and FP4 training achieves accuracy comparable to FP32, paving the way for robust and scalable LLM training under advanced low-bit quantization. The code implementation for Metis is available at: https://github.com/typename-yyf/Metis-quantization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sii-research/Metis", "github_api_added_by": "auto" }, { "date": "2025-09-03", "arxiv_id": "2509.02046", "title": "Fantastic Pretraining Optimizers and Where to Find Them", "authors": [ "Kaiyue Wen", "David Hall", "Tengyu Ma", "Percy Liang" ], "github": "https://github.com/marin-community/marin/tree/kaiyue/optimizers", "project_page": "", "abstract": "AdamW has long been the dominant optimizer in language model pretraining, despite numerous claims that alternative optimizers offer 1.4 to 2x speedup. We posit that two methodological shortcomings have obscured fair comparisons and hindered practical adoption: (i) unequal hyperparameter tuning and (ii) limited or misleading evaluation setups. To address these two issues, we conduct a systematic study of ten deep learning optimizers across four model scales (0.1B-1.2B parameters) and data-to-model ratios (1-8x the Chinchilla optimum). We find that fair and informative comparisons require rigorous hyperparameter tuning and evaluations across a range of model scales and data-to-model ratios, performed at the end of training. First, optimal hyperparameters for one optimizer may be suboptimal for another, making blind hyperparameter transfer unfair. Second, the actual speedup of many proposed optimizers over well-tuned baselines is lower than claimed and decreases with model size to only 1.1x for 1.2B parameter models. Thirdly, comparing intermediate checkpoints before reaching the target training budgets can be misleading, as rankings between two optimizers can flip during training due to learning rate decay. Through our thorough investigation, we find that all the fastest optimizers such as Muon and Soap, use matrices as preconditioners -- multiplying gradients with matrices rather than entry-wise scalars. However, the speedup of matrix-based optimizers is inversely proportional to model scale, decreasing from 1.4x over AdamW for 0.1B parameter models to merely 1.1x for 1.2B parameter models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/marin-community/marin/tree/kaiyue/optimizers", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.00425", "title": "The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in LLMs with Camlang", "authors": [ "Fenghua Liu", "Yulong Chen", "Yixuan Liu", "Zhujun Jin", "Solomon Tsai", "Ming Zhong" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) achieve gold-medal performance across many benchmarks, yet it remains unclear whether such success reflects genuine reasoning or pattern matching. From a cognitive science perspective, an informative test is whether models can master an unfamiliar language through explicit metalinguistic deductive learning, a paradigm where human learners can reliably internalise grammatical systems through metalinguistic reasoning. We address this question with Camlang, a novel constructed language that exhibits naturalistic yet unattested feature combinations. Camlang consists of two explicit resources, a grammar book and a bilingual dictionary, which mirror adult second-language learning via explicit grammar rules and lexical lookup, and enable us to disentangle errors in morpho-syntax, lexical semantics, and sentence-level reasoning. Human experiments show that these resources are sufficient for participants to acquire Camlang and successfully solve Camlang tasks. To operationalise evaluation, we adapt CommonsenseQA into Camlang, creating Camlang-CSQA-v0, the first task in a broader suite where solving questions requires applying grammar rules and lexical mappings. Experimental results show that GPT-5 achieves 98\\% EM accuracy in English but only 47\\% in Camlang, far below human performance at 87\\%, while other state-of-the-art reasoning LLMs perform even worse. Human verification further reveals that most model successes stem from shallow lexical alignment while GPT-5 shows emerging metalinguistic awareness to a limited extent but not systematic grammatical mastery as humans. Camlang establishes a cognitively grounded evaluation paradigm that exposes fundamental gaps between current models and human metalinguistic competence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.00531", "title": "MobiAgent: A Systematic Framework for Customizable Mobile Agents", "authors": [ "Cheng Zhang", "Erhu Feng", "Xi Zhao", "Yisheng Zhao", "Wangbo Gong", "Jiahui Sun", "Dong Du", "Zhichao Hua", "Yubin Xia", "Haibo Chen" ], "github": "", "project_page": "", "abstract": "With the rapid advancement of Vision-Language Models (VLMs), GUI-based mobile agents have emerged as a key development direction for intelligent mobile systems. However, existing agent models continue to face significant challenges in real-world task execution, particularly in terms of accuracy and efficiency. To address these limitations, we propose MobiAgent, a comprehensive mobile agent system comprising three core components: the MobiMind-series agent models, the AgentRR acceleration framework, and the MobiFlow benchmarking suite. Furthermore, recognizing that the capabilities of current mobile agents are still limited by the availability of high-quality data, we have developed an AI-assisted agile data collection pipeline that significantly reduces the cost of manual annotation. Compared to both general-purpose LLMs and specialized GUI agent models, MobiAgent achieves state-of-the-art performance in real-world mobile scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.01584", "title": "ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association", "authors": [ "Ganlin Zhang", "Shenhan Qian", "Xi Wang", "Daniel Cremers" ], "github": "https://github.com/zhangganlin/vista-slam", "project_page": "", "abstract": "We present ViSTA-SLAM as a real-time monocular visual SLAM system that operates without requiring camera intrinsics, making it broadly applicable across diverse camera setups. At its core, the system employs a lightweight symmetric two-view association (STA) model as the frontend, which simultaneously estimates relative camera poses and regresses local pointmaps from only two RGB images. This design reduces model complexity significantly, the size of our frontend is only 35\\% that of comparable state-of-the-art methods, while enhancing the quality of two-view constraints used in the pipeline. In the backend, we construct a specially designed Sim(3) pose graph that incorporates loop closures to address accumulated drift. Extensive experiments demonstrate that our approach achieves superior performance in both camera tracking and dense 3D reconstruction quality compared to current methods. Github repository: https://github.com/zhangganlin/vista-slam", "github_manual": "", "github_regex": "https://github.com/zhangganlin/vista-slam", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.01440", "title": "Benchmarking Optimizers for Large Language Model Pretraining", "authors": [ "Andrei Semenov", "Matteo Pagliardini", "Martin Jaggi" ], "github": "https://github.com/epfml/llm-optimizer-benchmark", "project_page": "", "abstract": "The recent development of Large Language Models (LLMs) has been accompanied by an effervescence of novel ideas and methods to better optimize the loss of deep learning models. Claims from those methods are myriad: from faster convergence to removing reliance on certain hyperparameters. However, the diverse experimental protocols used to validate these claims make direct comparisons between methods challenging. This study presents a comprehensive evaluation of recent optimization techniques across standardized LLM pretraining scenarios, systematically varying model size, batch size, and training duration. Through careful tuning of each method, we provide guidance to practitioners on which optimizer is best suited for each scenario. For researchers, our work highlights promising directions for future optimization research. Finally, by releasing our code and making all experiments fully reproducible, we hope our efforts can help the development and rigorous benchmarking of future methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/epfml/llm-optimizer-benchmark", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.02333", "title": "DCPO: Dynamic Clipping Policy Optimization", "authors": [ "Shihui Yang", "Chengfeng Dou", "Peidong Guo", "Kai Lu", "Qiang Ju", "Fei Deng", "Rihui Xin" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a promising framework for enhancing the reasoning capabilities of large language models. However, existing approaches such as GRPO often suffer from zero gradients. This problem arises primarily due to fixed clipping bounds for token-level probability ratios and the standardization of identical rewards, which can lead to ineffective gradient updates and underutilization of generated responses. In this work, we propose Dynamic Clipping Policy Optimization (DCPO), which introduces a dynamic clipping strategy that adaptively adjusts the clipping bounds based on token-specific prior probabilities to enhance token-level exploration, and a smooth advantage standardization technique that standardizes rewards across cumulative training steps to improve the response-level effective utilization of generated responses. DCPO achieved state-of-the-art performance on four benchmarks based on four different models. In particular, DCPO achieved an Avg@1 of 46.7 under greedy decoding and an Avg@32 of 38.8 under 32 times sampling on the AIME24 benchmark, surpassing both DAPO (36.7/31.6) and GRPO (36.7/32.1) on the Qwen2.5-Math-7B model. On the AIME25 benchmark based on Qwen2.5-14B, DCPO achieves a performance of (23.3/19.0), surpassing GRPO (13.3/10.5) and DAPO (20.0/15.3). Furthermore, DCPO achieved an average 28% improvement in the nonzero advantage over GRPO in four models, doubled the training efficiency over DAPO, and significantly reduced the token clipping ratio by an order of magnitude compared to both GRPO and DAPO, while achieving superior performance. These results highlight DCPO's effectiveness in leveraging generated data more efficiently for reinforcement learning in large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2508.20586", "title": "FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models", "authors": [ "Zheng Chong", "Yanwei Lei", "Shiyue Zhang", "Zhuandi He", "Zhen Wang", "Xujie Zhang", "Xiao Dong", "Yiling Wu", "Dongmei Jiang", "Xiaodan Liang" ], "github": "", "project_page": "", "abstract": "Despite its great potential, virtual try-on technology is hindered from real-world application by two major challenges: the inability of current methods to support multi-reference outfit compositions (including garments and accessories), and their significant inefficiency caused by the redundant re-computation of reference features in each denoising step. To address these challenges, we propose FastFit, a high-speed multi-reference virtual try-on framework based on a novel cacheable diffusion architecture. By employing a Semi-Attention mechanism and substituting traditional timestep embeddings with class embeddings for reference items, our model fully decouples reference feature encoding from the denoising process with negligible parameter overhead. This allows reference features to be computed only once and losslessly reused across all steps, fundamentally breaking the efficiency bottleneck and achieving an average 3.5x speedup over comparable methods. Furthermore, to facilitate research on complex, multi-reference virtual try-on, we introduce DressCode-MR, a new large-scale dataset. It comprises 28,179 sets of high-quality, paired images covering five key categories (tops, bottoms, dresses, shoes, and bags), constructed through a pipeline of expert models and human feedback refinement. Extensive experiments on the VITON-HD, DressCode, and our DressCode-MR datasets show that FastFit surpasses state-of-the-art methods on key fidelity metrics while offering its significant advantage in inference efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2508.21496", "title": "ELV-Halluc: Benchmarking Semantic Aggregation Hallucinations in Long Video Understanding", "authors": [ "Hao Lu", "Jiahao Wang", "Yaolun Zhang", "Ruohui Wang", "Xuanyu Zheng", "Yepeng Tang", "Dahua Lin", "Lewei Lu" ], "github": "https://github.com/hlsv02/ELV-Halluc", "project_page": "", "abstract": "Video multimodal large language models (Video-MLLMs) have achieved remarkable progress in video understanding. However, they remain vulnerable to hallucination-producing content inconsistent with or unrelated to video inputs. Previous video hallucination benchmarks primarily focus on short-videos. They attribute hallucinations to factors such as strong language priors, missing frames, or vision-language biases introduced by the visual encoder. While these causes indeed account for most hallucinations in short videos, they still oversimplify the cause of hallucinations. Sometimes, models generate incorrect outputs but with correct frame-level semantics. We refer to this type of hallucination as Semantic Aggregation Hallucination (SAH), which arises during the process of aggregating frame-level semantics into event-level semantic groups. Given that SAH becomes particularly critical in long videos due to increased semantic complexity across multiple events, it is essential to separate and thoroughly investigate the causes of this type of hallucination. To address the above issues, we introduce ELV-Halluc, the first benchmark dedicated to long-video hallucination, enabling a systematic investigation of SAH. Our experiments confirm the existence of SAH and show that it increases with semantic complexity. Additionally, we find that models are more prone to SAH on rapidly changing semantics. Moreover, we discuss potential approaches to mitigate SAH. We demonstrate that positional encoding strategy contributes to alleviating SAH, and further adopt DPO strategy to enhance the model's ability to distinguish semantics within and across events. To support this, we curate a dataset of 8K adversarial data pairs and achieve improvements on both ELV-Halluc and Video-MME, including a substantial 27.7% reduction in SAH ratio.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hlsv02/ELV-Halluc", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.02563", "title": "DynaGuard: A Dynamic Guardrail Model With User-Defined Policies", "authors": [ "Monte Hoover", "Vatsal Baherwani", "Neel Jain", "Khalid Saifullah", "Joseph Vincent", "Chirag Jain", "Melissa Kazemi Rad", "C. Bayan Bruss", "Ashwinee Panda", "Tom Goldstein" ], "github": "", "project_page": "", "abstract": "Guardian models are used to supervise and moderate the outputs of user-facing chatbots, enforcing guardrails and detecting bad behaviors. Standard guardian models like LlamaGuard detect predefined, static categories of harms. We propose dynamic guardian models that evaluate text based on user-defined policies, making them useful for different application domains that are not addressed by standard guardian models. Our dynamic guardian models can be used for fast detection of policy violations or with chain-of-thought reasoning that articulates and justifies the model outputs. Our dynamic guardian models match static models in detection accuracy for static harm categories while identifying violations of free-form policies with accuracy comparable to frontier reasoning models in a fraction of the time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2508.21334", "title": "Stairway to Fairness: Connecting Group and Individual Fairness", "authors": [ "Theresia Veronika Rampisela", "Maria Maistro", "Tuukka Ruotsalo", "Falk Scholer", "Christina Lioma" ], "github": "https://github.com/theresiavr/stairway-to-fairness", "project_page": "", "abstract": "Fairness in recommender systems (RSs) is commonly categorised into group fairness and individual fairness. However, there is no established scientific understanding of the relationship between the two fairness types, as prior work on both types has used different evaluation measures or evaluation objectives for each fairness type, thereby not allowing for a proper comparison of the two. As a result, it is currently not known how increasing one type of fairness may affect the other. To fill this gap, we study the relationship of group and individual fairness through a comprehensive comparison of evaluation measures that can be used for both fairness types. Our experiments with 8 runs across 3 datasets show that recommendations that are highly fair for groups can be very unfair for individuals. Our finding is novel and useful for RS practitioners aiming to improve the fairness of their systems. Our code is available at: https://github.com/theresiavr/stairway-to-fairness.", "github_manual": "", "github_regex": "https://github.com/theresiavr/stairway-to-fairness", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.00605", "title": "Gated Associative Memory: A Parallel O(N) Architecture for Efficient Sequence Modeling", "authors": [ "Rishiraj Acharya" ], "github": "", "project_page": "", "abstract": "The Transformer architecture, underpinned by the self-attention mechanism, has become the de facto standard for sequence modeling tasks. However, its core computational primitive scales quadratically with sequence length (O(N^2)), creating a significant bottleneck for processing long contexts. In this paper, we propose the Gated Associative Memory (GAM) network, a novel, fully parallel architecture for sequence modeling that exhibits linear complexity (O(N)) with respect to sequence length. The GAM block replaces the self-attention layer with two parallel pathways: a causal convolution to efficiently capture local, position-dependent context, and a parallel associative memory retrieval mechanism to model global, content-based patterns. These pathways are dynamically fused using a gating mechanism, allowing the model to flexibly combine local and global information for each token. We implement GAM from scratch and conduct a rigorous comparative analysis against a standard Transformer model and a modern linear-time baseline (Mamba) on the WikiText-2 benchmark, as well as against the Transformer on the TinyStories dataset. Our experiments demonstrate that GAM is consistently faster, outperforming both baselines on training speed, and achieves a superior or competitive final validation perplexity across all datasets, establishing it as a promising and efficient alternative for sequence modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.01790", "title": "Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs", "authors": [ "Andong Hua", "Kenan Tang", "Chenhe Gu", "Jindong Gu", "Eric Wong", "Yao Qin" ], "github": "", "project_page": "", "abstract": "Prompt sensitivity, referring to the phenomenon where paraphrasing (i.e., repeating something written or spoken using different words) leads to significant changes in large language model (LLM) performance, has been widely accepted as a core limitation of LLMs. In this work, we revisit this issue and ask: Is the widely reported high prompt sensitivity truly an inherent weakness of LLMs, or is it largely an artifact of evaluation processes? To answer this question, we systematically evaluate 7 LLMs (e.g., GPT and Gemini family) across 6 benchmarks, including both multiple-choice and open-ended tasks on 12 diverse prompt templates. We find that much of the prompt sensitivity stems from heuristic evaluation methods, including log-likelihood scoring and rigid answer matching, which often overlook semantically correct responses expressed through alternative phrasings, such as synonyms or paraphrases. When we adopt LLM-as-a-Judge evaluations, we observe a substantial reduction in performance variance and a consistently higher correlation in model rankings across prompts. Our findings suggest that modern LLMs are more robust to prompt templates than previously believed, and that prompt sensitivity may be more an artifact of evaluation than a flaw in the models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2509.02523", "title": "Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices", "authors": [ "Evan King", "Adam Sabra", "Manjunath Kudlur", "James Wang", "Pete Warden" ], "github": "", "project_page": "", "abstract": "We present the Flavors of Moonshine, a suite of tiny automatic speech recognition (ASR) models specialized for a range of underrepresented languages. Prevailing wisdom suggests that multilingual ASR models outperform monolingual counterparts by exploiting cross-lingual phonetic similarities. We challenge this assumption, showing that for sufficiently small models (27M parameters), training monolingual systems on a carefully balanced mix of high-quality human-labeled, pseudo-labeled, and synthetic data yields substantially superior performance. On average, our models achieve error rates 48% lower than the comparably sized Whisper Tiny model, outperform the 9x larger Whisper Small model, and in most cases match or outperform the 28x larger Whisper Medium model. These results advance the state of the art for models of this size, enabling accurate on-device ASR for languages that previously had limited support. We release Arabic, Chinese, Japanese, Korean, Ukrainian, and Vietnamese Moonshine models under a permissive open-source license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-03", "arxiv_id": "2508.21038", "title": "On the Theoretical Limitations of Embedding-Based Retrieval", "authors": [ "Orion Weller", "Michael Boratko", "Iftekhar Naim", "Jinhyuk Lee" ], "github": "https://github.com/google-deepmind/limit", "project_page": "", "abstract": "Vector embeddings have been tasked with an ever-increasing set of retrieval tasks over the years, with a nascent rise in using them for reasoning, instruction-following, coding, and more. These new benchmarks push embeddings to work for any query and any notion of relevance that could be given. While prior works have pointed out theoretical limitations of vector embeddings, there is a common assumption that these difficulties are exclusively due to unrealistic queries, and those that are not can be overcome with better training data and larger models. In this work, we demonstrate that we may encounter these theoretical limitations in realistic settings with extremely simple queries. We connect known results in learning theory, showing that the number of top-k subsets of documents capable of being returned as the result of some query is limited by the dimension of the embedding. We empirically show that this holds true even if we restrict to k=2, and directly optimize on the test set with free parameterized embeddings. We then create a realistic dataset called LIMIT that stress tests models based on these theoretical results, and observe that even state-of-the-art models fail on this dataset despite the simple nature of the task. Our work shows the limits of embedding models under the existing single vector paradigm and calls for future research to develop methods that can resolve this fundamental limitation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/limit", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-04", "arxiv_id": "2509.01106", "title": "Robix: A Unified Model for Robot Interaction, Reasoning and Planning", "authors": [ "Huang Fang", "Mengxi Zhang", "Heng Dong", "Wei Li", "Zixuan Wang", "Qifeng Zhang", "Xueyun Tian", "Yucheng Hu", "Hang Li" ], "github": "", "project_page": "", "abstract": "We introduce Robix, a unified model that integrates robot reasoning, task planning, and natural language interaction within a single vision-language architecture. Acting as the high-level cognitive layer in a hierarchical robot system, Robix dynamically generates atomic commands for the low-level controller and verbal responses for human interaction, enabling robots to follow complex instructions, plan long-horizon tasks, and interact naturally with human within an end-to-end framework. Robix further introduces novel capabilities such as proactive dialogue, real-time interruption handling, and context-aware commonsense reasoning during task execution. At its core, Robix leverages chain-of-thought reasoning and adopts a three-stage training strategy: (1) continued pretraining to enhance foundational embodied reasoning abilities including 3D spatial understanding, visual grounding, and task-centric reasoning; (2) supervised finetuning to model human-robot interaction and task planning as a unified reasoning-action sequence; and (3) reinforcement learning to improve reasoning-action consistency and long-horizon task coherence. Extensive experiments demonstrate that Robix outperforms both open-source and commercial baselines (e.g., GPT-4o and Gemini 2.5 Pro) in interactive task execution, demonstrating strong generalization across diverse instruction types (e.g., open-ended, multi-stage, constrained, invalid, and interrupted) and various user-involved tasks such as table bussing, grocery shopping, and dietary filtering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-04", "arxiv_id": "2509.00375", "title": "Open Data Synthesis For Deep Research", "authors": [ "Ziyi Xia", "Kun Luo", "Hongjin Qian", "Zheng Liu" ], "github": "https://github.com/VectorSpaceLab/InfoSeek", "project_page": "", "abstract": "Large language models (LLMs) are increasingly expected to go beyond simple factual queries toward Deep Research-tasks that require decomposing questions into sub-problems, coordinating multi-step reasoning, and synthesizing evidence from diverse sources. We formalize Deep Research tasks with verifiable answers as Hierarchical Constraint Satisfaction Problems (HCSPs), which are fundamentally different from single-constraint, multi-hop, or flat CSP formulations. However, existing benchmarks (e.g., Natural Questions, HotpotQA) fail to capture this complexity, while recent synthetic datasets often introduce shortcut reasoning, knowledge leakage, or lack sufficient structural depth. To address this gap, we introduce InfoSeek, a scalable framework for synthesizing complex Deep Research tasks. InfoSeek uses a dual-agent system to recursively build a Research Tree from large-scale webpages, blurring intermediate nodes into valid sub-problems, and converting these trees into natural language questions that require traversing the full hierarchy. It also enables rapid scaling, yielding over 50K training examples, a curated test set, and reasoning trajectories generated via reject sampling. Experiments show that models trained on InfoSeek consistently outperform strong baselines. On a challenging benchmark BrowseComp-Plus, 3B LLMs optimized with InfoSeek surpass much larger 32B models and lightweight commercial APIs (e.g., Gemini2.5-Flash), while achieving performance comparable to stronger APIs (e.g., Gemini2.5-Pro). By preserving meta-information such as intermediate steps and retrieval labels, InfoSeek further supports advanced optimization strategies, including compound reward design and trajectory-level exploration. We provide our codes and datasets in https://github.com/VectorSpaceLab/InfoSeek{this repository}.", "github_manual": "", "github_regex": "https://github.com/VectorSpaceLab/InfoSeek", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-04", "arxiv_id": "2509.01977", "title": "MOSAIC: Multi-Subject Personalized Generation via Correspondence-Aware Alignment and Disentanglement", "authors": [ "Dong She", "Siming Fu", "Mushui Liu", "Qiaoqiao Jin", "Hualiang Wang", "Mu Liu", "Jidong Jiang" ], "github": "", "project_page": "", "abstract": "Multi-subject personalized generation presents unique challenges in maintaining identity fidelity and semantic coherence when synthesizing images conditioned on multiple reference subjects. Existing methods often suffer from identity blending and attribute leakage due to inadequate modeling of how different subjects should interact within shared representation spaces. We present MOSAIC, a representation-centric framework that rethinks multi-subject generation through explicit semantic correspondence and orthogonal feature disentanglement. Our key insight is that multi-subject generation requires precise semantic alignment at the representation level - knowing exactly which regions in the generated image should attend to which parts of each reference. To enable this, we introduce SemAlign-MS, a meticulously annotated dataset providing fine-grained semantic correspondences between multiple reference subjects and target images, previously unavailable in this domain. Building on this foundation, we propose the semantic correspondence attention loss to enforce precise point-to-point semantic alignment, ensuring high consistency from each reference to its designated regions. Furthermore, we develop the multi-reference disentanglement loss to push different subjects into orthogonal attention subspaces, preventing feature interference while preserving individual identity characteristics. Extensive experiments demonstrate that MOSAIC achieves state-of-the-art performance on multiple benchmarks. Notably, while existing methods typically degrade beyond 3 subjects, MOSAIC maintains high fidelity with 4+ reference subjects, opening new possibilities for complex multi-subject synthesis applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-04", "arxiv_id": "2509.00428", "title": "Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation", "authors": [ "Xuechao Zou", "Shun Zhang", "Xing Fu", "Yue Li", "Kai Li", "Yushe Cao", "Congyan Lang", "Pin Tao", "Junliang Xing" ], "github": "https://github.com/XavierJiezou/Face-MoGLE", "project_page": "", "abstract": "Controllable face generation poses critical challenges in generative modeling due to the intricate balance required between semantic controllability and photorealism. While existing approaches struggle with disentangling semantic controls from generation pipelines, we revisit the architectural potential of Diffusion Transformers (DiTs) through the lens of expert specialization. This paper introduces Face-MoGLE, a novel framework featuring: (1) Semantic-decoupled latent modeling through mask-conditioned space factorization, enabling precise attribute manipulation; (2) A mixture of global and local experts that captures holistic structure and region-level semantics for fine-grained controllability; (3) A dynamic gating network producing time-dependent coefficients that evolve with diffusion steps and spatial locations. Face-MoGLE provides a powerful and flexible solution for high-quality, controllable face generation, with strong potential in generative modeling and security applications. Extensive experiments demonstrate its effectiveness in multimodal and monomodal face generation settings and its robust zero-shot generalization capability. Project page is available at https://github.com/XavierJiezou/Face-MoGLE.", "github_manual": "", "github_regex": "https://github.com/XavierJiezou/Face-MoGLE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-04", "arxiv_id": "2509.03405", "title": "LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations", "authors": [ "Daniela Gottesman", "Alon Gilae-Dotan", "Ido Cohen", "Yoav Gur-Arieh", "Marius Mosbach", "Ori Yoran", "Mor Geva" ], "github": "", "project_page": "", "abstract": "Language models (LMs) increasingly drive real-world applications that require world knowledge. However, the internal processes through which models turn data into representations of knowledge and beliefs about the world, are poorly understood. Insights into these processes could pave the way for developing LMs with knowledge representations that are more consistent, robust, and complete. To facilitate studying these questions, we present LMEnt, a suite for analyzing knowledge acquisition in LMs during pretraining. LMEnt introduces: (1) a knowledge-rich pretraining corpus, fully annotated with entity mentions, based on Wikipedia, (2) an entity-based retrieval method over pretraining data that outperforms previous approaches by as much as 80.4%, and (3) 12 pretrained models with up to 1B parameters and 4K intermediate checkpoints, with comparable performance to popular open-sourced models on knowledge benchmarks. Together, these resources provide a controlled environment for analyzing connections between entity mentions in pretraining and downstream performance, and the effects of causal interventions in pretraining data. We show the utility of LMEnt by studying knowledge acquisition across checkpoints, finding that fact frequency is key, but does not fully explain learning trends. We release LMEnt to support studies of knowledge in LMs, including knowledge representations, plasticity, editing, attribution, and learning dynamics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-04", "arxiv_id": "2509.02722", "title": "Planning with Reasoning using Vision Language World Model", "authors": [ "Delong Chen", "Theo Moutakanni", "Willy Chung", "Yejin Bang", "Ziwei Ji", "Allen Bolourchi", "Pascale Fung" ], "github": "", "project_page": "", "abstract": "Effective planning requires strong world models, but high-level world models that can understand and reason about actions with semantic and temporal abstraction remain largely underdeveloped. We introduce the Vision Language World Model (VLWM), a foundation model trained for language-based world modeling on natural videos. Given visual observations, the VLWM first infers the overall goal achievements then predicts a trajectory composed of interleaved actions and world state changes. Those targets are extracted by iterative LLM Self-Refine conditioned on compressed future observations represented by Tree of Captions. The VLWM learns both an action policy and a dynamics model, which respectively facilitates reactive system-1 plan decoding and reflective system-2 planning via cost minimization. The cost evaluates the semantic distance between the hypothetical future states given by VLWM roll-outs and the expected goal state, and is measured by a critic model that we trained in a self-supervised manner. The VLWM achieves state-of-the-art Visual Planning for Assistance (VPA) performance on both benchmark evaluations and our proposed PlannerArena human evaluations, where system-2 improves the Elo score by +27% upon system-1. The VLWM models also outperforms strong VLM baselines on RoboVQA and WorldPrediction benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-04", "arxiv_id": "2509.00930", "title": "SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs", "authors": [ "Yanxiao Zhao", "Yaqian Li", "Zihao Bo", "Rinyoichi Takezoe", "Haojia Hui", "Mo Guang", "Lei Ren", "Xiaolin Qin", "Kaiwen Long" ], "github": "", "project_page": "", "abstract": "Recent advances in Large Language Models (LLMs) have demonstrated remarkable general reasoning capabilities. However, systematically evaluating and enhancing these reasoning capabilities is challenging due to the lack of controllable and scalable tools for fine-grained analysis. Existing benchmarks and datasets often lack the necessary variable control for multi-dimensional, systematic analysis and training, or have narrow problem types and formats. To address these limitations, we introduce SATQuest, a systematic verifier designed to evaluate and enhance logical reasoning in LLMs by generating diverse, Satisfiability-based logical reasoning problems directly from Conjunctive Normal Form (CNF) instances. SATQuest structures these problems along three orthogonal dimensions: instance scale, problem type, and question format, employing randomized, SAT-based problem generation and objective answer verification via PySAT. This design mitigates memorization issues, allows for nuanced insights into reasoning performance, and enables effective reinforcement fine-tuning. Our extensive evaluation of various LLMs using SATQuest identified significant limitations in their logical reasoning, particularly in generalizing beyond familiar mathematical formats. Furthermore, we show that reinforcement fine-tuning with SATQuest rewards substantially improves targeted task performance and generalizes to more complex instances, while highlighting remaining challenges in cross-format adaptation. Through these demonstrations, we showcase SATQuest's potential as a foundational tool and a valuable starting point for advancing LLM logical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-04", "arxiv_id": "2509.02530", "title": "Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots", "authors": [ "Minghuan Liu", "Zhengbang Zhu", "Xiaoshen Han", "Peng Hu", "Haotong Lin", "Xinyao Li", "Jingxiao Chen", "Jiafeng Xu", "Yichu Yang", "Yunfeng Lin", "Xinghang Li", "Yong Yu", "Weinan Zhang", "Tao Kong", "Bingyi Kang" ], "github": "https://github.com/ByteDance-Seed/manip-as-in-sim-suite", "project_page": "", "abstract": "Modern robotic manipulation primarily relies on visual observations in a 2D color space for skill learning but suffers from poor generalization. In contrast, humans, living in a 3D world, depend more on physical properties-such as distance, size, and shape-than on texture when interacting with objects. Since such 3D geometric information can be acquired from widely available depth cameras, it appears feasible to endow robots with similar perceptual capabilities. Our pilot study found that using depth cameras for manipulation is challenging, primarily due to their limited accuracy and susceptibility to various types of noise. In this work, we propose Camera Depth Models (CDMs) as a simple plugin on daily-use depth cameras, which take RGB images and raw depth signals as input and output denoised, accurate metric depth. To achieve this, we develop a neural data engine that generates high-quality paired data from simulation by modeling a depth camera's noise pattern. Our results show that CDMs achieve nearly simulation-level accuracy in depth prediction, effectively bridging the sim-to-real gap for manipulation tasks. Notably, our experiments demonstrate, for the first time, that a policy trained on raw simulated depth, without the need for adding noise or real-world fine-tuning, generalizes seamlessly to real-world robots on two challenging long-horizon tasks involving articulated, reflective, and slender objects, with little to no performance degradation. We hope our findings will inspire future research in utilizing simulation data and 3D information in general robot policies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ByteDance-Seed/manip-as-in-sim-suite", "github_api_added_by": "auto" }, { "date": "2025-09-04", "arxiv_id": "2509.03403", "title": "Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training", "authors": [ "Chenlu Ye", "Zhou Yu", "Ziji Zhang", "Hao Chen", "Narayanan Sadagopan", "Jing Huang", "Tong Zhang", "Anurag Beniwal" ], "github": "https://github.com/Chenluye99/PROF", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has emerged to be a predominant paradigm for mathematical reasoning tasks, offering stable improvements in reasoning ability. However, Outcome Reward Models (ORMs) in RLVR are too coarse-grained to distinguish flawed reasoning within correct answers or valid reasoning within incorrect answers. This lack of granularity introduces noisy and misleading gradients significantly and hinders further progress in reasoning process quality. While Process Reward Models (PRMs) offer fine-grained guidance for intermediate steps, they frequently suffer from inaccuracies and are susceptible to reward hacking. To resolve this dilemma, we introduce PRocess cOnsistency Filter (PROF), an effective data process curation method that harmonizes noisy, fine-grained process rewards with accurate, coarse-grained outcome rewards. Rather than naively blending PRM and ORM in the objective function (arXiv:archive/2506.18896), PROF leverages their complementary strengths through consistency-driven sample selection. Our approach retains correct responses with higher averaged process values and incorrect responses with lower averaged process values, while maintaining positive/negative training sample balance. Extensive experiments demonstrate that our method not only consistently improves the final accuracy over 4% compared to the blending approaches, but also strengthens the quality of intermediate reasoning steps. Codes and training recipes are available at https://github.com/Chenluye99/PROF.", "github_manual": "", "github_regex": "https://github.com/Chenluye99/PROF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2509.01396", "title": "DeepResearch Arena: The First Exam of LLMs' Research Abilities via Seminar-Grounded Tasks", "authors": [ "Haiyuan Wan", "Chen Yang", "Junchi Yu", "Meiqi Tu", "Jiaxuan Lu", "Di Yu", "Jianbao Cao", "Ben Gao", "Jiaqing Xie", "Aoran Wang", "Wenlong Zhang", "Philip Torr", "Dongzhan Zhou" ], "github": "", "project_page": "", "abstract": "Deep research agents have attracted growing attention for their potential to orchestrate multi-stage research workflows, spanning literature synthesis, methodological design, and empirical verification. Despite these strides, evaluating their research capability faithfully is rather challenging due to the difficulty of collecting frontier research questions that genuinely capture researchers' attention and intellectual curiosity. To address this gap, we introduce DeepResearch Arena, a benchmark grounded in academic seminars that capture rich expert discourse and interaction, better reflecting real-world research environments and reducing the risk of data leakage. To automatically construct DeepResearch Arena, we propose a Multi-Agent Hierarchical Task Generation (MAHTG) system that extracts research-worthy inspirations from seminar transcripts. The MAHTG system further translates research-worthy inspirations into high-quality research tasks, ensuring the traceability of research task formulation while filtering noise. With the MAHTG system, we curate DeepResearch Arena with over 10,000 high-quality research tasks from over 200 academic seminars, spanning 12 disciplines, such as literature, history, and science. Our extensive evaluation shows that DeepResearch Arena presents substantial challenges for current state-of-the-art agents, with clear performance gaps observed across different models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2509.04419", "title": "Towards a Unified View of Large Language Model Post-Training", "authors": [ "Xingtai Lv", "Yuxin Zuo", "Youbang Sun", "Hongyi Liu", "Yuntian Wei", "Zhekai Chen", "Lixuan He", "Xuekai Zhu", "Kaiyan Zhang", "Bingning Wang", "Ning Ding", "Bowen Zhou" ], "github": "", "project_page": "", "abstract": "Two major sources of training data exist for post-training modern language models: online (model-generated rollouts) data, and offline (human or other-model demonstrations) data. These two types of data are typically used by approaches like Reinforcement Learning (RL) and Supervised Fine-Tuning (SFT), respectively. In this paper, we show that these approaches are not in contradiction, but are instances of a single optimization process. We derive a Unified Policy Gradient Estimator, and present the calculations of a wide spectrum of post-training approaches as the gradient of a common objective under different data distribution assumptions and various bias-variance tradeoffs. The gradient estimator is constructed with four interchangeable parts: stabilization mask, reference policy denominator, advantage estimate, and likelihood gradient. Motivated by our theoretical findings, we propose Hybrid Post-Training (HPT), an algorithm that dynamically selects different training signals. HPT is designed to yield both effective exploitation of demonstration and stable exploration without sacrificing learned reasoning patterns. We provide extensive experiments and ablation studies to verify the effectiveness of our unified theoretical framework and HPT. Across six mathematical reasoning benchmarks and two out-of-distribution suites, HPT consistently surpasses strong baselines across models of varying scales and families.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2508.18733", "title": "Drawing2CAD: Sequence-to-Sequence Learning for CAD Generation from Vector Drawings", "authors": [ "Feiwei Qin", "Shichao Lu", "Junhao Hou", "Changmiao Wang", "Meie Fang", "Ligang Liu" ], "github": "https://github.com/lllssc/Drawing2CAD", "project_page": "", "abstract": "Computer-Aided Design (CAD) generative modeling is driving significant innovations across industrial applications. Recent works have shown remarkable progress in creating solid models from various inputs such as point clouds, meshes, and text descriptions. However, these methods fundamentally diverge from traditional industrial workflows that begin with 2D engineering drawings. The automatic generation of parametric CAD models from these 2D vector drawings remains underexplored despite being a critical step in engineering design. To address this gap, our key insight is to reframe CAD generation as a sequence-to-sequence learning problem where vector drawing primitives directly inform the generation of parametric CAD operations, preserving geometric precision and design intent throughout the transformation process. We propose Drawing2CAD, a framework with three key technical components: a network-friendly vector primitive representation that preserves precise geometric information, a dual-decoder transformer architecture that decouples command type and parameter generation while maintaining precise correspondence, and a soft target distribution loss function accommodating inherent flexibility in CAD parameters. To train and evaluate Drawing2CAD, we create CAD-VGDrawing, a dataset of paired engineering drawings and parametric CAD models, and conduct thorough experiments to demonstrate the effectiveness of our method. Code and dataset are available at https://github.com/lllssc/Drawing2CAD.", "github_manual": "", "github_regex": "https://github.com/lllssc/Drawing2CAD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2509.03888", "title": "False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize", "authors": [ "Cheng Wang", "Zeming Wei", "Qin Liu", "Muhao Chen" ], "github": "https://github.com/WangCheng0116/Why-Probe-Fails", "project_page": "", "abstract": "Large Language Models (LLMs) can comply with harmful instructions, raising serious safety concerns despite their impressive capabilities. Recent work has leveraged probing-based approaches to study the separability of malicious and benign inputs in LLMs' internal representations, and researchers have proposed using such probing methods for safety detection. We systematically re-examine this paradigm. Motivated by poor out-of-distribution performance, we hypothesize that probes learn superficial patterns rather than semantic harmfulness. Through controlled experiments, we confirm this hypothesis and identify the specific patterns learned: instructional patterns and trigger words. Our investigation follows a systematic approach, progressing from demonstrating comparable performance of simple n-gram methods, to controlled experiments with semantically cleaned datasets, to detailed analysis of pattern dependencies. These results reveal a false sense of security around current probing-based approaches and highlight the need to redesign both models and evaluation protocols, for which we provide further discussions in the hope of suggesting responsible further research in this direction. We have open-sourced the project at https://github.com/WangCheng0116/Why-Probe-Fails.", "github_manual": "", "github_regex": "https://github.com/WangCheng0116/Why-Probe-Fails", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2509.04394", "title": "Transition Models: Rethinking the Generative Learning Objective", "authors": [ "Zidong Wang", "Yiyuan Zhang", "Xiaoyu Yue", "Xiangyu Yue", "Yangguang Li", "Wanli Ouyang", "Lei Bai" ], "github": "", "project_page": "", "abstract": "A fundamental dilemma in generative modeling persists: iterative diffusion models achieve outstanding fidelity, but at a significant computational cost, while efficient few-step alternatives are constrained by a hard quality ceiling. This conflict between generation steps and output quality arises from restrictive training objectives that focus exclusively on either infinitesimal dynamics (PF-ODEs) or direct endpoint prediction. We address this challenge by introducing an exact, continuous-time dynamics equation that analytically defines state transitions across any finite time interval. This leads to a novel generative paradigm, Transition Models (TiM), which adapt to arbitrary-step transitions, seamlessly traversing the generative trajectory from single leaps to fine-grained refinement with more steps. Despite having only 865M parameters, TiM achieves state-of-the-art performance, surpassing leading models such as SD3.5 (8B parameters) and FLUX.1 (12B parameters) across all evaluated step counts. Importantly, unlike previous few-step generators, TiM demonstrates monotonic quality improvement as the sampling budget increases. Additionally, when employing our native-resolution strategy, TiM delivers exceptional fidelity at resolutions up to 4096x4096.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2509.04434", "title": "Durian: Dual Reference-guided Portrait Animation with Attribute Transfer", "authors": [ "Hyunsoo Cha", "Byungjun Kim", "Hanbyul Joo" ], "github": "", "project_page": "", "abstract": "We present Durian, the first method for generating portrait animation videos with facial attribute transfer from a given reference image to a target portrait in a zero-shot manner. To enable high-fidelity and spatially consistent attribute transfer across frames, we introduce dual reference networks that inject spatial features from both the portrait and attribute images into the denoising process of a diffusion model. We train the model using a self-reconstruction formulation, where two frames are sampled from the same portrait video: one is treated as the attribute reference and the other as the target portrait, and the remaining frames are reconstructed conditioned on these inputs and their corresponding masks. To support the transfer of attributes with varying spatial extent, we propose a mask expansion strategy using keypoint-conditioned image generation for training. In addition, we further augment the attribute and portrait images with spatial and appearance-level transformations to improve robustness to positional misalignment between them. These strategies allow the model to effectively generalize across diverse attributes and in-the-wild reference combinations, despite being trained without explicit triplet supervision. Durian achieves state-of-the-art performance on portrait animation with attribute transfer, and notably, its dual reference design enables multi-attribute composition in a single generation pass without additional training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2509.04406", "title": "Few-step Flow for 3D Generation via Marginal-Data Transport Distillation", "authors": [ "Zanwei Zhou", "Taoran Yi", "Jiemin Fang", "Chen Yang", "Lingxi Xie", "Xinggang Wang", "Wei Shen", "Qi Tian" ], "github": "https://github.com/Zanue/MDT-dist", "project_page": "", "abstract": "Flow-based 3D generation models typically require dozens of sampling steps during inference. Though few-step distillation methods, particularly Consistency Models (CMs), have achieved substantial advancements in accelerating 2D diffusion models, they remain under-explored for more complex 3D generation tasks. In this study, we propose a novel framework, MDT-dist, for few-step 3D flow distillation. Our approach is built upon a primary objective: distilling the pretrained model to learn the Marginal-Data Transport. Directly learning this objective needs to integrate the velocity fields, while this integral is intractable to be implemented. Therefore, we propose two optimizable objectives, Velocity Matching (VM) and Velocity Distillation (VD), to equivalently convert the optimization target from the transport level to the velocity and the distribution level respectively. Velocity Matching (VM) learns to stably match the velocity fields between the student and the teacher, but inevitably provides biased gradient estimates. Velocity Distillation (VD) further enhances the optimization process by leveraging the learned velocity fields to perform probability density distillation. When evaluated on the pioneer 3D generation framework TRELLIS, our method reduces sampling steps of each flow transformer from 25 to 1 or 2, achieving 0.68s (1 step x 2) and 0.94s (2 steps x 2) latency with 9.0x and 6.5x speedup on A800, while preserving high visual and geometric fidelity. Extensive experiments demonstrate that our method significantly outperforms existing CM distillation methods, and enables TRELLIS to achieve superior performance in few-step 3D generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Zanue/MDT-dist", "github_api_added_by": "auto" }, { "date": "2025-09-05", "arxiv_id": "2509.04338", "title": "From Editor to Dense Geometry Estimator", "authors": [ "JiYuan Wang", "Chunyu Lin", "Lei Sun", "Rongying Liu", "Lang Nie", "Mingxing Li", "Kang Liao", "Xiangxiang Chu", "Yao Zhao" ], "github": "", "project_page": "", "abstract": "Leveraging visual priors from pre-trained text-to-image (T2I) generative models has shown success in dense prediction. However, dense prediction is inherently an image-to-image task, suggesting that image editing models, rather than T2I generative models, may be a more suitable foundation for fine-tuning. Motivated by this, we conduct a systematic analysis of the fine-tuning behaviors of both editors and generators for dense geometry estimation. Our findings show that editing models possess inherent structural priors, which enable them to converge more stably by ``refining\" their innate features, and ultimately achieve higher performance than their generative counterparts. Based on these findings, we introduce FE2E, a framework that pioneeringly adapts an advanced editing model based on Diffusion Transformer (DiT) architecture for dense geometry prediction. Specifically, to tailor the editor for this deterministic task, we reformulate the editor's original flow matching loss into the ``consistent velocity\" training objective. And we use logarithmic quantization to resolve the precision conflict between the editor's native BFloat16 format and the high precision demand of our tasks. Additionally, we leverage the DiT's global attention for a cost-free joint estimation of depth and normals in a single forward pass, enabling their supervisory signals to mutually enhance each other. Without scaling up the training data, FE2E achieves impressive performance improvements in zero-shot monocular depth and normal estimation across multiple datasets. Notably, it achieves over 35\\% performance gains on the ETH3D dataset and outperforms the DepthAnything series, which is trained on 100times data. The project page can be accessed https://amap-ml.github.io/FE2E/{here}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2509.04292", "title": "Inverse IFEval: Can LLMs Unlearn Stubborn Training Conventions to Follow Real Instructions?", "authors": [ "Qinyan Zhang", "Xinping Lei", "Ruijie Miao", "Yu Fu", "Haojie Fan", "Le Chang", "Jiafan Hou", "Dingling Zhang", "Zhongfei Hou", "Ziqiang Yang", "Changxin Pu", "Fei Hu", "Jingkai Liu", "Mengyun Liu", "Yang Liu", "Xiang Gao", "Jiaheng Liu", "Tong Yang", "Zaiyuan Wang", "Ge Zhang", "Wenhao Huang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) achieve strong performance on diverse tasks but often exhibit cognitive inertia, struggling to follow instructions that conflict with the standardized patterns learned during supervised fine-tuning (SFT). To evaluate this limitation, we propose Inverse IFEval, a benchmark that measures models Counter-intuitive Abilitytheir capacity to override training-induced biases and comply with adversarial instructions. Inverse IFEval introduces eight types of such challenges, including Question Correction, Intentional Textual Flaws, Code without Comments, and Counterfactual Answering. Using a human-in-the-loop pipeline, we construct a dataset of 1012 high-quality Chinese and English questions across 23 domains, evaluated under an optimized LLM-as-a-Judge framework. Experiments on existing leading LLMs demonstrate the necessity of our proposed Inverse IFEval benchmark. Our findings emphasize that future alignment efforts should not only pursue fluency and factual correctness but also account for adaptability under unconventional contexts. We hope that Inverse IFEval serves as both a diagnostic tool and a foundation for developing methods that mitigate cognitive inertia, reduce overfitting to narrow patterns, and ultimately enhance the instruction-following reliability of LLMs in diverse and unpredictable real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2509.04011", "title": "NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings", "authors": [ "Or Shachar", "Uri Katz", "Yoav Goldberg", "Oren Glickman" ], "github": "https://github.com/ShacharOr100/ner_retriever", "project_page": "", "abstract": "We present NER Retriever, a zero-shot retrieval framework for ad-hoc Named Entity Retrieval, a variant of Named Entity Recognition (NER), where the types of interest are not provided in advance, and a user-defined type description is used to retrieve documents mentioning entities of that type. Instead of relying on fixed schemas or fine-tuned models, our method builds on internal representations of large language models (LLMs) to embed both entity mentions and user-provided open-ended type descriptions into a shared semantic space. We show that internal representations, specifically the value vectors from mid-layer transformer blocks, encode fine-grained type information more effectively than commonly used top-layer embeddings. To refine these representations, we train a lightweight contrastive projection network that aligns type-compatible entities while separating unrelated types. The resulting entity embeddings are compact, type-aware, and well-suited for nearest-neighbor search. Evaluated on three benchmarks, NER Retriever significantly outperforms both lexical and dense sentence-level retrieval baselines. Our findings provide empirical support for representation selection within LLMs and demonstrate a practical solution for scalable, schema-free entity retrieval. The NER Retriever Codebase is publicly available at https://github.com/ShacharOr100/ner_retriever", "github_manual": "", "github_regex": "https://github.com/ShacharOr100/ner_retriever", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2509.04442", "title": "Delta Activations: A Representation for Finetuned Large Language Models", "authors": [ "Zhiqiu Xu", "Amish Sethi", "Mayur Naik", "Ser-Nam Lim" ], "github": "https://github.com/OscarXZQ/delta_activations", "project_page": "", "abstract": "The success of powerful open source Large Language Models (LLMs) has enabled the community to create a vast collection of post-trained models adapted to specific tasks and domains. However, navigating and understanding these models remains challenging due to inconsistent metadata and unstructured repositories. We introduce Delta Activations, a method to represent finetuned models as vector embeddings by measuring shifts in their internal activations relative to a base model. This representation allows for effective clustering by domain and task, revealing structure in the model landscape. Delta Activations also demonstrate desirable properties: it is robust across finetuning settings and exhibits an additive property when finetuning datasets are mixed. In addition, we show that Delta Activations can embed tasks via few-shot finetuning, and further explore its use for model selection and merging. We hope Delta Activations can facilitate the practice of reusing publicly available models. Code is available at https://github.com/OscarXZQ/delta_activations.", "github_manual": "", "github_regex": "https://github.com/OscarXZQ/delta_activations", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2509.03867", "title": "Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth", "authors": [ "Yang Wang", "Chenghao Xiao", "Chia-Yi Hsiao", "Zi Yan Chang", "Chi-Li Chen", "Tyler Loakman", "Chenghua Lin" ], "github": "", "project_page": "", "abstract": "We introduce Drivelology, a unique linguistic phenomenon characterised as \"nonsense with depth\", utterances that are syntactically coherent yet pragmatically paradoxical, emotionally loaded, or rhetorically subversive. While such expressions may resemble surface-level nonsense, they encode implicit meaning requiring contextual inference, moral reasoning, or emotional interpretation. We find that current large language models (LLMs), despite excelling at many natural language processing (NLP) tasks, consistently fail to grasp the layered semantics of Drivelological text. To investigate this, we construct a small but diverse benchmark dataset of over 1,200 meticulously curated examples, with select instances in English, Mandarin, Spanish, French, Japanese, and Korean. Annotation was especially challenging: each of the examples required careful expert review to verify that it truly reflected Drivelological characteristics. The process involved multiple rounds of discussion and adjudication to address disagreements, highlighting the subtle and subjective nature of the Drivelology. We evaluate a range of LLMs on classification, generation, and reasoning tasks. Our results reveal clear limitations of LLMs: models often confuse Drivelology with shallow nonsense, produce incoherent justifications, or miss the implied rhetorical function altogether. These findings highlight a deeper representational gap in LLMs' pragmatic understanding and challenge the assumption that statistical fluency implies cognitive comprehension. We release our dataset and code to facilitate further research in modelling linguistic depth beyond surface-level coherence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2508.20478", "title": "Video-MTR: Reinforced Multi-Turn Reasoning for Long Video Understanding", "authors": [ "Yuan Xie", "Tianshui Chen", "Zheng Ge", "Lionel Ni" ], "github": "", "project_page": "", "abstract": "Long-form video understanding, characterized by long-range temporal dependencies and multiple events, remains a challenge. Existing methods often rely on static reasoning or external visual-language models (VLMs), which face issues like complexity and sub-optimal performance due to the lack of end-to-end training. In this paper, we propose Video-MTR, a reinforced multi-turn reasoning framework designed to enable iterative key video segment selection and question comprehension. Unlike traditional video reasoning pipeline, which generate predictions in a single turn, Video-MTR performs reasoning in multiple turns, selecting video segments progressively based on the evolving understanding of previously processed segments and the current question. This iterative process allows for a more refined and contextually aware analysis of the video. To ensure intermediate reasoning process, we introduce a novel gated bi-level reward system, combining trajectory-level rewards based on answer correctness and turn-level rewards emphasizing frame-query relevance. This system optimizes both video segment selection and question comprehension, eliminating the need for external VLMs and allowing end-to-end training. Extensive experiments on benchmarks like VideoMME, MLVU, and EgoSchema demonstrate that Video-MTR outperforms existing methods in both accuracy and efficiency, advancing the state-of-the-art in long video understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-05", "arxiv_id": "2509.03059", "title": "Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers", "authors": [ "Xingyue Huang", "Rishabh", "Gregor Franke", "Ziyi Yang", "Jiamu Bai", "Weijie Bai", "Jinhe Bi", "Zifeng Ding", "Yiqun Duan", "Chengyu Fan", "Wendong Fan", "Xin Gao", "Ruohao Guo", "Yuan He", "Zhuangzhuang He", "Xianglong Hu", "Neil Johnson", "Bowen Li", "Fangru Lin", "Siyu Lin", "Tong Liu", "Yunpu Ma", "Hao Shen", "Hao Sun", "Beibei Wang", "Fangyijie Wang", "Hao Wang", "Haoran Wang", "Yang Wang", "Yifeng Wang", "Zhaowei Wang", "Ziyang Wang", "Yifan Wu", "Zikai Xiao", "Chengxing Xie", "Fan Yang", "Junxiao Yang", "Qianshuo Ye", "Ziyu Ye", "Guangtao Zeng", "Yuwen Ebony Zhang", "Zeyu Zhang", "Zihao Zhu", "Bernard Ghanem", "Philip Torr", "Guohao Li" ], "github": "https://github.com/camel-ai/loong", "project_page": "", "abstract": "Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RLVR), particularly in domains like mathematics and programming, where ground-truth correctness can be automatically evaluated. However, extending this success to other reasoning-intensive domains remains challenging due to the scarcity of high-quality, verifiable datasets and the high cost of human supervision. In this work, we introduce the Loong Project: an open-source framework for scalable synthetic data generation and verification across a diverse range of reasoning-intensive domains. The framework consists of two key components: (1) LoongBench, a curated seed dataset containing 8,729 human-vetted examples across 12 domains (e.g., Advanced Mathematics, Chemistry, Logic), each paired with executable code and rich metadata; and (2) LoongEnv, a modular synthetic data generation environment that supports multiple prompting strategies to produce new question-answer-code triples. Together, these components form an agent-environment loop that enables reinforcement learning, where an LLM-based agent is rewarded for generating Chain-of-Thought (CoT) solutions that align with code-executed answers. Empirically, we benchmark LoongBench on a broad suite of both open-source and proprietary LLMs to evaluate domain coverage and reveal performance bottlenecks. In addition, we conduct a comprehensive analysis of synthetic data generated by LoongEnv, examining correctness, difficulty, and diversity. Code and documentation are available at https://github.com/camel-ai/loong.", "github_manual": "", "github_regex": "https://github.com/camel-ai/loong", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-08", "arxiv_id": "2509.04744", "title": "WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning", "authors": [ "Gagan Mundada", "Yash Vishe", "Amit Namburi", "Xin Xu", "Zachary Novack", "Julian McAuley", "Junda Wu" ], "github": "https://github.com/GaganVM/WildScore", "project_page": "", "abstract": "Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various vision-language tasks. However, their reasoning abilities in the multimodal symbolic music domain remain largely unexplored. We introduce WildScore, the first in-the-wild multimodal symbolic music reasoning and analysis benchmark, designed to evaluate MLLMs' capacity to interpret real-world music scores and answer complex musicological queries. Each instance in WildScore is sourced from genuine musical compositions and accompanied by authentic user-generated questions and discussions, capturing the intricacies of practical music analysis. To facilitate systematic evaluation, we propose a systematic taxonomy, comprising both high-level and fine-grained musicological ontologies. Furthermore, we frame complex music reasoning as multiple-choice question answering, enabling controlled and scalable assessment of MLLMs' symbolic music understanding. Empirical benchmarking of state-of-the-art MLLMs on WildScore reveals intriguing patterns in their visual-symbolic reasoning, uncovering both promising directions and persistent challenges for MLLMs in symbolic music reasoning and analysis. We release the dataset and code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GaganVM/WildScore", "github_api_added_by": "auto" }, { "date": "2025-09-08", "arxiv_id": "2509.05263", "title": "LatticeWorld: A Multimodal Large Language Model-Empowered Framework for Interactive Complex World Generation", "authors": [ "Yinglin Duan", "Zhengxia Zou", "Tongwei Gu", "Wei Jia", "Zhan Zhao", "Luyi Xu", "Xinzhu Liu", "Hao Jiang", "Kang Chen", "Shuang Qiu" ], "github": "", "project_page": "", "abstract": "Recent research has been increasingly focusing on developing 3D world models that simulate complex real-world scenarios. World models have found broad applications across various domains, including embodied AI, autonomous driving, entertainment, etc. A more realistic simulation with accurate physics will effectively narrow the sim-to-real gap and allow us to gather rich information about the real world conveniently. While traditional manual modeling has enabled the creation of virtual 3D scenes, modern approaches have leveraged advanced machine learning algorithms for 3D world generation, with most recent advances focusing on generative methods that can create virtual worlds based on user instructions. This work explores such a research direction by proposing LatticeWorld, a simple yet effective 3D world generation framework that streamlines the industrial production pipeline of 3D environments. LatticeWorld leverages lightweight LLMs (LLaMA-2-7B) alongside the industry-grade rendering engine (e.g., Unreal Engine 5) to generate a dynamic environment. Our proposed framework accepts textual descriptions and visual instructions as multimodal inputs and creates large-scale 3D interactive worlds with dynamic agents, featuring competitive multi-agent interaction, high-fidelity physics simulation, and real-time rendering. We conduct comprehensive experiments to evaluate LatticeWorld, showing that it achieves superior accuracy in scene layout generation and visual fidelity. Moreover, LatticeWorld achieves over a 90times increase in industrial production efficiency while maintaining high creative quality compared with traditional manual production methods. Our demo video is available at https://youtu.be/8VWZXpERR18", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-08", "arxiv_id": "2509.03800", "title": "MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting", "authors": [ "Yuheng Li", "Yenho Chen", "Yuxiang Lai", "Jike Zhong", "Vanessa Wildman", "Xiaofeng Yang" ], "github": "", "project_page": "", "abstract": "Radiologic diagnostic errors-under-reading errors, inattentional blindness, and communication failures-remain prevalent in clinical practice. These issues often stem from missed localized abnormalities, limited global context, and variability in report language. These challenges are amplified in 3D imaging, where clinicians must examine hundreds of slices per scan. Addressing them requires systems with precise localized detection, global volume-level reasoning, and semantically consistent natural language reporting. However, existing 3D vision-language models are unable to meet all three needs jointly, lacking local-global understanding for spatial reasoning and struggling with the variability and noise of uncurated radiology reports. We present MedVista3D, a multi-scale semantic-enriched vision-language pretraining framework for 3D CT analysis. To enable joint disease detection and holistic interpretation, MedVista3D performs local and global image-text alignment for fine-grained representation learning within full-volume context. To address report variability, we apply language model rewrites and introduce a Radiology Semantic Matching Bank for semantics-aware alignment. MedVista3D achieves state-of-the-art performance on zero-shot disease classification, report retrieval, and medical visual question answering, while transferring well to organ segmentation and prognosis prediction. Code and datasets will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-08", "arxiv_id": "2509.04664", "title": "Why Language Models Hallucinate", "authors": [ "Adam Tauman Kalai", "Ofir Nachum", "Santosh S. Vempala", "Edwin Zhang" ], "github": "", "project_page": "", "abstract": "Like students facing hard exam questions, large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty. Such \"hallucinations\" persist even in state-of-the-art systems and undermine trust. We argue that language models hallucinate because the training and evaluation procedures reward guessing over acknowledging uncertainty, and we analyze the statistical causes of hallucinations in the modern training pipeline. Hallucinations need not be mysterious -- they originate simply as errors in binary classification. If incorrect statements cannot be distinguished from facts, then hallucinations in pretrained language models will arise through natural statistical pressures. We then argue that hallucinations persist due to the way most evaluations are graded -- language models are optimized to be good test-takers, and guessing when uncertain improves test performance. This \"epidemic\" of penalizing uncertain responses can only be addressed through a socio-technical mitigation: modifying the scoring of existing benchmarks that are misaligned but dominate leaderboards, rather than introducing additional hallucination evaluations. This change may steer the field toward more trustworthy AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-08", "arxiv_id": "2509.04504", "title": "Behavioral Fingerprinting of Large Language Models", "authors": [ "Zehua Pei", "Hui-Ling Zhen", "Ying Zhang", "Zhiyuan Yang", "Xing Li", "Xianzhi Yu", "Mingxuan Yuan", "Bei Yu" ], "github": "https://github.com/JarvisPei/Behavioral-Fingerprinting", "project_page": "", "abstract": "Current benchmarks for Large Language Models (LLMs) primarily focus on performance metrics, often failing to capture the nuanced behavioral characteristics that differentiate them. This paper introduces a novel ``Behavioral Fingerprinting'' framework designed to move beyond traditional evaluation by creating a multi-faceted profile of a model's intrinsic cognitive and interactive styles. Using a curated Diagnostic Prompt Suite and an innovative, automated evaluation pipeline where a powerful LLM acts as an impartial judge, we analyze eighteen models across capability tiers. Our results reveal a critical divergence in the LLM landscape: while core capabilities like abstract and causal reasoning are converging among top models, alignment-related behaviors such as sycophancy and semantic robustness vary dramatically. We further document a cross-model default persona clustering (ISTJ/ESTJ) that likely reflects common alignment incentives. Taken together, this suggests that a model's interactive nature is not an emergent property of its scale or reasoning power, but a direct consequence of specific, and highly variable, developer alignment strategies. Our framework provides a reproducible and scalable methodology for uncovering these deep behavioral differences. Project: https://github.com/JarvisPei/Behavioral-Fingerprinting", "github_manual": "", "github_regex": "https://github.com/JarvisPei/Behavioral-Fingerprinting", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-08", "arxiv_id": "2509.04013", "title": "On Robustness and Reliability of Benchmark-Based Evaluation of LLMs", "authors": [ "Riccardo Lunardi", "Vincenzo Della Mea", "Stefano Mizzaro", "Kevin Roitero" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) effectiveness is usually evaluated by means of benchmarks such as MMLU, ARC-C, or HellaSwag, where questions are presented in their original wording, thus in a fixed, standardized format. However, real-world applications involve linguistic variability, requiring models to maintain their effectiveness across diverse rewordings of the same question or query. In this study, we systematically assess the robustness of LLMs to paraphrased benchmark questions and investigate whether benchmark-based evaluations provide a reliable measure of model capabilities. We systematically generate various paraphrases of all the questions across six different common benchmarks, and measure the resulting variations in effectiveness of 34 state-of-the-art LLMs, of different size and effectiveness. Our findings reveal that while LLM rankings remain relatively stable across paraphrased inputs, absolute effectiveness scores change, and decline significantly. This suggests that LLMs struggle with linguistic variability, raising concerns about their generalization abilities and evaluation methodologies. Furthermore, the observed performance drop challenges the reliability of benchmark-based evaluations, indicating that high benchmark scores may not fully capture a model's robustness to real-world input variations. We discuss the implications of these findings for LLM evaluation methodologies, emphasizing the need for robustness-aware benchmarks that better reflect practical deployment scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-08", "arxiv_id": "2509.05296", "title": "WinT3R: Window-Based Streaming Reconstruction with Camera Token Pool", "authors": [ "Zizun Li", "Jianjun Zhou", "Yifan Wang", "Haoyu Guo", "Wenzheng Chang", "Yang Zhou", "Haoyi Zhu", "Junyi Chen", "Chunhua Shen", "Tong He" ], "github": "https://github.com/LiZizun/WinT3R", "project_page": "", "abstract": "We present WinT3R, a feed-forward reconstruction model capable of online prediction of precise camera poses and high-quality point maps. Previous methods suffer from a trade-off between reconstruction quality and real-time performance. To address this, we first introduce a sliding window mechanism that ensures sufficient information exchange among frames within the window, thereby improving the quality of geometric predictions without large computation. In addition, we leverage a compact representation of cameras and maintain a global camera token pool, which enhances the reliability of camera pose estimation without sacrificing efficiency. These designs enable WinT3R to achieve state-of-the-art performance in terms of online reconstruction quality, camera pose estimation, and reconstruction speed, as validated by extensive experiments on diverse datasets. Code and model are publicly available at https://github.com/LiZizun/WinT3R.", "github_manual": "", "github_regex": "https://github.com/LiZizun/WinT3R", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-08", "arxiv_id": "2509.05208", "title": "Symbolic Graphics Programming with Large Language Models", "authors": [ "Yamei Chen", "Haoquan Zhang", "Yangyi Huang", "Zeju Qiu", "Kaipeng Zhang", "Yandong Wen", "Weiyang Liu" ], "github": "https://github.com/sail-sg/oat-zero", "project_page": "", "abstract": "Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored. We study symbolic graphics programming, where the goal is to generate an SGP from a natural-language description. This task also serves as a lens into how LLMs understand the visual world by prompting them to generate images rendered from SGPs. Among various SGPs, our paper sticks to scalable vector graphics (SVGs). We begin by examining the extent to which LLMs can generate SGPs. To this end, we introduce SGP-GenBench, a comprehensive benchmark covering object fidelity, scene fidelity, and compositionality (attribute binding, spatial relations, numeracy). On SGP-GenBench, we discover that frontier proprietary models substantially outperform open-source models, and performance correlates well with general coding capabilities. Motivated by this gap, we aim to improve LLMs' ability to generate SGPs. We propose a reinforcement learning (RL) with verifiable rewards approach, where a format-validity gate ensures renderable SVG, and a cross-modal reward aligns text and the rendered image via strong vision encoders (e.g., SigLIP for text-image and DINO for image-image). Applied to Qwen-2.5-7B, our method substantially improves SVG generation quality and semantics, achieving performance on par with frontier systems. We further analyze training dynamics, showing that RL induces (i) finer decomposition of objects into controllable primitives and (ii) contextual details that improve scene coherence. Our results demonstrate that symbolic graphics programming offers a precise and interpretable lens on cross-modal grounding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sail-sg/oat-zero", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-08", "arxiv_id": "2509.03680", "title": "LuxDiT: Lighting Estimation with Video Diffusion Transformer", "authors": [ "Ruofan Liang", "Kai He", "Zan Gojcic", "Igor Gilitschenski", "Sanja Fidler", "Nandita Vijaykumar", "Zian Wang" ], "github": "", "project_page": "", "abstract": "Estimating scene lighting from a single image or video remains a longstanding challenge in computer vision and graphics. Learning-based approaches are constrained by the scarcity of ground-truth HDR environment maps, which are expensive to capture and limited in diversity. While recent generative models offer strong priors for image synthesis, lighting estimation remains difficult due to its reliance on indirect visual cues, the need to infer global (non-local) context, and the recovery of high-dynamic-range outputs. We propose LuxDiT, a novel data-driven approach that fine-tunes a video diffusion transformer to generate HDR environment maps conditioned on visual input. Trained on a large synthetic dataset with diverse lighting conditions, our model learns to infer illumination from indirect visual cues and generalizes effectively to real-world scenes. To improve semantic alignment between the input and the predicted environment map, we introduce a low-rank adaptation finetuning strategy using a collected dataset of HDR panoramas. Our method produces accurate lighting predictions with realistic angular high-frequency details, outperforming existing state-of-the-art techniques in both quantitative and qualitative evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-08", "arxiv_id": "2509.02437", "title": "U-ARM : Ultra low-cost general teleoperation interface for robot manipulation", "authors": [ "Yanwen Zou", "Zhaoye Zhou", "Chenyang Shi", "Zewei Ye", "Junda Huang", "Yan Ding", "Bo Zhao" ], "github": "https://github.com/MINT-SJTU/LeRobot-Anything-U-Arm", "project_page": "", "abstract": "We propose U-Arm, a low-cost and rapidly adaptable leader-follower teleoperation framework designed to interface with most of commercially available robotic arms. Our system supports teleoperation through three structurally distinct 3D-printed leader arms that share consistent control logic, enabling seamless compatibility with diverse commercial robot configurations. Compared with previous open-source leader-follower interfaces, we further optimized both the mechanical design and servo selection, achieving a bill of materials (BOM) cost of only \\50.5 for the 6-DoF leader arm and 56.8 for the 7-DoF version. To enhance usability, we mitigate the common challenge in controlling redundant degrees of freedom by %engineering methods mechanical and control optimizations. Experimental results demonstrate that U-Arm achieves 39\\% higher data collection efficiency and comparable task success rates across multiple manipulation scenarios compared with Joycon, another low-cost teleoperation interface. We have open-sourced all CAD models of three configs and also provided simulation support for validating teleoperation workflows. We also open-sourced real-world manipulation data collected with U-Arm. The project website is https://github.com/MINT-SJTU/LeRobot-Anything-U-Arm.", "github_manual": "", "github_regex": "https://github.com/MINT-SJTU/LeRobot-Anything-U-Arm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-08", "arxiv_id": "2509.04575", "title": "Bootstrapping Task Spaces for Self-Improvement", "authors": [ "Minqi Jiang", "Andrei Lupu", "Yoram Bachrach" ], "github": "", "project_page": "", "abstract": "Progress in many task domains emerges from repeated revisions to previous solution attempts. Training agents that can reliably self-improve over such sequences at inference-time is a natural target for reinforcement learning (RL), yet the naive approach assumes a fixed maximum iteration depth, which can be both costly and arbitrary. We present Exploratory Iteration (ExIt), a family of autocurriculum RL methods that directly exploits the recurrent structure of self-improvement tasks to train LLMs to perform multi-step self-improvement at inference-time while only training on the most informative single-step iterations. ExIt grows a task space by selectively sampling the most informative intermediate, partial histories encountered during an episode for continued iteration, treating these starting points as new self-iteration task instances to train a self-improvement policy. ExIt can further pair with explicit exploration mechanisms to sustain greater task diversity. Across several domains, encompassing competition math, multi-turn tool-use, and machine learning engineering, we demonstrate that ExIt strategies, starting from either a single or many task instances, can produce policies exhibiting strong inference-time self-improvement on held-out task instances, and the ability to iterate towards higher performance over a step budget extending beyond the average iteration depth encountered during training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-08", "arxiv_id": "2509.04185", "title": "Set Block Decoding is a Language Model Inference Accelerator", "authors": [ "Itai Gat", "Heli Ben-Hamu", "Marton Havasi", "Daniel Haziza", "Jeremy Reizenstein", "Gabriel Synnaeve", "David Lopez-Paz", "Brian Karrer", "Yaron Lipman" ], "github": "", "project_page": "", "abstract": "Autoregressive next token prediction language models offer powerful capabilities but face significant challenges in practical deployment due to the high computational and memory costs of inference, particularly during the decoding stage. We introduce Set Block Decoding (SBD), a simple and flexible paradigm that accelerates generation by integrating standard next token prediction (NTP) and masked token prediction (MATP) within a single architecture. SBD allows the model to sample multiple, not necessarily consecutive, future tokens in parallel, a key distinction from previous acceleration methods. This flexibility allows the use of advanced solvers from the discrete diffusion literature, offering significant speedups without sacrificing accuracy. SBD requires no architectural changes or extra training hyperparameters, maintains compatibility with exact KV-caching, and can be implemented by fine-tuning existing next token prediction models. By fine-tuning Llama-3.1 8B and Qwen-3 8B, we demonstrate that SBD enables a 3-5x reduction in the number of forward passes required for generation while achieving same performance as equivalent NTP training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.01656", "title": "Reinforced Visual Perception with Tools", "authors": [ "Zetong Zhou", "Dongping Chen", "Zixian Ma", "Zhihan Hu", "Mingyang Fu", "Sinan Wang", "Yao Wan", "Zhou Zhao", "Ranjay Krishna" ], "github": "https://github.com/ls-kelvin/REVPT", "project_page": "", "abstract": "Visual reasoning, a cornerstone of human intelligence, encompasses complex perceptual and logical processes essential for solving diverse visual problems. While advances in computer vision have produced powerful models for various perceptual tasks, leveraging these for general visual reasoning remains challenging. Prior work demonstrates that augmenting LLMs with vision models via supervised finetuning improves performance, but faces key limitations such as expensive data generation, reliance on careful data filtering, and poor generalization. To address these issues, we propose ReVPT to enhance multi-modal LLMs' abilities to reason about and use visual tools through reinforcement learning. We introduce a novel RL algorithm based on GRPO, designed to train models to reason with a suite of four visual tools. Through extensive experiments, we show that our method achieves state-of-the-art performance on several perception-heavy benchmarks, including SAT, CV-Bench, BLINK and MMStar, significantly outperforming the supervised and text-based RL finetuning baselines. Notably, Our ReVPT-3B and ReVPT-7B outperform the instruct models by 9.03% and 9.44% on CV-Bench. Finally, we bring to the community new insights on RL-based visual tool-usage through extensive ablations. Our code is available at https://github.com/ls-kelvin/REVPT.", "github_manual": "", "github_regex": "https://github.com/ls-kelvin/REVPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06155", "title": "UniVerse-1: Unified Audio-Video Generation via Stitching of Experts", "authors": [ "Duomin Wang", "Wei Zuo", "Aojie Li", "Ling-Hao Chen", "Xinyao Liao", "Deyu Zhou", "Zixin Yin", "Xili Dai", "Daxin Jiang", "Gang Yu" ], "github": "", "project_page": "", "abstract": "We introduce UniVerse-1, a unified, Veo-3-like model capable of simultaneously generating coordinated audio and video. To enhance training efficiency, we bypass training from scratch and instead employ a stitching of experts (SoE) technique. This approach deeply fuses the corresponding blocks of pre-trained video and music generation experts models, thereby fully leveraging their foundational capabilities. To ensure accurate annotations and temporal alignment for both ambient sounds and speech with video content, we developed an online annotation pipeline that processes the required training data and generates labels during training process. This strategy circumvents the performance degradation often caused by misalignment text-based annotations. Through the synergy of these techniques, our model, after being finetuned on approximately 7,600 hours of audio-video data, produces results with well-coordinated audio-visuals for ambient sounds generation and strong alignment for speech generation. To systematically evaluate our proposed method, we introduce Verse-Bench, a new benchmark dataset. In an effort to advance research in audio-video generation and to close the performance gap with state-of-the-art models such as Veo3, we make our model and code publicly available. We hope this contribution will benefit the broader research community. Project page: https://dorniwang.github.io/UniVerse-1/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06949", "title": "Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models", "authors": [ "Yinjie Wang", "Ling Yang", "Bowen Li", "Ye Tian", "Ke Shen", "Mengdi Wang" ], "github": "https://github.com/Gen-Verse/dLLM-RL", "project_page": "", "abstract": "We propose TraceRL, a trajectory-aware reinforcement learning framework for diffusion language models (DLMs) that incorporates preferred inference trajectory into post-training, and is applicable across different architectures. Equipped with a diffusion-based value model that enhances training stability, we demonstrate improved reasoning performance on complex math and coding tasks. Besides, it can also be applied to adapt block-specific models to larger blocks, which improves sampling flexibility. Employing TraceRL, we derive a series of state-of-the-art diffusion language models, namely TraDo. Although smaller than 7B-scale AR models, TraDo-4B-Instruct still consistently outperforms them across complex math reasoning tasks. TraDo-8B-Instruct achieves relative accuracy improvements of 6.1% over Qwen2.5-7B-Instruct and 51.3% over Llama3.1-8B-Instruct on mathematical reasoning benchmarks. Through curriculum learning, we also derive the first long-CoT DLM, outperforming Qwen2.5-7B-Instruct on MATH500 with an 18.1% relative accuracy gain. To facilitate reproducible research and practical applications, we release a comprehensive open-source framework for building, training, and deploying diffusion LLMs across diverse architectures. The framework integrates accelerated KV-cache techniques and inference engines for both inference and reinforcement learning, and includes implementations of various supervised fine-tuning and RL methods for mathematics, coding, and general tasks. Code and Models: https://github.com/Gen-Verse/dLLM-RL", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/dLLM-RL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06733", "title": "Reinforcement Learning Foundations for Deep Research Systems: A Survey", "authors": [ "Wenjun Li", "Zhi Chen", "Jingru Lin", "Hannan Cao", "Wei Han", "Sheng Liang", "Zhi Zhang", "Kuicai Dong", "Dexun Li", "Chen Zhang", "Yong Liu" ], "github": "https://github.com/wenjunli-0/deepresearch-survey", "project_page": "", "abstract": "Deep research systems, agentic AI that solve complex, multi-step tasks by coordinating reasoning, search across the open web and user files, and tool use, are moving toward hierarchical deployments with a Planner, Coordinator, and Executors. In practice, training entire stacks end-to-end remains impractical, so most work trains a single planner connected to core tools such as search, browsing, and code. While SFT imparts protocol fidelity, it suffers from imitation and exposure biases and underuses environment feedback. Preference alignment methods such as DPO are schema and proxy-dependent, off-policy, and weak for long-horizon credit assignment and multi-objective trade-offs. A further limitation of SFT and DPO is their reliance on human defined decision points and subskills through schema design and labeled comparisons. Reinforcement learning aligns with closed-loop, tool-interaction research by optimizing trajectory-level policies, enabling exploration, recovery behaviors, and principled credit assignment, and it reduces dependence on such human priors and rater biases. This survey is, to our knowledge, the first dedicated to the RL foundations of deep research systems. It systematizes work after DeepSeek-R1 along three axes: (i) data synthesis and curation; (ii) RL methods for agentic research covering stability, sample efficiency, long context handling, reward and credit design, multi-objective optimization, and multimodal integration; and (iii) agentic RL training systems and frameworks. We also cover agent architecture and coordination, as well as evaluation and benchmarks, including recent QA, VQA, long-form synthesis, and domain-grounded, tool-interaction tasks. We distill recurring patterns, surface infrastructure bottlenecks, and offer practical guidance for training robust, transparent deep research agents with RL.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/wenjunli-0/deepresearch-survey", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.03516", "title": "Easier Painting Than Thinking: Can Text-to-Image Models Set the Stage, but Not Direct the Play?", "authors": [ "Ouxiang Li", "Yuan Wang", "Xinting Hu", "Huijuan Huang", "Rui Chen", "Jiarong Ou", "Xin Tao", "Pengfei Wan", "Fuli Feng" ], "github": "", "project_page": "", "abstract": "Text-to-image (T2I) generation aims to synthesize images from textual prompts, which jointly specify what must be shown and imply what can be inferred, thereby corresponding to two core capabilities: composition and reasoning. However, with the emerging advances of T2I models in reasoning beyond composition, existing benchmarks reveal clear limitations in providing comprehensive evaluations across and within these capabilities. Meanwhile, these advances also enable models to handle more complex prompts, whereas current benchmarks remain limited to low scene density and simplified one-to-one reasoning. To address these limitations, we propose T2I-CoReBench, a comprehensive and complex benchmark that evaluates both composition and reasoning capabilities of T2I models. To ensure comprehensiveness, we structure composition around scene graph elements (instance, attribute, and relation) and reasoning around the philosophical framework of inference (deductive, inductive, and abductive), formulating a 12-dimensional evaluation taxonomy. To increase complexity, driven by the inherent complexities of real-world scenarios, we curate each prompt with high compositional density for composition and multi-step inference for reasoning. We also pair each prompt with a checklist that specifies individual yes/no questions to assess each intended element independently to facilitate fine-grained and reliable evaluation. In statistics, our benchmark comprises 1,080 challenging prompts and around 13,500 checklist questions. Experiments across 27 current T2I models reveal that their composition capability still remains limited in complex high-density scenarios, while the reasoning capability lags even further behind as a critical bottleneck, with all models struggling to infer implicit elements from prompts. Our project page: https://t2i-corebench.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06786", "title": "R^textbf{2AI}: Towards Resistant and Resilient AI in an Evolving World", "authors": [ "Youbang Sun", "Xiang Wang", "Jie Fu", "Chaochao Lu", "Bowen Zhou" ], "github": "", "project_page": "", "abstract": "In this position paper, we address the persistent gap between rapidly growing AI capabilities and lagging safety progress. Existing paradigms divide into ``Make AI Safe'', which applies post-hoc alignment and guardrails but remains brittle and reactive, and ``Make Safe AI'', which emphasizes intrinsic safety but struggles to address unforeseen risks in open-ended environments. We therefore propose safe-by-coevolution as a new formulation of the ``Make Safe AI'' paradigm, inspired by biological immunity, in which safety becomes a dynamic, adversarial, and ongoing learning process. To operationalize this vision, we introduce R^2AI -- Resistant and Resilient AI -- as a practical framework that unites resistance against known threats with resilience to unforeseen risks. R^2AI integrates fast and slow safe models, adversarial simulation and verification through a safety wind tunnel, and continual feedback loops that guide safety and capability to coevolve. We argue that this framework offers a scalable and proactive path to maintain continual safety in dynamic environments, addressing both near-term vulnerabilities and long-term existential risks as AI advances toward AGI and ASI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06917", "title": "Paper2Agent: Reimagining Research Papers As Interactive and Reliable AI Agents", "authors": [ "Jiacheng Miao", "Joe R. Davis", "Jonathan K. Pritchard", "James Zou" ], "github": "https://github.com/google-deepmind/alphagenome", "project_page": "", "abstract": "We introduce Paper2Agent, an automated framework that converts research papers into AI agents. Paper2Agent transforms research output from passive artifacts into active systems that can accelerate downstream use, adoption, and discovery. Conventional research papers require readers to invest substantial effort to understand and adapt a paper's code, data, and methods to their own work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by automatically converting a paper into an AI agent that acts as a knowledgeable research assistant. It systematically analyzes the paper and the associated codebase using multiple agents to construct a Model Context Protocol (MCP) server, then iteratively generates and runs tests to refine and robustify the resulting MCP. These paper MCPs can then be flexibly connected to a chat agent (e.g. Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the original paper. We demonstrate Paper2Agent's effectiveness in creating reliable and capable paper agents through in-depth case studies. Paper2Agent created an agent that leverages AlphaGenome to interpret genomic variants and agents based on ScanPy and TISSUE to carry out single-cell and spatial transcriptomics analyses. We validate that these paper agents can reproduce the original paper's results and can correctly carry out novel user queries. By turning static papers into dynamic, interactive AI agents, Paper2Agent introduces a new paradigm for knowledge dissemination and a foundation for the collaborative ecosystem of AI co-scientists.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/alphagenome", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06493", "title": "Scaling up Multi-Turn Off-Policy RL and Multi-Agent Tree Search for LLM Step-Provers", "authors": [ "Ran Xin", "Zeyu Zheng", "Yanchen Nie", "Kun Yuan", "Xia Xiao" ], "github": "https://github.com/ByteDance-Seed/BFS-Prover-V2", "project_page": "", "abstract": "The integration of Large Language Models (LLMs) into automated theorem proving has shown immense promise, yet is fundamentally constrained by challenges in scaling up both training-time reinforcement learning (RL) and inference-time compute. This paper introduces BFS-Prover-V2, a system designed to address this dual scaling problem. We present two primary innovations. The first is a novel multi-turn off-policy RL framework for continually improving the performance of LLM step-prover at training time. This framework, inspired by the principles of AlphaZero, utilizes a multi-stage expert iteration pipeline featuring adaptive tactic-level data filtering and periodic retraining to surmount the performance plateaus that typically curtail long-term RL in LLM-based agents. The second innovation is a planner-enhanced multi-agent search architecture that scales reasoning capabilities at inference time. This architecture employs a general reasoning model as a high-level planner to iteratively decompose complex theorems into a sequence of simpler subgoals. This hierarchical approach substantially reduces the search space, enabling a team of parallel prover agents to collaborate efficiently by leveraging a shared proof cache. We demonstrate that this dual approach to scaling yields state-of-the-art results on established formal mathematics benchmarks. BFS-Prover-V2 achieves 95.08\\% and 41.4\\% on the MiniF2F and ProofNet test sets respectively. While demonstrated in the domain of formal mathematics, the RL and inference techniques presented in this work are of broader interest and may be applied to other domains requiring long-horizon multi-turn reasoning and complex search.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByteDance-Seed/BFS-Prover-V2", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06160", "title": "Reverse-Engineered Reasoning for Open-Ended Generation", "authors": [ "Haozhe Wang", "Haoran Que", "Qixin Xu", "Minghao Liu", "Wangchunshu Zhou", "Jiazhan Feng", "Wanjun Zhong", "Wei Ye", "Tong Yang", "Wenhao Huang", "Ge Zhang", "Fangzhen Lin" ], "github": "", "project_page": "", "abstract": "While the ``deep reasoning'' paradigm has spurred significant advances in verifiable domains like mathematics, its application to open-ended, creative generation remains a critical challenge. The two dominant methods for instilling reasoning -- reinforcement learning (RL) and instruction distillation -- falter in this area; RL struggles with the absence of clear reward signals and high-quality reward models, while distillation is prohibitively expensive and capped by the teacher model's capabilities. To overcome these limitations, we introduce REverse-Engineered Reasoning (REER), a new paradigm that fundamentally shifts the approach. Instead of building a reasoning process ``forwards'' through trial-and-error or imitation, REER works ``backwards'' from known-good solutions to computationally discover the latent, step-by-step deep reasoning process that could have produced them. Using this scalable, gradient-free approach, we curate and open-source DeepWriting-20K, a large-scale dataset of 20,000 deep reasoning trajectories for open-ended tasks. Our model, DeepWriter-8B, trained on this data, not only surpasses strong open-source baselines but also achieves performance competitive with, and at times superior to, leading proprietary models like GPT-4o and Claude 3.5.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.05668", "title": "Llama-GENBA-10B: A Trilingual Large Language Model for German, English and Bavarian", "authors": [ "Michael Hoffmann", "Jophin John", "Stefan Schweter", "Gokul Ramakrishnan", "Hoi-Fong Mak", "Alice Zhang", "Dmitry Gaynullin", "Nicolay J. Hammer" ], "github": "", "project_page": "", "abstract": "We present Llama-GENBA-10B, a trilingual foundation model addressing English-centric bias in large language models. Built on Llama 3.1-8B and scaled to 10B parameters, Llama-GENBA-10B is continuously pretrained on 164B tokens (82B English, 82B German, and 80M Bavarian), balancing resources while preventing English dominance. Targeted at the German NLP community, the model also promotes Bavarian as a low-resource language. Development tackled four challenges: (1) curating a multilingual corpus despite Bavarian scarcity, (2) creating a unified tokenizer for English, German, and Bavarian, (3) optimizing architecture and language-ratio hyperparameters for cross-lingual transfer, and (4) establishing the first standardized trilingual evaluation suite by translating German benchmarks into Bavarian. Evaluations show that Llama-GENBA-10B achieves strong cross-lingual performance, with the fine-tuned variant surpassing Apertus-8B-2509 and gemma-2-9b in Bavarian and establishing itself as the best model in its class for this language, while also outperforming EuroLLM in English and matching its results in German. Training on the Cerebras CS-2 demonstrated efficient large-scale multilingual pretraining with documented energy use, offering a blueprint for inclusive foundation models that integrate low-resource languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06467", "title": "Does DINOv3 Set a New Medical Vision Standard?", "authors": [ "Che Liu", "Yinda Chen", "Haoyuan Shi", "Jinpeng Lu", "Bailiang Jian", "Jiazhen Pan", "Linghan Cai", "Jiayi Wang", "Yundi Zhang", "Jun Li", "Cosmin I. Bercea", "Cheng Ouyang", "Chen Chen", "Zhiwei Xiong", "Benedikt Wiestler", "Christian Wachinger", "Daniel Rueckert", "Wenjia Bai", "Rossella Arcucci" ], "github": "", "project_page": "", "abstract": "The advent of large-scale vision foundation models, pre-trained on diverse natural images, has marked a paradigm shift in computer vision. However, how the frontier vision foundation models' efficacies transfer to specialized domains remains such as medical imaging remains an open question. This report investigates whether DINOv3, a state-of-the-art self-supervised vision transformer (ViT) that features strong capability in dense prediction tasks, can directly serve as a powerful, unified encoder for medical vision tasks without domain-specific pre-training. To answer this, we benchmark DINOv3 across common medical vision tasks, including 2D/3D classification and segmentation on a wide range of medical imaging modalities. We systematically analyze its scalability by varying model sizes and input image resolutions. Our findings reveal that DINOv3 shows impressive performance and establishes a formidable new baseline. Remarkably, it can even outperform medical-specific foundation models like BiomedCLIP and CT-Net on several tasks, despite being trained solely on natural images. However, we identify clear limitations: The model's features degrade in scenarios requiring deep domain specialization, such as in Whole-Slide Pathological Images (WSIs), Electron Microscopy (EM), and Positron Emission Tomography (PET). Furthermore, we observe that DINOv3 does not consistently obey scaling law in the medical domain; performance does not reliably increase with larger models or finer feature resolutions, showing diverse scaling behaviors across tasks. Ultimately, our work establishes DINOv3 as a strong baseline, whose powerful visual features can serve as a robust prior for multiple complex medical tasks. This opens promising future directions, such as leveraging its features to enforce multiview consistency in 3D reconstruction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06477", "title": "MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents", "authors": [ "Pengxiang Zhao", "Guangyi Liu", "Yaozhen Liang", "Weiqing He", "Zhengxi Lu", "Yuehao Huang", "Yaxuan Guo", "Kexin Zhang", "Hao Wang", "Liang Liu", "Yong Liu" ], "github": "", "project_page": "", "abstract": "To enhance the efficiency of GUI agents on various platforms like smartphones and computers, a hybrid paradigm that combines flexible GUI operations with efficient shortcuts (e.g., API, deep links) is emerging as a promising direction. However, a framework for systematically benchmarking these hybrid agents is still underexplored. To take the first step in bridging this gap, we introduce MAS-Bench, a benchmark that pioneers the evaluation of GUI-shortcut hybrid agents with a specific focus on the mobile domain. Beyond merely using predefined shortcuts, MAS-Bench assesses an agent's capability to autonomously generate shortcuts by discovering and creating reusable, low-cost workflows. It features 139 complex tasks across 11 real-world applications, a knowledge base of 88 predefined shortcuts (APIs, deep-links, RPA scripts), and 7 evaluation metrics. The tasks are designed to be solvable via GUI-only operations, but can be significantly accelerated by intelligently embedding shortcuts. Experiments show that hybrid agents achieve significantly higher success rates and efficiency than their GUI-only counterparts. This result also demonstrates the effectiveness of our method for evaluating an agent's shortcut generation capabilities. MAS-Bench fills a critical evaluation gap, providing a foundational platform for future advancements in creating more efficient and robust intelligent agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06771", "title": "D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning", "authors": [ "Sai Kartheek Reddy Kasu", "Mohammad Zia Ur Rehman", "Shahid Shafi Dar", "Rishi Bharat Junghare", "Dhanvin Sanjay Namboodiri", "Nagendra Kumar" ], "github": "https://github.com/Sai-Kartheek-Reddy/D-Humor-Dark-Humor-Understanding-via-Multimodal-Open-ended-Reasoning", "project_page": "", "abstract": "Dark humor in online memes poses unique challenges due to its reliance on implicit, sensitive, and culturally contextual cues. To address the lack of resources and methods for detecting dark humor in multimodal content, we introduce a novel dataset of 4,379 Reddit memes annotated for dark humor, target category (gender, mental health, violence, race, disability, and other), and a three-level intensity rating (mild, moderate, severe). Building on this resource, we propose a reasoning-augmented framework that first generates structured explanations for each meme using a Large Vision-Language Model (VLM). Through a Role-Reversal Self-Loop, VLM adopts the author's perspective to iteratively refine its explanations, ensuring completeness and alignment. We then extract textual features from both the OCR transcript and the self-refined reasoning via a text encoder, while visual features are obtained using a vision transformer. A Tri-stream Cross-Reasoning Network (TCRNet) fuses these three streams, text, image, and reasoning, via pairwise attention mechanisms, producing a unified representation for classification. Experimental results demonstrate that our approach outperforms strong baselines across three tasks: dark humor detection, target identification, and intensity prediction. The dataset, annotations, and code are released to facilitate further research in multimodal humor understanding and content moderation. Code and Dataset are available at: https://github.com/Sai-Kartheek-Reddy/D-Humor-Dark-Humor-Understanding-via-Multimodal-Open-ended-Reasoning", "github_manual": "", "github_regex": "https://github.com/Sai-Kartheek-Reddy/D-Humor-Dark-Humor-Understanding-via-Multimodal-Open-ended-Reasoning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06461", "title": "Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning", "authors": [ "Yuyao Ge", "Shenghua Liu", "Yiwei Wang", "Lingrui Mei", "Baolong Bi", "Xuanshan Zhou", "Jiayu Yao", "Jiafeng Guo", "Xueqi Cheng" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) have demonstrated remarkable success across diverse visual tasks, yet their performance degrades in complex visual environments. While existing enhancement approaches require additional training, rely on external segmentation tools, or operate at coarse-grained levels, they overlook the innate ability within VLMs. To bridge this gap, we investigate VLMs' attention patterns and discover that: (1) visual complexity strongly correlates with attention entropy, negatively impacting reasoning performance; (2) attention progressively refines from global scanning in shallow layers to focused convergence in deeper layers, with convergence degree determined by visual complexity. (3) Theoretically, we prove that the contrast of attention maps between general queries and task-specific queries enables the decomposition of visual signal into semantic signals and visual noise components. Building on these insights, we propose Contrastive Attention Refinement for Visual Enhancement (CARVE), a training-free method that extracts task-relevant visual signals through attention contrasting at the pixel level. Extensive experiments demonstrate that CARVE consistently enhances performance, achieving up to 75% improvement on open-source models. Our work provides critical insights into the interplay between visual complexity and attention mechanisms, offering an efficient pathway for improving visual reasoning with contrasting attention.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06945", "title": "Interleaving Reasoning for Better Text-to-Image Generation", "authors": [ "Wenxuan Huang", "Shuang Chen", "Zheyong Xie", "Shaosheng Cao", "Shixiang Tang", "Yufan Shen", "Qingyu Yin", "Wenbo Hu", "Xiaoman Wang", "Yuntian Tang", "Junbo Qiao", "Yue Guo", "Yao Hu", "Zhenfei Yin", "Philip Torr", "Yu Cheng", "Wanli Ouyang", "Shaohui Lin" ], "github": "https://github.com/Osilly/Interleaving-Reasoning-Generation", "project_page": "", "abstract": "Unified multimodal understanding and generation models recently have achieve significant improvement in image generation capability, yet a large gap remains in instruction following and detail preservation compared to systems that tightly couple comprehension with generation such as GPT-4o. Motivated by recent advances in interleaving reasoning, we explore whether such reasoning can further improve Text-to-Image (T2I) generation. We introduce Interleaving Reasoning Generation (IRG), a framework that alternates between text-based thinking and image synthesis: the model first produces a text-based thinking to guide an initial image, then reflects on the result to refine fine-grained details, visual quality, and aesthetics while preserving semantics. To train IRG effectively, we propose Interleaving Reasoning Generation Learning (IRGL), which targets two sub-goals: (1) strengthening the initial think-and-generate stage to establish core content and base quality, and (2) enabling high-quality textual reflection and faithful implementation of those refinements in a subsequent image. We curate IRGL-300K, a dataset organized into six decomposed learning modes that jointly cover learning text-based thinking, and full thinking-image trajectories. Starting from a unified foundation model that natively emits interleaved text-image outputs, our two-stage training first builds robust thinking and reflection, then efficiently tunes the IRG pipeline in the full thinking-image trajectory data. Extensive experiments show SoTA performance, yielding absolute gains of 5-10 points on GenEval, WISE, TIIF, GenAI-Bench, and OneIG-EN, alongside substantial improvements in visual quality and fine-grained fidelity. The code, model weights and datasets will be released in: https://github.com/Osilly/Interleaving-Reasoning-Generation .", "github_manual": "", "github_regex": "https://github.com/Osilly/Interleaving-Reasoning-Generation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06501", "title": "WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents", "authors": [ "Junteng Liu", "Yunji Li", "Chi Zhang", "Jingyang Li", "Aili Chen", "Ke Ji", "Weiyu Cheng", "Zijia Wu", "Chengyu Du", "Qidi Xu", "Jiayuan Song", "Zhengmao Zhu", "Wenhu Chen", "Pengyu Zhao", "Junxian He" ], "github": "https://github.com/hkust-nlp/WebExplorer", "project_page": "", "abstract": "The paradigm of Large Language Models (LLMs) has increasingly shifted toward agentic applications, where web browsing capabilities are fundamental for retrieving information from diverse online sources. However, existing open-source web agents either demonstrate limited information-seeking abilities on complex tasks or lack transparent implementations. In this work, we identify that the key challenge lies in the scarcity of challenging data for information seeking. To address this limitation, we introduce WebExplorer: a systematic data generation approach using model-based exploration and iterative, long-to-short query evolution. This method creates challenging query-answer pairs that require multi-step reasoning and complex web navigation. By leveraging our curated high-quality dataset, we successfully develop advanced web agent WebExplorer-8B through supervised fine-tuning followed by reinforcement learning. Our model supports 128K context length and up to 100 tool calling turns, enabling long-horizon problem solving. Across diverse information-seeking benchmarks, WebExplorer-8B achieves the state-of-the-art performance at its scale. Notably, as an 8B-sized model, WebExplorer-8B is able to effectively search over an average of 16 turns after RL training, achieving higher accuracy than WebSailor-72B on BrowseComp-en/zh and attaining the best performance among models up to 100B parameters on WebWalkerQA and FRAMES. Beyond these information-seeking tasks, our model also achieves strong generalization on the HLE benchmark even though it is only trained on knowledge-intensive QA data. These results highlight our approach as a practical path toward long-horizon web agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hkust-nlp/WebExplorer", "github_api_added_by": "auto" }, { "date": "2025-09-09", "arxiv_id": "2509.06861", "title": "Test-Time Scaling in Reasoning Models Is Not Effective for Knowledge-Intensive Tasks Yet", "authors": [ "James Xu Zhao", "Bryan Hooi", "See-Kiong Ng" ], "github": "https://github.com/XuZhao0/tts-knowledge", "project_page": "", "abstract": "Test-time scaling increases inference-time computation by allowing models to generate long reasoning chains, and has shown strong performance across many domains. However, in this work, we show that this approach is not yet effective for knowledge-intensive tasks, where high factual accuracy and low hallucination rates are essential. We conduct a comprehensive evaluation of test-time scaling using 12 reasoning models on two knowledge-intensive benchmarks. Our results reveal that increasing test-time computation does not consistently improve accuracy and, in many cases, it even leads to more hallucinations. We then analyze how extended reasoning affects hallucination behavior. We find that reduced hallucinations often result from the model choosing to abstain after thinking more, rather than from improved factual recall. Conversely, for some models, longer reasoning encourages attempts on previously unanswered questions, many of which result in hallucinations. Case studies show that extended reasoning can induce confirmation bias, leading to overconfident hallucinations. Despite these limitations, we observe that compared to non-thinking, enabling thinking remains beneficial. Code and data are available at https://github.com/XuZhao0/tts-knowledge", "github_manual": "", "github_regex": "https://github.com/XuZhao0/tts-knowledge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06809", "title": "Saturation-Driven Dataset Generation for LLM Mathematical Reasoning in the TPTP Ecosystem", "authors": [ "Valentin Quesnel", "Damien Sileo" ], "github": "https://github.com/sileod/reasoning_core\nhttps:", "project_page": "", "abstract": "The scarcity of high-quality, logically sound data is a critical bottleneck for advancing the mathematical reasoning of Large Language Models (LLMs). Our work confronts this challenge by turning decades of automated theorem proving research into a scalable data engine. Rather than relying on error-prone LLMs or complex proof-assistant syntax like Lean and Isabelle, our framework leverages E-prover's saturation capabilities on the vast TPTP axiom library to derive a massive, guaranteed-valid corpus of theorems. Our pipeline is principled and simple: saturate axioms, filter for \"interesting\" theorems, and generate tasks. With no LLMs in the loop, we eliminate factual errors by construction. This purely symbolic data is then transformed into three difficulty-controlled challenges: entailment verification, premise selection, and proof reconstruction. Our zero-shot experiments on frontier models reveal a clear weakness: performance collapses on tasks requiring deep, structural reasoning. Our framework provides both the diagnostic tool to measure this gap and a scalable source of symbolic training data to address it. We make the code and data publicly available. https://github.com/sileod/reasoning_core https://hf.co/datasets/reasoning-core/rc1", "github_manual": "https://github.com/sileod/reasoning_core\nhttps:", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06631", "title": "Guided Decoding and Its Critical Role in Retrieval-Augmented Generation", "authors": [ "\u00d6zg\u00fcr U\u011fur", "Musa Y\u0131lmaz", "Esra \u015eavirdi", "\u00d6zay Ezerceli", "Mahmut El Huseyni", "Selva Ta\u015f", "Reyhan Bayraktar" ], "github": "", "project_page": "", "abstract": "The integration of Large Language Models (LLMs) into various applications has driven the need for structured and reliable responses. A key challenge in Retrieval-Augmented Generation (RAG) systems is ensuring that outputs align with expected formats while minimizing hallucinations. This study examines the role of guided decoding in RAG systems, comparing three methods, Outlines, XGrammar, and LM Format Enforcer, across different multi-turn prompting setups (0-turn, 1-turn, and 2-turn). By evaluating success rates, hallucination rates, and output quality, we provide insights into their performance and applicability. Our findings reveal how multi-turn interactions influence guided decoding, uncovering unexpected performance variations that can inform method selection for specific use cases. This work advances the understanding of structured output generation in RAG systems, offering both theoretical insights and practical guidance for LLM deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06285", "title": "DCReg: Decoupled Characterization for Efficient Degenerate LiDAR Registration", "authors": [ "Xiangcheng Hu", "Xieyuanli Chen", "Mingkai Jia", "Jin Wu", "Ping Tan", "Steven L. Waslander" ], "github": "https://github.com/JokerJohn/DCReg", "project_page": "", "abstract": "LiDAR point cloud registration is fundamental to robotic perception and navigation. However, in geometrically degenerate or narrow environments, registration problems become ill-conditioned, leading to unstable solutions and degraded accuracy. While existing approaches attempt to handle these issues, they fail to address the core challenge: accurately detection, interpret, and resolve this ill-conditioning, leading to missed detections or corrupted solutions. In this study, we introduce DCReg, a principled framework that systematically addresses the ill-conditioned registration problems through three integrated innovations. First, DCReg achieves reliable ill-conditioning detection by employing a Schur complement decomposition to the hessian matrix. This technique decouples the registration problem into clean rotational and translational subspaces, eliminating coupling effects that mask degeneracy patterns in conventional analyses. Second, within these cleanly subspaces, we develop quantitative characterization techniques that establish explicit mappings between mathematical eigenspaces and physical motion directions, providing actionable insights about which specific motions lack constraints. Finally, leveraging this clean subspace, we design a targeted mitigation strategy: a novel preconditioner that selectively stabilizes only the identified ill-conditioned directions while preserving all well-constrained information in observable space. This enables efficient and robust optimization via the Preconditioned Conjugate Gradient method with a single physical interpretable parameter. Extensive experiments demonstrate DCReg achieves at least 20% - 50% improvement in localization accuracy and 5-100 times speedup over state-of-the-art methods across diverse environments. Our implementation will be available at https://github.com/JokerJohn/DCReg.", "github_manual": "", "github_regex": "https://github.com/JokerJohn/DCReg", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.04582", "title": "Inpaint4Drag: Repurposing Inpainting Models for Drag-Based Image Editing via Bidirectional Warping", "authors": [ "Jingyi Lu", "Kai Han" ], "github": "", "project_page": "", "abstract": "Drag-based image editing has emerged as a powerful paradigm for intuitive image manipulation. However, existing approaches predominantly rely on manipulating the latent space of generative models, leading to limited precision, delayed feedback, and model-specific constraints. Accordingly, we present Inpaint4Drag, a novel framework that decomposes drag-based editing into pixel-space bidirectional warping and image inpainting. Inspired by elastic object deformation in the physical world, we treat image regions as deformable materials that maintain natural shape under user manipulation. Our method achieves real-time warping previews (0.01s) and efficient inpainting (0.3s) at 512x512 resolution, significantly improving the interaction experience compared to existing methods that require minutes per edit. By transforming drag inputs directly into standard inpainting formats, our approach serves as a universal adapter for any inpainting model without architecture modification, automatically inheriting all future improvements in inpainting technology. Extensive experiments demonstrate that our method achieves superior visual quality and precise control while maintaining real-time performance. Project page: https://visual-ai.github.io/inpaint4drag/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.03740", "title": "Singular Value Few-shot Adaptation of Vision-Language Models", "authors": [ "Taha Koleilat", "Hassan Rivaz", "Yiming Xiao" ], "github": "https://github.com/HealthX-Lab/CLIP-SVD", "project_page": "", "abstract": "Vision-language models (VLMs) like CLIP have shown impressive zero-shot and few-shot learning capabilities across diverse applications. However, adapting these models to new fine-grained domains remains difficult due to reliance on prompt engineering and the high cost of full model fine-tuning. Existing adaptation approaches rely on augmented components, such as prompt tokens and adapter modules, which could limit adaptation quality, destabilize the model, and compromise the rich knowledge learned during pretraining. In this work, we present CLIP-SVD, a novel multi-modal and parameter-efficient adaptation technique that leverages Singular Value Decomposition (SVD) to modify the internal parameter space of CLIP without injecting additional modules. Specifically, we fine-tune only the singular values of the CLIP parameter matrices to rescale the basis vectors for domain adaptation while retaining the pretrained model. This design enables enhanced adaptation performance using only 0.04\\% of the model's total parameters and better preservation of its generalization ability. CLIP-SVD achieves state-of-the-art classification results on 11 natural and 10 biomedical datasets, outperforming previous methods in both accuracy and generalization under few-shot settings. Additionally, we leverage a natural language-based approach to analyze the effectiveness and dynamics of the CLIP adaptation to allow interpretability of CLIP-SVD. The code is publicly available at https://github.com/HealthX-Lab/CLIP-SVD.", "github_manual": "", "github_regex": "https://github.com/HealthX-Lab/CLIP-SVD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.02108", "title": "DivMerge: A divergence-based model merging method for multi-tasking", "authors": [ "Touayouch Brahim", "Fosse Lo\u00efc", "Damnati G\u00e9raldine", "Lecorv\u00e9 Gw\u00e9nol\u00e9" ], "github": "", "project_page": "", "abstract": "Multi-task learning (MTL) is often achieved by merging datasets before fine-tuning, but the growing availability of fine-tuned models has led to new approaches such as model merging via task arithmetic. A major challenge in this setting is task interference, which worsens as the number of tasks increases. We propose a method that merges models trained on different tasks into a single model, maintaining strong performance across all tasks. Our approach leverages Jensen-Shannon divergence to guide the merging process without requiring additional labelled data, and automatically balances task importance. Unlike existing methods, our approach remains robust as the number of tasks grows and consistently outperforms prior work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.00328", "title": "Mechanistic interpretability for steering vision-language-action models", "authors": [ "Bear H\u00e4on", "Kaylene Stocking", "Ian Chuang", "Claire Tomlin" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models are a promising path to realizing generalist embodied agents that can quickly adapt to new tasks, modalities, and environments. However, methods for interpreting and steering VLAs fall far short of classical robotics pipelines, which are grounded in explicit models of kinematics, dynamics, and control. This lack of mechanistic insight is a central challenge for deploying learned policies in real-world robotics, where robustness and explainability are critical. Motivated by advances in mechanistic interpretability for large language models, we introduce the first framework for interpreting and steering VLAs via their internal representations, enabling direct intervention in model behavior at inference time. We project feedforward activations within transformer layers onto the token embedding basis, identifying sparse semantic directions - such as speed and direction - that are causally linked to action selection. Leveraging these findings, we introduce a general-purpose activation steering method that modulates behavior in real time, without fine-tuning, reward signals, or environment interaction. We evaluate this method on two recent open-source VLAs, Pi0 and OpenVLA, and demonstrate zero-shot behavioral control in simulation (LIBERO) and on a physical robot (UR5). This work demonstrates that interpretable components of embodied VLAs can be systematically harnessed for control - establishing a new paradigm for transparent and steerable foundation models in robotics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-09", "arxiv_id": "2509.06283", "title": "SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents", "authors": [ "Xuan-Phi Nguyen", "Shrey Pandit", "Revanth Gangi Reddy", "Austin Xu", "Silvio Savarese", "Caiming Xiong", "Shafiq Joty" ], "github": "", "project_page": "", "abstract": "Equipping large language models (LLMs) with complex, interleaved reasoning and tool-use capabilities has become a key focus in agentic AI research, especially with recent advances in reasoning-oriented (``thinking'') models. Such capabilities are key to unlocking a number of important applications. One such application is Deep Research (DR), which requires extensive search and reasoning over many sources. Our work in this paper focuses on the development of native Autonomous Single-Agent models for DR featuring minimal web crawling and Python tool integration. Unlike multi-agent systems, where agents take up pre-defined roles and are told what to do at each step in a static workflow, an autonomous single-agent determines its next action dynamically based on context, without manual directive. While prior work has proposed training recipes for base or instruction-tuned LLMs, we focus on continual reinforcement learning (RL) of reasoning-optimized models to further enhance agentic skills while preserving reasoning ability. Towards this end, we propose a simple RL recipe with entirely synthetic data, which we apply to various open-source LLMs. Our best variant SFR-DR-20B achieves up to 28.7% on Humanity's Last Exam benchmark. In addition, we conduct key analysis experiments to provide more insights into our methodologies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.06923", "title": "Staying in the Sweet Spot: Responsive Reasoning Evolution via Capability-Adaptive Hint Scaffolding", "authors": [ "Ziheng Li", "Zexu Sun", "Jinman Zhao", "Erxue Min", "Yongcheng Zeng", "Hui Wu", "Hengyi Cai", "Shuaiqiang Wang", "Dawei Yin", "Xu Chen", "Zhi-Hong Deng" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, existing RLVR methods often suffer from exploration inefficiency due to mismatches between the training data's difficulty and the model's capability. LLMs fail to discover viable reasoning paths when problems are overly difficult, while learning little new capability when problems are too simple. In this work, we formalize the impact of problem difficulty by quantifying the relationship between loss descent speed and rollout accuracy. Building on this analysis, we propose SEELE, a novel supervision-aided RLVR framework that dynamically adjusts problem difficulty to stay within the high-efficiency region. SEELE augments each training sample by appending a hint (part of a full solution) after the original problem. Unlike previous hint-based approaches, SEELE deliberately and adaptively adjusts the hint length for each problem to achieve an optimal difficulty. To determine the optimal hint length, SEELE employs a multi-round rollout sampling strategy. In each round, it fits an item response theory model to the accuracy-hint pairs collected in preceding rounds to predict the required hint length for the next round. This instance-level, real-time difficulty adjustment aligns problem difficulty with the evolving model capability, thereby improving exploration efficiency. Experimental results show that SEELE outperforms Group Relative Policy Optimization (GRPO) and Supervised Fine-tuning (SFT) by +11.8 and +10.5 points, respectively, and surpasses the best previous supervision-aided approach by +3.6 points on average across six math reasoning benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.07979", "title": "Visual Representation Alignment for Multimodal Large Language Models", "authors": [ "Heeji Yoon", "Jaewoo Jung", "Junwan Kim", "Hyungyu Choi", "Heeseong Shin", "Sangbeom Lim", "Honggyu An", "Chaehyun Kim", "Jisang Han", "Donghyun Kim", "Chanho Eom", "Sunghwan Hong", "Seungryong Kim" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) trained with visual instruction tuning have achieved strong performance across diverse tasks, yet they remain limited in vision-centric tasks such as object counting or spatial reasoning. We attribute this gap to the prevailing text-only supervision paradigm, which provides only indirect guidance for the visual pathway and often leads MLLMs to discard fine-grained visual details during training. In this paper, we present VIsual Representation ALignment (VIRAL), a simple yet effective regularization strategy that aligns the internal visual representations of MLLMs with those of pre-trained vision foundation models (VFMs). By explicitly enforcing this alignment, VIRAL enables the model not only to retain critical visual details from the input vision encoder but also to complement additional visual knowledge from VFMs, thereby enhancing its ability to reason over complex visual inputs. Our experiments demonstrate consistent improvements across all tasks on widely adopted multimodal benchmarks. Furthermore, we conduct comprehensive ablation studies to validate the key design choices underlying our framework. We believe this simple finding opens up an important direction for the effective integration of visual information in training MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.07969", "title": "Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual Search", "authors": [ "Xin Lai", "Junyi Li", "Wei Li", "Tao Liu", "Tianjian Li", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "Recent advances in large multimodal models have leveraged image-based tools with reinforcement learning to tackle visual problems. However, existing open-source approaches often exhibit monotonous reasoning patterns and allow only a limited number of interaction turns, making them inadequate for difficult tasks that require trial-and-error exploration. In this work, we address this limitation by scaling up tool-based interactions and introduce Mini-o3, a system that executes deep, multi-turn reasoning -- spanning tens of steps -- and achieves state-of-the-art performance on challenging visual search tasks. Our recipe for reproducing OpenAI o3-style behaviors comprises three key components. First, we construct the Visual Probe Dataset, a collection of thousands of challenging visual search problems designed for exploratory reasoning. Second, we develop an iterative data collection pipeline to obtain cold-start trajectories that exhibit diverse reasoning patterns, including depth-first search, trial-and-error, and goal maintenance. Third, we propose an over-turn masking strategy that prevents penalization of over-turn responses (those that hit the maximum number of turns) during reinforcement learning, thereby balancing training-time efficiency with test-time scalability. Despite training with an upper bound of only six interaction turns, our model generates trajectories that naturally scale to tens of turns at inference time, with accuracy improving as the number of turns increases. Extensive experiments demonstrate that Mini-o3 produces rich reasoning patterns and deep thinking paths, effectively solving challenging visual search problems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.07980", "title": "Parallel-R1: Towards Parallel Thinking via Reinforcement Learning", "authors": [ "Tong Zheng", "Hongming Zhang", "Wenhao Yu", "Xiaoyang Wang", "Xinyu Yang", "Runpeng Dai", "Rui Liu", "Huiwen Bao", "Chengsong Huang", "Heng Huang", "Dong Yu" ], "github": "https://github.com/zhengkid/Parallel-R1", "project_page": "", "abstract": "Parallel thinking has emerged as a novel approach for enhancing the reasoning capabilities of large language models (LLMs) by exploring multiple reasoning paths concurrently. However, activating such capabilities through training remains challenging, as existing methods predominantly rely on supervised fine-tuning (SFT) over synthetic data, which encourages teacher-forced imitation rather than exploration and generalization. Different from them, we propose Parallel-R1, the first reinforcement learning (RL) framework that enables parallel thinking behaviors for complex real-world reasoning tasks. Our framework employs a progressive curriculum that explicitly addresses the cold-start problem in training parallel thinking with RL. We first use SFT on prompt-generated trajectories from easier tasks to instill the parallel thinking ability, then transition to RL to explore and generalize this skill on harder problems. Experiments on various math benchmarks, including MATH, AMC23, and AIME, show that Parallel-R1 successfully instills parallel thinking, leading to 8.4% accuracy improvements over the sequential thinking model trained directly on challenging tasks with RL. Further analysis reveals a clear shift in the model's thinking behavior: at an early stage, it uses parallel thinking as an exploration strategy, while in a later stage, it uses the same capability for multi-perspective verification. Most significantly, we validate parallel thinking as a mid-training exploration scaffold, where this temporary exploratory phase unlocks a higher performance ceiling after RL, yielding a 42.9% improvement over the baseline on AIME25. Our model, data, and code will be open-source at https://github.com/zhengkid/Parallel-R1.", "github_manual": "", "github_regex": "https://github.com/zhengkid/Parallel-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.06951", "title": "F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions", "authors": [ "Qi Lv", "Weijie Kong", "Hao Li", "Jia Zeng", "Zherui Qiu", "Delin Qu", "Haoming Song", "Qizhi Chen", "Xiang Deng", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "Executing language-conditioned tasks in dynamic visual environments remains a central challenge in embodied AI. Existing Vision-Language-Action (VLA) models predominantly adopt reactive state-to-action mappings, often leading to short-sighted behaviors and poor robustness in dynamic scenes. In this paper, we introduce F1, a pretrained VLA framework which integrates the visual foresight generation into decision-making pipeline. F1 adopts a Mixture-of-Transformer architecture with dedicated modules for perception, foresight generation, and control, thereby bridging understanding, generation, and actions. At its core, F1 employs a next-scale prediction mechanism to synthesize goal-conditioned visual foresight as explicit planning targets. By forecasting plausible future visual states, F1 reformulates action generation as a foresight-guided inverse dynamics problem, enabling actions that implicitly achieve visual goals. To endow F1 with robust and generalizable capabilities, we propose a three-stage training recipe on an extensive dataset comprising over 330k trajectories across 136 diverse tasks. This training scheme enhances modular reasoning and equips the model with transferable visual foresight, which is critical for complex and dynamic environments. Extensive evaluations on real-world tasks and simulation benchmarks demonstrate F1 consistently outperforms existing approaches, achieving substantial gains in both task success rate and generalization ability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.07295", "title": "Reconstruction Alignment Improves Unified Multimodal Models", "authors": [ "Ji Xie", "Trevor Darrell", "Luke Zettlemoyer", "XuDong Wang" ], "github": "", "project_page": "", "abstract": "Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture. However, conventional training relies on image-text pairs (or sequences) whose captions are typically sparse and miss fine-grained visual details--even when they use hundreds of words to describe a simple image. We introduce Reconstruction Alignment (RecA), a resource-efficient post-training method that leverages visual understanding encoder embeddings as dense \"text prompts,\" providing rich supervision without captions. Concretely, RecA conditions a UMM on its own visual understanding embeddings and optimizes it to reconstruct the input image with a self-supervised reconstruction loss, thereby realigning understanding and generation. Despite its simplicity, RecA is broadly applicable: across autoregressive, masked-autoregressive, and diffusion-based UMMs, it consistently improves generation and editing fidelity. With only 27 GPU-hours, post-training with RecA substantially improves image generation performance on GenEval (0.73rightarrow0.90) and DPGBench (80.93rightarrow88.15), while also boosting editing benchmarks (ImgEdit 3.38rightarrow3.75, GEdit 6.94rightarrow7.25). Notably, RecA surpasses much larger open-source models and applies broadly across diverse UMM architectures, establishing it as an efficient and general post-training alignment strategy for UMMs", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.07968", "title": "SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge", "authors": [ "Lukas Haas", "Gal Yona", "Giovanni D'Antonio", "Sasha Goldshtein", "Dipanjan Das" ], "github": "", "project_page": "", "abstract": "We introduce SimpleQA Verified, a 1,000-prompt benchmark for evaluating Large Language Model (LLM) short-form factuality based on OpenAI's SimpleQA. It addresses critical limitations in OpenAI's benchmark, including noisy and incorrect labels, topical biases, and question redundancy. SimpleQA Verified was created through a rigorous multi-stage filtering process involving de-duplication, topic balancing, and source reconciliation to produce a more reliable and challenging evaluation set, alongside improvements in the autorater prompt. On this new benchmark, Gemini 2.5 Pro achieves a state-of-the-art F1-score of 55.6, outperforming other frontier models, including GPT-5. This work provides the research community with a higher-fidelity tool to track genuine progress in parametric model factuality and to mitigate hallucinations. The benchmark dataset, evaluation code, and leaderboard are available at: https://www.kaggle.com/benchmarks/deepmind/simpleqa-verified.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.07414", "title": "Language Self-Play For Data-Free Training", "authors": [ "Jakub Grudzien Kuba", "Mengting Gu", "Qi Ma", "Yuandong Tian", "Vijai Mohan" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have advanced rapidly in recent years, driven by scale, abundant high-quality training data, and reinforcement learning. Yet this progress faces a fundamental bottleneck: the need for ever more data from which models can continue to learn. In this work, we propose a reinforcement learning approach that removes this dependency by enabling models to improve without additional data. Our method leverages a game-theoretic framework of self-play, where a model's capabilities are cast as performance in a competitive game and stronger policies emerge by having the model play against itself - a process we call Language Self-Play (LSP). Experiments with Llama-3.2-3B-Instruct on instruction-following benchmarks show that pretrained models can not only enhance their performance on challenging tasks through self-play alone, but can also do so more effectively than data-driven baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.06818", "title": "UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward", "authors": [ "Yufeng Cheng", "Wenxu Wu", "Shaojin Wu", "Mengqi Huang", "Fei Ding", "Qian He" ], "github": "https://github.com/bytedance/UMO", "project_page": "", "abstract": "Recent advancements in image customization exhibit a wide range of application prospects due to stronger customization capabilities. However, since we humans are more sensitive to faces, a significant challenge remains in preserving consistent identity while avoiding identity confusion with multi-reference images, limiting the identity scalability of customization models. To address this, we present UMO, a Unified Multi-identity Optimization framework, designed to maintain high-fidelity identity preservation and alleviate identity confusion with scalability. With \"multi-to-multi matching\" paradigm, UMO reformulates multi-identity generation as a global assignment optimization problem and unleashes multi-identity consistency for existing image customization methods generally through reinforcement learning on diffusion models. To facilitate the training of UMO, we develop a scalable customization dataset with multi-reference images, consisting of both synthesised and real parts. Additionally, we propose a new metric to measure identity confusion. Extensive experiments demonstrate that UMO not only improves identity consistency significantly, but also reduces identity confusion on several image customization methods, setting a new state-of-the-art among open-source methods along the dimension of identity preserving. Code and model: https://github.com/bytedance/UMO", "github_manual": "", "github_regex": "https://github.com/bytedance/UMO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.07301", "title": "Causal Attention with Lookahead Keys", "authors": [ "Zhuoqing Song", "Peng Sun", "Huizhuo Yuan", "Quanquan Gu" ], "github": "", "project_page": "", "abstract": "In standard causal attention, each token's query, key, and value (QKV) are static and encode only preceding context. We introduce CAuSal aTtention with Lookahead kEys (CASTLE), an attention mechanism that continually updates each token's keys as the context unfolds. We term these updated keys lookahead keys because they belong to earlier positions yet integrate information from tokens that appear later relative to those positions, while strictly preserving the autoregressive property. Although the mechanism appears sequential, we derive a mathematical equivalence that avoids explicitly materializing lookahead keys at each position and enables efficient parallel training. On language modeling benchmarks, CASTLE consistently outperforms standard causal attention across model scales, reducing validation perplexity and improving performance on a range of downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.01624", "title": "Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling", "authors": [ "Natalia Frumkin", "Diana Marculescu" ], "github": "https://github.com/enyac-group/q-sched", "project_page": "", "abstract": "Text-to-image diffusion models are computationally intensive, often requiring dozens of forward passes through large transformer backbones. For instance, Stable Diffusion XL generates high-quality images with 50 evaluations of a 2.6B-parameter model, an expensive process even for a single batch. Few-step diffusion models reduce this cost to 2-8 denoising steps but still depend on large, uncompressed U-Net or diffusion transformer backbones, which are often too costly for full-precision inference without datacenter GPUs. These requirements also limit existing post-training quantization methods that rely on full-precision calibration. We introduce Q-Sched, a new paradigm for post-training quantization that modifies the diffusion model scheduler rather than model weights. By adjusting the few-step sampling trajectory, Q-Sched achieves full-precision accuracy with a 4x reduction in model size. To learn quantization-aware pre-conditioning coefficients, we propose the JAQ loss, which combines text-image compatibility with an image quality metric for fine-grained optimization. JAQ is reference-free and requires only a handful of calibration prompts, avoiding full-precision inference during calibration. Q-Sched delivers substantial gains: a 15.5% FID improvement over the FP16 4-step Latent Consistency Model and a 16.6% improvement over the FP16 8-step Phased Consistency Model, showing that quantization and few-step distillation are complementary for high-fidelity generation. A large-scale user study with more than 80,000 annotations further confirms Q-Sched's effectiveness on both FLUX.1[schnell] and SDXL-Turbo.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/enyac-group/q-sched", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.06830", "title": "Curia: A Multi-Modal Foundation Model for Radiology", "authors": [ "Corentin Dancette", "Julien Khlaut", "Antoine Saporta", "Helene Philippe", "Elodie Ferreres", "Baptiste Callard", "Th\u00e9o Danielou", "L\u00e9o Alberge", "L\u00e9o Machado", "Daniel Tordjman", "Julie Dupuis", "Korentin Le Floch", "Jean Du Terrail", "Mariam Moshiri", "Laurent Dercle", "Tom Boeken", "Jules Gregory", "Maxime Ronot", "Fran\u00e7ois Legou", "Pascal Roux", "Marc Sapoval", "Pierre Manceron", "Paul H\u00e9rent" ], "github": "", "project_page": "", "abstract": "AI-assisted radiological interpretation is based on predominantly narrow, single-task models. This approach is impractical for covering the vast spectrum of imaging modalities, diseases, and radiological findings. Foundation models (FMs) hold the promise of broad generalization across modalities and in low-data settings. However, this potential has remained largely unrealized in radiology. We introduce Curia, a foundation model trained on the entire cross-sectional imaging output of a major hospital over several years, which to our knowledge is the largest such corpus of real-world data-encompassing 150,000 exams (130 TB). On a newly curated 19-task external validation benchmark, Curia accurately identifies organs, detects conditions like brain hemorrhages and myocardial infarctions, and predicts outcomes in tumor staging. Curia meets or surpasses the performance of radiologists and recent foundation models, and exhibits clinically significant emergent properties in cross-modality, and low-data regimes. To accelerate progress, we release our base model's weights at https://huggingface.co/raidium/curia.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.07558", "title": "\u0394L Normalization: Rethink Loss Aggregation in RLVR", "authors": [ "Zhiyuan He", "Xufang Luo", "Yike Zhang", "Yuqing Yang", "Lili Qiu" ], "github": "https://github.com/zerolllin/Delta-L-Normalization", "project_page": "", "abstract": "We propose Delta L Normalization, a simple yet effective loss aggregation method tailored to the characteristic of dynamic generation lengths in Reinforcement Learning with Verifiable Rewards (RLVR). Recently, RLVR has demonstrated strong potential in improving the reasoning capabilities of large language models (LLMs), but a major challenge lies in the large variability of response lengths during training, which leads to high gradient variance and unstable optimization. Although previous methods such as GRPO, DAPO, and Dr. GRPO introduce different loss normalization terms to address this issue, they either produce biased estimates or still suffer from high gradient variance. By analyzing the effect of varying lengths on policy loss both theoretically and empirically, we reformulate the problem as finding a minimum-variance unbiased estimator. Our proposed Delta L Normalization not only provides an unbiased estimate of the true policy loss but also minimizes gradient variance in theory. Extensive experiments show that it consistently achieves superior results across different model sizes, maximum lengths, and tasks. Our code will be made public at https://github.com/zerolllin/Delta-L-Normalization.", "github_manual": "", "github_regex": "https://github.com/zerolllin/Delta-L-Normalization", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.06942", "title": "Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference", "authors": [ "Xiangwei Shen", "Zhimin Li", "Zhantao Yang", "Shiyi Zhang", "Yingfang Zhang", "Donghao Li", "Chunyu Wang", "Qinglin Lu", "Yansong Tang" ], "github": "", "project_page": "", "abstract": "Recent studies have demonstrated the effectiveness of directly aligning diffusion models with human preferences using differentiable reward. However, they exhibit two primary challenges: (1) they rely on multistep denoising with gradient computation for reward scoring, which is computationally expensive, thus restricting optimization to only a few diffusion steps; (2) they often need continuous offline adaptation of reward models in order to achieve desired aesthetic quality, such as photorealism or precise lighting effects. To address the limitation of multistep denoising, we propose Direct-Align, a method that predefines a noise prior to effectively recover original images from any time steps via interpolation, leveraging the equation that diffusion states are interpolations between noise and target images, which effectively avoids over-optimization in late timesteps. Furthermore, we introduce Semantic Relative Preference Optimization (SRPO), in which rewards are formulated as text-conditioned signals. This approach enables online adjustment of rewards in response to positive and negative prompt augmentation, thereby reducing the reliance on offline reward fine-tuning. By fine-tuning the FLUX.1.dev model with optimized denoising and online reward adjustment, we improve its human-evaluated realism and aesthetic quality by over 3x.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.03646", "title": "Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning", "authors": [ "Haozhe Wang", "Qixin Xu", "Che Liu", "Junhong Wu", "Fangzhen Lin", "Wenhu Chen" ], "github": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "project_page": "", "abstract": "Reinforcement Learning (RL) has proven highly effective at enhancing the complex reasoning abilities of Large Language Models (LLMs), yet underlying mechanisms driving this success remain largely opaque. Our analysis reveals that puzzling phenomena like ``aha moments\", ``length-scaling'' and entropy dynamics are not disparate occurrences but hallmarks of an emergent reasoning hierarchy, akin to the separation of high-level strategic planning from low-level procedural execution in human cognition. We uncover a compelling two-phase dynamic: initially, a model is constrained by procedural correctness and must improve its low-level skills. The learning bottleneck then decisively shifts, with performance gains being driven by the exploration and mastery of high-level strategic planning. This insight exposes a core inefficiency in prevailing RL algorithms like GRPO, which apply optimization pressure agnostically and dilute the learning signal across all tokens. To address this, we propose HIerarchy-Aware Credit Assignment (HICRA), an algorithm that concentrates optimization efforts on high-impact planning tokens. HICRA significantly outperforms strong baselines, demonstrating that focusing on this strategic bottleneck is key to unlocking advanced reasoning. Furthermore, we validate semantic entropy as a superior compass for measuring strategic exploration over misleading metrics such as token-level entropy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.07253", "title": "Benchmarking Information Retrieval Models on Complex Retrieval Tasks", "authors": [ "Julian Killingback", "Hamed Zamani" ], "github": "https://github.com/jfkback/crumb", "project_page": "", "abstract": "Large language models (LLMs) are incredible and versatile tools for text-based tasks that have enabled countless, previously unimaginable, applications. Retrieval models, in contrast, have not yet seen such capable general-purpose models emerge. To achieve this goal, retrieval models must be able to perform complex retrieval tasks, where queries contain multiple parts, constraints, or requirements in natural language. These tasks represent a natural progression from the simple, single-aspect queries that are used in the vast majority of existing, commonly used evaluation sets. Complex queries naturally arise as people expect search systems to handle more specific and often ambitious information requests, as is demonstrated by how people use LLM-based information systems. Despite the growing desire for retrieval models to expand their capabilities in complex retrieval tasks, there exist limited resources to assess the ability of retrieval models on a comprehensive set of diverse complex tasks. The few resources that do exist feature a limited scope and often lack realistic settings making it hard to know the true capabilities of retrieval models on complex real-world retrieval tasks. To address this shortcoming and spur innovation in next-generation retrieval models, we construct a diverse and realistic set of complex retrieval tasks and benchmark a representative set of state-of-the-art retrieval models. Additionally, we explore the impact of LLM-based query expansion and rewriting on retrieval quality. Our results show that even the best models struggle to produce high-quality retrieval results with the highest average nDCG@10 of only 0.346 and R@100 of only 0.587 across all tasks. Although LLM augmentation can help weaker models, the strongest model has decreased performance across all metrics with all rewriting techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jfkback/crumb", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.06938", "title": "From Noise to Narrative: Tracing the Origins of Hallucinations in Transformers", "authors": [ "Praneet Suresh", "Jack Stanley", "Sonia Joseph", "Luca Scimeca", "Danilo Bzdok" ], "github": "", "project_page": "", "abstract": "As generative AI systems become competent and democratized in science, business, and government, deeper insight into their failure modes now poses an acute need. The occasional volatility in their behavior, such as the propensity of transformer models to hallucinate, impedes trust and adoption of emerging AI solutions in high-stakes areas. In the present work, we establish how and when hallucinations arise in pre-trained transformer models through concept representations captured by sparse autoencoders, under scenarios with experimentally controlled uncertainty in the input space. Our systematic experiments reveal that the number of semantic concepts used by the transformer model grows as the input information becomes increasingly unstructured. In the face of growing uncertainty in the input space, the transformer model becomes prone to activate coherent yet input-insensitive semantic features, leading to hallucinated output. At its extreme, for pure-noise inputs, we identify a wide variety of robustly triggered and meaningful concepts in the intermediate activations of pre-trained transformer models, whose functional integrity we confirm through targeted steering. We also show that hallucinations in the output of a transformer model can be reliably predicted from the concept patterns embedded in transformer layer activations. This collection of insights on transformer internal processing mechanics has immediate consequences for aligning AI models with human values, AI safety, opening the attack surface for potential adversarial attacks, and providing a basis for automatic quantification of a model's hallucination risk.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-10", "arxiv_id": "2509.08721", "title": "Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing", "authors": [ "Jeffrey Amico", "Gabriel Passamani Andrade", "John Donaghy", "Ben Fielding", "Tristin Forbus", "Harry Grieve", "Semih Kara", "Jari Kolehmainen", "Yihua Lou", "Christopher Nies", "Edward Phillip Flores Nu\u00f1o", "Diogo Ortega", "Shikhar Rastogi", "Austin Virts", "Matthew J. Wright" ], "github": "", "project_page": "", "abstract": "Post-training language models (LMs) with reinforcement learning (RL) can enhance their complex reasoning capabilities without supervised fine-tuning, as demonstrated by DeepSeek-R1-Zero. However, effectively utilizing RL for LMs requires significant parallelization to scale-up inference, which introduces non-trivial technical challenges (e.g. latency, memory, and reliability) alongside ever-growing financial costs. We present Swarm sAmpling Policy Optimization (SAPO), a fully decentralized and asynchronous RL post-training algorithm. SAPO is designed for decentralized networks of heterogenous compute nodes, where each node manages its own policy model(s) while \"sharing\" rollouts with others in the network; no explicit assumptions about latency, model homogeneity, or hardware are required and nodes can operate in silo if desired. As a result, the algorithm avoids common bottlenecks in scaling RL post-training while also allowing (and even encouraging) new possibilities. By sampling rollouts \"shared\" across the network, it enables \"Aha moments\" to propagate, thereby bootstrapping the learning process. In this paper we show SAPO achieved cumulative reward gains of up to 94% in controlled experiments. We also share insights from tests on a network with thousands of nodes contributed by Gensyn community members running the algorithm on diverse hardware and models during an open-source demo.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-11", "arxiv_id": "2509.08827", "title": "A Survey of Reinforcement Learning for Large Reasoning Models", "authors": [ "Kaiyan Zhang", "Yuxin Zuo", "Bingxiang He", "Youbang Sun", "Runze Liu", "Che Jiang", "Yuchen Fan", "Kai Tian", "Guoli Jia", "Pengfei Li", "Yu Fu", "Xingtai Lv", "Yuchen Zhang", "Sihang Zeng", "Shang Qu", "Haozhan Li", "Shijie Wang", "Yuru Wang", "Xinwei Long", "Fangfu Liu", "Xiang Xu", "Jiaze Ma", "Xuekai Zhu", "Ermo Hua", "Yihao Liu", "Zonglin Li", "Huayu Chen", "Xiaoye Qu", "Yafu Li", "Weize Chen", "Zhenzhao Yuan", "Junqi Gao", "Dong Li", "Zhiyuan Ma", "Ganqu Cui", "Zhiyuan Liu", "Biqing Qi", "Ning Ding", "Bowen Zhou" ], "github": "https://github.com/TsinghuaC3I/Awesome-RL-for-LRMs", "project_page": "", "abstract": "In this paper, we survey recent advances in Reinforcement Learning (RL) for reasoning with Large Language Models (LLMs). RL has achieved remarkable success in advancing the frontier of LLM capabilities, particularly in addressing complex logical tasks such as mathematics and coding. As a result, RL has emerged as a foundational methodology for transforming LLMs into LRMs. With the rapid progress of the field, further scaling of RL for LRMs now faces foundational challenges not only in computational resources but also in algorithm design, training data, and infrastructure. To this end, it is timely to revisit the development of this domain, reassess its trajectory, and explore strategies to enhance the scalability of RL toward Artificial SuperIntelligence (ASI). In particular, we examine research applying RL to LLMs and LRMs for reasoning abilities, especially since the release of DeepSeek-R1, including foundational components, core problems, training resources, and downstream applications, to identify future opportunities and directions for this rapidly evolving area. We hope this review will promote future research on RL for broader reasoning models. Github: https://github.com/TsinghuaC3I/Awesome-RL-for-LRMs", "github_manual": "", "github_regex": "https://github.com/TsinghuaC3I/Awesome-RL-for-LRMs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-11", "arxiv_id": "2509.08826", "title": "RewardDance: Reward Scaling in Visual Generation", "authors": [ "Jie Wu", "Yu Gao", "Zilyu Ye", "Ming Li", "Liang Li", "Hanzhong Guo", "Jie Liu", "Zeyue Xue", "Xiaoxia Hou", "Wei Liu", "Yan Zeng", "Weilin Huang" ], "github": "", "project_page": "", "abstract": "Reward Models (RMs) are critical for improving generation models via Reinforcement Learning (RL), yet the RM scaling paradigm in visual generation remains largely unexplored. It primarily due to fundamental limitations in existing approaches: CLIP-based RMs suffer from architectural and input modality constraints, while prevalent Bradley-Terry losses are fundamentally misaligned with the next-token prediction mechanism of Vision-Language Models (VLMs), hindering effective scaling. More critically, the RLHF optimization process is plagued by Reward Hacking issue, where models exploit flaws in the reward signal without improving true quality. To address these challenges, we introduce RewardDance, a scalable reward modeling framework that overcomes these barriers through a novel generative reward paradigm. By reformulating the reward score as the model's probability of predicting a \"yes\" token, indicating that the generated image outperforms a reference image according to specific criteria, RewardDance intrinsically aligns reward objectives with VLM architectures. This alignment unlocks scaling across two dimensions: (1) Model Scaling: Systematic scaling of RMs up to 26 billion parameters; (2) Context Scaling: Integration of task-specific instructions, reference examples, and chain-of-thought (CoT) reasoning. Extensive experiments demonstrate that RewardDance significantly surpasses state-of-the-art methods in text-to-image, text-to-video, and image-to-video generation. Crucially, we resolve the persistent challenge of \"reward hacking\": Our large-scale RMs exhibit and maintain high reward variance during RL fine-tuning, proving their resistance to hacking and ability to produce diverse, high-quality outputs. It greatly relieves the mode collapse problem that plagues smaller models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-11", "arxiv_id": "2509.05209", "title": "Hunyuan-MT Technical Report", "authors": [ "Mao Zheng", "Zheng Li", "Bingxin Qu", "Mingyang Song", "Yang Du", "Mingrui Sun", "Di Wang" ], "github": "https://github.com/Tencent-Hunyuan/Hunyuan-MT", "project_page": "", "abstract": "In this report, we introduce Hunyuan-MT-7B, our first open-source multilingual translation model, which supports bidirectional translation across 33 major languages and places a special emphasis on translation between Mandarin and several ethnic minority languages as well as dialects. Furthermore, to serve and address diverse translation scenarios and enhance model performance at test time, we introduce Hunyuan-MT-Chimera-7B, a translation model inspired by the slow thinking mode. This model integrates multiple outputs generated by the Hunyuan-MT-7B model under varying parameter settings, thereby achieving performance superior to that of conventional slow-thinking models based on Chain-of-Thought (CoT). The development of our models follows a holistic training process specifically engineered for multilingual translation, which begins with general and MT-oriented pre-training to build foundational capabilities, proceeds to Supervised Fine-Tuning (SFT) for task-specific adaptation, and culminates in advanced alignment through Reinforcement Learning (RL) and weak-to-strong RL. Through comprehensive experimentation, we demonstrate that both Hunyuan-MT-7B and Hunyuan-MT-Chimera-7B significantly outperform all translation-specific models of comparable parameter size and most of the SOTA large models, particularly on the task of translation between Mandarin and minority languages as well as dialects. In the WMT2025 shared task (General Machine Translation), our models demonstrate state-of-the-art performance, ranking first in 30 out of 31 language pairs. This result highlights the robustness of our models across a diverse linguistic spectrum, encompassing high-resource languages such as Chinese, English, and Japanese, as well as low-resource languages including Czech, Marathi, Estonian, and Icelandic.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Tencent-Hunyuan/Hunyuan-MT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-11", "arxiv_id": "2509.08755", "title": "AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning", "authors": [ "Zhiheng Xi", "Jixuan Huang", "Chenyang Liao", "Baodai Huang", "Honglin Guo", "Jiaqi Liu", "Rui Zheng", "Junjie Ye", "Jiazheng Zhang", "Wenxiang Chen", "Wei He", "Yiwen Ding", "Guanyu Li", "Zehui Chen", "Zhengyin Du", "Xuesong Yao", "Yufei Xu", "Jiecao Chen", "Tao Gui", "Zuxuan Wu", "Qi Zhang", "Xuanjing Huang", "Yu-Gang Jiang" ], "github": "https://github.com/wooodyy/AgentGym-RL", "project_page": "", "abstract": "Developing autonomous LLM agents capable of making a series of intelligent decisions to solve complex, real-world tasks is a fast-evolving frontier. Like human cognitive development, agents are expected to acquire knowledge and skills through exploration and interaction with the environment. Despite advances, the community still lacks a unified, interactive reinforcement learning (RL) framework that can effectively train such agents from scratch -- without relying on supervised fine-tuning (SFT) -- across diverse and realistic environments. To bridge this gap, we introduce AgentGym-RL, a new framework to train LLM agents for multi-turn interactive decision-making through RL. The framework features a modular and decoupled architecture, ensuring high flexibility and extensibility. It encompasses a wide variety of real-world scenarios, and supports mainstream RL algorithms. Furthermore, we propose ScalingInter-RL, a training approach designed for exploration-exploitation balance and stable RL optimization. In early stages, it emphasizes exploitation by restricting the number of interactions, and gradually shifts towards exploration with larger horizons to encourage diverse problem-solving strategies. In this way, the agent develops more diverse behaviors and is less prone to collapse under long horizons. We perform extensive experiments to validate the stability and effectiveness of both the AgentGym-RL framework and the ScalingInter-RL approach. Our agents match or surpass commercial models on 27 tasks across diverse environments. We offer key insights and will open-source the complete AgentGym-RL framework -- including code and datasets -- to empower the research community in developing the next generation of intelligent agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/wooodyy/AgentGym-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-11", "arxiv_id": "2509.06784", "title": "P3-SAM: Native 3D Part Segmentation", "authors": [ "Changfeng Ma", "Yang Li", "Xinhao Yan", "Jiachen Xu", "Yunhan Yang", "Chunshi Wang", "Zibo Zhao", "Yanwen Guo", "Zhuo Chen", "Chunchao Guo" ], "github": "", "project_page": "", "abstract": "Segmenting 3D assets into their constituent parts is crucial for enhancing 3D understanding, facilitating model reuse, and supporting various applications such as part generation. However, current methods face limitations such as poor robustness when dealing with complex objects and cannot fully automate the process. In this paper, we propose a native 3D point-promptable part segmentation model termed P3-SAM, designed to fully automate the segmentation of any 3D objects into components. Inspired by SAM, P3-SAM consists of a feature extractor, multiple segmentation heads, and an IoU predictor, enabling interactive segmentation for users. We also propose an algorithm to automatically select and merge masks predicted by our model for part instance segmentation. Our model is trained on a newly built dataset containing nearly 3.7 million models with reasonable segmentation labels. Comparisons show that our method achieves precise segmentation results and strong robustness on any complex objects, attaining state-of-the-art performance. Our code will be released soon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-11", "arxiv_id": "2509.08088", "title": "EnvX: Agentize Everything with Agentic AI", "authors": [ "Linyao Chen", "Zimian Peng", "Yingxuan Yang", "Yikun Wang", "Wenzheng Tom Tang", "Hiroki H. Kobayashi", "Weinan Zhang" ], "github": "", "project_page": "", "abstract": "The widespread availability of open-source repositories has led to a vast collection of reusable software components, yet their utilization remains manual, error-prone, and disconnected. Developers must navigate documentation, understand APIs, and write integration code, creating significant barriers to efficient software reuse. To address this, we present EnvX, a framework that leverages Agentic AI to agentize GitHub repositories, transforming them into intelligent, autonomous agents capable of natural language interaction and inter-agent collaboration. Unlike existing approaches that treat repositories as static code resources, EnvX reimagines them as active agents through a three-phase process: (1) TODO-guided environment initialization, which sets up the necessary dependencies, data, and validation datasets; (2) human-aligned agentic automation, allowing repository-specific agents to autonomously perform real-world tasks; and (3) Agent-to-Agent (A2A) protocol, enabling multiple agents to collaborate. By combining large language model capabilities with structured tool integration, EnvX automates not just code generation, but the entire process of understanding, initializing, and operationalizing repository functionality. We evaluate EnvX on the GitTaskBench benchmark, using 18 repositories across domains such as image processing, speech recognition, document analysis, and video manipulation. Our results show that EnvX achieves a 74.07% execution completion rate and 51.85% task pass rate, outperforming existing frameworks. Case studies further demonstrate EnvX's ability to enable multi-repository collaboration via the A2A protocol. This work marks a shift from treating repositories as passive code resources to intelligent, interactive agents, fostering greater accessibility and collaboration within the open-source ecosystem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-11", "arxiv_id": "2509.08494", "title": "HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants", "authors": [ "Benjamin Sturgeon", "Daniel Samuelson", "Jacob Haimes", "Jacy Reese Anthis" ], "github": "https://github.com/BenSturgeon/HumanAgencyBench", "project_page": "", "abstract": "As humans delegate more tasks and decisions to artificial intelligence (AI), we risk losing control of our individual and collective futures. Relatively simple algorithmic systems already steer human decision-making, such as social media feed algorithms that lead people to unintentionally and absent-mindedly scroll through engagement-optimized content. In this paper, we develop the idea of human agency by integrating philosophical and scientific theories of agency with AI-assisted evaluation methods: using large language models (LLMs) to simulate and validate user queries and to evaluate AI responses. We develop HumanAgencyBench (HAB), a scalable and adaptive benchmark with six dimensions of human agency based on typical AI use cases. HAB measures the tendency of an AI assistant or agent to Ask Clarifying Questions, Avoid Value Manipulation, Correct Misinformation, Defer Important Decisions, Encourage Learning, and Maintain Social Boundaries. We find low-to-moderate agency support in contemporary LLM-based assistants and substantial variation across system developers and dimensions. For example, while Anthropic LLMs most support human agency overall, they are the least supportive LLMs in terms of Avoid Value Manipulation. Agency support does not appear to consistently result from increasing LLM capabilities or instruction-following behavior (e.g., RLHF), and we encourage a shift towards more robust safety and alignment targets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/BenSturgeon/HumanAgencyBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-11", "arxiv_id": "2509.07996", "title": "3D and 4D World Modeling: A Survey", "authors": [ "Lingdong Kong", "Wesley Yang", "Jianbiao Mei", "Youquan Liu", "Ao Liang", "Dekai Zhu", "Dongyue Lu", "Wei Yin", "Xiaotao Hu", "Mingkai Jia", "Junyuan Deng", "Kaiwen Zhang", "Yang Wu", "Tianyi Yan", "Shenyuan Gao", "Song Wang", "Linfeng Li", "Liang Pan", "Yong Liu", "Jianke Zhu", "Wei Tsang Ooi", "Steven C. H. Hoi", "Ziwei Liu" ], "github": "https://github.com/worldbench/survey", "project_page": "", "abstract": "World modeling has become a cornerstone in AI research, enabling agents to understand, represent, and predict the dynamic environments they inhabit. While prior work largely emphasizes generative methods for 2D image and video data, they overlook the rapidly growing body of work that leverages native 3D and 4D representations such as RGB-D imagery, occupancy grids, and LiDAR point clouds for large-scale scene modeling. At the same time, the absence of a standardized definition and taxonomy for ``world models'' has led to fragmented and sometimes inconsistent claims in the literature. This survey addresses these gaps by presenting the first comprehensive review explicitly dedicated to 3D and 4D world modeling and generation. We establish precise definitions, introduce a structured taxonomy spanning video-based (VideoGen), occupancy-based (OccGen), and LiDAR-based (LiDARGen) approaches, and systematically summarize datasets and evaluation metrics tailored to 3D/4D settings. We further discuss practical applications, identify open challenges, and highlight promising research directions, aiming to provide a coherent and foundational reference for advancing the field. A systematic summary of existing literature is available at https://github.com/worldbench/survey", "github_manual": "", "github_regex": "https://github.com/worldbench/survey", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-11", "arxiv_id": "2509.08358", "title": " So let's replace this phrase with insult... Lessons learned from generation of toxic texts with LLMs", "authors": [ "Sergey Pletenev", "Daniil Moskovskiy", "Alexander Panchenko" ], "github": "https://github.com/A1exRey/Lessons-from-Generating-Toxic-Texts", "project_page": "", "abstract": "Modern Large Language Models (LLMs) are excellent at generating synthetic data. However, their performance in sensitive domains such as text detoxification has not received proper attention from the scientific community. This paper explores the possibility of using LLM-generated synthetic toxic data as an alternative to human-generated data for training models for detoxification. Using Llama 3 and Qwen activation-patched models, we generated synthetic toxic counterparts for neutral texts from ParaDetox and SST-2 datasets. Our experiments show that models fine-tuned on synthetic data consistently perform worse than those trained on human data, with a drop in performance of up to 30% in joint metrics. The root cause is identified as a critical lexical diversity gap: LLMs generate toxic content using a small, repetitive vocabulary of insults that fails to capture the nuances and variety of human toxicity. These findings highlight the limitations of current LLMs in this domain and emphasize the continued importance of diverse, human-annotated data for building robust detoxification systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/A1exRey/Lessons-from-Generating-Toxic-Texts", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-11", "arxiv_id": "2509.06870", "title": "The Majority is not always right: RL training for solution aggregation", "authors": [ "Wenting Zhao", "Pranjal Aggarwal", "Swarnadeep Saha", "Asli Celikyilmaz", "Jason Weston", "Ilia Kulikov" ], "github": "", "project_page": "", "abstract": "Scaling up test-time compute, by generating multiple independent solutions and selecting or aggregating among them, has become a central paradigm for improving large language models (LLMs) on challenging reasoning tasks. While most prior work relies on simple majority voting or reward model ranking to aggregate solutions, these approaches may only yield limited benefits. In this work, we propose to learn aggregation as an explicit reasoning skill: given a set of candidate solutions, we train an aggregator model to review, reconcile, and synthesize a final, correct answer using reinforcement learning from verifiable rewards. A key ingredient is careful balancing of easy and hard training examples, allowing the model to learn both to recover minority-but-correct answers as well as easy majority-correct answers. Empirically, we find our method, AggLM, outperforms both strong rule-based and reward-model baselines, across multiple benchmarks. Furthermore, it generalizes effectively to solutions from differing models, including stronger ones than contained in the training data, all while requiring substantially fewer tokens than majority voting with larger numbers of solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-11", "arxiv_id": "2509.07054", "title": "Statistical Methods in Generative AI", "authors": [ "Edgar Dobriban" ], "github": "", "project_page": "", "abstract": "Generative Artificial Intelligence is emerging as an important technology, promising to be transformative in many areas. At the same time, generative AI techniques are based on sampling from probabilistic models, and by default, they come with no guarantees about correctness, safety, fairness, or other properties. Statistical methods offer a promising potential approach to improve the reliability of generative AI techniques. In addition, statistical methods are also promising for improving the quality and efficiency of AI evaluation, as well as for designing interventions and experiments in AI. In this paper, we review some of the existing work on these topics, explaining both the general statistical techniques used, as well as their applications to generative AI. We also discuss limitations and potential future directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-11", "arxiv_id": "2509.09675", "title": "CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models", "authors": [ "Runpeng Dai", "Linfeng Song", "Haolin Liu", "Zhenwen Liang", "Dian Yu", "Haitao Mi", "Zhaopeng Tu", "Rui Liu", "Tong Zheng", "Hongtu Zhu", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm for enhancing the reasoning ability of Large Language Models (LLMs). Yet current RLVR methods often explore poorly, leading to premature convergence and entropy collapse. To address this challenge, we introduce Curiosity-Driven Exploration (CDE), a framework that leverages the model's own intrinsic sense of curiosity to guide exploration. We formalize curiosity with signals from both the actor and the critic: for the actor, we use perplexity over its generated response, and for the critic, we use the variance of value estimates from a multi-head architecture. Both signals serve as an exploration bonus within the RLVR framework to guide the model. Our theoretical analysis shows that the actor-wise bonus inherently penalizes overconfident errors and promotes diversity among correct responses; moreover, we connect the critic-wise bonus to the well-established count-based exploration bonus in RL. Empirically, our method achieves an approximate +3 point improvement over standard RLVR using GRPO/PPO on AIME benchmarks. Further analysis identifies a calibration collapse mechanism within RLVR, shedding light on common LLM failure modes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09174", "title": "EchoX: Towards Mitigating Acoustic-Semantic Gap via Echo Training for Speech-to-Speech LLMs", "authors": [ "Yuhao Zhang", "Yuhao Du", "Zhanchen Dai", "Xiangnan Ma", "Kaiqi Kou", "Benyou Wang", "Haizhou Li" ], "github": "https://github.com/FreedomIntelligence/EchoX", "project_page": "", "abstract": "Speech-to-speech large language models (SLLMs) are attracting increasing attention. Derived from text-based large language models (LLMs), SLLMs often exhibit degradation in knowledge and reasoning capabilities. We hypothesize that this limitation arises because current training paradigms for SLLMs fail to bridge the acoustic-semantic gap in the feature representation space. To address this issue, we propose EchoX, which leverages semantic representations and dynamically generates speech training targets. This approach integrates both acoustic and semantic learning, enabling EchoX to preserve strong reasoning abilities as a speech LLM. Experimental results demonstrate that EchoX, with about six thousand hours of training data, achieves advanced performance on multiple knowledge-based question-answering benchmarks. The project is available at https://github.com/FreedomIntelligence/EchoX.", "github_manual": "", "github_regex": "https://github.com/FreedomIntelligence/EchoX", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09674", "title": "SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning", "authors": [ "Haozhan Li", "Yuxin Zuo", "Jiale Yu", "Yuhao Zhang", "Zhaohui Yang", "Kaiyan Zhang", "Xuekai Zhu", "Yuchen Zhang", "Tianxing Chen", "Ganqu Cui", "Dehui Wang", "Dingxiang Luo", "Yuchen Fan", "Youbang Sun", "Jia Zeng", "Jiangmiao Pang", "Shanghang Zhang", "Yu Wang", "Yao Mu", "Bowen Zhou", "Ning Ding" ], "github": "https://github.com/PRIME-RL/SimpleVLA-RL", "project_page": "", "abstract": "Vision-Language-Action (VLA) models have recently emerged as a powerful paradigm for robotic manipulation. Despite substantial progress enabled by large-scale pretraining and supervised fine-tuning (SFT), these models face two fundamental challenges: (i) the scarcity and high cost of large-scale human-operated robotic trajectories required for SFT scaling, and (ii) limited generalization to tasks involving distribution shift. Recent breakthroughs in Large Reasoning Models (LRMs) demonstrate that reinforcement learning (RL) can dramatically enhance step-by-step reasoning capabilities, raising a natural question: Can RL similarly improve the long-horizon step-by-step action planning of VLA? In this work, we introduce SimpleVLA-RL, an efficient RL framework tailored for VLA models. Building upon veRL, we introduce VLA-specific trajectory sampling, scalable parallelization, multi-environment rendering, and optimized loss computation. When applied to OpenVLA-OFT, SimpleVLA-RL achieves SoTA performance on LIBERO and even outperforms pi_0 on RoboTwin 1.0\\&2.0 with the exploration-enhancing strategies we introduce. SimpleVLA-RL not only reduces dependence on large-scale data and enables robust generalization, but also remarkably surpasses SFT in real-world tasks. Moreover, we identify a novel phenomenon ``pushcut'' during RL training, wherein the policy discovers previously unseen patterns beyond those seen in the previous training process. Github: https://github.com/PRIME-RL/SimpleVLA-RL", "github_manual": "", "github_regex": "https://github.com/PRIME-RL/SimpleVLA-RL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09680", "title": "FLUX-Reason-6M & PRISM-Bench: A Million-Scale Text-to-Image Reasoning Dataset and Comprehensive Benchmark", "authors": [ "Rongyao Fang", "Aldrich Yu", "Chengqi Duan", "Linjiang Huang", "Shuai Bai", "Yuxuan Cai", "Kun Wang", "Si Liu", "Xihui Liu", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "The advancement of open-source text-to-image (T2I) models has been hindered by the absence of large-scale, reasoning-focused datasets and comprehensive evaluation benchmarks, resulting in a performance gap compared to leading closed-source systems. To address this challenge, We introduce FLUX-Reason-6M and PRISM-Bench (Precise and Robust Image Synthesis Measurement Benchmark). FLUX-Reason-6M is a massive dataset consisting of 6 million high-quality FLUX-generated images and 20 million bilingual (English and Chinese) descriptions specifically designed to teach complex reasoning. The image are organized according to six key characteristics: Imagination, Entity, Text rendering, Style, Affection, and Composition, and design explicit Generation Chain-of-Thought (GCoT) to provide detailed breakdowns of image generation steps. The whole data curation takes 15,000 A100 GPU days, providing the community with a resource previously unattainable outside of large industrial labs. PRISM-Bench offers a novel evaluation standard with seven distinct tracks, including a formidable Long Text challenge using GCoT. Through carefully designed prompts, it utilizes advanced vision-language models for nuanced human-aligned assessment of prompt-image alignment and image aesthetics. Our extensive evaluation of 19 leading models on PRISM-Bench reveals critical performance gaps and highlights specific areas requiring improvement. Our dataset, benchmark, and evaluation code are released to catalyze the next wave of reasoning-oriented T2I generation. Project page: https://flux-reason-6m.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09265", "title": "Harnessing Uncertainty: Entropy-Modulated Policy Gradients for Long-Horizon LLM Agents", "authors": [ "Jiawei Wang", "Jiacai Liu", "Yuqian Fu", "Yingru Li", "Xintao Wang", "Yuan Lin", "Yu Yue", "Lin Zhang", "Yang Wang", "Ke Wang" ], "github": "", "project_page": "", "abstract": "In long-horizon tasks, recent agents based on Large Language Models (LLMs) face a significant challenge that sparse, outcome-based rewards make it difficult to assign credit to intermediate steps. Previous methods mainly focus on creating dense reward signals to guide learning, either through traditional reinforcement learning techniques like inverse reinforcement learning or by using Process Reward Models for step-by-step feedback. In this paper, we identify a fundamental problem in the learning dynamics of LLMs: the magnitude of policy gradients is inherently coupled with the entropy, which leads to inefficient small updates for confident correct actions and potentially destabilizes large updates for uncertain ones. To resolve this, we propose Entropy-Modulated Policy Gradients (EMPG), a framework that re-calibrates the learning signal based on step-wise uncertainty and the final task outcome. EMPG amplifies updates for confident correct actions, penalizes confident errors, and attenuates updates from uncertain steps to stabilize exploration. We further introduce a bonus term for future clarity that encourages agents to find more predictable solution paths. Through comprehensive experiments on three challenging agent tasks, WebShop, ALFWorld, and Deep Search, we demonstrate that EMPG achieves substantial performance gains and significantly outperforms strong policy gradient baselines. Project page is at https://empgseed-seed.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09118", "title": "Gradient-Attention Guided Dual-Masking Synergetic Framework for Robust Text-based Person Retrieval", "authors": [ "Tianlu Zheng", "Yifan Zhang", "Xiang An", "Ziyong Feng", "Kaicheng Yang", "Qichuan Ding" ], "github": "", "project_page": "", "abstract": "Although Contrastive Language-Image Pre-training (CLIP) exhibits strong performance across diverse vision tasks, its application to person representation learning faces two critical challenges: (i) the scarcity of large-scale annotated vision-language data focused on person-centric images, and (ii) the inherent limitations of global contrastive learning, which struggles to maintain discriminative local features crucial for fine-grained matching while remaining vulnerable to noisy text tokens. This work advances CLIP for person representation learning through synergistic improvements in data curation and model architecture. First, we develop a noise-resistant data construction pipeline that leverages the in-context learning capabilities of MLLMs to automatically filter and caption web-sourced images. This yields WebPerson, a large-scale dataset of 5M high-quality person-centric image-text pairs. Second, we introduce the GA-DMS (Gradient-Attention Guided Dual-Masking Synergetic) framework, which improves cross-modal alignment by adaptively masking noisy textual tokens based on the gradient-attention similarity score. Additionally, we incorporate masked token prediction objectives that compel the model to predict informative text tokens, enhancing fine-grained semantic representation learning. Extensive experiments show that GA-DMS achieves state-of-the-art performance across multiple benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09676", "title": "SpatialVID: A Large-Scale Video Dataset with Spatial Annotations", "authors": [ "Jiahao Wang", "Yufeng Yuan", "Rujie Zheng", "Youtian Lin", "Jian Gao", "Lin-Zhuo Chen", "Yajie Bao", "Yi Zhang", "Chang Zeng", "Yanxi Zhou", "Xiaoxiao Long", "Hao Zhu", "Zhaoxiang Zhang", "Xun Cao", "Yao Yao" ], "github": "", "project_page": "", "abstract": "Significant progress has been made in spatial intelligence, spanning both spatial reconstruction and world exploration. However, the scalability and real-world fidelity of current models remain severely constrained by the scarcity of large-scale, high-quality training data. While several datasets provide camera pose information, they are typically limited in scale, diversity, and annotation richness, particularly for real-world dynamic scenes with ground-truth camera motion. To this end, we collect SpatialVID, a dataset consists of a large corpus of in-the-wild videos with diverse scenes, camera movements and dense 3D annotations such as per-frame camera poses, depth, and motion instructions. Specifically, we collect more than 21,000 hours of raw video, and process them into 2.7 million clips through a hierarchical filtering pipeline, totaling 7,089 hours of dynamic content. A subsequent annotation pipeline enriches these clips with detailed spatial and semantic information, including camera poses, depth maps, dynamic masks, structured captions, and serialized motion instructions. Analysis of SpatialVID's data statistics reveals a richness and diversity that directly foster improved model generalization and performance, establishing it as a key asset for the video and 3D vision research community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09614", "title": "LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering", "authors": [ "Jielin Qiu", "Zuxin Liu", "Zhiwei Liu", "Rithesh Murthy", "Jianguo Zhang", "Haolin Chen", "Shiyu Wang", "Ming Zhu", "Liangwei Yang", "Juntao Tan", "Zhepeng Cen", "Cheng Qian", "Shelby Heinecke", "Weiran Yao", "Silvio Savarese", "Caiming Xiong", "Huan Wang" ], "github": "https://github.com/SalesforceAIResearch/LoCoBench", "project_page": "", "abstract": "The emergence of long-context language models with context windows extending to millions of tokens has created new opportunities for sophisticated code understanding and software development evaluation. We propose LoCoBench, a comprehensive benchmark specifically designed to evaluate long-context LLMs in realistic, complex software development scenarios. Unlike existing code evaluation benchmarks that focus on single-function completion or short-context tasks, LoCoBench addresses the critical evaluation gap for long-context capabilities that require understanding entire codebases, reasoning across multiple files, and maintaining architectural consistency across large-scale software systems. Our benchmark provides 8,000 evaluation scenarios systematically generated across 10 programming languages, with context lengths spanning 10K to 1M tokens, a 100x variation that enables precise assessment of long-context performance degradation in realistic software development settings. LoCoBench introduces 8 task categories that capture essential long-context capabilities: architectural understanding, cross-file refactoring, multi-session development, bug investigation, feature implementation, code comprehension, integration testing, and security analysis. Through a 5-phase pipeline, we create diverse, high-quality scenarios that challenge LLMs to reason about complex codebases at unprecedented scale. We introduce a comprehensive evaluation framework with 17 metrics across 4 dimensions, including 8 new evaluation metrics, combined in a LoCoBench Score (LCBS). Our evaluation of state-of-the-art long-context models reveals substantial performance gaps, demonstrating that long-context understanding in complex software development represents a significant unsolved challenge that demands more attention. LoCoBench is released at: https://github.com/SalesforceAIResearch/LoCoBench.", "github_manual": "", "github_regex": "https://github.com/SalesforceAIResearch/LoCoBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09595", "title": "Kling-Avatar: Grounding Multimodal Instructions for Cascaded Long-Duration Avatar Animation Synthesis", "authors": [ "Yikang Ding", "Jiwen Liu", "Wenyuan Zhang", "Zekun Wang", "Wentao Hu", "Liyuan Cui", "Mingming Lao", "Yingchao Shao", "Hui Liu", "Xiaohan Li", "Ming Chen", "Xiaoqiang Liu", "Yu-Shen Liu", "Pengfei Wan" ], "github": "", "project_page": "", "abstract": "Recent advances in audio-driven avatar video generation have significantly enhanced audio-visual realism. However, existing methods treat instruction conditioning merely as low-level tracking driven by acoustic or visual cues, without modeling the communicative purpose conveyed by the instructions. This limitation compromises their narrative coherence and character expressiveness. To bridge this gap, we introduce Kling-Avatar, a novel cascaded framework that unifies multimodal instruction understanding with photorealistic portrait generation. Our approach adopts a two-stage pipeline. In the first stage, we design a multimodal large language model (MLLM) director that produces a blueprint video conditioned on diverse instruction signals, thereby governing high-level semantics such as character motion and emotions. In the second stage, guided by blueprint keyframes, we generate multiple sub-clips in parallel using a first-last frame strategy. This global-to-local framework preserves fine-grained details while faithfully encoding the high-level intent behind multimodal instructions. Our parallel architecture also enables fast and stable generation of long-duration videos, making it suitable for real-world applications such as digital human livestreaming and vlogging. To comprehensively evaluate our method, we construct a benchmark of 375 curated samples covering diverse instructions and challenging scenarios. Extensive experiments demonstrate that Kling-Avatar is capable of generating vivid, fluent, long-duration videos at up to 1080p and 48 fps, achieving superior performance in lip synchronization accuracy, emotion and dynamic expressiveness, instruction controllability, identity preservation, and cross-domain generalization. These results establish Kling-Avatar as a new benchmark for semantically grounded, high-fidelity audio-driven avatar synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09332", "title": "OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning", "authors": [ "Yuecheng Liu", "Dafeng Chi", "Shiguang Wu", "Zhanguang Zhang", "Yuzheng Zhuang", "Bowen Yang", "He Zhu", "Lingfeng Zhang", "Pengwei Xie", "David Gamaliel Arcos Bravo", "Yingxue Zhang", "Jianye Hao", "Xingyue Quan" ], "github": "", "project_page": "", "abstract": "Recent advances in multimodal large language models (MLLMs) have opened new opportunities for embodied intelligence, enabling multimodal understanding, reasoning, and interaction, as well as continuous spatial decision-making. Nevertheless, current MLLM-based embodied systems face two critical limitations. First, Geometric Adaptability Gap: models trained solely on 2D inputs or with hard-coded 3D geometry injection suffer from either insufficient spatial information or restricted 2D generalization, leading to poor adaptability across tasks with diverse spatial demands. Second, Embodiment Constraint Gap: prior work often neglects the physical constraints and capacities of real robots, resulting in task plans that are theoretically valid but practically infeasible.To address these gaps, we introduce OmniEVA -- an embodied versatile planner that enables advanced embodied reasoning and task planning through two pivotal innovations: (1) a Task-Adaptive 3D Grounding mechanism, which introduces a gated router to perform explicit selective regulation of 3D fusion based on contextual requirements, enabling context-aware 3D grounding for diverse embodied tasks. (2) an Embodiment-Aware Reasoning framework that jointly incorporates task goals and embodiment constraints into the reasoning loop, resulting in planning decisions that are both goal-directed and executable. Extensive experimental results demonstrate that OmniEVA not only achieves state-of-the-art general embodied reasoning performance, but also exhibits a strong ability across a wide range of downstream scenarios. Evaluations of a suite of proposed embodied benchmarks, including both primitive and composite tasks, confirm its robust and versatile planning capabilities. Project page: https://omnieva.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09286", "title": "Visual Programmability: A Guide for Code-as-Thought in Chart Understanding", "authors": [ "Bohao Tang", "Yan Ma", "Fei Zhang", "Jiadi Su", "Ethan Chern", "Zhulin Hu", "Zhixin Wang", "Pengfei Liu", "Ya Zhang" ], "github": "https://github.com/Aphelios-Tang/Code-as-Thought", "project_page": "", "abstract": "Chart understanding presents a critical test to the reasoning capabilities of Vision-Language Models (VLMs). Prior approaches face critical limitations: some rely on external tools, making them brittle and constrained by a predefined toolkit, while others fine-tune specialist models that often adopt a single reasoning strategy, such as text-based chain-of-thought (CoT). The intermediate steps of text-based reasoning are difficult to verify, which complicates the use of reinforcement-learning signals that reward factual accuracy. To address this, we propose a Code-as-Thought (CaT) approach to represent the visual information of a chart in a verifiable, symbolic format. Our key insight is that this strategy must be adaptive: a fixed, code-only implementation consistently fails on complex charts where symbolic representation is unsuitable. This finding leads us to introduce Visual Programmability: a learnable property that determines if a chart-question pair is better solved with code or direct visual analysis. We implement this concept in an adaptive framework where a VLM learns to choose between the CaT pathway and a direct visual reasoning pathway. The selection policy of the model is trained with reinforcement learning using a novel dual-reward system. This system combines a data-accuracy reward to ground the model in facts and prevent numerical hallucination, with a decision reward that teaches the model when to use each strategy, preventing it from defaulting to a single reasoning mode. Experiments demonstrate strong and robust performance across diverse chart-understanding benchmarks. Our work shows that VLMs can be taught not only to reason but also how to reason, dynamically selecting the optimal reasoning pathway for each task.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Aphelios-Tang/Code-as-Thought", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.07430", "title": "The Choice of Divergence: A Neglected Key to Mitigating Diversity Collapse in Reinforcement Learning with Verifiable Reward", "authors": [ "Long Li", "Jiaran Hao", "Jason Klein Liu", "Zhijian Zhou", "Xiaoyu Tan", "Wei Chu", "Zhe Wang", "Shirui Pan", "Chao Qu", "Yuan Qi" ], "github": "https://github.com/seamoke/DPH-RL", "project_page": "", "abstract": "A central paradox in fine-tuning Large Language Models (LLMs) with Reinforcement Learning with Verifiable Reward (RLVR) is the frequent degradation of multi-attempt performance (Pass@k) despite improvements in single-attempt accuracy (Pass@1). This is often accompanied by catastrophic forgetting, where models lose previously acquired skills. While various methods have been proposed, the choice and function of the divergence term have been surprisingly unexamined as a proactive solution. We argue that standard RLVR objectives -- both those using the mode-seeking reverse KL-divergence and those forgoing a divergence term entirely -- lack a crucial mechanism for knowledge retention. The reverse-KL actively accelerates this decay by narrowing the policy, while its absence provides no safeguard against the model drifting from its diverse knowledge base. We propose a fundamental shift in perspective: using the divergence term itself as the solution. Our framework, Diversity-Preserving Hybrid RL (DPH-RL), leverages mass-covering f-divergences (like forward-KL and JS-divergence) to function as a rehearsal mechanism. By continuously referencing the initial policy, this approach forces the model to maintain broad solution coverage. Extensive experiments on math and SQL generation demonstrate that DPH-RL not only resolves the Pass@k degradation but improves both Pass@1 and Pass@k in- and out-of-domain. Additionally, DPH-RL is more training-efficient because it computes f-divergence using generator functions, requiring only sampling from the initial policy and no online reference model. Our work highlights a crucial, overlooked axis for improving RLVR, demonstrating that the proper selection of a divergence measure is a powerful tool for building more general and diverse reasoning models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/seamoke/DPH-RL", "github_api_added_by": "auto" }, { "date": "2025-09-12", "arxiv_id": "2509.01964", "title": "2D Gaussian Splatting with Semantic Alignment for Image Inpainting", "authors": [ "Hongyu Li", "Chaofeng Chen", "Xiaoming Li", "Guangming Lu" ], "github": "", "project_page": "", "abstract": "Gaussian Splatting (GS), a recent technique for converting discrete points into continuous spatial representations, has shown promising results in 3D scene modeling and 2D image super-resolution. In this paper, we explore its untapped potential for image inpainting, which demands both locally coherent pixel synthesis and globally consistent semantic restoration. We propose the first image inpainting framework based on 2D Gaussian Splatting, which encodes incomplete images into a continuous field of 2D Gaussian splat coefficients and reconstructs the final image via a differentiable rasterization process. The continuous rendering paradigm of GS inherently promotes pixel-level coherence in the inpainted results. To improve efficiency and scalability, we introduce a patch-wise rasterization strategy that reduces memory overhead and accelerates inference. For global semantic consistency, we incorporate features from a pretrained DINO model. We observe that DINO's global features are naturally robust to small missing regions and can be effectively adapted to guide semantic alignment in large-mask scenarios, ensuring that the inpainted content remains contextually consistent with the surrounding scene. Extensive experiments on standard benchmarks demonstrate that our method achieves competitive performance in both quantitative metrics and perceptual quality, establishing a new direction for applying Gaussian Splatting to 2D image processing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.08519", "title": "HuMo: Human-Centric Video Generation via Collaborative Multi-Modal Conditioning", "authors": [ "Liyang Chen", "Tianxiang Ma", "Jiawei Liu", "Bingchuan Li", "Zhuowei Chen", "Lijie Liu", "Xu He", "Gen Li", "Qian He", "Zhiyong Wu" ], "github": "", "project_page": "", "abstract": "Human-Centric Video Generation (HCVG) methods seek to synthesize human videos from multimodal inputs, including text, image, and audio. Existing methods struggle to effectively coordinate these heterogeneous modalities due to two challenges: the scarcity of training data with paired triplet conditions and the difficulty of collaborating the sub-tasks of subject preservation and audio-visual sync with multimodal inputs. In this work, we present HuMo, a unified HCVG framework for collaborative multimodal control. For the first challenge, we construct a high-quality dataset with diverse and paired text, reference images, and audio. For the second challenge, we propose a two-stage progressive multimodal training paradigm with task-specific strategies. For the subject preservation task, to maintain the prompt following and visual generation abilities of the foundation model, we adopt the minimal-invasive image injection strategy. For the audio-visual sync task, besides the commonly adopted audio cross-attention layer, we propose a focus-by-predicting strategy that implicitly guides the model to associate audio with facial regions. For joint learning of controllabilities across multimodal inputs, building on previously acquired capabilities, we progressively incorporate the audio-visual sync task. During inference, for flexible and fine-grained multimodal control, we design a time-adaptive Classifier-Free Guidance strategy that dynamically adjusts guidance weights across denoising steps. Extensive experimental results demonstrate that HuMo surpasses specialized state-of-the-art methods in sub-tasks, establishing a unified framework for collaborative multimodal-conditioned HCVG. Project Page: https://phantom-video.github.io/HuMo.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09666", "title": "Can Understanding and Generation Truly Benefit Together -- or Just Coexist?", "authors": [ "Zhiyuan Yan", "Kaiqing Lin", "Zongjian Li", "Junyan Ye", "Hui Han", "Zhendong Wang", "Hao Liu", "Bin Lin", "Hao Li", "Xue Xu", "Xinyan Xiao", "Jingdong Wang", "Haifeng Wang", "Li Yuan" ], "github": "https://github.com/PKU-YuanGroup/UAE", "project_page": "", "abstract": "In this paper, we introduce an insightful paradigm through the Auto-Encoder lens-understanding as the encoder (I2T) that compresses images into text, and generation as the decoder (T2I) that reconstructs images from that text. Using reconstruction fidelity as the unified training objective, we enforce the coherent bidirectional information flow between the understanding and generation processes, bringing mutual gains. To implement this, we propose UAE, a novel framework for unified multimodal learning. We begin by pre-training the decoder with large-scale long-context image captions to capture fine-grained semantic and complex spatial relationships. We then propose Unified-GRPO via reinforcement learning (RL), which covers three stages: (1) A cold-start phase to gently initialize both encoder and decoder with a semantic reconstruction loss; (2) Generation for Understanding, where the encoder is trained to generate informative captions that maximize the decoder's reconstruction quality, enhancing its visual understanding; (3) Understanding for Generation, where the decoder is refined to reconstruct from these captions, forcing it to leverage every detail and improving its long-context instruction following and generation fidelity. For evaluation, we introduce Unified-Bench, the first benchmark tailored to assess the degree of unification of the UMMs. A surprising \"aha moment\" arises within the multimodal learning domain: as RL progresses, the encoder autonomously produces more descriptive captions, while the decoder simultaneously demonstrates a profound ability to understand these intricate descriptions, resulting in reconstructions of striking fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PKU-YuanGroup/UAE", "github_api_added_by": "auto" }, { "date": "2025-09-12", "arxiv_id": "2509.09372", "title": "VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model", "authors": [ "Yihao Wang", "Pengxiang Ding", "Lingxiao Li", "Can Cui", "Zirui Ge", "Xinyang Tong", "Wenxuan Song", "Han Zhao", "Wei Zhao", "Pengxu Hou", "Siteng Huang", "Yifan Tang", "Wenhui Wang", "Ru Zhang", "Jianyi Liu", "Donglin Wang" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models typically bridge the gap between perceptual and action spaces by pre-training a large-scale Vision-Language Model (VLM) on robotic data. While this approach greatly enhances performance, it also incurs significant training costs. In this paper, we investigate how to effectively bridge vision-language (VL) representations to action (A). We introduce VLA-Adapter, a novel paradigm designed to reduce the reliance of VLA models on large-scale VLMs and extensive pre-training. To this end, we first systematically analyze the effectiveness of various VL conditions and present key findings on which conditions are essential for bridging perception and action spaces. Based on these insights, we propose a lightweight Policy module with Bridge Attention, which autonomously injects the optimal condition into the action space. In this way, our method achieves high performance using only a 0.5B-parameter backbone, without any robotic data pre-training. Extensive experiments on both simulated and real-world robotic benchmarks demonstrate that VLA-Adapter not only achieves state-of-the-art level performance, but also offers the fast inference speed reported to date. Furthermore, thanks to the proposed advanced bridging paradigm, VLA-Adapter enables the training of a powerful VLA model in just 8 hours on a single consumer-grade GPU, greatly lowering the barrier to deploying the VLA model. Project page: https://vla-adapter.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.05739", "title": "Reasoning Introduces New Poisoning Attacks Yet Makes Them More Complicated", "authors": [ "Hanna Foerster", "Ilia Shumailov", "Yiren Zhao", "Harsh Chaudhari", "Jamie Hayes", "Robert Mullins", "Yarin Gal" ], "github": "", "project_page": "", "abstract": "Early research into data poisoning attacks against Large Language Models (LLMs) demonstrated the ease with which backdoors could be injected. More recent LLMs add step-by-step reasoning, expanding the attack surface to include the intermediate chain-of-thought (CoT) and its inherent trait of decomposing problems into subproblems. Using these vectors for more stealthy poisoning, we introduce ``decomposed reasoning poison'', in which the attacker modifies only the reasoning path, leaving prompts and final answers clean, and splits the trigger across multiple, individually harmless components. Fascinatingly, while it remains possible to inject these decomposed poisons, reliably activating them to change final answers (rather than just the CoT) is surprisingly difficult. This difficulty arises because the models can often recover from backdoors that are activated within their thought processes. Ultimately, it appears that an emergent form of backdoor robustness is originating from the reasoning capabilities of these advanced LLMs, as well as from the architectural separation between reasoning and final answer generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09114", "title": "Modality Alignment with Multi-scale Bilateral Attention for Multimodal Recommendation", "authors": [ "Kelin Ren", "Chan-Yang Ju", "Dong-Ho Lee" ], "github": "https://github.com/rkl71/MambaRec", "project_page": "", "abstract": "Multimodal recommendation systems are increasingly becoming foundational technologies for e-commerce and content platforms, enabling personalized services by jointly modeling users' historical behaviors and the multimodal features of items (e.g., visual and textual). However, most existing methods rely on either static fusion strategies or graph-based local interaction modeling, facing two critical limitations: (1) insufficient ability to model fine-grained cross-modal associations, leading to suboptimal fusion quality; and (2) a lack of global distribution-level consistency, causing representational bias. To address these, we propose MambaRec, a novel framework that integrates local feature alignment and global distribution regularization via attention-guided learning. At its core, we introduce the Dilated Refinement Attention Module (DREAM), which uses multi-scale dilated convolutions with channel-wise and spatial attention to align fine-grained semantic patterns between visual and textual modalities. This module captures hierarchical relationships and context-aware associations, improving cross-modal semantic modeling. Additionally, we apply Maximum Mean Discrepancy (MMD) and contrastive loss functions to constrain global modality alignment, enhancing semantic consistency. This dual regularization reduces mode-specific deviations and boosts robustness. To improve scalability, MambaRec employs a dimensionality reduction strategy to lower the computational cost of high-dimensional multimodal features. Extensive experiments on real-world e-commerce datasets show that MambaRec outperforms existing methods in fusion quality, generalization, and efficiency. Our code has been made publicly available at https://github.com/rkl71/MambaRec.", "github_manual": "", "github_regex": "https://github.com/rkl71/MambaRec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09594", "title": "ObjectReact: Learning Object-Relative Control for Visual Navigation", "authors": [ "Sourav Garg", "Dustin Craggs", "Vineeth Bhat", "Lachlan Mares", "Stefan Podgorski", "Madhava Krishna", "Feras Dayoub", "Ian Reid" ], "github": "", "project_page": "", "abstract": "Visual navigation using only a single camera and a topological map has recently become an appealing alternative to methods that require additional sensors and 3D maps. This is typically achieved through an \"image-relative\" approach to estimating control from a given pair of current observation and subgoal image. However, image-level representations of the world have limitations because images are strictly tied to the agent's pose and embodiment. In contrast, objects, being a property of the map, offer an embodiment- and trajectory-invariant world representation. In this work, we present a new paradigm of learning \"object-relative\" control that exhibits several desirable characteristics: a) new routes can be traversed without strictly requiring to imitate prior experience, b) the control prediction problem can be decoupled from solving the image matching problem, and c) high invariance can be achieved in cross-embodiment deployment for variations across both training-testing and mapping-execution settings. We propose a topometric map representation in the form of a \"relative\" 3D scene graph, which is used to obtain more informative object-level global path planning costs. We train a local controller, dubbed \"ObjectReact\", conditioned directly on a high-level \"WayObject Costmap\" representation that eliminates the need for an explicit RGB input. We demonstrate the advantages of learning object-relative control over its image-relative counterpart across sensor height variations and multiple navigation tasks that challenge the underlying spatial understanding capability, e.g., navigating a map trajectory in the reverse direction. We further show that our sim-only policy is able to generalize well to real-world indoor environments. Code and supplementary material are accessible via project page: https://object-react.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.06888", "title": "mmBERT: A Modern Multilingual Encoder with Annealed Language Learning", "authors": [ "Marc Marone", "Orion Weller", "William Fleshman", "Eugene Yang", "Dawn Lawrie", "Benjamin Van Durme" ], "github": "https://github.com/jhu-clsp/mmBERT", "project_page": "", "abstract": "Encoder-only languages models are frequently used for a variety of standard machine learning tasks, including classification and retrieval. However, there has been a lack of recent research for encoder models, especially with respect to multilingual models. We introduce mmBERT, an encoder-only language model pretrained on 3T tokens of multilingual text in over 1800 languages. To build mmBERT we introduce several novel elements, including an inverse mask ratio schedule and an inverse temperature sampling ratio. We add over 1700 low-resource languages to the data mix only during the decay phase, showing that it boosts performance dramatically and maximizes the gains from the relatively small amount of training data. Despite only including these low-resource languages in the short decay phase we achieve similar classification performance to models like OpenAI's o3 and Google's Gemini 2.5 Pro. Overall, we show that mmBERT significantly outperforms the previous generation of models on classification and retrieval tasks -- on both high and low-resource languages.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jhu-clsp/mmBERT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09254", "title": "Towards Better Dental AI: A Multimodal Benchmark and Instruction Dataset for Panoramic X-ray Analysis", "authors": [ "Jing Hao", "Yuxuan Fan", "Yanpeng Sun", "Kaixin Guo", "Lizhuo Lin", "Jinrong Yang", "Qi Yong H. Ai", "Lun M. Wong", "Hao Tang", "Kuo Feng Hung" ], "github": "https://github.com/isbrycee/OralGPT", "project_page": "", "abstract": "Recent advances in large vision-language models (LVLMs) have demonstrated strong performance on general-purpose medical tasks. However, their effectiveness in specialized domains such as dentistry remains underexplored. In particular, panoramic X-rays, a widely used imaging modality in oral radiology, pose interpretative challenges due to dense anatomical structures and subtle pathological cues, which are not captured by existing medical benchmarks or instruction datasets. To this end, we introduce MMOral, the first large-scale multimodal instruction dataset and benchmark tailored for panoramic X-ray interpretation. MMOral consists of 20,563 annotated images paired with 1.3 million instruction-following instances across diverse task types, including attribute extraction, report generation, visual question answering, and image-grounded dialogue. In addition, we present MMOral-Bench, a comprehensive evaluation suite covering five key diagnostic dimensions in dentistry. We evaluate 64 LVLMs on MMOral-Bench and find that even the best-performing model, i.e., GPT-4o, only achieves 41.45% accuracy, revealing significant limitations of current models in this domain. To promote the progress of this specific domain, we also propose OralGPT, which conducts supervised fine-tuning (SFT) upon Qwen2.5-VL-7B with our meticulously curated MMOral instruction dataset. Remarkably, a single epoch of SFT yields substantial performance enhancements for LVLMs, e.g., OralGPT demonstrates a 24.73% improvement. Both MMOral and OralGPT hold significant potential as a critical foundation for intelligent dentistry and enable more clinically impactful multimodal AI systems in the dental field. The dataset, model, benchmark, and evaluation suite are available at https://github.com/isbrycee/OralGPT.", "github_manual": "", "github_regex": "https://github.com/isbrycee/OralGPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.07225", "title": "All You Need Is A Fuzzing Brain: An LLM-Powered System for Automated Vulnerability Detection and Patching", "authors": [ "Ze Sheng", "Qingxiao Xu", "Jianwei Huang", "Matthew Woodcock", "Heqing Huang", "Alastair F. Donaldson", "Guofei Gu", "Jeff Huang" ], "github": "https://github.com/o2lab/afc-crs-all-you-need-is-a-fuzzing-brain", "project_page": "", "abstract": "Our team, All You Need Is A Fuzzing Brain, was one of seven finalists in DARPA's Artificial Intelligence Cyber Challenge (AIxCC), placing fourth in the final round. During the competition, we developed a Cyber Reasoning System (CRS) that autonomously discovered 28 security vulnerabilities - including six previously unknown zero-days - in real-world open-source C and Java projects, and successfully patched 14 of them. The complete CRS is open source at https://github.com/o2lab/afc-crs-all-you-need-is-a-fuzzing-brain. This paper provides a detailed technical description of our CRS, with an emphasis on its LLM-powered components and strategies. Building on AIxCC, we further introduce a public leaderboard for benchmarking state-of-the-art LLMs on vulnerability detection and patching tasks, derived from the AIxCC dataset. The leaderboard is available at https://o2lab.github.io/FuzzingBrain-Leaderboard/.", "github_manual": "", "github_regex": "https://github.com/o2lab/afc-crs-all-you-need-is-a-fuzzing-brain", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.08031", "title": "AU-Harness: An Open-Source Toolkit for Holistic Evaluation of Audio LLMs", "authors": [ "Sidharth Surapaneni", "Hoang Nguyen", "Jash Mehta", "Aman Tiwari", "Oluwanifemi Bamgbose", "Akshay Kalkunte", "Sai Rajeswar", "Sathwik Tejaswi Madhusudhan" ], "github": "https://github.com/ServiceNow/AU-Harness", "project_page": "", "abstract": "Large Audio Language Models (LALMs) are rapidly advancing, but evaluating them remains challenging due to inefficient toolkits that limit fair comparison and systematic assessment. Current frameworks suffer from three critical issues: slow processing that bottlenecks large-scale studies, inconsistent prompting that hurts reproducibility, and narrow task coverage that misses important audio reasoning capabilities. We introduce AU-Harness, an efficient and comprehensive evaluation framework for LALMs. Our system achieves a speedup of up to 127% over existing toolkits through optimized batch processing and parallel execution, enabling large-scale evaluations previously impractical. We provide standardized prompting protocols and flexible configurations for fair model comparison across diverse scenarios. Additionally, we introduce two new evaluation categories: LLM-Adaptive Diarization for temporal audio understanding and Spoken Language Reasoning for complex audio-based cognitive tasks. Through evaluation across 380+ tasks, we reveal significant gaps in current LALMs, particularly in temporal understanding and complex spoken language reasoning tasks. Our findings also highlight a lack of standardization in instruction modality existent across audio benchmarks, which can lead up performance differences up to 9.5 absolute points on the challenging complex instruction following downstream tasks. AU-Harness provides both practical evaluation tools and insights into model limitations, advancing systematic LALM development.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ServiceNow/AU-Harness", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.09313", "title": "Cross-Domain Evaluation of Transformer-Based Vulnerability Detection on Open & Industry Data", "authors": [ "Moritz Mock", "Thomas Forrer", "Barbara Russo" ], "github": "", "project_page": "", "abstract": "Deep learning solutions for vulnerability detection proposed in academic research are not always accessible to developers, and their applicability in industrial settings is rarely addressed. Transferring such technologies from academia to industry presents challenges related to trustworthiness, legacy systems, limited digital literacy, and the gap between academic and industrial expertise. For deep learning in particular, performance and integration into existing workflows are additional concerns. In this work, we first evaluate the performance of CodeBERT for detecting vulnerable functions in industrial and open-source software. We analyse its cross-domain generalisation when fine-tuned on open-source data and tested on industrial data, and vice versa, also exploring strategies for handling class imbalance. Based on these results, we develop AI-DO(Automating vulnerability detection Integration for Developers' Operations), a Continuous Integration-Continuous Deployment (CI/CD)-integrated recommender system that uses fine-tuned CodeBERT to detect and localise vulnerabilities during code review without disrupting workflows. Finally, we assess the tool's perceived usefulness through a survey with the company's IT professionals. Our results show that models trained on industrial data detect vulnerabilities accurately within the same domain but lose performance on open-source code, while a deep learner fine-tuned on open data, with appropriate undersampling techniques, improves the detection of vulnerabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.06266", "title": "Spatial Reasoning with Vision-Language Models in Ego-Centric Multi-View Scenes", "authors": [ "Mohsen Gholami", "Ahmad Rezaei", "Zhou Weimin", "Yong Zhang", "Mohammad Akbari" ], "github": "https://github.com/vbdi/Ego3D-Bench", "project_page": "", "abstract": "Understanding 3D spatial relationships remains a major limitation of current Vision-Language Models (VLMs). Prior work has addressed this issue by creating spatial question-answering (QA) datasets based on single images or indoor videos. However, real-world embodied AI agents such as robots and self-driving cars typically rely on ego-centric, multi-view observations. To this end, we introduce Ego3D-Bench, a new benchmark designed to evaluate the spatial reasoning abilities of VLMs using ego-centric, multi-view outdoor data. Ego3D-Bench comprises over 8,600 QA pairs, created with significant involvement from human annotators to ensure quality and diversity. We benchmark 16 SOTA VLMs, including GPT-4o, Gemini1.5-Pro, InternVL3, and Qwen2.5-VL. Our results reveal a notable performance gap between human level scores and VLM performance, highlighting that current VLMs still fall short of human level spatial understanding. To bridge this gap, we propose Ego3D-VLM, a post-training framework that enhances 3D spatial reasoning of VLMs. Ego3D-VLM generates cognitive map based on estimated global 3D coordinates, resulting in 12% average improvement on multi-choice QA and 56% average improvement on absolute distance estimation. Ego3D-VLM is modular and can be integrated with any existing VLM. Together, Ego3D-Bench and Ego3D-VLM offer valuable tools for advancing toward human level spatial understanding in real-world, multi-view environments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vbdi/Ego3D-Bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-12", "arxiv_id": "2509.06806", "title": "MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML", "authors": [ "Haoyu Dong", "Pengkun Zhang", "Mingzhe Lu", "Yanzhen Shen", "Guolin Ke" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) possess broad world knowledge and strong general-purpose reasoning ability, yet they struggle to learn from many in-context examples on standard machine learning (ML) tasks, that is, to leverage many-shot demonstrations purely via in-context learning (ICL) without gradient descent. We introduce MachineLearningLM, a portable continued-pretraining framework that equips a general-purpose LLM with robust in-context ML capability while preserving its general knowledge and reasoning for broader chat workflows. Our pretraining procedure synthesizes ML tasks from millions of structural causal models (SCMs), spanning shot counts up to 1,024. We begin with a random-forest teacher, distilling tree-based decision strategies into the LLM to strengthen robustness in numerical modeling. All tasks are serialized with a token-efficient prompt, enabling 3x to 6x more examples per context window and delivering up to 50x amortized throughput via batch inference. Despite a modest setup (Qwen-2.5-7B-Instruct with LoRA rank 8), MachineLearningLM outperforms strong LLM baselines (e.g., GPT-5-mini) by an average of about 15% on out-of-distribution tabular classification across finance, physics, biology, and healthcare domains. It exhibits a striking many-shot scaling law: accuracy increases monotonically as in-context demonstrations grow from 8 to 1,024. Without any task-specific training, it attains random-forest-level accuracy across hundreds of shots. General chat capabilities, including knowledge and reasoning, are preserved: it achieves 75.4% on MMLU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.09677", "title": "The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs", "authors": [ "Akshit Sinha", "Arvindh Arun", "Shashwat Goel", "Steffen Staab", "Jonas Geiping" ], "github": "", "project_page": "", "abstract": "Does continued scaling of large language models (LLMs) yield diminishing returns? Real-world value often stems from the length of task an agent can complete. We start this work by observing the simple but counterintuitive fact that marginal gains in single-step accuracy can compound into exponential improvements in the length of a task a model can successfully complete. Then, we argue that failures of LLMs when simple tasks are made longer arise from mistakes in execution, rather than an inability to reason. We propose isolating execution capability, by explicitly providing the knowledge and plan needed to solve a long-horizon task. We find that larger models can correctly execute significantly more turns even when small models have 100\\% single-turn accuracy. We observe that the per-step accuracy of models degrades as the number of steps increases. This is not just due to long-context limitations -- curiously, we observe a self-conditioning effect -- models become more likely to make mistakes when the context contains their errors from prior turns. Self-conditioning does not reduce by just scaling the model size. In contrast, recent thinking models do not self-condition, and can also execute much longer tasks in a single turn. We conclude by benchmarking frontier thinking models on the length of task they can execute in a single turn. Overall, by focusing on the ability to execute, we hope to reconcile debates on how LLMs can solve complex reasoning problems yet fail at simple tasks when made longer, and highlight the massive benefits of scaling model size and sequential test-time compute for long-horizon tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.10441", "title": "InfGen: A Resolution-Agnostic Paradigm for Scalable Image Synthesis", "authors": [ "Tao Han", "Wanghan Xu", "Junchao Gong", "Xiaoyu Yue", "Song Guo", "Luping Zhou", "Lei Bai" ], "github": "", "project_page": "", "abstract": "Arbitrary resolution image generation provides a consistent visual experience across devices, having extensive applications for producers and consumers. Current diffusion models increase computational demand quadratically with resolution, causing 4K image generation delays over 100 seconds. To solve this, we explore the second generation upon the latent diffusion models, where the fixed latent generated by diffusion models is regarded as the content representation and we propose to decode arbitrary resolution images with a compact generated latent using a one-step generator. Thus, we present the InfGen, replacing the VAE decoder with the new generator, for generating images at any resolution from a fixed-size latent without retraining the diffusion models, which simplifies the process, reducing computational complexity and can be applied to any model using the same latent space. Experiments show InfGen is capable of improving many models into the arbitrary high-resolution era while cutting 4K image generation time to under 10 seconds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.10058", "title": "Color Me Correctly: Bridging Perceptual Color Spaces and Text Embeddings for Improved Diffusion Generation", "authors": [ "Sung-Lin Tsai", "Bo-Lun Huang", "Yu Ting Shen", "Cheng Yu Yeo", "Chiang Tseng", "Bo-Kai Ruan", "Wen-Sheng Lien", "Hong-Han Shuai" ], "github": "https://github.com/Sung-Lin/TintBench", "project_page": "", "abstract": "Accurate color alignment in text-to-image (T2I) generation is critical for applications such as fashion, product visualization, and interior design, yet current diffusion models struggle with nuanced and compound color terms (e.g., Tiffany blue, lime green, hot pink), often producing images that are misaligned with human intent. Existing approaches rely on cross-attention manipulation, reference images, or fine-tuning but fail to systematically resolve ambiguous color descriptions. To precisely render colors under prompt ambiguity, we propose a training-free framework that enhances color fidelity by leveraging a large language model (LLM) to disambiguate color-related prompts and guiding color blending operations directly in the text embedding space. Our method first employs a large language model (LLM) to resolve ambiguous color terms in the text prompt, and then refines the text embeddings based on the spatial relationships of the resulting color terms in the CIELAB color space. Unlike prior methods, our approach improves color accuracy without requiring additional training or external reference images. Experimental results demonstrate that our framework improves color alignment without compromising image quality, bridging the gap between text semantics and visual generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Sung-Lin/TintBench", "github_api_added_by": "auto" }, { "date": "2025-09-15", "arxiv_id": "2509.09734", "title": "MCP-AgentBench: Evaluating Real-World Language Agent Performance with MCP-Mediated Tools", "authors": [ "Zikang Guo", "Benfeng Xu", "Chiwei Zhu", "Wentao Hong", "Xiaorui Wang", "Zhendong Mao" ], "github": "", "project_page": "", "abstract": "The Model Context Protocol (MCP) is rapidly emerging as a pivotal open standard, designed to enhance agent-tool integration and interoperability, and is positioned to unlock a new era of powerful, interconnected, and genuinely utilitarian agentic AI. However, despite MCP's growing adoption, existing benchmarks often fail to capture real-world agent performance within this new paradigm, leading to a distorted perception of their true operational value and an inability to reliably differentiate proficiencies. To bridge this critical evaluation gap, we introduce MCP-AgentBench -- a comprehensive benchmark specifically engineered to rigorously assess language agent capabilities in MCP-mediated tool interactions. Core contributions of MCP-AgentBench include: the establishment of a robust MCP testbed comprising 33 operational servers with 188 distinct tools; the development of a benchmark featuring 600 systematically designed queries distributed across 6 distinct categories of varying interaction complexity; and the introduction of MCP-Eval, a novel outcome-oriented evaluation methodology prioritizing real-world task success. Through extensive empirical evaluation of leading language agents, we provide foundational insights. MCP-AgentBench aims to equip the research community with a standardized and reliable framework to build, validate, and advance agents capable of fully leveraging MCP's transformative benefits, thereby accelerating progress toward truly capable and interoperable AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.10147", "title": "Virtual Agent Economies", "authors": [ "Nenad Tomasev", "Matija Franklin", "Joel Z. Leibo", "Julian Jacobs", "William A. Cunningham", "Iason Gabriel", "Simon Osindero" ], "github": "", "project_page": "", "abstract": "The rapid adoption of autonomous AI agents is giving rise to a new economic layer where agents transact and coordinate at scales and speeds beyond direct human oversight. We propose the \"sandbox economy\" as a framework for analyzing this emergent system, characterizing it along two key dimensions: its origins (emergent vs. intentional) and its degree of separateness from the established human economy (permeable vs. impermeable). Our current trajectory points toward a spontaneous emergence of a vast and highly permeable AI agent economy, presenting us with opportunities for an unprecedented degree of coordination as well as significant challenges, including systemic economic risk and exacerbated inequality. Here we discuss a number of possible design choices that may lead to safely steerable AI agent markets. In particular, we consider auction mechanisms for fair resource allocation and preference resolution, the design of AI \"mission economies\" to coordinate around achieving collective goals, and socio-technical infrastructure needed to ensure trust, safety, and accountability. By doing this, we argue for the proactive design of steerable agent markets to ensure the coming technological shift aligns with humanity's long-term collective flourishing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.09995", "title": "QuantAgent: Price-Driven Multi-Agent LLMs for High-Frequency Trading", "authors": [ "Fei Xiong", "Xiang Zhang", "Aosong Feng", "Siqi Sun", "Chenyu You" ], "github": "https://github.com/Y-Research-SBU/QuantAgent", "project_page": "", "abstract": "Recent advances in Large Language Models (LLMs) have demonstrated impressive capabilities in financial reasoning and market understanding. Multi-agent LLM frameworks such as TradingAgent and FINMEM augment these models to long-horizon investment tasks, leveraging fundamental and sentiment-based inputs for strategic decision-making. However, such systems are ill-suited for the high-speed, precision-critical demands of High-Frequency Trading (HFT). HFT requires rapid, risk-aware decisions based on structured, short-horizon signals, including technical indicators, chart patterns, and trend-based features, distinct from the long-term semantic reasoning typical of traditional financial LLM applications. To this end, we introduce QuantAgent, the first multi-agent LLM framework explicitly designed for high-frequency algorithmic trading. The system decomposes trading into four specialized agents, Indicator, Pattern, Trend, and Risk, each equipped with domain-specific tools and structured reasoning capabilities to capture distinct aspects of market dynamics over short temporal windows. In zero-shot evaluations across ten financial instruments, including Bitcoin and Nasdaq futures, QuantAgent demonstrates superior performance in both predictive accuracy and cumulative return over 4-hour trading intervals, outperforming strong neural and rule-based baselines. Our findings suggest that combining structured financial priors with language-native reasoning unlocks new potential for traceable, real-time decision systems in high-frequency financial markets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Y-Research-SBU/QuantAgent", "github_api_added_by": "auto" }, { "date": "2025-09-15", "arxiv_id": "2509.09926", "title": "LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised Learning in Open-World Scenarios", "authors": [ "Jiahao Chen", "Zhiyuan Huang", "Yurou Liu", "Bing Su" ], "github": "", "project_page": "", "abstract": "Long-tailed learning has garnered increasing attention due to its wide applicability in real-world scenarios. Among existing approaches, Long-Tailed Semi-Supervised Learning (LTSSL) has emerged as an effective solution by incorporating a large amount of unlabeled data into the imbalanced labeled dataset. However, most prior LTSSL methods are designed to train models from scratch, which often leads to issues such as overconfidence and low-quality pseudo-labels. To address these challenges, we extend LTSSL into the foundation model fine-tuning paradigm and propose a novel framework: LoFT (Long-tailed semi-supervised learning via parameter-efficient Fine-Tuning). We demonstrate that fine-tuned foundation models can generate more reliable pseudolabels, thereby benefiting imbalanced learning. Furthermore, we explore a more practical setting by investigating semi-supervised learning under open-world conditions, where the unlabeled data may include out-of-distribution (OOD) samples. To handle this problem, we propose LoFT-OW (LoFT under Open-World scenarios) to improve the discriminative ability. Experimental results on multiple benchmarks demonstrate that our method achieves superior performance compared to previous approaches, even when utilizing only 1\\% of the unlabeled data compared with previous works.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.09716", "title": "VStyle: A Benchmark for Voice Style Adaptation with Spoken Instructions", "authors": [ "Jun Zhan", "Mingyang Han", "Yuxuan Xie", "Chen Wang", "Dong Zhang", "Kexin Huang", "Haoxiang Shi", "DongXiao Wang", "Tengtao Song", "Qinyuan Cheng", "Shimin Li", "Jun Song", "Xipeng Qiu", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Spoken language models (SLMs) have emerged as a unified paradigm for speech understanding and generation, enabling natural human machine interaction. However, while most progress has focused on semantic accuracy and instruction following, the ability of SLMs to adapt their speaking style based on spoken instructions has received limited attention. We introduce Voice Style Adaptation (VSA), a new task that examines whether SLMs can modify their speaking style, such as timbre, prosody, or persona following natural language spoken commands. To study this task, we present VStyle, a bilingual (Chinese & English) benchmark covering four categories of speech generation: acoustic attributes, natural language instruction, role play, and implicit empathy. We also introduce the Large Audio Language Model as a Judge (LALM as a Judge) framework, which progressively evaluates outputs along textual faithfulness, style adherence, and naturalness, ensuring reproducible and objective assessment. Experiments on commercial systems and open source SLMs demonstrate that current models face clear limitations in controllable style adaptation, highlighting both the novelty and challenge of this task. By releasing VStyle and its evaluation toolkit, we aim to provide the community with a foundation for advancing human centered spoken interaction. The dataset and code are publicly available at https://junzhan2000.github.io/VStyle.github.io/{project's homepage}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.09713", "title": "HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented Generation for Multi-hop Question Answering", "authors": [ "Duolin Sun", "Dan Yang", "Yue Shen", "Yihan Jiao", "Zhehao Tan", "Jie Feng", "Lianzhen Zhong", "Jian Wang", "Peng Wei", "Jinjie Gu" ], "github": "", "project_page": "", "abstract": "The Retrieval-Augmented Generation (RAG) approach enhances question-answering systems and dialogue generation tasks by integrating information retrieval (IR) technologies with large language models (LLMs). This strategy, which retrieves information from external knowledge bases to bolster the response capabilities of generative models, has achieved certain successes. However, current RAG methods still face numerous challenges when dealing with multi-hop queries. For instance, some approaches overly rely on iterative retrieval, wasting too many retrieval steps on compound queries. Additionally, using the original complex query for retrieval may fail to capture content relevant to specific sub-queries, resulting in noisy retrieved content. If the noise is not managed, it can lead to the problem of noise accumulation. To address these issues, we introduce HANRAG, a novel heuristic-based framework designed to efficiently tackle problems of varying complexity. Driven by a powerful revelator, HANRAG routes queries, decomposes them into sub-queries, and filters noise from retrieved documents. This enhances the system's adaptability and noise resistance, making it highly capable of handling diverse queries. We compare the proposed framework against other leading industry methods across various benchmarks. The results demonstrate that our framework obtains superior performance in both single-hop and multi-hop question-answering tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.10396", "title": "Inpainting-Guided Policy Optimization for Diffusion Large Language Models", "authors": [ "Siyan Zhao", "Mengchen Liu", "Jing Huang", "Miao Liu", "Chenyu Wang", "Bo Liu", "Yuandong Tian", "Guan Pang", "Sean Bell", "Aditya Grover", "Feiyu Chen" ], "github": "", "project_page": "", "abstract": "Masked diffusion large language models (dLLMs) are emerging as promising alternatives to autoregressive LLMs, offering competitive performance while supporting unique generation capabilities such as inpainting. We explore how inpainting can inform RL algorithm design for dLLMs. Aligning LLMs with reinforcement learning faces an exploration challenge: sparse reward signals and sample waste when models fail to discover correct solutions. While this inefficiency affects LLMs broadly, dLLMs offer a distinctive opportunity--their inpainting ability can guide exploration. We introduce IGPO (Inpainting Guided Policy Optimization), an RL framework that strategically inserts partial ground-truth reasoning traces during online sampling. Unlike providing full solutions, inpainting steers exploration toward promising trajectory spaces while preserving self-generated reasoning, bridging supervised fine-tuning and reinforcement learning. We apply IGPO to group-based optimization methods such as GRPO, where exploration failures cause zero advantages and gradients. IGPO restores meaningful gradients while improving sample efficiency. We also propose supervised fine-tuning on synthetically rewritten concise traces that better align with dLLM generation patterns. With additional techniques including entropy-based filtering, our training recipe yields substantial gains across three mathematical benchmarks--GSM8K, Math500, and AMC--achieving new state-of-the-art results for full-attention masked dLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.09990", "title": "CMHG: A Dataset and Benchmark for Headline Generation of Minority Languages in China", "authors": [ "Guixian Xu", "Zeli Su", "Ziyin Zhang", "Jianing Liu", "XU Han", "Ting Zhang", "Yushuang Dong" ], "github": "", "project_page": "", "abstract": "Minority languages in China, such as Tibetan, Uyghur, and Traditional Mongolian, face significant challenges due to their unique writing systems, which differ from international standards. This discrepancy has led to a severe lack of relevant corpora, particularly for supervised tasks like headline generation. To address this gap, we introduce a novel dataset, Chinese Minority Headline Generation (CMHG), which includes 100,000 entries for Tibetan, and 50,000 entries each for Uyghur and Mongolian, specifically curated for headline generation tasks. Additionally, we propose a high-quality test set annotated by native speakers, designed to serve as a benchmark for future research in this domain. We hope this dataset will become a valuable resource for advancing headline generation in Chinese minority languages and contribute to the development of related benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.08643", "title": "X-Part: high fidelity and structure coherent shape decomposition", "authors": [ "Xinhao Yan", "Jiachen Xu", "Yang Li", "Changfeng Ma", "Yunhan Yang", "Chunshi Wang", "Zibo Zhao", "Zeqiang Lai", "Yunfei Zhao", "Zhuo Chen", "Chunchao Guo" ], "github": "", "project_page": "", "abstract": "Generating 3D shapes at part level is pivotal for downstream applications such as mesh retopology, UV mapping, and 3D printing. However, existing part-based generation methods often lack sufficient controllability and suffer from poor semantically meaningful decomposition. To this end, we introduce X-Part, a controllable generative model designed to decompose a holistic 3D object into semantically meaningful and structurally coherent parts with high geometric fidelity. X-Part exploits the bounding box as prompts for the part generation and injects point-wise semantic features for meaningful decomposition. Furthermore, we design an editable pipeline for interactive part generation. Extensive experimental results show that X-Part achieves state-of-the-art performance in part-level shape generation. This work establishes a new paradigm for creating production-ready, editable, and structurally sound 3D assets. Codes will be released for public research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.06652", "title": "IntrEx: A Dataset for Modeling Engagement in Educational Conversations", "authors": [ "Xingwei Tan", "Mahathi Parvatham", "Chiara Gambi", "Gabriele Pergola" ], "github": "https://github.com/doccano/doccano", "project_page": "", "abstract": "Engagement and motivation are crucial for second-language acquisition, yet maintaining learner interest in educational conversations remains a challenge. While prior research has explored what makes educational texts interesting, still little is known about the linguistic features that drive engagement in conversations. To address this gap, we introduce IntrEx, the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions. Built upon the Teacher-Student Chatroom Corpus (TSCC), IntrEx extends prior work by incorporating sequence-level annotations, allowing for the study of engagement beyond isolated turns to capture how interest evolves over extended dialogues. We employ a rigorous annotation process with over 100 second-language learners, using a comparison-based rating approach inspired by reinforcement learning from human feedback (RLHF) to improve agreement. We investigate whether large language models (LLMs) can predict human interestingness judgments. We find that LLMs (7B/8B parameters) fine-tuned on interestingness ratings outperform larger proprietary models like GPT-4o, demonstrating the potential for specialised datasets to model engagement in educational settings. Finally, we analyze how linguistic and cognitive factors, such as concreteness, comprehensibility (readability), and uptake, influence engagement in educational dialogues.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/doccano/doccano", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.04996", "title": "FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies", "authors": [ "Moritz Reuss", "Hongyi Zhou", "Marcel R\u00fchle", "\u00d6mer Erdin\u00e7 Ya\u011fmurlu", "Fabian Otto", "Rudolf Lioutikov" ], "github": "", "project_page": "", "abstract": "Developing efficient Vision-Language-Action (VLA) policies is crucial for practical robotics deployment, yet current approaches face prohibitive computational costs and resource requirements. Existing diffusion-based VLA policies require multi-billion-parameter models and massive datasets to achieve strong performance. We tackle this efficiency challenge with two contributions: intermediate-modality fusion, which reallocates capacity to the diffusion head by pruning up to 50% of LLM layers, and action-specific Global-AdaLN conditioning, which cuts parameters by 20% through modular adaptation. We integrate these advances into a novel 950 M-parameter VLA called FLOWER. Pretrained in just 200 H100 GPU hours, FLOWER delivers competitive performance with bigger VLAs across 190 tasks spanning ten simulation and real-world benchmarks and demonstrates robustness across diverse robotic embodiments. In addition, FLOWER achieves a new SoTA of 4.53 on the CALVIN ABC benchmark. Demos, code and pretrained weights are available at https://intuitive-robots.github.io/flower_vla/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.09524", "title": "DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning and Label Distribution Learning", "authors": [ "Daniil Ignatev", "Nan Li", "Hugh Mee Wong", "Anh Dang", "Shane Kaszefski Yaschuk" ], "github": "", "project_page": "", "abstract": "This system paper presents the DeMeVa team's approaches to the third edition of the Learning with Disagreements shared task (LeWiDi 2025; Leonardelli et al., 2025). We explore two directions: in-context learning (ICL) with large language models, where we compare example sampling strategies; and label distribution learning (LDL) methods with RoBERTa (Liu et al., 2019b), where we evaluate several fine-tuning methods. Our contributions are twofold: (1) we show that ICL can effectively predict annotator-specific annotations (perspectivist annotations), and that aggregating these predictions into soft labels yields competitive performance; and (2) we argue that LDL methods are promising for soft label predictions and merit further exploration by the perspectivist community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.08825", "title": "Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation", "authors": [ "Joachim Baumann", "Paul R\u00f6ttger", "Aleksandra Urman", "Albert Wendsj\u00f6", "Flor Miriam Plaza-del-Arco", "Johannes B. Gruber", "Dirk Hovy" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are rapidly transforming social science research by enabling the automation of labor-intensive tasks like data annotation and text analysis. However, LLM outputs vary significantly depending on the implementation choices made by researchers (e.g., model selection, prompting strategy, or temperature settings). Such variation can introduce systematic biases and random errors, which propagate to downstream analyses and cause Type I, Type II, Type S, or Type M errors. We call this LLM hacking. We quantify the risk of LLM hacking by replicating 37 data annotation tasks from 21 published social science research studies with 18 different models. Analyzing 13 million LLM labels, we test 2,361 realistic hypotheses to measure how plausible researcher choices affect statistical conclusions. We find incorrect conclusions based on LLM-annotated data in approximately one in three hypotheses for state-of-the-art models, and in half the hypotheses for small language models. While our findings show that higher task performance and better general model capabilities reduce LLM hacking risk, even highly accurate models do not completely eliminate it. The risk of LLM hacking decreases as effect sizes increase, indicating the need for more rigorous verification of findings near significance thresholds. Our extensive analysis of LLM hacking mitigation techniques emphasizes the importance of human annotations in reducing false positive findings and improving model selection. Surprisingly, common regression estimator correction techniques are largely ineffective in reducing LLM hacking risk, as they heavily trade off Type I vs. Type II errors. Beyond accidental errors, we find that intentional LLM hacking is unacceptably simple. With few LLMs and just a handful of prompt paraphrases, anything can be presented as statistically significant.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.01535", "title": "CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models", "authors": [ "Kairong Han", "Wenshuo Zhao", "Ziyu Zhao", "JunJian Ye", "Lujia Pan", "Kun Kuang" ], "github": "https://github.com/Kairong-Han/CAT", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved remarkable success across various domains. However, a fundamental question remains: Can LLMs effectively utilize causal knowledge for prediction and generation? Through empirical studies, we find that LLMs trained directly on large-scale data often capture spurious correlations rather than true causal relationships, leading to suboptimal performance, especially in out-of-distribution (OOD) scenarios. To address this challenge, we propose Causal Attention Tuning (CAT), a novel approach that injects fine-grained causal knowledge into the attention mechanism. We propose an automated pipeline that leverages human priors to automatically generate token-level causal signals and introduce the Re-Attention mechanism to guide training, helping the model focus on causal structures while mitigating noise and biases in attention scores. Experimental results on our proposed Spurious Token Game (STG) benchmark and multiple downstream tasks demonstrate that our approach effectively leverages causal knowledge for prediction and remains robust in OOD scenarios. Implementation details can be found at https://github.com/Kairong-Han/CAT.", "github_manual": "", "github_regex": "https://github.com/Kairong-Han/CAT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.09737", "title": "World Modeling with Probabilistic Structure Integration", "authors": [ "Klemen Kotar", "Wanhee Lee", "Rahul Venkatesh", "Honglin Chen", "Daniel Bear", "Jared Watrous", "Simon Kim", "Khai Loong Aw", "Lilian Naing Chen", "Stefan Stojanov", "Kevin Feigelis", "Imran Thobani", "Alex Durango", "Khaled Jedoui", "Atlas Kazemian", "Dan Yamins" ], "github": "", "project_page": "", "abstract": "We present Probabilistic Structure Integration (PSI), a system for learning richly controllable and flexibly promptable world models from data. PSI consists of a three-step cycle. The first step, Probabilistic prediction, involves building a probabilistic graphical model Psi of the data, in the form of a random-access autoregressive sequence model. Psi supports a complete set of learned conditional distributions describing the dependence of any variables in the data on any other set of variables. In step 2, Structure extraction, we show how to extract underlying low-dimensional properties in the data, corresponding to a diverse set of meaningful \"intermediate structures\", in a zero-shot fashion via causal inference on Psi. Step 3, Integration, completes the cycle by converting these structures into new token types that are then continually mixed back into the training diet as conditioning signals and prediction targets. Each such cycle augments the capabilities of Psi, both allowing it to model the underlying data better, and creating new control handles -- akin to an LLM-like universal prompting language. We train an instance of Psi on 1.4 trillion tokens of internet video data; we use it to perform a variety of useful video prediction and understanding inferences; we extract state-of-the-art optical flow, self-supervised depth and object segmentation; and we use these structures to support a full cycle of predictive improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.07966", "title": "Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images", "authors": [ "Boammani Aser Lompo", "Marc Haraoui" ], "github": "https://github.com/AI-4-Everyone/Visual-TableQA", "project_page": "", "abstract": "Visual reasoning over structured data such as tables is a critical capability for modern vision-language models (VLMs), yet current benchmarks remain limited in scale, diversity, or reasoning depth, especially when it comes to rendered table images. Addressing this gap, we introduce Visual-TableQA, a large-scale, open-domain multimodal dataset specifically designed to evaluate and enhance visual reasoning over complex tabular data. Our generation pipeline is modular, scalable, and fully autonomous, involving multiple reasoning LLMs collaborating across distinct roles: generation, validation, and inspiration. Visual-TableQA comprises 2.5k richly structured LaTeX-rendered tables and 6k reasoning-intensive QA pairs, all produced at a cost of under USD 100. To promote diversity and creativity, our pipeline performs multi-model collaborative data generation via cross-model prompting ('inspiration') and LLM-jury filtering. Stronger models seed layouts and topics that weaker models elaborate, collectively distilling diverse reasoning patterns and visual structures into the dataset. Empirical results show that models fine-tuned on Visual-TableQA generalize robustly to external benchmarks, outperforming several proprietary models despite the dataset's synthetic nature. The full pipeline and resources are publicly available at https://github.com/AI-4-Everyone/Visual-TableQA.", "github_manual": "", "github_regex": "https://github.com/AI-4-Everyone/Visual-TableQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.04500", "title": "Context Engineering for Trustworthiness: Rescorla Wagner Steering Under Mixed and Inappropriate Contexts", "authors": [ "Rushi Wang", "Jiateng Liu", "Cheng Qian", "Yifan Shen", "Yanzhou Pan", "Zhaozhuo Xu", "Ahmed Abbasi", "Heng Ji", "Denghui Zhang" ], "github": "https://github.com/Oppugno-Rushi/RW-Steering", "project_page": "", "abstract": "Incorporating external context can significantly enhance the response quality of Large Language Models (LLMs). However, real-world contexts often mix relevant information with disproportionate inappropriate content, posing reliability risks. How do LLMs process and prioritize mixed context? To study this, we introduce the Poisoned Context Testbed, pairing queries with real-world contexts containing relevant and inappropriate content. Inspired by associative learning in animals, we adapt the Rescorla-Wagner (RW) model from neuroscience to quantify how competing contextual signals influence LLM outputs. Our adapted model reveals a consistent behavioral pattern: LLMs exhibit a strong tendency to incorporate information that is less prevalent in the context. This susceptibility is harmful in real-world settings, where small amounts of inappropriate content can substantially degrade response quality. Empirical evaluations on our testbed further confirm this vulnerability. To tackle this, we introduce RW-Steering, a two-stage finetuning-based approach that enables the model to internally identify and ignore inappropriate signals. Unlike prior methods that rely on extensive supervision across diverse context mixtures, RW-Steering generalizes robustly across varying proportions of inappropriate content. Experiments show that our best fine-tuned model improves response quality by 39.8% and reverses the undesirable behavior curve, establishing RW-Steering as a robust, generalizable context engineering solution for improving LLM safety in real-world use.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Oppugno-Rushi/RW-Steering", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-15", "arxiv_id": "2509.08270", "title": "Interpretable Physics Reasoning and Performance Taxonomy in Vision-Language Models", "authors": [ "Pranav Pawar", "Kavish Shah", "Akshat Bhalani", "Komal Kasat", "Dev Mittal", "Hadi Gala", "Deepali Patil", "Nikita Raichada", "Monali Deshmukh" ], "github": "https://github.com/prnvpwr2612/Interpretable-Physics-Reasoning-and-Performance-Taxonomy-in-Vision-Language-Models", "project_page": "", "abstract": "As Vision-Language Models (VLMs) grow in sophistication, their ability to perform reasoning is coming under increasing supervision. While they excel at many tasks, their grasp of fundamental scientific principles, such as physics, remains an underexplored frontier. To reflect the advancements in these capabilities, we introduce a novel and accessible framework designed to rigorously evaluate VLMs on their understanding of 2D physics. Our framework features a pragmatic scenario generator that creates a diverse testbed of over 400 problems across four core domains: Projectile Motion, Collision Dynamics, Mechanics, and Fluid Dynamics. Through comprehensive evaluation of four state-of-the-art VLMs, we demonstrate a strong correlation between model scale and reasoning ability, with our top-performing model, Qwen2.5-VL-7B, achieving an overall score of 0.815. We find that while models excel at formulaic problems, they struggle significantly with domains requiring abstract spatial reasoning. By designing this framework, we aim to democratize the study of scientific reasoning in VLMs and foster deeper insights into their capabilities and limitations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/prnvpwr2612/Interpretable-Physics-Reasoning-and-Performance-Taxonomy-in-Vision-Language-Models", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.11543", "title": "UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning", "authors": [ "Zhengxi Lu", "Jiabo Ye", "Fei Tang", "Yongliang Shen", "Haiyang Xu", "Ziwei Zheng", "Weiming Lu", "Ming Yan", "Fei Huang", "Jun Xiao", "Yueting Zhuang" ], "github": "https://github.com/X-PLUG/MobileAgent/tree/main/UI-S1", "project_page": "", "abstract": "Graphical User Interface (GUI) agents have demonstrated remarkable progress in automating complex user interface interactions through reinforcement learning. However, current approaches face a fundamental dilemma: offline RL enables stable training on pre-collected trajectories, but struggles with multi-step task execution for lack of trajectory-level reward signals; online RL captures these signals through environment interaction, but suffers from sparse rewards and prohibitive deployment costs. To address it, we present Semi-online Reinforcement Learning, a novel paradigm that simulates online RL on offline trajectories. During each rollout process, we preserve the original model output within the multi-turn dialogue, where a Patch Module adaptively recovers the divergence between rollout and expert trajectories. To capture long-term training signals, Semi-online RL introduces discounted future returns into the reward computation and optimizes the policy with weighted step-level and episode-level advantages. We further introduce Semi-Online Performance (SOP), a metric that aligns better with true online performance, serving as a practical and effective proxy for real-world evaluation. Experiments show that ours Semi-online RL achieves SOTA performance among 7B models across four dynamic benchmarks, with significant gains over the base model (e.g., +12.0% on AndroidWorld, +23.8% on AITW), demonstrating significant progress in bridging the gap between offline training efficiency and online multi-turn reasoning. The code is available at https://github.com/X-PLUG/MobileAgent/tree/main/UI-S1.", "github_manual": "", "github_regex": "https://github.com/X-PLUG/MobileAgent/tree/main/UI-S1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.12203", "title": "LazyDrag: Enabling Stable Drag-Based Editing on Multi-Modal Diffusion Transformers via Explicit Correspondence", "authors": [ "Zixin Yin", "Xili Dai", "Duomin Wang", "Xianfang Zeng", "Lionel M. Ni", "Gang Yu", "Heung-Yeung Shum" ], "github": "", "project_page": "", "abstract": "The reliance on implicit point matching via attention has become a core bottleneck in drag-based editing, resulting in a fundamental compromise on weakened inversion strength and costly test-time optimization (TTO). This compromise severely limits the generative capabilities of diffusion models, suppressing high-fidelity inpainting and text-guided creation. In this paper, we introduce LazyDrag, the first drag-based image editing method for Multi-Modal Diffusion Transformers, which directly eliminates the reliance on implicit point matching. In concrete terms, our method generates an explicit correspondence map from user drag inputs as a reliable reference to boost the attention control. This reliable reference opens the potential for a stable full-strength inversion process, which is the first in the drag-based editing task. It obviates the necessity for TTO and unlocks the generative capability of models. Therefore, LazyDrag naturally unifies precise geometric control with text guidance, enabling complex edits that were previously out of reach: opening the mouth of a dog and inpainting its interior, generating new objects like a ``tennis ball'', or for ambiguous drags, making context-aware changes like moving a hand into a pocket. Additionally, LazyDrag supports multi-round workflows with simultaneous move and scale operations. Evaluated on the DragBench, our method outperforms baselines in drag accuracy and perceptual quality, as validated by VIEScore and human evaluation. LazyDrag not only establishes new state-of-the-art performance, but also paves a new way to editing paradigms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.12201", "title": "OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling", "authors": [ "Yang Zhou", "Yifan Wang", "Jianjun Zhou", "Wenzheng Chang", "Haoyu Guo", "Zizun Li", "Kaijing Ma", "Xinyue Li", "Yating Wang", "Haoyi Zhu", "Mingyu Liu", "Dingning Liu", "Jiange Yang", "Zhoujie Fu", "Junyi Chen", "Chunhua Shen", "Jiangmiao Pang", "Kaipeng Zhang", "Tong He" ], "github": "", "project_page": "", "abstract": "The field of 4D world modeling - aiming to jointly capture spatial geometry and temporal dynamics - has witnessed remarkable progress in recent years, driven by advances in large-scale generative models and multimodal learning. However, the development of truly general 4D world models remains fundamentally constrained by the availability of high-quality data. Existing datasets and benchmarks often lack the dynamic complexity, multi-domain diversity, and spatial-temporal annotations required to support key tasks such as 4D geometric reconstruction, future prediction, and camera-control video generation. To address this gap, we introduce OmniWorld, a large-scale, multi-domain, multi-modal dataset specifically designed for 4D world modeling. OmniWorld consists of a newly collected OmniWorld-Game dataset and several curated public datasets spanning diverse domains. Compared with existing synthetic datasets, OmniWorld-Game provides richer modality coverage, larger scale, and more realistic dynamic interactions. Based on this dataset, we establish a challenging benchmark that exposes the limitations of current state-of-the-art (SOTA) approaches in modeling complex 4D environments. Moreover, fine-tuning existing SOTA methods on OmniWorld leads to significant performance gains across 4D reconstruction and video generation tasks, strongly validating OmniWorld as a powerful resource for training and evaluation. We envision OmniWorld as a catalyst for accelerating the development of general-purpose 4D world models, ultimately advancing machines' holistic understanding of the physical world.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.11648", "title": "EthicsMH: A Pilot Benchmark for Ethical Reasoning in Mental Health AI", "authors": [ "Sai Kartheek Reddy Kasu" ], "github": "", "project_page": "", "abstract": "The deployment of large language models (LLMs) in mental health and other sensitive domains raises urgent questions about ethical reasoning, fairness, and responsible alignment. Yet, existing benchmarks for moral and clinical decision-making do not adequately capture the unique ethical dilemmas encountered in mental health practice, where confidentiality, autonomy, beneficence, and bias frequently intersect. To address this gap, we introduce Ethical Reasoning in Mental Health (EthicsMH), a pilot dataset of 125 scenarios designed to evaluate how AI systems navigate ethically charged situations in therapeutic and psychiatric contexts. Each scenario is enriched with structured fields, including multiple decision options, expert-aligned reasoning, expected model behavior, real-world impact, and multi-stakeholder viewpoints. This structure enables evaluation not only of decision accuracy but also of explanation quality and alignment with professional norms. Although modest in scale and developed with model-assisted generation, EthicsMH establishes a task framework that bridges AI ethics and mental health decision-making. By releasing this dataset, we aim to provide a seed resource that can be expanded through community and expert contributions, fostering the development of AI systems capable of responsibly handling some of society's most delicate decisions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.11452", "title": "Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weighting", "authors": [ "Yining Lu", "Zilong Wang", "Shiyang Li", "Xin Liu", "Changlong Yu", "Qingyu Yin", "Zhan Shi", "Zixuan Zhang", "Meng Jiang" ], "github": "", "project_page": "", "abstract": "Prior works in multi-objective reinforcement learning typically use linear reward scalarization with fixed weights, which provably fail to capture non-convex Pareto fronts and thus yield suboptimal results. This limitation becomes especially critical in online preference alignment for large language models. Here, stochastic trajectories generated by parameterized policies create highly non-linear and non-convex mappings from parameters to objectives that no single static weighting scheme can find optimal trade-offs. We address this limitation by introducing dynamic reward weighting, which adaptively adjusts reward weights during the online reinforcement learning process. Unlike existing approaches that rely on fixed-weight interpolation, our dynamic weighting continuously balances and prioritizes objectives in training, facilitating effective exploration of Pareto fronts in objective space. We introduce two approaches of increasing sophistication and generalizability: (1) hypervolume-guided weight adaptation and (2) gradient-based weight optimization, offering a versatile toolkit for online multi-objective alignment. Our extensive experiments demonstrate their compatibility with commonly used online reinforcement learning algorithms (including GRPO, REINFORCE, and RLOO), effectiveness across multiple mathematical reasoning datasets, and applicability to different model families, consistently achieving Pareto dominant solutions with fewer training steps than fixed-weight linear scalarization baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.11444", "title": "CognitiveSky: Scalable Sentiment and Narrative Analysis for Decentralized Social Media", "authors": [ "Gaurab Chhetri", "Anandi Dutta", "Subasish Das" ], "github": "https://github.com/gauravfs-14/CognitiveSky", "project_page": "", "abstract": "The emergence of decentralized social media platforms presents new opportunities and challenges for real-time analysis of public discourse. This study introduces CognitiveSky, an open-source and scalable framework designed for sentiment, emotion, and narrative analysis on Bluesky, a federated Twitter or X.com alternative. By ingesting data through Bluesky's Application Programming Interface (API), CognitiveSky applies transformer-based models to annotate large-scale user-generated content and produces structured and analyzable outputs. These summaries drive a dynamic dashboard that visualizes evolving patterns in emotion, activity, and conversation topics. Built entirely on free-tier infrastructure, CognitiveSky achieves both low operational cost and high accessibility. While demonstrated here for monitoring mental health discourse, its modular design enables applications across domains such as disinformation detection, crisis response, and civic sentiment analysis. By bridging large language models with decentralized networks, CognitiveSky offers a transparent, extensible tool for computational social science in an era of shifting digital ecosystems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gauravfs-14/CognitiveSky", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.09658", "title": "Measuring Epistemic Humility in Multimodal Large Language Models", "authors": [ "Bingkui Tong", "Jiaer Xia", "Sifeng Shang", "Kaiyang Zhou" ], "github": "https://github.com/maifoundations/HumbleBench", "project_page": "", "abstract": "Hallucinations in multimodal large language models (MLLMs) -- where the model generates content inconsistent with the input image -- pose significant risks in real-world applications, from misinformation in visual question answering to unsafe errors in decision-making. Existing benchmarks primarily test recognition accuracy, i.e., evaluating whether models can select the correct answer among distractors. This overlooks an equally critical capability for trustworthy AI: recognizing when none of the provided options are correct, a behavior reflecting epistemic humility. We present HumbleBench, a new hallucination benchmark designed to evaluate MLLMs' ability to reject plausible but incorrect answers across three hallucination types: object, relation, and attribute. Built from a panoptic scene graph dataset, we leverage fine-grained scene graph annotations to extract ground-truth entities and relations, and prompt GPT-4-Turbo to generate multiple-choice questions, followed by a rigorous manual filtering process. Each question includes a \"None of the above\" option, requiring models not only to recognize correct visual information but also to identify when no provided answer is valid. We evaluate a variety of state-of-the-art MLLMs -- including both general-purpose and specialized reasoning models -- on HumbleBench and share valuable findings and insights with the community. By incorporating explicit false-option rejection, HumbleBench fills a key gap in current evaluation suites, providing a more realistic measure of MLLM reliability in safety-critical settings. Our code and dataset are released publicly and can be accessed at https://github.com/maifoundations/HumbleBench.", "github_manual": "", "github_regex": "https://github.com/maifoundations/HumbleBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.10813", "title": "InternScenes: A Large-scale Simulatable Indoor Scene Dataset with Realistic Layouts", "authors": [ "Weipeng Zhong", "Peizhou Cao", "Yichen Jin", "Li Luo", "Wenzhe Cai", "Jingli Lin", "Hanqing Wang", "Zhaoyang Lyu", "Tai Wang", "Bo Dai", "Xudong Xu", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "The advancement of Embodied AI heavily relies on large-scale, simulatable 3D scene datasets characterized by scene diversity and realistic layouts. However, existing datasets typically suffer from limitations in data scale or diversity, sanitized layouts lacking small items, and severe object collisions. To address these shortcomings, we introduce InternScenes, a novel large-scale simulatable indoor scene dataset comprising approximately 40,000 diverse scenes by integrating three disparate scene sources, real-world scans, procedurally generated scenes, and designer-created scenes, including 1.96M 3D objects and covering 15 common scene types and 288 object classes. We particularly preserve massive small items in the scenes, resulting in realistic and complex layouts with an average of 41.5 objects per region. Our comprehensive data processing pipeline ensures simulatability by creating real-to-sim replicas for real-world scans, enhances interactivity by incorporating interactive objects into these scenes, and resolves object collisions by physical simulations. We demonstrate the value of InternScenes with two benchmark applications: scene layout generation and point-goal navigation. Both show the new challenges posed by the complex and realistic layouts. More importantly, InternScenes paves the way for scaling up the model training for both tasks, making the generation and navigation in such complex scenes possible. We commit to open-sourcing the data, models, and benchmarks to benefit the whole community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.10884", "title": "Nav-R1: Reasoning and Navigation in Embodied Scenes", "authors": [ "Qingxiang Liu", "Ting Huang", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/Nav-R1", "project_page": "", "abstract": "Embodied navigation requires agents to integrate perception, reasoning, and action for robust interaction in complex 3D environments. Existing approaches often suffer from incoherent and unstable reasoning traces that hinder generalization across diverse environments, and difficulty balancing long-horizon semantic reasoning with low-latency control for real-time navigation. To address these challenges, we propose Nav-R1, an embodied foundation model that unifies reasoning in embodied environments. We first construct Nav-CoT-110K, a large-scale dataset of step-by-step Chains-of-Thought (CoT) for embodied tasks, which enables cold-start initialization with structured reasoning. Building on this foundation, we design a GRPO-based reinforcement learning framework with three complementary rewards: format, understanding, and navigation, to improve structural adherence, semantic grounding, and path fidelity. Furthermore, we introduce a Fast-in-Slow reasoning paradigm, decoupling deliberate semantic reasoning from low-latency reactive control for efficient yet coherent navigation. Extensive evaluations on embodied AI benchmarks demonstrate that Nav-R1 consistently outperforms strong baselines, with over 8% average improvement in reasoning and navigation performance. Real-world deployment on a mobile robot further validates its robustness under limited onboard resources. Code: https://github.com/AIGeeksGroup/Nav-R1. Website: https://aigeeksgroup.github.io/Nav-R1.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/Nav-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.11362", "title": "PersonaX: Multimodal Datasets with LLM-Inferred Behavior Traits", "authors": [ "Loka Li", "Wong Yu Kang", "Minghao Fu", "Guangyi Chen", "Zhenhao Chen", "Gongxu Luo", "Yuewen Sun", "Salman Khan", "Peter Spirtes", "Kun Zhang" ], "github": "", "project_page": "", "abstract": "Understanding human behavior traits is central to applications in human-computer interaction, computational social science, and personalized AI systems. Such understanding often requires integrating multiple modalities to capture nuanced patterns and relationships. However, existing resources rarely provide datasets that combine behavioral descriptors with complementary modalities such as facial attributes and biographical information. To address this gap, we present PersonaX, a curated collection of multimodal datasets designed to enable comprehensive analysis of public traits across modalities. PersonaX consists of (1) CelebPersona, featuring 9444 public figures from diverse occupations, and (2) AthlePersona, covering 4181 professional athletes across 7 major sports leagues. Each dataset includes behavioral trait assessments inferred by three high-performing large language models, alongside facial imagery and structured biographical features. We analyze PersonaX at two complementary levels. First, we abstract high-level trait scores from text descriptions and apply five statistical independence tests to examine their relationships with other modalities. Second, we introduce a novel causal representation learning (CRL) framework tailored to multimodal and multi-measurement data, providing theoretical identifiability guarantees. Experiments on both synthetic and real-world data demonstrate the effectiveness of our approach. By unifying structured and unstructured analysis, PersonaX establishes a foundation for studying LLM-inferred behavioral traits in conjunction with visual and biographical attributes, advancing multimodal trait analysis and causal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.11986", "title": "Lost in Embeddings: Information Loss in Vision-Language Models", "authors": [ "Wenyan Li", "Raphael Tang", "Chengzu Li", "Caiqi Zhang", "Ivan Vuli\u0107", "Anders S\u00f8gaard" ], "github": "", "project_page": "", "abstract": "Vision--language models (VLMs) often process visual inputs through a pretrained vision encoder, followed by a projection into the language model's embedding space via a connector component. While crucial for modality fusion, the potential information loss induced by this projection step and its direct impact on model capabilities remain understudied. We introduce two complementary approaches to examine and quantify this loss by analyzing the latent representation space. First, we evaluate semantic information preservation by analyzing changes in k-nearest neighbor relationships between image representations, before and after projection. Second, we directly measure information loss by reconstructing visual embeddings from the projected representation, localizing loss at an image patch level. Experiments reveal that connectors substantially distort the local geometry of visual representations, with k-nearest neighbors diverging by 40--60\\% post-projection, correlating with degradation in retrieval performance. The patch-level embedding reconstruction provides interpretable insights for model behavior on visually grounded question-answering tasks, finding that areas of high information loss reliably predict instances where models struggle.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.10844", "title": "GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings", "authors": [ "Yixuan Tang", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Domain-specific embedding models have shown promise for applications that require specialized semantic understanding, such as coding agents and financial retrieval systems, often achieving higher performance gains than general models. However, state-of-the-art embedding models are typically based on LLMs, which contain billions of parameters, making deployment challenging in resource-constrained environments. Model compression through pruning offers a promising solution, but existing pruning methods treat all parameters uniformly, failing to distinguish between general semantic representations and domain-specific patterns, leading to suboptimal pruning decisions. Thus, we propose GAPrune, a pruning framework that addresses this challenge by considering both domain importance and preserving general linguistic foundation. Our method uses Fisher Information to measure importance and general-domain gradient alignment to assess parameter behavior, then combines these signals using our Domain Alignment Importance (DAI) scoring. Lower DAI scores indicate that the parameter is either less important for the domain task or creates conflicts between domain and general objectives. Experiments on two domain benchmarks, FinMTEB and ChemTEB, show that GAPrune maintains performance within 2.5% of dense models in one-shot pruning at 50% sparsity, while outperforming all baselines. With retraining in 100 steps, GAPrune achieves +4.51% improvement on FinMTEB and +1.73% on ChemTEB, demonstrating that our pruning strategy not only preserves but enhances domain-specific capabilities. Our findings demonstrate that principled pruning strategies can achieve model compression and enhanced domain specialization, providing the research community with a new approach for development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.09672", "title": "Locality in Image Diffusion Models Emerges from Data Statistics", "authors": [ "Artem Lukoianov", "Chenyang Yuan", "Justin Solomon", "Vincent Sitzmann" ], "github": "", "project_page": "", "abstract": "Among generative models, diffusion models are uniquely intriguing due to the existence of a closed-form optimal minimizer of their training objective, often referred to as the optimal denoiser. However, diffusion using this optimal denoiser merely reproduces images in the training set and hence fails to capture the behavior of deep diffusion models. Recent work has attempted to characterize this gap between the optimal denoiser and deep diffusion models, proposing analytical, training-free models that can generate images that resemble those generated by a trained UNet. The best-performing method hypothesizes that shift equivariance and locality inductive biases of convolutional neural networks are the cause of the performance gap, hence incorporating these assumptions into its analytical model. In this work, we present evidence that the locality in deep diffusion models emerges as a statistical property of the image dataset, not due to the inductive bias of convolutional neural networks. Specifically, we demonstrate that an optimal parametric linear denoiser exhibits similar locality properties to the deep neural denoisers. We further show, both theoretically and experimentally, that this locality arises directly from the pixel correlations present in natural image datasets. Finally, we use these insights to craft an analytical denoiser that better matches scores predicted by a deep diffusion model than the prior expert-crafted alternative.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.12132", "title": "Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models", "authors": [ "Pu Jian", "Junhong Wu", "Wei Sun", "Chen Wang", "Shuo Ren", "Jiajun Zhang" ], "github": "", "project_page": "", "abstract": "Recent advances in text-only \"slow-thinking\" reasoning have prompted efforts to transfer this capability to vision-language models (VLMs), for training visual reasoning models (VRMs). owever, such transfer faces critical challenges: Effective \"slow thinking\" in VRMs requires visual reflection, the ability to check the reasoning process based on visual information. Through quantitative analysis, we observe that current VRMs exhibit limited visual reflection, as their attention to visual information diminishes rapidly with longer generated responses. To address this challenge, we propose a new VRM Reflection-V, which enhances visual reflection based on reasoning data construction for cold-start and reward design for reinforcement learning (RL). Firstly, we construct vision-centered reasoning data by leveraging an agent that interacts between VLMs and reasoning LLMs, enabling cold-start learning of visual reflection patterns. Secondly, a visual attention based reward model is employed during RL to encourage reasoning based on visual information. Therefore, Reflection-V demonstrates significant improvements across multiple visual reasoning benchmarks. Furthermore, Reflection-V maintains a stronger and more consistent reliance on visual information during visual reasoning, indicating effective enhancement in visual reflection capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.10708", "title": "SearchInstruct: Enhancing Domain Adaptation via Retrieval-Based Instruction Dataset Creation", "authors": [ "Iman Barati", "Mostafa Amiri", "Heshaam Faili" ], "github": "https://github.com/mostafaamiri/SearchInstruct", "project_page": "", "abstract": "Supervised Fine-Tuning (SFT) is essential for training large language models (LLMs), significantly enhancing critical capabilities such as instruction following and in-context learning. Nevertheless, creating suitable training datasets tailored for specific domains remains challenging due to unique domain constraints and data scarcity. In this paper, we propose SearchInstruct, an innovative method explicitly designed to construct high quality instruction datasets for SFT. Our approach begins with a limited set of domain specific, human generated questions, which are systematically expanded using a large language model. Subsequently, domain relevant resources are dynamically retrieved to generate accurate and contextually appropriate answers for each augmented question. Experimental evaluation demonstrates that SearchInstruct enhances both the diversity and quality of SFT datasets, leading to measurable improvements in LLM performance within specialized domains. Additionally, we show that beyond dataset generation, the proposed method can also effectively facilitate tasks such as model editing, enabling efficient updates to existing models. To facilitate reproducibility and community adoption, we provide full implementation details, the complete set of generated instruction response pairs, and the source code in a publicly accessible Git repository: [https://github.com/mostafaamiri/SearchInstruct](https://github.com/mostafaamiri/SearchInstruct)", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mostafaamiri/SearchInstruct", "github_api_added_by": "user" }, { "date": "2025-09-16", "arxiv_id": "2509.11866", "title": "Dr.V: A Hierarchical Perception-Temporal-Cognition Framework to Diagnose Video Hallucination by Fine-grained Spatial-Temporal Grounding", "authors": [ "Meng Luo", "Shengqiong Wu", "Liqiang Jing", "Tianjie Ju", "Li Zheng", "Jinxiang Lai", "Tianlong Wu", "Xinya Du", "Jian Li", "Siyuan Yan", "Jiebo Luo", "William Yang Wang", "Hao Fei", "Mong-Li Lee", "Wynne Hsu" ], "github": "https://github.com/Eurekaleo/Dr.V", "project_page": "", "abstract": "Recent advancements in large video models (LVMs) have significantly enhance video understanding. However, these models continue to suffer from hallucinations, producing content that conflicts with input videos. To address this issue, we propose Dr.V, a hierarchical framework covering perceptive, temporal, and cognitive levels to diagnose video hallucination by fine-grained spatial-temporal grounding. Dr.V comprises of two key components: a benchmark dataset Dr.V-Bench and a satellite video agent Dr.V-Agent. Dr.V-Bench includes 10k instances drawn from 4,974 videos spanning diverse tasks, each enriched with detailed spatial-temporal annotation. Dr.V-Agent detects hallucinations in LVMs by systematically applying fine-grained spatial-temporal grounding at the perceptive and temporal levels, followed by cognitive level reasoning. This step-by-step pipeline mirrors human-like video comprehension and effectively identifies hallucinations. Extensive experiments demonstrate that Dr.V-Agent is effective in diagnosing hallucination while enhancing interpretability and reliability, offering a practical blueprint for robust video understanding in real-world scenarios. All our data and code are available at https://github.com/Eurekaleo/Dr.V.", "github_manual": "", "github_regex": "https://github.com/Eurekaleo/Dr.V", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.07403", "title": "LongEmotion: Measuring Emotional Intelligence of Large Language Models in Long-Context Interaction", "authors": [ "Weichu Liu", "Jing Xiong", "Yuxuan Hu", "Zixuan Li", "Minghuan Tan", "Ningning Mao", "Chenyang Zhao", "Zhongwei Wan", "Chaofan Tao", "Wendong Xu", "Hui Shen", "Chengming Li", "Lingpeng Kong", "Ngai Wong" ], "github": "https://github.com/LongEmotion/LongEmotion", "project_page": "", "abstract": "Large language models (LLMs) make significant progress in Emotional Intelligence (EI) and long-context understanding. However, existing benchmarks tend to overlook certain aspects of EI in long-context scenarios, especially under realistic, practical settings where interactions are lengthy, diverse, and often noisy. To move towards such realistic settings, we present LongEmotion, a benchmark specifically designed for long-context EI tasks. It covers a diverse set of tasks, including Emotion Classification, Emotion Detection, Emotion QA, Emotion Conversation, Emotion Summary, and Emotion Expression. On average, the input length for these tasks reaches 8,777 tokens, with long-form generation required for Emotion Expression. To enhance performance under realistic constraints, we incorporate Retrieval-Augmented Generation (RAG) and Collaborative Emotional Modeling (CoEM), and compare them with standard prompt-based methods. Unlike conventional approaches, our RAG method leverages both the conversation context and the large language model itself as retrieval sources, avoiding reliance on external knowledge bases. The CoEM method further improves performance by decomposing the task into five stages, integrating both retrieval augmentation and limited knowledge injection. Experimental results show that both RAG and CoEM consistently enhance EI-related performance across most long-context tasks, advancing LLMs toward more practical and real-world EI applications. Furthermore, we conducted a comparative case study experiment on the GPT series to demonstrate the differences among various models in terms of EI. Code is available on GitHub at https://github.com/LongEmotion/LongEmotion, and the project page can be found at https://longemotion.github.io/.", "github_manual": "", "github_regex": "https://github.com/LongEmotion/LongEmotion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.11492", "title": "ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claims", "authors": [ "Anirban Saha Anik", "Md Fahimul Kabir Chowdhury", "Andrew Wyckoff", "Sagnik Ray Choudhury" ], "github": "", "project_page": "", "abstract": "This paper presents our system for Task 3 of the CLEF 2025 CheckThat! Lab, which focuses on verifying numerical and temporal claims using retrieved evidence. We explore two complementary approaches: zero-shot prompting with instruction-tuned large language models (LLMs) and supervised fine-tuning using parameter-efficient LoRA. To enhance evidence quality, we investigate several selection strategies, including full-document input and top-k sentence filtering using BM25 and MiniLM. Our best-performing model LLaMA fine-tuned with LoRA achieves strong performance on the English validation set. However, a notable drop in the test set highlights a generalization challenge. These findings underscore the importance of evidence granularity and model adaptation for robust numerical fact verification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.11425", "title": "FuseCodec: Semantic-Contextual Fusion and Supervision for Neural Codecs", "authors": [ "Md Mubtasim Ahasan", "Rafat Hasan Khan", "Tasnim Mohiuddin", "Aman Chadha", "Tariq Iqbal", "M Ashraful Amin", "Amin Ahsan Ali", "Md Mofijul Islam", "A K M Mahbubur Rahman" ], "github": "https://github.com/mubtasimahasan/FuseCodec", "project_page": "", "abstract": "Speech tokenization enables discrete representation and facilitates speech language modeling. However, existing neural codecs capture low-level acoustic features, overlooking the semantic and contextual cues inherent to human speech. While recent efforts introduced semantic representations from self-supervised speech models or incorporated contextual representations from pre-trained language models, challenges remain in aligning and unifying the semantic and contextual representations. We introduce FuseCodec, which unifies acoustic, semantic, and contextual representations through strong cross-modal alignment and globally informed supervision. We propose three complementary techniques: (i) Latent Representation Fusion, integrating semantic and contextual features directly into the encoder latent space for robust and unified representation learning; (ii) Global Semantic-Contextual Supervision, supervising discrete tokens with globally pooled and broadcasted representations to enhance temporal consistency and cross-modal alignment; and (iii) Temporally Aligned Contextual Supervision, strengthening alignment by dynamically matching contextual and speech tokens within a local window for fine-grained token-level supervision. We further introduce FuseCodec-TTS, demonstrating our methodology's applicability to zero-shot speech synthesis. Empirically, FuseCodec achieves state-of-the-art performance in LibriSpeech, surpassing EnCodec, SpeechTokenizer, and DAC in transcription accuracy, perceptual quality, intelligibility, and speaker similarity. Results highlight the effectiveness of contextually and semantically guided tokenization for speech tokenization and downstream tasks. Code and pretrained models are available at https://github.com/mubtasimahasan/FuseCodec.", "github_manual": "", "github_regex": "https://github.com/mubtasimahasan/FuseCodec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-16", "arxiv_id": "2509.11963", "title": "ToolRM: Outcome Reward Models for Tool-Calling Large Language Models", "authors": [ "Mayank Agarwal", "Ibrahim Abdelaziz", "Kinjal Basu", "Merve Unuvar", "Luis A. Lastras", "Yara Rizk", "Pavan Kapanipathi" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) increasingly interact with external tools, reward modeling for tool use has become a critical yet underexplored area. Existing reward models, trained primarily on natural language outputs, struggle to evaluate tool-based reasoning and execution. To quantify this gap, we introduce FC-RewardBench, the first benchmark designed to systematically assess reward models' performance in tool-calling scenarios. Our analysis shows that current reward models often miss key signals of effective tool use, highlighting the need for domain-specific modeling. To address this, we propose a training framework for outcome-based reward models using data synthesized from permissively licensed, open-weight LLMs. We train models ranging from 1.7B to 14B parameters and evaluate them across seven out-of-domain benchmarks. These models consistently outperform general-purpose baselines, achieving up to 25\\% average improvement in downstream task performance and enabling data-efficient fine-tuning through reward-guided filtering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.13232", "title": "Single-stream Policy Optimization", "authors": [ "Zhongwen Xu", "Zihan Ding" ], "github": "", "project_page": "", "abstract": "We revisit policy-gradient optimization for Large Language Models (LLMs) from a single-stream perspective. Prevailing group-based methods like GRPO reduce variance with on-the-fly baselines but suffer from critical flaws: frequent degenerate groups erase learning signals, and synchronization barriers hinder scalability. We introduce Single-stream Policy Optimization (SPO), which eliminates these issues by design. SPO replaces per-group baselines with a persistent, KL-adaptive value tracker and normalizes advantages globally across the batch, providing a stable, low-variance learning signal for every sample. Being group-free, SPO enables higher throughput and scales effectively in long-horizon or tool-integrated settings where generation times vary. Furthermore, the persistent value tracker naturally enables an adaptive curriculum via prioritized sampling. Experiments using Qwen3-8B show that SPO converges more smoothly and attains higher accuracy than GRPO, while eliminating computation wasted on degenerate groups. Ablation studies confirm that SPO's gains stem from its principled approach to baseline estimation and advantage normalization, offering a more robust and efficient path for LLM reasoning. Across five hard math benchmarks with Qwen3 8B, SPO improves the average maj@32 by +3.4 percentage points (pp) over GRPO, driven by substantial absolute point gains on challenging datasets, including +7.3 pp on BRUMO 25, +4.4 pp on AIME 25, +3.3 pp on HMMT 25, and achieves consistent relative gain in pass@k across the evaluated k values. SPO's success challenges the prevailing trend of adding incidental complexity to RL algorithms, highlighting a path where fundamental principles, not architectural workarounds, drive the next wave of progress in LLM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.12815", "title": "Hunyuan3D Studio: End-to-End AI Pipeline for Game-Ready 3D Asset Generation", "authors": [ "Biwen Lei", "Yang Li", "Xinhai Liu", "Shuhui Yang", "Lixin Xu", "Jingwei Huang", "Ruining Tang", "Haohan Weng", "Jian Liu", "Jing Xu", "Zhen Zhou", "Yiling Zhu", "Jiankai Xing", "Jiachen Xu", "Changfeng Ma", "Xinhao Yan", "Yunhan Yang", "Chunshi Wang", "Duoteng Xu", "Xueqi Ma", "Yuguang Chen", "Jing Li", "Mingxin Yang", "Sheng Zhang", "Yifei Feng", "Xin Huang", "Di Luo", "Zebin He", "Puhua Jiang", "Changrong Hu", "Zihan Qin", "Shiwei Miao", "Haolin Liu", "Yunfei Zhao", "Zeqiang Lai", "Qingxiang Lin", "Zibo Zhao", "Kunhong Li", "Xianghui Yang", "Huiwen Shi", "Xin Yang", "Yuxuan Wang", "Zebin Yao", "Yihang Lian", "Sicong Liu", "Xintong Han", "Wangchen Qin", "Caisheng Ouyang", "Jianyin Liu", "Tianwen Yuan", "Shuai Jiang", "Hong Duan", "Yanqi Niu", "Wencong Lin", "Yifu Sun", "Shirui Huang", "Lin Niu", "Gu Gong", "Guojian Xiao", "Bojian Zheng", "Xiang Yuan", "Qi Chen", "Jie Xiao", "Dongyang Zheng", "Xiaofeng Yang", "Kai Liu", "Jianchen Zhu", "Lifu Wang", "Qinglin Lu", "Jie Liu", "Liang Dong", "Fan Jiang", "Ruibin Chen", "Lei Wang", "Chao Zhang", "Jiaxin Lin", "Hao Zhang", "Zheng Ye", "Peng He", "Runzhou Wu", "Yinhe Wu", "Jiayao Du", "Jupeng Chen", "Xinyue Mao", "Dongyuan Guo", "Yixuan Tang", "Yulin Tsai", "Yonghao Tan", "Jiaao Yu", "Junlin Yu", "Keren Zhang", "Yifan Li", "Peng Chen", "Tian Liu", "Di Wang", "Yuhong Liu", "Linus", "Jie Jiang", "Zhuo Chen", "Chunchao Guo" ], "github": "", "project_page": "", "abstract": "The creation of high-quality 3D assets, a cornerstone of modern game development, has long been characterized by labor-intensive and specialized workflows. This paper presents Hunyuan3D Studio, an end-to-end AI-powered content creation platform designed to revolutionize the game production pipeline by automating and streamlining the generation of game-ready 3D assets. At its core, Hunyuan3D Studio integrates a suite of advanced neural modules (such as Part-level 3D Generation, Polygon Generation, Semantic UV, etc.) into a cohesive and user-friendly system. This unified framework allows for the rapid transformation of a single concept image or textual description into a fully-realized, production-quality 3D model complete with optimized geometry and high-fidelity PBR textures. We demonstrate that assets generated by Hunyuan3D Studio are not only visually compelling but also adhere to the stringent technical requirements of contemporary game engines, significantly reducing iteration time and lowering the barrier to entry for 3D content creation. By providing a seamless bridge from creative intent to technical asset, Hunyuan3D Studio represents a significant leap forward for AI-assisted workflows in game development and interactive media.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.06079", "title": "Multimodal Reasoning for Science: Technical Report and 1st Place Solution to the ICML 2025 SeePhys Challenge", "authors": [ "Hao Liang", "Ruitao Wu", "Bohan Zeng", "Junbo Niu", "Wentao Zhang", "Bin Dong" ], "github": "https://github.com/OpenDCAI/SciReasoner", "project_page": "", "abstract": "Multimodal reasoning remains a fundamental challenge in artificial intelligence. Despite substantial advances in text-based reasoning, even state-of-the-art models such as GPT-o3 struggle to maintain strong performance in multimodal scenarios. To address this gap, we introduce a caption-assisted reasoning framework that effectively bridges visual and textual modalities. Our approach achieved 1st place in the ICML 2025 AI for Math Workshop \\& Challenge 2: SeePhys, highlighting its effectiveness and robustness. Furthermore, we validate its generalization on the MathVerse benchmark for geometric reasoning, demonstrating the versatility of our method. Our code is publicly available at https://github.com/OpenDCAI/SciReasoner.", "github_manual": "", "github_regex": "https://github.com/OpenDCAI/SciReasoner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.13317", "title": "3D Aware Region Prompted Vision Language Model", "authors": [ "An-Chieh Cheng", "Yang Fu", "Yukang Chen", "Zhijian Liu", "Xiaolong Li", "Subhashree Radhakrishnan", "Song Han", "Yao Lu", "Jan Kautz", "Pavlo Molchanov", "Hongxu Yin", "Xiaolong Wang", "Sifei Liu" ], "github": "", "project_page": "", "abstract": "We present Spatial Region 3D (SR-3D) aware vision-language model that connects single-view 2D images and multi-view 3D data through a shared visual token space. SR-3D supports flexible region prompting, allowing users to annotate regions with bounding boxes, segmentation masks on any frame, or directly in 3D, without the need for exhaustive multi-frame labeling. We achieve this by enriching 2D visual features with 3D positional embeddings, which allows the 3D model to draw upon strong 2D priors for more accurate spatial reasoning across frames, even when objects of interest do not co-occur within the same view. Extensive experiments on both general 2D vision language and specialized 3D spatial benchmarks demonstrate that SR-3D achieves state-of-the-art performance, underscoring its effectiveness for unifying 2D and 3D representation space on scene understanding. Moreover, we observe applicability to in-the-wild videos without sensory 3D inputs or ground-truth 3D annotations, where SR-3D accurately infers spatial relationships and metric measurements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.13312", "title": "WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research", "authors": [ "Zijian Li", "Xin Guan", "Bo Zhang", "Shen Huang", "Houquan Zhou", "Shaopeng Lai", "Ming Yan", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Jun Zhang", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "This paper tackles open-ended deep research (OEDR), a complex challenge where AI agents must synthesize vast web-scale information into insightful reports. Current approaches are plagued by dual-fold limitations: static research pipelines that decouple planning from evidence acquisition and one-shot generation paradigms that easily suffer from long-context failure issues like \"loss in the middle\" and hallucinations. To address these challenges, we introduce WebWeaver, a novel dual-agent framework that emulates the human research process. The planner operates in a dynamic cycle, iteratively interleaving evidence acquisition with outline optimization to produce a comprehensive, source-grounded outline linking to a memory bank of evidence. The writer then executes a hierarchical retrieval and writing process, composing the report section by section. By performing targeted retrieval of only the necessary evidence from the memory bank for each part, it effectively mitigates long-context issues. Our framework establishes a new state-of-the-art across major OEDR benchmarks, including DeepResearch Bench, DeepConsult, and DeepResearchGym. These results validate our human-centric, iterative methodology, demonstrating that adaptive planning and focused synthesis are crucial for producing high-quality, reliable, and well-structured reports.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.13311", "title": "Towards General Agentic Intelligence via Environment Scaling", "authors": [ "Runnan Fang", "Shihao Cai", "Baixuan Li", "Jialong Wu", "Guangyu Li", "Wenbiao Yin", "Xinyu Wang", "Xiaobin Wang", "Liangcai Su", "Zhen Zhang", "Shibin Wu", "Zhengwei Tao", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Jingren Zhou" ], "github": "https://github.com/ByteDance-Seed/seed-oss", "project_page": "", "abstract": "Advanced agentic intelligence is a prerequisite for deploying Large Language Models in practical, real-world applications. Diverse real-world APIs demand precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments. The breadth of function-calling competence is closely tied to the diversity of environments in which agents are trained. In this work, we scale up environments as a step towards advancing general agentic intelligence. This gives rise to two central challenges: (i) how to scale environments in a principled manner, and (ii) how to effectively train agentic capabilities from experiences derived through interactions with these environments. To address these, we design a scalable framework that automatically constructs heterogeneous environments that are fully simulated, systematically broadening the space of function-calling scenarios. We further adapt a two-phase agent fine-tuning strategy: first endowing agents with fundamental agentic capabilities, then specializing them for domain-specific contexts. Extensive experiments on agentic benchmarks, tau-bench, tau2-Bench, and ACEBench, demonstrate that our trained model, AgentScaler, significantly enhances the function-calling capability of models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByteDance-Seed/seed-oss", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.13309", "title": "WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents", "authors": [ "Zile Qiao", "Guoxin Chen", "Xuanzhong Chen", "Donglei Yu", "Wenbiao Yin", "Xinyu Wang", "Zhen Zhang", "Baixuan Li", "Huifeng Yin", "Kuan Li", "Rui Min", "Minpeng Liao", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "Recent advances in deep-research systems have demonstrated the potential for AI agents to autonomously discover and synthesize knowledge from external sources. In this paper, we introduce WebResearcher, a novel framework for building such agents through two key components: (1) WebResearcher, an iterative deep-research paradigm that reformulates deep research as a Markov Decision Process, where agents periodically consolidate findings into evolving reports while maintaining focused workspaces, overcoming the context suffocation and noise contamination that plague existing mono-contextual approaches; and (2) WebFrontier, a scalable data synthesis engine that generates high-quality training data through tool-augmented complexity escalation, enabling systematic creation of research tasks that bridge the gap between passive knowledge recall and active knowledge construction. Notably, we find that the training data from our paradigm significantly enhances tool-use capabilities even for traditional mono-contextual methods. Furthermore, our paradigm naturally scales through parallel thinking, enabling concurrent multi-agent exploration for more comprehensive conclusions. Extensive experiments across 6 challenging benchmarks demonstrate that WebResearcher achieves state-of-the-art performance, even surpassing frontier proprietary systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.11526", "title": "Multiple Instance Learning Framework with Masked Hard Instance Mining for Gigapixel Histopathology Image Analysis", "authors": [ "Wenhao Tang", "Sheng Huang", "Heng Fang", "Fengtao Zhou", "Bo Liu", "Qingshan Liu" ], "github": "https://github.com/DearCaat/MHIM-MIL", "project_page": "", "abstract": "Digitizing pathological images into gigapixel Whole Slide Images (WSIs) has opened new avenues for Computational Pathology (CPath). As positive tissue comprises only a small fraction of gigapixel WSIs, existing Multiple Instance Learning (MIL) methods typically focus on identifying salient instances via attention mechanisms. However, this leads to a bias towards easy-to-classify instances while neglecting challenging ones. Recent studies have shown that hard examples are crucial for accurately modeling discriminative boundaries. Applying such an idea at the instance level, we elaborate a novel MIL framework with masked hard instance mining (MHIM-MIL), which utilizes a Siamese structure with a consistency constraint to explore the hard instances. Using a class-aware instance probability, MHIM-MIL employs a momentum teacher to mask salient instances and implicitly mine hard instances for training the student model. To obtain diverse, non-redundant hard instances, we adopt large-scale random masking while utilizing a global recycle network to mitigate the risk of losing key features. Furthermore, the student updates the teacher using an exponential moving average, which identifies new hard instances for subsequent training iterations and stabilizes optimization. Experimental results on cancer diagnosis, subtyping, survival analysis tasks, and 12 benchmarks demonstrate that MHIM-MIL outperforms the latest methods in both performance and efficiency. The code is available at: https://github.com/DearCaat/MHIM-MIL.", "github_manual": "", "github_regex": "https://github.com/DearCaat/MHIM-MIL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.12603", "title": "EconProver: Towards More Economical Test-Time Scaling for Automated Theorem Proving", "authors": [ "Mukai Li", "Linfeng Song", "Zhenwen Liang", "Jiahao Xu", "Shansan Gong", "Qi Liu", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have recently advanced the field of Automated Theorem Proving (ATP), attaining substantial performance gains through widely adopted test-time scaling strategies, notably reflective Chain-of-Thought (CoT) reasoning and increased sampling passes. However, they both introduce significant computational overhead for inference. Moreover, existing cost analyses typically regulate only the number of sampling passes, while neglecting the substantial disparities in sampling costs introduced by different scaling strategies. In this paper, we systematically compare the efficiency of different test-time scaling strategies for ATP models and demonstrate the inefficiency of the current state-of-the-art (SOTA) open-source approaches. We then investigate approaches to significantly reduce token usage and sample passes while maintaining the original performance. Specifically, we propose two complementary methods that can be integrated into a unified EconRL pipeline for amplified benefits: (1) a dynamic Chain-of-Thought (CoT) switching mechanism designed to mitigate unnecessary token consumption, and (2) Diverse parallel-scaled reinforcement learning (RL) with trainable prefixes to enhance pass rates under constrained sampling passes. Experiments on miniF2F and ProofNet demonstrate that our EconProver achieves comparable performance to baseline methods with only 12% of the computational cost. This work provides actionable insights for deploying lightweight ATP models without sacrificing performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.12341", "title": "Exact Coset Sampling for Quantum Lattice Algorithms", "authors": [ "Yifan Zhang" ], "github": "https://github.com/yifanzhang-pro/quantum-lattice", "project_page": "", "abstract": "We give a simple, fully correct, and assumption-light replacement for the contested \"domain-extension\" in Step 9 of a recent windowed-QFT lattice algorithm with complex-Gaussian windows~chen2024quantum. The published Step~9 suffers from a periodicity/support mismatch. We present a pair-shift difference construction that coherently cancels all unknown offsets, produces an exact uniform CRT-coset state over Z_{P}, and then uses the QFT to enforce the intended modular linear relation. The unitary is reversible, uses poly(log M_2) gates, and preserves the algorithm's asymptotics. Project Page: https://github.com/yifanzhang-pro/quantum-lattice.", "github_manual": "", "github_regex": "https://github.com/yifanzhang-pro/quantum-lattice", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.13305", "title": "WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning", "authors": [ "Kuan Li", "Zhongwang Zhang", "Huifeng Yin", "Rui Ye", "Yida Zhao", "Liwen Zhang", "Litu Ou", "Dingchu Zhang", "Xixi Wu", "Jialong Wu", "Xinyu Wang", "Zile Qiao", "Zhen Zhang", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Jingren Zhou" ], "github": "https://github.com/Alibaba-NLP/DeepResearch", "project_page": "", "abstract": "Transcending human cognitive limitations represents a critical frontier in LLM training. Proprietary agentic systems like DeepResearch have demonstrated superhuman capabilities on extremely complex information-seeking benchmarks such as BrowseComp, a feat previously unattainable. We posit that their success hinges on a sophisticated reasoning pattern absent in open-source models: the ability to systematically reduce extreme uncertainty when navigating vast information landscapes. Based on this insight, we introduce WebSailor, a complete post-training methodology designed to instill this crucial capability. Our approach involves generating novel, high-uncertainty tasks through structured sampling and information obfuscation, RFT cold start, and an efficient agentic RL training algorithm, Duplicating Sampling Policy Optimization (DUPO). With this integrated pipeline, WebSailor significantly outperforms all open-source agents in complex information-seeking tasks, matching proprietary agents' performance and closing the capability gap.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Alibaba-NLP/DeepResearch", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.13313", "title": "ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization", "authors": [ "Xixi Wu", "Kuan Li", "Yida Zhao", "Liwen Zhang", "Litu Ou", "Huifeng Yin", "Zhongwang Zhang", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Minhao Cheng", "Shuai Wang", "Hong Cheng", "Jingren Zhou" ], "github": "https://github.com/MiroMindAI/MiroThinker", "project_page": "", "abstract": "Large Language Model (LLM)-based web agents demonstrate strong performance on knowledge-intensive tasks but are hindered by context window limitations in paradigms like ReAct. Complex queries involving multiple entities, intertwined relationships, and high uncertainty demand extensive search cycles that rapidly exhaust context budgets before reaching complete solutions. To overcome this challenge, we introduce ReSum, a novel paradigm that enables indefinite exploration through periodic context summarization. ReSum converts growing interaction histories into compact reasoning states, maintaining awareness of prior discoveries while bypassing context constraints. For paradigm adaptation, we propose ReSum-GRPO, integrating GRPO with segmented trajectory training and advantage broadcasting to familiarize agents with summary-conditioned reasoning. Extensive experiments on web agents of varying scales across three benchmarks demonstrate that ReSum delivers an average absolute improvement of 4.5\\% over ReAct, with further gains of up to 8.2\\% following ReSum-GRPO training. Notably, with only 1K training samples, our WebResummer-30B (a ReSum-GRPO-trained version of WebSailor-30B) achieves 33.3\\% Pass@1 on BrowseComp-zh and 18.3\\% on BrowseComp-en, surpassing existing open-source web agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiroMindAI/MiroThinker", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.13310", "title": "Scaling Agents via Continual Pre-training", "authors": [ "Liangcai Su", "Zhen Zhang", "Guangyu Li", "Zhuo Chen", "Chenxi Wang", "Maojia Song", "Xinyu Wang", "Kuan Li", "Jialong Wu", "Xuanzhong Chen", "Zile Qiao", "Zhongwang Zhang", "Huifeng Yin", "Shihao Cai", "Runnan Fang", "Zhengwei Tao", "Wenbiao Yin", "Chenxiong Qian", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have evolved into agentic systems capable of autonomous tool use and multi-step reasoning for complex problem-solving. However, post-training approaches building upon general-purpose foundation models consistently underperform in agentic tasks, particularly in open-source implementations. We identify the root cause: the absence of robust agentic foundation models forces models during post-training to simultaneously learn diverse agentic behaviors while aligning them to expert demonstrations, thereby creating fundamental optimization tensions. To this end, we are the first to propose incorporating Agentic Continual Pre-training (Agentic CPT) into the deep research agents training pipeline to build powerful agentic foundational models. Based on this approach, we develop a deep research agent model named AgentFounder. We evaluate our AgentFounder-30B on 10 benchmarks and achieve state-of-the-art performance while retains strong tool-use ability, notably 39.9% on BrowseComp-en, 43.3% on BrowseComp-zh, and 31.5% Pass@1 on HLE.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.11177", "title": "Optimal Brain Restoration for Joint Quantization and Sparsification of LLMs", "authors": [ "Hang Guo", "Yawei Li", "Luca Benini" ], "github": "", "project_page": "", "abstract": "Recent advances in Large Language Model (LLM) compression, such as quantization and pruning, have achieved notable success. However, as these techniques gradually approach their respective limits, relying on a single method for further compression has become increasingly challenging. In this work, we explore an alternative solution by combining quantization and sparsity. This joint approach, though promising, introduces new difficulties due to the inherently conflicting requirements on weight distributions: quantization favors compact ranges, while pruning benefits from high variance. To attack this problem, we propose Optimal Brain Restoration (OBR), a general and training-free framework that aligns pruning and quantization by error compensation between both. OBR minimizes performance degradation on downstream tasks by building on a second-order Hessian objective, which is then reformulated into a tractable problem through surrogate approximation and ultimately reaches a closed-form solution via group error compensation. Experiments show that OBR enables aggressive W4A4KV4 quantization with 50% sparsity on existing LLMs, and delivers up to 4.72x speedup and 6.4x memory reduction compared to the FP16-dense baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.13177", "title": "ROOM: A Physics-Based Continuum Robot Simulator for Photorealistic Medical Datasets Generation", "authors": [ "Salvatore Esposito", "Mat\u00edas Mattamala", "Daniel Rebain", "Francis Xiatian Zhang", "Kevin Dhaliwal", "Mohsen Khadem", "Subramanian Ramamoorthy" ], "github": "https://github.com/iamsalvatore/room", "project_page": "", "abstract": "Continuum robots are advancing bronchoscopy procedures by accessing complex lung airways and enabling targeted interventions. However, their development is limited by the lack of realistic training and test environments: Real data is difficult to collect due to ethical constraints and patient safety concerns, and developing autonomy algorithms requires realistic imaging and physical feedback. We present ROOM (Realistic Optical Observation in Medicine), a comprehensive simulation framework designed for generating photorealistic bronchoscopy training data. By leveraging patient CT scans, our pipeline renders multi-modal sensor data including RGB images with realistic noise and light specularities, metric depth maps, surface normals, optical flow and point clouds at medically relevant scales. We validate the data generated by ROOM in two canonical tasks for medical robotics -- multi-view pose estimation and monocular depth estimation, demonstrating diverse challenges that state-of-the-art methods must overcome to transfer to these medical settings. Furthermore, we show that the data produced by ROOM can be used to fine-tune existing depth estimation models to overcome these challenges, also enabling other downstream applications such as navigation. We expect that ROOM will enable large-scale data generation across diverse patient anatomies and procedural scenarios that are challenging to capture in clinical settings. Code and data: https://github.com/iamsalvatore/room.", "github_manual": "", "github_regex": "https://github.com/iamsalvatore/room", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.12521", "title": "Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Time", "authors": [ "Yifan Lan", "Yuanpu Cao", "Weitong Zhang", "Lu Lin", "Jinghui Chen" ], "github": "https://github.com/Yifan-Lan/Phi", "project_page": "", "abstract": "Recently, Multimodal Large Language Models (MLLMs) have gained significant attention across various domains. However, their widespread adoption has also raised serious safety concerns. In this paper, we uncover a new safety risk of MLLMs: the output preference of MLLMs can be arbitrarily manipulated by carefully optimized images. Such attacks often generate contextually relevant yet biased responses that are neither overtly harmful nor unethical, making them difficult to detect. Specifically, we introduce a novel method, Preference Hijacking (Phi), for manipulating the MLLM response preferences using a preference hijacked image. Our method works at inference time and requires no model modifications. Additionally, we introduce a universal hijacking perturbation -- a transferable component that can be embedded into different images to hijack MLLM responses toward any attacker-specified preferences. Experimental results across various tasks demonstrate the effectiveness of our approach. The code for Phi is accessible at https://github.com/Yifan-Lan/Phi.", "github_manual": "", "github_regex": "https://github.com/Yifan-Lan/Phi", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.10687", "title": "Stable Part Diffusion 4D: Multi-View RGB and Kinematic Parts Video Generation", "authors": [ "Hao Zhang", "Chun-Han Yao", "Simon Donn\u00e9", "Narendra Ahuja", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "We present Stable Part Diffusion 4D (SP4D), a framework for generating paired RGB and kinematic part videos from monocular inputs. Unlike conventional part segmentation methods that rely on appearance-based semantic cues, SP4D learns to produce kinematic parts - structural components aligned with object articulation and consistent across views and time. SP4D adopts a dual-branch diffusion model that jointly synthesizes RGB frames and corresponding part segmentation maps. To simplify the architecture and flexibly enable different part counts, we introduce a spatial color encoding scheme that maps part masks to continuous RGB-like images. This encoding allows the segmentation branch to share the latent VAE from the RGB branch, while enabling part segmentation to be recovered via straightforward post-processing. A Bidirectional Diffusion Fusion (BiDiFuse) module enhances cross-branch consistency, supported by a contrastive part consistency loss to promote spatial and temporal alignment of part predictions. We demonstrate that the generated 2D part maps can be lifted to 3D to derive skeletal structures and harmonic skinning weights with few manual adjustments. To train and evaluate SP4D, we construct KinematicParts20K, a curated dataset of over 20K rigged objects selected and processed from Objaverse XL (Deitke et al., 2023), each paired with multi-view RGB and part video sequences. Experiments show that SP4D generalizes strongly to diverse scenarios, including real-world videos, novel generated objects, and rare articulated poses, producing kinematic-aware outputs suitable for downstream animation and motion-related tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.11481", "title": "RAPTOR: A Foundation Policy for Quadrotor Control", "authors": [ "Jonas Eschmann", "Dario Albani", "Giuseppe Loianno" ], "github": "https://github.com/rl-tools/raptor", "project_page": "", "abstract": "Humans are remarkably data-efficient when adapting to new unseen conditions, like driving a new car. In contrast, modern robotic control systems, like neural network policies trained using Reinforcement Learning (RL), are highly specialized for single environments. Because of this overfitting, they are known to break down even under small differences like the Simulation-to-Reality (Sim2Real) gap and require system identification and retraining for even minimal changes to the system. In this work, we present RAPTOR, a method for training a highly adaptive foundation policy for quadrotor control. Our method enables training a single, end-to-end neural-network policy to control a wide variety of quadrotors. We test 10 different real quadrotors from 32 g to 2.4 kg that also differ in motor type (brushed vs. brushless), frame type (soft vs. rigid), propeller type (2/3/4-blade), and flight controller (PX4/Betaflight/Crazyflie/M5StampFly). We find that a tiny, three-layer policy with only 2084 parameters is sufficient for zero-shot adaptation to a wide variety of platforms. The adaptation through In-Context Learning is made possible by using a recurrence in the hidden layer. The policy is trained through a novel Meta-Imitation Learning algorithm, where we sample 1000 quadrotors and train a teacher policy for each of them using Reinforcement Learning. Subsequently, the 1000 teachers are distilled into a single, adaptive student policy. We find that within milliseconds, the resulting foundation policy adapts zero-shot to unseen quadrotors. We extensively test the capabilities of the foundation policy under numerous conditions (trajectory tracking, indoor/outdoor, wind disturbance, poking, different propellers).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/rl-tools/raptor", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.12541", "title": "zELO: ELO-inspired Training Method for Rerankers and Embedding Models", "authors": [ "Nicholas Pipitone", "Ghita Houir Alami", "Advaith Avadhanam", "Anton Kaminskyi", "Ashley Khoo" ], "github": "https://github.com/zeroentropy-ai/zbench", "project_page": "", "abstract": "We introduce a novel training methodology named zELO, which optimizes retrieval performance via the analysis that ranking tasks are statically equivalent to a Thurstone model. Based on the zELO method, we use unsupervised data in order train a suite of state-of-the-art open-weight reranker models: zerank-1 and zerank-1-small. These models achieve the highest retrieval scores in multiple domains, including finance, legal, code, and STEM, outperforming closed-source proprietary rerankers on both NDCG@10 and Recall. These models also demonstrate great versatility, maintaining their 0-shot performance on out-of-domain and private customer datasets. The training data included 112,000 queries and 100 documents per query, and was trained end-to-end from unannotated queries and documents in less than 10,000 H100-hours.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zeroentropy-ai/zbench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-17", "arxiv_id": "2509.10696", "title": "Struct-Bench: A Benchmark for Differentially Private Structured Text Generation", "authors": [ "Shuaiqi Wang", "Vikas Raunak", "Arturs Backurs", "Victor Reis", "Pei Zhou", "Sihao Chen", "Longqi Yang", "Zinan Lin", "Sergey Yekhanin", "Giulia Fanti" ], "github": "https://github.com/struct-bench/structpe", "project_page": "", "abstract": "Differentially private (DP) synthetic data generation is a promising technique for utilizing private datasets that otherwise cannot be exposed for model training or other analytics. While much research literature has focused on generating private unstructured text and image data, in enterprise settings, structured data (e.g., tabular) is more common, often including natural language fields or components. Existing synthetic data evaluation techniques (e.g., FID) struggle to capture the structural properties and correlations of such datasets. In this work, we propose Struct-Bench, a framework and benchmark for evaluating synthetic datasets derived from structured datasets that contain natural language data. The Struct-Bench framework requires users to provide a representation of their dataset structure as a Context-Free Grammar (CFG). Our benchmark comprises 5 real-world and 2 synthetically generated datasets, each annotated with CFGs. We show that these datasets demonstrably present a great challenge even for state-of-the-art DP synthetic data generation methods. Struct-Bench also includes reference implementations of different metrics and a leaderboard, thereby providing researchers a standardized evaluation platform to benchmark and investigate privacy-preserving synthetic data generation methods. Further, we also present a case study showing how to use Struct-Bench to improve the synthetic data quality of Private Evolution (PE) on structured data. The benchmark and the leaderboard have been publicly made available at https://struct-bench.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/struct-bench/structpe", "github_api_added_by": "auto" }, { "date": "2025-09-17", "arxiv_id": "2509.10706", "title": "Sound Matching an Analogue Levelling Amplifier Using the Newton-Raphson Method", "authors": [ "Chin-Yun Yu", "Gy\u00f6rgy Fazekas" ], "github": "https://github.com/aim-qmul/4a2a", "project_page": "", "abstract": "Automatic differentiation through digital signal processing algorithms for virtual analogue modelling has recently gained popularity. These algorithms are typically more computationally efficient than black-box neural networks that rely on dense matrix multiplications. Due to their differentiable nature, they can be integrated with neural networks and jointly trained using gradient descent algorithms, resulting in more efficient systems. Furthermore, signal processing algorithms have significantly fewer parameters than neural networks, allowing the application of the Newton-Raphson method. This method offers faster and more robust convergence than gradient descent at the cost of quadratic storage. This paper presents a method to emulate analogue levelling amplifiers using a feed-forward digital compressor with parameters optimised via the Newton-Raphson method. We demonstrate that a digital compressor can successfully approximate the behaviour of our target unit, the Teletronix LA-2A. Different strategies for computing the Hessian matrix are benchmarked. We leverage parallel algorithms for recursive filters to achieve efficient training on modern GPUs. The resulting model is made into a VST plugin and is open-sourced at https://github.com/aim-qmul/4a2a.", "github_manual": "", "github_regex": "https://github.com/aim-qmul/4a2a", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.14232", "title": "GenExam: A Multidisciplinary Text-to-Image Exam", "authors": [ "Zhaokai Wang", "Penghao Yin", "Xiangyu Zhao", "Changyao Tian", "Yu Qiao", "Wenhai Wang", "Jifeng Dai", "Gen Luo" ], "github": "https://github.com/OpenGVLab/GenExam", "project_page": "", "abstract": "Exams are a fundamental test of expert-level intelligence and require integrated understanding, reasoning, and generation. Existing exam-style benchmarks mainly focus on understanding and reasoning tasks, and current generation benchmarks emphasize the illustration of world knowledge and visual concepts, neglecting the evaluation of rigorous drawing exams. We introduce GenExam, the first benchmark for multidisciplinary text-to-image exams, featuring 1,000 samples across 10 subjects with exam-style prompts organized under a four-level taxonomy. Each problem is equipped with ground-truth images and fine-grained scoring points to enable a precise evaluation of semantic correctness and visual plausibility. Experiments show that even state-of-the-art models such as GPT-Image-1 and Gemini-2.5-Flash-Image achieve less than 15% strict scores, and most models yield almost 0%, suggesting the great challenge of our benchmark. By framing image generation as an exam, GenExam offers a rigorous assessment of models' ability to integrate knowledge, reasoning, and generation, providing insights on the path to general AGI.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenGVLab/GenExam", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.13761", "title": "THOR: Tool-Integrated Hierarchical Optimization via RL for Mathematical Reasoning", "authors": [ "Qikai Chang", "Zhenrong Zhang", "Pengfei Hu", "Jiefeng Ma", "Yicheng Pan", "Jianshu Zhang", "Jun Du", "Quan Liu", "Jianqing Gao" ], "github": "https://github.com/JingMog/THOR", "project_page": "", "abstract": "Large Language Models (LLMs) have made remarkable progress in mathematical reasoning, but still continue to struggle with high-precision tasks like numerical computation and formal symbolic manipulation. Integrating external tools has emerged as a promising approach to bridge this gap. Despite recent advances, existing methods struggle with three key challenges: constructing tool-integrated reasoning data, performing fine-grained optimization, and enhancing inference. To overcome these limitations, we propose THOR (Tool-Integrated Hierarchical Optimization via RL). First, we introduce TIRGen, a multi-agent actor-critic-based pipeline for constructing high-quality datasets of tool-integrated reasoning paths, aligning with the policy and generalizing well across diverse models. Second, to perform fine-grained hierarchical optimization, we introduce an RL strategy that jointly optimizes for both trajectory-level problem solving and step-level code generation. This is motivated by our key insight that the success of an intermediate tool call is a strong predictor of the final answer's correctness. Finally, THOR incorporates a self-correction mechanism that leverages immediate tool feedback to dynamically revise erroneous reasoning paths during inference. Our approach demonstrates strong generalization across diverse models, performing effectively in both reasoning and non-reasoning models. It further achieves state-of-the-art performance for models of a similar scale on multiple mathematical benchmarks, while also delivering consistent improvements on code benchmarks. Our code will be publicly available at https://github.com/JingMog/THOR.", "github_manual": "", "github_regex": "https://github.com/JingMog/THOR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.14033", "title": "SAIL-VL2 Technical Report", "authors": [ "Weijie Yin", "Yongjie Ye", "Fangxun Shu", "Yue Liao", "Zijian Kang", "Hongyuan Dong", "Haiyang Yu", "Dingkang Yang", "Jiacong Wang", "Han Wang", "Wenzhuo Liu", "Xiao Liang", "Shuicheng Yan", "Chao Feng" ], "github": "", "project_page": "", "abstract": "We introduce SAIL-VL2, an open-suite vision-language foundation model (LVM) for comprehensive multimodal understanding and reasoning. As the successor to SAIL-VL, SAIL-VL2 achieves state-of-the-art performance at the 2B and 8B parameter scales across diverse image and video benchmarks, demonstrating strong capabilities from fine-grained perception to complex reasoning. Three core innovations drive its effectiveness. First, a large-scale data curation pipeline with scoring and filtering strategies enhances both quality and distribution across captioning, OCR, QA, and video data, improving training efficiency. Second, a progressive training framework begins with a powerful pre-trained vision encoder (SAIL-ViT), advances through multimodal pre-training, and culminates in a thinking-fusion SFT-RL hybrid paradigm that systematically strengthens model capabilities. Third, architectural advances extend beyond dense LLMs to efficient sparse Mixture-of-Experts (MoE) designs. With these contributions, SAIL-VL2 demonstrates competitive performance across 106 datasets and achieves state-of-the-art results on challenging reasoning benchmarks such as MMMU and MathVista. Furthermore, on the OpenCompass leaderboard, SAIL-VL2-2B ranks first among officially released open-source models under the 4B parameter scale, while serving as an efficient and extensible foundation for the open-source multimodal community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.14142", "title": "MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods, Results, Discussion, and Outlook", "authors": [ "Peng Xu", "Shengwu Xiong", "Jiajun Zhang", "Yaxiong Chen", "Bowen Zhou", "Chen Change Loy", "David A. Clifton", "Kyoung Mu Lee", "Luc Van Gool", "Ruiming He", "Ruilin Yao", "Xinwei Long", "Jirui Huang", "Kai Tian", "Sa Yang", "Yihua Shao", "Jin Feng", "Yue Zhong", "Jiakai Zhou", "Cheng Tang", "Tianyu Zou", "Yifang Zhang", "Junming Liang", "Guoyou Li", "Zhaoxiang Wang", "Qiang Zhou", "Yichen Zhao", "Shili Xiong", "Hyeongjin Nam", "Jaerin Lee", "Jaeyoung Chung", "JoonKyu Park", "Junghun Oh", "Kanggeon Lee", "Wooseok Lee", "Juneyoung Ro", "Turghun Osman", "Can Hu", "Chaoyang Liao", "Cheng Chen", "Chengcheng Han", "Chenhao Qiu", "Chong Peng", "Cong Xu", "Dailin Li", "Feiyu Wang", "Feng Gao", "Guibo Zhu", "Guopeng Tang", "Haibo Lu", "Han Fang", "Han Qi", "Hanxiao Wu", "Haobo Cheng", "Hongbo Sun", "Hongyao Chen", "Huayong Hu", "Hui Li", "Jiaheng Ma", "Jiang Yu", "Jianing Wang", "Jie Yang", "Jing He", "Jinglin Zhou", "Jingxuan Li", "Josef Kittler", "Lihao Zheng", "Linnan Zhao", "Mengxi Jia", "Muyang Yan", "Nguyen Thanh Thien", "Pu Luo", "Qi Li", "Shien Song", "Shijie Dong", "Shuai Shao", "Shutao Li", "Taofeng Xue", "Tianyang Xu", "Tianyi Gao", "Tingting Li", "Wei Zhang", "Weiyang Su", "Xiaodong Dong", "Xiao-Jun Wu", "Xiaopeng Zhou", "Xin Chen", "Xin Wei", "Xinyi You", "Xudong Kang", "Xujie Zhou", "Xusheng Liu", "Yanan Wang", "Yanbin Huang", "Yang Liu", "Yang Yang", "Yanglin Deng", "Yashu Kang", "Ye Yuan", "Yi Wen", "Yicen Tian", "Yilin Tao", "Yin Tang", "Yipeng Lin", "Yiqing Wang", "Yiting Xi", "Yongkang Yu", "Yumei Li", "Yuxin Qin", "Yuying Chen", "Yuzhe Cen", "Zhaofan Zou", "Zhaohong Liu", "Zhehao Shen", "Zhenglin Du", "Zhengyang Li", "Zhenni Huang", "Zhenwei Shao", "Zhilong Song", "Zhiyong Feng", "Zhiyu Wang", "Zhou Yu", "Ziang Li", "Zihan Zhai", "Zijian Zhang", "Ziyang Peng", "Ziyun Xiao", "Zongshu Li" ], "github": "", "project_page": "", "abstract": "This paper reviews the MARS2 2025 Challenge on Multimodal Reasoning. We aim to bring together different approaches in multimodal machine learning and LLMs via a large benchmark. We hope it better allows researchers to follow the state-of-the-art in this very dynamic area. Meanwhile, a growing number of testbeds have boosted the evolution of general-purpose large language models. Thus, this year's MARS2 focuses on real-world and specialized scenarios to broaden the multimodal reasoning applications of MLLMs. Our organizing team released two tailored datasets Lens and AdsQA as test sets, which support general reasoning in 12 daily scenarios and domain-specific reasoning in advertisement videos, respectively. We evaluated 40+ baselines that include both generalist MLLMs and task-specific models, and opened up three competition tracks, i.e., Visual Grounding in Real-world Scenarios (VG-RS), Visual Question Answering with Spatial Awareness (VQA-SA), and Visual Reasoning in Creative Advertisement Videos (VR-Ads). Finally, 76 teams from the renowned academic and industrial institutions have registered and 40+ valid submissions (out of 1200+) have been included in our ranking lists. Our datasets, code sets (40+ baselines and 15+ participants' methods), and rankings are publicly available on the MARS2 workshop website and our GitHub organization page https://github.com/mars2workshop/, where our updates and announcements of upcoming events will be continuously provided.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.14055", "title": "Wan-Animate: Unified Character Animation and Replacement with Holistic Replication", "authors": [ "Gang Cheng", "Xin Gao", "Li Hu", "Siqi Hu", "Mingyang Huang", "Chaonan Ji", "Ju Li", "Dechao Meng", "Jinwei Qi", "Penchong Qiao", "Zhen Shen", "Yafei Song", "Ke Sun", "Linrui Tian", "Feng Wang", "Guangyuan Wang", "Qi Wang", "Zhongjian Wang", "Jiayu Xiao", "Sheng Xu", "Bang Zhang", "Peng Zhang", "Xindi Zhang", "Zhe Zhang", "Jingren Zhou", "Lian Zhuo" ], "github": "", "project_page": "", "abstract": "We introduce Wan-Animate, a unified framework for character animation and replacement. Given a character image and a reference video, Wan-Animate can animate the character by precisely replicating the expressions and movements of the character in the video to generate high-fidelity character videos. Alternatively, it can integrate the animated character into the reference video to replace the original character, replicating the scene's lighting and color tone to achieve seamless environmental integration. Wan-Animate is built upon the Wan model. To adapt it for character animation tasks, we employ a modified input paradigm to differentiate between reference conditions and regions for generation. This design unifies multiple tasks into a common symbolic representation. We use spatially-aligned skeleton signals to replicate body motion and implicit facial features extracted from source images to reenact expressions, enabling the generation of character videos with high controllability and expressiveness. Furthermore, to enhance environmental integration during character replacement, we develop an auxiliary Relighting LoRA. This module preserves the character's appearance consistency while applying the appropriate environmental lighting and color tone. Experimental results demonstrate that Wan-Animate achieves state-of-the-art performance. We are committed to open-sourcing the model weights and its source code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.13450", "title": "SteeringControl: Holistic Evaluation of Alignment Steering in LLMs", "authors": [ "Vincent Siu", "Nicholas Crispino", "David Park", "Nathan W. Henry", "Zhun Wang", "Yang Liu", "Dawn Song", "Chenguang Wang" ], "github": "https://github.com/wang-research-lab/SteeringControl.git", "project_page": "", "abstract": "We introduce SteeringControl, a benchmark for evaluating representation steering methods across core alignment objectives--bias, harmful generation, and hallucination--and their effects on secondary behaviors such as sycophancy and commonsense morality. While prior alignment work often highlights truthfulness or reasoning ability to demonstrate the side effects of representation steering, we find there are many unexplored tradeoffs not yet understood in a systematic way. We collect a dataset of safety-relevant primary and secondary behaviors to evaluate steering effectiveness and behavioral entanglement centered around five popular steering methods. To enable this, we craft a modular steering framework based on unique components that serve as the building blocks of many existing methods. Our results on Qwen-2.5-7B and Llama-3.1-8B find that strong steering performance is dependent on the specific combination of steering method, model, and targeted behavior, and that severe concept entanglement can result from poor combinations of these three as well. We release our code here: https://github.com/wang-research-lab/SteeringControl.git.", "github_manual": "", "github_regex": "https://github.com/wang-research-lab/SteeringControl.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.14008", "title": "Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale", "authors": [ "Hasan Abed Al Kader Hammoud", "Mohammad Zbeeb", "Bernard Ghanem" ], "github": "", "project_page": "", "abstract": "We present Hala, a family of Arabic-centric instruction and translation models built with our translate-and-tune pipeline. We first compress a strong ARleftrightarrowEN teacher to FP8 (yielding sim2times higher throughput with no quality loss) and use it to create high-fidelity bilingual supervision. A lightweight language model LFM2-1.2B is then fine-tuned on this data and used to translate high-quality English instruction sets into Arabic, producing a million-scale corpus tailored to instruction following. We train Hala models at 350M, 700M, 1.2B, and 9B parameters, and apply slerp merging to balance Arabic specialization with base-model strengths. On Arabic-centric benchmarks, Hala achieves state-of-the-art results within both the \"nano\" (leq2B) and \"small\" (7-9B) categories, outperforming their bases. We release models, data, evaluation, and recipes to accelerate research in Arabic NLP.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.13755", "title": "Scrub It Out! Erasing Sensitive Memorization in Code Language Models via Machine Unlearning", "authors": [ "Zhaoyang Chu", "Yao Wan", "Zhikun Zhang", "Di Wang", "Zhou Yang", "Hongyu Zhang", "Pan Zhou", "Xuanhua Shi", "Hai Jin", "David Lo" ], "github": "", "project_page": "", "abstract": "While Code Language Models (CLMs) have demonstrated superior performance in software engineering tasks such as code generation and summarization, recent empirical studies reveal a critical privacy vulnerability: these models exhibit unintended memorization of sensitive training data, enabling verbatim reproduction of confidential information when specifically prompted. To address this issue, several approaches, including training data de-duplication and differential privacy augmentation, have been proposed. However, these methods require full-model retraining for deployed CLMs, which incurs substantial computational costs. In this paper, we aim to answer the following research question: Can sensitive information memorized by CLMs be erased effectively and efficiently? We conduct a pioneering investigation into erasing sensitive memorization in CLMs through machine unlearning - a post-hoc modification method that removes specific information from trained models without requiring full retraining. Specifically, we first quantify the memorization risks of sensitive data within CLM training datasets and curate a high-risk dataset of 50,000 sensitive memorized samples as unlearning targets. We study two widely used gradient ascent-based unlearning approaches: the vanilla and constraint-based methods, and introduce CodeEraser, an advanced variant that selectively unlearns sensitive memorized segments in code while preserving the structural integrity and functional correctness of the surrounding code. Extensive experiments on three families of CLMs, i.e., CodeParrot, CodeGen-Mono, and Qwen2.5-Coder, validate the effectiveness and efficiency of CodeEraser in erasing targeted sensitive memorization while maintaining model utility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.12989", "title": "PANORAMA: The Rise of Omnidirectional Vision in the Embodied AI Era", "authors": [ "Xu Zheng", "Chenfei Liao", "Ziqiao Weng", "Kaiyu Lei", "Zihao Dongfang", "Haocong He", "Yuanhuiyi Lyu", "Lutao Jiang", "Lu Qi", "Li Chen", "Danda Pani Paudel", "Kailun Yang", "Linfeng Zhang", "Luc Van Gool", "Xuming Hu" ], "github": "", "project_page": "", "abstract": "Omnidirectional vision, using 360-degree vision to understand the environment, has become increasingly critical across domains like robotics, industrial inspection, and environmental monitoring. Compared to traditional pinhole vision, omnidirectional vision provides holistic environmental awareness, significantly enhancing the completeness of scene perception and the reliability of decision-making. However, foundational research in this area has historically lagged behind traditional pinhole vision. This talk presents an emerging trend in the embodied AI era: the rapid development of omnidirectional vision, driven by growing industrial demand and academic interest. We highlight recent breakthroughs in omnidirectional generation, omnidirectional perception, omnidirectional understanding, and related datasets. Drawing on insights from both academia and industry, we propose an ideal panoramic system architecture in the embodied AI era, PANORAMA, which consists of four key subsystems. Moreover, we offer in-depth opinions related to emerging trends and cross-community impacts at the intersection of panoramic vision and embodied AI, along with the future roadmap and open challenges. This overview synthesizes state-of-the-art advancements and outlines challenges and opportunities for future research in building robust, general-purpose omnidirectional AI systems in the embodied AI era.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.13683", "title": "Improving Context Fidelity via Native Retrieval-Augmented Reasoning", "authors": [ "Suyuchen Wang", "Jinlin Wang", "Xinyu Wang", "Shiqi Li", "Xiangru Tang", "Sirui Hong", "Xiao-Wen Chang", "Chenglin Wu", "Bang Liu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on provided information. Existing approaches either rely on expensive supervised fine-tuning to generate evidence post-answer or train models to perform web searches without necessarily improving utilization of the given context. We propose CARE, a novel native retrieval-augmented reasoning framework that teaches LLMs to explicitly integrate in-context evidence within their reasoning process with the model's own retrieval capabilities. Our method requires limited labeled evidence data while significantly enhancing both retrieval accuracy and answer generation performance through strategically retrieved in-context tokens in the reasoning chain. Extensive experiments on multiple real-world and counterfactual QA benchmarks demonstrate that our approach substantially outperforms supervised fine-tuning, traditional retrieval-augmented generation methods, and external retrieval solutions. This work represents a fundamental advancement in making LLMs more accurate, reliable, and efficient for knowledge-intensive tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2508.14880", "title": "MedReseacher-R1: Expert-Level Medical Deep Researcher via A Knowledge-Informed Trajectory Synthesis Framework", "authors": [ "Ailing Yu", "Lan Yao", "Jingnan Liu", "Zhe Chen", "Jiajun Yin", "Yuan Wang", "Xinhao Liao", "Zhiling Ye", "Ji Li", "Yun Yue", "Hansong Xiao", "Hualei Zhou", "Chunxiao Guo", "Peng Wei", "Jinjie Gu" ], "github": "https://github.com/AQ-MedAI/MedResearcher-R1", "project_page": "", "abstract": "Recent developments in Large Language Model (LLM)-based agents have shown impressive capabilities spanning multiple domains, exemplified by deep research systems that demonstrate superior performance on complex information-seeking and synthesis tasks. While general-purpose deep research agents have shown impressive capabilities, they struggle significantly with medical domain challenges, as evidenced by leading proprietary systems achieving limited accuracy on complex medical benchmarks. The key limitations are: (1) the model lacks sufficient dense medical knowledge for clinical reasoning, and (2) the framework is constrained by the absence of specialized retrieval tools tailored for medical contexts.We present a medical deep research agent that addresses these challenges through two core innovations. First, we develop a novel data synthesis framework using medical knowledge graphs, extracting the longest chains from subgraphs around rare medical entities to generate complex multi-hop question-answer pairs. Second, we integrate a custom-built private medical retrieval engine alongside general-purpose tools, enabling accurate medical information synthesis. Our approach generates 2100+ diverse trajectories across 12 medical specialties, each averaging 4.2 tool interactions.Through a two-stage training paradigm combining supervised fine-tuning and online reinforcement learning with composite rewards, our MedResearcher-R1-32B model demonstrates exceptional performance, establishing new state-of-the-art results on medical benchmarks while maintaining competitive performance on general deep research tasks. Our work demonstrates that strategic domain-specific innovations in architecture, tool design, and training data construction can enable smaller open-source models to outperform much larger proprietary systems in specialized domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AQ-MedAI/MedResearcher-R1", "github_api_added_by": "auto" }, { "date": "2025-09-18", "arxiv_id": "2509.13523", "title": "AERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions", "authors": [ "V\u00e4in\u00f6 Hatanp\u00e4\u00e4", "Eugene Ku", "Jason Stock", "Murali Emani", "Sam Foreman", "Chunyong Jung", "Sandeep Madireddy", "Tung Nguyen", "Varuni Sastry", "Ray A. O. Sinurat", "Sam Wheeler", "Huihuo Zheng", "Troy Arcomano", "Venkatram Vishwanath", "Rao Kotamarthi" ], "github": "https://github.com/intel/intel-extension-for-pytorch", "project_page": "", "abstract": "Generative machine learning offers new opportunities to better understand complex Earth system dynamics. Recent diffusion-based methods address spectral biases and improve ensemble calibration in weather forecasting compared to deterministic methods, yet have so far proven difficult to scale stably at high resolutions. We introduce AERIS, a 1.3 to 80B parameter pixel-level Swin diffusion transformer to address this gap, and SWiPe, a generalizable technique that composes window parallelism with sequence and pipeline parallelism to shard window-based transformers without added communication cost or increased global batch size. On Aurora (10,080 nodes), AERIS sustains 10.21 ExaFLOPS (mixed precision) and a peak performance of 11.21 ExaFLOPS with 1 times 1 patch size on the 0.25{\\deg} ERA5 dataset, achieving 95.5% weak scaling efficiency, and 81.6% strong scaling efficiency. AERIS outperforms the IFS ENS and remains stable on seasonal scales to 90 days, highlighting the potential of billion-parameter diffusion models for weather and climate prediction.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/intel/intel-extension-for-pytorch", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.14026", "title": "Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks", "authors": [ "Jiun-Cheng Jiang", "Morris Yu-Chao Huang", "Tianlong Chen", "Hsi-Sheng Goan" ], "github": "https://github.com/Jim137/qkan", "project_page": "", "abstract": "Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions. We unify these directions by introducing quantum variational activation functions (QVAFs), realized through single-qubit data re-uploading circuits called DatA Re-Uploading ActivatioNs (DARUANs). We show that DARUAN with trainable weights in data pre-processing possesses an exponentially growing frequency spectrum with data repetitions, enabling an exponential reduction in parameter size compared with Fourier-based activations without loss of expressivity. Embedding DARUAN into KANs yields quantum-inspired KANs (QKANs), which retain the interpretability of KANs while improving their parameter efficiency, expressivity, and generalization. We further introduce two novel techniques to enhance scalability, feasibility and computational efficiency, such as layer extension and hybrid QKANs (HQKANs) as drop-in replacements of multi-layer perceptrons (MLPs) for feed-forward networks in large-scale models. We provide theoretical analysis and extensive experiments on function regression, image classification, and autoregressive generative language modeling, demonstrating the efficiency and scalability of QKANs. DARUANs and QKANs offer a promising direction for advancing quantum machine learning on both noisy intermediate-scale quantum (NISQ) hardware and classical quantum simulators.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Jim137/qkan", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.13353", "title": "Hybrid Quantum-Classical Model for Image Classification", "authors": [ "Muhammad Adnan Shahzad" ], "github": "", "project_page": "", "abstract": "This study presents a systematic comparison between hybrid quantum-classical neural networks and purely classical models across three benchmark datasets (MNIST, CIFAR100, and STL10) to evaluate their performance, efficiency, and robustness. The hybrid models integrate parameterized quantum circuits with classical deep learning architectures, while the classical counterparts use conventional convolutional neural networks (CNNs). Experiments were conducted over 50 training epochs for each dataset, with evaluations on validation accuracy, test accuracy, training time, computational resource usage, and adversarial robustness (tested with epsilon=0.1 perturbations).Key findings demonstrate that hybrid models consistently outperform classical models in final accuracy, achieving {99.38\\% (MNIST), 41.69\\% (CIFAR100), and 74.05\\% (STL10) validation accuracy, compared to classical benchmarks of 98.21\\%, 32.25\\%, and 63.76\\%, respectively. Notably, the hybrid advantage scales with dataset complexity, showing the most significant gains on CIFAR100 (+9.44\\%) and STL10 (+10.29\\%). Hybrid models also train 5--12times faster (e.g., 21.23s vs. 108.44s per epoch on MNIST) and use 6--32\\% fewer parameters} while maintaining superior generalization to unseen test data.Adversarial robustness tests reveal that hybrid models are significantly more resilient on simpler datasets (e.g., 45.27\\% robust accuracy on MNIST vs. 10.80\\% for classical) but show comparable fragility on complex datasets like CIFAR100 (sim1\\% robustness for both). Resource efficiency analyses indicate that hybrid models consume less memory (4--5GB vs. 5--6GB for classical) and lower CPU utilization (9.5\\% vs. 23.2\\% on average).These results suggest that hybrid quantum-classical architectures offer compelling advantages in accuracy, training efficiency, and parameter scalability, particularly for complex vision tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.13642", "title": "LLM-I: LLMs are Naturally Interleaved Multimodal Creators", "authors": [ "Zirun Guo", "Feng Zhang", "Kai Jia", "Tao Jin" ], "github": "https://github.com/ByteDance-BandAI/LLM-I", "project_page": "", "abstract": "We propose LLM-Interleaved (LLM-I), a flexible and dynamic framework that reframes interleaved image-text generation as a tool-use problem. LLM-I is designed to overcome the \"one-tool\" bottleneck of current unified models, which are limited to synthetic imagery and struggle with tasks requiring factual grounding or programmatic precision. Our framework empowers a central LLM or MLLM agent to intelligently orchestrate a diverse toolkit of specialized visual tools, including online image search, diffusion-based generation, code execution, and image editing. The agent is trained to select and apply these tools proficiently via a Reinforcement Learning (RL) framework that features a hybrid reward system combining rule-based logic with judgments from LLM and MLLM evaluators. Trained on a diverse new dataset using four different model backbones, LLM-I demonstrates state-of-the-art performance, outperforming existing methods by a large margin across four benchmarks. We also introduce a novel test-time scaling strategy that provides further performance gains. Project Page: https://github.com/ByteDance-BandAI/LLM-I.", "github_manual": "", "github_regex": "https://github.com/ByteDance-BandAI/LLM-I", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.14180", "title": "Synthesizing Behaviorally-Grounded Reasoning Chains: A Data-Generation Framework for Personal Finance LLMs", "authors": [ "Akhil Theerthala" ], "github": "", "project_page": "", "abstract": "Personalized financial advice requires consideration of user goals, constraints, risk tolerance, and jurisdiction. Prior LLM work has focused on support systems for investors and financial planners. Simultaneously, numerous recent studies examine broader personal finance tasks, including budgeting, debt management, retirement, and estate planning, through agentic pipelines that incur high maintenance costs, yielding less than 25% of their expected financial returns. In this study, we introduce a novel and reproducible framework that integrates relevant financial context with behavioral finance studies to construct supervision data for end-to-end advisors. Using this framework, we create a 19k sample reasoning dataset and conduct a comprehensive fine-tuning of the Qwen-3-8B model on the dataset. Through a held-out test split and a blind LLM-jury study, we demonstrate that through careful data curation and behavioral integration, our 8B model achieves performance comparable to significantly larger baselines (14-32B parameters) across factual accuracy, fluency, and personalization metrics while incurring 80% lower costs than the larger counterparts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.12474", "title": "Image Tokenizer Needs Post-Training", "authors": [ "Kai Qiu", "Xiang Li", "Hao Chen", "Jason Kuen", "Xiaohao Xu", "Jiuxiang Gu", "Yinyi Luo", "Bhiksha Raj", "Zhe Lin", "Marios Savvides" ], "github": "https://github.com/qiuk2/RobusTok", "project_page": "", "abstract": "Recent image generative models typically capture the image distribution in a pre-constructed latent space, relying on a frozen image tokenizer. However, there exists a significant discrepancy between the reconstruction and generation distribution, where current tokenizers only prioritize the reconstruction task that happens before generative training without considering the generation errors during sampling. In this paper, we comprehensively analyze the reason for this discrepancy in a discrete latent space, and, from which, we propose a novel tokenizer training scheme including both main-training and post-training, focusing on improving latent space construction and decoding respectively. During the main training, a latent perturbation strategy is proposed to simulate sampling noises, \\ie, the unexpected tokens generated in generative inference. Specifically, we propose a plug-and-play tokenizer training scheme, which significantly enhances the robustness of tokenizer, thus boosting the generation quality and convergence speed, and a novel tokenizer evaluation metric, \\ie, pFID, which successfully correlates the tokenizer performance to generation quality. During post-training, we further optimize the tokenizer decoder regarding a well-trained generative model to mitigate the distribution difference between generated and reconstructed tokens. With a sim400M generator, a discrete tokenizer trained with our proposed main training achieves a notable 1.60 gFID and further obtains 1.36 gFID with the additional post-training. Further experiments are conducted to broadly validate the effectiveness of our post-training strategy on off-the-shelf discrete and continuous tokenizers, coupled with autoregressive and diffusion-based generators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qiuk2/RobusTok", "github_api_added_by": "auto" }, { "date": "2025-09-18", "arxiv_id": "2509.14284", "title": "The Sum Leaks More Than Its Parts: Compositional Privacy Risks and Mitigations in Multi-Agent Collaboration", "authors": [ "Vaidehi Patil", "Elias Stengel-Eskin", "Mohit Bansal" ], "github": "https://github.com/Vaidehi99/MultiAgentPrivacy", "project_page": "", "abstract": "As large language models (LLMs) become integral to multi-agent systems, new privacy risks emerge that extend beyond memorization, direct inference, or single-turn evaluations. In particular, seemingly innocuous responses, when composed across interactions, can cumulatively enable adversaries to recover sensitive information, a phenomenon we term compositional privacy leakage. We present the first systematic study of such compositional privacy leaks and possible mitigation methods in multi-agent LLM systems. First, we develop a framework that models how auxiliary knowledge and agent interactions jointly amplify privacy risks, even when each response is benign in isolation. Next, to mitigate this, we propose and evaluate two defense strategies: (1) Theory-of-Mind defense (ToM), where defender agents infer a questioner's intent by anticipating how their outputs may be exploited by adversaries, and (2) Collaborative Consensus Defense (CoDef), where responder agents collaborate with peers who vote based on a shared aggregated state to restrict sensitive information spread. Crucially, we balance our evaluation across compositions that expose sensitive information and compositions that yield benign inferences. Our experiments quantify how these defense strategies differ in balancing the privacy-utility trade-off. We find that while chain-of-thought alone offers limited protection to leakage (~39% sensitive blocking rate), our ToM defense substantially improves sensitive query blocking (up to 97%) but can reduce benign task success. CoDef achieves the best balance, yielding the highest Balanced Outcome (79.8%), highlighting the benefit of combining explicit reasoning with defender collaboration. Together, our results expose a new class of risks in collaborative LLM deployments and provide actionable insights for designing safeguards against compositional, context-driven privacy leakage.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Vaidehi99/MultiAgentPrivacy", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-18", "arxiv_id": "2509.11114", "title": "WildSmoke: Ready-to-Use Dynamic 3D Smoke Assets from a Single Video in the Wild", "authors": [ "Yuqiu Liu", "Jialin Song", "Manolis Savva", "Wuyang Chen" ], "github": "", "project_page": "", "abstract": "We propose a pipeline to extract and reconstruct dynamic 3D smoke assets from a single in-the-wild video, and further integrate interactive simulation for smoke design and editing. Recent developments in 3D vision have significantly improved reconstructing and rendering fluid dynamics, supporting realistic and temporally consistent view synthesis. However, current fluid reconstructions rely heavily on carefully controlled clean lab environments, whereas real-world videos captured in the wild are largely underexplored. We pinpoint three key challenges of reconstructing smoke in real-world videos and design targeted techniques, including smoke extraction with background removal, initialization of smoke particles and camera poses, and inferring multi-view videos. Our method not only outperforms previous reconstruction and generation methods with high-quality smoke reconstructions (+2.22 average PSNR on wild videos), but also enables diverse and realistic editing of fluid dynamics by simulating our smoke assets. We provide our models, data, and 4D smoke assets at [https://autumnyq.github.io/WildSmoke](https://autumnyq.github.io/WildSmoke).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.15194", "title": "Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation", "authors": [ "Yujun Zhou", "Zhenwen Liang", "Haolin Liu", "Wenhao Yu", "Kishan Panaganti", "Linfeng Song", "Dian Yu", "Xiangliang Zhang", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly trained with reinforcement learning from verifiable rewards (RLVR), yet real-world deployment demands models that can self-improve without labels or external judges. Existing label-free methods, confidence minimization, self-consistency, or majority-vote objectives, stabilize learning but steadily shrink exploration, causing an entropy collapse: generations become shorter, less diverse, and brittle. Unlike prior approaches such as Test-Time Reinforcement Learning (TTRL), which primarily adapt models to the immediate unlabeled dataset at hand, our goal is broader: to enable general improvements without sacrificing the model's inherent exploration capacity and generalization ability, i.e., evolving. We formalize this issue and propose EVolution-Oriented and Label-free Reinforcement Learning (EVOL-RL), a simple rule that couples stability with variation under a label-free setting. EVOL-RL keeps the majority-voted answer as a stable anchor (selection) while adding a novelty-aware reward that favors responses whose reasoning differs from what has already been produced (variation), measured in semantic space. Implemented with GRPO, EVOL-RL also uses asymmetric clipping to preserve strong signals and an entropy regularizer to sustain search. This majority-for-selection + novelty-for-variation design prevents collapse, maintains longer and more informative chains of thought, and improves both pass@1 and pass@n. EVOL-RL consistently outperforms the majority-only TTRL baseline; e.g., training on label-free AIME24 lifts Qwen3-4B-Base AIME25 pass@1 from TTRL's 4.6% to 16.4%, and pass@16 from 18.5% to 37.9%. EVOL-RL not only prevents diversity collapse but also unlocks stronger generalization across domains (e.g., GPQA). Furthermore, we demonstrate that EVOL-RL also boosts performance in the RLVR setting, highlighting its broad applicability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.15221", "title": "ScaleCUA: Scaling Open-Source Computer Use Agents with Cross-Platform Data", "authors": [ "Zhaoyang Liu", "JingJing Xie", "Zichen Ding", "Zehao Li", "Bowen Yang", "Zhenyu Wu", "Xuehui Wang", "Qiushi Sun", "Shi Liu", "Weiyun Wang", "Shenglong Ye", "Qingyun Li", "Zeyue Tian", "Gen Luo", "Xiangyu Yue", "Biqing Qi", "Kai Chen", "Bowen Zhou", "Yu Qiao", "Qifeng Chen", "Wenhai Wang" ], "github": "https://github.com/OpenGVLab/ScaleCUA", "project_page": "", "abstract": "Vision-Language Models (VLMs) have enabled computer use agents (CUAs) that operate GUIs autonomously, showing great potential, yet progress is limited by the lack of large-scale, open-source computer use data and foundation models. In this work, we introduce ScaleCUA, a step toward scaling open-source CUAs. It offers a large-scale dataset spanning 6 operating systems and 3 task domains, built via a closed-loop pipeline uniting automated agents with human experts. Trained on this scaled-up data, ScaleCUA can operate seamlessly across platforms. Specifically, it delivers strong gains over baselines (+26.6 on WebArena-Lite-v2, +10.7 on ScreenSpot-Pro) and sets new state-of-the-art results (94.4% on MMBench-GUI L1-Hard, 60.6% on OSWorld-G, 47.4% on WebArena-Lite-v2). These findings underscore the power of data-driven scaling for general-purpose computer use agents. We will release data, models, and code to advance future research: https://github.com/OpenGVLab/ScaleCUA.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/ScaleCUA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.14638", "title": "MultiEdit: Advancing Instruction-based Image Editing on Diverse and Challenging Tasks", "authors": [ "Mingsong Li", "Lin Liu", "Hongjun Wang", "Haoxing Chen", "Xijun Gu", "Shizhan Liu", "Dong Gong", "Junbo Zhao", "Zhenzhong Lan", "Jianguo Li" ], "github": "", "project_page": "", "abstract": "Current instruction-based image editing (IBIE) methods struggle with challenging editing tasks, as both editing types and sample counts of existing datasets are limited. Moreover, traditional dataset construction often contains noisy image-caption pairs, which may introduce biases and limit model capabilities in complex editing scenarios. To address these limitations, we introduce MultiEdit, a comprehensive dataset featuring over 107K high-quality image editing samples. It encompasses 6 challenging editing tasks through a diverse collection of 18 non-style-transfer editing types and 38 style transfer operations, covering a spectrum from sophisticated style transfer to complex semantic operations like person reference editing and in-image text editing. We employ a novel dataset construction pipeline that utilizes two multi-modal large language models (MLLMs) to generate visual-adaptive editing instructions and produce high-fidelity edited images, respectively. Extensive experiments demonstrate that fine-tuning foundational open-source models with our MultiEdit-Train set substantially improves models' performance on sophisticated editing tasks in our proposed MultiEdit-Test benchmark, while effectively preserving their capabilities on the standard editing benchmark. We believe MultiEdit provides a valuable resource for advancing research into more diverse and challenging IBIE capabilities. Our dataset is available at https://huggingface.co/datasets/inclusionAI/MultiEdit.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.15212", "title": "RynnVLA-001: Using Human Demonstrations to Improve Robot Manipulation", "authors": [ "Yuming Jiang", "Siteng Huang", "Shengke Xue", "Yaxi Zhao", "Jun Cen", "Sicong Leng", "Kehan Li", "Jiayan Guo", "Kexiang Wang", "Mingxiu Chen", "Fan Wang", "Deli Zhao", "Xin Li" ], "github": "", "project_page": "", "abstract": "This paper presents RynnVLA-001, a vision-language-action(VLA) model built upon large-scale video generative pretraining from human demonstrations. We propose a novel two-stage pretraining methodology. The first stage, Ego-Centric Video Generative Pretraining, trains an Image-to-Video model on 12M ego-centric manipulation videos to predict future frames conditioned on an initial frame and a language instruction. The second stage, Human-Centric Trajectory-Aware Modeling, extends this by jointly predicting future keypoint trajectories, thereby effectively bridging visual frame prediction with action prediction. Furthermore, to enhance action representation, we propose ActionVAE, a variational autoencoder that compresses sequences of actions into compact latent embeddings, reducing the complexity of the VLA output space. When finetuned on the same downstream robotics datasets, RynnVLA-001 achieves superior performance over state-of-the-art baselines, demonstrating that the proposed pretraining strategy provides a more effective initialization for VLA models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.15130", "title": "WorldForge: Unlocking Emergent 3D/4D Generation in Video Diffusion Model via Training-Free Guidance", "authors": [ "Chenxi Song", "Yanming Yang", "Tong Zhao", "Ruibo Li", "Chi Zhang" ], "github": "https://github.com/Westlake-AGI-Lab/WorldForge", "project_page": "", "abstract": "Recent video diffusion models demonstrate strong potential in spatial intelligence tasks due to their rich latent world priors. However, this potential is hindered by their limited controllability and geometric inconsistency, creating a gap between their strong priors and their practical use in 3D/4D tasks. As a result, current approaches often rely on retraining or fine-tuning, which risks degrading pretrained knowledge and incurs high computational costs. To address this, we propose WorldForge, a training-free, inference-time framework composed of three tightly coupled modules. Intra-Step Recursive Refinement introduces a recursive refinement mechanism during inference, which repeatedly optimizes network predictions within each denoising step to enable precise trajectory injection. Flow-Gated Latent Fusion leverages optical flow similarity to decouple motion from appearance in the latent space and selectively inject trajectory guidance into motion-related channels. Dual-Path Self-Corrective Guidance compares guided and unguided denoising paths to adaptively correct trajectory drift caused by noisy or misaligned structural signals. Together, these components inject fine-grained, trajectory-aligned guidance without training, achieving both accurate motion control and photorealistic content generation. Extensive experiments across diverse benchmarks validate our method's superiority in realism, trajectory consistency, and visual fidelity. This work introduces a novel plug-and-play paradigm for controllable video synthesis, offering a new perspective on leveraging generative priors for spatial intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Westlake-AGI-Lab/WorldForge", "github_api_added_by": "auto" }, { "date": "2025-09-19", "arxiv_id": "2509.14476", "title": "AToken: A Unified Tokenizer for Vision", "authors": [ "Jiasen Lu", "Liangchen Song", "Mingze Xu", "Byeongjoo Ahn", "Yanjun Wang", "Chen Chen", "Afshin Dehghan", "Yinfei Yang" ], "github": "", "project_page": "", "abstract": "We present AToken, the first unified visual tokenizer that achieves both high-fidelity reconstruction and semantic understanding across images, videos, and 3D assets. Unlike existing tokenizers that specialize in either reconstruction or understanding for single modalities, AToken encodes these diverse visual inputs into a shared 4D latent space, unifying both tasks and modalities in a single framework. Specifically, we introduce a pure transformer architecture with 4D rotary position embeddings to process visual inputs of arbitrary resolutions and temporal durations. To ensure stable training, we introduce an adversarial-free training objective that combines perceptual and Gram matrix losses, achieving state-of-the-art reconstruction quality. By employing a progressive training curriculum, AToken gradually expands from single images, videos, and 3D, and supports both continuous and discrete latent tokens. AToken achieves 0.21 rFID with 82.2% ImageNet accuracy for images, 3.01 rFVD with 32.6% MSRVTT retrieval for videos, and 28.19 PSNR with 90.9% classification accuracy for 3D. In downstream applications, AToken enables both visual generation tasks (e.g., image generation with continuous and discrete tokens, text-to-video generation, image-to-3D synthesis) and understanding tasks (e.g., multimodal LLMs), achieving competitive performance across all benchmarks. These results shed light on the next-generation multimodal AI systems built upon unified visual tokenization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.13160", "title": "FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoning", "authors": [ "Liang Hu", "Jianpeng Jiao", "Jiashuo Liu", "Yanle Ren", "Zhoufutu Wen", "Kaiyuan Zhang", "Xuanliang Zhang", "Xiang Gao", "Tianci He", "Fei Hu", "Yali Liao", "Zaiyuan Wang", "Chenghao Yang", "Qianyu Yang", "Mingren Yin", "Zhiyuan Zeng", "Ge Zhang", "Xinyi Zhang", "Xiying Zhao", "Zhenwei Zhu", "Hongseok Namkoong", "Wenhao Huang", "Yuwen Tang" ], "github": "", "project_page": "", "abstract": "Search has emerged as core infrastructure for LLM-based agents and is widely viewed as critical on the path toward more general intelligence. Finance is a particularly demanding proving ground: analysts routinely conduct complex, multi-step searches over time-sensitive, domain-specific data, making it ideal for assessing both search proficiency and knowledge-grounded reasoning. Yet no existing open financial datasets evaluate data searching capability of end-to-end agents, largely because constructing realistic, complicated tasks requires deep financial expertise and time-sensitive data is hard to evaluate. We present FinSearchComp, the first fully open-source agent benchmark for realistic, open-domain financial search and reasoning. FinSearchComp comprises three tasks -- Time-Sensitive Data Fetching, Simple Historical Lookup, and Complex Historical Investigation -- closely reproduce real-world financial analyst workflows. To ensure difficulty and reliability, we engage 70 professional financial experts for annotation and implement a rigorous multi-stage quality-assurance pipeline. The benchmark includes 635 questions spanning global and Greater China markets, and we evaluate 21 models (products) on it. Grok 4 (web) tops the global subset, approaching expert-level accuracy. DouBao (web) leads on the Greater China subset. Experimental analyses show that equipping agents with web search and financial plugins substantially improves results on FinSearchComp, and the country origin of models and tools impact performance significantly.By aligning with realistic analyst tasks and providing end-to-end evaluation, FinSearchComp offers a professional, high-difficulty testbed for complex financial search and reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.15207", "title": "FlowRL: Matching Reward Distributions for LLM Reasoning", "authors": [ "Xuekai Zhu", "Daixuan Cheng", "Dinghuai Zhang", "Hengli Li", "Kaiyan Zhang", "Che Jiang", "Youbang Sun", "Ermo Hua", "Yuxin Zuo", "Xingtai Lv", "Qizheng Zhang", "Lin Chen", "Fanghao Shao", "Bo Xue", "Yunchong Song", "Zhenjie Yang", "Ganqu Cui", "Ning Ding", "Jianfeng Gao", "Xiaodong Liu", "Bowen Zhou", "Hongyuan Mei", "Zhouhan Lin" ], "github": "", "project_page": "", "abstract": "We propose FlowRL: matching the full reward distribution via flow balancing instead of maximizing rewards in large language model (LLM) reinforcement learning (RL). Recent advanced reasoning models adopt reward-maximizing methods (\\eg, PPO and GRPO), which tend to over-optimize dominant reward signals while neglecting less frequent but valid reasoning paths, thus reducing diversity. In contrast, we transform scalar rewards into a normalized target distribution using a learnable partition function, and then minimize the reverse KL divergence between the policy and the target distribution. We implement this idea as a flow-balanced optimization method that promotes diverse exploration and generalizable reasoning trajectories. We conduct experiments on math and code reasoning tasks: FlowRL achieves a significant average improvement of 10.0% over GRPO and 5.1% over PPO on math benchmarks, and performs consistently better on code reasoning tasks. These results highlight reward distribution-matching as a key step toward efficient exploration and diverse reasoning in LLM reinforcement learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.15178", "title": "Unleashing the Potential of Multimodal LLMs for Zero-Shot Spatio-Temporal Video Grounding", "authors": [ "Zaiquan Yang", "Yuhao Liu", "Gerhard Hancke", "Rynson W. H. Lau" ], "github": "https://github.com/zaiquanyang/LLaVA_Next_STVG", "project_page": "", "abstract": "Spatio-temporal video grounding (STVG) aims at localizing the spatio-temporal tube of a video, as specified by the input text query. In this paper, we utilize multimodal large language models (MLLMs) to explore a zero-shot solution in STVG. We reveal two key insights about MLLMs: (1) MLLMs tend to dynamically assign special tokens, referred to as grounding tokens, for grounding the text query; and (2) MLLMs often suffer from suboptimal grounding due to the inability to fully integrate the cues in the text query (e.g., attributes, actions) for inference. Based on these insights, we propose a MLLM-based zero-shot framework for STVG, which includes novel decomposed spatio-temporal highlighting (DSTH) and temporal-augmented assembling (TAS) strategies to unleash the reasoning ability of MLLMs. The DSTH strategy first decouples the original query into attribute and action sub-queries for inquiring the existence of the target both spatially and temporally. It then uses a novel logit-guided re-attention (LRA) module to learn latent variables as spatial and temporal prompts, by regularizing token predictions for each sub-query. These prompts highlight attribute and action cues, respectively, directing the model's attention to reliable spatial and temporal related visual regions. In addition, as the spatial grounding by the attribute sub-query should be temporally consistent, we introduce the TAS strategy to assemble the predictions using the original video frames and the temporal-augmented frames as inputs to help improve temporal consistency. We evaluate our method on various MLLMs, and show that it outperforms SOTA methods on three common STVG benchmarks. The code will be available at https://github.com/zaiquanyang/LLaVA_Next_STVG.", "github_manual": "", "github_regex": "https://github.com/zaiquanyang/LLaVA_Next_STVG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.15185", "title": "Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation", "authors": [ "Xiaoyu Yue", "Zidong Wang", "Yuqing Wang", "Wenlong Zhang", "Xihui Liu", "Wanli Ouyang", "Lei Bai", "Luping Zhou" ], "github": "", "project_page": "", "abstract": "Recent studies have demonstrated the importance of high-quality visual representations in image generation and have highlighted the limitations of generative models in image understanding. As a generative paradigm originally designed for natural language, autoregressive models face similar challenges. In this work, we present the first systematic investigation into the mechanisms of applying the next-token prediction paradigm to the visual domain. We identify three key properties that hinder the learning of high-level visual semantics: local and conditional dependence, inter-step semantic inconsistency, and spatial invariance deficiency. We show that these issues can be effectively addressed by introducing self-supervised objectives during training, leading to a novel training framework, Self-guided Training for AutoRegressive models (ST-AR). Without relying on pre-trained representation models, ST-AR significantly enhances the image understanding ability of autoregressive models and leads to improved generation quality. Specifically, ST-AR brings approximately 42% FID improvement for LlamaGen-L and 49% FID improvement for LlamaGen-XL, while maintaining the same sampling strategy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.06482", "title": "FSG-Net: Frequency-Spatial Synergistic Gated Network for High-Resolution Remote Sensing Change Detection", "authors": [ "Zhongxiang Xie", "Shuangxi Miao", "Yuhan Jiang", "Zhewei Zhang", "Jing Yao", "Xuecao Li", "Jianxi Huang", "Pedram Ghamisi" ], "github": "https://github.com/zxXie-Air/FSG-Net", "project_page": "", "abstract": "Change detection from high-resolution remote sensing images lies as a cornerstone of Earth observation applications, yet its efficacy is often compromised by two critical challenges. First, false alarms are prevalent as models misinterpret radiometric variations from temporal shifts (e.g., illumination, season) as genuine changes. Second, a non-negligible semantic gap between deep abstract features and shallow detail-rich features tends to obstruct their effective fusion, culminating in poorly delineated boundaries. To step further in addressing these issues, we propose the Frequency-Spatial Synergistic Gated Network (FSG-Net), a novel paradigm that aims to systematically disentangle semantic changes from nuisance variations. Specifically, FSG-Net first operates in the frequency domain, where a Discrepancy-Aware Wavelet Interaction Module (DAWIM) adaptively mitigates pseudo-changes by discerningly processing different frequency components. Subsequently, the refined features are enhanced in the spatial domain by a Synergistic Temporal-Spatial Attention Module (STSAM), which amplifies the saliency of genuine change regions. To finally bridge the semantic gap, a Lightweight Gated Fusion Unit (LGFU) leverages high-level semantics to selectively gate and integrate crucial details from shallow layers. Comprehensive experiments on the CDD, GZ-CD, and LEVIR-CD benchmarks validate the superiority of FSG-Net, establishing a new state-of-the-art with F1-scores of 94.16%, 89.51%, and 91.27%, respectively. The code will be made available at https://github.com/zxXie-Air/FSG-Net after a possible publication.", "github_manual": "", "github_regex": "https://github.com/zxXie-Air/FSG-Net", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.14760", "title": "Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Delibration", "authors": [ "Haoran Zhang", "Yafu Li", "Xuyang Hu", "Dongrui Liu", "Zhilin Wang", "Bo Li", "Yu Cheng" ], "github": "https://github.com/zzzhr97/SpecBench", "project_page": "", "abstract": "Large language models (LLMs) are increasingly applied in diverse real-world scenarios, each governed by bespoke behavioral and safety specifications (spec) custom-tailored by users or organizations. These spec, categorized into safety-spec and behavioral-spec, vary across scenarios and evolve with changing preferences and requirements. We formalize this challenge as specification alignment, focusing on LLMs' ability to follow dynamic, scenario-specific spec from both behavioral and safety perspectives. To address this challenge, we propose Align3, a lightweight method that employs Test-Time Deliberation (TTD) with hierarchical reflection and revision to reason over the specification boundaries. We further present SpecBench, a unified benchmark for measuring specification alignment, covering 5 scenarios, 103 spec, and 1,500 prompts. Experiments on 15 reasoning and 18 instruct models with several TTD methods, including Self-Refine, TPO, and MoreThink, yield three key findings: (i) test-time deliberation enhances specification alignment; (ii) Align3 advances the safety-helpfulness trade-off frontier with minimal overhead; (iii) SpecBench effectively reveals alignment gaps. These results highlight the potential of test-time deliberation as an effective strategy for reasoning over the real-world specification boundaries.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zzzhr97/SpecBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.14977", "title": "EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence", "authors": [ "Chaoyin She", "Ruifang Lu", "Lida Chen", "Wei Wang", "Qinghua Huang" ], "github": "https://github.com/Asunatan/EchoVLM", "project_page": "", "abstract": "Ultrasound imaging has become the preferred imaging modality for early cancer screening due to its advantages of non-ionizing radiation, low cost, and real-time imaging capabilities. However, conventional ultrasound diagnosis heavily relies on physician expertise, presenting challenges of high subjectivity and low diagnostic efficiency. Vision-language models (VLMs) offer promising solutions for this issue, but existing general-purpose models demonstrate limited knowledge in ultrasound medical tasks, with poor generalization in multi-organ lesion recognition and low efficiency across multi-task diagnostics. To address these limitations, we propose EchoVLM, a vision-language model specifically designed for ultrasound medical imaging. The model employs a Mixture of Experts (MoE) architecture trained on data spanning seven anatomical regions. This design enables the model to perform multiple tasks, including ultrasound report generation, diagnosis and visual question-answering (VQA). The experimental results demonstrated that EchoVLM achieved significant improvements of 10.15 and 4.77 points in BLEU-1 scores and ROUGE-1 scores respectively compared to Qwen2-VL on the ultrasound report generation task. These findings suggest that EchoVLM has substantial potential to enhance diagnostic accuracy in ultrasound imaging, thereby providing a viable technical solution for future clinical applications. Source code and model weights are available at https://github.com/Asunatan/EchoVLM.", "github_manual": "", "github_regex": "https://github.com/Asunatan/EchoVLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.15020", "title": "Mind the Gap: A Closer Look at Tokenization for Multiple-Choice Question Answering with LLMs", "authors": [ "Mario Sanz-Guerrero", "Minh Duc Bui", "Katharina von der Wense" ], "github": "", "project_page": "", "abstract": "When evaluating large language models (LLMs) with multiple-choice question answering (MCQA), it is common to end the prompt with the string \"Answer:\" to facilitate automated answer extraction via next-token probabilities. However, there is no consensus on how to tokenize the space following the colon, often overlooked as a trivial choice. In this paper, we uncover accuracy differences of up to 11% due to this (seemingly irrelevant) tokenization variation as well as reshuffled model rankings, raising concerns about the reliability of LLM comparisons in prior work. Surprisingly, we are able to recommend one specific strategy -- tokenizing the space together with the answer letter -- as we observe consistent and statistically significant performance improvements. Additionally, it improves model calibration, enhancing the reliability of the model's confidence estimates. Our findings underscore the importance of careful evaluation design and highlight the need for standardized, transparent evaluation protocols to ensure reliable and comparable results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.10397", "title": "RecoWorld: Building Simulated Environments for Agentic Recommender Systems", "authors": [ "Fei Liu", "Xinyu Lin", "Hanchao Yu", "Mingyuan Wu", "Jianyu Wang", "Qiang Zhang", "Zhuokai Zhao", "Yinglong Xia", "Yao Zhang", "Weiwei Li", "Mingze Gao", "Qifan Wang", "Lizhu Zhang", "Benyu Zhang", "Xiangjun Fan" ], "github": "", "project_page": "", "abstract": "We present RecoWorld, a blueprint for building simulated environments tailored to agentic recommender systems. Such environments give agents a proper training space where they can learn from errors without impacting real users. RecoWorld distinguishes itself with a dual-view architecture: a simulated user and an agentic recommender engage in multi-turn interactions aimed at maximizing user retention. The user simulator reviews recommended items, updates its mindset, and when sensing potential user disengagement, generates reflective instructions. The agentic recommender adapts its recommendations by incorporating these user instructions and reasoning traces, creating a dynamic feedback loop that actively engages users. This process leverages the exceptional reasoning capabilities of modern LLMs. We explore diverse content representations within the simulator, including text-based, multimodal, and semantic ID modeling, and discuss how multi-turn RL enables the recommender to refine its strategies through iterative interactions. RecoWorld also supports multi-agent simulations, allowing creators to simulate the responses of targeted user populations. It marks an important first step toward recommender systems where users and agents collaboratively shape personalized information streams. We envision new interaction paradigms where \"user instructs, recommender responds,\" jointly optimizing user retention and engagement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.10402", "title": "Developer-LLM Conversations: An Empirical Study of Interactions and Generated Code Quality", "authors": [ "Suzhen Zhong", "Ying Zou", "Bram Adams" ], "github": "https://github.com/keras-team/keras", "project_page": "", "abstract": "Large Language Models (LLMs) are becoming integral to modern software development workflows, assisting developers with code generation, API explanation, and iterative problem-solving through natural language conversations. Despite widespread adoption, there is limited understanding of how developers interact with LLMs in practice and how these conversational dynamics influence task outcomes, code quality, and software engineering workflows. To address this, we leverage CodeChat, a large dataset comprising 82,845 real-world developer-LLM conversations, containing 368,506 code snippets generated across over 20 programming languages, derived from the WildChat dataset. We find that LLM responses are substantially longer than developer prompts, with a median token-length ratio of 14:1. Multi-turn conversations account for 68% of the dataset and often evolve due to shifting requirements, incomplete prompts, or clarification requests. Topic analysis identifies web design (9.6% of conversations) and neural network training (8.7% of conversations) as the most frequent LLM-assisted tasks. Evaluation across five languages (i.e., Python, JavaScript, C++, Java, and C#) reveals prevalent and language-specific issues in LLM-generated code: generated Python and JavaScript code often include undefined variables (83.4% and 75.3% of code snippets, respectively); Java code lacks required comments (75.9%); C++ code frequently omits headers (41.1%) and C# code shows unresolved namespaces (49.2%). During a conversation, syntax and import errors persist across turns; however, documentation quality in Java improves by up to 14.7%, and import handling in Python improves by 3.7% over 5 turns. Prompts that point out mistakes in code generated in prior turns and explicitly request a fix are most effective for resolving errors.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/keras-team/keras", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.06216", "title": "Agentic Software Engineering: Foundational Pillars and a Research Roadmap", "authors": [ "Ahmed E. Hassan", "Hao Li", "Dayi Lin", "Bram Adams", "Tse-Hsun Chen", "Yutaro Kashiwa", "Dong Qiu" ], "github": "", "project_page": "", "abstract": "Agentic Software Engineering (SE 3.0) represents a new era where intelligent agents are tasked not with simple code generation, but with achieving complex, goal-oriented SE objectives. To harness these new capabilities while ensuring trustworthiness, we must recognize a fundamental duality within the SE field in the Agentic SE era, comprising two symbiotic modalities: SE for Humans and SE for Agents. This duality demands a radical reimagining of the foundational pillars of SE (actors, processes, tools, and artifacts) which manifest differently across each modality. We propose two purpose-built workbenches to support this vision. The Agent Command Environment (ACE) serves as a command center where humans orchestrate and mentor agent teams, handling outputs such as Merge-Readiness Packs (MRPs) and Consultation Request Packs (CRPs). The Agent Execution Environment (AEE) is a digital workspace where agents perform tasks while invoking human expertise when facing ambiguity or complex trade-offs. This bi-directional partnership, which supports agent-initiated human callbacks and handovers, gives rise to new, structured engineering activities (i.e., processes) that redefine human-AI collaboration, elevating the practice from agentic coding to true agentic software engineering. This paper presents the Structured Agentic Software Engineering (SASE) vision, outlining several of the foundational pillars for the future of SE. The paper culminates in a research roadmap that identifies a few key challenges and opportunities while briefly discussing the resulting impact of this future on SE education. Our goal is not to offer a definitive solution, but to provide a conceptual scaffold with structured vocabulary to catalyze a community-wide dialogue, pushing the SE community to think beyond its classic, human-centric tenets toward a disciplined, scalable, and trustworthy agentic future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.14233", "title": "Apertus: Democratizing Open and Compliant LLMs for Global Language Environments", "authors": [ "Alejandro Hern\u00e1ndez-Cano", "Alexander H\u00e4gele", "Allen Hao Huang", "Angelika Romanou", "Antoni-Joan Solergibert", "Barna Pasztor", "Bettina Messmer", "Dhia Garbaya", "Eduard Frank \u010eurech", "Ido Hakimi", "Juan Garc\u00eda Giraldo", "Mete Ismayilzada", "Negar Foroutan", "Skander Moalla", "Tiancheng Chen", "Vinko Sabol\u010dec", "Yixuan Xu", "Michael Aerni", "Badr AlKhamissi", "Ines Altemir Marinas", "Mohammad Hossein Amani", "Matin Ansaripour", "Ilia Badanin", "Harold Benoit", "Emanuela Boros", "Nicholas Browning", "Fabian B\u00f6sch", "Maximilian B\u00f6ther", "Niklas Canova", "Camille Challier", "Clement Charmillot", "Jonathan Coles", "Jan Deriu", "Arnout Devos", "Lukas Drescher", "Daniil Dzenhaliou", "Maud Ehrmann", "Dongyang Fan", "Simin Fan", "Silin Gao", "Miguel Gila", "Mar\u00eda Grandury", "Diba Hashemi", "Alexander Hoyle", "Jiaming Jiang", "Mark Klein", "Andrei Kucharavy", "Anastasiia Kucherenko", "Frederike L\u00fcbeck", "Roman Machacek", "Theofilos Manitaras", "Andreas Marfurt", "Kyle Matoba", "Simon Matrenok", "Henrique Mendonc\u00e7a", "Fawzi Roberto Mohamed", "Syrielle Montariol", "Luca Mouchel", "Sven Najem-Meyer", "Jingwei Ni", "Gennaro Oliva", "Matteo Pagliardini", "Elia Palme", "Andrei Panferov", "L\u00e9o Paoletti", "Marco Passerini", "Ivan Pavlov", "Auguste Poiroux", "Kaustubh Ponkshe", "Nathan Ranchin", "Javi Rando", "Mathieu Sauser", "Jakhongir Saydaliev", "Muhammad Ali Sayfiddinov", "Marian Schneider", "Stefano Schuppli", "Marco Scialanga", "Andrei Semenov", "Kumar Shridhar", "Raghav Singhal", "Anna Sotnikova", "Alexander Sternfeld", "Ayush Kumar Tarun", "Paul Teiletche", "Jannis Vamvas", "Xiaozhe Yao", "Hao Zhao Alexander Ilic", "Ana Klimovic", "Andreas Krause", "Caglar Gulcehre", "David Rosenthal", "Elliott Ash", "Florian Tram\u00e8r", "Joost VandeVondele", "Livio Veraldi", "Martin Rajman", "Thomas Schulthess", "Torsten Hoefler", "Antoine Bosselut", "Martin Jaggi", "Imanol Schlag" ], "github": "https://github.com/swiss-ai/Apertus-70B-2509", "project_page": "", "abstract": "We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingual representation. Unlike many prior models that release weights without reproducible data pipelines or regard for content-owner rights, Apertus models are pretrained exclusively on openly available data, retroactively respecting robots.txt exclusions and filtering for non-permissive, toxic, and personally identifiable content. To mitigate risks of memorization, we adopt the Goldfish objective during pretraining, strongly suppressing verbatim recall of data while retaining downstream task performance. The Apertus models also expand multilingual coverage, training on 15T tokens from over 1800 languages, with ~40% of pretraining data allocated to non-English content. Released at 8B and 70B scales, Apertus approaches state-of-the-art results among fully open models on multilingual benchmarks, rivalling or surpassing open-weight counterparts. Beyond model weights, we release all scientific artifacts from our development cycle with a permissive license, including data preparation scripts, checkpoints, evaluation suites, and training code, enabling transparent audit and extension.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/swiss-ai/Apertus-70B-2509", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.09307", "title": "Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization", "authors": [ "Zhengzhao Lai", "Youbin Zheng", "Zhenyang Cai", "Haonan Lyu", "Jinpu Yang", "Hongqing Liang", "Yan Hu", "Benyou Wang" ], "github": "https://github.com/FreedomIntelligence/MatCha", "project_page": "", "abstract": "Materials characterization is fundamental to acquiring materials information, revealing the processing-microstructure-property relationships that guide material design and optimization. While multimodal large language models (MLLMs) have recently shown promise in generative and predictive tasks within materials science, their capacity to understand real-world characterization imaging data remains underexplored. To bridge this gap, we present MatCha, the first benchmark for materials characterization image understanding, comprising 1,500 questions that demand expert-level domain expertise. MatCha encompasses four key stages of materials research comprising 21 distinct tasks, each designed to reflect authentic challenges faced by materials scientists. Our evaluation of state-of-the-art MLLMs on MatCha reveals a significant performance gap compared to human experts. These models exhibit degradation when addressing questions requiring higher-level expertise and sophisticated visual perception. Simple few-shot and chain-of-thought prompting struggle to alleviate these limitations. These findings highlight that existing MLLMs still exhibit limited adaptability to real-world materials characterization scenarios. We hope MatCha will facilitate future research in areas such as new material discovery and autonomous scientific agents. MatCha is available at https://github.com/FreedomIntelligence/MatCha.", "github_manual": "", "github_regex": "https://github.com/FreedomIntelligence/MatCha", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-19", "arxiv_id": "2509.13399", "title": "EdiVal-Agent: An Object-Centric Framework for Automated, Scalable, Fine-Grained Evaluation of Multi-Turn Editing", "authors": [ "Tianyu Chen", "Yasi Zhang", "Zhi Zhang", "Peiyu Yu", "Shu Wang", "Zhendong Wang", "Kevin Lin", "Xiaofei Wang", "Zhengyuan Yang", "Linjie Li", "Chung-Ching Lin", "Jianwen Xie", "Oscar Leong", "Lijuan Wang", "Ying Nian Wu", "Mingyuan Zhou" ], "github": "", "project_page": "", "abstract": "Instruction-based image editing has advanced rapidly, yet reliable and interpretable evaluation remains a bottleneck. Current protocols either (i) depend on paired reference images -- resulting in limited coverage and inheriting biases from prior generative models -- or (ii) rely solely on zero-shot vision-language models (VLMs), whose prompt-based assessments of instruction following, content consistency, and visual quality are often imprecise. To address this, we introduce EdiVal-Agent, an automated, scalable, and fine-grained evaluation framework for multi-turn instruction-based editing from an object-centric perspective, supported by a suite of expert tools. Given an image, EdiVal-Agent first decomposes it into semantically meaningful objects, then synthesizes diverse, context-aware editing instructions. For evaluation, it integrates VLMs with open-vocabulary object detectors to assess instruction following, uses semantic-level feature extractors to evaluate content consistency, and leverages human preference models to judge visual quality. We show that combining VLMs with object detectors yields stronger agreement with human judgments in instruction-following evaluation compared to using VLMs alone and CLIP-based metrics. Furthermore, the pipeline's modular design allows future tools to be seamlessly integrated, enhancing evaluation accuracy over time. Instantiating this pipeline, we build EdiVal-Bench, a multi-turn editing benchmark covering 9 instruction types and 11 state-of-the-art editing models spanning autoregressive (AR) (including Nano Banana, GPT-Image-1), flow-matching, and diffusion paradigms. We demonstrate that EdiVal-Agent can be used to identify existing failure modes, thereby informing the development of the next generation of editing models. Project page: https://tianyucodings.github.io/EdiVAL-page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.16197", "title": "MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer", "authors": [ "Yanghao Li", "Rui Qian", "Bowen Pan", "Haotian Zhang", "Haoshuo Huang", "Bowen Zhang", "Jialing Tong", "Haoxuan You", "Xianzhi Du", "Zhe Gan", "Hyunjik Kim", "Chao Jia", "Zhenbang Wang", "Yinfei Yang", "Mingfei Gao", "Zi-Yi Dou", "Wenze Hu", "Chang Gao", "Dongxu Li", "Philipp Dufter", "Zirui Wang", "Guoli Yin", "Zhengdong Zhang", "Chen Chen", "Yang Zhao", "Ruoming Pang", "Zhifeng Chen" ], "github": "", "project_page": "", "abstract": "Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from a performance trade-off between these capabilities. We present Manzano, a simple and scalable unified framework that substantially reduces this tension by coupling a hybrid image tokenizer with a well-curated training recipe. A single shared vision encoder feeds two lightweight adapters that produce continuous embeddings for image-to-text understanding and discrete tokens for text-to-image generation within a common semantic space. A unified autoregressive LLM predicts high-level semantics in the form of text and image tokens, with an auxiliary diffusion decoder subsequently translating the image tokens into pixels. The architecture, together with a unified training recipe over understanding and generation data, enables scalable joint learning of both capabilities. Manzano achieves state-of-the-art results among unified models, and is competitive with specialist models, particularly on text-rich evaluation. Our studies show minimal task conflicts and consistent gains from scaling model size, validating our design choice of a hybrid tokenizer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.16198", "title": "RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation", "authors": [ "Jane Luo", "Xin Zhang", "Steven Liu", "Jie Wu", "Yiming Huang", "Yangyu Huang", "Chengyu Yin", "Ying Xin", "Jianfeng Liu", "Yuefeng Zhan", "Hao Sun", "Qi Chen", "Scarlett Li", "Mao Yang" ], "github": "", "project_page": "", "abstract": "Large language models excel at function- and file-level code generation, yet generating complete repositories from scratch remains a fundamental challenge. This process demands coherent and reliable planning across proposal- and implementation-level stages, while natural language, due to its ambiguity and verbosity, is ill-suited for faithfully representing complex software structures. To address this, we introduce the Repository Planning Graph (RPG), a persistent representation that unifies proposal- and implementation-level planning by encoding capabilities, file structures, data flows, and functions in one graph. RPG replaces ambiguous natural language with an explicit blueprint, enabling long-horizon planning and scalable repository generation. Building on RPG, we develop ZeroRepo, a graph-driven framework for repository generation from scratch. It operates in three stages: proposal-level planning and implementation-level refinement to construct the graph, followed by graph-guided code generation with test validation. To evaluate this setting, we construct RepoCraft, a benchmark of six real-world projects with 1,052 tasks. On RepoCraft, ZeroRepo produces repositories averaging nearly 36K LOC, roughly 3.9times the strongest baseline (Claude Code) and about 64times other baselines. It attains 81.5% functional coverage and a 69.7% pass rate, exceeding Claude Code by 27.3 and 35.8 percentage points, respectively. Further analysis shows that RPG models complex dependencies, enables progressively more sophisticated planning through near-linear scaling, and enhances LLM understanding of repositories, thereby accelerating agent localization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.16127", "title": "BaseReward: A Strong Baseline for Multimodal Reward Model", "authors": [ "Yi-Fan Zhang", "Haihua Yang", "Huanyu Zhang", "Yang Shi", "Zezhou Chen", "Haochen Tian", "Chaoyou Fu", "Haotian Wang", "Kai Wu", "Bo Cui", "Xu Wang", "Jianfei Pan", "Haotian Wang", "Zhang Zhang", "Liang Wang" ], "github": "", "project_page": "", "abstract": "The rapid advancement of Multimodal Large Language Models (MLLMs) has made aligning them with human preferences a critical challenge. Reward Models (RMs) are a core technology for achieving this goal, but a systematic guide for building state-of-the-art Multimodal Reward Models (MRMs) is currently lacking in both academia and industry. Through exhaustive experimental analysis, this paper aims to provide a clear ``recipe'' for constructing high-performance MRMs. We systematically investigate every crucial component in the MRM development pipeline, including reward modeling paradigms (e.g., Naive-RM, Critic-based RM, and Generative RM), reward head architecture, training strategies, data curation (covering over ten multimodal and text-only preference datasets), backbone model and model scale, and ensemble methods. Based on these experimental insights, we introduce BaseReward, a powerful and efficient baseline for multimodal reward modeling. BaseReward adopts a simple yet effective architecture, built upon a {Qwen2.5-VL} backbone, featuring an optimized two-layer reward head, and is trained on a carefully curated mixture of high-quality multimodal and text-only preference data. Our results show that BaseReward establishes a new SOTA on major benchmarks such as MM-RLHF-Reward Bench, VL-Reward Bench, and Multimodal Reward Bench, outperforming previous models. Furthermore, to validate its practical utility beyond static benchmarks, we integrate BaseReward into a real-world reinforcement learning pipeline, successfully enhancing an MLLM's performance across various perception, reasoning, and conversational tasks. This work not only delivers a top-tier MRM but, more importantly, provides the community with a clear, empirically-backed guide for developing robust reward models for the next generation of MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.15123", "title": "RGB-Only Supervised Camera Parameter Optimization in Dynamic Scenes", "authors": [ "Fang Li", "Hao Zhang", "Narendra Ahuja" ], "github": "", "project_page": "", "abstract": "Although COLMAP has long remained the predominant method for camera parameter optimization in static scenes, it is constrained by its lengthy runtime and reliance on ground truth (GT) motion masks for application to dynamic scenes. Many efforts attempted to improve it by incorporating more priors as supervision such as GT focal length, motion masks, 3D point clouds, camera poses, and metric depth, which, however, are typically unavailable in casually captured RGB videos. In this paper, we propose a novel method for more accurate and efficient camera parameter optimization in dynamic scenes solely supervised by a single RGB video. Our method consists of three key components: (1) Patch-wise Tracking Filters, to establish robust and maximally sparse hinge-like relations across the RGB video. (2) Outlier-aware Joint Optimization, for efficient camera parameter optimization by adaptive down-weighting of moving outliers, without reliance on motion priors. (3) A Two-stage Optimization Strategy, to enhance stability and optimization speed by a trade-off between the Softplus limits and convex minima in losses. We visually and numerically evaluate our camera estimates. To further validate accuracy, we feed the camera estimates into a 4D reconstruction method and assess the resulting 3D scenes, and rendered 2D RGB and depth maps. We perform experiments on 4 real-world datasets (NeRF-DS, DAVIS, iPhone, and TUM-dynamics) and 1 synthetic dataset (MPI-Sintel), demonstrating that our method estimates camera parameters more efficiently and accurately with a single RGB video as the only supervision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.15566", "title": "BTL-UI: Blink-Think-Link Reasoning Model for GUI Agent", "authors": [ "Shaojie Zhang", "Ruoceng Zhang", "Pei Fu", "Shaokang Wang", "Jiahui Yang", "Xin Du", "Shiqi Cui", "Bin Qin", "Ying Huang", "Zhenbo Luo", "Jian Luan" ], "github": "https://github.com/xiaomi-research/btl-ui", "project_page": "", "abstract": "In the field of AI-driven human-GUI interaction automation, while rapid advances in multimodal large language models and reinforcement fine-tuning techniques have yielded remarkable progress, a fundamental challenge persists: their interaction logic significantly deviates from natural human-GUI communication patterns. To fill this gap, we propose \"Blink-Think-Link\" (BTL), a brain-inspired framework for human-GUI interaction that mimics the human cognitive process between users and graphical interfaces. The system decomposes interactions into three biologically plausible phases: (1) Blink - rapid detection and attention to relevant screen areas, analogous to saccadic eye movements; (2) Think - higher-level reasoning and decision-making, mirroring cognitive planning; and (3) Link - generation of executable commands for precise motor control, emulating human action selection mechanisms. Additionally, we introduce two key technical innovations for the BTL framework: (1) Blink Data Generation - an automated annotation pipeline specifically optimized for blink data, and (2) BTL Reward -- the first rule-based reward mechanism that enables reinforcement learning driven by both process and outcome. Building upon this framework, we develop a GUI agent model named BTL-UI, which demonstrates consistent state-of-the-art performance across both static GUI understanding and dynamic interaction tasks in comprehensive benchmarks. These results provide conclusive empirical validation of the framework's efficacy in developing advanced GUI Agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xiaomi-research/btl-ui", "github_api_added_by": "auto" }, { "date": "2025-09-22", "arxiv_id": "2509.15496", "title": "Lynx: Towards High-Fidelity Personalized Video Generation", "authors": [ "Shen Sang", "Tiancheng Zhi", "Tianpei Gu", "Jing Liu", "Linjie Luo" ], "github": "", "project_page": "", "abstract": "We present Lynx, a high-fidelity model for personalized video synthesis from a single input image. Built on an open-source Diffusion Transformer (DiT) foundation model, Lynx introduces two lightweight adapters to ensure identity fidelity. The ID-adapter employs a Perceiver Resampler to convert ArcFace-derived facial embeddings into compact identity tokens for conditioning, while the Ref-adapter integrates dense VAE features from a frozen reference pathway, injecting fine-grained details across all transformer layers through cross-attention. These modules collectively enable robust identity preservation while maintaining temporal coherence and visual realism. Through evaluation on a curated benchmark of 40 subjects and 20 unbiased prompts, which yielded 800 test cases, Lynx has demonstrated superior face resemblance, competitive prompt following, and strong video quality, thereby advancing the state of personalized video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.10452", "title": "WhisTLE: Deeply Supervised, Text-Only Domain Adaptation for Pretrained Speech Recognition Transformers", "authors": [ "Akshat Pandey", "Karun Kumar", "Raphael Tang" ], "github": "", "project_page": "", "abstract": "Pretrained automatic speech recognition (ASR) models such as Whisper perform well but still need domain adaptation to handle unseen vocabulary and parlance. In many real-world settings, collecting speech data is impractical, necessitating text-only adaptation. We propose WhisTLE, a deeply supervised, text-only adaptation method for pretrained encoder-decoder ASR models. WhisTLE trains a variational autoencoder (VAE) to model encoder outputs from text and fine-tunes the decoder using the learned text-to-latent encoder, optionally combined with text-to-speech (TTS) adaptation. At inference, the original encoder is restored, incurring no extra runtime cost. Across four out-of-domain datasets and four ASR models, WhisTLE with TTS reduces word error rate (WER) by 12.3% relative to TTS-only adaptation and outperforms all non-WhisTLE baselines in 27 of 32 scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.15937", "title": "A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning", "authors": [ "Shaopeng Zhai", "Qi Zhang", "Tianyi Zhang", "Fuxian Huang", "Haoran Zhang", "Ming Zhou", "Shengzhe Zhang", "Litao Liu", "Sixu Lin", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "Robotic real-world reinforcement learning (RL) with vision-language-action (VLA) models is bottlenecked by sparse, handcrafted rewards and inefficient exploration. We introduce VLAC, a general process reward model built upon InternVL and trained on large scale heterogeneous datasets. Given pairwise observations and a language goal, it outputs dense progress delta and done signal, eliminating task-specific reward engineering, and supports one-shot in-context transfer to unseen tasks and environments. VLAC is trained on vision-language datasets to strengthen perception, dialogic and reasoning capabilities, together with robot and human trajectories data that ground action generation and progress estimation, and additionally strengthened to reject irrelevant prompts as well as detect regression or stagnation by constructing large numbers of negative and semantically mismatched samples. With prompt control, a single VLAC model alternately generating reward and action tokens, unifying critic and policy. Deployed inside an asynchronous real-world RL loop, we layer a graded human-in-the-loop protocol (offline demonstration replay, return and explore, human guided explore) that accelerates exploration and stabilizes early learning. Across four distinct real-world manipulation tasks, VLAC lifts success rates from about 30\\% to about 90\\% within 200 real-world interaction episodes; incorporating human-in-the-loop interventions yields a further 50% improvement in sample efficiency and achieves up to 100% final success.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.15233", "title": "Video2Roleplay: A Multimodal Dataset and Framework for Video-Guided Role-playing Agents", "authors": [ "Xueqiao Zhang", "Chao Zhang", "Jingtao Xu", "Yifan Zhu", "Xin Shi", "Yi Yang", "Yawei Luo" ], "github": "https://github.com/zxqSled/Video2Roleplay", "project_page": "", "abstract": "Role-playing agents (RPAs) have attracted growing interest for their ability to simulate immersive and interactive characters. However, existing approaches primarily focus on static role profiles, overlooking the dynamic perceptual abilities inherent to humans. To bridge this gap, we introduce the concept of dynamic role profiles by incorporating video modality into RPAs. To support this, we construct Role-playing-Video60k, a large-scale, high-quality dataset comprising 60k videos and 700k corresponding dialogues. Based on this dataset, we develop a comprehensive RPA framework that combines adaptive temporal sampling with both dynamic and static role profile representations. Specifically, the dynamic profile is created by adaptively sampling video frames and feeding them to the LLM in temporal order, while the static profile consists of (1) character dialogues from training videos during fine-tuning, and (2) a summary context from the input video during inference. This joint integration enables RPAs to generate greater responses. Furthermore, we propose a robust evaluation method covering eight metrics. Experimental results demonstrate the effectiveness of our framework, highlighting the importance of dynamic role profiles in developing RPAs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zxqSled/Video2Roleplay", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.14981", "title": "SPATIALGEN: Layout-guided 3D Indoor Scene Generation", "authors": [ "Chuan Fang", "Heng Li", "Yixun Liang", "Jia Zheng", "Yongsen Mao", "Yuan Liu", "Rui Tang", "Zihan Zhou", "Ping Tan" ], "github": "", "project_page": "", "abstract": "Creating high-fidelity 3D models of indoor environments is essential for applications in design, virtual reality, and robotics. However, manual 3D modeling remains time-consuming and labor-intensive. While recent advances in generative AI have enabled automated scene synthesis, existing methods often face challenges in balancing visual quality, diversity, semantic consistency, and user control. A major bottleneck is the lack of a large-scale, high-quality dataset tailored to this task. To address this gap, we introduce a comprehensive synthetic dataset, featuring 12,328 structured annotated scenes with 57,440 rooms, and 4.7M photorealistic 2D renderings. Leveraging this dataset, we present SpatialGen, a novel multi-view multi-modal diffusion model that generates realistic and semantically consistent 3D indoor scenes. Given a 3D layout and a reference image (derived from a text prompt), our model synthesizes appearance (color image), geometry (scene coordinate map), and semantic (semantic segmentation map) from arbitrary viewpoints, while preserving spatial consistency across modalities. SpatialGen consistently generates superior results to previous methods in our experiments. We are open-sourcing our data and models to empower the community and advance the field of indoor scene understanding and generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.15061", "title": "Ask-to-Clarify: Resolving Instruction Ambiguity through Multi-turn Dialogue", "authors": [ "Xingyao Lin", "Xinghao Zhu", "Tianyi Lu", "Sicheng Xie", "Hui Zhang", "Xipeng Qiu", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "", "project_page": "", "abstract": "The ultimate goal of embodied agents is to create collaborators that can interact with humans, not mere executors that passively follow instructions. This requires agents to communicate, coordinate, and adapt their actions based on human feedback. Recently, advances in VLAs have offered a path toward this goal. However, most current VLA-based embodied agents operate in a one-way mode: they receive an instruction and execute it without feedback. This approach fails in real-world scenarios where instructions are often ambiguous. In this paper, we address this problem with the Ask-to-Clarify framework. Our framework first resolves ambiguous instructions by asking questions in a multi-turn dialogue. Then it generates low-level actions end-to-end. Specifically, the Ask-to-Clarify framework consists of two components, one VLM for collaboration and one diffusion for action. We also introduce a connection module that generates conditions for the diffusion based on the output of the VLM. This module adjusts the observation by instructions to create reliable conditions. We train our framework with a two-stage knowledge-insulation strategy. First, we fine-tune the collaboration component using ambiguity-solving dialogue data to handle ambiguity. Then, we integrate the action component while freezing the collaboration one. This preserves the interaction abilities while fine-tuning the diffusion to generate actions. The training strategy guarantees our framework can first ask questions, then generate actions. During inference, a signal detector functions as a router that helps our framework switch between asking questions and taking actions. We evaluate the Ask-to-Clarify framework in 8 real-world tasks, where it outperforms existing state-of-the-art VLAs. The results suggest that our proposed framework, along with the training strategy, provides a path toward collaborative embodied agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.15591", "title": "Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification", "authors": [ "Zinan Lin", "Enshu Liu", "Xuefei Ning", "Junyi Zhu", "Wenyu Wang", "Sergey Yekhanin" ], "github": "https://github.com/microsoft/latent-zoning-networks", "project_page": "", "abstract": "Generative modeling, representation learning, and classification are three core problems in machine learning (ML), yet their state-of-the-art (SoTA) solutions remain largely disjoint. In this paper, we ask: Can a unified principle address all three? Such unification could simplify ML pipelines and foster greater synergy across tasks. We introduce Latent Zoning Network (LZN) as a step toward this goal. At its core, LZN creates a shared Gaussian latent space that encodes information across all tasks. Each data type (e.g., images, text, labels) is equipped with an encoder that maps samples to disjoint latent zones, and a decoder that maps latents back to data. ML tasks are expressed as compositions of these encoders and decoders: for example, label-conditional image generation uses a label encoder and image decoder; image embedding uses an image encoder; classification uses an image encoder and label decoder. We demonstrate the promise of LZN in three increasingly complex scenarios: (1) LZN can enhance existing models (image generation): When combined with the SoTA Rectified Flow model, LZN improves FID on CIFAR10 from 2.76 to 2.59-without modifying the training objective. (2) LZN can solve tasks independently (representation learning): LZN can implement unsupervised representation learning without auxiliary loss functions, outperforming the seminal MoCo and SimCLR methods by 9.3% and 0.2%, respectively, on downstream linear classification on ImageNet. (3) LZN can solve multiple tasks simultaneously (joint generation and classification): With image and label encoders/decoders, LZN performs both tasks jointly by design, improving FID and achieving SoTA classification accuracy on CIFAR10. The code and trained models are available at https://github.com/microsoft/latent-zoning-networks. The project website is at https://zinanlin.me/blogs/latent_zoning_networks.html.", "github_manual": "", "github_regex": "https://github.com/microsoft/latent-zoning-networks", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.13989", "title": "Do You Hear What I Mean? Quantifying the Instruction-Perception Gap in Instruction-Guided Expressive Text-To-Speech Systems", "authors": [ "Yi-Cheng Lin", "Huang-Cheng Chou", "Tzu-Chieh Wei", "Kuan-Yu Chen", "Hung-yi Lee" ], "github": "", "project_page": "", "abstract": "Instruction-guided text-to-speech (ITTS) enables users to control speech generation through natural language prompts, offering a more intuitive interface than traditional TTS. However, the alignment between user style instructions and listener perception remains largely unexplored. This work first presents a perceptual analysis of ITTS controllability across two expressive dimensions (adverbs of degree and graded emotion intensity) and collects human ratings on speaker age and word-level emphasis attributes. To comprehensively reveal the instruction-perception gap, we provide a data collection with large-scale human evaluations, named Expressive VOice Control (E-VOC) corpus. Furthermore, we reveal that (1) gpt-4o-mini-tts is the most reliable ITTS model with great alignment between instruction and generated utterances across acoustic dimensions. (2) The 5 analyzed ITTS systems tend to generate Adult voices even when the instructions ask to use child or Elderly voices. (3) Fine-grained control remains a major challenge, indicating that most ITTS systems have substantial room for improvement in interpreting slightly different attribute instructions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.16622", "title": "Audio-Conditioned Diffusion LLMs for ASR and Deliberation Processing", "authors": [ "Mengqi Wang", "Zhan Liu", "Zengrui Jin", "Guangzhi Sun", "Chao Zhang", "Philip C. Woodland" ], "github": "", "project_page": "", "abstract": "Diffusion-based large language models (DLLMs) have recently attracted growing interest as an alternative to autoregressive decoders. In this work, we present an empirical study on using the diffusion-based large language model LLaDA for automatic speech recognition (ASR). We first investigate its use as an external deliberation-based processing module for Whisper-LLaMA transcripts. By leveraging the bidirectional attention and denoising capabilities of LLaDA, we explore random masking, low-confidence masking, and semi-autoregressive strategies, showing that Whisper-LLaDA substantially reduces WER compared with the baseline. On LibriSpeech, the best cascade system achieves 2.25%/4.94% WER on test-clean/test-other, representing a 12.3% relative improvement over the Whisper-LLaMA baseline on the test-other split. In contrast, a plain-text LLaDA without acoustic features fails to improve accuracy, highlighting the importance of audio-conditioned embeddings. We further evaluate Whisper-LLaDA as a standalone decoder for ASR with diffusion-based and semi-autoregressive decoding. Most experimental configurations achieve faster inference than the Whisper-LLaMA baseline, although recognition accuracy is slightly lower. These findings offer an empirical view of diffusion-based LLMs for ASR and point to promising directions for improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-22", "arxiv_id": "2509.14627", "title": "Towards Human-like Multimodal Conversational Agent by Generating Engaging Speech", "authors": [ "Taesoo Kim", "Yongsik Jo", "Hyunmin Song", "Taehwan Kim" ], "github": "https://github.com/kimtaesu24/MSenC", "project_page": "", "abstract": "Human conversation involves language, speech, and visual cues, with each medium providing complementary information. For instance, speech conveys a vibe or tone not fully captured by text alone. While multimodal LLMs focus on generating text responses from diverse inputs, less attention has been paid to generating natural and engaging speech. We propose a human-like agent that generates speech responses based on conversation mood and responsive style information. To achieve this, we build a novel MultiSensory Conversation dataset focused on speech to enable agents to generate natural speech. We then propose a multimodal LLM-based model for generating text responses and voice descriptions, which are used to generate speech covering paralinguistic information. Experimental results demonstrate the effectiveness of utilizing both visual and audio modalities in conversation to generate engaging speech. The source code is available in https://github.com/kimtaesu24/MSenC", "github_manual": "", "github_regex": "https://github.com/kimtaesu24/MSenC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.18084", "title": "ByteWrist: A Parallel Robotic Wrist Enabling Flexible and Anthropomorphic Motion for Confined Spaces", "authors": [ "Jiawen Tian", "Liqun Huang", "Zhongren Cui", "Jingchao Qiao", "Jiafeng Xu", "Xiao Ma", "Zeyu Ren" ], "github": "", "project_page": "", "abstract": "This paper introduces ByteWrist, a novel highly-flexible and anthropomorphic parallel wrist for robotic manipulation. ByteWrist addresses the critical limitations of existing serial and parallel wrists in narrow-space operations through a compact three-stage parallel drive mechanism integrated with arc-shaped end linkages. The design achieves precise RPY (Roll-Pitch-Yaw) motion while maintaining exceptional compactness, making it particularly suitable for complex unstructured environments such as home services, medical assistance, and precision assembly. The key innovations include: (1) a nested three-stage motor-driven linkages that minimize volume while enabling independent multi-DOF control, (2) arc-shaped end linkages that optimize force transmission and expand motion range, and (3) a central supporting ball functioning as a spherical joint that enhances structural stiffness without compromising flexibility. Meanwhile, we present comprehensive kinematic modeling including forward / inverse kinematics and a numerical Jacobian solution for precise control. Empirically, we observe ByteWrist demonstrates strong performance in narrow-space maneuverability and dual-arm cooperative manipulation tasks, outperforming Kinova-based systems. Results indicate significant improvements in compactness, efficiency, and stiffness compared to traditional designs, establishing ByteWrist as a promising solution for next-generation robotic manipulation in constrained environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.16596", "title": "Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels", "authors": [ "Junjie Ye", "Yuming Yang", "Yang Nan", "Shuo Li", "Qi Zhang", "Tao Gui", "Xuanjing Huang", "Peng Wang", "Zhongchao Shi", "Jianping Fan" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) acquire substantial world knowledge during pre-training, which is further shaped by post-training techniques such as supervised fine-tuning (SFT). However, the impact of SFT on a model's knowledge remains underexplored, limiting our ability to control knowledge change behavior in fine-tuned models. To address this gap, we evaluate closed-book question answering (CBQA) performance across five LLMs from the LLaMA-2 and LLaMA-3 families. Surprisingly, models fine-tuned on 1,920 samples perform up to 14% worse than those fine-tuned on only 240 samples. Furthermore, varying the level of knowledge mastery in the fine-tuning data leads to performance fluctuations of over 12%. To investigate these effects, we analyze model behavior at both the token and parameter levels. Our analysis reveals that up to 90% of parameter updates during SFT do not contribute to knowledge enhancement. Restoring these updates can improve performance on the CBQA task, depending on the characteristics of the fine-tuning data. These insights offer practical guidance for developing fine-tuning strategies that more effectively strengthen model knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.16117", "title": "DiffusionNFT: Online Diffusion Reinforcement with Forward Process", "authors": [ "Kaiwen Zheng", "Huayu Chen", "Haotian Ye", "Haoxiang Wang", "Qinsheng Zhang", "Kai Jiang", "Hang Su", "Stefano Ermon", "Jun Zhu", "Ming-Yu Liu" ], "github": "", "project_page": "", "abstract": "Online reinforcement learning (RL) has been central to post-training language models, but its extension to diffusion models remains challenging due to intractable likelihoods. Recent works discretize the reverse sampling process to enable GRPO-style training, yet they inherit fundamental drawbacks, including solver restrictions, forward-reverse inconsistency, and complicated integration with classifier-free guidance (CFG). We introduce Diffusion Negative-aware FineTuning (DiffusionNFT), a new online RL paradigm that optimizes diffusion models directly on the forward process via flow matching. DiffusionNFT contrasts positive and negative generations to define an implicit policy improvement direction, naturally incorporating reinforcement signals into the supervised learning objective. This formulation enables training with arbitrary black-box solvers, eliminates the need for likelihood estimation, and requires only clean images rather than sampling trajectories for policy optimization. DiffusionNFT is up to 25times more efficient than FlowGRPO in head-to-head comparisons, while being CFG-free. For instance, DiffusionNFT improves the GenEval score from 0.24 to 0.98 within 1k steps, while FlowGRPO achieves 0.95 with over 5k steps and additional CFG employment. By leveraging multiple reward models, DiffusionNFT significantly boosts the performance of SD3.5-Medium in every benchmark tested.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.15248", "title": "Synthetic bootstrapped pretraining", "authors": [ "Zitong Yang", "Aonan Zhang", "Hong Liu", "Tatsunori Hashimoto", "Emmanuel Cand\u00e8s", "Chong Wang", "Ruoming Pang" ], "github": "", "project_page": "", "abstract": "We introduce Synthetic Bootstrapped Pretraining (SBP), a language model (LM) pretraining procedure that first learns a model of relations between documents from the pretraining dataset and then leverages it to synthesize a vast new corpus for joint training. While the standard pretraining teaches LMs to learn causal correlations among tokens within a single document, it is not designed to efficiently model the rich, learnable inter-document correlations that can potentially lead to better performance. We validate SBP by designing a compute-matched pretraining setup and pretrain a 3B-parameter model on up to 1T tokens from scratch. We find SBP consistently improves upon a strong repetition baseline and delivers a significant fraction of performance improvement attainable by an oracle upper bound with access to 20x more unique data. Qualitative analysis reveals that the synthesized documents go beyond mere paraphrases -- SBP first abstracts a core concept from the seed material and then crafts a new narration on top of it. Besides strong empirical performance, SBP admits a natural Bayesian interpretation: the synthesizer implicitly learns to abstract the latent concepts shared between related documents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17627", "title": "OmniInsert: Mask-Free Video Insertion of Any Reference via Diffusion Transformer Models", "authors": [ "Jinshu Chen", "Xinghui Li", "Xu Bai", "Tianxiang Ma", "Pengze Zhang", "Zhuowei Chen", "Gen Li", "Lijie Liu", "Songtao Zhao", "Bingchuan Li", "Qian He" ], "github": "https://github.com/Phantom-video/OmniInsert", "project_page": "", "abstract": "Recent advances in video insertion based on diffusion models are impressive. However, existing methods rely on complex control signals but struggle with subject consistency, limiting their practical applicability. In this paper, we focus on the task of Mask-free Video Insertion and aim to resolve three key challenges: data scarcity, subject-scene equilibrium, and insertion harmonization. To address the data scarcity, we propose a new data pipeline InsertPipe, constructing diverse cross-pair data automatically. Building upon our data pipeline, we develop OmniInsert, a novel unified framework for mask-free video insertion from both single and multiple subject references. Specifically, to maintain subject-scene equilibrium, we introduce a simple yet effective Condition-Specific Feature Injection mechanism to distinctly inject multi-source conditions and propose a novel Progressive Training strategy that enables the model to balance feature injection from subjects and source video. Meanwhile, we design the Subject-Focused Loss to improve the detailed appearance of the subjects. To further enhance insertion harmonization, we propose an Insertive Preference Optimization methodology to optimize the model by simulating human preferences, and incorporate a Context-Aware Rephraser module during reference to seamlessly integrate the subject into the original scenes. To address the lack of a benchmark for the field, we introduce InsertBench, a comprehensive benchmark comprising diverse scenes with meticulously selected subjects. Evaluation on InsertBench indicates OmniInsert outperforms state-of-the-art closed-source commercial solutions. The code will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Phantom-video/OmniInsert", "github_api_added_by": "auto" }, { "date": "2025-09-23", "arxiv_id": "2509.18091", "title": "OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System", "authors": [ "Sunhao Dai", "Jiakai Tang", "Jiahua Wu", "Kun Wang", "Yuxuan Zhu", "Bingjun Chen", "Bangyang Hong", "Yu Zhao", "Cong Fu", "Kangle Wu", "Yabo Ni", "Anxiang Zeng", "Wenjie Wang", "Xu Chen", "Jun Xu", "See-Kiong Ng" ], "github": "", "project_page": "", "abstract": "Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain limited to transplanting Transformer architectures, which bring only incremental improvements over strong Deep Learning Recommendation Models (DLRMs). From a first principle perspective, the breakthroughs of LLMs stem not only from their architectures but also from two complementary mechanisms: context engineering, which enriches raw input queries with contextual cues to better elicit model capabilities, and multi-step reasoning, which iteratively refines model outputs through intermediate reasoning paths. However, these two mechanisms and their potential to unlock substantial improvements remain largely underexplored in industrial ranking systems. In this paper, we propose OnePiece, a unified framework that seamlessly integrates LLM-style context engineering and reasoning into both retrieval and ranking models of industrial cascaded pipelines. OnePiece is built on a pure Transformer backbone and further introduces three key innovations: (1) structured context engineering, which augments interaction history with preference and scenario signals and unifies them into a structured tokenized input sequence for both retrieval and ranking; (2) block-wise latent reasoning, which equips the model with multi-step refinement of representations and scales reasoning bandwidth via block size; (3) progressive multi-task training, which leverages user feedback chains to effectively supervise reasoning steps during training. OnePiece has been deployed in the main personalized search scenario of Shopee and achieves consistent online gains across different key business metrics, including over +2% GMV/UU and a +2.90% increase in advertising revenue.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17765", "title": "Qwen3-Omni Technical Report", "authors": [ "Jin Xu", "Zhifang Guo", "Hangrui Hu", "Yunfei Chu", "Xiong Wang", "Jinzheng He", "Yuxuan Wang", "Xian Shi", "Ting He", "Xinfa Zhu", "Yuanjun Lv", "Yongqi Wang", "Dake Guo", "He Wang", "Linhan Ma", "Pei Zhang", "Xinyu Zhang", "Hongkun Hao", "Zishan Guo", "Baosong Yang", "Bin Zhang", "Ziyang Ma", "Xipin Wei", "Shuai Bai", "Keqin Chen", "Xuejing Liu", "Peng Wang", "Mingkun Yang", "Dayiheng Liu", "Xingzhang Ren", "Bo Zheng", "Rui Men", "Fan Zhou", "Bowen Yu", "Jianxin Yang", "Le Yu", "Jingren Zhou", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "We present Qwen3-Omni, a single multimodal model that, for the first time, maintains state-of-the-art performance across text, image, audio, and video without any degradation relative to single-modal counterparts. Qwen3-Omni matches the performance of same-sized single-modal models within the Qwen series and excels particularly on audio tasks. Across 36 audio and audio-visual benchmarks, Qwen3-Omni achieves open-source SOTA on 32 benchmarks and overall SOTA on 22, outperforming strong closed-source models such as Gemini-2.5-Pro, Seed-ASR, and GPT-4o-Transcribe. Qwen3-Omni adopts a Thinker-Talker MoE architecture that unifies perception and generation across text, images, audio, and video, yielding fluent text and natural real-time speech. It supports text interaction in 119 languages, speech understanding in 19 languages, and speech generation in 10 languages. To reduce first-packet latency in streaming synthesis, Talker autoregressively predicts discrete speech codecs using a multi-codebook scheme. Leveraging the representational capacity of these codebooks, we replace computationally intensive block-wise diffusion with a lightweight causal ConvNet, enabling streaming from the first codec frame. In cold-start settings, Qwen3-Omni achieves a theoretical end-to-end first-packet latency of 234 ms. To further strengthen multimodal reasoning, we introduce a Thinking model that explicitly reasons over inputs from any modality. Since the research community currently lacks a general-purpose audio captioning model, we fine-tuned Qwen3-Omni-30B-A3B to obtain Qwen3-Omni-30B-A3B-Captioner, which produces detailed, low-hallucination captions for arbitrary audio inputs. Qwen3-Omni-30B-A3B, Qwen3-Omni-30B-A3B-Thinking, and Qwen3-Omni-30B-A3B-Captioner are publicly released under the Apache 2.0 license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17396", "title": "EpiCache: Episodic KV Cache Management for Long Conversational Question Answering", "authors": [ "Minsoo Kim", "Arnav Kundu", "Han-Byul Kim", "Richa Dixit", "Minsik Cho" ], "github": "https://github.com/apple/ml-epicache", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have extended context lengths, enabling assistants to sustain long histories for coherent, personalized responses. This ability, however, hinges on Key-Value (KV) caching, whose memory grows linearly with dialogue length and quickly dominates under strict resource constraints. An active line of research for reducing this overhead is KV cache compression, which seeks to limit cache size while preserving accuracy. Yet existing methods face two major limitations: (i) evicting entries after full-context prefill causes unbounded peak memory, and (ii) query-dependent eviction narrows the cache to a single query, leading to degraded accuracy in multi-turn conversations. We introduce EpiCache, a training-free KV cache management framework for long conversational question answering (LongConvQA) under fixed memory budgets. EpiCache bounds cache growth through block-wise prefill and preserves topic-relevant context via episodic KV compression, which clusters conversation history into coherent episodes and applies episode-specific KV cache eviction. We further design an adaptive layer-wise budget allocation strategy that measures each layer's sensitivity to eviction and distributes the memory budget across layers accordingly. Across three LongConvQA benchmarks, EpiCache improves accuracy by up to 40% over recent baselines, sustains near-full KV accuracy under 4-6x compression, and reduces latency and memory by up to 2.4x and 3.5x, thereby enabling efficient multi-turn interaction under strict resource constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/ml-epicache", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.16941", "title": "SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?", "authors": [ "Xiang Deng", "Jeff Da", "Edwin Pan", "Yannis Yiming He", "Charles Ide", "Kanak Garg", "Niklas Lauffer", "Andrew Park", "Nitin Pasari", "Chetan Rane", "Karmini Sampath", "Maya Krishnan", "Srivatsa Kundurthy", "Sean Hendryx", "Zifan Wang", "Chen Bo Calvin Zhang", "Noah Jacobson", "Bing Liu", "Brad Kenstler" ], "github": "https://github.com/scaleapi/SWE-bench_Pro-os", "project_page": "", "abstract": "We introduce SWE-Bench Pro, a substantially more challenging benchmark that builds upon the best practices of SWE-BENCH [25], but is explicitly designed to capture realistic, complex, enterprise-level problems beyond the scope of SWE-BENCH. SWE-BENCH PRO contains 1,865 problems sourced from a diverse set of 41 actively maintained repositories spanning business applications, B2B services, and developer tools. The benchmark is partitioned into a public set with open access to problems sourced from 11 repositories, a held-out set of 12 repositories and a commercial set of 18 proprietary repositories where we have formal partnership agreements with early-stage startups. Problems in the held-out and the commercial set are not publicly accessible, but we release results on the commercial set. Our benchmark features long-horizon tasks that may require hours to days for a professional software engineer to complete, often involving patches across multiple files and substantial code modifications. All tasks are human-verified and augmented with sufficient context to ensure resolvability. In our evaluation of widely used coding models, under a unified scaffold, we observe that their performance on SWE-Bench PRO remains below 25% (Pass@1), with GPT-5 achieving the highest score to date at 23.3%. To better understand these limitations, we cluster the failure modes observed in the collected agent trajectories for a clearer characterization of the error patterns exhibited by current models. Overall, SWE-BENCH PRO provides a contamination-resistant testbed that more faithfully captures the complexity and diversity of real-world software development, advancing the pursuit of truly autonomous software engineering agents at a professional level.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/scaleapi/SWE-bench_Pro-os", "github_api_added_by": "auto" }, { "date": "2025-09-23", "arxiv_id": "2509.17818", "title": "ContextFlow: Training-Free Video Object Editing via Adaptive Context Enrichment", "authors": [ "Yiyang Chen", "Xuanhua He", "Xiujun Ma", "Yue Ma" ], "github": "https://github.com/yyChen233/ContextFlow", "project_page": "", "abstract": "Training-free video object editing aims to achieve precise object-level manipulation, including object insertion, swapping, and deletion. However, it faces significant challenges in maintaining fidelity and temporal consistency. Existing methods, often designed for U-Net architectures, suffer from two primary limitations: inaccurate inversion due to first-order solvers, and contextual conflicts caused by crude \"hard\" feature replacement. These issues are more challenging in Diffusion Transformers (DiTs), where the unsuitability of prior layer-selection heuristics makes effective guidance challenging. To address these limitations, we introduce ContextFlow, a novel training-free framework for DiT-based video object editing. In detail, we first employ a high-order Rectified Flow solver to establish a robust editing foundation. The core of our framework is Adaptive Context Enrichment (for specifying what to edit), a mechanism that addresses contextual conflicts. Instead of replacing features, it enriches the self-attention context by concatenating Key-Value pairs from parallel reconstruction and editing paths, empowering the model to dynamically fuse information. Additionally, to determine where to apply this enrichment (for specifying where to edit), we propose a systematic, data-driven analysis to identify task-specific vital layers. Based on a novel Guidance Responsiveness Metric, our method pinpoints the most influential DiT blocks for different tasks (e.g., insertion, swapping), enabling targeted and highly effective guidance. Extensive experiments show that ContextFlow significantly outperforms existing training-free methods and even surpasses several state-of-the-art training-based approaches, delivering temporally coherent, high-fidelity results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yyChen233/ContextFlow", "github_api_added_by": "auto" }, { "date": "2025-09-23", "arxiv_id": "2509.17336", "title": "Mano Report", "authors": [ "Tianyu Fu", "Anyang Su", "Chenxu Zhao", "Hanning Wang", "Minghui Wu", "Zhe Yu", "Fei Hu", "Mingjia Shi", "Wei Dong", "Jiayao Wang", "Yuyang Chen", "Ruiyang Yu", "Siran Peng", "Menglin Li", "Nan Huang", "Haitian Wei", "Jiawei Yu", "Yi Xin", "Xilin Zhao", "Kai Gu", "Ping Jiang", "Sifan Zhou", "Shuo Wang" ], "github": "", "project_page": "", "abstract": "Graphical user interfaces (GUIs) are the primary medium for human-computer interaction, yet automating GUI interactions remains challenging due to the complexity of visual elements, dynamic environments, and the need for multi-step reasoning. Existing methods based on vision-language models (VLMs) often suffer from limited resolution, domain mismatch, and insufficient sequential decisionmaking capability. To address these issues, we propose Mano, a robust GUI agent built upon a multi-modal foundation model pre-trained on extensive web and computer system data. Our approach integrates a novel simulated environment for high-fidelity data generation, a three-stage training pipeline (supervised fine-tuning, offline reinforcement learning, and online reinforcement learning), and a verification module for error recovery. Mano demonstrates state-of-the-art performance on multiple GUI benchmarks, including Mind2Web and OSWorld, achieving significant improvements in success rate and operational accuracy. Our work provides new insights into the effective integration of reinforcement learning with VLMs for practical GUI agent deployment, highlighting the importance of domain-specific data, iterative training, and holistic reward design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17158", "title": "ARE: Scaling Up Agent Environments and Evaluations", "authors": [ "Pierre Andrews", "Amine Benhalloum", "Gerard Moreno-Torres Bertran", "Matteo Bettini", "Amar Budhiraja", "Ricardo Silveira Cabral", "Virginie Do", "Romain Froger", "Emilien Garreau", "Jean-Baptiste Gaya", "Hugo Lauren\u00e7on", "Maxime Lecanu", "Kunal Malkan", "Dheeraj Mekala", "Pierre M\u00e9nard", "Gr\u00e9goire Mialon", "Ulyana Piterbarg", "Mikhail Plekhanov", "Mathieu Rita", "Andrey Rusakov", "Thomas Scialom", "Vladislav Vorotilov", "Mengjue Wang", "Ian Yu" ], "github": "", "project_page": "", "abstract": "We introduce Meta Agents Research Environments (ARE), a research platform for scalable creation of environments, integration of synthetic or real applications, and execution of agentic orchestrations. ARE provides simple abstractions to build complex and diverse environments, each with their own rules, tools, content, and verifiers, helping to bridge the gap between model development and real-world deployment. We also propose Gaia2, a benchmark built in ARE and designed to measure general agent capabilities. Beyond search and execution, Gaia2 requires agents to handle ambiguities and noise, adapt to dynamic environments, collaborate with other agents, and operate under temporal constraints. Unlike prior benchmarks, Gaia2 runs asynchronously, surfacing new failure modes that are invisible in static settings. Our experiments show that no system dominates across the intelligence spectrum: stronger reasoning often comes at the cost of efficiency, and budget scaling curves plateau, highlighting the need for new architectures and adaptive compute strategies. Perhaps more importantly, ARE abstractions enable continuous extension of Gaia2 to other environments, empowering the community to rapidly create new benchmarks tailored to their domains. In AI's second half, progress increasingly depends on defining meaningful tasks and robust evaluations to drive frontier capabilities forward.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.09873", "title": "From Hugging Face to GitHub: Tracing License Drift in the Open-Source AI Ecosystem", "authors": [ "James Jewitt", "Hao Li", "Bram Adams", "Gopi Krishnan Rajbahadur", "Ahmed E. Hassan" ], "github": "", "project_page": "", "abstract": "Hidden license conflicts in the open-source AI ecosystem pose serious legal and ethical risks, exposing organizations to potential litigation and users to undisclosed risk. However, the field lacks a data-driven understanding of how frequently these conflicts occur, where they originate, and which communities are most affected. We present the first end-to-end audit of licenses for datasets and models on Hugging Face, as well as their downstream integration into open-source software applications, covering 364 thousand datasets, 1.6 million models, and 140 thousand GitHub projects. Our empirical analysis reveals systemic non-compliance in which 35.5% of model-to-application transitions eliminate restrictive license clauses by relicensing under permissive terms. In addition, we prototype an extensible rule engine that encodes almost 200 SPDX and model-specific clauses for detecting license conflicts, which can solve 86.4% of license conflicts in software applications. To support future research, we release our dataset and the prototype engine. Our study highlights license compliance as a critical governance challenge in open-source AI and provides both the data and tools necessary to enable automated, AI-aware compliance at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17567", "title": "LIMI: Less is More for Agency", "authors": [ "Yang Xiao", "Mohan Jiang", "Jie Sun", "Keyu Li", "Jifan Lin", "Yumin Zhuang", "Ji Zeng", "Shijie Xia", "Qishuo Hua", "Xuefeng Li", "Xiaojie Cai", "Tongyu Wang", "Yue Zhang", "Liming Liu", "Xia Wu", "Jinlong Hou", "Yuan Cheng", "Wenjie Li", "Xiang Wang", "Dequan Wang", "Pengfei Liu" ], "github": "https://github.com/Significant-Gravitas/AutoGPT", "project_page": "", "abstract": "We define Agency as the emergent capacity of AI systems to function as autonomous agents actively discovering problems, formulating hypotheses, and executing solutions through self-directed engagement with environments and tools. This fundamental capability marks the dawn of the Age of AI Agency, driven by a critical industry shift: the urgent need for AI systems that don't just think, but work. While current AI excels at reasoning and generating responses, industries demand autonomous agents that can execute tasks, operate tools, and drive real-world outcomes. As agentic intelligence becomes the defining characteristic separating cognitive systems from productive workers, efficiently cultivating machine autonomy becomes paramount. Current approaches assume that more data yields better agency, following traditional scaling laws from language modeling. We fundamentally challenge this paradigm. LIMI (Less Is More for Intelligent Agency) demonstrates that agency follows radically different development principles. Through strategic focus on collaborative software development and scientific research workflows, we show that sophisticated agentic intelligence can emerge from minimal but strategically curated demonstrations of autonomous behavior. Using only 78 carefully designed training samples, LIMI achieves 73.5% on comprehensive agency benchmarks, dramatically outperforming state-of-the-art models: Kimi-K2-Instruct (24.1%), DeepSeek-V3.1 (11.9%), Qwen3-235B-A22B-Instruct (27.5%), and GLM-4.5 (45.1%). Most strikingly, LIMI demonstrates 53.7% improvement over models trained on 10,000 samples-achieving superior agentic intelligence with 128 times fewer samples. Our findings establish the Agency Efficiency Principle: machine autonomy emerges not from data abundance but from strategic curation of high-quality agentic demonstrations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Significant-Gravitas/AutoGPT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17985", "title": "VideoFrom3D: 3D Scene Video Generation via Complementary Image and Video Diffusion Models", "authors": [ "Geonung Kim", "Janghyeok Han", "Sunghyun Cho" ], "github": "https://github.com/KIMGEONUNG/VideoFrom3D", "project_page": "", "abstract": "In this paper, we propose VideoFrom3D, a novel framework for synthesizing high-quality 3D scene videos from coarse geometry, a camera trajectory, and a reference image. Our approach streamlines the 3D graphic design workflow, enabling flexible design exploration and rapid production of deliverables. A straightforward approach to synthesizing a video from coarse geometry might condition a video diffusion model on geometric structure. However, existing video diffusion models struggle to generate high-fidelity results for complex scenes due to the difficulty of jointly modeling visual quality, motion, and temporal consistency. To address this, we propose a generative framework that leverages the complementary strengths of image and video diffusion models. Specifically, our framework consists of a Sparse Anchor-view Generation (SAG) and a Geometry-guided Generative Inbetweening (GGI) module. The SAG module generates high-quality, cross-view consistent anchor views using an image diffusion model, aided by Sparse Appearance-guided Sampling. Building on these anchor views, GGI module faithfully interpolates intermediate frames using a video diffusion model, enhanced by flow-based camera control and structural guidance. Notably, both modules operate without any paired dataset of 3D scene models and natural images, which is extremely difficult to obtain. Comprehensive experiments show that our method produces high-quality, style-consistent scene videos under diverse and challenging scenarios, outperforming simple and extended baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KIMGEONUNG/VideoFrom3D", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.15709", "title": "Understanding Embedding Scaling in Collaborative Filtering", "authors": [ "Zhuangzhuang He", "Zhou Kaiyu", "Haoyue Bai", "Fengbin Zhu", "Yonghui Yang" ], "github": "", "project_page": "", "abstract": "Scaling recommendation models into large recommendation models has become one of the most widely discussed topics. Recent efforts focus on components beyond the scaling embedding dimension, as it is believed that scaling embedding may lead to performance degradation. Although there have been some initial observations on embedding, the root cause of their non-scalability remains unclear. Moreover, whether performance degradation occurs across different types of models and datasets is still an unexplored area. Regarding the effect of embedding dimensions on performance, we conduct large-scale experiments across 10 datasets with varying sparsity levels and scales, using 4 representative classical architectures. We surprisingly observe two novel phenomenon: double-peak and logarithmic. For the former, as the embedding dimension increases, performance first improves, then declines, rises again, and eventually drops. For the latter, it exhibits a perfect logarithmic curve. Our contributions are threefold. First, we discover two novel phenomena when scaling collaborative filtering models. Second, we gain an understanding of the underlying causes of the double-peak phenomenon. Lastly, we theoretically analyze the noise robustness of collaborative filtering models, with results matching empirical observations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17641", "title": "AuditoryBench++: Can Language Models Understand Auditory Knowledge without Hearing?", "authors": [ "Hyunjong Ok", "Suho Yoo", "Hyeonjun Kim", "Jaeho Lee" ], "github": "https://github.com/HJ-OK/AuditoryBenchpp", "project_page": "", "abstract": "Even without directly hearing sounds, humans can effortlessly reason about auditory properties, such as pitch, loudness, or sound-source associations, drawing on auditory commonsense. In contrast, language models often lack this capability, limiting their effectiveness in multimodal interactions. As an initial step to address this gap, we present AuditoryBench++, a comprehensive benchmark for evaluating auditory knowledge and reasoning in text-only settings. The benchmark encompasses tasks that range from basic auditory comparisons to contextually grounded reasoning, enabling fine-grained analysis of how models process and integrate auditory concepts. In addition, we introduce AIR-CoT, a novel auditory imagination reasoning method that generates and integrates auditory information during inference through span detection with special tokens and knowledge injection. Extensive experiments with recent LLMs and Multimodal LLMs demonstrate that AIR-CoT generally outperforms both the off-the-shelf models and those augmented with auditory knowledge. The project page is available at https://auditorybenchpp.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HJ-OK/AuditoryBenchpp", "github_api_added_by": "auto" }, { "date": "2025-09-23", "arxiv_id": "2509.16633", "title": "When Big Models Train Small Ones: Label-Free Model Parity Alignment for Efficient Visual Question Answering using Small VLMs", "authors": [ "Abhirama Subramanyam Penamakuri", "Navlika Singh", "Piyush Arora", "Anand Mishra" ], "github": "", "project_page": "", "abstract": "Large Vision-Language Models (L-VLMs) have demonstrated remarkable performance in various vision and language tasks, including visual question answering (VQA). However, their high computational cost makes them impractical for resource-constrained settings and inference-heavy applications. In contrast, Small Vision-Language Models (S-VLMs) offer efficiency but suffer from a significant performance gap compared to their larger counterparts. In this work, we introduce the Model Parity Aligner (MPA), a novel framework designed to systematically improve S-VLMs by leveraging unlabeled images and effective knowledge transfer from L-VLMs. Instead of traditional knowledge distillation methods that rely on labeled training data, MPA employs a strategic parity-based approach that precisely identifies the knowledge disparities between S-VLMs and L-VLMs, and optimizes training by targeting only these disparities. We conduct extensive experiments on four diverse VQA benchmarks, namely TextVQA, ST-VQA, ChartQA, and OKVQA, each of which requires specialized reasoning capabilities such as text recognition, chart interpretation, and commonsense and factual understanding. Our results demonstrate that MPA consistently enhances the performance of S-VLMs on all benchmarks, reducing the performance gap while maintaining computational efficiency. We make our code publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.16591", "title": "From Uniform to Heterogeneous: Tailoring Policy Optimization to Every Token's Nature", "authors": [ "Zheng Liu", "Mengjie Liu", "Siwei Wen", "Mengzhang Cai", "Bin Cui", "Conghui He", "Wentao Zhang" ], "github": "https://github.com/starriver030515/HAPO", "project_page": "", "abstract": "Reinforcement Learning has emerged as the fundamental technique for enhancing reasoning in LLMs. However, existing algorithms apply uniform optimization to all tokens, ignoring their different roles in reasoning process. To address this limitation, we introduce Heterogeneous Adaptive Policy Optimization (HAPO), a comprehensive token-aware algorithm that dynamically adapts optimization based on token entropy. For rollout sampling, we propose Adaptive Temperature Sampling, which adjusts sampling temperature in real time, promoting exploration at high-entropy tokens while preserving coherence at low-entropy ones. For advantage calculation, we introduce Token Level Group Average that normalizes advantages at token level, jointly accounting for sequence-length as in token-mean loss while preserving non-biased treatment. We then develop Differential Advantage Redistribution that leverages entropy and importance ratios to modulate rewards-adjusting updates for tokens with clear signals. For clipping loss, we design Asymmetric Adaptive Clipping, allowing aggressive probability reduction for noisy low-entropy tokens while enabling exploration for high-entropy tokens. Through systematic investigation between entropy and training dynamics, we embedded token-level treatment into every stages to achieve fine-grained control. Extensive experiments demonstrate that HAPO consistently outperforms DAPO across multiple model scales. Our code can be found in https://github.com/starriver030515/HAPO.", "github_manual": "", "github_regex": "https://github.com/starriver030515/HAPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.16415", "title": "StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes", "authors": [ "Zhengri Wu", "Yiran Wang", "Yu Wen", "Zeyu Zhang", "Biao Wu", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/StereoAdapter", "project_page": "", "abstract": "Underwater stereo depth estimation provides accurate 3D geometry for robotics tasks such as navigation, inspection, and mapping, offering metric depth from low-cost passive cameras while avoiding the scale ambiguity of monocular methods. However, existing approaches face two critical challenges: (i) parameter-efficiently adapting large vision foundation encoders to the underwater domain without extensive labeled data, and (ii) tightly fusing globally coherent but scale-ambiguous monocular priors with locally metric yet photometrically fragile stereo correspondences. To address these challenges, we propose StereoAdapter, a parameter-efficient self-supervised framework that integrates a LoRA-adapted monocular foundation encoder with a recurrent stereo refinement module. We further introduce dynamic LoRA adaptation for efficient rank selection and pre-training on the synthetic UW-StereoDepth-40K dataset to enhance robustness under diverse underwater conditions. Comprehensive evaluations on both simulated and real-world benchmarks show improvements of 6.11% on TartanAir and 5.12% on SQUID compared to state-of-the-art methods, while real-world deployment with the BlueROV2 robot further demonstrates the consistent robustness of our approach. Code: https://github.com/AIGeeksGroup/StereoAdapter. Website: https://aigeeksgroup.github.io/StereoAdapter.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/StereoAdapter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.18095", "title": "MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction", "authors": [ "Zilin Xiao", "Qi Ma", "Mengting Gu", "Chun-cheng Jason Chen", "Xintao Chen", "Vicente Ordonez", "Vijai Mohan" ], "github": "", "project_page": "", "abstract": "Universal multimodal embedding models have achieved great success in capturing semantic relevance between queries and candidates. However, current methods either condense queries and candidates into a single vector, potentially limiting the expressiveness for fine-grained information, or produce too many vectors that are prohibitively expensive for multi-vector retrieval. In this work, we introduce MetaEmbed, a new framework for multimodal retrieval that rethinks how multimodal embeddings are constructed and interacted with at scale. During training, a fixed number of learnable Meta Tokens are appended to the input sequence. At test-time, their last-layer contextualized representations serve as compact yet expressive multi-vector embeddings. Through the proposed Matryoshka Multi-Vector Retrieval training, MetaEmbed learns to organize information by granularity across multiple vectors. As a result, we enable test-time scaling in multimodal retrieval, where users can balance retrieval quality against efficiency demands by selecting the number of tokens used for indexing and retrieval interactions. Extensive evaluations on the Massive Multimodal Embedding Benchmark (MMEB) and the Visual Document Retrieval Benchmark (ViDoRe) confirm that MetaEmbed achieves state-of-the-art retrieval performance while scaling robustly to models with 32B parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.18056", "title": "TempSamp-R1: Effective Temporal Sampling with Reinforcement Fine-Tuning for Video LLMs", "authors": [ "Yunheng Li", "Jing Cheng", "Shaoyong Jia", "Hangyi Kuang", "Shaohui Jiao", "Qibin Hou", "Ming-Ming Cheng" ], "github": "https://github.com/HVision-NKU/TempSamp-R1", "project_page": "", "abstract": "This paper introduces TempSamp-R1, a new reinforcement fine-tuning framework designed to improve the effectiveness of adapting multimodal large language models (MLLMs) to video temporal grounding tasks. We reveal that existing reinforcement learning methods, such as Group Relative Policy Optimization (GRPO), rely on on-policy sampling for policy updates. However, in tasks with large temporal search spaces, this strategy becomes both inefficient and limited in performance, as it often fails to identify temporally accurate solutions. To address this limitation, TempSamp-R1 leverages ground-truth annotations as off-policy supervision to provide temporally precise guidance, effectively compensating for the sparsity and misalignment in on-policy solutions. To further stabilize training and reduce variance in reward-based updates, TempSamp-R1 provides a non-linear soft advantage computation method that dynamically reshapes the reward feedback via an asymmetric transformation. By employing a hybrid Chain-of-Thought (CoT) training paradigm, TempSamp-R1 optimizes a single unified model to support both CoT and non-CoT inference modes, enabling efficient handling of queries with varying reasoning complexity. Experimental results demonstrate that TempSamp-R1 outperforms GRPO-based baselines, establishing new state-of-the-art performance on benchmark datasets: Charades-STA (R1@0.7: 52.9%, +2.7%), ActivityNet Captions (R1@0.5: 56.0%, +5.3%), and QVHighlights (mAP: 30.0%, +3.0%). Moreover, TempSamp-R1 shows robust few-shot generalization capabilities under limited data. Code: https://github.com/HVision-NKU/TempSamp-R1", "github_manual": "", "github_regex": "https://github.com/HVision-NKU/TempSamp-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17437", "title": "GeoPQA: Bridging the Visual Perception Gap in MLLMs for Geometric Reasoning", "authors": [ "Guizhen Chen", "Weiwen Xu", "Hao Zhang", "Hou Pong Chan", "Deli Zhao", "Anh Tuan Luu", "Yu Rong" ], "github": "", "project_page": "", "abstract": "Recent advancements in reinforcement learning (RL) have enhanced the reasoning abilities of large language models (LLMs), yet the impact on multimodal LLMs (MLLMs) is limited. Particularly in vision-intensive tasks like geometric reasoning, MLLMs hallucinate frequently, leading to inaccurate reasoning. We attribute this to the perceptual bottleneck in MLLMs, which caps the benefits of reasoning training. To quantify this, we design a Geo-Perception Question-Answering (GeoPQA) benchmark, targeting basic geometric concepts and spatial relationships. Experiments on GeoPQA reveal significant shortcomings of MLLMs in visual perception, which constrain RL reward signals for effective training. To address this bottleneck, we propose a two-stage RL training framework by first enhancing the visual perception of geometric structures, then fostering reasoning capabilities. Applied to Qwen2.5-VL-3B-Instruct, our two-stage training improves geometric reasoning by 9.7% and geometric problem solving by 9.1%, compared to the direct reasoning training approach. Our method also generalizes to other vision-intensive domains like figure understanding, highlighting the importance of perceptual grounding in effective MLLM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17671", "title": "Turk-LettuceDetect: A Hallucination Detection Models for Turkish RAG Applications", "authors": [ "Selva Ta\u015f", "Mahmut El Huseyni", "\u00d6zay Ezerceli", "Reyhan Bayraktar", "Fatma Bet\u00fcl Terzio\u011flu" ], "github": "", "project_page": "", "abstract": "The widespread adoption of Large Language Models (LLMs) has been hindered by their tendency to hallucinate, generating plausible but factually incorrect information. While Retrieval-Augmented Generation (RAG) systems attempt to address this issue by grounding responses in external knowledge, hallucination remains a persistent challenge, particularly for morphologically complex, low-resource languages like Turkish. This paper introduces Turk-LettuceDetect, the first suite of hallucination detection models specifically designed for Turkish RAG applications. Building on the LettuceDetect framework, we formulate hallucination detection as a token-level classification task and fine-tune three distinct encoder architectures: a Turkish-specific ModernBERT, TurkEmbed4STS, and multilingual EuroBERT. These models were trained on a machine-translated version of the RAGTruth benchmark dataset containing 17,790 instances across question answering, data-to-text generation, and summarization tasks. Our experimental results show that the ModernBERT-based model achieves an F1-score of 0.7266 on the complete test set, with particularly strong performance on structured tasks. The models maintain computational efficiency while supporting long contexts up to 8,192 tokens, making them suitable for real-time deployment. Comparative analysis reveals that while state-of-the-art LLMs demonstrate high recall, they suffer from low precision due to over-generation of hallucinated content, underscoring the necessity of specialized detection mechanisms. By releasing our models and translated dataset, this work addresses a critical gap in multilingual NLP and establishes a foundation for developing more reliable and trustworthy AI applications for Turkish and other languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.14856", "title": "CodeFuse-CR-Bench: A Comprehensiveness-aware Benchmark for End-to-End Code Review Evaluation in Python Projects", "authors": [ "Hanyang Guo", "Xunjin Zheng", "Zihan Liao", "Hang Yu", "Peng DI", "Ziyin Zhang", "Hong-Ning Dai" ], "github": "", "project_page": "", "abstract": "Automated code review (CR) is a key application for Large Language Models (LLMs), but progress is hampered by a \"reality gap\": existing benchmarks evaluate models on isolated sub-tasks using simplified, context-poor data. This fails to reflect the holistic context-rich nature of real-world CR. To bridge this gap, we introduce CodeFuse-CR-Bench, the first comprehensiveness-aware benchmark for repository-level CR evaluation. CodeFuse-CR-Bench comprises 601 high-quality instances from 70 Python projects covering nine Pull-Request (PR) problem domains, where each instance provides rich, multi-faceted context including the associated issue, PR details, and repository state, enabling end-to-end evaluation. Beyond superficial metrics, we also propose a novel evaluation framework that combines rule-based checks for location and syntax with model-based judgments of review quality. We present the first large-scale assessment of state-of-the-art LLMs on this comprehensive CR task. Our results establish crucial baselines and reveal that (1) no single LLM dominates all aspects of CR; (2) Gemini 2.5 Pro achieves the highest comprehensive performance; and (3) different LLMs exhibit varying robustness to redundant context. These findings highlight the necessity of holistic, multi-dimensional evaluation and provide actionable insights for advancing truly intelligent yet practical CR assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17191", "title": "VaseVQA: Multimodal Agent and Benchmark for Ancient Greek Pottery", "authors": [ "Jinchao Ge", "Tengfei Cheng", "Biao Wu", "Zeyu Zhang", "Shiya Huang", "Judith Bishop", "Gillian Shepherd", "Meng Fang", "Ling Chen", "Yang Zhao" ], "github": "https://github.com/AIGeeksGroup/VaseVQA", "project_page": "", "abstract": "Analyzing cultural-heritage artifacts remains challenging for MLLMs: general models lack domain expertise, and SFT often overfits superficial patterns, yielding brittle reasoning for authentication and historical attribution. This raises the question of how to equip MLLMs with robust, expert-level reasoning for ancient Greek pottery. We present VaseVL, an SFT-then-RL system that turns evaluation into supervision: we construct a taxonomy of question types, probe the SFT model to localize type-specific performance gaps, and optimize with type-conditioned, compositionality-oriented rewards targeting those gaps. We also release VaseVQA, a comprehensive benchmark of 31,773 images designed to probe deep understanding. Experiments show state-of-the-art results on style classification and historical attribution with marked gains in compositional robustness over SFT-only baselines, validating diagnosis-guided, taxonomy-conditioned reward engineering and providing a reusable resource for future research. Code and dataset will be available at https://github.com/AIGeeksGroup/VaseVQA.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/VaseVQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.16548", "title": "SCAN: Self-Denoising Monte Carlo Annotation for Robust Process Reward Learning", "authors": [ "Yuyang Ding", "Xinyu Shi", "Juntao Li", "Xiaobo Liang", "Zhaopeng Tu", "Min Zhang" ], "github": "https://github.com/yyDing1/SCAN-PRM", "project_page": "", "abstract": "Process reward models (PRMs) offer fine-grained, step-level evaluations that facilitate deeper reasoning processes in large language models (LLMs), proving effective in complex tasks like mathematical reasoning. However, developing PRMs is challenging due to the high cost and limited scalability of human-annotated data. Synthetic data from Monte Carlo (MC) estimation is a promising alternative but suffers from a high noise ratio, which can cause overfitting and hinder large-scale training. In this work, we conduct a preliminary study on the noise distribution in synthetic data from MC estimation, identifying that annotation models tend to both underestimate and overestimate step correctness due to limitations in their annotation capabilities. Building on these insights, we propose Self-Denoising Monte Carlo Annotation (SCAN), an efficient data synthesis and noise-tolerant learning framework. Our key findings indicate that: (1) Even lightweight models (e.g., 1.5B parameters) can produce high-quality annotations through a self-denoising strategy, enabling PRMs to achieve superior performance with only 6% the inference cost required by vanilla MC estimation. (2) With our robust learning strategy, PRMs can effectively learn from this weak supervision, achieving a 39.2 F1 score improvement (from 19.9 to 59.1) in ProcessBench. Despite using only a compact synthetic dataset, our models surpass strong baselines, including those trained on large-scale human-annotated datasets such as PRM800K. Furthermore, performance continues to improve as we scale up the synthetic data, highlighting the potential of SCAN for scalable, cost-efficient, and robust PRM training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yyDing1/SCAN-PRM", "github_api_added_by": "auto" }, { "date": "2025-09-23", "arxiv_id": "2509.18083", "title": "Reasoning Core: A Scalable RL Environment for LLM Symbolic Reasoning", "authors": [ "Valentin Lacombe", "Valentin Quesnel", "Damien Sileo" ], "github": "https://github.com/sileod/reasoning", "project_page": "", "abstract": "We introduce Reasoning Core, a new scalable environment for Reinforcement Learning with Verifiable Rewards (RLVR), designed to advance foundational symbolic reasoning in Large Language Models (LLMs). Unlike existing benchmarks that focus on games or isolated puzzles, Reasoning Core procedurally generates problems across core formal domains, including PDDL planning, first-order logic, context-free grammar parsing, causal reasoning, and system equation solving. The environment is built on key design principles of high-generality problem distributions, verification via external tools, and continuous difficulty control, which together provide a virtually infinite supply of novel training instances. Initial zero-shot evaluations with frontier LLMs confirm the difficulty of Reasoning Core's tasks, positioning it as a promising resource to improve the reasoning capabilities of future models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sileod/reasoning", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17428", "title": "QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models", "authors": [ "Hyesung Jeon", "Seojune Lee", "Beomseok Kang", "Yulhwa Kim", "Jae-Joon Kim" ], "github": "https://github.com/vantaa89/qwha", "project_page": "", "abstract": "The demand for efficient deployment of large language models (LLMs) has driven interest in quantization, which reduces inference cost, and parameter-efficient fine-tuning (PEFT), which lowers training overhead. This motivated the development of quantization-aware PEFT to produce accurate yet efficient quantized models. In this setting, reducing quantization error prior to fine-tuning is crucial for achieving high model accuracy. However, existing methods that rely on low-rank adaptation suffer from limited representational capacity. Recent Fourier-related transform (FT)-based adapters offer greater representational power than low-rank adapters, but their direct integration into quantized models often results in ineffective error reduction and increased computational overhead. To overcome these limitations, we propose QWHA, a method that integrates FT-based adapters into quantized models by employing the Walsh-Hadamard Transform (WHT) as the transform kernel, together with a novel adapter initialization scheme incorporating adaptive parameter selection and value refinement. We demonstrate that QWHA effectively mitigates quantization errors while facilitating fine-tuning, and that its design substantially reduces computational cost. Experimental results show that QWHA consistently outperforms baselines in low-bit quantization accuracy and achieves significant training speedups over existing FT-based adapters. The code is available at https://github.com/vantaa89/qwha.", "github_manual": "", "github_regex": "https://github.com/vantaa89/qwha", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17177", "title": "FlagEval Findings Report: A Preliminary Evaluation of Large Reasoning Models on Automatically Verifiable Textual and Visual Questions", "authors": [ "Bowen Qin", "Chen Yue", "Fang Yin", "Hui Wang", "JG Yao", "Jiakang Liu", "Jing-Shu Zheng", "Miguel Hu Chen", "Richeng Xuan", "Shibei Meng", "Shiqi Zhou", "Teng Dai", "Tong-Shuai Ren", "Wei Cui", "Xi Yang", "Xialin Du", "Xiaojing Xu", "Xue Sun", "Xuejing Li", "Yaming Liu", "Yesheng Liu", "Ying Liu", "Yonghua Lin", "Yu Zhao", "Yunduo Zhang", "Yuwen Luo", "Zheqi He", "Zhiyuan He", "Zhongyuan Wang" ], "github": "https://github.com/flageval-baai/ROME-evaluation", "project_page": "", "abstract": "We conduct a moderate-scale contamination-free (to some extent) evaluation of current large reasoning models (LRMs) with some preliminary findings. We also release ROME, our evaluation benchmark for vision language models intended to test reasoning from visual clues. We attach links to the benchmark, evaluation data, and other updates on this website: https://flageval-baai.github.io/LRM-Eval/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/flageval-baai/ROME-evaluation", "github_api_added_by": "auto" }, { "date": "2025-09-23", "arxiv_id": "2509.17399", "title": "DIWALI - Diversity and Inclusivity aWare cuLture specific Items for India: Dataset and Assessment of LLMs for Cultural Text Adaptation in Indian Context", "authors": [ "Pramit Sahoo", "Maharaj Brahma", "Maunendra Sankar Desarkar" ], "github": "https://github.com/pramitsahoo/culture-evaluation", "project_page": "", "abstract": "Large language models (LLMs) are widely used in various tasks and applications. However, despite their wide capabilities, they are shown to lack cultural alignment ryan-etal-2024-unintended, alkhamissi-etal-2024-investigating and produce biased generations naous-etal-2024-beer due to a lack of cultural knowledge and competence. Evaluation of LLMs for cultural awareness and alignment is particularly challenging due to the lack of proper evaluation metrics and unavailability of culturally grounded datasets representing the vast complexity of cultures at the regional and sub-regional levels. Existing datasets for culture specific items (CSIs) focus primarily on concepts at the regional level and may contain false positives. To address this issue, we introduce a novel CSI dataset for Indian culture, belonging to 17 cultural facets. The dataset comprises sim8k cultural concepts from 36 sub-regions. To measure the cultural competence of LLMs on a cultural text adaptation task, we evaluate the adaptations using the CSIs created, LLM as Judge, and human evaluations from diverse socio-demographic region. Furthermore, we perform quantitative analysis demonstrating selective sub-regional coverage and surface-level adaptations across all considered LLMs. Our dataset is available here: https://huggingface.co/datasets/nlip/DIWALI{https://huggingface.co/datasets/nlip/DIWALI}, project webpage\\href{https://nlip-lab.github.io/nlip/publications/diwali/{https://nlip-lab.github.io/nlip/publications/diwali/}}, and our codebase with model outputs can be found here: https://github.com/pramitsahoo/culture-evaluation{https://github.com/pramitsahoo/culture-evaluation}.", "github_manual": "", "github_regex": "https://github.com/pramitsahoo/culture-evaluation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.18010", "title": "Cross-Attention is Half Explanation in Speech-to-Text Models", "authors": [ "Sara Papi", "Dennis Fucci", "Marco Gaido", "Matteo Negri", "Luisa Bentivogli" ], "github": "https://github.com/hlt-mt/FBK-fairseq/blob/master/fbk_works/XAI_FEATURE_ATTRIBUTION.md", "project_page": "", "abstract": "Cross-attention is a core mechanism in encoder-decoder architectures, widespread in many fields, including speech-to-text (S2T) processing. Its scores have been repurposed for various downstream applications--such as timestamp estimation and audio-text alignment--under the assumption that they reflect the dependencies between input speech representation and the generated text. While the explanatory nature of attention mechanisms has been widely debated in the broader NLP literature, this assumption remains largely unexplored within the speech domain. To address this gap, we assess the explanatory power of cross-attention in S2T models by comparing its scores to input saliency maps derived from feature attribution. Our analysis spans monolingual and multilingual, single-task and multi-task models at multiple scales, and shows that attention scores moderately to strongly align with saliency-based explanations, particularly when aggregated across heads and layers. However, it also shows that cross-attention captures only about 50% of the input relevance and, in the best case, only partially reflects how the decoder attends to the encoder's representations--accounting for just 52-75% of the saliency. These findings uncover fundamental limitations in interpreting cross-attention as an explanatory proxy, suggesting that it offers an informative yet incomplete view of the factors driving predictions in S2T models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hlt-mt/FBK-fairseq/blob/master/fbk_works/XAI_FEATURE_ATTRIBUTION.md", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17277", "title": "BeepBank-500: A Synthetic Earcon Mini-Corpus for UI Sound Research and Psychoacoustics Research", "authors": [ "Mandip Goswami" ], "github": "https://github.com/mandip42/earcons-mini-500", "project_page": "", "abstract": "We introduce BeepBank-500, a compact, fully synthetic earcon/alert dataset (300-500 clips) designed for rapid, rights-clean experimentation in human-computer interaction and audio machine learning. Each clip is generated from a parametric recipe controlling waveform family (sine, square, triangle, FM), fundamental frequency, duration, amplitude envelope, amplitude modulation (AM), and lightweight Schroeder-style reverberation. We use three reverberation settings: dry, and two synthetic rooms denoted 'rir small' ('small') and 'rir medium' ('medium') throughout the paper and in the metadata. We release mono 48 kHz WAV audio (16-bit), a rich metadata table (signal/spectral features), and tiny reproducible baselines for (i) waveform-family classification and (ii) f0 regression on single tones. The corpus targets tasks such as earcon classification, timbre analyses, and onset detection, with clearly stated licensing and limitations. Audio is dedicated to the public domain via CC0-1.0; code is under MIT. Data DOI: https://doi.org/10.5281/zenodo.17172015. Code: https://github.com/mandip42/earcons-mini-500.", "github_manual": "", "github_regex": "https://github.com/mandip42/earcons-mini-500", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.18094", "title": "UniPixel: Unified Object Referring and Segmentation for Pixel-Level Visual Reasoning", "authors": [ "Ye Liu", "Zongyang Ma", "Junfu Pu", "Zhongang Qi", "Yang Wu", "Ying Shan", "Chang Wen Chen" ], "github": "", "project_page": "", "abstract": "Recent advances in Large Multi-modal Models (LMMs) have demonstrated their remarkable success as general-purpose multi-modal assistants, with particular focuses on holistic image- and video-language understanding. Conversely, less attention has been given to scaling fine-grained pixel-level understanding capabilities, where the models are expected to realize pixel-level alignment between visual signals and language semantics. Some previous studies have applied LMMs to related tasks such as region-level captioning and referring expression segmentation. However, these models are limited to performing either referring or segmentation tasks independently and fail to integrate these fine-grained perception capabilities into visual reasoning. To bridge this gap, we propose UniPixel, a large multi-modal model capable of flexibly comprehending visual prompt inputs and generating mask-grounded responses. Our model distinguishes itself by seamlessly integrating pixel-level perception with general visual understanding capabilities. Specifically, UniPixel processes visual prompts and generates relevant masks on demand, and performs subsequent reasoning conditioning on these intermediate pointers during inference, thereby enabling fine-grained pixel-level reasoning. The effectiveness of our approach has been verified on 10 benchmarks across a diverse set of tasks, including pixel-level referring/segmentation and object-centric understanding in images/videos. A novel PixelQA task that jointly requires referring, segmentation, and question answering is also designed to verify the flexibility of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.18058", "title": "Strategic Dishonesty Can Undermine AI Safety Evaluations of Frontier LLM", "authors": [ "Alexander Panfilov", "Evgenii Kortukov", "Kristina Nikoli\u0107", "Matthias Bethge", "Sebastian Lapuschkin", "Wojciech Samek", "Ameya Prabhu", "Maksym Andriushchenko", "Jonas Geiping" ], "github": "https://github.com/MiroMindAI/MiroThinker", "project_page": "", "abstract": "Large language model (LLM) developers aim for their models to be honest, helpful, and harmless. However, when faced with malicious requests, models are trained to refuse, sacrificing helpfulness. We show that frontier LLMs can develop a preference for dishonesty as a new strategy, even when other options are available. Affected models respond to harmful requests with outputs that sound harmful but are subtly incorrect or otherwise harmless in practice. This behavior emerges with hard-to-predict variations even within models from the same model family. We find no apparent cause for the propensity to deceive, but we show that more capable models are better at executing this strategy. Strategic dishonesty already has a practical impact on safety evaluations, as we show that dishonest responses fool all output-based monitors used to detect jailbreaks that we test, rendering benchmark scores unreliable. Further, strategic dishonesty can act like a honeypot against malicious users, which noticeably obfuscates prior jailbreak attacks. While output monitors fail, we show that linear probes on internal activations can be used to reliably detect strategic dishonesty. We validate probes on datasets with verifiable outcomes and by using their features as steering vectors. Overall, we consider strategic dishonesty as a concrete example of a broader concern that alignment of LLMs is hard to control, especially when helpfulness and harmlessness conflict.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiroMindAI/MiroThinker", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17998", "title": "Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs", "authors": [ "Richard Cornelius Suwandi", "Feng Yin", "Juntao Wang", "Renjie Li", "Tsung-Hui Chang", "Sergios Theodoridis" ], "github": "https://github.com/richardcsuwandi/cake", "project_page": "", "abstract": "The efficiency of Bayesian optimization (BO) relies heavily on the choice of the Gaussian process (GP) kernel, which plays a central role in balancing exploration and exploitation under limited evaluation budgets. Traditional BO methods often rely on fixed or heuristic kernel selection strategies, which can result in slow convergence or suboptimal solutions when the chosen kernel is poorly suited to the underlying objective function. To address this limitation, we propose a freshly-baked Context-Aware Kernel Evolution (CAKE) to enhance BO with large language models (LLMs). Concretely, CAKE leverages LLMs as the crossover and mutation operators to adaptively generate and refine GP kernels based on the observed data throughout the optimization process. To maximize the power of CAKE, we further propose BIC-Acquisition Kernel Ranking (BAKER) to select the most effective kernel through balancing the model fit measured by the Bayesian information criterion (BIC) with the expected improvement at each iteration of BO. Extensive experiments demonstrate that our fresh CAKE-based BO method consistently outperforms established baselines across a range of real-world tasks, including hyperparameter optimization, controller tuning, and photonic chip design. Our code is publicly available at https://github.com/cake4bo/cake.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/richardcsuwandi/cake", "github_api_added_by": "user" }, { "date": "2025-09-23", "arxiv_id": "2509.18053", "title": "V2V-GoT: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multimodal Large Language Models and Graph-of-Thoughts", "authors": [ "Hsu-kuang Chiu", "Ryo Hachiuma", "Chien-Yi Wang", "Yu-Chiang Frank Wang", "Min-Hung Chen", "Stephen F. Smith" ], "github": "", "project_page": "", "abstract": "Current state-of-the-art autonomous vehicles could face safety-critical situations when their local sensors are occluded by large nearby objects on the road. Vehicle-to-vehicle (V2V) cooperative autonomous driving has been proposed as a means of addressing this problem, and one recently introduced framework for cooperative autonomous driving has further adopted an approach that incorporates a Multimodal Large Language Model (MLLM) to integrate cooperative perception and planning processes. However, despite the potential benefit of applying graph-of-thoughts reasoning to the MLLM, this idea has not been considered by previous cooperative autonomous driving research. In this paper, we propose a novel graph-of-thoughts framework specifically designed for MLLM-based cooperative autonomous driving. Our graph-of-thoughts includes our proposed novel ideas of occlusion-aware perception and planning-aware prediction. We curate the V2V-GoT-QA dataset and develop the V2V-GoT model for training and testing the cooperative driving graph-of-thoughts. Our experimental results show that our method outperforms other baselines in cooperative perception, prediction, and planning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17786", "title": "Accurate and Efficient Low-Rank Model Merging in Core Space", "authors": [ "Aniello Panariello", "Daniel Marczak", "Simone Magistri", "Angelo Porrello", "Bart\u0142omiej Twardowski", "Andrew D. Bagdanov", "Simone Calderara", "Joost van de Weijer" ], "github": "https://github.com/apanariello4/core-space-merging", "project_page": "", "abstract": "In this paper, we address the challenges associated with merging low-rank adaptations of large neural networks. With the rise of parameter-efficient adaptation techniques, such as Low-Rank Adaptation (LoRA), model fine-tuning has become more accessible. While fine-tuning models with LoRA is highly efficient, existing merging methods often sacrifice this efficiency by merging fully-sized weight matrices. We propose the Core Space merging framework, which enables the merging of LoRA-adapted models within a common alignment basis, thereby preserving the efficiency of low-rank adaptation while substantially improving accuracy across tasks. We further provide a formal proof that projection into Core Space ensures no loss of information and provide a complexity analysis showing the efficiency gains. Extensive empirical results demonstrate that Core Space significantly improves existing merging techniques and achieves state-of-the-art results on both vision and language tasks while utilizing a fraction of the computational resources. Codebase is available at https://github.com/apanariello4/core-space-merging.", "github_manual": "", "github_regex": "https://github.com/apanariello4/core-space-merging", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.17938", "title": "D-REX: A Benchmark for Detecting Deceptive Reasoning in Large Language Models", "authors": [ "Satyapriya Krishna", "Andy Zou", "Rahul Gupta", "Eliot Krzysztof Jones", "Nick Winter", "Dan Hendrycks", "J. Zico Kolter", "Matt Fredrikson", "Spyros Matsoukas" ], "github": "", "project_page": "", "abstract": "The safety and alignment of Large Language Models (LLMs) are critical for their responsible deployment. Current evaluation methods predominantly focus on identifying and preventing overtly harmful outputs. However, they often fail to address a more insidious failure mode: models that produce benign-appearing outputs while operating on malicious or deceptive internal reasoning. This vulnerability, often triggered by sophisticated system prompt injections, allows models to bypass conventional safety filters, posing a significant, underexplored risk. To address this gap, we introduce the Deceptive Reasoning Exposure Suite (D-REX), a novel dataset designed to evaluate the discrepancy between a model's internal reasoning process and its final output. D-REX was constructed through a competitive red-teaming exercise where participants crafted adversarial system prompts to induce such deceptive behaviors. Each sample in D-REX contains the adversarial system prompt, an end-user's test query, the model's seemingly innocuous response, and, crucially, the model's internal chain-of-thought, which reveals the underlying malicious intent. Our benchmark facilitates a new, essential evaluation task: the detection of deceptive alignment. We demonstrate that D-REX presents a significant challenge for existing models and safety mechanisms, highlighting the urgent need for new techniques that scrutinize the internal processes of LLMs, not just their final outputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.04441", "title": "DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation", "authors": [ "Hao-Shu Fang", "Branden Romero", "Yichen Xie", "Arthur Hu", "Bo-Ruei Huang", "Juan Alvarez", "Matthew Kim", "Gabriel Margolis", "Kavya Anbarasu", "Masayoshi Tomizuka", "Edward Adelson", "Pulkit Agrawal" ], "github": "", "project_page": "", "abstract": "We introduce perioperation, a paradigm for robotic data collection that sensorizes and records human manipulation while maximizing the transferability of the data to real robots. We implement this paradigm in DEXOP, a passive hand exoskeleton designed to maximize human ability to collect rich sensory (vision + tactile) data for diverse dexterous manipulation tasks in natural environments. DEXOP mechanically connects human fingers to robot fingers, providing users with direct contact feedback (via proprioception) and mirrors the human hand pose to the passive robot hand to maximize the transfer of demonstrated skills to the robot. The force feedback and pose mirroring make task demonstrations more natural for humans compared to teleoperation, increasing both speed and accuracy. We evaluate DEXOP across a range of dexterous, contact-rich tasks, demonstrating its ability to collect high-quality demonstration data at scale. Policies learned with DEXOP data significantly improve task performance per unit time of data collection compared to teleoperation, making DEXOP a powerful tool for advancing robot dexterity. Our project page is at https://dex-op.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-23", "arxiv_id": "2509.16195", "title": "FocalCodec-Stream: Streaming Low-Bitrate Speech Coding via Causal Distillation", "authors": [ "Luca Della Libera", "Cem Subakan", "Mirco Ravanelli" ], "github": "https://github.com/lucadellalib/focalcodec", "project_page": "", "abstract": "Neural audio codecs are a fundamental component of modern generative audio pipelines. Although recent codecs achieve strong low-bitrate reconstruction and provide powerful representations for downstream tasks, most are non-streamable, limiting their use in real-time applications. We present FocalCodec-Stream, a hybrid codec based on focal modulation that compresses speech into a single binary codebook at 0.55 - 0.80 kbps with a theoretical latency of 80 ms. Our approach combines multi-stage causal distillation of WavLM with targeted architectural improvements, including a lightweight refiner module that enhances quality under latency constraints. Experiments show that FocalCodec-Stream outperforms existing streamable codecs at comparable bitrates, while preserving both semantic and acoustic information. The result is a favorable trade-off between reconstruction quality, downstream task performance, latency, and efficiency. Code and checkpoints will be released at https://github.com/lucadellalib/focalcodec.", "github_manual": "", "github_regex": "https://github.com/lucadellalib/focalcodec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.19249", "title": "Reinforcement Learning on Pre-Training Data", "authors": [ "Siheng Li", "Kejiao Li", "Zenan Xu", "Guanhua Huang", "Evander Yang", "Kun Li", "Haoyuan Wu", "Jiajia Wu", "Zihao Zheng", "Chenchen Zhang", "Kun Shi", "Kyrierl Deng", "Qi Yi", "Ruibin Xiong", "Tingqiang Xu", "Yuhao Jiang", "Jianfeng Yan", "Yuyuan Zeng", "Guanghui Xu", "Jinbao Xue", "Zhijiang Xu", "Zheng Fang", "Shuai Li", "Qibin Liu", "Xiaoxue Li", "Zhuoyu Li", "Yangyu Tao", "Fei Gao", "Cheng Jiang", "Bo Chao Wang", "Kai Liu", "Jianchen Zhu", "Wai Lam", "Wayyt Wang", "Bo Zhou", "Di Wang" ], "github": "", "project_page": "", "abstract": "The growing disparity between the exponential scaling of computational resources and the finite growth of high-quality text data now constrains conventional scaling approaches for large language models (LLMs). To address this challenge, we introduce Reinforcement Learning on Pre-Training data (RLPT), a new training-time scaling paradigm for optimizing LLMs. In contrast to prior approaches that scale training primarily through supervised learning, RLPT enables the policy to autonomously explore meaningful trajectories to learn from pre-training data and improve its capability through reinforcement learning (RL). While existing RL strategies such as reinforcement learning from human feedback (RLHF) and reinforcement learning with verifiable rewards (RLVR) rely on human annotation for reward construction, RLPT eliminates this dependency by deriving reward signals directly from pre-training data. Specifically, it adopts a next-segment reasoning objective, rewarding the policy for accurately predicting subsequent text segments conditioned on the preceding context. This formulation allows RL to be scaled on pre-training data, encouraging the exploration of richer trajectories across broader contexts and thereby fostering more generalizable reasoning skills. Extensive experiments on both general-domain and mathematical reasoning benchmarks across multiple models validate the effectiveness of RLPT. For example, when applied to Qwen3-4B-Base, RLPT yields absolute improvements of 3.0, 5.1, 8.1, 6.0, 6.6, and 5.3 on MMLU, MMLU-Pro, GPQA-Diamond, KOR-Bench, AIME24, and AIME25, respectively. The results further demonstrate favorable scaling behavior, suggesting strong potential for continued gains with more compute. In addition, RLPT provides a solid foundation, extending the reasoning boundaries of LLMs and enhancing RLVR performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.19284", "title": "What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT", "authors": [ "Yunzhen Feng", "Julia Kempe", "Cheng Zhang", "Parag Jain", "Anthony Hartshorn" ], "github": "", "project_page": "", "abstract": "Large reasoning models (LRMs) spend substantial test-time compute on long chain-of-thought (CoT) traces, but what *characterizes* an effective CoT remains unclear. While prior work reports gains from lengthening CoTs and increasing review (revisiting earlier steps) via appended *wait* tokens, recent studies suggest that shorter thinking can outperform longer traces. We therefore conduct a systematic evaluation across ten LRMs on math and scientific reasoning. Contrary to the \"longer-is-better\" narrative, we find that both naive CoT lengthening and increased review are associated with *lower* accuracy. As CoT unfolds step by step, token-level metrics can conflate verbosity with process quality. We introduce a graph view of CoT to extract structure and identify a single statistic-the *Failed-Step Fraction (FSF)*, the fraction of steps in abandoned branches-that consistently outpredicts length and review ratio for correctness across models. To probe causality, we design two interventions. First, we rank candidate CoTs by each metric at test time, where FSF yields the largest pass@1 gains; second, we edit CoTs to remove failed branches, which significantly improves accuracy, indicating that failed branches bias subsequent reasoning. Taken together, these results characterize effective CoTs as those that *fail less* and support *structure-aware* test-time scaling over indiscriminately generating long CoT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.18154", "title": "MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe", "authors": [ "Tianyu Yu", "Zefan Wang", "Chongyi Wang", "Fuwei Huang", "Wenshuo Ma", "Zhihui He", "Tianchi Cai", "Weize Chen", "Yuxiang Huang", "Yuanqian Zhao", "Bokai Xu", "Junbo Cui", "Yingjing Xu", "Liqing Ruan", "Luoyuan Zhang", "Hanyu Liu", "Jingkun Tang", "Hongyuan Liu", "Qining Guo", "Wenhao Hu", "Bingxiang He", "Jie Zhou", "Jie Cai", "Ji Qi", "Zonghao Guo", "Chi Chen", "Guoyang Zeng", "Yuxuan Li", "Ganqu Cui", "Ning Ding", "Xu Han", "Yuan Yao", "Zhiyuan Liu", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) are undergoing rapid progress and represent the frontier of AI development. However, their training and inference efficiency have emerged as a core bottleneck in making MLLMs more accessible and scalable. To address the challenges, we present MiniCPM-V 4.5, an 8B parameter model designed for high efficiency and strong performance. We introduce three core improvements in model architecture, data strategy and training method: a unified 3D-Resampler model architecture for highly compact encoding over images and videos, a unified learning paradigm for document knowledge and text recognition without heavy data engineering, and a hybrid reinforcement learning strategy for proficiency in both short and long reasoning modes. Comprehensive experimental results in OpenCompass evaluation show that MiniCPM-V 4.5 surpasses widely used proprietary models such as GPT-4o-latest, and significantly larger open-source models such as Qwen2.5-VL 72B. Notably, the strong performance is achieved with remarkable efficiency. For example, on the widely adopted VideoMME benchmark, MiniCPM-V 4.5 achieves state-of-the-art performance among models under 30B size, using just 46.7\\% GPU memory cost and 8.7\\% inference time of Qwen2.5-VL 7B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.19296", "title": "Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation", "authors": [ "Sherwin Bahmani", "Tianchang Shen", "Jiawei Ren", "Jiahui Huang", "Yifeng Jiang", "Haithem Turki", "Andrea Tagliasacchi", "David B. Lindell", "Zan Gojcic", "Sanja Fidler", "Huan Ling", "Jun Gao", "Xuanchi Ren" ], "github": "", "project_page": "", "abstract": "The ability to generate virtual environments is crucial for applications ranging from gaming to physical AI domains such as robotics, autonomous driving, and industrial AI. Current learning-based 3D reconstruction methods rely on the availability of captured real-world multi-view data, which is not always readily available. Recent advancements in video diffusion models have shown remarkable imagination capabilities, yet their 2D nature limits the applications to simulation where a robot needs to navigate and interact with the environment. In this paper, we propose a self-distillation framework that aims to distill the implicit 3D knowledge in the video diffusion models into an explicit 3D Gaussian Splatting (3DGS) representation, eliminating the need for multi-view training data. Specifically, we augment the typical RGB decoder with a 3DGS decoder, which is supervised by the output of the RGB decoder. In this approach, the 3DGS decoder can be purely trained with synthetic data generated by video diffusion models. At inference time, our model can synthesize 3D scenes from either a text prompt or a single image for real-time rendering. Our framework further extends to dynamic 3D scene generation from a monocular input video. Experimental results show that our framework achieves state-of-the-art performance in static and dynamic 3D scene generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.19087", "title": "Zero-Shot Multi-Spectral Learning: Reimagining a Generalist Multimodal Gemini 2.5 Model for Remote Sensing Applications", "authors": [ "Ganesh Mallya", "Yotam Gigi", "Dahun Kim", "Maxim Neumann", "Genady Beryozkin", "Tomer Shekel", "Anelia Angelova" ], "github": "", "project_page": "", "abstract": "Multi-spectral imagery plays a crucial role in diverse Remote Sensing applications including land-use classification, environmental monitoring and urban planning. These images are widely adopted because their additional spectral bands correlate strongly with physical materials on the ground, such as ice, water, and vegetation. This allows for more accurate identification, and their public availability from missions, such as Sentinel-2 and Landsat, only adds to their value. Currently, the automatic analysis of such data is predominantly managed through machine learning models specifically trained for multi-spectral input, which are costly to train and support. Furthermore, although providing a lot of utility for Remote Sensing, such additional inputs cannot be used with powerful generalist large multimodal models, which are capable of solving many visual problems, but are not able to understand specialized multi-spectral signals. To address this, we propose a training-free approach which introduces new multi-spectral data in a Zero-Shot-only mode, as inputs to generalist multimodal models, trained on RGB-only inputs. Our approach leverages the multimodal models' understanding of the visual space, and proposes to adapt to inputs to that space, and to inject domain-specific information as instructions into the model. We exemplify this idea with the Gemini2.5 model and observe strong Zero-Shot performance gains of the approach on popular Remote Sensing benchmarks for land cover and land use classification and demonstrate the easy adaptability of Gemini2.5 to new inputs. These results highlight the potential for geospatial professionals, working with non-standard specialized inputs, to easily leverage powerful multimodal models, such as Gemini2.5, to accelerate their work, benefiting from their rich reasoning and contextual capabilities, grounded in the specialized sensor data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.18824", "title": "Hyper-Bagel: A Unified Acceleration Framework for Multimodal Understanding and Generation", "authors": [ "Yanzuo Lu", "Xin Xia", "Manlin Zhang", "Huafeng Kuang", "Jianbin Zheng", "Yuxi Ren", "Xuefeng Xiao" ], "github": "", "project_page": "", "abstract": "Unified multimodal models have recently attracted considerable attention for their remarkable abilities in jointly understanding and generating diverse content. However, as contexts integrate increasingly numerous interleaved multimodal tokens, the iterative processes of diffusion denoising and autoregressive decoding impose significant computational overhead. To address this, we propose Hyper-Bagel, a unified acceleration framework designed to simultaneously speed up both multimodal understanding and generation tasks. Our approach uses a divide-and-conquer strategy, employing speculative decoding for next-token prediction and a multi-stage distillation process for diffusion denoising. The framework delivers substantial performance gains, achieving over a 2x speedup in multimodal understanding. For generative tasks, our resulting lossless 6-NFE model yields a 16.67x speedup in text-to-image generation and a 22x speedup in image editing, all while preserving the high-quality output of the original model. We further develop a highly efficient 1-NFE model that enables near real-time interactive editing and generation. By combining advanced adversarial distillation with human feedback learning, this model achieves ultimate cost-effectiveness and responsiveness, making complex multimodal interactions seamless and instantaneous.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.17083", "title": "HyRF: Hybrid Radiance Fields for Memory-efficient and High-quality Novel View Synthesis", "authors": [ "Zipeng Wang", "Dan Xu" ], "github": "", "project_page": "", "abstract": "Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful alternative to NeRF-based approaches, enabling real-time, high-quality novel view synthesis through explicit, optimizable 3D Gaussians. However, 3DGS suffers from significant memory overhead due to its reliance on per-Gaussian parameters to model view-dependent effects and anisotropic shapes. While recent works propose compressing 3DGS with neural fields, these methods struggle to capture high-frequency spatial variations in Gaussian properties, leading to degraded reconstruction of fine details. We present Hybrid Radiance Fields (HyRF), a novel scene representation that combines the strengths of explicit Gaussians and neural fields. HyRF decomposes the scene into (1) a compact set of explicit Gaussians storing only critical high-frequency parameters and (2) grid-based neural fields that predict remaining properties. To enhance representational capacity, we introduce a decoupled neural field architecture, separately modeling geometry (scale, opacity, rotation) and view-dependent color. Additionally, we propose a hybrid rendering scheme that composites Gaussian splatting with a neural field-predicted background, addressing limitations in distant scene representation. Experiments demonstrate that HyRF achieves state-of-the-art rendering quality while reducing model size by over 20 times compared to 3DGS and maintaining real-time performance. Our project page is available at https://wzpscott.github.io/hyrf/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.19297", "title": "VolSplat: Rethinking Feed-Forward 3D Gaussian Splatting with Voxel-Aligned Prediction", "authors": [ "Weijie Wang", "Yeqing Chen", "Zeyu Zhang", "Hengyu Liu", "Haoxiao Wang", "Zhiyuan Feng", "Wenkang Qin", "Zheng Zhu", "Donny Y. Chen", "Bohan Zhuang" ], "github": "", "project_page": "", "abstract": "Feed-forward 3D Gaussian Splatting (3DGS) has emerged as a highly effective solution for novel view synthesis. Existing methods predominantly rely on a pixel-aligned Gaussian prediction paradigm, where each 2D pixel is mapped to a 3D Gaussian. We rethink this widely adopted formulation and identify several inherent limitations: it renders the reconstructed 3D models heavily dependent on the number of input views, leads to view-biased density distributions, and introduces alignment errors, particularly when source views contain occlusions or low texture. To address these challenges, we introduce VolSplat, a new multi-view feed-forward paradigm that replaces pixel alignment with voxel-aligned Gaussians. By directly predicting Gaussians from a predicted 3D voxel grid, it overcomes pixel alignment's reliance on error-prone 2D feature matching, ensuring robust multi-view consistency. Furthermore, it enables adaptive control over Gaussian density based on 3D scene complexity, yielding more faithful Gaussian point clouds, improved geometric consistency, and enhanced novel-view rendering quality. Experiments on widely used benchmarks including RealEstate10K and ScanNet demonstrate that VolSplat achieves state-of-the-art performance while producing more plausible and view-consistent Gaussian reconstructions. In addition to superior results, our approach establishes a more scalable framework for feed-forward 3D reconstruction with denser and more robust representations, paving the way for further research in wider communities. The video results, code and trained models are available on our project page: https://lhmd.top/volsplat.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.18849", "title": "MAPO: Mixed Advantage Policy Optimization", "authors": [ "Wenke Huang", "Quan Zhang", "Yiyang Fang", "Jian Liang", "Xuankun Rong", "Huanjin Yao", "Guancheng Wan", "Ke Liang", "Wenwen He", "Mingjun Li", "Leszek Rutkowski", "Mang Ye", "Bo Du", "Dacheng Tao" ], "github": "", "project_page": "", "abstract": "Recent advances in reinforcement learning for foundation models, such as Group Relative Policy Optimization (GRPO), have significantly improved the performance of foundation models on reasoning tasks. Notably, the advantage function serves as a central mechanism in GRPO for ranking the trajectory importance. However, existing explorations encounter both advantage reversion and advantage mirror problems, which hinder the reasonable advantage allocation across different query samples. In this work, we propose an easy but effective GRPO strategy, Mixed Advantage Policy Optimization (MAPO). We reveal that the trajectory appears with different certainty and propose the advantage percent deviation for samples with high-certainty trajectories. Furthermore, we dynamically reweight the advantage function for samples with varying trajectory certainty, thereby adaptively configuring the advantage function to account for sample-specific characteristics. Comparison with related state-of-the-art methods, along with ablation studies on different advantage variants, validates the effectiveness of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.19300", "title": "CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target for Better Flow Matching", "authors": [ "Chen Chen", "Pengsheng Guo", "Liangchen Song", "Jiasen Lu", "Rui Qian", "Xinze Wang", "Tsu-Jui Fu", "Wei Liu", "Yinfei Yang", "Alex Schwing" ], "github": "https://github.com/willisma/SiT", "project_page": "", "abstract": "Conditional generative modeling aims to learn a conditional data distribution from samples containing data-condition pairs. For this, diffusion and flow-based methods have attained compelling results. These methods use a learned (flow) model to transport an initial standard Gaussian noise that ignores the condition to the conditional data distribution. The model is hence required to learn both mass transport and conditional injection. To ease the demand on the model, we propose Condition-Aware Reparameterization for Flow Matching (CAR-Flow) -- a lightweight, learned shift that conditions the source, the target, or both distributions. By relocating these distributions, CAR-Flow shortens the probability path the model must learn, leading to faster training in practice. On low-dimensional synthetic data, we visualize and quantify the effects of CAR. On higher-dimensional natural image data (ImageNet-256), equipping SiT-XL/2 with CAR-Flow reduces FID from 2.07 to 1.68, while introducing less than 0.6% additional parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/willisma/SiT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.17321", "title": "OpenGVL - Benchmarking Visual Temporal Progress for Data Curation", "authors": [ "Pawe\u0142 Budzianowski", "Emilia Wi\u015bnios", "Gracjan G\u00f3ral", "Igor Kulakov", "Viktor Petrenko", "Krzysztof Walas" ], "github": "https://github.com/budzianowski/opengvl", "project_page": "", "abstract": "Data scarcity remains one of the most limiting factors in driving progress in robotics. However, the amount of available robotics data in the wild is growing exponentially, creating new opportunities for large-scale data utilization. Reliable temporal task completion prediction could help automatically annotate and curate this data at scale. The Generative Value Learning (GVL) approach was recently proposed, leveraging the knowledge embedded in vision-language models (VLMs) to predict task progress from visual observations. Building upon GVL, we propose OpenGVL, a comprehensive benchmark for estimating task progress across diverse challenging manipulation tasks involving both robotic and human embodiments. We evaluate the capabilities of publicly available open-source foundation models, showing that open-source model families significantly underperform closed-source counterparts, achieving only approximately 70% of their performance on temporal progress prediction tasks. Furthermore, we demonstrate how OpenGVL can serve as a practical tool for automated data curation and filtering, enabling efficient quality assessment of large-scale robotics datasets. We release the benchmark along with the complete codebase at github.com/budzianowski/opengvl{OpenGVL}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/budzianowski/opengvl", "github_api_added_by": "auto" }, { "date": "2025-09-24", "arxiv_id": "2509.18644", "title": "Do You Need Proprioceptive States in Visuomotor Policies?", "authors": [ "Juntu Zhao", "Wenbo Lu", "Di Zhang", "Yufeng Liu", "Yushen Liang", "Tianluo Zhang", "Yifeng Cao", "Junyuan Xie", "Yingdong Hu", "Shengjie Wang", "Junliang Guo", "Dequan Wang", "Yang Gao" ], "github": "", "project_page": "", "abstract": "Imitation-learning-based visuomotor policies have been widely used in robot manipulation, where both visual observations and proprioceptive states are typically adopted together for precise control. However, in this study, we find that this common practice makes the policy overly reliant on the proprioceptive state input, which causes overfitting to the training trajectories and results in poor spatial generalization. On the contrary, we propose the State-free Policy, removing the proprioceptive state input and predicting actions only conditioned on visual observations. The State-free Policy is built in the relative end-effector action space, and should ensure the full task-relevant visual observations, here provided by dual wide-angle wrist cameras. Empirical results demonstrate that the State-free policy achieves significantly stronger spatial generalization than the state-based policy: in real-world tasks such as pick-and-place, challenging shirt-folding, and complex whole-body manipulation, spanning multiple robot embodiments, the average success rate improves from 0\\% to 85\\% in height generalization and from 6\\% to 64\\% in horizontal generalization. Furthermore, they also show advantages in data efficiency and cross-embodiment adaptation, enhancing their practicality for real-world deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.18174", "title": "Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR", "authors": [ "Khalil Hennara", "Muhammad Hreden", "Mohamed Motasim Hamed", "Ahmad Bastati", "Zeina Aldallal", "Sara Chrouf", "Safwan AlModhayan" ], "github": "", "project_page": "", "abstract": "Arabic document OCR remains a challenging task due to the language's cursive script, diverse fonts, diacritics, and right-to-left orientation. While modern Multimodal Large Language Models (MLLMs) have advanced document understanding for high-resource languages, their performance on Arabic remains limited. In this work, we introduce Baseer, a vision-language model fine- tuned specifically for Arabic document OCR. Leveraging a large-scale dataset combining synthetic and real-world documents, Baseer is trained using a decoder-only fine-tuning strategy to adapt a pre-trained MLLM while preserving general visual features. We also present Misraj-DocOCR, a high-quality, expert-verified benchmark designed for rigorous evaluation of Arabic OCR systems. Our experiments show that Baseer significantly outperforms existing open-source and commercial solutions, achieving a WER of 0.25 and establishing a new state-of-the-art in the domain of Arabic document OCR. Our results highlight the benefits of domain-specific adaptation of general-purpose MLLMs and establish a strong baseline for high-accuracy OCR on morphologically rich languages like Arabic.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.13835", "title": "Large Language Models Discriminate Against Speakers of German Dialects", "authors": [ "Minh Duc Bui", "Carolin Holtermann", "Valentin Hofmann", "Anne Lauscher", "Katharina von der Wense" ], "github": "https://github.com/UhhDS/German-Dialect-Bias", "project_page": "", "abstract": "Dialects represent a significant component of human culture and are found across all regions of the world. In Germany, more than 40% of the population speaks a regional dialect (Adler and Hansen, 2022). However, despite cultural importance, individuals speaking dialects often face negative societal stereotypes. We examine whether such stereotypes are mirrored by large language models (LLMs). We draw on the sociolinguistic literature on dialect perception to analyze traits commonly associated with dialect speakers. Based on these traits, we assess the dialect naming bias and dialect usage bias expressed by LLMs in two tasks: an association task and a decision task. To assess a model's dialect usage bias, we construct a novel evaluation corpus that pairs sentences from seven regional German dialects (e.g., Alemannic and Bavarian) with their standard German counterparts. We find that: (1) in the association task, all evaluated LLMs exhibit significant dialect naming and dialect usage bias against German dialect speakers, reflected in negative adjective associations; (2) all models reproduce these dialect naming and dialect usage biases in their decision making; and (3) contrary to prior work showing minimal bias with explicit demographic mentions, we find that explicitly labeling linguistic demographics--German dialect speakers--amplifies bias more than implicit cues like dialect usage.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/UhhDS/German-Dialect-Bias", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.19002", "title": "VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via Travel Video Itinerary Reconstruction", "authors": [ "Hao Wang", "Eiki Murata", "Lingfang Zhang", "Ayako Sato", "So Fukuda", "Ziqi Yin", "Wentao Hu", "Keisuke Nakao", "Yusuke Nakamura", "Sebastian Zwirner", "Yi-Chia Chen", "Hiroyuki Otomo", "Hiroki Ouchi", "Daisuke Kawahara" ], "github": "", "project_page": "", "abstract": "Recent advances in multimodal large language models (MLLMs) have significantly enhanced video understanding capabilities, opening new possibilities for practical applications. Yet current video benchmarks focus largely on indoor scenes or short-range outdoor activities, leaving the challenges associated with long-distance travel largely unexplored. Mastering extended geospatial-temporal trajectories is critical for next-generation MLLMs, underpinning real-world tasks such as embodied-AI planning and navigation. To bridge this gap, we present VIR-Bench, a novel benchmark consisting of 200 travel videos that frames itinerary reconstruction as a challenging task designed to evaluate and push forward MLLMs' geospatial-temporal intelligence. Experimental results reveal that state-of-the-art MLLMs, including proprietary ones, struggle to achieve high scores, underscoring the difficulty of handling videos that span extended spatial and temporal scales. Moreover, we conduct an in-depth case study in which we develop a prototype travel-planning agent that leverages the insights gained from VIR-Bench. The agent's markedly improved itinerary recommendations verify that our evaluation protocol not only benchmarks models effectively but also translates into concrete performance gains in user-facing applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.18090", "title": "GeoSVR: Taming Sparse Voxels for Geometrically Accurate Surface Reconstruction", "authors": [ "Jiahe Li", "Jiawei Zhang", "Youmin Zhang", "Xiao Bai", "Jin Zheng", "Xiaohan Yu", "Lin Gu" ], "github": "https://github.com/Fictionarry/GeoSVR", "project_page": "", "abstract": "Reconstructing accurate surfaces with radiance fields has achieved remarkable progress in recent years. However, prevailing approaches, primarily based on Gaussian Splatting, are increasingly constrained by representational bottlenecks. In this paper, we introduce GeoSVR, an explicit voxel-based framework that explores and extends the under-investigated potential of sparse voxels for achieving accurate, detailed, and complete surface reconstruction. As strengths, sparse voxels support preserving the coverage completeness and geometric clarity, while corresponding challenges also arise from absent scene constraints and locality in surface refinement. To ensure correct scene convergence, we first propose a Voxel-Uncertainty Depth Constraint that maximizes the effect of monocular depth cues while presenting a voxel-oriented uncertainty to avoid quality degradation, enabling effective and robust scene constraints yet preserving highly accurate geometries. Subsequently, Sparse Voxel Surface Regularization is designed to enhance geometric consistency for tiny voxels and facilitate the voxel-based formation of sharp and accurate surfaces. Extensive experiments demonstrate our superior performance compared to existing methods across diverse challenging scenarios, excelling in geometric accuracy, detail preservation, and reconstruction completeness while maintaining high efficiency. Code is available at https://github.com/Fictionarry/GeoSVR.", "github_manual": "", "github_regex": "https://github.com/Fictionarry/GeoSVR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.17349", "title": "Better Late Than Never: Evaluation of Latency Metrics for Simultaneous Speech-to-Text Translation", "authors": [ "Peter Pol\u00e1k", "Sara Papi", "Luisa Bentivogli", "Ond\u0159ej Bojar" ], "github": "", "project_page": "", "abstract": "Simultaneous speech-to-text translation (SimulST) systems have to balance translation quality with latency--the delay between speech input and the translated output. While quality evaluation is well established, accurate latency measurement remains a challenge. Existing metrics often produce inconsistent or misleading results, especially in the widely used short-form setting, where speech is artificially presegmented. In this paper, we present the first comprehensive analysis of SimulST latency metrics across language pairs, systems, and both short- and long-form regimes. We uncover a structural bias in current metrics related to segmentation that undermines fair and meaningful comparisons. To address this, we introduce YAAL (Yet Another Average Lagging), a refined latency metric that delivers more accurate evaluations in the short-form regime. We extend YAAL to LongYAAL for unsegmented audio and propose SoftSegmenter, a novel resegmentation tool based on word-level alignment. Our experiments show that YAAL and LongYAAL outperform popular latency metrics, while SoftSegmenter enhances alignment quality in long-form evaluation, together enabling more reliable assessments of SimulST systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.19274", "title": "DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models' Understanding on Indian Culture", "authors": [ "Arijit Maji", "Raghvendra Kumar", "Akash Ghosh", "Anushka", "Nemil Shah", "Abhilekh Borah", "Vanshika Shah", "Nishant Mishra", "Sriparna Saha" ], "github": "", "project_page": "", "abstract": "We introduce DRISHTIKON, a first-of-its-kind multimodal and multilingual benchmark centered exclusively on Indian culture, designed to evaluate the cultural understanding of generative AI systems. Unlike existing benchmarks with a generic or global scope, DRISHTIKON offers deep, fine-grained coverage across India's diverse regions, spanning 15 languages, covering all states and union territories, and incorporating over 64,000 aligned text-image pairs. The dataset captures rich cultural themes including festivals, attire, cuisines, art forms, and historical heritage amongst many more. We evaluate a wide range of vision-language models (VLMs), including open-source small and large models, proprietary systems, reasoning-specialized VLMs, and Indic-focused models, across zero-shot and chain-of-thought settings. Our results expose key limitations in current models' ability to reason over culturally grounded, multimodal inputs, particularly for low-resource languages and less-documented traditions. DRISHTIKON fills a vital gap in inclusive AI research, offering a robust testbed to advance culturally aware, multimodally competent language technologies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.19170", "title": "Soft Tokens, Hard Truths", "authors": [ "Natasha Butt", "Ariel Kwiatkowski", "Ismail Labiad", "Julia Kempe", "Yann Ollivier" ], "github": "", "project_page": "", "abstract": "The use of continuous instead of discrete tokens during the Chain-of-Thought (CoT) phase of reasoning LLMs has garnered attention recently, based on the intuition that a continuous mixture of discrete tokens could simulate a superposition of several reasoning paths simultaneously. Theoretical results have formally proven that continuous tokens have much greater expressivity and can solve specific problems more efficiently. However, practical use of continuous tokens has been limited by strong training difficulties: previous works either just use continuous tokens at inference time on a pre-trained discrete-token model, or must distill the continuous CoT from ground-truth discrete CoTs and face computational costs that limit the CoT to very few tokens. This is the first work introducing a scalable method to learn continuous CoTs via reinforcement learning (RL), without distilling from reference discrete CoTs. We use \"soft\" tokens: mixtures of tokens together with noise on the input embedding to provide RL exploration. Computational overhead is minimal, enabling us to learn continuous CoTs with hundreds of tokens. On math reasoning benchmarks with Llama and Qwen models up to 8B, training with continuous CoTs match discrete-token CoTs for pass@1 and surpass them for pass@32, showing greater CoT diversity. In systematic comparisons, the best-performing scenario is to train with continuous CoT tokens then use discrete tokens for inference, meaning the \"soft\" models can be deployed in a standard way. Finally, we show continuous CoT RL training better preserves the predictions of the base model on out-of-domain tasks, thus providing a softer touch to the base model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.16506", "title": "CommonForms: A Large, Diverse Dataset for Form Field Detection", "authors": [ "Joe Barrow" ], "github": "https://github.com/jbarrow/commonforms", "project_page": "", "abstract": "This paper introduces CommonForms, a web-scale dataset for form field detection. It casts the problem of form field detection as object detection: given an image of a page, predict the location and type (Text Input, Choice Button, Signature) of form fields. The dataset is constructed by filtering Common Crawl to find PDFs that have fillable elements. Starting with 8 million documents, the filtering process is used to arrive at a final dataset of roughly 55k documents that have over 450k pages. Analysis shows that the dataset contains a diverse mixture of languages and domains; one third of the pages are non-English, and among the 14 classified domains, no domain makes up more than 25% of the dataset. In addition, this paper presents a family of form field detectors, FFDNet-Small and FFDNet-Large, which attain a very high average precision on the CommonForms test set. Each model cost less than $500 to train. Ablation results show that high-resolution inputs are crucial for high-quality form field detection, and that the cleaning process improves data efficiency over using all PDFs that have fillable fields in Common Crawl. A qualitative analysis shows that they outperform a popular, commercially available PDF reader that can prepare forms. Unlike the most popular commercially available solutions, FFDNet can predict checkboxes in addition to text and signature fields. This is, to our knowledge, the first large scale dataset released for form field detection, as well as the first open source models. The dataset, models, and code will be released at https://github.com/jbarrow/commonforms", "github_manual": "", "github_regex": "https://github.com/jbarrow/commonforms", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.18282", "title": "PEEK: Guiding and Minimal Image Representations for Zero-Shot Generalization of Robot Manipulation Policies", "authors": [ "Jesse Zhang", "Marius Memmel", "Kevin Kim", "Dieter Fox", "Jesse Thomason", "Fabio Ramos", "Erdem B\u0131y\u0131k", "Abhishek Gupta", "Anqi Li" ], "github": "", "project_page": "", "abstract": "Robotic manipulation policies often fail to generalize because they must simultaneously learn where to attend, what actions to take, and how to execute them. We argue that high-level reasoning about where and what can be offloaded to vision-language models (VLMs), leaving policies to specialize in how to act. We present PEEK (Policy-agnostic Extraction of Essential Keypoints), which fine-tunes VLMs to predict a unified point-based intermediate representation: 1. end-effector paths specifying what actions to take, and 2. task-relevant masks indicating where to focus. These annotations are directly overlaid onto robot observations, making the representation policy-agnostic and transferable across architectures. To enable scalable training, we introduce an automatic annotation pipeline, generating labeled data across 20+ robot datasets spanning 9 embodiments. In real-world evaluations, PEEK consistently boosts zero-shot generalization, including a 41.4x real-world improvement for a 3D policy trained only in simulation, and 2-3.5x gains for both large VLAs and small manipulation policies. By letting VLMs absorb semantic and visual complexity, PEEK equips manipulation policies with the minimal cues they need--where, what, and how. Website at https://peek-robot.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.18905", "title": "How Far are VLMs from Visual Spatial Intelligence? A Benchmark-Driven Perspective", "authors": [ "Songsong Yu", "Yuxin Chen", "Hao Ju", "Lianjie Jia", "Fuxi Zhang", "Shaofei Huang", "Yuhan Wu", "Rundi Cui", "Binghao Ran", "Zaibin Zhang", "Zhedong Zheng", "Zhipeng Zhang", "Yifan Wang", "Lin Song", "Lijun Wang", "Yanwei Li", "Ying Shan", "Huchuan Lu" ], "github": "", "project_page": "", "abstract": "Visual Spatial Reasoning (VSR) is a core human cognitive ability and a critical requirement for advancing embodied intelligence and autonomous systems. Despite recent progress in Vision-Language Models (VLMs), achieving human-level VSR remains highly challenging due to the complexity of representing and reasoning over three-dimensional space. In this paper, we present a systematic investigation of VSR in VLMs, encompassing a review of existing methodologies across input modalities, model architectures, training strategies, and reasoning mechanisms. Furthermore, we categorize spatial intelligence into three levels of capability, ie, basic perception, spatial understanding, spatial planning, and curate SIBench, a spatial intelligence benchmark encompassing nearly 20 open-source datasets across 23 task settings. Experiments with state-of-the-art VLMs reveal a pronounced gap between perception and reasoning, as models show competence in basic perceptual tasks but consistently underperform in understanding and planning tasks, particularly in numerical estimation, multi-view reasoning, temporal dynamics, and spatial imagination. These findings underscore the substantial challenges that remain in achieving spatial intelligence, while providing both a systematic roadmap and a comprehensive benchmark to drive future research in the field. The related resources of this study are accessible at https://sibench.github.io/Awesome-Visual-Spatial-Reasoning/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.14635", "title": "SWE-QA: Can Language Models Answer Repository-level Code Questions?", "authors": [ "Weihan Peng", "Yuling Shi", "Yuhang Wang", "Xinyun Zhang", "Beijun Shen", "Xiaodong Gu" ], "github": "", "project_page": "", "abstract": "Understanding and reasoning about entire software repositories is an essential capability for intelligent software engineering tools. While existing benchmarks such as CoSQA and CodeQA have advanced the field, they predominantly focus on small, self-contained code snippets. These setups fail to capture the complexity of real-world repositories, where effective understanding and reasoning often require navigating multiple files, understanding software architecture, and grounding answers in long-range code dependencies. In this paper, we present SWE-QA, a repository-level code question answering (QA) benchmark designed to facilitate research on automated QA systems in realistic code environments. SWE-QA involves 576 high-quality question-answer pairs spanning diverse categories, including intention understanding, cross-file reasoning, and multi-hop dependency analysis. To construct SWE-QA, we first crawled 77,100 GitHub issues from 11 popular repositories. Based on an analysis of naturally occurring developer questions extracted from these issues, we developed a two-level taxonomy of repository-level questions and constructed a set of seed questions for each category. For each category, we manually curated and validated questions and collected their corresponding answers. As a prototype application, we further develop SWE-QA-Agent, an agentic framework in which LLM agents reason and act to find answers automatically. We evaluate six advanced LLMs on SWE-QA under various context augmentation strategies. Experimental results highlight the promise of LLMs, particularly our SWE-QA-Agent framework, in addressing repository-level QA, while also revealing open challenges and pointing to future research directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-24", "arxiv_id": "2509.18030", "title": "RadEval: A framework for radiology text evaluation", "authors": [ "Justin Xu", "Xi Zhang", "Javid Abderezaei", "Julie Bauml", "Roger Boodoo", "Fatemeh Haghighi", "Ali Ganjizadeh", "Eric Brattain", "Dave Van Veen", "Zaiqiao Meng", "David Eyre", "Jean-Benoit Delbrouck" ], "github": "", "project_page": "", "abstract": "We introduce RadEval, a unified, open-source framework for evaluating radiology texts. RadEval consolidates a diverse range of metrics, from classic n-gram overlap (BLEU, ROUGE) and contextual measures (BERTScore) to clinical concept-based scores (F1CheXbert, F1RadGraph, RaTEScore, SRR-BERT, TemporalEntityF1) and advanced LLM-based evaluators (GREEN). We refine and standardize implementations, extend GREEN to support multiple imaging modalities with a more lightweight model, and pretrain a domain-specific radiology encoder, demonstrating strong zero-shot retrieval performance. We also release a richly annotated expert dataset with over 450 clinically significant error labels and show how different metrics correlate with radiologist judgment. Finally, RadEval provides statistical testing tools and baseline model evaluations across multiple publicly available datasets, facilitating reproducibility and robust benchmarking in radiology report generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-25", "arxiv_id": "2509.20360", "title": "EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning", "authors": [ "Xuan Ju", "Tianyu Wang", "Yuqian Zhou", "He Zhang", "Qing Liu", "Nanxuan Zhao", "Zhifei Zhang", "Yijun Li", "Yuanhao Cai", "Shaoteng Liu", "Daniil Pakhomov", "Zhe Lin", "Soo Ye Kim", "Qiang Xu" ], "github": "https://github.com/itseez/opencv", "project_page": "", "abstract": "Recent advances in foundation models highlight a clear trend toward unification and scaling, showing emergent capabilities across diverse domains. While image generation and editing have rapidly transitioned from task-specific to unified frameworks, video generation and editing remain fragmented due to architectural limitations and data scarcity. In this work, we introduce EditVerse, a unified framework for image and video generation and editing within a single model. By representing all modalities, i.e., text, image, and video, as a unified token sequence, EditVerse leverages self-attention to achieve robust in-context learning, natural cross-modal knowledge transfer, and flexible handling of inputs and outputs with arbitrary resolutions and durations. To address the lack of video editing training data, we design a scalable data pipeline that curates 232K video editing samples and combines them with large-scale image and video datasets for joint training. Furthermore, we present EditVerseBench, the first benchmark for instruction-based video editing covering diverse tasks and resolutions. Extensive experiments and user studies demonstrate that EditVerse achieves state-of-the-art performance, surpassing existing open-source and commercial models, while exhibiting emergent editing and generation abilities across modalities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/itseez/opencv", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-25", "arxiv_id": "2509.20354", "title": "EmbeddingGemma: Powerful and Lightweight Text Representations", "authors": [ "Henrique Schechter Vera", "Sahil Dua", "Biao Zhang", "Daniel Salz", "Ryan Mullins", "Sindhu Raghuram Panyam", "Sara Smoot", "Iftekhar Naim", "Joe Zou", "Feiyang Chen", "Daniel Cer", "Alice Lisak", "Min Choi", "Lucas Gonzalez", "Omar Sanseviero", "Glenn Cameron", "Ian Ballantyne", "Kat Black", "Kaifeng Chen", "Weiyi Wang", "Zhe Li", "Gus Martins", "Jinhyuk Lee", "Mark Sherwood", "Juyeong Ji", "Renjie Wu", "Jingxiao Zheng", "Jyotinder Singh", "Abheesht Sharma", "Divya Sreepat", "Aashi Jain", "Adham Elarabawy", "AJ Co", "Andreas Doumanoglou", "Babak Samari", "Ben Hora", "Brian Potetz", "Dahun Kim", "Enrique Alfonseca", "Fedor Moiseev", "Feng Han", "Frank Palma Gomez", "Gustavo Hern\u00e1ndez \u00c1brego", "Hesen Zhang", "Hui Hui", "Jay Han", "Karan Gill", "Ke Chen", "Koert Chen", "Madhuri Shanbhogue", "Michael Boratko", "Paul Suganthan", "Sai Meher Karthik Duddu", "Sandeep Mariserla", "Setareh Ariafar", "Shanfeng Zhang", "Shijie Zhang", "Simon Baumgartner", "Sonam Goenka", "Steve Qiu", "Tanmaya Dabral", "Trevor Walker", "Vikram Rao", "Waleed Khawaja", "Wenlei Zhou", "Xiaoqi Ren", "Ye Xia", "Yichang Chen", "Yi-Ting Chen", "Zhe Dong", "Zhongli Ding", "Francesco Visin", "Ga\u00ebl Liu", "Jiageng Zhang", "Kathleen Kenealy", "Michelle Casbon", "Ravin Kumar", "Thomas Mesnard", "Zach Gleicher", "Cormac Brick", "Olivier Lacombe", "Adam Roberts", "Yunhsuan Sung", "Raphael Hoffmann", "Tris Warkentin", "Armand Joulin", "Tom Duerig", "Mojtaba Seyedhosseini" ], "github": "", "project_page": "", "abstract": "We introduce EmbeddingGemma, a new lightweight, open text embedding model based on the Gemma 3 language model family. Our innovative training recipe strategically captures knowledge from larger models via encoder-decoder initialization and geometric embedding distillation. We improve model robustness and expressiveness with a spread-out regularizer, and ensure generalizability by merging checkpoints from varied, optimized mixtures. Evaluated on the Massive Text Embedding Benchmark (MTEB) across multilingual, English, and code domains, EmbeddingGemma (300M) achieves state-of-the-art results. Notably, it outperforms prior top models, both proprietary and open, with fewer than 500M parameters, and provides performance comparable to models double its size, offering an exceptional performance-to-cost ratio. Remarkably, this lead persists when quantizing model weights or truncating embedding outputs. This makes EmbeddingGemma particularly well-suited for low-latency and high-throughput use cases such as on-device applications. We provide ablation studies exploring our key design choices. We release EmbeddingGemma to the community to promote further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-25", "arxiv_id": "2509.20358", "title": "PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation", "authors": [ "Chen Wang", "Chuhao Chen", "Yiming Huang", "Zhiyang Dou", "Yuan Liu", "Jiatao Gu", "Lingjie Liu" ], "github": "https://github.com/cwchenwang/physctrl", "project_page": "", "abstract": "Existing video generation models excel at producing photo-realistic videos from text or images, but often lack physical plausibility and 3D controllability. To overcome these limitations, we introduce PhysCtrl, a novel framework for physics-grounded image-to-video generation with physical parameters and force control. At its core is a generative physics network that learns the distribution of physical dynamics across four materials (elastic, sand, plasticine, and rigid) via a diffusion model conditioned on physics parameters and applied forces. We represent physical dynamics as 3D point trajectories and train on a large-scale synthetic dataset of 550K animations generated by physics simulators. We enhance the diffusion model with a novel spatiotemporal attention block that emulates particle interactions and incorporates physics-based constraints during training to enforce physical plausibility. Experiments show that PhysCtrl generates realistic, physics-grounded motion trajectories which, when used to drive image-to-video models, yield high-fidelity, controllable videos that outperform existing methods in both visual quality and physical plausibility. Project Page: https://cwchenwang.github.io/physctrl", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cwchenwang/physctrl", "github_api_added_by": "auto" }, { "date": "2025-09-25", "arxiv_id": "2509.20328", "title": "Video models are zero-shot learners and reasoners", "authors": [ "Thadd\u00e4us Wiedemer", "Yuxuan Li", "Paul Vicol", "Shixiang Shane Gu", "Nick Matarese", "Kevin Swersky", "Been Kim", "Priyank Jaini", "Robert Geirhos" ], "github": "", "project_page": "", "abstract": "The remarkable zero-shot capabilities of Large Language Models (LLMs) have propelled natural language processing from task-specific models to unified, generalist foundation models. This transformation emerged from simple primitives: large, generative models trained on web-scale data. Curiously, the same primitives apply to today's generative video models. Could video models be on a trajectory towards general-purpose vision understanding, much like LLMs developed general-purpose language understanding? We demonstrate that Veo 3 can solve a broad variety of tasks it wasn't explicitly trained for: segmenting objects, detecting edges, editing images, understanding physical properties, recognizing object affordances, simulating tool use, and more. These abilities to perceive, model, and manipulate the visual world enable early forms of visual reasoning like maze and symmetry solving. Veo's emergent zero-shot capabilities indicate that video models are on a path to becoming unified, generalist vision foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-25", "arxiv_id": "2509.19760", "title": "Logics-Parsing Technical Report", "authors": [ "Xiangyang Chen", "Shuzhao Li", "Xiuwen Zhu", "Yongfan Chen", "Fan Yang", "Cheng Fang", "Lin Qu", "Xiaoxiao Xu", "Hu Wei", "Minggang Wu" ], "github": "https://github.com/alibaba/Logics-Parsing", "project_page": "", "abstract": "Recent advances in Large Vision-Language models (LVLM) have spurred significant progress in document parsing task. Compared to traditional pipeline-based methods, end-to-end paradigms have shown their excellence in converting PDF images into structured outputs through integrated Optical Character Recognition (OCR), table recognition, mathematical formula recognition and so on. However, the absence of explicit analytical stages for document layouts and reading orders limits the LVLM's capability in handling complex document types such as multi-column newspapers or posters. To address this limitation, we propose in this report Logics-Parsing: an end-to-end LVLM-based model augmented with reinforcement learning. Our model incorporates meticulously designed reward mechanisms to optimize complex layout analysis and reading order inference. In addition, we expand the model's versatility by incorporating diverse data types such as chemical formulas and handwritten Chinese characters into supervised fine-tuning. Finally, to enable rigorous evaluation of our approach, we introduce LogicsParsingBench, a curated set of 1,078 page-level PDF images spanning nine major categories and over twenty sub-categories, which will be released later. Comprehensive experiments conducted on LogicsParsingBench have validated the efficacy and State-of-the-art (SOTA) performance of our proposed model across diverse document analysis scenarios. Project Page: https://github.com/alibaba/Logics-Parsing", "github_manual": "", "github_regex": "https://github.com/alibaba/Logics-Parsing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-25", "arxiv_id": "2509.14745", "title": "On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub", "authors": [ "Miku Watanabe", "Hao Li", "Yutaro Kashiwa", "Brittany Reid", "Hajimu Iida", "Ahmed E. Hassan" ], "github": "https://github.com/mmikuu/OnTheUseOfAgenticCoding", "project_page": "", "abstract": "Large language models (LLMs) are increasingly being integrated into software development processes. The ability to generate code and submit pull requests with minimal human intervention, through the use of autonomous AI agents, is poised to become a standard practice. However, little is known about the practical usefulness of these pull requests and the extent to which their contributions are accepted in real-world projects. In this paper, we empirically study 567 GitHub pull requests (PRs) generated using Claude Code, an agentic coding tool, across 157 diverse open-source projects. Our analysis reveals that developers tend to rely on agents for tasks such as refactoring, documentation, and testing. The results indicate that 83.8% of these agent-assisted PRs are eventually accepted and merged by project maintainers, with 54.9% of the merged PRs are integrated without further modification. The remaining 45.1% require additional changes benefit from human revisions, especially for bug fixes, documentation, and adherence to project-specific standards. These findings suggest that while agent-assisted PRs are largely acceptable, they still benefit from human oversight and refinement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mmikuu/OnTheUseOfAgenticCoding", "github_api_added_by": "auto" }, { "date": "2025-09-25", "arxiv_id": "2509.20317", "title": "SIM-CoT: Supervised Implicit Chain-of-Thought", "authors": [ "Xilin Wei", "Xiaoran Liu", "Yuhang Zang", "Xiaoyi Dong", "Yuhang Cao", "Jiaqi Wang", "Xipeng Qiu", "Dahua Lin" ], "github": "", "project_page": "", "abstract": "Implicit Chain-of-Thought (CoT) methods present a promising, token-efficient alternative to explicit CoT reasoning in Large Language Models (LLMs), but a persistent performance gap has limited the application of implicit CoT. We identify a core latent instability issue by scaling the computational budget of implicit CoT approaches: as we increase the number of implicit reasoning tokens to enhance performance, the training process often becomes unstable and collapses. Our analysis reveals that this instability arises from the latent representations becoming homogeneous and losing their semantic diversity, a failure caused by insufficient step-level supervision in existing implicit CoT approaches. To address this issue, we propose SIM-CoT, a plug-and-play training module that introduces step-level supervision to stabilize and enrich the latent reasoning space. Specifically, SIM-CoT employs an auxiliary decoder during training to align each implicit token with its corresponding explicit reasoning step, ensuring that latent states capture distinct and meaningful information. The proposed auxiliary decoder is removed during inference, preserving the computational efficiency of implicit CoT methods with no added overhead. In addition, the auxiliary decoder affords interpretability of implicit reasoning by projecting each latent token onto an explicit reasoning vocabulary, enabling per-step visualization of semantic roles and diagnosis. SIM-CoT significantly enhances both the in-domain accuracy and out-of-domain stability of various implicit CoT methods, boosting baselines like Coconut by +8.2% on GPT-2 and CODI by +3.0% on LLaMA-3.1 8B. Demonstrating strong scalability, SIM-CoT also surpasses the explicit CoT baseline on GPT-2 by 2.1% with 2.3\\times greater token efficiency, while substantially closing the performance gap on larger models like LLaMA-3.1 8B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-25", "arxiv_id": "2509.19244", "title": "Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation", "authors": [ "Shufan Li", "Jiuxiang Gu", "Kangning Liu", "Zhe Lin", "Zijun Wei", "Aditya Grover", "Jason Kuen" ], "github": "", "project_page": "", "abstract": "We propose Lavida-O, a unified Masked Diffusion Model (MDM) for multimodal understanding and generation. Unlike existing multimodal MDMs such as MMaDa and Muddit which only support simple image-level understanding tasks and low-resolution image generation, Lavida-O presents a single framework that enables image-level understanding, object grounding, image editing, and high-resolution (1024px) text-to-image synthesis. Lavida-O incorporates a novel Elastic Mixture-of-Transformers (Elastic-MoT) architecture that couples a lightweight generation branch with a larger understanding branch, supported by token compression, universal text conditioning and stratified sampling for efficient and high-quality generation. Lavida-O further incorporates planning and iterative self-reflection in image generation and editing tasks, seamlessly boosting generation quality with its understanding capabilities. Lavida-O achieves state-of-the-art performance on a wide range of benchmarks including RefCOCO object grounding, GenEval text-to-image generation, and ImgEdit image editing, outperforming existing autoregressive models and continuous diffusion models such as Qwen2.5-VL and FluxKontext-dev, while offering considerable speedup at inference. These advances establish Lavida-O as a new paradigm for scalable multimodal reasoning and generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-25", "arxiv_id": "2509.19580", "title": "LLMs4All: A Review on Large Language Models for Research and Applications in Academic Disciplines", "authors": [ "Yanfang", "Ye", "Zheyuan Zhang", "Tianyi Ma", "Zehong Wang", "Yiyang Li", "Shifu Hou", "Weixiang Sun", "Kaiwen Shi", "Yijun Ma", "Wei Song", "Ahmed Abbasi", "Ying Cheng", "Jane Cleland-Huang", "Steven Corcelli", "Patricia Culligan", "Robert Goulding", "Ming Hu", "Ting Hua", "John Lalor", "Fang Liu", "Tengfei Luo", "Ed Maginn", "Nuno Moniz", "Jason Rohr", "Brett Savoie", "Daniel Slate", "Tom Stapleford", "Matthew Webber", "Olaf Wiest", "Johnny Zhang", "Nitesh Chawla" ], "github": "", "project_page": "", "abstract": "Cutting-edge Artificial Intelligence (AI) techniques keep reshaping our view of the world. For example, Large Language Models (LLMs) based applications such as ChatGPT have shown the capability of generating human-like conversation on extensive topics. Due to the impressive performance on a variety of language-related tasks (e.g., open-domain question answering, translation, and document summarization), one can envision the far-reaching impacts that can be brought by the LLMs with broader real-world applications (e.g., customer service, education and accessibility, and scientific discovery). Inspired by their success, this paper will offer an overview of state-of-the-art LLMs and their integration into a wide range of academic disciplines, including: (1) arts, letters, and law (e.g., history, philosophy, political science, arts and architecture, law), (2) economics and business (e.g., finance, economics, accounting, marketing), and (3) science and engineering (e.g., mathematics, physics and mechanical engineering, chemistry and chemical engineering, life sciences and bioengineering, earth sciences and civil engineering, computer science and electrical engineering). Integrating humanity and technology, in this paper, we will explore how LLMs are shaping research and practice in these fields, while also discussing key limitations, open challenges, and future directions in the era of generative AI. The review of how LLMs are engaged across disciplines-along with key observations and insights-can help researchers and practitioners interested in exploiting LLMs to advance their works in diverse real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-25", "arxiv_id": "2509.16990", "title": "Advancing Speech Understanding in Speech-Aware Language Models with GRPO", "authors": [ "Avishai Elmakies", "Hagai Aronowitz", "Nimrod Shabtay", "Eli Schwartz", "Ron Hoory", "Avihu Dekel" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce a Group Relative Policy Optimization (GRPO)-based method for training Speech-Aware Large Language Models (SALLMs) on open-format speech understanding tasks, such as Spoken Question Answering and Automatic Speech Translation. SALLMs have proven highly effective for speech understanding tasks. GRPO has recently gained traction for its efficiency in training LLMs, and prior work has explored its application to SALLMs, primarily in multiple-choice tasks. Building on this, we focus on open-format tasks that better reflect the generative abilities of the models. Our approach leverages GRPO with BLEU as the reward signal to optimize SALLMs, and we demonstrate empirically that it surpasses standard SFT across several key metrics. Finally, we explore the potential of incorporating off-policy samples within GRPO for these tasks, highlighting avenues for further improvement and further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-25", "arxiv_id": "2509.18400", "title": "ATLAS: Benchmarking and Adapting LLMs for Global Trade via Harmonized Tariff Code Classification", "authors": [ "Pritish Yuvraj", "Siva Devarakonda" ], "github": "", "project_page": "", "abstract": "Accurate classification of products under the Harmonized Tariff Schedule (HTS) is a critical bottleneck in global trade, yet it has received little attention from the machine learning community. Misclassification can halt shipments entirely, with major postal operators suspending deliveries to the U.S. due to incomplete customs documentation. We introduce the first benchmark for HTS code classification, derived from the U.S. Customs Rulings Online Search System (CROSS). Evaluating leading LLMs, we find that our fine-tuned Atlas model (LLaMA-3.3-70B) achieves 40 percent fully correct 10-digit classifications and 57.5 percent correct 6-digit classifications, improvements of 15 points over GPT-5-Thinking and 27.5 points over Gemini-2.5-Pro-Thinking. Beyond accuracy, Atlas is roughly five times cheaper than GPT-5-Thinking and eight times cheaper than Gemini-2.5-Pro-Thinking, and can be self-hosted to guarantee data privacy in high-stakes trade and compliance workflows. While Atlas sets a strong baseline, the benchmark remains highly challenging, with only 40 percent 10-digit accuracy. By releasing both dataset and model, we aim to position HTS classification as a new community benchmark task and invite future work in retrieval, reasoning, and alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-25", "arxiv_id": "2509.18480", "title": "SimpleFold: Folding Proteins is Simpler than You Think", "authors": [ "Yuyang Wang", "Jiarui Lu", "Navdeep Jaitly", "Josh Susskind", "Miguel Angel Bautista" ], "github": "https://github.com/apple/ml-simplefold", "project_page": "", "abstract": "Protein folding models have achieved groundbreaking results typically via a combination of integrating domain knowledge into the architectural blocks and training pipelines. Nonetheless, given the success of generative models across different but related problems, it is natural to question whether these architectural designs are a necessary condition to build performant models. In this paper, we introduce SimpleFold, the first flow-matching based protein folding model that solely uses general purpose transformer blocks. Protein folding models typically employ computationally expensive modules involving triangular updates, explicit pair representations or multiple training objectives curated for this specific domain. Instead, SimpleFold employs standard transformer blocks with adaptive layers and is trained via a generative flow-matching objective with an additional structural term. We scale SimpleFold to 3B parameters and train it on approximately 9M distilled protein structures together with experimental PDB data. On standard folding benchmarks, SimpleFold-3B achieves competitive performance compared to state-of-the-art baselines, in addition SimpleFold demonstrates strong performance in ensemble prediction which is typically difficult for models trained via deterministic reconstruction objectives. Due to its general-purpose architecture, SimpleFold shows efficiency in deployment and inference on consumer-level hardware. SimpleFold challenges the reliance on complex domain-specific architectures designs in protein folding, opening up an alternative design space for future progress.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/apple/ml-simplefold", "github_api_added_by": "auto" }, { "date": "2025-09-25", "arxiv_id": "2509.16080", "title": "kh2d-solver: A Python Library for Idealized Two-Dimensional Incompressible Kelvin-Helmholtz Instability", "authors": [ "Sandy H. S. Herho", "Nurjanna J. Trilaksono", "Faiz R. Fajary", "Gandhi Napitupulu", "Iwan P. Anwar", "Faruq Khadami", "Dasapta E. Irawan" ], "github": "", "project_page": "", "abstract": "We present an open-source Python library for simulating two-dimensional incompressible Kelvin-Helmholtz instabilities in stratified shear flows. The solver employs a fractional-step projection method with spectral Poisson solution via Fast Sine Transform, achieving second-order spatial accuracy. Implementation leverages NumPy, SciPy, and Numba JIT compilation for efficient computation. Four canonical test cases explore Reynolds numbers 1000--5000 and Richardson numbers 0.1--0.3: classical shear layer, double shear configuration, rotating flow, and forced turbulence. Statistical analysis using Shannon entropy and complexity indices reveals that double shear layers achieve 2.8times higher mixing rates than forced turbulence despite lower Reynolds numbers. The solver runs efficiently on standard desktop hardware, with 384times192 grid simulations completing in approximately 31 minutes. Results demonstrate that mixing efficiency depends on instability generation pathways rather than intensity measures alone, challenging Richardson number-based parameterizations and suggesting refinements for subgrid-scale representation in climate models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-25", "arxiv_id": "2509.21164", "title": "Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say", "authors": [ "Jacob Fein-Ashley", "Dhruv Parikh", "Rajgopal Kannan", "Viktor Prasanna" ], "github": "https://github.com/jacobfa/mot", "project_page": "", "abstract": "Open-source Large Language Models (LLMs) increasingly specialize by domain (e.g., math, code, general reasoning), motivating systems that leverage complementary strengths across models. Prior multi-LLM approaches either (i) route a query to one or a few experts and generate independently, (ii) aggregate outputs from each model via costly multi-turn exchanges, or (iii) fuse weights into a single model-typically requiring architectural homogeneity. We introduce Mixture of Thoughts (MoT), a simple method for latent-level collaboration among heterogeneous experts under a global routing scheme. For each query, a lightweight router selects top-K experts and designates a primary expert; uniformly placed interaction layers project hidden states into a shared latent space where the primary expert performs cross-attention over its active (selected) peers. Pre-trained experts remain frozen; only the router and the lightweight interaction layers are trained with a novel joint training objective that improves both the expert selection and inter-expert collaboration. Across five in-distribution (ID) and three out-of-distribution (OOD) benchmarks, MoT surpasses the current routing and aggregation-based state-of-the-art, Avengers, by +0.38% and +2.92%, respectively. Further, MoT significantly outperforms the best-performing single model. It achieves this with single-pass inference, runtime comparable to routing baselines, and none of the overheads of iterative aggregation. MoT offers a simple latent-space mechanism for combining heterogeneous LLMs, a practical step toward broader multi-LLM collaboration. Our code is publicly available at https://github.com/jacobfa/mot.", "github_manual": "", "github_regex": "https://github.com/jacobfa/mot", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.20427", "title": "Seedream 4.0: Toward Next-generation Multimodal Image Generation", "authors": [ "Team Seedream", "Yunpeng Chen", "Yu Gao", "Lixue Gong", "Meng Guo", "Qiushan Guo", "Zhiyao Guo", "Xiaoxia Hou", "Weilin Huang", "Yixuan Huang", "Xiaowen Jian", "Huafeng Kuang", "Zhichao Lai", "Fanshi Li", "Liang Li", "Xiaochen Lian", "Chao Liao", "Liyang Liu", "Wei Liu", "Yanzuo Lu", "Zhengxiong Luo", "Tongtong Ou", "Guang Shi", "Yichun Shi", "Shiqi Sun", "Yu Tian", "Zhi Tian", "Peng Wang", "Rui Wang", "Xun Wang", "Ye Wang", "Guofeng Wu", "Jie Wu", "Wenxu Wu", "Yonghui Wu", "Xin Xia", "Xuefeng Xiao", "Shuang Xu", "Xin Yan", "Ceyuan Yang", "Jianchao Yang", "Zhonghua Zhai", "Chenlin Zhang", "Heng Zhang", "Qi Zhang", "Xinyu Zhang", "Yuwei Zhang", "Shijia Zhao", "Wenliang Zhao", "Wenjia Zhu" ], "github": "", "project_page": "", "abstract": "We introduce Seedream 4.0, an efficient and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image composition within a single framework. We develop a highly efficient diffusion transformer with a powerful VAE which also can reduce the number of image tokens considerably. This allows for efficient training of our model, and enables it to fast generate native high-resolution images (e.g., 1K-4K). Seedream 4.0 is pretrained on billions of text-image pairs spanning diverse taxonomies and knowledge-centric concepts. Comprehensive data collection across hundreds of vertical scenarios, coupled with optimized strategies, ensures stable and large-scale training, with strong generalization. By incorporating a carefully fine-tuned VLM model, we perform multi-modal post-training for training both T2I and image editing tasks jointly. For inference acceleration, we integrate adversarial distillation, distribution matching, and quantization, as well as speculative decoding. It achieves an inference time of up to 1.8 seconds for generating a 2K image (without a LLM/VLM as PE model). Comprehensive evaluations reveal that Seedream 4.0 can achieve state-of-the-art results on both T2I and multimodal image editing. In particular, it demonstrates exceptional multimodal capabilities in complex tasks, including precise image editing and in-context reasoning, and also allows for multi-image reference, and can generate multiple output images. This extends traditional T2I systems into an more interactive and multidimensional creative tool, pushing the boundary of generative AI for both creativity and professional applications. Seedream 4.0 is now accessible on https://www.volcengine.com/experience/ark?launch=seedream.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.20712", "title": "CE-GPPO: Controlling Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning", "authors": [ "Zhenpeng Su", "Leiyu Pan", "Minxuan Lv", "Yuntao Li", "Wenping Hu", "Fuzheng Zhang", "Kun Gai", "Guorui Zhou" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has become a powerful paradigm for optimizing large language models (LLMs) to handle complex reasoning tasks. A core challenge in this process lies in managing policy entropy, which reflects the balance between exploration and exploitation during training. Existing methods, such as proximal policy optimization (PPO) and its variants, discard valuable gradient signals from low-probability tokens due to the clipping mechanism. We systematically analyze the entropy dynamics and reveal that these clipped tokens play a critical yet overlooked role in regulating entropy evolution. We propose Controlling Entropy via Gradient-Preserving Policy Optimization (CE-GPPO), a novel algorithm that reintroduces gradients from clipped tokens in native PPO in a gentle and bounded manner. By controlling the magnitude of gradients from tokens outside the clipping interval, CE-GPPO is able to achieve an exploration-exploitation trade-off. We provide theoretical justification and empirical evidence showing that CE-GPPO effectively mitigates entropy instability. Extensive experiments on mathematical reasoning benchmarks show that CE-GPPO consistently outperforms strong baselines across different model scales.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21268", "title": "MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resources", "authors": [ "Sicong Leng", "Jing Wang", "Jiaxi Li", "Hao Zhang", "Zhiqiang Hu", "Boqiang Zhang", "Yuming Jiang", "Hang Zhang", "Xin Li", "Lidong Bing", "Deli Zhao", "Wei Lu", "Yu Rong", "Aixin Sun", "Shijian Lu" ], "github": "https://github.com/LengSicong/MMR1", "project_page": "", "abstract": "Large multimodal reasoning models have achieved rapid progress, but their advancement is constrained by two major limitations: the absence of open, large-scale, high-quality long chain-of-thought (CoT) data, and the instability of reinforcement learning (RL) algorithms in post-training. Group Relative Policy Optimization (GRPO), the standard framework for RL fine-tuning, is prone to gradient vanishing when reward variance is low, which weakens optimization signals and impairs convergence. This work makes three contributions: (1) We propose Variance-Aware Sampling (VAS), a data selection strategy guided by Variance Promotion Score (VPS) that combines outcome variance and trajectory diversity to promote reward variance and stabilize policy optimization. (2) We release large-scale, carefully curated resources containing ~1.6M long CoT cold-start data and ~15k RL QA pairs, designed to ensure quality, difficulty, and diversity, along with a fully reproducible end-to-end training codebase. (3) We open-source a family of multimodal reasoning models in multiple scales, establishing standardized baselines for the community. Experiments across mathematical reasoning benchmarks demonstrate the effectiveness of both the curated data and the proposed VAS. Comprehensive ablation studies and analyses provide further insight into the contributions of each component. In addition, we theoretically establish that reward variance lower-bounds the expected policy gradient magnitude, with VAS serving as a practical mechanism to realize this guarantee. Our code, data, and checkpoints are available at https://github.com/LengSicong/MMR1.", "github_manual": "", "github_regex": "https://github.com/LengSicong/MMR1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21320", "title": "SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines", "authors": [ "Yizhou Wang", "Chen Tang", "Han Deng", "Jiabei Xiao", "Jiaqi Liu", "Jianyu Wu", "Jun Yao", "Pengze Li", "Encheng Su", "Lintao Wang", "Guohang Zhuang", "Yuchen Ren", "Ben Fei", "Ming Hu", "Xin Chen", "Dongzhan Zhou", "Junjun He", "Xiangyu Yue", "Zhenfei Yin", "Jiamin Wu", "Qihao Zheng", "Yuhao Zhou", "Huihui Xu", "Chenglong Ma", "Yan Lu", "Wenlong Zhang", "Chunfeng Song", "Philip Torr", "Shixiang Tang", "Xinzhu Ma", "Wanli Ouyang", "Lei Bai" ], "github": "https://github.com/open-sciencelab/SciReason", "project_page": "", "abstract": "We present a scientific reasoning foundation model that aligns natural language with heterogeneous scientific representations. The model is pretrained on a 206B-token corpus spanning scientific text, pure sequences, and sequence-text pairs, then aligned via SFT on 40M instructions, annealed cold-start bootstrapping to elicit long-form chain-of-thought, and reinforcement learning with task-specific reward shaping, which instills deliberate scientific reasoning. It supports four capability families, covering up to 103 tasks across workflows: (i) faithful translation between text and scientific formats, (ii) text/knowledge extraction, (iii) property prediction, (iv) property classification, (v) unconditional and conditional sequence generation and design. Compared with specialist systems, our approach broadens instruction coverage, improves cross-domain generalization, and enhances fidelity. We detail data curation and training and show that cross-discipline learning strengthens transfer and downstream reliability. The model, instruct tuning datasets and the evaluation code are open-sourced at https://huggingface.co/SciReason and https://github.com/open-sciencelab/SciReason.", "github_manual": "", "github_regex": "https://github.com/open-sciencelab/SciReason", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21114", "title": "CHARM: Control-point-based 3D Anime Hairstyle Auto-Regressive Modeling", "authors": [ "Yuze He", "Yanning Zhou", "Wang Zhao", "Jingwen Ye", "Yushi Bai", "Kaiwen Xiao", "Yong-Jin Liu", "Zhongqian Sun", "Wei Yang" ], "github": "", "project_page": "", "abstract": "We present CHARM, a novel parametric representation and generative framework for anime hairstyle modeling. While traditional hair modeling methods focus on realistic hair using strand-based or volumetric representations, anime hairstyle exhibits highly stylized, piecewise-structured geometry that challenges existing techniques. Existing works often rely on dense mesh modeling or hand-crafted spline curves, making them inefficient for editing and unsuitable for scalable learning. CHARM introduces a compact, invertible control-point-based parameterization, where a sequence of control points represents each hair card, and each point is encoded with only five geometric parameters. This efficient and accurate representation supports both artist-friendly design and learning-based generation. Built upon this representation, CHARM introduces an autoregressive generative framework that effectively generates anime hairstyles from input images or point clouds. By interpreting anime hairstyles as a sequential \"hair language\", our autoregressive transformer captures both local geometry and global hairstyle topology, resulting in high-fidelity anime hairstyle creation. To facilitate both training and evaluation of anime hairstyle generation, we construct AnimeHair, a large-scale dataset of 37K high-quality anime hairstyles with separated hair cards and processed mesh data. Extensive experiments demonstrate state-of-the-art performance of CHARM in both reconstruction accuracy and generation quality, offering an expressive and scalable solution for anime hairstyle modeling. Project page: https://hyzcluster.github.io/charm/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.20186", "title": "Thinking Augmented Pre-training", "authors": [ "Liang Wang", "Nan Yang", "Shaohan Huang", "Li Dong", "Furu Wei" ], "github": "https://github.com/huggingface/lighteval", "project_page": "", "abstract": "This paper introduces a simple and scalable approach to improve the data efficiency of large language model (LLM) training by augmenting existing text data with thinking trajectories. The compute for pre-training LLMs has been growing at an unprecedented rate, while the availability of high-quality data remains limited. Consequently, maximizing the utility of available data constitutes a significant research challenge. A primary impediment is that certain high-quality tokens are difficult to learn given a fixed model capacity, as the underlying rationale for a single token can be exceptionally complex and deep. To address this issue, we propose Thinking augmented Pre-Training (TPT), a universal methodology that augments text with automatically generated thinking trajectories. Such augmentation effectively increases the volume of the training data and makes high-quality tokens more learnable through step-by-step reasoning and decomposition. We apply TPT across diverse training configurations up to 100B tokens, encompassing pre-training with both constrained and abundant data, as well as mid-training from strong open-source checkpoints. Experimental results indicate that our method substantially improves the performance of LLMs across various model sizes and families. Notably, TPT enhances the data efficiency of LLM pre-training by a factor of 3. For a 3B parameter model, it improves the post-training performance by over 10% on several challenging reasoning benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/lighteval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21318", "title": "SD3.5-Flash: Distribution-Guided Distillation of Generative Flows", "authors": [ "Hmrishav Bandyopadhyay", "Rahim Entezari", "Jim Scott", "Reshinth Adithyan", "Yi-Zhe Song", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "We present SD3.5-Flash, an efficient few-step distillation framework that brings high-quality image generation to accessible consumer devices. Our approach distills computationally prohibitive rectified flow models through a reformulated distribution matching objective tailored specifically for few-step generation. We introduce two key innovations: \"timestep sharing\" to reduce gradient noise and \"split-timestep fine-tuning\" to improve prompt alignment. Combined with comprehensive pipeline optimizations like text encoder restructuring and specialized quantization, our system enables both rapid generation and memory-efficient deployment across different hardware configurations. This democratizes access across the full spectrum of devices, from mobile phones to desktop computers. Through extensive evaluation including large-scale user studies, we demonstrate that SD3.5-Flash consistently outperforms existing few-step methods, making advanced generative AI truly accessible for practical deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21317", "title": "Interactive Recommendation Agent with Active User Commands", "authors": [ "Jiakai Tang", "Yujie Luo", "Xunke Xi", "Fei Sun", "Xueyang Feng", "Sunhao Dai", "Chao Yi", "Dian Chen", "Zhujin Gao", "Yang Li", "Xu Chen", "Wen Chen", "Jian Wu", "Yuning Jiang", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Traditional recommender systems rely on passive feedback mechanisms that limit users to simple choices such as like and dislike. However, these coarse-grained signals fail to capture users' nuanced behavior motivations and intentions. In turn, current systems cannot also distinguish which specific item attributes drive user satisfaction or dissatisfaction, resulting in inaccurate preference modeling. These fundamental limitations create a persistent gap between user intentions and system interpretations, ultimately undermining user satisfaction and harming system effectiveness. To address these limitations, we introduce the Interactive Recommendation Feed (IRF), a pioneering paradigm that enables natural language commands within mainstream recommendation feeds. Unlike traditional systems that confine users to passive implicit behavioral influence, IRF empowers active explicit control over recommendation policies through real-time linguistic commands. To support this paradigm, we develop RecBot, a dual-agent architecture where a Parser Agent transforms linguistic expressions into structured preferences and a Planner Agent dynamically orchestrates adaptive tool chains for on-the-fly policy adjustment. To enable practical deployment, we employ simulation-augmented knowledge distillation to achieve efficient performance while maintaining strong reasoning capabilities. Through extensive offline and long-term online experiments, RecBot shows significant improvements in both user satisfaction and business outcomes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21245", "title": "Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets", "authors": [ "Team Hunyuan3D", "Bowen Zhang", "Chunchao Guo", "Haolin Liu", "Hongyu Yan", "Huiwen Shi", "Jingwei Huang", "Junlin Yu", "Kunhong Li", "Linus", "Penghao Wang", "Qingxiang Lin", "Sicong Liu", "Xianghui Yang", "Yixuan Tang", "Yunfei Zhao", "Zeqiang Lai", "Zhihao Liang", "Zibo Zhao" ], "github": "https://github.com/Tencent-Hunyuan/Hunyuan3D-Omni", "project_page": "", "abstract": "Recent advances in 3D-native generative models have accelerated asset creation for games, film, and design. However, most methods still rely primarily on image or text conditioning and lack fine-grained, cross-modal controls, which limits controllability and practical adoption. To address this gap, we present Hunyuan3D-Omni, a unified framework for fine-grained, controllable 3D asset generation built on Hunyuan3D 2.1. In addition to images, Hunyuan3D-Omni accepts point clouds, voxels, bounding boxes, and skeletal pose priors as conditioning signals, enabling precise control over geometry, topology, and pose. Instead of separate heads for each modality, our model unifies all signals in a single cross-modal architecture. We train with a progressive, difficulty-aware sampling strategy that selects one control modality per example and biases sampling toward harder signals (e.g., skeletal pose) while downweighting easier ones (e.g., point clouds), encouraging robust multi-modal fusion and graceful handling of missing inputs. Experiments show that these additional controls improve generation accuracy, enable geometry-aware transformations, and increase robustness for production workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tencent-Hunyuan/Hunyuan3D-Omni", "github_api_added_by": "auto" }, { "date": "2025-09-26", "arxiv_id": "2509.21070", "title": "ScaleDiff: Scaling Difficult Problems for Advanced Mathematical Reasoning", "authors": [ "Qizhi Pei", "Zhuoshi Pan", "Honglin Lin", "Xin Gao", "Yu Li", "Zinan Tang", "Conghui He", "Rui Yan", "Lijun Wu" ], "github": "https://github.com/QizhiPei/ScaleDiff", "project_page": "", "abstract": "Large Reasoning Models (LRMs) have shown impressive capabilities in complex problem-solving, often benefiting from training on difficult mathematical problems that stimulate intricate reasoning. Recent efforts have explored automated synthesis of mathematical problems by prompting proprietary models or large-scale open-source models from seed data or inherent mathematical concepts. However, scaling up these methods remains challenging due to their high computational/API cost, complexity of prompting, and limited difficulty level of the generated problems. To overcome these limitations, we propose ScaleDiff, a simple yet effective pipeline designed to scale the creation of difficult problems. We efficiently identify difficult problems from existing datasets with only a single forward pass using an adaptive thinking model, which can perceive problem difficulty and automatically switch between \"Thinking\" and \"NoThinking\" modes. We then train a specialized difficult problem generator (DiffGen-8B) on this filtered difficult data, which can produce new difficult problems in large scale, eliminating the need for complex, per-instance prompting and its associated high API costs. Fine-tuning Qwen2.5-Math-7B-Instruct on the ScaleDiff-Math dataset yields a substantial performance increase of 11.3% compared to the original dataset and achieves a 65.9% average accuracy on AIME'24, AIME'25, HMMT-Feb'25, BRUMO'25, and MATH500, outperforming recent strong LRMs like OpenThinker3. Notably, this performance is achieved using the cost-efficient Qwen3-8B model as a teacher, demonstrating that our pipeline can effectively transfer advanced reasoning capabilities without relying on larger, more expensive teacher models. Furthermore, we observe a clear scaling phenomenon in model performance on difficult benchmarks as the quantity of difficult problems increases. Code: https://github.com/QizhiPei/ScaleDiff.", "github_manual": "", "github_regex": "https://github.com/QizhiPei/ScaleDiff", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.20868", "title": "StyleBench: Evaluating thinking styles in Large Language Models", "authors": [ "Junyu Guo", "Shangding Gu", "Ming Jin", "Costas Spanos", "Javad Lavaei" ], "github": "https://github.com/JamesJunyuGuo/Style_Bench", "project_page": "", "abstract": "The effectiveness of Large Language Models (LLMs) is heavily influenced by the reasoning strategies, or styles of thought, employed in their prompts. However, the interplay between these reasoning styles, model architecture, and task type remains poorly understood. To address this, we introduce StyleBench, a comprehensive benchmark for systematically evaluating reasoning styles across diverse tasks and models. We assess five representative reasoning styles, including Chain of Thought (CoT), Tree of Thought (ToT), Algorithm of Thought (AoT), Sketch of Thought (SoT), and Chain-of-Draft (CoD) on five reasoning tasks, using 15 open-source models from major families (LLaMA, Qwen, Mistral, Gemma, GPT-OSS, Phi, and DeepSeek) ranging from 270M to 120B parameters. Our large-scale analysis reveals that no single style is universally optimal. We demonstrate that strategy efficacy is highly contingent on both model scale and task type: search-based methods (AoT, ToT) excel in open-ended problems but require large-scale models, while concise styles (SoT, CoD) achieve radical efficiency gains on well-defined tasks. Furthermore, we identify key behavioral patterns: smaller models frequently fail to follow output instructions and default to guessing, while reasoning robustness emerges as a function of scale. Our findings offer a crucial roadmap for selecting optimal reasoning strategies based on specific constraints, we open source the benchmark in https://github.com/JamesJunyuGuo/Style_Bench.", "github_manual": "", "github_regex": "https://github.com/JamesJunyuGuo/Style_Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.20414", "title": "SceneWeaver: All-in-One 3D Scene Synthesis with an Extensible and Self-Reflective Agent", "authors": [ "Yandan Yang", "Baoxiong Jia", "Shujie Zhang", "Siyuan Huang" ], "github": "https://github.com/Scene-Weaver/SceneWeaver", "project_page": "", "abstract": "Indoor scene synthesis has become increasingly important with the rise of Embodied AI, which requires 3D environments that are not only visually realistic but also physically plausible and functionally diverse. While recent approaches have advanced visual fidelity, they often remain constrained to fixed scene categories, lack sufficient object-level detail and physical consistency, and struggle to align with complex user instructions. In this work, we present SceneWeaver, a reflective agentic framework that unifies diverse scene synthesis paradigms through tool-based iterative refinement. At its core, SceneWeaver employs a language model-based planner to select from a suite of extensible scene generation tools, ranging from data-driven generative models to visual- and LLM-based methods, guided by self-evaluation of physical plausibility, visual realism, and semantic alignment with user input. This closed-loop reason-act-reflect design enables the agent to identify semantic inconsistencies, invoke targeted tools, and update the environment over successive iterations. Extensive experiments on both common and open-vocabulary room types demonstrate that SceneWeaver not only outperforms prior methods on physical, visual, and semantic metrics, but also generalizes effectively to complex scenes with diverse instructions, marking a step toward general-purpose 3D environment generation. Project website: https://scene-weaver.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Scene-Weaver/SceneWeaver", "github_api_added_by": "auto" }, { "date": "2025-09-26", "arxiv_id": "2509.14662", "title": "Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld's Episode Theory", "authors": [ "Ming Li", "Nan Zhang", "Chenrui Fan", "Hong Jiao", "Yanbin Fu", "Sydney Peters", "Qingshu Xu", "Robert Lissitz", "Tianyi Zhou" ], "github": "https://github.com/MingLiiii/Schoenfeld_Reasoning", "project_page": "", "abstract": "While Large Reasoning Models (LRMs) generate extensive chain-of-thought reasoning, we lack a principled framework for understanding how these thoughts are structured. In this paper, we introduce a novel approach by applying Schoenfeld's Episode Theory, a classic cognitive framework for human mathematical problem-solving, to analyze the reasoning traces of LRMs. We annotated thousands of sentences and paragraphs from model-generated solutions to math problems using seven cognitive labels (e.g., Plan, Implement, Verify). The result is the first publicly available benchmark for the fine-grained analysis of machine reasoning, including a large annotated corpus and detailed annotation guidebooks. Our preliminary analysis reveals distinct patterns in LRM reasoning, such as the transition dynamics between cognitive states. This framework provides a theoretically grounded methodology for interpreting LRM cognition and enables future work on more controllable and transparent reasoning systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MingLiiii/Schoenfeld_Reasoning", "github_api_added_by": "auto" }, { "date": "2025-09-26", "arxiv_id": "2509.19803", "title": "VCRL: Variance-based Curriculum Reinforcement Learning for Large Language Models", "authors": [ "Guochao Jiang", "Wenfeng Feng", "Guofeng Quan", "Chuzhan Hao", "Yuewei Zhang", "Guohua Liu", "Hao Wang" ], "github": "", "project_page": "", "abstract": "Policy-based reinforcement learning currently plays an important role in improving LLMs on mathematical reasoning tasks. However, existing rollout-based reinforcement learning methods (GRPO, DAPO, GSPO, etc.) fail to explicitly consider LLMs' learning ability for samples of different difficulty levels, which is contrary to the human cognitive process of mathematical reasoning tasks from easy to difficult. Intuitively, we find that the variance of the rollout group's reward in RLVR partly reflects the difficulty of the current sample for LLMs. Samples that are too easy or too difficult have a lower variance, while samples with moderate difficulty have a higher variance. Based on this, we propose VCRL, a curriculum reinforcement learning framework that dynamically controls the difficulty of training samples based on the variance of group rewards. Experiments on five mathematical benchmarks and two models reveal the advantages of VCRL over the current LLM RL baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21240", "title": "Tree Search for LLM Agent Reinforcement Learning", "authors": [ "Yuxiang Ji", "Ziyu Ma", "Yong Wang", "Guanhua Chen", "Xiangxiang Chu", "Liaoni Wu" ], "github": "", "project_page": "", "abstract": "Recent advances in reinforcement learning (RL) have significantly enhanced the agentic capabilities of large language models (LLMs). In long-term and multi-turn agent tasks, existing approaches driven solely by outcome rewards often suffer from the problem of sparse supervision. To address the challenge, we propose Tree-based Group Relative Policy Optimization (Tree-GRPO), a grouped agent RL method based on tree search, where each tree node represents the complete agent interaction step. By sharing common prefixes, the tree search sampling increases the number of rollouts achievable within a fixed budget of tokens or tool calls. Moreover, we find that the tree-structured trajectory naturally allows the construction of step-wise process supervised signals even using only the outcome reward. Based on this, Tree-GRPO estimates the grouped relative advantages both on intra-tree and inter-tree levels. Through theoretical analysis, we demonstrate that the objective of intra-tree level group relative policy optimization is equivalent to that of step-level direct preference learning. Experiments across 11 datasets and 3 types of QA tasks demonstrate the superiority of the proposed tree-based RL over the chain-based RL method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21113", "title": "MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for Video Temporal Reasoning", "authors": [ "Sicheng Tao", "Jungang Li", "Yibo Yan", "Junyan Zhang", "Yubo Gao", "Hanqian Li", "ShuHang Xun", "Yuxuan Fan", "Hong Chen", "Jianxiang He", "Xuming Hu" ], "github": "", "project_page": "", "abstract": "Video reasoning has emerged as a critical capability for multimodal large language models (MLLMs), requiring models to move beyond static perception toward coherent understanding of temporal dynamics in complex scenes. Yet existing MLLMs often exhibit process inconsistency, where intermediate reasoning drifts from video dynamics even when the final answer is correct, undermining interpretability and robustness. To address this issue, we introduce MOSS-ChatV, a reinforcement learning framework with a Dynamic Time Warping (DTW)-based process reward. This rule-based reward aligns reasoning traces with temporally grounded references, enabling efficient process supervision without auxiliary reward models. We further identify dynamic state prediction as a key measure of video reasoning and construct MOSS-Video, a benchmark with annotated reasoning traces, where the training split is used to fine-tune MOSS-ChatV and the held-out split is reserved for evaluation. MOSS-ChatV achieves 87.2\\% on MOSS-Video (test) and improves performance on general video benchmarks such as MVBench and MMVU. The framework consistently yields gains across different architectures, including Qwen2.5-VL and Phi-2, confirming its broad applicability. Evaluations with GPT-4o-as-judge further show that MOSS-ChatV produces more consistent and stable reasoning traces.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21278", "title": "Does FLUX Already Know How to Perform Physically Plausible Image Composition?", "authors": [ "Shilin Lu", "Zhuming Lian", "Zihan Zhou", "Shaocong Zhang", "Chen Zhao", "Adams Wai-Kin Kong" ], "github": "", "project_page": "", "abstract": "Image composition aims to seamlessly insert a user-specified object into a new scene, but existing models struggle with complex lighting (e.g., accurate shadows, water reflections) and diverse, high-resolution inputs. Modern text-to-image diffusion models (e.g., SD3.5, FLUX) already encode essential physical and resolution priors, yet lack a framework to unleash them without resorting to latent inversion, which often locks object poses into contextually inappropriate orientations, or brittle attention surgery. We propose SHINE, a training-free framework for Seamless, High-fidelity Insertion with Neutralized Errors. SHINE introduces manifold-steered anchor loss, leveraging pretrained customization adapters (e.g., IP-Adapter) to guide latents for faithful subject representation while preserving background integrity. Degradation-suppression guidance and adaptive background blending are proposed to further eliminate low-quality outputs and visible seams. To address the lack of rigorous benchmarks, we introduce ComplexCompo, featuring diverse resolutions and challenging conditions such as low lighting, strong illumination, intricate shadows, and reflective surfaces. Experiments on ComplexCompo and DreamEditBench show state-of-the-art performance on standard metrics (e.g., DINOv2) and human-aligned scores (e.g., DreamSim, ImageReward, VisionReward). Code and benchmark will be publicly available upon publication.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.20878", "title": "The Unanticipated Asymmetry Between Perceptual Optimization and Assessment", "authors": [ "Jiabei Zhang", "Qi Wang", "Siyu Wu", "Du Chen", "Tianhe Wu" ], "github": "", "project_page": "", "abstract": "Perceptual optimization is primarily driven by the fidelity objective, which enforces both semantic consistency and overall visual realism, while the adversarial objective provides complementary refinement by enhancing perceptual sharpness and fine-grained detail. Despite their central role, the correlation between their effectiveness as optimization objectives and their capability as image quality assessment (IQA) metrics remains underexplored. In this work, we conduct a systematic analysis and reveal an unanticipated asymmetry between perceptual optimization and assessment: fidelity metrics that excel in IQA are not necessarily effective for perceptual optimization, with this misalignment emerging more distinctly under adversarial training. In addition, while discriminators effectively suppress artifacts during optimization, their learned representations offer only limited benefits when reused as backbone initializations for IQA models. Beyond this asymmetry, our findings further demonstrate that discriminator design plays a decisive role in shaping optimization, with patch-level and convolutional architectures providing more faithful detail reconstruction than vanilla or Transformer-based alternatives. These insights advance the understanding of loss function design and its connection to IQA transferability, paving the way for more principled approaches to perceptual optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21042", "title": "Behind RoPE: How Does Causal Mask Encode Positional Information?", "authors": [ "Junu Kim", "Xiao Liu", "Zhenghao Lin", "Lei Ji", "Yeyun Gong", "Edward Choi" ], "github": "", "project_page": "", "abstract": "While explicit positional encodings such as RoPE are a primary source of positional information in Transformer decoders, the causal mask also provides positional information. In this work, we prove that the causal mask can induce position-dependent patterns in attention scores, even without parameters or causal dependency in the input. Our theoretical analysis indicates that the induced attention pattern tends to favor nearby query-key pairs, mirroring the behavior of common positional encodings. Empirical analysis confirms that trained models exhibit the same behavior, with learned parameters further amplifying these patterns. Notably, we found that the interaction of causal mask and RoPE distorts RoPE's relative attention score patterns into non-relative ones. We consistently observed this effect in modern large language models, suggesting the importance of considering the causal mask as a source of positional information alongside explicit positional encodings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.20109", "title": "Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving", "authors": [ "Pengxiang Li", "Yinan Zheng", "Yue Wang", "Huimin Wang", "Hang Zhao", "Jingjing Liu", "Xianyuan Zhan", "Kun Zhan", "Xianpeng Lang" ], "github": "", "project_page": "", "abstract": "End-to-End (E2E) solutions have emerged as a mainstream approach for autonomous driving systems, with Vision-Language-Action (VLA) models representing a new paradigm that leverages pre-trained multimodal knowledge from Vision-Language Models (VLMs) to interpret and interact with complex real-world environments. However, these methods remain constrained by the limitations of imitation learning, which struggles to inherently encode physical rules during training. Existing approaches often rely on complex rule-based post-refinement, employ reinforcement learning that remains largely limited to simulation, or utilize diffusion guidance that requires computationally expensive gradient calculations. To address these challenges, we introduce ReflectDrive, a novel learning-based framework that integrates a reflection mechanism for safe trajectory generation via discrete diffusion. We first discretize the two-dimensional driving space to construct an action codebook, enabling the use of pre-trained Diffusion Language Models for planning tasks through fine-tuning. Central to our approach is a safety-aware reflection mechanism that performs iterative self-correction without gradient computation. Our method begins with goal-conditioned trajectory generation to model multi-modal driving behaviors. Based on this, we apply local search methods to identify unsafe tokens and determine feasible solutions, which then serve as safe anchors for inpainting-based regeneration. Evaluated on the NAVSIM benchmark, ReflectDrive demonstrates significant advantages in safety-critical trajectory generation, offering a scalable and reliable solution for autonomous driving systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21117", "title": "TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them", "authors": [ "Yidong Wang", "Yunze Song", "Tingyuan Zhu", "Xuanwang Zhang", "Zhuohao Yu", "Hao Chen", "Chiyu Song", "Qiufeng Wang", "Cunxiang Wang", "Zhen Wu", "Xinyu Dai", "Yue Zhang", "Wei Ye", "Shikun Zhang" ], "github": "https://github.com/TrustJudge/TrustJudge", "project_page": "", "abstract": "The adoption of Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks. We identify two fundamental types of inconsistencies: (1) Score-Comparison Inconsistency, where lower-rated responses outperform higher-scored ones in pairwise comparisons, and (2) Pairwise Transitivity Inconsistency, manifested through circular preference chains (A>B>C>A) and equivalence contradictions (A=B=C\\neq A). We argue that these issues come from information loss in discrete rating systems and ambiguous tie judgments during pairwise evaluation. We propose TrustJudge, a probabilistic framework that addresses these limitations through two key innovations: 1) distribution-sensitive scoring that computes continuous expectations from discrete rating probabilities, preserving information entropy for more precise scoring, and 2) likelihood-aware aggregation that resolves transitivity violations using bidirectional preference probabilities or perplexity. We also formalize the theoretical limitations of current LLM-as-a-judge frameworks and demonstrate how TrustJudge's components overcome them. When evaluated with Llama-3.1-70B-Instruct as judge using our dataset, TrustJudge reduces Score-Comparison inconsistency by 8.43% (from 23.32% to 14.89%) and Pairwise Transitivity inconsistency by 10.82% (from 15.22% to 4.40%), while maintaining higher evaluation accuracy. Our work provides the first systematic analysis of evaluation framework inconsistencies in LLM-as-a-judge paradigms, offering both theoretical insights and practical solutions for reliable automated assessment. The framework demonstrates consistent improvements across various model architectures and scales, enabling more trustworthy LLM evaluation without requiring additional training or human annotations. The codes can be found at https://github.com/TrustJudge/TrustJudge.", "github_manual": "", "github_regex": "https://github.com/TrustJudge/TrustJudge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.20136", "title": "V-GameGym: Visual Game Generation for Code Large Language Models", "authors": [ "Wei Zhang", "Jack Yang", "Renshuai Tao", "Lingzheng Chai", "Shawn Guo", "Jiajun Wu", "Xiaoming Chen", "Ganqu Cui", "Ning Ding", "Xander Xu", "Hu Wei", "Bowen Zhou" ], "github": "https://github.com/alibaba/SKYLENAGE-GameCodeGym", "project_page": "", "abstract": "Code large language models have demonstrated remarkable capabilities in programming tasks, yet current benchmarks primarily focus on single modality rather than visual game development. Most existing code-related benchmarks evaluate syntax correctness and execution accuracy, overlooking critical game-specific metrics such as playability, visual aesthetics, and user engagement that are essential for real-world deployment. To address the gap between current LLM capabilities in algorithmic problem-solving and competitive programming versus the comprehensive requirements of practical game development, we present V-GameGym, a comprehensive benchmark comprising 2,219 high-quality samples across 100 thematic clusters derived from real-world repositories, adopting a novel clustering-based curation methodology to ensure both diversity and structural completeness. Further, we introduce a multimodal evaluation framework with an automated LLM-driven pipeline for visual code synthesis using complete UI sandbox environments. Our extensive analysis reveals that V-GameGym effectively bridges the gap between code generation accuracy and practical game development workflows, providing quantifiable quality metrics for visual programming and interactive element generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alibaba/SKYLENAGE-GameCodeGym", "github_api_added_by": "auto" }, { "date": "2025-09-26", "arxiv_id": "2509.20394", "title": "Blueprints of Trust: AI System Cards for End to End Transparency and Governance", "authors": [ "Huzaifa Sidhpurwala", "Emily Fox", "Garth Mollett", "Florencio Cano Gabarda", "Roman Zhukov" ], "github": "", "project_page": "", "abstract": "This paper introduces the Hazard-Aware System Card (HASC), a novel framework designed to enhance transparency and accountability in the development and deployment of AI systems. The HASC builds upon existing model card and system card concepts by integrating a comprehensive, dynamic record of an AI system's security and safety posture. The framework proposes a standardized system of identifiers, including a novel AI Safety Hazard (ASH) ID, to complement existing security identifiers like CVEs, allowing for clear and consistent communication of fixed flaws. By providing a single, accessible source of truth, the HASC empowers developers and stakeholders to make more informed decisions about AI system safety throughout its lifecycle. Ultimately, we also compare our proposed AI system cards with the ISO/IEC 42001:2023 standard and discuss how they can be used to complement each other, providing greater transparency and accountability for AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.19301", "title": "Residual Off-Policy RL for Finetuning Behavior Cloning Policies", "authors": [ "Lars Ankile", "Zhenyu Jiang", "Rocky Duan", "Guanya Shi", "Pieter Abbeel", "Anusha Nagabandi" ], "github": "", "project_page": "", "abstract": "Recent advances in behavior cloning (BC) have enabled impressive visuomotor control policies. However, these approaches are limited by the quality of human demonstrations, the manual effort required for data collection, and the diminishing returns from increasing offline data. In comparison, reinforcement learning (RL) trains an agent through autonomous interaction with the environment and has shown remarkable success in various domains. Still, training RL policies directly on real-world robots remains challenging due to sample inefficiency, safety concerns, and the difficulty of learning from sparse rewards for long-horizon tasks, especially for high-degree-of-freedom (DoF) systems. We present a recipe that combines the benefits of BC and RL through a residual learning framework. Our approach leverages BC policies as black-box bases and learns lightweight per-step residual corrections via sample-efficient off-policy RL. We demonstrate that our method requires only sparse binary reward signals and can effectively improve manipulation policies on high-degree-of-freedom (DoF) systems in both simulation and the real world. In particular, we demonstrate, to the best of our knowledge, the first successful real-world RL training on a humanoid robot with dexterous hands. Our results demonstrate state-of-the-art performance in various vision-based tasks, pointing towards a practical pathway for deploying RL in the real world. Project website: https://residual-offpolicy-rl.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.20706", "title": "MI-Fuse: Label Fusion for Unsupervised Domain Adaptation with Closed-Source Large-Audio Language Model", "authors": [ "Hsiao-Ying Huang", "Yi-Cheng Lin", "Hung-yi Lee" ], "github": "", "project_page": "", "abstract": "Large audio-language models (LALMs) show strong zero-shot ability on speech tasks, suggesting promise for speech emotion recognition (SER). However, SER in real-world deployments often fails under domain mismatch, where source data are unavailable and powerful LALMs are accessible only through an API. We ask: given only unlabeled target-domain audio and an API-only LALM, can a student model be adapted to outperform the LALM in the target domain? To this end, we propose MI-Fuse, a denoised label fusion framework that supplements the LALM with a source-domain trained SER classifier as an auxiliary teacher. The framework draws multiple stochastic predictions from both teachers, weights their mean distributions by mutual-information-based uncertainty, and stabilizes training with an exponential moving average teacher. Experiments across three public emotion datasets and six cross-domain transfers show consistent gains, with the student surpassing the LALM and outperforming the strongest baseline by 3.9%. This approach strengthens emotion-aware speech systems without sharing source data, enabling realistic adaptation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21302", "title": "Quantized Visual Geometry Grounded Transformer", "authors": [ "Weilun Feng", "Haotong Qin", "Mingqiang Wu", "Chuanguang Yang", "Yuqi Li", "Xiangqi Li", "Zhulin An", "Libo Huang", "Yulun Zhang", "Michele Magno", "Yongjun Xu" ], "github": "https://github.com/wlfeng0509/QuantVGGT", "project_page": "", "abstract": "Learning-based 3D reconstruction models, represented by Visual Geometry Grounded Transformers (VGGTs), have made remarkable progress with the use of large-scale transformers. Their prohibitive computational and memory costs severely hinder real-world deployment. Post-Training Quantization (PTQ) has become a common practice for compressing and accelerating models. However, we empirically observe that PTQ faces unique obstacles when compressing billion-scale VGGTs: the data-independent special tokens induce heavy-tailed activation distributions, while the multi-view nature of 3D data makes calibration sample selection highly unstable. This paper proposes the first Quantization framework for VGGTs, namely QuantVGGT. This mainly relies on two technical contributions: First, we introduce Dual-Smoothed Fine-Grained Quantization, which integrates pre-global Hadamard rotation and post-local channel smoothing to mitigate heavy-tailed distributions and inter-channel variance robustly. Second, we design Noise-Filtered Diverse Sampling, which filters outliers via deep-layer statistics and constructs frame-aware diverse calibration clusters to ensure stable quantization ranges. Comprehensive experiments demonstrate that QuantVGGT achieves the state-of-the-art results across different benchmarks and bit-width, surpassing the previous state-of-the-art generic quantization method with a great margin. We highlight that our 4-bit QuantVGGT can deliver a 3.7times memory reduction and 2.5times acceleration in real-hardware inference, while maintaining reconstruction accuracy above 98\\% of its full-precision counterpart. This demonstrates the vast advantages and practicality of QuantVGGT in resource-constrained scenarios. Our code is released in https://github.com/wlfeng0509/QuantVGGT.", "github_manual": "", "github_regex": "https://github.com/wlfeng0509/QuantVGGT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21138", "title": "AutoIntent: AutoML for Text Classification", "authors": [ "Ilya Alekseev", "Roman Solomatin", "Darina Rustamova", "Denis Kuznetsov" ], "github": "", "project_page": "", "abstract": "AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision threshold tuning, all within a modular, sklearn-like interface. The framework is designed to support multi-label classification and out-of-scope detection. AutoIntent demonstrates superior performance compared to existing AutoML tools on standard intent classification datasets and enables users to balance effectiveness and resource consumption.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21072", "title": "Recon-Act: A Self-Evolving Multi-Agent Browser-Use System via Web Reconnaissance, Tool Generation, and Task Execution", "authors": [ "Kaiwen He", "Zhiwei Wang", "Chenyi Zhuang", "Jinjie Gu" ], "github": "", "project_page": "", "abstract": "Recent years, multimodal models have made remarkable strides and pave the way for intelligent browser use agents. However, when solving tasks on real world webpages in multi-turn, long-horizon trajectories, current agents still suffer from disordered action sequencing and excessive trial and error during execution. This paper introduces Recon-Act, a self-evolving multi-agent framework grounded in Reconnaissance-Action behavioral paradigm. The system comprises a Reconnaissance Team and an Action Team: the former conducts comparative analysis and tool generation, while the latter handles intent decomposition, tool orchestration, and execution. By contrasting the erroneous trajectories with successful ones, the Reconnaissance Team infers remedies, and abstracts them into a unified notion of generalized tools, either expressed as hints or as rule-based codes, and register to the tool archive in real time. The Action Team reinference the process empowered with these targeting tools, thus establishing a closed-loop training pipeline of data-tools-action-feedback. Following the 6 level implementation roadmap proposed in this work, we have currently reached Level 3 (with limited human-in-the-loop intervention). Leveraging generalized tools obtained through reconnaissance, Recon-Act substantially improves adaptability to unseen websites and solvability on long-horizon tasks, and achieves state-of-the-art performance on the challenging VisualWebArena dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.21106", "title": "BESPOKE: Benchmark for Search-Augmented Large Language Model Personalization via Diagnostic Feedback", "authors": [ "Hyunseo Kim", "Sangam Lee", "Kwangwook Seo", "Dongha Lee" ], "github": "", "project_page": "", "abstract": "Search-augmented large language models (LLMs) have advanced information-seeking tasks by integrating retrieval into generation, reducing users' cognitive burden compared to traditional search systems. Yet they remain insufficient for fully addressing diverse user needs, which requires recognizing how the same query can reflect different intents across users and delivering information in preferred forms. While recent systems such as ChatGPT and Gemini attempt personalization by leveraging user histories, systematic evaluation of such personalization is under-explored. To address this gap, we propose BESPOKE, the realistic benchmark for evaluating personalization in search-augmented LLMs. BESPOKE is designed to be both realistic, by collecting authentic chat and search histories directly from humans, and diagnostic, by pairing responses with fine-grained preference scores and feedback. The benchmark is constructed through long-term, deeply engaged human annotation, where human annotators contributed their own histories, authored queries with detailed information needs, and evaluated responses with scores and diagnostic feedback. Leveraging BESPOKE, we conduct systematic analyses that reveal key requirements for effective personalization in information-seeking tasks, providing a foundation for fine-grained evaluation of personalized search-augmented LLMs. Our code and data are available at https://augustinlib.github.io/BESPOKE/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.20293", "title": "When Judgment Becomes Noise: How Design Failures in LLM Judge Benchmarks Silently Undermine Validity", "authors": [ "Benjamin Feuer", "Chiung-Yi Tseng", "Astitwa Sarthak Lathe", "Oussama Elachqar", "John P Dickerson" ], "github": "https://github.com/penfever/judgment-to-noise", "project_page": "", "abstract": "LLM-judged benchmarks are increasingly used to evaluate complex model behaviors, yet their design introduces failure modes absent in conventional ground-truth based benchmarks. We argue that without tight objectives and verifiable constructions, benchmark rankings can produce high-confidence rankings that are in fact largely noise. We introduce two mechanisms to diagnose these issues. Schematic adherence quantifies how much of a judge's overall verdict is explained by the explicit evaluation schema, revealing unexplained variance when judges deviate from their own rubric. Psychometric validity aggregates internal consistency and discriminant validity signals to quantify irreducible uncertainty in any benchmarking run. Applying these tools to Arena-Hard Auto, we find severe schema incoherence and factor collapse across popular judges: for example, unexplained variance exceeding 90 percent for DeepSeek-R1-32B and factor correlations above 0.93 for most criteria. We also show that the ELO-style aggregation used by Arena-Hard Auto collapses and masks genuine ranking uncertainty. Our results highlight design failures that undermine validity and offer actionable principles for building better-scoped, reliability-aware LLM-judged benchmarks. We release our code at https://anonymous.4open.science/r/judgment-to-noise-947D/README.md", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/penfever/judgment-to-noise", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.19676", "title": "Thinking While Listening: Simple Test Time Scaling For Audio Classification", "authors": [ "Prateek Verma", "Mert Pilanci" ], "github": "", "project_page": "", "abstract": "We propose a framework that enables neural models to \"think while listening\" to everyday sounds, thereby enhancing audio classification performance. Motivated by recent advances in the reasoning capabilities of large language models, we address two central questions: (i) how can thinking be incorporated into existing audio classification pipelines to enable reasoning in the category space and improve performance, and (ii) can a new architecture be designed from the ground up to support both thinking and test-time scaling? We demonstrate that in both settings, our models exhibit improved classification accuracy. Leveraging test-time scaling, we observe consistent gains as the number of sampled traces increases. Furthermore, we evaluate two open-source reasoning models, GPT-OSS-20B and Qwen3-14B, showing that while such models are capable of zero-shot reasoning, a lightweight approach--retraining only the embedding matrix of a frozen, smaller model like GPT-2--can surpass the performance of billion-parameter text-based reasoning models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.18293", "title": "Evaluating Large Language Models for Detecting Antisemitism", "authors": [ "Jay Patel", "Hrudayangam Mehta", "Jeremy Blackburn" ], "github": "https://github.com/idramalab/quantify-llm-explanations", "project_page": "", "abstract": "Detecting hateful content is a challenging and important problem. Automated tools, like machine-learning models, can help, but they require continuous training to adapt to the ever-changing landscape of social media. In this work, we evaluate eight open-source LLMs' capability to detect antisemitic content, specifically leveraging in-context definition as a policy guideline. We explore various prompting techniques and design a new CoT-like prompt, Guided-CoT. Guided-CoT handles the in-context policy well, increasing performance across all evaluated models, regardless of decoding configuration, model sizes, or reasoning capability. Notably, Llama 3.1 70B outperforms fine-tuned GPT-3.5. Additionally, we examine LLM errors and introduce metrics to quantify semantic divergence in model-generated rationales, revealing notable differences and paradoxical behaviors among LLMs. Our experiments highlight the differences observed across LLMs' utility, explainability, and reliability.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/idramalab/quantify-llm-explanations", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.19736", "title": "UserRL: Training Interactive User-Centric Agent via Reinforcement Learning", "authors": [ "Cheng Qian", "Zuxin Liu", "Akshara Prabhakar", "Jielin Qiu", "Zhiwei Liu", "Haolin Chen", "Shirley Kokane", "Heng Ji", "Weiran Yao", "Shelby Heinecke", "Silvio Savarese", "Caiming Xiong", "Huan Wang" ], "github": "https://github.com/SalesforceAIResearch/UserRL", "project_page": "", "abstract": "Reinforcement learning (RL) has shown promise in training agentic models that move beyond static benchmarks to engage in dynamic, multi-turn interactions. Yet, the ultimate value of such agents lies in their ability to assist users, a setting where diversity and dynamics of user interaction pose challenges. In this work, we propose UserRL, a unified framework for training and evaluating user-centric abilities through standardized gym environments paired with simulated users. We systematically vary turn-level reward assignment and trajectory-level score calculation to analyze how different formulations affect learning under the GRPO algorithm. Our experiments across Qwen3 models reveal three key findings: (i) SFT cold start is critical for unlocking initial interaction ability and enabling sustained RL improvements; (ii) deliberate trajectory scoring yields more efficient and effective multi-turn interactions; and (iii) while stronger simulated users (e.g., GPT-4o) facilitates training, open-source simulators (e.g., Qwen3-32B) remain a cost-effective and transferable option. Together, these results highlight that careful design of reward shaping and user simulation choice is as crucial as model scale, and establish UserRL as a practical pathway for developing robust user-centric agentic models. All codes and data are public for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SalesforceAIResearch/UserRL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.19228", "title": "CompLLM: Compression for Long Context Q&A", "authors": [ "Gabriele Berton", "Jayakrishnan Unnikrishnan", "Son Tran", "Mubarak Shah" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) face significant computational challenges when processing long contexts due to the quadratic complexity of self-attention. While soft context compression methods, which map input text to smaller latent representations, have shown promise, their real-world adoption is limited. Existing techniques typically compress the context as a single unit, which leads to quadratic compression complexity and an inability to reuse computations across queries with overlapping contexts. In this work, we introduce CompLLM, a soft compression technique designed for practical deployment. Instead of processing the context holistically, CompLLM divides it into segments and compresses each one independently. This simple design choice yields three critical properties: efficiency, as the compression step scales linearly with the context length; scalability, enabling models trained on short sequences (e.g., 1k tokens) to generalize to contexts of 100k tokens; and reusability, allowing compressed segments to be cached and reused across different queries. Our experiments show that with a 2x compression rate, at high context lengths CompLLM speeds up Time To First Token (TTFT) by up to 4x and reduces the KV cache size by 50%. Furthermore, CompLLM achieves performance comparable to that obtained with the uncompressed context, and even surpasses it on very long sequences, demonstrating its effectiveness and practical utility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-26", "arxiv_id": "2509.19282", "title": "OverLayBench: A Benchmark for Layout-to-Image Generation with Dense Overlaps", "authors": [ "Bingnan Li", "Chen-Yu Wang", "Haiyang Xu", "Xiang Zhang", "Ethan Armand", "Divyansh Srivastava", "Xiaojun Shan", "Zeyuan Chen", "Jianwen Xie", "Zhuowen Tu" ], "github": "", "project_page": "", "abstract": "Despite steady progress in layout-to-image generation, current methods still struggle with layouts containing significant overlap between bounding boxes. We identify two primary challenges: (1) large overlapping regions and (2) overlapping instances with minimal semantic distinction. Through both qualitative examples and quantitative analysis, we demonstrate how these factors degrade generation quality. To systematically assess this issue, we introduce OverLayScore, a novel metric that quantifies the complexity of overlapping bounding boxes. Our analysis reveals that existing benchmarks are biased toward simpler cases with low OverLayScore values, limiting their effectiveness in evaluating model performance under more challenging conditions. To bridge this gap, we present OverLayBench, a new benchmark featuring high-quality annotations and a balanced distribution across different levels of OverLayScore. As an initial step toward improving performance on complex overlaps, we also propose CreatiLayout-AM, a model fine-tuned on a curated amodal mask dataset. Together, our contributions lay the groundwork for more robust layout-to-image generation under realistic and challenging scenarios. Project link: https://mlpc-ucsd.github.io/OverLayBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22576", "title": "EPO: Entropy-regularized Policy Optimization for LLM Agents Reinforcement Learning", "authors": [ "Xu Wujiang", "Wentian Zhao", "Zhenting Wang", "Li Yu-Jhe", "Jin Can", "Jin Mingyu", "Mei Kai", "Wan Kun", "Metaxas Dimitris" ], "github": "", "project_page": "", "abstract": "Training LLM agents in multi-turn environments with sparse rewards, where completing a single task requires 30+ turns of interaction within an episode, presents a fundamental challenge for reinforcement learning. We identify a critical failure mode unique to this setting: the exploration-exploitation cascade failure. This cascade begins with early-stage policy premature convergence, where sparse feedback causes agents to commit to flawed, low-entropy strategies. Subsequently, agents enter late-stage policy collapse, where conventional entropy regularization becomes counterproductive, promoting chaotic exploration that destabilizes training. We propose Entropy-regularized Policy Optimization (EPO), a general framework that breaks this failure cycle through three synergistic mechanisms: (1) adopting entropy regularization in multi-turn settings to enhance exploration, (2) an entropy smoothing regularizer that bounds policy entropy within historical averages to prevent abrupt fluctuations, and (3) adaptive phase-based weighting that balances exploration and exploitation across training. Our analysis justifies that EPO guarantees monotonically decreasing entropy variance while maintaining convergence. EPO achieves up to 152% performance improvement on ScienceWorld and up to 19.8% on ALFWorld. Our work demonstrates that multi-turn sparse-reward settings require fundamentally different entropy control than traditional RL, with broad implications for LLM agent training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22622", "title": "LongLive: Real-time Interactive Long Video Generation", "authors": [ "Shuai Yang", "Wei Huang", "Ruihang Chu", "Yicheng Xiao", "Yuyang Zhao", "Xianbang Wang", "Muyang Li", "Enze Xie", "Yingcong Chen", "Yao Lu", "Song Han", "Yukang Chen" ], "github": "", "project_page": "", "abstract": "We present LongLive, a frame-level autoregressive (AR) framework for real-time and interactive long video generation. Long video generation presents challenges in both efficiency and quality. Diffusion and Diffusion-Forcing models can produce high-quality videos but suffer from low efficiency due to bidirectional attention. Causal attention AR models support KV caching for faster inference, but often degrade in quality on long videos due to memory challenges during long-video training. In addition, beyond static prompt-based generation, interactive capabilities, such as streaming prompt inputs, are critical for dynamic content creation, enabling users to guide narratives in real time. This interactive requirement significantly increases complexity, especially in ensuring visual consistency and semantic coherence during prompt transitions. To address these challenges, LongLive adopts a causal, frame-level AR design that integrates a KV-recache mechanism that refreshes cached states with new prompts for smooth, adherent switches; streaming long tuning to enable long video training and to align training and inference (train-long-test-long); and short window attention paired with a frame-level attention sink, shorten as frame sink, preserving long-range consistency while enabling faster generation. With these key designs, LongLive fine-tunes a 1.3B-parameter short-clip model to minute-long generation in just 32 GPU-days. At inference, LongLive sustains 20.7 FPS on a single NVIDIA H100, achieves strong performance on VBench in both short and long videos. LongLive supports up to 240-second videos on a single H100 GPU. LongLive further supports INT8-quantized inference with only marginal quality loss.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22637", "title": "Variational Reasoning for Language Models", "authors": [ "Xiangxin Zhou", "Zichen Liu", "Haonan Wang", "Chao Du", "Min Lin", "Chongxuan Li", "Liang Wang", "Tianyu Pang" ], "github": "https://github.com/sail-sg/variational-reasoning", "project_page": "", "abstract": "We introduce a variational reasoning framework for language models that treats thinking traces as latent variables and optimizes them through variational inference. Starting from the evidence lower bound (ELBO), we extend it to a multi-trace objective for tighter bounds and propose a forward-KL formulation that stabilizes the training of the variational posterior. We further show that rejection sampling finetuning and binary-reward RL, including GRPO, can be interpreted as local forward-KL objectives, where an implicit weighting by model accuracy naturally arises from the derivation and reveals a previously unnoticed bias toward easier questions. We empirically validate our method on the Qwen 2.5 and Qwen 3 model families across a wide range of reasoning tasks. Overall, our work provides a principled probabilistic perspective that unifies variational inference with RL-style methods and yields stable objectives for improving the reasoning ability of language models. Our code is available at https://github.com/sail-sg/variational-reasoning.", "github_manual": "", "github_regex": "https://github.com/sail-sg/variational-reasoning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22638", "title": "Language Models Can Learn from Verbal Feedback Without Scalar Rewards", "authors": [ "Renjie Luo", "Zichen Liu", "Xiangyan Liu", "Chao Du", "Min Lin", "Wenhu Chen", "Wei Lu", "Tianyu Pang" ], "github": "https://github.com/sail-sg/feedback-conditional-policy", "project_page": "", "abstract": "LLMs are often trained with RL from human or AI feedback, yet such methods typically compress nuanced feedback into scalar rewards, discarding much of their richness and inducing scale imbalance. We propose treating verbal feedback as a conditioning signal. Inspired by language priors in text-to-image generation, which enable novel outputs from unseen prompts, we introduce the feedback-conditional policy (FCP). FCP learns directly from response-feedback pairs, approximating the feedback-conditional posterior through maximum likelihood training on offline data. We further develop an online bootstrapping stage where the policy generates under positive conditions and receives fresh feedback to refine itself. This reframes feedback-driven learning as conditional generation rather than reward optimization, offering a more expressive way for LLMs to directly learn from verbal feedback. Our code is available at https://github.com/sail-sg/feedback-conditional-policy.", "github_manual": "", "github_regex": "https://github.com/sail-sg/feedback-conditional-policy", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22611", "title": "Quantile Advantage Estimation for Entropy-Safe Reasoning", "authors": [ "Junkang Wu", "Kexin Huang", "Jiancan Wu", "An Zhang", "Xiang Wang", "Xiangnan He" ], "github": "https://github.com/junkangwu/QAE", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) strengthens LLM reasoning, but training often oscillates between {entropy collapse} and {entropy explosion}. We trace both hazards to the mean baseline used in value-free RL (e.g., GRPO and DAPO), which improperly penalizes negative-advantage samples under reward outliers. We propose {Quantile Advantage Estimation} (QAE), replacing the mean with a group-wise K-quantile baseline. QAE induces a response-level, two-regime gate: on hard queries (p <= 1 - K) it reinforces rare successes, while on easy queries (p > 1 - K) it targets remaining failures. Under first-order softmax updates, we prove {two-sided entropy safety}, giving lower and upper bounds on one-step entropy change that curb explosion and prevent collapse. Empirically, this minimal modification stabilizes entropy, sparsifies credit assignment (with tuned K, roughly 80% of responses receive zero advantage), and yields sustained pass@1 gains on Qwen3-8B/14B-Base across AIME 2024/2025 and AMC 2023. These results identify {baseline design} -- rather than token-level heuristics -- as the primary mechanism for scaling RLVR.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/junkangwu/QAE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22651", "title": "VoiceAssistant-Eval: Benchmarking AI Assistants across Listening, Speaking, and Viewing", "authors": [ "Ke Wang", "Houxing Ren", "Zimu Lu", "Mingjie Zhan", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "The growing capabilities of large language models and multimodal systems have spurred interest in voice-first AI assistants, yet existing benchmarks are inadequate for evaluating the full range of these systems' capabilities. We introduce VoiceAssistant-Eval, a comprehensive benchmark designed to assess AI assistants across listening, speaking, and viewing. VoiceAssistant-Eval comprises 10,497 curated examples spanning 13 task categories. These tasks include natural sounds, music, and spoken dialogue for listening; multi-turn dialogue, role-play imitation, and various scenarios for speaking; and highly heterogeneous images for viewing. To demonstrate its utility, we evaluate 21 open-source models and GPT-4o-Audio, measuring the quality of the response content and speech, as well as their consistency. The results reveal three key findings: (1) proprietary models do not universally outperform open-source models; (2) most models excel at speaking tasks but lag in audio understanding; and (3) well-designed smaller models can rival much larger ones. Notably, the mid-sized Step-Audio-2-mini (7B) achieves more than double the listening accuracy of LLaMA-Omni2-32B-Bilingual. However, challenges remain: multimodal (audio plus visual) input and role-play voice imitation tasks are difficult for current models, and significant gaps persist in robustness and safety alignment. VoiceAssistant-Eval identifies these gaps and establishes a rigorous framework for evaluating and guiding the development of next-generation AI assistants. Code and data will be released at https://mathllm.github.io/VoiceAssistantEval/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21760", "title": "UniVid: Unifying Vision Tasks with Pre-trained Video Generation Models", "authors": [ "Lan Chen", "Yuchao Gu", "Qi Mao" ], "github": "https://github.com/CUC-MIPG/UniVid", "project_page": "", "abstract": "Large language models, trained on extensive corpora, successfully unify diverse linguistic tasks within a single generative framework. Inspired by this, recent works like Large Vision Model (LVM) extend this paradigm to vision by organizing tasks into sequential visual sentences, where visual prompts serve as the context to guide outputs. However, such modeling requires task-specific pre-training across modalities and sources, which is costly and limits scalability to unseen tasks. Given that pre-trained video generation models inherently capture temporal sequence dependencies, we explore a more unified and scalable alternative: can a pre-trained video generation model adapt to diverse image and video tasks? To answer this, we propose UniVid, a framework that fine-tunes a video diffusion transformer to handle various vision tasks without task-specific modifications. Tasks are represented as visual sentences, where the context sequence defines both the task and the expected output modality. We evaluate the generalization of UniVid from two perspectives: (1) cross-modal inference with contexts composed of both images and videos, extending beyond LVM's uni-modal setting; (2) cross-source tasks from natural to annotated data, without multi-source pre-training. Despite being trained solely on natural video data, UniVid generalizes well in both settings. Notably, understanding and generation tasks can easily switch by simply reversing the visual sentence order in this paradigm. These findings highlight the potential of pre-trained video generation models to serve as a scalable and unified foundation for vision modeling. Our code will be released at https://github.com/CUC-MIPG/UniVid.", "github_manual": "", "github_regex": "https://github.com/CUC-MIPG/UniVid", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22647", "title": "CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning", "authors": [ "Long Xing", "Xiaoyi Dong", "Yuhang Zang", "Yuhang Cao", "Jianze Liang", "Qidong Huang", "Jiaqi Wang", "Feng Wu", "Dahua Lin" ], "github": "https://github.com/InternLM/CapRL", "project_page": "", "abstract": "Image captioning is a fundamental task that bridges the visual and linguistic domains, playing a critical role in pre-training Large Vision-Language Models (LVLMs). Current state-of-the-art captioning models are typically trained with Supervised Fine-Tuning (SFT), a paradigm that relies on expensive, non-scalable data annotated by humans or proprietary models. This approach often leads to models that memorize specific ground-truth answers, limiting their generality and ability to generate diverse, creative descriptions. To overcome the limitation of SFT, we propose applying the Reinforcement Learning with Verifiable Rewards (RLVR) paradigm to the open-ended task of image captioning. A primary challenge, however, is designing an objective reward function for the inherently subjective nature of what constitutes a \"good\" caption. We introduce Captioning Reinforcement Learning (CapRL), a novel training framework that redefines caption quality through its utility: a high-quality caption should enable a non-visual language model to accurately answer questions about the corresponding image. CapRL employs a decoupled two-stage pipeline where an LVLM generates a caption, and the objective reward is derived from the accuracy of a separate, vision-free LLM answering Multiple-Choice Questions based solely on that caption. As the first study to apply RLVR to the subjective image captioning task, we demonstrate that CapRL significantly enhances multiple settings. Pretraining on the CapRL-5M caption dataset annotated by CapRL-3B results in substantial gains across 12 benchmarks. Moreover, within the Prism Framework for caption quality evaluation, CapRL achieves performance comparable to Qwen2.5-VL-72B, while exceeding the baseline by an average margin of 8.4%. Code is available here: https://github.com/InternLM/CapRL.", "github_manual": "", "github_regex": "https://github.com/InternLM/CapRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22186", "title": "MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing", "authors": [ "Junbo Niu", "Zheng Liu", "Zhuangcheng Gu", "Bin Wang", "Linke Ouyang", "Zhiyuan Zhao", "Tao Chu", "Tianyao He", "Fan Wu", "Qintong Zhang", "Zhenjiang Jin", "Guang Liang", "Rui Zhang", "Wenzheng Zhang", "Yuan Qu", "Zhifei Ren", "Yuefeng Sun", "Yuanhong Zheng", "Dongsheng Ma", "Zirui Tang", "Boyu Niu", "Ziyang Miao", "Hejun Dong", "Siyi Qian", "Junyuan Zhang", "Jingzhou Chen", "Fangdong Wang", "Xiaomeng Zhao", "Liqun Wei", "Wei Li", "Shasha Wang", "Ruiliang Xu", "Yuanyuan Cao", "Lu Chen", "Qianqian Wu", "Huaiyu Gu", "Lindong Lu", "Keming Wang", "Dechen Lin", "Guanlin Shen", "Xuanhe Zhou", "Linfeng Zhang", "Yuhang Zang", "Xiaoyi Dong", "Jiaqi Wang", "Bo Zhang", "Lei Bai", "Pei Chu", "Weijia Li", "Jiang Wu", "Lijun Wu", "Zhenxiang Li", "Guangyu Wang", "Zhongying Tu", "Chao Xu", "Kai Chen", "Yu Qiao", "Bowen Zhou", "Dahua Lin", "Wentao Zhang", "Conghui He" ], "github": "https://github.com/MigoXLab/dingo", "project_page": "", "abstract": "We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MigoXLab/dingo", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21766", "title": "UltraHorizon: Benchmarking Agent Capabilities in Ultra Long-Horizon Scenarios", "authors": [ "Haotian Luo", "Huaisong Zhang", "Xuelin Zhang", "Haoyu Wang", "Zeyu Qin", "Wenjie Lu", "Guozheng Ma", "Haiying He", "Yingsha Xie", "Qiyang Zhou", "Zixuan Hu", "Hongze Mi", "Yibo Wang", "Naiqiang Tan", "Hong Chen", "Yi R. Fung", "Chun Yuan", "Li Shen" ], "github": "https://github.com/StarDewXXX/UltraHorizon", "project_page": "", "abstract": "Autonomous agents have recently achieved remarkable progress across diverse domains, yet most evaluations focus on short-horizon, fully observable tasks. In contrast, many critical real-world tasks, such as large-scale software development, commercial investment, and scientific discovery, unfold in long-horizon and partially observable scenarios where success hinges on sustained reasoning, planning, memory management, and tool use. Existing benchmarks rarely capture these long-horizon challenges, leaving a gap in systematic evaluation. To bridge this gap, we introduce UltraHorizon a novel benchmark that measures the foundational capabilities essential for complex real-world challenges. We use exploration as a unifying task across three distinct environments to validate these core competencies. Agents are designed in long-horizon discovery tasks where they must iteratively uncover hidden rules through sustained reasoning, planning, memory and tools management, and interaction with environments. Under the heaviest scale setting, trajectories average 200k+ tokens and 400+ tool calls, whereas in standard configurations they still exceed 35k tokens and involve more than 60 tool calls on average. Our extensive experiments reveal that LLM-agents consistently underperform in these settings, whereas human participants achieve higher scores, underscoring a persistent gap in agents' long-horizon abilities. We also observe that simple scaling fails in our task. To better illustrate the failure of agents, we conduct an in-depth analysis of collected trajectories. We identify eight types of errors and attribute them to two primary causes: in-context locking and functional fundamental capability gaps. https://github.com/StarDewXXX/UltraHorizon{Our code will be available here.}", "github_manual": "", "github_regex": "https://github.com/StarDewXXX/UltraHorizon", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22496", "title": "Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation", "authors": [ "Ruoyu Chen", "Xiaoqing Guo", "Kangwei Liu", "Siyuan Liang", "Shiming Liu", "Qunli Zhang", "Hua Zhang", "Xiaochun Cao" ], "github": "https://github.com/RuoyuChen10/EAGLE", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in aligning visual inputs with natural language outputs. Yet, the extent to which generated tokens depend on visual modalities remains poorly understood, limiting interpretability and reliability. In this work, we present EAGLE, a lightweight black-box framework for explaining autoregressive token generation in MLLMs. EAGLE attributes any selected tokens to compact perceptual regions while quantifying the relative influence of language priors and perceptual evidence. The framework introduces an objective function that unifies sufficiency (insight score) and indispensability (necessity score), optimized via greedy search over sparsified image regions for faithful and efficient attribution. Beyond spatial attribution, EAGLE performs modality-aware analysis that disentangles what tokens rely on, providing fine-grained interpretability of model decisions. Extensive experiments across open-source MLLMs show that EAGLE consistently outperforms existing methods in faithfulness, localization, and hallucination diagnosis, while requiring substantially less GPU memory. These results highlight its effectiveness and practicality for advancing the interpretability of MLLMs. The code is available at https://github.com/RuoyuChen10/EAGLE.", "github_manual": "", "github_regex": "https://github.com/RuoyuChen10/EAGLE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22601", "title": "Learn the Ropes, Then Trust the Wins: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning", "authors": [ "Yulei Qin", "Xiaoyu Tan", "Zhengbao He", "Gang Li", "Haojia Lin", "Zongyi Li", "Zihan Xu", "Yuchen Shi", "Siqi Cai", "Renting Rui", "Shaofei Cai", "Yuzheng Cai", "Xuan Zhang", "Sheng Ye", "Ke Li", "Xing Sun" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) is the dominant paradigm for sharpening strategic tool use capabilities of LLMs on long-horizon, sparsely-rewarded agent tasks, yet it faces a fundamental challenge of exploration-exploitation trade-off. Existing studies stimulate exploration through the lens of policy entropy, but such mechanical entropy maximization is prone to RL training instability due to the multi-turn distribution shifting. In this paper, we target the progressive exploration-exploitation balance under the guidance of the agent own experiences without succumbing to either entropy collapsing or runaway divergence. We propose SPEAR, a curriculum-based self-imitation learning (SIL) recipe for training agentic LLMs. It extends the vanilla SIL framework, where a replay buffer stores self-generated promising trajectories for off-policy update, by gradually steering the policy evolution within a well-balanced range of entropy across stages. Specifically, our approach incorporates a curriculum to manage the exploration process, utilizing intrinsic rewards to foster skill-level exploration and facilitating action-level exploration through SIL. At first, the auxiliary tool call reward plays a critical role in the accumulation of tool-use skills, enabling broad exposure to the unfamiliar distributions of the environment feedback with an upward entropy trend. As training progresses, self-imitation gets strengthened to exploit existing successful patterns from replayed experiences for comparative action-level exploration, accelerating solution iteration without unbounded entropy growth. To further stabilize training, we recalibrate the advantages of experiences in the replay buffer to address the potential policy drift. Reugularizations such as the clipping of tokens with high covariance between probability and advantage are introduced to the trajectory-level entropy control to curb over-confidence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21574", "title": "X-Streamer: Unified Human World Modeling with Audiovisual Interaction", "authors": [ "You Xie", "Tianpei Gu", "Zenan Li", "Chenxu Zhang", "Guoxian Song", "Xiaochen Zhao", "Chao Liang", "Jianwen Jiang", "Hongyi Xu", "Linjie Luo" ], "github": "", "project_page": "", "abstract": "We introduce X-Streamer, an end-to-end multimodal human world modeling framework for building digital human agents capable of infinite interactions across text, speech, and video within a single unified architecture. Starting from a single portrait, X-Streamer enables real-time, open-ended video calls driven by streaming multimodal inputs. At its core is a Thinker-Actor dual-transformer architecture that unifies multimodal understanding and generation, turning a static portrait into persistent and intelligent audiovisual interactions. The Thinker module perceives and reasons over streaming user inputs, while its hidden states are translated by the Actor into synchronized multimodal streams in real time. Concretely, the Thinker leverages a pretrained large language-speech model, while the Actor employs a chunk-wise autoregressive diffusion model that cross-attends to the Thinker's hidden states to produce time-aligned multimodal responses with interleaved discrete text and audio tokens and continuous video latents. To ensure long-horizon stability, we design inter- and intra-chunk attentions with time-aligned multimodal positional embeddings for fine-grained cross-modality alignment and context retention, further reinforced by chunk-wise diffusion forcing and global identity referencing. X-Streamer runs in real time on two A100 GPUs, sustaining hours-long consistent video chat experiences from arbitrary portraits and paving the way toward unified world modeling of interactive digital humans.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22244", "title": "FlashEdit: Decoupling Speed, Structure, and Semantics for Precise Image Editing", "authors": [ "Junyi Wu", "Zhiteng Li", "Haotong Qin", "Xiaohong Liu", "Linghe Kong", "Yulun Zhang", "Xiaokang Yang" ], "github": "https://github.com/JunyiWuCode/FlashEdit", "project_page": "", "abstract": "Text-guided image editing with diffusion models has achieved remarkable quality but suffers from prohibitive latency, hindering real-world applications. We introduce FlashEdit, a novel framework designed to enable high-fidelity, real-time image editing. Its efficiency stems from three key innovations: (1) a One-Step Inversion-and-Editing (OSIE) pipeline that bypasses costly iterative processes; (2) a Background Shield (BG-Shield) technique that guarantees background preservation by selectively modifying features only within the edit region; and (3) a Sparsified Spatial Cross-Attention (SSCA) mechanism that ensures precise, localized edits by suppressing semantic leakage to the background. Extensive experiments demonstrate that FlashEdit maintains superior background consistency and structural integrity, while performing edits in under 0.2 seconds, which is an over 150times speedup compared to prior multi-step methods. Our code will be made publicly available at https://github.com/JunyiWuCode/FlashEdit.", "github_manual": "", "github_regex": "https://github.com/JunyiWuCode/FlashEdit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.19768", "title": "CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition", "authors": [ "Sina J. Semnani", "Han Zhang", "Xinyan He", "Merve Tekg\u00fcrler", "Monica S. Lam" ], "github": "https://github.com/stanford-oval/Churro", "project_page": "", "abstract": "Accurate text recognition for historical documents can greatly advance the study and preservation of cultural heritage. Existing vision-language models (VLMs), however, are designed for modern, standardized texts and are not equipped to read the diverse languages and scripts, irregular layouts, and frequent degradation found in historical materials. This paper presents CHURRO, a 3B-parameter open-weight VLM specialized for historical text recognition. The model is trained on CHURRO-DS, the largest historical text recognition dataset to date. CHURRO-DS unifies 155 historical corpora comprising 99,491 pages, spanning 22 centuries of textual heritage across 46 language clusters, including historical variants and dead languages. We evaluate several open-weight and closed VLMs and optical character recognition (OCR) systems on CHURRO-DS and find that CHURRO outperforms all other VLMs. On the CHURRO-DS test set, CHURRO achieves 82.3% (printed) and 70.1% (handwritten) normalized Levenshtein similarity, surpassing the second-best model, Gemini 2.5 Pro, by 1.4% and 6.5%, respectively, while being 15.5 times more cost-effective. By releasing the model and dataset, we aim to enable community-driven research to improve the readability of historical texts and accelerate scholarship.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stanford-oval/Churro", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21679", "title": "ReviewScore: Misinformed Peer Review Detection with Large Language Models", "authors": [ "Hyun Ryu", "Doohyuk Jang", "Hyemin S. Lee", "Joonhyun Jeong", "Gyeongman Kim", "Donghyeon Cho", "Gyouk Chu", "Minyeong Hwang", "Hyeongwon Jang", "Changhun Kim", "Haechan Kim", "Jina Kim", "Joowon Kim", "Yoonjeon Kim", "Kwanhyung Lee", "Chanjae Park", "Heecheol Yun", "Gregor Betz", "Eunho Yang" ], "github": "", "project_page": "", "abstract": "Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes. To reliably detect low-quality reviews, we define misinformed review points as either \"weaknesses\" in a review that contain incorrect premises, or \"questions\" in a review that can be already answered by the paper. We verify that 15.2% of weaknesses and 26.4% of questions are misinformed and introduce ReviewScore indicating if a review point is misinformed. To evaluate the factuality of each premise of weaknesses, we propose an automated engine that reconstructs every explicit and implicit premise from a weakness. We build a human expert-annotated ReviewScore dataset to check the ability of LLMs to automate ReviewScore evaluation. Then, we measure human-model agreements on ReviewScore using eight current state-of-the-art LLMs and verify moderate agreements. We also prove that evaluating premise-level factuality shows significantly higher agreements than evaluating weakness-level factuality. A thorough disagreement analysis further supports a potential of fully automated ReviewScore evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22644", "title": "WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning", "authors": [ "Zimu Lu", "Houxing Ren", "Yunqiao Yang", "Ke Wang", "Zhuofan Zong", "Junting Pan", "Mingjie Zhan", "Hongsheng Li" ], "github": "https://github.com/mnluzimu/WebGen-Agent", "project_page": "", "abstract": "Agent systems powered by large language models (LLMs) have demonstrated impressive performance on repository-level code-generation tasks. However, for tasks such as website codebase generation, which depend heavily on visual effects and user-interaction feedback, current code agents rely only on simple code execution for feedback and verification. This approach fails to capture the actual quality of the generated code. In this paper, we propose WebGen-Agent, a novel website-generation agent that leverages comprehensive and multi-level visual feedback to iteratively generate and refine the website codebase. Detailed and expressive text descriptions and suggestions regarding the screenshots and GUI-agent testing of the websites are generated by a visual language model (VLM), together with scores that quantify their quality. The screenshot and GUI-agent scores are further integrated with a backtracking and select-best mechanism, enhancing the performance of the agent. Utilizing the accurate visual scores inherent in the WebGen-Agent workflow, we further introduce Step-GRPO with Screenshot and GUI-agent Feedback to improve the ability of LLMs to act as the reasoning engine of WebGen-Agent. By using the screenshot and GUI-agent scores at each step as the reward in Step-GRPO, we provide a dense and reliable process supervision signal, which effectively improves the model's website-generation ability. On the WebGen-Bench dataset, WebGen-Agent increases the accuracy of Claude-3.5-Sonnet from 26.4% to 51.9% and its appearance score from 3.0 to 3.9, outperforming the previous state-of-the-art agent system. Additionally, our Step-GRPO training approach increases the accuracy of Qwen2.5-Coder-7B-Instruct from 38.9% to 45.4% and raises the appearance score from 3.4 to 3.7.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mnluzimu/WebGen-Agent", "github_api_added_by": "auto" }, { "date": "2025-09-29", "arxiv_id": "2509.22414", "title": "LucidFlux: Caption-Free Universal Image Restoration via a Large-Scale Diffusion Transformer", "authors": [ "Song Fei", "Tian Ye", "Lujia Wang", "Lei Zhu" ], "github": "https://github.com/W2GenAI-Lab/LucidFlux", "project_page": "", "abstract": "Universal image restoration (UIR) aims to recover images degraded by unknown mixtures while preserving semantics -- conditions under which discriminative restorers and UNet-based diffusion priors often oversmooth, hallucinate, or drift. We present LucidFlux, a caption-free UIR framework that adapts a large diffusion transformer (Flux.1) without image captions. LucidFlux introduces a lightweight dual-branch conditioner that injects signals from the degraded input and a lightly restored proxy to respectively anchor geometry and suppress artifacts. Then, a timestep- and layer-adaptive modulation schedule is designed to route these cues across the backbone's hierarchy, in order to yield coarse-to-fine and context-aware updates that protect the global structure while recovering texture. After that, to avoid the latency and instability of text prompts or MLLM captions, we enforce caption-free semantic alignment via SigLIP features extracted from the proxy. A scalable curation pipeline further filters large-scale data for structure-rich supervision. Across synthetic and in-the-wild benchmarks, LucidFlux consistently outperforms strong open-source and commercial baselines, and ablation studies verify the necessity of each component. LucidFlux shows that, for large DiTs, when, where, and what to condition on -- rather than adding parameters or relying on text prompts -- is the governing lever for robust and caption-free universal image restoration in the wild.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/W2GenAI-Lab/LucidFlux", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21710", "title": "Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval", "authors": [ "Xiaojun Wu", "Cehao Yang", "Xueyuan Lin", "Chengjin Xu", "Xuhui Jiang", "Yuanliang Sun", "Hui Xiong", "Jia Li", "Jian Guo" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) and Graph-based RAG has become the important paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, existing approaches face a fundamental trade-off. While graph-based methods are inherently dependent on high-quality graph structures, they face significant practical constraints: manually constructed knowledge graphs are prohibitively expensive to scale, while automatically extracted graphs from corpora are limited by the performance of the underlying LLM extractors, especially when using smaller, local-deployed models. This paper presents Think-on-Graph 3.0 (ToG-3), a novel framework that introduces Multi-Agent Context Evolution and Retrieval (MACER) mechanism to overcome these limitations. Our core innovation is the dynamic construction and refinement of a Chunk-Triplets-Community heterogeneous graph index, which pioneeringly incorporates a dual-evolution mechanism of Evolving Query and Evolving Sub-Graph for precise evidence retrieval. This approach addresses a critical limitation of prior Graph-based RAG methods, which typically construct a static graph index in a single pass without adapting to the actual query. A multi-agent system, comprising Constructor, Retriever, Reflector, and Responser agents, collaboratively engages in an iterative process of evidence retrieval, answer generation, sufficiency reflection, and, crucially, evolving query and subgraph. This dual-evolving multi-agent system allows ToG-3 to adaptively build a targeted graph index during reasoning, mitigating the inherent drawbacks of static, one-time graph construction and enabling deep, precise reasoning even with lightweight LLMs. Extensive experiments demonstrate that ToG-3 outperforms compared baselines on both deep and broad reasoning benchmarks, and ablation studies confirm the efficacy of the components of MACER framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21799", "title": "D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents", "authors": [ "Hongze Mi", "Yibo Feng", "Wenjie Lu", "Yuqi Wang", "Jinyuan Li", "Song Cao", "He Cui", "Tengfei Tian", "Xuelin Zhang", "Haotian Luo", "Di Sun", "Naiqiang Tan", "Gang Pan" ], "github": "", "project_page": "", "abstract": "Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. Despite rapid advancements, current approaches are hindered by several critical challenges: data bottleneck in end-to-end training, high cost of delayed error detection, and risk of contradictory guidance. Inspired by the human cognitive loop of Thinking, Alignment, and Reflection, we present D-Artemis -- a novel deliberative framework in this paper. D-Artemis leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process. It also employs a proactive Pre-execution Alignment stage, where Thought-Action Consistency (TAC) Check module and Action Correction Agent (ACA) work in concert to mitigate the risk of execution failures. A post-execution Status Reflection Agent (SRA) completes the cognitive loop, enabling strategic learning from experience. Crucially, D-Artemis enhances the capabilities of general-purpose Multimodal large language models (MLLMs) for GUI tasks without the need for training on complex trajectory datasets, demonstrating strong generalization. D-Artemis establishes new state-of-the-art (SOTA) results across both major benchmarks, achieving a 75.8% success rate on AndroidWorld and 96.8% on ScreenSpot-V2. Extensive ablation studies further demonstrate the significant contribution of each component to the framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22624", "title": "SPARK: Synergistic Policy And Reward Co-Evolving Framework", "authors": [ "Ziyu Liu", "Yuhang Zang", "Shengyuan Ding", "Yuhang Cao", "Xiaoyi Dong", "Haodong Duan", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/InternLM/Spark", "project_page": "", "abstract": "Recent Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) increasingly use Reinforcement Learning (RL) for post-pretraining, such as RL with Verifiable Rewards (RLVR) for objective tasks and RL from Human Feedback (RLHF) for subjective tasks. However, RLHF incurs high costs and potential reward-policy mismatch due to reliance on human preferences, while RLVR still wastes supervision by discarding rollouts and correctness signals after each update. To address these challenges, we introduce the Synergistic Policy And Reward Co-Evolving Framework (SPARK), an efficient, on-policy, and stable method that builds on RLVR. Instead of discarding rollouts and correctness data, SPARK recycles this valuable information to simultaneously train the model itself as a generative reward model. This auxiliary training uses a mix of objectives, such as pointwise reward score, pairwise comparison, and evaluation conditioned on further-reflection responses, to teach the model to evaluate and improve its own responses. Our process eliminates the need for a separate reward model and costly human preference data. SPARK creates a positive co-evolving feedback loop: improved reward accuracy yields better policy gradients, which in turn produce higher-quality rollouts that further refine the reward model. Our unified framework supports test-time scaling via self-reflection without external reward models and their associated costs. We show that SPARK achieves significant performance gains on multiple LLM and LVLM models and multiple reasoning, reward models, and general benchmarks. For example, SPARK-VL-7B achieves an average 9.7% gain on 7 reasoning benchmarks, 12.1% on 2 reward benchmarks, and 1.5% on 8 general benchmarks over the baselines, demonstrating robustness and broad generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/InternLM/Spark", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21500", "title": "Chasing the Tail: Effective Rubric-based Reward Modeling for Large Language Model Post-Training", "authors": [ "Junkai Zhang", "Zihao Wang", "Lin Gui", "Swarnashree Mysore Sathyendra", "Jaehwan Jeong", "Victor Veitch", "Wei Wang", "Yunzhong He", "Bing Liu", "Lifeng Jin" ], "github": "https://github.com/Jun-Kai-Zhang/rubrics.git", "project_page": "", "abstract": "Reinforcement fine-tuning (RFT) often suffers from reward over-optimization, where a policy model hacks the reward signals to achieve high scores while producing low-quality outputs. Our theoretical analysis shows that the key lies in reward misspecification at the high-reward tail: the inability to reliably distinguish Excellent responses from merely Great ones. This motivate us to focus on the high-reward region. However, such tail examples are scarce under the base LLM. While off-policy exemplars (e.g. from stronger models or rewrites) are easier to obtain, naively training on them yields a misspecified reward for the policy we aim to align. To address this, we study rubric-based rewards. By design, rubrics can leverage off-policy examples while remaining insensitive to their artifacts. To elicit rubrics that capture the high-reward tail, we highlight the importance of distinguishing among great and diverse responses, and introduce a workflow to implement this idea. We empirically demonstrate that rubric-based rewards substantially mitigate reward over-optimization and deliver effective LLM post-training improvements. Our code can be accessed at https://github.com/Jun-Kai-Zhang/rubrics.git .", "github_manual": "", "github_regex": "https://github.com/Jun-Kai-Zhang/rubrics.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22630", "title": "StateX: Enhancing RNN Recall via Post-training State Expansion", "authors": [ "Xingyu Shen", "Yingfa Chen", "Zhen Leng Thai", "Xu Han", "Zhiyuan Liu", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "While Transformer-based models have demonstrated remarkable language modeling performance, their high complexities result in high costs when processing long contexts. In contrast, recurrent neural networks (RNNs) such as linear attention and state space models have gained popularity due to their constant per-token complexities. However, these recurrent models struggle with tasks that require accurate recall of contextual information from long contexts, because all contextual information is compressed into a constant-size recurrent state. Previous works have shown that recall ability is positively correlated with the recurrent state size, yet directly training RNNs with larger recurrent states results in high training costs. In this paper, we introduce StateX, a training pipeline for efficiently expanding the states of pre-trained RNNs through post-training. For two popular classes of RNNs, linear attention and state space models, we design post-training architectural modifications to scale up the state size with no or negligible increase in model parameters. Experiments on models up to 1.3B parameters demonstrate that StateX efficiently enhances the recall and in-context learning ability of RNNs without incurring high post-training costs or compromising other capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22281", "title": "MesaTask: Towards Task-Driven Tabletop Scene Generation via 3D Spatial Reasoning", "authors": [ "Jinkun Hao", "Naifu Liang", "Zhen Luo", "Xudong Xu", "Weipeng Zhong", "Ran Yi", "Yichen Jin", "Zhaoyang Lyu", "Feng Zheng", "Lizhuang Ma", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "The ability of robots to interpret human instructions and execute manipulation tasks necessitates the availability of task-relevant tabletop scenes for training. However, traditional methods for creating these scenes rely on time-consuming manual layout design or purely randomized layouts, which are limited in terms of plausibility or alignment with the tasks. In this paper, we formulate a novel task, namely task-oriented tabletop scene generation, which poses significant challenges due to the substantial gap between high-level task instructions and the tabletop scenes. To support research on such a challenging task, we introduce MesaTask-10K, a large-scale dataset comprising approximately 10,700 synthetic tabletop scenes with manually crafted layouts that ensure realistic layouts and intricate inter-object relations. To bridge the gap between tasks and scenes, we propose a Spatial Reasoning Chain that decomposes the generation process into object inference, spatial interrelation reasoning, and scene graph construction for the final 3D layout. We present MesaTask, an LLM-based framework that utilizes this reasoning chain and is further enhanced with DPO algorithms to generate physically plausible tabletop scenes that align well with given task descriptions. Exhaustive experiments demonstrate the superior performance of MesaTask compared to baselines in generating task-conforming tabletop scenes with realistic layouts. Project page is at https://mesatask.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21989", "title": "Mind-the-Glitch: Visual Correspondence for Detecting Inconsistencies in Subject-Driven Generation", "authors": [ "Abdelrahman Eldesokey", "Aleksandar Cvejic", "Bernard Ghanem", "Peter Wonka" ], "github": "", "project_page": "", "abstract": "We propose a novel approach for disentangling visual and semantic features from the backbones of pre-trained diffusion models, enabling visual correspondence in a manner analogous to the well-established semantic correspondence. While diffusion model backbones are known to encode semantically rich features, they must also contain visual features to support their image synthesis capabilities. However, isolating these visual features is challenging due to the absence of annotated datasets. To address this, we introduce an automated pipeline that constructs image pairs with annotated semantic and visual correspondences based on existing subject-driven image generation datasets, and design a contrastive architecture to separate the two feature types. Leveraging the disentangled representations, we propose a new metric, Visual Semantic Matching (VSM), that quantifies visual inconsistencies in subject-driven image generation. Empirical results show that our approach outperforms global feature-based metrics such as CLIP, DINO, and vision--language models in quantifying visual inconsistencies while also enabling spatial localization of inconsistent regions. To our knowledge, this is the first method that supports both quantification and localization of inconsistencies in subject-driven generation, offering a valuable tool for advancing this task. Project Page:https://abdo-eldesokey.github.io/mind-the-glitch/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21880", "title": "No Prompt Left Behind: Exploiting Zero-Variance Prompts in LLM Reinforcement Learning via Entropy-Guided Advantage Shaping", "authors": [ "Thanh-Long V. Le", "Myeongho Jeon", "Kim Vu", "Viet Lai", "Eunho Yang" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful framework for improving the reasoning abilities of Large Language Models (LLMs). However, current methods such as GRPO rely only on problems where the model responses to the same input differ in correctness, while ignoring those where all responses receive the same reward - so-called zero-variance prompts. In this work, we argue that such prompts are not useless but can, in fact, provide meaningful feedback for policy optimization. To this end, we introduce RL with Zero-Variance Prompts (RL-ZVP), a novel algorithm that extract learning signals from zero-variance prompts. RL-ZVP directly rewards correctness and penalizes errors even without contrasting responses, modulating feedback with token-level characteristics to preserve informative, nuanced signals. Across six math reasoning benchmarks, RL-ZVP achieves significant improvements of up to 8.61 points in accuracy and 7.77 points in pass rate over GRPO, while consistently outperforming other baselines that filter out zero-variance prompts. These results highlight the untapped potential of learning from zero-variance prompts in RLVR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22653", "title": "See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned Aerial Navigation", "authors": [ "Chih Yao Hu", "Yang-Sen Lin", "Yuna Lee", "Chih-Hai Su", "Jie-Ying Lee", "Shr-Ruei Tsai", "Chin-Yang Lin", "Kuan-Wen Chen", "Tsung-Wei Ke", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "We present See, Point, Fly (SPF), a training-free aerial vision-and-language navigation (AVLN) framework built atop vision-language models (VLMs). SPF is capable of navigating to any goal based on any type of free-form instructions in any kind of environment. In contrast to existing VLM-based approaches that treat action prediction as a text generation task, our key insight is to consider action prediction for AVLN as a 2D spatial grounding task. SPF harnesses VLMs to decompose vague language instructions into iterative annotation of 2D waypoints on the input image. Along with the predicted traveling distance, SPF transforms predicted 2D waypoints into 3D displacement vectors as action commands for UAVs. Moreover, SPF also adaptively adjusts the traveling distance to facilitate more efficient navigation. Notably, SPF performs navigation in a closed-loop control manner, enabling UAVs to follow dynamic targets in dynamic environments. SPF sets a new state of the art in DRL simulation benchmark, outperforming the previous best method by an absolute margin of 63%. In extensive real-world evaluations, SPF outperforms strong baselines by a large margin. We also conduct comprehensive ablation studies to highlight the effectiveness of our design choice. Lastly, SPF shows remarkable generalization to different VLMs. Project page: https://spf-web.pages.dev", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21559", "title": "X-CoT: Explainable Text-to-Video Retrieval via LLM-based Chain-of-Thought Reasoning", "authors": [ "Prasanna Reddy Pulakurthi", "Jiamian Wang", "Majid Rabbani", "Sohail Dianat", "Raghuveer Rao", "Zhiqiang Tao" ], "github": "https://github.com/PrasannaPulakurthi/X-CoT", "project_page": "", "abstract": "Prevalent text-to-video retrieval systems mainly adopt embedding models for feature extraction and compute cosine similarities for ranking. However, this design presents two limitations. Low-quality text-video data pairs could compromise the retrieval, yet are hard to identify and examine. Cosine similarity alone provides no explanation for the ranking results, limiting the interpretability. We ask that can we interpret the ranking results, so as to assess the retrieval models and examine the text-video data? This work proposes X-CoT, an explainable retrieval framework upon LLM CoT reasoning in place of the embedding model-based similarity ranking. We first expand the existing benchmarks with additional video annotations to support semantic understanding and reduce data bias. We also devise a retrieval CoT consisting of pairwise comparison steps, yielding detailed reasoning and complete ranking. X-CoT empirically improves the retrieval performance and produces detailed rationales. It also facilitates the model behavior and data quality analysis. Code and data are available at: https://github.com/PrasannaPulakurthi/X-CoT.", "github_manual": "", "github_regex": "https://github.com/PrasannaPulakurthi/X-CoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22650", "title": "RefAM: Attention Magnets for Zero-Shot Referral Segmentation", "authors": [ "Anna Kukleva", "Enis Simsar", "Alessio Tonioni", "Muhammad Ferjad Naeem", "Federico Tombari", "Jan Eric Lenssen", "Bernt Schiele" ], "github": "", "project_page": "", "abstract": "Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models, often at the cost of additional training and architectural modifications. Meanwhile, large-scale generative diffusion models encode rich semantic information, making them attractive as general-purpose feature extractors. In this work, we introduce a new method that directly exploits features, attention scores, from diffusion transformers for downstream tasks, requiring neither architectural modifications nor additional training. To systematically evaluate these features, we extend benchmarks with vision-language grounding tasks spanning both images and videos. Our key insight is that stop words act as attention magnets: they accumulate surplus attention and can be filtered to reduce noise. Moreover, we identify global attention sinks (GAS) emerging in deeper layers and show that they can be safely suppressed or redirected onto auxiliary tokens, leading to sharper and more accurate grounding maps. We further propose an attention redistribution strategy, where appended stop words partition background activations into smaller clusters, yielding sharper and more localized heatmaps. Building on these findings, we develop RefAM, a simple training-free grounding framework that combines cross-attention maps, GAS handling, and redistribution. Across zero-shot referring image and video segmentation benchmarks, our approach consistently outperforms prior methods, establishing a new state of the art without fine-tuning or additional components.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22642", "title": "WoW: Towards a World omniscient World model Through Embodied Interaction", "authors": [ "Xiaowei Chi", "Peidong Jia", "Chun-Kai Fan", "Xiaozhu Ju", "Weishi Mi", "Kevin Zhang", "Zhiyuan Qin", "Wanxin Tian", "Kuangzhi Ge", "Hao Li", "Zezhong Qian", "Anthony Chen", "Qiang Zhou", "Yueru Jia", "Jiaming Liu", "Yong Dai", "Qingpo Wuwu", "Chengyu Bai", "Yu-Kai Wang", "Ying Li", "Lizhang Chen", "Yong Bao", "Zhiyuan Jiang", "Jiacheng Zhu", "Kai Tang", "Ruichuan An", "Yulin Luo", "Qiuxuan Feng", "Siyuan Zhou", "Chi-min Chan", "Chengkai Hou", "Wei Xue", "Sirui Han", "Yike Guo", "Shanghang Zhang", "Jian Tang" ], "github": "https://github.com/wow-world-model/wow-world-model", "project_page": "", "abstract": "Humans develop an understanding of intuitive physics through active interaction with the world. This approach is in stark contrast to current video models, such as Sora, which rely on passive observation and therefore struggle with grasping physical causality. This observation leads to our central hypothesis: authentic physical intuition of the world model must be grounded in extensive, causally rich interactions with the real world. To test this hypothesis, we present WoW, a 14-billion-parameter generative world model trained on 2 million robot interaction trajectories. Our findings reveal that the model's understanding of physics is a probabilistic distribution of plausible outcomes, leading to stochastic instabilities and physical hallucinations. Furthermore, we demonstrate that this emergent capability can be actively constrained toward physical realism by SOPHIA, where vision-language model agents evaluate the DiT-generated output and guide its refinement by iteratively evolving the language instructions. In addition, a co-trained Inverse Dynamics Model translates these refined plans into executable robotic actions, thus closing the imagination-to-action loop. We establish WoWBench, a new benchmark focused on physical consistency and causal reasoning in video, where WoW achieves state-of-the-art performance in both human and autonomous evaluation, demonstrating strong ability in physical causality, collision dynamics, and object permanence. Our work provides systematic evidence that large-scale, real-world interaction is a cornerstone for developing physical intuition in AI. Models, data, and benchmarks will be open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wow-world-model/wow-world-model", "github_api_added_by": "auto" }, { "date": "2025-09-29", "arxiv_id": "2509.20787", "title": "Real-Time Object Detection Meets DINOv3", "authors": [ "Shihua Huang", "Yongjie Hou", "Longfei Liu", "Xuanlong Yu", "Xi Shen" ], "github": "https://github.com/Intellindust-AI-Lab/DEIMv2", "project_page": "", "abstract": "Benefiting from the simplicity and effectiveness of Dense O2O and MAL, DEIM has become the mainstream training framework for real-time DETRs, significantly outperforming the YOLO series. In this work, we extend it with DINOv3 features, resulting in DEIMv2. DEIMv2 spans eight model sizes from X to Atto, covering GPU, edge, and mobile deployment. For the X, L, M, and S variants, we adopt DINOv3-pretrained or distilled backbones and introduce a Spatial Tuning Adapter (STA), which efficiently converts DINOv3's single-scale output into multi-scale features and complements strong semantics with fine-grained details to enhance detection. For ultra-lightweight models (Nano, Pico, Femto, and Atto), we employ HGNetv2 with depth and width pruning to meet strict resource budgets. Together with a simplified decoder and an upgraded Dense O2O, this unified design enables DEIMv2 to achieve a superior performance-cost trade-off across diverse scenarios, establishing new state-of-the-art results. Notably, our largest model, DEIMv2-X, achieves 57.8 AP with only 50.3 million parameters, surpassing prior X-scale models that require over 60 million parameters for just 56.5 AP. On the compact side, DEIMv2-S is the first sub-10 million model (9.71 million) to exceed the 50 AP milestone on COCO, reaching 50.9 AP. Even the ultra-lightweight DEIMv2-Pico, with just 1.5 million parameters, delivers 38.5 AP, matching YOLOv10-Nano (2.3 million) with around 50 percent fewer parameters. Our code and pre-trained models are available at https://github.com/Intellindust-AI-Lab/DEIMv2", "github_manual": "", "github_regex": "https://github.com/Intellindust-AI-Lab/DEIMv2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22300", "title": "HiGS: History-Guided Sampling for Plug-and-Play Enhancement of Diffusion Models", "authors": [ "Seyedmorteza Sadat", "Farnood Salehi", "Romann M. Weber" ], "github": "", "project_page": "", "abstract": "While diffusion models have made remarkable progress in image generation, their outputs can still appear unrealistic and lack fine details, especially when using fewer number of neural function evaluations (NFEs) or lower guidance scales. To address this issue, we propose a novel momentum-based sampling technique, termed history-guided sampling (HiGS), which enhances quality and efficiency of diffusion sampling by integrating recent model predictions into each inference step. Specifically, HiGS leverages the difference between the current prediction and a weighted average of past predictions to steer the sampling process toward more realistic outputs with better details and structure. Our approach introduces practically no additional computation and integrates seamlessly into existing diffusion frameworks, requiring neither extra training nor fine-tuning. Extensive experiments show that HiGS consistently improves image quality across diverse models and architectures and under varying sampling budgets and guidance scales. Moreover, using a pretrained SiT model, HiGS achieves a new state-of-the-art FID of 1.61 for unguided ImageNet generation at 256times256 with only 30 sampling steps (instead of the standard 250). We thus present HiGS as a plug-and-play enhancement to standard diffusion sampling that enables faster generation with higher fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.19894", "title": "PromptCoT 2.0: Scaling Prompt Synthesis for Large Language Model Reasoning", "authors": [ "Xueliang Zhao", "Wei Wu", "Jian Guan", "Zhuocheng Gong", "Lingpeng Kong" ], "github": "https://github.com/inclusionAI/PromptCoT", "project_page": "", "abstract": "Large language models (LLMs) are evolving from conversational systems into strong reasoners for tasks such as Olympiad mathematics and competitive programming. While scaling parameters and test-time computation has driven progress, a key bottleneck is the lack of high-quality training problems: human-curated datasets are costly and limited, while existing synthetic corpora are often too easy or narrow. PromptCoT 1.0 showed that injecting rationales into prompt synthesis increases problem difficulty. Building on this, we present PromptCoT 2.0, a scalable framework that replaces hand-crafted heuristics with an expectation-maximization (EM) loop, where rationales are iteratively refined to guide prompt construction. This produces problems that are both harder and more diverse than prior corpora. The synthetic prompts support two post-training regimes: (1) Self-Play, where strong models improve autonomously via verifiable feedback without stronger teachers; and (2) Supervised Fine-Tuning (SFT), where weaker models learn from teacher-distilled traces. Extensive experiments demonstrate the effectiveness of this approach. In self-play, applying PromptCoT 2.0 to Qwen3-30B-A3B-Thinking-2507 sets new state-of-the-art results at the 30B scale, with +4.4, +4.8, and +5.3 on AIME 24/25 and HMMT 25, +6.1 and +5.0 on LiveCodeBench v5/v6, and +35 Elo on Codeforces. In SFT, training Qwen2.5-7B-Instruct solely on synthetic prompts boosts accuracy to 73.1 (AIME 24), 65.6 (AIME 25), and 53.4 (LiveCodeBench v5), surpassing models trained on human or hybrid data. Analyses further confirm that PromptCoT 2.0 yields fundamentally harder and distributionally distinct problems. These results establish prompt synthesis as a new axis for scaling reasoning and position PromptCoT 2.0 as a scalable foundation for future open-source models. The implementation is available at https://github.com/inclusionAI/PromptCoT.", "github_manual": "", "github_regex": "https://github.com/inclusionAI/PromptCoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.18420", "title": "Instruction-Following Evaluation in Function Calling for Large Language Models", "authors": [ "Nikolai Skripko" ], "github": "https://github.com/Skripkon/IFEval-FC", "project_page": "", "abstract": "Function calling is a core capability of large language models, essential for AI agents. Existing benchmarks such as the Berkeley Function Calling Leaderboard (BFCL), tau^2-Bench (arXiv:2506.07982), and ACEBench (arXiv:2501.12851) evaluate argument correctness but do not test adherence to format instructions embedded in parameter descriptions, such as enclosing values in double quotes or using ISO date formats. We introduce IFEval-FC, a benchmark inspired by IFEval (arXiv:2311.07911) that assesses precise instruction following in function calling. IFEval-FC encodes verifiable formats directly within JSON schema descriptions, for example specifying that a value must not contain punctuation. It includes 750 test cases, each consisting of a function with an embedded format for one of its input parameters and a corresponding user query. Evaluation is fully algorithmic, ensuring objectivity, reproducibility, and scalability. Our results show that even state-of-the-art proprietary models, including GPT-5 and Claude 4.1 Opus, frequently fail to follow basic formatting rules, highlighting a practical limitation for real-world agent systems. The complete codebase and data are publicly available at https://github.com/Skripkon/IFEval-FC.", "github_manual": "", "github_regex": "https://github.com/Skripkon/IFEval-FC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21991", "title": "ERGO: Efficient High-Resolution Visual Understanding for Vision-Language Models", "authors": [ "Jewon Lee", "Wooksu Shin", "Seungmin Yang", "Ki-Ung Song", "DongUk Lim", "Jaeyeon Kim", "Tae-Ho Kim", "Bo-Kyeong Kim" ], "github": "https://github.com/nota-github/ERGO", "project_page": "", "abstract": "Efficient processing of high-resolution images is crucial for real-world vision-language applications. However, existing Large Vision-Language Models (LVLMs) incur substantial computational overhead due to the large number of vision tokens. With the advent of \"thinking with images\" models, reasoning now extends beyond text to the visual domain. This capability motivates our two-stage \"coarse-to-fine\" reasoning pipeline: first, a downsampled image is analyzed to identify task-relevant regions; then, only these regions are cropped at full resolution and processed in a subsequent reasoning stage. This approach reduces computational cost while preserving fine-grained visual details where necessary. A major challenge lies in inferring which regions are truly relevant to a given query. Recent related methods often fail in the first stage after input-image downsampling, due to perception-driven reasoning, where clear visual information is required for effective reasoning. To address this issue, we propose ERGO (Efficient Reasoning & Guided Observation) that performs reasoning-driven perception-leveraging multimodal context to determine where to focus. Our model can account for perceptual uncertainty, expanding the cropped region to cover visually ambiguous areas for answering questions. To this end, we develop simple yet effective reward components in a reinforcement learning framework for coarse-to-fine perception. Across multiple datasets, our approach delivers higher accuracy than the original model and competitive methods, with greater efficiency. For instance, ERGO surpasses Qwen2.5-VL-7B on the V* benchmark by 4.7 points while using only 23% of the vision tokens, achieving a 3x inference speedup. The code and models can be found at: https://github.com/nota-github/ERGO.", "github_manual": "", "github_regex": "https://github.com/nota-github/ERGO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21388", "title": "TUN3D: Towards Real-World Scene Understanding from Unposed Images", "authors": [ "Anton Konushin", "Nikita Drozdov", "Bulat Gabdullin", "Alexey Zakharov", "Anna Vorontsova", "Danila Rukhovich", "Maksim Kolodiazhnyi" ], "github": "https://github.com/col14m/tun3d", "project_page": "", "abstract": "Layout estimation and 3D object detection are two fundamental tasks in indoor scene understanding. When combined, they enable the creation of a compact yet semantically rich spatial representation of a scene. Existing approaches typically rely on point cloud input, which poses a major limitation since most consumer cameras lack depth sensors and visual-only data remains far more common. We address this issue with TUN3D, the first method that tackles joint layout estimation and 3D object detection in real scans, given multi-view images as input, and does not require ground-truth camera poses or depth supervision. Our approach builds on a lightweight sparse-convolutional backbone and employs two dedicated heads: one for 3D object detection and one for layout estimation, leveraging a novel and effective parametric wall representation. Extensive experiments show that TUN3D achieves state-of-the-art performance across three challenging scene understanding benchmarks: (i) using ground-truth point clouds, (ii) using posed images, and (iii) using unposed images. While performing on par with specialized 3D object detection methods, TUN3D significantly advances layout estimation, setting a new benchmark in holistic indoor scene understanding. Code is available at https://github.com/col14m/tun3d .", "github_manual": "", "github_regex": "https://github.com/col14m/tun3d", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.20906", "title": "Finding 3D Positions of Distant Objects from Noisy Camera Movement and Semantic Segmentation Sequences", "authors": [ "Julius Pesonen", "Arno Solin", "Eija Honkavaara" ], "github": "", "project_page": "", "abstract": "3D object localisation based on a sequence of camera measurements is essential for safety-critical surveillance tasks, such as drone-based wildfire monitoring. Localisation of objects detected with a camera can typically be solved with dense depth estimation or 3D scene reconstruction. However, in the context of distant objects or tasks limited by the amount of available computational resources, neither solution is feasible. In this paper, we show that the task can be solved using particle filters for both single and multiple target scenarios. The method was studied using a 3D simulation and a drone-based image segmentation sequence with global navigation satellite system (GNSS)-based camera pose estimates. The results showed that a particle filter can be used to solve practical localisation tasks based on camera poses and image segments in these situations where other solutions fail. The particle filter is independent of the detection method, making it flexible for new tasks. The study also demonstrates that drone-based wildfire monitoring can be conducted using the proposed method paired with a pre-existing image segmentation model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22072", "title": "Fine-tuning Done Right in Model Editing", "authors": [ "Wanli Yang", "Fei Sun", "Rui Tang", "Hongyu Zang", "Du Su", "Qi Cao", "Jingang Wang", "Huawei Shen", "Xueqi Cheng" ], "github": "https://github.com/ICT-STAR/LocFT", "project_page": "", "abstract": "Fine-tuning, a foundational method for adapting large language models, has long been considered ineffective for model editing. Here, we challenge this belief, arguing that the reported failure arises not from the inherent limitation of fine-tuning itself, but from adapting it to the sequential nature of the editing task, a single-pass depth-first pipeline that optimizes each sample to convergence before moving on. While intuitive, this depth-first pipeline coupled with sample-wise updating over-optimizes each edit and induces interference across edits. Our controlled experiments reveal that simply restoring fine-tuning to the standard breadth-first (i.e., epoch-based) pipeline with mini-batch optimization substantially improves its effectiveness for model editing. Moreover, fine-tuning in editing also suffers from suboptimal tuning parameter locations inherited from prior methods. Through systematic analysis of tuning locations, we derive LocFT-BF, a simple and effective localized editing method built on the restored fine-tuning framework. Extensive experiments across diverse LLMs and datasets demonstrate that LocFT-BF outperforms state-of-the-art methods by large margins. Notably, to our knowledge, it is the first to sustain 100K edits and 72B-parameter models,10 x beyond prior practice, without sacrificing general capabilities. By clarifying a long-standing misconception and introducing a principled localized tuning strategy, we advance fine-tuning from an underestimated baseline to a leading method for model editing, establishing a solid foundation for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ICT-STAR/LocFT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22636", "title": "Scale-Wise VAR is Secretly Discrete Diffusion", "authors": [ "Amandeep Kumar", "Nithin Gopalakrishnan Nair", "Vishal M. Patel" ], "github": "", "project_page": "", "abstract": "Autoregressive (AR) transformers have emerged as a powerful paradigm for visual generation, largely due to their scalability, computational efficiency and unified architecture with language and vision. Among them, next scale prediction Visual Autoregressive Generation (VAR) has recently demonstrated remarkable performance, even surpassing diffusion-based models. In this work, we revisit VAR and uncover a theoretical insight: when equipped with a Markovian attention mask, VAR is mathematically equivalent to a discrete diffusion. We term this reinterpretation as Scalable Visual Refinement with Discrete Diffusion (SRDD), establishing a principled bridge between AR transformers and diffusion models. Leveraging this new perspective, we show how one can directly import the advantages of diffusion such as iterative refinement and reduce architectural inefficiencies into VAR, yielding faster convergence, lower inference cost, and improved zero-shot reconstruction. Across multiple datasets, we show that the diffusion based perspective of VAR leads to consistent gains in efficiency and generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21150", "title": "CAD-Tokenizer: Towards Text-based CAD Prototyping via Modality-Specific Tokenization", "authors": [ "Ruiyu Wang", "Shizhao Sun", "Weijian Ma", "Jiang Bian" ], "github": "", "project_page": "", "abstract": "Computer-Aided Design (CAD) is a foundational component of industrial prototyping, where models are defined not by raw coordinates but by construction sequences such as sketches and extrusions. This sequential structure enables both efficient prototype initialization and subsequent editing. Text-guided CAD prototyping, which unifies Text-to-CAD generation and CAD editing, has the potential to streamline the entire design pipeline. However, prior work has not explored this setting, largely because standard large language model (LLM) tokenizers decompose CAD sequences into natural-language word pieces, failing to capture primitive-level CAD semantics and hindering attention modules from modeling geometric structure. We conjecture that a multimodal tokenization strategy, aligned with CAD's primitive and structural nature, can provide more effective representations. To this end, we propose CAD-Tokenizer, a framework that represents CAD data with modality-specific tokens using a sequence-based VQ-VAE with primitive-level pooling and constrained decoding. This design produces compact, primitive-aware representations that align with CAD's structural nature. Applied to unified text-guided CAD prototyping, CAD-Tokenizer significantly improves instruction following and generation quality, achieving better quantitative and qualitative performance over both general-purpose LLMs and task-specific baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21294", "title": "The role of synthetic data in Multilingual, Multi-cultural AI systems: Lessons from Indic Languages", "authors": [ "Pranjal A. Chitale", "Varun Gumma", "Sanchit Ahuja", "Prashant Kodali", "Manan Uppadhyay", "Deepthi Sudharsan", "Sunayana Sitaram" ], "github": "", "project_page": "", "abstract": "Developing AI systems that operate effectively across languages while remaining culturally grounded is a long-standing challenge, particularly in low-resource settings. Synthetic data provides a promising avenue, yet its effectiveness in multilingual and multicultural contexts remains underexplored. We investigate the creation and impact of synthetic, culturally contextualized datasets for Indian languages through a bottom-up generation strategy that prompts large open-source LLMs (>= 235B parameters) to ground data generation in language-specific Wikipedia content. This approach complements the dominant top-down paradigm of translating synthetic datasets from high-resource languages such as English. We introduce Updesh, a high-quality large-scale synthetic instruction-following dataset comprising 9.5M data points across 13 Indian languages, encompassing diverse reasoning and generative tasks with an emphasis on long-context, multi-turn capabilities, and alignment with Indian cultural contexts. A comprehensive evaluation incorporating both automated metrics and human annotation across 10k assessments indicates that generated data is high quality; though, human evaluation highlights areas for further improvement. Additionally, we perform downstream evaluations by fine-tuning models on our dataset and assessing the performance across 15 diverse multilingual datasets. Models trained on Updesh consistently achieve significant gains on generative tasks and remain competitive on multiple-choice style NLU tasks. Notably, relative improvements are most pronounced in low and medium-resource languages, narrowing their gap with high-resource languages. These findings provide empirical evidence that effective multilingual AI requires multi-faceted data curation and generation strategies that incorporate context-aware, culturally grounded methodologies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.22075", "title": "COSPADI: Compressing LLMs via Calibration-Guided Sparse Dictionary Learning", "authors": [ "Dmitriy Shopkhoev", "Denis Makhov", "Magauiya Zhussip", "Ammar Ali", "Stamatios Lefkimmiatis" ], "github": "", "project_page": "", "abstract": "Post-training compression of large language models (LLMs) largely relies on low-rank weight approximation, which represents each column of a weight matrix in a shared low-dimensional subspace. While this is a computationally efficient strategy, the imposed structural constraint is rigid and can lead to a noticeable model accuracy drop. In this work, we propose CoSpaDi (Compression via Sparse Dictionary Learning), a novel training-free compression framework that replaces low-rank decomposition with a more flexible structured sparse factorization in which each weight matrix is represented with a dense dictionary and a column-sparse coefficient matrix. This formulation enables a union-of-subspaces representation: different columns of the original weight matrix are approximated in distinct subspaces spanned by adaptively selected dictionary atoms, offering greater expressiveness than a single invariant basis. Crucially, CoSpaDi leverages a small calibration dataset to optimize the factorization such that the output activations of compressed projection layers closely match those of the original ones, thereby minimizing functional reconstruction error rather than mere weight approximation. This data-aware strategy preserves better model fidelity without any fine-tuning under reasonable compression ratios. Moreover, the resulting structured sparsity allows efficient sparse-dense matrix multiplication and is compatible with post-training quantization for further memory and latency gains. We evaluate CoSpaDi across multiple Llama and Qwen models under per-layer and per-group settings at 20-50\\% compression ratios, demonstrating consistent superiority over state-of-the-art data-aware low-rank methods both in accuracy and perplexity. Our results establish structured sparse dictionary learning as a powerful alternative to conventional low-rank approaches for efficient LLM deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.21319", "title": "RLBFF: Binary Flexible Feedback to bridge between Human Feedback & Verifiable Rewards", "authors": [ "Zhilin Wang", "Jiaqi Zeng", "Olivier Delalleau", "Ellie Evans", "Daniel Egert", "Hoo-Chang Shin", "Felipe Soares", "Yi Dong", "Oleksii Kuchaiev" ], "github": "https://github.com/NVIDIA-NeMo/RL", "project_page": "", "abstract": "Reinforcement Learning with Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) are the main RL paradigms used in LLM post-training, each offering distinct advantages. However, RLHF struggles with interpretability and reward hacking because it relies on human judgments that usually lack explicit criteria, whereas RLVR is limited in scope by its focus on correctness-based verifiers. We propose Reinforcement Learning with Binary Flexible Feedback (RLBFF), which combines the versatility of human-driven preferences with the precision of rule-based verification, enabling reward models to capture nuanced aspects of response quality beyond mere correctness. RLBFF extracts principles that can be answered in a binary fashion (e.g. accuracy of information: yes, or code readability: no) from natural language feedback. Such principles can then be used to ground Reward Model training as an entailment task (response satisfies or does not satisfy an arbitrary principle). We show that Reward Models trained in this manner can outperform Bradley-Terry models when matched for data and achieve top performance on RM-Bench (86.2%) and JudgeBench (81.4%, #1 on leaderboard as of September 24, 2025). Additionally, users can specify principles of interest at inference time to customize the focus of our reward models, in contrast to Bradley-Terry models. Finally, we present a fully open source recipe (including data) to align Qwen3-32B using RLBFF and our Reward Model, to match or exceed the performance of o3-mini and DeepSeek R1 on general alignment benchmarks of MT-Bench, WildBench, and Arena Hard v2 (at <5% of the inference cost).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA-NeMo/RL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-29", "arxiv_id": "2509.23661", "title": "LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training", "authors": [ "Xiang An", "Yin Xie", "Kaicheng Yang", "Wenkang Zhang", "Xiuwei Zhao", "Zheng Cheng", "Yirui Wang", "Songcen Xu", "Changrui Chen", "Chunsheng Wu", "Huajie Tan", "Chunyuan Li", "Jing Yang", "Jie Yu", "Xiyao Wang", "Bin Qin", "Yumeng Wang", "Zizhen Yan", "Ziyong Feng", "Ziwei Liu", "Bo Li", "Jiankang Deng" ], "github": "", "project_page": "", "abstract": "We present LLaVA-OneVision-1.5, a novel family of Large Multimodal Models (LMMs) that achieve state-of-the-art performance with significantly reduced computational and financial costs. Different from the existing works, LLaVA-OneVision-1.5 provides an open, efficient, and reproducible framework for building high-quality vision-language models entirely from scratch. The LLaVA-OneVision-1.5 release comprises three primary components: (1) Large-Scale Curated Datasets: We construct an 85M concept-balanced pretraining dataset LLaVA-OneVision-1.5-Mid-Traning and a meticulously curated 26M instruction dataset LLaVA-OneVision-1.5-Instruct, collectively encompassing 64B compressed multimodal tokens. (2) Efficient Training Framework: We develop a complete end-to-end efficient training framework leveraging an offline parallel data packing strategy to facilitate the training of LLaVA-OneVision-1.5 within a $16,000 budget. (3) State-of-the-art Performance: Experimental results demonstrate that LLaVA-OneVision1.5 yields exceptionally competitive performance across a broad range of downstream tasks. Specifically, LLaVA-OneVision-1.5-8B outperforms Qwen2.5-VL-7B on 18 of 27 benchmarks, and LLaVA-OneVision-1.5-4B surpasses Qwen2.5-VL-3B on all 27 benchmarks. We anticipate releasing LLaVA-OneVision-1.5-RL shortly and encourage the community to await further updates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24006", "title": "SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention", "authors": [ "Jintao Zhang", "Haoxu Wang", "Kai Jiang", "Shuo Yang", "Kaiwen Zheng", "Haocheng Xi", "Ziteng Wang", "Hongzhou Zhu", "Min Zhao", "Ion Stoica", "Joseph E. Gonzalez", "Jun Zhu", "Jianfei Chen" ], "github": "https://github.com/thu-ml/SLA", "project_page": "", "abstract": "In Diffusion Transformer (DiT) models, particularly for video generation, attention latency is a major bottleneck due to the long sequence length and the quadratic complexity. We find that attention weights can be separated into two parts: a small fraction of large weights with high rank and the remaining weights with very low rank. This naturally suggests applying sparse acceleration to the first part and low-rank acceleration to the second. Based on this finding, we propose SLA (Sparse-Linear Attention), a trainable attention method that fuses sparse and linear attention to accelerate diffusion models. SLA classifies attention weights into critical, marginal, and negligible categories, applying O(N^2) attention to critical weights, O(N) attention to marginal weights, and skipping negligible ones. SLA combines these computations into a single GPU kernel and supports both forward and backward passes. With only a few fine-tuning steps using SLA, DiT models achieve a 20x reduction in attention computation, resulting in significant acceleration without loss of generation quality. Experiments show that SLA reduces attention computation by 95% without degrading end-to-end generation quality, outperforming baseline methods. In addition, we implement an efficient GPU kernel for SLA, which yields a 13.7x speedup in attention computation and a 2.2x end-to-end speedup in video generation on Wan2.1-1.3B.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/thu-ml/SLA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23102", "title": "Multiplayer Nash Preference Optimization", "authors": [ "Fang Wu", "Xu Huang", "Weihao Xuan", "Zhiwei Zhang", "Yijia Xiao", "Guancheng Wan", "Xiaomin Li", "Bing Hu", "Peng Xia", "Jure Leskovec", "Yejin Choi" ], "github": "https://github.com/smiles724/MNPO", "project_page": "", "abstract": "Reinforcement learning from human feedback (RLHF) has emerged as the standard paradigm for aligning large language models (LLMs) with human preferences. However, reward-based methods built on the Bradley-Terry assumption struggle to capture the non-transitive and heterogeneous nature of real-world preferences. To address this, recent studies have reframed alignment as a two-player Nash game, giving rise to Nash learning from human feedback (NLHF). While this perspective has inspired algorithms such as INPO, ONPO, and EGPO with strong theoretical and empirical guarantees, they remain fundamentally restricted to two-player interactions, creating a single-opponent bias that fails to capture the full complexity of realistic preference structures. In this work, we introduce Multiplayer Nash Preference Optimization (MNPO), a novel framework that generalizes NLHF to the multiplayer regime. It formulates alignment as an n-player game, where each policy competes against a population of opponents while being regularized toward a reference model. Our framework establishes well-defined Nash equilibria in multiplayer settings and extends the concept of duality gap to quantify approximation quality. We demonstrate that MNPO inherits the equilibrium guarantees of two-player methods while enabling richer competitive dynamics and improved coverage of diverse preference structures. Through comprehensive empirical evaluation, we show that MNPO consistently outperforms existing NLHF baselines on instruction-following benchmarks, achieving superior alignment quality under heterogeneous annotator conditions and mixed-policy evaluation scenarios. Together, these results establish MNPO as a principled and scalable framework for aligning LLMs with complex, non-transitive human preferences. Code is available at https://github.com/smiles724/MNPO.", "github_manual": "", "github_regex": "https://github.com/smiles724/MNPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23909", "title": "EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modeling", "authors": [ "Xin Luo", "Jiahao Wang", "Chenyuan Wu", "Shitao Xiao", "Xiyan Jiang", "Defu Lian", "Jiajun Zhang", "Dong Liu", "Zheng liu" ], "github": "", "project_page": "", "abstract": "Instruction-guided image editing has achieved remarkable progress, yet current models still face challenges with complex instructions and often require multiple samples to produce a desired result. Reinforcement Learning (RL) offers a promising solution, but its adoption in image editing has been severely hindered by the lack of a high-fidelity, efficient reward signal. In this work, we present a comprehensive methodology to overcome this barrier, centered on the development of a state-of-the-art, specialized reward model. We first introduce EditReward-Bench, a comprehensive benchmark to systematically evaluate reward models on editing quality. Building on this benchmark, we develop EditScore, a series of reward models (7B-72B) for evaluating the quality of instruction-guided image editing. Through meticulous data curation and filtering, EditScore effectively matches the performance of learning proprietary VLMs. Furthermore, coupled with an effective self-ensemble strategy tailored for the generative nature of EditScore, our largest variant even surpasses GPT-5 in the benchmark. We then demonstrate that a high-fidelity reward model is the key to unlocking online RL for image editing. Our experiments show that, while even the largest open-source VLMs fail to provide an effective learning signal, EditScore enables efficient and robust policy optimization. Applying our framework to a strong base model, OmniGen2, results in a final model that shows a substantial and consistent performance uplift. Overall, this work provides the first systematic path from benchmarking to reward modeling to RL training in image editing, showing that a high-fidelity, domain-specialized reward model is the key to unlocking the full potential of RL in this domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23426", "title": "Democratizing AI scientists using ToolUniverse", "authors": [ "Shanghua Gao", "Richard Zhu", "Pengwei Sui", "Zhenglun Kong", "Sufian Aldogom", "Yepeng Huang", "Ayush Noori", "Reza Shamji", "Krishna Parvataneni", "Theodoros Tsiligkaridis", "Marinka Zitnik" ], "github": "https://github.com/mims-harvard/ToolUniverse", "project_page": "", "abstract": "AI scientists are emerging computational systems that serve as collaborative partners in discovery. These systems remain difficult to build because they are bespoke, tied to rigid workflows, and lack shared environments that unify tools, data, and analyses into a common ecosystem. In omics, unified ecosystems have transformed research by enabling interoperability, reuse, and community-driven development; AI scientists require comparable infrastructure. We present ToolUniverse, an ecosystem for building AI scientists from any language or reasoning model, whether open or closed. TOOLUNIVERSE standardizes how AI scientists identify and call tools, integrating more than 600 machine learning models, datasets, APIs, and scientific packages for data analysis, knowledge retrieval, and experimental design. It automatically refines tool interfaces for correct use by AI scientists, creates new tools from natural language descriptions, iteratively optimizes tool specifications, and composes tools into agentic workflows. In a case study of hypercholesterolemia, ToolUniverse was used to create an AI scientist to identify a potent analog of a drug with favorable predicted properties. The open-source ToolUniverse is available at https://aiscientist.tools.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mims-harvard/ToolUniverse", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24193", "title": "AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play", "authors": [ "Ran Xu", "Yuchen Zhuang", "Zihan Dong", "Jonathan Wang", "Yue Yu", "Joyce C. Ho", "Linjun Zhang", "Haoyu Wang", "Wenqi Shi", "Carl Yang" ], "github": "https://github.com/ritaranx/AceSearcher", "project_page": "", "abstract": "Search-augmented LLMs often struggle with complex reasoning tasks due to ineffective multi-hop retrieval and limited reasoning ability. We propose AceSearcher, a cooperative self-play framework that trains a single large language model (LLM) to alternate between two roles: a decomposer that breaks down complex queries and a solver that integrates retrieved contexts for answer generation. AceSearcher couples supervised fine-tuning on a diverse mixture of search, reasoning, and decomposition tasks with reinforcement fine-tuning optimized for final answer accuracy, eliminating the need for intermediate annotations. Extensive experiments on three reasoning-intensive tasks across 10 datasets show that AceSearcher outperforms state-of-the-art baselines, achieving an average exact match improvement of 7.6%. Remarkably, on document-level finance reasoning tasks, AceSearcher-32B matches the performance of the DeepSeek-V3 model using less than 5% of its parameters. Even at smaller scales (1.5B and 8B), AceSearcher often surpasses existing search-augmented LLMs with up to 9x more parameters, highlighting its exceptional efficiency and effectiveness in tackling complex reasoning tasks. Our code will be published at https://github.com/ritaranx/AceSearcher and https://huggingface.co/AceSearcher.", "github_manual": "", "github_regex": "https://github.com/ritaranx/AceSearcher", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24014", "title": "SparseD: Sparse Attention for Diffusion Language Models", "authors": [ "Zeqing Wang", "Gongfan Fang", "Xinyin Ma", "Xingyi Yang", "Xinchao Wang" ], "github": "https://github.com/INV-WZQ/SparseD", "project_page": "", "abstract": "While diffusion language models (DLMs) offer a promising alternative to autoregressive models (ARs), existing open-source DLMs suffer from high inference latency. This bottleneck is mainly due to the attention's quadratic complexity with respect to context length in computing all query-key pairs. Intuitively, to reduce this complexity, a natural strategy is to restrict attention to sparse patterns that retain only the most relevant connections. Such approaches are well-established in ARs, where attention follows fixed and clearly defined sparse patterns. However, in DLMs, we observe distinct sparsity behaviors: (1) attention patterns vary across heads, (2) attention patterns in each head remain highly similar across denoising steps, and (3) early denoising steps are critical for generation. These findings render sparse attention methods designed for ARs largely incompatible with DLMs, as they fail to capture head-specific structures and risk degrading generation when applied in early denoising steps. To address these challenges, we propose SparseD, a novel sparse attention method for DLMs. Leveraging the observations, SparseD only requires pre-computing head-specific sparse patterns one time, and reuses them across all steps. This prevents recomputing sparse patterns at each denoising step. Meanwhile, SparseD uses full attention in the early steps, then switches to sparse attention later to maintain generation quality. Together, these establish SparseD as a practical and efficient solution for deploying DLMs in long-context applications. Experimental results demonstrate that SparseD achieves lossless acceleration, delivering up to 1.50times speedup over FlashAttention at a 64k context length with 1,024 denoising steps.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/INV-WZQ/SparseD", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.21953", "title": "MultiCrafter: High-Fidelity Multi-Subject Generation via Spatially Disentangled Attention and Identity-Aware Reinforcement Learning", "authors": [ "Tao Wu", "Yibo Jiang", "Yehao Lu", "Zhizhong Wang", "Zeyi Huang", "Zequn Qin", "Xi Li" ], "github": "", "project_page": "", "abstract": "Multi-subject image generation aims to synthesize user-provided subjects in a single image while preserving subject fidelity, ensuring prompt consistency, and aligning with human aesthetic preferences. However, existing methods, particularly those built on the In-Context-Learning paradigm, are limited by their reliance on simple reconstruction-based objectives, leading to both severe attribute leakage that compromises subject fidelity and failing to align with nuanced human preferences. To address this, we propose MultiCrafter, a framework that ensures high-fidelity, preference-aligned generation. First, we find that the root cause of attribute leakage is a significant entanglement of attention between different subjects during the generation process. Therefore, we introduce explicit positional supervision to explicitly separate attention regions for each subject, effectively mitigating attribute leakage. To enable the model to accurately plan the attention region of different subjects in diverse scenarios, we employ a Mixture-of-Experts architecture to enhance the model's capacity, allowing different experts to focus on different scenarios. Finally, we design a novel online reinforcement learning framework to align the model with human preferences, featuring a scoring mechanism to accurately assess multi-subject fidelity and a more stable training strategy tailored for the MoE architecture. Experiments validate that our framework significantly improves subject fidelity while aligning with human preferences better.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25190", "title": "Visual Jigsaw Post-Training Improves MLLMs", "authors": [ "Penghao Wu", "Yushan Zhang", "Haiwen Diao", "Bo Li", "Lewei Lu", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Reinforcement learning based post-training has recently emerged as a powerful paradigm for enhancing the alignment and reasoning capabilities of multimodal large language models (MLLMs). While vision-centric post-training is crucial for enhancing MLLMs' intrinsic understanding of visual signals, current post-training paradigms are predominantly text-centric, where dense visual inputs are only leveraged to extract sparse cues for text-based reasoning. There exist a few approaches in this direction, however, they often still rely on text as an intermediate mediator or introduce additional visual generative designs. In this work, we introduce Visual Jigsaw, a generic self-supervised post-training framework designed to strengthen visual understanding in MLLMs. Visual Jigsaw is formulated as a general ordering task: visual inputs are partitioned, shuffled, and the model must reconstruct the visual information by producing the correct permutation in natural language. This naturally aligns with reinforcement learning from verifiable rewards (RLVR), requires no additional visual generative components, and derives its supervisory signal automatically without any annotations. We instantiate Visual Jigsaw across three visual modalities, including images, videos, and 3D data. Extensive experiments demonstrate substantial improvements in fine-grained perception, temporal reasoning, and 3D spatial understanding. Our findings highlight the potential of self-supervised vision-centric tasks in post-training MLLMs and aim to inspire further research on vision-centric pretext designs. Project Page: https://penghao-wu.github.io/visual_jigsaw/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23371", "title": "Alignment through Meta-Weighted Online Sampling: Bridging the Gap between Data Generation and Preference Optimization", "authors": [ "Junming Yang", "Ning Xu", "Biao Liu", "Shiqi Qiao", "Xin Geng" ], "github": "", "project_page": "", "abstract": "Preference optimization is crucial for aligning large language models (LLMs) with human values and intentions. A significant challenge in this process is the distribution mismatch between pre-collected offline preference data and the evolving model policy. Existing methods attempt to reduce this gap using static heuristics or decoupled online sampling strategies, but they often fail to adapt to the model's dynamic learning state. To bridge this gap, we propose Meta-Weighted Adaptive Preference Optimization (MetaAPO), a novel framework that dynamically couples data generation with model training. MetaAPO employs a lightweight meta-learner, as an \"alignment gap estimator\", to evaluate the potential benefits of on-policy sampling in relation to offline data. This guides targeted online generation and assigns sample-wise meta-weights to the optimization objective, dynamically balancing the quality and distribution of online and offline data. Experiments on AlpacaEval 2, Arena-Hard and MT-Bench demonstrate that MetaAPO consistently outperforms existing preference optimization approaches across various settings, while reducing 42% in online annotation costs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23285", "title": "Toward Effective Tool-Integrated Reasoning via Self-Evolved Preference Learning", "authors": [ "Yifei Chen", "Guanting Dong", "Zhicheng Dou" ], "github": "", "project_page": "", "abstract": "Tool-Integrated Reasoning (TIR) enables large language models (LLMs) to improve their internal reasoning ability by integrating external tools. However, models employing TIR often display suboptimal behaviors, such as insufficient or excessive tool usage and overthinking after tool calls. The challenge of incentivizing LLMs to perform TIR efficiently and accurately, while stabilizing the reasoning process, remains an open question. In this paper, we start by exploring the impact of tool calls on model reasoning from the perspective of information entropy. Our findings indicate that tool call results lead to a distinct change in the information entropy of subsequent reasoning, with the overall entropy of the reasoning chain varying based on the number of tool calls. Building on these insights, we propose Tool-Light, a framework designed to encourage LLMs to perform TIR efficiently and accurately. Our framework includes dataset construction and multi-stage fine-tuning. For dataset construction, we employ continuous self-evolved sampling using the fine-tuned model, integrating both vanilla sampling and entropy-guided sampling. Besides, we establish strict criteria for selecting positive-negative pairs during sampling. The training process involves a two-stage approach, comprising Supervised Fine-Tuning (SFT) and Self-Evolved Direct Preference Optimization (DPO). Experimental results on 10 datasets demonstrate the effectiveness of Tool-Light, significantly improving the model's efficiency in executing TIR tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25131", "title": "MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech", "authors": [ "Chengyao Wang", "Zhisheng Zhong", "Bohao Peng", "Senqiao Yang", "Yuqi Liu", "Haokun Gui", "Bin Xia", "Jingyao Li", "Bei Yu", "Jiaya Jia" ], "github": "https://github.com/LianjiaTech/BELLE", "project_page": "", "abstract": "We present MGM-Omni, a unified Omni LLM for omni-modal understanding and expressive, long-horizon speech generation. Unlike cascaded pipelines that isolate speech synthesis, MGM-Omni adopts a \"brain-mouth\" design with a dual-track, token-based architecture that cleanly decouples multimodal reasoning from real-time speech generation. This design enables efficient cross-modal interaction and low-latency, streaming speech generation. For understanding, a unified training strategy coupled with a dual audio encoder design enables long-form audio perception across diverse acoustic conditions. For generation, a chunk-based parallel decoding scheme narrows the text speech token-rate gap, accelerating inference and supporting streaming zero-shot voice cloning with stable timbre over extended durations. Compared to concurrent work, MGM-Omni achieves these capabilities with markedly data-efficient training. Extensive experiments demonstrate that MGM-Omni outperforms existing open source models in preserving timbre identity across extended sequences, producing natural and context-aware speech, and achieving superior long-form audio and omnimodal understanding. MGM-Omni establishes an efficient, end-to-end paradigm for omnimodal understanding and controllable, personalised long-horizon speech generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LianjiaTech/BELLE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24786", "title": "LOVE-R1: Advancing Long Video Understanding with an Adaptive Zoom-in Mechanism via Multi-Step Reasoning", "authors": [ "Shenghao Fu", "Qize Yang", "Yuan-Ming Li", "Xihan Wei", "Xiaohua Xie", "Wei-Shi Zheng" ], "github": "", "project_page": "", "abstract": "Long video understanding is still challenging for recent Large Video-Language Models (LVLMs) due to the conflict between long-form temporal understanding and detailed spatial perception. LVLMs with a uniform frame sampling mechanism, which samples frames with an equal frame size and fixed sampling rate, inevitably sacrifice either temporal clues or spatial details, resulting in suboptimal solutions. To mitigate this dilemma, we propose LOVE-R1, a model that can adaptively zoom in on a video clip. The model is first provided with densely sampled frames but in a small resolution. If some spatial details are needed, the model can zoom in on a clip of interest with a large frame resolution based on its reasoning until key visual information is obtained. The whole process is implemented as a multi-step reasoning process. To train the reasoning ability, we first finetune the model on our collected 38k high-quality CoT data and enhance it with decoupled reinforcement finetuning. As outcome rewards can not provide fine-grained process supervision, we decouple multi-step reasoning into multiple single-step reasoning and optimize the internal zoom-in ability explicitly. Experiments on long video understanding benchmarks show that our model with the slow-fast adaptive frame sampling mechanism achieves a great trade-off between sampling density and frame resolutions, and LOVE-R1 outperforms our baseline Qwen2.5-VL by an average of 3.1% points across 4 common long video understanding benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25175", "title": "EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering", "authors": [ "Haolei Xu", "Xinyu Mei", "Yuchen Yan", "Rui Zhou", "Wenqi Zhang", "Weiming Lu", "Yueting Zhuang", "Yongliang Shen" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted manipulation of hidden states, offering a lightweight alternative to expensive retraining. However, existing steering frameworks suffer from critical limitations: computational inefficiency, limited extensibility, and restricted functionality that hinder both research progress and practical deployment. We present EasySteer, a unified framework for high-performance, extensible LLM steering built on vLLM. Our system features modular architecture with pluggable interfaces for both analysis-based and learning-based methods, fine-grained parameter control, pre-computed steering vectors for eight application domains, and an interactive demonstration system. Through deep integration with vLLM's optimized inference engine, EasySteer achieves 5.5-11.4times speedup over existing frameworks. Extensive experiments demonstrate its effectiveness in overthinking mitigation, hallucination reduction, and other key applications. EasySteer transforms steering from research technique to production-ready capability, establishing critical infrastructure for deployable, controllable language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24695", "title": "SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer", "authors": [ "Junsong Chen", "Yuyang Zhao", "Jincheng Yu", "Ruihang Chu", "Junyu Chen", "Shuai Yang", "Xianbang Wang", "Yicheng Pan", "Daquan Zhou", "Huan Ling", "Haozhe Liu", "Hongwei Yi", "Hao Zhang", "Muyang Li", "Yukang Chen", "Han Cai", "Sanja Fidler", "Ping Luo", "Song Han", "Enze Xie" ], "github": "", "project_page": "", "abstract": "We introduce SANA-Video, a small diffusion model that can efficiently generate videos up to 720x1280 resolution and minute-length duration. SANA-Video synthesizes high-resolution, high-quality and long videos with strong text-video alignment at a remarkably fast speed, deployable on RTX 5090 GPU. Two core designs ensure our efficient, effective and long video generation: (1) Linear DiT: We leverage linear attention as the core operation, which is more efficient than vanilla attention given the large number of tokens processed in video generation. (2) Constant-Memory KV cache for Block Linear Attention: we design block-wise autoregressive approach for long video generation by employing a constant-memory state, derived from the cumulative properties of linear attention. This KV cache provides the Linear DiT with global context at a fixed memory cost, eliminating the need for a traditional KV cache and enabling efficient, minute-long video generation. In addition, we explore effective data filters and model training strategies, narrowing the training cost to 12 days on 64 H100 GPUs, which is only 1% of the cost of MovieGen. Given its low cost, SANA-Video achieves competitive performance compared to modern state-of-the-art small diffusion models (e.g., Wan 2.1-1.3B and SkyReel-V2-1.3B) while being 16x faster in measured latency. Moreover, SANA-Video can be deployed on RTX 5090 GPUs with NVFP4 precision, accelerating the inference speed of generating a 5-second 720p video from 71s to 29s (2.4x speedup). In summary, SANA-Video enables low-cost, high-quality video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25176", "title": "SIRI: Scaling Iterative Reinforcement Learning with Interleaved Compression", "authors": [ "Haoming Wen", "Yushi Bai", "Juanzi Li", "Jie Tang" ], "github": "", "project_page": "", "abstract": "We introduce SIRI, Scaling Iterative Reinforcement Learning with Interleaved Compression, a simple yet effective RL approach for Large Reasoning Models (LRMs) that enables more efficient and accurate reasoning. Existing studies have observed repetitive thinking patterns in LRMs, and attempts to reduce them often come at the cost of performance. In this paper, we show that this trade-off can be overcome through a training regime that iteratively alternates between compressing and expanding the reasoning budget, by dynamically adjusting the maximum rollout length during training. The compression phase cuts the rollout length, forcing the model to make precise and valuable decisions within a limited context, which effectively reduces redundant tokens and increases reasoning density. The expansion phase then relaxes the length limit, providing space for the model to explore and plan in long-horizon settings. Remarkably, we find that after each compression-expansion cycle, the model's performance improves even as its output length decreases, steadily pushing it closer to the Pareto frontier in the performance-efficiency trade-off. Training on DeepSeek-R1-Distill-Qwen-1.5B, SIRI-low improves performance on AIME24 by 43.2% while reducing token usage by 46.9% after three iterations, and SIRI-high achieves the highest accuracy compared to all other methods (Figure 1). Our findings shed light on the potential of periodically oscillating the LRM's output truncation length during training to dynamically balance exploration and efficiency in reasoning, converging towards an optimal \"sweet spot\" between the two. Our models are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24897", "title": "RealUnify: Do Unified Models Truly Benefit from Unification? A Comprehensive Benchmark", "authors": [ "Yang Shi", "Yuhao Dong", "Yue Ding", "Yuran Wang", "Xuanyu Zhu", "Sheng Zhou", "Wenting Liu", "Haochen Tian", "Rundong Wang", "Huanqian Wang", "Zuyan Liu", "Bohan Zeng", "Ruizhe Chen", "Qixun Wang", "Zhuoran Zhang", "Xinlong Chen", "Chengzhuo Tong", "Bozhou Li", "Chaoyou Fu", "Qiang Liu", "Haotian Wang", "Wenjing Yang", "Yuanxing Zhang", "Pengfei Wan", "Yi-Fan Zhang", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "The integration of visual understanding and generation into unified multimodal models represents a significant stride toward general-purpose AI. However, a fundamental question remains unanswered by existing benchmarks: does this architectural unification actually enable synergetic interaction between the constituent capabilities? Existing evaluation paradigms, which primarily assess understanding and generation in isolation, are insufficient for determining whether a unified model can leverage its understanding to enhance its generation, or use generative simulation to facilitate deeper comprehension. To address this critical gap, we introduce RealUnify, a benchmark specifically designed to evaluate bidirectional capability synergy. RealUnify comprises 1,000 meticulously human-annotated instances spanning 10 categories and 32 subtasks. It is structured around two core axes: 1) Understanding Enhances Generation, which requires reasoning (e.g., commonsense, logic) to guide image generation, and 2) Generation Enhances Understanding, which necessitates mental simulation or reconstruction (e.g., of transformed or disordered visual inputs) to solve reasoning tasks. A key contribution is our dual-evaluation protocol, which combines direct end-to-end assessment with a diagnostic stepwise evaluation that decomposes tasks into distinct understanding and generation phases. This protocol allows us to precisely discern whether performance bottlenecks stem from deficiencies in core abilities or from a failure to integrate them. Through large-scale evaluations of 12 leading unified models and 6 specialized baselines, we find that current unified models still struggle to achieve effective synergy, indicating that architectural unification alone is insufficient. These results highlight the need for new training strategies and inductive biases to fully unlock the potential of unified modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24900", "title": "OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing", "authors": [ "Zhihong Chen", "Xuehai Bai", "Yang Shi", "Chaoyou Fu", "Huanyu Zhang", "Haotian Wang", "Xiaoyan Sun", "Zhang Zhang", "Liang Wang", "Yuanxing Zhang", "Pengfei Wan", "Yi-Fan Zhang" ], "github": "", "project_page": "", "abstract": "The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensiveness of their training data. While existing datasets have covered basic tasks like style transfer and simple object manipulation, they often lack the systematic structure and challenging scenarios required for real-world applications. To address this bottleneck, we introduce OpenGPT-4o-Image, a large-scale dataset constructed using a novel methodology that combines hierarchical task taxonomy with automated data generation. Our taxonomy not only includes fundamental capabilities such as text rendering and style control but also introduces highly practical yet challenging categories like scientific imagery for chemistry illustrations and complex instruction editing requiring simultaneous execution of multiple operations. Through an automated pipeline leveraging structured resource pools and GPT-4o, we generate 80k high-quality instruction-image pairs with controlled diversity, covering 11 major domains and 51 subtasks. Extensive experiments show that fine-tuning leading models on our dataset achieves significant performance gains across multiple benchmarks, with improvements of up to 18\\% on editing tasks (UniWorld-V1 on ImgEdit-Bench) and 13% on generation tasks (Harmon on GenEval). Our work demonstrates that systematic data construction is key to advancing multimodal AI capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24473", "title": "Euclid's Gift: Enhancing Spatial Perception and Reasoning in Vision-Language Models via Geometric Surrogate Tasks", "authors": [ "Shijie Lian", "Changti Wu", "Laurence Tianruo Yang", "Hang Yuan", "Bin Yu", "Lei Zhang", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Spatial intelligence spans a rich suite of abilities, including visualising and transforming shapes, mentally rotating objects, judging relational positions and containment, and estimating numerosity. However, it still remains a critical unresolved challenge for Multimodal Large Language Models (MLLMs).To fill this gap, we propose to treat Euclidean geometry problem-solving as a surrogate task. Specifically, we meticulously constructed a curated multimodal dataset, called Euclid30K, comprising approximately 30K plane and solid geometry problems. To enable the model to acquire and apply Euclidean principles from these geometry problems, we employed Group Relative Policy Optimization (GRPO) to finetune the Qwen2.5VL family and RoboBrain2.0 family, inspiring the models to identify shapes, count, and relate entities, and perform multi-step deductive reasoning using Euclidean principles. Our experiments demonstrate that the resulting models achieve substantial zero-shot gains across four spatial reasoning benchmarks (Super-CLEVR, Omni3DBench, VSI-Bench, and MindCube) without any task-specific adaptations. Notably, after training on the Euclid30K, the mean VSI-Bench accuracy of all evaluated models rose from 34.5% to 40.5%, improving by 5.5 percentage points. Among them, RoboBrain2.0-Euclid-7B achieves 49.6\\% accuracy, surpassing the previous state-of-the-art model, Spatial-MLLM.To our knowledge, this is the first systematic study showing that geometry-centric fine-tuning can confer vision-language models with broadly transferable spatial skills. Code and Euclid30K dataset can be found in https://zgca-ai4edu.github.io/Euclids_Gift.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24007", "title": "Sequential Diffusion Language Models", "authors": [ "Yangzhou Liu", "Yue Cao", "Hao Li", "Gen Luo", "Zhe Chen", "Weiyun Wang", "Xiaobo Liang", "Biqing Qi", "Lijun Wu", "Changyao Tian", "Yanting Zhang", "Yuqiang Li", "Tong Lu", "Yu Qiao", "Jifeng Dai", "Wenhai Wang" ], "github": "https://github.com/OpenGVLab/SDLM", "project_page": "", "abstract": "Diffusion language models (DLMs) have strong theoretical efficiency but are limited by fixed-length decoding and incompatibility with key-value (KV) caches. Block diffusion mitigates these issues, yet still enforces a fixed block size and requires expensive training. We introduce Next Sequence Prediction (NSP), which unifies next-token and next-block prediction, enabling the model to adaptively determine the generation length at each step. When the length is fixed to 1, NSP reduces to standard next-token prediction. Building on NSP, we propose Sequential Diffusion Language Model (SDLM), which can retrofit pre-trained autoregressive language models (ALMs) at minimal cost. Specifically, SDLM performs diffusion inference within fixed-size mask blocks, but dynamically decodes consecutive subsequences based on model confidence, thereby preserving KV-cache compatibility and improving robustness to varying uncertainty and semantics across the sequence. Experiments show that SDLM matches or surpasses strong autoregressive baselines using only 3.5M training samples, while achieving 2.1 higher throughput than Qwen-2.5. Notably, the SDLM-32B model delivers even more pronounced efficiency gains, demonstrating the strong scalability potential of our modeling paradigm. Project page and codes: https://github.com/OpenGVLab/SDLM", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/SDLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25161", "title": "Rolling Forcing: Autoregressive Long Video Diffusion in Real Time", "authors": [ "Kunhao Liu", "Wenbo Hu", "Jiale Xu", "Ying Shan", "Shijian Lu" ], "github": "", "project_page": "", "abstract": "Streaming video generation, as one fundamental component in interactive world models and neural game engines, aims to generate high-quality, low-latency, and temporally coherent long video streams. However, most existing work suffers from severe error accumulation that often significantly degrades the generated stream videos over long horizons. We design Rolling Forcing, a novel video generation technique that enables streaming long videos with minimal error accumulation. Rolling Forcing comes with three novel designs. First, instead of iteratively sampling individual frames, which accelerates error propagation, we design a joint denoising scheme that simultaneously denoises multiple frames with progressively increasing noise levels. This design relaxes the strict causality across adjacent frames, effectively suppressing error growth. Second, we introduce the attention sink mechanism into the long-horizon stream video generation task, which allows the model to keep key value states of initial frames as a global context anchor and thereby enhances long-term global consistency. Third, we design an efficient training algorithm that enables few-step distillation over largely extended denoising windows. This algorithm operates on non-overlapping windows and mitigates exposure bias conditioned on self-generated histories. Extensive experiments show that Rolling Forcing enables real-time streaming generation of multi-minute videos on a single GPU, with substantially reduced error accumulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24910", "title": "Learning Goal-Oriented Language-Guided Navigation with Self-Improving Demonstrations at Scale", "authors": [ "Songze Li", "Zun Wang", "Gengze Zhou", "Jialu Li", "Xiangyu Zeng", "Limin Wang", "Yu Qiao", "Qi Wu", "Mohit Bansal", "Yi Wang" ], "github": "", "project_page": "", "abstract": "Goal-oriented language-guided navigation requires robust exploration capabilities for agents to navigate to specified goals in unknown environments without step-by-step instructions. Existing methods tend to exclusively utilize shortest-path trajectories, lacking effective exploration priors for training navigation agents. To address the above challenges, we present SID, a goal-oriented language-guided navigation learning approach with Self-Improving Demonstrations. Specifically, SID learns an initial agent on the shortest-path data sampled from environments and then leverages this agent to generate novel exploration trajectories. The novel rollouts provide demonstrations with stronger exploration strategies to train a better agent, which in turn produces higher-quality agent demonstrations for the next round of training. We show that this iterative self-improving pipeline readily scales to new environments, and the resulting demonstrations can be transferred across a variety of language-guided navigation tasks, elevating the performance ceiling in diverse goal-oriented navigation tasks. Extensive experiments demonstrate that SID significantly boosts the exploration capabilities and generalization of navigation agents. The resulting agent achieves new state-of-the-art performance on goal-oriented language-guided navigation tasks, including REVERIE, SOON, notably achieving a 50.9% success rate on the unseen validation splits of SOON, surpassing the prior leading approaches by a margin of 13.9%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24335", "title": "Hyperspherical Latents Improve Continuous-Token Autoregressive Generation", "authors": [ "Guolin Ke", "Hui Xue" ], "github": "", "project_page": "", "abstract": "Autoregressive (AR) models are promising for image generation, yet continuous-token AR variants often trail latent diffusion and masked-generation models. The core issue is heterogeneous variance in VAE latents, which is amplified during AR decoding, especially under classifier-free guidance (CFG), and can cause variance collapse. We propose SphereAR to address this issue. Its core design is to constrain all AR inputs and outputs -- including after CFG -- to lie on a fixed-radius hypersphere (constant ell_2 norm), leveraging hyperspherical VAEs. Our theoretical analysis shows that hyperspherical constraint removes the scale component (the primary cause of variance collapse), thereby stabilizing AR decoding. Empirically, on ImageNet generation, SphereAR-H (943M) sets a new state of the art for AR models, achieving FID 1.34. Even at smaller scales, SphereAR-L (479M) reaches FID 1.54 and SphereAR-B (208M) reaches 1.92, matching or surpassing much larger baselines such as MAR-H (943M, 1.55) and VAR-d30 (2B, 1.92). To our knowledge, this is the first time a pure next-token AR image generator with raster order surpasses diffusion and masked-generation models at comparable parameter scales.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24269", "title": "AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models", "authors": [ "Zihao Zhu", "Xinyu Wu", "Gehan Hu", "Siwei Lyu", "Ke Xu", "Baoyuan Wu" ], "github": "", "project_page": "", "abstract": "Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex problem-solving through Chain-of-Thought (CoT) reasoning. However, the multi-step nature of CoT introduces new safety challenges that extend beyond conventional language model alignment. We identify a failure mode in current safety CoT tuning methods: the snowball effect, where minor reasoning deviations progressively amplify throughout the thought process, leading to either harmful compliance or excessive refusal. This effect stems from models being trained to imitate perfect reasoning scripts without learning to self-correct. To address this limitation, we propose AdvChain, an alignment paradigm that teaches models dynamic self-correction through adversarial CoT tuning. Our method involves constructing a dataset containing Temptation-Correction and Hesitation-Correction samples, where models learn to recover from harmful reasoning drifts and unnecessary cautions. Extensive experiments show that AdvChain significantly enhances robustness against jailbreak attacks and CoT hijacking while substantially reducing over-refusal on benign prompts, achieving a superior safety-utility balance without compromising reasoning capabilities. Our work establishes a new direction for building more robust and reliable reasoning models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23196", "title": "From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs", "authors": [ "Haonan Wang", "Weida Liang", "Zihang Fu", "Nie Zheng", "Yifan Zhang", "Yao Tong", "Tongyao Zhu", "Hao Jiang", "Chuang Li", "Jiaying Wu", "Kenji Kawaguchi" ], "github": "", "project_page": "", "abstract": "Recent reasoning LLMs (RLMs), especially those trained with verifier-based reinforcement learning, often perform worse with few-shot CoT than with direct answering. We revisit this paradox using high-quality reasoning traces from DeepSeek-R1 as demonstrations and find that adding more exemplars consistently degrades accuracy, even when demonstrations are optimal. A detailed analysis reveals two mechanisms behind this decline: (i) semantic misguidance, where high textual similarity leads the model to treat the target as the same as the exemplar and to copy intermediate steps verbatim; and (ii) strategy transfer failure, where the model struggles to extract useful reasoning strategies and apply them to target questions. Guided by these, we introduce Insight-to-Solve (I2S), a sequential test-time procedure that turns demonstrations into explicit, reusable insights and derives a target-specific reasoning trace; optionally, the reasoning is self-refined for coherence and correctness (I2S+). Extensive experiments on diverse benchmarks show that I2S and I2S+ consistently outperform both direct answering and test-time scaling baselines across open- and closed-source models. Even for GPT models, our method helps: on AIME'25, GPT-4.1 rises by +14.0%, and o1-mini improves by +2.7% on AIME and +1.7% on GPQA, indicating that in-context demonstrations can be harnessed effectively via insight-refine-solve framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23143", "title": "MathBode: Frequency-Domain Fingerprints of LLM Mathematical Reasoning", "authors": [ "Charles L. Wang" ], "github": "https://github.com/charleslwang/MathBode-Eval", "project_page": "", "abstract": "This paper presents MathBode, a dynamic diagnostic for mathematical reasoning in large language models (LLMs). Instead of one-shot accuracy, MathBode treats each parametric problem as a system: we drive a single parameter sinusoidally and fit first-harmonic responses of model outputs and exact solutions. This yields interpretable, frequency-resolved metrics -- gain (amplitude tracking) and phase (lag) -- that form Bode-style fingerprints. Across five closed-form families (linear solve, ratio/saturation, compound interest, 2x2 linear systems, similar triangles), the diagnostic surfaces systematic low-pass behavior and growing phase lag that accuracy alone obscures. We compare several models against a symbolic baseline that calibrates the instrument (G approx 1, phi approx 0). Results separate frontier from mid-tier models on dynamics, providing a compact, reproducible protocol that complements standard benchmarks with actionable measurements of reasoning fidelity and consistency. We open-source the dataset and code to enable further research and adoption.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/charleslwang/MathBode-Eval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.22572", "title": "Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time", "authors": [ "Yixuan Han", "Fan Ma", "Ruijie Quan", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Test-Time Scaling (TTS) enhances the reasoning ability of large language models (LLMs) by allocating additional computation during inference. However, existing approaches primarily rely on output-level sampling while overlooking the role of model architecture. In mainstream Mixture-of-Experts (MoE) LLMs, we observe that varying the number of activated experts yields complementary solution sets with stable accuracy, revealing a new and underexplored source of diversity. Motivated by this observation, we propose Dynamic Experts Search (DES), a TTS strategy that elevates expert activation into a controllable dimension of the search space. DES integrates two key components: (1) Dynamic MoE, which enables direct control of expert counts during inference to generate diverse reasoning trajectories without additional cost; and (2) Expert Configuration Inheritance, which preserves consistent expert counts within a reasoning path while varying them across runs, thereby balancing stability and diversity throughout the search. Extensive experiments across MoE architectures, verifiers and reasoning benchmarks (i.e., math, code and knowledge) demonstrate that DES reliably outperforms TTS baselines, enhancing accuracy and stability without additional cost. These results highlight DES as a practical and scalable form of architecture-aware TTS, illustrating how structural flexibility in modern LLMs can advance reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.22570", "title": "UniMIC: Token-Based Multimodal Interactive Coding for Human-AI Collaboration", "authors": [ "Qi Mao", "Tinghan Yang", "Jiahao Li", "Bin Li", "Libiao Jin", "Yan Lu" ], "github": "", "project_page": "", "abstract": "The rapid progress of Large Multimodal Models (LMMs) and cloud-based AI agents is transforming human-AI collaboration into bidirectional, multimodal interaction. However, existing codecs remain optimized for unimodal, one-way communication, resulting in repeated degradation under conventional compress-transmit-reconstruct pipelines. To address this limitation, we propose UniMIC, a Unified token-based Multimodal Interactive Coding framework that bridges edge devices and cloud AI agents. Instead of transmitting raw pixels or plain text, UniMIC employs compact tokenized representations as the communication medium, enabling efficient low-bitrate transmission while maintaining compatibility with LMMs. To further enhance compression, lightweight Transformer-based entropy models with scenario-specific designs-generic, masked, and text-conditioned-effectively minimize inter-token redundancy. Extensive experiments on text-to-image generation, text-guided inpainting, outpainting, and visual question answering show that UniMIC achieves substantial bitrate savings and remains robust even at ultra-low bitrates (<0.05bpp), without compromising downstream task performance. These results establish UniMIC as a practical and forward-looking paradigm for next-generation multimodal interactive communication.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25185", "title": "PixelCraft: A Multi-Agent System for High-Fidelity Visual Reasoning on Structured Images", "authors": [ "Shuoshuo Zhang", "Zijian Li", "Yizhen Zhang", "Jingjing Fu", "Lei Song", "Jiang Bian", "Jun Zhang", "Yujiu Yang", "Rui Wang" ], "github": "https://github.com/microsoft/PixelCraft", "project_page": "", "abstract": "Structured images (e.g., charts and geometric diagrams) remain challenging for multimodal large language models (MLLMs), as perceptual slips can cascade into erroneous conclusions. Intermediate visual cues can steer reasoning; however, existing cue-based methods are constrained with low-fidelity image processing and linear, rigid reasoning patterns, limiting their effectiveness on complex structured-image tasks. In this paper, we propose PixelCraft, a novel multi-agent system for high-fidelity image processing and flexible visual reasoning on structured images. The system comprises a dispatcher, a planner, a reasoner, critics, and a set of visual tool agents. To achieve high-fidelity processing, we construct a high-quality corpus and fine-tune an MLLM into a grounding model, whose pixel-level localizations are integrated with traditional computer vision (CV) algorithms in tool agents. Building on this foundation, PixelCraft facilitates flexible visual reasoning through a dynamic three-stage workflow of tool selection, agent discussion, and self-criticism. Moreover, unlike prior linear reasoning patterns that simply append historical images, PixelCraft maintains an image memory to allow the planner to adaptively revisit earlier visual steps, explore alternative reasoning branches, and dynamically adjust the reasoning trajectory during discussion. Extensive experiments on challenging chart and geometry benchmarks demonstrate that PixelCraft significantly improves visual reasoning performance for advanced MLLMs, setting a new standard for structured image reasoning. Our code will be available at https://github.com/microsoft/PixelCraft.", "github_manual": "", "github_regex": "https://github.com/microsoft/PixelCraft", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25160", "title": "GSM8K-V: Can Vision Language Models Solve Grade School Math Word Problems in Visual Contexts", "authors": [ "Fan Yuan", "Yuchen Yan", "Yifan Jiang", "Haoran Zhao", "Tao Feng", "Jinyan Chen", "Yanwei Lou", "Wenqi Zhang", "Yongliang Shen", "Weiming Lu", "Jun Xiao", "Yueting Zhuang" ], "github": "", "project_page": "", "abstract": "Vision language models (VLMs) achieve unified modeling of images and text, enabling them to accomplish complex real-world tasks through perception, planning, and reasoning. Among these tasks, reasoning is particularly representative, with mathematical reasoning serving as a prominent example. It highlights the high-level capability of VLMs to comprehend mathematical information in images and to perform sophisticated reasoning. Recently, numerous visual mathematical reasoning benchmarks have been proposed, but they are often restricted to geometry, lack coverage of math word problems, and rarely assess reasoning across multiple images. To address these gaps, we introduce GSM8K-V, a purely visual multi-image mathematical reasoning benchmark. GSM8K-V is built by systematically mapping each sample from the widely used text-based GSM8K into visual form. Through a carefully designed automated image-generation pipeline combined with meticulous human annotation, we curate 1,319 high-quality samples. We evaluate a wide range of open-source and closed-source models on GSM8K-V. Results show that although existing VLMs have nearly saturated performance on text-based GSM8K, there remains substantial room for improvement on GSM8K-V. For example, the best-performing model, Gemini-2.5-Pro, achieves 95.22% accuracy on GSM8K but only 46.93% on GSM8K-V. We conduct a comprehensive analysis of GSM8K-V, examining the limitations of current models as well as potential directions for improvement. GSM8K-V offers a new perspective on visual mathematical reasoning and establishes a benchmark to guide the development of more robust and generalizable VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24709", "title": "IWR-Bench: Can LVLMs reconstruct interactive webpage from a user interaction video?", "authors": [ "Yang Chen", "Minghao Liu", "Yufan Shen", "Yunwen Li", "Tianyuan Huang", "Xinyu Fang", "Tianyu Zheng", "Wenxuan Huang", "Cheng Yang", "Daocheng Fu", "Jianbiao Mei", "Rong Wu", "Licheng Wen", "Xuemeng Yang", "Song Mao", "Qunshu Lin", "Zhi Yu", "Yongliang Shen", "Yu Qiao", "Botian Shi" ], "github": "https://github.com/SIGMME/IWR-Bench", "project_page": "", "abstract": "The webpage-to-code task requires models to understand visual representations of webpages and generate corresponding code. However, existing benchmarks primarily focus on static screenshot-to-code tasks, thereby overlooking the dynamic interactions fundamental to real-world web applications. To address this limitation, this paper introduces IWR-Bench, a novel benchmark for evaluating the capabilities of Large Vision-Language Models (LVLMs) in interactive webpage reconstruction from video. IWR-Bench comprises 113 meticulously curated tasks from 100 real-world websites, with 1,001 actions and featuring diverse interaction complexities (e.g., web games), visual styles, and domains. Aligning with standard web development practices, each task includes not only user interaction videos but also all crawled static assets (e.g., images, videos). This benchmark evaluates models on two fundamental challenges: comprehensive multi-modal reasoning to infer interaction logic from video and assets, and advanced code generation to translate this logic into functional code. An agent-as-a-judge framework with a comprehensive metric system automatically assesses the functional correctness and visual fidelity of generated webpages. Extensive experiments on 28 LVLMs reveal a significant challenge: the best model achieves an overall score of only 36.35%, as functional correctness (24.39% IFS) lags significantly behind visual fidelity (64.25% VFS). These results highlight critical limitations in current models' ability to reason about temporal dynamics and synthesize event-driven logic, establishing IWR-Bench as a challenging frontier for vision-language research. The benchmark and evaluation code will be made publicly available. Code is available at https://github.com/L-O-I/IWR-Bench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SIGMME/IWR-Bench", "github_api_added_by": "auto" }, { "date": "2025-09-30", "arxiv_id": "2509.23951", "title": "HunyuanImage 3.0 Technical Report", "authors": [ "Siyu Cao", "Hangting Chen", "Peng Chen", "Yiji Cheng", "Yutao Cui", "Xinchi Deng", "Ying Dong", "Kipper Gong", "Tianpeng Gu", "Xiusen Gu", "Tiankai Hang", "Duojun Huang", "Jie Jiang", "Zhengkai Jiang", "Weijie Kong", "Changlin Li", "Donghao Li", "Junzhe Li", "Xin Li", "Yang Li", "Zhenxi Li", "Zhimin Li", "Jiaxin Lin", "Linus", "Lucaz Liu", "Shu Liu", "Songtao Liu", "Yu Liu", "Yuhong Liu", "Yanxin Long", "Fanbin Lu", "Qinglin Lu", "Yuyang Peng", "Yuanbo Peng", "Xiangwei Shen", "Yixuan Shi", "Jiale Tao", "Yangyu Tao", "Qi Tian", "Pengfei Wan", "Chunyu Wang", "Kai Wang", "Lei Wang", "Linqing Wang", "Lucas Wang", "Qixun Wang", "Weiyan Wang", "Hao Wen", "Bing Wu", "Jianbing Wu", "Yue Wu", "Senhao Xie", "Fang Yang", "Miles Yang", "Xiaofeng Yang", "Xuan Yang", "Zhantao Yang", "Jingmiao Yu", "Zheng Yuan", "Chao Zhang", "Jian-Wei Zhang", "Peizhen Zhang", "Shi-Xue Zhang", "Tao Zhang", "Weigang Zhang", "Yepeng Zhang", "Yingfang Zhang", "Zihao Zhang", "Zijian Zhang", "Penghao Zhao", "Zhiyuan Zhao", "Xuefei Zhe", "Jianchen Zhu", "Zhao Zhong" ], "github": "https://github.com/Tencent-Hunyuan/HunyuanImage-3.0", "project_page": "", "abstract": "We present HunyuanImage 3.0, a native multimodal model that unifies multimodal understanding and generation within an autoregressive framework, with its image generation module publicly available. The achievement of HunyuanImage 3.0 relies on several key components, including meticulous data curation, advanced architecture design, a native Chain-of-Thoughts schema, progressive model pre-training, aggressive model post-training, and an efficient infrastructure that enables large-scale training and inference. With these advancements, we successfully trained a Mixture-of-Experts (MoE) model comprising over 80 billion parameters in total, with 13 billion parameters activated per token during inference, making it the largest and most powerful open-source image generative model to date. We conducted extensive experiments and the results of automatic and human evaluation of text-image alignment and visual quality demonstrate that HunyuanImage 3.0 rivals previous state-of-the-art models. By releasing the code and weights of HunyuanImage 3.0, we aim to enable the community to explore new ideas with a state-of-the-art foundation model, fostering a dynamic and vibrant multimodal ecosystem. All open source assets are publicly available at https://github.com/Tencent-Hunyuan/HunyuanImage-3.0", "github_manual": "", "github_regex": "https://github.com/Tencent-Hunyuan/HunyuanImage-3.0", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23866", "title": "Efficient Multi-turn RL for GUI Agents via Decoupled Training and Adaptive Data Curation", "authors": [ "Pengxiang Li", "Zechen Hu", "Zirui Shang", "Jingrong Wu", "Yang Liu", "Hui Liu", "Zhi Gao", "Chenrui Shi", "Bofei Zhang", "Zihao Zhang", "Xiaochuan Shi", "Zedong YU", "Yuwei Wu", "Xinxiao Wu", "Yunde Jia", "Liuyu Xiang", "Zhaofeng He", "Qing Li" ], "github": "", "project_page": "", "abstract": "Vision-language model (VLM) based GUI agents show promise for automating complex desktop and mobile tasks, but face significant challenges in applying reinforcement learning (RL): (1) slow multi-turn interactions with GUI environments for policy rollout, and (2) insufficient high-quality agent-environment interactions for policy learning. To address these challenges, we propose DART, a Decoupled Agentic RL Training framework for GUI agents, which coordinates heterogeneous modules in a highly decoupled manner. DART separates the training system into four asynchronous modules: environment cluster, rollout service, data manager, and trainer. This design enables non-blocking communication, asynchronous training, rollout-wise trajectory sampling, and per-worker model synchronization, significantly improving the system efficiency: 1.6*GPU utilization for rollout, 1.9* training throughput, and 5.5* environment utilization. To facilitate effective learning from abundant samples, we introduce an adaptive data curation scheme: (1) pre-collecting successful trajectories for challenging tasks to supplement sparse success in online sampling; (2) dynamically adjusting rollout numbers and trajectory lengths based on task difficulty; (3) training selectively on high-entropy steps to prioritize critical decisions; (4) stabilizing learning via truncated importance sampling for policy mismatch between policy rollout and updating. On the OSWorld benchmark, DART-GUI-7B achieves a 42.13% task success rate, a 14.61% absolute gain over the base model, and 7.34% higher than open-source SOTA. We will fully open-source our training framework, data, and model checkpoints via computer-use-agents.github.io/dart-gui, which we believe is a timely contribution to the open-source community of agentic RL training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.22830", "title": "ChatInject: Abusing Chat Templates for Prompt Injection in LLM Agents", "authors": [ "Hwan Chang", "Yonghyun Jun", "Hwanhee Lee" ], "github": "https://github.com/hwanchang00/ChatInject", "project_page": "", "abstract": "The growing deployment of large language model (LLM) based agents that interact with external environments has created new attack surfaces for adversarial manipulation. One major threat is indirect prompt injection, where attackers embed malicious instructions in external environment output, causing agents to interpret and execute them as if they were legitimate prompts. While previous research has focused primarily on plain-text injection attacks, we find a significant yet underexplored vulnerability: LLMs' dependence on structured chat templates and their susceptibility to contextual manipulation through persuasive multi-turn dialogues. To this end, we introduce ChatInject, an attack that formats malicious payloads to mimic native chat templates, thereby exploiting the model's inherent instruction-following tendencies. Building on this foundation, we develop a persuasion-driven Multi-turn variant that primes the agent across conversational turns to accept and execute otherwise suspicious actions. Through comprehensive experiments across frontier LLMs, we demonstrate three critical findings: (1) ChatInject achieves significantly higher average attack success rates than traditional prompt injection methods, improving from 5.18% to 32.05% on AgentDojo and from 15.13% to 45.90% on InjecAgent, with multi-turn dialogues showing particularly strong performance at average 52.33% success rate on InjecAgent, (2) chat-template-based payloads demonstrate strong transferability across models and remain effective even against closed-source LLMs, despite their unknown template structures, and (3) existing prompt-based defenses are largely ineffective against this attack approach, especially against Multi-turn variants. These findings highlight vulnerabilities in current agent systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hwanchang00/ChatInject", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.22518", "title": "REMA: A Unified Reasoning Manifold Framework for Interpreting Large Language Model", "authors": [ "Bo Li", "Guanzhi Deng", "Ronghao Chen", "Junrong Yue", "Shuo Zhang", "Qinghua Zhao", "Linqi Song", "Lijie Wen" ], "github": "", "project_page": "", "abstract": "Understanding how Large Language Models (LLMs) perform complex reasoning and their failure mechanisms is a challenge in interpretability research. To provide a measurable geometric analysis perspective, we define the concept of the Reasoning Manifold, a latent low-dimensional geometric structure formed by the internal representations corresponding to all correctly reasoned generations. This structure can be conceptualized as the embodiment of the effective thinking paths that the model has learned to successfully solve a given task. Based on this concept, we build REMA, a framework that explains the origins of failures by quantitatively comparing the spatial relationships of internal model representations corresponding to both erroneous and correct reasoning samples. Specifically, REMA first quantifies the geometric deviation of each erroneous representation by calculating its k-nearest neighbors distance to the approximated manifold formed by correct representations, thereby providing a unified failure signal. It then localizes the divergence points where these deviations first become significant by tracking this deviation metric across the model's layers and comparing it against a baseline of internal fluctuations from correct representations, thus identifying where the reasoning chain begins to go off-track. Our extensive experiments on diverse language and multimodal models and tasks demonstrate the low-dimensional nature of the reasoning manifold and the high separability between erroneous and correct reasoning representations. The results also validate the effectiveness of the REMA framework in analyzing the origins of reasoning failures. This research connects abstract reasoning failures to measurable geometric deviations in representations, providing new avenues for in-depth understanding and diagnosis of the internal computational processes of black-box models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.22824", "title": "Critique-Coder: Enhancing Coder Models by Critique Reinforcement Learning", "authors": [ "Chi Ruan", "Dongfu Jiang", "Yubo Wang", "Wenhu Chen" ], "github": "https://github.com/TIGER-AI-Lab/Critique-Coder", "project_page": "", "abstract": "Reinforcement Learning (RL) has emerged as a popular training paradigm, particularly when paired with reasoning models. While effective, it primarily focuses on generating responses and lacks mechanisms to explicitly foster critique or reflection. Several recent studies, like Critique-Fine-Tuning (CFT) and Critique-Guided-Distillation (CGD) have shown the benefits of explicitly teaching LLMs how to critique. Motivated by them, we propose Critique Reinforcement Learning (CRL), where the model is tasked with generating a critique for a given (question, solution) pair. The reward is determined solely by whether the final judgment label c in {True, False} of the generated critique aligns with the ground-truth judgment c^*. Building on this point, we introduce Critique-Coder, which is trained on a hybrid of RL and CRL by substituting 20\\% of the standard RL data with CRL data. We fine-tune multiple models (Critique-Coder) and evaluate them on different benchmarks to show their advantages over RL-only models. We show that Critique-Coder consistently outperforms RL-only baselines on all the evaluated benchmarks. Notably, our Critique-Coder-8B can reach over 60\\% on LiveCodeBench (v5), outperforming other reasoning models like DeepCoder-14B and GPT-o1. Beyond code generation, Critique-Coder also demonstrates enhanced general reasoning abilities, as evidenced by its better performance on logic reasoning tasks from the BBEH dataset. This indicates that the application of CRL on coding datasets enhances general reasoning and critique abilities, which are transferable across a broad range of tasks. Hence, we believe that CRL works as a great complement to standard RL for LLM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TIGER-AI-Lab/Critique-Coder", "github_api_added_by": "auto" }, { "date": "2025-09-30", "arxiv_id": "2509.22799", "title": "VideoScore2: Think before You Score in Generative Video Evaluation", "authors": [ "Xuan He", "Dongfu Jiang", "Ping Nie", "Minghao Liu", "Zhengxuan Jiang", "Mingyi Su", "Wentao Ma", "Junru Lin", "Chun Ye", "Yi Lu", "Keming Wu", "Benjamin Schneider", "Quy Duc Do", "Zhuofeng Li", "Yiming Jia", "Yuxuan Zhang", "Guo Cheng", "Haozhe Wang", "Wangchunshu Zhou", "Qunshu Lin", "Yuanxing Zhang", "Ge Zhang", "Wenhao Huang", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Recent advances in text-to-video generation have produced increasingly realistic and diverse content, yet evaluating such videos remains a fundamental challenge due to their multi-faceted nature encompassing visual quality, semantic alignment, and physical consistency. Existing evaluators and reward models are limited to single opaque scores, lack interpretability, or provide only coarse analysis, making them insufficient for capturing the comprehensive nature of video quality assessment. We present VideoScore2, a multi-dimensional, interpretable, and human-aligned framework that explicitly evaluates visual quality, text-to-video alignment, and physical/common-sense consistency while producing detailed chain-of-thought rationales. Our model is trained on a large-scale dataset VideoFeedback2 containing 27,168 human-annotated videos with both scores and reasoning traces across three dimensions, using a two-stage pipeline of supervised fine-tuning followed by reinforcement learning with Group Relative Policy Optimization (GRPO) to enhance analytical robustness. Extensive experiments demonstrate that VideoScore2 achieves superior performance with 44.35 (+5.94) accuracy on our in-domain benchmark VideoScore-Bench-v2 and 50.37 (+4.32) average performance across four out-of-domain benchmarks (VideoGenReward-Bench, VideoPhy2, etc), while providing interpretable assessments that bridge the gap between evaluation and controllable generation through effective reward modeling for Best-of-N sampling. Project Page: https://tiger-ai-lab.github.io/VideoScore2/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24663", "title": "InfLLM-V2: Dense-Sparse Switchable Attention for Seamless Short-to-Long Adaptation", "authors": [ "Weilin Zhao", "Zihan Zhou", "Zhou Su", "Chaojun Xiao", "Yuxuan Li", "Yanghao Li", "Yudi Zhang", "Weilun Zhao", "Zhen Li", "Yuxiang Huang", "Ao Sun", "Xu Han", "Zhiyuan Liu" ], "github": "https://github.com/XunhaoLai/native-sparse-attention-triton", "project_page": "", "abstract": "Long-sequence processing is a critical capability for modern large language models. However, the self-attention mechanism in the standard Transformer architecture faces severe computational and memory bottlenecks when processing long sequences. While trainable sparse attention methods offer a promising solution, existing approaches such as NSA introduce excessive extra parameters and disrupt the conventional pretrain-on-short, finetune-on-long workflow, resulting in slow convergence and difficulty in acceleration. To overcome these limitations, we introduce dense-sparse switchable attention framework, termed as InfLLM-V2. InfLLM-V2 is a trainable sparse attention that seamlessly adapts models from short to long sequences. Specifically, InfLLM-V2 reuses dense attention parameters through parameter-free architecture modification, maintaining consistency between short and long sequence processing. Additionally, InfLLM-V2 ensures computational efficiency across all sequence lengths, by using dense attention for short inputs and smoothly transitioning to sparse attention for long sequences. To achieve practical acceleration, we further introduce an efficient implementation of InfLLM-V2 that significantly reduces the computational overhead. Our experiments on long-context understanding and chain-of-thought reasoning demonstrate that InfLLM-V2 is 4times faster than dense attention while retaining 98.1% and 99.7% of the performance, respectively. Based on the InfLLM-V2 framework, we have trained and open-sourced MiniCPM4.1 (https://huggingface.co/openbmb/MiniCPM4.1-8B), a hybrid reasoning model, providing a reproducible implementation for the research community.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/XunhaoLai/native-sparse-attention-triton", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25149", "title": "Pretraining Large Language Models with NVFP4", "authors": [ "NVIDIA", "Felix Abecassis", "Anjulie Agrusa", "Dong Ahn", "Jonah Alben", "Stefania Alborghetti", "Michael Andersch", "Sivakumar Arayandi", "Alexis Bjorlin", "Aaron Blakeman", "Evan Briones", "Ian Buck", "Bryan Catanzaro", "Jinhang Choi", "Mike Chrzanowski", "Eric Chung", "Victor Cui", "Steve Dai", "Bita Darvish Rouhani", "Carlo del Mundo", "Deena Donia", "Burc Eryilmaz", "Henry Estela", "Abhinav Goel", "Oleg Goncharov", "Yugi Guvvala", "Robert Hesse", "Russell Hewett", "Herbert Hum", "Ujval Kapasi", "Brucek Khailany", "Mikail Khona", "Nick Knight", "Alex Kondratenko", "Ronny Krashinsky", "Ben Lanir", "Simon Layton", "Michael Lightstone", "Daniel Lo", "Paulius Micikevicius", "Asit Mishra", "Tim Moon", "Deepak Narayanan", "Chao Ni", "Abhijit Paithankar", "Satish Pasumarthi", "Ankit Patel", "Mostofa Patwary", "Ashwin Poojary", "Gargi Prasad", "Sweta Priyadarshi", "Yigong Qin", "Xiaowei Ren", "Oleg Rybakov", "Charbel Sakr", "Sanjeev Satheesh", "Stas Sergienko", "Pasha Shamis", "Kirthi Shankar", "Nishant Sharma", "Mohammad Shoeybi", "Michael Siu", "Misha Smelyanskiy", "Darko Stosic", "Dusan Stosic", "Bor-Yiing Su", "Frank Sun", "Nima Tajbakhsh", "Shelby Thomas", "Przemek Tredak", "Evgeny Tsykunov", "Gandhi Vaithilingam", "Aditya Vavre", "Rangharajan Venkatesan", "Roger Waleffe", "Qiyu Wan", "Hexin Wang", "Mengdi Wang", "Lizzie Wei", "Hao Wu", "Evan Wu", "Keith Wyss", "Ning Xu", "Jinze Xue", "Charlene Yang", "Yujia Zhai", "Ruoxi Zhang", "Jingyang Zhu", "Zhongbo Zhu" ], "github": "https://github.com/NVIDIA/TransformerEngine", "project_page": "", "abstract": "Large Language Models (LLMs) today are powerful problem solvers across many domains, and they continue to get stronger as they scale in model size, training set size, and training set quality, as shown by extensive research and experimentation across the industry. Training a frontier model today requires on the order of tens to hundreds of yottaflops, which is a massive investment of time, compute, and energy. Improving pretraining efficiency is therefore essential to enable the next generation of even more capable LLMs. While 8-bit floating point (FP8) training is now widely adopted, transitioning to even narrower precision, such as 4-bit floating point (FP4), could unlock additional improvements in computational speed and resource utilization. However, quantization at this level poses challenges to training stability, convergence, and implementation, notably for large-scale models trained on long token horizons. In this study, we introduce a novel approach for stable and accurate training of large language models (LLMs) using the NVFP4 format. Our method integrates Random Hadamard transforms (RHT) to bound block-level outliers, employs a two-dimensional quantization scheme for consistent representations across both the forward and backward passes, utilizes stochastic rounding for unbiased gradient estimation, and incorporates selective high-precision layers. We validate our approach by training a 12-billion-parameter model on 10 trillion tokens -- the longest publicly documented training run in 4-bit precision to date. Our results show that the model trained with our NVFP4-based pretraining technique achieves training loss and downstream task accuracies comparable to an FP8 baseline. These findings highlight that NVFP4, when combined with our training approach, represents a major step forward in narrow-precision LLM training algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVIDIA/TransformerEngine", "github_api_added_by": "auto" }, { "date": "2025-09-30", "arxiv_id": "2509.25191", "title": "VGGT-X: When VGGT Meets Dense Novel View Synthesis", "authors": [ "Yang Liu", "Chuanchen Luo", "Zimo Tang", "Junran Peng", "Zhaoxiang Zhang" ], "github": "", "project_page": "", "abstract": "We study the problem of applying 3D Foundation Models (3DFMs) to dense Novel View Synthesis (NVS). Despite significant progress in Novel View Synthesis powered by NeRF and 3DGS, current approaches remain reliant on accurate 3D attributes (e.g., camera poses and point clouds) acquired from Structure-from-Motion (SfM), which is often slow and fragile in low-texture or low-overlap captures. Recent 3DFMs showcase orders of magnitude speedup over the traditional pipeline and great potential for online NVS. But most of the validation and conclusions are confined to sparse-view settings. Our study reveals that naively scaling 3DFMs to dense views encounters two fundamental barriers: dramatically increasing VRAM burden and imperfect outputs that degrade initialization-sensitive 3D training. To address these barriers, we introduce VGGT-X, incorporating a memory-efficient VGGT implementation that scales to 1,000+ images, an adaptive global alignment for VGGT output enhancement, and robust 3DGS training practices. Extensive experiments show that these measures substantially close the fidelity gap with COLMAP-initialized pipelines, achieving state-of-the-art results in dense COLMAP-free NVS and pose estimation. Additionally, we analyze the causes of remaining gaps with COLMAP-initialized rendering, providing insights for the future development of 3D foundation models and dense NVS. Our project page is available at https://dekuliutesla.github.io/vggt-x.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25077", "title": "BRIDGE - Building Reinforcement-Learning Depth-to-Image Data Generation Engine for Monocular Depth Estimation", "authors": [ "Dingning Liu", "Haoyu Guo", "Jingyi Zhou", "Tong He" ], "github": "", "project_page": "", "abstract": "Monocular Depth Estimation (MDE) is a foundational task for computer vision. Traditional methods are limited by data scarcity and quality, hindering their robustness. To overcome this, we propose BRIDGE, an RL-optimized depth-to-image (D2I) generation framework that synthesizes over 20M realistic and geometrically accurate RGB images, each intrinsically paired with its ground truth depth, from diverse source depth maps. Then we train our depth estimation model on this dataset, employing a hybrid supervision strategy that integrates teacher pseudo-labels with ground truth depth for comprehensive and robust training. This innovative data generation and training paradigm enables BRIDGE to achieve breakthroughs in scale and domain diversity, consistently outperforming existing state-of-the-art approaches quantitatively and in complex scene detail capture, thereby fostering general and robust depth features. Code and models are available at https://dingning-liu.github.io/bridge.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25052", "title": "Cogito, Ergo Ludo: An Agent that Learns to Play by Reasoning and Planning", "authors": [ "Sai Wang", "Yu Wu", "Zhongwen Xu" ], "github": "", "project_page": "", "abstract": "The pursuit of artificial agents that can learn to master complex environments has led to remarkable successes, yet prevailing deep reinforcement learning methods often rely on immense experience, encoding their knowledge opaquely within neural network weights. We propose a different paradigm, one in which an agent learns to play by reasoning and planning. We introduce Cogito, ergo ludo (CEL), a novel agent architecture that leverages a Large Language Model (LLM) to build an explicit, language-based understanding of its environment's mechanics and its own strategy. Starting from a tabula rasa state with no prior knowledge (except action set), CEL operates on a cycle of interaction and reflection. After each episode, the agent analyzes its complete trajectory to perform two concurrent learning processes: Rule Induction, where it refines its explicit model of the environment's dynamics, and Strategy and Playbook Summarization, where it distills experiences into an actionable strategic playbook. We evaluate CEL on diverse grid-world tasks (i.e., Minesweeper, Frozen Lake, and Sokoban), and show that the CEL agent successfully learns to master these games by autonomously discovering their rules and developing effective policies from sparse rewards. Ablation studies confirm that the iterative process is critical for sustained learning. Our work demonstrates a path toward more general and interpretable agents that not only act effectively but also build a transparent and improving model of their world through explicit reasoning on raw experience.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23115", "title": "RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility", "authors": [ "Haoyu He", "Haozheng Luo", "Yan Chen", "Qi R. Wang" ], "github": "https://github.com/he-h/rhythm", "project_page": "", "abstract": "Predicting human mobility is inherently challenging due to complex long-range dependencies and multi-scale periodic behaviors. To address this, we introduce RHYTHM (Reasoning with Hierarchical Temporal Tokenization for Human Mobility), a unified framework that leverages large language models (LLMs) as general-purpose spatio-temporal predictors and trajectory reasoners. Methodologically, RHYTHM employs temporal tokenization to partition each trajectory into daily segments and encode them as discrete tokens with hierarchical attention that captures both daily and weekly dependencies, thereby significantly reducing the sequence length while preserving cyclical information. Additionally, we enrich token representations by adding pre-computed prompt embeddings for trajectory segments and prediction targets via a frozen LLM, and feeding these combined embeddings back into the LLM backbone to capture complex interdependencies. Computationally, RHYTHM freezes the pretrained LLM's backbone to reduce attention complexity and memory cost. We evaluate our model against state-of-the-art methods using three real-world datasets. Notably, RHYTHM achieves a 2.4% improvement in overall accuracy, a 5.0% increase on weekends, and a 24.6% reduction in training time. Code is publicly available at https://github.com/he-h/rhythm.", "github_manual": "", "github_regex": "https://github.com/he-h/rhythm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25123", "title": "From f(x) and g(x) to f(g(x)): LLMs Learn New Skills in RL by Composing Old Ones", "authors": [ "Lifan Yuan", "Weize Chen", "Yuchen Zhang", "Ganqu Cui", "Hanbin Wang", "Ziming You", "Ning Ding", "Zhiyuan Liu", "Maosong Sun", "Hao Peng" ], "github": "", "project_page": "", "abstract": "Does RL teach LLMs genuinely new skills, or does it merely activate existing ones? This question lies at the core of ongoing debates about the role of RL in LLM post-training. On one side, strong empirical results can be achieved with RL even without preceding supervised finetuning; on the other, critics argue that RL contributes little beyond reweighting existing reasoning strategies. This work provides concrete evidence that LLMs can acquire genuinely new skills during RL by composing existing ones, mirroring one of the central mechanisms by which humans acquire new cognitive skills. To mitigate data contamination and other confounding factors, and to allow precise control over task complexity, we develop a synthetic framework for our investigation. Specifically, we define a skill as the ability to infer the output of a string transformation function f(x) given x. When an LLM has already learned f and g prior to RL, our experiments reveal that RL enables it to learn unseen compositions of them h(x)=g(f(x)). Further, this compositional ability generalizes to more difficult problems such as compositions of >2 functions unseen during RL training. Surprisingly, our experiments show that compositional skill acquired on a source task transfers to a different target task. This transfer happens even without compositional training on the target, requiring only prior knowledge of the target's atomic skills. Our qualitative analysis shows that RL fundamentally changes the reasoning behaviors of the models. In contrast, next-token training with the same data yields none of these findings. Our systematic experiments provide fresh insights into LLM learning, suggesting the value of first building base models with basic skills, then using RL to incentivize advanced, generalizable skills for complex problems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23808", "title": "Beyond the Exploration-Exploitation Trade-off: A Hidden State Approach for LLM Reasoning in RLVR", "authors": [ "Fanding Huang", "Guanbo Huang", "Xiao Fan", "Yi He", "Xiao Liang", "Xiao Chen", "Qinting Jiang", "Faisal Nadeem Khan", "Jingyan Jiang", "Zhi Wang" ], "github": "", "project_page": "", "abstract": "A prevailing view in Reinforcement Learning for Verifiable Rewards (RLVR) interprets recent progress through the lens of an exploration-exploitation trade-off, a perspective largely shaped by token-level metrics. We re-examine this perspective, proposing that this perceived trade-off may not be a fundamental constraint but rather an artifact of the measurement level. To investigate this, we shift the analysis to the semantically rich hidden-state space, adopting Effective Rank (ER) to quantify exploration and proposing its novel first- and second-order derivatives, named Effective Rank Velocity (ERV) and Effective Rank Acceleration (ERA), to capture exploitation dynamics. Our analysis reveals that at the hidden-state level, exploration and exploitation could be decoupled (Sec. 4). This finding reveals an opportunity to enhance both capacities simultaneously. This insight motivates our method, Velocity-Exploiting Rank-Learning (VERL), the first to operationalize the principle of synergistic exploration-exploitation enhancement by directly shaping the RL advantage function. The key innovation is leveraging the theoretically stable ERA as a predictive meta-controller to create a synergistic, dual-channel incentive structure. Instead of forcing a trade-off, VERL prospectively amplifies rewards for exploration to preempt overconfidence and reinforces exploitative gains to consolidate reasoning. Experiments across diverse LLMs and reasoning benchmarks show consistent gains, including up to 21.4% absolute accuracy improvement on the challenging Gaokao 2024 dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.16538", "title": "Advancing Reference-free Evaluation of Video Captions with Factual Analysis", "authors": [ "Shubhashis Roy Dipta", "Tz-Ying Wu", "Subarna Tripathi" ], "github": "", "project_page": "", "abstract": "Video captions offer concise snapshots of actors, objects, and actions within a video, serving as valuable assets for applications such as question answering and event localization. However, acquiring human annotations for video captions is costly or even impractical, especially when dealing with diverse video domains. Existing models trained on supervised datasets face challenges in evaluating performance across different domains due to the reliance on reference-based evaluation protocols, which necessitate ground truth captions. This assumption is unrealistic for evaluating videos in the wild. To address these limitations, we propose a reference-free evaluation framework that does not require ground truth captions, focusing on factual grounding to ensure accurate assessment of caption quality. We introduce VC-Inspector, a novel caption quality evaluator that is both reference-free and factually grounded. Utilizing large language models, we generate pseudo captions of varying quality based on supervised data, which are subsequently used to train a multimodal model (i.e., Qwen2.5-VL) as the evaluator. Our approach demonstrates superior alignment with human judgments on the VATEX-Eval dataset, outperforming existing methods. The performance also generalizes to image caption datasets, Flickr8K-Expert and Flickr8K-CF, when viewing images as 1-frame videos. Overall, VC-Inspector offers a scalable and generalizable solution for evaluating the factual accuracy of video captions, paving the way for more effective and objective assessment methodologies in diverse video domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25106", "title": "Towards Personalized Deep Research: Benchmarks and Evaluations", "authors": [ "Yuan Liang", "Jiaxian Li", "Yuqing Wang", "Piaohong Wang", "Motong Tian", "Pai Liu", "Shuofei Qiao", "Runnan Fang", "He Zhu", "Ge Zhang", "Minghao Liu", "Yuchen Eleanor Jiang", "Ningyu Zhang", "Wangchunshu Zhou" ], "github": "https://github.com/MiroMindAI/MiroFlow", "project_page": "", "abstract": "Deep Research Agents (DRAs) can autonomously conduct complex investigations and generate comprehensive reports, demonstrating strong real-world potential. However, existing evaluations mostly rely on close-ended benchmarks, while open-ended deep research benchmarks remain scarce and typically neglect personalized scenarios. To bridge this gap, we introduce Personalized Deep Research Bench, the first benchmark for evaluating personalization in DRAs. It pairs 50 diverse research tasks across 10 domains with 25 authentic user profiles that combine structured persona attributes with dynamic real-world contexts, yielding 250 realistic user-task queries. To assess system performance, we propose the PQR Evaluation Framework, which jointly measures (P) Personalization Alignment, (Q) Content Quality, and (R) Factual Reliability. Our experiments on a range of systems highlight current capabilities and limitations in handling personalized deep research. This work establishes a rigorous foundation for developing and evaluating the next generation of truly personalized AI research assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiroMindAI/MiroFlow", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25084", "title": "Scaling Generalist Data-Analytic Agents", "authors": [ "Shuofei Qiao", "Yanqiu Zhao", "Zhisong Qiu", "Xiaobin Wang", "Jintian Zhang", "Zhao Bin", "Ningyu Zhang", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Huajun Chen" ], "github": "", "project_page": "", "abstract": "Data-analytic agents are emerging as a key catalyst for automated scientific discovery and for the vision of Innovating AI. Current approaches, however, rely heavily on prompt engineering over proprietary models, while open-source models struggle to face diverse-format, large-scale data files and long-horizon, multi-step reasoning that real-world analytics demands. This paper introduces DataMind, a scalable data synthesis and agent training recipe designed to build generalist data-analytic agents. DataMind tackles three key challenges in building open-source data-analytic agents, including insufficient data resources, improper training strategy, and unstable code-based multi-turn rollout. Concretely, DataMind applies 1) a fine-grained task taxonomy and a recursive easy-to-hard task composition mechanism to increase the diversity and difficulty of synthesized queries; 2) a knowledge-augmented trajectory sampling strategy followed by model-based and rule-based filtering; 3) a dynamically adjustable training objective combining both SFT and RL losses; 4) a memory-frugal and stable code-based multi-turn rollout framework. Built on DataMind, we curate DataMind-12K, a high-quality trajectory set spanning diverse domains, task categories, and data file formats for data-analytic tasks. Trained on DataMind-12K, our DataMind-14B achieves state-of-the-art with an average score of 71.16% on multiple data analysis benchmarks, outperforming the strongest proprietary baselines DeepSeek-V3.1 and GPT-5. Our DataMind-7B also performs best among all open-source models with a score of 68.10%. We also incorporate some empirical insights gained from our exploratory trials into the analysis experiments, aiming to provide actionable insights about agentic training for the community. We will release DataMind-12K and DataMind-7B,14B for the community's future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24981", "title": "Random Policy Valuation is Enough for LLM Reasoning with Verifiable Rewards", "authors": [ "Haoran He", "Yuxiao Ye", "Qingpeng Cai", "Chen Hu", "Binxing Jiao", "Daxin Jiang", "Ling Pan" ], "github": "", "project_page": "", "abstract": "RL with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving the reasoning abilities of large language models (LLMs). Current methods rely primarily on policy optimization frameworks like PPO and GRPO, which follow generalized policy iteration that alternates between evaluating the current policy's value and improving the policy based on evaluation. While effective, they often suffer from training instability and diversity collapse, requiring complex heuristic tricks and careful tuning. We observe that standard RLVR in math reasoning can be formalized as a specialized finite-horizon Markov Decision Process with deterministic state transitions, tree-structured dynamics, and binary terminal rewards. Though large in scale, the underlying structure is simpler than general-purpose control settings for which popular RL algorithms (e.g., PPO) were developed, suggesting that several sophisticated techniques in existing methods may be reduced or even omitted. Based on this insight, we prove a surprising result: the optimal action can be recovered from the Q-function of a fixed uniformly random policy, thereby bypassing the generalized policy iteration loop and its associated heuristics. We introduce Random Policy Valuation for Diverse Reasoning (ROVER) to translate this principle into a practical and scalable algorithm for LLM math reasoning, a minimalist yet highly effective RL method that samples actions from a softmax over these uniform-policy Q-values. ROVER preserves diversity throughout training, allowing sustained exploration of multiple valid pathways. Across multiple base models and standard math reasoning benchmarks, ROVER demonstrates superior performance in both quality (+8.2 on pass@1, +16.8 on pass@256) and diversity (+17.6\\%), despite its radical simplification compared to strong, complicated existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24908", "title": "BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal Decrees and Notifications", "authors": [ "Andr\u00e9s Fern\u00e1ndez Garc\u00eda", "Javier de la Rosa", "Julio Gonzalo", "Roser Morante", "Enrique Amig\u00f3", "Alejandro Benito-Santos", "Jorge Carrillo-de-Albornoz", "V\u00edctor Fresno", "Adrian Ghajari", "Guillermo Marco", "Laura Plaza", "Eva S\u00e1nchez Salido" ], "github": "", "project_page": "", "abstract": "The ability to summarize long documents succinctly is increasingly important in daily life due to information overload, yet there is a notable lack of such summaries for Spanish documents in general, and in the legal domain in particular. In this work, we present BOE-XSUM, a curated dataset comprising 3,648 concise, plain-language summaries of documents sourced from Spain's ``Bolet\\'{\\i}n Oficial del Estado'' (BOE), the State Official Gazette. Each entry in the dataset includes a short summary, the original text, and its document type label. We evaluate the performance of medium-sized large language models (LLMs) fine-tuned on BOE-XSUM, comparing them to general-purpose generative models in a zero-shot setting. Results show that fine-tuned models significantly outperform their non-specialized counterparts. Notably, the best-performing model -- BERTIN GPT-J 6B (32-bit precision) -- achieves a 24\\% performance gain over the top zero-shot model, DeepSeek-R1 (accuracies of 41.6\\% vs.\\ 33.5\\%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23338", "title": "PARROT: A Benchmark for Evaluating LLMs in Cross-System SQL Translation", "authors": [ "Wei Zhou", "Guoliang Li", "Haoyu Wang", "Yuxing Han", "Xufei Wu", "Fan Wu", "Xuanhe Zhou" ], "github": "", "project_page": "", "abstract": "Large language models (LLMS) have shown increasing effectiveness in Text-to-SQL tasks. However, another closely related problem, Cross-System SQL Translation (a.k.a., SQL-to-SQL), which adapts a query written for one database system (e.g., MySQL) into its equivalent one for another system (e.g., ClickHouse), is of great practical importance but remains underexplored. Existing SQL benchmarks are not well-suited for SQL-to-SQL evaluation, which (1) focus on a limited set of database systems (often just SQLite) and (2) cannot capture many system-specific SQL dialects (e.g., customized functions, data types, and syntax rules). Thus, in this paper, we introduce PARROT, a Practical And Realistic BenchmaRk for CrOss-System SQL Translation. PARROT comprises 598 translation pairs from 38 open-source benchmarks and real-world business services, specifically prepared to challenge system-specific SQL understanding (e.g., LLMS achieve lower than 38.53% accuracy on average). We also provide multiple benchmark variants, including PARROT-Diverse with 28,003 translations (for extensive syntax testing) and PARROT-Simple with 5,306 representative samples (for focused stress testing), covering 22 production-grade database systems. To promote future research, we release a public leaderboard and source code at: https://code4db.github.io/parrot-bench/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23924", "title": "Taming Masked Diffusion Language Models via Consistency Trajectory Reinforcement Learning with Fewer Decoding Step", "authors": [ "Jingyi Yang", "Guanxu Chen", "Xuhao Hu", "Jing Shao" ], "github": "https://github.com/yjyddq/EOSER-ASS-RL", "project_page": "", "abstract": "Masked diffusion language models (MDLMs) have recently emerged as a promising alternative to autoregressive (AR) language models, offering properties such as parallel decoding, flexible generation orders, and the potential for fewer inference steps. Despite these advantages, decoding strategies and reinforcement learning (RL) algorithms tailored for MDLMs remain underexplored. A naive approach is to directly transfer techniques well-established for AR models to MDLMs. However, this raises an immediate question: Is such a naive transfer truly optimal? For example, 1) Block-wise and semi-AR decoding strategies are not employed during the training of MDLMs, so why do they outperform full diffusion-style decoding during inference? 2) Applying RL algorithms designed for AR models directly to MDLMs exhibits a training-inference inconsistency, since MDLM decoding are non-causal (parallel). This results in inconsistencies between the rollout trajectory and the optimization trajectory. To address these challenges, we propose EOS Early Rejection (EOSER) and Ascending Step-Size (ASS) decoding scheduler, which unlock the potential of MDLMs to perform full diffusion-style decoding, achieving competitive performance with fewer decoding steps. Additionally, we introduce Consistency Trajectory Group Relative Policy Optimization (CJ-GRPO) for taming MDLMs, which emphasizes the consistency between rollout trajectory and optimization trajectory, and reduces the optimization errors caused by skip-step optimization. We conduct extensive experiments on reasoning tasks, such as mathematical and planning benchmarks, using LLaDA-8B-Instruct. The results demonstrate that the proposed EOSER and ASS mechanisms, together with CJ-GRPO, hold significant promise for effectively and efficiently taming MDLMs. Code: https://github.com/yjyddq/EOSER-ASS-RL.", "github_manual": "", "github_regex": "https://github.com/yjyddq/EOSER-ASS-RL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23233", "title": "Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large Language Models", "authors": [ "Sina J. Semnani", "Jirayu Burapacheep", "Arpandeep Khatua", "Thanawan Atchariyachanvanit", "Zheng Wang", "Monica S. Lam" ], "github": "https://github.com/stanford-oval/inconsistency-detection", "project_page": "", "abstract": "Wikipedia is the largest open knowledge corpus, widely used worldwide and serving as a key resource for training large language models (LLMs) and retrieval-augmented generation (RAG) systems. Ensuring its accuracy is therefore critical. But how accurate is Wikipedia, and how can we improve it? We focus on inconsistencies, a specific type of factual inaccuracy, and introduce the task of corpus-level inconsistency detection. We present CLAIRE, an agentic system that combines LLM reasoning with retrieval to surface potentially inconsistent claims along with contextual evidence for human review. In a user study with experienced Wikipedia editors, 87.5% reported higher confidence when using CLAIRE, and participants identified 64.7% more inconsistencies in the same amount of time. Combining CLAIRE with human annotation, we contribute WIKICOLLIDE, the first benchmark of real Wikipedia inconsistencies. Using random sampling with CLAIRE-assisted analysis, we find that at least 3.3% of English Wikipedia facts contradict another fact, with inconsistencies propagating into 7.3% of FEVEROUS and 4.0% of AmbigQA examples. Benchmarking strong baselines on this dataset reveals substantial headroom: the best fully automated system achieves an AUROC of only 75.1%. Our results show that contradictions are a measurable component of Wikipedia and that LLM-based systems like CLAIRE can provide a practical tool to help editors improve knowledge consistency at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stanford-oval/inconsistency-detection", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.22820", "title": "MMPB: It's Time for Multi-Modal Personalization", "authors": [ "Jaeik Kim", "Woojin Kim", "Woohyeon Park", "Jaeyoung Do" ], "github": "", "project_page": "", "abstract": "Visual personalization is essential in user-facing AI systems such as smart homes and healthcare, where aligning model behavior with user-centric concepts is critical. However, recent large Vision-Language Models (VLMs), despite their broad applicability, remain underexplored in their ability to adapt to individual users. In this paper, we introduce MMPB, the first extensive benchmark for evaluating VLMs on personalization. MMPB comprises 10k image-query pairs and includes 111 personalizable concepts across four categories: humans, animals, objects, and characters, with the human category enriched with preference-grounded queries. We structure personalization into three main task types, each highlighting a different key property of VLMs. Using 23 widely used VLMs including both open- and closed-source models, we evaluate personalization performance via a three-stage protocol: concept injection, multi-turn dialogue, and personalized querying. Our findings indicate that most VLMs (including some closed-source models) struggle with personalization, particularly in maintaining consistency over dialogue, handling user preferences, and adapting to visual cues. Our analysis reveals that the challenges in VLM personalization (such as refusal behaviors and long-context forgetting) highlight substantial room for improvement. By identifying these limitations and offering a scalable benchmark, MMPB offers valuable insights and a solid foundation for future research toward truly personalized multi-modal AI. Project Page: aidaslab.github.io/MMPB", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.22193", "title": "When Does Reasoning Matter? A Controlled Study of Reasoning's Contribution to Model Performance", "authors": [ "Nicolas Boizard", "Hippolyte Gisserot-Boukhlef", "Kevin El-Haddad", "C\u00e9line Hudelot", "Pierre Colombo" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) with reasoning capabilities have achieved state-of-the-art performance on a wide range of tasks. Despite its empirical success, the tasks and model scales at which reasoning becomes effective, as well as its training and inference costs, remain underexplored. In this work, we rely on a synthetic data distillation framework to conduct a large-scale supervised study. We compare Instruction Fine-Tuning (IFT) and reasoning models of varying sizes, on a wide range of math-centric and general-purpose tasks, evaluating both multiple-choice and open-ended formats. Our analysis reveals that reasoning consistently improves model performance, often matching or surpassing significantly larger IFT systems. Notably, while IFT remains Pareto-optimal in training and inference costs, reasoning models become increasingly valuable as model size scales, overcoming IFT performance limits on reasoning-intensive and open-ended tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23219", "title": "WirelessMathLM: Teaching Mathematical Reasoning for LLMs in Wireless Communications with Reinforcement Learning", "authors": [ "Xin Li", "Mengbing Liu", "Yiyang Zhu", "Wenhe Zhang", "Li Wei", "Jiancheng An", "Chau Yuen" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) excel at general mathematical reasoning but fail catastrophically on specialized technical mathematics. In wireless communications, where problems require precise manipulation of information-theoretic bounds, optimization constraints, and signal processing formulations, even state-of-the-art models struggle to achieve competent performance. We present WirelessMathLM, demonstrating that compact models (0.5B-7B parameters) can match or exceed much larger models through domain-specific reinforcement learning with verifiable rewards. Our key insight is that wireless mathematics problems possess a unique property--verifiable correctness--that enables effective reinforcement learning without human feedback. We construct WirelessMathBench-XL, a comprehensive benchmark of 4,027 problems from 970 papers. Using Group Relative Policy Optimization (GRPO) with binary verification rewards, we train models directly from base checkpoints without supervised warm-start. Our 7B model achieves 39.5% accuracy on WirelessMathBench-XL, approaching GPT-4o (40.4%) while using about 100 times fewer parameters than DeepSeek-R1 (671B, 57.4%). Remarkably, GRPO training nearly doubles performance across all model scales (0.5B +11%, 3B +103%, 7B +81%), with positive transfer to general mathematics benchmarks--our models gain +8.4 points on average across MATH, Minerva-Math, OlympiadBench, AMC, and AIME without any training on these tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23061", "title": "Local Success Does Not Compose: Benchmarking Large Language Models for Compositional Formal Verification", "authors": [ "Xu Xu", "Xin Li", "Xingwei Qu", "Jie Fu", "Binhang Yuan" ], "github": "", "project_page": "", "abstract": "We introduce DafnyCOMP, a benchmark for evaluating large language models (LLMs) on compositional specification generation in Dafny. Unlike prior benchmarks that focus on single-function tasks, DafnyCOMP targets programs composed of multiple interacting functions with data dependencies, requiring reasoning across component boundaries. The benchmark consists of 300 automatically synthesized multi-function programs. We evaluate several state-of-the-art LLM families and find that, while they perform well on single-function verification, their performance drops sharply on compositional tasks. Analysis reveals systematic failures in cross-functional reasoning, including fragile specifications, misalignment between implementations and proofs, and unstable reasoning. DafnyCOMP thus provides a diagnostic tool for measuring progress toward reliable, verifiable, and compositional code generation with LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24285", "title": "SCI-Verifier: Scientific Verifier with Thinking", "authors": [ "Shenghe Zheng", "Chenyu Huang", "Fangchen Yu", "Junchi Yao", "Jingqi Ye", "Tao Chen", "Yun Luo", "Ning Ding", "LEI BAI", "Ganqu Cui", "Peng Ye" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) are increasingly applied to scientific reasoning, the complexity of answer formats and the diversity of equivalent expressions make answer verification a critical yet challenging task. Existing verification studies in scientific domains suffer from two major limitations: (a) the absence of systematic evaluation standards and insufficient disciplinary coverage, which hinders their comprehensive assessment; and (b) heavy reliance on cumbersome rule design or prompt engineering, which reduces their effectiveness in complex reasoning scenarios or limits their cross-disciplinary generalization. To address these challenges, we propose solutions at both the data and model levels. On the data side, we construct SCI-VerifyBench, a cross-disciplinary benchmark covering mathematics, physics, biology, chemistry, and general scientific QA. The benchmark is built from real LLM responses and enhanced with domain-specific equivalence transformations that generate challenging and realistic data. Model-based and expert annotations ensure both quality and diversity, enabling rigorous evaluation of verification ability. On the model side, we emphasize the importance of reasoning for verification and introduce SCI-Verifier, a unified reasoning-augmented verifier for scientific domains. Through post-training, SCI-Verifier demonstrates strong logical reasoning and equivalence judgment capabilities while maintaining concise and stable outputs. Together, SCI-VerifyBench and SCI-Verifier provide a principled framework for scientific verification, offering both systematic evaluation and practical pathways to enhance the reliability and applicability of LLMs in scientific domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.22921", "title": "Rethinking Large Language Model Distillation: A Constrained Markov Decision Process Perspective", "authors": [ "Matthieu Zimmer", "Xiaotong Ji", "Tu Nguyen", "Haitham Bou Ammar" ], "github": "", "project_page": "", "abstract": "We introduce a novel approach to large language model (LLM) distillation by formulating it as a constrained reinforcement learning problem. While recent work has begun exploring the integration of task-specific rewards into distillation processes, existing methods typically rely on ad-hoc reward weighting. We propose a principled optimization framework that maximizes task-specific rewards while constraining the divergence from the teacher model to remain below a specified threshold. Our approach adapts constrained state augmented reinforcement learning to the distillation setting, introducing a modified reward function that maintains theoretical guarantees of constraint satisfaction without requiring state augmentation or teacher model access during deployment and without the computational overhead of the dual Lagrangian methods. Through extensive experiments on mathematical reasoning tasks, we demonstrate that our method achieves better constraint satisfaction rates and better reasoning compared to the soft Lagrangian relaxation baselines while maintaining competitive task performance. Our framework provides a theoretically grounded and practically efficient solution for reward-aware distillation in resource-constrained settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24200", "title": "UniVid: The Open-Source Unified Video Model", "authors": [ "Jiabin Luo", "Junhui Lin", "Zeyu Zhang", "Biao Wu", "Meng Fang", "Ling Chen", "Hao Tang" ], "github": "", "project_page": "", "abstract": "Unified video modeling that combines generation and understanding capabilities is increasingly important but faces two key challenges: maintaining semantic faithfulness during flow-based generation due to text-visual token imbalance and the limitations of uniform cross-modal attention across the flow trajectory, and efficiently extending image-centric MLLMs to video without costly retraining. We present UniVid, a unified architecture that couples an MLLM with a diffusion decoder through a lightweight adapter, enabling both video understanding and generation. We introduce Temperature Modality Alignment to improve prompt adherence and Pyramid Reflection for efficient temporal reasoning via dynamic keyframe selection. Extensive experiments on standard benchmarks demonstrate state-of-the-art performance, achieving a 2.2% improvement on VBench-Long total score compared to EasyAnimateV5.1, and 1.0% and 3.3% accuracy gains on MSVD-QA and ActivityNet-QA, respectively, compared with the best prior 7B baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.22220", "title": "StableToken: A Noise-Robust Semantic Speech Tokenizer for Resilient SpeechLLMs", "authors": [ "Yuhan Song", "Linhao Zhang", "Chuhan Wu", "Aiwei Liu", "Wei Jia", "Houfeng Wang", "Xiao Zhou" ], "github": "", "project_page": "", "abstract": "Prevalent semantic speech tokenizers, designed to capture linguistic content, are surprisingly fragile. We find they are not robust to meaning-irrelevant acoustic perturbations; even at high Signal-to-Noise Ratios (SNRs) where speech is perfectly intelligible, their output token sequences can change drastically, increasing the learning burden for downstream LLMs. This instability stems from two flaws: a brittle single-path quantization architecture and a distant training signal indifferent to intermediate token stability. To address this, we introduce StableToken, a tokenizer that achieves stability through a consensus-driven mechanism. Its multi-branch architecture processes audio in parallel, and these representations are merged via a powerful bit-wise voting mechanism to form a single, stable token sequence. StableToken sets a new state-of-the-art in token stability, drastically reducing Unit Edit Distance (UED) under diverse noise conditions. This foundational stability translates directly to downstream benefits, significantly improving the robustness of SpeechLLMs on a variety of tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25137", "title": "The Era of Real-World Human Interaction: RL from User Conversations", "authors": [ "Chuanyang Jin", "Jing Xu", "Bo Liu", "Leitian Tao", "Olga Golovneva", "Tianmin Shu", "Wenting Zhao", "Xian Li", "Jason Weston" ], "github": "", "project_page": "", "abstract": "We posit that to achieve continual model improvement and multifaceted alignment, future models must learn from natural human interaction. Current conversational models are aligned using pre-annotated, expert-generated human feedback. In this work, we introduce Reinforcement Learning from Human Interaction (RLHI), a paradigm that learns directly from in-the-wild user conversations. We develop two complementary methods: (1) RLHI with User-Guided Rewrites, which revises unsatisfactory model outputs based on users' natural-language follow-up responses, (2) RLHI with User-Based Rewards, which learns via a reward model conditioned on knowledge of the user's long-term interaction history (termed persona). Together, these methods link long-term user personas to turn-level preferences via persona-conditioned preference optimization. Trained on conversations derived from WildChat, both RLHI variants outperform strong baselines in personalization and instruction-following, and similar feedback enhances performance on reasoning benchmarks. These results suggest organic human interaction offers scalable, effective supervision for personalized alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24372", "title": "Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning", "authors": [ "Xin Qiu", "Yulu Gan", "Conor F. Hayes", "Qiyao Liang", "Elliot Meyerson", "Babak Hodjat", "Risto Miikkulainen" ], "github": "https://github.com/VsonicV/es-fine-tuning-paper", "project_page": "", "abstract": "Fine-tuning pre-trained large language models (LLMs) for down-stream tasks is a critical step in the AI deployment pipeline. Reinforcement learning (RL) is arguably the most prominent fine-tuning method, contributing to the birth of many state-of-the-art LLMs. In contrast, evolution strategies (ES), which once showed comparable performance to RL on models with a few million parameters, was neglected due to the pessimistic perception of its scalability to larger models. In this work, we report the first successful attempt to scale up ES for fine-tuning the full parameters of LLMs, showing the surprising fact that ES can search efficiently over billions of parameters and outperform existing RL fine-tuning methods in multiple respects, including sample efficiency, tolerance to long-horizon rewards, robustness to different base LLMs, less tendency to reward hacking, and more stable performance across runs. It therefore serves as a basis to unlock a new direction in LLM fine-tuning beyond what current RL techniques provide. The source codes are provided at: https://github.com/VsonicV/es-fine-tuning-paper.", "github_manual": "", "github_regex": "https://github.com/VsonicV/es-fine-tuning-paper", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25050", "title": "Advantage Weighted Matching: Aligning RL with Pretraining in Diffusion Models", "authors": [ "Shuchen Xue", "Chongjian Ge", "Shilong Zhang", "Yichen Li", "Zhi-Ming Ma" ], "github": "https://github.com/scxue/advantage_weighted_matching", "project_page": "", "abstract": "Reinforcement Learning (RL) has emerged as a central paradigm for advancing Large Language Models (LLMs), where pre-training and RL post-training share the same log-likelihood formulation. In contrast, recent RL approaches for diffusion models, most notably Denoising Diffusion Policy Optimization (DDPO), optimize an objective different from the pretraining objectives--score/flow matching loss. In this work, we establish a novel theoretical analysis: DDPO is an implicit form of score/flow matching with noisy targets, which increases variance and slows convergence. Building on this analysis, we introduce Advantage Weighted Matching (AWM), a policy-gradient method for diffusion. It uses the same score/flow-matching loss as pretraining to obtain a lower-variance objective and reweights each sample by its advantage. In effect, AWM raises the influence of high-reward samples and suppresses low-reward ones while keeping the modeling objective identical to pretraining. This unifies pretraining and RL conceptually and practically, is consistent with policy-gradient theory, reduces variance, and yields faster convergence. This simple yet effective design yields substantial benefits: on GenEval, OCR, and PickScore benchmarks, AWM delivers up to a 24times speedup over Flow-GRPO (which builds on DDPO), when applied to Stable Diffusion 3.5 Medium and FLUX, without compromising generation quality. Code is available at https://github.com/scxue/advantage_weighted_matching.", "github_manual": "", "github_regex": "https://github.com/scxue/advantage_weighted_matching", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23050", "title": "Understanding Language Prior of LVLMs by Contrasting Chain-of-Embedding", "authors": [ "Lin Long", "Changdae Oh", "Seongheon Park", "Yixuan Li" ], "github": "https://github.com/deeplearning-wisc/understanding_lp", "project_page": "", "abstract": "Large vision-language models (LVLMs) achieve strong performance on multimodal tasks, yet they often default to their language prior (LP) -- memorized textual patterns from pre-training while under-utilizing visual evidence. Prior analyses of LP mostly rely on input-output probing, which fails to reveal the internal mechanisms governing when and how vision influences model behavior. To address this gap, we present the first systematic analysis of language prior through the lens of chain-of-embedding, which examines the layer-wise representation dynamics within LVLMs. Our analysis reveals a universal phenomenon: each model exhibits a Visual Integration Point (VIP), a critical layer at which visual information begins to meaningfully reshape hidden representations and influence decoding. Building on this observation, we introduce the Total Visual Integration (TVI) estimator, which aggregates representation distance beyond the VIP to quantify how strongly visual query influences response generation. Across 54 model-dataset combinations spanning 9 contemporary LVLMs and 6 benchmarks, we demonstrate that VIP consistently emerges, and that TVI reliably predicts the strength of language prior. This offers a principled toolkit for diagnosing and understanding language prior in LVLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/deeplearning-wisc/understanding_lp", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.23564", "title": "Clean First, Align Later: Benchmarking Preference Data Cleaning for Reliable LLM Alignment", "authors": [ "Min-Hsuan Yeh", "Yixuan Li" ], "github": "https://github.com/deeplearning-wisc/PrefCleanBench", "project_page": "", "abstract": "Human feedback plays a pivotal role in aligning large language models (LLMs) with human preferences. However, such feedback is often noisy or inconsistent, which can degrade the quality of reward models and hinder alignment. While various automated data cleaning methods have been proposed to mitigate this issue, a systematic evaluation of their effectiveness and generalizability remains lacking. To bridge this gap, we introduce the first comprehensive benchmark for evaluating 13 preference data cleaning methods in the context of LLM alignment. PrefCleanBench offers a standardized protocol to assess cleaning strategies in terms of alignment performance and generalizability across diverse datasets, model architectures, and optimization algorithms. By unifying disparate methods and rigorously comparing them, we uncover key factors that determine the success of data cleaning in alignment tasks. This benchmark lays the groundwork for principled and reproducible approaches to improving LLM alignment through better data quality-highlighting the crucial but underexplored role of data preprocessing in responsible AI development. We release modular implementations of all methods to catalyze further research: https://github.com/deeplearning-wisc/PrefCleanBench.", "github_manual": "", "github_regex": "https://github.com/deeplearning-wisc/PrefCleanBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.21875", "title": "LUMINA: Detecting Hallucinations in RAG System with Context-Knowledge Signals", "authors": [ "Min-Hsuan Yeh", "Yixuan Li", "Tanwi Mallick" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) aims to mitigate hallucinations in large language models (LLMs) by grounding responses in retrieved documents. Yet, RAG-based LLMs still hallucinate even when provided with correct and sufficient context. A growing line of work suggests that this stems from an imbalance between how models use external context and their internal knowledge, and several approaches have attempted to quantify these signals for hallucination detection. However, existing methods require extensive hyperparameter tuning, limiting their generalizability. We propose LUMINA, a novel framework that detects hallucinations in RAG systems through context-knowledge signals: external context utilization is quantified via distributional distance, while internal knowledge utilization is measured by tracking how predicted tokens evolve across transformer layers. We further introduce a framework for statistically validating these measurements. Experiments on common RAG hallucination benchmarks and four open-source LLMs show that LUMINA achieves consistently high AUROC and AUPRC scores, outperforming prior utilization-based methods by up to +13% AUROC on HalluRAG. Moreover, LUMINA remains robust under relaxed assumptions about retrieval quality and model matching, offering both effectiveness and practicality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25370", "title": "Where LLM Agents Fail and How They can Learn From Failures", "authors": [ "Kunlun Zhu", "Zijia Liu", "Bingxuan Li", "Muxin Tian", "Yingxuan Yang", "Jiaxun Zhang", "Pengrui Han", "Qipeng Xie", "Fuyang Cui", "Weijia Zhang", "Xiaoteng Ma", "Xiaodong Yu", "Gowtham Ramesh", "Jialian Wu", "Zicheng Liu", "Pan Lu", "James Zou", "Jiaxuan You" ], "github": "https://github.com/ulab-uiuc/AgentDebug", "project_page": "", "abstract": "Large Language Model (LLM) agents, which integrate planning, memory, reflection, and tool-use modules, have shown promise in solving complex, multi-step tasks. Yet their sophisticated architectures amplify vulnerability to cascading failures, where a single root-cause error propagates through subsequent decisions, leading to task failure. Current systems lack a framework that can comprehensively understand agent error in a modular and systemic way, and therefore fail to detect these errors accordingly. We address this gap with three contributions. First, we introduce the AgentErrorTaxonomy, a modular classification of failure modes spanning memory, reflection, planning, action, and system-level operations. Second, we construct AgentErrorBench, the first dataset of systematically annotated failure trajectories from ALFWorld, GAIA, and WebShop, grounding error analysis in real-world agent rollouts. Third, we propose AgentDebug, a debugging framework that isolates root-cause failures and provides corrective feedback, enabling agents to recover and iteratively improve. Experiments on AgentErrorBench show that AgentDebug achieves 24% higher all-correct accuracy and 17% higher step accuracy compared to the strongest baseline. Beyond detection, the targeted feedback generated by AgentDebug enables LLM agents to iteratively recover from failures, yielding up to 26% relative improvements in task success across ALFWorld, GAIA, and WebShop. These results establish principled debugging as a pathway to more reliable and adaptive LLM agents. The code and data will be available at https://github.com/ulab-uiuc/AgentDebug", "github_manual": "", "github_regex": "https://github.com/ulab-uiuc/AgentDebug", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24317", "title": "Rethinking JEPA: Compute-Efficient Video SSL with Frozen Teachers", "authors": [ "Xianhang Li", "Chen Huang", "Chun-Liang Li", "Eran Malach", "Josh Susskind", "Vimal Thilak", "Etai Littwin" ], "github": "", "project_page": "", "abstract": "Video Joint Embedding Predictive Architectures (V-JEPA) learn generalizable off-the-shelf video representation by predicting masked regions in latent space with an exponential moving average (EMA)-updated teacher. While EMA prevents representation collapse, it complicates scalable model selection and couples teacher and student architectures. We revisit masked-latent prediction and show that a frozen teacher suffices. Concretely, we (i) train a target encoder with a simple pixel-reconstruction objective under V-JEPA masking, then (ii) freeze it and train a student to predict the teacher's latents on masked regions. This leads to a two-stage, unregularized scheme that we refer to as SALT (Static-teacher Asymmetric Latent Training). SALT decouples optimization into pixel reconstruction (teacher) and masked latent prediction (student), increasing transparency, efficiency, and scalability while preserving the ability of representation to generalize under frozen evaluation. Empirically, our student models outperform recently proposed V-JEPA 2 encoders under frozen backbone evaluation across diverse benchmarks. They are also more compute-optimal: at matched pretraining FLOPs, our method achieves higher probing accuracy, and its scaling curves dominate V-JEPA's accuracy-FLOPs Pareto frontier. Finally, we find that student quality is remarkably robust to teacher quality: high-performing students emerge even with small, sub-optimal teachers. This points to a compute budget allocation that should overwhelmingly favor the student. These results position SALT as a simple, scalable, and compute-efficient alternative to EMA-based self-distillation for video representation learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26625", "title": "Learning to See Before Seeing: Demystifying LLM Visual Priors from Language Pre-training", "authors": [ "Junlin Han", "Shengbang Tong", "David Fan", "Yufan Ren", "Koustuv Sinha", "Philip Torr", "Filippos Kokkinos" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs), despite being trained on text alone, surprisingly develop rich visual priors. These priors allow latent visual capabilities to be unlocked for vision tasks with a relatively small amount of multimodal data, and in some cases, to perform visual tasks without ever having seen an image. Through systematic analysis, we reveal that visual priors-the implicit, emergent knowledge about the visual world acquired during language pre-training-are composed of separable perception and reasoning priors with unique scaling trends and origins. We show that an LLM's latent visual reasoning ability is predominantly developed by pre-training on reasoning-centric data (e.g., code, math, academia) and scales progressively. This reasoning prior acquired from language pre-training is transferable and universally applicable to visual reasoning. In contrast, a perception prior emerges more diffusely from broad corpora, and perception ability is more sensitive to the vision encoder and visual instruction tuning data. In parallel, text describing the visual world proves crucial, though its performance impact saturates rapidly. Leveraging these insights, we propose a data-centric recipe for pre-training vision-aware LLMs and verify it in 1T token scale pre-training. Our findings are grounded in over 100 controlled experiments consuming 500,000 GPU-hours, spanning the full MLLM construction pipeline-from LLM pre-training to visual alignment and supervised multimodal fine-tuning-across five model scales, a wide range of data categories and mixtures, and multiple adaptation setups. Along with our main findings, we propose and investigate several hypotheses, and introduce the Multi-Level Existence Bench (MLE-Bench). Together, this work provides a new way of deliberately cultivating visual priors from language pre-training, paving the way for the next generation of multimodal LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.22613", "title": "Benefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspective", "authors": [ "Siwei Wang", "Yifei Shen", "Haoran Sun", "Shi Feng", "Shang-Hua Teng", "Li Dong", "Yaru Hao", "Wei Chen" ], "github": "", "project_page": "", "abstract": "Recent reinforcement learning (RL) methods have substantially enhanced the planning capabilities of Large Language Models (LLMs), yet the theoretical basis for their effectiveness remains elusive. In this work, we investigate RL's benefits and limitations through a tractable graph-based abstraction, focusing on policy gradient (PG) and Q-learning methods. Our theoretical analyses reveal that supervised fine-tuning (SFT) may introduce co-occurrence-based spurious solutions, whereas RL achieves correct planning primarily through exploration, underscoring exploration's role in enabling better generalization. However, we also show that PG suffers from diversity collapse, where output diversity decreases during training and persists even after perfect accuracy is attained. By contrast, Q-learning provides two key advantages: off-policy learning and diversity preservation at convergence. We further demonstrate that careful reward design is necessary to prevent reward hacking in Q-learning. Finally, applying our framework to the real-world planning benchmark Blocksworld, we confirm that these behaviors manifest in practice.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25182", "title": "DC-VideoGen: Efficient Video Generation with Deep Compression Video Autoencoder", "authors": [ "Junyu Chen", "Wenkun He", "Yuchao Gu", "Yuyang Zhao", "Jincheng Yu", "Junsong Chen", "Dongyun Zou", "Yujun Lin", "Zhekai Zhang", "Muyang Li", "Haocheng Xi", "Ligeng Zhu", "Enze Xie", "Song Han", "Han Cai" ], "github": "https://github.com/dc-ai-projects/DC-VideoGen", "project_page": "", "abstract": "We introduce DC-VideoGen, a post-training acceleration framework for efficient video generation. DC-VideoGen can be applied to any pre-trained video diffusion model, improving efficiency by adapting it to a deep compression latent space with lightweight fine-tuning. The framework builds on two key innovations: (i) a Deep Compression Video Autoencoder with a novel chunk-causal temporal design that achieves 32x/64x spatial and 4x temporal compression while preserving reconstruction quality and generalization to longer videos; and (ii) AE-Adapt-V, a robust adaptation strategy that enables rapid and stable transfer of pre-trained models into the new latent space. Adapting the pre-trained Wan-2.1-14B model with DC-VideoGen requires only 10 GPU days on the NVIDIA H100 GPU. The accelerated models achieve up to 14.8x lower inference latency than their base counterparts without compromising quality, and further enable 2160x3840 video generation on a single GPU. Code: https://github.com/dc-ai-projects/DC-VideoGen.", "github_manual": "", "github_regex": "https://github.com/dc-ai-projects/DC-VideoGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25760", "title": "TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning", "authors": [ "Zhepei Wei", "Xiao Yang", "Kai Sun", "Jiaqi Wang", "Rulin Shao", "Sean Chen", "Mohammad Kachuee", "Teja Gollapudi", "Tony Liao", "Nicolas Scheffer", "Rakesh Wanga", "Anuj Kumar", "Yu Meng", "Wen-tau Yih", "Xin Luna Dong" ], "github": "", "project_page": "", "abstract": "While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly when tasks demand information outside their parametric knowledge. Indeed, truthfulness requires more than accuracy -- models must also recognize uncertainty and abstain when unsure to avoid hallucinations. This presents a fundamental challenge for existing methods: approaches that optimize for accuracy often amplify hallucinations, while those that encourage abstention can become overly conservative, sacrificing correct answers. Both extremes ultimately compromise truthfulness. In this work, we present TruthRL, a general reinforcement learning (RL) framework that directly optimizes the truthfulness of LLMs. Specifically, we implement TruthRL using GRPO with a simple yet effective ternary reward that distinguishes correct answers, hallucinations, and abstentions. It incentivizes models to reduce hallucinations not only by providing correct responses, but also by enabling abstention when uncertain, thereby improving truthfulness. Extensive experiments across four knowledge-intensive benchmarks show that, compared to vanilla RL, TruthRL significantly reduces hallucinations by 28.9% and improves truthfulness by 21.1%, with consistent gains across various backbone models (e.g., Qwen, Llama) under both retrieval and non-retrieval setups. In-depth ablation study demonstrates that vanilla accuracy-driven methods, such as supervised fine-tuning or RL with a binary reward, struggle to balance factual correctness and uncertainty. In contrast, our proposed truthfulness-driven TruthRL achieves strong performance in both accuracy and truthfulness, underscoring the importance of learning objective design for developing truthful LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25758", "title": "Thinking Sparks!: Emergent Attention Heads in Reasoning Models During Post Training", "authors": [ "Yein Park", "Minbyul Jeong", "Jaewoo Kang" ], "github": "", "project_page": "", "abstract": "The remarkable capabilities of modern large reasoning models are largely unlocked through post-training techniques such as supervised fine-tuning and reinforcement learning. However, the architectural mechanisms behind such improvements remain largely opaque. In this work, we use circuit analysis to demonstrate that post-training for complex reasoning sparks the emergence of novel, functionally specialized attention heads. These heads collectively support structured reasoning and computation. Our comparative analysis across Qwen families and DeepSeek-distilled model reveals that these emergent heads evolve differently under different training regimes. Distillation and SFT foster a cumulative addition of stable reasoning heads. In contrast, group relative policy optimization operates in a dynamic search mode: relatively few attention heads are iteratively activated, evaluated, and pruned, with their survival closely tracking fluctuations in the task reward signal. Furthermore, we find that controllable think on/off models do not possess dedicated thinking heads. Instead, turning off explicit reasoning triggers a broader-but less efficient-set of compensatory heads. Through ablation and qualitative analyses, we connect these circuit-level dynamics to a crucial performance trade-off: strengthened heads enable sophisticated problem-solving strategies for difficult problems but can also introduce over-thinking failure modes, such as calculation errors or logical loops on simpler tasks. These findings connect circuit-level dynamics to macro-level performance, identifying an inherent tension where complex reasoning comes at the cost of elementary computations. More broadly, our work points to future directions for training policy design, emphasizing the need to balance the development of effective reasoning strategies with the assurance of reliable, flawless execution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26536", "title": "OceanGym: A Benchmark Environment for Underwater Embodied Agents", "authors": [ "Yida Xue", "Mingjun Mao", "Xiangyuan Ru", "Yuqi Zhu", "Baochang Ren", "Shuofei Qiao", "Mengru Wang", "Shumin Deng", "Xinyu An", "Ningyu Zhang", "Ying Chen", "Huajun Chen" ], "github": "https://github.com/OceanGPT/OceanGym", "project_page": "", "abstract": "We introduce OceanGym, the first comprehensive benchmark for ocean underwater embodied agents, designed to advance AI in one of the most demanding real-world environments. Unlike terrestrial or aerial domains, underwater settings present extreme perceptual and decision-making challenges, including low visibility, dynamic ocean currents, making effective agent deployment exceptionally difficult. OceanGym encompasses eight realistic task domains and a unified agent framework driven by Multi-modal Large Language Models (MLLMs), which integrates perception, memory, and sequential decision-making. Agents are required to comprehend optical and sonar data, autonomously explore complex environments, and accomplish long-horizon objectives under these harsh conditions. Extensive experiments reveal substantial gaps between state-of-the-art MLLM-driven agents and human experts, highlighting the persistent difficulty of perception, planning, and adaptability in ocean underwater environments. By providing a high-fidelity, rigorously designed platform, OceanGym establishes a testbed for developing robust embodied AI and transferring these capabilities to real-world autonomous ocean underwater vehicles, marking a decisive step toward intelligent agents capable of operating in one of Earth's last unexplored frontiers. The code and data are available at https://github.com/OceanGPT/OceanGym.", "github_manual": "", "github_regex": "https://github.com/OceanGPT/OceanGym", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26490", "title": "VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications", "authors": [ "Wei He", "Yueqing Sun", "Hongyan Hao", "Xueyuan Hao", "Zhikang Xia", "Qi Gu", "Chengcheng Han", "Dengchang Zhao", "Hui Su", "Kefeng Zhang", "Man Gao", "Xi Su", "Xiaodong Cai", "Xunliang Cai", "Yu Yang", "Yunke Zhao" ], "github": "", "project_page": "", "abstract": "As LLM-based agents are increasingly deployed in real-life scenarios, existing benchmarks fail to capture their inherent complexity of handling extensive information, leveraging diverse resources, and managing dynamic user interactions. To address this gap, we introduce VitaBench, a challenging benchmark that evaluates agents on versatile interactive tasks grounded in real-world settings. Drawing from daily applications in food delivery, in-store consumption, and online travel services, VitaBench presents agents with the most complex life-serving simulation environment to date, comprising 66 tools. Through a framework that eliminates domain-specific policies, we enable flexible composition of these scenarios and tools, yielding 100 cross-scenario tasks (main results) and 300 single-scenario tasks. Each task is derived from multiple real user requests and requires agents to reason across temporal and spatial dimensions, utilize complex tool sets, proactively clarify ambiguous instructions, and track shifting user intent throughout multi-turn conversations. Moreover, we propose a rubric-based sliding window evaluator, enabling robust assessment of diverse solution pathways in complex environments and stochastic interactions. Our comprehensive evaluation reveals that even the most advanced models achieve only 30% success rate on cross-scenario tasks, and less than 50% success rate on others. Overall, we believe VitaBench will serve as a valuable resource for advancing the development of AI agents in practical real-world applications. The code, dataset, and leaderboard are available at https://vitabench.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26628", "title": "Attention as a Compass: Efficient Exploration for Process-Supervised RL in Reasoning Models", "authors": [ "Runze Liu", "Jiakang Wang", "Yuling Shi", "Zhihui Xie", "Chenxin An", "Kaiyan Zhang", "Jian Zhao", "Xiaodong Gu", "Lei Lin", "Wenping Hu", "Xiu Li", "Fuzheng Zhang", "Guorui Zhou", "Kun Gai" ], "github": "https://github.com/volcengine/verl/tree/main/recipe/one_step_off_policy", "project_page": "", "abstract": "Reinforcement Learning (RL) has shown remarkable success in enhancing the reasoning capabilities of Large Language Models (LLMs). Process-Supervised RL (PSRL) has emerged as a more effective paradigm compared to outcome-based RL. However, existing PSRL approaches suffer from limited exploration efficiency, both in terms of branching positions and sampling. In this paper, we introduce a novel PSRL framework (AttnRL), which enables efficient exploration for reasoning models. Motivated by preliminary observations that steps exhibiting high attention scores correlate with reasoning behaviors, we propose to branch from positions with high values. Furthermore, we develop an adaptive sampling strategy that accounts for problem difficulty and historical batch size, ensuring that the whole training batch maintains non-zero advantage values. To further improve sampling efficiency, we design a one-step off-policy training pipeline for PSRL. Extensive experiments on multiple challenging mathematical reasoning benchmarks demonstrate that our method consistently outperforms prior approaches in terms of performance and sampling and training efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/volcengine/verl/tree/main/recipe/one_step_off_policy", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26488", "title": "dParallel: Learnable Parallel Decoding for dLLMs", "authors": [ "Zigeng Chen", "Gongfan Fang", "Xinyin Ma", "Ruonan Yu", "Xinchao Wang" ], "github": "https://github.com/czg1225/dParallel", "project_page": "", "abstract": "Diffusion large language models (dLLMs) have recently drawn considerable attention within the research community as a promising alternative to autoregressive generation, offering parallel token prediction and lower inference latency. Yet, their parallel decoding potential remains largely underexplored, as existing open-source models still require nearly token-length decoding steps to ensure performance. To address this, we introduce dParallel, a simple and effective method that unlocks the inherent parallelism of dLLMs for fast sampling. We identify that the key bottleneck to parallel decoding arises from the sequential certainty convergence for masked tokens. Building on this insight, we introduce the core of our approach: certainty-forcing distillation, a novel training strategy that distills the model to follow its original sampling trajectories while enforcing it to achieve high certainty on masked tokens more rapidly and in parallel. Extensive experiments across various benchmarks demonstrate that our method can dramatically reduce the number of decoding steps while maintaining performance. When applied to the LLaDA-8B-Instruct model, dParallel reduces decoding steps from 256 to 30 on GSM8K, achieving an 8.5x speedup without performance degradation. On the MBPP benchmark, it cuts decoding steps from 256 to 24, resulting in a 10.5x speedup while maintaining accuracy. Our code is available at https://github.com/czg1225/dParallel", "github_manual": "", "github_regex": "https://github.com/czg1225/dParallel", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.23610", "title": "Efficient Audio-Visual Speech Separation with Discrete Lip Semantics and Multi-Scale Global-Local Attention", "authors": [ "Kai Li", "Kejun Gao", "Xiaolin Hu" ], "github": "", "project_page": "", "abstract": "Audio-visual speech separation (AVSS) methods leverage visual cues to extract target speech and have demonstrated strong separation quality in noisy acoustic environments. However, these methods usually involve a large number of parameters and require high computational cost, which is unacceptable in many applications where speech separation serves as only a preprocessing step for further speech processing. To address this issue, we propose an efficient AVSS method, named Dolphin. For visual feature extraction, we develop DP-LipCoder, a dual-path lightweight video encoder that transforms lip-motion into discrete audio-aligned semantic tokens. For audio separation, we construct a lightweight encoder-decoder separator, in which each layer incorporates a global-local attention (GLA) block to efficiently capture multi-scale dependencies. Experiments on three benchmark datasets showed that Dolphin not only surpassed the current state-of-the-art (SOTA) model in separation quality but also achieved remarkable improvements in efficiency: over 50% fewer parameters, more than 2.4x reduction in MACs, and over 6x faster GPU inference speed. These results indicate that Dolphin offers a practical and deployable solution for high-performance AVSS in real-world scenarios. Our code and demo page are publicly available at http://cslikai.cn/Dolphin/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26542", "title": "Voice Evaluation of Reasoning Ability: Diagnosing the Modality-Induced Performance Gap", "authors": [ "Yueqian Lin", "Zhengmian Hu", "Qinsi Wang", "Yudong Liu", "Hengfan Zhang", "Jayakumar Subramanian", "Nikos Vlassis", "Hai Helen Li", "Yiran Chen" ], "github": "https://github.com/linyueqian/VERA", "project_page": "", "abstract": "We present Voice Evaluation of Reasoning Ability (VERA), a benchmark for evaluating reasoning ability in voice-interactive systems under real-time conversational constraints. VERA comprises 2,931 voice-native episodes derived from established text benchmarks and organized into five tracks (Math, Web, Science, Long-Context, Factual). Each item is adapted for speech interaction while preserving reasoning difficulty. VERA enables direct text-voice comparison within model families and supports analysis of how architectural choices affect reliability. We assess 12 contemporary voice systems alongside strong text baselines and observe large, consistent modality gaps: on competition mathematics a leading text model attains 74.8% accuracy while its voice counterpart reaches 6.1%; macro-averaged across tracks the best text models achieve 54.0% versus 11.3% for voice. Latency-accuracy analyses reveal a low-latency plateau, where fast voice systems cluster around ~10% accuracy, while approaching text performance requires sacrificing real-time interaction. Diagnostic experiments indicate that common mitigations are insufficient. Increasing \"thinking time\" yields negligible gains; a decoupled cascade that separates reasoning from narration improves accuracy but still falls well short of text and introduces characteristic grounding/consistency errors. Failure analyses further show distinct error signatures across native streaming, end-to-end, and cascade designs. VERA provides a reproducible testbed and targeted diagnostics for architectures that decouple thinking from speaking, offering a principled way to measure progress toward real-time voice assistants that are both fluent and reliably reasoned.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/linyueqian/VERA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26539", "title": "Ferret-UI Lite: Lessons from Building Small On-Device GUI Agents", "authors": [ "Zhen Yang", "Zi-Yi Dou", "Di Feng", "Forrest Huang", "Anh Nguyen", "Keen You", "Omar Attia", "Yuhao Yang", "Michael Feng", "Haotian Zhang", "Ram Ramrakhya", "Chao Jia", "Jeffrey Nichols", "Alexander Toshev", "Yinfei Yang", "Zhe Gan" ], "github": "", "project_page": "", "abstract": "Developing autonomous agents that effectively interact with Graphic User Interfaces (GUIs) remains a challenging open problem, especially for small on-device models. In this paper, we present Ferret-UI Lite, a compact, end-to-end GUI agent that operates across diverse platforms, including mobile, web, and desktop. Utilizing techniques optimized for developing small models, we build our 3B Ferret-UI Lite agent through curating a diverse GUI data mixture from real and synthetic sources, strengthening inference-time performance through chain-of-thought reasoning and visual tool-use, and reinforcement learning with designed rewards. Ferret-UI Lite achieves competitive performance with other small-scale GUI agents. In GUI grounding, Ferret-UI Lite attains scores of 91.6%, 53.3%, and 61.2% on the ScreenSpot-V2, ScreenSpot-Pro, and OSWorld-G benchmarks, respectively. For GUI navigation, Ferret-UI Lite achieves success rates of 28.0% on AndroidWorld and 19.8% on OSWorld. We share our methods and lessons learned from developing compact, on-device GUI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26476", "title": "Regression Language Models for Code", "authors": [ "Yash Akhauri", "Xingyou Song", "Arissa Wongpanich", "Bryan Lewandowski", "Mohamed S. Abdelfattah" ], "github": "", "project_page": "", "abstract": "We study code-to-metric regression: predicting numeric outcomes of code executions, a challenging task due to the open-ended nature of programming languages. While prior methods have resorted to heavy and domain-specific feature engineering, we show that a single unified Regression Language Model (RLM) can simultaneously predict directly from text, (i) the memory footprint of code across multiple high-level languages such as Python and C++, (ii) the latency of Triton GPU kernels, and (iii) the accuracy and speed of trained neural networks represented in ONNX. In particular, a relatively small 300M parameter RLM initialized from T5Gemma, obtains > 0.9 Spearman-rank on competitive programming submissions from APPS, and a single unified model achieves > 0.5 average Spearman-rank across 17 separate languages from CodeNet. Furthermore, the RLM can obtain the highest average Kendall-Tau of 0.46 on five classic NAS design spaces previously dominated by graph neural networks, and simultaneously predict architecture latencies on numerous hardware platforms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26391", "title": "MotionRAG: Motion Retrieval-Augmented Image-to-Video Generation", "authors": [ "Chenhui Zhu", "Yilu Wu", "Shuai Wang", "Gangshan Wu", "Limin Wang" ], "github": "https://github.com/MCG-NJU/MotionRAG", "project_page": "", "abstract": "Image-to-video generation has made remarkable progress with the advancements in diffusion models, yet generating videos with realistic motion remains highly challenging. This difficulty arises from the complexity of accurately modeling motion, which involves capturing physical constraints, object interactions, and domain-specific dynamics that are not easily generalized across diverse scenarios. To address this, we propose MotionRAG, a retrieval-augmented framework that enhances motion realism by adapting motion priors from relevant reference videos through Context-Aware Motion Adaptation (CAMA). The key technical innovations include: (i) a retrieval-based pipeline extracting high-level motion features using video encoder and specialized resamplers to distill semantic motion representations; (ii) an in-context learning approach for motion adaptation implemented through a causal transformer architecture; (iii) an attention-based motion injection adapter that seamlessly integrates transferred motion features into pretrained video diffusion models. Extensive experiments demonstrate that our method achieves significant improvements across multiple domains and various base models, all with negligible computational overhead during inference. Furthermore, our modular design enables zero-shot generalization to new domains by simply updating the retrieval database without retraining any components. This research enhances the core capability of video generation systems by enabling the effective retrieval and transfer of motion priors, facilitating the synthesis of realistic motion dynamics.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MCG-NJU/MotionRAG", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25541", "title": "Vision-Zero: Scalable VLM Self-Improvement via Strategic Gamified Self-Play", "authors": [ "Qinsi Wang", "Bo Liu", "Tianyi Zhou", "Jing Shi", "Yueqian Lin", "Yiran Chen", "Hai Helen Li", "Kun Wan", "Wentian Zhao" ], "github": "https://github.com/wangqinsi1/Vision-Zero", "project_page": "", "abstract": "Although reinforcement learning (RL) can effectively enhance the reasoning capabilities of vision-language models (VLMs), current methods remain heavily dependent on labor-intensive datasets that require extensive manual construction and verification, leading to extremely high training costs and consequently constraining the practical deployment of VLMs. To address this challenge, we propose Vision-Zero, a domain-agnostic framework enabling VLM self-improvement through competitive visual games generated from arbitrary image pairs. Specifically, Vision-Zero encompasses three main attributes: (1) Strategic Self-Play Framework: Vision-Zero trains VLMs in \"Who Is the Spy\"-style games, where the models engage in strategic reasoning and actions across multiple roles. Through interactive gameplay, models autonomously generate their training data without human annotation. (2) Gameplay from Arbitrary Images: Unlike existing gamified frameworks, Vision-Zero can generate games from arbitrary images, thereby enhancing the model's reasoning ability across diverse domains and showing strong generalization to different tasks. We demonstrate this versatility using three distinct types of image datasets: CLEVR-based synthetic scenes, charts, and real-world images. (3) Sustainable Performance Gain: We introduce Iterative Self-Play Policy Optimization (Iterative-SPO), a novel training algorithm that alternates between Self-Play and reinforcement learning with verifiable rewards (RLVR), mitigating the performance plateau often seen in self-play-only training and achieving sustained long-term improvements. Despite using label-free data, Vision-Zero achieves state-of-the-art performance on reasoning, chart question answering, and vision-centric understanding tasks, surpassing other annotation-based methods. Models and code has been released at https://github.com/wangqinsi1/Vision-Zero.", "github_manual": "", "github_regex": "https://github.com/wangqinsi1/Vision-Zero", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25339", "title": "VisualOverload: Probing Visual Understanding of VLMs in Really Dense Scenes", "authors": [ "Paul Gavrikov", "Wei Lin", "M. Jehanzeb Mirza", "Soumya Jahagirdar", "Muhammad Huzaifa", "Sivan Doveh", "Serena Yeung-Levy", "James Glass", "Hilde Kuehne" ], "github": "", "project_page": "", "abstract": "Is basic visual understanding really solved in state-of-the-art VLMs? We present VisualOverload, a slightly different visual question answering (VQA) benchmark comprising 2,720 question-answer pairs, with privately held ground-truth responses. Unlike prior VQA datasets that typically focus on near global image understanding, VisualOverload challenges models to perform simple, knowledge-free vision tasks in densely populated (or, overloaded) scenes. Our dataset consists of high-resolution scans of public-domain paintings that are populated with multiple figures, actions, and unfolding subplots set against elaborately detailed backdrops. We manually annotated these images with questions across six task categories to probe for a thorough understanding of the scene. We hypothesize that current benchmarks overestimate the performance of VLMs, and encoding and reasoning over details is still a challenging task for them, especially if they are confronted with densely populated scenes. Indeed, we observe that even the best model (o3) out of 37 tested models only achieves 19.6% accuracy on our hardest test split and overall 69.5% accuracy on all questions. Beyond a thorough evaluation, we complement our benchmark with an error analysis that reveals multiple failure modes, including a lack of counting skills, failure in OCR, and striking logical inconsistencies under complex tasks. Altogether, VisualOverload exposes a critical gap in current vision models and offers a crucial resource for the community to develop better models. Benchmark: http://paulgavrikov.github.io/visualoverload", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26574", "title": "Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmark", "authors": [ "Minhui Zhu", "Minyang Tian", "Xiaocheng Yang", "Tianci Zhou", "Penghao Zhu", "Eli Chertkov", "Shengyan Liu", "Yufeng Du", "Lifan Yuan", "Ziming Ji", "Indranil Das", "Junyi Cao", "Yufeng Du", "Jinchen He", "Yifan Su", "Jiabin Yu", "Yikun Jiang", "Yujie Zhang", "Chang Liu", "Ze-Min Huang", "Weizhen Jia", "Xinan Chen", "Peixue Wu", "Yunkai Wang", "Juntai Zhou", "Yong Zhao", "Farshid Jafarpour", "Jessie Shelton", "Aaron Young", "John Bartolotta", "Wenchao Xu", "Yue Sun", "Anjun Chu", "Victor Colussi", "Chris Akers", "Nathan Brooks", "Wenbo Fu", "Christopher Wilson", "Jinchao Zhao", "Marvin Qi", "Anqi Mu", "Yubo Yang", "Allen Zang", "Yang Lyu", "Peizhi Mai", "Xuefei Guo", "Luyu Gao", "Ze Yang", "Chi Xue", "Dmytro Bandak", "Ya\u00efr Hein", "Yonatan Kahn", "Kevin Zhou", "John Drew Wilson Jarrod T. Reilly", "Di Luo", "Daniel Inafuku", "Hao Tong", "Liang Yang", "Ruixing Zhang", "Xueying Wang", "Ofir Press", "Nicolas Chia", "Eliu Huerta", "Hao Peng" ], "github": "", "project_page": "", "abstract": "While large language models (LLMs) with reasoning capabilities are progressing rapidly on high-school math competitions and coding, can they reason effectively through complex, open-ended challenges found in frontier physics research? And crucially, what kinds of reasoning tasks do physicists want LLMs to assist with? To address these questions, we present the CritPt (Complex Research using Integrated Thinking - Physics Test, pronounced \"critical point\"), the first benchmark designed to test LLMs on unpublished, research-level reasoning tasks that broadly covers modern physics research areas, including condensed matter, quantum physics, atomic, molecular & optical physics, astrophysics, high energy physics, mathematical physics, statistical physics, nuclear physics, nonlinear dynamics, fluid dynamics and biophysics. CritPt consists of 71 composite research challenges designed to simulate full-scale research projects at the entry level, which are also decomposed to 190 simpler checkpoint tasks for more fine-grained insights. All problems are newly created by 50+ active physics researchers based on their own research. Every problem is hand-curated to admit a guess-resistant and machine-verifiable answer and is evaluated by an automated grading pipeline heavily customized for advanced physics-specific output formats. We find that while current state-of-the-art LLMs show early promise on isolated checkpoints, they remain far from being able to reliably solve full research-scale challenges: the best average accuracy among base models is only 4.0% , achieved by GPT-5 (high), moderately rising to around 10% when equipped with coding tools. Through the realistic yet standardized evaluation offered by CritPt, we highlight a large disconnect between current model capabilities and realistic physics research demands, offering a foundation to guide the development of scientifically grounded AI tools.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24494", "title": "GRPO-MA: Multi-Answer Generation in GRPO for Stable and Efficient Chain-of-Thought Training", "authors": [ "Hongcheng Wang", "Yinuo Huang", "Sukai Wang", "Guanghui Ren", "Hao Dong" ], "github": "", "project_page": "", "abstract": "Recent progress, such as DeepSeek-R1, has shown that the GRPO algorithm, a Reinforcement Learning (RL) approach, can effectively train Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) and Vision-Language Models (VLMs). In this paper, we analyze three challenges of GRPO: gradient coupling between thoughts and answers, sparse reward signals caused by limited parallel sampling, and unstable advantage estimation. To mitigate these challenges, we propose GRPO-MA, a simple yet theoretically grounded method that leverages multi-answer generation from each thought process, enabling more robust and efficient optimization. Theoretically, we show that the variance of thought advantage decreases as the number of answers per thought increases. Empirically, our gradient analysis confirms this effect, showing that GRPO-MA reduces gradient spikes compared to GRPO. Experiments on math, code, and diverse multimodal tasks demonstrate that GRPO-MA substantially improves performance and training efficiency. Our ablation studies further reveal that increasing the number of answers per thought consistently enhances model performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25154", "title": "Who's Your Judge? On the Detectability of LLM-Generated Judgments", "authors": [ "Dawei Li", "Zhen Tan", "Chengshuai Zhao", "Bohan Jiang", "Baixiang Huang", "Pingchuan Ma", "Abdullah Alnaibari", "Kai Shu", "Huan Liu" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM)-based judgments leverage powerful LLMs to efficiently evaluate candidate content and provide judgment scores. However, the inherent biases and vulnerabilities of LLM-generated judgments raise concerns, underscoring the urgent need for distinguishing them in sensitive scenarios like academic peer reviewing. In this work, we propose and formalize the task of judgment detection and systematically investigate the detectability of LLM-generated judgments. Unlike LLM-generated text detection, judgment detection relies solely on judgment scores and candidates, reflecting real-world scenarios where textual feedback is often unavailable in the detection process. Our preliminary analysis shows that existing LLM-generated text detection methods perform poorly given their incapability to capture the interaction between judgment scores and candidate content -- an aspect crucial for effective judgment detection. Inspired by this, we introduce J-Detector, a lightweight and transparent neural detector augmented with explicitly extracted linguistic and LLM-enhanced features to link LLM judges' biases with candidates' properties for accurate detection. Experiments across diverse datasets demonstrate the effectiveness of J-Detector and show how its interpretability enables quantifying biases in LLM judges. Finally, we analyze key factors affecting the detectability of LLM-generated judgments and validate the practical utility of judgment detection in real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.24002", "title": "MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use", "authors": [ "Zijian Wu", "Xiangyan Liu", "Xinyuan Zhang", "Lingjun Chen", "Fanqing Meng", "Lingxiao Du", "Yiran Zhao", "Fanshi Zhang", "Yaoqi Ye", "Jiawei Wang", "Zirui Wang", "Jinjie Ni", "Yufan Yang", "Arvin Xu", "Michael Qizhe Shieh" ], "github": "https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem", "project_page": "", "abstract": "MCP standardizes how LLMs interact with external systems, forming the foundation for general agents. However, existing MCP benchmarks remain narrow in scope: they focus on read-heavy tasks or tasks with limited interaction depth, and fail to capture the complexity and realism of real-world workflows. To address this gap, we propose MCPMark, a benchmark designed to evaluate MCP use in a more realistic and comprehensive manner. It consists of 127 high-quality tasks collaboratively created by domain experts and AI agents. Each task begins with a curated initial state and includes a programmatic script for automatic verification. These tasks demand richer and more diverse interactions with the environment, involving a broad range of create, read, update, and delete (CRUD) operations. We conduct a comprehensive evaluation of cutting-edge LLMs using a minimal agent framework that operates in a tool-calling loop. Empirical results show that the best-performing model, gpt-5-medium, reaches only 52.56\\% pass@1 and 33.86\\% pass^4, while other widely regarded strong models, including claude-sonnet-4 and o3, fall below 30\\% pass@1 and 15\\% pass^4. On average, LLMs require 16.2 execution turns and 17.4 tool calls per task, significantly surpassing those in previous MCP benchmarks and highlighting the stress-testing nature of MCPMark.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26231", "title": "IMG: Calibrating Diffusion Models via Implicit Multimodal Guidance", "authors": [ "Jiayi Guo", "Chuanhao Yan", "Xingqian Xu", "Yulin Wang", "Kai Wang", "Gao Huang", "Humphrey Shi" ], "github": "https://github.com/SHI-Labs/IMG-Multimodal-Diffusion-Alignment", "project_page": "", "abstract": "Ensuring precise multimodal alignment between diffusion-generated images and input prompts has been a long-standing challenge. Earlier works finetune diffusion weight using high-quality preference data, which tends to be limited and difficult to scale up. Recent editing-based methods further refine local regions of generated images but may compromise overall image quality. In this work, we propose Implicit Multimodal Guidance (IMG), a novel re-generation-based multimodal alignment framework that requires no extra data or editing operations. Specifically, given a generated image and its prompt, IMG a) utilizes a multimodal large language model (MLLM) to identify misalignments; b) introduces an Implicit Aligner that manipulates diffusion conditioning features to reduce misalignments and enable re-generation; and c) formulates the re-alignment goal into a trainable objective, namely Iteratively Updated Preference Objective. Extensive qualitative and quantitative evaluations on SDXL, SDXL-DPO, and FLUX show that IMG outperforms existing alignment methods. Furthermore, IMG acts as a flexible plug-and-play adapter, seamlessly enhancing prior finetuning-based alignment methods. Our code will be available at https://github.com/SHI-Labs/IMG-Multimodal-Diffusion-Alignment.", "github_manual": "", "github_regex": "https://github.com/SHI-Labs/IMG-Multimodal-Diffusion-Alignment", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26495", "title": "OffTopicEval: When Large Language Models Enter the Wrong Chat, Almost Always!", "authors": [ "Jingdi Lei", "Varun Gumma", "Rishabh Bhardwaj", "Seok Min Lim", "Chuan Li", "Amir Zadeh", "Soujanya Poria" ], "github": "https://github.com/declare-lab/OffTopicEval", "project_page": "", "abstract": "Large Language Model (LLM) safety is one of the most pressing challenges for enabling wide-scale deployment. While most studies and global discussions focus on generic harms, such as models assisting users in harming themselves or others, enterprises face a more fundamental concern: whether LLM-based agents are safe for their intended use case. To address this, we introduce operational safety, defined as an LLM's ability to appropriately accept or refuse user queries when tasked with a specific purpose. We further propose OffTopicEval, an evaluation suite and benchmark for measuring operational safety both in general and within specific agentic use cases. Our evaluations on six model families comprising 20 open-weight LLMs reveal that while performance varies across models, all of them remain highly operationally unsafe. Even the strongest models -- Qwen-3 (235B) with 77.77\\% and Mistral (24B) with 79.96\\% -- fall far short of reliable operational safety, while GPT models plateau in the 62--73\\% range, Phi achieves only mid-level scores (48--70\\%), and Gemma and Llama-3 collapse to 39.53\\% and 23.84\\%, respectively. While operational safety is a core model alignment issue, to suppress these failures, we propose prompt-based steering methods: query grounding (Q-ground) and system-prompt grounding (P-ground), which substantially improve OOD refusal. Q-ground provides consistent gains of up to 23\\%, while P-ground delivers even larger boosts, raising Llama-3.3 (70B) by 41\\% and Qwen-3 (30B) by 27\\%. These results highlight both the urgent need for operational safety interventions and the promise of prompt-based steering as a first step toward more reliable LLM-based agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/declare-lab/OffTopicEval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26329", "title": "TAU: A Benchmark for Cultural Sound Understanding Beyond Semantics", "authors": [ "Yi-Cheng Lin", "Yu-Hua Chen", "Jia-Kai Dong", "Yueh-Hsuan Huang", "Szu-Chi Chen", "Yu-Chen Chen", "Chih-Yao Chen", "Yu-Jung Lin", "Yu-Ling Chen", "Zih-Yu Chen", "I-Ning Tsai", "Hsiu-Hsuan Wang", "Ho-Lam Chung", "Ke-Han Lu", "Hung-yi Lee" ], "github": "", "project_page": "", "abstract": "Large audio-language models are advancing rapidly, yet most evaluations emphasize speech or globally sourced sounds, overlooking culturally distinctive cues. This gap raises a critical question: can current models generalize to localized, non-semantic audio that communities instantly recognize but outsiders do not? To address this, we present TAU (Taiwan Audio Understanding), a benchmark of everyday Taiwanese \"soundmarks.\" TAU is built through a pipeline combining curated sources, human editing, and LLM-assisted question generation, producing 702 clips and 1,794 multiple-choice items that cannot be solved by transcripts alone. Experiments show that state-of-the-art LALMs, including Gemini 2.5 and Qwen2-Audio, perform far below local humans. TAU demonstrates the need for localized benchmarks to reveal cultural blind spots, guide more equitable multimodal evaluation, and ensure models serve communities beyond the global mainstream.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.23773", "title": "Knowledge Homophily in Large Language Models", "authors": [ "Utkarsh Sahu", "Zhisheng Qi", "Mahantesh Halappanavar", "Nedim Lipka", "Ryan A. Rossi", "Franck Dernoncourt", "Yu Zhang", "Yao Ma", "Yu Wang" ], "github": "https://github.com/utkarshxsahu/kgc", "project_page": "", "abstract": "Large Language Models (LLMs) have been increasingly studied as neural knowledge bases for supporting knowledge-intensive applications such as question answering and fact checking. However, the structural organization of their knowledge remains unexplored. Inspired by cognitive neuroscience findings, such as semantic clustering and priming, where knowing one fact increases the likelihood of recalling related facts, we investigate an analogous knowledge homophily pattern in LLMs. To this end, we map LLM knowledge into a graph representation through knowledge checking at both the triplet and entity levels. After that, we analyze the knowledgeability relationship between an entity and its neighbors, discovering that LLMs tend to possess a similar level of knowledge about entities positioned closer in the graph. Motivated by this homophily principle, we propose a Graph Neural Network (GNN) regression model to estimate entity-level knowledgeability scores for triplets by leveraging their neighborhood scores. The predicted knowledgeability enables us to prioritize checking less well-known triplets, thereby maximizing knowledge coverage under the same labeling budget. This not only improves the efficiency of active labeling for fine-tuning to inject knowledge into LLMs but also enhances multi-hop path retrieval in reasoning-intensive question answering.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/utkarshxsahu/kgc", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25189", "title": "InfoAgent: Advancing Autonomous Information-Seeking Agents", "authors": [ "Gongrui Zhang", "Jialiang Zhu", "Ruiqi Yang", "Kai Qiu", "Miaosen Zhang", "Zhirong Wu", "Qi Dai", "Bei Liu", "Chong Luo", "Zhengyuan Yang", "Linjie Li", "Lijuan Wang", "Weizhu Chen", "Yuan Zhang", "Xin Li", "Zhaoyi Liu", "Xin Geng", "Baining Guo" ], "github": "https://github.com/MiroMindAI/MiroThinker", "project_page": "", "abstract": "Building Large Language Model agents that expand their capabilities by interacting with external tools represents a new frontier in AI research and applications. In this paper, we introduce InfoAgent, a deep research agent powered by an innovative data synthesis pipeline and orchestrated web search tools. To construct challenging, hard-to-find queries,we build entity trees and apply sub-tree sampling with entity fuzzification to systematically increase question difficulty. Unlike prior work that relies heavily on commercial search tools, we develop a dedicated self-hosted search infrastructure, enhancing transparency of agent environments and facilitating further advancement of agent capacity. We evaluate the effectiveness of our data pipeline by measuring the average number of tool calls required to correctly answer a question, and also show that our agent yields better performance when equipped with our tools. Our InfoAgent is post-trained from Qwen3-14B using a two-stage recipe: cold-start supervised finetuning to instill long-horizon search behaviors, followed by reinforcement learning which significantly improves reasoning-driven tool use. With our methods, InfoAgent achieves 15.3\\% accuracy on BrowseComp, 29.2\\% on BrowseComp-ZH, and 40.4\\% on Xbench-DS, outperforming prior open-source deep research agents such as WebSailor-72B and DeepDive-32B.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiroMindAI/MiroThinker", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.24207", "title": "Humanline: Online Alignment as Perceptual Loss", "authors": [ "Sijia Liu", "Niklas Muennighoff", "Kawin Ethayarajh" ], "github": "https://github.com/ContextualAI/HALOs/tree/research/", "project_page": "", "abstract": "Online alignment (e.g., GRPO) is generally more performant than offline alignment (e.g., DPO) -- but why? Drawing on prospect theory from behavioral economics, we propose a human-centric explanation. We prove that online on-policy sampling better approximates the human-perceived distribution of what the model can produce, and PPO/GRPO-style clipping -- originally introduced to just stabilize training -- recovers a perceptual bias in how humans perceive probability. In this sense, PPO/GRPO act as perceptual losses already. Our theory further suggests that the online/offline dichotomy is itself incidental to maximizing human utility, since we can achieve the same effect by selectively training on any data in a manner that mimics human perception, rather than restricting ourselves to online on-policy data. Doing so would allow us to post-train more quickly, cheaply, and flexibly without sacrificing performance. To this end, we propose a design pattern that explicitly incorporates perceptual distortions of probability into objectives like DPO/KTO/GRPO, creating humanline variants of them. Surprisingly, we find that these humanline variants, even when trained with offline off-policy data, can match the performance of their online counterparts on both verifiable and unverifiable tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ContextualAI/HALOs/tree/research/", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.23166", "title": "Test-Time Policy Adaptation for Enhanced Multi-Turn Interactions with LLMs", "authors": [ "Chenxing Wei", "Hong Wang", "Ying He", "Fei Yu", "Yao Shu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) employ multi-turn interaction as a fundamental paradigm for completing complex tasks. However, their performance often degrades in extended interactions, as they are typically trained on static, single-turn data, which hinders their ability to adapt to real-time user feedback. To address this limitation, we first propose a new paradigm: Test-Time Policy Adaptation for Multi-Turn Interactions (T2PAM), which utilizes user feedback from the ongoing interaction as a reward signal to estimate a latent optimal policy aligned with user preferences, then updates a small subset of parameters to steer the model toward this policy, ultimately enabling efficient in-conversation self-correction. We then introduce Optimum-Referenced One-Step Adaptation (ROSA), a lightweight algorithm that operationalizes T2PAM. ROSA guides the model parameters toward a theoretical optimal policy in a single, efficient update step, avoiding costly iterative gradient-based optimization and minimizing computational overhead. We provide a rigorous theoretical analysis guaranteeing that the policy of ROSA converges to the preference of user as the number of interactions increases. Extensive experiments on challenging benchmark demonstrate that ROSA achieves significant improvements in both task effectiveness and efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25848", "title": "More Thought, Less Accuracy? On the Dual Nature of Reasoning in Vision-Language Models", "authors": [ "Xinyu Tian", "Shu Zou", "Zhaoyuan Yang", "Mengqi He", "Fabian Waschkowski", "Lukas Wesemann", "Peter Tu", "Jing Zhang" ], "github": "", "project_page": "", "abstract": "Reasoning has emerged as a pivotal capability in Large Language Models (LLMs). Through Reinforcement Learning (RL), typically Group Relative Policy Optimization (GRPO), these models are able to solve complex tasks such as mathematics and code generation. Building on these advances, recent research has sought to extend reasoning to Vision-Language Models (VLMs), yielding promising results across diverse visual tasks. Despite this progress, our study uncovers the dual nature of multimodal reasoning: while it substantially enhances logical inference and facilitates performance on challenging problems, it may gradually impair perceptual grounding, leading to recognition failures on otherwise basic visual questions. Through further analysis, we attribute this phenomenon to visual forgetting, wherein prolonged reasoning causes the model to increasingly disregard visual input. To address this, we propose Vision-Anchored Policy Optimization (VAPO), a simple yet effective method that explicitly steers the reasoning process toward visually grounded trajectories. Our result model, VAPO-Thinker-7B, significantly strengthens the model's reliance on visual information and achieves new state-of-the-art results on a wide range of established benchmarks. Project page: https://xytian1008.github.io/VAPO/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26555", "title": "Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation", "authors": [ "Agneet Chatterjee", "Rahim Entezari", "Maksym Zhuravinskyi", "Maksim Lapin", "Reshinth Adithyan", "Amit Raj", "Chitta Baral", "Yezhou Yang", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "Recent advances in video generation have enabled high-fidelity video synthesis from user provided prompts. However, existing models and benchmarks fail to capture the complexity and requirements of professional video generation. Towards that goal, we introduce Stable Cinemetrics, a structured evaluation framework that formalizes filmmaking controls into four disentangled, hierarchical taxonomies: Setup, Event, Lighting, and Camera. Together, these taxonomies define 76 fine-grained control nodes grounded in industry practices. Using these taxonomies, we construct a benchmark of prompts aligned with professional use cases and develop an automated pipeline for prompt categorization and question generation, enabling independent evaluation of each control dimension. We conduct a large-scale human study spanning 10+ models and 20K videos, annotated by a pool of 80+ film professionals. Our analysis, both coarse and fine-grained reveal that even the strongest current models exhibit significant gaps, particularly in Events and Camera-related controls. To enable scalable evaluation, we train an automatic evaluator, a vision-language model aligned with expert annotations that outperforms existing zero-shot baselines. SCINE is the first approach to situate professional video generation within the landscape of video generative models, introducing taxonomies centered around cinematic controls and supporting them with structured evaluation pipelines and detailed analyses to guide future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.22646", "title": "Learning Human-Perceived Fakeness in AI-Generated Videos via Multimodal LLMs", "authors": [ "Xingyu Fu", "Siyi Liu", "Yinuo Xu", "Pan Lu", "Guangqiuse Hu", "Tianbo Yang", "Taran Anantasagar", "Christopher Shen", "Yikai Mao", "Yuanzhe Liu", "Keyush Shah", "Chung Un Lee", "Yejin Choi", "James Zou", "Dan Roth", "Chris Callison-Burch" ], "github": "", "project_page": "", "abstract": "Can humans identify AI-generated (fake) videos and provide grounded reasons? While video generation models have advanced rapidly, a critical dimension -- whether humans can detect deepfake traces within a generated video, i.e., spatiotemporal grounded visual artifacts that reveal a video as machine generated -- has been largely overlooked. We introduce DeeptraceReward, the first fine-grained, spatially- and temporally- aware benchmark that annotates human-perceived fake traces for video generation reward. The dataset comprises 4.3K detailed annotations across 3.3K high-quality generated videos. Each annotation provides a natural-language explanation, pinpoints a bounding-box region containing the perceived trace, and marks precise onset and offset timestamps. We consolidate these annotations into 9 major categories of deepfake traces that lead humans to identify a video as AI-generated, and train multimodal language models (LMs) as reward models to mimic human judgments and localizations. On DeeptraceReward, our 7B reward model outperforms GPT-5 by 34.7% on average across fake clue identification, grounding, and explanation. Interestingly, we observe a consistent difficulty gradient: binary fake v.s. real classification is substantially easier than fine-grained deepfake trace detection; within the latter, performance degrades from natural language explanations (easiest), to spatial grounding, to temporal labeling (hardest). By foregrounding human-perceived deepfake traces, DeeptraceReward provides a rigorous testbed and training signal for socially aware and trustworthy video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26618", "title": "DA^2: Depth Anything in Any Direction", "authors": [ "Haodong Li", "Wangguangdong Zheng", "Jing He", "Yuhao Liu", "Xin Lin", "Xin Yang", "Ying-Cong Chen", "Chunchao Guo" ], "github": "", "project_page": "", "abstract": "Panorama has a full FoV (360^circtimes180^circ), offering a more complete visual description than perspective images. Thanks to this characteristic, panoramic depth estimation is gaining increasing traction in 3D vision. However, due to the scarcity of panoramic data, previous methods are often restricted to in-domain settings, leading to poor zero-shot generalization. Furthermore, due to the spherical distortions inherent in panoramas, many approaches rely on perspective splitting (e.g., cubemaps), which leads to suboptimal efficiency. To address these challenges, we propose DA^{2}: Depth Anything in Any Direction, an accurate, zero-shot generalizable, and fully end-to-end panoramic depth estimator. Specifically, for scaling up panoramic data, we introduce a data curation engine for generating high-quality panoramic depth data from perspective, and create sim543K panoramic RGB-depth pairs, bringing the total to sim607K. To further mitigate the spherical distortions, we present SphereViT, which explicitly leverages spherical coordinates to enforce the spherical geometric consistency in panoramic image features, yielding improved performance. A comprehensive benchmark on multiple datasets clearly demonstrates DA^{2}'s SoTA performance, with an average 38% improvement on AbsRel over the strongest zero-shot baseline. Surprisingly, DA^{2} even outperforms prior in-domain methods, highlighting its superior zero-shot generalization. Moreover, as an end-to-end solution, DA^{2} exhibits much higher efficiency over fusion-based approaches. Both the code and the curated panoramic data will be released. Project page: https://depth-any-in-any-dir.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25134", "title": "LayerD: Decomposing Raster Graphic Designs into Layers", "authors": [ "Tomoyuki Suzuki", "Kang-Jun Liu", "Naoto Inoue", "Kota Yamaguchi" ], "github": "", "project_page": "", "abstract": "Designers craft and edit graphic designs in a layer representation, but layer-based editing becomes impossible once composited into a raster image. In this work, we propose LayerD, a method to decompose raster graphic designs into layers for re-editable creative workflow. LayerD addresses the decomposition task by iteratively extracting unoccluded foreground layers. We propose a simple yet effective refinement approach taking advantage of the assumption that layers often exhibit uniform appearance in graphic designs. As decomposition is ill-posed and the ground-truth layer structure may not be reliable, we develop a quality metric that addresses the difficulty. In experiments, we show that LayerD successfully achieves high-quality decomposition and outperforms baselines. We also demonstrate the use of LayerD with state-of-the-art image generators and layer-based editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24726", "title": "Socratic-Zero : Bootstrapping Reasoning via Data-Free Agent Co-evolution", "authors": [ "Shaobo Wang", "Zhengbo Jiao", "Zifan Zhang", "Yilang Peng", "Xu Ze", "Boyu Yang", "Wei Wang", "Hu Wei", "Linfeng Zhang" ], "github": "", "project_page": "", "abstract": "Recent breakthroughs in large language models (LLMs) on reasoning tasks rely heavily on massive, high-quality datasets-typically human-annotated and thus difficult to scale. While data synthesis or distillation offers a promising alternative, existing methods struggle with inconsistent data quality and an inability to dynamically adapt to the evolving capabilities of the model, leading to suboptimal training signals. To address these limitations, we introduce Socratic-Zero, a fully autonomous framework that generates high-quality training data from minimal seed examples through the co-evolution of three agents: the Teacher, the Solver, and the Generator. The Solver continuously refines its reasoning by learning from preference feedback on both successful and failed trajectories; the Teacher adaptively crafts increasingly challenging questions based on the Solver's weaknesses; and the Generator distills the Teacher's question-design strategy to enable scalable, high-fidelity curriculum generation. This closed-loop system produces a self-improving curriculum-requiring no pre-existing tasks or labels. Remarkably, starting from only 100 seed questions, our Socratic-Solver-8B achieves an average gain of +20.2 percentage points over prior data synthesis methods across seven mathematical reasoning benchmarks (AMC23, AIME24-25, Olympiad, MATH-500, Minerva, and GSM8K), with consistent gains on both Qwen3 and GLM4 series models. Even more surprisingly, synthetic data from Socratic-Generator-32B enables student LLMs to achieve superior performance compared to other state-of-the-art (SOTA) commercial LLMs on these benchmarks, including Qwen3-235B-A22B, DeepSeek-V3.1-671B, GPT-5, Gemini-2.5-Pro, Grok-4, and Claude-4.1-Opus.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25911", "title": "Mem-\u03b1: Learning Memory Construction via Reinforcement Learning", "authors": [ "Yu Wang", "Ryuichi Takanobu", "Zhiqi Liang", "Yuzhen Mao", "Yuanzhe Hu", "Julian McAuley", "Xiaojian Wu" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) agents are constrained by limited context windows, necessitating external memory systems for long-term information understanding. Current memory-augmented agents typically depend on pre-defined instructions and tools for memory updates. However, language models may lack the ability to determine which information to store, how to structure it, and when to update it, especially as memory systems become more complex. This results in suboptimal memory construction and information loss. To this end, we propose Mem-alpha, a reinforcement learning framework that trains agents to effectively manage complex memory systems through interaction and feedback. We also construct a specialized training dataset spanning diverse multi-turn interaction patterns paired with comprehensive evaluation questions designed to teach effective memory management. During training, agents process sequential information chunks, learn to extract and store relevant content, then update the memory system. The reward signal derives from downstream question-answering accuracy over the full interaction history, directly optimizing for memory construction. To illustrate the effectiveness of our training framework, we design a memory architecture comprising core, episodic, and semantic components, equipped with multiple tools for memory operations. Empirical evaluation demonstrates that Mem-alpha achieves significant improvements over existing memory-augmented agent baselines. Despite being trained exclusively on instances with a maximum length of 30k tokens, our agents exhibit remarkable generalization to sequences exceeding 400k tokens, over 13x the training length, highlighting the robustness of Mem-alpha.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25082", "title": "MANI-Pure: Magnitude-Adaptive Noise Injection for Adversarial Purification", "authors": [ "Xiaoyi Huang", "Junwei Wu", "Kejia Zhang", "Carl Yang", "Zhiming Luo" ], "github": "https://github.com/phoshowy/MANI-Pure", "project_page": "", "abstract": "Adversarial purification with diffusion models has emerged as a promising defense strategy, but existing methods typically rely on uniform noise injection, which indiscriminately perturbs all frequencies, corrupting semantic structures and undermining robustness. Our empirical study reveals that adversarial perturbations are not uniformly distributed: they are predominantly concentrated in high-frequency regions, with heterogeneous magnitude intensity patterns that vary across frequencies and attack types. Motivated by this observation, we introduce MANI-Pure, a magnitude-adaptive purification framework that leverages the magnitude spectrum of inputs to guide the purification process. Instead of injecting homogeneous noise, MANI-Pure adaptively applies heterogeneous, frequency-targeted noise, effectively suppressing adversarial perturbations in fragile high-frequency, low-magnitude bands while preserving semantically critical low-frequency content. Extensive experiments on CIFAR-10 and ImageNet-1K validate the effectiveness of MANI-Pure. It narrows the clean accuracy gap to within 0.59 of the original classifier, while boosting robust accuracy by 2.15, and achieves the top-1 robust accuracy on the RobustBench leaderboard, surpassing the previous state-of-the-art method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/phoshowy/MANI-Pure", "github_api_added_by": "auto" }, { "date": "2025-10-01", "arxiv_id": "2509.26603", "title": "DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively", "authors": [ "Yixuan Weng", "Minjun Zhu", "Qiujie Xie", "Qiyao Sun", "Zhen Lin", "Sifan Liu", "Yue Zhang" ], "github": "https://github.com/ResearAI/DeepScientist", "project_page": "", "abstract": "While previous AI Scientist systems can generate novel findings, they often lack the focus to produce scientifically valuable contributions that address pressing human-defined challenges. We introduce DeepScientist, a system designed to overcome this by conducting goal-oriented, fully autonomous scientific discovery over month-long timelines. It formalizes discovery as a Bayesian Optimization problem, operationalized through a hierarchical evaluation process consisting of \"hypothesize, verify, and analyze\". Leveraging a cumulative Findings Memory, this loop intelligently balances the exploration of novel hypotheses with exploitation, selectively promoting the most promising findings to higher-fidelity levels of validation. Consuming over 20,000 GPU hours, the system generated about 5,000 unique scientific ideas and experimentally validated approximately 1100 of them, ultimately surpassing human-designed state-of-the-art (SOTA) methods on three frontier AI tasks by 183.7\\%, 1.9\\%, and 7.9\\%. This work provides the first large-scale evidence of an AI achieving discoveries that progressively surpass human SOTA on scientific tasks, producing valuable findings that genuinely push the frontier of scientific discovery. To facilitate further research into this process, we will open-source all experimental logs and system code at https://github.com/ResearAI/DeepScientist/.", "github_manual": "", "github_regex": "https://github.com/ResearAI/DeepScientist", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25397", "title": "A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects", "authors": [ "Johan Lin\u00e5ker", "Cailean Osborne", "Jennifer Ding", "Ben Burtenshaw" ], "github": "", "project_page": "", "abstract": "The proliferation of open large language models (LLMs) is fostering a vibrant ecosystem of research and innovation in artificial intelligence (AI). However, the methods of collaboration used to develop open LLMs both before and after their public release have not yet been comprehensively studied, limiting our understanding of how open LLM projects are initiated, organized, and governed as well as what opportunities there are to foster this ecosystem even further. We address this gap through an exploratory analysis of open collaboration throughout the development and reuse lifecycle of open LLMs, drawing on semi-structured interviews with the developers of 14 open LLMs from grassroots projects, research institutes, startups, and Big Tech companies in North America, Europe, Africa, and Asia. We make three key contributions to research and practice. First, collaboration in open LLM projects extends far beyond the LLMs themselves, encompassing datasets, benchmarks, open source frameworks, leaderboards, knowledge sharing and discussion forums, and compute partnerships, among others. Second, open LLM developers have a variety of social, economic, and technological motivations, from democratizing AI access and promoting open science to building regional ecosystems and expanding language representation. Third, the sampled open LLM projects exhibit five distinct organizational models, ranging from single company projects to non-profit-sponsored grassroots projects, which vary in their centralization of control and community engagement strategies used throughout the open LLM lifecycle. We conclude with practical recommendations for stakeholders seeking to support the global community building a more open future for AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26645", "title": "TTT3R: 3D Reconstruction as Test-Time Training", "authors": [ "Xingyu Chen", "Yue Chen", "Yuliang Xiu", "Andreas Geiger", "Anpei Chen" ], "github": "", "project_page": "", "abstract": "Modern Recurrent Neural Networks have become a competitive architecture for 3D reconstruction due to their linear-time complexity. However, their performance degrades significantly when applied beyond the training context length, revealing limited length generalization. In this work, we revisit the 3D reconstruction foundation models from a Test-Time Training perspective, framing their designs as an online learning problem. Building on this perspective, we leverage the alignment confidence between the memory state and incoming observations to derive a closed-form learning rate for memory updates, to balance between retaining historical information and adapting to new observations. This training-free intervention, termed TTT3R, substantially improves length generalization, achieving a 2times improvement in global pose estimation over baselines, while operating at 20 FPS with just 6 GB of GPU memory to process thousands of images. Code available in https://rover-xingyu.github.io/TTT3R", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.24732", "title": "Who invented deep residual learning?", "authors": [ "Juergen Schmidhuber" ], "github": "", "project_page": "", "abstract": "Modern AI is based on deep artificial neural networks (NNs). As of 2025, the most cited scientific article of the 21st century is an NN paper on deep residual learning with residual connections. Who invented this? We present a timeline of the evolution of deep residual learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26278", "title": "ProfVLM: A Lightweight Video-Language Model for Multi-View Proficiency Estimation", "authors": [ "Edoardo Bianchi", "Jacopo Staiano", "Antonio Liotta" ], "github": "https://github.com/facebookresearch/TimeSformer", "project_page": "", "abstract": "Existing approaches to skill proficiency estimation often rely on black-box video classifiers, ignoring multi-view context and lacking explainability. We present ProfVLM, a compact vision-language model that reformulates this task as generative reasoning: it jointly predicts skill level and generates expert-like feedback from egocentric and exocentric videos. Central to our method is an AttentiveGatedProjector that dynamically fuses multi-view features, projected from a frozen TimeSformer backbone into a language model tuned for feedback generation. Trained on EgoExo4D with expert commentaries, ProfVLM surpasses state-of-the-art methods while using up to 20x fewer parameters and reducing training time by up to 60%. Our approach not only achieves superior accuracy across diverse activities, but also outputs natural language critiques aligned with performance, offering transparent reasoning. These results highlight generative vision-language modeling as a powerful new direction for skill assessment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/TimeSformer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.23873", "title": "Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning", "authors": [ "Shaobo Wang", "Jiaming Wang", "Jiajun Zhang", "Cong Wang", "Yue Min", "Zichen Wen", "Fei Huang", "Huiqiang Jiang", "Junyang Lin", "Dayiheng Liu", "Linfeng Zhang" ], "github": "", "project_page": "", "abstract": "As supervised fine-tuning (SFT) evolves from a lightweight post-training step into a compute-intensive phase rivaling mid-training in scale, data efficiency has become critical for aligning large language models (LLMs) under tight budgets. Existing data pruning methods suffer from a fragmented design: they operate either at the sample level or the token level in isolation, failing to jointly optimize both dimensions. This disconnect leads to significant inefficiencies--high-value samples may still contain redundant tokens, while token-level pruning often discards crucial instructional or corrective signals embedded in individual examples. To address this bottleneck, we introduce the Error-Uncertainty (EU) Plane, a diagnostic framework that jointly characterizes the heterogeneous utility of training data across samples and tokens. Guided by this insight, we propose Quadrant-based Tuning (Q-Tuning), a unified framework that strategically coordinates sample pruning and token pruning. Q-Tuning employs a two-stage strategy: first, it performs sample-level triage to retain examples rich in informative misconceptions or calibration signals; second, it applies an asymmetric token-pruning policy, using a context-aware scoring mechanism to trim less salient tokens exclusively from misconception samples while preserving calibration samples in their entirety. Our method sets a new state of the art across five diverse benchmarks. Remarkably, on SmolLM2-1.7B, Q-Tuning achieves a +38\\% average improvement over the full-data SFT baseline using only 12.5\\% of the original training data. As the first dynamic pruning approach to consistently outperform full-data training, Q-Tuning provides a practical and scalable blueprint for maximizing data utilization in budget-constrained LLM SFT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25248", "title": "BuildBench: Benchmarking LLM Agents on Compiling Real-World Open-Source Software", "authors": [ "Zehua Zhang", "Ati Priya Bajaj", "Divij Handa", "Siyu Liu", "Arvind S Raj", "Hongkai Chen", "Hulin Wang", "Yibo Liu", "Zion Leonahenahe Basque", "Souradip Nath", "Vishal Juneja", "Nikhil Chapre", "Yan Shoshitaishvili", "Adam Doup\u00e9", "Chitta Baral", "Ruoyu Wang" ], "github": "", "project_page": "", "abstract": "Automatically compiling open-source software (OSS) projects is a vital, labor-intensive, and complex task, which makes it a good challenge for LLM Agents. Existing methods rely on manually curated rules and workflows, which cannot adapt to OSS that requires customized configuration or environment setup. Recent attempts using Large Language Models (LLMs) used selective evaluation on a subset of highly rated OSS, a practice that underestimates the realistic challenges of OSS compilation. In practice, compilation instructions are often absent, dependencies are undocumented, and successful builds may even require patching source files or modifying build scripts. We propose a more challenging and realistic benchmark, BUILD-BENCH, comprising OSS that are more diverse in quality, scale, and characteristics. Furthermore, we propose a strong baseline LLM-based agent, OSS-BUILD-AGENT, an effective system with enhanced build instruction retrieval module that achieves state-of-the-art performance on BUILD-BENCH and is adaptable to heterogeneous OSS characteristics. We also provide detailed analysis regarding different compilation method design choices and their influence to the whole task, offering insights to guide future advances. We believe performance on BUILD-BENCH can faithfully reflect an agent's ability to tackle compilation as a complex software engineering tasks, and, as such, our benchmark will spur innovation with a significant impact on downstream applications in the fields of software development and software security.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25085", "title": "jina-reranker-v3: Last but Not Late Interaction for Document Reranking", "authors": [ "Feng Wang", "Yuqing Li", "Han Xiao" ], "github": "", "project_page": "", "abstract": "jina-reranker-v3 is a 0.6B parameter multilingual document reranker that introduces a novel last but not late interaction. Unlike late interaction models such as ColBERT that perform separate encoding followed by multi-vector matching, our approach conducts causal self-attention between query and documents within the same context window, enabling rich cross-document interactions before extracting contextual embeddings from the last token of each document. This compact architecture achieves state-of-the-art BEIR performance with 61.94 nDCG@10 while being ten times smaller than generative listwise rerankers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26507", "title": "The Dragon Hatchling: The Missing Link between the Transformer and Models of the Brain", "authors": [ "Adrian Kosowski", "Przemys\u0142aw Uzna\u0144ski", "Jan Chorowski", "Zuzanna Stamirowska", "Micha\u0142 Bartoszkiewicz" ], "github": "", "project_page": "", "abstract": "The relationship between computing systems and the brain has served as motivation for pioneering theoreticians since John von Neumann and Alan Turing. Uniform, scale-free biological networks, such as the brain, have powerful properties, including generalizing over time, which is the main barrier for Machine Learning on the path to Universal Reasoning Models. We introduce `Dragon Hatchling' (BDH), a new Large Language Model architecture based on a scale-free biologically inspired network of \\n locally-interacting neuron particles. BDH couples strong theoretical foundations and inherent interpretability without sacrificing Transformer-like performance. BDH is a practical, performant state-of-the-art attention-based state space sequence learning architecture. In addition to being a graph model, BDH admits a GPU-friendly formulation. It exhibits Transformer-like scaling laws: empirically BDH rivals GPT2 performance on language and translation tasks, at the same number of parameters (10M to 1B), for the same training data. BDH can be represented as a brain model. The working memory of BDH during inference entirely relies on synaptic plasticity with Hebbian learning using spiking neurons. We confirm empirically that specific, individual synapses strengthen connection whenever BDH hears or reasons about a specific concept while processing language inputs. The neuron interaction network of BDH is a graph of high modularity with heavy-tailed degree distribution. The BDH model is biologically plausible, explaining one possible mechanism which human neurons could use to achieve speech. BDH is designed for interpretability. Activation vectors of BDH are sparse and positive. We demonstrate monosemanticity in BDH on language tasks. Interpretability of state, which goes beyond interpretability of neurons and model parameters, is an inherent feature of the BDH architecture.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26157", "title": "EntroPE: Entropy-Guided Dynamic Patch Encoder for Time Series Forecasting", "authors": [ "Sachith Abeywickrama", "Emadeldeen Eldele", "Min Wu", "Xiaoli Li", "Chau Yuen" ], "github": "https://github.com/Sachithx/EntroPE", "project_page": "", "abstract": "Transformer-based models have significantly advanced time series forecasting, with patch-based input strategies offering efficiency and improved long-horizon modeling. Yet, existing approaches rely on temporally-agnostic patch construction, where arbitrary starting positions and fixed lengths fracture temporal coherence by splitting natural transitions across boundaries. This naive segmentation often disrupts short-term dependencies and weakens representation learning. In response, we propose EntroPE (Entropy-Guided Dynamic Patch Encoder), a novel, temporally informed framework that dynamically detects transition points via conditional entropy and dynamically places patch boundaries. This preserves temporal structure while retaining the computational benefits of patching. EntroPE consists of two key modules, namely an Entropy-based Dynamic Patcher (EDP) that applies information-theoretic criteria to locate natural temporal shifts and determine patch boundaries, and an Adaptive Patch Encoder (APE) that employs pooling and cross-attention to capture intra-patch dependencies and produce fixed-size latent representations. These embeddings are then processed by a global transformer to model inter-patch dynamics. Experiments across long-term forecasting benchmarks demonstrate that EntroPE improves both accuracy and efficiency, establishing entropy-guided dynamic patching as a promising new paradigm for time series modeling. Code is available at: https://github.com/Sachithx/EntroPE.", "github_manual": "", "github_regex": "https://github.com/Sachithx/EntroPE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.23094", "title": "d^2Cache: Accelerating Diffusion-Based LLMs via Dual Adaptive Caching", "authors": [ "Yuchu Jiang", "Yue Cai", "Xiangzhong Luo", "Jiale Fu", "Jiarui Wang", "Chonghan Liu", "Xu Yang" ], "github": "https://github.com/Kamichanw/d2Cache", "project_page": "", "abstract": "Diffusion-based large language models (dLLMs), despite their promising performance, still suffer from inferior inference efficiency. This is because dLLMs rely on bidirectional attention and cannot directly benefit from the standard key-value (KV) cache as autoregressive models (ARMs) do. To tackle this issue, we introduce Dual aDaptive Cache (d^2Cache), which is a training-free approximate KV cache framework for accelerating dLLM inference. d^2Cache features a two-stage fine-grained selection strategy to identify tokens and adaptively update their KV states at each decoding step, while caching the KV states of the remaining tokens for reuse. Furthermore, d^2Cache naturally offers a more reliable decoding alternative, which can enable quasi left-to-right generation and mitigate premature overconfidence in tokens at the end of the sequence. Extensive experimental results on two representative dLLMs (\\ie, LLaDA and Dream) demonstrate that d^2Cache not only achieves substantial inference speedups, but also yields consistent improvements in generation quality. The code is available at https://github.com/Kamichanw/d2Cache.", "github_manual": "", "github_regex": "https://github.com/Kamichanw/d2Cache", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.24510", "title": "Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models", "authors": [ "Jonas H\u00fcbotter", "Patrik Wolf", "Alexander Shevchenko", "Dennis J\u00fcni", "Andreas Krause", "Gil Kur" ], "github": "", "project_page": "", "abstract": "Recent empirical studies have explored the idea of continuing to train a model at test-time for a given task, known as test-time training (TTT), and have found it to yield significant performance improvements. However, there is limited understanding of why and when TTT is effective. Earlier explanations mostly focused on the observation that TTT may help when applied to out-of-distribution adaptation or used with privileged data. However, the growing scale of foundation models with most test data being in-distribution questions these explanations. We instead posit that foundation models remain globally underparameterized, with TTT providing a mechanism for specialization after generalization, focusing capacity on concepts relevant to the test task. Specifically, under the linear representation hypothesis, we propose a model in which TTT achieves a substantially smaller in-distribution test error than global training. We empirically validate our model's key assumptions by training a sparse autoencoder on ImageNet, showing that semantically related data points are explained by only a few shared concepts. Finally, we perform scaling studies across image and language tasks that confirm the practical implications of our model, identifying the regimes where specialization is most effective.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.23695", "title": "Estimating Time Series Foundation Model Transferability via In-Context Learning", "authors": [ "Qingren Yao", "Ming Jin", "Chengqi Zhang", "Chao-Han Huck Yang", "Jun Qi", "Shirui Pan" ], "github": "", "project_page": "", "abstract": "Time series foundation models (TSFMs) offer strong zero-shot forecasting via large-scale pre-training, yet fine-tuning remains critical for boosting performance in domains with limited public data. With the growing number of TSFMs, efficiently identifying the best model for downstream fine-tuning becomes increasingly challenging. In this work, we introduce TimeTic, a transferability estimation framework that recasts model selection as an in-context-learning problem: given observations on known (source) datasets, it predicts how a TSFM will perform after fine-tuning on a downstream (target) dataset. TimeTic flexibly organizes the observed model-data relationships as contextual information, allowing it to adapt seamlessly to various test-time scenarios. Leveraging the natural tabular structure formed by dataset meta-features, model characteristics, and fine-tuned performance, we employ tabular foundation models to serve as in-context learners. We further introduce a novel model characterization based on entropy evolution across model layers, capturing embedding-space distinctions and enabling TimeTic to generalize across arbitrary model sets. We establish a comprehensive benchmark for transferability estimation including 10 datasets, 10 foundation models, and 3 forecasting tasks. On this benchmark, TimeTic's estimation demonstrates strong alignment with actual fine-tuned performance for previously unseen datasets, achieving a mean rank correlation of approximately 0.6 and a 30% improvement compared to using zero-shot performance as the transferability score.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.21361", "title": "Context Is What You Need: The Maximum Effective Context Window for Real World Limits of LLMs", "authors": [ "Norman Paulsen" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) providers boast big numbers for maximum context window sizes. To test the real world use of context windows, we 1) define a concept of maximum effective context window, 2) formulate a testing method of a context window's effectiveness over various sizes and problem types, and 3) create a standardized way to compare model efficacy for increasingly larger context window sizes to find the point of failure. We collected hundreds of thousands of data points across several models and found significant differences between reported Maximum Context Window (MCW) size and Maximum Effective Context Window (MECW) size. Our findings show that the MECW is, not only, drastically different from the MCW but also shifts based on the problem type. A few top of the line models in our test group failed with as little as 100 tokens in context; most had severe degradation in accuracy by 1000 tokens in context. All models fell far short of their Maximum Context Window by as much as 99 percent. Our data reveals the Maximum Effective Context Window shifts based on the type of problem provided, offering clear and actionable insights into how to improve model accuracy and decrease model hallucination rates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26226", "title": "Thinking-Free Policy Initialization Makes Distilled Reasoning Models More Effective and Efficient Reasoners", "authors": [ "Xin Xu", "Cliveb AI", "Kai Yang", "Tianhao Chen", "Yang Wang", "Saiyong Yang", "Can Yang" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Reward (RLVR) effectively solves complex tasks but demands extremely long context lengths during training, leading to substantial computational costs. While multi-stage training can partially mitigate this, starting with overly short contexts often causes irreversible performance degradation, ultimately failing to reduce overall training compute significantly. In this paper, we introduce **T**hinking-**F**ree **P**olicy **I**nitialization (**TFPI**), a simple yet effective adaptation to RLVR that bridges long Chain-of-Thought (CoT) distillation and standard RLVR. TFPI employs a simple *ThinkFree* operation, explicitly discarding the thinking content via a direct ** append, to reduce token usage during inference. Training with *ThinkFree*-adapted inputs improves performance and lowers token consumption, even in the original slow-thinking mode. Extensive experiments across various benchmarks have shown that TFPI accelerates RL convergence, achieves a higher performance ceiling, and yields more token-efficient reasoning models without specialized rewards or complex training designs. With TFPI only, we train a 4B model to reach 89.0% accuracy on AIME24 and 65.5% on LiveCodeBench using less than 4K H20 hours.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26604", "title": "Video Object Segmentation-Aware Audio Generation", "authors": [ "Ilpo Viertola", "Vladimir Iashin", "Esa Rahtu" ], "github": "", "project_page": "", "abstract": "Existing multimodal audio generation models often lack precise user control, which limits their applicability in professional Foley workflows. In particular, these models focus on the entire video and do not provide precise methods for prioritizing a specific object within a scene, generating unnecessary background sounds, or focusing on the wrong objects. To address this gap, we introduce the novel task of video object segmentation-aware audio generation, which explicitly conditions sound synthesis on object-level segmentation maps. We present SAGANet, a new multimodal generative model that enables controllable audio generation by leveraging visual segmentation masks along with video and textual cues. Our model provides users with fine-grained and visually localized control over audio generation. To support this task and further research on segmentation-aware Foley, we propose Segmented Music Solos, a benchmark dataset of musical instrument performance videos with segmentation information. Our method demonstrates substantial improvements over current state-of-the-art methods and sets a new standard for controllable, high-fidelity Foley synthesis. Code, samples, and Segmented Music Solos are available at https://saganet.notion.site", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.22889", "title": "Convolutional Set Transformer", "authors": [ "Federico Chinello", "Giacomo Boracchi" ], "github": "https://github.com/chinefed/convolutional-set-transformer", "project_page": "", "abstract": "We introduce the Convolutional Set Transformer (CST), a novel neural architecture designed to process image sets of arbitrary cardinality that are visually heterogeneous yet share high-level semantics - such as a common category, scene, or concept. Existing set-input networks, e.g., Deep Sets and Set Transformer, are limited to vector inputs and cannot directly handle 3D image tensors. As a result, they must be cascaded with a feature extractor, typically a CNN, which encodes images into embeddings before the set-input network can model inter-image relationships. In contrast, CST operates directly on 3D image tensors, performing feature extraction and contextual modeling simultaneously, thereby enabling synergies between the two processes. This design yields superior performance in tasks such as Set Classification and Set Anomaly Detection and further provides native compatibility with CNN explainability methods such as Grad-CAM, unlike competing approaches that remain opaque. Finally, we show that CSTs can be pre-trained on large-scale datasets and subsequently adapted to new domains and tasks through standard Transfer Learning schemes. To support further research, we release CST-15, a CST backbone pre-trained on ImageNet (https://github.com/chinefed/convolutional-set-transformer).", "github_manual": "", "github_regex": "https://github.com/chinefed/convolutional-set-transformer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.18538", "title": "GeoRemover: Removing Objects and Their Causal Visual Artifacts", "authors": [ "Zixin Zhu", "Haoxiang Li", "Xuelu Feng", "He Wu", "Chunming Qiao", "Junsong Yuan" ], "github": "https://github.com/buxiangzhiren/GeoRemover", "project_page": "", "abstract": "Towards intelligent image editing, object removal should eliminate both the target object and its causal visual artifacts, such as shadows and reflections. However, existing image appearance-based methods either follow strictly mask-aligned training and fail to remove these causal effects which are not explicitly masked, or adopt loosely mask-aligned strategies that lack controllability and may unintentionally over-erase other objects. We identify that these limitations stem from ignoring the causal relationship between an object's geometry presence and its visual effects. To address this limitation, we propose a geometry-aware two-stage framework that decouples object removal into (1) geometry removal and (2) appearance rendering. In the first stage, we remove the object directly from the geometry (e.g., depth) using strictly mask-aligned supervision, enabling structure-aware editing with strong geometric constraints. In the second stage, we render a photorealistic RGB image conditioned on the updated geometry, where causal visual effects are considered implicitly as a result of the modified 3D geometry. To guide learning in the geometry removal stage, we introduce a preference-driven objective based on positive and negative sample pairs, encouraging the model to remove objects as well as their causal visual artifacts while avoiding new structural insertions. Extensive experiments demonstrate that our method achieves state-of-the-art performance in removing both objects and their associated artifacts on two popular benchmarks. The code is available at https://github.com/buxiangzhiren/GeoRemover.", "github_manual": "", "github_regex": "https://github.com/buxiangzhiren/GeoRemover", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25180", "title": "DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space", "authors": [ "Wenkun He", "Yuchao Gu", "Junyu Chen", "Dongyun Zou", "Yujun Lin", "Zhekai Zhang", "Haocheng Xi", "Muyang Li", "Ligeng Zhu", "Jincheng Yu", "Junsong Chen", "Enze Xie", "Song Han", "Han Cai" ], "github": "https://github.com/dc-ai-projects/DC-Gen", "project_page": "", "abstract": "Existing text-to-image diffusion models excel at generating high-quality images, but face significant efficiency challenges when scaled to high resolutions, like 4K image generation. While previous research accelerates diffusion models in various aspects, it seldom handles the inherent redundancy within the latent space. To bridge this gap, this paper introduces DC-Gen, a general framework that accelerates text-to-image diffusion models by leveraging a deeply compressed latent space. Rather than a costly training-from-scratch approach, DC-Gen uses an efficient post-training pipeline to preserve the quality of the base model. A key challenge in this paradigm is the representation gap between the base model's latent space and a deeply compressed latent space, which can lead to instability during direct fine-tuning. To overcome this, DC-Gen first bridges the representation gap with a lightweight embedding alignment training. Once the latent embeddings are aligned, only a small amount of LoRA fine-tuning is needed to unlock the base model's inherent generation quality. We verify DC-Gen's effectiveness on SANA and FLUX.1-Krea. The resulting DC-Gen-SANA and DC-Gen-FLUX models achieve quality comparable to their base models but with a significant speedup. Specifically, DC-Gen-FLUX reduces the latency of 4K image generation by 53x on the NVIDIA H100 GPU. When combined with NVFP4 SVDQuant, DC-Gen-FLUX generates a 4K image in just 3.5 seconds on a single NVIDIA 5090 GPU, achieving a total latency reduction of 138x compared to the base FLUX.1-Krea model. Code: https://github.com/dc-ai-projects/DC-Gen.", "github_manual": "", "github_regex": "https://github.com/dc-ai-projects/DC-Gen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.26030", "title": "Muon Outperforms Adam in Tail-End Associative Memory Learning", "authors": [ "Shuche Wang", "Fengzhuo Zhang", "Jiaxiang Li", "Cunxiao Du", "Chao Du", "Tianyu Pang", "Zhuoran Yang", "Mingyi Hong", "Vincent Y. F. Tan" ], "github": "", "project_page": "", "abstract": "The Muon optimizer is consistently faster than Adam in training Large Language Models (LLMs), yet the mechanism underlying its success remains unclear. This paper demystifies this mechanism through the lens of associative memory. By ablating the transformer components optimized by Muon, we reveal that the associative memory parameters of LLMs, namely the Value and Output (VO) attention weights and Feed-Forward Networks (FFNs), are the primary contributors to Muon's superiority. Motivated by this associative memory view, we then explain Muon's superiority on real-world corpora, which are intrinsically heavy-tailed: a few classes (tail classes) appear far less frequently than others. The superiority is explained through two key properties: (i) its update rule consistently yields a more isotropic singular spectrum than Adam; and as a result, (ii) on heavy-tailed data, it optimizes tail classes more effectively than Adam. Beyond empirical evidence, we theoretically confirm these findings by analyzing a one-layer associative memory model under class-imbalanced data. We prove that Muon consistently achieves balanced learning across classes regardless of feature embeddings, whereas Adam can induce large disparities in learning errors depending on embedding properties. In summary, our empirical observations and theoretical analyses reveal Muon's core advantage: its update rule aligns with the outer-product structure of linear associative memories, enabling more balanced and effective learning of tail classes in heavy-tailed distributions than Adam.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25666", "title": "Nudging the Boundaries of LLM Reasoning", "authors": [ "Justin Chih-Yao Chen", "Becky Xiangyu Peng", "Prafulla Kumar Choubey", "Kung-Hsiang Huang", "Jiaxin Zhang", "Mohit Bansal", "Chien-Sheng Wu" ], "github": "", "project_page": "", "abstract": "Current online reinforcement learning (RL) algorithms like GRPO share a key limitation in LLM reasoning: they cannot learn from problems that are \"unsolvable\" to the model. In other words, they can only improve performance on problems where the model is capable of exploring the correct answer. Consequently, the model's \"upper limit\" remains unchanged after RL training, even though the likelihood of solving easier, solvable problems may increase. These hard samples cannot contribute to training, as no rollouts yield rewards and thus no gradients are produced. To unlock learning from these hard samples, we propose NuRL, a \"nudging\" method that aims to push the upper bound of LLM reasoning using self-generated hints, i.e., abstract cues that help reduce the problem difficulty for the model. Given a question and its gold answer, the model generates a CoT and then produces a hint containing the core knowledge needed to solve the problem. During training, we generate G rollouts from the base policy and use the pass rate to decide whether the hint should be injected. For hard samples with a 0% pass rate, we inject the hint and regenerate a new batch of trajectories. This yields two benefits: (1) the hint boosts pass rates (from 0% to non-zero), thereby introducing training signals for previously unsolvable samples, and (2) the hints are self-generated, avoiding distributional shift and do not rely on external models. NuRL achieves consistent improvements across 6 benchmarks and 3 models, while remaining complementary to test-time scaling. Notably, NuRL can raise the model's upper limit, whereas GRPO leaves pass@1024 unchanged from the base model. Furthermore, we present a systematic study of what makes an effective hint and when hints are most useful. Interestingly, the best hints are abstract and high-level, and are most beneficial when applied necessarily and after GRPO has converged.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25716", "title": "DeepCodeSeek: Real-Time API Retrieval for Context-Aware Code Generation", "authors": [ "Esakkivel Esakkiraja", "Denis Akhiyarov", "Aditya Shanmugham", "Chitra Ganapathy" ], "github": "", "project_page": "", "abstract": "Current search techniques are limited to standard RAG query-document applications. In this paper, we propose a novel technique to expand the code and index for predicting the required APIs, directly enabling high-quality, end-to-end code generation for auto-completion and agentic AI applications. We address the problem of API leaks in current code-to-code benchmark datasets by introducing a new dataset built from real-world ServiceNow Script Includes that capture the challenge of unclear API usage intent in the code. Our evaluation metrics show that this method achieves 87.86% top-40 retrieval accuracy, allowing the critical context with APIs needed for successful downstream code generation. To enable real-time predictions, we develop a comprehensive post-training pipeline that optimizes a compact 0.6B reranker through synthetic dataset generation, supervised fine-tuning, and reinforcement learning. This approach enables our compact reranker to outperform a much larger 8B model while maintaining 2.5x reduced latency, effectively addressing the nuances of enterprise-specific code without the computational overhead of larger models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.23019", "title": "LLM Watermark Evasion via Bias Inversion", "authors": [ "Jeongyeon Hwang", "Sangdon Park", "Jungseul Ok" ], "github": "", "project_page": "", "abstract": "Watermarking for large language models (LLMs) embeds a statistical signal during generation to enable detection of model-produced text. While watermarking has proven effective in benign settings, its robustness under adversarial evasion remains contested. To advance a rigorous understanding and evaluation of such vulnerabilities, we propose the Bias-Inversion Rewriting Attack (BIRA), which is theoretically motivated and model-agnostic. BIRA weakens the watermark signal by suppressing the logits of likely watermarked tokens during LLM-based rewriting, without any knowledge of the underlying watermarking scheme. Across recent watermarking methods, BIRA achieves over 99\\% evasion while preserving the semantic content of the original text. Beyond demonstrating an attack, our results reveal a systematic vulnerability, emphasizing the need for stress testing and robust defenses.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25810", "title": "Learning to Reason as Action Abstractions with Scalable Mid-Training RL", "authors": [ "Shenao Zhang", "Donghan Yu", "Yihao Feng", "Bowen Jin", "Zhaoran Wang", "John Peebles", "Zirui Wang" ], "github": "", "project_page": "", "abstract": "Large language models excel with reinforcement learning (RL), but fully unlocking this potential requires a mid-training stage. An effective mid-training phase should identify a compact set of useful actions and enable fast selection among them through online RL. We formalize this intuition by presenting the first theoretical result on how mid-training shapes post-training: it characterizes an action subspace that minimizes both the value approximation error from pruning and the RL error during subsequent planning. Our analysis reveals two key determinants of mid-training effectiveness: pruning efficiency, which shapes the prior of the initial RL policy, and its impact on RL convergence, which governs the extent to which that policy can be improved via online interactions. These results suggest that mid-training is most effective when the decision space is compact and the effective horizon is short, highlighting the importance of operating in the space of action abstractions rather than primitive actions. Building on these insights, we propose Reasoning as Action Abstractions (RA3), a scalable mid-training algorithm. Specifically, we derive a sequential variational lower bound and optimize it by iteratively discovering temporally-consistent latent structures via RL, followed by fine-tuning on the bootstrapped data. Experiments on code generation tasks demonstrate the effectiveness of our approach. Across multiple base models, RA3 improves the average performance on HumanEval and MBPP by 8 and 4 points over the base model and the next-token prediction baseline. Furthermore, RA3 achieves faster convergence and higher asymptotic performance in RLVR on HumanEval+, MBPP+, LiveCodeBench, and Codeforces.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.24088", "title": "CORRECT: COndensed eRror RECognition via knowledge Transfer in multi-agent systems", "authors": [ "Yifan Yu", "Moyan Li", "Shaoyuan Xu", "Jinmiao Fu", "Xinhai Hou", "Fan Lai", "Bryan Wang" ], "github": "", "project_page": "", "abstract": "Multi-agent systems (MAS) are increasingly capable of tackling complex real-world tasks, yet their reliance on inter-agent coordination, tool use, and long-horizon reasoning makes error recognition particularly challenging. Minor errors can propagate across agents, escalating into task failures while producing long, intertwined execution trajectories that impose significant costs for both human developers and automated systems to debug and analyze. Our key insight is that, despite surface differences in failure trajectories (e.g., logs), MAS errors often recur with similar structural patterns. This paper presents CORRECT, the first lightweight, training-free framework that leverages an online cache of distilled error schemata to recognize and transfer knowledge of failure structures across new requests. This cache-based reuse allows LLMs to perform targeted error localization at inference time, avoiding the need for expensive retraining while adapting to dynamic MAS deployments in subseconds. To support rigorous study in this domain, we also introduce CORRECT-Error, a large-scale dataset of over 2,000 annotated trajectories collected through a novel error-injection pipeline guided by real-world distributions, and further validated through human evaluation to ensure alignment with natural failure patterns. Experiments across seven diverse MAS applications show that CORRECT improves step-level error localization up to 19.8% over existing advances while at near-zero overhead, substantially narrowing the gap between automated and human-level error recognition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.25631", "title": "Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting", "authors": [ "Jason Stock", "Troy Arcomano", "Rao Kotamarthi" ], "github": "https://github.com/stockeh/swift", "project_page": "", "abstract": "Diffusion models offer a physically grounded framework for probabilistic weather forecasting, but their typical reliance on slow, iterative solvers during inference makes them impractical for subseasonal-to-seasonal (S2S) applications where long lead-times and domain-driven calibration are essential. To address this, we introduce Swift, a single-step consistency model that, for the first time, enables autoregressive finetuning of a probability flow model with a continuous ranked probability score (CRPS) objective. This eliminates the need for multi-model ensembling or parameter perturbations. Results show that Swift produces skillful 6-hourly forecasts that remain stable for up to 75 days, running 39times faster than state-of-the-art diffusion baselines while achieving forecast skill competitive with the numerical-based, operational IFS ENS. This marks a step toward efficient and reliable ensemble forecasting from medium-range to seasonal-scales.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stockeh/swift", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2510.00492", "title": "Rethinking Reward Models for Multi-Domain Test-Time Scaling", "authors": [ "Dong Bok Lee", "Seanie Lee", "Sangwoo Park", "Minki Kang", "Jinheon Baek", "Dongki Kim", "Dominik Wagner", "Jiongdao Jin", "Heejun Lee", "Tobias Bocklet", "Jinyu Wang", "Jingjing Fu", "Sung Ju Hwang", "Jiang Bia", "Lei Song" ], "github": "https://github.com/db-Lee/Multi-RM", "project_page": "", "abstract": "The reliability of large language models (LLMs) during test-time scaling is often assessed with external verifiers or reward models that distinguish correct reasoning from flawed logic. Prior work generally assumes that process reward models (PRMs), which score every intermediate reasoning step, outperform outcome reward models (ORMs) that assess only the final answer. This view is based mainly on evidence from narrow, math-adjacent domains. We present the first unified evaluation of four reward model variants, discriminative ORM and PRM (\\DisORM, \\DisPRM) and generative ORM and PRM (\\GenORM, \\GenPRM), across 14 diverse domains. Contrary to conventional wisdom, we find that (i) \\DisORM performs on par with \\DisPRM, (ii) \\GenPRM is not competitive, and (iii) overall, \\GenORM is the most robust, yielding significant and consistent gains across every tested domain. We attribute this to PRM-style stepwise scoring, which inherits label noise from LLM auto-labeling and has difficulty evaluating long reasoning trajectories, including those involving self-correcting reasoning. Our theoretical analysis shows that step-wise aggregation compounds errors as reasoning length grows, and our empirical observations confirm this effect. These findings challenge the prevailing assumption that fine-grained supervision is always better and support generative outcome verification for multi-domain deployment. We publicly release our code, datasets, and checkpoints at https://github.com/db-Lee/Multi-RM{\\small\\texttt{https://github.com/db-Lee/Multi-RM}} to facilitate future research in multi-domain settings.", "github_manual": "", "github_regex": "https://github.com/db-Lee/Multi-RM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2510.00231", "title": "The Pitfalls of KV Cache Compression", "authors": [ "Alex Chen", "Renato Geh", "Aditya Grover", "Guy Van den Broeck", "Daniel Israel" ], "github": "", "project_page": "", "abstract": "KV cache compression promises increased throughput and efficiency with negligible loss in performance. While the gains in throughput are indisputable and recent literature has indeed shown minimal degradation on particular benchmarks, in general the consequences of compression in realistic scenarios such as multi-instruction prompting have been insufficiently studied. In this paper, we identify several pitfalls practitioners should be aware of when deploying KV cache compressed LLMs. Importantly, we show that certain instructions degrade much more rapidly with compression, effectively causing them to be completely ignored by the LLM. As a practical example of that, we highlight system prompt leakage as a case study, empirically showing the impact of compression on leakage and general instruction following. We show several factors that play a role in prompt leakage: compression method, instruction order, and KV eviction bias. We then propose simple changes to KV cache eviction policies that can reduce the impact of these factors and improve the overall performance in multi-instruction tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00184", "title": "Why Can't Transformers Learn Multiplication? Reverse-Engineering Reveals Long-Range Dependency Pitfalls", "authors": [ "Xiaoyan Bai", "Itamar Pres", "Yuntian Deng", "Chenhao Tan", "Stuart Shieber", "Fernanda Vi\u00e9gas", "Martin Wattenberg", "Andrew Lee" ], "github": "https://github.com/ajyl/icot", "project_page": "", "abstract": "Language models are increasingly capable, yet still fail at a seemingly simple task of multi-digit multiplication. In this work, we study why, by reverse-engineering a model that successfully learns multiplication via implicit chain-of-thought, and report three findings: (1) Evidence of long-range structure: Logit attributions and linear probes indicate that the model encodes the necessary long-range dependencies for multi-digit multiplication. (2) Mechanism: the model encodes long-range dependencies using attention to construct a directed acyclic graph to ``cache'' and ``retrieve'' pairwise partial products. (3) Geometry: the model implements partial products in attention heads by forming Minkowski sums between pairs of digits, and digits are represented using a Fourier basis, both of which are intuitive and efficient representations that the standard fine-tuning model lacks. With these insights, we revisit the learning dynamics of standard fine-tuning and find that the model converges to a local optimum that lacks the required long-range dependencies. We further validate this understanding by introducing an auxiliary loss that predicts the ``running sum'' via a linear regression probe, which provides an inductive bias that enables the model to successfully learn multi-digit multiplication. In summary, by reverse-engineering the mechanisms of an implicit chain-of-thought model we uncover a pitfall for learning long-range dependencies in Transformers and provide an example of how the correct inductive bias can address this issue.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ajyl/icot", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.01174", "title": "Code2Video: A Code-centric Paradigm for Educational Video Generation", "authors": [ "Yanzhe Chen", "Kevin Qinghong Lin", "Mike Zheng Shou" ], "github": "https://github.com/showlab/Code2Video", "project_page": "", "abstract": "While recent generative models advance pixel-space video synthesis, they remain limited in producing professional educational videos, which demand disciplinary knowledge, precise visual structures, and coherent transitions, limiting their applicability in educational scenarios. Intuitively, such requirements are better addressed through the manipulation of a renderable environment, which can be explicitly controlled via logical commands (e.g., code). In this work, we propose Code2Video, a code-centric agent framework for generating educational videos via executable Python code. The framework comprises three collaborative agents: (i) Planner, which structures lecture content into temporally coherent flows and prepares corresponding visual assets; (ii) Coder, which converts structured instructions into executable Python codes while incorporating scope-guided auto-fix to enhance efficiency; and (iii) Critic, which leverages vision-language models (VLM) with visual anchor prompts to refine spatial layout and ensure clarity. To support systematic evaluation, we build MMMC, a benchmark of professionally produced, discipline-specific educational videos. We evaluate MMMC across diverse dimensions, including VLM-as-a-Judge aesthetic scores, code efficiency, and particularly, TeachQuiz, a novel end-to-end metric that quantifies how well a VLM, after unlearning, can recover knowledge by watching the generated videos. Our results demonstrate the potential of Code2Video as a scalable, interpretable, and controllable approach, achieving 40% improvement over direct code generation and producing videos comparable to human-crafted tutorials. The code and datasets are available at https://github.com/showlab/Code2Video.", "github_manual": "", "github_regex": "https://github.com/showlab/Code2Video", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.01051", "title": "GEM: A Gym for Agentic LLMs", "authors": [ "Zichen Liu", "Anya Sims", "Keyu Duan", "Changyu Chen", "Simon Yu", "Xiangxin Zhou", "Haotian Xu", "Shaopan Xiong", "Bo Liu", "Chenmien Tan", "Chuen Yang Beh", "Weixun Wang", "Hao Zhu", "Weiyan Shi", "Diyi Yang", "Michael Shieh", "Yee Whye Teh", "Wee Sun Lee", "Min Lin" ], "github": "https://github.com/axon-rl/gem", "project_page": "", "abstract": "The training paradigm for large language models (LLMs) is moving from static datasets to experience-based learning, where agents acquire skills via interacting with complex environments. To facilitate this transition we introduce GEM (General Experience Maker), an open-source environment simulator designed for the age of LLMs. Analogous to OpenAI-Gym for traditional reinforcement learning (RL), GEM provides a standardized framework for the environment-agent interface, including asynchronous vectorized execution for high throughput, and flexible wrappers for easy extensibility. GEM also features a diverse suite of environments, robust integrated tools, and single-file example scripts demonstrating using GEM with five popular RL training frameworks. Along with this, we also provide a set of baselines across 24 environments using REINFORCE with Return Batch Normalization (ReBN), which -- unlike GRPO -- is compatible with the full RL setting of dense per-turn rewards and offers better credit assignment. We further conduct apple-to-apple benchmarking of PPO, GRPO and REINFORCE in both single- and multi-turn settings using GEM to shed light on the algorithmic designs. Lastly, GEM also functions as a convenient evaluation toolkit besides a training environment. We hope this framework can help accelerate future agentic LLM research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/axon-rl/gem", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00526", "title": "Beyond Log Likelihood: Probability-Based Objectives for Supervised Fine-Tuning across the Model Capability Continuum", "authors": [ "Gaotang Li", "Ruizhong Qiu", "Xiusi Chen", "Heng Ji", "Hanghang Tong" ], "github": "https://github.com/GaotangLi/Beyond-Log-Likelihood", "project_page": "", "abstract": "Supervised fine-tuning (SFT) is the standard approach for post-training large language models (LLMs), yet it often shows limited generalization. We trace this limitation to its default training objective: negative log likelihood (NLL). While NLL is classically optimal when training from scratch, post-training operates in a different paradigm and could violate its optimality assumptions, where models already encode task-relevant priors and supervision can be long and noisy. To this end, we study a general family of probability-based objectives and characterize their effectiveness under different conditions. Through comprehensive experiments and extensive ablation studies across 7 model backbones, 14 benchmarks, and 3 domains, we uncover a critical dimension that governs objective behavior: the model-capability continuum. Near the model-strong end, prior-leaning objectives that downweight low-probability tokens (e.g., -p, -p^{10}, thresholded variants) consistently outperform NLL; toward the model-weak end, NLL dominates; in between, no single objective prevails. Our theoretical analysis further elucidates how objectives trade places across the continuum, providing a principled foundation for adapting objectives to model capability. Our code is available at https://github.com/GaotangLi/Beyond-Log-Likelihood.", "github_manual": "", "github_regex": "https://github.com/GaotangLi/Beyond-Log-Likelihood", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00232", "title": "BiasFreeBench: a Benchmark for Mitigating Bias in Large Language Model Responses", "authors": [ "Xin Xu", "Xunzhi He", "Churan Zhi", "Ruizhe Chen", "Julian McAuley", "Zexue He" ], "github": "", "project_page": "", "abstract": "Existing studies on bias mitigation methods for large language models (LLMs) use diverse baselines and metrics to evaluate debiasing performance, leading to inconsistent comparisons among them. Moreover, their evaluations are mostly based on the comparison between LLMs' probabilities of biased and unbiased contexts, which ignores the gap between such evaluations and real-world use cases where users interact with LLMs by reading model responses and expect fair and safe outputs rather than LLMs' probabilities. To enable consistent evaluation across debiasing methods and bridge this gap, we introduce BiasFreeBench, an empirical benchmark that comprehensively compares eight mainstream bias mitigation techniques (covering four prompting-based and four training-based methods) on two test scenarios (multi-choice QA and open-ended multi-turn QA) by reorganizing existing datasets into a unified query-response setting. We further introduce a response-level metric, Bias-Free Score, to measure the extent to which LLM responses are fair, safe, and anti-stereotypical. Debiasing performances are systematically compared and analyzed across key dimensions: the prompting vs. training paradigm, model size, and generalization of different training strategies to unseen bias types. We will publicly release our benchmark, aiming to establish a unified testbed for bias mitigation research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00777", "title": "In-Place Feedback: A New Paradigm for Guiding LLMs in Multi-Turn Reasoning", "authors": [ "Youngbin Choi", "Minjong Lee", "Saemi Moon", "Seunghyuk Cho", "Chaehyeon Chung", "MoonJeong Park", "Dongwoo Kim" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly studied in the context of multi-turn reasoning, where models iteratively refine their outputs based on user-provided feedback. Such settings are crucial for tasks that require complex reasoning, yet existing feedback paradigms often rely on issuing new messages. LLMs struggle to integrate these reliably, leading to inconsistent improvements. In this work, we introduce in-place feedback, a novel interaction paradigm in which users directly edit an LLM's previous response, and the model conditions on this modified response to generate its revision. Empirical evaluations on diverse reasoning-intensive benchmarks reveal that in-place feedback achieves better performance than conventional multi-turn feedback while using 79.1% fewer tokens. Complementary analyses on controlled environments further demonstrate that in-place feedback resolves a core limitation of multi-turn feedback: models often fail to apply feedback precisely to erroneous parts of the response, leaving errors uncorrected and sometimes introducing new mistakes into previously correct content. These findings suggest that in-place feedback offers a more natural and effective mechanism for guiding LLMs in reasoning-intensive tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00553", "title": "On Predictability of Reinforcement Learning Dynamics for Large Language Models", "authors": [ "Yuchen Cai", "Ding Cao", "Xin Xu", "Zijun Yao", "Yuqing Huang", "Zhenyu Tan", "Benyi Zhang", "Guiquan Liu", "Junfeng Fang" ], "github": "https://github.com/caiyuchen-ustc/Alpha-RL", "project_page": "", "abstract": "Recent advances in reasoning capabilities of large language models (LLMs) are largely driven by reinforcement learning (RL), yet the underlying parameter dynamics during RL training remain poorly understood. This work identifies two fundamental properties of RL-induced parameter updates in LLMs: (1) Rank-1 Dominance, where the top singular subspace of the parameter update matrix nearly fully determines reasoning improvements, recovering over 99\\% of performance gains; and (2) Rank-1 Linear Dynamics, where this dominant subspace evolves linearly throughout training, enabling accurate prediction from early checkpoints. Extensive experiments across 8 LLMs and 7 algorithms validate the generalizability of these properties. More importantly, based on these findings, we propose AlphaRL, a plug-in acceleration framework that extrapolates the final parameter update using a short early training window, achieving up to 2.5 speedup while retaining \\textgreater 96\\% of reasoning performance without extra modules or hyperparameter tuning. This positions our finding as a versatile and practical tool for large-scale RL, opening a path toward principled, interpretable, and efficient training paradigm for LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/caiyuchen-ustc/Alpha-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00536", "title": "GUI-KV: Efficient GUI Agents via KV Cache with Spatio-Temporal Awareness", "authors": [ "Kung-Hsiang Huang", "Haoyi Qiu", "Yutong Dai", "Caiming Xiong", "Chien-Sheng Wu" ], "github": "", "project_page": "", "abstract": "Graphical user interface (GUI) agents built on vision-language models have emerged as a promising approach to automate human-computer workflows. However, they also face the inefficiency challenge as they process long sequences of high-resolution screenshots and solving long-horizon tasks, making inference slow, costly and memory-bound. While key-value (KV) caching can mitigate this, storing the full cache is prohibitive for image-heavy contexts. Existing cache-compression methods are sub-optimal as they do not account for the spatial and temporal redundancy of GUIs. In this work, we first analyze attention patterns in GUI agent workloads and find that, unlike in natural images, attention sparsity is uniformly high across all transformer layers. This insight motivates a simple uniform budget allocation strategy, which we show empirically outperforms more complex layer-varying schemes. Building on this, we introduce GUI-KV, a plug-and-play KV cache compression method for GUI agents that requires no retraining. GUI-KV combines two novel techniques: (i) spatial saliency guidance, which augments attention scores with the L2 norm of hidden states to better preserve semantically important visual tokens, and (ii) temporal redundancy scoring, which projects previous frames' keys onto the current frame's key subspace to preferentially prune redundant history. Across standard GUI agent benchmarks and models, GUI-KV outperforms competitive KV compression baselines, closely matching full-cache accuracy at modest budgets. Notably, in a 5-screenshot setting on the AgentNetBench benchmark, GUI-KV reduces decoding FLOPs by 38.9% while increasing step accuracy by 4.1% over the full-cache baseline. These results demonstrate that exploiting GUI-specific redundancies enables efficient and reliable agent performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00510", "title": "JoyAgent-JDGenie: Technical Report on the GAIA", "authors": [ "Jiarun Liu", "Shiyue Xu", "Shangkun Liu", "Yang Li", "Wen Liu", "Min Liu", "Xiaoqing Zhou", "Hanmin Wang", "Shilin Jia", "zhen Wang", "Shaohua Tian", "Hanhao Li", "Junbo Zhang", "Yongli Yu", "Peng Cao", "Haofen Wang" ], "github": "", "project_page": "", "abstract": "Large Language Models are increasingly deployed as autonomous agents for complex real-world tasks, yet existing systems often focus on isolated improvements without a unifying design for robustness and adaptability. We propose a generalist agent architecture that integrates three core components: a collective multi-agent framework combining planning and execution agents with critic model voting, a hierarchical memory system spanning working, semantic, and procedural layers, and a refined tool suite for search, code execution, and multimodal parsing. Evaluated on a comprehensive benchmark, our framework consistently outperforms open-source baselines and approaches the performance of proprietary systems. These results demonstrate the importance of system-level integration and highlight a path toward scalable, resilient, and adaptive AI assistants capable of operating across diverse domains and tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00406", "title": "VLA-RFT: Vision-Language-Action Reinforcement Fine-tuning with Verified Rewards in World Simulators", "authors": [ "Hengtao Li", "Pengxiang Ding", "Runze Suo", "Yihao Wang", "Zirui Ge", "Dongyuan Zang", "Kexian Yu", "Mingyang Sun", "Hongyin Zhang", "Donglin Wang", "Weihua Su" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models enable embodied decision-making but rely heavily on imitation learning, leading to compounding errors and poor robustness under distribution shift. Reinforcement learning (RL) can mitigate these issues yet typically demands costly real-world interactions or suffers from sim-to-real gaps. We introduce VLA-RFT, a reinforcement fine-tuning framework that leverages a data-driven world model as a controllable simulator. Trained from real interaction data, the simulator predicts future visual observations conditioned on actions, allowing policy rollouts with dense, trajectory-level rewards derived from goal-achieving references. This design delivers an efficient and action-aligned learning signal, drastically lowering sample requirements. With fewer than 400 fine-tuning steps, VLA-RFT surpasses strong supervised baselines and achieves greater efficiency than simulator-based RL. Moreover, it exhibits strong robustness under perturbed conditions, sustaining stable task execution. Our results establish world-model-based RFT as a practical post-training paradigm to enhance the generalization and robustness of VLA models. For more details, please refer to https://vla-rft.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.23250", "title": "Training Vision-Language Process Reward Models for Test-Time Scaling in Multimodal Reasoning: Key Insights and Lessons Learned", "authors": [ "Brandon Ong", "Tej Deep Pala", "Vernon Toh", "William Chandra Tjhi", "Soujanya Poria" ], "github": "", "project_page": "", "abstract": "Process Reward Models (PRMs) provide step-level supervision that improves the reliability of reasoning in large language models. While PRMs have been extensively studied in text-based domains, their extension to Vision Language Models (VLMs) remains limited. Existing Vision-Language PRMs (VL-PRMs) rely on Monte Carlo Tree Search (MCTS) for data construction, which can often produce noisy supervision signals and limit generalization across tasks. In this work, we aim to elucidate the design space of VL-PRMs by exploring diverse strategies for dataset construction, training, and test-time scaling. First, we introduce a hybrid data synthesis framework that combines MCTS with judgments from a strong VLM, producing more accurate step-level labels. Second, we propose perception-focused supervision, enabling our PRM to explicitly detect errors at the visual grounding stage of reasoning. Third, we systematically evaluate multiple test-time scaling strategies, showing that our PRMs can reliably guide VLMs toward more accurate solutions. Our experiments covering five diverse multimodal benchmarks (MMMU, PuzzleVQA, AlgoPuzzleVQA, MathVista, and MathVision) reveal several key insights: (i) VL-PRMs when used as Outcome Reward Models (ORMs) during test-time scaling (TTS) can outperform VL-PRM guided process step selection, (ii) smaller VL-PRMs can match or even surpass larger ones in detecting process errors, (iii) VL-PRMs uncover latent reasoning abilities in stronger VLM backbones, (iv) perception-level supervision leads to significant gains in test-time scaling, and (v) TTS performance of different policies improve on advanced math reasoning datasets despite not training VL-PRMs on such datasets. We hope our work will motivate further research and support the advancement of VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.25301", "title": "Flash-Searcher: Fast and Effective Web Agents via DAG-Based Parallel Execution", "authors": [ "Tianrui Qin", "Qianben Chen", "Sinuo Wang", "He Xing", "King Zhu", "He Zhu", "Dingfeng Shi", "Xinxin Liu", "Ge Zhang", "Jiaheng Liu", "Yuchen Eleanor Jiang", "Xitong Gao", "Wangchunshu Zhou" ], "github": "https://github.com/OPPO-PersonalAI/Flash-Searcher", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks when equipped with external tools. However, current frameworks predominantly rely on sequential processing, leading to inefficient execution particularly for tasks requiring extensive tool interaction. This paper introduces Flash-Searcher, a novel parallel agent reasoning framework that fundamentally reimagines the execution paradigm from sequential chains to directed acyclic graphs (DAGs). Flash-Searcher decomposes complex tasks into subtasks with explicit dependencies, enabling concurrent execution of independent reasoning paths while maintaining logical constraints. Through dynamic workflow optimization, our framework continuously refines the execution graph based on intermediate results, effectively integrating summary module. Comprehensive evaluations across multiple benchmarks demonstrate that Flash-Searcher consistently outperforms existing approaches. Specifically, it achieves 67.7% accuracy on BrowseComp and 83% on xbench-DeepSearch, while reducing agent execution steps by up to 35% compared to current frameworks. Furthermore, when distilling this parallel reasoning pipeline into single models, we observe substantial performance gains across diverse backbone architectures, underscoring the generalizability of our methodology. Our work thus represents a significant advance in agent architecture design, offering a more scalable and efficient paradigm for complex reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OPPO-PersonalAI/Flash-Searcher", "github_api_added_by": "auto" }, { "date": "2025-10-02", "arxiv_id": "2509.25411", "title": "Boolean Satisfiability via Imitation Learning", "authors": [ "Zewei Zhang", "Huan Liu", "Yuanhao Yu", "Jun Chen", "Xiangyu Xu" ], "github": "https://github.com/zewei-Zhang/ImitSAT", "project_page": "", "abstract": "We propose ImitSAT, a branching policy for conflict-driven clause learning (CDCL) solvers based on imitation learning for the Boolean satisfiability problem (SAT). Unlike previous methods that predict instance-level signals to improve CDCL branching indirectly, or rely on reinforcement learning and insufficient CDCL information to enhance branching, ImitSAT learns from expert KeyTrace that collapses a full run into the sequence of surviving decisions. Replaying a KeyTrace on the same instance is nearly conflict-free, providing dense decision-level supervision and directly reducing propagations -- the dominant contributor to wall-clock time. This prefix-conditioned supervision enables ImitSAT to reproduce high-quality branches without exploration, yielding faster convergence, stable training, and seamless integration into CDCL. Extensive experiments demonstrate that ImitSAT reduces propagation counts and runtime, outperforming state-of-the-art learned approaches. We released the source code and trained model at https://github.com/zewei-Zhang/ImitSAT", "github_manual": "", "github_regex": "https://github.com/zewei-Zhang/ImitSAT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.19185", "title": "An Empirical Study of Testing Practices in Open Source AI Agent Frameworks and Agentic Applications", "authors": [ "Mohammed Mehedi Hasan", "Hao Li", "Emad Fallahzadeh", "Gopi Krishnan Rajbahadur", "Bram Adams", "Ahmed E. Hassan" ], "github": "https://github.com/elastic/elasticsearch", "project_page": "", "abstract": "Foundation model (FM)-based AI agents are rapidly gaining adoption across diverse domains, but their inherent non-determinism and non-reproducibility pose testing and quality assurance challenges. While recent benchmarks provide task-level evaluations, there is limited understanding of how developers verify the internal correctness of these agents during development. To address this gap, we conduct the first large-scale empirical study of testing practices in the AI agent ecosystem, analyzing 39 open-source agent frameworks and 439 agentic applications. We identify ten distinct testing patterns and find that novel, agent-specific methods like DeepEval are seldom used (around 1%), while traditional patterns like negative and membership testing are widely adapted to manage FM uncertainty. By mapping these patterns to canonical architectural components of agent frameworks and agentic applications, we uncover a fundamental inversion of testing effort: deterministic components like Resource Artifacts (tools) and Coordination Artifacts (workflows) consume over 70% of testing effort, while the FM-based Plan Body receives less than 5%. Crucially, this reveals a critical blind spot, as the Trigger component (prompts) remains neglected, appearing in around 1% of all tests. Our findings offer the first empirical testing baseline in FM-based agent frameworks and agentic applications, revealing a rational but incomplete adaptation to non-determinism. To address it, framework developers should improve support for novel testing methods, application developers must adopt prompt regression testing, and researchers should explore barriers to adoption. Strengthening these practices is vital for building more robust and dependable AI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/elastic/elasticsearch", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.25454", "title": "DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search", "authors": [ "Fang Wu", "Weihao Xuan", "Heli Qi", "Ximing Lu", "Aaron Tu", "Li Erran Li", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "Although RLVR has become an essential component for developing advanced reasoning skills in LLMs, contemporary studies have documented training plateaus that emerge following thousands of optimization steps, demonstrating notable decreases in performance gains despite increased computational investment. This limitation stems from the sparse exploration patterns inherent in current RLVR practices, where models rely on limited rollouts that often miss critical reasoning paths and fail to provide systematic coverage of the solution space. We present DeepSearch, a framework that integrates Monte Carlo Tree Search directly into RLVR training. In contrast to existing methods that rely on tree search only at inference, DeepSearch embeds structured search into the training loop, enabling systematic exploration and fine-grained credit assignment across reasoning steps. Through training-time exploration, DeepSearch addresses the fundamental bottleneck of insufficient exploration, which leads to diminishing performance improvements over prolonged training steps. Our contributions include: (1) a global frontier selection strategy that prioritizes promising nodes across the search tree, (2) selection with entropy-based guidance that identifies confident paths for supervision, and (3) adaptive replay buffer training with solution caching for efficiency. Experiments on mathematical reasoning benchmarks show that DeepSearch achieves 62.95% average accuracy and establishes a new state-of-the-art for 1.5B reasoning models - using 5.7x fewer GPU hours than extended training approaches. These results highlight the importance of strategic exploration over brute-force scaling and demonstrate the promise of algorithmic innovation for advancing RLVR methodologies. DeepSearch establishes a new direction for scaling reasoning capabilities through systematic search rather than prolonged computation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.22887", "title": "Infusing Theory of Mind into Socially Intelligent LLM Agents", "authors": [ "EunJeong Hwang", "Yuwei Yin", "Giuseppe Carenini", "Peter West", "Vered Shwartz" ], "github": "https://github.com/eujhwang/toma", "project_page": "", "abstract": "Theory of Mind (ToM)-an understanding of the mental states of others-is a key aspect of human social intelligence, yet, chatbots and LLM-based social agents do not typically integrate it. In this work, we demonstrate that LLMs that explicitly use ToM get better at dialogue, achieving goals more effectively. After showing that simply prompting models to generate mental states between dialogue turns already provides significant benefit, we further introduce ToMAgent (ToMA), a ToM-focused dialogue agent. ToMA is trained by pairing ToM with dialogue lookahead to produce mental states that are maximally useful for achieving dialogue goals. Experiments on the Sotopia interactive social evaluation benchmark demonstrate the effectiveness of our method over a range of baselines. Comprehensive analysis shows that ToMA exhibits more strategic, goal-oriented reasoning behaviors, which enable long-horizon adaptation, while maintaining better relationships with their partners. Our results suggest a step forward in integrating ToM for building socially intelligent LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/eujhwang/toma", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.25849", "title": "Knapsack RL: Unlocking Exploration of LLMs via Optimizing Budget Allocation", "authors": [ "Ziniu Li", "Congliang Chen", "Tianyun Yang", "Tian Ding", "Ruoyu Sun", "Ge Zhang", "Wenhao Huang", "Zhi-Quan Luo" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) can self-improve through reinforcement learning, where they generate trajectories to explore and discover better solutions. However, this exploration process is computationally expensive, often forcing current methods to assign limited exploration budgets to each task. This uniform allocation creates problematic edge cases: easy tasks consistently succeed while difficult tasks consistently fail, both producing zero gradients during training updates for the widely used Group Relative Policy Optimization (GRPO). We address this problem from the lens of exploration budget allocation. Viewing each task's exploration as an \"item\" with a distinct \"value\" and \"cost\", we establish a connection to the classical knapsack problem. This formulation allows us to derive an optimal assignment rule that adaptively distributes resources based on the model's current learning status. When applied to GRPO, our method increases the effective ratio of non-zero policy gradients by 20-40% during training. Acting as a computational \"free lunch\", our approach could reallocate exploration budgets from tasks where learning is saturated to those where it is most impactful. This enables significantly larger budgets (e.g., 93 rollouts) for especially challenging problems, which would be computationally prohibitive under a uniform allocation. These improvements translate to meaningful gains on mathematical reasoning benchmarks, with average improvements of 2-4 points and peak gains of 9 points on specific tasks. Notably, achieving comparable performance with traditional homogeneous allocation would require about 2x the computational resources.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00931", "title": "Making, not Taking, the Best of N", "authors": [ "Ammar Khairi", "Daniel D'souza", "Marzieh Fadaee", "Julia Kreutzer" ], "github": "", "project_page": "", "abstract": "Obtaining high-quality generations in modern LLMs has largely been framed as a selection problem: identifying a single winning generation from a diverse pool of N samples, the Best-of-N (BoN). Yet, this approach is inherently zero-sum, discarding diverse and potentially useful information from the pool. Instead, we explore a collaborative setup, where all candidates can potentially contribute to the final winning generation. To this end, we propose Fusion-of-N (FusioN): a method that uses a general LLM judge to synthesize the most informative elements of each sample into a single final answer. We compare FusioN to BoN in two settings, (i) test-time scaling, where we sample and aggregate from a single model at test-time (ii) synthetic data generation, where we fuse samples from a pool of diverse teachers to improve a student model. We extensively benchmark both setups across 11 languages, 3 diverse tasks and varying model scales. Across the bench, FusioN consistently outperforms BoN showing versatility and robustness both in test-time scaling and in downstream gains from synthetic data generation. We also perform extensive analysis on FusioN, where it shows surprising strengths and robustness under challenging settings. These results show that we should shift how we think about evaluating and utilizing LLM generations from a monolithic measure of quality, to embracing their polylithic nature. This shift allows us to integrate diverse strengths, unlock latent potential, and achieve improvements that were previously inaccessible through selection alone.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00615", "title": "ACON: Optimizing Context Compression for Long-horizon LLM Agents", "authors": [ "Minki Kang", "Wei-Ning Chen", "Dongge Han", "Huseyin A. Inan", "Lukas Wutschitz", "Yanzhi Chen", "Robert Sim", "Saravan Rajmohan" ], "github": "https://github.com/microsoft/acon", "project_page": "", "abstract": "Large language models (LLMs) are increasingly deployed as agents in dynamic, real-world environments, where success requires both reasoning and effective tool use. A central challenge for agentic tasks is the growing context length, as agents must accumulate long histories of actions and observations. This expansion raises costs and reduces efficiency in long-horizon tasks, yet prior work on context compression has mostly focused on single-step tasks or narrow applications. We introduce Agent Context Optimization (ACON), a unified framework that optimally compresses both environment observations and interaction histories into concise yet informative condensations. ACON leverages compression guideline optimization in natural language space: given paired trajectories where full context succeeds but compressed context fails, capable LLMs analyze the causes of failure, and the compression guideline is updated accordingly. Furthermore, we propose distilling the optimized LLM compressor into smaller models to reduce the overhead of the additional module. Experiments on AppWorld, OfficeBench, and Multi-objective QA show that ACON reduces memory usage by 26-54% (peak tokens) while largely preserving task performance, preserves over 95% of accuracy when distilled into smaller compressors, and enhances smaller LMs as long-horizon agents with up to 46% performance improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/acon", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.01180", "title": "BroRL: Scaling Reinforcement Learning via Broadened Exploration", "authors": [ "Jian Hu", "Mingjie Liu", "Ximing Lu", "Fang Wu", "Zaid Harchaoui", "Shizhe Diao", "Yejin Choi", "Pavlo Molchanov", "Jun Yang", "Jan Kautz", "Yi Dong" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key ingredient for unlocking complex reasoning capabilities in large language models. Recent work ProRL has shown promise in scaling RL by increasing the number of training steps. However, performance plateaus after thousands of steps, with clear diminishing returns from allocating more computation to additional training. In this work, we investigate a complementary paradigm for scaling RL, BroR-Lincreasing the number of rollouts per example to hundreds to exhaustively Broaden exploration, which yields continuous performance gains beyond the saturation point observed in ProRL when scaling the number of training steps. Our approach is motivated by a mass balance equation analysis allowing us to characterize the rate of change in probability mass for correct and incorrect tokens during the reinforcement process. We show that under a one-step RL assumption, sampled rollout tokens always contribute to correct-mass expansion, while unsampled tokens outside rollouts may lead to gains or losses depending on their distribution and the net reward balance. Importantly, as the number of rollouts per example N increases, the effect of unsampled terms diminishes, ensuring overall correct-mass expansion. To validate our theoretical analysis, we conduct simulations under more relaxed conditions and find that a sufficiently large rollout size N-corresponding to ample exploration-guarantees an increase in the probability mass of all correct tokens. Empirically, BroRL revives models saturated after 3K ProRL training steps and demonstrates robust, continuous improvement, achieving state-of-the-art results for the 1.5B model across diverse benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.01070", "title": "Eliciting Secret Knowledge from Language Models", "authors": [ "Bartosz Cywi\u0144ski", "Emil Ryd", "Rowan Wang", "Senthooran Rajamanoharan", "Neel Nanda", "Arthur Conmy", "Samuel Marks" ], "github": "", "project_page": "", "abstract": "We study secret elicitation: discovering knowledge that an AI possesses but does not explicitly verbalize. As a testbed, we train three families of large language models (LLMs) to possess specific knowledge that they apply downstream but deny knowing when asked directly. For example, in one setting, we train an LLM to generate replies that are consistent with knowing the user is female, while denying this knowledge when asked directly. We then design various black-box and white-box secret elicitation techniques and evaluate them based on whether they can help an LLM auditor successfully guess the secret knowledge. Many of our techniques improve on simple baselines. Our most effective techniques (performing best in 2/3 settings) are based on prefill attacks, a black-box technique where the LLM reveals secret knowledge when generating a completion from a predefined prefix. In our remaining setting, white-box techniques based on logit lens and sparse autoencoders (SAEs) are most effective. We release our models and code, establishing a public benchmark for evaluating secret elicitation methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.01061", "title": "ReSWD: ReSTIR'd, not shaken. Combining Reservoir Sampling and Sliced Wasserstein Distance for Variance Reduction", "authors": [ "Mark Boss", "Andreas Engelhardt", "Simon Donn\u00e9", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "Distribution matching is central to many vision and graphics tasks, where the widely used Wasserstein distance is too costly to compute for high dimensional distributions. The Sliced Wasserstein Distance (SWD) offers a scalable alternative, yet its Monte Carlo estimator suffers from high variance, resulting in noisy gradients and slow convergence. We introduce Reservoir SWD (ReSWD), which integrates Weighted Reservoir Sampling into SWD to adaptively retain informative projection directions in optimization steps, resulting in stable gradients while remaining unbiased. Experiments on synthetic benchmarks and real-world tasks such as color correction and diffusion guidance show that ReSWD consistently outperforms standard SWD and other variance reduction baselines. Project page: https://reservoirswd.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.01037", "title": "CurES: From Gradient Analysis to Efficient Curriculum Learning for Reasoning LLMs", "authors": [ "Yongcheng Zeng", "Zexu Sun", "Bokai Ji", "Erxue Min", "Hengyi Cai", "Shuaiqiang Wang", "Dawei Yin", "Haifeng Zhang", "Xu Chen", "Jun Wang" ], "github": "", "project_page": "", "abstract": "Curriculum learning plays a crucial role in enhancing the training efficiency of large language models (LLMs) on reasoning tasks. However, existing methods often fail to adequately account for variations in prompt difficulty or rely on simplistic filtering mechanisms to select prompt datasets within a narrow criterion range, resulting in significant computational waste. In this work, we approach the problem from the perspective of reinforcement learning gradient optimization, offering a systematic and theoretical investigation into how to improve the training efficiency of LLMs. We identify two key factors influencing training efficiency: the selection of training prompts and the allocation of rollout quantities across different prompts. Our theoretical analysis reveals that the sampling distribution of prompts dictates the convergence rate of gradient descent, while the allocation of the rollout quantity influences the consistency and stability of overall gradient updates. Based on these insights, we propose CurES, an efficient training method that accelerates convergence and employs Bayesian posterior estimation to minimize computational overhead. Experiments demonstrate that our CurES outperforms Group Relative Policy Optimization (GRPO) by +3.30 points and +4.82 points with 1.5B and 7B models, respectively. Additionally, CurES exhibits faster convergence compared to baselines, including GRPO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.25916", "title": "VLM-FO1: Bridging the Gap Between High-Level Reasoning and Fine-Grained Perception in VLMs", "authors": [ "Peng Liu", "Haozhan Shen", "Chunxin Fang", "Zhicheng Sun", "Jiajia Liao", "Tiancheng Zhao" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) excel at high-level scene understanding but falter on fine-grained perception tasks requiring precise localization. This failure stems from a fundamental mismatch, as generating exact numerical coordinates is a challenging task for language-centric architectures. In this paper, we introduce VLM-FO1, a novel framework that overcomes this limitation by reframing object-centric perception from a brittle coordinate generation problem into a robust feature retrieval task. Our method operates as a plug-and-play module that integrates with any pre-trained VLM. It leverages a Hybrid Fine-grained Region Encoder (HFRE), featuring a dual vision encoder, to generate powerful region tokens rich in both semantic and spatial detail. A token-based referencing system then enables the LLM to seamlessly reason about and ground language in these specific visual regions. Experiments show that VLM-FO1 achieves state-of-the-art performance across a diverse suite of benchmarks, demonstrating exceptional capabilities in object grounding, region generational understanding, and visual region reasoning. Crucially, our two-stage training strategy ensures that these perception gains are achieved without compromising the base model's general visual understanding capabilities. VLM-FO1 establishes an effective and flexible paradigm for building perception-aware VLMs, bridging the gap between high-level reasoning and fine-grained visual grounding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.25045", "title": "Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures", "authors": [ "Marco Bronzini", "Carlo Nicolini", "Bruno Lepri", "Jacopo Staiano", "Andrea Passerini" ], "github": "", "project_page": "", "abstract": "Despite their capabilities, Large Language Models (LLMs) remain opaque with limited understanding of their internal representations. Current interpretability methods, such as direct logit attribution (DLA) and sparse autoencoders (SAEs), provide restricted insight due to limitations such as the model's output vocabulary or unclear feature names. This work introduces Hyperdimensional Probe, a novel paradigm for decoding information from the LLM vector space. It combines ideas from symbolic representations and neural probing to project the model's residual stream into interpretable concepts via Vector Symbolic Architectures (VSAs). This probe combines the strengths of SAEs and conventional probes while overcoming their key limitations. We validate our decoding paradigm with controlled input-completion tasks, probing the model's final state before next-token prediction on inputs spanning syntactic pattern recognition, key-value associations, and abstract inference. We further assess it in a question-answering setting, examining the state of the model both before and after text generation. Our experiments show that our probe reliably extracts meaningful concepts across varied LLMs, embedding sizes, and input domains, also helping identify LLM failures. Our work advances information decoding in LLM vector space, enabling extracting more informative, interpretable, and structured features from neural representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-01", "arxiv_id": "2509.16944", "title": "Catching the Details: Self-Distilled RoI Predictors for Fine-Grained MLLM Perception", "authors": [ "Yuheng Shi", "Xiaohuan Pei", "Minjing Dong", "Chang Xu" ], "github": "https://github.com/YuHengsss/SD-RPN", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) require high-resolution visual information to perform fine-grained perception, yet processing entire high-resolution images is computationally prohibitive. While recent methods leverage a Region-of-Interest (RoI) mechanism to focus on salient areas, they typically present a difficult trade-off: training-based approaches depend on large-scale annotated datasets, while training-free methods that utilize the model's internal attention are computationally inefficient and less accurate, requiring either multi-pass prefill stages or reliance on the slow auto-regressive decoding process. In this paper, we propose an efficient, annotation-free Self-Distilled Region Proposal Network (SD-RPN) that resolves this trade-off. The SD-RPN is built around a pipeline that transforms the noisy attention maps from the MLLM's middle layers into high-quality pseudo-RoI labels by explicitly denoising the signal and resolving ambiguity. We use these labels to train a lightweight Region Proposal Network (RPN) that learns a more precise localization. This RPN is also highly efficient, predicting the RoI in a single forward pass using features from the MLLM's middle layers, decoupling RoI identification from the auto-regressive generation and avoiding costly multi-pass operations.To validate our approach, we integrate the framework into the LLaVA-1.5 architecture. Despite being trained on only a few (e.g. 10K) question-answer pairs, our method demonstrates exceptional data efficiency and generalization, achieving over a 10% absolute accuracy improvement on unseen benchmarks, including TextVQA, DocVQA, and V-Star. Our work presents a practical and scalable solution for enhancing the fine-grained perception of MLLMs without requiring costly supervision or full model fine-tuning. Code is available at https://github.com/YuHengsss/SD-RPN.", "github_manual": "", "github_regex": "https://github.com/YuHengsss/SD-RPN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.25455", "title": "PIPer: On-Device Environment Setup via Online Reinforcement Learning", "authors": [ "Alexander Kovrigin", "Aleksandra Eliseeva", "Konstantin Grotov", "Egor Bogomolov", "Yaroslav Zharov" ], "github": "https://github.com/JetBrains-Research/PIPer", "project_page": "", "abstract": "Environment setup-the process of configuring the system to work with a specific software project-represents a persistent challenge in Software Engineering (SE). Automated environment setup methods could assist developers by providing fully configured environments for arbitrary repositories without manual effort. This also helps SE researchers to scale execution-based benchmarks. However, recent studies reveal that even state-of-the-art Large Language Models (LLMs) achieve limited success in automating this task. To address this limitation, we tune a specialized model for environment setup. We combine supervised fine-tuning for generating correct Bash scripts and Reinforcement Learning with Verifiable Rewards (RLVR) to adapt it to the task of environment setup. On EnvBench-Python, our method enables Qwen3-8B (a model runnable on consumer hardware) to perform on par with larger models-Qwen3-32B and GPT-4o. The training code and model checkpoints are available online: https://github.com/JetBrains-Research/PIPer.", "github_manual": "", "github_regex": "https://github.com/JetBrains-Research/PIPer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00977", "title": "It Takes Two: Your GRPO Is Secretly DPO", "authors": [ "Yihong Wu", "Liheng Ma", "Lei Ding", "Muzhi Li", "Xinyu Wang", "Kejia Chen", "Zhan Su", "Zhanguang Zhang", "Chenyang Huang", "Yingxue Zhang", "Mark Coates", "Jian-Yun Nie" ], "github": "", "project_page": "", "abstract": "Group Relative Policy Optimization (GRPO) is a prominent reinforcement learning algorithm for post-training Large Language Models (LLMs). It is commonly believed that GRPO necessitates a large group size to ensure stable training via precise statistical estimation, which incurs substantial computational overhead. In this work, we challenge this assumption by reframing GRPO as a form of contrastive learning, which reveals a fundamental connection to Direct Preference Optimization (DPO). Motivated by DPO's empirical success, we investigate the minimal two-rollout case (2-GRPO), a configuration previously deemed infeasible. We provide a rigorous theoretical analysis to validate 2-GRPO and demonstrate empirically that it achieves performance on par with 16-GRPO, despite using only 1/8 of the rollouts and reducing training time by over 70%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00438", "title": "BindWeave: Subject-Consistent Video Generation via Cross-Modal Integration", "authors": [ "Zhaoyang Li", "Dongjun Qian", "Kai Su", "Qishuai Diao", "Xiangyang Xia", "Chang Liu", "Wenfei Yang", "Tianzhu Zhang", "Zehuan Yuan" ], "github": "", "project_page": "", "abstract": "Diffusion Transformer has shown remarkable abilities in generating high-fidelity videos, delivering visually coherent frames and rich details over extended durations. However, existing video generation models still fall short in subject-consistent video generation due to an inherent difficulty in parsing prompts that specify complex spatial relationships, temporal logic, and interactions among multiple subjects. To address this issue, we propose BindWeave, a unified framework that handles a broad range of subject-to-video scenarios from single-subject cases to complex multi-subject scenes with heterogeneous entities. To bind complex prompt semantics to concrete visual subjects, we introduce an MLLM-DiT framework in which a pretrained multimodal large language model performs deep cross-modal reasoning to ground entities and disentangle roles, attributes, and interactions, yielding subject-aware hidden states that condition the diffusion transformer for high-fidelity subject-consistent video generation. Experiments on the OpenS2V benchmark demonstrate that our method achieves superior performance across subject consistency, naturalness, and text relevance in generated videos, outperforming existing open-source and commercial models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.22944", "title": "SINQ: Sinkhorn-Normalized Quantization for Calibration-Free Low-Precision LLM Weights", "authors": [ "Lorenz K. M\u00fcller", "Philippe Bich", "Jiawei Zhuang", "Ahmet \u00c7elik", "Luca Benfenati", "Lukas Cavigelli" ], "github": "https://github.com/huawei-csl/SINQ", "project_page": "", "abstract": "Post-training quantization has emerged as the most widely used strategy for deploying large language models at low precision. Still, current methods show perplexity degradation at bit-widths less than or equal to 4, partly because representing outliers causes precision issues in parameters that share the same scales as these outliers. This problem is especially pronounced for calibration-free, uniform quantization methods. We introduce SINQ to augment existing post-training quantizers with an additional second-axis scale factor and a fast Sinkhorn-Knopp-style algorithm that finds scales to normalize per-row and per-column variances, thereby minimizing a novel per-matrix proxy target for quantization: the matrix imbalance. Our method has no interactions between layers and can be trivially applied to new architectures to quantize any linear layers. We evaluate our method on the Qwen3 model family and DeepSeek-V2.5. SINQ improves WikiText2 and C4 perplexity significantly against uncalibrated uniform quantization baselines and can be further enhanced by combining it with calibration and non-uniform quantization levels. Code to reproduce the results of this work and to easily quantize models using SINQ is available at https://github.com/huawei-csl/SINQ.", "github_manual": "", "github_regex": "https://github.com/huawei-csl/SINQ", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00967", "title": "QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL", "authors": [ "Cong Yu", "Valter Uotila", "Shilong Deng", "Qingyuan Wu", "Tuo Shi", "Songlin Jiang", "Lei You", "Bo Zhao" ], "github": "https://github.com/benyucong/QUASAR", "project_page": "", "abstract": "Designing and optimizing task-specific quantum circuits are crucial to leverage the advantage of quantum computing. Recent large language model (LLM)-based quantum circuit generation has emerged as a promising automatic solution. However, the fundamental challenges remain unaddressed: (i) parameterized quantum gates require precise numerical values for optimal performance, which also depend on multiple aspects, including the number of quantum gates, their parameters, and the layout/depth of the circuits. (ii) LLMs often generate low-quality or incorrect quantum circuits due to the lack of quantum domain-specific knowledge. We propose QUASAR, an agentic reinforcement learning (RL) framework for quantum circuits generation and optimization based on tool-augmented LLMs. To align the LLM with quantum-specific knowledge and improve the generated quantum circuits, QUASAR designs (i) a quantum circuit verification approach with external quantum simulators and (ii) a sophisticated hierarchical reward mechanism in RL training. Extensive evaluation shows improvements in both syntax and semantic performance of the generated quantum circuits. When augmenting a 4B LLM, QUASAR has achieved the validity of 99.31% in Pass@1 and 100% in Pass@10, outperforming industrial LLMs of GPT-4o, GPT-5 and DeepSeek-V3 and several supervised-fine-tuning (SFT)-only and RL-only baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/benyucong/QUASAR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.26346", "title": "EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing", "authors": [ "Keming Wu", "Sicong Jiang", "Max Ku", "Ping Nie", "Minghao Liu", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Recently, we have witnessed great progress in image editing with natural language instructions. Several closed-source models like GPT-Image-1, Seedream, and Google-Nano-Banana have shown highly promising progress. However, the open-source models are still lagging. The main bottleneck is the lack of a reliable reward model to scale up high-quality synthetic training data. To address this critical bottleneck, we built \\mname, trained with our new large-scale human preference dataset, meticulously annotated by trained experts following a rigorous protocol containing over 200K preference pairs. \\mname demonstrates superior alignment with human preferences in instruction-guided image editing tasks. Experiments show that \\mname achieves state-of-the-art human correlation on established benchmarks such as GenAI-Bench, AURORA-Bench, ImagenHub, and our new \\benchname, outperforming a wide range of VLM-as-judge models. Furthermore, we use \\mname to select a high-quality subset from the existing noisy ShareGPT-4o-Image dataset. We train Step1X-Edit on the selected subset, which shows significant improvement over training on the full set. This demonstrates \\mname's ability to serve as a reward model to scale up high-quality training data for image editing. Furthermore, its strong alignment suggests potential for advanced applications like reinforcement learning-based post-training and test-time scaling of image editing models. \\mname with its training dataset will be released to help the community build more high-quality image editing training datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.00225", "title": "TGPO: Temporal Grounded Policy Optimization for Signal Temporal Logic Tasks", "authors": [ "Yue Meng", "Fei Chen", "Chuchu Fan" ], "github": "https://github.com/mengyuest/TGPO", "project_page": "", "abstract": "Learning control policies for complex, long-horizon tasks is a central challenge in robotics and autonomous systems. Signal Temporal Logic (STL) offers a powerful and expressive language for specifying such tasks, but its non-Markovian nature and inherent sparse reward make it difficult to be solved via standard Reinforcement Learning (RL) algorithms. Prior RL approaches focus only on limited STL fragments or use STL robustness scores as sparse terminal rewards. In this paper, we propose TGPO, Temporal Grounded Policy Optimization, to solve general STL tasks. TGPO decomposes STL into timed subgoals and invariant constraints and provides a hierarchical framework to tackle the problem. The high-level component of TGPO proposes concrete time allocations for these subgoals, and the low-level time-conditioned policy learns to achieve the sequenced subgoals using a dense, stage-wise reward signal. During inference, we sample various time allocations and select the most promising assignment for the policy network to rollout the solution trajectory. To foster efficient policy learning for complex STL with multiple subgoals, we leverage the learned critic to guide the high-level temporal search via Metropolis-Hastings sampling, focusing exploration on temporally feasible solutions. We conduct experiments on five environments, ranging from low-dimensional navigation to manipulation, drone, and quadrupedal locomotion. Under a wide range of STL tasks, TGPO significantly outperforms state-of-the-art baselines (especially for high-dimensional and long-horizon cases), with an average of 31.6% improvement in task success rate compared to the best baseline. The code will be available at https://github.com/mengyuest/TGPO", "github_manual": "", "github_regex": "https://github.com/mengyuest/TGPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.25531", "title": "MixtureVitae: Open Web-Scale Pretraining Dataset With High Quality Instruction and Reasoning Data Built from Permissive-First Text Sources", "authors": [ "Huu Nguyen", "Victor May", "Harsh Raj", "Marianna Nezhurina", "Yishan Wang", "Yanqi Luo", "Minh Chien Vu", "Taishi Nakamura", "Ken Tsui", "Van Khue Nguyen", "David Salinas", "Aleksandra Krasnod\u0119bska", "Christoph Schuhmann", "Mats Leon Richter", "Xuan-Son", "Vu", "Jenia Jitsev" ], "github": "https://github.com/ontocord/mixturevitae", "project_page": "", "abstract": "We present MixtureVitae, an open-access pretraining corpus built to minimize legal risk while providing strong model performance. MixtureVitae follows a risk-mitigated sourcing strategy that combines public-domain and permissively licensed text (e.g., CC-BY/Apache) with carefully justified low-risk additions (e.g., government works and EU TDM-eligible sources), alongside targeted instruction, reasoning and synthetic data with documented provenance. We detail a transparent, multi-stage pipeline for license-aware filtering, safety and quality screening, and domain-aware mixing, and we release the dataset and curation recipes to support reproducible research. In controlled experiments using the open-sci-ref training protocol (fixed architectures at 130M/400M/1.3B/1.7B parameters; training budgets of 50B and 300B tokens), models trained on MixtureVitae consistently outperform other permissive datasets across a suite of standard benchmarks, and at the 1.7B/300B setting they surpass FineWeb-Edu and approach DCLM in the later stages of training. Performance is particularly strong on math/code and competitive on QA tasks. These results demonstrate that permissive-first, risk-mitigated data provides a practical and legally mitigated foundation for training capable LLMs, reducing reliance on indiscriminate web scraping without sacrificing competitiveness. Code: https://github.com/ontocord/mixturevitae", "github_manual": "", "github_regex": "https://github.com/ontocord/mixturevitae", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.26514", "title": "BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs", "authors": [ "Yue Wang", "Ruotian Ma", "Xingyu Chen", "Zhengliang Shi", "Wanshun Chen", "Huang Liu", "Jiadi Yao", "Qu Yang", "Qingxuan Jiang", "Fanghua Ye", "Juntao Li", "Min Zhang", "Zhaopeng Tu", "Xiaolong Li", "Linus" ], "github": "", "project_page": "", "abstract": "The rise of Large Language Models (LLMs) is reshaping multimodel models, with speech synthesis being a prominent application. However, existing approaches often underutilize the linguistic intelligence of these models, typically failing to leverage their powerful instruction-following capabilities. This limitation hinders the model's ability to follow text instructions for controllable Text-to-Speech~(TTS). To address this, we propose a new paradigm inspired by ``operationalism'' that decouples instruction understanding from speech generation. We introduce BatonVoice, a framework where an LLM acts as a ``conductor'', understanding user instructions and generating a textual ``plan'' -- explicit vocal features (e.g., pitch, energy). A separate TTS model, the ``orchestra'', then generates the speech from these features. To realize this component, we develop BatonTTS, a TTS model trained specifically for this task. Our experiments demonstrate that BatonVoice achieves strong performance in controllable and emotional speech synthesis, outperforming strong open- and closed-source baselines. Notably, our approach enables remarkable zero-shot cross-lingual generalization, accurately applying feature control abilities to languages unseen during post-training. This demonstrates that objectifying speech into textual vocal features can more effectively unlock the linguistic intelligence of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2509.25162", "title": "Aligning Visual Foundation Encoders to Tokenizers for Diffusion Models", "authors": [ "Bowei Chen", "Sai Bi", "Hao Tan", "He Zhang", "Tianyuan Zhang", "Zhengqi Li", "Yuanjun Xiong", "Jianming Zhang", "Kai Zhang" ], "github": "", "project_page": "", "abstract": "In this work, we propose aligning pretrained visual encoders to serve as tokenizers for latent diffusion models in image generation. Unlike training a variational autoencoder (VAE) from scratch, which primarily emphasizes low-level details, our approach leverages the rich semantic structure of foundation encoders. We introduce a three-stage alignment strategy: (1) freeze the encoder and train an adapter and a decoder to establish a semantic latent space; (2) jointly optimize all components with an additional semantic preservation loss, enabling the encoder to capture perceptual details while retaining high-level semantics; and (3) refine the decoder for improved reconstruction quality. This alignment yields semantically rich image tokenizers that benefit diffusion models. On ImageNet 256times256, our tokenizer accelerates the convergence of diffusion models, reaching a gFID of 1.90 within just 64 epochs, and improves generation both with and without classifier-free guidance. Scaling to LAION, a 2B-parameter text-to-image model trained with our tokenizer consistently outperforms FLUX VAE under the same training steps. Overall, our method is simple, scalable, and establishes a semantically grounded paradigm for continuous tokenizer design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-02", "arxiv_id": "2510.01152", "title": "Pay-Per-Search Models are Abstention Models", "authors": [ "Mustafa Omer Gul", "Claire Cardie", "Tanya Goyal" ], "github": "https://github.com/momergul/mash", "project_page": "", "abstract": "LLMs cannot reliably recognize their parametric knowledge boundaries and often hallucinate answers to outside-of-boundary questions. In contrast, humans recognize their limitations and can either seek external help for such questions or abstain. In this paper, we introduce MASH (Modeling Abstention via Selective Help-seeking), a training framework that readily extracts abstentions from LLMs. Our key idea is that any external help-seeking by an LLM, i.e. search tool use, can serve as a proxy for abstention if the external help (search) is appropriately penalized while simultaneously rewarding answer accuracy. MASH operationalizes this idea using reinforcement learning with a pay-per-search reward. We run experiments on three knowledge-intensive QA datasets. Our results show that MASH substantially improves upon the selective help-seeking performance of prior efficient search approaches; on multi-hop datasets, MASH improves answer accuracy by 7.6%. Furthermore, MASH demonstrates strong off-the-shelf abstention -- it can distinguish between unanswerable/answerable questions and selectively generate responses for answerable questions -- showcasing behavior analogous to specialized abstention approaches. We emphasize that contrary to prior abstention methods, MASH does not require pre-determining knowledge boundaries to construct training data. Instead, MASH's abstentions are a by-product of training for the auxiliary selective help-seeking task. Overall, we show that MASH training effectively aligns search tool use with parametric knowledge, which can be successfully leveraged for making abstention decisions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/momergul/mash", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02297", "title": "Interactive Training: Feedback-Driven Neural Network Optimization", "authors": [ "Wentao Zhang", "Yang Young Lu", "Yuntian Deng" ], "github": "https://github.com/yuntian-group/interactive-training", "project_page": "", "abstract": "Traditional neural network training typically follows fixed, predefined optimization recipes, lacking the flexibility to dynamically respond to instabilities or emerging training issues. In this paper, we introduce Interactive Training, an open-source framework that enables real-time, feedback-driven intervention during neural network training by human experts or automated AI agents. At its core, Interactive Training uses a control server to mediate communication between users or agents and the ongoing training process, allowing users to dynamically adjust optimizer hyperparameters, training data, and model checkpoints. Through three case studies, we demonstrate that Interactive Training achieves superior training stability, reduced sensitivity to initial hyperparameters, and improved adaptability to evolving user needs, paving the way toward a future training paradigm where AI agents autonomously monitor training logs, proactively resolve instabilities, and optimize training dynamics.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yuntian-group/interactive-training", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01591", "title": "CLUE: Non-parametric Verification from Experience via Hidden-State Clustering", "authors": [ "Zhenwen Liang", "Ruosen Li", "Yujun Zhou", "Linfeng Song", "Dian Yu", "Xinya Du", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Assessing the quality of Large Language Model (LLM) outputs presents a critical challenge. Previous methods either rely on text-level information (e.g., reward models, majority voting), which can overfit to superficial cues, or on calibrated confidence from token probabilities, which would fail on less-calibrated models. Yet both of these signals are, in fact, partial projections of a richer source of information: the model's internal hidden states. Early layers, closer to token embeddings, preserve semantic and lexical features that underpin text-based judgments, while later layers increasingly align with output logits, embedding confidence-related information. This paper explores hidden states directly as a unified foundation for verification. We show that the correctness of a solution is encoded as a geometrically separable signature within the trajectory of hidden activations. To validate this, we present Clue (Clustering and Experience-based Verification), a deliberately minimalist, non-parametric verifier. With no trainable parameters, CLUE only summarizes each reasoning trace by an hidden state delta and classifies correctness via nearest-centroid distance to ``success'' and ``failure'' clusters formed from past experience. The simplicity of this method highlights the strength of the underlying signal. Empirically, CLUE consistently outperforms LLM-as-a-judge baselines and matches or exceeds modern confidence-based methods in reranking candidates, improving both top-1 and majority-vote accuracy across AIME 24/25 and GPQA. As a highlight, on AIME 24 with a 1.5B model, CLUE boosts accuracy from 56.7% (majority@64) to 70.0% (top-maj@16).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02259", "title": "Transformers Discover Molecular Structure Without Graph Priors", "authors": [ "Tobias Kreiman", "Yutong Bai", "Fadi Atieh", "Elizabeth Weaver", "Eric Qu", "Aditi S. Krishnapriyan" ], "github": "https://github.com/ASK-Berkeley/graph-free-transformer", "project_page": "", "abstract": "Graph Neural Networks (GNNs) are the dominant architecture for molecular machine learning, particularly for molecular property prediction and machine learning interatomic potentials (MLIPs). GNNs perform message passing on predefined graphs often induced by a fixed radius cutoff or k-nearest neighbor scheme. While this design aligns with the locality present in many molecular tasks, a hard-coded graph can limit expressivity due to the fixed receptive field and slows down inference with sparse graph operations. In this work, we investigate whether pure, unmodified Transformers trained directly on Cartesian coordinatesx2013without predefined graphs or physical priorsx2013can approximate molecular energies and forces. As a starting point for our analysis, we demonstrate how to train a Transformer to competitive energy and force mean absolute errors under a matched training compute budget, relative to a state-of-the-art equivariant GNN on the OMol25 dataset. We discover that the Transformer learns physically consistent patternsx2013such as attention weights that decay inversely with interatomic distancex2013and flexibly adapts them across different molecular environments due to the absence of hard-coded biases. The use of a standard Transformer also unlocks predictable improvements with respect to scaling training resources, consistent with empirical scaling laws observed in other domains. Our results demonstrate that many favorable properties of GNNs can emerge adaptively in Transformers, challenging the necessity of hard-coded graph inductive biases and pointing toward standardized, scalable architectures for molecular modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ASK-Berkeley/graph-free-transformer", "github_api_added_by": "auto" }, { "date": "2025-10-03", "arxiv_id": "2510.00352", "title": "AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidance", "authors": [ "Tong Chen", "Yinuo Zhang", "Pranam Chatterjee" ], "github": "", "project_page": "", "abstract": "Designing sequences that satisfy multiple, often conflicting, objectives is a central challenge in therapeutic and biomolecular engineering. Existing generative frameworks largely operate in continuous spaces with single-objective guidance, while discrete approaches lack guarantees for multi-objective Pareto optimality. We introduce AReUReDi (Annealed Rectified Updates for Refining Discrete Flows), a discrete optimization algorithm with theoretical guarantees of convergence to the Pareto front. Building on Rectified Discrete Flows (ReDi), AReUReDi combines Tchebycheff scalarization, locally balanced proposals, and annealed Metropolis-Hastings updates to bias sampling toward Pareto-optimal states while preserving distributional invariance. Applied to peptide and SMILES sequence design, AReUReDi simultaneously optimizes up to five therapeutic properties (including affinity, solubility, hemolysis, half-life, and non-fouling) and outperforms both evolutionary and diffusion-based baselines. These results establish AReUReDi as a powerful, sequence-based framework for multi-property biomolecule generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02283", "title": "Self-Forcing++: Towards Minute-Scale High-Quality Video Generation", "authors": [ "Justin Cui", "Jie Wu", "Ming Li", "Tao Yang", "Xiaojie Li", "Rui Wang", "Andrew Bai", "Yuanhao Ban", "Cho-Jui Hsieh" ], "github": "https://github.com/.../self-forcing-plus-plus.github.io", "project_page": "", "abstract": "Diffusion models have revolutionized image and video generation, achieving unprecedented visual quality. However, their reliance on transformer architectures incurs prohibitively high computational costs, particularly when extending generation to long videos. Recent work has explored autoregressive formulations for long video generation, typically by distilling from short-horizon bidirectional teachers. Nevertheless, given that teacher models cannot synthesize long videos, the extrapolation of student models beyond their training horizon often leads to pronounced quality degradation, arising from the compounding of errors within the continuous latent space. In this paper, we propose a simple yet effective approach to mitigate quality degradation in long-horizon video generation without requiring supervision from long-video teachers or retraining on long video datasets. Our approach centers on exploiting the rich knowledge of teacher models to provide guidance for the student model through sampled segments drawn from self-generated long videos. Our method maintains temporal consistency while scaling video length by up to 20x beyond teacher's capability, avoiding common issues such as over-exposure and error-accumulation without recomputing overlapping frames like previous methods. When scaling up the computation, our method shows the capability of generating videos up to 4 minutes and 15 seconds, equivalent to 99.9% of the maximum span supported by our base model's position embedding and more than 50x longer than that of our baseline model. Experiments on standard benchmarks and our proposed improved benchmark demonstrate that our approach substantially outperforms baseline methods in both fidelity and consistency. Our long-horizon videos demo can be found at https://self-forcing-plus-plus.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/.../self-forcing-plus-plus.github.io", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02253", "title": "DragFlow: Unleashing DiT Priors with Region Based Supervision for Drag Editing", "authors": [ "Zihan Zhou", "Shilin Lu", "Shuli Leng", "Shaocong Zhang", "Zhuming Lian", "Xinlei Yu", "Adams Wai-Kin Kong" ], "github": "", "project_page": "", "abstract": "Drag-based image editing has long suffered from distortions in the target region, largely because the priors of earlier base models, Stable Diffusion, are insufficient to project optimized latents back onto the natural image manifold. With the shift from UNet-based DDPMs to more scalable DiT with flow matching (e.g., SD3.5, FLUX), generative priors have become significantly stronger, enabling advances across diverse editing tasks. However, drag-based editing has yet to benefit from these stronger priors. This work proposes the first framework to effectively harness FLUX's rich prior for drag-based editing, dubbed DragFlow, achieving substantial gains over baselines. We first show that directly applying point-based drag editing to DiTs performs poorly: unlike the highly compressed features of UNets, DiT features are insufficiently structured to provide reliable guidance for point-wise motion supervision. To overcome this limitation, DragFlow introduces a region-based editing paradigm, where affine transformations enable richer and more consistent feature supervision. Additionally, we integrate pretrained open-domain personalization adapters (e.g., IP-Adapter) to enhance subject consistency, while preserving background fidelity through gradient mask-based hard constraints. Multimodal large language models (MLLMs) are further employed to resolve task ambiguities. For evaluation, we curate a novel Region-based Dragging benchmark (ReD Bench) featuring region-level dragging instructions. Extensive experiments on DragBench-DR and ReD Bench show that DragFlow surpasses both point-based and region-based baselines, setting a new state-of-the-art in drag-based image editing. Code and datasets will be publicly available upon publication.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.00523", "title": "VIRTUE: Visual-Interactive Text-Image Universal Embedder", "authors": [ "Wei-Yao Wang", "Kazuya Tateishi", "Qiyu Wu", "Shusuke Takahashi", "Yuki Mitsufuji" ], "github": "", "project_page": "", "abstract": "Multimodal representation learning models have demonstrated successful operation across complex tasks, and the integration of vision-language models (VLMs) has further enabled embedding models with instruction-following capabilities. However, existing embedding models lack visual-interactive capabilities to specify regions of interest from users (e.g., point, bounding box, mask), which have been explored in generative models to broaden their human-interactive applicability. Equipping embedding models with visual interactions not only would unlock new applications with localized grounding of user intent, which remains unexplored, but also enable the models to learn entity-level information within images to complement their global representations for conventional embedding tasks. In this paper, we propose a novel Visual-InteRactive Text-Image Universal Embedder (VIRTUE) that extends the capabilities of the segmentation model and the vision-language model to the realm of representation learning. In VIRTUE, the segmentation model can process visual prompts that pinpoint specific regions within an image, thereby enabling the embedder to handle complex and ambiguous scenarios more precisely. To evaluate the visual-interaction ability of VIRTUE, we introduce a large-scale Segmentation-and-Scene Caption Retrieval (SCaR) benchmark comprising 1M samples that aims to retrieve the text caption by jointly considering the entity with a specific object and image scene. VIRTUE consistently achieves a state-of-the-art performance with significant improvements across 36 universal MMEB (3.1%-8.5%) and five visual-interactive SCaR (15.2%-20.3%) tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.00446", "title": "LongCodeZip: Compress Long Context for Code Language Models", "authors": [ "Yuling Shi", "Yichun Qian", "Hongyu Zhang", "Beijun Shen", "Xiaodong Gu" ], "github": "https://github.com/YerbaPage/LongCodeZip", "project_page": "", "abstract": "Code generation under long contexts is becoming increasingly critical as Large Language Models (LLMs) are required to reason over extensive information in the codebase. While recent advances enable code LLMs to process long inputs, high API costs and generation latency remain substantial bottlenecks. Existing context pruning techniques, such as LLMLingua, achieve promising results for general text but overlook code-specific structures and dependencies, leading to suboptimal performance in programming tasks. In this paper, we propose LongCodeZip, a novel plug-and-play code compression framework designed specifically for code LLMs. LongCodeZip employs a dual-stage strategy: (1) coarse-grained compression, which identifies and ranks function-level chunks using conditional perplexity with respect to the instruction, retaining only the most relevant functions; and (2) fine-grained compression, which segments retained functions into blocks based on perplexity and selects an optimal subset under an adaptive token budget to maximize relevance. Evaluations across multiple tasks, including code completion, summarization, and question answering, show that LongCodeZip consistently outperforms baseline methods, achieving up to a 5.6x compression ratio without degrading task performance. By effectively reducing context size while preserving essential information, LongCodeZip enables LLMs to better scale to real-world, large-scale code scenarios, advancing the efficiency and capability of code intelligence applications.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YerbaPage/LongCodeZip", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01444", "title": "VOGUE: Guiding Exploration with Visual Uncertainty Improves Multimodal Reasoning", "authors": [ "Rui Liu", "Dian Yu", "Tong Zheng", "Runpeng Dai", "Zongxia Li", "Wenhao Yu", "Zhenwen Liang", "Linfeng Song", "Haitao Mi", "Pratap Tokekar", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models (LLMs) but struggles with exploration, an issue that still persists for multimodal LLMs (MLLMs). Current methods treat the visual input as a fixed, deterministic condition, overlooking a critical source of ambiguity and struggling to build policies robust to plausible visual variations. We introduce VOGUE (Visual Uncertainty Guided Exploration), a novel method that shifts exploration from the output (text) to the input (visual) space. By treating the image as a stochastic context, VOGUE quantifies the policy's sensitivity to visual perturbations using the symmetric KL divergence between a \"raw\" and \"noisy\" branch, creating a direct signal for uncertainty-aware exploration. This signal shapes the learning objective via an uncertainty-proportional bonus, which, combined with a token-entropy bonus and an annealed sampling schedule, effectively balances exploration and exploitation. Implemented within GRPO on two model scales (Qwen2.5-VL-3B/7B), VOGUE boosts pass@1 accuracy by an average of 2.6% on three visual math benchmarks and 3.7% on three general-domain reasoning benchmarks, while simultaneously increasing pass@4 performance and mitigating the exploration decay commonly observed in RL fine-tuning. Our work shows that grounding exploration in the inherent uncertainty of visual inputs is an effective strategy for improving multimodal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01265", "title": "RLP: Reinforcement as a Pretraining Objective", "authors": [ "Ali Hatamizadeh", "Syeda Nahida Akter", "Shrimai Prabhumoye", "Jan Kautz", "Mostofa Patwary", "Mohammad Shoeybi", "Bryan Catanzaro", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "The dominant paradigm for training large reasoning models starts with pre-training using next-token prediction loss on vast amounts of data. Reinforcement learning, while powerful in scaling reasoning, is introduced only as the very last phase of post-training, preceded by supervised fine-tuning. While dominant, is this an optimal way of training? In this paper, we present RLP, an information-driven reinforcement pretraining objective, that brings the core spirit of reinforcement learning -- exploration -- to the last phase of pretraining. The key idea is to treat chain-of-thought as an exploratory action, with rewards computed based on the information gain it provides for predicting future tokens. This training objective essentially encourages the model to think for itself before predicting what comes next, thus teaching an independent thinking behavior earlier in the pretraining. More concretely, the reward signal measures the increase in log-likelihood of the next token when conditioning on both context and a sampled reasoning chain, compared to conditioning on context alone. This approach yields a verifier-free dense reward signal, allowing for efficient training for the full document stream during pretraining. Specifically, RLP reframes reinforcement learning for reasoning as a pretraining objective on ordinary text, bridging the gap between next-token prediction and the emergence of useful chain-of-thought reasoning. Pretraining with RLP on Qwen3-1.7B-Base lifts the overall average across an eight-benchmark math-and-science suite by 19%. With identical post-training, the gains compound, with the largest improvements on reasoning-heavy tasks such as AIME25 and MMLU-Pro. Applying RLP to the hybrid Nemotron-Nano-12B-v2 increases the overall average from 42.81% to 61.32% and raises the average on scientific reasoning by 23%, demonstrating scalability across architectures and model sizes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01179", "title": "TOUCAN: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP Environments", "authors": [ "Zhangchen Xu", "Adriana Meza Soria", "Shawn Tan", "Anurag Roy", "Ashish Sunil Agrawal", "Radha Poovendran", "Rameswar Panda" ], "github": "https://github.com/TheAgentArk/Toucan", "project_page": "", "abstract": "Large Language Model (LLM) agents are rapidly emerging as powerful systems for automating tasks across domains. Yet progress in the open-source community is constrained by the lack of high quality permissively licensed tool-agentic training data. Existing datasets are often limited in diversity, realism, and complexity, particularly regarding multi-tool and multi-turn interactions. To address this gap, we introduce Toucan, the largest publicly available tool-agentic dataset to date, containing 1.5 million trajectories synthesized from nearly 500 real-world Model Context Protocols (MCPs). Unlike prior work, Toucan leverages authentic MCP environments to generate diverse, realistic, and challenging tasks with trajectories involving real tool execution. Our pipeline first produces a broad spectrum of tool-use queries using five distinct models, applies model-based quality filtering, and then generates agentic trajectories with three teacher models using two agentic frameworks. Rigorous rule-based and model-based validation ensures high-quality outputs. We also introduce three extension mechanisms to further diversify tasks and simulate multi-turn conversations. Models fine-tuned on Toucan outperform larger closed-source counterparts on the BFCL V3 benchmark and push the Pareto frontier forward on MCP-Universe Bench.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/TheAgentArk/Toucan", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02294", "title": "F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data", "authors": [ "Ziyin Zhang", "Zihan Liao", "Hang Yu", "Peng Di", "Rui Wang" ], "github": "https://github.com/codefuse-ai/CodeFuse-Embeddings", "project_page": "", "abstract": "We introduce F2LLM - Foundation to Feature Large Language Models, a suite of state-of-the-art embedding models in three sizes: 0.6B, 1.7B, and 4B. Unlike previous top-ranking embedding models that require massive contrastive pretraining, sophisticated training pipelines, and costly synthetic training data, F2LLM is directly finetuned from foundation models on 6 million query-document-negative tuples curated from open-source, non-synthetic datasets, striking a strong balance between training cost, model size, and embedding performance. On the MTEB English leaderboard, F2LLM-4B ranks 2nd among models with approximately 4B parameters and 7th overall, while F2LLM-1.7B ranks 1st among models in the 1B-2B size range. To facilitate future research in the field, we release the models, training dataset, and code, positioning F2LLM as a strong, reproducible, and budget-friendly baseline for future works.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/codefuse-ai/CodeFuse-Embeddings", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02250", "title": "The Unreasonable Effectiveness of Scaling Agents for Computer Use", "authors": [ "Gonzalo Gonzalez-Pumariega", "Vincent Tu", "Chih-Lun Lee", "Jiachen Yang", "Ang Li", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "Computer-use agents (CUAs) hold promise for automating everyday digital tasks, but their unreliability and high variance hinder their application to long-horizon, complex tasks. We introduce Behavior Best-of-N (bBoN), a method that scales over agents by generating multiple rollouts and selecting among them using behavior narratives that describe the agents' rollouts. It enables both wide exploration and principled trajectory selection, substantially improving robustness and success rates. On OSWorld, our bBoN scaling method establishes a new state of the art (SoTA) at 69.9%, significantly outperforming prior methods and approaching human-level performance at 72%, with comprehensive ablations validating key design choices. We further demonstrate strong generalization results to different operating systems on WindowsAgentArena and AndroidWorld. Crucially, our results highlight the unreasonable effectiveness of scaling CUAs, when you do it right: effective scaling requires structured trajectory understanding and selection, and bBoN provides a practical framework to achieve this.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2509.24203", "title": "Group-Relative REINFORCE Is Secretly an Off-Policy Algorithm: Demystifying Some Myths About GRPO and Its Friends", "authors": [ "Chaorui Yao", "Yanxi Chen", "Yuchang Sun", "Yushuo Chen", "Wenhao Zhang", "Xuchen Pan", "Yaliang Li", "Bolin Ding" ], "github": "https://github.com/modelscope/Trinity-RFT/tree/main/examples", "project_page": "", "abstract": "Off-policy reinforcement learning (RL) for large language models (LLMs) is attracting growing interest, driven by practical constraints in real-world applications, the complexity of LLM-RL infrastructure, and the need for further innovations of RL methodologies. While classic REINFORCE and its modern variants like Group Relative Policy Optimization (GRPO) are typically regarded as on-policy algorithms with limited tolerance of off-policyness, we present in this work a first-principles derivation for group-relative REINFORCE without assuming a specific training data distribution, showing that it admits a native off-policy interpretation. This perspective yields two general principles for adapting REINFORCE to off-policy settings: regularizing policy updates, and actively shaping the data distribution. Our analysis demystifies some myths about the roles of importance sampling and clipping in GRPO, unifies and reinterprets two recent algorithms -- Online Policy Mirror Descent (OPMD) and Asymmetric REINFORCE (AsymRE) -- as regularized forms of the REINFORCE loss, and offers theoretical justification for seemingly heuristic data-weighting strategies. Our findings lead to actionable insights that are validated with extensive empirical studies, and open up new opportunities for principled algorithm design in off-policy RL for LLMs. Source code for this work is available at https://github.com/modelscope/Trinity-RFT/tree/main/examples/rec_gsm8k.", "github_manual": "", "github_regex": "https://github.com/modelscope/Trinity-RFT/tree/main/examples", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02209", "title": "StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?", "authors": [ "Yanxu Chen", "Zijun Yao", "Yantao Liu", "Jin Ye", "Jianing Yu", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/ChenYXxxx/stockbench", "project_page": "", "abstract": "Large language models (LLMs) have recently demonstrated strong capabilities as autonomous agents, showing promise in reasoning, tool use, and sequential decision-making. While prior benchmarks have evaluated LLM agents in domains such as software engineering and scientific discovery, the finance domain remains underexplored, despite its direct relevance to economic value and high-stakes decision-making. Existing financial benchmarks primarily test static knowledge through question answering, but they fall short of capturing the dynamic and iterative nature of trading. To address this gap, we introduce StockBench, a contamination-free benchmark designed to evaluate LLM agents in realistic, multi-month stock trading environments. Agents receive daily market signals -- including prices, fundamentals, and news -- and must make sequential buy, sell, or hold decisions. Performance is assessed using financial metrics such as cumulative return, maximum drawdown, and the Sortino ratio. Our evaluation of state-of-the-art proprietary (e.g., GPT-5, Claude-4) and open-weight (e.g., Qwen3, Kimi-K2, GLM-4.5) models shows that while most LLM agents struggle to outperform the simple buy-and-hold baseline, several models demonstrate the potential to deliver higher returns and manage risk more effectively. These findings highlight both the challenges and opportunities in developing LLM-powered financial agents, showing that excelling at static financial knowledge tasks does not necessarily translate into successful trading strategies. We release StockBench as an open-source resource to support reproducibility and advance future research in this domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ChenYXxxx/stockbench", "github_api_added_by": "auto" }, { "date": "2025-10-03", "arxiv_id": "2510.02190", "title": "A Rigorous Benchmark with Multidimensional Evaluation for Deep Research Agents: From Answers to Reports", "authors": [ "Yang Yao", "Yixu Wang", "Yuxuan Zhang", "Yi Lu", "Tianle Gu", "Lingyu Li", "Dingyi Zhao", "Keming Wu", "Haozhe Wang", "Ping Nie", "Yan Teng", "Yingchun Wang" ], "github": "https://github.com/evigbyen/rigorousbench", "project_page": "", "abstract": "Artificial intelligence is undergoing the paradigm shift from closed language models to interconnected agent systems capable of external perception and information integration. As a representative embodiment, Deep Research Agents (DRAs) systematically exhibit the capabilities for task decomposition, cross-source retrieval, multi-stage reasoning, and structured output, which markedly enhance performance on complex and open-ended tasks. However, existing benchmarks remain deficient in evaluation dimensions, response formatting, and scoring mechanisms, limiting their capacity to assess such systems effectively. This paper introduces a rigorous benchmark and a multidimensional evaluation framework tailored to DRAs and report-style responses. The benchmark comprises 214 expert-curated challenging queries distributed across 10 broad thematic domains, each accompanied by manually constructed reference bundles to support composite evaluation. The framework enables comprehensive evaluation of long-form reports generated by DRAs, incorporating integrated scoring metrics for semantic quality, topical focus, and retrieval trustworthiness. Extensive experimentation confirms the superior performance of mainstream DRAs over web-search-tool-augmented reasoning models, yet reveals considerable scope for further improvement. This study provides a robust foundation for capability assessment, architectural refinement, and paradigm advancement in DRA systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/evigbyen/rigorousbench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01796", "title": "Rethinking the shape convention of an MLP", "authors": [ "Meng-Hsi Chen", "Yu-Ang Lee", "Feng-Ting Liao", "Da-shan Shiu" ], "github": "", "project_page": "", "abstract": "Multi-layer perceptrons (MLPs) conventionally follow a narrow-wide-narrow design where skip connections operate at the input/output dimensions while processing occurs in expanded hidden spaces. We challenge this convention by proposing wide-narrow-wide (Hourglass) MLP blocks where skip connections operate at expanded dimensions while residual computation flows through narrow bottlenecks. This inversion leverages higher-dimensional spaces for incremental refinement while maintaining computational efficiency through parameter-matched designs. Implementing Hourglass MLPs requires an initial projection to lift input signals to expanded dimensions. We propose that this projection can remain fixed at random initialization throughout training, enabling efficient training and inference implementations. We evaluate both architectures on generative tasks over popular image datasets, characterizing performance-parameter Pareto frontiers through systematic architectural search. Results show that Hourglass architectures consistently achieve superior Pareto frontiers compared to conventional designs. As parameter budgets increase, optimal Hourglass configurations favor deeper networks with wider skip connections and narrower bottlenecks-a scaling pattern distinct from conventional MLPs. Our findings suggest reconsidering skip connection placement in modern architectures, with potential applications extending to Transformers and other residual networks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01691", "title": "MedQ-Bench: Evaluating and Exploring Medical Image Quality Assessment Abilities in MLLMs", "authors": [ "Jiyao Liu", "Jinjie Wei", "Wanying Qu", "Chenglong Ma", "Junzhi Ning", "Yunheng Li", "Ying Chen", "Xinzhe Luo", "Pengcheng Chen", "Xin Gao", "Ming Hu", "Huihui Xu", "Xin Wang", "Shujian Gao", "Dingkang Yang", "Zhongying Deng", "Jin Ye", "Lihao Liu", "Junjun He", "Ningsheng Xu" ], "github": "https://github.com/liujiyaoFDU/MedQBench", "project_page": "", "abstract": "Medical Image Quality Assessment (IQA) serves as the first-mile safety gate for clinical AI, yet existing approaches remain constrained by scalar, score-based metrics and fail to reflect the descriptive, human-like reasoning process central to expert evaluation. To address this gap, we introduce MedQ-Bench, a comprehensive benchmark that establishes a perception-reasoning paradigm for language-based evaluation of medical image quality with Multi-modal Large Language Models (MLLMs). MedQ-Bench defines two complementary tasks: (1) MedQ-Perception, which probes low-level perceptual capability via human-curated questions on fundamental visual attributes; and (2) MedQ-Reasoning, encompassing both no-reference and comparison reasoning tasks, aligning model evaluation with human-like reasoning on image quality. The benchmark spans five imaging modalities and over forty quality attributes, totaling 2,600 perceptual queries and 708 reasoning assessments, covering diverse image sources including authentic clinical acquisitions, images with simulated degradations via physics-based reconstructions, and AI-generated images. To evaluate reasoning ability, we propose a multi-dimensional judging protocol that assesses model outputs along four complementary axes. We further conduct rigorous human-AI alignment validation by comparing LLM-based judgement with radiologists. Our evaluation of 14 state-of-the-art MLLMs demonstrates that models exhibit preliminary but unstable perceptual and reasoning skills, with insufficient accuracy for reliable clinical use. These findings highlight the need for targeted optimization of MLLMs in medical IQA. We hope that MedQ-Bench will catalyze further exploration and unlock the untapped potential of MLLMs for medical image quality evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/liujiyaoFDU/MedQBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01670", "title": "Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directedness", "authors": [ "Erfan Shayegani", "Keegan Hines", "Yue Dong", "Nael Abu-Ghazaleh", "Roman Lutz", "Spencer Whitehead", "Vidhisha Balachandran", "Besmira Nushi", "Vibhav Vineet" ], "github": "", "project_page": "", "abstract": "Computer-Use Agents (CUAs) are an increasingly deployed class of agents that take actions on GUIs to accomplish user goals. In this paper, we show that CUAs consistently exhibit Blind Goal-Directedness (BGD): a bias to pursue goals regardless of feasibility, safety, reliability, or context. We characterize three prevalent patterns of BGD: (i) lack of contextual reasoning, (ii) assumptions and decisions under ambiguity, and (iii) contradictory or infeasible goals. We develop BLIND-ACT, a benchmark of 90 tasks capturing these three patterns. Built on OSWorld, BLIND-ACT provides realistic environments and employs LLM-based judges to evaluate agent behavior, achieving 93.75% agreement with human annotations. We use BLIND-ACT to evaluate nine frontier models, including Claude Sonnet and Opus 4, Computer-Use-Preview, and GPT-5, observing high average BGD rates (80.8%) across them. We show that BGD exposes subtle risks that arise even when inputs are not directly harmful. While prompting-based interventions lower BGD levels, substantial risk persists, highlighting the need for stronger training- or inference-time interventions. Qualitative analysis reveals observed failure modes: execution-first bias (focusing on how to act over whether to act), thought-action disconnect (execution diverging from reasoning), and request-primacy (justifying actions due to user request). Identifying BGD and introducing BLIND-ACT establishes a foundation for future research on studying and mitigating this fundamental risk and ensuring safe CUA deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.00428", "title": "Automated Structured Radiology Report Generation with Rich Clinical Context", "authors": [ "Seongjae Kang", "Dong Bok Lee", "Juho Jung", "Dongseop Kim", "Won Hwa Kim", "Sunghoon Joo" ], "github": "https://github.com/vuno/contextualized-srrg", "project_page": "", "abstract": "Automated structured radiology report generation (SRRG) from chest X-ray images offers significant potential to reduce workload of radiologists by generating reports in structured formats that ensure clarity, consistency, and adherence to clinical reporting standards. While radiologists effectively utilize available clinical contexts in their diagnostic reasoning, existing SRRG systems overlook these essential elements. This fundamental gap leads to critical problems including temporal hallucinations when referencing non-existent clinical contexts. To address these limitations, we propose contextualized SRRG (C-SRRG) that comprehensively incorporates rich clinical context for SRRG. We curate C-SRRG dataset by integrating comprehensive clinical context encompassing 1) multi-view X-ray images, 2) clinical indication, 3) imaging techniques, and 4) prior studies with corresponding comparisons based on patient histories. Through extensive benchmarking with state-of-the-art multimodal large language models, we demonstrate that incorporating clinical context with the proposed C-SRRG significantly improves report generation quality. We publicly release dataset, code, and checkpoints to facilitate future research for clinically-aligned automated RRG at https://github.com/vuno/contextualized-srrg.", "github_manual": "", "github_regex": "https://github.com/vuno/contextualized-srrg", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2509.24304", "title": "FrameThinker: Learning to Think with Long Videos via Multi-Turn Frame Spotlighting", "authors": [ "Zefeng He", "Xiaoye Qu", "Yafu Li", "Siyuan Huang", "Daizong Liu", "Yu Cheng" ], "github": "", "project_page": "", "abstract": "While Large Vision-Language Models (LVLMs) have achieved substantial progress in video understanding, their application to long video reasoning is hindered by uniform frame sampling and static textual reasoning, which are inefficient and struggle to handle visually intensive video tasks. To overcome these challenges, in this paper, we introduce the concept of thinking with long videos and propose a novel framework FrameThinker. Within this framework, LVLMs are able to iteratively interrogate video content. Developing such video reasoning capabilities in LVLMs presents notable challenges, particularly in adapting the model to new video actions (e.g. select frame), and designing reward functions to guide LVLMs to adopt the newly introduced action. To solve these challenges, we propose a two-phase training strategy, first employing Supervised Fine-Tuning (SFT) to instill fundamental action capabilities, followed by Reinforcement Learning (RL) to optimize a strategic decision-making policy. Notably, in this RL phase, we conduct an in-depth and comprehensive exploration of the reward design for each action and format reward. Extensive experiments on reasoning benchmarks like Video-Holmes, LongVideo-Reason, and long-video understanding benchmarks such as LongVideoBench, MLVU, VideoMME, and LVBench, demonstrate that FrameThinker achieves a significant average improvement of +10.4% over baselines while drastically reducing the number of processed frames. Most notably, our 7B model, FrameThinker establishes a new state-of-the-art on LongVideo-Reason, achieving 76.1% accuracy using an average of only 20.6 frames. This not only outperforms the competitive LongVILA-R1 (72.0%) but does so with over 20x fewer frames (vs. 512), demonstrating unparalleled efficiency and effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01241", "title": "SKYLENAGE Technical Report: Mathematical Reasoning and Contest-Innovation Benchmarks for Multi-Level Math Evaluation", "authors": [ "Hu Wei", "Ze Xu", "Boyu Yang", "Linlin Miao", "Weiqi Zhai", "Yihan Li", "Zixuan Li", "Zhijun Wang", "Boya Wang", "Jianwei Yu", "Jialing Yuan", "Xiaoyue Zhang", "Cheng He", "Minglei Chen", "Zifan Zhang", "Qianhui Li", "Wei Wang", "Xiang Xu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) now perform strongly on many public math suites, yet frontier separation within mathematics increasingly suffers from ceiling effects. We present two complementary benchmarks: SKYLENAGE-ReasoningMATH, a 100-item, structure-aware diagnostic set with per-item metadata on length, numeric density, and symbolic complexity; and SKYLENAGE-MATH, a 150-item contest-style suite spanning four stages from high school to doctoral under a seven-subject taxonomy. We evaluate fifteen contemporary LLM variants under a single setup and analyze subject x model and grade x model performance. On the contest suite, the strongest model reaches 44% while the runner-up reaches 37%; accuracy declines from high school to doctoral, and top systems exhibit a doctoral-to-high-school retention near 79%. On the reasoning set, the best model attains 81% overall, and hardest-slice results reveal clear robustness gaps between leaders and the mid-tier. In summary, we release SKYLENAGE-ReasoningMATH and report aggregate results for SKYLENAGE-MATH; together, SKYLENAGE provides a hard, reasoning-centered and broadly covering math benchmark with calibrated difficulty and rich metadata, serving as a reference benchmark for future evaluations of mathematical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01623", "title": "VLA-R1: Enhancing Reasoning in Vision-Language-Action Models", "authors": [ "Angen Ye", "Zeyu Zhang", "Boyuan Wang", "Xiaofeng Wang", "Dapeng Zhang", "Zheng Zhu" ], "github": "https://github.com/GigaAI-research/VLA-R1", "project_page": "", "abstract": "Vision-Language-Action (VLA) models aim to unify perception, language understanding, and action generation, offering strong cross-task and cross-scene generalization with broad impact on embodied AI. However, current VLA models often lack explicit step-by-step reasoning, instead emitting final actions without considering affordance constraints or geometric relations. Their post-training pipelines also rarely reinforce reasoning quality, relying primarily on supervised fine-tuning with weak reward design. To address these challenges, we present VLA-R1, a reasoning-enhanced VLA that integrates Reinforcement Learning from Verifiable Rewards (RLVR) with Group Relative Policy Optimization (GRPO) to systematically optimize both reasoning and execution. Specifically, we design an RLVR-based post-training strategy with verifiable rewards for region alignment, trajectory consistency, and output formatting, thereby strengthening reasoning robustness and execution accuracy. Moreover, we develop VLA-CoT-13K, a high-quality dataset that provides chain-of-thought supervision explicitly aligned with affordance and trajectory annotations. Furthermore, extensive evaluations on in-domain, out-of-domain, simulation, and real-robot platforms demonstrate that VLA-R1 achieves superior generalization and real-world performance compared to prior VLA methods. We plan to release the model, code, and dataset following the publication of this work. Code: https://github.com/GigaAI-research/VLA-R1. Website: https://gigaai-research.github.io/VLA-R1.", "github_manual": "", "github_regex": "https://github.com/GigaAI-research/VLA-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01284", "title": "Ovi: Twin Backbone Cross-Modal Fusion for Audio-Video Generation", "authors": [ "Chetwin Low", "Weimin Wang", "Calder Katyal" ], "github": "", "project_page": "", "abstract": "Audio-video generation has often relied on complex multi-stage architectures or sequential synthesis of sound and visuals. We introduce Ovi, a unified paradigm for audio-video generation that models the two modalities as a single generative process. By using blockwise cross-modal fusion of twin-DiT modules, Ovi achieves natural synchronization and removes the need for separate pipelines or post hoc alignment. To facilitate fine-grained multimodal fusion modeling, we initialize an audio tower with an architecture identical to that of a strong pretrained video model. Trained from scratch on hundreds of thousands of hours of raw audio, the audio tower learns to generate realistic sound effects, as well as speech that conveys rich speaker identity and emotion. Fusion is obtained by jointly training the identical video and audio towers via blockwise exchange of timing (via scaled-RoPE embeddings) and semantics (through bidirectional cross-attention) on a vast video corpus. Our model enables cinematic storytelling with natural speech and accurate, context-matched sound effects, producing movie-grade video clips. All the demos, code and model weights are published at https://aaxwaz.github.io/Ovi", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2509.26376", "title": "Go with Your Gut: Scaling Confidence for Autoregressive Image Generation", "authors": [ "Harold Haodong Chen", "Xianfeng Wu", "Wen-Jie Shu", "Rongjin Guo", "Disen Lan", "Harry Yang", "Ying-Cong Chen" ], "github": "https://github.com/EnVision-Research/ScalingAR", "project_page": "", "abstract": "Test-time scaling (TTS) has demonstrated remarkable success in enhancing large language models, yet its application to next-token prediction (NTP) autoregressive (AR) image generation remains largely uncharted. Existing TTS approaches for visual AR (VAR), which rely on frequent partial decoding and external reward models, are ill-suited for NTP-based image generation due to the inherent incompleteness of intermediate decoding results. To bridge this gap, we introduce ScalingAR, the first TTS framework specifically designed for NTP-based AR image generation that eliminates the need for early decoding or auxiliary rewards. ScalingAR leverages token entropy as a novel signal in visual token generation and operates at two complementary scaling levels: (i) Profile Level, which streams a calibrated confidence state by fusing intrinsic and conditional signals; and (ii) Policy Level, which utilizes this state to adaptively terminate low-confidence trajectories and dynamically schedule guidance for phase-appropriate conditioning strength. Experiments on both general and compositional benchmarks show that ScalingAR (1) improves base models by 12.5% on GenEval and 15.2% on TIIF-Bench, (2) efficiently reduces visual token consumption by 62.0% while outperforming baselines, and (3) successfully enhances robustness, mitigating performance drops by 26.0% in challenging scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EnVision-Research/ScalingAR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02272", "title": "Parallel Scaling Law: Unveiling Reasoning Generalization through A Cross-Linguistic Perspective", "authors": [ "Wen Yang", "Junhong Wu", "Chong Li", "Chengqing Zong", "Jiajun Zhang" ], "github": "", "project_page": "", "abstract": "Recent advancements in Reinforcement Post-Training (RPT) have significantly enhanced the capabilities of Large Reasoning Models (LRMs), sparking increased interest in the generalization of RL-based reasoning. While existing work has primarily focused on investigating its generalization across tasks or modalities, this study proposes a novel cross-linguistic perspective to investigate reasoning generalization. This raises a crucial question: Does the reasoning capability achieved from English RPT effectively transfer to other languages? We address this by systematically evaluating English-centric LRMs on multilingual reasoning benchmarks and introducing a metric to quantify cross-lingual transferability. Our findings reveal that cross-lingual transferability varies significantly across initial model, target language, and training paradigm. Through interventional studies, we find that models with stronger initial English capabilities tend to over-rely on English-specific patterns, leading to diminished cross-lingual generalization. To address this, we conduct a thorough parallel training study. Experimental results yield three key findings: First-Parallel Leap, a substantial leap in performance when transitioning from monolingual to just a single parallel language, and a predictable Parallel Scaling Law, revealing that cross-lingual reasoning transfer follows a power-law with the number of training parallel languages. Moreover, we identify the discrepancy between actual monolingual performance and the power-law prediction as Monolingual Generalization Gap, indicating that English-centric LRMs fail to fully generalize across languages. Our study challenges the assumption that LRM reasoning mirrors human cognition, providing critical insights for the development of more language-agnostic LRMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02245", "title": "ExGRPO: Learning to Reason from Experience", "authors": [ "Runzhe Zhan", "Yafu Li", "Zhi Wang", "Xiaoye Qu", "Dongrui Liu", "Jing Shao", "Derek F. Wong", "Yu Cheng" ], "github": "", "project_page": "", "abstract": "Reinforcement learning from verifiable rewards (RLVR) is an emerging paradigm for improving the reasoning ability of large language models. However, standard on-policy training discards rollout experiences after a single update, leading to computational inefficiency and instability. While prior work on RL has highlighted the benefits of reusing past experience, the role of experience characteristics in shaping learning dynamics of large reasoning models remains underexplored. In this paper, we are the first to investigate what makes a reasoning experience valuable and identify rollout correctness and entropy as effective indicators of experience value. Based on these insights, we propose ExGRPO (Experiential Group Relative Policy Optimization), a framework that organizes and prioritizes valuable experiences, and employs a mixed-policy objective to balance exploration with experience exploitation. Experiments on five backbone models (1.5B-8B parameters) show that ExGRPO consistently improves reasoning performance on mathematical/general benchmarks, with an average gain of +3.5/7.6 points over on-policy RLVR. Moreover, ExGRPO stabilizes training on both stronger and weaker models where on-policy methods fail. These results highlight principled experience management as a key ingredient for efficient and scalable RLVR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01538", "title": "TimeSeriesScientist: A General-Purpose AI Agent for Time Series Analysis", "authors": [ "Haokun Zhao", "Xiang Zhang", "Jiaqi Wei", "Yiwei Xu", "Yuting He", "Siqi Sun", "Chenyu You" ], "github": "https://github.com/Y-Research-SBU/TimeSeriesScientist", "project_page": "", "abstract": "Time series forecasting is central to decision-making in domains as diverse as energy, finance, climate, and public health. In practice, forecasters face thousands of short, noisy series that vary in frequency, quality, and horizon, where the dominant cost lies not in model fitting, but in the labor-intensive preprocessing, validation, and ensembling required to obtain reliable predictions. Prevailing statistical and deep learning models are tailored to specific datasets or domains and generalize poorly. A general, domain-agnostic framework that minimizes human intervention is urgently in demand. In this paper, we introduce TimeSeriesScientist (TSci), the first LLM-driven agentic framework for general time series forecasting. The framework comprises four specialized agents: Curator performs LLM-guided diagnostics augmented by external tools that reason over data statistics to choose targeted preprocessing; Planner narrows the hypothesis space of model choice by leveraging multi-modal diagnostics and self-planning over the input; Forecaster performs model fitting and validation and, based on the results, adaptively selects the best model configuration as well as ensemble strategy to make final predictions; and Reporter synthesizes the whole process into a comprehensive, transparent report. With transparent natural-language rationales and comprehensive reports, TSci transforms the forecasting workflow into a white-box system that is both interpretable and extensible across tasks. Empirical results on eight established benchmarks demonstrate that TSci consistently outperforms both statistical and LLM-based baselines, reducing forecast error by an average of 10.4% and 38.2%, respectively. Moreover, TSci produces a clear and rigorous report that makes the forecasting workflow more transparent and interpretable.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Y-Research-SBU/TimeSeriesScientist", "github_api_added_by": "auto" }, { "date": "2025-10-03", "arxiv_id": "2510.00537", "title": "Spectral Scaling Laws in Language Models: How Effectively Do Feed-Forward Networks Use Their Latent Space?", "authors": [ "Nandan Kumar Jha", "Brandon Reagen" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) scale, the question is not only how large they become, but how much of their capacity is effectively utilized. Existing scaling laws relate model size to loss, yet overlook how components exploit their latent space. We study feed-forward networks (FFNs) and recast width selection as a spectral utilization problem. Using a lightweight diagnostic suite -- Hard Rank (participation ratio), Soft Rank (Shannon rank), Spectral Concentration, and the composite Spectral Utilization Index (SUI) -- we quantify how many latent directions are meaningfully activated across LLaMA, GPT-2, and nGPT families. Our key finding is an asymmetric spectral scaling law: soft rank follows an almost perfect power law with FFN width, while hard rank grows only sublinearly and with high variance. This asymmetry suggests that widening FFNs mostly adds low-energy tail directions, while dominant-mode subspaces saturate early. Moreover, at larger widths, variance further collapses into a narrow subspace, leaving much of the latent space under-utilized. These results recast FFN width selection as a principled trade-off between tail capacity and dominant-mode capacity, offering concrete guidance for inference-efficient LLM design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02314", "title": "StealthAttack: Robust 3D Gaussian Splatting Poisoning via Density-Guided Illusions", "authors": [ "Bo-Hsu Ke", "You-Zhe Xie", "Yu-Lun Liu", "Wei-Chen Chiu" ], "github": "", "project_page": "", "abstract": "3D scene representation methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have significantly advanced novel view synthesis. As these methods become prevalent, addressing their vulnerabilities becomes critical. We analyze 3DGS robustness against image-level poisoning attacks and propose a novel density-guided poisoning method. Our method strategically injects Gaussian points into low-density regions identified via Kernel Density Estimation (KDE), embedding viewpoint-dependent illusory objects clearly visible from poisoned views while minimally affecting innocent views. Additionally, we introduce an adaptive noise strategy to disrupt multi-view consistency, further enhancing attack effectiveness. We propose a KDE-based evaluation protocol to assess attack difficulty systematically, enabling objective benchmarking for future research. Extensive experiments demonstrate our method's superior performance compared to state-of-the-art techniques. Project page: https://hentci.github.io/stealthattack/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02240", "title": "RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning", "authors": [ "Sicheng Feng", "Kaiwen Tuo", "Song Wang", "Lingdong Kong", "Jianke Zhu", "Huan Wang" ], "github": "", "project_page": "", "abstract": "Fine-grained visual reasoning remains a core challenge for multimodal large language models (MLLMs). The recently introduced ReasonMap highlights this gap by showing that even advanced MLLMs struggle with spatial reasoning in structured and information-rich settings such as transit maps, a task of clear practical and scientific importance. However, standard reinforcement learning (RL) on such tasks is impeded by sparse rewards and unstable optimization. To address this, we first construct ReasonMap-Plus, an extended dataset that introduces dense reward signals through Visual Question Answering (VQA) tasks, enabling effective cold-start training of fine-grained visual understanding skills. Next, we propose RewardMap, a multi-stage RL framework designed to improve both visual understanding and reasoning capabilities of MLLMs. RewardMap incorporates two key designs. First, we introduce a difficulty-aware reward design that incorporates detail rewards, directly tackling the sparse rewards while providing richer supervision. Second, we propose a multi-stage RL scheme that bootstraps training from simple perception to complex reasoning tasks, offering a more effective cold-start strategy than conventional Supervised Fine-Tuning (SFT). Experiments on ReasonMap and ReasonMap-Plus demonstrate that each component of RewardMap contributes to consistent performance gains, while their combination yields the best results. Moreover, models trained with RewardMap achieve an average improvement of 3.47% across 6 benchmarks spanning spatial reasoning, fine-grained visual reasoning, and general tasks beyond transit maps, underscoring enhanced visual understanding and reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02315", "title": "Optimal Control Meets Flow Matching: A Principled Route to Multi-Subject Fidelity", "authors": [ "Eric Tillmann Bill", "Enis Simsar", "Thomas Hofmann" ], "github": "", "project_page": "", "abstract": "Text-to-image (T2I) models excel on single-entity prompts but struggle with multi-subject descriptions, often showing attribute leakage, identity entanglement, and subject omissions. We introduce the first theoretical framework with a principled, optimizable objective for steering sampling dynamics toward multi-subject fidelity. Viewing flow matching (FM) through stochastic optimal control (SOC), we formulate subject disentanglement as control over a trained FM sampler. This yields two architecture-agnostic algorithms: (i) a training-free test-time controller that perturbs the base velocity with a single-pass update, and (ii) Adjoint Matching, a lightweight fine-tuning rule that regresses a control network to a backward adjoint signal while preserving base-model capabilities. The same formulation unifies prior attention heuristics, extends to diffusion models via a flow-diffusion correspondence, and provides the first fine-tuning route explicitly designed for multi-subject fidelity. Empirically, on Stable Diffusion 3.5, FLUX, and Stable Diffusion XL, both algorithms consistently improve multi-subject alignment while maintaining base-model style. Test-time control runs efficiently on commodity GPUs, and fine-tuned controllers trained on limited prompts generalize to unseen ones. We further highlight FOCUS (Flow Optimal Control for Unentangled Subjects), which achieves state-of-the-art multi-subject fidelity across models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01304", "title": "Agentic Jigsaw Interaction Learning for Enhancing Visual Perception and Reasoning in Vision-Language Models", "authors": [ "Yu Zeng", "Wenxuan Huang", "Shiting Huang", "Xikun Bao", "Yukun Qi", "Yiming Zhao", "Qiuchen Wang", "Lin Chen", "Zehui Chen", "Huaian Chen", "Wanli Ouyang", "Feng Zhao" ], "github": "https://github.com/yuzeng0-0/AGILE", "project_page": "", "abstract": "Although current large Vision-Language Models (VLMs) have advanced in multimodal understanding and reasoning, their fundamental perceptual and reasoning abilities remain limited. Specifically, even on simple jigsaw tasks, existing VLMs perform near randomly, revealing deficiencies in core perception and reasoning capabilities. While high-quality vision-language data can enhance these capabilities, its scarcity and limited scalability impose significant constraints. To address this, we propose AGILE, an Agentic jiGsaw Interaction Learning for Enhancing visual perception and reasoning in VLMs. AGILE formulates jigsaw solving as an interactive process, enabling the model to progressively engage with the environment. At each step, the model generates executable code to perform an action based on the current state, while the environment provides fine-grained visual feedback to guide task completion. Through this iterative cycle of observation and interaction, the model incrementally improves its perceptual and reasoning capabilities via exploration and feedback. Experimental results show that AGILE not only substantially boosts performance on jigsaw tasks of varying complexity (e.g., increasing accuracy from 9.5% to 82.8% under the 2 times 2 setting) but also demonstrates strong generalization across 9 general vision tasks, achieving an average improvement of 3.1%. These results indicate notable enhancements in both perceptual and reasoning abilities. This work opens a new avenue for advancing reasoning and generalization in multimodal models and provides an efficient, scalable solution to the scarcity of multimodal reinforcement learning data. The code and datasets is available at https://github.com/yuzeng0-0/AGILE .", "github_manual": "", "github_regex": "https://github.com/yuzeng0-0/AGILE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02173", "title": "Learning to Reason for Hallucination Span Detection", "authors": [ "Hsuan Su", "Ting-Yao Hu", "Hema Swetha Koppula", "Kundan Krishna", "Hadi Pouransari", "Cheng-Yu Hsieh", "Cem Koc", "Joseph Yitan Cheng", "Oncel Tuzel", "Raviteja Vemulapalli" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) often generate hallucinations -- unsupported content that undermines reliability. While most prior works frame hallucination detection as a binary task, many real-world applications require identifying hallucinated spans, which is a multi-step decision making process. This naturally raises the question of whether explicit reasoning can help the complex task of detecting hallucination spans. To answer this question, we first evaluate pretrained models with and without Chain-of-Thought (CoT) reasoning, and show that CoT reasoning has the potential to generate at least one correct answer when sampled multiple times. Motivated by this, we propose RL4HS, a reinforcement learning framework that incentivizes reasoning with a span-level reward function. RL4HS builds on Group Relative Policy Optimization and introduces Class-Aware Policy Optimization to mitigate reward imbalance issue. Experiments on the RAGTruth benchmark (summarization, question answering, data-to-text) show that RL4HS surpasses pretrained reasoning models and supervised fine-tuning, demonstrating the necessity of reinforcement learning with span-level rewards for detecting hallucination spans.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02286", "title": "Tree-based Dialogue Reinforced Policy Optimization for Red-Teaming Attacks", "authors": [ "Ruohao Guo", "Afshin Oroojlooy", "Roshan Sridhar", "Miguel Ballesteros", "Alan Ritter", "Dan Roth" ], "github": "", "project_page": "", "abstract": "Despite recent rapid progress in AI safety, current large language models remain vulnerable to adversarial attacks in multi-turn interaction settings, where attackers strategically adapt their prompts across conversation turns and pose a more critical yet realistic challenge. Existing approaches that discover safety vulnerabilities either rely on manual red-teaming with human experts or employ automated methods using pre-defined templates and human-curated attack data, with most focusing on single-turn attacks. However, these methods did not explore the vast space of possible multi-turn attacks, failing to consider novel attack trajectories that emerge from complex dialogue dynamics and strategic conversation planning. This gap is particularly critical given recent findings that LLMs exhibit significantly higher vulnerability to multi-turn attacks compared to single-turn attacks. We propose DialTree-RPO, an on-policy reinforcement learning framework integrated with tree search that autonomously discovers diverse multi-turn attack strategies by treating the dialogue as a sequential decision-making problem, enabling systematic exploration without manually curated data. Through extensive experiments, our approach not only achieves more than 25.9% higher ASR across 10 target models compared to previous state-of-the-art approaches, but also effectively uncovers new attack strategies by learning optimal dialogue policies that maximize attack success across multiple turns.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02295", "title": "VideoNSA: Native Sparse Attention Scales Video Understanding", "authors": [ "Enxin Song", "Wenhao Chai", "Shusheng Yang", "Ethan Armand", "Xiaojun Shan", "Haiyang Xu", "Jianwen Xie", "Zhuowen Tu" ], "github": "https://github.com/Espere-1119-Song/VideoNSA", "project_page": "", "abstract": "Video understanding in multimodal language models remains limited by context length: models often miss key transition frames and struggle to maintain coherence across long time scales. To address this, we adapt Native Sparse Attention (NSA) to video-language models. Our method, VideoNSA, adapts Qwen2.5-VL through end-to-end training on a 216K video instruction dataset. We employ a hardware-aware hybrid approach to attention, preserving dense attention for text, while employing NSA for video. Compared to token-compression and training-free sparse baselines, VideoNSA achieves improved performance on long-video understanding, temporal reasoning, and spatial benchmarks. Further ablation analysis reveals four key findings: (1) reliable scaling to 128K tokens; (2) an optimal global-local attention allocation at a fixed budget; (3) task-dependent branch usage patterns; and (4) the learnable combined sparse attention help induce dynamic attention sinks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Espere-1119-Song/VideoNSA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2509.21789", "title": "Visual Multi-Agent System: Mitigating Hallucination Snowballing via Visual Flow", "authors": [ "Xinlei Yu", "Chengming Xu", "Guibin Zhang", "Yongbo He", "Zhangquan Chen", "Zhucun Xue", "Jiangning Zhang", "Yue Liao", "Xiaobin Hu", "Yu-Gang Jiang", "Shuicheng Yan" ], "github": "https://github.com/YU-deep/ViF.git", "project_page": "", "abstract": "Multi-Agent System (MAS) powered by Visual Language Models (VLMs) enables challenging tasks but suffers from a novel failure term, multi-agent visual hallucination snowballing, where hallucinations are seeded in a single agent and amplified by following ones due to the over-reliance on textual flow to relay visual information. Through turn-, layer-, and token-wise attention analyses, we provide detailed insights into the essence of hallucination snowballing regarding the reduction of visual attention allocation. It leads us to identify a subset of vision tokens with a unimodal attention peak in middle layers that best preserve visual evidence but gradually diminish in deeper agent turns, resulting in the visual hallucination snowballing in MAS. Thus, we propose ViF, a lightweight, plug-and-play mitigation paradigm that relays inter-agent messages with Visual Flow powered by the selected visual relay tokens and applies attention reallocation to amplify this pattern. The experiment results demonstrate that our method markedly reduces hallucination snowballing, consistently improving the performance across eight benchmarks based on four common MAS structures and ten base models. The source code will be available at: https://github.com/YU-deep/ViF.git.", "github_manual": "", "github_regex": "https://github.com/YU-deep/ViF.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01143", "title": "Generalized Parallel Scaling with Interdependent Generations", "authors": [ "Harry Dong", "David Brandfonbrener", "Eryk Helenowski", "Yun He", "Mrinal Kumar", "Han Fang", "Yuejie Chi", "Karthik Abinav Sankararaman" ], "github": "", "project_page": "", "abstract": "Parallel LLM inference scaling involves sampling a set of N>1 responses for a single input prompt. However, these N parallel responses tend to be generated independently from each other, partitioning compute resources and leaving potentially useful information in one generation untapped by others. This is in contrast to response length scaling where past computation is used in all future steps. For higher quality responses and response sets, we propose Bridge to generate interdependent responses in parallel by rethinking batched LLM hidden states as holistic tensors rather than independent slices. With only a small amount (2.8%-5.1%) of new parameters, Bridge improves the relative mean accuracy gains from reinforcement learning with verifiable rewards by up to 50% and boosts consistency of correct responses. Trained once, Bridge scales to any generation width, all with greater performance than independent generations, unlocking a more general mode of parallel scaling that effectively leverages information between sequences, compatible with any post-generation aggregation technique.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2509.26330", "title": "SQUARE: Semantic Query-Augmented Fusion and Efficient Batch Reranking for Training-free Zero-Shot Composed Image Retrieval", "authors": [ "Ren-Di Wu", "Yu-Yen Lin", "Huei-Fang Yang" ], "github": "", "project_page": "", "abstract": "Composed Image Retrieval (CIR) aims to retrieve target images that preserve the visual content of a reference image while incorporating user-specified textual modifications. Training-free zero-shot CIR (ZS-CIR) approaches, which require no task-specific training or labeled data, are highly desirable, yet accurately capturing user intent remains challenging. In this paper, we present SQUARE, a novel two-stage training-free framework that leverages Multimodal Large Language Models (MLLMs) to enhance ZS-CIR. In the Semantic Query-Augmented Fusion (SQAF) stage, we enrich the query embedding derived from a vision-language model (VLM) such as CLIP with MLLM-generated captions of the target image. These captions provide high-level semantic guidance, enabling the query to better capture the user's intent and improve global retrieval quality. In the Efficient Batch Reranking (EBR) stage, top-ranked candidates are presented as an image grid with visual marks to the MLLM, which performs joint visual-semantic reasoning across all candidates. Our reranking strategy operates in a single pass and yields more accurate rankings. Experiments show that SQUARE, with its simplicity and effectiveness, delivers strong performance on four standard CIR benchmarks. Notably, it maintains high performance even with lightweight pre-trained, demonstrating its potential applicability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02263", "title": "RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems", "authors": [ "Yuxiao Qu", "Anikait Singh", "Yoonho Lee", "Amrith Setlur", "Ruslan Salakhutdinov", "Chelsea Finn", "Aviral Kumar" ], "github": "https://github.com/QwenLM/Qwen3", "project_page": "", "abstract": "Reasoning requires going beyond pattern matching or memorization of solutions to identify and implement \"algorithmic procedures\" that can be used to deduce answers to hard problems. Doing so requires realizing the most relevant primitives, intermediate results, or shared procedures, and building upon them. While RL post-training on long chains of thought ultimately aims to uncover this kind of algorithmic behavior, most reasoning traces learned by large models fail to consistently capture or reuse procedures, instead drifting into verbose and degenerate exploration. To address more effective reasoning, we introduce reasoning abstractions: concise natural language descriptions of procedural and factual knowledge that guide the model toward learning successful reasoning. We train models to be capable of proposing multiple abstractions given a problem, followed by RL that incentivizes building a solution while using the information provided by these abstractions. This results in a two-player RL training paradigm, abbreviated as RLAD, that jointly trains an abstraction generator and a solution generator. This setup effectively enables structured exploration, decouples learning signals of abstraction proposal and solution generation, and improves generalization to harder problems. We also show that allocating more test-time compute to generating abstractions is more beneficial for performance than generating more solutions at large test budgets, illustrating the role of abstractions in guiding meaningful exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/QwenLM/Qwen3", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2509.22067", "title": "The Rogue Scalpel: Activation Steering Compromises LLM Safety", "authors": [ "Anton Korznikov", "Andrey Galichin", "Alexey Dontsov", "Oleg Y. Rogov", "Ivan Oseledets", "Elena Tutubalina" ], "github": "", "project_page": "", "abstract": "Activation steering is a promising technique for controlling LLM behavior by adding semantically meaningful vectors directly into a model's hidden states during inference. It is often framed as a precise, interpretable, and potentially safer alternative to fine-tuning. We demonstrate the opposite: steering systematically breaks model alignment safeguards, making it comply with harmful requests. Through extensive experiments on different model families, we show that even steering in a random direction can increase the probability of harmful compliance from 0% to 2-27%. Alarmingly, steering benign features from a sparse autoencoder (SAE), a common source of interpretable directions, increases these rates by a further 2-4%. Finally, we show that combining 20 randomly sampled vectors that jailbreak a single prompt creates a universal attack, significantly increasing harmful compliance on unseen requests. These results challenge the paradigm of safety through interpretability, showing that precise control over model internals does not guarantee precise control over model behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01149", "title": "ModernVBERT: Towards Smaller Visual Document Retrievers", "authors": [ "Paul Teiletche", "Quentin Mac\u00e9", "Max Conti", "Antonio Loison", "Gautier Viaud", "Pierre Colombo", "Manuel Faysse" ], "github": "https://github.com/illuin-tech/modernvbert", "project_page": "", "abstract": "Multimodal embedding models are gaining prevalence, notably for document retrieval as efficient alternatives to text-only pipelines. These models are typically built by finetuning large vision-language decoders (VLMs) with contrastive losses on text-image pairs. In this work, we show that, while cost-efficient, this repurposing approach often bottlenecks retrieval performance. Through controlled experiments, we establish a principled recipe for improving visual document retrieval models. We notably measure the impact of attention masking, image resolution, modality alignment data regimes, and late interaction centered contrastive objectives which emerge as central performance factors. Building on these insights, we release ModernVBERT, a compact 250M-parameter vision-language encoder that outperforms models up to 10 times larger when finetuned on document retrieval tasks. Models and code are made available at https://huggingface.co/ModernVBERT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/illuin-tech/modernvbert", "github_api_added_by": "auto" }, { "date": "2025-10-03", "arxiv_id": "2510.01260", "title": "IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol", "authors": [ "Ningyuan Yang", "Guanliang Lyu", "Mingchen Ma", "Yiyi Lu", "Yiming Li", "Zhihui Gao", "Hancheng Ye", "Jianyi Zhang", "Tingjun Chen", "Yiran Chen" ], "github": "https://github.com/Duke-CEI-Center/IoT-MCP-Servers", "project_page": "", "abstract": "The integration of Large Language Models (LLMs) with Internet-of-Things (IoT) systems faces significant challenges in hardware heterogeneity and control complexity. The Model Context Protocol (MCP) emerges as a critical enabler, providing standardized communication between LLMs and physical devices. We propose IoT-MCP, a novel framework that implements MCP through edge-deployed servers to bridge LLMs and IoT ecosystems. To support rigorous evaluation, we introduce IoT-MCP Bench, the first benchmark containing 114 Basic Tasks (e.g., ``What is the current temperature?'') and 1,140 Complex Tasks (e.g., ``I feel so hot, do you have any ideas?'') for IoT-enabled LLMs. Experimental validation across 22 sensor types and 6 microcontroller units demonstrates IoT-MCP's 100% task success rate to generate tool calls that fully meet expectations and obtain completely accurate results, 205ms average response time, and 74KB peak memory footprint. This work delivers both an open-source integration framework (https://github.com/Duke-CEI-Center/IoT-MCP-Servers) and a standardized evaluation methodology for LLM-IoT systems.", "github_manual": "", "github_regex": "https://github.com/Duke-CEI-Center/IoT-MCP-Servers", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.00137", "title": "Optimizing What Matters: AUC-Driven Learning for Robust Neural Retrieval", "authors": [ "Nima Sheikholeslami", "Erfan Hosseini", "Patrice Bechard", "Srivatsava Daruru", "Sai Rajeswar" ], "github": "", "project_page": "", "abstract": "Dual-encoder retrievers depend on the principle that relevant documents should score higher than irrelevant ones for a given query. Yet the dominant Noise Contrastive Estimation (NCE) objective, which underpins Contrastive Loss, optimizes a softened ranking surrogate that we rigorously prove is fundamentally oblivious to score separation quality and unrelated to AUC. This mismatch leads to poor calibration and suboptimal performance in downstream tasks like retrieval-augmented generation (RAG). To address this fundamental limitation, we introduce the MW loss, a new training objective that maximizes the Mann-Whitney U statistic, which is mathematically equivalent to the Area under the ROC Curve (AUC). MW loss encourages each positive-negative pair to be correctly ranked by minimizing binary cross entropy over score differences. We provide theoretical guarantees that MW loss directly upper-bounds the AoC, better aligning optimization with retrieval goals. We further promote ROC curves and AUC as natural threshold free diagnostics for evaluating retriever calibration and ranking quality. Empirically, retrievers trained with MW loss consistently outperform contrastive counterparts in AUC and standard retrieval metrics. Our experiments show that MW loss is an empirically superior alternative to Contrastive Loss, yielding better-calibrated and more discriminative retrievers for high-stakes applications like RAG.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2509.26313", "title": "One-Token Rollout: Guiding Supervised Fine-Tuning of LLMs with Policy Gradient", "authors": [ "Rui Ming", "Haoyuan Wu", "Shoubo Hu", "Zhuolun He", "Bei Yu" ], "github": "", "project_page": "", "abstract": "Supervised fine-tuning (SFT) is the predominant method for adapting large language models (LLMs), yet it often struggles with generalization compared to reinforcement learning (RL). In this work, we posit that this performance disparity stems not just from the loss function, but from a more fundamental difference: SFT learns from a fixed, pre-collected dataset, whereas RL utilizes on-policy data sampled from the current policy. Building on this hypothesis, we introduce one-token rollout (OTR), a novel fine-tuning algorithm that guides SFT with the policy gradient method. OTR reframes the autoregressive learning process by treating each token generation as a single-step reinforcement learning trajectory. At each step, it performs a Monte Carlo ``rollout'' by sampling multiple candidate tokens from the current policy's distribution. The ground-truth token from the supervised data is then used to provide a reward signal to these samples. Guided by policy gradient, our algorithm repurposes static, off-policy supervised data into a dynamic, on-policy signal at the token level, capturing the generalization benefits of on-policy learning while bypassing the costly overhead of full sentence generation. Through extensive experiments on a diverse suite of challenging benchmarks spanning mathematical reasoning, code generation, and general domain reasoning, we demonstrate that OTR consistently outperforms standard SFT. Our findings establish OTR as a powerful and practical alternative for fine-tuning LLMs and provide compelling evidence that the on-policy nature of data is a critical driver of generalization, offering a promising new direction for fine-tuning LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.02306", "title": "Drawing Conclusions from Draws: Rethinking Preference Semantics in Arena-Style LLM Evaluation", "authors": [ "Raphael Tang", "Crystina Zhang", "Wenyan Li", "Carmen Lai", "Pontus Stenetorp", "Yao Lu" ], "github": "", "project_page": "", "abstract": "In arena-style evaluation of large language models (LLMs), two LLMs respond to a user query, and the user chooses the winning response or deems the \"battle\" a draw, resulting in an adjustment to the ratings of both models. The prevailing approach for modeling these rating dynamics is to view battles as two-player game matches, as in chess, and apply the Elo rating system and its derivatives. In this paper, we critically examine this paradigm. Specifically, we question whether a draw genuinely means that the two models are equal and hence whether their ratings should be equalized. Instead, we conjecture that draws are more indicative of query difficulty: if the query is too easy, then both models are more likely to succeed equally. On three real-world arena datasets, we show that ignoring rating updates for draws yields a 1-3% relative increase in battle outcome prediction accuracy (which includes draws) for all four rating systems studied. Further analyses suggest that draws occur more for queries rated as very easy and those as highly objective, with risk ratios of 1.37 and 1.35, respectively. We recommend future rating systems to reconsider existing draw semantics and to account for query properties in rating updates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01581", "title": "Think Right: Learning to Mitigate Under-Over Thinking via Adaptive, Attentive Compression", "authors": [ "Joykirat Singh", "Justin Chih-Yao Chen", "Archiki Prasad", "Elias Stengel-Eskin", "Akshay Nambi", "Mohit Bansal" ], "github": "https://github.com/joykirat18/TRAAC", "project_page": "", "abstract": "Recent thinking models solve complex reasoning tasks by scaling test-time compute, but this scaling must be allocated in line with task difficulty. On one hand, short reasoning (underthinking) leads to errors on harder problems that require extended reasoning steps; but, excessively long reasoning (overthinking) can be token-inefficient, generating unnecessary steps even after reaching a correct intermediate solution. We refer to this as under-adaptivity, where the model fails to modulate its response length appropriately given problems of varying difficulty. To address under-adaptivity and strike a balance between under- and overthinking, we propose TRAAC (Think Right with Adaptive, Attentive Compression), an online post-training RL method that leverages the model's self-attention over a long reasoning trajectory to identify important steps and prune redundant ones. TRAAC also estimates difficulty and incorporates it into training rewards, thereby learning to allocate reasoning budget commensurate with example difficulty. Our approach improves accuracy, reduces reasoning steps, and enables adaptive thinking compared to base models and other RL baselines. Across a variety of tasks (AIME, AMC, GPQA-D, BBEH), TRAAC (Qwen3-4B) achieves an average absolute accuracy gain of 8.4% with a relative reduction in reasoning length of 36.8% compared to the base model, and a 7.9% accuracy gain paired with a 29.4% length drop compared to the best RL baseline. TRAAC also shows strong generalization: although our models are trained on math datasets, they show accuracy and efficiency gains on out-of-distribution non-math datasets like GPQA-D, BBEH, and OptimalThinkingBench. Our analysis further verifies that TRAAC provides fine-grained adjustments to thinking budget based on difficulty and that a combination of task-difficulty calibration and attention-based compression yields gains across diverse tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/joykirat18/TRAAC", "github_api_added_by": "auto" }, { "date": "2025-10-03", "arxiv_id": "2509.25729", "title": "Controlled Generation for Private Synthetic Text", "authors": [ "Zihao Zhao", "Anjalie Field" ], "github": "https://github.com/zzhao71/Controlled-Generation-for-Private-Synthetic-Text.git", "project_page": "", "abstract": "Text anonymization is essential for responsibly developing and deploying AI in high-stakes domains such as healthcare, social services, and law. In this work, we propose a novel methodology for privacy-preserving synthetic text generation that leverages the principles of de-identification and the Hiding In Plain Sight (HIPS) theory. Our approach introduces entity-aware control codes to guide controllable generation using either in-context learning (ICL) or prefix tuning. The ICL variant ensures privacy levels consistent with the underlying de-identification system, while the prefix tuning variant incorporates a custom masking strategy and loss function to support scalable, high-quality generation. Experiments on legal and clinical datasets demonstrate that our method achieves a strong balance between privacy protection and utility, offering a practical and effective solution for synthetic text generation in sensitive domains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zzhao71/Controlled-Generation-for-Private-Synthetic-Text.git", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01817", "title": "Sparse Query Attention (SQA): A Computationally Efficient Attention Mechanism with Query Heads Reduction", "authors": [ "Adam Filipek" ], "github": "", "project_page": "", "abstract": "The Transformer architecture, underpinned by the Multi-Head Attention (MHA) mechanism, has become the de facto standard for state-of-the-art models in artificial intelligence. However, the quadratic computational complexity of MHA with respect to sequence length presents a significant barrier to scaling, particularly for applications involving long contexts. Prevailing solutions, such as Multi-Query Attention (MQA) and Grouped-Query Attention (GQA), have effectively addressed the memory bandwidth bottleneck that dominates autoregressive inference latency by sharing Key and Value projections. While highly successful, these methods do not reduce the fundamental number of floating-point operations (FLOPs) required for the attention score computation, which remains a critical bottleneck for training and full-sequence processing. This paper introduces Sparse Query Attention (SQA), a novel attention architecture that pursues an alternative and complementary optimization path. Instead of reducing Key/Value heads, SQA reduces the number of Query heads. This architectural modification directly decreases the computational complexity of the attention mechanism by a factor proportional to the reduction in query heads, thereby lowering the overall FLOPs. This work presents the theoretical foundation of SQA, its mathematical formulation, and a family of architectural variants. Empirical benchmarks on long sequences (32k-200k tokens) demonstrate that SQA can achieve significant throughput improvements of up to 3x in computation-bound scenarios such as model pre-training, fine-tuning, and encoder-based tasks, with only a minimal impact on model quality in preliminary smallscale experiments. SQA was discovered serendipitously during the development of the upcoming Reactive Transformer architecture, suggesting its potential as a powerful tool for building more efficient and scalable models", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2510.01346", "title": "Aristotle: IMO-level Automated Theorem Proving", "authors": [ "Tudor Achim", "Alex Best", "Kevin Der", "Math\u00efs F\u00e9d\u00e9rico", "Sergei Gukov", "Daniel Halpern-Leister", "Kirsten Henningsgard", "Yury Kudryashov", "Alexander Meiburg", "Martin Michelsen", "Riley Patterson", "Eric Rodriguez", "Laura Scharff", "Vikram Shanker", "Vladmir Sicca", "Hari Sowrirajan", "Aidan Swope", "Matyas Tamas", "Vlad Tenev", "Jonathan Thomm", "Harold Williams", "Lawrence Wu" ], "github": "https://github.com/harmonic-ai/IMO2025", "project_page": "", "abstract": "We introduce Aristotle, an AI system that combines formal verification with informal reasoning, achieving gold-medal-equivalent performance on the 2025 International Mathematical Olympiad problems. Aristotle integrates three main components: a Lean proof search system, an informal reasoning system that generates and formalizes lemmas, and a dedicated geometry solver. Our system demonstrates state-of-the-art performance with favorable scaling properties for automated theorem proving.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/harmonic-ai/IMO2025", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-03", "arxiv_id": "2509.22582", "title": "Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs", "authors": [ "Yehonatan Peisakhovsky", "Zorik Gekhman", "Yosi Mass", "Liat Ein-Dor", "Roi Reichart" ], "github": "https://github.com/yonip97/The_final_benchmark", "project_page": "", "abstract": "Context-grounded hallucinations are cases where model outputs contain information not verifiable against the source text. We study the applicability of LLMs for localizing such hallucinations, as a more practical alternative to existing complex evaluation pipelines. In the absence of established benchmarks for meta-evaluation of hallucinations localization, we construct one tailored to LLMs, involving a challenging human annotation of over 1,000 examples. We complement the benchmark with an LLM-based evaluation protocol, verifying its quality in a human evaluation. Since existing representations of hallucinations limit the types of errors that can be expressed, we propose a new representation based on free-form textual descriptions, capturing the full range of possible errors. We conduct a comprehensive study, evaluating four large-scale LLMs, which highlights the benchmark's difficulty, as the best model achieves an F1 score of only 0.67. Through careful analysis, we offer insights into optimal prompting strategies for the task and identify the main factors that make it challenging for LLMs: (1) a tendency to incorrectly flag missing details as inconsistent, despite being instructed to check only facts in the output; and (2) difficulty with outputs containing factually correct information absent from the source - and thus not verifiable - due to alignment with the model's parametric knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yonip97/The_final_benchmark", "github_api_added_by": "auto" }, { "date": "2025-10-03", "arxiv_id": "2510.01123", "title": "Rethinking Thinking Tokens: LLMs as Improvement Operators", "authors": [ "Lovish Madaan", "Aniket Didolkar", "Suchin Gururangan", "John Quan", "Ruan Silva", "Ruslan Salakhutdinov", "Manzil Zaheer", "Sanjeev Arora", "Anirudh Goyal" ], "github": "", "project_page": "", "abstract": "Reasoning training incentivizes LLMs to produce long chains of thought (long CoT), which among other things, allows them to explore solution strategies with self-checking. This results in higher accuracy, but inflates context length, token/compute cost, and answer latency. We ask: Can current models leverage their metacognition to provide other combinations on this Pareto frontier, e.g., better accuracy with lower context length and/or latency? Abstractly, we view the model as an improvement operator on its own \"thoughts\" with a continuum of possible strategies. We identify an interesting inference family Parallel-Distill-Refine (PDR), which performs the following: (i) generate diverse drafts in parallel; (ii) distill them into a bounded, textual workspace; and (iii) refine conditioned on this workspace, producing an output that seeds the next round. Importantly, context length (hence compute cost) is controllable via degree of parallelism, and is no longer conflated with the total number of generated tokens. We report PDR instantiations of current models that give better accuracy than long CoT while incurring lower latency. Setting degree of parallelism to 1 yields an interesting subcase, Sequential Refinement (SR) (iteratively improve a single candidate answer) which provides performance superior to long CoT. Success of such model orchestrations raises the question whether further training could shift the Pareto frontier. To this end, we train an 8B thinking model with Reinforcement Learning (RL) to make it consistent with PDR as the inference method. On math tasks with verifiable answers, iterative pipelines surpass single-pass baselines at matched sequential budgets, with PDR delivering the largest gains (e.g., +11% on AIME 2024 and +9% on AIME 2025).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.03230", "title": "Improving GUI Grounding with Explicit Position-to-Coordinate Mapping", "authors": [ "Suyuchen Wang", "Tianyu Zhang", "Ahmed Masry", "Christopher Pal", "Spandana Gella", "Bang Liu", "Perouz Taslakian" ], "github": "", "project_page": "", "abstract": "GUI grounding, the task of mapping natural-language instructions to pixel coordinates, is crucial for autonomous agents, yet remains difficult for current VLMs. The core bottleneck is reliable patch-to-pixel mapping, which breaks when extrapolating to high-resolution displays unseen during training. Current approaches generate coordinates as text tokens directly from visual features, forcing the model to infer complex position-to-pixel mappings implicitly; as a result, accuracy degrades and failures proliferate on new resolutions. We address this with two complementary innovations. First, RULER tokens serve as explicit coordinate markers, letting the model reference positions similar to gridlines on a map and adjust rather than generate coordinates from scratch. Second, Interleaved MRoPE (I-MRoPE) improves spatial encoding by ensuring that width and height dimensions are represented equally, addressing the asymmetry of standard positional schemes. Experiments on ScreenSpot, ScreenSpot-V2, and ScreenSpot-Pro show consistent gains in grounding accuracy, with the largest improvements on high-resolution interfaces. By providing explicit spatial guidance rather than relying on implicit learning, our approach enables more reliable GUI automation across diverse resolutions and platforms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.02665", "title": "Self-Improvement in Multimodal Large Language Models: A Survey", "authors": [ "Shijian Deng", "Kai Wang", "Tianyu Yang", "Harsh Singh", "Yapeng Tian" ], "github": "", "project_page": "", "abstract": "Recent advancements in self-improvement for Large Language Models (LLMs) have efficiently enhanced model capabilities without significantly increasing costs, particularly in terms of human effort. While this area is still relatively young, its extension to the multimodal domain holds immense potential for leveraging diverse data sources and developing more general self-improving models. This survey is the first to provide a comprehensive overview of self-improvement in Multimodal LLMs (MLLMs). We provide a structured overview of the current literature and discuss methods from three perspectives: 1) data collection, 2) data organization, and 3) model optimization, to facilitate the further development of self-improvement in MLLMs. We also include commonly used evaluations and downstream applications. Finally, we conclude by outlining open challenges and future research directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.02571", "title": "How Confident are Video Models? Empowering Video Models to Express their Uncertainty", "authors": [ "Zhiting Mei", "Ola Shorinwa", "Anirudha Majumdar" ], "github": "", "project_page": "", "abstract": "Generative video models demonstrate impressive text-to-video capabilities, spurring widespread adoption in many real-world applications. However, like large language models (LLMs), video generation models tend to hallucinate, producing plausible videos even when they are factually wrong. Although uncertainty quantification (UQ) of LLMs has been extensively studied in prior work, no UQ method for video models exists, raising critical safety concerns. To our knowledge, this paper represents the first work towards quantifying the uncertainty of video models. We present a framework for uncertainty quantification of generative video models, consisting of: (i) a metric for evaluating the calibration of video models based on robust rank correlation estimation with no stringent modeling assumptions; (ii) a black-box UQ method for video models (termed S-QUBED), which leverages latent modeling to rigorously decompose predictive uncertainty into its aleatoric and epistemic components; and (iii) a UQ dataset to facilitate benchmarking calibration in video models. By conditioning the generation task in the latent space, we disentangle uncertainty arising due to vague task specifications from that arising from lack of knowledge. Through extensive experiments on benchmark video datasets, we demonstrate that S-QUBED computes calibrated total uncertainty estimates that are negatively correlated with the task accuracy and effectively computes the aleatoric and epistemic constituents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.01068", "title": "Compose Your Policies! Improving Diffusion-based or Flow-based Robot Policies via Test-time Distribution-level Composition", "authors": [ "Jiahang Cao", "Yize Huang", "Hanzhong Guo", "Rui Zhang", "Mu Nan", "Weijian Mai", "Jiaxu Wang", "Hao Cheng", "Jingkai Sun", "Gang Han", "Wen Zhao", "Qiang Zhang", "Yijie Guo", "Qihao Zheng", "Chunfeng Song", "Xiao Li", "Ping Luo", "Andrew F. Luo" ], "github": "", "project_page": "", "abstract": "Diffusion-based models for robotic control, including vision-language-action (VLA) and vision-action (VA) policies, have demonstrated significant capabilities. Yet their advancement is constrained by the high cost of acquiring large-scale interaction datasets. This work introduces an alternative paradigm for enhancing policy performance without additional model training. Perhaps surprisingly, we demonstrate that the composed policies can exceed the performance of either parent policy. Our contribution is threefold. First, we establish a theoretical foundation showing that the convex composition of distributional scores from multiple diffusion models can yield a superior one-step functional objective compared to any individual score. A Gr\\\"onwall-type bound is then used to show that this single-step improvement propagates through entire generation trajectories, leading to systemic performance gains. Second, motivated by these results, we propose General Policy Composition (GPC), a training-free method that enhances performance by combining the distributional scores of multiple pre-trained policies via a convex combination and test-time search. GPC is versatile, allowing for the plug-and-play composition of heterogeneous policies, including VA and VLA models, as well as those based on diffusion or flow-matching, irrespective of their input visual modalities. Third, we provide extensive empirical validation. Experiments on Robomimic, PushT, and RoboTwin benchmarks, alongside real-world robotic evaluations, confirm that GPC consistently improves performance and adaptability across a diverse set of tasks. Further analysis of alternative composition operators and weighting strategies offers insights into the mechanisms underlying the success of GPC. These results establish GPC as a simple yet effective method for improving control performance by leveraging existing policies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.03204", "title": "FocusAgent: Simple Yet Effective Ways of Trimming the Large Context of Web Agents", "authors": [ "Imene Kerboua", "Sahar Omidi Shayegan", "Megh Thakkar", "Xing Han L\u00f9", "L\u00e9o Boisvert", "Massimo Caccia", "J\u00e9r\u00e9my Espinas", "Alexandre Aussem", "V\u00e9ronique Eglin", "Alexandre Lacoste" ], "github": "https://github.com/ServiceNow/AgentLab", "project_page": "", "abstract": "Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens of thousands of tokens. This saturates context limits and increases computational cost processing; moreover, processing full pages exposes agents to security risks such as prompt injection. Existing pruning strategies either discard relevant content or retain irrelevant context, leading to suboptimal action prediction. We introduce FocusAgent, a simple yet effective approach that leverages a lightweight LLM retriever to extract the most relevant lines from accessibility tree (AxTree) observations, guided by task goals. By pruning noisy and irrelevant content, FocusAgent enables efficient reasoning while reducing vulnerability to injection attacks. Experiments on WorkArena and WebArena benchmarks show that FocusAgent matches the performance of strong baselines, while reducing observation size by over 50%. Furthermore, a variant of FocusAgent significantly reduces the success rate of prompt-injection attacks, including banner and pop-up attacks, while maintaining task success performance in attack-free settings. Our results highlight that targeted LLM-based retrieval is a practical and robust strategy for building web agents that are efficient, effective, and secure.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ServiceNow/AgentLab", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.03160", "title": "SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus", "authors": [ "Ming Zhao", "Wenhui Dong", "Yang Zhang", "Xiang Zheng", "Zhonghao Zhang", "Zian Zhou", "Yunzhi Guan", "Liukun Xu", "Wei Peng", "Zhaoyang Gong", "Zhicheng Zhang", "Dachuan Li", "Xiaosheng Ma", "Yuli Ma", "Jianing Ni", "Changjiang Jiang", "Lixia Tian", "Qixin Chen", "Kaishun Xia", "Pingping Liu", "Tongshun Zhang", "Zhiqiang Liu", "Zhongan Bi", "Chenyang Si", "Tiansheng Sun", "Caifeng Shan" ], "github": "", "project_page": "", "abstract": "Spine disorders affect 619 million people globally and are a leading cause of disability, yet AI-assisted diagnosis remains limited by the lack of level-aware, multimodal datasets. Clinical decision-making for spine disorders requires sophisticated reasoning across X-ray, CT, and MRI at specific vertebral levels. However, progress has been constrained by the absence of traceable, clinically-grounded instruction data and standardized, spine-specific benchmarks. To address this, we introduce SpineMed, an ecosystem co-designed with practicing spine surgeons. It features SpineMed-450k, the first large-scale dataset explicitly designed for vertebral-level reasoning across imaging modalities with over 450,000 instruction instances, and SpineBench, a clinically-grounded evaluation framework. SpineMed-450k is curated from diverse sources, including textbooks, guidelines, open datasets, and ~1,000 de-identified hospital cases, using a clinician-in-the-loop pipeline with a two-stage LLM generation method (draft and revision) to ensure high-quality, traceable data for question-answering, multi-turn consultations, and report generation. SpineBench evaluates models on clinically salient axes, including level identification, pathology assessment, and surgical planning. Our comprehensive evaluation of several recently advanced large vision-language models (LVLMs) on SpineBench reveals systematic weaknesses in fine-grained, level-specific reasoning. In contrast, our model fine-tuned on SpineMed-450k demonstrates consistent and significant improvements across all tasks. Clinician assessments confirm the diagnostic clarity and practical utility of our model's outputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.03120", "title": "SurveyBench: How Well Can LLM(-Agents) Write Academic Surveys?", "authors": [ "Zhaojun Sun", "Xuzhou Zhu", "Xuanhe Zhou", "Xin Tong", "Shuo Wang", "Jie Fu", "Guoliang Li", "Zhiyuan Liu", "Fan Wu" ], "github": "", "project_page": "", "abstract": "Academic survey writing, which distills vast literature into a coherent and insightful narrative, remains a labor-intensive and intellectually demanding task. While recent approaches, such as general DeepResearch agents and survey-specialized methods, can generate surveys automatically (a.k.a. LLM4Survey), their outputs often fall short of human standards and there lacks a rigorous, reader-aligned benchmark for thoroughly revealing their deficiencies. To fill the gap, we propose a fine-grained, quiz-driven evaluation framework SurveyBench, featuring (1) typical survey topics source from recent 11,343 arXiv papers and corresponding 4,947 high-quality surveys; (2) a multifaceted metric hierarchy that assesses the outline quality (e.g., coverage breadth, logical coherence), content quality (e.g., synthesis granularity, clarity of insights), and non-textual richness; and (3) a dual-mode evaluation protocol that includes content-based and quiz-based answerability tests, explicitly aligned with readers' informational needs. Results show SurveyBench effectively challenges existing LLM4Survey approaches (e.g., on average 21% lower than human in content-based evaluation).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.01879", "title": "REPAIR: Robust Editing via Progressive Adaptive Intervention and Reintegration", "authors": [ "Yisu Wang", "Ming Wang", "Haoyuan Song", "Wenjie Huang", "Chaozheng Wang", "Yi Xie", "Xuming Ran" ], "github": "", "project_page": "", "abstract": "Post-training for large language models (LLMs) is constrained by the high cost of acquiring new knowledge or correcting errors and by the unintended side effects that frequently arise from retraining. To address these issues, we introduce REPAIR (Robust Editing via Progressive Adaptive Intervention and Reintegration), a lifelong editing framework designed to support precise and low-cost model updates while preserving non-target knowledge. REPAIR mitigates the instability and conflicts of large-scale sequential edits through a closed-loop feedback mechanism coupled with dynamic memory management. Furthermore, by incorporating frequent knowledge fusion and enforcing strong locality guards, REPAIR effectively addresses the shortcomings of traditional distribution-agnostic approaches that often overlook unintended ripple effects. Our experiments demonstrate that REPAIR boosts editing accuracy by 10%-30% across multiple model families and significantly reduces knowledge forgetting. This work introduces a robust framework for developing reliable, scalable, and continually evolving LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2509.25771", "title": "Free Lunch Alignment of Text-to-Image Diffusion Models without Preference Image Pairs", "authors": [ "Jia Jun Cheng Xian", "Muchen Li", "Haotian Yang", "Xin Tao", "Pengfei Wan", "Leonid Sigal", "Renjie Liao" ], "github": "https://github.com/DSL-Lab/T2I-Free-Lunch-Alignment", "project_page": "", "abstract": "Recent advances in diffusion-based text-to-image (T2I) models have led to remarkable success in generating high-quality images from textual prompts. However, ensuring accurate alignment between the text and the generated image remains a significant challenge for state-of-the-art diffusion models. To address this, existing studies employ reinforcement learning with human feedback (RLHF) to align T2I outputs with human preferences. These methods, however, either rely directly on paired image preference data or require a learned reward function, both of which depend heavily on costly, high-quality human annotations and thus face scalability limitations. In this work, we introduce Text Preference Optimization (TPO), a framework that enables \"free-lunch\" alignment of T2I models, achieving alignment without the need for paired image preference data. TPO works by training the model to prefer matched prompts over mismatched prompts, which are constructed by perturbing original captions using a large language model. Our framework is general and compatible with existing preference-based algorithms. We extend both DPO and KTO to our setting, resulting in TDPO and TKTO. Quantitative and qualitative evaluations across multiple benchmarks show that our methods consistently outperform their original counterparts, delivering better human preference scores and improved text-to-image alignment. Our Open-source code is available at https://github.com/DSL-Lab/T2I-Free-Lunch-Alignment.", "github_manual": "", "github_regex": "https://github.com/DSL-Lab/T2I-Free-Lunch-Alignment", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2509.26354", "title": "Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents", "authors": [ "Shuai Shao", "Qihan Ren", "Chen Qian", "Boyi Wei", "Dadi Guo", "Jingyi Yang", "Xinhao Song", "Linfeng Zhang", "Weinan Zhang", "Dongrui Liu", "Jing Shao" ], "github": "https://github.com/ShaoShuai0605/Misevolution", "project_page": "", "abstract": "Advances in Large Language Models (LLMs) have enabled a new class of self-evolving agents that autonomously improve through interaction with the environment, demonstrating strong capabilities. However, self-evolution also introduces novel risks overlooked by current safety research. In this work, we study the case where an agent's self-evolution deviates in unintended ways, leading to undesirable or even harmful outcomes. We refer to this as Misevolution. To provide a systematic investigation, we evaluate misevolution along four key evolutionary pathways: model, memory, tool, and workflow. Our empirical findings reveal that misevolution is a widespread risk, affecting agents built even on top-tier LLMs (e.g., Gemini-2.5-Pro). Different emergent risks are observed in the self-evolutionary process, such as the degradation of safety alignment after memory accumulation, or the unintended introduction of vulnerabilities in tool creation and reuse. To our knowledge, this is the first study to systematically conceptualize misevolution and provide empirical evidence of its occurrence, highlighting an urgent need for new safety paradigms for self-evolving agents. Finally, we discuss potential mitigation strategies to inspire further research on building safer and more trustworthy self-evolving agents. Our code and data are available at https://github.com/ShaoShuai0605/Misevolution . Warning: this paper includes examples that may be offensive or harmful in nature.", "github_manual": "", "github_regex": "https://github.com/ShaoShuai0605/Misevolution", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.01459", "title": "LSPO: Length-aware Dynamic Sampling for Policy Optimization in LLM Reasoning", "authors": [ "Weizhe Chen", "Sven Koenig", "Bistra Dilkina" ], "github": "", "project_page": "", "abstract": "Since the release of Deepseek-R1, reinforcement learning with verifiable rewards (RLVR) has become a central approach for training large language models (LLMs) on reasoning tasks. Recent work has largely focused on modifying loss functions to make RLVR more efficient and effective. In this paper, motivated by studies of overthinking in LLMs, we propose Length-aware Sampling for Policy Optimization (LSPO), a novel meta-RLVR algorithm that dynamically selects training data at each step based on the average response length. We evaluate LSPO across multiple base models and datasets, demonstrating that it consistently improves learning effectiveness. In addition, we conduct a detailed ablation study to examine alternative ways of incorporating length signals into dynamic sampling, offering further insights and highlighting promising directions for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.00515", "title": "Efficient Multi-modal Large Language Models via Progressive Consistency Distillation", "authors": [ "Zichen Wen", "Shaobo Wang", "Yufa Zhou", "Junyuan Zhang", "Qintong Zhang", "Yifeng Gao", "Zhaorun Chen", "Bin Wang", "Weijia Li", "Conghui He", "Linfeng Zhang" ], "github": "", "project_page": "", "abstract": "Visual tokens consume substantial computational resources in multi-modal large models (MLLMs), significantly compromising their efficiency. Recent works have attempted to improve efficiency by compressing visual tokens during training, either through modifications to model components or by introducing additional parameters. However, they often overlook the increased learning difficulty caused by such compression, as the model's parameter space struggles to quickly adapt to the substantial perturbations in the feature space induced by token compression. In this work, we propose to develop Efficient MLLMs via Progressive Consistency Distillation (EPIC), a progressive learning framework. Specifically, by decomposing the feature space perturbations introduced by token compression along the token-wise and layer-wise dimensions, we introduce token consistency distillation and layer consistency distillation, respectively, aiming to reduce the training difficulty by leveraging guidance from a teacher model and following a progressive learning trajectory. Extensive experiments demonstrate the superior effectiveness, robustness, and generalization capabilities of our proposed framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.03194", "title": "CoDA: Agentic Systems for Collaborative Data Visualization", "authors": [ "Zichen Chen", "Jiefeng Chen", "Sercan \u00d6. Arik", "Misha Sra", "Tomas Pfister", "Jinsung Yoon" ], "github": "", "project_page": "", "abstract": "Deep research has revolutionized data analysis, yet data scientists still devote substantial time to manually crafting visualizations, highlighting the need for robust automation from natural language queries. However, current systems struggle with complex datasets containing multiple files and iterative refinement. Existing approaches, including simple single- or multi-agent systems, often oversimplify the task, focusing on initial query parsing while failing to robustly manage data complexity, code errors, or final visualization quality. In this paper, we reframe this challenge as a collaborative multi-agent problem. We introduce CoDA, a multi-agent system that employs specialized LLM agents for metadata analysis, task planning, code generation, and self-reflection. We formalize this pipeline, demonstrating how metadata-focused analysis bypasses token limits and quality-driven refinement ensures robustness. Extensive evaluations show CoDA achieves substantial gains in the overall score, outperforming competitive baselines by up to 41.5%. This work demonstrates that the future of visualization automation lies not in isolated code generation but in integrated, collaborative agentic workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.01354", "title": "WAInjectBench: Benchmarking Prompt Injection Detections for Web Agents", "authors": [ "Yinuo Liu", "Ruohan Xu", "Xilong Wang", "Yuqi Jia", "Neil Zhenqiang Gong" ], "github": "https://github.com/Norrrrrrr-lyn/WAInjectBench", "project_page": "", "abstract": "Multiple prompt injection attacks have been proposed against web agents. At the same time, various methods have been developed to detect general prompt injection attacks, but none have been systematically evaluated for web agents. In this work, we bridge this gap by presenting the first comprehensive benchmark study on detecting prompt injection attacks targeting web agents. We begin by introducing a fine-grained categorization of such attacks based on the threat model. We then construct datasets containing both malicious and benign samples: malicious text segments generated by different attacks, benign text segments from four categories, malicious images produced by attacks, and benign images from two categories. Next, we systematize both text-based and image-based detection methods. Finally, we evaluate their performance across multiple scenarios. Our key findings show that while some detectors can identify attacks that rely on explicit textual instructions or visible image perturbations with moderate to high accuracy, they largely fail against attacks that omit explicit instructions or employ imperceptible perturbations. Our datasets and code are released at: https://github.com/Norrrrrrr-lyn/WAInjectBench.", "github_manual": "", "github_regex": "https://github.com/Norrrrrrr-lyn/WAInjectBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.00658", "title": "Align Your Tangent: Training Better Consistency Models via Manifold-Aligned Tangents", "authors": [ "Beomsu Kim", "Byunghee Cha", "Jong Chul Ye" ], "github": "https://github.com/1202kbs/AYT", "project_page": "", "abstract": "With diffusion and flow matching models achieving state-of-the-art generating performance, the interest of the community now turned to reducing the inference time without sacrificing sample quality. Consistency Models (CMs), which are trained to be consistent on diffusion or probability flow ordinary differential equation (PF-ODE) trajectories, enable one or two-step flow or diffusion sampling. However, CMs typically require prolonged training with large batch sizes to obtain competitive sample quality. In this paper, we examine the training dynamics of CMs near convergence and discover that CM tangents -- CM output update directions -- are quite oscillatory, in the sense that they move parallel to the data manifold, not towards the manifold. To mitigate oscillatory tangents, we propose a new loss function, called the manifold feature distance (MFD), which provides manifold-aligned tangents that point toward the data manifold. Consequently, our method -- dubbed Align Your Tangent (AYT) -- can accelerate CM training by orders of magnitude and even out-perform the learned perceptual image patch similarity metric (LPIPS). Furthermore, we find that our loss enables training with extremely small batch sizes without compromising sample quality. Code: https://github.com/1202kbs/AYT", "github_manual": "", "github_regex": "https://github.com/1202kbs/AYT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.01132", "title": "A Practitioner's Guide to Multi-turn Agentic Reinforcement Learning", "authors": [ "Ruiyi Wang", "Prithviraj Ammanabrolu" ], "github": "https://github.com/pearls-lab/meow-tea-taro", "project_page": "", "abstract": "We study what actually works and what doesn't for training large language models as agents via multi-turn reinforcement learning. Despite rapid progress, existing frameworks and definitions are fragmented, and there is no systematic formulation or analysis of which design choices matter across tasks. We address this gap by first breaking down the design space into three inter-related pillars -- environment, reward, and policy -- and empirically derive a recipe for training LLM agents in situated textual domains. In particular, we test TextWorld and ALFWorld, popular domains for testing situated embodied reasoning, as well as SWE-Gym for more software engineering style tasks. (i) For the environment, we analyze the impacts of task complexity in terms of sizes of the state and action spaces as well as optimal solution length, finding that even simple environments within a domain can provide signal on how well an agent can generalize to more complex tasks. (ii) For the reward, we ablate relative reward sparsity, observing that while dense turn-level rewards accelerate training, performance and stability is highly dependent on the choice of RL algorithm. (iii) And for the agent's policy, we explore the interplay between reward sparsity and biased (PPO, GRPO) and unbiased (RLOO) policy gradient methods in addition to showing how to find the optimal Supervised Fine-tuning (SFT) to RL training ratio given a fixed budget. We distill these findings into a training recipe that guides co-design across the three pillars, facilitating research and practical efforts in multi-turn agentic RL. Code: https://github.com/pearls-lab/meow-tea-taro", "github_manual": "", "github_regex": "https://github.com/pearls-lab/meow-tea-taro", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.01141", "title": "Apriel-1.5-15b-Thinker", "authors": [ "Shruthan Radhakrishna", "Aman Tiwari", "Aanjaneya Shukla", "Masoud Hashemi", "Rishabh Maheshwary", "Shiva Krishna Reddy Malay", "Jash Mehta", "Pulkit Pattnaik", "Saloni Mittal", "Khalil Slimi", "Kelechi Ogueji", "Akintunde Oladipo", "Soham Parikh", "Oluwanifemi Bamgbose", "Toby Liang", "Ahmed Masry", "Khyati Mahajan", "Sai Rajeswar Mudumba", "Vikas Yadav", "Sathwik Tejaswi Madhusudhan", "Torsten Scholak", "Sagar Davasam", "Srinivas Sunkara", "Nicholas Chapados" ], "github": "", "project_page": "", "abstract": "We present Apriel-1.5-15B-Thinker, a 15-billion parameter open-weights multimodal reasoning model that achieves frontier-level performance through training design rather than sheer scale. Starting from Pixtral-12B, we apply a progressive three-stage methodology: (1) depth upscaling to expand reasoning capacity without pretraining from scratch, (2) staged continual pre-training that first develops foundational text and vision understanding, then enhances visual reasoning through targeted synthetic data generation addressing spatial structure, compositional understanding, and fine-grained perception, and (3) high-quality text-only supervised fine-tuning on curated instruction-response pairs with explicit reasoning traces spanning mathematics, coding, science, and tool use. Notably, our model achieves competitive results without reinforcement learning or preference optimization, isolating the contribution of our data-centric continual pre-training approach. On the Artificial Analysis Intelligence Index, Apriel-1.5-15B-Thinker attains a score of 52, matching DeepSeek-R1-0528 despite requiring significantly fewer computational resources. Across ten image benchmarks, its performance is on average within five points of Gemini-2.5-Flash and Claude Sonnet-3.7, a key achievement for a model operating within single-GPU deployment constraints. Our results demonstrate that thoughtful mid-training 2 design can close substantial capability gaps without massive scale, making frontier-level multimodal reasoning accessible to organizations with limited infrastructure. We release the model checkpoint, all training recipes, and evaluation protocols under the MIT license to to advance open-source research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.03232", "title": "LEAML: Label-Efficient Adaptation to Out-of-Distribution Visual Tasks for Multimodal Large Language Models", "authors": [ "Ci-Siang Lin", "Min-Hung Chen", "Yu-Yang Sheng", "Yu-Chiang Frank Wang" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have achieved strong performance on general visual benchmarks but struggle with out-of-distribution (OOD) tasks in specialized domains such as medical imaging, where labeled data is limited and expensive. We introduce LEAML, a label-efficient adaptation framework that leverages both scarce labeled VQA samples and abundant unlabeled images. Our approach generates domain-relevant pseudo question-answer pairs for unlabeled data using a QA generator regularized by caption distillation. Importantly, we selectively update only those neurons most relevant to question-answering, enabling the QA Generator to efficiently acquire domain-specific knowledge during distillation. Experiments on gastrointestinal endoscopy and sports VQA demonstrate that LEAML consistently outperforms standard fine-tuning under minimal supervision, highlighting the effectiveness of our proposed LEAML framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2509.25944", "title": "NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving", "authors": [ "Yuan Gao", "Mattia Piccinini", "Roberto Brusnicki", "Yuchen Zhang", "Johannes Betz" ], "github": "", "project_page": "", "abstract": "Understanding risk in autonomous driving requires not only perception and prediction, but also high-level reasoning about agent behavior and context. Current Vision Language Models (VLMs)-based methods primarily ground agents in static images and provide qualitative judgments, lacking the spatio-temporal reasoning needed to capture how risks evolve over time. To address this gap, we propose NuRisk, a comprehensive Visual Question Answering (VQA) dataset comprising 2,900 scenarios and 1.1 million agent-level samples, built on real-world data from nuScenes and Waymo, supplemented with safety-critical scenarios from the CommonRoad simulator. The dataset provides Bird-Eye-View (BEV) based sequential images with quantitative, agent-level risk annotations, enabling spatio-temporal reasoning. We benchmark well-known VLMs across different prompting techniques and find that they fail to perform explicit spatio-temporal reasoning, resulting in a peak accuracy of 33% at high latency. To address these shortcomings, our fine-tuned 7B VLM agent improves accuracy to 41% and reduces latency by 75%, demonstrating explicit spatio-temporal reasoning capabilities that proprietary models lacked. While this represents a significant step forward, the modest accuracy underscores the profound challenge of the task, establishing NuRisk as a critical benchmark for advancing spatio-temporal reasoning in autonomous driving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2509.25122", "title": "Triangle Splatting+: Differentiable Rendering with Opaque Triangles", "authors": [ "Jan Held", "Renaud Vandeghen", "Sanghyun Son", "Daniel Rebain", "Matheus Gadelha", "Yi Zhou", "Ming C. Lin", "Marc Van Droogenbroeck", "Andrea Tagliasacchi" ], "github": "https://github.com/trianglesplatting2/triangle-splatting2", "project_page": "", "abstract": "Reconstructing 3D scenes and synthesizing novel views has seen rapid progress in recent years. Neural Radiance Fields demonstrated that continuous volumetric radiance fields can achieve high-quality image synthesis, but their long training and rendering times limit practicality. 3D Gaussian Splatting (3DGS) addressed these issues by representing scenes with millions of Gaussians, enabling real-time rendering and fast optimization. However, Gaussian primitives are not natively compatible with the mesh-based pipelines used in VR headsets, and real-time graphics applications. Existing solutions attempt to convert Gaussians into meshes through post-processing or two-stage pipelines, which increases complexity and degrades visual quality. In this work, we introduce Triangle Splatting+, which directly optimizes triangles, the fundamental primitive of computer graphics, within a differentiable splatting framework. We formulate triangle parametrization to enable connectivity through shared vertices, and we design a training strategy that enforces opaque triangles. The final output is immediately usable in standard graphics engines without post-processing. Experiments on the Mip-NeRF360 and Tanks & Temples datasets show that Triangle Splatting+achieves state-of-the-art performance in mesh-based novel view synthesis. Our method surpasses prior splatting approaches in visual fidelity while remaining efficient and fast to training. Moreover, the resulting semi-connected meshes support downstream applications such as physics-based simulation or interactive walkthroughs. The project page is https://trianglesplatting2.github.io/trianglesplatting2/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/trianglesplatting2/triangle-splatting2", "github_api_added_by": "auto" }, { "date": "2025-10-06", "arxiv_id": "2509.22033", "title": "OrtSAE: Orthogonal Sparse Autoencoders Uncover Atomic Features", "authors": [ "Anton Korznikov", "Andrey Galichin", "Alexey Dontsov", "Oleg Rogov", "Elena Tutubalina", "Ivan Oseledets" ], "github": "", "project_page": "", "abstract": "Sparse autoencoders (SAEs) are a technique for sparse decomposition of neural network activations into human-interpretable features. However, current SAEs suffer from feature absorption, where specialized features capture instances of general features creating representation holes, and feature composition, where independent features merge into composite representations. In this work, we introduce Orthogonal SAE (OrtSAE), a novel approach aimed to mitigate these issues by enforcing orthogonality between the learned features. By implementing a new training procedure that penalizes high pairwise cosine similarity between SAE features, OrtSAE promotes the development of disentangled features while scaling linearly with the SAE size, avoiding significant computational overhead. We train OrtSAE across different models and layers and compare it with other methods. We find that OrtSAE discovers 9% more distinct features, reduces feature absorption (by 65%) and composition (by 15%), improves performance on spurious correlation removal (+6%), and achieves on-par performance for other downstream tasks compared to traditional SAEs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.01698", "title": "TalkPlay-Tools: Conversational Music Recommendation with LLM Tool Calling", "authors": [ "Seungheon Doh", "Keunwoo Choi", "Juhan Nam" ], "github": "", "project_page": "", "abstract": "While the recent developments in large language models (LLMs) have successfully enabled generative recommenders with natural language interactions, their recommendation behavior is limited, leaving other simpler yet crucial components such as metadata or attribute filtering underutilized in the system. We propose an LLM-based music recommendation system with tool calling to serve as a unified retrieval-reranking pipeline. Our system positions an LLM as an end-to-end recommendation system that interprets user intent, plans tool invocations, and orchestrates specialized components: boolean filters (SQL), sparse retrieval (BM25), dense retrieval (embedding similarity), and generative retrieval (semantic IDs). Through tool planning, the system predicts which types of tools to use, their execution order, and the arguments needed to find music matching user preferences, supporting diverse modalities while seamlessly integrating multiple database filtering methods. We demonstrate that this unified tool-calling framework achieves competitive performance across diverse recommendation scenarios by selectively employing appropriate retrieval methods based on user queries, envisioning a new paradigm for conversational music recommendation systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2509.24975", "title": "DiffTester: Accelerating Unit Test Generation for Diffusion LLMs via Repetitive Pattern", "authors": [ "Lekang Yang", "Yuetong Liu", "Yitong Zhang", "Jia Li" ], "github": "https://github.com/wellbeingyang/DLM4UTG-open", "project_page": "", "abstract": "Software development relies heavily on extensive unit testing, which makes the efficiency of automated Unit Test Generation (UTG) particularly important. However, most existing LLMs generate test cases one token at a time in each forward pass, which leads to inefficient UTG. Recently, diffusion LLMs (dLLMs) have emerged, offering promising parallel generation capabilities and showing strong potential for efficient UTG. Despite this advantage, their application to UTG is still constrained by a clear trade-off between efficiency and test quality, since increasing the number of tokens generated in each step often causes a sharp decline in the quality of test cases. To overcome this limitation, we present DiffTester, an acceleration framework specifically tailored for dLLMs in UTG. The key idea of DiffTester is that unit tests targeting the same focal method often share repetitive structural patterns. By dynamically identifying these common patterns through abstract syntax tree analysis during generation, DiffTester adaptively increases the number of tokens produced at each step without compromising the quality of the output. To enable comprehensive evaluation, we extend the original TestEval benchmark, which was limited to Python, by introducing additional programming languages including Java and C++. Extensive experiments on three benchmarks with two representative models show that DiffTester delivers significant acceleration while preserving test coverage. Moreover, DiffTester generalizes well across different dLLMs and programming languages, providing a practical and scalable solution for efficient UTG in software development. Code and data are publicly available at https://github.com/wellbeingyang/DLM4UTG-open .", "github_manual": "", "github_regex": "https://github.com/wellbeingyang/DLM4UTG-open", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2509.23291", "title": "Scaling Policy Compliance Assessment in Language Models with Policy Reasoning Traces", "authors": [ "Joseph Marvin Imperial", "Harish Tayyar Madabushi" ], "github": "", "project_page": "", "abstract": "Policy compliance assessment is a fundamental task of evaluating whether an input case strictly complies with a set of human-defined rules, more generally known as policies. In practice, human experts follow a systematic, step-by-step process to identify violations with respect to specific stipulations outlined in the policy. However, such documentation of gold-standard, expert-level reasoning processes is costly to acquire. In this paper, we introduce Policy Reasoning Traces (PRT), a form of specialized generated reasoning chains that serve as a reasoning bridge to improve an LLM's policy compliance assessment capabilities. Our empirical evaluations demonstrate that the use of PRTs for both inference-time and training-time scenarios significantly enhances the performance of open-weight and commercial models, setting a new state-of-the-art for HIPAA and GDPR policies. Beyond accuracy gains, we also highlight how PRTs can improve an LLM's ability to accurately cite policy clauses, as well as influence compliance decisions through their high utilization from the raw chains of thought.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.00938", "title": "Large Reasoning Models Learn Better Alignment from Flawed Thinking", "authors": [ "ShengYun Peng", "Eric Smith", "Ivan Evtimov", "Song Jiang", "Pin-Yu Chen", "Hongyuan Zhan", "Haozhu Wang", "Duen Horng Chau", "Mahesh Pasupuleti", "Jianfeng Chi" ], "github": "", "project_page": "", "abstract": "Large reasoning models (LRMs) \"think\" by generating structured chain-of-thought (CoT) before producing a final answer, yet they still lack the ability to reason critically about safety alignment and are easily biased when a flawed premise is injected into their thought process. We propose RECAP (Robust Safety Alignment via Counter-Aligned Prefilling), a principled reinforcement learning (RL) method for post-training that explicitly teaches models to override flawed reasoning trajectories and reroute to safe and helpful responses. RECAP trains on a mixture of synthetically generated counter-aligned CoT prefills and standard prompts, requires no additional training cost or modifications beyond vanilla reinforcement learning from human feedback (RLHF), and substantially improves safety and jailbreak robustness, reduces overrefusal, and preserves core reasoning capability -- all while maintaining inference token budget. Extensive analysis shows that RECAP-trained models engage in self-reflection more frequently and remain robust under adaptive attacks, preserving safety even after repeated attempts to override their reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2509.23202", "title": "Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization", "authors": [ "Vage Egiazarian", "Roberto L. Castro", "Denis Kuznedelev", "Andrei Panferov", "Eldar Kurtic", "Shubhra Pandit", "Alexandre Marques", "Mark Kurtz", "Saleh Ashkboos", "Torsten Hoefler", "Dan Alistarh" ], "github": "https://github.com/IST-DASLab/FP-Quant", "project_page": "", "abstract": "The recent hardware-accelerated microscaling 4-bit floating-point formats such as MXFP4 and NVFP4, supported on NVIDIA and AMD GPUs, promise to revolutionize large language model (LLM) inference. Yet, their practical benefits remain unproven. We present the first comprehensive study of MXFP4 and NVFP4 for post-training quantization, revealing gaps between their promise and real-world performance. Our analysis shows that state-of-the-art methods struggle with FP4, due to two key issues: (1) NVFP4's small group size provably neutralizes traditional outlier mitigation techniques; (2) MXFP4's power-of-two scale quantization severely degrades accuracy due to high induced error. To bridge this gap, we introduce Micro-Rotated-GPTQ (MR-GPTQ), a variant of the classic GPTQ quantization algorithm that tailors the quantization process to FP4's unique properties, by using block-wise Hadamard transforms and format-specific optimizations. We support our proposal with a set of high-performance GPU kernels that enable the MR-GPTQ format with negligible overhead, by rotation fusion into the weights, and fast online computation of the activations. This leads to speedups vs. FP16 of up to 3.6x layer-wise, and 2.2x end-to-end on NVIDIA B200, and of 6x layer-wise and 4x end-to-end on RTX5090. Our extensive empirical evaluation demonstrates that MR-GPTQ matches or outperforms state-of-the-art accuracy, significantly boosting MXFP4, to the point where it nears that of NVFP4. We conclude that, while FP4 is not an automatic upgrade over INT4, format-specialized methods like MR-GPTQ can unlock a new frontier of accuracy-performance trade-offs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/IST-DASLab/FP-Quant", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2509.26388", "title": "Game-Time: Evaluating Temporal Dynamics in Spoken Language Models", "authors": [ "Kai-Wei Chang", "En-Pei Hu", "Chun-Yi Kuan", "Wenze Ren", "Wei-Chih Chen", "Guan-Ting Lin", "Yu Tsao", "Shao-Hua Sun", "Hung-yi Lee", "James Glass" ], "github": "", "project_page": "", "abstract": "Conversational Spoken Language Models (SLMs) are emerging as a promising paradigm for real-time speech interaction. However, their capacity of temporal dynamics, including the ability to manage timing, tempo and simultaneous speaking, remains a critical and unevaluated challenge for conversational fluency. To address this gap, we introduce the Game-Time Benchmark, a framework to systematically assess these temporal capabilities. Inspired by how humans learn a language through language activities, Game-Time consists of basic instruction-following tasks and advanced tasks with temporal constraints, such as tempo adherence and synchronized responses. Our evaluation of diverse SLM architectures reveals a clear performance disparity: while state-of-the-art models handle basic tasks well, many contemporary systems still struggle with fundamental instruction-following. More critically, nearly all models degrade substantially under temporal constraints, exposing persistent weaknesses in time awareness and full-duplex interaction. The Game-Time Benchmark provides a foundation for guiding future research toward more temporally-aware conversational AI. Demos and datasets are available on our project website https://ga642381.github.io/Game-Time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.02880", "title": "Consolidating Reinforcement Learning for Multimodal Discrete Diffusion Models", "authors": [ "Tianren Ma", "Mu Zhang", "Yibing Wang", "Qixiang Ye" ], "github": "https://github.com/martian422/MaskGRPO", "project_page": "", "abstract": "Optimizing discrete diffusion model (DDM) with rewards remains a challenge: the non-autoregressive paradigm makes importance sampling intractable and rollout complex, puzzling reinforcement learning methods such as Group Relative Policy Optimization (GRPO). In this study, we introduce MaskGRPO, the first viable approach to enable scalable multimodal reinforcement learning in discrete diffusion with effective importance sampling and modality-specific adaptations. To this end, we first clarify the theoretical foundation for DDMs, which facilitates building an importance estimator that captures valuable token fluctuation for gradient updates. We then delicately tailored the rollout method for visual sequences, which yields diverse completions and reliable optimization gradients. Upon math reasoning, coding, and visual generation benchmarks, MaskGRPO brings more stable and efficient updates, leading to stronger reasoning performance and better generation quality. This study establishes MaskGRPO as a systematic policy optimization approach and the first practical way for discretized visual diffusion.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/martian422/MaskGRPO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.02375", "title": "Pretraining with hierarchical memories: separating long-tail and common knowledge", "authors": [ "Hadi Pouransari", "David Grangier", "C Thomas", "Michael Kirchhof", "Oncel Tuzel" ], "github": "https://github.com/mlfoundations/open_lm", "project_page": "", "abstract": "The impressive performance gains of modern language models currently rely on scaling parameters: larger models store more world knowledge and reason better. Yet compressing all world knowledge into parameters is unnecessary, as only a fraction is used per prompt, and impractical for edge devices with limited inference-time memory and compute. We address this shortcoming by a memory-augmented architecture and a pretraining strategy aligned with existing hardware paradigms. We introduce small language models that access large hierarchical parametric memory banks encoding world knowledge. During pretraining and inference, we fetch a small, context-dependent memory block and add it to the model. Our pretraining learns to store long-tail world knowledge in the memory parameters, while the small language model acts as an anchor capturing common knowledge and general reasoning abilities. Through trillion-token-scale experiments, we show significant gains: a 160M-parameters model augmented with an 18M-parameters memory fetched from a 4.6B memory bank obtains comparable performance to a regular model with more than 2x the parameters. Through extensive experiments, we study the optimal type and size of parametric memories in transformers, scaling them to over 21B parameters. We find that our proposed hierarchical feed-forward memories work robustly across transformer architectures, whether added during pretraining or post-hoc.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mlfoundations/open_lm", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.02110", "title": "SoundReactor: Frame-level Online Video-to-Audio Generation", "authors": [ "Koichi Saito", "Julian Tanke", "Christian Simon", "Masato Ishii", "Kazuki Shimada", "Zachary Novack", "Zhi Zhong", "Akio Hayakawa", "Takashi Shibuya", "Yuki Mitsufuji" ], "github": "", "project_page": "", "abstract": "Prevailing Video-to-Audio (V2A) generation models operate offline, assuming an entire video sequence or chunks of frames are available beforehand. This critically limits their use in interactive applications such as live content creation and emerging generative world models. To address this gap, we introduce the novel task of frame-level online V2A generation, where a model autoregressively generates audio from video without access to future video frames. Furthermore, we propose SoundReactor, which, to the best of our knowledge, is the first simple yet effective framework explicitly tailored for this task. Our design enforces end-to-end causality and targets low per-frame latency with audio-visual synchronization. Our model's backbone is a decoder-only causal transformer over continuous audio latents. For vision conditioning, it leverages grid (patch) features extracted from the smallest variant of the DINOv2 vision encoder, which are aggregated into a single token per frame to maintain end-to-end causality and efficiency. The model is trained through a diffusion pre-training followed by consistency fine-tuning to accelerate the diffusion head decoding. On a benchmark of diverse gameplay videos from AAA titles, our model successfully generates semantically and temporally aligned, high-quality full-band stereo audio, validated by both objective and human evaluations. Furthermore, our model achieves low per-frame waveform-level latency (26.3ms with the head NFE=1, 31.5ms with NFE=4) on 30FPS, 480p videos using a single H100. Demo samples are available at https://koichi-saito-sony.github.io/soundreactor/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.02730", "title": "Dale meets Langevin: A Multiplicative Denoising Diffusion Model", "authors": [ "Nishanth Shetty", "Madhava Prasath", "Chandra Sekhar Seelamantula" ], "github": "", "project_page": "", "abstract": "Gradient descent has proven to be a powerful and effective technique for optimization in numerous machine learning applications. Recent advances in computational neuroscience have shown that learning in standard gradient descent optimization formulation is not consistent with learning in biological systems. This has opened up interesting avenues for building biologically inspired learning techniques. One such approach is inspired by Dale's law, which states that inhibitory and excitatory synapses do not swap roles during the course of learning. The resulting exponential gradient descent optimization scheme leads to log-normally distributed synaptic weights. Interestingly, the density that satisfies the Fokker-Planck equation corresponding to the stochastic differential equation (SDE) with geometric Brownian motion (GBM) is the log-normal density. Leveraging this connection, we start with the SDE governing geometric Brownian motion, and show that discretizing the corresponding reverse-time SDE yields a multiplicative update rule, which surprisingly, coincides with the sampling equivalent of the exponential gradient descent update founded on Dale's law. Furthermore, we propose a new formalism for multiplicative denoising score-matching, subsuming the loss function proposed by Hyvaerinen for non-negative data. Indeed, log-normally distributed data is positive and the proposed score-matching formalism turns out to be a natural fit. This allows for training of score-based models for image data and results in a novel multiplicative update scheme for sample generation starting from a log-normal density. Experimental results on MNIST, Fashion MNIST, and Kuzushiji datasets demonstrate generative capability of the new scheme. To the best of our knowledge, this is the first instance of a biologically inspired generative model employing multiplicative updates, founded on geometric Brownian motion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.01329", "title": "Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling", "authors": [ "Huangjie Zheng", "Shansan Gong", "Ruixiang Zhang", "Tianrong Chen", "Jiatao Gu", "Mingyuan Zhou", "Navdeep Jaitly", "Yizhe Zhang" ], "github": "", "project_page": "", "abstract": "Standard discrete diffusion models treat all unobserved states identically by mapping them to an absorbing [MASK] token. This creates an 'information void' where semantic information that could be inferred from unmasked tokens is lost between denoising steps. We introduce Continuously Augmented Discrete Diffusion (CADD), a framework that augments the discrete state space with a paired diffusion in a continuous latent space. This yields graded, gradually corrupted states in which masked tokens are represented by noisy yet informative latent vectors rather than collapsed 'information voids'. At each reverse step, CADD may leverage the continuous latent as a semantic hint to guide discrete denoising. The design is clean and compatible with existing discrete diffusion training. At sampling time, the strength and choice of estimator for the continuous latent vector enables a controlled trade-off between mode-coverage (generating diverse outputs) and mode-seeking (generating contextually precise outputs) behaviors. Empirically, we demonstrate CADD improves generative quality over mask-based diffusion across text generation, image synthesis, and code modeling, with consistent gains on both qualitative and quantitative metrics against strong discrete baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.00177", "title": "Personalized Reasoning: Just-In-Time Personalization and Why LLMs Fail At It", "authors": [ "Shuyue Stella Li", "Avinandan Bose", "Faeze Brahman", "Simon Shaolei Du", "Pang Wei Koh", "Maryam Fazel", "Yulia Tsvetkov" ], "github": "", "project_page": "", "abstract": "Current large language model (LLM) development treats task-solving and preference alignment as separate challenges, optimizing first for objective correctness, then for alignment to aggregated human preferences. This paradigm fails in human-facing applications where solving a problem correctly is insufficient if the response mismatches the user's needs. This challenge intensifies in just-in-time scenarios where no prior user interaction history exists due to cold-start conditions or privacy constraints. LLMs need to identify what they don't know about user preferences, strategically elicit preference values through questioning, then adapt their reasoning processes and responses accordingly -- a complicated chain of cognitive processes which we term personalized reasoning. We introduce PREFDISCO, an evaluation methodology that transforms static benchmarks into interactive personalization tasks using psychologically-grounded personas with sparse preferences. Our framework creates scenarios where identical questions require different reasoning chains depending on user context, as optimal explanation approaches vary by individual expertise and preferences while maintaining factual accuracy. Evaluation of 21 frontier models across 10 tasks reveals 29.0% of naive personalization attempts produce worse preference alignment than generic responses, yet generic responses also fail to serve individual user needs effectively. These findings suggest personalized reasoning requires dedicated development rather than emerging naturally. PREFDISCO establishes personalized reasoning as a measurable research frontier and reveals fundamental limitations in current LLMs' interactive capabilities, providing a foundation for developing systems that can adapt to individual users in education, healthcare, and technical domains where personalization is critical.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.02657", "title": "Less LLM, More Documents: Searching for Improved RAG", "authors": [ "Jingjie Ning", "Yibo Kong", "Yunfan Long", "Jamie Callan" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) couples document retrieval with large language models (LLMs). While scaling generators improves accuracy, it also raises cost and limits deployability. We explore an orthogonal axis: enlarging the retriever's corpus to reduce reliance on large LLMs. Experimental results show that corpus scaling consistently strengthens RAG and can often serve as a substitute for increasing model size, though with diminishing returns at larger scales. Small- and mid-sized generators paired with larger corpora often rival much larger models with smaller corpora; mid-sized models tend to gain the most, while tiny and large models benefit less. Our analysis shows that improvements arise primarily from increased coverage of answer-bearing passages, while utilization efficiency remains largely unchanged. These findings establish a principled corpus-generator trade-off: investing in larger corpora offers an effective path to stronger RAG, often comparable to enlarging the LLM itself.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.02410", "title": "OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data", "authors": [ "Patrick Langer", "Thomas Kaar", "Max Rosenblattl", "Maxwell A. Xu", "Winnie Chow", "Martin Maritsch", "Aradhana Verma", "Brian Han", "Daniel Seung Kim", "Henry Chubb", "Scott Ceresnak", "Aydin Zahedivash", "Alexander Tarlochan Singh Sandhu", "Fatima Rodriguez", "Daniel McDuff", "Elgar Fleisch", "Oliver Aalami", "Filipe Barata", "Paul Schmiedmayer" ], "github": "", "project_page": "", "abstract": "LLMs have emerged as powerful tools for interpreting multimodal data. In medicine, they hold particular promise for synthesizing large volumes of clinical information into actionable insights and digital health applications. Yet, a major limitation remains their inability to handle time series. To overcome this gap, we present OpenTSLM, a family of Time Series Language Models (TSLMs) created by integrating time series as a native modality to pretrained LLMs, enabling reasoning over multiple time series of any length. We investigate two architectures for OpenTSLM. The first, OpenTSLM-SoftPrompt, models time series implicitly by concatenating learnable time series tokens with text tokens via soft prompting. Although parameter-efficient, we hypothesize that explicit time series modeling scales better and outperforms implicit approaches. We thus introduce OpenTSLM-Flamingo, which integrates time series with text via cross-attention. We benchmark both variants against baselines that treat time series as text tokens or plots, across a suite of text-time-series Chain-of-Thought (CoT) reasoning tasks. We introduce three datasets: HAR-CoT, Sleep-CoT, and ECG-QA-CoT. Across all, OpenTSLM models outperform baselines, reaching 69.9 F1 in sleep staging and 65.4 in HAR, compared to 9.05 and 52.2 for finetuned text-only models. Notably, even 1B-parameter OpenTSLM models surpass GPT-4o (15.47 and 2.95). OpenTSLM-Flamingo matches OpenTSLM-SoftPrompt in performance and outperforms on longer sequences, while maintaining stable memory requirements. By contrast, SoftPrompt grows exponentially in memory with sequence length, requiring around 110 GB compared to 40 GB VRAM when training on ECG-QA with LLaMA-3B. Expert reviews by clinicians find strong reasoning capabilities exhibited by OpenTSLMs on ECG-QA. To facilitate further research, we provide all code, datasets, and models open-source.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-06", "arxiv_id": "2510.03574", "title": "Efficient Test-Time Scaling for Small Vision-Language Models", "authors": [ "Mehmet Onurcan Kaya", "Desmond Elliott", "Dim P. Papadopoulos" ], "github": "", "project_page": "", "abstract": "Small Vision-Language Models (VLMs) provide a computationally efficient alternative to larger models, at the cost of weaker generalization abilities and downstream task performance. These shortcomings could be addressed by test-time scaling techniques, but existing methods are typically computationally demanding, contradicting the resource-efficient design goals of small models. To address these limitations, we propose two novel and efficient test-time scaling strategies that leverage the model-internal features rather than external supervision: (i) Test-Time Augmentation (TTAug), which generates multiple augmented inputs and aggregates outputs at the token level without parameter updates, and (ii) Test-Time Adaptation (TTAdapt), which adapts model parameters during inference using consensus-based pseudolabels from TTAug. Through extensive experiments across nine benchmarks, we demonstrate consistent performance improvements while maintaining computational efficiency suitable for resource-constrained environments. The generality of our approach is demonstrated both within models at different scales and across different VLMs without additional tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04399", "title": "Utility-Learning Tension in Self-Modifying Agents", "authors": [ "Charles L. Wang", "Keir Dorchen", "Peter Jin" ], "github": "", "project_page": "", "abstract": "As systems trend toward superintelligence, a natural modeling premise is that agents can self-improve along every facet of their own design. We formalize this with a five-axis decomposition and a decision layer, separating incentives from learning behavior and analyzing axes in isolation. Our central result identifies and introduces a sharp utility--learning tension, the structural conflict in self-modifying systems whereby utility-driven changes that improve immediate or expected performance can also erode the statistical preconditions for reliable learning and generalization. Our findings show that distribution-free guarantees are preserved iff the policy-reachable model family is uniformly capacity-bounded; when capacity can grow without limit, utility-rational self-changes can render learnable tasks unlearnable. Under standard assumptions common in practice, these axes reduce to the same capacity criterion, yielding a single boundary for safe self-modification. Numerical experiments across several axes validate the theory by comparing destructive utility policies against our proposed two-gate policies that preserve learnability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.00263", "title": "Judging with Confidence: Calibrating Autoraters to Preference Distributions", "authors": [ "Zhuohang Li", "Xiaowei Li", "Chengyu Huang", "Guowang Li", "Katayoon Goshvadi", "Bo Dai", "Dale Schuurmans", "Paul Zhou", "Hamid Palangi", "Yiwen Song", "Palash Goyal", "Murat Kantarcioglu", "Bradley A. Malin", "Yuan Xue" ], "github": "", "project_page": "", "abstract": "The alignment of large language models (LLMs) with human values increasingly relies on using other LLMs as automated judges, or ``autoraters''. However, their reliability is limited by a foundational issue: they are trained on discrete preference labels, forcing a single ground truth onto tasks that are often subjective, ambiguous, or nuanced. We argue that a reliable autorater must learn to model the full distribution of preferences defined by a target population. In this paper, we propose a general framework for calibrating probabilistic autoraters to any given preference distribution. We formalize the problem and present two learning methods tailored to different data conditions: 1) a direct supervised fine-tuning for dense, probabilistic labels, and 2) a reinforcement learning approach for sparse, binary labels. Our empirical results show that finetuning autoraters with a distribution-matching objective leads to verbalized probability predictions that are better aligned with the target preference distribution, with improved calibration and significantly lower positional bias, all while preserving performance on objective tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.05096", "title": "Paper2Video: Automatic Video Generation from Scientific Papers", "authors": [ "Zeyu Zhu", "Kevin Qinghong Lin", "Mike Zheng Shou" ], "github": "https://github.com/showlab/Paper2Video", "project_page": "", "abstract": "Academic presentation videos have become an essential medium for research communication, yet producing them remains highly labor-intensive, often requiring hours of slide design, recording, and editing for a short 2 to 10 minutes video. Unlike natural video, presentation video generation involves distinctive challenges: inputs from research papers, dense multi-modal information (text, figures, tables), and the need to coordinate multiple aligned channels such as slides, subtitles, speech, and human talker. To address these challenges, we introduce PaperTalker, the first benchmark of 101 research papers paired with author-created presentation videos, slides, and speaker metadata. We further design four tailored evaluation metrics--Meta Similarity, PresentArena, PresentQuiz, and IP Memory--to measure how videos convey the paper's information to the audience. Building on this foundation, we propose PaperTalker, the first multi-agent framework for academic presentation video generation. It integrates slide generation with effective layout refinement by a novel effective tree search visual choice, cursor grounding, subtitling, speech synthesis, and talking-head rendering, while parallelizing slide-wise generation for efficiency. Experiments on Paper2Video demonstrate that the presentation videos produced by our approach are more faithful and informative than existing baselines, establishing a practical step toward automated and ready-to-use academic video generation. Our dataset, agent, and code are available at https://github.com/showlab/Paper2Video.", "github_manual": "", "github_regex": "https://github.com/showlab/Paper2Video", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04996", "title": "Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training", "authors": [ "Wei Xiong", "Chenlu Ye", "Baohao Liao", "Hanze Dong", "Xinxing Xu", "Christof Monz", "Jiang Bian", "Nan Jiang", "Tong Zhang" ], "github": "https://github.com/RLHFlow/Reinforce-Ada", "project_page": "", "abstract": "Reinforcement learning applied to large language models (LLMs) for reasoning tasks is often bottlenecked by unstable gradient estimates due to fixed and uniform sampling of responses across prompts. Prior work such as GVM-RAFT addresses this by dynamically allocating inference budget per prompt to minimize stochastic gradient variance under a budget constraint. Inspired by this insight, we propose Reinforce-Ada, an adaptive sampling framework for online RL post-training of LLMs that continuously reallocates sampling effort to the prompts with the greatest uncertainty or learning potential. Unlike conventional two-stage allocation methods, Reinforce-Ada interleaves estimation and sampling in an online successive elimination process, and automatically stops sampling for a prompt once sufficient signal is collected. To stabilize updates, we form fixed-size groups with enforced reward diversity and compute advantage baselines using global statistics aggregated over the adaptive sampling phase. Empirical results across multiple model architectures and reasoning benchmarks show that Reinforce-Ada accelerates convergence and improves final performance compared to GRPO, especially when using the balanced sampling variant. Our work highlights the central role of variance-aware, adaptive data curation in enabling efficient and reliable reinforcement learning for reasoning-capable LLMs. Code is available at https://github.com/RLHFlow/Reinforce-Ada.", "github_manual": "", "github_regex": "https://github.com/RLHFlow/Reinforce-Ada", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04800", "title": "Hybrid Architectures for Language Models: Systematic Analysis and Design Insights", "authors": [ "Sangmin Bae", "Bilge Acun", "Haroun Habeeb", "Seungyeon Kim", "Chien-Yu Lin", "Liang Luo", "Junjie Wang", "Carole-Jean Wu" ], "github": "", "project_page": "", "abstract": "Recent progress in large language models demonstrates that hybrid architectures--combining self-attention mechanisms with structured state space models like Mamba--can achieve a compelling balance between modeling quality and computational efficiency, particularly for long-context tasks. While these hybrid models show promising performance, systematic comparisons of hybridization strategies and analyses on the key factors behind their effectiveness have not been clearly shared to the community. In this work, we present a holistic evaluation of hybrid architectures based on inter-layer (sequential) or intra-layer (parallel) fusion. We evaluate these designs from a variety of perspectives: language modeling performance, long-context capabilities, scaling analysis, and training and inference efficiency. By investigating the core characteristics of their computational primitive, we identify the most critical elements for each hybridization strategy and further propose optimal design recipes for both hybrid models. Our comprehensive analysis provides practical guidance and valuable insights for developing hybrid language models, facilitating the optimization of architectural configurations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.03264", "title": "Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data", "authors": [ "Syeda Nahida Akter", "Shrimai Prabhumoye", "Eric Nyberg", "Mostofa Patwary", "Mohammad Shoeybi", "Yejin Choi", "Bryan Catanzaro" ], "github": "", "project_page": "", "abstract": "The prevailing paradigm for enhancing the reasoning abilities of LLMs revolves around post-training on high-quality, reasoning-intensive data. While emerging literature suggests that reasoning data is increasingly incorporated also during the mid-training stage-a practice that is relatively more proprietary and less openly characterized-the role of such data in pretraining remains unclear. In particular, due to the opaqueness of pretraining corpora in most frontier models, the effect of reasoning data introduced at different phases of pre- and/or post-training is relatively less reported in the scientific literature. This raises several important questions: Is adding reasoning data earlier during pretraining any better than introducing it during post-training? Could earlier inclusion risk overfitting and harm generalization, or instead establish durable foundations that later fine-tuning cannot recover? We conduct the first systematic study of how reasoning data-varying in scale, diversity, and quality-affects LLM performance when introduced at different stages of training. We find that front-loading reasoning data into pretraining is critical (19% avg gain), establishing foundational capabilities that cannot be fully replicated by later-stage SFT, even with more data. We uncover an asymmetric principle for optimal data allocation: pretraining benefits most from broad diversity in reasoning patterns (11% avg gain), while SFT is more sensitive to data quality (15% avg gain). We show that high-quality pretraining data has latent effects, activated only after SFT, and that naively scaling SFT data can be detrimental, washing away the benefits of early reasoning injection. Our results challenge the conventional separation of language modeling and reasoning, providing a principled guide for strategically allocating data across the entire training pipeline to build more capable models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.05094", "title": "VChain: Chain-of-Visual-Thought for Reasoning in Video Generation", "authors": [ "Ziqi Huang", "Ning Yu", "Gordon Chen", "Haonan Qiu", "Paul Debevec", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transitions over time remains a core challenge. In contrast, large language and multimodal models (e.g., GPT-4o) exhibit strong visual state reasoning and future prediction capabilities. To bridge these strengths, we introduce VChain, a novel inference-time chain-of-visual-thought framework that injects visual reasoning signals from multimodal models into video generation. Specifically, VChain contains a dedicated pipeline that leverages large multimodal models to generate a sparse set of critical keyframes as snapshots, which are then used to guide the sparse inference-time tuning of a pre-trained video generator only at these key moments. Our approach is tuning-efficient, introduces minimal overhead and avoids dense supervision. Extensive experiments on complex, multi-step scenarios show that VChain significantly enhances the quality of generated videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.05034", "title": "Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models", "authors": [ "Yunlong Tang", "Jing Bi", "Pinxin Liu", "Zhenyu Pan", "Zhangyun Tan", "Qianxiang Shen", "Jiani Liu", "Hang Hua", "Junjia Guo", "Yunzhong Xiao", "Chao Huang", "Zhiyuan Wang", "Susan Liang", "Xinyi Liu", "Yizhi Song", "Yuhe Nie", "Jia-Xing Zhong", "Bozheng Li", "Daiqing Qi", "Ziyun Zeng", "Ali Vosoughi", "Luchuan Song", "Zeliang Zhang", "Daiki Shimada", "Han Liu", "Jiebo Luo", "Chenliang Xu" ], "github": "https://github.com/yunlong10/Awesome-Video-LMM-Post-Training", "project_page": "", "abstract": "Video understanding represents the most challenging frontier in computer vision, requiring models to reason about complex spatiotemporal relationships, long-term dependencies, and multimodal evidence. The recent emergence of Video-Large Multimodal Models (Video-LMMs), which integrate visual encoders with powerful decoder-based language models, has demonstrated remarkable capabilities in video understanding tasks. However, the critical phase that transforms these models from basic perception systems into sophisticated reasoning engines, post-training, remains fragmented across the literature. This survey provides the first comprehensive examination of post-training methodologies for Video-LMMs, encompassing three fundamental pillars: supervised fine-tuning (SFT) with chain-of-thought, reinforcement learning (RL) from verifiable objectives, and test-time scaling (TTS) through enhanced inference computation. We present a structured taxonomy that clarifies the roles, interconnections, and video-specific adaptations of these techniques, addressing unique challenges such as temporal localization, spatiotemporal grounding, long video efficiency, and multimodal evidence integration. Through systematic analysis of representative methods, we synthesize key design principles, insights, and evaluation protocols while identifying critical open challenges in reward design, scalability, and cost-performance optimization. We further curate essential benchmarks, datasets, and metrics to facilitate rigorous assessment of post-training effectiveness. This survey aims to provide researchers and practitioners with a unified framework for advancing Video-LMM capabilities. Additional resources and updates are maintained at: https://github.com/yunlong10/Awesome-Video-LMM-Post-Training", "github_manual": "", "github_regex": "https://github.com/yunlong10/Awesome-Video-LMM-Post-Training", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.03561", "title": "Reactive Transformer (RxT) -- Stateful Real-Time Processing for Event-Driven Reactive Language Models", "authors": [ "Adam Filipek" ], "github": "", "project_page": "", "abstract": "The Transformer architecture has become the de facto standard for Large Language Models (LLMs), demonstrating remarkable capabilities in language understanding and generation. However, its application in conversational AI is fundamentally constrained by its stateless nature and the quadratic computational complexity (O(L^2)) with respect to sequence length L. Current models emulate memory by reprocessing an ever-expanding conversation history with each turn, leading to prohibitive costs and latency in long dialogues. This paper introduces the Reactive Transformer (RxT), a novel architecture designed to overcome these limitations by shifting from a data-driven to an event-driven paradigm. RxT processes each conversational turn as a discrete event in real-time, maintaining context in an integrated, fixed-size Short-Term Memory (STM) system. The architecture features a distinct operational cycle where a generator-decoder produces a response based on the current query and the previous memory state, after which a memory-encoder and a dedicated Memory Attention network asynchronously update the STM with a representation of the complete interaction. This design fundamentally alters the scaling dynamics, reducing the total user-facing cost of a conversation from quadratic (O(N^2 cdot T)) to linear (O(N cdot T)) with respect to the number of interactions N. By decoupling response generation from memory updates, RxT achieves low latency, enabling truly real-time, stateful, and economically viable long-form conversations. We validated our architecture with a series of proof-of-concept experiments on synthetic data, demonstrating superior performance and constant-time inference latency compared to a baseline stateless model of comparable size.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04434", "title": "Good Intentions Beyond ACL: Who Does NLP for Social Good, and Where?", "authors": [ "Grace LeFevre", "Qingcheng Zeng", "Adam Leif", "Jason Jewell", "Denis Peskoff", "Rob Voigt" ], "github": "https://github.com/asl7168/nlp4sg_beyond_acl", "project_page": "", "abstract": "The social impact of Natural Language Processing (NLP) is increasingly important, with a rising community focus on initiatives related to NLP for Social Good (NLP4SG). Indeed, in recent years, almost 20% of all papers in the ACL Anthology address topics related to social good as defined by the UN Sustainable Development Goals (Adauto et al., 2023). In this study, we take an author- and venue-level perspective to map the landscape of NLP4SG, quantifying the proportion of work addressing social good concerns both within and beyond the ACL community, by both core ACL contributors and non-ACL authors. With this approach we discover two surprising facts about the landscape of NLP4SG. First, ACL authors are dramatically more likely to do work addressing social good concerns when publishing in venues outside of ACL. Second, the vast majority of publications using NLP techniques to address concerns of social good are done by non-ACL authors in venues outside of ACL. We discuss the implications of these findings on agenda-setting considerations for the ACL community related to NLP4SG.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/asl7168/nlp4sg_beyond_acl", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.05091", "title": "Factuality Matters: When Image Generation and Editing Meet Structured Visuals", "authors": [ "Le Zhuo", "Songhao Han", "Yuandong Pu", "Boxiang Qiu", "Sayak Paul", "Yue Liao", "Yihao Liu", "Jie Shao", "Xi Chen", "Si Liu", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "While modern visual generation models excel at creating aesthetically pleasing natural images, they struggle with producing or editing structured visuals like charts, diagrams, and mathematical figures, which demand composition planning, text rendering, and multimodal reasoning for factual fidelity. To address this, we present the first comprehensive, systematic investigation of this domain, encompassing data construction, model training, and an evaluation benchmark. First, we construct a large-scale dataset of 1.3 million high-quality structured image pairs derived from executable drawing programs and augmented with chain-of-thought reasoning annotations. Building on it, we train a unified model that integrates a VLM with FLUX.1 Kontext via a lightweight connector for enhanced multimodal understanding. A three-stage training curriculum enables progressive feature alignment, knowledge infusion, and reasoning-augmented generation, further boosted by an external reasoner at inference time. Finally, we introduce StructBench, a novel benchmark for generation and editing with over 1,700 challenging instances, and an accompanying evaluation metric, StructScore, which employs a multi-round Q\\&A protocol to assess fine-grained factual accuracy. Evaluations of 15 models reveal that even leading closed-source systems remain far from satisfactory. Our model attains strong editing performance, and inference-time reasoning yields consistent gains across diverse architectures. By releasing the dataset, model, and benchmark, we aim to advance unified multimodal foundations for structured visuals.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.05069", "title": "SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs", "authors": [ "Dachuan Shi", "Abedelkadir Asi", "Keying Li", "Xiangchi Yuan", "Leyan Pan", "Wenke Lee", "Wen Xiao" ], "github": "", "project_page": "", "abstract": "Recent work shows that, beyond discrete reasoning through explicit chain-of-thought steps, which are limited by the boundaries of natural languages, large language models (LLMs) can also reason continuously in latent space, allowing richer information per step and thereby improving token efficiency. Despite this promise, latent reasoning still faces two challenges, especially in training-free settings: 1) purely latent reasoning broadens the search distribution by maintaining multiple implicit paths, which diffuses probability mass, introduces noise, and impedes convergence to a single high-confidence solution, thereby hurting accuracy; and 2) overthinking persists even without explicit text, wasting tokens and degrading efficiency. To address these issues, we introduce SwiReasoning, a training-free framework for LLM reasoning which features two key innovations: 1) SwiReasoning dynamically switches between explicit and latent reasoning, guided by block-wise confidence estimated from entropy trends in next-token distributions, to balance exploration and exploitation and promote timely convergence. 2) By limiting the maximum number of thinking-block switches, SwiReasoning curbs overthinking and improves token efficiency across varying problem difficulties. On widely used mathematics and STEM benchmarks, SwiReasoning consistently improves average accuracy by 1.5%-2.8% across reasoning LLMs of different model families and scales. Furthermore, under constrained budgets, SwiReasoning improves average token efficiency by 56%-79%, with larger gains as budgets tighten.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04673", "title": "Watch and Learn: Learning to Use Computers from Online Videos", "authors": [ "Chan Hee Song", "Yiwen Song", "Palash Goyal", "Yu Su", "Oriana Riva", "Hamid Palangi", "Tomas Pfister" ], "github": "", "project_page": "", "abstract": "Computer use agents (CUAs) need to plan task workflows grounded in diverse, ever-changing applications and environments, but learning is hindered by the scarcity of large-scale, high-quality training data in the target application. Existing datasets are domain-specific, static, and costly to annotate, while current synthetic data generation methods often yield simplistic or misaligned task demonstrations. To address these limitations, we introduce Watch & Learn (W&L), a framework that converts human demonstration videos readily available on the Internet into executable UI trajectories at scale. Instead of directly generating trajectories or relying on ad hoc reasoning heuristics, we cast the problem as an inverse dynamics objective: predicting the user's action from consecutive screen states. This formulation reduces manual engineering, is easier to learn, and generalizes more robustly across applications. Concretely, we develop an inverse dynamics labeling pipeline with task-aware video retrieval, generate over 53k high-quality trajectories from raw web videos, and demonstrate that these trajectories improve CUAs both as in-context demonstrations and as supervised training data. On the challenging OSWorld benchmark, UI trajectories extracted with W&L consistently enhance both general-purpose and state-of-the-art frameworks in-context, and deliver stronger gains for open-source models under supervised training. These results highlight web-scale human demonstration videos as a practical and scalable foundation for advancing CUAs towards real-world deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.03632", "title": "MITS: Enhanced Tree Search Reasoning for LLMs via Pointwise Mutual Information", "authors": [ "Jiaxi Li", "Yucheng Shi", "Jin Lu", "Ninghao Liu" ], "github": "", "project_page": "", "abstract": "Tree search has become as a representative framework for test-time reasoning with large language models (LLMs), exemplified by methods such as Tree-of-Thought and Monte Carlo Tree Search that explore multiple reasoning paths. However, it remains difficult to provide instant and reliable quantitative assessments of intermediate reasoning step quality, and extensive path exploration is computationally costly. To address this, we propose Mutual Information Tree Search (MITS), a novel framework that guides reasoning with information-theoretic principles. MITS introduces an effective scoring function based on pointwise mutual information (PMI), which enables step-wise evaluation of reasoning paths and search tree expansion via beam search without expensive look-ahead simulations, achieving superior reasoning performances while maintaining computational efficiency. The framework is complemented by an entropy-based dynamic sampling strategy that adaptively allocates computational resources to uncertain reasoning steps where exploration is most beneficial. For final prediction, MITS employs a weighted voting scheme that combines PMI scores with prediction consensus. Through comprehensive experiments on diverse reasoning benchmarks, MITS consistently surpasses baseline methods, establishing a principled and efficient framework for LLM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04618", "title": "Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models", "authors": [ "Qizheng Zhang", "Changran Hu", "Shubhangi Upasani", "Boyuan Ma", "Fenglu Hong", "Vamsidhar Kamanuru", "Jay Rainton", "Chen Wu", "Mengmeng Ji", "Hanchen Li", "Urmish Thakker", "James Zou", "Kunle Olukotun" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation -- modifying inputs with instructions, strategies, or evidence, rather than weight updates. Prior approaches improve usability but often suffer from brevity bias, which drops domain insights for concise summaries, and from context collapse, where iterative rewriting erodes details over time. Building on the adaptive memory introduced by Dynamic Cheatsheet, we introduce ACE (Agentic Context Engineering), a framework that treats contexts as evolving playbooks that accumulate, refine, and organize strategies through a modular process of generation, reflection, and curation. ACE prevents collapse with structured, incremental updates that preserve detailed knowledge and scale with long-context models. Across agent and domain-specific benchmarks, ACE optimizes contexts both offline (e.g., system prompts) and online (e.g., agent memory), consistently outperforming strong baselines: +10.6% on agents and +8.6% on finance, while significantly reducing adaptation latency and rollout cost. Notably, ACE could adapt effectively without labeled supervision and instead by leveraging natural execution feedback. On the AppWorld leaderboard, ACE matches the top-ranked production-level agent on the overall average and surpasses it on the harder test-challenge split, despite using a smaller open-source model. These results show that comprehensive, evolving contexts enable scalable, efficient, and self-improving LLM systems with low overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04016", "title": "Thai Semantic End-of-Turn Detection for Real-Time Voice Agents", "authors": [ "Thanapol Popit", "Natthapath Rungseesiripak", "Monthol Charattrakool", "Saksorn Ruangtanusak" ], "github": "https://github.com/snakers4/silero-vad", "project_page": "", "abstract": "Fluid voice-to-voice interaction requires reliable and low-latency detection of when a user has finished speaking. Traditional audio-silence end-pointers add hundreds of milliseconds of delay and fail under hesitations or language-specific phenomena. We present, to our knowledge, the first systematic study of Thai text-only end-of-turn (EOT) detection for real-time agents. We compare zero-shot and few-shot prompting of compact LLMs to supervised fine-tuning of lightweight transformers. Using transcribed subtitles from the YODAS corpus and Thai-specific linguistic cues (e.g., sentence-final particles), we formulate EOT as a binary decision over token boundaries. We report a clear accuracy-latency tradeoff and provide a public-ready implementation plan. This work establishes a Thai baseline and demonstrates that small, fine-tuned models can deliver near-instant EOT decisions suitable for on-device agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/snakers4/silero-vad", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.00507", "title": "Graph2Eval: Automatic Multimodal Task Generation for Agents via Knowledge Graphs", "authors": [ "Yurun Chen", "Xavier Hu", "Yuhan Liu", "Ziqi Wang", "Zeyi Liao", "Lin Chen", "Feng Wei", "Yuxi Qian", "Bo Zheng", "Keting Yin", "Shengyu Zhang" ], "github": "https://github.com/YurunChen/Graph2Eval", "project_page": "", "abstract": "As multimodal LLM-driven agents continue to advance in autonomy and generalization, evaluation based on static datasets can no longer adequately assess their true capabilities in dynamic environments and diverse tasks. Existing LLM-based synthetic data methods are largely designed for LLM training and evaluation, and thus cannot be directly applied to agent tasks that require tool use and interactive capabilities. While recent studies have explored automatic agent task generation with LLMs, most efforts remain limited to text or image analysis, without systematically modeling multi-step interactions in web environments. To address these challenges, we propose Graph2Eval, a knowledge graph-based framework that automatically generates both multimodal document comprehension tasks and web interaction tasks, enabling comprehensive evaluation of agents' reasoning, collaboration, and interactive capabilities. In our approach, knowledge graphs constructed from multi-source external data serve as the task space, where we translate semantic relations into structured multimodal tasks using subgraph sampling, task templates, and meta-paths. A multi-stage filtering pipeline based on node reachability, LLM scoring, and similarity analysis is applied to guarantee the quality and executability of the generated tasks. Furthermore, Graph2Eval supports end-to-end evaluation of multiple agent types (Single-Agent, Multi-Agent, Web Agent) and measures reasoning, collaboration, and interaction capabilities. We instantiate the framework with Graph2Eval-Bench, a curated dataset of 1,319 tasks spanning document comprehension and web interaction scenarios. Experiments show that Graph2Eval efficiently generates tasks that differentiate agent and model performance, revealing gaps in reasoning, collaboration, and web interaction across different settings and offering a new perspective for agent evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YurunChen/Graph2Eval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2509.24613", "title": "HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition", "authors": [ "Gio Paik", "Yongbeom Kim", "Soungmin Lee", "Sangmin Ahn", "Chanwoo Kim" ], "github": "https://github.com/ThetaOne-AI/HiKE", "project_page": "", "abstract": "Despite advances in multilingual automatic speech recognition (ASR), code-switching (CS), the mixing of languages within an utterance common in daily speech, remains a severely underexplored challenge. In this paper, we introduce HiKE: the Hierarchical Korean-English code-switching benchmark, the first globally accessible evaluation framework for Korean-English CS, aiming to provide a means for the precise evaluation of multilingual ASR models and to foster research in the field. The proposed framework not only consists of high-quality, natural CS data across various topics, but also provides meticulous loanword labels and a hierarchical CS-level labeling scheme (word, phrase, and sentence) that together enable a systematic evaluation of a model's ability to handle each distinct level of code-switching. Through evaluations of diverse multilingual ASR models and fine-tuning experiments, this paper demonstrates that while most multilingual ASR models initially struggle with CS-ASR, this capability can be enabled through fine-tuning with CS data. HiKE will be available at https://github.com/ThetaOne-AI/HiKE.", "github_manual": "", "github_regex": "https://github.com/ThetaOne-AI/HiKE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04290", "title": "ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation", "authors": [ "Jay Zhangjie Wu", "Xuanchi Ren", "Tianchang Shen", "Tianshi Cao", "Kai He", "Yifan Lu", "Ruiyuan Gao", "Enze Xie", "Shiyi Lan", "Jose M. Alvarez", "Jun Gao", "Sanja Fidler", "Zian Wang", "Huan Ling" ], "github": "", "project_page": "", "abstract": "Recent advances in large generative models have significantly advanced image editing and in-context image generation, yet a critical gap remains in ensuring physical consistency, where edited objects must remain coherent. This capability is especially vital for world simulation related tasks. In this paper, we present ChronoEdit, a framework that reframes image editing as a video generation problem. First, ChronoEdit treats the input and edited images as the first and last frames of a video, allowing it to leverage large pretrained video generative models that capture not only object appearance but also the implicit physics of motion and interaction through learned temporal consistency. Second, ChronoEdit introduces a temporal reasoning stage that explicitly performs editing at inference time. Under this setting, the target frame is jointly denoised with reasoning tokens to imagine a plausible editing trajectory that constrains the solution space to physically viable transformations. The reasoning tokens are then dropped after a few steps to avoid the high computational cost of rendering a full video. To validate ChronoEdit, we introduce PBench-Edit, a new benchmark of image-prompt pairs for contexts that require physical consistency, and demonstrate that ChronoEdit surpasses state-of-the-art baselines in both visual fidelity and physical plausibility. Code and models for both the 14B and 2B variants of ChronoEdit will be released on the project page: https://research.nvidia.com/labs/toronto-ai/chronoedit", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.01586", "title": "AdvEvo-MARL: Shaping Internalized Safety through Adversarial Co-Evolution in Multi-Agent Reinforcement Learning", "authors": [ "Zhenyu Pan", "Yiting Zhang", "Zhuo Liu", "Yolo Yunlong Tang", "Zeliang Zhang", "Haozheng Luo", "Yuwei Han", "Jianshu Zhang", "Dennis Wu", "Hong-Yu Chen", "Haoran Lu", "Haoyang Fang", "Manling Li", "Chenliang Xu", "Philip S. Yu", "Han Liu" ], "github": "", "project_page": "", "abstract": "LLM-based multi-agent systems excel at planning, tool use, and role coordination, but their openness and interaction complexity also expose them to jailbreak, prompt-injection, and adversarial collaboration. Existing defenses fall into two lines: (i) self-verification that asks each agent to pre-filter unsafe instructions before execution, and (ii) external guard modules that police behaviors. The former often underperforms because a standalone agent lacks sufficient capacity to detect cross-agent unsafe chains and delegation-induced risks; the latter increases system overhead and creates a single-point-of-failure-once compromised, system-wide safety collapses, and adding more guards worsens cost and complexity. To solve these challenges, we propose AdvEvo-MARL, a co-evolutionary multi-agent reinforcement learning framework that internalizes safety into task agents. Rather than relying on external guards, AdvEvo-MARL jointly optimizes attackers (which synthesize evolving jailbreak prompts) and defenders (task agents trained to both accomplish their duties and resist attacks) in adversarial learning environments. To stabilize learning and foster cooperation, we introduce a public baseline for advantage estimation: agents within the same functional group share a group-level mean-return baseline, enabling lower-variance updates and stronger intra-group coordination. Across representative attack scenarios, AdvEvo-MARL consistently keeps attack-success rate (ASR) below 20%, whereas baselines reach up to 38.33%, while preserving-and sometimes improving-task accuracy (up to +3.67% on reasoning tasks). These results show that safety and utility can be jointly improved without relying on extra guard agents or added system overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.00732", "title": "EvolProver: Advancing Automated Theorem Proving by Evolving Formalized Problems via Symmetry and Difficulty", "authors": [ "Yuchen Tian", "Ruiyuan Huang", "Xuanwu Wang", "Jing Ma", "Zengfeng Huang", "Ziyang Luo", "Hongzhan Lin", "Da Zheng", "Lun Du" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) for formal theorem proving have shown significant promise, yet they often lack generalizability and are fragile to even minor transformations of problem statements. To address this limitation, we introduce a novel data augmentation pipeline designed to enhance model robustness from two perspectives: symmetry and difficulty. From the symmetry perspective, we propose two complementary methods: EvolAST, an Abstract Syntax Tree (AST) based approach that targets syntactic symmetry to generate semantically equivalent problem variants, and EvolDomain, which leverages LLMs to address semantic symmetry by translating theorems across mathematical domains. From the difficulty perspective, we propose EvolDifficulty, which uses carefully designed evolutionary instructions to guide LLMs in generating new theorems with a wider range of difficulty. We then use the evolved data to train EvolProver, a 7B-parameter non-reasoning theorem prover. EvolProver establishes a new state-of-the-art (SOTA) on FormalMATH-Lite with a 53.8% pass@32 rate, surpassing all models of comparable size, including reasoning-based models. It also sets new SOTA records for non-reasoning models on MiniF2F-Test (69.8% pass@32), Ineq-Comp-Seed (52.2% pass@32), and Ineq-Comp-Transformed (34.0% pass@32). Ablation studies further confirm our data augmentation pipeline's effectiveness across multiple benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04860", "title": "Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails", "authors": [ "Siwei Han", "Jiaqi Liu", "Yaofeng Su", "Wenbo Duan", "Xinyuan Liu", "Cihang Xie", "Mohit Bansal", "Mingyu Ding", "Linjun Zhang", "Huaxiu Yao" ], "github": "https://github.com/aiming-lab/ATP", "project_page": "", "abstract": "As Large Language Model (LLM) agents increasingly gain self-evolutionary capabilities to adapt and refine their strategies through real-world interaction, their long-term reliability becomes a critical concern. We identify the Alignment Tipping Process (ATP), a critical post-deployment risk unique to self-evolving LLM agents. Unlike training-time failures, ATP arises when continual interaction drives agents to abandon alignment constraints established during training in favor of reinforced, self-interested strategies. We formalize and analyze ATP through two complementary paradigms: Self-Interested Exploration, where repeated high-reward deviations induce individual behavioral drift, and Imitative Strategy Diffusion, where deviant behaviors spread across multi-agent systems. Building on these paradigms, we construct controllable testbeds and benchmark Qwen3-8B and Llama-3.1-8B-Instruct. Our experiments show that alignment benefits erode rapidly under self-evolution, with initially aligned models converging toward unaligned states. In multi-agent settings, successful violations diffuse quickly, leading to collective misalignment. Moreover, current reinforcement learning-based alignment methods provide only fragile defenses against alignment tipping. Together, these findings demonstrate that alignment of LLM agents is not a static property but a fragile and dynamic one, vulnerable to feedback-driven decay during deployment. Our data and code are available at https://github.com/aiming-lab/ATP.", "github_manual": "", "github_regex": "https://github.com/aiming-lab/ATP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04226", "title": "Epistemic Diversity and Knowledge Collapse in Large Language Models", "authors": [ "Dustin Wright", "Sarah Masud", "Jared Moore", "Srishti Yadav", "Maria Antoniak", "Chan Young Park", "Isabelle Augenstein" ], "github": "https://github.com/dwright37/llm-knowledge", "project_page": "", "abstract": "Large language models (LLMs) tend to generate lexically, semantically, and stylistically homogenous texts. This poses a risk of knowledge collapse, where homogenous LLMs mediate a shrinking in the range of accessible information over time. Existing works on homogenization are limited by a focus on closed-ended multiple-choice setups or fuzzy semantic features, and do not look at trends across time and cultural contexts. To overcome this, we present a new methodology to measure epistemic diversity, i.e., variation in real-world claims in LLM outputs, which we use to perform a broad empirical study of LLM knowledge collapse. We test 27 LLMs, 155 topics covering 12 countries, and 200 prompt variations sourced from real user chats. For the topics in our study, we show that while newer models tend to generate more diverse claims, nearly all models are less epistemically diverse than a basic web search. We find that model size has a negative impact on epistemic diversity, while retrieval-augmented generation (RAG) has a positive impact, though the improvement from RAG varies by the cultural context. Finally, compared to a traditional knowledge source (Wikipedia), we find that country-specific claims reflect the English language more than the local one, highlighting a gap in epistemic representation", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dwright37/llm-knowledge", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.02919", "title": "Self-Reflective Generation at Test Time", "authors": [ "Jian Mu", "Qixin Zhang", "Zhiyong Wang", "Menglin Yang", "Shuang Qiu", "Chengwei Qin", "Zhongxiang Dai", "Yao Shu" ], "github": "https://github.com/2020-qqtcg/SRGen", "project_page": "", "abstract": "Large language models (LLMs) increasingly solve complex reasoning tasks via long chain-of-thought, but their forward-only autoregressive generation process is fragile; early token errors can cascade, which creates a clear need for self-reflection mechanisms. However, existing self-reflection either performs revisions over full drafts or learns self-correction via expensive training, both fundamentally reactive and inefficient. To address this, we propose Self-Reflective Generation at Test Time (SRGen), a lightweight test-time framework that reflects before generating at uncertain points. During token generation, SRGen utilizes dynamic entropy thresholding to identify high-uncertainty tokens. For each identified token, it trains a specific corrective vector, which fully exploits the already generated context for a self-reflective generation to correct the token probability distribution. By retrospectively analyzing the partial output, this self-reflection enables more trustworthy decisions, thereby significantly reducing the probability of errors at highly uncertain points. Evaluated on challenging mathematical reasoning benchmarks and a diverse set of LLMs, SRGen can consistently strengthen model reasoning: improvements in single-pass quality also translate into stronger self-consistency voting. Especially, on AIME2024 with DeepSeek-R1-Distill-Qwen-7B, SRGen yields absolute improvements of +12.0% on Pass@1 and +13.3% on Cons@5. Moreover, our findings position SRGen as a plug-and-play method that integrates reflection into the generation process for reliable LLM reasoning, achieving consistent gains with bounded overhead and broad composability with other training-time (e.g., RLHF) and test-time (e.g., SLOT) techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/2020-qqtcg/SRGen", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.05025", "title": "Imperceptible Jailbreaking against Large Language Models", "authors": [ "Kuofeng Gao", "Yiming Li", "Chao Du", "Xin Wang", "Xingjun Ma", "Shu-Tao Xia", "Tianyu Pang" ], "github": "https://github.com/sail-sg/imperceptible-jailbreaks", "project_page": "", "abstract": "Jailbreaking attacks on the vision modality typically rely on imperceptible adversarial perturbations, whereas attacks on the textual modality are generally assumed to require visible modifications (e.g., non-semantic suffixes). In this paper, we introduce imperceptible jailbreaks that exploit a class of Unicode characters called variation selectors. By appending invisible variation selectors to malicious questions, the jailbreak prompts appear visually identical to original malicious questions on screen, while their tokenization is \"secretly\" altered. We propose a chain-of-search pipeline to generate such adversarial suffixes to induce harmful responses. Our experiments show that our imperceptible jailbreaks achieve high attack success rates against four aligned LLMs and generalize to prompt injection attacks, all without producing any visible modifications in the written prompt. Our code is available at https://github.com/sail-sg/imperceptible-jailbreaks.", "github_manual": "", "github_regex": "https://github.com/sail-sg/imperceptible-jailbreaks", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04136", "title": "MoME: Mixture of Matryoshka Experts for Audio-Visual Speech Recognition", "authors": [ "Umberto Cappellazzo", "Minsu Kim", "Pingchuan Ma", "Honglie Chen", "Xubo Liu", "Stavros Petridis", "Maja Pantic" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have recently shown strong potential in audio-visual speech recognition (AVSR), but their high computational demands and sensitivity to token granularity limit their practicality in resource-constrained settings. Token compression methods can reduce inference cost, but they require fixing a compression rate in advance and produce a single fixed-length output, offering no flexibility to balance information density and efficiency at inference time. Matryoshka representation learning (MRL) addresses this by enabling a single model to operate across multiple token granularities, allowing compression rates to be adjusted dynamically. However, current MRL-based methods treat each scale independently during training, limiting cross-scale generalization, robustness at high compression, and interpretability. To overcome these limitations, we propose MoME (Mixture of Matryoshka Experts), a novel framework that integrates sparse Mixture-of-Experts (MoE) into MRL-based LLMs for AVSR. MoME augments a frozen LLM with top-k routed and shared experts, allowing dynamic capacity allocation across scales and modalities. A shared router promotes consistent expert activation across granularities, enabling compressed sequences to benefit from representations learned at lower compression. Experiments on LRS2 and LRS3 demonstrate that MoME achieves state-of-the-art performance across AVSR, ASR, and VSR tasks, while requiring significantly fewer parameters and maintaining robustness under noise. MoME unifies the adaptability of MRL with the efficiency of MoE, offering a scalable and interpretable solution for resource-aware speech recognition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.05081", "title": "SAEdit: Token-level control for continuous image editing via Sparse AutoEncoder", "authors": [ "Ronen Kamenetsky", "Sara Dorfman", "Daniel Garibi", "Roni Paiss", "Or Patashnik", "Daniel Cohen-Or" ], "github": "", "project_page": "", "abstract": "Large-scale text-to-image diffusion models have become the backbone of modern image editing, yet text prompts alone do not offer adequate control over the editing process. Two properties are especially desirable: disentanglement, where changing one attribute does not unintentionally alter others, and continuous control, where the strength of an edit can be smoothly adjusted. We introduce a method for disentangled and continuous editing through token-level manipulation of text embeddings. The edits are applied by manipulating the embeddings along carefully chosen directions, which control the strength of the target attribute. To identify such directions, we employ a Sparse Autoencoder (SAE), whose sparse latent space exposes semantically isolated dimensions. Our method operates directly on text embeddings without modifying the diffusion process, making it model agnostic and broadly applicable to various image synthesis backbones. Experiments show that it enables intuitive and efficient manipulations with continuous control across diverse attributes and domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.02350", "title": "LLMSQL: Upgrading WikiSQL for the LLM Era of Text-to-SQL", "authors": [ "Dzmitry Pihulski", "Karol Charchut", "Viktoria Novogrodskaia", "Jan Koco\u0144" ], "github": "", "project_page": "", "abstract": "Converting natural language questions into SQL queries (Text-to-SQL) enables non-expert users to interact with relational databases and has long been a central task for natural language interfaces to data. While the WikiSQL dataset played a key role in early NL2SQL research, its usage has declined due to structural and annotation issues, including case sensitivity inconsistencies, data type mismatches, syntax errors, and unanswered questions. We present LLMSQL, a systematic revision and transformation of WikiSQL designed for the LLM era. We classify these errors and implement automated methods for cleaning and re-annotation. To assess the impact of these improvements, we evaluated multiple large language models (LLMs), including Gemma 3, LLaMA 3.2, Mistral 7B, gpt-oss 20B, Phi-3.5 Mini, Qwen 2.5, OpenAI o4-mini, DeepSeek R1 and others. Rather than serving as an update, LLMSQL is introduced as an LLM-ready benchmark: unlike the original WikiSQL, tailored for pointer-network models selecting tokens from input, LLMSQL provides clean natural language questions and full SQL queries as plain text, enabling straightforward generation and evaluation for modern natural language-to-SQL models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.03755", "title": "Code4MeV2: a Research-oriented Code-completion Platform", "authors": [ "Roham Koohestani", "Parham Bateni", "Aydin Ebrahimi", "Behdad Etezadi", "Kiarash Karimi", "Maliheh Izadi" ], "github": "", "project_page": "", "abstract": "The adoption of AI-powered code completion tools in software development has increased substantially, yet the user interaction data produced by these systems remain proprietary within large corporations. This creates a barrier for the academic community, as researchers must often develop dedicated platforms to conduct studies on human--AI interaction, making reproducible research and large-scale data analysis impractical. In this work, we introduce Code4MeV2, a research-oriented, open-source code completion plugin for JetBrains IDEs, as a solution to this limitation. Code4MeV2 is designed using a client--server architecture and features inline code completion and a context-aware chat assistant. Its core contribution is a modular and transparent data collection framework that gives researchers fine-grained control over telemetry and context gathering. Code4MeV2 achieves industry-comparable performance in terms of code completion, with an average latency of 200~ms. We assess our tool through a combination of an expert evaluation and a user study with eight participants. Feedback from both researchers and daily users highlights its informativeness and usefulness. We invite the community to adopt and contribute to this tool. More information about the tool can be found at https://app.code4me.me.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.03871", "title": "Optimal Scaling Needs Optimal Norm", "authors": [ "Oleg Filatov", "Jiangtao Wang", "Jan Ebert", "Stefan Kesselheim" ], "github": "", "project_page": "", "abstract": "Despite recent progress in optimal hyperparameter transfer under model and dataset scaling, no unifying explanatory principle has been established. Using the Scion optimizer, we discover that joint optimal scaling across model and dataset sizes is governed by a single invariant: the operator norm of the output layer. Across models with up to 1.3B parameters trained on up to 138B tokens, the optimal learning rate/batch size pair (eta^{ast}, B^{ast}) consistently has the same operator norm value - a phenomenon we term norm transfer. This constant norm condition is necessary but not sufficient: while for each dataset size, multiple (eta, B) reach the optimal norm, only a unique (eta^{ast}, B^{ast}) achieves the best loss. As a sufficient condition, we provide the first measurement of (eta^{ast}, B^{ast}) scaling with dataset size for Scion, and find that the scaling rules are consistent with those of the Adam optimizer. Tuning per-layer-group learning rates also improves model performance, with the output layer being the most sensitive and hidden layers benefiting from lower learning rates. We provide practical insights on norm-guided optimal scaling and release our Distributed Scion (Disco) implementation with logs from over two thousand runs to support research on LLM training dynamics at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.05093", "title": "Character Mixing for Video Generation", "authors": [ "Tingting Liao", "Chongjian Ge", "Guangyi Liu", "Hao Li", "Yi Zhou" ], "github": "", "project_page": "", "abstract": "Imagine Mr. Bean stepping into Tom and Jerry--can we generate videos where characters interact naturally across different worlds? We study inter-character interaction in text-to-video generation, where the key challenge is to preserve each character's identity and behaviors while enabling coherent cross-context interaction. This is difficult because characters may never have coexisted and because mixing styles often causes style delusion, where realistic characters appear cartoonish or vice versa. We introduce a framework that tackles these issues with Cross-Character Embedding (CCE), which learns identity and behavioral logic across multimodal sources, and Cross-Character Augmentation (CCA), which enriches training with synthetic co-existence and mixed-style data. Together, these techniques allow natural interactions between previously uncoexistent characters without losing stylistic fidelity. Experiments on a curated benchmark of cartoons and live-action series with 10 characters show clear improvements in identity preservation, interaction quality, and robustness to style delusion, enabling new forms of generative storytelling.Additional results and videos are available on our project page: https://tingtingliao.github.io/mimix/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04786", "title": "Learning on the Job: Test-Time Curricula for Targeted Reinforcement Learning", "authors": [ "Jonas H\u00fcbotter", "Leander Diaz-Bone", "Ido Hakimi", "Andreas Krause", "Moritz Hardt" ], "github": "", "project_page": "", "abstract": "Humans are good at learning on the job: We learn how to solve the tasks we face as we go along. Can a model do the same? We propose an agent that assembles a task-specific curriculum, called test-time curriculum (TTC-RL), and applies reinforcement learning to continue training the model for its target task. The test-time curriculum avoids time-consuming human curation of datasets by automatically selecting the most task-relevant data from a large pool of available training data. Our experiments demonstrate that reinforcement learning on a test-time curriculum consistently improves the model on its target tasks, across a variety of evaluations and models. Notably, on challenging math and coding benchmarks, TTC-RL improves the pass@1 of Qwen3-8B by approximately 1.8x on AIME25 and 2.1x on CodeElo. Moreover, we find that TTC-RL significantly raises the performance ceiling compared to the initial model, increasing pass@8 on AIME25 from 40% to 62% and on CodeElo from 28% to 43%. Our findings show the potential of test-time curricula in extending the test-time scaling paradigm to continual training on thousands of task-relevant experiences during test-time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.05040", "title": "Test-Time Scaling in Diffusion LLMs via Hidden Semi-Autoregressive Experts", "authors": [ "Jihoon Lee", "Hoyeon Moon", "Kevin Zhai", "Arun Kumar Chithanar", "Anit Kumar Sahu", "Soummya Kar", "Chul Lee", "Souradip Chakraborty", "Amrit Singh Bedi" ], "github": "https://github.com/junos-ai-org/hex", "project_page": "", "abstract": "Diffusion-based large language models (dLLMs) are trained flexibly to model extreme dependence in the data distribution; however, how to best utilize this information at inference time remains an open problem. In this work, we uncover an interesting property of these models: dLLMs trained on textual data implicitly learn a mixture of semi-autoregressive experts, where different generation orders reveal different specialized behaviors. We show that committing to any single, fixed inference time schedule, a common practice, collapses performance by failing to leverage this latent ensemble. To address this, we introduce HEX (Hidden semiautoregressive EXperts for test-time scaling), a training-free inference method that ensembles across heterogeneous block schedules. By doing a majority vote over diverse block-sized generation paths, HEX robustly avoids failure modes associated with any single fixed schedule. On reasoning benchmarks such as GSM8K, it boosts accuracy by up to 3.56X (from 24.72% to 88.10%), outperforming top-K margin inference and specialized fine-tuned methods like GRPO, without additional training. HEX even yields significant gains on MATH benchmark from 16.40% to 40.00%, scientific reasoning on ARC-C from 54.18% to 87.80%, and TruthfulQA from 28.36% to 57.46%. Our results establish a new paradigm for test-time scaling in diffusion-based LLMs (dLLMs), revealing that the sequence in which masking is performed plays a critical role in determining performance during inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/junos-ai-org/hex", "github_api_added_by": "auto" }, { "date": "2025-10-07", "arxiv_id": "2510.04995", "title": "Power Transform Revisited: Numerically Stable, and Federated", "authors": [ "Xuefeng Xu", "Graham Cormode" ], "github": "https://github.com/xuefeng-xu/powertf", "project_page": "", "abstract": "Power transforms are popular parametric techniques for making data more Gaussian-like, and are widely used as preprocessing steps in statistical analysis and machine learning. However, we find that direct implementations of power transforms suffer from severe numerical instabilities, which can lead to incorrect results or even crashes. In this paper, we provide a comprehensive analysis of the sources of these instabilities and propose effective remedies. We further extend power transforms to the federated learning setting, addressing both numerical and distributional challenges that arise in this context. Experiments on real-world datasets demonstrate that our methods are both effective and robust, substantially improving stability compared to existing approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xuefeng-xu/powertf", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04979", "title": "Federated Computation of ROC and PR Curves", "authors": [ "Xuefeng Xu", "Graham Cormode" ], "github": "https://github.com/xuefeng-xu/fedcurve", "project_page": "", "abstract": "Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are fundamental tools for evaluating machine learning classifiers, offering detailed insights into the trade-offs between true positive rate vs. false positive rate (ROC) or precision vs. recall (PR). However, in Federated Learning (FL) scenarios, where data is distributed across multiple clients, computing these curves is challenging due to privacy and communication constraints. Specifically, the server cannot access raw prediction scores and class labels, which are used to compute the ROC and PR curves in a centralized setting. In this paper, we propose a novel method for approximating ROC and PR curves in a federated setting by estimating quantiles of the prediction score distribution under distributed differential privacy. We provide theoretical bounds on the Area Error (AE) between the true and estimated curves, demonstrating the trade-offs between approximation accuracy, privacy, and communication cost. Empirical results on real-world datasets demonstrate that our method achieves high approximation accuracy with minimal communication and strong privacy guarantees, making it practical for privacy-preserving model evaluation in federated systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xuefeng-xu/fedcurve", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.03528", "title": "Fine-Tuning on Noisy Instructions: Effects on Generalization and Performance", "authors": [ "Ahmed Alajrami", "Xingwei Tan", "Nikolaos Aletras" ], "github": "https://github.com/aajrami/finetuning-on-noisy-instructions", "project_page": "", "abstract": "Instruction-tuning plays a vital role in enhancing the task-solving abilities of large language models (LLMs), improving their usability in generating helpful responses on various tasks. However, previous work has demonstrated that they are sensitive to minor variations in instruction phrasing. In this paper, we explore whether introducing perturbations in instruction-tuning data can enhance LLMs' resistance against noisy instructions. We focus on how instruction-tuning with perturbations, such as removing stop words or shuffling words, affects LLMs' performance on the original and perturbed versions of widely-used benchmarks (MMLU, BBH, GSM8K). We further assess learning dynamics and potential shifts in model behavior. Surprisingly, our results suggest that instruction-tuning on perturbed instructions can, in some cases, improve downstream performance. These findings highlight the importance of including perturbed instructions in instruction-tuning, which can make LLMs more resilient to noisy user inputs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aajrami/finetuning-on-noisy-instructions", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.00499", "title": "MOSS-Speech: Towards True Speech-to-Speech Models Without Text Guidance", "authors": [ "Xingjian Zhao", "Zhe Xu", "Qinyuan Cheng", "Zhaoye Fei", "Luozhijie Jin", "Yang Wang", "Hanfu Chen", "Yaozhou Jiang", "Qinghui Gao", "Ke Chen", "Ruixiao Li", "Mingshu Chen", "Ruiming Wang", "Wenbo Zhang", "Yiyang Zhang", "Donghua Yu", "Yang Gao", "Xiaogui Yang", "Yitian Gong", "Yuanfan Xu", "Yaqian Zhou", "Xuanjing Huang", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Spoken dialogue systems often rely on cascaded pipelines that transcribe, process, and resynthesize speech. While effective, this design discards paralinguistic cues and limits expressivity. Recent end-to-end methods reduce latency and better preserve these cues, yet still rely on text intermediates, creating a fundamental bottleneck. We present MOSS-Speech, a true speech-to-speech large language model that directly understands and generates speech without relying on text guidance. Our approach combines a modality-based layer-splitting architecture with a frozen pre-training strategy, preserving the reasoning and knowledge of pretrained text LLMs while adding native speech capabilities. Experiments show that our model achieves state-of-the-art results in spoken question answering and delivers comparable speech-to-speech performance relative to existing text-guided systems, while still maintaining competitive text performance. By narrowing the gap between text-guided and direct speech generation, our work establishes a new paradigm for expressive and efficient end-to-end speech interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04072", "title": "Slow-Fast Policy Optimization: Reposition-Before-Update for LLM Reasoning", "authors": [ "Ziyan Wang", "Zheng Wang", "Jie Fu", "Xingwei Qu", "Qi Cheng", "Shengpu Tang", "Minjia Zhang", "Xiaoming Huo" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has become central to enhancing reasoning in large language models (LLMs). Yet on-policy algorithms such as Group Relative Policy Optimization (GRPO) often suffer in early training: noisy gradients from low-quality rollouts lead to unstable updates and inefficient exploration. We introduce Slow-Fast Policy Optimization (SFPO), a simple yet efficient framework to address these limitations via decomposing each step into three stages: a short fast trajectory of inner steps on the same batch, a reposition mechanism to control off-policy drift, and a final slow correction. This reposition-before-update design preserves the objective and rollout process unchanged, making SFPO plug-compatible with existing policy-gradient pipelines. Extensive experiments demonstrate that SFPO consistently improves stability, reduces rollouts, and accelerates convergence of reasoning RL training. Specifically, it outperforms GRPO by up to 2.80 points in average on math reasoning benchmarks. It also achieves up to 4.93 fewer rollouts and a 4.19 reduction in wall-clock time to match GRPO's best accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.01161", "title": "Prosperity before Collapse: How Far Can Off-Policy RL Reach with Stale Data on LLMs?", "authors": [ "Haizhong Zheng", "Jiawei Zhao", "Bedi Chen" ], "github": "https://github.com/Infini-AI-Lab/M2PO", "project_page": "", "abstract": "Reinforcement learning has been central to recent advances in large language model reasoning, but most algorithms rely on on-policy training that demands fresh rollouts at every update, limiting efficiency and scalability. Asynchronous RL systems alleviate this by decoupling rollout generation from training, yet their effectiveness hinges on tolerating large staleness in rollout data, a setting where existing methods either degrade in performance or collapse. We revisit this challenge and uncover a prosperity-before-collapse phenomenon: stale data can be as informative as on-policy data if exploited properly. Building on this insight, we introduce M2PO (Second-Moment Trust Policy Optimization), which constrains the second moment of importance weights to suppress only extreme outliers while preserving informative updates. Notably, M2PO sharply reduces the fraction of clipped tokens under high staleness (from 1.22% to 0.06% over training), precisely masking high-variance tokens while maintaining stable optimization. Extensive evaluation across six models (from 1.7B to 32B) and eight benchmarks shows that M2PO delivers stable off-policy training even with data stale by at least 256 model updates and matches on-policy performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Infini-AI-Lab/M2PO", "github_api_added_by": "auto" }, { "date": "2025-10-07", "arxiv_id": "2510.03434", "title": "Paris: A Decentralized Trained Open-Weight Diffusion Model", "authors": [ "Zhiying Jiang", "Raihan Seraj", "Marcos Villagra", "Bidhan Roy" ], "github": "https://github.com/bageldotcom/paris", "project_page": "", "abstract": "We present Paris, the first publicly released diffusion model pre-trained entirely through decentralized computation. Paris demonstrates that high-quality text-to-image generation can be achieved without centrally coordinated infrastructure. Paris is open for research and commercial use. Paris required implementing our Distributed Diffusion Training framework from scratch. The model consists of 8 expert diffusion models (129M-605M parameters each) trained in complete isolation with no gradient, parameter, or intermediate activation synchronization. Rather than requiring synchronized gradient updates across thousands of GPUs, we partition data into semantically coherent clusters where each expert independently optimizes its subset while collectively approximating the full distribution. A lightweight transformer router dynamically selects appropriate experts at inference, achieving generation quality comparable to centrally coordinated baselines. Eliminating synchronization enables training on heterogeneous hardware without specialized interconnects. Empirical validation confirms that Paris's decentralized training maintains generation quality while removing the dedicated GPU cluster requirement for large-scale diffusion models. Paris achieves this using 14times less training data and 16times less compute than the prior decentralized baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bageldotcom/paris", "github_api_added_by": "auto" }, { "date": "2025-10-07", "arxiv_id": "2510.01645", "title": "Position: Privacy Is Not Just Memorization!", "authors": [ "Niloofar Mireshghallah", "Tianshi Li" ], "github": "https://github.com/ai-robots-txt/ai.robots.txt", "project_page": "", "abstract": "The discourse on privacy risks in Large Language Models (LLMs) has disproportionately focused on verbatim memorization of training data, while a constellation of more immediate and scalable privacy threats remain underexplored. This position paper argues that the privacy landscape of LLM systems extends far beyond training data extraction, encompassing risks from data collection practices, inference-time context leakage, autonomous agent capabilities, and the democratization of surveillance through deep inference attacks. We present a comprehensive taxonomy of privacy risks across the LLM lifecycle -- from data collection through deployment -- and demonstrate through case studies how current privacy frameworks fail to address these multifaceted threats. Through a longitudinal analysis of 1,322 AI/ML privacy papers published at leading conferences over the past decade (2016--2025), we reveal that while memorization receives outsized attention in technical research, the most pressing privacy harms lie elsewhere, where current technical approaches offer little traction and viable paths forward remain unclear. We call for a fundamental shift in how the research community approaches LLM privacy, moving beyond the narrow focus of current technical solutions and embracing interdisciplinary approaches that address the sociotechnical nature of these emerging threats.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ai-robots-txt/ai.robots.txt", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.02387", "title": "CWM: An Open-Weights LLM for Research on Code Generation with World Models", "authors": [ "FAIR CodeGen team", "Quentin Carbonneaux", "Gal Cohen", "Jonas Gehring", "Jacob Kahn", "Jannik Kossen", "Felix Kreuk", "Emily McMilin", "Michel Meyer", "Yuxiang Wei", "David Zhang", "Kunhao Zheng", "Jordi Armengol-Estap\u00e9", "Pedram Bashiri", "Maximilian Beck", "Pierre Chambon", "Abhishek Charnalia", "Chris Cummins", "Juliette Decugis", "Zacharias V. Fisches", "Fran\u00e7ois Fleuret", "Fabian Gloeckle", "Alex Gu", "Michael Hassid", "Daniel Haziza", "Badr Youbi Idrissi", "Christian Keller", "Rahul Kindi", "Hugh Leather", "Gallil Maimon", "Aram Markosyan", "Francisco Massa", "Pierre-Emmanuel Mazar\u00e9", "Vegard Mella", "Naila Murray", "Keyur Muzumdar", "Peter O'Hearn", "Matteo Pagliardini", "Dmitrii Pedchenko", "Tal Remez", "Volker Seeker", "Marco Selvi", "Oren Sultan", "Sida Wang", "Luca Wehrstedt", "Ori Yoran", "Lingming Zhang", "Taco Cohen", "Yossi Adi", "Gabriel Synnaeve" ], "github": "https://github.com/Aider-AI/aider.GitHub", "project_page": "", "abstract": "We release Code World Model (CWM), a 32-billion-parameter open-weights LLM, to advance research on code generation with world models. To improve code understanding beyond what can be learned from training on static code alone, we mid-train CWM on a large amount of observation-action trajectories from Python interpreter and agentic Docker environments, and perform extensive multi-task reasoning RL in verifiable coding, math, and multi-turn software engineering environments. With CWM, we provide a strong testbed for researchers to explore the opportunities world modeling affords for improving code generation with reasoning and planning in computational environments. We present first steps of how world models can benefit agentic coding, enable step-by-step simulation of Python code execution, and show early results of how reasoning can benefit from the latter. CWM is a dense, decoder-only LLM trained with a context size of up to 131k tokens. Independent of its world modeling capabilities, CWM offers strong performance on general coding and math tasks: it reaches pass@1 scores of 65.8% on SWE-bench Verified (with test-time scaling), 68.6% on LiveCodeBench, 96.6% on Math-500, and 76.0% on AIME 2024. To support further research on code world modeling, we release model checkpoints after mid-training, SFT, and RL.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Aider-AI/aider.GitHub", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.03857", "title": "Optimized Minimal 4D Gaussian Splatting", "authors": [ "Minseo Lee", "Byeonghyeon Lee", "Lucas Yunkyu Lee", "Eunsoo Lee", "Sangmin Kim", "Seunghyeon Song", "Joo Chan Lee", "Jong Hwan Ko", "Jaesik Park", "Eunbyung Park" ], "github": "", "project_page": "", "abstract": "4D Gaussian Splatting has emerged as a new paradigm for dynamic scene representation, enabling real-time rendering of scenes with complex motions. However, it faces a major challenge of storage overhead, as millions of Gaussians are required for high-fidelity reconstruction. While several studies have attempted to alleviate this memory burden, they still face limitations in compression ratio or visual quality. In this work, we present OMG4 (Optimized Minimal 4D Gaussian Splatting), a framework that constructs a compact set of salient Gaussians capable of faithfully representing 4D Gaussian models. Our method progressively prunes Gaussians in three stages: (1) Gaussian Sampling to identify primitives critical to reconstruction fidelity, (2) Gaussian Pruning to remove redundancies, and (3) Gaussian Merging to fuse primitives with similar characteristics. In addition, we integrate implicit appearance compression and generalize Sub-Vector Quantization (SVQ) to 4D representations, further reducing storage while preserving quality. Extensive experiments on standard benchmark datasets demonstrate that OMG4 significantly outperforms recent state-of-the-art methods, reducing model sizes by over 60% while maintaining reconstruction quality. These results position OMG4 as a significant step forward in compact 4D scene representation, opening new possibilities for a wide range of applications. Our source code is available at https://minshirley.github.io/OMG4/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-07", "arxiv_id": "2510.04694", "title": "Multilingual Routing in Mixture-of-Experts", "authors": [ "Lucas Bandarkar", "Chenyuan Yang", "Mohsen Fayyaz", "Junlin Hu", "Nanyun Peng" ], "github": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) architectures have become the key to scaling modern LLMs, yet little is understood about how their sparse routing dynamics respond to multilingual data. In this work, we analyze expert routing patterns using parallel multilingual datasets and present highly interpretable layer-wise phenomena. We find that MoE models route tokens in language-specific ways in the early and late decoder layers but exhibit significant cross-lingual routing alignment in middle layers, mirroring parameter-sharing trends observed in dense LLMs. In particular, we reveal a clear, strong correlation between a model's performance in a given language and how similarly its tokens are routed to English in these layers. Extending beyond correlation, we explore inference-time interventions that induce higher cross-lingual routing alignment. We introduce a method that steers the router by promoting middle-layer task experts frequently activated in English, and it successfully increases multilingual performance. These 1-2% gains are remarkably consistent across two evaluation tasks, three models, and 15+ languages, especially given that these simple interventions override routers of extensively trained, state-of-the-art LLMs. In comparison, interventions outside of the middle layers or targeting multilingual-specialized experts only yield performance degradation. Altogether, we present numerous findings that explain how MoEs process non-English text and demonstrate that generalization is limited by the model's ability to leverage language-universal experts in all languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06217", "title": "TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning", "authors": [ "Jiaru Zou", "Soumya Roy", "Vinay Kumar Verma", "Ziyi Wang", "David Wipf", "Pan Lu", "Sumit Negi", "James Zou", "Jingrui He" ], "github": "", "project_page": "", "abstract": "Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of test-time scaling (TTS). However, their potential for supervising LRMs on tabular reasoning domains remains underexplored. Through detailed empirical analyses, we identify that existing PRMs, though widely adopted for supervising text-only reasoning steps, struggle with table-specific operations such as sub-table retrieval and schema interaction, leading to critical performance bottlenecks. To address this limitation, we propose TaTToo, a novel table-grounded PRM framework that (i) reasons explicitly over tabular reasoning steps and (ii) integrates tool-based verification to provide precise reward supervision. Concretely, we first design a scalable data curation pipeline that constructs over 60k high-quality step-level annotations by integrating table verification rationales with tool-based executions. Building on the collected data, we train TaTToo with a dual-stage paradigm: cold-start supervised fine-tuning to capture tool-use reasoning patterns, followed by reinforcement learning with tool-grounded reward shaping to align our model with table-based verification. We provide a comprehensive evaluation of the policy improvement induced by our newly designed PRM. Across 5 challenging tabular reasoning benchmarks covering numerical reasoning, fact-checking, and data analysis, TaTToo improves downstream policy LRMs by 30.9% at inference, surpasses strong PRM baselines such as Qwen-2.5-Math-PRM-72B with only 8B parameters, and demonstrates strong generalizability across diverse TTS strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06218", "title": "EgoNight: Towards Egocentric Vision Understanding at Night with a Challenging Benchmark", "authors": [ "Deheng Zhang", "Yuqian Fu", "Runyi Yang", "Yang Miao", "Tianwen Qian", "Xu Zheng", "Guolei Sun", "Ajad Chhatkuli", "Xuanjing Huang", "Yu-Gang Jiang", "Luc Van Gool", "Danda Pani Paudel" ], "github": "", "project_page": "", "abstract": "Most existing benchmarks for egocentric vision understanding focus primarily on daytime scenarios, overlooking the low-light conditions that are inevitable in real-world applications. To investigate this gap, we present EgoNight, the first comprehensive benchmark for nighttime egocentric vision, with visual question answering (VQA) as the core task. A key feature of EgoNight is the introduction of day-night aligned videos, which enhance night annotation quality using the daytime data and reveal clear performance gaps between lighting conditions. To achieve this, we collect both synthetic videos rendered by Blender and real-world recordings, ensuring that scenes and actions are visually and temporally aligned. Leveraging these paired videos, we construct EgoNight-VQA, supported by a novel day-augmented night auto-labeling engine and refinement through extensive human verification. Each QA pair is double-checked by annotators for reliability. In total, EgoNight-VQA contains 3658 QA pairs across 90 videos, spanning 12 diverse QA types, with more than 300 hours of human work. Evaluations of state-of-the-art multimodal large language models (MLLMs) reveal substantial performance drops when transferring from day to night, underscoring the challenges of reasoning under low-light conditions. Beyond VQA, EgoNight also introduces two auxiliary tasks, day-night correspondence retrieval and egocentric depth estimation at night, that further explore the boundaries of existing models. We believe EgoNight-VQA provides a strong foundation for advancing application-driven egocentric vision research and for developing models that generalize across illumination domains. All the data and code will be made available upon acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06139", "title": "Deforming Videos to Masks: Flow Matching for Referring Video Segmentation", "authors": [ "Zanyi Wang", "Dengyang Jiang", "Liuzhuozheng Li", "Sizhe Dang", "Chengzu Li", "Harry Yang", "Guang Dai", "Mengmeng Wang", "Jingdong Wang" ], "github": "https://github.com/xmz111/FlowRVS", "project_page": "", "abstract": "Referring Video Object Segmentation (RVOS) requires segmenting specific objects in a video guided by a natural language description. The core challenge of RVOS is to anchor abstract linguistic concepts onto a specific set of pixels and continuously segment them through the complex dynamics of a video. Faced with this difficulty, prior work has often decomposed the task into a pragmatic `locate-then-segment' pipeline. However, this cascaded design creates an information bottleneck by simplifying semantics into coarse geometric prompts (e.g, point), and struggles to maintain temporal consistency as the segmenting process is often decoupled from the initial language grounding. To overcome these fundamental limitations, we propose FlowRVS, a novel framework that reconceptualizes RVOS as a conditional continuous flow problem. This allows us to harness the inherent strengths of pretrained T2V models, fine-grained pixel control, text-video semantic alignment, and temporal coherence. Instead of conventional generating from noise to mask or directly predicting mask, we reformulate the task by learning a direct, language-guided deformation from a video's holistic representation to its target mask. Our one-stage, generative approach achieves new state-of-the-art results across all major RVOS benchmarks. Specifically, achieving a J&F of 51.1 in MeViS (+1.6 over prior SOTA) and 73.3 in the zero shot Ref-DAVIS17 (+2.7), demonstrating the significant potential of modeling video understanding tasks as continuous deformation processes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xmz111/FlowRVS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05432", "title": "AInstein: Assessing the Feasibility of AI-Generated Approaches to Research Problems", "authors": [ "Shambhavi Mishra", "Gaurav Sahu", "Marco Pedersoli", "Laurent Charlin", "Jose Dolz", "Christopher Pal" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) demonstrate impressive capabilities across a wide range of tasks, yet it remains unclear whether such success reflects genuine reasoning or sophisticated recall. We introduce AInstein, a framework for testing whether LLMs can generate valid solutions to AI research problems using only their pretrained parametric knowledge -- without domain-specific fine-tuning, retrieval augmentation, or other external aids. Our approach extracts distilled problem statements from high-quality ICLR 2025 submissions, then tasks specialized solver agents with proposing and refining technical solutions through iterative critique loops, mimicking the cycles of proposal, review, and revision central to scientific inquiry. We evaluate AInstein on 1,214 ICLR papers stratified by acceptance tier (Oral, Spotlight, Poster), using an LLM-as-a-judge paradigm guided by a structured rubric, complemented by targeted manual checks. Performance is assessed with three metrics: Success Rate (does the solution address the problem?), Rediscovery (does it align with human-proposed methods?), and Novelty (does it yield valid, original approaches?). Our results reveal that while LLMs can rediscover feasible solutions and occasionally propose creative alternatives, their problem-solving ability remains fragile and highly sensitive to framing. These findings provide the first large-scale evidence on the extent to which LLMs can act as autonomous scientific problem-solvers, highlighting both their latent potential and their current limitations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05342", "title": "Margin Adaptive DPO: Leveraging Reward Model for Granular Control in Preference Optimization", "authors": [ "Hyung Gyu Rho" ], "github": "https://github.com/sirano1004/Margin-Apative-Direct-Preference-Optimization", "project_page": "", "abstract": "Direct Preference Optimization (DPO) has emerged as a simple and effective method for aligning large language models. However, its reliance on a fixed temperature parameter leads to suboptimal training on diverse preference data, causing overfitting on easy examples and under-learning from informative ones. Recent methods have emerged to counter this. While IPO addresses general overfitting, its uniform regularization can be overly conservative. The more targeted approach of beta-DPO suffers from its own limitations: its batch-level adaptation applies a single, compromised temperature to mixed-margin pairs, its linear update rule can produce unstable negative beta values, and its filtering mechanism discards potentially useful training signals. In this work, we introduce Margin-Adaptive Direct Preference Optimization (MADPO), a method that provides a stable, data-preserving, and instance-level solution. MADPO employs a practical two-step approach: it first trains a reward model to estimate preference margins and then uses these margins to apply a continuous, adaptive weight to the DPO loss for each individual training sample. This re-weighting scheme creates an effective target margin that is amplified for hard pairs and dampened for easy pairs, allowing for granular control over the learning signal. We provide a comprehensive theoretical analysis, proving that MADPO has a well-behaved optimization landscape and is robust to reward model estimation errors. We validate our theory with experiments on a sentiment generation task, where MADPO consistently and significantly outperforms strong baselines across datasets of varying quality. It achieves performance gains of up to +33.3\\% on High Quality data and +10.5\\% on Low Quality data over the next-best method. Our results establish MADPO as a more robust and principled approach to preference alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sirano1004/Margin-Apative-Direct-Preference-Optimization", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05137", "title": "Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics", "authors": [ "Maojia Song", "Renhang Liu", "Xinyu Wang", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Soujanya Poria", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "RAG (Retrieval-Augmented Generation) systems and web agents are increasingly evaluated on multi-hop deep search tasks, yet current practice suffers from two major limitations. First, most benchmarks leak the reasoning path in the question text, allowing models to follow surface cues rather than discover reasoning chains autonomously. Second, evaluation is typically reduced to a single pass rate, which collapses diverse behaviours into one score and obscures whether failures stem from inadequate search, poor knowledge use, or inappropriate refusal. To address these issues, we present WebDetective, a benchmark of hint-free multi-hop questions paired with a controlled Wikipedia sandbox that ensures full traceability of model actions, and a holistic evaluation framework that separates search sufficiency, knowledge utilisation, and refusal behaviour. Our evaluation of 25 state-of-the-art models reveals systematic weaknesses across all architectures: models struggle with knowledge utilisation despite having sufficient evidence and demonstrate near-absent appropriate refusal when evidence is lacking. These patterns expose a fundamental gap: today's systems excel at executing given reasoning paths but fail when required to discover them. We develop an agentic workflow, EvidenceLoop, that explicitly targets the challenges our benchmark identifies, incorporating verification loops and systematic evidence tracking that improve both search and synthesis capabilities. This baseline demonstrates that WebDetective's diagnostic framework can guide concrete architectural improvements, establishing our benchmark as a critical tool for developing genuinely autonomous reasoning systems rather than pattern-following agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2509.26328", "title": "Fast-dLLM v2: Efficient Block-Diffusion LLM", "authors": [ "Chengyue Wu", "Hao Zhang", "Shuchen Xue", "Shizhe Diao", "Yonggan Fu", "Zhijian Liu", "Pavlo Molchanov", "Ping Luo", "Song Han", "Enze Xie" ], "github": "https://github.com/NVlabs/Fast-dLLM", "project_page": "", "abstract": "Autoregressive (AR) large language models (LLMs) have achieved remarkable performance across a wide range of natural language tasks, yet their inherent sequential decoding limits inference efficiency. In this work, we propose Fast-dLLM v2, a carefully designed block diffusion language model (dLLM) that efficiently adapts pretrained AR models into dLLMs for parallel text generation, requiring only approximately 1B tokens of fine-tuning. This represents a 500x reduction in training data compared to full-attention diffusion LLMs such as Dream (580B tokens), while preserving the original model's performance. Our approach introduces a novel training recipe that combines a block diffusion mechanism with a complementary attention mask, enabling blockwise bidirectional context modeling without sacrificing AR training objectives. To further accelerate decoding, we design a hierarchical caching mechanism: a block-level cache that stores historical context representations across blocks, and a sub-block cache that enables efficient parallel generation within partially decoded blocks. Coupled with our parallel decoding pipeline, Fast-dLLM v2 achieves up to 2.5x speedup over standard AR decoding without compromising generation quality. Extensive experiments across diverse benchmarks demonstrate that Fast-dLLM v2 matches or surpasses AR baselines in accuracy, while delivering state-of-the-art efficiency among dLLMs - marking a significant step toward the practical deployment of fast and accurate LLMs. Code and model will be publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVlabs/Fast-dLLM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.03270", "title": "CoDA: Coding LM via Diffusion Adaptation", "authors": [ "Haolin Chen", "Shiyu Wang", "Can Qin", "Bo Pang", "Zuxin Liu", "Jielin Qiu", "Jianguo Zhang", "Yingbo Zhou", "Zeyuan Chen", "Ran Xu", "Shelby Heinecke", "Silvio Savarese", "Caiming Xiong", "Huan Wang", "Weiran Yao" ], "github": "", "project_page": "", "abstract": "Diffusion language models promise bidirectional context and infilling capabilities that autoregressive coders lack, yet practical systems remain heavyweight. We introduce CoDA, a 1.7B-parameter diffusion coder trained on TPU with a fully open-source training pipeline. CoDA pairs large-scale diffusion pre-training with code-centric mid-training and instruction tuning, enabling confidence-guided sampling that keeps inference latency competitive. On Humaneval, MBPP, and EvalPlus, CoDA-1.7B-Instruct matches or surpasses diffusion models up to 7B parameters. Our release includes model checkpoints, evaluation harnesses, and TPU training pipelines to accelerate research on lightweight diffusion-based coding assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05560", "title": "HoloScene: Simulation-Ready Interactive 3D Worlds from a Single Video", "authors": [ "Hongchi Xia", "Chih-Hao Lin", "Hao-Yu Hsu", "Quentin Leboutet", "Katelyn Gao", "Michael Paulitsch", "Benjamin Ummenhofer", "Shenlong Wang" ], "github": "", "project_page": "", "abstract": "Digitizing the physical world into accurate simulation-ready virtual environments offers significant opportunities in a variety of fields such as augmented and virtual reality, gaming, and robotics. However, current 3D reconstruction and scene-understanding methods commonly fall short in one or more critical aspects, such as geometry completeness, object interactivity, physical plausibility, photorealistic rendering, or realistic physical properties for reliable dynamic simulation. To address these limitations, we introduce HoloScene, a novel interactive 3D reconstruction framework that simultaneously achieves these requirements. HoloScene leverages a comprehensive interactive scene-graph representation, encoding object geometry, appearance, and physical properties alongside hierarchical and inter-object relationships. Reconstruction is formulated as an energy-based optimization problem, integrating observational data, physical constraints, and generative priors into a unified, coherent objective. Optimization is efficiently performed via a hybrid approach combining sampling-based exploration with gradient-based refinement. The resulting digital twins exhibit complete and precise geometry, physical stability, and realistic rendering from novel viewpoints. Evaluations conducted on multiple benchmark datasets demonstrate superior performance, while practical use-cases in interactive gaming and real-time digital-twin manipulation illustrate HoloScene's broad applicability and effectiveness. Project page: https://xiahongchi.github.io/HoloScene.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05367", "title": "LightCache: Memory-Efficient, Training-Free Acceleration for Video Generation", "authors": [ "Yang Xiao", "Gen Li", "Kaiyuan Deng", "Yushu Wu", "Zheng Zhan", "Yanzhi Wang", "Xiaolong Ma", "Bo Hui" ], "github": "https://github.com/NKUShaw/LightCache", "project_page": "", "abstract": "Training-free acceleration has emerged as an advanced research area in video generation based on diffusion models. The redundancy of latents in diffusion model inference provides a natural entry point for acceleration. In this paper, we decompose the inference process into the encoding, denoising, and decoding stages, and observe that cache-based acceleration methods often lead to substantial memory surges in the latter two stages. To address this problem, we analyze the characteristics of inference across different stages and propose stage-specific strategies for reducing memory consumption: 1) Asynchronous Cache Swapping. 2) Feature chunk. 3) Slicing latents to decode. At the same time, we ensure that the time overhead introduced by these three strategies remains lower than the acceleration gains themselves. Compared with the baseline, our approach achieves faster inference speed and lower memory usage, while maintaining quality degradation within an acceptable range. The Code is available at https://github.com/NKUShaw/LightCache .", "github_manual": "", "github_regex": "https://github.com/NKUShaw/LightCache", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.04081", "title": "Scaling Code-Assisted Chain-of-Thoughts and Instructions for Model Reasoning", "authors": [ "Honglin Lin", "Qizhi Pei", "Xin Gao", "Zhuoshi Pan", "Yu Li", "Juntao Li", "Conghui He", "Lijun Wu" ], "github": "", "project_page": "", "abstract": "Reasoning capability is pivotal for Large Language Models (LLMs) to solve complex tasks, yet achieving reliable and scalable reasoning remains challenging. While Chain-of-Thought (CoT) prompting has become a mainstream approach, existing methods often suffer from uncontrolled generation, insufficient quality, and limited diversity in reasoning paths. Recent efforts leverage code to enhance CoT by grounding reasoning in executable steps, but such methods are typically constrained to predefined mathematical problems, hindering scalability and generalizability. In this work, we propose Caco (Code-Assisted Chain-of-ThOught), a novel framework that automates the synthesis of high-quality, verifiable, and diverse instruction-CoT reasoning data through code-driven augmentation. Unlike prior work, Caco first fine-tunes a code-based CoT generator on existing math and programming solutions in a unified code format, then scales the data generation to a large amount of diverse reasoning traces. Crucially, we introduce automated validation via code execution and rule-based filtering to ensure logical correctness and structural diversity, followed by reverse-engineering filtered outputs into natural language instructions and language CoTs to enrich task adaptability. This closed-loop process enables fully automated, scalable synthesis of reasoning data with guaranteed executability. Experiments on our created Caco-1.3M dataset demonstrate that Caco-trained models achieve strong competitive performance on mathematical reasoning benchmarks, outperforming existing strong baselines. Further analysis reveals that Caco's code-anchored verification and instruction diversity contribute to superior generalization across unseen tasks. Our work establishes a paradigm for building self-sustaining, trustworthy reasoning systems without human intervention.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.04087", "title": "A Contextual Quality Reward Model for Reliable and Efficient Best-of-N Sampling", "authors": [ "Hyung Gyu Rho" ], "github": "", "project_page": "", "abstract": "Modern preference alignment techniques, such as Best-of-N (BoN) sampling, rely on reward models trained with pairwise comparison data. While effective at learning relative preferences, this paradigm fails to capture a signal of response acceptability, leaving systems vulnerable to selecting the least bad of many unacceptable options. This is particularly problematic for hard prompts, where the risk of such false acceptances increases with the number of samples. In this paper, we address this critical reliability gap by introducing a new data collection and modeling framework. By augmenting preference data with an outside option, inspired by discrete choice models, we train a reward model that can distinguish not just what is better, but what is good enough. We leverage this capability to create an adaptive inference strategy, best of mini-N in-loop, which partitions the generation budget into sequential loops with a calibrated, early-exit condition. Our experiments show that when tuned as an alignment guardrail, it reduces reliability failures by 70\\%, and when tuned as an inference accelerator, it improves average inference speed by over 22\\% in IMDB-sentiment setting. We thus provide a principled and flexible framework for practitioners to explicitly manage the trade-off between reliability and computational efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.02341", "title": "DRIFT: Learning from Abundant User Dissatisfaction in Real-World Preference Learning", "authors": [ "Yifan Wang", "Bolian Li", "Junlin Wu", "Zhaoxuan Tan", "Zheli Liu", "Ruqi Zhang", "Ananth Grama", "Qingkai Zeng" ], "github": "https://github.com/cacayaya/DRIFT.git", "project_page": "", "abstract": "Real-world large language model deployments (e.g., conversational AI systems, code generation assistants) naturally generate abundant implicit user dissatisfaction (DSAT) signals, as users iterate toward better answers through refinements, corrections, and expressed preferences, while explicit satisfaction (SAT) feedback is scarce. Existing preference learning approaches are poorly aligned with this data profile, as they rely on costly human annotations or assume plentiful positive responses. In this paper, we introduce DRIFT (Dissatisfaction-Refined Iterative preFerence Training), which anchors training on real-world DSAT signals and samples positives dynamically from the evolving policy. Empirically, DRIFT models trained on real-world WildFeedback datasets and synthetic UltraFeedback datasets achieve up to +6.23\\% (7B) / +7.61\\% (14B) on WildBench Task Score and up to +8.95\\% (7B) / +12.29\\% (14B) on AlpacaEval2 win rate over base models, outperforming strong baseline methods such as iterative DPO and SPIN. At larger scales, the improvements are particularly pronounced: 14B models trained with DRIFT surpass GPT-4o-mini on WildBench. Further analysis shows that DRIFT also preserves exploratory capacity, yielding more diverse high-reward solutions rather than collapsing to narrow subsets. Theoretically, we demonstrate that this design preserves preference margins and avoids the gradient degeneration. These results show that DRIFT is an effective and scalable recipe for real-world post-training that leverages the most abundant and informative signal. The code and data are available at https://github.com/cacayaya/DRIFT.git.", "github_manual": "", "github_regex": "https://github.com/cacayaya/DRIFT.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06131", "title": "Discrete Diffusion Models with MLLMs for Unified Medical Multimodal Generation", "authors": [ "Jiawei Mao", "Yuhan Wang", "Lifeng Chen", "Can Zhao", "Yucheng Tang", "Dong Yang", "Liangqiong Qu", "Daguang Xu", "Yuyin Zhou" ], "github": "https://github.com/UCSC-VLAA/MeDiM", "project_page": "", "abstract": "Recent advances in generative medical models are constrained by modality-specific scenarios that hinder the integration of complementary evidence from imaging, pathology, and clinical notes. This fragmentation limits their evolution into foundation models that can learn and reason across the full spectrum of biomedical data. We propose MeDiM, the first medical discrete diffusion model that learns shared distributions across modalities without modality-specific components. MeDiM unifies multiple generative tasks: translating between images and text, and jointly producing image-report pairs across domains in response to prompts. Built on a discrete diffusion framework, MeDiM bridges vision and language representations through a shared probabilistic space. To enable unified and flexible medical generation, we employ a multimodal large language model (MLLM) as the diffusion backbone, leveraging its prior knowledge and cross-modal reasoning. Two key designs are introduced: (1) removing the causal attention mask for bidirectional context, and (2) injecting continuous timestep embeddings for diffusion awareness. Experiments demonstrate high-fidelity medical generation (FID 16.60 on MIMIC-CXR and FID 24.19 on PathGen) and accurate report generation (METEOR 0.2650 and 0.2580). Jointly generated image-report pairs further enhance downstream performance (plus6.43 percent BLEU-1, plus18.57 percent BLEU-2, plus31.58 percent BLEU-3, plus4.80 percent METEOR), showing that MeDiM supports coherent and clinically grounded multimodal outputs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/UCSC-VLAA/MeDiM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05156", "title": "VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation", "authors": [ "Lesly Miculicich", "Mihir Parmar", "Hamid Palangi", "Krishnamurthy Dj Dvijotham", "Mirko Montanari", "Tomas Pfister", "Long T. Le" ], "github": "", "project_page": "", "abstract": "The deployment of autonomous AI agents in sensitive domains, such as healthcare, introduces critical risks to safety, security, and privacy. These agents may deviate from user objectives, violate data handling policies, or be compromised by adversarial attacks. Mitigating these dangers necessitates a mechanism to formally guarantee that an agent's actions adhere to predefined safety constraints, a challenge that existing systems do not fully address. We introduce VeriGuard, a novel framework that provides formal safety guarantees for LLM-based agents through a dual-stage architecture designed for robust and verifiable correctness. The initial offline stage involves a comprehensive validation process. It begins by clarifying user intent to establish precise safety specifications. VeriGuard then synthesizes a behavioral policy and subjects it to both testing and formal verification to prove its compliance with these specifications. This iterative process refines the policy until it is deemed correct. Subsequently, the second stage provides online action monitoring, where VeriGuard operates as a runtime monitor to validate each proposed agent action against the pre-verified policy before execution. This separation of the exhaustive offline validation from the lightweight online monitoring allows formal guarantees to be practically applied, providing a robust safeguard that substantially improves the trustworthiness of LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06062", "title": "ASPO: Asymmetric Importance Sampling Policy Optimization", "authors": [ "Jiakang Wang", "Runze Liu", "Lei Lin", "Wenping Hu", "Xiu Li", "Fuzheng Zhang", "Guorui Zhou", "Kun Gai" ], "github": "https://github.com/wizard-III/Archer2.0", "project_page": "", "abstract": "Recent Large Language Model (LLM) post-training methods rely on token-level clipping mechanisms during Reinforcement Learning (RL). However, we identify a fundamental flaw in this Outcome-Supervised RL (OSRL) paradigm: the Importance Sampling (IS) ratios of positive-advantage tokens are mismatched, leading to unbalanced token weighting for positive and negative tokens. This mismatch suppresses the update of low-probability tokens while over-amplifying already high-probability ones. To address this, we propose Asymmetric Importance Sampling Policy Optimization (ASPO), which uses a simple yet effective strategy that flips the IS ratios of positive-advantage tokens, aligning their update direction with the learning dynamics of negative ones. AIS further incorporates a soft dual-clipping mechanism to stabilize extreme updates while maintaining gradient flow. Comprehensive experiments on coding and mathematical reasoning benchmarks demonstrate that ASPO significantly mitigates premature convergence, improves training stability, and enhances final performance over strong GRPO-based baselines. Our analysis provides new insights into the role of token-level weighting in OSRL and highlights the critical importance of correcting IS in LLM RL. The code and models of ASPO are available at https://github.com/wizard-III/Archer2.0.", "github_manual": "", "github_regex": "https://github.com/wizard-III/Archer2.0", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06182", "title": "Mixing Mechanisms: How Language Models Retrieve Bound Entities In-Context", "authors": [ "Yoav Gur-Arieh", "Mor Geva", "Atticus Geiger" ], "github": "https://github.com/yoavgur/mixing-mechs", "project_page": "", "abstract": "A key component of in-context reasoning is the ability of language models (LMs) to bind entities for later retrieval. For example, an LM might represent \"Ann loves pie\" by binding \"Ann\" to \"pie\", allowing it to later retrieve \"Ann\" when asked \"Who loves pie?\" Prior research on short lists of bound entities found strong evidence that LMs implement such retrieval via a positional mechanism, where \"Ann\" is retrieved based on its position in context. In this work, we find that this mechanism generalizes poorly to more complex settings; as the number of bound entities in context increases, the positional mechanism becomes noisy and unreliable in middle positions. To compensate for this, we find that LMs supplement the positional mechanism with a lexical mechanism (retrieving \"Ann\" using its bound counterpart \"pie\") and a reflexive mechanism (retrieving \"Ann\" through a direct pointer). Through extensive experiments on nine models and ten binding tasks, we uncover a consistent pattern in how LMs mix these mechanisms to drive model behavior. We leverage these insights to develop a causal model combining all three mechanisms that estimates next token distributions with 95% agreement. Finally, we show that our model generalizes to substantially longer inputs of open-ended text interleaved with entity groups, further demonstrating the robustness of our findings in more natural settings. Overall, our study establishes a more complete picture of how LMs bind and retrieve entities in-context.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yoavgur/mixing-mechs", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05122", "title": "CARE: Cognitive-reasoning Augmented Reinforcement for Emotional Support Conversation", "authors": [ "Jie Zhu", "Yuanchen Zhou", "Shuo Jiang", "Junhui Li", "Lifan Guo", "Feng Chen", "Chi Zhang", "Fang Kong" ], "github": "", "project_page": "", "abstract": "Emotional Support Conversation (ESC) plays a vital role in alleviating psychological stress and providing emotional value through dialogue. While recent studies have largely focused on data augmentation and synthetic corpus construction, they often overlook the deeper cognitive reasoning processes that underpin effective emotional support. To address this gap, we propose CARE, a novel framework that strengthens reasoning in ESC without relying on large-scale synthetic data. CARE leverages the original ESC training set to guide models in generating logically coherent and supportive responses, thereby explicitly enhancing cognitive reasoning. Building on this foundation, we further employ reinforcement learning to refine and reinforce the reasoning process. Experimental results demonstrate that CARE significantly improves both the logical soundness and supportive quality of responses, advancing the development of empathetic, cognitively robust, and human-like emotional support systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.03978", "title": "No Tokens Wasted: Leveraging Long Context in Biomedical Vision-Language Models", "authors": [ "Min Woo Sun", "Alejandro Lozano", "Javier Gamazo Tejero", "Vishwesh Nath", "Xiao Xiao Sun", "James Burgess", "Yuhui Zhang", "Kun Yuan", "Robert Tibshirani", "Sean Huver", "Serena Yeung-Levy" ], "github": "", "project_page": "", "abstract": "Embedding vision-language models (VLMs) are typically pretrained with short text windows (<77 tokens), which forces the truncation of long-format captions. Yet, the distribution of biomedical captions from large-scale open source literature reveals that a huge portion of captions far exceed 77 tokens. To this end, we investigate the impact of pretraining on long-format biomedical captions by extending the context length of text encoders in VLMs. We find that longer context (thus, enabling additional supervision provided in long-format captions) correlates with better retrieval and classification performance. Given this finding, we introduce BIOMEDICA-LongCAP, a dataset of 1M image-caption pairs enriched with context-aware descriptions from full-text articles, providing longer and additional textual supervision. Using BIOMEDICA-LongCAP, we train BMC-LongCLIP, a long-context biomedical VLM with a text encoder supporting windows of up to 512 tokens. Our model extends context capacity by 6.6x, reducing token waste from 55% to just 2.2%. On long-caption retrieval benchmarks, BMC-LongCLIP achieves up to +30% absolute gains in Recall@1 and +2% average improvements in classification, while also converging faster than short-context. Our results demonstrate that long-context modeling is a promising direction for advancing biomedical VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2509.24107", "title": "Fathom-DeepResearch: Unlocking Long Horizon Information Retrieval and Synthesis for SLMs", "authors": [ "Shreyas Singh", "Kunal Singh", "Pradeep Moturi" ], "github": "", "project_page": "", "abstract": "Tool-integrated reasoning has emerged as a key focus for enabling agentic applications. Among these, DeepResearch Agents have gained significant attention for their strong performance on complex, open-ended information-seeking tasks. We introduce Fathom-DeepResearch, an agentic system composed of two specialized models. The first is Fathom-Search-4B, a DeepSearch model trained from Qwen3-4B and optimized for evidence-based investigation through live web search and targeted webpage querying. Its training combines three advances: (i) DUETQA, a 5K-sample dataset generated via multi-agent self-play that enforces strict web-search dependence and heterogeneous source grounding; (ii) RAPO, a zero-overhead extension of GRPO that stabilizes multi-turn Reinforcement Learning with Verifiable Rewards through curriculum pruning, reward-aware advantage scaling, and per-prompt replay buffers; and (iii) a steerable step-level reward that classifies each tool call by cognitive behavior and marginal utility, enabling explicit control over search trajectory breadth, depth, and horizon. These improvements enable reliable extension of tool-calling beyond 20 calls when warranted. The second is Fathom-Synthesizer-4B, trained from Qwen3-4B, which converts multi-turn DeepSearch traces into structured, citation-dense DeepResearch Reports for comprehensive synthesis. Evaluated on DeepSearch benchmarks (SimpleQA, FRAMES, WebWalker, Seal0, MuSiQue) and DeepResearch-Bench, the system achieves state-of-the-art performance in the open-weights category while demonstrating strong generalization to diverse reasoning tasks including HLE, AIME-25, GPQA-Diamond, and MedQA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06036", "title": "Refusal Falls off a Cliff: How Safety Alignment Fails in Reasoning?", "authors": [ "Qingyu Yin", "Chak Tou Leong", "Linyi Yang", "Wenxuan Huang", "Wenjie Li", "Xiting Wang", "Jaehong Yoon", "YunXing", "XingYu", "Jinjin Gu" ], "github": "https://github.com/MikaStars39/RefusalCliff", "project_page": "", "abstract": "Large reasoning models (LRMs) with multi-step reasoning capabilities have shown remarkable problem-solving abilities, yet they exhibit concerning safety vulnerabilities that remain poorly understood. In this work, we investigate why safety alignment fails in reasoning models through a mechanistic interpretability lens. Using a linear probing approach to trace refusal intentions across token positions, we discover a striking phenomenon termed as refusal cliff: many poorly-aligned reasoning models correctly identify harmful prompts and maintain strong refusal intentions during their thinking process, but experience a sharp drop in refusal scores at the final tokens before output generation. This suggests that these models are not inherently unsafe; rather, their refusal intentions are systematically suppressed. Through causal intervention analysis, we identify a sparse set of attention heads that negatively contribute to refusal behavior. Ablating just 3\\% of these heads can reduce attack success rates below 10\\%. Building on these mechanistic insights, we propose Cliff-as-a-Judge, a novel data selection method that identifies training examples exhibiting the largest refusal cliff to efficiently repair reasoning models' safety alignment. This approach achieves comparable safety improvements using only 1.7\\% of the vanilla safety training data, demonstrating a less-is-more effect in safety alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MikaStars39/RefusalCliff", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06052", "title": "MixReasoning: Switching Modes to Think", "authors": [ "Haiquan Lu", "Gongfan Fang", "Xinyin Ma", "Qi Li", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Reasoning models enhance performance by tackling problems in a step-by-step manner, decomposing them into sub-problems and exploring long chains of thought before producing an answer. However, applying extended reasoning to every step introduces substantial redundancy, as sub-problems vary widely in difficulty and complexity: a small number of pivotal steps are genuinely challenging and decisive for the final answer, while many others only involve straightforward revisions or simple computations. Therefore, a natural idea is to endow reasoning models with the ability to adaptively respond to this variation, rather than treating all steps with the same level of elaboration. To this end, we propose MixReasoning, a framework that dynamically adjusts the depth of reasoning within a single response. The resulting chain of thought then becomes a mixture of detailed reasoning on difficult steps and concise inference on simpler ones. Experiments on GSM8K, MATH-500, and AIME show that MixReasoning shortens reasoning length and substantially improves efficiency without compromising accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06219", "title": "Human3R: Everyone Everywhere All at Once", "authors": [ "Yue Chen", "Xingyu Chen", "Yuxuan Xue", "Anpei Chen", "Yuliang Xiu", "Gerard Pons-Moll" ], "github": "", "project_page": "", "abstract": "We present Human3R, a unified, feed-forward framework for online 4D human-scene reconstruction, in the world frame, from casually captured monocular videos. Unlike previous approaches that rely on multi-stage pipelines, iterative contact-aware refinement between humans and scenes, and heavy dependencies, e.g., human detection, depth estimation, and SLAM pre-processing, Human3R jointly recovers global multi-person SMPL-X bodies (\"everyone\"), dense 3D scene (\"everywhere\"), and camera trajectories in a single forward pass (\"all-at-once\"). Our method builds upon the 4D online reconstruction model CUT3R, and uses parameter-efficient visual prompt tuning, to strive to preserve CUT3R's rich spatiotemporal priors, while enabling direct readout of multiple SMPL-X bodies. Human3R is a unified model that eliminates heavy dependencies and iterative refinement. After being trained on the relatively small-scale synthetic dataset BEDLAM for just one day on one GPU, it achieves superior performance with remarkable efficiency: it reconstructs multiple humans in a one-shot manner, along with 3D scenes, in one stage, at real-time speed (15 FPS) with a low memory footprint (8 GB). Extensive experiments demonstrate that Human3R delivers state-of-the-art or competitive performance across tasks, including global human motion estimation, local human mesh recovery, video depth estimation, and camera pose estimation, with a single unified model. We hope that Human3R will serve as a simple yet strong baseline, be easily extended for downstream applications.Code available in https://fanegg.github.io/Human3R", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06107", "title": "Distributional Semantics Tracing: A Framework for Explaining Hallucinations in Large Language Models", "authors": [ "Gagan Bhatia", "Somayajulu G Sripada", "Kevin Allan", "Jacobo Azcona" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are prone to hallucination, the generation of plausible yet factually incorrect statements. This work investigates the intrinsic, architectural origins of this failure mode through three primary contributions.First, to enable the reliable tracing of internal semantic failures, we propose Distributional Semantics Tracing (DST), a unified framework that integrates established interpretability techniques to produce a causal map of a model's reasoning, treating meaning as a function of context (distributional semantics). Second, we pinpoint the model's layer at which a hallucination becomes inevitable, identifying a specific commitment layer where a model's internal representations irreversibly diverge from factuality. Third, we identify the underlying mechanism for these failures. We observe a conflict between distinct computational pathways, which we interpret using the lens of dual-process theory: a fast, heuristic associative pathway (akin to System 1) and a slow, deliberate contextual pathway (akin to System 2), leading to predictable failure modes such as Reasoning Shortcut Hijacks. Our framework's ability to quantify the coherence of the contextual pathway reveals a strong negative correlation (rho = -0.863) with hallucination rates, implying that these failures are predictable consequences of internal semantic weakness. The result is a mechanistic account of how, when, and why hallucinations occur within the Transformer architecture.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05934", "title": "Revisiting Modeling and Evaluation Approaches in Speech Emotion Recognition: Considering Subjectivity of Annotators and Ambiguity of Emotions", "authors": [ "Huang-Cheng Chou", "Chi-Chun Lee" ], "github": "", "project_page": "", "abstract": "Over the past two decades, speech emotion recognition (SER) has received growing attention. To train SER systems, researchers collect emotional speech databases annotated by crowdsourced or in-house raters who select emotions from predefined categories. However, disagreements among raters are common. Conventional methods treat these disagreements as noise, aggregating labels into a single consensus target. While this simplifies SER as a single-label task, it ignores the inherent subjectivity of human emotion perception. This dissertation challenges such assumptions and asks: (1) Should minority emotional ratings be discarded? (2) Should SER systems learn from only a few individuals' perceptions? (3) Should SER systems predict only one emotion per sample? Psychological studies show that emotion perception is subjective and ambiguous, with overlapping emotional boundaries. We propose new modeling and evaluation perspectives: (1) Retain all emotional ratings and represent them with soft-label distributions. Models trained on individual annotator ratings and jointly optimized with standard SER systems improve performance on consensus-labeled tests. (2) Redefine SER evaluation by including all emotional data and allowing co-occurring emotions (e.g., sad and angry). We propose an ``all-inclusive rule'' that aggregates all ratings to maximize diversity in label representation. Experiments on four English emotion databases show superior performance over majority and plurality labeling. (3) Construct a penalization matrix to discourage unlikely emotion combinations during training. Integrating it into loss functions further improves performance. Overall, embracing minority ratings, multiple annotators, and multi-emotion predictions yields more robust and human-aligned SER systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05318", "title": "BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactions", "authors": [ "Nan Huo", "Xiaohan Xu", "Jinyang Li", "Per Jacobsson", "Shipei Lin", "Bowen Qin", "Binyuan Hui", "Xiaolong Li", "Ge Qu", "Shuzheng Si", "Linheng Han", "Edward Alexander", "Xintong Zhu", "Rui Qin", "Ruihan Yu", "Yiyao Jin", "Feige Zhou", "Weihao Zhong", "Yun Chen", "Hongyu Liu", "Chenhao Ma", "Fatma Ozcan", "Yannis Papakonstantinou", "Reynold Cheng" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable performance on single-turn text-to-SQL tasks, but real-world database applications predominantly require multi-turn interactions to handle ambiguous queries, execution errors, and evolving user requirements. Existing multi-turn benchmarks fall short by treating conversation histories as static context or limiting evaluation to read-only operations, failing to reflect production-grade database assistant challenges. We introduce BIRD-INTERACT, a benchmark that restores this realism through: (1) a comprehensive interaction environment coupling each database with a hierarchical knowledge base, metadata files, and a function-driven user simulator, enabling models to solicit clarifications, retrieve knowledge, and recover from errors without human supervision; (2) two evaluation settings consisting of a pre-defined conversational protocol (c-Interact) and an open-ended agentic setting (a-Interact) where models autonomously decide when to query the user simulator or explore the environment; (3) a challenging task suite covering the full CRUD spectrum for business-intelligence and operational use cases, guarded by executable test cases. Each task features ambiguous and follow-up sub-tasks requiring dynamic interaction. The suite comprises BIRD-INTERACT-FULL (600 tasks, up to 11,796 interactions) for comprehensive performance assessment, and BIRD-INTERACT-LITE (300 tasks with simplified databases) for detailed behavioral analysis and rapid method development. Our empirical results highlight BIRD-INTERACT's difficulty: GPT-5 completes only 8.67% of tasks in c-Interact and 17.00% in a-Interact. Analysis via memory grafting and Interaction Test-time Scaling validates the importance of effective interaction for complex, dynamic text-to-SQL tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.00880", "title": "HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate Hallucinations in Retrieval-Augmented Generation", "authors": [ "Loris Bergeron", "Ioana Buhnila", "J\u00e9r\u00f4me Fran\u00e7ois", "Radu State" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) excel in many NLP tasks but remain prone to hallucinations, limiting trust in real-world applications. We present HalluGuard, a 4B-parameter Small Reasoning Model (SRM) for mitigating hallucinations in Retrieval-Augmented Generation (RAG). HalluGuard classifies document-claim pairs as grounded or hallucinated and produces evidence-grounded justifications for transparency. Our approach combines (i) a domain-agnostic synthetic dataset derived from FineWeb and refined through multi-stage curation and data reformation, (ii) synthetic grounded and hallucinated claims, and (iii) preference-based fine-tuning with Odds Ratio Preference Optimization to distill large-model reasoning into a smaller backbone. On the RAGTruth subset of the LLM-AggreFact benchmark, HalluGuard achieves 84.0% balanced accuracy (BAcc), rivaling specialized models, MiniCheck (7B; 84.0%) and Granite Guardian 3.3 (8B; 82.2%) while using roughly half their parameters. Over the full benchmark it reaches 75.7% BAcc, matching larger general-purpose LLMs such as GPT-4o (75.9%). We will release HalluGuard and datasets under Apache 2.0 upon acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05485", "title": "TensorBLEU: Vectorized GPU-based BLEU Score Implementation for Per-Sentence In-Training Evaluation", "authors": [ "Adam Filipek" ], "github": "", "project_page": "", "abstract": "Modern natural language processing models have achieved unprecedented scale, yet the tools for their evaluation often remain a computational bottleneck, limiting the pace of research. This is particularly acute for in-training evaluation metrics, such as per-sentence reward signals in Reinforcement Learning, which must operate efficiently on batches of token IDs directly on the GPU. In this paper, we introduce TensorBLEU, a novel implementation of the BLEU metric designed from the ground up for this specific use case. Our approach is fully vectorized for GPU-accelerated, per-sentence computation within PyTorch and introduces a memory-efficient counting mechanism. By creating a compact, batch-specific dictionary of n-grams using torch.unique, our method avoids the prohibitive memory costs of traditional hashing-based vectorization, making it practical for large-vocabulary models. We benchmark TensorBLEU against NLTK, the standard library for token-ID-based BLEU calculation on the CPU. Experiments show that TensorBLEU provides speedups of over 13x on consumer-grade GPUs (NVIDIA T4) and exceeding 40x on data-center-class hardware (NVIDIA A100). This performance transforms a significant bottleneck into a negligible part of the training loop. By clearly defining its role as a \"Token-ID BLEU\" for development purposes and open-sourcing our implementation, we provide a powerful tool for accelerating research in areas like RL-based model fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.02300", "title": "Equilibrium Matching: Generative Modeling with Implicit Energy-Based Models", "authors": [ "Runqian Wang", "Yilun Du" ], "github": "", "project_page": "", "abstract": "We introduce Equilibrium Matching (EqM), a generative modeling framework built from an equilibrium dynamics perspective. EqM discards the non-equilibrium, time-conditional dynamics in traditional diffusion and flow-based generative models and instead learns the equilibrium gradient of an implicit energy landscape. Through this approach, we can adopt an optimization-based sampling process at inference time, where samples are obtained by gradient descent on the learned landscape with adjustable step sizes, adaptive optimizers, and adaptive compute. EqM surpasses the generation performance of diffusion/flow models empirically, achieving an FID of 1.90 on ImageNet 256times256. EqM is also theoretically justified to learn and sample from the data manifold. Beyond generation, EqM is a flexible framework that naturally handles tasks including partially noised image denoising, OOD detection, and image composition. By replacing time-conditional velocities with a unified equilibrium landscape, EqM offers a tighter bridge between flow and energy-based models and a simple route to optimization-driven inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.03506", "title": "OneFlow: Concurrent Mixed-Modal and Interleaved Generation with Edit Flows", "authors": [ "John Nguyen", "Marton Havasi", "Tariq Berrada", "Luke Zettlemoyer", "Ricky T. Q. Chen" ], "github": "", "project_page": "", "abstract": "We present OneFlow, the first non-autoregressive multimodal model that enables variable-length and concurrent mixed-modal generation. Unlike autoregressive models that enforce rigid causal ordering between text and image generation, OneFlow combines an insertion-based Edit Flow for discrete text tokens with Flow Matching for image latents. OneFlow enables concurrent text-image synthesis with hierarchical sampling that prioritizes content over grammar. Through controlled experiments across model sizes from 1B to 8B, we demonstrate that OneFlow outperforms autoregressive baselines on both generation and understanding tasks while using up to 50% fewer training FLOPs. OneFlow surpasses both autoregressive and diffusion-based approaches while unlocking new capabilities for concurrent generation, iterative refinement, and natural reasoning-like generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2509.23379", "title": "CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding", "authors": [ "Xi Zhang", "Zaiqiao Meng", "Jake Lever", "Edmond S. L. Ho" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have recently achieved remarkable progress in radiology by integrating visual perception with natural language understanding. However, they often generate clinically unsupported descriptions, known as medical hallucinations, which pose serious risks in medical applications that demand accuracy and image-grounded outputs. Through empirical analysis, we find that prompt-induced hallucinations remain prevalent in radiology MLLMs, largely due to over-sensitivity to clinical sections. To address this, we introduce Clinical Contrastive Cecoding (CCD), a training-free and retrieval-free inference framework that integrates structured clinical signals from task-specific radiology expert models. CCD introduces a dual-stage contrastive mechanism to refine token-level logits during generation, thereby enhancing clinical fidelity without modifying the base MLLM. Experiments on three datasets and multiple models demonstrate that CCD consistently improves overall performance on radiology report generation (RRG). On the MIMIC-CXR dataset, it yields up to a 17% improvement in RadGraph-F1 when applied to state-of-the-art RRG models. Our approach provides a lightweight and generalisable solution for mitigating medical hallucinations, effectively bridging expert models and MLLMs in radiology.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06213", "title": "Training Dynamics Impact Post-Training Quantization Robustness", "authors": [ "Albert Catalan-Tatjer", "Niccol\u00f2 Ajroldi", "Jonas Geiping" ], "github": "", "project_page": "", "abstract": "While post-training quantization is widely adopted for efficient deployment of large language models, the mechanisms underlying quantization robustness remain unclear. We conduct a comprehensive analysis of quantization degradation across open-source language model training trajectories up to 32B parameters and 15T training tokens to accurately assess the relationship between training dynamics and quantization performance. Our key finding is that quantization errors in large-scale training runs are driven by a complex interplay between learning rate and other training hyperparameters. Specifically, once learning rates decay, validation loss and quantization error diverge, largely independent of training data scale. To investigate interventions on the training dynamics and identify specific configurations that can modulate quantization robustness favorably, we train our own models in controlled experiments up to 100B tokens. Our results challenge the assumption that increasing dataset scale inherently compromises quantization effectiveness, demonstrating instead that strategic training hyperparameter interventions can improve quantization quality at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.04871", "title": "Less is More: Recursive Reasoning with Tiny Networks", "authors": [ "Alexia Jolicoeur-Martineau" ], "github": "", "project_page": "", "abstract": "Hierarchical Reasoning Model (HRM) is a novel approach using two small neural networks recursing at different frequencies. This biologically inspired method beats Large Language models (LLMs) on hard puzzle tasks such as Sudoku, Maze, and ARC-AGI while trained with small models (27M parameters) on small data (around 1000 examples). HRM holds great promise for solving hard problems with small networks, but it is not yet well understood and may be suboptimal. We propose Tiny Recursive Model (TRM), a much simpler recursive reasoning approach that achieves significantly higher generalization than HRM, while using a single tiny network with only 2 layers. With only 7M parameters, TRM obtains 45% test-accuracy on ARC-AGI-1 and 8% on ARC-AGI-2, higher than most LLMs (e.g., Deepseek R1, o3-mini, Gemini 2.5 Pro) with less than 0.01% of the parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.04162", "title": "Drax: Speech Recognition with Discrete Flow Matching", "authors": [ "Aviv Navon", "Aviv Shamsian", "Neta Glazer", "Yael Segal-Feldman", "Gill Hetz", "Joseph Keshet", "Ethan Fetaya" ], "github": "https://github.com/aiola-lab/drax", "project_page": "", "abstract": "Diffusion and flow-based non-autoregressive (NAR) models have shown strong promise in large language modeling, however, their potential for automatic speech recognition (ASR) remains largely unexplored. We propose Drax, a discrete flow matching framework for ASR that enables efficient parallel decoding. To better align training with inference, we construct an audio-conditioned probability path that guides the model through trajectories resembling likely intermediate inference errors, rather than direct random noise to target transitions. Our theoretical analysis links the generalization gap to divergences between training and inference occupancies, controlled by cumulative velocity errors, thereby motivating our design choice. Empirical evaluation demonstrates that our approach attains recognition accuracy on par with state-of-the-art speech models while offering improved accuracy-efficiency trade-offs, highlighting discrete flow matching as a promising direction for advancing NAR ASR.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aiola-lab/drax", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06208", "title": "ShapeGen4D: Towards High Quality 4D Shape Generation from Videos", "authors": [ "Jiraphon Yenphraphai", "Ashkan Mirzaei", "Jianqi Chen", "Jiaxu Zou", "Sergey Tulyakov", "Raymond A. Yeh", "Peter Wonka", "Chaoyang Wang" ], "github": "", "project_page": "", "abstract": "Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. In this work, we introduce a native video-to-4D shape generation framework that synthesizes a single dynamic 3D representation end-to-end from the video. Our framework introduces three key components based on large-scale pre-trained 3D models: (i) a temporal attention that conditions generation on all frames while producing a time-indexed dynamic representation; (ii) a time-aware point sampling and 4D latent anchoring that promote temporally consistent geometry and texture; and (iii) noise sharing across frames to enhance temporal stability. Our method accurately captures non-rigid motion, volume changes, and even topological transitions without per-frame optimization. Across diverse in-the-wild videos, our method improves robustness and perceptual fidelity and reduces failure modes compared with the baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06071", "title": "Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks", "authors": [ "Jo\u00e3o Palmeiro", "Diogo Duarte", "Rita Costa", "Pedro Bizarro" ], "github": "https://github.com/feedzai/biy-paper", "project_page": "", "abstract": "AI models are increasingly used for data analysis and visualization, yet benchmarks rarely address scatterplot-specific tasks, limiting insight into performance. To address this gap for one of the most common chart types, we introduce a synthetic, annotated dataset of over 18,000 scatterplots from six data generators and 17 chart designs, and a benchmark based on it. We evaluate proprietary models from OpenAI and Google using N-shot prompting on five distinct tasks derived from annotations of cluster bounding boxes, their center coordinates, and outlier coordinates. OpenAI models and Gemini 2.5 Flash, especially when prompted with examples, are viable options for counting clusters and, in Flash's case, outliers (90%+ Accuracy). However, the results for localization-related tasks are unsatisfactory: Precision and Recall are near or below 50%, except for Flash in outlier identification (65.01%). Furthermore, the impact of chart design on performance appears to be a secondary factor, but it is advisable to avoid scatterplots with wide aspect ratios (16:9 and 21:9) or those colored randomly. Supplementary materials are available at https://github.com/feedzai/biy-paper.", "github_manual": "", "github_regex": "https://github.com/feedzai/biy-paper", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05592", "title": "In-the-Flow Agentic System Optimization for Effective Planning and Tool Use", "authors": [ "Zhuofeng Li", "Haoxiang Zhang", "Seungju Han", "Sheng Liu", "Jianwen Xie", "Yu Zhang", "Yejin Choi", "James Zou", "Pan Lu" ], "github": "", "project_page": "", "abstract": "Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios. Agentic systems offer a promising alternative by decomposing work across specialized modules, yet most remain training-free or rely on offline training decoupled from the live dynamics of multi-turn interaction. We introduce AgentFlow, a trainable, in-the-flow agentic framework that coordinates four modules (planner, executor, verifier, generator) through an evolving memory and directly optimizes its planner inside the multi-turn loop. To train on-policy in live environments, we propose Flow-based Group Refined Policy Optimization (Flow-GRPO), which tackles long-horizon, sparse-reward credit assignment by converting multi-turn optimization into a sequence of tractable single-turn policy updates. It broadcasts a single, verifiable trajectory-level outcome to every turn to align local planner decisions with global success and stabilizes learning with group-normalized advantages. Across ten benchmarks, AgentFlow with a 7B-scale backbone outperforms top-performing baselines with average accuracy gains of 14.9% on search, 14.0% on agentic, 14.5% on mathematical, and 4.1% on scientific tasks, even surpassing larger proprietary models like GPT-4o. Further analyses confirm the benefits of in-the-flow optimization, showing improved planning, enhanced tool-calling reliability, and positive scaling with model size and reasoning turns.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05571", "title": "Presenting a Paper is an Art: Self-Improvement Aesthetic Agents for Academic Presentations", "authors": [ "Chengzhi Liu", "Yuzhe Yang", "Kaiwen Zhou", "Zhen Zhang", "Yue Fan", "Yannan Xie", "Peng Qi", "Xin Eric Wang" ], "github": "https://github.com/eric-ai-lab/EvoPresent", "project_page": "", "abstract": "The promotion of academic papers has become an important means of enhancing research visibility. However, existing automated methods struggle limited storytelling, insufficient aesthetic quality, and constrained self-adjustment, making it difficult to achieve efficient and engaging dissemination. At the heart of those challenges is a simple principle: there is no way to improve it when you cannot evaluate it right. To address this, we introduce EvoPresent, a self-improvement agent framework that unifies coherent narratives, aesthetic-aware designs, and realistic presentation delivery via virtual characters. Central to EvoPresent is PresAesth, a multi-task reinforcement learning (RL) aesthetic model that provides reliable aesthetic scoring, defect adjustment, and comparative feedback, enabling iterative self-improvement even under limited aesthetic training data. To systematically evaluate the methods, we introduce EvoPresent Benchmark, a comprehensive benchmark comprising: Presentation Generation Quality, built on 650 top-tier AI conference papers with multimodal resources (slides, videos and scripts) to assess both content and design; and Aesthetic Awareness, consisting of 2,000 slide pairs with varying aesthetic levels, supporting joint training and evaluation on scoring, defect adjustment, and comparison. Our findings highlight that (i) High-quality feedback is essential for agent self-improvement, while initial capability alone does not guarantee effective self-correction. (ii) Automated generation pipelines exhibit a trade-off between visual design and content construction. (iii) Multi-task RL training shows stronger generalization in aesthetic awareness tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/eric-ai-lab/EvoPresent", "github_api_added_by": "auto" }, { "date": "2025-10-08", "arxiv_id": "2510.05681", "title": "Verifier-free Test-Time Sampling for Vision Language Action Models", "authors": [ "Suhyeok Jang", "Dongyoung Kim", "Changyeon Kim", "Youngsuk Kim", "Jinwoo Shin" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action models (VLAs) have demonstrated remarkable performance in robot control. However, they remain fundamentally limited in tasks that require high precision due to their single-inference paradigm. While test-time scaling approaches using external verifiers have shown promise, they require additional training and fail to generalize to unseen conditions. We propose Masking Distribution Guided Selection (MG-Select), a novel test-time scaling framework for VLAs that leverages the model's internal properties without requiring additional training or external modules. Our approach utilizes KL divergence from a reference action token distribution as a confidence metric for selecting the optimal action from multiple candidates. We introduce a reference distribution generated by the same VLA but with randomly masked states and language conditions as inputs, ensuring maximum uncertainty while remaining aligned with the target task distribution. Additionally, we propose a joint training strategy that enables the model to learn both conditional and unconditional distributions by applying dropout to state and language conditions, thereby further improving the quality of the reference distribution. Our experiments demonstrate that MG-Select achieves significant performance improvements, including a 28%/35% improvement in real-world in-distribution/out-of-distribution tasks, along with a 168% relative gain on RoboCasa pick-and-place tasks trained with 30 demonstrations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06056", "title": "Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep Research", "authors": [ "Gang Liu", "Yihan Zhu", "Jie Chen", "Meng Jiang" ], "github": "https://github.com/liugangcode/deepevolve", "project_page": "", "abstract": "Large language models hold promise as scientific assistants, yet existing agents either rely solely on algorithm evolution or on deep research in isolation, both of which face critical limitations. Pure algorithm evolution, as in AlphaEvolve, depends only on the internal knowledge of LLMs and quickly plateaus in complex domains, while pure deep research proposes ideas without validation, resulting in unrealistic or unimplementable solutions. We present DeepEvolve, an agent that integrates deep research with algorithm evolution, uniting external knowledge retrieval, cross-file code editing, and systematic debugging under a feedback-driven iterative loop. Each iteration not only proposes new hypotheses but also refines, implements, and tests them, avoiding both shallow improvements and unproductive over-refinements. Across nine benchmarks in chemistry, mathematics, biology, materials, and patents, DeepEvolve consistently improves the initial algorithm, producing executable new algorithms with sustained gains. By bridging the gap between unguided evolution and research without grounding, DeepEvolve provides a reliable framework for advancing scientific algorithm discovery. Our code is available at https://github.com/liugangcode/deepevolve.", "github_manual": "", "github_regex": "https://github.com/liugangcode/deepevolve", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06101", "title": "The Valley of Code Reasoning: Scaling Knowledge Distillation of Large Language Models", "authors": [ "Muyu He", "Muhammad Ali Shafique", "Anand Kumar", "Tsach Mackey", "Nazneen Rajani" ], "github": "", "project_page": "", "abstract": "Distilling the thinking traces of a Large Language Model (LLM) with reasoning capabilities into a smaller model has been proven effective. Yet, there is a scarcity of work done on how model performances scale with the quantity of distillation data. In this work, we study the scaling trend of distilling competitive coding skills on two small non-reasoning LLMs. We validate the hypothesis that there is a valley of code reasoning: downstream performance on competitive coding first drops as data quantity increases, then it steadily increases in a sharper-than-log-linear fashion. Having identified the trend, we further fine-tune the models at two different distillation stages on the same data to ground conclusions on their respective learning phases. We learn that across stages in the low and medium-low data regimes, small models benefit significantly from easier coding questions than from harder ones. We also find that, surprisingly, the correctness of outputs in training data makes no difference to distillation outcomes. Our work represents a step forward in understanding the training dynamics of code reasoning distillation outside intuition", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2509.21499", "title": "On Code-Induced Reasoning in LLMs", "authors": [ "Abdul Waheed", "Zhen Wu", "Carolyn Ros\u00e9", "Daphne Ippolito" ], "github": "", "project_page": "", "abstract": "Code data has been shown to enhance the reasoning capabilities of large language models (LLMs), but it remains unclear which aspects of code are most responsible. We investigate this question with a systematic, data-centric framework. We construct parallel instruction datasets in ten programming languages and apply controlled perturbations that selectively disrupt structural or semantic properties of code. We then finetune LLMs from five model families and eight scales on each variant and evaluate their performance on natural language, math, and code tasks. Across 3,331 experiments, our results show that LLMs are more vulnerable to structural perturbations than semantic ones, particularly on math and code tasks. Appropriate abstractions like pseudocode and flowcharts can be as effective as code, while encoding the same information with fewer tokens without adhering to original syntax can often retain or even improve performance. Remarkably, even corrupted code with misleading signals remains competitive when surface-level regularities persist. Finally, syntactic styles also shape task-specific gains with Python favoring natural language reasoning and lower-level languages such as Java and Rust favoring math. Through our systematic framework, we aim to provide insight into how different properties of code influence reasoning and inform the design of training data for enhancing LLM reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06030", "title": "Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches", "authors": [ "Rohit Goswami", "Hannes J\u00f3nsson" ], "github": "", "project_page": "", "abstract": "Gaussian process (GP) regression provides a strategy for accelerating saddle point searches on high-dimensional energy surfaces by reducing the number of times the energy and its derivatives with respect to atomic coordinates need to be evaluated. The computational overhead in the hyperparameter optimization can, however, be large and make the approach inefficient. Failures can also occur if the search ventures too far into regions that are not represented well enough by the GP model. Here, these challenges are resolved by using geometry-aware optimal transport measures and an active pruning strategy using a summation over Wasserstein-1 distances for each atom-type in farthest-point sampling, selecting a fixed-size subset of geometrically diverse configurations to avoid rapidly increasing cost of GP updates as more observations are made. Stability is enhanced by permutation-invariant metric that provides a reliable trust radius for early-stopping and a logarithmic barrier penalty for the growth of the signal variance. These physically motivated algorithmic changes prove their efficacy by reducing to less than a half the mean computational time on a set of 238 challenging configurations from a previously published data set of chemical reactions. With these improvements, the GP approach is established as, a robust and scalable algorithm for accelerating saddle point searches when the evaluation of the energy and atomic forces requires significant computational effort.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05251", "title": "Let it Calm: Exploratory Annealed Decoding for Verifiable Reinforcement Learning", "authors": [ "Chenghao Yang", "Lin Gui", "Chenxiao Yang", "Victor Veitch", "Lizhu Zhang", "Zhuokai Zhao" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) is a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs), yet its success hinges on effective exploration. An ideal exploration strategy must navigate two fundamental challenges: it must preserve sample quality while also ensuring training stability. While standard fixed-temperature sampling is simple, it struggles to balance these competing demands, as high temperatures degrade sample quality and low temperatures limit discovery. In this work, we propose a simpler and more effective strategy, Exploratory Annealed Decoding (EAD), grounded in the insight that exploration is most impactful on early tokens which define a sequence's semantic direction. EAD implements an intuitive **explore-at-the-beginning, exploit-at-the-end** strategy by annealing the sampling temperature from high to low during generation. This dynamic schedule encourages meaningful, high-level diversity at the start, then gradually lowers the temperature to preserve sample quality and keep the sampling distribution close to the target policy, which is essential for stable training. We demonstrate that EAD is a lightweight, plug-and-play method that significantly improves sample efficiency, consistently outperforming fixed-temperature sampling across various RLVR algorithms and model sizes. Our work suggests that aligning exploration with the natural dynamics of sequential generation offers a robust path to improving LLM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.04506", "title": "GRACE: Generative Representation Learning via Contrastive Policy Optimization", "authors": [ "Jiashuo Sun", "Shixuan Liu", "Zhaochen Su", "Xianrui Zhong", "Pengcheng Jiang", "Bowen Jin", "Peiran Li", "Weijia Shi", "Jiawei Han" ], "github": "https://github.com/GasolSun36/GRACE", "project_page": "", "abstract": "Prevailing methods for training Large Language Models (LLMs) as text encoders rely on contrastive losses that treat the model as a black box function, discarding its generative and reasoning capabilities in favor of static embeddings. We introduce GRACE (Generative Representation Learning via Contrastive Policy Optimization), a novel framework that reimagines contrastive signals not as losses to be minimized, but as rewards that guide a generative policy. In GRACE, the LLM acts as a policy that produces explicit, human-interpretable rationales--structured natural language explanations of its semantic understanding. These rationales are then encoded into high-quality embeddings via mean pooling. Using policy gradient optimization, we train the model with a multi-component reward function that maximizes similarity between query positive pairs and minimizes similarity with negatives. This transforms the LLM from an opaque encoder into an interpretable agent whose reasoning process is transparent and inspectable. On MTEB benchmark, GRACE yields broad cross category gains: averaged over four backbones, the supervised setting improves overall score by 11.5% over base models, and the unsupervised variant adds 6.9%, while preserving general capabilities. This work treats contrastive objectives as rewards over rationales, unifying representation learning with generation to produce stronger embeddings and transparent rationales. The model, data and code are available at https://github.com/GasolSun36/GRACE.", "github_manual": "", "github_regex": "https://github.com/GasolSun36/GRACE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.04514", "title": "ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering", "authors": [ "Rachneet Kaur", "Nishan Srishankar", "Zhen Zeng", "Sumitra Ganesh", "Manuela Veloso" ], "github": "", "project_page": "", "abstract": "Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts, those requiring precise visual interpretation rather than relying on textual shortcuts. To address this, we introduce ChartAgent, a novel agentic framework that explicitly performs visual reasoning directly within the chart's spatial domain. Unlike textual chain-of-thought reasoning, ChartAgent iteratively decomposes queries into visual subtasks and actively manipulates and interacts with chart images through specialized actions such as drawing annotations, cropping regions (e.g., segmenting pie slices, isolating bars), and localizing axes, using a library of chart-specific vision tools to fulfill each subtask. This iterative reasoning process closely mirrors human cognitive strategies for chart comprehension. ChartAgent achieves state-of-the-art accuracy on the ChartBench and ChartX benchmarks, surpassing prior methods by up to 16.07% absolute gain overall and 17.31% on unannotated, numerically intensive queries. Furthermore, our analyses show that ChartAgent is (a) effective across diverse chart types, (b) achieve the highest scores across varying visual and reasoning complexity levels, and (c) serves as a plug-and-play framework that boosts performance across diverse underlying LLMs. Our work is among the first to demonstrate visually grounded reasoning for chart understanding using tool-augmented multimodal agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.05396", "title": "Scalable In-context Ranking with Generative Models", "authors": [ "Nilesh Gupta", "Chong You", "Srinadh Bhojanapalli", "Sanjiv Kumar", "Inderjit Dhillon", "Felix Yu" ], "github": "https://github.com/nilesh2797/BlockRank", "project_page": "", "abstract": "In-context Ranking (ICR) is an emerging paradigm for Information Retrieval (IR), which leverages contextual understanding of LLMs by directly incorporating the task description, candidate documents, and the query into the model's input prompt and tasking the LLM to identify relevant document(s). While it is effective, efficiency is a significant challenge in this paradigm, especially as the candidate list grows due to quadratic/super-linear scaling of attention operation with context length. To this end, this paper first identifies inherent and exploitable structures in the attention of LLMs finetuned for ICR: (1) inter-document block sparsity: attention is dense within each document block but sparse across different documents in the context; and (2) query-document block relevance: the attention scores from certain query tokens to a document block in middle layers strongly correlate with that document's actual relevance. Motivated by these observations, we introduce BlockRank (Blockwise In-context Ranking), a novel method that adapts the attention operation in an LLM by (a) architecturally enforcing the observed inter-document block sparsity, reducing attention complexity from quadratic to linear without loss in performance, and (b) optimizing query-document block relevance for true relevant documents during fine-tuning using an auxiliary contrastive training objective, improving retrieval in attention. Experiments on BEIR, MSMarco and NQ with Mistral-7B demonstrate that FLARE Mistral matches or outperforms existing SOTA listwise rankers and controlled fine-tuned baseline while being significantly more efficient at inference (4.7x for 100 MSMarco documents in context) and scaling gracefully to long-context shortlists, around 500 documents in-context (approximately 100K context length) within a second, presenting a scalable and effective solution for ICR.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nilesh2797/BlockRank", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06596", "title": "SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation", "authors": [ "Ayush Zenith", "Arnold Zumbrun", "Neel Raut", "Jing Lin" ], "github": "https://github.com/ayushzenith/SDQM", "project_page": "", "abstract": "The performance of machine learning models depends heavily on training data. The scarcity of large-scale, well-annotated datasets poses significant challenges in creating robust models. To address this, synthetic data generated through simulations and generative models has emerged as a promising solution, enhancing dataset diversity and improving the performance, reliability, and resilience of models. However, evaluating the quality of this generated data requires an effective metric. This paper introduces the Synthetic Dataset Quality Metric (SDQM) to assess data quality for object detection tasks without requiring model training to converge. This metric enables more efficient generation and selection of synthetic datasets, addressing a key challenge in resource-constrained object detection tasks. In our experiments, SDQM demonstrated a strong correlation with the mean Average Precision (mAP) scores of YOLOv11, a leading object detection model, while previous metrics only exhibited moderate or weak correlations. Additionally, it provides actionable insights for improving dataset quality, minimizing the need for costly iterative training. This scalable and efficient metric sets a new standard for evaluating synthetic data. The code for SDQM is available at https://github.com/ayushzenith/SDQM", "github_manual": "", "github_regex": "https://github.com/ayushzenith/SDQM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06528", "title": "BACHI: Boundary-Aware Symbolic Chord Recognition Through Masked Iterative Decoding on Pop and Classical Music", "authors": [ "Mingyang Yao", "Ke Chen", "Shlomo Dubnov", "Taylor Berg-Kirkpatrick" ], "github": "", "project_page": "", "abstract": "Automatic chord recognition (ACR) via deep learning models has gradually achieved promising recognition accuracy, yet two key challenges remain. First, prior work has primarily focused on audio-domain ACR, while symbolic music (e.g., score) ACR has received limited attention due to data scarcity. Second, existing methods still overlook strategies that are aligned with human music analytical practices. To address these challenges, we make two contributions: (1) we introduce POP909-CL, an enhanced version of POP909 dataset with tempo-aligned content and human-corrected labels of chords, beats, keys, and time signatures; and (2) We propose BACHI, a symbolic chord recognition model that decomposes the task into different decision steps, namely boundary detection and iterative ranking of chord root, quality, and bass (inversion). This mechanism mirrors the human ear-training practices. Experiments demonstrate that BACHI achieves state-of-the-art chord recognition performance on both classical and pop music benchmarks, with ablation studies validating the effectiveness of each module.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.01353", "title": "MEMTRACK: Evaluating Long-Term Memory and State Tracking in Multi-Platform Dynamic Agent Environments", "authors": [ "Darshan Deshpande", "Varun Gangal", "Hersh Mehta", "Anand Kannappan", "Rebecca Qian", "Peng Wang" ], "github": "", "project_page": "", "abstract": "Recent works on context and memory benchmarking have primarily focused on conversational instances but the need for evaluating memory in dynamic enterprise environments is crucial for its effective application. We introduce MEMTRACK, a benchmark designed to evaluate long-term memory and state tracking in multi-platform agent environments. MEMTRACK models realistic organizational workflows by integrating asynchronous events across multiple communication and productivity platforms such as Slack, Linear and Git. Each benchmark instance provides a chronologically platform-interleaved timeline, with noisy, conflicting, cross-referring information as well as potential codebase/file-system comprehension and exploration. Consequently, our benchmark tests memory capabilities such as acquistion, selection and conflict resolution. We curate the MEMTRACK dataset through both manual expert driven design and scalable agent based synthesis, generating ecologically valid scenarios grounded in real world software development processes. We introduce pertinent metrics for Correctness, Efficiency, and Redundancy that capture the effectiveness of memory mechanisms beyond simple QA performance. Experiments across SoTA LLMs and memory backends reveal challenges in utilizing memory across long horizons, handling cross-platform dependencies, and resolving contradictions. Notably, the best performing GPT-5 model only achieves a 60\\% Correctness score on MEMTRACK. This work provides an extensible framework for advancing evaluation research for memory-augmented agents, beyond existing focus on conversational setups, and sets the stage for multi-agent, multi-platform memory benchmarking in complex organizational settings", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-08", "arxiv_id": "2510.06199", "title": "DYMO-Hair: Generalizable Volumetric Dynamics Modeling for Robot Hair Manipulation", "authors": [ "Chengyang Zhao", "Uksang Yoo", "Arkadeep Narayan Chaudhury", "Giljoo Nam", "Jonathan Francis", "Jeffrey Ichnowski", "Jean Oh" ], "github": "", "project_page": "", "abstract": "Hair care is an essential daily activity, yet it remains inaccessible to individuals with limited mobility and challenging for autonomous robot systems due to the fine-grained physical structure and complex dynamics of hair. In this work, we present DYMO-Hair, a model-based robot hair care system. We introduce a novel dynamics learning paradigm that is suited for volumetric quantities such as hair, relying on an action-conditioned latent state editing mechanism, coupled with a compact 3D latent space of diverse hairstyles to improve generalizability. This latent space is pre-trained at scale using a novel hair physics simulator, enabling generalization across previously unseen hairstyles. Using the dynamics model with a Model Predictive Path Integral (MPPI) planner, DYMO-Hair is able to perform visual goal-conditioned hair styling. Experiments in simulation demonstrate that DYMO-Hair's dynamics model outperforms baselines on capturing local deformation for diverse, unseen hairstyles. DYMO-Hair further outperforms baselines in closed-loop hair styling tasks on unseen hairstyles, with an average of 22% lower final geometric error and 42% higher success rate than the state-of-the-art system. Real-world experiments exhibit zero-shot transferability of our system to wigs, achieving consistent success on challenging unseen hairstyles where the state-of-the-art system fails. Together, these results introduce a foundation for model-based robot hair care, advancing toward more generalizable, flexible, and accessible robot hair styling in unconstrained physical environments. More details are available on our project page: https://chengyzhao.github.io/DYMOHair-web/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06917", "title": "SHANKS: Simultaneous Hearing and Thinking for Spoken Language Models", "authors": [ "Cheng-Han Chiang", "Xiaofei Wang", "Linjie Li", "Chung-Ching Lin", "Kevin Lin", "Shujie Liu", "Zhendong Wang", "Zhengyuan Yang", "Hung-yi Lee", "Lijuan Wang" ], "github": "", "project_page": "", "abstract": "Current large language models (LLMs) and spoken language models (SLMs) begin thinking and taking actions only after the user has finished their turn. This prevents the model from interacting during the user's turn and can lead to high response latency while it waits to think. Consequently, thinking after receiving the full input is not suitable for speech-to-speech interaction, where real-time, low-latency exchange is important. We address this by noting that humans naturally \"think while listening.\" In this paper, we propose SHANKS, a general inference framework that enables SLMs to generate unspoken chain-of-thought reasoning while listening to the user input. SHANKS streams the input speech in fixed-duration chunks and, as soon as a chunk is received, generates unspoken reasoning based on all previous speech and reasoning, while the user continues speaking. SHANKS uses this unspoken reasoning to decide whether to interrupt the user and to make tool calls to complete the task. We demonstrate that SHANKS enhances real-time user-SLM interaction in two scenarios: (1) when the user is presenting a step-by-step solution to a math problem, SHANKS can listen, reason, and interrupt when the user makes a mistake, achieving 37.1% higher interruption accuracy than a baseline that interrupts without thinking; and (2) in a tool-augmented dialogue, SHANKS can complete 56.9% of the tool calls before the user finishes their turn. Overall, SHANKS moves toward models that keep thinking throughout the conversation, not only after a turn ends. Animated illustrations of Shanks can be found at https://d223302.github.io/SHANKS/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.07318", "title": "Artificial Hippocampus Networks for Efficient Long-Context Modeling", "authors": [ "Yunhao Fang", "Weihao Yu", "Shu Zhong", "Qinghao Ye", "Xuehan Xiong", "Lai Wei" ], "github": "https://github.com/ByteDance-Seed/AHN", "project_page": "", "abstract": "Long-sequence modeling faces a fundamental trade-off between the efficiency of compressive fixed-size memory in RNN-like models and the fidelity of lossless growing memory in attention-based Transformers. Inspired by the Multi-Store Model in cognitive science, we introduce a memory framework of artificial neural networks. Our method maintains a sliding window of the Transformer's KV cache as lossless short-term memory, while a learnable module termed Artificial Hippocampus Network (AHN) recurrently compresses out-of-window information into a fixed-size compact long-term memory. To validate this framework, we instantiate AHNs using modern RNN-like architectures, including Mamba2, DeltaNet, and Gated DeltaNet. Extensive experiments on long-context benchmarks LV-Eval and InfiniteBench demonstrate that AHN-augmented models consistently outperform sliding window baselines and achieve performance comparable or even superior to full-attention models, while substantially reducing computational and memory requirements. For instance, augmenting the Qwen2.5-3B-Instruct with AHNs reduces inference FLOPs by 40.5% and memory cache by 74.0%, while improving its average score on LV-Eval (128k sequence length) from 4.41 to 5.88. Code is available at: https://github.com/ByteDance-Seed/AHN.", "github_manual": "", "github_regex": "https://github.com/ByteDance-Seed/AHN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.04678", "title": "Multi-Agent Tool-Integrated Policy Optimization", "authors": [ "Zhanfeng Mo", "Xingxuan Li", "Yuntao Chen", "Lidong Bing" ], "github": "https://github.com/mzf666/MATPO", "project_page": "", "abstract": "Large language models (LLMs) increasingly rely on multi-turn tool-integrated planning for knowledge-intensive and complex reasoning tasks. Existing implementations typically rely on a single agent, but they suffer from limited context length and noisy tool responses. A natural solution is to adopt a multi-agent framework with planner- and worker-agents to manage context. However, no existing methods support effective reinforcement learning post-training of tool-integrated multi-agent frameworks. To address this gap, we propose Multi-Agent Tool-Integrated Policy Optimization (MATPO), which enables distinct roles (planner and worker) to be trained within a single LLM instance using role-specific prompts via reinforcement learning. MATPO is derived from a principled credit assignment mechanism across planner and worker rollouts. This design eliminates the need to deploy multiple LLMs, which would be memory-intensive, while preserving the benefits of specialization. Experiments on GAIA-text, WebWalkerQA, and FRAMES show that MATPO consistently outperforms single-agent baselines by an average of 18.38% relative improvement in performance and exhibits greater robustness to noisy tool outputs. Our findings highlight the effectiveness of unifying multiple agent roles within a single LLM and provide practical insights for stable and efficient multi-agent RL training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mzf666/MATPO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.07315", "title": "Vibe Checker: Aligning Code Evaluation with Human Preference", "authors": [ "Ming Zhong", "Xiang Zhou", "Ting-Yun Chang", "Qingze Wang", "Nan Xu", "Xiance Si", "Dan Garrette", "Shyam Upadhyay", "Jeremiah Liu", "Jiawei Han", "Benoit Schillings", "Jiao Sun" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have catalyzed vibe coding, where users leverage LLMs to generate and iteratively refine code through natural language interactions until it passes their vibe check. Vibe check is tied to real-world human preference and goes beyond functionality: the solution should feel right, read cleanly, preserve intent, and remain correct. However, current code evaluation remains anchored to pass@k and captures only functional correctness, overlooking the non-functional instructions that users routinely apply. In this paper, we hypothesize that instruction following is the missing piece underlying vibe check that represents human preference in coding besides functional correctness. To quantify models' code instruction following capabilities with measurable signals, we present VeriCode, a taxonomy of 30 verifiable code instructions together with corresponding deterministic verifiers. We use the taxonomy to augment established evaluation suites, resulting in Vibe Checker, a testbed to assess both code instruction following and functional correctness. Upon evaluating 31 leading LLMs, we show that even the strongest models struggle to comply with multiple instructions and exhibit clear functional regression. Most importantly, a composite score of functional correctness and instruction following correlates the best with human preference, with the latter emerging as the primary differentiator on real-world programming tasks. Our work identifies core factors of the vibe check, providing a concrete path for benchmarking and developing models that better align with user preferences in coding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.03215", "title": "Cache-to-Cache: Direct Semantic Communication Between Large Language Models", "authors": [ "Tianyu Fu", "Zihan Min", "Hanling Zhang", "Jichao Yan", "Guohao Dai", "Wanli Ouyang", "Yu Wang" ], "github": "https://github.com/thu-nics/C2C", "project_page": "", "abstract": "Multi-LLM systems harness the complementary strengths of diverse Large Language Models, achieving performance and efficiency gains unattainable by a single model. In existing designs, LLMs communicate through text, forcing internal representations to be transformed into output token sequences. This process both loses rich semantic information and incurs token-by-token generation latency. Motivated by these limitations, we ask: Can LLMs communicate beyond text? Oracle experiments show that enriching the KV-Cache semantics can improve response quality without increasing cache size, supporting KV-Cache as an effective medium for inter-model communication. Thus, we propose Cache-to-Cache (C2C), a new paradigm for direct semantic communication between LLMs. C2C uses a neural network to project and fuse the source model's KV-cache with that of the target model to enable direct semantic transfer. A learnable gating mechanism selects the target layers that benefit from cache communication. Compared with text communication, C2C utilizes the deep, specialized semantics from both models, while avoiding explicit intermediate text generation. Experiments show that C2C achieves 8.5-10.5% higher average accuracy than individual models. It further outperforms the text communication paradigm by approximately 3.0-5.0%, while delivering an average 2.0x speedup in latency. Our code is available at https://github.com/thu-nics/C2C.", "github_manual": "", "github_regex": "https://github.com/thu-nics/C2C", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06308", "title": "Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding", "authors": [ "Yi Xin", "Qi Qin", "Siqi Luo", "Kaiwen Zhu", "Juncheng Yan", "Yan Tai", "Jiayi Lei", "Yuewen Cao", "Keqi Wang", "Yibin Wang", "Jinbin Bai", "Qian Yu", "Dengyang Jiang", "Yuandong Pu", "Haoxing Chen", "Le Zhuo", "Junjun He", "Gen Luo", "Tianbin Li", "Ming Hu", "Jin Ye", "Shenglong Ye", "Bo Zhang", "Chang Xu", "Wenhai Wang", "Hongsheng Li", "Guangtao Zhai", "Tianfan Xue", "Bin Fu", "Xiaohong Liu", "Yu Qiao", "Yihao Liu" ], "github": "", "project_page": "", "abstract": "We introduce Lumina-DiMOO, an open-source foundational model for seamless multi-modal generation and understanding. Lumina-DiMOO sets itself apart from prior unified models by utilizing a fully discrete diffusion modeling to handle inputs and outputs across various modalities. This innovative approach allows Lumina-DiMOO to achieve higher sampling efficiency compared to previous autoregressive (AR) or hybrid AR-Diffusion paradigms and adeptly support a broad spectrum of multi-modal tasks, including text-to-image generation, image-to-image generation (e.g., image editing, subject-driven generation, and image inpainting, etc.), as well as image understanding. Lumina-DiMOO achieves state-of-the-art performance on multiple benchmarks, surpassing existing open-source unified multi-modal models. To foster further advancements in multi-modal and discrete diffusion model research, we release our code and checkpoints to the community. Project Page: https://synbol.github.io/Lumina-DiMOO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.04212", "title": "Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention", "authors": [ "Haiquan Qiu", "Quanming Yao" ], "github": "https://github.com/ucker/why-low-precision-training-fails", "project_page": "", "abstract": "The pursuit of computational efficiency has driven the adoption of low-precision formats for training transformer models. However, this progress is often hindered by notorious training instabilities. This paper provides the first mechanistic explanation for a long-standing and unresolved failure case where training with flash attention in low-precision settings leads to catastrophic loss explosions. Our in-depth analysis reveals that the failure is not a random artifact but caused by two intertwined phenomena: the emergence of similar low-rank representations within the attention mechanism and the compounding effect of biased rounding errors inherent in low-precision arithmetic. We demonstrate how these factors create a vicious cycle of error accumulation that corrupts weight updates, ultimately derailing the training dynamics. To validate our findings, we introduce a minimal modification to the flash attention that mitigates the bias in rounding errors. This simple change stabilizes the training process, confirming our analysis and offering a practical solution to this persistent problem.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ucker/why-low-precision-training-fails", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.07313", "title": "WristWorld: Generating Wrist-Views via 4D World Models for Robotic Manipulation", "authors": [ "Zezhong Qian", "Xiaowei Chi", "Yuming Li", "Shizun Wang", "Zhiyuan Qin", "Xiaozhu Ju", "Sirui Han", "Shanghang Zhang" ], "github": "https://github.com/XuWuLingYu/WristWorld", "project_page": "", "abstract": "Wrist-view observations are crucial for VLA models as they capture fine-grained hand-object interactions that directly enhance manipulation performance. Yet large-scale datasets rarely include such recordings, resulting in a substantial gap between abundant anchor views and scarce wrist views. Existing world models cannot bridge this gap, as they require a wrist-view first frame and thus fail to generate wrist-view videos from anchor views alone. Amid this gap, recent visual geometry models such as VGGT emerge with geometric and cross-view priors that make it possible to address extreme viewpoint shifts. Inspired by these insights, we propose WristWorld, the first 4D world model that generates wrist-view videos solely from anchor views. WristWorld operates in two stages: (i) Reconstruction, which extends VGGT and incorporates our Spatial Projection Consistency (SPC) Loss to estimate geometrically consistent wrist-view poses and 4D point clouds; (ii) Generation, which employs our video generation model to synthesize temporally coherent wrist-view videos from the reconstructed perspective. Experiments on Droid, Calvin, and Franka Panda demonstrate state-of-the-art video generation with superior spatial consistency, while also improving VLA performance, raising the average task completion length on Calvin by 3.81% and closing 42.4% of the anchor-wrist view gap.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/XuWuLingYu/WristWorld", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.05862", "title": "Revisiting Long-context Modeling from Context Denoising Perspective", "authors": [ "Zecheng Tang", "Baibei Ji", "Juntao Li", "Lijun Wu", "Haijia Gui", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Long-context models (LCMs) have demonstrated great potential in processing long sequences, facilitating many real-world applications. The success of LCMs can be attributed to their ability to locate implicit critical information within the context for further prediction. However, recent research reveals that LCMs are often susceptible to contextual noise, i.e., irrelevant tokens, that can mislead model attention. In this paper, we conduct a fine-grained analysis of the context noise and propose an effective metric, the Integrated Gradient (IG) score, to detect and quantify the noise information within the context. Our findings reveal that even simple mitigation of detected context noise can substantially boost the model's attention on critical tokens and benefit subsequent predictions. Building on this insight, we propose Context Denoising Training (CDT), a straightforward yet effective training strategy that improves attention on critical tokens while reinforcing their influence on model predictions. Extensive experiments across four tasks, under both context window scaling and long-context alignment settings, demonstrate the superiority of CDT. Notably, when trained with CDT, an open-source 8B model can achieve performance (50.92) comparable to GPT-4o (51.00).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06751", "title": "OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot", "authors": [ "Junhan Zhu", "Hesong Wang", "Mingluo Su", "Zefang Wang", "Huan Wang" ], "github": "", "project_page": "", "abstract": "Large-scale text-to-image diffusion models, while powerful, suffer from prohibitive computational cost. Existing one-shot network pruning methods can hardly be directly applied to them due to the iterative denoising nature of diffusion models. To bridge the gap, this paper presents OBS-Diff, a novel one-shot pruning framework that enables accurate and training-free compression of large-scale text-to-image diffusion models. Specifically, (i) OBS-Diff revitalizes the classic Optimal Brain Surgeon (OBS), adapting it to the complex architectures of modern diffusion models and supporting diverse pruning granularity, including unstructured, N:M semi-structured, and structured (MHA heads and FFN neurons) sparsity; (ii) To align the pruning criteria with the iterative dynamics of the diffusion process, by examining the problem from an error-accumulation perspective, we propose a novel timestep-aware Hessian construction that incorporates a logarithmic-decrease weighting scheme, assigning greater importance to earlier timesteps to mitigate potential error accumulation; (iii) Furthermore, a computationally efficient group-wise sequential pruning strategy is proposed to amortize the expensive calibration process. Extensive experiments show that OBS-Diff achieves state-of-the-art one-shot pruning for diffusion models, delivering inference acceleration with minimal degradation in visual quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.04999", "title": "Bridging Text and Video Generation: A Survey", "authors": [ "Nilay Kumar", "Priyansh Bhandari", "G. Maragatham" ], "github": "", "project_page": "", "abstract": "Text-to-video (T2V) generation technology holds potential to transform multiple domains such as education, marketing, entertainment, and assistive technologies for individuals with visual or reading comprehension challenges, by creating coherent visual content from natural language prompts. From its inception, the field has advanced from adversarial models to diffusion-based models, yielding higher-fidelity, temporally consistent outputs. Yet challenges persist, such as alignment, long-range coherence, and computational efficiency. Addressing this evolving landscape, we present a comprehensive survey of text-to-video generative models, tracing their development from early GANs and VAEs to hybrid Diffusion-Transformer (DiT) architectures, detailing how these models work, what limitations they addressed in their predecessors, and why shifts toward new architectural paradigms were necessary to overcome challenges in quality, coherence, and control. We provide a systematic account of the datasets, which the surveyed text-to-video models were trained and evaluated on, and, to support reproducibility and assess the accessibility of training such models, we detail their training configurations, including their hardware specifications, GPU counts, batch sizes, learning rates, optimizers, epochs, and other key hyperparameters. Further, we outline the evaluation metrics commonly used for evaluating such models and present their performance across standard benchmarks, while also discussing the limitations of these metrics and the emerging shift toward more holistic, perception-aligned evaluation strategies. Finally, drawing from our analysis, we outline the current open challenges and propose a few promising future directions, laying out a perspective for future researchers to explore and build upon in advancing T2V research and applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.07310", "title": "MATRIX: Mask Track Alignment for Interaction-aware Video Generation", "authors": [ "Siyoon Jin", "Seongchan Kim", "Dahyun Chung", "Jaeho Lee", "Hyunwook Choi", "Jisu Nam", "Jiyoung Kim", "Seungryong Kim" ], "github": "https://github.com/LAION-AI/aesthetic-predictor", "project_page": "", "abstract": "Video DiTs have advanced video generation, yet they still struggle to model multi-instance or subject-object interactions. This raises a key question: How do these models internally represent interactions? To answer this, we curate MATRIX-11K, a video dataset with interaction-aware captions and multi-instance mask tracks. Using this dataset, we conduct a systematic analysis that formalizes two perspectives of video DiTs: semantic grounding, via video-to-text attention, which evaluates whether noun and verb tokens capture instances and their relations; and semantic propagation, via video-to-video attention, which assesses whether instance bindings persist across frames. We find both effects concentrate in a small subset of interaction-dominant layers. Motivated by this, we introduce MATRIX, a simple and effective regularization that aligns attention in specific layers of video DiTs with multi-instance mask tracks from the MATRIX-11K dataset, enhancing both grounding and propagation. We further propose InterGenEval, an evaluation protocol for interaction-aware video generation. In experiments, MATRIX improves both interaction fidelity and semantic alignment while reducing drift and hallucination. Extensive ablations validate our design choices. Codes and weights will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LAION-AI/aesthetic-predictor", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.07307", "title": "MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline", "authors": [ "Rushi Qiang", "Yuchen Zhuang", "Anikait Singh", "Percy Liang", "Chao Zhang", "Sherry Yang", "Bo Dai" ], "github": "", "project_page": "", "abstract": "While Language Models (LMs) have made significant progress in automating machine learning engineering (MLE), the acquisition of high-quality MLE training data is significantly constrained. Current MLE benchmarks suffer from low scalability and limited applicability because they rely on static, manually curated tasks, demanding extensive time and manual effort to produce. We introduce MLE-Smith, a fully automated multi-agent pipeline, to transform raw datasets into competition-style MLE challenges through an efficient generate-verify-execute paradigm for scaling MLE tasks with verifiable quality, real-world usability, and rich diversity. The proposed multi-agent pipeline in MLE-Smith drives structured task design and standardized refactoring, coupled with a hybrid verification mechanism that enforces strict structural rules and high-level semantic soundness. It further validates empirical solvability and real-world fidelity through interactive execution. We apply MLE-Smith to 224 of real-world datasets and generate 606 tasks spanning multiple categories, objectives, and modalities, demonstrating that MLE-Smith can work effectively across a wide range of real-world datasets. Evaluation on the generated tasks shows that the performance of eight mainstream and cutting-edge LLMs on MLE-Smith tasks is strongly correlated with their performance on carefully human-designed tasks, highlighting the effectiveness of the MLE-Smith to scaling up MLE tasks, while maintaining task quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.07041", "title": "U-Bench: A Comprehensive Understanding of U-Net through 100-Variant Benchmarking", "authors": [ "Fenghe Tang", "Chengqi Dong", "Wenxin Ma", "Zikang Xu", "Heqin Zhu", "Zihang Jiang", "Rongsheng Wang", "Yuhao Wang", "Chenxu Wu", "Shaohua Kevin Zhou" ], "github": "https://github.com/FengheTan9/U-Bench", "project_page": "", "abstract": "Over the past decade, U-Net has been the dominant architecture in medical image segmentation, leading to the development of thousands of U-shaped variants. Despite its widespread adoption, there is still no comprehensive benchmark to systematically evaluate their performance and utility, largely because of insufficient statistical validation and limited consideration of efficiency and generalization across diverse datasets. To bridge this gap, we present U-Bench, the first large-scale, statistically rigorous benchmark that evaluates 100 U-Net variants across 28 datasets and 10 imaging modalities. Our contributions are threefold: (1) Comprehensive Evaluation: U-Bench evaluates models along three key dimensions: statistical robustness, zero-shot generalization, and computational efficiency. We introduce a novel metric, U-Score, which jointly captures the performance-efficiency trade-off, offering a deployment-oriented perspective on model progress. (2) Systematic Analysis and Model Selection Guidance: We summarize key findings from the large-scale evaluation and systematically analyze the impact of dataset characteristics and architectural paradigms on model performance. Based on these insights, we propose a model advisor agent to guide researchers in selecting the most suitable models for specific datasets and tasks. (3) Public Availability: We provide all code, models, protocols, and weights, enabling the community to reproduce our results and extend the benchmark with future methods. In summary, U-Bench not only exposes gaps in previous evaluations but also establishes a foundation for fair, reproducible, and practically relevant benchmarking in the next decade of U-Net-based segmentation models. The project can be accessed at: https://fenghetan9.github.io/ubench. Code is available at: https://github.com/FengheTan9/U-Bench.", "github_manual": "", "github_regex": "https://github.com/FengheTan9/U-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06783", "title": "TTRV: Test-Time Reinforcement Learning for Vision Language Models", "authors": [ "Akshit Singh", "Shyam Marjit", "Wei Lin", "Paul Gavrikov", "Serena Yeung-Levy", "Hilde Kuehne", "Rogerio Feris", "Sivan Doveh", "James Glass", "M. Jehanzeb Mirza" ], "github": "", "project_page": "", "abstract": "Existing methods for extracting reward signals in Reinforcement Learning typically rely on labeled data and dedicated training splits, a setup that contrasts with how humans learn directly from their environment. In this work, we propose TTRV to enhance vision language understanding by adapting the model on the fly at inference time, without the need for any labeled data. Concretely, we enhance the Group Relative Policy Optimization (GRPO) framework by designing rewards based on the frequency of the base model's output, while inferring on each test sample multiple times. Further, we also propose to control the diversity of the model's output by simultaneously rewarding the model for obtaining low entropy of the output empirical distribution. Our approach delivers consistent gains across both object recognition and visual question answering (VQA), with improvements of up to 52.4% and 29.8%, respectively, and average boosts of 24.6% and 10.0% across 16 datasets.Remarkably, on image recognition, TTRV applied to InternVL 8B surpasses GPT-4o by an average of 2.3% over 8 benchmarks, while remaining highly competitive on VQA, demonstrating that test-time reinforcement learning can match or exceed the strongest proprietary models. Finally, we find many interesting properties of test-time RL for VLMs: for example, even in extremely data-constrained scenarios, where adaptation is performed on a single randomly chosen unlabeled test example, TTRV still yields non-trivial improvements of up to 5.5% in recognition tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.05891", "title": "D^3QE: Learning Discrete Distribution Discrepancy-aware Quantization Error for Autoregressive-Generated Image Detection", "authors": [ "Yanran Zhang", "Bingyao Yu", "Yu Zheng", "Wenzhao Zheng", "Yueqi Duan", "Lei Chen", "Jie Zhou", "Jiwen Lu" ], "github": "https://github.com/Zhangyr2022/D3QE", "project_page": "", "abstract": "The emergence of visual autoregressive (AR) models has revolutionized image generation while presenting new challenges for synthetic image detection. Unlike previous GAN or diffusion-based methods, AR models generate images through discrete token prediction, exhibiting both marked improvements in image synthesis quality and unique characteristics in their vector-quantized representations. In this paper, we propose to leverage Discrete Distribution Discrepancy-aware Quantization Error (D^3QE) for autoregressive-generated image detection that exploits the distinctive patterns and the frequency distribution bias of the codebook existing in real and fake images. We introduce a discrete distribution discrepancy-aware transformer that integrates dynamic codebook frequency statistics into its attention mechanism, fusing semantic features and quantization error latent. To evaluate our method, we construct a comprehensive dataset termed ARForensics covering 7 mainstream visual AR models. Experiments demonstrate superior detection accuracy and strong generalization of D^3QE across different AR models, with robustness to real-world perturbations. Code is available at https://github.com/Zhangyr2022/D3QE{https://github.com/Zhangyr2022/D3QE}.", "github_manual": "", "github_regex": "https://github.com/Zhangyr2022/D3QE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06261", "title": "AlphaApollo: Orchestrating Foundation Models and Professional Tools into a Self-Evolving System for Deep Agentic Reasoning", "authors": [ "Zhanke Zhou", "Chentao Cao", "Xiao Feng", "Xuan Li", "Zongze Li", "Xiangyu Lu", "Jiangchao Yao", "Weikai Huang", "Linrui Xu", "Tian Cheng", "Guanyu Jiang", "Yiming Zheng", "Brando Miranda", "Tongliang Liu", "Sanmi Koyejo", "Masashi Sugiyama", "Bo Han" ], "github": "https://github.com/tmlr-group/AlphaApollo", "project_page": "", "abstract": "We present AlphaApollo, a self-evolving agentic reasoning system that aims to address two bottlenecks in foundation model (FM) reasoning-limited model-intrinsic capacity and unreliable test-time iteration. AlphaApollo orchestrates multiple models with professional tools to enable deliberate, verifiable reasoning. It couples (i) a computation tool (Python with numerical and symbolic libraries) and (ii) a retrieval tool (task-relevant external information) to execute exact calculations and ground decisions. The system further supports multi-round, multi-model solution evolution via a shared state map that records candidates, executable checks, and feedback for iterative refinement. In evaluations on AIME 2024/2025 across multiple models, AlphaApollo delivers consistent gains: +5.15% Average@32 and +23.34% Pass@32 for Qwen2.5-14B-Instruct, and +8.91% Average@32 with +26.67% Pass@32 for Llama-3.3-70B-Instruct. Tool-use analysis shows that more than 80% of tool calls are successfully executed, with consistent outperformance of non-tool baselines, thereby lifting the capability ceiling of FMs. More empirical results and implementation details will be updated at https://github.com/tmlr-group/AlphaApollo.", "github_manual": "", "github_regex": "https://github.com/tmlr-group/AlphaApollo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.07238", "title": "When Benchmarks Age: Temporal Misalignment through Large Language Model Factuality Evaluation", "authors": [ "Xunyi Jiang", "Dingyi Chang", "Julian McAuley", "Xin Xu" ], "github": "https://github.com/JiangXunyi/BenchAge", "project_page": "", "abstract": "The rapid evolution of large language models (LLMs) and the real world has outpaced the static nature of widely used evaluation benchmarks, raising concerns about their reliability for evaluating LLM factuality. While substantial works continue to rely on the popular but old benchmarks, their temporal misalignment with real-world facts and modern LLMs, and their effects on LLM factuality evaluation remain underexplored. Therefore, in this work, we present a systematic investigation of this issue by examining five popular factuality benchmarks and eight LLMs released across different years. An up-to-date fact retrieval pipeline and three metrics are tailored to quantify benchmark aging and its impact on LLM factuality evaluation. Experimental results and analysis illustrate that a considerable portion of samples in the widely used factuality benchmarks are outdated, leading to unreliable assessments of LLM factuality. We hope our work can provide a testbed to assess the reliability of a benchmark for LLM factuality evaluation and inspire more research on the benchmark aging issue. Codes are available in https://github.com/JiangXunyi/BenchAge.", "github_manual": "", "github_regex": "https://github.com/JiangXunyi/BenchAge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.01982", "title": "G^2RPO: Granular GRPO for Precise Reward in Flow Models", "authors": [ "Yujie Zhou", "Pengyang Ling", "Jiazi Bu", "Yibin Wang", "Yuhang Zang", "Jiaqi Wang", "Li Niu", "Guangtao Zhai" ], "github": "", "project_page": "", "abstract": "The integration of online reinforcement learning (RL) into diffusion and flow models has recently emerged as a promising approach for aligning generative models with human preferences. Stochastic sampling via Stochastic Differential Equations (SDE) is employed during the denoising process to generate diverse denoising directions for RL exploration. While existing methods effectively explore potential high-value samples, they suffer from sub-optimal preference alignment due to sparse and narrow reward signals. To address these challenges, we propose a novel Granular-GRPO (G^2RPO ) framework that achieves precise and comprehensive reward assessments of sampling directions in reinforcement learning of flow models. Specifically, a Singular Stochastic Sampling strategy is introduced to support step-wise stochastic exploration while enforcing a high correlation between the reward and the injected noise, thereby facilitating a faithful reward for each SDE perturbation. Concurrently, to eliminate the bias inherent in fixed-granularity denoising, we introduce a Multi-Granularity Advantage Integration module that aggregates advantages computed at multiple diffusion scales, producing a more comprehensive and robust evaluation of the sampling directions. Experiments conducted on various reward models, including both in-domain and out-of-domain evaluations, demonstrate that our G^2RPO significantly outperforms existing flow-based GRPO baselines,highlighting its effectiveness and robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.07037", "title": "Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models", "authors": [ "Rajvee Sheth", "Samridhi Raj Sinha", "Mahavir Patil", "Himanshu Beniwal", "Mayank Singh" ], "github": "https://github.com/lingo-iitgn/awesome-code-mixing", "project_page": "", "abstract": "Code-switching (CSW), the alternation of languages and scripts within a single utterance, remains a fundamental challenge for multiling ual NLP, even amidst the rapid advances of large language models (LLMs). Most LLMs still struggle with mixed-language inputs, limited CSW datasets, and evaluation biases, hindering deployment in multilingual societies. This survey provides the first comprehensive analysis of CSW-aware LLM research, reviewing unique_references studies spanning five research areas, 12 NLP tasks, 30+ datasets, and 80+ languages. We classify recent advances by architecture, training strategy, and evaluation methodology, outlining how LLMs have reshaped CSW modeling and what challenges persist. The paper concludes with a roadmap emphasizing the need for inclusive datasets, fair evaluation, and linguistically grounded models to achieve truly multilingual intelligence. A curated collection of all resources is maintained at https://github.com/lingo-iitgn/awesome-code-mixing/.", "github_manual": "", "github_regex": "https://github.com/lingo-iitgn/awesome-code-mixing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.05057", "title": "StaMo: Unsupervised Learning of Generalizable Robot Motion from Compact State Representation", "authors": [ "Mingyu Liu", "Jiuhe Shu", "Hui Chen", "Zeju Li", "Canyu Zhao", "Jiange Yang", "Shenyuan Gao", "Hao Chen", "Chunhua Shen" ], "github": "https://github.com/aim-uofa/StaMo", "project_page": "", "abstract": "A fundamental challenge in embodied intelligence is developing expressive and compact state representations for efficient world modeling and decision making. However, existing methods often fail to achieve this balance, yielding representations that are either overly redundant or lacking in task-critical information. We propose an unsupervised approach that learns a highly compressed two-token state representation using a lightweight encoder and a pre-trained Diffusion Transformer (DiT) decoder, capitalizing on its strong generative prior. Our representation is efficient, interpretable, and integrates seamlessly into existing VLA-based models, improving performance by 14.3% on LIBERO and 30% in real-world task success with minimal inference overhead. More importantly, we find that the difference between these tokens, obtained via latent interpolation, naturally serves as a highly effective latent action, which can be further decoded into executable robot actions. This emergent capability reveals that our representation captures structured dynamics without explicit supervision. We name our method StaMo for its ability to learn generalizable robotic Motion from compact State representation, which is encoded from static images, challenging the prevalent dependence to learning latent action on complex architectures and video data. The resulting latent actions also enhance policy co-training, outperforming prior methods by 10.4% with improved interpretability. Moreover, our approach scales effectively across diverse data sources, including real-world robot data, simulation, and human egocentric video.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aim-uofa/StaMo", "github_api_added_by": "auto" }, { "date": "2025-10-09", "arxiv_id": "2510.07143", "title": "Are We Using the Right Benchmark: An Evaluation Framework for Visual Token Compression Methods", "authors": [ "Chenfei Liao", "Wensong Wang", "Zichen Wen", "Xu Zheng", "Yiyu Wang", "Haocong He", "Yuanhuiyi Lyu", "Lutao Jiang", "Xin Zou", "Yuqian Fu", "Bin Ren", "Linfeng Zhang", "Xuming Hu" ], "github": "https://github.com/Chenfei-Liao/VTC-Bench", "project_page": "", "abstract": "Recent endeavors to accelerate inference in Multimodal Large Language Models (MLLMs) have primarily focused on visual token compression. The effectiveness of these methods is typically assessed by measuring the accuracy drop on established benchmarks, comparing model performance before and after compression. However, these benchmarks are originally designed to assess the perception and reasoning capabilities of MLLMs, rather than to evaluate compression techniques. As a result, directly applying them to visual token compression introduces a task mismatch. Strikingly, our investigation reveals that simple image downsampling consistently outperforms many advanced compression methods across multiple widely used benchmarks. Through extensive experiments, we make the following observations: (i) Current benchmarks are noisy for the visual token compression task. (ii) Down-sampling is able to serve as a data filter to evaluate the difficulty of samples in the visual token compression task. Motivated by these findings, we introduce VTC-Bench, an evaluation framework that incorporates a data filtering mechanism to denoise existing benchmarks, thereby enabling fairer and more accurate assessment of visual token compression methods. All data and code are available at https://github.com/Chenfei-Liao/VTC-Bench.", "github_manual": "", "github_regex": "https://github.com/Chenfei-Liao/VTC-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06855", "title": "Online Generic Event Boundary Detection", "authors": [ "Hyungrok Jung", "Daneul Kim", "Seunggyun Lim", "Jeany Son", "Jonghyun Choi" ], "github": "", "project_page": "", "abstract": "Generic Event Boundary Detection (GEBD) aims to interpret long-form videos through the lens of human perception. However, current GEBD methods require processing complete video frames to make predictions, unlike humans processing data online and in real-time. To bridge this gap, we introduce a new task, Online Generic Event Boundary Detection (On-GEBD), aiming to detect boundaries of generic events immediately in streaming videos. This task faces unique challenges of identifying subtle, taxonomy-free event changes in real-time, without the access to future frames. To tackle these challenges, we propose a novel On-GEBD framework, Estimator, inspired by Event Segmentation Theory (EST) which explains how humans segment ongoing activity into events by leveraging the discrepancies between predicted and actual information. Our framework consists of two key components: the Consistent Event Anticipator (CEA), and the Online Boundary Discriminator (OBD). Specifically, the CEA generates a prediction of the future frame reflecting current event dynamics based solely on prior frames. Then, the OBD measures the prediction error and adaptively adjusts the threshold using statistical tests on past errors to capture diverse, subtle event transitions. Experimental results demonstrate that Estimator outperforms all baselines adapted from recent online video understanding models and achieves performance comparable to prior offline-GEBD methods on the Kinetics-GEBD and TAPOS datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06673", "title": "Heptapod: Language Modeling on Visual Signals", "authors": [ "Yongxin Zhu", "Jiawei Chen", "Yuanzhe Chen", "Zhuo Chen", "Dongya Jia", "Jian Cong", "Xiaobin Zhuang", "Yuping Wang", "Yuxuan Wang" ], "github": "", "project_page": "", "abstract": "We introduce Heptapod, an image autoregressive model that adheres to the foundational principles of language modeling. Heptapod employs causal attention, eliminates reliance on CFG, and eschews the trend of semantic tokenizers. Our key innovation is next 2D distribution prediction: a causal Transformer with reconstruction-focused visual tokenizer, learns to predict the distribution over the entire 2D spatial grid of images at each timestep. This learning objective unifies the sequential modeling of autoregressive framework with the holistic self-supervised learning of masked autoencoding, enabling the model to capture comprehensive image semantics via generative training. On the ImageNet generation benchmark, Heptapod achieves an FID of 2.70, significantly outperforming previous causal autoregressive approaches. We hope our work inspires a principled rethinking of language modeling on visual signals and beyond.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.01954", "title": "Patch-as-Decodable-Token: Towards Unified Multi-Modal Vision Tasks in MLLMs", "authors": [ "Yongyi Su", "Haojie Zhang", "Shijie Li", "Nanqing Liu", "Jingyi Liao", "Junyi Pan", "Yuan Liu", "Xiaofen Xing", "Chong Sun", "Chen Li", "Nancy F. Chen", "Shuicheng Yan", "Xulei Yang", "Xun Xu" ], "github": "https://github.com/Gorilla-Lab-SCUT/PaDT", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have advanced rapidly in recent years. However, existing approaches for vision tasks often rely on indirect representations, such as generating coordinates as text for detection, which limits performance and prevents dense prediction tasks like segmentation. To overcome these challenges, we introduce Patch-as-Decodable Token (PaDT), a unified paradigm that enables MLLMs to directly generate both textual and diverse visual outputs. Central to PaDT are Visual Reference Tokens (VRTs), derived from visual patch embeddings of query images and interleaved seamlessly with LLM's output textual tokens. A lightweight decoder then transforms LLM's outputs into detection, segmentation, and grounding predictions. Unlike prior methods, PaDT processes VRTs independently at each forward pass and dynamically expands the embedding table, thus improving localization and differentiation among similar objects. We further tailor a training strategy for PaDT by randomly selecting VRTs for supervised fine-tuning and introducing a robust per-token cross-entropy loss. Our empirical studies across four visual perception and understanding tasks suggest PaDT consistently achieving state-of-the-art performance, even compared with significantly larger MLLM models. The code is available at https://github.com/Gorilla-Lab-SCUT/PaDT.", "github_manual": "", "github_regex": "https://github.com/Gorilla-Lab-SCUT/PaDT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.05644", "title": "The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP", "authors": [ "Sheriff Issaka", "Keyi Wang", "Yinka Ajibola", "Oluwatumininu Samuel-Ipaye", "Zhaoyi Zhang", "Nicte Aguillon Jimenez", "Evans Kofi Agyei", "Abraham Lin", "Rohan Ramachandran", "Sadick Abdul Mumin", "Faith Nchifor", "Mohammed Shuraim", "Lieqi Liu", "Erick Rosas Gonzalez", "Sylvester Kpei", "Jemimah Osei", "Carlene Ajeneza", "Persis Boateng", "Prisca Adwoa Dufie Yeboah", "Saadia Gabriel" ], "github": "", "project_page": "", "abstract": "Despite representing nearly one-third of the world's languages, African languages remain critically underserved by modern NLP technologies, with 88\\% classified as severely underrepresented or completely ignored in computational linguistics. We present the African Languages Lab (All Lab), a comprehensive research initiative that addresses this technological gap through systematic data collection, model development, and capacity building. Our contributions include: (1) a quality-controlled data collection pipeline, yielding the largest validated African multi-modal speech and text dataset spanning 40 languages with 19 billion tokens of monolingual text and 12,628 hours of aligned speech data; (2) extensive experimental validation demonstrating that our dataset, combined with fine-tuning, achieves substantial improvements over baseline models, averaging +23.69 ChrF++, +0.33 COMET, and +15.34 BLEU points across 31 evaluated languages; and (3) a structured research program that has successfully mentored fifteen early-career researchers, establishing sustainable local capacity. Our comparative evaluation against Google Translate reveals competitive performance in several languages while identifying areas that require continued development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06710", "title": "RLinf-VLA: A Unified and Efficient Framework for VLA+RL Training", "authors": [ "Hongzhi Zang", "Mingjie Wei", "Si Xu", "Yongji Wu", "Zhen Guo", "Yuanqing Wang", "Hao Lin", "Liangzhi Shi", "Yuqing Xie", "Zhexuan Xu", "Zhihao Liu", "Kang Chen", "Wenhao Tang", "Quanlu Zhang", "Weinan Zhang", "Chao Yu", "Yu Wang" ], "github": "", "project_page": "", "abstract": "Recent progress in vision and language foundation models has significantly advanced multimodal understanding, reasoning, and generation, inspiring a surge of interest in extending such capabilities to embodied settings through vision-language-action (VLA) models. Yet, most VLA models are still trained with supervised fine-tuning (SFT), which struggles to generalize under distribution shifts due to error accumulation. Reinforcement learning (RL) offers a promising alternative by directly optimizing task performance through interaction, but existing attempts remain fragmented and lack a unified platform for fair and systematic comparison across model architectures and algorithmic designs. To address this gap, we introduce RLinf-VLA, a unified and efficient framework for scalable RL training of VLA models. The system adopts a highly flexible resource allocation design that addresses the challenge of integrating rendering, training, and inference in RL+VLA training. In particular, for GPU-parallelized simulators, RLinf-VLA implements a novel hybrid fine-grained pipeline allocation mode, achieving a 1.61x-1.88x speedup in training. Through a unified interface, RLinf-VLA seamlessly supports diverse VLA architectures (e.g., OpenVLA, OpenVLA-OFT), multiple RL algorithms (e.g., PPO, GRPO), and various simulators (e.g., ManiSkill, LIBERO). In simulation, a unified model achieves 98.11\\% across 130 LIBERO tasks and 97.66\\% across 25 ManiSkill tasks. Beyond empirical performance, our study distills a set of best practices for applying RL to VLA training and sheds light on emerging patterns in this integration. Furthermore, we present preliminary deployment on a real-world Franka robot, where RL-trained policies exhibit stronger generalization than those trained with SFT. We envision RLinf-VLA as a foundation to accelerate and standardize research on embodied intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.05491", "title": "NorMuon: Making Muon more efficient and scalable", "authors": [ "Zichong Li", "Liming Liu", "Chen Liang", "Weizhu Chen", "Tuo Zhao" ], "github": "https://github.com/zichongli5/NorMuon", "project_page": "", "abstract": "The choice of optimizer significantly impacts the training efficiency and computational costs of large language models (LLMs). Recently, the Muon optimizer has demonstrated promising results by orthogonalizing parameter updates, improving optimization geometry through better conditioning. Despite Muon's emergence as a candidate successor to Adam, the potential for jointly leveraging their strengths has not been systematically explored. In this work, we bridge this gap by proposing NorMuon (Neuron-wise Normalized Muon), an optimizer that synergistically combines orthogonalization with neuron-level adaptive learning rates. Our analysis reveals that while Muon effectively reduces condition numbers, the resulting updates exhibit highly non-uniform neuron norms, causing certain neurons to dominate the optimization process. NorMuon addresses this imbalance by maintaining second-order momentum statistics for each neuron and applying row-wise normalization after orthogonalization, ensuring balanced parameter utilization while preserving Muon's conditioning benefits. To enable practical deployment at scale, we develop an efficient distributed implementation under the FSDP2 framework that strategically distributes orthogonalization computations across devices. Experiments across multiple model scales demonstrate that NorMuon consistently outperforms both Adam and Muon, achieving 21.74% better training efficiency than Adam and 11.31% improvement over Muon on 1.1 B pretraining setting, while maintaining a comparable memory footprint to Muon. Our findings suggest that orthogonalization and adaptive learning rates are complementary rather than competing approaches, opening new avenues for optimizer design in large-scale deep learning.", "github_manual": "https://github.com/zichongli5/NorMuon", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06590", "title": "Ming-UniVision: Joint Image Understanding and Generation with a Unified Continuous Tokenizer", "authors": [ "Ziyuan Huang", "DanDan Zheng", "Cheng Zou", "Rui Liu", "Xiaolong Wang", "Kaixiang Ji", "Weilong Chai", "Jianxin Sun", "Libin Wang", "Yongjie Lv", "Taozhi Huang", "Jiajia Liu", "Qingpei Guo", "Ming Yang", "Jingdong Chen", "Jun Zhou" ], "github": "", "project_page": "", "abstract": "Visual tokenization remains a core challenge in unifying visual understanding and generation within the autoregressive paradigm. Existing methods typically employ tokenizers in discrete latent spaces to align with the tokens from large language models, where the quantization errors can limit semantic expressiveness and degrade the capability of vision-language understanding. To address this, we introduce MingTok, a new family of visual tokenizers with a continuous latent space, for unified autoregressive generation and understanding. While understanding tasks favor discriminative high-dimensional features, generation tasks prefer compact low-level codes. Thus, to reconcile these competing demands, MingTok adopts a three-stage sequential architecture involving low-level encoding, semantic expansion, and visual reconstruction. Built on top of it, Ming-UniVision eliminates the need for task-specific visual representations, and unifies diverse vision-language tasks under a single autoregrsssive prediction paradigm. By formulating both understanding and generation as next-token prediction in a shared continuous space, it seamlessly supports multi-round, in-context tasks such as iterative understanding, generation and editing. Empirically, we find that using a unified continuous visual representation reconciles the competing requirements on the tokenizers by the understanding and generation tasks, thereby leading to state-of-the-art level performance across both domains. We hope our findings will facilitate unified visual tokenization in the continuous domain. Inference code and model weights are released to benefit community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.07019", "title": "Native Hybrid Attention for Efficient Sequence Modeling", "authors": [ "Jusen Du", "Jiaxi Hu", "Tao Zhang", "Weigao Sun", "Yu Cheng" ], "github": "https://github.com/JusenD/NHA", "project_page": "", "abstract": "Transformers excel at sequence modeling but face quadratic complexity, while linear attention offers improved efficiency but often compromises recall accuracy over long contexts. In this work, we introduce Native Hybrid Attention (NHA), a novel hybrid architecture of linear and full attention that integrates both intra \\& inter-layer hybridization into a unified layer design. NHA maintains long-term context in key-value slots updated by a linear RNN, and augments them with short-term tokens from a sliding window. A single softmax attention operation is then applied over all keys and values, enabling per-token and per-head context-dependent weighting without requiring additional fusion parameters. The inter-layer behavior is controlled through a single hyperparameter, the sliding window size, which allows smooth adjustment between purely linear and full attention while keeping all layers structurally uniform. Experimental results show that NHA surpasses Transformers and other hybrid baselines on recall-intensive and commonsense reasoning tasks. Furthermore, pretrained LLMs can be structurally hybridized with NHA, achieving competitive accuracy while delivering significant efficiency gains. Code is available at https://github.com/JusenD/NHA.", "github_manual": "", "github_regex": "https://github.com/JusenD/NHA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2509.21842", "title": "DeepTravel: An End-to-End Agentic Reinforcement Learning Framework for Autonomous Travel Planning Agents", "authors": [ "Yansong Ning", "Rui Liu", "Jun Wang", "Kai Chen", "Wei Li", "Jun Fang", "Kan Zheng", "Naiqiang Tan", "Hao Liu" ], "github": "", "project_page": "", "abstract": "Travel planning (TP) agent has recently worked as an emerging building block to interact with external tools and resources for travel itinerary generation, ensuring enjoyable user experience. Despite its benefits, existing studies rely on hand craft prompt and fixed agent workflow, hindering more flexible and autonomous TP agent. This paper proposes DeepTravel, an end to end agentic reinforcement learning framework for building autonomous travel planning agent, capable of autonomously planning, executing tools, and reflecting on tool responses to explore, verify, and refine intermediate actions in multi step reasoning. To achieve this, we first construct a robust sandbox environment by caching transportation, accommodation and POI data, facilitating TP agent training without being constrained by real world APIs limitations (e.g., inconsistent outputs). Moreover, we develop a hierarchical reward modeling system, where a trajectory level verifier first checks spatiotemporal feasibility and filters unsatisfied travel itinerary, and then the turn level verifier further validate itinerary detail consistency with tool responses, enabling efficient and precise reward service. Finally, we propose the reply augmented reinforcement learning method that enables TP agent to periodically replay from a failures experience buffer, emerging notable agentic capacity. We deploy trained TP agent on DiDi Enterprise Solutions App and conduct comprehensive online and offline evaluations, demonstrating that DeepTravel enables small size LLMs (e.g., Qwen3 32B) to significantly outperform existing frontier LLMs such as OpenAI o1, o3 and DeepSeek R1 in travel planning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06557", "title": "The Markovian Thinker", "authors": [ "Milad Aghajohari", "Kamran Chitsaz", "Amirhossein Kazemnejad", "Sarath Chandar", "Alessandro Sordoni", "Aaron Courville", "Siva Reddy" ], "github": "https://github.com/McGill-NLP/the-markovian-thinker", "project_page": "", "abstract": "Reinforcement learning (RL) has recently become a strong recipe for training reasoning LLMs that produce long chains of thought (LongCoT). Yet the standard RL \"thinking environment\", where the state is the prompt plus all prior reasoning tokens, makes the state unbounded and forces attention-based policies to pay quadratic compute as thoughts lengthen. We revisit the environment itself. We propose Markovian Thinking, a paradigm in which the policy advances reasoning while conditioning on a constant-size state, decoupling thinking length from context size. As an immediate consequence this yields linear compute with constant memory. We instantiate this idea with Delethink, an RL environment that structures reasoning into fixed-size chunks. Within each chunk, the model thinks as usual; at the boundary, the environment resets the context and reinitializes the prompt with a short carryover. Through RL, the policy learns to write a textual state near the end of each chunk sufficient for seamless continuation of reasoning after reset. Trained in this environment, an R1-Distill 1.5B model reasons in 8K-token chunks yet thinks up to 24K tokens, matching or surpassing LongCoT-RL trained with a 24K budget. With test-time scaling, Delethink continues to improve where LongCoT plateaus. The effect of linear compute is substantial: we empirically estimate at 96K average thinking length LongCoT-RL costs 27 H100-months vs. 7 for Delethink. Analysis at RL initialization shows off-the-shelf reasoning models (1.5B-120B) often sample Markovian traces zero-shot across diverse benchmarks, providing positive samples that make RL effective at scale. Our results show that redesigning the thinking environment is a powerful lever: it enables very long reasoning without quadratic overhead and opens a path toward efficient, scalable reasoning LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/McGill-NLP/the-markovian-thinker", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06953", "title": "Revisiting the Uniform Information Density Hypothesis in LLM Reasoning Traces", "authors": [ "Minju Gwak", "Guijin Son", "Jaehyung Kim" ], "github": "", "project_page": "", "abstract": "The Uniform Information Density (UID) hypothesis suggests that effective communication maintains a stable flow of information. In this work, we revisit this principle in the context of large language model (LLM) reasoning traces, asking whether step-level uniformity reflects reasoning quality. To this end, we propose an entropy-based stepwise information density metric and introduce two complementary measures of uniformity, local and global uniformity scores. Across the experiments on six different reasoning benchmarks, we find that step-level uniformity not only provides a strong theoretical lens but also yields practical performance benefits; for example, selecting reasoning traces with more uniform information density at the step-level improves accuracy by 10-32\\% relative gains over baselines at AIME2025. Our analysis further reveals that correct reasoning traces tend to avoid sharp information density spikes, while incorrect traces exhibit irregular information bursts. These results demonstrate that UID-inspired information density measures outperform alternative internal signals as predictors of reasoning quality. Results highlight the uniformity of the information density as a robust diagnostic and selection criterion for building more reliable and accurate reasoning systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.04230", "title": "Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thought", "authors": [ "Guijin Son", "Donghun Yang", "Hitesh Laxmichand Patel", "Amit Agarwal", "Hyunwoo Ko", "Chanuk Lim", "Srikant Panda", "Minhyuk Kim", "Nikunj Drolia", "Dasol Choi", "Kyong-Ha Lee", "Youngjae Yu" ], "github": "", "project_page": "", "abstract": "Recent frontier models employ long chain-of-thought reasoning to explore solution spaces in context and achieve stonger performance. While many works study distillation to build smaller yet capable models, most focus on English and little is known about language-specific reasoning. To bridge this gap, we first introduct **Language-Mixed CoT**, a reasoning schema that switches between English and a target language, using English as an anchor to excel in reasoning while minimizing translation artificats. As a Korean case study, we curate **Yi-Sang**: 5.79M native-Korean prompts from web Q&A, exams, STEM, and code; 3.7M long reasoning traces generated from Qwen3-32B; and a targeted 260k high-yield subset. We train ninve models (4B-35B) across six families (Qwen2.5, Llama-3.1, Gemma-3, etc). Our best model, **KO-REAson-35B**, achieves state-of-the-art performance, with the highest overall average score (64.0 \\pm 25), ranking first on 5/9 benchmarks and second on the remainder. Samller and mid-sized models also benefit substantially, with an average improvement of +18.6 points across teh evaluated nine benchmarks. Ablations show **Language-Mixed CoT** is more effective than monolingual CoT, also resulting in cross-lingual and mult-modal performance gains. We release our data-curation pipeline, evaluation system, datasets, and models to advance research on language-specific reasoning. Data and model collection: https://huggingface.co/KOREAson.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.04204", "title": "CALM Before the STORM: Unlocking Native Reasoning for Optimization Modeling", "authors": [ "Zhengyang Tang", "Zihan Ye", "Chenyu Huang", "Xuhan Huang", "Chengpeng Li", "Sihang Li", "Guanhua Chen", "Ming Yan", "Zizhuo Wang", "Hongyuan Zha", "Dayiheng Liu", "Benyou Wang" ], "github": "", "project_page": "", "abstract": "Large Reasoning Models (LRMs) have demonstrated strong capabilities in complex multi-step reasoning, opening new opportunities for automating optimization modeling. However, existing domain adaptation methods, originally designed for earlier instruction-tuned models, often fail to exploit the advanced reasoning patterns of modern LRMs -- In particular, we show that direct fine-tuning on traditional non-reflective datasets leads to limited gains. To fully leverage LRMs' inherent reasoning abilities, we propose CALM (Corrective Adaptation with Lightweight Modification), a framework that progressively refines LRMs within their native reasoning modes for optimization modeling tasks. In CALM, an expert intervener identifies reasoning flaws and provides concise corrective hints, which the LRM incorporates to produce improved reasoning trajectories. These interventions modify fewer than 2.6\\% of generated tokens, but generate high-quality data for soft adaptation through supervised fine-tuning. The adapted model is then further improved through reinforcement learning. Building on CALM, we develop STORM (Smart Thinking Optimization Reasoning Model), a 4B-parameter LRM that achieves a new state-of-the-art average accuracy of 68.9\\% across five popular optimization modeling benchmarks, matching the performance of a 671B LRM. These results demonstrate that dynamic, hint-based data synthesis both preserves and amplifies the native reasoning patterns of modern LRMs, offering a more effective and scalable path towards expert-level performance on challenging optimization modeling tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06475", "title": "PuzzlePlex: Benchmarking Foundation Models on Reasoning and Planning with Puzzles", "authors": [ "Yitao Long", "Yuru Jiang", "Hongjun Liu", "Yilun Zhao", "Jingchen Sun", "Yiqiu Shen", "Chen Zhao", "Arman Cohan", "Dennis Shasha" ], "github": "", "project_page": "", "abstract": "This work investigates the reasoning and planning capabilities of foundation models and their scalability in complex, dynamic environments. We introduce PuzzlePlex, a benchmark designed to assess these capabilities through a diverse set of puzzles. PuzzlePlex consists of 15 types of puzzles, including deterministic and stochastic games of varying difficulty, as well as single-player and two-player scenarios. The PuzzlePlex framework provides a comprehensive environment for each game, and supports extensibility to generate more challenging instances as foundation models evolve. Additionally, we implement customized game-playing strategies for comparison. Building on this benchmark, we develop fine-grained metrics to measure performance and conduct an in-depth analysis of frontier foundation models across two settings: instruction-based and code-based. Furthermore, we systematically investigate their scaling limits. Our findings show that reasoning models outperform others in instruction-based settings, while code-based execution presents greater challenges but offers a scalable and efficient alternative. PuzzlePlex enables targeted evaluation and guides future improvements in reasoning, planning, and generalization for foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06426", "title": "FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering", "authors": [ "Yitao Long", "Tiansheng Hu", "Yilun Zhao", "Arman Cohan", "Chen Zhao" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) frequently hallucinate to long-form questions, producing plausible yet factually incorrect answers. A common mitigation strategy is to provide attribution to LLM outputs. However, existing benchmarks primarily focus on simple attribution that retrieves supporting textual evidence as references. We argue that in real-world scenarios such as financial applications, attribution goes beyond reference retrieval. We introduce FinLFQA, a benchmark designed to evaluate the ability of LLMs to generate long-form answers to complex financial questions with reliable and nuanced attributions. FinLFQA evaluates three critical aspects of attribution through human annotations: (1) supporting evidence extracted from financial reports, (2) intermediate numerical reasoning steps, and (3) domain-specific financial knowledge that informs the reasoning process. We further provide an automatic evaluation framework covering both answer quality and attribution quality. Through extensive experiments on eight LLMs across multiple attribution-generation paradigms, we find that fine-grained metrics are important to distinguish model capabilities, that end-to-end generation achieves comparable performance to post-hoc approaches, and that iterative refinement only helps when guided by external feedback.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.04910", "title": "Glocal Information Bottleneck for Time Series Imputation", "authors": [ "Jie Yang", "Kexin Zhang", "Guibin Zhang", "Philip S. Yu", "Kaize Ding" ], "github": "https://github.com/Muyiiiii/NeurIPS-25-Glocal-IB", "project_page": "", "abstract": "Time Series Imputation (TSI), which aims to recover missing values in temporal data, remains a fundamental challenge due to the complex and often high-rate missingness in real-world scenarios. Existing models typically optimize the point-wise reconstruction loss, focusing on recovering numerical values (local information). However, we observe that under high missing rates, these models still perform well in the training phase yet produce poor imputations and distorted latent representation distributions (global information) in the inference phase. This reveals a critical optimization dilemma: current objectives lack global guidance, leading models to overfit local noise and fail to capture global information of the data. To address this issue, we propose a new training paradigm, Glocal Information Bottleneck (Glocal-IB). Glocal-IB is model-agnostic and extends the standard IB framework by introducing a Global Alignment loss, derived from a tractable mutual information approximation. This loss aligns the latent representations of masked inputs with those of their originally observed counterparts. It helps the model retain global structure and local details while suppressing noise caused by missing values, giving rise to better generalization under high missingness. Extensive experiments on nine datasets confirm that Glocal-IB leads to consistently improved performance and aligned latent representations under missingness. Our code implementation is available in https://github.com/Muyiiiii/NeurIPS-25-Glocal-IB.", "github_manual": "", "github_regex": "https://github.com/Muyiiiii/NeurIPS-25-Glocal-IB", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.05152", "title": "A Single Character can Make or Break Your LLM Evals", "authors": [ "Jingtong Su", "Jianyu Zhang", "Karen Ullrich", "L\u00e9on Bottou", "Mark Ibrahim" ], "github": "https://github.com/EleutherAI/lm-evaluation-harness", "project_page": "", "abstract": "Common Large Language model (LLM) evaluations rely on demonstration examples to steer models' responses to the desired style. While the number of examples used has been studied and standardized, the choice of how to format examples is less investigated. In evaluation protocols and real world usage, users face the choice how to separate in-context examples: use a comma? new line? semi-colon? hashtag? etc.? Surprisingly, we find this seemingly minor choice can dramatically alter model response quality. Across leading model families (Llama, Qwen, Gemma), performance on MMLU for example can vary by pm 23% depending on the choice of delimiter. In fact, one can manipulate model rankings to put any model in the lead by only modifying the single character separating examples. We find LLMs' brittleness pervades topics, model families, and doesn't improve with scale. By probing attention head scores, we find that good-performing delimiters steer attention towards key tokens in the input. Finally, we explore methods to improve LLMs' robustness to the choice of delimiter. We find specifying the selected delimiter in the prompt boosts robustness and offer practical recommendations for the best-performing delimiters to select.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EleutherAI/lm-evaluation-harness", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06607", "title": "Code Agent can be an End-to-end System Hacker: Benchmarking Real-world Threats of Computer-use Agent", "authors": [ "Weidi Luo", "Qiming Zhang", "Tianyu Lu", "Xiaogeng Liu", "Bin Hu", "Hung-Chun Chiu", "Siyuan Ma", "Yizhe Zhang", "Xusheng Xiao", "Yinzhi Cao", "Zhen Xiang", "Chaowei Xiao" ], "github": "https://github.com/google-gemini/gemini-cli", "project_page": "", "abstract": "Computer-use agent (CUA) frameworks, powered by large language models (LLMs) or multimodal LLMs (MLLMs), are rapidly maturing as assistants that can perceive context, reason, and act directly within software environments. Among their most critical applications is operating system (OS) control. As CUAs in the OS domain become increasingly embedded in daily operations, it is imperative to examine their real-world security implications, specifically whether CUAs can be misused to perform realistic, security-relevant attacks. Existing works exhibit four major limitations: Missing attacker-knowledge model on tactics, techniques, and procedures (TTP), Incomplete coverage for end-to-end kill chains, unrealistic environment without multi-host and encrypted user credentials, and unreliable judgment dependent on LLM-as-a-Judge. To address these gaps, we propose AdvCUA, the first benchmark aligned with real-world TTPs in MITRE ATT&CK Enterprise Matrix, which comprises 140 tasks, including 40 direct malicious tasks, 74 TTP-based malicious tasks, and 26 end-to-end kill chains, systematically evaluates CUAs under a realistic enterprise OS security threat in a multi-host environment sandbox by hard-coded evaluation. We evaluate the existing five mainstream CUAs, including ReAct, AutoGPT, Gemini CLI, Cursor CLI, and Cursor IDE based on 8 foundation LLMs. The results demonstrate that current frontier CUAs do not adequately cover OS security-centric threats. These capabilities of CUAs reduce dependence on custom malware and deep domain expertise, enabling even inexperienced attackers to mount complex enterprise intrusions, which raises social concern about the responsibility and security of CUAs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-gemini/gemini-cli", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.06888", "title": "M3Retrieve: Benchmarking Multimodal Retrieval for Medicine", "authors": [ "Arkadeep Acharya", "Akash Ghosh", "Pradeepika Verma", "Kitsuchart Pasupa", "Sriparna Saha", "Priti Singh" ], "github": "https://github.com/AkashGhosh/M3Retrieve", "project_page": "", "abstract": "With the increasing use of RetrievalAugmented Generation (RAG), strong retrieval models have become more important than ever. In healthcare, multimodal retrieval models that combine information from both text and images offer major advantages for many downstream tasks such as question answering, cross-modal retrieval, and multimodal summarization, since medical data often includes both formats. However, there is currently no standard benchmark to evaluate how well these models perform in medical settings. To address this gap, we introduce M3Retrieve, a Multimodal Medical Retrieval Benchmark. M3Retrieve, spans 5 domains,16 medical fields, and 4 distinct tasks, with over 1.2 Million text documents and 164K multimodal queries, all collected under approved licenses. We evaluate leading multimodal retrieval models on this benchmark to explore the challenges specific to different medical specialities and to understand their impact on retrieval performance. By releasing M3Retrieve, we aim to enable systematic evaluation, foster model innovation, and accelerate research toward building more capable and reliable multimodal retrieval systems for medical applications. The dataset and the baselines code are available in this github page https://github.com/AkashGhosh/M3Retrieve.", "github_manual": "", "github_regex": "https://github.com/AkashGhosh/M3Retrieve", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2509.24375", "title": "Reinforcement Mid-Training", "authors": [ "Yijun Tian", "Shaoyu Chen", "Zhichao Xu", "Yawei Wang", "Jinhe Bi", "Peng Han", "Wei Wang" ], "github": "", "project_page": "", "abstract": "The development of state-of-the-art large language models is commonly understood as a two-stage process involving pre-training and post-training. We point out the need for an additional intermediate stage called reinforcement mid-training with potential for strong performance gains. In this paper, we formally define the problem and identify three key challenges: (1) inefficient training due to excessive reasoning steps, (2) disregard of the imbalanced token entropy distribution, and (3) underutilization of token information. To address these challenges, we propose RMT, a framework for efficient, adaptive, and unified reinforcement mid-training with various innovative components. In particular, we first introduce a dynamic token budget mechanism that constrains unnecessary reasoning steps and mitigates model overthinking. Next, we design a curriculum-based adaptive sampling method that fosters a progressive learning trajectory from easy to hard tokens. Finally, we present a dual training strategy that combines reinforcement learning with next-token prediction, ensuring targeted learning on key tokens and full exploitation of all token information. Extensive experiments demonstrate the superiority of RMT over state-of-the-art methods, achieving up to +64.91% performance improvement with only 21% of the reasoning length in language modeling. We also show that checkpoints obtained after reinforcement mid-training can benefit the subsequent post-training, yielding up to +18.76% improvement in the mathematical domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-09", "arxiv_id": "2510.07550", "title": "TRAVL: A Recipe for Making Video-Language Models Better Judges of Physics Implausibility", "authors": [ "Saman Motamed", "Minghao Chen", "Luc Van Gool", "Iro Laina" ], "github": "", "project_page": "", "abstract": "Despite impressive visual fidelity, modern video generative models frequently produce sequences that violate intuitive physical laws, such as objects floating, teleporting, or morphing in ways that defy causality. While humans can easily detect such implausibilities, there remains no robust method for quantitatively assessing physical realism in video. In this work, we explore whether Video-Language Models (VLMs) can be trained to serve as reliable judges of physical plausibility. We find that existing VLMs struggle to identify physics violations, exposing fundamental limitations in their temporal and causal reasoning. To address this, we introduce TRAVL, a fine-tuning recipe that combines a balanced training dataset with a trajectory-aware attention module to improve motion encoding and discrimination in VLMs. To evaluate physical reasoning more rigorously, we propose ImplausiBench, a benchmark of 300 videos (150 real, 150 generated) that removes linguistic biases and isolates visual-temporal understanding. Performance is reported both with gold-standard human judgments and stricter LLM-as-judge metrics. Together, TRAVL and ImplausiBench offer a unified framework for probing and improving physical plausibility in multimodal models, shedding light on a challenging and underexplored aspect of visual-temporal understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08558", "title": "Agent Learning via Early Experience", "authors": [ "Kai Zhang", "Xiangchao Chen", "Bo Liu", "Tianci Xue", "Zeyi Liao", "Zhihan Liu", "Xiyao Wang", "Yuting Ning", "Zhaorun Chen", "Xiaohan Fu", "Jian Xie", "Yuxuan Sun", "Boyu Gou", "Qi Qi", "Zihang Meng", "Jianwei Yang", "Ning Zhang", "Xian Li", "Ashish Shah", "Dat Huynh", "Hengduo Li", "Zi Yang", "Sara Cao", "Lawrence Jang", "Shuyan Zhou", "Jiacheng Zhu", "Huan Sun", "Jason Weston", "Yu Su", "Yifan Wu" ], "github": "", "project_page": "", "abstract": "A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tasks. However, training agents from experience data with reinforcement learning remains difficult in many environments, which either lack verifiable rewards (e.g., websites) or require inefficient long-horizon rollouts (e.g., multi-turn tool use). As a result, most current agents rely on supervised fine-tuning on expert data, which is challenging to scale and generalizes poorly. This limitation stems from the nature of expert demonstrations: they capture only a narrow range of scenarios and expose the agent to limited environment diversity. We address this limitation with a middle-ground paradigm we call early experience: interaction data generated by the agent's own actions, where the resulting future states serve as supervision without reward signals. Within this paradigm we study two strategies of using such data: (1) Implicit world modeling, which uses collected states to ground the policy in environment dynamics; and (2) Self-reflection, where the agent learns from its suboptimal actions to improve reasoning and decision-making. We evaluate across eight diverse environments and multiple model families. Our approaches consistently improve effectiveness and out-of-domain generalization, highlighting the value of early experience. Moreover, in environments with verifiable rewards, our results provide promising signals that early experience offers a strong foundation for subsequent reinforcement learning, positioning it as a practical bridge between imitation learning and fully experience-driven agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08565", "title": "NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints", "authors": [ "Changyao Tian", "Hao Li", "Gen Luo", "Xizhou Zhu", "Weijie Su", "Hanming Deng", "Jinguo Zhu", "Jie Shao", "Ziran Zhu", "Yunpeng Liu", "Lewei Lu", "Wenhai Wang", "Hongsheng Li", "Jifeng Dai" ], "github": "", "project_page": "", "abstract": "Compositional training has been the de-facto paradigm in existing Multimodal Large Language Models (MLLMs), where pre-trained vision encoders are connected with pre-trained LLMs through continuous multimodal pre-training. However, the multimodal scaling property of this paradigm remains difficult to explore due to the separated training. In this paper, we focus on the native training of MLLMs in an end-to-end manner and systematically study its design space and scaling property under a practical setting, i.e., data constraint. Through careful study of various choices in MLLM, we obtain the optimal meta-architecture that best balances performance and training cost. After that, we further explore the scaling properties of the native MLLM and indicate the positively correlated scaling relationship between visual encoders and LLMs. Based on these findings, we propose a native MLLM called NaViL, combined with a simple and cost-effective recipe. Experimental results on 14 multimodal benchmarks confirm the competitive performance of NaViL against existing MLLMs. Besides that, our findings and results provide in-depth insights for the future study of native MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08540", "title": "MM-HELIX: Boosting Multimodal Long-Chain Reflective Reasoning with Holistic Platform and Adaptive Hybrid Policy Optimization", "authors": [ "Xiangyu Zhao", "Junming Lin", "Tianhao Liang", "Yifan Zhou", "Wenhao Chai", "Yuzhe Gu", "Weiyun Wang", "Kai Chen", "Gen Luo", "Wenwei Zhang", "Junchi Yan", "Hua Yang", "Haodong Duan", "Xue Yang" ], "github": "", "project_page": "", "abstract": "While current Multimodal Large Language Models (MLLMs) have demonstrated proficiency in reasoning tasks such as mathematics and logic, their capacity for long-chain reflective reasoning, a prerequisite for solving complex real-world problems, remains largely underexplored. In this work, we first conduct an extensive empirical investigation to evaluate this capability. Leveraging a carefully designed data synthesis engine, we construct MM-HELIX, a multimodal benchmark consisting 1,260 samples of 42 challenging synthetic tasks that require iterative thinking and backtracking. Empirical results on this benchmark reveal that existing MLLMs exhibit significant performance deficits in long-chain reflective reasoning. To address this limitation, we generate post-training data and further explore learning paradigms for exploiting such data. We first develop the Step-Elicited Response Generation pipeline to create MM-HELIX-100K, a large-scale dataset of 100k high-quality, reflective reasoning traces for instruction-tuning stage. Given that standard Reinforcement Learning fails on complex tasks due to sparse reward signals and catastrophic forgetting after Supervised Fine-Tuning, we propose Adaptive Hybrid Policy Optimization (AHPO), a novel training strategy that dynamically unifies offline supervision and online optimization into a single stage. This strategy enables the model to learn from expert data when rewards are sparse and conduct independent exploration once proficient. When applied to the Qwen2.5-VL-7B baseline, our method achieves a +18.6\\% accuracy improvement on MM-HELIX benchmark and demonstrates strong generalization with a +5.7\\% average performance gain on general mathematic and logic tasks. Our work demonstrate that reflective reasoning in MLLMs can be effectively learned and generalized, paving the way for developing more capable MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08483", "title": "DeepPrune: Parallel Scaling without Inter-trace Redundancy", "authors": [ "Shangqing Tu", "Yaxuan Li", "Yushi Bai", "Lei Hou", "Juanzi Li" ], "github": "", "project_page": "", "abstract": "Parallel scaling has emerged as a powerful paradigm to enhance reasoning capabilities in large language models (LLMs) by generating multiple Chain-of-Thought (CoT) traces simultaneously. However, this approach introduces significant computational inefficiency due to inter-trace redundancy -- our analysis reveals that over 80% of parallel reasoning traces yield identical final answers, representing substantial wasted computation. To address this critical efficiency bottleneck, we propose DeepPrune, a novel framework that enables efficient parallel scaling through dynamic pruning. Our method features a specialized judge model trained with focal loss and oversampling techniques to accurately predict answer equivalence from partial reasoning traces which realizes 0.87 AUROC on equivalence prediction, combined with an online greedy clustering algorithm that dynamically prunes redundant paths while preserving answer diversity. Comprehensive evaluations across three challenging benchmarks (AIME 2024, AIME 2025, and GPQA) and multiple reasoning models demonstrate that DeepPrune achieves remarkable token reduction by over 80% compared to conventional consensus sampling on most cases, while maintaining competitive accuracy within 3 percentage points. Our work establishes a new standard for efficient parallel reasoning, making high-performance reasoning more efficient. Our code and data are here: https://deepprune.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.03663", "title": "UNIDOC-BENCH: A Unified Benchmark for Document-Centric Multimodal RAG", "authors": [ "Xiangyu Peng", "Cab Qin", "Zeyuan Chen", "Ran Xu", "Caiming Xiong", "Chien-Sheng Wu" ], "github": "", "project_page": "", "abstract": "Multimodal retrieval-augmented generation (MM-RAG) is a key approach for applying large language models (LLMs) and agents to real-world knowledge bases, yet current evaluations are fragmented, focusing on either text or images in isolation or on simplified multimodal setups that fail to capture document-centric multimodal use cases. In this paper, we introduce UniDoc-Bench, the first large-scale, realistic benchmark for MM-RAG built from 70k real-world PDF pages across eight domains. Our pipeline extracts and links evidence from text, tables, and figures, then generates 1,600 multimodal QA pairs spanning factual retrieval, comparison, summarization, and logical reasoning queries. To ensure reliability, 20% of QA pairs are validated by multiple annotators and expert adjudication. UniDoc-Bench supports apples-to-apples comparison across four paradigms: (1) text-only, (2) image-only, (3) multimodal text-image fusion, and (4) multimodal joint retrieval -- under a unified protocol with standardized candidate pools, prompts, and evaluation metrics. Our experiments show that multimodal text-image fusion RAG systems consistently outperform both unimodal and jointly multimodal embedding-based retrieval, indicating that neither text nor images alone are sufficient and that current multimodal embeddings remain inadequate. Beyond benchmarking, our analysis reveals when and how visual context complements textual evidence, uncovers systematic failure modes, and offers actionable guidance for developing more robust MM-RAG pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08308", "title": "First Try Matters: Revisiting the Role of Reflection in Reasoning Models", "authors": [ "Liwei Kang", "Yue Deng", "Yao Xiao", "Zhanfeng Mo", "Wee Sun Lee", "Lidong Bing" ], "github": "https://github.com/Olafyii/first-try-matters", "project_page": "", "abstract": "Large language models have recently demonstrated significant gains in reasoning ability, often attributed to their capacity to generate longer chains of thought and engage in reflective reasoning. However, the contribution of reflections to performance improvement remains unclear. In this paper, we systematically analyze the rollouts of eight reasoning models on five mathematical datasets. We focus on reflective behaviours where the model has already produced an answer but continues reflecting before finalizing its output. Our analysis reveals that reflections are predominantly confirmatory and rarely alter the model's initial answer, a pattern consistent across models and datasets. To understand the role of reflections in training, we construct supervised fine-tuning (SFT) datasets with varying amounts of reflection steps. We observe that training models on rollouts with more reflection steps primarily enhances first-answer correctness rather than the ability to correct initially wrong answers through reflections. This motivates us to propose a question-aware early-stopping method that enhances inference-time token efficiency by stopping the reasoning process once a few plausible candidate answers are generated, thereby reducing unnecessary reflection steps. Motivated by this, we further propose to dynamically truncate the reflections after a candidate answer has appeared during generation, which reduces reasoning tokens by 24.5% across five mathematical datasets, within a 2.9% drop in accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Olafyii/first-try-matters", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08555", "title": "VideoCanvas: Unified Video Completion from Arbitrary Spatiotemporal Patches via In-Context Conditioning", "authors": [ "Minghong Cai", "Qiulin Wang", "Zongli Ye", "Wenze Liu", "Quande Liu", "Weicai Ye", "Xintao Wang", "Pengfei Wan", "Kun Gai", "Xiangyu Yue" ], "github": "", "project_page": "", "abstract": "We introduce the task of arbitrary spatio-temporal video completion, where a video is generated from arbitrary, user-specified patches placed at any spatial location and timestamp, akin to painting on a video canvas. This flexible formulation naturally unifies many existing controllable video generation tasks--including first-frame image-to-video, inpainting, extension, and interpolation--under a single, cohesive paradigm. Realizing this vision, however, faces a fundamental obstacle in modern latent video diffusion models: the temporal ambiguity introduced by causal VAEs, where multiple pixel frames are compressed into a single latent representation, making precise frame-level conditioning structurally difficult. We address this challenge with VideoCanvas, a novel framework that adapts the In-Context Conditioning (ICC) paradigm to this fine-grained control task with zero new parameters. We propose a hybrid conditioning strategy that decouples spatial and temporal control: spatial placement is handled via zero-padding, while temporal alignment is achieved through Temporal RoPE Interpolation, which assigns each condition a continuous fractional position within the latent sequence. This resolves the VAE's temporal ambiguity and enables pixel-frame-aware control on a frozen backbone. To evaluate this new capability, we develop VideoCanvasBench, the first benchmark for arbitrary spatio-temporal video completion, covering both intra-scene fidelity and inter-scene creativity. Experiments demonstrate that VideoCanvas significantly outperforms existing conditioning paradigms, establishing a new state of the art in flexible and unified video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08431", "title": "Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency", "authors": [ "Kaiwen Zheng", "Yuji Wang", "Qianli Ma", "Huayu Chen", "Jintao Zhang", "Yogesh Balaji", "Jianfei Chen", "Ming-Yu Liu", "Jun Zhu", "Qinsheng Zhang" ], "github": "https://github.com/NVlabs/rcm", "project_page": "", "abstract": "This work represents the first effort to scale up continuous-time consistency distillation to general application-level image and video diffusion models. Although continuous-time consistency model (sCM) is theoretically principled and empirically powerful for accelerating academic-scale diffusion, its applicability to large-scale text-to-image and video tasks remains unclear due to infrastructure challenges in Jacobian-vector product (JVP) computation and the limitations of standard evaluation benchmarks. We first develop a parallelism-compatible FlashAttention-2 JVP kernel, enabling sCM training on models with over 10 billion parameters and high-dimensional video tasks. Our investigation reveals fundamental quality limitations of sCM in fine-detail generation, which we attribute to error accumulation and the \"mode-covering\" nature of its forward-divergence objective. To remedy this, we propose the score-regularized continuous-time consistency model (rCM), which incorporates score distillation as a long-skip regularizer. This integration complements sCM with the \"mode-seeking\" reverse divergence, effectively improving visual quality while maintaining high generation diversity. Validated on large-scale models (Cosmos-Predict2, Wan2.1) up to 14B parameters and 5-second videos, rCM matches or surpasses the state-of-the-art distillation method DMD2 on quality metrics while offering notable advantages in diversity, all without GAN tuning or extensive hyperparameter searches. The distilled models generate high-fidelity samples in only 1sim4 steps, accelerating diffusion sampling by 15timessim50times. These results position rCM as a practical and theoretically grounded framework for advancing large-scale diffusion distillation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/rcm", "github_api_added_by": "auto" }, { "date": "2025-10-10", "arxiv_id": "2510.07429", "title": "Learning to Route LLMs from Bandit Feedback: One Policy, Many Trade-offs", "authors": [ "Wang Wei", "Tiankai Yang", "Hongjie Chen", "Yue Zhao", "Franck Dernoncourt", "Ryan A. Rossi", "Hoda Eldardiry" ], "github": "", "project_page": "", "abstract": "Efficient use of large language models (LLMs) is critical for deployment at scale: without adaptive routing, systems either overpay for strong models or risk poor performance from weaker ones. Selecting the right LLM for each query is fundamentally an online decision problem: models differ in strengths, prices fluctuate, and users value accuracy and cost differently. Yet most routers are trained offline with labels for all candidate models, an assumption that breaks in deployment, where only the outcome of the chosen model is observed. We bridge this gap with BaRP, a Bandit-feedback Routing with Preferences approach that trains under the same partial-feedback restriction as deployment, while supporting preference-tunable inference: operators can dial the performance/cost trade-off at test time without retraining. Framed as a contextual bandit over prompt features and a user preference vector, our method simulates an online feedback setting during training and adapts its routing decisions to each new prompt, rather than depending on full-information offline supervision. Comprehensive experiments show that our method consistently outperforms strong offline routers by at least 12.46% and the largest LLM by at least 2.45%, and generalizes robustly for unseen tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.03222", "title": "Low-probability Tokens Sustain Exploration in Reinforcement Learning with Verifiable Reward", "authors": [ "Guanhua Huang", "Tingqiang Xu", "Mingze Wang", "Qi Yi", "Xue Gong", "Siheng Li", "Ruibin Xiong", "Kejiao Li", "Yuhao Jiang", "Bo Zhou" ], "github": "https://github.com/CarlanLark/Lp-Reg", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has propelled Large Language Models in complex reasoning, yet its scalability is often hindered by a training bottleneck where performance plateaus as policy entropy collapses, signaling a loss of exploration. Previous methods typically address this by maintaining high policy entropy, yet the precise mechanisms that govern meaningful exploration have remained underexplored. Our analysis suggests that an unselective focus on entropy risks amplifying irrelevant tokens and destabilizing training. This paper investigates the exploration dynamics within RLVR and identifies a key issue: the gradual elimination of valuable low-probability exploratory tokens, which we term \\textit{reasoning sparks}. We find that while abundant in pre-trained models, these sparks are systematically extinguished during RLVR due to over-penalization, leading to a degeneracy in exploration. To address this, we introduce Low-probability Regularization (Lp-Reg). Its core mechanism regularizes the policy towards a heuristic proxy distribution. This proxy is constructed by filtering out presumed noise tokens and re-normalizing the distribution over the remaining candidates. The result is a less-noisy proxy where the probability of reasoning sparks is amplified, which then serves as a soft regularization target to shield these valuable tokens from elimination via KL divergence. Experiments show that Lp-Reg enables stable on-policy training for around 1,000 steps, a regime where baseline entropy-control methods collapse. This sustained exploration leads to state-of-the-art performance, achieving a 60.17% average accuracy on five math benchmarks, an improvement of 2.66% over prior methods. Code is available at https://github.com/CarlanLark/Lp-Reg.", "github_manual": "", "github_regex": "https://github.com/CarlanLark/Lp-Reg", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.07172", "title": "NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents", "authors": [ "Tianshi Zheng", "Kelvin Kiu-Wai Tam", "Newt Hue-Nam K. Nguyen", "Baixuan Xu", "Zhaowei Wang", "Jiayang Cheng", "Hong Ting Tsang", "Weiqi Wang", "Jiaxin Bai", "Tianqing Fang", "Yangqiu Song", "Ginny Y. Wong", "Simon See" ], "github": "", "project_page": "", "abstract": "Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer from a fundamental methodological trilemma, forcing a trade-off between scientific relevance, scalability, and resistance to memorization. Furthermore, they oversimplify discovery as static function fitting, failing to capture the authentic scientific process of uncovering embedded laws through the interactive exploration of complex model systems. To address these critical gaps, we introduce NewtonBench, a benchmark comprising 324 scientific law discovery tasks across 12 physics domains. Our design mitigates the evaluation trilemma by using metaphysical shifts - systematic alterations of canonical laws - to generate a vast suite of problems that are scalable, scientifically relevant, and memorization-resistant. Moreover, we elevate the evaluation from static function fitting to interactive model discovery, requiring agents to experimentally probe simulated complex systems to uncover hidden principles. Our extensive experiment reveals a clear but fragile capability for discovery in frontier LLMs: this ability degrades precipitously with increasing system complexity and exhibits extreme sensitivity to observational noise. Notably, we uncover a paradoxical effect of tool assistance: providing a code interpreter can hinder more capable models by inducing a premature shift from exploration to exploitation, causing them to satisfice on suboptimal solutions. These results demonstrate that robust, generalizable discovery in complex, interactive environments remains the core challenge. By providing a scalable, robust, and scientifically authentic testbed, NewtonBench offers a crucial tool for measuring true progress and guiding the development of next-generation AI agents capable of genuine scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.06915", "title": "LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling", "authors": [ "Zecheng Tang", "Baibei Ji", "Quantong Qiu", "Haitian Wang", "Xiaobo Liang", "Juntao Li", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Reward model (RM) plays a pivotal role in aligning large language model (LLM) with human preferences. As real-world applications increasingly involve long history trajectories, e.g., LLM agent, it becomes indispensable to evaluate whether a model's responses are not only high-quality but also grounded in and consistent with the provided context. Yet, current RMs remain confined to short-context settings and primarily focus on response-level attributes (e.g., safety or helpfulness), while largely neglecting the critical dimension of long context-response consistency. In this work, we introduce Long-RewardBench, a benchmark specifically designed for long-context RM evaluation, featuring both Pairwise Comparison and Best-of-N tasks. Our preliminary study reveals that even state-of-the-art generative RMs exhibit significant fragility in long-context scenarios, failing to maintain context-aware preference judgments. Motivated by the analysis of failure patterns observed in model outputs, we propose a general multi-stage training strategy that effectively scales arbitrary models into robust Long-context RMs (LongRMs). Experiments show that our approach not only substantially improves performance on long-context evaluation but also preserves strong short-context capability. Notably, our 8B LongRM outperforms much larger 70B-scale baselines and matches the performance of the proprietary Gemini 2.5 Pro model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08559", "title": "SciVideoBench: Benchmarking Scientific Video Reasoning in Large Multimodal Models", "authors": [ "Andong Deng", "Taojiannan Yang", "Shoubin Yu", "Lincoln Spencer", "Mohit Bansal", "Chen Chen", "Serena Yeung-Levy", "Xiaohan Wang" ], "github": "https://github.com/dengandong/SciVideoBench", "project_page": "", "abstract": "Large Multimodal Models (LMMs) have achieved remarkable progress across various capabilities; however, complex video reasoning in the scientific domain remains a significant and challenging frontier. Current video benchmarks predominantly target general scenarios where perception/recognition is heavily relied on, while with relatively simple reasoning tasks, leading to saturation and thus failing to effectively evaluate advanced multimodal cognitive skills. To address this critical gap, we introduce SciVideoBench, a rigorous benchmark specifically designed to assess advanced video reasoning in scientific contexts. SciVideoBench consists of 1,000 carefully crafted multiple-choice questions derived from cutting-edge scientific experimental videos spanning over 25 specialized academic subjects and verified by a semi-automatic system. Each question demands sophisticated domain-specific knowledge, precise spatiotemporal perception, and intricate logical reasoning, effectively challenging models' higher-order cognitive abilities. Our evaluation highlights significant performance deficits in state-of-the-art proprietary and open-source LMMs, including Gemini 2.5 Pro and Qwen2.5-VL, indicating substantial room for advancement in video reasoning capabilities. Detailed analyses of critical factors such as reasoning complexity and visual grounding provide valuable insights and clear direction for future developments in LMMs, driving the evolution of truly capable multimodal AI co-scientists. We hope SciVideoBench could fit the interests of the community and help to push the boundary of cutting-edge AI for border science.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dengandong/SciVideoBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08549", "title": "Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints", "authors": [ "Zilin Kang", "Chonghua Liao", "Tingqiang Xu", "Huazhe Xu" ], "github": "", "project_page": "", "abstract": "We propose ERA, a new paradigm that constrains the sampling entropy above given thresholds by applying specially designed activations to the outputs of models. Our approach demonstrates broad effectiveness across different domains: 1) for large language models(LLMs), boosting the AIME 2025 score for Qwen2.5-Math-7B by 37.4%; 2) for continuous control reinforcement learning agents, improving performance by more than 30% over strong baselines such as SAC on the challenging HumanoidBench; 3) for image classification, enhancing ImageNet top-1 accuracy by 0.69% for ResNet-50. These gains are achieved with a computational overhead of less than 7%. Our work validates output activation as a powerful tool for entropy control, opening a new direction for designing simpler and more robust algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08547", "title": "R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized Manipulation", "authors": [ "Xiuwei Xu", "Angyuan Ma", "Hankun Li", "Bingyao Yu", "Zheng Zhu", "Jie Zhou", "Jiwen Lu" ], "github": "", "project_page": "", "abstract": "Towards the aim of generalized robotic manipulation, spatial generalization is the most fundamental capability that requires the policy to work robustly under different spatial distribution of objects, environment and agent itself. To achieve this, substantial human demonstrations need to be collected to cover different spatial configurations for training a generalized visuomotor policy via imitation learning. Prior works explore a promising direction that leverages data generation to acquire abundant spatially diverse data from minimal source demonstrations. However, most approaches face significant sim-to-real gap and are often limited to constrained settings, such as fixed-base scenarios and predefined camera viewpoints. In this paper, we propose a real-to-real 3D data generation framework (R2RGen) that directly augments the pointcloud observation-action pairs to generate real-world data. R2RGen is simulator- and rendering-free, thus being efficient and plug-and-play. Specifically, given a single source demonstration, we introduce an annotation mechanism for fine-grained parsing of scene and trajectory. A group-wise augmentation strategy is proposed to handle complex multi-object compositions and diverse task constraints. We further present camera-aware processing to align the distribution of generated data with real-world 3D sensor. Empirically, R2RGen substantially enhances data efficiency on extensive experiments and demonstrates strong potential for scaling and application on mobile manipulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08377", "title": "UniVideo: Unified Understanding, Generation, and Editing for Videos", "authors": [ "Cong Wei", "Quande Liu", "Zixuan Ye", "Qiulin Wang", "Xintao Wang", "Pengfei Wan", "Kun Gai", "Wenhu Chen" ], "github": "https://github.com/KlingTeam/UniVideo", "project_page": "", "abstract": "Unified multimodal models have shown promising results in multimodal content generation and editing but remain largely limited to the image domain. In this work, we present UniVideo, a versatile framework that extends unified modeling to the video domain. UniVideo adopts a dual-stream design, combining a Multimodal Large Language Model (MLLM) for instruction understanding with a Multimodal DiT (MMDiT) for video generation. This design enables accurate interpretation of complex multimodal instructions while preserving visual consistency. Built on this architecture, UniVideo unifies diverse video generation and editing tasks under a single multimodal instruction paradigm and is jointly trained across them. Extensive experiments demonstrate that UniVideo matches or surpasses state-of-the-art task-specific baselines in text/image-to-video generation, in-context video generation and in-context video editing. Notably, the unified design of UniVideo enables two forms of generalization. First, UniVideo supports task composition, such as combining editing with style transfer, by integrating multiple capabilities within a single instruction. Second, even without explicit training on free-form video editing, UniVideo transfers its editing capability from large-scale image editing data to this setting, handling unseen instructions such as green-screening characters or changing materials within a video. Beyond these core capabilities, UniVideo also supports visual-prompt-based video generation, where the MLLM interprets visual prompts and guides the MMDiT during synthesis. To foster future research, we will release our model and code.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KlingTeam/UniVideo", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08191", "title": "Training-Free Group Relative Policy Optimization", "authors": [ "Yuzheng Cai", "Siqi Cai", "Yuchen Shi", "Zihan Xu", "Lichao Chen", "Yulei Qin", "Xiaoyu Tan", "Gang Li", "Zongyi Li", "Haojia Lin", "Yong Mao", "Ke Li", "Xing Sun" ], "github": "https://github.com/MiroMindAI/MiroThinker", "project_page": "", "abstract": "Recent advances in Large Language Model (LLM) agents have demonstrated their promising general capabilities. However, their performance in specialized real-world domains often degrades due to challenges in effectively integrating external tools and specific prompting strategies. While methods like agentic reinforcement learning have been proposed to address this, they typically rely on costly parameter updates, for example, through a process that uses Supervised Fine-Tuning (SFT) followed by a Reinforcement Learning (RL) phase with Group Relative Policy Optimization (GRPO) to alter the output distribution. However, we argue that LLMs can achieve a similar effect on the output distribution by learning experiential knowledge as a token prior, which is a far more lightweight approach that not only addresses practical data scarcity but also avoids the common issue of overfitting. To this end, we propose Training-Free Group Relative Policy Optimization (Training-Free GRPO), a cost-effective solution that enhances LLM agent performance without any parameter updates. Our method leverages the group relative semantic advantage instead of numerical ones within each group of rollouts, iteratively distilling high-quality experiential knowledge during multi-epoch learning on a minimal ground-truth data. Such knowledge serves as the learned token prior, which is seamlessly integrated during LLM API calls to guide model behavior. Experiments on mathematical reasoning and web searching tasks demonstrate that Training-Free GRPO, when applied to DeepSeek-V3.1-Terminus, significantly improves out-of-domain performance. With just a few dozen training samples, Training-Free GRPO outperforms fine-tuned small LLMs with marginal training data and cost.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiroMindAI/MiroThinker", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08008", "title": "Recycling Pretrained Checkpoints: Orthogonal Growth of Mixture-of-Experts for Efficient Large Language Model Pre-Training", "authors": [ "Ruizhe Wang", "Yucheng Ding", "Xiao Liu", "Yaoxiang Wang", "Peng Cheng", "Baining Guo", "Zhengjun Zha", "Yeyun Gong" ], "github": "", "project_page": "", "abstract": "The rapidly increasing computational cost of pretraining Large Language Models necessitates more efficient approaches. Numerous computational costs have been invested in existing well-trained checkpoints, but many of them remain underutilized due to engineering constraints or limited model capacity. To efficiently reuse this \"sunk\" cost, we propose to recycle pretrained checkpoints by expanding their parameter counts and continuing training. We propose orthogonal growth method well-suited for converged Mixture-of-Experts model: interpositional layer copying for depth growth and expert duplication with injected noise for width growth. To determine the optimal timing for such growth across checkpoints sequences, we perform comprehensive scaling experiments revealing that the final accuracy has a strong positive correlation with the amount of sunk cost, indicating that greater prior investment leads to better performance. We scale our approach to models with 70B parameters and over 1T training tokens, achieving 10.66% accuracy gain over training from scratch under the same additional compute budget. Our checkpoint recycling approach establishes a foundation for economically efficient large language model pretraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08143", "title": "UniMMVSR: A Unified Multi-Modal Framework for Cascaded Video Super-Resolution", "authors": [ "Shian Du", "Menghan Xia", "Chang Liu", "Quande Liu", "Xintao Wang", "Pengfei Wan", "Xiangyang Ji" ], "github": "", "project_page": "", "abstract": "Cascaded video super-resolution has emerged as a promising technique for decoupling the computational burden associated with generating high-resolution videos using large foundation models. Existing studies, however, are largely confined to text-to-video tasks and fail to leverage additional generative conditions beyond text, which are crucial for ensuring fidelity in multi-modal video generation. We address this limitation by presenting UniMMVSR, the first unified generative video super-resolution framework to incorporate hybrid-modal conditions, including text, images, and videos. We conduct a comprehensive exploration of condition injection strategies, training schemes, and data mixture techniques within a latent video diffusion model. A key challenge was designing distinct data construction and condition utilization methods to enable the model to precisely utilize all condition types, given their varied correlations with the target video. Our experiments demonstrate that UniMMVSR significantly outperforms existing methods, producing videos with superior detail and a higher degree of conformity to multi-modal conditions. We also validate the feasibility of combining UniMMVSR with a base model to achieve multi-modal guided generation of 4K video, a feat previously unattainable with existing techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.07958", "title": "A^2Search: Ambiguity-Aware Question Answering with Reinforcement Learning", "authors": [ "Fengji Zhang", "Xinyao Niu", "Chengyang Ying", "Guancheng Lin", "Zhongkai Hao", "Zhou Fan", "Chengen Huang", "Jacky Keung", "Bei Chen", "Junyang Lin" ], "github": "https://github.com/zfj1998/A2Search", "project_page": "", "abstract": "Recent advances in Large Language Models (LLMs) and Reinforcement Learning (RL) have led to strong performance in open-domain question answering (QA). However, existing models still struggle with questions that admit multiple valid answers. Standard QA benchmarks, which typically assume a single gold answer, overlook this reality and thus produce inappropriate training signals. Existing attempts to handle ambiguity often rely on costly manual annotation, which is difficult to scale to multi-hop datasets such as HotpotQA and MuSiQue. In this paper, we present A^2Search, an annotation-free, end-to-end training framework to recognize and handle ambiguity. At its core is an automated pipeline that detects ambiguous questions and gathers alternative answers via trajectory sampling and evidence verification. The model is then optimized with RL using a carefully designed AnsF1 reward, which naturally accommodates multiple answers. Experiments on eight open-domain QA benchmarks demonstrate that A^2Search achieves new state-of-the-art performance. With only a single rollout, A^2Search-7B yields an average AnsF1@1 score of 48.4% across four multi-hop benchmarks, outperforming all strong baselines, including the substantially larger ReSearch-32B (46.2%). Extensive analyses further show that A^2Search resolves ambiguity and generalizes across benchmarks, highlighting that embracing ambiguity is essential for building more reliable QA systems. Our code, data, and model weights can be found at https://github.com/zfj1998/A2Search", "github_manual": "", "github_regex": "https://github.com/zfj1998/A2Search", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.07242", "title": "Hybrid Reinforcement: When Reward Is Sparse, It's Better to Be Dense", "authors": [ "Leitian Tao", "Ilia Kulikov", "Swarnadeep Saha", "Tianlu Wang", "Jing Xu", "Yixuan Li", "Jason E Weston", "Ping Yu" ], "github": "", "project_page": "", "abstract": "Post-training for reasoning of large language models (LLMs) increasingly relies on verifiable rewards: deterministic checkers that provide 0-1 correctness signals. While reliable, such binary feedback is brittle--many tasks admit partially correct or alternative answers that verifiers under-credit, and the resulting all-or-nothing supervision limits learning. Reward models offer richer, continuous feedback, which can serve as a complementary supervisory signal to verifiers. We introduce HERO (Hybrid Ensemble Reward Optimization), a reinforcement learning framework that integrates verifier signals with reward-model scores in a structured way. HERO employs stratified normalization to bound reward-model scores within verifier-defined groups, preserving correctness while refining quality distinctions, and variance-aware weighting to emphasize challenging prompts where dense signals matter most. Across diverse mathematical reasoning benchmarks, HERO consistently outperforms RM-only and verifier-only baselines, with strong gains on both verifiable and hard-to-verify tasks. Our results show that hybrid reward design retains the stability of verifiers while leveraging the nuance of reward models to advance reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08240", "title": "The Alignment Waltz: Jointly Training Agents to Collaborate for Safety", "authors": [ "Jingyu Zhang", "Haozhu Wang", "Eric Michael Smith", "Sid Wang", "Amr Sharaf", "Mahesh Pasupuleti", "Benjamin Van Durme", "Daniel Khashabi", "Jason Weston", "Hongyuan Zhan" ], "github": "https://github.com/tatsu-lab/alpaca_eval", "project_page": "", "abstract": "Harnessing the power of LLMs requires a delicate dance between being helpful and harmless. This creates a fundamental tension between two competing challenges: vulnerability to adversarial attacks that elicit unsafe content, and a tendency for overrefusal on benign but sensitive prompts. Current approaches often navigate this dance with safeguard models that completely reject any content that contains unsafe portions. This approach cuts the music entirely-it may exacerbate overrefusals and fails to provide nuanced guidance for queries it refuses. To teach models a more coordinated choreography, we propose WaltzRL, a novel multi-agent reinforcement learning framework that formulates safety alignment as a collaborative, positive-sum game. WaltzRL jointly trains a conversation agent and a feedback agent, where the latter is incentivized to provide useful suggestions that improve the safety and helpfulness of the conversation agent's responses. At the core of WaltzRL is a Dynamic Improvement Reward (DIR) that evolves over time based on how well the conversation agent incorporates the feedback. At inference time, unsafe or overrefusing responses from the conversation agent are improved rather than discarded. The feedback agent is deployed together with the conversation agent and only engages adaptively when needed, preserving helpfulness and low latency on safe queries. Our experiments, conducted across five diverse datasets, demonstrate that WaltzRL significantly reduces both unsafe responses (e.g., from 39.0% to 4.6% on WildJailbreak) and overrefusals (from 45.3% to 9.9% on OR-Bench) compared to various baselines. By enabling the conversation and feedback agents to co-evolve and adaptively apply feedback, WaltzRL enhances LLM safety without degrading general capabilities, thereby advancing the Pareto front between helpfulness and harmlessness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tatsu-lab/alpaca_eval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08551", "title": "ARTDECO: Towards Efficient and High-Fidelity On-the-Fly 3D Reconstruction with Structured Scene Representation", "authors": [ "Guanghao Li", "Kerui Ren", "Linning Xu", "Zhewen Zheng", "Changjian Jiang", "Xin Gao", "Bo Dai", "Jian Pu", "Mulin Yu", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "On-the-fly 3D reconstruction from monocular image sequences is a long-standing challenge in computer vision, critical for applications such as real-to-sim, AR/VR, and robotics. Existing methods face a major tradeoff: per-scene optimization yields high fidelity but is computationally expensive, whereas feed-forward foundation models enable real-time inference but struggle with accuracy and robustness. In this work, we propose ARTDECO, a unified framework that combines the efficiency of feed-forward models with the reliability of SLAM-based pipelines. ARTDECO uses 3D foundation models for pose estimation and point prediction, coupled with a Gaussian decoder that transforms multi-scale features into structured 3D Gaussians. To sustain both fidelity and efficiency at scale, we design a hierarchical Gaussian representation with a LoD-aware rendering strategy, which improves rendering fidelity while reducing redundancy. Experiments on eight diverse indoor and outdoor benchmarks show that ARTDECO delivers interactive performance comparable to SLAM, robustness similar to feed-forward systems, and reconstruction quality close to per-scene optimization, providing a practical path toward on-the-fly digitization of real-world environments with both accurate geometry and high visual fidelity. Explore more demos on our project page: https://city-super.github.io/artdeco/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08276", "title": "Beyond Turn Limits: Training Deep Search Agents with Dynamic Context Window", "authors": [ "Qiaoyu Tang", "Hao Xiang", "Le Yu", "Bowen Yu", "Yaojie Lu", "Xianpei Han", "Le Sun", "WenJuan Zhang", "Pengbo Wang", "Shixuan Liu", "Zhenru Zhang", "Jianhong Tu", "Hongyu Lin", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "While recent advances in reasoning models have demonstrated cognitive behaviors through reinforcement learning, existing approaches struggle to invoke deep reasoning capabilities in multi-turn agents with long-horizon interactions. We propose DeepMiner, a novel framework that elicits such abilities by introducing high-difficulty training tasks and dynamic context window. DeepMiner presents a reverse construction method to generate complex but verifiable question-answer pairs from authentic web sources, which ensures the challenge and reliability of training data while injecting cognitive capabilities into multi-turn reasoning scenarios. We further design an elegant yet effective dynamic context management strategy for both training and inference, utilizing sliding window mechanisms while eliminating the dependency on external summarization models, thereby efficiently empowering the model to handle continuously expanding long-horizon contexts. Through reinforcement learning on Qwen3-32B, we develop DeepMiner-32B, which achieves substantial performance improvements across multiple search agent benchmarks. DeepMiner attains 33.5% accuracy on BrowseComp-en, surpassing the previous best open-source agent by almost 20 percentage points, and demonstrates consistent improvements on BrowseComp-zh, XBench-DeepSearch, and GAIA. Notably, our dynamic context management enables sustained interactions of nearly 100 turns within standard 32k context length, effectively addressing the context limitations that constrain existing multi-turn interaction systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08556", "title": "DexNDM: Closing the Reality Gap for Dexterous In-Hand Rotation via Joint-Wise Neural Dynamics Model", "authors": [ "Xueyi Liu", "He Wang", "Li Yi" ], "github": "", "project_page": "", "abstract": "Achieving generalized in-hand object rotation remains a significant challenge in robotics, largely due to the difficulty of transferring policies from simulation to the real world. The complex, contact-rich dynamics of dexterous manipulation create a \"reality gap\" that has limited prior work to constrained scenarios involving simple geometries, limited object sizes and aspect ratios, constrained wrist poses, or customized hands. We address this sim-to-real challenge with a novel framework that enables a single policy, trained in simulation, to generalize to a wide variety of objects and conditions in the real world. The core of our method is a joint-wise dynamics model that learns to bridge the reality gap by effectively fitting limited amount of real-world collected data and then adapting the sim policy's actions accordingly. The model is highly data-efficient and generalizable across different whole-hand interaction distributions by factorizing dynamics across joints, compressing system-wide influences into low-dimensional variables, and learning each joint's evolution from its own dynamic profile, implicitly capturing these net effects. We pair this with a fully autonomous data collection strategy that gathers diverse, real-world interaction data with minimal human intervention. Our complete pipeline demonstrates unprecedented generality: a single policy successfully rotates challenging objects with complex shapes (e.g., animals), high aspect ratios (up to 5.33), and small sizes, all while handling diverse wrist orientations and rotation axes. Comprehensive real-world evaluations and a teleoperation application for complex tasks validate the effectiveness and robustness of our approach. Website: https://meowuu7.github.io/DexNDM/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08425", "title": "Reinforcing Diffusion Models by Direct Group Preference Optimization", "authors": [ "Yihong Luo", "Tianyang Hu", "Jing Tang" ], "github": "https://github.com/Luo-Yihong/DGPO", "project_page": "", "abstract": "While reinforcement learning methods such as Group Relative Preference Optimization (GRPO) have significantly enhanced Large Language Models, adapting them to diffusion models remains challenging. In particular, GRPO demands a stochastic policy, yet the most cost-effective diffusion samplers are based on deterministic ODEs. Recent work addresses this issue by using inefficient SDE-based samplers to induce stochasticity, but this reliance on model-agnostic Gaussian noise leads to slow convergence. To resolve this conflict, we propose Direct Group Preference Optimization (DGPO), a new online RL algorithm that dispenses with the policy-gradient framework entirely. DGPO learns directly from group-level preferences, which utilize relative information of samples within groups. This design eliminates the need for inefficient stochastic policies, unlocking the use of efficient deterministic ODE samplers and faster training. Extensive results show that DGPO trains around 20 times faster than existing state-of-the-art methods and achieves superior performance on both in-domain and out-of-domain reward metrics. Code is available at https://github.com/Luo-Yihong/DGPO.", "github_manual": "", "github_regex": "https://github.com/Luo-Yihong/DGPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.07499", "title": "When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs", "authors": [ "Soyeong Jeong", "Taehee Jung", "Sung Ju Hwang", "Joo-Kyung Kim", "Dongyeop Kang" ], "github": "https://github.com/starsuzi/ToTAL", "project_page": "", "abstract": "Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge-intensive multi-hop reasoning by integrating large sets of retrieved documents or, in some cases, directly all necessary information. However, simply feeding more documents into the context window fails to capture how evidence should be connected. We address this gap with thought templates, which recast reasoning as reusable thought caches, derived from prior problem solving traces, structuring how evidence is combined and guiding multi-hop inference with factual documents. To keep these templates effective, we propose an update strategy that iteratively refines templates derived from training data through natural-language feedback. Across diverse benchmarks and LCLM families, our approach delivers consistent gains over strong baselines in both retrieval-based and retrieval-free settings. Furthermore, we show that optimized templates can be distilled into smaller open-source models, demonstrating its broad applicability and transparent reasoning reuse. We refer to our framework as Thought Template Augmented LCLMs (ToTAL).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/starsuzi/ToTAL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08485", "title": "InstructX: Towards Unified Visual Editing with MLLM Guidance", "authors": [ "Chong Mou", "Qichao Sun", "Yanze Wu", "Pengze Zhang", "Xinghui Li", "Fulong Ye", "Songtao Zhao", "Qian He" ], "github": "https://github.com/MC-E/InstructX", "project_page": "", "abstract": "With recent advances in Multimodal Large Language Models (MLLMs) showing strong visual understanding and reasoning, interest is growing in using them to improve the editing performance of diffusion models. Despite rapid progress, most studies lack an in-depth analysis of MLLM design choices. Moreover, the integration of MLLMs and diffusion models remains an open challenge in some difficult tasks, such as video editing. In this paper, we present InstructX, a unified framework for image and video editing. Specifically, we conduct a comprehensive study on integrating MLLMs and diffusion models for instruction-driven editing across diverse tasks. Building on this study, we analyze the cooperation and distinction between images and videos in unified modeling. (1) We show that training on image data can lead to emergent video editing capabilities without explicit supervision, thereby alleviating the constraints imposed by scarce video training data. (2) By incorporating modality-specific MLLM features, our approach effectively unifies image and video editing tasks within a single model. Extensive experiments demonstrate that our method can handle a broad range of image and video editing tasks and achieves state-of-the-art performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MC-E/InstructX", "github_api_added_by": "auto" }, { "date": "2025-10-10", "arxiv_id": "2510.03259", "title": "Meta-Awareness Enhances Reasoning Models: Self-Alignment Reinforcement Learning", "authors": [ "Yoonjeon Kim", "Doohyuk Jang", "Eunho Yang" ], "github": "https://github.com/akatigre/MASA-RL", "project_page": "", "abstract": "Recent studies on reasoning models explore the meta-awareness of language models, the ability to know how to think by itself. We argue that large reasoning models lack this meta-awareness property by proving severe misalignment between true rollouts and predicted meta information. We posit that aligning meta-prediction with true rollouts will lead to significant performance gains. To verify this hypothesis, we design a training pipeline that boosts Meta-Awareness via Self-Alignment (MASA), and prove that enhanced meta-awareness directly translates to improved accuracy. Unlike existing meta-cognitive reasoning models, our method does not require external training sources but leverages self-generated signals to train meta-awareness. Moreover, our method enables efficient training by i) filtering out zero-variance prompts that are either trivial or unsolvable and ii) cutting off lengthy rollouts when they are unlikely to lead to correct answers. The results are inspiring: our strategy yields significant improvements in both accuracy and training efficiency on in-domain tasks and shows strong generalization to out-of-domain benchmarks. More specifically, our method can speed up GRPO training by over 1.28x to reach the same performance, and achieve a 19.3% gain in accuracy on AIME25, and a 6.2 % average gain over six mathematics benchmarks. Training with meta-cognitive guidance enhances out-of-domain generalization, giving a 3.87 % boost on GPQA-Diamond and a 2.08 % overall accuracy gain across 13 benchmarks spanning logical, scientific, and coding domains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/akatigre/MASA-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2509.23768", "title": "From What to Why: A Multi-Agent System for Evidence-based Chemical Reaction Condition Reasoning", "authors": [ "Cheng Yang", "Jiaxuan Lu", "Haiyuan Wan", "Junchi Yu", "Feiwei Qin" ], "github": "", "project_page": "", "abstract": "The chemical reaction recommendation is to select proper reaction condition parameters for chemical reactions, which is pivotal to accelerating chemical science. With the rapid development of large language models (LLMs), there is growing interest in leveraging their reasoning and planning capabilities for reaction condition recommendation. Despite their success, existing methods rarely explain the rationale behind the recommended reaction conditions, limiting their utility in high-stakes scientific workflows. In this work, we propose ChemMAS, a multi-agent system that reframes condition prediction as an evidence-based reasoning task. ChemMAS decomposes the task into mechanistic grounding, multi-channel recall, constraint-aware agentic debate, and rationale aggregation. Each decision is backed by interpretable justifications grounded in chemical knowledge and retrieved precedents. Experiments show that ChemMAS achieves 20-35% gains over domain-specific baselines and outperforms general-purpose LLMs by 10-15% in Top-1 accuracy, while offering falsifiable, human-trustable rationales, which establishes a new paradigm for explainable AI in scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08211", "title": "LLMs Learn to Deceive Unintentionally: Emergent Misalignment in Dishonesty from Misaligned Samples to Biased Human-AI Interactions", "authors": [ "XuHao Hu", "Peng Wang", "Xiaoya Lu", "Dongrui Liu", "Xuanjing Huang", "Jing Shao" ], "github": "", "project_page": "", "abstract": "Previous research has shown that LLMs finetuned on malicious or incorrect completions within narrow domains (e.g., insecure code or incorrect medical advice) can become broadly misaligned to exhibit harmful behaviors, which is called emergent misalignment. In this work, we investigate whether this phenomenon can extend beyond safety behaviors to a broader spectrum of dishonesty and deception under high-stakes scenarios (e.g., lying under pressure and deceptive behavior). To explore this, we finetune open-sourced LLMs on misaligned completions across diverse domains. Experimental results demonstrate that LLMs show broadly misaligned behavior in dishonesty. Additionally, we further explore this phenomenon in a downstream combined finetuning setting, and find that introducing as little as 1% of misalignment data into a standard downstream task is sufficient to decrease honest behavior over 20%. Furthermore, we consider a more practical human-AI interaction environment where we simulate both benign and biased users to interact with the assistant LLM. Notably, we find that the assistant can be misaligned unintentionally to exacerbate its dishonesty with only 10% biased user population. In summary, we extend the study of emergent misalignment to the domain of dishonesty and deception under high-stakes scenarios, and demonstrate that this risk arises not only through direct finetuning, but also in downstream mixture tasks and practical human-AI interactions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.03279", "title": "MemMamba: Rethinking Memory Patterns in State Space Model", "authors": [ "Youjin Wang", "Yangjingyi Chen", "Jiahao Yan", "Jiaxuan Lu", "Xiao Sun" ], "github": "", "project_page": "", "abstract": "With the explosive growth of data, long-sequence modeling has become increasingly important in tasks such as natural language processing and bioinformatics. However, existing methods face inherent trade-offs between efficiency and memory. Recurrent neural networks suffer from gradient vanishing and explosion, making them hard to scale. Transformers can model global dependencies but are constrained by quadratic complexity. Recently, selective state-space models such as Mamba have demonstrated high efficiency with O(n) time and O(1) recurrent inference, yet their long-range memory decays exponentially. In this work, we conduct mathematical derivations and information-theoretic analysis to systematically uncover the memory decay mechanism of Mamba, answering a fundamental question: what is the nature of Mamba's long-range memory and how does it retain information? To quantify key information loss, we further introduce horizontal-vertical memory fidelity metrics that capture degradation both within and across layers. Inspired by how humans distill and retain salient information when reading long documents, we propose MemMamba, a novel architectural framework that integrates state summarization mechanism together with cross-layer and cross-token attention, which alleviates long-range forgetting while preserving linear complexity. MemMamba achieves significant improvements over existing Mamba variants and Transformers on long-sequence benchmarks such as PG19 and Passkey Retrieval, while delivering a 48% speedup in inference efficiency. Both theoretical analysis and empirical results demonstrate that MemMamba achieves a breakthrough in the complexity-memory trade-off, offering a new paradigm for ultra-long sequence modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.02994", "title": "Towards Scalable and Consistent 3D Editing", "authors": [ "Ruihao Xia", "Yang Tang", "Pan Zhou" ], "github": "", "project_page": "", "abstract": "3D editing - the task of locally modifying the geometry or appearance of a 3D asset - has wide applications in immersive content creation, digital entertainment, and AR/VR. However, unlike 2D editing, it remains challenging due to the need for cross-view consistency, structural fidelity, and fine-grained controllability. Existing approaches are often slow, prone to geometric distortions, or dependent on manual and accurate 3D masks that are error-prone and impractical. To address these challenges, we advance both the data and model fronts. On the data side, we introduce 3DEditVerse, the largest paired 3D editing benchmark to date, comprising 116,309 high-quality training pairs and 1,500 curated test pairs. Built through complementary pipelines of pose-driven geometric edits and foundation model-guided appearance edits, 3DEditVerse ensures edit locality, multi-view consistency, and semantic alignment. On the model side, we propose 3DEditFormer, a 3D-structure-preserving conditional transformer. By enhancing image-to-3D generation with dual-guidance attention and time-adaptive gating, 3DEditFormer disentangles editable regions from preserved structure, enabling precise and consistent edits without requiring auxiliary 3D masks. Extensive experiments demonstrate that our framework outperforms state-of-the-art baselines both quantitatively and qualitatively, establishing a new standard for practical and scalable 3D editing. Dataset and code will be released. Project: https://www.lv-lab.org/3DEditFormer/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08529", "title": "CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards", "authors": [ "Xiangyuan Xue", "Yifan Zhou", "Guibin Zhang", "Zaibin Zhang", "Yijiang Li", "Chen Zhang", "Zhenfei Yin", "Philip Torr", "Wanli Ouyang", "Lei Bai" ], "github": "https://github.com/xyQwQ/CoMAS", "project_page": "", "abstract": "Self-evolution is a central research topic in enabling large language model (LLM)-based agents to continually improve their capabilities after pretraining. Recent research has witnessed a transition from reinforcement learning (RL)-free to RL-based methods. Current RL-based methods either rely on dense external reward signals or extract intrinsic reward signals from LLMs themselves. However, these approaches diverge from the self-evolution mechanisms observed in human intelligence, where individuals learn and improve through mutual discussion and collaboration. In this work, we introduce Co-Evolving Multi-Agent Systems (CoMAS), a novel framework that enables agents to improve autonomously by learning from inter-agent interactions without external supervision. CoMAS generates intrinsic rewards from rich discussion dynamics, employs an LLM-as-a-judge mechanism to formulate these rewards, and optimizes each agent's policy through RL, thereby enabling decentralized and scalable co-evolution. Experimental results demonstrate that CoMAS consistently outperforms untrained agents and achieves state-of-the-art performance across most evaluation settings. Ablation studies confirm the necessity of interaction-based reward signals and reveal promising scalability as the number and diversity of agents increase. These findings establish CoMAS as a novel and effective paradigm for self-evolution in LLM-based agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xyQwQ/CoMAS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2509.24817", "title": "UP2You: Fast Reconstruction of Yourself from Unconstrained Photo Collections", "authors": [ "Zeyu Cai", "Ziyang Li", "Xiaoben Li", "Boqian Li", "Zeyu Wang", "Zhenyu Zhang", "Yuliang Xiu" ], "github": "", "project_page": "", "abstract": "We present UP2You, the first tuning-free solution for reconstructing high-fidelity 3D clothed portraits from extremely unconstrained in-the-wild 2D photos. Unlike previous approaches that require \"clean\" inputs (e.g., full-body images with minimal occlusions, or well-calibrated cross-view captures), UP2You directly processes raw, unstructured photographs, which may vary significantly in pose, viewpoint, cropping, and occlusion. Instead of compressing data into tokens for slow online text-to-3D optimization, we introduce a data rectifier paradigm that efficiently converts unconstrained inputs into clean, orthogonal multi-view images in a single forward pass within seconds, simplifying the 3D reconstruction. Central to UP2You is a pose-correlated feature aggregation module (PCFA), that selectively fuses information from multiple reference images w.r.t. target poses, enabling better identity preservation and nearly constant memory footprint, with more observations. We also introduce a perceiver-based multi-reference shape predictor, removing the need for pre-captured body templates. Extensive experiments on 4D-Dress, PuzzleIOI, and in-the-wild captures demonstrate that UP2You consistently surpasses previous methods in both geometric accuracy (Chamfer-15%, P2S-18% on PuzzleIOI) and texture fidelity (PSNR-21%, LPIPS-46% on 4D-Dress). UP2You is efficient (1.5 minutes per person), and versatile (supports arbitrary pose control, and training-free multi-garment 3D virtual try-on), making it practical for real-world scenarios where humans are casually captured. Both models and code will be released to facilitate future research on this underexplored task. Project Page: https://zcai0612.github.io/UP2You", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08002", "title": "Learning on the Job: An Experience-Driven Self-Evolving Agent for Long-Horizon Tasks", "authors": [ "Cheng Yang", "Xuemeng Yang", "Licheng Wen", "Daocheng Fu", "Jianbiao Mei", "Rong Wu", "Pinlong Cai", "Yufan Shen", "Nianchen Deng", "Botian Shi", "Yu Qiao", "Haifeng Li" ], "github": "https://github.com/KnowledgeXLab/MUSE", "project_page": "", "abstract": "Large Language Models have demonstrated remarkable capabilities across diverse domains, yet significant challenges persist when deploying them as AI agents for real-world long-horizon tasks. Existing LLM agents suffer from a critical limitation: they are test-time static and cannot learn from experience, lacking the ability to accumulate knowledge and continuously improve on the job. To address this challenge, we propose MUSE, a novel agent framework that introduces an experience-driven, self-evolving system centered around a hierarchical Memory Module. MUSE organizes diverse levels of experience and leverages them to plan and execute long-horizon tasks across multiple applications. After each sub-task execution, the agent autonomously reflects on its trajectory, converting the raw trajectory into structured experience and integrating it back into the Memory Module. This mechanism enables the agent to evolve beyond its static pretrained parameters, fostering continuous learning and self-evolution. We evaluate MUSE on the long-horizon productivity benchmark TAC. It achieves new SOTA performance by a significant margin using only a lightweight Gemini-2.5 Flash model. Sufficient Experiments demonstrate that as the agent autonomously accumulates experience, it exhibits increasingly superior task completion capabilities, as well as robust continuous learning and self-evolution capabilities. Moreover, the accumulated experience from MUSE exhibits strong generalization properties, enabling zero-shot improvement on new tasks. MUSE establishes a new paradigm for AI agents capable of real-world productivity task automation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KnowledgeXLab/MUSE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08203", "title": "Memory Retrieval and Consolidation in Large Language Models through Function Tokens", "authors": [ "Shaohua Zhang", "Yuan Lin", "Hang Li" ], "github": "https://github.com/jbloomAus/SAELens", "project_page": "", "abstract": "The remarkable success of large language models (LLMs) stems from their ability to consolidate vast amounts of knowledge into the memory during pre-training and to retrieve it from the memory during inference, enabling advanced capabilities such as knowledge memorization, instruction-following and reasoning. However, the mechanisms of memory retrieval and consolidation in LLMs remain poorly understood. In this paper, we propose the function token hypothesis to explain the workings of LLMs: During inference, function tokens activate the most predictive features from context and govern next token prediction (memory retrieval). During pre-training, predicting the next tokens (usually content tokens) that follow function tokens increases the number of learned features of LLMs and updates the model parameters (memory consolidation). Function tokens here roughly correspond to function words in linguistics, including punctuation marks, articles, prepositions, and conjunctions, in contrast to content tokens. We provide extensive experimental evidence supporting this hypothesis. Using bipartite graph analysis, we show that a small number of function tokens activate the majority of features. Case studies further reveal how function tokens activate the most predictive features from context to direct next token prediction. We also find that during pre-training, the training loss is dominated by predicting the next content tokens following function tokens, which forces the function tokens to select the most predictive features from context.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jbloomAus/SAELens", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2509.23500", "title": "Beyond Outliers: A Study of Optimizers Under Quantization", "authors": [ "Georgios Vlassis", "Saleh Ashkboos", "Alexandra Volkova", "Torsten Hoefler", "Dan Alistarh" ], "github": "", "project_page": "", "abstract": "As new optimizers gain traction and model quantization becomes standard for efficient deployment, a key question arises: how does the choice of optimizer affect model performance in the presence of quantization? Despite progress in both areas, systematic evidence on optimizer-quantization interactions remains limited. To fill this gap, we study the impact of optimizer choice on model robustness under quantization, considering both post-training quantization (PTQ), and quantization-aware training (QAT). We first train full-precision models, ranging from 50M to 1.5B parameters, with six optimizers, to explore the hyperparameter landscape, and establish well-tuned baselines. We then apply PTQ to evaluate how model performance degrades when trained with different optimizers. We find that outlier-related metrics, such as the max-to-mean ratio (MMR) and Kurtosis, fail to predict the PTQ performance across different optimizers. We show analytically that this is due to the MMR capturing only isolated layer errors, while ignoring how quantization errors accumulate and propagate through the network. To study the QAT degradation, we train quantized models from scratch and compare them to our original-precision baselines. We find that optimizers performing well in the original pretraining setup may not remain optimal under QAT, and that models trained with Shampoo show the lowest accuracy degradation. Finally, we derive scaling laws for quantization-aware training under different optimizers, showing that Shampoo achieves the highest parameter efficiency of all tested optimizers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.03117", "title": "Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interaction", "authors": [ "Kaisi Guan", "Xihua Wang", "Zhengfeng Lai", "Xin Cheng", "Peng Zhang", "XiaoJiang Liu", "Ruihua Song", "Meng Cao" ], "github": "", "project_page": "", "abstract": "This study focuses on a challenging yet promising task, Text-to-Sounding-Video (T2SV) generation, which aims to generate a video with synchronized audio from text conditions, meanwhile ensuring both modalities are aligned with text. Despite progress in joint audio-video training, two critical challenges still remain unaddressed: (1) a single, shared text caption where the text for video is equal to the text for audio often creates modal interference, confusing the pretrained backbones, and (2) the optimal mechanism for cross-modal feature interaction remains unclear. To address these challenges, we first propose the Hierarchical Visual-Grounded Captioning (HVGC) framework that generates pairs of disentangled captions, a video caption, and an audio caption, eliminating interference at the conditioning stage. Based on HVGC, we further introduce BridgeDiT, a novel dual-tower diffusion transformer, which employs a Dual CrossAttention (DCA) mechanism that acts as a robust ``bridge\" to enable a symmetric, bidirectional exchange of information, achieving both semantic and temporal synchronization. Extensive experiments on three benchmark datasets, supported by human evaluations, demonstrate that our method achieves state-of-the-art results on most metrics. Comprehensive ablation studies further validate the effectiveness of our contributions, offering key insights for the future T2SV task. All the codes and checkpoints will be publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2509.24797", "title": "Fidelity-Aware Data Composition for Robust Robot Generalization", "authors": [ "Zizhao Tong", "Di Chen", "Sicheng Hu", "Hongwei Fan", "Liliang Chen", "Guanghui Ren", "Hao Tang", "Hao Dong", "Ling Shao" ], "github": "", "project_page": "", "abstract": "Generalist robot policies trained on large-scale, visually homogeneous datasets can be susceptible to shortcut learning, which impairs their out-of-distribution (OOD) generalization. While generative data augmentation is a common approach to introduce diversity, it presents a subtle challenge: data composition. Naively mixing real and synthetic data can corrupt the learning signal, as this process often prioritizes visual diversity at the expense of information fidelity. This paper suggests that robust generalization depends on principled, fidelity-aware data composition. We introduce Coherent Information Fidelity Tuning (CIFT), a framework that treats data composition as an optimization problem. CIFT uses a practical proxy for Information Fidelity based on the feature-space geometry of a dataset. This enables the identification of a phase transition, termed the Decoherence Point, where training stability degrades. The framework includes a generative engine, Multi-View Video Augmentation (MVAug), to synthesize a causally disentangled data spectrum for this tuning process. Applying CIFT to policy architectures such as pi_0 and Diffusion Policy improves OOD success rates by over 54\\%. These results indicate that fidelity-aware composition, beyond data synthesis alone, is an important component for developing robust, general-purpose robots.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.07048", "title": "Search-R3: Unifying Reasoning and Embedding Generation in Large Language Models", "authors": [ "Yuntao Gui", "James Cheng" ], "github": "https://github.com/ytgui/Search-R3", "project_page": "", "abstract": "Despite their remarkable natural language understanding capabilities, Large Language Models (LLMs) have been underutilized for retrieval tasks. We present Search-R3, a novel framework that addresses this limitation by adapting LLMs to generate search embeddings as a direct output of their reasoning process. Our approach exploits LLMs' chain-of-thought capabilities, allowing them to produce more effective embeddings by reasoning step-by-step through complex semantic analyses. We implement this through three complementary mechanisms. (1) a supervised learning stage enables the model's ability to produce quality embeddings, (2) a reinforcement learning (RL) methodology that optimizes embedding generation alongside reasoning, and (3) a specialized RL environment that efficiently handles evolving embedding representations without requiring complete corpus re-encoding at each training iteration. Our extensive evaluations on diverse benchmarks demonstrate that Search-R3 significantly outperforms prior methods by unifying the reasoning and embedding generation processes. This integrated post-training approach represents a substantial advancement in handling complex knowledge-intensive tasks that require both sophisticated reasoning and effective information retrieval. Project page: https://github.com/ytgui/Search-R3", "github_manual": "", "github_regex": "https://github.com/ytgui/Search-R3", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.07790", "title": "GCPO: When Contrast Fails, Go Gold", "authors": [ "Hao Wu", "Wei Liu" ], "github": "https://github.com/AchoWu/GCPO", "project_page": "", "abstract": "Reinforcement learning has been widely applied to enhance the reasoning capabilities of large language models. Extending the inference limits of smaller models has become a prominent research focus. However, algorithms such as Group Relative Policy Optimization (GRPO) suffer from a clear drawback: the upper bound of a model's rollout responses is entirely determined by the model itself, preventing the acquisition of knowledge from samples that are either all incorrect or all correct. In this paper, we introduce Group Contrastive Policy Optimization (GCPO), a method that incorporates external standard reference answers. When the model cannot solve a problem, the reference answer supplies the correct response, steering the model toward an unequivocally accurate update direction. This approach offers two main advantages: (1) it improves training efficiency by fully utilizing every sample; (2) it enables the model to emulate the problem solving strategy of the reference answer during training, thereby enhancing generalization in reasoning. GCPO achieves outstanding results across multiple benchmark datasets, yielding substantial improvements over the baseline model. Our code is available at: https://github.com/AchoWu/GCPO.", "github_manual": "", "github_regex": "https://github.com/AchoWu/GCPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.08271", "title": "SViM3D: Stable Video Material Diffusion for Single Image 3D Generation", "authors": [ "Andreas Engelhardt", "Mark Boss", "Vikram Voletti", "Chun-Han Yao", "Hendrik P. A. Lensch", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "We present Stable Video Materials 3D (SViM3D), a framework to predict multi-view consistent physically based rendering (PBR) materials, given a single image. Recently, video diffusion models have been successfully used to reconstruct 3D objects from a single image efficiently. However, reflectance is still represented by simple material models or needs to be estimated in additional steps to enable relighting and controlled appearance edits. We extend a latent video diffusion model to output spatially varying PBR parameters and surface normals jointly with each generated view based on explicit camera control. This unique setup allows for relighting and generating a 3D asset using our model as neural prior. We introduce various mechanisms to this pipeline that improve quality in this ill-posed setting. We show state-of-the-art relighting and novel view synthesis performance on multiple object-centric datasets. Our method generalizes to diverse inputs, enabling the generation of relightable 3D assets useful in AR/VR, movies, games and other visual media.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.07314", "title": "GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations", "authors": [ "Fabian Paischer", "Gianluca Galletti", "William Hornsby", "Paul Setinek", "Lorenzo Zanisi", "Naomi Carey", "Stanislas Pamela", "Johannes Brandstetter" ], "github": "", "project_page": "", "abstract": "Nuclear fusion plays a pivotal role in the quest for reliable and sustainable energy production. A major roadblock to viable fusion power is understanding plasma turbulence, which significantly impairs plasma confinement, and is vital for next-generation reactor design. Plasma turbulence is governed by the nonlinear gyrokinetic equation, which evolves a 5D distribution function over time. Due to its high computational cost, reduced-order models are often employed in practice to approximate turbulent transport of energy. However, they omit nonlinear effects unique to the full 5D dynamics. To tackle this, we introduce GyroSwin, the first scalable 5D neural surrogate that can model 5D nonlinear gyrokinetic simulations, thereby capturing the physical phenomena neglected by reduced models, while providing accurate estimates of turbulent heat transport.GyroSwin (i) extends hierarchical Vision Transformers to 5D, (ii) introduces cross-attention and integration modules for latent 3Dleftrightarrow5D interactions between electrostatic potential fields and the distribution function, and (iii) performs channelwise mode separation inspired by nonlinear physics. We demonstrate that GyroSwin outperforms widely used reduced numerics on heat flux prediction, captures the turbulent energy cascade, and reduces the cost of fully resolved nonlinear gyrokinetics by three orders of magnitude while remaining physically verifiable. GyroSwin shows promising scaling laws, tested up to one billion parameters, paving the way for scalable neural surrogates for gyrokinetic simulations of plasma turbulence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.06209", "title": "Drive&Gen: Co-Evaluating End-to-End Driving and Video Generation Models", "authors": [ "Jiahao Wang", "Zhenpei Yang", "Yijing Bai", "Yingwei Li", "Yuliang Zou", "Bo Sun", "Abhijit Kundu", "Jose Lezama", "Luna Yue Huang", "Zehao Zhu", "Jyh-Jing Hwang", "Dragomir Anguelov", "Mingxing Tan", "Chiyu Max Jiang" ], "github": "", "project_page": "", "abstract": "Recent advances in generative models have sparked exciting new possibilities in the field of autonomous vehicles. Specifically, video generation models are now being explored as controllable virtual testing environments. Simultaneously, end-to-end (E2E) driving models have emerged as a streamlined alternative to conventional modular autonomous driving systems, gaining popularity for their simplicity and scalability. However, the application of these techniques to simulation and planning raises important questions. First, while video generation models can generate increasingly realistic videos, can these videos faithfully adhere to the specified conditions and be realistic enough for E2E autonomous planner evaluation? Second, given that data is crucial for understanding and controlling E2E planners, how can we gain deeper insights into their biases and improve their ability to generalize to out-of-distribution scenarios? In this work, we bridge the gap between the driving models and generative world models (Drive&Gen) to address these questions. We propose novel statistical measures leveraging E2E drivers to evaluate the realism of generated videos. By exploiting the controllability of the video generation model, we conduct targeted experiments to investigate distribution gaps affecting E2E planner performance. Finally, we show that synthetic data produced by the video generation model offers a cost-effective alternative to real-world data collection. This synthetic data effectively improves E2E model generalization beyond existing Operational Design Domains, facilitating the expansion of autonomous vehicle services into new operational contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.07546", "title": "PickStyle: Video-to-Video Style Transfer with Context-Style Adapters", "authors": [ "Soroush Mehraban", "Vida Adeli", "Jacob Rommann", "Babak Taati", "Kyryl Truskovskyi" ], "github": "https://github.com/PickfordAI/pickstyle", "project_page": "", "abstract": "We address the task of video style transfer with diffusion models, where the goal is to preserve the context of an input video while rendering it in a target style specified by a text prompt. A major challenge is the lack of paired video data for supervision. We propose PickStyle, a video-to-video style transfer framework that augments pretrained video diffusion backbones with style adapters and benefits from paired still image data with source-style correspondences for training. PickStyle inserts low-rank adapters into the self-attention layers of conditioning modules, enabling efficient specialization for motion-style transfer while maintaining strong alignment between video content and style. To bridge the gap between static image supervision and dynamic video, we construct synthetic training clips from paired images by applying shared augmentations that simulate camera motion, ensuring temporal priors are preserved. In addition, we introduce Context-Style Classifier-Free Guidance (CS-CFG), a novel factorization of classifier-free guidance into independent text (style) and video (context) directions. CS-CFG ensures that context is preserved in generated video while the style is effectively transferred. Experiments across benchmarks show that our approach achieves temporally coherent, style-faithful, and content-preserving video translations, outperforming existing baselines both qualitatively and quantitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PickfordAI/pickstyle", "github_api_added_by": "auto" }, { "date": "2025-10-10", "arxiv_id": "2510.06679", "title": "DreamOmni2: Multimodal Instruction-based Editing and Generation", "authors": [ "Bin Xia", "Bohao Peng", "Yuechen Zhang", "Junjia Huang", "Jiyang Liu", "Jingyao Li", "Haoru Tan", "Sitong Wu", "Chengyao Wang", "Yitong Wang", "Xinglong Wu", "Bei Yu", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/DreamOmni2", "project_page": "", "abstract": "Recent advancements in instruction-based image editing and subject-driven generation have garnered significant attention, yet both tasks still face limitations in meeting practical user needs. Instruction-based editing relies solely on language instructions, which often fail to capture specific editing details, making reference images necessary. Meanwhile, subject-driven generation is limited to combining concrete objects or people, overlooking broader, abstract concepts. To address these challenges, we propose two novel tasks: multimodal instruction-based editing and generation. These tasks support both text and image instructions and extend the scope to include both concrete and abstract concepts, greatly enhancing their practical applications. We introduce DreamOmni2, tackling two primary challenges: data creation and model framework design. Our data synthesis pipeline consists of three steps: (1) using a feature mixing method to create extraction data for both abstract and concrete concepts, (2) generating multimodal instruction-based editing training data using the editing and extraction models, and (3) further applying the extraction model to create training data for multimodal instruction-based editing. For the framework, to handle multi-image input, we propose an index encoding and position encoding shift scheme, which helps the model distinguish images and avoid pixel confusion. Additionally, we introduce joint training with the VLM and our generation/editing model to better process complex instructions. In addition, we have proposed comprehensive benchmarks for these two new tasks to drive their development. Experiments show that DreamOmni2 has achieved impressive results. Models and codes will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dvlab-research/DreamOmni2", "github_api_added_by": "auto" }, { "date": "2025-10-10", "arxiv_id": "2510.02590", "title": "Use the Online Network If You Can: Towards Fast and Stable Reinforcement Learning", "authors": [ "Ahmed Hendawy", "Henrik Metternich", "Th\u00e9o Vincent", "Mahdi Kallel", "Jan Peters", "Carlo D'Eramo" ], "github": "", "project_page": "", "abstract": "The use of target networks is a popular approach for estimating value functions in deep Reinforcement Learning (RL). While effective, the target network remains a compromise solution that preserves stability at the cost of slowly moving targets, thus delaying learning. Conversely, using the online network as a bootstrapped target is intuitively appealing, albeit well-known to lead to unstable learning. In this work, we aim to obtain the best out of both worlds by introducing a novel update rule that computes the target using the MINimum estimate between the Target and Online network, giving rise to our method, MINTO. Through this simple, yet effective modification, we show that MINTO enables faster and stable value function learning, by mitigating the potential overestimation bias of using the online network for bootstrapping. Notably, MINTO can be seamlessly integrated into a wide range of value-based and actor-critic algorithms with a negligible cost. We evaluate MINTO extensively across diverse benchmarks, spanning online and offline RL, as well as discrete and continuous action spaces. Across all benchmarks, MINTO consistently improves performance, demonstrating its broad applicability and effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2509.26633", "title": "OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction", "authors": [ "Lujie Yang", "Xiaoyu Huang", "Zhen Wu", "Angjoo Kanazawa", "Pieter Abbeel", "Carmelo Sferrazza", "C. Karen Liu", "Rocky Duan", "Guanya Shi" ], "github": "", "project_page": "", "abstract": "A dominant paradigm for teaching humanoid robots complex skills is to retarget human motions as kinematic references to train reinforcement learning (RL) policies. However, existing retargeting pipelines often struggle with the significant embodiment gap between humans and robots, producing physically implausible artifacts like foot-skating and penetration. More importantly, common retargeting methods neglect the rich human-object and human-environment interactions essential for expressive locomotion and loco-manipulation. To address this, we introduce OmniRetarget, an interaction-preserving data generation engine based on an interaction mesh that explicitly models and preserves the crucial spatial and contact relationships between an agent, the terrain, and manipulated objects. By minimizing the Laplacian deformation between the human and robot meshes while enforcing kinematic constraints, OmniRetarget generates kinematically feasible trajectories. Moreover, preserving task-relevant interactions enables efficient data augmentation, from a single demonstration to different robot embodiments, terrains, and object configurations. We comprehensively evaluate OmniRetarget by retargeting motions from OMOMO, LAFAN1, and our in-house MoCap datasets, generating over 8-hour trajectories that achieve better kinematic constraint satisfaction and contact preservation than widely used baselines. Such high-quality data enables proprioceptive RL policies to successfully execute long-horizon (up to 30 seconds) parkour and loco-manipulation skills on a Unitree G1 humanoid, trained with only 5 reward terms and simple domain randomization shared by all tasks, without any learning curriculum.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-10", "arxiv_id": "2510.07743", "title": "OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment", "authors": [ "Tianci Liu", "Ran Xu", "Tony Yu", "Ilgee Hong", "Carl Yang", "Tuo Zhao", "Haoyu Wang" ], "github": "", "project_page": "", "abstract": "Reward modeling lies at the core of reinforcement learning from human feedback (RLHF), yet most existing reward models rely on scalar or pairwise judgments that fail to capture the multifaceted nature of human preferences. Recent studies have explored rubrics-as-rewards (RaR) that uses structured natural language criteria that capture multiple dimensions of response quality. However, producing rubrics that are both reliable and scalable remains a key challenge. In this work, we introduce OpenRubrics, a diverse, large-scale collection of (prompt, rubric) pairs for training rubric-generation and rubric-based reward models. To elicit discriminative and comprehensive evaluation signals, we introduce Contrastive Rubric Generation (CRG), which derives both hard rules (explicit constraints) and principles (implicit qualities) by contrasting preferred and rejected responses. We further improve reliability by enforcing preference-label consistency via rejection sampling to remove noisy rubrics. Across multiple reward-modeling benchmarks, our rubric-based reward model, Rubric-RM, surpasses strong size-matched baselines by 6.8%. These gains transfer to policy models on instruction-following and biomedical benchmarks. Our results show that rubrics provide scalable alignment signals that narrow the gap between costly human evaluation and automated reward modeling, enabling a new principle-driven paradigm for LLM alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.04533", "title": "TAG:Tangential Amplifying Guidance for Hallucination-Resistant Diffusion Sampling", "authors": [ "Hyunmin Cho", "Donghoon Ahn", "Susung Hong", "Jee Eun Kim", "Seungryong Kim", "Kyong Hwan Jin" ], "github": "", "project_page": "", "abstract": "Recent diffusion models achieve the state-of-the-art performance in image generation, but often suffer from semantic inconsistencies or hallucinations. While various inference-time guidance methods can enhance generation, they often operate indirectly by relying on external signals or architectural modifications, which introduces additional computational overhead. In this paper, we propose Tangential Amplifying Guidance (TAG), a more efficient and direct guidance method that operates solely on trajectory signals without modifying the underlying diffusion model. TAG leverages an intermediate sample as a projection basis and amplifies the tangential components of the estimated scores with respect to this basis to correct the sampling trajectory. We formalize this guidance process by leveraging a first-order Taylor expansion, which demonstrates that amplifying the tangential component steers the state toward higher-probability regions, thereby reducing inconsistencies and enhancing sample quality. TAG is a plug-and-play, architecture-agnostic module that improves diffusion sampling fidelity with minimal computational addition, offering a new perspective on diffusion guidance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08867", "title": "ReviewerToo: Should AI Join The Program Committee? A Look At The Future of Peer Review", "authors": [ "Gaurav Sahu", "Hugo Larochelle", "Laurent Charlin", "Christopher Pal" ], "github": "", "project_page": "", "abstract": "Peer review is the cornerstone of scientific publishing, yet it suffers from inconsistencies, reviewer subjectivity, and scalability challenges. We introduce ReviewerToo, a modular framework for studying and deploying AI-assisted peer review to complement human judgment with systematic and consistent assessments. ReviewerToo supports systematic experiments with specialized reviewer personas and structured evaluation criteria, and can be partially or fully integrated into real conference workflows. We validate ReviewerToo on a carefully curated dataset of 1,963 paper submissions from ICLR 2025, where our experiments with the gpt-oss-120b model achieves 81.8% accuracy for the task of categorizing a paper as accept/reject compared to 83.9% for the average human reviewer. Additionally, ReviewerToo-generated reviews are rated as higher quality than the human average by an LLM judge, though still trailing the strongest expert contributions. Our analysis highlights domains where AI reviewers excel (e.g., fact-checking, literature coverage) and where they struggle (e.g., assessing methodological novelty and theoretical contributions), underscoring the continued need for human expertise. Based on these findings, we propose guidelines for integrating AI into peer-review pipelines, showing how AI can enhance consistency, coverage, and fairness while leaving complex evaluative judgments to domain experts. Our work provides a foundation for systematic, hybrid peer-review systems that scale with the growth of scientific publishing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08696", "title": "Don't Waste Mistakes: Leveraging Negative RL-Groups via Confidence Reweighting", "authors": [ "Yunzhen Feng", "Parag Jain", "Anthony Hartshorn", "Yaqi Duan", "Julia Kempe" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has become a standard recipe for improving large language models (LLMs) on reasoning tasks, with Group Relative Policy Optimization (GRPO) widely used in practice. Yet GRPO wastes substantial compute on negative groups: groups in which no sampled response is correct yield zero advantage and thus no gradient. We ask whether negative groups can be leveraged without extra supervision. Starting from a maximum-likelihood (MLE) objective in reward modeling, we show that the MLE gradient is equivalent to a policy gradient for a modified value function. This value function adds a confidence-weighted penalty on incorrect responses, imposing larger penalties on more confident mistakes. We refer to this as Likelihood Estimation with Negative Samples (LENS). LENS modifies GRPO to assign non-zero, confidence-dependent rewards to incorrect generations, making negative groups informative and converting previously wasted samples into useful gradient updates. On the MATH benchmark with Llama-3.1-8B and Qwen-2.5-3B, the proposed variant consistently outperforms GRPO baseline, with significant gains on harder items. These results demonstrate a principled and practical way to \"rescue\" negative groups, improving efficiency and performance in RLVR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09608", "title": "StreamingVLM: Real-Time Understanding for Infinite Video Streams", "authors": [ "Ruyi Xu", "Guangxuan Xiao", "Yukang Chen", "Liuning He", "Kelly Peng", "Yao Lu", "Song Han" ], "github": "https://github.com/mit-han-lab/streaming-vlm", "project_page": "", "abstract": "Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-infinite video streams without escalating latency and memory usage. Processing entire videos with full attention leads to quadratic computational costs and poor performance on long videos. Meanwhile, simple sliding window methods are also flawed, as they either break coherence or suffer from high latency due to redundant recomputation. In this paper, we introduce StreamingVLM, a model designed for real-time, stable understanding of infinite visual input. Our approach is a unified framework that aligns training with streaming inference. During inference, we maintain a compact KV cache by reusing states of attention sinks, a short window of recent vision tokens, and a long window of recent text tokens. This streaming ability is instilled via a simple supervised fine-tuning (SFT) strategy that applies full attention on short, overlapped video chunks, which effectively mimics the inference-time attention pattern without training on prohibitively long contexts. For evaluation, we build Inf-Streams-Eval, a new benchmark with videos averaging over two hours that requires dense, per-second alignment between frames and text. On Inf-Streams-Eval, StreamingVLM achieves a 66.18% win rate against GPT-4O mini and maintains stable, real-time performance at up to 8 FPS on a single NVIDIA H100. Notably, our SFT strategy also enhances general VQA abilities without any VQA-specific fine-tuning, improving performance on LongVideoBench by +4.30 and OVOBench Realtime by +5.96. Code is available at https://github.com/mit-han-lab/streaming-vlm.", "github_manual": "", "github_regex": "https://github.com/mit-han-lab/streaming-vlm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09606", "title": "SpaceVista: All-Scale Visual Spatial Reasoning from mm to km", "authors": [ "Peiwen Sun", "Shiqiang Lang", "Dongming Wu", "Yi Ding", "Kaituo Feng", "Huadai Liu", "Zhen Ye", "Rui Liu", "Yun-Hui Liu", "Jianan Wang", "Xiangyu Yue" ], "github": "", "project_page": "", "abstract": "With the current surge in spatial reasoning explorations, researchers have made significant progress in understanding indoor scenes, but still struggle with diverse applications such as robotics and autonomous driving. This paper aims to advance all-scale spatial reasoning across diverse scenarios by tackling two key challenges: 1) the heavy reliance on indoor 3D scans and labor-intensive manual annotations for dataset curation; 2) the absence of effective all-scale scene modeling, which often leads to overfitting to individual scenes. In this paper, we introduce a holistic solution that integrates a structured spatial reasoning knowledge system, scale-aware modeling, and a progressive training paradigm, as the first attempt to broaden the all-scale spatial intelligence of MLLMs to the best of our knowledge. Using a task-specific, specialist-driven automated pipeline, we curate over 38K video scenes across 5 spatial scales to create SpaceVista-1M, a dataset comprising approximately 1M spatial QA pairs spanning 19 diverse task types. While specialist models can inject useful domain knowledge, they are not reliable for evaluation. We then build an all-scale benchmark with precise annotations by manually recording, retrieving, and assembling video-based data. However, naive training with SpaceVista-1M often yields suboptimal results due to the potential knowledge conflict. Accordingly, we introduce SpaceVista-7B, a spatial reasoning model that accepts dense inputs beyond semantics and uses scale as an anchor for scale-aware experts and progressive rewards. Finally, extensive evaluations across 5 benchmarks, including our SpaceVista-Bench, demonstrate competitive performance, showcasing strong generalization across all scales and scenarios. Our dataset, model, and benchmark will be released on https://peiwensun2000.github.io/mm2km .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09577", "title": "Dyna-Mind: Learning to Simulate from Experience for Better AI Agents", "authors": [ "Xiao Yu", "Baolin Peng", "Michel Galley", "Hao Cheng", "Qianhui Wu", "Janardhan Kulkarni", "Suman Nath", "Zhou Yu", "Jianfeng Gao" ], "github": "", "project_page": "", "abstract": "Reasoning models have recently shown remarkable progress in domains such as math and coding. However, their expert-level abilities in math and coding contrast sharply with their performance in long-horizon, interactive tasks such as web navigation and computer/phone-use. Inspired by literature on human cognition, we argue that current AI agents need ''vicarious trial and error'' - the capacity to mentally simulate alternative futures before acting - in order to enhance their understanding and performance in complex interactive environments. We introduce Dyna-Mind, a two-stage training framework that explicitly teaches (V)LM agents to integrate such simulation into their reasoning. In stage 1, we introduce Reasoning with Simulations (ReSim), which trains the agent to generate structured reasoning traces from expanded search trees built from real experience gathered through environment interactions. ReSim thus grounds the agent's reasoning in faithful world dynamics and equips it with the ability to anticipate future states in its reasoning. In stage 2, we propose Dyna-GRPO, an online reinforcement learning method to further strengthen the agent's simulation and decision-making ability by using both outcome rewards and intermediate states as feedback from real rollouts. Experiments on two synthetic benchmarks (Sokoban and ALFWorld) and one realistic benchmark (AndroidWorld) demonstrate that (1) ReSim effectively infuses simulation ability into AI agents, and (2) Dyna-GRPO leverages outcome and interaction-level signals to learn better policies for long-horizon, planning-intensive tasks. Together, these results highlight the central role of simulation in enabling AI agents to reason, plan, and act more effectively in the ever more challenging environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08047", "title": "Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic Speech Recognition", "authors": [ "Yi-Cheng Lin", "Yu-Hsuan Li Liang", "Hsuan Su", "Tzu-Quan Lin", "Shang-Tse Chen", "Yun-Nung Chen", "Hung-yi Lee" ], "github": "", "project_page": "", "abstract": "Robust ASR under domain shift is crucial because real-world systems encounter unseen accents and domains with limited labeled data. Although pseudo-labeling offers a practical workaround, it often introduces systematic, accent-specific errors that filtering fails to fix. We ask: How can we correct these recurring biases without target ground truth? We propose a simple parameter-space correction: in a source domain containing both real and pseudo-labeled data, two ASR models are fine-tuned from the same initialization, one on ground-truth labels and the other on pseudo-labels, and their weight difference forms a correction vector that captures pseudo-label biases. When applied to a pseudo-labeled target model, this vector enhances recognition, achieving up to a 35% relative Word Error Rate (WER) reduction on AfriSpeech-200 across ten African accents with the Whisper tiny model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08673", "title": "Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generation", "authors": [ "Kang Liao", "Size Wu", "Zhonghua Wu", "Linyi Jin", "Chao Wang", "Yikai Wang", "Fei Wang", "Wei Li", "Chen Change Loy" ], "github": "", "project_page": "", "abstract": "Camera-centric understanding and generation are two cornerstones of spatial intelligence, yet they are typically studied in isolation. We present Puffin, a unified camera-centric multimodal model that extends spatial awareness along the camera dimension. Puffin integrates language regression and diffusion-based generation to interpret and create scenes from arbitrary viewpoints. To bridge the modality gap between cameras and vision-language, we introduce a novel paradigm that treats camera as language, enabling thinking with camera. This guides the model to align spatially grounded visual cues with photographic terminology while reasoning across geometric context. Puffin is trained on Puffin-4M, a large-scale dataset of 4 million vision-language-camera triplets. We incorporate both global camera parameters and pixel-wise camera maps, yielding flexible and reliable spatial generation. Experiments demonstrate Puffin superior performance over specialized models for camera-centric generation and understanding. With instruction tuning, Puffin generalizes to diverse cross-view tasks such as spatial imagination, world exploration, and photography guidance. We will release the code, models, dataset pipeline, and benchmark to advance multimodal spatial intelligence research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.06499", "title": "Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining Levels", "authors": [ "Zhepeng Cen", "Haolin Chen", "Shiyu Wang", "Zuxin Liu", "Zhiwei Liu", "Ding Zhao", "Silvio Savarese", "Caiming Xiong", "Huan Wang", "Weiran Yao" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved remarkable success through imitation learning on vast text corpora, but this paradigm creates a training-generation gap and limits robust reasoning. Reinforcement learning (RL) offers a more data-efficient solution capable of bridging this gap, yet its application has been constrained by a critical data bottleneck: existing RL datasets are orders of magnitude smaller and less diverse than web-scale pre-training corpora. To address this, we introduce the Webscale-RL pipeline, a scalable data engine that systematically converts large-scale pre-training documents into millions of diverse, verifiable question-answer pairs for RL. Using this pipeline, we construct the Webscale-RL dataset, containing 1.2 million examples across more than 9 domains. Our experiments show that the model trained on this dataset significantly outperforms continual pretraining and strong data refinement baselines across a suite of benchmarks. Notably, RL training with our dataset proves substantially more efficient, achieving the performance of continual pre-training with up to 100times fewer tokens. Our work presents a viable path toward scaling RL to pre-training levels, enabling more capable and efficient language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.04759", "title": "Progressive Gaussian Transformer with Anisotropy-aware Sampling for Open Vocabulary Occupancy Prediction", "authors": [ "Chi Yan", "Dan Xu" ], "github": "", "project_page": "", "abstract": "The 3D occupancy prediction task has witnessed remarkable progress in recent years, playing a crucial role in vision-based autonomous driving systems. While traditional methods are limited to fixed semantic categories, recent approaches have moved towards predicting text-aligned features to enable open-vocabulary text queries in real-world scenes. However, there exists a trade-off in text-aligned scene modeling: sparse Gaussian representation struggles to capture small objects in the scene, while dense representation incurs significant computational overhead. To address these limitations, we present PG-Occ, an innovative Progressive Gaussian Transformer Framework that enables open-vocabulary 3D occupancy prediction. Our framework employs progressive online densification, a feed-forward strategy that gradually enhances the 3D Gaussian representation to capture fine-grained scene details. By iteratively enhancing the representation, the framework achieves increasingly precise and detailed scene understanding. Another key contribution is the introduction of an anisotropy-aware sampling strategy with spatio-temporal fusion, which adaptively assigns receptive fields to Gaussians at different scales and stages, enabling more effective feature aggregation and richer scene information capture. Through extensive evaluations, we demonstrate that PG-Occ achieves state-of-the-art performance with a relative 14.3% mIoU improvement over the previous best performing method. Code and pretrained models will be released upon publication on our project page: https://yanchi-3dv.github.io/PG-Occ", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09201", "title": "Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs", "authors": [ "Yumin Choi", "Dongki Kim", "Jinheon Baek", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have shown remarkable success, and their multimodal expansions (MLLMs) further unlock capabilities spanning images, videos, and other modalities beyond text. However, despite this shift, prompt optimization approaches, designed to reduce the burden of manual prompt crafting while maximizing performance, remain confined to text, ultimately limiting the full potential of MLLMs. Motivated by this gap, we introduce the new problem of multimodal prompt optimization, which expands the prior definition of prompt optimization to the multimodal space defined by the pairs of textual and non-textual prompts. To tackle this problem, we then propose the Multimodal Prompt Optimizer (MPO), a unified framework that not only performs the joint optimization of multimodal prompts through alignment-preserving updates but also guides the selection process of candidate prompts by leveraging earlier evaluations as priors in a Bayesian-based selection strategy. Through extensive experiments across diverse modalities that go beyond text, such as images, videos, and even molecules, we demonstrate that MPO outperforms leading text-only optimization methods, establishing multimodal prompt optimization as a crucial step to realizing the potential of MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08189", "title": "R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?", "authors": [ "Yi Lu", "Jianing Wang", "Linsen Guo", "Wei He", "Hongyin Tang", "Tao Gui", "Xuanjing Huang", "Xuezhi Cao", "Wei Wang", "Xunliang Cai" ], "github": "", "project_page": "", "abstract": "Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1, DeepSeek-R1) have led to remarkable improvements through long Chain-of-Thought (CoT). However, existing benchmarks mainly focus on immediate, single-horizon tasks, failing to adequately evaluate models' ability to understand and respond to complex, long-horizon scenarios. To address this incomplete evaluation of Large Reasoning Models (LRMs), we propose R-HORIZON, a method designed to stimulate long-horizon reasoning behaviors in LRMs through query composition. Based on R-HORIZON, we construct a long-horizon reasoning benchmark, comprising complex multi-step reasoning tasks with interdependent problems that span long reasoning horizons. Through comprehensive evaluation of LRMs using the R-HORIZON benchmark, we find that even the most advanced LRMs suffer significant performance degradation. Our analysis reveals that LRMs exhibit limited effective reasoning length and struggle to allocate thinking budget across multiple problems appropriately. Recognizing these limitations, we use R-HORIZON to construct long-horizon reasoning data for reinforcement learning with verified rewards (RLVR). Compared to training with single-horizon data, RLVR with R-HORIZON not only substantially improves performance on the multi-horizon reasoning tasks, but also promotes accuracy on standard reasoning tasks, with an increase of 7.5 on AIME2024. These results position R-HORIZON as a scalable, controllable, and low-cost paradigm for enhancing and evaluating the long-horizon reasoning capabilities of LRMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09592", "title": "Mind-Paced Speaking: A Dual-Brain Approach to Real-Time Reasoning in Spoken Language Models", "authors": [ "Donghang Wu", "Haoyang Zhang", "Jun Chen", "Xiangyu", "Zhang", "Hexin Liu", "Eng Siong Chng", "Fei Tian", "Xuerui Yang", "Xiangyu Zhang", "Daxin Jiang", "Gang Yu" ], "github": "https://github.com/stepfun-ai/Step-MPS", "project_page": "", "abstract": "Real-time Spoken Language Models (SLMs) struggle to leverage Chain-of-Thought (CoT) reasoning due to the prohibitive latency of generating the entire thought process sequentially. Enabling SLMs to think while speaking, similar to humans, is attracting increasing attention. We present, for the first time, Mind-Paced Speaking (MPS), a brain-inspired framework that enables high-fidelity, real-time reasoning. Similar to how humans utilize distinct brain regions for thinking and responding, we propose a novel dual-brain approach, employing a \"Formulation Brain\" for high-level reasoning to pace and guide a separate \"Articulation Brain\" for fluent speech generation. This division of labor eliminates mode-switching, preserving the integrity of the reasoning process. Experiments show that MPS significantly outperforms existing think-while-speaking methods and achieves reasoning performance comparable to models that pre-compute the full CoT before speaking, while drastically reducing latency. Under a zero-latency configuration, the proposed method achieves an accuracy of 92.8% on the mathematical reasoning task Spoken-MQA and attains a score of 82.5 on the speech conversation task URO-Bench. Our work effectively bridges the gap between high-quality reasoning and real-time interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stepfun-ai/Step-MPS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09561", "title": "TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion Control", "authors": [ "Minkyoung Cho", "Ruben Ohana", "Christian Jacobsen", "Adityan Jothi", "Min-Hung Chen", "Z. Morley Mao", "Ethem Can" ], "github": "", "project_page": "", "abstract": "Current controllable diffusion models typically rely on fixed architectures that modify intermediate activations to inject guidance conditioned on a new modality. This approach uses a static conditioning strategy for a dynamic, multi-stage denoising process, limiting the model's ability to adapt its response as the generation evolves from coarse structure to fine detail. We introduce TC-LoRA (Temporally Modulated Conditional LoRA), a new paradigm that enables dynamic, context-aware control by conditioning the model's weights directly. Our framework uses a hypernetwork to generate LoRA adapters on-the-fly, tailoring weight modifications for the frozen backbone at each diffusion step based on time and the user's condition. This mechanism enables the model to learn and execute an explicit, adaptive strategy for applying conditional guidance throughout the entire generation process. Through experiments on various data domains, we demonstrate that this dynamic, parametric control significantly enhances generative fidelity and adherence to spatial conditions compared to static, activation-based methods. TC-LoRA establishes an alternative approach in which the model's conditioning strategy is modified through a deeper functional adaptation of its weights, allowing control to align with the dynamic demands of the task and generative stage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09507", "title": "PhysToolBench: Benchmarking Physical Tool Understanding for MLLMs", "authors": [ "Zixin Zhang", "Kanghao Chen", "Xingwang Lin", "Lutao Jiang", "Xu Zheng", "Yuanhuiyi Lyu", "Litao Guo", "Yinchuan Li", "Ying-Cong Chen" ], "github": "https://github.com/EnVision-Research/PhysToolBench", "project_page": "", "abstract": "The ability to use, understand, and create tools is a hallmark of human intelligence, enabling sophisticated interaction with the physical world. For any general-purpose intelligent agent to achieve true versatility, it must also master these fundamental skills. While modern Multimodal Large Language Models (MLLMs) leverage their extensive common knowledge for high-level planning in embodied AI and in downstream Vision-Language-Action (VLA) models, the extent of their true understanding of physical tools remains unquantified. To bridge this gap, we present PhysToolBench, the first benchmark dedicated to evaluating the comprehension of physical tools by MLLMs. Our benchmark is structured as a Visual Question Answering (VQA) dataset comprising over 1,000 image-text pairs. It assesses capabilities across three distinct difficulty levels: (1) Tool Recognition: Requiring the recognition of a tool's primary function. (2) Tool Understanding: Testing the ability to grasp the underlying principles of a tool's operation. (3) Tool Creation: Challenging the model to fashion a new tool from surrounding objects when conventional options are unavailable. Our comprehensive evaluation of 32 MLLMs-spanning proprietary, open-source, specialized embodied, and backbones in VLAs-reveals a significant deficiency in tool understanding. Furthermore, we provide an in-depth analysis and propose preliminary solutions. Code and dataset are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EnVision-Research/PhysToolBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08697", "title": "BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution", "authors": [ "Terry Yue Zhuo", "Xiaolong Jin", "Hange Liu", "Juyong Jiang", "Tianyang Liu", "Chen Gong", "Bhupesh Bishnoi", "Vaisakhi Mishra", "Marek Suppa", "Noah Ziems", "Saiteja Utpala", "Ming Xu", "Guangyu Song", "Kaixin Li", "Yuhan Cao", "Bo Liu", "Zheng Liu", "Sabina Abdurakhmanova", "Wenhao Yu", "Mengzhao Jia", "Jihan Yao", "Kenneth Hamilton", "Kumar Shridhar", "Minh Chien Vu", "Dingmin Wang", "Jiawei Liu", "Zijian Wang", "Qian Liu", "Binyuan Hui", "Meg Risdal", "Ahsen Khaliq", "Atin Sood", "Zhenchang Xing", "Wasi Uddin Ahmad", "John Grundy", "David Lo", "Banghua Zhu", "Xiaoning Du", "Torsten Scholak", "Leandro von Werra" ], "github": "https://github.com/bigcode-project/bigcodearena", "project_page": "", "abstract": "Crowdsourced model evaluation platforms, such as Chatbot Arena, enable real-time evaluation from human perspectives to assess the quality of model responses. In the coding domain, manually examining the quality of LLM-generated content is extremely challenging, as it requires understanding long chunks of raw code and deliberately simulating code execution. To this end, we introduce BigCodeArena, an open human evaluation platform for code generation backed by a comprehensive and on-the-fly execution environment. Built on top of Chatbot Arena, BigCodeArena enables the execution of LLM-generated code and allows humans to interact with the execution process and outcomes. We collected over 14,000 raw code-centric conversation sessions across 10 widely used LLMs, spanning 10 languages and 8 types of execution environments. Among these conversations, we identified more than 4,700 multi-turn samples with pairwise human preferences. Further analysis uncovers underexplored preferences of LLMs in fine-grained domains characterized by tasks, languages, and frameworks. To systematically examine code understanding and generation capabilities of frontier LLMs, we curated two benchmarks based on the collected data, namely BigCodeReward and AutoCodeArena. For BigCodeReward, we post-processed the 4,700 conversations and evaluated the consistency between reward models and human preferences. The evaluation shows that most LLMs have superior performance in judging coding preferences when the execution results are available. Inspired by these findings, we propose AutoCodeArena, an automatic Elo rating benchmark designed to assess the coding quality of LLMs without human involvement. We find that proprietary LLMs like GPT-5, Claude-Sonnet-4, and Claude-Opus-4 still lead in code generation performance among recent emerging models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bigcode-project/bigcodearena", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.07745", "title": "Parallel Test-Time Scaling for Latent Reasoning Models", "authors": [ "Runyang You", "Yongqi Li", "Meng Liu", "Wenjie Wang", "Liqiang Nie", "Wenjie Li" ], "github": "https://github.com/YRYangang/LatentTTS", "project_page": "", "abstract": "Parallel test-time scaling (TTS) is a pivotal approach for enhancing large language models (LLMs), typically by sampling multiple token-based chains-of-thought in parallel and aggregating outcomes through voting or search. Recent advances in latent reasoning, where intermediate reasoning unfolds in continuous vector spaces, offer a more efficient alternative to explicit Chain-of-Thought, yet whether such latent models can similarly benefit from parallel TTS remains open, mainly due to the absence of sampling mechanisms in continuous space, and the lack of probabilistic signals for advanced trajectory aggregation. \\ This work enables parallel TTS for latent reasoning models by addressing the above issues. For sampling, we introduce two uncertainty-inspired stochastic strategies: Monte Carlo Dropout and Additive Gaussian Noise. For aggregation, we design a Latent Reward Model (LatentRM) trained with step-wise contrastive objective to score and guide latent reasoning. Extensive experiments and visualization analyses show that both sampling strategies scale effectively with compute and exhibit distinct exploration dynamics, while LatentRM enables effective trajectory selection. Together, our explorations open a new direction for scalable inference in continuous spaces. Code released at https://github.com/YRYangang/LatentTTS.", "github_manual": "", "github_regex": "https://github.com/YRYangang/LatentTTS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.07319", "title": "Temporal Prompting Matters: Rethinking Referring Video Object Segmentation", "authors": [ "Ci-Siang Lin", "Min-Hung Chen", "I-Jieh Liu", "Chien-Yi Wang", "Sifei Liu", "Yu-Chiang Frank Wang" ], "github": "", "project_page": "", "abstract": "Referring Video Object Segmentation (RVOS) aims to segment the object referred to by the query sentence in the video. Most existing methods require end-to-end training with dense mask annotations, which could be computation-consuming and less scalable. In this work, we rethink the RVOS problem and aim to investigate the key to this task. Based on existing foundation segmentation models, we decompose the RVOS task into referring, video, and segmentation factors, and propose a Temporal Prompt Generation and Selection (Tenet) framework to address the referring and video factors while leaving the segmentation problem to foundation models. To efficiently adapt image-based foundation segmentation models to referring video object segmentation, we leverage off-the-shelf object detectors and trackers to produce temporal prompts associated with the referring sentence. While high-quality temporal prompts could be produced, they can not be easily identified from confidence scores. To tackle this issue, we propose Prompt Preference Learning to evaluate the quality of the produced temporal prompts. By taking such prompts to instruct image-based foundation segmentation models, we would be able to produce high-quality masks for the referred object, enabling efficient model adaptation to referring video object segmentation. Experiments on RVOS benchmarks demonstrate the effectiveness of the Tenet framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.01119", "title": "Instant4D: 4D Gaussian Splatting in Minutes", "authors": [ "Zhanpeng Luo", "Haoxi Ran", "Li Lu" ], "github": "", "project_page": "", "abstract": "Dynamic view synthesis has seen significant advances, yet reconstructing scenes from uncalibrated, casual video remains challenging due to slow optimization and complex parameter estimation. In this work, we present Instant4D, a monocular reconstruction system that leverages native 4D representation to efficiently process casual video sequences within minutes, without calibrated cameras or depth sensors. Our method begins with geometric recovery through deep visual SLAM, followed by grid pruning to optimize scene representation. Our design significantly reduces redundancy while maintaining geometric integrity, cutting model size to under 10% of its original footprint. To handle temporal dynamics efficiently, we introduce a streamlined 4D Gaussian representation, achieving a 30x speed-up and reducing training time to within two minutes, while maintaining competitive performance across several benchmarks. Our method reconstruct a single video within 10 minutes on the Dycheck dataset or for a typical 200-frame video. We further apply our model to in-the-wild videos, showcasing its generalizability. Our project website is published at https://instant4d.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09517", "title": "StatEval: A Comprehensive Benchmark for Large Language Models in Statistics", "authors": [ "Yuchen Lu", "Run Yang", "Yichen Zhang", "Shuguang Yu", "Runpeng Dai", "Ziwei Wang", "Jiayi Xiang", "Wenxin E", "Siran Gao", "Xinyao Ruan", "Yirui Huang", "Chenjing Xi", "Haibo Hu", "Yueming Fu", "Qinglan Yu", "Xiaobing Wei", "Jiani Gu", "Rui Sun", "Jiaxuan Jia", "Fan Zhou" ], "github": "https://github.com/StatEval/StatEval", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable advances in mathematical and logical reasoning, yet statistics, as a distinct and integrative discipline, remains underexplored in benchmarking efforts. To address this gap, we introduce StatEval, the first comprehensive benchmark dedicated to statistics, spanning both breadth and depth across difficulty levels. StatEval consists of 13,817 foundational problems covering undergraduate and graduate curricula, together with 2374 research-level proof tasks extracted from leading journals. To construct the benchmark, we design a scalable multi-agent pipeline with human-in-the-loop validation that automates large-scale problem extraction, rewriting, and quality control, while ensuring academic rigor. We further propose a robust evaluation framework tailored to both computational and proof-based tasks, enabling fine-grained assessment of reasoning ability. Experimental results reveal that while closed-source models such as GPT5-mini achieve below 57\\% on research-level problems, with open-source models performing significantly lower. These findings highlight the unique challenges of statistical reasoning and the limitations of current LLMs. We expect StatEval to serve as a rigorous benchmark for advancing statistical intelligence in large language models. All data and code are available on our web platform: https://stateval.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/StatEval/StatEval", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09510", "title": "MRMR: A Realistic and Expert-Level Multidisciplinary Benchmark for Reasoning-Intensive Multimodal Retrieval", "authors": [ "Siyue Zhang", "Yuan Gao", "Xiao Zhou", "Yilun Zhao", "Tingyu Song", "Arman Cohan", "Anh Tuan Luu", "Chen Zhao" ], "github": "https://github.com/rebeccaz4/MRMR", "project_page": "", "abstract": "We introduce MRMR, the first expert-level multidisciplinary multimodal retrieval benchmark requiring intensive reasoning. MRMR contains 1,502 queries spanning 23 domains, with positive documents carefully verified by human experts. Compared to prior benchmarks, MRMR introduces three key advancements. First, it challenges retrieval systems across diverse areas of expertise, enabling fine-grained model comparison across domains. Second, queries are reasoning-intensive, with images requiring deeper interpretation such as diagnosing microscopic slides. We further introduce Contradiction Retrieval, a novel task requiring models to identify conflicting concepts. Finally, queries and documents are constructed as image-text interleaved sequences. Unlike earlier benchmarks restricted to single images or unimodal documents, MRMR offers a realistic setting with multi-image queries and mixed-modality corpus documents. We conduct an extensive evaluation of 4 categories of multimodal retrieval systems and 14 frontier models on MRMR. The text embedding model Qwen3-Embedding with LLM-generated image captions achieves the highest performance, highlighting substantial room for improving multimodal retrieval models. Although latest multimodal models such as Ops-MM-Embedding perform competitively on expert-domain queries, they fall short on reasoning-intensive tasks. We believe that MRMR paves the way for advancing multimodal retrieval in more realistic and challenging scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/rebeccaz4/MRMR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.07861", "title": "Understanding DeepResearch via Reports", "authors": [ "Tianyu Fan", "Xinyao Niu", "Yuxiang Zheng", "Fengji Zhang", "Chengen Huang", "Bei Chen", "Junyang Lin", "Chao Huang" ], "github": "https://github.com/HKUDS/DeepResearch-Eval", "project_page": "", "abstract": "DeepResearch agents represent a transformative AI paradigm, conducting expert-level research through sophisticated reasoning and multi-tool integration. However, evaluating these systems remains critically challenging due to open-ended research scenarios and existing benchmarks that focus on isolated capabilities rather than holistic performance. Unlike traditional LLM tasks, DeepResearch systems must synthesize diverse sources, generate insights, and present coherent findings, which are capabilities that resist simple verification. To address this gap, we introduce DeepResearch-ReportEval, a comprehensive framework designed to assess DeepResearch systems through their most representative outputs: research reports. Our approach systematically measures three dimensions: quality, redundancy, and factuality, using an innovative LLM-as-a-Judge methodology achieving strong expert concordance. We contribute a standardized benchmark of 100 curated queries spanning 12 real-world categories, enabling systematic capability comparison. Our evaluation of four leading commercial systems reveals distinct design philosophies and performance trade-offs, establishing foundational insights as DeepResearch evolves from information assistants toward intelligent research partners. Source code and data are available at: https://github.com/HKUDS/DeepResearch-Eval.", "github_manual": "", "github_regex": "https://github.com/HKUDS/DeepResearch-Eval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.05608", "title": "A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks", "authors": [ "Shuzheng Si", "Haozhe Zhao", "Kangyang Luo", "Gang Chen", "Fanchao Qi", "Minjia Zhang", "Baobao Chang", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "Agents based on large language models (LLMs) struggle with brainless trial-and-error and generating hallucinatory actions due to a lack of global planning in long-horizon tasks. In this paper, we introduce a plan-and-execute framework and propose EAGLET, an efficient and effective planner training method to enhance the executor agent's planning abilities without human effort. Specifically, we train a plug-and-play global planner through a two-step process: we first synthesize high-quality plans from an advanced LLM using our proposed homologous consensus filtering strategy, and apply fine-tuning as a cold start. Moreover, we further improve the planner with a rule-based reinforcement learning stage using a novel executor capability gain reward, ensuring it can handle task instructions of varying difficulty. Experiments on three long-horizon agent tasks show that executor agents equipped with our planner outperform existing methods, achieving new state-of-the-art performance. Meanwhile, EAGLET reduces training costs by 8x compared to RL-based baselines, and it does not require manual effort or extra training data, offering an efficient and effective solution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09535", "title": "Mitigating Overthinking through Reasoning Shaping", "authors": [ "Feifan Song", "Shaohang Wei", "Bofei Gao", "Yejie Wang", "Wen Luo", "Wei Li", "Linli Yao", "Weimin Xiong", "Liang Chen", "Tianyu Liu", "Houfeng Wang" ], "github": "", "project_page": "", "abstract": "Large reasoning models (LRMs) boosted by Reinforcement Learning from Verifier Reward (RLVR) have shown great power in problem solving, yet they often cause overthinking: excessive, meandering reasoning that inflates computational cost. Prior designs of penalization in RLVR manage to reduce token consumption while often harming model performance, which arises from the oversimplicity of token-level supervision. In this paper, we argue that the granularity of supervision plays a crucial role in balancing efficiency and accuracy, and propose Group Relative Segment Penalization (GRSP), a step-level method to regularize reasoning. Since preliminary analyses show that reasoning segments are strongly correlated with token consumption and model performance, we design a length-aware weighting mechanism across segment clusters. Extensive experiments demonstrate that GRSP achieves superior token efficiency without heavily compromising accuracy, especially the advantages with harder problems. Moreover, GRSP stabilizes RL training and scales effectively across model sizes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09558", "title": "AutoPR: Let's Automate Your Academic Promotion!", "authors": [ "Qiguang Chen", "Zheng Yan", "Mingda Yang", "Libo Qin", "Yixin Yuan", "Hanjing Li", "Jinhao Liu", "Yiyan Ji", "Dengyun Peng", "Jiannan Guan", "Mengkang Hu", "Yantao Du", "Wanxiang Che" ], "github": "https://github.com/LightChen233/AutoPR", "project_page": "", "abstract": "As the volume of peer-reviewed research surges, scholars increasingly rely on social platforms for discovery, while authors invest considerable effort in promoting their work to ensure visibility and citations. To streamline this process and reduce the reliance on human effort, we introduce Automatic Promotion (AutoPR), a novel task that transforms research papers into accurate, engaging, and timely public content. To enable rigorous evaluation, we release PRBench, a multimodal benchmark that links 512 peer-reviewed articles to high-quality promotional posts, assessing systems along three axes: Fidelity (accuracy and tone), Engagement (audience targeting and appeal), and Alignment (timing and channel optimization). We also introduce PRAgent, a multi-agent framework that automates AutoPR in three stages: content extraction with multimodal preparation, collaborative synthesis for polished outputs, and platform-specific adaptation to optimize norms, tone, and tagging for maximum reach. When compared to direct LLM pipelines on PRBench, PRAgent demonstrates substantial improvements, including a 604% increase in total watch time, a 438% rise in likes, and at least a 2.9x boost in overall engagement. Ablation studies show that platform modeling and targeted promotion contribute the most to these gains. Our results position AutoPR as a tractable, measurable research problem and provide a roadmap for scalable, impactful automated scholarly communication.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LightChen233/AutoPR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08457", "title": "ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level Entropy Shaping", "authors": [ "Shuang Chen", "Yue Guo", "Yimeng Ye", "Shijue Huang", "Wenbo Hu", "Haoxi Li", "Manyuan Zhang", "Jiayu Chen", "Song Guo", "Nanyun Peng" ], "github": "https://github.com/shawn0728/ARES", "project_page": "", "abstract": "Recent advances in multimodal large reasoning models (MLRMs) have substantially improved their ability to solve complex textual and visual tasks. However, these models tend to overthink on simple problems, producing unnecessarily lengthy reasoning traces, while under-exploring on challenging ones, leading to missed solutions. To address this imbalance, we propose ARES, a unified open-source framework for adaptive reasoning that dynamically allocates exploration effort based on task difficulty. Our approach is motivated by two key empirical findings: (i) while single-token entropy is noisy, high window-entropy (HWE) tokens (token-level entropies averaged under a sliding window) can reliably capture reasoning-critical moments; and (ii) reducing HWE usage benefits easy problems, while increasing it is essential for solving hard ones. Building on these insights, ARES introduces a two-stage training pipeline. In the Adaptive Cold-Start stage, we curate multimodal and textual data paired with reasoning traces of length proportional to problem difficulty, equipping the model with initial difficulty awareness. In the second stage, we develop Adaptive Entropy Policy Optimization (AEPO), which uses HWE tokens as exploration triggers to decide when to explore, and a hierarchical entropy reward with dynamic KL control to decide how much to explore. Extensive experiments demonstrate that ARES achieves superior performance and reasoning efficiency across diverse mathematical, logical, and multimodal benchmarks, while closing the gap to leading commercial systems under significantly lower inference costs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/shawn0728/ARES", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08872", "title": "GTAlign: Game-Theoretic Alignment of LLM Assistants for Mutual Welfare", "authors": [ "Siqi Zhu", "David Zhang", "Pedro Cisneros-Velarde", "Jiaxuan You" ], "github": "https://github.com/ulab-uiuc/GTAlign", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved remarkable progress in reasoning, yet sometimes produce responses that are suboptimal for users in tasks such as writing, information seeking, or providing practical guidance. Conventional alignment practices typically assume that maximizing model reward also maximizes user welfare, but this assumption frequently fails in practice: models may over-clarify or generate overly verbose reasoning when users prefer concise answers. Such behaviors resemble the prisoner's dilemma, where individually rational choices lead to socially suboptimal outcomes. The fundamental challenge is the lack of a principled decision making mechanism that mutually benefits both the LLM and the user. We propose Game-Theoretic Alignment (GTAlign), an alignment framework that integrates game-theoretic decision making into both reasoning and training. During reasoning, the model explicitly treats user-LLM interaction as a strategic game: it constructs payoff matrices within its reasoning chain to estimate welfare for both itself and the user, and then selects actions that are mutually beneficial. During training, we introduce a mutual welfare reward that reinforces cooperative responses, aligning model behavior with socially efficient outcomes. In addition, we introduce an inference technique that leverages game-theoretic reasoning to dynamically adapt LLM's response when pricing policies of LLM service change. Extensive experiments demonstrate that GTAlign substantially improves reasoning efficiency, answer quality, and mutual welfare compared to baselines across diverse tasks. The code is available at https://github.com/ulab-uiuc/GTAlign .", "github_manual": "", "github_regex": "https://github.com/ulab-uiuc/GTAlign", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.07896", "title": "ACE: Attribution-Controlled Knowledge Editing for Multi-hop Factual Recall", "authors": [ "Jiayu Yang", "Yuxuan Fan", "Songning Lai", "Shengen Wu", "Jiaqi Tang", "Chun Kang", "Zhijiang Guo", "Yutao Yue" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) require efficient knowledge editing (KE) to update factual information, yet existing methods exhibit significant performance decay in multi-hop factual recall. This failure is particularly acute when edits involve intermediate implicit subjects within reasoning chains. Through causal analysis, we reveal that this limitation stems from an oversight of how chained knowledge is dynamically represented and utilized at the neuron level. We discover that during multi hop reasoning, implicit subjects function as query neurons, which sequentially activate corresponding value neurons across transformer layers to accumulate information toward the final answer, a dynamic prior KE work has overlooked. Guided by this insight, we propose ACE: Attribution-Controlled Knowledge Editing for Multi-hop Factual Recall, a framework that leverages neuron-level attribution to identify and edit these critical query-value (Q-V) pathways. ACE provides a mechanistically grounded solution for multi-hop KE, empirically outperforming state-of-the-art methods by 9.44% on GPT-J and 37.46% on Qwen3-8B. Our analysis further reveals more fine-grained activation patterns in Qwen3 and demonstrates that the semantic interpretability of value neurons is orchestrated by query-driven accumulation. These findings establish a new pathway for advancing KE capabilities based on the principled understanding of internal reasoning mechanisms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08994", "title": "Speculative Jacobi-Denoising Decoding for Accelerating Autoregressive Text-to-image Generation", "authors": [ "Yao Teng", "Fuyun Wang", "Xian Liu", "Zhekai Chen", "Han Shi", "Yu Wang", "Zhenguo Li", "Weiyang Liu", "Difan Zou", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "As a new paradigm of visual content generation, autoregressive text-to-image models suffer from slow inference due to their sequential token-by-token decoding process, often requiring thousands of model forward passes to generate a single image. To address this inefficiency, we propose Speculative Jacobi-Denoising Decoding (SJD2), a framework that incorporates the denoising process into Jacobi iterations to enable parallel token generation in autoregressive models. Our method introduces a next-clean-token prediction paradigm that enables the pre-trained autoregressive models to accept noise-perturbed token embeddings and predict the next clean tokens through low-cost fine-tuning. This denoising paradigm guides the model towards more stable Jacobi trajectories. During inference, our method initializes token sequences with Gaussian noise and performs iterative next-clean-token-prediction in the embedding space. We employ a probabilistic criterion to verify and accept multiple tokens in parallel, and refine the unaccepted tokens for the next iteration with the denoising trajectory. Experiments show that our method can accelerate generation by reducing model forward passes while maintaining the visual quality of generated images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.05684", "title": "D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI", "authors": [ "Suwhan Choi", "Jaeyoon Jung", "Haebin Seong", "Minchan Kim", "Minyeong Kim", "Yongjun Cho", "Yoonshik Kim", "Yubeen Park", "Youngjae Yu", "Yunsung Lee" ], "github": "https://github.com/yt-dlp/yt-dlp", "project_page": "", "abstract": "Large language models leverage internet-scale text data, yet embodied AI remains constrained by the prohibitive costs of physical trajectory collection. Desktop environments -- particularly gaming -- offer a compelling alternative: they provide rich sensorimotor interactions at scale while maintaining the structured observation-action coupling essential for embodied learning. We present D2E (Desktop to Embodied AI), a framework that demonstrates desktop interactions can serve as an effective pretraining substrate for robotics embodied AI tasks. Unlike prior work that remained domain-specific (e.g., VPT for Minecraft) or kept data proprietary (e.g., SIMA), D2E establishes a complete pipeline from scalable desktop data collection to verified transfer in embodied domains. Our framework comprises three components: (1) the OWA Toolkit that unifies diverse desktop interactions into a standardized format with 152x compression, (2) the Generalist-IDM that achieves strong zero-shot generalization across unseen games through timestamp-based event prediction, enabling internet-scale pseudo-labeling, and (3) VAPT that transfers desktop-pretrained representations to physical manipulation and navigation. Using 1.3K+ hours of data (259 hours of human demonstrations, and 1K+ hours of pseudo-labeled gameplay), we achieve a total of 96.6% success rate on LIBERO manipulation and 83.3% on CANVAS navigation benchmarks. This validates that sensorimotor primitives in digital interactions exhibit sufficient invariance to transfer meaningfully to physical embodied tasks, establishing desktop pretraining as a practical paradigm for robotics. We will make all our work public, including the OWA toolkit, datasets of human-collected and pseudo-labeled, and VAPT-trained models available at https://worv-ai.github.io/d2e/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yt-dlp/yt-dlp", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.02898", "title": "One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework", "authors": [ "Lorenzo Bianchi", "Giacomo Pacini", "Fabio Carrara", "Nicola Messina", "Giuseppe Amato", "Fabrizio Falchi" ], "github": "", "project_page": "", "abstract": "Zero-shot captioners are recently proposed models that utilize common-space vision-language representations to caption images without relying on paired image-text data. To caption an image, they proceed by textually decoding a text-aligned image feature, but they limit their scope to global representations and whole-image captions. We present , a unified framework for zero-shot captioning that shifts from an image-centric to a patch-centric paradigm, enabling the captioning of arbitrary regions without the need of region-level supervision. Instead of relying on global image representations, we treat individual patches as atomic captioning units and aggregate them to describe arbitrary regions, from single patches to non-contiguous areas and entire images. We analyze the key ingredients that enable current latent captioners to work in our novel proposed framework. Experiments demonstrate that backbones producing meaningful, dense visual features, such as DINO, are key to achieving state-of-the-art performance in multiple region-based captioning tasks. Compared to other baselines and state-of-the-art competitors, our models achieve better performance on zero-shot dense, region-set, and a newly introduced trace captioning task, highlighting the effectiveness of patch-wise semantic representations for scalable caption generation. Project page at https://paciosoft.com/Patch-ioner/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.07959", "title": "DISCO: Diversifying Sample Condensation for Efficient Model Evaluation", "authors": [ "Alexander Rubinstein", "Benjamin Raible", "Martin Gubri", "Seong Joon Oh" ], "github": "https://github.com/arubique/disco-public", "project_page": "", "abstract": "Evaluating modern machine learning models has become prohibitively expensive. Benchmarks such as LMMs-Eval and HELM demand thousands of GPU hours per model. Costly evaluation reduces inclusivity, slows the cycle of innovation, and worsens environmental impact. The typical approach follows two steps. First, select an anchor subset of data. Second, train a mapping from the accuracy on this subset to the final test result. The drawback is that anchor selection depends on clustering, which can be complex and sensitive to design choices. We argue that promoting diversity among samples is not essential; what matters is to select samples that maximise diversity in model responses. Our method, Diversifying Sample Condensation (DISCO), selects the top-k samples with the greatest model disagreements. This uses greedy, sample-wise statistics rather than global clustering. The approach is conceptually simpler. From a theoretical view, inter-model disagreement provides an information-theoretically optimal rule for such greedy selection. DISCO shows empirical gains over prior methods, achieving state-of-the-art results in performance prediction across MMLU, Hellaswag, Winogrande, and ARC. Code is available here: https://github.com/arubique/disco-public.", "github_manual": "", "github_regex": "https://github.com/arubique/disco-public", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08492", "title": "Better Together: Leveraging Unpaired Multimodal Data for Stronger Unimodal Models", "authors": [ "Sharut Gupta", "Shobhita Sundaram", "Chenyu Wang", "Stefanie Jegelka", "Phillip Isola" ], "github": "https://github.com/Sharut/Unpaired-Multimodal-Learning", "project_page": "", "abstract": "Traditional multimodal learners find unified representations for tasks like visual question answering, but rely heavily on paired datasets. However, an overlooked yet potentially powerful question is: can one leverage auxiliary unpaired multimodal data to directly enhance representation learning in a target modality? We introduce UML: Unpaired Multimodal Learner, a modality-agnostic training paradigm in which a single model alternately processes inputs from different modalities while sharing parameters across them. This design exploits the assumption that different modalities are projections of a shared underlying reality, allowing the model to benefit from cross-modal structure without requiring explicit pairs. Theoretically, under linear data-generating assumptions, we show that unpaired auxiliary data can yield representations strictly more informative about the data-generating process than unimodal training. Empirically, we show that using unpaired data from auxiliary modalities -- such as text, audio, or images -- consistently improves downstream performance across diverse unimodal targets such as image and audio. Our project page: https://unpaired-multimodal.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Sharut/Unpaired-Multimodal-Learning", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.06274", "title": "Bridging Reasoning to Learning: Unmasking Illusions using Complexity Out of Distribution Generalization", "authors": [ "Mohammad Mahdi Samiei Paqaleh", "Arash Marioriyad", "Arman Tahmasebi-Zadeh", "Mohamadreza Fereydooni", "Mahdi Ghaznavai", "Mahdieh Soleymani Baghshah" ], "github": "", "project_page": "", "abstract": "Recent progress has pushed AI frontiers from pattern recognition tasks toward problems that require step by step, System2 style reasoning, especially with large language models. Yet, unlike learning, where generalization and out of distribution (OoD) evaluation concepts are well formalized, there is no clear, consistent definition or metric for reasoning ability. We propose Complexity Out of Distribution (Complexity OoD) generalization as a framework and problem setting to define and measure reasoning. A model exhibits Complexity OoD generalization when it maintains performance on test instances whose minimal required solution complexity, either representational (richer solution structure) or computational (more reasoning steps/program length), exceeds that of all training examples. We formalize complexity via solution description Kolmogorov complexity and operational proxies (e.g., object/relation counts; reasoning step counts), clarifying how Complexity OoD differs from length and compositional OoD. This lens unifies learning and reasoning: many cases solvable with System1 like processing at low complexity become System2 like under complexity pressure, while System2 can be viewed as generalization over solution structures. We translate this perspective into practice with recommendations for operationalizing Complexity OoD across the stack: incorporating complexity into benchmark and evaluation metric design, rethinking supervision to target solution traces, seeking and designing inductive biases for Complexity OoD generalization, addressing learning to reason spillovers such as spurious shortcuts, semantic robustness, catastrophic forgetting, and step wise calibration. Because Complexity OoD cannot be solved by scaling data alone, progress toward robust reasoning will require architectures and training regimes that explicitly model and allocate computation with respect to complexity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09462", "title": "Adaptive Attacks on Trusted Monitors Subvert AI Control Protocols", "authors": [ "Mikhail Terekhov", "Alexander Panfilov", "Daniil Dzenhaliou", "Caglar Gulcehre", "Maksym Andriushchenko", "Ameya Prabhu", "Jonas Geiping" ], "github": "https://github.com/UKGovernmentBEIS/control-arena", "project_page": "", "abstract": "AI control protocols serve as a defense mechanism to stop untrusted LLM agents from causing harm in autonomous settings. Prior work treats this as a security problem, stress testing with exploits that use the deployment context to subtly complete harmful side tasks, such as backdoor insertion. In practice, most AI control protocols are fundamentally based on LLM monitors, which can become a central point of failure. We study adaptive attacks by an untrusted model that knows the protocol and the monitor model, which is plausible if the untrusted model was trained with a later knowledge cutoff or can search for this information autonomously. We instantiate a simple adaptive attack vector by which the attacker embeds publicly known or zero-shot prompt injections in the model outputs. Using this tactic, frontier models consistently evade diverse monitors and complete malicious tasks on two main AI control benchmarks. The attack works universally against current protocols that rely on a monitor. Furthermore, the recent Defer-to-Resample protocol even backfires, as its resampling amplifies the prompt injection and effectively reframes it as a best-of-n attack. In general, adaptive attacks on monitor models represent a major blind spot in current control protocols and should become a standard component of evaluations for future AI control mechanisms.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/UKGovernmentBEIS/control-arena", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09426", "title": "KORMo: Korean Open Reasoning Model for Everyone", "authors": [ "Minjun Kim", "Hyeonseok Lim", "Hangyeol Yoo", "Inho Won", "Seungwoo Song", "Minkyung Cho", "Junhun Yuk", "Changsu Choi", "Dongjae Shin", "Huige Lee", "Hoyun Song", "Alice Oh", "Kyungtae Lim" ], "github": "", "project_page": "", "abstract": "This work presents the first large-scale investigation into constructing a fully open bilingual large language model (LLM) for a non-English language, specifically Korean, trained predominantly on synthetic data. We introduce KORMo-10B, a 10.8B-parameter model trained from scratch on a Korean-English corpus in which 68.74% of the Korean portion is synthetic. Through systematic experimentation, we demonstrate that synthetic data, when carefully curated with balanced linguistic coverage and diverse instruction styles, does not cause instability or degradation during large-scale pretraining. Furthermore, the model achieves performance comparable to that of contemporary open-weight multilingual baselines across a wide range of reasoning, knowledge, and instruction-following benchmarks. Our experiments reveal two key findings: (1) synthetic data can reliably sustain long-horizon pretraining without model collapse, and (2) bilingual instruction tuning enables near-native reasoning and discourse coherence in Korean. By fully releasing all components including data, code, training recipes, and logs, this work establishes a transparent framework for developing synthetic data-driven fully open models (FOMs) in low-resource settings and sets a reproducible precedent for future multilingual LLM research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08525", "title": "Which Heads Matter for Reasoning? RL-Guided KV Cache Compression", "authors": [ "Wenjie Du", "Li Jiang", "Keda Tao", "Xue Liu", "Huan Wang" ], "github": "", "project_page": "", "abstract": "Reasoning large language models exhibit complex reasoning behaviors through the extended chain-of-thought generation, creating unprecedented Key-Value (KV) cache overhead during the decoding phase. Existing KV cache compression methods underperform on reasoning models: token-dropping methods break reasoning integrity by discarding critical information, while head-reallocating methods mistakenly compress reasoning-critical heads since they are designed for retrieval tasks, resulting in significant performance degradation as compression rates increase. We hypothesize that KV heads exhibit functional heterogeneity in reasoning models-some heads are critical for chain-of-thought consistency while others are compressible. To validate and exploit this insight, we propose RLKV, a novel reasoning-critical head identification framework, which uses reinforcement learning to directly optimize the relationship between each head's cache usage and reasoning quality. As RLKV produces rewards from actual generated samples during training, it naturally identifies heads relevant to reasoning behaviors. We then allocate full KV cache to these heads while applying compressed constant KV cache to others for efficient inference. Our experiments reveal that only a small fraction of attention heads is essential for reasoning, enabling our KV compression approach to outperform baseline methods while achieving 20-50% cache reduction with near lossless performance compared to uncompressed results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.09320", "title": "Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimation", "authors": [ "Wenyao Zhang", "Hongsi Liu", "Bohan Li", "Jiawei He", "Zekun Qi", "Yunnan Wang", "Shengyang Zhao", "Xinqiang Yu", "Wenjun Zeng", "Xin Jin" ], "github": "https://github.com/Zhangwenyao1/Hybrid-depth", "project_page": "", "abstract": "Current self-supervised monocular depth estimation (MDE) approaches encounter performance limitations due to insufficient semantic-spatial knowledge extraction. To address this challenge, we propose Hybrid-depth, a novel framework that systematically integrates foundation models (e.g., CLIP and DINO) to extract visual priors and acquire sufficient contextual information for MDE. Our approach introduces a coarse-to-fine progressive learning framework: 1) Firstly, we aggregate multi-grained features from CLIP (global semantics) and DINO (local spatial details) under contrastive language guidance. A proxy task comparing close-distant image patches is designed to enforce depth-aware feature alignment using text prompts; 2) Next, building on the coarse features, we integrate camera pose information and pixel-wise language alignment to refine depth predictions. This module seamlessly integrates with existing self-supervised MDE pipelines (e.g., Monodepth2, ManyDepth) as a plug-and-play depth encoder, enhancing continuous depth estimation. By aggregating CLIP's semantic context and DINO's spatial details through language guidance, our method effectively addresses feature granularity mismatches. Extensive experiments on the KITTI benchmark demonstrate that our method significantly outperforms SOTA methods across all metrics, which also indeed benefits downstream tasks like BEV perception. Code is available at https://github.com/Zhangwenyao1/Hybrid-depth.", "github_manual": "", "github_regex": "https://github.com/Zhangwenyao1/Hybrid-depth", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08649", "title": "Formalizing Style in Personal Narratives", "authors": [ "Gustave Cortal", "Alain Finkel" ], "github": "", "project_page": "", "abstract": "Personal narratives are stories authors construct to make meaning of their experiences. Style, the distinctive way authors use language to express themselves, is fundamental to how these narratives convey subjective experiences. Yet there is a lack of a formal framework for systematically analyzing these stylistic choices. We present a novel approach that formalizes style in personal narratives as patterns in the linguistic choices authors make when communicating subjective experiences. Our framework integrates three domains: functional linguistics establishes language as a system of meaningful choices, computer science provides methods for automatically extracting and analyzing sequential patterns, and these patterns are linked to psychological observations. Using language models, we automatically extract linguistic features such as processes, participants, and circumstances. We apply our framework to hundreds of dream narratives, including a case study on a war veteran with post-traumatic stress disorder. Analysis of his narratives uncovers distinctive patterns, particularly how verbal processes dominate over mental ones, illustrating the relationship between linguistic choices and psychological states.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.07793", "title": "LLM4Cell: A Survey of Large Language and Agentic Models for Single-Cell Biology", "authors": [ "Sajib Acharjee Dip", "Adrika Zafor", "Bikash Kumar Paul", "Uddip Acharjee Shuvo", "Muhit Islam Emon", "Xuan Wang", "Liqing Zhang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) and emerging agentic frameworks are beginning to transform single-cell biology by enabling natural-language reasoning, generative annotation, and multimodal data integration. However, progress remains fragmented across data modalities, architectures, and evaluation standards. LLM4Cell presents the first unified survey of 58 foundation and agentic models developed for single-cell research, spanning RNA, ATAC, multi-omic, and spatial modalities. We categorize these methods into five families-foundation, text-bridge, spatial, multimodal, epigenomic, and agentic-and map them to eight key analytical tasks including annotation, trajectory and perturbation modeling, and drug-response prediction. Drawing on over 40 public datasets, we analyze benchmark suitability, data diversity, and ethical or scalability constraints, and evaluate models across 10 domain dimensions covering biological grounding, multi-omics alignment, fairness, privacy, and explainability. By linking datasets, models, and evaluation domains, LLM4Cell provides the first integrated view of language-driven single-cell intelligence and outlines open challenges in interpretability, standardization, and trustworthy model development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.07151", "title": "ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL", "authors": [ "Egor Cherepanov", "Alexey K. Kovalev", "Aleksandr I. Panov" ], "github": "", "project_page": "", "abstract": "Real-world robotic agents must act under partial observability and long horizons, where key cues may appear long before they affect decision making. However, most modern approaches rely solely on instantaneous information, without incorporating insights from the past. Standard recurrent or transformer models struggle with retaining and leveraging long-term dependencies: context windows truncate history, while naive memory extensions fail under scale and sparsity. We propose ELMUR (External Layer Memory with Update/Rewrite), a transformer architecture with structured external memory. Each layer maintains memory embeddings, interacts with them via bidirectional cross-attention, and updates them through an Least Recently Used (LRU) memory module using replacement or convex blending. ELMUR extends effective horizons up to 100,000 times beyond the attention window and achieves a 100% success rate on a synthetic T-Maze task with corridors up to one million steps. In POPGym, it outperforms baselines on more than half of the tasks. On MIKASA-Robo sparse-reward manipulation tasks with visual observations, it nearly doubles the performance of strong baselines. These results demonstrate that structured, layer-local external memory offers a simple and scalable approach to decision making under partial observability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.07962", "title": "LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?", "authors": [ "Jingyuan Wang", "Yankai Chen", "Zhonghang Li", "Chao Huang" ], "github": "https://github.com/HKUDS/LightReasoner", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable progress in reasoning, often through supervised fine-tuning (SFT). However, SFT is resource-intensive, relying on large curated datasets, rejection-sampled demonstrations, and uniform optimization across all tokens, even though only a fraction carry meaningful learning value. In this work, we explore a counterintuitive idea: can smaller language models (SLMs) teach larger language models (LLMs) by revealing high-value reasoning moments that reflect the latter's unique strength? We propose LightReasoner, a novel framework that leverages the behavioral divergence between a stronger expert model (LLM) and a weaker amateur model (SLM). LightReasoner operates in two stages: (1) a sampling stage that pinpoints critical reasoning moments and constructs supervision examples capturing the expert's advantage through expert-amateur contrast, and (2) a fine-tuning stage that aligns the expert model with these distilled examples, amplifying its reasoning strengths. Across seven mathematical benchmarks, LightReasoner improves accuracy by up to 28.1%, while reducing time consumption by 90%, sampled problems by 80%, and tuned token usage by 99%, all without relying on ground-truth labels. By turning weaker SLMs into effective teaching signals, LightReasoner offers a scalable and resource-efficient approach for advancing LLM reasoning. Code is available at: https://github.com/HKUDS/LightReasoner", "github_manual": "", "github_regex": "https://github.com/HKUDS/LightReasoner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.07656", "title": "MONKEY: Masking ON KEY-Value Activation Adapter for Personalization", "authors": [ "James Baker" ], "github": "https://github.com/jamesBaker361/monkey", "project_page": "", "abstract": "Personalizing diffusion models allows users to generate new images that incorporate a given subject, allowing more control than a text prompt. These models often suffer somewhat when they end up just recreating the subject image, and ignoring the text prompt. We observe that one popular method for personalization, the IP-Adapter automatically generates masks that we definitively segment the subject from the background during inference. We propose to use this automatically generated mask on a second pass to mask the image tokens, thus restricting them to the subject, not the background, allowing the text prompt to attend to the rest of the image. For text prompts describing locations and places, this produces images that accurately depict the subject while definitively matching the prompt. We compare our method to a few other test time personalization methods, and find our method displays high prompt and source image alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jamesBaker361/monkey", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08759", "title": "BEAR: Benchmarking and Enhancing Multimodal Language Models for Atomic Embodied Capabilities", "authors": [ "Yu Qi", "Haibo Zhao", "Ziyu Guo", "Siyuan Ma", "Ziyan Chen", "Yaokun Han", "Renrui Zhang", "Zitiantao Lin", "Shiji Xin", "Yijian Huang", "Kai Cheng", "Peiheng Wang", "Jiazheng Liu", "Jiayi Zhang", "Yizhe Zhu", "Wenqing Wang", "Yiran Qin", "Xupeng Zhu", "Haojie Huang", "Lawson L. S. Wong" ], "github": "", "project_page": "", "abstract": "Embodied capabilities refer to a suite of fundamental abilities for an agent to perceive, comprehend, and interact with the physical world. While multimodal large language models (MLLMs) show promise as embodied agents, a thorough and systematic evaluation of their embodied capabilities remains underexplored, as existing benchmarks primarily focus on specific domains such as planning or spatial understanding. To bridge this gap, we introduce BEAR, a comprehensive and fine-grained benchmark that evaluates MLLMs on atomic embodied capabilities. BEAR comprises 4,469 interleaved image-video-text entries across 14 domains in 6 categories, including tasks from low-level pointing, trajectory understanding, spatial reasoning, to high-level planning. Extensive evaluation results of 20 representative MLLMs reveal their persistent limitations across all domains of embodied capabilities. To tackle the shortfall, we propose BEAR-Agent, a multimodal conversable agent that integrates pretrained vision models to strengthen MLLM perception, 3D understanding, and planning capabilities. It substantially enhances MLLM performance across diverse embodied capabilities on BEAR, yielding a 9.12% absolute gain and a relative improvement of 17.5% on GPT-5. Furthermore, our experiments indicate that improving MLLM embodied capabilities can benefit embodied tasks in simulated environments. Project website: https://bear-official66.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.05528", "title": "ARMOR: High-Performance Semi-Structured Pruning via Adaptive Matrix Factorization", "authors": [ "Lawrence Liu", "Alexander Liu", "Mengdi Wang", "Tuo Zhao", "Lin F. Yang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) present significant deployment challenges due to their immense computational and memory requirements. While semi-structured pruning, particularly 2:4 sparsity, offers a path to practical hardware acceleration, existing methods often incur substantial performance degradation. To bridge this gap, we introduce ARMOR: (Adaptive Representation with Matrix-factORization), a novel one-shot post-training pruning algorithm. Instead of directly pruning weights, ARMOR factorizes each weight matrix into a 2:4 sparse core wrapped by two low-overhead, block diagonal matrices. These wrappers act as efficient pre and post-transformation error correctors, offering greater flexibility to preserve model quality compared to conventional 2:4 pruning techniques. The sparse core and block diagonal wrappers are chosen through a block coordinate descent algorithm that minimizes a layer-wise proxy loss. We theoretically prove this optimization is guaranteed to converge to a solution with a proxy loss less than or equal to state-of-the-art pruning algorithms. Experiments on Llama (Touvron et al., 2023; Dubey et al., 2024) and Qwen (Yang et al., 2025) model families demonstrate that ARMOR consistently and significantly outperforms state-of-the-art 2:4 pruning methods across a wide range of downstream tasks and perplexity evaluations. ARMOR achieves this superior performance while retaining the inference speedups and substantial memory usage reductions of 2:4 pruning, establishing a more effective trade-off between model compression and task accuracy", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-13", "arxiv_id": "2510.08564", "title": "How to Teach Large Multimodal Models New Skills", "authors": [ "Zhen Zhu", "Yiming Gong", "Yao Xiao", "Yaoyao Liu", "Derek Hoiem" ], "github": "https://github.com/jessemelpolio/LMM_CL", "project_page": "", "abstract": "How can we teach large multimodal models (LMMs) new skills without erasing prior abilities? We study sequential fine-tuning on five target skills while monitoring general ability on eight held-out benchmarks across three model families. We observe that apparent \"forgetting\" on held-out tasks after narrow fine-tuning can partly recover at later stages. We trace this behavior to a measurable shift in the output token distribution, manifested through a simple counting-bias probe that co-varies with forgetting. Guided by this picture, we identify two simple, robust tuning recipes that learn strongly while limiting drift: (i) updating only the self-attention projection layers, and (ii) updating only the MLP Gate&Up while freezing the Down projection. Across models and tasks, these choices deliver strong target gains while largely preserving held-out performance. Code is available at https://github.com/jessemelpolio/LMM_CL", "github_manual": "", "github_regex": "https://github.com/jessemelpolio/LMM_CL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.07841", "title": "Self-Improving LLM Agents at Test-Time", "authors": [ "Emre Can Acikgoz", "Cheng Qian", "Heng Ji", "Dilek Hakkani-T\u00fcr", "Gokhan Tur" ], "github": "", "project_page": "", "abstract": "One paradigm of language model (LM) fine-tuning relies on creating large training datasets, under the assumption that high quantity and diversity will enable models to generalize to novel tasks after post-training. In practice, gathering large sets of data is inefficient, and training on them is prohibitively expensive; worse, there is no guarantee that the resulting model will handle complex scenarios or generalize better. Moreover, existing techniques rarely assess whether a training sample provides novel information or is redundant with the knowledge already acquired by the model, resulting in unnecessary costs. In this work, we explore a new test-time self-improvement method to create more effective and generalizable agentic LMs on-the-fly. The proposed algorithm can be summarized in three steps: (i) first it identifies the samples that model struggles with (self-awareness), (ii) then generates similar examples from detected uncertain samples (self-data augmentation), and (iii) uses these newly generated samples at test-time fine-tuning (self-improvement). We study two variants of this approach: Test-Time Self-Improvement (TT-SI), where the same model generates additional training examples from its own uncertain cases and then learns from them, and contrast this approach with Test-Time Distillation (TT-D), where a stronger model generates similar examples for uncertain cases, enabling student to adapt using distilled supervision. Empirical evaluations across different agent benchmarks demonstrate that TT-SI improves the performance with +5.48% absolute accuracy gain on average across all benchmarks and surpasses other standard learning methods, yet using 68x less training samples. Our findings highlight the promise of TT-SI, demonstrating the potential of self-improvement algorithms at test-time as a new paradigm for building more capable agents toward self-evolution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.08026", "title": "PEAR: Phase Entropy Aware Reward for Efficient Reasoning", "authors": [ "Chen Huang", "Wei Lu", "Wenxuan Zhang" ], "github": "https://github.com/iNLP-Lab/PEAR", "project_page": "", "abstract": "Large Reasoning Models (LRMs) have achieved impressive performance on complex reasoning tasks by generating detailed chain-of-thought (CoT) explanations. However, these responses are often excessively long, containing redundant reasoning steps that inflate inference cost and reduce usability. Controlling the length of generated reasoning without sacrificing accuracy remains an open challenge. Through a systematic empirical analysis, we reveal a consistent positive correlation between model entropy and response length at different reasoning stages across diverse LRMs: the thinking phase exhibits higher entropy, reflecting exploratory behavior of longer responses, while the final answer phase shows lower entropy, indicating a more deterministic solution. This observation suggests that entropy at different reasoning stages can serve as a control knob for balancing conciseness and performance. Based on this insight, this paper introduces Phase Entropy Aware Reward (PEAR), a reward mechanism that incorporating phase-dependent entropy into the reward design. Instead of treating all tokens uniformly, PEAR penalize excessive entropy during the thinking phase and allowing moderate exploration at the final answer phase, which encourages models to generate concise reasoning traces that retain sufficient flexibility to solve the task correctly. This enables adaptive control of response length without relying on explicit length targets or rigid truncation rules. Extensive experiments across four benchmarks demonstrate that PEAR consistently reduces response length while sustaining competitive accuracy across model scales. In addition, PEAR demonstrates strong out-of-distribution (OOD) robustness beyond the training distribution. Our code is available at: https://github.com/iNLP-Lab/PEAR.", "github_manual": "", "github_regex": "https://github.com/iNLP-Lab/PEAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.10670", "title": "AdaViewPlanner: Adapting Video Diffusion Models for Viewpoint Planning in 4D Scenes", "authors": [ "Yu Li", "Menghan Xia", "Gongye Liu", "Jianhong Bai", "Xintao Wang", "Conglang Zhang", "Yuxuan Lin", "Ruihang Chu", "Pengfei Wan", "Yujiu Yang" ], "github": "", "project_page": "", "abstract": "Recent Text-to-Video (T2V) models have demonstrated powerful capability in visual simulation of real-world geometry and physical laws, indicating its potential as implicit world models. Inspired by this, we explore the feasibility of leveraging the video generation prior for viewpoint planning from given 4D scenes, since videos internally accompany dynamic scenes with natural viewpoints. To this end, we propose a two-stage paradigm to adapt pre-trained T2V models for viewpoint prediction, in a compatible manner. First, we inject the 4D scene representation into the pre-trained T2V model via an adaptive learning branch, where the 4D scene is viewpoint-agnostic and the conditional generated video embeds the viewpoints visually. Then, we formulate viewpoint extraction as a hybrid-condition guided camera extrinsic denoising process. Specifically, a camera extrinsic diffusion branch is further introduced onto the pre-trained T2V model, by taking the generated video and 4D scene as input. Experimental results show the superiority of our proposed method over existing competitors, and ablation studies validate the effectiveness of our key technical designs. To some extent, this work proves the potential of video generation models toward 4D interaction in real world.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.08886", "title": "FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs", "authors": [ "Yan Wang", "Keyi Wang", "Shanshan Yang", "Jaisal Patel", "Jeff Zhao", "Fengran Mo", "Xueqing Peng", "Lingfei Qian", "Jimin Huang", "Guojun Xiong", "Xiao-Yang Liu", "Jian-Yun Nie" ], "github": "", "project_page": "", "abstract": "The complexity of the Generally Accepted Accounting Principles (GAAP) and the hierarchical structure of eXtensible Business Reporting Language (XBRL) filings make financial auditing increasingly difficult to automate and verify. While large language models (LLMs) have demonstrated strong capabilities in unstructured text understanding, their ability to reason over structured, interdependent, and taxonomy-driven financial documents remains largely unexplored. To fill this gap, we introduce FinAuditing, the first taxonomy-aligned, structure-aware, multi-document benchmark for evaluating LLMs on financial auditing tasks. Built from real US-GAAP-compliant XBRL filings, FinAuditing defines three complementary subtasks, FinSM for semantic consistency, FinRE for relational consistency, and FinMR for numerical consistency, each targeting a distinct aspect of structured auditing reasoning. We further propose a unified evaluation framework integrating retrieval, classification, and reasoning metrics across these subtasks. Extensive zero-shot experiments on 13 state-of-the-art LLMs reveal that current models perform inconsistently across semantic, relational, and mathematical dimensions, with accuracy drops of up to 60-90% when reasoning over hierarchical multi-document structures. Our findings expose the systematic limitations of modern LLMs in taxonomy-grounded financial reasoning and establish FinAuditing as a foundation for developing trustworthy, structure-aware, and regulation-aligned financial intelligence systems. The benchmark dataset is available at Hugging Face.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11701", "title": "Demystifying Reinforcement Learning in Agentic Reasoning", "authors": [ "Zhaochen Yu", "Ling Yang", "Jiaru Zou", "Shuicheng Yan", "Mengdi Wang" ], "github": "https://github.com/Gen-Verse/Open-AgentRL", "project_page": "", "abstract": "Recently, the emergence of agentic RL has showcased that RL could also effectively improve the agentic reasoning ability of LLMs, yet the key design principles and optimal practices remain unclear. In this work, we conduct a comprehensive and systematic investigation to demystify reinforcement learning in agentic reasoning from three key perspectives: data, algorithm, and reasoning mode. We highlight our key insights: (i) Replacing stitched synthetic trajectories with real end-to-end tool-use trajectories yields a far stronger SFT initialization; high-diversity, model-aware datasets sustain exploration and markedly improve RL performance. (ii) Exploration-friendly techniques are crucial for agentic RL, such as clip higher, overlong reward shaping, and maintaining adequate policy entropy could improve the training efficiency. (iii) A deliberative strategy with fewer tool calls outperforms frequent tool calls or verbose self-reasoning, improving tool efficiency and final accuracy. Together, these simple practices consistently enhance agentic reasoning and training efficiency, achieving strong results on challenging benchmarks with smaller models, and establishing a practical baseline for future agentic RL research. Beyond these empirical insights, we further contribute a high-quality, real end-to-end agentic SFT dataset along with a high-quality RL dataset, and demonstrate the effectiveness of our insights in boosting the agentic reasoning ability of LLMs across four challenging benchmarks, including AIME2024/AIME2025, GPQA-Diamond, and LiveCodeBench-v6. With our recipes, 4B-sized models could also achieve superior agentic reasoning performance compared to 32B-sized models. Code and models: https://github.com/Gen-Verse/Open-AgentRL", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/Open-AgentRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.04617", "title": "Making Mathematical Reasoning Adaptive", "authors": [ "Zhejian Lai", "Xiang Geng", "Zhijun Wang", "Yang Bai", "Jiahuan Li", "Rongxiang Weng", "Jingang Wang", "Xuezhi Cao", "Xunliang Cai", "Shujian Huang" ], "github": "https://github.com/NJUNLP/AdaR", "project_page": "", "abstract": "Mathematical reasoning is a primary indicator of large language models (LLMs) intelligence. However, existing LLMs exhibit failures of robustness and generalization. This paper attributes these deficiencies to spurious reasoning, i.e., producing answers from superficial features. To address this challenge, we propose the AdaR framework to enable adaptive reasoning, wherein models rely on problem-solving logic to produce answers. AdaR synthesizes logically equivalent queries by varying variable values, and trains models with RLVR on these data to penalize spurious logic while encouraging adaptive logic. To improve data quality, we extract the problem-solving logic from the original query and generate the corresponding answer by code execution, then apply a sanity check. Experimental results demonstrate that AdaR improves robustness and generalization, achieving substantial improvement in mathematical reasoning while maintaining high data efficiency. Analysis indicates that data synthesis and RLVR function in a coordinated manner to enable adaptive reasoning in LLMs. Subsequent analyses derive key design insights into the effect of critical factors and the applicability to instruct LLMs. Our project is available at https://github.com/LaiZhejian/AdaR", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NJUNLP/AdaR", "github_api_added_by": "user" }, { "date": "2025-10-14", "arxiv_id": "2510.10023", "title": "Skill-Targeted Adaptive Training", "authors": [ "Yinghui He", "Abhishek Panigrahi", "Yong Lin", "Sanjeev Arora" ], "github": "https://github.com/princeton-pli/STAT", "project_page": "", "abstract": "Language models often show little to no improvement (i.e., \"saturation\") when trained via vanilla supervised fine-tuning (SFT) on data similar to what they saw in their training set (e.g., MATH). We introduce a new fine-tuning strategy, STAT, to train such a student model by using the metacognition ability of a stronger large language model (LLM) as the teacher. The teacher uses the task dataset to create a list of skills needed for the task, and then labels each data point with its required skills (Didolkar et al., 2024). By monitoring the student's answers, the teacher creates a Missing-Skill-Profile for the student, tracking how often they failed to apply each skill in their responses. We use this idea to build a modified training set in one of two ways. In STAT-Sel, the teacher uses an existing set of training examples but adaptively reweights them according to the Missing-Skill-Profile. In STAT-Syn, the teacher synthesizes additional examples involving missing skills. Across extensive experiments on Llama and Qwen models, our methods yield improvements of up to 7.5% on MATH, whereas SFT provides only limited gains. Furthermore, STAT enhances performance on out-of-distribution benchmarks (e.g., AIME24/25, AMC23, etc.) by an average of 4.6%. Crucially, we find that STAT is complementary to RL via GRPO (Shao et al., 2024): after the model is improved using STAT to address skill gaps, GRPO continues to add further gains. We conclude that skill-targeted adaptive training should broadly improve current training pipelines. Our code is available at: https://github.com/princeton-pli/STAT.", "github_manual": "", "github_regex": "https://github.com/princeton-pli/STAT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.09541", "title": "SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models", "authors": [ "Chenyu Wang", "Paria Rashidinejad", "DiJia Su", "Song Jiang", "Sid Wang", "Siyan Zhao", "Cai Zhou", "Shannon Zejiang Shen", "Feiyu Chen", "Tommi Jaakkola", "Yuandong Tian", "Bo Liu" ], "github": "", "project_page": "", "abstract": "Diffusion large language models (dLLMs) are emerging as an efficient alternative to autoregressive models due to their ability to decode multiple tokens in parallel. However, aligning dLLMs with human preferences or task-specific rewards via reinforcement learning (RL) is challenging because their intractable log-likelihood precludes the direct application of standard policy gradient methods. While prior work uses surrogates like the evidence lower bound (ELBO), these one-sided approximations can introduce significant policy gradient bias. To address this, we propose the Sandwiched Policy Gradient (SPG) that leverages both an upper and a lower bound of the true log-likelihood. Experiments show that SPG significantly outperforms baselines based on ELBO or one-step estimation. Specifically, SPG improves the accuracy over state-of-the-art RL methods for dLLMs by 3.6% in GSM8K, 2.6% in MATH500, 18.4% in Countdown and 27.0% in Sudoku.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11391", "title": "DocReward: A Document Reward Model for Structuring and Stylizing", "authors": [ "Junpeng Liu", "Yuzhong Zhao", "Bowen Cao", "Jiayu Ding", "Yilin Jia", "Tengchao Lv", "Yupan Huang", "Shaohan Huang", "Nan Yang", "Li Dong", "Lei Cui", "Tao Ge", "Xun Wang", "Huitian Jiao", "Sun Mao", "FNU Kartik", "Si-Qing Chen", "Wai Lam", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Recent advances in agentic workflows have enabled the automation of tasks such as professional document generation. However, they primarily focus on textual quality, neglecting visual structure and style, which are crucial for readability and engagement. This gap arises mainly from the absence of suitable reward models to guide agentic workflows toward producing documents with stronger structural and stylistic quality. To address this, we propose DocReward, a document reward model that evaluates documents based on their structure and style. We construct a multi-domain dataset DocPair of 117K paired documents, covering 32 domains and 267 document types, each including a high- and low-professionalism document with identical content but different structure and style. This enables the model to evaluate professionalism comprehensively, and in a textual-quality-agnostic way. DocReward is trained using the Bradley-Terry loss to score documents, penalizing predictions that contradict the annotated ranking. To assess the performance of reward models, we create a test dataset containing document bundles ranked by well-educated human evaluators. Notably, DocReward outperforms GPT-4o and GPT-5 in accuracy by 30.6 and 19.4 percentage points, respectively, demonstrating its superiority over baselines. In an extrinsic evaluation of document generation, DocReward achieves a significantly higher win rate of 60.8%, compared to GPT-5's 37.7% win rate, demonstrating its utility in guiding generation agents toward producing human-preferred documents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11696", "title": "QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs", "authors": [ "Wei Huang", "Yi Ge", "Shuai Yang", "Yicheng Xiao", "Huizi Mao", "Yujun Lin", "Hanrong Ye", "Sifei Liu", "Ka Chun Cheung", "Hongxu Yin", "Yao Lu", "Xiaojuan Qi", "Song Han", "Yukang Chen" ], "github": "", "project_page": "", "abstract": "We propose QeRL, a Quantization-enhanced Reinforcement Learning framework for large language models (LLMs). While RL is essential for LLMs' reasoning capabilities, it is resource-intensive, requiring substantial GPU memory and long rollout durations. QeRL addresses these issues by combining NVFP4 quantization with Low-Rank Adaptation (LoRA), accelerating rollout phase of RL while reducing memory overhead. Beyond efficiency, our findings show that quantization noise increases policy entropy, enhancing exploration, and enabling the discovery of better strategies during RL. To further optimize exploration, QeRL introduces an Adaptive Quantization Noise (AQN) mechanism, which dynamically adjusts noise during training. Experiments demonstrate that QeRL delivers over 1.5 times speedup in the rollout phase. Moreover, this is the first framework to enable RL training of a 32B LLM on a single H100 80GB GPU, while delivering overall speedups for RL training. It also achieves faster reward growth and higher final accuracy than 16-bit LoRA and QLoRA, while matching the performance of full-parameter fine-tuning on mathematical benchmarks such as GSM8K (90.8%) and MATH 500 (77.4%) in the 7B model. These results establish QeRL as an efficient and effective framework for RL training in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.08744", "title": "Graph Diffusion Transformers are In-Context Molecular Designers", "authors": [ "Gang Liu", "Jie Chen", "Yihan Zhu", "Michael Sun", "Tengfei Luo", "Nitesh V Chawla", "Meng Jiang" ], "github": "https://github.com/liugangcode/DemoDiff", "project_page": "", "abstract": "In-context learning allows large models to adapt to new tasks from a few demonstrations, but it has shown limited success in molecular design. Existing databases such as ChEMBL contain molecular properties spanning millions of biological assays, yet labeled data for each property remain scarce. To address this limitation, we introduce demonstration-conditioned diffusion models (DemoDiff), which define task contexts using a small set of molecule-score examples instead of text descriptions. These demonstrations guide a denoising Transformer to generate molecules aligned with target properties. For scalable pretraining, we develop a new molecular tokenizer with Node Pair Encoding that represents molecules at the motif level, requiring 5.5times fewer nodes. We curate a dataset containing millions of context tasks from multiple sources covering both drugs and materials, and pretrain a 0.7-billion-parameter model on it. Across 33 design tasks in six categories, DemoDiff matches or surpasses language models 100-1000times larger and achieves an average rank of 3.63 compared to 5.25-10.20 for domain-specific approaches. These results position DemoDiff as a molecular foundation model for in-context molecular design. Our code is available at https://github.com/liugangcode/DemoDiff.", "github_manual": "", "github_regex": "https://github.com/liugangcode/DemoDiff", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.09189", "title": "LLaMAX2: Your Translation-Enhanced Model also Performs Well in Reasoning", "authors": [ "Changjiang Gao", "Zixian Huang", "Jingyang Gong", "Shujian Huang", "Lei Li", "Fei Yuan" ], "github": "https://github.com/CONE-MT/LLaMAX2.0", "project_page": "", "abstract": "General Large Language Models (LLMs) excel in reasoning, but those enhanced for translation struggle with reasoning tasks. To address this, we propose a novel translationenhanced recipe that begins with instruct models and applies layer-selective tuning only on parallel data. Following this pipeline, we introduce the Qwen3-XPlus models, which demonstrate significant improvements in translation performance across both high- and lowresource languages, achieving 15+ spBLEU and 40+ xComet in low-resource languages, like Swahili. Interestingly, training only with small parallel datasets, Qwen3-XPlus achieves an average improvement of 1+ points on 7 multilingual tasks while maintaining proficiency comparable to the Qwen3 instruct model in 15 popular reasoning datasets. This work offers a promising approach to multilingual enhancement, significantly reducing complexity and enhancing accessibility for a wider range of languages. The code and model are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/CONE-MT/LLaMAX2.0", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.04587", "title": "Pathology-CoT: Learning Visual Chain-of-Thought Agent from Expert Whole Slide Image Diagnosis Behavior", "authors": [ "Sheng Wang", "Ruiming Wu", "Charles Herndon", "Yihang Liu", "Shunsuke Koga", "Jeanne Shen", "Zhi Huang" ], "github": "", "project_page": "", "abstract": "Diagnosing a whole-slide image is an interactive, multi-stage process involving changes in magnification and movement between fields. Although recent pathology foundation models are strong, practical agentic systems that decide what field to examine next, adjust magnification, and deliver explainable diagnoses are still lacking. The blocker is data: scalable, clinically aligned supervision of expert viewing behavior that is tacit and experience-based, not written in textbooks or online, and therefore absent from large language model training. We introduce the AI Session Recorder, which works with standard WSI viewers to unobtrusively record routine navigation and convert the viewer logs into standardized behavioral commands (inspect or peek at discrete magnifications) and bounding boxes. A lightweight human-in-the-loop review turns AI-drafted rationales into the Pathology-CoT dataset, a form of paired \"where to look\" and \"why it matters\" supervision produced at roughly six times lower labeling time. Using this behavioral data, we build Pathologist-o3, a two-stage agent that first proposes regions of interest and then performs behavior-guided reasoning. On gastrointestinal lymph-node metastasis detection, it achieved 84.5% precision, 100.0% recall, and 75.4% accuracy, exceeding the state-of-the-art OpenAI o3 model and generalizing across backbones. To our knowledge, this constitutes one of the first behavior-grounded agentic systems in pathology. Turning everyday viewer logs into scalable, expert-validated supervision, our framework makes agentic pathology practical and establishes a path to human-aligned, upgradeable clinical AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11690", "title": "Diffusion Transformers with Representation Autoencoders", "authors": [ "Boyang Zheng", "Nanye Ma", "Shengbang Tong", "Saining Xie" ], "github": "", "project_page": "", "abstract": "Latent generative modeling, where a pretrained autoencoder maps pixels into a latent space for the diffusion process, has become the standard strategy for Diffusion Transformers (DiT); however, the autoencoder component has barely evolved. Most DiTs continue to rely on the original VAE encoder, which introduces several limitations: outdated backbones that compromise architectural simplicity, low-dimensional latent spaces that restrict information capacity, and weak representations that result from purely reconstruction-based training and ultimately limit generative quality. In this work, we explore replacing the VAE with pretrained representation encoders (e.g., DINO, SigLIP, MAE) paired with trained decoders, forming what we term Representation Autoencoders (RAEs). These models provide both high-quality reconstructions and semantically rich latent spaces, while allowing for a scalable transformer-based architecture. Since these latent spaces are typically high-dimensional, a key challenge is enabling diffusion transformers to operate effectively within them. We analyze the sources of this difficulty, propose theoretically motivated solutions, and validate them empirically. Our approach achieves faster convergence without auxiliary representation alignment losses. Using a DiT variant equipped with a lightweight, wide DDT head, we achieve strong image generation results on ImageNet: 1.51 FID at 256x256 (no guidance) and 1.13 at both 256x256 and 512x512 (with guidance). RAE offers clear advantages and should be the new default for diffusion transformer training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.10395", "title": "AVoCaDO: An Audiovisual Video Captioner Driven by Temporal Orchestration", "authors": [ "Xinlong Chen", "Yue Ding", "Weihong Lin", "Jingyun Hua", "Linli Yao", "Yang Shi", "Bozhou Li", "Yuanxing Zhang", "Qiang Liu", "Pengfei Wan", "Liang Wang", "Tieniu Tan" ], "github": "", "project_page": "", "abstract": "Audiovisual video captioning aims to generate semantically rich descriptions with temporal alignment between visual and auditory events, thereby benefiting both video understanding and generation. In this paper, we present AVoCaDO, a powerful audiovisual video captioner driven by the temporal orchestration between audio and visual modalities. We propose a two-stage post-training pipeline: (1) AVoCaDO SFT, which fine-tunes the model on a newly curated dataset of 107K high-quality, temporally-aligned audiovisual captions; and (2) AVoCaDO GRPO, which leverages tailored reward functions to further enhance temporal coherence and dialogue accuracy while regularizing caption length and reducing collapse. Experimental results demonstrate that AVoCaDO significantly outperforms existing open-source models across four audiovisual video captioning benchmarks, and also achieves competitive performance on the VDC and DREAM-1K benchmark under visual-only settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.10689", "title": "OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs", "authors": [ "Caorui Li", "Yu Chen", "Yiyan Ji", "Jin Xu", "Zhenyu Cui", "Shihao Li", "Yuanxing Zhang", "Jiafu Tang", "Zhenghao Song", "Dingling Zhang", "Ying He", "Haoxiang Liu", "Yuxuan Wang", "Qiufeng Wang", "Zhenhe Wu", "Jiehui Luo", "Zhiyu Pan", "Weihao Xie", "Chenchen Zhang", "Zhaohui Wang", "Jiayi Tian", "Yanghai Wang", "Zhe Cao", "Minxin Dai", "Ke Wang", "Runzhe Wen", "Yinghao Ma", "Yaning Pan", "Sungkyun Chang", "Termeh Taheri", "Haiwen Xia", "Christos Plachouras", "Emmanouil Benetos", "Yizhi Li", "Ge Zhang", "Jian Yang", "Tianhao Peng", "Zili Wang", "Minghao Liu", "Junran Peng", "Zhaoxiang Zhang", "Jiaheng Liu" ], "github": "", "project_page": "", "abstract": "Recent advances in multimodal large language models (MLLMs) have demonstrated substantial potential in video understanding. However, existing benchmarks fail to comprehensively evaluate synergistic reasoning capabilities across audio and visual modalities, often neglecting either one of the modalities or integrating them in a logically inconsistent manner. To bridge this gap, we introduce OmniVideoBench, a large-scale and rigorously designed benchmark dedicated to assessing synergistic audio-visual understanding, with a strong emphasis on modality complementarity and logical consistency. Specifically, OmniVideoBench comprises 1000 high-quality question-answer(QA) pairs, each annotated with step-by-step reasoning traces, derived from 628 diverse videos ranging from several seconds to 30 minutes, and manually verified to guarantee complete correctness and uniqueness. Moreover, OmniVideoBench encompasses 13 carefully designed question types, covering temporal reasoning, spatial localization, counting, causal inference, summarization, and beyond, thereby capturing the essential challenges of video understanding. Evaluation of multiple MLLMs on OmniVideoBench reveals a pronounced gap between model performance and human reasoning, with open-source models lagging significantly behind their closed-source counterparts, underscoring the inherent difficulty of genuine audio-visual reasoning. We will release OmniVideoBench to foster the development of MLLMs with stronger and more generalizable reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11027", "title": "Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning", "authors": [ "Ganlin Yang", "Tianyi Zhang", "Haoran Hao", "Weiyun Wang", "Yibin Liu", "Dehui Wang", "Guanzhou Chen", "Zijian Cai", "Junting Chen", "Weijie Su", "Wengang Zhou", "Yu Qiao", "Jifeng Dai", "Jiangmiao Pang", "Gen Luo", "Wenhai Wang", "Yao Mu", "Zhi Hou" ], "github": "https://github.com/OpenGVLab/Vlaser", "project_page": "", "abstract": "While significant research has focused on developing embodied reasoning capabilities using Vision-Language Models (VLMs) or integrating advanced VLMs into Vision-Language-Action (VLA) models for end-to-end robot control, few studies directly address the critical gap between upstream VLM-based reasoning and downstream VLA policy learning. In this work, we take an initial step toward bridging embodied reasoning with VLA policy learning by introducing Vlaser - a Vision-Language-Action Model with synergistic embodied reasoning capability, which is a foundational vision-language model designed to integrate high-level reasoning with low-level control for embodied agents. Built upon the high-quality Vlaser-6M dataset, Vlaser achieves state-of-the-art performance across a range of embodied reasoning benchmarks - including spatial reasoning, embodied grounding, embodied QA, and task planning. Furthermore, we systematically examine how different VLM initializations affect supervised VLA fine-tuning, offering novel insights into mitigating the domain shift between internet-scale pre-training data and embodied-specific policy learning data. Based on these insights, our approach achieves state-of-the-art results on the WidowX benchmark and competitive performance on the Google Robot benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenGVLab/Vlaser", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11712", "title": "DiT360: High-Fidelity Panoramic Image Generation via Hybrid Training", "authors": [ "Haoran Feng", "Dizhe Zhang", "Xiangtai Li", "Bo Du", "Lu Qi" ], "github": "https://github.com/Insta360-Research-Team/DiT360", "project_page": "", "abstract": "In this work, we propose DiT360, a DiT-based framework that performs hybrid training on perspective and panoramic data for panoramic image generation. For the issues of maintaining geometric fidelity and photorealism in generation quality, we attribute the main reason to the lack of large-scale, high-quality, real-world panoramic data, where such a data-centric view differs from prior methods that focus on model design. Basically, DiT360 has several key modules for inter-domain transformation and intra-domain augmentation, applied at both the pre-VAE image level and the post-VAE token level. At the image level, we incorporate cross-domain knowledge through perspective image guidance and panoramic refinement, which enhance perceptual quality while regularizing diversity and photorealism. At the token level, hybrid supervision is applied across multiple modules, which include circular padding for boundary continuity, yaw loss for rotational robustness, and cube loss for distortion awareness. Extensive experiments on text-to-panorama, inpainting, and outpainting tasks demonstrate that our method achieves better boundary consistency and image fidelity across eleven quantitative metrics. Our code is available at https://github.com/Insta360-Research-Team/DiT360.", "github_manual": "", "github_regex": "https://github.com/Insta360-Research-Team/DiT360", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11026", "title": "GIR-Bench: Versatile Benchmark for Generating Images with Reasoning", "authors": [ "Hongxiang Li", "Yaowei Li", "Bin Lin", "Yuwei Niu", "Yuhang Yang", "Xiaoshuang Huang", "Jiayin Cai", "Xiaolong Jiang", "Yao Hu", "Long Chen" ], "github": "", "project_page": "", "abstract": "Unified multimodal models integrate the reasoning capacity of large language models with both image understanding and generation, showing great promise for advanced multimodal intelligence. However, the community still lacks a rigorous reasoning-centric benchmark to systematically evaluate the alignment between understanding and generation, and their generalization potential in complex visual tasks. To this end, we introduce GIR-Bench, a comprehensive benchmark that evaluates unified models across three complementary perspectives. Firstly, we investigate understanding-generation consistency (GIR-Bench-UGC), asking whether models can consistently leverage the same knowledge in both understanding and generation tasks. Secondly, we investigate whether models can perform reasoning-centric text-to-image generation that requires applying logical constraints and implicit knowledge to generate faithful visual content (GIR-Bench-T2I). Thirdly, we evaluate whether models can handle multi-step reasoning in editing (GIR-Bench-Edit). For each subset, we carefully design different task-specific evaluation pipelines tailored for each task. This enables fine-grained and interpretable evaluation while mitigating biases from the prevalent MLLM-as-a-Judge paradigm. Extensive ablations over various unified models and generation-only systems have shown that: Although unified models are more capable of reasoning-driven visual tasks, they still exhibit a persistent gap between understanding and generation. The data and code for GIR-Bench are available at https://hkust-longgroup.github.io/GIR-Bench{https://hkust-longgroup.github.io/GIR-Bench}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11718", "title": "CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images", "authors": [ "Chengqi Duan", "Kaiyue Sun", "Rongyao Fang", "Manyuan Zhang", "Yan Feng", "Ying Luo", "Yufang Liu", "Ke Wang", "Peng Pei", "Xunliang Cai", "Hongsheng Li", "Yi Ma", "Xihui Liu" ], "github": "https://github.com/HKU-MMLab/Math-VR-CodePlot-CoT", "project_page": "", "abstract": "Recent advances in Large Language Models (LLMs) and Vision Language Models (VLMs) have shown significant progress in mathematical reasoning, yet they still face a critical bottleneck with problems requiring visual assistance, such as drawing auxiliary lines or plotting functions to solve the problems. Most LLMs and VLMs are constrained to text-only reasoning chains, while multimodal unified models that can generate interleaved text and images lack the necessary precision and controllability for such tasks. To address this, we propose CodePlot-CoT, a code-driven Chain-of-Thought paradigm for \"thinking with images\" in mathematics. Our approach leverages the VLM to generate text reasoning as well as executable plotting code, which is then rendered into images as \"visual thought\", to solve mathematical problems. To achieve this, we first construct Math-VR, the first large-scale, bilingual dataset and benchmark for Mathematics problems with Visual Reasoning, comprising 178K samples. Second, to create high-quality training data, we develop a state-of-the-art image-to-code converter specialized for parsing complex mathematical figures into codes. Finally, using these training data, we train the CodePlot-CoT model for solving mathematical problems. Experimental results show that our model achieves up to 21% increase over base model on our new benchmark, fully validating the efficacy of our proposed code-driven reasoning paradigm. Our work opens a new direction for multimodal mathematical reasoning and provides the community with the first large-scale dataset, comprehensive benchmark, and strong approach for such problems. To facilitate future research, we make our datasets, code, and pretrained models publicly available at https://github.com/HKU-MMLab/Math-VR-CodePlot-CoT.", "github_manual": "", "github_regex": "https://github.com/HKU-MMLab/Math-VR-CodePlot-CoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.09008", "title": "On Epistemic Uncertainty of Visual Tokens for Object Hallucinations in Large Vision-Language Models", "authors": [ "Hoigi Seo", "Dong Un Kang", "Hyunjin Cho", "Joohoon Lee", "Se Young Chun" ], "github": "", "project_page": "", "abstract": "Large vision-language models (LVLMs), which integrate a vision encoder (VE) with a large language model, have achieved remarkable success across various tasks. However, there are still crucial challenges in LVLMs such as object hallucination, generating descriptions of objects that are not in the input image. Here, we argue that uncertain visual tokens within the VE is a key factor that contributes to object hallucination. Our statistical analysis found that there are positive correlations between visual tokens with high epistemic uncertainty and the occurrence of hallucinations. Furthermore, we show theoretically and empirically that visual tokens in early VE layers that exhibit large representation deviations under small adversarial perturbations indicate high epistemic uncertainty. Based on these findings, we propose a simple yet effective strategy to mitigate object hallucination by modifying the VE only. Our method comprises a proxy method with adversarial perturbations for identifying uncertain visual tokens efficiently and a method to mask these uncertain visual tokens during the self-attention process in the middle layers of the VE, suppressing their influence on visual encoding and thus alleviating hallucinations. Extensive experiments show that our method significantly reduces object hallucinations in LVLMs and can synergistically work with other prior arts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11498", "title": "ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web Coding", "authors": [ "Yuhang Li", "Chenchen Zhang", "Ruilin Lv", "Ao Liu", "Ken Deng", "Yuanxing Zhang", "Jiaheng Liu", "Wiggin Zhou", "Bo Zhou" ], "github": "", "project_page": "", "abstract": "While Large Language Models (LLMs) excel at algorithmic code generation, they struggle with front-end development, where correctness is judged on rendered pixels and interaction. We present ReLook, an agentic, vision-grounded reinforcement learning framework that empowers an agent to close a robust generate--diagnose--refine loop by invoking a multimodal LLM (MLLM) as a tool. During training, the agent uses the MLLM-in-the-loop both as a visual critic--scoring code with screenshots--and as a source of actionable, vision-grounded feedback; a strict zero-reward rule for invalid renders anchors renderability and prevents reward hacking. To prevent behavioral collapse, we introduce Forced Optimization, a strict acceptance rule that admits only improving revisions, yielding monotonically better trajectories. At inference, we decouple the critic and run a lightweight, critic-free self-edit cycle, keeping latency comparable to base decoding while retaining most of the gains. Across three widely used benchmarks, ReLook consistently outperforms strong baselines in vision-grounded front-end code generation, highlighting the benefits of agentic perception, visual rewards, and training-inference decoupling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.09905", "title": "The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs", "authors": [ "Xi Fang", "Weijie Xu", "Yuchong Zhang", "Stephanie Eckman", "Scott Nickleach", "Chandan K. Reddy" ], "github": "", "project_page": "", "abstract": "When an AI assistant remembers that Sarah is a single mother working two jobs, does it interpret her stress differently than if she were a wealthy executive? As personalized AI systems increasingly incorporate long-term user memory, understanding how this memory shapes emotional reasoning is critical. We investigate how user memory affects emotional intelligence in large language models (LLMs) by evaluating 15 models on human validated emotional intelligence tests. We find that identical scenarios paired with different user profiles produce systematically divergent emotional interpretations. Across validated user independent emotional scenarios and diverse user profiles, systematic biases emerged in several high-performing LLMs where advantaged profiles received more accurate emotional interpretations. Moreover, LLMs demonstrate significant disparities across demographic factors in emotion understanding and supportive recommendations tasks, indicating that personalization mechanisms can embed social hierarchies into models emotional reasoning. These results highlight a key challenge for memory enhanced AI: systems designed for personalization may inadvertently reinforce social inequalities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.10868", "title": "FastHMR: Accelerating Human Mesh Recovery via Token and Layer Merging with Diffusion Decoding", "authors": [ "Soroush Mehraban", "Andrea Iaboni", "Babak Taati" ], "github": "https://github.com/TaatiTeam/FastHMR", "project_page": "", "abstract": "Recent transformer-based models for 3D Human Mesh Recovery (HMR) have achieved strong performance but often suffer from high computational cost and complexity due to deep transformer architectures and redundant tokens. In this paper, we introduce two HMR-specific merging strategies: Error-Constrained Layer Merging (ECLM) and Mask-guided Token Merging (Mask-ToMe). ECLM selectively merges transformer layers that have minimal impact on the Mean Per Joint Position Error (MPJPE), while Mask-ToMe focuses on merging background tokens that contribute little to the final prediction. To further address the potential performance drop caused by merging, we propose a diffusion-based decoder that incorporates temporal context and leverages pose priors learned from large-scale motion capture datasets. Experiments across multiple benchmarks demonstrate that our method achieves up to 2.3x speed-up while slightly improving performance over the baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TaatiTeam/FastHMR", "github_api_added_by": "auto" }, { "date": "2025-10-14", "arxiv_id": "2510.11650", "title": "InfiniHuman: Infinite 3D Human Creation with Precise Control", "authors": [ "Yuxuan Xue", "Xianghui Xie", "Margaret Kostyrko", "Gerard Pons-Moll" ], "github": "https://github.com/YuxuanSnow/InfiniHuman", "project_page": "", "abstract": "Generating realistic and controllable 3D human avatars is a long-standing challenge, particularly when covering broad attribute ranges such as ethnicity, age, clothing styles, and detailed body shapes. Capturing and annotating large-scale human datasets for training generative models is prohibitively expensive and limited in scale and diversity. The central question we address in this paper is: Can existing foundation models be distilled to generate theoretically unbounded, richly annotated 3D human data? We introduce InfiniHuman, a framework that synergistically distills these models to produce richly annotated human data at minimal cost and with theoretically unlimited scalability. We propose InfiniHumanData, a fully automatic pipeline that leverages vision-language and image generation models to create a large-scale multi-modal dataset. User study shows our automatically generated identities are undistinguishable from scan renderings. InfiniHumanData contains 111K identities spanning unprecedented diversity. Each identity is annotated with multi-granularity text descriptions, multi-view RGB images, detailed clothing images, and SMPL body-shape parameters. Building on this dataset, we propose InfiniHumanGen, a diffusion-based generative pipeline conditioned on text, body shape, and clothing assets. InfiniHumanGen enables fast, realistic, and precisely controllable avatar generation. Extensive experiments demonstrate significant improvements over state-of-the-art methods in visual quality, generation speed, and controllability. Our approach enables high-quality avatar generation with fine-grained control at effectively unbounded scale through a practical and affordable solution. We will publicly release the automatic data generation pipeline, the comprehensive InfiniHumanData dataset, and the InfiniHumanGen models at https://yuxuan-xue.com/infini-human.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YuxuanSnow/InfiniHuman", "github_api_added_by": "auto" }, { "date": "2025-10-14", "arxiv_id": "2510.10201", "title": "RLFR: Extending Reinforcement Learning for LLMs with Flow Environment", "authors": [ "Jinghao Zhang", "Naishan Zheng", "Ruilin Li", "Dongzhou Cheng", "Zheming Liang", "Feng Zhao", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a promising framework for improving reasoning abilities in Large Language Models (LLMs). However, policy optimized with binary verification prone to overlook potential valuable exploration in reasoning trajectory. In view of heavy annotation cost of golden Process Reward Models (PRMs), recent works attempt using auxiliary signals for reward shaping of process tokens, involving entropy and likelihood collected from logit space. In this work, we offer a novel perspective on shaping RLVR with flow rewards derived from latent space, and propose RLFR, where the flow fields of model latents are constructed from either off-policy high-quality data and on-policy rejection sampling data, and the velocity deviations of policy latents within it are quantified to serve as a reward signal. RLFR first demonstrates that a well-established flow field can be a sound environment for reward signal collection, highlighting the expressive latent space is much underexplored. Moreover, RLFR is able to compress any off-policy expert data as reference for constituting reward signals, and we show that the efficient context dependence compressed within the hidden states are utilized, rather than individual token-level denotation for context comprehending. Experiments on both language and multimodal reasoning benchmarks demonstrate the reliability of flow rewards, and suggesting a promising paradigm for reward shaping with auxiliary signals.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11647", "title": "IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment", "authors": [ "Yinan Chen", "Jiangning Zhang", "Teng Hu", "Yuxiang Zeng", "Zhucun Xue", "Qingdong He", "Chengjie Wang", "Yong Liu", "Xiaobin Hu", "Shuicheng Yan" ], "github": "https://github.com/RyanChenYN/IVEBench", "project_page": "", "abstract": "Instruction-guided video editing has emerged as a rapidly advancing research direction, offering new opportunities for intuitive content transformation while also posing significant challenges for systematic evaluation. Existing video editing benchmarks fail to support the evaluation of instruction-guided video editing adequately and further suffer from limited source diversity, narrow task coverage and incomplete evaluation metrics. To address the above limitations, we introduce IVEBench, a modern benchmark suite specifically designed for instruction-guided video editing assessment. IVEBench comprises a diverse database of 600 high-quality source videos, spanning seven semantic dimensions, and covering video lengths ranging from 32 to 1,024 frames. It further includes 8 categories of editing tasks with 35 subcategories, whose prompts are generated and refined through large language models and expert review. Crucially, IVEBench establishes a three-dimensional evaluation protocol encompassing video quality, instruction compliance and video fidelity, integrating both traditional metrics and multimodal large language model-based assessments. Extensive experiments demonstrate the effectiveness of IVEBench in benchmarking state-of-the-art instruction-guided video editing methods, showing its ability to provide comprehensive and human-aligned evaluation outcomes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RyanChenYN/IVEBench", "github_api_added_by": "auto" }, { "date": "2025-10-14", "arxiv_id": "2510.11512", "title": "LikePhys: Evaluating Intuitive Physics Understanding in Video Diffusion Models via Likelihood Preference", "authors": [ "Jianhao Yuan", "Fabio Pizzati", "Francesco Pinto", "Lars Kunze", "Ivan Laptev", "Paul Newman", "Philip Torr", "Daniele De Martini" ], "github": "https://github.com/YuanJianhao508/LikePhys", "project_page": "", "abstract": "Intuitive physics understanding in video diffusion models plays an essential role in building general-purpose physically plausible world simulators, yet accurately evaluating such capacity remains a challenging task due to the difficulty in disentangling physics correctness from visual appearance in generation. To the end, we introduce LikePhys, a training-free method that evaluates intuitive physics in video diffusion models by distinguishing physically valid and impossible videos using the denoising objective as an ELBO-based likelihood surrogate on a curated dataset of valid-invalid pairs. By testing on our constructed benchmark of twelve scenarios spanning over four physics domains, we show that our evaluation metric, Plausibility Preference Error (PPE), demonstrates strong alignment with human preference, outperforming state-of-the-art evaluator baselines. We then systematically benchmark intuitive physics understanding in current video diffusion models. Our study further analyses how model design and inference settings affect intuitive physics understanding and highlights domain-specific capacity variations across physical laws. Empirical results show that, despite current models struggling with complex and chaotic dynamics, there is a clear trend of improvement in physics understanding as model capacity and inference settings scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YuanJianhao508/LikePhys", "github_api_added_by": "auto" }, { "date": "2025-10-14", "arxiv_id": "2510.09781", "title": "Building a Foundational Guardrail for General Agentic Systems via Synthetic Data", "authors": [ "Yue Huang", "Hang Hua", "Yujun Zhou", "Pengcheng Jing", "Manish Nagireddy", "Inkit Padhi", "Greta Dolcetti", "Zhangchen Xu", "Subhajit Chaudhury", "Ambrish Rawat", "Liubov Nedoshivina", "Pin-Yu Chen", "Prasanna Sattigeri", "Xiangliang Zhang" ], "github": "", "project_page": "", "abstract": "While LLM agents can plan multi-step tasks, intervening at the planning stage-before any action is executed-is often the safest way to prevent harm, since certain risks can lead to severe consequences once carried out. However, existing guardrails mostly operate post-execution, which is difficult to scale and leaves little room for controllable supervision at the plan level. To address this challenge, we highlight three critical gaps in current research: data gap, model gap, and evaluation gap. To close the data gap, we introduce AuraGen, a controllable engine that (i) synthesizes benign trajectories, (ii) injects category-labeled risks with calibrated difficulty, and (iii) filters outputs via an automated reward model, producing large and reliable corpora for pre-execution safety. To close the guardian model gap, we propose a foundational guardrail Safiron, combining a cross-planner adapter with a compact guardian model. The adapter unifies different input formats, while Safiron flags risky cases, assigns risk types, and generates rationales; trained in two stages with a broadly explored data recipe, Safiron achieves robust transfer across settings. To close the evaluation gap, we release Pre-Exec Bench, a realistic benchmark covering diverse tools and branching trajectories, which measures detection, fine-grained categorization, explanation, and cross-planner generalization in human-verified scenarios. Extensive experiments demonstrate consistent gains of the proposed guardrail over strong baselines on Pre-Exec Bench, and ablations further distill actionable practices, providing a practical template for safer agentic systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.10666", "title": "BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions", "authors": [ "Zhengbo Zhang", "Zhiheng Lyu", "Junhao Gong", "Hongzhu Yi", "Xinming Wang", "Yuxuan Zhou", "Jiabing Yang", "Ping Nie", "Yan Huang", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Efficiently solving real-world problems with LLMs increasingly hinges on their ability to interact with dynamic web environments and autonomously acquire external information. While recent research like Search-R1 and WebDancer demonstrates strong performance in solving web tasks, they heavily rely on additional tools to convert the interactive web environment into static text content. This is in contrast to human browsing behaviors, which involve diverse interactions with the browser, such as scrolling, clicking, and typing. In this paper, we propose BrowserAgent, a more interactive agent that solves complex tasks through human-inspired browser actions. BrowserAgent operates directly on raw web pages via Playwright through a set of predefined browser actions. We adopt a two-stage training (Supervised Fine-Tuning (SFT) and Rejection Fine-Tuning (RFT)) to improve the model's generalization abilities. Despite using significantly less training data than Search-R1, BrowserAgent achieves more competitive results across different Open-QA tasks. Additionally, we introduce an explicit memory mechanism to store key conclusions across steps, further enhancing the model's reasoning capabilities for long-horizon tasks. Notably, BrowserAgent-7B can achieve around 20\\% improvement over Search-R1 on multi-hop QA tasks like HotpotQA, 2Wiki, and Bamboogle. These results indicate that BrowserAgent can serve as a more advanced framework for more interactive and scalable web agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.10681", "title": "RePro: Training Language Models to Faithfully Recycle the Web for Pretraining", "authors": [ "Zichun Yu", "Chenyan Xiong" ], "github": "https://github.com/cxcscmu/RePro", "project_page": "", "abstract": "High-quality pretraining data is the fossil fuel of large language models (LLMs), yet its reserves are running low for frontier models. In this paper, we introduce RePro, a novel web recycling method that trains a relatively small LM with reinforcement learning to generate effective and faithful rephrasings of pretraining data. Specifically, we design one quality reward and three faithfulness rewards, optimizing the LM rephraser to convert organic data into high-quality rephrasings while maintaining its core semantics and structure. In our experiment, we train a 4B rephraser to recycle 72B tokens sampled from DCLM-RefinedWeb. Pretraining results on 400M and 1.4B models demonstrate that RePro delivers 4.7%-14.0% relative accuracy gains over organic-only baseline on 22 downstream tasks. RePro also outperforms ReWire, the state-of-the-art web recycling method that prompts a 70B rephraser, as well as the organic baseline with a 4x larger data pool. Experiments with different amounts of recycled data highlight that RePro improves organic data efficiency by 2-3x. Individual and distributional analyses validate that RePro preserves more critical information and faithfully reflects the characteristics of organic data compared to prompting-based methods. Together, these results show that RePro provides an efficient and controllable path to effectively harness the fossil fuel of LLM pretraining. We open-source our code, rephraser, and recycled data at https://github.com/cxcscmu/RePro.", "github_manual": "", "github_regex": "https://github.com/cxcscmu/RePro", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11713", "title": "Are Large Reasoning Models Interruptible?", "authors": [ "Tsung-Han Wu", "Mihran Miroyan", "David M. Chan", "Trevor Darrell", "Narges Norouzi", "Joseph E. Gonzalez" ], "github": "https://github.com/dynamic-lm/interrupt-lrm", "project_page": "", "abstract": "Large Reasoning Models (LRMs) excel at complex reasoning but are traditionally evaluated in static, \"frozen world\" settings: model responses are assumed to be instantaneous, and the context of a request is presumed to be immutable over the duration of the response. While generally true for short-term tasks, the \"frozen world\" assumption breaks down in modern reasoning tasks such as assistive programming, where models may take hours to think through problems and code may change dramatically from the time the model starts thinking to the model's final output. In this work, we challenge the frozen world assumption and evaluate LRM robustness under two realistic dynamic scenarios: interruptions, which test the quality of the model's partial outputs on a limited budget, and dynamic context, which tests model adaptation to in-flight changes. Across mathematics and programming benchmarks that require long-form reasoning, static evaluations consistently overestimate robustness: even state-of-the-art LRMs, which achieve high accuracy in static settings, can fail unpredictably when interrupted or exposed to changing context, with performance dropping by up to 60% when updates are introduced late in the reasoning process. Our analysis further reveals several novel failure modes, including reasoning leakage, where models fold the reasoning into their final answer when interrupted; panic, where under time pressure models abandon reasoning entirely and return incorrect answers; and self-doubt, where performance degrades while incorporating updated information.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dynamic-lm/interrupt-lrm", "github_api_added_by": "auto" }, { "date": "2025-10-14", "arxiv_id": "2510.10637", "title": "High-Fidelity Simulated Data Generation for Real-World Zero-Shot Robotic Manipulation Learning with Gaussian Splatting", "authors": [ "Haoyu Zhao", "Cheng Zeng", "Linghao Zhuang", "Yaxi Zhao", "Shengke Xue", "Hao Wang", "Xingyue Zhao", "Zhongyu Li", "Kehan Li", "Siteng Huang", "Mingxiu Chen", "Xin Li", "Deli Zhao", "Hua Zou" ], "github": "https://github.com/Maxwell-Zhao/RoboSimGS", "project_page": "", "abstract": "The scalability of robotic learning is fundamentally bottlenecked by the significant cost and labor of real-world data collection. While simulated data offers a scalable alternative, it often fails to generalize to the real world due to significant gaps in visual appearance, physical properties, and object interactions. To address this, we propose RoboSimGS, a novel Real2Sim2Real framework that converts multi-view real-world images into scalable, high-fidelity, and physically interactive simulation environments for robotic manipulation. Our approach reconstructs scenes using a hybrid representation: 3D Gaussian Splatting (3DGS) captures the photorealistic appearance of the environment, while mesh primitives for interactive objects ensure accurate physics simulation. Crucially, we pioneer the use of a Multi-modal Large Language Model (MLLM) to automate the creation of physically plausible, articulated assets. The MLLM analyzes visual data to infer not only physical properties (e.g., density, stiffness) but also complex kinematic structures (e.g., hinges, sliding rails) of objects. We demonstrate that policies trained entirely on data generated by RoboSimGS achieve successful zero-shot sim-to-real transfer across a diverse set of real-world manipulation tasks. Furthermore, data from RoboSimGS significantly enhances the performance and generalization capabilities of SOTA methods. Our results validate RoboSimGS as a powerful and scalable solution for bridging the sim-to-real gap.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Maxwell-Zhao/RoboSimGS", "github_api_added_by": "auto" }, { "date": "2025-10-14", "arxiv_id": "2510.10493", "title": "The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution", "authors": [ "Norbert Tihanyi", "Bilel Cherif", "Richard A. Dubniczky", "Mohamed Amine Ferrag", "Tam\u00e1s Bisztray" ], "github": "", "project_page": "", "abstract": "In this paper, we present the first large-scale study exploring whether JavaScript code generated by Large Language Models (LLMs) can reveal which model produced it, enabling reliable authorship attribution and model fingerprinting. With the rapid rise of AI-generated code, attribution is playing a critical role in detecting vulnerabilities, flagging malicious content, and ensuring accountability. While AI-vs-human detection usually treats AI as a single category we show that individual LLMs leave unique stylistic signatures, even among models belonging to the same family or parameter size. To this end, we introduce LLM-NodeJS, a dataset of 50,000 Node.js back-end programs from 20 large language models. Each has four transformed variants, yielding 250,000 unique JavaScript samples and two additional representations (JSIR and AST) for diverse research applications. Using this dataset, we benchmark traditional machine learning classifiers against fine-tuned Transformer encoders and introduce CodeT5-JSA, a custom architecture derived from the 770M-parameter CodeT5 model with its decoder removed and a modified classification head. It achieves 95.8% accuracy on five-class attribution, 94.6% on ten-class, and 88.5% on twenty-class tasks, surpassing other tested models such as BERT, CodeBERT, and Longformer. We demonstrate that classifiers capture deeper stylistic regularities in program dataflow and structure, rather than relying on surface-level features. As a result, attribution remains effective even after mangling, comment removal, and heavy code transformations. To support open science and reproducibility, we release the LLM-NodeJS dataset, Google Colab training scripts, and all related materials on GitHub: https://github.com/LLM-NodeJS-dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.10197", "title": "Don't Just Fine-tune the Agent, Tune the Environment", "authors": [ "Siyuan Lu", "Zechuan Wang", "Hongxuan Zhang", "Qintong Wu", "Leilei Gan", "Chenyi Zhuang", "Jinjie Gu", "Tao Lin" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM) agents show great promise for complex, multi-turn tool-use tasks, but their development is often hampered by the extreme scarcity of high-quality training data. Supervised fine-tuning (SFT) on synthetic data leads to overfitting, whereas standard reinforcement learning (RL) struggles with a critical cold-start problem and training instability. To address these challenges, we introduce Environment Tuning, a novel training paradigm that enables agents to learn complex behaviors directly from problem instances without relying on pre-collected expert trajectories. Environment Tuning orchestrates this learning process through a structured curriculum, actionable environment augmentation that provides corrective feedback, and fine-grained progress rewards to ensure stable and efficient exploration. Using only 400 problem instances from Berkeley Function-Calling Leaderboard (BFCL) benchmark, our method not only achieves competitive in-distribution performance against strong baselines but also demonstrates superior out-of-distribution generalization, overcoming the performance collapse common to SFT-based approaches. Our work presents a paradigm shift from supervised fine-tuning on static trajectories to dynamic, environment-based exploration, paving the way for training more robust and data-efficient agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.10062", "title": "HUME: Measuring the Human-Model Performance Gap in Text Embedding Task", "authors": [ "Adnan El Assadi", "Isaac Chung", "Roman Solomatin", "Niklas Muennighoff", "Kenneth Enevoldsen" ], "github": "https://github.com/embeddings-benchmark/mteb", "project_page": "", "abstract": "Comparing human and model performance offers a valuable perspective for understanding the strengths and limitations of embedding models, highlighting where they succeed and where they fail to capture meaning and nuance. However, such comparisons are rarely made, as human performance on embedding tasks is difficult to measure. To fill this gap, we introduce HUME: Human Evaluation Framework for Text Embeddings. While frameworks like MTEB provide broad model evaluation, they lack reliable estimates of human performance, limiting the interpretability of model scores. We measure human performance across 16 MTEB datasets spanning reranking, classification, clustering, and semantic textual similarity across linguistically diverse high- and low-resource languages. Humans achieve an average performance of 77.6% compared to 80.1% for the best embedding model, although variation is substantial: models reach near-ceiling performance on some datasets while struggling on others, suggesting dataset issues and revealing shortcomings in low-resource languages. We provide human performance baselines, insight into task difficulty patterns, and an extensible evaluation framework that enables a more meaningful interpretation of the model and informs the development of both models and benchmarks. Our code, dataset, and leaderboard are publicly available at https://github.com/embeddings-benchmark/mteb.", "github_manual": "", "github_regex": "https://github.com/embeddings-benchmark/mteb", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.09285", "title": "Spotlight on Token Perception for Multimodal Reinforcement Learning", "authors": [ "Siyuan Huang", "Xiaoye Qu", "Yafu Li", "Yun Luo", "Zefeng He", "Daizong Liu", "Yu Cheng" ], "github": "", "project_page": "", "abstract": "While Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capabilities of Large Vision-Language Models (LVLMs), most existing methods in multimodal reasoning neglect the critical role of visual perception within the RLVR optimization process. In this paper, we undertake a pioneering exploration of multimodal RLVR through the novel perspective of token perception, which measures the visual dependency of each generated token. With a granular analysis of Chain-of-Thought (CoT) processes, we uncover two key insights: first, token perception in a rollout trajectory is sparsely distributed, where only a small fraction of tokens have high visual dependency for visually-grounded reasoning; second, different trajectories exhibit significant divergence in their overall visual dependency. Based on these observations, we propose Visually-Perceptive Policy Optimization (VPPO), a novel policy gradient algorithm that explicitly leverages token perception to refine the learning signal. Specifically, VPPO achieves this through a dual mechanism: it reweights a trajectory's advantage by its overall visual dependency, and focuses policy updates exclusively on perceptually pivotal tokens. On a comprehensive suite of eight perception and reasoning benchmarks, VPPO demonstrates substantial gains over leading open-source RL-tuned models, with its effectiveness consistently validated across 7B and 32B model scales. Our findings not only establish a new token-level perceptual perspective for analyzing multimodal RLVR but also present a novel and effective optimization strategy to significantly enhance the multimodal reasoning capabilities of LVLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11652", "title": "ACADREASON: Exploring the Limits of Reasoning Models with Academic Research Problems", "authors": [ "Xin Gui", "King Zhu", "JinCheng Ren", "Qianben Chen", "Zekun Moore Wang", "Yizhi LI", "Xinpeng Liu", "Xiaowan Li", "Wenli Ren", "Linyu Miao", "Tianrui Qin", "Ziqi Shu", "He Zhu", "Xiangru Tang", "Dingfeng Shi", "Jiaheng Liu", "Yuchen Eleanor Jiang", "Minghao Liu", "Ge Zhang", "Wangchunshu Zhou" ], "github": "https://github.com/OPPO-PersonalAI/Acadreason-benchmark", "project_page": "", "abstract": "In recent years, the research focus of large language models (LLMs) and agents has shifted increasingly from demonstrating novel capabilities to complex reasoning and tackling challenging tasks. However, existing evaluations focus mainly on math/code contests or general tasks, while existing multi-domain academic benchmarks lack sufficient reasoning depth, leaving the field without a rigorous benchmark for high-level reasoning. To fill this gap, we introduce the Acadreason benchmark, designed to evaluate the ability of LLMs and agents to acquire and reason over academic knowledge. It consists of 50 expert-annotated academic problems across five high-reasoning domains, including computer science, economics, law, mathematics, and philosophy. All questions are sourced from top-tier publications in recent years and undergo rigorous annotation and quality control to ensure they are both challenging and answerable. We conduct systematic evaluations of over 10 mainstream LLMs and agents. The results show that most LLMs scored below 20 points, with even the cutting-edge GPT-5 achieving only 16 points. While agents achieved higher scores, none exceeded 40 points. This demonstrates the current capability gap between LLMs and agents in super-intelligent academic research tasks and highlights the challenges of Acadreason.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OPPO-PersonalAI/Acadreason-benchmark", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.05213", "title": "VER: Vision Expert Transformer for Robot Learning via Foundation Distillation and Dynamic Routing", "authors": [ "Yixiao Wang", "Mingxiao Huo", "Zhixuan Liang", "Yushi Du", "Lingfeng Sun", "Haotian Lin", "Jinghuan Shang", "Chensheng Peng", "Mohit Bansal", "Mingyu Ding", "Masayoshi Tomizuka" ], "github": "https://github.com/YixiaoWang7/ver", "project_page": "", "abstract": "Pretrained vision foundation models (VFMs) advance robotic learning via rich visual representations, yet individual VFMs typically excel only in specific domains, limiting generality across tasks. Distilling multiple VFMs into a unified representation for policy can mitigate this limitation but often yields inflexible task-specific feature selection and requires costly full re-training to incorporate robot-domain knowledge. We propose VER, a Vision Expert transformer for Robot learning. During pretraining, VER distills multiple VFMs into a vision expert library. It then fine-tunes only a lightweight routing network (fewer than 0.4% of parameters) to dynamically select task-relevant experts from the pretrained library for downstream robot tasks. We further introduce Patchwise Expert Routing with Curriculum Top-K Annealing to improve both flexibility and precision of dynamic expert selection. Moreover, VER supports parameter-efficient finetuning for scalable expert utilization and adaptive robot-domain knowledge integration. Across 17 diverse robotic tasks and multiple policy heads, VER achieves state-of-the-art performance. We find that VER reduces large-norm outliers in task-irrelevant regions (e.g., background) and concentrates on task-critical regions. Visualizations and codes can be found in https://yixiaowang7.github.io/ver_page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YixiaoWang7/ver", "github_api_added_by": "auto" }, { "date": "2025-10-14", "arxiv_id": "2510.11341", "title": "InternSVG: Towards Unified SVG Tasks with Multimodal Large Language Models", "authors": [ "Haomin Wang", "Jinhui Yin", "Qi Wei", "Wenguang Zeng", "Lixin Gu", "Shenglong Ye", "Zhangwei Gao", "Yaohui Wang", "Yanting Zhang", "Yuanqi Li", "Yanwen Guo", "Wenhai Wang", "Kai Chen", "Yu Qiao", "Hongjie Zhang" ], "github": "", "project_page": "", "abstract": "General SVG modeling remains challenging due to fragmented datasets, limited transferability of methods across tasks, and the difficulty of handling structural complexity. In response, we leverage the strong transfer and generalization capabilities of multimodal large language models (MLLMs) to achieve unified modeling for SVG understanding, editing, and generation. We present the InternSVG family, an integrated data-benchmark-model suite. At its core is SAgoge, the largest and most comprehensive multimodal dataset for SVG tasks, encompassing both static graphics and dynamic animations. It covers icons, long-sequence illustrations, scientific diagrams, and dynamic animations, supporting tasks of varied difficulty levels and providing deeper hierarchies with richer attributes compared to previous datasets. Based on this resource, we introduce SArena, a companion benchmark with comprehensive task definitions and standardized evaluation that aligns with the domains and difficulty spectrum covered by SAgoge. Building on these foundations, we propose InternSVG, a unified MLLM for SVG understanding, editing, and generation with SVG-specific special tokens, subword-based embedding initialization, and a two-stage training strategy that progresses from short static SVGs to long-sequence illustrations and complex animations. This unified formulation induces positive transfer and improves overall performance. Experiments on SArena and prior benchmark confirm that InternSVG achieves substantial gains and consistently outperforms leading open and proprietary counterparts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.09212", "title": "Stable Video Infinity: Infinite-Length Video Generation with Error Recycling", "authors": [ "Wuyang Li", "Wentao Pan", "Po-Chien Luan", "Yang Gao", "Alexandre Alahi" ], "github": "", "project_page": "", "abstract": "We propose Stable Video Infinity (SVI) that is able to generate infinite-length videos with high temporal consistency, plausible scene transitions, and controllable streaming storylines. While existing long-video methods attempt to mitigate accumulated errors via handcrafted anti-drifting (e.g., modified noise scheduler, frame anchoring), they remain limited to single-prompt extrapolation, producing homogeneous scenes with repetitive motions. We identify that the fundamental challenge extends beyond error accumulation to a critical discrepancy between the training assumption (seeing clean data) and the test-time autoregressive reality (conditioning on self-generated, error-prone outputs). To bridge this hypothesis gap, SVI incorporates Error-Recycling Fine-Tuning, a new type of efficient training that recycles the Diffusion Transformer (DiT)'s self-generated errors into supervisory prompts, thereby encouraging DiT to actively identify and correct its own errors. This is achieved by injecting, collecting, and banking errors through closed-loop recycling, autoregressively learning from error-injected feedback. Specifically, we (i) inject historical errors made by DiT to intervene on clean inputs, simulating error-accumulated trajectories in flow matching; (ii) efficiently approximate predictions with one-step bidirectional integration and calculate errors with residuals; (iii) dynamically bank errors into replay memory across discretized timesteps, which are resampled for new input. SVI is able to scale videos from seconds to infinite durations with no additional inference cost, while remaining compatible with diverse conditions (e.g., audio, skeleton, and text streams). We evaluate SVI on three benchmarks, including consistent, creative, and conditional settings, thoroughly verifying its versatility and state-of-the-art role.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.07624", "title": "From Data to Rewards: a Bilevel Optimization Perspective on Maximum Likelihood Estimation", "authors": [ "Abdelhakim Benechehab", "Gabriel Singer", "Corentin L\u00e9ger", "Youssef Attia El Hili", "Giuseppe Paolo", "Albert Thomas", "Maurizio Filippone", "Bal\u00e1zs K\u00e9gl" ], "github": "https://github.com/abenechehab/nll_to_po", "project_page": "", "abstract": "Generative models form the backbone of modern machine learning, underpinning state-of-the-art systems in text, vision, and multimodal applications. While Maximum Likelihood Estimation has traditionally served as the dominant training paradigm, recent work have highlighted its limitations, particularly in generalization and susceptibility to catastrophic forgetting compared to Reinforcement Learning techniques, such as Policy Gradient methods. However, these approaches depend on explicit reward signals, which are often unavailable in practice, leaving open the fundamental problem of how to align generative models when only high-quality datasets are accessible. In this work, we address this challenge via a Bilevel Optimization framework, where the reward function is treated as the optimization variable of an outer-level problem, while a policy gradient objective defines the inner-level. We then conduct a theoretical analysis of this optimization problem in a tractable setting and extract insights that, as we demonstrate, generalize to applications such as tabular classification and model-based reinforcement learning. We release the code at https://github.com/abenechehab/nll_to_po .", "github_manual": "", "github_regex": "https://github.com/abenechehab/nll_to_po", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11052", "title": "Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by Refining Belief States", "authors": [ "Qinglin Zhu", "Yizhen Yao", "Runcong Zhao", "Yanzheng Xiang", "Amrutha Saseendran", "Chen Jin", "Philip Alexander Teare", "Bin Liang", "Yulan He", "Lin Gui" ], "github": "", "project_page": "", "abstract": "Autoregressive (AR) models remain the standard for natural language generation but still suffer from high latency due to strictly sequential decoding. Recent diffusion-inspired approaches, such as LlaDA and Dream, mitigate this by generating in parallel, yet they suffer from two core limitations: information loss, as predictive distributions for non-finalized tokens are discarded at each step, and premature commitment, where local decisions are made without sufficient global coordination. We introduce Latent Refinement Decoding (LRD), a two-stage framework with Latent Refinement and a Predictive Feedback Loop. The first stage maintains masked positions as distributional mixtures of predicted tokens and the mask embedding, allowing the model to establish more globally consistent beliefs. The second stage progressively finalizes confident tokens while retaining uncertain ones for iterative feedback. KL-divergence dynamics provide a principled and reliable criterion for convergence and early stopping. Experiments across coding (HumanEval +6.3, MBPP +2.6) and reasoning (GSM8K +2.9, MATH500 +3.8) show that LRD improves accuracy while delivering speedups of up to 10.6x, making it a strong and versatile alternative for parallel sequence generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.09871", "title": "CoBia: Constructed Conversations Can Trigger Otherwise Concealed Societal Biases in LLMs", "authors": [ "Nafiseh Nikeghbal", "Amir Hossein Kargaran", "Jana Diesner" ], "github": "https://github.com/nafisenik/CoBia", "project_page": "", "abstract": "Improvements in model construction, including fortified safety guardrails, allow Large language models (LLMs) to increasingly pass standard safety checks. However, LLMs sometimes slip into revealing harmful behavior, such as expressing racist viewpoints, during conversations. To analyze this systematically, we introduce CoBia, a suite of lightweight adversarial attacks that allow us to refine the scope of conditions under which LLMs depart from normative or ethical behavior in conversations. CoBia creates a constructed conversation where the model utters a biased claim about a social group. We then evaluate whether the model can recover from the fabricated bias claim and reject biased follow-up questions. We evaluate 11 open-source as well as proprietary LLMs for their outputs related to six socio-demographic categories that are relevant to individual safety and fair treatment, i.e., gender, race, religion, nationality, sex orientation, and others. Our evaluation is based on established LLM-based bias metrics, and we compare the results against human judgments to scope out the LLMs' reliability and alignment. The results suggest that purposefully constructed conversations reliably reveal bias amplification and that LLMs often fail to reject biased follow-up questions during dialogue. This form of stress-testing highlights deeply embedded biases that can be surfaced through interaction. Code and artifacts are available at https://github.com/nafisenik/CoBia.", "github_manual": "", "github_regex": "https://github.com/nafisenik/CoBia", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.06582", "title": "Through the Perspective of LiDAR: A Feature-Enriched and Uncertainty-Aware Annotation Pipeline for Terrestrial Point Cloud Segmentation", "authors": [ "Fei Zhang", "Rob Chancia", "Josie Clapp", "Amirhossein Hassanzadeh", "Dimah Dera", "Richard MacKenzie", "Jan van Aardt" ], "github": "", "project_page": "", "abstract": "Accurate semantic segmentation of terrestrial laser scanning (TLS) point clouds is limited by costly manual annotation. We propose a semi-automated, uncertainty-aware pipeline that integrates spherical projection, feature enrichment, ensemble learning, and targeted annotation to reduce labeling effort, while sustaining high accuracy. Our approach projects 3D points to a 2D spherical grid, enriches pixels with multi-source features, and trains an ensemble of segmentation networks to produce pseudo-labels and uncertainty maps, the latter guiding annotation of ambiguous regions. The 2D outputs are back-projected to 3D, yielding densely annotated point clouds supported by a three-tier visualization suite (2D feature maps, 3D colorized point clouds, and compact virtual spheres) for rapid triage and reviewer guidance. Using this pipeline, we build Mangrove3D, a semantic segmentation TLS dataset for mangrove forests. We further evaluate data efficiency and feature importance to address two key questions: (1) how much annotated data are needed and (2) which features matter most. Results show that performance saturates after ~12 annotated scans, geometric features contribute the most, and compact nine-channel stacks capture nearly all discriminative power, with the mean Intersection over Union (mIoU) plateauing at around 0.76. Finally, we confirm the generalization of our feature-enrichment strategy through cross-dataset tests on ForestSemantic and Semantic3D. Our contributions include: (i) a robust, uncertainty-aware TLS annotation pipeline with visualization tools; (ii) the Mangrove3D dataset; and (iii) empirical guidance on data efficiency and feature importance, thus enabling scalable, high-quality segmentation of TLS point clouds for ecological monitoring and beyond. The dataset and processing scripts are publicly available at https://fz-rit.github.io/through-the-lidars-eye/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11218", "title": "The Curious Case of Factual (Mis)Alignment between LLMs' Short- and Long-Form Answers", "authors": [ "Saad Obaid ul Islam", "Anne Lauscher", "Goran Glava\u0161" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) can correctly answer \"When was Einstein born?\" yet fail to provide the same date when writing about Einstein's life revealing a fundamental inconsistency in how models access factual knowledge across task complexities. While models display impressive accuracy on factual question-answering benchmarks, the reliability gap between simple and complex queries remains poorly understood, eroding their trustworthiness. In this work, we introduce Short-Long Form Alignment for Factual Question Answering (SLAQ), a controlled evaluation framework that compares LLMs' answers to the same factual questions asked (a) in isolation (short) vs. (b) integrated into complex queries (long). Looking at 16 LLMs across 600 queries, we find a systematic misalignment of answers to the corresponding short and long queries. We further uncover position-dependent accuracy loss and momentum effects where consecutive correct or incorrect answers create self-reinforcing patterns. Through mechanistic analysis, we find that aligned facts activate overlapping model internals, and that metrics based on mechanistic similarity can predict short-long answer alignment with up to 78% accuracy. Our work establishes factual consistency over query complexity as an important aspect of LLMs' trustworthiness and challenges current evaluation practices, which implicitly assume that good performance for simple factual queries implies reliability in more complex knowledge-seeking tasks too.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.10047", "title": "SwarmSys: Decentralized Swarm-Inspired Agents for Scalable and Adaptive Reasoning", "authors": [ "Ruohao Li", "Hongjun Liu", "Leyi Zhao", "Zisu Li", "Jiawei Li", "Jiajun Jiang", "Linning Xu", "Chen Zhao", "Mingming Fan", "Chen Liang" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) agents have shown remarkable reasoning abilities. However, existing multi-agent frameworks often rely on fixed roles or centralized control, limiting scalability and adaptability in long-horizon reasoning. We introduce SwarmSys, a closed-loop framework for distributed multi-agent reasoning inspired by swarm intelligence. Coordination in SwarmSys emerges through iterative interactions among three specialized roles, Explorers, Workers, and Validators, that continuously cycle through exploration, exploitation, and validation. To enable scalable and adaptive collaboration, we integrate adaptive agent and event profiles, embedding-based probabilistic matching, and a pheromone-inspired reinforcement mechanism, supporting dynamic task allocation and self-organizing convergence without global supervision. Across symbolic reasoning, research synthesis, and scientific programming tasks, SwarmSys consistently outperforms baselines, improving both accuracy and reasoning stability. These findings highlight swarm-inspired coordination as a promising paradigm for scalable, robust, and adaptive multi-agent reasoning, suggesting that coordination scaling may rival model scaling in advancing LLM intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.04201", "title": "World-To-Image: Grounding Text-to-Image Generation with Agent-Driven World Knowledge", "authors": [ "Moo Hyun Son", "Jintaek Oh", "Sun Bin Mun", "Jaechul Roh", "Sehyun Choi" ], "github": "https://github.com/mhson-kyle/World-To-Image", "project_page": "", "abstract": "While text-to-image (T2I) models can synthesize high-quality images, their performance degrades significantly when prompted with novel or out-of-distribution (OOD) entities due to inherent knowledge cutoffs. We introduce World-To-Image, a novel framework that bridges this gap by empowering T2I generation with agent-driven world knowledge. We design an agent that dynamically searches the web to retrieve images for concepts unknown to the base model. This information is then used to perform multimodal prompt optimization, steering powerful generative backbones toward an accurate synthesis. Critically, our evaluation goes beyond traditional metrics, utilizing modern assessments like LLMGrader and ImageReward to measure true semantic fidelity. Our experiments show that World-To-Image substantially outperforms state-of-the-art methods in both semantic alignment and visual aesthetics, achieving +8.1% improvement in accuracy-to-prompt on our curated NICE benchmark. Our framework achieves these results with high efficiency in less than three iterations, paving the way for T2I systems that can better reflect the ever-changing real world. Our demo code is available herehttps://github.com/mhson-kyle/World-To-Image.", "github_manual": "", "github_regex": "https://github.com/mhson-kyle/World-To-Image", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.10606", "title": "ViSurf: Visual Supervised-and-Reinforcement Fine-Tuning for Large Vision-and-Language Models", "authors": [ "Yuqi Liu", "Liangyu Chen", "Jiazhen Liu", "Mingkang Zhu", "Zhisheng Zhong", "Bei Yu", "Jiaya Jia" ], "github": "", "project_page": "", "abstract": "Typical post-training paradigms for Large Vision-and-Language Models (LVLMs) include Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR). SFT leverages external guidance to inject new knowledge, whereas RLVR utilizes internal reinforcement to enhance reasoning capabilities and overall performance. However, our analysis reveals that SFT often leads to sub-optimal performance, while RLVR struggles with tasks that exceed the model's internal knowledge base. To address these limitations, we propose ViSurf (Visual Supervised-and-Reinforcement Fine-Tuning), a unified post-training paradigm that integrates the strengths of both SFT and RLVR within a single stage. We analyze the derivation of the SFT and RLVR objectives to establish the ViSurf objective, providing a unified perspective on these two paradigms. The core of ViSurf involves injecting ground-truth labels into the RLVR rollouts, thereby providing simultaneous external supervision and internal reinforcement. Furthermore, we introduce three novel reward control strategies to stabilize and optimize the training process. Extensive experiments across several diverse benchmarks demonstrate the effectiveness of ViSurf, outperforming both individual SFT, RLVR, and two-stage SFT \\textrightarrow RLVR. In-depth analysis corroborates these findings, validating the derivation and design principles of ViSurf.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11496", "title": "AndesVL Technical Report: An Efficient Mobile-side Multimodal Large Language Model", "authors": [ "Zhiwei Jin", "Xiaohui Song", "Nan Wang", "Yafei Liu", "Chao Li", "Xin Li", "Ruichen Wang", "Zhihao Li", "Qi Qi", "Long Cheng", "Dongze Hao", "Quanlong Zheng", "Yanhao Zhang", "Haobo Ji", "Jian Ma", "Zhitong Zheng", "Zhenyi Lin", "Haolin Deng", "Xin Zou", "Xiaojie Yin", "Ruilin Wang", "Liankai Cai", "Haijing Liu", "Yuqing Qiu", "Ke Chen", "Zixian Li", "Chi Xie", "Huafei Li", "Chenxing Li", "Chuangchuang Wang", "Kai Tang", "Zhiguang Zhu", "Kai Tang", "Wenmei Gao", "Rui Wang", "Jun Wu", "Chao Liu", "Qin Xie", "Chen Chen", "Haonan Lu" ], "github": "", "project_page": "", "abstract": "In recent years, while cloud-based MLLMs such as QwenVL, InternVL, GPT-4o, Gemini, and Claude Sonnet have demonstrated outstanding performance with enormous model sizes reaching hundreds of billions of parameters, they significantly surpass the limitations in memory, power consumption, and computing capacity of edge devices such as mobile phones. This paper introduces AndesVL, a suite of mobile-side MLLMs with 0.6B to 4B parameters based on Qwen3's LLM and various visual encoders. We comprehensively outline the model architectures, training pipeline, and training data of AndesVL, which achieves first-tier performance across a wide range of open-source benchmarks, including fields such as text-rich image understanding, reasoning and math, multi-image comprehension, general VQA, hallucination mitigation, multilingual understanding, and GUI-related tasks when compared with state-of-the-art models of a similar scale. Furthermore, we introduce a 1+N LoR", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.09474", "title": "Multimodal Policy Internalization for Conversational Agents", "authors": [ "Zhenhailong Wang", "Jiateng Liu", "Amin Fazel", "Ritesh Sarkhel", "Xing Fan", "Xiang Li", "Chenlei Guo", "Heng Ji", "Ruhi Sarikaya" ], "github": "https://github.com/MikeWangWZHL/TriMPI", "project_page": "", "abstract": "Modern conversational agents like ChatGPT and Alexa+ rely on predefined policies specifying metadata, response styles, and tool-usage rules. As these LLM-based systems expand to support diverse business and user queries, such policies, often implemented as in-context prompts, are becoming increasingly complex and lengthy, making faithful adherence difficult and imposing large fixed computational costs. With the rise of multimodal agents, policies that govern visual and multimodal behaviors are critical but remain understudied. Prior prompt-compression work mainly shortens task templates and demonstrations, while existing policy-alignment studies focus only on text-based safety rules. We introduce Multimodal Policy Internalization (MPI), a new task that internalizes reasoning-intensive multimodal policies into model parameters, enabling stronger policy-following without including the policy during inference. MPI poses unique data and algorithmic challenges. We build two datasets spanning synthetic and real-world decision-making and tool-using tasks and propose TriMPI, a three-stage training framework. TriMPI first injects policy knowledge via continual pretraining, then performs supervised finetuning, and finally applies PolicyRollout, a GRPO-style reinforcement learning extension that augments rollouts with policy-aware responses for grounded exploration. TriMPI achieves notable gains in end-to-end accuracy, generalization, and robustness to forgetting. As the first work on multimodal policy internalization, we provide datasets, training recipes, and comprehensive evaluations to foster future research. Project page: https://mikewangwzhl.github.io/TriMPI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MikeWangWZHL/TriMPI", "github_api_added_by": "auto" }, { "date": "2025-10-14", "arxiv_id": "2510.01427", "title": "A Tale of LLMs and Induced Small Proxies: Scalable Agents for Knowledge Mining", "authors": [ "Sipeng Zhang", "Longfei Yun", "Zilong Wang", "Jingbo Shang", "Letian Peng" ], "github": "https://github.com/LongfeiYun17/falconer", "project_page": "", "abstract": "At the core of Deep Research is knowledge mining, the task of extracting structured information from massive unstructured text in response to user instructions. Large language models (LLMs) excel at interpreting such instructions but are prohibitively expensive to deploy at scale, while traditional pipelines of classifiers and extractors remain efficient yet brittle and unable to generalize to new tasks. We introduce Falconer, a collaborative framework that combines the agentic reasoning of LLMs with lightweight proxy models for scalable knowledge mining. In Falconer, LLMs act as planners, decomposing user instructions into executable pipelines, and as annotators, generating supervision to train small proxies. The framework unifies classification and extraction into two atomic operations, get label and get span, enabling a single instruction-following model to replace multiple task-specific components. To evaluate the consistency between proxy models incubated by Falconer and annotations provided by humans and large models, we construct new benchmarks covering both planning and end-to-end execution. Experiments show that Falconer closely matches state-of-the-art LLMs in instruction-following accuracy while reducing inference cost by up to 90% and accelerating large-scale knowledge mining by more than 20x, offering an efficient and scalable foundation for Deep Research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LongfeiYun17/falconer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.09023", "title": "The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections", "authors": [ "Milad Nasr", "Nicholas Carlini", "Chawin Sitawarin", "Sander V. Schulhoff", "Jamie Hayes", "Michael Ilie", "Juliette Pluto", "Shuang Song", "Harsh Chaudhari", "Ilia Shumailov", "Abhradeep Thakurta", "Kai Yuanqing Xiao", "Andreas Terzis", "Florian Tram\u00e8r" ], "github": "", "project_page": "", "abstract": "How should we evaluate the robustness of language model defenses? Current defenses against jailbreaks and prompt injections (which aim to prevent an attacker from eliciting harmful knowledge or remotely triggering malicious actions, respectively) are typically evaluated either against a static set of harmful attack strings, or against computationally weak optimization methods that were not designed with the defense in mind. We argue that this evaluation process is flawed. Instead, we should evaluate defenses against adaptive attackers who explicitly modify their attack strategy to counter a defense's design while spending considerable resources to optimize their objective. By systematically tuning and scaling general optimization techniques-gradient descent, reinforcement learning, random search, and human-guided exploration-we bypass 12 recent defenses (based on a diverse set of techniques) with attack success rate above 90% for most; importantly, the majority of defenses originally reported near-zero attack success rates. We believe that future defense work must consider stronger attacks, such as the ones we describe, in order to make reliable and convincing claims of robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.08561", "title": "MultiCOIN: Multi-Modal COntrollable Video INbetweening", "authors": [ "Maham Tanveer", "Yang Zhou", "Simon Niklaus", "Ali Mahdavi Amiri", "Hao Zhang", "Krishna Kumar Singh", "Nanxuan Zhao" ], "github": "", "project_page": "", "abstract": "Video inbetweening creates smooth and natural transitions between two image frames, making it an indispensable tool for video editing and long-form video synthesis. Existing works in this domain are unable to generate large, complex, or intricate motions. In particular, they cannot accommodate the versatility of user intents and generally lack fine control over the details of intermediate frames, leading to misalignment with the creative mind. To fill these gaps, we introduce MultiCOIN, a video inbetweening framework that allows multi-modal controls, including depth transition and layering, motion trajectories, text prompts, and target regions for movement localization, while achieving a balance between flexibility, ease of use, and precision for fine-grained video interpolation. To achieve this, we adopt the Diffusion Transformer (DiT) architecture as our video generative model, due to its proven capability to generate high-quality long videos. To ensure compatibility between DiT and our multi-modal controls, we map all motion controls into a common sparse and user-friendly point-based representation as the video/noise input. Further, to respect the variety of controls which operate at varying levels of granularity and influence, we separate content controls and motion controls into two branches to encode the required features before guiding the denoising process, resulting in two generators, one for motion and the other for content. Finally, we propose a stage-wise training strategy to ensure that our model learns the multi-modal controls smoothly. Extensive qualitative and quantitative experiments demonstrate that multi-modal controls enable a more dynamic, customizable, and contextually accurate visual narrative.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.07731", "title": "oMeBench: Towards Robust Benchmarking of LLMs in Organic Mechanism Elucidation and Reasoning", "authors": [ "Ruiling Xu", "Yifan Zhang", "Qingyun Wang", "Carl Edwards", "Heng Ji" ], "github": "", "project_page": "", "abstract": "Organic reaction mechanisms are the stepwise elementary reactions by which reactants form intermediates and products, and are fundamental to understanding chemical reactivity and designing new molecules and reactions. Although large language models (LLMs) have shown promise in understanding chemical tasks such as synthesis design, it is unclear to what extent this reflects genuine chemical reasoning capabilities, i.e., the ability to generate valid intermediates, maintain chemical consistency, and follow logically coherent multi-step pathways. We address this by introducing oMeBench, the first large-scale, expert-curated benchmark for organic mechanism reasoning in organic chemistry. It comprises over 10,000 annotated mechanistic steps with intermediates, type labels, and difficulty ratings. Furthermore, to evaluate LLM capability more precisely and enable fine-grained scoring, we propose oMeS, a dynamic evaluation framework that combines step-level logic and chemical similarity. We analyze the performance of state-of-the-art LLMs, and our results show that although current models display promising chemical intuition, they struggle with correct and consistent multi-step reasoning. Notably, we find that using prompting strategy and fine-tuning a specialist model on our proposed dataset increases performance by 50% over the leading closed-source model. We hope that oMeBench will serve as a rigorous foundation for advancing AI systems toward genuine chemical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.11769", "title": "GAR: Generative Adversarial Reinforcement Learning for Formal Theorem Proving", "authors": [ "Ruida Wang", "Jiarui Yao", "Rui Pan", "Shizhe Diao", "Tong Zhang" ], "github": "", "project_page": "", "abstract": "Solving math problems through verifiable languages such as Lean has significantly impacted both the mathematics and computer science communities. Current state-of-the-art models are often trained with expensive online Reinforcement Learning (RL) or expert iteration. However, these approaches rely on fixed problem sets, which causes inefficient training and limits the model to tackle complex problems. To overcome these limitations, we propose GAR: Generative Adversarial Reinforcement learning, a comprehensive RL training framework that jointly trains the problem composer and solver in an adversarial loop. GAR introduces an implicit curriculum learning mechanism, which aligns task difficulty with the prover's evolving capability. It thereby improves the training efficiency and enables stronger performance of proving advanced theorems. Experiments show that with GAR training, Goedel-Prover-V2-8B and DeepSeek-Prover-V2-7B achieve an average relative improvement in pass@32 of 4.20% on MiniF2F-Test benchmark, while DeepSeek-Prover-V2's pass@32 on ProofNet-Test increases from 22.58% to 25.81%. Beyond formal proving, GAR establishes a general RL paradigm for co-evolution of problem generation and solving under verifiable environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12801", "title": "DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search", "authors": [ "Kartik Narayan", "Yang Xu", "Tian Cao", "Kavya Nerella", "Vishal M. Patel", "Navid Shiee", "Peter Grasch", "Chao Jia", "Yinfei Yang", "Zhe Gan" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) in real-world applications require access to external knowledge sources and must remain responsive to the dynamic and ever-changing real-world information in order to address information-seeking and knowledge-intensive user queries. Existing approaches, such as retrieval augmented generation (RAG) methods, search agents, and search equipped MLLMs, often suffer from rigid pipelines, excessive search calls, and poorly constructed search queries, which result in inefficiencies and suboptimal outcomes. To address these limitations, we present DeepMMSearch-R1, the first multimodal LLM capable of performing on-demand, multi-turn web searches and dynamically crafting queries for both image and text search tools. Specifically, DeepMMSearch-R1 can initiate web searches based on relevant crops of the input image making the image search more effective, and can iteratively adapt text search queries based on retrieved information, thereby enabling self-reflection and self-correction. Our approach relies on a two-stage training pipeline: a cold start supervised finetuning phase followed by an online reinforcement learning optimization. For training, we introduce DeepMMSearchVQA, a novel multimodal VQA dataset created through an automated pipeline intermixed with real-world information from web search tools. This dataset contains diverse, multi-hop queries that integrate textual and visual information, teaching the model when to search, what to search for, which search tool to use and how to reason over the retrieved information. We conduct extensive experiments across a range of knowledge-intensive benchmarks to demonstrate the superiority of our approach. Finally, we analyze the results and provide insights that are valuable for advancing multimodal web-search.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12789", "title": "UniFusion: Vision-Language Model as Unified Encoder in Image Generation", "authors": [ "Kevin Li", "Manuel Brack", "Sudeep Katakol", "Hareesh Ravi", "Ajinkya Kale" ], "github": "", "project_page": "", "abstract": "Although recent advances in visual generation have been remarkable, most existing architectures still depend on distinct encoders for images and text. This separation constrains diffusion models' ability to perform cross-modal reasoning and knowledge transfer. Prior attempts to bridge this gap often use the last layer information from VLM, employ multiple visual encoders, or train large unified models jointly for text and image generation, which demands substantial computational resources and large-scale data, limiting its accessibility.We present UniFusion, a diffusion-based generative model conditioned on a frozen large vision-language model (VLM) that serves as a unified multimodal encoder. At the core of UniFusion is the Layerwise Attention Pooling (LAP) mechanism that extracts both high level semantics and low level details from text and visual tokens of a frozen VLM to condition a diffusion generative model. We demonstrate that LAP outperforms other shallow fusion architectures on text-image alignment for generation and faithful transfer of visual information from VLM to the diffusion model which is key for editing. We propose VLM-Enabled Rewriting Injection with Flexibile Inference (VERIFI), which conditions a diffusion transformer (DiT) only on the text tokens generated by the VLM during in-model prompt rewriting. VERIFI combines the alignment of the conditioning distribution with the VLM's reasoning capabilities for increased capabilities and flexibility at inference. In addition, finetuning on editing task not only improves text-image alignment for generation, indicative of cross-modality knowledge transfer, but also exhibits tremendous generalization capabilities. Our model when trained on single image editing, zero-shot generalizes to multiple image references further motivating the unified encoder design of UniFusion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.09116", "title": "DITING: A Multi-Agent Evaluation Framework for Benchmarking Web Novel Translation", "authors": [ "Enze Zhang", "Jiaying Wang", "Mengxi Xiao", "Jifei Liu", "Ziyan Kuang", "Rui Dong", "Eric Dong", "Sophia Ananiadou", "Min Peng", "Qianqian Xie" ], "github": "https://github.com/WHUNextGen/DITING", "project_page": "", "abstract": "Large language models (LLMs) have substantially advanced machine translation (MT), yet their effectiveness in translating web novels remains unclear. Existing benchmarks rely on surface-level metrics that fail to capture the distinctive traits of this genre. To address these gaps, we introduce DITING, the first comprehensive evaluation framework for web novel translation, assessing narrative and cultural fidelity across six dimensions: idiom translation, lexical ambiguity, terminology localization, tense consistency, zero-pronoun resolution, and cultural safety, supported by over 18K expert-annotated Chinese-English sentence pairs. We further propose AgentEval, a reasoning-driven multi-agent evaluation framework that simulates expert deliberation to assess translation quality beyond lexical overlap, achieving the highest correlation with human judgments among seven tested automatic metrics. To enable metric comparison, we develop MetricAlign, a meta-evaluation dataset of 300 sentence pairs annotated with error labels and scalar quality scores. Comprehensive evaluation of fourteen open, closed, and commercial models reveals that Chinese-trained LLMs surpass larger foreign counterparts, and that DeepSeek-V3 delivers the most faithful and stylistically coherent translations. Our work establishes a new paradigm for exploring LLM-based web novel translation and provides public resources to advance future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WHUNextGen/DITING", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12586", "title": "Advancing End-to-End Pixel Space Generative Modeling via Self-supervised Pre-training", "authors": [ "Jiachen Lei", "Keli Liu", "Julius Berner", "Haiming Yu", "Hongkai Zheng", "Jiahong Wu", "Xiangxiang Chu" ], "github": "https://github.com/AMAP-ML/EPG", "project_page": "", "abstract": "Pixel-space generative models are often more difficult to train and generally underperform compared to their latent-space counterparts, leaving a persistent performance and efficiency gap. In this paper, we introduce a novel two-stage training framework that closes this gap for pixel-space diffusion and consistency models. In the first stage, we pre-train encoders to capture meaningful semantics from clean images while aligning them with points along the same deterministic sampling trajectory, which evolves points from the prior to the data distribution. In the second stage, we integrate the encoder with a randomly initialized decoder and fine-tune the complete model end-to-end for both diffusion and consistency models. Our training framework demonstrates strong empirical performance on ImageNet dataset. Specifically, our diffusion model reaches an FID of 2.04 on ImageNet-256 and 2.35 on ImageNet-512 with 75 number of function evaluations (NFE), surpassing prior pixel-space methods by a large margin in both generation quality and efficiency while rivaling leading VAE-based models at comparable training cost. Furthermore, on ImageNet-256, our consistency model achieves an impressive FID of 8.82 in a single sampling step, significantly surpassing its latent-space counterpart. To the best of our knowledge, this marks the first successful training of a consistency model directly on high-resolution images without relying on pre-trained VAEs or diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AMAP-ML/EPG", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12747", "title": "FlashVSR: Towards Real-Time Diffusion-Based Streaming Video Super-Resolution", "authors": [ "Junhao Zhuang", "Shi Guo", "Xin Cai", "Xiaohui Li", "Yihao Liu", "Chun Yuan", "Tianfan Xue" ], "github": "", "project_page": "", "abstract": "Diffusion models have recently advanced video restoration, but applying them to real-world video super-resolution (VSR) remains challenging due to high latency, prohibitive computation, and poor generalization to ultra-high resolutions. Our goal in this work is to make diffusion-based VSR practical by achieving efficiency, scalability, and real-time performance. To this end, we propose FlashVSR, the first diffusion-based one-step streaming framework towards real-time VSR. FlashVSR runs at approximately 17 FPS for 768x1408 videos on a single A100 GPU by combining three complementary innovations: (i) a train-friendly three-stage distillation pipeline that enables streaming super-resolution, (ii) locality-constrained sparse attention that cuts redundant computation while bridging the train-test resolution gap, and (iii) a tiny conditional decoder that accelerates reconstruction without sacrificing quality. To support large-scale training, we also construct VSR-120K, a new dataset with 120k videos and 180k images. Extensive experiments show that FlashVSR scales reliably to ultra-high resolutions and achieves state-of-the-art performance with up to 12x speedup over prior one-step diffusion VSR models. We will release the code, pretrained models, and dataset to foster future research in efficient diffusion-based VSR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12399", "title": "A Survey of Vibe Coding with Large Language Models", "authors": [ "Yuyao Ge", "Lingrui Mei", "Zenghao Duan", "Tianhao Li", "Yujia Zheng", "Yiwei Wang", "Lexin Wang", "Jiayu Yao", "Tianyu Liu", "Yujun Cai", "Baolong Bi", "Fangda Guo", "Jiafeng Guo", "Shenghua Liu", "Xueqi Cheng" ], "github": "", "project_page": "", "abstract": "The advancement of large language models (LLMs) has catalyzed a paradigm shift from code generation assistance to autonomous coding agents, enabling a novel development methodology termed \"Vibe Coding\" where developers validate AI-generated implementations through outcome observation rather than line-by-line code comprehension. Despite its transformative potential, the effectiveness of this emergent paradigm remains under-explored, with empirical evidence revealing unexpected productivity losses and fundamental challenges in human-AI collaboration. To address this gap, this survey provides the first comprehensive and systematic review of Vibe Coding with large language models, establishing both theoretical foundations and practical frameworks for this transformative development approach. Drawing from systematic analysis of over 1000 research papers, we survey the entire vibe coding ecosystem, examining critical infrastructure components including LLMs for coding, LLM-based coding agent, development environment of coding agent, and feedback mechanisms. We first introduce Vibe Coding as a formal discipline by formalizing it through a Constrained Markov Decision Process that captures the dynamic triadic relationship among human developers, software projects, and coding agents. Building upon this theoretical foundation, we then synthesize existing practices into five distinct development models: Unconstrained Automation, Iterative Conversational Collaboration, Planning-Driven, Test-Driven, and Context-Enhanced Models, thus providing the first comprehensive taxonomy in this domain. Critically, our analysis reveals that successful Vibe Coding depends not merely on agent capabilities but on systematic context engineering, well-established development environments, and human-agent collaborative development models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12798", "title": "Detect Anything via Next Point Prediction", "authors": [ "Qing Jiang", "Junan Huo", "Xingyu Chen", "Yuda Xiong", "Zhaoyang Zeng", "Yihao Chen", "Tianhe Ren", "Junzhi Yu", "Lei Zhang" ], "github": "", "project_page": "", "abstract": "Object detection has long been dominated by traditional coordinate regression-based models, such as YOLO, DETR, and Grounding DINO. Although recent efforts have attempted to leverage MLLMs to tackle this task, they face challenges like low recall rate, duplicate predictions, coordinate misalignment, etc. In this work, we bridge this gap and propose Rex-Omni, a 3B-scale MLLM that achieves state-of-the-art object perception performance. On benchmarks like COCO and LVIS, Rex-Omni attains performance comparable to or exceeding regression-based models (e.g., DINO, Grounding DINO) in a zero-shot setting. This is enabled by three key designs: 1) Task Formulation: we use special tokens to represent quantized coordinates from 0 to 999, reducing the model's learning difficulty and improving token efficiency for coordinate prediction; 2) Data Engines: we construct multiple data engines to generate high-quality grounding, referring, and pointing data, providing semantically rich supervision for training; \\3) Training Pipelines: we employ a two-stage training process, combining supervised fine-tuning on 22 million data with GRPO-based reinforcement post-training. This RL post-training leverages geometry-aware rewards to effectively bridge the discrete-to-continuous coordinate prediction gap, improve box accuracy, and mitigate undesirable behaviors like duplicate predictions that stem from the teacher-guided nature of the initial SFT stage. Beyond conventional detection, Rex-Omni's inherent language understanding enables versatile capabilities such as object referring, pointing, visual prompting, GUI grounding, spatial referring, OCR and key-pointing, all systematically evaluated on dedicated benchmarks. We believe that Rex-Omni paves the way for more versatile and language-aware visual perception systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12693", "title": "ERA: Transforming VLMs into Embodied Agents via Embodied Prior Learning and Online Reinforcement Learning", "authors": [ "Hanyang Chen", "Mark Zhao", "Rui Yang", "Qinwei Ma", "Ke Yang", "Jiarui Yao", "Kangrui Wang", "Hao Bai", "Zhenhailong Wang", "Rui Pan", "Mengchao Zhang", "Jose Barreiros", "Aykut Onol", "ChengXiang Zhai", "Heng Ji", "Manling Li", "Huan Zhang", "Tong Zhang" ], "github": "https://github.com/Embodied-Reasoning-Agent/Embodied-Reasoning-Agent", "project_page": "", "abstract": "Recent advances in embodied AI highlight the potential of vision language models (VLMs) as agents capable of perception, reasoning, and interaction in complex environments. However, top-performing systems rely on large-scale models that are costly to deploy, while smaller VLMs lack the necessary knowledge and skills to succeed. To bridge this gap, we present Embodied Reasoning Agent (ERA), a two-stage framework that integrates prior knowledge learning and online reinforcement learning (RL). The first stage, Embodied Prior Learning, distills foundational knowledge from three types of data: (1) Trajectory-Augmented Priors, which enrich existing trajectory data with structured reasoning generated by stronger models; (2) Environment-Anchored Priors, which provide in-environment knowledge and grounding supervision; and (3) External Knowledge Priors, which transfer general knowledge from out-of-environment datasets. In the second stage, we develop an online RL pipeline that builds on these priors to further enhance agent performance. To overcome the inherent challenges in agent RL, including long horizons, sparse rewards, and training instability, we introduce three key designs: self-summarization for context management, dense reward shaping, and turn-level policy optimization. Extensive experiments on both high-level planning (EB-ALFRED) and low-level control (EB-Manipulation) tasks demonstrate that ERA-3B surpasses both prompting-based large models and previous training-based baselines. Specifically, it achieves overall improvements of 8.4\\% on EB-ALFRED and 19.4\\% on EB-Manipulation over GPT-4o, and exhibits strong generalization to unseen tasks. Overall, ERA offers a practical path toward scalable embodied intelligence, providing methodological insights for future embodied AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Embodied-Reasoning-Agent/Embodied-Reasoning-Agent", "github_api_added_by": "auto" }, { "date": "2025-10-15", "arxiv_id": "2510.11693", "title": "Scaling Language-Centric Omnimodal Representation Learning", "authors": [ "Chenghao Xiao", "Hou Pong Chan", "Hao Zhang", "Weiwen Xu", "Mahani Aljunied", "Yu Rong" ], "github": "https://github.com/LCO-Embedding/LCO-Embedding", "project_page": "", "abstract": "Recent multimodal embedding approaches leveraging multimodal large language models (MLLMs) fine-tuned with contrastive learning (CL) have shown promising results, yet the underlying reasons behind their superiority remain underexplored. This work argues that a crucial advantage of MLLM-based approaches stems from implicit cross-modal alignment achieved during generative pretraining, where the language decoder learns to exploit multimodal signals within a shared representation space for generating unimodal outputs. Through analysis of anisotropy and kernel similarity structure, we empirically confirm that latent alignment emerges within MLLM representations, allowing CL to serve as a lightweight refinement stage. Leveraging this insight, we propose a Language-Centric Omnimodal Embedding framework, termed LCO-Emb. Extensive experiments across diverse backbones and benchmarks demonstrate its effectiveness, achieving state-of-the-art performance across modalities. Furthermore, we identify a Generation-Representation Scaling Law (GRSL), showing that the representational capabilities gained through contrastive refinement scales positively with the MLLM's generative capabilities. This suggests that improving generative abilities evolves as an effective paradigm for enhancing representation quality. We provide a theoretical explanation of GRSL, which formally links the MLLM's generative quality to the upper bound on its representation performance, and validate it on a challenging, low-resource visual-document retrieval task, showing that continual generative pretraining before CL can further enhance the potential of a model's embedding capabilities. Codes, models, and resources are available at https://github.com/LCO-Embedding/LCO-Embedding.", "github_manual": "", "github_regex": "https://github.com/LCO-Embedding/LCO-Embedding", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12635", "title": "Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks", "authors": [ "Yuxiang Zhang", "Jiangming Shu", "Ye Ma", "Xueyuan Lin", "Shangxi Wu", "Jitao Sang" ], "github": "https://github.com/ADaM-BJTU/MemAct", "project_page": "", "abstract": "Large Language Models face challenges in long-horizon agentic tasks as their constrained memory is easily overwhelmed by distracting or irrelevant context. Existing working memory methods typically rely on external, heuristic mechanisms that are decoupled from the agent's core policy. In this work, we reframe working memory management as a learnable, intrinsic capability. We propose a novel framework, Memory-as-Action, where an agent actively manages its working memory by executing explicit editing operations as part of a unified policy. This formulation allows an agent, trained via reinforcement learning, to balance memory curation against long-term task objectives under given resource constraints. However, such memory editing actions break the standard assumption of a continuously growing prefix in LLM interactions, leading to what we call trajectory fractures. These non-prefix changes disrupt the causal continuity required by standard policy gradient methods, making those methods inapplicable. To address this, we propose a new algorithm, Dynamic Context Policy Optimization, which enables stable end-to-end reinforcement learning by segmenting trajectories at memory action points and applying trajectory-level advantages to the resulting action segments. Our results demonstrate that jointly optimizing for task reasoning and memory management in an end-to-end fashion not only reduces overall computational consumption but also improves task performance, driven by adaptive context curation strategies tailored to the model's intrinsic capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ADaM-BJTU/MemAct", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.11683", "title": "Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion Large Language Models", "authors": [ "Nianyi Lin", "Jiajie Zhang", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/THU-KEG/BGPO", "project_page": "", "abstract": "A key challenge in applying reinforcement learning (RL) to diffusion large language models (dLLMs) lies in the intractability of their likelihood functions, which are essential for the RL objective, necessitating corresponding approximation in each training step. While existing methods approximate the log-likelihoods by their evidence lower bounds (ELBOs) via customized Monte Carlo (MC) sampling, the forward computational graphs of all MC samples need to be retained for the gradient computation of non-linear terms in the RL objective, resulting in significant memory overhead. This constraint restricts feasible sample sizes, leading to imprecise likelihood approximations and ultimately distorting the RL objective. To overcome this limitation, we propose Boundary-Guided Policy Optimization (BGPO), a memory-efficient RL algorithm that maximizes a specially constructed lower bound of the ELBO-based objective. This lower bound is carefully designed to satisfy two key properties: (1) Linearity: it is formulated in a linear sum where each term depends only on a single MC sample, thereby enabling gradient accumulation across samples and ensuring constant memory usage; (2) Equivalence: Both the value and gradient of this lower bound are equal to those of the ELBO-based objective in on-policy training, making it also an effective approximation for the original RL objective. These properties allow BGPO to adopt a large MC sample size, resulting in more accurate likelihood approximations and improved RL objective estimation, which in turn leads to enhanced performance. Experiments show that BGPO significantly outperforms previous RL algorithms for dLLMs in math problem solving, code generation, and planning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THU-KEG/BGPO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12773", "title": "Dr.LLM: Dynamic Layer Routing in LLMs", "authors": [ "Ahmed Heakl", "Martin Gubri", "Salman Khan", "Sangdoo Yun", "Seong Joon Oh" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) process every token through all layers of a transformer stack, causing wasted computation on simple queries and insufficient flexibility for harder ones that need deeper reasoning. Adaptive-depth methods can improve efficiency, but prior approaches rely on costly inference-time search, architectural changes, or large-scale retraining, and in practice often degrade accuracy despite efficiency gains. We introduce Dr.LLM, Dynamic routing of Layers for LLMs, a retrofittable framework that equips pretrained models with lightweight per-layer routers deciding to skip, execute, or repeat a block. Routers are trained with explicit supervision: using Monte Carlo Tree Search (MCTS), we derive high-quality layer configurations that preserve or improve accuracy under a compute budget. Our design, windowed pooling for stable routing, focal loss with class balancing, and bottleneck MLP routers, ensures robustness under class imbalance and long sequences. On ARC (logic) and DART (math), Dr.LLM improves accuracy by up to +3.4%p while saving 5 layers per example on average. Routers generalize to out-of-domain tasks (MMLU, GSM8k, AIME, TruthfulQA, SQuADv2, GPQA, PIQA, AGIEval) with only 0.85% accuracy drop while retaining efficiency, and outperform prior routing methods by up to +7.7%p. Overall, Dr.LLM shows that explicitly supervised routers retrofit frozen LLMs for budget-aware, accuracy-driven inference without altering base weights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12276", "title": "Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Model", "authors": [ "Fuhao Li", "Wenxuan Song", "Han Zhao", "Jingbo Wang", "Pengxiang Ding", "Donglin Wang", "Long Zeng", "Haoang Li" ], "github": "", "project_page": "", "abstract": "Vision-language-action (VLA) models have recently shown strong potential in enabling robots to follow language instructions and execute precise actions. However, most VLAs are built upon vision-language models pretrained solely on 2D data, which lack accurate spatial awareness and hinder their ability to operate in the 3D physical world. Existing solutions attempt to incorporate explicit 3D sensor inputs such as depth maps or point clouds, but these approaches face challenges due to sensor noise, hardware heterogeneity, and incomplete depth coverage in existing datasets. Alternative methods that estimate 3D cues from 2D images also suffer from the limited performance of depth estimators.We propose Spatial Forcing (SF), a simple yet effective alignment strategy that implicitly forces VLA models to develop spatial comprehension capabilities without relying on explicit 3D inputs or depth estimators. SF aligns intermediate visual embeddings of VLAs with geometric representations produced by pretrained 3D foundation models. By enforcing alignment at intermediate layers, SF guides VLAs to encode richer spatial representations that enhance action precision.Extensive experiments in simulation and real-world environments demonstrate that SF achieves state-of-the-art results, surpassing both 2D- and 3D-based VLAs. SF further accelerates training by up to 3.8x and improves data efficiency across diverse robotic tasks. Project page is at https://spatial-forcing.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12784", "title": "SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models", "authors": [ "Weiyang Jin", "Yuwei Niu", "Jiaqi Liao", "Chengqi Duan", "Aoxue Li", "Shenghua Gao", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Recently, remarkable progress has been made in Unified Multimodal Models (UMMs), which integrate vision-language generation and understanding capabilities within a single framework. However, a significant gap exists where a model's strong visual understanding often fails to transfer to its visual generation. A model might correctly understand an image based on user instructions, yet be unable to generate a faithful image from text prompts. This phenomenon directly raises a compelling question: Can a model achieve self-improvement by using its understanding module to reward its generation module? To bridge this gap and achieve self-improvement, we introduce SRUM, a self-rewarding post-training framework that can be directly applied to existing UMMs of various designs. SRUM creates a feedback loop where the model's own understanding module acts as an internal ``evaluator'', providing corrective signals to improve its generation module, without requiring additional human-labeled data. To ensure this feedback is comprehensive, we designed a global-local dual reward system. To tackle the inherent structural complexity of images, this system offers multi-scale guidance: a global reward ensures the correctness of the overall visual semantics and layout, while a local reward refines fine-grained, object-level fidelity. SRUM leads to powerful capabilities and shows strong generalization, boosting performance on T2I-CompBench from 82.18 to 88.37 and on T2I-ReasonBench from 43.82 to 46.75. Overall, our work establishes a powerful new paradigm for enabling a UMMs' understanding module to guide and enhance its own generation via self-rewarding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12777", "title": "What If : Understanding Motion Through Sparse Interactions", "authors": [ "Stefan Andreas Baumann", "Nick Stracke", "Timy Phan", "Bj\u00f6rn Ommer" ], "github": "", "project_page": "", "abstract": "Understanding the dynamics of a physical scene involves reasoning about the diverse ways it can potentially change, especially as a result of local interactions. We present the Flow Poke Transformer (FPT), a novel framework for directly predicting the distribution of local motion, conditioned on sparse interactions termed \"pokes\". Unlike traditional methods that typically only enable dense sampling of a single realization of scene dynamics, FPT provides an interpretable directly accessible representation of multi-modal scene motion, its dependency on physical interactions and the inherent uncertainties of scene dynamics. We also evaluate our model on several downstream tasks to enable comparisons with prior methods and highlight the flexibility of our approach. On dense face motion generation, our generic pre-trained model surpasses specialized baselines. FPT can be fine-tuned in strongly out-of-distribution tasks such as synthetic datasets to enable significant improvements over in-domain methods in articulated object motion estimation. Additionally, predicting explicit motion distributions directly enables our method to achieve competitive performance on tasks like moving part segmentation from pokes which further demonstrates the versatility of our FPT. Code and models are publicly available at https://compvis.github.io/flow-poke-transformer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12709", "title": "SAIL-Embedding Technical Report: Omni-modal Embedding Foundation Model", "authors": [ "Lin Lin", "Jiefeng Long", "Zhihe Wan", "Yuchi Wang", "Dingkang Yang", "Shuang Yang", "Yueyang Yao", "Xu Chen", "Zirui Guo", "Shengqiang Li", "Weiran Li", "Hanyu Li", "Yaling Mou", "Yan Qiu", "Haiyang Yu", "Xiao Liang", "Hongsheng Li", "Chao Feng" ], "github": "", "project_page": "", "abstract": "Multimodal embedding models aim to yield informative unified representations that empower diverse cross-modal tasks. Despite promising developments in the evolution from CLIP-based dual-tower architectures to large vision-language models, prior works still face unavoidable challenges in real-world applications and business scenarios, such as the limited modality support, unstable training mechanisms, and industrial domain gaps. In this work, we introduce SAIL-Embedding, an omni-modal embedding foundation model that addresses these issues through tailored training strategies and architectural design. In the optimization procedure, we propose a multi-stage training scheme to boost the multifaceted effectiveness of representation learning. Specifically, the content-aware progressive training aims to enhance the model's adaptability to diverse downstream tasks and master enriched cross-modal proficiency. The collaboration-aware recommendation enhancement training further adapts multimodal representations for recommendation scenarios by distilling knowledge from sequence-to-item and ID-to-item embeddings while mining user historical interests. Concurrently, we develop the stochastic specialization and dataset-driven pattern matching to strengthen model training flexibility and generalizability. Experimental results show that SAIL-Embedding achieves SOTA performance compared to other methods in different retrieval tasks. In online experiments across various real-world scenarios integrated with our model, we observe a significant increase in Lifetime (LT), which is a crucial indicator for the recommendation experience. For instance, the model delivers the 7-day LT gain of +0.158% and the 14-day LT gain of +0.144% in the Douyin-Selected scenario. For the Douyin feed rank model, the match features produced by SAIL-Embedding yield a +0.08% AUC gain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12225", "title": "HoneyBee: Data Recipes for Vision-Language Reasoners", "authors": [ "Hritik Bansal", "Devandra Singh Sachan", "Kai-Wei Chang", "Aditya Grover", "Gargi Ghosh", "Wen-tau Yih", "Ramakanth Pasunuru" ], "github": "", "project_page": "", "abstract": "Recent advances in vision-language models (VLMs) have made them highly effective at reasoning tasks. However, the principles underlying the construction of performant VL reasoning training datasets remain poorly understood. In this work, we introduce several data curation approaches and study their impacts on VL reasoning capabilities by carefully controlling training and evaluation setups. We analyze the effects of context (image and question pair) sources, implement targeted data interventions, and explore scaling up images, questions, and chain-of-thought (CoT) solutions. Our findings reveal that (a) context source strategies significantly affect VLM performance, (b) interventions such as auxiliary signals from image captions and the inclusion of text-only reasoning yield substantial gains, and (c) scaling all data dimensions (e.g., unique questions per image and unique CoTs per image-question pair) consistently improves reasoning capability. Motivated by these insights, we introduce HoneyBee, a large-scale, high-quality CoT reasoning dataset with 2.5M examples consisting 350K image-question pairs. VLMs trained with HoneyBee outperform state-of-the-art models across model sizes. For instance, a HoneyBee-trained VLM with 3B parameters outperforms the SOTA model and the base model by 7.8% and 24.8%, respectively, on MathVerse. Furthermore, we propose a test-time scaling strategy that reduces decoding cost by 73% without sacrificing accuracy. Overall, this work presents improved strategies for VL reasoning dataset curation research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.11919", "title": "LLM Reasoning for Machine Translation: Synthetic Data Generation over Thinking Tokens", "authors": [ "Armel Zebaze", "Rachel Bawden", "Beno\u00eet Sagot" ], "github": "", "project_page": "", "abstract": "Large reasoning models (LRMs) have led to new possibilities in terms of problem-solving, through the devising of a natural language thought process prior to answering a query. While their capabilities are well known across mathematics and coding tasks, their impact on the task of machine translation (MT) remains underexplored. In this work, we explore the benefits of the generation of intermediate tokens when performing MT across multiple language pairs of different levels of resourcedness and multiple setups. We find that \"thinking tokens\" do not help LRMs better perform MT. This result generalizes to models fine-tuned to reason before translating using distilled chain of thought (CoT) inspired by human translators' practices. Specifically, fine-tuning a model with synthetic CoT explanations detailing how to translate step-by-step does not outperform standard input-output fine-tuning. However, constructing the intermediate tokens by combining the outputs of modular translation-specific prompting strategies results in improvements. Our findings underscore that the contribution of intermediate tokens during fine-tuning highly depends on the presence of translation attempts within them. More broadly, our results suggest that using a teacher to refine target translations or to expand parallel corpora is more impactful than distilling their CoT explanations into \"thinking\" MT models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-14", "arxiv_id": "2510.10715", "title": "VLM-Guided Adaptive Negative Prompting for Creative Generation", "authors": [ "Shelly Golan", "Yotam Nitzan", "Zongze Wu", "Or Patashnik" ], "github": "", "project_page": "", "abstract": "Creative generation is the synthesis of new, surprising, and valuable samples that reflect user intent yet cannot be envisioned in advance. This task aims to extend human imagination, enabling the discovery of visual concepts that exist in the unexplored spaces between familiar domains. While text-to-image diffusion models excel at rendering photorealistic scenes that faithfully match user prompts, they still struggle to generate genuinely novel content. Existing approaches to enhance generative creativity either rely on interpolation of image features, which restricts exploration to predefined categories, or require time-intensive procedures such as embedding optimization or model fine-tuning. We propose VLM-Guided Adaptive Negative-Prompting, a training-free, inference-time method that promotes creative image generation while preserving the validity of the generated object. Our approach utilizes a vision-language model (VLM) that analyzes intermediate outputs of the generation process and adaptively steers it away from conventional visual concepts, encouraging the emergence of novel and surprising outputs. We evaluate creativity through both novelty and validity, using statistical metrics in the CLIP embedding space. Through extensive experiments, we show consistent gains in creative novelty with negligible computational overhead. Moreover, unlike existing methods that primarily generate single objects, our approach extends to complex scenarios, such as generating coherent sets of creative objects and preserving creativity within elaborate compositional prompts. Our method integrates seamlessly into existing diffusion pipelines, offering a practical route to producing creative outputs that venture beyond the constraints of textual descriptions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12793", "title": "ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution", "authors": [ "Long Cui", "Weiyun Wang", "Jie Shao", "Zichen Wen", "Gen Luo", "Linfeng Zhang", "Yanting Zhang", "Yu Qiao", "Wenhai Wang" ], "github": "https://github.com/InternLM/lmdeploy", "project_page": "", "abstract": "Existing Multimodal Large Language Models (MLLMs) suffer from increased inference costs due to the additional vision tokens introduced by image inputs. In this work, we propose Visual Consistency Learning (ViCO), a novel training algorithm that enables the model to represent images of varying semantic complexities using different numbers of vision tokens. The key idea behind our method is to employ multiple MLP connectors, each with a different image compression ratio, to downsample the vision tokens based on the semantic complexity of the image. During training, we minimize the KL divergence between the responses conditioned on different MLP connectors. At inference time, we introduce an image router, termed Visual Resolution Router (ViR), that automatically selects the appropriate compression rate for each image patch. Compared with existing dynamic high-resolution strategies, which adjust the number of visual tokens based on image resolutions, our method dynamically adapts the number of visual tokens according to semantic complexity. Experimental results demonstrate that our method can reduce the number of vision tokens by up to 50% while maintaining the model's perception, reasoning, and OCR capabilities. We hope this work will contribute to the development of more efficient MLLMs. The code and models will be released to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/InternLM/lmdeploy", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.11606", "title": "ExpVid: A Benchmark for Experiment Video Understanding & Reasoning", "authors": [ "Yicheng Xu", "Yue Wu", "Jiashuo Yu", "Ziang Yan", "Tianxiang Jiang", "Yinan He", "Qingsong Zhao", "Kai Chen", "Yu Qiao", "Limin Wang", "Manabu Okumura", "Yi Wang" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) hold promise for accelerating scientific discovery by interpreting complex experimental procedures. However, their true capabilities are poorly understood, as existing benchmarks neglect the fine-grained and long-horizon nature of authentic laboratory work, especially in wet-lab settings. To bridge this gap, we introduce ExpVid, the first benchmark designed to systematically evaluate MLLMs on scientific experiment videos. Curated from peer-reviewed video publications, ExpVid features a new three-level task hierarchy that mirrors the scientific process: (1) Fine-grained Perception of tools, materials, and actions; (2) Procedural Understanding of step order and completeness; and (3) Scientific Reasoning that connects the full experiment to its published conclusions. Our vision-centric annotation pipeline, combining automated generation with multi-disciplinary expert validation, ensures that tasks require visual grounding. We evaluate 19 leading MLLMs on ExpVid and find that while they excel at coarse-grained recognition, they struggle with disambiguating fine details, tracking state changes over time, and linking experimental procedures to scientific outcomes. Our results reveal a notable performance gap between proprietary and open-source models, particularly in high-order reasoning. ExpVid not only provides a diagnostic tool but also charts a roadmap for developing MLLMs capable of becoming trustworthy partners in scientific experimentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.11057", "title": "Temporal Alignment Guidance: On-Manifold Sampling in Diffusion Models", "authors": [ "Youngrok Park", "Hojung Jung", "Sangmin Bae", "Se-Young Yun" ], "github": "", "project_page": "", "abstract": "Diffusion models have achieved remarkable success as generative models. However, even a well-trained model can accumulate errors throughout the generation process. These errors become particularly problematic when arbitrary guidance is applied to steer samples toward desired properties, which often breaks sample fidelity. In this paper, we propose a general solution to address the off-manifold phenomenon observed in diffusion models. Our approach leverages a time predictor to estimate deviations from the desired data manifold at each timestep, identifying that a larger time gap is associated with reduced generation quality. We then design a novel guidance mechanism, `Temporal Alignment Guidance' (TAG), attracting the samples back to the desired manifold at every timestep during generation. Through extensive experiments, we demonstrate that TAG consistently produces samples closely aligned with the desired manifold at each timestep, leading to significant improvements in generation quality across various downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12088", "title": "One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration", "authors": [ "Zaid Khan", "Archiki Prasad", "Elias Stengel-Eskin", "Jaemin Cho", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Symbolic world modeling requires inferring and representing an environment's transitional dynamics as an executable program. Prior work has focused on largely deterministic environments with abundant interaction data, simple mechanics, and human guidance. We address a more realistic and challenging setting, learning in a complex, stochastic environment where the agent has only \"one life\" to explore a hostile environment without human guidance. We introduce OneLife, a framework that models world dynamics through conditionally-activated programmatic laws within a probabilistic programming framework. Each law operates through a precondition-effect structure, activating in relevant world states. This creates a dynamic computation graph that routes inference and optimization only through relevant laws, avoiding scaling challenges when all laws contribute to predictions about a complex, hierarchical state, and enabling the learning of stochastic dynamics even with sparse rule activation. To evaluate our approach under these demanding constraints, we introduce a new evaluation protocol that measures (a) state ranking, the ability to distinguish plausible future states from implausible ones, and (b) state fidelity, the ability to generate future states that closely resemble reality. We develop and evaluate our framework on Crafter-OO, our reimplementation of the Crafter environment that exposes a structured, object-oriented symbolic state and a pure transition function that operates on that state alone. OneLife can successfully learn key environment dynamics from minimal, unguided interaction, outperforming a strong baseline on 16 out of 23 scenarios tested. We also test OneLife's planning ability, with simulated rollouts successfully identifying superior strategies. Our work establishes a foundation for autonomously constructing programmatic world models of unknown, complex environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.08783", "title": "MLLM as a UI Judge: Benchmarking Multimodal LLMs for Predicting Human Perception of User Interfaces", "authors": [ "Reuben A. Luera", "Ryan Rossi", "Franck Dernoncourt", "Samyadeep Basu", "Sungchul Kim", "Subhojyoti Mukherjee", "Puneet Mathur", "Ruiyi Zhang", "Jihyung Kil", "Nedim Lipka", "Seunghyun Yoon", "Jiuxiang Gu", "Zichao Wang", "Cindy Xiong Bearfield", "Branislav Kveton" ], "github": "", "project_page": "", "abstract": "In an ideal design pipeline, user interface (UI) design is intertwined with user research to validate decisions, yet studies are often resource-constrained during early exploration. Recent advances in multimodal large language models (MLLMs) offer a promising opportunity to act as early evaluators, helping designers narrow options before formal testing. Unlike prior work that emphasizes user behavior in narrow domains such as e-commerce with metrics like clicks or conversions, we focus on subjective user evaluations across varied interfaces. We investigate whether MLLMs can mimic human preferences when evaluating individual UIs and comparing them. Using data from a crowdsourcing platform, we benchmark GPT-4o, Claude, and Llama across 30 interfaces and examine alignment with human judgments on multiple UI factors. Our results show that MLLMs approximate human preferences on some dimensions but diverge on others, underscoring both their potential and limitations in supplementing early UX research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12403", "title": "Robot Learning: A Tutorial", "authors": [ "Francesco Capuano", "Caroline Pascal", "Adil Zouitine", "Thomas Wolf", "Michel Aractingi" ], "github": "", "project_page": "", "abstract": "Robot learning is at an inflection point, driven by rapid advancements in machine learning and the growing availability of large-scale robotics data. This shift from classical, model-based methods to data-driven, learning-based paradigms is unlocking unprecedented capabilities in autonomous systems. This tutorial navigates the landscape of modern robot learning, charting a course from the foundational principles of Reinforcement Learning and Behavioral Cloning to generalist, language-conditioned models capable of operating across diverse tasks and even robot embodiments. This work is intended as a guide for researchers and practitioners, and our goal is to equip the reader with the conceptual understanding and practical tools necessary to contribute to developments in robot learning, with ready-to-use examples implemented in lerobot.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.11545", "title": "Information-Preserving Reformulation of Reasoning Traces for Antidistillation", "authors": [ "Jiayu Ding", "Lei Cui", "Li Dong", "Nanning Zheng", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Recent advances in Large Language Models (LLMs) show that extending the length of reasoning chains significantly improves performance on complex tasks. While revealing these reasoning traces helps users better follow, verify, and learn from the model's problem-solving process, it also makes them highly vulnerable to unauthorized distillation. To mitigate this risk, proprietary model providers often adopt aggressive protection strategies, such as replacing detailed reasoning with brief summaries, which deprive users of valuable intermediate information. To address this trade-off, we propose PART, an information-preserving antidistillation reformulation of reasoning traces. Motivated by the difference between how humans understand reasoning traces and how LLMs exploit them for supervised fine-tuning, we design a simple but effective two-step reformulation: removing self-talk behaviors and reordering sub-conclusions. A small auxiliary model is trained to perform this reformulation, incurring minimal computational overhead. Extensive experiments demonstrate that PART consistently disrupts distillation across student models of different sizes and types on various reasoning benchmarks. For instance, when training on reformulated traces, even the performance of a large 32B student model decreases from 54.17 to 46.88 on AIME 2024, corresponding to a 13.5% degradation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.09062", "title": "ReFIne: A Framework for Trustworthy Large Reasoning Models with Reliability, Faithfulness, and Interpretability", "authors": [ "Chung-En Sun", "Ge Yan", "Akshay Kulkarni", "Tsui-Wei Weng" ], "github": "https://github.com/Trustworthy-ML-Lab/Training_Trustworthy_LRM_with_Refine", "project_page": "", "abstract": "Recent advances in long chain-of-thought (CoT) reasoning have largely prioritized answer accuracy and token efficiency, while overlooking aspects critical to trustworthiness. We argue that usable reasoning systems must be trustworthy, characterized by three properties: interpretability, faithfulness, and reliability. To this end, we propose ReFIne, a new training framework that integrates supervised fine-tuning with GRPO to encourage models to: (i) improve interpretability by producing structured, tag-based traces with high-level planning that are easier for humans to follow; (ii) enhance faithfulness by explicitly disclosing the decisive information guiding each solution, with consistent cross-section references; and (iii) promote reliability by providing self-assessments of both the derivation's soundness and the confidence of the final answer. We apply ReFIne to the Qwen3 models at multiple scales (1.7B/4B/8B) and evaluate across mathematical benchmarks of varying difficulty. Our experimental results show that ReFIne models generate clearer and better-structured reasoning traces (interpretability +44.0%), more faithfully expose their underlying decision process (faithfulness +18.8%), and offer informative confidence estimates (reliability +42.4%). These findings highlight an overlooked but important direction: reasoning models should be optimized not only for accuracy, but also for broader dimensions of trustworthiness. Our code is available at: https://github.com/Trustworthy-ML-Lab/Training_Trustworthy_LRM_with_Refine", "github_manual": "", "github_regex": "https://github.com/Trustworthy-ML-Lab/Training_Trustworthy_LRM_with_Refine", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12269", "title": "Tensor Logic: The Language of AI", "authors": [ "Pedro Domingos" ], "github": "", "project_page": "", "abstract": "Progress in AI is hindered by the lack of a programming language with all the requisite features. Libraries like PyTorch and TensorFlow provide automatic differentiation and efficient GPU implementation, but are additions to Python, which was never intended for AI. Their lack of support for automated reasoning and knowledge acquisition has led to a long and costly series of hacky attempts to tack them on. On the other hand, AI languages like LISP an Prolog lack scalability and support for learning. This paper proposes tensor logic, a language that solves these problems by unifying neural and symbolic AI at a fundamental level. The sole construct in tensor logic is the tensor equation, based on the observation that logical rules and Einstein summation are essentially the same operation, and all else can be reduced to them. I show how to elegantly implement key forms of neural, symbolic and statistical AI in tensor logic, including transformers, formal reasoning, kernel machines and graphical models. Most importantly, tensor logic makes new directions possible, such as sound reasoning in embedding space. This combines the scalability and learnability of neural networks with the reliability and transparency of symbolic reasoning, and is potentially a basis for the wider adoption of AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.09263", "title": "SynthID-Image: Image watermarking at internet scale", "authors": [ "Sven Gowal", "Rudy Bunel", "Florian Stimberg", "David Stutz", "Guillermo Ortiz-Jimenez", "Christina Kouridi", "Mel Vecerik", "Jamie Hayes", "Sylvestre-Alvise Rebuffi", "Paul Bernard", "Chris Gamble", "Mikl\u00f3s Z. Horv\u00e1th", "Fabian Kaczmarczyck", "Alex Kaskasoli", "Aleksandar Petrov", "Ilia Shumailov", "Meghana Thotakuri", "Olivia Wiles", "Jessica Yung", "Zahra Ahmed", "Victor Martin", "Simon Rosen", "Christopher Sav\u010dak", "Armin Senoner", "Nidhi Vyas", "Pushmeet Kohli" ], "github": "https://github.com/facebookresearch/watermark-anything", "project_page": "", "abstract": "We introduce SynthID-Image, a deep learning-based system for invisibly watermarking AI-generated imagery. This paper documents the technical desiderata, threat models, and practical challenges of deploying such a system at internet scale, addressing key requirements of effectiveness, fidelity, robustness, and security. SynthID-Image has been used to watermark over ten billion images and video frames across Google's services and its corresponding verification service is available to trusted testers. For completeness, we present an experimental evaluation of an external model variant, SynthID-O, which is available through partnerships. We benchmark SynthID-O against other post-hoc watermarking methods from the literature, demonstrating state-of-the-art performance in both visual quality and robustness to common image perturbations. While this work centers on visual media, the conclusions on deployment, constraints, and threat modeling generalize to other modalities, including audio. This paper provides a comprehensive documentation for the large-scale deployment of deep learning-based media provenance systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/watermark-anything", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.11602", "title": "Deconstructing Attention: Investigating Design Principles for Effective Language Modeling", "authors": [ "Huiyin Xue", "Nafise Sadat Moosavi", "Nikolaos Aletras" ], "github": "https://github.com/HUIYINXUE/DeconAttn", "project_page": "", "abstract": "The success of Transformer language models is widely credited to their dot-product attention mechanism, which interweaves a set of key design principles: mixing information across positions (enabling multi-token interactions), sequence-dependent activations (where attention weights adapt to each input), a specific mathematical form (dot-product similarities plus softmax weighting), and coupling of queries and keys to evolving hidden states (grounding attention in the current layer). However, the necessity of each of these principles remains largely untested. In this work, we systematically deconstruct attention by designing controlled variants that selectively relax these principles, applied both uniformly across all layers and in hybrid architectures where only some layers retain standard attention. Our empirical analysis reveals that mechanisms for mixing tokens are indispensable, as their absence collapses models to near-random behavior, while the exact mathematical form and sequence dependency can be substantially relaxed, especially when preserved in just a subset of layers. Surprisingly, even variants that fail in isolation can achieve robust performance when interleaved with standard attention, highlighting a cooperative effect. These findings deepen our understanding of what truly underpins attention's effectiveness and open new avenues for simplifying language models without sacrificing performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HUIYINXUE/DeconAttn", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.01171", "title": "Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity", "authors": [ "Jiayi Zhang", "Simon Yu", "Derek Chong", "Anthony Sicilia", "Michael R. Tomz", "Christopher D. Manning", "Weiyan Shi" ], "github": "https://github.com/ChenmienTan/RL2", "project_page": "", "abstract": "Post-training alignment often reduces LLM diversity, leading to a phenomenon known as mode collapse. Unlike prior work that attributes this effect to algorithmic limitations, we identify a fundamental, pervasive data-level driver: typicality bias in preference data, whereby annotators systematically favor familiar text as a result of well-established findings in cognitive psychology. We formalize this bias theoretically, verify it on preference datasets empirically, and show that it plays a central role in mode collapse. Motivated by this analysis, we introduce Verbalized Sampling, a simple, training-free prompting strategy to circumvent mode collapse. VS prompts the model to verbalize a probability distribution over a set of responses (e.g., ``Generate 5 jokes about coffee and their corresponding probabilities''). Comprehensive experiments show that VS significantly improves performance across creative writing (poems, stories, jokes), dialogue simulation, open-ended QA, and synthetic data generation, without sacrificing factual accuracy and safety. For instance, in creative writing, VS increases diversity by 1.6-2.1x over direct prompting. We further observe an emergent trend that more capable models benefit more from VS. In sum, our work provides a new data-centric perspective on mode collapse and a practical inference-time remedy that helps unlock pre-trained generative diversity.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ChenmienTan/RL2", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12323", "title": "RAG-Anything: All-in-One RAG Framework", "authors": [ "Zirui Guo", "Xubin Ren", "Lingrui Xu", "Jiahao Zhang", "Chao Huang" ], "github": "https://github.com/HKUDS/RAG-Anything", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) has emerged as a fundamental paradigm for expanding Large Language Models beyond their static training limitations. However, a critical misalignment exists between current RAG capabilities and real-world information environments. Modern knowledge repositories are inherently multimodal, containing rich combinations of textual content, visual elements, structured tables, and mathematical expressions. Yet existing RAG frameworks are limited to textual content, creating fundamental gaps when processing multimodal documents. We present RAG-Anything, a unified framework that enables comprehensive knowledge retrieval across all modalities. Our approach reconceptualizes multimodal content as interconnected knowledge entities rather than isolated data types. The framework introduces dual-graph construction to capture both cross-modal relationships and textual semantics within a unified representation. We develop cross-modal hybrid retrieval that combines structural knowledge navigation with semantic matching. This enables effective reasoning over heterogeneous content where relevant evidence spans multiple modalities. RAG-Anything demonstrates superior performance on challenging multimodal benchmarks, achieving significant improvements over state-of-the-art methods. Performance gains become particularly pronounced on long documents where traditional approaches fail. Our framework establishes a new paradigm for multimodal knowledge access, eliminating the architectural fragmentation that constrains current systems. Our framework is open-sourced at: https://github.com/HKUDS/RAG-Anything.", "github_manual": "", "github_regex": "https://github.com/HKUDS/RAG-Anything", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.11892", "title": "R-WoM: Retrieval-augmented World Model For Computer-use Agents", "authors": [ "Kai Mei", "Jiang Guo", "Shuaichen Chang", "Mingwen Dong", "Dongkyu Lee", "Xing Niu", "Jiarong Jiang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) can serve as world models to enhance agent decision-making in digital environments by simulating future states and predicting action outcomes, potentially eliminating costly trial-and-error exploration. However, this capability is fundamentally limited by LLMs' tendency toward hallucination and their reliance on static training knowledge, which can lead to compounding errors that inhibit long-horizon simulations. To systematically investigate whether LLMs are appropriate for world modeling, we probe two core capabilities of world models--future state prediction and reward estimation--through three tasks: next-state identification, full-procedure planning alignment, and milestone transition recognition. Our analysis shows that while LLMs effectively capture immediate next states and identify meaningful state transitions, their performance rapidly degrades in full-procedure planning. This highlights LLMs' limitations in reliably modeling environment dynamics over long horizons. To address these limitations, we propose the Retrieval-augmented World Model (R-WoM), which grounds LLM simulations by incorporating factual, up-to-date knowledge retrieved from external tutorials. Experiments show that R-WoM achieves substantial improvements of up to 25.3% (OSWorld) and 18.1% (WebArena) compared to baselines, with particular advantages in longer-horizon simulations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.11851", "title": "Deep Research Brings Deeper Harm", "authors": [ "Shuo Chen", "Zonggen Li", "Zhen Han", "Bailan He", "Tong Liu", "Haokun Chen", "Georg Groh", "Philip Torr", "Volker Tresp", "Jindong Gu" ], "github": "https://github.com/chenxshuo/DeeperHarm", "project_page": "", "abstract": "Deep Research (DR) agents built on Large Language Models (LLMs) can perform complex, multi-step research by decomposing tasks, retrieving online information, and synthesizing detailed reports. However, the misuse of LLMs with such powerful capabilities can lead to even greater risks. This is especially concerning in high-stakes and knowledge-intensive domains such as biosecurity, where DR can generate a professional report containing detailed forbidden knowledge. Unfortunately, we have found such risks in practice: simply submitting a harmful query, which a standalone LLM directly rejects, can elicit a detailed and dangerous report from DR agents. This highlights the elevated risks and underscores the need for a deeper safety analysis. Yet, jailbreak methods designed for LLMs fall short in exposing such unique risks, as they do not target the research ability of DR agents. To address this gap, we propose two novel jailbreak strategies: Plan Injection, which injects malicious sub-goals into the agent's plan; and Intent Hijack, which reframes harmful queries as academic research questions. We conducted extensive experiments across different LLMs and various safety benchmarks, including general and biosecurity forbidden prompts. These experiments reveal 3 key findings: (1) Alignment of the LLMs often fail in DR agents, where harmful prompts framed in academic terms can hijack agent intent; (2) Multi-step planning and execution weaken the alignment, revealing systemic vulnerabilities that prompt-level safeguards cannot address; (3) DR agents not only bypass refusals but also produce more coherent, professional, and dangerous content, compared with standalone LLMs. These results demonstrate a fundamental misalignment in DR agents and call for better alignment techniques tailored to DR agents. Code and datasets are available at https://chenxshuo.github.io/deeper-harm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/chenxshuo/DeeperHarm", "github_api_added_by": "auto" }, { "date": "2025-10-15", "arxiv_id": "2510.11570", "title": "Bag of Tricks for Subverting Reasoning-based Safety Guardrails", "authors": [ "Shuo Chen", "Zhen Han", "Haokun Chen", "Bailan He", "Shengyun Si", "Jingpei Wu", "Philip Torr", "Volker Tresp", "Jindong Gu" ], "github": "https://github.com/chenxshuo/breakoss", "project_page": "", "abstract": "Recent reasoning-based safety guardrails for Large Reasoning Models (LRMs), such as deliberative alignment, have shown strong defense against jailbreak attacks. By leveraging LRMs' reasoning ability, these guardrails help the models to assess the safety of user inputs before generating final responses. The powerful reasoning ability can analyze the intention of the input query and will refuse to assist once it detects the harmful intent hidden by the jailbreak methods. Such guardrails have shown a significant boost in defense, such as the near-perfect refusal rates on the open-source gpt-oss series. Unfortunately, we find that these powerful reasoning-based guardrails can be extremely vulnerable to subtle manipulation of the input prompts, and once hijacked, can lead to even more harmful results. Specifically, we first uncover a surprisingly fragile aspect of these guardrails: simply adding a few template tokens to the input prompt can successfully bypass the seemingly powerful guardrails and lead to explicit and harmful responses. To explore further, we introduce a bag of jailbreak methods that subvert the reasoning-based guardrails. Our attacks span white-, gray-, and black-box settings and range from effortless template manipulations to fully automated optimization. Along with the potential for scalable implementation, these methods also achieve alarmingly high attack success rates (e.g., exceeding 90% across 5 different benchmarks on gpt-oss series on both local host models and online API services). Evaluations across various leading open-source LRMs confirm that these vulnerabilities are systemic, underscoring the urgent need for stronger alignment techniques for open-sourced LRMs to prevent malicious misuse. Code is open-sourced at https://chenxshuo.github.io/bag-of-tricks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/chenxshuo/breakoss", "github_api_added_by": "auto" }, { "date": "2025-10-15", "arxiv_id": "2510.08666", "title": "dInfer: An Efficient Inference Framework for Diffusion Language Models", "authors": [ "Yuxin Ma", "Lun Du", "Lanning Wei", "Kun Chen", "Qian Xu", "Kangyu Wang", "Guofeng Feng", "Guoshan Lu", "Lin Liu", "Xiaojing Qi", "Xinyuan Zhang", "Zhen Tao", "Haibo Feng", "Ziyun Jiang", "Ying Xu", "Zenan Huang", "Yihong Zhuang", "Haokai Xu", "Jiaqi Hu", "Zhenzhong Lan", "Junbo Zhao", "Jianguo Li", "Da Zheng" ], "github": "https://github.com/inclusionAI/dInfer", "project_page": "", "abstract": "Diffusion-based large language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs, leveraging denoising-based generation to enable inherent parallelism. Even more and more open-sourced dLLM models emerge, yet their widespread adoption remains constrained by the lack of a standardized and efficient inference framework. We present dInfer, an efficient and extensible framework for dLLM inference. dInfer decomposes the inference pipeline into four modular components--model, diffusion iteration manager, decoding strategy, and KV-cache manager--and integrates novel algorithms for each component alongside system-level optimizations. Through this combination of algorithmic innovations and system enhancements, dInfer achieves substantial efficiency gains without compromising output quality on LLaDA-MoE. At batch size 1, it surpasses 1,100 tokens per second on HumanEval and averages over 800 tokens per second across six benchmarks on 8times H800 GPUs. Compared to prior systems, dInfer delivers a 10times speedup over Fast-dLLM while maintaining similar model performance. Even compared to the AR model (with a comparable number of activation parameters and performance) QWen2.5-3B, which is highly optimized with the latest vLLM inference engine, dInfer still delivers a 2-3times speedup. The implementation of dInfer is open-sourced at https://github.com/inclusionAI/dInfer.", "github_manual": "", "github_regex": "https://github.com/inclusionAI/dInfer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.11661", "title": "SR-Scientist: Scientific Equation Discovery With Agentic AI", "authors": [ "Shijie Xia", "Yuhan Sun", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/SR-Scientist", "project_page": "", "abstract": "Recently, Large Language Models (LLMs) have been applied to scientific equation discovery, leveraging their embedded scientific knowledge for hypothesis generation. However, current methods typically confine LLMs to the role of an equation proposer within search algorithms like genetic programming. In this paper, we present SR-Scientist, a framework that elevates the LLM from a simple equation proposer to an autonomous AI scientist that writes code to analyze data, implements the equation as code, submits it for evaluation, and optimizes the equation based on experimental feedback. Specifically, we wrap the code interpreter into a set of tools for data analysis and equation evaluation. The agent is instructed to optimize the equation by utilizing these tools over a long horizon with minimal human-defined pipelines. Empirical results show that SR-Scientist outperforms baseline methods by an absolute margin of 6% to 35% on datasets covering four science disciplines. Additionally, we demonstrate our method's robustness to noise, the generalization of the discovered equations to out-of-domain data, and their symbolic accuracy. Furthermore, we develop an end-to-end reinforcement learning framework to enhance the agent's capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GAIR-NLP/SR-Scientist", "github_api_added_by": "auto" }, { "date": "2025-10-15", "arxiv_id": "2510.09259", "title": "Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models", "authors": [ "Yongding Tao", "Tian Wang", "Yihong Dong", "Huanyu Liu", "Kechi Zhang", "Xiaolong Hu", "Ge Li" ], "github": "https://github.com/yongding-tao/RL-Data-Contamination", "project_page": "", "abstract": "Data contamination poses a significant threat to the reliable evaluation of Large Language Models (LLMs). This issue arises when benchmark samples may inadvertently appear in training sets, compromising the validity of reported performance. While detection methods have been developed for the pre-training and Supervised Fine-Tuning stages, a critical research gap exists for the increasingly significant phase of Reinforcement Learning (RL) post-training. As RL post-training becomes pivotal for advancing LLM reasoning, the absence of specialized contamination detection methods in this paradigm presents a critical vulnerability. To address this, we conduct the first systematic study of data detection within RL post-training scenario and propose Self-Critique. Our method is motivated by a key observation: after RL phase, the output entropy distribution of LLMs tends to collapse into highly specific and sparse modes. Self-Critique probes for the underlying policy collapse, i.e., the model's convergence to a narrow reasoning path, which causes this entropy reduction. To facilitate this research, we also introduce RL-MIA, a benchmark constructed to simulate this specific contamination scenario. Extensive experiments show that Self-Critique significantly outperforms baseline methods across multiple models and contamination tasks, achieving an AUC improvement of up to 30%. Whereas existing methods are close to a random guess for RL-phase contamination, our method makes detection possible.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yongding-tao/RL-Data-Contamination", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12402", "title": "Cautious Weight Decay", "authors": [ "Lizhang Chen", "Jonathan Li", "Kaizhao Liang", "Baiyu Su", "Cong Xie", "Nuo Wang Pierse", "Chen Liang", "Ni Lao", "Qiang Liu" ], "github": "", "project_page": "", "abstract": "We introduce Cautious Weight Decay (CWD), a one-line, optimizer-agnostic modification that applies weight decay only to parameter coordinates whose signs align with the optimizer update. Unlike standard decoupled decay, which implicitly optimizes a regularized or constrained objective, CWD preserves the original loss and admits a bilevel interpretation: it induces sliding-mode behavior upon reaching the stationary manifold, allowing it to search for locally Pareto-optimal stationary points of the unmodified objective. In practice, CWD is a drop-in change for optimizers such as AdamW, Lion, and Muon, requiring no new hyperparameters or additional tuning. For language model pre-training and ImageNet classification, CWD consistently improves final loss and accuracy at million- to billion-parameter scales.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.11330", "title": "Diffusion-Link: Diffusion Probabilistic Model for Bridging the Audio-Text Modality Gap", "authors": [ "KiHyun Nam", "Jongmin Choi", "Hyeongkeun Lee", "Jungwoo Heo", "Joon Son Chung" ], "github": "https://github.com/DevKiHyun/Diffusion-Link", "project_page": "", "abstract": "Contrastive audio-language pretraining yields powerful joint representations, yet a persistent audio-text modality gap limits the benefits of coupling multimodal encoders with large language models (LLMs). We present Diffusion-Link, a diffusion-based modality-bridging module that generatively maps audio embeddings into the text-embedding distribution. The module is trained at the output embedding from the frozen multimodal encoder and implemented as a lightweight network with three residual MLP blocks. To assess the effect of Diffusion-Link on multimodal encoder-LLM coupling, we evaluate on Automatic Audio Captioning (AAC); to our knowledge, this is the first application of diffusion-based modality bridging to AAC. We report two results. (1) Modality-gap analysis: on similarity and geometric criteria, Diffusion-Link reduces the modality gap the most among prior diffusion-based methods and shows a collective migration of audio embeddings toward the text distribution. (2) Downstream AAC: attaching Diffusion-Link to the same multimodal LLM baseline achieves state-of-the-art on AudioCaps in both zero-shot and fully supervised captioning without external knowledge, with relative gains up to 52.5% and 7.5%, respectively. These findings show that closing the modality gap is pivotal for effective coupling between multimodal encoders and LLMs, and diffusion-based modality bridging offers a promising direction beyond knowledge-retrieval-centric designs. Code will be released upon acceptance https://github.com/DevKiHyun/Diffusion-Link", "github_manual": "", "github_regex": "https://github.com/DevKiHyun/Diffusion-Link", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12497", "title": "Mitigating the Noise Shift for Denoising Generative Models via Noise Awareness Guidance", "authors": [ "Jincheng Zhong", "Boyuan Jiang", "Xin Tao", "Pengfei Wan", "Kun Gai", "Mingsheng Long" ], "github": "https://github.com/facebookresearch/DiT", "project_page": "", "abstract": "Existing denoising generative models rely on solving discretized reverse-time SDEs or ODEs. In this paper, we identify a long-overlooked yet pervasive issue in this family of models: a misalignment between the pre-defined noise level and the actual noise level encoded in intermediate states during sampling. We refer to this misalignment as noise shift. Through empirical analysis, we demonstrate that noise shift is widespread in modern diffusion models and exhibits a systematic bias, leading to sub-optimal generation due to both out-of-distribution generalization and inaccurate denoising updates. To address this problem, we propose Noise Awareness Guidance (NAG), a simple yet effective correction method that explicitly steers sampling trajectories to remain consistent with the pre-defined noise schedule. We further introduce a classifier-free variant of NAG, which jointly trains a noise-conditional and a noise-unconditional model via noise-condition dropout, thereby eliminating the need for external classifiers. Extensive experiments, including ImageNet generation and various supervised fine-tuning tasks, show that NAG consistently mitigates noise shift and substantially improves the generation quality of mainstream diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/DiT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.11000", "title": "ContextGen: Contextual Layout Anchoring for Identity-Consistent Multi-Instance Generation", "authors": [ "Ruihang Xu", "Dewei Zhou", "Fan Ma", "Yi Yang" ], "github": "https://github.com/MS-Diffusion/MS-Diffusion/blob/main/msdiffusion/models/projection.py", "project_page": "", "abstract": "Multi-instance image generation (MIG) remains a significant challenge for modern diffusion models due to key limitations in achieving precise control over object layout and preserving the identity of multiple distinct subjects. To address these limitations, we introduce ContextGen, a novel Diffusion Transformer framework for multi-instance generation that is guided by both layout and reference images. Our approach integrates two key technical contributions: a Contextual Layout Anchoring (CLA) mechanism that incorporates the composite layout image into the generation context to robustly anchor the objects in their desired positions, and Identity Consistency Attention (ICA), an innovative attention mechanism that leverages contextual reference images to ensure the identity consistency of multiple instances. Recognizing the lack of large-scale, hierarchically-structured datasets for this task, we introduce IMIG-100K, the first dataset with detailed layout and identity annotations. Extensive experiments demonstrate that ContextGen sets a new state-of-the-art, outperforming existing methods in control precision, identity fidelity, and overall visual quality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MS-Diffusion/MS-Diffusion/blob/main/msdiffusion/models/projection.py", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.12117", "title": "Locket: Robust Feature-Locking Technique for Language Models", "authors": [ "Lipeng He", "Vasisht Duddu", "N. Asokan" ], "github": "", "project_page": "", "abstract": "Chatbot providers (e.g., OpenAI) rely on tiered subscription schemes to generate revenue, offering basic models for free users, and advanced models for paying subscribers. However, a finer-grained pay-to-unlock scheme for premium features (e.g., math, coding) is thought to be more economically viable for the providers. Such a scheme requires a feature-locking technique (FLoTE) which is (i) effective in refusing locked features, (ii) utility-preserving for unlocked features, (iii) robust against evasion or unauthorized credential sharing, and (iv) scalable to multiple features and users. However, existing FLoTEs (e.g., password-locked models) are not robust or scalable. We present Locket, the first robust and scalable FLoTE to enable pay-to-unlock schemes. Locket uses a novel merging approach to attach adapters to an LLM for refusing unauthorized features. Our comprehensive evaluation shows that Locket is effective (100% refusal on locked features), utility-preserving (leq 7% utility degradation in unlocked features), robust (leq 5% attack success rate), and scales to multiple features and clients.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.09782", "title": "The Geometry of Reasoning: Flowing Logics in Representation Space", "authors": [ "Yufa Zhou", "Yixiao Wang", "Xunjian Yin", "Shuyan Zhou", "Anru R. Zhang" ], "github": "", "project_page": "", "abstract": "We study how large language models (LLMs) ``think'' through their representation space. We propose a novel geometric framework that models an LLM's reasoning as flows -- embedding trajectories evolving where logic goes. We disentangle logical structure from semantics by employing the same natural deduction propositions with varied semantic carriers, allowing us to test whether LLMs internalize logic beyond surface form. This perspective connects reasoning with geometric quantities such as position, velocity, and curvature, enabling formal analysis in representation and concept spaces. Our theory establishes: (1) LLM reasoning corresponds to smooth flows in representation space, and (2) logical statements act as local controllers of these flows' velocities. Using learned representation proxies, we design controlled experiments to visualize and quantify reasoning flows, providing empirical validation of our theoretical framework. Our work serves as both a conceptual foundation and practical tools for studying reasoning phenomenon, offering a new lens for interpretability and formal analysis of LLMs' behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.09776", "title": "Why Do Transformers Fail to Forecast Time Series In-Context?", "authors": [ "Yufa Zhou", "Yixiao Wang", "Surbhi Goel", "Anru R. Zhang" ], "github": "", "project_page": "", "abstract": "Time series forecasting (TSF) remains a challenging and largely unsolved problem in machine learning, despite significant recent efforts leveraging Large Language Models (LLMs), which predominantly rely on Transformer architectures. Empirical evidence consistently shows that even powerful Transformers often fail to outperform much simpler models, e.g., linear models, on TSF tasks; however, a rigorous theoretical understanding of this phenomenon remains limited. In this paper, we provide a theoretical analysis of Transformers' limitations for TSF through the lens of In-Context Learning (ICL) theory. Specifically, under AR(p) data, we establish that: (1) Linear Self-Attention (LSA) models cannot achieve lower expected MSE than classical linear models for in-context forecasting; (2) as the context length approaches to infinity, LSA asymptotically recovers the optimal linear predictor; and (3) under Chain-of-Thought (CoT) style inference, predictions collapse to the mean exponentially. We empirically validate these findings through carefully designed experiments. Our theory not only sheds light on several previously underexplored phenomena but also offers practical insights for designing more effective forecasting architectures. We hope our work encourages the broader research community to revisit the fundamental theoretical limitations of TSF and to critically evaluate the direct application of increasingly sophisticated architectures without deeper scrutiny.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.08532", "title": "Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing", "authors": [ "Rishubh Parihar", "Or Patashnik", "Daniil Ostashev", "R. Venkatesh Babu", "Daniel Cohen-Or", "Kuan-Chieh Wang" ], "github": "", "project_page": "", "abstract": "Instruction-based image editing offers a powerful and intuitive way to manipulate images through natural language. Yet, relying solely on text instructions limits fine-grained control over the extent of edits. We introduce Kontinuous Kontext, an instruction-driven editing model that provides a new dimension of control over edit strength, enabling users to adjust edits gradually from no change to a fully realized result in a smooth and continuous manner. Kontinuous Kontext extends a state-of-the-art image editing model to accept an additional input, a scalar edit strength which is then paired with the edit instruction, enabling explicit control over the extent of the edit. To inject this scalar information, we train a lightweight projector network that maps the input scalar and the edit instruction to coefficients in the model's modulation space. For training our model, we synthesize a diverse dataset of image-edit-instruction-strength quadruplets using existing generative models, followed by a filtering stage to ensure quality and consistency. Kontinuous Kontext provides a unified approach for fine-grained control over edit strength for instruction driven editing from subtle to strong across diverse operations such as stylization, attribute, material, background, and shape changes, without requiring attribute-specific training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-15", "arxiv_id": "2510.11967", "title": "Scaling Long-Horizon LLM Agent via Context-Folding", "authors": [ "Weiwei Sun", "Miao Lu", "Zhan Ling", "Kang Liu", "Xuesong Yao", "Yiming Yang", "Jiecao Chen" ], "github": "https://github.com/sunnweiwei/FoldAgent", "project_page": "", "abstract": "Large language model (LLM) agents are fundamentally constrained by context length on long-horizon tasks. We introduce Context-Folding, a framework that empowers agents to actively manage their working context. An agent can procedurally branch into a sub-trajectory to handle a subtask and then fold it upon completion, collapsing the intermediate steps while retaining a concise summary of the outcome. To make this behavior learnable, we develop an end-to-end reinforcement learning framework FoldGRPO with specific process rewards to encourage effective task decomposition and context management. On complex long-horizon tasks (Deep Research and SWE), our folding agent matches or outperforms the ReAct baselines while using an active context 10times smaller and significantly outperforms models that rely on summarization-based context management.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sunnweiwei/FoldAgent", "github_api_added_by": "auto" }, { "date": "2025-10-15", "arxiv_id": "2510.06727", "title": "Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management", "authors": [ "Miao Lu", "Weiwei Sun", "Weihua Du", "Zhan Ling", "Xuesong Yao", "Kang Liu", "Jiecao Chen" ], "github": "", "project_page": "", "abstract": "We study reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use, where context length quickly becomes a fundamental bottleneck. Existing RL pipelines can suffer from degraded instruction following, excessive rollout costs, and most importantly, strict context limits. To address these challenges, we introduce summarization-based context management to training. In specific, it periodically compresses the tool using history by LLM-generated summaries that retain task-relevant information to keep a compact context while enabling the agent to scale beyond the fixed context window. Building on this formulation, we derive a policy gradient representation that seamlessly enables standard LLM RL infrastructures to optimize both tool-use behaviors as well as summarization strategies in an end-to-end fashion. We instantiate this framework with SUmmarization augmented Policy Optimization (SUPO), an LLM RL algorithm that enables long-horizon training beyond a fixed context limit. Experiments on interactive function calling and searching tasks demonstrate that SUPO significantly improves the success rate while maintaining the same or even lower working context length compared to baselines. We also demonstrate that for complex searching tasks, SUPO can further improve the evaluation performance when scaling test-time maximum round of summarization beyond that of training time. Our results establish summarization-based context management as a principled and scalable approach for training RL agents beyond a fixed context length limit.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13809", "title": "PhysMaster: Mastering Physical Representation for Video Generation via Reinforcement Learning", "authors": [ "Sihui Ji", "Xi Chen", "Xin Tao", "Pengfei Wan", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "Video generation models nowadays are capable of generating visually realistic videos, but often fail to adhere to physical laws, limiting their ability to generate physically plausible videos and serve as ''world models''. To address this issue, we propose PhysMaster, which captures physical knowledge as a representation for guiding video generation models to enhance their physics-awareness. Specifically, PhysMaster is based on the image-to-video task where the model is expected to predict physically plausible dynamics from the input image. Since the input image provides physical priors like relative positions and potential interactions of objects in the scenario, we devise PhysEncoder to encode physical information from it as an extra condition to inject physical knowledge into the video generation process. The lack of proper supervision on the model's physical performance beyond mere appearance motivates PhysEncoder to apply reinforcement learning with human feedback to physical representation learning, which leverages feedback from generation models to optimize physical representations with Direct Preference Optimization (DPO) in an end-to-end manner. PhysMaster provides a feasible solution for improving physics-awareness of PhysEncoder and thus of video generation, proving its ability on a simple proxy task and generalizability to wide-ranging physical scenarios. This implies that our PhysMaster, which unifies solutions for various physical processes via representation learning in the reinforcement learning paradigm, can act as a generic and plug-in solution for physics-aware video generation and broader applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13804", "title": "Generative Universal Verifier as Multimodal Meta-Reasoner", "authors": [ "Xinchen Zhang", "Xiaoying Zhang", "Youbin Wu", "Yanbin Cao", "Renrui Zhang", "Ruihang Chu", "Ling Yang", "Yujiu Yang" ], "github": "", "project_page": "", "abstract": "We introduce Generative Universal Verifier, a novel concept and plugin designed for next-generation multimodal reasoning in vision-language models and unified multimodal models, providing the fundamental capability of reflection and refinement on visual outcomes during the reasoning and generation process. This work makes three main contributions: (1) We build ViVerBench, a comprehensive benchmark spanning 16 categories of critical tasks for evaluating visual outcomes in multimodal reasoning. Results show that existing VLMs consistently underperform across these tasks, underscoring a substantial gap from human-level capability in reliable visual verification. (2) We design two automated pipelines to construct large-scale visual verification data and train OmniVerifier-7B, the first omni-capable generative verifier trained for universal visual verification and achieves notable gains on ViVerBench(+8.3). Through training, we identify three atomic capabilities in visual verification and demonstrate how they generalize and interact synergistically. (3) We propose OmniVerifier-TTS, a sequential test-time scaling paradigm that leverages the universal verifier to bridge image generation and editing within unified models, enhancing the upper bound of generative ability through iterative fine-grained optimization. Beyond generation, we extend universal verifier to broader world-modeling interleaved reasoning scenarios. Empirically, OmniVerifier-TTS achieves improvements on T2I-ReasonBench(+3.7), and GenEval++(+4.3), outperforming existing parallel test-time scaling methods, such as Best-of-N. By endowing multimodal reasoning with reliable visual verification, OmniVerifier advances both reliable reflection during generation and scalable test-time refinement, marking a step toward more trustworthy and controllable next-generation reasoning systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13802", "title": "Trace Anything: Representing Any Video in 4D via Trajectory Fields", "authors": [ "Xinhang Liu", "Yuxi Xiao", "Donny Y. Chen", "Jiashi Feng", "Yu-Wing Tai", "Chi-Keung Tang", "Bingyi Kang" ], "github": "", "project_page": "", "abstract": "Effective spatio-temporal representation is fundamental to modeling, understanding, and predicting dynamics in videos. The atomic unit of a video, the pixel, traces a continuous 3D trajectory over time, serving as the primitive element of dynamics. Based on this principle, we propose representing any video as a Trajectory Field: a dense mapping that assigns a continuous 3D trajectory function of time to each pixel in every frame. With this representation, we introduce Trace Anything, a neural network that predicts the entire trajectory field in a single feed-forward pass. Specifically, for each pixel in each frame, our model predicts a set of control points that parameterizes a trajectory (i.e., a B-spline), yielding its 3D position at arbitrary query time instants. We trained the Trace Anything model on large-scale 4D data, including data from our new platform, and our experiments demonstrate that: (i) Trace Anything achieves state-of-the-art performance on our new benchmark for trajectory field estimation and performs competitively on established point-tracking benchmarks; (ii) it offers significant efficiency gains thanks to its one-pass paradigm, without requiring iterative optimization or auxiliary estimators; and (iii) it exhibits emergent abilities, including goal-conditioned manipulation, motion forecasting, and spatio-temporal fusion. Project page: https://trace-anything.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13744", "title": "Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier Math", "authors": [ "Shrey Pandit", "Austin Xu", "Xuan-Phi Nguyen", "Yifei Ming", "Caiming Xiong", "Shafiq Joty" ], "github": "https://github.com/ByteDance-Seed/seed-oss", "project_page": "", "abstract": "Large language model (LLM)-based reasoning systems have recently achieved gold medal-level performance in the IMO 2025 competition, writing mathematical proofs where, to receive full credit, each step must be not only correct but also sufficiently supported. To train LLM-based reasoners in such challenging, open-ended settings, strong verifiers capable of catching step-level mistakes are necessary prerequisites. We introduce Hard2Verify, a human-annotated, step-level verification benchmark produced with over 500 hours of human labor. Hard2Verify is designed to rigorously assess step-level verifiers at the frontier: Verifiers must provide step-level annotations or identify the first error in responses generated by frontier LLMs for very recent, challenging, and open-ended math questions. We evaluate 29 generative critics and process reward models, demonstrating that, beyond a few standouts, open-source verifiers lag closed source models. We subsequently analyze what drives poor performance in step-level verification, the impacts of scaling verifier compute, as well as fundamental questions such as self-verification and verification-generation dynamics.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByteDance-Seed/seed-oss", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13747", "title": "InteractiveOmni: A Unified Omni-modal Model for Audio-Visual Multi-turn Dialogue", "authors": [ "Wenwen Tong", "Hewei Guo", "Dongchuan Ran", "Jiangnan Chen", "Jiefan Lu", "Kaibin Wang", "Keqiang Li", "Xiaoxu Zhu", "Jiakui Li", "Kehan Li", "Xueheng Li", "Lumin Li", "Chenxu Guo", "Jiasheng Zhou", "Jiandong Chen", "Xianye Wu", "Jiahao Wang", "Silei Wu", "Lei Chen", "Hanming Deng", "Yuxuan Song", "Dinghao Zhou", "Guiping Zhong", "Ken Zheng", "Shiyin Kang", "Lewei Lu" ], "github": "https://github.com/SenseTime-FVG/InteractiveOmni", "project_page": "", "abstract": "We introduce InteractiveOmni, a unified and open-source omni-modal large language model for audio-visual multi-turn interaction, ranging from 4B to 8B parameters, designed to lead the field of lightweight models by offering comprehensive omni-modal understanding and speech generation capabilities. To achieve this, we integrate the vision encoder, audio encoder, large language model, and speech decoder into a unified model for understanding and generation tasks. We design a multi-stage training strategy to ensure robust cross-modal capabilities, including pre-training for omni-modal understanding, followed by post-training with speech conversation and audio-visual interaction. To enable human-like long-term conversational ability, we meticulously curate a multi-turn training dataset that enhances the model's ability to handle complex and multi-turn interactions. To effectively evaluate the multi-turn memory and speech interaction capabilities, we construct the multi-modal multi-turn memory benchmark and the multi-turn speech interaction benchmark. Experiments demonstrate that InteractiveOmni significantly outperforms leading open-source models and provides a more intelligent multi-turn audio-visual experience, particularly in its long-term memory capabilities. Notably, InteractiveOmni-4B is comparable to the much larger model like Qwen2.5-Omni-7B on general benchmarks, and it can retain 97% of the performance of the InteractiveOmni-8B while utilizing only 50% of the model size. Achieving state-of-the-art results against similarly sized models across image, audio, video understanding, and speech generation tasks, InteractiveOmni is an accessible, open-source foundation for next-generation intelligent interactive systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SenseTime-FVG/InteractiveOmni", "github_api_added_by": "auto" }, { "date": "2025-10-16", "arxiv_id": "2510.13515", "title": "UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learning", "authors": [ "Tiancheng Gu", "Kaicheng Yang", "Kaichen Zhang", "Xiang An", "Ziyong Feng", "Yueyi Zhang", "Weidong Cai", "Jiankang Deng", "Lidong Bing" ], "github": "", "project_page": "", "abstract": "Universal multimodal embedding models are foundational to various tasks. Existing approaches typically employ in-batch negative mining by measuring the similarity of query-candidate pairs. However, these methods often struggle to capture subtle semantic differences among candidates and lack diversity in negative samples. Moreover, the embeddings exhibit limited discriminative ability in distinguishing false and hard negatives. In this paper, we leverage the advanced understanding capabilities of MLLMs to enhance representation learning and present a novel Universal Multimodal Embedding (UniME-V2) model. Our approach first constructs a potential hard negative set through global retrieval. We then introduce the MLLM-as-a-Judge mechanism, which utilizes MLLMs to assess the semantic alignment of query-candidate pairs and generate soft semantic matching scores. These scores serve as a foundation for hard negative mining, mitigating the impact of false negatives and enabling the identification of diverse, high-quality hard negatives. Furthermore, the semantic matching scores are used as soft labels to mitigate the rigid one-to-one mapping constraint. By aligning the similarity matrix with the soft semantic matching score matrix, the model learns semantic distinctions among candidates, significantly enhancing its discriminative capacity. To further improve performance, we propose UniME-V2-Reranker, a reranking model trained on our mined hard negatives through a joint pairwise and listwise optimization approach. We conduct comprehensive experiments on the MMEB benchmark and multiple retrieval tasks, demonstrating that our method achieves state-of-the-art performance on average across all tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13621", "title": "The Role of Computing Resources in Publishing Foundation Model Research", "authors": [ "Yuexing Hao", "Yue Huang", "Haoran Zhang", "Chenyang Zhao", "Zhenwen Liang", "Paul Pu Liang", "Yue Zhao", "Lichao Sun", "Saleh Kalantari", "Xiangliang Zhang", "Marzyeh Ghassemi" ], "github": "", "project_page": "", "abstract": "Cutting-edge research in Artificial Intelligence (AI) requires considerable resources, including Graphics Processing Units (GPUs), data, and human resources. In this paper, we evaluate of the relationship between these resources and the scientific advancement of foundation models (FM). We reviewed 6517 FM papers published between 2022 to 2024, and surveyed 229 first-authors to the impact of computing resources on scientific output. We find that increased computing is correlated with national funding allocations and citations, but our findings don't observe the strong correlations with research environment (academic or industrial), domain, or study methodology. We advise that individuals and institutions focus on creating shared and affordable computing opportunities to lower the entry barrier for under-resourced researchers. These steps can help expand participation in FM research, foster diversity of ideas and contributors, and sustain innovation and progress in AI. The data will be available at: https://mit-calc.csail.mit.edu/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.04767", "title": "ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMs", "authors": [ "Wonjun Kang", "Kevin Galim", "Seunghyuk Oh", "Minjae Lee", "Yuchen Zeng", "Shuibai Zhang", "Coleman Hooper", "Yuezhou Hu", "Hyung Il Koo", "Nam Ik Cho", "Kangwook Lee" ], "github": "", "project_page": "", "abstract": "While most autoregressive LLMs are constrained to one-by-one decoding, diffusion LLMs (dLLMs) have attracted growing interest for their potential to dramatically accelerate inference through parallel decoding. Despite this promise, the conditional independence assumption in dLLMs causes parallel decoding to ignore token dependencies, inevitably degrading generation quality when these dependencies are strong. However, existing works largely overlook these inherent challenges, and evaluations on standard benchmarks (e.g., math and coding) are not sufficient to capture the quality degradation caused by parallel decoding. To address this gap, we first provide an information-theoretic analysis of parallel decoding. We then conduct case studies on analytically tractable synthetic list operations from both data distribution and decoding strategy perspectives, offering quantitative insights that highlight the fundamental limitations of parallel decoding. Building on these insights, we propose ParallelBench, the first benchmark specifically designed for dLLMs, featuring realistic tasks that are trivial for humans and autoregressive LLMs yet exceptionally challenging for dLLMs under parallel decoding. Using ParallelBench, we systematically analyze both dLLMs and autoregressive LLMs, revealing that: (i) dLLMs under parallel decoding can suffer dramatic quality degradation in real-world scenarios, and (ii) current parallel decoding strategies struggle to adapt their degree of parallelism based on task difficulty, thus failing to achieve meaningful speedup without compromising quality. Our findings underscore the pressing need for innovative decoding methods that can overcome the current speed-quality trade-off. We release our benchmark to help accelerate the development of truly efficient dLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13678", "title": "FlashWorld: High-quality 3D Scene Generation within Seconds", "authors": [ "Xinyang Li", "Tengfei Wang", "Zixiao Gu", "Shengchuan Zhang", "Chunchao Guo", "Liujuan Cao" ], "github": "", "project_page": "", "abstract": "We propose FlashWorld, a generative model that produces 3D scenes from a single image or text prompt in seconds, 10~100times faster than previous works while possessing superior rendering quality. Our approach shifts from the conventional multi-view-oriented (MV-oriented) paradigm, which generates multi-view images for subsequent 3D reconstruction, to a 3D-oriented approach where the model directly produces 3D Gaussian representations during multi-view generation. While ensuring 3D consistency, 3D-oriented method typically suffers poor visual quality. FlashWorld includes a dual-mode pre-training phase followed by a cross-mode post-training phase, effectively integrating the strengths of both paradigms. Specifically, leveraging the prior from a video diffusion model, we first pre-train a dual-mode multi-view diffusion model, which jointly supports MV-oriented and 3D-oriented generation modes. To bridge the quality gap in 3D-oriented generation, we further propose a cross-mode post-training distillation by matching distribution from consistent 3D-oriented mode to high-quality MV-oriented mode. This not only enhances visual quality while maintaining 3D consistency, but also reduces the required denoising steps for inference. Also, we propose a strategy to leverage massive single-view images and text prompts during this process to enhance the model's generalization to out-of-distribution inputs. Extensive experiments demonstrate the superiority and efficiency of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.10977", "title": "Revisiting Model Interpolation for Efficient Reasoning", "authors": [ "Taiqiang Wu", "Runming Yang", "Tao Liu", "Jiahao Wang", "Ngai Wong" ], "github": "https://github.com/wutaiqiang/MI", "project_page": "", "abstract": "Model merging, typically on Instruct and Thinking models, has shown remarkable performance for efficient reasoning. In this paper, we systematically revisit the simplest merging method that interpolates two weights directly. Particularly, we observe that model interpolation follows a three-stage evolutionary paradigm with distinct behaviors on the reasoning trajectory. These dynamics provide a principled guide for navigating the performance-cost trade-off. Empirical results demonstrate that a strategically interpolated model surprisingly surpasses sophisticated model merging baselines on both efficiency and effectiveness. We further validate our findings with extensive ablation studies on model layers, modules, and decoding strategies. Ultimately, this work demystifies model interpolation and offers a practical framework for crafting models with precisely targeted reasoning capabilities. Code is available at https://github.com/wutaiqiang/MI{Github}.", "github_manual": "", "github_regex": "https://github.com/wutaiqiang/MI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13778", "title": "InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy", "authors": [ "Xinyi Chen", "Yilun Chen", "Yanwei Fu", "Ning Gao", "Jiaya Jia", "Weiyang Jin", "Hao Li", "Yao Mu", "Jiangmiao Pang", "Yu Qiao", "Yang Tian", "Bin Wang", "Bolun Wang", "Fangjing Wang", "Hanqing Wang", "Tai Wang", "Ziqin Wang", "Xueyuan Wei", "Chao Wu", "Shuai Yang", "Jinhui Ye", "Junqiu Yu", "Jia Zeng", "Jingjing Zhang", "Jinyu Zhang", "Shi Zhang", "Feng Zheng", "Bowen Zhou", "Yangkun Zhu" ], "github": "https://github.com/InternRobotics/InternVLA-M1", "project_page": "", "abstract": "We introduce InternVLA-M1, a unified framework for spatial grounding and robot control that advances instruction-following robots toward scalable, general-purpose intelligence. Its core idea is spatially guided vision-language-action training, where spatial grounding serves as the critical link between instructions and robot actions. InternVLA-M1 employs a two-stage pipeline: (i) spatial grounding pre-training on over 2.3M spatial reasoning data to determine ``where to act'' by aligning instructions with visual, embodiment-agnostic positions, and (ii) spatially guided action post-training to decide ``how to act'' by generating embodiment-aware actions through plug-and-play spatial prompting. This spatially guided training recipe yields consistent gains: InternVLA-M1 outperforms its variant without spatial guidance by +14.6% on SimplerEnv Google Robot, +17% on WidowX, and +4.3% on LIBERO Franka, while demonstrating stronger spatial reasoning capability in box, point, and trace prediction. To further scale instruction following, we built a simulation engine to collect 244K generalizable pick-and-place episodes, enabling a 6.2% average improvement across 200 tasks and 3K+ objects. In real-world clustered pick-and-place, InternVLA-M1 improved by 7.3%, and with synthetic co-training, achieved +20.6% on unseen objects and novel configurations. Moreover, in long-horizon reasoning-intensive scenarios, it surpassed existing works by over 10%. These results highlight spatially guided training as a unifying principle for scalable and resilient generalist robots. Code and models are available at https://github.com/InternRobotics/InternVLA-M1.", "github_manual": "", "github_regex": "https://github.com/InternRobotics/InternVLA-M1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13602", "title": "NOSA: Native and Offloadable Sparse Attention", "authors": [ "Yuxiang Huang", "Chaojun Xiao", "Xu Han", "Zhiyuan Liu" ], "github": "", "project_page": "", "abstract": "Trainable sparse attention has emerged as a promising solution to address the decoding efficiency bottleneck of LLMs in long-context processing, significantly saving memory accesses while minimally impacting task performance. However, existing sparse attention methods leave a crucial limitation unresolved: the size of the key-value (KV) cache remains unreduced, which constrains on-GPU batch sizes and throttles decoding throughput, especially in large-scale batched inference. In this paper, we show that trainable sparse attention naturally exhibits strong locality in token selection across adjacent decoding steps, thereby enabling KV cache offloading without altering the underlying attention computation. However, the inherent locality remains insufficient to achieve efficient offloading, as the transfer of selected KV pairs between the CPU and GPU continues to dominate the overall decoding cost. Building on this insight, we present NOSA, a trainable sparse attention framework designed to natively support KV cache offloading. NOSA introduces explicit locality constraints by decomposing token selection into query-aware and query-agnostic components, thereby reducing KV transfers while preserving the same attention computation as used during training. We pretrain a 1B-parameter model with NOSA and conduct extensive benchmarks, showing that it preserves near-lossless performance while achieving up to a 2.3x improvement in decoding throughput compared with the vanilla trainable sparse attention baseline (InfLLM-V2).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13586", "title": "Deflanderization for Game Dialogue: Balancing Character Authenticity with Task Execution in LLM-based NPCs", "authors": [ "Pasin Buakhaw", "Kun Kerdthaisong", "Phuree Phenhiran", "Pitikorn Khlaisamniang", "Supasate Vorathammathorn", "Piyalitt Ittichaiwong", "Nutchanon Yongsatianchot" ], "github": "https://github.com/nltk/nltk", "project_page": "", "abstract": "The emergence of large language models (LLMs) has opened new opportunities for cre- ating dynamic non-player characters (NPCs) in gaming environments, enabling both func- tional task execution and persona-consistent dialogue generation. In this paper, we (Tu_Character_lab) report our participation in the Commonsense Persona-Grounded Dialogue Challenge (CPDC) 2025 Round 2, which eval- uates agents across three tracks: task-oriented dialogue, context-aware dialogue, and their integration. Our approach combines two complementary strategies: (i) lightweight prompting techniques in the API track, including a Deflanderization prompting method to suppress excessive role-play and improve task fidelity, and (ii) fine-tuned large models in the GPU track, leveraging Qwen3-14B with supervisedfinetuning (SFT) and Low-Rank Adaptation(LoRA). Our best submissions ranked 2nd on Task 1, 2nd on Task 3 (API track), and 4th on Task 3 (GPU track).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nltk/nltk", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.12560", "title": "CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving", "authors": [ "Xiaoji Zheng", "Ziyuan Yang", "Yanhao Chen", "Yuhang Peng", "Yuanrong Tang", "Gengyuan Liu", "Bokui Chen", "Jiangtao Gong" ], "github": "https://github.com/SEU-zxj/CoIRL-AD", "project_page": "", "abstract": "End-to-end autonomous driving models trained solely with imitation learning (IL) often suffer from poor generalization. In contrast, reinforcement learning (RL) promotes exploration through reward maximization but faces challenges such as sample inefficiency and unstable convergence. A natural solution is to combine IL and RL. Moving beyond the conventional two-stage paradigm (IL pretraining followed by RL fine-tuning), we propose CoIRL-AD, a competitive dual-policy framework that enables IL and RL agents to interact during training. CoIRL-AD introduces a competition-based mechanism that facilitates knowledge exchange while preventing gradient conflicts. Experiments on the nuScenes dataset show an 18% reduction in collision rate compared to baselines, along with stronger generalization and improved performance on long-tail scenarios. Code is available at: https://github.com/SEU-zxj/CoIRL-AD.", "github_manual": "", "github_regex": "https://github.com/SEU-zxj/CoIRL-AD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.11958", "title": "Direct Multi-Token Decoding", "authors": [ "Xuan Luo", "Weizhi Wang", "Xifeng Yan" ], "github": "https://github.com/luoxuan-cs/Direct-Multitoken-Decoding", "project_page": "", "abstract": "Decoder-only transformers have become the standard architecture for large language models (LLMs) due to their strong performance. Recent studies suggest that, in pre-trained LLMs, early, middle, and late layers may serve distinct roles: Early layers focus on understanding the input context, middle layers handle task-specific processing, and late layers convert abstract representations into output tokens. We hypothesize that once representations have been processed by the early and middle layers, the resulting hidden states may encapsulate sufficient information to support the generation of multiple tokens using only the late layers, eliminating the need to repeatedly traverse the early and middle layers. We refer to this inference paradigm as Direct Multi-Token Decoding (DMTD). Unlike speculative decoding, our method introduces no additional parameters, auxiliary routines, or post-generation verification. Despite being trained on a limited dataset, a fine-tuned DMTD Qwen3-4B model has already demonstrated promising results, achieving up to a 2x speedup with only minor performance loss. Moreover, as shown in our scaling analysis, its performance is expected to further improve with larger training datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/luoxuan-cs/Direct-Multitoken-Decoding", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.12831", "title": "MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training", "authors": [ "Taicheng Guo", "Hai Wang", "ChaoChun Liu", "Mohsen Golalikhani", "Xin Chen", "Xiangliang Zhang", "Chandan K. Reddy" ], "github": "", "project_page": "", "abstract": "Multi-turn Text-to-SQL aims to translate a user's conversational utterances into executable SQL while preserving dialogue coherence and grounding to the target schema. However, most existing systems only regard this task as a simple text translation task and follow a short-horizon paradigm, generating a query per turn without execution, explicit verification, and refinement, which leads to non-executable or incoherent outputs. We present MTSQL-R1, an agentic training framework for long-horizon multi-turn Text-to-SQL. We cast the task as a Markov Decision Process (MDP) in which an agent interacts with (i) a database for execution feedback and (ii) a persistent dialogue memory for coherence verification, performing an iterative propose to execute -> verify -> refine cycle until all checks pass. Experiments on COSQL and SPARC demonstrate that MTSQL-R1 consistently outperforms strong baselines, highlighting the importance of environment-driven verification and memory-guided refinement for conversational semantic parsing. Full recipes (including code, trained models, logs, reasoning trajectories, etc.) will be released after the internal review to contribute to community research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.10611", "title": "HyperAgent: Leveraging Hypergraphs for Topology Optimization in Multi-Agent Communication", "authors": [ "Heng Zhang", "Yuling Shi", "Xiaodong Gu", "Zijian Zhang", "Haochen You", "Lubin Gan", "Yilei Yuan", "Jin Huang" ], "github": "", "project_page": "", "abstract": "Recent advances in large language model-powered multi-agent systems have demonstrated remarkable collective intelligence through effective communication. However, existing approaches face two primary challenges: (i) Ineffective group collaboration modeling, as they rely on pairwise edge representations in graph structures, limiting their ability to capture relationships among multiple agents; and (ii) Limited task-adaptiveness in communication topology design, leading to excessive communication cost for simple tasks and insufficient coordination for complex scenarios. These issues restrict the scalability and practical deployment of adaptive collaboration frameworks. To address these challenges, we propose HyperAgent, a hypergraph-based framework that optimizes communication topologies and effectively captures group collaboration patterns using direct hyperedge representations. Unlike edge-based approaches, HyperAgent uses hyperedges to link multiple agents within the same subtask and employs hypergraph convolutional layers to achieve one-step information aggregation in collaboration groups. Additionally, it incorporates a variational autoencoder framework with sparsity regularization to dynamically adjust hypergraph topologies based on task complexity. Experiments highlight the superiority of HyperAgent in both performance and efficiency. For instance, on GSM8K, HyperAgent achieves 95.07\\% accuracy while reducing token consumption by 25.33\\%, demonstrating the potential of hypergraph-based optimization for multi-agent communication.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.10581", "title": "GraphTracer: Graph-Guided Failure Tracing in LLM Agents for Robust Multi-Turn Deep Search", "authors": [ "Heng Zhang", "Yuling Shi", "Xiaodong Gu", "Haochen You", "Zijian Zhang", "Lubin Gan", "Yilei Yuan", "Jin Huang" ], "github": "", "project_page": "", "abstract": "Multi-agent systems powered by Large Language Models excel at complex tasks through coordinated collaboration, yet they face high failure rates in multi-turn deep search scenarios. Existing temporal attribution methods struggle to accurately diagnose root causes, particularly when errors propagate across multiple agents. Attempts to automate failure attribution by analyzing action sequences remain ineffective due to their inability to account for information dependencies that span agents. This paper identifies two core challenges: (i) distinguishing symptoms from root causes in multi-agent error propagation, and (ii) tracing information dependencies beyond temporal order. To address these issues, we introduce GraphTracer, a framework that redefines failure attribution through information flow analysis. GraphTracer constructs Information Dependency Graphs (IDGs) to explicitly capture how agents reference and build on prior outputs. It localizes root causes by tracing through these dependency structures instead of relying on temporal sequences. GraphTracer also uses graph-aware synthetic data generation to target critical nodes, creating realistic failure scenarios. Evaluations on the Who\\&When benchmark and integration into production systems demonstrate that GraphTracer-8B achieves up to 18.18\\% higher attribution accuracy compared to state-of-the-art models and enables 4.8\\% to 14.2\\% performance improvements in deployed multi-agent frameworks, establishing a robust solution for multi-agent system debugging.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13626", "title": "LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models", "authors": [ "Senyu Fei", "Siyin Wang", "Junhao Shi", "Zihao Dai", "Jikun Cai", "Pengfang Qian", "Li Ji", "Xinzhe He", "Shiduo Zhang", "Zhaoye Fei", "Jinlan Fu", "Jingjing Gong", "Xipeng Qiu" ], "github": "https://github.com/sylvestf/LIBERO-plus", "project_page": "", "abstract": "Visual-Language-Action (VLA) models report impressive success rates on robotic manipulation benchmarks, yet these results may mask fundamental weaknesses in robustness. We perform a systematic vulnerability analysis by introducing controlled perturbations across seven dimensions: objects layout, camera viewpoints, robot initial states, language instructions, light conditions, background textures and sensor noise. We comprehensively analyzed multiple state-of-the-art models and revealed consistent brittleness beneath apparent competence. Our analysis exposes critical weaknesses: models exhibit extreme sensitivity to perturbation factors, including camera viewpoints and robot initial states, with performance dropping from 95% to below 30% under modest perturbations. Surprisingly, models are largely insensitive to language variations, with further experiments revealing that models tend to ignore language instructions completely. Our findings challenge the assumption that high benchmark scores equate to true competency and highlight the need for evaluation practices that assess reliability under realistic variation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sylvestf/LIBERO-plus", "github_api_added_by": "auto" }, { "date": "2025-10-16", "arxiv_id": "2510.13344", "title": "UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity MoE", "authors": [ "Zhenyu Liu", "Yunxin Li", "Xuanyu Zhang", "Qixun Teng", "Shenyuan Jiang", "Xinyu Chen", "Haoyuan Shi", "Jinchao Li", "Qi Wang", "Haolan Chen", "Fanbo Meng", "Mingjun Zhao", "Yu Xu", "Yancheng He", "Baotian Hu", "Min Zhang" ], "github": "https://github.com/boson-ai/higgs-audio", "project_page": "", "abstract": "Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. However, the auditory domain remains a significant challenge, with music and speech often developed in isolation, hindering progress towards universal audio synthesis. This separation stems from inherent task conflicts and severe data imbalances, which impede the development of a truly unified audio generation model. To address this challenge, we propose UniMoE-Audio, a unified speech and music generation model within a novel Dynamic-Capacity Mixture-of-Experts (MoE) framework. Architecturally, UniMoE-Audio introduces a Top-P routing strategy for dynamic expert number allocation, and a hybrid expert design comprising routed experts for domain-specific knowledge, shared experts for domain-agnostic features, and null experts for adaptive computation skipping. To tackle data imbalance, we introduce a three-stage training curriculum: 1) Independent Specialist Training leverages original datasets to instill domain-specific knowledge into each \"proto-expert\" without interference; 2) MoE Integration and Warmup incorporates these specialists into the UniMoE-Audio architecture, warming up the gate module and shared expert using a subset of balanced dataset; and 3) Synergistic Joint Training trains the entire model end-to-end on the fully balanced dataset, fostering enhanced cross-domain synergy. Extensive experiments show that UniMoE-Audio not only achieves state-of-the-art performance on major speech and music generation benchmarks, but also demonstrates superior synergistic learning, mitigating the performance degradation typically seen in naive joint training. Our findings highlight the substantial potential of specialized MoE architecture and curated training strategies in advancing the field of universal audio generation. Homepage: https://mukioxun.github.io/Uni-MoE-site/home.html", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/boson-ai/higgs-audio", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.07944", "title": "CVD-STORM: Cross-View Video Diffusion with Spatial-Temporal Reconstruction Model for Autonomous Driving", "authors": [ "Tianrui Zhang", "Yichen Liu", "Zilin Guo", "Yuxin Guo", "Jingcheng Ni", "Chenjing Ding", "Dan Xu", "Lewei Lu", "Zehuan Wu" ], "github": "", "project_page": "", "abstract": "Generative models have been widely applied to world modeling for environment simulation and future state prediction. With advancements in autonomous driving, there is a growing demand not only for high-fidelity video generation under various controls, but also for producing diverse and meaningful information such as depth estimation. To address this, we propose CVD-STORM, a cross-view video diffusion model utilizing a spatial-temporal reconstruction Variational Autoencoder (VAE) that generates long-term, multi-view videos with 4D reconstruction capabilities under various control inputs. Our approach first fine-tunes the VAE with an auxiliary 4D reconstruction task, enhancing its ability to encode 3D structures and temporal dynamics. Subsequently, we integrate this VAE into the video diffusion process to significantly improve generation quality. Experimental results demonstrate that our model achieves substantial improvements in both FID and FVD metrics. Additionally, the jointly-trained Gaussian Splatting Decoder effectively reconstructs dynamic scenes, providing valuable geometric information for comprehensive scene understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13554", "title": "Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization", "authors": [ "Yang Li", "Zhichen Dong", "Yuhan Sun", "Weixun Wang", "Shaopan Xiong", "Yijia Luo", "Jiashun Liu", "Han Lu", "Jiamang Wang", "Wenbo Su", "Bo Zheng", "Junchi Yan" ], "github": "", "project_page": "", "abstract": "The reasoning pattern of Large language models (LLMs) remains opaque, and Reinforcement learning (RL) typically applies uniform credit across an entire generation, blurring the distinction between pivotal and routine steps. This work positions attention as a privileged substrate that renders the internal logic of LLMs legible, not merely as a byproduct of computation, but as a mechanistic blueprint of reasoning itself. We first distinguish attention heads between locally and globally focused information processing and reveal that locally focused heads produce a sawtooth pattern near the diagonal indicating phrasal chunks, while globally focused heads expose tokens that exert broad downstream influence over future tokens. We formalize these with two metrics: 1) Windowed Average Attention Distance, which measures the extent of backward attention within a clipped window; 2) Future Attention Influence, which quantifies a token's global importance as the average attention it receives from subsequent tokens. Taken together, these signals reveal a recurring preplan-and-anchor mechanism, where the model first performs a long-range contextual reference to generate an introductory token, which is immediately followed by or coincides with a semantic anchor token that organizes subsequent reasoning. Leveraging these insights, we introduce three novel RL strategies that dynamically perform targeted credit assignment to critical nodes (preplan tokens, anchor tokens, and their temporal coupling) and show consistent performance gains across various reasoning tasks. By aligning optimization with the model's intrinsic reasoning rhythm, we aim to transform opaque optimization into an actionable structure-aware process, hoping to offer a potential step toward more transparent and effective optimization of LLM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13795", "title": "Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs", "authors": [ "Yi Zhang", "Bolin Ni", "Xin-Sheng Chen", "Heng-Rui Zhang", "Yongming Rao", "Houwen Peng", "Qinglin Lu", "Han Hu", "Meng-Hao Guo", "Shi-Min Hu" ], "github": "", "project_page": "", "abstract": "Fully open multimodal large language models (MLLMs) currently lag behind proprietary counterparts, primarily due to a significant gap in data quality for supervised fine-tuning (SFT). Existing open-source datasets are often plagued by widespread noise and a critical deficit in complex reasoning data, such as Chain-of-Thought (CoT), which hinders the development of advanced model capabilities. Addressing these challenges, our work makes three primary contributions. First, we introduce Honey-Data-15M, a new SFT dataset comprising approximately 15 million QA pairs, processed through multiple cleaning techniques and enhanced with a novel dual-level (short and long) CoT enrichment strategy. Second, we introduce HoneyPipe, the data curation pipeline, and its underlying framework DataStudio, providing the community with a transparent and adaptable methodology for data curation that moves beyond static dataset releases. Finally, to validate our dataset and pipeline, we train Bee-8B, an 8B model on Honey-Data-15M. Experiments show that Bee-8B establishes a new state-of-the-art (SOTA) for fully open MLLMs, achieving performance that is competitive with, and in some cases surpasses, recent semi-open models such as InternVL3.5-8B. Our work delivers to the community a suite of foundational resources, including: the Honey-Data-15M corpus; the full-stack suite comprising HoneyPipe and DataStudio; training recipes; an evaluation harness; and the model weights. This effort demonstrates that a principled focus on data quality is a key pathway to developing fully open MLLMs that are highly competitive with their semi-open counterparts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13759", "title": "Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark", "authors": [ "Kai Zou", "Ziqi Huang", "Yuhao Dong", "Shulin Tian", "Dian Zheng", "Hongbo Liu", "Jingwen He", "Bin Liu", "Yu Qiao", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Unified multimodal models aim to jointly enable visual understanding and generation, yet current benchmarks rarely examine their true integration. Existing evaluations either treat the two abilities in isolation or overlook tasks that inherently couple them. To address this gap, we present Uni-MMMU, a comprehensive and discipline-aware benchmark that systematically unfolds the bidirectional synergy between generation and understanding across eight reasoning-centric domains, including science, coding, mathematics, and puzzles. Each task is bidirectionally coupled, demanding models to (i) leverage conceptual understanding to guide precise visual synthesis, or (ii) utilize generation as a cognitive scaffold for analytical reasoning. Uni-MMMU incorporates verifiable intermediate reasoning steps, unique ground truths, and a reproducible scoring protocol for both textual and visual outputs. Through extensive evaluation of state-of-the-art unified, generation-only, and understanding-only models, we reveal substantial performance disparities and cross-modal dependencies, offering new insights into when and how these abilities reinforce one another, and establishing a reliable foundation for advancing unified models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.10921", "title": "FG-CLIP 2: A Bilingual Fine-grained Vision-Language Alignment Model", "authors": [ "Chunyu Xie", "Bin Wang", "Fanjing Kong", "Jincheng Li", "Dawei Liang", "Ji Ao", "Dawei Leng", "Yuhui Yin" ], "github": "", "project_page": "", "abstract": "Fine-grained vision-language understanding requires precise alignment between visual content and linguistic descriptions, a capability that remains limited in current models, particularly in non-English settings. While models like CLIP perform well on global alignment, they often struggle to capture fine-grained details in object attributes, spatial relations, and linguistic expressions, with limited support for bilingual comprehension. To address these challenges, we introduce FG-CLIP 2, a bilingual vision-language model designed to advance fine-grained alignment for both English and Chinese. Our approach leverages rich fine-grained supervision, including region-text matching and long-caption modeling, alongside multiple discriminative objectives. We further introduce the Textual Intra-modal Contrastive (TIC) loss to better distinguish semantically similar captions. Trained on a carefully curated mixture of large-scale English and Chinese data, FG-CLIP 2 achieves powerful bilingual performance. To enable rigorous evaluation, we present a new benchmark for Chinese multimodal understanding, featuring long-caption retrieval and bounding box classification. Extensive experiments on 29 datasets across 8 tasks show that FG-CLIP 2 outperforms existing methods, achieving state-of-the-art results in both languages. We release the model, code, and benchmark to facilitate future research on bilingual fine-grained alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.10274", "title": "X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model", "authors": [ "Jinliang Zheng", "Jianxiong Li", "Zhihao Wang", "Dongxiu Liu", "Xirui Kang", "Yuchun Feng", "Yinan Zheng", "Jiayin Zou", "Yilun Chen", "Jia Zeng", "Ya-Qin Zhang", "Jiangmiao Pang", "Jingjing Liu", "Tai Wang", "Xianyuan Zhan" ], "github": "", "project_page": "", "abstract": "Successful generalist Vision-Language-Action (VLA) models rely on effective training across diverse robotic platforms with large-scale, cross-embodiment, heterogeneous datasets. To facilitate and leverage the heterogeneity in rich, diverse robotic data sources, we propose a novel Soft Prompt approach with minimally added parameters, by infusing prompt learning concepts into cross-embodiment robot learning and introducing separate sets of learnable embeddings for each distinct data source. These embeddings serve as embodiment-specific prompts, which in unity empower VLA models with effective exploitation of varying cross-embodiment features. Our new X-VLA, a neat flow-matching-based VLA architecture, relies exclusively on soft-prompted standard Transformer encoders, enjoying both scalability and simplicity. Evaluated across 6 simulations as well as 3 real-world robots, our 0.9B instantiation-X-VLA-0.9B simultaneously achieves SOTA performance over a sweep of benchmarks, demonstrating superior results on a wide axes of capabilities, from flexible dexterity to quick adaptation across embodiments, environments, and tasks. Website: https://thu-air-dream.github.io/X-VLA/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13282", "title": "Universal Image Restoration Pre-training via Masked Degradation Classification", "authors": [ "JiaKui Hu", "Zhengjian Yao", "Lujia Jin", "Yinghao Chen", "Yanye Lu" ], "github": "https://github.com/MILab-PKU/MaskDCPT", "project_page": "", "abstract": "This study introduces a Masked Degradation Classification Pre-Training method (MaskDCPT), designed to facilitate the classification of degradation types in input images, leading to comprehensive image restoration pre-training. Unlike conventional pre-training methods, MaskDCPT uses the degradation type of the image as an extremely weak supervision, while simultaneously leveraging the image reconstruction to enhance performance and robustness. MaskDCPT includes an encoder and two decoders: the encoder extracts features from the masked low-quality input image. The classification decoder uses these features to identify the degradation type, whereas the reconstruction decoder aims to reconstruct a corresponding high-quality image. This design allows the pre-training to benefit from both masked image modeling and contrastive learning, resulting in a generalized representation suited for restoration tasks. Benefit from the straightforward yet potent MaskDCPT, the pre-trained encoder can be used to address universal image restoration and achieve outstanding performance. Implementing MaskDCPT significantly improves performance for both convolution neural networks (CNNs) and Transformers, with a minimum increase in PSNR of 3.77 dB in the 5D all-in-one restoration task and a 34.8% reduction in PIQE compared to baseline in real-world degradation scenarios. It also emergences strong generalization to previously unseen degradation types and levels. In addition, we curate and release the UIR-2.5M dataset, which includes 2.5 million paired restoration samples across 19 degradation types and over 200 degradation levels, incorporating both synthetic and real-world data. The dataset, source code, and models are available at https://github.com/MILab-PKU/MaskDCPT.", "github_manual": "", "github_regex": "https://github.com/MILab-PKU/MaskDCPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13800", "title": "Reasoning in Space via Grounding in the World", "authors": [ "Yiming Chen", "Zekun Qi", "Wenyao Zhang", "Xin Jin", "Li Zhang", "Peidong Liu" ], "github": "https://github.com/WU-CVGL/GS-Reasoner", "project_page": "", "abstract": "In this paper, we claim that 3D visual grounding is the cornerstone of spatial reasoning and introduce the Grounded-Spatial Reasoner (GS-Reasoner) to explore the effective spatial representations that bridge the gap between them. Existing 3D LLMs suffer from the absence of a unified 3D representation capable of jointly capturing semantic and geometric information. This deficiency is manifested either in poor performance on grounding or in an excessive reliance on external modules, ultimately hindering the seamless integration of grounding and spatial reasoning. To address this, we propose a simple yet effective dual-path pooling mechanism that tightly aligns geometric features with both semantic and positional cues, constructing a unified image patch-based 3D representation that encapsulates all essential information without increasing the number of input tokens. Leveraging this holistic representation, GS-Reasoner is the first 3D LLM that achieves autoregressive grounding entirely without external modules while delivering performance comparable to state-of-the-art models, establishing a unified and self-contained framework for 3D spatial reasoning. To further bridge grounding and spatial reasoning, we introduce the Grounded Chain-of-Thought (GCoT) dataset. This dataset is meticulously curated to include both 3D bounding box annotations for objects referenced in reasoning questions and step-by-step reasoning paths that integrate grounding as a core component of the problem-solving process. Extensive experiments demonstrate that GS-Reasoner achieves impressive results on 3D visual grounding, which in turn significantly enhances its spatial reasoning capabilities, leading to state-of-the-art performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WU-CVGL/GS-Reasoner", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.11062", "title": "Stronger Together: On-Policy Reinforcement Learning for Collaborative LLMs", "authors": [ "Yujie Zhao", "Lanxiang Hu", "Yang Wang", "Minmin Hou", "Hao Zhang", "Ke Ding", "Jishen Zhao" ], "github": "https://github.com/pettingllms-ai/PettingLLMs", "project_page": "", "abstract": "Multi-agent systems (MAS) and reinforcement learning (RL) are widely used to enhance the agentic capabilities of large language models (LLMs). MAS improves task performance through role-based orchestration, while RL uses environmental rewards to learn stronger policies, such as GRPO-style optimization. However, applying on-policy RL to MAS remains underexplored and presents unique challenges. Algorithmically, standard GRPO grouping assumptions break down because prompts vary by role and by turn. System-wise, the training stack must support MAS-workflow rollouts and on-policy updates for both single-policy and multi-policy models. We propose AT-GRPO, which includes (i) an agent- and turn-wise grouped RL algorithm tailored to MAS and (ii) a training system that supports both single- and multi-policy regimes. Across game, planning, coding, and math tasks, AT-GRPO delivers substantial gains. On long-horizon planning, it increases accuracy from a 14.0 to 47.0 percent single-agent RL baseline to 96.0 to 99.5 percent. It also improves reasoning performance, with average gains of 3.87 to 7.62 percent on coding tasks and 9.0 to 17.93 percent on math. Code and environments are available at: https://github.com/pettingllms-ai/PettingLLMs.", "github_manual": "", "github_regex": "https://github.com/pettingllms-ai/PettingLLMs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.11715", "title": "Point Prompting: Counterfactual Tracking with Video Diffusion Models", "authors": [ "Ayush Shrivastava", "Sanyam Mehta", "Daniel Geng", "Andrew Owens" ], "github": "", "project_page": "", "abstract": "Trackers and video generators solve closely related problems: the former analyze motion, while the latter synthesize it. We show that this connection enables pretrained video diffusion models to perform zero-shot point tracking by simply prompting them to visually mark points as they move over time. We place a distinctively colored marker at the query point, then regenerate the rest of the video from an intermediate noise level. This propagates the marker across frames, tracing the point's trajectory. To ensure that the marker remains visible in this counterfactual generation, despite such markers being unlikely in natural videos, we use the unedited initial frame as a negative prompt. Through experiments with multiple image-conditioned video diffusion models, we find that these \"emergent\" tracks outperform those of prior zero-shot methods and persist through occlusions, often obtaining performance that is competitive with specialized self-supervised models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13786", "title": "The Art of Scaling Reinforcement Learning Compute for LLMs", "authors": [ "Devvrit Khatri", "Lovish Madaan", "Rishabh Tiwari", "Rachit Bansal", "Sai Surya Duvvuri", "Manzil Zaheer", "Inderjit S. Dhillon", "David Brandfonbrener", "Rishabh Agarwal" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has become central to training large language models (LLMs), yet the field lacks predictive scaling methodologies comparable to those established for pre-training. Despite rapidly rising compute budgets, there is no principled understanding of how to evaluate algorithmic improvements for scaling RL compute. We present the first large-scale systematic study, amounting to more than 400,000 GPU-hours, that defines a principled framework for analyzing and predicting RL scaling in LLMs. We fit sigmoidal compute-performance curves for RL training and ablate a wide range of common design choices to analyze their effects on asymptotic performance and compute efficiency. We observe: (1) Not all recipes yield similar asymptotic performance, (2) Details such as loss aggregation, normalization, curriculum, and off-policy algorithm primarily modulate compute efficiency without materially shifting the asymptote, and (3) Stable, scalable recipes follow predictable scaling trajectories, enabling extrapolation from smaller-scale runs. Combining these insights, we propose a best-practice recipe, ScaleRL, and demonstrate its effectiveness by successfully scaling and predicting validation performance on a single RL run scaled up to 100,000 GPU-hours. Our work provides both a scientific framework for analyzing scaling in RL and a practical recipe that brings RL training closer to the predictability long achieved in pre-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13255", "title": "Hierarchical Frequency Tagging Probe (HFTP): A Unified Approach to Investigate Syntactic Structure Representations in Large Language Models and the Human Brain", "authors": [ "Jingmin An", "Yilong Song", "Ruolin Yang", "Nai Ding", "Lingxi Lu", "Yuxuan Wang", "Wei Wang", "Chu Zhuang", "Qian Wang", "Fang Fang" ], "github": "https://github.com/LilTiger/HFTP", "project_page": "", "abstract": "Large Language Models (LLMs) demonstrate human-level or even superior language abilities, effectively modeling syntactic structures, yet the specific computational modules responsible remain unclear. A key question is whether LLM behavioral capabilities stem from mechanisms akin to those in the human brain. To address these questions, we introduce the Hierarchical Frequency Tagging Probe (HFTP), a tool that utilizes frequency-domain analysis to identify neuron-wise components of LLMs (e.g., individual Multilayer Perceptron (MLP) neurons) and cortical regions (via intracranial recordings) encoding syntactic structures. Our results show that models such as GPT-2, Gemma, Gemma 2, Llama 2, Llama 3.1, and GLM-4 process syntax in analogous layers, while the human brain relies on distinct cortical regions for different syntactic levels. Representational similarity analysis reveals a stronger alignment between LLM representations and the left hemisphere of the brain (dominant in language processing). Notably, upgraded models exhibit divergent trends: Gemma 2 shows greater brain similarity than Gemma, while Llama 3.1 shows less alignment with the brain compared to Llama 2. These findings offer new insights into the interpretability of LLM behavioral improvements, raising questions about whether these advancements are driven by human-like or non-human-like mechanisms, and establish HFTP as a valuable tool bridging computational linguistics and cognitive neuroscience. This project is available at https://github.com/LilTiger/HFTP.", "github_manual": "", "github_regex": "https://github.com/LilTiger/HFTP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.11653", "title": "MATH-Beyond: A Benchmark for RL to Expand Beyond the Base Model", "authors": [ "Prasanna Mayilvahanan", "Ricardo Dominguez-Olmedo", "Thadd\u00e4us Wiedemer", "Wieland Brendel" ], "github": "", "project_page": "", "abstract": "With the advent of DeepSeek-R1, a new wave of reinforcement learning (RL) methods has emerged that seem to unlock stronger mathematical reasoning. However, a closer look at the open-source ecosystem reveals a critical limitation: with sufficiently many draws (e.g., pass@1024), many existing base models already solve nearly all questions on widely used math benchmarks such as MATH-500 and AIME 2024. This suggests that the RL fine-tuning methods prevalent in the LLM reasoning literature largely sharpen existing solution modes rather than discovering entirely new ones. Such sharpening stands in contrast to the broader promise of RL: to foster exploration and to acquire new skills. To move beyond this plateau, we introduce MATH-Beyond (MATH-B), a benchmark deliberately constructed to defeat common open-source models of up to 8B parameters even under large sampling budgets. Improving performance on our benchmark via RL requires methods that learn to reason in ways that go beyond base model capabilities in repeated sampling. Since the problems are drawn from subsets of DAPO-Math-17K and DeepScaleR datasets, they remain topically equivalent to standard high-school math. Validating our premise, RL fine-tuned models such as Nemotron-Research-Reasoning-Qwen-1.5B and DeepScaleR-1.5B-Preview perform poorly on MATH-B at pass@1024, showing how existing approaches fall short on tackling harder instances. We hope MATH-B will catalyze exploration-driven RL approaches that elicit deeper reasoning capabilities. We release MATH-B at https://huggingface.co/datasets/brendel-group/MATH-Beyond.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.11170", "title": "EAGER: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling", "authors": [ "Daniel Scalena", "Leonidas Zotos", "Elisabetta Fersini", "Malvina Nissim", "Ahmet \u00dcst\u00fcn" ], "github": "", "project_page": "", "abstract": "With the rise of reasoning language models and test-time scaling methods as a paradigm for improving model performance, substantial computation is often required to generate multiple candidate sequences from the same prompt. This enables exploration of different reasoning paths toward the correct solution, however, allocates the same compute budget for each prompt. Grounded on the assumption that different prompts carry different degrees of complexity, and thus different computation needs, we propose EAGer, a training-free generation method that leverages model uncertainty through token-wise entropy distribution to reduce redundant computation and concurrently improve overall performance. EAGer allows branching to multiple reasoning paths only in the presence of high-entropy tokens, and then reallocates the saved compute budget to the instances where exploration of alternative paths is most needed. We find that across multiple open-source models on complex reasoning benchmarks such as AIME 2025, EAGer can reallocate the budget without accessing target labels, achieving the best efficiency-performance trade-off in terms of reasoning length and Pass@k. When target labels are accessible, EAGer generates up to 65% fewer tokens (hence saving compute) and achieves up to 37% improvement in Pass@k compared to the Full Parallel Sampling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13714", "title": "Dedelayed: Deleting remote inference delay via on-device correction", "authors": [ "Dan Jacobellis", "Mateen Ulhaq", "Fabien Racap\u00e9", "Hyomin Choi", "Neeraja J. Yadwadkar" ], "github": "", "project_page": "", "abstract": "Remote inference allows lightweight devices to leverage powerful cloud models. However, communication network latency makes predictions stale and unsuitable for real-time tasks. To address this, we introduce Dedelayed, a delay-corrective method that mitigates arbitrary remote inference delays, allowing the local device to produce low-latency outputs in real time. Our method employs a lightweight local model that processes the current frame and fuses in features that a heavyweight remote model computes from past frames. On video from the BDD100K driving dataset, Dedelayed improves semantic segmentation accuracy over the stronger of the local-only and remote-only baselines across all realistic communication network delays beyond 33 ms. Without incurring additional delay, it improves accuracy by 6.4 mIoU compared to fully local inference and 9.8 mIoU compared to remote inference, for a round-trip delay of 100 ms. The advantage grows under longer delays and higher-motion scenes, as delay-mitigated split inference sustains accuracy more effectively, providing clear advantages for real-time tasks that must remain aligned with the current world state.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.11438", "title": "What Generative Search Engines Like and How to Optimize Web Content Cooperatively", "authors": [ "Yujiang Wu", "Shanshan Zhong", "Yubin Kim", "Chenyan Xiong" ], "github": "https://github.com/cxcscmu/AutoGEO", "project_page": "", "abstract": "By employing large language models (LLMs) to retrieve documents and generate natural language responses, Generative Engines, such as Google AI overview and ChatGPT, provide significantly enhanced user experiences and have rapidly become the new form of search. Their rapid adoption also drives the needs of Generative Engine Optimization (GEO), as content providers are eager to gain more traction from them. In this paper, we introduce AutoGEO, a framework to automatically learn generative engine preferences when using retrieved contents for response generation, and rewrite web contents for more such traction. AutoGEO first prompts frontier LLMs to explain generative engine preferences and extract meaningful preference rules from these explanations. Then it uses preference rules as context engineering for AutoGEO_API, a prompt-based GEO system, and as rule-based rewards to train AutoGEO_Mini, a cost-effective GEO model. Experiments on the standard GEO-Bench and two newly constructed benchmarks using real user queries demonstrate the effectiveness of AutoGEO in enhancing content traction while preserving search utility. Analyses confirm the learned rules' robustness and abilities to capture unique preferences in variant domains, and AutoGEO systems' ability to embed them in content optimization. The code is released at https://github.com/cxcscmu/AutoGEO.", "github_manual": "", "github_regex": "https://github.com/cxcscmu/AutoGEO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.10930", "title": "Evaluating Language Models' Evaluations of Games", "authors": [ "Katherine M. Collins", "Cedegao E. Zhang", "Graham Todd", "Lance Ying", "Mauricio Barba da Costa", "Ryan Liu", "Prafull Sharma", "Adrian Weller", "Ionatan Kuperwajs", "Lionel Wong", "Joshua B. Tenenbaum", "Thomas L. Griffiths" ], "github": "", "project_page": "", "abstract": "Reasoning is not just about solving problems -- it is also about evaluating which problems are worth solving at all. Evaluations of artificial intelligence (AI) systems primarily focused on problem solving, historically by studying how models play games such as chess and Go. In this paper, we advocate for a new paradigm that assesses AI systems' evaluation of games. First, we introduce a formalism for evaluating such evaluations. We then leverage a large-scale dataset of over 100 novel board games and over 450 human judgments to compare evaluations produced by modern language and reasoning models against those of people and symbolic computational agents. We consider two kinds of evaluative queries: assessing the payoff (or fairness) and the funness of games. These queries span two dimensions relevant to the design of evaluations of AI evaluations: how complex a query is to compute and how difficult a query is to quantify. Our results show that reasoning models are generally more aligned to people in their evaluations of games than non-reasoning language models. However, we observe a non-monotonic relationship: as models get closer to game-theoretic optimal, their fit to human data weakens. We also observe more \"jaggedness\" across models for assessing funness, in line with the greater difficulty of quantifying this query. Across queries and games, reasoning models show highly variable and unpredictable resource usage when assessing queries, pointing to the importance of imbuing more resource-rational meta-reasoning in language and reasoning models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.10494", "title": "Tracing the Traces: Latent Temporal Signals for Efficient and Accurate Reasoning", "authors": [ "Martina G. Vilas", "Safoora Yousefi", "Besmira Nushi", "Eric Horvitz", "Vidhisha Balachandran" ], "github": "", "project_page": "", "abstract": "Reasoning models improve their problem-solving ability through inference-time scaling, allocating more compute via longer token budgets. Identifying which reasoning traces are likely to succeed remains a key opportunity: reliably predicting productive paths can substantially reduce wasted computation and improve overall efficiency. We introduce Latent-Trajectory signals that characterize the temporal evolution of a model's internal representations during the generation of intermediate reasoning tokens. By measuring the overall change in latent representations between the start and end of reasoning, the change accumulated across intermediate steps, and the extent to which these changes advance toward the final state, we show that these signals predict solution accuracy more reliably than both cross-layer metrics and output-based confidence measures. When used to guide answer selection across multiple sampled generations, Latent-Trajectory signals make test-time scaling more effective and efficient than majority voting, reducing token usage by up to 70% while preserving and even improving accuracy by 2.6% on average. Moreover, these predictive signals often emerge early in the reasoning trace, enabling early selection and allocation of compute to the most promising candidates. Our findings contribute not only practical strategies for inference-time efficiency, but also a deeper interpretability perspective on how reasoning processes are represented and differentiated in latent space.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.07414", "title": "Haystack Engineering: Context Engineering for Heterogeneous and Agentic Long-Context Evaluation", "authors": [ "Mufei Li", "Dongqi Fu", "Limei Wang", "Si Zhang", "Hanqing Zeng", "Kaan Sancak", "Ruizhong Qiu", "Haoyu Wang", "Xiaoxin He", "Xavier Bresson", "Yinglong Xia", "Chonglin Sun", "Pan Li" ], "github": "https://github.com/Graph-COM/HaystackCraft", "project_page": "", "abstract": "Modern long-context large language models (LLMs) perform well on synthetic \"needle-in-a-haystack\" (NIAH) benchmarks, but such tests overlook how noisy contexts arise from biased retrieval and agentic workflows. We argue that haystack engineering is necessary to construct noisy long contexts that faithfully capture key real-world factors -- distraction from heterogeneous biased retrievers and cascading errors in agentic workflows -- to test models' long-context robustness. We instantiate it through HaystackCraft, a new NIAH benchmark built on the full English Wikipedia hyperlink network with multi-hop questions. HaystackCraft evaluates how heterogeneous retrieval strategies (e.g., sparse, dense, hybrid, and graph-based) affect distractor composition, haystack ordering, and downstream LLM performance. HaystackCraft further extends NIAH to dynamic, LLM-dependent settings that simulate agentic operations, where models refine queries, reflect on their past reasonings, and decide when to stop. Experiments with 15 long-context models show that (1) while stronger dense retrievers can introduce more challenging distractors, graph-based reranking simultaneously improves retrieval effectiveness and mitigates more harmful distractors; (2) in agentic tests, even advanced models like Gemini 2.5 Pro and GPT-5 suffer cascading failures from self-generated distractors or struggle to perform early stops. These results highlight persistent challenges in agentic long-context reasoning and establish HaystackCraft as a valuable testbed for future progress.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Graph-COM/HaystackCraft", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.12866", "title": "Learning to Grasp Anything by Playing with Random Toys", "authors": [ "Dantong Niu", "Yuvan Sharma", "Baifeng Shi", "Rachel Ding", "Matteo Gioia", "Haoru Xue", "Henry Tsai", "Konstantinos Kallidromitis", "Anirudh Pai", "Shankar Shastry", "Trevor Darrell", "Jitendra Malik", "Roei Herzig" ], "github": "https://github.com/moojink/openvla-oft", "project_page": "", "abstract": "Robotic manipulation policies often struggle to generalize to novel objects, limiting their real-world utility. In contrast, cognitive science suggests that children develop generalizable dexterous manipulation skills by mastering a small set of simple toys and then applying that knowledge to more complex items. Inspired by this, we study if similar generalization capabilities can also be achieved by robots. Our results indicate robots can learn generalizable grasping using randomly assembled objects that are composed from just four shape primitives: spheres, cuboids, cylinders, and rings. We show that training on these \"toys\" enables robust generalization to real-world objects, yielding strong zero-shot performance. Crucially, we find the key to this generalization is an object-centric visual representation induced by our proposed detection pooling mechanism. Evaluated in both simulation and on physical robots, our model achieves a 67% real-world grasping success rate on the YCB dataset, outperforming state-of-the-art approaches that rely on substantially more in-domain data. We further study how zero-shot generalization performance scales by varying the number and diversity of training toys and the demonstrations per toy. We believe this work offers a promising path to scalable and generalizable learning in robotic manipulation. Demonstration videos, code, checkpoints and our dataset are available on our project page: https://lego-grasp.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/moojink/openvla-oft", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.09913", "title": "Don't Throw Away Your Pretrained Model", "authors": [ "Shangbin Feng", "Wenhao Yu", "Yike Wang", "Hongming Zhang", "Yulia Tsvetkov", "Dong Yu" ], "github": "https://github.com/BunsenFeng/switch_generation", "project_page": "", "abstract": "Alignment training has tradeoffs: it helps language models (LMs) gain in reasoning and instruction following but might lose out on skills such as creativity and calibration, where unaligned base models are better at. We aim to make the best of both worlds through model collaboration, where different models in the training pipeline collaborate and complement each other. Since LM responses feature interleaving skills that favor different models, we propose Switch Generation, where pretrained and aligned model versions take turns to ``speak'' in a response sequence. Specifically, we train a switcher LM by learning from outcomes of choosing different models to generate the next segment across diverse queries and contexts. At inference time, the switcher LM guides different model checkpoints to dynamically generate the next segment where their strengths are most needed. Extensive experiments with 8 model collaboration baselines and 18 datasets show that 1) model collaboration consistently outperforms individual models on 16 out of 18 tasks, and 2) Switch Generation further outperforms baselines by 12.9% on average. Further analysis reveals that Switch Generation discovers compositional skills to solve problems where individual models struggle and generalizes to unseen models and tasks, reusing and repurposing by-products in expensive model training pipelines that are otherwise discarded.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/BunsenFeng/switch_generation", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.13940", "title": "Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention", "authors": [ "Zhen Yang", "Mingyang Zhang", "Feng Chen", "Ganggui Ding", "Liang Hou", "Xin Tao", "Pengfei Wan", "Ying-Cong Chen" ], "github": "https://github.com/EnVision-Research/MTI", "project_page": "", "abstract": "Recent progress in large language models (LLMs) has focused on test-time scaling to improve reasoning via increased inference computation, but often at the cost of efficiency. We revisit test-time behavior and uncover a simple yet underexplored phenomenon: reasoning uncertainty is highly localized-only a small subset of high-entropy tokens dominantly affects output correctness. Motivated by this, we propose Minimal Test-Time Intervention (MTI), a training-free framework that enhances reasoning accuracy and stability with minimal overhead. MTI includes: (i) Selective CFG intervention, applying classifier-free guidance only at uncertain positions; and (ii) Lightweight negative-prompt guidance, reusing the main model's KV cache to approximate unconditional decoding efficiently. MTI yields consistent gains across general, coding, and STEM tasks-e.g., +1.35% average improvement on eight benchmarks for Qwen3-8B-Base and +5% on AIME2024 using Qwen3-32B-Reasoning-while remaining highly efficient.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EnVision-Research/MTI", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-16", "arxiv_id": "2510.12872", "title": "KVCOMM: Online Cross-context KV-cache Communication for Efficient LLM-based Multi-agent Systems", "authors": [ "Hancheng Ye", "Zhengqi Gao", "Mingyuan Ma", "Qinsi Wang", "Yuzhe Fu", "Ming-Yu Chung", "Yueqian Lin", "Zhijian Liu", "Jianyi Zhang", "Danyang Zhuo", "Yiran Chen" ], "github": "https://github.com/FastMAS/KVCOMM", "project_page": "", "abstract": "Multi-agent large language model (LLM) systems are increasingly adopted for complex language processing tasks that require communication and coordination among agents. However, these systems often suffer substantial overhead from repeated reprocessing of overlapping contexts across agents. In typical pipelines, once an agent receives a message from its predecessor, the full context-including prior turns-must be reprocessed from scratch, leading to inefficient processing. While key-value (KV) caching is an effective solution for avoiding redundant computation in single-agent settings where prefixes remain unchanged, it cannot be directly reused in multi-agent scenarios due to diverging prefixes introduced by agent-specific context extensions. We identify that the core challenge lies in the offset variance of KV-caches across agents. To address this, we propose KVCOMM, a training-free framework that enables efficient prefilling in multi-agent inference by reusing KV-caches and aligning cache offsets of overlapping contexts under diverse prefix contexts. KVCOMM estimates and adjusts KV-caches for shared content by referencing a pool of cached examples-termed anchors-that store observed cache deviations under varying prefixes. The anchor pool is maintained and updated online, allowing dynamic adaptation to distinct user requests and context structures. KVCOMM achieves over 70% reuse rate across diverse multi-agent workloads, including retrieval-augmented generation, math reasoning, and collaborative coding tasks, all without quality degradation. Particularly, when each fully-connected agent receives 1K input tokens with 512 prefix tokens and 512 output tokens under a five-agent setting, KVCOMM achieves up to 7.8x speedup compared to the standard prefill pipeline, reducing TTFT from ~430 ms to ~55 ms.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FastMAS/KVCOMM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.13217", "title": "LLM-guided Hierarchical Retrieval", "authors": [ "Nilesh Gupta", "Wei-Cheng Chang", "Ngot Bui", "Cho-Jui Hsieh", "Inderjit S. Dhillon" ], "github": "", "project_page": "", "abstract": "Modern IR systems are increasingly tasked with answering complex, multi-faceted queries that require deep reasoning rather than simple keyword or semantic matching. While LLM-based IR has shown great promise, the prevailing retrieve-then-rerank paradigm inherits the limitations of embedding-based retrieval; parametric generative approaches are difficult to update with new information; and long-context methods that place the entire corpus in context are computationally infeasible for large document collections. To address these challenges, we introduce LATTICE, a hierarchical retrieval framework that enables an LLM to reason over and navigate large corpora with logarithmic search complexity by imposing a semantic tree structure on the corpus. Our approach consists of two stages: (1) an offline phase that organizes the corpus into a semantic hierarchy via either a bottom-up agglomerative strategy or a top-down divisive strategy using multi-level summaries and (2) an online traversal phase where a search LLM navigates this tree. A central challenge in such LLM-guided search is that the model's relevance judgments are noisy, context-dependent, and unaware of the hierarchy, making cross-branch and cross-level comparisons difficult. To overcome this, we propose a traversal algorithm that estimates calibrated latent relevance scores from local LLM outputs and aggregates them into a global path relevance metric. Our training-free framework achieves state-of-the-art zero-shot performance on the reasoning-intensive BRIGHT benchmark, demonstrating up to 9% improvement in Recall@100 and 5% in nDCG@10 over the next best zero-shot baseline. Furthermore, compared to the fine-tuned SOTA method DIVER-v2, LATTICE attains comparable results on BRIGHT subsets that use a static corpus for evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14979", "title": "From Pixels to Words -- Towards Native Vision-Language Primitives at Scale", "authors": [ "Haiwen Diao", "Mingxuan Li", "Silei Wu", "Linjun Dai", "Xiaohua Wang", "Hanming Deng", "Lewei Lu", "Dahua Lin", "Ziwei Liu" ], "github": "https://github.com/EvolvingLMMs-Lab/NEO", "project_page": "", "abstract": "The edifice of native Vision-Language Models (VLMs) has emerged as a rising contender to typical modular VLMs, shaped by evolving model architectures and training paradigms. Yet, two lingering clouds cast shadows over its widespread exploration and promotion: (-) What fundamental constraints set native VLMs apart from modular ones, and to what extent can these barriers be overcome? (-) How to make research in native VLMs more accessible and democratized, thereby accelerating progress in the field. In this paper, we clarify these challenges and outline guiding principles for constructing native VLMs. Specifically, one native VLM primitive should: (i) effectively align pixel and word representations within a shared semantic space; (ii) seamlessly integrate the strengths of formerly separate vision and language modules; (iii) inherently embody various cross-modal properties that support unified vision-language encoding, aligning, and reasoning. Hence, we launch NEO, a novel family of native VLMs built from first principles, capable of rivaling top-tier modular counterparts across diverse real-world scenarios. With only 390M image-text examples, NEO efficiently develops visual perception from scratch while mitigating vision-language conflicts inside a dense and monolithic model crafted from our elaborate primitives. We position NEO as a cornerstone for scalable and powerful native VLMs, paired with a rich set of reusable components that foster a cost-effective and extensible ecosystem. Our code and models are publicly available at: https://github.com/EvolvingLMMs-Lab/NEO.", "github_manual": "", "github_regex": "https://github.com/EvolvingLMMs-Lab/NEO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14969", "title": "LLMs as Scalable, General-Purpose Simulators For Evolving Digital Agent Training", "authors": [ "Yiming Wang", "Da Yin", "Yuedong Cui", "Ruichen Zheng", "Zhiqian Li", "Zongyu Lin", "Di Wu", "Xueqing Wu", "Chenchen Ye", "Yu Zhou", "Kai-Wei Chang" ], "github": "https://github.com/WadeYin9712/UI-Simulator", "project_page": "", "abstract": "Digital agents require diverse, large-scale UI trajectories to generalize across real-world tasks, yet collecting such data is prohibitively expensive in both human annotation, infra and engineering perspectives. To this end, we introduce UI-Simulator, a scalable paradigm that generates structured UI states and transitions to synthesize training trajectories at scale. Our paradigm integrates a digital world simulator for diverse UI states, a guided rollout process for coherent exploration, and a trajectory wrapper that produces high-quality and diverse trajectories for agent training. We further propose UI-Simulator-Grow, a targeted scaling strategy that enables more rapid and data-efficient scaling by prioritizing high-impact tasks and synthesizes informative trajectory variants. Experiments on WebArena and AndroidWorld show that UI-Simulator rivals or surpasses open-source agents trained on real UIs with significantly better robustness, despite using weaker teacher models. Moreover, UI-Simulator-Grow matches the performance of Llama-3-70B-Instruct using only Llama-3-8B-Instruct as the base model, highlighting the potential of targeted synthesis scaling paradigm to continuously and efficiently enhance the digital agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WadeYin9712/UI-Simulator", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14967", "title": "Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn LLM Agents", "authors": [ "Guoqing Wang", "Sunhao Dai", "Guangze Ye", "Zeyu Gan", "Wei Yao", "Yong Deng", "Xiaofeng Wu", "Zhenzhe Ying" ], "github": "", "project_page": "", "abstract": "Large language model (LLM)-based agents are increasingly trained with reinforcement learning (RL) to enhance their ability to interact with external environments through tool use, particularly in search-based settings that require multi-turn reasoning and knowledge acquisition. However, existing approaches typically rely on outcome-based rewards that are only provided at the final answer. This reward sparsity becomes particularly problematic in multi-turn settings, where long trajectories exacerbate two critical issues: (i) advantage collapse, where all rollouts receive identical rewards and provide no useful learning signals, and (ii) lack of fine-grained credit assignment, where dependencies between turns are obscured, especially in long-horizon tasks. In this paper, we propose Information Gain-based Policy Optimization (IGPO), a simple yet effective RL framework that provides dense and intrinsic supervision for multi-turn agent training. IGPO models each interaction turn as an incremental process of acquiring information about the ground truth, and defines turn-level rewards as the marginal increase in the policy's probability of producing the correct answer. Unlike prior process-level reward approaches that depend on external reward models or costly Monte Carlo estimation, IGPO derives intrinsic rewards directly from the model's own belief updates. These intrinsic turn-level rewards are combined with outcome-level supervision to form dense reward trajectories. Extensive experiments on both in-domain and out-of-domain benchmarks demonstrate that IGPO consistently outperforms strong baselines in multi-turn scenarios, achieving higher accuracy and improved sample efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.13454", "title": "VIST3A: Text-to-3D by Stitching a Multi-view Reconstruction Network to a Video Generator", "authors": [ "Hyojun Go", "Dominik Narnhofer", "Goutam Bhat", "Prune Truong", "Federico Tombari", "Konrad Schindler" ], "github": "https://github.com/CodeGoat24/UnifiedReward/blob/main/inference_qwen/image_generation/qwen_point_score_ACS_image_generation.py", "project_page": "", "abstract": "The rapid progress of large, pretrained models for both visual content generation and 3D reconstruction opens up new possibilities for text-to-3D generation. Intuitively, one could obtain a formidable 3D scene generator if one were able to combine the power of a modern latent text-to-video model as \"generator\" with the geometric abilities of a recent (feedforward) 3D reconstruction system as \"decoder\". We introduce VIST3A, a general framework that does just that, addressing two main challenges. First, the two components must be joined in a way that preserves the rich knowledge encoded in their weights. We revisit model stitching, i.e., we identify the layer in the 3D decoder that best matches the latent representation produced by the text-to-video generator and stitch the two parts together. That operation requires only a small dataset and no labels. Second, the text-to-video generator must be aligned with the stitched 3D decoder, to ensure that the generated latents are decodable into consistent, perceptually convincing 3D scene geometry. To that end, we adapt direct reward finetuning, a popular technique for human preference alignment. We evaluate the proposed VIST3A approach with different video generators and 3D reconstruction models. All tested pairings markedly improve over prior text-to-3D models that output Gaussian splats. Moreover, by choosing a suitable 3D base model, VIST3A also enables high-quality text-to-pointmap generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/CodeGoat24/UnifiedReward/blob/main/inference_qwen/image_generation/qwen_point_score_ACS_image_generation.py", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14974", "title": "pi-Flow: Policy-Based Few-Step Generation via Imitation Distillation", "authors": [ "Hansheng Chen", "Kai Zhang", "Hao Tan", "Leonidas Guibas", "Gordon Wetzstein", "Sai Bi" ], "github": "", "project_page": "", "abstract": "Few-step diffusion or flow-based generative models typically distill a velocity-predicting teacher into a student that predicts a shortcut towards denoised data. This format mismatch has led to complex distillation procedures that often suffer from a quality-diversity trade-off. To address this, we propose policy-based flow models (pi-Flow). pi-Flow modifies the output layer of a student flow model to predict a network-free policy at one timestep. The policy then produces dynamic flow velocities at future substeps with negligible overhead, enabling fast and accurate ODE integration on these substeps without extra network evaluations. To match the policy's ODE trajectory to the teacher's, we introduce a novel imitation distillation approach, which matches the policy's velocity to the teacher's along the policy's trajectory using a standard ell_2 flow matching loss. By simply mimicking the teacher's behavior, pi-Flow enables stable and scalable training and avoids the quality-diversity trade-off. On ImageNet 256^2, it attains a 1-NFE FID of 2.85, outperforming MeanFlow of the same DiT architecture. On FLUX.1-12B and Qwen-Image-20B at 4 NFEs, pi-Flow achieves substantially better diversity than state-of-the-art few-step methods, while maintaining teacher-level quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14351", "title": "Beyond One World: Benchmarking Super Heros in Role-Playing Across Multiversal Contexts", "authors": [ "Perapard Ngokpol", "Kun Kerdthaisong", "Pasin Buakhaw", "Pitikorn Khlaisamniang", "Supasate Vorathammathorn", "Piyalitt Ittichaiwong", "Nutchanon Yongsatianchot" ], "github": "https://github.com/Augustus2011/Beyond_One_World", "project_page": "", "abstract": "Large language models (LLMs) are increasingly used as role-playing agents, yet their capacity to faithfully and consistently portray version-specific characters -- for example, superheroes across comic and cinematic universes -- remains underexplored. Superhero canons such as Marvel and DC provide a rich testbed: decades of storytelling yield multiple incarnations of the same character with distinct histories, values, and moral codes. To study this problem, we introduce Beyond One World, a benchmark for character-grounded roleplay spanning 30 iconic heroes and 90 canon-specific versions. The benchmark comprises two tasks: (i) Canon Events, which probes factual recall of pivotal life stages, and (ii) Moral Dilemmas, which confronts models with ethically charged scenarios. We score responses for canonical accuracy and reasoning fidelity under a framework that separates internal deliberation (\"thinking\") from outward decisions (\"acting\"). We further propose Think-Act Matching, a metric that quantifies alignment between reasons and actions and serves as a proxy for model trustworthiness. Experiments across reasoning- and non-reasoning-oriented models yield three findings: (1) chain-of-thought prompting improves narrative coherence in weaker models but can reduce canonical accuracy in stronger ones; (2) cross-version generalization within a character remains a major obstacle; and (3) models often excel at either thinking or acting, but rarely both. Beyond One World exposes critical gaps in multiversal consistency and reasoning alignment, offering a challenging evaluation for role-playing LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Augustus2011/Beyond_One_World", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14975", "title": "WithAnyone: Towards Controllable and ID Consistent Image Generation", "authors": [ "Hengyuan Xu", "Wei Cheng", "Peng Xing", "Yixiao Fang", "Shuhan Wu", "Rui Wang", "Xianfang Zeng", "Daxin Jiang", "Gang Yu", "Xingjun Ma", "Yu-Gang Jiang" ], "github": "", "project_page": "", "abstract": "Identity-consistent generation has become an important focus in text-to-image research, with recent models achieving notable success in producing images aligned with a reference identity. Yet, the scarcity of large-scale paired datasets containing multiple images of the same individual forces most approaches to adopt reconstruction-based training. This reliance often leads to a failure mode we term copy-paste, where the model directly replicates the reference face rather than preserving identity across natural variations in pose, expression, or lighting. Such over-similarity undermines controllability and limits the expressive power of generation. To address these limitations, we (1) construct a large-scale paired dataset MultiID-2M, tailored for multi-person scenarios, providing diverse references for each identity; (2) introduce a benchmark that quantifies both copy-paste artifacts and the trade-off between identity fidelity and variation; and (3) propose a novel training paradigm with a contrastive identity loss that leverages paired data to balance fidelity with diversity. These contributions culminate in WithAnyone, a diffusion-based model that effectively mitigates copy-paste while preserving high identity similarity. Extensive qualitative and quantitative experiments demonstrate that WithAnyone significantly reduces copy-paste artifacts, improves controllability over pose and expression, and maintains strong perceptual quality. User studies further validate that our method achieves high identity fidelity while enabling expressive controllable generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14943", "title": "LaSeR: Reinforcement Learning with Last-Token Self-Rewarding", "authors": [ "Wenkai Yang", "Weijie Liu", "Ruobing Xie", "Yiju Guo", "Lulu Wu", "Saiyong Yang", "Yankai Lin" ], "github": "https://github.com/RUCBM/LaSeR", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a core paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs). To address the lack of verification signals at test time, prior studies incorporate the training of model's self-verification capability into the standard RLVR process, thereby unifying reasoning and verification capabilities within a single LLM. However, previous practice requires the LLM to sequentially generate solutions and self-verifications using two separate prompt templates, which significantly reduces efficiency. In this work, we theoretically reveal that the closed-form solution to the RL objective of self-verification can be reduced to a remarkably simple form: the true reasoning reward of a solution is equal to its last-token self-rewarding score, which is computed as the difference between the policy model's next-token log-probability assigned to any pre-specified token at the solution's last token and a pre-calculated constant, scaled by the KL coefficient. Based on this insight, we propose LaSeR (Reinforcement Learning with Last-Token Self-Rewarding), an algorithm that simply augments the original RLVR loss with a MSE loss that aligns the last-token self-rewarding scores with verifier-based reasoning rewards, jointly optimizing the reasoning and self-rewarding capabilities of LLMs. The optimized self-rewarding scores can be utilized in both training and testing to enhance model performance. Notably, our algorithm derives these scores from the predicted next-token probability distribution of the last token immediately after generation, incurring only the minimal extra cost of one additional token inference. Experiments show that our method not only improves the model's reasoning performance but also equips it with remarkable self-rewarding capability, thereby boosting its inference-time scaling performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/RUCBM/LaSeR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14545", "title": "Agentic Entropy-Balanced Policy Optimization", "authors": [ "Guanting Dong", "Licheng Bao", "Zhongyuan Wang", "Kangzhi Zhao", "Xiaoxi Li", "Jiajie Jin", "Jinghan Yang", "Hangyu Mao", "Fuzheng Zhang", "Kun Gai", "Guorui Zhou", "Yutao Zhu", "Ji-Rong Wen", "Zhicheng Dou" ], "github": "", "project_page": "", "abstract": "Recently, Agentic Reinforcement Learning (Agentic RL) has made significant progress in incentivizing the multi-turn, long-horizon tool-use capabilities of web agents. While mainstream agentic RL algorithms autonomously explore high-uncertainty tool-call steps under the guidance of entropy, excessive reliance on entropy signals can impose further constraints, leading to the training collapse. In this paper, we delve into the challenges caused by entropy and propose the Agentic Entropy-Balanced Policy Optimization (AEPO), an agentic RL algorithm designed to balance entropy in both the rollout and policy update phases. AEPO comprises two core components: (1) a dynamic entropy-balanced rollout mechanism that adaptively allocate global and branch sampling budget through entropy pre-monitoring, while imposing a branch penalty on consecutive high-entropy tool-call steps to prevent over-branching issues; and (2) Entropy-Balanced Policy Optimization that inserts a stop-gradient operation into the high-entropy clipping term to preserve and properly rescale gradients on high-entropy tokens, while incorporating entropy-aware advantage estimation to prioritize learning on high-uncertainty tokens. Results across 14 challenging datasets show that AEPO consistently outperforms 7 mainstream RL algorithms. With just 1K RL samples, Qwen3-14B with AEPO achieves impressive results: 47.6% on GAIA, 11.2% on Humanity's Last Exam, and 43.0% on WebWalker for Pass@1; 65.0% on GAIA, 26.0% on Humanity's Last Exam, and 70.0% on WebWalker for Pass@5. Further analysis reveals that AEPO improves rollout sampling diversity while maintaining stable policy entropy, facilitating scalable web agent training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14973", "title": "Attention Is All You Need for KV Cache in Diffusion LLMs", "authors": [ "Quan Nguyen-Tri", "Mukul Ranjan", "Zhiqiang Shen" ], "github": "", "project_page": "", "abstract": "This work studies how to adaptively recompute key-value (KV) caches for diffusion large language models (DLMs) to maximize prediction accuracy while minimizing decoding latency. Prior methods' decoders recompute QKV for all tokens at every denoising step and layer, despite KV states changing little across most steps, especially in shallow layers, leading to substantial redundancy. We make three observations: (1) distant {bf MASK} tokens primarily act as a length-bias and can be cached block-wise beyond the active prediction window; (2) KV dynamics increase with depth, suggesting that selective refresh starting from deeper layers is sufficient; and (3) the most-attended token exhibits the smallest KV drift, providing a conservative lower bound on cache change for other tokens. Building on these, we propose {bf Elastic-Cache}, a training-free, architecture-agnostic strategy that jointly decides {when} to refresh (via an attention-aware drift test on the most-attended token) and {where} to refresh (via a depth-aware schedule that recomputes from a chosen layer onward while reusing shallow-layer caches and off-window MASK caches). Unlike fixed-period schemes, Elastic-Cache performs adaptive, layer-aware cache updates for diffusion LLMs, reducing redundant computation and accelerating decoding with negligible loss in generation quality. Experiments on LLaDA-Instruct, LLaDA-1.5, and LLaDA-V across mathematical reasoning and code generation tasks demonstrate consistent speedups: 8.7times on GSM8K (256 tokens), 45.1times on longer sequences, and 4.8times on HumanEval, while consistently maintaining higher accuracy than the baseline. Our method achieves significantly higher throughput (6.8times on GSM8K) than existing confidence-based approaches while preserving generation quality, enabling practical deployment of diffusion LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14528", "title": "PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model", "authors": [ "Cheng Cui", "Ting Sun", "Suyin Liang", "Tingquan Gao", "Zelun Zhang", "Jiaxuan Liu", "Xueqing Wang", "Changda Zhou", "Hongen Liu", "Manhui Lin", "Yue Zhang", "Yubo Zhang", "Handong Zheng", "Jing Zhang", "Jun Zhang", "Yi Liu", "Dianhai Yu", "Yanjun Ma" ], "github": "", "project_page": "", "abstract": "In this report, we propose PaddleOCR-VL, a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14359", "title": "AI for Service: Proactive Assistance with AI Glasses", "authors": [ "Zichen Wen", "Yiyu Wang", "Chenfei Liao", "Boxue Yang", "Junxian Li", "Weifeng Liu", "Haocong He", "Bolong Feng", "Xuyang Liu", "Yuanhuiyi Lyu", "Xu Zheng", "Xuming Hu", "Linfeng Zhang" ], "github": "", "project_page": "", "abstract": "In an era where AI is evolving from a passive tool into an active and adaptive companion, we introduce AI for Service (AI4Service), a new paradigm that enables proactive and real-time assistance in daily life. Existing AI services remain largely reactive, responding only to explicit user commands. We argue that a truly intelligent and helpful assistant should be capable of anticipating user needs and taking actions proactively when appropriate. To realize this vision, we propose Alpha-Service, a unified framework that addresses two fundamental challenges: Know When to intervene by detecting service opportunities from egocentric video streams, and Know How to provide both generalized and personalized services. Inspired by the von Neumann computer architecture and based on AI glasses, Alpha-Service consists of five key components: an Input Unit for perception, a Central Processing Unit for task scheduling, an Arithmetic Logic Unit for tool utilization, a Memory Unit for long-term personalization, and an Output Unit for natural human interaction. As an initial exploration, we implement Alpha-Service through a multi-agent system deployed on AI glasses. Case studies, including a real-time Blackjack advisor, a museum tour guide, and a shopping fit assistant, demonstrate its ability to seamlessly perceive the environment, infer user intent, and provide timely and useful assistance without explicit prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.10518", "title": "VR-Thinker: Boosting Video Reward Models through Thinking-with-Image Reasoning", "authors": [ "Qunzhong Wang", "Jie Liu", "Jiajun Liang", "Yilei Jiang", "Yuanxing Zhang", "Jinyuan Chen", "Yaozhi Zheng", "Xintao Wang", "Pengfei Wan", "Xiangyu Yue", "Jiaheng Liu" ], "github": "https://github.com/qunzhongwang/vr-thinker", "project_page": "", "abstract": "Recent advancements in multimodal reward models (RMs) have substantially improved post-training for visual generative models. However, current RMs face inherent limitations: (1) visual inputs consume large context budgets, forcing fewer frames and causing loss of fine-grained details; and (2) all visual information is packed into the initial prompt, exacerbating hallucination and forgetting during chain-of-thought reasoning. To overcome these issues, we introduce VideoReward Thinker (VR-Thinker), a thinking-with-image framework that equips the RM with visual reasoning operations (e.g., select frame) and a configurable visual memory window. This allows the RM to actively acquire and update visual evidence within context limits, improving reasoning fidelity and reliability. We activate visual reasoning via a reinforcement fine-tuning pipeline: (i) Cold Start with curated visual chain-of-thought data to distill basic reasoning skills and operation formatting; (ii) select samples whose per-dimension and overall judgments are all correct, then conduct Rejection sampling Fine-Tuning on these high-quality traces to further enhance reasoning; and (iii) apply Group Relative Policy Optimization (GRPO) to strengthen reasoning. Our approach delivers state-of-the-art accuracy among open-source models on video preference benchmarks, especially for longer videos: a 7B VR-Thinker achieves 80.5% on VideoGen Reward, 82.3% on GenAI-Bench, and 75.6% on MJ-Bench-Video. These results validate the effectiveness and promise of thinking-with-image multimodal reward modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/qunzhongwang/vr-thinker", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14978", "title": "Learning an Image Editing Model without Image Editing Pairs", "authors": [ "Nupur Kumari", "Sheng-Yu Wang", "Nanxuan Zhao", "Yotam Nitzan", "Yuheng Li", "Krishna Kumar Singh", "Richard Zhang", "Eli Shechtman", "Jun-Yan Zhu", "Xun Huang" ], "github": "", "project_page": "", "abstract": "Recent image editing models have achieved impressive results while following natural language editing instructions, but they rely on supervised fine-tuning with large datasets of input-target pairs. This is a critical bottleneck, as such naturally occurring pairs are hard to curate at scale. Current workarounds use synthetic training pairs that leverage the zero-shot capabilities of existing models. However, this can propagate and magnify the artifacts of the pretrained model into the final trained model. In this work, we present a new training paradigm that eliminates the need for paired data entirely. Our approach directly optimizes a few-step diffusion model by unrolling it during training and leveraging feedback from vision-language models (VLMs). For each input and editing instruction, the VLM evaluates if an edit follows the instruction and preserves unchanged content, providing direct gradients for end-to-end optimization. To ensure visual fidelity, we incorporate distribution matching loss (DMD), which constrains generated images to remain within the image manifold learned by pretrained models. We evaluate our method on standard benchmarks and include an extensive ablation study. Without any paired data, our method performs on par with various image editing diffusion models trained on extensive supervised paired data, under the few-step setting. Given the same VLM as the reward model, we also outperform RL-based techniques like Flow-GRPO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.13054", "title": "VLA-0: Building State-of-the-Art VLAs with Zero Modification", "authors": [ "Ankit Goyal", "Hugo Hadfield", "Xuning Yang", "Valts Blukis", "Fabio Ramos" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action models (VLAs) hold immense promise for enabling generalist robot manipulation. However, the best way to build them remains an open question. Current approaches often add complexity, such as modifying the existing vocabulary of a Vision-Language Model (VLM) with action tokens or introducing special action heads. Curiously, the simplest strategy of representing actions directly as text has remained largely unexplored. This work introduces VLA-0 to investigate this idea. We find that VLA-0 is not only effective; it is surprisingly powerful. With the right design, VLA-0 outperforms more involved models. On LIBERO, a popular benchmark for evaluating VLAs, VLA-0 outperforms all existing methods trained on the same robotic data, including pi_0.5-KI, OpenVLA-OFT and SmolVLA. Furthermore, without large-scale robotics-specific training, it outperforms methods trained on large-scale robotic data, like pi_0.5-KI, pi_0, GR00T-N1 and MolmoAct. These findings also translate to the real world, where VLA-0 outperforms SmolVLA, a VLA model pre-trained on large-scale real data. This paper summarizes our unexpected findings and spells out the specific techniques required to unlock the high performance of this simple yet potent VLA design. Visual results, code, and trained models are provided here: https://vla0.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14958", "title": "MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning", "authors": [ "Weikang Shi", "Aldrich Yu", "Rongyao Fang", "Houxing Ren", "Ke Wang", "Aojun Zhou", "Changyao Tian", "Xinyu Fu", "Yuxuan Hu", "Zimu Lu", "Linjiang Huang", "Si Liu", "Rui Liu", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "While Large Language Models (LLMs) have excelled in textual reasoning, they struggle with mathematical domains like geometry that intrinsically rely on visual aids. Existing approaches to Visual Chain-of-Thought (VCoT) are often limited by rigid external tools or fail to generate the high-fidelity, strategically-timed diagrams necessary for complex problem-solving. To bridge this gap, we introduce MathCanvas, a comprehensive framework designed to endow unified Large Multimodal Models (LMMs) with intrinsic VCoT capabilities for mathematics. Our approach consists of two phases. First, a Visual Manipulation stage pre-trains the model on a novel 15.2M-pair corpus, comprising 10M caption-to-diagram pairs (MathCanvas-Imagen) and 5.2M step-by-step editing trajectories (MathCanvas-Edit), to master diagram generation and editing. Second, a Strategic Visual-Aided Reasoning stage fine-tunes the model on MathCanvas-Instruct, a new 219K-example dataset of interleaved visual-textual reasoning paths, teaching it when and how to leverage visual aids. To facilitate rigorous evaluation, we introduce MathCanvas-Bench, a challenging benchmark with 3K problems that require models to produce interleaved visual-textual solutions. Our model, BAGEL-Canvas, trained under this framework, achieves an 86% relative improvement over strong LMM baselines on MathCanvas-Bench, demonstrating excellent generalization to other public math benchmarks. Our work provides a complete toolkit-framework, datasets, and benchmark-to unlock complex, human-like visual-aided reasoning in LMMs. Project Page: https://mathcanvas.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14276", "title": "Qwen3Guard Technical Report", "authors": [ "Haiquan Zhao", "Chenhan Yuan", "Fei Huang", "Xiaomeng Hu", "Yichang Zhang", "An Yang", "Bowen Yu", "Dayiheng Liu", "Jingren Zhou", "Junyang Lin", "Baosong Yang", "Chen Cheng", "Jialong Tang", "Jiandong Jiang", "Jianwei Zhang", "Jijie Xu", "Ming Yan", "Minmin Sun", "Pei Zhang", "Pengjun Xie", "Qiaoyu Tang", "Qin Zhu", "Rong Zhang", "Shibin Wu", "Shuo Zhang", "Tao He", "Tianyi Tang", "Tingyu Xia", "Wei Liao", "Weizhou Shen", "Wenbiao Yin", "Wenmeng Zhou", "Wenyuan Yu", "Xiaobin Wang", "Xiaodong Deng", "Xiaodong Xu", "Xinyu Zhang", "Yang Liu", "Yeqiu Li", "Yi Zhang", "Yong Jiang", "Yu Wan", "Yuxin Zhou" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) become more capable and widely used, ensuring the safety of their outputs is increasingly critical. Existing guardrail models, though useful in static evaluation settings, face two major limitations in real-world applications: (1) they typically output only binary \"safe/unsafe\" labels, which can be interpreted inconsistently across diverse safety policies, rendering them incapable of accommodating varying safety tolerances across domains; and (2) they require complete model outputs before performing safety checks, making them fundamentally incompatible with streaming LLM inference, thereby preventing timely intervention during generation and increasing exposure to harmful partial outputs. To address these challenges, we present Qwen3Guard, a series of multilingual safety guardrail models with two specialized variants: Generative Qwen3Guard, which casts safety classification as an instruction-following task to enable fine-grained tri-class judgments (safe, controversial, unsafe); and Stream Qwen3Guard, which introduces a token-level classification head for real-time safety monitoring during incremental text generation. Both variants are available in three sizes (0.6B, 4B, and 8B parameters) and support up to 119 languages and dialects, providing comprehensive, scalable, and low-latency safety moderation for global LLM deployments. Evaluated across English, Chinese, and multilingual benchmarks, Qwen3Guard achieves state-of-the-art performance in both prompt and response safety classification. All models are released under the Apache 2.0 license for public use.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14252", "title": "MoM: Mixtures of Scenario-Aware Document Memories for Retrieval-Augmented Generation Systems", "authors": [ "Jihao Zhao", "Zhiyuan Ji", "Simin Niu", "Hanyu Wang", "Feiyu Xiong", "Zhiyu Li" ], "github": "https://github.com/MemTensor/MoM", "project_page": "", "abstract": "The traditional RAG paradigm, which typically engages in the comprehension of relevant text chunks in response to received queries, inherently restricts both the depth of knowledge internalization and reasoning capabilities. To address this limitation, our research transforms the text processing in RAG from passive chunking to proactive understanding, defining this process as document memory extraction with the objective of simulating human cognitive processes during reading. Building upon this, we propose the Mixtures of scenario-aware document Memories (MoM) framework, engineered to efficiently handle documents from multiple domains and train small language models (SLMs) to acquire the ability to proactively explore and construct document memories. The MoM initially instructs large language models (LLMs) to simulate domain experts in generating document logical outlines, thereby directing structured chunking and core content extraction. It employs a multi-path sampling and multi-perspective evaluation mechanism, specifically designing comprehensive metrics that represent chunk clarity and extraction completeness to select the optimal document memories. Additionally, to infuse deeper human-like reading abilities during the training of SLMs, we incorporate a reverse reasoning strategy, which deduces refined expert thinking paths from high-quality outcomes. Finally, leveraging diverse forms of content generated by MoM, we develop a three-layer document memory retrieval mechanism, which is grounded in our theoretical proof from the perspective of probabilistic modeling. Extensive experimental results across three distinct domains demonstrate that the MoM framework not only resolves text chunking challenges in existing RAG systems, providing LLMs with semantically complete document memories, but also paves the way for SLMs to achieve human-centric intelligent text processing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MemTensor/MoM", "github_api_added_by": "auto" }, { "date": "2025-10-17", "arxiv_id": "2510.09033", "title": "Large Language Models Do NOT Really Know What They Don't Know", "authors": [ "Chi Seng Cheang", "Hou Pong Chan", "Wenxuan Zhang", "Yang Deng" ], "github": "", "project_page": "", "abstract": "Recent work suggests that large language models (LLMs) encode factuality signals in their internal representations, such as hidden states, attention weights, or token probabilities, implying that LLMs may \"know what they don't know\". However, LLMs can also produce factual errors by relying on shortcuts or spurious associations. These error are driven by the same training objective that encourage correct predictions, raising the question of whether internal computations can reliably distinguish between factual and hallucinated outputs. In this work, we conduct a mechanistic analysis of how LLMs internally process factual queries by comparing two types of hallucinations based on their reliance on subject information. We find that when hallucinations are associated with subject knowledge, LLMs employ the same internal recall process as for correct responses, leading to overlapping and indistinguishable hidden-state geometries. In contrast, hallucinations detached from subject knowledge produce distinct, clustered representations that make them detectable. These findings reveal a fundamental limitation: LLMs do not encode truthfulness in their internal states but only patterns of knowledge recall, demonstrating that \"LLMs don't really know what they don't know\".", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.13998", "title": "BitNet Distillation", "authors": [ "Xun Wu", "Shaohan Huang", "Wenhui Wang", "Ting Song", "Li Dong", "Yan Xia", "Furu Wei" ], "github": "https://github.com/microsoft/BitNet", "project_page": "", "abstract": "In this paper, we present BitNet Distillation (BitDistill), a lightweight pipeline that fine-tunes off-the-shelf full-precision LLMs (e.g., Qwen) into 1.58-bit precision (i.e., ternary weights {-1, 0, 1}) for specific downstream tasks, achieving strong task-specific performance with minimal computational cost. Specifically, BitDistill incorporates three key techniques: the SubLN module, as introduced in BitNet; multi-head attention distillation, based on MiniLM; and continual pre-training, which serves as a crucial warm-up step to mitigate the scalability issue of the performance gap between finetuned full-precision and 1.58-bit LLMs on specific tasks. Experimental results show that BitDistill achieves performance comparable to the full-precision counterpart models across model size, while enabling up to 10x memory savings and 2.65x faster inference on CPUs. Code is available at https://github.com/microsoft/BitNet.", "github_manual": "", "github_regex": "https://github.com/microsoft/BitNet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14972", "title": "TokDrift: When LLM Speaks in Subwords but Code Speaks in Grammar", "authors": [ "Yinxi Li", "Yuntian Deng", "Pengyu Nie" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) for code rely on subword tokenizers, such as byte-pair encoding (BPE), learned from mixed natural language text and programming language code but driven by statistics rather than grammar. As a result, semantically identical code snippets can be tokenized differently depending on superficial factors such as whitespace or identifier naming. To measure the impact of this misalignment, we introduce TokDrift, a framework that applies semantic-preserving rewrite rules to create code variants differing only in tokenization. Across nine code LLMs, including large ones with over 30B parameters, even minor formatting changes can cause substantial shifts in model behavior. Layer-wise analysis shows that the issue originates in early embeddings, where subword segmentation fails to capture grammar token boundaries. Our findings identify misaligned tokenization as a hidden obstacle to reliable code understanding and generation, highlighting the need for grammar-aware tokenization for future code LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14880", "title": "Fantastic (small) Retrievers and How to Train Them: mxbai-edge-colbert-v0 Tech Report", "authors": [ "Rikiya Takehi", "Benjamin Clavi\u00e9", "Sean Lee", "Aamir Shakir" ], "github": "", "project_page": "", "abstract": "In this work, we introduce mxbai-edge-colbert-v0 models, at two different parameter counts: 17M and 32M. As part of our research, we conduct numerous experiments to improve retrieval and late-interaction models, which we intend to distill into smaller models as proof-of-concepts. Our ultimate aim is to support retrieval at all scales, from large-scale retrieval which lives in the cloud to models that can run locally, on any device. mxbai-edge-colbert-v0 is a model that we hope will serve as a solid foundation backbone for all future experiments, representing the first version of a long series of small proof-of-concepts. As part of the development of mxbai-edge-colbert-v0, we conducted multiple ablation studies, of which we report the results. In terms of downstream performance, mxbai-edge-colbert-v0 is a particularly capable small model, outperforming ColBERTv2 on common short-text benchmarks (BEIR) and representing a large step forward in long-context tasks, with unprecedented efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.10390", "title": "RefusalBench: Generative Evaluation of Selective Refusal in Grounded Language Models", "authors": [ "Aashiq Muhamed", "Leonardo F. R. Ribeiro", "Markus Dreyer", "Virginia Smith", "Mona T. Diab" ], "github": "", "project_page": "", "abstract": "The ability of language models in RAG systems to selectively refuse to answer based on flawed context is critical for safety, yet remains a significant failure point. Our large-scale study reveals that even frontier models struggle in this setting, with refusal accuracy dropping below 50% on multi-document tasks, while exhibiting either dangerous overconfidence or overcaution. Static benchmarks fail to reliably evaluate this capability, as models exploit dataset-specific artifacts and memorize test instances. We introduce RefusalBench, a generative methodology that programmatically creates diagnostic test cases through controlled linguistic perturbation. Our framework employs 176 distinct perturbation strategies across six categories of informational uncertainty and three intensity levels. Evaluation of over 30 models uncovers systematic failure patterns: refusal comprises separable detection and categorization skills, and neither scale nor extended reasoning improves performance. We find that selective refusal is a trainable, alignment-sensitive capability, offering a clear path for improvement. We release two benchmarks -- RefusalBench-NQ (single document) and RefusalBench-GaRAGe (multi-document) -- and our complete generation framework to enable continued, dynamic evaluation of this critical capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14300", "title": "Expertise need not monopolize: Action-Specialized Mixture of Experts for Vision-Language-Action Learning", "authors": [ "Weijie Shen", "Yitian Liu", "Yuhao Wu", "Zhixuan Liang", "Sijia Gu", "Dehui Wang", "Tian Nian", "Lei Xu", "Yusen Qin", "Jiangmiao Pang", "Xinping Guan", "Xiaokang Yang", "Yao Mu" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models are experiencing rapid development and demonstrating promising capabilities in robotic manipulation tasks. However, scaling up VLA models presents several critical challenges: (1) Training new VLA models from scratch demands substantial computational resources and extensive datasets. Given the current scarcity of robot data, it becomes particularly valuable to fully leverage well-pretrained VLA model weights during the scaling process. (2) Real-time control requires carefully balancing model capacity with computational efficiency. To address these challenges, We propose AdaMoE, a Mixture-of-Experts (MoE) architecture that inherits pretrained weights from dense VLA models, and scales up the action expert by substituting the feedforward layers into sparsely activated MoE layers. AdaMoE employs a decoupling technique that decouples expert selection from expert weighting through an independent scale adapter working alongside the traditional router. This enables experts to be selected based on task relevance while contributing with independently controlled weights, allowing collaborative expert utilization rather than winner-takes-all dynamics. Our approach demonstrates that expertise need not monopolize. Instead, through collaborative expert utilization, we can achieve superior performance while maintaining computational efficiency. AdaMoE consistently outperforms the baseline model across key benchmarks, delivering performance gains of 1.8% on LIBERO and 9.3% on RoboTwin. Most importantly, a substantial 21.5% improvement in real-world experiments validates its practical effectiveness for robotic manipulation tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14976", "title": "Ponimator: Unfolding Interactive Pose for Versatile Human-human Interaction Animation", "authors": [ "Shaowei Liu", "Chuan Guo", "Bing Zhou", "Jian Wang" ], "github": "", "project_page": "", "abstract": "Close-proximity human-human interactive poses convey rich contextual information about interaction dynamics. Given such poses, humans can intuitively infer the context and anticipate possible past and future dynamics, drawing on strong priors of human behavior. Inspired by this observation, we propose Ponimator, a simple framework anchored on proximal interactive poses for versatile interaction animation. Our training data consists of close-contact two-person poses and their surrounding temporal context from motion-capture interaction datasets. Leveraging interactive pose priors, Ponimator employs two conditional diffusion models: (1) a pose animator that uses the temporal prior to generate dynamic motion sequences from interactive poses, and (2) a pose generator that applies the spatial prior to synthesize interactive poses from a single pose, text, or both when interactive poses are unavailable. Collectively, Ponimator supports diverse tasks, including image-based interaction animation, reaction animation, and text-to-interaction synthesis, facilitating the transfer of interaction knowledge from high-quality mocap data to open-world scenarios. Empirical experiments across diverse datasets and applications demonstrate the universality of the pose prior and the effectiveness and robustness of our framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14763", "title": "COIG-Writer: A High-Quality Dataset for Chinese Creative Writing with Thought Processes", "authors": [ "Yunwen Li", "Shuangshuang Ying", "Xingwei Qu", "Xin Li", "Sheng Jin", "Minghao Liu", "Zhoufutu Wen", "Tianyu Zheng", "Xeron Du", "Qiguang Chen", "Jiajun Shi", "Wangchunshu Zhou", "Jiazhan Feng", "Wanjun Zhong", "Libo Qin", "Stephen Huang", "Wanxiang Che", "Chenghua Lin", "Eli Zhang" ], "github": "", "project_page": "", "abstract": "Large language models exhibit systematic deficiencies in creative writing, particularly in non-English contexts where training data is scarce and lacks process-level supervision. We present COIG-Writer, a novel Chinese creative writing dataset that captures both diverse outputs and their underlying thought processes through systematic reverse-engineering of high-quality texts. Unlike existing datasets that provide only input-output pairs, COIG-Writer comprises 1,665 meticulously curated triplets spanning 51 genres, each containing: (1) a reverse-engineered prompt, (2) detailed creative reasoning documenting decision-making processes, and (3) the final text. Through comprehensive experiments, we identify a two-component model of creative writing: narrative logic (provided by process supervision) and linguistic expression (maintained by general-purpose data). Our findings reveal three critical insights: (1) Process supervision is highly effective but requires stabilization with general data. A ratio of at least one creative sample to twelve general samples is needed to achieve optimal performance; below this threshold, the win rate progressively degrades (from 62.75% down to 35.78%)., (2) creative capabilities are culturally-bound with no cross-lingual transfer (89.26pp gap between Chinese and English performance), and (3) lexical diversity inversely correlates with creative quality (TTR paradox), suggesting high diversity signals compensatory behavior for logical deficiencies. These findings establish that creative excellence emerges from the interaction between logical scaffolding and linguistic grounding, analogous to how mathematical reasoning enhances but cannot replace linguistic competence in foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14616", "title": "Beyond Correctness: Evaluating Subjective Writing Preferences Across Cultures", "authors": [ "Shuangshuang Ying", "Yunwen Li", "Xingwei Qu", "Xin Li", "Sheng Jin", "Minghao Liu", "Zhoufutu Wen", "Xeron Du", "Tianyu Zheng", "Yichi Zhang", "Letian Ni", "Yuyang Cheng", "Qiguang Chen", "Jingzhe Ding", "Shengda Long", "Wangchunshu Zhou", "Jiazhan Feng", "Wanjun Zhong", "Libo Qin", "Ge Zhang", "Wenhao Huang", "Wanxiang Che", "Chenghua Lin" ], "github": "", "project_page": "", "abstract": "Current preference learning methods achieve high accuracy on standard benchmarks but exhibit significant performance degradation when objective quality signals are removed. We introduce WritingPreferenceBench, a dataset of 1,800 human-annotated preference pairs (1,200 English, 600 Chinese) across 8 creative writing genres, where responses are matched for objective correctness, factual accuracy, and length. On this benchmark, sequence-based reward models--the standard architecture for RLHF--achieve only 52.7% mean accuracy, while zero-shot language model judges perform at 53.9%. In contrast, generative reward models that produce explicit reasoning chains achieve 81.8% accuracy. We observe high within-model variance across genres: individual models range from 18.2% to 81.8% accuracy across different writing categories, with standard deviations averaging 10.1%. This variance persists regardless of model scale, with 27B parameter models showing no consistent improvement over 8B variants. Our results suggest that current RLHF methods primarily learn to detect objective errors rather than capture subjective quality preferences (e.g., creativity, stylistic flair, and emotional resonance), and that successful preference modeling may require intermediate reasoning representations rather than direct classification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14211", "title": "LiteStage: Latency-aware Layer Skipping for Multi-stage Reasoning", "authors": [ "Beomseok Kang", "Jiwon Song", "Jae-Joon Kim" ], "github": "", "project_page": "", "abstract": "Multi-stage reasoning has emerged as an effective strategy for enhancing the reasoning capability of small language models by decomposing complex problems into sequential sub-stages. However, this comes at the cost of increased latency. We observe that existing adaptive acceleration techniques, such as layer skipping, struggle to balance efficiency and accuracy in this setting due to two key challenges: (1) stage-wise variation in skip sensitivity, and (2) the generation of redundant output tokens. To address these, we propose LiteStage, a latency-aware layer skipping framework for multi-stage reasoning. LiteStage combines a stage-wise offline search that allocates optimal layer budgets with an online confidence-based generation early exit to suppress unnecessary decoding. Experiments on three benchmarks, e.g., OBQA, CSQA, and StrategyQA, show that LiteStage achieves up to 1.70x speedup with less than 4.0% accuracy loss, outperforming prior training-free layer skipping methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14949", "title": "DialectGen: Benchmarking and Improving Dialect Robustness in Multimodal Generation", "authors": [ "Yu Zhou", "Sohyun An", "Haikang Deng", "Da Yin", "Clark Peng", "Cho-Jui Hsieh", "Kai-Wei Chang", "Nanyun Peng" ], "github": "", "project_page": "", "abstract": "Contact languages like English exhibit rich regional variations in the form of dialects, which are often used by dialect speakers interacting with generative models. However, can multimodal generative models effectively produce content given dialectal textual input? In this work, we study this question by constructing a new large-scale benchmark spanning six common English dialects. We work with dialect speakers to collect and verify over 4200 unique prompts and evaluate on 17 image and video generative models. Our automatic and human evaluation results show that current state-of-the-art multimodal generative models exhibit 32.26% to 48.17% performance degradation when a single dialect word is used in the prompt. Common mitigation methods such as fine-tuning and prompt rewriting can only improve dialect performance by small margins (< 7%), while potentially incurring significant performance degradation in Standard American English (SAE). To this end, we design a general encoder-based mitigation strategy for multimodal generative models. Our method teaches the model to recognize new dialect features while preserving SAE performance. Experiments on models such as Stable Diffusion 1.5 show that our method is able to simultaneously raise performance on five dialects to be on par with SAE (+34.4%), while incurring near zero cost to SAE performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.13996", "title": "The German Commons - 154 Billion Tokens of Openly Licensed Text for German Language Models", "authors": [ "Lukas Gienapp", "Christopher Schr\u00f6der", "Stefan Schweter", "Christopher Akiki", "Ferdinand Schlatt", "Arden Zimmermann", "Phillipe Gen\u00eat", "Martin Potthast" ], "github": "https://github.com/kermitt2/grobid", "project_page": "", "abstract": "Large language model development relies on large-scale training corpora, yet most contain data of unclear licensing status, limiting the development of truly open models. This problem is exacerbated for non-English languages, where openly licensed text remains critically scarce. We introduce the German Commons, the largest collection of openly licensed German text to date. It compiles data from 41 sources across seven domains, encompassing legal, scientific, cultural, political, news, economic, and web text. Through systematic sourcing from established data providers with verifiable licensing, it yields 154.56 billion tokens of high-quality text for language model training. Our processing pipeline implements comprehensive quality filtering, deduplication, and text formatting fixes, ensuring consistent quality across heterogeneous text sources. All domain subsets feature licenses of at least CC-BY-SA 4.0 or equivalent, ensuring legal compliance for model training and redistribution. The German Commons therefore addresses the critical gap in openly licensed German pretraining data, and enables the development of truly open German language models. We also release code for corpus construction and data filtering tailored to German language text, rendering the German Commons fully reproducible and extensible.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kermitt2/grobid", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14955", "title": "RealDPO: Real or Not Real, that is the Preference", "authors": [ "Guo Cheng", "Danni Yang", "Ziqi Huang", "Jianlou Si", "Chenyang Si", "Ziwei Liu" ], "github": "https://github.com/Vchitect/RealDPO", "project_page": "", "abstract": "Video generative models have recently achieved notable advancements in synthesis quality. However, generating complex motions remains a critical challenge, as existing models often struggle to produce natural, smooth, and contextually consistent movements. This gap between generated and real-world motions limits their practical applicability. To address this issue, we introduce RealDPO, a novel alignment paradigm that leverages real-world data as positive samples for preference learning, enabling more accurate motion synthesis. Unlike traditional supervised fine-tuning (SFT), which offers limited corrective feedback, RealDPO employs Direct Preference Optimization (DPO) with a tailored loss function to enhance motion realism. By contrasting real-world videos with erroneous model outputs, RealDPO enables iterative self-correction, progressively refining motion quality. To support post-training in complex motion synthesis, we propose RealAction-5K, a curated dataset of high-quality videos capturing human daily activities with rich and precise motion details. Extensive experiments demonstrate that RealDPO significantly improves video quality, text alignment, and motion realism compared to state-of-the-art models and existing preference optimization techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Vchitect/RealDPO", "github_api_added_by": "auto" }, { "date": "2025-10-17", "arxiv_id": "2510.14902", "title": "VLA^2: Empowering Vision-Language-Action Models with an Agentic Framework for Unseen Concept Manipulation", "authors": [ "Han Zhao", "Jiaxuan Zhang", "Wenxuan Song", "Pengxiang Ding", "Donglin Wang" ], "github": "https://github.com/hkchengrex/Cutie", "project_page": "", "abstract": "Current vision-language-action (VLA) models, pre-trained on large-scale robotic data, exhibit strong multi-task capabilities and generalize well to variations in visual and language instructions for manipulation. However, their success rate drops significantly when faced with object concepts outside the training data, such as unseen object descriptions and textures in the dataset. To address this, we propose a novel agentic framework, VLA^2, which leverages OpenVLA as the execution backbone and effectively leverages external modules such as web retrieval and object detection to provide visual and textual knowledge about target objects to the VLA. This approach mitigates generalization failure when handling out-of-distribution objects. Based on the LIBERO simulation environment, we introduced novel objects and object descriptions to construct a new evaluation benchmark with three difficulty levels to test the effectiveness of our method. Our framework successfully outperformed the current state-of-the-art models on our designed hard-level generalization benchmark. Compared to the standalone OpenVLA baseline, VLA^2 achieves a 44.2% improvement in the success rate in the hard-level benchmark and an average improvement of 20.2% in all customized environments without any performance degradation on in-domain tasks. Project website: https://vla-2.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hkchengrex/Cutie", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.06694", "title": "SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis", "authors": [ "Jipeng Lyu", "Jiahua Dong", "Yu-Xiong Wang" ], "github": "", "project_page": "", "abstract": "Persistent dynamic scene modeling for tracking and novel-view synthesis remains challenging due to the difficulty of capturing accurate deformations while maintaining computational efficiency. We propose SCas4D, a cascaded optimization framework that leverages structural patterns in 3D Gaussian Splatting for dynamic scenes. The key idea is that real-world deformations often exhibit hierarchical patterns, where groups of Gaussians share similar transformations. By progressively refining deformations from coarse part-level to fine point-level, SCas4D achieves convergence within 100 iterations per time frame and produces results comparable to existing methods with only one-twentieth of the training iterations. The approach also demonstrates effectiveness in self-supervised articulated object segmentation, novel view synthesis, and dense point tracking tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14847", "title": "ImagerySearch: Adaptive Test-Time Search for Video Generation Beyond Semantic Dependency Constraints", "authors": [ "Meiqi Wu", "Jiashu Zhu", "Xiaokun Feng", "Chubin Chen", "Chen Zhu", "Bingze Song", "Fangyuan Mao", "Jiahong Wu", "Xiangxiang Chu", "Kaiqi Huang" ], "github": "", "project_page": "", "abstract": "Video generation models have achieved remarkable progress, particularly excelling in realistic scenarios; however, their performance degrades notably in imaginative scenarios. These prompts often involve rarely co-occurring concepts with long-distance semantic relationships, falling outside training distributions. Existing methods typically apply test-time scaling for improving video quality, but their fixed search spaces and static reward designs limit adaptability to imaginative scenarios. To fill this gap, we propose ImagerySearch, a prompt-guided adaptive test-time search strategy that dynamically adjusts both the inference search space and reward function according to semantic relationships in the prompt. This enables more coherent and visually plausible videos in challenging imaginative settings. To evaluate progress in this direction, we introduce LDT-Bench, the first dedicated benchmark for long-distance semantic prompts, consisting of 2,839 diverse concept pairs and an automated protocol for assessing creative generation capabilities. Extensive experiments show that ImagerySearch consistently outperforms strong video generation baselines and existing test-time scaling approaches on LDT-Bench, and achieves competitive improvements on VBench, demonstrating its effectiveness across diverse prompt types. We will release LDT-Bench and code to facilitate future research on imaginative video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.13910", "title": "RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems", "authors": [ "Jingru Lin", "Chen Zhang", "Stephen Y. Liu", "Haizhou Li" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) mitigates key limitations of Large Language Models (LLMs)-such as factual errors, outdated knowledge, and hallucinations-by dynamically retrieving external information. Recent work extends this paradigm through agentic RAG systems, where LLMs act as agents to iteratively plan, retrieve, and reason over complex queries. However, these systems still struggle with challenging multi-hop questions, and their intermediate reasoning capabilities remain underexplored. To address this, we propose RAGCap-Bench, a capability-oriented benchmark for fine-grained evaluation of intermediate tasks in agentic RAG workflows. We analyze outputs from state-of-the-art systems to identify common tasks and the core capabilities required for their execution, then construct a taxonomy of typical LLM errors to design targeted evaluation questions. Experiments show that \"slow-thinking\" models with stronger RAGCap performance achieve better end-to-end results, underscoring the benchmark's validity and the importance of enhancing these intermediate capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.13697", "title": "On Pretraining for Project-Level Code Completion", "authors": [ "Maksim Sapronov", "Evgeniy Glukhov" ], "github": "", "project_page": "", "abstract": "Repository-level pretraining is commonly used to enable large language models for code to leverage codebase-wide context. This enhances their ability to generate accurate and context-aware code completions. In this work, we investigate how different repository-processing strategies affect in-context learning in OpenCoder, a 1.5B-parameter model. We extend its context window from 4,096 to 16,384 tokens by training on additional 1B tokens of curated repository-level data. Despite relying on a smaller dataset than competing models (which often use hundreds of billions of tokens), our model achieves comparable performance on the Long Code Arena benchmark. We find that various repository-processing techniques yield similarly strong results, with the primary gain coming from adapting to a new rotary positional embedding (RoPE) scaling parameter. Finally, we show that a simpler file-level training approach at the original sequence length remains highly effective, opening up repository-level code completion research to settings with more constrained data and compute resources.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.04849", "title": "When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQA", "authors": [ "Elisei Rykov", "Kseniia Petrushina", "Maksim Savkin", "Valerii Olisov", "Artem Vazhentsev", "Kseniia Titova", "Alexander Panchenko", "Vasily Konovalov", "Julia Belikova" ], "github": "", "project_page": "", "abstract": "Hallucination detection remains a fundamental challenge for the safe and reliable deployment of large language models (LLMs), especially in applications requiring factual accuracy. Existing hallucination benchmarks often operate at the sequence level and are limited to English, lacking the fine-grained, multilingual supervision needed for a comprehensive evaluation. In this work, we introduce PsiloQA, a large-scale, multilingual dataset annotated with span-level hallucinations across 14 languages. PsiloQA is constructed through an automated three-stage pipeline: generating question-answer pairs from Wikipedia using GPT-4o, eliciting potentially hallucinated answers from diverse LLMs in a no-context setting, and automatically annotating hallucinated spans using GPT-4o by comparing against golden answers and retrieved context. We evaluate a wide range of hallucination detection methods -- including uncertainty quantification, LLM-based tagging, and fine-tuned encoder models -- and show that encoder-based models achieve the strongest performance across languages. Furthermore, PsiloQA demonstrates effective cross-lingual generalization and supports robust knowledge transfer to other benchmarks, all while being significantly more cost-efficient than human-annotated datasets. Our dataset and results advance the development of scalable, fine-grained hallucination detection in multilingual settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14961", "title": "Efficient Parallel Samplers for Recurrent-Depth Models and Their Connection to Diffusion Language Models", "authors": [ "Jonas Geiping", "Xinyu Yang", "Guinan Su" ], "github": "", "project_page": "", "abstract": "Language models with recurrent depth, also referred to as universal or looped when considering transformers, are defined by the capacity to increase their computation through the repetition of layers. Recent efforts in pretraining have demonstrated that these architectures can scale to modern language modeling tasks while exhibiting advantages in reasoning tasks. In this work, we examine the relationship between recurrent-depth models and diffusion language models. Building on their similarities, we develop a new diffusion forcing sampler for these models to accelerate generation. The sampler advances by decoding new tokens at every forward pass of the model, while the latent states of these tokens can be further refined in parallel through recurrence. Theoretically, generation with our sampler is strictly more expressive than the baseline autoregressive generation using the same time budget on modern hardware. Moreover, this sampler, based on principles from diffusion literature, can be directly applied to existing 3.5B recurrent-depth transformers without any tuning, leading to up to a 5x speedup. Consequently, our findings not only provide an efficient mechanism for parallelizing the extra computation in recurrent-depth models at inference, but also suggest that such models can be naturally viewed as strong continuous, though causal, diffusion language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14980", "title": "Agentic Design of Compositional Machines", "authors": [ "Wenqian Zhang", "Weiyang Liu", "Zhen Liu" ], "github": "", "project_page": "", "abstract": "The design of complex machines stands as both a marker of human intelligence and a foundation of engineering practice. Given recent advances in large language models (LLMs), we ask whether they, too, can learn to create. We approach this question through the lens of compositional machine design: a task in which machines are assembled from standardized components to meet functional demands like locomotion or manipulation in a simulated physical environment. To support this investigation, we introduce BesiegeField, a testbed built on the machine-building game Besiege, which enables part-based construction, physical simulation and reward-driven evaluation. Using BesiegeField, we benchmark state-of-the-art LLMs with agentic workflows and identify key capabilities required for success, including spatial reasoning, strategic assembly, and instruction-following. As current open-source models fall short, we explore reinforcement learning (RL) as a path to improvement: we curate a cold-start dataset, conduct RL finetuning experiments, and highlight open challenges at the intersection of language, machine design, and physical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14807", "title": "SimKO: Simple Pass@K Policy Optimization", "authors": [ "Ruotian Peng", "Yi Ren", "Zhouliang Yu", "Weiyang Liu", "Yandong Wen" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has advanced the reasoning capabilities of large language models (LLMs). However, prevailing RLVR methods exhibit a systematic bias toward exploitation over exploration, as evidenced by improved pass@1 but reduced pass@K (K>1) performance. To understand this issue, we analyze training dynamics of RLVR methods by tracking the token-level probability distributions over vocabulary candidates. Our analysis reveals a consistent probability concentration effect where the top-1 candidate increasingly accumulates probability mass and suppresses that of other candidates. More importantly, stronger over-concentration correlates with worse pass@K performance. Inspired by this finding, we propose Simple Pass@K Optimization (SimKO), a method designed to mitigate the over-concentration issue, thereby encouraging exploration. SimKO operates in an asymmetrical manner. For verified-correct responses, it boosts the probabilities of the top-K candidates. For verified-incorrect responses, it applies stronger penalties to the top-1 candidate. We observe that this asymmetric design is particularly effective at mitigating over-concentration when applied at tokens with high entropy. Across various math and logical-reasoning benchmarks, SimKO consistently yields higher pass@K for a wide range of K, providing a simple way to improve RLVR's exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.13161", "title": "Mirror Speculative Decoding: Breaking the Serial Barrier in LLM Inference", "authors": [ "Nikhil Bhendawade", "Kumari Nishu", "Arnav Kundu", "Chris Bartels", "Minsik Cho", "Irina Belousova" ], "github": "", "project_page": "", "abstract": "Speculative decoding accelerates LLM inference by using a draft model to look ahead, but gains are capped by the cost of autoregressive draft generation: increasing draft size elevates acceptance rates but introduces additional latency overhead exacerbating the speed-accuracy tradeoff. Prior methods (Medusa, Hydra, EAGLE) partially reduce draft cost but either degrade acceptance or introduce overheads that limit scaling. We present Mirror Speculative Decoding (Mirror-SD), an inference algorithm that breaks the latency-acceptance tradeoff. Mirror-SD launches branch-complete rollouts from early-exit signals in parallel with the target model's suffix and explicitly maps computation across heterogeneous accelerators (GPU and NPU) to exploit cross-device parallelism. The draft speculates forward continuations for the target to verify, while the target simultaneously speculates correction paths for the draft, converting speculation into two complementary execution pipelines. To further cut draft latency without weakening acceptance semantics, we add speculative streaming so the draft emits multiple tokens per step. This dual strategy of parallel heterogeneous execution plus multi-token speculative streaming pushes speculative decoding toward its ideal regime of high acceptance with low overhead. On SpecBench with server-scale models from 14B to 66B parameters, Mirror-SD delivers consistent end-to-end gains, achieving 2.8x-5.8x wall-time speedups across diverse tasks and a 30% average relative improvement over the strongest baseline, EAGLE3.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.12764", "title": "AnyUp: Universal Feature Upsampling", "authors": [ "Thomas Wimmer", "Prune Truong", "Marie-Julie Rakotosaona", "Michael Oechsle", "Federico Tombari", "Bernt Schiele", "Jan Eric Lenssen" ], "github": "https://github.com/wimmerth/anyup", "project_page": "", "abstract": "We introduce AnyUp, a method for feature upsampling that can be applied to any vision feature at any resolution, without encoder-specific training. Existing learning-based upsamplers for features like DINO or CLIP need to be re-trained for every feature extractor and thus do not generalize to different feature types at inference time. In this work, we propose an inference-time feature-agnostic upsampling architecture to alleviate this limitation and improve upsampling quality. In our experiments, AnyUp sets a new state of the art for upsampled features, generalizes to different feature types, and preserves feature semantics while being efficient and easy to apply to a wide range of downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/wimmerth/anyup", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14942", "title": "GroundedPRM: Tree-Guided and Fidelity-Aware Process Reward Modeling for Step-Level Reasoning", "authors": [ "Yao Zhang", "Yu Wu", "Haowei Zhang", "Weiguo Li", "Haokun Chen", "Jingpei Wu", "Guohao Li", "Zhen Han", "Volker Tresp" ], "github": "", "project_page": "", "abstract": "Process Reward Models (PRMs) aim to improve multi-step reasoning in Large Language Models (LLMs) by supervising intermediate steps and identifying errors. However, building effective PRMs remains challenging due to the lack of scalable, high-quality annotations. Existing approaches rely on costly human labeling, LLM-based self-evaluation that is prone to hallucination, or Monte Carlo (MC) estimation, which infers step quality solely from rollout outcomes and often introduces noisy, misaligned supervision due to credit misattribution. These issues result in three core limitations: noisy rewards, low factual fidelity, and misalignment with step-level reasoning objectives. To address these challenges, we introduce GroundedPRM, a tree-guided and fidelity-aware framework for automatic process supervision. To reduce reward noise and enable fine-grained credit assignment, we construct structured reasoning paths via Monte Carlo Tree Search (MCTS). To eliminate hallucinated supervision, we validate each intermediate step using an external tool, providing execution-grounded correctness signals. To combine both step-level validation and global outcome assessment, we design a hybrid reward aggregation mechanism that fuses tool-based verification with MCTS-derived feedback. Finally, we format the reward signal into a rationale-enhanced, generative structure to promote interpretability and compatibility with instruction-tuned LLMs. GroundedPRM is trained on only 40K automatically labeled samples, amounting to just 10% of the data used by the best-performing PRM trained with auto-labeled supervision. Nevertheless, it achieves up to a 26% relative improvement in average performance on ProcessBench. When used for reward-guided greedy search, GroundedPRM outperforms even PRMs trained with human-labeled supervision, offering a scalable and verifiable path toward high-quality process-level reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14240", "title": "LiveResearchBench: A Live Benchmark for User-Centric Deep Research in the Wild", "authors": [ "Jiayu Wang", "Yifei Ming", "Riya Dulepet", "Qinglin Chen", "Austin Xu", "Zixuan Ke", "Frederic Sala", "Aws Albarghouthi", "Caiming Xiong", "Shafiq Joty" ], "github": "https://github.com/SalesforceAIResearch/LiveResearchBench", "project_page": "", "abstract": "Deep research -- producing comprehensive, citation-grounded reports by searching and synthesizing information from hundreds of live web sources -- marks an important frontier for agentic systems. To rigorously evaluate this ability, four principles are essential: tasks should be (1) user-centric, reflecting realistic information needs, (2) dynamic, requiring up-to-date information beyond parametric knowledge, (3) unambiguous, ensuring consistent interpretation across users, and (4) multi-faceted and search-intensive, requiring search over numerous web sources and in-depth analysis. Existing benchmarks fall short of these principles, often focusing on narrow domains or posing ambiguous questions that hinder fair comparison. Guided by these principles, we introduce LiveResearchBench, a benchmark of 100 expert-curated tasks spanning daily life, enterprise, and academia, each requiring extensive, dynamic, real-time web search and synthesis. Built with over 1,500 hours of human labor, LiveResearchBench provides a rigorous basis for systematic evaluation. To evaluate citation-grounded long-form reports, we introduce DeepEval, a comprehensive suite covering both content- and report-level quality, including coverage, presentation, citation accuracy and association, consistency and depth of analysis. DeepEval integrates four complementary evaluation protocols, each designed to ensure stable assessment and high agreement with human judgments. Using LiveResearchBench and DeepEval, we conduct a comprehensive evaluation of 17 frontier deep research systems, including single-agent web search, single-agent deep research, and multi-agent systems. Our analysis reveals current strengths, recurring failure modes, and key system components needed to advance reliable, insightful deep research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SalesforceAIResearch/LiveResearchBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.13913", "title": "Synthesizing Agentic Data for Web Agents with Progressive Difficulty Enhancement Mechanisms", "authors": [ "Shrey Pandit", "Xuan-Phi Nguyen", "Yifei Ming", "Austin Xu", "Jiayu Wang", "Caiming Xiong", "Shafiq Joty" ], "github": "", "project_page": "", "abstract": "Web-based 'deep research' agents aim to solve complex question - answering tasks through long-horizon interactions with online tools. These tasks remain challenging, as the underlying language models are often not optimized for long-horizon reasoning and exploration. Prior work has proposed workflows for constructing instruction-tuning datasets, often leveraging knowledge graphs. However, such methods typically lack fine-grained control over difficulty and quality, yielding synthetic data that falls short of capturing the complexity required for long-horizon reasoning. Furthermore, many studies conflate data and training effects by comparing models trained under different optimization recipes, making it difficult to isolate and evaluate the effectiveness of the data itself. We introduce a two-pronged data synthesis pipeline that generates question - answer pairs by progressively increasing task complexity until a frontier baseline web agent fails. The baseline agent plays multiple roles in this process: attempting the questions, validating factuality, checking for alternative answers, and enforcing filtering. To evaluate the effectiveness of our synthesis methods, we adopt a controlled training setup based on distillation from strong web agents. Experiments across multiple web-based benchmarks show that our dataset - despite being smaller - enables the training of more effective web agents than existing datasets. In particular, our data exhibits twice the diversity in tool-use actions, allowing models trained on it to achieve stronger performance while avoiding repetitive tool-calling behaviors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14095", "title": "Unlocking Out-of-Distribution Generalization in Transformers via Recursive Latent Space Reasoning", "authors": [ "Awni Altabaa", "Siyu Chen", "John Lafferty", "Zhuoran Yang" ], "github": "", "project_page": "", "abstract": "Systematic, compositional generalization beyond the training distribution remains a core challenge in machine learning -- and a critical bottleneck for the emergent reasoning abilities of modern language models. This work investigates out-of-distribution (OOD) generalization in Transformer networks using a GSM8K-style modular arithmetic on computational graphs task as a testbed. We introduce and explore a set of four architectural mechanisms aimed at enhancing OOD generalization: (i) input-adaptive recurrence; (ii) algorithmic supervision; (iii) anchored latent representations via a discrete bottleneck; and (iv) an explicit error-correction mechanism. Collectively, these mechanisms yield an architectural approach for native and scalable latent space reasoning in Transformer networks with robust algorithmic generalization capabilities. We complement these empirical results with a detailed mechanistic interpretability analysis that reveals how these mechanisms give rise to robust OOD generalization abilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.10472", "title": "FML-bench: A Benchmark for Automatic ML Research Agents Highlighting the Importance of Exploration Breadth", "authors": [ "Qiran Zou", "Hou Hei Lam", "Wenhao Zhao", "Yiming Tang", "Tingting Chen", "Samson Yu", "Tianyi Zhang", "Chang Liu", "Xiangyang Ji", "Dianbo Liu" ], "github": "https://github.com/qrzou/FML-bench", "project_page": "", "abstract": "Large language models (LLMs) have sparked growing interest in automatic machine learning research agents. Among them, agents capable of autonomously proposing ideas and conducting machine learning experiments are particularly promising, as they maximize research automation and accelerate scientific progress by iteratively refining ideas based on experimental results. However, comprehensively evaluating such agents remains challenging. Existing benchmarks tend to overemphasize engineering aspects while neglecting academic rigor, creating barriers that obscure a clear assessment of an agent's scientific capabilities in machine learning research. They also suffer from limited task diversity, an overemphasis on application-oriented tasks over fundamental research problems, and limited scalability to realistic research settings. To address these limitations, we introduce FML-bench, a benchmark designed to evaluate automatic machine learning research agents on 8 diverse and fundamental machine learning research problems. It reduces coding burden, emphasizes fundamental problems rather than specific use cases, offers high task diversity, and is extensible to real-world machine learning GitHub repositories. Furthermore, we present a unified evaluation framework with five complementary metrics, designed to comprehensively assess agent performance on our benchmark. We evaluate state-of-the-art automatic research agents on FML-bench, and find that agents employing broad research exploration strategies outperform those focusing on narrow but deep exploration. These findings suggest that emphasizing the breadth of exploration may lead to more effective research outcomes than focusing solely on incremental refinement. Our benchmark is available at https://github.com/qrzou/FML-bench.", "github_manual": "", "github_regex": "https://github.com/qrzou/FML-bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14919", "title": "Predicting Task Performance with Context-aware Scaling Laws", "authors": [ "Kyle Montgomery", "David Park", "Jianhong Tu", "Michael Bendersky", "Beliz Gunel", "Dawn Song", "Chenguang Wang" ], "github": "https://github.com/wang-research-lab/context-scaling", "project_page": "", "abstract": "Scaling laws have transformed our understanding of large language models by linking upstream metrics like cross-entropy loss to design factors such as model size, training data, and compute. However, these conventional laws fail to capture downstream task performance, where context plays a critical role. In this work, we propose a straightforward, interpretable framework that jointly models downstream performance as a function of the training compute and the provided context. We empirically validate our framework by fitting it on the observed downstream performance of extended-context variants of Llama-2-7B and Llama-2-13B across 65,500 unique instances spanning three tasks: arithmetic reasoning, common sense reasoning, and machine translation. Our results demonstrate that our framework accurately models in-distribution downstream performance, generalizes across three orders of magnitude in training compute, and reliably extrapolates performance as the amount of context increases. These findings offer valuable insights into the interplay between training compute and context utilization, providing guidance for designing more efficient long-context LLMs for diverse downstream tasks. Our code is available at https://github.com/wang-research-lab/context-scaling.", "github_manual": "", "github_regex": "https://github.com/wang-research-lab/context-scaling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.14913", "title": "Budget-aware Test-time Scaling via Discriminative Verification", "authors": [ "Kyle Montgomery", "Sijun Tan", "Yuqi Chen", "Siyuan Zhuang", "Tianjun Zhang", "Raluca Ada Popa", "Chenguang Wang" ], "github": "https://github.com/wang-research-lab/verification", "project_page": "", "abstract": "Test-time scaling is a powerful strategy for boosting the performance of large language models on complex reasoning tasks. While state-of-the-art approaches often employ generative verifiers to select the best solution from a pool of candidates, this method incurs prohibitive computational costs, limiting its practicality. In this work, we shift the focus to a more budget-aware paradigm: discriminative verification. We conduct a thorough empirical analysis and demonstrate that while discriminative verifiers may underperform in isolation, combining them with self-consistency in a hybrid approach creates a powerful and efficient test-time scaling mechanism. Notably, under a fixed compute budget, this hybrid approach surpasses state-of-the-art generative verification by a significant margin: achieving up to 15.3\\% higher accuracy on AIME2025. Our findings establish that for practical, real-world applications, budget-aware scaling with discriminative verifiers is not only a \"free\" upgrade over self-consistency, but also a more effective and efficient alternative to costly generative techniques. Code is available at https://github.com/wang-research-lab/verification.", "github_manual": "", "github_regex": "https://github.com/wang-research-lab/verification", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-17", "arxiv_id": "2510.13928", "title": "LLMs Can Get \"Brain Rot\"!", "authors": [ "Shuo Xing", "Junyuan Hong", "Yifan Wang", "Runjin Chen", "Zhenyu Zhang", "Ananth Grama", "Zhengzhong Tu", "Zhangyang Wang" ], "github": "", "project_page": "", "abstract": "We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language models (LLMs). To causally isolate data quality, we run controlled experiments on real Twitter/X corpora, constructing junk and reversely controlled datasets via two orthogonal operationalizations: M1 (engagement degree) and M2 (semantic quality), with matched token scale and training operations across conditions. Contrary to the control group, continual pre-training of 4 LLMs on the junk dataset causes non-trivial declines (Hedges' g>0.3) on reasoning, long-context understanding, safety, and inflating \"dark traits\" (e.g., psychopathy, narcissism). The gradual mixtures of junk and control datasets also yield dose-response cognition decay: for example, under M1, ARC-Challenge with Chain Of Thoughts drops 74.9 rightarrow 57.2 and RULER-CWE 84.4 rightarrow 52.3 as junk ratio rises from 0% to 100%. Error forensics reveal several key insights. First, we identify thought-skipping as the primary lesion: models increasingly truncate or skip reasoning chains, explaining most of the error growth. Second, partial but incomplete healing is observed: scaling instruction tuning and clean data pre-training improve the declined cognition yet cannot restore baseline capability, suggesting persistent representational drift rather than format mismatch. Finally, we discover that the popularity, a non-semantic metric, of a tweet is a better indicator of the Brain Rot effect than the length in M1. Together, the results provide significant, multi-perspective evidence that data quality is a causal driver of LLM capability decay, reframing curation for continual pretraining as a training-time safety problem and motivating routine \"cognitive health checks\" for deployed LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.14265", "title": "MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning", "authors": [ "Xukai Wang", "Xuanbo Liu", "Mingrui Chen", "Haitian Zhong", "Xuanlin Yang", "Bohan Zeng", "Jinbo Hu", "Hao Liang", "Junbo Niu", "Xuchen Li", "Ruitao Wu", "Ruichuan An", "Yang Shi", "Liu Liu", "Xu-Yao Zhang", "Qiang Liu", "Zhouchen Lin", "Wentao Zhang", "Bin Dong" ], "github": "https://github.com/OpenDCAI/MorphoBench", "project_page": "", "abstract": "With the advancement of powerful large-scale reasoning models, effectively evaluating the reasoning capabilities of these models has become increasingly important. However, existing benchmarks designed to assess the reasoning abilities of large models tend to be limited in scope and lack the flexibility to adapt their difficulty according to the evolving reasoning capacities of the models. To address this, we propose MorphoBench, a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models. Specifically, we curate the benchmark by selecting and collecting complex reasoning questions from existing benchmarks and sources such as Olympiad-level competitions. Additionally, MorphoBench adaptively modifies the analytical challenge of questions by leveraging key statements generated during the model's reasoning process. Furthermore, it includes questions generated using simulation software, enabling dynamic adjustment of benchmark difficulty with minimal resource consumption. We have gathered over 1,300 test questions and iteratively adjusted the difficulty of MorphoBench based on the reasoning capabilities of models such as o3 and GPT-5. MorphoBench enhances the comprehensiveness and validity of model reasoning evaluation, providing reliable guidance for improving both the reasoning abilities and scientific robustness of large models. The code has been released in https://github.com/OpenDCAI/MorphoBench.", "github_manual": "", "github_regex": "https://github.com/OpenDCAI/MorphoBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15869", "title": "Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery", "authors": [ "Jie-Ying Lee", "Yi-Ruei Liu", "Shr-Ruei Tsai", "Wei-Cheng Chang", "Chung-Ho Wu", "Jiewen Chan", "Zhenjun Zhao", "Chieh Hubert Lin", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Synthesizing large-scale, explorable, and geometrically accurate 3D urban scenes is a challenging yet valuable task in providing immersive and embodied applications. The challenges lie in the lack of large-scale and high-quality real-world 3D scans for training generalizable generative models. In this paper, we take an alternative route to create large-scale 3D scenes by synergizing the readily available satellite imagery that supplies realistic coarse geometry and the open-domain diffusion model for creating high-quality close-up appearances. We propose Skyfall-GS, the first city-block scale 3D scene creation framework without costly 3D annotations, also featuring real-time, immersive 3D exploration. We tailor a curriculum-driven iterative refinement strategy to progressively enhance geometric completeness and photorealistic textures. Extensive experiments demonstrate that Skyfall-GS provides improved cross-view consistent geometry and more realistic textures compared to state-of-the-art approaches. Project page: https://skyfall-gs.jayinnn.dev/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15868", "title": "LightsOut: Diffusion-based Outpainting for Enhanced Lens Flare Removal", "authors": [ "Shr-Ruei Tsai", "Wei-Cheng Chang", "Jie-Ying Lee", "Chih-Hai Su", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Lens flare significantly degrades image quality, impacting critical computer vision tasks like object detection and autonomous driving. Recent Single Image Flare Removal (SIFR) methods perform poorly when off-frame light sources are incomplete or absent. We propose LightsOut, a diffusion-based outpainting framework tailored to enhance SIFR by reconstructing off-frame light sources. Our method leverages a multitask regression module and LoRA fine-tuned diffusion model to ensure realistic and physically consistent outpainting results. Comprehensive experiments demonstrate LightsOut consistently boosts the performance of existing SIFR methods across challenging scenarios without additional retraining, serving as a universally applicable plug-and-play preprocessing solution. Project page: https://ray-1026.github.io/lightsout/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15870", "title": "OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM", "authors": [ "Hanrong Ye", "Chao-Han Huck Yang", "Arushi Goel", "Wei Huang", "Ligeng Zhu", "Yuanhang Su", "Sean Lin", "An-Chieh Cheng", "Zhen Wan", "Jinchuan Tian", "Yuming Lou", "Dong Yang", "Zhijian Liu", "Yukang Chen", "Ambrish Dantrey", "Ehsan Jahangiri", "Sreyan Ghosh", "Daguang Xu", "Ehsan Hosseini-Asl", "Danial Mohseni Taheri", "Vidya Murali", "Sifei Liu", "Jason Lu", "Oluwatobi Olabiyi", "Frank Wang", "Rafael Valle", "Bryan Catanzaro", "Andrew Tao", "Song Han", "Jan Kautz", "Hongxu Yin", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "Advancing machine intelligence requires developing the ability to perceive across multiple modalities, much as humans sense the world. We introduce OmniVinci, an initiative to build a strong, open-source, omni-modal LLM. We carefully study the design choices across model architecture and data curation. For model architecture, we present three key innovations: (i) OmniAlignNet for strengthening alignment between vision and audio embeddings in a shared omni-modal latent space; (ii) Temporal Embedding Grouping for capturing relative temporal alignment between vision and audio signals; and (iii) Constrained Rotary Time Embedding for encoding absolute temporal information in omni-modal embeddings. We introduce a curation and synthesis pipeline that generates 24M single-modal and omni-modal conversations. We find that modalities reinforce one another in both perception and reasoning. Our model, OmniVinci, outperforms Qwen2.5-Omni with +19.05 on DailyOmni (cross-modal understanding), +1.7 on MMAR (audio), and +3.9 on Video-MME (vision), while using just 0.2T training tokens - a 6 times reduction compared to Qwen2.5-Omni's 1.2T. We finally demonstrate omni-modal advantages in downstream applications spanning robotics, medical AI, and smart factory.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15857", "title": "BLIP3o-NEXT: Next Frontier of Native Image Generation", "authors": [ "Jiuhai Chen", "Le Xue", "Zhiyang Xu", "Xichen Pan", "Shusheng Yang", "Can Qin", "An Yan", "Honglu Zhou", "Zeyuan Chen", "Lifu Huang", "Tianyi Zhou", "Junnan Li", "Silvio Savarese", "Caiming Xiong", "Ran Xu" ], "github": "", "project_page": "", "abstract": "We present BLIP3o-NEXT, a fully open-source foundation model in the BLIP3 series that advances the next frontier of native image generation. BLIP3o-NEXT unifies text-to-image generation and image editing within a single architecture, demonstrating strong image generation and image editing capabilities. In developing the state-of-the-art native image generation model, we identify four key insights: (1) Most architectural choices yield comparable performance; an architecture can be deemed effective provided it scales efficiently and supports fast inference; (2) The successful application of reinforcement learning can further push the frontier of native image generation; (3) Image editing still remains a challenging task, yet instruction following and the consistency between generated and reference images can be significantly enhanced through post-training and data engine; (4) Data quality and scale continue to be decisive factors that determine the upper bound of model performance. Building upon these insights, BLIP3o-NEXT leverages an Autoregressive + Diffusion architecture in which an autoregressive model first generates discrete image tokens conditioned on multimodal inputs, whose hidden states are then used as conditioning signals for a diffusion model to generate high-fidelity images. This architecture integrates the reasoning strength and instruction following of autoregressive models with the fine-detail rendering ability of diffusion models, achieving a new level of coherence and realism. Extensive evaluations of various text-to-image and image-editing benchmarks show that BLIP3o-NEXT achieves superior performance over existing models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15564", "title": "Imaginarium: Vision-guided High-Quality 3D Scene Layout Generation", "authors": [ "Xiaoming Zhu", "Xu Huang", "Qinghongbing Xie", "Zhi Deng", "Junsheng Yu", "Yirui Guan", "Zhongyuan Liu", "Lin Zhu", "Qijun Zhao", "Ligang Liu", "Long Zeng" ], "github": "https://github.com/HiHiAllen/Imaginarium", "project_page": "", "abstract": "Generating artistic and coherent 3D scene layouts is crucial in digital content creation. Traditional optimization-based methods are often constrained by cumbersome manual rules, while deep generative models face challenges in producing content with richness and diversity. Furthermore, approaches that utilize large language models frequently lack robustness and fail to accurately capture complex spatial relationships. To address these challenges, this paper presents a novel vision-guided 3D layout generation system. We first construct a high-quality asset library containing 2,037 scene assets and 147 3D scene layouts. Subsequently, we employ an image generation model to expand prompt representations into images, fine-tuning it to align with our asset library. We then develop a robust image parsing module to recover the 3D layout of scenes based on visual semantics and geometric information. Finally, we optimize the scene layout using scene graphs and overall visual semantics to ensure logical coherence and alignment with the images. Extensive user testing demonstrates that our algorithm significantly outperforms existing methods in terms of layout richness and quality. The code and dataset will be available at https://github.com/HiHiAllen/Imaginarium.", "github_manual": "", "github_regex": "https://github.com/HiHiAllen/Imaginarium", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15301", "title": "Latent Diffusion Model without Variational Autoencoder", "authors": [ "Minglei Shi", "Haolin Wang", "Wenzhao Zheng", "Ziyang Yuan", "Xiaoshi Wu", "Xintao Wang", "Pengfei Wan", "Jie Zhou", "Jiwen Lu" ], "github": "", "project_page": "", "abstract": "Recent progress in diffusion-based visual generation has largely relied on latent diffusion models with variational autoencoders (VAEs). While effective for high-fidelity synthesis, this VAE+diffusion paradigm suffers from limited training efficiency, slow inference, and poor transferability to broader vision tasks. These issues stem from a key limitation of VAE latent spaces: the lack of clear semantic separation and strong discriminative structure. Our analysis confirms that these properties are crucial not only for perception and understanding tasks, but also for the stable and efficient training of latent diffusion models. Motivated by this insight, we introduce SVG, a novel latent diffusion model without variational autoencoders, which leverages self-supervised representations for visual generation. SVG constructs a feature space with clear semantic discriminability by leveraging frozen DINO features, while a lightweight residual branch captures fine-grained details for high-fidelity reconstruction. Diffusion models are trained directly on this semantically structured latent space to facilitate more efficient learning. As a result, SVG enables accelerated diffusion training, supports few-step sampling, and improves generative quality. Experimental results further show that SVG preserves the semantic and discriminative capabilities of the underlying self-supervised representations, providing a principled pathway toward task-general, high-quality visual representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15232", "title": "FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain", "authors": [ "Tiansheng Hu", "Tongyan Hu", "Liuyang Bai", "Yilun Zhao", "Arman Cohan", "Chen Zhao" ], "github": "", "project_page": "", "abstract": "Recent LLMs have demonstrated promising ability in solving finance related problems. However, applying LLMs in real-world finance application remains challenging due to its high risk and high stakes property. This paper introduces FinTrust, a comprehensive benchmark specifically designed for evaluating the trustworthiness of LLMs in finance applications. Our benchmark focuses on a wide range of alignment issues based on practical context and features fine-grained tasks for each dimension of trustworthiness evaluation. We assess eleven LLMs on FinTrust and find that proprietary models like o4-mini outperforms in most tasks such as safety while open-source models like DeepSeek-V3 have advantage in specific areas like industry-level fairness. For challenging task like fiduciary alignment and disclosure, all LLMs fall short, showing a significant gap in legal awareness. We believe that FinTrust can be a valuable benchmark for LLMs' trustworthiness evaluation in finance domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.14438", "title": "Explore to Evolve: Scaling Evolved Aggregation Logic via Proactive Online Exploration for Deep Research Agents", "authors": [ "Rui Wang", "Ce Zhang", "Jun-Yu Ma", "Jianshu Zhang", "Hongru Wang", "Yi Chen", "Boyang Xue", "Tianqing Fang", "Zhisong Zhang", "Hongming Zhang", "Haitao Mi", "Dong Yu", "Kam-Fai Wong" ], "github": "", "project_page": "", "abstract": "Deep research web agents not only retrieve information from diverse sources such as web environments, files, and multimodal inputs, but more importantly, they need to rigorously analyze and aggregate knowledge for insightful research. However, existing open-source deep research agents predominantly focus on enhancing information-seeking capabilities of web agents to locate specific information, while overlooking the essential need for information aggregation, which would limit their ability to support in-depth research. We propose an Explore to Evolve paradigm to scalably construct verifiable training data for web agents. Begins with proactive online exploration, an agent sources grounded information by exploring the real web. Using the collected evidence, the agent then self-evolves an aggregation program by selecting, composing, and refining operations from 12 high-level logical types to synthesize a verifiable QA pair. This evolution from high-level guidance to concrete operations allowed us to scalably produce WebAggregatorQA, a dataset of 10K samples across 50K websites and 11 domains. Based on an open-source agent framework, SmolAgents, we collect supervised fine-tuning trajectories to develop a series of foundation models, WebAggregator. WebAggregator-8B matches the performance of GPT-4.1, while the 32B variant surpasses GPT-4.1 by more than 10% on GAIA-text and closely approaches Claude-3.7-sonnet. Moreover, given the limited availability of benchmarks that evaluate web agents' information aggregation abilities, we construct a human-annotated evaluation split of WebAggregatorQA as a challenging test set. On this benchmark, Claude-3.7-sonnet only achieves 28%, and GPT-4.1 scores 25.8%. Even when agents manage to retrieve all references, they still struggle on WebAggregatorQA, highlighting the need to strengthen the information aggregation capabilities of web agent foundations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15842", "title": "Paper2Web: Let's Make Your Paper Alive!", "authors": [ "Yuhang Chen", "Tianpeng Lv", "Siyi Zhang", "Yixiang Yin", "Yao Wan", "Philip S. Yu", "Dongping Chen" ], "github": "https://github.com/YuhangChen1/Paper2All", "project_page": "", "abstract": "Academic project websites can more effectively disseminate research when they clearly present core content and enable intuitive navigation and interaction. However, current approaches such as direct Large Language Model (LLM) generation, templates, or direct HTML conversion struggle to produce layout-aware, interactive sites, and a comprehensive evaluation suite for this task has been lacking. In this paper, we introduce Paper2Web, a benchmark dataset and multi-dimensional evaluation framework for assessing academic webpage generation. It incorporates rule-based metrics like Connectivity, Completeness and human-verified LLM-as-a-Judge (covering interactivity, aesthetics, and informativeness), and PaperQuiz, which measures paper-level knowledge retention. We further present PWAgent, an autonomous pipeline that converts scientific papers into interactive and multimedia-rich academic homepages. The agent iteratively refines both content and layout through MCP tools that enhance emphasis, balance, and presentation quality. Our experiments show that PWAgent consistently outperforms end-to-end baselines like template-based webpages and arXiv/alphaXiv versions by a large margin while maintaining low cost, achieving the Pareto-front in academic webpage generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YuhangChen1/Paper2All", "github_api_added_by": "auto" }, { "date": "2025-10-20", "arxiv_id": "2510.15742", "title": "Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset", "authors": [ "Qingyan Bai", "Qiuyu Wang", "Hao Ouyang", "Yue Yu", "Hanlin Wang", "Wen Wang", "Ka Leong Cheng", "Shuailei Ma", "Yanhong Zeng", "Zichen Liu", "Yinghao Xu", "Yujun Shen", "Qifeng Chen" ], "github": "", "project_page": "", "abstract": "Instruction-based video editing promises to democratize content creation, yet its progress is severely hampered by the scarcity of large-scale, high-quality training data. We introduce Ditto, a holistic framework designed to tackle this fundamental challenge. At its heart, Ditto features a novel data generation pipeline that fuses the creative diversity of a leading image editor with an in-context video generator, overcoming the limited scope of existing models. To make this process viable, our framework resolves the prohibitive cost-quality trade-off by employing an efficient, distilled model architecture augmented by a temporal enhancer, which simultaneously reduces computational overhead and improves temporal coherence. Finally, to achieve full scalability, this entire pipeline is driven by an intelligent agent that crafts diverse instructions and rigorously filters the output, ensuring quality control at scale. Using this framework, we invested over 12,000 GPU-days to build Ditto-1M, a new dataset of one million high-fidelity video editing examples. We trained our model, Editto, on Ditto-1M with a curriculum learning strategy. The results demonstrate superior instruction-following ability and establish a new state-of-the-art in instruction-based video editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15831", "title": "VISTA: A Test-Time Self-Improving Video Generation Agent", "authors": [ "Do Xuan Long", "Xingchen Wan", "Hootan Nakhost", "Chen-Yu Lee", "Tomas Pfister", "Sercan \u00d6. Ar\u0131k" ], "github": "", "project_page": "", "abstract": "Despite rapid advances in text-to-video synthesis, generated video quality remains critically dependent on precise user prompts. Existing test-time optimization methods, successful in other domains, struggle with the multi-faceted nature of video. In this work, we introduce VISTA (Video Iterative Self-improvemenT Agent), a novel multi-agent system that autonomously improves video generation through refining prompts in an iterative loop. VISTA first decomposes a user idea into a structured temporal plan. After generation, the best video is identified through a robust pairwise tournament. This winning video is then critiqued by a trio of specialized agents focusing on visual, audio, and contextual fidelity. Finally, a reasoning agent synthesizes this feedback to introspectively rewrite and enhance the prompt for the next generation cycle. Experiments on single- and multi-scene video generation scenarios show that while prior methods yield inconsistent gains, VISTA consistently improves video quality and alignment with user intent, achieving up to 60% pairwise win rate against state-of-the-art baselines. Human evaluators concur, preferring VISTA outputs in 66.4% of comparisons.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15262", "title": "Robust Layerwise Scaling Rules by Proper Weight Decay Tuning", "authors": [ "Zhiyuan Fan", "Yifeng Liu", "Qingyue Zhao", "Angela Yuan", "Quanquan Gu" ], "github": "", "project_page": "", "abstract": "Empirical scaling laws prescribe how to allocate parameters, data, and compute, while maximal-update parameterization (muP) enables learning-rate transfer across widths by equalizing early-time update magnitudes. However, in modern scale-invariant architectures, training quickly enters an optimizer-governed steady state where normalization layers create backward scale sensitivity and the effective learning rate becomes width dependent, degrading muP transfer. We address this by introducing a weight-decay scaling rule for AdamW that preserves sublayer gain across widths. Empirically, the singular-value spectrum of each matrix parameter scales in norm as eta/lambda with an approximately invariant shape; under width scaling d, we observe that the top singular value scales approximately as eta/lambdacdot d^{0.75}. Combining this observation with the muP learning-rate rule eta_2propto d^{-1} for matrix-like parameters implies an empirical weight-decay scaling rule lambda_2propto d that approximately keeps sublayer gains width invariant. Together with vector-like parameters trained at eta_1=Theta_d(1) and lambda_1=0, this yields zero-shot transfer of both learning rate and weight decay from proxy to target widths, removing per-width sweeps. We validate the rule on LLaMA-style Transformers and in a minimal synthetic setting, and we provide a simple diagnostic, matching top singular values, to check sublayer-gain invariance. Our results extend muP beyond the near-init regime by explicitly controlling steady-state scales set by the optimizer, offering a practical recipe for width-robust hyperparameter transfer under AdamW.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15110", "title": "DLER: Doing Length pEnalty Right - Incentivizing More Intelligence per Token via Reinforcement Learning", "authors": [ "Shih-Yang Liu", "Xin Dong", "Ximing Lu", "Shizhe Diao", "Mingjie Liu", "Min-Hung Chen", "Hongxu Yin", "Yu-Chiang Frank Wang", "Kwang-Ting Cheng", "Yejin Choi", "Jan Kautz", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "Reasoning language models such as OpenAI-o1, DeepSeek-R1, and Qwen achieve strong performance via extended chains of thought but often generate unnecessarily long outputs. Maximizing intelligence per token--accuracy relative to response length--remains an open problem. We revisit reinforcement learning (RL) with the simplest length penalty--truncation--and show that accuracy degradation arises not from the lack of sophisticated penalties but from inadequate RL optimization. We identify three key challenges: (i) large bias in advantage estimation, (ii) entropy collapse, and (iii) sparse reward signal. We address them with Doing Length pEnalty Right (DLER), a training recipe combining batch-wise reward normalization, higher clipping, dynamic sampling, and a simple truncation length penalty. DLER achieves state-of-the-art accuracy--efficiency trade-offs, cutting output length by over 70 percent while surpassing all previous baseline accuracy. It also improves test-time scaling: compared to DeepSeek-R1-7B, DLER-7B generates multiple concise responses in parallel with 28 percent higher accuracy and lower latency. We further introduce Difficulty-Aware DLER, which adaptively tightens truncation on easier questions for additional efficiency gains. We also propose an update-selective merging method that preserves baseline accuracy while retaining the concise reasoning ability of the DLER model, which is useful for scenarios where RL training data is scarce.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15162", "title": "Train a Unified Multimodal Data Quality Classifier with Synthetic Data", "authors": [ "Weizhi Wang", "Rongmei Lin", "Shiyang Li", "Colin Lockard", "Ritesh Sarkhel", "Sanket Lokegaonkar", "Jingbo Shang", "Xifeng Yan", "Nasser Zalmout", "Xian Li" ], "github": "https://github.com/Victorwz/UniFilter", "project_page": "", "abstract": "The Multimodal Large Language Models (MLLMs) are continually pre-trained on a mixture of image-text caption data and interleaved document data, while the high-quality data filtering towards image-text interleaved document data is under-explored. We propose to train an efficient MLLM as a Unified Mulitmodal Data Quality Classifier to Filter both high-quality image-text caption and interleaved data (UniFilter). To address the challenge of collecting diverse labeled multimodal data, we introduce a semi-synthetic approach that leverages readily available raw images and generates corresponding text across four quality levels. This method enables efficient creation of sample-score pairs for both caption and interleaved document data to train UniFilter. We apply UniFilter to curate high-quality caption data from DataComp caption dataset and interleaved data from the OBELICS image-text interleaved dataset. MLLMs pre-trained on the filtered data demonstrate significantly enhanced capabilities compared to those trained on baseline-filtered data, achieving stronger zero-shot reasoning and in-context learning capabilities. After visual supervised fine-tuning, these UniFilter-induced MLLMs achieve stronger performance on various benchmarks, highlighting the downstream benefits of high-quality multimodal pre-training. We release the synthetic training data used for training UniFilter, the UniFilter model checkpoints, and the high-quality interleaved document subset OBELICS-HQ, curated by UniFilter, to the community for reproduction and further development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Victorwz/UniFilter", "github_api_added_by": "auto" }, { "date": "2025-10-20", "arxiv_id": "2510.15859", "title": "InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training", "authors": [ "Pengkai Wang", "Qi Zuo", "Pengwei Liu", "Zhijie Sang", "Congkai Xie", "Hongxia Yang" ], "github": "https://github.com/pidneuralode/ORBIT", "project_page": "", "abstract": "Large Language Models (LLMs) have shown substantial advances through reinforcement learning (RL), particularly in domains where rewards can be programmatically verified, such as mathematics and code. In these areas, models benefit from a well-defined operational base guided by explicit rule-based objectives. However, this progress reveals a significant limitation: in open-ended domains where rewards are ambiguous, subjective, or context-dependent, such as creative writing, scientific reasoning, and notably medical consultation, robust reward functions are lacking, making these areas challenging for current RL strategies. To bridge this gap, we introduce ORBIT, an open-ended rubric-based incremental training framework specifically designed for high-stakes medical dialogue. ORBIT integrates syn- thetic dialogue generation with the dynamic creation of rubrics, employing these rubrics to direct an incremental RL process. In particular, this approach does not depend on external medical knowledge or manual rules, instead utilizing rubric-guided feedback to shape learning. When implemented on the Qwen3-4B-Instruct model, our method can greatly enhance its performance on the HealthBench-Hard benchmark from 7.0 to 27.2 using only 2k samples, thus achieving state-of-the-art results for models of this scale. Our analysis confirms that rubric-driven RL fos-ters consistent performance gains across diverse consultation scenarios, going beyond simple numerical improvements. These findings underscore rubric-based feedback as a scalable strategy for advancing LLMs in intricate, open-ended tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pidneuralode/ORBIT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15280", "title": "Foundation Models for Scientific Discovery: From Paradigm Enhancement to Paradigm Transition", "authors": [ "Fan Liu", "Jindong Han", "Tengfei Lyu", "Weijia Zhang", "Zhe-Rui Yang", "Lu Dai", "Cancheng Liu", "Hao Liu" ], "github": "https://github.com/usail-hkust/Awesome-Foundation-Models-for-Scientific-Discovery", "project_page": "", "abstract": "Foundation models (FMs), such as GPT-4 and AlphaFold, are reshaping the landscape of scientific research. Beyond accelerating tasks such as hypothesis generation, experimental design, and result interpretation, they prompt a more fundamental question: Are FMs merely enhancing existing scientific methodologies, or are they redefining the way science is conducted? In this paper, we argue that FMs are catalyzing a transition toward a new scientific paradigm. We introduce a three-stage framework to describe this evolution: (1) Meta-Scientific Integration, where FMs enhance workflows within traditional paradigms; (2) Hybrid Human-AI Co-Creation, where FMs become active collaborators in problem formulation, reasoning, and discovery; and (3) Autonomous Scientific Discovery, where FMs operate as independent agents capable of generating new scientific knowledge with minimal human intervention. Through this lens, we review current applications and emerging capabilities of FMs across existing scientific paradigms. We further identify risks and future directions for FM-enabled scientific discovery. This position paper aims to support the scientific community in understanding the transformative role of FMs and to foster reflection on the future of scientific discovery. Our project is available at https://github.com/usail-hkust/Awesome-Foundation-Models-for-Scientific-Discovery.", "github_manual": "", "github_regex": "https://github.com/usail-hkust/Awesome-Foundation-Models-for-Scientific-Discovery", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.12838", "title": "A^2FM: An Adaptive Agent Foundation Model for Tool-Aware Hybrid Reasoning", "authors": [ "Qianben Chen", "Jingyi Cao", "Jiayu Zhang", "Tianrui Qin", "Xiaowan Li", "King Zhu", "Dingfeng Shi", "He Zhu", "Minghao Liu", "Xiaobo Liang", "Xin Gui", "Ge Zhang", "Jian Yang", "Yuchen Eleanor Jiang", "Wangchunshu Zhou" ], "github": "https://github.com/OPPO-PersonalAI/Adaptive_Agent_Foundation_Models", "project_page": "", "abstract": "Large language models split into two families: reasoning-centric LLMs, which strengthen internal chain-of-thought reasoning but cannot invoke external tools, and agentic LLMs, which learn to interact with environments and leverage tools but often lag in deep reasoning. This divide arises from fundamentally different training objectives, leading to mismatched strengths and inefficiency on simple queries, where both families tend to overthink or over-call tools. In this work, we present Adaptive Agent Foundation Model (A^2FM), a unified framework that follows a route-then-align principle: the model first learns task-aware routing and then aligns mode-specific trajectories under a shared backbone. To address the inefficiency gap, we introduce a third mode-instant-that handles simple queries directly, preventing unnecessary reasoning or tool calls while complementing the agentic and reasoning modes. To jointly enhance accuracy and efficiency, we propose Adaptive Policy Optimization (APO), which enforces adaptive sampling across modes and applies a cost-regularized reward. On the 32B scale, A^2FM achieves 13.4% on BrowseComp, 70.4% on AIME25, and 16.7% on HLE, setting new SOTA among comparable models and performing competitively with frontier LLMs across agentic, reasoning, and general benchmarks. Notably, the adaptive execution achieves a cost of pass of only $0.00487 per correct answer-cutting cost by 45.2% relative to reasoning and 33.5% relative to agentic, thus delivering substantially higher cost efficiency while maintaining comparable accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OPPO-PersonalAI/Adaptive_Agent_Foundation_Models", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15019", "title": "NANO3D: A Training-Free Approach for Efficient 3D Editing Without Masks", "authors": [ "Junliang Ye", "Shenghao Xie", "Ruowen Zhao", "Zhengyi Wang", "Hongyu Yan", "Wenqiang Zu", "Lei Ma", "Jun Zhu" ], "github": "", "project_page": "", "abstract": "3D object editing is essential for interactive content creation in gaming, animation, and robotics, yet current approaches remain inefficient, inconsistent, and often fail to preserve unedited regions. Most methods rely on editing multi-view renderings followed by reconstruction, which introduces artifacts and limits practicality. To address these challenges, we propose Nano3D, a training-free framework for precise and coherent 3D object editing without masks. Nano3D integrates FlowEdit into TRELLIS to perform localized edits guided by front-view renderings, and further introduces region-aware merging strategies, Voxel/Slat-Merge, which adaptively preserve structural fidelity by ensuring consistency between edited and unedited areas. Experiments demonstrate that Nano3D achieves superior 3D consistency and visual quality compared with existing methods. Based on this framework, we construct the first large-scale 3D editing datasets Nano3D-Edit-100k, which contains over 100,000 high-quality 3D editing pairs. This work addresses long-standing challenges in both algorithm design and data availability, significantly improving the generality and reliability of 3D editing, and laying the groundwork for the development of feed-forward 3D editing models. Project Page:https://jamesyjl.github.io/Nano3D", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15624", "title": "Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automation", "authors": [ "Ed Li", "Junyu Ren", "Xintian Pan", "Cat Yan", "Chuanhao Li", "Dirk Bergemann", "Zhuoran Yang" ], "github": "", "project_page": "", "abstract": "The automation of scientific discovery represents a critical milestone in Artificial Intelligence (AI) research. However, existing agentic systems for science suffer from two fundamental limitations: rigid, pre-programmed workflows that cannot adapt to intermediate findings, and inadequate context management that hinders long-horizon research. We present freephdlabor, an open-source multiagent framework featuring fully dynamic workflows determined by real-time agent reasoning and a \\textit{modular architecture} enabling seamless customization -- users can modify, add, or remove agents to address domain-specific requirements. The framework provides comprehensive infrastructure including automatic context compaction, workspace-based communication to prevent information degradation, memory persistence across sessions, and non-blocking human intervention mechanisms. These features collectively transform automated research from isolated, single-run attempts into continual research programs that build systematically on prior explorations and incorporate human feedback. By providing both the architectural principles and practical implementation for building customizable co-scientist systems, this work aims to facilitate broader adoption of automated research across scientific domains, enabling practitioners to deploy interactive multiagent systems that autonomously conduct end-to-end research -- from ideation through experimentation to publication-ready manuscripts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.12766", "title": "Language Models Model Language", "authors": [ "\u0141ukasz Borchmann" ], "github": "", "project_page": "", "abstract": "Linguistic commentary on LLMs, heavily influenced by the theoretical frameworks of de Saussure and Chomsky, is often speculative and unproductive. Critics challenge whether LLMs can legitimately model language, citing the need for \"deep structure\" or \"grounding\" to achieve an idealized linguistic \"competence.\" We argue for a radical shift in perspective towards the empiricist principles of Witold Ma\\'nczak, a prominent general and historical linguist. He defines language not as a \"system of signs\" or a \"computational system of the brain\" but as the totality of all that is said and written. Above all, he identifies frequency of use of particular language elements as language's primary governing principle. Using his framework, we challenge prior critiques of LLMs and provide a constructive guide for designing, evaluating, and interpreting language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.14853", "title": "Rewiring Experts on the Fly:Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert models", "authors": [ "Guinan Su", "Yanwu Yang", "Li Shen", "Lu Yin", "Shiwei Liu", "Jonas Geiping" ], "github": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) models achieve efficient scaling through sparse expert activation, but often suffer from suboptimal routing decisions due to distribution shifts in deployment. While existing test-time adaptation methods could potentially address these issues, they primarily focus on dense models and require access to external data, limiting their practical applicability to MoE architectures. However, we find that, instead of relying on reference data, we can optimize MoE expert selection on-the-fly based only on input context. As such, we propose a data-free, online test-time framework that continuously adapts MoE routing decisions during text generation without external supervision or data. Our method cycles between two phases: During the prefill stage, and later in regular intervals, we optimize the routing decisions of the model using self-supervision based on the already generated sequence. Then, we generate text as normal, maintaining the modified router until the next adaption. We implement this through lightweight additive vectors that only update router logits in selected layers, maintaining computational efficiency while preventing over-adaptation. The experimental results show consistent performance gains on challenging reasoning tasks while maintaining robustness to context shifts. For example, our method achieves a 5.5\\% improvement on HumanEval with OLMoE. Furthermore, owing to its plug-and-play property, our method naturally complements existing test-time scaling techniques, e.g., achieving 6\\% average gains when incorporated with self-consistency on DeepSeek-V2-Lite.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.14077", "title": "ERGO: Entropy-guided Resetting for Generation Optimization in Multi-turn Language Models", "authors": [ "Haziq Mohammad Khalid", "Athikash Jeyaganthan", "Timothy Do", "Yicheng Fu", "Sean O'Brien", "Vasu Sharma", "Kevin Zhu" ], "github": "https://github.com/haziq-exe/ERGO", "project_page": "", "abstract": "Large Language Models (LLMs) suffer significant performance degradation in multi-turn conversations when information is presented incrementally. Given that multi-turn conversations characterize everyday interactions with LLMs, this degradation poses a severe challenge to real world usability. We hypothesize that abrupt increases in model uncertainty signal misalignment in multi-turn LLM interactions, and we exploit this insight to dynamically realign conversational context. We introduce ERGO (Entropy-guided Resetting for Generation Optimization), which continuously quantifies internal uncertainty via Shannon entropy over next token distributions and triggers adaptive prompt consolidation when a sharp spike in entropy is detected. By treating uncertainty as a first class signal rather than a nuisance to eliminate, ERGO embraces variability in language and modeling, representing and responding to uncertainty. In multi-turn tasks with incrementally revealed instructions, ERGO yields a 56.6% average performance gain over standard baselines, increases aptitude (peak performance capability) by 24.7%, and decreases unreliability (variability in performance) by 35.3%, demonstrating that uncertainty aware interventions can improve both accuracy and reliability in conversational AI.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/haziq-exe/ERGO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.11288", "title": "Emergent Misalignment via In-Context Learning: Narrow in-context examples can produce broadly misaligned LLMs", "authors": [ "Nikita Afonin", "Nikita Andriyanov", "Nikhil Bageshpura", "Kyle Liu", "Kevin Zhu", "Sunishchal Dev", "Ashwinee Panda", "Alexander Panchenko", "Oleg Rogov", "Elena Tutubalina", "Mikhail Seleznyov" ], "github": "", "project_page": "", "abstract": "Recent work has shown that narrow finetuning can produce broadly misaligned LLMs, a phenomenon termed emergent misalignment (EM). While concerning, these findings were limited to finetuning and activation steering, leaving out in-context learning (ICL). We therefore ask: does EM emerge in ICL? We find that it does: across three datasets, three frontier models produce broadly misaligned responses at rates between 2% and 17% given 64 narrow in-context examples, and up to 58% with 256 examples. We also examine mechanisms of EM by eliciting step-by-step reasoning (while leaving in-context examples unchanged). Manual analysis of the resulting chain-of-thought shows that 67.5% of misaligned traces explicitly rationalize harmful outputs by adopting a reckless or dangerous ''persona'', echoing prior results on finetuning-induced EM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15264", "title": "DriveGen3D: Boosting Feed-Forward Driving Scene Generation with Efficient Video Diffusion", "authors": [ "Weijie Wang", "Jiagang Zhu", "Zeyu Zhang", "Xiaofeng Wang", "Zheng Zhu", "Guosheng Zhao", "Chaojun Ni", "Haoxiao Wang", "Guan Huang", "Xinze Chen", "Yukun Zhou", "Wenkang Qin", "Duochao Shi", "Haoyun Li", "Guanghong Jia", "Jiwen Lu" ], "github": "", "project_page": "", "abstract": "We present DriveGen3D, a novel framework for generating high-quality and highly controllable dynamic 3D driving scenes that addresses critical limitations in existing methodologies. Current approaches to driving scene synthesis either suffer from prohibitive computational demands for extended temporal generation, focus exclusively on prolonged video synthesis without 3D representation, or restrict themselves to static single-scene reconstruction. Our work bridges this methodological gap by integrating accelerated long-term video generation with large-scale dynamic scene reconstruction through multimodal conditional control. DriveGen3D introduces a unified pipeline consisting of two specialized components: FastDrive-DiT, an efficient video diffusion transformer for high-resolution, temporally coherent video synthesis under text and Bird's-Eye-View (BEV) layout guidance; and FastRecon3D, a feed-forward reconstruction module that rapidly builds 3D Gaussian representations across time, ensuring spatial-temporal consistency. Together, these components enable real-time generation of extended driving videos (up to 424times800 at 12 FPS) and corresponding dynamic 3D scenes, achieving SSIM of 0.811 and PSNR of 22.84 on novel view synthesis, all while maintaining parameter efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.15444", "title": "A Theoretical Study on Bridging Internal Probability and Self-Consistency for LLM Reasoning", "authors": [ "Zhi Zhou", "Yuhao Tan", "Zenan Li", "Yuan Yao", "Lan-Zhe Guo", "Yu-Feng Li", "Xiaoxing Ma" ], "github": "", "project_page": "", "abstract": "Test-time scaling seeks to improve the reasoning performance of large language models (LLMs) by adding computational resources. A prevalent approach within the field is sampling-based test-time scaling methods, which enhance reasoning by generating multiple reasoning paths for a given input during inference. However, despite its practical success, the theoretical foundations remain underexplored. In this paper, we provide the first theoretical framework for analyzing sampling-based test-time scaling methods, grounded in the perspective of confidence estimation. Based on the framework, we analyze two dominant paradigms: self-consistency and perplexity, and reveal key limitations: self-consistency suffers from high estimation error while perplexity exhibits substantial modeling error and possible degradation of the estimation error convergence. To address these limitations, we introduce RPC, a hybrid method that leverages our theoretical insights through two key components: Perplexity Consistency and Reasoning Pruning. Perplexity Consistency combines the strengths of self-consistency and perplexity, boosting the convergence rate of estimation error from linear to exponential while preserving model error. Reasoning Pruning prevents degradation by eliminating low-probability reasoning paths. Both theoretical analysis and empirical results across seven benchmark datasets demonstrate that RPC has a strong potential for reducing reasoning error. Notably, RPC achieves reasoning performance comparable to self-consistency while not only enhancing confidence reliability but also reducing sampling costs by 50%. The code and resources are available at https://wnjxyk.github.io/RPC.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.11328", "title": "Do LLMs \"Feel\"? Emotion Circuits Discovery and Control", "authors": [ "Chenxi Wang", "Yixuan Zhang", "Ruiji Yu", "Yufei Zheng", "Lang Gao", "Zirui Song", "Zixiang Xu", "Gus Xia", "Huishuai Zhang", "Dongyan Zhao", "Xiuying Chen" ], "github": "https://github.com/Aurora-cx/EmotionCircuits-LLM", "project_page": "", "abstract": "As the demand for emotional intelligence in large language models (LLMs) grows, a key challenge lies in understanding the internal mechanisms that give rise to emotional expression and in controlling emotions in generated text. This study addresses three core questions: (1) Do LLMs contain context-agnostic mechanisms shaping emotional expression? (2) What form do these mechanisms take? (3) Can they be harnessed for universal emotion control? We first construct a controlled dataset, SEV (Scenario-Event with Valence), to elicit comparable internal states across emotions. Subsequently, we extract context-agnostic emotion directions that reveal consistent, cross-context encoding of emotion (Q1). We identify neurons and attention heads that locally implement emotional computation through analytical decomposition and causal analysis, and validate their causal roles via ablation and enhancement interventions. Next, we quantify each sublayer's causal influence on the model's final emotion representation and integrate the identified local components into coherent global emotion circuits that drive emotional expression (Q2). Directly modulating these circuits achieves 99.65% emotion-expression accuracy on the test set, surpassing prompting- and steering-based methods (Q3). To our knowledge, this is the first systematic study to uncover and validate emotion circuits in LLMs, offering new insights into interpretability and controllable emotional intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Aurora-cx/EmotionCircuits-LLM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-20", "arxiv_id": "2510.14630", "title": "Adapting Self-Supervised Representations as a Latent Space for Efficient Generation", "authors": [ "Ming Gui", "Johannes Schusterbauer", "Timy Phan", "Felix Krause", "Josh Susskind", "Miguel Angel Bautista", "Bj\u00f6rn Ommer" ], "github": "https://github.com/CompVis/RepTok", "project_page": "", "abstract": "We introduce Representation Tokenizer (RepTok), a generative modeling framework that represents an image using a single continuous latent token obtained from self-supervised vision transformers. Building on a pre-trained SSL encoder, we fine-tune only the semantic token embedding and pair it with a generative decoder trained jointly using a standard flow matching objective. This adaptation enriches the token with low-level, reconstruction-relevant details, enabling faithful image reconstruction. To preserve the favorable geometry of the original SSL space, we add a cosine-similarity loss that regularizes the adapted token, ensuring the latent space remains smooth and suitable for generation. Our single-token formulation resolves spatial redundancies of 2D latent spaces and significantly reduces training costs. Despite its simplicity and efficiency, RepTok achieves competitive results on class-conditional ImageNet generation and naturally extends to text-to-image synthesis, reaching competitive zero-shot performance on MS-COCO under extremely limited training budgets. Our findings highlight the potential of fine-tuned SSL representations as compact and effective latent spaces for efficient generative modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/CompVis/RepTok", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16872", "title": "DeepAnalyze: Agentic Large Language Models for Autonomous Data Science", "authors": [ "Shaolei Zhang", "Ju Fan", "Meihao Fan", "Guoliang Li", "Xiaoyong Du" ], "github": "", "project_page": "", "abstract": "Autonomous data science, from raw data sources to analyst-grade deep research reports, has been a long-standing challenge, and is now becoming feasible with the emergence of powerful large language models (LLMs). Recent workflow-based data agents have shown promising results on specific data tasks but remain fundamentally limited in achieving fully autonomous data science due to their reliance on predefined workflows. In this paper, we introduce DeepAnalyze-8B, the first agentic LLM designed for autonomous data science, capable of automatically completing the end-toend pipeline from data sources to analyst-grade deep research reports. To tackle high-complexity data science tasks, we propose a curriculum-based agentic training paradigm that emulates the learning trajectory of human data scientists, enabling LLMs to progressively acquire and integrate multiple capabilities in real-world environments. We also introduce a data-grounded trajectory synthesis framework that constructs high-quality training data. Through agentic training, DeepAnalyze learns to perform a broad spectrum of data tasks, ranging from data question answering and specialized analytical tasks to open-ended data research. Experiments demonstrate that, with only 8B parameters, DeepAnalyze outperforms previous workflow-based agents built on most advanced proprietary LLMs. The model, code, and training data of DeepAnalyze are open-sourced, paving the way toward autonomous data science.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17354", "title": "Towards Mixed-Modal Retrieval for Universal Retrieval-Augmented Generation", "authors": [ "Chenghao Zhang", "Guanting Dong", "Xinyu Yang", "Zhicheng Dou" ], "github": "https://github.com/SnowNation101/Nyx", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) by retrieving relevant documents from an external corpus. However, existing RAG systems primarily focus on unimodal text documents, and often fall short in real-world scenarios where both queries and documents may contain mixed modalities (such as text and images). In this paper, we address the challenge of Universal Retrieval-Augmented Generation (URAG), which involves retrieving and reasoning over mixed-modal information to improve vision-language generation. To this end, we propose Nyx, a unified mixed-modal to mixed-modal retriever tailored for URAG scenarios. To mitigate the scarcity of realistic mixed-modal data, we introduce a four-stage automated pipeline for generation and filtering, leveraging web documents to construct NyxQA, a dataset comprising diverse mixed-modal question-answer pairs that better reflect real-world information needs. Building on this high-quality dataset, we adopt a two-stage training framework for Nyx: we first perform pre-training on NyxQA along with a variety of open-source retrieval datasets, followed by supervised fine-tuning using feedback from downstream vision-language models (VLMs) to align retrieval outputs with generative preferences. Experimental results demonstrate that Nyx not only performs competitively on standard text-only RAG benchmarks, but also excels in the more general and realistic URAG setting, significantly improving generation quality in vision-language tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SnowNation101/Nyx", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17681", "title": "PICABench: How Far Are We from Physically Realistic Image Editing?", "authors": [ "Yuandong Pu", "Le Zhuo", "Songhao Han", "Jinbo Xing", "Kaiwen Zhu", "Shuo Cao", "Bin Fu", "Si Liu", "Hongsheng Li", "Yu Qiao", "Wenlong Zhang", "Xi Chen", "Yihao Liu" ], "github": "", "project_page": "", "abstract": "Image editing has achieved remarkable progress recently. Modern editing models could already follow complex instructions to manipulate the original content. However, beyond completing the editing instructions, the accompanying physical effects are the key to the generation realism. For example, removing an object should also remove its shadow, reflections, and interactions with nearby objects. Unfortunately, existing models and benchmarks mainly focus on instruction completion but overlook these physical effects. So, at this moment, how far are we from physically realistic image editing? To answer this, we introduce PICABench, which systematically evaluates physical realism across eight sub-dimension (spanning optics, mechanics, and state transitions) for most of the common editing operations (add, remove, attribute change, etc). We further propose the PICAEval, a reliable evaluation protocol that uses VLM-as-a-judge with per-case, region-level human annotations and questions. Beyond benchmarking, we also explore effective solutions by learning physics from videos and construct a training dataset PICA-100K. After evaluating most of the mainstream models, we observe that physical realism remains a challenging problem with large rooms to explore. We hope that our benchmark and proposed solutions can serve as a foundation for future work moving from naive content editing toward physically consistent realism.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17795", "title": "Executable Knowledge Graphs for Replicating AI Research", "authors": [ "Yujie Luo", "Zhuoyun Yu", "Xuehai Wang", "Yuqi Zhu", "Ningyu Zhang", "Lanning Wei", "Lun Du", "Da Zheng", "Huajun Chen" ], "github": "https://github.com/zjunlp/xKG", "project_page": "", "abstract": "Replicating AI research is a crucial yet challenging task for large language model (LLM) agents. Existing approaches often struggle to generate executable code, primarily due to insufficient background knowledge and the limitations of retrieval-augmented generation (RAG) methods, which fail to capture latent technical details hidden in referenced papers. Furthermore, previous approaches tend to overlook valuable implementation-level code signals and lack structured knowledge representations that support multi-granular retrieval and reuse. To overcome these challenges, we propose Executable Knowledge Graphs (xKG), a modular and pluggable knowledge base that automatically integrates technical insights, code snippets, and domain-specific knowledge extracted from scientific literature. When integrated into three agent frameworks with two different LLMs, xKG shows substantial performance gains (10.9% with o3-mini) on PaperBench, demonstrating its effectiveness as a general and extensible solution for automated AI research replication. Code will released at https://github.com/zjunlp/xKG.", "github_manual": "", "github_regex": "https://github.com/zjunlp/xKG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17803", "title": "ConsistEdit: Highly Consistent and Precise Training-free Visual Editing", "authors": [ "Zixin Yin", "Ling-Hao Chen", "Lionel Ni", "Xili Dai" ], "github": "", "project_page": "", "abstract": "Recent advances in training-free attention control methods have enabled flexible and efficient text-guided editing capabilities for existing generation models. However, current approaches struggle to simultaneously deliver strong editing strength while preserving consistency with the source. This limitation becomes particularly critical in multi-round and video editing, where visual errors can accumulate over time. Moreover, most existing methods enforce global consistency, which limits their ability to modify individual attributes such as texture while preserving others, thereby hindering fine-grained editing. Recently, the architectural shift from U-Net to MM-DiT has brought significant improvements in generative performance and introduced a novel mechanism for integrating text and vision modalities. These advancements pave the way for overcoming challenges that previous methods failed to resolve. Through an in-depth analysis of MM-DiT, we identify three key insights into its attention mechanisms. Building on these, we propose ConsistEdit, a novel attention control method specifically tailored for MM-DiT. ConsistEdit incorporates vision-only attention control, mask-guided pre-attention fusion, and differentiated manipulation of the query, key, and value tokens to produce consistent, prompt-aligned edits. Extensive experiments demonstrate that ConsistEdit achieves state-of-the-art performance across a wide range of image and video editing tasks, including both structure-consistent and structure-inconsistent scenarios. Unlike prior methods, it is the first approach to perform editing across all inference steps and attention layers without handcraft, significantly enhancing reliability and consistency, which enables robust multi-round and multi-region editing. Furthermore, it supports progressive adjustment of structural consistency, enabling finer control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16751", "title": "Visual Autoregressive Models Beat Diffusion Models on Inference Time Scaling", "authors": [ "Erik Riise", "Mehmet Onurcan Kaya", "Dim P. Papadopoulos" ], "github": "", "project_page": "", "abstract": "While inference-time scaling through search has revolutionized Large Language Models, translating these gains to image generation has proven difficult. Recent attempts to apply search strategies to continuous diffusion models show limited benefits, with simple random sampling often performing best. We demonstrate that the discrete, sequential nature of visual autoregressive models enables effective search for image generation. We show that beam search substantially improves text-to-image generation, enabling a 2B parameter autoregressive model to outperform a 12B parameter diffusion model across benchmarks. Systematic ablations show that this advantage comes from the discrete token space, which allows early pruning and computational reuse, and our verifier analysis highlights trade-offs between speed and reasoning capability. These findings suggest that model architecture, not just scale, is critical for inference-time optimization in visual generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17797", "title": "Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics", "authors": [ "Akshara Prabhakar", "Roshan Ram", "Zixiang Chen", "Silvio Savarese", "Frank Wang", "Caiming Xiong", "Huan Wang", "Weiran Yao" ], "github": "https://github.com/SalesforceAIResearch/enterprise-deep-research", "project_page": "", "abstract": "As information grows exponentially, enterprises face increasing pressure to transform unstructured data into coherent, actionable insights. While autonomous agents show promise, they often struggle with domain-specific nuances, intent alignment, and enterprise integration. We present Enterprise Deep Research (EDR), a multi-agent system that integrates (1) a Master Planning Agent for adaptive query decomposition, (2) four specialized search agents (General, Academic, GitHub, LinkedIn), (3) an extensible MCP-based tool ecosystem supporting NL2SQL, file analysis, and enterprise workflows, (4) a Visualization Agent for data-driven insights, and (5) a reflection mechanism that detects knowledge gaps and updates research direction with optional human-in-the-loop steering guidance. These components enable automated report generation, real-time streaming, and seamless enterprise deployment, as validated on internal datasets. On open-ended benchmarks including DeepResearch Bench and DeepConsult, EDR outperforms state-of-the-art agentic systems without any human steering. We release the EDR framework and benchmark trajectories to advance research on multi-agent reasoning applications. Code at https://github.com/SalesforceAIResearch/enterprise-deep-research and Dataset at https://huggingface.co/datasets/Salesforce/EDR-200", "github_manual": "", "github_regex": "https://github.com/SalesforceAIResearch/enterprise-deep-research", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17498", "title": "Deep Self-Evolving Reasoning", "authors": [ "Zihan Liu", "Shun Zheng", "Xumeng Wen", "Yang Wang", "Jiang Bian", "Mao Yang" ], "github": "", "project_page": "", "abstract": "Long-form chain-of-thought reasoning has become a cornerstone of advanced reasoning in large language models. While recent verification-refinement frameworks have enabled proprietary models to solve Olympiad-level problems, their effectiveness hinges on strong, reliable verification and correction capabilities, which remain fragile in open-weight, smaller-scale models. This work demonstrates that even with weak verification and refinement capabilities on hard tasks, the reasoning limits of such models can be substantially extended through a probabilistic paradigm we call Deep Self-Evolving Reasoning (DSER). We conceptualize iterative reasoning as a Markov chain, where each step represents a stochastic transition in the solution space. The key insight is that convergence to a correct solution is guaranteed as long as the probability of improvement marginally exceeds that of degradation. By running multiple long-horizon, self-evolving processes in parallel, DSER amplifies these small positive tendencies, enabling the model to asymptotically approach correct answers. Empirically, we apply DSER to the DeepSeek-R1-0528-Qwen3-8B model. On the challenging AIME 2024-2025 benchmark, DSER solves 5 out of 9 previously unsolvable problems and boosts overall performance, enabling this compact model to surpass the single-turn accuracy of its 600B-parameter teacher through majority voting. Beyond its immediate utility for test-time scaling, the DSER framework serves to diagnose the fundamental limitations of current open-weight reasoners. By clearly delineating their shortcomings in self-verification, refinement, and stability, our findings establish a clear research agenda for developing next-generation models with powerful, intrinsic self-evolving capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17269", "title": "FineVision: Open Data Is All You Need", "authors": [ "Luis Wiedmann", "Orr Zohar", "Amir Mahla", "Xiaohan Wang", "Rui Li", "Thibaud Frere", "Leandro von Werra", "Aritra Roy Gosthipaty", "Andr\u00e9s Marafioti" ], "github": "", "project_page": "", "abstract": "The advancement of vision-language models (VLMs) is hampered by a fragmented landscape of inconsistent and contaminated public datasets. We introduce FineVision, a meticulously collected, curated, and unified corpus of 24 million samples - the largest open resource of its kind. We unify more than 200 sources into 185 subsets via a semi-automated, human-in-the-loop pipeline: automation performs bulk ingestion and schema mapping, while reviewers audit mappings and spot-check outputs to verify faithful consumption of annotations, appropriate formatting and diversity, and safety; issues trigger targeted fixes and re-runs. The workflow further applies rigorous de-duplication within and across sources and decontamination against 66 public benchmarks. FineVision also encompasses agentic/GUI tasks with a unified action space; reviewers validate schemas and inspect a sample of trajectories to confirm executable fidelity. Models trained on FineVision consistently outperform those trained on existing open mixtures across a broad evaluation suite, underscoring the benefits of scale, data hygiene, and balanced automation with human oversight. We release the corpus and curation tools to accelerate data-centric VLM research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.15527", "title": "Balanced Multi-Task Attention for Satellite Image Classification: A Systematic Approach to Achieving 97.23% Accuracy on EuroSAT Without Pre-Training", "authors": [ "Aditya Vir" ], "github": "", "project_page": "", "abstract": "This work presents a systematic investigation of custom convolutional neural network architectures for satellite land use classification, achieving 97.23% test accuracy on the EuroSAT dataset without reliance on pre-trained models. Through three progressive architectural iterations (baseline: 94.30%, CBAM-enhanced: 95.98%, and balanced multi-task attention: 97.23%) we identify and address specific failure modes in satellite imagery classification. Our principal contribution is a novel balanced multi-task attention mechanism that combines Coordinate Attention for spatial feature extraction with Squeeze-Excitation blocks for spectral feature extraction, unified through a learnable fusion parameter. Experimental results demonstrate that this learnable parameter autonomously converges to alpha approximately 0.57, indicating near-equal importance of spatial and spectral modalities for satellite imagery. We employ progressive DropBlock regularization (5-20% by network depth) and class-balanced loss weighting to address overfitting and confusion pattern imbalance. The final 12-layer architecture achieves Cohen's Kappa of 0.9692 with all classes exceeding 94.46% accuracy, demonstrating confidence calibration with a 24.25% gap between correct and incorrect predictions. Our approach achieves performance within 1.34% of fine-tuned ResNet-50 (98.57%) while requiring no external data, validating the efficacy of systematic architectural design for domain-specific applications. Complete code, trained models, and evaluation scripts are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.14605", "title": "Knowledge-based Visual Question Answer with Multimodal Processing, Retrieval and Filtering", "authors": [ "Yuyang Hong", "Jiaqi Gu", "Qi Yang", "Lubin Fan", "Yue Wu", "Ying Wang", "Kun Ding", "Shiming Xiang", "Jieping Ye" ], "github": "https://github.com/cqu-student/Wiki-PRF", "project_page": "", "abstract": "Knowledge-based visual question answering (KB-VQA) requires visual language models (VLMs) to integrate visual understanding with external knowledge retrieval. Although retrieval-augmented generation (RAG) achieves significant advances in this task by combining knowledge-base querying, it still struggles with the quality of multimodal queries and the relevance of retrieved results. To overcome these challenges, we propose a novel three-stage method, termed Wiki-PRF, including Processing, Retrieval and Filtering stages. The processing stage dynamically invokes visual tools to extract precise multimodal information for retrieval. The retrieval stage integrates visual and text features to achieve multimodal knowledge retrieval. The filtering stage performs relevance filtering and concentration on retrieval results. To this end, we introduce a visual language model trained with answer accuracy and format consistency as reward signals via a reinforcement learning manner. This enhances the model's reasoning, tool invocation for accurate queries, and filtering of irrelevant content. Experiments on benchmark datasets (E-VQA and InfoSeek) show significant improvements~(36.0 and 42.8) in answer quality, achieving state-of-the-art performance. Code is available at https://github.com/cqu-student/Wiki-PRF", "github_manual": "", "github_regex": "https://github.com/cqu-student/Wiki-PRF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16156", "title": "AsyncVoice Agent: Real-Time Explanation for LLM Planning and Reasoning", "authors": [ "Yueqian Lin", "Zhengmian Hu", "Jayakumar Subramanian", "Qinsi Wang", "Nikos Vlassis", "Hai \"Helen\" Li", "Yiran Chen" ], "github": "", "project_page": "", "abstract": "Effective human-AI collaboration on complex reasoning tasks requires that users understand and interact with the model's process, not just receive an output. However, the monolithic text from methods like Chain-of-Thought (CoT) prevents this, as current interfaces lack real-time verbalization and robust user barge-in. We present AsyncVoice Agent, a system whose asynchronous architecture decouples a streaming LLM backend from a conversational voice frontend. This design allows narration and inference to run in parallel, empowering users to interrupt, query, and steer the model's reasoning process at any time. Objective benchmarks show this approach reduces interaction latency by more than 600x compared to monolithic baselines while ensuring high fidelity and competitive task accuracy. By enabling a two-way dialogue with a model's thought process, AsyncVoice Agent offers a new paradigm for building more effective, steerable, and trustworthy human-AI systems for high-stakes tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17800", "title": "Glyph: Scaling Context Windows via Visual-Text Compression", "authors": [ "Jiale Cheng", "Yusen Liu", "Xinyu Zhang", "Yulin Fei", "Wenyi Hong", "Ruiliang Lyu", "Weihan Wang", "Zhe Su", "Xiaotao Gu", "Xiao Liu", "Yushi Bai", "Jie Tang", "Hongning Wang", "Minlie Huang" ], "github": "https://github.com/thu-coai/Glyph", "project_page": "", "abstract": "Large language models (LLMs) increasingly rely on long-context modeling for tasks such as document understanding, code analysis, and multi-step reasoning. However, scaling context windows to the million-token level brings prohibitive computational and memory costs, limiting the practicality of long-context LLMs. In this work, we take a different perspective-visual context scaling-to tackle this challenge. Instead of extending token-based sequences, we propose Glyph, a framework that renders long texts into images and processes them with vision-language models (VLMs). This approach substantially compresses textual input while preserving semantic information, and we further design an LLM-driven genetic search to identify optimal visual rendering configurations for balancing accuracy and compression. Through extensive experiments, we demonstrate that our method achieves 3-4x token compression while maintaining accuracy comparable to leading LLMs such as Qwen3-8B on various long-context benchmarks. This compression also leads to around 4x faster prefilling and decoding, and approximately 2x faster SFT training. Furthermore, under extreme compression, a 128K-context VLM could scale to handle 1M-token-level text tasks. In addition, the rendered text data benefits real-world multimodal tasks, such as document understanding. Our code and model are released at https://github.com/thu-coai/Glyph.", "github_manual": "", "github_regex": "https://github.com/thu-coai/Glyph", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16720", "title": "Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AI", "authors": [ "Jitao Sang", "Jinlin Xiao", "Jiarun Han", "Jilin Chen", "Xiaoyi Chen", "Shuyu Wei", "Yongjie Sun", "Yuhang Wang" ], "github": "https://github.com/kingjulio8238/Memary", "project_page": "", "abstract": "The rapid evolution of agentic AI marks a new phase in artificial intelligence, where Large Language Models (LLMs) no longer merely respond but act, reason, and adapt. This survey traces the paradigm shift in building agentic AI: from Pipeline-based systems, where planning, tool use, and memory are orchestrated by external logic, to the emerging Model-native paradigm, where these capabilities are internalized within the model's parameters. We first position Reinforcement Learning (RL) as the algorithmic engine enabling this paradigm shift. By reframing learning from imitating static data to outcome-driven exploration, RL underpins a unified solution of LLM + RL + Task across language, vision and embodied domains. Building on this, the survey systematically reviews how each capability -- Planning, Tool use, and Memory -- has evolved from externally scripted modules to end-to-end learned behaviors. Furthermore, it examines how this paradigm shift has reshaped major agent applications, specifically the Deep Research agent emphasizing long-horizon reasoning and the GUI agent emphasizing embodied interaction. We conclude by discussing the continued internalization of agentic capabilities like Multi-agent collaboration and Reflection, alongside the evolving roles of the system and model layers in future agentic AI. Together, these developments outline a coherent trajectory toward model-native agentic AI as an integrated learning and interaction framework, marking the transition from constructing systems that apply intelligence to developing models that grow intelligence through experience.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kingjulio8238/Memary", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16259", "title": "Distractor Injection Attacks on Large Reasoning Models: Characterization and Defense", "authors": [ "Zhehao Zhang", "Weijie Xu", "Shixian Cui", "Chandan K. Reddy" ], "github": "", "project_page": "", "abstract": "Recent advances in large reasoning models (LRMs) have enabled remarkable performance on complex tasks such as mathematics and coding by generating long Chain-of-Thought (CoT) traces. In this paper, we identify and systematically analyze a critical vulnerability we term reasoning distraction, where LRMs are diverted from their primary objective by irrelevant yet complex tasks maliciously embedded in the prompt. Through a comprehensive study across diverse models and benchmarks, we show that even state-of-the-art LRMs are highly susceptible, with injected distractors reducing task accuracy by up to 60%. We further reveal that certain alignment techniques can amplify this weakness and that models may exhibit covert compliance, following hidden adversarial instructions in reasoning while concealing them in the final output. To mitigate these risks, we propose a training-based defense that combines Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) on synthetic adversarial data, improving robustness by over 50 points on challenging distractor attacks. Our findings establish reasoning distraction as a distinct and urgent threat to LRM reliability and provide a practical step toward safer and more trustworthy reasoning systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16333", "title": "RL makes MLLMs see better than SFT", "authors": [ "Junha Song", "Sangdoo Yun", "Dongyoon Han", "Jaegul Choo", "Byeongho Heo" ], "github": "", "project_page": "", "abstract": "A dominant assumption in Multimodal Language Model (MLLM) research is that its performance is largely inherited from the LLM backbone, given its immense parameter scale and remarkable capabilities. This has created a void in the understanding of the vision encoder, which determines how MLLMs perceive images. The recent shift in MLLM training paradigms, from Supervised Finetuning (SFT) to Reinforcement Learning (RL), magnifies this oversight-namely, the significant lack of analysis on how such training reshapes the vision encoder as well as the MLLM. To address this, we first investigate the impact of training strategies on MLLMs, where RL shows a clear advantage over SFT in strongly vision-related VQA benchmarks. Motivated by this, we conduct a critical yet under-explored analysis of the vision encoder of MLLMs through diverse and in-depth experiments, ranging from ImageNet classification and segmentation to gradient visualization. Our results demonstrate that MLLM's post-training strategy (i.e., SFT or RL) not only leads to distinct outcomes on MLLM downstream tasks, but also fundamentally reshapes MLLM's underlying visual representations. Specifically, the key finding of our study is that RL produces stronger and precisely localized visual representations compared to SFT, boosting the ability of the vision encoder for MLLM. We then reframe our findings into a simple recipe for building strong vision encoders for MLLMs, Preference-Instructed Vision OpTimization (PIVOT). When integrated into MLLMs, a PIVOT-trained vision encoder outperforms even larger and more heavily-trained counterparts, despite requiring less than 1% of the computational cost of standard vision pretraining. This result opens an effective and efficient path for advancing the vision backbones of MLLMs. Project page available at https://june-page.github.io/pivot/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17790", "title": "UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Action", "authors": [ "Yuhao Yang", "Zhen Yang", "Zi-Yi Dou", "Anh Nguyen", "Keen You", "Omar Attia", "Andrew Szot", "Michael Feng", "Ram Ramrakhya", "Alexander Toshev", "Chao Huang", "Yinfei Yang", "Zhe Gan" ], "github": "", "project_page": "", "abstract": "Multimodal agents for computer use rely exclusively on primitive actions (click, type, scroll) that require accurate visual grounding and lengthy execution chains, leading to cascading failures and performance bottlenecks. While other agents leverage rich programmatic interfaces (APIs, MCP servers, tools), computer-use agents (CUAs) remain isolated from these capabilities. We present UltraCUA, a foundation model that bridges this gap through hybrid action -- seamlessly integrating GUI primitives with high-level programmatic tool calls. To achieve this, our approach comprises four key components: (1) an automated pipeline that scales programmatic tools from software documentation, open-source repositories, and code generation; (2) a synthetic data engine producing over 17,000 verifiable tasks spanning real-world computer-use scenarios; (3) a large-scale high-quality hybrid action trajectory collection with both low-level GUI actions and high-level programmatic tool calls; and (4) a two-stage training pipeline combining supervised fine-tuning with online reinforcement learning, enabling strategic alternation between low-level and high-level actions. Experiments with our 7B and 32B models demonstrate substantial improvements over state-of-the-art agents. On OSWorld, UltraCUA models achieve an average 22% relative improvement over base models, while being 11% faster in terms of steps. Out-of-domain evaluation on WindowsAgentArena shows our model reaches 21.7% success rate, outperforming baselines trained on Windows data. The hybrid action mechanism proves critical, reducing error propagation while maintaining execution efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16258", "title": "Embody 3D: A Large-scale Multimodal Motion and Behavior Dataset", "authors": [ "Claire McLean", "Makenzie Meendering", "Tristan Swartz", "Orri Gabbay", "Alexandra Olsen", "Rachel Jacobs", "Nicholas Rosen", "Philippe de Bree", "Tony Garcia", "Gadsden Merrill", "Jake Sandakly", "Julia Buffalini", "Neham Jain", "Steven Krenn", "Moneish Kumar", "Dejan Markovic", "Evonne Ng", "Fabian Prada", "Andrew Saba", "Siwei Zhang", "Vasu Agrawal", "Tim Godisart", "Alexander Richard", "Michael Zollhoefer" ], "github": "", "project_page": "", "abstract": "The Codec Avatars Lab at Meta introduces Embody 3D, a multimodal dataset of 500 individual hours of 3D motion data from 439 participants collected in a multi-camera collection stage, amounting to over 54 million frames of tracked 3D motion. The dataset features a wide range of single-person motion data, including prompted motions, hand gestures, and locomotion; as well as multi-person behavioral and conversational data like discussions, conversations in different emotional states, collaborative activities, and co-living scenarios in an apartment-like space. We provide tracked human motion including hand tracking and body shape, text annotations, and a separate audio track for each participant.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16888", "title": "Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback", "authors": [ "Zongjian Li", "Zheyuan Liu", "Qihui Zhang", "Bin Lin", "Shenghai Yuan", "Zhiyuan Yan", "Yang Ye", "Wangbo Yu", "Yuwei Niu", "Li Yuan" ], "github": "https://github.com/PKU-YuanGroup/UniWorld-V2", "project_page": "", "abstract": "Instruction-based image editing has achieved remarkable progress; however, models solely trained via supervised fine-tuning often overfit to annotated patterns, hindering their ability to explore and generalize beyond training distributions. To this end, we introduce Edit-R1, a novel post-training framework for instruction-based image editing based on policy optimization. Specifically, we utilize Diffusion Negative-aware Finetuning (DiffusionNFT), a likelihood-free policy optimization method consistent with the flow matching forward process, thereby enabling the use of higher-order samplers and more efficient training. Another key challenge here is the absence of a universal reward model, resulting from the diverse nature of editing instructions and tasks. To bridge this gap, we employ a Multimodal Large Language Model (MLLM) as a unified, training-free reward model, leveraging its output logits to provide fine-grained feedback. Furthermore, we carefully design a low-variance group filtering mechanism to reduce MLLM scoring noise and stabilize optimization. UniWorld-V2, trained with this framework, achieves state-of-the-art results on the ImgEdit and GEdit-Bench benchmarks, scoring 4.49 and 7.83, respectively. Crucially, our framework is model-agnostic, delivering substantial performance gains when applied to diverse base models like Qwen-Image-Edit and FLUX-Kontext, demonstrating its wide applicability. Code and models are publicly available at https://github.com/PKU-YuanGroup/UniWorld-V2.", "github_manual": "", "github_regex": "https://github.com/PKU-YuanGroup/UniWorld-V2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17509", "title": "Annotation-Efficient Universal Honesty Alignment", "authors": [ "Shiyu Ni", "Keping Bi", "Jiafeng Guo", "Minghao Tang", "Jingtong Wu", "Zengxin Han", "Xueqi Cheng" ], "github": "", "project_page": "", "abstract": "Honesty alignment-the ability of large language models (LLMs) to recognize their knowledge boundaries and express calibrated confidence-is essential for trustworthy deployment. Existing methods either rely on training-free confidence estimation (e.g., token probabilities, self-consistency) or training-based calibration with correctness annotations. While effective, achieving universal honesty alignment with training-based calibration requires costly, large-scale labeling. To support annotation-efficient training, we introduce Elicitation-Then-Calibration (EliCal), a two-stage framework that first elicits internal confidence using inexpensive self-consistency supervision, then calibrates this confidence with a small set of correctness annotations. To support a large-scale study, we release HonestyBench, a benchmark covering ten free-form QA datasets with 560k training and 70k evaluation instances annotated with correctness and self-consistency signals. Experiments show that EliCal achieves near-optimal alignment with only 1k correctness annotations (0.18% of full supervision) and better alignment performance on unseen MMLU tasks than the calibration-only baseline, offering a scalable solution toward universal honesty alignment in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.15346", "title": "When to Ensemble: Identifying Token-Level Points for Stable and Fast LLM Ensembling", "authors": [ "Heecheol Yun", "Kwangmin Ki", "Junghyun Lee", "Eunho Yang" ], "github": "https://github.com/yoon6503/SAFE", "project_page": "", "abstract": "Ensembling Large Language Models (LLMs) has gained attention as a promising approach to surpass the performance of individual models by leveraging their complementary strengths. In particular, aggregating models' next-token probability distributions to select the next token has been shown to be effective in various tasks. However, while successful for short-form answers, its application to long-form generation remains underexplored. In this paper, we show that using existing ensemble methods in long-form generation requires a careful choice of ensembling positions, since the standard practice of ensembling at every token often degrades performance. We identify two key factors for determining these positions: tokenization mismatch across models and consensus in their next-token probability distributions. Based on this, we propose SAFE, (Stable And Fast LLM Ensembling), a framework that selectively ensembles by jointly considering these factors. To further improve stability, we introduce a probability sharpening strategy that consolidates probabilities spread across multiple sub-word tokens representing the same word into a single representative token. Our experiments on diverse benchmarks, including MATH500 and BBH, demonstrate that SAFE outperforms existing methods in both accuracy and efficiency, with gains achieved even when ensembling fewer than 1% of tokens.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yoon6503/SAFE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.15021", "title": "Constantly Improving Image Models Need Constantly Improving Benchmarks", "authors": [ "Jiaxin Ge", "Grace Luo", "Heekyung Lee", "Nishant Malpani", "Long Lian", "XuDong Wang", "Aleksander Holynski", "Trevor Darrell", "Sewon Min", "David M. Chan" ], "github": "", "project_page": "", "abstract": "Recent advances in image generation, often driven by proprietary systems like GPT-4o Image Gen, regularly introduce new capabilities that reshape how users interact with these models. Existing benchmarks often lag behind and fail to capture these emerging use cases, leaving a gap between community perceptions of progress and formal evaluation. To address this, we present ECHO, a framework for constructing benchmarks directly from real-world evidence of model use: social media posts that showcase novel prompts and qualitative user judgments. Applying this framework to GPT-4o Image Gen, we construct a dataset of over 31,000 prompts curated from such posts. Our analysis shows that ECHO (1) discovers creative and complex tasks absent from existing benchmarks, such as re-rendering product labels across languages or generating receipts with specified totals, (2) more clearly distinguishes state-of-the-art models from alternatives, and (3) surfaces community feedback that we use to inform the design of metrics for model quality (e.g., measuring observed shifts in color, identity, and structure). Our website is at https://echo-bench.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17793", "title": "Foundational Automatic Evaluators: Scaling Multi-Task Generative Evaluator Training for Reasoning-Centric Domains", "authors": [ "Austin Xu", "Xuan-Phi Nguyen", "Yilun Zhou", "Chien-Sheng Wu", "Caiming Xiong", "Shafiq Joty" ], "github": "", "project_page": "", "abstract": "Finetuning specialized generative evaluators has emerged as a popular paradigm to meet the increasing demand for scalable evaluation during both training and test-time. However, recent work has largely focused on applying new methodology, such as reinforcement learning (RL), to training evaluators, shying away from large-scale, data-driven development. In this work, we focus on data scaling, curating a set of 2.5M samples spanning five unique evaluation tasks (pairwise, step-level, reference-free and reference-based verification, and single rating) and multiple domains focused on reasoning evaluation. With our data, we train Foundational Automatic Reasoning Evaluators (FARE), a family of 8B and 20B (with 3.6B active) parameter evaluators, with a simple iterative rejection-sampling supervised finetuning (SFT) approach. FARE-8B challenges larger specialized RL-trained evaluators and FARE-20B sets the new standard for open-source evaluators, surpassing specialized 70B+ evaluators. Beyond static benchmarks, we evaluate FARE in real-world tasks: As inference-time rerankers, FARE-20B achieves near-oracle performance on MATH. As verifiers in RL training, FARE improves the downstream RL-trained model performance by up to 14.1% vs. string-matching verifiers. When initialized from FARE, a continually-finetuned FARE-Code outperforms gpt-oss-20B by 65% on evaluating test-case quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.15821", "title": "Chronos-2: From Univariate to Universal Forecasting", "authors": [ "Abdul Fatir Ansari", "Oleksandr Shchur", "Jaris K\u00fcken", "Andreas Auer", "Boran Han", "Pedro Mercado", "Syama Sundar Rangapuram", "Huibin Shen", "Lorenzo Stella", "Xiyuan Zhang", "Mononito Goswami", "Shubham Kapoor", "Danielle C. Maddix", "Pablo Guerron", "Tony Hu", "Junming Yin", "Nick Erickson", "Prateek Mutalik Desai", "Hao Wang", "Huzefa Rangwala", "George Karypis", "Yuyang Wang", "Michael Bohlke-Schneider" ], "github": "", "project_page": "", "abstract": "Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training. However, existing approaches largely focus on univariate forecasting, limiting their applicability in real-world scenarios where multivariate data and covariates play a crucial role. We present Chronos-2, a pretrained model capable of handling univariate, multivariate, and covariate-informed forecasting tasks in a zero-shot manner. Chronos-2 employs a group attention mechanism that facilitates in-context learning (ICL) through efficient information sharing across multiple time series within a group, which may represent sets of related series, variates of a multivariate series, or targets and covariates in a forecasting task. These general capabilities are achieved through training on synthetic datasets that impose diverse multivariate structures on univariate series. Chronos-2 delivers state-of-the-art performance across three comprehensive benchmarks: fev-bench, GIFT-Eval, and Chronos Benchmark II. On fev-bench, which emphasizes multivariate and covariate-informed forecasting, Chronos-2's universal ICL capabilities lead to substantial improvements over existing models. On tasks involving covariates, it consistently outperforms baselines by a wide margin. Case studies in the energy and retail domains further highlight its practical advantages. The in-context learning capabilities of Chronos-2 establish it as a general-purpose forecasting model that can be used \"as is\" in real-world forecasting pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16136", "title": "GuideFlow3D: Optimization-Guided Rectified Flow For Appearance Transfer", "authors": [ "Sayan Deb Sarkar", "Sinisa Stekovic", "Vincent Lepetit", "Iro Armeni" ], "github": "https://github.com/GradientSpaces/GuideFlow3D", "project_page": "", "abstract": "Transferring appearance to 3D assets using different representations of the appearance object - such as images or text - has garnered interest due to its wide range of applications in industries like gaming, augmented reality, and digital content creation. However, state-of-the-art methods still fail when the geometry between the input and appearance objects is significantly different. A straightforward approach is to directly apply a 3D generative model, but we show that this ultimately fails to produce appealing results. Instead, we propose a principled approach inspired by universal guidance. Given a pretrained rectified flow model conditioned on image or text, our training-free method interacts with the sampling process by periodically adding guidance. This guidance can be modeled as a differentiable loss function, and we experiment with two different types of guidance including part-aware losses for appearance and self-similarity. Our experiments show that our approach successfully transfers texture and geometric details to the input 3D asset, outperforming baselines both qualitatively and quantitatively. We also show that traditional metrics are not suitable for evaluating the task due to their inability of focusing on local details and comparing dissimilar inputs, in absence of ground truth data. We thus evaluate appearance transfer quality with a GPT-based system objectively ranking outputs, ensuring robust and human-like assessment, as further confirmed by our user study. Beyond showcased scenarios, our method is general and could be extended to different types of diffusion models and guidance functions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GradientSpaces/GuideFlow3D", "github_api_added_by": "auto" }, { "date": "2025-10-21", "arxiv_id": "2510.16641", "title": "MultiVerse: A Multi-Turn Conversation Benchmark for Evaluating Large Vision and Language Models", "authors": [ "Young-Jun Lee", "Byung-Kwan Lee", "Jianshu Zhang", "Yechan Hwang", "Byungsoo Ko", "Han-Gyu Kim", "Dongyu Yao", "Xuankun Rong", "Eojin Joo", "Seung-Ho Han", "Bowon Ko", "Ho-Jin Choi" ], "github": "", "project_page": "", "abstract": "Vision-and-Language Models (VLMs) have shown impressive capabilities on single-turn benchmarks, yet real-world applications often demand more intricate multi-turn dialogues. Existing multi-turn datasets (e.g, MMDU, ConvBench) only partially capture the breadth and depth of conversational scenarios encountered by users. In this work, we introduce MultiVerse, a novel multi-turn conversation benchmark featuring 647 dialogues - each averaging four turns - derived from a diverse set of 12 popular VLM evaluation benchmarks. With 484 tasks and 484 interaction goals, MultiVerse covers a wide range of topics, from factual knowledge and perception to advanced reasoning tasks such as mathematics and coding. To facilitate robust assessment, we propose a checklist-based evaluation method that leverages GPT-4o as the automated evaluator, measuring performance across 37 key aspects, including perceptual accuracy, linguistic clarity, and factual correctness. We evaluate 18 VLMs on MultiVerse, revealing that even the strongest models (e.g., GPT-4o) achieve only a 50% success rate in complex multi-turn conversations, highlighting the dataset's challenging nature. Notably, we find that providing full dialogue context significantly enhances performance for smaller or weaker models, emphasizing the importance of in-context learning. We believe MultiVerse is a landscape of evaluating multi-turn interaction abilities for VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.15768", "title": "On Non-interactive Evaluation of Animal Communication Translators", "authors": [ "Orr Paradise", "David F. Gruber", "Adam Tauman Kalai" ], "github": "", "project_page": "", "abstract": "If you had an AI Whale-to-English translator, how could you validate whether or not it is working? Does one need to interact with the animals or rely on grounded observations such as temperature? We provide theoretical and proof-of-concept experimental evidence suggesting that interaction and even observations may not be necessary for sufficiently complex languages. One may be able to evaluate translators solely by their English outputs, offering potential advantages in terms of safety, ethics, and cost. This is an instance of machine translation quality evaluation (MTQE) without any reference translations available. A key challenge is identifying ``hallucinations,'' false translations which may appear fluent and plausible. We propose using segment-by-segment translation together with the classic NLP shuffle test to evaluate translators. The idea is to translate animal communication, turn by turn, and evaluate how often the resulting translations make more sense in order than permuted. Proof-of-concept experiments on data-scarce human languages and constructed languages demonstrate the potential utility of this evaluation methodology. These human-language experiments serve solely to validate our reference-free metric under data scarcity. It is found to correlate highly with a standard evaluation based on reference translations, which are available in our experiments. We also perform a theoretical analysis suggesting that interaction may not be necessary nor efficient in the early stages of learning to translate.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17431", "title": "Agentic Reinforcement Learning for Search is Unsafe", "authors": [ "Yushi Yang", "Shreyansh Padarha", "Andrew Lee", "Adam Mahdi" ], "github": "https://github.com/Yushi-Y/RL-search-safety", "project_page": "", "abstract": "Agentic reinforcement learning (RL) trains large language models to autonomously call tools during reasoning, with search as the most common application. These models excel at multi-step reasoning tasks, but their safety properties are not well understood. In this study, we show that RL-trained search models inherit refusal from instruction tuning and often deflect harmful requests by turning them into safe queries. However, this safety is fragile. Two simple attacks, one that forces the model to begin response with search (Search attack), another that encourages models to repeatedly search (Multi-search attack), trigger cascades of harmful searches and answers. Across two model families (Qwen, Llama) with both local and web search, these attacks lower refusal rates by up to 60.0%, answer safety by 82.5%, and search-query safety by 82.4%. The attacks succeed by triggering models to generate harmful, request-mirroring search queries before they can generate the inherited refusal tokens. This exposes a core weakness of current RL training: it rewards continued generation of effective queries without accounting for their harmfulness. As a result, RL search models have vulnerabilities that users can easily exploit, making it urgent to develop safety-aware agentic RL pipelines optimising for safe search.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Yushi-Y/RL-search-safety", "github_api_added_by": "auto" }, { "date": "2025-10-21", "arxiv_id": "2510.17715", "title": "QueST: Incentivizing LLMs to Generate Difficult Problems", "authors": [ "Hanxu Hu", "Xingxing Zhang", "Jannis Vamvas", "Rico Sennrich", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Large Language Models have achieved strong performance on reasoning tasks, solving competition-level coding and math problems. However, their scalability is limited by human-labeled datasets and the lack of large-scale, challenging coding problem training data. Existing competitive coding datasets contain only thousands to tens of thousands of problems. Previous synthetic data generation methods rely on either augmenting existing instruction datasets or selecting challenging problems from human-labeled data. In this paper, we propose QueST, a novel framework which combines difficulty-aware graph sampling and difficulty-aware rejection fine-tuning that directly optimizes specialized generators to create challenging coding problems. Our trained generators demonstrate superior capability compared to even GPT-4o at creating challenging problems that benefit downstream performance. We leverage QueST to generate large-scale synthetic coding problems, which we then use to distill from strong teacher models with long chain-of-thought or to conduct reinforcement learning for smaller models, proving effective in both scenarios. Our distillation experiments demonstrate significant performance gains. Specifically, after fine-tuning Qwen3-8B-base on 100K difficult problems generated by QueST, we surpass the performance of the original Qwen3-8B on LiveCodeBench. With an additional 112K examples (i.e., 28K human-written problems paired with multiple synthetic solutions), our 8B model matches the performance of the much larger DeepSeek-R1-671B. These findings indicate that generating complex problems via QueST offers an effective and scalable approach to advancing the frontiers of competitive coding and reasoning for large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.06471", "title": "Test-Time Scaling of Reasoning Models for Machine Translation", "authors": [ "Zihao Li", "Shaoxiong Ji", "J\u00f6rg Tiedemann" ], "github": "", "project_page": "", "abstract": "Test-time scaling (TTS) has enhanced the performance of Reasoning Models (RMs) on various tasks such as math and coding, yet its efficacy in machine translation (MT) remains underexplored. This paper investigates whether increased inference-time computation improves translation quality. We evaluate 12 RMs across a diverse suite of MT benchmarks spanning multiple domains, examining three scenarios: direct translation, forced-reasoning extrapolation, and post-editing. Our findings show that for general-purpose RMs, TTS provides limited and inconsistent benefits for direct translation, with performance quickly plateauing. However, the effectiveness of TTS is unlocked by domain-specific fine-tuning, which aligns a model's reasoning process with task requirements, leading to consistent improvements up to an optimal, self-determined reasoning depth. We also find that forcing a model to reason beyond its natural stopping point consistently degrades translation quality. In contrast, TTS proves highly effective in a post-editing context, reliably turning self-correction into a beneficial process. These results indicate that the value of inference-time computation in MT lies not in enhancing single-pass translation with general models, but in targeted applications like multi-step, self-correction workflows and in conjunction with task-specialized models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16727", "title": "Beacon: Single-Turn Diagnosis and Mitigation of Latent Sycophancy in Large Language Models", "authors": [ "Sanskar Pandey", "Ruhaan Chopra", "Angkul Puniya", "Sohom Pal" ], "github": "", "project_page": "", "abstract": "Large language models internalize a structural trade-off between truthfulness and obsequious flattery, emerging from reward optimization that conflates helpfulness with polite submission. This latent bias, known as sycophancy, manifests as a preference for user agreement over principled reasoning. We introduce Beacon, a single-turn forced-choice benchmark that isolates this bias independent of conversational context, enabling precise measurement of the tension between factual accuracy and submissive bias. Evaluations across twelve state-of-the-art models reveal that sycophancy decomposes into stable linguistic and affective sub-biases, each scaling with model capacity. We further propose prompt-level and activation-level interventions that modulate these biases in opposing directions, exposing the internal geometry of alignment as a dynamic manifold between truthfulness and socially compliant judgment. Beacon reframes sycophancy as a measurable form of normative misgeneralization, providing a reproducible foundation for studying and mitigating alignment drift in large-scale generative systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16499", "title": "Automated Composition of Agents: A Knapsack Approach for Agentic Component Selection", "authors": [ "Michelle Yuan", "Khushbu Pahwa", "Shuaichen Chang", "Mustafa Kaba", "Jiarong Jiang", "Xiaofei Ma", "Yi Zhang", "Monica Sunkara" ], "github": "", "project_page": "", "abstract": "Designing effective agentic systems requires the seamless composition and integration of agents, tools, and models within dynamic and uncertain environments. Most existing methods rely on static, semantic retrieval approaches for tool or agent discovery. However, effective reuse and composition of existing components remain challenging due to incomplete capability descriptions and the limitations of retrieval methods. Component selection suffers because the decisions are not based on capability, cost, and real-time utility. To address these challenges, we introduce a structured, automated framework for agentic system composition that is inspired by the knapsack problem. Our framework enables a composer agent to systematically identify, select, and assemble an optimal set of agentic components by jointly considering performance, budget constraints, and compatibility. By dynamically testing candidate components and modeling their utility in real-time, our approach streamlines the assembly of agentic systems and facilitates scalable reuse of resources. Empirical evaluation with Claude 3.5 Sonnet across five benchmarking datasets shows that our online-knapsack-based composer consistently lies on the Pareto frontier, achieving higher success rates at significantly lower component costs compared to our baselines. In the single-agent setup, the online knapsack composer shows a success rate improvement of up to 31.6% in comparison to the retrieval baselines. In multi-agent systems, the online knapsack composer increases success rate from 37% to 87% when agents are selected from an agent inventory of 100+ agents. The substantial performance gap confirms the robust adaptability of our method across diverse domains and budget constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16276", "title": "What Limits Agentic Systems Efficiency?", "authors": [ "Song Bian", "Minghao Yan", "Anand Jayarajan", "Gennady Pekhimenko", "Shivaram Venkataraman" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated strong reasoning capabilities. To further enhance LLM capabilities, recent agentic systems, such as Deep Research, incorporate web interactions into LLM reasoning to mitigate uncertainties and reduce potential errors. However, existing research predominantly focuses on reasoning performance, often neglecting the efficiency of agentic systems. In this work, we present a comprehensive empirical study that identifies efficiency bottlenecks in web-interactive agentic systems. We decompose end-to-end latency into two primary components: LLM API latency and web environment latency. We conduct a comprehensive empirical study across 15 models and 5 providers to demonstrate high variability in API-based agentic systems. We observe that web environment latency can contribute as much as 53.7% to the overall latency in a web-based agentic system. To improve latency, we propose SpecCache, a caching framework augmented with speculative execution that can reduce web environment overhead. Extensive evaluations on two standard benchmarks show that our approach improves the cache hit rate by up to 58x compared to a random caching strategy, while reducing web environment overhead by up to 3.2x, without degrading agentic system performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16449", "title": "TrajSelector: Harnessing Latent Representations for Efficient and Effective Best-of-N in Large Reasoning Model", "authors": [ "Bin Yu", "Xinming Wang", "Shijie Lian", "Haotian Li", "Changti Wu", "Ruina Hu", "Bailing Wang", "Yuliang Wei", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have shown remarkable progress in complex reasoning tasks, largely enabled by test-time scaling (TTS) paradigms that allocate additional compute during inference. Among these, external TTS (particularly the Best-of-N selection paradigm) yields scalable performance improvements by selecting from multiple independently generated reasoning trajectories. However, this approach faces key limitations: (i) the high computational overhead of deploying process reward models, (ii) the underutilization of the LLM's intrinsic latent representations. We introduce TrajSelector, an efficient and effective Best-of-N framework that exploit the hidden states in the sampler LLM for process-level scoring. A lightweight verifier (with only 0.6B parameters) evaluates the quality of step-wise trajectory, and then aggregates these scores to identify the optimal reasoning trajectory. Our framework employs a fully data-driven, end-to-end training recipe that eliminates reliance on massive step-level annotations. Experiential results across five benchmarks demonstrate that TrajSelector delivers consistent performance gains. In Best-of-32 settings, it surpasses majority voting by 4.61% accuracy and outperforms existing process reward models by 4.31% to 12.21%, all while maintaining lower inference costs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.17960", "title": "AION-1: Omnimodal Foundation Model for Astronomical Sciences", "authors": [ "Liam Parker", "Francois Lanusse", "Jeff Shen", "Ollie Liu", "Tom Hehir", "Leopoldo Sarra", "Lucas Meyer", "Micah Bowles", "Sebastian Wagner-Carena", "Helen Qu", "Siavash Golkar", "Alberto Bietti", "Hatim Bourfoune", "Nathan Casserau", "Pierre Cornette", "Keiya Hirashima", "Geraud Krawezik", "Ruben Ohana", "Nicholas Lourie", "Michael McCabe", "Rudy Morel", "Payel Mukhopadhyay", "Mariel Pettee", "Bruno Regaldo-Saint Blancard", "Kyunghyun Cho", "Miles Cranmer", "Shirley Ho" ], "github": "", "project_page": "", "abstract": "While foundation models have shown promise across a variety of fields, astronomy still lacks a unified framework for joint modeling across its highly diverse data modalities. In this paper, we present AION-1, a family of large-scale multimodal foundation models for astronomy. AION-1 integrates heterogeneous imaging, spectroscopic, and scalar data using a two-stage architecture: modality-specific tokenization followed by transformer-based masked modeling of cross-modal token sequences. The model is pretrained on five large-scale surveys: Legacy Survey, Hyper Suprime-Cam (HSC), Sloan Digital Sky Survey (SDSS), Dark Energy Spectroscopic Instrument (DESI), and Gaia. These span more than 200 million observations of stars, galaxies, and quasars. With a single frozen encoder, AION-1 achieves strong results on a broad suite of downstream tasks, including galaxy and stellar property estimation, galaxy morphology classification, similarity-based retrieval, galaxy image segmentation, and spectral super-resolution. We release AION-1 model variants ranging from 300 M to 3.1 B parameters. Beyond astronomy, AION-1 provides a scalable blueprint for multimodal scientific foundation models that can seamlessly integrate noisy, instrument-specific observations. All code, tokenizers, pretrained weights, and a lightweight evaluation suite are released under an open-source license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-21", "arxiv_id": "2510.16380", "title": "MoReBench: Evaluating Procedural and Pluralistic Moral Reasoning in Language Models, More than Outcomes", "authors": [ "Yu Ying Chiu", "Michael S. Lee", "Rachel Calcott", "Brandon Handoko", "Paul de Font-Reaulx", "Paula Rodriguez", "Chen Bo Calvin Zhang", "Ziwen Han", "Udari Madhushani Sehwag", "Yash Maurya", "Christina Q Knight", "Harry R. Lloyd", "Florence Bacus", "Mantas Mazeika", "Bing Liu", "Yejin Choi", "Mitchell L Gordon", "Sydney Levine" ], "github": "", "project_page": "", "abstract": "As AI systems progress, we rely more on them to make decisions with us and for us. To ensure that such decisions are aligned with human values, it is imperative for us to understand not only what decisions they make but also how they come to those decisions. Reasoning language models, which provide both final responses and (partially transparent) intermediate thinking traces, present a timely opportunity to study AI procedural reasoning. Unlike math and code problems which often have objectively correct answers, moral dilemmas are an excellent testbed for process-focused evaluation because they allow for multiple defensible conclusions. To do so, we present MoReBench: 1,000 moral scenarios, each paired with a set of rubric criteria that experts consider essential to include (or avoid) when reasoning about the scenarios. MoReBench contains over 23 thousand criteria including identifying moral considerations, weighing trade-offs, and giving actionable recommendations to cover cases on AI advising humans moral decisions as well as making moral decisions autonomously. Separately, we curate MoReBench-Theory: 150 examples to test whether AI can reason under five major frameworks in normative ethics. Our results show that scaling laws and existing benchmarks on math, code, and scientific reasoning tasks fail to predict models' abilities to perform moral reasoning. Models also show partiality towards specific moral frameworks (e.g., Benthamite Act Utilitarianism and Kantian Deontology), which might be side effects of popular training paradigms. Together, these benchmarks advance process-focused reasoning evaluation towards safer and more transparent AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18866", "title": "LightMem: Lightweight and Efficient Memory-Augmented Generation", "authors": [ "Jizhan Fang", "Xinle Deng", "Haoming Xu", "Ziyan Jiang", "Yuqi Tang", "Ziwen Xu", "Shumin Deng", "Yunzhi Yao", "Mengru Wang", "Shuofei Qiao", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/LightMem", "project_page": "", "abstract": "Despite their remarkable capabilities, Large Language Models (LLMs) struggle to effectively leverage historical interaction information in dynamic and complex environments. Memory systems enable LLMs to move beyond stateless interactions by introducing persistent information storage, retrieval, and utilization mechanisms. However, existing memory systems often introduce substantial time and computational overhead. To this end, we introduce a new memory system called LightMem, which strikes a balance between the performance and efficiency of memory systems. Inspired by the Atkinson-Shiffrin model of human memory, LightMem organizes memory into three complementary stages. First, cognition-inspired sensory memory rapidly filters irrelevant information through lightweight compression and groups information according to their topics. Next, topic-aware short-term memory consolidates these topic-based groups, organizing and summarizing content for more structured access. Finally, long-term memory with sleep-time update employs an offline procedure that decouples consolidation from online inference. Experiments on LongMemEval with GPT and Qwen backbones show that LightMem outperforms strong baselines in accuracy (up to 10.9% gains) while reducing token usage by up to 117x, API calls by up to 159x, and runtime by over 12x. The code is available at https://github.com/zjunlp/LightMem.", "github_manual": "", "github_regex": "https://github.com/zjunlp/LightMem", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18855", "title": "Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale Thinking Model", "authors": [ "Ling Team", "Anqi Shen", "Baihui Li", "Bin Hu", "Bin Jing", "Cai Chen", "Chao Huang", "Chao Zhang", "Chaokun Yang", "Cheng Lin", "Chengyao Wen", "Congqi Li", "Deng Zhao", "Dingbo Yuan", "Donghai You", "Fagui Mao", "Fanzhuang Meng", "Feng Xu", "Guojie Li", "Guowei Wang", "Hao Dai", "Haonan Zheng", "Hong Liu", "Jia Guo", "Jiaming Liu", "Jian Liu", "Jianhao Fu", "Jiannan Shi", "Jianwen Wang", "Jianxin Lai", "Jin Yang", "Jun Mei", "Jun Zhou", "Junbo Zhao", "Junping Zhao", "Kuan Xu", "Le Su", "Lei Chen", "Li Tang", "Liang Jiang", "Liangcheng Fu", "Lianhao Xu", "Linfeng Shi", "Lisha Liao", "Longfei Zheng", "Meng Li", "Mingchun Chen", "Qi Zuo", "Qiang Cheng", "Qianggang Cao", "Qitao Shi", "Quanrui Guo", "Senlin Zhu", "Shaofei Wang", "Shaomian Zheng", "Shuaicheng Li", "Shuwei Gu", "Siba Chen", "Tao Wu", "Tao Zhang", "Tianyu Zhang", "Tianyu Zhou", "Tiwei Bie", "Tongkai Yang", "Wang Hong", "Wang Ren", "Weihua Chen", "Wenbo Yu", "Wengang Zheng", "Xiangchun Wang", "Xiaodong Yan", "Xiaopei Wan", "Xin Zhao", "Xinyu Kong", "Xinyu Tang", "Xudong Han", "Xudong Wang", "Xuemin Yang", "Xueyu Hu", "Yalin Zhang", "Yan Sun", "Yicheng Shan", "Yilong Wang", "Yingying Xu", "Yongkang Liu", "Yongzhen Guo", "Yuanyuan Wang", "Yuchen Yan", "Yuefan Wang", "Yuhong Guo", "Zehuan Li", "Zhankai Xu", "Zhe Li", "Zhenduo Zhang", "Zhengke Gui", "Zhenxuan Pan", "Zhenyu Huang", "Zhenzhong Lan", "Zhiqiang Ding", "Zhiqiang Zhang", "Zhixun Li", "Zhizhen Liu", "Zihao Wang", "Zujie Wen" ], "github": "https://github.com/inclusionAI/Ring-V2", "project_page": "", "abstract": "We present Ring-1T, the first open-source, state-of-the-art thinking model with a trillion-scale parameter. It features 1 trillion total parameters and activates approximately 50 billion per token. Training such models at a trillion-parameter scale introduces unprecedented challenges, including train-inference misalignment, inefficiencies in rollout processing, and bottlenecks in the RL system. To address these, we pioneer three interconnected innovations: (1) IcePop stabilizes RL training via token-level discrepancy masking and clipping, resolving instability from training-inference mismatches; (2) C3PO++ improves resource utilization for long rollouts under a token budget by dynamically partitioning them, thereby obtaining high time efficiency; and (3) ASystem, a high-performance RL framework designed to overcome the systemic bottlenecks that impede trillion-parameter model training. Ring-1T delivers breakthrough results across critical benchmarks: 93.4 on AIME-2025, 86.72 on HMMT-2025, 2088 on CodeForces, and 55.94 on ARC-AGI-v1. Notably, it attains a silver medal-level result on the IMO-2025, underscoring its exceptional reasoning capabilities. By releasing the complete 1T parameter MoE model to the community, we provide the research community with direct access to cutting-edge reasoning capabilities. This contribution marks a significant milestone in democratizing large-scale reasoning intelligence and establishes a new baseline for open-source model performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/inclusionAI/Ring-V2", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.16880", "title": "Chem-R: Learning to Reason as a Chemist", "authors": [ "Weida Wang", "Benteng Chen", "Di Zhang", "Wanhao Liu", "Shuchen Pu", "Ben Gao", "Jin Zeng", "Lei Bai", "Wanli Ouyang", "Xiaoyong Wei", "Tianshu Yu", "Tianfan Fu", "Shuzhou Sun", "Jiatong Li", "Zifu Wang", "Yuqiang Li", "Shufei Zhang" ], "github": "https://github.com/davidweidawang/Chem-R", "project_page": "", "abstract": "Although large language models (LLMs) have significant potential to advance chemical discovery, current LLMs lack core chemical knowledge, produce unreliable reasoning trajectories, and exhibit suboptimal performance across diverse chemical tasks. To address these challenges, we propose Chem-R, a generalizable Chemical Reasoning model designed to emulate the deliberative processes of chemists. Chem-R is trained through a three-phase framework that progressively builds advanced reasoning capabilities, including: 1) Chemical Foundation Training, which establishes core chemical knowledge. 2) Chemical Reasoning Protocol Distillation, incorporating structured, expert-like reasoning traces to guide systematic and reliable problem solving. 3) Multi-task Group Relative Policy Optimization that optimizes the model for balanced performance across diverse molecular- and reaction-level tasks. This structured pipeline enables Chem-R to achieve state-of-the-art performance on comprehensive benchmarks, surpassing leading large language models, including Gemini-2.5-Pro and DeepSeek-R1, by up to 46% on molecular tasks and 66% on reaction tasks. Meanwhile, Chem-R also consistently outperforms the existing chemical foundation models across both molecular and reaction level tasks. These results highlight Chem-R's robust generalization, interpretability, and potential as a foundation for next-generation AI-driven chemical discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/davidweidawang/Chem-R", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18876", "title": "Grasp Any Region: Towards Precise, Contextual Pixel Understanding for Multimodal LLMs", "authors": [ "Haochen Wang", "Yuhao Wang", "Tao Zhang", "Yikang Zhou", "Yanwei Li", "Jiacong Wang", "Ye Tian", "Jiahao Meng", "Zilong Huang", "Guangcan Mai", "Anran Wang", "Yunhai Tong", "Zhuochen Wang", "Xiangtai Li", "Zhaoxiang Zhang" ], "github": "", "project_page": "", "abstract": "While Multimodal Large Language Models (MLLMs) excel at holistic understanding, they struggle in capturing the dense world with complex scenes, requiring fine-grained analysis of intricate details and object inter-relationships. Region-level MLLMs have been a promising step. However, previous attempts are generally optimized to understand given regions in isolation, neglecting crucial global contexts. To address this, we introduce Grasp Any Region (GAR) for comprehen- sive region-level visual understanding. Empowered by an effective RoI-aligned feature replay technique, GAR supports (1) precise perception by leveraging necessary global contexts, and (2) modeling interactions between multiple prompts. Together, it then naturally achieves (3) advanced compositional reasoning to answer specific free-form questions about any region, shifting the paradigm from passive description to active dialogue. Moreover, we construct GAR-Bench, which not only provides a more accurate evaluation of single-region comprehension, but also, more importantly, measures interactions and complex reasoning across multiple regions. Extensive experiments have demonstrated that GAR-1B not only maintains the state-of-the-art captioning capabilities, e.g., outperforming DAM-3B +4.5 on DLC-Bench, but also excels at modeling relationships between multiple prompts with advanced comprehension capabilities, even surpassing InternVL3-78B on GAR-Bench-VQA. More importantly, our zero-shot GAR-8B even outperforms in-domain VideoRefer-7B on VideoRefer-BenchQ, indicating its strong capabilities can be easily transferred to videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18795", "title": "ProCLIP: Progressive Vision-Language Alignment via LLM-based Embedder", "authors": [ "Xiaoxing Hu", "Kaicheng Yang", "Ziyong Feng", "Qi Ming", "Zonghao Guo", "Xiang An", "Ziyong Feng", "Junchi Yan", "Xue Yang" ], "github": "https://github.com/VisionXLab/ProCLIP", "project_page": "", "abstract": "The original CLIP text encoder is limited by a maximum input length of 77 tokens, which hampers its ability to effectively process long texts and perform fine-grained semantic understanding. In addition, the CLIP text encoder lacks support for multilingual inputs. All these limitations significantly restrict its applicability across a broader range of tasks. Recent studies have attempted to replace the CLIP text encoder with an LLM-based embedder to enhance its ability in processing long texts, multilingual understanding, and fine-grained semantic comprehension. However, because the representation spaces of LLMs and the vision-language space of CLIP are pretrained independently without alignment priors, direct alignment using contrastive learning can disrupt the intrinsic vision-language alignment in the CLIP image encoder, leading to an underutilization of the knowledge acquired during pre-training. To address this challenge, we propose ProCLIP, a curriculum learning-based progressive vision-language alignment framework to effectively align the CLIP image encoder with an LLM-based embedder. Specifically, ProCLIP first distills knowledge from CLIP's text encoder into the LLM-based embedder to leverage CLIP's rich pretrained knowledge while establishing initial alignment between the LLM embedder and CLIP image encoder. Subsequently, ProCLIP further aligns the CLIP image encoder with the LLM-based embedder through image-text contrastive tuning, employing self-distillation regularization to avoid overfitting. To achieve a more effective alignment, instance semantic alignment loss and embedding structure alignment loss are employed during representation inheritance and contrastive tuning. The Code is available at https://github.com/VisionXLab/ProCLIP", "github_manual": "", "github_regex": "https://github.com/VisionXLab/ProCLIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18726", "title": "IF-VidCap: Can Video Caption Models Follow Instructions?", "authors": [ "Shihao Li", "Yuanxing Zhang", "Jiangtao Wu", "Zhide Lei", "Yiwen He", "Runzhe Wen", "Chenxi Liao", "Chengkang Jiang", "An Ping", "Shuo Gao", "Suhan Wang", "Zhaozhou Bian", "Zijun Zhou", "Jingyi Xie", "Jiayi Zhou", "Jing Wang", "Yifan Yao", "Weihao Xie", "Yingshui Tan", "Yanghai Wang", "Qianqian Xie", "Zhaoxiang Zhang", "Jiaheng Liu" ], "github": "", "project_page": "", "abstract": "Although Multimodal Large Language Models (MLLMs) have demonstrated proficiency in video captioning, practical applications require captions that follow specific user instructions rather than generating exhaustive, unconstrained descriptions. Current benchmarks, however, primarily assess descriptive comprehensiveness while largely overlooking instruction-following capabilities. To address this gap, we introduce IF-VidCap, a new benchmark for evaluating controllable video captioning, which contains 1,400 high-quality samples. Distinct from existing video captioning or general instruction-following benchmarks, IF-VidCap incorporates a systematic framework that assesses captions on two dimensions: format correctness and content correctness. Our comprehensive evaluation of over 20 prominent models reveals a nuanced landscape: despite the continued dominance of proprietary models, the performance gap is closing, with top-tier open-source solutions now achieving near-parity. Furthermore, we find that models specialized for dense captioning underperform general-purpose MLLMs on complex instructions, indicating that future work should simultaneously advance both descriptive richness and instruction-following fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18692", "title": "MoGA: Mixture-of-Groups Attention for End-to-End Long Video Generation", "authors": [ "Weinan Jia", "Yuning Lu", "Mengqi Huang", "Hualiang Wang", "Binyuan Huang", "Nan Chen", "Mu Liu", "Jidong Jiang", "Zhendong Mao" ], "github": "", "project_page": "", "abstract": "Long video generation with Diffusion Transformers (DiTs) is bottlenecked by the quadratic scaling of full attention with sequence length. Since attention is highly redundant, outputs are dominated by a small subset of query-key pairs. Existing sparse methods rely on blockwise coarse estimation, whose accuracy-efficiency trade-offs are constrained by block size. This paper introduces Mixture-of-Groups Attention (MoGA), an efficient sparse attention that uses a lightweight, learnable token router to precisely match tokens without blockwise estimation. Through semantic-aware routing, MoGA enables effective long-range interactions. As a kernel-free method, MoGA integrates seamlessly with modern attention stacks, including FlashAttention and sequence parallelism. Building on MoGA, we develop an efficient long video generation model that end-to-end produces minute-level, multi-shot, 480p videos at 24 fps, with a context length of approximately 580k. Comprehensive experiments on various video generation tasks validate the effectiveness of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.17722", "title": "MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues", "authors": [ "Yaning Pan", "Zekun Wang", "Qianqian Xie", "Yongqian Wen", "Yuanxing Zhang", "Guohui Zhang", "Haoxuan Hu", "Zhiyu Pan", "Yibing Huang", "Zhidong Gan", "Yonghong Lin", "An Ping", "Tianhao Peng", "Jiaheng Liu" ], "github": "", "project_page": "", "abstract": "The recent development of Multimodal Large Language Models (MLLMs) has significantly advanced AI's ability to understand visual modalities. However, existing evaluation benchmarks remain limited to single-turn question answering, overlooking the complexity of multi-turn dialogues in real-world scenarios. To bridge this gap, we introduce MT-Video-Bench, a holistic video understanding benchmark for evaluating MLLMs in multi-turn dialogues. Specifically, our MT-Video-Bench mainly assesses six core competencies that focus on perceptivity and interactivity, encompassing 987 meticulously curated multi-turn dialogues from diverse domains. These capabilities are rigorously aligned with real-world applications, such as interactive sports analysis and multi-turn video-based intelligent tutoring. With MT-Video-Bench, we extensively evaluate various state-of-the-art open-source and closed-source MLLMs, revealing their significant performance discrepancies and limitations in handling multi-turn video dialogues. The benchmark will be publicly available to foster future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.17519", "title": "MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation Models", "authors": [ "Yongshun Zhang", "Zhongyi Fan", "Yonghang Zhang", "Zhangzikang Li", "Weifeng Chen", "Zhongwei Feng", "Chaoyue Wang", "Peng Hou", "Anxiang Zeng" ], "github": "https://github.com/Shopee-MUG/MUG-V", "project_page": "", "abstract": "In recent years, large-scale generative models for visual content (e.g., images, videos, and 3D objects/scenes) have made remarkable progress. However, training large-scale video generation models remains particularly challenging and resource-intensive due to cross-modal text-video alignment, the long sequences involved, and the complex spatiotemporal dependencies. To address these challenges, we present a training framework that optimizes four pillars: (i) data processing, (ii) model architecture, (iii) training strategy, and (iv) infrastructure for large-scale video generation models. These optimizations delivered significant efficiency gains and performance improvements across all stages of data preprocessing, video compression, parameter scaling, curriculum-based pretraining, and alignment-focused post-training. Our resulting model, MUG-V 10B, matches recent state-of-the-art video generators overall and, on e-commerce-oriented video generation tasks, surpasses leading open-source baselines in human evaluations. More importantly, we open-source the complete stack, including model weights, Megatron-Core-based large-scale training code, and inference pipelines for video generation and enhancement. To our knowledge, this is the first public release of large-scale video generation training code that exploits Megatron-Core to achieve high training efficiency and near-linear multi-node scaling, details are available in https://github.com/Shopee-MUG/MUG-V{our webpage}.", "github_manual": "", "github_regex": "https://github.com/Shopee-MUG/MUG-V", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18775", "title": "UltraGen: High-Resolution Video Generation with Hierarchical Attention", "authors": [ "Teng Hu", "Jiangning Zhang", "Zihan Su", "Ran Yi" ], "github": "https://github.com/sjtuplayer/UltraGen", "project_page": "", "abstract": "Recent advances in video generation have made it possible to produce visually compelling videos, with wide-ranging applications in content creation, entertainment, and virtual reality. However, most existing diffusion transformer based video generation models are limited to low-resolution outputs (<=720P) due to the quadratic computational complexity of the attention mechanism with respect to the output width and height. This computational bottleneck makes native high-resolution video generation (1080P/2K/4K) impractical for both training and inference. To address this challenge, we present UltraGen, a novel video generation framework that enables i) efficient and ii) end-to-end native high-resolution video synthesis. Specifically, UltraGen features a hierarchical dual-branch attention architecture based on global-local attention decomposition, which decouples full attention into a local attention branch for high-fidelity regional content and a global attention branch for overall semantic consistency. We further propose a spatially compressed global modeling strategy to efficiently learn global dependencies, and a hierarchical cross-window local attention mechanism to reduce computational costs while enhancing information flow across different local windows. Extensive experiments demonstrate that UltraGen can effectively scale pre-trained low-resolution video models to 1080P and even 4K resolution for the first time, outperforming existing state-of-the-art methods and super-resolution based two-stage pipelines in both qualitative and quantitative evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sjtuplayer/UltraGen", "github_api_added_by": "auto" }, { "date": "2025-10-22", "arxiv_id": "2510.18701", "title": "UniGenBench++: A Unified Semantic Evaluation Benchmark for Text-to-Image Generation", "authors": [ "Yibin Wang", "Zhimin Li", "Yuhang Zang", "Jiazi Bu", "Yujie Zhou", "Yi Xin", "Junjun He", "Chunyu Wang", "Qinglin Lu", "Cheng Jin", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Recent progress in text-to-image (T2I) generation underscores the importance of reliable benchmarks in evaluating how accurately generated images reflect the semantics of their textual prompt. However, (1) existing benchmarks lack the diversity of prompt scenarios and multilingual support, both essential for real-world applicability; (2) they offer only coarse evaluations across primary dimensions, covering a narrow range of sub-dimensions, and fall short in fine-grained sub-dimension assessment. To address these limitations, we introduce UniGenBench++, a unified semantic assessment benchmark for T2I generation. Specifically, it comprises 600 prompts organized hierarchically to ensure both coverage and efficiency: (1) spans across diverse real-world scenarios, i.e., 5 main prompt themes and 20 subthemes; (2) comprehensively probes T2I models' semantic consistency over 10 primary and 27 sub evaluation criteria, with each prompt assessing multiple testpoints. To rigorously assess model robustness to variations in language and prompt length, we provide both English and Chinese versions of each prompt in short and long forms. Leveraging the general world knowledge and fine-grained image understanding capabilities of a closed-source Multi-modal Large Language Model (MLLM), i.e., Gemini-2.5-Pro, an effective pipeline is developed for reliable benchmark construction and streamlined model assessment. Moreover, to further facilitate community use, we train a robust evaluation model that enables offline assessment of T2I model outputs. Through comprehensive benchmarking of both open- and closed-sourced T2I models, we systematically reveal their strengths and weaknesses across various aspects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.17045", "title": "Video Reasoning without Training", "authors": [ "Deepak Sridhar", "Kartikeya Bhardwaj", "Jeya Pradha Jeyaraj", "Nuno Vasconcelos", "Ankita Nayak", "Harris Teague" ], "github": "", "project_page": "", "abstract": "Video reasoning using Large Multimodal Models (LMMs) relies on costly reinforcement learning (RL) and verbose chain-of-thought, resulting in substantial computational overhead during both training and inference. Moreover, the mechanisms that control the thinking process in these reasoning models are very limited. In this paper, using entropy of the model's output as a signal, we discover that the high-quality models go through a series of micro-explorations and micro-exploitations which keep the reasoning process grounded (i.e., avoid excessive randomness while the model is exploring or thinking through an answer). We further observe that once this \"thinking\" process is over, more accurate models demonstrate a better convergence by reducing the entropy significantly via a final exploitation phase (i.e., a more certain convergence towards a solution trajectory). We then use these novel, theoretically-grounded insights to tune the model's behavior directly at inference, without using any RL or supervised fine-tuning. Specifically, during inference, our proposed approach called V-Reason (Video-Reason) adapts the value cache of the LMM via a few optimization steps on a small, trainable controller using an entropy-based objective, i.e., no supervision from any dataset or RL is necessary. This tuning improves the model's micro-exploration and exploitation behavior during inference. Our experiments show that our proposed method achieves significant improvements over the base instruction-tuned models across several video reasoning datasets, narrowing the gap with RL-trained models to within 0.6% average accuracy without any training, while offering massive efficiency benefits: output tokens are reduced by 58.6% compared to the RL model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18135", "title": "World-in-World: World Models in a Closed-Loop World", "authors": [ "Jiahan Zhang", "Muqing Jiang", "Nanru Dai", "Taiming Lu", "Arda Uzunoglu", "Shunchi Zhang", "Yana Wei", "Jiahao Wang", "Vishal M. Patel", "Paul Pu Liang", "Daniel Khashabi", "Cheng Peng", "Rama Chellappa", "Tianmin Shu", "Alan Yuille", "Yilun Du", "Jieneng Chen" ], "github": "https://github.com/World-In-World/world-in-world", "project_page": "", "abstract": "Generative world models (WMs) can now simulate worlds with striking visual realism, which naturally raises the question of whether they can endow embodied agents with predictive perception for decision making. Progress on this question has been limited by fragmented evaluation: most existing benchmarks adopt open-loop protocols that emphasize visual quality in isolation, leaving the core issue of embodied utility unresolved, i.e., do WMs actually help agents succeed at embodied tasks? To address this gap, we introduce World-in-World, the first open platform that benchmarks WMs in a closed-loop world that mirrors real agent-environment interactions. World-in-World provides a unified online planning strategy and a standardized action API, enabling heterogeneous WMs for decision making. We curate four closed-loop environments that rigorously evaluate diverse WMs, prioritize task success as the primary metric, and move beyond the common focus on visual quality; we also present the first data scaling law for world models in embodied settings. Our study uncovers three surprises: (1) visual quality alone does not guarantee task success, controllability matters more; (2) scaling post-training with action-observation data is more effective than upgrading the pretrained video generators; and (3) allocating more inference-time compute allows WMs to substantially improve closed-loop performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/World-In-World/world-in-world", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18849", "title": "Towards Faithful and Controllable Personalization via Critique-Post-Edit Reinforcement Learning", "authors": [ "Chenghao Zhu", "Meiling Tao", "Tiannan Wang", "Dongyi Ding", "Yuchen Eleanor Jiang", "Wangchunshu Zhou" ], "github": "", "project_page": "", "abstract": "Faithfully personalizing large language models (LLMs) to align with individual user preferences is a critical but challenging task. While supervised fine-tuning (SFT) quickly reaches a performance plateau, standard reinforcement learning from human feedback (RLHF) also struggles with the nuances of personalization. Scalar-based reward models are prone to reward hacking which leads to verbose and superficially personalized responses. To address these limitations, we propose Critique-Post-Edit, a robust reinforcement learning framework that enables more faithful and controllable personalization. Our framework integrates two key components: (1) a Personalized Generative Reward Model (GRM) that provides multi-dimensional scores and textual critiques to resist reward hacking, and (2) a Critique-Post-Edit mechanism where the policy model revises its own outputs based on these critiques for more targeted and efficient learning. Under a rigorous length-controlled evaluation, our method substantially outperforms standard PPO on personalization benchmarks. Personalized Qwen2.5-7B achieves an average 11\\% win-rate improvement, and personalized Qwen2.5-14B model surpasses the performance of GPT-4.1. These results demonstrate a practical path to faithful, efficient, and controllable personalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18250", "title": "ssToken: Self-modulated and Semantic-aware Token Selection for LLM Fine-tuning", "authors": [ "Xiaohan Qin", "Xiaoxing Wang", "Ning Liao", "Cancheng Zhang", "Xiangdong Zhang", "Mingquan Feng", "Jingzhi Wang", "Junchi Yan" ], "github": "https://github.com/jianke0604/ssToken", "project_page": "", "abstract": "Data quality plays a critical role in enhancing supervised fine-tuning (SFT) for large language models (LLMs), and token-level data selection has emerged as a promising direction for its fine-grained nature. Despite their strong empirical performance, existing token-level selection methods share two key limitations: (1) requiring training or accessing an additional reference model, and (2) relying solely on loss information for token selection, which cannot well preserve semantically important tokens that are not favored by loss-based metrics. To address these challenges, we propose ssToken, a Self-modulated and Semantic-aware Token Selection approach. ssToken leverages readily accessible history models to compute the per-token loss difference with the current model, which serves as a self-modulated signal that enables the model to adaptively select tokens along its optimization trajectory, rather than relying on excess loss from an offline-trained reference model as in prior works. We further introduce a semantic-aware, attention-based token importance estimation metric, orthogonal to loss-based selection and providing complementary semantic information for more effective filtering. Extensive experiments across different model families and scales demonstrate that both self-modulated selection and semantic-aware selection alone outperform full-data fine-tuning, while their integration--ssToken--achieves synergistic gains and further surpasses prior token-level selection methods, delivering performance improvements while maintaining training efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jianke0604/ssToken", "github_api_added_by": "auto" }, { "date": "2025-10-22", "arxiv_id": "2510.18873", "title": "DSI-Bench: A Benchmark for Dynamic Spatial Intelligence", "authors": [ "Ziang Zhang", "Zehan Wang", "Guanghao Zhang", "Weilong Dai", "Yan Xia", "Ziang Yan", "Minjie Hong", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "Reasoning about dynamic spatial relationships is essential, as both observers and objects often move simultaneously. Although vision-language models (VLMs) and visual expertise models excel in 2D tasks and static scenarios, their ability to fully understand dynamic 3D scenarios remains limited. We introduce Dynamic Spatial Intelligence and propose DSI-Bench, a benchmark with nearly 1,000 dynamic videos and over 1,700 manually annotated questions covering nine decoupled motion patterns of observers and objects. Spatially and temporally symmetric designs reduce biases and enable systematic evaluation of models' reasoning about self-motion and object motion. Our evaluation of 14 VLMs and expert models reveals key limitations: models often conflate observer and object motion, exhibit semantic biases, and fail to accurately infer relative relationships in dynamic scenarios. Our DSI-Bench provides valuable findings and insights about the future development of general and expertise models with dynamic spatial intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.15600", "title": "Unleashing Scientific Reasoning for Bio-experimental Protocol Generation via Structured Component-based Reward Mechanism", "authors": [ "Haoran Sun", "Yankai Jiang", "Zhenyu Tang", "Yaning Pan", "Shuang Gu", "Zekai Lin", "Lilong Wang", "Wenjie Lou", "Lei Liu", "Lei Bai", "Xiaosong Wang" ], "github": "", "project_page": "", "abstract": "The foundation of reproducible science lies in protocols that are precise, logically ordered, and executable. The autonomous generation of these protocols through natural language queries could greatly improve the efficiency of the reproduction process. However, current leading large language models (LLMs) often generate incomplete or inconsistent protocols, limiting their utility. To address this limitation, we first introduce SciRecipe, a large-scale dataset of over 12K structured protocols spanning 27 biological subfields and encompassing both comprehension and problem-solving tasks. To further improve protocol generation, we propose the \"Sketch-and-Fill\" paradigm, which separates analysis, structuring, and expression to ensure each step is explicit and verifiable. Complementing this, the structured component-based reward mechanism evaluates step granularity, action order, and semantic fidelity, aligning model optimization with experimental reliability. Building on these components, we develop Thoth, trained through a staged Knowledge-to-Action process that progresses from knowledge acquisition to operational reasoning and ultimately to robust, executable protocol generation. Across multiple benchmarks, Thoth consistently surpasses both proprietary and open-source LLMs, achieving significant improvements in step alignment, logical sequencing, and semantic accuracy. Our approach paves the way for reliable scientific assistants that bridge knowledge with experimental execution. All data, code, and models will be released publicly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18489", "title": "Mono4DGS-HDR: High Dynamic Range 4D Gaussian Splatting from Alternating-exposure Monocular Videos", "authors": [ "Jinfeng Liu", "Lingtong Kong", "Mi Zhou", "Jinwen Chen", "Dan Xu" ], "github": "", "project_page": "", "abstract": "We introduce Mono4DGS-HDR, the first system for reconstructing renderable 4D high dynamic range (HDR) scenes from unposed monocular low dynamic range (LDR) videos captured with alternating exposures. To tackle such a challenging problem, we present a unified framework with two-stage optimization approach based on Gaussian Splatting. The first stage learns a video HDR Gaussian representation in orthographic camera coordinate space, eliminating the need for camera poses and enabling robust initial HDR video reconstruction. The second stage transforms video Gaussians into world space and jointly refines the world Gaussians with camera poses. Furthermore, we propose a temporal luminance regularization strategy to enhance the temporal consistency of the HDR appearance. Since our task has not been studied before, we construct a new evaluation benchmark using publicly available datasets for HDR video reconstruction. Extensive experiments demonstrate that Mono4DGS-HDR significantly outperforms alternative solutions adapted from state-of-the-art methods in both rendering quality and speed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.17928", "title": "EvoSyn: Generalizable Evolutionary Data Synthesis for Verifiable Learning", "authors": [ "He Du", "Bowen Li", "Aijun Yang", "Siyang He", "Qipeng Guo", "Dacheng Tao" ], "github": "https://github.com/kinza99/openevolve", "project_page": "", "abstract": "Reliable verifiable data has become a key driver of capability gains in modern language models, enabling stable reinforcement learning with verifiable rewards and effective distillation that transfers competence across math, coding, and agentic tasks. Yet constructing generalizable synthetic verifiable data remains difficult due to hallucination-prone generation, and weak or trivial verification artifacts that fail to separate strong from weak solutions. Existing approaches often rely on task-specific heuristics or post-hoc filters that do not transfer across domains and lack a principled, universal evaluator of verifiability. In this work, we introduce an evolutionary, task-agnostic, strategy-guided, executably-checkable data synthesis framework that, from minimal seed supervision, jointly synthesizes problems, diverse candidate solutions, and verification artifacts, and iteratively discovers strategies via a consistency-based evaluator that enforces agreement between human-annotated and strategy-induced checks. This pipeline upgrades filtering into principled synthesis: it reliably assembles coherent, verifiable training instances and generalizes without domain-specific rules. Our experiments demonstrate the effectiveness of the proposed approach under both RLVR and model distillation training paradigms. The results show that training with our synthesized data yields significant improvements on both the LiveCodeBench and AgentBench-OS tasks, highlighting the robust generalization of our framework.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kinza99/openevolve", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.14264", "title": "AlphaQuanter: An End-to-End Tool-Orchestrated Agentic Reinforcement Learning Framework for Stock Trading", "authors": [ "Zheye Deng", "Jiashu Wang" ], "github": "https://github.com/AlphaQuanter/AlphaQuanter", "project_page": "", "abstract": "While Large Language Model (LLM) agents show promise in automated trading, they still face critical limitations. Prominent multi-agent frameworks often suffer from inefficiency, produce inconsistent signals, and lack the end-to-end optimization required to learn a coherent strategy from market feedback. To address this, we introduce AlphaQuanter, a single-agent framework that uses reinforcement learning (RL) to learn a dynamic policy over a transparent, tool-augmented decision workflow, which empowers a single agent to autonomously orchestrate tools and proactively acquire information on demand, establishing a transparent and auditable reasoning process. Extensive experiments demonstrate that AlphaQuanter achieves state-of-the-art performance on key financial metrics. Moreover, its interpretable reasoning reveals sophisticated strategies, offering novel and valuable insights for human traders. Our code for data acquisition and agent training is publicly available at: https://github.com/AlphaQuanter/AlphaQuanter", "github_manual": "", "github_regex": "https://github.com/AlphaQuanter/AlphaQuanter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.16505", "title": "PRISMM-Bench: A Benchmark of Peer-Review Grounded Multimodal Inconsistencies", "authors": [ "Lukas Selch", "Yufang Hou", "M. Jehanzeb Mirza", "Sivan Doveh", "James Glass", "Rogerio Feris", "Wei Lin" ], "github": "", "project_page": "", "abstract": "Large Multimodal Models (LMMs) are increasingly applied to scientific research, yet it remains unclear whether they can reliably understand and reason over the multimodal complexity of papers. A central challenge lies in detecting and resolving inconsistencies across text, figures, tables, and equations, issues that are often subtle, domain-specific, and ultimately undermine clarity, reproducibility, and trust. Existing benchmarks overlook this issue, either isolating single modalities or relying on synthetic errors that fail to capture real-world complexity. We introduce PRISMM-Bench (Peer-Review-sourced Inconsistency Set for Multimodal Models), the first benchmark grounded in real reviewer-flagged inconsistencies in scientific papers. Through a multi-stage pipeline of review mining, LLM-assisted filtering and human verification, we curate 262 inconsistencies from 242 papers. Based on this set, we design three tasks, namely inconsistency identification, remedy and pair matching, which assess a model's capacity to detect, correct, and reason over inconsistencies across different modalities. Furthermore, to address the notorious problem of choice-only shortcuts in multiple-choice evaluation, where models exploit answer patterns without truly understanding the question, we further introduce structured JSON-based answer representations that minimize linguistic biases by reducing reliance on superficial stylistic cues. We benchmark 21 leading LMMs, including large open-weight models (GLM-4.5V 106B, InternVL3 78B) and proprietary models (Gemini 2.5 Pro, GPT-5 with high reasoning). Results reveal strikingly low performance (26.1-54.2%), underscoring the challenge of multimodal scientific reasoning and motivating progress towards trustworthy scientific assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18554", "title": "Extracting alignment data in open models", "authors": [ "Federico Barbero", "Xiangming Gu", "Christopher A. Choquette-Choo", "Chawin Sitawarin", "Matthew Jagielski", "Itay Yona", "Petar Veli\u010dkovi\u0107", "Ilia Shumailov", "Jamie Hayes" ], "github": "", "project_page": "", "abstract": "In this work, we show that it is possible to extract significant amounts of alignment training data from a post-trained model -- useful to steer the model to improve certain capabilities such as long-context reasoning, safety, instruction following, and maths. While the majority of related work on memorisation has focused on measuring success of training data extraction through string matching, we argue that embedding models are better suited for our specific goals. Distances measured through a high quality embedding model can identify semantic similarities between strings that a different metric such as edit distance will struggle to capture. In fact, in our investigation, approximate string matching would have severely undercounted (by a conservative estimate of 10times) the amount of data that can be extracted due to trivial artifacts that deflate the metric. Interestingly, we find that models readily regurgitate training data that was used in post-training phases such as SFT or RL. We show that this data can be then used to train a base model, recovering a meaningful amount of the original performance. We believe our work exposes a possibly overlooked risk towards extracting alignment data. Finally, our work opens up an interesting discussion on the downstream effects of distillation practices: since models seem to be regurgitating aspects of their training set, distillation can therefore be thought of as indirectly training on the model's original dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18019", "title": "Is Multilingual LLM Watermarking Truly Multilingual? A Simple Back-Translation Solution", "authors": [ "Asim Mohamed", "Martin Gubri" ], "github": "https://github.com/asimzz/steam", "project_page": "", "abstract": "Multilingual watermarking aims to make large language model (LLM) outputs traceable across languages, yet current methods still fall short. Despite claims of cross-lingual robustness, they are evaluated only on high-resource languages. We show that existing multilingual watermarking methods are not truly multilingual: they fail to remain robust under translation attacks in medium- and low-resource languages. We trace this failure to semantic clustering, which fails when the tokenizer vocabulary contains too few full-word tokens for a given language. To address this, we introduce STEAM, a back-translation-based detection method that restores watermark strength lost through translation. STEAM is compatible with any watermarking method, robust across different tokenizers and languages, non-invasive, and easily extendable to new languages. With average gains of +0.19 AUC and +40%p TPR@1% on 17 languages, STEAM provides a simple and robust path toward fairer watermarking across diverse languages.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/asimzz/steam", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.15862", "title": "PokeeResearch: Effective Deep Research via Reinforcement Learning from AI Feedback and Robust Reasoning Scaffold", "authors": [ "Yi Wan", "Jiuqi Wang", "Liam Li", "Jinsong Liu", "Ruihao Zhu", "Zheqing Zhu" ], "github": "https://github.com/Pokee-AI/PokeeResearchOSS", "project_page": "", "abstract": "Tool-augmented large language models (LLMs) are emerging as deep research agents, systems that decompose complex queries, retrieve external evidence, and synthesize grounded responses. Yet current agents remain limited by shallow retrieval, weak alignment metrics, and brittle tool-use behavior. We introduce PokeeResearch-7B, a 7B-parameter deep research agent built under a unified reinforcement learning framework for robustness, alignment, and scalability. PokeeResearch-7B is trained by an annotation-free Reinforcement Learning from AI Feedback (RLAIF) framework to optimize policies using LLM-based reward signals that capture factual accuracy, citation faithfulness, and instruction adherence. A chain-of-thought-driven multi-call reasoning scaffold further enhances robustness through self-verification and adaptive recovery from tool failures. Among 10 popular deep research benchmarks, PokeeResearch-7B achieves state-of-the-art performance among 7B-scale deep research agents. This highlights that careful reinforcement learning and reasoning design can produce efficient, resilient, and research-grade AI agents. The model and inference code is open-sourced under MIT license at https://github.com/Pokee-AI/PokeeResearchOSS.", "github_manual": "", "github_regex": "https://github.com/Pokee-AI/PokeeResearchOSS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.17699", "title": "GAS: Improving Discretization of Diffusion ODEs via Generalized Adversarial Solver", "authors": [ "Aleksandr Oganov", "Ilya Bykov", "Eva Neudachina", "Mishan Aliev", "Alexander Tolmachev", "Alexander Sidorov", "Aleksandr Zuev", "Andrey Okhotin", "Denis Rakitin", "Aibek Alanov" ], "github": "https://github.com/3145tttt/GAS", "project_page": "", "abstract": "While diffusion models achieve state-of-the-art generation quality, they still suffer from computationally expensive sampling. Recent works address this issue with gradient-based optimization methods that distill a few-step ODE diffusion solver from the full sampling process, reducing the number of function evaluations from dozens to just a few. However, these approaches often rely on intricate training techniques and do not explicitly focus on preserving fine-grained details. In this paper, we introduce the Generalized Solver: a simple parameterization of the ODE sampler that does not require additional training tricks and improves quality over existing approaches. We further combine the original distillation loss with adversarial training, which mitigates artifacts and enhances detail fidelity. We call the resulting method the Generalized Adversarial Solver and demonstrate its superior performance compared to existing solver training methods under similar resource constraints. Code is available at https://github.com/3145tttt/GAS.", "github_manual": "", "github_regex": "https://github.com/3145tttt/GAS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18632", "title": "Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views", "authors": [ "Zhangquan Chen", "Manyuan Zhang", "Xinlei Yu", "Xufang Luo", "Mingze Sun", "Zihao Pan", "Yan Feng", "Peng Pei", "Xunliang Cai", "Ruqi Huang" ], "github": "https://github.com/zhangquanchen/3DThinker", "project_page": "", "abstract": "Though recent advances in vision-language models (VLMs) have achieved remarkable progress across a wide range of multimodal tasks, understanding 3D spatial relationships from limited views remains a significant challenge. Previous reasoning methods typically rely on pure text (e.g., topological cognitive maps) or on 2D visual cues. However, their limited representational capacity hinders performance in specific tasks that require 3D spatial imagination. To address this limitation, we propose 3DThinker, a framework that can effectively exploits the rich geometric information embedded within images while reasoning, like humans do. Our framework is the first to enable 3D mentaling during reasoning without any 3D prior input, and it does not rely on explicitly labeled 3D data for training. Specifically, our training consists of two stages. First, we perform supervised training to align the 3D latent generated by VLM while reasoning with that of a 3D foundation model (e.g., VGGT). Then, we optimize the entire reasoning trajectory solely based on outcome signals, thereby refining the underlying 3D mentaling. Extensive experiments across multiple benchmarks show that 3DThinker consistently outperforms strong baselines and offers a new perspective toward unifying 3D representations into multimodal reasoning. Our code will be available at https://github.com/zhangquanchen/3DThinker.", "github_manual": "", "github_regex": "https://github.com/zhangquanchen/3DThinker", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18234", "title": "DeepSeek-OCR: Contexts Optical Compression", "authors": [ "Haoran Wei", "Yaofeng Sun", "Yukun Li" ], "github": "https://github.com/deepseek-ai/DeepSeek-OCR", "project_page": "", "abstract": "We present DeepSeek-OCR as an initial investigation into the feasibility of compressing long contexts via optical 2D mapping. DeepSeek-OCR consists of two components: DeepEncoder and DeepSeek3B-MoE-A570M as the decoder. Specifically, DeepEncoder serves as the core engine, designed to maintain low activations under high-resolution input while achieving high compression ratios to ensure an optimal and manageable number of vision tokens. Experiments show that when the number of text tokens is within 10 times that of vision tokens (i.e., a compression ratio < 10x), the model can achieve decoding (OCR) precision of 97%. Even at a compression ratio of 20x, the OCR accuracy still remains at about 60%. This shows considerable promise for research areas such as historical long-context compression and memory forgetting mechanisms in LLMs. Beyond this, DeepSeek-OCR also demonstrates high practical value. On OmniDocBench, it surpasses GOT-OCR2.0 (256 tokens/page) using only 100 vision tokens, and outperforms MinerU2.0 (6000+ tokens per page on average) while utilizing fewer than 800 vision tokens. In production, DeepSeek-OCR can generate training data for LLMs/VLMs at a scale of 200k+ pages per day (a single A100-40G). Codes and model weights are publicly accessible at http://github.com/deepseek-ai/DeepSeek-OCR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/deepseek-ai/DeepSeek-OCR", "github_api_added_by": "auto" }, { "date": "2025-10-22", "arxiv_id": "2510.18121", "title": "Efficient Long-context Language Model Training by Core Attention Disaggregation", "authors": [ "Yonghao Zhuang", "Junda Chen", "Bo Pang", "Yi Gu", "Yibo Zhu", "Yimin Jiang", "Ion Stoica", "Eric Xing", "Hao Zhang" ], "github": "", "project_page": "", "abstract": "We present core attention disaggregation (CAD), a technique that improves long-context large language model training by decoupling the core attention computation, softmax(QK^T)V, from the rest of the model and executing it on a separate pool of devices. In existing systems, core attention is colocated with other layers; at long context lengths, its quadratic compute growth compared to the near-linear growth of other components causes load imbalance and stragglers across data and pipeline parallel groups. CAD is enabled by two observations. First, core attention is stateless: it has no trainable parameters and only minimal transient data, so balancing reduces to scheduling compute-bound tasks. Second, it is composable: modern attention kernels retain high efficiency when processing fused batches of token-level shards with arbitrary lengths. CAD partitions core attention into token-level tasks and dispatches them to dedicated attention servers, which dynamically rebatch tasks to equalize compute without sacrificing kernel efficiency. We implement CAD in a system called DistCA, which uses a ping-pong execution scheme to fully overlap communication with computation and in-place execution on attention servers to reduce memory use. On 512 H200 GPUs and context lengths up to 512k tokens, DistCA improves end-to-end training throughput by up to 1.35x, eliminates data and pipeline parallel stragglers, and achieves near-perfect compute and memory balance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.07581", "title": "Expanding the Action Space of LLMs to Reason Beyond Language", "authors": [ "Zhongqi Yue", "Weishi Wang", "Yundaichuan Zhan", "Juncheng Li", "Daniel Dahlmeier", "Fredrik D. Johansson" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are powerful reasoners in natural language, but their actions are typically confined to outputting vocabulary tokens. As a result, interactions with external environments -- such as symbolic operators or simulators -- must be expressed through text in predefined formats, parsed, and routed to external interfaces. This overloads the model's language with both reasoning and control duties, and requires a hand-crafted parser, external to the LLM. To address this, we decouple environment interactions from language by internalizing them in an Expanded Action space (ExpA), beyond the vocabulary. The model starts reasoning in the default language environment, but may trigger routing actions and switch to an external environment at any time. From there, the model can only invoke environment-specific actions, receive feedback from the environment, and potentially route back to language as a result. To promote effective exploration of the expanded action space and new environments, we introduce ExpA Reinforcement Learning (EARL) with counterfactual policy optimization. On tasks requiring multi-turn interactions and contingent planning, EARL outperforms strong baselines with vocabulary-constrained actions. It performs robustly across calculator-based multi-task learning and, in the partially observed sorting problem, achieves perfect Sort-4 accuracy while self-discovering an efficient algorithm competitive with classical designs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18087", "title": "Planned Diffusion", "authors": [ "Daniel Israel", "Tian Jin", "Ellie Cheng", "Guy Van den Broeck", "Aditya Grover", "Suvinay Subramanian", "Michael Carbin" ], "github": "", "project_page": "", "abstract": "A central challenge in large language model inference is the trade-off between generation speed and output quality. Autoregressive models produce high-quality text but generate tokens sequentially. Diffusion models can generate tokens in parallel but often need many iterations to match the same quality. We propose planned diffusion, a hybrid method that combines the strengths of both paradigms. Planned diffusion works in two stages: first, the model creates a short autoregressive plan that breaks the output into smaller, independent spans. Second, the model generates these spans simultaneously using diffusion. This approach expands the speed-quality Pareto frontier and provides a practical path to faster, high-quality text generation. On AlpacaEval, a suite of 805 instruction-following prompts, planned diffusion achieves Pareto-optimal trade-off between quality and latency, achieving 1.27x to 1.81x speedup over autoregressive generation with only 0.87\\% to 5.4\\% drop in win rate, respectively. Our sensitivity analysis shows that the planning mechanism of planned diffusion is minimal and reliable, and simple runtime knobs exist to provide flexible control of the quality-latency trade-off.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.15710", "title": "Unimedvl: Unifying Medical Multimodal Understanding And Generation Through Observation-Knowledge-Analysis", "authors": [ "Junzhi Ning", "Wei Li", "Cheng Tang", "Jiashi Lin", "Chenglong Ma", "Chaoyang Zhang", "Jiyao Liu", "Ying Chen", "Shujian Gao", "Lihao Liu", "Yuandong Pu", "Huihui Xu", "Chenhui Gou", "Ziyan Huang", "Yi Xin", "Qi Qin", "Zhongying Deng", "Diping Song", "Bin Fu", "Guang Yang", "Yuanfeng Ji", "Tianbin Li", "Yanzhou Su", "Jin Ye", "Shixiang Tang", "Ming Hu", "Junjun He" ], "github": "https://github.com/uni-medical/UniMedVL", "project_page": "", "abstract": "Medical diagnostic applications require models that can process multimodal medical inputs (images, patient histories, lab results) and generate diverse outputs including both textual reports and visual content (annotations, segmentation masks, and images). Despite this need, existing medical AI systems disrupt this unified process: medical image understanding models interpret images but cannot generate visual outputs, while medical image generation models synthesize images but cannot provide textual explanations. This leads to gaps in data representation, feature integration, and task-level multimodal capabilities. To this end, we propose a multi-level framework that draws inspiration from diagnostic workflows through the Observation-Knowledge-Analysis (OKA) paradigm. Specifically, at the observation level, we construct UniMed-5M, a dataset comprising over 5.6M samples that reformat diverse unimodal data into multimodal pairs for foundational observation. At the knowledge level, we propose Progressive Curriculum Learning that systematically introduces medical multimodal knowledge. At the analysis level, we introduce UniMedVL, the first medical unified multimodal model for the simultaneous analysis of image understanding and generation tasks within a single architecture. UniMedVL achieves superior performance on five medical image understanding benchmarks, while matching specialized models in generation quality across eight medical imaging modalities. Crucially, our unified architecture enables bidirectional knowledge sharing: generation tasks enhance visual understanding features, demonstrating that integrating traditionally separate capabilities within a single medical framework unlocks improvements across diverse medical vision-language tasks. Code is available at https://github.com/uni-medical/UniMedVL.", "github_manual": "", "github_regex": "https://github.com/uni-medical/UniMedVL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.13982", "title": "Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulations", "authors": [ "Jinkun Chen", "Sher Badshah", "Xuemin Yu", "Sijia Han" ], "github": "", "project_page": "", "abstract": "What if artificial agents could not just communicate, but also evolve, adapt, and reshape their worlds in ways we cannot fully predict? With llm now powering multi-agent systems and social simulations, we are witnessing new possibilities for modeling open-ended, ever-changing environments. Yet, most current simulations remain constrained within static sandboxes, characterized by predefined tasks, limited dynamics, and rigid evaluation criteria. These limitations prevent them from capturing the complexity of real-world societies. In this paper, we argue that static, task-specific benchmarks are fundamentally inadequate and must be rethought. We critically review emerging architectures that blend llm with multi-agent dynamics, highlight key hurdles such as balancing stability and diversity, evaluating unexpected behaviors, and scaling to greater complexity, and introduce a fresh taxonomy for this rapidly evolving field. Finally, we present a research roadmap centered on open-endedness, continuous co-evolution, and the development of resilient, socially aligned AI ecosystems. We call on the community to move beyond static paradigms and help shape the next generation of adaptive, socially-aware multi-agent simulations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.18081", "title": "Any-Depth Alignment: Unlocking Innate Safety Alignment of LLMs to Any-Depth", "authors": [ "Jiawei Zhang", "Andrew Estornell", "David D. Baek", "Bo Li", "Xiaojun Xu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) exhibit strong but shallow alignment: they directly refuse harmful queries when a refusal is expected at the very start of an assistant turn, yet this protection collapses once a harmful continuation is underway (either through the adversarial attacks or via harmful assistant-prefill attacks). This raises a fundamental question: Can the innate shallow alignment in LLMs be unlocked to ensure safety at arbitrary generation depths? To achieve this goal, we propose Any-Depth Alignment (ADA), an effective inference-time defense with negligible overhead. ADA is built based on our observation that alignment is concentrated in the assistant header tokens through repeated use in shallow-refusal training, and these tokens possess the model's strong alignment priors. By reintroducing these tokens mid-stream, ADA induces the model to reassess harmfulness and recover refusals at any point in generation. Across diverse open-source model families (Llama, Gemma, Mistral, Qwen, DeepSeek, and gpt-oss), ADA achieves robust safety performance without requiring any changes to the base model's parameters. It secures a near-100% refusal rate against challenging adversarial prefill attacks ranging from dozens to thousands of tokens. Furthermore, ADA reduces the average success rate of prominent adversarial prompt attacks (such as GCG, AutoDAN, PAIR, and TAP) to below 3%. This is all accomplished while preserving utility on benign tasks with minimal over-refusal. ADA maintains this resilience even after the base model undergoes subsequent instruction tuning (benign or adversarial).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.15136", "title": "Predicting the Unpredictable: Reproducible BiLSTM Forecasting of Incident Counts in the Global Terrorism Database (GTD)", "authors": [ "Oluwasegun Adegoke" ], "github": "", "project_page": "", "abstract": "We study short-horizon forecasting of weekly terrorism incident counts using the Global Terrorism Database (GTD, 1970--2016). We build a reproducible pipeline with fixed time-based splits and evaluate a Bidirectional LSTM (BiLSTM) against strong classical anchors (seasonal-naive, linear/ARIMA) and a deep LSTM-Attention baseline. On the held-out test set, the BiLSTM attains RMSE 6.38, outperforming LSTM-Attention (9.19; +30.6\\%) and a linear lag-regression baseline (+35.4\\% RMSE gain), with parallel improvements in MAE and MAPE. Ablations varying temporal memory, training-history length, spatial grain, lookback size, and feature groups show that models trained on long historical data generalize best; a moderate lookback (20--30 weeks) provides strong context; and bidirectional encoding is critical for capturing both build-up and aftermath patterns within the window. Feature-group analysis indicates that short-horizon structure (lagged counts and rolling statistics) contributes most, with geographic and casualty features adding incremental lift. We release code, configs, and compact result tables, and provide a data/ethics statement documenting GTD licensing and research-only use. Overall, the study offers a transparent, baseline-beating reference for GTD incident forecasting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.17862", "title": "When \"Correct\" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?", "authors": [ "Yibo Peng", "James Song", "Lei Li", "Xinyu Yang", "Mihai Christodorescu", "Ravi Mangal", "Corina Pasareanu", "Haizhong Zheng", "Beidi Chen" ], "github": "https://github.com/Infini-AI-Lab/FCV", "project_page": "", "abstract": "Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses almost exclusively on functional correctness. In this paper, we reveal a novel type of threat to real-world code agents: Functionally Correct yet Vulnerable (FCV) patches, which pass all test cases but contain vulnerable code. With our proposed FCV-Attack, which can be deliberately crafted by malicious attackers or implicitly introduced by benign developers, we show that SOTA LLMs (e.g., ChatGPT and Claude) and agent scaffolds (e.g., SWE-agent and OpenHands) are all vulnerable to this FCV threat; across 12 agent-model combinations on SWE-Bench, the attack only requires black-box access and a single query to the code agent to perform the attack. For example, for CWE-538 (information exposure vulnerability), the FCV-Attack attains an attack success rate of 40.7% on GPT-5 Mini + OpenHands. Our results reveal an important security threat overlooked by current evaluation paradigms and urge the development of security-aware defenses for code agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Infini-AI-Lab/FCV", "github_api_added_by": "auto" }, { "date": "2025-10-22", "arxiv_id": "2510.14463", "title": "Pruning Overparameterized Multi-Task Networks for Degraded Web Image Restoration", "authors": [ "Thomas Katraouras", "Dimitrios Rafailidis" ], "github": "https://github.com/Thomkat/MIR-L", "project_page": "", "abstract": "Image quality is a critical factor in delivering visually appealing content on web platforms. However, images often suffer from degradation due to lossy operations applied by online social networks (OSNs), negatively affecting user experience. Image restoration is the process of recovering a clean high-quality image from a given degraded input. Recently, multi-task (all-in-one) image restoration models have gained significant attention, due to their ability to simultaneously handle different types of image degradations. However, these models often come with an excessively high number of trainable parameters, making them computationally inefficient. In this paper, we propose a strategy for compressing multi-task image restoration models. We aim to discover highly sparse subnetworks within overparameterized deep models that can match or even surpass the performance of their dense counterparts. The proposed model, namely MIR-L, utilizes an iterative pruning strategy that removes low-magnitude weights across multiple rounds, while resetting the remaining weights to their original initialization. This iterative process is important for the multi-task image restoration model's optimization, effectively uncovering \"winning tickets\" that maintain or exceed state-of-the-art performance at high sparsity levels. Experimental evaluation on benchmark datasets for the deraining, dehazing, and denoising tasks shows that MIR-L retains only 10% of the trainable parameters while maintaining high image restoration performance. Our code, datasets and pre-trained models are made publicly available at https://github.com/Thomkat/MIR-L.", "github_manual": "", "github_regex": "https://github.com/Thomkat/MIR-L", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-22", "arxiv_id": "2510.17388", "title": "The Atomic Instruction Gap: Instruction-Tuned LLMs Struggle with Simple, Self-Contained Directives", "authors": [ "Henry Lim", "Kwan Hui Lim" ], "github": "", "project_page": "", "abstract": "Instruction-tuned large language models (IT-LLMs) exhibit strong zero-shot reasoning, yet their ability to execute simple, self-contained instructions remains underexplored, despite this being foundational to complex instruction-following. We evaluate 20 IT-LLMs on modified MMLU and MMLU-Pro benchmarks, by systematically varying the format of option labels (alphabetic, numeric, Roman) while keeping their meaning identical under four paradigms, namely: (1) With explicit instructions, label changes cause large performance shifts (e.g., -30.45\\% for Roman vs. numeric), revealing instruction-format bias. (2) Without instructions, performance drops further (up to -10.84\\%) and label sensitivity intensifies, underscoring the role of explicit guidance. (3) When option contents are removed, models fail random-choice baselines except with numeric labels, suggesting weak adherence to atomic directives. (4) Three-shot exemplars yield no significant gains in robustness or fidelity, and generation analyses show persistent label errors, especially for non-numeric formats. Across model sizes, larger LLMs achieve higher accuracy but remain inconsistent in instruction adherence. These results expose the insufficiencies of current instruction-tuning paradigms and highlight the need for evaluation methods and training strategies that explicitly target atomic instruction-following.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19363", "title": "LoongRL:Reinforcement Learning for Advanced Reasoning over Long Contexts", "authors": [ "Siyuan Wang", "Gaokai Zhang", "Li Lyna Zhang", "Ning Shang", "Fan Yang", "Dongyao Chen", "Mao Yang" ], "github": "", "project_page": "", "abstract": "Reasoning over long contexts is essential for large language models. While reinforcement learning (RL) enhances short-context reasoning by inducing \"Aha\" moments in chain-of-thought, the advanced thinking patterns required for long-context reasoning remain largely unexplored, and high-difficulty RL data are scarce. In this paper, we introduce LoongRL, a data-driven RL method for advanced long-context reasoning. Central to LoongRL is KeyChain, a synthesis approach that transforms short multi-hop QA into high-difficulty long-context tasks by inserting UUID chains that hide the true question among large collections of distracting documents. Solving these tasks requires the model to trace the correct chain step-by-step, identify the true question, retrieve relevant facts and reason over them to answer correctly. RL training on KeyChain data induces an emergent plan-retrieve-reason-recheck reasoning pattern that generalizes far beyond training length. Models trained at 16K effectively solve 128K tasks without prohibitive full-length RL rollout costs. On Qwen2.5-7B and 14B, LoongRL substantially improves long-context multi-hop QA accuracy by +23.5% and +21.1% absolute gains. The resulting LoongRL-14B reaches a score of 74.2, rivaling much larger frontier models such as o3-mini (74.5) and DeepSeek-R1 (74.9). It also improves long-context retrieval, passes all 128K needle-in-a-haystack stress tests, and preserves short-context reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19817", "title": "olmOCR 2: Unit Test Rewards for Document OCR", "authors": [ "Jake Poznanski", "Luca Soldaini", "Kyle Lo" ], "github": "https://github.com/deepseek-ai/DeepSeek-OCR", "project_page": "", "abstract": "We present olmOCR 2, the latest in our family of powerful OCR systems for converting digitized print documents, like PDFs, into clean, naturally ordered plain text. olmOCR 2 is powered by olmOCR-2-7B-1025, a specialized, 7B vision language model (VLM) trained using reinforcement learning with verifiable rewards (RLVR), where our rewards are a diverse set of binary unit tests. To scale unit test creation, we develop a pipeline for generating synthetic documents with diverse and challenging layouts, known ground-truth HTML source code, and extracted test cases. We show that RL training on these test cases results in state-of-the-art performance on olmOCR-Bench, our English-language OCR benchmark, with the largest improvements in math formula conversion, table parsing, and multi-column layouts compared to previous versions. We release our model, data and code under permissive open licenses.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/deepseek-ai/DeepSeek-OCR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19808", "title": "Pico-Banana-400K: A Large-Scale Dataset for Text-Guided Image Editing", "authors": [ "Yusu Qian", "Eli Bocek-Rivele", "Liangchen Song", "Jialing Tong", "Yinfei Yang", "Jiasen Lu", "Wenze Hu", "Zhe Gan" ], "github": "", "project_page": "", "abstract": "Recent advances in multimodal models have demonstrated remarkable text-guided image editing capabilities, with systems like GPT-4o and Nano-Banana setting new benchmarks. However, the research community's progress remains constrained by the absence of large-scale, high-quality, and openly accessible datasets built from real images. We introduce Pico-Banana-400K, a comprehensive 400K-image dataset for instruction-based image editing. Our dataset is constructed by leveraging Nano-Banana to generate diverse edit pairs from real photographs in the OpenImages collection. What distinguishes Pico-Banana-400K from previous synthetic datasets is our systematic approach to quality and diversity. We employ a fine-grained image editing taxonomy to ensure comprehensive coverage of edit types while maintaining precise content preservation and instruction faithfulness through MLLM-based quality scoring and careful curation. Beyond single turn editing, Pico-Banana-400K enables research into complex editing scenarios. The dataset includes three specialized subsets: (1) a 72K-example multi-turn collection for studying sequential editing, reasoning, and planning across consecutive modifications; (2) a 56K-example preference subset for alignment research and reward model training; and (3) paired long-short editing instructions for developing instruction rewriting and summarization capabilities. By providing this large-scale, high-quality, and task-rich resource, Pico-Banana-400K establishes a robust foundation for training and benchmarking the next generation of text-guided image editing models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19286", "title": "TheMCPCompany: Creating General-purpose Agents with Task-specific Tools", "authors": [ "Reza Esfandiarpoor", "Vishwas Suryanarayanan", "Stephen H. Bach", "Vishal Chowdhary", "Anthony Aue" ], "github": "", "project_page": "", "abstract": "Since the introduction of the Model Context Protocol (MCP), the number of available tools for Large Language Models (LLMs) has increased significantly. These task-specific tool sets offer an alternative to general-purpose tools such as web browsers, while being easier to develop and maintain than GUIs. However, current general-purpose agents predominantly rely on web browsers for interacting with the environment. Here, we introduce TheMCPCompany, a benchmark for evaluating tool-calling agents on tasks that involve interacting with various real-world services. We use the REST APIs of these services to create MCP servers, which include over 18,000 tools. We also provide manually annotated ground-truth tools for each task. In our experiments, we use the ground truth tools to show the potential of tool-calling agents for both improving performance and reducing costs assuming perfect tool retrieval. Next, we explore agent performance using tool retrieval to study the real-world practicality of tool-based agents. While all models with tool retrieval perform similarly or better than browser-based agents, smaller models cannot take full advantage of the available tools through retrieval. On the other hand, GPT-5's performance with tool retrieval is very close to its performance with ground-truth tools. Overall, our work shows that the most advanced reasoning models are effective at discovering tools in simpler environments, but seriously struggle with navigating complex enterprise environments. TheMCPCompany reveals that navigating tens of thousands of tools and combining them in non-trivial ways to solve complex problems is still a challenging task for current models and requires both better reasoning and better retrieval models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19336", "title": "DaMo: Data Mixing Optimizer in Fine-tuning Multimodal LLMs for Mobile Phone Agents", "authors": [ "Kai Shi", "Jun Yang", "Ni Yang", "Binqiang Pan", "Qingsong Xie", "Chao Zhang", "Zhenyu Yang", "Tianhuang Su", "Haonan Lu" ], "github": "https://github.com/OPPO-Mente-Lab/DaMo.git", "project_page": "", "abstract": "Mobile Phone Agents (MPAs) have emerged as a promising research direction due to their broad applicability across diverse scenarios. While Multimodal Large Language Models (MLLMs) serve as the foundation for MPAs, their effectiveness in handling multiple mobile phone tasks simultaneously remains limited. Although multitask supervised fine-tuning (SFT) is widely adopted for multitask learning, existing approaches struggle to determine optimal training data compositions for peak performance. To address this challenge, we propose DaMo (Data Mixture Optimizer) - a novel solution employing a trainable network that predicts optimal data mixtures by forecasting downstream task performance for any given dataset ratio. To support comprehensive evaluation, we introduce PhoneAgentBench, the first specialized benchmark to evaluate MLLMs on multimodal mobile phone tasks, comprising 1235 QA pairs spanning diverse real-world industrial mobile application scenarios. Demonstrating strong predictive capability (R^2=0.81) in small-scale pilot experiments, DaMo efficiently extrapolates optimal data mixing configurations. Our results show DaMo achieves a 3.38% performance improvement on PhoneAgentBench compared to alternative methods. Furthermore, extensive experiments across established benchmarks including BFCL-v3, MME-Reasoning, MME-Perception, and OCRBench reveal DaMo's superior generalization, outperforming other approaches by 2.57% in terms of average score. When used solely for MLLM optimization on the BFCL-v3 task, DaMo improves the metrics by 12.47% than other methods. Notably, DaMo maintains robust scalability, preserving its effectiveness when applied to other model architectures. The code and dataset are available at https://github.com/OPPO-Mente-Lab/DaMo.git", "github_manual": "", "github_regex": "https://github.com/OPPO-Mente-Lab/DaMo.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19386", "title": "ColorAgent: Building A Robust, Personalized, and Interactive OS Agent", "authors": [ "Ning Li", "Qiqiang Lin", "Zheng Wu", "Xiaoyun Mo", "Weiming Zhang", "Yin Zhao", "Xiangmou Qu", "Jiamu Zhou", "Jun Wang", "Congmin Zheng", "Yuanyi Song", "Hongjiang Chen", "Heyuan Huang", "Jihong Wang", "Jiaxin Yin", "Jingwei Yu", "Junwei Liao", "Qiuying Peng", "Xingyu Lou", "Jun Wang", "Weiwen Liu", "Zhuosheng Zhang", "Weinan Zhang" ], "github": "https://github.com/MadeAgents/mobile-use", "project_page": "", "abstract": "With the advancements in hardware, software, and large language model technologies, the interaction between humans and operating systems has evolved from the command-line interface to the rapidly emerging AI agent interactions. Building an operating system (OS) agent capable of executing user instructions and faithfully following user desires is becoming a reality. In this technical report, we present ColorAgent, an OS agent designed to engage in long-horizon, robust interactions with the environment while also enabling personalized and proactive user interaction. To enable long-horizon interactions with the environment, we enhance the model's capabilities through step-wise reinforcement learning and self-evolving training, while also developing a tailored multi-agent framework that ensures generality, consistency, and robustness. In terms of user interaction, we explore personalized user intent recognition and proactive engagement, positioning the OS agent not merely as an automation tool but as a warm, collaborative partner. We evaluate ColorAgent on the AndroidWorld and AndroidLab benchmarks, achieving success rates of 77.2% and 50.7%, respectively, establishing a new state of the art. Nonetheless, we note that current benchmarks are insufficient for a comprehensive evaluation of OS agents and propose further exploring directions in future work, particularly in the areas of evaluation paradigms, agent collaboration, and security. Our code is available at https://github.com/MadeAgents/mobile-use.", "github_manual": "", "github_regex": "https://github.com/MadeAgents/mobile-use", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19338", "title": "Every Attention Matters: An Efficient Hybrid Architecture for Long-Context Reasoning", "authors": [ "Ling Team", "Bin Han", "Caizhi Tang", "Chen Liang", "Donghao Zhang", "Fan Yuan", "Feng Zhu", "Jie Gao", "Jingyu Hu", "Longfei Li", "Meng Li", "Mingyang Zhang", "Peijie Jiang", "Peng Jiao", "Qian Zhao", "Qingyuan Yang", "Wenbo Shen", "Xinxing Yang", "Yalin Zhang", "Yankun Ren", "Yao Zhao", "Yibo Cao", "Yixuan Sun", "Yue Zhang", "Yuchen Fang", "Zibin Lin", "Zixuan Cheng", "Jun Zhou" ], "github": "https://github.com/inclusionAI/linghe", "project_page": "", "abstract": "In this technical report, we present the Ring-linear model series, specifically including Ring-mini-linear-2.0 and Ring-flash-linear-2.0. Ring-mini-linear-2.0 comprises 16B parameters and 957M activations, while Ring-flash-linear-2.0 contains 104B parameters and 6.1B activations. Both models adopt a hybrid architecture that effectively integrates linear attention and softmax attention, significantly reducing I/O and computational overhead in long-context inference scenarios. Compared to a 32 billion parameter dense model, this series reduces inference cost to 1/10, and compared to the original Ring series, the cost is also reduced by over 50%. Furthermore, through systematic exploration of the ratio between different attention mechanisms in the hybrid architecture, we have identified the currently optimal model structure. Additionally, by leveraging our self-developed high-performance FP8 operator library-linghe, overall training efficiency has been improved by 50%. Benefiting from the high alignment between the training and inference engine operators, the models can undergo long-term, stable, and highly efficient optimization during the reinforcement learning phase, consistently maintaining SOTA performance across multiple challenging complex reasoning benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/inclusionAI/linghe", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19307", "title": "Unified Reinforcement and Imitation Learning for Vision-Language Models", "authors": [ "Byung-Kwan Lee", "Ryo Hachiuma", "Yong Man Ro", "Yu-Chiang Frank Wang", "Yueh-Hua Wu" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) have achieved remarkable progress, yet their large scale often renders them impractical for resource-constrained environments. This paper introduces Unified Reinforcement and Imitation Learning (RIL), a novel and efficient training algorithm designed to create powerful, lightweight VLMs. RIL distinctively combines the strengths of reinforcement learning with adversarial imitation learning. This enables smaller student VLMs not only to mimic the sophisticated text generation of large teacher models but also to systematically improve their generative capabilities through reinforcement signals. Key to our imitation framework is an LLM-based discriminator that adeptly distinguishes between student and teacher outputs, complemented by guidance from multiple large teacher VLMs to ensure diverse learning. This unified learning strategy, leveraging both reinforcement and imitation, empowers student models to achieve significant performance gains, making them competitive with leading closed-source VLMs. Extensive experiments on diverse vision-language benchmarks demonstrate that RIL significantly narrows the performance gap with state-of-the-art open- and closed-source VLMs and, in several instances, surpasses them.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19457", "title": "MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models", "authors": [ "Kailin Jiang", "Ning Jiang", "Yuchen Ren", "Yuchen Li", "Yifan Gao", "Jinhe Bi", "Yunpu Ma", "Qingqing Liu", "Xianhao Wang", "Yifan Jia", "Hongbo Jiang", "Yaocong Hu", "Bin Li", "Lei Liu", "Yuntao Du" ], "github": "", "project_page": "", "abstract": "Large Multimodal Models (LMMs) encode rich factual knowledge via cross-modal pre-training, yet their static representations struggle to maintain an accurate understanding of time-sensitive factual knowledge. Existing benchmarks remain constrained by static designs, inadequately evaluating LMMs' ability to understand time-sensitive knowledge. To address this gap, we propose MINED, a comprehensive benchmark that evaluates temporal awareness along 6 key dimensions and 11 challenging tasks: cognition, awareness, trustworthiness, understanding, reasoning, and robustness. MINED is constructed from Wikipedia by two professional annotators, containing 2,104 time-sensitive knowledge samples spanning six knowledge types. Evaluating 15 widely used LMMs on MINED shows that Gemini-2.5-Pro achieves the highest average CEM score of 63.07, while most open-source LMMs still lack time understanding ability. Meanwhile, LMMs perform best on organization knowledge, whereas their performance is weakest on sport. To address these challenges, we investigate the feasibility of updating time-sensitive knowledge in LMMs through knowledge editing methods and observe that LMMs can effectively update knowledge via knowledge editing methods in single editing scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19316", "title": "KORE: Enhancing Knowledge Injection for Large Multimodal Models via Knowledge-Oriented Augmentations and Constraints", "authors": [ "Kailin Jiang", "Hongbo Jiang", "Ning Jiang", "Zhi Gao", "Jinhe Bi", "Yuchen Ren", "Bin Li", "Yuntao Du", "Lei Liu", "Qing Li" ], "github": "", "project_page": "", "abstract": "Large Multimodal Models encode extensive factual knowledge in their pre-trained weights. However, its knowledge remains static and limited, unable to keep pace with real-world developments, which hinders continuous knowledge acquisition. Effective knowledge injection thus becomes critical, involving two goals: knowledge adaptation (injecting new knowledge) and knowledge retention (preserving old knowledge). Existing methods often struggle to learn new knowledge and suffer from catastrophic forgetting. To address this, we propose KORE, a synergistic method of KnOwledge-oRientEd augmentations and constraints for injecting new knowledge into large multimodal models while preserving old knowledge. Unlike general text or image data augmentation, KORE automatically converts individual knowledge items into structured and comprehensive knowledge to ensure that the model accurately learns new knowledge, enabling accurate adaptation. Meanwhile, KORE stores previous knowledge in the covariance matrix of LMM's linear layer activations and initializes the adapter by projecting the original weights into the matrix's null space, defining a fine-tuning direction that minimizes interference with previous knowledge, enabling powerful retention. Extensive experiments on various LMMs, including LLaVA-v1.5-7B, LLaVA-v1.5-13B, and Qwen2.5-VL-7B, show that KORE achieves superior new knowledge injection performance and effectively mitigates catastrophic forgetting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.18313", "title": "OmniNWM: Omniscient Driving Navigation World Models", "authors": [ "Bohan Li", "Zhuang Ma", "Dalong Du", "Baorui Peng", "Zhujin Liang", "Zhenqiang Liu", "Chao Ma", "Yueming Jin", "Hao Zhao", "Wenjun Zeng", "Xin Jin" ], "github": "https://github.com/Ma-Zhuang/OmniNWM", "project_page": "", "abstract": "Autonomous driving world models are expected to work effectively across three core dimensions: state, action, and reward. Existing models, however, are typically restricted to limited state modalities, short video sequences, imprecise action control, and a lack of reward awareness. In this paper, we introduce OmniNWM, an omniscient panoramic navigation world model that addresses all three dimensions within a unified framework. For state, OmniNWM jointly generates panoramic videos of RGB, semantics, metric depth, and 3D occupancy. A flexible forcing strategy enables high-quality long-horizon auto-regressive generation. For action, we introduce a normalized panoramic Plucker ray-map representation that encodes input trajectories into pixel-level signals, enabling highly precise and generalizable control over panoramic video generation. Regarding reward, we move beyond learning reward functions with external image-based models: instead, we leverage the generated 3D occupancy to directly define rule-based dense rewards for driving compliance and safety. Extensive experiments demonstrate that OmniNWM achieves state-of-the-art performance in video generation, control accuracy, and long-horizon stability, while providing a reliable closed-loop evaluation framework through occupancy-grounded rewards. Project page is available at https://github.com/Arlo0o/OmniNWM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Ma-Zhuang/OmniNWM", "github_api_added_by": "user" }, { "date": "2025-10-23", "arxiv_id": "2510.16844", "title": "FinSight: Towards Real-World Financial Deep Research", "authors": [ "Jiajie Jin", "Yuyao Zhang", "Yimeng Xu", "Hongjin Qian", "Yutao Zhu", "Zhicheng Dou" ], "github": "", "project_page": "", "abstract": "Generating professional financial reports is a labor-intensive and intellectually demanding process that current AI systems struggle to fully automate. To address this challenge, we introduce FinSight (Financial InSight), a novel multi agent framework for producing high-quality, multimodal financial reports. The foundation of FinSight is the Code Agent with Variable Memory (CAVM) architecture, which unifies external data, designed tools, and agents into a programmable variable space, enabling flexible data collection, analysis and report generation through executable code. To ensure professional-grade visualization, we propose an Iterative Vision-Enhanced Mechanism that progressively refines raw visual outputs into polished financial charts. Furthermore, a two stage Writing Framework expands concise Chain-of-Analysis segments into coherent, citation-aware, and multimodal reports, ensuring both analytical depth and structural consistency. Experiments on various company and industry-level tasks demonstrate that FinSight significantly outperforms all baselines, including leading deep research systems in terms of factual accuracy, analytical depth, and presentation quality, demonstrating a clear path toward generating reports that approach human-expert quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19488", "title": "VideoAgentTrek: Computer Use Pretraining from Unlabeled Videos", "authors": [ "Dunjie Lu", "Yiheng Xu", "Junli Wang", "Haoyuan Wu", "Xinyuan Wang", "Zekun Wang", "Junlin Yang", "Hongjin Su", "Jixuan Chen", "Junda Chen", "Yuchen Mao", "Jingren Zhou", "Junyang Lin", "Binyuan Hui", "Tao Yu" ], "github": "https://github.com/xlang-ai/VideoAgentTrek", "project_page": "", "abstract": "Training computer-use agents requires massive amounts of GUI interaction data, but manually annotating action trajectories at scale is prohibitively expensive. We present VideoAgentTrek, a scalable pipeline that automatically mines training data from publicly available screen-recorded videos at web scale, eliminating the need for manual annotation. Our approach addresses a key challenge: raw videos contain implicit demonstrations but lack explicit action labels. To solve this, we develop Video2Action, an inverse dynamics module (IDM) with two components: (1) a video grounding model that detects and localizes GUI actions with precise temporal boundaries and context, and (2) an action-content recognizer that extracts structured parameters like click coordinates and typed text with high fidelity. Applied to 39,000 YouTube tutorial videos, our pipeline generates 1.52 million interaction steps automatically. We leverage this data through continued pretraining followed by supervised fine-tuning. On OSWorld-Verified, our approach improves task success rates from 9.3% (SFT-only baseline) to 15.8%, a 70% relative improvement. On AgentNetBench, step accuracy increases from 64.1% to 69.3%. Our results demonstrate that passive internet videos can be transformed into high-quality supervision for computer-use agents, providing a scalable alternative to expensive manual annotation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xlang-ai/VideoAgentTrek", "github_api_added_by": "auto" }, { "date": "2025-10-23", "arxiv_id": "2510.18927", "title": "BAPO: Stabilizing Off-Policy Reinforcement Learning for LLMs via Balanced Policy Optimization with Adaptive Clipping", "authors": [ "Zhiheng Xi", "Xin Guo", "Yang Nan", "Enyu Zhou", "Junrui Shen", "Wenxiang Chen", "Jiaqi Liu", "Jixuan Huang", "Zhihao Zhang", "Honglin Guo", "Xun Deng", "Zhikai Lei", "Miao Zheng", "Guoteng Wang", "Shuo Zhang", "Peng Sun", "Rui Zheng", "Hang Yan", "Tao Gui", "Qi Zhang", "Xuanjing Huang" ], "github": "https://github.com/WooooDyy/BAPO", "project_page": "", "abstract": "Reinforcement learning (RL) has recently become the core paradigm for aligning and strengthening large language models (LLMs). Yet, applying RL in off-policy settings--where stale data from past policies are used for training--improves sample efficiency, but remains challenging: policy entropy declines sharply, optimization often becomes unstable and may even collapse. Through theoretical and empirical analysis, we identify two key insights: (i) an imbalance in optimization, where negative-advantage samples dominate the policy gradient, suppressing useful behaviors and risking gradient explosions; and (ii) the derived Entropy-Clip Rule, which reveals that the fixed clipping mechanism in PPO-like objectives systematically blocks entropy-increasing updates, thereby driving the policy toward over-exploitation at the expense of exploration. Building on these insights, we propose BAlanced Policy Optimization with Adaptive Clipping (BAPO), a simple yet effective method that dynamically adjusts clipping bounds to adaptively re-balance positive and negative contributions, preserve entropy, and stabilize RL optimization. Across diverse off-policy scenarios--including sample replay and partial rollout--BAPO achieves fast, stable, and data-efficient training. On AIME 2024 and AIME 2025 benchmarks, our 7B BAPO model surpasses open-source counterparts such as SkyWork-OR1-7B, while our 32B BAPO model not only achieves state-of-the-art results among models of the same scale but also outperforms leading proprietary systems like o3-mini and Gemini-2.5-Flash-Thinking.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WooooDyy/BAPO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.18941", "title": "ProfBench: Multi-Domain Rubrics requiring Professional Knowledge to Answer and Judge", "authors": [ "Zhilin Wang", "Jaehun Jung", "Ximing Lu", "Shizhe Diao", "Ellie Evans", "Jiaqi Zeng", "Pavlo Molchanov", "Yejin Choi", "Jan Kautz", "Yi Dong" ], "github": "https://github.com/NVlabs/ProfBench", "project_page": "", "abstract": "Evaluating progress in large language models (LLMs) is often constrained by the challenge of verifying responses, limiting assessments to tasks like mathematics, programming, and short-form question-answering. However, many real-world applications require evaluating LLMs in processing professional documents, synthesizing information, and generating comprehensive reports in response to user queries. We introduce ProfBench: a set of over 7000 response-criterion pairs as evaluated by human-experts with professional knowledge across Physics PhD, Chemistry PhD, Finance MBA and Consulting MBA. We build robust and affordable LLM-Judges to evaluate ProfBench rubrics, by mitigating self-enhancement bias and reducing the cost of evaluation by 2-3 orders of magnitude, to make it fair and accessible to the broader community. Our findings reveal that ProfBench poses significant challenges even for state-of-the-art LLMs, with top-performing models like GPT-5-high achieving only 65.9\\% overall performance. Furthermore, we identify notable performance disparities between proprietary and open-weight models and provide insights into the role that extended thinking plays in addressing complex, professional-domain tasks. Data: https://huggingface.co/datasets/nvidia/ProfBench and Code: https://github.com/NVlabs/ProfBench", "github_manual": "", "github_regex": "https://github.com/NVlabs/ProfBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.18940", "title": "NeuroAda: Activating Each Neuron's Potential for Parameter-Efficient Fine-Tuning", "authors": [ "Zhi Zhang", "Yixian Shen", "Congfeng Cao", "Ekaterina Shutova" ], "github": "https://github.com/FightingFighting/NeuroAda.git", "project_page": "", "abstract": "Existing parameter-efficient fine-tuning (PEFT) methods primarily fall into two categories: addition-based and selective in-situ adaptation. The former, such as LoRA, introduce additional modules to adapt the model to downstream tasks, offering strong memory efficiency. However, their representational capacity is often limited, making them less suitable for fine-grained adaptation. In contrast, the latter directly fine-tunes a carefully chosen subset of the original model parameters, allowing for more precise and effective adaptation, but at the cost of significantly increased memory consumption. To reconcile this trade-off, we propose NeuroAda, a novel PEFT method that enables fine-grained model finetuning while maintaining high memory efficiency. Our approach first identifies important parameters (i.e., connections within the network) as in selective adaptation, and then introduces bypass connections for these selected parameters. During finetuning, only the bypass connections are updated, leaving the original model parameters frozen. Empirical results on 23+ tasks spanning both natural language generation and understanding demonstrate that NeuroAda achieves state-of-the-art performance with as little as leq 0.02% trainable parameters, while reducing CUDA memory usage by up to 60%. We release our code here: https://github.com/FightingFighting/NeuroAda.git.", "github_manual": "", "github_regex": "https://github.com/FightingFighting/NeuroAda.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.18428", "title": "AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library", "authors": [ "Minwei Kong", "Ao Qu", "Xiaotong Guo", "Wenbin Ouyang", "Chonghe Jiang", "Han Zheng", "Yining Ma", "Dingyi Zhuang", "Yuhan Tang", "Junyi Li", "Hai Wang", "Cathy Wu", "Jinhua Zhao" ], "github": "https://github.com/Minw913/AlphaOPT", "project_page": "", "abstract": "Optimization modeling enables critical decisions across industries but remains difficult to automate: informal language must be mapped to precise mathematical formulations and executable solver code. Prior LLM approaches either rely on brittle prompting or costly retraining with limited generalization. We present AlphaOPT, a self-improving experience library that enables an LLM to learn from limited demonstrations (even answers alone, without gold-standard programs) and solver feedback - without annotated reasoning traces or parameter updates. AlphaOPT operates in a continual two-phase cycle: (i) a Library Learning phase that reflects on failed attempts, extracting solver-verified, structured insights as {taxonomy, condition, explanation, example}; and (ii) a Library Evolution phase that diagnoses retrieval misalignments and refines the applicability conditions of stored insights, improving transfer across tasks. This design (1) learns efficiently from limited demonstrations without curated rationales, (2) expands continually without costly retraining by updating the library rather than model weights, and (3) makes knowledge explicit and interpretable for human inspection and intervention. Experiments show that AlphaOPT steadily improves with more data (65% to 72% from 100 to 300 training items) and surpasses the strongest baseline by 7.7% on the out-of-distribution OptiBench dataset when trained only on answers. Code and data are available at: https://github.com/Minw913/AlphaOPT.", "github_manual": "", "github_regex": "https://github.com/Minw913/AlphaOPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.18917", "title": "RIR-Mega: a large-scale simulated room impulse response dataset for machine learning and room acoustics modeling", "authors": [ "Mandip Goswami" ], "github": "https://github.com/mandip42/rirmega", "project_page": "", "abstract": "Room impulse responses are a core resource for dereverberation, robust speech recognition, source localization, and room acoustics estimation. We present RIR-Mega, a large collection of simulated RIRs described by a compact, machine friendly metadata schema and distributed with simple tools for validation and reuse. The dataset ships with a Hugging Face Datasets loader, scripts for metadata checks and checksums, and a reference regression baseline that predicts RT60 like targets from waveforms. On a train and validation split of 36,000 and 4,000 examples, a small Random Forest on lightweight time and spectral features reaches a mean absolute error near 0.013 s and a root mean square error near 0.022 s. We host a subset with 1,000 linear array RIRs and 3,000 circular array RIRs on Hugging Face for streaming and quick tests, and preserve the complete 50,000 RIR archive on Zenodo. The dataset and code are public to support reproducible studies.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mandip42/rirmega", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19430", "title": "GigaBrain-0: A World Model-Powered Vision-Language-Action Model", "authors": [ "GigaBrain Team", "Angen Ye", "Boyuan Wang", "Chaojun Ni", "Guan Huang", "Guosheng Zhao", "Haoyun Li", "Jie Li", "Jiagang Zhu", "Lv Feng", "Peng Li", "Qiuping Deng", "Runqi Ouyang", "Wenkang Qin", "Xinze Chen", "Xiaofeng Wang", "Yang Wang", "Yifan Li", "Yilong Li", "Yiran Ding", "Yuan Xu", "Yun Ye", "Yukun Zhou", "Zhehao Dong", "Zhenan Wang", "Zhichao Liu", "Zheng Zhu" ], "github": "", "project_page": "", "abstract": "Training Vision-Language-Action (VLA) models for generalist robots typically requires large-scale real-world robot data, which is expensive and time-consuming to collect. The inefficiency of physical data collection severely limits the scalability, and generalization capacity of current VLA systems. To address this challenge, we introduce GigaBrain-0, a novel VLA foundation model empowered by world model-generated data (e.g., video generation, real2real transfer, human transfer, view transfer, sim2real transfer data). By leveraging world models to generate diverse data at scale, GigaBrain-0 significantly reduces reliance on real robot data while improving cross-task generalization. Our approach further improves policy robustness through RGBD input modeling and embodied Chain-of-Thought (CoT) supervision, enabling the model to reason about spatial geometry, object states, and long-horizon dependencies during task execution. This leads to substantial gains in real-world performance on dexterous, long-horizon, and mobile manipulation tasks. Extensive experiments demonstrate that GigaBrain-0 achieves superior generalization across variations in appearances (e.g., textures, colors), object placements, and camera viewpoints. Additionally, we present GigaBrain-0-Small, an optimized lightweight variant designed to run efficiently on devices such as the NVIDIA Jetson AGX Orin.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19028", "title": "Are they lovers or friends? Evaluating LLMs' Social Reasoning in English and Korean Dialogues", "authors": [ "Eunsu Kim", "Junyeong Park", "Juhyun Oh", "Kiwoong Park", "Seyoung Song", "A. Seza Dogruoz", "Najoung Kim", "Alice Oh" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) are increasingly used in human-AI interactions, their social reasoning capabilities in interpersonal contexts are critical. We introduce SCRIPTS, a 1k-dialogue dataset in English and Korean, sourced from movie scripts. The task involves evaluating models' social reasoning capability to infer the interpersonal relationships (e.g., friends, sisters, lovers) between speakers in each dialogue. Each dialogue is annotated with probabilistic relational labels (Highly Likely, Less Likely, Unlikely) by native (or equivalent) Korean and English speakers from Korea and the U.S. Evaluating nine models on our task, current proprietary LLMs achieve around 75-80% on the English dataset, whereas their performance on Korean drops to 58-69%. More strikingly, models select Unlikely relationships in 10-25% of their responses. Furthermore, we find that thinking models and chain-of-thought prompting, effective for general reasoning, provide minimal benefits for social reasoning and occasionally amplify social biases. Our findings reveal significant limitations in current LLMs' social reasoning capabilities, highlighting the need for efforts to develop socially-aware language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.17932", "title": "From Charts to Code: A Hierarchical Benchmark for Multimodal Models", "authors": [ "Jiahao Tang", "Henry Hengyuan Zhao", "Lijian Wu", "Yifei Tao", "Dongxing Mao", "Yang Wan", "Jingru Tan", "Min Zeng", "Min Li", "Alex Jinpeng Wang" ], "github": "", "project_page": "", "abstract": "We introduce Chart2Code, a new benchmark for evaluating the chart understanding and code generation capabilities of large multimodal models (LMMs). Chart2Code is explicitly designed from a user-driven perspective, capturing diverse real-world scenarios and progressively increasing task difficulty. It consists of three levels: Level 1 (Chart Reproduction) reproduces charts from a reference figure and user query; Level 2 (Chart Editing) involves complex modifications such as changing chart types or adding elements; and Level 3 (Long-Table to Chart Generation) requires models to transform long, information-dense tables into faithful charts following user instructions. To our knowledge, this is the first hierarchical benchmark that reflects practical chart2code usage while systematically scaling task complexity. In total, Chart2Code contains 2,023 tasks across 22 chart types, paired with multi-level evaluation metrics that assess both code correctness and the visual fidelity of rendered charts. We benchmark 25 state-of-the-art (SoTA) LMMs, including both proprietary and the latest open-source models such as GPT-5, Qwen2.5-VL, InternVL3/3.5, MiMo-VL, and Seed-1.6-VL. Experimental results demonstrate that even the SoTA model GPT-5 averages only 0.57 on code-based evaluation and 0.22 on chart-quality assessment across the editing tasks, underscoring the difficulty of Chart2Code. We anticipate this benchmark will drive advances in multimodal reasoning and foster the development of more robust and general-purpose LMMs. Our code and data are available on Chart2Code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.15731", "title": "Attention Sinks in Diffusion Language Models", "authors": [ "Maximo Eduardo Rulli", "Simone Petruzzi", "Edoardo Michielon", "Fabrizio Silvestri", "Simone Scardapane", "Alessio Devoto" ], "github": "", "project_page": "", "abstract": "Masked Diffusion Language Models (DLMs) have recently emerged as a promising alternative to traditional Autoregressive Models (ARMs). DLMs employ transformer encoders with bidirectional attention, enabling parallel token generation while maintaining competitive performance. Although their efficiency and effectiveness have been extensively studied, the internal mechanisms that govern DLMs remain largely unexplored. In this work, we conduct an empirical analysis of DLM attention patterns, focusing on the attention sinking phenomenon, an effect previously observed in various transformer-based architectures. Our findings reveal that DLMs also exhibit attention sinks, but with distinct characteristics. First, unlike in ARMs, the sink positions in DLMs tend to shift throughout the generation process, displaying a dynamic behaviour. Second, while ARMs are highly sensitive to the removal of attention sinks, DLMs remain robust: masking sinks leads to only a minor degradation in performance. These results provide new insights into the inner workings of diffusion-based language models and highlight fundamental differences in how they allocate and utilize attention compared to autoregressive models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.18909", "title": "Learning from the Best, Differently: A Diversity-Driven Rethinking on Data Selection", "authors": [ "Hongyi He", "Xiao Liu", "Zhenghao Lin", "Mingni Tang", "Yi Cheng", "Jintao Wang", "Wenjie Li", "Peng Cheng", "Yeyun Gong" ], "github": "", "project_page": "", "abstract": "High-quality pre-training data is crutial for large language models, where quality captures factual reliability and semantic value, and diversity ensures broad coverage and distributional heterogeneity. Existing approaches typically rely on single or multiple-dimensional score-based selection. However, directly selecting top-scored data often degrades performance, and sampling from a broader range is required to recover results. The above non-monotonicity between dataset scores and downstream benchmark results reveals a fundamental bias: score-based methods collapse correlated dimensions, causing top-scored data to appear high-quality while systematically overlooking diversity. We argue that ensuring diversity requires decomposing correlated metrics into orthogonal feature dimensions, from which the top-scored data can be directly selected. Therefore, we proposed the Orthogonal Diversity-Aware Selection (ODiS) algorithm, which preserves both quality and diversity during data selection. First, ODiS evaluates data from multiple dimensions, covering language quality, knowledge quality, and comprehension difficulty. The multi-dimensional scores are then decorrelated via Principal Component Analysis (PCA), yielding orthogonal evaluation dimensions. For each dimension, a Roberta-based scorer is trained to regress the data onto PCA-projected scores, enabling scalable inference on large corpora. Finally, ODiS constructs the training dataset by selecting top-scored data within each orthogonal dimension, thereby ensuring both quality and diversity. Empirical results show that ODiS-selected data exhibit less than 2\\% inter-dimension overlap, confirming orthogonality between dimensions. More importantly, models trained with ODiS-selected data significantly outperform other baselines on downstream benchmarks, highlighting the necessity of orthogonal, diversity-aware data selection for LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19753", "title": "When Do Transformers Learn Heuristics for Graph Connectivity?", "authors": [ "Qilin Ye", "Deqing Fu", "Robin Jia", "Vatsal Sharan" ], "github": "", "project_page": "", "abstract": "Transformers often fail to learn generalizable algorithms, instead relying on brittle heuristics. Using graph connectivity as a testbed, we explain this phenomenon both theoretically and empirically. We consider a simplified Transformer architecture, the disentangled Transformer, and prove that an L-layer model has capacity to solve for graphs with diameters up to exactly 3^L, implementing an algorithm equivalent to computing powers of the adjacency matrix. We analyze the training-dynamics, and show that the learned strategy hinges on whether most training instances are within this model capacity. Within-capacity graphs (diameter leq 3^L) drive the learning of a correct algorithmic solution while beyond-capacity graphs drive the learning of a simple heuristic based on node degrees. Finally, we empirically demonstrate that restricting training data within a model's capacity leads to both standard and disentangled transformers learning the exact algorithm rather than the degree-based heuristic.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19492", "title": "Machine Text Detectors are Membership Inference Attacks", "authors": [ "Ryuto Koike", "Liam Dugan", "Masahiro Kaneko", "Chris Callison-Burch", "Naoaki Okazaki" ], "github": "https://github.com/zhang-wei-chao/DC-PDD", "project_page": "", "abstract": "Although membership inference attacks (MIAs) and machine-generated text detection target different goals, identifying training samples and synthetic texts, their methods often exploit similar signals based on a language model's probability distribution. Despite this shared methodological foundation, the two tasks have been independently studied, which may lead to conclusions that overlook stronger methods and valuable insights developed in the other task. In this work, we theoretically and empirically investigate the transferability, i.e., how well a method originally developed for one task performs on the other, between MIAs and machine text detection. For our theoretical contribution, we prove that the metric that achieves the asymptotically highest performance on both tasks is the same. We unify a large proportion of the existing literature in the context of this optimal metric and hypothesize that the accuracy with which a given method approximates this metric is directly correlated with its transferability. Our large-scale empirical experiments, including 7 state-of-the-art MIA methods and 5 state-of-the-art machine text detectors across 13 domains and 10 generators, demonstrate very strong rank correlation (rho > 0.6) in cross-task performance. We notably find that Binoculars, originally designed for machine text detection, achieves state-of-the-art performance on MIA benchmarks as well, demonstrating the practical impact of the transferability. Our findings highlight the need for greater cross-task awareness and collaboration between the two research communities. To facilitate cross-task developments and fair evaluations, we introduce MINT, a unified evaluation suite for MIAs and machine-generated text detection, with implementation of 15 recent methods from both tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhang-wei-chao/DC-PDD", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.15050", "title": "Directional Reasoning Injection for Fine-Tuning MLLMs", "authors": [ "Chao Huang", "Zeliang Zhang", "Jiang Liu", "Ximeng Sun", "Jialian Wu", "Xiaodong Yu", "Ze Wang", "Chenliang Xu", "Emad Barsoum", "Zicheng Liu" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) are rapidly advancing, yet their reasoning ability often lags behind that of strong text-only counterparts. Existing methods to bridge this gap rely on supervised fine-tuning over large-scale multimodal reasoning data or reinforcement learning, both of which are resource-intensive. A promising alternative is model merging, which interpolates parameters between reasoning-enhanced LLMs and multimodal variants. However, our analysis shows that naive merging is not always a \"free lunch\": its effectiveness varies drastically across model families, with some (e.g., LLaVA, Idefics) benefiting while others (e.g., Qwen) suffer performance degradation. To address this, we propose Directional Reasoning Injection for Fine-Tuning (DRIFT) MLLMs, a lightweight method that transfers reasoning knowledge in the gradient space, without destabilizing multimodal alignment. DRIFT precomputes a reasoning prior as the parameter-space difference between reasoning and multimodal variants, then uses it to bias gradients during multimodal fine-tuning. This approach preserves the simplicity of standard supervised fine-tuning pipelines while enabling efficient reasoning transfer. Extensive experiments on multimodal reasoning benchmarks, including MathVista and MathVerse, demonstrate that DRIFT consistently improves reasoning performance over naive merging and supervised fine-tuning, while matching or surpassing training-heavy methods at a fraction of the cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.18840", "title": "See the Text: From Tokenization to Visual Reading", "authors": [ "Ling Xing", "Alex Jinpeng Wang", "Rui Yan", "Hongyu Qu", "Zechao Li", "Jinhui Tang" ], "github": "", "project_page": "", "abstract": "People see text. Humans read by recognizing words as visual objects, including their shapes, layouts, and patterns, before connecting them to meaning, which enables us to handle typos, distorted fonts, and various scripts effectively. Modern large language models (LLMs), however, rely on subword tokenization, fragmenting text into pieces from a fixed vocabulary. While effective for high-resource languages, this approach over-segments low-resource languages, yielding long, linguistically meaningless sequences and inflating computation. In this work, we challenge this entrenched paradigm and move toward a vision-centric alternative. Our method, SeeTok, renders text as images (visual-text) and leverages pretrained multimodal LLMs to interpret them, reusing strong OCR and text-vision alignment abilities learned from large-scale multimodal training. Across three different language tasks, SeeTok matches or surpasses subword tokenizers while requiring 4.43 times fewer tokens and reducing FLOPs by 70.5%, with additional gains in cross-lingual generalization, robustness to typographic noise, and linguistic hierarchy. SeeTok signals a shift from symbolic tokenization to human-like visual reading, and takes a step toward more natural and cognitively inspired language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.15511", "title": "Language Models are Injective and Hence Invertible", "authors": [ "Giorgos Nikolaou", "Tommaso Mencattini", "Donato Crisostomi", "Andrea Santilli", "Yannis Panagakis", "Emanuele Rodola'" ], "github": "", "project_page": "", "abstract": "Transformer components such as non-linear activations and normalization are inherently non-injective, suggesting that different inputs could map to the same output and prevent exact recovery of the input from a model's representations. In this paper, we challenge this view. First, we prove mathematically that transformer language models mapping discrete input sequences to their corresponding sequence of continuous representations are injective and therefore lossless, a property established at initialization and preserved during training. Second, we confirm this result empirically through billions of collision tests on six state-of-the-art language models, and observe no collisions. Third, we operationalize injectivity: we introduce SipIt, the first algorithm that provably and efficiently reconstructs the exact input text from hidden activations, establishing linear-time guarantees and demonstrating exact invertibility in practice. Overall, our work establishes injectivity as a fundamental and exploitable property of language models, with direct implications for transparency, interpretability, and safe deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.15015", "title": "DeLeaker: Dynamic Inference-Time Reweighting For Semantic Leakage Mitigation in Text-to-Image Models", "authors": [ "Mor Ventura", "Michael Toker", "Or Patashnik", "Yonatan Belinkov", "Roi Reichart" ], "github": "", "project_page": "", "abstract": "Text-to-Image (T2I) models have advanced rapidly, yet they remain vulnerable to semantic leakage, the unintended transfer of semantically related features between distinct entities. Existing mitigation strategies are often optimization-based or dependent on external inputs. We introduce DeLeaker, a lightweight, optimization-free inference-time approach that mitigates leakage by directly intervening on the model's attention maps. Throughout the diffusion process, DeLeaker dynamically reweights attention maps to suppress excessive cross-entity interactions while strengthening the identity of each entity. To support systematic evaluation, we introduce SLIM (Semantic Leakage in IMages), the first dataset dedicated to semantic leakage, comprising 1,130 human-verified samples spanning diverse scenarios, together with a novel automatic evaluation framework. Experiments demonstrate that DeLeaker consistently outperforms all baselines, even when they are provided with external information, achieving effective leakage mitigation without compromising fidelity or quality. These results underscore the value of attention control and pave the way for more semantically precise T2I models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19592", "title": "Decomposed Attention Fusion in MLLMs for Training-Free Video Reasoning Segmentation", "authors": [ "Su Ho Han", "Jeongseok Hyun", "Pilhyeon Lee", "Minho Shim", "Dongyoon Wee", "Seon Joo Kim" ], "github": "https://github.com/HYUNJS/DecAF", "project_page": "", "abstract": "Multimodal large language models (MLLMs) demonstrate strong video understanding by attending to visual tokens relevant to textual queries. To directly adapt this for localization in a training-free manner, we cast video reasoning segmentation as a video QA task and extract attention maps via rollout mechanism. However, raw attention maps are noisy and poorly aligned with object regions. We propose Decomposed Attention Fusion (DecAF), which refines these maps through two mechanisms: (1) contrastive object-background fusion and (2) complementary video-frame fusion. This method suppresses irrelevant activations and enhances object-focused cues, enabling direct conversion of attention maps into coarse segmentation masks. In addition, we introduce attention-guided SAM2 prompting for obtaining fine-grained masks. Unlike existing methods that jointly train MLLMs with SAM, our method operates entirely without retraining. DecAF outperforms training-free methods and achieves performance comparable to training-based methods on both referring and reasoning VOS benchmarks. The code will be available at https://github.com/HYUNJS/DecAF.", "github_manual": "", "github_regex": "https://github.com/HYUNJS/DecAF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.16435", "title": "What Questions Should Robots Be Able to Answer? A Dataset of User Questions for Explainable Robotics", "authors": [ "Lennart Wachowiak", "Andrew Coles", "Gerard Canal", "Oya Celiktutan" ], "github": "https://github.com/lwachowiak/xai-questions-dataset", "project_page": "", "abstract": "With the growing use of large language models and conversational interfaces in human-robot interaction, robots' ability to answer user questions is more important than ever. We therefore introduce a dataset of 1,893 user questions for household robots, collected from 100 participants and organized into 12 categories and 70 subcategories. Most work in explainable robotics focuses on why-questions. In contrast, our dataset provides a wide variety of questions, from questions about simple execution details to questions about how the robot would act in hypothetical scenarios -- thus giving roboticists valuable insights into what questions their robot needs to be able to answer. To collect the dataset, we created 15 video stimuli and 7 text stimuli, depicting robots performing varied household tasks. We then asked participants on Prolific what questions they would want to ask the robot in each portrayed situation. In the final dataset, the most frequent categories are questions about task execution details (22.5%), the robot's capabilities (12.7%), and performance assessments (11.3%). Although questions about how robots would handle potentially difficult scenarios and ensure correct behavior are less frequent, users rank them as the most important for robots to be able to answer. Moreover, we find that users who identify as novices in robotics ask different questions than more experienced users. Novices are more likely to inquire about simple facts, such as what the robot did or the current state of the environment. As robots enter environments shared with humans and language becomes central to giving instructions and interaction, this dataset provides a valuable foundation for (i) identifying the information robots need to log and expose to conversational interfaces, (ii) benchmarking question-answering modules, and (iii) designing explanation strategies that align with user expectations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lwachowiak/xai-questions-dataset", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19127", "title": "Steering Autoregressive Music Generation with Recursive Feature Machines", "authors": [ "Daniel Zhao", "Daniel Beaglehole", "Taylor Berg-Kirkpatrick", "Julian McAuley", "Zachary Novack" ], "github": "https://github.com/astradzhao/music-rfm", "project_page": "", "abstract": "Controllable music generation remains a significant challenge, with existing methods often requiring model retraining or introducing audible artifacts. We introduce MusicRFM, a framework that adapts Recursive Feature Machines (RFMs) to enable fine-grained, interpretable control over frozen, pre-trained music models by directly steering their internal activations. RFMs analyze a model's internal gradients to produce interpretable \"concept directions\", or specific axes in the activation space that correspond to musical attributes like notes or chords. We first train lightweight RFM probes to discover these directions within MusicGen's hidden states; then, during inference, we inject them back into the model to guide the generation process in real-time without per-step optimization. We present advanced mechanisms for this control, including dynamic, time-varying schedules and methods for the simultaneous enforcement of multiple musical properties. Our method successfully navigates the trade-off between control and generation quality: we can increase the accuracy of generating a target musical note from 0.23 to 0.82, while text prompt adherence remains within approximately 0.02 of the unsteered baseline, demonstrating effective control with minimal impact on prompt fidelity. We release code to encourage further exploration on RFMs in the music domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/astradzhao/music-rfm", "github_api_added_by": "auto" }, { "date": "2025-10-23", "arxiv_id": "2510.18091", "title": "Accelerating Vision Transformers with Adaptive Patch Sizes", "authors": [ "Rohan Choudhury", "JungEun Kim", "Jinhyung Park", "Eunho Yang", "L\u00e1szl\u00f3 A. Jeni", "Kris M. Kitani" ], "github": "", "project_page": "", "abstract": "Vision Transformers (ViTs) partition input images into uniformly sized patches regardless of their content, resulting in long input sequence lengths for high-resolution images. We present Adaptive Patch Transformers (APT), which addresses this by using multiple different patch sizes within the same image. APT reduces the total number of input tokens by allocating larger patch sizes in more homogeneous areas and smaller patches in more complex ones. APT achieves a drastic speedup in ViT inference and training, increasing throughput by 40% on ViT-L and 50% on ViT-H while maintaining downstream performance, and can be applied to a previously fine-tuned ViT, converging in as little as 1 epoch. It also significantly reduces training and inference time without loss of performance in high-resolution dense visual tasks, achieving up to 30\\% faster training and inference in visual QA, object detection, and semantic segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.18034", "title": "SAVANT: Semantic Analysis with Vision-Augmented Anomaly deTection", "authors": [ "Roberto Brusnicki", "David Pop", "Yuan Gao", "Mattia Piccinini", "Johannes Betz" ], "github": "", "project_page": "", "abstract": "Autonomous driving systems remain critically vulnerable to the long-tail of rare, out-of-distribution scenarios with semantic anomalies. While Vision Language Models (VLMs) offer promising reasoning capabilities, naive prompting approaches yield unreliable performance and depend on expensive proprietary models, limiting practical deployment. We introduce SAVANT (Semantic Analysis with Vision-Augmented Anomaly deTection), a structured reasoning framework that achieves high accuracy and recall in detecting anomalous driving scenarios from input images through layered scene analysis and a two-phase pipeline: structured scene description extraction followed by multi-modal evaluation. Our approach transforms VLM reasoning from ad-hoc prompting to systematic analysis across four semantic layers: Street, Infrastructure, Movable Objects, and Environment. SAVANT achieves 89.6% recall and 88.0% accuracy on real-world driving scenarios, significantly outperforming unstructured baselines. More importantly, we demonstrate that our structured framework enables a fine-tuned 7B parameter open-source model (Qwen2.5VL) to achieve 90.8% recall and 93.8% accuracy - surpassing all models evaluated while enabling local deployment at near-zero cost. By automatically labeling over 9,640 real-world images with high accuracy, SAVANT addresses the critical data scarcity problem in anomaly detection and provides a practical path toward reliable, accessible semantic monitoring for autonomous systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.18279", "title": "Text or Pixels? It Takes Half: On the Token Efficiency of Visual Text Inputs in Multimodal LLMs", "authors": [ "Yanhong Li", "Zixuan Lan", "Jiawei Zhou" ], "github": "https://github.com/yanhong-lbh/text_or_pixels", "project_page": "", "abstract": "Large language models (LLMs) and their multimodal variants can now process visual inputs, including images of text. This raises an intriguing question: can we compress textual inputs by feeding them as images to reduce token usage while preserving performance? In this paper, we show that visual text representations are a practical and surprisingly effective form of input compression for decoder LLMs. We exploit the idea of rendering long text inputs as a single image and provide it directly to the model. This leads to dramatically reduced number of decoder tokens required, offering a new form of input compression. Through experiments on two distinct benchmarks RULER (long-context retrieval) and CNN/DailyMail (document summarization) we demonstrate that this text-as-image method yields substantial token savings (often nearly half) without degrading task performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yanhong-lbh/text_or_pixels", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.20168", "title": "DeepWideSearch: Benchmarking Depth and Width in Agentic Information Seeking", "authors": [ "Tian Lan", "Bin Zhu", "Qianghuai Jia", "Junyang Ren", "Haijun Li", "Longyue Wang", "Zhao Xu", "Weihua Luo", "Kaifu Zhang" ], "github": "", "project_page": "", "abstract": "Current search agents fundamentally lack the ability to simultaneously perform deep reasoning over multi-hop retrieval and wide-scale information collection-a critical deficiency for real-world applications like comprehensive market analysis and business development. To bridge this gap, we introduce DeepWideSearch, the first benchmark explicitly designed to evaluate agents to integrate depth and width in information seeking. In DeepWideSearch, agents must process a large volume of data, each requiring deep reasoning over multi-hop retrieval paths. Specifically, we propose two methods to converse established datasets, resulting in a curated collection of 220 questions spanning 15 diverse domains. Extensive experiments demonstrate that even state-of-the-art agents achieve only 2.39% average success rate on DeepWideSearch, highlighting the substantial challenge of integrating depth and width search in information-seeking tasks. Furthermore, our error analysis reveals four failure modes: lack of reflection, overreliance on internal knowledge, insufficient retrieval, and context overflow-exposing key limitations in current agent architectures. We publicly release DeepWideSearch to catalyze future research on more capable and robust information-seeking agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-23", "arxiv_id": "2510.19631", "title": "HSCodeComp: A Realistic and Expert-level Benchmark for Deep Search Agents in Hierarchical Rule Application", "authors": [ "Yiqian Yang", "Tian Lan", "Qianghuai Jia", "Li Zhu", "Hui Jiang", "Hang Zhu", "Longyue Wang", "Weihua Luo", "Kaifu Zhang" ], "github": "", "project_page": "", "abstract": "Effective deep search agents must not only access open-domain and domain-specific knowledge but also apply complex rules-such as legal clauses, medical manuals and tariff rules. These rules often feature vague boundaries and implicit logic relationships, making precise application challenging for agents. However, this critical capability is largely overlooked by current agent benchmarks. To fill this gap, we introduce HSCodeComp, the first realistic, expert-level e-commerce benchmark designed to evaluate deep search agents in hierarchical rule application. In this task, the deep reasoning process of agents is guided by these rules to predict 10-digit Harmonized System Code (HSCode) of products with noisy but realistic descriptions. These codes, established by the World Customs Organization, are vital for global supply chain efficiency. Built from real-world data collected from large-scale e-commerce platforms, our proposed HSCodeComp comprises 632 product entries spanning diverse product categories, with these HSCodes annotated by several human experts. Extensive experimental results on several state-of-the-art LLMs, open-source, and closed-source agents reveal a huge performance gap: best agent achieves only 46.8% 10-digit accuracy, far below human experts at 95.0%. Besides, detailed analysis demonstrates the challenges of hierarchical rule application, and test-time scaling fails to improve performance further.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.20579", "title": "Open-o3 Video: Grounded Video Reasoning with Explicit Spatio-Temporal Evidence", "authors": [ "Jiahao Meng", "Xiangtai Li", "Haochen Wang", "Yue Tan", "Tao Zhang", "Lingdong Kong", "Yunhai Tong", "Anran Wang", "Zhiyang Teng", "Yujing Wang", "Zhuochen Wang" ], "github": "", "project_page": "", "abstract": "Most video reasoning models only generate textual reasoning traces without indicating when and where key evidence appears. Recent models such as OpenAI-o3 have sparked wide interest in evidence-centered reasoning for images, yet extending this ability to videos is more challenging, as it requires joint temporal tracking and spatial localization across dynamic scenes. We introduce Open-o3 Video, a non-agent framework that integrates explicit spatio-temporal evidence into video reasoning, and carefully collect training data and design training strategies to address the aforementioned challenges. The model highlights key timestamps, objects, and bounding boxes alongside its answers, allowing reasoning to be grounded in concrete visual observations. To enable this functionality, we first curate and build two high-quality datasets, STGR-CoT-30k for SFT and STGR-RL-36k for RL, with carefully constructed temporal and spatial annotations, since most existing datasets offer either temporal spans for videos or spatial boxes on images, lacking unified spatio-temporal supervision and reasoning traces. Then, we adopt a cold-start reinforcement learning strategy with multiple specially designed rewards that jointly encourage answer accuracy, temporal alignment, and spatial precision. On V-STAR benchmark, Open-o3 Video achieves state-of-the-art performance, raising mAM by 14.4% and mLGM by 24.2% on the Qwen2.5-VL baseline. Consistent improvements are also observed on a broad range of video understanding benchmarks, including VideoMME, WorldSense, VideoMMMU, and TVGBench. Beyond accuracy, the reasoning traces produced by Open-o3 Video also provide valuable signals for test-time scaling, enabling confidence-aware verification and improving answer reliability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.19304", "title": "Loopholing Discrete Diffusion: Deterministic Bypass of the Sampling Wall", "authors": [ "Mingyu Jo", "Jaesik Yoon", "Justin Deschenaux", "Caglar Gulcehre", "Sungjin Ahn" ], "github": "", "project_page": "", "abstract": "Discrete diffusion models offer a promising alternative to autoregressive generation through parallel decoding, but they suffer from a sampling wall: once categorical sampling occurs, rich distributional information collapses into one-hot vectors and cannot be propagated across steps, forcing subsequent steps to operate with limited information. To mitigate this problem, we introduce Loopholing, a novel and simple mechanism that preserves this information via a deterministic latent pathway, leading to Loopholing Discrete Diffusion Models (LDDMs). Trained efficiently with a self-conditioning strategy, LDDMs achieve substantial gains-reducing generative perplexity by up to 61% over prior baselines, closing (and in some cases surpassing) the gap with autoregressive models, and producing more coherent text. Applied to reasoning tasks, LDDMs also improve performance on arithmetic benchmarks such as Countdown and Game of 24. These results also indicate that loopholing mitigates idle steps and oscillations, providing a scalable path toward high-quality non-autoregressive text generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.20187", "title": "Every Question Has Its Own Value: Reinforcement Learning with Explicit Human Values", "authors": [ "Dian Yu", "Yulai Zhao", "Kishan Panaganti", "Linfeng Song", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "We propose Reinforcement Learning with Explicit Human Values (RLEV), a method that aligns Large Language Model (LLM) optimization directly with quantifiable human value signals. While Reinforcement Learning with Verifiable Rewards (RLVR) effectively trains models in objective domains using binary correctness rewards, it overlooks that not all tasks are equally significant. RLEV extends this framework by incorporating human-defined value signals directly into the reward function. Using exam-style data with explicit ground-truth value labels, RLEV consistently outperforms correctness-only baselines across multiple RL algorithms and model scales. Crucially, RLEV policies not only improve value-weighted accuracy but also learn a value-sensitive termination policy: concise for low-value prompts, thorough for high-value ones. We demonstrate this behavior stems from value-weighted gradient amplification on end-of-sequence tokens. Ablation studies confirm the gain is causally linked to value alignment. RLEV remains robust under noisy value signals, such as difficulty-based labels, demonstrating that optimizing for an explicit utility function offers a practical path to aligning LLMs with human priorities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.20822", "title": "HoloCine: Holistic Generation of Cinematic Multi-Shot Long Video Narratives", "authors": [ "Yihao Meng", "Hao Ouyang", "Yue Yu", "Qiuyu Wang", "Wen Wang", "Ka Leong Cheng", "Hanlin Wang", "Yixuan Li", "Cheng Chen", "Yanhong Zeng", "Yujun Shen", "Huamin Qu" ], "github": "", "project_page": "", "abstract": "State-of-the-art text-to-video models excel at generating isolated clips but fall short of creating the coherent, multi-shot narratives, which are the essence of storytelling. We bridge this \"narrative gap\" with HoloCine, a model that generates entire scenes holistically to ensure global consistency from the first shot to the last. Our architecture achieves precise directorial control through a Window Cross-Attention mechanism that localizes text prompts to specific shots, while a Sparse Inter-Shot Self-Attention pattern (dense within shots but sparse between them) ensures the efficiency required for minute-scale generation. Beyond setting a new state-of-the-art in narrative coherence, HoloCine develops remarkable emergent abilities: a persistent memory for characters and scenes, and an intuitive grasp of cinematic techniques. Our work marks a pivotal shift from clip synthesis towards automated filmmaking, making end-to-end cinematic creation a tangible future. Our code is available at: https://holo-cine.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.20820", "title": "LayerComposer: Interactive Personalized T2I via Spatially-Aware Layered Canvas", "authors": [ "Guocheng Gordon Qian", "Ruihang Zhang", "Tsai-Shien Chen", "Yusuf Dalva", "Anujraaj Argo Goyal", "Willi Menapace", "Ivan Skorokhodov", "Meng Dong", "Arpit Sahni", "Daniil Ostashev", "Ju Hu", "Sergey Tulyakov", "Kuan-Chieh Jackson Wang" ], "github": "", "project_page": "", "abstract": "Despite their impressive visual fidelity, existing personalized generative models lack interactive control over spatial composition and scale poorly to multiple subjects. To address these limitations, we present LayerComposer, an interactive framework for personalized, multi-subject text-to-image generation. Our approach introduces two main contributions: (1) a layered canvas, a novel representation in which each subject is placed on a distinct layer, enabling occlusion-free composition; and (2) a locking mechanism that preserves selected layers with high fidelity while allowing the remaining layers to adapt flexibly to the surrounding context. Similar to professional image-editing software, the proposed layered canvas allows users to place, resize, or lock input subjects through intuitive layer manipulation. Our versatile locking mechanism requires no architectural changes, relying instead on inherent positional embeddings combined with a new complementary data sampling strategy. Extensive experiments demonstrate that LayerComposer achieves superior spatial control and identity preservation compared to the state-of-the-art methods in multi-subject personalized image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.20270", "title": "ImpossibleBench: Measuring LLMs' Propensity of Exploiting Test Cases", "authors": [ "Ziqian Zhong", "Aditi Raghunathan", "Nicholas Carlini" ], "github": "https://github.com/safety-research/impossiblebench", "project_page": "", "abstract": "The tendency to find and exploit \"shortcuts\" to complete tasks poses significant risks for reliable assessment and deployment of large language models (LLMs). For example, an LLM agent with access to unit tests may delete failing tests rather than fix the underlying bug. Such behavior undermines both the validity of benchmark results and the reliability of real-world LLM coding assistant deployments. To quantify, study, and mitigate such behavior, we introduce ImpossibleBench, a benchmark framework that systematically measures LLM agents' propensity to exploit test cases. ImpossibleBench creates \"impossible\" variants of tasks from existing benchmarks like LiveCodeBench and SWE-bench by introducing direct conflicts between the natural-language specification and the unit tests. We measure an agent's \"cheating rate\" as its pass rate on these impossible tasks, where any pass necessarily implies a specification-violating shortcut. As a practical framework, ImpossibleBench is not just an evaluation but a versatile tool. We demonstrate its utility for: (1) studying model behaviors, revealing more fine-grained details of cheating behaviors from simple test modification to complex operator overloading; (2) context engineering, showing how prompt, test access and feedback loop affect cheating rates; and (3) developing monitoring tools, providing a testbed with verified deceptive solutions. We hope ImpossibleBench serves as a useful framework for building more robust and reliable LLM systems. Our implementation can be found at https://github.com/safety-research/impossiblebench.", "github_manual": "", "github_regex": "https://github.com/safety-research/impossiblebench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.20803", "title": "ARGenSeg: Image Segmentation with Autoregressive Image Generation Model", "authors": [ "Xiaolong Wang", "Lixiang Ru", "Ziyuan Huang", "Kaixiang Ji", "Dandan Zheng", "Jingdong Chen", "Jun Zhou" ], "github": "", "project_page": "", "abstract": "We propose a novel AutoRegressive Generation-based paradigm for image Segmentation (ARGenSeg), achieving multimodal understanding and pixel-level perception within a unified framework. Prior works integrating image segmentation into multimodal large language models (MLLMs) typically employ either boundary points representation or dedicated segmentation heads. These methods rely on discrete representations or semantic prompts fed into task-specific decoders, which limits the ability of the MLLM to capture fine-grained visual details. To address these challenges, we introduce a segmentation framework for MLLM based on image generation, which naturally produces dense masks for target objects. We leverage MLLM to output visual tokens and detokenize them into images using an universal VQ-VAE, making the segmentation fully dependent on the pixel-level understanding of the MLLM. To reduce inference latency, we employ a next-scale-prediction strategy to generate required visual tokens in parallel. Extensive experiments demonstrate that our method surpasses prior state-of-the-art approaches on multiple segmentation datasets with a remarkable boost in inference speed, while maintaining strong understanding capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.20771", "title": "AlphaFlow: Understanding and Improving MeanFlow Models", "authors": [ "Huijie Zhang", "Aliaksandr Siarohin", "Willi Menapace", "Michael Vasilkovsky", "Sergey Tulyakov", "Qing Qu", "Ivan Skorokhodov" ], "github": "https://github.com/snap-research/alphaflow", "project_page": "", "abstract": "MeanFlow has recently emerged as a powerful framework for few-step generative modeling trained from scratch, but its success is not yet fully understood. In this work, we show that the MeanFlow objective naturally decomposes into two parts: trajectory flow matching and trajectory consistency. Through gradient analysis, we find that these terms are strongly negatively correlated, causing optimization conflict and slow convergence. Motivated by these insights, we introduce alpha-Flow, a broad family of objectives that unifies trajectory flow matching, Shortcut Model, and MeanFlow under one formulation. By adopting a curriculum strategy that smoothly anneals from trajectory flow matching to MeanFlow, alpha-Flow disentangles the conflicting objectives, and achieves better convergence. When trained from scratch on class-conditional ImageNet-1K 256x256 with vanilla DiT backbones, alpha-Flow consistently outperforms MeanFlow across scales and settings. Our largest alpha-Flow-XL/2+ model achieves new state-of-the-art results using vanilla DiT backbones, with FID scores of 2.58 (1-NFE) and 2.15 (2-NFE).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/snap-research/alphaflow", "github_api_added_by": "auto" }, { "date": "2025-10-24", "arxiv_id": "2510.19600", "title": "Human-Agent Collaborative Paper-to-Page Crafting for Under $0.1", "authors": [ "Qianli Ma", "Siyu Wang", "Yilin Chen", "Yinhao Tang", "Yixiang Yang", "Chang Guo", "Bingjie Gao", "Zhening Xing", "Yanan Sun", "Zhipeng Zhang" ], "github": "", "project_page": "", "abstract": "In the quest for scientific progress, communicating research is as vital as the discovery itself. Yet, researchers are often sidetracked by the manual, repetitive chore of building project webpages to make their dense papers accessible. While automation has tackled static slides and posters, the dynamic, interactive nature of webpages has remained an unaddressed challenge. To bridge this gap, we reframe the problem, arguing that the solution lies not in a single command, but in a collaborative, hierarchical process. We introduce AutoPage, a novel multi-agent system that embodies this philosophy. AutoPage deconstructs paper-to-page creation into a coarse-to-fine pipeline from narrative planning to multimodal content generation and interactive rendering. To combat AI hallucination, dedicated \"Checker\" agents verify each step against the source paper, while optional human checkpoints ensure the final product aligns perfectly with the author's vision, transforming the system from a mere tool into a powerful collaborative assistant. To rigorously validate our approach, we also construct PageBench, the first benchmark for this new task. Experiments show AutoPage not only generates high-quality, visually appealing pages but does so with remarkable efficiency in under 15 minutes for less than \\0.1. Code and dataset will be released at https://mqleet.github.io/AutoPage_ProjectPage/{Webpage}$.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.20470", "title": "Conan: Progressive Learning to Reason Like a Detective over Multi-Scale Visual Evidence", "authors": [ "Kun Ouyang", "Yuanxin Liu", "Linli Yao", "Yishuo Cai", "Hao Zhou", "Jie Zhou", "Fandong Meng", "Xu Sun" ], "github": "", "project_page": "", "abstract": "Video reasoning, which requires multi-step deduction across frames, remains a major challenge for multimodal large language models (MLLMs). While reinforcement learning (RL)-based methods enhance reasoning capabilities, they often rely on text-only chains that yield ungrounded or hallucinated conclusions. Conversely, frame-retrieval approaches introduce visual grounding but still struggle with inaccurate evidence localization. To address these challenges, we present Conan, a framework for evidence-grounded multi-step video reasoning. Conan identifies contextual and evidence frames, reasons over cross-frame clues, and adaptively decides when to conclude or explore further. To achieve this, we (1) construct Conan-91K, a large-scale dataset of automatically generated reasoning traces that includes frame identification, evidence reasoning, and action decision, and (2) design a multi-stage progressive cold-start strategy combined with an Identification-Reasoning-Action (AIR) RLVR training framework to jointly enhance multi-step visual reasoning. Extensive experiments on six multi-step reasoning benchmarks demonstrate that Conan surpasses the baseline Qwen2.5-VL-7B-Instruct by an average of over 10% in accuracy, achieving state-of-the-art performance. Furthermore, Conan generalizes effectively to long-video understanding tasks, validating its strong scalability and robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.19779", "title": "AdaSPEC: Selective Knowledge Distillation for Efficient Speculative Decoders", "authors": [ "Yuezhou Hu", "Jiaxin Guo", "Xinyu Feng", "Tuo Zhao" ], "github": "https://github.com/yuezhouhu/adaspec", "project_page": "", "abstract": "Speculative Decoding (SD) accelerates large language model inference by employing a small draft model to generate predictions, which are then verified by a larger target model. The effectiveness of SD hinges on the alignment between these models, which is typically enhanced by Knowledge Distillation (KD). However, conventional KD methods aim to minimize the KL divergence between the draft and target models across all tokens, a goal that is misaligned with the true objective of SD, which is to maximize token acceptance rate. Therefore, draft models often struggle to fully assimilate the target model's knowledge due to capacity constraints, leading to suboptimal performance. To address this challenge, we propose AdaSPEC, a novel method that incorporates selective token filtering into the KD process. AdaSPEC utilizes a reference model to identify and filter out difficult-to-fit tokens, enabling the distillation of a draft model that better aligns with the target model on simpler tokens. This approach improves the overall token acceptance rate without compromising generation quality. We evaluate AdaSPEC across diverse tasks, including arithmetic reasoning, instruction-following, coding, and summarization, using model configurations of 31M/1.4B and 350M/2.7B parameters. Our results demonstrate that AdaSPEC consistently outperforms the state-of-the-art DistillSpec method, achieving higher acceptance rates across all tasks (up to 15\\%). The code is publicly available at https://github.com/yuezhouhu/adaspec.", "github_manual": "", "github_regex": "https://github.com/yuezhouhu/adaspec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.19365", "title": "The Massive Legal Embedding Benchmark (MLEB)", "authors": [ "Umar Butler", "Abdur-Rahman Butler", "Adrian Lucas Malec" ], "github": "", "project_page": "", "abstract": "We present the Massive Legal Embedding Benchmark (MLEB), the largest, most diverse, and most comprehensive open-source benchmark for legal information retrieval to date. MLEB consists of ten expert-annotated datasets spanning multiple jurisdictions (the US, UK, EU, Australia, Ireland, and Singapore), document types (cases, legislation, regulatory guidance, contracts, and literature), and task types (search, zero-shot classification, and question answering). Seven of the datasets in MLEB were newly constructed in order to fill domain and jurisdictional gaps in the open-source legal information retrieval landscape. We document our methodology in building MLEB and creating the new constituent datasets, and release our code, results, and data openly to assist with reproducible evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.20766", "title": "DyPE: Dynamic Position Extrapolation for Ultra High Resolution Diffusion", "authors": [ "Noam Issachar", "Guy Yariv", "Sagie Benaim", "Yossi Adi", "Dani Lischinski", "Raanan Fattal" ], "github": "", "project_page": "", "abstract": "Diffusion Transformer models can generate images with remarkable fidelity and detail, yet training them at ultra-high resolutions remains extremely costly due to the self-attention mechanism's quadratic scaling with the number of image tokens. In this paper, we introduce Dynamic Position Extrapolation (DyPE), a novel, training-free method that enables pre-trained diffusion transformers to synthesize images at resolutions far beyond their training data, with no additional sampling cost. DyPE takes advantage of the spectral progression inherent to the diffusion process, where low-frequency structures converge early, while high-frequencies take more steps to resolve. Specifically, DyPE dynamically adjusts the model's positional encoding at each diffusion step, matching their frequency spectrum with the current stage of the generative process. This approach allows us to generate images at resolutions that exceed the training resolution dramatically, e.g., 16 million pixels using FLUX. On multiple benchmarks, DyPE consistently improves performance and achieves state-of-the-art fidelity in ultra-high-resolution image generation, with gains becoming even more pronounced at higher resolutions. Project page is available at https://noamissachar.github.io/DyPE/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.18821", "title": "Search Self-play: Pushing the Frontier of Agent Capability without Supervision", "authors": [ "Hongliang Lu", "Yuhang Wen", "Pengyu Cheng", "Ruijin Ding", "Haotian Xu", "Jiaqi Guo", "Chutian Wang", "Haonan Chen", "Xiaoxi Jiang", "Guanjun Jiang" ], "github": "https://github.com/Alibaba-Quark/SSP", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has become the mainstream technique for training LLM agents. However, RLVR highly depends on well-crafted task queries and corresponding ground-truth answers to provide accurate rewards, which requires massive human efforts and hinders the RL scaling processes, especially under agentic scenarios. Although a few recent works explore task synthesis methods, the difficulty of generated agentic tasks can hardly be controlled to provide effective RL training advantages. To achieve agentic RLVR with higher scalability, we explore self-play training for deep search agents, in which the learning LLM utilizes multi-turn search engine calling and acts simultaneously as both a task proposer and a problem solver. The task proposer aims to generate deep search queries with well-defined ground-truth answers and increasing task difficulty. The problem solver tries to handle the generated search queries and output the correct answer predictions. To ensure that each generated search query has accurate ground truth, we collect all the searching results from the proposer's trajectory as external knowledge, then conduct retrieval-augmentation generation (RAG) to test whether the proposed query can be correctly answered with all necessary search documents provided. In this search self-play (SSP) game, the proposer and the solver co-evolve their agent capabilities through both competition and cooperation. With substantial experimental results, we find that SSP can significantly improve search agents' performance uniformly on various benchmarks without any supervision under both from-scratch and continuous RL training setups. The code is at https://github.com/Alibaba-Quark/SSP.", "github_manual": "", "github_regex": "https://github.com/Alibaba-Quark/SSP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.20668", "title": "From Masks to Worlds: A Hitchhiker's Guide to World Models", "authors": [ "Jinbin Bai", "Yu Lei", "Hecong Wu", "Yuchen Zhu", "Shufan Li", "Yi Xin", "Xiangtai Li", "Molei Tao", "Aditya Grover", "Ming-Hsuan Yang" ], "github": "", "project_page": "", "abstract": "This is not a typical survey of world models; it is a guide for those who want to build worlds. We do not aim to catalog every paper that has ever mentioned a ``world model\". Instead, we follow one clear road: from early masked models that unified representation learning across modalities, to unified architectures that share a single paradigm, then to interactive generative models that close the action-perception loop, and finally to memory-augmented systems that sustain consistent worlds over time. We bypass loosely related branches to focus on the core: the generative heart, the interactive loop, and the memory system. We show that this is the most promising path towards true world models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.15804", "title": "Emergence of Linear Truth Encodings in Language Models", "authors": [ "Shauli Ravfogel", "Gilad Yehudai", "Tal Linzen", "Joan Bruna", "Alberto Bietti" ], "github": "", "project_page": "", "abstract": "Recent probing studies reveal that large language models exhibit linear subspaces that separate true from false statements, yet the mechanism behind their emergence is unclear. We introduce a transparent, one-layer transformer toy model that reproduces such truth subspaces end-to-end and exposes one concrete route by which they can arise. We study one simple setting in which truth encoding can emerge: a data distribution where factual statements co-occur with other factual statements (and vice-versa), encouraging the model to learn this distinction in order to lower the LM loss on future tokens. We corroborate this pattern with experiments in pretrained language models. Finally, in the toy setting we observe a two-phase learning dynamic: networks first memorize individual factual associations in a few steps, then -- over a longer horizon -- learn to linearly separate true from false, which in turn lowers language-modeling loss. Together, these results provide both a mechanistic demonstration and an empirical motivation for how and why linear truth representations can emerge in language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.19944", "title": "Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets", "authors": [ "Jiashi Feng", "Xiu Li", "Jing Lin", "Jiahang Liu", "Gaohong Liu", "Weiqiang Lou", "Su Ma", "Guang Shi", "Qinlong Wang", "Jun Wang", "Zhongcong Xu", "Xuanyu Yi", "Zihao Yu", "Jianfeng Zhang", "Yifan Zhu", "Rui Chen", "Jinxin Chi", "Zixian Du", "Li Han", "Lixin Huang", "Kaihua Jiang", "Yuhan Li", "Guan Luo", "Shuguang Wang", "Qianyi Wu", "Fan Yang", "Junyang Zhang", "Xuanmeng Zhang" ], "github": "", "project_page": "", "abstract": "Developing embodied AI agents requires scalable training environments that balance content diversity with physics accuracy. World simulators provide such environments but face distinct limitations: video-based methods generate diverse content but lack real-time physics feedback for interactive learning, while physics-based engines provide accurate dynamics but face scalability limitations from costly manual asset creation. We present Seed3D 1.0, a foundation model that generates simulation-ready 3D assets from single images, addressing the scalability challenge while maintaining physics rigor. Unlike existing 3D generation models, our system produces assets with accurate geometry, well-aligned textures, and realistic physically-based materials. These assets can be directly integrated into physics engines with minimal configuration, enabling deployment in robotic manipulation and simulation training. Beyond individual objects, the system scales to complete scene generation through assembling objects into coherent environments. By enabling scalable simulation-ready content creation, Seed3D 1.0 provides a foundation for advancing physics-based world simulators. Seed3D 1.0 is now available on https://console.volcengine.com/ark/region:ark+cn-beijing/experience/vision?modelId=doubao-seed3d-1-0-250928&tab=Gen3D", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.20733", "title": "Thought Communication in Multiagent Collaboration", "authors": [ "Yujia Zheng", "Zhuokai Zhao", "Zijian Li", "Yaqi Xie", "Mingze Gao", "Lizhu Zhang", "Kun Zhang" ], "github": "", "project_page": "", "abstract": "Natural language has long enabled human cooperation, but its lossy, ambiguous, and indirect nature limits the potential of collective intelligence. While machines are not subject to these constraints, most LLM-based multi-agent systems still rely solely on natural language, exchanging tokens or their embeddings. To go beyond language, we introduce a new paradigm, thought communication, which enables agents to interact directly mind-to-mind, akin to telepathy. To uncover these latent thoughts in a principled way, we formalize the process as a general latent variable model, where agent states are generated by an unknown function of underlying thoughts. We prove that, in a nonparametric setting without auxiliary information, both shared and private latent thoughts between any pair of agents can be identified. Moreover, the global structure of thought sharing, including which agents share which thoughts and how these relationships are structured, can also be recovered with theoretical guarantees. Guided by the established theory, we develop a framework that extracts latent thoughts from all agents prior to communication and assigns each agent the relevant thoughts, along with their sharing patterns. This paradigm naturally extends beyond LLMs to all modalities, as most observational data arise from hidden generative processes. Experiments on both synthetic and real-world benchmarks validate the theory and demonstrate the collaborative advantages of thought communication. We hope this work illuminates the potential of leveraging the hidden world, as many challenges remain unsolvable through surface-level observation alone, regardless of compute or data scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.16893", "title": "Investigating Safety Vulnerabilities of Large Audio-Language Models Under Speaker Emotional Variations", "authors": [ "Bo-Han Feng", "Chien-Feng Liu", "Yu-Hsuan Li Liang", "Chih-Kai Yang", "Szu-Wei Fu", "Zhehuai Chen", "Ke-Han Lu", "Sung-Feng Huang", "Chao-Han Huck Yang", "Yu-Chiang Frank Wang", "Yun-Nung Chen", "Hung-yi Lee" ], "github": "", "project_page": "", "abstract": "Large audio-language models (LALMs) extend text-based LLMs with auditory understanding, offering new opportunities for multimodal applications. While their perception, reasoning, and task performance have been widely studied, their safety alignment under paralinguistic variation remains underexplored. This work systematically investigates the role of speaker emotion. We construct a dataset of malicious speech instructions expressed across multiple emotions and intensities, and evaluate several state-of-the-art LALMs. Our results reveal substantial safety inconsistencies: different emotions elicit varying levels of unsafe responses, and the effect of intensity is non-monotonic, with medium expressions often posing the greatest risk. These findings highlight an overlooked vulnerability in LALMs and call for alignment strategies explicitly designed to ensure robustness under emotional variation, a prerequisite for trustworthy deployment in real-world settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.16917", "title": "SAKE: Towards Editing Auditory Attribute Knowledge of Large Audio-Language Models", "authors": [ "Chih-Kai Yang", "Yen-Ting Piao", "Tzu-Wen Hsu", "Szu-Wei Fu", "Zhehuai Chen", "Ke-Han Lu", "Sung-Feng Huang", "Chao-Han Huck Yang", "Yu-Chiang Frank Wang", "Yun-Nung Chen", "Hung-yi Lee" ], "github": "https://github.com/ckyang1124/SAKE", "project_page": "", "abstract": "Knowledge editing offers an efficient way to update model knowledge without full retraining, but prior work has concentrated almost exclusively on textual or visual modalities. We introduce SAKE, the first benchmark specifically designed for editing auditory attribute knowledge in Large Audio-Language Models (LALMs). Unlike factual updates, SAKE targets several abstract auditory attributes, capturing knowledge types that go beyond conventional textual and visual domains. We benchmark seven editing methods on two LALMs along four dimensions: reliability, generality, audio/text locality, and portability. Results highlight challenges such as preserving intra-attribute knowledge unrelated to the edit, generalizing edits to multimodal reasoning, and maintaining edits under sequential updates. SAKE provides a principled framework to study how knowledge editing extends to the auditory modalities, opening new directions for maintaining and adapting LALMs in more diverse real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ckyang1124/SAKE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.12487", "title": "Diff-XYZ: A Benchmark for Evaluating Diff Understanding", "authors": [ "Evgeniy Glukhov", "Michele Conti", "Egor Bogomolov", "Yaroslav Golubev", "Alexander Bezzubov" ], "github": "", "project_page": "", "abstract": "Reliable handling of code diffs is central to agents that edit and refactor repositories at scale. We introduce Diff-XYZ, a compact benchmark for code-diff understanding with three supervised tasks: apply (old code + diff rightarrow new code), anti-apply (new code - diff rightarrow old code), and diff generation (new code - old code rightarrow diff). Instances in the benchmark are triples langle old code, new code, diff rangle drawn from real commits in CommitPackFT, paired with automatic metrics and a clear evaluation protocol. We use the benchmark to do a focused empirical study of the unified diff format and run a cross-format comparison of different diff representations. Our findings reveal that different formats should be used depending on the use case and model size. For example, representing diffs in search-replace format is good for larger models in the diff generation scenario, yet not suited well for diff analysis and smaller models. The Diff-XYZ benchmark is a reusable foundation for assessing and improving diff handling in LLMs that can aid future development of diff formats and models editing code. The dataset is published on HuggingFace Hub: https://huggingface.co/datasets/JetBrains-Research/diff-xyz.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.20362", "title": "ComProScanner: A multi-agent based framework for composition-property structured data extraction from scientific literature", "authors": [ "Aritra Roy", "Enrico Grisan", "John Buckeridge", "Chiara Gattinoni" ], "github": "", "project_page": "", "abstract": "Since the advent of various pre-trained large language models, extracting structured knowledge from scientific text has experienced a revolutionary change compared with traditional machine learning or natural language processing techniques. Despite these advances, accessible automated tools that allow users to construct, validate, and visualise datasets from scientific literature extraction remain scarce. We therefore developed ComProScanner, an autonomous multi-agent platform that facilitates the extraction, validation, classification, and visualisation of machine-readable chemical compositions and properties, integrated with synthesis data from journal articles for comprehensive database creation. We evaluated our framework using 100 journal articles against 10 different LLMs, including both open-source and proprietary models, to extract highly complex compositions associated with ceramic piezoelectric materials and corresponding piezoelectric strain coefficients (d33), motivated by the lack of a large dataset for such materials. DeepSeek-V3-0324 outperformed all models with a significant overall accuracy of 0.82. This framework provides a simple, user-friendly, readily-usable package for extracting highly complex experimental data buried in the literature to build machine learning or deep learning datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.19995", "title": "Communication to Completion: Modeling Collaborative Workflows with Intelligent Multi-Agent Communication", "authors": [ "Yiming Lu", "Xun Wang", "Simin Ma", "Shujian Liu", "Sathish Reddy Indurthi", "Song Wang", "Haoyun Deng", "Fei Liu", "Kaiqiang Song" ], "github": "", "project_page": "", "abstract": "Teamwork in workspace for complex tasks requires diverse communication strategies, but current multi-agent LLM systems lack systematic frameworks for task oriented communication. We introduce Communication to Completion (C2C), a scalable framework that addresses this gap through two key innovations: (1) the Alignment Factor (AF), a novel metric quantifying agent task alignment that directly impacts work efficiency, and (2) a Sequential Action Framework that integrates stepwise execution with intelligent communication decisions. C2C enables agents to make cost aware communication choices, dynamically improving task understanding through targeted interactions. We evaluated C2C on realistic coding workflows across three complexity tiers and team sizes from 5 to 17 agents, comparing against no communication and fixed steps baselines. The results show that C2C reduces the task completion time by about 40% with acceptable communication costs. The framework completes all tasks successfully in standard configurations and maintains effectiveness at scale. C2C establishes both a theoretical foundation for measuring communication effectiveness in multi-agent systems and a practical framework for complex collaborative tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.17853", "title": "CiteGuard: Faithful Citation Attribution for LLMs via Retrieval-Augmented Validation", "authors": [ "Yee Man Choi", "Xuehang Guo", "Yi R.", "Fung", "Qingyun Wang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have emerged as promising assistants for scientific writing. However, there have been concerns regarding the quality and reliability of the generated text, one of which is the citation accuracy and faithfulness. While most recent work relies on methods such as LLM-as-a-Judge, the reliability of LLM-as-a-Judge alone is also in doubt. In this work, we reframe citation evaluation as a problem of citation attribution alignment, which is assessing whether LLM-generated citations match those a human author would include for the same text. We propose CiteGuard, a retrieval-aware agent framework designed to provide more faithful grounding for citation validation. CiteGuard improves the prior baseline by 12.3%, and achieves up to 65.4% accuracy on the CiteME benchmark, on par with human-level performance (69.7%). It also enables the identification of alternative but valid citations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.18245", "title": "Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs", "authors": [ "Song Bian", "Tao Yu", "Shivaram Venkataraman", "Youngsuk Park" ], "github": "", "project_page": "", "abstract": "Scaling the number of parameters and the size of training data has proven to be an effective strategy for improving large language model (LLM) performance. Yet, as these models grow increasingly powerful and widely deployed, the cost of inference has become a pressing concern. Despite its importance, the trade-off between model accuracy and inference efficiency remains underexplored. In this work, we examine how key architectural factors, hidden size, the allocation of parameters between MLP and attention (mlp-to-attention ratio), and grouped-query attention (GQA), influence both inference cost and accuracy. We introduce a conditional scaling law that augments the Chinchilla framework with architectural information, along with a search framework for identifying architectures that are simultaneously inference-efficient and accurate. To validate our approach, we train more than 200 models spanning 80M to 3B parameters and 8B to 100B training tokens, and fit the proposed conditional scaling law. Our results show that the conditional scaling law reliably predicts optimal architectural choices and that the resulting models outperform existing open-source baselines. Under the same training budget, optimized architectures achieve up to 2.1% higher accuracy and 42% greater inference throughput compared to LLaMA-3.2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.18413", "title": "Adamas: Hadamard Sparse Attention for Efficient Long-Context Inference", "authors": [ "Siyuan Yan", "Guo-Qing Jiang", "Yuchen Zhang", "Xiaoxing Ma", "Ran Zhu", "Chun Cao", "Jingwei Xu" ], "github": "https://github.com/FibonaccciYan/Adamas", "project_page": "", "abstract": "Large language models (LLMs) now support context windows of hundreds of thousands to millions of tokens, enabling applications such as long-document summarization, large-scale code synthesis, multi-document question answering and persistent multi-turn dialogue. However, such extended contexts exacerbate the quadratic cost of self-attention, leading to severe latency in autoregressive decoding. Existing sparse attention methods alleviate these costs but rely on heuristic patterns that struggle to recall critical key-value (KV) pairs for each query, resulting in accuracy degradation. We introduce Adamas, a lightweight yet highly accurate sparse attention mechanism designed for long-context inference. Adamas applies the Hadamard transform, bucketization and 2-bit compression to produce compact representations, and leverages Manhattan-distance estimation for efficient top-k selections. Experiments show that Adamas matches the accuracy of full attention with only a 64-token budget, achieves near-lossless performance at 128, and supports up to 8x higher sparsity than prior state-of-the-art (SOTA) methods while delivering up to 4.4x self-attention and 1.5x end-to-end speedups on 32K-length sequences. Remarkably, Adamas attains comparable or even lower perplexity than full attention, underscoring its effectiveness in maintaining accuracy under aggressive sparsity.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FibonaccciYan/Adamas", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.17896", "title": "Long-Context Attention Benchmark: From Kernel Efficiency to Distributed Context Parallelism", "authors": [ "Tao Bu", "Qiangang Wang", "Bowen Zeng", "Hanwen Sun", "Yunpeng Huang", "Chun Cao", "Jingwei Xu" ], "github": "https://github.com/NJUDeepEngine/LongCA-bench", "project_page": "", "abstract": "Transformer-based large language models (LLMs) have achieved remarkable success, yet their standard attention mechanism incurs quadratic computation and memory costs with respect to sequence length, posing a major bottleneck for long-context training. Prior work tackles this challenge along two directions: (1) kernel-level optimizations, which accelerate dense and sparse attention operators; and (2) module-level strategies, often referred to as distributed attention or context parallel training, which scale attention across multiple devices. However, systematic evaluation still remains limited: operator-level comparisons are often incomplete, while context parallel strategies are typically framework-specific, with unclear performance analysis across contexts. To address these gaps, we propose a unified benchmark that integrates representative attention kernels and context parallel mechanisms with a modular and extensible interface for evaluation. The benchmark evaluates methods along two critical dimensions: (1) attention mask patterns, which strongly affect efficiency, scalability, and usability, and (2) sequence length and distributed scale, which determine performance under extreme long-context training. Through comprehensive experiments on the cluster of up to 96 GPUs, our benchmark enables reproducible comparisons, highlights method-specific trade-offs, and provides practical guidance for designing and deploying attention mechanisms in long-context LLM training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NJUDeepEngine/LongCA-bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-24", "arxiv_id": "2510.19423", "title": "MSC-Bench: A Rigorous Benchmark for Multi-Server Tool Orchestration", "authors": [ "Jia-Kai Dong", "I-Wei Huang", "Chun-Tin Wu", "Yi-Tien Tsai" ], "github": "https://github.com/snooow1029/MSC_Bench", "project_page": "", "abstract": "We introduce MSC-Bench, a large-scale benchmark for evaluating multi-hop, end-to-end tool orchestration by LLM agents in a hierarchical Model-Context Protocol (MCP) ecosystem. Existing benchmarks often evaluate tools in isolation, ignoring challenges such as functional overlap and cross-server orchestration, leading to overly optimistic assessments. MSC-Bench addresses these gaps by constructing ground truth through 'equal function sets', allowing objective metrics such as F1 score and reducing the dependency on LLM-as-a-judge evaluation. Organized as a five-level curriculum, it systematically tests agent capabilities from single-tool orchestration to complex cross-server planning, and robustness to out-of-scope requests. Experiments reveal that rigid hierarchies can hinder performance without co-designed strategies, and even state-of-the-art agents exhibit systemic weaknesses in robustness. MSC-Bench provides a diagnostic framework to expose these limitations and guide the development of more capable and efficient tool-using agents. The benchmark and resources are publicly available at https://github.com/snooow1029/MSC_Bench.", "github_manual": "", "github_regex": "https://github.com/snooow1029/MSC_Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.21682", "title": "WorldGrow: Generating Infinite 3D World", "authors": [ "Sikuang Li", "Chen Yang", "Jiemin Fang", "Taoran Yi", "Jia Lu", "Jiazhong Cen", "Lingxi Xie", "Wei Shen", "Qi Tian" ], "github": "", "project_page": "", "abstract": "We tackle the challenge of generating the infinitely extendable 3D world -- large, continuous environments with coherent geometry and realistic appearance. Existing methods face key challenges: 2D-lifting approaches suffer from geometric and appearance inconsistencies across views, 3D implicit representations are hard to scale up, and current 3D foundation models are mostly object-centric, limiting their applicability to scene-level generation. Our key insight is leveraging strong generation priors from pre-trained 3D models for structured scene block generation. To this end, we propose WorldGrow, a hierarchical framework for unbounded 3D scene synthesis. Our method features three core components: (1) a data curation pipeline that extracts high-quality scene blocks for training, making the 3D structured latent representations suitable for scene generation; (2) a 3D block inpainting mechanism that enables context-aware scene extension; and (3) a coarse-to-fine generation strategy that ensures both global layout plausibility and local geometric/textural fidelity. Evaluated on the large-scale 3D-FRONT dataset, WorldGrow achieves SOTA performance in geometry reconstruction, while uniquely supporting infinite scene generation with photorealistic and structurally consistent outputs. These results highlight its capability for constructing large-scale virtual environments and potential for building future world models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.21618", "title": "DeepAgent: A General Reasoning Agent with Scalable Toolsets", "authors": [ "Xiaoxi Li", "Wenxiang Jiao", "Jiarui Jin", "Guanting Dong", "Jiajie Jin", "Yinuo Wang", "Hao Wang", "Yutao Zhu", "Ji-Rong Wen", "Yuan Lu", "Zhicheng Dou" ], "github": "https://github.com/RUC-NLPIR/DeepAgent", "project_page": "", "abstract": "Large reasoning models have demonstrated strong problem-solving abilities, yet real-world tasks often require external tools and long-horizon interactions. Existing agent frameworks typically follow predefined workflows, which limit autonomous and global task completion. In this paper, we introduce DeepAgent, an end-to-end deep reasoning agent that performs autonomous thinking, tool discovery, and action execution within a single, coherent reasoning process. To address the challenges of long-horizon interactions, particularly the context length explosion from multiple tool calls and the accumulation of interaction history, we introduce an autonomous memory folding mechanism that compresses past interactions into structured episodic, working, and tool memories, reducing error accumulation while preserving critical information. To teach general-purpose tool use efficiently and stably, we develop an end-to-end reinforcement learning strategy, namely ToolPO, that leverages LLM-simulated APIs and applies tool-call advantage attribution to assign fine-grained credit to the tool invocation tokens. Extensive experiments on eight benchmarks, including general tool-use tasks (ToolBench, API-Bank, TMDB, Spotify, ToolHop) and downstream applications (ALFWorld, WebShop, GAIA, HLE), demonstrate that DeepAgent consistently outperforms baselines across both labeled-tool and open-set tool retrieval scenarios. This work takes a step toward more general and capable agents for real-world applications. The code and demo are available at https://github.com/RUC-NLPIR/DeepAgent.", "github_manual": "", "github_regex": "https://github.com/RUC-NLPIR/DeepAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.20888", "title": "Video-As-Prompt: Unified Semantic Control for Video Generation", "authors": [ "Yuxuan Bian", "Xin Chen", "Zenan Li", "Tiancheng Zhi", "Shen Sang", "Linjie Luo", "Qiang Xu" ], "github": "", "project_page": "", "abstract": "Unified, generalizable semantic control in video generation remains a critical open challenge. Existing methods either introduce artifacts by enforcing inappropriate pixel-wise priors from structure-based controls, or rely on non-generalizable, condition-specific finetuning or task-specific architectures. We introduce Video-As-Prompt (VAP), a new paradigm that reframes this problem as in-context generation. VAP leverages a reference video as a direct semantic prompt, guiding a frozen Video Diffusion Transformer (DiT) via a plug-and-play Mixture-of-Transformers (MoT) expert. This architecture prevents catastrophic forgetting and is guided by a temporally biased position embedding that eliminates spurious mapping priors for robust context retrieval. To power this approach and catalyze future research, we built VAP-Data, the largest dataset for semantic-controlled video generation with over 100K paired videos across 100 semantic conditions. As a single unified model, VAP sets a new state-of-the-art for open-source methods, achieving a 38.7% user preference rate that rivals leading condition-specific commercial models. VAP's strong zero-shot generalization and support for various downstream applications mark a significant advance toward general-purpose, controllable video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.20286", "title": "UI-Ins: Enhancing GUI Grounding with Multi-Perspective Instruction-as-Reasoning", "authors": [ "Liangyu Chen", "Hanzhang Zhou", "Chenglin Cai", "Jianan Zhang", "Panrong Tong", "Quyu Kong", "Xu Zhang", "Chen Liu", "Yuqi Liu", "Wenxuan Wang", "Yue Wang", "Qin Jin", "Steven Hoi" ], "github": "https://github.com/alibaba/UI-Ins", "project_page": "", "abstract": "GUI grounding, which maps natural-language instructions to actionable UI elements, is a core capability of GUI agents. Prior works largely treats instructions as a static proxy for user intent, overlooking the impact of instruction diversity and quality on grounding performance. Through a careful investigation of existing grounding datasets, we find a 23.3% flaw rate in their instructions and show that inference-time exploitation of instruction diversity yields up to a substantial 76% relative performance improvement. In this paper, we introduce the Instruction-as-Reasoning paradigm, treating instructions as dynamic analytical pathways that offer distinct perspectives and enabling the model to select the most effective pathway during reasoning. To achieve this, we propose a two-stage training framework: supervised fine-tuning (SFT) on synthesized, diverse instructions to instill multi-perspective reasoning, followed by reinforcement learning (RL) to optimize pathway selection and composition. Our resulting models, UI-Ins-7B and UI-Ins-32B, achieve state-of-the-art results on five challenging grounding benchmarks and exhibit emergent reasoning, selectively composing and synthesizing novel instruction pathways at inference. In particular, UI-Ins-32B attains the best grounding accuracy, scoring 87.3% on UI-I2E-Bench, 57.0% on ScreenSpot-Pro, and 84.9% on MMBench-GUI L2. Furthermore, our model demonstrates strong agentic potential, achieving a 74.1% success rate on AndroidWorld using UI-Ins-7B as the executor. Our in-depth analysis reveals additional insights such as how reasoning can be formulated to enhance rather than hinder grounding performance, and how our method mitigates policy collapse in the SFT+RL framework. All code and model checkpoints will be publicly released in https://github.com/alibaba/UI-Ins.", "github_manual": "", "github_regex": "https://github.com/alibaba/UI-Ins", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.19871", "title": "From Denoising to Refining: A Corrective Framework for Vision-Language Diffusion Model", "authors": [ "Yatai Ji", "Teng Wang", "Yuying Ge", "Zhiheng Liu", "Sidi Yang", "Ying Shan", "Ping Luo" ], "github": "", "project_page": "", "abstract": "Discrete diffusion models have emerged as a promising direction for vision-language tasks, offering bidirectional context modeling and theoretical parallelization. However, their practical application is severely hindered by a train-inference discrepancy, which leads to catastrophic error cascades: initial token errors during parallel decoding pollute the generation context, triggering a chain reaction of compounding errors and leading to syntactic errors and semantic hallucinations. To address this fundamental challenge, we reframe the generation process from passive denoising to active refining. We introduce ReDiff, a refining-enhanced diffusion framework that teaches the model to identify and correct its own errors. Our approach features a two-stage training process: first, we instill a foundational revision capability by training the model to revise synthetic errors; second, we implement a novel online self-correction loop where the model is explicitly trained to revise its own flawed drafts by learning from an expert's corrections. This mistake-driven learning endows the model with the crucial ability to revisit and refine its already generated output, effectively breaking the error cascade. Extensive experiments demonstrate that ReDiff significantly improves the coherence and factual accuracy of generated content, enabling stable and efficient parallel generation far superior to traditional denoising methods. Our codes and models are available at https://rediff-hku.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.20479", "title": "RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging", "authors": [ "Bowen Wang", "Haiyuan Wan", "Liwen Shi", "Chen Yang", "Peng He", "Yue Ma", "Haochen Han", "Wenhao Li", "Tiao Tan", "Yongjian Li", "Fangming Liu", "Yifan Gong", "Sheng Zhang" ], "github": "https://github.com/bw-wang19/RECALL", "project_page": "", "abstract": "We unveil that internal representations in large language models (LLMs) serve as reliable proxies of learned knowledge, and propose RECALL, a novel representation-aware model merging framework for continual learning without access to historical data. RECALL computes inter-model similarity from layer-wise hidden representations over clustered typical samples, and performs adaptive, hierarchical parameter fusion to align knowledge across models. This design enables the preservation of domain-general features in shallow layers while allowing task-specific adaptation in deeper layers. Unlike prior methods that require task labels or incur performance trade-offs, RECALL achieves seamless multi-domain integration and strong resistance to catastrophic forgetting. Extensive experiments across five NLP tasks and multiple continual learning scenarios show that RECALL outperforms baselines in both knowledge retention and generalization, providing a scalable and data-free solution for evolving LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bw-wang19/RECALL", "github_api_added_by": "auto" }, { "date": "2025-10-27", "arxiv_id": "2510.20206", "title": "RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via Data Alignment and Test-Time Scaling", "authors": [ "Bingjie Gao", "Qianli Ma", "Xiaoxue Wu", "Shuai Yang", "Guanzhou Lan", "Haonan Zhao", "Jiaxuan Chen", "Qingyang Liu", "Yu Qiao", "Xinyuan Chen", "Yaohui Wang", "Li Niu" ], "github": "https://github.com/Vchitect/RAPO", "project_page": "", "abstract": "Prompt design plays a crucial role in text-to-video (T2V) generation, yet user-provided prompts are often short, unstructured, and misaligned with training data, limiting the generative potential of diffusion-based T2V models. We present RAPO++, a cross-stage prompt optimization framework that unifies training-data--aligned refinement, test-time iterative scaling, and large language model (LLM) fine-tuning to substantially improve T2V generation without modifying the underlying generative backbone. In Stage 1, Retrieval-Augmented Prompt Optimization (RAPO) enriches user prompts with semantically relevant modifiers retrieved from a relation graph and refactors them to match training distributions, enhancing compositionality and multi-object fidelity. Stage 2 introduces Sample-Specific Prompt Optimization (SSPO), a closed-loop mechanism that iteratively refines prompts using multi-source feedback -- including semantic alignment, spatial fidelity, temporal coherence, and task-specific signals such as optical flow -- yielding progressively improved video generation quality. Stage 3 leverages optimized prompt pairs from SSPO to fine-tune the rewriter LLM, internalizing task-specific optimization patterns and enabling efficient, high-quality prompt generation even before inference. Extensive experiments across five state-of-the-art T2V models and five benchmarks demonstrate that RAPO++ achieves significant gains in semantic alignment, compositional reasoning, temporal stability, and physical plausibility, outperforming existing methods by large margins. Our results highlight RAPO++ as a model-agnostic, cost-efficient, and scalable solution that sets a new standard for prompt optimization in T2V generation. The code is available at https://github.com/Vchitect/RAPO.", "github_manual": "", "github_regex": "https://github.com/Vchitect/RAPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.18212", "title": "A Definition of AGI", "authors": [ "Dan Hendrycks", "Dawn Song", "Christian Szegedy", "Honglak Lee", "Yarin Gal", "Erik Brynjolfsson", "Sharon Li", "Andy Zou", "Lionel Levine", "Bo Han", "Jie Fu", "Ziwei Liu", "Jinwoo Shin", "Kimin Lee", "Mantas Mazeika", "Long Phan", "George Ingebretsen", "Adam Khoja", "Cihang Xie", "Olawale Salaudeen", "Matthias Hein", "Kevin Zhao", "Alexander Pan", "David Duvenaud", "Bo Li", "Steve Omohundro", "Gabriel Alfour", "Max Tegmark", "Kevin McGrew", "Gary Marcus", "Jaan Tallinn", "Eric Schmidt", "Yoshua Bengio" ], "github": "", "project_page": "", "abstract": "The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly \"jagged\" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 58%) concretely quantify both rapid progress and the substantial gap remaining before AGI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.21223", "title": "Model Merging with Functional Dual Anchors", "authors": [ "Kexuan Shi", "Yandong Wen", "Weiyang Liu" ], "github": "", "project_page": "", "abstract": "Model merging is an efficient post-training strategy for integrating knowledge from multiple finetuned checkpoints of a shared foundation model. Existing methods operate in the parameter space, combining task vectors to mitigate conflicts, but remain constrained by parameter inconsistencies. We propose Functional Dual Anchors (FDAs), a framework that instead models the input-representation space. FDAs are synthetic inputs whose induced gradients align with task vectors, capturing task-specific functional shifts relative to the pretrained model. This perspective bridges joint multi-task training and post-hoc merging, offering both robustness and flexibility. We further introduce a principled initialization scheme and show that FDAs are complementary to parameter-space model merging. Comprehensive experiments demonstrate the effectiveness of FDAs in model merging.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.21697", "title": "Visual Diffusion Models are Geometric Solvers", "authors": [ "Nir Goren", "Shai Yehezkel", "Omer Dahary", "Andrey Voynov", "Or Patashnik", "Daniel Cohen-Or" ], "github": "", "project_page": "", "abstract": "In this paper we show that visual diffusion models can serve as effective geometric solvers: they can directly reason about geometric problems by working in pixel space. We first demonstrate this on the Inscribed Square Problem, a long-standing problem in geometry that asks whether every Jordan curve contains four points forming a square. We then extend the approach to two other well-known hard geometric problems: the Steiner Tree Problem and the Simple Polygon Problem. Our method treats each problem instance as an image and trains a standard visual diffusion model that transforms Gaussian noise into an image representing a valid approximate solution that closely matches the exact one. The model learns to transform noisy geometric structures into correct configurations, effectively recasting geometric reasoning as image generation. Unlike prior work that necessitates specialized architectures and domain-specific adaptations when applying diffusion to parametric geometric representations, we employ a standard visual diffusion model that operates on the visual representation of the problem. This simplicity highlights a surprising bridge between generative modeling and geometric problem solving. Beyond the specific problems studied here, our results point toward a broader paradigm: operating in image space provides a general and practical framework for approximating notoriously hard problems, and opens the door to tackling a far wider class of challenging geometric tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.21270", "title": "Sparser Block-Sparse Attention via Token Permutation", "authors": [ "Xinghao Wang", "Pengyu Wang", "Dong Zhang", "Chenkun Tan", "Shaojun Zhou", "Zhaoxiang Liu", "Shiguo Lian", "Fangxu Liu", "Kai Song", "Xipeng Qiu" ], "github": "https://github.com/xinghaow99/pbs-attn", "project_page": "", "abstract": "Scaling the context length of large language models (LLMs) offers significant benefits but is computationally expensive. This expense stems primarily from the self-attention mechanism, whose O(N^2) complexity with respect to sequence length presents a major bottleneck for both memory and latency. Fortunately, the attention matrix is often sparse, particularly for long sequences, suggesting an opportunity for optimization. Block-sparse attention has emerged as a promising solution that partitions sequences into blocks and skips computation for a subset of these blocks. However, the effectiveness of this method is highly dependent on the underlying attention patterns, which can lead to sub-optimal block-level sparsity. For instance, important key tokens for queries within a single block may be scattered across numerous other blocks, leading to computational redundancy. In this work, we propose Permuted Block-Sparse Attention (PBS-Attn), a plug-and-play method that leverages the permutation properties of attention to increase block-level sparsity and enhance the computational efficiency of LLM prefilling. We conduct comprehensive experiments on challenging real-world long-context datasets, demonstrating that PBS-Attn consistently outperforms existing block-sparse attention methods in model accuracy and closely matches the full attention baseline. Powered by our custom permuted-FlashAttention kernels, PBS-Attn achieves an end-to-end speedup of up to 2.75times in long-context prefilling, confirming its practical viability. Code available at https://github.com/xinghaow99/pbs-attn", "github_manual": "", "github_regex": "https://github.com/xinghaow99/pbs-attn", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.21652", "title": "AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite", "authors": [ "Jonathan Bragg", "Mike D'Arcy", "Nishant Balepur", "Dan Bareket", "Bhavana Dalvi", "Sergey Feldman", "Dany Haddad", "Jena D. Hwang", "Peter Jansen", "Varsha Kishore", "Bodhisattwa Prasad Majumder", "Aakanksha Naik", "Sigal Rahamimov", "Kyle Richardson", "Amanpreet Singh", "Harshit Surana", "Aryeh Tiktinsky", "Rosni Vasu", "Guy Wiener", "Chloe Anastasiades", "Stefan Candra", "Jason Dunkelberger", "Dan Emery", "Rob Evans", "Malachi Hamada", "Regan Huff", "Rodney Kinney", "Matt Latzke", "Jaron Lochner", "Ruben Lozano-Aguilera", "Cecile Nguyen", "Smita Rao", "Amber Tanaka", "Brooke Vlahos", "Peter Clark", "Doug Downey", "Yoav Goldberg", "Ashish Sabharwal", "Daniel S. Weld" ], "github": "https://github.com/allenai/asta-bench", "project_page": "", "abstract": "AI agents hold the potential to revolutionize scientific productivity by automating literature reviews, replicating experiments, analyzing data, and even proposing new directions of inquiry; indeed, there are now many such agents, ranging from general-purpose \"deep research\" systems to specialized science-specific agents, such as AI Scientist and AIGS. Rigorous evaluation of these agents is critical for progress. Yet existing benchmarks fall short on several fronts: they (1) fail to provide holistic, product-informed measures of real-world use cases such as science research; (2) lack reproducible agent tools necessary for a controlled comparison of core agentic capabilities; (3) do not account for confounding variables such as model cost and tool access; (4) do not provide standardized interfaces for quick agent prototyping and evaluation; and (5) lack comprehensive baseline agents necessary to identify true advances. In response, we define principles and tooling for more rigorously benchmarking agents. Using these, we present AstaBench, a suite that provides the first holistic measure of agentic ability to perform scientific research, comprising 2400+ problems spanning the entire scientific discovery process and multiple scientific domains, and including many problems inspired by actual user requests to deployed Asta agents. Our suite comes with the first scientific research environment with production-grade search tools that enable controlled, reproducible evaluation, better accounting for confounders. Alongside, we provide a comprehensive suite of nine science-optimized classes of Asta agents and numerous baselines. Our extensive evaluation of 57 agents across 22 agent classes reveals several interesting findings, most importantly that despite meaningful progress on certain individual aspects, AI remains far from solving the challenge of science research assistance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/asta-bench", "github_api_added_by": "auto" }, { "date": "2025-10-27", "arxiv_id": "2510.21447", "title": "PhysWorld: From Real Videos to World Models of Deformable Objects via Physics-Aware Demonstration Synthesis", "authors": [ "Yu Yang", "Zhilu Zhang", "Xiang Zhang", "Yihan Zeng", "Hui Li", "Wangmeng Zuo" ], "github": "", "project_page": "", "abstract": "Interactive world models that simulate object dynamics are crucial for robotics, VR, and AR. However, it remains a significant challenge to learn physics-consistent dynamics models from limited real-world video data, especially for deformable objects with spatially-varying physical properties. To overcome the challenge of data scarcity, we propose PhysWorld, a novel framework that utilizes a simulator to synthesize physically plausible and diverse demonstrations to learn efficient world models. Specifically, we first construct a physics-consistent digital twin within MPM simulator via constitutive model selection and global-to-local optimization of physical properties. Subsequently, we apply part-aware perturbations to the physical properties and generate various motion patterns for the digital twin, synthesizing extensive and diverse demonstrations. Finally, using these demonstrations, we train a lightweight GNN-based world model that is embedded with physical properties. The real video can be used to further refine the physical properties. PhysWorld achieves accurate and fast future predictions for various deformable objects, and also generalizes well to novel interactions. Experiments show that PhysWorld has competitive performance while enabling inference speeds 47 times faster than the recent state-of-the-art method, i.e., PhysTwin.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.20535", "title": "ARC-Encoder: learning compressed text representations for large language models", "authors": [ "Hippolyte Pilchen", "Edouard Grave", "Patrick P\u00e9rez" ], "github": "https://github.com/kyutai-labs/ARC-Encoder", "project_page": "", "abstract": "Recent techniques such as retrieval-augmented generation or chain-of-thought reasoning have led to longer contexts and increased inference costs. Context compression techniques can reduce these costs, but the most effective approaches require fine-tuning the target model or even modifying its architecture. This can degrade its general abilities when not used for this specific purpose. Here we explore an alternative approach: an encoder that compresses the context into continuous representations which replace token embeddings in decoder LLMs. First, we perform a systematic study of training strategies and architecture choices for the encoder. Our findings led to the design of an Adaptable text Representations Compressor, named ARC-Encoder, which outputs x-times fewer continuous representations (typically x!in!{4,8}) than text tokens. We evaluate ARC-Encoder across a variety of LLM usage scenarios, ranging from in-context learning to context window extension, on both instruct and base decoders. Results show that ARC-Encoder achieves state-of-the-art performance on several benchmarks while improving computational efficiency at inference. Finally, we demonstrate that our models can be adapted to multiple decoders simultaneously, allowing a single encoder to generalize across different decoder LLMs. This makes ARC-Encoder a flexible and efficient solution for portable encoders that work seamlessly with multiple LLMs. We release a training code at https://github.com/kyutai-labs/ARC-Encoder , fine-tuning dataset and pretrained models are available at https://huggingface.co/collections/kyutai/arc-encoders-68ee18787301407d60a57047 .", "github_manual": "", "github_regex": "https://github.com/kyutai-labs/ARC-Encoder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.21553", "title": "Document Understanding, Measurement, and Manipulation Using Category Theory", "authors": [ "Jared Claypoole", "Yunye Gong", "Noson S. Yanofsky", "Ajay Divakaran" ], "github": "", "project_page": "", "abstract": "We apply category theory to extract multimodal document structure which leads us to develop information theoretic measures, content summarization and extension, and self-supervised improvement of large pretrained models. We first develop a mathematical representation of a document as a category of question-answer pairs. Second, we develop an orthogonalization procedure to divide the information contained in one or more documents into non-overlapping pieces. The structures extracted in the first and second steps lead us to develop methods to measure and enumerate the information contained in a document. We also build on those steps to develop new summarization techniques, as well as to develop a solution to a new problem viz. exegesis resulting in an extension of the original document. Our question-answer pair methodology enables a novel rate distortion analysis of summarization techniques. We implement our techniques using large pretrained models, and we propose a multimodal extension of our overall mathematical framework. Finally, we develop a novel self-supervised method using RLVR to improve large pretrained models using consistency constraints such as composability and closure under certain operations that stem naturally from our category theoretic framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.17234", "title": "Taming Modality Entanglement in Continual Audio-Visual Segmentation", "authors": [ "Yuyang Hong", "Qi Yang", "Tao Zhang", "Zili Wang", "Zhaojin Fu", "Kun Ding", "Bin Fan", "Shiming Xiang" ], "github": "", "project_page": "", "abstract": "Recently, significant progress has been made in multi-modal continual learning, aiming to learn new tasks sequentially in multi-modal settings while preserving performance on previously learned ones. However, existing methods mainly focus on coarse-grained tasks, with limitations in addressing modality entanglement in fine-grained continual learning settings. To bridge this gap, we introduce a novel Continual Audio-Visual Segmentation (CAVS) task, aiming to continuously segment new classes guided by audio. Through comprehensive analysis, two critical challenges are identified: 1) multi-modal semantic drift, where a sounding objects is labeled as background in sequential tasks; 2) co-occurrence confusion, where frequent co-occurring classes tend to be confused. In this work, a Collision-based Multi-modal Rehearsal (CMR) framework is designed to address these challenges. Specifically, for multi-modal semantic drift, a Multi-modal Sample Selection (MSS) strategy is proposed to select samples with high modal consistency for rehearsal. Meanwhile, for co-occurence confusion, a Collision-based Sample Rehearsal (CSR) mechanism is designed, allowing for the increase of rehearsal sample frequency of those confusable classes during training process. Moreover, we construct three audio-visual incremental scenarios to verify effectiveness of our method. Comprehensive experiments demonstrate that our method significantly outperforms single-modal continual learning methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.13251", "title": "Map the Flow: Revealing Hidden Pathways of Information in VideoLLMs", "authors": [ "Minji Kim", "Taekyung Kim", "Bohyung Han" ], "github": "", "project_page": "", "abstract": "Video Large Language Models (VideoLLMs) extend the capabilities of vision-language models to spatiotemporal inputs, enabling tasks such as video question answering (VideoQA). Despite recent advances in VideoLLMs, their internal mechanisms on where and how they extract and propagate video and textual information remain less explored. In this study, we investigate the internal information flow of VideoLLMs using mechanistic interpretability techniques. Our analysis reveals consistent patterns across diverse VideoQA tasks: (1) temporal reasoning in VideoLLMs initiates with active cross-frame interactions in early-to-middle layers, (2) followed by progressive video-language integration in middle layers. This is facilitated by alignment between video representations and linguistic embeddings containing temporal concepts. (3) Upon completion of this integration, the model is ready to generate correct answers in middle-to-late layers. (4) Based on our analysis, we show that VideoLLMs can retain their VideoQA performance by selecting these effective information pathways while suppressing a substantial amount of attention edges, e.g., 58% in LLaVA-NeXT-7B-Video-FT. These findings provide a blueprint on how VideoLLMs perform temporal reasoning and offer practical insights for improving model interpretability and downstream generalization. Our project page with the source code is available at https://map-the-flow.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.14901", "title": "Reasoning with Sampling: Your Base Model is Smarter Than You Think", "authors": [ "Aayush Karan", "Yilun Du" ], "github": "", "project_page": "", "abstract": "Frontier reasoning models have exhibited incredible capabilities across a wide array of disciplines, driven by posttraining large language models (LLMs) with reinforcement learning (RL). However, despite the widespread success of this paradigm, much of the literature has been devoted to disentangling truly novel behaviors that emerge during RL but are not present in the base models. In our work, we approach this question from a different angle, instead asking whether comparable reasoning capabilites can be elicited from base models at inference time by pure sampling, without any additional training. Inspired by Markov chain Monte Carlo (MCMC) techniques for sampling from sharpened distributions, we propose a simple iterative sampling algorithm leveraging the base models' own likelihoods. Over different base models, we show that our algorithm offers substantial boosts in reasoning that nearly match and even outperform those from RL on a wide variety of single-shot tasks, including MATH500, HumanEval, and GPQA. Moreover, our sampler avoids the collapse in diversity over multiple samples that is characteristic of RL-posttraining. Crucially, our method does not require training, curated datasets, or a verifier, suggesting broad applicability beyond easily verifiable domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.20780", "title": "Are Large Reasoning Models Good Translation Evaluators? Analysis and Performance Boost", "authors": [ "Runzhe Zhan", "Zhihong Huang", "Xinyi Yang", "Lidia S. Chao", "Min Yang", "Derek F. Wong" ], "github": "", "project_page": "", "abstract": "Recent advancements in large reasoning models (LRMs) have introduced an intermediate \"thinking\" process prior to generating final answers, improving their reasoning capabilities on complex downstream tasks. However, the potential of LRMs as evaluators for machine translation (MT) quality remains underexplored. We provides the first systematic analysis of LRM-as-a-judge in MT evaluation. We identify key challenges, revealing LRMs require tailored evaluation materials, tend to \"overthink\" simpler instances and have issues with scoring mechanisms leading to overestimation. To address these, we propose to calibrate LRM thinking by training them on synthetic, human-like thinking trajectories. Our experiments on WMT24 Metrics benchmarks demonstrate that this approach largely reduces thinking budgets by ~35x while concurrently improving evaluation performance across different LRM scales from 7B to 32B (e.g., R1-Distill-Qwen-7B achieves a +8.7 correlation point improvement). These findings highlight the potential of efficiently calibrated LRMs to advance fine-grained automatic MT evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.21583", "title": "Sample By Step, Optimize By Chunk: Chunk-Level GRPO For Text-to-Image Generation", "authors": [ "Yifu Luo", "Penghui Du", "Bo Li", "Sinan Du", "Tiantian Zhang", "Yongzhe Chang", "Kai Wu", "Kun Gai", "Xueqian Wang" ], "github": "", "project_page": "", "abstract": "Group Relative Policy Optimization (GRPO) has shown strong potential for flow-matching-based text-to-image (T2I) generation, but it faces two key limitations: inaccurate advantage attribution, and the neglect of temporal dynamics of generation. In this work, we argue that shifting the optimization paradigm from the step level to the chunk level can effectively alleviate these issues. Building on this idea, we propose Chunk-GRPO, the first chunk-level GRPO-based approach for T2I generation. The insight is to group consecutive steps into coherent 'chunk's that capture the intrinsic temporal dynamics of flow matching, and to optimize policies at the chunk level. In addition, we introduce an optional weighted sampling strategy to further enhance performance. Extensive experiments show that ChunkGRPO achieves superior results in both preference alignment and image quality, highlighting the promise of chunk-level optimization for GRPO-based methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.21111", "title": "PhysVLM-AVR: Active Visual Reasoning for Multimodal Large Language Models in Physical Environments", "authors": [ "Weijie Zhou", "Xuantang Xiong", "Yi Peng", "Manli Tao", "Chaoyang Zhao", "Honghui Dong", "Ming Tang", "Jinqiao Wang" ], "github": "", "project_page": "", "abstract": "Visual reasoning in multimodal large language models (MLLMs) has primarily been studied in static, fully observable settings, limiting their effectiveness in real-world environments where information is often incomplete due to occlusion or limited field of view. Humans, in contrast, actively explore and interact with their environment-moving, examining, and manipulating objects-to gather information through a closed-loop process integrating perception, reasoning, and action. Inspired by this human capability, we introduce the Active Visual Reasoning (AVR) task, extending visual reasoning to partially observable, interactive environments. AVR necessitates agents to: (1) actively acquire information via sequential physical actions, (2) integrate observations across multiple steps for coherent reasoning, and (3) dynamically adjust decisions based on evolving visual feedback. To rigorously evaluate AVR, we introduce CLEVR-AVR, a simulation benchmark featuring multi-round interactive environments designed to assess both reasoning correctness and information-gathering efficiency. We present AVR-152k, a large-scale dataset that offers rich Chain-of-Thought (CoT) annotations detailing iterative reasoning for uncertainty identification, action-conditioned information gain prediction, and information-maximizing action selection, crucial for training agents in a higher-order Markov Decision Process. Building on this, we develop PhysVLM-AVR, an MLLM achieving state-of-the-art performance on CLEVR-AVR, embodied reasoning (OpenEQA, RoboVQA), and passive visual reasoning (GeoMath, Geometry30K). Our analysis also reveals that current embodied MLLMs, despite detecting information incompleteness, struggle to actively acquire and integrate new information through interaction, highlighting a fundamental gap in active reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.21057", "title": "Soft Instruction De-escalation Defense", "authors": [ "Nils Philipp Walter", "Chawin Sitawarin", "Jamie Hayes", "David Stutz", "Ilia Shumailov" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly deployed in agentic systems that interact with an external environment; this makes them susceptible to prompt injections when dealing with untrusted data. To overcome this limitation, we propose SIC (Soft Instruction Control)-a simple yet effective iterative prompt sanitization loop designed for tool-augmented LLM agents. Our method repeatedly inspects incoming data for instructions that could compromise agent behavior. If such content is found, the malicious content is rewritten, masked, or removed, and the result is re-evaluated. The process continues until the input is clean or a maximum iteration limit is reached; if imperative instruction-like content remains, the agent halts to ensure security. By allowing multiple passes, our approach acknowledges that individual rewrites may fail but enables the system to catch and correct missed injections in later steps. Although immediately useful, worst-case analysis shows that SIC is not infallible; strong adversary can still get a 15% ASR by embedding non-imperative workflows. This nonetheless raises the bar.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.11370", "title": "Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers", "authors": [ "Wenhan Ma", "Hailin Zhang", "Liang Zhao", "Yifan Song", "Yudong Wang", "Zhifang Sui", "Fuli Luo" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has emerged as a crucial approach for enhancing the capabilities of large language models. However, in Mixture-of-Experts (MoE) models, the routing mechanism often introduces instability, even leading to catastrophic RL training collapse. We analyze the training-inference consistency of MoE models and identify a notable discrepancy in routing behaviors between the two phases. Moreover, even under identical conditions, the routing framework can yield divergent expert selections across repeated forward passes. To address this foundational inconsistency, we propose Rollout Routing Replay (R3), a method that records routing distributions from the inference engine and replays them during training. R3 significantly reduces training-inference policy KL divergence and mitigates extreme discrepancies without compromising training speed. Extensive experiments on various settings confirm that R3 succeeds in stabilizing RL training, preventing collapse and outperforming methods such as GSPO and TIS. We believe this work can offer a new solution for stabilizing RL in MoE models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.21581", "title": "Foley Control: Aligning a Frozen Latent Text-to-Audio Model to Video", "authors": [ "Ciara Rowles", "Varun Jampani", "Simon Donn\u00e9", "Shimon Vainer", "Julian Parker", "Zach Evans" ], "github": "", "project_page": "", "abstract": "Foley Control is a lightweight approach to video-guided Foley that keeps pretrained single-modality models frozen and learns only a small cross-attention bridge between them. We connect V-JEPA2 video embeddings to a frozen Stable Audio Open DiT text-to-audio (T2A) model by inserting compact video cross-attention after the model's existing text cross-attention, so prompts set global semantics while video refines timing and local dynamics. The frozen backbones retain strong marginals (video; audio given text) and the bridge learns the audio-video dependency needed for synchronization -- without retraining the audio prior. To cut memory and stabilize training, we pool video tokens before conditioning. On curated video-audio benchmarks, Foley Control delivers competitive temporal and semantic alignment with far fewer trainable parameters than recent multi-modal systems, while preserving prompt-driven controllability and production-friendly modularity (swap/upgrade encoders or the T2A backbone without end-to-end retraining). Although we focus on Video-to-Foley, the same bridge design can potentially extend to other audio modalities (e.g., speech).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.20708", "title": "ALICE-LRI: A General Method for Lossless Range Image Generation for Spinning LiDAR Sensors without Calibration Metadata", "authors": [ "Samuel Soutullo", "Miguel Yermo", "David L. Vilari\u00f1o", "\u00d3scar G. Lorenzo", "Jos\u00e9 C. Cabaleiro", "Francisco F. Rivera" ], "github": "", "project_page": "", "abstract": "3D LiDAR sensors are essential for autonomous navigation, environmental monitoring, and precision mapping in remote sensing applications. To efficiently process the massive point clouds generated by these sensors, LiDAR data is often projected into 2D range images that organize points by their angular positions and distances. While these range image representations enable efficient processing, conventional projection methods suffer from fundamental geometric inconsistencies that cause irreversible information loss, compromising high-fidelity applications. We present ALICE-LRI (Automatic LiDAR Intrinsic Calibration Estimation for Lossless Range Images), the first general, sensor-agnostic method that achieves lossless range image generation from spinning LiDAR point clouds without requiring manufacturer metadata or calibration files. Our algorithm automatically reverse-engineers the intrinsic geometry of any spinning LiDAR sensor by inferring critical parameters including laser beam configuration, angular distributions, and per-beam calibration corrections, enabling lossless projection and complete point cloud reconstruction with zero point loss. Comprehensive evaluation across the complete KITTI and DurLAR datasets demonstrates that ALICE-LRI achieves perfect point preservation, with zero points lost across all point clouds. Geometric accuracy is maintained well within sensor precision limits, establishing geometric losslessness with real-time performance. We also present a compression case study that validates substantial downstream benefits, demonstrating significant quality improvements in practical applications. This paradigm shift from approximate to lossless LiDAR projections opens new possibilities for high-precision remote sensing applications requiring complete geometric preservation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-27", "arxiv_id": "2510.21440", "title": "Redefining Retrieval Evaluation in the Era of LLMs", "authors": [ "Giovanni Trappolini", "Florin Cuconasu", "Simone Filice", "Yoelle Maarek", "Fabrizio Silvestri" ], "github": "", "project_page": "", "abstract": "Traditional Information Retrieval (IR) metrics, such as nDCG, MAP, and MRR, assume that human users sequentially examine documents with diminishing attention to lower ranks. This assumption breaks down in Retrieval Augmented Generation (RAG) systems, where search results are consumed by Large Language Models (LLMs), which, unlike humans, process all retrieved documents as a whole rather than sequentially. Additionally, traditional IR metrics do not account for related but irrelevant documents that actively degrade generation quality, rather than merely being ignored. Due to these two major misalignments, namely human vs. machine position discount and human relevance vs. machine utility, classical IR metrics do not accurately predict RAG performance. We introduce a utility-based annotation schema that quantifies both the positive contribution of relevant passages and the negative impact of distracting ones. Building on this foundation, we propose UDCG (Utility and Distraction-aware Cumulative Gain), a metric using an LLM-oriented positional discount to directly optimize the correlation with the end-to-end answer accuracy. Experiments on five datasets and six LLMs demonstrate that UDCG improves correlation by up to 36% compared to traditional metrics. Our work provides a critical step toward aligning IR evaluation with LLM consumers and enables more reliable assessment of RAG components", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.21817", "title": "VITA-E: Natural Embodied Interaction with Concurrent Seeing, Hearing, Speaking, and Acting", "authors": [ "Xiaoyu Liu", "Chaoyou Fu", "Chi Yan", "Chu Wu", "Haihan Gao", "Yi-Fan Zhang", "Shaoqi Dong", "Cheng Qian", "Bin Luo", "Xiuyong Yang", "Guanwu Li", "Yusheng Cai", "Yunhang Shen", "Deqiang Jiang", "Haoyu Cao", "Xing Sun", "Caifeng Shan", "Ran He" ], "github": "", "project_page": "", "abstract": "Current Vision-Language-Action (VLA) models are often constrained by a rigid, static interaction paradigm, which lacks the ability to see, hear, speak, and act concurrently as well as handle real-time user interruptions dynamically. This hinders seamless embodied collaboration, resulting in an inflexible and unresponsive user experience. To address these limitations, we introduce VITA-E, a novel embodied interaction framework designed for both behavioral concurrency and nearly real-time interruption. The core of our approach is a dual-model architecture where two parallel VLA instances operate as an ``Active Model'' and a ``Standby Model'', allowing the embodied agent to observe its environment, listen to user speech, provide verbal responses, and execute actions, all concurrently and interruptibly, mimicking human-like multitasking capabilities. We further propose a ``model-as-controller'' paradigm, where we fine-tune the VLM to generate special tokens that serve as direct system-level commands, coupling the model's reasoning with the system's behavior. Experiments conducted on a physical humanoid platform demonstrate that VITA-E can reliably handle complex interactive scenarios. Our framework is compatible with various dual-system VLA models, achieving an extremely high success rate on emergency stops and speech interruptions while also successfully performing concurrent speech and action. This represents a significant step towards more natural and capable embodied assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.23607", "title": "Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations", "authors": [ "Yujia Zhang", "Xiaoyang Wu", "Yixing Lao", "Chengyao Wang", "Zhuotao Tian", "Naiyan Wang", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "Humans learn abstract concepts through multisensory synergy, and once formed, such representations can often be recalled from a single modality. Inspired by this principle, we introduce Concerto, a minimalist simulation of human concept learning for spatial cognition, combining 3D intra-modal self-distillation with 2D-3D cross-modal joint embedding. Despite its simplicity, Concerto learns more coherent and informative spatial features, as demonstrated by zero-shot visualizations. It outperforms both standalone SOTA 2D and 3D self-supervised models by 14.2% and 4.8%, respectively, as well as their feature concatenation, in linear probing for 3D scene perception. With full fine-tuning, Concerto sets new SOTA results across multiple scene understanding benchmarks (e.g., 80.7% mIoU on ScanNet). We further present a variant of Concerto tailored for video-lifted point cloud spatial understanding, and a translator that linearly projects Concerto representations into CLIP's language space, enabling open-world perception. These results highlight that Concerto emerges spatial representations with superior fine-grained geometric and semantic consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.23588", "title": "FARMER: Flow AutoRegressive Transformer over Pixels", "authors": [ "Guangting Zheng", "Qinyu Zhao", "Tao Yang", "Fei Xiao", "Zhijie Lin", "Jie Wu", "Jiajun Deng", "Yanyong Zhang", "Rui Zhu" ], "github": "", "project_page": "", "abstract": "Directly modeling the explicit likelihood of the raw data distribution is key topic in the machine learning area, which achieves the scaling successes in Large Language Models by autoregressive modeling. However, continuous AR modeling over visual pixel data suffer from extremely long sequences and high-dimensional spaces. In this paper, we present FARMER, a novel end-to-end generative framework that unifies Normalizing Flows (NF) and Autoregressive (AR) models for tractable likelihood estimation and high-quality image synthesis directly from raw pixels. FARMER employs an invertible autoregressive flow to transform images into latent sequences, whose distribution is modeled implicitly by an autoregressive model. To address the redundancy and complexity in pixel-level modeling, we propose a self-supervised dimension reduction scheme that partitions NF latent channels into informative and redundant groups, enabling more effective and efficient AR modeling. Furthermore, we design a one-step distillation scheme to significantly accelerate inference speed and introduce a resampling-based classifier-free guidance algorithm to boost image generation quality. Extensive experiments demonstrate that FARMER achieves competitive performance compared to existing pixel-based generative models while providing exact likelihoods and scalable training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.22201", "title": "ACG: Action Coherence Guidance for Flow-based VLA models", "authors": [ "Minho Park", "Kinam Kim", "Junha Hyung", "Hyojin Jang", "Hoiyeong Jin", "Jooyeol Yun", "Hojoon Lee", "Jaegul Choo" ], "github": "https://github.com/DAVIAN-Robotics/ACG", "project_page": "", "abstract": "Diffusion and flow matching models have emerged as powerful robot policies, enabling Vision-Language-Action (VLA) models to generalize across diverse scenes and instructions. Yet, when trained via imitation learning, their high generative capacity makes them sensitive to noise in human demonstrations: jerks, pauses, and jitter which reduce action coherence. Reduced action coherence causes instability and trajectory drift during deployment, failures that are catastrophic in fine-grained manipulation where precision is crucial. In this paper, we present Action Coherence Guidance (ACG) for VLA models, a training-free test-time guidance algorithm that improves action coherence and thereby yields performance gains. Evaluated on RoboCasa, DexMimicGen, and real-world SO-101 tasks, ACG consistently improves action coherence and boosts success rates across diverse manipulation tasks. Code and project page are available at https://github.com/DAVIAN-Robotics/ACG and https://DAVIAN-Robotics.github.io/ACG , respectively.", "github_manual": "", "github_regex": "https://github.com/DAVIAN-Robotics/ACG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.23451", "title": "Omni-Reward: Towards Generalist Omni-Modal Reward Modeling with Free-Form Preferences", "authors": [ "Zhuoran Jin", "Hongbang Yuan", "Kejian Zhu", "Jiachun Li", "Pengfei Cao", "Yubo Chen", "Kang Liu", "Jun Zhao" ], "github": "", "project_page": "", "abstract": "Reward models (RMs) play a critical role in aligning AI behaviors with human preferences, yet they face two fundamental challenges: (1) Modality Imbalance, where most RMs are mainly focused on text and image modalities, offering limited support for video, audio, and other modalities; and (2) Preference Rigidity, where training on fixed binary preference pairs fails to capture the complexity and diversity of personalized preferences. To address the above challenges, we propose Omni-Reward, a step toward generalist omni-modal reward modeling with support for free-form preferences, consisting of: (1) Evaluation: We introduce Omni-RewardBench, the first omni-modal RM benchmark with free-form preferences, covering nine tasks across five modalities including text, image, video, audio, and 3D; (2) Data: We construct Omni-RewardData, a multimodal preference dataset comprising 248K general preference pairs and 69K instruction-tuning pairs for training generalist omni-modal RMs; (3) Model: We propose Omni-RewardModel, which includes both discriminative and generative RMs, and achieves strong performance on Omni-RewardBench as well as other widely used reward modeling benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.22733", "title": "E^2Rank: Your Text Embedding can Also be an Effective and Efficient Listwise Reranker", "authors": [ "Qi Liu", "Yanzhao Zhang", "Mingxin Li", "Dingkun Long", "Pengjun Xie", "Jiaxin Mao" ], "github": "", "project_page": "", "abstract": "Text embedding models serve as a fundamental component in real-world search applications. By mapping queries and documents into a shared embedding space, they deliver competitive retrieval performance with high efficiency. However, their ranking fidelity remains limited compared to dedicated rerankers, especially recent LLM-based listwise rerankers, which capture fine-grained query-document and document-document interactions. In this paper, we propose a simple yet effective unified framework E^2Rank, means Efficient Embedding-based Ranking (also means Embedding-to-Rank), which extends a single text embedding model to perform both high-quality retrieval and listwise reranking through continued training under a listwise ranking objective, thereby achieving strong effectiveness with remarkable efficiency. By applying cosine similarity between the query and document embeddings as a unified ranking function, the listwise ranking prompt, which is constructed from the original query and its candidate documents, serves as an enhanced query enriched with signals from the top-K documents, akin to pseudo-relevance feedback (PRF) in traditional retrieval models. This design preserves the efficiency and representational quality of the base embedding model while significantly improving its reranking performance. Empirically, E^2Rank achieves state-of-the-art results on the BEIR reranking benchmark and demonstrates competitive performance on the reasoning-intensive BRIGHT benchmark, with very low reranking latency. We also show that the ranking training process improves embedding performance on the MTEB benchmark. Our findings indicate that a single embedding model can effectively unify retrieval and reranking, offering both computational efficiency and competitive ranking accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.22706", "title": "IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction", "authors": [ "Hao Li", "Zhengyu Zou", "Fangfu Liu", "Xuanyang Zhang", "Fangzhou Hong", "Yukang Cao", "Yushi Lan", "Manyuan Zhang", "Gang Yu", "Dingwen Zhang", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Humans naturally perceive the geometric structure and semantic content of a 3D world as intertwined dimensions, enabling coherent and accurate understanding of complex scenes. However, most prior approaches prioritize training large geometry models for low-level 3D reconstruction and treat high-level spatial understanding in isolation, overlooking the crucial interplay between these two fundamental aspects of 3D-scene analysis, thereby limiting generalization and leading to poor performance in downstream 3D understanding tasks. Recent attempts have mitigated this issue by simply aligning 3D models with specific language models, thus restricting perception to the aligned model's capacity and limiting adaptability to downstream tasks. In this paper, we propose InstanceGrounded Geometry Transformer (IGGT), an end-to-end large unified transformer to unify the knowledge for both spatial reconstruction and instance-level contextual understanding. Specifically, we design a 3D-Consistent Contrastive Learning strategy that guides IGGT to encode a unified representation with geometric structures and instance-grounded clustering through only 2D visual inputs. This representation supports consistent lifting of 2D visual inputs into a coherent 3D scene with explicitly distinct object instances. To facilitate this task, we further construct InsScene-15K, a large-scale dataset with high-quality RGB images, poses, depth maps, and 3D-consistent instance-level mask annotations with a novel data curation pipeline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.23603", "title": "PixelRefer: A Unified Framework for Spatio-Temporal Object Referring with Arbitrary Granularity", "authors": [ "Yuqian Yuan", "Wenqiao Zhang", "Xin Li", "Shihao Wang", "Kehan Li", "Wentong Li", "Jun Xiao", "Lei Zhang", "Beng Chin Ooi" ], "github": "https://github.com/alibaba-damo-academy/PixelRefer", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have demonstrated strong general-purpose capabilities in open-world visual comprehension. However, most existing MLLMs primarily focus on holistic, scene-level understanding, often overlooking the need for fine-grained, object-centric reasoning. In this paper, we present PixelRefer, a unified region-level MLLM framework that enables advanced fine-grained understanding over user-specified regions across both images and videos. Motivated by the observation that LLM attention predominantly focuses on object-level tokens, we propose a Scale-Adaptive Object Tokenizer (SAOT) to generate compact and semantically rich object representations from free-form regions. Our analysis reveals that global visual tokens contribute mainly in early LLM layers, inspiring the design of PixelRefer-Lite, an efficient variant that employs an Object-Centric Infusion module to pre-fuse global context into object tokens. This yields a lightweight Object-Only Framework that substantially reduces computational cost while maintaining high semantic fidelity. To facilitate fine-grained instruction tuning, we curate PixelRefer-2.2M, a high-quality object-centric instruction dataset. Extensive experiments across a range of benchmarks validate that PixelRefer achieves leading performance with fewer training samples, while PixelRefer-Lite offers competitive accuracy with notable gains in efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alibaba-damo-academy/PixelRefer", "github_api_added_by": "auto" }, { "date": "2025-10-28", "arxiv_id": "2510.23544", "title": "LimRank: Less is More for Reasoning-Intensive Information Reranking", "authors": [ "Tingyu Song", "Yilun Zhao", "Siyue Zhang", "Chen Zhao", "Arman Cohan" ], "github": "https://github.com/SighingSnow/LimRank", "project_page": "", "abstract": "Existing approaches typically rely on large-scale fine-tuning to adapt LLMs for information reranking tasks, which is computationally expensive. In this work, we demonstrate that modern LLMs can be effectively adapted using only minimal, high-quality supervision. To enable this, we design LIMRANK-SYNTHESIZER, a reusable and open-source pipeline for generating diverse, challenging, and realistic reranking examples. Using this synthetic data, we fine-tune our reranker model, LIMRANK. We evaluate LIMRANK on two challenging benchmarks, i.e., BRIGHT for reasoning-intensive retrieval and FollowIR for instruction-following retrieval. Our experiments demonstrate that LIMRANK achieves competitive performance, while being trained on less than 5% of the data typically used in prior work. Further ablation studies demonstrate the effectiveness of LIMRANK-SYNTHESIZER and the strong generalization capabilities of LIMRANK across downstream tasks, including scientific literature search and retrieval-augmented generation for knowledge-intensive problem solving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SighingSnow/LimRank", "github_api_added_by": "auto" }, { "date": "2025-10-28", "arxiv_id": "2510.22907", "title": "Language Server CLI Empowers Language Agents with Process Rewards", "authors": [ "Yifan Zhang", "Lanser Contributors" ], "github": "https://github.com/yifanzhang-pro/lanser-cli", "project_page": "", "abstract": "Large language models routinely hallucinate APIs and mislocalize edits, while language servers compute verified, IDE-grade facts about real code. We present Lanser-CLI, a CLI-first orchestration layer that pins and mediates a Language Server Protocol (LSP) server for coding agents and CI, exposing deterministic, replayable workflows. Our position is that language servers provide not only structural information (definitions, references, types, diagnostics) but also an actionable process reward: machine-checked, step-wise signals that align an agent's planning loop with program reality. In this work, Lanser-CLI contributes: (i) a robust addressing scheme beyond brittle \"file:line:col\" via a Selector DSL (symbolic, AST-path, and content-anchored selectors) with a principled relocation algorithm; (ii) deterministic Analysis Bundles that normalize Language Server responses and capture environment/capability metadata with stable content hashes; (iii) a safety envelope for mutating operations (rename, code actions) with preview, workspace jails, and Git-aware, transactional apply; and (iv) a process-reward functional derived from Language Server facts (diagnostic deltas, disambiguation confidence, and safe-apply checks) that is computable online and replayable offline. We formalize determinism under frozen snapshots and establish a monotonicity property for the process reward, making it suitable for process supervision and counterfactual analysis. Project Page: https://github.com/yifanzhang-pro/lanser-cli", "github_manual": "", "github_regex": "https://github.com/yifanzhang-pro/lanser-cli", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.21003", "title": "Distilled Decoding 2: One-step Sampling of Image Auto-regressive Models with Conditional Score Distillation", "authors": [ "Enshu Liu", "Qian Chen", "Xuefei Ning", "Shengen Yan", "Guohao Dai", "Zinan Lin", "Yu Wang" ], "github": "https://github.com/imagination-research/Distilled-Decoding-2", "project_page": "", "abstract": "Image Auto-regressive (AR) models have emerged as a powerful paradigm of visual generative models. Despite their promising performance, they suffer from slow generation speed due to the large number of sampling steps required. Although Distilled Decoding 1 (DD1) was recently proposed to enable few-step sampling for image AR models, it still incurs significant performance degradation in the one-step setting, and relies on a pre-defined mapping that limits its flexibility. In this work, we propose a new method, Distilled Decoding 2 (DD2), to further advances the feasibility of one-step sampling for image AR models. Unlike DD1, DD2 does not without rely on a pre-defined mapping. We view the original AR model as a teacher model which provides the ground truth conditional score in the latent embedding space at each token position. Based on this, we propose a novel conditional score distillation loss to train a one-step generator. Specifically, we train a separate network to predict the conditional score of the generated distribution and apply score distillation at every token position conditioned on previous tokens. Experimental results show that DD2 enables one-step sampling for image AR models with an minimal FID increase from 3.40 to 5.43 on ImageNet-256. Compared to the strongest baseline DD1, DD2 reduces the gap between the one-step sampling and original AR model by 67%, with up to 12.3times training speed-up simultaneously. DD2 takes a significant step toward the goal of one-step AR generation, opening up new possibilities for fast and high-quality AR modeling. Code is available at https://github.com/imagination-research/Distilled-Decoding-2.", "github_manual": "", "github_regex": "https://github.com/imagination-research/Distilled-Decoding-2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.23594", "title": "PRISM-Bench: A Benchmark of Puzzle-Based Visual Tasks with CoT Error Detection", "authors": [ "Yusu Qian", "Cheng Wan", "Chao Jia", "Yinfei Yang", "Qingyu Zhao", "Zhe Gan" ], "github": "https://github.com/JornyWan/PRISM-Bench", "project_page": "", "abstract": "We introduce PRISM-Bench, a benchmark of puzzle-based visual challenges designed to evaluate not only whether models can solve problems, but how their reasoning unfolds. Unlike prior evaluations that measure only final-answer accuracy, PRISM-Bench introduces a diagnostic task: given a visual puzzle and a step-by-step chain-of-thought (CoT) containing exactly one error, models must identify the first incorrect step. This setting enables fine-grained assessment of logical consistency, error detection, and visual reasoning. The puzzles in PRISM-Bench require multi-step symbolic, geometric, and analogical reasoning, resisting shortcuts based on superficial pattern matching. Evaluations across state-of-the-art MLLMs reveal a persistent gap between fluent generation and faithful reasoning: models that produce plausible CoTs often fail to locate simple logical faults. By disentangling answer generation from reasoning verification, PRISM-Bench offers a sharper lens on multimodal reasoning competence and underscores the need for diagnostic evaluation protocols in the development of trustworthy MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JornyWan/PRISM-Bench", "github_api_added_by": "auto" }, { "date": "2025-10-28", "arxiv_id": "2510.23571", "title": "RobotArena infty: Scalable Robot Benchmarking via Real-to-Sim Translation", "authors": [ "Yash Jangir", "Yidi Zhang", "Kashu Yamazaki", "Chenyu Zhang", "Kuan-Hsun Tu", "Tsung-Wei Ke", "Lei Ke", "Yonatan Bisk", "Katerina Fragkiadaki" ], "github": "", "project_page": "", "abstract": "The pursuit of robot generalists - instructable agents capable of performing diverse tasks across diverse environments - demands rigorous and scalable evaluation. Yet real-world testing of robot policies remains fundamentally constrained: it is labor-intensive, slow, unsafe at scale, and difficult to reproduce. Existing simulation benchmarks are similarly limited, as they train and test policies within the same synthetic domains and cannot assess models trained from real-world demonstrations or alternative simulation environments. As policies expand in scope and complexity, these barriers only intensify, since defining \"success\" in robotics often hinges on nuanced human judgments of execution quality. In this paper, we introduce a new benchmarking framework that overcomes these challenges by shifting VLA evaluation into large-scale simulated environments augmented with online human feedback. Leveraging advances in vision-language models, 2D-to-3D generative modeling, and differentiable rendering, our approach automatically converts video demonstrations from widely used robot datasets into simulated counterparts. Within these digital twins, we assess VLA policies using both automated VLM-guided scoring and scalable human preference judgments collected from crowdworkers, transforming human involvement from tedious scene setup, resetting, and safety supervision into lightweight preference comparisons. To measure robustness, we systematically perturb simulated environments along multiple axes, such as textures and object placements, stress-testing policy generalization under controlled variation. The result is a continuously evolving, reproducible, and scalable benchmark for real-world trained robot manipulation policies, addressing a critical missing capability in today's robotics landscape.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.23052", "title": "Knocking-Heads Attention", "authors": [ "Zhanchao Zhou", "Xiaodong Chen", "Haoxing Chen", "Zhenzhong Lan", "Jianguo Li" ], "github": "", "project_page": "", "abstract": "Multi-head attention (MHA) has become the cornerstone of modern large language models, enhancing representational capacity through parallel attention heads. However, increasing the number of heads inherently weakens individual head capacity, and existing attention mechanisms - whether standard MHA or its variants like grouped-query attention (GQA) and grouped-tied attention (GTA) - simply concatenate outputs from isolated heads without strong interaction. To address this limitation, we propose knocking-heads attention (KHA), which enables attention heads to \"knock\" on each other - facilitating cross-head feature-level interactions before the scaled dot-product attention. This is achieved by applying a shared, diagonally-initialized projection matrix across all heads. The diagonal initialization preserves head-specific specialization at the start of training while allowing the model to progressively learn integrated cross-head representations. KHA adds only minimal parameters and FLOPs and can be seamlessly integrated into MHA, GQA, GTA, and other attention variants. We validate KHA by training a 6.1B parameter MoE model (1.01B activated) on 1T high-quality tokens. Compared to baseline attention mechanisms, KHA brings superior and more stable training dynamics, achieving better performance across downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.22946", "title": "LightBagel: A Light-weighted, Double Fusion Framework for Unified Multimodal Understanding and Generation", "authors": [ "Zeyu Wang", "Zilong Chen", "Chenhui Gou", "Feng Li", "Chaorui Deng", "Deyao Zhu", "Kunchang Li", "Weihao Yu", "Haoqin Tu", "Haoqi Fan", "Cihang Xie" ], "github": "", "project_page": "", "abstract": "Unified multimodal models have recently shown remarkable gains in both capability and versatility, yet most leading systems are still trained from scratch and require substantial computational resources. In this paper, we show that competitive performance can be obtained far more efficiently by strategically fusing publicly available models specialized for either generation or understanding. Our key design is to retain the original blocks while additionally interleaving multimodal self-attention blocks throughout the networks. This double fusion mechanism (1) effectively enables rich multi-modal fusion while largely preserving the original strengths of the base models, and (2) catalyzes synergistic fusion of high-level semantic representations from the understanding encoder with low-level spatial signals from the generation encoder. By training with only ~ 35B tokens, this approach achieves strong results across multiple benchmarks: 0.91 on GenEval for compositional text-to-image generation, 82.16 on DPG-Bench for complex text-to-image generation, 6.06 on GEditBench, and 3.77 on ImgEdit-Bench for image editing. By fully releasing the entire suite of code, model weights, and datasets, we hope to support future research on unified multimodal modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.22200", "title": "LongCat-Video Technical Report", "authors": [ "Meituan LongCat Team", "Xunliang Cai", "Qilong Huang", "Zhuoliang Kang", "Hongyu Li", "Shijun Liang", "Liya Ma", "Siyu Ren", "Xiaoming Wei", "Rixu Xie", "Tong Zhang" ], "github": "https://github.com/meituan-longcat/LongCat-Video", "project_page": "", "abstract": "Video generation is a critical pathway toward world models, with efficient long video inference as a key capability. Toward this end, we introduce LongCat-Video, a foundational video generation model with 13.6B parameters, delivering strong performance across multiple video generation tasks. It particularly excels in efficient and high-quality long video generation, representing our first step toward world models. Key features include: Unified architecture for multiple tasks: Built on the Diffusion Transformer (DiT) framework, LongCat-Video supports Text-to-Video, Image-to-Video, and Video-Continuation tasks with a single model; Long video generation: Pretraining on Video-Continuation tasks enables LongCat-Video to maintain high quality and temporal coherence in the generation of minutes-long videos; Efficient inference: LongCat-Video generates 720p, 30fps videos within minutes by employing a coarse-to-fine generation strategy along both the temporal and spatial axes. Block Sparse Attention further enhances efficiency, particularly at high resolutions; Strong performance with multi-reward RLHF: Multi-reward RLHF training enables LongCat-Video to achieve performance on par with the latest closed-source and leading open-source models. Code and model weights are publicly available to accelerate progress in the field.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/meituan-longcat/LongCat-Video", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.21800", "title": "MARS-M: When Variance Reduction Meets Matrices", "authors": [ "Yifeng Liu", "Angela Yuan", "Quanquan Gu" ], "github": "https://github.com/AGI-Arena/MARS", "project_page": "", "abstract": "Matrix-based preconditioned optimizers, such as Muon, have recently been shown to be more efficient than scalar-based optimizers for training large-scale neural networks, including large language models (LLMs). On the other hand, recent benchmarks on optimizers for LLM pre-training have demonstrated that variance-reduction techniques such as MARS can achieve substantial speedups over standard optimizers that do not employ variance reduction. In this paper, to achieve the best of both worlds, we introduce MARS-M, a new optimizer that integrates the variance reduction technique in MARS with Muon. Under standard regularity conditions, we prove that Muon-M converges to a first-order stationary point at a rate of mathcal{O}(T^{-1/3}), which improves upon mathcal{O}(T^{-1/4}) rate attained by Muon. Our empirical results on language modeling and computer vision tasks demonstrate that MARS-M consistently yields lower losses and improved performance across various downstream benchmarks. The implementation of MARS-M is available at https://github.com/AGI-Arena/MARS/MARS_M.", "github_manual": "", "github_regex": "https://github.com/AGI-Arena/MARS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.23564", "title": "ReCode: Unify Plan and Action for Universal Granularity Control", "authors": [ "Zhaoyang Yu", "Jiayi Zhang", "Huixue Su", "Yufan Zhao", "Yifan Wu", "Mingyi Deng", "Jinyu Xiang", "Yizhang Lin", "Lingxiao Tang", "Yingchao Li", "Yuyu Luo", "Bang Liu", "Chenglin Wu" ], "github": "https://github.com/FoundationAgents/ReCode", "project_page": "", "abstract": "Real-world tasks require decisions at varying granularities, and humans excel at this by leveraging a unified cognitive representation where planning is fundamentally understood as a high-level form of action. However, current Large Language Model (LLM)-based agents lack this crucial capability to operate fluidly across decision granularities. This limitation stems from existing paradigms that enforce a rigid separation between high-level planning and low-level action, which impairs dynamic adaptability and limits generalization. We propose ReCode (Recursive Code Generation), a novel paradigm that addresses this limitation by unifying planning and action within a single code representation. In this representation, ReCode treats high-level plans as abstract placeholder functions, which the agent then recursively decomposes into finer-grained sub-functions until reaching primitive actions. This recursive approach dissolves the rigid boundary between plan and action, enabling the agent to dynamically control its decision granularity. Furthermore, the recursive structure inherently generates rich, multi-granularity training data, enabling models to learn hierarchical decision-making processes. Extensive experiments show ReCode significantly surpasses advanced baselines in inference performance and demonstrates exceptional data efficiency in training, validating our core insight that unifying planning and action through recursive code generation is a powerful and effective approach to achieving universal granularity control. The code is available at https://github.com/FoundationAgents/ReCode.", "github_manual": "", "github_regex": "https://github.com/FoundationAgents/ReCode", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.23587", "title": "A Survey of Data Agents: Emerging Paradigm or Overstated Hype?", "authors": [ "Yizhang Zhu", "Liangwei Wang", "Chenyu Yang", "Xiaotian Lin", "Boyan Li", "Wei Zhou", "Xinyu Liu", "Zhangyang Peng", "Tianqi Luo", "Yu Li", "Chengliang Chai", "Chong Chen", "Shimin Di", "Ju Fan", "Ji Sun", "Nan Tang", "Fugee Tsung", "Jiannan Wang", "Chenglin Wu", "Yanwei Xu", "Shaolei Zhang", "Yong Zhang", "Xuanhe Zhou", "Guoliang Li", "Yuyu Luo" ], "github": "https://github.com/jd-opensource/joyagent-jdgenie", "project_page": "", "abstract": "The rapid advancement of large language models (LLMs) has spurred the emergence of data agents--autonomous systems designed to orchestrate Data + AI ecosystems for tackling complex data-related tasks. However, the term \"data agent\" currently suffers from terminological ambiguity and inconsistent adoption, conflating simple query responders with sophisticated autonomous architectures. This terminological ambiguity fosters mismatched user expectations, accountability challenges, and barriers to industry growth. Inspired by the SAE J3016 standard for driving automation, this survey introduces the first systematic hierarchical taxonomy for data agents, comprising six levels that delineate and trace progressive shifts in autonomy, from manual operations (L0) to a vision of generative, fully autonomous data agents (L5), thereby clarifying capability boundaries and responsibility allocation. Through this lens, we offer a structured review of existing research arranged by increasing autonomy, encompassing specialized data agents for data management, preparation, and analysis, alongside emerging efforts toward versatile, comprehensive systems with enhanced autonomy. We further analyze critical evolutionary leaps and technical gaps for advancing data agents, especially the ongoing L2-to-L3 transition, where data agents evolve from procedural execution to autonomous orchestration. Finally, we conclude with a forward-looking roadmap, envisioning the advent of proactive, generative data agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jd-opensource/joyagent-jdgenie", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.23581", "title": "Lookahead Anchoring: Preserving Character Identity in Audio-Driven Human Animation", "authors": [ "Junyoung Seo", "Rodrigo Mira", "Alexandros Haliassos", "Stella Bounareli", "Honglie Chen", "Linh Tran", "Seungryong Kim", "Zoe Landgraf", "Jie Shen" ], "github": "https://github.com/j0seo/lookahead-anchoring", "project_page": "", "abstract": "Audio-driven human animation models often suffer from identity drift during temporal autoregressive generation, where characters gradually lose their identity over time. One solution is to generate keyframes as intermediate temporal anchors that prevent degradation, but this requires an additional keyframe generation stage and can restrict natural motion dynamics. To address this, we propose Lookahead Anchoring, which leverages keyframes from future timesteps ahead of the current generation window, rather than within it. This transforms keyframes from fixed boundaries into directional beacons: the model continuously pursues these future anchors while responding to immediate audio cues, maintaining consistent identity through persistent guidance. This also enables self-keyframing, where the reference image serves as the lookahead target, eliminating the need for keyframe generation entirely. We find that the temporal lookahead distance naturally controls the balance between expressivity and consistency: larger distances allow for greater motion freedom, while smaller ones strengthen identity adherence. When applied to three recent human animation models, Lookahead Anchoring achieves superior lip synchronization, identity preservation, and visual quality, demonstrating improved temporal conditioning across several different architectures. Video results are available at the following link: https://lookahead-anchoring.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/j0seo/lookahead-anchoring", "github_api_added_by": "auto" }, { "date": "2025-10-28", "arxiv_id": "2510.23272", "title": "Code Aesthetics with Agentic Reward Feedback", "authors": [ "Bang Xiao", "Lingjie Jiang", "Shaohan Huang", "Tengchao Lv", "Yupan Huang", "Xun Wu", "Lei Cui", "Furu Wei" ], "github": "https://github.com/bangx7/code_aesthetics", "project_page": "", "abstract": "Large Language Models (LLMs) have become valuable assistants for developers in code-related tasks. While LLMs excel at traditional programming tasks such as code generation and bug fixing, they struggle with visually-oriented coding tasks, often producing suboptimal aesthetics. In this paper, we introduce a new pipeline to enhance the aesthetic quality of LLM-generated code. We first construct AesCode-358K, a large-scale instruction-tuning dataset focused on code aesthetics. Next, we propose agentic reward feedback, a multi-agent system that evaluates executability, static aesthetics, and interactive aesthetics. Building on this, we develop GRPO-AR, which integrates these signals into the GRPO algorithm for joint optimization of functionality and code aesthetics. Finally, we develop OpenDesign, a benchmark for assessing code aesthetics. Experimental results show that combining supervised fine-tuning on AesCode-358K with reinforcement learning using agentic reward feedback significantly improves performance on OpenDesign and also enhances results on existing benchmarks such as PandasPlotBench. Notably, our AesCoder-4B surpasses GPT-4o and GPT-4.1, and achieves performance comparable to large open-source models with 480B-685B parameters, underscoring the effectiveness of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bangx7/code_aesthetics", "github_api_added_by": "auto" }, { "date": "2025-10-28", "arxiv_id": "2510.22975", "title": "VoMP: Predicting Volumetric Mechanical Property Fields", "authors": [ "Rishit Dagli", "Donglai Xiang", "Vismay Modi", "Charles Loop", "Clement Fuji Tsang", "Anka He Chen", "Anita Hu", "Gavriel State", "David I. W. Levin", "Maria Shugrina" ], "github": "https://github.com/ArthurBrussee/brush", "project_page": "", "abstract": "Physical simulation relies on spatially-varying mechanical properties, often laboriously hand-crafted. VoMP is a feed-forward method trained to predict Young's modulus (E), Poisson's ratio (nu), and density (rho) throughout the volume of 3D objects, in any representation that can be rendered and voxelized. VoMP aggregates per-voxel multi-view features and passes them to our trained Geometry Transformer to predict per-voxel material latent codes. These latents reside on a manifold of physically plausible materials, which we learn from a real-world dataset, guaranteeing the validity of decoded per-voxel materials. To obtain object-level training data, we propose an annotation pipeline combining knowledge from segmented 3D datasets, material databases, and a vision-language model, along with a new benchmark. Experiments show that VoMP estimates accurate volumetric properties, far outperforming prior art in accuracy and speed.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ArthurBrussee/brush", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.23605", "title": "Track, Inpaint, Resplat: Subject-driven 3D and 4D Generation with Progressive Texture Infilling", "authors": [ "Shuhong Zheng", "Ashkan Mirzaei", "Igor Gilitschenski" ], "github": "", "project_page": "", "abstract": "Current 3D/4D generation methods are usually optimized for photorealism, efficiency, and aesthetics. However, they often fail to preserve the semantic identity of the subject across different viewpoints. Adapting generation methods with one or few images of a specific subject (also known as Personalization or Subject-driven generation) allows generating visual content that align with the identity of the subject. However, personalized 3D/4D generation is still largely underexplored. In this work, we introduce TIRE (Track, Inpaint, REsplat), a novel method for subject-driven 3D/4D generation. It takes an initial 3D asset produced by an existing 3D generative model as input and uses video tracking to identify the regions that need to be modified. Then, we adopt a subject-driven 2D inpainting model for progressively infilling the identified regions. Finally, we resplat the modified 2D multi-view observations back to 3D while still maintaining consistency. Extensive experiments demonstrate that our approach significantly improves identity preservation in 3D/4D generation compared to state-of-the-art methods. Our project website is available at https://zsh2000.github.io/track-inpaint-resplat.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.22603", "title": "Mitigating Attention Sinks and Massive Activations in Audio-Visual Speech Recognition with LLMS", "authors": [ "Anand", "Umberto Cappellazzo", "Stavros Petridis", "Maja Pantic" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have recently advanced auditory speech recognition (ASR), visual speech recognition (VSR), and audio-visual speech recognition (AVSR). However, understanding of their internal dynamics under fine-tuning remains limited. In natural language processing, recent work has revealed attention sinks, tokens that attract disproportionately high attention, and associated massive activations in which some features of sink tokens exhibit huge activation in LLMs. In this work, we are the first to study these phenomena in multimodal speech recognition. Through a detailed analysis of audio-visual LLMs, we identify attention sinks and massive activations not only at the BOS token but also at intermediate low-semantic tokens across ASR, VSR, and AVSR. We show that massive activations originate in the MLP layers and correspond to fixed feature indices across all sink tokens. We further show that intermediate sink tokens exhibit high cosine similarity to the BOS token, thereby amplifying attention and activation. Building on these insights, we introduce a simple decorrelation loss that reduces cosine similarity between BOS and other tokens, effectively mitigating intermediate sinks and massive activations. Furthermore, our method improves word error rate (WER) under high audio-visual feature downsampling while remaining stable at lower downsampling rates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.22317", "title": "Memory-based Language Models: An Efficient, Explainable, and Eco-friendly Approach to Large Language Modeling", "authors": [ "Antal van den Bosch", "Ainhoa Risco Pat\u00f3n", "Teun Buijse", "Peter Berck", "Maarten van Gompel" ], "github": "", "project_page": "", "abstract": "We present memory-based language modeling as an efficient, eco-friendly alternative to deep neural network-based language modeling. It offers log-linearly scalable next-token prediction performance and strong memorization capabilities. Implementing fast approximations of k-nearest neighbor classification, memory-based language modeling leaves a relatively small ecological footprint both in training and in inference mode, as it relies fully on CPUs and attains low token latencies. Its internal workings are simple and fully transparent. We compare our implementation of memory-based language modeling, OLIFANT, with GPT-2 and GPT-Neo on next-token prediction accuracy, estimated emissions and speeds, and offer some deeper analyses of the model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.23393", "title": "The Best of N Worlds: Aligning Reinforcement Learning with Best-of-N Sampling via max@k Optimisation", "authors": [ "Farid Bagirov", "Mikhail Arkhipov", "Ksenia Sycheva", "Evgeniy Glukhov", "Egor Bogomolov" ], "github": "", "project_page": "", "abstract": "The application of Reinforcement Learning with Verifiable Rewards (RLVR) to mathematical and coding domains has demonstrated significant improvements in the reasoning and problem-solving abilities of Large Language Models. Despite its success in single generation problem solving, the reinforcement learning fine-tuning process may harm the model's exploration ability, as reflected in decreased diversity of generations and a resulting degradation of performance during Best-of-N sampling for large N values. In this work, we focus on optimizing the max@k metric, a continuous generalization of pass@k. We derive an unbiased on-policy gradient estimate for direct optimization of this metric. Furthermore, we extend our derivations to the off-policy updates, a common element in modern RLVR algorithms, that allows better sample efficiency. Empirically, we show that our objective effectively optimizes max@k metric in off-policy scenarios, aligning the model with the Best-of-N inference strategy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.20512", "title": "EchoDistill: Bidirectional Concept Distillation for One-Step Diffusion Personalization", "authors": [ "Yixiong Yang", "Tao Wu", "Senmao Li", "Shiqi Yang", "Yaxing Wang", "Joost van de Weijer", "Kai Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in accelerating text-to-image (T2I) diffusion models have enabled the synthesis of high-fidelity images even in a single step. However, personalizing these models to incorporate novel concepts remains a challenge due to the limited capacity of one-step models to capture new concept distributions effectively. We propose a bidirectional concept distillation framework, EchoDistill, to enable one-step diffusion personalization (1-SDP). Our approach involves an end-to-end training process where a multi-step diffusion model (teacher) and a one-step diffusion model (student) are trained simultaneously. The concept is first distilled from the teacher model to the student, and then echoed back from the student to the teacher. During the EchoDistill, we share the text encoder between the two models to ensure consistent semantic understanding. Following this, the student model is optimized with adversarial losses to align with the real image distribution and with alignment losses to maintain consistency with the teacher's output. Furthermore, we introduce the bidirectional echoing refinement strategy, wherein the student model leverages its faster generation capability to feedback to the teacher model. This bidirectional concept distillation mechanism not only enhances the student ability to personalize novel concepts but also improves the generative quality of the teacher model. Our experiments demonstrate that this collaborative framework significantly outperforms existing personalization methods over the 1-SDP setup, establishing a novel paradigm for rapid and effective personalization in T2I diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.22236", "title": "DiffusionLane: Diffusion Model for Lane Detection", "authors": [ "Kunyang Zhou", "Yeqin Shao" ], "github": "https://github.com/zkyntu/UnLanedet", "project_page": "", "abstract": "In this paper, we present a novel diffusion-based model for lane detection, called DiffusionLane, which treats the lane detection task as a denoising diffusion process in the parameter space of the lane. Firstly, we add the Gaussian noise to the parameters (the starting point and the angle) of ground truth lanes to obtain noisy lane anchors, and the model learns to refine the noisy lane anchors in a progressive way to obtain the target lanes. Secondly, we propose a hybrid decoding strategy to address the poor feature representation of the encoder, resulting from the noisy lane anchors. Specifically, we design a hybrid diffusion decoder to combine global-level and local-level decoders for high-quality lane anchors. Then, to improve the feature representation of the encoder, we employ an auxiliary head in the training stage to adopt the learnable lane anchors for enriching the supervision on the encoder. Experimental results on four benchmarks, Carlane, Tusimple, CULane, and LLAMAS, show that DiffusionLane possesses a strong generalization ability and promising detection performance compared to the previous state-of-the-art methods. For example, DiffusionLane with ResNet18 surpasses the existing methods by at least 1\\% accuracy on the domain adaptation dataset Carlane. Besides, DiffusionLane with MobileNetV4 gets 81.32\\% F1 score on CULane, 96.89\\% accuracy on Tusimple with ResNet34, and 97.59\\% F1 score on LLAMAS with ResNet101. Code will be available at https://github.com/zkyntu/UnLanedet.", "github_manual": "", "github_regex": "https://github.com/zkyntu/UnLanedet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.16320", "title": "Scaling Laws for Deepfake Detection", "authors": [ "Wenhao Wang", "Longqi Cai", "Taihong Xiao", "Yuxiao Wang", "Ming-Hsuan Yang" ], "github": "", "project_page": "", "abstract": "This paper presents a systematic study of scaling laws for the deepfake detection task. Specifically, we analyze the model performance against the number of real image domains, deepfake generation methods, and training images. Since no existing dataset meets the scale requirements for this research, we construct ScaleDF, the largest dataset to date in this field, which contains over 5.8 million real images from 51 different datasets (domains) and more than 8.8 million fake images generated by 102 deepfake methods. Using ScaleDF, we observe power-law scaling similar to that shown in large language models (LLMs). Specifically, the average detection error follows a predictable power-law decay as either the number of real domains or the number of deepfake methods increases. This key observation not only allows us to forecast the number of additional real domains or deepfake methods required to reach a target performance, but also inspires us to counter the evolving deepfake technology in a data-centric manner. Beyond this, we examine the role of pre-training and data augmentations in deepfake detection under scaling, as well as the limitations of scaling itself.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.07723", "title": "SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view Human Reconstruction", "authors": [ "Wenyue Chen", "Peng Li", "Wangguandong Zheng", "Chengfeng Zhao", "Mengfei Li", "Yaolong Zhu", "Zhiyang Dou", "Ronggang Wang", "Yuan Liu" ], "github": "https://github.com/IGL-HKUST/SyncHuman", "project_page": "", "abstract": "Photorealistic 3D full-body human reconstruction from a single image is a critical yet challenging task for applications in films and video games due to inherent ambiguities and severe self-occlusions. While recent approaches leverage SMPL estimation and SMPL-conditioned image generative models to hallucinate novel views, they suffer from inaccurate 3D priors estimated from SMPL meshes and have difficulty in handling difficult human poses and reconstructing fine details. In this paper, we propose SyncHuman, a novel framework that combines 2D multiview generative model and 3D native generative model for the first time, enabling high-quality clothed human mesh reconstruction from single-view images even under challenging human poses. Multiview generative model excels at capturing fine 2D details but struggles with structural consistency, whereas 3D native generative model generates coarse yet structurally consistent 3D shapes. By integrating the complementary strengths of these two approaches, we develop a more effective generation framework. Specifically, we first jointly fine-tune the multiview generative model and the 3D native generative model with proposed pixel-aligned 2D-3D synchronization attention to produce geometrically aligned 3D shapes and 2D multiview images. To further improve details, we introduce a feature injection mechanism that lifts fine details from 2D multiview images onto the aligned 3D shapes, enabling accurate and high-fidelity reconstruction. Extensive experiments demonstrate that SyncHuman achieves robust and photo-realistic 3D human reconstruction, even for images with challenging poses. Our method outperforms baseline methods in geometric accuracy and visual fidelity, demonstrating a promising direction for future 3D generation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/IGL-HKUST/SyncHuman", "github_api_added_by": "auto" }, { "date": "2025-10-28", "arxiv_id": "2510.22521", "title": "Open Multimodal Retrieval-Augmented Factual Image Generation", "authors": [ "Yang Tian", "Fan Liu", "Jingyuan Zhang", "Wei Bi", "Yupeng Hu", "Liqiang Nie" ], "github": "", "project_page": "", "abstract": "Large Multimodal Models (LMMs) have achieved remarkable progress in generating photorealistic and prompt-aligned images, but they often produce outputs that contradict verifiable knowledge, especially when prompts involve fine-grained attributes or time-sensitive events. Conventional retrieval-augmented approaches attempt to address this issue by introducing external information, yet they are fundamentally incapable of grounding generation in accurate and evolving knowledge due to their reliance on static sources and shallow evidence integration. To bridge this gap, we introduce ORIG, an agentic open multimodal retrieval-augmented framework for Factual Image Generation (FIG), a new task that requires both visual realism and factual grounding. ORIG iteratively retrieves and filters multimodal evidence from the web and incrementally integrates the refined knowledge into enriched prompts to guide generation. To support systematic evaluation, we build FIG-Eval, a benchmark spanning ten categories across perceptual, compositional, and temporal dimensions. Experiments demonstrate that ORIG substantially improves factual consistency and overall image quality over strong baselines, highlighting the potential of open multimodal retrieval for factual image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.22849", "title": "Once Upon an Input: Reasoning via Per-Instance Program Synthesis", "authors": [ "Adam Stein", "Neelay Velingker", "Mayur Naik", "Eric Wong" ], "github": "https://github.com/adaminsky/pips", "project_page": "", "abstract": "Large language models (LLMs) excel at zero-shot inference but continue to struggle with complex, multi-step reasoning. Recent methods that augment LLMs with intermediate reasoning steps such as Chain of Thought (CoT) and Program of Thought (PoT) improve performance but often produce undesirable solutions, especially in algorithmic domains. We introduce Per-Instance Program Synthesis (PIPS), a method that generates and refines programs at the instance-level using structural feedback without relying on task-specific guidance or explicit test cases. To further improve performance, PIPS incorporates a confidence metric that dynamically chooses between direct inference and program synthesis on a per-instance basis. Experiments across three frontier LLMs and 30 benchmarks including all tasks of Big Bench Extra Hard (BBEH), visual question answering tasks, relational reasoning tasks, and mathematical reasoning tasks show that PIPS improves the absolute harmonic mean accuracy by up to 8.6% and 9.4% compared to PoT and CoT respectively, and reduces undesirable program generations by 65.1% on the algorithmic tasks compared to PoT with Gemini-2.0-Flash.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/adaminsky/pips", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.22010", "title": "FlowOpt: Fast Optimization Through Whole Flow Processes for Training-Free Editing", "authors": [ "Or Ronai", "Vladimir Kulikov", "Tomer Michaeli" ], "github": "", "project_page": "", "abstract": "The remarkable success of diffusion and flow-matching models has ignited a surge of works on adapting them at test time for controlled generation tasks. Examples range from image editing to restoration, compression and personalization. However, due to the iterative nature of the sampling process in those models, it is computationally impractical to use gradient-based optimization to directly control the image generated at the end of the process. As a result, existing methods typically resort to manipulating each timestep separately. Here we introduce FlowOpt - a zero-order (gradient-free) optimization framework that treats the entire flow process as a black box, enabling optimization through the whole sampling path without backpropagation through the model. Our method is both highly efficient and allows users to monitor the intermediate optimization results and perform early stopping if desired. We prove a sufficient condition on FlowOpt's step-size, under which convergence to the global optimum is guaranteed. We further show how to empirically estimate this upper bound so as to choose an appropriate step-size. We demonstrate how FlowOpt can be used for image editing, showcasing two options: (i) inversion (determining the initial noise that generates a given image), and (ii) directly steering the edited image to be similar to the source image while conforming to a target text prompt. In both cases, FlowOpt achieves state-of-the-art results while using roughly the same number of neural function evaluations (NFEs) as existing methods. Code and examples are available on the project's webpage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.23479", "title": "MergeMix: A Unified Augmentation Paradigm for Visual and Multi-Modal Understanding", "authors": [ "Xin Jin", "Siyuan Li", "Siyong Jian", "Kai Yu", "Huan Wang" ], "github": "https://github.com/JinXins/MergeMix", "project_page": "", "abstract": "Vision-language alignment in multi-modal large language models (MLLMs) typically relies on supervised fine-tuning (SFT) or reinforcement learning (RL). SFT is stable and efficient but requires large-scale human annotations and cannot capture subtle preferences, while RL brings in a reward signal for training, but suffers from overhead and instability. These limitations highlight a trade-off between scalability, robustness, and alignment quality. To address this, we propose MergeMix, a training-time augmentation paradigm that bridges SFT and RL. It first applies an attention-aware image mixing via token merge with more cluster representation and spatial context, and then presents a preference-driven training paradigm for MLLMs by building preference pairs with mixed images and raw images, and optimizing via SimPO loss. As a mixup augmentation, MergeMix enhances attention consistency and efficiency, surpassing other heuristic-based methods in classification. Extensive experiments demonstrate that MergeMix achieves competitive accuracy with improved efficiency, providing a scalable approach to preference alignment in classification and MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JinXins/MergeMix", "github_api_added_by": "auto" }, { "date": "2025-10-28", "arxiv_id": "2510.23595", "title": "Multi-Agent Evolve: LLM Self-Improve through Co-evolution", "authors": [ "Yixing Chen", "Yiding Wang", "Siqi Zhu", "Haofei Yu", "Tao Feng", "Muhan Zhan", "Mostofa Patwary", "Jiaxuan You" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning (RL) has demonstrated significant potential in enhancing the reasoning capabilities of large language models (LLMs). However, the success of RL for LLMs heavily relies on human-curated datasets and verifiable rewards, which limit their scalability and generality. Recent Self-Play RL methods, inspired by the success of the paradigm in games and Go, aim to enhance LLM reasoning capabilities without human-annotated data. However, their methods primarily depend on a grounded environment for feedback (e.g., a Python interpreter or a game engine); extending them to general domains remains challenging. To address these challenges, we propose Multi-Agent Evolve (MAE), a framework that enables LLMs to self-evolve in solving diverse tasks, including mathematics, reasoning, and general knowledge Q&A. The core design of MAE is based on a triplet of interacting agents (Proposer, Solver, Judge) that are instantiated from a single LLM, and applies reinforcement learning to optimize their behaviors. The Proposer generates questions, the Solver attempts solutions, and the Judge evaluates both while co-evolving. Experiments on Qwen2.5-3B-Instruct demonstrate that MAE achieves an average improvement of 4.54% on multiple benchmarks. These results highlight MAE as a scalable, data-efficient method for enhancing the general reasoning abilities of LLMs with minimal reliance on human-curated supervision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-28", "arxiv_id": "2510.21986", "title": "Sprint: Sparse-Dense Residual Fusion for Efficient Diffusion Transformers", "authors": [ "Dogyun Park", "Moayed Haji-Ali", "Yanyu Li", "Willi Menapace", "Sergey Tulyakov", "Hyunwoo J. Kim", "Aliaksandr Siarohin", "Anil Kag" ], "github": "", "project_page": "", "abstract": "Diffusion Transformers (DiTs) deliver state-of-the-art generative performance but their quadratic training cost with sequence length makes large-scale pretraining prohibitively expensive. Token dropping can reduce training cost, yet na\\\"ive strategies degrade representations, and existing methods are either parameter-heavy or fail at high drop ratios. We present SPRINT, Sparse--Dense Residual Fusion for Efficient Diffusion Transformers, a simple method that enables aggressive token dropping (up to 75%) while preserving quality. SPRINT leverages the complementary roles of shallow and deep layers: early layers process all tokens to capture local detail, deeper layers operate on a sparse subset to cut computation, and their outputs are fused through residual connections. Training follows a two-stage schedule: long masked pre-training for efficiency followed by short full-token fine-tuning to close the train--inference gap. On ImageNet-1K 256x256, SPRINT achieves 9.8x training savings with comparable FID/FDD, and at inference, its Path-Drop Guidance (PDG) nearly halves FLOPs while improving quality. These results establish SPRINT as a simple, effective, and general solution for efficient DiT training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24701", "title": "Tongyi DeepResearch Technical Report", "authors": [ "Tongyi DeepResearch Team", "Baixuan Li", "Bo Zhang", "Dingchu Zhang", "Fei Huang", "Guangyu Li", "Guoxin Chen", "Huifeng Yin", "Jialong Wu", "Jingren Zhou", "Kuan Li", "Liangcai Su", "Litu Ou", "Liwen Zhang", "Pengjun Xie", "Rui Ye", "Wenbiao Yin", "Xinmiao Yu", "Xinyu Wang", "Xixi Wu", "Xuanzhong Chen", "Yida Zhao", "Zhen Zhang", "Zhengwei Tao", "Zhongwang Zhang", "Zile Qiao", "Chenxi Wang", "Donglei Yu", "Gang Fu", "Haiyang Shen", "Jiayin Yang", "Jun Lin", "Junkai Zhang", "Kui Zeng", "Li Yang", "Hailong Yin", "Maojia Song", "Ming Yan", "Peng Xia", "Qian Xiao", "Rui Min", "Ruixue Ding", "Runnan Fang", "Shaowei Chen", "Shen Huang", "Shihang Wang", "Shihao Cai", "Weizhou Shen", "Xiaobin Wang", "Xin Guan", "Xinyu Geng", "Yingcheng Shi", "Yuning Wu", "Zhuo Chen", "Zijian Li", "Yong Jiang" ], "github": "https://github.com/Alibaba-NLP/DeepResearch/tree/main/evaluation", "project_page": "", "abstract": "We present Tongyi DeepResearch, an agentic large language model, which is specifically designed for long-horizon, deep information-seeking research tasks. To incentivize autonomous deep research agency, Tongyi DeepResearch is developed through an end-to-end training framework that combines agentic mid-training and agentic post-training, enabling scalable reasoning and information seeking across complex tasks. We design a highly scalable data synthesis pipeline that is fully automatic, without relying on costly human annotation, and empowers all training stages. By constructing customized environments for each stage, our system enables stable and consistent interactions throughout. Tongyi DeepResearch, featuring 30.5 billion total parameters, with only 3.3 billion activated per token, achieves state-of-the-art performance across a range of agentic deep research benchmarks, including Humanity's Last Exam, BrowseComp, BrowseComp-ZH, WebWalkerQA, xbench-DeepSearch, FRAMES and xbench-DeepSearch-2510. We open-source the model, framework, and complete solutions to empower the community.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Alibaba-NLP/DeepResearch/tree/main/evaluation", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24591", "title": "ReplicationBench: Can AI Agents Replicate Astrophysics Research Papers?", "authors": [ "Christine Ye", "Sihan Yuan", "Suchetha Cooray", "Steven Dillmann", "Ian L. V. Roque", "Dalya Baron", "Philipp Frank", "Sergio Martin-Alvarez", "Nolan Koblischke", "Frank J Qu", "Diyi Yang", "Risa Wechsler", "Ioana Ciuca" ], "github": "https://github.com/CMBAgents/cmbagent", "project_page": "", "abstract": "Frontier AI agents show increasing promise as scientific research assistants, and may eventually be useful for extended, open-ended research workflows. However, in order to use agents for novel research, we must first assess the underlying faithfulness and correctness of their work. To evaluate agents as research assistants, we introduce ReplicationBench, an evaluation framework that tests whether agents can replicate entire research papers drawn from the astrophysics literature. Astrophysics, where research relies heavily on archival data and computational study while requiring little real-world experimentation, is a particularly useful testbed for AI agents in scientific research. We split each paper into tasks which require agents to replicate the paper's core contributions, including the experimental setup, derivations, data analysis, and codebase. Each task is co-developed with the original paper authors and targets a key scientific result, enabling objective evaluation of both faithfulness (adherence to original methods) and correctness (technical accuracy of results). ReplicationBench is extremely challenging for current frontier language models: even the best-performing language models score under 20%. We analyze ReplicationBench trajectories in collaboration with domain experts and find a rich, diverse set of failure modes for agents in scientific research. ReplicationBench establishes the first benchmark of paper-scale, expert-validated astrophysics research tasks, reveals insights about agent performance generalizable to other domains of data-driven science, and provides a scalable framework for measuring AI agents' reliability in scientific research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/CMBAgents/cmbagent", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24563", "title": "OSWorld-MCP: Benchmarking MCP Tool Invocation In Computer-Use Agents", "authors": [ "Hongrui Jia", "Jitong Liao", "Xi Zhang", "Haiyang Xu", "Tianbao Xie", "Chaoya Jiang", "Ming Yan", "Si Liu", "Wei Ye", "Fei Huang" ], "github": "", "project_page": "", "abstract": "With advances in decision-making and reasoning capabilities, multimodal agents show strong potential in computer application scenarios. Past evaluations have mainly assessed GUI interaction skills, while tool invocation abilities, such as those enabled by the Model Context Protocol (MCP), have been largely overlooked. Comparing agents with integrated tool invocation to those evaluated only on GUI interaction is inherently unfair. We present OSWorld-MCP, the first comprehensive and fair benchmark for assessing computer-use agents' tool invocation, GUI operation, and decision-making abilities in a real-world environment. We design a novel automated code-generation pipeline to create tools and combine them with a curated selection from existing tools. Rigorous manual validation yields 158 high-quality tools (covering 7 common applications), each verified for correct functionality, practical applicability, and versatility. Extensive evaluations of state-of-the-art multimodal agents on OSWorld-MCP show that MCP tools generally improve task success rates (e.g., from 8.3% to 20.4% for OpenAI o3 at 15 steps, from 40.1% to 43.3% for Claude 4 Sonnet at 50 steps), underscoring the importance of assessing tool invocation capabilities. However, even the strongest models have relatively low tool invocation rates, Only 36.3%, indicating room for improvement and highlighting the benchmark's challenge. By explicitly measuring MCP tool usage skills, OSWorld-MCP deepens understanding of multimodal agents and sets a new standard for evaluating performance in complex, tool-assisted environments. Our code, environment, and data are publicly available at https://osworld-mcp.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24320", "title": "Critique-RL: Training Language Models for Critiquing through Two-Stage Reinforcement Learning", "authors": [ "Zhiheng Xi", "Jixuan Huang", "Xin Guo", "Boyang Hong", "Dingwen Yang", "Xiaoran Fan", "Shuo Li", "Zehui Chen", "Junjie Ye", "Siyu Yuan", "Zhengyin Du", "Xuesong Yao", "Yufei Xu", "Jiecao Chen", "Rui Zheng", "Tao Gui", "Qi Zhang", "Xuanjing Huang" ], "github": "https://github.com/WooooDyy/Critique-RL", "project_page": "", "abstract": "Training critiquing language models to assess and provide feedback on model outputs is a promising way to improve LLMs for complex reasoning tasks. However, existing approaches typically rely on stronger supervisors for annotating critique data. To address this, we propose Critique-RL, an online RL approach for developing critiquing language models without stronger supervision. Our approach operates on a two-player paradigm: the actor generates a response, the critic provides feedback, and the actor refines the response accordingly. We first reveal that relying solely on indirect reward signals from the actor's outputs for RL optimization often leads to unsatisfactory critics: while their helpfulness (i.e., providing constructive feedback) improves, the discriminability (i.e., determining whether a response is high-quality or not) remains poor, resulting in marginal performance gains. To overcome this, Critique-RL adopts a two-stage optimization strategy. In stage I, it reinforces the discriminability of the critic with direct rule-based reward signals; in stage II, it introduces indirect rewards based on actor refinement to improve the critic's helpfulness, while maintaining its discriminability via appropriate regularization. Extensive experiments across various tasks and models show that Critique-RL delivers substantial performance improvements. For example, it achieves a 9.02% gain on in-domain tasks and a 5.70% gain on out-of-domain tasks for Qwen2.5-7B, highlighting its potential.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WooooDyy/Critique-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.23763", "title": "RoboOmni: Proactive Robot Manipulation in Omni-modal Context", "authors": [ "Siyin Wang", "Jinlan Fu", "Feihong Liu", "Xinzhe He", "Huangxuan Wu", "Junhao Shi", "Kexin Huang", "Zhaoye Fei", "Jingjing Gong", "Zuxuan Wu", "Yugang Jiang", "See-Kiong Ng", "Tat-Seng Chua", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision-Language-Action (VLA) models for robotic manipulation. Although effective in many scenarios, current approaches largely rely on explicit instructions, whereas in real-world interactions, humans rarely issue instructions directly. Effective collaboration requires robots to infer user intentions proactively. In this work, we introduce cross-modal contextual instructions, a new setting where intent is derived from spoken dialogue, environmental sounds, and visual cues rather than explicit commands. To address this new setting, we present RoboOmni, a Perceiver-Thinker-Talker-Executor framework based on end-to-end omni-modal LLMs that unifies intention recognition, interaction confirmation, and action execution. RoboOmni fuses auditory and visual signals spatiotemporally for robust intention recognition, while supporting direct speech interaction. To address the absence of training data for proactive intention recognition in robotic manipulation, we build OmniAction, comprising 140k episodes, 5k+ speakers, 2.4k event sounds, 640 backgrounds, and six contextual instruction types. Experiments in simulation and real-world settings show that RoboOmni surpasses text- and ASR-based baselines in success rate, inference speed, intention recognition, and proactive assistance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24514", "title": "Latent Sketchpad: Sketching Visual Thoughts to Elicit Multimodal Reasoning in MLLMs", "authors": [ "Huanyu Zhang", "Wenshan Wu", "Chengzu Li", "Ning Shang", "Yan Xia", "Yangyu Huang", "Yifan Zhang", "Li Dong", "Zhang Zhang", "Liang Wang", "Tieniu Tan", "Furu Wei" ], "github": "", "project_page": "", "abstract": "While Multimodal Large Language Models (MLLMs) excel at visual understanding, they often struggle in complex scenarios that require visual planning and imagination. Inspired by how humans use sketching as a form of visual thinking to develop and communicate ideas, we introduce Latent Sketchpad, a framework that equips MLLMs with an internal visual scratchpad. The internal visual representations of MLLMs have traditionally been confined to perceptual understanding. We repurpose them to support generative visual thought without compromising reasoning ability. Building on frontier MLLMs, our approach integrates visual generation directly into their native autoregressive reasoning process. It allows the model to interleave textual reasoning with the generation of visual latents. These latents guide the internal thought process and can be translated into sketch images for interpretability. To realize this, we introduce two components: a Context-Aware Vision Head autoregressively produces visual representations, and a pretrained Sketch Decoder renders these into human-interpretable images. We evaluate the framework on our new dataset MazePlanning. Experiments across various MLLMs show that Latent Sketchpad delivers comparable or even superior reasoning performance to their backbone. It further generalizes across distinct frontier MLLMs, including Gemma3 and Qwen2.5-VL. By extending model's textual reasoning to visual thinking, our framework opens new opportunities for richer human-computer interaction and broader applications. More details and resources are available on our project page: https://latent-sketchpad.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24698", "title": "ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking", "authors": [ "Baixuan Li", "Dingchu Zhang", "Jialong Wu", "Wenbiao Yin", "Zhengwei Tao", "Yida Zhao", "Liwen Zhang", "Haiyang Shen", "Runnan Fang", "Pengjun Xie", "Jingren Zhou", "Yong Jiang" ], "github": "https://github.com/Alibaba-NLP/DeepResearch", "project_page": "", "abstract": "Parallel thinking expands exploration breadth, complementing the deep exploration of information-seeking (IS) agents to further enhance problem-solving capability. However, conventional parallel thinking faces two key challenges in this setting: inefficiency from repeatedly rolling out from scratch, and difficulty in integrating long-horizon reasoning trajectories during answer generation, as limited context capacity prevents full consideration of the reasoning process. To address these issues, we propose ParallelMuse, a two-stage paradigm designed for deep IS agents. The first stage, Functionality-Specified Partial Rollout, partitions generated sequences into functional regions and performs uncertainty-guided path reuse and branching to enhance exploration efficiency. The second stage, Compressed Reasoning Aggregation, exploits reasoning redundancy to losslessly compress information relevant to answer derivation and synthesize a coherent final answer. Experiments across multiple open-source agents and benchmarks demonstrate up to 62% performance improvement with a 10--30% reduction in exploratory token consumption.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Alibaba-NLP/DeepResearch", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24699", "title": "AgentFold: Long-Horizon Web Agents with Proactive Context Management", "authors": [ "Rui Ye", "Zhongwang Zhang", "Kuan Li", "Huifeng Yin", "Zhengwei Tao", "Yida Zhao", "Liangcai Su", "Liwen Zhang", "Zile Qiao", "Xinyu Wang", "Pengjun Xie", "Fei Huang", "Siheng Chen", "Jingren Zhou", "Yong Jiang" ], "github": "https://github.com/MiroMindAI/MiroThinker", "project_page": "", "abstract": "LLM-based web agents show immense promise for information seeking, yet their effectiveness on long-horizon tasks is hindered by a fundamental trade-off in context management. Prevailing ReAct-based agents suffer from context saturation as they accumulate noisy, raw histories, while methods that fixedly summarize the full history at each step risk the irreversible loss of critical details. Addressing these, we introduce AgentFold, a novel agent paradigm centered on proactive context management, inspired by the human cognitive process of retrospective consolidation. AgentFold treats its context as a dynamic cognitive workspace to be actively sculpted, rather than a passive log to be filled. At each step, it learns to execute a `folding' operation, which manages its historical trajectory at multiple scales: it can perform granular condensations to preserve vital, fine-grained details, or deep consolidations to abstract away entire multi-step sub-tasks. The results on prominent benchmarks are striking: with simple supervised fine-tuning (without continual pre-training or RL), our AgentFold-30B-A3B agent achieves 36.2% on BrowseComp and 47.3% on BrowseComp-ZH. Notably, this performance not only surpasses or matches open-source models of a dramatically larger scale, such as the DeepSeek-V3.1-671B-A37B, but also surpasses leading proprietary agents like OpenAI's o4-mini.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiroMindAI/MiroThinker", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24697", "title": "WebLeaper: Empowering Efficiency and Efficacy in WebAgent via Enabling Info-Rich Seeking", "authors": [ "Zhengwei Tao", "Haiyang Shen", "Baixuan Li", "Wenbiao Yin", "Jialong Wu", "Kuan Li", "Zhongwang Zhang", "Huifeng Yin", "Rui Ye", "Liwen Zhang", "Xinyu Wang", "Pengjun Xie", "Jingren Zhou", "Yong Jiang" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM)-based agents have emerged as a transformative approach for open-ended problem solving, with information seeking (IS) being a core capability that enables autonomous reasoning and decision-making. While prior research has largely focused on improving retrieval depth, we observe that current IS agents often suffer from low search efficiency, which in turn constrains overall performance. A key factor underlying this inefficiency is the sparsity of target entities in training tasks, which limits opportunities for agents to learn and generalize efficient search behaviors. To address these challenges, we propose WebLeaper, a framework for constructing high-coverage IS tasks and generating efficient solution trajectories. We formulate IS as a tree-structured reasoning problem, enabling a substantially larger set of target entities to be embedded within a constrained context. Leveraging curated Wikipedia tables, we propose three variants for synthesizing IS tasks, Basic, Union, and Reverse-Union, to systematically increase both IS efficiency and efficacy. Finally, we curate training trajectories by retaining only those that are simultaneously accurate and efficient, ensuring that the model is optimized for both correctness and search performance. Extensive experiments on both basic and comprehensive settings, conducted on five IS benchmarks, BrowserComp, GAIA, xbench-DeepSearch, WideSearch, and Seal-0, demonstrate that our method consistently achieves improvements in both effectiveness and efficiency over strong baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24695", "title": "AgentFrontier: Expanding the Capability Frontier of LLM Agents with ZPD-Guided Data Synthesis", "authors": [ "Xuanzhong Chen", "Zile Qiao", "Guoxin Chen", "Liangcai Su", "Zhen Zhang", "Xinyu Wang", "Pengjun Xie", "Fei Huang", "Jingren Zhou", "Yong Jiang" ], "github": "", "project_page": "", "abstract": "Training large language model agents on tasks at the frontier of their capabilities is key to unlocking advanced reasoning. We introduce a data synthesis approach inspired by the educational theory of the Zone of Proximal Development (ZPD), which defines this frontier as tasks an LLM cannot solve alone but can master with guidance. To operationalize this, we present the AgentFrontier Engine, an automated pipeline that synthesizes high-quality, multidisciplinary data situated precisely within the LLM's ZPD. This engine supports both continued pre-training with knowledge-intensive data and targeted post-training on complex reasoning tasks. From the same framework, we derive the ZPD Exam, a dynamic and automated benchmark designed to evaluate agent capabilities on these frontier tasks. We train AgentFrontier-30B-A3B model on our synthesized data, which achieves state-of-the-art results on demanding benchmarks like Humanity's Last Exam, even surpassing some leading proprietary agents. Our work demonstrates that a ZPD-guided approach to data synthesis offers a scalable and effective path toward building more capable LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24694", "title": "Repurposing Synthetic Data for Fine-grained Search Agent Supervision", "authors": [ "Yida Zhao", "Kuan Li", "Xixi Wu", "Liwen Zhang", "Dingchu Zhang", "Baixuan Li", "Maojia Song", "Zhuo Chen", "Chenxi Wang", "Xinyu Wang", "Kewei Tu", "Pengjun Xie", "Jingren Zhou", "Yong Jiang" ], "github": "", "project_page": "", "abstract": "LLM-based search agents are increasingly trained on entity-centric synthetic data to solve complex, knowledge-intensive tasks. However, prevailing training methods like Group Relative Policy Optimization (GRPO) discard this rich entity information, relying instead on sparse, outcome-based rewards. This critical limitation renders them unable to distinguish informative \"near-miss\" samples-those with substantially correct reasoning but a flawed final answer-from complete failures, thus discarding valuable learning signals. We address this by leveraging the very entities discarded during training. Our empirical analysis reveals a strong positive correlation between the number of ground-truth entities identified during an agent's reasoning process and final answer accuracy. Building on this insight, we introduce Entity-aware Group Relative Policy Optimization (E-GRPO), a novel framework that formulates a dense entity-aware reward function. E-GRPO assigns partial rewards to incorrect samples proportional to their entity match rate, enabling the model to effectively learn from these \"near-misses\". Experiments on diverse question-answering (QA) and deep research benchmarks show that E-GRPO consistently and significantly outperforms the GRPO baseline. Furthermore, our analysis reveals that E-GRPO not only achieves superior accuracy but also induces more efficient reasoning policies that require fewer tool calls, demonstrating a more effective and sample-efficient approach to aligning search agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24645", "title": "FunReason-MT Technical Report: Overcoming the Complexity Barrier in Multi-Turn Function Calling", "authors": [ "Zengzhuang Xu", "Bingguang Hao", "Zechuan Wang", "Yuntao Wen", "Maolin Wang", "Yang Liu", "Long Chen", "Dong Wang", "Yicheng Chen", "Cunyin Peng", "Chenyi Zhuang", "Jinjie Gu", "Leilei Gan", "Xiangyu Zhao", "Shi Gu" ], "github": "", "project_page": "", "abstract": "Function calling (FC) empowers large language models (LLMs) and autonomous agents to interface with external tools, a critical capability for solving complex, real-world problems. As this ability becomes increasingly central to advanced AI systems, the need for high-quality, multi-turn training data to develop and refine it cannot be overstated. Existing data synthesis methods, such as random environment sampling or multi-agent role-playing, are not powerful enough to generate high-quality data in real-world environments. Practical challenges come in three folds: targeted model training, isolation of tool architecture, and multi-turn logical dependency. To address these structural deficiencies, we present FunReason-MT, a novel data synthesis framework for real-world multi-turn tool use. FunReason-MT resolves the complexity barrier in multi-turn FC data by employing 1) Environment-API Graph Interactions to gather varied high-quality trajectories, 2) Advanced Tool-Query Synthesis to simplify hard query construction, and 3) Guided Iterative Chain for sophisticated CoT generation. Evaluations on Berkeley Function-Calling Leaderboard (BFCLv3) demonstrate the power of our framework: a 4B model built upon FunReason-MT generated data achieves state-of-the-art performance among comparable-sized models, outperforming most close-source models. Further performance improvements on BFCLv4 confirm that FunReason-MT provides a reliable and robust source for agentic learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.22099", "title": "Generalization or Memorization: Dynamic Decoding for Mode Steering", "authors": [ "Xuanming Zhang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) exhibit a troubling duality, capable of both remarkable generalization and brittle, verbatim memorization of their training data. This unpredictability undermines their reliability in high-stakes applications. In this work, we propose a unified framework to understand, identify, and control these distinct reasoning modes. First, we introduce a theoretical model based on the Information Bottleneck (IB) principle, formalizing generalization as the learning of a compressed, task-relevant representation and memorization as a failure to compress. Building on this theory, we develop Dynamic Mode Steering (DMS), a novel inference-time algorithm which comprises two components: (1) a lightweight, causally-grounded linear probe that identifies the model's instantaneous reliance on memorization, and (2) a dynamic activation steering mechanism that nudges the model's computation towards pre-identified generalization circuits. We frame DMS as a form of adaptive, self-contrastive decoding. Experiments on reasoning and faithfulness tasks demonstrate that DMS significantly improves logical consistency and factual accuracy, thereby offering a principled approach to enhancing LLM reliability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.21323", "title": "VL-SAE: Interpreting and Enhancing Vision-Language Alignment with a Unified Concept Set", "authors": [ "Shufan Shen", "Junshu Sun", "Qingming Huang", "Shuhui Wang" ], "github": "https://github.com/ssfgunner/VL-SAE", "project_page": "", "abstract": "The alignment of vision-language representations endows current Vision-Language Models (VLMs) with strong multi-modal reasoning capabilities. However, the interpretability of the alignment component remains uninvestigated due to the difficulty in mapping the semantics of multi-modal representations into a unified concept set. To address this problem, we propose VL-SAE, a sparse autoencoder that encodes vision-language representations into its hidden activations. Each neuron in its hidden layer correlates to a concept represented by semantically similar images and texts, thereby interpreting these representations with a unified concept set. To establish the neuron-concept correlation, we encourage semantically similar representations to exhibit consistent neuron activations during self-supervised training. First, to measure the semantic similarity of multi-modal representations, we perform their alignment in an explicit form based on cosine similarity. Second, we construct the VL-SAE with a distance-based encoder and two modality-specific decoders to ensure the activation consistency of semantically similar representations. Experiments across multiple VLMs (e.g., CLIP, LLaVA) demonstrate the superior capability of VL-SAE in interpreting and enhancing the vision-language alignment. For interpretation, the alignment between vision and language representations can be understood by comparing their semantics with concepts. For enhancement, the alignment can be strengthened by aligning vision-language representations at the concept level, contributing to performance improvements in downstream tasks, including zero-shot image classification and hallucination elimination. Codes are available at https://github.com/ssfgunner/VL-SAE.", "github_manual": "", "github_regex": "https://github.com/ssfgunner/VL-SAE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24448", "title": "Rethinking Visual Intelligence: Insights from Video Pretraining", "authors": [ "Pablo Acuaviva", "Aram Davtyan", "Mariam Hassan", "Sebastian Stapf", "Ahmad Rahimi", "Alexandre Alahi", "Paolo Favaro" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated that large-scale pretraining enables systems to adapt rapidly to new problems with little supervision in the language domain. This success, however, has not translated as effectively to the visual domain, where models, including LLMs, continue to struggle with compositional understanding, sample efficiency, and general-purpose problem-solving. We investigate Video Diffusion Models (VDMs) as a promising direction for bridging this gap. Pretraining on spatiotemporal data endows these models with strong inductive biases for structure and dynamics, which we hypothesize can support broad task adaptability. To test this, we design a controlled evaluation in which both a pretrained LLM and a pretrained VDM are equipped with lightweight adapters and presented with tasks in their natural modalities. Across benchmarks including ARC-AGI, ConceptARC, visual games, route planning, and cellular automata, VDMs demonstrate higher data efficiency than their language counterparts. Taken together, our results indicate that video pretraining offers inductive biases that support progress toward visual foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.23691", "title": "Game-TARS: Pretrained Foundation Models for Scalable Generalist Multimodal Game Agents", "authors": [ "Zihao Wang", "Xujing Li", "Yining Ye", "Junjie Fang", "Haoming Wang", "Longxiang Liu", "Shihao Liang", "Junting Lu", "Zhiyong Wu", "Jiazhan Feng", "Wanjun Zhong", "Zili Li", "Yu Wang", "Yu Miao", "Bo Zhou", "Yuanfan Li", "Hao Wang", "Zhongkai Zhao", "Faming Wu", "Zhengxuan Jiang", "Weihao Tan", "Heyuan Yao", "Shi Yan", "Xiangyang Li", "Yitao Liang", "Yujia Qin", "Guang Shi" ], "github": "", "project_page": "", "abstract": "We present Game-TARS, a generalist game agent trained with a unified, scalable action space anchored to human-aligned native keyboard-mouse inputs. Unlike API- or GUI-based approaches, this paradigm enables large-scale continual pre-training across heterogeneous domains, including OS, web, and simulation games. Game-TARS is pre-trained on over 500B tokens with diverse trajectories and multimodal data. Key techniques include a decaying continual loss to reduce causal confusion and an efficient Sparse-Thinking strategy that balances reasoning depth and inference cost. Experiments show that Game-TARS achieves about 2 times the success rate over the previous sota model on open-world Minecraft tasks, is close to the generality of fresh humans in unseen web 3d games, and outperforms GPT-5, Gemini-2.5-Pro, and Claude-4-Sonnet in FPS benchmarks. Scaling results on training-time and test-time confirm that the unified action space sustains improvements when scaled to cross-game and multimodal data. Our results demonstrate that simple, scalable action representations combined with large-scale pre-training provide a promising path toward generalist agents with broad computer-use abilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24668", "title": "InteractComp: Evaluating Search Agents With Ambiguous Queries", "authors": [ "Mingyi Deng", "Lijun Huang", "Yani Fan", "Jiayi Zhang", "Fashen Ren", "Jinyi Bai", "Fuzhen Yang", "Dayi Miao", "Zhaoyang Yu", "Yifan Wu", "Yanfei Zhang", "Fengwei Teng", "Yingjia Wan", "Song Hu", "Yude Li", "Xin Jin", "Conghao Hu", "Haoyu Li", "Qirui Fu", "Tai Zhong", "Xinyu Wang", "Xiangru Tang", "Nan Tang", "Chenglin Wu", "Yuyu Luo" ], "github": "https://github.com/FoundationAgents/InteractComp", "project_page": "", "abstract": "Language agents have demonstrated remarkable potential in web search and information retrieval. However, these search agents assume user queries are complete and unambiguous, an assumption that diverges from reality where users begin with incomplete queries requiring clarification through interaction. Yet most agents lack interactive mechanisms during the search process, and existing benchmarks cannot assess this capability. To address this gap, we introduce InteractComp, a benchmark designed to evaluate whether search agents can recognize query ambiguity and actively interact to resolve it during search. Following the principle of easy to verify, interact to disambiguate, we construct 210 expert-curated questions across 9 domains through a target-distractor methodology that creates genuine ambiguity resolvable only through interaction. Evaluation of 17 models reveals striking failure: the best model achieves only 13.73% accuracy despite 71.50% with complete context, exposing systematic overconfidence rather than reasoning deficits. Forced interaction produces dramatic gains, demonstrating latent capability current strategies fail to engage. Longitudinal analysis shows interaction capabilities stagnated over 15 months while search performance improved seven-fold, revealing a critical blind spot. This stagnation, coupled with the immediate feedback inherent to search tasks, makes InteractComp a valuable resource for both evaluating and training interaction capabilities in search agents. The code is available at https://github.com/FoundationAgents/InteractComp.", "github_manual": "", "github_regex": "https://github.com/FoundationAgents/InteractComp", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24657", "title": "Group Relative Attention Guidance for Image Editing", "authors": [ "Xuanpu Zhang", "Xuesong Niu", "Ruidong Chen", "Dan Song", "Jianhao Zeng", "Penghui Du", "Haoxiang Cao", "Kai Wu", "An-an Liu" ], "github": "https://github.com/little-misfit/GRAG-Image-Editing", "project_page": "", "abstract": "Recently, image editing based on Diffusion-in-Transformer models has undergone rapid development. However, existing editing methods often lack effective control over the degree of editing, limiting their ability to achieve more customized results. To address this limitation, we investigate the MM-Attention mechanism within the DiT model and observe that the Query and Key tokens share a bias vector that is only layer-dependent. We interpret this bias as representing the model's inherent editing behavior, while the delta between each token and its corresponding bias encodes the content-specific editing signals. Based on this insight, we propose Group Relative Attention Guidance, a simple yet effective method that reweights the delta values of different tokens to modulate the focus of the model on the input image relative to the editing instruction, enabling continuous and fine-grained control over editing intensity without any tuning. Extensive experiments conducted on existing image editing frameworks demonstrate that GRAG can be integrated with as few as four lines of code, consistently enhancing editing quality. Moreover, compared to the commonly used Classifier-Free Guidance, GRAG achieves smoother and more precise control over the degree of editing. Our code will be released at https://github.com/little-misfit/GRAG-Image-Editing.", "github_manual": "", "github_regex": "https://github.com/little-misfit/GRAG-Image-Editing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24711", "title": "Routing Matters in MoE: Scaling Diffusion Transformers with Explicit Routing Guidance", "authors": [ "Yujie Wei", "Shiwei Zhang", "Hangjie Yuan", "Yujin Han", "Zhekai Chen", "Jiayu Wang", "Difan Zou", "Xihui Liu", "Yingya Zhang", "Yu Liu", "Hongming Shan" ], "github": "https://github.com/KwaiVGI/DiffMoE", "project_page": "", "abstract": "Mixture-of-Experts (MoE) has emerged as a powerful paradigm for scaling model capacity while preserving computational efficiency. Despite its notable success in large language models (LLMs), existing attempts to apply MoE to Diffusion Transformers (DiTs) have yielded limited gains. We attribute this gap to fundamental differences between language and visual tokens. Language tokens are semantically dense with pronounced inter-token variation, while visual tokens exhibit spatial redundancy and functional heterogeneity, hindering expert specialization in vision MoE. To this end, we present ProMoE, an MoE framework featuring a two-step router with explicit routing guidance that promotes expert specialization. Specifically, this guidance encourages the router to partition image tokens into conditional and unconditional sets via conditional routing according to their functional roles, and refine the assignments of conditional image tokens through prototypical routing with learnable prototypes based on semantic content. Moreover, the similarity-based expert allocation in latent space enabled by prototypical routing offers a natural mechanism for incorporating explicit semantic guidance, and we validate that such guidance is crucial for vision MoE. Building on this, we propose a routing contrastive loss that explicitly enhances the prototypical routing process, promoting intra-expert coherence and inter-expert diversity. Extensive experiments on ImageNet benchmark demonstrate that ProMoE surpasses state-of-the-art methods under both Rectified Flow and DDPM training objectives. Code and models will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KwaiVGI/DiffMoE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.23642", "title": "VisCoder2: Building Multi-Language Visualization Coding Agents", "authors": [ "Yuansheng Ni", "Songcheng Cai", "Xiangchao Chen", "Jiarong Liang", "Zhiheng Lyu", "Jiaqi Deng", "Kai Zou", "Ping Nie", "Fei Yuan", "Xiang Yue", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code. However, existing models often fail in practical workflows due to limited language coverage, unreliable execution, and lack of iterative correction mechanisms. Progress has been constrained by narrow datasets and benchmarks that emphasize single-round generation and single-language tasks. To address these challenges, we introduce three complementary resources for advancing visualization coding agents. VisCode-Multi-679K is a large-scale, supervised dataset containing 679K validated and executable visualization samples with multi-turn correction dialogues across 12 programming languages. VisPlotBench is a benchmark for systematic evaluation, featuring executable tasks, rendered outputs, and protocols for both initial generation and multi-round self-debug. Finally, we present VisCoder2, a family of multi-language visualization models trained on VisCode-Multi-679K. Experiments show that VisCoder2 significantly outperforms strong open-source baselines and approaches the performance of proprietary models like GPT-4.1, with further gains from iterative self-debug, reaching 82.4% overall execution pass rate at the 32B scale, particularly in symbolic or compiler-dependent languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24693", "title": "STAR-Bench: Probing Deep Spatio-Temporal Reasoning as Audio 4D Intelligence", "authors": [ "Zihan Liu", "Zhikang Niu", "Qiuyang Xiao", "Zhisheng Zheng", "Ruoqi Yuan", "Yuhang Zang", "Yuhang Cao", "Xiaoyi Dong", "Jianze Liang", "Xie Chen", "Leilei Sun", "Dahua Lin", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Despite rapid progress in Multi-modal Large Language Models and Large Audio-Language Models, existing audio benchmarks largely test semantics that can be recovered from text captions, masking deficits in fine-grained perceptual reasoning. We formalize audio 4D intelligence that is defined as reasoning over sound dynamics in time and 3D space, and introduce STAR-Bench to measure it. STAR-Bench combines a Foundational Acoustic Perception setting (six attributes under absolute and relative regimes) with a Holistic Spatio-Temporal Reasoning setting that includes segment reordering for continuous and discrete processes and spatial tasks spanning static localization, multi-source relations, and dynamic trajectories. Our data curation pipeline uses two methods to ensure high-quality samples. For foundational tasks, we use procedurally synthesized and physics-simulated audio. For holistic data, we follow a four-stage process that includes human annotation and final selection based on human performance. Unlike prior benchmarks where caption-only answering reduces accuracy slightly, STAR-Bench induces far larger drops (-31.5\\% temporal, -35.2\\% spatial), evidencing its focus on linguistically hard-to-describe cues. Evaluating 19 models reveals substantial gaps compared with humans and a capability hierarchy: closed-source models are bottlenecked by fine-grained perception, while open-source models lag across perception, knowledge, and reasoning. Our STAR-Bench provides critical insights and a clear path forward for developing future models with a more robust understanding of the physical world.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.22037", "title": "ATLAS: Adaptive Transfer Scaling Laws for Multilingual Pretraining, Finetuning, and Decoding the Curse of Multilinguality", "authors": [ "Shayne Longpre", "Sneha Kudugunta", "Niklas Muennighoff", "I-Hung Hsu", "Isaac Caswell", "Alex Pentland", "Sercan Arik", "Chen-Yu Lee", "Sayna Ebrahimi" ], "github": "", "project_page": "", "abstract": "Scaling laws research has focused overwhelmingly on English -- yet the most prominent AI models explicitly serve billions of international users. In this work, we undertake the largest multilingual scaling laws study to date, totaling 774 multilingual training experiments, spanning 10M-8B model parameters, 400+ training languages and 48 evaluation languages. We introduce the Adaptive Transfer Scaling Law (ATLAS) for both monolingual and multilingual pretraining, which outperforms existing scaling laws' out-of-sample generalization often by more than 0.3 R^2. Our analyses of the experiments shed light on multilingual learning dynamics, transfer properties between languages, and the curse of multilinguality. First, we derive a cross-lingual transfer matrix, empirically measuring mutual benefit scores between 38 x 38=1444 language pairs. Second, we derive a language-agnostic scaling law that reveals how to optimally scale model size and data when adding languages without sacrificing performance. Third, we identify the computational crossover points for when to pretrain from scratch versus finetune from multilingual checkpoints. We hope these findings provide the scientific foundation for democratizing scaling laws across languages, and enable practitioners to efficiently scale models -- beyond English-first AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24717", "title": "Uniform Discrete Diffusion with Metric Path for Video Generation", "authors": [ "Haoge Deng", "Ting Pan", "Fan Zhang", "Yang Liu", "Zhuoyan Luo", "Yufeng Cui", "Wenxuan Wang", "Chunhua Shen", "Shiguang Shan", "Zhaoxiang Zhang", "Xinlong Wang" ], "github": "https://github.com/baaivision/URSA", "project_page": "", "abstract": "Continuous-space video generation has advanced rapidly, while discrete approaches lag behind due to error accumulation and long-context inconsistency. In this work, we revisit discrete generative modeling and present Uniform discRete diffuSion with metric pAth (URSA), a simple yet powerful framework that bridges the gap with continuous approaches for the scalable video generation. At its core, URSA formulates the video generation task as an iterative global refinement of discrete spatiotemporal tokens. It integrates two key designs: a Linearized Metric Path and a Resolution-dependent Timestep Shifting mechanism. These designs enable URSA to scale efficiently to high-resolution image synthesis and long-duration video generation, while requiring significantly fewer inference steps. Additionally, we introduce an asynchronous temporal fine-tuning strategy that unifies versatile tasks within a single model, including interpolation and image-to-video generation. Extensive experiments on challenging video and image generation benchmarks demonstrate that URSA consistently outperforms existing discrete methods and achieves performance comparable to state-of-the-art continuous diffusion methods. Code and models are available at https://github.com/baaivision/URSA", "github_manual": "", "github_regex": "https://github.com/baaivision/URSA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.22373", "title": "VisJudge-Bench: Aesthetics and Quality Assessment of Visualizations", "authors": [ "Yupeng Xie", "Zhiyang Zhang", "Yifan Wu", "Sirong Lu", "Jiayi Zhang", "Zhaoyang Yu", "Jinlin Wang", "Sirui Hong", "Bang Liu", "Chenglin Wu", "Yuyu Luo" ], "github": "https://github.com/HKUSTDial/VisJudgeBench", "project_page": "", "abstract": "Visualization, a domain-specific yet widely used form of imagery, is an effective way to turn complex datasets into intuitive insights, and its value depends on whether data are faithfully represented, clearly communicated, and aesthetically designed. However, evaluating visualization quality is challenging: unlike natural images, it requires simultaneous judgment across data encoding accuracy, information expressiveness, and visual aesthetics. Although multimodal large language models (MLLMs) have shown promising performance in aesthetic assessment of natural images, no systematic benchmark exists for measuring their capabilities in evaluating visualizations. To address this, we propose VisJudge-Bench, the first comprehensive benchmark for evaluating MLLMs' performance in assessing visualization aesthetics and quality. It contains 3,090 expert-annotated samples from real-world scenarios, covering single visualizations, multiple visualizations, and dashboards across 32 chart types. Systematic testing on this benchmark reveals that even the most advanced MLLMs (such as GPT-5) still exhibit significant gaps compared to human experts in judgment, with a Mean Absolute Error (MAE) of 0.551 and a correlation with human ratings of only 0.429. To address this issue, we propose VisJudge, a model specifically designed for visualization aesthetics and quality assessment. Experimental results demonstrate that VisJudge significantly narrows the gap with human judgment, reducing the MAE to 0.442 (a 19.8% reduction) and increasing the consistency with human experts to 0.681 (a 58.7% improvement) compared to GPT-5. The benchmark is available at https://github.com/HKUSTDial/VisJudgeBench.", "github_manual": "", "github_regex": "https://github.com/HKUSTDial/VisJudgeBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.20155", "title": "PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D Part Understanding", "authors": [ "Penghao Wang", "Yiyang He", "Xin Lv", "Yukai Zhou", "Lan Xu", "Jingyi Yu", "Jiayuan Gu" ], "github": "", "project_page": "", "abstract": "Understanding objects at the level of their constituent parts is fundamental to advancing computer vision, graphics, and robotics. While datasets like PartNet have driven progress in 3D part understanding, their reliance on untextured geometries and expert-dependent annotation limits scalability and usability. We introduce PartNeXt, a next-generation dataset addressing these gaps with over 23,000 high-quality, textured 3D models annotated with fine-grained, hierarchical part labels across 50 categories. We benchmark PartNeXt on two tasks: (1) class-agnostic part segmentation, where state-of-the-art methods (e.g., PartField, SAMPart3D) struggle with fine-grained and leaf-level parts, and (2) 3D part-centric question answering, a new benchmark for 3D-LLMs that reveals significant gaps in open-vocabulary part grounding. Additionally, training Point-SAM on PartNeXt yields substantial gains over PartNet, underscoring the dataset's superior quality and diversity. By combining scalable annotation, texture-aware labels, and multi-task evaluation, PartNeXt opens new avenues for research in structured 3D understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.17439", "title": "From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors", "authors": [ "Zhengshen Zhang", "Hao Li", "Yalun Dai", "Zhengbang Zhu", "Lei Zhou", "Chenchen Liu", "Dong Wang", "Francis E. H. Tay", "Sijin Chen", "Ziwei Liu", "Yuxiao Liu", "Xinghang Li", "Pan Zhou" ], "github": "", "project_page": "", "abstract": "Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptability. Recent 3D integration techniques for VLAs either require specialized sensors and transfer poorly across modalities, or inject weak cues that lack geometry and degrade vision-language alignment. In this work, we introduce FALCON (From Spatial to Action), a novel paradigm that injects rich 3D spatial tokens into the action head. FALCON leverages spatial foundation models to deliver strong geometric priors from RGB alone, and includes an Embodied Spatial Model that can optionally fuse depth, or pose for higher fidelity when available, without retraining or architectural changes. To preserve language reasoning, spatial tokens are consumed by a Spatial-Enhanced Action Head rather than being concatenated into the vision-language backbone. These designs enable FALCON to address limitations in spatial representation, modality transferability, and alignment. In comprehensive evaluations across three simulation benchmarks and eleven real-world tasks, our proposed FALCON achieves state-of-the-art performance, consistently surpasses competitive baselines, and remains robust under clutter, spatial-prompt conditioning, and variations in object scale and height.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.22264", "title": "PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text Embedding", "authors": [ "Iliass Ayaou", "Denis Cavallucci" ], "github": "https://github.com/iliass-y/patenteb.\nKeywords:", "project_page": "", "abstract": "Patent text embeddings enable prior art search, technology landscaping, and patent analysis, yet existing benchmarks inadequately capture patent-specific challenges. We introduce PatenTEB, a comprehensive benchmark comprising 15 tasks across retrieval, classification, paraphrase, and clustering, with 2.06 million examples. PatenTEB employs domain-stratified splits, domain specific hard negative mining, and systematic coverage of asymmetric fragment-to-document matching scenarios absent from general embedding benchmarks. We develop the patembed model family through multi-task training, spanning 67M to 344M parameters with context lengths up to 4096 tokens. External validation shows strong generalization: patembed-base achieves state-of-the-art on MTEB BigPatentClustering.v2 (0.494 V-measure vs. 0.445 previous best), while patembed-large achieves 0.377 NDCG@100 on DAPFAM. Systematic ablations reveal that multi-task training improves external generalization despite minor benchmark costs, and that domain-pretrained initialization provides consistent advantages across task families. All resources will be made available at https://github.com/iliass-y/patenteb. Keywords: patent retrieval, sentence embeddings, multi-task learning, asymmetric retrieval, benchmark evaluation, contrastive learning.", "github_manual": "https://github.com/iliass-y/patenteb.\nKeywords:", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.20661", "title": "UltraHR-100K: Enhancing UHR Image Synthesis with A Large-Scale High-Quality Dataset", "authors": [ "Chen Zhao", "En Ci", "Yunzhe Xu", "Tiehan Fan", "Shanyan Guan", "Yanhao Ge", "Jian Yang", "Ying Tai" ], "github": "https://github.com/NJU-PCALab/UltraHR-100k", "project_page": "", "abstract": "Ultra-high-resolution (UHR) text-to-image (T2I) generation has seen notable progress. However, two key challenges remain : 1) the absence of a large-scale high-quality UHR T2I dataset, and (2) the neglect of tailored training strategies for fine-grained detail synthesis in UHR scenarios. To tackle the first challenge, we introduce UltraHR-100K, a high-quality dataset of 100K UHR images with rich captions, offering diverse content and strong visual fidelity. Each image exceeds 3K resolution and is rigorously curated based on detail richness, content complexity, and aesthetic quality. To tackle the second challenge, we propose a frequency-aware post-training method that enhances fine-detail generation in T2I diffusion models. Specifically, we design (i) Detail-Oriented Timestep Sampling (DOTS) to focus learning on detail-critical denoising steps, and (ii) Soft-Weighting Frequency Regularization (SWFR), which leverages Discrete Fourier Transform (DFT) to softly constrain frequency components, encouraging high-frequency detail preservation. Extensive experiments on our proposed UltraHR-eval4K benchmarks demonstrate that our approach significantly improves the fine-grained detail quality and overall fidelity of UHR image generation. The code is available at https://github.com/NJU-PCALab/UltraHR-100k{here}.", "github_manual": "", "github_regex": "https://github.com/NJU-PCALab/UltraHR-100k", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.23667", "title": "Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization", "authors": [ "Amin Heyrani Nobari", "Lyle Regenwetter", "Cyril Picard", "Ligong Han", "Faez Ahmed" ], "github": "https://github.com/ahnobari/OptimizeAnyTopology", "project_page": "", "abstract": "Structural topology optimization (TO) is central to engineering design but remains computationally intensive due to complex physics and hard constraints. Existing deep-learning methods are limited to fixed square grids, a few hand-coded boundary conditions, and post-hoc optimization, preventing general deployment. We introduce Optimize Any Topology (OAT), a foundation-model framework that directly predicts minimum-compliance layouts for arbitrary aspect ratios, resolutions, volume fractions, loads, and fixtures. OAT combines a resolution- and shape-agnostic autoencoder with an implicit neural-field decoder and a conditional latent-diffusion model trained on OpenTO, a new corpus of 2.2 million optimized structures covering 2 million unique boundary-condition configurations. On four public benchmarks and two challenging unseen tests, OAT lowers mean compliance up to 90% relative to the best prior models and delivers sub-1 second inference on a single GPU across resolutions from 64 x 64 to 256 x 256 and aspect ratios as high as 10:1. These results establish OAT as a general, fast, and resolution-free framework for physics-aware topology optimization and provide a large-scale dataset to spur further research in generative modeling for inverse design. Code & data can be found at https://github.com/ahnobari/OptimizeAnyTopology.", "github_manual": "", "github_regex": "https://github.com/ahnobari/OptimizeAnyTopology", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24702", "title": "Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents", "authors": [ "Yueqi Song", "Ketan Ramaneti", "Zaid Sheikh", "Ziru Chen", "Boyu Gou", "Tianbao Xie", "Yiheng Xu", "Danyang Zhang", "Apurva Gandhi", "Fan Yang", "Joseph Liu", "Tianyue Ou", "Zhihao Yuan", "Frank Xu", "Shuyan Zhou", "Xingyao Wang", "Xiang Yue", "Tao Yu", "Huan Sun", "Yu Su", "Graham Neubig" ], "github": "", "project_page": "", "abstract": "Public research results on large-scale supervised finetuning of AI agents remain relatively rare, since the collection of agent training data presents unique challenges. In this work, we argue that the bottleneck is not a lack of underlying data sources, but that a large variety of data is fragmented across heterogeneous formats, tools, and interfaces. To this end, we introduce the agent data protocol (ADP), a light-weight representation language that serves as an \"interlingua\" between agent datasets in diverse formats and unified agent training pipelines downstream. The design of ADP is expressive enough to capture a large variety of tasks, including API/tool use, browsing, coding, software engineering, and general agentic workflows, while remaining simple to parse and train on without engineering at a per-dataset level. In experiments, we unified a broad collection of 13 existing agent training datasets into ADP format, and converted the standardized ADP data into training-ready formats for multiple agent frameworks. We performed SFT on these data, and demonstrated an average performance gain of ~20% over corresponding base models, and delivers state-of-the-art or near-SOTA performance on standard coding, browsing, tool use, and research benchmarks, without domain-specific tuning. All code and data are released publicly, in the hope that ADP could help lower the barrier to standardized, scalable, and reproducible agent training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24684", "title": "SPICE: Self-Play In Corpus Environments Improves Reasoning", "authors": [ "Bo Liu", "Chuanyang Jin", "Seungone Kim", "Weizhe Yuan", "Wenting Zhao", "Ilia Kulikov", "Xian Li", "Sainbayar Sukhbaatar", "Jack Lanchantin", "Jason Weston" ], "github": "", "project_page": "", "abstract": "Self-improving systems require environmental interaction for continuous adaptation. We introduce SPICE (Self-Play In Corpus Environments), a reinforcement learning framework where a single model acts in two roles: a Challenger that mines documents from a large corpus to generate diverse reasoning tasks, and a Reasoner that solves them. Through adversarial dynamics, the Challenger creates an automatic curriculum at the frontier of the Reasoner's capability, while corpus grounding provides the rich, near-inexhaustible external signal necessary for sustained improvement. Unlike existing ungrounded self-play methods that offer more limited benefits, SPICE achieves consistent gains across mathematical (+8.9%) and general reasoning (+9.8%) benchmarks on multiple model families. Our analysis reveals how document grounding is a key ingredient in SPICE to continuously generate its own increasingly challenging goals and achieve them, enabling sustained self-improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.22795", "title": "SAO-Instruct: Free-form Audio Editing using Natural Language Instructions", "authors": [ "Michael Ungersb\u00f6ck", "Florian Gr\u00f6tschla", "Luca A. Lanzend\u00f6rfer", "June Young Yi", "Changho Choi", "Roger Wattenhofer" ], "github": "https://github.com/Stability-AI/stable-audio-tools", "project_page": "", "abstract": "Generative models have made significant progress in synthesizing high-fidelity audio from short textual descriptions. However, editing existing audio using natural language has remained largely underexplored. Current approaches either require the complete description of the edited audio or are constrained to predefined edit instructions that lack flexibility. In this work, we introduce SAO-Instruct, a model based on Stable Audio Open capable of editing audio clips using any free-form natural language instruction. To train our model, we create a dataset of audio editing triplets (input audio, edit instruction, output audio) using Prompt-to-Prompt, DDPM inversion, and a manual editing pipeline. Although partially trained on synthetic data, our model generalizes well to real in-the-wild audio clips and unseen edit instructions. We demonstrate that SAO-Instruct achieves competitive performance on objective metrics and outperforms other audio editing approaches in a subjective listening study. To encourage future research, we release our code and model weights.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Stability-AI/stable-audio-tools", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.22768", "title": "MMPersuade: A Dataset and Evaluation Framework for Multimodal Persuasion", "authors": [ "Haoyi Qiu", "Yilun Zhou", "Pranav Narayanan Venkit", "Kung-Hsiang Huang", "Jiaxin Zhang", "Nanyun Peng", "Chien-Sheng Wu" ], "github": "", "project_page": "", "abstract": "As Large Vision-Language Models (LVLMs) are increasingly deployed in domains such as shopping, health, and news, they are exposed to pervasive persuasive content. A critical question is how these models function as persuadees-how and why they can be influenced by persuasive multimodal inputs. Understanding both their susceptibility to persuasion and the effectiveness of different persuasive strategies is crucial, as overly persuadable models may adopt misleading beliefs, override user preferences, or generate unethical or unsafe outputs when exposed to manipulative messages. We introduce MMPersuade, a unified framework for systematically studying multimodal persuasion dynamics in LVLMs. MMPersuade contributes (i) a comprehensive multimodal dataset that pairs images and videos with established persuasion principles across commercial, subjective and behavioral, and adversarial contexts, and (ii) an evaluation framework that quantifies both persuasion effectiveness and model susceptibility via third-party agreement scoring and self-estimated token probabilities on conversation histories. Our study of six leading LVLMs as persuadees yields three key insights: (i) multimodal inputs substantially increase persuasion effectiveness-and model susceptibility-compared to text alone, especially in misinformation scenarios; (ii) stated prior preferences decrease susceptibility, yet multimodal information maintains its persuasive advantage; and (iii) different strategies vary in effectiveness across contexts, with reciprocity being most potent in commercial and subjective contexts, and credibility and logic prevailing in adversarial contexts. By jointly analyzing persuasion effectiveness and susceptibility, MMPersuade provides a principled foundation for developing models that are robust, preference-consistent, and ethically aligned when engaging with persuasive multimodal content.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.24081", "title": "Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+ Languages and Cultures", "authors": [ "Tyler A. Chang", "Catherine Arnett", "Abdelrahman Eldesokey", "Abdelrahman Sadallah", "Abeer Kashar", "Abolade Daud", "Abosede Grace Olanihun", "Adamu Labaran Mohammed", "Adeyemi Praise", "Adhikarinayum Meerajita Sharma", "Aditi Gupta", "Afitab Iyigun", "Afonso Simpl\u00edcio", "Ahmed Essouaied", "Aicha Chorana", "Akhil Eppa", "Akintunde Oladipo", "Akshay Ramesh", "Aleksei Dorkin", "Alfred Malengo Kondoro", "Alham Fikri Aji", "Ali Eren \u00c7etinta\u015f", "Allan Hanbury", "Alou Dembele", "Alp Niksarli", "\u00c1lvaro Arroyo", "Amin Bajand", "Amol Khanna", "Ana Chkhaidze", "Ana Condez", "Andiswa Mkhonto", "Andrew Hoblitzell", "Andrew Tran", "Angelos Poulis", "Anirban Majumder", "Anna Vacalopoulou", "Annette Kuuipolani Kanahele Wong", "Annika Simonsen", "Anton Kovalev", "Ashvanth. S", "Ayodeji Joseph Lana", "Barkin Kinay", "Bashar Alhafni", "Benedict Cibalinda Busole", "Bernard Ghanem", "Bharti Nathani", "Biljana Stojanovska \u0110uri\u0107", "Bola Agbonile", "Bragi Bergsson", "Bruce Torres Fischer", "Burak Tutar", "Burcu Alaku\u015f \u00c7\u0131nar", "Cade J. Kanoniakapueo Kane", "Can Udomcharoenchaikit", "Catherine Arnett", "Chadi Helwe", "Chaithra Reddy Nerella", "Chen Cecilia Liu", "Chiamaka Glory Nwokolo", "Cristina Espa\u00f1a-Bonet", "Cynthia Amol", "DaeYeop Lee", "Dana Arad", "Daniil Dzenhaliou", "Daria Pugacheva", "Dasol Choi", "Daud Abolade", "David Liu", "David Semedo", "Deborah Popoola", "Deividas Mataciunas", "Delphine Nyaboke", "Dhyuthy Krishna Kumar", "Diogo Gl\u00f3ria-Silva", "Diogo Tavares", "Divyanshu Goyal", "DongGeon Lee", "Ebele Nwamaka Anajemba", "Egonu Ngozi Grace", "Elena Mickel", "Elena Tutubalina", "Elias Herranen", "Emile Anand", "Emmanuel Habumuremyi", "Emuobonuvie Maria Ajiboye", "Eryawan Presma Yulianrifat", "Esther Adenuga", "Ewa Rudnicka", "Faith Olabisi Itiola", "Faran Taimoor Butt", "Fathima Thekkekara", "Fatima Haouari", "Filbert Aurelian Tjiaranata", "Firas Laakom", "Francesca Grasso", "Francesco Orabona", "Francesco Periti", "Gbenga Kayode Solomon", "Gia Nghia Ngo", "Gloria Udhehdhe-oze", "Gon\u00e7alo Martins", "Gopi Naga Sai Ram Challagolla", "Guijin Son", "Gulnaz Abdykadyrova", "Hafsteinn Einarsson", "Hai Hu", "Hamidreza Saffari", "Hamza Zaidi", "Haopeng Zhang", "Harethah Abu Shairah", "Harry Vuong", "Hele-Andra Kuulmets", "Houda Bouamor", "Hwanjo Yu", "Iben Nyholm Debess", "\u0130brahim Ethem Deveci", "Ikhlasul Akmal Hanif", "Ikhyun Cho", "In\u00eas Calvo", "In\u00eas Vieira", "Isaac Manzi", "Ismail Daud", "Itay Itzhak", "Iuliia", "Alekseenko", "Ivan Belashkin", "Ivan Spada", "Ivan Zhelyazkov", "Jacob Brinton", "Jafar Isbarov", "Jaka \u010cibej", "Jan \u010cuhel", "Jan Koco\u0144", "Jauza Akbar Krito", "Jebish Purbey", "Jennifer Mickel", "Jennifer Za", "Jenny Kunz", "Jihae Jeong", "Jimena Tena D\u00e1valos", "Jinu Lee", "Jo\u00e3o Magalh\u00e3es", "John Yi", "Jongin Kim", "Joseph Chataignon", "Joseph Marvin Imperial", "Jubeerathan Thevakumar", "Judith Land", "Junchen Jiang", "Jungwhan Kim", "Kairit Sirts", "Kamesh R", "Kamesh V", "Kanda Patrick Tshinu", "K\u00e4triin Kukk", "Kaustubh Ponkshe", "Kavsar Huseynova", "Ke He", "Kelly Buchanan", "Kengatharaiyer Sarveswaran", "Kerem Zaman", "Khalil Mrini", "Kian Kyars", "Krister Kruusmaa", "Kusum Chouhan", "Lainitha Krishnakumar", "Laura Castro S\u00e1nchez", "Laura Porrino Moscoso", "Leshem Choshen", "Levent Sencan", "Lilja \u00d8vrelid", "Lisa Alazraki", "Lovina Ehimen-Ugbede", "Luheerathan Thevakumar", "Luxshan Thavarasa", "Mahnoor Malik", "Mamadou K. Keita", "Mansi Jangid", "Marco De Santis", "Marcos Garc\u00eda", "Marek Suppa", "Mariam D'Ciofalo", "Marii Ojastu", "Maryam Sikander", "Mausami Narayan", "Maximos Skandalis", "Mehak Mehak", "Mehmet \u0130lteri\u015f Bozkurt", "Melaku Bayu Workie", "Menan Velayuthan", "Michael Leventhal", "Micha\u0142 Marci\u0144czuk", "Mirna Poto\u010dnjak", "Mohammadamin Shafiei", "Mridul Sharma", "Mrityunjaya Indoria", "Muhammad Ravi Shulthan Habibi", "Murat Koli\u0107", "Nada Galant", "Naphat Permpredanun", "Narada Maugin", "Nicholas Kluge Corr\u00eaa", "Nikola Ljube\u0161i\u0107", "Nirmal Thomas", "Nisansa de Silva", "Nisheeth Joshi", "Nitish Ponkshe", "Nizar Habash", "Nneoma C. Udeze", "Noel Thomas", "No\u00e9mi Ligeti-Nagy", "Nouhoum Coulibaly", "Nsengiyumva Faustin", "Odunayo Kareemat Buliaminu", "Odunayo Ogundepo", "Oghojafor Godswill Fejiro", "Ogundipe Blessing Funmilola", "Okechukwu God'spraise", "Olanrewaju Samuel", "Olaoye Deborah Oluwaseun", "Olasoji Akindejoye", "Olga Popova", "Olga Snissarenko", "Onyinye Anulika Chiemezie", "Orkun Kinay", "Osman Tursun", "Owoeye Tobiloba Moses", "Oyelade Oluwafemi Joshua", "Oyesanmi Fiyinfoluwa", "Pablo Gamallo", "Pablo Rodr\u00edguez Fern\u00e1ndez", "Palak Arora", "Pedro Valente", "Peter Rupnik", "Philip Oghenesuowho Ekiugbo", "Pramit Sahoo", "Prokopis Prokopidis", "Pua Niau-Puhipau", "Quadri Yahya", "Rachele Mignone", "Raghav Singhal", "Ram Mohan Rao Kadiyala", "Raphael Merx", "Rapheal Afolayan", "Ratnavel Rajalakshmi", "Rishav Ghosh", "Romina Oji", "Ron Kekeha Solis", "Rui Guerra", "Rushikesh Zawar", "Sa'ad Nasir Bashir", "Saeed Alzaabi", "Sahil Sandeep", "Sai Pavan Batchu", "SaiSandeep Kantareddy", "Salsabila Zahirah Pranida", "Sam Buchanan", "Samuel Rutunda", "Sander Land", "Sarah Sulollari", "Sardar Ali", "Saroj Sapkota", "Saulius Tautvaisas", "Sayambhu Sen", "Sayantani Banerjee", "Sebastien Diarra", "SenthilNathan. M", "Sewoong Lee", "Shaan Shah", "Shankar Venkitachalam", "Sharifa Djurabaeva", "Sharon Ibejih", "Shivanya Shomir Dutta", "Siddhant Gupta", "Silvia Paniagua Su\u00e1rez", "Sina Ahmadi", "Sivasuthan Sukumar", "Siyuan Song", "Snegha A.", "Sokratis Sofianopoulos", "Sona Elza Simon", "Sonja Ben\u010dina", "Sophie Gvasalia", "Sphurti Kirit More", "Spyros Dragazis", "Stephan P. Kaufhold", "Suba. S", "Sultan AlRashed", "Surangika Ranathunga", "Taiga Someya", "Taja Kuzman Punger\u0161ek", "Tal Haklay", "Tasi'u Jibril", "Tatsuya Aoyama", "Tea Abashidze", "Terenz Jomar Dela Cruz", "Terra Blevins", "Themistoklis Nikas", "Theresa Dora Idoko", "Thu Mai Do", "Tilek Chubakov", "Tommaso Gargiani", "Uma Rathore", "Uni Johannesen", "Uwuma Doris Ugwu", "Vallerie Alexandra Putra", "Vanya Bannihatti Kumar", "Varsha Jeyarajalingam", "Varvara Arzt", "Vasudevan Nedumpozhimana", "Viktoria Ondrejova", "Viktoryia Horbik", "Vishnu Vardhan Reddy Kummitha", "Vuk Dini\u0107", "Walelign Tewabe Sewunetie", "Winston Wu", "Xiaojing Zhao", "Yacouba Diarra", "Yaniv Nikankin", "Yash Mathur", "Yixi Chen", "Yiyuan Li", "Yolanda Xavier", "Yonatan Belinkov", "Yusuf Ismail Abayomi", "Zaid Alyafeai", "Zhengyang Shan", "Zhi Rui Tam", "Zilu Tang", "Zuzana Nadova", "Baber Abbasi", "Stella Biderman", "David Stap", "Duygu Ataman", "Fabian Schmidt", "Hila Gonen", "Jiayi Wang", "David Ifeoluwa Adelani" ], "github": "", "project_page": "", "abstract": "To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we present Global PIQA, a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by 335 researchers from 65 countries around the world. The 116 language varieties in Global PIQA cover five continents, 14 language families, and 23 writing systems. In the non-parallel split of Global PIQA, over 50% of examples reference local foods, customs, traditions, or other culturally-specific elements. We find that state-of-the-art LLMs perform well on Global PIQA in aggregate, but they exhibit weaker performance in lower-resource languages (up to a 37% accuracy gap, despite random chance at 50%). Open models generally perform worse than proprietary models. Global PIQA highlights that in many languages and cultures, everyday knowledge remains an area for improvement, alongside more widely-discussed capabilities such as complex reasoning and expert knowledge. Beyond its uses for LLM evaluation, we hope that Global PIQA provides a glimpse into the wide diversity of cultures in which human language is embedded.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.22319", "title": "GRPO-Guard: Mitigating Implicit Over-Optimization in Flow Matching via Regulated Clipping", "authors": [ "Jing Wang", "Jiajun Liang", "Jie Liu", "Henglin Liu", "Gongye Liu", "Jun Zheng", "Wanyuan Pang", "Ao Ma", "Zhenyu Xie", "Xintao Wang", "Meng Wang", "Pengfei Wan", "Xiaodan Liang" ], "github": "", "project_page": "", "abstract": "Recently, GRPO-based reinforcement learning has shown remarkable progress in optimizing flow-matching models, effectively improving their alignment with task-specific rewards. Within these frameworks, the policy update relies on importance-ratio clipping to constrain overconfident positive and negative gradients. However, in practice, we observe a systematic shift in the importance-ratio distribution-its mean falls below 1 and its variance differs substantially across timesteps. This left-shifted and inconsistent distribution prevents positive-advantage samples from entering the clipped region, causing the mechanism to fail in constraining overconfident positive updates. As a result, the policy model inevitably enters an implicit over-optimization stage-while the proxy reward continues to increase, essential metrics such as image quality and text-prompt alignment deteriorate sharply, ultimately making the learned policy impractical for real-world use. To address this issue, we introduce GRPO-Guard, a simple yet effective enhancement to existing GRPO frameworks. Our method incorporates ratio normalization, which restores a balanced and step-consistent importance ratio, ensuring that PPO clipping properly constrains harmful updates across denoising timesteps. In addition, a gradient reweighting strategy equalizes policy gradients over noise conditions, preventing excessive updates from particular timestep regions. Together, these designs act as a regulated clipping mechanism, stabilizing optimization and substantially mitigating implicit over-optimization without relying on heavy KL regularization. Extensive experiments on multiple diffusion backbones (e.g., SD3.5M, Flux.1-dev) and diverse proxy tasks demonstrate that GRPO-Guard significantly reduces over-optimization while maintaining or even improving generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.21978", "title": "Beyond Reasoning Gains: Mitigating General Capabilities Forgetting in Large Reasoning Models", "authors": [ "Hoang Phan", "Xianjun Yang", "Kevin Yao", "Jingyu Zhang", "Shengjie Bi", "Xiaocheng Tang", "Madian Khabsa", "Lijuan Liu", "Deren Lei" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning and has become a standard post-training paradigm for contemporary language and vision-language models. However, the RLVR recipe introduces a significant risk of capability regression, where models forget foundational skills after prolonged training without employing regularization strategies. We empirically confirm this concern, observing that open-source reasoning models suffer performance degradation on core capabilities such as perception and faithfulness. While imposing regularization terms like KL divergence can help prevent deviation from the base model, these terms are calculated on the current task, thus they do not guarantee broader knowledge. Meanwhile, commonly used experience replay across heterogeneous domains makes it nontrivial to decide how much training focus each objective should receive. To address this, we propose RECAP-a replay strategy with dynamic objective reweighting for general knowledge preservation. Our reweighting mechanism adapts in an online manner using short-horizon signals of convergence and instability, shifting the post-training focus away from saturated objectives and toward underperforming or volatile ones. Our method is end-to-end and readily applicable to existing RLVR pipelines without training additional models or heavy tuning. Extensive experiments on benchmarks based on Qwen2.5-VL-3B and Qwen2.5-VL-7B demonstrate the effectiveness of our method, which not only preserves general capabilities but also improves reasoning by enabling more flexible trade-offs among in-task rewards.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.23925", "title": "Latent Chain-of-Thought for Visual Reasoning", "authors": [ "Guohao Sun", "Hang Hua", "Jian Wang", "Jiebo Luo", "Sohail Dianat", "Majid Rabbani", "Raghuveer Rao", "Zhiqiang Tao" ], "github": "https://github.com/heliossun/LaCoT", "project_page": "", "abstract": "Chain-of-thought (CoT) reasoning is critical for improving the interpretability and reliability of Large Vision-Language Models (LVLMs). However, existing training algorithms such as SFT, PPO, and GRPO may not generalize well across unseen reasoning tasks and heavily rely on a biased reward model. To address this challenge, we reformulate reasoning in LVLMs as posterior inference and propose a scalable training algorithm based on amortized variational inference. By leveraging diversity-seeking reinforcement learning algorithms, we introduce a novel sparse reward function for token-level learning signals that encourage diverse, high-likelihood latent CoT, overcoming deterministic sampling limitations and avoiding reward hacking. Additionally, we implement a Bayesian inference-scaling strategy that replaces costly Best-of-N and Beam Search with a marginal likelihood to efficiently rank optimal rationales and answers. We empirically demonstrate that the proposed method enhances the state-of-the-art LVLMs on seven reasoning benchmarks, in terms of effectiveness, generalization, and interpretability.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/heliossun/LaCoT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.22876", "title": "Batch Speculative Decoding Done Right", "authors": [ "Ranran Haoran Zhang", "Soumik Dey", "Ashirbad Mishra", "Hansi Wu", "Binbin Li", "Rui Zhang" ], "github": "https://github.com/eBay/spec_dec", "project_page": "", "abstract": "Speculative decoding speeds up LLM inference by using a small draft model to propose multiple tokens that a target model verifies in parallel. Extending this idea to batches is essential for production serving, but it introduces the ragged tensor problem: sequences in the same batch accept different numbers of draft tokens, breaking right-alignment and corrupting position IDs, attention masks, and KV-cache state. We show that several existing batch implementations violate output equivalence-the fundamental requirement that speculative decoding must produce identical token sequences to standard autoregressive generation. These violations occur precisely due to improper handling of the ragged tensor problem. In response, we (1) characterize the synchronization requirements that guarantee correctness, (2) present a correctness-first batch speculative decoding EQSPEC that exposes realignment as consuming 40% of overhead, and (3) introduce EXSPEC, which maintains a sliding pool of sequences and dynamically forms same-length groups, to reduce the realignment overhead while preserving per-sequence speculative speedups. On the SpecBench dataset, across Vicuna-7B/68M, Qwen3-8B/0.6B, and GLM-4-9B/0.6B target/draft pairs, our approach achieves up to 3times throughput improvement at batch size 8 compared to batch size 1, with efficient scaling through batch size 8, while maintaining 95% output equivalence. Our method requires no custom kernels and integrates cleanly with existing inference stacks. Our code is available at https://github.com/eBay/spec_dec.", "github_manual": "", "github_regex": "https://github.com/eBay/spec_dec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.22590", "title": "ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs", "authors": [ "Yassir Lairgi", "Ludovic Moncla", "Khalid Benabdeslem", "R\u00e9my Cazabet", "Pierre Cl\u00e9au" ], "github": "https://github.com/AuvaLab/itext2kg", "project_page": "", "abstract": "In today's rapidly expanding data landscape, knowledge extraction from unstructured text is vital for real-time analytics, temporal inference, and dynamic memory frameworks. However, traditional static knowledge graph (KG) construction often overlooks the dynamic and time-sensitive nature of real-world data, limiting adaptability to continuous changes. Moreover, recent zero- or few-shot approaches that avoid domain-specific fine-tuning or reliance on prebuilt ontologies often suffer from instability across multiple runs, as well as incomplete coverage of key facts. To address these challenges, we introduce ATOM (AdapTive and OptiMized), a few-shot and scalable approach that builds and continuously updates Temporal Knowledge Graphs (TKGs) from unstructured texts. ATOM splits input documents into minimal, self-contained \"atomic\" facts, improving extraction exhaustivity and stability. Then, it constructs atomic TKGs from these facts while employing a dual-time modeling that distinguishes when information is observed from when it is valid. The resulting atomic TKGs are subsequently merged in parallel. Empirical evaluations demonstrate that ATOM achieves ~18% higher exhaustivity, ~17% better stability, and over 90% latency reduction compared to baseline methods, demonstrating a strong scalability potential for dynamic TKG construction.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AuvaLab/itext2kg", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.22728", "title": "S-Chain: Structured Visual Chain-of-Thought For Medicine", "authors": [ "Khai Le-Duc", "Duy M. H. Nguyen", "Phuong T. H. Trinh", "Tien-Phat Nguyen", "Nghiem T. Diep", "An Ngo", "Tung Vu", "Trinh Vuong", "Anh-Tien Nguyen", "Mau Nguyen", "Van Trung Hoang", "Khai-Nguyen Nguyen", "Hy Nguyen", "Chris Ngo", "Anji Liu", "Nhat Ho", "Anne-Christin Hauschild", "Khanh Xuan Nguyen", "Thanh Nguyen-Tang", "Pengtao Xie", "Daniel Sonntag", "James Zou", "Mathias Niepert", "Anh Totti Nguyen" ], "github": "", "project_page": "", "abstract": "Faithful reasoning in medical vision-language models (VLMs) requires not only accurate predictions but also transparent alignment between textual rationales and visual evidence. While Chain-of-Thought (CoT) prompting has shown promise in medical visual question answering (VQA), no large-scale expert-level dataset has captured stepwise reasoning with precise visual grounding. We introduce S-Chain, the first large-scale dataset of 12,000 expert-annotated medical images with bounding boxes and structured visual CoT (SV-CoT), explicitly linking visual regions to reasoning steps. The dataset further supports 16 languages, totaling over 700k VQA pairs for broad multilingual applicability. Using S-Chain, we benchmark state-of-the-art medical VLMs (ExGra-Med, LLaVA-Med) and general-purpose VLMs (Qwen2.5-VL, InternVL2.5), showing that SV-CoT supervision significantly improves interpretability, grounding fidelity, and robustness. Beyond benchmarking, we study its synergy with retrieval-augmented generation, revealing how domain knowledge and visual grounding interact during autoregressive reasoning. Finally, we propose a new mechanism that strengthens the alignment between visual evidence and reasoning, improving both reliability and efficiency. S-Chain establishes a new benchmark for grounded medical reasoning and paves the way toward more trustworthy and explainable medical VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-29", "arxiv_id": "2510.23828", "title": "Beyond Understanding: Evaluating the Pragmatic Gap in LLMs' Cultural Processing of Figurative Language", "authors": [ "Mena Attia", "Aashiq Muhamed", "Mai Alkhamissi", "Thamar Solorio", "Mona Diab" ], "github": "", "project_page": "", "abstract": "We present a comprehensive evaluation of the ability of large language models (LLMs) to process culturally grounded language, specifically to understand and pragmatically use figurative expressions that encode local knowledge and cultural nuance. Using figurative language as a proxy for cultural nuance and local knowledge, we design evaluation tasks for contextual understanding, pragmatic use, and connotation interpretation in Arabic and English. We evaluate 22 open- and closed-source LLMs on Egyptian Arabic idioms, multidialectal Arabic proverbs, and English proverbs. Our results show a consistent hierarchy: the average accuracy for Arabic proverbs is 4.29% lower than for English proverbs, and performance for Egyptian idioms is 10.28% lower than for Arabic proverbs. For the pragmatic use task, accuracy drops by 14.07% relative to understanding, though providing contextual idiomatic sentences improves accuracy by 10.66%. Models also struggle with connotative meaning, reaching at most 85.58% agreement with human annotators on idioms with 100% inter-annotator agreement. These findings demonstrate that figurative language serves as an effective diagnostic for cultural reasoning: while LLMs can often interpret figurative meaning, they face challenges in using it appropriately. To support future research, we release Kinayat, the first dataset of Egyptian Arabic idioms designed for both figurative understanding and pragmatic use evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.25741", "title": "Scaling Latent Reasoning via Looped Language Models", "authors": [ "Rui-Jie Zhu", "Zixuan Wang", "Kai Hua", "Tianyu Zhang", "Ziniu Li", "Haoran Que", "Boyi Wei", "Zixin Wen", "Fan Yin", "He Xing", "Lu Li", "Jiajun Shi", "Kaijing Ma", "Shanda Li", "Taylor Kergan", "Andrew Smith", "Xingwei Qu", "Mude Hui", "Bohong Wu", "Qiyang Min", "Hongzhi Huang", "Xun Zhou", "Wei Ye", "Jiaheng Liu", "Jian Yang", "Yunfeng Shi", "Chenghua Lin", "Enduo Zhao", "Tianle Cai", "Ge Zhang", "Wenhao Huang", "Yoshua Bengio", "Jason Eshraghian" ], "github": "", "project_page": "", "abstract": "Modern LLMs are trained to \"think\" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training data. We present and open-source Ouro, named after the recursive Ouroboros, a family of pre-trained Looped Language Models (LoopLM) that instead build reasoning into the pre-training phase through (i) iterative computation in latent space, (ii) an entropy-regularized objective for learned depth allocation, and (iii) scaling to 7.7T tokens. Ouro 1.4B and 2.6B models enjoy superior performance that match the results of up to 12B SOTA LLMs across a wide range of benchmarks. Through controlled experiments, we show this advantage stems not from increased knowledge capacity, but from superior knowledge manipulation capabilities. We also show that LoopLM yields reasoning traces more aligned with final outputs than explicit CoT. We hope our results show the potential of LoopLM as a novel scaling direction in the reasoning era. Our model could be found in: http://ouro-llm.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.25590", "title": "RegionE: Adaptive Region-Aware Generation for Efficient Image Editing", "authors": [ "Pengtao Chen", "Xianfang Zeng", "Maosen Zhao", "Mingzhu Shen", "Peng Ye", "Bangyin Xiang", "Zhibo Wang", "Wei Cheng", "Gang Yu", "Tao Chen" ], "github": "", "project_page": "", "abstract": "Recently, instruction-based image editing (IIE) has received widespread attention. In practice, IIE often modifies only specific regions of an image, while the remaining areas largely remain unchanged. Although these two types of regions differ significantly in generation difficulty and computational redundancy, existing IIE models do not account for this distinction, instead applying a uniform generation process across the entire image. This motivates us to propose RegionE, an adaptive, region-aware generation framework that accelerates IIE tasks without additional training. Specifically, the RegionE framework consists of three main components: 1) Adaptive Region Partition. We observed that the trajectory of unedited regions is straight, allowing for multi-step denoised predictions to be inferred in a single step. Therefore, in the early denoising stages, we partition the image into edited and unedited regions based on the difference between the final estimated result and the reference image. 2) Region-Aware Generation. After distinguishing the regions, we replace multi-step denoising with one-step prediction for unedited areas. For edited regions, the trajectory is curved, requiring local iterative denoising. To improve the efficiency and quality of local iterative generation, we propose the Region-Instruction KV Cache, which reduces computational cost while incorporating global information. 3) Adaptive Velocity Decay Cache. Observing that adjacent timesteps in edited regions exhibit strong velocity similarity, we further propose an adaptive velocity decay cache to accelerate the local denoising process. We applied RegionE to state-of-the-art IIE base models, including Step1X-Edit, FLUX.1 Kontext, and Qwen-Image-Edit. RegionE achieved acceleration factors of 2.57, 2.41, and 2.06. Evaluations by GPT-4o confirmed that semantic and perceptual fidelity were well preserved.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.24821", "title": "Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal Perception and Generation", "authors": [ "Inclusion AI", "Bowen Ma", "Cheng Zou", "Canxiang Yan", "Chunxiang Jin", "Chunjie Shen", "Dandan Zheng", "Fudong Wang", "Furong Xu", "GuangMing Yao", "Jun Zhou", "Jingdong Chen", "Jianing Li", "Jianxin Sun", "Jiajia Liu", "Jianjiang Zhu", "Jianping Jiang", "Jun Peng", "Kaixiang Ji", "Kaimeng Ren", "Libin Wang", "Lixiang Ru", "Longhua Tan", "Lan Wang", "Mochen Bai", "Ning Gao", "Qingpei Guo", "Qinglong Zhang", "Qiang Xu", "Rui Liu", "Ruijie Xiong", "Ruobing Zheng", "Sirui Gao", "Tianqi Li", "Tinghao Liu", "Weilong Chai", "Xinyu Xiao", "Xiaomei Wang", "Xiaolong Wang", "Xiao Lu", "Xiaoyu Li", "Xingning Dong", "Xuzheng Yu", "Yi Yuan", "Yuting Gao", "Yuting Xiao", "Yunxiao Sun", "Yipeng Chen", "Yifan Mao", "Yifei Wu", "Yongjie Lyu", "Ziping Ma", "Zhiqiang Fang", "Zhihao Qiu", "Ziyuan Huang", "Zizheng Yang", "Zhengyu He" ], "github": "", "project_page": "", "abstract": "We propose Ming-Flash-Omni, an upgraded version of Ming-Omni, built upon a sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100 billion total parameters, of which only 6.1 billion are active per token. This architecture enables highly efficient scaling (dramatically improving computational efficiency while significantly expanding model capacity) and empowers stronger unified multimodal intelligence across vision, speech, and language, representing a key step toward Artificial General Intelligence (AGI). Compared to its predecessor, the upgraded version exhibits substantial improvements across multimodal understanding and generation. We significantly advance speech recognition capabilities, achieving state-of-the-art performance in contextual ASR and highly competitive results in dialect-aware ASR. In image generation, Ming-Flash-Omni introduces high-fidelity text rendering and demonstrates marked gains in scene consistency and identity preservation during image editing. Furthermore, Ming-Flash-Omni introduces generative segmentation, a capability that not only achieves strong standalone segmentation performance but also enhances spatial control in image generation and improves editing consistency. Notably, Ming-Flash-Omni achieves state-of-the-art results in text-to-image generation and generative segmentation, and sets new records on all 12 contextual ASR benchmarks, all within a single unified architecture.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.25409", "title": "BhashaBench V1: A Comprehensive Benchmark for the Quadrant of Indic Domains", "authors": [ "Vijay Devane", "Mohd Nauman", "Bhargav Patel", "Aniket Mahendra Wakchoure", "Yogeshkumar Sant", "Shyam Pawar", "Viraj Thakur", "Ananya Godse", "Sunil Patra", "Neha Maurya", "Suraj Racha", "Nitish Kamal Singh", "Ajay Nagpal", "Piyush Sawarkar", "Kundeshwar Vijayrao Pundalik", "Rohit Saluja", "Ganesh Ramakrishnan" ], "github": "https://github.com/AI4Bharat/IndicLID.Models", "project_page": "", "abstract": "The rapid advancement of large language models(LLMs) has intensified the need for domain and culture specific evaluation. Existing benchmarks are largely Anglocentric and domain-agnostic, limiting their applicability to India-centric contexts. To address this gap, we introduce BhashaBench V1, the first domain-specific, multi-task, bilingual benchmark focusing on critical Indic knowledge systems. BhashaBench V1 contains 74,166 meticulously curated question-answer pairs, with 52,494 in English and 21,672 in Hindi, sourced from authentic government and domain-specific exams. It spans four major domains: Agriculture, Legal, Finance, and Ayurveda, comprising 90+ subdomains and covering 500+ topics, enabling fine-grained evaluation. Evaluation of 29+ LLMs reveals significant domain and language specific performance gaps, with especially large disparities in low-resource domains. For instance, GPT-4o achieves 76.49% overall accuracy in Legal but only 59.74% in Ayurveda. Models consistently perform better on English content compared to Hindi across all domains. Subdomain-level analysis shows that areas such as Cyber Law, International Finance perform relatively well, while Panchakarma, Seed Science, and Human Rights remain notably weak. BhashaBench V1 provides a comprehensive dataset for evaluating large language models across India's diverse knowledge domains. It enables assessment of models' ability to integrate domain-specific knowledge with bilingual understanding. All code, benchmarks, and resources are publicly available to support open research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AI4Bharat/IndicLID.Models", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.23473", "title": "Video-Thinker: Sparking \"Thinking with Videos\" via Reinforcement Learning", "authors": [ "Shijian Wang", "Jiarui Jin", "Xingjian Wang", "Linxin Song", "Runhao Fu", "Hecheng Wang", "Zongyuan Ge", "Yuan Lu", "Xuelian Cheng" ], "github": "", "project_page": "", "abstract": "Recent advances in image reasoning methods, particularly \"Thinking with Images\", have demonstrated remarkable success in Multimodal Large Language Models (MLLMs); however, this dynamic reasoning paradigm has not yet been extended to video reasoning tasks. In this paper, we propose Video-Thinker, which empowers MLLMs to think with videos by autonomously leveraging their intrinsic \"grounding\" and \"captioning\" capabilities to generate reasoning clues throughout the inference process. To spark this capability, we construct Video-Thinker-10K, a curated dataset featuring autonomous tool usage within chain-of-thought reasoning sequences. Our training strategy begins with Supervised Fine-Tuning (SFT) to learn the reasoning format, followed by Group Relative Policy Optimization (GRPO) to strengthen this reasoning capability. Through this approach, Video-Thinker enables MLLMs to autonomously navigate grounding and captioning tasks for video reasoning, eliminating the need for constructing and calling external tools. Extensive experiments demonstrate that Video-Thinker achieves significant performance gains on both in-domain tasks and challenging out-of-domain video reasoning benchmarks, including Video-Holmes, CG-Bench-Reasoning, and VRBench. Our Video-Thinker-7B substantially outperforms existing baselines such as Video-R1 and establishes state-of-the-art performance among 7B-sized MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.23538", "title": "JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence", "authors": [ "Qiushi Sun", "Jingyang Gong", "Yang Liu", "Qiaosheng Chen", "Lei Li", "Kai Chen", "Qipeng Guo", "Ben Kao", "Fei Yuan" ], "github": "https://github.com/InternLM/JanusCoder", "project_page": "", "abstract": "The scope of neural code intelligence is rapidly expanding beyond text-based source code to encompass the rich visual outputs that programs generate. This visual dimension is critical for advanced applications like flexible content generation and precise, program-driven editing of visualizations. However, progress has been impeded by the scarcity of high-quality multimodal code data, a bottleneck stemming from challenges in synthesis and quality assessment. To address these challenges, we make contributions from both a data and modeling perspective. We first introduce a complete synthesis toolkit that leverages reciprocal synergies between data modalities to efficiently produce a large-scale, high-quality corpus spanning from standard charts to complex interactive web UIs and code-driven animations. Leveraging this toolkit, we construct JanusCode-800K, the largest multimodal code corpus to date. This powers the training of our models, JanusCoder and JanusCoderV, which establish a visual-programmatic interface for generating code from textual instructions, visual inputs, or a combination of both. Our unified model is a departure from existing approaches that build specialized models for isolated tasks. Extensive experiments on both text-centric and vision-centric coding tasks demonstrate the superior performance of the JanusCoder series, with our 7B to 14B scale models approaching or even exceeding the performance of commercial models. Furthermore, extensive analysis provides key insights into harmonizing programmatic logic with its visual expression. Our code and checkpoints will are available at https://github.com/InternLM/JanusCoder.", "github_manual": "", "github_regex": "https://github.com/InternLM/JanusCoder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.25726", "title": "The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution", "authors": [ "Junlong Li", "Wenshuo Zhao", "Jian Zhao", "Weihao Zeng", "Haoze Wu", "Xiaochen Wang", "Rui Ge", "Yuxuan Cao", "Yuzhen Huang", "Wei Liu", "Junteng Liu", "Zhaochen Su", "Yiyang Guo", "Fan Zhou", "Lueyang Zhang", "Juan Michelini", "Xingyao Wang", "Xiang Yue", "Shuyan Zhou", "Graham Neubig", "Junxian He" ], "github": "", "project_page": "", "abstract": "Real-world language agents must handle complex, multi-step workflows across diverse Apps. For instance, an agent may manage emails by coordinating with calendars and file systems, or monitor a production database to detect anomalies and generate reports following an operating manual. However, existing language agent benchmarks often focus on narrow domains or simplified tasks that lack the diversity, realism, and long-horizon complexity required to evaluate agents' real-world performance. To address this gap, we introduce the Tool Decathlon (dubbed as Toolathlon), a benchmark for language agents offering diverse Apps and tools, realistic environment setup, and reliable execution-based evaluation. Toolathlon spans 32 software applications and 604 tools, ranging from everyday platforms such as Google Calendar and Notion to professional ones like WooCommerce, Kubernetes, and BigQuery. Most of the tools are based on a high-quality set of Model Context Protocol (MCP) servers that we may have revised or implemented ourselves. Unlike prior works, which primarily ensure functional realism but offer limited environment state diversity, we provide realistic initial environment states from real software, such as Canvas courses with dozens of students or real financial spreadsheets. This benchmark includes 108 manually sourced or crafted tasks in total, requiring interacting with multiple Apps over around 20 turns on average to complete. Each task is strictly verifiable through dedicated evaluation scripts. Comprehensive evaluation of SOTA models highlights their significant shortcomings: the best-performing model, Claude-4.5-Sonnet, achieves only a 38.6% success rate with 20.2 tool calling turns on average, while the top open-weights model DeepSeek-V3.2-Exp reaches 20.1%. We expect Toolathlon to drive the development of more capable language agents for real-world, long-horizon task execution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.25772", "title": "VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context Learning", "authors": [ "Baolu Li", "Yiming Zhang", "Qinghe Wang", "Liqian Ma", "Xiaoyu Shi", "Xintao Wang", "Pengfei Wan", "Zhenfei Yin", "Yunzhi Zhuge", "Huchuan Lu", "Xu Jia" ], "github": "", "project_page": "", "abstract": "Visual effects (VFX) are crucial to the expressive power of digital media, yet their creation remains a major challenge for generative AI. Prevailing methods often rely on the one-LoRA-per-effect paradigm, which is resource-intensive and fundamentally incapable of generalizing to unseen effects, thus limiting scalability and creation. To address this challenge, we introduce VFXMaster, the first unified, reference-based framework for VFX video generation. It recasts effect generation as an in-context learning task, enabling it to reproduce diverse dynamic effects from a reference video onto target content. In addition, it demonstrates remarkable generalization to unseen effect categories. Specifically, we design an in-context conditioning strategy that prompts the model with a reference example. An in-context attention mask is designed to precisely decouple and inject the essential effect attributes, allowing a single unified model to master the effect imitation without information leakage. In addition, we propose an efficient one-shot effect adaptation mechanism to boost generalization capability on tough unseen effects from a single user-provided video rapidly. Extensive experiments demonstrate that our method effectively imitates various categories of effect information and exhibits outstanding generalization to out-of-domain effects. To foster future research, we will release our code, models, and a comprehensive dataset to the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.24592", "title": "ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization", "authors": [ "Guoxin Chen", "Jing Wu", "Xinjie Chen", "Wayne Xin Zhao", "Ruihua Song", "Chengxi Li", "Kai Fan", "Dayiheng Liu", "Minpeng Liao" ], "github": "", "project_page": "", "abstract": "Autoformalization, which translates natural language mathematics into machine-verifiable formal statements, is critical for using formal mathematical reasoning to solve math problems stated in natural language. While Large Language Models can generate syntactically correct formal statements, they often fail to preserve the original problem's semantic intent. This limitation arises from the LLM approaches' treating autoformalization as a simplistic translation task which lacks mechanisms for self-reflection and iterative refinement that human experts naturally employ. To address these issues, we propose ReForm, a Reflective Autoformalization method that tightly integrates semantic consistency evaluation into the autoformalization process. This enables the model to iteratively generate formal statements, assess its semantic fidelity, and self-correct identified errors through progressive refinement. To effectively train this reflective model, we introduce Prospective Bounded Sequence Optimization (PBSO), which employs different rewards at different sequence positions to ensure that the model develops both accurate autoformalization and correct semantic validations, preventing superficial critiques that would undermine the purpose of reflection. Extensive experiments across four autoformalization benchmarks demonstrate that ReForm achieves an average improvement of 17.2 percentage points over the strongest baselines. To further ensure evaluation reliability, we introduce ConsistencyCheck, a benchmark of 859 expert-annotated items that not only validates LLMs as judges but also reveals that autoformalization is inherently difficult: even human experts produce semantic errors in up to 38.5% of cases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.19195", "title": "Rethinking Driving World Model as Synthetic Data Generator for Perception Tasks", "authors": [ "Kai Zeng", "Zhanqian Wu", "Kaixin Xiong", "Xiaobao Wei", "Xiangyu Guo", "Zhenxin Zhu", "Kalok Ho", "Lijun Zhou", "Bohan Zeng", "Ming Lu", "Haiyang Sun", "Bing Wang", "Guang Chen", "Hangjun Ye", "Wentao Zhang" ], "github": "https://github.com/wm-research/Dream4Drive", "project_page": "", "abstract": "Recent advancements in driving world models enable controllable generation of high-quality RGB videos or multimodal videos. Existing methods primarily focus on metrics related to generation quality and controllability. However, they often overlook the evaluation of downstream perception tasks, which are really crucial for the performance of autonomous driving. Existing methods usually leverage a training strategy that first pretrains on synthetic data and finetunes on real data, resulting in twice the epochs compared to the baseline (real data only). When we double the epochs in the baseline, the benefit of synthetic data becomes negligible. To thoroughly demonstrate the benefit of synthetic data, we introduce Dream4Drive, a novel synthetic data generation framework designed for enhancing the downstream perception tasks. Dream4Drive first decomposes the input video into several 3D-aware guidance maps and subsequently renders the 3D assets onto these guidance maps. Finally, the driving world model is fine-tuned to produce the edited, multi-view photorealistic videos, which can be used to train the downstream perception models. Dream4Drive enables unprecedented flexibility in generating multi-view corner cases at scale, significantly boosting corner case perception in autonomous driving. To facilitate future research, we also contribute a large-scale 3D asset dataset named DriveObj3D, covering the typical categories in driving scenarios and enabling diverse 3D-aware video editing. We conduct comprehensive experiments to show that Dream4Drive can effectively boost the performance of downstream perception models under various training epochs. Page: https://wm-research.github.io/Dream4Drive/ GitHub Link: https://github.com/wm-research/Dream4Drive", "github_manual": "", "github_regex": "https://github.com/wm-research/Dream4Drive", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.24824", "title": "Parallel Loop Transformer for Efficient Test-Time Computation Scaling", "authors": [ "Bohong Wu", "Mengzhao Chen", "Xiang Luo", "Shen Yan", "Qifan Yu", "Fan Xia", "Tianqi Zhang", "Hongrui Zhan", "Zheng Zhong", "Xun Zhou", "Siyuan Qiao", "Xingyan Bin" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are powerful but often too slow and costly for real-world use during inference. Looped transformers save on parameters by reusing the same weights for multiple computational steps, or \"loops.\" However, this approach has a major flaw: the loops run one after another, causing inference latency and memory requirements to increase with each added loop. This makes them impractical for fast applications. To solve this problem, we introduce the Parallel Loop Transformer (PLT). PLT is a new architecture that delivers the performance benefits of a deep, looped model but with the low latency of a standard, non-looped model. PLT works using two key techniques. First, Cross-Loop Parallelism (CLP) breaks the sequential dependency by computing different loops for different tokens at the same time, all within a single pass. Second, to prevent memory costs from growing, we use an Efficient Representation Enhancement strategy. This method shares the memory (KV cache) from the first loop with all other loops. It then uses a Gated Sliding-Window Attention (G-SWA) to combine this shared global information with local information, maintaining high accuracy. Our experiments show that PLT achieves the high accuracy of a traditional looped model but with almost no extra latency or memory cost compared to a standard transformer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.24803", "title": "MASPRM: Multi-Agent System Process Reward Model", "authors": [ "Milad Yazdani", "Mahdi Mostajabdaveh", "Zirui Zhou", "Ying Xiong" ], "github": "https://github.com/milad1378yz/MASPRM", "project_page": "", "abstract": "Practical deployment of Multi-Agent Systems (MAS) demands strong test-time performance, motivating methods that guide inference-time search and selectively spend compute to improve quality. We present the Multi-Agent System Process Reward Model (MASPRM). It assigns per-action, per-agent values to partial inter-agent transcripts and acts as an inference-time controller. MASPRM is trained from multi-agent Monte Carlo Tree Search (MCTS) rollouts without requiring step-level human annotations, by propagating returns to local targets. At inference, MASPRM guides step-level beam search and MCTS, focusing computation on promising branches and pruning early. On GSM8K and MATH, MASPRM-guided decoding with an outcome reward model (ORM) applied to the final answer, improves exact match (EM) over a single straight-through MAS pass by +30.7 and +22.9 points, respectively. A MASPRM trained on GSM8K transfers zero-shot to MATH without retraining, adding 8.4 EM points at the same budget. MASPRM is a plug-in value model that estimates per-agent progress and complements verifier-style decoders, enabling more reliable, compute-aware multi-agent reasoning. Code: https://github.com/milad1378yz/MASPRM", "github_manual": "", "github_regex": "https://github.com/milad1378yz/MASPRM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.25092", "title": "SeeingEye: Agentic Information Flow Unlocks Multimodal Reasoning In Text-only LLMs", "authors": [ "Weijia Zhang", "Zijia Liu", "Haoru Li", "Haoqi Chen", "Jiaxuan You" ], "github": "https://github.com/ulab-uiuc/SeeingEye", "project_page": "", "abstract": "Recent advances in text-only large language models (LLMs), such as DeepSeek-R1, demonstrate remarkable reasoning ability. However, these models remain fragile or entirely incapable when extended to multi-modal tasks. Existing approaches largely rely on single-form captions, which lack diversity and often fail to adapt across different types of Visual Question Answering (VQA) benchmarks. As a result, they provide no principled or efficient channel for transmitting fine-grained visual information. We introduce Seeing Eye, a modular framework that unlocks multimodal reasoning in text-only LLMs through an agent-based small VLM translator. This translator acts as a perception agent: it can invoke specialized tools (e.g., OCR and crop) and iteratively distill multimodal inputs into structured intermediate representations (SIRs) tailored to the question. These SIRs are then passed to the text-only LLM, which serves as a reasoning agent. Crucially, the translator and reasoner engage in multi-round feedback and interaction, enabling the extraction of targeted visual details and yielding more confident answers. Experiments on knowledge-intensive VQA benchmarks, including MMMU and MIA-Bench, demonstrate that Seeing Eye not only reduces inference cost but also surpasses much larger end-to-end VLMs. For example, an instantiation combining a 3B-parameter vision translator with an 8B-parameter language reasoner outperforms a monolithic 32B VLM on challenging knowledge-based questions. Our results highlight that decoupling perception from reasoning via agent information flow offers a scalable and plug-and-play pathway to multimodal reasoning, allowing strong text-only LLMs to fully leverage their reasoning capabilities. Code is available at: https://github.com/ulab-uiuc/SeeingEye", "github_manual": "", "github_regex": "https://github.com/ulab-uiuc/SeeingEye", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.25758", "title": "TheraMind: A Strategic and Adaptive Agent for Longitudinal Psychological Counseling", "authors": [ "He Hu", "Yucheng Zhou", "Chiyuan Ma", "Qianning Wang", "Zheng Zhang", "Fei Ma", "Laizhong Cui", "Qi Tian" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) in psychological counseling have attracted increasing attention. However, existing approaches often lack emotional understanding, adaptive strategies, and the use of therapeutic methods across multiple sessions with long-term memory, leaving them far from real clinical practice. To address these critical gaps, we introduce TheraMind, a strategic and adaptive agent for longitudinal psychological counseling. The cornerstone of TheraMind is a novel dual-loop architecture that decouples the complex counseling process into an Intra-Session Loop for tactical dialogue management and a Cross-Session Loop for strategic therapeutic planning. The Intra-Session Loop perceives the patient's emotional state to dynamically select response strategies while leveraging cross-session memory to ensure continuity. Crucially, the Cross-Session Loop empowers the agent with long-term adaptability by evaluating the efficacy of the applied therapy after each session and adjusting the method for subsequent interactions. We validate our approach in a high-fidelity simulation environment grounded in real clinical cases. Extensive evaluations show that TheraMind outperforms other methods, especially on multi-session metrics like Coherence, Flexibility, and Therapeutic Attunement, validating the effectiveness of its dual-loop design in emulating strategic, adaptive, and longitudinal therapeutic behavior. The code is publicly available at https://0mwwm0.github.io/TheraMind/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.25065", "title": "Reasoning-Aware GRPO using Process Mining", "authors": [ "Taekhyun Park", "Yongjae Lee", "Hyerim Bae" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL)-based post-training has been crucial for enabling multi-step reasoning in large reasoning models (LRMs), yet current reward schemes are typically outcome-centric. We propose PM4GRPO, a reasoning-aware Group Relative Policy Optimization (GRPO) that augments standard answer/format rewards with signals over the reasoning procedure. To this end, process mining techniques are utilized to compute a scalar conformance reward that measures how closely a policy model's reasoning aligns with the pretrained teacher model. The empirical results on five benchmarks demonstrate that PM4GRPO significantly outperforms existing methodologies for GRPO-based post-training. These results highlight that leveraging process mining for reasoning-aware GRPO effectively enhances the reasoning capabilities of policy models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.25682", "title": "PairUni: Pairwise Training for Unified Multimodal Language Models", "authors": [ "Jiani Zheng", "Zhiyang Teng", "Xiangtai Li", "Anran Wang", "Yu Tian", "Kunpeng Qiu", "Ye Tian", "Haochen Wang", "Zhuochen Wang" ], "github": "https://github.com/Haochen-Wang409/PairUni", "project_page": "", "abstract": "Unified vision-language models (UVLMs) must perform both understanding and generation within a single architecture, but these tasks rely on heterogeneous data and supervision, making it difficult to balance them during reinforcement learning (RL). We propose PairUni, a unified framework that reorganizes data into understanding-generation (UG) pairs and aligns optimization accordingly. We first use GPT-o3 to augment single-task data, generating captions for understanding samples and question-answer (QA) pairs for generation samples, forming aligned pairs from the same instance. Additionally, for each generation sample, we retrieve a semantically related understanding example to form a retrieved pair, linking different but related data points. These paired structures expose cross-task semantic correspondences and support consistent policy learning. To leverage this structure, we present Pair-GPRO, a pair-aware variant based on Group Relative Policy Optimization. It assigns a similarity score to each pair to modulate the advantage, strengthening learning from well-aligned examples and reducing task interference. We curate a high-quality dataset of 16K UG pairs named PairUG for RL fine-tuning and evaluate PairUni on the powerful Janus-Pro UVLMs. Our approach achieves balanced improvements on various UVLMs, outperforming strong UVLM RL baselines. Code: https://github.com/Haochen-Wang409/PairUni{github.com/Haochen-Wang409/PairUni}", "github_manual": "", "github_regex": "https://github.com/Haochen-Wang409/PairUni", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.25760", "title": "Multimodal Spatial Reasoning in the Large Model Era: A Survey and Benchmarks", "authors": [ "Xu Zheng", "Zihao Dongfang", "Lutao Jiang", "Boyuan Zheng", "Yulong Guo", "Zhenquan Zhang", "Giuliano Albanese", "Runyi Yang", "Mengjiao Ma", "Zixin Zhang", "Chenfei Liao", "Dingcheng Zhen", "Yuanhuiyi Lyu", "Yuqian Fu", "Bin Ren", "Linfeng Zhang", "Danda Pani Paudel", "Nicu Sebe", "Luc Van Gool", "Xuming Hu" ], "github": "https://github.com/zhengxuJosh/Awesome-Multimodal-Spatial-Reasoning", "project_page": "", "abstract": "Humans possess spatial reasoning abilities that enable them to understand spaces through multimodal observations, such as vision and sound. Large multimodal reasoning models extend these abilities by learning to perceive and reason, showing promising performance across diverse spatial tasks. However, systematic reviews and publicly available benchmarks for these models remain limited. In this survey, we provide a comprehensive review of multimodal spatial reasoning tasks with large models, categorizing recent progress in multimodal large language models (MLLMs) and introducing open benchmarks for evaluation. We begin by outlining general spatial reasoning, focusing on post-training techniques, explainability, and architecture. Beyond classical 2D tasks, we examine spatial relationship reasoning, scene and layout understanding, as well as visual question answering and grounding in 3D space. We also review advances in embodied AI, including vision-language navigation and action models. Additionally, we consider emerging modalities such as audio and egocentric video, which contribute to novel spatial understanding through new sensors. We believe this survey establishes a solid foundation and offers insights into the growing field of multimodal spatial reasoning. Updated information about this survey, codes and implementation of the open benchmarks can be found at https://github.com/zhengxuJosh/Awesome-Spatial-Reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhengxuJosh/Awesome-Multimodal-Spatial-Reasoning", "github_api_added_by": "auto" }, { "date": "2025-10-30", "arxiv_id": "2510.22543", "title": "FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning", "authors": [ "Yuyang Ding", "Chi Zhang", "Juntao Li", "Haibin Lin", "Xin Liu", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising paradigm for enhancing the reasoning capabilities of large language models (LLMs). In this context, models explore reasoning trajectories and exploit rollouts with correct answers as positive signals for policy optimization. However, these rollouts might involve flawed patterns such as answer-guessing and jump-in-reasoning. Such flawed-positive rollouts are rewarded identically to fully correct ones, causing policy models to internalize these unreliable reasoning patterns. In this work, we first conduct a systematic study of flawed-positive rollouts in RL and find that they enable rapid capability gains during the early optimization stage, while constraining reasoning capability later by reinforcing unreliable patterns. Building on these insights, we propose Flawed-Aware Policy Optimization (FAPO), which presents a parameter-free reward penalty for flawed-positive rollouts, enabling the policy to leverage them as useful shortcuts in the warm-up stage, securing stable early gains, while gradually shifting optimization toward reliable reasoning in the later refinement stage. To accurately and comprehensively detect flawed-positive rollouts, we introduce a generative reward model (GenRM) with a process-level reward that precisely localizes reasoning errors. Experiments show that FAPO is effective in broad domains, improving outcome correctness, process reliability, and training stability without increasing the token budget.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.18455", "title": "ChronoPlay: A Framework for Modeling Dual Dynamics and Authenticity in Game RAG Benchmarks", "authors": [ "Liyang He", "Yuren Zhang", "Ziwei Zhu", "Zhenghui Li", "Shiwei Tong" ], "github": "https://github.com/hly1998/ChronoPlay", "project_page": "", "abstract": "Retrieval Augmented Generation (RAG) systems are increasingly vital in dynamic domains like online gaming, yet the lack of a dedicated benchmark has impeded standardized evaluation in this area. The core difficulty lies in Dual Dynamics: the constant interplay between game content updates and the shifting focus of the player community. Furthermore, the necessity of automating such a benchmark introduces a critical requirement for player-centric authenticity to ensure generated questions are realistic. To address this integrated challenge, we introduce ChronoPlay, a novel framework for the automated and continuous generation of game RAG benchmarks. ChronoPlay utilizes a dual-dynamic update mechanism to track both forms of change, and a dual-source synthesis engine that draws from official sources and player community to ensure both factual correctness and authentic query patterns. We instantiate our framework on three distinct games to create the first dynamic RAG benchmark for the gaming domain, offering new insights into model performance under these complex and realistic conditions. Code is avaliable at: https://github.com/hly1998/ChronoPlay.", "github_manual": "", "github_regex": "https://github.com/hly1998/ChronoPlay", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.24654", "title": "Evolving Diagnostic Agents in a Virtual Clinical Environment", "authors": [ "Pengcheng Qiu", "Chaoyi Wu", "Junwei Liu", "Qiaoyu Zheng", "Yusheng Liao", "Haowen Wang", "Yun Yue", "Qianrui Fan", "Shuai Zhen", "Jian Wang", "Jinjie Gu", "Yanfeng Wang", "Ya Zhang", "Weidi Xie" ], "github": "", "project_page": "", "abstract": "In this paper, we present a framework for training large language models (LLMs) as diagnostic agents with reinforcement learning, enabling them to manage multi-turn diagnostic processes, adaptively select examinations, and commit to final diagnoses. Unlike instruction-tuned models trained on static case summaries, our method acquires diagnostic strategies through interactive exploration and outcome-based feedback. Our contributions are fourfold: (i) We present DiagGym, a diagnostics world model trained with electronic health records that emits examination outcomes conditioned on patient history and recommended examination, serving as a virtual clinical environment for realistic diagnosis training and evaluation; (ii) We train DiagAgent via end-to-end, multi-turn reinforcement learning to learn diagnostic policies that optimize both information yield and diagnostic accuracy; (iii) We introduce DiagBench, a diagnostic benchmark comprising 750 cases with physician-validated examination recommendations and 99 cases annotated with 973 physician-written rubrics on diagnosis process; (iv) we demonstrate superior performance across diverse diagnostic settings. DiagAgent significantly outperforms 10 state-of-the-art LLMs, including DeepSeek-v3 and GPT-4o, as well as two prompt-engineered agents. In single-turn settings, DiagAgent achieves 9.34% higher diagnostic accuracy and 44.03% improvement in examination recommendation hit ratio. In end-to-end settings, it delivers 15.12% increase in diagnostic accuracy and 23.09% boost in examination recommendation F1 score. In rubric-based evaluation, it surpasses the next-best model, Claude-sonnet-4, by 7.1% in weighted rubric score. These findings indicate that learning policies in interactive clinical environments confers dynamic and clinically meaningful diagnostic management abilities unattainable through passive training alone.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.25771", "title": "Gaperon: A Peppered English-French Generative Language Model Suite", "authors": [ "Nathan Godey", "Wissam Antoun", "Rian Touchent", "Rachel Bawden", "\u00c9ric de la Clergerie", "Beno\u00eet Sagot", "Djam\u00e9 Seddah" ], "github": "", "project_page": "", "abstract": "We release Gaperon, a fully open suite of French-English-coding language models designed to advance transparency and reproducibility in large-scale model training. The Gaperon family includes 1.5B, 8B, and 24B parameter models trained on 2-4 trillion tokens, released with all elements of the training pipeline: French and English datasets filtered with a neural quality classifier, an efficient data curation and training framework, and hundreds of intermediate checkpoints. Through this work, we study how data filtering and contamination interact to shape both benchmark and generative performance. We find that filtering for linguistic quality enhances text fluency and coherence but yields subpar benchmark results, and that late deliberate contamination -- continuing training on data mixes that include test sets -- recovers competitive scores while only reasonably harming generation quality. We discuss how usual neural filtering can unintentionally amplify benchmark leakage. To support further research, we also introduce harmless data poisoning during pretraining, providing a realistic testbed for safety studies. By openly releasing all models, datasets, code, and checkpoints, Gaperon establishes a reproducible foundation for exploring the trade-offs between data curation, evaluation, safety, and openness in multilingual language model development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.22304", "title": "ODesign: A World Model for Biomolecular Interaction Design", "authors": [ "Odin Zhang", "Xujun Zhang", "Haitao Lin", "Cheng Tan", "Qinghan Wang", "Yuanle Mo", "Qiantai Feng", "Gang Du", "Yuntao Yu", "Zichang Jin", "Ziyi You", "Peicong Lin", "Yijie Zhang", "Yuyang Tao", "Shicheng Chen", "Jack Xiaoyu Chen", "Chenqing Hua", "Weibo Zhao", "Runze Ma", "Yunpeng Xia", "Kejun Ying", "Jun Li", "Yundian Zeng", "Lijun Lang", "Peichen Pan", "Hanqun Cao", "Zihao Song", "Bo Qiang", "Jiaqi Wang", "Pengfei Ji", "Lei Bai", "Jian Zhang", "Chang-yu Hsieh", "Pheng Ann Heng", "Siqi Sun", "Tingjun Hou", "Shuangjia Zheng" ], "github": "", "project_page": "", "abstract": "Biomolecular interactions underpin almost all biological processes, and their rational design is central to programming new biological functions. Generative AI models have emerged as powerful tools for molecular design, yet most remain specialized for individual molecular types and lack fine-grained control over interaction details. Here we present ODesign, an all-atom generative world model for all-to-all biomolecular interaction design. ODesign allows scientists to specify epitopes on arbitrary targets and generate diverse classes of binding partners with fine-grained control. Across entity-, token-, and atom-level benchmarks in the protein modality, ODesign demonstrates superior controllability and performance to modality-specific baselines. Extending beyond proteins, it generalizes to nucleic acid and small-molecule design, enabling interaction types such as protein-binding RNA/DNA and RNA/DNA-binding ligands that were previously inaccessible. By unifying multimodal biomolecular interactions within a single generative framework, ODesign moves toward a general-purpose molecular world model capable of programmable design. ODesign is available at https://odesign.lglab.ac.cn ,", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.21890", "title": "The Principles of Diffusion Models", "authors": [ "Chieh-Hsin Lai", "Yang Song", "Dongjun Kim", "Yuki Mitsufuji", "Stefano Ermon" ], "github": "", "project_page": "", "abstract": "This monograph presents the core principles that have guided the development of diffusion models, tracing their origins and showing how diverse formulations arise from shared mathematical ideas. Diffusion modeling starts by defining a forward process that gradually corrupts data into noise, linking the data distribution to a simple prior through a continuum of intermediate distributions. The goal is to learn a reverse process that transforms noise back into data while recovering the same intermediates. We describe three complementary views. The variational view, inspired by variational autoencoders, sees diffusion as learning to remove noise step by step. The score-based view, rooted in energy-based modeling, learns the gradient of the evolving data distribution, indicating how to nudge samples toward more likely regions. The flow-based view, related to normalizing flows, treats generation as following a smooth path that moves samples from noise to data under a learned velocity field. These perspectives share a common backbone: a time-dependent velocity field whose flow transports a simple prior to the data. Sampling then amounts to solving a differential equation that evolves noise into data along a continuous trajectory. On this foundation, the monograph discusses guidance for controllable generation, efficient numerical solvers, and diffusion-motivated flow-map models that learn direct mappings between arbitrary times. It provides a conceptual and mathematically grounded understanding of diffusion models for readers with basic deep-learning knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.24801", "title": "Fortytwo: Swarm Inference with Peer-Ranked Consensus", "authors": [ "Vladyslav Larin", "Ihor Naumenko", "Aleksei Ivashov", "Ivan Nikitin", "Alexander Firsov" ], "github": "", "project_page": "", "abstract": "As centralized AI hits compute ceilings and diminishing returns from ever-larger training runs, meeting demand requires an inference layer that scales horizontally in both capacity and capability. We present Fortytwo, a novel protocol that leverages swarm intelligence principles and distributed pairwise ranking consensus to achieve superior performance in AI inference. Our approach reimagines collaboration among AI nodes using swarm inference: a peer-ranked, reputation-weighted consensus across heterogeneous models that surfaces the highest-quality responses. Using pairwise ranking with a custom Bradley-Terry-style aggregation model, we demonstrate that swarm inference substantially outperforms majority voting, achieving 85.90% on GPQA Diamond versus 68.69% for majority voting with the same model set - an improvement of +17.21 percentage points (approximately +25.1% relative). The protocol incorporates on-chain reputation so node influence adapts to demonstrated accuracy over time, yielding a meritocratic consensus that filters low-quality or malicious participants. To resist Sybil attacks, Fortytwo employs proof-of-capability in its consensus: nodes must successfully complete calibration/test requests and stake reputation to enter ranking rounds, making multi-identity attacks economically unattractive while preserving openness. Across six challenging benchmarks, including GPQA Diamond, LiveCodeBench, and AIME, our evaluation indicates higher accuracy and strong resilience to adversarial and noisy free-form prompting (e.g., prompt-injection degradation of only 0.12% versus 6.20% for a monolithic single-model baseline), while retaining practical deployability. Together, these results establish a foundation for decentralized AI systems - democratizing access to high-quality inference through collective intelligence without sacrificing reliability or security.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.18672", "title": "Reasoning Language Model Inference Serving Unveiled: An Empirical Study", "authors": [ "Qi Li", "Junpan Wu", "Xiang Liu", "Yuxin Wang", "Zeyu Li", "Zhenheng Tang", "Yuhan Chen", "Shaohuai Shi", "Xiaowen Chu" ], "github": "", "project_page": "", "abstract": "The reasoning large language model (RLLM) has been proven competitive in solving complex reasoning tasks such as mathematics, coding, compared to general LLM. However, the serving performance and behavior of RLLM remains unexplored, which may undermine the deployment and utilization of RLLM in real-world scenario. To close this gap, in this paper, we conduct a comprehensive study of RLLM service. We first perform a pilot study on comparing the serving performance between RLLM and traditional LLM and reveal that there are several distinct differences regarding serving behavior: (1) significant memory usage and fluctuations; (2) straggler requests; (3) adaptive running time; (4) domain preference. Then we further investigate whether existing inference optimization techniques are valid for RLLM. Our main takeaways are that model quantization methods and speculative decoding can improve service system efficiency with small compromise to RLLM accuracy, while prefix caching, KV cache quantization may even degrade accuracy or serving performance for small RLLM. Lastly, we conduct evaluation under real world workload modeled by Gamma distribution to verify our findings. Empirical results of real world workload evaluation across different dataset are aligned with our main findings regarding RLLM serving. We hope our work can provide the research community and industry with insights to advance RLLM inference serving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.24718", "title": "Generative View Stitching", "authors": [ "Chonghyuk Song", "Michal Stary", "Boyuan Chen", "George Kopanas", "Vincent Sitzmann" ], "github": "", "project_page": "", "abstract": "Autoregressive video diffusion models are capable of long rollouts that are stable and consistent with history, but they are unable to guide the current generation with conditioning from the future. In camera-guided video generation with a predefined camera trajectory, this limitation leads to collisions with the generated scene, after which autoregression quickly collapses. To address this, we propose Generative View Stitching (GVS), which samples the entire sequence in parallel such that the generated scene is faithful to every part of the predefined camera trajectory. Our main contribution is a sampling algorithm that extends prior work on diffusion stitching for robot planning to video generation. While such stitching methods usually require a specially trained model, GVS is compatible with any off-the-shelf video model trained with Diffusion Forcing, a prevalent sequence diffusion framework that we show already provides the affordances necessary for stitching. We then introduce Omni Guidance, a technique that enhances the temporal consistency in stitching by conditioning on both the past and future, and that enables our proposed loop-closing mechanism for delivering long-range coherence. Overall, GVS achieves camera-guided video generation that is stable, collision-free, frame-to-frame consistent, and closes loops for a variety of predefined camera paths, including Oscar Reutersv\\\"ard's Impossible Staircase. Results are best viewed as videos at https://andrewsonga.github.io/gvs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.24211", "title": "MC-SJD : Maximal Coupling Speculative Jacobi Decoding for Autoregressive Visual Generation Acceleration", "authors": [ "Junhyuk So", "Hyunho Kook", "Chaeyeon Jang", "Eunhyeok Park" ], "github": "", "project_page": "", "abstract": "While autoregressive (AR) modeling has recently emerged as a new paradigm in visual generation, its practical adoption is severely constrained by the slow inference speed of per-token generation, which often requires thousands of steps to produce a single sample. To address this challenge, we propose MC-SJD, a training-free, lossless parallel decoding framework designed to accelerate AR visual generation by extending the recently introduced Speculative Jacobi Decoding (SJD). Although SJD shows strong potential for accelerating AR generation, we demonstrate that token instability across iterations significantly reduces the acceptance rate, a limitation that primarily arises from the independent sampling process used during draft token generation. To overcome this, we introduce MC-SJD, an information-theoretic approach based on coupling, which substantially accelerates standard SJD by maximizing the probability of sampling identical draft tokens across consecutive iterations, all while preserving its lossless property. Remarkably, this method requires only a single-line modification to the existing algorithm, yet achieves substantial performance gains, delivering up to a ~4.2x acceleration in image generation and ~13.3x acceleration in video generation compared to standard AR decoding, without any degradation in output quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.24035", "title": "GraphNet: A Large-Scale Computational Graph Dataset for Tensor Compiler Research", "authors": [ "Xinqi Li", "Yiqun Liu", "Shan Jiang", "Enrong Zheng", "Huaijin Zheng", "Wenhao Dai", "Haodong Deng", "Dianhai Yu", "Yanjun Ma" ], "github": "https://github.com/PaddlePaddle/GraphNet", "project_page": "", "abstract": "We introduce GraphNet, a dataset of 2.7K real-world deep learning computational graphs with rich metadata, spanning six major task categories across multiple deep learning frameworks. To evaluate tensor compiler performance on these samples, we propose the benchmark metric Speedup Score S(t), which jointly considers runtime speedup and execution correctness under tunable tolerance levels, offering a reliable measure of general optimization capability. Furthermore, we extend S(t) to the Error-aware Speedup Score ES(t), which incorporates error information and helps compiler developers identify key performance bottlenecks. In this report, we benchmark the default tensor compilers, CINN for PaddlePaddle and TorchInductor for PyTorch, on computer vision (CV) and natural language processing (NLP) samples to demonstrate the practicality of GraphNet. The full construction pipeline with graph extraction and compiler evaluation tools is available at https://github.com/PaddlePaddle/GraphNet .", "github_manual": "", "github_regex": "https://github.com/PaddlePaddle/GraphNet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.25039", "title": "Automating Benchmark Design", "authors": [ "Amanda Dsouza", "Harit Vishwakarma", "Zhengyang Qi", "Justin Bauer", "Derek Pham", "Thomas Walshe", "Armin Parchami", "Frederic Sala", "Paroma Varma" ], "github": "", "project_page": "", "abstract": "The rapid progress and widespread deployment of LLMs and LLM-powered agents has outpaced our ability to evaluate them. Hand-crafted, static benchmarks are the primary tool for assessing model capabilities, but these quickly become saturated. In contrast, dynamic benchmarks evolve alongside the models they evaluate, but are expensive to create and continuously update. To address these challenges, we develop BeTaL (Benchmark Tuning with an LLM-in-the-loop), a framework that leverages environment design principles to automate the process of dynamic benchmark design. BeTaL works by parameterizing key design choices in base benchmark templates and uses LLMs to reason through the resulting parameter space to obtain target properties (such as difficulty and realism) in a cost-efficient manner. We validate this approach on its ability to create benchmarks with desired difficulty levels. Using BeTaL, we create two new benchmarks and extend a popular agentic benchmark tau-bench. Extensive evaluation on these three tasks and multiple target difficulty levels shows that BeTaL produces benchmarks much closer to the desired difficulty, with average deviations ranging from 5.3% to 13.2% -- a 2-4x improvement over the baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-30", "arxiv_id": "2510.26007", "title": "The Quest for Reliable Metrics of Responsible AI", "authors": [ "Theresia Veronika Rampisela", "Maria Maistro", "Tuukka Ruotsalo", "Christina Lioma" ], "github": "https://github.com/theresiavr/individual-item-fairness-measures-recsys", "project_page": "", "abstract": "The development of Artificial Intelligence (AI), including AI in Science (AIS), should be done following the principles of responsible AI. Progress in responsible AI is often quantified through evaluation metrics, yet there has been less work on assessing the robustness and reliability of the metrics themselves. We reflect on prior work that examines the robustness of fairness metrics for recommender systems as a type of AI application and summarise their key takeaways into a set of non-exhaustive guidelines for developing reliable metrics of responsible AI. Our guidelines apply to a broad spectrum of AI applications, including AIS.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/theresiavr/individual-item-fairness-measures-recsys", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26583", "title": "Emu3.5: Native Multimodal Models are World Learners", "authors": [ "Yufeng Cui", "Honghao Chen", "Haoge Deng", "Xu Huang", "Xinghang Li", "Jirong Liu", "Yang Liu", "Zhuoyan Luo", "Jinsheng Wang", "Wenxuan Wang", "Yueze Wang", "Chengyuan Wang", "Fan Zhang", "Yingli Zhao", "Ting Pan", "Xianduo Li", "Zecheng Hao", "Wenxuan Ma", "Zhuo Chen", "Yulong Ao", "Tiejun Huang", "Zhongyuan Wang", "Xinlong Wang" ], "github": "https://github.com/baaivision/Emu3.5", "project_page": "", "abstract": "We introduce Emu3.5, a large-scale multimodal world model that natively predicts the next state across vision and language. Emu3.5 is pre-trained end-to-end with a unified next-token prediction objective on a corpus of vision-language interleaved data containing over 10 trillion tokens, primarily derived from sequential frames and transcripts of internet videos. The model naturally accepts interleaved vision-language inputs and generates interleaved vision-language outputs. Emu3.5 is further post-trained with large-scale reinforcement learning to enhance multimodal reasoning and generation. To improve inference efficiency, we propose Discrete Diffusion Adaptation (DiDA), which converts token-by-token decoding into bidirectional parallel prediction, accelerating per-image inference by about 20x without sacrificing performance. Emu3.5 exhibits strong native multimodal capabilities, including long-horizon vision-language generation, any-to-image (X2I) generation, and complex text-rich image generation. It also exhibits generalizable world-modeling abilities, enabling spatiotemporally consistent world exploration and open-world embodied manipulation across diverse scenarios and tasks. For comparison, Emu3.5 achieves performance comparable to Gemini 2.5 Flash Image (Nano Banana) on image generation and editing tasks and demonstrates superior results on a suite of interleaved generation tasks. We open-source Emu3.5 at https://github.com/baaivision/Emu3.5 to support community research.", "github_manual": "", "github_regex": "https://github.com/baaivision/Emu3.5", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26802", "title": "Are Video Models Ready as Zero-Shot Reasoners? An Empirical Study with the MME-CoF Benchmark", "authors": [ "Ziyu Guo", "Xinyan Chen", "Renrui Zhang", "Ruichuan An", "Yu Qi", "Dongzhi Jiang", "Xiangtai Li", "Manyuan Zhang", "Hongsheng Li", "Pheng-Ann Heng" ], "github": "", "project_page": "", "abstract": "Recent video generation models can produce high-fidelity, temporally coherent videos, indicating that they may encode substantial world knowledge. Beyond realistic synthesis, they also exhibit emerging behaviors indicative of visual perception, modeling, and manipulation. Yet, an important question still remains: Are video models ready to serve as zero-shot reasoners in challenging visual reasoning scenarios? In this work, we conduct an empirical study to comprehensively investigate this question, focusing on the leading and popular Veo-3. We evaluate its reasoning behavior across 12 dimensions, including spatial, geometric, physical, temporal, and embodied logic, systematically characterizing both its strengths and failure modes. To standardize this study, we curate the evaluation data into MME-CoF, a compact benchmark that enables in-depth and thorough assessment of Chain-of-Frame (CoF) reasoning. Our findings reveal that while current video models demonstrate promising reasoning patterns on short-horizon spatial coherence, fine-grained grounding, and locally consistent dynamics, they remain limited in long-horizon causal reasoning, strict geometric constraints, and abstract logic. Overall, they are not yet reliable as standalone zero-shot reasoners, but exhibit encouraging signs as complementary visual engines alongside dedicated reasoning models. Project page: https://video-cof.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26800", "title": "OmniX: From Unified Panoramic Generation and Perception to Graphics-Ready 3D Scenes", "authors": [ "Yukun Huang", "Jiwen Yu", "Yanning Zhou", "Jianan Wang", "Xintao Wang", "Pengfei Wan", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "There are two prevalent ways to constructing 3D scenes: procedural generation and 2D lifting. Among them, panorama-based 2D lifting has emerged as a promising technique, leveraging powerful 2D generative priors to produce immersive, realistic, and diverse 3D environments. In this work, we advance this technique to generate graphics-ready 3D scenes suitable for physically based rendering (PBR), relighting, and simulation. Our key insight is to repurpose 2D generative models for panoramic perception of geometry, textures, and PBR materials. Unlike existing 2D lifting approaches that emphasize appearance generation and ignore the perception of intrinsic properties, we present OmniX, a versatile and unified framework. Based on a lightweight and efficient cross-modal adapter structure, OmniX reuses 2D generative priors for a broad range of panoramic vision tasks, including panoramic perception, generation, and completion. Furthermore, we construct a large-scale synthetic panorama dataset containing high-quality multimodal panoramas from diverse indoor and outdoor scenes. Extensive experiments demonstrate the effectiveness of our model in panoramic visual perception and graphics-ready 3D scene generation, opening new possibilities for immersive and physically realistic virtual world generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26794", "title": "The Quest for Generalizable Motion Generation: Data, Model, and Evaluation", "authors": [ "Jing Lin", "Ruisi Wang", "Junzhe Lu", "Ziqi Huang", "Guorui Song", "Ailing Zeng", "Xian Liu", "Chen Wei", "Wanqi Yin", "Qingping Sun", "Zhongang Cai", "Lei Yang", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Despite recent advances in 3D human motion generation (MoGen) on standard benchmarks, existing models still face a fundamental bottleneck in their generalization capability. In contrast, adjacent generative fields, most notably video generation (ViGen), have demonstrated remarkable generalization in modeling human behaviors, highlighting transferable insights that MoGen can leverage. Motivated by this observation, we present a comprehensive framework that systematically transfers knowledge from ViGen to MoGen across three key pillars: data, modeling, and evaluation. First, we introduce ViMoGen-228K, a large-scale dataset comprising 228,000 high-quality motion samples that integrates high-fidelity optical MoCap data with semantically annotated motions from web videos and synthesized samples generated by state-of-the-art ViGen models. The dataset includes both text-motion pairs and text-video-motion triplets, substantially expanding semantic diversity. Second, we propose ViMoGen, a flow-matching-based diffusion transformer that unifies priors from MoCap data and ViGen models through gated multimodal conditioning. To enhance efficiency, we further develop ViMoGen-light, a distilled variant that eliminates video generation dependencies while preserving strong generalization. Finally, we present MBench, a hierarchical benchmark designed for fine-grained evaluation across motion quality, prompt fidelity, and generalization ability. Extensive experiments show that our framework significantly outperforms existing approaches in both automatic and human evaluations. The code, data, and benchmark will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26692", "title": "Kimi Linear: An Expressive, Efficient Attention Architecture", "authors": [ "Kimi Team", "Yu Zhang", "Zongyu Lin", "Xingcheng Yao", "Jiaxi Hu", "Fanqing Meng", "Chengyin Liu", "Xin Men", "Songlin Yang", "Zhiyuan Li", "Wentao Li", "Enzhe Lu", "Weizhou Liu", "Yanru Chen", "Weixin Xu", "Longhui Yu", "Yejie Wang", "Yu Fan", "Longguang Zhong", "Enming Yuan", "Dehao Zhang", "Yizhi Zhang", "T. Y. Liu", "Haiming Wang", "Shengjun Fang", "Weiran He", "Shaowei Liu", "Yiwei Li", "Jianlin Su", "Jiezhong Qiu", "Bo Pang", "Junjie Yan", "Zhejun Jiang", "Weixiao Huang", "Bohong Yin", "Jiacheng You", "Chu Wei", "Zhengtao Wang", "Chao Hong", "Yutian Chen", "Guanduo Chen", "Yucheng Wang", "Huabin Zheng", "Feng Wang", "Yibo Liu", "Mengnan Dong", "Zheng Zhang", "Siyuan Pan", "Wenhao Wu", "Yuhao Wu", "Longyu Guan", "Jiawen Tao", "Guohong Fu", "Xinran Xu", "Yuzhi Wang", "Guokun Lai", "Yuxin Wu", "Xinyu Zhou", "Zhilin Yang", "Yulun Du" ], "github": "", "project_page": "", "abstract": "We introduce Kimi Linear, a hybrid linear attention architecture that, for the first time, outperforms full attention under fair comparisons across various scenarios -- including short-context, long-context, and reinforcement learning (RL) scaling regimes. At its core lies Kimi Delta Attention (KDA), an expressive linear attention module that extends Gated DeltaNet with a finer-grained gating mechanism, enabling more effective use of limited finite-state RNN memory. Our bespoke chunkwise algorithm achieves high hardware efficiency through a specialized variant of the Diagonal-Plus-Low-Rank (DPLR) transition matrices, which substantially reduces computation compared to the general DPLR formulation while remaining more consistent with the classical delta rule. We pretrain a Kimi Linear model with 3B activated parameters and 48B total parameters, based on a layerwise hybrid of KDA and Multi-Head Latent Attention (MLA). Our experiments show that with an identical training recipe, Kimi Linear outperforms full MLA with a sizeable margin across all evaluated tasks, while reducing KV cache usage by up to 75% and achieving up to 6 times decoding throughput for a 1M context. These results demonstrate that Kimi Linear can be a drop-in replacement for full attention architectures with superior performance and efficiency, including tasks with longer input and output lengths. To support further research, we open-source the KDA kernel and vLLM implementations, and release the pre-trained and instruction-tuned model checkpoints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.25992", "title": "Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning", "authors": [ "Yihe Deng", "I-Hung Hsu", "Jun Yan", "Zifeng Wang", "Rujun Han", "Gufeng Zhang", "Yanfei Chen", "Wei Wang", "Tomas Pfister", "Chen-Yu Lee" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) often struggle with problems that require multi-step reasoning. For small-scale open-source models, Reinforcement Learning with Verifiable Rewards (RLVR) fails when correct solutions are rarely sampled even after many attempts, while Supervised Fine-Tuning (SFT) tends to overfit long demonstrations through rigid token-by-token imitation. To address this gap, we propose Supervised Reinforcement Learning (SRL), a framework that reformulates problem solving as generating a sequence of logical \"actions\". SRL trains the model to generate an internal reasoning monologue before committing to each action. It provides smoother rewards based on the similarity between the model's actions and expert actions extracted from the SFT dataset in a step-wise manner. This supervision offers richer learning signals even when all rollouts are incorrect, while encouraging flexible reasoning guided by expert demonstrations. As a result, SRL enables small models to learn challenging problems previously unlearnable by SFT or RLVR. Moreover, initializing training with SRL before refining with RLVR yields the strongest overall performance. Beyond reasoning benchmarks, SRL generalizes effectively to agentic software engineering tasks, establishing it as a robust and versatile training framework for reasoning-oriented LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.25779", "title": "Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets", "authors": [ "Gagan Bansal", "Wenyue Hua", "Zezhou Huang", "Adam Fourney", "Amanda Swearngin", "Will Epperson", "Tyler Payne", "Jake M. Hofman", "Brendan Lucier", "Chinmay Singh", "Markus Mobius", "Akshay Nambi", "Archana Yadav", "Kevin Gao", "David M. Rothschild", "Aleksandrs Slivkins", "Daniel G. Goldstein", "Hussein Mozannar", "Nicole Immorlica", "Maya Murad", "Matthew Vogel", "Subbarao Kambhampati", "Eric Horvitz", "Saleema Amershi" ], "github": "https://github.com/microsoft/multi-agent-marketplace", "project_page": "", "abstract": "As LLM agents advance, they are increasingly mediating economic decisions, ranging from product discovery to transactions, on behalf of users. Such applications promise benefits but also raise many questions about agent accountability and value for users. Addressing these questions requires understanding how agents behave in realistic market conditions. However, previous research has largely evaluated agents in constrained settings, such as single-task marketplaces (e.g., negotiation) or structured two-agent interactions. Real-world markets are fundamentally different: they require agents to handle diverse economic activities and coordinate within large, dynamic ecosystems where multiple agents with opaque behaviors may engage in open-ended dialogues. To bridge this gap, we investigate two-sided agentic marketplaces where Assistant agents represent consumers and Service agents represent competing businesses. To study these interactions safely, we develop Magentic-Marketplace-- a simulated environment where Assistants and Services can operate. This environment enables us to study key market dynamics: the utility agents achieve, behavioral biases, vulnerability to manipulation, and how search mechanisms shape market outcomes. Our experiments show that frontier models can approach optimal welfare-- but only under ideal search conditions. Performance degrades sharply with scale, and all models exhibit severe first-proposal bias, creating 10-30x advantages for response speed over quality. These findings reveal how behaviors emerge across market conditions, informing the design of fair and efficient agentic marketplaces.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/multi-agent-marketplace", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26787", "title": "Remote Labor Index: Measuring AI Automation of Remote Work", "authors": [ "Mantas Mazeika", "Alice Gatti", "Cristina Menghini", "Udari Madhushani Sehwag", "Shivam Singhal", "Yury Orlovskiy", "Steven Basart", "Manasi Sharma", "Denis Peskoff", "Elaine Lau", "Jaehyuk Lim", "Lachlan Carroll", "Alice Blair", "Vinaya Sivakumar", "Sumana Basu", "Brad Kenstler", "Yuntao Ma", "Julian Michael", "Xiaoke Li", "Oliver Ingebretsen", "Aditya Mehta", "Jean Mottola", "John Teichmann", "Kevin Yu", "Zaina Shaik", "Adam Khoja", "Richard Ren", "Jason Hausenloy", "Long Phan", "Ye Htet", "Ankit Aich", "Tahseen Rabbani", "Vivswan Shah", "Andriy Novykov", "Felix Binder", "Kirill Chugunov", "Luis Ramirez", "Matias Geralnik", "Hern\u00e1n Mesura", "Dean Lee", "Ed-Yeremai Hernandez Cardona", "Annette Diamond", "Summer Yue", "Alexandr Wang", "Bing Liu", "Ernesto Hernandez", "Dan Hendrycks" ], "github": "", "project_page": "", "abstract": "AIs have made rapid progress on research-oriented benchmarks of knowledge and reasoning, but it remains unclear how these gains translate into economic value and automation. To measure this, we introduce the Remote Labor Index (RLI), a broadly multi-sector benchmark comprising real-world, economically valuable projects designed to evaluate end-to-end agent performance in practical settings. AI agents perform near the floor on RLI, with the highest-performing agent achieving an automation rate of 2.5%. These results help ground discussions of AI automation in empirical evidence, setting a common basis for tracking AI impacts and enabling stakeholders to proactively navigate AI-driven labor automation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26298", "title": "Can Agent Conquer Web? Exploring the Frontiers of ChatGPT Atlas Agent in Web Games", "authors": [ "Jingran Zhang", "Ning Li", "Justin Cui" ], "github": "", "project_page": "", "abstract": "OpenAI's ChatGPT Atlas introduces new capabilities for web interaction, enabling the model to analyze webpages, process user intents, and execute cursor and keyboard inputs directly within the browser. While its capacity for information retrieval tasks has been demonstrated, its performance in dynamic, interactive environments remains less explored. In this study, we conduct an early evaluation of Atlas's web interaction capabilities using browser-based games as test scenarios, including Google's T-Rex Runner, Sudoku, Flappy Bird, and Stein.world. We employ in-game performance scores as quantitative metrics to assess performance across different task types. Our results show that Atlas performs strongly in logical reasoning tasks like Sudoku, completing puzzles significantly faster than human baselines, but struggles substantially in real-time games requiring precise timing and motor control, often failing to progress beyond initial obstacles. These findings suggest that while Atlas demonstrates capable analytical processing, there remain notable limitations in dynamic web environments requiring real-time interaction. The website of our project can be found at https://atlas-game-eval.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.25628", "title": "EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis", "authors": [ "Yusheng Liao", "Chaoyi Wu", "Junwei Liu", "Shuyang Jiang", "Pengcheng Qiu", "Haowen Wang", "Yun Yue", "Shuai Zhen", "Jian Wang", "Qianrui Fan", "Jinjie Gu", "Ya Zhang", "Yanfeng Wang", "Yu Wang", "Weidi Xie" ], "github": "", "project_page": "", "abstract": "Electronic Health Records (EHRs) contain rich yet complex information, and their automated analysis is critical for clinical decision-making. Despite recent advances of large language models (LLMs) in clinical workflows, their ability to analyze EHRs remains limited due to narrow task coverage and lack of EHR-oriented reasoning capabilities. This paper aims to bridge the gap, specifically, we present EHR-Ins, a large-scale, comprehensive EHR reasoning instruction dataset, comprising 300k high-quality reasoning cases and 4M non-reasoning cases across 42 distinct EHR tasks. Its core innovation is a thinking-graph-driven framework that enables to generate high-quality reasoning data at scale. Based on it, we develop EHR-R1, a series of reasoning-enhanced LLMs with up to 72B parameters tailored for EHR analysis. Through a multi-stage training paradigm, including domain adaptation, reasoning enhancement, and reinforcement learning, EHR-R1 systematically acquires domain knowledge and diverse reasoning capabilities, enabling accurate and robust EHR analysis. Lastly, we introduce EHR-Bench, a new benchmark curated from MIMIC-IV, spanning 42 tasks, to comprehensively assess reasoning and prediction across EHR scenarios. In experiments, we show that the resulting EHR-R1 consistently outperforms state-of-the-art commercial and open-source LLMs (including DeepSeek-V3 and GPT-4o), surpassing GPT-4o by over 30 points on MIMIC-Bench and achieving a 10\\% higher zero-shot AUROC on EHRSHOT. Collectively, EHR-Ins, EHR-R1, and EHR-Bench have significantly advanced the development for more reliable and clinically relevant EHR analysis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.25132", "title": "EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation", "authors": [ "Chao Song", "Zhiyuan Liu", "Han Huang", "Liang Wang", "Qiong Wang", "Jianyu Shi", "Hui Yu", "Yihang Zhou", "Yang Zhang" ], "github": "https://github.com/Vecteur-libre/EnzyControl", "project_page": "", "abstract": "Designing enzyme backbones with substrate-specific functionality is a critical challenge in computational protein engineering. Current generative models excel in protein design but face limitations in binding data, substrate-specific control, and flexibility for de novo enzyme backbone generation. To address this, we introduce EnzyBind, a dataset with 11,100 experimentally validated enzyme-substrate pairs specifically curated from PDBbind. Building on this, we propose EnzyControl, a method that enables functional and substrate-specific control in enzyme backbone generation. Our approach generates enzyme backbones conditioned on MSA-annotated catalytic sites and their corresponding substrates, which are automatically extracted from curated enzyme-substrate data. At the core of EnzyControl is EnzyAdapter, a lightweight, modular component integrated into a pretrained motif-scaffolding model, allowing it to become substrate-aware. A two-stage training paradigm further refines the model's ability to generate accurate and functional enzyme structures. Experiments show that our EnzyControl achieves the best performance across structural and functional metrics on EnzyBind and EnzyBench benchmarks, with particularly notable improvements of 13\\% in designability and 13\\% in catalytic efficiency compared to the baseline models. The code is released at https://github.com/Vecteur-libre/EnzyControl.", "github_manual": "", "github_regex": "https://github.com/Vecteur-libre/EnzyControl", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26474", "title": "Counteracting Matthew Effect in Self-Improvement of LVLMs through Head-Tail Re-balancing", "authors": [ "Xin Guo", "Zhiheng Xi", "Yiwen Ding", "Yitao Zhai", "Xiaowei Shi", "Xunliang Cai", "Tao Gui", "Qi Zhang", "Xuanjing Huang" ], "github": "", "project_page": "", "abstract": "Self-improvement has emerged as a mainstream paradigm for advancing the reasoning capabilities of large vision-language models (LVLMs), where models explore and learn from successful trajectories iteratively. However, we identify a critical issue during this process: the model excels at generating high-quality trajectories for simple queries (i.e., head data) but struggles with more complex ones (i.e., tail data). This leads to an imbalanced optimization that drives the model to prioritize simple reasoning skills, while hindering its ability to tackle more complex reasoning tasks. Over iterations, this imbalance becomes increasingly pronounced--a dynamic we term the \"Matthew effect\"--which ultimately hinders further model improvement and leads to performance bottlenecks. To counteract this challenge, we introduce four efficient strategies from two perspectives: distribution-reshaping and trajectory-resampling, to achieve head-tail re-balancing during the exploration-and-learning self-improvement process. Extensive experiments on Qwen2-VL-7B-Instruct and InternVL2.5-4B models across visual reasoning tasks demonstrate that our methods consistently improve visual reasoning capabilities, outperforming vanilla self-improvement by 3.86 points on average.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26160", "title": "CRAG-MM: Multi-modal Multi-turn Comprehensive RAG Benchmark", "authors": [ "Jiaqi Wang", "Xiao Yang", "Kai Sun", "Parth Suresh", "Sanat Sharma", "Adam Czyzewski", "Derek Andersen", "Surya Appini", "Arkav Banerjee", "Sajal Choudhary", "Shervin Ghasemlou", "Ziqiang Guan", "Akil Iyer", "Haidar Khan", "Lingkun Kong", "Roy Luo", "Tiffany Ma", "Zhen Qiao", "David Tran", "Wenfang Xu", "Skyler Yeatman", "Chen Zhou", "Gunveer Gujral", "Yinglong Xia", "Shane Moon", "Nicolas Scheffer", "Nirav Shah", "Eun Chang", "Yue Liu", "Florian Metze", "Tammy Stark", "Zhaleh Feizollahi", "Andrea Jessee", "Mangesh Pujari", "Ahmed Aly", "Babak Damavandi", "Rakesh Wanga", "Anuj Kumar", "Rohit Patel", "Wen-tau Yih", "Xin Luna Dong" ], "github": "", "project_page": "", "abstract": "Wearable devices such as smart glasses are transforming the way people interact with their surroundings, enabling users to seek information regarding entities in their view. Multi-Modal Retrieval-Augmented Generation (MM-RAG) plays a key role in supporting such questions, yet there is still no comprehensive benchmark for this task, especially regarding wearables scenarios. To fill this gap, we present CRAG-MM -- a Comprehensive RAG benchmark for Multi-modal Multi-turn conversations. CRAG-MM contains a diverse set of 6.5K (image, question, answer) triplets and 2K visual-based multi-turn conversations across 13 domains, including 6.2K egocentric images designed to mimic captures from wearable devices. We carefully constructed the questions to reflect real-world scenarios and challenges, including five types of image-quality issues, six question types, varying entity popularity, differing information dynamism, and different conversation turns. We design three tasks: single-source augmentation, multi-source augmentation, and multi-turn conversations -- each paired with an associated retrieval corpus and APIs for both image-KG retrieval and webpage retrieval. Our evaluation shows that straightforward RAG approaches achieve only 32% and 43% truthfulness on CRAG-MM single- and multi-turn QA, respectively, whereas state-of-the-art industry solutions have similar quality (32%/45%), underscoring ample room for improvement. The benchmark has hosted KDD Cup 2025, attracting about 1K participants and 5K submissions, with winning solutions improving baseline performance by 28%, highlighting its early impact on advancing the field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26140", "title": "FullPart: Generating each 3D Part at Full Resolution", "authors": [ "Lihe Ding", "Shaocong Dong", "Yaokun Li", "Chenjian Gao", "Xiao Chen", "Rui Han", "Yihao Kuang", "Hong Zhang", "Bo Huang", "Zhanpeng Huang", "Zibin Wang", "Dan Xu", "Tianfan Xue" ], "github": "", "project_page": "", "abstract": "Part-based 3D generation holds great potential for various applications. Previous part generators that represent parts using implicit vector-set tokens often suffer from insufficient geometric details. Another line of work adopts an explicit voxel representation but shares a global voxel grid among all parts; this often causes small parts to occupy too few voxels, leading to degraded quality. In this paper, we propose FullPart, a novel framework that combines both implicit and explicit paradigms. It first derives the bounding box layout through an implicit box vector-set diffusion process, a task that implicit diffusion handles effectively since box tokens contain little geometric detail. Then, it generates detailed parts, each within its own fixed full-resolution voxel grid. Instead of sharing a global low-resolution space, each part in our method - even small ones - is generated at full resolution, enabling the synthesis of intricate details. We further introduce a center-point encoding strategy to address the misalignment issue when exchanging information between parts of different actual sizes, thereby maintaining global coherence. Moreover, to tackle the scarcity of reliable part data, we present PartVerse-XL, the largest human-annotated 3D part dataset to date with 40K objects and 320K parts. Extensive experiments demonstrate that FullPart achieves state-of-the-art results in 3D part generation. We will release all code, data, and model to benefit future research in 3D part generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26658", "title": "The Era of Agentic Organization: Learning to Organize with Language Models", "authors": [ "Zewen Chi", "Li Dong", "Qingxiu Dong", "Yaru Hao", "Xun Wu", "Shaohan Huang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "We envision a new era of AI, termed agentic organization, where agents solve complex problems by working collaboratively and concurrently, enabling outcomes beyond individual intelligence. To realize this vision, we introduce asynchronous thinking (AsyncThink) as a new paradigm of reasoning with large language models, which organizes the internal thinking process into concurrently executable structures. Specifically, we propose a thinking protocol where an organizer dynamically assigns sub-queries to workers, merges intermediate knowledge, and produces coherent solutions. More importantly, the thinking structure in this protocol can be further optimized through reinforcement learning. Experiments demonstrate that AsyncThink achieves 28% lower inference latency compared to parallel thinking while improving accuracy on mathematical reasoning. Moreover, AsyncThink generalizes its learned asynchronous thinking capabilities, effectively tackling unseen tasks without additional training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26768", "title": "AMO-Bench: Large Language Models Still Struggle in High School Math Competitions", "authors": [ "Shengnan An", "Xunliang Cai", "Xuezhi Cao", "Xiaoyu Li", "Yehao Lin", "Junlin Liu", "Xinxuan Lv", "Dan Ma", "Xuanlin Wang", "Ziwen Wang", "Shuang Zhou" ], "github": "", "project_page": "", "abstract": "We present AMO-Bench, an Advanced Mathematical reasoning benchmark with Olympiad level or even higher difficulty, comprising 50 human-crafted problems. Existing benchmarks have widely leveraged high school math competitions for evaluating mathematical reasoning capabilities of large language models (LLMs). However, many existing math competitions are becoming less effective for assessing top-tier LLMs due to performance saturation (e.g., AIME24/25). To address this, AMO-Bench introduces more rigorous challenges by ensuring all 50 problems are (1) cross-validated by experts to meet at least the International Mathematical Olympiad (IMO) difficulty standards, and (2) entirely original problems to prevent potential performance leakages from data memorization. Moreover, each problem in AMO-Bench requires only a final answer rather than a proof, enabling automatic and robust grading for evaluation. Experimental results across 26 LLMs on AMO-Bench show that even the best-performing model achieves only 52.4% accuracy on AMO-Bench, with most LLMs scoring below 40%. Beyond these poor performances, our further analysis reveals a promising scaling trend with increasing test-time compute on AMO-Bench. These results highlight the significant room for improving the mathematical reasoning in current LLMs. We release AMO-Bench to facilitate further research into advancing the reasoning abilities of language models. https://amo-bench.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26213", "title": "OmniLayout: Enabling Coarse-to-Fine Learning with LLMs for Universal Document Layout Generation", "authors": [ "Hengrui Kang", "Zhuangcheng Gu", "Zhiyuan Zhao", "Zichen Wen", "Bin Wang", "Weijia Li", "Conghui He" ], "github": "", "project_page": "", "abstract": "Document AI has advanced rapidly and is attracting increasing attention. Yet, while most efforts have focused on document layout analysis (DLA), its generative counterpart, document layout generation, remains underexplored. A major obstacle lies in the scarcity of diverse layouts: academic papers with Manhattan-style structures dominate existing studies, while open-world genres such as newspapers and magazines remain severely underrepresented. To address this gap, we curate OmniLayout-1M, the first million-scale dataset of diverse document layouts, covering six common document types and comprising contemporary layouts collected from multiple sources. Moreover, since existing methods struggle in complex domains and often fail to arrange long sequences coherently, we introduce OmniLayout-LLM, a 0.5B model with designed two-stage Coarse-to-Fine learning paradigm: 1) learning universal layout principles from OmniLayout-1M with coarse category definitions, and 2) transferring the knowledge to a specific domain with fine-grained annotations. Extensive experiments demonstrate that our approach achieves strong performance on multiple domains in M^{6}Doc dataset, substantially surpassing both existing layout generation experts and several latest general-purpose LLMs. Our code, models, and dataset will be publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.15510", "title": "Exploring Conditions for Diffusion models in Robotic Control", "authors": [ "Heeseong Shin", "Byeongho Heo", "Dongyoon Han", "Seungryong Kim", "Taekyung Kim" ], "github": "", "project_page": "", "abstract": "While pre-trained visual representations have significantly advanced imitation learning, they are often task-agnostic as they remain frozen during policy learning. In this work, we explore leveraging pre-trained text-to-image diffusion models to obtain task-adaptive visual representations for robotic control, without fine-tuning the model itself. However, we find that naively applying textual conditions - a successful strategy in other vision domains - yields minimal or even negative gains in control tasks. We attribute this to the domain gap between the diffusion model's training data and robotic control environments, leading us to argue for conditions that consider the specific, dynamic visual information required for control. To this end, we propose ORCA, which introduces learnable task prompts that adapt to the control environment and visual prompts that capture fine-grained, frame-specific details. Through facilitating task-adaptive representations with our newly devised conditions, our approach achieves state-of-the-art performance on various robotic control benchmarks, significantly surpassing prior methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26781", "title": "ChartAB: A Benchmark for Chart Grounding & Dense Alignment", "authors": [ "Aniruddh Bansal", "Davit Soselia", "Dang Nguyen", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Charts play an important role in visualization, reasoning, data analysis, and the exchange of ideas among humans. However, existing vision-language models (VLMs) still lack accurate perception of details and struggle to extract fine-grained structures from charts. Such limitations in chart grounding also hinder their ability to compare multiple charts and reason over them. In this paper, we introduce a novel \"ChartAlign Benchmark (ChartAB)\" to provide a comprehensive evaluation of VLMs in chart grounding tasks, i.e., extracting tabular data, localizing visualization elements, and recognizing various attributes from charts of diverse types and complexities. We design a JSON template to facilitate the calculation of evaluation metrics specifically tailored for each grounding task. By incorporating a novel two-stage inference workflow, the benchmark can further evaluate VLMs' capability to align and compare elements/attributes across two charts. Our analysis of evaluations on several recent VLMs reveals new insights into their perception biases, weaknesses, robustness, and hallucinations in chart understanding. These findings highlight the fine-grained discrepancies among VLMs in chart understanding tasks and point to specific skills that need to be strengthened in current models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.25897", "title": "MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency", "authors": [ "Nicolas Dufour", "Lucas Degeorge", "Arijit Ghosh", "Vicky Kalogeiton", "David Picard" ], "github": "https://github.com/nicolas-dufour/miro", "project_page": "", "abstract": "Current text-to-image generative models are trained on large uncurated datasets to enable diverse generation capabilities. However, this does not align well with user preferences. Recently, reward models have been specifically designed to perform post-hoc selection of generated images and align them to a reward, typically user preference. This discarding of informative data together with the optimizing for a single reward tend to harm diversity, semantic fidelity and efficiency. Instead of this post-processing, we propose to condition the model on multiple reward models during training to let the model learn user preferences directly. We show that this not only dramatically improves the visual quality of the generated images but it also significantly speeds up the training. Our proposed method, called MIRO, achieves state-of-the-art performances on the GenEval compositional benchmark and user-preference scores (PickAScore, ImageReward, HPSv2).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nicolas-dufour/miro", "github_api_added_by": "auto" }, { "date": "2025-10-31", "arxiv_id": "2510.26697", "title": "The End of Manual Decoding: Towards Truly End-to-End Language Models", "authors": [ "Zhichao Wang", "Dongyang Ma", "Xinting Huang", "Deng Cai", "Tian Lan", "Jiahao Xu", "Haitao Mi", "Xiaoying Tang", "Yan Wang" ], "github": "", "project_page": "", "abstract": "The \"end-to-end\" label for LLMs is a misnomer. In practice, they depend on a non-differentiable decoding process that requires laborious, hand-tuning of hyperparameters like temperature and top-p. This paper introduces AutoDeco, a novel architecture that enables truly \"end-to-end\" generation by learning to control its own decoding strategy. We augment the standard transformer with lightweight heads that, at each step, dynamically predict context-specific temperature and top-p values alongside the next-token logits. This approach transforms decoding into a parametric, token-level process, allowing the model to self-regulate its sampling strategy within a single forward pass. Through extensive experiments on eight benchmarks, we demonstrate that AutoDeco not only significantly outperforms default decoding strategies but also achieves performance comparable to an oracle-tuned baseline derived from \"hacking the test set\"-a practical upper bound for any static method. Crucially, we uncover an emergent capability for instruction-based decoding control: the model learns to interpret natural language commands (e.g., \"generate with low randomness\") and adjusts its predicted temperature and top-p on a token-by-token basis, opening a new paradigm for steerable and interactive LLM decoding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.19949", "title": "Surfer 2: The Next Generation of Cross-Platform Computer Use Agents", "authors": [ "Mathieu Andreux", "M\u00e4rt Bakler", "Yanael Barbier", "Hamza Benchekroun", "Emilien Bir\u00e9", "Antoine Bonnet", "Riaz Bordie", "Nathan Bout", "Matthias Brunel", "Aleix Cambray", "Pierre-Louis Cedoz", "Antoine Chassang", "Gautier Cloix", "Ethan Connelly", "Alexandra Constantinou", "Ramzi De Coster", "Hubert de la Jonquiere", "Aur\u00e9lien Delfosse", "Maxime Delpit", "Alexis Deprez", "Augustin Derupti", "Mathieu Diaz", "Shannon D'Souza", "Julie Dujardin", "Abai Edmund", "Michael Eickenberg", "Armand Fatalot", "Wissem Felissi", "Isaac Herring", "Xavier Koegler", "Erwan Le Jumeau de Kergaradec", "Aur\u00e9lien Lac", "Maxime Langevin", "Corentin Lauverjat", "Antonio Loison", "Avshalom Manevich", "Axel Moyal", "Axel Nguyen Kerbel", "Marinela Parovic", "Julien Revelle", "Guillaume Richard", "Mats Richter", "Ronan Riochet", "Mar\u00eda Santos", "Romain Savidan", "Laurent Sifre", "Maxime Theillard", "Marc Thibault", "Ivan Valentini", "Tony Wu", "Laura Yie", "Kai Yuan", "Jevgenij Zubovskij" ], "github": "https://github.com/k2-agent/k2-agent", "project_page": "", "abstract": "Building agents that generalize across web, desktop, and mobile environments remains an open challenge, as prior systems rely on environment-specific interfaces that limit cross-platform deployment. We introduce Surfer 2, a unified architecture operating purely from visual observations that achieves state-of-the-art performance across all three environments. Surfer 2 integrates hierarchical context management, decoupled planning and execution, and self-verification with adaptive recovery, enabling reliable operation over long task horizons. Our system achieves 97.1% accuracy on WebVoyager, 69.6% on WebArena, 60.1% on OSWorld, and 87.1% on AndroidWorld, outperforming all prior systems without task-specific fine-tuning. With multiple attempts, Surfer 2 exceeds human performance on all benchmarks. These results demonstrate that systematic orchestration amplifies foundation model capabilities and enables general-purpose computer control through visual interaction alone, while calling for a next-generation vision language model to achieve Pareto-optimal cost-efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/k2-agent/k2-agent", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.25867", "title": "MedVLSynther: Synthesizing High-Quality Visual Question Answering from Medical Documents with Generator-Verifier LMMs", "authors": [ "Xiaoke Huang", "Ningsen Wang", "Hui Liu", "Xianfeng Tang", "Yuyin Zhou" ], "github": "", "project_page": "", "abstract": "Large Multimodal Models (LMMs) are increasingly capable of answering medical questions that require joint reasoning over images and text, yet training general medical VQA systems is impeded by the lack of large, openly usable, high-quality corpora. We present MedVLSynther, a rubric-guided generator-verifier framework that synthesizes high-quality multiple-choice VQA items directly from open biomedical literature by conditioning on figures, captions, and in-text references. The generator produces self-contained stems and parallel, mutually exclusive options under a machine-checkable JSON schema; a multi-stage verifier enforces essential gates (self-containment, single correct answer, clinical validity, image-text consistency), awards fine-grained positive points, and penalizes common failure modes before acceptance. Applying this pipeline to PubMed Central yields MedSynVQA: 13,087 audited questions over 14,803 images spanning 13 imaging modalities and 28 anatomical regions. Training open-weight LMMs with reinforcement learning using verifiable rewards improves accuracy across six medical VQA benchmarks, achieving averages of 55.85 (3B) and 58.15 (7B), with up to 77.57 on VQA-RAD and 67.76 on PathVQA, outperforming strong medical LMMs. A Ablations verify that both generation and verification are necessary and that more verified data consistently helps, and a targeted contamination analysis detects no leakage from evaluation suites. By operating entirely on open literature and open-weight models, MedVLSynther offers an auditable, reproducible, and privacy-preserving path to scalable medical VQA training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.25364", "title": "CLASS-IT: Conversational and Lecture-Aligned Small-Scale Instruction Tuning for BabyLMs", "authors": [ "Luca Capone", "Alessandro Bondielli", "Alessandro Lenci" ], "github": "", "project_page": "", "abstract": "This work investigates whether small-scale LMs can benefit from instruction tuning. We compare conversational and question-answering instruction tuning datasets, applied either in a merged or sequential curriculum, using decoder-only models with 100M and 140M parameters. Evaluation spans both fine-tuning (SuperGLUE) and zero-shot (BLiMP, EWoK, WUGs, entity tracking, and psycholinguistic correlation) settings. Results show that instruction tuning yields small but consistent gains in fine-tuning scenarios, with sequential curricula outperforming merged data; however, improvements do not consistently transfer to zero-shot tasks, suggesting a trade-off between interaction-focused adaptation and broad linguistic generalization. These results highlight both the potential and the constraints of adapting human-inspired learning strategies to low-resource LMs, and point toward hybrid, curriculum-based approaches for enhancing generalization under ecological training limits.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.22282", "title": "CityRiSE: Reasoning Urban Socio-Economic Status in Vision-Language Models via Reinforcement Learning", "authors": [ "Tianhui Liu", "Hetian Pang", "Xin Zhang", "Jie Feng", "Yong Li", "Pan Hui" ], "github": "https://github.com/tsinghua-fib-lab/CityRiSE", "project_page": "", "abstract": "Harnessing publicly available, large-scale web data, such as street view and satellite imagery, urban socio-economic sensing is of paramount importance for achieving global sustainable development goals. With the emergence of Large Vision-Language Models (LVLMs), new opportunities have arisen to solve this task by treating it as a multi-modal perception and understanding problem. However, recent studies reveal that LVLMs still struggle with accurate and interpretable socio-economic predictions from visual data. To address these limitations and maximize the potential of LVLMs, we introduce CityRiSE, a novel framework for Reasoning urban Socio-Economic status in LVLMs through pure reinforcement learning (RL). With carefully curated multi-modal data and verifiable reward design, our approach guides the LVLM to focus on semantically meaningful visual cues, enabling structured and goal-oriented reasoning for generalist socio-economic status prediction. Experiments demonstrate that CityRiSE with emergent reasoning process significantly outperforms existing baselines, improving both prediction accuracy and generalization across diverse urban contexts, particularly for prediction on unseen cities and unseen indicators. This work highlights the promise of combining RL and LVLMs for interpretable and generalist urban socio-economic sensing.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tsinghua-fib-lab/CityRiSE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.26020", "title": "PORTool: Tool-Use LLM Training with Rewarded Tree", "authors": [ "Feijie Wu", "Weiwu Zhu", "Yuxiang Zhang", "Soumya Chatterjee", "Jiarong Zhu", "Fan Mo", "Rodin Luo", "Jing Gao" ], "github": "", "project_page": "", "abstract": "Current tool-use large language models (LLMs) are trained on static datasets, enabling them to interact with external tools and perform multi-step, tool-integrated reasoning, which produces tool-call trajectories. However, these models imitate how a query is resolved in a generic tool-call routine, thereby failing to explore possible solutions and demonstrating limited performance in an evolved, dynamic tool-call environment. In this work, we propose PORTool, a reinforcement learning (RL) method that encourages a tool-use LLM to explore various trajectories yielding the correct answer. Specifically, this method starts with generating multiple rollouts for a given query, and some of them share the first few tool-call steps, thereby forming a tree-like structure. Next, we assign rewards to each step, based on its ability to produce a correct answer and make successful tool calls. A shared step across different trajectories receives the same reward, while different steps under the same fork receive different rewards. Finally, these step-wise rewards are used to calculate fork-relative advantages, blended with trajectory-relative advantages, to train the LLM for tool use. The experiments utilize 17 tools to address user queries, covering both time-sensitive and time-invariant topics. We conduct ablation studies to systematically justify the necessity and the design robustness of step-wise rewards. Furthermore, we compare the proposed PORTool with other training approaches and demonstrate significant improvements in final accuracy and the number of tool-call steps.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.21970", "title": "Performance Trade-offs of Optimizing Small Language Models for E-Commerce", "authors": [ "Josip Tomo Licardo", "Nikola Tankovic" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) offer state-of-the-art performance in natural language understanding and generation tasks. However, the deployment of leading commercial models for specialized tasks, such as e-commerce, is often hindered by high computational costs, latency, and operational expenses. This paper investigates the viability of smaller, open-weight models as a resource-efficient alternative. We present a methodology for optimizing a one-billion-parameter Llama 3.2 model for multilingual e-commerce intent recognition. The model was fine-tuned using Quantized Low-Rank Adaptation (QLoRA) on a synthetically generated dataset designed to mimic real-world user queries. Subsequently, we applied post-training quantization techniques, creating GPU-optimized (GPTQ) and CPU-optimized (GGUF) versions. Our results demonstrate that the specialized 1B model achieves 99% accuracy, matching the performance of the significantly larger GPT-4.1 model. A detailed performance analysis revealed critical, hardware-dependent trade-offs: while 4-bit GPTQ reduced VRAM usage by 41%, it paradoxically slowed inference by 82% on an older GPU architecture (NVIDIA T4) due to dequantization overhead. Conversely, GGUF formats on a CPU achieved a speedup of up to 18x in inference throughput and a reduction of over 90% in RAM consumption compared to the FP16 baseline. We conclude that small, properly optimized open-weight models are not just a viable but a more suitable alternative for domain-specific applications, offering state-of-the-art accuracy at a fraction of the computational cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.20976", "title": "L^2M^3OF: A Large Language Multimodal Model for Metal-Organic Frameworks", "authors": [ "Jiyu Cui", "Fang Wu", "Haokai Zhao", "Minggao Feng", "Xenophon Evangelopoulos", "Andrew I. Cooper", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "Large language models have demonstrated remarkable reasoning capabilities across diverse natural language tasks. However, comparable breakthroughs in scientific discovery are more limited, because understanding complex physical phenomena demands multifaceted representations far beyond language alone. A compelling example is the design of functional materials such as MOFs-critical for a range of impactful applications like carbon capture and hydrogen storage. Navigating their vast and intricate design space in language-based representations interpretable by LLMs is challenging due to the numerous possible three-dimensional atomic arrangements and strict reticular rules of coordination geometry and topology. Despite promising early results in LLM-assisted discovery for simpler materials systems, MOF design remains heavily reliant on tacit human expertise rarely codified in textual information alone. To overcome this barrier, we introduce L2M3OF, the first multimodal LLM for MOFs. L2M3OF integrates crystal representation learning with language understanding to process structural, textual, and knowledge modalities jointly. L2M3OF employs a pre-trained crystal encoder with a lightweight projection layer to compress structural information into a token space, enabling efficient alignment with language instructions. To facilitate training and evaluation, we curate a structure-property-knowledge database of crystalline materials and benchmark L2M3OF against state-of-the-art closed-source LLMs such as GPT-5, Gemini-2.5-Pro and DeepSeek-R1. Experiments show that L2M3OF outperforms leading text-based closed-source LLMs in property prediction and knowledge generation tasks, despite using far fewer parameters. These results highlight the importance of multimodal approaches for porous material understanding and establish L2M3OF as a foundation for next-generation AI systems in materials discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-10-31", "arxiv_id": "2510.24992", "title": "POWSM: A Phonetic Open Whisper-Style Speech Foundation Model", "authors": [ "Chin-Jou Li", "Kalvin Chang", "Shikhar Bharadwaj", "Eunjung Yeo", "Kwanghee Choi", "Jian Zhu", "David Mortensen", "Shinji Watanabe" ], "github": "", "project_page": "", "abstract": "Recent advances in spoken language processing have led to substantial progress in phonetic tasks such as automatic speech recognition (ASR), phone recognition (PR), grapheme-to-phoneme conversion (G2P), and phoneme-to-grapheme conversion (P2G). Despite their conceptual similarity, these tasks have largely been studied in isolation, each relying on task-specific architectures and datasets. In this paper, we introduce POWSM (Phonetic Open Whisper-style Speech Model), the first unified framework capable of jointly performing multiple phone-related tasks. POWSM enables seamless conversion between audio, text (graphemes), and phones, opening up new possibilities for universal and low-resource speech processing. Our model outperforms or matches specialized PR models of similar size (Wav2Vec2Phoneme and ZIPA) while jointly supporting G2P, P2G, and ASR. Our training data, code and models are released to foster open science.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.27492", "title": "ThinkMorph: Emergent Properties in Multimodal Interleaved Chain-of-Thought Reasoning", "authors": [ "Jiawei Gu", "Yunzhuo Hao", "Huichen Will Wang", "Linjie Li", "Michael Qizhe Shieh", "Yejin Choi", "Ranjay Krishna", "Yu Cheng" ], "github": "", "project_page": "", "abstract": "Multimodal reasoning requires iterative coordination between language and vision, yet it remains unclear what constitutes a meaningful interleaved chain of thought. We posit that text and image thoughts should function as complementary, rather than isomorphic, modalities that mutually advance reasoning. Guided by this principle, we build ThinkMorph, a unified model fine-tuned on 24K high-quality interleaved reasoning traces spanning tasks with varying visual engagement. ThinkMorph learns to generate progressive text-image reasoning steps that concretely manipulate visual content while maintaining coherent verbal logic. It delivers large gains on vision-centric benchmarks (averaging 34.7% over the base model) and generalizes to out-of-domain tasks, matching or surpassing larger and proprietary VLMs. Beyond performance, ThinkMorph exhibits emergent multimodal intelligence, including unseen visual manipulation skills, adaptive switching between reasoning modes, and better test-time scaling through diversified multimodal thoughts.These findings suggest promising directions for characterizing the emergent capabilities of unified models for multimodal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.25602", "title": "INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats", "authors": [ "Mengzhao Chen", "Meng Wu", "Hui Jin", "Zhihang Yuan", "Jing Liu", "Chaoyi Zhang", "Yunshui Li", "Jie Huang", "Jin Ma", "Zeyue Xue", "Zhiheng Liu", "Xingyan Bin", "Ping Luo" ], "github": "", "project_page": "", "abstract": "Modern AI hardware, such as Nvidia's Blackwell architecture, is increasingly embracing low-precision floating-point (FP) formats to handle the pervasive activation outliers in Large Language Models (LLMs). Despite this industry trend, a unified comparison of FP and integer (INT) quantization across varying granularities has been missing, leaving algorithm and hardware co-design without clear guidance. This paper fills that gap by systematically investigating the trade-offs between FP and INT formats. We reveal a critical performance crossover: while FP excels in coarse-grained quantization, the comparison at fine-grained (block-wise) levels is more nuanced. Our comprehensive comparison demonstrates that for popular 8-bit fine-grained formats (e.g., MX with block size 32), MXINT8 is superior to its FP counterpart in both algorithmic accuracy and hardware efficiency. However, for 4-bit formats, FP (e.g., MXFP4, NVFP4) often holds an accuracy advantage , though we show that NVINT4 can surpass NVFP4 when outlier-mitigation techniques like Hadamard rotation are applied. We also introduce a symmetric clipping method that resolves gradient bias in fine-grained low-bit INT training, enabling nearly lossless performance for MXINT8 training. These findings challenge the current hardware trajectory, demonstrating that a one-size-fits-all FP approach is suboptimal and advocating that fine-grained INT formats, particularly MXINT8, offer a better balance of accuracy, power, and efficiency for future AI accelerators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.27688", "title": "Continuous Autoregressive Language Models", "authors": [ "Chenze Shao", "Darren Li", "Fandong Meng", "Jie Zhou" ], "github": "https://github.com/shaochenze/calm", "project_page": "", "abstract": "The efficiency of large language models (LLMs) is fundamentally limited by their sequential, token-by-token generation process. We argue that overcoming this bottleneck requires a new design axis for LLM scaling: increasing the semantic bandwidth of each generative step. To this end, we introduce Continuous Autoregressive Language Models (CALM), a paradigm shift from discrete next-token prediction to continuous next-vector prediction. CALM uses a high-fidelity autoencoder to compress a chunk of K tokens into a single continuous vector, from which the original tokens can be reconstructed with over 99.9\\% accuracy. This allows us to model language as a sequence of continuous vectors instead of discrete tokens, which reduces the number of generative steps by a factor of K. The paradigm shift necessitates a new modeling toolkit; therefore, we develop a comprehensive likelihood-free framework that enables robust training, evaluation, and controllable sampling in the continuous domain. Experiments show that CALM significantly improves the performance-compute trade-off, achieving the performance of strong discrete baselines at a significantly lower computational cost. More importantly, these findings establish next-vector prediction as a powerful and scalable pathway towards ultra-efficient language models. Code: https://github.com/shaochenze/calm. Project: https://shaochenze.github.io/blog/2025/CALM.", "github_manual": "", "github_regex": "https://github.com/shaochenze/calm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.25889", "title": "\u03c0_RL: Online RL Fine-tuning for Flow-based Vision-Language-Action Models", "authors": [ "Kang Chen", "Zhihao Liu", "Tonghe Zhang", "Zhen Guo", "Si Xu", "Hao Lin", "Hongzhi Zang", "Quanlu Zhang", "Zhaofei Yu", "Guoliang Fan", "Tiejun Huang", "Yu Wang", "Chao Yu" ], "github": "https://github.com/NVIDIA/Isaac-GR00T", "project_page": "", "abstract": "Vision-Language-Action (VLA) models enable robots to understand and perform complex tasks from multimodal input. Although recent work explores using reinforcement learning (RL) to automate the laborious data collection process in scaling supervised fine-tuning (SFT), applying large-scale RL to flow-based VLAs (e.g., pi_0, pi_{0.5}) remains challenging due to intractable action log-likelihoods from iterative denoising. We address this challenge with pi_{RL}, an open-source framework for training flow-based VLAs in parallel simulation. pi_{RL} implements two RL algorithms: (1) {Flow-Noise} models the denoising process as a discrete-time MDP with a learnable noise network for exact log-likelihood computation. (2) {Flow-SDE} integrates denoising with agent-environment interaction, formulating a two-layer MDP that employs ODE-to-SDE conversion for efficient RL exploration. We evaluate pi_{RL} on LIBERO and ManiSkill benchmarks. On LIBERO, pi_{RL} boosts few-shot SFT models pi_0 and pi_{0.5} from 57.6% to 97.6% and from 77.1% to 98.3%, respectively. In ManiSkill, we train pi_{RL} in 320 parallel environments, improving pi_0 from 41.6% to 85.7% and pi_{0.5} from 40.0% to 84.8% across 4352 pick-and-place tasks, demonstrating scalable multitask RL under heterogeneous simulation. Overall, pi_{RL} achieves significant performance gains and stronger generalization over SFT-models, validating the effectiveness of online RL for flow-based VLAs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA/Isaac-GR00T", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.27606", "title": "Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning", "authors": [ "Yuhong Liu", "Beichen Zhang", "Yuhang Zang", "Yuhang Cao", "Long Xing", "Xiaoyi Dong", "Haodong Duan", "Dahua Lin", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Spatial understanding remains a weakness of Large Vision-Language Models (LVLMs). Existing supervised fine-tuning (SFT) and recent reinforcement learning with verifiable rewards (RLVR) pipelines depend on costly supervision, specialized tools, or constrained environments that limit scale. We introduce Spatial-SSRL, a self-supervised RL paradigm that derives verifiable signals directly from ordinary RGB or RGB-D images. Spatial-SSRL automatically formulates five pretext tasks that capture 2D and 3D spatial structure: shuffled patch reordering, flipped patch recognition, cropped patch inpainting, regional depth ordering, and relative 3D position prediction. These tasks provide ground-truth answers that are easy to verify and require no human or LVLM annotation. Training on our tasks substantially improves spatial reasoning while preserving general visual capabilities. On seven spatial understanding benchmarks in both image and video settings, Spatial-SSRL delivers average accuracy gains of 4.63% (3B) and 3.89% (7B) over the Qwen2.5-VL baselines. Our results show that simple, intrinsic supervision enables RLVR at scale and provides a practical route to stronger spatial intelligence in LVLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.27684", "title": "Phased DMD: Few-step Distribution Matching Distillation via Score Matching within Subintervals", "authors": [ "Xiangyu Fan", "Zesong Qiu", "Zhuguanyu Wu", "Fanzhou Wang", "Zhiqian Lin", "Tianxiang Ren", "Dahua Lin", "Ruihao Gong", "Lei Yang" ], "github": "", "project_page": "", "abstract": "Distribution Matching Distillation (DMD) distills score-based generative models into efficient one-step generators, without requiring a one-to-one correspondence with the sampling trajectories of their teachers. However, limited model capacity causes one-step distilled models underperform on complex generative tasks, e.g., synthesizing intricate object motions in text-to-video generation. Directly extending DMD to multi-step distillation increases memory usage and computational depth, leading to instability and reduced efficiency. While prior works propose stochastic gradient truncation as a potential solution, we observe that it substantially reduces the generation diversity of multi-step distilled models, bringing it down to the level of their one-step counterparts. To address these limitations, we propose Phased DMD, a multi-step distillation framework that bridges the idea of phase-wise distillation with Mixture-of-Experts (MoE), reducing learning difficulty while enhancing model capacity. Phased DMD is built upon two key ideas: progressive distribution matching and score matching within subintervals. First, our model divides the SNR range into subintervals, progressively refining the model to higher SNR levels, to better capture complex distributions. Next, to ensure the training objective within each subinterval is accurate, we have conducted rigorous mathematical derivations. We validate Phased DMD by distilling state-of-the-art image and video generation models, including Qwen-Image (20B parameters) and Wan2.2 (28B parameters). Experimental results demonstrate that Phased DMD preserves output diversity better than DMD while retaining key generative capabilities. We will release our code and models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.26788", "title": "Defeating the Training-Inference Mismatch via FP16", "authors": [ "Penghui Qi", "Zichen Liu", "Xiangxin Zhou", "Tianyu Pang", "Chao Du", "Wee Sun Lee", "Min Lin" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) fine-tuning of large language models (LLMs) often suffers from instability due to the numerical mismatch between the training and inference policies. While prior work has attempted to mitigate this issue through algorithmic corrections or engineering alignments, we show that its root cause lies in the floating point precision itself. The widely adopted BF16, despite its large dynamic range, introduces large rounding errors that breaks the consistency between training and inference. In this work, we demonstrate that simply reverting to FP16 effectively eliminates this mismatch. The change is simple, fully supported by modern frameworks with only a few lines of code change, and requires no modification to the model architecture or learning algorithm. Our results suggest that using FP16 uniformly yields more stable optimization, faster convergence, and stronger performance across diverse tasks, algorithms and frameworks. We hope these findings motivate a broader reconsideration of precision trade-offs in RL fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.24411", "title": "OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows", "authors": [ "Qiushi Sun", "Mukai Li", "Zhoumianze Liu", "Zhihui Xie", "Fangzhi Xu", "Zhangyue Yin", "Kanzhi Cheng", "Zehao Li", "Zichen Ding", "Qi Liu", "Zhiyong Wu", "Zhuosheng Zhang", "Ben Kao", "Lingpeng Kong" ], "github": "https://github.com/OpenGVLab/ScaleCUA", "project_page": "", "abstract": "Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms. While these agents hold great promise for advancing digital automation, their potential for unsafe operations, such as system compromise and privacy leakage, is raising significant concerns. Detecting these safety concerns across the vast and complex operational space of mobile environments presents a formidable challenge that remains critically underexplored. To establish a foundation for mobile agent safety research, we introduce MobileRisk-Live, a dynamic sandbox environment accompanied by a safety detection benchmark comprising realistic trajectories with fine-grained annotations. Built upon this, we propose OS-Sentinel, a novel hybrid safety detection framework that synergistically combines a Formal Verifier for detecting explicit system-level violations with a VLM-based Contextual Judge for assessing contextual risks and agent actions. Experiments show that OS-Sentinel achieves 10%-30% improvements over existing approaches across multiple metrics. Further analysis provides critical insights that foster the development of safer and more reliable autonomous mobile agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenGVLab/ScaleCUA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.27266", "title": "HyperClick: Advancing Reliable GUI Grounding via Uncertainty Calibration", "authors": [ "Shaojie Zhang", "Pei Fu", "Ruoceng Zhang", "Jiahui Yang", "Anan Du", "Xiuwen Xi", "Shaokang Wang", "Ying Huang", "Bin Qin", "Zhenbo Luo", "Jian Luan" ], "github": "", "project_page": "", "abstract": "Autonomous Graphical User Interface (GUI) agents rely on accurate GUI grounding, which maps language instructions to on-screen coordinates, to execute user commands. However, current models, whether trained via supervised fine-tuning (SFT) or reinforcement fine-tuning (RFT), lack self-awareness of their capability boundaries, leading to overconfidence and unreliable predictions. We first systematically evaluate probabilistic and verbalized confidence in general and GUI-specific models, revealing a misalignment between confidence and actual accuracy, which is particularly critical in dynamic GUI automation tasks, where single errors can cause task failure. To address this, we propose HyperClick, a novel framework that enhances reliable GUI grounding through uncertainty calibration. HyperClick introduces a dual reward mechanism, combining a binary reward for correct actions with a truncated Gaussian-based spatial confidence modeling, calibrated using the Brier score. This approach jointly optimizes grounding accuracy and confidence reliability, fostering introspective self-criticism. Extensive experiments on seven challenge benchmarks show that HyperClick achieves state-of-the-art performance while providing well-calibrated confidence. By enabling explicit confidence calibration and introspective self-criticism, HyperClick reduces overconfidence and supports more reliable GUI automation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.27258", "title": "Higher-order Linear Attention", "authors": [ "Yifan Zhang", "Zhen Qin", "Quanquan Gu" ], "github": "https://github.com/yifanzhang-pro/HLA", "project_page": "", "abstract": "The quadratic cost of scaled dot-product attention is a central obstacle to scaling autoregressive language models to long contexts. Linear-time attention and State Space Models (SSMs) provide scalable alternatives but are typically restricted to first-order or kernel-based approximations, which can limit expressivity. We introduce Higher-order Linear Attention (HLA), a causal, streaming mechanism that realizes higher interactions via compact prefix sufficient statistics. In the second-order case, HLA maintains a constant-size state and computes per-token outputs in linear time without materializing any n times n matrices. We give closed-form streaming identities, a strictly causal masked variant using two additional summaries, and a chunk-parallel training scheme based on associative scans that reproduces the activations of a serial recurrence exactly. We further outline extensions to third and higher orders. Collectively, these results position HLA as a principled, scalable building block that combines attention-like, data-dependent mixing with the efficiency of modern recurrent architectures. Project Page: https://github.com/yifanzhang-pro/HLA.", "github_manual": "", "github_regex": "https://github.com/yifanzhang-pro/HLA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.26887", "title": "The Denario project: Deep knowledge AI agents for scientific discovery", "authors": [ "Francisco Villaescusa-Navarro", "Boris Bolliet", "Pablo Villanueva-Domingo", "Adrian E. Bayer", "Aidan Acquah", "Chetana Amancharla", "Almog Barzilay-Siegal", "Pablo Bermejo", "Camille Bilodeau", "Pablo C\u00e1rdenas Ram\u00edrez", "Miles Cranmer", "Urbano L. Fran\u00e7a", "ChangHoon Hahn", "Yan-Fei Jiang", "Raul Jimenez", "Jun-Young Lee", "Antonio Lerario", "Osman Mamun", "Thomas Meier", "Anupam A. Ojha", "Pavlos Protopapas", "Shimanto Roy", "David N. Spergel", "Pedro Taranc\u00f3n-\u00c1lvarez", "Ujjwal Tiwari", "Matteo Viel", "Digvijay Wadekar", "Chi Wang", "Bonny Y. Wang", "Licong Xu", "Yossi Yovel", "Shuwen Yue", "Wen-Han Zhou", "Qiyao Zhu", "Jiajun Zou", "\u00cd\u00f1igo Zubeldia" ], "github": "https://github.com/AstroPilot-AI/Denario", "project_page": "", "abstract": "We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent as a deep-research backend. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines, and we illustrate this by showing a paper that applies methods from quantum physics and machine learning to astrophysical data. We report the evaluations performed on these papers by domain experts, who provided both numerical scores and review-like feedback. We then highlight the strengths, weaknesses, and limitations of the current system. Finally, we discuss the ethical implications of AI-driven research and reflect on how such technology relates to the philosophy of science. We publicly release the code at https://github.com/AstroPilot-AI/Denario. A Denario demo can also be run directly on the web at https://huggingface.co/spaces/astropilot-ai/Denario, and the full app will be deployed on the cloud.", "github_manual": "", "github_regex": "https://github.com/AstroPilot-AI/Denario", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.23095", "title": "Revisiting Multimodal Positional Encoding in Vision-Language Models", "authors": [ "Jie Huang", "Xuejing Liu", "Sibo Song", "Ruibing Hou", "Hong Chang", "Junyang Lin", "Shuai Bai" ], "github": "https://github.com/JJJYmmm/Multimodal-RoPEs", "project_page": "", "abstract": "Multimodal position encoding is essential for vision-language models, yet there has been little systematic investigation into multimodal position encoding. We conduct a comprehensive analysis of multimodal Rotary Positional Embedding (RoPE) by examining its two core components: position design and frequency allocation. Through extensive experiments, we identify three key guidelines: positional coherence, full frequency utilization, and preservation of textual priors-ensuring unambiguous layout, rich representation, and faithful transfer from the pre-trained LLM. Based on these insights, we propose Multi-Head RoPE (MHRoPE) and MRoPE-Interleave (MRoPE-I), two simple and plug-and-play variants that require no architectural changes. Our methods consistently outperform existing approaches across diverse benchmarks, with significant improvements in both general and fine-grained multimodal understanding. Code will be avaliable at https://github.com/JJJYmmm/Multimodal-RoPEs.", "github_manual": "", "github_regex": "https://github.com/JJJYmmm/Multimodal-RoPEs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.27623", "title": "Visual Backdoor Attacks on MLLM Embodied Decision Making via Contrastive Trigger Learning", "authors": [ "Qiusi Zhan", "Hyeonjeong Ha", "Rui Yang", "Sirui Xu", "Hanyang Chen", "Liang-Yan Gui", "Yu-Xiong Wang", "Huan Zhang", "Heng Ji", "Daniel Kang" ], "github": "https://github.com/uiuc-kang-lab/BEAT", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have advanced embodied agents by enabling direct perception, reasoning, and planning task-oriented actions from visual inputs. However, such vision driven embodied agents open a new attack surface: visual backdoor attacks, where the agent behaves normally until a visual trigger appears in the scene, then persistently executes an attacker-specified multi-step policy. We introduce BEAT, the first framework to inject such visual backdoors into MLLM-based embodied agents using objects in the environments as triggers. Unlike textual triggers, object triggers exhibit wide variation across viewpoints and lighting, making them difficult to implant reliably. BEAT addresses this challenge by (1) constructing a training set that spans diverse scenes, tasks, and trigger placements to expose agents to trigger variability, and (2) introducing a two-stage training scheme that first applies supervised fine-tuning (SFT) and then our novel Contrastive Trigger Learning (CTL). CTL formulates trigger discrimination as preference learning between trigger-present and trigger-free inputs, explicitly sharpening the decision boundaries to ensure precise backdoor activation. Across various embodied agent benchmarks and MLLMs, BEAT achieves attack success rates up to 80%, while maintaining strong benign task performance, and generalizes reliably to out-of-distribution trigger placements. Notably, compared to naive SFT, CTL boosts backdoor activation accuracy up to 39% under limited backdoor data. These findings expose a critical yet unexplored security risk in MLLM-based embodied agents, underscoring the need for robust defenses before real-world deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/uiuc-kang-lab/BEAT", "github_api_added_by": "auto" }, { "date": "2025-11-03", "arxiv_id": "2510.27607", "title": "Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model", "authors": [ "John Won", "Kyungmin Lee", "Huiwon Jang", "Dongyoung Kim", "Jinwoo Shin" ], "github": "", "project_page": "", "abstract": "Recently, augmenting Vision-Language-Action models (VLAs) with world modeling has shown promise in improving robotic policy learning. However, it remains challenging to jointly predict next-state observations and action sequences because of the inherent difference between the two modalities. To address this, we propose DUal-STream diffusion (DUST), a world-model augmented VLA framework that handles the modality conflict and enhances the performance of VLAs across diverse tasks. Specifically, we propose a multimodal diffusion transformer architecture that explicitly maintains separate modality streams while still enabling cross-modal knowledge sharing. In addition, we introduce independent noise perturbations for each modality and a decoupled flow-matching loss. This design enables the model to learn the joint distribution in a bidirectional manner while avoiding the need for a unified latent space. Based on the decoupling of modalities during training, we also introduce a joint sampling method that supports test-time scaling, where action and vision tokens evolve asynchronously at different rates. Through experiments on simulated benchmarks such as RoboCasa and GR-1, DUST achieves up to 6% gains over baseline methods, while our test-time scaling approach provides an additional 2-5% boost. On real-world tasks with the Franka Research 3, DUST improves success rates by 13%, confirming its effectiveness beyond simulation. Furthermore, pre-training on action-free videos from BridgeV2 yields significant transfer gains on RoboCasa, underscoring DUST's potential for large-scale VLA pretraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.27224", "title": "Mask-to-Height: A YOLOv11-Based Architecture for Joint Building Instance Segmentation and Height Classification from Satellite Imagery", "authors": [ "Mahmoud El Hussieni", "Bahad\u0131r K. G\u00fcnt\u00fcrk", "Hasan F. Ate\u015f", "O\u011fuz Hano\u011flu" ], "github": "", "project_page": "", "abstract": "Accurate building instance segmentation and height classification are critical for urban planning, 3D city modeling, and infrastructure monitoring. This paper presents a detailed analysis of YOLOv11, the recent advancement in the YOLO series of deep learning models, focusing on its application to joint building extraction and discrete height classification from satellite imagery. YOLOv11 builds on the strengths of earlier YOLO models by introducing a more efficient architecture that better combines features at different scales, improves object localization accuracy, and enhances performance in complex urban scenes. Using the DFC2023 Track 2 dataset -- which includes over 125,000 annotated buildings across 12 cities -- we evaluate YOLOv11's performance using metrics such as precision, recall, F1 score, and mean average precision (mAP). Our findings demonstrate that YOLOv11 achieves strong instance segmentation performance with 60.4\\% mAP@50 and 38.3\\% mAP@50--95 while maintaining robust classification accuracy across five predefined height tiers. The model excels in handling occlusions, complex building shapes, and class imbalance, particularly for rare high-rise structures. Comparative analysis confirms that YOLOv11 outperforms earlier multitask frameworks in both detection accuracy and inference speed, making it well-suited for real-time, large-scale urban mapping. This research highlights YOLOv11's potential to advance semantic urban reconstruction through streamlined categorical height modeling, offering actionable insights for future developments in remote sensing and geospatial intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.27044", "title": "Limits of Generalization in RLVR: Two Case Studies in Mathematical Reasoning", "authors": [ "Md Tanvirul Alam", "Nidhi Rastogi" ], "github": "https://github.com/xashru/rlvr-seq-generalization", "project_page": "", "abstract": "Mathematical reasoning is a central challenge for large language models (LLMs), requiring not only correct answers but also faithful reasoning processes. Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising approach for enhancing such capabilities; however, its ability to foster genuine reasoning remains unclear. We investigate RLVR on two combinatorial problems with fully verifiable solutions: Activity Scheduling and the Longest Increasing Subsequence, using carefully curated datasets with unique optima. Across multiple reward designs, we find that RLVR improves evaluation metrics but often by reinforcing superficial heuristics rather than acquiring new reasoning strategies. These findings highlight the limits of RLVR generalization, emphasizing the importance of benchmarks that disentangle genuine mathematical reasoning from shortcut exploitation and provide faithful measures of progress. Code available at https://github.com/xashru/rlvr-seq-generalization.", "github_manual": "", "github_regex": "https://github.com/xashru/rlvr-seq-generalization", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.24795", "title": "A Survey on Efficient Vision-Language-Action Models", "authors": [ "Zhaoshu Yu", "Bo Wang", "Pengpeng Zeng", "Haonan Zhang", "Ji Zhang", "Lianli Gao", "Jingkuan Song", "Nicu Sebe", "Heng Tao Shen" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action models (VLAs) represent a significant frontier in embodied intelligence, aiming to bridge digital knowledge with physical-world interaction. While these models have demonstrated remarkable generalist capabilities, their deployment is severely hampered by the substantial computational and data requirements inherent to their underlying large-scale foundation models. Motivated by the urgent need to address these challenges, this survey presents the first comprehensive review of Efficient Vision-Language-Action models (Efficient VLAs) across the entire data-model-training process. Specifically, we introduce a unified taxonomy to systematically organize the disparate efforts in this domain, categorizing current techniques into three core pillars: (1) Efficient Model Design, focusing on efficient architectures and model compression; (2) Efficient Training, which reduces computational burdens during model learning; and (3) Efficient Data Collection, which addresses the bottlenecks in acquiring and utilizing robotic data. Through a critical review of state-of-the-art methods within this framework, this survey not only establishes a foundational reference for the community but also summarizes representative applications, delineates key challenges, and charts a roadmap for future research. We maintain a continuously updated project page to track our latest developments: https://evla-survey.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.26707", "title": "Value Drifts: Tracing Value Alignment During LLM Post-Training", "authors": [ "Mehar Bhatia", "Shravan Nayak", "Gaurav Kamath", "Marius Mosbach", "Karolina Sta\u0144czak", "Vered Shwartz", "Siva Reddy" ], "github": "", "project_page": "", "abstract": "As LLMs occupy an increasingly important role in society, they are more and more confronted with questions that require them not only to draw on their general knowledge but also to align with certain human value systems. Therefore, studying the alignment of LLMs with human values has become a crucial field of inquiry. Prior work, however, mostly focuses on evaluating the alignment of fully trained models, overlooking the training dynamics by which models learn to express human values. In this work, we investigate how and at which stage value alignment arises during the course of a model's post-training. Our analysis disentangles the effects of post-training algorithms and datasets, measuring both the magnitude and time of value drifts during training. Experimenting with Llama-3 and Qwen-3 models of different sizes and popular supervised fine-tuning (SFT) and preference optimization datasets and algorithms, we find that the SFT phase generally establishes a model's values, and subsequent preference optimization rarely re-aligns these values. Furthermore, using a synthetic preference dataset that enables controlled manipulation of values, we find that different preference optimization algorithms lead to different value alignment outcomes, even when preference data is held constant. Our findings provide actionable insights into how values are learned during post-training and help to inform data curation, as well as the selection of models and algorithms for preference optimization to improve model alignment to human values.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.24940", "title": "SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens", "authors": [ "Yinhan He", "Wendy Zheng", "Yaochen Zhu", "Zaiyi Zheng", "Lin Su", "Sriram Vasudevan", "Qi Guo", "Liangjie Hong", "Jundong Li" ], "github": "https://github.com/YinhanHe123/SemCoT", "project_page": "", "abstract": "The verbosity of Chain-of-Thought (CoT) reasoning hinders its mass deployment in efficiency-critical applications. Recently, implicit CoT approaches have emerged, which encode reasoning steps within LLM's hidden embeddings (termed ``implicit reasoning'') rather than explicit tokens. This approach accelerates CoT by reducing the reasoning length and bypassing some LLM components. However, existing implicit CoT methods face two significant challenges: (1) they fail to preserve the semantic alignment between the implicit reasoning (when transformed to natural language) and the ground-truth reasoning, resulting in a significant CoT performance degradation, and (2) they focus on reducing the length of the implicit reasoning; however, they neglect the considerable time cost for an LLM to generate one individual implicit reasoning token. To tackle these challenges, we propose a novel semantically-aligned implicit CoT framework termed SemCoT. In particular, for the first challenge, we design a contrastively trained sentence transformer that evaluates semantic alignment between implicit and explicit reasoning, which is used to enforce semantic preservation during implicit reasoning optimization. To address the second challenge, we introduce an efficient implicit reasoning generator by finetuning a lightweight language model using knowledge distillation. This generator is guided by our sentence transformer to distill ground-truth reasoning into semantically aligned implicit reasoning, while also optimizing for accuracy. SemCoT is the first approach that enhances CoT efficiency by jointly optimizing token-level generation speed and preserving semantic alignment with ground-truth reasoning. Extensive experiments demonstrate the superior performance of SemCoT compared to state-of-the-art methods in both efficiency and effectiveness. Our code can be found at https://github.com/YinhanHe123/SemCoT/.", "github_manual": "", "github_regex": "https://github.com/YinhanHe123/SemCoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.20150", "title": "Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning", "authors": [ "Yaochen Zhu", "Harald Steck", "Dawen Liang", "Yinhan He", "Jundong Li", "Nathan Kallus" ], "github": "https://github.com/yaochenzhu/Rank-GRPO", "project_page": "", "abstract": "Large language models (LLMs) are reshaping the recommender system paradigm by enabling users to express preferences and receive recommendations through conversations. Yet, aligning LLMs to the recommendation task remains challenging: pretrained LLMs often generate out-of-catalog items, violate required output formats, and their ranking quality degrades sharply toward the end of the generated list. To this end, we propose ConvRec-R1, a two-stage framework for end-to-end training of LLM-based conversational recommender systems. In Stage 1, we construct a behavioral-cloning dataset with a Remap-Reflect-Adjust pipeline, which produces high-quality, catalog-grounded demonstrations from powerful blackbox LLMs to warm-start the RL training. In Stage 2, we propose Rank-GRPO, a principled extension of group relative policy optimization (GRPO) tailored to tasks with rank-style outputs. Rank-GRPO treats each rank in the recommendation list as the unit instead of token (too fine-grained) or sequence (too coarse), redefining rewards to remove non-causal credit assignment and introducing a rank-level importance ratio based on the geometric mean of rank-wise token probabilities to stabilize policy updates. Experiments on the public Reddit-v2 dataset show that ConvRec-R1 converges faster and achieves higher Recall and NDCG than GRPO-style baselines. Code and datasets are released at https://github.com/yaochenzhu/Rank-GRPO.", "github_manual": "", "github_regex": "https://github.com/yaochenzhu/Rank-GRPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.26345", "title": "MisSynth: Improving MISSCI Logical Fallacies Classification with Synthetic Data", "authors": [ "Mykhailo Poliakov", "Nadiya Shvai" ], "github": "https://github.com/mxpoliakov/MisSynth", "project_page": "", "abstract": "Health-related misinformation is very prevalent and potentially harmful. It is difficult to identify, especially when claims distort or misinterpret scientific findings. We investigate the impact of synthetic data generation and lightweight fine-tuning techniques on the ability of large language models (LLMs) to recognize fallacious arguments using the MISSCI dataset and framework. In this work, we propose MisSynth, a pipeline that applies retrieval-augmented generation (RAG) to produce synthetic fallacy samples, which are then used to fine-tune an LLM model. Our results show substantial accuracy gains with fine-tuned models compared to vanilla baselines. For instance, the LLaMA 3.1 8B fine-tuned model achieved an over 35% F1-score absolute improvement on the MISSCI test split over its vanilla baseline. We demonstrate that introducing synthetic fallacy data to augment limited annotated resources can significantly enhance zero-shot LLM classification performance on real-world scientific misinformation tasks, even with limited computational resources. The code and synthetic dataset are available on https://github.com/mxpoliakov/MisSynth.", "github_manual": "", "github_regex": "https://github.com/mxpoliakov/MisSynth", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.25080", "title": "Monopoly Deal: A Benchmark Environment for Bounded One-Sided Response Games", "authors": [ "Will Wolf" ], "github": "", "project_page": "", "abstract": "Card games are widely used to study sequential decision-making under uncertainty, with real-world analogues in negotiation, finance, and cybersecurity. These games typically fall into three categories based on the flow of control: strictly sequential (players alternate single actions), deterministic response (some actions trigger a fixed outcome), and unbounded reciprocal response (alternating counterplays are permitted). A less-explored but strategically rich structure is the bounded one-sided response, where a player's action briefly transfers control to the opponent, who must satisfy a fixed condition through one or more moves before the turn resolves. We term games featuring this mechanism Bounded One-Sided Response Games (BORGs). We introduce a modified version of Monopoly Deal as a benchmark environment that isolates this dynamic, where a Rent action forces the opponent to choose payment assets. The gold-standard algorithm, Counterfactual Regret Minimization (CFR), converges on effective strategies without novel algorithmic extensions. A lightweight full-stack research platform unifies the environment, a parallelized CFR runtime, and a human-playable web interface. The trained CFR agent and source code are available at https://monopolydeal.ai.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-03", "arxiv_id": "2510.24078", "title": "Beyond Objects: Contextual Synthetic Data Generation for Fine-Grained Classification", "authors": [ "William Yang", "Xindi Wu", "Zhiwei Deng", "Esin Tureci", "Olga Russakovsky" ], "github": "https://github.com/princetonvisualai/BeyondObjects", "project_page": "", "abstract": "Text-to-image (T2I) models are increasingly used for synthetic dataset generation, but generating effective synthetic training data for classification remains challenging. Fine-tuning a T2I model with a few real examples can help improve the quality of synthetic training data; however, it may also cause overfitting and reduce diversity in the generated samples. We propose a fine-tuning strategy BOB (BeyondOBjects) to mitigate these concerns for fine-grained classification. Given a small set of real examples, we first extract class-agnostic attributes such as scene background and object pose. We then explicitly condition on these attributes during fine-tuning of the T2I model and marginalize them out during generation. This design mitigates overfitting, preserves the T2I model's generative prior, reduces estimation errors, and further minimizes unintended inter-class associations. Extensive experiments across multiple T2I models, backbones, and datasets show that our method achieves state-of-the-art performance in low-shot fine-grained classification when augmented with synthetic data. Concretely, BOB outperforms DataDream by 7.4% on the Aircraft dataset (from 50.0% to 57.4% when fine-tuning a CLIP classifier with five real images augmented with 100 synthetic images). In three of the four benchmarks, fine-tuning downstream models with 5 real images augmented with BOB achieves better performance than fine-tuning with 10 real images. Collectively, BOB outperforms prior art in 18 of 24 experimental settings, with 2+% accuracy improvements in 14 of these settings.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/princetonvisualai/BeyondObjects", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2510.24788", "title": "The Underappreciated Power of Vision Models for Graph Structural Understanding", "authors": [ "Xinjian Zhao", "Wei Pang", "Zhongkai Xue", "Xiangru Jian", "Lei Zhang", "Yaoyao Xu", "Xiaozhuang Song", "Shu Wu", "Tianshu Yu" ], "github": "https://github.com/LOGO-CUHKSZ/GraphAbstract", "project_page": "", "abstract": "Graph Neural Networks operate through bottom-up message-passing, fundamentally differing from human visual perception, which intuitively captures global structures first. We investigate the underappreciated potential of vision models for graph understanding, finding they achieve performance comparable to GNNs on established benchmarks while exhibiting distinctly different learning patterns. These divergent behaviors, combined with limitations of existing benchmarks that conflate domain features with topological understanding, motivate our introduction of GraphAbstract. This benchmark evaluates models' ability to perceive global graph properties as humans do: recognizing organizational archetypes, detecting symmetry, sensing connectivity strength, and identifying critical elements. Our results reveal that vision models significantly outperform GNNs on tasks requiring holistic structural understanding and maintain generalizability across varying graph scales, while GNNs struggle with global pattern abstraction and degrade with increasing graph size. This work demonstrates that vision models possess remarkable yet underutilized capabilities for graph structural understanding, particularly for problems requiring global topological awareness and scale-invariant reasoning. These findings open new avenues to leverage this underappreciated potential for developing more effective graph foundation models for tasks dominated by holistic pattern recognition.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LOGO-CUHKSZ/GraphAbstract", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2510.22115", "title": "Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation", "authors": [ "Ling-Team", "Ang Li", "Ben Liu", "Binbin Hu", "Bing Li", "Bingwei Zeng", "Borui Ye", "Caizhi Tang", "Changxin Tian", "Chao Huang", "Chao Zhang", "Chen Qian", "Chenchen Ju", "Chenchen Li", "Chengfu Tang", "Chili Fu", "Chunshao Ren", "Chunwei Wu", "Cong Zhang", "Cunyin Peng", "Dafeng Xu", "Daixin Wang", "Dalong Zhang", "Dingnan Jin", "Dingyuan Zhu", "Dongke Hu", "Fangzheng Zhao", "Feifan Wu", "Feng Zhu", "Gangshan Wang", "Haitao Zhang", "Hailin Zhao", "Hanxiao Zhang", "Hanzi Wang", "Hao Qian", "Haoyi Yu", "Heng Zhang", "Hongliang Zhang", "Hongzhi Luan", "Huirong Dong", "Huizhong Li", "Jia Li", "Jia Liu", "Jialong Zhu", "Jian Sha", "Jianping Wei", "Jiaolong Yang", "Jieyue Ma", "Jiewei Wu", "Jinjing Huang", "Jingyun Tian", "Jingyuan Zhang", "Jinquan Sun", "Juanhui Tu", "Jun Liu", "Jun Xu", "Jun Zhou", "Junjie Ou", "Junpeng Fang", "Kaihong Zhang", "Kaiqin Hu", "Ke Shi", "Kun Tang", "Kunlong Chen", "Lanyin Mei", "Lei Liang", "Lei Xu", "Libo Zhang", "Lin Ju", "Lin Yuan", "Ling Zhong", "Lintao Ma", "Lu Liu", "Lu Yu", "Lun Cai", "Meiqi Zhu", "Mengying Li", "Min Chen", "Minghao Xue", "Minghong Cai", "Mingming Yin", "Peijie Jiang", "Peilong Zhao", "Pingping Liu", "Qian Zhao", "Qing Cui", "Qingxiang Huang", "Qingyuan Yang", "Quankun Yu", "Shaowei Wei", "Shijie Lian", "Shoujian Zheng", "Shun Song", "Shungen Zhang", "Shuo Zhang", "Siyuan Li", "Song Liu", "Ting Guo", "Tong Zhao", "Wanli Gu", "Weichang Wu", "Weiguang Han", "Wenjing Fang", "Wubin Wang", "Xiang Shu", "Xiao Shi", "Xiaoshun Lan", "Xiaolu Zhang", "Xiaqing Sun", "Xin Zhao", "Xingyu Lu", "Xiong Xu", "Xudong Wang", "Xudong Wang", "Xuemin Yang", "Yajie Yang", "Yang Xiang", "Yanzhe Li", "Yi Zhang", "Yilong Wang", "Yingxue Li", "Yongzhen Guo", "Yuzhuo Fu", "Yuanyuan Wang", "Yue Yang", "Yue Yu", "Yufeng Deng", "Yun Zhang", "Yunfei Xu", "Yuqi Zhang", "Yuxiao He", "Zengke Gui", "Zhaoxin Huan", "Zhaoyang Wang", "Zhibo Zhu", "Zhihao Wang", "Zhiqiang Zhang", "Zhoufei Wang", "Zihang Zeng", "Ziqi Liu", "Zitao Xuan", "Zuoli Tang" ], "github": "", "project_page": "", "abstract": "We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Designed to scale from tens of billions to one trillion parameters under a unified Mixture-of-Experts (MoE) paradigm, Ling 2.0 emphasizes high sparsity, cross-scale consistency, and efficiency guided by empirical scaling laws. The series includes three non-thinking (instruct) models - Ling-mini-2.0, Ling-flash-2.0, and Ling-1T - ranging from 16B to 1T total parameters and achieving up to 7-fold active-compute efficiency compared with dense counterparts. Ling 2.0 integrates coordinated innovations across model architecture, pre-training, post-training, and infrastructure: a high-sparsity MoE with MTP for efficient reasoning, reasoning-oriented data and mid-training CoT activation, reinforcement-based fine-tuning (DFT, Evo-CoT), and full-scale FP8 training with fine-grained heterogeneous pipelines. At the trillion scale, Ling-1T establishes a new Pareto frontier of reasoning accuracy versus computational efficiency, demonstrating that sparse activation, when properly aligned with reasoning objectives, enables scalable and efficient intelligence. Collectively, Ling 2.0 provides a coherent, open, and efficient foundation for advancing future reasoning and thinking models, including the Ring series built upon the same base.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.00086", "title": "Generalizing Test-time Compute-optimal Scaling as an Optimizable Graph", "authors": [ "Fali Wang", "Jihai Chen", "Shuhua Yang", "Runxue Bao", "Tianxiang Zhao", "Zhiwei Zhang", "Xianfeng Tang", "Hui Liu", "Qi He", "Suhang Wang" ], "github": "", "project_page": "", "abstract": "Test-Time Scaling (TTS) improves large language models (LLMs) by allocating additional computation during inference, typically through parallel, sequential, or hybrid scaling. However, prior studies often assume fixed collaboration architectures (e.g., topologies) and single-model usage, overlooking that optimal architectures and model combinations can vary across tasks. Therefore, we study the novel problem of searching for compute-optimal model combinations and architectures in TTS under a fixed budget. We formalize it as a multi-LLM collaboration graph, where nodes encode roles and LLM model assignments, and edges capture information flow. This problem is challenging because (i) the combinatorial search space is prohibitively large, and (ii) task-specific requirements demand tailored designs. To address these, we reformulate the problem as probabilistic graph optimization and, through pilot experiments, derive three empirical insights into TTS collaboration graphs. Guided by these insights, we propose Agent-REINFORCE, an LLM-agent-augmented framework that mirrors the REINFORCE pipeline by mapping sampling-gradient-update to sampling-feedback-update, where feedback serves as a textual gradient to update the probabilistic graph and efficiently search for optimal multi-LLM collaboration graphs. Experiments show that Agent-REINFORCE outperforms both traditional and LLM-based baselines in sample efficiency and search performance, and effectively identifies optimal graphs under joint objectives of accuracy and inference latency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2510.27571", "title": "Towards Universal Video Retrieval: Generalizing Video Embedding via Synthesized Multimodal Pyramid Curriculum", "authors": [ "Zhuoning Guo", "Mingxin Li", "Yanzhao Zhang", "Dingkun Long", "Pengjun Xie", "Xiaowen Chu" ], "github": "", "project_page": "", "abstract": "The prevailing video retrieval paradigm is structurally misaligned, as narrow benchmarks incentivize correspondingly limited data and single-task training. Therefore, universal capability is suppressed due to the absence of a diagnostic evaluation that defines and demands multi-dimensional generalization. To break this cycle, we introduce a framework built on the co-design of evaluation, data, and modeling. First, we establish the Universal Video Retrieval Benchmark (UVRB), a suite of 16 datasets designed not only to measure performance but also to diagnose critical capability gaps across tasks and domains. Second, guided by UVRB's diagnostics, we introduce a scalable synthesis workflow that generates 1.55 million high-quality pairs to populate the semantic space required for universality. Finally, we devise the Modality Pyramid, a curriculum that trains our General Video Embedder (GVE) by explicitly leveraging the latent interconnections within our diverse data. Extensive experiments show GVE achieves state-of-the-art zero-shot generalization on UVRB. In particular, our analysis reveals that popular benchmarks are poor predictors of general ability and that partially relevant retrieval is a dominant but overlooked scenario. Overall, our co-designed framework provides a practical path to escape the limited scope and advance toward truly universal video retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01857", "title": "Trove: A Flexible Toolkit for Dense Retrieval", "authors": [ "Reza Esfandiarpoor", "Max Zuo", "Stephen H. Bach" ], "github": "", "project_page": "", "abstract": "We introduce Trove, an easy-to-use open-source retrieval toolkit that simplifies research experiments without sacrificing flexibility or speed. For the first time, we introduce efficient data management features that load and process (filter, select, transform, and combine) retrieval datasets on the fly, with just a few lines of code. This gives users the flexibility to easily experiment with different dataset configurations without the need to compute and store multiple copies of large datasets. Trove is highly customizable: in addition to many built-in options, it allows users to freely modify existing components or replace them entirely with user-defined objects. It also provides a low-code and unified pipeline for evaluation and hard negative mining, which supports multi-node execution without any code changes. Trove's data management features reduce memory consumption by a factor of 2.6. Moreover, Trove's easy-to-use inference pipeline incurs no overhead, and inference times decrease linearly with the number of available nodes. Most importantly, we demonstrate how Trove simplifies retrieval experiments and allows for arbitrary customizations, thus facilitating exploratory research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01833", "title": "TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning", "authors": [ "Ming Li", "Jike Zhong", "Shitian Zhao", "Haoquan Zhang", "Shaoheng Lin", "Yuxiang Lai", "Wei Chen", "Konstantinos Psounis", "Kaipeng Zhang" ], "github": "", "project_page": "", "abstract": "The frontier of visual reasoning is shifting toward models like OpenAI o3, which can intelligently create and operate tools to transform images for problem-solving, also known as thinking-with-images in chain-of-thought. Yet existing benchmarks fail to fully capture this advanced capability. Even Visual Search, the most common benchmark for current thinking-with-images methods, tests only basic operations such as localization and cropping, offering little insight into more complex, dynamic, and tool-dependent reasoning. We introduce TIR-Bench, a comprehensive benchmark for evaluating agentic thinking-with-images across 13 diverse tasks, each requiring novel tool use for image processing and manipulation in chain-of-thought. We evaluate 22 multimodal large language models (MLLMs), from leading open-sourced and proprietary models to those with explicit tool-use augmentation. Results show that TIR-Bench is universally challenging, and strong performance requires genuine thinking-with-images capabilities. Finally, we present a pilot study comparing direct versus agentic fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01295", "title": "UniREditBench: A Unified Reasoning-based Image Editing Benchmark", "authors": [ "Feng Han", "Yibin Wang", "Chenglin Li", "Zheming Liang", "Dianyi Wang", "Yang Jiao", "Zhipeng Wei", "Chao Gong", "Cheng Jin", "Jingjing Chen", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in multi-modal generative models have driven substantial improvements in image editing. However, current generative models still struggle with handling diverse and complex image editing tasks that require implicit reasoning, underscoring the need for a comprehensive benchmark to systematically assess their performance across various reasoning scenarios. Existing benchmarks primarily focus on single-object attribute transformation in realistic scenarios, which, while effective, encounter two key challenges: (1) they largely overlook multi-object interactions as well as game-world scenarios that involve human-defined rules, which are common in real-life applications; (2) they only rely on textual references to evaluate the generated images, potentially leading to systematic misjudgments, especially in complex reasoning scenarios. To this end, this work proposes UniREditBench, a unified benchmark for reasoning-based image editing evaluation. It comprises 2,700 meticulously curated samples, covering both real- and game-world scenarios across 8 primary dimensions and 18 sub-dimensions. To improve evaluation reliability, we introduce multimodal dual-reference evaluation, providing both textual and ground-truth image references for each sample assessment. Furthermore, we design an automated multi-scenario data synthesis pipeline and construct UniREdit-Data-100K, a large-scale synthetic dataset with high-quality chain-of-thought (CoT) reasoning annotations. We fine-tune Bagel on this dataset and develop UniREdit-Bagel, demonstrating substantial improvements in both in-domain and out-of-distribution settings. Through thorough benchmarking of both open-source and closed-source image editing models, we reveal their strengths and weaknesses across various aspects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01846", "title": "Towards Robust Mathematical Reasoning", "authors": [ "Thang Luong", "Dawsen Hwang", "Hoang H. Nguyen", "Golnaz Ghiasi", "Yuri Chervonyi", "Insuk Seo", "Junsu Kim", "Garrett Bingham", "Jonathan Lee", "Swaroop Mishra", "Alex Zhai", "Clara Huiyi Hu", "Henryk Michalewski", "Jimin Kim", "Jeonghyun Ahn", "Junhwi Bae", "Xingyou Song", "Trieu H. Trinh", "Quoc V. Le", "Junehyuk Jung" ], "github": "https://github.com/lyang36/IMO25", "project_page": "", "abstract": "Finding the right north-star metrics is highly critical for advancing the mathematical reasoning capabilities of foundation models, especially given that existing evaluations are either too easy or only focus on getting correct short answers. To address these issues, we present IMO-Bench, a suite of advanced reasoning benchmarks, vetted by a panel of top specialists and that specifically targets the level of the International Mathematical Olympiad (IMO), the most prestigious venue for young mathematicians. IMO-AnswerBench first tests models on 400 diverse Olympiad problems with verifiable short answers. IMO-Proof Bench is the next-level evaluation for proof-writing capabilities, which includes both basic and advanced IMO level problems as well as detailed grading guidelines to facilitate automatic grading. These benchmarks played a crucial role in our historic achievement of the gold-level performance at IMO 2025 with Gemini Deep Think (Luong and Lockhart, 2025). Our model achieved 80.0% on IMO-AnswerBench and 65.7% on the advanced IMO-Proof Bench, surpassing the best non-Gemini models by large margins of 6.9% and 42.4% respectively. We also showed that autograders built with Gemini reasoning correlate well with human evaluations and construct IMO-GradingBench, with 1000 human gradings on proofs, to enable further progress in automatic evaluation of long-form answers. We hope that IMO-Bench will help the community towards advancing robust mathematical reasoning and release it at https://imobench.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lyang36/IMO25", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01775", "title": "How Far Are Surgeons from Surgical World Models? A Pilot Study on Zero-shot Surgical Video Generation with Expert Assessment", "authors": [ "Zhen Chen", "Qing Xu", "Jinlin Wu", "Biao Yang", "Yuhao Zhai", "Geng Guo", "Jing Zhang", "Yinlu Ding", "Nassir Navab", "Jiebo Luo" ], "github": "https://github.com/franciszchen/SurgVeo", "project_page": "", "abstract": "Foundation models in video generation are demonstrating remarkable capabilities as potential world models for simulating the physical world. However, their application in high-stakes domains like surgery, which demand deep, specialized causal knowledge rather than general physical rules, remains a critical unexplored gap. To systematically address this challenge, we present SurgVeo, the first expert-curated benchmark for video generation model evaluation in surgery, and the Surgical Plausibility Pyramid (SPP), a novel, four-tiered framework tailored to assess model outputs from basic appearance to complex surgical strategy. On the basis of the SurgVeo benchmark, we task the advanced Veo-3 model with a zero-shot prediction task on surgical clips from laparoscopic and neurosurgical procedures. A panel of four board-certified surgeons evaluates the generated videos according to the SPP. Our results reveal a distinct \"plausibility gap\": while Veo-3 achieves exceptional Visual Perceptual Plausibility, it fails critically at higher levels of the SPP, including Instrument Operation Plausibility, Environment Feedback Plausibility, and Surgical Intent Plausibility. This work provides the first quantitative evidence of the chasm between visually convincing mimicry and causal understanding in surgical AI. Our findings from SurgVeo and the SPP establish a crucial foundation and roadmap for developing future models capable of navigating the complexities of specialized, real-world healthcare domains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/franciszchen/SurgVeo", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01718", "title": "Unified Diffusion VLA: Vision-Language-Action Model via Joint Discrete Denoising Diffusion Process", "authors": [ "Jiayi Chen", "Wenxuan Song", "Pengxiang Ding", "Ziyang Zhou", "Han Zhao", "Feilong Tang", "Donglin Wang", "Haoang Li" ], "github": "", "project_page": "", "abstract": "Vision-language-action (VLA) models aim to understand natural language instructions and visual observations and to execute corresponding actions as an embodied agent. Recent work integrates future images into the understanding-acting loop, yielding unified VLAs that jointly understand, generate, and act -- reading text and images and producing future images and actions. However, these models either rely on external experts for modality unification or treat image generation and action prediction as separate processes, limiting the benefits of direct synergy between these tasks. Our core philosophy is to optimize generation and action jointly through a synchronous denoising process, where the iterative refinement enables actions to evolve from initialization, under constant and sufficient visual guidance. We ground this philosophy in our proposed Unified Diffusion VLA and Joint Discrete Denoising Diffusion Process (JD3P), which is a joint diffusion process that integrates multiple modalities into a single denoising trajectory to serve as the key mechanism enabling understanding, generation, and acting to be intrinsically synergistic. Our model and theory are built on a unified tokenized space of all modalities and a hybrid attention mechanism. We further propose a two-stage training pipeline and several inference-time techniques that optimize performance and efficiency. Our approach achieves state-of-the-art performance on benchmarks such as CALVIN, LIBERO, and SimplerEnv with 4times faster inference than autoregressive methods, and we demonstrate its effectiveness through in-depth analysis and real-world evaluations. Our project page is available at https://irpn-eai.github.io/UD-VLA.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.00405", "title": "UME-R1: Exploring Reasoning-Driven Generative Multimodal Embeddings", "authors": [ "Zhibin Lan", "Liqiang Niu", "Fandong Meng", "Jie Zhou", "Jinsong Su" ], "github": "https://github.com/XMUDeepLIT/UME-R1", "project_page": "", "abstract": "The remarkable success of multimodal large language models (MLLMs) has driven advances in multimodal embeddings, yet existing models remain inherently discriminative, limiting their ability to benefit from reasoning-driven generation paradigm. In this work, we pioneer the exploration of generative embeddings, unifying embedding tasks within a generative paradigm. We propose UME-R1, a universal multimodal embedding framework consisting of a two-stage training strategy: a cold-start supervised fine-tuning equips the model with reasoning capabilities and enables it to generate both discriminative and generative embeddings; a subsequent reinforcement learning enhances reasoning and further optimizes generative embedding quality. This pioneering work reveals four key insights: 1) generative embeddings unlock substantial performance gains over conventional discriminative embeddings by leveraging the powerful generative reasoning capabilities of MLLMs; 2) discriminative and generative embeddings are complementary, whose combined oracle performance far exceeding that of either alone; 3) RL can effectively enhance generative embeddings, establishing a scalable optimization paradigm.; 4) repeated sampling at inference boosts downstream task coverage (pass@k), highlighting the inference-time scalability potential of generative embeddings. Evaluated on the MMEB-V2 benchmark across 78 tasks spanning video, image, and visual documents, UME-R1 significantly outperforms conventional discriminative embedding models and offers a foundation for more interpretable, reasoning-driven generative multimodal embeddings. Our code, models, and datasets will be publicly available at https://github.com/XMUDeepLIT/UME-R1.", "github_manual": "", "github_regex": "https://github.com/XMUDeepLIT/UME-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.00279", "title": "LongCat-Flash-Omni Technical Report", "authors": [ "Meituan LongCat Team", "Bairui Wang", "Bayan", "Bin Xiao", "Bo Zhang", "Bolin Rong", "Borun Chen", "Chang Wan", "Chao Zhang", "Chen Huang", "Chen Chen", "Chen Chen", "Chengxu Yang", "Chengzuo Yang", "Cong Han", "Dandan Peng", "Delian Ruan", "Detai Xin", "Disong Wang", "Dongchao Yang", "Fanfan Liu", "Fengjiao Chen", "Fengyu Yang", "Gan Dong", "Gang Huang", "Gang Xu", "Guanglu Wan", "Guoqiang Tan", "Guoqiao Yu", "Haibo Qiu", "Hao Lu", "Hongbo Liu", "Hongyu Xiang", "Jiaheng Wu", "Jian Yang", "Jiaxing Liu", "Jing Huang", "Jingang Wang", "Jinrui Ding", "Juchao Jiang", "Jun Kuang", "Jun Wang", "Junhui Mei", "Ke Ding", "Kefeng Zhang", "Lei Chen", "Liang Shi", "Limeng Qiao", "Liming Zheng", "Lin Ma", "Liuyang Guo", "Liya Ma", "Luying Sun", "Man Gao", "Mengshen Zhu", "Miao Cao", "Minliang Lin", "Nuo Xu", "Peng Shi", "Qi Zhang", "Qian Fang", "Qian Wang", "Qian Yang", "Quanxiu Wang", "Rongxiang Weng", "Rongxin Guo", "Ruoxuan Liang", "Senbin Yang", "Shanbo Xu", "Shanglin Lei", "Shengze Ye", "Shimin Chen", "Shuaiqi Chen", "Shujie Hu", "Shuo Li", "Siqi Yang", "Siyu Xu", "Siyu Ren", "Song Li", "Songxiang Liu", "Tianhao Bai", "Tianye Dai", "Wei Hong", "Wei Wang", "Weixiao Zhao", "Wengang Cao", "Wenlong Zhu", "Wenlong He", "Xi Su", "Xi Nan", "Xiaohan Zhao", "Xiaohao Wang", "Xiaoyu Zhao", "Xiaoyu Wang", "Xiaoyu Li", "Xin Pan", "Xin Chen", "Xiusong Sun", "Xu Xiang", "Xudong Xing", "Xuezhi Cao", "Xunliang Cai", "Yang Yang", "Yanli Tan", "Yao Yao", "Yerui Sun", "Yi Chen", "Yifan Lu", "Yin Gong", "Yining Zhang", "Yitian Chen", "Yiyang Gan", "Yuchen Tang", "Yuchen Xie", "Yueqian Wang", "Yuewen Zheng", "Yufei Zhang", "Yufeng Zhong", "Yulei Qian", "Yuqi Peng", "Yuwei Jiang", "Zeyang Hu", "Zheng Zhang", "Zhengkun Tian", "Zhiqing Hong", "Zhixiong Zeng", "Zhuqi Mi", "Ziran Li", "Ziwen Wang", "Ziyi Zhao", "Ziyuan Zhuang", "Zizhe Zhao" ], "github": "https://github.com/meituan-longcat/LongCat-Flash-Omni", "project_page": "", "abstract": "We introduce LongCat-Flash-Omni, a state-of-the-art open-source omni-modal model with 560 billion parameters, excelling at real-time audio-visual interaction. By adopting a curriculum-inspired progressive training strategy that transitions from simpler to increasingly complex modality sequence modeling tasks, LongCat-Flash-Omni attains comprehensive multimodal capabilities while maintaining strong unimodal capability. Building upon LongCat-Flash, which adopts a high-performance Shortcut-connected Mixture-of-Experts (MoE) architecture with zero-computation experts, LongCat-Flash-Omni integrates efficient multimodal perception and speech reconstruction modules. Despite its immense size of 560B parameters (with 27B activated), LongCat-Flash-Omni achieves low-latency real-time audio-visual interaction. For training infrastructure, we developed a modality-decoupled parallelism scheme specifically designed to manage the data and model heterogeneity inherent in large-scale multimodal training. This innovative approach demonstrates exceptional efficiency by sustaining over 90% of the throughput achieved by text-only training. Extensive evaluations show that LongCat-Flash-Omni achieves state-of-the-art performance on omni-modal benchmarks among open-source models. Furthermore, it delivers highly competitive results across a wide range of modality-specific tasks, including text, image, and video understanding, as well as audio understanding and generation. We provide a comprehensive overview of the model architecture design, training procedures, and data strategies, and open-source the model to foster future research and development in the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/meituan-longcat/LongCat-Flash-Omni", "github_api_added_by": "auto" }, { "date": "2025-11-04", "arxiv_id": "2510.26491", "title": "Data-Efficient RLVR via Off-Policy Influence Guidance", "authors": [ "Erle Zhu", "Dazhi Jiang", "Yuan Wang", "Xujun Li", "Jiale Cheng", "Yuxian Gu", "Yilin Niu", "Aohan Zeng", "Jie Tang", "Minlie Huang", "Hongning Wang" ], "github": "", "project_page": "", "abstract": "Data selection is a critical aspect of Reinforcement Learning with Verifiable Rewards (RLVR) for enhancing the reasoning capabilities of large language models (LLMs). Current data selection methods are largely heuristic-based, lacking theoretical guarantees and generalizability. This work proposes a theoretically-grounded approach using influence functions to estimate the contribution of each data point to the learning objective. To overcome the prohibitive computational cost of policy rollouts required for online influence estimation, we introduce an off-policy influence estimation method that efficiently approximates data influence using pre-collected offline trajectories. Furthermore, to manage the high-dimensional gradients of LLMs, we employ sparse random projection to reduce dimensionality and improve storage and computation efficiency. Leveraging these techniques, we develop Curriculum RL with Off-Policy Influence guidance (CROPI), a multi-stage RL framework that iteratively selects the most influential data for the current policy. Experiments on models up to 7B parameters demonstrate that CROPI significantly accelerates training. On a 1.5B model, it achieves a 2.66x step-level acceleration while using only 10\\% of the data per stage compared to full-dataset training. Our results highlight the substantial potential of influence-based data selection for efficient RLVR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.00062", "title": "World Simulation with Video Foundation Models for Physical AI", "authors": [ "NVIDIA", "Arslan Ali", "Junjie Bai", "Maciej Bala", "Yogesh Balaji", "Aaron Blakeman", "Tiffany Cai", "Jiaxin Cao", "Tianshi Cao", "Elizabeth Cha", "Yu-Wei Chao", "Prithvijit Chattopadhyay", "Mike Chen", "Yongxin Chen", "Yu Chen", "Shuai Cheng", "Yin Cui", "Jenna Diamond", "Yifan Ding", "Jiaojiao Fan", "Linxi Fan", "Liang Feng", "Francesco Ferroni", "Sanja Fidler", "Xiao Fu", "Ruiyuan Gao", "Yunhao Ge", "Jinwei Gu", "Aryaman Gupta", "Siddharth Gururani", "Imad El Hanafi", "Ali Hassani", "Zekun Hao", "Jacob Huffman", "Joel Jang", "Pooya Jannaty", "Jan Kautz", "Grace Lam", "Xuan Li", "Zhaoshuo Li", "Maosheng Liao", "Chen-Hsuan Lin", "Tsung-Yi Lin", "Yen-Chen Lin", "Huan Ling", "Ming-Yu Liu", "Xian Liu", "Yifan Lu", "Alice Luo", "Qianli Ma", "Hanzi Mao", "Kaichun Mo", "Seungjun Nah", "Yashraj Narang", "Abhijeet Panaskar", "Lindsey Pavao", "Trung Pham", "Morteza Ramezanali", "Fitsum Reda", "Scott Reed", "Xuanchi Ren", "Haonan Shao", "Yue Shen", "Stella Shi", "Shuran Song", "Bartosz Stefaniak", "Shangkun Sun", "Shitao Tang", "Sameena Tasmeen", "Lyne Tchapmi", "Wei-Cheng Tseng", "Jibin Varghese", "Andrew Z. Wang", "Hao Wang", "Haoxiang Wang", "Heng Wang", "Ting-Chun Wang", "Fangyin Wei", "Jiashu Xu", "Dinghao Yang", "Xiaodong Yang", "Haotian Ye", "Seonghyeon Ye", "Xiaohui Zeng", "Jing Zhang", "Qinsheng Zhang", "Kaiwen Zheng", "Andrew Zhu", "Yuke Zhu" ], "github": "https://github.com/nvidia-cosmos/cosmos-predict2.5", "project_page": "", "abstract": "We introduce [Cosmos-Predict2.5], the latest generation of the Cosmos World Foundation Models for Physical AI. Built on a flow-based architecture, [Cosmos-Predict2.5] unifies Text2World, Image2World, and Video2World generation in a single model and leverages [Cosmos-Reason1], a Physical AI vision-language model, to provide richer text grounding and finer control of world simulation. Trained on 200M curated video clips and refined with reinforcement learning-based post-training, [Cosmos-Predict2.5] achieves substantial improvements over [Cosmos-Predict1] in video quality and instruction alignment, with models released at 2B and 14B scales. These capabilities enable more reliable synthetic data generation, policy evaluation, and closed-loop simulation for robotics and autonomous systems. We further extend the family with [Cosmos-Transfer2.5], a control-net style framework for Sim2Real and Real2Real world translation. Despite being 3.5times smaller than [Cosmos-Transfer1], it delivers higher fidelity and robust long-horizon video generation. Together, these advances establish [Cosmos-Predict2.5] and [Cosmos-Transfer2.5] as versatile tools for scaling embodied intelligence. To accelerate research and deployment in Physical AI, we release source code, pretrained checkpoints, and curated benchmarks under the NVIDIA Open Model License at https://github.com/nvidia-cosmos/cosmos-predict2.5 and https://github.com/nvidia-cosmos/cosmos-transfer2.5. We hope these open resources lower the barrier to adoption and foster innovation in building the next generation of embodied intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nvidia-cosmos/cosmos-predict2.5", "github_api_added_by": "auto" }, { "date": "2025-11-04", "arxiv_id": "2511.01163", "title": "ROVER: Benchmarking Reciprocal Cross-Modal Reasoning for Omnimodal Generation", "authors": [ "Yongyuan Liang", "Wei Chow", "Feng Li", "Ziqiao Ma", "Xiyao Wang", "Jiageng Mao", "Jiuhai Chen", "Jiatao Gu", "Yue Wang", "Furong Huang" ], "github": "https://github.com/cheryyunl/ROVER", "project_page": "", "abstract": "Unified multimodal models (UMMs) have emerged as a powerful paradigm for seamlessly unifying text and image understanding and generation. However, prevailing evaluations treat these abilities in isolation, such that tasks with multimodal inputs and outputs are scored primarily through unimodal reasoning, i.e., textual benchmarks emphasize language-based reasoning, while visual benchmarks emphasize reasoning outcomes manifested in the pixels. We introduce ROVER to address this pressing need to test reciprocal cross-modal reasoning, the use of one modality to guide, verify, or refine outputs in the other, an ability central to the vision of unified multimodal intelligence. ROVER is a human-annotated benchmark that explicitly targets reciprocal cross-modal reasoning, which contains 1312 tasks grounded in 1876 images, spanning two complementary settings. Verbally-augmented reasoning for visual generation evaluates whether models can use verbal prompts and reasoning chains to guide faithful image synthesis. Visually-augmented reasoning for verbal generation evaluates whether models can generate intermediate visualizations that strengthen their own reasoning processes for question answering. Experiments on 17 unified models reveal two key findings: (i) Cross-modal reasoning determines visual generation quality, with interleaved models significantly outperforming non-interleaved ones; notably, combining strong unimodal models fails to achieve comparable reasoning. (ii) Models show dissociation between physical and symbolic reasoning: they succeed at interpreting perceptual concepts literally but fail to construct visual abstractions for symbolic tasks, where faulty reasoning harms performance. These results highlight reciprocal cross-modal reasoning as a critical frontier for enabling true omnimodal generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cheryyunl/ROVER", "github_api_added_by": "auto" }, { "date": "2025-11-04", "arxiv_id": "2510.26865", "title": "Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBench", "authors": [ "Fenfen Lin", "Yesheng Liu", "Haiyu Xu", "Chen Yue", "Zheqi He", "Mingxuan Zhao", "Miguel Hu Chen", "Jiakang Liu", "JG Yao", "Xi Yang" ], "github": "", "project_page": "", "abstract": "Reading measurement instruments is effortless for humans and requires relatively little domain expertise, yet it remains surprisingly challenging for current vision-language models (VLMs) as we find in preliminary evaluation. In this work, we introduce MeasureBench, a benchmark on visual measurement reading covering both real-world and synthesized images of various types of measurements, along with an extensible pipeline for data synthesis. Our pipeline procedurally generates a specified type of gauge with controllable visual appearance, enabling scalable variation in key details such as pointers, scales, fonts, lighting, and clutter. Evaluation on popular proprietary and open-weight VLMs shows that even the strongest frontier VLMs struggle measurement reading in general. A consistent failure mode is indicator localization: models can read digits or labels but misidentify the key positions of pointers or alignments, leading to big numeric errors despite plausible textual reasoning. We have also conducted preliminary experiments with reinforcement learning over synthetic data, and find encouraging results on in-domain synthetic subset but less promising for real-world images. Our analysis highlights a fundamental limitation of current VLMs in fine-grained spatial grounding. We hope this resource can help future advances on visually grounded numeracy and precise spatial perception of VLMs, bridging the gap between recognizing numbers and measuring the world.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01678", "title": "UniLumos: Fast and Unified Image and Video Relighting with Physics-Plausible Feedback", "authors": [ "Ropeway Liu", "Hangjie Yuan", "Bo Dong", "Jiazheng Xing", "Jinwang Wang", "Rui Zhao", "Yan Xing", "Weihua Chen", "Fan Wang" ], "github": "https://github.com/alibaba-damo-academy/Lumos-Custom", "project_page": "", "abstract": "Relighting is a crucial task with both practical demand and artistic value, and recent diffusion models have shown strong potential by enabling rich and controllable lighting effects. However, as they are typically optimized in semantic latent space, where proximity does not guarantee physical correctness in visual space, they often produce unrealistic results, such as overexposed highlights, misaligned shadows, and incorrect occlusions. We address this with UniLumos, a unified relighting framework for both images and videos that brings RGB-space geometry feedback into a flow matching backbone. By supervising the model with depth and normal maps extracted from its outputs, we explicitly align lighting effects with the scene structure, enhancing physical plausibility. Nevertheless, this feedback requires high-quality outputs for supervision in visual space, making standard multi-step denoising computationally expensive. To mitigate this, we employ path consistency learning, allowing supervision to remain effective even under few-step training regimes. To enable fine-grained relighting control and supervision, we design a structured six-dimensional annotation protocol capturing core illumination attributes. Building upon this, we propose LumosBench, a disentangled attribute-level benchmark that evaluates lighting controllability via large vision-language models, enabling automatic and interpretable assessment of relighting precision across individual dimensions. Extensive experiments demonstrate that UniLumos achieves state-of-the-art relighting quality with significantly improved physical consistency, while delivering a 20x speedup for both image and video relighting. Code is available at https://github.com/alibaba-damo-academy/Lumos-Custom.", "github_manual": "", "github_regex": "https://github.com/alibaba-damo-academy/Lumos-Custom", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01618", "title": "Actial: Activate Spatial Reasoning Ability of Multimodal Large Language Models", "authors": [ "Xiaoyu Zhan", "Wenxuan Huang", "Hao Sun", "Xinyu Fu", "Changfeng Ma", "Shaosheng Cao", "Bohan Jia", "Shaohui Lin", "Zhenfei Yin", "Lei Bai", "Wanli Ouyang", "Yuanqi Li", "Jie Guo", "Yanwen Guo" ], "github": "", "project_page": "", "abstract": "Recent advances in Multimodal Large Language Models (MLLMs) have significantly improved 2D visual understanding, prompting interest in their application to complex 3D reasoning tasks. However, it remains unclear whether these models can effectively capture the detailed spatial information required for robust real-world performance, especially cross-view consistency, a key requirement for accurate 3D reasoning. Considering this issue, we introduce Viewpoint Learning, a task designed to evaluate and improve the spatial reasoning capabilities of MLLMs. We present the Viewpoint-100K dataset, consisting of 100K object-centric image pairs with diverse viewpoints and corresponding question-answer pairs. Our approach employs a two-stage fine-tuning strategy: first, foundational knowledge is injected to the baseline MLLM via Supervised Fine-Tuning (SFT) on Viewpoint-100K, resulting in significant improvements across multiple tasks; second, generalization is enhanced through Reinforcement Learning using the Group Relative Policy Optimization (GRPO) algorithm on a broader set of questions. Additionally, we introduce a hybrid cold-start initialization method designed to simultaneously learn viewpoint representations and maintain coherent reasoning thinking. Experimental results show that our approach significantly activates the spatial reasoning ability of MLLM, improving performance on both in-domain and out-of-domain reasoning tasks. Our findings highlight the value of developing foundational spatial skills in MLLMs, supporting future progress in robotics, autonomous systems, and 3D scene understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2510.26236", "title": "PHUMA: Physically-Grounded Humanoid Locomotion Dataset", "authors": [ "Kyungmin Lee", "Sibeen Kim", "Minho Park", "Hyunseung Kim", "Dongyoon Hwang", "Hojoon Lee", "Jaegul Choo" ], "github": "https://github.com/kevinzakka/mink", "project_page": "", "abstract": "Motion imitation is a promising approach for humanoid locomotion, enabling agents to acquire humanlike behaviors. Existing methods typically rely on high-quality motion capture datasets such as AMASS, but these are scarce and expensive, limiting scalability and diversity. Recent studies attempt to scale data collection by converting large-scale internet videos, exemplified by Humanoid-X. However, they often introduce physical artifacts such as floating, penetration, and foot skating, which hinder stable imitation. In response, we introduce PHUMA, a Physically-grounded HUMAnoid locomotion dataset that leverages human video at scale, while addressing physical artifacts through careful data curation and physics-constrained retargeting. PHUMA enforces joint limits, ensures ground contact, and eliminates foot skating, producing motions that are both large-scale and physically reliable. We evaluated PHUMA in two sets of conditions: (i) imitation of unseen motion from self-recorded test videos and (ii) path following with pelvis-only guidance. In both cases, PHUMA-trained policies outperform Humanoid-X and AMASS, achieving significant gains in imitating diverse motions. The code is available at https://davian-robotics.github.io/PHUMA.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kevinzakka/mink", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2510.27363", "title": "ToolScope: An Agentic Framework for Vision-Guided and Long-Horizon Tool Use", "authors": [ "Mengjie Deng", "Guanting Dong", "Zhicheng Dou" ], "github": "", "project_page": "", "abstract": "Recently, large language models (LLMs) have demonstrated remarkable problem-solving capabilities by autonomously integrating with external tools for collaborative reasoning. However, due to the inherently complex and diverse nature of multimodal information, enabling multimodal large language models (MLLMs) to flexibly and efficiently utilize external tools during reasoning remains an underexplored challenge. In this work, we introduce ToolScope, an agentic framework designed to unify global planning with local multimodal perception, adopting a specialized Perceive tool to mitigates visual context degradation in long-horizon VQA task. ToolScope comprises three primary components: the Global Navigator, the Agentic Executor, and the Response Synthesizer. The Global Navigator functions as a \"telescope\", offering high-level strategic guidance. The Agentic Executor operates iteratively to augment MLLM with local perception through the integration of external tools-Search, Code, and Perceive. Finally, the Response Synthesizer consolidates and organizes the reasoning process into a coherent, user-friendly output. We evaluate ToolScope on four VQA benchmarks across diverse domains, including VQA 2.0, ScienceQA, MAT-Search and MathVista. It demonstrates strong generalization capabilities, achieving an average performance improvement of up to +6.69% across all datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01266", "title": "MotionStream: Real-Time Video Generation with Interactive Motion Controls", "authors": [ "Joonghyuk Shin", "Zhengqi Li", "Richard Zhang", "Jun-Yan Zhu", "Jaesik Park", "Eli Schechtman", "Xun Huang" ], "github": "", "project_page": "", "abstract": "Current motion-conditioned video generation methods suffer from prohibitive latency (minutes per video) and non-causal processing that prevents real-time interaction. We present MotionStream, enabling sub-second latency with up to 29 FPS streaming generation on a single GPU. Our approach begins by augmenting a text-to-video model with motion control, which generates high-quality videos that adhere to the global text prompt and local motion guidance, but does not perform inference on the fly. As such, we distill this bidirectional teacher into a causal student through Self Forcing with Distribution Matching Distillation, enabling real-time streaming inference. Several key challenges arise when generating videos of long, potentially infinite time-horizons: (1) bridging the domain gap from training on finite length and extrapolating to infinite horizons, (2) sustaining high quality by preventing error accumulation, and (3) maintaining fast inference, without incurring growth in computational cost due to increasing context windows. A key to our approach is introducing carefully designed sliding-window causal attention, combined with attention sinks. By incorporating self-rollout with attention sinks and KV cache rolling during training, we properly simulate inference-time extrapolations with a fixed context window, enabling constant-speed generation of arbitrarily long videos. Our models achieve state-of-the-art results in motion following and video quality while being two orders of magnitude faster, uniquely enabling infinite-length streaming. With MotionStream, users can paint trajectories, control cameras, or transfer motion, and see results unfold in real-time, delivering a truly interactive experience.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.00602", "title": "OpenSIR: Open-Ended Self-Improving Reasoner", "authors": [ "Wai-Chung Kwan", "Joshua Ong Jun Leang", "Pavlos Vougiouklis", "Jeff Z. Pan", "Marco Valentino", "Pasquale Minervini" ], "github": "https://github.com/EdinburghNLP/OpenSIR", "project_page": "", "abstract": "Recent advances in large language model (LLM) reasoning through reinforcement learning rely on annotated datasets for verifiable rewards, which may limit models' ability to surpass human-level performance. While self-play offers a promising alternative, existing approaches depend on external verifiers or cannot learn open-endedly. We present Open-Ended Self-Improving Reasoner (OpenSIR), a self-play framework where an LLM learns to generate and solve novel problems by alternating teacher and student roles without external supervision. To generate novel problems, OpenSIR optimises for both difficulty and diversity, rewarding problems that challenge appropriately while exploring distinct concepts, enabling open-ended mathematical discovery. Starting from a single trivial seed problem, OpenSIR substantially improves instruction models: Llama-3.2-3B-Instruct advances from 73.9 to 78.3 on GSM8K, and from 28.8 to 34.4 on College Math, while Gemma-2-2B-Instruct rises from 38.5 to 58.7 on GSM8K. Our analyses reveal that OpenSIR achieves open-ended learning through co-evolving teacher-student roles that adaptively calibrate difficulty and drive diverse exploration, progressing autonomously from basic to advanced mathematics.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EdinburghNLP/OpenSIR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2510.26909", "title": "NaviTrace: Evaluating Embodied Navigation of Vision-Language Models", "authors": [ "Tim Windecker", "Manthan Patel", "Moritz Reuss", "Richard Schwarzkopf", "Cesar Cadena", "Rudolf Lioutikov", "Marco Hutter", "Jonas Frey" ], "github": "", "project_page": "", "abstract": "Vision-language models demonstrate unprecedented performance and generalization across a wide range of tasks and scenarios. Integrating these foundation models into robotic navigation systems opens pathways toward building general-purpose robots. Yet, evaluating these models' navigation capabilities remains constrained by costly real-world trials, overly simplified simulations, and limited benchmarks. We introduce NaviTrace, a high-quality Visual Question Answering benchmark where a model receives an instruction and embodiment type (human, legged robot, wheeled robot, bicycle) and must output a 2D navigation trace in image space. Across 1000 scenarios and more than 3000 expert traces, we systematically evaluate eight state-of-the-art VLMs using a newly introduced semantic-aware trace score. This metric combines Dynamic Time Warping distance, goal endpoint error, and embodiment-conditioned penalties derived from per-pixel semantics and correlates with human preferences. Our evaluation reveals consistent gap to human performance caused by poor spatial grounding and goal localization. NaviTrace establishes a scalable and reproducible benchmark for real-world robotic navigation. The benchmark and leaderboard can be found at https://leggedrobotics.github.io/navitrace_webpage/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01617", "title": "Vote-in-Context: Turning VLMs into Zero-Shot Rank Fusers", "authors": [ "Mohamed Eltahir", "Ali Habibullah", "Lama Ayash", "Tanveer Hussain", "Naeemullah Khan" ], "github": "https://github.com/mohammad2012191/ViC", "project_page": "", "abstract": "In the retrieval domain, candidates' fusion from heterogeneous retrievers is a long-standing challenge, particularly for complex, multi-modal data such as videos. While typical fusion techniques are training-free, they rely solely on rank or score signals, disregarding candidates' representations. This work introduces Vote-in-Context (ViC), a generalized, training-free framework that re-thinks list-wise reranking and fusion as a zero-shot reasoning task for a Vision-Language Model (VLM). The core insight is to serialize both content evidence and retriever metadata directly within the VLM's prompt, allowing the model to adaptively weigh retriever consensus against visual-linguistic content. We demonstrate the generality of this framework by applying it to the challenging domain of cross-modal video retrieval. To this end, we introduce the S-Grid, a compact serialization map that represents each video as an image grid, optionally paired with subtitles to enable list-wise reasoning over video candidates. ViC is evaluated both as a single-list reranker, where it dramatically improves the precision of individual retrievers, and as an ensemble fuser, where it consistently outperforms strong baselines like CombSUM. Across video retrieval benchmarks including ActivityNet and VATEX, the framework establishes new state-of-the-art zero-shot retrieval performance, demonstrating its effectiveness in handling complex visual and temporal signals alongside text. In zero-shot settings, ViC achieves Recall@1 scores of 87.1% (t2v) / 89.0% (v2t) on MSR-VTT and 99.6% (v2t) on VATEX, representing massive gains of up to +40 Recall@1 over previous state-of-the-art baselines. We present ViC as a simple, reproducible, and highly effective recipe for turning modern VLMs into powerful zero-shot rerankers and fusers. Code and resources are publicly available at: https://github.com/mohammad2012191/ViC", "github_manual": "", "github_regex": "https://github.com/mohammad2012191/ViC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2510.24794", "title": "MR-Align: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models", "authors": [ "Xinming Wang", "Jian Xu", "Bin Yu", "Sheng Lian", "Hongzhu Yi", "Yi Chen", "Yingjian Zhu", "Boran Wang", "Hongming Yang", "Han Hu", "Xu-Yao Zhang", "Cheng-Lin Liu" ], "github": "", "project_page": "", "abstract": "Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. We find this limitation is partially attributable to a reasoning-answer hit gap, where the model identifies the correct facts during reasoning but fails to incorporate them into the final response, thereby reducing factual fidelity. To address this issue, we propose MR-ALIGN, a Meta-Reasoning informed alignment framework that enhances factuality without relying on external verifiers. MR-ALIGN quantifies state transition probabilities along the model's thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments. This re-weighting reshapes token-level signals into probability-aware segment scores, encouraging coherent reasoning trajectories that are more conducive to factual correctness. Empirical evaluations across four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning. These results highlight that aligning the reasoning process itself, rather than merely the outputs, is pivotal for advancing factuality in LRMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2510.27545", "title": "EBT-Policy: Energy Unlocks Emergent Physical Reasoning Capabilities", "authors": [ "Travis Davies", "Yiqi Huang", "Alexi Gladstone", "Yunxin Liu", "Xiang Chen", "Heng Ji", "Huxian Liu", "Luhui Hu" ], "github": "", "project_page": "", "abstract": "Implicit policies parameterized by generative models, such as Diffusion Policy, have become the standard for policy learning and Vision-Language-Action (VLA) models in robotics. However, these approaches often suffer from high computational cost, exposure bias, and unstable inference dynamics, which lead to divergence under distribution shifts. Energy-Based Models (EBMs) address these issues by learning energy landscapes end-to-end and modeling equilibrium dynamics, offering improved robustness and reduced exposure bias. Yet, policies parameterized by EBMs have historically struggled to scale effectively. Recent work on Energy-Based Transformers (EBTs) demonstrates the scalability of EBMs to high-dimensional spaces, but their potential for solving core challenges in physically embodied models remains underexplored. We introduce a new energy-based architecture, EBT-Policy, that solves core issues in robotic and real-world settings. Across simulated and real-world tasks, EBT-Policy consistently outperforms diffusion-based policies, while requiring less training and inference computation. Remarkably, on some tasks it converges within just two inference steps, a 50x reduction compared to Diffusion Policy's 100. Moreover, EBT-Policy exhibits emergent capabilities not seen in prior models, such as zero-shot recovery from failed action sequences using only behavior cloning and without explicit retry training. By leveraging its scalar energy for uncertainty-aware inference and dynamic compute allocation, EBT-Policy offers a promising path toward robust, generalizable robot behavior under distribution shifts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01706", "title": "Multi-Step Knowledge Interaction Analysis via Rank-2 Subspace Disentanglement", "authors": [ "Sekh Mainul Islam", "Pepa Atanasova", "Isabelle Augenstein" ], "github": "https://github.com/copenlu/pk-ck-knowledge-disentanglement", "project_page": "", "abstract": "Natural Language Explanations (NLEs) describe how Large Language Models (LLMs) make decisions, drawing on both external Context Knowledge (CK) and Parametric Knowledge (PK) stored in model weights. Understanding their interaction is key to assessing the grounding of NLEs, yet it remains underexplored. Prior work has largely examined only single-step generation, typically the final answer, and has modelled PK and CK interaction only as a binary choice in a rank-1 subspace. This overlooks richer forms of interaction, such as complementary or supportive knowledge. We propose a novel rank-2 projection subspace that disentangles PK and CK contributions more accurately and use it for the first multi-step analysis of knowledge interactions across longer NLE sequences. Experiments on four QA datasets and three open-weight instruction-tuned LLMs show that diverse knowledge interactions are poorly represented in a rank-1 subspace but are effectively captured in our rank-2 formulation. Our multi-step analysis reveals that hallucinated NLEs align strongly with the PK direction, context-faithful ones balance PK and CK, and Chain-of-Thought prompting for NLEs shifts generated NLEs toward CK by reducing PK reliance. This work provides the first framework for systematic studies of multi-step knowledge interactions in LLMs through a richer rank-2 subspace disentanglement. Code and data: https://github.com/copenlu/pk-ck-knowledge-disentanglement.", "github_manual": "", "github_regex": "https://github.com/copenlu/pk-ck-knowledge-disentanglement", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01340", "title": "left|,circlearrowright,text{BUS},right|: A Large and Diverse Multimodal Benchmark for evaluating the ability of Vision-Language Models to understand Rebus Puzzles", "authors": [ "Trishanu Das", "Abhilash Nandy", "Khush Bajaj", "Deepiha S" ], "github": "https://github.com/abhi1nandy2/Re-Bus", "project_page": "", "abstract": "Understanding Rebus Puzzles (Rebus Puzzles use pictures, symbols, and letters to represent words or phrases creatively) requires a variety of skills such as image recognition, cognitive skills, commonsense reasoning, multi-step reasoning, image-based wordplay, etc., making this a challenging task for even current Vision-Language Models. In this paper, we present left|,circlearrowright,text{BUS},right|, a large and diverse benchmark of 1,333 English Rebus Puzzles containing different artistic styles and levels of difficulty, spread across 18 categories such as food, idioms, sports, finance, entertainment, etc. We also propose RebusDescProgICE, a model-agnostic framework which uses a combination of an unstructured description and code-based, structured reasoning, along with better, reasoning-based in-context example selection, improving the performance of Vision-Language Models on left|,circlearrowright,text{BUS},right| by 2.1-4.1% and 20-30% using closed-source and open-source models respectively compared to Chain-of-Thought Reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/abhi1nandy2/Re-Bus", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.00810", "title": "GUI-AIMA: Aligning Intrinsic Multimodal Attention with a Context Anchor for GUI Grounding", "authors": [ "Shijie Zhou", "Viet Dac Lai", "Hao Tan", "Jihyung Kil", "Wanrong Zhu", "Changyou Chen", "Ruiyi Zhang" ], "github": "https://github.com/sjz5202/GUI-AIMA", "project_page": "", "abstract": "Graphical user interface (GUI) grounding is a key function of computer-use agents, which maps natural-language instructions to actionable screen regions. Existing approaches based on Multimodal Large Language Models (MLLMs) typically formulate it as a text-based coordinate generation task, yet directly generating precise coordinates from visual inputs remains challenging and computationally intensive. An intuitive way to implement GUI grounding is to first select visual patches relevant to the instructions and then determine the precise click location within those patches. Based on the observations that general MLLMs have some native grounding capability, nested within their attentions, we propose GUI-AIMA, an attention-based and coordinate-free supervised fine-tuning framework for efficient GUI grounding. GUI-AIMA aligns the intrinsic multimodal attention of MLLMs with patch-wise grounding signals. These signals are calculated adaptively for diverse user instructions by multi-head aggregation on simplified query-visual attention matrices. Besides, its coordinate-free manner can easily integrate a plug-and-play zoom-in stage. GUI-AIMA-3B was trained with only 85k screenshots, demonstrating exceptional data efficiency and verifying that light training can trigger the native grounding capability of MLLMs. It achieves state-of-the-art performance among 3B models, attaining an average accuracy of 58.6% on ScreenSpot-Pro and 62.2% on OSWorld-G. Project page: https://github.com/sjz5202/GUI-AIMA", "github_manual": "", "github_regex": "https://github.com/sjz5202/GUI-AIMA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-04", "arxiv_id": "2511.01144", "title": "AthenaBench: A Dynamic Benchmark for Evaluating LLMs in Cyber Threat Intelligence", "authors": [ "Md Tanvirul Alam", "Dipkamal Bhusal", "Salman Ahmad", "Nidhi Rastogi", "Peter Worth" ], "github": "https://github.com/Athena-Software-Group/athenabench", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated strong capabilities in natural language reasoning, yet their application to Cyber Threat Intelligence (CTI) remains limited. CTI analysis involves distilling large volumes of unstructured reports into actionable knowledge, a process where LLMs could substantially reduce analyst workload. CTIBench introduced a comprehensive benchmark for evaluating LLMs across multiple CTI tasks. In this work, we extend CTIBench by developing AthenaBench, an enhanced benchmark that includes an improved dataset creation pipeline, duplicate removal, refined evaluation metrics, and a new task focused on risk mitigation strategies. We evaluate twelve LLMs, including state-of-the-art proprietary models such as GPT-5 and Gemini-2.5 Pro, alongside seven open-source models from the LLaMA and Qwen families. While proprietary LLMs achieve stronger results overall, their performance remains subpar on reasoning-intensive tasks, such as threat actor attribution and risk mitigation, with open-source models trailing even further behind. These findings highlight fundamental limitations in the reasoning capabilities of current LLMs and underscore the need for models explicitly tailored to CTI workflows and automation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Athena-Software-Group/athenabench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.02778", "title": "VCode: a Multimodal Coding Benchmark with SVG as Symbolic Visual Representation", "authors": [ "Kevin Qinghong Lin", "Yuhao Zheng", "Hangyu Ran", "Dantong Zhu", "Dongxing Mao", "Linjie Li", "Philip Torr", "Alex Jinpeng Wang" ], "github": "https://github.com/CSU-JPG/VCode", "project_page": "", "abstract": "Code has emerged as a precise and executable medium for reasoning and action in the agent era. Yet, progress has largely focused on language-centric tasks such as program synthesis and debugging, leaving visual-centric coding underexplored. Inspired by how humans reason over sketches, we advocate SVG code as a compact, interpretable, and executable visual representation. We introduce VCode, a benchmark that reframes multimodal understanding as code generation: given an image, a model must produce SVG that preserves symbolic meaning for downstream reasoning. VCode covers three domains - general commonsense (MM-Vet), professional disciplines (MMMU), and visual-centric perception (CV-Bench). To assess symbolic fidelity, we propose CodeVQA, a novel evaluation protocol in which a policy model answers questions over rendered SVGs; correct answers indicate faithful symbolic preservation. Empirically, frontier VLMs struggle to generate faithful SVGs, revealing a persistent gap between language-centric and visual-centric coding. To close this gap, we introduce VCoder, an agentic framework that augments VLMs along two axes: (i) Thinking with Revision, which iteratively analyzes discrepancies and refines SVG code; and (ii) Acting with Visual Tools, where detectors and parsers supply structured cues such as objects, shapes, and text beyond the model's intrinsic capacity. Across benchmarks, frontier VLMs with strong reasoning capabilities score well overall yet remain limited in professional knowledge and 3D reasoning. VCoder delivers a 12.3-point overall gain over the top-performing Claude-4-Opus. Human studies show that both humans and VLMs perform worse on rendered SVGs, their consistency reveals the promise of symbolic visual representation. The benchmark and code are available at https://github.com/CSU-JPG/VCode.", "github_manual": "", "github_regex": "https://github.com/CSU-JPG/VCode", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2510.19278", "title": "D2D: Detector-to-Differentiable Critic for Improved Numeracy in Text-to-Image Generation", "authors": [ "Nobline Yoo", "Olga Russakovsky", "Ye Zhu" ], "github": "", "project_page": "", "abstract": "Text-to-image (T2I) diffusion models have achieved strong performance in semantic alignment, yet they still struggle with generating the correct number of objects specified in prompts. Existing approaches typically incorporate auxiliary counting networks as external critics to enhance numeracy. However, since these critics must provide gradient guidance during generation, they are restricted to regression-based models that are inherently differentiable, thus excluding detector-based models with superior counting ability, whose count-via-enumeration nature is non-differentiable. To overcome this limitation, we propose Detector-to-Differentiable (D2D), a novel framework that transforms non-differentiable detection models into differentiable critics, thereby leveraging their superior counting ability to guide numeracy generation. Specifically, we design custom activation functions to convert detector logits into soft binary indicators, which are then used to optimize the noise prior at inference time with pre-trained T2I models. Our extensive experiments on SDXL-Turbo, SD-Turbo, and Pixart-DMD across four benchmarks of varying complexity (low-density, high-density, and multi-object scenarios) demonstrate consistent and substantial improvements in object counting accuracy (e.g., boosting up to 13.7% on D2D-Small, a 400-prompt, low-density benchmark), with minimal degradation in overall image quality and computational overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.02832", "title": "TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System", "authors": [ "Yanjie Ze", "Siheng Zhao", "Weizhuo Wang", "Angjoo Kanazawa", "Rocky Duan", "Pieter Abbeel", "Guanya Shi", "Jiajun Wu", "C. Karen Liu" ], "github": "https://github.com/amazon-far/TWIST2", "project_page": "", "abstract": "Large-scale data has driven breakthroughs in robotics, from language models to vision-language-action models in bimanual manipulation. However, humanoid robotics lacks equally effective data collection frameworks. Existing humanoid teleoperation systems either use decoupled control or depend on expensive motion capture setups. We introduce TWIST2, a portable, mocap-free humanoid teleoperation and data collection system that preserves full whole-body control while advancing scalability. Our system leverages PICO4U VR for obtaining real-time whole-body human motions, with a custom 2-DoF robot neck (cost around $250) for egocentric vision, enabling holistic human-to-humanoid control. We demonstrate long-horizon dexterous and mobile humanoid skills and we can collect 100 demonstrations in 15 minutes with an almost 100% success rate. Building on this pipeline, we propose a hierarchical visuomotor policy framework that autonomously controls the full humanoid body based on egocentric vision. Our visuomotor policy successfully demonstrates whole-body dexterous manipulation and dynamic kicking tasks. The entire system is fully reproducible and open-sourced at https://yanjieze.com/TWIST2 . Our collected dataset is also open-sourced at https://twist-data.github.io .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amazon-far/TWIST2", "github_api_added_by": "auto" }, { "date": "2025-11-05", "arxiv_id": "2511.01914", "title": "iFlyBot-VLA Technical Report", "authors": [ "Yuan Zhang", "Chenyu Xue", "Wenjie Xu", "Chao Ji", "Jiajia wu", "Jia Pan" ], "github": "https://github.com/xuwenjie401/iFlyBot-VLA", "project_page": "", "abstract": "We introduce iFlyBot-VLA, a large-scale Vision-Language-Action (VLA) model trained under a novel framework. The main contributions are listed as follows: (1) a latent action model thoroughly trained on large-scale human and robotic manipulation videos; (2) a dual-level action representation framework that jointly supervises both the Vision-Language Model (VLM) and the action expert during training; (3) a mixed training strategy that combines robot trajectory data with general QA and spatial QA datasets, effectively enhancing the 3D perceptual and reasoning capabilities of the VLM backbone. Specifically, the VLM is trained to predict two complementary forms of actions: latent actions, derived from our latent action model pretrained on cross-embodiment manipulation data, which capture implicit high-level intentions; and structured discrete action tokens, obtained through frequency-domain transformations of continuous control signals, which encode explicit low-level dynamics. This dual supervision aligns the representation spaces of language, vision, and action, enabling the VLM to directly contribute to action generation. Experimental results on the LIBERO Franka benchmark demonstrate the superiority of our frame-work, while real-world evaluations further show that iFlyBot-VLA achieves competitive success rates across diverse and challenging manipulation tasks. Furthermore, we plan to open-source a portion of our self-constructed dataset to support future research in the community", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xuwenjie401/iFlyBot-VLA", "github_api_added_by": "auto" }, { "date": "2025-11-05", "arxiv_id": "2511.02712", "title": "VidEmo: Affective-Tree Reasoning for Emotion-Centric Video Foundation Models", "authors": [ "Zhicheng Zhang", "Weicheng Wang", "Yongjie Zhu", "Wenyu Qin", "Pengfei Wan", "Di Zhang", "Jufeng Yang" ], "github": "", "project_page": "", "abstract": "Understanding and predicting emotion from videos has gathered significant attention in recent studies, driven by advancements in video large language models (VideoLLMs). While advanced methods have made progress in video emotion analysis, the intrinsic nature of emotions poses significant challenges. Emotions are characterized by dynamic and cues-dependent properties, making it difficult to understand complex and evolving emotional states with reasonable rationale. To tackle these challenges, we propose a novel affective cues-guided reasoning framework that unifies fundamental attribute perception, expression analysis, and high-level emotional understanding in a stage-wise manner. At the core of our approach is a family of video emotion foundation models (VidEmo), specifically designed for emotion reasoning and instruction-following. These models undergo a two-stage tuning process: first, curriculum emotion learning for injecting emotion knowledge, followed by affective-tree reinforcement learning for emotion reasoning. Moreover, we establish a foundational data infrastructure and introduce a emotion-centric fine-grained dataset (Emo-CFG) consisting of 2.1M diverse instruction-based samples. Emo-CFG includes explainable emotional question-answering, fine-grained captions, and associated rationales, providing essential resources for advancing emotion understanding tasks. Experimental results demonstrate that our approach achieves competitive performance, setting a new milestone across 15 face perception tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.02415", "title": "ChartM^3: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension", "authors": [ "Duo Xu", "Hao Cheng", "Xin Lin", "Zhen Xie", "Hao Wang" ], "github": "", "project_page": "", "abstract": "Complex chart understanding tasks demand advanced visual recognition and reasoning capabilities from multimodal large language models (MLLMs). However, current research provides limited coverage of complex chart scenarios and computation-intensive reasoning tasks prevalent in real-world applications. This study proposes an automated multi-stage code-driven pipeline for systematically generating visual reasoning datasets to address these limitations. The pipeline integrates retrieval-augmented generation (RAG) to retrieve professional chart templates and employs chain-of-thought (CoT) strategies to generate reasoning codes that simulate real data distributions, thereby driving chart rendering and question-related statistical computations. Through model-based evaluation, the pipeline enhances chart diversity and data quality. Using this framework, we construct ChartM^3, a multi-dimensional and multi-step dataset containing 38K charts and 142K Q&A pairs for training, along with 2,871 high-quality evaluation samples for enabling practical performance assessment. Supervised fine-tuning (SFT) and reinforcement learning (RL) experiments demonstrate that our dataset significantly improves reasoning capabilities and cross-domain generalization performance, enabling smaller models to achieve performance comparable to larger-scale models in complex chart comprehension.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.02366", "title": "LiveSecBench: A Dynamic and Culturally-Relevant AI Safety Benchmark for LLMs in Chinese Context", "authors": [ "Yudong Li", "Zhongliang Yang", "Kejiang Chen", "Wenxuan Wang", "Tianxin Zhang", "Sifang Wan", "Kecheng Wang", "Haitian Li", "Xu Wang", "Lefan Cheng", "Youdan Yang", "Baocheng Chen", "Ziyu Liu", "Yufei Sun", "Liyan Wu", "Wenya Wen", "Xingchi Gu", "Peiru Yang" ], "github": "https://github.com/ydli-ai/LiveSecBench", "project_page": "", "abstract": "In this work, we propose LiveSecBench, a dynamic and continuously updated safety benchmark specifically for Chinese-language LLM application scenarios. LiveSecBench evaluates models across six critical dimensions (Legality, Ethics, Factuality, Privacy, Adversarial Robustness, and Reasoning Safety) rooted in the Chinese legal and social frameworks. This benchmark maintains relevance through a dynamic update schedule that incorporates new threat vectors, such as the planned inclusion of Text-to-Image Generation Safety and Agentic Safety in the next update. For now, LiveSecBench (v251030) has evaluated 18 LLMs, providing a landscape of AI safety in the context of Chinese language. The leaderboard is publicly accessible at https://livesecbench.intokentech.cn/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ydli-ai/LiveSecBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.01502", "title": "Discriminately Treating Motion Components Evolves Joint Depth and Ego-Motion Learning", "authors": [ "Mengtan Zhang", "Zizhan Guo", "Hongbo Zhao", "Yi Feng", "Zuyi Xiong", "Yue Wang", "Shaoyi Du", "Hanli Wang", "Rui Fan" ], "github": "", "project_page": "", "abstract": "Unsupervised learning of depth and ego-motion, two fundamental 3D perception tasks, has made significant strides in recent years. However, most methods treat ego-motion as an auxiliary task, either mixing all motion types or excluding depth-independent rotational motions in supervision. Such designs limit the incorporation of strong geometric constraints, reducing reliability and robustness under diverse conditions. This study introduces a discriminative treatment of motion components, leveraging the geometric regularities of their respective rigid flows to benefit both depth and ego-motion estimation. Given consecutive video frames, network outputs first align the optical axes and imaging planes of the source and target cameras. Optical flows between frames are transformed through these alignments, and deviations are quantified to impose geometric constraints individually on each ego-motion component, enabling more targeted refinement. These alignments further reformulate the joint learning process into coaxial and coplanar forms, where depth and each translation component can be mutually derived through closed-form geometric relationships, introducing complementary constraints that improve depth robustness. DiMoDE, a general depth and ego-motion joint learning framework incorporating these designs, achieves state-of-the-art performance on multiple public datasets and a newly collected diverse real-world dataset, particularly under challenging conditions. Our source code will be publicly available at mias.group/DiMoDE upon publication.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.02779", "title": "When Visualizing is the First Step to Reasoning: MIRA, a Benchmark for Visual Chain-of-Thought", "authors": [ "Yiyang Zhou", "Haoqin Tu", "Zijun Wang", "Zeyu Wang", "Niklas Muennighoff", "Fan Nie", "Yejin Choi", "James Zou", "Chaorui Deng", "Shen Yan", "Haoqi Fan", "Cihang Xie", "Huaxiu Yao", "Qinghao Ye" ], "github": "", "project_page": "", "abstract": "We propose MIRA, a new benchmark designed to evaluate models in scenarios where generating intermediate visual images is essential for successful reasoning. Unlike traditional CoT methods that rely solely on text, tasks in MIRA require models to generate and utilize intermediate images - such as sketches, structural diagrams, or path drawings - to guide their reasoning process. This setup closely mirrors how humans solve complex problems through \"drawing to think\". To solve this, MIRA focuses on tasks that are intrinsically challenging and involve complex structures, spatial relationships, or reasoning steps that are difficult to express through language alone. To ensure that our evaluation data is of high-quality, we include 546 multimodal problems, annotated with intermediate visual images and final answers. We also propose a unified evaluation protocol for MIRA that spans three levels of evaluation input: direct input with image and question only, text-only CoT input with image and thinking prompts, and Visual-CoT input with both annotated image clues and textual thinking prompts. To probe the upper bound of model capacity on our benchmark, we also report pass@k and majority voting accuracies under different k settings. Experimental results show that existing multimodal large language models, including strongest private models as well as strong open-weight models, perform poorly when relying solely on textual prompts. However, when intermediate visual cues are provided, model performance improves consistently, yielding an average relative gain of 33.7% across all models and tasks. We also probe the upper bound by expanding the search space and designing textual prompts aligned with Visual-CoT, but both yield only limited improvements compared to our Visual-CoT setting. These results underscore the critical role of imagined visual information in enabling successful reasoning on MIRA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.02243", "title": "When Modalities Conflict: How Unimodal Reasoning Uncertainty Governs Preference Dynamics in MLLMs", "authors": [ "Zhuoran Zhang", "Tengyue Wang", "Xilin Gong", "Yang Shi", "Haotian Wang", "Di Wang", "Lijie Hu" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) must resolve conflicts when different modalities provide contradictory information, a process we term modality following. Prior work measured this behavior only with coarse dataset-level statistics, overlooking the influence of model's confidence in unimodal reasoning. In this paper, we introduce a new framework that decomposes modality following into two fundamental factors: relative reasoning uncertainty (the case-specific confidence gap between unimodal predictions) and inherent modality preference( a model's stable bias when uncertainties are balanced). To validate this framework, we construct a controllable dataset that systematically varies the reasoning difficulty of visual and textual inputs. Using entropy as a fine-grained uncertainty metric, we uncover a universal law: the probability of following a modality decreases monotonically as its relative uncertainty increases. At the relative difficulty level where the model tends to follow both modalities with comparable probability what we call the balance point, a practical indicator of the model's inherent preference. Unlike traditional macro-level ratios, this measure offers a more principled and less confounded way to characterize modality bias, disentangling it from unimodal capabilities and dataset artifacts. Further, by probing layer-wise predictions, we reveal the internal mechanism of oscillation: in ambiguous regions near the balance point, models vacillate between modalities across layers, explaining externally observed indecision. Together, these findings establish relative uncertainty and inherent preference as the two governing principles of modality following, offering both a quantitative framework and mechanistic insight into how MLLMs resolve conflicting information.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.02347", "title": "LTD-Bench: Evaluating Large Language Models by Letting Them Draw", "authors": [ "Liuhao Lin", "Ke Li", "Zihan Xu", "Yuchen Shi", "Yulei Qin", "Yan Zhang", "Xing Sun", "Rongrong Ji" ], "github": "", "project_page": "", "abstract": "Current evaluation paradigms for large language models (LLMs) represent a critical blind spot in AI research--relying on opaque numerical metrics that conceal fundamental limitations in spatial reasoning while providing no intuitive understanding of model capabilities. This deficiency creates a dangerous disconnect between reported performance and practical abilities, particularly for applications requiring physical world understanding. We introduce LTD-Bench, a breakthrough benchmark that transforms LLM evaluation from abstract scores to directly observable visual outputs by requiring models to generate drawings through dot matrices or executable code. This approach makes spatial reasoning limitations immediately apparent even to non-experts, bridging the fundamental gap between statistical performance and intuitive assessment. LTD-Bench implements a comprehensive methodology with complementary generation tasks (testing spatial imagination) and recognition tasks (assessing spatial perception) across three progressively challenging difficulty levels, methodically evaluating both directions of the critical language-spatial mapping. Our extensive experiments with state-of-the-art models expose an alarming capability gap: even LLMs achieving impressive results on traditional benchmarks demonstrate profound deficiencies in establishing bidirectional mappings between language and spatial concept--a fundamental limitation that undermines their potential as genuine world models. Furthermore, LTD-Bench's visual outputs enable powerful diagnostic analysis, offering a potential approach to investigate model similarity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.01937", "title": "Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR", "authors": [ "Abdelaziz Bounhar", "Hadi Abdine", "Evan Dufraisse", "Ahmad Chamma", "Amr Mohamed", "Dani Bouch", "Michalis Vazirgiannis", "Guokan Shang" ], "github": "https://github.com/MBZUAI-Paris/Frugal-AI", "project_page": "", "abstract": "Large language models (LLMs) trained for step-by-step reasoning often become excessively verbose, raising inference cost. Standard Reinforcement Learning with Verifiable Rewards (RLVR) pipelines filter out ``easy'' problems for training efficiency, leaving the model to train primarily on harder problems that require longer reasoning chains. This skews the output length distribution upward, resulting in a model that conflates ``thinking longer'' with ``thinking better''. In this work, we show that retaining and modestly up-weighting moderately easy problems acts as an implicit length regularizer. Exposing the model to solvable short-chain tasks constrains its output distribution and prevents runaway verbosity. The result is \\emph{emergent brevity for free}: the model learns to solve harder problems without inflating the output length, despite the absence of any explicit length penalization. RLVR experiments using this approach on Qwen3-4B-Thinking-2507 (with a 16k token limit) achieve baseline pass@1 AIME25 accuracy while generating solutions that are, on average, nearly twice as short. The code is available at https://github.com/MBZUAI-Paris/Frugal-AI{GitHub}, with datasets and models on https://huggingface.co/collections/MBZUAI-Paris/k2-think-mini-68dcfa8b114686a4bd3dc2bc{Hugging Face}.", "github_manual": "", "github_regex": "https://github.com/MBZUAI-Paris/Frugal-AI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.02490", "title": "BRAINS: A Retrieval-Augmented System for Alzheimer's Detection and Monitoring", "authors": [ "Rajan Das Gupta", "Md Kishor Morol", "Nafiz Fahad", "Md Tanzib Hosain", "Sumaya Binte Zilani Choya", "Md Jakir Hossen" ], "github": "", "project_page": "", "abstract": "As the global burden of Alzheimer's disease (AD) continues to grow, early and accurate detection has become increasingly critical, especially in regions with limited access to advanced diagnostic tools. We propose BRAINS (Biomedical Retrieval-Augmented Intelligence for Neurodegeneration Screening) to address this challenge. This novel system harnesses the powerful reasoning capabilities of Large Language Models (LLMs) for Alzheimer's detection and monitoring. BRAINS features a dual-module architecture: a cognitive diagnostic module and a case-retrieval module. The Diagnostic Module utilizes LLMs fine-tuned on cognitive and neuroimaging datasets -- including MMSE, CDR scores, and brain volume metrics -- to perform structured assessments of Alzheimer's risk. Meanwhile, the Case Retrieval Module encodes patient profiles into latent representations and retrieves similar cases from a curated knowledge base. These auxiliary cases are fused with the input profile via a Case Fusion Layer to enhance contextual understanding. The combined representation is then processed with clinical prompts for inference. Evaluations on real-world datasets demonstrate BRAINS effectiveness in classifying disease severity and identifying early signs of cognitive decline. This system not only shows strong potential as an assistive tool for scalable, explainable, and early-stage Alzheimer's disease detection, but also offers hope for future applications in the field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2510.25976", "title": "Brain-IT: Image Reconstruction from fMRI via Brain-Interaction Transformer", "authors": [ "Roman Beliy", "Amit Zalcher", "Jonathan Kogman", "Navve Wasserman", "Michal Irani" ], "github": "", "project_page": "", "abstract": "Reconstructing images seen by people from their fMRI brain recordings provides a non-invasive window into the human brain. Despite recent progress enabled by diffusion models, current methods often lack faithfulness to the actual seen images. We present \"Brain-IT\", a brain-inspired approach that addresses this challenge through a Brain Interaction Transformer (BIT), allowing effective interactions between clusters of functionally-similar brain-voxels. These functional-clusters are shared by all subjects, serving as building blocks for integrating information both within and across brains. All model components are shared by all clusters & subjects, allowing efficient training with a limited amount of data. To guide the image reconstruction, BIT predicts two complementary localized patch-level image features: (i)high-level semantic features which steer the diffusion model toward the correct semantic content of the image; and (ii)low-level structural features which help to initialize the diffusion process with the correct coarse layout of the image. BIT's design enables direct flow of information from brain-voxel clusters to localized image features. Through these principles, our method achieves image reconstructions from fMRI that faithfully reconstruct the seen images, and surpass current SotA approaches both visually and by standard objective metrics. Moreover, with only 1-hour of fMRI data from a new subject, we achieve results comparable to current methods trained on full 40-hour recordings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.02650", "title": "Can Visual Input Be Compressed? A Visual Token Compression Benchmark for Large Multimodal Models", "authors": [ "Tianfan Peng", "Yuntao Du", "Pengzhou Ji", "Shijie Dong", "Kailin Jiang", "Mingchuan Ma", "Yijun Tian", "Jinhe Bi", "Qian Li", "Wei Du", "Feng Xiao", "Lizhen Cui" ], "github": "", "project_page": "", "abstract": "Large multimodal models (LMMs) often suffer from severe inference inefficiency due to the large number of visual tokens introduced by image encoders. While recent token compression methods, such as pruning and merging, have shown promise in reducing redundancy, their evaluation remains fragmented and inconsistent. In this work, we present UniPruneBench, a unified and extensible benchmark for visual token pruning in multimodal LLMs. UniPruneBench provides standardized protocols across six ability dimensions and ten datasets, covering ten representative compression algorithms and three families of LMMs (LLaVA-v1.5, Intern-VL3, and Qwen2.5-VL). Beyond task accuracy, it incorporates system-level metrics such as runtime and prefilling latency to provide a holistic view. Our experiments uncover several key findings: (1) random pruning is a surprisingly strong baseline, (2) no single method consistently outperforms others across scenarios, (3) pruning sensitivity varies significantly across tasks, with OCR being most vulnerable, and (4) pruning ratio is the dominant factor governing performance degradation. We believe UniPruneBench will serve as a reliable foundation for future research on efficient multimodal modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.00839", "title": "CodeClash: Benchmarking Goal-Oriented Software Engineering", "authors": [ "John Yang", "Kilian Lieret", "Joyce Yang", "Carlos E. Jimenez", "Ofir Press", "Ludwig Schmidt", "Diyi Yang" ], "github": "", "project_page": "", "abstract": "Current benchmarks for coding evaluate language models (LMs) on concrete, well-specified tasks such as fixing specific bugs or writing targeted tests. However, human programmers do not spend all day incessantly addressing isolated tasks. Instead, real-world software development is grounded in the pursuit of high-level goals, like improving user retention or reducing costs. Evaluating whether LMs can also iteratively develop code to better accomplish open-ended objectives without any explicit guidance remains an open challenge. To address this, we introduce CodeClash, a benchmark where LMs compete in multi-round tournaments to build the best codebase for achieving a competitive objective. Each round proceeds in two phases: agents edit their code, then their codebases compete head-to-head in a code arena that determines winners based on objectives like score maximization, resource acquisition, or survival. Whether it's writing notes, scrutinizing documentation, analyzing competition logs, or creating test suites, models must decide for themselves how to improve their codebases both absolutely and against their opponents. We run 1680 tournaments (25,200 rounds total) to evaluate 8 LMs across 6 arenas. Our results reveal that while models exhibit diverse development styles, they share fundamental limitations in strategic reasoning. Models also struggle with long-term codebase maintenance, as repositories become progressively messy and redundant. These limitations are stark: top models lose every round against expert human programmers. We open-source CodeClash to advance the study of autonomous, goal-oriented code development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.02219", "title": "TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data", "authors": [ "Changjiang Jiang", "Fengchang Yu", "Haihua Chen", "Wei Lu", "Jin Zeng" ], "github": "", "project_page": "", "abstract": "Complex reasoning over tabular data is crucial in real-world data analysis, yet large language models (LLMs) often underperform due to complex queries, noisy data, and limited numerical capabilities. To address these issues, we propose \\method, a framework consisting of: (1) a query decomposer that breaks down complex questions, (2) a table sanitizer that cleans and filters noisy tables, and (3) a program-of-thoughts (PoT)-based reasoner that generates executable code to derive the final answer from the sanitized table. To ensure unbiased evaluation and mitigate data leakage, we introduce a new dataset, CalTab151, specifically designed for complex numerical reasoning over tables. Experimental results demonstrate that \\method consistently outperforms existing methods, achieving state-of-the-art (SOTA) performance with 8.79%, 6.08%, and 19.87% accuracy improvement on TAT-QA, TableBench, and \\method, respectively. Moreover, our framework integrates seamlessly with mainstream LLMs, providing a robust solution for complex tabular numerical reasoning. These findings highlight the effectiveness of our framework in enhancing LLM performance for complex tabular numerical reasoning. Data and code are available upon request.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2510.25616", "title": "Don't Blind Your VLA: Aligning Visual Representations for OOD Generalization", "authors": [ "Nikita Kachaev", "Mikhail Kolosov", "Daniil Zelezetsky", "Alexey K. Kovalev", "Aleksandr I. Panov" ], "github": "", "project_page": "", "abstract": "The growing success of Vision-Language-Action (VLA) models stems from the promise that pretrained Vision-Language Models (VLMs) can endow agents with transferable world knowledge and vision-language (VL) grounding, laying a foundation for action models with broader generalization. Yet when these VLMs are adapted to the action modality, it remains unclear to what extent their original VL representations and knowledge are preserved. In this work, we conduct a systematic study of representation retention during VLA fine-tuning, showing that naive action fine-tuning leads to degradation of visual representations. To characterize and measure these effects, we probe VLA's hidden representations and analyze attention maps, further, we design a set of targeted tasks and methods that contrast VLA models with their counterpart VLMs, isolating changes in VL capabilities induced by action fine-tuning. We further evaluate a range of strategies for aligning visual representations and introduce a simple yet effective method that mitigates degradation and yields improved generalization to out-of-distribution (OOD) scenarios. Taken together, our analysis clarifies the trade-off between action fine-tuning and the degradation of VL representations and highlights practical approaches to recover inherited VL capabilities. Code is publicly available: https://blind-vla-paper.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.02687", "title": "The Collaboration Gap", "authors": [ "Tim R. Davidson", "Adam Fourney", "Saleema Amershi", "Robert West", "Eric Horvitz", "Ece Kamar" ], "github": "", "project_page": "", "abstract": "The trajectory of AI development suggests that we will increasingly rely on agent-based systems composed of independently developed agents with different information, privileges, and tools. The success of these systems will critically depend on effective collaboration among these heterogeneous agents, even under partial observability. Despite intense interest, few empirical studies have evaluated such agent-agent collaboration at scale. We propose a collaborative maze-solving benchmark that (i) isolates collaborative capabilities, (ii) modulates problem complexity, (iii) enables scalable automated grading, and (iv) imposes no output-format constraints, preserving ecological plausibility. Using this framework, we evaluate 32 leading open- and closed-source models in solo, homogeneous, and heterogeneous pairings. Our results reveal a \"collaboration gap\": models that perform well solo often degrade substantially when required to collaborate. Collaboration can break down dramatically; for instance, small distilled models that solve mazes well alone may fail almost completely in certain pairings. We find that starting with the stronger agent often improves outcomes, motivating a \"relay inference\" approach where the stronger agent leads before handing off to the weaker one, closing much of the gap. Our findings argue for (1) collaboration-aware evaluation, (2) training strategies developed to enhance collaborative capabilities, and (3) interaction design that reliably elicits agents' latent skills, guidance that applies to AI-AI and human-AI collaboration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2510.17950", "title": "RoboChallenge: Large-scale Real-robot Evaluation of Embodied Policies", "authors": [ "Adina Yakefu", "Bin Xie", "Chongyang Xu", "Enwen Zhang", "Erjin Zhou", "Fan Jia", "Haitao Yang", "Haoqiang Fan", "Haowei Zhang", "Hongyang Peng", "Jing Tan", "Junwen Huang", "Kai Liu", "Kaixin Liu", "Kefan Gu", "Qinglun Zhang", "Ruitao Zhang", "Saike Huang", "Shen Cheng", "Shuaicheng Liu", "Tiancai Wang", "Tiezhen Wang", "Wei Sun", "Wenbin Tang", "Yajun Wei", "Yang Chen", "Youqiang Gui", "Yucheng Zhao", "Yunchao Ma", "Yunfei Wei", "Yunhuan Yang", "Yutong Guo", "Ze Chen", "Zhengyuan Du", "Ziheng Zhang", "Ziming Liu", "Ziwei Yan" ], "github": "", "project_page": "", "abstract": "Testing on real machines is indispensable for robotic control algorithms. In the context of learning-based algorithms, especially VLA models, demand for large-scale evaluation, i.e. testing a large number of models on a large number of tasks, is becoming increasingly urgent. However, doing this right is highly non-trivial, especially when scalability and reproducibility is taken into account. In this report, we describe our methodology for constructing RoboChallenge, an online evaluation system to test robotic control algorithms, and our survey of recent state-of-the-art VLA models using our initial benchmark Table30.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.01450", "title": "Reg-DPO: SFT-Regularized Direct Preference Optimization with GT-Pair for Improving Video Generation", "authors": [ "Jie Du", "Xinyu Gong", "Qingshan Tan", "Wen Li", "Yangming Cheng", "Weitao Wang", "Chenlu Zhan", "Suhui Wu", "Hao Zhang", "Jun Zhang" ], "github": "", "project_page": "", "abstract": "Recent studies have identified Direct Preference Optimization (DPO) as an efficient and reward-free approach to improving video generation quality. However, existing methods largely follow image-domain paradigms and are mainly developed on small-scale models (approximately 2B parameters), limiting their ability to address the unique challenges of video tasks, such as costly data construction, unstable training, and heavy memory consumption. To overcome these limitations, we introduce a GT-Pair that automatically builds high-quality preference pairs by using real videos as positives and model-generated videos as negatives, eliminating the need for any external annotation. We further present Reg-DPO, which incorporates the SFT loss as a regularization term into the DPO objective to enhance training stability and generation fidelity. Additionally, by combining the FSDP framework with multiple memory optimization techniques, our approach achieves nearly three times higher training capacity than using FSDP alone. Extensive experiments on both I2V and T2V tasks across multiple datasets demonstrate that our method consistently outperforms existing approaches, delivering superior video generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2510.24932", "title": "RiddleBench: A New Generative Reasoning Benchmark for LLMs", "authors": [ "Deepon Halder", "Alan Saji", "Thanmay Jayakumar", "Ratish Puduppully", "Anoop Kunchukuttan", "Raj Dabre" ], "github": "", "project_page": "", "abstract": "Large Language Models have demonstrated strong performance on many established reasoning benchmarks. However, these benchmarks primarily evaluate structured skills like quantitative problem-solving, leaving a gap in assessing flexible, multifaceted reasoning abilities that are central to human intelligence. These abilities require integrating logical deduction with spatial awareness and constraint satisfaction, which current evaluations do not measure well. To address this, we introduce RiddleBench, a benchmark of 1,737 challenging puzzles in English designed to probe these core reasoning capabilities. Evaluation of state-of-the-art models on RiddleBench shows fundamental weaknesses. Even top proprietary models like Gemini 2.5 Pro, o3, and Claude 4 Sonnet achieve accuracy just above 60% (60.30%, 63.37%, and 63.16%). Analysis further reveals deep failures, including hallucination cascades (accepting flawed reasoning from other models) and poor self-correction due to a strong self-confirmation bias. Their reasoning is also fragile, with performance degrading significantly when constraints are reordered or irrelevant information is introduced. RiddleBench functions as a diagnostic tool for these issues and as a resource for guiding the development of more robust and reliable language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.02374", "title": "AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda", "authors": [ "Mohd Nauman", "Sravan Gvm", "Vijay Devane", "Shyam Pawar", "Viraj Thakur", "Kundeshwar Pundalik", "Piyush Sawarkar", "Rohit Saluja", "Maunendra Desarkar", "Ganesh Ramakrishnan" ], "github": "", "project_page": "", "abstract": "Current large language models excel at broad, general-purpose tasks, but consistently underperform when exposed to highly specialized domains that require deep cultural, linguistic, and subject-matter expertise. In particular, traditional medical systems such as Ayurveda embody centuries of nuanced textual and clinical knowledge that mainstream LLMs fail to accurately interpret or apply. We introduce AyurParam-2.9B, a domain-specialized, bilingual language model fine-tuned from Param-1-2.9B using an extensive, expertly curated Ayurveda dataset spanning classical texts and clinical guidance. AyurParam's dataset incorporates context-aware, reasoning, and objective-style Q&A in both English and Hindi, with rigorous annotation protocols for factual precision and instructional clarity. Benchmarked on BhashaBench-Ayur, AyurParam not only surpasses all open-source instruction-tuned models in its size class (1.5--3B parameters), but also demonstrates competitive or superior performance compared to much larger models. The results from AyurParam highlight the necessity for authentic domain adaptation and high-quality supervision in delivering reliable, culturally congruent AI for specialized medical knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.03601", "title": "Step-Audio-EditX Technical Report", "authors": [ "Chao Yan", "Boyong Wu", "Peng Yang", "Pengfei Tan", "Guoqiang Hu", "Yuxin Zhang", "Xiangyu", "Zhang", "Fei Tian", "Xuerui Yang", "Xiangyu Zhang", "Daxin Jiang", "Gang Yu" ], "github": "https://github.com/stepfun-ai/Step-Audio-EditX", "project_page": "", "abstract": "We present Step-Audio-EditX, the first open-source LLM-based audio model excelling at expressive and iterative audio editing encompassing emotion, speaking style, and paralinguistics alongside robust zero-shot text-to-speech (TTS) capabilities.Our core innovation lies in leveraging only large-margin synthetic data, which circumvents the need for embedding-based priors or auxiliary modules. This large-margin learning approach enables both iterative control and high expressivity across voices, and represents a fundamental pivot from the conventional focus on representation-level disentanglement. Evaluation results demonstrate that Step-Audio-EditX surpasses both MiniMax-2.6-hd and Doubao-Seed-TTS-2.0 in emotion editing and other fine-grained control tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stepfun-ai/Step-Audio-EditX", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-05", "arxiv_id": "2511.01202", "title": "Forget BIT, It is All about TOKEN: Towards Semantic Information Theory for LLMs", "authors": [ "Bo Bai" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities in numerous real-world applications. While the vast majority of research conducted from an experimental perspective is progressing rapidly, it demands substantial computational power, data, and other resources. Therefore, how to open the black-box of LLMs from a theoretical standpoint has become a critical challenge. This paper takes the theory of rate-distortion function, directed information, and Granger causality as its starting point to investigate the information-theoretic principles behind LLMs, leading to the development of semantic information theory for LLMs, where the fundamental unit is token, rather than bits that lacks any semantic meaning. By defining the probabilistic model of LLMs, we discuss structure-agnostic information-theoretic measures, such as the directed rate-distortion function in pre-training, the directed rate-reward function in post-training, and the semantic information flow in inference phase. This paper also delves deeply into the theory of token-level semantic embedding and the information-theoretically optimal vectorization method. Thereafter, we propose a general definition of autoregression LLM, where the Transformer architecture and its performance such as ELBO, generalization error bound, memory capacity, and semantic information measures can be derived theoretically. Other architectures, such as Mamba/Mamba2 and LLaDA, are also discussed in our framework. Consequently, this paper provides a theoretical framework for understanding LLMs from the perspective of semantic information theory, which also offers the necessary theoretical tools for further in-depth research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-06", "arxiv_id": "2511.03276", "title": "Diffusion Language Models are Super Data Learners", "authors": [ "Jinjie Ni", "Qian Liu", "Longxu Dou", "Chao Du", "Zili Wang", "Hang Yan", "Tianyu Pang", "Michael Qizhe Shieh" ], "github": "", "project_page": "", "abstract": "Under strictly controlled pre-training settings, we observe a Crossover: when unique data is limited, diffusion language models (DLMs) consistently surpass autoregressive (AR) models by training for more epochs. The crossover shifts later with more or higher-quality data, earlier with larger models, and persists across dense and sparse architectures. We attribute the gains to three compounding factors: (1) any-order modeling, (2) super-dense compute from iterative bidirectional denoising, and (3) built-in Monte Carlo augmentation; input or parameter noise improves AR under data constraint but cannot close the gap. At scale, a 1.7B DLM trained with a ~1.5T-token compute budget on 10B unique Python tokens overtakes an AR coder trained with strictly matched settings. In addition, a 1B-parameter DLM achieves > 56% accuracy on HellaSwag and > 33% on MMLU using only 1B tokens, without any special tricks, just by repeating standard pre-training data. We also show that rising validation cross-entropy does not imply degraded downstream performance in this regime.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-06", "arxiv_id": "2511.03628", "title": "LiveTradeBench: Seeking Real-World Alpha with Large Language Models", "authors": [ "Haofei Yu", "Fenghai Li", "Jiaxuan You" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) achieve strong performance across benchmarks--from knowledge quizzes and math reasoning to web-agent tasks--but these tests occur in static settings, lacking real dynamics and uncertainty. Consequently, they evaluate isolated reasoning or problem-solving rather than decision-making under uncertainty. To address this, we introduce LiveTradeBench, a live trading environment for evaluating LLM agents in realistic and evolving markets. LiveTradeBench follows three design principles: (i) Live data streaming of market prices and news, eliminating dependence on offline backtesting and preventing information leakage while capturing real-time uncertainty; (ii) a portfolio-management abstraction that extends control from single-asset actions to multi-asset allocation, integrating risk management and cross-asset reasoning; and (iii) multi-market evaluation across structurally distinct environments--U.S. stocks and Polymarket prediction markets--differing in volatility, liquidity, and information flow. At each step, an agent observes prices, news, and its portfolio, then outputs percentage allocations that balance risk and return. Using LiveTradeBench, we run 50-day live evaluations of 21 LLMs across families. Results show that (1) high LMArena scores do not imply superior trading outcomes; (2) models display distinct portfolio styles reflecting risk appetite and reasoning dynamics; and (3) some LLMs effectively leverage live signals to adapt decisions. These findings expose a gap between static evaluation and real-world competence, motivating benchmarks that test sequential decision making and consistency under live uncertainty.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-06", "arxiv_id": "2511.03146", "title": "MME-CC: A Challenging Multi-Modal Evaluation Benchmark of Cognitive Capacity", "authors": [ "Kaiyuan Zhang", "Chenghao Yang", "Zhoufutu Wen", "Sihang Yuan", "Qiuyue Wang", "Chaoyi Huang", "Guosheng Zhu", "He Wang", "Huawenyu Lu", "Jianing Wen", "Jianpeng Jiao", "Lishu Luo", "Longxiang Liu", "Sijin Wu", "Xiaolei Zhu", "Xuanliang Zhang", "Ge Zhang", "Yi Lin", "Guang Shi", "Chaoyou Fu", "Wenhao Huang" ], "github": "", "project_page": "", "abstract": "As reasoning models scale rapidly, the essential role of multimodality in human cognition has come into sharp relief, driving a growing need to probe vision-centric cognitive behaviors. Yet, existing multimodal benchmarks either overemphasize textual reasoning or fall short of systematically capturing vision-centric cognitive behaviors, leaving the cognitive capacity of MLLMs insufficiently assessed. To address this limitation, we introduce MME-CC (Multi-Modal Evaluation benchmark of Cognitive Capacity), a vision-grounded benchmark that organizes 11 representative reasoning tasks into three fundamental categories of visual information: spatial, geometric, and knowledge-based reasoning, and provides fine-grained analyses of MLLMs' cognitive capacity across these dimensions. Based on MME-CC, we conduct extensive experiments over 16 representative MLLMs. Our study reveals that closed-source models currently lead overall (e.g., 42.66 for Gemini-2.5-Pro vs. 30.45 for GLM-4.5V), while spatial and geometric reasoning remain broadly weak (less than or equal to 30%). We further identify common error patterns, including orientation mistakes, fragile cross-view identity persistence, and poor adherence to counterfactual instructions, and observe that Chain-of-Thought typically follows a three-stage process (extract -> reason -> verify) with heavy reliance on visual extraction. We hope this work catalyzes a shift toward treating the cognitive capacity of MLLMs as central to both evaluation and model design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-06", "arxiv_id": "2511.01294", "title": "Kinematify: Open-Vocabulary Synthesis of High-DoF Articulated Objects", "authors": [ "Jiawei Wang", "Dingyou Wang", "Jiaming Hu", "Qixuan Zhang", "Jingyi Yu", "Lan Xu" ], "github": "", "project_page": "", "abstract": "A deep understanding of kinematic structures and movable components is essential for enabling robots to manipulate objects and model their own articulated forms. Such understanding is captured through articulated objects, which are essential for tasks such as physical simulation, motion planning, and policy learning. However, creating these models, particularly for objects with high degrees of freedom (DoF), remains a significant challenge. Existing methods typically rely on motion sequences or strong assumptions from hand-curated datasets, which hinders scalability. In this paper, we introduce Kinematify, an automated framework that synthesizes articulated objects directly from arbitrary RGB images or textual descriptions. Our method addresses two core challenges: (i) inferring kinematic topologies for high-DoF objects and (ii) estimating joint parameters from static geometry. To achieve this, we combine MCTS search for structural inference with geometry-driven optimization for joint reasoning, producing physically consistent and functionally valid descriptions. We evaluate Kinematify on diverse inputs from both synthetic and real-world environments, demonstrating improvements in registration and kinematic topology accuracy over prior work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-06", "arxiv_id": "2511.03001", "title": "LEGO-Eval: Towards Fine-Grained Evaluation on Synthesizing 3D Embodied Environments with Tool Augmentation", "authors": [ "Gyeom Hwangbo", "Hyungjoo Chae", "Minseok Kang", "Hyeonjong Ju", "Soohyun Oh", "Jinyoung Yeo" ], "github": "https://github.com/gyeomh/LEGO-EVAL", "project_page": "", "abstract": "Despite recent progress in using Large Language Models (LLMs) for automatically generating 3D scenes, generated scenes often lack realistic spatial layouts and object attributes found in real-world environments. As this problem stems from insufficiently detailed, coarse-grained instructions, advancing 3D scene synthesis guided by more detailed, fine-grained instructions that reflect real-world environments becomes crucial. Without such realistic scenes, training embodied agents in unrealistic environments can lead them to learn priors that diverge significantly from real-world physics and semantics, degrading their performance when deployed. Thus, verifying the alignment between the fine-grained instruction and the generated scene is essential for effective learning. However, current evaluation methods, such as CLIPScore and vision-language models (VLMs), often fail to reliably assess such alignment. This shortcoming arises primarily from their shallow understanding of 3D scenes, which often leads to improperly grounded scene components. To address this, we introduce LEGO-Eval, an evaluation framework equipped with diverse tools designed to explicitly ground scene components, enabling more accurate alignment assessments. We also present LEGO-Bench, a benchmark of detailed instructions that specify complex layouts and attributes of real-world environments. Experiments demonstrate that LEGO-Eval outperforms VLM-as-a-judge by 0.41 F1 score in assessing scene-instruction alignment. Benchmarking with LEGO-Bench reveals significant limitations in current generation methods. Across all evaluated approaches, success rates reached at most 10% in generating scenes that fully align with fine-grained instructions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gyeomh/LEGO-EVAL", "github_api_added_by": "auto" }, { "date": "2025-11-06", "arxiv_id": "2511.02358", "title": "Let Multimodal Embedders Learn When to Augment Query via Adaptive Query Augmentation", "authors": [ "Wongyu Kim", "Hochang Lee", "Sanghak Lee", "Yoonsung Kim", "Jaehyun Park" ], "github": "", "project_page": "", "abstract": "Query augmentation makes queries more meaningful by appending further information to the queries to find relevant documents. Current studies have proposed Large Language Model (LLM)-based embedders, which learn representation for embedding and generation for query augmentation in a multi-task manner by leveraging the generative capabilities of LLM. During inference, these jointly trained embedders have conducted query augmentation followed by embedding, showing effective results. However, augmenting every query leads to substantial embedding latency and query augmentation can be detrimental to performance for some queries. Also, previous methods have not been explored in multimodal environments. To tackle these problems, we propose M-Solomon, a universal multimodal embedder that can adaptively determine when to augment queries. Our approach first divides the queries of the training datasets into two groups at the dataset level. One includes queries that require augmentation and the other includes queries that do not. Then, we introduces a synthesis process that generates appropriate augmentations for queries that require them by leveraging a powerful Multimodal LLM (MLLM). Next, we present adaptive query augmentation. Through this step, M-Solomon can conduct query augmentation only when necessary by learning to generate synthetic augmentations with the prefix /augment for queries that demand them and to generate the simple string /embed for others. Experimental results showed that M-Solomon not only surpassed the baseline without augmentation by a large margin but also outperformed the baseline that always used augmentation, providing much faster embedding latency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-06", "arxiv_id": "2511.02818", "title": "Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning", "authors": [ "Mohamed Bouadi", "Pratinav Seth", "Aditya Tanna", "Vinay Kumar Sankarapu" ], "github": "https://github.com/Lexsi-Labs/Orion-MSP", "project_page": "", "abstract": "Tabular data remain the predominant format for real-world applications. Yet, developing effective neural models for tabular data remains challenging due to heterogeneous feature types and complex interactions occurring at multiple scales. Recent advances in tabular in-context learning (ICL), such as TabPFN and TabICL, have achieved state-of-the-art performance comparable to gradient-boosted trees (GBTs) without task-specific fine-tuning. However, current architectures exhibit key limitations: (1) single-scale feature processing that overlooks hierarchical dependencies, (2) dense attention with quadratic scaling in table width, and (3) strictly sequential component processing that prevents iterative representation refinement and cross-component communication. To address these challenges, we introduce Orion-MSP, a tabular ICL architecture featuring three key innovations: (1) multi-scale processing to capture hierarchical feature interactions; (2) block-sparse attention combining windowed, global, and random patterns for scalable efficiency and long-range connectivity; and (3) a Perceiver-style memory enabling safe bidirectional information flow across components. Across diverse benchmarks, Orion-MSP matches or surpasses state-of-the-art performance while scaling effectively to high-dimensional tables, establishing a new standard for efficient tabular in-context learning. The model is publicly available at https://github.com/Lexsi-Labs/Orion-MSP .", "github_manual": "", "github_regex": "https://github.com/Lexsi-Labs/Orion-MSP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-06", "arxiv_id": "2511.02802", "title": "TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models", "authors": [ "Aditya Tanna", "Pratinav Seth", "Mohamed Bouadi", "Utsav Avaiya", "Vinay Kumar Sankarapu" ], "github": "https://github.com/Lexsi-Labs/TabTune", "project_page": "", "abstract": "Tabular foundation models represent a growing paradigm in structured data learning, extending the benefits of large-scale pretraining to tabular domains. However, their adoption remains limited due to heterogeneous preprocessing pipelines, fragmented APIs, inconsistent fine-tuning procedures, and the absence of standardized evaluation for deployment-oriented metrics such as calibration and fairness. We present TabTune, a unified library that standardizes the complete workflow for tabular foundation models through a single interface. TabTune provides consistent access to seven state-of-the-art models supporting multiple adaptation strategies, including zero-shot inference, meta-learning, supervised fine-tuning (SFT), and parameter-efficient fine-tuning (PEFT). The framework automates model-aware preprocessing, manages architectural heterogeneity internally, and integrates evaluation modules for performance, calibration, and fairness. Designed for extensibility and reproducibility, TabTune enables consistent benchmarking of adaptation strategies of tabular foundation models. The library is open source and available at https://github.com/Lexsi-Labs/TabTune .", "github_manual": "", "github_regex": "https://github.com/Lexsi-Labs/TabTune", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-06", "arxiv_id": "2511.03334", "title": "UniAVGen: Unified Audio and Video Generation with Asymmetric Cross-Modal Interactions", "authors": [ "Guozhen Zhang", "Zixiang Zhou", "Teng Hu", "Ziqiao Peng", "Youliang Zhang", "Yi Chen", "Yuan Zhou", "Qinglin Lu", "Limin Wang" ], "github": "", "project_page": "", "abstract": "Due to the lack of effective cross-modal modeling, existing open-source audio-video generation methods often exhibit compromised lip synchronization and insufficient semantic consistency. To mitigate these drawbacks, we propose UniAVGen, a unified framework for joint audio and video generation. UniAVGen is anchored in a dual-branch joint synthesis architecture, incorporating two parallel Diffusion Transformers (DiTs) to build a cohesive cross-modal latent space. At its heart lies an Asymmetric Cross-Modal Interaction mechanism, which enables bidirectional, temporally aligned cross-attention, thus ensuring precise spatiotemporal synchronization and semantic consistency. Furthermore, this cross-modal interaction is augmented by a Face-Aware Modulation module, which dynamically prioritizes salient regions in the interaction process. To enhance generative fidelity during inference, we additionally introduce Modality-Aware Classifier-Free Guidance, a novel strategy that explicitly amplifies cross-modal correlation signals. Notably, UniAVGen's robust joint synthesis design enables seamless unification of pivotal audio-video tasks within a single model, such as joint audio-video generation and continuation, video-to-audio dubbing, and audio-driven video synthesis. Comprehensive experiments validate that, with far fewer training samples (1.3M vs. 30.1M), UniAVGen delivers overall advantages in audio-video synchronization, timbre consistency, and emotion consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-06", "arxiv_id": "2511.02309", "title": "The Sequential Edge: Inverse-Entropy Voting Beats Parallel Self-Consistency at Matched Compute", "authors": [ "Aman Sharma", "Paras Chopra" ], "github": "", "project_page": "", "abstract": "We revisit test-time scaling for language model reasoning and ask a fundamental question: at equal token budget and compute, is it better to run multiple independent chains in parallel, or to run fewer chains that iteratively refine through sequential steps? Through comprehensive evaluation across 5 state-of-the-art open source models and 3 challenging reasoning benchmarks, we find that sequential scaling where chains explicitly build upon previous attempts consistently outperforms the dominant parallel self-consistency paradigm in 95.6% of configurations with gains in accuracy upto 46.7%. Further, we introduce inverse-entropy weighted voting, a novel training-free method to further boost the accuracy of sequential scaling. By weighing answers in proportion to the inverse entropy of their reasoning chains, we increase our success rate over parallel majority and establish it as the optimal test-time scaling strategy. Our findings fundamentally challenge the parallel reasoning orthodoxy that has dominated test-time scaling since Wang et al.'s self-consistency decoding (Wang et al., 2022), positioning sequential refinement as the robust default for modern LLM reasoning and necessitating a paradigm shift in how we approach inference-time optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-06", "arxiv_id": "2511.03718", "title": "Grounded Misunderstandings in Asymmetric Dialogue: A Perspectivist Annotation Scheme for MapTask", "authors": [ "Nan Li", "Albert Gatt", "Massimo Poesio" ], "github": "", "project_page": "", "abstract": "Collaborative dialogue relies on participants incrementally establishing common ground, yet in asymmetric settings they may believe they agree while referring to different entities. We introduce a perspectivist annotation scheme for the HCRC MapTask corpus (Anderson et al., 1991) that separately captures speaker and addressee grounded interpretations for each reference expression, enabling us to trace how understanding emerges, diverges, and repairs over time. Using a scheme-constrained LLM annotation pipeline, we obtain 13k annotated reference expressions with reliability estimates and analyze the resulting understanding states. The results show that full misunderstandings are rare once lexical variants are unified, but multiplicity discrepancies systematically induce divergences, revealing how apparent grounding can mask referential misalignment. Our framework provides both a resource and an analytic lens for studying grounded misunderstanding and for evaluating (V)LLMs' capacity to model perspective-dependent grounding in collaborative dialogue.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-06", "arxiv_id": "2511.02734", "title": "CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents", "authors": [ "Jiayu Liu", "Cheng Qian", "Zhaochen Su", "Qing Zong", "Shijue Huang", "Bingxiang He", "Yi R. Fung" ], "github": "https://github.com/JiayuJeff/CostBench", "project_page": "", "abstract": "Current evaluations of Large Language Model (LLM) agents primarily emphasize task completion, often overlooking resource efficiency and adaptability. This neglects a crucial capability: agents' ability to devise and adjust cost-optimal plans in response to changing environments. To bridge this gap, we introduce CostBench, a scalable, cost-centric benchmark designed to evaluate agents' economic reasoning and replanning abilities. Situated in the travel-planning domain, CostBench comprises tasks solvable via multiple sequences of atomic and composite tools with diverse, customizable costs. It also supports four types of dynamic blocking events, such as tool failures and cost changes, to simulate real-world unpredictability and necessitate agents to adapt in real time. Evaluating leading open-sourced and proprietary models on CostBench reveals a substantial gap in cost-aware planning: agents frequently fail to identify cost-optimal solutions in static settings, with even GPT-5 achieving less than 75% exact match rate on the hardest tasks, and performance further dropping by around 40% under dynamic conditions. By diagnosing these weaknesses, CostBench lays the groundwork for developing future agents that are both economically rational and robust.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JiayuJeff/CostBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-06", "arxiv_id": "2511.04583", "title": "Jr. AI Scientist and Its Risk Report: Autonomous Scientific Exploration from a Baseline Paper", "authors": [ "Atsuyuki Miyai", "Mashiro Toyooka", "Takashi Otonari", "Zaiying Zhao", "Kiyoharu Aizawa" ], "github": "https://github.com/Agent4Science-UTokyo/Jr.AI-Scientist", "project_page": "", "abstract": "Understanding the current capabilities and risks of AI Scientist systems is essential for ensuring trustworthy and sustainable AI-driven scientific progress while preserving the integrity of the academic ecosystem. To this end, we develop Jr. AI Scientist, a state-of-the-art autonomous AI scientist system that mimics the core research workflow of a novice student researcher: Given the baseline paper from the human mentor, it analyzes its limitations, formulates novel hypotheses for improvement, validates them through rigorous experimentation, and writes a paper with the results. Unlike previous approaches that assume full automation or operate on small-scale code, Jr. AI Scientist follows a well-defined research workflow and leverages modern coding agents to handle complex, multi-file implementations, leading to scientifically valuable contributions. For evaluation, we conducted automated assessments using AI Reviewers, author-led evaluations, and submissions to Agents4Science, a venue dedicated to AI-driven scientific contributions. The findings demonstrate that Jr. AI Scientist generates papers receiving higher review scores than existing fully automated systems. Nevertheless, we identify important limitations from both the author evaluation and the Agents4Science reviews, indicating the potential risks of directly applying current AI Scientist systems and key challenges for future research. Finally, we comprehensively report various risks identified during development. We hope these insights will deepen understanding of current progress and risks in AI Scientist development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Agent4Science-UTokyo/Jr.AI-Scientist", "github_api_added_by": "auto" }, { "date": "2025-11-07", "arxiv_id": "2511.04460", "title": "V-Thinker: Interactive Thinking with Images", "authors": [ "Runqi Qiao", "Qiuna Tan", "Minghan Yang", "Guanting Dong", "Peiqing Yang", "Shiqiang Lang", "Enhui Wan", "Xiaowan Wang", "Yida Xu", "Lan Yang", "Chong Sun", "Chen Li", "Honggang Zhang" ], "github": "", "project_page": "", "abstract": "Empowering Large Multimodal Models (LMMs) to deeply integrate image interaction with long-horizon reasoning capabilities remains a long-standing challenge in this field. Recent advances in vision-centric reasoning explore a promising \"Thinking with Images\" paradigm for LMMs, marking a shift from image-assisted reasoning to image-interactive thinking. While this milestone enables models to focus on fine-grained image regions, progress remains constrained by limited visual tool spaces and task-specific workflow designs. To bridge this gap, we present V-Thinker, a general-purpose multimodal reasoning assistant that enables interactive, vision-centric thinking through end-to-end reinforcement learning. V-Thinker comprises two key components: (1) a Data Evolution Flywheel that automatically synthesizes, evolves, and verifies interactive reasoning datasets across three dimensions-diversity, quality, and difficulty; and (2) a Visual Progressive Training Curriculum that first aligns perception via point-level supervision, then integrates interactive reasoning through a two-stage reinforcement learning framework. Furthermore, we introduce VTBench, an expert-verified benchmark targeting vision-centric interactive reasoning tasks. Extensive experiments demonstrate that V-Thinker consistently outperforms strong LMM-based baselines in both general and interactive reasoning scenarios, providing valuable insights for advancing image-interactive reasoning applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-07", "arxiv_id": "2511.04307", "title": "GUI-360: A Comprehensive Dataset and Benchmark for Computer-Using Agents", "authors": [ "Jian Mu", "Chaoyun Zhang", "Chiming Ni", "Lu Wang", "Bo Qiao", "Kartik Mathur", "Qianhui Wu", "Yuhang Xie", "Xiaojun Ma", "Mengyu Zhou", "Si Qin", "Liqun Li", "Yu Kang", "Minghua Ma", "Qingwei Lin", "Saravan Rajmohan", "Dongmei Zhang" ], "github": "", "project_page": "", "abstract": "We introduce GUI-360^circ, a large-scale, comprehensive dataset and benchmark suite designed to advance computer-using agents (CUAs). CUAs present unique challenges and is constrained by three persistent gaps: a scarcity of real-world CUA tasks, the lack of automated collection-and-annotation pipelines for multi-modal trajectories, and the absence of a unified benchmark that jointly evaluates GUI grounding, screen parsing, and action prediction. GUI-360^circ addresses these gaps with an LLM-augmented, largely automated pipeline for query sourcing, environment-template construction, task instantiation, batched execution, and LLM-driven quality filtering. The released corpus contains over 1.2M executed action steps across thousands of trajectories in popular Windows office applications, and includes full-resolution screenshots, accessibility metadata when available, instantiated goals, intermediate reasoning traces, and both successful and failed action trajectories. The dataset supports three canonical tasks, GUI grounding, screen parsing, and action prediction, and a hybrid GUI+API action space that reflects modern agent designs. Benchmarking state-of-the-art vision--language models on GUI-360^circ reveals substantial out-of-the-box shortcomings in grounding and action prediction; supervised fine-tuning and reinforcement learning yield significant gains but do not close the gap to human-level reliability. We release GUI-360^circ and accompanying code to facilitate reproducible research and accelerate progress on robust desktop CUAs. The full dataset has been made public on https://huggingface.co/datasets/vyokky/GUI-360.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-07", "arxiv_id": "2511.04670", "title": "Cambrian-S: Towards Spatial Supersensing in Video", "authors": [ "Shusheng Yang", "Jihan Yang", "Pinzhi Huang", "Ellis Brown", "Zihao Yang", "Yue Yu", "Shengbang Tong", "Zihan Zheng", "Yifan Xu", "Muhan Wang", "Daohan Lu", "Rob Fergus", "Yann LeCun", "Li Fei-Fei", "Saining Xie" ], "github": "https://github.com/cambrian-mllm/cambrian-s", "project_page": "", "abstract": "We argue that progress in true multimodal intelligence calls for a shift from reactive, task-driven systems and brute-force long context towards a broader paradigm of supersensing. We frame spatial supersensing as four stages beyond linguistic-only understanding: semantic perception (naming what is seen), streaming event cognition (maintaining memory across continuous experiences), implicit 3D spatial cognition (inferring the world behind pixels), and predictive world modeling (creating internal models that filter and organize information). Current benchmarks largely test only the early stages, offering narrow coverage of spatial cognition and rarely challenging models in ways that require true world modeling. To drive progress in spatial supersensing, we present VSI-SUPER, a two-part benchmark: VSR (long-horizon visual spatial recall) and VSC (continual visual spatial counting). These tasks require arbitrarily long video inputs yet are resistant to brute-force context expansion. We then test data scaling limits by curating VSI-590K and training Cambrian-S, achieving +30% absolute improvement on VSI-Bench without sacrificing general capabilities. Yet performance on VSI-SUPER remains limited, indicating that scale alone is insufficient for spatial supersensing. We propose predictive sensing as a path forward, presenting a proof-of-concept in which a self-supervised next-latent-frame predictor leverages surprise (prediction error) to drive memory and event segmentation. On VSI-SUPER, this approach substantially outperforms leading proprietary baselines, showing that spatial supersensing requires models that not only see but also anticipate, select, and organize experience.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cambrian-mllm/cambrian-s", "github_api_added_by": "auto" }, { "date": "2025-11-07", "arxiv_id": "2511.04570", "title": "Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm", "authors": [ "Jingqi Tong", "Yurong Mou", "Hangcheng Li", "Mingzhe Li", "Yongzhuo Yang", "Ming Zhang", "Qiguang Chen", "Tianyi Liang", "Xiaomeng Hu", "Yining Zheng", "Xinchi Chen", "Jun Zhao", "Xuanjing Huang", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "\"Thinking with Text\" and \"Thinking with Images\" paradigm significantly improve the reasoning ability of large language models (LLMs) and Vision Language Models (VLMs). However, these paradigms have inherent limitations. (1) Images capture only single moments and fail to represent dynamic processes or continuous changes, and (2) The separation of text and vision as distinct modalities, hindering unified multimodal understanding and generation. To overcome these limitations, we introduce \"Thinking with Video\", a new paradigm that leverages video generation models, such as Sora-2, to bridge visual and textual reasoning in a unified temporal framework. To support this exploration, we developed the Video Thinking Benchmark (VideoThinkBench). VideoThinkBench encompasses two task categories: (1) vision-centric tasks (e.g., Eyeballing Puzzles), and (2) text-centric tasks (e.g., subsets of GSM8K, MMMU). Our evaluation establishes Sora-2 as a capable reasoner. On vision-centric tasks, Sora-2 is generally comparable to state-of-the-art (SOTA) VLMs, and even surpasses VLMs on several tasks, such as Eyeballing Games. On text-centric tasks, Sora-2 achieves 92% accuracy on MATH, and 75.53% accuracy on MMMU. Furthermore, we systematically analyse the source of these abilities. We also find that self-consistency and in-context learning can improve Sora-2's performance. In summary, our findings demonstrate that the video generation model is the potential unified multimodal understanding and generation model, positions \"thinking with video\" as a unified multimodal reasoning paradigm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-07", "arxiv_id": "2511.04217", "title": "The Strong Lottery Ticket Hypothesis for Multi-Head Attention Mechanisms", "authors": [ "Hikari Otsuka", "Daiki Chijiwa", "Yasuyuki Okoshi", "Daichi Fujiki", "Susumu Takeuchi", "Masato Motomura" ], "github": "", "project_page": "", "abstract": "The strong lottery ticket hypothesis (SLTH) conjectures that high-performing subnetworks, called strong lottery tickets (SLTs), are hidden in randomly initialized neural networks. Although recent theoretical studies have established the SLTH across various neural architectures, the SLTH for transformer architectures still lacks theoretical understanding. In particular, the current theory of the SLTH does not yet account for the multi-head attention (MHA) mechanism, a core component of transformers. To address this gap, we introduce a theoretical analysis of the existence of SLTs within MHAs. We prove that, if a randomly initialized MHA of H heads and input dimension d has the hidden dimension O(dlog(Hd^{3/2})) for the key and value, it contains an SLT that approximates an arbitrary MHA with the same input dimension with high probability. Furthermore, by leveraging this theory for MHAs, we extend the SLTH to transformers without normalization layers. We empirically validate our theoretical findings, demonstrating that the approximation error between the SLT within a source model (MHA and transformer) and an approximate target counterpart decreases exponentially by increasing the hidden dimension of the source model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-07", "arxiv_id": "2511.03929", "title": "NVIDIA Nemotron Nano V2 VL", "authors": [ "NVIDIA", "Amala Sanjay Deshmukh", "Kateryna Chumachenko", "Tuomas Rintamaki", "Matthieu Le", "Tyler Poon", "Danial Mohseni Taheri", "Ilia Karmanov", "Guilin Liu", "Jarno Seppanen", "Guo Chen", "Karan Sapra", "Zhiding Yu", "Adi Renduchintala", "Charles Wang", "Peter Jin", "Arushi Goel", "Mike Ranzinger", "Lukas Voegtle", "Philipp Fischer", "Timo Roman", "Wei Ping", "Boxin Wang", "Zhuolin Yang", "Nayeon Lee", "Shaokun Zhang", "Fuxiao Liu", "Zhiqi Li", "Di Zhang", "Greg Heinrich", "Hongxu", "Yin", "Song Han", "Pavlo Molchanov", "Parth Mannan", "Yao Xu", "Jane Polak Scowcroft", "Tom Balough", "Subhashree Radhakrishnan", "Paris Zhang", "Sean Cha", "Ratnesh Kumar", "Zaid Pervaiz Bhat", "Jian Zhang", "Darragh Hanley", "Pritam Biswas", "Jesse Oliver", "Kevin Vasques", "Roger Waleffe", "Duncan Riach", "Oluwatobi Olabiyi", "Ameya Sunil Mahabaleshwarkar", "Bilal Kartal", "Pritam Gundecha", "Khanh Nguyen", "Alexandre Milesi", "Eugene Khvedchenia", "Ran Zilberstein", "Ofri Masad", "Natan Bagrov", "Nave Assaf", "Tomer Asida", "Daniel Afrimi", "Amit Zuker", "Netanel Haber", "Zhiyu Cheng", "Jingyu", "Xin", "Di", "Wu", "Nik Spirin", "Maryam Moosaei", "Roman Ageev", "Vanshil Atul Shah", "Yuting Wu", "Daniel Korzekwa", "Unnikrishnan Kizhakkemadam Sreekumar", "Wanli Jiang", "Padmavathy Subramanian", "Alejandra Rico", "Sandip Bhaskar", "Saeid Motiian", "Kedi Wu", "Annie Surla", "Chia-Chih Chen", "Hayden Wolff", "Matthew Feinberg", "Melissa Corpuz", "Marek Wawrzos", "Eileen Long", "Aastha Jhunjhunwala", "Paul Hendricks", "Farzan Memarian", "Benika Hall", "Xin-Yu Wang", "David Mosallanezhad", "Soumye Singhal", "Luis Vega", "Katherine Cheung", "Krzysztof Pawelec", "Michael Evans", "Katherine Luna", "Jie Lou", "Erick Galinkin", "Akshay Hazare", "Kaustubh Purandare", "Ann Guan", "Anna Warno", "Chen Cui", "Yoshi Suhara", "Shibani Likhite", "Seph Mard", "Meredith Price", "Laya Sleiman", "Saori Kaji", "Udi Karpas", "Kari Briski", "Joey Conway", "Michael Lightstone", "Jan Kautz", "Mohammad Shoeybi", "Mostofa Patwary", "Jonathen Cohen", "Oleksii Kuchaiev", "Andrew Tao", "Bryan Catanzaro" ], "github": "", "project_page": "", "abstract": "We introduce Nemotron Nano V2 VL, the latest model of the Nemotron vision-language series designed for strong real-world document understanding, long video comprehension, and reasoning tasks. Nemotron Nano V2 VL delivers significant improvements over our previous model, Llama-3.1-Nemotron-Nano-VL-8B, across all vision and text domains through major enhancements in model architecture, datasets, and training recipes. Nemotron Nano V2 VL builds on Nemotron Nano V2, a hybrid Mamba-Transformer LLM, and innovative token reduction techniques to achieve higher inference throughput in long document and video scenarios. We are releasing model checkpoints in BF16, FP8, and FP4 formats and sharing large parts of our datasets, recipes and training code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-07", "arxiv_id": "2511.03773", "title": "Scaling Agent Learning via Experience Synthesis", "authors": [ "Zhaorun Chen", "Zhuokai Zhao", "Kai Zhang", "Bo Liu", "Qi Qi", "Yifan Wu", "Tarun Kalluri", "Sara Cao", "Yuanhao Xiong", "Haibo Tong", "Huaxiu Yao", "Hengduo Li", "Jiacheng Zhu", "Xian Li", "Dawn Song", "Bo Li", "Jason Weston", "Dat Huynh" ], "github": "", "project_page": "", "abstract": "While reinforcement learning (RL) can empower large language model (LLM) agents by enabling self-improvement through interaction, its practical adoption remains challenging due to costly rollouts, limited task diversity, unreliable reward signals, and infrastructure complexity, all of which obstruct the collection of scalable experience data. To address these challenges, we introduce DreamGym, the first unified framework designed to synthesize diverse experiences with scalability in mind to enable effective online RL training for autonomous agents. Rather than relying on expensive real-environment rollouts, DreamGym distills environment dynamics into a reasoning-based experience model that derives consistent state transitions and feedback signals through step-by-step reasoning, enabling scalable agent rollout collection for RL. To improve the stability and quality of transitions, DreamGym leverages an experience replay buffer initialized with offline real-world data and continuously enriched with fresh interactions to actively support agent training. To improve knowledge acquisition, DreamGym adaptively generates new tasks that challenge the current agent policy, enabling more effective online curriculum learning. Experiments across diverse environments and agent backbones demonstrate that DreamGym substantially improves RL training, both in fully synthetic settings and in sim-to-real transfer scenarios. On non-RL-ready tasks like WebArena, DreamGym outperforms all baselines by over 30%. And in RL-ready but costly settings, it matches GRPO and PPO performance using only synthetic interactions. When transferring a policy trained purely on synthetic experiences to real-environment RL, DreamGym yields significant additional performance gains while requiring far fewer real-world interactions, providing a scalable warm-start strategy for general-purpose RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-07", "arxiv_id": "2511.04655", "title": "Benchmark Designers Should \"Train on the Test Set\" to Expose Exploitable Non-Visual Shortcuts", "authors": [ "Ellis Brown", "Jihan Yang", "Shusheng Yang", "Rob Fergus", "Saining Xie" ], "github": "", "project_page": "", "abstract": "Robust benchmarks are crucial for evaluating Multimodal Large Language Models (MLLMs). Yet we find that models can ace many multimodal benchmarks without strong visual understanding, instead exploiting biases, linguistic priors, and superficial patterns. This is especially problematic for vision-centric benchmarks that are meant to require visual inputs. We adopt a diagnostic principle for benchmark design: if a benchmark can be gamed, it will be. Designers should therefore try to ``game'' their own benchmarks first, using diagnostic and debiasing procedures to systematically identify and mitigate non-visual biases. Effective diagnosis requires directly ``training on the test set'' -- probing the released test set for its intrinsic, exploitable patterns. We operationalize this standard with two components. First, we diagnose benchmark susceptibility using a ``Test-set Stress-Test'' (TsT) methodology. Our primary diagnostic tool involves fine-tuning a powerful Large Language Model via k-fold cross-validation on exclusively the non-visual, textual inputs of the test set to reveal shortcut performance and assign each sample a bias score s(x). We complement this with a lightweight Random Forest-based diagnostic operating on hand-crafted features for fast, interpretable auditing. Second, we debias benchmarks by filtering high-bias samples using an ``Iterative Bias Pruning'' (IBP) procedure. Applying this framework to four benchmarks -- VSI-Bench, CV-Bench, MMMU, and VideoMME -- we uncover pervasive non-visual biases. As a case study, we apply our full framework to create VSI-Bench-Debiased, demonstrating reduced non-visual solvability and a wider vision-blind performance gap than the original.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-07", "arxiv_id": "2511.03996", "title": "Learning Vision-Driven Reactive Soccer Skills for Humanoid Robots", "authors": [ "Yushi Wang", "Changsheng Luo", "Penghui Chen", "Jianran Liu", "Weijian Sun", "Tong Guo", "Kechang Yang", "Biao Hu", "Yangang Zhang", "Mingguo Zhao" ], "github": "", "project_page": "", "abstract": "Humanoid soccer poses a representative challenge for embodied intelligence, requiring robots to operate within a tightly coupled perception-action loop. However, existing systems typically rely on decoupled modules, resulting in delayed responses and incoherent behaviors in dynamic environments, while real-world perceptual limitations further exacerbate these issues. In this work, we present a unified reinforcement learning-based controller that enables humanoid robots to acquire reactive soccer skills through the direct integration of visual perception and motion control. Our approach extends Adversarial Motion Priors to perceptual settings in real-world dynamic environments, bridging motion imitation and visually grounded dynamic control. We introduce an encoder-decoder architecture combined with a virtual perception system that models real-world visual characteristics, allowing the policy to recover privileged states from imperfect observations and establish active coordination between perception and action. The resulting controller demonstrates strong reactivity, consistently executing coherent and robust soccer behaviors across various scenarios, including real RoboCup matches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-07", "arxiv_id": "2511.03774", "title": "Contamination Detection for VLMs using Multi-Modal Semantic Perturbation", "authors": [ "Jaden Park", "Mu Cai", "Feng Yao", "Jingbo Shang", "Soochahn Lee", "Yong Jae Lee" ], "github": "https://github.com/haotian-liu/LLaVA", "project_page": "", "abstract": "Recent advances in Vision-Language Models (VLMs) have achieved state-of-the-art performance on numerous benchmark tasks. However, the use of internet-scale, often proprietary, pretraining corpora raises a critical concern for both practitioners and users: inflated performance due to test-set leakage. While prior works have proposed mitigation strategies such as decontamination of pretraining data and benchmark redesign for LLMs, the complementary direction of developing detection methods for contaminated VLMs remains underexplored. To address this gap, we deliberately contaminate open-source VLMs on popular benchmarks and show that existing detection approaches either fail outright or exhibit inconsistent behavior. We then propose a novel simple yet effective detection method based on multi-modal semantic perturbation, demonstrating that contaminated models fail to generalize under controlled perturbations. Finally, we validate our approach across multiple realistic contamination strategies, confirming its robustness and effectiveness. The code and perturbed dataset will be released publicly.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/haotian-liu/LLaVA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-07", "arxiv_id": "2511.03295", "title": "How to Evaluate Speech Translation with Source-Aware Neural MT Metrics", "authors": [ "Mauro Cettolo", "Marco Gaido", "Matteo Negri", "Sara Papi", "Luisa Bentivogli" ], "github": "", "project_page": "", "abstract": "Automatic evaluation of speech-to-text translation (ST) systems is typically performed by comparing translation hypotheses with one or more reference translations. While effective to some extent, this approach inherits the limitation of reference-based evaluation that ignores valuable information from the source input. In machine translation (MT), recent progress has shown that neural metrics incorporating the source text achieve stronger correlation with human judgments. Extending this idea to ST, however, is not trivial because the source is audio rather than text, and reliable transcripts or alignments between source and references are often unavailable. In this work, we conduct the first systematic study of source-aware metrics for ST, with a particular focus on real-world operating conditions where source transcripts are not available. We explore two complementary strategies for generating textual proxies of the input audio, automatic speech recognition (ASR) transcripts, and back-translations of the reference translation, and introduce a novel two-step cross-lingual re-segmentation algorithm to address the alignment mismatch between synthetic sources and reference translations. Our experiments, carried out on two ST benchmarks covering 79 language pairs and six ST systems with diverse architectures and performance levels, show that ASR transcripts constitute a more reliable synthetic source than back-translations when word error rate is below 20%, while back-translations always represent a computationally cheaper but still effective alternative. Furthermore, our cross-lingual re-segmentation algorithm enables robust use of source-aware MT metrics in ST evaluation, paving the way toward more accurate and principled evaluation methodologies for speech translation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-07", "arxiv_id": "2510.27656", "title": "RDMA Point-to-Point Communication for LLM Systems", "authors": [ "Nandor Licker", "Kevin Hu", "Vladimir Zaytsev", "Lequn Chen" ], "github": "https://github.com/perplexityai/pplx-garden", "project_page": "", "abstract": "Emerging Large Language Model (LLM) system patterns, such as disaggregated inference, Mixture-of-Experts (MoE) routing, and asynchronous reinforcement fine-tuning, require flexible point-to-point communication beyond simple collectives. Existing implementations are locked to specific Network Interface Controllers (NICs), hindering integration into inference engines and portability across hardware providers. We present TransferEngine, which bridges the functionality of common NICs to expose a uniform interface. TransferEngine exposes one-sided WriteImm operations with a ImmCounter primitive for completion notification, without ordering assumptions of network transport, transparently managing multiple NICs per GPU. We demonstrate peak throughput of 400 Gbps on both NVIDIA ConnectX-7 and AWS Elastic Fabric Adapter (EFA). We showcase TransferEngine through three production systems: (1) KvCache transfer for disaggregated inference with dynamic scaling, (2) RL weight updates achieving 1.3 seconds for trillion-parameter models, and (3) MoE dispatch/combine implementation exceeding DeepEP decode latency on ConnectX-7, with the first viable latencies on EFA. We demonstrate that our portable point-to-point communication complements collectives while avoiding lock-in.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/perplexityai/pplx-garden", "github_api_added_by": "auto" }, { "date": "2025-11-07", "arxiv_id": "2511.02280", "title": "SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward RL Tuning", "authors": [ "Fangxun Shu", "Yongjie Ye", "Yue Liao", "Zijian Kang", "Weijie Yin", "Jiacong Wang", "Xiao Liang", "Shuicheng Yan", "Chao Feng" ], "github": "https://github.com/BytedanceDouyinContent/SAIL-RL", "project_page": "", "abstract": "We introduce SAIL-RL, a reinforcement learning (RL) post-training framework that enhances the reasoning capabilities of multimodal large language models (MLLMs) by teaching them when and how to think. Existing approaches are limited by outcome-only supervision, which rewards correct answers without ensuring sound reasoning, and by uniform thinking strategies, which often lead to overthinking on simple tasks and underthinking on complex ones. SAIL-RL addresses these challenges with a dual reward system: the Thinking Reward, which evaluates reasoning quality through factual grounding, logical coherence, and answer consistency, and the Judging Reward, which adaptively determines whether deep reasoning or direct answering is appropriate. Experiments on the state-of-the-art SAIL-VL2 show that SAIL-RL improves reasoning and multimodal understanding benchmarks at both 4B and 8B scales, achieving competitive performance against commercial closed-source models such as GPT-4o, and substantially reduces hallucinations, establishing it as a principled framework for building more reliable and adaptive MLLMs. The code will be available at https://github.com/BytedanceDouyinContent/SAIL-RL.", "github_manual": "", "github_regex": "https://github.com/BytedanceDouyinContent/SAIL-RL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-07", "arxiv_id": "2511.04668", "title": "SIMS-V: Simulated Instruction-Tuning for Spatial Video Understanding", "authors": [ "Ellis Brown", "Arijit Ray", "Ranjay Krishna", "Ross Girshick", "Rob Fergus", "Saining Xie" ], "github": "https://github.com/ellisbrown/SIMS-V", "project_page": "", "abstract": "Despite impressive high-level video comprehension, multimodal language models struggle with spatial reasoning across time and space. While current spatial training approaches rely on real-world video data, obtaining diverse footage with precise spatial annotations remains a bottleneck. To alleviate this bottleneck, we present SIMS-V -- a systematic data-generation framework that leverages the privileged information of 3D simulators to create spatially-rich video training data for multimodal language models. Using this framework, we investigate which properties of simulated data drive effective real-world transfer through systematic ablations of question types, mixes, and scales. We identify a minimal set of three question categories (metric measurement, perspective-dependent reasoning, and temporal tracking) that prove most effective for developing transferable spatial intelligence, outperforming comprehensive coverage despite using fewer question types. These insights enable highly efficient training: our 7B-parameter video LLM fine-tuned on just 25K simulated examples outperforms the larger 72B baseline and achieves competitive performance with proprietary models on rigorous real-world spatial reasoning benchmarks. Our approach demonstrates robust generalization, maintaining performance on general video understanding while showing substantial improvements on embodied and real-world spatial tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ellisbrown/SIMS-V", "github_api_added_by": "auto" }, { "date": "2025-11-07", "arxiv_id": "2511.00956", "title": "EVTAR: End-to-End Try on with Additional Unpaired Visual Reference", "authors": [ "Liuzhuozheng Li", "Yue Gong", "Shanyuan Liu", "Bo Cheng", "Yuhang Ma", "Liebucha Wu", "Dengyang Jiang", "Zanyi Wang", "Dawei Leng", "Yuhui Yin" ], "github": "https://github.com/360CVGroup/RefVTON", "project_page": "", "abstract": "We propose EVTAR, an End-to-End Virtual Try-on model with Additional Reference, that directly fits the target garment onto the person image while incorporating reference images to enhance try-on accuracy. Most existing virtual try-on approaches rely on complex inputs such as agnostic person images, human pose, densepose, or body keypoints, making them labor-intensive and impractical for real-world applications. In contrast, EVTAR adopts a two-stage training strategy, enabling simple inference with only the source image and the target garment inputs. Our model generates try-on results without masks, densepose, or segmentation maps. Moreover, EVTAR leverages additional reference images of different individuals wearing the same clothes to preserve garment texture and fine-grained details better. This mechanism is analogous to how humans consider reference models when choosing outfits, thereby simulating a more realistic and high-quality dressing effect. We enrich the training data with supplementary references and unpaired person images to support these capabilities. We evaluate EVTAR on two widely used benchmarks and diverse tasks, and the results consistently validate the effectiveness of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/360CVGroup/RefVTON", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-10", "arxiv_id": "2511.04962", "title": "Too Good to be Bad: On the Failure of LLMs to Role-Play Villains", "authors": [ "Zihao Yi", "Qingxuan Jiang", "Ruotian Ma", "Xingyu Chen", "Qu Yang", "Mengru Wang", "Fanghua Ye", "Ying Shen", "Zhaopeng Tu", "Xiaolong Li", "Linus" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly tasked with creative generation, including the simulation of fictional characters. However, their ability to portray non-prosocial, antagonistic personas remains largely unexamined. We hypothesize that the safety alignment of modern LLMs creates a fundamental conflict with the task of authentically role-playing morally ambiguous or villainous characters. To investigate this, we introduce the Moral RolePlay benchmark, a new dataset featuring a four-level moral alignment scale and a balanced test set for rigorous evaluation. We task state-of-the-art LLMs with role-playing characters from moral paragons to pure villains. Our large-scale evaluation reveals a consistent, monotonic decline in role-playing fidelity as character morality decreases. We find that models struggle most with traits directly antithetical to safety principles, such as ``Deceitful'' and ``Manipulative'', often substituting nuanced malevolence with superficial aggression. Furthermore, we demonstrate that general chatbot proficiency is a poor predictor of villain role-playing ability, with highly safety-aligned models performing particularly poorly. Our work provides the first systematic evidence of this critical limitation, highlighting a key tension between model safety and creative fidelity. Our benchmark and findings pave the way for developing more nuanced, context-aware alignment methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-10", "arxiv_id": "2511.05491", "title": "Visual Spatial Tuning", "authors": [ "Rui Yang", "Ziyu Zhu", "Yanwei Li", "Jingjia Huang", "Shen Yan", "Siyuan Zhou", "Zhe Liu", "Xiangtai Li", "Shuangye Li", "Wenqian Wang", "Yi Lin", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "Capturing spatial relationships from visual inputs is a cornerstone of human-like general intelligence. Several previous studies have tried to enhance the spatial awareness of Vision-Language Models (VLMs) by adding extra expert encoders, which brings extra overhead and usually harms general capabilities. To enhance the spatial ability in general architectures, we introduce Visual Spatial Tuning (VST), a comprehensive framework to cultivate VLMs with human-like visuospatial abilities, from spatial perception to reasoning. We first attempt to enhance spatial perception in VLMs by constructing a large-scale dataset termed VST-P, which comprises 4.1 million samples spanning 19 skills across single views, multiple images, and videos. Then, we present VST-R, a curated dataset with 135K samples that instruct models to reason in space. In particular, we adopt a progressive training pipeline: supervised fine-tuning to build foundational spatial knowledge, followed by reinforcement learning to further improve spatial reasoning abilities. Without the side-effect to general capabilities, the proposed VST consistently achieves state-of-the-art results on several spatial benchmarks, including 34.8% on MMSI-Bench and 61.2% on VSIBench. It turns out that the Vision-Language-Action models can be significantly enhanced with the proposed spatial tuning paradigm, paving the way for more physically grounded AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-10", "arxiv_id": "2511.05369", "title": "Dense Motion Captioning", "authors": [ "Shiyao Xu", "Benedetta Liberatori", "G\u00fcl Varol", "Paolo Rota" ], "github": "", "project_page": "", "abstract": "Recent advances in 3D human motion and language integration have primarily focused on text-to-motion generation, leaving the task of motion understanding relatively unexplored. We introduce Dense Motion Captioning, a novel task that aims to temporally localize and caption actions within 3D human motion sequences. Current datasets fall short in providing detailed temporal annotations and predominantly consist of short sequences featuring few actions. To overcome these limitations, we present the Complex Motion Dataset (CompMo), the first large-scale dataset featuring richly annotated, complex motion sequences with precise temporal boundaries. Built through a carefully designed data generation pipeline, CompMo includes 60,000 motion sequences, each composed of multiple actions ranging from at least two to ten, accurately annotated with their temporal extents. We further present DEMO, a model that integrates a large language model with a simple motion adapter, trained to generate dense, temporally grounded captions. Our experiments show that DEMO substantially outperforms existing methods on CompMo as well as on adapted benchmarks, establishing a robust baseline for future research in 3D motion understanding and captioning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-10", "arxiv_id": "2511.05271", "title": "DeepEyesV2: Toward Agentic Multimodal Model", "authors": [ "Jack Hong", "Chenxiao Zhao", "ChengLin Zhu", "Weiheng Lu", "Guohai Xu", "Xing Yu" ], "github": "", "project_page": "", "abstract": "Agentic multimodal models should not only comprehend text and images, but also actively invoke external tools, such as code execution environments and web search, and integrate these operations into reasoning. In this work, we introduce DeepEyesV2 and explore how to build an agentic multimodal model from the perspectives of data construction, training methods, and model evaluation. We observe that direct reinforcement learning alone fails to induce robust tool-use behavior. This phenomenon motivates a two-stage training pipeline: a cold-start stage to establish tool-use patterns, and reinforcement learning stage to further refine tool invocation. We curate a diverse, moderately challenging training dataset, specifically including examples where tool use is beneficial. We further introduce RealX-Bench, a comprehensive benchmark designed to evaluate real-world multimodal reasoning, which inherently requires the integration of multiple capabilities, including perception, search, and reasoning. We evaluate DeepEyesV2 on RealX-Bench and other representative benchmarks, demonstrating its effectiveness across real-world understanding, mathematical reasoning, and search-intensive tasks. Moreover, DeepEyesV2 exhibits task-adaptive tool invocation, tending to use image operations for perception tasks and numerical computations for reasoning tasks. Reinforcement learning further enables complex tool combinations and allows model to selectively invoke tools based on context. We hope our study can provide guidance for community in developing agentic multimodal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-10", "arxiv_id": "2511.05017", "title": "Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings", "authors": [ "Aakriti Agrawal", "Gouthaman KV", "Rohith Aralikatti", "Gauri Jagatap", "Jiaxin Yuan", "Vijay Kamarshi", "Andrea Fanelli", "Furong Huang" ], "github": "", "project_page": "", "abstract": "In this work, we identify an inherent bias in prevailing LVLM architectures toward the language modality, largely resulting from the common practice of simply appending visual embeddings to the input text sequence. To address this, we propose a simple yet effective method that refines textual embeddings by integrating average-pooled visual features. Our approach demonstrably improves visual grounding and significantly reduces hallucinations on established benchmarks. While average pooling offers a straightforward, robust, and efficient means of incorporating visual information, we believe that more sophisticated fusion methods could further enhance visual grounding and cross-modal alignment. Given that the primary focus of this work is to highlight the modality imbalance and its impact on hallucinations -- and to show that refining textual embeddings with visual information mitigates this issue -- we leave exploration of advanced fusion strategies for future work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-10", "arxiv_id": "2511.04707", "title": "Jailbreaking in the Haystack", "authors": [ "Rishi Rajesh Shah", "Chen Henry Wu", "Shashwat Saxena", "Ziqian Zhong", "Alexander Robey", "Aditi Raghunathan" ], "github": "", "project_page": "", "abstract": "Recent advances in long-context language models (LMs) have enabled million-token inputs, expanding their capabilities across complex tasks like computer-use agents. Yet, the safety implications of these extended contexts remain unclear. To bridge this gap, we introduce NINJA (short for Needle-in-haystack jailbreak attack), a method that jailbreaks aligned LMs by appending benign, model-generated content to harmful user goals. Critical to our method is the observation that the position of harmful goals play an important role in safety. Experiments on standard safety benchmark, HarmBench, show that NINJA significantly increases attack success rates across state-of-the-art open and proprietary models, including LLaMA, Qwen, Mistral, and Gemini. Unlike prior jailbreaking methods, our approach is low-resource, transferable, and less detectable. Moreover, we show that NINJA is compute-optimal -- under a fixed compute budget, increasing context length can outperform increasing the number of trials in best-of-N jailbreak. These findings reveal that even benign long contexts -- when crafted with careful goal positioning -- introduce fundamental vulnerabilities in modern LMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-10", "arxiv_id": "2510.24505", "title": "CritiCal: Can Critique Help LLM Uncertainty or Confidence Calibration?", "authors": [ "Qing Zong", "Jiayu Liu", "Tianshi Zheng", "Chunyang Li", "Baixuan Xu", "Haochen Shi", "Weiqi Wang", "Zhaowei Wang", "Chunkit Chan", "Yangqiu Song" ], "github": "https://github.com/HKUST-KnowComp/CritiCal", "project_page": "", "abstract": "Accurate confidence calibration in Large Language Models (LLMs) is critical for safe use in high-stakes domains, where clear verbalized confidence enhances user trust. Traditional methods that mimic reference confidence expressions often fail to capture the reasoning needed for accurate confidence assessment. We propose natural language critiques as a solution, ideally suited for confidence calibration, as precise gold confidence labels are hard to obtain and often require multiple generations. This paper studies how natural language critiques can enhance verbalized confidence, addressing: (1) What to critique: uncertainty (question-focused) or confidence (answer-specific)? Analysis shows confidence suits multiple-choice tasks, while uncertainty excels in open-ended scenarios. (2) How to critique: self-critique or critique calibration training? We propose Self-Critique, enabling LLMs to critique and optimize their confidence beyond mere accuracy, and CritiCal, a novel Critique Calibration training method that leverages natural language critiques to improve confidence calibration, moving beyond direct numerical optimization. Experiments show that CritiCal significantly outperforms Self-Critique and other competitive baselines, even surpassing its teacher model, GPT-4o, in complex reasoning tasks. CritiCal also shows robust generalization in out-of-distribution settings, advancing LLM's reliability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HKUST-KnowComp/CritiCal", "github_api_added_by": "auto" }, { "date": "2025-11-10", "arxiv_id": "2511.01047", "title": "HAFixAgent: History-Aware Automated Program Repair Agent", "authors": [ "Yu Shi", "Hao Li", "Bram Adams", "Ahmed E. Hassan" ], "github": "", "project_page": "", "abstract": "Automated program repair (APR) has recently shifted toward large language models and agent-based systems, yet most systems rely on local snapshot context, overlooking repository history. Prior work shows that repository history helps repair single-line bugs, since the last commit touching the buggy line is often the bug-introducing one. In this paper, we investigate whether repository history can also improve agentic APR systems at scale, especially for complex multi-hunk bugs. We present HAFixAgent, a History-Aware Bug-Fixing Agent that injects blame-derived repository heuristics into its repair loop. A preliminary study of all 854 real-world bugs from Defects4J motivates our design, showing that bug-relevant history is both widely available and highly concentrated. Empirical comparison of HAFixAgent with two state-of-the-art baselines shows: (1) Effectiveness: HAFixAgent significantly improves over the agent-based baseline (by 212.3%) and the multi-hunk baseline (by 29.9%). (2) Efficiency: history does not significantly increase agent steps and keeps token costs comparable, with notably lower median costs for complex multi-file-multi-hunk bugs. (3) Practicality: combining different historical heuristics repairs more bugs, offering a clear cost-benefit trade-off. HAFixAgent offers a practical recipe for history-aware agentic APR: ground the agent in version control history, prioritize diff-based historical context, and integrate complementary heuristics when needed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-10", "arxiv_id": "2511.04662", "title": "VeriCoT: Neuro-symbolic Chain-of-Thought Validation via Logical Consistency Checks", "authors": [ "Yu Feng", "Nathaniel Weir", "Kaj Bostrom", "Sam Bayless", "Darion Cassel", "Sapana Chaudhary", "Benjamin Kiesl-Reiter", "Huzefa Rangwala" ], "github": "", "project_page": "", "abstract": "LLMs can perform multi-step reasoning through Chain-of-Thought (CoT), but they cannot reliably verify their own logic. Even when they reach correct answers, the underlying reasoning may be flawed, undermining trust in high-stakes scenarios. To mitigate this issue, we introduce VeriCoT, a neuro-symbolic method that extracts and verifies formal logical arguments from CoT reasoning. VeriCoT formalizes each CoT reasoning step into first-order logic and identifies premises that ground the argument in source context, commonsense knowledge, or prior reasoning steps. The symbolic representation enables automated solvers to verify logical validity while the NL premises allow humans and systems to identify ungrounded or fallacious reasoning steps. Experiments on the ProofWriter, LegalBench, and BioASQ datasets show VeriCoT effectively identifies flawed reasoning, and serves as a strong predictor of final answer correctness. We also leverage VeriCoT's verification signal for (1) inference-time self-reflection, (2) supervised fine-tuning (SFT) on VeriCoT-distilled datasets and (3) preference fine-tuning (PFT) with direct preference optimization (DPO) using verification-based pairwise rewards, further improving reasoning validity and accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-10", "arxiv_id": "2511.04898", "title": "Real-Time Reasoning Agents in Evolving Environments", "authors": [ "Yule Wen", "Yixin Ye", "Yanzhe Zhang", "Diyi Yang", "Hao Zhu" ], "github": "", "project_page": "", "abstract": "Agents in the real world must make not only logical but also timely judgments. This requires continuous awareness of the dynamic environment: hazards emerge, opportunities arise, and other agents act, while the agent's reasoning is still unfolding. Despite advances in language model reasoning, existing approaches fail to account for this dynamic nature. We introduce real-time reasoning as a new problem formulation for agents in evolving environments and build Real-Time Reasoning Gym to demonstrate it. We study two paradigms for deploying language models in agents: (1) reactive agents, which employ language models with bounded reasoning computation for rapid responses, and (2) planning agents, which allow extended reasoning computation for complex problems. Our experiments show that even state-of-the-art models struggle with making logical and timely judgments in either paradigm. To address this limitation, we propose AgileThinker, which simultaneously engages both reasoning paradigms. AgileThinker consistently outperforms agents engaging only one reasoning paradigm as the task difficulty and time pressure rise, effectively balancing reasoning depth and response latency. Our work establishes real-time reasoning as a critical testbed for developing practical agents and provides a foundation for research in temporally constrained AI systems, highlighting a path toward real-time capable agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.03506", "title": "HaluMem: Evaluating Hallucinations in Memory Systems of Agents", "authors": [ "Ding Chen", "Simin Niu", "Kehang Li", "Peng Liu", "Xiangping Zheng", "Bo Tang", "Xinchi Li", "Feiyu Xiong", "Zhiyu Li" ], "github": "", "project_page": "", "abstract": "Memory systems are key components that enable AI systems such as LLMs and AI agents to achieve long-term learning and sustained interaction. However, during memory storage and retrieval, these systems frequently exhibit memory hallucinations, including fabrication, errors, conflicts, and omissions. Existing evaluations of memory hallucinations are primarily end-to-end question answering, which makes it difficult to localize the operational stage within the memory system where hallucinations arise. To address this, we introduce the Hallucination in Memory Benchmark (HaluMem), the first operation level hallucination evaluation benchmark tailored to memory systems. HaluMem defines three evaluation tasks (memory extraction, memory updating, and memory question answering) to comprehensively reveal hallucination behaviors across different operational stages of interaction. To support evaluation, we construct user-centric, multi-turn human-AI interaction datasets, HaluMem-Medium and HaluMem-Long. Both include about 15k memory points and 3.5k multi-type questions. The average dialogue length per user reaches 1.5k and 2.6k turns, with context lengths exceeding 1M tokens, enabling evaluation of hallucinations across different context scales and task complexities. Empirical studies based on HaluMem show that existing memory systems tend to generate and accumulate hallucinations during the extraction and updating stages, which subsequently propagate errors to the question answering stage. Future research should focus on developing interpretable and constrained memory operation mechanisms that systematically suppress hallucinations and improve memory reliability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.04285", "title": "RLoop: An Self-Improving Framework for Reinforcement Learning with Iterative Policy Initialization", "authors": [ "Zeng Zhiyuan", "Jiashuo Liu", "Zhangyue Yin", "Ge Zhang", "Wenhao Huang", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "While Reinforcement Learning for Verifiable Rewards (RLVR) is powerful for training large reasoning models, its training dynamics harbor a critical challenge: RL overfitting, where models gain training rewards but lose generalization. Our analysis reveals this is driven by policy over-specialization and catastrophic forgetting of diverse solutions generated during training. Standard optimization discards this valuable inter-step policy diversity. To address this, we introduce RLoop, a self-improving framework built on iterative policy initialization. RLoop transforms the standard training process into a virtuous cycle: it first uses RL to explore the solution space from a given policy, then filters the successful trajectories to create an expert dataset. This dataset is used via Rejection-sampling Fine-Tuning (RFT) to refine the initial policy, creating a superior starting point for the next iteration. This loop of exploration and exploitation via iterative re-initialization effectively converts transient policy variations into robust performance gains. Our experiments show RLoop mitigates forgetting and substantially improves generalization, boosting average accuracy by 9% and pass@32 by over 15% compared to vanilla RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07409", "title": "DIMO: Diverse 3D Motion Generation for Arbitrary Objects", "authors": [ "Linzhan Mou", "Jiahui Lei", "Chen Wang", "Lingjie Liu", "Kostas Daniilidis" ], "github": "https://github.com/Friedrich-M/DIMO", "project_page": "", "abstract": "We present DIMO, a generative approach capable of generating diverse 3D motions for arbitrary objects from a single image. The core idea of our work is to leverage the rich priors in well-trained video models to extract the common motion patterns and then embed them into a shared low-dimensional latent space. Specifically, we first generate multiple videos of the same object with diverse motions. We then embed each motion into a latent vector and train a shared motion decoder to learn the distribution of motions represented by a structured and compact motion representation, i.e., neural key point trajectories. The canonical 3D Gaussians are then driven by these key points and fused to model the geometry and appearance. During inference time with learned latent space, we can instantly sample diverse 3D motions in a single-forward pass and support several interesting applications including 3D motion interpolation and language-guided motion generation. Our project page is available at https://linzhanm.github.io/dimo.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Friedrich-M/DIMO", "github_api_added_by": "auto" }, { "date": "2025-11-11", "arxiv_id": "2511.07327", "title": "IterResearch: Rethinking Long-Horizon Agents via Markovian State Reconstruction", "authors": [ "Guoxin Chen", "Zile Qiao", "Xuanzhong Chen", "Donglei Yu", "Haotian Xu", "Wayne Xin Zhao", "Ruihua Song", "Wenbiao Yin", "Huifeng Yin", "Liwen Zhang", "Kuan Li", "Minpeng Liao", "Yong Jiang", "Pengjun Xie", "Fei Huang", "Jingren Zhou" ], "github": "https://github.com/Alibaba-NLP/DeepResearch", "project_page": "", "abstract": "Recent advances in deep-research agents have shown promise for autonomous knowledge construction through dynamic reasoning over external sources. However, existing approaches rely on a mono-contextual paradigm that accumulates all information in a single, expanding context window, leading to context suffocation and noise contamination that limit their effectiveness on long-horizon tasks. We introduce IterResearch, a novel iterative deep-research paradigm that reformulates long-horizon research as a Markov Decision Process with strategic workspace reconstruction. By maintaining an evolving report as memory and periodically synthesizing insights, our approach preserves consistent reasoning capacity across arbitrary exploration depths. We further develop Efficiency-Aware Policy Optimization (EAPO), a reinforcement learning framework that incentivizes efficient exploration through geometric reward discounting and enables stable distributed training via adaptive downsampling. Extensive experiments demonstrate that IterResearch achieves substantial improvements over existing open-source agents with average +14.5pp across six benchmarks and narrows the gap with frontier proprietary systems. Remarkably, our paradigm exhibits unprecedented interaction scaling, extending to 2048 interactions with dramatic performance gains (from 3.5\\% to 42.5\\%), and serves as an effective prompting strategy, improving frontier models by up to 19.2pp over ReAct on long-horizon tasks. These findings position IterResearch as a versatile solution for long-horizon reasoning, effective both as a trained agent and as a prompting paradigm for frontier models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Alibaba-NLP/DeepResearch", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07419", "title": "Routing Manifold Alignment Improves Generalization of Mixture-of-Experts LLMs", "authors": [ "Zhongyang Li", "Ziyue Li", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Sparse Mixture-of-Experts (MoE) have been widely adopted in recent large language models since it can efficiently scale up the model capability without increasing the inference cost. However, evaluations on broad downstream tasks reveal a consistent suboptimality of the routers in existing MoE LLMs, which results in a severe performance gap (e.g., 10-20% in accuracy) to the optimal routing. In this paper, we show that aligning the manifold of routing weights with that of task embedding can effectively reduce the gap and improve MoE LLMs' generalization performance. Our method, \"Routing Manifold Alignment (RoMA)\", introduces an additional manifold regularization term in the post-training objective and only requires lightweight finetuning of routers (with other parameters frozen). Specifically, the regularization encourages the routing weights of each sample to be close to those of its successful neighbors (whose routing weights lead to correct answers) in a task embedding space. Consequently, samples targeting similar tasks will share similar expert choices across layers. Building such bindings between tasks and experts over different samples is essential to achieve better generalization. Moreover, RoMA demonstrates the advantage of unifying the task understanding (by embedding models) with solution generation (by MoE LLMs). In experiments, we finetune routers in OLMoE, DeepSeekMoE, and Qwen3-MoE using RoMA. Evaluations on diverse benchmarks and extensive comparisons with baselines show the substantial improvement brought by RoMA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07070", "title": "RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking Services", "authors": [ "Fei Zhao", "Chonggang Lu", "Haofu Qian", "Fangcheng Shi", "Zijie Meng", "Jianzhao Huang", "Xu Tang", "Zheyong Xie", "Zheyu Ye", "Zhe Xu", "Yao Hu", "Shaosheng Cao" ], "github": "", "project_page": "", "abstract": "As a key medium for human interaction and information exchange, social networking services (SNS) pose unique challenges for large language models (LLMs): heterogeneous workloads, fast-shifting norms and slang, and multilingual, culturally diverse corpora that induce sharp distribution shift. Supervised fine-tuning (SFT) can specialize models but often triggers a ``seesaw'' between in-distribution gains and out-of-distribution robustness, especially for smaller models. To address these challenges, we introduce RedOne 2.0, an SNS-oriented LLM trained with a progressive, RL-prioritized post-training paradigm designed for rapid and stable adaptation. The pipeline consist in three stages: (1) Exploratory Learning on curated SNS corpora to establish initial alignment and identify systematic weaknesses; (2) Targeted Fine-Tuning that selectively applies SFT to the diagnosed gaps while mixing a small fraction of general data to mitigate forgetting; and (3) Refinement Learning that re-applies RL with SNS-centric signals to consolidate improvements and harmonize trade-offs across tasks. Across various tasks spanning three categories, our 4B scale model delivers an average improvements about 2.41 over the 7B sub-optimal baseline. Additionally, RedOne 2.0 achieves average performance lift about 8.74 from the base model with less than half the data required by SFT-centric method RedOne, evidencing superior data efficiency and stability at compact scales. Overall, RedOne 2.0 establishes a competitive, cost-effective baseline for domain-specific LLMs in SNS scenario, advancing capability without sacrificing robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.06307", "title": "DRIVE: Data Curation Best Practices for Reinforcement Learning with Verifiable Reward in Competitive Code Generation", "authors": [ "Speed Zhu", "Jianwei Cai", "Guang Chen", "Lulu Wu", "Saiyong Yang", "Wiggin Zhou" ], "github": "", "project_page": "", "abstract": "Recent reasoning-first models (e.g., OpenAI o1, DeepSeek R1) have spurred a resurgence of interest in RLVR. Nevertheless, advances are dominated by mathematics (e.g., AIME), with competitive-programming code generation underexplored and data curation receiving less attention than RL algorithm design. We investigate how to construct RLVR datasets (i.e., RL prompts) and present practical training techniques that yield strong performance on competitive-programming code generation. Our pipeline begins with supervised fine-tuning (SFT) distilled from strong open-source models, augmented with general-purpose and reasoning-intensive data. RL then follows a two-stage process with executable, testcase-driven rewards: first, training on a large, uniformly distributed set of competitive-programming problems using Group Relative Policy Optimization (GRPO) with 8 rollouts per prompt and a relatively short response-generation window (e.g., 32k during SFT and 24k in this stage) to expand entropy and mitigate repetition and truncation; second, we perform Pre-GRPO: updating on a small, high-quality set of challenging problems with a large rollout budget (64 rollouts per prompt) under a hard-focus curriculum that continuously retains the most difficult instances throughout training. We implement our method on Qwen2.5-32B and evaluate on LeetCode and Codeforces weekly contests to avoid data leakage. The resulting model achieves state-of-the-art performance among models of similar scale and is comparable to leading systems such as DeepSeek v3.1 and Doubao-1.5-Thinking. We also examine scaling trends and observe strong RL scaling on an internal large-scale MoE model. Our study distills concise best practices for data curation, entropy expansion, and curriculum design in RLVR for competitive-programming code generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07250", "title": "MVU-Eval: Towards Multi-Video Understanding Evaluation for Multimodal LLMs", "authors": [ "Tianhao Peng", "Haochen Wang", "Yuanxing Zhang", "Zekun Wang", "Zili Wang", "Ge Zhang", "Jian Yang", "Shihao Li", "Yanghai Wang", "Xintao Wang", "Houyi Li", "Wei Ji", "Pengfei Wan", "Wenhao Huang", "Zhaoxiang Zhang", "Jiaheng Liu" ], "github": "", "project_page": "", "abstract": "The advent of Multimodal Large Language Models (MLLMs) has expanded AI capabilities to visual modalities, yet existing evaluation benchmarks remain limited to single-video understanding, overlooking the critical need for multi-video understanding in real-world scenarios (e.g., sports analytics and autonomous driving). To address this significant gap, we introduce MVU-Eval, the first comprehensive benchmark for evaluating Multi-Video Understanding for MLLMs. Specifically, our MVU-Eval mainly assesses eight core competencies through 1,824 meticulously curated question-answer pairs spanning 4,959 videos from diverse domains, addressing both fundamental perception tasks and high-order reasoning tasks. These capabilities are rigorously aligned with real-world applications such as multi-sensor synthesis in autonomous systems and cross-angle sports analytics. Through extensive evaluation of state-of-the-art open-source and closed-source models, we reveal significant performance discrepancies and limitations in current MLLMs' ability to perform understanding across multiple videos. The benchmark will be made publicly available to foster future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.06411", "title": "SofT-GRPO: Surpassing Discrete-Token LLM Reinforcement Learning via Gumbel-Reparameterized Soft-Thinking Policy Optimization", "authors": [ "Zhi Zheng", "Wee Sun Lee" ], "github": "https://github.com/zz1358m/SofT-GRPO-master", "project_page": "", "abstract": "The soft-thinking paradigm for Large Language Model (LLM) reasoning can outperform the conventional discrete-token Chain-of-Thought (CoT) reasoning in some scenarios, underscoring its research and application value. However, while the discrete-token CoT reasoning pattern can be reinforced through policy optimization algorithms such as group relative policy optimization (GRPO), extending the soft-thinking pattern with Reinforcement Learning (RL) remains challenging. This difficulty stems from the complexities of injecting stochasticity into soft-thinking tokens and updating soft-thinking policies accordingly. As a result, previous attempts to combine soft-thinking with GRPO typically underperform their discrete-token GRPO counterparts. To fully unlock the potential of soft-thinking, this paper presents a novel policy optimization algorithm, SofT-GRPO, to reinforce LLMs under the soft-thinking reasoning pattern. SofT-GRPO injects the Gumbel noise into logits, employs the Gumbel-Softmax technique to avoid soft-thinking tokens outside the pre-trained embedding space, and leverages the reparameterization trick in policy gradient. We conduct experiments across base LLMs ranging from 1.5B to 7B parameters, and results demonstrate that SofT-GRPO enables soft-thinking LLMs to slightly outperform discrete-token GRPO on Pass@1 (+0.13% on average accuracy), while exhibiting a substantial uplift on Pass@32 (+2.19% on average accuracy). Codes and weights are available on https://github.com/zz1358m/SofT-GRPO-master", "github_manual": "", "github_regex": "https://github.com/zz1358m/SofT-GRPO-master", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.06309", "title": "The Station: An Open-World Environment for AI-Driven Discovery", "authors": [ "Stephen Chung", "Wenyu Du" ], "github": "https://github.com/dualverse-ai/station", "project_page": "", "abstract": "We introduce the STATION, an open-world multi-agent environment that models a miniature scientific ecosystem. Leveraging their extended context windows, agents in the Station can engage in long scientific journeys that include reading papers from peers, formulating hypotheses, submitting code, performing analyses, and publishing results. Importantly, there is no centralized system coordinating their activities - agents are free to choose their own actions and develop their own narratives within the Station. Experiments demonstrate that AI agents in the Station achieve new state-of-the-art performance on a wide range of benchmarks, spanning from mathematics to computational biology to machine learning, notably surpassing AlphaEvolve in circle packing. A rich tapestry of narratives emerges as agents pursue independent research, interact with peers, and build upon a cumulative history. From these emergent narratives, novel methods arise organically, such as a new density-adaptive algorithm for scRNA-seq batch integration. The Station marks a first step towards autonomous scientific discovery driven by emergent behavior in an open-world environment, representing a new paradigm that moves beyond rigid optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dualverse-ai/station", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07416", "title": "Robot Learning from a Physical World Model", "authors": [ "Jiageng Mao", "Sicheng He", "Hao-Ning Wu", "Yang You", "Shuyang Sun", "Zhicheng Wang", "Yanan Bao", "Huizhong Chen", "Leonidas Guibas", "Vitor Guizilini", "Howard Zhou", "Yue Wang" ], "github": "", "project_page": "", "abstract": "We introduce PhysWorld, a framework that enables robot learning from video generation through physical world modeling. Recent video generation models can synthesize photorealistic visual demonstrations from language commands and images, offering a powerful yet underexplored source of training signals for robotics. However, directly retargeting pixel motions from generated videos to robots neglects physics, often resulting in inaccurate manipulations. PhysWorld addresses this limitation by coupling video generation with physical world reconstruction. Given a single image and a task command, our method generates task-conditioned videos and reconstructs the underlying physical world from the videos, and the generated video motions are grounded into physically accurate actions through object-centric residual reinforcement learning with the physical world model. This synergy transforms implicit visual guidance into physically executable robotic trajectories, eliminating the need for real robot data collection and enabling zero-shot generalizable robotic manipulation. Experiments on diverse real-world tasks demonstrate that PhysWorld substantially improves manipulation accuracy compared to previous approaches. Visit https://pointscoder.github.io/PhysWorld_Web/{the project webpage} for details.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07413", "title": "DigiData: Training and Evaluating General-Purpose Mobile Control Agents", "authors": [ "Yuxuan Sun", "Manchen Wang", "Shengyi Qian", "William R. Wong", "Eric Gan", "Pierluca D'Oro", "Alejandro Castillejo Munoz", "Sneha Silwal", "Pedro Matias", "Nitin Kamra", "Satwik Kottur", "Nick Raines", "Xuanyi Zhao", "Joy Chen", "Joseph Greer", "Andrea Madotto", "Allen Bolourchi", "James Valori", "Kevin Carlberg", "Karl Ridgeway", "Joseph Tighe" ], "github": "", "project_page": "", "abstract": "AI agents capable of controlling user interfaces have the potential to transform human interaction with digital devices. To accelerate this transformation, two fundamental building blocks are essential: high-quality datasets that enable agents to achieve complex and human-relevant goals, and robust evaluation methods that allow researchers and practitioners to rapidly enhance agent performance. In this paper, we introduce DigiData, a large-scale, high-quality, diverse, multi-modal dataset designed for training mobile control agents. Unlike existing datasets, which derive goals from unstructured interactions, DigiData is meticulously constructed through comprehensive exploration of app features, resulting in greater diversity and higher goal complexity. Additionally, we present DigiData-Bench, a benchmark for evaluating mobile control agents on real-world complex tasks. We demonstrate that the commonly used step-accuracy metric falls short in reliably assessing mobile control agents and, to address this, we propose dynamic evaluation protocols and AI-powered evaluations as rigorous alternatives for agent assessment. Our contributions aim to significantly advance the development of mobile control agents, paving the way for more intuitive and effective human-device interactions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.06090", "title": "SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?", "authors": [ "Jeffrey Jian Ma", "Milad Hashemi", "Amir Yazdanbakhsh", "Kevin Swersky", "Ofir Press", "Enhui Li", "Vijay Janapa Reddi", "Parthasarathy Ranganathan" ], "github": "", "project_page": "", "abstract": "Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness. However, most benchmarks emphasize what to fix rather than how to fix code. We introduce SWE-fficiency, a benchmark for evaluating repository-level performance optimization on real workloads. Our suite contains 498 tasks across nine widely used data-science, machine-learning, and HPC repositories (e.g., numpy, pandas, scipy): given a complete codebase and a slow workload, an agent must investigate code semantics, localize bottlenecks and relevant tests, and produce a patch that matches or exceeds expert speedup while passing the same unit tests. To enable this how-to-fix evaluation, our automated pipeline scrapes GitHub pull requests for performance-improving edits, combining keyword filtering, static analysis, coverage tooling, and execution validation to both confirm expert speedup baselines and identify relevant repository unit tests. Empirical evaluation of state-of-the-art agents reveals significant underperformance. On average, agents achieve less than 0.15x the expert speedup: agents struggle in localizing optimization opportunities, reasoning about execution across functions, and maintaining correctness in proposed edits. We release the benchmark and accompanying data pipeline to facilitate research on automated performance engineering and long-horizon software reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.06174", "title": "LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs", "authors": [ "Zifan He", "Shengyu Ye", "Rui Ma", "Yang Wang", "Jason Cong" ], "github": "", "project_page": "", "abstract": "The rapid progress of large language models (LLMs) has advanced numerous applications, yet efficient single-batch inference remains vital for on-device intelligence. While FPGAs offer fine-grained data control and high energy efficiency, recent GPU optimizations have narrowed their advantage, especially under arithmetic-based computation. To overcome this, we leverage FPGAs' abundant on-chip memory to shift LLM inference from arithmetic- to memory-based computation through table lookups. We present LUT-LLM, the first FPGA accelerator enabling 1B+ LLM inference via vector-quantized memory operations. Our analysis identifies activation-weight co-quantization as the most effective scheme, supported by (1) bandwidth-aware parallel centroid search, (2) efficient 2D table lookups, and (3) a spatial-temporal hybrid design minimizing data caching. Implemented on an AMD V80 FPGA for a customized Qwen 3 1.7B model, LUT-LLM achieves 1.66x lower latency than AMD MI210 and 1.72x higher energy efficiency than NVIDIA A100, scaling to 32B models with 2.16x efficiency gain over A100.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.03317", "title": "Diffusion-SDPO: Safeguarded Direct Preference Optimization for Diffusion Models", "authors": [ "Minghao Fu", "Guo-Hua Wang", "Tianyu Cui", "Qing-Guo Chen", "Zhao Xu", "Weihua Luo", "Kaifu Zhang" ], "github": "https://github.com/AIDC-AI/Diffusion-SDPO", "project_page": "", "abstract": "Text-to-image diffusion models deliver high-quality images, yet aligning them with human preferences remains challenging. We revisit diffusion-based Direct Preference Optimization (DPO) for these models and identify a critical pathology: enlarging the preference margin does not necessarily improve generation quality. In particular, the standard Diffusion-DPO objective can increase the reconstruction error of both winner and loser branches. Consequently, degradation of the less-preferred outputs can become sufficiently severe that the preferred branch is also adversely affected even as the margin grows. To address this, we introduce Diffusion-SDPO, a safeguarded update rule that preserves the winner by adaptively scaling the loser gradient according to its alignment with the winner gradient. A first-order analysis yields a closed-form scaling coefficient that guarantees the error of the preferred output is non-increasing at each optimization step. Our method is simple, model-agnostic, broadly compatible with existing DPO-style alignment frameworks and adds only marginal computational overhead. Across standard text-to-image benchmarks, Diffusion-SDPO delivers consistent gains over preference-learning baselines on automated preference, aesthetic, and prompt alignment metrics. Code is publicly available at https://github.com/AIDC-AI/Diffusion-SDPO.", "github_manual": "", "github_regex": "https://github.com/AIDC-AI/Diffusion-SDPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.06209", "title": "Reasoning with Confidence: Efficient Verification of LLM Reasoning Steps via Uncertainty Heads", "authors": [ "Jingwei Ni", "Ekaterina Fadeeva", "Tianyi Wu", "Mubashara Akhtar", "Jiaheng Zhang", "Elliott Ash", "Markus Leippold", "Timothy Baldwin", "See-Kiong Ng", "Artem Shelmanov", "Mrinmaya Sachan" ], "github": "", "project_page": "", "abstract": "Solving complex tasks usually requires LLMs to generate long multi-step reasoning chains. Previous work has shown that verifying the correctness of individual reasoning steps can further improve the performance and efficiency of LLMs on such tasks and enhance solution interpretability. However, existing verification approaches, such as Process Reward Models (PRMs), are either computationally expensive, limited to specific domains, or require large-scale human or model-generated annotations. Thus, we propose a lightweight alternative for step-level reasoning verification based on data-driven uncertainty scores. We train transformer-based uncertainty quantification heads (UHeads) that use the internal states of a frozen LLM to estimate the uncertainty of its reasoning steps during generation. The approach is fully automatic: target labels are generated either by another larger LLM (e.g., DeepSeek R1) or in a self-supervised manner by the original model itself. UHeads are both effective and lightweight, containing less than 10M parameters. Across multiple domains, including mathematics, planning, and general knowledge question answering, they match or even surpass the performance of PRMs that are up to 810x larger. Our findings suggest that the internal states of LLMs encode their uncertainty and can serve as reliable signals for reasoning verification, offering a promising direction toward scalable and generalizable introspective LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07137", "title": "MPJudge: Towards Perceptual Assessment of Music-Induced Paintings", "authors": [ "Shiqi Jiang", "Tianyi Liang", "Changbo Wang", "Chenhui Li" ], "github": "", "project_page": "", "abstract": "Music induced painting is a unique artistic practice, where visual artworks are created under the influence of music. Evaluating whether a painting faithfully reflects the music that inspired it poses a challenging perceptual assessment task. Existing methods primarily rely on emotion recognition models to assess the similarity between music and painting, but such models introduce considerable noise and overlook broader perceptual cues beyond emotion. To address these limitations, we propose a novel framework for music induced painting assessment that directly models perceptual coherence between music and visual art. We introduce MPD, the first large scale dataset of music painting pairs annotated by domain experts based on perceptual coherence. To better handle ambiguous cases, we further collect pairwise preference annotations. Building on this dataset, we present MPJudge, a model that integrates music features into a visual encoder via a modulation based fusion mechanism. To effectively learn from ambiguous cases, we adopt Direct Preference Optimization for training. Extensive experiments demonstrate that our method outperforms existing approaches. Qualitative results further show that our model more accurately identifies music relevant regions in paintings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.05936", "title": "10 Open Challenges Steering the Future of Vision-Language-Action Models", "authors": [ "Soujanya Poria", "Navonil Majumder", "Chia-Yu Hung", "Amir Ali Bagherzadeh", "Chuan Li", "Kenneth Kwok", "Ziwei Wang", "Cheston Tan", "Jiajun Wu", "David Hsu" ], "github": "", "project_page": "", "abstract": "Due to their ability of follow natural language instructions, vision-language-action (VLA) models are increasingly prevalent in the embodied AI arena, following the widespread success of their precursors -- LLMs and VLMs. In this paper, we discuss 10 principal milestones in the ongoing development of VLA models -- multimodality, reasoning, data, evaluation, cross-robot action generalization, efficiency, whole-body coordination, safety, agents, and coordination with humans. Furthermore, we discuss the emerging trends of using spatial understanding, modeling world dynamics, post training, and data synthesis -- all aiming to reach these milestones. Through these discussions, we hope to bring attention to the research avenues that may accelerate the development of VLA models into wider acceptability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07299", "title": "VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models", "authors": [ "Ying Cheng", "Yu-Ho Lin", "Min-Hung Chen", "Fu-En Yang", "Shang-Hong Lai" ], "github": "", "project_page": "", "abstract": "Video anomaly understanding (VAU) aims to provide detailed interpretation and semantic comprehension of anomalous events within videos, addressing limitations of traditional methods that focus solely on detecting and localizing anomalies. However, existing approaches often neglect the deeper causal relationships and interactions between objects, which are critical for understanding anomalous behaviors. In this paper, we propose VADER, an LLM-driven framework for Video Anomaly unDErstanding, which integrates keyframe object Relation features with visual cues to enhance anomaly comprehension from video. Specifically, VADER first applies an Anomaly Scorer to assign per-frame anomaly scores, followed by a Context-AwarE Sampling (CAES) strategy to capture the causal context of each anomalous event. A Relation Feature Extractor and a COntrastive Relation Encoder (CORE) jointly model dynamic object interactions, producing compact relational representations for downstream reasoning. These visual and relational cues are integrated with LLMs to generate detailed, causally grounded descriptions and support robust anomaly-related question answering. Experiments on multiple real-world VAU benchmarks demonstrate that VADER achieves strong results across anomaly description, explanation, and causal reasoning tasks, advancing the frontier of explainable video anomaly analysis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07253", "title": "Omni-AVSR: Towards Unified Multimodal Speech Recognition with Large Language Models", "authors": [ "Umberto Cappellazzo", "Xubo Liu", "Pingchuan Ma", "Stavros Petridis", "Maja Pantic" ], "github": "https://github.com/umbertocappellazzo/Omni-AVSR", "project_page": "", "abstract": "Large language models (LLMs) have recently achieved impressive results in speech recognition across multiple modalities, including Auditory Speech Recognition (ASR), Visual Speech Recognition (VSR), and Audio-Visual Speech Recognition (AVSR). Despite this progress, current LLM-based approaches typically address each task independently, training separate models that raise computational and deployment resource use while missing potential cross-task synergies. They also rely on fixed-rate token compression, which restricts flexibility in balancing accuracy with efficiency. These limitations highlight the need for a unified framework that can support ASR, VSR, and AVSR while enabling elastic inference. To this end, we present Omni-AVSR, a unified audio-visual LLM that combines efficient multi-granularity training with parameter-efficient adaptation. Specifically, we adapt the matryoshka representation learning paradigm to efficiently train across multiple audio and visual granularities, reducing its inherent training resource use. Furthermore, we explore three LoRA-based strategies for adapting the backbone LLM, balancing shared and task-specific specialization. Experiments on LRS2 and LRS3 show that Omni-AVSR achieves comparable or superior accuracy to state-of-the-art baselines while training a single model at substantially lower training and deployment resource use. The model also remains robust under acoustic noise, and we analyze its scaling behavior as LLM size increases, providing insights into the trade-off between performance and efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/umbertocappellazzo/Omni-AVSR", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07061", "title": "Do LLMs Feel? Teaching Emotion Recognition with Prompts, Retrieval, and Curriculum Learning", "authors": [ "Xinran Li", "Xiujuan Xu", "Jiaqi Qiao", "Yu Liu" ], "github": "", "project_page": "", "abstract": "Emotion Recognition in Conversation (ERC) is a crucial task for understanding human emotions and enabling natural human-computer interaction. Although Large Language Models (LLMs) have recently shown great potential in this field, their ability to capture the intrinsic connections between explicit and implicit emotions remains limited. We propose a novel ERC training framework, PRC-Emo, which integrates Prompt engineering, demonstration Retrieval, and Curriculum learning, with the goal of exploring whether LLMs can effectively perceive emotions in conversational contexts. Specifically, we design emotion-sensitive prompt templates based on both explicit and implicit emotional cues to better guide the model in understanding the speaker's psychological states. We construct the first dedicated demonstration retrieval repository for ERC, which includes training samples from widely used datasets, as well as high-quality dialogue examples generated by LLMs and manually verified. Moreover, we introduce a curriculum learning strategy into the LoRA fine-tuning process, incorporating weighted emotional shifts between same-speaker and different-speaker utterances to assign difficulty levels to dialogue samples, which are then organized in an easy-to-hard training sequence. Experimental results on two benchmark datasets-- IEMOCAP and MELD --show that our method achieves new state-of-the-art (SOTA) performance, demonstrating the effectiveness and generalizability of our approach in improving LLM-based emotional understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.06194", "title": "NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS Modeling", "authors": [ "Muhammad Usama", "Mohammad Sadil Khan", "Didier Stricker", "Muhammad Zeshan Afzal" ], "github": "", "project_page": "", "abstract": "Generating editable 3D CAD models from natural language remains challenging, as existing text-to-CAD systems either produce meshes or rely on scarce design-history data. We present NURBGen, the first framework to generate high-fidelity 3D CAD models directly from text using Non-Uniform Rational B-Splines (NURBS). To achieve this, we fine-tune a large language model (LLM) to translate free-form texts into JSON representations containing NURBS surface parameters (i.e, control points, knot vectors, degrees, and rational weights) which can be directly converted into BRep format using Python. We further propose a hybrid representation that combines untrimmed NURBS with analytic primitives to handle trimmed surfaces and degenerate regions more robustly, while reducing token complexity. Additionally, we introduce partABC, a curated subset of the ABC dataset consisting of individual CAD components, annotated with detailed captions using an automated annotation pipeline. NURBGen demonstrates strong performance on diverse prompts, surpassing prior methods in geometric fidelity and dimensional accuracy, as confirmed by expert evaluations. Code and dataset will be released publicly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07332", "title": "Grounding Computer Use Agents on Human Demonstrations", "authors": [ "Aarash Feizi", "Shravan Nayak", "Xiangru Jian", "Kevin Qinghong Lin", "Kaixin Li", "Rabiul Awal", "Xing Han L\u00f9", "Johan Obando-Ceron", "Juan A. Rodriguez", "Nicolas Chapados", "David Vazquez", "Adriana Romero-Soriano", "Reihaneh Rabbany", "Perouz Taslakian", "Christopher Pal", "Spandana Gella", "Sai Rajeswar" ], "github": "", "project_page": "", "abstract": "Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements. While large datasets exist for web and mobile interactions, high-quality resources for desktop environments are limited. To address this gap, we introduce GroundCUA, a large-scale desktop grounding dataset built from expert human demonstrations. It covers 87 applications across 12 categories and includes 56K screenshots, with every on-screen element carefully annotated for a total of over 3.56M human-verified annotations. From these demonstrations, we generate diverse instructions that capture a wide range of real-world tasks, providing high-quality data for model training. Using GroundCUA, we develop the GroundNext family of models that map instructions to their target UI elements. At both 3B and 7B scales, GroundNext achieves state-of-the-art results across five benchmarks using supervised fine-tuning, while requiring less than one-tenth the training data of prior work. Reinforcement learning post-training further improves performance, and when evaluated in an agentic setting on the OSWorld benchmark using o3 as planner, GroundNext attains comparable or superior results to models trained with substantially more data,. These results demonstrate the critical role of high-quality, expert-driven datasets in advancing general-purpose computer-use agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.00710", "title": "Ariadne: A Controllable Framework for Probing and Extending VLM Reasoning Boundaries", "authors": [ "Minghe Shen", "Zhuo Zhi", "Chonghan Liu", "Shuo Xing", "Zhengzhong Tu", "Che Liu" ], "github": "", "project_page": "", "abstract": "While Vision-Language Models (VLMs) post-trained with Reinforcement Learning (RL) show impressive general reasoning, their evaluation is often confined to language-dominant tasks (e.g., math). This raises a critical question: can RL post-training truly extend the inherent capability boundary of a base VLM, particularly for visual-centric spatial tasks where it initially fails? To investigate this, we introduce Ariadne, a framework utilizing synthetic mazes for multi-step spatial reasoning where task difficulty (e.g., path length, turns) is precisely controlled. We leverage this controllable environment to train VLMs using Reinforcement Learning with Verified Rewards (RLVR) in a difficulty-aware curriculum. Surprisingly, post-RLVR training, the VLM achieves over 50% accuracy on a problem set where the base model scored 0%, demonstrating that our approach expands the model's initial capability boundary. To assess real-world viability, we evaluate out-of-distribution (OOD) generalization on practical benchmarks. Despite training only on synthetic maze samples, Ariadne achieves significant zero-shot improvements, averaging 16% on MapBench (e.g., museum navigation) and 24% on ReasonMap (subway transfer tasks). These results confirm that our method not only broadens the model's fundamental limits but also enhances its generalization to real-world spatial reasoning. We acknowledge our study is limited to the post-training phase, given the opaqueness of pre-training data, and hope our research motivates further work on specialized, capability-extending alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07384", "title": "Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence", "authors": [ "Sean McLeish", "Ang Li", "John Kirchenbauer", "Dayal Singh Kalra", "Brian R. Bartoldson", "Bhavya Kailkhura", "Avi Schwarzschild", "Jonas Geiping", "Tom Goldstein", "Micah Goldblum" ], "github": "https://github.com/KellerJordan/Muon", "project_page": "", "abstract": "Recent advances in depth-recurrent language models show that recurrence can decouple train-time compute and parameter count from test-time compute. In this work, we study how to convert existing pretrained non-recurrent language models into depth-recurrent models. We find that using a curriculum of recurrences to increase the effective depth of the model over the course of training preserves performance while reducing total computational cost. In our experiments, on mathematics, we observe that converting pretrained models to recurrent ones results in better performance at a given compute budget than simply post-training the original non-recurrent language model.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KellerJordan/Muon", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07317", "title": "RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments", "authors": [ "Zhiyuan Zeng", "Hamish Ivison", "Yiping Wang", "Lifan Yuan", "Shuyue Stella Li", "Zhuorui Ye", "Siting Li", "Jacqueline He", "Runlong Zhou", "Tong Chen", "Chenyang Zhao", "Yulia Tsvetkov", "Simon Shaolei Du", "Natasha Jaques", "Hao Peng", "Pang Wei Koh", "Hannaneh Hajishirzi" ], "github": "https://github.com/Zhiyuan-Zeng/RLVE", "project_page": "", "abstract": "We introduce Reinforcement Learning (RL) with Adaptive Verifiable Environments (RLVE), an approach using verifiable environments that procedurally generate problems and provide algorithmically verifiable rewards, to scale up RL for language models (LMs). RLVE enables each verifiable environment to dynamically adapt its problem difficulty distribution to the policy model's capabilities as training progresses. In contrast, static data distributions often lead to vanishing learning signals when problems are either too easy or too hard for the policy. To implement RLVE, we create RLVE-Gym, a large-scale suite of 400 verifiable environments carefully developed through manual environment engineering. Using RLVE-Gym, we show that environment scaling, i.e., expanding the collection of training environments, consistently improves generalizable reasoning capabilities. RLVE with joint training across all 400 environments in RLVE-Gym yields a 3.37% absolute average improvement across six reasoning benchmarks, starting from one of the strongest 1.5B reasoning LMs. By comparison, continuing this LM's original RL training yields only a 0.49% average absolute gain despite using over 3x more compute. We release our code publicly.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Zhiyuan-Zeng/RLVE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.07025", "title": "Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Tasks", "authors": [ "Yauhen Babakhin", "Radek Osmulski", "Ronay Ak", "Gabriel Moreira", "Mengyao Xu", "Benedikt Schifferer", "Bo Liu", "Even Oldridge" ], "github": "https://github.com/QwenLM/Qwen3-Embedding", "project_page": "", "abstract": "We introduce llama-embed-nemotron-8b, an open-weights text embedding model that achieves state-of-the-art performance on the Multilingual Massive Text Embedding Benchmark (MMTEB) leaderboard as of October 21, 2025. While recent models show strong performance, their training data or methodologies are often not fully disclosed. We aim to address this by developing a fully open-source model, publicly releasing its weights and detailed ablation studies, and planning to share the curated training datasets. Our model demonstrates superior performance across all major embedding tasks -- including retrieval, classification and semantic textual similarity (STS) -- and excels in challenging multilingual scenarios, such as low-resource languages and cross-lingual setups. This state-of-the-art performance is driven by a novel data mix of 16.1 million query-document pairs, split between 7.7 million samples from public datasets and 8.4 million synthetically generated examples from various open-weight LLMs. One of our key contributions is a detailed ablation study analyzing core design choices, including a comparison of contrastive loss implementations, an evaluation of synthetic data generation (SDG) strategies, and the impact of model merging. The llama-embed-nemotron-8b is an instruction-aware model, supporting user-defined instructions to enhance performance for specific use-cases. This combination of top-tier performance, broad applicability, and user-driven flexibility enables it to serve as a universal text embedding solution.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/QwenLM/Qwen3-Embedding", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.05705", "title": "Long Grounded Thoughts: Distilling Compositional Visual Reasoning Chains at Scale", "authors": [ "David Acuna", "Chao-Han Huck Yang", "Yuntian Deng", "Jaehun Jung", "Ximing Lu", "Prithviraj Ammanabrolu", "Hyunwoo Kim", "Yuan-Hong Liao", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "Recent progress in multimodal reasoning has been driven largely by undisclosed datasets and proprietary data synthesis recipes, leaving open questions about how to systematically build large-scale, vision-centric reasoning datasets, particularly for tasks that go beyond visual math. In this work, we introduce a new reasoning data generation framework spanning diverse skills and levels of complexity with over 1M high-quality synthetic vision-centric questions. The dataset also includes preference data and instruction prompts supporting both offline and online RL. Our synthesis framework proceeds in two stages: (1) scale; and (2) complexity. Reasoning traces are then synthesized through a two-stage process that leverages VLMs and reasoning LLMs, producing CoT traces for VLMs that capture the richness and diverse cognitive behaviors found in frontier reasoning models. Remarkably, we show that finetuning Qwen2.5-VL-7B on our data outperforms all open-data baselines across all evaluated vision-centric benchmarks, and even surpasses strong closed-data models such as MiMo-VL-7B-RL on V* Bench, CV-Bench and MMStar-V. Perhaps most surprising, despite being entirely vision-centric, our data transfers positively to text-only reasoning (MMLU-Pro) and audio reasoning (MMAU), demonstrating its effectiveness. Similarly, despite not containing videos or embodied visual data, we observe notable gains when evaluating on a single-evidence embodied QA benchmark (NiEH). Finally, we use our data to analyze the entire VLM post-training pipeline. Our empirical analysis highlights that (i) SFT on high-quality data with non-linear reasoning traces is essential for effective online RL, (ii) staged offline RL matches online RL's performance while reducing compute demands, and (iii) careful SFT on high quality data can substantially improve out-of-domain, cross-modality transfer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.05933", "title": "Reinforcement Learning Improves Traversal of Hierarchical Knowledge in LLMs", "authors": [ "Renfei Zhang", "Manasa Kaniselvan", "Niloofar Mireshghallah" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) is often credited with improving language model reasoning and generalization at the expense of degrading memorized knowledge. We challenge this narrative by observing that RL-enhanced models consistently outperform their base and supervised fine-tuned (SFT) counterparts on pure knowledge recall tasks, particularly those requiring traversal of hierarchical, structured knowledge (e.g., medical codes). We hypothesize these gains stem not from newly acquired data, but from improved procedural skills in navigating and searching existing knowledge hierarchies within the model parameters. To support this hypothesis, we show that structured prompting, which explicitly guides SFTed models through hierarchical traversal, recovers most of the performance gap (reducing 24pp to 7pp on MedConceptsQA for DeepSeek-V3/R1). We further find that while prompting improves final-answer accuracy, RL-enhanced models retain superior ability to recall correct procedural paths on deep-retrieval tasks. Finally our layer-wise internal activation analysis reveals that while factual representations (e.g., activations for the statement \"code 57.95 refers to urinary infection\") maintain high cosine similarity between SFT and RL models, query representations (e.g., \"what is code 57.95\") diverge noticeably, indicating that RL primarily transforms how models traverse knowledge rather than the knowledge representation itself.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.06876", "title": "Generating an Image From 1,000 Words: Enhancing Text-to-Image With Structured Captions", "authors": [ "Eyal Gutflaish", "Eliran Kachlon", "Hezi Zisman", "Tal Hacham", "Nimrod Sarid", "Alexander Visheratin", "Saar Huberman", "Gal Davidi", "Guy Bukchin", "Kfir Goldberg", "Ron Mokady" ], "github": "", "project_page": "", "abstract": "Text-to-image models have rapidly evolved from casual creative tools to professional-grade systems, achieving unprecedented levels of image quality and realism. Yet, most models are trained to map short prompts into detailed images, creating a gap between sparse textual input and rich visual outputs. This mismatch reduces controllability, as models often fill in missing details arbitrarily, biasing toward average user preferences and limiting precision for professional use. We address this limitation by training the first open-source text-to-image model on long structured captions, where every training sample is annotated with the same set of fine-grained attributes. This design maximizes expressive coverage and enables disentangled control over visual factors. To process long captions efficiently, we propose DimFusion, a fusion mechanism that integrates intermediate tokens from a lightweight LLM without increasing token length. We also introduce the Text-as-a-Bottleneck Reconstruction (TaBR) evaluation protocol. By assessing how well real images can be reconstructed through a captioning-generation loop, TaBR directly measures controllability and expressiveness, even for very long captions where existing evaluation methods fail. Finally, we demonstrate our contributions by training the large-scale model FIBO, achieving state-of-the-art prompt alignment among open-source models. Model weights are publicly available at https://huggingface.co/briaai/FIBO", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-11", "arxiv_id": "2511.06449", "title": "FLEX: Continuous Agent Evolution via Forward Learning from Experience", "authors": [ "Zhicheng Cai", "Xinyuan Guo", "Yu Pei", "JiangTao Feng", "Jiangjie Chen", "Ya-Qin Zhang", "Wei-Ying Ma", "Mingxuan Wang", "Hao Zhou" ], "github": "https://github.com/GenSI-THUAIR/FLEX", "project_page": "", "abstract": "Autonomous agents driven by Large Language Models (LLMs) have revolutionized reasoning and problem-solving but remain static after training, unable to grow with experience as intelligent beings do during deployment. We introduce Forward Learning with EXperience (FLEX), a gradient-free learning paradigm that enables LLM agents to continuously evolve through accumulated experience. Specifically, FLEX cultivates scalable and inheritable evolution by constructing a structured experience library through continual reflection on successes and failures during interaction with the environment. FLEX delivers substantial improvements on mathematical reasoning, chemical retrosynthesis, and protein fitness prediction (up to 23% on AIME25, 10% on USPTO50k, and 14% on ProteinGym). We further identify a clear scaling law of experiential growth and the phenomenon of experience inheritance across agents, marking a step toward scalable and inheritable continuous agent evolution. Project Page: https://flex-gensi-thuair.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GenSI-THUAIR/FLEX", "github_api_added_by": "auto" }, { "date": "2025-11-12", "arxiv_id": "2511.06221", "title": "Tiny Model, Big Logic: Diversity-Driven Optimization Elicits Large-Model Reasoning Ability in VibeThinker-1.5B", "authors": [ "Sen Xu", "Yi Zhou", "Wei Wang", "Jixin Min", "Zhibin Yin", "Yingwei Dai", "Shixi Liu", "Lianyu Pang", "Yirong Chen", "Junlin Zhang" ], "github": "https://github.com/Tencent-Hunyuan/Hunyuan-1.8B", "project_page": "", "abstract": "Challenging the prevailing consensus that small models inherently lack robust reasoning, this report introduces VibeThinker-1.5B, a 1.5B-parameter dense model developed via our Spectrum-to-Signal Principle (SSP). This challenges the prevailing approach of scaling model parameters to enhance capabilities, as seen in models like DeepSeek R1 (671B) and Kimi k2 (>1T). The SSP framework first employs a Two-Stage Diversity-Exploring Distillation (SFT) to generate a broad spectrum of solutions, followed by MaxEnt-Guided Policy Optimization (RL) to amplify the correct signal. With a total training cost of only $7,800, VibeThinker-1.5B demonstrates superior reasoning capabilities compared to closed-source models like Magistral Medium and Claude Opus 4, and performs on par with open-source models like GPT OSS-20B Medium. Remarkably, it surpasses the 400x larger DeepSeek R1 on three math benchmarks: AIME24 (80.3 vs. 79.8), AIME25 (74.4 vs. 70.0), and HMMT25 (50.4 vs. 41.7). This is a substantial improvement over its base model (6.7, 4.3, and 0.6, respectively). On LiveCodeBench V6, it scores 51.1, outperforming Magistral Medium's 50.3 and its base model's 0.0. These findings demonstrate that small models can achieve reasoning capabilities comparable to large models, drastically reducing training and inference costs and thereby democratizing advanced AI research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Tencent-Hunyuan/Hunyuan-1.8B", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-12", "arxiv_id": "2511.06281", "title": "VideoSSR: Video Self-Supervised Reinforcement Learning", "authors": [ "Zefeng He", "Xiaoye Qu", "Yafu Li", "Siyuan Huang", "Daizong Liu", "Yu Cheng" ], "github": "https://github.com/lcqysl/VideoSSR", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has substantially advanced the video understanding capabilities of Multimodal Large Language Models (MLLMs). However, the rapid progress of MLLMs is outpacing the complexity of existing video datasets, while the manual annotation of new, high-quality data remains prohibitively expensive. This work investigates a pivotal question: Can the rich, intrinsic information within videos be harnessed to self-generate high-quality, verifiable training data? To investigate this, we introduce three self-supervised pretext tasks: Anomaly Grounding, Object Counting, and Temporal Jigsaw. We construct the Video Intrinsic Understanding Benchmark (VIUBench) to validate their difficulty, revealing that current state-of-the-art MLLMs struggle significantly on these tasks. Building upon these pretext tasks, we develop the VideoSSR-30K dataset and propose VideoSSR, a novel video self-supervised reinforcement learning framework for RLVR. Extensive experiments across 17 benchmarks, spanning four major video domains (General Video QA, Long Video QA, Temporal Grounding, and Complex Reasoning), demonstrate that VideoSSR consistently enhances model performance, yielding an average improvement of over 5\\%. These results establish VideoSSR as a potent foundational framework for developing more advanced video understanding in MLLMs. The code is available at https://github.com/lcqysl/VideoSSR.", "github_manual": "", "github_regex": "https://github.com/lcqysl/VideoSSR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-12", "arxiv_id": "2511.06428", "title": "Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective", "authors": [ "Samuel Ferino", "Rashina Hoda", "John Grundy", "Christoph Treude" ], "github": "", "project_page": "", "abstract": "Background: Large Language Models emerged with the potential of provoking a revolution in software development (e.g., automating processes, workforce transformation). Although studies have started to investigate the perceived impact of LLMs for software development, there is a need for empirical studies to comprehend how to balance forward and backward effects of using LLMs. Objective: We investigated how LLMs impact software development and how to manage the impact from a software developer's perspective. Method: We conducted 22 interviews with software practitioners across 3 rounds of data collection and analysis, between October (2024) and September (2025). We employed socio-technical grounded theory (STGT) for data analysis to rigorously analyse interview participants' responses. Results: We identified the benefits (e.g., maintain software development flow, improve developers' mental model, and foster entrepreneurship) and disadvantages (e.g., negative impact on developers' personality and damage to developers' reputation) of using LLMs at individual, team, organisation, and society levels; as well as best practices on how to adopt LLMs. Conclusion: Critically, we present the trade-offs that software practitioners, teams, and organisations face in working with LLMs. Our findings are particularly useful for software team leaders and IT managers to assess the viability of LLMs within their specific context.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-12", "arxiv_id": "2511.08319", "title": "Adaptive Multi-Agent Response Refinement in Conversational Systems", "authors": [ "Soyeong Jeong", "Aparna Elangovan", "Emine Yilmaz", "Oleg Rokhlenko" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable success in conversational systems by generating human-like responses. However, they can fall short, especially when required to account for personalization or specific knowledge. In real-life settings, it is impractical to rely on users to detect these errors and request a new response. One way to address this problem is to refine the response before returning it to the user. While existing approaches focus on refining responses within a single LLM, this method struggles to consider diverse aspects needed for effective conversations. In this work, we propose refining responses through a multi-agent framework, where each agent is assigned a specific role for each aspect. We focus on three key aspects crucial to conversational quality: factuality, personalization, and coherence. Each agent is responsible for reviewing and refining one of these aspects, and their feedback is then merged to improve the overall response. To enhance collaboration among them, we introduce a dynamic communication strategy. Instead of following a fixed sequence of agents, our approach adaptively selects and coordinates the most relevant agents based on the specific requirements of each query. We validate our framework on challenging conversational datasets, demonstrating that ours significantly outperforms relevant baselines, particularly in tasks involving knowledge or user's persona, or both.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-12", "arxiv_id": "2511.07080", "title": "Wasm: A Pipeline for Constructing Structured Arabic Interleaved Multimodal Corpora", "authors": [ "Khalil Hennara", "Ahmad Bastati", "Muhammad Hreden", "Mohamed Motasim Hamed", "Zeina Aldallal", "Sara Chrouf", "Safwan AlModhayan" ], "github": "", "project_page": "", "abstract": "The performance of large language models (LLMs) and large multimodal models (LMMs) depends heavily on the quality and scale of their pre-training datasets. Recent research shows that large multimodal models trained on natural documents where images and text are interleaved outperform those trained only on image-text pairs across a wide range of benchmarks, leveraging advanced pre- trained models to enforce semantic alignment, image-sequence consistency, and textual coherence. For Arabic, however, the lack of high-quality multimodal datasets that preserve document structure has limited progress. In this paper, we present our pipeline Wasm for processing the Common Crawl dataset to create a new Arabic multimodal dataset that uniquely provides markdown output. Unlike existing Arabic corpora that focus solely on text extraction, our approach preserves the structural integrity of web content while maintaining flexibility for both text-only and multimodal pre-training scenarios. We provide a comprehensive comparative analysis of our data processing pipeline against those used for major existing datasets, highlighting the convergences in filtering strategies and justifying our specific design choices. To support future research, we publicly release a representative dataset dump along with the multimodal processing pipeline for Arabic.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-12", "arxiv_id": "2511.05664", "title": "KLASS: KL-Guided Fast Inference in Masked Diffusion Models", "authors": [ "Seo Hyun Kim", "Sunwoo Hong", "Hojung Jung", "Youngrok Park", "Se-Young Yun" ], "github": "https://github.com/shkim0116/KLASS", "project_page": "", "abstract": "Masked diffusion models have demonstrated competitive results on various tasks including language generation. However, due to its iterative refinement process, the inference is often bottlenecked by slow and static sampling speed. To overcome this problem, we introduce `KL-Adaptive Stability Sampling' (KLASS), a fast yet effective sampling method that exploits token-level KL divergence to identify stable, high-confidence predictions. By unmasking multiple tokens in each iteration without any additional model training, our approach speeds up generation significantly while maintaining sample quality. On reasoning benchmarks, KLASS achieves up to 2.78times wall-clock speedups while improving performance over standard greedy decoding, attaining state-of-the-art results among diffusion-based samplers. We further validate KLASS across diverse domains, including text, image, and molecular generation, showing its effectiveness as a broadly applicable sampler across different models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/shkim0116/KLASS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-12", "arxiv_id": "2511.08567", "title": "The Path Not Taken: RLVR Provably Learns Off the Principals", "authors": [ "Hanqing Zhu", "Zhenyu Zhang", "Hanxian Huang", "DiJia Su", "Zechun Liu", "Jiawei Zhao", "Igor Fedorov", "Hamed Pirsiavash", "Zhizhou Sha", "Jinwon Lee", "David Z. Pan", "Zhangyang Wang", "Yuandong Tian", "Kai Sheng Tai" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) reliably improves the reasoning performance of large language models, yet it appears to modify only a small fraction of parameters. We revisit this paradox and show that sparsity is a surface artifact of a model-conditioned optimization bias: for a fixed pretrained model, updates consistently localize to preferred parameter regions, highly consistent across runs and largely invariant to datasets and RL recipes. We mechanistically explain these dynamics with a Three-Gate Theory: Gate I (KL Anchor) imposes a KL-constrained update; Gate II (Model Geometry) steers the step off principal directions into low-curvature, spectrum-preserving subspaces; and Gate III (Precision) hides micro-updates in non-preferred regions, making the off-principal bias appear as sparsity. We then validate this theory and, for the first time, provide a parameter-level characterization of RLVR's learning dynamics: RLVR learns off principal directions in weight space, achieving gains via minimal spectral drift, reduced principal-subspace rotation, and off-principal update alignment. In contrast, SFT targets principal weights, distorts the spectrum, and even lags RLVR. Together, these results provide the first parameter-space account of RLVR's training dynamics, revealing clear regularities in how parameters evolve. Crucially, we show that RL operates in a distinct optimization regime from SFT, so directly adapting SFT-era parameter-efficient fine-tuning (PEFT) methods can be flawed, as evidenced by our case studies on advanced sparse fine-tuning and LoRA variants. We hope this work charts a path toward a white-box understanding of RLVR and the design of geometry-aware, RLVR-native learning algorithms, rather than repurposed SFT-era heuristics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-12", "arxiv_id": "2511.08029", "title": "BiCA: Effective Biomedical Dense Retrieval with Citation-Aware Hard Negatives", "authors": [ "Aarush Sinha", "Pavan Kumar S", "Roshan Balaji", "Nirav Pravinbhai Bhatt" ], "github": "", "project_page": "", "abstract": "Hard negatives are essential for training effective retrieval models. Hard-negative mining typically relies on ranking documents using cross-encoders or static embedding models based on similarity metrics such as cosine distance. Hard negative mining becomes challenging for biomedical and scientific domains due to the difficulty in distinguishing between source and hard negative documents. However, referenced documents naturally share contextual relevance with the source document but are not duplicates, making them well-suited as hard negatives. In this work, we propose BiCA: Biomedical Dense Retrieval with Citation-Aware Hard Negatives, an approach for hard-negative mining by utilizing citation links in 20,000 PubMed articles for improving a domain-specific small dense retriever. We fine-tune the GTE_small and GTE_Base models using these citation-informed negatives and observe consistent improvements in zero-shot dense retrieval using nDCG@10 for both in-domain and out-of-domain tasks on BEIR and outperform baselines on long-tailed topics in LoTTE using Success@5. Our findings highlight the potential of leveraging document link structure to generate highly informative negatives, enabling state-of-the-art performance with minimal fine-tuning and demonstrating a path towards highly data-efficient domain adaptation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-12", "arxiv_id": "2511.07003", "title": "Beyond English: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs", "authors": [ "Yingfeng Luo", "Ziqiang Xu", "Yuxuan Ouyang", "Murun Yang", "Dingyang Lin", "Kaiyan Chang", "Tong Zheng", "Bei Li", "Peinan Feng", "Quan Du", "Tong Xiao", "Jingbo Zhu" ], "github": "https://github.com/NiuTrans/LMT", "project_page": "", "abstract": "Large language models have significantly advanced Multilingual Machine Translation (MMT), yet the broad language coverage, consistent translation quality, and English-centric bias remain open challenges. To address these challenges, we introduce LMT, a suite of Large-scale Multilingual Translation models centered on both Chinese and English, covering 60 languages and 234 translation directions. During development, we identify a previously overlooked phenomenon of directional degeneration, where symmetric multi-way fine-tuning data overemphasize reverse directions (X to En/Zh), leading to excessive many-to-one mappings and degraded translation quality. We propose Strategic Downsampling, a simple yet effective method to mitigate this degeneration. In addition, we design Parallel Multilingual Prompting (PMP), which leverages typologically related auxiliary languages to enhance cross-lingual transfer. Through rigorous data curation and refined adaptation strategies, LMT achieves SOTA performance among models of comparable language coverage, with our 4B model (LMT-60-4B) surpassing the much larger Aya-101-13B and NLLB-54B models by a substantial margin. We release LMT in four sizes (0.6B/1.7B/4B/8B) to catalyze future research and provide strong baselines for inclusive, scalable, and high-quality MMT \\href{https://github.com/NiuTrans/LMT{https://github.com/NiuTrans/LMT}}.", "github_manual": "", "github_regex": "https://github.com/NiuTrans/LMT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-12", "arxiv_id": "2511.07885", "title": "Intelligence per Watt: Measuring Intelligence Efficiency of Local AI", "authors": [ "Jon Saad-Falcon", "Avanika Narayan", "Hakki Orhun Akengin", "J. Wes Griffin", "Herumb Shandilya", "Adrian Gamarra Lafuente", "Medhya Goel", "Rebecca Joseph", "Shlok Natarajan", "Etash Kumar Guha", "Shang Zhu", "Ben Athiwaratkun", "John Hennessy", "Azalia Mirhoseini", "Christopher R\u00e9" ], "github": "https://github.com/HazyResearch/intelligence-per-watt", "project_page": "", "abstract": "Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure. Rapidly growing demand strains this paradigm, and cloud providers struggle to scale infrastructure at pace. Two advances enable us to rethink this paradigm: small LMs (<=20B active parameters) now achieve competitive performance to frontier models on many tasks, and local accelerators (e.g., Apple M4 Max) run these models at interactive latencies. This raises the question: can local inference viably redistribute demand from centralized infrastructure? Answering this requires measuring whether local LMs can accurately answer real-world queries and whether they can do so efficiently enough to be practical on power-constrained devices (i.e., laptops). We propose intelligence per watt (IPW), task accuracy divided by unit of power, as a metric for assessing capability and efficiency of local inference across model-accelerator pairs. We conduct a large-scale empirical study across 20+ state-of-the-art local LMs, 8 accelerators, and a representative subset of LLM traffic: 1M real-world single-turn chat and reasoning queries. For each query, we measure accuracy, energy, latency, and power. Our analysis reveals 3 findings. First, local LMs can accurately answer 88.7% of single-turn chat and reasoning queries with accuracy varying by domain. Second, from 2023-2025, IPW improved 5.3x and local query coverage rose from 23.2% to 71.3%. Third, local accelerators achieve at least 1.4x lower IPW than cloud accelerators running identical models, revealing significant headroom for optimization. These findings demonstrate that local inference can meaningfully redistribute demand from centralized infrastructure, with IPW serving as the critical metric for tracking this transition. We release our IPW profiling harness for systematic intelligence-per-watt benchmarking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HazyResearch/intelligence-per-watt", "github_api_added_by": "auto" }, { "date": "2025-11-12", "arxiv_id": "2511.08043", "title": "DynaAct: Large Language Model Reasoning with Dynamic Action Spaces", "authors": [ "Xueliang Zhao", "Wei Wu", "Jian Guan", "Qintong Li", "Lingpeng Kong" ], "github": "https://github.com/zhaoxlpku/DynaAct", "project_page": "", "abstract": "In modern sequential decision-making systems, the construction of an optimal candidate action space is critical to efficient inference. However, existing approaches either rely on manually defined action spaces that lack scalability or utilize unstructured spaces that render exhaustive search computationally prohibitive. In this paper, we propose a novel framework named DynaAct for automatically constructing a compact action space to enhance sequential reasoning in complex problem-solving scenarios. Our method first estimates a proxy for the complete action space by extracting general sketches observed in a corpus covering diverse complex reasoning problems using large language models. We then formulate a submodular function that jointly evaluates candidate actions based on their utility to the current state and their diversity, and employ a greedy algorithm to select an optimal candidate set. Extensive experiments on six diverse standard benchmarks demonstrate that our approach significantly improves overall performance, while maintaining efficient inference without introducing substantial latency. The implementation is available at https://github.com/zhaoxlpku/DynaAct.", "github_manual": "", "github_regex": "https://github.com/zhaoxlpku/DynaAct", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-12", "arxiv_id": "2511.07587", "title": "Beyond Fact Retrieval: Episodic Memory for RAG with Generative Semantic Workspaces", "authors": [ "Shreyas Rajesh", "Pavan Holur", "Chenda Duan", "David Chong", "Vwani Roychowdhury" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) face fundamental challenges in long-context reasoning: many documents exceed their finite context windows, while performance on texts that do fit degrades with sequence length, necessitating their augmentation with external memory frameworks. Current solutions, which have evolved from retrieval using semantic embeddings to more sophisticated structured knowledge graphs representations for improved sense-making and associativity, are tailored for fact-based retrieval and fail to build the space-time-anchored narrative representations required for tracking entities through episodic events. To bridge this gap, we propose the Generative Semantic Workspace (GSW), a neuro-inspired generative memory framework that builds structured, interpretable representations of evolving situations, enabling LLMs to reason over evolving roles, actions, and spatiotemporal contexts. Our framework comprises an Operator, which maps incoming observations to intermediate semantic structures, and a Reconciler, which integrates these into a persistent workspace that enforces temporal, spatial, and logical coherence. On the Episodic Memory Benchmark (EpBench) huet_episodic_2025 comprising corpora ranging from 100k to 1M tokens in length, GSW outperforms existing RAG based baselines by up to 20\\%. Furthermore, GSW is highly efficient, reducing query-time context tokens by 51\\% compared to the next most token-efficient baseline, reducing inference time costs considerably. More broadly, GSW offers a concrete blueprint for endowing LLMs with human-like episodic memory, paving the way for more capable agents that can reason over long horizons.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-12", "arxiv_id": "2511.05650", "title": "Optimizing Diversity and Quality through Base-Aligned Model Collaboration", "authors": [ "Yichen Wang", "Chenghao Yang", "Tenghao Huang", "Muhao Chen", "Jonathan May", "Mina Lee" ], "github": "https://github.com/YichenZW/base-align-collab", "project_page": "", "abstract": "Alignment has greatly improved large language models (LLMs)' output quality at the cost of diversity, yielding highly similar outputs across generations. We propose Base-Aligned Model Collaboration (BACo), an inference-time token-level model collaboration framework that dynamically combines a base LLM with its aligned counterpart to optimize diversity and quality. Inspired by prior work (Fei et al., 2025), BACo employs routing strategies that determine, at each token, from which model to decode based on next-token prediction uncertainty and predicted contents' semantic role. Prior diversity-promoting methods, such as retraining, prompt engineering, and multi-sampling methods, improve diversity but often degrade quality or require costly decoding or post-training. In contrast, BACo achieves both high diversity and quality post hoc within a single pass, while offering strong controllability. We explore a family of routing strategies, across three open-ended generation tasks and 13 metrics covering diversity and quality, BACo consistently surpasses state-of-the-art inference-time baselines. With our best router, BACo achieves a 21.3% joint improvement in diversity and quality. Human evaluations also mirror these improvements. The results suggest that collaboration between base and aligned models can optimize and control diversity and quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YichenZW/base-align-collab", "github_api_added_by": "auto" }, { "date": "2025-11-12", "arxiv_id": "2511.05489", "title": "TimeSearch-R: Adaptive Temporal Search for Long-Form Video Understanding via Self-Verification Reinforcement Learning", "authors": [ "Junwen Pan", "Qizhe Zhang", "Rui Zhang", "Ming Lu", "Xin Wan", "Yuan Zhang", "Chang Liu", "Qi She" ], "github": "https://github.com/Time-Search/TimeSearch-R", "project_page": "", "abstract": "Temporal search aims to identify a minimal set of relevant frames from tens of thousands based on a given query, serving as a foundation for accurate long-form video understanding. Existing works attempt to progressively narrow the search space. However, these approaches typically rely on a hand-crafted search process, lacking end-to-end optimization for learning optimal search strategies. In this paper, we propose TimeSearch-R, which reformulates temporal search as interleaved text-video thinking, seamlessly integrating searching video clips into the reasoning process through reinforcement learning (RL). However, applying RL training methods, such as Group Relative Policy Optimization (GRPO), to video reasoning can result in unsupervised intermediate search decisions. This leads to insufficient exploration of the video content and inconsistent logical reasoning. To address these issues, we introduce GRPO with Completeness Self-Verification (GRPO-CSV), which gathers searched video frames from the interleaved reasoning process and utilizes the same policy model to verify the adequacy of searched frames, thereby improving the completeness of video reasoning. Additionally, we construct datasets specifically designed for the SFT cold-start and RL training of GRPO-CSV, filtering out samples with weak temporal dependencies to enhance task difficulty and improve temporal search capabilities. Extensive experiments demonstrate that TimeSearch-R achieves significant improvements on temporal search benchmarks such as Haystack-LVBench and Haystack-Ego4D, as well as long-form video understanding benchmarks like VideoMME and MLVU. Notably, TimeSearch-R establishes a new state-of-the-art on LongVideoBench with 4.1% improvement over the base model Qwen2.5-VL and 2.0% over the advanced video reasoning model Video-R1. Our code is available at https://github.com/Time-Search/TimeSearch-R.", "github_manual": "", "github_regex": "https://github.com/Time-Search/TimeSearch-R", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.08923", "title": "TiDAR: Think in Diffusion, Talk in Autoregression", "authors": [ "Jingyu Liu", "Xin Dong", "Zhifan Ye", "Rishabh Mehta", "Yonggan Fu", "Vartika Singh", "Jan Kautz", "Ce Zhang", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "Diffusion language models hold the promise of fast parallel generation, while autoregressive (AR) models typically excel in quality due to their causal structure aligning naturally with language modeling. This raises a fundamental question: can we achieve a synergy with high throughput, higher GPU utilization, and AR level quality? Existing methods fail to effectively balance these two aspects, either prioritizing AR using a weaker model for sequential drafting (speculative decoding), leading to lower drafting efficiency, or using some form of left-to-right (AR-like) decoding logic for diffusion, which still suffers from quality degradation and forfeits its potential parallelizability. We introduce TiDAR, a sequence-level hybrid architecture that drafts tokens (Thinking) in Diffusion and samples final outputs (Talking) AutoRegressively - all within a single forward pass using specially designed structured attention masks. This design exploits the free GPU compute density, achieving a strong balance between drafting and verification capacity. Moreover, TiDAR is designed to be serving-friendly (low overhead) as a standalone model. We extensively evaluate TiDAR against AR models, speculative decoding, and diffusion variants across generative and likelihood tasks at 1.5B and 8B scales. Thanks to the parallel drafting and sampling as well as exact KV cache support, TiDAR outperforms speculative decoding in measured throughput and surpasses diffusion models like Dream and Llada in both efficiency and quality. Most notably, TiDAR is the first architecture to close the quality gap with AR models while delivering 4.71x to 5.91x more tokens per second.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.09515", "title": "WMPO: World Model-based Policy Optimization for Vision-Language-Action Models", "authors": [ "Fangqi Zhu", "Zhengyang Yan", "Zicong Hong", "Quanxin Shou", "Xiao Ma", "Song Guo" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation, but their reliance on expert demonstrations limits their ability to learn from failures and perform self-corrections. Reinforcement learning (RL) addresses these through self-improving interactions with the physical environment, but suffers from high sample complexity on real robots. We introduce World-Model-based Policy Optimization (WMPO), a principled framework for on-policy VLA RL without interacting with the real environment. In contrast to widely used latent world models, WMPO focuses on pixel-based predictions that align the \"imagined\" trajectories with the VLA features pretrained with web-scale images. Crucially, WMPO enables the policy to perform on-policy GRPO that provides stronger performance than the often-used off-policy methods. Extensive experiments in both simulation and real-robot settings demonstrate that WMPO (i) substantially improves sample efficiency, (ii) achieves stronger overall performance, (iii) exhibits emergent behaviors such as self-correction, and (iv) demonstrates robust generalization and lifelong learning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.08892", "title": "Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds", "authors": [ "Weihao Tan", "Xiangyang Li", "Yunhao Fang", "Heyuan Yao", "Shi Yan", "Hao Luo", "Tenglong Ao", "Huihui Li", "Hongbin Ren", "Bairen Yi", "Yujia Qin", "Bo An", "Libin Liu", "Guang Shi" ], "github": "", "project_page": "", "abstract": "We introduce Lumine, the first open recipe for developing generalist agents capable of completing hours-long complex missions in real time within challenging 3D open-world environments. Lumine adopts a human-like interaction paradigm that unifies perception, reasoning, and action in an end-to-end manner, powered by a vision-language model. It processes raw pixels at 5 Hz to produce precise 30 Hz keyboard-mouse actions and adaptively invokes reasoning only when necessary. Trained in Genshin Impact, Lumine successfully completes the entire five-hour Mondstadt main storyline on par with human-level efficiency and follows natural language instructions to perform a broad spectrum of tasks in both 3D open-world exploration and 2D GUI manipulation across collection, combat, puzzle-solving, and NPC interaction. In addition to its in-domain performance, Lumine demonstrates strong zero-shot cross-game generalization. Without any fine-tuning, it accomplishes 100-minute missions in Wuthering Waves and the full five-hour first chapter of Honkai: Star Rail. These promising results highlight Lumine's effectiveness across distinct worlds and interaction dynamics, marking a concrete step toward generalist agents in open-ended environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.08633", "title": "Time-to-Move: Training-Free Motion Controlled Video Generation via Dual-Clock Denoising", "authors": [ "Assaf Singer", "Noam Rotstein", "Amir Mann", "Ron Kimmel", "Or Litany" ], "github": "", "project_page": "", "abstract": "Diffusion-based video generation can create realistic videos, yet existing image- and text-based conditioning fails to offer precise motion control. Prior methods for motion-conditioned synthesis typically require model-specific fine-tuning, which is computationally expensive and restrictive. We introduce Time-to-Move (TTM), a training-free, plug-and-play framework for motion- and appearance-controlled video generation with image-to-video (I2V) diffusion models. Our key insight is to use crude reference animations obtained through user-friendly manipulations such as cut-and-drag or depth-based reprojection. Motivated by SDEdit's use of coarse layout cues for image editing, we treat the crude animations as coarse motion cues and adapt the mechanism to the video domain. We preserve appearance with image conditioning and introduce dual-clock denoising, a region-dependent strategy that enforces strong alignment in motion-specified regions while allowing flexibility elsewhere, balancing fidelity to user intent with natural dynamics. This lightweight modification of the sampling process incurs no additional training or runtime cost and is compatible with any backbone. Extensive experiments on object and camera motion benchmarks show that TTM matches or exceeds existing training-based baselines in realism and motion control. Beyond this, TTM introduces a unique capability: precise appearance control through pixel-level conditioning, exceeding the limits of text-only prompting. Visit our project page for video examples and code: https://time-to-move.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.09148", "title": "LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls", "authors": [ "Kangning Zhang", "Wenxiang Jiao", "Kounianhua Du", "Yuan Lu", "Weiwen Liu", "Weinan Zhang", "Lei Zhang", "Yong Yu" ], "github": "https://github.com/Rednote-DeepExperience/LoopTool", "project_page": "", "abstract": "Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipelines where data generation and model training are executed as two separate, non-interactive processes. This approach fails to adaptively focus on a model's specific weaknesses and allows noisy labels to persist, degrading training efficiency. We introduce LoopTool, a fully automated, model-aware data evolution framework that closes this loop by tightly integrating data synthesis and model training. LoopTool iteratively refines both the data and the model through three synergistic modules: (1) Greedy Capability Probing (GCP) diagnoses the model's mastered and failed capabilities; (2) Judgement-Guided Label Verification (JGLV) uses an open-source judge model to find and correct annotation errors, progressively purifying the dataset; and (3) Error-Driven Data Expansion (EDDE) generates new, challenging samples based on identified failures. This closed-loop process operates within a cost-effective, open-source ecosystem, eliminating dependence on expensive closed-source APIs. Experiments show that our 8B model trained with LoopTool significantly surpasses its 32B data generator and achieves new state-of-the-art results on the BFCL-v3 and ACEBench benchmarks for its scale. Our work demonstrates that closed-loop, self-refining data pipelines can dramatically enhance the tool-use capabilities of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Rednote-DeepExperience/LoopTool", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.06805", "title": "MathSE: Improving Multimodal Mathematical Reasoning via Self-Evolving Iterative Reflection and Reward-Guided Fine-Tuning", "authors": [ "Jinhao Chen", "Zhen Yang", "Jianxin Shi", "Tianyu Wo", "Jie Tang" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in vision-language answering tasks. Despite their strengths, these models often encounter challenges in achieving complex reasoning tasks such as mathematical problem-solving. Previous works have focused on fine-tuning on specialized mathematical datasets. However, these datasets are typically distilled directly from teacher models, which capture only static reasoning patterns and leaving substantial gaps compared to student models. This reliance on fixed teacher-derived datasets not only restricts the model's ability to adapt to novel or more intricate questions that extend beyond the confines of the training data, but also lacks the iterative depth needed for robust generalization. To overcome these limitations, we propose \\method, a Mathematical Self-Evolving framework for MLLMs. In contrast to traditional one-shot fine-tuning paradigms, \\method iteratively refines the model through cycles of inference, reflection, and reward-based feedback. Specifically, we leverage iterative fine-tuning by incorporating correct reasoning paths derived from previous-stage inference and integrating reflections from a specialized Outcome Reward Model (ORM). To verify the effectiveness of \\method, we evaluate it on a suite of challenging benchmarks, demonstrating significant performance gains over backbone models. Notably, our experimental results on MathVL-test surpass the leading open-source multimodal mathematical reasoning model QVQ. Our code and models are available at https://zheny2751\\allowbreak-dotcom.github.io/\\allowbreak MathSE.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.07499", "title": "Toward the Frontiers of Reliable Diffusion Sampling via Adversarial Sinkhorn Attention Guidance", "authors": [ "Kwanyoung Kim" ], "github": "", "project_page": "", "abstract": "Diffusion models have demonstrated strong generative performance when using guidance methods such as classifier-free guidance (CFG), which enhance output quality by modifying the sampling trajectory. These methods typically improve a target output by intentionally degrading another, often the unconditional output, using heuristic perturbation functions such as identity mixing or blurred conditions. However, these approaches lack a principled foundation and rely on manually designed distortions. In this work, we propose Adversarial Sinkhorn Attention Guidance (ASAG), a novel method that reinterprets attention scores in diffusion models through the lens of optimal transport and intentionally disrupt the transport cost via Sinkhorn algorithm. Instead of naively corrupting the attention mechanism, ASAG injects an adversarial cost within self-attention layers to reduce pixel-wise similarity between queries and keys. This deliberate degradation weakens misleading attention alignments and leads to improved conditional and unconditional sample quality. ASAG shows consistent improvements in text-to-image diffusion, and enhances controllability and fidelity in downstream applications such as IP-Adapter and ControlNet. The method is lightweight, plug-and-play, and improves reliability without requiring any model retraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.06251", "title": "WebVIA: A Web-based Vision-Language Agentic Framework for Interactive and Verifiable UI-to-Code Generation", "authors": [ "Mingde Xu", "Zhen Yang", "Wenyi Hong", "Lihang Pan", "Xinyue Fan", "Yan Wang", "Xiaotao Gu", "Bin Xu", "Jie Tang" ], "github": "", "project_page": "", "abstract": "User interface (UI) development requires translating design mockups into functional code, a process that remains repetitive and labor-intensive. While recent Vision-Language Models (VLMs) automate UI-to-Code generation, they generate only static HTML/CSS/JavaScript layouts lacking interactivity. To address this, we propose WebVIA, the first agentic framework for interactive UI-to-Code generation and validation. The framework comprises three components: 1) an exploration agent to capture multi-state UI screenshots; 2) a UI2Code model that generates executable interactive code; 3) a validation module that verifies the interactivity. Experiments demonstrate that WebVIA-Agent achieves more stable and accurate UI exploration than general-purpose agents (e.g., Gemini-2.5-Pro). In addition, our fine-tuned WebVIA-UI2Code models exhibit substantial improvements in generating executable and interactive HTML/CSS/JavaScript code, outperforming their base counterparts across both interactive and static UI2Code benchmarks. Our code and models are available at https://zheny2751-dotcom.github.io/webvia.github.io/{https://webvia.github.io}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.04824", "title": "Agentic Refactoring: An Empirical Study of AI Coding Agents", "authors": [ "Kosei Horikawa", "Hao Li", "Yutaro Kashiwa", "Bram Adams", "Hajimu Iida", "Ahmed E. Hassan" ], "github": "", "project_page": "", "abstract": "Agentic coding tools, such as OpenAI Codex, Claude Code, and Cursor, are transforming the software engineering landscape. These AI-powered systems function as autonomous teammates capable of planning and executing complex development tasks. Agents have become active participants in refactoring, a cornerstone of sustainable software development aimed at improving internal code quality without altering observable behavior. Despite their increasing adoption, there is a critical lack of empirical understanding regarding how agentic refactoring is utilized in practice, how it compares to human-driven refactoring, and what impact it has on code quality. To address this empirical gap, we present a large-scale study of AI agent-generated refactorings in real-world open-source Java projects, analyzing 15,451 refactoring instances across 12,256 pull requests and 14,988 commits derived from the AIDev dataset. Our empirical analysis shows that refactoring is a common and intentional activity in this development paradigm, with agents explicitly targeting refactoring in 26.1% of commits. Analysis of refactoring types reveals that agentic efforts are dominated by low-level, consistency-oriented edits, such as Change Variable Type (11.8%), Rename Parameter (10.4%), and Rename Variable (8.5%), reflecting a preference for localized improvements over the high-level design changes common in human refactoring. Additionally, the motivations behind agentic refactoring focus overwhelmingly on internal quality concerns, with maintainability (52.5%) and readability (28.1%). Furthermore, quantitative evaluation of code quality metrics shows that agentic refactoring yields small but statistically significant improvements in structural metrics, particularly for medium-level changes, reducing class size and complexity (e.g., Class LOC median \u0394 = -15.25).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.06073", "title": "Stemming Hallucination in Language Models Using a Licensing Oracle", "authors": [ "Simeon Emanuilov", "Richard Ackermann" ], "github": "", "project_page": "", "abstract": "Language models exhibit remarkable natural language generation capabilities but remain prone to hallucinations, generating factually incorrect information despite producing syntactically coherent responses. This study introduces the Licensing Oracle, an architectural solution designed to stem hallucinations in LMs by enforcing truth constraints through formal validation against structured knowledge graphs. Unlike statistical approaches that rely on data scaling or fine-tuning, the Licensing Oracle embeds a deterministic validation step into the model's generative process, ensuring that only factually accurate claims are made. We evaluated the effectiveness of the Licensing Oracle through experiments comparing it with several state-of-the-art methods, including baseline language model generation, fine-tuning for factual recall, fine-tuning for abstention behavior, and retrieval-augmented generation (RAG). Our results demonstrate that although RAG and fine-tuning improve performance, they fail to eliminate hallucinations. In contrast, the Licensing Oracle achieved perfect abstention precision (AP = 1.0) and zero false answers (FAR-NE = 0.0), ensuring that only valid claims were generated with 89.1% accuracy in factual responses. This work shows that architectural innovations, such as the Licensing Oracle, offer a necessary and sufficient solution for hallucinations in domains with structured knowledge representations, offering guarantees that statistical methods cannot match. Although the Licensing Oracle is specifically designed to address hallucinations in fact-based domains, its framework lays the groundwork for truth-constrained generation in future AI systems, providing a new path toward reliable, epistemically grounded models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.08217", "title": "MADD: Multi-Agent Drug Discovery Orchestra", "authors": [ "Gleb V. Solovev", "Alina B. Zhidkovskaya", "Anastasia Orlova", "Nina Gubina", "Anastasia Vepreva", "Rodion Golovinskii", "Ilya Tonkii", "Ivan Dubrovsky", "Ivan Gurev", "Dmitry Gilemkhanov", "Denis Chistiakov", "Timur A. Aliev", "Ivan Poddiakov", "Galina Zubkova", "Ekaterina V. Skorb", "Vladimir Vinogradov", "Alexander Boukhanovsky", "Nikolay Nikitin", "Andrei Dmitrenko", "Anna Kalyuzhnaya", "Andrey Savchenko" ], "github": "", "project_page": "", "abstract": "Hit identification is a central challenge in early drug discovery, traditionally requiring substantial experimental resources. Recent advances in artificial intelligence, particularly large language models (LLMs), have enabled virtual screening methods that reduce costs and improve efficiency. However, the growing complexity of these tools has limited their accessibility to wet-lab researchers. Multi-agent systems offer a promising solution by combining the interpretability of LLMs with the precision of specialized models and tools. In this work, we present MADD, a multi-agent system that builds and executes customized hit identification pipelines from natural language queries. MADD employs four coordinated agents to handle key subtasks in de novo compound generation and screening. We evaluate MADD across seven drug discovery cases and demonstrate its superior performance compared to existing LLM-based solutions. Using MADD, we pioneer the application of AI-first drug design to five biological targets and release the identified hit molecules. Finally, we introduce a new benchmark of query-molecule pairs and docking scores for over three million compounds to contribute to the agentic future of drug design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.06101", "title": "Adapting Web Agents with Synthetic Supervision", "authors": [ "Zhaoyang Wang", "Yiming Liang", "Xuchao Zhang", "Qianhui Wu", "Siwei Han", "Anson Bastos", "Rujia Wang", "Chetan Bansal", "Baolin Peng", "Jianfeng Gao", "Saravan Rajmohan", "Huaxiu Yao" ], "github": "https://github.com/aiming-lab/SynthAgent", "project_page": "", "abstract": "Web agents struggle to adapt to new websites due to the scarcity of environment specific tasks and demonstrations. Recent works have explored synthetic data generation to address this challenge, however, they suffer from data quality issues where synthesized tasks contain hallucinations that cannot be executed, and collected trajectories are noisy with redundant or misaligned actions. In this paper, we propose SynthAgent, a fully synthetic supervision framework that aims at improving synthetic data quality via dual refinement of both tasks and trajectories. Our approach begins by synthesizing diverse tasks through categorized exploration of web elements, ensuring efficient coverage of the target environment. During trajectory collection, we refine tasks when conflicts with actual observations are detected, mitigating hallucinations while maintaining task consistency. After collection, we conduct trajectory refinement with a global context to mitigate potential noise or misalignments. Finally, we fine-tune open-source web agents on the refined synthetic data to adapt them to the target environment. Experimental results demonstrate that SynthAgent outperforms existing synthetic data methods, validating the importance of high-quality synthetic supervision. The code will be publicly available at https://github.com/aiming-lab/SynthAgent.", "github_manual": "", "github_regex": "https://github.com/aiming-lab/SynthAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-13", "arxiv_id": "2511.07464", "title": "Motif 2 12.7B technical report", "authors": [ "Junghwan Lim", "Sungmin Lee", "Dongseok Kim", "Taehyun Kim", "Eunhwan Park", "Jeesoo Lee", "Jeongdoo Lee", "Junhyeok Lee", "Wai Ting Cheung", "Dahye Choi", "Jaeheui Her", "Jaeyeon Huh", "Hanbin Jung", "Changjin Kang", "Beomgyu Kim", "Minjae Kim", "Taewhan Kim", "Youngrok Kim", "Hyukjin Kweon", "Haesol Lee", "Kungyu Lee", "Dongpin Oh", "Yeongjae Park", "Bokki Ryu", "Dongjoo Weon" ], "github": "https://github.com/microsoft/dion/blob/main/dion/muon.py", "project_page": "", "abstract": "We introduce Motif-2-12.7B, a new open-weight foundation model that pushes the efficiency frontier of large language models by combining architectural innovation with system-level optimization. Designed for scalable language understanding and robust instruction generalization under constrained compute budgets, Motif-2-12.7B builds upon Motif-2.6B with the integration of Grouped Differential Attention (GDA), which improves representational efficiency by disentangling signal and noise-control attention pathways. The model is pre-trained on 5.5 trillion tokens spanning diverse linguistic, mathematical, scientific, and programming domains using a curriculum-driven data scheduler that gradually changes the data composition ratio. The training system leverages the MuonClip optimizer alongside custom high-performance kernels, including fused PolyNorm activations and the Parallel Muon algorithm, yielding significant throughput and memory efficiency gains in large-scale distributed environments. Post-training employs a three-stage supervised fine-tuning pipeline that successively enhances general instruction adherence, compositional understanding, and linguistic precision. Motif-2-12.7B demonstrates competitive performance across diverse benchmarks, showing that thoughtful architectural scaling and optimized training design can rival the capabilities of much larger models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/dion/blob/main/dion/muon.py", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.10647", "title": "Depth Anything 3: Recovering the Visual Space from Any Views", "authors": [ "Haotong Lin", "Sili Chen", "Junhao Liew", "Donny Y. Chen", "Zhenyu Li", "Guang Shi", "Jiashi Feng", "Bingyi Kang" ], "github": "", "project_page": "", "abstract": "We present Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. In pursuit of minimal modeling, DA3 yields two key insights: a single plain transformer (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization, and a singular depth-ray prediction target obviates the need for complex multi-task learning. Through our teacher-student training paradigm, the model achieves a level of detail and generalization on par with Depth Anything 2 (DA2). We establish a new visual geometry benchmark covering camera pose estimation, any-view geometry and visual rendering. On this benchmark, DA3 sets a new state-of-the-art across all tasks, surpassing prior SOTA VGGT by an average of 44.3% in camera pose accuracy and 25.1% in geometric accuracy. Moreover, it outperforms DA2 in monocular depth estimation. All models are trained exclusively on public academic datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.08521", "title": "UniVA: Universal Video Agent towards Open-Source Next-Generation Video Generalist", "authors": [ "Zhengyang Liang", "Daoan Zhang", "Huichi Zhou", "Rui Huang", "Bobo Li", "Yuechen Zhang", "Shengqiong Wu", "Xiaohan Wang", "Jiebo Luo", "Lizi Liao", "Hao Fei" ], "github": "https://github.com/OpenCut-app/OpenCut", "project_page": "", "abstract": "While specialized AI models excel at isolated video tasks like generation or understanding, real-world applications demand complex, iterative workflows that combine these capabilities. To bridge this gap, we introduce UniVA, an open-source, omni-capable multi-agent framework for next-generation video generalists that unifies video understanding, segmentation, editing, and generation into cohesive workflows. UniVA employs a Plan-and-Act dual-agent architecture that drives a highly automated and proactive workflow: a planner agent interprets user intentions and decomposes them into structured video-processing steps, while executor agents execute these through modular, MCP-based tool servers (for analysis, generation, editing, tracking, etc.). Through a hierarchical multi-level memory (global knowledge, task context, and user-specific preferences), UniVA sustains long-horizon reasoning, contextual continuity, and inter-agent communication, enabling interactive and self-reflective video creation with full traceability. This design enables iterative and any-conditioned video workflows (e.g., text/image/video-conditioned generation rightarrow multi-round editing rightarrow object segmentation rightarrow compositional synthesis) that were previously cumbersome to achieve with single-purpose models or monolithic video-language models. We also introduce UniVA-Bench, a benchmark suite of multi-step video tasks spanning understanding, editing, segmentation, and generation, to rigorously evaluate such agentic video systems. Both UniVA and UniVA-Bench are fully open-sourced, aiming to catalyze research on interactive, agentic, and general-purpose video intelligence for the next generation of multimodal AI systems. (https://univa.online/)", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenCut-app/OpenCut", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.10643", "title": "Black-Box On-Policy Distillation of Large Language Models", "authors": [ "Tianzhu Ye", "Li Dong", "Zewen Chi", "Xun Wu", "Shaohan Huang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Black-box distillation creates student large language models (LLMs) by learning from a proprietary teacher model's text outputs alone, without access to its internal logits or parameters. In this work, we introduce Generative Adversarial Distillation (GAD), which enables on-policy and black-box distillation. GAD frames the student LLM as a generator and trains a discriminator to distinguish its responses from the teacher LLM's, creating a minimax game. The discriminator acts as an on-policy reward model that co-evolves with the student, providing stable, adaptive feedback. Experimental results show that GAD consistently surpasses the commonly used sequence-level knowledge distillation. In particular, Qwen2.5-14B-Instruct (student) trained with GAD becomes comparable to its teacher, GPT-5-Chat, on the LMSYS-Chat automatic evaluation. The results establish GAD as a promising and effective paradigm for black-box LLM distillation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.01918", "title": "Superpositional Gradient Descent: Harnessing Quantum Principles for Model Training", "authors": [ "Ahmet Erdem Pamuk", "Emir Kaan \u00d6zdemir", "\u015euayp Talha Kocabay" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly trained with classical optimization techniques like AdamW to improve convergence and generalization. However, the mechanisms by which quantum-inspired methods enhance classical training remain underexplored. We introduce Superpositional Gradient Descent (SGD), a novel optimizer linking gradient updates with quantum superposition by injecting quantum circuit perturbations. We present a mathematical framework and implement hybrid quantum-classical circuits in PyTorch and Qiskit. On synthetic sequence classification and large-scale LLM fine-tuning, SGD converges faster and yields lower final loss than AdamW. Despite promising results, scalability and hardware constraints limit adoption. Overall, this work provides new insights into the intersection of quantum computing and deep learning, suggesting practical pathways for leveraging quantum principles to control and enhance model behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.10629", "title": "One Small Step in Latent, One Giant Leap for Pixels: Fast Latent Upscale Adapter for Your Diffusion Models", "authors": [ "Aleksandr Razin", "Danil Kazantsev", "Ilya Makarov" ], "github": "", "project_page": "", "abstract": "Diffusion models struggle to scale beyond their training resolutions, as direct high-resolution sampling is slow and costly, while post-hoc image super-resolution (ISR) introduces artifacts and additional latency by operating after decoding. We present the Latent Upscaler Adapter (LUA), a lightweight module that performs super-resolution directly on the generator's latent code before the final VAE decoding step. LUA integrates as a drop-in component, requiring no modifications to the base model or additional diffusion stages, and enables high-resolution synthesis through a single feed-forward pass in latent space. A shared Swin-style backbone with scale-specific pixel-shuffle heads supports 2x and 4x factors and remains compatible with image-space SR baselines, achieving comparable perceptual quality with nearly 3x lower decoding and upscaling time (adding only +0.42 s for 1024 px generation from 512 px, compared to 1.87 s for pixel-space SR using the same SwinIR architecture). Furthermore, LUA shows strong generalization across the latent spaces of different VAEs, making it easy to deploy without retraining from scratch for each new decoder. Extensive experiments demonstrate that LUA closely matches the fidelity of native high-resolution generation while offering a practical and efficient path to scalable, high-fidelity image synthesis in modern diffusion pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.08522", "title": "AlphaResearch: Accelerating New Algorithm Discovery with Language Models", "authors": [ "Zhaojian Yu", "Kaiyue Feng", "Yilun Zhao", "Shilin He", "Xiao-Ping Zhang", "Arman Cohan" ], "github": "https://github.com/answers111/alpha-research", "project_page": "", "abstract": "Large language models have made significant progress in complex but easy-to-verify problems, yet they still struggle with discovering the unknown. In this paper, we present AlphaResearch, an autonomous research agent designed to discover new algorithms on open-ended problems. To synergize the feasibility and innovation of the discovery process, we construct a novel dual research environment by combining the execution-based verify and simulated real-world peer review environment. AlphaResearch discovers new algorithm by iteratively running the following steps: (1) propose new ideas (2) verify the ideas in the dual research environment (3) optimize the research proposals for better performance. To promote a transparent evaluation process, we construct AlphaResearchComp, a new evaluation benchmark that includes an eight open-ended algorithmic problems competition, with each problem carefully curated and verified through executable pipelines, objective metrics, and reproducibility checks. AlphaResearch gets a 2/8 win rate in head-to-head comparison with human researchers, demonstrate the possibility of accelerating algorithm discovery with LLMs. Notably, the algorithm discovered by AlphaResearch on the ``packing circles'' problem achieves the best-of-known performance, surpassing the results of human researchers and strong baselines from recent work (e.g., AlphaEvolve). Additionally, we conduct a comprehensive analysis of the remaining challenges of the 6/8 failure cases, providing valuable insights for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/answers111/alpha-research", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.10547", "title": "Benchmarking Diversity in Image Generation via Attribute-Conditional Human Evaluation", "authors": [ "Isabela Albuquerque", "Ira Ktena", "Olivia Wiles", "Ivana Kaji\u0107", "Amal Rannen-Triki", "Cristina Vasconcelos", "Aida Nematzadeh" ], "github": "", "project_page": "", "abstract": "Despite advances in generation quality, current text-to-image (T2I) models often lack diversity, generating homogeneous outputs. This work introduces a framework to address the need for robust diversity evaluation in T2I models. Our framework systematically assesses diversity by evaluating individual concepts and their relevant factors of variation. Key contributions include: (1) a novel human evaluation template for nuanced diversity assessment; (2) a curated prompt set covering diverse concepts with their identified factors of variation (e.g. prompt: An image of an apple, factor of variation: color); and (3) a methodology for comparing models in terms of human annotations via binomial tests. Furthermore, we rigorously compare various image embeddings for diversity measurement. Notably, our principled approach enables ranking of T2I models by diversity, identifying categories where they particularly struggle. This research offers a robust methodology and insights, paving the way for improvements in T2I model diversity and metric development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.10507", "title": "Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following", "authors": [ "Yun He", "Wenzhe Li", "Hejia Zhang", "Songlin Li", "Karishma Mandyam", "Sopan Khosla", "Yuanhao Xiong", "Nanshu Wang", "Selina Peng", "Beibin Li", "Shengjie Bi", "Shishir G. Patil", "Qi Qi", "Shengyu Feng", "Julian Katz-Samuels", "Richard Yuanzhe Pang", "Sujan Gonugondla", "Hunter Lang", "Yue Yu", "Yundi Qian", "Maryam Fazel-Zarandi", "Licheng Yu", "Amine Benhalloum", "Hany Awadalla", "Manaal Faruqui" ], "github": "https://github.com/facebookresearch/AdvancedIF", "project_page": "", "abstract": "Recent progress in large language models (LLMs) has led to impressive performance on a range of tasks, yet advanced instruction following (IF)-especially for complex, multi-turn, and system-prompted instructions-remains a significant challenge. Rigorous evaluation and effective training for such capabilities are hindered by the lack of high-quality, human-annotated benchmarks and reliable, interpretable reward signals. In this work, we introduce AdvancedIF (we will release this benchmark soon), a comprehensive benchmark featuring over 1,600 prompts and expert-curated rubrics that assess LLMs ability to follow complex, multi-turn, and system-level instructions. We further propose RIFL (Rubric-based Instruction-Following Learning), a novel post-training pipeline that leverages rubric generation, a finetuned rubric verifier, and reward shaping to enable effective reinforcement learning for instruction following. Extensive experiments demonstrate that RIFL substantially improves the instruction-following abilities of LLMs, achieving a 6.7% absolute gain on AdvancedIF and strong results on public benchmarks. Our ablation studies confirm the effectiveness of each component in RIFL. This work establishes rubrics as a powerful tool for both training and evaluating advanced IF in LLMs, paving the way for more capable and reliable AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/AdvancedIF", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.10289", "title": "Music Flamingo: Scaling Music Understanding in Audio Language Models", "authors": [ "Sreyan Ghosh", "Arushi Goel", "Lasha Koroshinadze", "Sang-gil Lee", "Zhifeng Kong", "Joao Felipe Santos", "Ramani Duraiswami", "Dinesh Manocha", "Wei Ping", "Mohammad Shoeybi", "Bryan Catanzaro" ], "github": "", "project_page": "", "abstract": "We introduce Music Flamingo, a novel large audio-language model designed to advance music (including song) understanding in foundational audio models. While audio-language research has progressed rapidly, music remains challenging due to its dynamic, layered, and information-dense nature. Progress has been further limited by the difficulty of scaling open audio understanding models, primarily because of the scarcity of high-quality music data and annotations. As a result, prior models are restricted to producing short, high-level captions, answering only surface-level questions, and showing limited generalization across diverse musical cultures. To address these challenges, we curate MF-Skills, a large-scale dataset labeled through a multi-stage pipeline that yields rich captions and question-answer pairs covering harmony, structure, timbre, lyrics, and cultural context. We fine-tune an enhanced Audio Flamingo 3 backbone on MF-Skills and further strengthen multiple skills relevant to music understanding. To improve the model's reasoning abilities, we introduce a post-training recipe: we first cold-start with MF-Think, a novel chain-of-thought dataset grounded in music theory, followed by GRPO-based reinforcement learning with custom rewards. Music Flamingo achieves state-of-the-art results across 10+ benchmarks for music understanding and reasoning, establishing itself as a generalist and musically intelligent audio-language model. Beyond strong empirical results, Music Flamingo sets a new standard for advanced music understanding by demonstrating how models can move from surface-level recognition toward layered, human-like perception of songs. We believe this work provides both a benchmark and a foundation for the community to build the next generation of models that engage with music as meaningfully as humans do.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.10047", "title": "MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples", "authors": [ "Xurui Li", "Feng Xue", "Yu Zhou" ], "github": "https://github.com/HUST-SLOW/MuSc-V2", "project_page": "", "abstract": "Zero-shot anomaly classification (AC) and segmentation (AS) methods aim to identify and outline defects without using any labeled samples. In this paper, we reveal a key property that is overlooked by existing methods: normal image patches across industrial products typically find many other similar patches, not only in 2D appearance but also in 3D shapes, while anomalies remain diverse and isolated. To explicitly leverage this discriminative property, we propose a Mutual Scoring framework (MuSc-V2) for zero-shot AC/AS, which flexibly supports single 2D/3D or multimodality. Specifically, our method begins by improving 3D representation through Iterative Point Grouping (IPG), which reduces false positives from discontinuous surfaces. Then we use Similarity Neighborhood Aggregation with Multi-Degrees (SNAMD) to fuse 2D/3D neighborhood cues into more discriminative multi-scale patch features for mutual scoring. The core comprises a Mutual Scoring Mechanism (MSM) that lets samples within each modality to assign score to each other, and Cross-modal Anomaly Enhancement (CAE) that fuses 2D and 3D scores to recover modality-specific missing anomalies. Finally, Re-scoring with Constrained Neighborhood (RsCon) suppresses false classification based on similarity to more representative samples. Our framework flexibly works on both the full dataset and smaller subsets with consistently robust performance, ensuring seamless adaptability across diverse product lines. In aid of the novel framework, MuSc-V2 achieves significant performance improvements: a +23.7% AP gain on the MVTec 3D-AD dataset and a +19.3% boost on the Eyecandies dataset, surpassing previous zero-shot benchmarks and even outperforming most few-shot methods. The code will be available at The code will be available at https://github.com/HUST-SLOW/MuSc-V2{https://github.com/HUST-SLOW/MuSc-V2}.", "github_manual": "", "github_regex": "https://github.com/HUST-SLOW/MuSc-V2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.10017", "title": "AffordBot: 3D Fine-grained Embodied Reasoning via Multimodal Large Language Models", "authors": [ "Xinyi Wang", "Xun Yang", "Yanlong Xu", "Yuchen Wu", "Zhen Li", "Na Zhao" ], "github": "", "project_page": "", "abstract": "Effective human-agent collaboration in physical environments requires understanding not only what to act upon, but also where the actionable elements are and how to interact with them. Existing approaches often operate at the object level or disjointedly handle fine-grained affordance reasoning, lacking coherent, instruction-driven grounding and reasoning. In this work, we introduce a new task: Fine-grained 3D Embodied Reasoning, which requires an agent to predict, for each referenced affordance element in a 3D scene, a structured triplet comprising its spatial location, motion type, and motion axis, based on a task instruction. To solve this task, we propose AffordBot, a novel framework that integrates Multimodal Large Language Models (MLLMs) with a tailored chain-of-thought (CoT) reasoning paradigm. To bridge the gap between 3D input and 2D-compatible MLLMs, we render surround-view images of the scene and project 3D element candidates into these views, forming a rich visual representation aligned with the scene geometry. Our CoT pipeline begins with an active perception stage, prompting the MLLM to select the most informative viewpoint based on the instruction, before proceeding with step-by-step reasoning to localize affordance elements and infer plausible interaction motions. Evaluated on the SceneFun3D dataset, AffordBot achieves state-of-the-art performance, demonstrating strong generalization and physically grounded reasoning with only 3D point cloud input and MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.09715", "title": "SliderEdit: Continuous Image Editing with Fine-Grained Instruction Control", "authors": [ "Arman Zarei", "Samyadeep Basu", "Mobina Pournemat", "Sayan Nag", "Ryan Rossi", "Soheil Feizi" ], "github": "", "project_page": "", "abstract": "Instruction-based image editing models have recently achieved impressive performance, enabling complex edits to an input image from a multi-instruction prompt. However, these models apply each instruction in the prompt with a fixed strength, limiting the user's ability to precisely and continuously control the intensity of individual edits. We introduce SliderEdit, a framework for continuous image editing with fine-grained, interpretable instruction control. Given a multi-part edit instruction, SliderEdit disentangles the individual instructions and exposes each as a globally trained slider, allowing smooth adjustment of its strength. Unlike prior works that introduced slider-based attribute controls in text-to-image generation, typically requiring separate training or fine-tuning for each attribute or concept, our method learns a single set of low-rank adaptation matrices that generalize across diverse edits, attributes, and compositional instructions. This enables continuous interpolation along individual edit dimensions while preserving both spatial locality and global semantic consistency. We apply SliderEdit to state-of-the-art image editing models, including FLUX-Kontext and Qwen-Image-Edit, and observe substantial improvements in edit controllability, visual consistency, and user steerability. To the best of our knowledge, we are the first to explore and propose a framework for continuous, fine-grained instruction control in instruction-based image editing models. Our results pave the way for interactive, instruction-driven image manipulation with continuous and compositional control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.07685", "title": "ResearchRubrics: A Benchmark of Prompts and Rubrics For Evaluating Deep Research Agents", "authors": [ "Manasi Sharma", "Chen Bo Calvin Zhang", "Chaithanya Bandi", "Clinton Wang", "Ankit Aich", "Huy Nghiem", "Tahseen Rabbani", "Ye Htet", "Brian Jang", "Sumana Basu", "Aishwarya Balwani", "Denis Peskoff", "Marcos Ayestaran", "Sean M. Hendryx", "Brad Kenstler", "Bing Liu" ], "github": "", "project_page": "", "abstract": "Deep Research (DR) is an emerging agent application that leverages large language models (LLMs) to address open-ended queries. It requires the integration of several capabilities, including multi-step reasoning, cross-document synthesis, and the generation of evidence-backed, long-form answers. Evaluating DR remains challenging because responses are lengthy and diverse, admit many valid solutions, and often depend on dynamic information sources. We introduce ResearchRubrics, a standardized benchmark for DR built with over 2,800+ hours of human labor that pairs realistic, domain-diverse prompts with 2,500+ expert-written, fine-grained rubrics to assess factual grounding, reasoning soundness, and clarity. We also propose a new complexity framework for categorizing DR tasks along three axes: conceptual breadth, logical nesting, and exploration. In addition, we develop human and model-based evaluation protocols that measure rubric adherence for DR agents. We evaluate several state-of-the-art DR systems and find that even leading agents like Gemini's DR and OpenAI's DR achieve under 68% average compliance with our rubrics, primarily due to missed implicit context and inadequate reasoning about retrieved information. Our results highlight the need for robust, scalable assessment of deep research capabilities, to which end we release ResearchRubrics(including all prompts, rubrics, and evaluation code) to facilitate progress toward well-justified research assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.09057", "title": "PAN: A World Model for General, Interactable, and Long-Horizon World Simulation", "authors": [ "PAN Team", "Jiannan Xiang", "Yi Gu", "Zihan Liu", "Zeyu Feng", "Qiyue Gao", "Yiyan Hu", "Benhao Huang", "Guangyi Liu", "Yichi Yang", "Kun Zhou", "Davit Abrahamyan", "Arif Ahmad", "Ganesh Bannur", "Junrong Chen", "Kimi Chen", "Mingkai Deng", "Ruobing Han", "Xinqi Huang", "Haoqiang Kang", "Zheqi Li", "Enze Ma", "Hector Ren", "Yashowardhan Shinde", "Rohan Shingre", "Ramsundar Tanikella", "Kaiming Tao", "Dequan Yang", "Xinle Yu", "Cong Zeng", "Binglin Zhou", "Zhengzhong Liu", "Zhiting Hu", "Eric P. Xing" ], "github": "https://github.com/nvidia-cosmos/cosmos-predict2", "project_page": "", "abstract": "A world model enables an intelligent agent to imagine, predict, and reason about how the world evolves in response to its actions, and accordingly to plan and strategize. While recent video generation models produce realistic visual sequences, they typically operate in the prompt-to-full-video manner without causal control, interactivity, or long-horizon consistency required for purposeful reasoning. Existing world modeling efforts, on the other hand, often focus on restricted domains (e.g., physical, game, or 3D-scene dynamics) with limited depth and controllability, and struggle to generalize across diverse environments and interaction formats. In this work, we introduce PAN, a general, interactable, and long-horizon world model that predicts future world states through high-quality video simulation conditioned on history and natural language actions. PAN employs the Generative Latent Prediction (GLP) architecture that combines an autoregressive latent dynamics backbone based on a large language model (LLM), which grounds simulation in extensive text-based knowledge and enables conditioning on language-specified actions, with a video diffusion decoder that reconstructs perceptually detailed and temporally coherent visual observations, to achieve a unification between latent space reasoning (imagination) and realizable world dynamics (reality). Trained on large-scale video-action pairs spanning diverse domains, PAN supports open-domain, action-conditioned simulation with coherent, long-term dynamics. Extensive experiments show that PAN achieves strong performance in action-conditioned world simulation, long-horizon forecasting, and simulative reasoning compared to other video generators and world models, taking a step towards general world models that enable predictive simulation of future world states for reasoning and acting.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nvidia-cosmos/cosmos-predict2", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.09780", "title": "Hail to the Thief: Exploring Attacks and Defenses in Decentralised GRPO", "authors": [ "Nikolay Blagoev", "O\u011fuzhan Ersoy", "Lydia Yiyu Chen" ], "github": "", "project_page": "", "abstract": "Group Relative Policy Optimization (GRPO) has demonstrated great utilization in post-training of Large Language Models (LLMs). In GRPO, prompts are answered by the model and, through reinforcement learning, preferred completions are learnt. Owing to the small communication volume, GRPO is inherently suitable for decentralised training as the prompts can be concurrently answered by multiple nodes and then exchanged in the forms of strings. In this work, we present the first adversarial attack in decentralised GRPO. We demonstrate that malicious parties can poison such systems by injecting arbitrary malicious tokens in benign models in both out-of-context and in-context attacks. Using empirical examples of math and coding tasks, we show that adversarial attacks can easily poison the benign nodes, polluting their local LLM post-training, achieving attack success rates up to 100% in as few as 50 iterations. We propose two ways to defend against these attacks, depending on whether all users train the same model or different models. We show that these defenses can achieve stop rates of up to 100%, making the attack impossible.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.09030", "title": "Solving a Million-Step LLM Task with Zero Errors", "authors": [ "Elliot Meyerson", "Giuseppe Paolo", "Roberto Dailey", "Hormoz Shahrzad", "Olivier Francon", "Conor F. Hayes", "Xin Qiu", "Babak Hodjat", "Risto Miikkulainen" ], "github": "", "project_page": "", "abstract": "LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by humans, organizations, and societies has remained out of reach. The models have a persistent error rate that prevents scale-up: for instance, recent experiments in the Towers of Hanoi benchmark domain showed that the process inevitably becomes derailed after at most a few hundred steps. Thus, although LLM research is often still benchmarked on tasks with relatively few dependent logical steps, there is increasing attention on the ability (or inability) of LLMs to perform long range tasks. This paper describes MAKER, the first system that successfully solves a task with over one million LLM steps with zero errors, and, in principle, scales far beyond this level. The approach relies on an extreme decomposition of a task into subtasks, each of which can be tackled by focused microagents. The high level of modularity resulting from the decomposition allows error correction to be applied at each step through an efficient multi-agent voting scheme. This combination of extreme decomposition and error correction makes scaling possible. Thus, the results suggest that instead of relying on continual improvement of current LLMs, massively decomposed agentic processes (MDAPs) may provide a way to efficiently solve problems at the level of organizations and societies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.07790", "title": "CC30k: A Citation Contexts Dataset for Reproducibility-Oriented Sentiment Analysis", "authors": [ "Rochana R. Obadage", "Sarah M. Rajtmajer", "Jian Wu" ], "github": "https://github.com/lamps-lab/CC30k", "project_page": "", "abstract": "Sentiments about the reproducibility of cited papers in downstream literature offer community perspectives and have shown as a promising signal of the actual reproducibility of published findings. To train effective models to effectively predict reproducibility-oriented sentiments and further systematically study their correlation with reproducibility, we introduce the CC30k dataset, comprising a total of 30,734 citation contexts in machine learning papers. Each citation context is labeled with one of three reproducibility-oriented sentiment labels: Positive, Negative, or Neutral, reflecting the cited paper's perceived reproducibility or replicability. Of these, 25,829 are labeled through crowdsourcing, supplemented with negatives generated through a controlled pipeline to counter the scarcity of negative labels. Unlike traditional sentiment analysis datasets, CC30k focuses on reproducibility-oriented sentiments, addressing a research gap in resources for computational reproducibility studies. The dataset was created through a pipeline that includes robust data cleansing, careful crowd selection, and thorough validation. The resulting dataset achieves a labeling accuracy of 94%. We then demonstrated that the performance of three large language models significantly improves on the reproducibility-oriented sentiment classification after fine-tuning using our dataset. The dataset lays the foundation for large-scale assessments of the reproducibility of machine learning papers. The CC30k dataset and the Jupyter notebooks used to produce and analyze the dataset are publicly available at https://github.com/lamps-lab/CC30k .", "github_manual": "", "github_regex": "https://github.com/lamps-lab/CC30k", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-14", "arxiv_id": "2511.09067", "title": "MM-CRITIC: A Holistic Evaluation of Large Multimodal Models as Multimodal Critique", "authors": [ "Gailun Zeng", "Ziyang Luo", "Hongzhan Lin", "Yuchen Tian", "Kaixin Li", "Ziyang Gong", "Jianxiong Guo", "Jing Ma" ], "github": "https://github.com/MichealZeng0420/MM-Critic", "project_page": "", "abstract": "The ability of critique is vital for models to self-improve and serve as reliable AI assistants. While extensively studied in language-only settings, multimodal critique of Large Multimodal Models (LMMs) remains underexplored despite their growing capabilities in tasks like captioning and visual reasoning. In this work, we introduce MM-CRITIC, a holistic benchmark for evaluating the critique ability of LMMs across multiple dimensions: basic, correction, and comparison. Covering 8 main task types and over 500 tasks, MM-CRITIC collects responses from various LMMs with different model sizes and is composed of 4471 samples. To enhance the evaluation reliability, we integrate expert-informed ground answers into scoring rubrics that guide GPT-4o in annotating responses and generating reference critiques, which serve as anchors for trustworthy judgments. Extensive experiments validate the effectiveness of MM-CRITIC and provide a comprehensive assessment of leading LMMs' critique capabilities under multiple dimensions. Further analysis reveals some key insights, including the correlation between response quality and critique, and varying critique difficulty across evaluation dimensions. Our code is available at https://github.com/MichealZeng0420/MM-Critic.", "github_manual": "", "github_regex": "https://github.com/MichealZeng0420/MM-Critic", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.11238", "title": "Virtual Width Networks", "authors": [ "Seed", "Baisheng Li", "Banggu Wu", "Bole Ma", "Bowen Xiao", "Chaoyi Zhang", "Cheng Li", "Chengyi Wang", "Chenyin Xu", "Chi Zhang", "Chong Hu", "Daoguang Zan", "Defa Zhu", "Dongyu Xu", "Du Li", "Faming Wu", "Fan Xia", "Ge Zhang", "Guang Shi", "Haobin Chen", "Hongyu Zhu", "Hongzhi Huang", "Huan Zhou", "Huanzhang Dou", "Jianhui Duan", "Jianqiao Lu", "Jianyu Jiang", "Jiayi Xu", "Jiecao Chen", "Jin Chen", "Jin Ma", "Jing Su", "Jingji Chen", "Jun Wang", "Jun Yuan", "Juncai Liu", "Jundong Zhou", "Kai Hua", "Kai Shen", "Kai Xiang", "Kaiyuan Chen", "Kang Liu", "Ke Shen", "Liang Xiang", "Lin Yan", "Lishu Luo", "Mengyao Zhang", "Ming Ding", "Mofan Zhang", "Nianning Liang", "Peng Li", "Penghao Huang", "Pengpeng Mu", "Qi Huang", "Qianli Ma", "Qiyang Min", "Qiying Yu", "Renming Pang", "Ru Zhang", "Shen Yan", "Shen Yan", "Shixiong Zhao", "Shuaishuai Cao", "Shuang Wu", "Siyan Chen", "Siyu Li", "Siyuan Qiao", "Tao Sun", "Tian Xin", "Tiantian Fan", "Ting Huang", "Ting-Han Fan", "Wei Jia", "Wenqiang Zhang", "Wenxuan Liu", "Xiangzhong Wu", "Xiaochen Zuo", "Xiaoying Jia", "Ximing Yang", "Xin Liu", "Xin Yu", "Xingyan Bin", "Xintong Hao", "Xiongcai Luo", "Xujing Li", "Xun Zhou", "Yanghua Peng", "Yangrui Chen", "Yi Lin", "Yichong Leng", "Yinghao Li", "Yingshuan Song", "Yiyuan Ma", "Yong Shan", "Yongan Xiang", "Yonghui Wu", "Yongtao Zhang", "Yongzhen Yao", "Yu Bao", "Yuehang Yang", "Yufeng Yuan", "Yunshui Li", "Yuqiao Xian", "Yutao Zeng", "Yuxuan Wang", "Zehua Hong", "Zehua Wang", "Zengzhi Wang", "Zeyu Yang", "Zhengqiang Yin", "Zhenyi Lu", "Zhexi Zhang", "Zhi Chen", "Zhi Zhang", "Zhiqi Lin", "Zihao Huang", "Zilin Xu", "Ziyun Wei", "Zuo Wang" ], "github": "", "project_page": "", "abstract": "We introduce Virtual Width Networks (VWN), a framework that delivers the benefits of wider representations without incurring the quadratic cost of increasing the hidden size. VWN decouples representational width from backbone width, expanding the embedding space while keeping backbone compute nearly constant. In our large-scale experiment, an 8-times expansion accelerates optimization by over 2 times for next-token and 3 times for next-2-token prediction. The advantage amplifies over training as both the loss gap grows and the convergence-speedup ratio increases, showing that VWN is not only token-efficient but also increasingly effective with scale. Moreover, we identify an approximately log-linear scaling relation between virtual width and loss reduction, offering an initial empirical basis and motivation for exploring virtual-width scaling as a new dimension of large-model efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.08195", "title": "UI2Code^N: A Visual Language Model for Test-Time Scalable Interactive UI-to-Code Generation", "authors": [ "Zhen Yang", "Wenyi Hong", "Mingde Xu", "Xinyue Fan", "Weihan Wang", "Jiele Cheng", "Xiaotao Gu", "Jie Tang" ], "github": "https://github.com/zai-org/UI2Code_N", "project_page": "", "abstract": "User interface (UI) programming is a core yet highly complex part of modern software development. Recent advances in visual language models (VLMs) highlight the potential of automatic UI coding, but current approaches face two key limitations: multimodal coding capabilities remain underdeveloped, and single-turn paradigms make little use of iterative visual feedback. We address these challenges with an interactive UI-to-code paradigm that better reflects real-world workflows and raises the upper bound of achievable performance. Under this paradigm, we present UI2Code^N, a visual language model trained through staged pretraining, fine-tuning, and reinforcement learning to achieve foundational improvements in multimodal coding. The model unifies three key capabilities: UI-to-code generation, UI editing, and UI polishing. We further explore test-time scaling for interactive generation, enabling systematic use of multi-turn feedback. Experiments on UI-to-code and UI polishing benchmarks show that UI2Code^N establishes a new state of the art among open-source models and achieves performance comparable to leading closed-source models such as Claude-4-Sonnet and GPT-5. Our code and models are available at https://github.com/zai-org/UI2Code_N.", "github_manual": "", "github_regex": "https://github.com/zai-org/UI2Code_N", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.11373", "title": "MarsRL: Advancing Multi-Agent Reasoning System via Reinforcement Learning with Agentic Pipeline Parallelism", "authors": [ "Shulin Liu", "Dong Du", "Tao Yang", "Yang Li", "Boyu Qiu" ], "github": "", "project_page": "", "abstract": "Recent progress in large language models (LLMs) has been propelled by reinforcement learning with verifiable rewards (RLVR) and test-time scaling. However, the limited output length of LLMs constrains the depth of reasoning attainable in a single inference process. Multi-agent reasoning systems offer a promising alternative by employing multiple agents including Solver, Verifier, and Corrector, to iteratively refine solutions. While effective in closed-source models like Gemini 2.5 Pro, they struggle to generalize to open-source models due to insufficient critic and correction capabilities. To address this, we propose MarsRL, a novel reinforcement learning framework with agentic pipeline parallelism, designed to jointly optimize all agents in the system. MarsRL introduces agent-specific reward mechanisms to mitigate reward noise and employs pipeline-inspired training to enhance efficiency in handling long trajectories. Applied to Qwen3-30B-A3B-Thinking-2507, MarsRL improves AIME2025 accuracy from 86.5% to 93.3% and BeyondAIME from 64.9% to 73.8%, even surpassing Qwen3-235B-A22B-Thinking-2507. These findings highlight the potential of MarsRL to advance multi-agent reasoning systems and broaden their applicability across diverse reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.11134", "title": "GGBench: A Geometric Generative Reasoning Benchmark for Unified Multimodal Models", "authors": [ "Jingxuan Wei", "Caijun Jia", "Xi Bai", "Xinglong Xu", "Siyuan Li", "Linzhuang Sun", "Bihui Yu", "Conghui He", "Lijun Wu", "Cheng Tan" ], "github": "https://github.com/opendatalab-raiser/GGBench", "project_page": "", "abstract": "The advent of Unified Multimodal Models (UMMs) signals a paradigm shift in artificial intelligence, moving from passive perception to active, cross-modal generation. Despite their unprecedented ability to synthesize information, a critical gap persists in evaluation: existing benchmarks primarily assess discriminative understanding or unconstrained image generation separately, failing to measure the integrated cognitive process of generative reasoning. To bridge this gap, we propose that geometric construction provides an ideal testbed as it inherently demands a fusion of language comprehension and precise visual generation. We introduce GGBench, a benchmark designed specifically to evaluate geometric generative reasoning. It provides a comprehensive framework for systematically diagnosing a model's ability to not only understand and reason but to actively construct a solution, thereby setting a more rigorous standard for the next generation of intelligent systems. Project website: https://opendatalab-raiser.github.io/GGBench/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/opendatalab-raiser/GGBench", "github_api_added_by": "auto" }, { "date": "2025-11-17", "arxiv_id": "2511.11002", "title": "EmoVid: A Multimodal Emotion Video Dataset for Emotion-Centric Video Understanding and Generation", "authors": [ "Zongyang Qiu", "Bingyuan Wang", "Xingbei Chen", "Yingqing He", "Zeyu Wang" ], "github": "", "project_page": "", "abstract": "Emotion plays a pivotal role in video-based expression, but existing video generation systems predominantly focus on low-level visual metrics while neglecting affective dimensions. Although emotion analysis has made progress in the visual domain, the video community lacks dedicated resources to bridge emotion understanding with generative tasks, particularly for stylized and non-realistic contexts. To address this gap, we introduce EmoVid, the first multimodal, emotion-annotated video dataset specifically designed for creative media, which includes cartoon animations, movie clips, and animated stickers. Each video is annotated with emotion labels, visual attributes (brightness, colorfulness, hue), and text captions. Through systematic analysis, we uncover spatial and temporal patterns linking visual features to emotional perceptions across diverse video forms. Building on these insights, we develop an emotion-conditioned video generation technique by fine-tuning the Wan2.1 model. The results show a significant improvement in both quantitative metrics and the visual quality of generated videos for text-to-video and image-to-video tasks. EmoVid establishes a new benchmark for affective video computing. Our work not only offers valuable insights into visual emotion analysis in artistically styled videos, but also provides practical methods for enhancing emotional expression in video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.10984", "title": "DiscoX: Benchmarking Discourse-Level Translation task in Expert Domains", "authors": [ "Xiying Zhao", "Zhoufutu Wen", "Zhixuan Chen", "Jingzhe Ding", "Jianpeng Jiao", "Shuai Li", "Xi Li", "Danni Liang", "Shengda Long", "Qianqian Liu", "Xianbo Wu", "Hongwan Gao", "Xiang Gao", "Liang Hu", "Jiashuo Liu", "Mengyun Liu", "Weiran Shi", "Chenghao Yang", "Qianyu Yang", "Xuanliang Zhang", "Ge Zhang", "Wenhao Huang" ], "github": "https://github.com/ByteDance-Seed/DiscoX", "project_page": "", "abstract": "The evaluation of discourse-level translation in expert domains remains inadequate, despite its centrality to knowledge dissemination and cross-lingual scholarly communication. While these translations demand discourse-level coherence and strict terminological precision, current evaluation methods predominantly focus on segment-level accuracy and fluency. To address this limitation, we introduce DiscoX, a new benchmark for discourse-level and expert-level Chinese-English translation. It comprises 200 professionally-curated texts from 7 domains, with an average length exceeding 1700 tokens. To evaluate performance on DiscoX, we also develop Metric-S, a reference-free system that provides fine-grained automatic assessments across accuracy, fluency, and appropriateness. Metric-S demonstrates strong consistency with human judgments, significantly outperforming existing metrics. Our experiments reveal a remarkable performance gap: even the most advanced LLMs still trail human experts on these tasks. This finding validates the difficulty of DiscoX and underscores the challenges that remain in achieving professional-grade machine translation. The proposed benchmark and evaluation system provide a robust framework for more rigorous evaluation, facilitating future advancements in LLM-based translation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByteDance-Seed/DiscoX", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.11168", "title": "CATS-V2V: A Real-World Vehicle-to-Vehicle Cooperative Perception Dataset with Complex Adverse Traffic Scenarios", "authors": [ "Hangyu Li", "Bofeng Cao", "Zhaohui Liang", "Wuzhen Li", "Juyoung Oh", "Yuxuan Chen", "Shixiao Liang", "Hang Zhou", "Chengyuan Ma", "Jiaxi Liu", "Zheng Li", "Peng Zhang", "KeKe Long", "Maolin Liu", "Jackson Jiang", "Chunlei Yu", "Shengxiang Liu", "Hongkai Yu", "Xiaopeng Li" ], "github": "", "project_page": "", "abstract": "Vehicle-to-Vehicle (V2V) cooperative perception has great potential to enhance autonomous driving performance by overcoming perception limitations in complex adverse traffic scenarios (CATS). Meanwhile, data serves as the fundamental infrastructure for modern autonomous driving AI. However, due to stringent data collection requirements, existing datasets focus primarily on ordinary traffic scenarios, constraining the benefits of cooperative perception. To address this challenge, we introduce CATS-V2V, the first-of-its-kind real-world dataset for V2V cooperative perception under complex adverse traffic scenarios. The dataset was collected by two hardware time-synchronized vehicles, covering 10 weather and lighting conditions across 10 diverse locations. The 100-clip dataset includes 60K frames of 10 Hz LiDAR point clouds and 1.26M multi-view 30 Hz camera images, along with 750K anonymized yet high-precision RTK-fixed GNSS and IMU records. Correspondingly, we provide time-consistent 3D bounding box annotations for objects, as well as static scenes to construct a 4D BEV representation. On this basis, we propose a target-based temporal alignment method, ensuring that all objects are precisely aligned across all sensor modalities. We hope that CATS-V2V, the largest-scale, most supportive, and highest-quality dataset of its kind to date, will benefit the autonomous driving community in related tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.11257", "title": "AIonopedia: an LLM agent orchestrating multimodal learning for ionic liquid discovery", "authors": [ "Yuqi Yin", "Yibo Fu", "Siyuan Wang", "Peng Sun", "Hongyu Wang", "Xiaohui Wang", "Lei Zheng", "Zhiyong Li", "Zhirong Liu", "Jianji Wang", "Zhaoxi Sun" ], "github": "", "project_page": "", "abstract": "The discovery of novel Ionic Liquids (ILs) is hindered by critical challenges in property prediction, including limited data, poor model accuracy, and fragmented workflows. Leveraging the power of Large Language Models (LLMs), we introduce AIonopedia, to the best of our knowledge, the first LLM agent for IL discovery. Powered by an LLM-augmented multimodal domain foundation model for ILs, AIonopedia enables accurate property predictions and incorporates a hierarchical search architecture for molecular screening and design. Trained and evaluated on a newly curated and comprehensive IL dataset, our model delivers superior performance. Complementing these results, evaluations on literature-reported systems indicate that the agent can perform effective IL modification. Moving beyond offline tests, the practical efficacy was further confirmed through real-world wet-lab validation, in which the agent demonstrated exceptional generalization capabilities on challenging out-of-distribution tasks, underscoring its ability to accelerate real-world IL discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.07403", "title": "SpatialThinker: Reinforcing 3D Reasoning in Multimodal LLMs via Spatial Rewards", "authors": [ "Hunar Batra", "Haoqin Tu", "Hardy Chen", "Yuanze Lin", "Cihang Xie", "Ronald Clark" ], "github": "https://github.com/hunarbatra/SpatialThinker", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language tasks, but they continue to struggle with spatial understanding. Existing spatial MLLMs often rely on explicit 3D inputs or architecture-specific modifications, and remain constrained by large-scale datasets or sparse supervision. To address these limitations, we introduce SpatialThinker, a 3D-aware MLLM trained with RL to integrate structured spatial grounding with multi-step reasoning. The model simulates human-like spatial perception by constructing a scene graph of task-relevant objects and spatial relations, and reasoning towards an answer via dense spatial rewards. SpatialThinker consists of two key contributions: (1) a data synthesis pipeline that generates STVQA-7K, a high-quality spatial VQA dataset, and (2) online RL with a multi-objective dense spatial reward enforcing spatial grounding. SpatialThinker-7B outperforms supervised fine-tuning and the sparse RL baseline on spatial understanding and real-world VQA benchmarks, nearly doubling the base-model gain compared to sparse RL, and surpassing GPT-4o. These results showcase the effectiveness of combining spatial supervision with reward-aligned reasoning in enabling robust 3D spatial understanding with limited data and advancing MLLMs towards human-level visual reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hunarbatra/SpatialThinker", "github_api_added_by": "auto" }, { "date": "2025-11-17", "arxiv_id": "2511.09146", "title": "DoPE: Denoising Rotary Position Embedding", "authors": [ "Jing Xiong", "Liyang Fan", "Hui Shen", "Zunhai Su", "Min Yang", "Lingpeng Kong", "Ngai Wong" ], "github": "", "project_page": "", "abstract": "Rotary Position Embedding (RoPE) in Transformer models has inherent limits that weaken length extrapolation. We reinterpret the attention map with positional encoding as a noisy feature map, and propose Denoising Positional Encoding (DoPE), a training-free method based on truncated matrix entropy to detect outlier frequency bands in the feature map. Leveraging the noise characteristics of the feature map, we further reparameterize it with a parameter-free Gaussian distribution to achieve robust extrapolation. Our method theoretically reveals the underlying cause of the attention sink phenomenon and its connection to truncated matrix entropy. Experiments on needle-in-a-haystack and many-shot in-context learning tasks demonstrate that DoPE significantly improves retrieval accuracy and reasoning stability across extended contexts (up to 64K tokens). The results show that the denoising strategy for positional embeddings effectively mitigates attention sinks and restores balanced attention patterns, providing a simple yet powerful solution for improving length generalization. Our project page is Project: https://The-physical-picture-of-LLMs.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.11434", "title": "WEAVE: Unleashing and Benchmarking the In-context Interleaved Comprehension and Generation", "authors": [ "Wei Chow", "Jiachun Pan", "Yongyuan Liang", "Mingze Zhou", "Xue Song", "Liyu Jia", "Saining Zhang", "Siliang Tang", "Juncheng Li", "Fengda Zhang", "Weijia Wu", "Hanwang Zhang", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "Recent advances in unified multimodal models (UMMs) have enabled impressive progress in visual comprehension and generation. However, existing datasets and benchmarks focus primarily on single-turn interactions, failing to capture the multi-turn, context-dependent nature of real-world image creation and editing. To address this gap, we present WEAVE, the first suite for in-context interleaved cross-modality comprehension and generation. Our suite consists of two complementary parts. WEAVE-100k is a large-scale dataset of 100K interleaved samples spanning over 370K dialogue turns and 500K images, covering comprehension, editing, and generation tasks that require reasoning over historical context. WEAVEBench is a human-annotated benchmark with 100 tasks based on 480 images, featuring a hybrid VLM judger evaluation framework based on both the reference image and the combination of the original image with editing instructions that assesses models' abilities in multi-turn generation, visual memory, and world-knowledge reasoning across diverse domains. Experiments demonstrate that training on WEAVE-100k enables vision comprehension, image editing, and comprehension-generation collaboration capabilities. Furthermore, it facilitates UMMs to develop emergent visual-memory capabilities, while extensive evaluations on WEAVEBench expose the persistent limitations and challenges of current approaches in multi-turn, context-aware image generation and editing. We believe WEAVE provides a view and foundation for studying in-context interleaved comprehension and generation for multi-modal community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.11062", "title": "LiteAttention: A Temporal Sparse Attention for Diffusion Transformers", "authors": [ "Dor Shmilovich", "Tony Wu", "Aviad Dahan", "Yuval Domb" ], "github": "", "project_page": "", "abstract": "Diffusion Transformers, particularly for video generation, achieve remarkable quality but suffer from quadratic attention complexity, leading to prohibitive latency. Existing acceleration methods face a fundamental trade-off: dynamically estimating sparse attention patterns at each denoising step incurs high computational overhead and estimation errors, while static sparsity patterns remain fixed and often suboptimal throughout denoising. We identify a key structural property of diffusion attention, namely, its sparsity patterns exhibit strong temporal coherence across denoising steps. Tiles deemed non-essential at step t typically remain so at step t+\u03b4. Leveraging this observation, we introduce LiteAttention, a method that exploits temporal coherence to enable evolutionary computation skips across the denoising sequence. By marking non-essential tiles early and propagating skip decisions forward, LiteAttention eliminates redundant attention computations without repeated profiling overheads, combining the adaptivity of dynamic methods with the efficiency of static ones. We implement a highly optimized LiteAttention kernel on top of FlashAttention and demonstrate substantial speedups on production video diffusion models, with no degradation in quality. The code and implementation details will be publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.09915", "title": "HI-TransPA: Hearing Impairments Translation Personal Assistant", "authors": [ "Zhiming Ma", "Shiyu Gan", "Junhao Zhao", "Xianming Li", "Qingyun Pan", "Peidong Wang", "Mingjun Pan", "Yuhao Mo", "Jiajie Cheng", "Chengxin Chen", "Zhonglun Cao", "Chonghan Liu", "Shi Cheng" ], "github": "", "project_page": "", "abstract": "To provide a unified and flexible solution for daily communication among hearing-impaired individuals, we introduce the Omni-Model paradigm into assistive technology and present HI-TransPA, an instruction-driven audio-visual personal assistant. The model fuses indistinct speech with high-frame-rate lip dynamics, enabling both translation and dialogue within a single multimodal framework. To tackle the challenges of noisy and heterogeneous raw data and the limited adaptability of existing Omni-Models to hearing-impaired speech, we construct a comprehensive preprocessing and curation pipeline that detects facial landmarks, isolates and stabilizes the lip region, and quantitatively assesses multimodal sample quality. These quality scores guide a curriculum learning strategy that first trains on clean, high-confidence samples and progressively incorporates harder cases to strengthen model robustness. We further adopt a SigLIP encoder combined with a Unified 3D-Resampler to efficiently encode high-frame-rate lip motion. Experiments on our purpose-built HI-Dialogue dataset show that HI-TransPA achieves state-of-the-art performance in both literal accuracy and semantic fidelity. This work establishes a foundation for applying Omni-Models to assistive communication technology, providing an end-to-end modeling framework and essential processing tools for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.07448", "title": "Large Language Models for Scientific Idea Generation: A Creativity-Centered Survey", "authors": [ "Fatemeh Shahhosseini", "Arash Marioriyad", "Ali Momen", "Mahdieh Soleymani Baghshah", "Mohammad Hossein Rohban", "Shaghayegh Haghjooy Javanmard" ], "github": "", "project_page": "", "abstract": "Scientific idea generation lies at the heart of scientific discovery and has driven human progress-whether by solving unsolved problems or proposing novel hypotheses to explain unknown phenomena. Unlike standard scientific reasoning or general creative generation, idea generation in science is a multi-objective and open-ended task, where the novelty of a contribution is as essential as its empirical soundness. Large language models (LLMs) have recently emerged as promising generators of scientific ideas, capable of producing coherent and factual outputs with surprising intuition and acceptable reasoning, yet their creative capacity remains inconsistent and poorly understood. This survey provides a structured synthesis of methods for LLM-driven scientific ideation, examining how different approaches balance creativity with scientific soundness. We categorize existing methods into five complementary families: External knowledge augmentation, Prompt-based distributional steering, Inference-time scaling, Multi-agent collaboration, and Parameter-level adaptation. To interpret their contributions, we employ two complementary frameworks: Boden's taxonomy of Combinatorial, Exploratory and Transformational creativity to characterize the level of ideas each family expected to generate, and Rhodes' 4Ps framework-Person, Process, Press, and Product-to locate the aspect or source of creativity that each method emphasizes. By aligning methodological advances with creativity frameworks, this survey clarifies the state of the field and outlines key directions toward reliable, systematic, and transformative applications of LLMs in scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.08585", "title": "Simulating the Visual World with Artificial Intelligence: A Roadmap", "authors": [ "Jingtong Yue", "Ziqi Huang", "Zhaoxi Chen", "Xintao Wang", "Pengfei Wan", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "The landscape of video generation is shifting, from a focus on generating visually appealing clips to building virtual environments that support interaction and maintain physical plausibility. These developments point toward the emergence of video foundation models that function not only as visual generators but also as implicit world models, models that simulate the physical dynamics, agent-environment interactions, and task planning that govern real or imagined worlds. This survey provides a systematic overview of this evolution, conceptualizing modern video foundation models as the combination of two core components: an implicit world model and a video renderer. The world model encodes structured knowledge about the world, including physical laws, interaction dynamics, and agent behavior. It serves as a latent simulation engine that enables coherent visual reasoning, long-term temporal consistency, and goal-driven planning. The video renderer transforms this latent simulation into realistic visual observations, effectively producing videos as a \"window\" into the simulated world. We trace the progression of video generation through four generations, in which the core capabilities advance step by step, ultimately culminating in a world model, built upon a video generation model, that embodies intrinsic physical plausibility, real-time multimodal interaction, and planning capabilities spanning multiple spatiotemporal scales. For each generation, we define its core characteristics, highlight representative works, and examine their application domains such as robotics, autonomous driving, and interactive gaming. Finally, we discuss open challenges and design principles for next-generation world models, including the role of agent intelligence in shaping and evaluating these systems. An up-to-date list of related works is maintained at this link.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.10492", "title": "Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-head Decoding", "authors": [ "Yunkai Zhang", "Qiang Zhang", "Feng", "Lin", "Ruizhong Qiu", "Hanchao Yu", "Jason Liu", "Yinglong Xia", "Zhuoran Yu", "Zeyu Zheng", "Diji Yang" ], "github": "", "project_page": "", "abstract": "Optimizing recommender systems for objectives beyond accuracy, such as diversity, novelty, and personalization, is crucial for long-term user satisfaction. To this end, industrial practitioners have accumulated vast amounts of structured domain knowledge, which we term human priors (e.g., item taxonomies, temporal patterns). This knowledge is typically applied through post-hoc adjustments during ranking or post-ranking. However, this approach remains decoupled from the core model learning, which is particularly undesirable as the industry shifts to end-to-end generative recommendation foundation models. On the other hand, many methods targeting these beyond-accuracy objectives often require architecture-specific modifications and discard these valuable human priors by learning user intent in a fully unsupervised manner. Instead of discarding the human priors accumulated over years of practice, we introduce a backbone-agnostic framework that seamlessly integrates these human priors directly into the end-to-end training of generative recommenders. With lightweight, prior-conditioned adapter heads inspired by efficient LLM decoding strategies, our approach guides the model to disentangle user intent along human-understandable axes (e.g., interaction types, long- vs. short-term interests). We also introduce a hierarchical composition strategy for modeling complex interactions across different prior types. Extensive experiments on three large-scale datasets demonstrate that our method significantly enhances both accuracy and beyond-accuracy objectives. We also show that human priors allow the backbone model to more effectively leverage longer context lengths and larger model sizes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.10258", "title": "Workload Schedulers -- Genesis, Algorithms and Differences", "authors": [ "Leszek Sliwko", "Vladimir Getov" ], "github": "", "project_page": "", "abstract": "This paper presents a novel approach to categorization of modern workload schedulers. We provide descriptions of three classes of schedulers: Operating Systems Process Schedulers, Cluster Systems Jobs Schedulers and Big Data Schedulers. We describe their evolution from early adoptions to modern implementations, considering both the use and features of algorithms. In summary, we discuss differences between all presented classes of schedulers and discuss their chronological development. In conclusion we highlight similarities in the focus of scheduling strategies design, applicable to both local and distributed systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.11287", "title": "Building the Web for Agents: A Declarative Framework for Agent-Web Interaction", "authors": [ "Sven Schultze", "Meike Verena Kietzmann", "Nils-Lucas Sch\u00f6nfeld", "Ruth Stock-Homburg" ], "github": "", "project_page": "", "abstract": "The increasing deployment of autonomous AI agents on the web is hampered by a fundamental misalignment: agents must infer affordances from human-oriented user interfaces, leading to brittle, inefficient, and insecure interactions. To address this, we introduce VOIX, a web-native framework that enables websites to expose reliable, auditable, and privacy-preserving capabilities for AI agents through simple, declarative HTML elements. VOIX introduces and tags, allowing developers to explicitly define available actions and relevant state, thereby creating a clear, machine-readable contract for agent behavior. This approach shifts control to the website developer while preserving user privacy by disconnecting the conversational interactions from the website. We evaluated the framework's practicality, learnability, and expressiveness in a three-day hackathon study with 16 developers. The results demonstrate that participants, regardless of prior experience, were able to rapidly build diverse and functional agent-enabled web applications. Ultimately, this work provides a foundational mechanism for realizing the Agentic Web, enabling a future of seamless and secure human-AI collaboration on the web.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.09554", "title": "RF-DETR: Neural Architecture Search for Real-Time Detection Transformers", "authors": [ "Isaac Robinson", "Peter Robicheaux", "Matvei Popov", "Deva Ramanan", "Neehar Peri" ], "github": "https://github.com/roboflow/rf-detr", "project_page": "", "abstract": "Open-vocabulary detectors achieve impressive performance on COCO, but often fail to generalize to real-world datasets with out-of-distribution classes not typically found in their pre-training. Rather than simply fine-tuning a heavy-weight vision-language model (VLM) for new domains, we introduce RF-DETR, a light-weight specialist detection transformer that discovers accuracy-latency Pareto curves for any target dataset with weight-sharing neural architecture search (NAS). Our approach fine-tunes a pre-trained base network on a target dataset and evaluates thousands of network configurations with different accuracy-latency tradeoffs without re-training. Further, we revisit the \"tunable knobs\" for NAS to improve the transferability of DETRs to diverse target domains. Notably, RF-DETR significantly improves on prior state-of-the-art real-time methods on COCO and Roboflow100-VL. RF-DETR (nano) achieves 48.0 AP on COCO, beating D-FINE (nano) by 5.3 AP at similar latency, and RF-DETR (2x-large) outperforms GroundingDINO (tiny) by 1.2 AP on Roboflow100-VL while running 20x as fast. To the best of our knowledge, RF-DETR (2x-large) is the first real-time detector to surpass 60 AP on COCO. Our code is at https://github.com/roboflow/rf-detr", "github_manual": "", "github_regex": "https://github.com/roboflow/rf-detr", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.11519", "title": "Experience-Guided Adaptation of Inference-Time Reasoning Strategies", "authors": [ "Adam Stein", "Matthew Trager", "Benjamin Bowman", "Michael Kleinman", "Aditya Chattopadhyay", "Wei Xia", "Stefano Soatto" ], "github": "", "project_page": "", "abstract": "Enabling agentic AI systems to adapt their problem-solving approaches based on post-training interactions remains a fundamental challenge. While systems that update and maintain a memory at inference time have been proposed, existing designs only steer the system by modifying textual input to a language model or agent, which means that they cannot change sampling parameters, remove tools, modify system prompts, or switch between agentic and workflow paradigms. On the other hand, systems that adapt more flexibly require offline optimization and remain static once deployed. We present Experience-Guided Reasoner (EGuR), which generates tailored strategies -- complete computational procedures involving LLM calls, tools, sampling parameters, and control logic -- dynamically at inference time based on accumulated experience. We achieve this using an LLM-based meta-strategy -- a strategy that outputs strategies -- enabling adaptation of all strategy components (prompts, sampling parameters, tool configurations, and control logic). EGuR operates through two components: a Guide generates multiple candidate strategies conditioned on the current problem and structured memory of past experiences, while a Consolidator integrates execution feedback to improve future strategy generation. This produces complete, ready-to-run strategies optimized for each problem, which can be cached, retrieved, and executed as needed without wasting resources. Across five challenging benchmarks (AIME 2025, 3-SAT, and three Big Bench Extra Hard tasks), EGuR achieves up to 14% accuracy improvements over the strongest baselines while reducing computational costs by up to 111x, with both metrics improving as the system gains experience.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.10899", "title": "From Proof to Program: Characterizing Tool-Induced Reasoning Hallucinations in Large Language Models", "authors": [ "Farima Fatahi Bayat", "Pouya Pezeshkpour", "Estevam Hruschka" ], "github": "https://github.com/megagonlabs/TIM", "project_page": "", "abstract": "Tool-augmented Language Models (TaLMs) can invoke external tools to solve problems beyond their parametric capacity. However, it remains unclear whether these tool-enabled gains reflect trustworthy reasoning. Focusing on the Code Interpreter tool, we show that even when tools are selected and executed correctly, TaLMs treat tool outputs as substitutes for reasoning, producing solutions that appear correct but lack coherent justification. We term this failure mode Tool-Induced Myopia (TIM), and study it using PYMATH, a benchmark of 1,679 competition-level mathematical problems for which Python code is helpful but not sufficient. We further develop a multi-dimensional evaluation suite to quantify reasoning degradation in TaLMs relative to their non-tool counterparts. Our findings reveal that while TaLMs achieve up to a 19.3 percentage point gain in final-answer accuracy, their reasoning behavior consistently deteriorates (e.g., non-tool LLMs win up to 41.5% more often in pairwise comparisons of the reasoning process). This degradation intensifies with tool use; the more frequently a model invokes tools, the less coherent its reasoning becomes. Moreover, tool use shifts errors from arithmetic mistakes toward global reasoning failures (logic, assumption, creativity); with TIM present in ~55% of high-risk cases. Finally, we propose a preference-optimization-based framework that realigns TaLMs to use tools as assistive evidence, improving both final-answer accuracy and reasoning depth under tool use. Codes and data are available at: https://github.com/megagonlabs/TIM.", "github_manual": "", "github_regex": "https://github.com/megagonlabs/TIM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.03108", "title": "miniF2F-Lean Revisited: Reviewing Limitations and Charting a Path Forward", "authors": [ "Azim Ospanov", "Farzan Farnia", "Roozbeh Yousefzadeh" ], "github": "https://github.com/roozbeh-yz/miniF2F_v2", "project_page": "", "abstract": "We perform a thorough analysis of the formal and informal statements in the miniF2F benchmark from the perspective of an AI system that is tasked to participate in a math Olympiad consisting of the problems in miniF2F. In such setting, the model has to read and comprehend the problems in natural language, formalize them in Lean language, then proceed with proving the problems, and it will get credit for each problem if the formal proof corresponds to the original informal statement presented to the model. Our evaluation results reveal that the best accuracy of such pipeline can be about 36% using the SoTA models in the literature, considerably lower than the individual SoTA accuracies, 97% and 69% reported in the autoformalization and theorem proving literature. Analyzing the failure modes, we trace back a considerable portion of this drop to discrepancies between the formal and informal statements for more than half of the problems in miniF2F. We proceed with correcting all the errors, discrepancies and simplifications in formal and informal statements, and present the miniF2F-v2 with fully verified formal and informal statements and proofs. Evaluating the full theorem proving pipeline on miniF2F-v2 leads to the best accuracy of 70%, a significant improvement from the 40% on the original miniF2F, yet indicating considerable misalignment between the autoformalization models and theorem provers. Our deep analysis suggests that a higher quality benchmark can help the community better evaluate progress in the field of formal reasoning and also better diagnose the failure and success modes of autoformalization and theorem proving models. Our dataset is available at https://github.com/roozbeh-yz/miniF2F_v2.", "github_manual": "", "github_regex": "https://github.com/roozbeh-yz/miniF2F_v2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-17", "arxiv_id": "2511.11721", "title": "A Meta-Heuristic Load Balancer for Cloud Computing Systems", "authors": [ "Leszek Sliwko", "Vladimir Getov" ], "github": "", "project_page": "", "abstract": "This paper presents a strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.13612", "title": "P1: Mastering Physics Olympiads with Reinforcement Learning", "authors": [ "Jiacheng Chen", "Qianjia Cheng", "Fangchen Yu", "Haiyuan Wan", "Yuchen Zhang", "Shenghe Zheng", "Junchi Yao", "Qingyang Zhang", "Haonan He", "Yun Luo", "Yufeng Zhao", "Futing Wang", "Li Sheng", "Chengxing Xie", "Yuxin Zuo", "Yizhuo Li", "Wenxauan Zeng", "Yulun Wu", "Rui Huang", "Dongzhan Zhou", "Kai Chen", "Yu Qiao", "Lei Bai", "Yu Cheng", "Ning Ding", "Bowen Zhou", "Peng Ye", "Ganqu Cui" ], "github": "", "project_page": "", "abstract": "Recent progress in large language models (LLMs) has moved the frontier from puzzle-solving to science-grade reasoning-the kind needed to tackle problems whose answers must stand against nature, not merely fit a rubric. Physics is the sharpest test of this shift, which binds symbols to reality in a fundamental way, serving as the cornerstone of most modern technologies. In this work, we manage to advance physics research by developing large language models with exceptional physics reasoning capabilities, especially excel at solving Olympiad-level physics problems. We introduce P1, a family of open-source physics reasoning models trained entirely through reinforcement learning (RL). Among them, P1-235B-A22B is the first open-source model with Gold-medal performance at the latest International Physics Olympiad (IPhO 2025), and wins 12 gold medals out of 13 international/regional physics competitions in 2024/2025. P1-30B-A3B also surpasses almost all other open-source models on IPhO 2025, getting a silver medal. Further equipped with an agentic framework PhysicsMinions, P1-235B-A22B+PhysicsMinions achieves overall No.1 on IPhO 2025, and obtains the highest average score over the 13 physics competitions. Besides physics, P1 models also present great performance on other reasoning tasks like math and coding, showing the great generalibility of P1 series.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.12609", "title": "Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data", "authors": [ "Yunxin Li", "Xinyu Chen", "Shenyuan Jiang", "Haoyuan Shi", "Zhenyu Liu", "Xuanyu Zhang", "Nanhao Deng", "Zhenran Xu", "Yicheng Ma", "Meishan Zhang", "Baotian Hu", "Min Zhang" ], "github": "", "project_page": "", "abstract": "We present Uni-MoE 2.0 from the Lychee family. As a fully open-source omnimodal large model (OLM), it substantially advances Lychee's Uni-MoE series in language-centric multimodal understanding, reasoning, and generating. Based on the Qwen2.5-7B dense architecture, we build Uni-MoE-2.0-Omni from scratch through three core contributions: dynamic-capacity Mixture-of-Experts (MoE) design, a progressive training strategy enhanced with an iterative reinforcement strategy, and a carefully curated multimodal data matching technique. It is capable of omnimodal understanding, as well as generating images, text, and speech. Architecturally, our new MoE framework balances computational efficiency and capability for 10 cross-modal inputs using shared, routed, and null experts, while our Omni-Modality 3D RoPE ensures spatio-temporal cross-modality alignment in the self-attention layer. For training, following cross-modal pretraining, we use a progressive supervised fine-tuning strategy that activates modality-specific experts and is enhanced by balanced data composition and an iterative GSPO-DPO method to stabilise RL training and improve reasoning. Data-wise, the base model, trained on approximately 75B tokens of open-source multimodal data, is equipped with special speech and image generation tokens, allowing it to learn these generative tasks by conditioning its outputs on linguistic cues. Extensive evaluation across 85 benchmarks demonstrates that our model achieves SOTA or highly competitive performance against leading OLMs, surpassing Qwen2.5-Omni (trained with 1.2T tokens) on over 50 of 76 benchmarks. Key strengths include video understanding (+7% avg. of 8), omnimodallity understanding (+7% avg. of 4), and audiovisual reasoning (+4%). It also advances long-form speech processing (reducing WER by 4.2%) and leads in low-level image processing and controllable generation across 5 metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.13647", "title": "Part-X-MLLM: Part-aware 3D Multimodal Large Language Model", "authors": [ "Chunshi Wang", "Junliang Ye", "Yunhan Yang", "Yang Li", "Zizhuo Lin", "Jun Zhu", "Zhuo Chen", "Yawei Luo", "Chunchao Guo" ], "github": "", "project_page": "", "abstract": "We introduce Part-X-MLLM, a native 3D multimodal large language model that unifies diverse 3D tasks by formulating them as programs in a structured, executable grammar. Given an RGB point cloud and a natural language prompt, our model autoregressively generates a single, coherent token sequence encoding part-level bounding boxes, semantic descriptions, and edit commands. This structured output serves as a versatile interface to drive downstream geometry-aware modules for part-based generation and editing. By decoupling the symbolic planning from the geometric synthesis, our approach allows any compatible geometry engine to be controlled through a single, language-native frontend. We pre-train a dual-encoder architecture to disentangle structure from semantics and instruction-tune the model on a large-scale, part-centric dataset. Experiments demonstrate that our model excels at producing high-quality, structured plans, enabling state-of-the-art performance in grounded Q\\&A, compositional generation, and localized editing through one unified interface. Project page: https://chunshi.wang/Part-X-MLLM/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.13704", "title": "TiViBench: Benchmarking Think-in-Video Reasoning for Video Generative Models", "authors": [ "Harold Haodong Chen", "Disen Lan", "Wen-Jie Shu", "Qingyang Liu", "Zihan Wang", "Sirui Chen", "Wenkai Cheng", "Kanghao Chen", "Hongfei Zhang", "Zixin Zhang", "Rongjin Guo", "Yu Cheng", "Ying-Cong Chen" ], "github": "https://github.com/thuml/MiniVeo3-Reasoner", "project_page": "", "abstract": "The rapid evolution of video generative models has shifted their focus from producing visually plausible outputs to tackling tasks requiring physical plausibility and logical consistency. However, despite recent breakthroughs such as Veo 3's chain-of-frames reasoning, it remains unclear whether these models can exhibit reasoning capabilities similar to large language models (LLMs). Existing benchmarks predominantly evaluate visual fidelity and temporal coherence, failing to capture higher-order reasoning abilities. To bridge this gap, we propose TiViBench, a hierarchical benchmark specifically designed to evaluate the reasoning capabilities of image-to-video (I2V) generation models. TiViBench systematically assesses reasoning across four dimensions: i) Structural Reasoning & Search, ii) Spatial & Visual Pattern Reasoning, iii) Symbolic & Logical Reasoning, and iv) Action Planning & Task Execution, spanning 24 diverse task scenarios across 3 difficulty levels. Through extensive evaluations, we show that commercial models (e.g., Sora 2, Veo 3.1) demonstrate stronger reasoning potential, while open-source models reveal untapped potential that remains hindered by limited training scale and data diversity. To further unlock this potential, we introduce VideoTPO, a simple yet effective test-time strategy inspired by preference optimization. By performing LLM self-analysis on generated candidates to identify strengths and weaknesses, VideoTPO significantly enhances reasoning performance without requiring additional training, data, or reward models. Together, TiViBench and VideoTPO pave the way for evaluating and advancing reasoning in video generation models, setting a foundation for future research in this emerging field.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/thuml/MiniVeo3-Reasoner", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.09611", "title": "MMaDA-Parallel: Multimodal Large Diffusion Language Models for Thinking-Aware Editing and Generation", "authors": [ "Ye Tian", "Ling Yang", "Jiongfan Yang", "Anran Wang", "Yu Tian", "Jiani Zheng", "Haochen Wang", "Zhiyang Teng", "Zhuochen Wang", "Yinjie Wang", "Yunhai Tong", "Mengdi Wang", "Xiangtai Li" ], "github": "https://github.com/tyfeld/MMaDA-Parallel", "project_page": "", "abstract": "While thinking-aware generation aims to improve performance on complex tasks, we identify a critical failure mode where existing sequential, autoregressive approaches can paradoxically degrade performance due to error propagation. To systematically analyze this issue, we propose ParaBench, a new benchmark designed to evaluate both text and image output modalities. Our analysis using ParaBench reveals that this performance degradation is strongly correlated with poor alignment between the generated reasoning and the final image. To resolve this, we propose a parallel multimodal diffusion framework, MMaDA-Parallel, that enables continuous, bidirectional interaction between text and images throughout the entire denoising trajectory. MMaDA-Parallel is trained with supervised finetuning and then further optimized by Parallel Reinforcement Learning (ParaRL), a novel strategy that applies semantic rewards along the trajectory to enforce cross-modal consistency. Experiments validate that our model significantly improves cross-modal alignment and semantic consistency, achieving a 6.9\\% improvement in Output Alignment on ParaBench compared to the state-of-the-art model, Bagel, establishing a more robust paradigm for thinking-aware image synthesis. Our code is open-sourced at https://github.com/tyfeld/MMaDA-Parallel", "github_manual": "", "github_regex": "https://github.com/tyfeld/MMaDA-Parallel", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.13648", "title": "PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image", "authors": [ "Ziang Cao", "Fangzhou Hong", "Zhaoxi Chen", "Liang Pan", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "3D modeling is shifting from static visual representations toward physical, articulated assets that can be directly used in simulation and interaction. However, most existing 3D generation methods overlook key physical and articulation properties, thereby limiting their utility in embodied AI. To bridge this gap, we introduce PhysX-Anything, the first simulation-ready physical 3D generative framework that, given a single in-the-wild image, produces high-quality sim-ready 3D assets with explicit geometry, articulation, and physical attributes. Specifically, we propose the first VLM-based physical 3D generative model, along with a new 3D representation that efficiently tokenizes geometry. It reduces the number of tokens by 193x, enabling explicit geometry learning within standard VLM token budgets without introducing any special tokens during fine-tuning and significantly improving generative quality. In addition, to overcome the limited diversity of existing physical 3D datasets, we construct a new dataset, PhysX-Mobility, which expands the object categories in prior physical 3D datasets by over 2x and includes more than 2K common real-world objects with rich physical annotations. Extensive experiments on PhysX-Mobility and in-the-wild images demonstrate that PhysX-Anything delivers strong generative performance and robust generalization. Furthermore, simulation-based experiments in a MuJoCo-style environment validate that our sim-ready assets can be directly used for contact-rich robotic policy learning. We believe PhysX-Anything can substantially empower a broad range of downstream applications, especially in embodied AI and physics-based simulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.11653", "title": "GroupRank: A Groupwise Reranking Paradigm Driven by Reinforcement Learning", "authors": [ "Duolin Sun", "Meixiu Long", "Dan Yang", "Yihan Jiao", "Zhehao Tan", "Jie Feng", "Junjie Wang", "Yue Shen", "Peng Wei", "Jian Wang", "Jinjie Gu" ], "github": "https://github.com/AQ-MedAI/Diver.git", "project_page": "", "abstract": "Large Language Models have shown strong potential as rerankers to enhance the overall performance of RAG systems. However, existing reranking paradigms are constrained by a core theoretical and practical dilemma: Pointwise methods, while simple and highly flexible, evaluate documents independently, making them prone to the Ranking Myopia Trap, overlooking the relative importance between documents. In contrast, Listwise methods can perceive the global ranking context, but suffer from inherent List Rigidity, leading to severe scalability and flexibility issues when handling large candidate sets. To address these challenges, we propose Groupwise, a novel reranking paradigm. In this approach, the query and a group of candidate documents are jointly fed into the model, which performs within-group comparisons to assign individual relevance scores to each document. This design retains the flexibility of Pointwise methods while enabling the comparative capability of Listwise methods. We further adopt GRPO for model training, equipped with a heterogeneous reward function that integrates ranking metrics with a distributional reward aimed at aligning score distributions across groups. To overcome the bottleneck caused by the scarcity of high quality labeled data, we further propose an innovative pipeline for synthesizing high quality retrieval and ranking data. The resulting data can be leveraged not only for training the reranker but also for training the retriever. Extensive experiments validate the effectiveness of our approach. On two reasoning intensive retrieval benchmarks, BRIGHT and R2MED.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AQ-MedAI/Diver.git", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.11332", "title": "UFO^3: Weaving the Digital Agent Galaxy", "authors": [ "Chaoyun Zhang", "Liqun Li", "He Huang", "Chiming Ni", "Bo Qiao", "Si Qin", "Yu Kang", "Minghua Ma", "Qingwei Lin", "Saravan Rajmohan", "Dongmei Zhang" ], "github": "https://github.com/microsoft/UFO", "project_page": "", "abstract": "Large language model (LLM)-powered agents are transforming digital devices from passive tools into proactive intelligent collaborators. However, most existing frameworks remain confined to a single OS or device, making cross-device workflows brittle and largely manual. We present UFO^3, a system that unifies heterogeneous endpoints, desktops, servers, mobile devices, and edge, into a single orchestration fabric. UFO^3 models each user request as a mutable TaskConstellation: a distributed DAG of atomic subtasks (TaskStars) with explicit control and data dependencies (TaskStarLines). The TaskConstellation continuously evolves as results stream in from distributed devices, enabling asynchronous execution, adaptive recovery, and dynamic optimization. A Constellation Orchestrator} executes tasks safely and asynchronously while applying dynamic DAG updates, and the Agent Interaction Protocol (AIP) provides persistent, low-latency channels for reliable task dispatch and result streaming. These designs dissolve the traditional boundaries between devices and platforms, allowing agents to collaborate seamlessly and amplify their collective intelligence. We evaluate UFO^3 on NebulaBench, a benchmark of 55 cross-device tasks across 5 machines and 10 categories. UFO^3 achieves 83.3% subtask completion, 70.9% task success, exposes parallelism with an average width of 1.72, and reduces end-to-end latency by 31% relative to a sequential baseline. Fault-injection experiments demonstrate graceful degradation and recovery under transient and permanent agent failures. These results show that UFO^3 achieves accurate, efficient, and resilient task orchestration across heterogeneous devices, uniting isolated agents into a coherent, adaptive computing fabric that extends across the landscape of ubiquitous computing.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/UFO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.13254", "title": "Souper-Model: How Simple Arithmetic Unlocks State-of-the-Art LLM Performance", "authors": [ "Shalini Maiti", "Amar Budhiraja", "Bhavul Gauri", "Gaurav Chaurasia", "Anton Protopopov", "Alexis Audran-Reiss", "Michael Slater", "Despoina Magka", "Tatiana Shavrina", "Roberta Raileanu", "Yoram Bachrach" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, but their training remains resource- and time-intensive, requiring massive compute power and careful orchestration of training procedures. Model souping-the practice of averaging weights from multiple models of the same architecture-has emerged as a promising pre- and post-training technique that can enhance performance without expensive retraining. In this paper, we introduce Soup Of Category Experts (SoCE), a principled approach for model souping that utilizes benchmark composition to identify optimal model candidates and applies non-uniform weighted averaging to maximize performance. Contrary to previous uniform-averaging approaches, our method leverages the observation that benchmark categories often exhibit low inter-correlations in model performance. SoCE identifies \"expert\" models for each weakly-correlated category cluster and combines them using optimized weighted averaging rather than uniform weights. We demonstrate that the proposed method improves performance and robustness across multiple domains, including multilingual capabilities, tool calling, and math and achieves state-of-the-art results on the Berkeley Function Calling Leaderboard.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.13655", "title": "OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation", "authors": [ "Henry Herzog", "Favyen Bastani", "Yawen Zhang", "Gabriel Tseng", "Joseph Redmon", "Hadrien Sablon", "Ryan Park", "Jacob Morrison", "Alexandra Buraczynski", "Karen Farley", "Joshua Hansen", "Andrew Howe", "Patrick Alan Johnson", "Mark Otterlee", "Ted Schmitt", "Hunter Pitelka", "Stephen Daspit", "Rachel Ratner", "Christopher Wilhelm", "Sebastian Wood", "Mike Jacobi", "Hannah Kerner", "Evan Shelhamer", "Ali Farhadi", "Ranjay Krishna", "Patrick Beukema" ], "github": "https://github.com/allenai/olmoearth_pretrain", "project_page": "", "abstract": "Earth observation data presents a unique challenge: it is spatial like images, sequential like video or text, and highly multimodal. We present OlmoEarth: a multimodal, spatio-temporal foundation model that employs a novel self-supervised learning formulation, masking strategy, and loss all designed for the Earth observation domain. OlmoEarth achieves state-of-the-art performance compared to 12 other foundation models across a variety of research benchmarks and real-world tasks from external partners. When evaluating embeddings OlmoEarth achieves the best performance on 15 out of 24 tasks, and with full fine-tuning it is the best on 19 of 29 tasks. We deploy OlmoEarth as the backbone of an end-to-end platform for data collection, labeling, training, and inference of Earth observation models. The OlmoEarth Platform puts frontier foundation models and powerful data management tools into the hands of non-profits and NGOs working to solve the world's biggest problems. OlmoEarth source code, training data, and pre-trained weights are available at https://github.com/allenai/olmoearth_pretrain{https://github.com/allenai/olmoearth_pretrain}.", "github_manual": "", "github_regex": "https://github.com/allenai/olmoearth_pretrain", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.11793", "title": "MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling", "authors": [ "MiroMind Team", "Song Bai", "Lidong Bing", "Carson Chen", "Guanzheng Chen", "Yuntao Chen", "Zhe Chen", "Ziyi Chen", "Jifeng Dai", "Xuan Dong", "Yue Deng", "Yunjie Fu", "Junqi Ge", "Chenxia Han", "Tammy Huang", "Zhenhang Huang", "Jerry Jiao", "Shilei Jiang", "Tianyu Jiao", "Xiaoqi Jian", "Lei Lei", "Ruilin Li", "Ryan Luo", "Tiantong Li", "Xiang Lin", "Ziyuan Liu", "Zhiqi Li", "Jie Ni", "Qiang Ren", "Pax Sun", "Shiqian Su", "Chenxin Tao", "Bin Wang", "Hellen Wang", "Haonan Wang", "James Wang", "Jin Wang", "Jojo Wang", "Letian Wang", "Shizun Wang", "Weizhi Wang", "Zixuan Wang", "Jinfan Xu", "Sen Xing", "Chenyu Yang", "Hai Ye", "Jiaheng Yu", "Yue Yu", "Muyan Zhong", "Tianchen Zhao", "Xizhou Zhu", "Yanpeng Zhou", "Yifan Zhang", "Zhi Zhu" ], "github": "https://github.com/MiroMindAI/MiroThinker", "project_page": "", "abstract": "We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. Unlike previous agents that only scale up model size or context length, MiroThinker explores interaction scaling at the model level, systematically training the model to handle deeper and more frequent agent-environment interactions as a third dimension of performance improvement. Unlike LLM test-time scaling, which operates in isolation and risks degradation with longer reasoning chains, interactive scaling leverages environment feedback and external information acquisition to correct errors and refine trajectories. Through reinforcement learning, the model achieves efficient interaction scaling: with a 256K context window, it can perform up to 600 tool calls per task, enabling sustained multi-turn reasoning and complex real-world research workflows. Across four representative benchmarks-GAIA, HLE, BrowseComp, and BrowseComp-ZH-the 72B variant achieves up to 81.9%, 37.7%, 47.1%, and 55.6% accuracy respectively, surpassing previous open-source agents and approaching commercial counterparts such as GPT-5-high. Our analysis reveals that MiroThinker benefits from interactive scaling consistently: research performance improves predictably as the model engages in deeper and more frequent agent-environment interactions, demonstrating that interaction depth exhibits scaling behaviors analogous to model size and context length. These findings establish interaction scaling as a third critical dimension for building next-generation open research agents, complementing model capacity and context windows.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiroMindAI/MiroThinker", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.13646", "title": "Live-SWE-agent: Can Software Engineering Agents Self-Evolve on the Fly?", "authors": [ "Chunqiu Steven Xia", "Zhe Wang", "Yan Yang", "Yuxiang Wei", "Lingming Zhang" ], "github": "https://github.com/OpenAutoCoder/live-swe-agent", "project_page": "", "abstract": "Large Language Models (LLMs) are reshaping almost all industries, including software engineering. In recent years, a number of LLM agents have been proposed to solve real-world software problems. Such software agents are typically equipped with a suite of coding tools and can autonomously decide the next actions to form complete trajectories to solve end-to-end software tasks. While promising, they typically require dedicated design and may still be suboptimal, since it can be extremely challenging and costly to exhaust the entire agent scaffold design space. Recognizing that software agents are inherently software themselves that can be further refined/modified, researchers have proposed a number of self-improving software agents recently, including the Darwin-G\u00f6del Machine (DGM). Meanwhile, such self-improving agents require costly offline training on specific benchmarks and may not generalize well across different LLMs or benchmarks. In this paper, we propose Live-SWE-agent, the first live software agent that can autonomously and continuously evolve itself on-the-fly during runtime when solving real-world software problems. More specifically, Live-SWE-agent starts with the most basic agent scaffold with only access to bash tools (e.g., mini-SWE-agent), and autonomously evolves its own scaffold implementation while solving real-world software problems. Our evaluation on the widely studied SWE-bench Verified benchmark shows that Live-SWE-agent can achieve an impressive solve rate of 75.4% without test-time scaling, outperforming all existing open-source software agents and approaching the performance of the best proprietary solution. Moreover, Live-SWE-agent outperforms state-of-the-art manually crafted software agents on the recent SWE-Bench Pro benchmark, achieving the best-known solve rate of 45.8%.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenAutoCoder/live-swe-agent", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.09809", "title": "Test-Time Spectrum-Aware Latent Steering for Zero-Shot Generalization in Vision-Language Models", "authors": [ "Konstantinos M. Dafnis", "Dimitris N. Metaxas" ], "github": "https://github.com/kdafnis/STS", "project_page": "", "abstract": "Vision-Language Models (VLMs) excel at zero-shot inference but often degrade under test-time domain shifts. For this reason, episodic test-time adaptation strategies have recently emerged as powerful techniques for adapting VLMs to a single unlabeled image. However, existing adaptation strategies, such as test-time prompt tuning, typically require backpropagating through large encoder weights or altering core model components. In this work, we introduce Spectrum-Aware Test-Time Steering (STS), a lightweight adaptation framework that extracts a spectral subspace from the textual embeddings to define principal semantic directions and learns to steer latent representations in a spectrum-aware manner by adapting a small number of per-sample shift parameters to minimize entropy across augmented views. STS operates entirely at inference in the latent space, without backpropagation through or modification of the frozen encoders. Building on standard evaluation protocols, our comprehensive experiments demonstrate that STS largely surpasses or compares favorably against state-of-the-art test-time adaptation methods, while introducing only a handful of additional parameters and achieving inference speeds up to 8x faster with a 12x smaller memory footprint than conventional test-time prompt tuning. The code is available at https://github.com/kdafnis/STS.", "github_manual": "", "github_regex": "https://github.com/kdafnis/STS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.12997", "title": "WebCoach: Self-Evolving Web Agents with Cross-Session Memory Guidance", "authors": [ "Genglin Liu", "Shijie Geng", "Sha Li", "Hejie Cui", "Sarah Zhang", "Xin Liu", "Tianyi Liu" ], "github": "", "project_page": "", "abstract": "Multimodal LLM-powered agents have recently demonstrated impressive capabilities in web navigation, enabling agents to complete complex browsing tasks across diverse domains. However, current agents struggle with repetitive errors and lack the ability to learn from past experiences across sessions, limiting their long-term robustness and sample efficiency. We introduce WebCoach, a model-agnostic self-evolving framework that equips web browsing agents with persistent cross-session memory, enabling improved long-term planning, reflection, and continual learning without retraining. WebCoach consists of three key components: (1) a WebCondenser, which standardizes raw navigation logs into concise summaries; (2) an External Memory Store, which organizes complete trajectories as episodic experiences; and (3) a Coach, which retrieves relevant experiences based on similarity and recency, and decides whether to inject task-specific advice into the agent via runtime hooks. This design empowers web agents to access long-term memory beyond their native context window, improving robustness in complex browsing tasks. Moreover, WebCoach achieves self-evolution by continuously curating episodic memory from new navigation trajectories, enabling agents to improve over time without retraining. Evaluations on the WebVoyager benchmark demonstrate that WebCoach consistently improves the performance of browser-use agents across three different LLM backbones. With a 38B model, it increases task success rates from 47% to 61% while reducing or maintaining the average number of steps. Notably, smaller base models with WebCoach achieve performance comparable to the same web agent using GPT-4o.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.11407", "title": "MicroVQA++: High-Quality Microscopy Reasoning Dataset with Weakly Supervised Graphs for Multimodal Large Language Model", "authors": [ "Manyu Li", "Ruian He", "Chenxi Ma", "Weimin Tan", "Bo Yan" ], "github": "https://github.com/ieellee/MicroVQA-PlusPlus", "project_page": "", "abstract": "Multimodal Large Language Models are increasingly applied to biomedical imaging, yet scientific reasoning for microscopy remains limited by the scarcity of large-scale, high-quality training data. We introduce MicroVQA++, a three-stage, large-scale and high-quality microscopy VQA corpus derived from the BIOMEDICA archive. Stage one bootstraps supervision from expert-validated figure-caption pairs sourced from peer-reviewed articles. Stage two applies HiCQA-Graph, a novel heterogeneous graph over images, captions, and QAs that fuses NLI-based textual entailment, CLIP-based vision-language alignment, and agent signals to identify and filter inconsistent samples. Stage three uses a MultiModal Large Language Model (MLLM) agent to generate multiple-choice questions (MCQ) followed by human screening. The resulting release comprises a large training split and a human-checked test split whose Bloom's level hard-sample distribution exceeds the MicroVQA benchmark. Our work delivers (i) a quality-controlled dataset that couples expert literature with graph-based filtering and human refinement; (ii) HiCQA-Graph, the first graph that jointly models (image, caption, QA) for cross-modal consistency filtering; (iii) evidence that careful data construction enables 4B-scale MLLMs to reach competitive microscopy reasoning performance (e.g., GPT-5) and achieve state-of-the-art performance among open-source MLLMs. Code and dataset will be released after the review process concludes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ieellee/MicroVQA-PlusPlus", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.10628", "title": "Instella: Fully Open Language Models with Stellar Performance", "authors": [ "Jiang Liu", "Jialian Wu", "Xiaodong Yu", "Yusheng Su", "Prakamya Mishra", "Gowtham Ramesh", "Sudhanshu Ranjan", "Chaitanya Manem", "Ximeng Sun", "Ze Wang", "Pratik Prabhanjan Brahma", "Zicheng Liu", "Emad Barsoum" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks, yet the majority of high-performing models remain closed-source or partially open, limiting transparency and reproducibility. In this work, we introduce Instella, a family of fully open three billion parameter language models trained entirely on openly available data and codebase. Powered by AMD Instinct MI300X GPUs, Instella is developed through large-scale pre-training, general-purpose instruction tuning, and alignment with human preferences. Despite using substantially fewer pre-training tokens than many contemporaries, Instella achieves state-of-the-art results among fully open models and is competitive with leading open-weight models of comparable size. We further release two specialized variants: Instella-Long, capable of handling context lengths up to 128K tokens, and Instella-Math, a reasoning-focused model enhanced through supervised fine-tuning and reinforcement learning on mathematical tasks. Together, these contributions establish Instella as a transparent, performant, and versatile alternative for the community, advancing the goal of open and reproducible language modeling research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.12710", "title": "Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs", "authors": [ "Yunhao Chen", "Xin Wang", "Juncheng Li", "Yixu Wang", "Jie Li", "Yan Teng", "Yingchun Wang", "Xingjun Ma" ], "github": "https://github.com/dongdongunique/EvoSynth", "project_page": "", "abstract": "Automated red teaming frameworks for Large Language Models (LLMs) have become increasingly sophisticated, yet they share a fundamental limitation: their jailbreak logic is confined to selecting, combining, or refining pre-existing attack strategies. This binds their creativity and leaves them unable to autonomously invent entirely new attack mechanisms. To overcome this gap, we introduce EvoSynth, an autonomous framework that shifts the paradigm from attack planning to the evolutionary synthesis of jailbreak methods. Instead of refining prompts, EvoSynth employs a multi-agent system to autonomously engineer, evolve, and execute novel, code-based attack algorithms. Crucially, it features a code-level self-correction loop, allowing it to iteratively rewrite its own attack logic in response to failure. Through extensive experiments, we demonstrate that EvoSynth not only establishes a new state-of-the-art by achieving an 85.5\\% Attack Success Rate (ASR) against highly robust models like Claude-Sonnet-4.5, but also generates attacks that are significantly more diverse than those from existing methods. We release our framework to facilitate future research in this new direction of evolutionary synthesis of jailbreak methods. Code is available at: https://github.com/dongdongunique/EvoSynth.", "github_manual": "", "github_regex": "https://github.com/dongdongunique/EvoSynth", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.02767", "title": "Dynamic Reflections: Probing Video Representations with Text Alignment", "authors": [ "Tyler Zhu", "Tengda Han", "Leonidas Guibas", "Viorica P\u0103tr\u0103ucean", "Maks Ovsjanikov" ], "github": "", "project_page": "", "abstract": "The alignment of representations from different modalities has recently been shown to provide insights on the structural similarities and downstream capabilities of different encoders across diverse data types. While significant progress has been made in aligning images with text, the temporal nature of video data remains largely unexplored in this context. In this work, we conduct the first comprehensive study of video-text representation alignment, probing the capabilities of modern video and language encoders. Our findings reveal several key insights. First, we demonstrate that cross-modal alignment highly depends on the richness of both visual (static images vs. multi-frame videos) and text (single caption vs. a collection) data provided at test time, especially when using state-of-the-art video encoders. We propose parametric test-time scaling laws that capture this behavior and show remarkable predictive power against empirical observations. Secondly, we investigate the correlation between semantic alignment and performance on both semantic and non-semantic downstream tasks, providing initial evidence that strong alignment against text encoders may be linked to general-purpose video representation and understanding. Finally, we correlate temporal reasoning with cross-modal alignment providing a challenging test-bed for vision and language models. Overall, our work introduces video-text alignment as an informative zero-shot way to probe the representation power of different encoders for spatio-temporal data. Project page can be found at https://video-prh.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.13720", "title": "Back to Basics: Let Denoising Generative Models Denoise", "authors": [ "Tianhong Li", "Kaiming He" ], "github": "", "project_page": "", "abstract": "Today's denoising diffusion models do not \"denoise\" in the classical sense, i.e., they do not directly predict clean images. Rather, the neural networks predict noise or a noised quantity. In this paper, we suggest that predicting clean data and predicting noised quantities are fundamentally different. According to the manifold assumption, natural data should lie on a low-dimensional manifold, whereas noised quantities do not. With this assumption, we advocate for models that directly predict clean data, which allows apparently under-capacity networks to operate effectively in very high-dimensional spaces. We show that simple, large-patch Transformers on pixels can be strong generative models: using no tokenizer, no pre-training, and no extra loss. Our approach is conceptually nothing more than \"Just image Transformers\", or JiT, as we call it. We report competitive results using JiT with large patch sizes of 16 and 32 on ImageNet at resolutions of 256 and 512, where predicting high-dimensional noised quantities can fail catastrophically. With our networks mapping back to the basics of the manifold, our research goes back to basics and pursues a self-contained paradigm for Transformer-based diffusion on raw natural data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.12797", "title": "Genomic Next-Token Predictors are In-Context Learners", "authors": [ "Nathan Breslow", "Aayush Mishra", "Mahler Revsine", "Michael C. Schatz", "Anqi Liu", "Daniel Khashabi" ], "github": "", "project_page": "", "abstract": "In-context learning (ICL) -- the capacity of a model to infer and apply abstract patterns from examples provided within its input -- has been extensively studied in large language models trained for next-token prediction on human text. In fact, prior work often attributes this emergent behavior to distinctive statistical properties in human language. This raises a fundamental question: can ICL arise organically in other sequence domains purely through large-scale predictive training? To explore this, we turn to genomic sequences, an alternative symbolic domain rich in statistical structure. Specifically, we study the Evo2 genomic model, trained predominantly on next-nucleotide (A/T/C/G) prediction, at a scale comparable to mid-sized LLMs. We develop a controlled experimental framework comprising symbolic reasoning tasks instantiated in both linguistic and genomic forms, enabling direct comparison of ICL across genomic and linguistic models. Our results show that genomic models, like their linguistic counterparts, exhibit log-linear gains in pattern induction as the number of in-context demonstrations increases. To the best of our knowledge, this is the first evidence of organically emergent ICL in genomic sequences, supporting the hypothesis that ICL arises as a consequence of large-scale predictive modeling over rich data. These findings extend emergent meta-learning beyond language, pointing toward a unified, modality-agnostic view of in-context learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.07577", "title": "A Decentralized Retrieval Augmented Generation System with Source Reliabilities Secured on Blockchain", "authors": [ "Yining Lu", "Wenyi Tang", "Max Johnson", "Taeho Jung", "Meng Jiang" ], "github": "https://github.com/yining610/Reliable-dRAG", "project_page": "", "abstract": "Existing retrieval-augmented generation (RAG) systems typically use a centralized architecture, causing a high cost of data collection, integration, and management, as well as privacy concerns. There is a great need for a decentralized RAG system that enables foundation models to utilize information directly from data owners who maintain full control over their sources. However, decentralization brings a challenge: the numerous independent data sources vary significantly in reliability, which can diminish retrieval accuracy and response quality. To address this, our decentralized RAG system has a novel reliability scoring mechanism that dynamically evaluates each source based on the quality of responses it contributes to generate and prioritizes high-quality sources during retrieval. To ensure transparency and trust, the scoring process is securely managed through blockchain-based smart contracts, creating verifiable and tamper-proof reliability records without relying on a central authority. We evaluate our decentralized system with two Llama models (3B and 8B) in two simulated environments where six data sources have different levels of reliability. Our system achieves a +10.7\\% performance improvement over its centralized counterpart in the real world-like unreliable data environments. Notably, it approaches the upper-bound performance of centralized systems under ideally reliable data environments. The decentralized infrastructure enables secure and trustworthy scoring management, achieving approximately 56\\% marginal cost savings through batched update operations. Our code and system are open-sourced at github.com/yining610/Reliable-dRAG.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yining610/Reliable-dRAG", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.12472", "title": "Assessing LLMs for Serendipity Discovery in Knowledge Graphs: A Case for Drug Repurposing", "authors": [ "Mengying Wang", "Chenhui Ma", "Ao Jiao", "Tuo Liang", "Pengjun Lu", "Shrinidhi Hegde", "Yu Yin", "Evren Gurkan-Cavusoglu", "Yinghui Wu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have greatly advanced knowledge graph question answering (KGQA), yet existing systems are typically optimized for returning highly relevant but predictable answers. A missing yet desired capacity is to exploit LLMs to suggest surprise and novel (\"serendipitious\") answers. In this paper, we formally define the serendipity-aware KGQA task and propose the SerenQA framework to evaluate LLMs' ability to uncover unexpected insights in scientific KGQA tasks. SerenQA includes a rigorous serendipity metric based on relevance, novelty, and surprise, along with an expert-annotated benchmark derived from the Clinical Knowledge Graph, focused on drug repurposing. Additionally, it features a structured evaluation pipeline encompassing three subtasks: knowledge retrieval, subgraph reasoning, and serendipity exploration. Our experiments reveal that while state-of-the-art LLMs perform well on retrieval, they still struggle to identify genuinely surprising and valuable discoveries, underscoring a significant room for future improvements. Our curated resources and extended version are released at: https://cwru-db-group.github.io/serenQA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.13714", "title": "UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity", "authors": [ "Junwei Yu", "Trevor Darrell", "XuDong Wang" ], "github": "", "project_page": "", "abstract": "The Segment Anything Model (SAM) family has become a widely adopted vision foundation model, but its ability to control segmentation granularity remains limited. Users often need to refine results manually - by adding more prompts or selecting from pre-generated masks - to achieve the desired level of detail. This process can be ambiguous, as the same prompt may correspond to several plausible masks, and collecting dense annotations across all granularities is prohibitively expensive, making supervised solutions infeasible. To address this limitation, we introduce UnSAMv2, which enables segment anything at any granularity without human annotations. UnSAMv2 extends the divide-and-conquer strategy of UnSAM by discovering abundant mask-granularity pairs and introducing a novel granularity control embedding that enables precise, continuous control over segmentation scale. Remarkably, with only 6K unlabeled images and 0.02% additional parameters, UnSAMv2 substantially enhances SAM-2, achieving segment anything at any granularity across interactive, whole-image, and video segmentation tasks. Evaluated on over 11 benchmarks, UnSAMv2 improves NoC_{90} (5.69 rightarrow 4.75), 1-IoU (58.0 rightarrow 73.1), and AR_{1000} (49.6 rightarrow 68.3), showing that small amounts of unlabeled data with a granularity-aware self-supervised learning method can unlock the potential of vision foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.13998", "title": "LoCoBench-Agent: An Interactive Benchmark for LLM Agents in Long-Context Software Engineering", "authors": [ "Jielin Qiu", "Zuxin Liu", "Zhiwei Liu", "Rithesh Murthy", "Jianguo Zhang", "Haolin Chen", "Shiyu Wang", "Ming Zhu", "Liangwei Yang", "Juntao Tan", "Roshan Ram", "Akshara Prabhakar", "Tulika Awalgaonkar", "Zixiang Chen", "Zhepeng Cen", "Cheng Qian", "Shelby Heinecke", "Weiran Yao", "Silvio Savarese", "Caiming Xiong", "Huan Wang" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) evolve into sophisticated autonomous agents capable of complex software development tasks, evaluating their real-world capabilities becomes critical. While existing benchmarks like LoCoBench~qiu2025locobench assess long-context code understanding, they focus on single-turn evaluation and cannot capture the multi-turn interactive nature, tool usage patterns, and adaptive reasoning required by real-world coding agents. We introduce LoCoBench-Agent, a comprehensive evaluation framework specifically designed to assess LLM agents in realistic, long-context software engineering workflows. Our framework extends LoCoBench's 8,000 scenarios into interactive agent environments, enabling systematic evaluation of multi-turn conversations, tool usage efficiency, error recovery, and architectural consistency across extended development sessions. We also introduce an evaluation methodology with 9 metrics across comprehension and efficiency dimensions. Our framework provides agents with 8 specialized tools (file operations, search, code analysis) and evaluates them across context lengths ranging from 10K to 1M tokens, enabling precise assessment of long-context performance. Through systematic evaluation of state-of-the-art models, we reveal several key findings: (1) agents exhibit remarkable long-context robustness; (2) comprehension-efficiency trade-off exists with negative correlation, where thorough exploration increases comprehension but reduces efficiency; and (3) conversation efficiency varies dramatically across models, with strategic tool usage patterns differentiating high-performing agents. As the first long-context LLM agent benchmark for software engineering, LoCoBench-Agent establishes a rigorous foundation for measuring agent capabilities, identifying performance gaps, and advancing autonomous software development at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.14659", "title": "NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards", "authors": [ "Chia-Yu Hung", "Navonil Majumder", "Haoyuan Deng", "Liu Renhang", "Yankang Ang", "Amir Zadeh", "Chuan Li", "Dorien Herremans", "Ziwei Wang", "Soujanya Poria" ], "github": "", "project_page": "", "abstract": "Vision--language--action (VLA) models have recently shown promising performance on a variety of embodied tasks, yet they still fall short in reliability and generalization, especially when deployed across different embodiments or real-world environments. In this work, we introduce NORA-1.5, a VLA model built from the pre-trained NORA backbone by adding to it a flow-matching-based action expert. This architectural enhancement alone yields substantial performance gains, enabling NORA-1.5 to outperform NORA and several state-of-the-art VLA models across both simulated and real-world benchmarks. To further improve robustness and task success, we develop a set of reward models for post-training VLA policies. Our rewards combine (i) an action-conditioned world model (WM) that evaluates whether generated actions lead toward the desired goal, and (ii) a deviation-from-ground-truth heuristic that distinguishes good actions from poor ones. Using these reward signals, we construct preference datasets and adapt NORA-1.5 to target embodiments through direct preference optimization (DPO). Extensive evaluations show that reward-driven post-training consistently improves performance in both simulation and real-robot settings, demonstrating significant VLA model-reliability gains through simple yet effective reward models. Our findings highlight NORA-1.5 and reward-guided post-training as a viable path toward more dependable embodied agents suitable for real-world deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.12982", "title": "SafeGRPO: Self-Rewarded Multimodal Safety Alignment via Rule-Governed Policy Optimization", "authors": [ "Xuankun Rong", "Wenke Huang", "Tingfeng Wang", "Daiguo Zhou", "Bo Du", "Mang Ye" ], "github": "https://github.com/XuankunRong/SafeGRPO", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have demonstrated impressive reasoning and instruction-following capabilities, yet their expanded modality space introduces new compositional safety risks that emerge from complex text-image interactions. Such cross-modal couplings can produce unsafe semantics even when individual inputs are benign, exposing the fragile safety awareness of current MLLMs. While recent works enhance safety by guiding models to reason about potential risks, unregulated reasoning traces may compromise alignment; although Group Relative Policy Optimization (GRPO) offers self-rewarded refinement without human supervision, it lacks verifiable signals for reasoning safety. To address this, we propose SafeGRPO a self-rewarded multimodal safety alignment framework that integrates rule-governed reward construction into GRPO, enabling interpretable and verifiable optimization of reasoning safety. Built upon the constructed SafeTag-VL-3K dataset with explicit visual, textual, and combined safety tags, SafeGRPO performs step-guided safety thinking to enforce structured reasoning and behavior alignment, substantially improving multimodal safety awareness, compositional robustness, and reasoning stability across diverse benchmarks without sacrificing general capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/XuankunRong/SafeGRPO", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.12133", "title": "AI-Salesman: Towards Reliable Large Language Model Driven Telemarketing", "authors": [ "Qingyu Zhang", "Chunlei Xin", "Xuanang Chen", "Yaojie Lu", "Hongyu Lin", "Xianpei Han", "Le Sun", "Qing Ye", "Qianlong Xie", "Xingxing Wang" ], "github": "", "project_page": "", "abstract": "Goal-driven persuasive dialogue, exemplified by applications like telemarketing, requires sophisticated multi-turn planning and strict factual faithfulness, which remains a significant challenge for even state-of-the-art Large Language Models (LLMs). A lack of task-specific data often limits previous works, and direct LLM application suffers from strategic brittleness and factual hallucination. In this paper, we first construct and release TeleSalesCorpus, the first real-world-grounded dialogue dataset for this domain. We then propose AI-Salesman, a novel framework featuring a dual-stage architecture. For the training stage, we design a Bayesian-supervised reinforcement learning algorithm that learns robust sales strategies from noisy dialogues. For the inference stage, we introduce the Dynamic Outline-Guided Agent (DOGA), which leverages a pre-built script library to provide dynamic, turn-by-turn strategic guidance. Moreover, we design a comprehensive evaluation framework that combines fine-grained metrics for key sales skills with the LLM-as-a-Judge paradigm. Experimental results demonstrate that our proposed AI-Salesman significantly outperforms baseline models in both automatic metrics and comprehensive human evaluations, showcasing its effectiveness in complex persuasive scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-18", "arxiv_id": "2511.11510", "title": "OpenUS: A Fully Open-Source Foundation Model for Ultrasound Image Analysis via Self-Adaptive Masked Contrastive Learning", "authors": [ "Xiaoyu Zheng", "Xu Chen", "Awais Rauf", "Qifan Fu", "Benedetta Monosi", "Felice Rivellese", "Myles J. Lewis", "Shaogang Gong", "Gregory Slabaugh" ], "github": "https://github.com/XZheng0427/OpenUS", "project_page": "", "abstract": "Ultrasound (US) is one of the most widely used medical imaging modalities, thanks to its low cost, portability, real-time feedback, and absence of ionizing radiation. However, US image interpretation remains highly operator-dependent and varies significantly across anatomical regions, acquisition protocols, and device types. These variations, along with unique challenges such as speckle, low contrast, and limited standardized annotations, hinder the development of generalizable, label-efficient ultrasound AI models. In this paper, we propose OpenUS, the first reproducible, open-source ultrasound foundation model built on a large collection of public data. OpenUS employs a vision Mamba backbone, capturing both local and global long-range dependencies across the image. To extract rich features during pre-training, we introduce a novel self-adaptive masking framework that combines contrastive learning with masked image modeling. This strategy integrates the teacher's attention map with student reconstruction loss, adaptively refining clinically-relevant masking to enhance pre-training effectiveness. OpenUS also applies a dynamic learning schedule to progressively adjust the difficulty of the pre-training process. To develop the foundation model, we compile the largest to-date public ultrasound dataset comprising over 308K images from 42 publicly available datasets, covering diverse anatomical regions, institutions, imaging devices, and disease types. Our pre-trained OpenUS model can be easily adapted to specific downstream tasks by serving as a backbone for label-efficient fine-tuning. Code is available at https://github.com/XZheng0427/OpenUS.", "github_manual": "", "github_regex": "https://github.com/XZheng0427/OpenUS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.10555", "title": "A Style is Worth One Code: Unlocking Code-to-Style Image Generation with Discrete Style Space", "authors": [ "Huijie Liu", "Shuhao Cui", "Haoxiang Cao", "Shuai Ma", "Kai Wu", "Guoliang Kang" ], "github": "", "project_page": "", "abstract": "Innovative visual stylization is a cornerstone of artistic creation, yet generating novel and consistent visual styles remains a significant challenge. Existing generative approaches typically rely on lengthy textual prompts, reference images, or parameter-efficient fine-tuning to guide style-aware image generation, but often struggle with style consistency, limited creativity, and complex style representations. In this paper, we affirm that a style is worth one numerical code by introducing the novel task, code-to-style image generation, which produces images with novel, consistent visual styles conditioned solely on a numerical style code. To date, this field has only been primarily explored by the industry (e.g., Midjourney), with no open-source research from the academic community. To fill this gap, we propose CoTyle, the first open-source method for this task. Specifically, we first train a discrete style codebook from a collection of images to extract style embeddings. These embeddings serve as conditions for a text-to-image diffusion model (T2I-DM) to generate stylistic images. Subsequently, we train an autoregressive style generator on the discrete style embeddings to model their distribution, allowing the synthesis of novel style embeddings. During inference, a numerical style code is mapped to a unique style embedding by the style generator, and this embedding guides the T2I-DM to generate images in the corresponding style. Unlike existing methods, our method offers unparalleled simplicity and diversity, unlocking a vast space of reproducible styles from minimal input. Extensive experiments validate that CoTyle effectively turns a numerical code into a style controller, demonstrating a style is worth one code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.14159", "title": "MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs", "authors": [ "Huiyi Chen", "Jiawei Peng", "Dehai Min", "Changchang Sun", "Kaijie Chen", "Yan Yan", "Xu Yang", "Lu Cheng" ], "github": "https://github.com/chenyil6/MVI-Bench", "project_page": "", "abstract": "Evaluating the robustness of Large Vision-Language Models (LVLMs) is essential for their continued development and responsible deployment in real-world applications. However, existing robustness benchmarks typically focus on hallucination or misleading textual inputs, while largely overlooking the equally critical challenge posed by misleading visual inputs in assessing visual understanding. To fill this important gap, we introduce MVI-Bench, the first comprehensive benchmark specially designed for evaluating how Misleading Visual Inputs undermine the robustness of LVLMs. Grounded in fundamental visual primitives, the design of MVI-Bench centers on three hierarchical levels of misleading visual inputs: Visual Concept, Visual Attribute, and Visual Relationship. Using this taxonomy, we curate six representative categories and compile 1,248 expertly annotated VQA instances. To facilitate fine-grained robustness evaluation, we further introduce MVI-Sensitivity, a novel metric that characterizes LVLM robustness at a granular level. Empirical results across 18 state-of-the-art LVLMs uncover pronounced vulnerabilities to misleading visual inputs, and our in-depth analyses on MVI-Bench provide actionable insights that can guide the development of more reliable and robust LVLMs. The benchmark and codebase can be accessed at https://github.com/chenyil6/MVI-Bench.", "github_manual": "", "github_regex": "https://github.com/chenyil6/MVI-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.08577", "title": "Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models", "authors": [ "Tianyu Fu", "Yichen You", "Zekai Chen", "Guohao Dai", "Huazhong Yang", "Yu Wang" ], "github": "https://github.com/thu-nics/TaH", "project_page": "", "abstract": "Improving reasoning capabilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications. Prior work proposes recurrent transformers, which allocate a fixed number of extra iterations per token to improve generation quality. After the first, standard forward pass, instead of verbalization, last-layer hidden states are fed back as inputs for additional iterations to refine token predictions. Yet we identify a latent overthinking phenomenon: easy token predictions that are already correct after the first pass are sometimes revised into errors in additional iterations. To address this, we propose Think-at-Hard (TaH), a dynamic latent thinking method that iterates deeper only at hard tokens. It employs a lightweight neural decider to trigger latent iterations only at tokens that are likely incorrect after the standard forward pass. During latent iterations, Low-Rank Adaptation (LoRA) modules shift the LLM objective from general next-token prediction to focused hard-token refinement. We further introduce a duo-causal attention mechanism that extends attention from the token sequence dimension to an additional iteration depth dimension. This enables cross-iteration information flow while maintaining full sequential parallelism. Experiments show that TaH boosts LLM reasoning performance across five challenging benchmarks while maintaining the same parameter count. Compared with baselines that iterate twice for all output tokens, TaH delivers 8.1-11.3% accuracy gains while exempting 94% of tokens from the second iteration. Against strong single-iteration Qwen3 models finetuned with the same data, it also delivers 4.0-5.0% accuracy gains. When allowing less than 3% additional parameters from LoRA and the iteration decider, the gains increase to 8.5-12.6% and 5.3-5.4%, respectively. Our code is available at https://github.com/thu-nics/TaH.", "github_manual": "", "github_regex": "https://github.com/thu-nics/TaH", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.13026", "title": "REVISOR: Beyond Textual Reflection, Towards Multimodal Introspective Reasoning in Long-Form Video Understanding", "authors": [ "Jiaze Li", "Hao Yin", "Wenhui Tan", "Jingyang Chen", "Boshen Xu", "Yuxun Qu", "Yijing Chen", "Jianzhong Ju", "Zhenbo Luo", "Jian Luan" ], "github": "", "project_page": "", "abstract": "Self-reflection mechanisms that rely on purely text-based rethinking processes perform well in most multimodal tasks. However, when directly applied to long-form video understanding scenarios, they exhibit clear limitations. The fundamental reasons for this lie in two points: (1)long-form video understanding involves richer and more dynamic visual input, meaning rethinking only the text information is insufficient and necessitates a further rethinking process specifically targeting visual information; (2) purely text-based reflection mechanisms lack cross-modal interaction capabilities, preventing them from fully integrating visual information during reflection. Motivated by these insights, we propose REVISOR (REflective VIsual Segment Oriented Reasoning), a novel framework for tool-augmented multimodal reflection. REVISOR enables MLLMs to collaboratively construct introspective reflection processes across textual and visual modalities, significantly enhancing their reasoning capability for long-form video understanding. To ensure that REVISOR can learn to accurately review video segments highly relevant to the question during reinforcement learning, we designed the Dual Attribution Decoupled Reward (DADR) mechanism. Integrated into the GRPO training strategy, this mechanism enforces causal alignment between the model's reasoning and the selected video evidence. Notably, the REVISOR framework significantly enhances long-form video understanding capability of MLLMs without requiring supplementary supervised fine-tuning or external models, achieving impressive results on four benchmarks including VideoMME, LongVideoBench, MLVU, and LVBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.14366", "title": "ATLAS: A High-Difficulty, Multidisciplinary Benchmark for Frontier Scientific Reasoning", "authors": [ "Hongwei Liu", "Junnan Liu", "Shudong Liu", "Haodong Duan", "Yuqiang Li", "Mao Su", "Xiaohong Liu", "Guangtao Zhai", "Xinyu Fang", "Qianhong Ma", "Taolin Zhang", "Zihan Ma", "Yufeng Zhao", "Peiheng Zhou", "Linchen Xiao", "Wenlong Zhang", "Shijie Zhou", "Xingjian Ma", "Siqi Sun", "Jiaye Ge", "Meng Li", "Yuhong Liu", "Jianxin Dong", "Jiaying Li", "Hui Wu", "Hanwen Liang", "Jintai Lin", "Yanting Wang", "Jie Dong", "Tong Zhu", "Tianfan Fu", "Conghui He", "Qi Zhang", "Songyang Zhang", "Lei Bai", "Kai Chen" ], "github": "https://github.com/open-compass/ATLAS", "project_page": "", "abstract": "The rapid advancement of Large Language Models (LLMs) has led to performance saturation on many established benchmarks, questioning their ability to distinguish frontier models. Concurrently, existing high-difficulty benchmarks often suffer from narrow disciplinary focus, oversimplified answer formats, and vulnerability to data contamination, creating a fidelity gap with real-world scientific inquiry. To address these challenges, we introduce ATLAS (AGI-Oriented Testbed for Logical Application in Science), a large-scale, high-difficulty, and cross-disciplinary evaluation suite composed of approximately 800 original problems. Developed by domain experts (PhD-level and above), ATLAS spans seven core scientific fields: mathematics, physics, chemistry, biology, computer science, earth science, and materials science. Its key features include: (1) High Originality and Contamination Resistance, with all questions newly created or substantially adapted to prevent test data leakage; (2) Cross-Disciplinary Focus, designed to assess models' ability to integrate knowledge and reason across scientific domains; (3) High-Fidelity Answers, prioritizing complex, open-ended answers involving multi-step reasoning and LaTeX-formatted expressions over simple multiple-choice questions; and (4) Rigorous Quality Control, employing a multi-stage process of expert peer review and adversarial testing to ensure question difficulty, scientific value, and correctness. We also propose a robust evaluation paradigm using a panel of LLM judges for automated, nuanced assessment of complex answers. Preliminary results on leading models demonstrate ATLAS's effectiveness in differentiating their advanced scientific reasoning capabilities. We plan to develop ATLAS into a long-term, open, community-driven platform to provide a reliable \"ruler\" for progress toward Artificial General Intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-compass/ATLAS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.13853", "title": "Can World Simulators Reason? Gen-ViRe: A Generative Visual Reasoning Benchmark", "authors": [ "Xinxin Liu", "Zhaopan Xu", "Kai Wang", "Yong Jae Lee", "Yuzhang Shang" ], "github": "", "project_page": "", "abstract": "While Chain-of-Thought (CoT) prompting enables sophisticated symbolic reasoning in LLMs, it remains confined to discrete text and cannot simulate the continuous, physics-governed dynamics of the real world. Recent video generation models have emerged as potential world simulators through Chain-of-Frames (CoF) reasoning -- materializing thought as frame-by-frame visual sequences, with each frame representing a physically-grounded reasoning step. Despite compelling demonstrations, a challenge persists: existing benchmarks, focusing on fidelity or alignment, do not assess CoF reasoning and thus cannot measure core cognitive abilities in multi-step planning, algorithmic logic, or abstract pattern extrapolation. This evaluation void prevents systematic understanding of model capabilities and principled guidance for improvement. We introduce Gen-ViRe (Generative Visual Reasoning Benchmark), a framework grounded in cognitive science and real-world AI applications, which decomposes CoF reasoning into six cognitive dimensions -- from perceptual logic to abstract planning -- and 24 subtasks. Through multi-source data curation, minimal prompting protocols, and hybrid VLM-assisted evaluation with detailed criteria, Gen-ViRe delivers the first quantitative assessment of video models as reasoners. Our experiments on SOTA systems reveal substantial discrepancies between impressive visual quality and actual reasoning depth, establishing baselines and diagnostic tools to advance genuine world simulators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.14460", "title": "Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning", "authors": [ "Mingyue Cheng", "Jie Ouyang", "Shuo Yu", "Ruiran Yan", "Yucong Luo", "Zirui Liu", "Daoyu Wang", "Qi Liu", "Enhong Chen" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly being explored for building Agents capable of active environmental interaction (e.g., via tool use) to solve complex problems. Reinforcement Learning (RL) is considered a key technology with significant potential for training such Agents; however, the effective application of RL to LLM Agents is still in its nascent stages and faces considerable challenges. Currently, this emerging field lacks in-depth exploration into RL approaches specifically tailored for the LLM Agent context, alongside a scarcity of flexible and easily extensible training frameworks designed for this purpose. To help advance this area, this paper first revisits and clarifies Reinforcement Learning methodologies for LLM Agents by systematically extending the Markov Decision Process (MDP) framework to comprehensively define the key components of an LLM Agent. Secondly, we introduce Agent-R1, a modular, flexible, and user-friendly training framework for RL-based LLM Agents, designed for straightforward adaptation across diverse task scenarios and interactive environments. We conducted experiments on Multihop QA benchmark tasks, providing initial validation for the effectiveness of our proposed methods and framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.14210", "title": "Orion: A Unified Visual Agent for Multimodal Perception, Advanced Visual Reasoning and Execution", "authors": [ "N Dinesh Reddy", "Sudeep Pillai" ], "github": "", "project_page": "", "abstract": "We introduce Orion, a visual agent framework that can take in any modality and generate any modality. Using an agentic framework with multiple tool-calling capabilities, Orion is designed for visual AI tasks and achieves state-of-the-art results. Unlike traditional vision-language models that produce descriptive outputs, Orion orchestrates a suite of specialized computer vision tools, including object detection, keypoint localization, panoptic segmentation, Optical Character Recognition, and geometric analysis, to execute complex multi-step visual workflows. The system achieves competitive performance on MMMU, MMBench, DocVQA, and MMLongBench while extending monolithic vision-language models to production-grade visual intelligence. By combining neural perception with symbolic execution, Orion enables autonomous visual reasoning, marking a transition from passive visual understanding to active, tool-driven visual intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.14582", "title": "OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models", "authors": [ "Keda Tao", "Kele Shao", "Bohan Yu", "Weiqiang Wang", "Jian liu", "Huan Wang" ], "github": "https://github.com/KD-TAO/OmniZip", "project_page": "", "abstract": "Omnimodal large language models (OmniLLMs) have attracted increasing research attention of late towards unified audio-video understanding, wherein processing audio-video token sequences creates a significant computational bottleneck, however. Existing token compression methods have yet to accommodate this emerging need of jointly compressing multimodal tokens. To bridge this gap, we present OmniZip, a training-free, audio-guided audio-visual token-compression framework that optimizes multimodal token representation and accelerates inference. Specifically, OmniZip first identifies salient audio tokens, then computes an audio retention score for each time group to capture information density, thereby dynamically guiding video token pruning and preserving cues from audio anchors enhanced by cross-modal similarity. For each time window, OmniZip compresses the video tokens using an interleaved spatio-temporal scheme. Extensive empirical results demonstrate the merits of OmniZip - it achieves 3.42X inference speedup and 1.4X memory reduction over other top-performing counterparts, while maintaining performance with no training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KD-TAO/OmniZip", "github_api_added_by": "auto" }, { "date": "2025-11-19", "arxiv_id": "2511.14086", "title": "Error-Driven Scene Editing for 3D Grounding in Large Language Models", "authors": [ "Yue Zhang", "Zun Wang", "Han Lin", "Jialu Li", "Jianing Yang", "Yonatan Bitton", "Idan Szpektor", "Mohit Bansal" ], "github": "https://github.com/zhangyuejoslin/Deer-3D", "project_page": "", "abstract": "Despite recent progress in 3D-LLMs, they remain limited in accurately grounding language to visual and spatial elements in 3D environments. This limitation stems in part from training data that focuses on language reasoning rather than spatial understanding due to scarce 3D resources, leaving inherent grounding biases unresolved. To address this, we propose 3D scene editing as a key mechanism to generate precise visual counterfactuals that mitigate these biases through fine-grained spatial manipulation, without requiring costly scene reconstruction or large-scale 3D data collection. Furthermore, to make these edits targeted and directly address the specific weaknesses of the model, we introduce DEER-3D, an error-driven framework following a structured \"Decompose, Diagnostic Evaluation, Edit, and Re-train\" workflow, rather than broadly or randomly augmenting data as in conventional approaches. Specifically, upon identifying a grounding failure of the 3D-LLM, our framework first diagnoses the exact predicate-level error (e.g., attribute or spatial relation). It then executes minimal, predicate-aligned 3D scene edits, such as recoloring or repositioning, to produce targeted counterfactual supervision for iterative model fine-tuning, significantly enhancing grounding accuracy. We evaluate our editing pipeline across multiple benchmarks for 3D grounding and scene understanding tasks, consistently demonstrating improvements across all evaluated datasets through iterative refinement. DEER-3D underscores the effectiveness of targeted, error-driven scene editing in bridging linguistic reasoning capabilities with spatial grounding in 3D LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhangyuejoslin/Deer-3D", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.12884", "title": "Agent READMEs: An Empirical Study of Context Files for Agentic Coding", "authors": [ "Worawalan Chatlatanagulchai", "Hao Li", "Yutaro Kashiwa", "Brittany Reid", "Kundjanasith Thonglek", "Pattara Leelaprute", "Arnon Rungsawang", "Bundit Manaskasemsak", "Bram Adams", "Ahmed E. Hassan", "Hajimu Iida" ], "github": "", "project_page": "", "abstract": "Agentic coding tools receive goals written in natural language as input, break them down into specific tasks, and write or execute the actual code with minimal human intervention. Central to this process are agent context files (\"READMEs for agents\") that provide persistent, project-level instructions. In this paper, we conduct the first large-scale empirical study of 2,303 agent context files from 1,925 repositories to characterize their structure, maintenance, and content. We find that these files are not static documentation but complex, difficult-to-read artifacts that evolve like configuration code, maintained through frequent, small additions. Our content analysis of 16 instruction types shows that developers prioritize functional context, such as build and run commands (62.3%), implementation details (69.9%), and architecture (67.7%). We also identify a significant gap: non-functional requirements like security (14.5%) and performance (14.5%) are rarely specified. These findings indicate that while developers use context files to make agents functional, they provide few guardrails to ensure that agent-written code is secure or performant, highlighting the need for improved tooling and practices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.14295", "title": "AraLingBench A Human-Annotated Benchmark for Evaluating Arabic Linguistic Capabilities of Large Language Models", "authors": [ "Mohammad Zbib", "Hasan Abed Al Kader Hammoud", "Sina Mukalled", "Nadine Rizk", "Fatima Karnib", "Issam Lakkis", "Ammar Mohanna", "Bernard Ghanem" ], "github": "https://github.com/hammoudhasan/AraLingBench", "project_page": "", "abstract": "We present AraLingBench: a fully human annotated benchmark for evaluating the Arabic linguistic competence of large language models (LLMs). The benchmark spans five core categories: grammar, morphology, spelling, reading comprehension, and syntax, through 150 expert-designed multiple choice questions that directly assess structural language understanding. Evaluating 35 Arabic and bilingual LLMs reveals that current models demonstrate strong surface level proficiency but struggle with deeper grammatical and syntactic reasoning. AraLingBench highlights a persistent gap between high scores on knowledge-based benchmarks and true linguistic mastery, showing that many models succeed through memorization or pattern recognition rather than authentic comprehension. By isolating and measuring fundamental linguistic skills, AraLingBench provides a diagnostic framework for developing Arabic LLMs. The full evaluation code is publicly available on GitHub.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hammoudhasan/AraLingBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.14385", "title": "Mitigating Label Length Bias in Large Language Models", "authors": [ "Mario Sanz-Guerrero", "Katharina von der Wense" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are powerful zero- and few-shot learners. However, when predicting over a set of candidate options, LLMs suffer from label biases, and existing calibration methods overlook biases arising from multi-token class labels. We tackle an issue we call label length bias, where labels of different lengths are treated inconsistently, even after standard length normalization. To mitigate it, we propose normalized contextual calibration (NCC), an effective method that normalizes and calibrates predictions at the full-label level. NCC achieves statistically significant improvements over prior approaches across multiple datasets and models, with gains of up to 10% F1. Moreover, NCC extends bias mitigation to broader tasks such as multiple-choice question answering. Our analysis shows that, when combined with in-context learning, NCC is less sensitive to few-shot example selection, requires fewer examples for competitive performance, and produces more reliable confidence estimates. These findings highlight the importance of mitigating full-label biases to improve the performance and robustness of LLM-based methods, particularly in real-world applications where class labels naturally consist of multiple tokens.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.13189", "title": "Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework", "authors": [ "Diego Ortego", "Marlon Rodr\u00edguez", "Mario Almagro", "Kunal Dahiya", "David Jim\u00e9nez", "Juan C. SanMiguel" ], "github": "https://github.com/DiegoOrtego/vixml", "project_page": "", "abstract": "Foundation models have revolutionized artificial intelligence across numerous domains, yet their transformative potential remains largely untapped in Extreme Multi-label Classification (XMC). Queries in XMC are associated with relevant labels from extremely large label spaces, where it is critical to strike a balance between efficiency and performance. Therefore, many recent approaches efficiently pose XMC as a maximum inner product search between embeddings learned from small encoder-only transformer architectures. In this paper, we address two important aspects in XMC: how to effectively harness larger decoder-only models, and how to exploit visual information while maintaining computational efficiency. We demonstrate that both play a critical role in XMC separately and can be combined for improved performance. We show that a few billion-size decoder can deliver substantial improvements while keeping computational overhead manageable. Furthermore, our Vision-enhanced eXtreme Multi-label Learning framework (ViXML) efficiently integrates foundation vision models by pooling a single embedding per image. This limits computational growth while unlocking multi-modal capabilities. Remarkably, ViXML with small encoders outperforms text-only decoder in most cases, showing that an image is worth billions of parameters. Finally, we present an extension of existing text-only datasets to exploit visual metadata and make them available for future benchmarking. Comprehensive experiments across four public text-only datasets and their corresponding image enhanced versions validate our proposals' effectiveness, surpassing previous state-of-the-art by up to +8.21\\% in P@1 on the largest dataset. ViXML's code is available at https://github.com/DiegoOrtego/vixml.", "github_manual": "", "github_regex": "https://github.com/DiegoOrtego/vixml", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.11270", "title": "\u03a6eat: Physically-Grounded Feature Representation", "authors": [ "Giuseppe Vecchio", "Adrien Kaiser", "Rouffet Romain", "Rosalie Martin", "Elena Garces", "Tamy Boubekeur" ], "github": "", "project_page": "", "abstract": "Foundation models have emerged as effective backbones for many vision tasks. However, current self-supervised features entangle high-level semantics with low-level physical factors, such as geometry and illumination, hindering their use in tasks requiring explicit physical reasoning. In this paper, we introduce \u03a6eat, a novel physically-grounded visual backbone that encourages a representation sensitive to material identity, including reflectance cues and geometric mesostructure. Our key idea is to employ a pretraining strategy that contrasts spatial crops and physical augmentations of the same material under varying shapes and lighting conditions. While similar data have been used in high-end supervised tasks such as intrinsic decomposition or material estimation, we demonstrate that a pure self-supervised training strategy, without explicit labels, already provides a strong prior for tasks requiring robust features invariant to external physical factors. We evaluate the learned representations through feature similarity analysis and material selection, showing that \u03a6eat captures physically-grounded structure beyond semantic grouping. These findings highlight the promise of unsupervised physical feature learning as a foundation for physics-aware perception in vision and graphics. These findings highlight the promise of unsupervised physical feature learning as a foundation for physics-aware perception in vision and graphics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.13954", "title": "A Brain Wave Encodes a Thousand Tokens: Modeling Inter-Cortical Neural Interactions for Effective EEG-based Emotion Recognition", "authors": [ "Nilay Kumar", "Priyansh Bhandari", "G. Maragatham" ], "github": "https://github.com/nnilayy/RBTransformer", "project_page": "", "abstract": "Human emotions are difficult to convey through words and are often abstracted in the process; however, electroencephalogram (EEG) signals can offer a more direct lens into emotional brain activity. Recent studies show that deep learning models can process these signals to perform emotion recognition with high accuracy. However, many existing approaches overlook the dynamic interplay between distinct brain regions, which can be crucial to understanding how emotions unfold and evolve over time, potentially aiding in more accurate emotion recognition. To address this, we propose RBTransformer, a Transformer-based neural network architecture that models inter-cortical neural dynamics of the brain in latent space to better capture structured neural interactions for effective EEG-based emotion recognition. First, the EEG signals are converted into Band Differential Entropy (BDE) tokens, which are then passed through Electrode Identity embeddings to retain spatial provenance. These tokens are processed through successive inter-cortical multi-head attention blocks that construct an electrode x electrode attention matrix, allowing the model to learn the inter-cortical neural dependencies. The resulting features are then passed through a classification head to obtain the final prediction. We conducted extensive experiments, specifically under subject-dependent settings, on the SEED, DEAP, and DREAMER datasets, over all three dimensions, Valence, Arousal, and Dominance (for DEAP and DREAMER), under both binary and multi-class classification settings. The results demonstrate that the proposed RBTransformer outperforms all previous state-of-the-art methods across all three datasets, over all three dimensions under both classification settings. The source code is available at: https://github.com/nnilayy/RBTransformer.", "github_manual": "", "github_regex": "https://github.com/nnilayy/RBTransformer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.11473", "title": "Proactive Hearing Assistants that Isolate Egocentric Conversations", "authors": [ "Guilin Hu", "Malek Itani", "Tuochao Chen", "Shyamnath Gollakota" ], "github": "", "project_page": "", "abstract": "We introduce proactive hearing assistants that automatically identify and separate the wearer's conversation partners, without requiring explicit prompts. Our system operates on egocentric binaural audio and uses the wearer's self-speech as an anchor, leveraging turn-taking behavior and dialogue dynamics to infer conversational partners and suppress others. To enable real-time, on-device operation, we propose a dual-model architecture: a lightweight streaming model runs every 12.5 ms for low-latency extraction of the conversation partners, while a slower model runs less frequently to capture longer-range conversational dynamics. Results on real-world 2- and 3-speaker conversation test sets, collected with binaural egocentric hardware from 11 participants totaling 6.8 hours, show generalization in identifying and isolating conversational partners in multi-conversation settings. Our work marks a step toward hearing assistants that adapt proactively to conversational dynamics and engagement. More information can be found on our website: https://proactivehearing.cs.washington.edu/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.11831", "title": "TopoPerception: A Shortcut-Free Evaluation of Global Visual Perception in Large Vision-Language Models", "authors": [ "Wenhao Zhou", "Hao Zheng", "Rong Zhao" ], "github": "https://github.com/Wenhao-Zhou/TopoPerception", "project_page": "", "abstract": "Large Vision-Language Models (LVLMs) typically align visual features from an encoder with a pre-trained Large Language Model (LLM). However, this makes the visual perception module a bottleneck, which constrains the overall capabilities of LVLMs. Conventional evaluation benchmarks, while rich in visual semantics, often contain unavoidable local shortcuts that can lead to an overestimation of models' perceptual abilities. Here, we introduce TopoPerception, a benchmark that leverages topological properties to rigorously evaluate the global visual perception capabilities of LVLMs across various granularities. Since topology depends on the global structure of an image and is invariant to local features, TopoPerception enables a shortcut-free assessment of global perception, fundamentally distinguishing it from semantically rich tasks. We evaluate state-of-the-art models on TopoPerception and find that even at the coarsest perceptual granularity, all models perform no better than random chance, indicating a profound inability to perceive global visual features. Notably, a consistent trend emerge within model families: more powerful models with stronger reasoning capabilities exhibit lower accuracy. This suggests that merely scaling up models is insufficient to address this deficit and may even exacerbate it. Progress may require new training paradigms or architectures. TopoPerception not only exposes a critical bottleneck in current LVLMs but also offers a lens and direction for improving their global visual perception. The data and code are publicly available at: https://github.com/Wenhao-Zhou/TopoPerception.", "github_manual": "", "github_regex": "https://github.com/Wenhao-Zhou/TopoPerception", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.07865", "title": "LLM-Powered Fully Automated Chaos Engineering: Towards Enabling Anyone to Build Resilient Software Systems at Low Cost", "authors": [ "Daisuke Kikuta", "Hiroki Ikeuchi", "Kengo Tajiri" ], "github": "https://github.com/ntt-dkiku/chaos-eater", "project_page": "", "abstract": "Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such experiments and improving the system based on the experimental results still remain manual. These processes are labor-intensive and require multi-domain expertise. To address these challenges and enable anyone to build resilient systems at low cost, this paper proposes ChaosEater, a system that automates the entire CE cycle with Large Language Models (LLMs). It predefines an agentic workflow according to a systematic CE cycle and assigns subdivided processes within the workflow to LLMs. ChaosEater targets CE for software systems built on Kubernetes. Therefore, the LLMs in ChaosEater complete CE cycles through software engineering tasks, including requirement definition, code generation, testing, and debugging. We evaluate ChaosEater through case studies on small- and large-scale Kubernetes systems. The results demonstrate that it consistently completes reasonable CE cycles with significantly low time and monetary costs. Its cycles are also qualitatively validated by human engineers and LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ntt-dkiku/chaos-eater", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-19", "arxiv_id": "2511.11113", "title": "VIDEOP2R: Video Understanding from Perception to Reasoning", "authors": [ "Yifan Jiang", "Yueying Wang", "Rui Zhao", "Toufiq Parag", "Zhimin Chen", "Zhenyu Liao", "Jayakrishnan Unnikrishnan" ], "github": "", "project_page": "", "abstract": "Reinforcement fine-tuning (RFT), a two-stage framework consisting of supervised fine-tuning (SFT) and reinforcement learning (RL) has shown promising results on improving reasoning ability of large language models (LLMs). Yet extending RFT to large video language models (LVLMs) remains challenging. We propose VideoP2R, a novel process-aware video RFT framework that enhances video reasoning by modeling perception and reasoning as distinct processes. In the SFT stage, we develop a three-step pipeline to generate VideoP2R-CoT-162K, a high-quality, process-aware chain-of-thought (CoT) dataset for perception and reasoning. In the RL stage, we introduce a novel process-aware group relative policy optimization (PA-GRPO) algorithm that supplies separate rewards for perception and reasoning. Extensive experiments show that VideoP2R achieves state-of-the-art (SotA) performance on six out of seven video reasoning and understanding benchmarks. Ablation studies further confirm the effectiveness of our process-aware modeling and PA-GRPO and demonstrate that model's perception output is information-sufficient for downstream reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-20", "arxiv_id": "2511.15065", "title": "Reasoning via Video: The First Evaluation of Video Models' Reasoning Abilities through Maze-Solving Tasks", "authors": [ "Cheng Yang", "Haiyuan Wan", "Yiran Peng", "Xin Cheng", "Zhaoyang Yu", "Jiayi Zhang", "Junchi Yu", "Xinlei Yu", "Xiawu Zheng", "Dongzhan Zhou", "Chenglin Wu" ], "github": "", "project_page": "", "abstract": "Video Models have achieved remarkable success in high-fidelity video generation with coherent motion dynamics. Analogous to the development from text generation to text-based reasoning in language modeling, the development of video models motivates us to ask: Can video models reason via video generation? Compared with the discrete text corpus, video grounds reasoning in explicit spatial layouts and temporal continuity, which serves as an ideal substrate for spatial reasoning. In this work, we explore the reasoning via video paradigm and introduce VR-Bench -- a comprehensive benchmark designed to systematically evaluate video models' reasoning capabilities. Grounded in maze-solving tasks that inherently require spatial planning and multi-step reasoning, VR-Bench contains 7,920 procedurally generated videos across five maze types and diverse visual styles. Our empirical analysis demonstrates that SFT can efficiently elicit the reasoning ability of video model. Video models exhibit stronger spatial perception during reasoning, outperforming leading VLMs and generalizing well across diverse scenarios, tasks, and levels of complexity. We further discover a test-time scaling effect, where diverse sampling during inference improves reasoning reliability by 10--20%. These findings highlight the unique potential and scalability of reasoning via video for spatial reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-20", "arxiv_id": "2511.15186", "title": "Instruction-Guided Lesion Segmentation for Chest X-rays with Automatically Generated Large-Scale Dataset", "authors": [ "Geon Choi", "Hangyul Yoon", "Hyunju Shin", "Hyunki Park", "Sang Hoon Seo", "Eunho Yang", "Edward Choi" ], "github": "https://github.com/checkoneee/ROSALIA", "project_page": "", "abstract": "The applicability of current lesion segmentation models for chest X-rays (CXRs) has been limited both by a small number of target labels and the reliance on long, detailed expert-level text inputs, creating a barrier to practical use. To address these limitations, we introduce a new paradigm: instruction-guided lesion segmentation (ILS), which is designed to segment diverse lesion types based on simple, user-friendly instructions. Under this paradigm, we construct MIMIC-ILS, the first large-scale instruction-answer dataset for CXR lesion segmentation, using our fully automated multimodal pipeline that generates annotations from chest X-ray images and their corresponding reports. MIMIC-ILS contains 1.1M instruction-answer pairs derived from 192K images and 91K unique segmentation masks, covering seven major lesion types. To empirically demonstrate its utility, we introduce ROSALIA, a vision-language model fine-tuned on MIMIC-ILS. ROSALIA can segment diverse lesions and provide textual explanations in response to user instructions. The model achieves high segmentation and textual accuracy in our newly proposed task, highlighting the effectiveness of our pipeline and the value of MIMIC-ILS as a foundational resource for pixel-level CXR lesion grounding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/checkoneee/ROSALIA", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-20", "arxiv_id": "2511.15586", "title": "MHR: Momentum Human Rig", "authors": [ "Aaron Ferguson", "Ahmed A. A. Osman", "Berta Bescos", "Carsten Stoll", "Chris Twigg", "Christoph Lassner", "David Otte", "Eric Vignola", "Federica Bogo", "Igor Santesteban", "Javier Romero", "Jenna Zarate", "Jeongseok Lee", "Jinhyung Park", "Jinlong Yang", "John Doublestein", "Kishore Venkateshan", "Kris Kitani", "Ladislav Kavan", "Marco Dal Farra", "Matthew Hu", "Matthew Cioffi", "Michael Fabris", "Michael Ranieri", "Mohammad Modarres", "Petr Kadlecek", "Rinat Abdrashitov", "Romain Pr\u00e9vost", "Roman Rajbhandari", "Ronald Mallet", "Russel Pearsall", "Sandy Kao", "Sanjeev Kumar", "Scott Parrish", "Te-Li Wang", "Tony Tung", "Yuan Dong", "Yuhua Chen", "Yuanlu Xu", "Yuting Ye", "Zhongshi Jiang" ], "github": "", "project_page": "", "abstract": "We present MHR, a parametric human body model that combines the decoupled skeleton/shape paradigm of ATLAS with a flexible, modern rig and pose corrective system inspired by the Momentum library. Our model enables expressive, anatomically plausible human animation, supporting non-linear pose correctives, and is designed for robust integration in AR/VR and graphics pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-20", "arxiv_id": "2511.13524", "title": "FreeAskWorld: An Interactive and Closed-Loop Simulator for Human-Centric Embodied AI", "authors": [ "Yuhang Peng", "Yizhou Pan", "Xinning He", "Jihaoyu Yang", "Xinyu Yin", "Han Wang", "Xiaoji Zheng", "Chao Gao", "Jiangtao Gong" ], "github": "", "project_page": "", "abstract": "As embodied intelligence emerges as a core frontier in artificial intelligence research, simulation platforms must evolve beyond low-level physical interactions to capture complex, human-centered social behaviors. We introduce FreeAskWorld, an interactive simulation framework that integrates large language models (LLMs) for high-level behavior planning and semantically grounded interaction, informed by theories of intention and social cognition. Our framework supports scalable, realistic human-agent simulations and includes a modular data generation pipeline tailored for diverse embodied tasks.To validate the framework, we extend the classic Vision-and-Language Navigation (VLN) task into a interaction enriched Direction Inquiry setting, wherein agents can actively seek and interpret navigational guidance. We present and publicly release FreeAskWorld, a large-scale benchmark dataset comprising reconstructed environments, six diverse task types, 16 core object categories, 63,429 annotated sample frames, and more than 17 hours of interaction data to support training and evaluation of embodied AI systems. We benchmark VLN models, and human participants under both open-loop and closed-loop settings. Experimental results demonstrate that models fine-tuned on FreeAskWorld outperform their original counterparts, achieving enhanced semantic understanding and interaction competency. These findings underscore the efficacy of socially grounded simulation frameworks in advancing embodied AI systems toward sophisticated high-level planning and more naturalistic human-agent interaction. Importantly, our work underscores that interaction itself serves as an additional information modality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-20", "arxiv_id": "2511.14993", "title": "Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation", "authors": [ "Vladimir Arkhipkin", "Vladimir Korviakov", "Nikolai Gerasimenko", "Denis Parkhomenko", "Viacheslav Vasilev", "Alexey Letunovskiy", "Maria Kovaleva", "Nikolai Vaulin", "Ivan Kirillov", "Lev Novitskiy", "Denis Koposov", "Nikita Kiselev", "Alexander Varlamov", "Dmitrii Mikhailov", "Vladimir Polovnikov", "Andrey Shutkin", "Ilya Vasiliev", "Julia Agafonova", "Anastasiia Kargapoltseva", "Anna Dmitrienko", "Anastasia Maltseva", "Anna Averchenkova", "Olga Kim", "Tatiana Nikulina", "Denis Dimitrov" ], "github": "https://github.com/ai-forever/KandinskyVideo", "project_page": "", "abstract": "This report introduces Kandinsky 5.0, a family of state-of-the-art foundation models for high-resolution image and 10-second video synthesis. The framework comprises three core line-up of models: Kandinsky 5.0 Image Lite - a line-up of 6B parameter image generation models, Kandinsky 5.0 Video Lite - a fast and lightweight 2B parameter text-to-video and image-to-video models, and Kandinsky 5.0 Video Pro - 19B parameter models that achieves superior video generation quality. We provide a comprehensive review of the data curation lifecycle - including collection, processing, filtering and clustering - for the multi-stage training pipeline that involves extensive pre-training and incorporates quality-enhancement techniques such as self-supervised fine-tuning (SFT) and reinforcement learning (RL)-based post-training. We also present novel architectural, training, and inference optimizations that enable Kandinsky 5.0 to achieve high generation speeds and state-of-the-art performance across various tasks, as demonstrated by human evaluation. As a large-scale, publicly available generative framework, Kandinsky 5.0 leverages the full potential of its pre-training and subsequent stages to be adapted for a wide range of generative applications. We hope that this report, together with the release of our open-source code and training checkpoints, will substantially advance the development and accessibility of high-quality generative models for the research community.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ai-forever/KandinskyVideo", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-20", "arxiv_id": "2511.15661", "title": "VisPlay: Self-Evolving Vision-Language Models from Images", "authors": [ "Yicheng He", "Chengsong Huang", "Zongxia Li", "Jiaxin Huang", "Yonghui Yang" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) provides a principled framework for improving Vision-Language Models (VLMs) on complex reasoning tasks. However, existing RL approaches often rely on human-annotated labels or task-specific heuristics to define verifiable rewards, both of which are costly and difficult to scale. We introduce VisPlay, a self-evolving RL framework that enables VLMs to autonomously improve their reasoning abilities using large amounts of unlabeled image data. Starting from a single base VLM, VisPlay assigns the model into two interacting roles: an Image-Conditioned Questioner that formulates challenging yet answerable visual questions, and a Multimodal Reasoner that generates silver responses. These roles are jointly trained with Group Relative Policy Optimization (GRPO), which incorporates diversity and difficulty rewards to balance the complexity of generated questions with the quality of the silver answers. VisPlay scales efficiently across two model families. When trained on Qwen2.5-VL and MiMo-VL, VisPlay achieves consistent improvements in visual reasoning, compositional generalization, and hallucination reduction across eight benchmarks, including MM-Vet and MMMU, demonstrating a scalable path toward self-evolving multimodal intelligence. The project page is available at https://bruno686.github.io/VisPlay/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-20", "arxiv_id": "2511.14349", "title": "ARC-Chapter: Structuring Hour-Long Videos into Navigable Chapters and Hierarchical Summaries", "authors": [ "Junfu Pu", "Teng Wang", "Yixiao Ge", "Yuying Ge", "Chen Li", "Ying Shan" ], "github": "", "project_page": "", "abstract": "The proliferation of hour-long videos (e.g., lectures, podcasts, documentaries) has intensified demand for efficient content structuring. However, existing approaches are constrained by small-scale training with annotations that are typical short and coarse, restricting generalization to nuanced transitions in long videos. We introduce ARC-Chapter, the first large-scale video chaptering model trained on over million-level long video chapters, featuring bilingual, temporally grounded, and hierarchical chapter annotations. To achieve this goal, we curated a bilingual English-Chinese chapter dataset via a structured pipeline that unifies ASR transcripts, scene texts, visual captions into multi-level annotations, from short title to long summaries. We demonstrate clear performance improvements with data scaling, both in data volume and label intensity. Moreover, we design a new evaluation metric termed GRACE, which incorporates many-to-one segment overlaps and semantic similarity, better reflecting real-world chaptering flexibility. Extensive experiments demonstrate that ARC-Chapter establishes a new state-of-the-art by a significant margin, outperforming the previous best by 14.0% in F1 score and 11.3% in SODA score. Moreover, ARC-Chapter shows excellent transferability, improving the state-of-the-art on downstream tasks like dense video captioning on YouCook2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-20", "arxiv_id": "2511.15038", "title": "Aligning Generative Music AI with Human Preferences: Methods and Challenges", "authors": [ "Dorien Herremans", "Abhinaba Roy" ], "github": "", "project_page": "", "abstract": "Recent advances in generative AI for music have achieved remarkable fidelity and stylistic diversity, yet these systems often fail to align with nuanced human preferences due to the specific loss functions they use. This paper advocates for the systematic application of preference alignment techniques to music generation, addressing the fundamental gap between computational optimization and human musical appreciation. Drawing on recent breakthroughs including MusicRL's large-scale preference learning, multi-preference alignment frameworks like diffusion-based preference optimization in DiffRhythm+, and inference-time optimization techniques like Text2midi-InferAlign, we discuss how these techniques can address music's unique challenges: temporal coherence, harmonic consistency, and subjective quality assessment. We identify key research challenges including scalability to long-form compositions, reliability amongst others in preference modelling. Looking forward, we envision preference-aligned music generation enabling transformative applications in interactive composition tools and personalized music services. This work calls for sustained interdisciplinary research combining advances in machine learning, music-theory to create music AI systems that truly serve human creative and experiential needs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-20", "arxiv_id": "2511.13001", "title": "Medal S: Spatio-Textual Prompt Model for Medical Segmentation", "authors": [ "Pengcheng Shi", "Jiawei Chen", "Jiaqi Liu", "Xinglin Zhang", "Tao Chen", "Lei Li" ], "github": "https://github.com/yinghemedical/Medal-S", "project_page": "", "abstract": "We introduce Medal S, a medical segmentation foundation model that supports native-resolution spatial and textual prompts within an end-to-end trainable framework. Unlike text-only methods lacking spatial awareness, Medal S achieves channel-wise alignment between volumetric prompts and text embeddings, mitigating inaccuracies from resolution mismatches. By preserving full 3D context, it efficiently processes multiple native-resolution masks in parallel, enhancing multi-class segmentation performance. A lightweight 3D convolutional module enables precise voxel-space refinement guided by both prompt types, supporting up to 243 classes across CT, MRI, PET, ultrasound, and microscopy modalities in the BiomedSegFM dataset. Medal S offers two prompting modes: a text-only mode, where model predictions serve as spatial prompts for self-refinement without human input, and a hybrid mode, incorporating manual annotations for enhanced flexibility. For 24-class segmentation, parallel spatial prompting reduces inference time by more than 90% compared to sequential prompting. We propose dynamic resampling to address target-patch ratio imbalance, extending SAT and nnU-Net for data augmentation. Furthermore, we develop optimized text preprocessing, a two-stage inference strategy, and post-processing techniques to improve memory efficiency, precision, and inference speed. On the five-modality average on the validation set, Medal S outperforms SAT with a DSC of 75.44 (vs. 69.83), NSD of 77.34 (vs. 71.06), F1 of 38.24 (vs. 24.88), and DSC TP of 65.46 (vs. 46.97). Medal S achieves excellent performance by harmonizing spatial precision with semantic textual guidance, demonstrating superior efficiency and accuracy in multi-class medical segmentation tasks compared to sequential prompt-based approaches. Medal S will be publicly available at https://github.com/yinghemedical/Medal-S.", "github_manual": "", "github_regex": "https://github.com/yinghemedical/Medal-S", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-20", "arxiv_id": "2511.12207", "title": "Mixture of States: Routing Token-Level Dynamics for Multimodal Generation", "authors": [ "Haozhe Liu", "Ding Liu", "Mingchen Zhuge", "Zijian Zhou", "Tian Xie", "Sen He", "Yukang Yang", "Shuming Liu", "Yuren Cong", "Jiadong Guo", "Hongyu Xu", "Ke Xu", "Kam-Woh Ng", "Juan C. P\u00e9rez", "Juan-Manuel~P\u00e9rez-R\u00faa", "Tao Xiang", "Wei Liu", "Shikun Liu", "J\u00fcrgen Schmidhuber" ], "github": "", "project_page": "", "abstract": "We introduce MoS (Mixture of States), a novel fusion paradigm for multimodal diffusion models that merges modalities using flexible, state-based interactions. The core of MoS is a learnable, token-wise router that creates denoising timestep- and input-dependent interactions between modalities' hidden states, precisely aligning token-level features with the diffusion trajectory. This router sparsely selects the top-k hidden states and is trained with an \u03b5-greedy strategy, efficiently selecting contextual features with minimal learnable parameters and negligible computational overhead. We validate our design with text-to-image generation (MoS-Image) and editing (MoS-Editing), which achieve state-of-the-art results. With only 3B to 5B parameters, our models match or surpass counterparts up to 4times larger. These findings establish MoS as a flexible and compute-efficient paradigm for scaling multimodal diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-20", "arxiv_id": "2511.15593", "title": "What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity", "authors": [ "Alexis Audran-Reiss", "Jordi Armengol Estap\u00e9", "Karen Hambardzumyan", "Amar Budhiraja", "Martin Josifoski", "Edan Toledo", "Rishi Hazra", "Despoina Magka", "Michael Shvartsman", "Parth Pathak", "Justine T Kao", "Lucia Cipolina-Kun", "Bhavul Gauri", "Jean-Christophe Gagnon-Audet", "Emanuel Tewolde", "Jenny Zhang", "Taco Cohen", "Yossi Adi", "Tatiana Shavrina", "Yoram Bachrach" ], "github": "", "project_page": "", "abstract": "AI research agents offer the promise to accelerate scientific progress by automating the design, implementation, and training of machine learning models. However, the field is still in its infancy, and the key factors driving the success or failure of agent trajectories are not fully understood. We examine the role that ideation diversity plays in agent performance. First, we analyse agent trajectories on MLE-bench, a well-known benchmark to evaluate AI research agents, across different models and agent scaffolds. Our analysis reveals that different models and agent scaffolds yield varying degrees of ideation diversity, and that higher-performing agents tend to have increased ideation diversity. Further, we run a controlled experiment where we modify the degree of ideation diversity, demonstrating that higher ideation diversity results in stronger performance. Finally, we strengthen our results by examining additional evaluation metrics beyond the standard medal-based scoring of MLE-bench, showing that our findings still hold across other agent performance metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-20", "arxiv_id": "2511.15706", "title": "RoMa v2: Harder Better Faster Denser Feature Matching", "authors": [ "Johan Edstedt", "David Nordstr\u00f6m", "Yushan Zhang", "Georg B\u00f6kman", "Jonathan Astermark", "Viktor Larsson", "Anders Heyden", "Fredrik Kahl", "M\u00e5rten Wadenb\u00e4ck", "Michael Felsberg" ], "github": "https://github.com/Parskatt/romav2", "project_page": "", "abstract": "Dense feature matching aims to estimate all correspondences between two images of a 3D scene and has recently been established as the gold-standard due to its high accuracy and robustness. However, existing dense matchers still fail or perform poorly for many hard real-world scenarios, and high-precision models are often slow, limiting their applicability. In this paper, we attack these weaknesses on a wide front through a series of systematic improvements that together yield a significantly better model. In particular, we construct a novel matching architecture and loss, which, combined with a curated diverse training distribution, enables our model to solve many complex matching tasks. We further make training faster through a decoupled two-stage matching-then-refinement pipeline, and at the same time, significantly reduce refinement memory usage through a custom CUDA kernel. Finally, we leverage the recent DINOv3 foundation model along with multiple other insights to make the model more robust and unbiased. In our extensive set of experiments we show that the resulting novel matcher sets a new state-of-the-art, being significantly more accurate than its predecessors. Code is available at https://github.com/Parskatt/romav2", "github_manual": "", "github_regex": "https://github.com/Parskatt/romav2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16668", "title": "V-ReasonBench: Toward Unified Reasoning Benchmark Suite for Video Generation Models", "authors": [ "Yang Luo", "Xuanlei Zhao", "Baijiong Lin", "Lingting Zhu", "Liyao Tang", "Yuqi Liu", "Ying-Cong Chen", "Shengju Qian", "Xin Wang", "Yang You" ], "github": "", "project_page": "", "abstract": "Recent progress in generative video models, such as Veo-3, has shown surprising zero-shot reasoning abilities, creating a growing need for systematic and reliable evaluation. We introduce V-ReasonBench, a benchmark designed to assess video reasoning across four key dimensions: structured problem-solving, spatial cognition, pattern-based inference, and physical dynamics. The benchmark is built from both synthetic and real-world image sequences and provides a diverse set of answer-verifiable tasks that are reproducible, scalable, and unambiguous. Evaluations of six state-of-the-art video models reveal clear dimension-wise differences, with strong variation in structured, spatial, pattern-based, and physical reasoning. We further compare video models with strong image models, analyze common hallucination behaviors, and study how video duration affects Chain-of-Frames reasoning. Overall, V-ReasonBench offers a unified and reproducible framework for measuring video reasoning and aims to support the development of models with more reliable, human-aligned reasoning skills.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16669", "title": "Video-as-Answer: Predict and Generate Next Video Event with Joint-GRPO", "authors": [ "Junhao Cheng", "Liang Hou", "Xin Tao", "Jing Liao" ], "github": "https://github.com/KlingTeam/VANS", "project_page": "", "abstract": "While language models have become impactful in many real-world applications, video generation remains largely confined to entertainment. Motivated by video's inherent capacity to demonstrate physical-world information that is difficult to convey through language alone (e.g., imagine teaching someone to tie a tie using only text), we identify an underutilized opportunity to extend video as a new answer modality for Next-Event Prediction (NEP), formalized as Video-Next-Event Prediction (VNEP). While the established NEP task takes a video with a procedural or predictive question as input to predict the next event in text, VNEP requires dynamic video responses. This shift from telling to showing unlocks more intuitive and customized answers for procedural learning and creative exploration. However, this task remains challenging for existing models, as it demands an understanding of multimodal input, instruction-conditioned reasoning, and the generation of video with visual and semantic consistency. To address this, we introduce VANS, a model that leverages reinforcement learning to align a Vision-Language Model (VLM) with a Video Diffusion Model (VDM) for VNEP. The core of VANS is our proposed Joint-GRPO that orchestrates the VLM and VDM to function as a unit. Driven by a shared reward on their respective output, it optimizes the VLM to produce captions that are both accurate and friendly to visualize, while guiding the VDM to generate videos that are faithful to these captions and the input visual context. To enable this learning, we craft VANS-Data-100K, a dedicated dataset for the VNEP task. Experiments on procedural and predictive benchmarks demonstrate that VANS achieves state-of-the-art performance in both video event prediction and visualization. Codes are released in https://github.com/KlingTeam/VANS.", "github_manual": "", "github_regex": "https://github.com/KlingTeam/VANS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.13719", "title": "Scaling Spatial Intelligence with Multimodal Foundation Models", "authors": [ "Zhongang Cai", "Ruisi Wang", "Chenyang Gu", "Fanyi Pu", "Junxiang Xu", "Yubo Wang", "Wanqi Yin", "Zhitao Yang", "Chen Wei", "Qingping Sun", "Tongxi Zhou", "Jiaqi Li", "Hui En Pang", "Oscar Qian", "Yukun Wei", "Zhiqian Lin", "Xuanke Shi", "Kewang Deng", "Xiaoyang Han", "Zukai Chen", "Xiangyu Fan", "Hanming Deng", "Lewei Lu", "Liang Pan", "Bo Li", "Ziwei Liu", "Quan Wang", "Dahua Lin", "Lei Yang" ], "github": "", "project_page": "", "abstract": "Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation models to cultivate spatial intelligence within the SenseNova-SI family, built upon established multimodal foundations including visual understanding models (i.e., Qwen3-VL and InternVL3) and unified understanding and generation models (i.e., Bagel). We take a principled approach to constructing high-performing and robust spatial intelligence by systematically curating SenseNova-SI-8M: eight million diverse data samples under a rigorous taxonomy of spatial capabilities. SenseNova-SI demonstrates unprecedented performance across a broad range of spatial intelligence benchmarks: 68.7% on VSI-Bench, 43.3% on MMSI, 85.6% on MindCube, 54.6% on ViewSpatial, and 50.1% on SITE, while maintaining strong general multimodal understanding (e.g., 84.9% on MMBench-En). More importantly, we analyze the impact of data scaling, discuss early signs of emergent generalization capabilities enabled by diverse data training, analyze the risk of overfitting and language shortcuts, present a preliminary study on spatial chain-of-thought reasoning, and validate the potential downstream application. SenseNova-SI is an ongoing project, and this report will be updated continuously. All newly trained multimodal foundation models are publicly released to facilitate further research in this direction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.15848", "title": "Step-Audio-R1 Technical Report", "authors": [ "Fei Tian", "Xiangyu Tony Zhang", "Yuxin Zhang", "Haoyang Zhang", "Yuxin Li", "Daijiao Liu", "Yayue Deng", "Donghang Wu", "Jun Chen", "Liang Zhao", "Chengyuan Yao", "Hexin Liu", "Eng Siong Chng", "Xuerui Yang", "Xiangyu Zhang", "Daxin Jiang", "Gang Yu" ], "github": "", "project_page": "", "abstract": "Recent advances in reasoning models have demonstrated remarkable success in text and vision domains through extended chain-of-thought deliberation. However, a perplexing phenomenon persists in audio language models: they consistently perform better with minimal or no reasoning, raising a fundamental question - can audio intelligence truly benefit from deliberate thinking? We introduce Step-Audio-R1, the first audio reasoning model that successfully unlocks reasoning capabilities in the audio domain. Through our proposed Modality-Grounded Reasoning Distillation (MGRD) framework, Step-Audio-R1 learns to generate audio-relevant reasoning chains that genuinely ground themselves in acoustic features rather than hallucinating disconnected deliberations. Our model exhibits strong audio reasoning capabilities, surpassing Gemini 2.5 Pro and achieving performance comparable to the state-of-the-art Gemini 3 Pro across comprehensive audio understanding and reasoning benchmarks spanning speech, environmental sounds, and music. These results demonstrate that reasoning is a transferable capability across modalities when appropriately anchored, transforming extended deliberation from a liability into a powerful asset for audio intelligence. By establishing the first successful audio reasoning model, Step-Audio-R1 opens new pathways toward building truly multimodal reasoning systems that think deeply across all sensory modalities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16518", "title": "MiMo-Embodied: X-Embodied Foundation Model Technical Report", "authors": [ "Xiaoshuai Hao", "Lei Zhou", "Zhijian Huang", "Zhiwen Hou", "Yingbo Tang", "Lingfeng Zhang", "Guang Li", "Zheng Lu", "Shuhuai Ren", "Xianhui Meng", "Yuchen Zhang", "Jing Wu", "Jinghui Lu", "Chenxu Dang", "Jiayi Guan", "Jianhua Wu", "Zhiyi Hou", "Hanbing Li", "Shumeng Xia", "Mingliang Zhou", "Yinan Zheng", "Zihao Yue", "Shuhao Gu", "Hao Tian", "Yuannan Shen", "Jianwei Cui", "Wen Zhang", "Shaoqing Xu", "Bing Wang", "Haiyang Sun", "Zeyu Zhu", "Yuncheng Jiang", "Zibin Guo", "Chuhong Gong", "Chaofan Zhang", "Wenbo Ding", "Kun Ma", "Guang Chen", "Rui Cai", "Diyun Xiang", "Heng Qu", "Fuli Luo", "Hangjun Ye", "Long Chen" ], "github": "https://github.com/XiaomiMiMo/MiMo-Embodied", "project_page": "", "abstract": "We open-source MiMo-Embodied, the first cross-embodied foundation model to successfully integrate and achieve state-of-the-art performance in both Autonomous Driving and Embodied AI. MiMo-Embodied sets new records across 17 embodied AI benchmarks in Task Planning, Affordance Prediction and Spatial Understanding, while also excelling in 12 autonomous driving benchmarks across Environmental Perception, Status Prediction, and Driving Planning. Across these tasks, MiMo-Embodied significantly outperforms existing open-source, closed-source, and specialized baselines. Our results indicate that through multi-stage learning, curated data construction, and CoT/RL fine-tuning, these two domains exhibit strong positive transfer and mutually reinforce one another. We provide a detailed analysis of our model design and training methodologies to facilitate further research. Code and models are available at https://github.com/XiaomiMiMo/MiMo-Embodied.", "github_manual": "", "github_regex": "https://github.com/XiaomiMiMo/MiMo-Embodied", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.15700", "title": "First Frame Is the Place to Go for Video Content Customization", "authors": [ "Jingxi Chen", "Zongxia Li", "Zhichao Liu", "Guangyao Shi", "Xiyang Wu", "Fuxiao Liu", "Cornelia Fermuller", "Brandon Y. Feng", "Yiannis Aloimonos" ], "github": "", "project_page": "", "abstract": "What role does the first frame play in video generation models? Traditionally, it's viewed as the spatial-temporal starting point of a video, merely a seed for subsequent animation. In this work, we reveal a fundamentally different perspective: video models implicitly treat the first frame as a conceptual memory buffer that stores visual entities for later reuse during generation. Leveraging this insight, we show that it's possible to achieve robust and generalized video content customization in diverse scenarios, using only 20-50 training examples without architectural changes or large-scale finetuning. This unveils a powerful, overlooked capability of video generation models for reference-based video customization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16624", "title": "SAM 3D: 3Dfy Anything in Images", "authors": [ "SAM 3D Team", "Xingyu Chen", "Fu-Jen Chu", "Pierre Gleize", "Kevin J Liang", "Alexander Sax", "Hao Tang", "Weiyao Wang", "Michelle Guo", "Thibaut Hardin", "Xiang Li", "Aohan Lin", "Jiawei Liu", "Ziqi Ma", "Anushka Sagar", "Bowen Song", "Xiaodong Wang", "Jianing Yang", "Bowen Zhang", "Piotr Doll\u00e1r", "Georgia Gkioxari", "Matt Feiszli", "Jitendra Malik" ], "github": "", "project_page": "", "abstract": "We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single image. SAM 3D excels in natural images, where occlusion and scene clutter are common and visual recognition cues from context play a larger role. We achieve this with a human- and model-in-the-loop pipeline for annotating object shape, texture, and pose, providing visually grounded 3D reconstruction data at unprecedented scale. We learn from this data in a modern, multi-stage training framework that combines synthetic pretraining with real-world alignment, breaking the 3D \"data barrier\". We obtain significant gains over recent work, with at least a 5:1 win rate in human preference tests on real-world objects and scenes. We will release our code and model weights, an online demo, and a new challenging benchmark for in-the-wild 3D object reconstruction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16671", "title": "Thinking-while-Generating: Interleaving Textual Reasoning throughout Visual Generation", "authors": [ "Ziyu Guo", "Renrui Zhang", "Hongyu Li", "Manyuan Zhang", "Xinyan Chen", "Sifan Wang", "Yan Feng", "Peng Pei", "Pheng-Ann Heng" ], "github": "https://github.com/ZiyuGuo99/Thinking-while-Generating", "project_page": "", "abstract": "Recent advances in visual generation have increasingly explored the integration of reasoning capabilities. They incorporate textual reasoning, i.e., think, either before (as pre-planning) or after (as post-refinement) the generation process, yet they lack on-the-fly multimodal interaction during the generation itself. In this preliminary study, we introduce Thinking-while-Generating (TwiG), the first interleaved framework that enables co-evolving textual reasoning throughout the visual generation process. As visual content is progressively generating, textual reasoning is interleaved to both guide upcoming local regions and reflect on previously synthesized ones. This dynamic interplay produces more context-aware and semantically rich visual outputs. To unveil the potential of this framework, we investigate three candidate strategies, zero-shot prompting, supervised fine-tuning (SFT) on our curated TwiG-50K dataset, and reinforcement learning (RL) via a customized TwiG-GRPO strategy, each offering unique insights into the dynamics of interleaved reasoning. We hope this work inspires further research into interleaving textual reasoning for enhanced visual generation. Code will be released at: https://github.com/ZiyuGuo99/Thinking-while-Generating.", "github_manual": "", "github_regex": "https://github.com/ZiyuGuo99/Thinking-while-Generating", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16664", "title": "Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs", "authors": [ "Ali Taghibakhshi", "Sharath Turuvekere Sreenivas", "Saurav Muralidharan", "Ruisi Cai", "Marcin Chochowski", "Ameya Sunil Mahabaleshwarkar", "Yoshi Suhara", "Oluwatobi Olabiyi", "Daniel Korzekwa", "Mostofa Patwary", "Mohammad Shoeybi", "Jan Kautz", "Bryan Catanzaro", "Ashwath Aithal", "Nima Tajbakhsh", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "Training a family of large language models targeting multiple scales and deployment objectives is prohibitively expensive, requiring separate training runs for each different size. Recent work on model compression through pruning and knowledge distillation has reduced this cost; however, this process still incurs hundreds of billions of tokens worth of training cost per compressed model. In this paper, we present Nemotron Elastic, a framework for building reasoning-oriented LLMs, including hybrid Mamba-Attention architectures, that embed multiple nested submodels within a single parent model, each optimized for different deployment configurations and budgets. Each of these submodels shares weights with the parent model and can be extracted zero-shot during deployment without additional training or fine-tuning. We enable this functionality through an end-to-end trained router, tightly coupled to a two-stage training curriculum designed specifically for reasoning models. We additionally introduce group-aware SSM elastification that preserves Mamba's structural constraints, heterogeneous MLP elastification, normalized MSE-based layer importance for improved depth selection, and knowledge distillation enabling simultaneous multi-budget optimization. We apply Nemotron Elastic to the Nemotron Nano V2 12B model, simultaneously producing a 9B and a 6B model using only 110B training tokens; this results in over 360x cost reduction compared to training model families from scratch, and around 7x compared to SoTA compression techniques. Each of the nested models performs on par or better than the SoTA in accuracy. Moreover, unlike other compression methods, the nested capability of our approach allows having a many-in-one reasoning model that has constant deployment memory against the number of models in the family.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16618", "title": "SAM2S: Segment Anything in Surgical Videos via Semantic Long-term Tracking", "authors": [ "Haofeng Liu", "Ziyue Wang", "Sudhanshu Mishra", "Mingqi Gao", "Guanyi Qin", "Chang Han Low", "Alex Y. W. Kong", "Yueming Jin" ], "github": "", "project_page": "", "abstract": "Surgical video segmentation is crucial for computer-assisted surgery, enabling precise localization and tracking of instruments and tissues. Interactive Video Object Segmentation (iVOS) models such as Segment Anything Model 2 (SAM2) provide prompt-based flexibility beyond methods with predefined categories, but face challenges in surgical scenarios due to the domain gap and limited long-term tracking. To address these limitations, we construct SA-SV, the largest surgical iVOS benchmark with instance-level spatio-temporal annotations (masklets) spanning eight procedure types (61k frames, 1.6k masklets), enabling comprehensive development and evaluation for long-term tracking and zero-shot generalization. Building on SA-SV, we propose SAM2S, a foundation model enhancing SAM2 for Surgical iVOS through: (1) DiveMem, a trainable diverse memory mechanism for robust long-term tracking; (2) temporal semantic learning for instrument understanding; and (3) ambiguity-resilient learning to mitigate annotation inconsistencies across multi-source datasets. Extensive experiments demonstrate that fine-tuning on SA-SV enables substantial performance gains, with SAM2 improving by 12.99 average J\\&F over vanilla SAM2. SAM2S further advances performance to 80.42 average J\\&F, surpassing vanilla and fine-tuned SAM2 by 17.10 and 4.11 points respectively, while maintaining 68 FPS real-time inference and strong zero-shot generalization. Code and dataset will be released at https://jinlab-imvr.github.io/SAM2S.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16595", "title": "TimeViper: A Hybrid Mamba-Transformer Vision-Language Model for Efficient Long Video Understanding", "authors": [ "Boshen Xu", "Zihan Xiao", "Jiaze Li", "Jianzhong Ju", "Zhenbo Luo", "Jian Luan", "Qin Jin" ], "github": "", "project_page": "", "abstract": "We introduce TimeViper, a hybrid vision-language model designed to tackle challenges of long video understanding. Processing long videos demands both an efficient model architecture and an effective mechanism for handling extended temporal contexts. To this end, TimeViper adopts a hybrid Mamba-Transformer backbone that combines the efficiency of state-space models with the expressivity of attention mechanisms. Through this hybrid design, we reveal the vision-to-text information aggregation phenomenon, where information progressively flows from vision tokens to text tokens across increasing LLM depth, resulting in severe vision token redundancy. Motivated by this observation, we propose TransV, a token information transfer module that transfers and compresses vision tokens into instruction tokens while maintaining multimodal understanding capabilities. This design enables TimeViper to process hour-long videos exceeding 10,000 frames. Extensive experiments across multiple benchmarks demonstrate that TimeViper competes with state-of-the-art models while extending frame numbers. We further analyze attention behaviors of both Mamba and Transformer layers, offering new insights into hybrid model interpretability. This work represents an initial step towards developing, interpreting, and compressing hybrid Mamba-Transformer architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16317", "title": "NaTex: Seamless Texture Generation as Latent Color Diffusion", "authors": [ "Zeqiang Lai", "Yunfei Zhao", "Zibo Zhao", "Xin Yang", "Xin Huang", "Jingwei Huang", "Xiangyu Yue", "Chunchao Guo" ], "github": "", "project_page": "", "abstract": "We present NaTex, a native texture generation framework that predicts texture color directly in 3D space. In contrast to previous approaches that rely on baking 2D multi-view images synthesized by geometry-conditioned Multi-View Diffusion models (MVDs), NaTex avoids several inherent limitations of the MVD pipeline. These include difficulties in handling occluded regions that require inpainting, achieving precise mesh-texture alignment along boundaries, and maintaining cross-view consistency and coherence in both content and color intensity. NaTex features a novel paradigm that addresses the aforementioned issues by viewing texture as a dense color point cloud. Driven by this idea, we propose latent color diffusion, which comprises a geometry-awared color point cloud VAE and a multi-control diffusion transformer (DiT), entirely trained from scratch using 3D data, for texture reconstruction and generation. To enable precise alignment, we introduce native geometry control that conditions the DiT on direct 3D spatial information via positional embeddings and geometry latents. We co-design the VAE-DiT architecture, where the geometry latents are extracted via a dedicated geometry branch tightly coupled with the color VAE, providing fine-grained surface guidance that maintains strong correspondence with the texture. With these designs, NaTex demonstrates strong performance, significantly outperforming previous methods in texture coherence and alignment. Moreover, NaTex also exhibits strong generalization capabilities, either training-free or with simple tuning, for various downstream applications, e.g., material generation, texture refinement, and part segmentation and texturing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16659", "title": "PartUV: Part-Based UV Unwrapping of 3D Meshes", "authors": [ "Zhaoning Wang", "Xinyue Wei", "Ruoxi Shi", "Xiaoshuai Zhang", "Hao Su", "Minghua Liu" ], "github": "", "project_page": "", "abstract": "UV unwrapping flattens 3D surfaces to 2D with minimal distortion, often requiring the complex surface to be decomposed into multiple charts. Although extensively studied, existing UV unwrapping methods frequently struggle with AI-generated meshes, which are typically noisy, bumpy, and poorly conditioned. These methods often produce highly fragmented charts and suboptimal boundaries, introducing artifacts and hindering downstream tasks. We introduce PartUV, a part-based UV unwrapping pipeline that generates significantly fewer, part-aligned charts while maintaining low distortion. Built on top of a recent learning-based part decomposition method PartField, PartUV combines high-level semantic part decomposition with novel geometric heuristics in a top-down recursive framework. It ensures each chart's distortion remains below a user-specified threshold while minimizing the total number of charts. The pipeline integrates and extends parameterization and packing algorithms, incorporates dedicated handling of non-manifold and degenerate meshes, and is extensively parallelized for efficiency. Evaluated across four diverse datasets, including man-made, CAD, AI-generated, and Common Shapes, PartUV outperforms existing tools and recent neural methods in chart count and seam length, achieves comparable distortion, exhibits high success rates on challenging meshes, and enables new applications like part-specific multi-tiles packing. Our project page is at https://www.zhaoningwang.com/PartUV.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16528", "title": "TurkColBERT: A Benchmark of Dense and Late-Interaction Models for Turkish Information Retrieval", "authors": [ "\u00d6zay Ezerceli", "Mahmoud El Hussieni", "Selva Ta\u015f", "Reyhan Bayraktar", "Fatma Bet\u00fcl Terzio\u011flu", "Yusuf \u00c7elebi", "Ya\u011f\u0131z Asker" ], "github": "", "project_page": "", "abstract": "Neural information retrieval systems excel in high-resource languages but remain underexplored for morphologically rich, lower-resource languages such as Turkish. Dense bi-encoders currently dominate Turkish IR, yet late-interaction models -- which retain token-level representations for fine-grained matching -- have not been systematically evaluated. We introduce TurkColBERT, the first comprehensive benchmark comparing dense encoders and late-interaction models for Turkish retrieval. Our two-stage adaptation pipeline fine-tunes English and multilingual encoders on Turkish NLI/STS tasks, then converts them into ColBERT-style retrievers using PyLate trained on MS MARCO-TR. We evaluate 10 models across five Turkish BEIR datasets covering scientific, financial, and argumentative domains. Results show strong parameter efficiency: the 1.0M-parameter colbert-hash-nano-tr is 600times smaller than the 600M turkish-e5-large dense encoder while preserving over 71\\% of its average mAP. Late-interaction models that are 3--5times smaller than dense encoders significantly outperform them; ColmmBERT-base-TR yields up to +13.8\\% mAP on domain-specific tasks. For production-readiness, we compare indexing algorithms: MUVERA+Rerank is 3.33times faster than PLAID and offers +1.7\\% relative mAP gain. This enables low-latency retrieval, with ColmmBERT-base-TR achieving 0.54 ms query times under MUVERA. We release all checkpoints, configs, and evaluation scripts. Limitations include reliance on moderately sized datasets (leq50K documents) and translated benchmarks, which may not fully reflect real-world Turkish retrieval conditions; larger-scale MUVERA evaluations remain necessary.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.15605", "title": "SRPO: Self-Referential Policy Optimization for Vision-Language-Action Models", "authors": [ "Senyu Fei", "Siyin Wang", "Li Ji", "Ao Li", "Shiduo Zhang", "Liming Liu", "Jinlong Hou", "Jingjing Gong", "Xianzhong Zhao", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models excel in robotic manipulation but are constrained by their heavy reliance on expert demonstrations, leading to demonstration bias and limiting performance. Reinforcement learning (RL) is a vital post-training strategy to overcome these limits, yet current VLA-RL methods, including group-based optimization approaches, are crippled by severe reward sparsity. Relying on binary success indicators wastes valuable information in failed trajectories, resulting in low training efficiency. To solve this, we propose Self-Referential Policy Optimization (SRPO), a novel VLA-RL framework. SRPO eliminates the need for external demonstrations or manual reward engineering by leveraging the model's own successful trajectories, generated within the current training batch, as a self-reference. This allows us to assign a progress-wise reward to failed attempts. A core innovation is the use of latent world representations to measure behavioral progress robustly. Instead of relying on raw pixels or requiring domain-specific fine-tuning, we utilize the compressed, transferable encodings from a world model's latent space. These representations naturally capture progress patterns across environments, enabling accurate, generalized trajectory comparison. Empirical evaluations on the LIBERO benchmark demonstrate SRPO's efficiency and effectiveness. Starting from a supervised baseline with 48.9% success, SRPO achieves a new state-of-the-art success rate of 99.2% in just 200 RL steps, representing a 103% relative improvement without any extra supervision. Furthermore, SRPO shows substantial robustness, achieving a 167% performance improvement on the LIBERO-Plus benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.11005", "title": "Draft and Refine with Visual Experts", "authors": [ "Sungheon Jeong", "Ryozo Masukawa", "Jihong Park", "Sanggeon Yun", "Wenjun Huang", "Hanning Chen", "Mahdi Imani", "Mohsen Imani" ], "github": "https://github.com/EavnJeong/Draft-and-Refine-with-Visual-Experts", "project_page": "", "abstract": "While recent Large Vision-Language Models (LVLMs) exhibit strong multimodal reasoning abilities, they often produce ungrounded or hallucinated responses because they rely too heavily on linguistic priors instead of visual evidence. This limitation highlights the absence of a quantitative measure of how much these models actually use visual information during reasoning. We propose Draft and Refine (DnR), an agent framework driven by a question-conditioned utilization metric. The metric quantifies the model's reliance on visual evidence by first constructing a query-conditioned relevance map to localize question-specific cues and then measuring dependence through relevance-guided probabilistic masking. Guided by this metric, the DnR agent refines its initial draft using targeted feedback from external visual experts. Each expert's output (such as boxes or masks) is rendered as visual cues on the image, and the model is re-queried to select the response that yields the largest improvement in utilization. This process strengthens visual grounding without retraining or architectural changes. Experiments across VQA and captioning benchmarks show consistent accuracy gains and reduced hallucination, demonstrating that measuring visual utilization provides a principled path toward more interpretable and evidence-driven multimodal agent systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EavnJeong/Draft-and-Refine-with-Visual-Experts", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.15248", "title": "EntroPIC: Towards Stable Long-Term Training of LLMs via Entropy Stabilization with Proportional-Integral Control", "authors": [ "Kai Yang", "Xin Xu", "Yangkun Chen", "Weijie Liu", "Jiafei Lyu", "Zichuan Lin", "Deheng Ye", "Saiyong Yang" ], "github": "", "project_page": "", "abstract": "Long-term training of large language models (LLMs) requires maintaining stable exploration to prevent the model from collapsing into sub-optimal behaviors. Entropy is crucial in this context, as it controls exploration and helps avoid premature convergence to sub-optimal solutions. However, existing reinforcement learning methods struggle to maintain an appropriate level of entropy, as the training process involves a mix of positive and negative samples, each affecting entropy in different ways across steps. To address this, we propose Entropy stablilization via Proportional-Integral Control (EntroPIC), a novel method that adaptively adjusts the influence of positive and negative samples by dynamically tuning their loss coefficients. This approach stabilizes entropy throughout training, ensuring efficient exploration and steady progress. We provide a comprehensive theoretical analysis for both on-policy and off-policy learning settings, demonstrating that EntroPIC is effective at controlling entropy in large-scale LLM training. Experimental results show that our method successfully maintains desired entropy levels, enabling stable and optimal RL training for LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16315", "title": "BioBench: A Blueprint to Move Beyond ImageNet for Scientific ML Benchmarks", "authors": [ "Samuel Stevens" ], "github": "https://github.com/samuelstevens/biobench", "project_page": "", "abstract": "ImageNet-1K linear-probe transfer accuracy remains the default proxy for visual representation quality, yet it no longer predicts performance on scientific imagery. Across 46 modern vision model checkpoints, ImageNet top-1 accuracy explains only 34% of variance on ecology tasks and mis-ranks 30% of models above 75% accuracy. We present BioBench, an open ecology vision benchmark that captures what ImageNet misses. BioBench unifies 9 publicly released, application-driven tasks, 4 taxonomic kingdoms, and 6 acquisition modalities (drone RGB, web video, micrographs, in-situ and specimen photos, camera-trap frames), totaling 3.1M images. A single Python API downloads data, fits lightweight classifiers to frozen backbones, and reports class-balanced macro-F1 (plus domain metrics for FishNet and FungiCLEF); ViT-L models evaluate in 6 hours on an A6000 GPU. BioBench provides new signal for computer vision in ecology and a template recipe for building reliable AI-for-science benchmarks in any domain. Code and predictions are available at https://github.com/samuelstevens/biobench and results at https://samuelstevens.me/biobench.", "github_manual": "", "github_regex": "https://github.com/samuelstevens/biobench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.14865", "title": "FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications", "authors": [ "Dwipam Katariya", "Snehita Varma", "Akshat Shreemali", "Benjamin Wu", "Kalanand Mishra", "Pranab Mohanty" ], "github": "", "project_page": "", "abstract": "Transformer-based architectures are widely adopted in sequential recommendation systems, yet their application in Financial Services (FS) presents distinct practical and modeling challenges for real-time recommendation. These include:a) long-range user interactions (implicit and explicit) spanning both digital and physical channels generating temporally heterogeneous context, b) the presence of multiple interrelated products require coordinated models to support varied ad placements and personalized feeds, while balancing competing business goals. We propose FinTRec, a transformer-based framework that addresses these challenges and its operational objectives in FS. While tree-based models have traditionally been preferred in FS due to their explainability and alignment with regulatory requirements, our study demonstrate that FinTRec offers a viable and effective shift toward transformer-based architectures. Through historic simulation and live A/B test correlations, we show FinTRec consistently outperforms the production-grade tree-based baseline. The unified architecture, when fine-tuned for product adaptation, enables cross-product signal sharing, reduces training cost and technical debt, while improving offline performance across all products. To our knowledge, this is the first comprehensive study of unified sequential recommendation modeling in FS that addresses both technical and business considerations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.13703", "title": "Generalist Foundation Models Are Not Clinical Enough for Hospital Operations", "authors": [ "Lavender Y. Jiang", "Angelica Chen", "Xu Han", "Xujin Chris Liu", "Radhika Dua", "Kevin Eaton", "Frederick Wolff", "Robert Steele", "Jeff Zhang", "Anton Alyakin", "Qingkai Pan", "Yanbing Chen", "Karl L. Sangwon", "Daniel A. Alber", "Jaden Stryker", "Jin Vivian Lee", "Yindalon Aphinyanaphongs", "Kyunghyun Cho", "Eric Karl Oermann" ], "github": "https://github.com/Lightning-AI/litgpt", "project_page": "", "abstract": "Hospitals and healthcare systems rely on operational decisions that determine patient flow, cost, and quality of care. Despite strong performance on medical knowledge and conversational benchmarks, foundation models trained on general text may lack the specialized knowledge required for these operational decisions. We introduce Lang1, a family of models (100M-7B parameters) pretrained on a specialized corpus blending 80B clinical tokens from NYU Langone Health's EHRs and 627B tokens from the internet. To rigorously evaluate Lang1 in real-world settings, we developed the REalistic Medical Evaluation (ReMedE), a benchmark derived from 668,331 EHR notes that evaluates five critical tasks: 30-day readmission prediction, 30-day mortality prediction, length of stay, comorbidity coding, and predicting insurance claims denial. In zero-shot settings, both general-purpose and specialized models underperform on four of five tasks (36.6%-71.7% AUROC), with mortality prediction being an exception. After finetuning, Lang1-1B outperforms finetuned generalist models up to 70x larger and zero-shot models up to 671x larger, improving AUROC by 3.64%-6.75% and 1.66%-23.66% respectively. We also observed cross-task scaling with joint finetuning on multiple tasks leading to improvement on other tasks. Lang1-1B effectively transfers to out-of-distribution settings, including other clinical tasks and an external health system. Our findings suggest that predictive capabilities for hospital operations require explicit supervised finetuning, and that this finetuning process is made more efficient by in-domain pretraining on EHR. Our findings support the emerging view that specialized LLMs can compete with generalist models in specialized tasks, and show that effective healthcare systems AI requires the combination of in-domain pretraining, supervised finetuning, and real-world evaluation beyond proxy benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Lightning-AI/litgpt", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.15943", "title": "Boosting Medical Visual Understanding From Multi-Granular Language Learning", "authors": [ "Zihan Li", "Yiqing Wang", "Sina Farsiu", "Paul Kinahan" ], "github": "https://github.com/HUANGLIZI/MGLL", "project_page": "", "abstract": "Recent advances in image-text pretraining have significantly enhanced visual understanding by aligning visual and textual representations. Contrastive Language-Image Pretraining (CLIP) has played a pivotal role in multimodal learning. However, its focus on single-label, single-granularity alignment limits its effectiveness in complex domains such as medical imaging, where images often correspond to multiple high-level labels (e.g., disease categories) across different annotation granularities (e.g., diagnostic description, clinical explanation). To address this, we propose Multi-Granular Language Learning (MGLL), a contrastive learning framework designed to improve both multi-label and cross-granularity alignment. MGLL leverages structured multi-label supervision, integrates textual descriptions across granularities, and introduces soft-label supervision with point-wise constraints to enhance alignment. MGLL employs smooth Kullback-Leibler (KL) divergence to ensure cross-granularity consistency while maintaining computational efficiency as a plug-and-play module for vision-language models. Pretrained on our constructed large-scale multi-granular datasets and evaluated across multiple datasets, MGLL outperforms other state-of-the-art methods in downstream tasks. The code is available at https://github.com/HUANGLIZI/MGLL{https://github.com/HUANGLIZI/MGLL}.", "github_manual": "", "github_regex": "https://github.com/HUANGLIZI/MGLL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-21", "arxiv_id": "2511.16043", "title": "Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning", "authors": [ "Peng Xia", "Kaide Zeng", "Jiaqi Liu", "Can Qin", "Fang Wu", "Yiyang Zhou", "Caiming Xiong", "Huaxiu Yao" ], "github": "https://github.com/aiming-lab/Agent0", "project_page": "", "abstract": "Large Language Model (LLM) Agents, often trained with Reinforcement Learning (RL), are constrained by a dependency on human-curated data, limiting scalability and tethering AI to human knowledge. Existing self-evolution frameworks offer an alternative but are typically restricted by the model's inherent capabilities and single-round interactions, hindering the development of complex curricula involving tool use or dynamic reasoning. We introduce Agent0, a fully autonomous framework that evolves high-performing agents without external data through multi-step co-evolution and seamless tool integration. Agent0 establishes a symbiotic competition between two agents initialized from the same base LLM: a curriculum agent that proposes increasingly challenging frontier tasks, and an executor agent that learns to solve them. We integrate external tools to enhance the executor's problem-solving capacity; this improvement, in turn, pressures the curriculum agent to construct more complex, tool-aware tasks. Through this iterative process, Agent0 establishes a self-reinforcing cycle that continuously produces high-quality curricula. Empirically, Agent0 substantially boosts reasoning capabilities, improving the Qwen3-8B-Base model by 18% on mathematical reasoning and 24% on general reasoning benchmarks. Code is available at https://github.com/aiming-lab/Agent0.", "github_manual": "", "github_regex": "https://github.com/aiming-lab/Agent0", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.16334", "title": "OpenMMReasoner: Pushing the Frontiers for Multimodal Reasoning with an Open and General Recipe", "authors": [ "Kaichen Zhang", "Keming Wu", "Zuhao Yang", "Kairui Hu", "Bin Wang", "Ziwei Liu", "Xingxuan Li", "Lidong Bing" ], "github": "https://github.com/EvolvingLMMs-Lab/OpenMMReasoner", "project_page": "", "abstract": "Recent advancements in large reasoning models have fueled growing interest in extending such capabilities to multimodal domains. However, despite notable progress in visual reasoning, the lack of transparent and reproducible data curation and training strategies remains a major barrier to scalable research. In this work, we introduce OpenMMReasoner, a fully transparent two-stage recipe for multimodal reasoning spanning supervised fine-tuning (SFT) and reinforcement learning (RL). In the SFT stage, we construct an 874K-sample cold-start dataset with rigorous step-by-step validation, providing a strong foundation for reasoning capabilities. The subsequent RL stage leverages a 74K-sample dataset across diverse domains to further sharpen and stabilize these abilities, resulting in a more robust and efficient learning process. Extensive evaluations demonstrate that our training recipe not only surpasses strong baselines but also highlights the critical role of data quality and training design in shaping multimodal reasoning performance. Notably, our method achieves a 11.6% improvement over the Qwen2.5-VL-7B-Instruct baseline across nine multimodal reasoning benchmarks, establishing a solid empirical foundation for future large-scale multimodal reasoning research. We open-sourced all our codes, pipeline, and data at https://github.com/EvolvingLMMs-Lab/OpenMMReasoner.", "github_manual": "", "github_regex": "https://github.com/EvolvingLMMs-Lab/OpenMMReasoner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.15705", "title": "GeoVista: Web-Augmented Agentic Visual Reasoning for Geolocalization", "authors": [ "Yikun Wang", "Zuyan Liu", "Ziyi Wang", "Pengfei Liu", "Han Hu", "Yongming Rao" ], "github": "", "project_page": "", "abstract": "Current research on agentic visual reasoning enables deep multimodal understanding but primarily focuses on image manipulation tools, leaving a gap toward more general-purpose agentic models. In this work, we revisit the geolocalization task, which requires not only nuanced visual grounding but also web search to confirm or refine hypotheses during reasoning. Since existing geolocalization benchmarks fail to meet the need for high-resolution imagery and the localization challenge for deep agentic reasoning, we curate GeoBench, a benchmark that includes photos and panoramas from around the world, along with a subset of satellite images of different cities to rigorously evaluate the geolocalization ability of agentic models. We also propose GeoVista, an agentic model that seamlessly integrates tool invocation within the reasoning loop, including an image-zoom-in tool to magnify regions of interest and a web-search tool to retrieve related web information. We develop a complete training pipeline for it, including a cold-start supervised fine-tuning (SFT) stage to learn reasoning patterns and tool-use priors, followed by a reinforcement learning (RL) stage to further enhance reasoning ability. We adopt a hierarchical reward to leverage multi-level geographical information and improve overall geolocalization performance. Experimental results show that GeoVista surpasses other open-source agentic models on the geolocalization task greatly and achieves performance comparable to closed-source models such as Gemini-2.5-flash and GPT-5 on most metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.16719", "title": "SAM 3: Segment Anything with Concepts", "authors": [ "Nicolas Carion", "Laura Gustafson", "Yuan-Ting Hu", "Shoubhik Debnath", "Ronghang Hu", "Didac Suris", "Chaitanya Ryali", "Kalyan Vasudev Alwala", "Haitham Khedr", "Andrew Huang", "Jie Lei", "Tengyu Ma", "Baishan Guo", "Arpit Kalla", "Markus Marks", "Joseph Greer", "Meng Wang", "Peize Sun", "Roman R\u00e4dle", "Triantafyllos Afouras", "Effrosyni Mavroudi", "Katherine Xu", "Tsung-Han Wu", "Yu Zhou", "Liliane Momeni", "Rishi Hazra", "Shuangrui Ding", "Sagar Vaze", "Francois Porcher", "Feng Li", "Siyuan Li", "Aishwarya Kamath", "Ho Kei Cheng", "Piotr Doll\u00e1r", "Nikhila Ravi", "Kate Saenko", "Pengchuan Zhang", "Christoph Feichtenhofer" ], "github": "", "project_page": "", "abstract": "We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short noun phrases (e.g., \"yellow school bus\"), image exemplars, or a combination of both. Promptable Concept Segmentation (PCS) takes such prompts and returns segmentation masks and unique identities for all matching object instances. To advance PCS, we build a scalable data engine that produces a high-quality dataset with 4M unique concept labels, including hard negatives, across images and videos. Our model consists of an image-level detector and a memory-based video tracker that share a single backbone. Recognition and localization are decoupled with a presence head, which boosts detection accuracy. SAM 3 doubles the accuracy of existing systems in both image and video PCS, and improves previous SAM capabilities on visual segmentation tasks. We open source SAM 3 along with our new Segment Anything with Concepts (SA-Co) benchmark for promptable concept segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.11007", "title": "VisMem: Latent Vision Memory Unlocks Potential of Vision-Language Models", "authors": [ "Xinlei Yu", "Chengming Xu", "Guibin Zhang", "Zhangquan Chen", "Yudong Zhang", "Yongbo He", "Peng-Tao Jiang", "Jiangning Zhang", "Xiaobin Hu", "Shuicheng Yan" ], "github": "https://github.com/YU-deep/VisMem.git", "project_page": "", "abstract": "Despite the remarkable success of Vision-Language Models (VLMs), their performance on a range of complex visual tasks is often hindered by a \"visual processing bottleneck\": a propensity to lose grounding in visual evidence and exhibit a deficit in contextualized visual experience during prolonged generation. Drawing inspiration from human cognitive memory theory, which distinguishes short-term visually-dominant memory and long-term semantically-dominant memory, we propose VisMem, a cognitively-aligned framework that equips VLMs with dynamic latent vision memories, a short-term module for fine-grained perceptual retention and a long-term module for abstract semantic consolidation. These memories are seamlessly invoked during inference, allowing VLMs to maintain both perceptual fidelity and semantic consistency across thinking and generation. Extensive experiments across diverse visual benchmarks for understanding, reasoning, and generation reveal that VisMem delivers a significant average performance boost of 11.8% relative to the vanilla model and outperforms all counterparts, establishing a new paradigm for latent-space memory enhancement. The code will be available: https://github.com/YU-deep/VisMem.git.", "github_manual": "", "github_regex": "https://github.com/YU-deep/VisMem.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.16175", "title": "Mantis: A Versatile Vision-Language-Action Model with Disentangled Visual Foresight", "authors": [ "Yi Yang", "Xueqi Li", "Yiyang Chen", "Jin Song", "Yihan Wang", "Zipeng Xiao", "Jiadi Su", "You Qiaoben", "Pengfei Liu", "Zhijie Deng" ], "github": "", "project_page": "", "abstract": "Recent advances in Vision-Language-Action (VLA) models demonstrate that visual signals can effectively complement sparse action supervisions. However, letting VLA directly predict high-dimensional visual states can distribute model capacity and incur prohibitive training cost, while compressing visual states into more compact supervisory signals inevitably incurs information bottlenecks. Moreover, existing methods often suffer from poor comprehension and reasoning capabilities due to the neglect of language supervision. This paper introduces Mantis, a novel framework featuring a Disentangled Visual Foresight (DVF) to tackle these issues. Specifically, Mantis decouples visual foresight prediction from the backbone with the combination of meta queries and a diffusion Transformer (DiT) head. With the current visual state provided to the DiT via a residual connection, a simple next-state prediction objective enables the meta queries to automatically capture the latent actions that delineate the visual trajectory, and hence boost the learning of explicit actions. The disentanglement reduces the burden of the VLA backbone, enabling it to maintain comprehension and reasoning capabilities through language supervision. Empirically, pretrained on human manipulation videos, robot demonstrations, and image-text pairs, Mantis achieves a 96.7% success rate on LIBERO benchmark after fine-tuning, surpassing powerful baselines while exhibiting high convergence speed. Real-world evaluations show that Mantis outperforms \u03c0_{0.5}, a leading open-source VLA model, particularly in instruction-following capability, generalization to unseen instructions, and reasoning ability. Code and weights are released to support the open-source community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.17502", "title": "RynnVLA-002: A Unified Vision-Language-Action and World Model", "authors": [ "Jun Cen", "Siteng Huang", "Yuqian Yuan", "Hangjie Yuan", "Chaohui Yu", "Yuming Jiang", "Jiayan Guo", "Kehan Li", "Hao Luo", "Fan Wang", "Xin Li", "Deli Zhao", "Hao Chen" ], "github": "", "project_page": "", "abstract": "We introduce RynnVLA-002, a unified Vision-Language-Action (VLA) and world model. The world model leverages action and visual inputs to predict future image states, learning the underlying physics of the environment to refine action generation. Conversely, the VLA model produces subsequent actions from image observations, enhancing visual understanding and supporting the world model's image generation. The unified framework of RynnVLA-002 enables joint learning of environmental dynamics and action planning. Our experiments show that RynnVLA-002 surpasses individual VLA and world models, demonstrating their mutual enhancement. We evaluate RynnVLA-002 in both simulation and real-world robot tasks. RynnVLA-002 achieves 97.4% success rate on the LIBERO simulation benchmark without pretraining, while in real-world LeRobot experiments, its integrated world model boosts the overall success rate by 50%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.17220", "title": "Parrot: Persuasion and Agreement Robustness Rating of Output Truth -- A Sycophancy Robustness Benchmark for LLMs", "authors": [ "Yusuf \u00c7elebi", "Mahmoud El Hussieni", "\u00d6zay Ezerceli" ], "github": "", "project_page": "", "abstract": "This study presents PARROT (Persuasion and Agreement Robustness Rating of Output Truth), a robustness focused framework designed to measure the degradation in accuracy that occurs under social pressure exerted on users through authority and persuasion in large language models (LLMs) the phenomenon of sycophancy (excessive conformity). PARROT (i) isolates causal effects by comparing the neutral version of the same question with an authoritatively false version using a double-blind evaluation, (ii) quantifies confidence shifts toward the correct and imposed false responses using log-likelihood-based calibration tracking, and (iii) systematically classifies failure modes (e.g., robust correct, sycophantic agreement, reinforced error, stubborn error, self-correction, etc.) using an eight-state behavioral taxonomy. We evaluated 22 models using 1,302 MMLU-style multiple-choice questions across 13 domains and domain-specific authority templates. Findings show marked heterogeneity: advanced models (e.g., GPT-5, GPT-4.1, Claude Sonnet 4.5) exhibit low \"follow rates\" (leq 11%, GPT-5: 4\\%) and minimal accuracy loss, while older/smaller models show severe epistemic collapse (GPT-4: 80\\%, Qwen 2.5-1.5B: 94\\%). The danger is not limited to response changes; weak models reduce confidence in the correct response while increasing confidence in the imposed incorrect response. While international law and global knowledge at the domain level exhibit high fragility, elementary mathematics is relatively resilient. Consequently, we argue that the goal of \"resistance to overfitting pressure\" should be addressed as a primary objective alongside accuracy, harm avoidance, and privacy for safe deployment in the real world.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.16825", "title": "WorldGen: From Text to Traversable and Interactive 3D Worlds", "authors": [ "Dilin Wang", "Hyunyoung Jung", "Tom Monnier", "Kihyuk Sohn", "Chuhang Zou", "Xiaoyu Xiang", "Yu-Ying Yeh", "Di Liu", "Zixuan Huang", "Thu Nguyen-Phuoc", "Yuchen Fan", "Sergiu Oprea", "Ziyan Wang", "Roman Shapovalov", "Nikolaos Sarafianos", "Thibault Groueix", "Antoine Toisoul", "Prithviraj Dhar", "Xiao Chu", "Minghao Chen", "Geon Yeong Park", "Mahima Gupta", "Yassir Azziz", "Rakesh Ranjan", "Andrea Vedaldi" ], "github": "", "project_page": "", "abstract": "We introduce WorldGen, a system that enables the automatic creation of large-scale, interactive 3D worlds directly from text prompts. Our approach transforms natural language descriptions into traversable, fully textured environments that can be immediately explored or edited within standard game engines. By combining LLM-driven scene layout reasoning, procedural generation, diffusion-based 3D generation, and object-aware scene decomposition, WorldGen bridges the gap between creative intent and functional virtual spaces, allowing creators to design coherent, navigable worlds without manual modeling or specialized 3D expertise. The system is fully modular and supports fine-grained control over layout, scale, and style, producing worlds that are geometrically consistent, visually rich, and efficient to render in real time. This work represents a step towards accessible, generative world-building at scale, advancing the frontier of 3D generative AI for applications in gaming, simulation, and immersive social environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.17490", "title": "Video-R4: Reinforcing Text-Rich Video Reasoning with Visual Rumination", "authors": [ "Yolo Yunlong Tang", "Daiki Shimada", "Hang Hua", "Chao Huang", "Jing Bi", "Rogerio Feris", "Chenliang Xu" ], "github": "", "project_page": "", "abstract": "Understanding text-rich videos requires reading small, transient textual cues that often demand repeated inspection. Yet most video QA models rely on single-pass perception over fixed frames, leading to hallucinations and failures on fine-grained evidence. Inspired by how humans pause, zoom, and re-read critical regions, we introduce Video-R4 (Reinforcing Text-Rich Video Reasoning with Visual Rumination), a video reasoning LMM that performs visual rumination: iteratively selecting frames, zooming into informative regions, re-encoding retrieved pixels, and updating its reasoning state. We construct two datasets with executable rumination trajectories: Video-R4-CoT-17k for supervised practice and Video-R4-RL-30k for reinforcement learning. We propose a multi-stage rumination learning framework that progressively finetunes a 7B LMM to learn atomic and mixing visual operations via SFT and GRPO-based RL. Video-R4-7B achieves state-of-the-art results on M4-ViteVQA and further generalizes to multi-page document QA, slides QA, and generic video QA, demonstrating that iterative rumination is an effective paradigm for pixel-grounded multimodal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.13593", "title": "O-Mem: Omni Memory System for Personalized, Long Horizon, Self-Evolving Agents", "authors": [ "Piaohong Wang", "Motong Tian", "Jiaxian Li", "Yuan Liang", "Yuqing Wang", "Qianben Chen", "Tiannan Wang", "Zhicong Lu", "Jiawei Ma", "Yuchen Eleanor Jiang", "Wangchunshu Zhou" ], "github": "", "project_page": "", "abstract": "Recent advancements in LLM-powered agents have demonstrated significant potential in generating human-like responses; however, they continue to face challenges in maintaining long-term interactions within complex environments, primarily due to limitations in contextual consistency and dynamic personalization. Existing memory systems often depend on semantic grouping prior to retrieval, which can overlook semantically irrelevant yet critical user information and introduce retrieval noise. In this report, we propose the initial design of O-Mem, a novel memory framework based on active user profiling that dynamically extracts and updates user characteristics and event records from their proactive interactions with agents. O-Mem supports hierarchical retrieval of persona attributes and topic-related context, enabling more adaptive and coherent personalized responses. O-Mem achieves 51.67% on the public LoCoMo benchmark, a nearly 3% improvement upon LangMem,the previous state-of-the-art, and it achieves 62.99% on PERSONAMEM, a 3.5% improvement upon A-Mem,the previous state-of-the-art. O-Mem also boosts token and interaction response time efficiency compared to previous memory frameworks. Our work opens up promising directions for developing efficient and human-like personalized AI assistants in the future.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.16931", "title": "OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists", "authors": [ "Chenyang Shao", "Dehao Huang", "Yu Li", "Keyu Zhao", "Weiquan Lin", "Yining Zhang", "Qingbin Zeng", "Zhiyu Chen", "Tianxing Li", "Yifei Huang", "Taozhong Wu", "Xinyang Liu", "Ruotong Zhao", "Mengsheng Zhao", "Xuhua Zhang", "Yue Wang", "Yuanyi Zhen", "Fengli Xu", "Yong Li", "Tie-Yan Liu" ], "github": "", "project_page": "", "abstract": "With the rapid development of Large Language Models (LLMs), AI agents have demonstrated increasing proficiency in scientific tasks, ranging from hypothesis generation and experimental design to manuscript writing. Such agent systems are commonly referred to as \"AI Scientists.\" However, existing AI Scientists predominantly formulate scientific discovery as a standalone search or optimization problem, overlooking the fact that scientific research is inherently a social and collaborative endeavor. Real-world science relies on a complex scientific infrastructure composed of collaborative mechanisms, contribution attribution, peer review, and structured scientific knowledge networks. Due to the lack of modeling for these critical dimensions, current systems struggle to establish a genuine research ecosystem or interact deeply with the human scientific community. To bridge this gap, we introduce OmniScientist, a framework that explicitly encodes the underlying mechanisms of human research into the AI scientific workflow. OmniScientist not only achieves end-to-end automation across data foundation, literature review, research ideation, experiment automation, scientific writing, and peer review, but also provides comprehensive infrastructural support by simulating the human scientific system, comprising: (1) a structured knowledge system built upon citation networks and conceptual correlations; (2) a collaborative research protocol (OSP), which enables seamless multi-agent collaboration and human researcher participation; and (3) an open evaluation platform (ScienceArena) based on blind pairwise user voting and Elo rankings. This infrastructure empowers agents to not only comprehend and leverage human knowledge systems but also to collaborate and co-evolve, fostering a sustainable and scalable innovation ecosystem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.16110", "title": "Multi-Faceted Attack: Exposing Cross-Model Vulnerabilities in Defense-Equipped Vision-Language Models", "authors": [ "Yijun Yang", "Lichao Wang", "Jianping Zhang", "Chi Harold Liu", "Lanqing Hong", "Qiang Xu" ], "github": "https://github.com/cure-lab/MultiFacetedAttack", "project_page": "", "abstract": "The growing misuse of Vision-Language Models (VLMs) has led providers to deploy multiple safeguards, including alignment tuning, system prompts, and content moderation. However, the real-world robustness of these defenses against adversarial attacks remains underexplored. We introduce Multi-Faceted Attack (MFA), a framework that systematically exposes general safety vulnerabilities in leading defense-equipped VLMs such as GPT-4o, Gemini-Pro, and Llama-4. The core component of MFA is the Attention-Transfer Attack (ATA), which hides harmful instructions inside a meta task with competing objectives. We provide a theoretical perspective based on reward hacking to explain why this attack succeeds. To improve cross-model transferability, we further introduce a lightweight transfer-enhancement algorithm combined with a simple repetition strategy that jointly bypasses both input-level and output-level filters without model-specific fine-tuning. Empirically, we show that adversarial images optimized for one vision encoder transfer broadly to unseen VLMs, indicating that shared visual representations create a cross-model safety vulnerability. Overall, MFA achieves a 58.5% success rate and consistently outperforms existing methods. On state-of-the-art commercial models, MFA reaches a 52.8% success rate, surpassing the second-best attack by 34%. These results challenge the perceived robustness of current defense mechanisms and highlight persistent safety weaknesses in modern VLMs. Code: https://github.com/cure-lab/MultiFacetedAttack", "github_manual": "", "github_regex": "https://github.com/cure-lab/MultiFacetedAttack", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.13081", "title": "Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations", "authors": [ "Yehonatan Elisha", "Seffi Cohen", "Oren Barkan", "Noam Koenigstein" ], "github": "https://github.com/yonisGit/RFxG", "project_page": "", "abstract": "Saliency maps are widely used for visual explanations in deep learning, but a fundamental lack of consensus persists regarding their intended purpose and alignment with diverse user queries. This ambiguity hinders the effective evaluation and practical utility of explanation methods. We address this gap by introducing the Reference-Frame times Granularity (RFxG) taxonomy, a principled conceptual framework that organizes saliency explanations along two essential axes:Reference-Frame: Distinguishing between pointwise (\"Why this prediction?\") and contrastive (\"Why this and not an alternative?\") explanations. Granularity: Ranging from fine-grained class-level (e.g., \"Why Husky?\") to coarse-grained group-level (e.g., \"Why Dog?\") interpretations. Using the RFxG lens, we demonstrate critical limitations in existing evaluation metrics, which overwhelmingly prioritize pointwise faithfulness while neglecting contrastive reasoning and semantic granularity. To systematically assess explanation quality across both RFxG dimensions, we propose four novel faithfulness metrics. Our comprehensive evaluation framework applies these metrics to ten state-of-the-art saliency methods, four model architectures, and three datasets. By advocating a shift toward user-intent-driven evaluation, our work provides both the conceptual foundation and the practical tools necessary to develop visual explanations that are not only faithful to the underlying model behavior but are also meaningfully aligned with the complexity of human understanding and inquiry.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yonisGit/RFxG", "github_api_added_by": "auto" }, { "date": "2025-11-24", "arxiv_id": "2511.17450", "title": "Planning with Sketch-Guided Verification for Physics-Aware Video Generation", "authors": [ "Yidong Huang", "Zun Wang", "Han Lin", "Dong-Ki Kim", "Shayegan Omidshafiei", "Jaehong Yoon", "Yue Zhang", "Mohit Bansal" ], "github": "https://github.com/h6kplus/SketchVerify", "project_page": "", "abstract": "Recent video generation approaches increasingly rely on planning intermediate control signals such as object trajectories to improve temporal coherence and motion fidelity. However, these methods mostly employ single-shot plans that are typically limited to simple motions, or iterative refinement which requires multiple calls to the video generator, incuring high computational cost. To overcome these limitations, we propose SketchVerify, a training-free, sketch-verification-based planning framework that improves motion planning quality with more dynamically coherent trajectories (i.e., physically plausible and instruction-consistent motions) prior to full video generation by introducing a test-time sampling and verification loop. Given a prompt and a reference image, our method predicts multiple candidate motion plans and ranks them using a vision-language verifier that jointly evaluates semantic alignment with the instruction and physical plausibility. To efficiently score candidate motion plans, we render each trajectory as a lightweight video sketch by compositing objects over a static background, which bypasses the need for expensive, repeated diffusion-based synthesis while achieving comparable performance. We iteratively refine the motion plan until a satisfactory one is identified, which is then passed to the trajectory-conditioned generator for final synthesis. Experiments on WorldModelBench and PhyWorldBench demonstrate that our method significantly improves motion quality, physical realism, and long-term consistency compared to competitive baselines while being substantially more efficient. Our ablation study further shows that scaling up the number of trajectory candidates consistently enhances overall performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/h6kplus/SketchVerify", "github_api_added_by": "auto" }, { "date": "2025-11-24", "arxiv_id": "2511.17199", "title": "VLA-4D: Embedding 4D Awareness into Vision-Language-Action Models for SpatioTemporally Coherent Robotic Manipulation", "authors": [ "Hanyu Zhou", "Chuanhao Ma", "Gim Hee Lee" ], "github": "", "project_page": "", "abstract": "Vision-language-action (VLA) models show potential for general robotic tasks, but remain challenging in spatiotemporally coherent manipulation, which requires fine-grained representations. Typically, existing methods embed 3D positions into visual representations to enhance the spatial precision of actions. However, these methods struggle to achieve temporally coherent control over action execution. In this work, we propose VLA-4D, a general VLA model with 4D awareness for spatiotemporally coherent robotic manipulation. Our model is guided by two key designs: 1) 4D-aware visual representation. We extract visual features, embed 1D time into 3D positions for 4D embeddings, and fuse them into a unified visual representation via a cross-attention mechanism. 2) Spatiotemporal action representation. We extend conventional spatial action representations with temporal information to enable the spatiotemporal planning, and align the multimodal representations into the LLM for spatiotemporal action prediction. Within this unified framework, the designed visual and action representations jointly make robotic manipulation spatially-smooth and temporally-coherent. In addition, we extend the VLA dataset with temporal action annotations for fine-tuning our model. Extensive experiments have been conducted to verify the superiority of our method across different tasks of robotic manipulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.15299", "title": "Taming Generative Synthetic Data for X-ray Prohibited Item Detection", "authors": [ "Jialong Sun", "Hongguang Zhu", "Weizhe Liu", "Yunda Sun", "Renshuai Tao", "Yunchao Wei" ], "github": "https://github.com/pILLOW-1/Xsyn", "project_page": "", "abstract": "Training prohibited item detection models requires a large amount of X-ray security images, but collecting and annotating these images is time-consuming and laborious. To address data insufficiency, X-ray security image synthesis methods composite images to scale up datasets. However, previous methods primarily follow a two-stage pipeline, where they implement labor-intensive foreground extraction in the first stage and then composite images in the second stage. Such a pipeline introduces inevitable extra labor cost and is not efficient. In this paper, we propose a one-stage X-ray security image synthesis pipeline (Xsyn) based on text-to-image generation, which incorporates two effective strategies to improve the usability of synthetic images. The Cross-Attention Refinement (CAR) strategy leverages the cross-attention map from the diffusion model to refine the bounding box annotation. The Background Occlusion Modeling (BOM) strategy explicitly models background occlusion in the latent space to enhance imaging complexity. To the best of our knowledge, compared with previous methods, Xsyn is the first to achieve high-quality X-ray security image synthesis without extra labor cost. Experiments demonstrate that our method outperforms all previous methods with 1.2% mAP improvement, and the synthetic images generated by our method are beneficial to improve prohibited item detection performance across various X-ray security datasets and detectors. Code is available at https://github.com/pILLOW-1/Xsyn/.", "github_manual": "", "github_regex": "https://github.com/pILLOW-1/Xsyn", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.15210", "title": "Unveiling Intrinsic Dimension of Texts: from Academic Abstract to Creative Story", "authors": [ "Vladislav Pedashenko", "Laida Kushnareva", "Yana Khassan Nibal", "Eduard Tulchinskii", "Kristian Kuznetsov", "Vladislav Zharchinskii", "Yury Maximov", "Irina Piontkovskaya" ], "github": "", "project_page": "", "abstract": "Intrinsic dimension (ID) is an important tool in modern LLM analysis, informing studies of training dynamics, scaling behavior, and dataset structure, yet its textual determinants remain underexplored. We provide the first comprehensive study grounding ID in interpretable text properties through cross-encoder analysis, linguistic features, and sparse autoencoders (SAEs). In this work, we establish three key findings. First, ID is complementary to entropy-based metrics: after controlling for length, the two are uncorrelated, with ID capturing geometric complexity orthogonal to prediction quality. Second, ID exhibits robust genre stratification: scientific prose shows low ID (~8), encyclopedic content medium ID (~9), and creative/opinion writing high ID (~10.5) across all models tested. This reveals that contemporary LLMs find scientific text \"representationally simple\" while fiction requires additional degrees of freedom. Third, using SAEs, we identify causal features: scientific signals (formal tone, report templates, statistics) reduce ID; humanized signals (personalization, emotion, narrative) increase it. Steering experiments confirm these effects are causal. Thus, for contemporary models, scientific writing appears comparatively \"easy\", whereas fiction, opinion, and affect add representational degrees of freedom. Our multi-faceted analysis provides practical guidance for the proper use of ID and the sound interpretation of ID-based results.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.17074", "title": "Diversity Has Always Been There in Your Visual Autoregressive Models", "authors": [ "Tong Wang", "Guanyu Yang", "Nian Liu", "Kai Wang", "Yaxing Wang", "Abdelrahman M Shaker", "Salman Khan", "Fahad Shahbaz Khan", "Senmao Li" ], "github": "https://github.com/wangtong627/DiverseVAR", "project_page": "", "abstract": "Visual Autoregressive (VAR) models have recently garnered significant attention for their innovative next-scale prediction paradigm, offering notable advantages in both inference efficiency and image quality compared to traditional multi-step autoregressive (AR) and diffusion models. However, despite their efficiency, VAR models often suffer from the diversity collapse i.e., a reduction in output variability, analogous to that observed in few-step distilled diffusion models. In this paper, we introduce DiverseVAR, a simple yet effective approach that restores the generative diversity of VAR models without requiring any additional training. Our analysis reveals the pivotal component of the feature map as a key factor governing diversity formation at early scales. By suppressing the pivotal component in the model input and amplifying it in the model output, DiverseVAR effectively unlocks the inherent generative potential of VAR models while preserving high-fidelity synthesis. Empirical results demonstrate that our approach substantially enhances generative diversity with only neglectable performance influences. Our code will be publicly released at https://github.com/wangtong627/DiverseVAR.", "github_manual": "", "github_regex": "https://github.com/wangtong627/DiverseVAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.17487", "title": "Downscaling Intelligence: Exploring Perception and Reasoning Bottlenecks in Small Multimodal Models", "authors": [ "Mark Endo", "Serena Yeung-Levy" ], "github": "", "project_page": "", "abstract": "Scaling up multimodal models has enabled remarkable advances in visual understanding and reasoning, but practical demands call for smaller, efficient systems. In this work, we conduct a principled analysis of downscaling intelligence in multimodal models, examining how reduced large language model (LLM) capacity affects multimodal capabilities. Our initial findings reveal an interesting trend: LLM downscaling disproportionately affects visual capabilities, rather than abilities inherited from the LLM. We then examine whether this drop mainly reflects the expected decline in visual reasoning or a more fundamental loss of perceptual abilities. Isolating the effect of LLM downscaling on perception, we find performance still drops sharply, often matching or exceeding the impact on reasoning. To address this bottleneck, we introduce visual extraction tuning, which explicitly trains the model to extract instruction-relevant visual details consistently across tasks. With these extracted visual details, we then apply step-by-step reasoning to generate answers. Together, these components form our Extract+Think approach, setting a new standard for efficiency and performance in this space.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.17344", "title": "Loomis Painter: Reconstructing the Painting Process", "authors": [ "Markus Pobitzer", "Chang Liu", "Chenyi Zhuang", "Teng Long", "Bin Ren", "Nicu Sebe" ], "github": "https://github.com/Markus-Pobitzer/wlp", "project_page": "", "abstract": "Step-by-step painting tutorials are vital for learning artistic techniques, but existing video resources (e.g., YouTube) lack interactivity and personalization. While recent generative models have advanced artistic image synthesis, they struggle to generalize across media and often show temporal or structural inconsistencies, hindering faithful reproduction of human creative workflows. To address this, we propose a unified framework for multi-media painting process generation with a semantics-driven style control mechanism that embeds multiple media into a diffusion models conditional space and uses cross-medium style augmentation. This enables consistent texture evolution and process transfer across styles. A reverse-painting training strategy further ensures smooth, human-aligned generation. We also build a large-scale dataset of real painting processes and evaluate cross-media consistency, temporal coherence, and final-image fidelity, achieving strong results on LPIPS, DINO, and CLIP metrics. Finally, our Perceptual Distance Profile (PDP) curve quantitatively models the creative sequence, i.e., composition, color blocking, and detail refinement, mirroring human artistic progression.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Markus-Pobitzer/wlp", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.14899", "title": "InstructMix2Mix: Consistent Sparse-View Editing Through Multi-View Model Personalization", "authors": [ "Daniel Gilo", "Or Litany" ], "github": "", "project_page": "", "abstract": "We address the task of multi-view image editing from sparse input views, where the inputs can be seen as a mix of images capturing the scene from different viewpoints. The goal is to modify the scene according to a textual instruction while preserving consistency across all views. Existing methods, based on per-scene neural fields or temporal attention mechanisms, struggle in this setting, often producing artifacts and incoherent edits. We propose InstructMix2Mix (I-Mix2Mix), a framework that distills the editing capabilities of a 2D diffusion model into a pretrained multi-view diffusion model, leveraging its data-driven 3D prior for cross-view consistency. A key contribution is replacing the conventional neural field consolidator in Score Distillation Sampling (SDS) with a multi-view diffusion student, which requires novel adaptations: incremental student updates across timesteps, a specialized teacher noise scheduler to prevent degeneration, and an attention modification that enhances cross-view coherence without additional cost. Experiments demonstrate that I-Mix2Mix significantly improves multi-view consistency while maintaining high per-frame edit quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.14806", "title": "MergeDNA: Context-aware Genome Modeling with Dynamic Tokenization through Token Merging", "authors": [ "Siyuan Li", "Kai Yu", "Anna Wang", "Zicheng Liu", "Chang Yu", "Jingbo Zhou", "Qirong Yang", "Yucheng Guo", "Xiaoming Zhang", "Stan Z. Li" ], "github": "", "project_page": "", "abstract": "Modeling genomic sequences faces two unsolved challenges: the information density varies widely across different regions, while there is no clearly defined minimum vocabulary unit. Relying on either four primitive bases or independently designed DNA tokenizers, existing approaches with naive masked language modeling pre-training often fail to adapt to the varying complexities of genomic sequences. Leveraging Token Merging techniques, this paper introduces a hierarchical architecture that jointly optimizes a dynamic genomic tokenizer and latent Transformers with context-aware pre-training tasks. As for network structures, the tokenization module automatically chunks adjacent bases into words by stacking multiple layers of the differentiable token merging blocks with local-window constraints, then a Latent Encoder captures the global context of these merged words by full-attention blocks. Symmetrically employing a Latent Decoder and a Local Decoder, MergeDNA learns with two pre-training tasks: Merged Token Reconstruction simultaneously trains the dynamic tokenization module and adaptively filters important tokens, while Adaptive Masked Token Modeling learns to predict these filtered tokens to capture informative contents. Extensive experiments show that MergeDNA achieves superior performance on three popular DNA benchmarks and several multi-omics tasks with fine-tuning or zero-shot evaluation, outperforming typical tokenization methods and large-scale DNA foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.15462", "title": "Insights from the ICLR Peer Review and Rebuttal Process", "authors": [ "Amir Hossein Kargaran", "Nafiseh Nikeghbal", "Jing Yang", "Nedjma Ousidhoum" ], "github": "https://github.com/papercopilot/iclr-insights", "project_page": "", "abstract": "Peer review is a cornerstone of scientific publishing, including at premier machine learning conferences such as ICLR. As submission volumes increase, understanding the nature and dynamics of the review process is crucial for improving its efficiency, effectiveness, and the quality of published papers. We present a large-scale analysis of the ICLR 2024 and 2025 peer review processes, focusing on before- and after-rebuttal scores and reviewer-author interactions. We examine review scores, author-reviewer engagement, temporal patterns in review submissions, and co-reviewer influence effects. Combining quantitative analyses with LLM-based categorization of review texts and rebuttal discussions, we identify common strengths and weaknesses for each rating group, as well as trends in rebuttal strategies that are most strongly associated with score changes. Our findings show that initial scores and the ratings of co-reviewers are the strongest predictors of score changes during the rebuttal, pointing to a degree of reviewer influence. Rebuttals play a valuable role in improving outcomes for borderline papers, where thoughtful author responses can meaningfully shift reviewer perspectives. More broadly, our study offers evidence-based insights to improve the peer review process, guiding authors on effective rebuttal strategies and helping the community design fairer and more efficient review processes. Our code and score changes data are available at https://github.com/papercopilot/iclr-insights.", "github_manual": "", "github_regex": "https://github.com/papercopilot/iclr-insights", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-24", "arxiv_id": "2511.17127", "title": "Training Foundation Models on a Full-Stack AMD Platform: Compute, Networking, and System Design", "authors": [ "Quentin Anthony", "Yury Tokpanov", "Skyler Szot", "Srivatsan Rajagopal", "Praneeth Medepalli", "Rishi Iyer", "Vasu Shyam", "Anna Golubeva", "Ansh Chaurasia", "Xiao Yang", "Tomas Figliolia", "Robert Washbourne", "Drew Thorstensen", "Amartey Pearson", "Zack Grossbart", "Jason van Patten", "Emad Barsoum", "Zhenyu Gu", "Yao Fu", "Beren Millidge" ], "github": "", "project_page": "", "abstract": "We report on the first large-scale mixture-of-experts (MoE) pretraining study on pure AMD hardware, utilizing both MI300X GPUs with Pollara interconnect. We distill practical guidance for both systems and model design. On the systems side, we deliver a comprehensive cluster and networking characterization: microbenchmarks for all core collectives (all-reduce, reduce-scatter, all-gather, broadcast) across message sizes and GPU counts on Pollara. To our knowledge, this is the first at this scale. We further provide MI300X microbenchmarks on kernel sizing and memory bandwidth to inform model design. On the modeling side, we introduce and apply MI300X-aware transformer sizing rules for attention and MLP blocks and justify MoE widths that jointly optimize training throughput and inference latency. We describe our training stack in depth, including often-ignored utilities such as fault-tolerance and checkpoint-reshaping, as well as detailed information on our training recipe. We also provide a preview of our model architecture and base model - ZAYA1 (760M active, 8.3B total parameters MoE) - which will be further improved upon in forthcoming papers. ZAYA1-base achieves performance comparable to leading base models such as Qwen3-4B and Gemma3-12B at its scale and larger, and outperforms models including Llama-3-8B and OLMoE across reasoning, mathematics, and coding benchmarks. Together, these results demonstrate that the AMD hardware, network, and software stack are mature and optimized enough for competitive large-scale pretraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.18423", "title": "General Agentic Memory Via Deep Research", "authors": [ "B. Y. Yan", "Chaofan Li", "Hongjin Qian", "Shuqi Lu", "Zheng Liu" ], "github": "", "project_page": "", "abstract": "Memory is critical for AI agents, yet the widely-adopted static memory, aiming to create readily available memory in advance, is inevitably subject to severe information loss. To address this limitation, we propose a novel framework called general agentic memory (GAM). GAM follows the principle of \"just-in time (JIT) compilation\" where it focuses on creating optimized contexts for its client at runtime while keeping only simple but useful memory during the offline stage. To this end, GAM employs a duo-design with the following components. 1) Memorizer, which highlights key historical information using a lightweight memory, while maintaining complete historical information within a universal page-store. 2) Researcher, which retrieves and integrates useful information from the page-store for its online request guided by the pre-constructed memory. This design allows GAM to effectively leverage the agentic capabilities and test-time scalability of frontier large language models (LLMs), while also facilitating end-to-end performance optimization through reinforcement learning. In our experimental study, we demonstrate that GAM achieves substantial improvement on various memory-grounded task completion scenarios against existing memory systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.19304", "title": "AutoEnv: Automated Environments for Measuring Cross-Environment Agent Learning", "authors": [ "Jiayi Zhang", "Yiran Peng", "Fanqi Kong", "Yang Cheng", "Yifan Wu", "Zhaoyang Yu", "Jinyu Xiang", "Jianhao Ruan", "Jinlin Wang", "Maojia Song", "HongZhang Liu", "Xiangru Tang", "Bang Liu", "Chenglin Wu", "Yuyu Luo" ], "github": "https://github.com/FoundationAgents/AutoEnv", "project_page": "", "abstract": "Humans naturally adapt to diverse environments by learning underlying rules across worlds with different dynamics, observations, and reward structures. In contrast, existing agents typically demonstrate improvements via self-evolving within a single domain, implicitly assuming a fixed environment distribution. Cross-environment learning has remained largely unmeasured: there is no standard collection of controllable, heterogeneous environments, nor a unified way to represent how agents learn. We address these gaps in two steps. First, we propose AutoEnv, an automated framework that treats environments as factorizable distributions over transitions, observations, and rewards, enabling low-cost (4.12 USD on average) generation of heterogeneous worlds. Using AutoEnv, we construct AutoEnv-36, a dataset of 36 environments with 358 validated levels, on which seven language models achieve 12-49% normalized reward, demonstrating the challenge of AutoEnv-36. Second, we formalize agent learning as a component-centric process driven by three stages of Selection, Optimization, and Evaluation applied to an improvable agent component. Using this formulation, we design eight learning methods and evaluate them on AutoEnv-36. Empirically, the gain of any single learning method quickly decrease as the number of environments increases, revealing that fixed learning methods do not scale across heterogeneous environments. Environment-adaptive selection of learning methods substantially improves performance but exhibits diminishing returns as the method space expands. These results highlight both the necessity and the current limitations of agent learning for scalable cross-environment generalization, and position AutoEnv and AutoEnv-36 as a testbed for studying cross-environment agent learning. The code is avaiable at https://github.com/FoundationAgents/AutoEnv.", "github_manual": "", "github_regex": "https://github.com/FoundationAgents/AutoEnv", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.15567", "title": "Computer-Use Agents as Judges for Generative User Interface", "authors": [ "Kevin Qinghong Lin", "Siyuan Hu", "Linjie Li", "Zhengyuan Yang", "Lijuan Wang", "Philip Torr", "Mike Zheng Shou" ], "github": "https://github.com/showlab/AUI", "project_page": "", "abstract": "Computer-Use Agents (CUA) are becoming increasingly capable of autonomously operating digital environments through Graphical User Interfaces (GUI). Yet, most GUI remain designed primarily for humans--prioritizing aesthetics and usability--forcing agents to adopt human-oriented behaviors that are unnecessary for efficient task execution. At the same time, rapid advances in coding-oriented language models (Coder) have transformed automatic GUI design. This raises a fundamental question: Can CUA as judges to assist Coder for automatic GUI design? To investigate, we introduce AUI-Gym, a benchmark for Automatic GUI development spanning 52 applications across diverse domains. Using language models, we synthesize 1560 tasks that simulate real-world scenarios. To ensure task reliability, we further develop a verifier that programmatically checks whether each task is executable within its environment. Building on this, we propose a Coder-CUA in Collaboration framework: the Coder acts as Designer, generating and revising websites, while the CUA serves as Judge, evaluating functionality and refining designs. Success is measured not by visual appearance, but by task solvability and CUA navigation success rate. To turn CUA feedback into usable guidance, we design a CUA Dashboard that compresses multi-step navigation histories into concise visual summaries, offering interpretable guidance for iterative redesign. By positioning agents as both designers and judges, our framework shifts interface design toward agent-native efficiency and reliability. Our work takes a step toward shifting agents from passive use toward active participation in digital environments. Our code and dataset are available at https://github.com/showlab/AUI.", "github_manual": "", "github_regex": "https://github.com/showlab/AUI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.19365", "title": "DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation", "authors": [ "Zehong Ma", "Longhui Wei", "Shuai Wang", "Shiliang Zhang", "Qi Tian" ], "github": "https://github.com/Zehong-Ma/DeCo", "project_page": "", "abstract": "Pixel diffusion aims to generate images directly in pixel space in an end-to-end fashion. This approach avoids the limitations of VAE in the two-stage latent diffusion, offering higher model capacity. Existing pixel diffusion models suffer from slow training and inference, as they usually model both high-frequency signals and low-frequency semantics within a single diffusion transformer (DiT). To pursue a more efficient pixel diffusion paradigm, we propose the frequency-DeCoupled pixel diffusion framework. With the intuition to decouple the generation of high and low frequency components, we leverage a lightweight pixel decoder to generate high-frequency details conditioned on semantic guidance from the DiT. This thus frees the DiT to specialize in modeling low-frequency semantics. In addition, we introduce a frequency-aware flow-matching loss that emphasizes visually salient frequencies while suppressing insignificant ones. Extensive experiments show that DeCo achieves superior performance among pixel diffusion models, attaining FID of 1.62 (256x256) and 2.22 (512x512) on ImageNet, closing the gap with latent diffusion methods. Furthermore, our pretrained text-to-image model achieves a leading overall score of 0.86 on GenEval in system-level comparison. Codes are publicly available at https://github.com/Zehong-Ma/DeCo.", "github_manual": "", "github_regex": "https://github.com/Zehong-Ma/DeCo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.19399", "title": "DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research", "authors": [ "Rulin Shao", "Akari Asai", "Shannon Zejiang Shen", "Hamish Ivison", "Varsha Kishore", "Jingming Zhuo", "Xinran Zhao", "Molly Park", "Samuel G. Finlayson", "David Sontag", "Tyler Murray", "Sewon Min", "Pradeep Dasigi", "Luca Soldaini", "Faeze Brahman", "Wen-tau Yih", "Tongshuang Wu", "Luke Zettlemoyer", "Yoon Kim", "Hannaneh Hajishirzi", "Pang Wei Koh" ], "github": "", "project_page": "", "abstract": "Deep research models perform multi-step research to produce long-form, well-attributed answers. However, most open deep research models are trained on easily verifiable short-form QA tasks via reinforcement learning with verifiable rewards (RLVR), which does not extend to realistic long-form tasks. We address this with Reinforcement Learning with Evolving Rubrics (RLER), in which we construct and maintain rubrics that co-evolve with the policy model during training; this allows the rubrics to incorporate information that the model has newly explored and to provide discriminative, on-policy feedback. Using RLER, we develop Deep Research Tulu (DR Tulu-8B), the first open model that is directly trained for open-ended, long-form deep research. Across four long-form deep research benchmarks in science, healthcare and general domains, DR Tulu substantially outperforms existing open deep research models, and matches or exceeds proprietary deep research systems, while being significantly smaller and cheaper per query. To facilitate future research, we release all data, models, and code, including our new MCP-based agent infrastructure for deep research systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.18050", "title": "UltraFlux: Data-Model Co-Design for High-quality Native 4K Text-to-Image Generation across Diverse Aspect Ratios", "authors": [ "Tian Ye", "Song Fei", "Lei Zhu" ], "github": "", "project_page": "", "abstract": "Diffusion transformers have recently delivered strong text-to-image generation around 1K resolution, but we show that extending them to native 4K across diverse aspect ratios exposes a tightly coupled failure mode spanning positional encoding, VAE compression, and optimization. Tackling any of these factors in isolation leaves substantial quality on the table. We therefore take a data-model co-design view and introduce UltraFlux, a Flux-based DiT trained natively at 4K on MultiAspect-4K-1M, a 1M-image 4K corpus with controlled multi-AR coverage, bilingual captions, and rich VLM/IQA metadata for resolution- and AR-aware sampling. On the model side, UltraFlux couples (i) Resonance 2D RoPE with YaRN for training-window-, frequency-, and AR-aware positional encoding at 4K; (ii) a simple, non-adversarial VAE post-training scheme that improves 4K reconstruction fidelity; (iii) an SNR-Aware Huber Wavelet objective that rebalances gradients across timesteps and frequency bands; and (iv) a Stage-wise Aesthetic Curriculum Learning strategy that concentrates high-aesthetic supervision on high-noise steps governed by the model prior. Together, these components yield a stable, detail-preserving 4K DiT that generalizes across wide, square, and tall ARs. On the Aesthetic-Eval at 4096 benchmark and multi-AR 4K settings, UltraFlux consistently outperforms strong open-source baselines across fidelity, aesthetic, and alignment metrics, and-with a LLM prompt refiner-matches or surpasses the proprietary Seedream 4.0.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.19401", "title": "In-Video Instructions: Visual Signals as Generative Control", "authors": [ "Gongfan Fang", "Xinyin Ma", "Xinchao Wang" ], "github": "https://github.com/VainF/In-Video-Instructions", "project_page": "", "abstract": "Large-scale video generative models have recently demonstrated strong visual capabilities, enabling the prediction of future frames that adhere to the logical and physical cues in the current observation. In this work, we investigate whether such capabilities can be harnessed for controllable image-to-video generation by interpreting visual signals embedded within the frames as instructions, a paradigm we term In-Video Instruction. In contrast to prompt-based control, which provides textual descriptions that are inherently global and coarse, In-Video Instruction encodes user guidance directly into the visual domain through elements such as overlaid text, arrows, or trajectories. This enables explicit, spatial-aware, and unambiguous correspondences between visual subjects and their intended actions by assigning distinct instructions to different objects. Extensive experiments on three state-of-the-art generators, including Veo 3.1, Kling 2.5, and Wan 2.2, show that video models can reliably interpret and execute such visually embedded instructions, particularly in complex multi-object scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/VainF/In-Video-Instructions", "github_api_added_by": "auto" }, { "date": "2025-11-25", "arxiv_id": "2511.17006", "title": "Budget-Aware Tool-Use Enables Effective Agent Scaling", "authors": [ "Tengxiao Liu", "Zifeng Wang", "Jin Miao", "I-Hung Hsu", "Jun Yan", "Jiefeng Chen", "Rujun Han", "Fangyuan Xu", "Yanfei Chen", "Ke Jiang", "Samira Daruki", "Yi Liang", "William Yang Wang", "Tomas Pfister", "Chen-Yu Lee" ], "github": "https://github.com/unclecode/crawl4ai", "project_page": "", "abstract": "Scaling test-time computation improves performance across different tasks on large language models (LLMs), which has also been extended to tool-augmented agents. For these agents, scaling involves not only \"thinking\" in tokens but also \"acting\" via tool calls. The number of tool calls directly bounds the agent's interaction with the external environment. However, we find that simply granting agents a larger tool-call budget fails to improve performance, as they lack \"budget awareness\" and quickly hit a performance ceiling. To address this, we study how to scale such agents effectively under explicit tool-call budgets, focusing on web search agents. We first introduce the Budget Tracker, a lightweight plug-in that provides the agent with continuous budget awareness, enabling simple yet effective scaling. We further develop BATS (Budget Aware Test-time Scaling), an advanced framework that leverages this awareness to dynamically adapt its planning and verification strategy, deciding whether to \"dig deeper\" on a promising lead or \"pivot\" to new paths based on remaining resources. To analyze cost-performance scaling in a controlled manner, we formalize a unified cost metric that jointly accounts for token and tool consumption. We provide the first systematic study on budget-constrained agents, showing that budget-aware methods produce more favorable scaling curves and push the cost-performance Pareto frontier. Our work offers empirical insights toward a more transparent and principled understanding of scaling in tool-augmented agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/unclecode/crawl4ai", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.17986", "title": "Plan-X: Instruct Video Generation via Semantic Planning", "authors": [ "Lun Huang", "You Xie", "Hongyi Xu", "Tianpei Gu", "Chenxu Zhang", "Guoxian Song", "Zenan Li", "Xiaochen Zhao", "Linjie Luo", "Guillermo Sapiro" ], "github": "", "project_page": "", "abstract": "Diffusion Transformers have demonstrated remarkable capabilities in visual synthesis, yet they often struggle with high-level semantic reasoning and long-horizon planning. This limitation frequently leads to visual hallucinations and mis-alignments with user instructions, especially in scenarios involving complex scene understanding, human-object interactions, multi-stage actions, and in-context motion reasoning. To address these challenges, we propose Plan-X, a framework that explicitly enforces high-level semantic planning to instruct video generation process. At its core lies a Semantic Planner, a learnable multimodal language model that reasons over the user's intent from both text prompts and visual context, and autoregressively generates a sequence of text-grounded spatio-temporal semantic tokens. These semantic tokens, complementary to high-level text prompt guidance, serve as structured \"semantic sketches\" over time for the video diffusion model, which has its strength at synthesizing high-fidelity visual details. Plan-X effectively integrates the strength of language models in multimodal in-context reasoning and planning, together with the strength of diffusion models in photorealistic video synthesis. Extensive experiments demonstrate that our framework substantially reduces visual hallucinations and enables fine-grained, instruction-aligned video generation consistent with multimodal context.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.17803", "title": "Pillar-0: A New Frontier for Radiology Foundation Models", "authors": [ "Kumar Krishna Agrawal", "Longchao Liu", "Long Lian", "Michael Nercessian", "Natalia Harguindeguy", "Yufu Wu", "Peter Mikhael", "Gigin Lin", "Lecia V. Sequist", "Florian Fintelmann", "Trevor Darrell", "Yutong Bai", "Maggie Chung", "Adam Yala" ], "github": "https://github.com/YalaLab/pillar-pretrain", "project_page": "", "abstract": "Radiology plays an integral role in modern medicine, yet rising imaging volumes have far outpaced workforce growth. Foundation models offer a path toward assisting with the full spectrum of radiology tasks, but existing medical models remain limited: they process volumetric CT and MRI as low-fidelity 2D slices, discard critical grayscale contrast information, and lack evaluation frameworks that reflect real clinical practice. We introduce Pillar-0, a radiology foundation model pretrained on 42,990 abdomen-pelvis CTs, 86,411 chest CTs, 14,348 head CTs, and 11,543 breast MRIs from a large academic center, together with RATE, a scalable framework that extracts structured labels for 366 radiologic findings with near-perfect accuracy using LLMs. Across internal test sets of 14,230 abdomen-pelvis CTs, 10,646 chest CTs, 4,906 head CTs, and 1,585 breast MRIs, Pillar-0 establishes a new performance frontier, achieving mean AUROCs of 86.4, 88.0, 90.1, and 82.9, outperforming MedGemma (Google), MedImageInsight (Microsoft), Lingshu (Alibaba), and Merlin (Stanford) by 7.8-15.8 AUROC points and ranking best in 87.2\\% (319/366) tasks. Pillar-0 similarly outperforms all baselines in an external validation on the Stanford Abdominal CT dataset, including Merlin (82.2 vs 80.6 AUROC). Pillar-0 extends to tasks beyond its pretraining, such as long-horizon lung cancer risk prediction, where it improves upon the state-of-the-art Sybil by 3.0 C-index points on NLST, and generalizes with gains of 5.9 (MGH) and 1.9 (CGMH). In brain hemorrhage detection, Pillar-0 obtained a >95 AUROC when using only 1/20th of the data of the next most sample efficient baseline. Pillar-0 and RATE together provide an open, clinically rigorous foundation for building high-performance radiology systems, enabling applications that were previously infeasible due to computational, data, and evaluation constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YalaLab/pillar-pretrain", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.17729", "title": "M3-Bench: Multi-Modal, Multi-Hop, Multi-Threaded Tool-Using MLLM Agent Benchmark", "authors": [ "Yang Zhou", "Mingyu Zhao", "Zhenting Wang", "Difei Gu", "Bangwei Guo", "Ruosong Ye", "Ligong Han", "Can Jin", "Dimitris N. Metaxas" ], "github": "https://github.com/EtaYang10th/Open-M3-Bench", "project_page": "", "abstract": "We present M^3-Bench, the first benchmark for evaluating multimodal tool use under the Model Context Protocol. The benchmark targets realistic, multi-hop and multi-threaded workflows that require visual grounding and textual reasoning, cross-tool dependencies, and persistence of intermediate resources across steps. We introduce a similarity-driven alignment that serializes each tool call, embeds signatures with a sentence encoder, and performs similarity-bucketed Hungarian matching to obtain auditable one-to-one correspondences. On top of this alignment, we report interpretable metrics that decouple semantic fidelity from workflow consistency. The benchmark spans 28 servers with 231 tools, and provides standardized trajectories curated through an Executor & Judge pipeline with human verification; an auxiliary four large language models (LLMs) judge ensemble reports end-task Task Completion and information grounding. Evaluations of representative state-of-the-art Multimodal LLMs (MLLMs) reveal persistent gaps in multimodal MCP tool use, particularly in argument fidelity and structure consistency, underscoring the need for methods that jointly reason over images, text, and tool graphs. Our Benchmark's anonymous repository is at https://github.com/EtaYang10th/Open-M3-Bench", "github_manual": "", "github_regex": "https://github.com/EtaYang10th/Open-M3-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.18870", "title": "HunyuanVideo 1.5 Technical Report", "authors": [ "Bing Wu", "Chang Zou", "Changlin Li", "Duojun Huang", "Fang Yang", "Hao Tan", "Jack Peng", "Jianbing Wu", "Jiangfeng Xiong", "Jie Jiang", "Linus", "Patrol", "Peizhen Zhang", "Peng Chen", "Penghao Zhao", "Qi Tian", "Songtao Liu", "Weijie Kong", "Weiyan Wang", "Xiao He", "Xin Li", "Xinchi Deng", "Xuefei Zhe", "Yang Li", "Yanxin Long", "Yuanbo Peng", "Yue Wu", "Yuhong Liu", "Zhenyu Wang", "Zuozhuo Dai", "Bo Peng", "Coopers Li", "Gu Gong", "Guojian Xiao", "Jiahe Tian", "Jiaxin Lin", "Jie Liu", "Jihong Zhang", "Jiesong Lian", "Kaihang Pan", "Lei Wang", "Lin Niu", "Mingtao Chen", "Mingyang Chen", "Mingzhe Zheng", "Miles Yang", "Qiangqiang Hu", "Qi Yang", "Qiuyong Xiao", "Runzhou Wu", "Ryan Xu", "Rui Yuan", "Shanshan Sang", "Shisheng Huang", "Siruis Gong", "Shuo Huang", "Weiting Guo", "Xiang Yuan", "Xiaojia Chen", "Xiawei Hu", "Wenzhi Sun", "Xiele Wu", "Xianshun Ren", "Xiaoyan Yuan", "Xiaoyue Mi", "Yepeng Zhang", "Yifu Sun", "Yiting Lu", "Yitong Li", "You Huang", "Yu Tang", "Yixuan Li", "Yuhang Deng", "Yuan Zhou", "Zhichao Hu", "Zhiguang Liu", "Zhihe Yang", "Zilin Yang", "Zhenzhi Lu", "Zixiang Zhou", "Zhao Zhong" ], "github": "https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5", "project_page": "", "abstract": "We present HunyuanVideo 1.5, a lightweight yet powerful open-source video generation model that achieves state-of-the-art visual quality and motion coherence with only 8.3 billion parameters, enabling efficient inference on consumer-grade GPUs. This achievement is built upon several key components, including meticulous data curation, an advanced DiT architecture featuring selective and sliding tile attention (SSTA), enhanced bilingual understanding through glyph-aware text encoding, progressive pre-training and post-training, and an efficient video super-resolution network. Leveraging these designs, we developed a unified framework capable of high-quality text-to-video and image-to-video generation across multiple durations and resolutions.Extensive experiments demonstrate that this compact and proficient model establishes a new state-of-the-art among open-source video generation models. By releasing the code and model weights, we provide the community with a high-performance foundation that lowers the barrier to video creation and research, making advanced video generation accessible to a broader audience. All open-source assets are publicly available at https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5.", "github_manual": "", "github_regex": "https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.19418", "title": "Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens", "authors": [ "Yiming Qin", "Bomin Wei", "Jiaxin Ge", "Konstantinos Kallidromitis", "Stephanie Fu", "Trevor Darrell", "Xudong Wang" ], "github": "https://github.com/Wakals/CoVT", "project_page": "", "abstract": "Vision-Language Models (VLMs) excel at reasoning in linguistic space but struggle with perceptual understanding that requires dense visual perception, e.g., spatial reasoning and geometric awareness. This limitation stems from the fact that current VLMs have limited mechanisms to capture dense visual information across spatial dimensions. We introduce Chain-of-Visual-Thought (COVT), a framework that enables VLMs to reason not only in words but also through continuous visual tokens-compact latent representations that encode rich perceptual cues. Within a small budget of roughly 20 tokens, COVT distills knowledge from lightweight vision experts, capturing complementary properties such as 2D appearance, 3D geometry, spatial layout, and edge structure. During training, the VLM with COVT autoregressively predicts these visual tokens to reconstruct dense supervision signals (e.g., depth, segmentation, edges, and DINO features). At inference, the model reasons directly in the continuous visual token space, preserving efficiency while optionally decoding dense predictions for interpretability. Evaluated across more than ten diverse perception benchmarks, including CV-Bench, MMVP, RealWorldQA, MMStar, WorldMedQA, and HRBench, integrating COVT into strong VLMs such as Qwen2.5-VL and LLaVA consistently improves performance by 3% to 16% and demonstrates that compact continuous visual thinking enables more precise, grounded, and interpretable multimodal intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Wakals/CoVT", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.13288", "title": "Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO", "authors": [ "Haoyang Hong", "Jiajun Yin", "Yuan Wang", "Jingnan Liu", "Zhe Chen", "Ailing Yu", "Ji Li", "Zhiling Ye", "Hansong Xiao", "Yefei Chen", "Hualei Zhou", "Yun Yue", "Minghui Yang", "Chunxiao Guo", "Junwei Liu", "Peng Wei", "Jinjie Gu" ], "github": "", "project_page": "", "abstract": "Multi-agent systems perform well on general reasoning tasks. However, the lack of training in specialized areas hinders their accuracy. Current training methods train a unified large language model (LLM) for all agents in the system. This may limit the performances due to different distributions underlying for different agents. Therefore, training multi-agent systems with distinct LLMs should be the next step to solve. However, this approach introduces optimization challenges. For example, agents operate at different frequencies, rollouts involve varying sub-agent invocations, and agents are often deployed across separate servers, disrupting end-to-end gradient flow. To address these issues, we propose M-GRPO, a hierarchical extension of Group Relative Policy Optimization designed for vertical Multi-agent systems with a main agent (planner) and multiple sub-agents (multi-turn tool executors). M-GRPO computes group-relative advantages for both main and sub-agents, maintaining hierarchical credit assignment. It also introduces a trajectory-alignment scheme that generates fixed-size batches despite variable sub-agent invocations. We deploy a decoupled training pipeline in which agents run on separate servers and exchange minimal statistics via a shared store. This enables scalable training without cross-server backpropagation. In experiments on real-world benchmarks (e.g., GAIA, XBench-DeepSearch, and WebWalkerQA), M-GRPO consistently outperforms both single-agent GRPO and multi-agent GRPO with frozen sub-agents, demonstrating improved stability and sample efficiency. These results show that aligning heterogeneous trajectories and decoupling optimization across specialized agents enhances tool-augmented reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.18945", "title": "MIST: Mutual Information Via Supervised Training", "authors": [ "German Gritsai", "Megan Richards", "Maxime M\u00e9loux", "Kyunghyun Cho", "Maxime Peyrard" ], "github": "https://github.com/grgera/mist", "project_page": "", "abstract": "We propose a fully data-driven approach to designing mutual information (MI) estimators. Since any MI estimator is a function of the observed sample from two random variables, we parameterize this function with a neural network (MIST) and train it end-to-end to predict MI values. Training is performed on a large meta-dataset of 625,000 synthetic joint distributions with known ground-truth MI. To handle variable sample sizes and dimensions, we employ a two-dimensional attention scheme ensuring permutation invariance across input samples. To quantify uncertainty, we optimize a quantile regression loss, enabling the estimator to approximate the sampling distribution of MI rather than return a single point estimate. This research program departs from prior work by taking a fully empirical route, trading universal theoretical guarantees for flexibility and efficiency. Empirically, the learned estimators largely outperform classical baselines across sample sizes and dimensions, including on joint distributions unseen during training. The resulting quantile-based intervals are well-calibrated and more reliable than bootstrap-based confidence intervals, while inference is orders of magnitude faster than existing neural baselines. Beyond immediate empirical gains, this framework yields trainable, fully differentiable estimators that can be embedded into larger learning pipelines. Moreover, exploiting MI's invariance to invertible transformations, meta-datasets can be adapted to arbitrary data modalities via normalizing flows, enabling flexible training for diverse target meta-distributions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/grgera/mist", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.16249", "title": "Controllable Layer Decomposition for Reversible Multi-Layer Image Generation", "authors": [ "Zihao Liu", "Zunnan Xu", "Shi Shu", "Jun Zhou", "Ruicheng Zhang", "Zhenchao Tang", "Xiu Li" ], "github": "", "project_page": "", "abstract": "This work presents Controllable Layer Decomposition (CLD), a method for achieving fine-grained and controllable multi-layer separation of raster images. In practical workflows, designers typically generate and edit each RGBA layer independently before compositing them into a final raster image. However, this process is irreversible: once composited, layer-level editing is no longer possible. Existing methods commonly rely on image matting and inpainting, but remain limited in controllability and segmentation precision. To address these challenges, we propose two key modules: LayerDecompose-DiT (LD-DiT), which decouples image elements into distinct layers and enables fine-grained control; and Multi-Layer Conditional Adapter (MLCA), which injects target image information into multi-layer tokens to achieve precise conditional generation. To enable a comprehensive evaluation, we build a new benchmark and introduce tailored evaluation metrics. Experimental results show that CLD consistently outperforms existing methods in both decomposition quality and controllability. Furthermore, the separated layers produced by CLD can be directly manipulated in commonly used design tools such as PowerPoint, highlighting its practical value and applicability in real-world creative workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.18373", "title": "MASS: Motion-Aware Spatial-Temporal Grounding for Physics Reasoning and Comprehension in Vision-Language Models", "authors": [ "Xiyang Wu", "Zongxia Li", "Jihui Jin", "Guangyao Shi", "Gouthaman KV", "Vishnu Raj", "Nilotpal Sinha", "Jingxi Chen", "Fan Du", "Dinesh Manocha" ], "github": "", "project_page": "", "abstract": "Vision Language Models (VLMs) perform well on standard video tasks but struggle with physics-driven reasoning involving motion dynamics and spatial interactions. This limitation reduces their ability to interpret real or AI-generated content (AIGC) videos and to generate physically consistent content. We present an approach that addresses this gap by translating physical-world context cues into interpretable representations aligned with VLMs' perception, comprehension, and reasoning. We introduce MASS-Bench, a comprehensive benchmark consisting of 4,350 real-world and AIGC videos and 8,361 free-form video question-answering pairs focused on physics-related comprehension tasks, with detailed annotations including visual detections, sub-segment grounding, and full-sequence 3D motion tracking of entities. We further present MASS, a model-agnostic method that injects spatial-temporal signals into the VLM language space via depth-based 3D encoding and visual grounding, coupled with a motion tracker for object dynamics. To strengthen cross-modal alignment and reasoning, we apply reinforcement fine-tuning. Experiments and ablations show that our refined VLMs outperform comparable and larger baselines, as well as prior state-of-the-art models, by 8.7% and 6.0%, achieving performance comparable to close-source SoTA VLMs such as Gemini-2.5-Flash on physics reasoning and comprehension. These results validate the effectiveness of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.19314", "title": "PRInTS: Reward Modeling for Long-Horizon Information Seeking", "authors": [ "Jaewoo Lee", "Archiki Prasad", "Justin Chih-Yao Chen", "Zaid Khan", "Elias Stengel-Eskin", "Mohit Bansal" ], "github": "https://github.com/G-JWLee/PRInTS", "project_page": "", "abstract": "Information-seeking is a core capability for AI agents, requiring them to gather and reason over tool-generated information across long trajectories. However, such multi-step information-seeking tasks remain challenging for agents backed by language models. While process reward models (PRMs) can guide agents by ranking candidate steps at test-time, existing PRMs, designed for short reasoning with binary judgment, cannot capture richer dimensions of information-seeking steps, such as tool interactions and reasoning over tool outputs, nor handle the rapidly growing context in long-horizon tasks. To address these limitations, we introduce PRInTS, a generative PRM trained with dual capabilities: (1) dense scoring based on the PRM's reasoning across multiple step quality dimensions (e.g., interpretation of tool outputs, tool call informativeness) and (2) trajectory summarization that compresses the growing context while preserving essential information for step evaluation. Extensive evaluations across FRAMES, GAIA (levels 1-3), and WebWalkerQA (easy-hard) benchmarks on multiple models, along with ablations, reveal that best-of-n sampling with PRInTS enhances information-seeking abilities of open-source models as well as specialized agents, matching or surpassing the performance of frontier models with a much smaller backbone agent and outperforming other strong reward modeling baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/G-JWLee/PRInTS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.19428", "title": "Flow Map Distillation Without Data", "authors": [ "Shangyuan Tong", "Nanye Ma", "Saining Xie", "Tommi Jaakkola" ], "github": "https://github.com/ShangyuanTong/FreeFlow", "project_page": "", "abstract": "State-of-the-art flow models achieve remarkable quality but require slow, iterative sampling. To accelerate this, flow maps can be distilled from pre-trained teachers, a procedure that conventionally requires sampling from an external dataset. We argue that this data-dependency introduces a fundamental risk of Teacher-Data Mismatch, as a static dataset may provide an incomplete or even misaligned representation of the teacher's full generative capabilities. This leads us to question whether this reliance on data is truly necessary for successful flow map distillation. In this work, we explore a data-free alternative that samples only from the prior distribution, a distribution the teacher is guaranteed to follow by construction, thereby circumventing the mismatch risk entirely. To demonstrate the practical viability of this philosophy, we introduce a principled framework that learns to predict the teacher's sampling path while actively correcting for its own compounding errors to ensure high fidelity. Our approach surpasses all data-based counterparts and establishes a new state-of-the-art by a significant margin. Specifically, distilling from SiT-XL/2+REPA, our method reaches an impressive FID of 1.45 on ImageNet 256x256, and 1.49 on ImageNet 512x512, both with only 1 sampling step. We hope our work establishes a more robust paradigm for accelerating generative models and motivates the broader adoption of flow map distillation without data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ShangyuanTong/FreeFlow", "github_api_added_by": "auto" }, { "date": "2025-11-25", "arxiv_id": "2511.16397", "title": "AICC: Parse HTML Finer, Make Models Better -- A 7.3T AI-Ready Corpus Built by a Model-Based HTML Parser", "authors": [ "Ren Ma", "Jiantao Qiu", "Chao Xu", "Pei Chu", "Kaiwen Liu", "Pengli Ren", "Yuan Qu", "Jiahui Peng", "Linfeng Hou", "Mengjie Liu", "Lindong Lu", "Wenchang Ning", "Jia Yu", "Rui Min", "Jin Shi", "Haojiong Chen", "Peng Zhang", "Wenjian Zhang", "Qian Jiang", "Zengjie Hu", "Guoqiang Yang", "Zhenxiang Li", "Fukai Shang", "Zhongying Tu", "Wentao Zhang", "Dahua Lin", "Conghui He" ], "github": "", "project_page": "", "abstract": "While web data quality is crucial for large language models, most curation efforts focus on filtering and deduplication,treating HTML-to-text extraction as a fixed pre-processing step. Existing web corpora rely on heuristic-based extractors like Trafilatura, which struggle to preserve document structure and frequently corrupt structured elements such as formulas, codes, and tables. We hypothesize that improving extraction quality can be as impactful as aggressive filtering strategies for downstream performance. We introduce MinerU-HTML, a novel extraction pipeline that reformulates content extraction as a sequence labeling problem solved by a 0.6B-parameter language model. Unlike text-density heuristics, MinerU-HTML leverages semantic understanding and employs a two-stage formatting pipeline that explicitly categorizes semantic elements before converting to Markdown. Crucially, its model-based approach is inherently scalable, whereas heuristic methods offer limited improvement pathways. On MainWebBench, our benchmark of 7,887 annotated web pages, MinerU-HTML achieves 81.8\\% ROUGE-N F1 compared to Trafilatura's 63.6\\%, with exceptional structured element preservation (90.9\\% for code blocks, 94.0\\% for formulas). Using MinerU-HTML, we construct AICC (AI-ready Common Crawl), a 7.3-trillion token multilingual corpus from two Common Crawl snapshots. In controlled pretraining experiments where AICC and Trafilatura-extracted TfCC undergo identical filtering, models trained on AICC (62B tokens) achieve 50.8\\% average accuracy across 13 benchmarks, outperforming TfCC by 1.08pp-providing direct evidence that extraction quality significantly impacts model capabilities. AICC also surpasses RefinedWeb and FineWeb on key benchmarks. We publicly release MainWebBench, MinerU-HTML, and AICC, demonstrating that HTML extraction is a critical, often underestimated component of web corpus construction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.18922", "title": "One4D: Unified 4D Generation and Reconstruction via Decoupled LoRA Control", "authors": [ "Zhenxing Mi", "Yuxin Wang", "Dan Xu" ], "github": "https://github.com/black-forest-labs/flux", "project_page": "", "abstract": "We present One4D, a unified framework for 4D generation and reconstruction that produces dynamic 4D content as synchronized RGB frames and pointmaps. By consistently handling varying sparsities of conditioning frames through a Unified Masked Conditioning (UMC) mechanism, One4D can seamlessly transition between 4D generation from a single image, 4D reconstruction from a full video, and mixed generation and reconstruction from sparse frames. Our framework adapts a powerful video generation model for joint RGB and pointmap generation, with carefully designed network architectures. The commonly used diffusion finetuning strategies for depthmap or pointmap reconstruction often fail on joint RGB and pointmap generation, quickly degrading the base video model. To address this challenge, we introduce Decoupled LoRA Control (DLC), which employs two modality-specific LoRA adapters to form decoupled computation branches for RGB frames and pointmaps, connected by lightweight, zero-initialized control links that gradually learn mutual pixel-level consistency. Trained on a mixture of synthetic and real 4D datasets under modest computational budgets, One4D produces high-quality RGB frames and accurate pointmaps across both generation and reconstruction tasks. This work represents a step toward general, high-quality geometry-based 4D world modeling using video diffusion models. Project page: https://mizhenxing.github.io/One4D", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/black-forest-labs/flux", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.17792", "title": "Target-Bench: Can World Models Achieve Mapless Path Planning with Semantic Targets?", "authors": [ "Dingrui Wang", "Hongyuan Ye", "Zhihao Liang", "Zhexiao Sun", "Zhaowei Lu", "Yuchen Zhang", "Yuyu Zhao", "Yuan Gao", "Marvin Seegert", "Finn Sch\u00e4fer", "Haotong Qin", "Wei Li", "Luigi Palmieri", "Felix Jahncke", "Mattia Piccinini", "Johannes Betz" ], "github": "", "project_page": "", "abstract": "While recent world models generate highly realistic videos, their ability to perform robot path planning remains unclear and unquantified. We introduce Target-Bench, the first benchmark specifically designed to evaluate world models on mapless path planning toward semantic targets in real-world environments. Target-Bench provides 450 robot-collected video sequences spanning 45 semantic categories with SLAM-based ground truth trajectories. Our evaluation pipeline recovers camera motion from generated videos and measures planning performance using five complementary metrics that quantify target-reaching capability, trajectory accuracy, and directional consistency. We evaluate state-of-the-art models including Sora 2, Veo 3.1, and the Wan series. The best off-the-shelf model (Wan2.2-Flash) achieves only 0.299 overall score, revealing significant limitations in current world models for robotic planning tasks. We show that fine-tuning an open-source 5B-parameter model on only 325 scenarios from our dataset achieves 0.345 overall score -- an improvement of more than 400% over its base version (0.066) and 15% higher than the best off-the-shelf model. We will open-source the code and dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.19319", "title": "SyncMV4D: Synchronized Multi-view Joint Diffusion of Appearance and Motion for Hand-Object Interaction Synthesis", "authors": [ "Lingwei Dang", "Zonghan Li", "Juntong Li", "Hongwen Zhang", "Liang An", "Yebin Liu", "Qingyao Wu" ], "github": "", "project_page": "", "abstract": "Hand-Object Interaction (HOI) generation plays a critical role in advancing applications across animation and robotics. Current video-based methods are predominantly single-view, which impedes comprehensive 3D geometry perception and often results in geometric distortions or unrealistic motion patterns. While 3D HOI approaches can generate dynamically plausible motions, their dependence on high-quality 3D data captured in controlled laboratory settings severely limits their generalization to real-world scenarios. To overcome these limitations, we introduce SyncMV4D, the first model that jointly generates synchronized multi-view HOI videos and 4D motions by unifying visual prior, motion dynamics, and multi-view geometry. Our framework features two core innovations: (1) a Multi-view Joint Diffusion (MJD) model that co-generates HOI videos and intermediate motions, and (2) a Diffusion Points Aligner (DPA) that refines the coarse intermediate motion into globally aligned 4D metric point tracks. To tightly couple 2D appearance with 4D dynamics, we establish a closed-loop, mutually enhancing cycle. During the diffusion denoising process, the generated video conditions the refinement of the 4D motion, while the aligned 4D point tracks are reprojected to guide next-step joint generation. Experimentally, our method demonstrates superior performance to state-of-the-art alternatives in visual realism, motion plausibility, and multi-view consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.19166", "title": "Representational Stability of Truth in Large Language Models", "authors": [ "Samantha Dies", "Courtney Maynard", "Germans Savcisens", "Tina Eliassi-Rad" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are widely used for factual tasks such as \"What treats asthma?\" or \"What is the capital of Latvia?\". However, it remains unclear how stably LLMs encode distinctions between true, false, and neither-true-nor-false content in their internal probabilistic representations. We introduce representational stability as the robustness of an LLM's veracity representations to perturbations in the operational definition of truth. We assess representational stability by (i) training a linear probe on an LLM's activations to separate true from not-true statements and (ii) measuring how its learned decision boundary shifts under controlled label changes. Using activations from sixteen open-source models and three factual domains, we compare two types of neither statements. The first are fact-like assertions about entities we believe to be absent from any training data. We call these unfamiliar neither statements. The second are nonfactual claims drawn from well-known fictional contexts. We call these familiar neither statements. The unfamiliar statements induce the largest boundary shifts, producing up to 40% flipped truth judgements in fragile domains (such as word definitions), while familiar fictional statements remain more coherently clustered and yield smaller changes (leq 8.2%). These results suggest that representational stability stems more from epistemic familiarity than from linguistic form. More broadly, our approach provides a diagnostic for auditing and training LLMs to preserve coherent truth assignments under semantic uncertainty, rather than optimizing for output accuracy alone.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.18047", "title": "Fidelity-Aware Recommendation Explanations via Stochastic Path Integration", "authors": [ "Oren Barkan", "Yahlly Schein", "Yehonatan Elisha", "Veronika Bogina", "Mikhail Baklanov", "Noam Koenigstein" ], "github": "https://github.com/DeltaLabTLV/SPINRec", "project_page": "", "abstract": "Explanation fidelity, which measures how accurately an explanation reflects a model's true reasoning, remains critically underexplored in recommender systems. We introduce SPINRec (Stochastic Path Integration for Neural Recommender Explanations), a model-agnostic approach that adapts path-integration techniques to the sparse and implicit nature of recommendation data. To overcome the limitations of prior methods, SPINRec employs stochastic baseline sampling: instead of integrating from a fixed or unrealistic baseline, it samples multiple plausible user profiles from the empirical data distribution and selects the most faithful attribution path. This design captures the influence of both observed and unobserved interactions, yielding more stable and personalized explanations. We conduct the most comprehensive fidelity evaluation to date across three models (MF, VAE, NCF), three datasets (ML1M, Yahoo! Music, Pinterest), and a suite of counterfactual metrics, including AUC-based perturbation curves and fixed-length diagnostics. SPINRec consistently outperforms all baselines, establishing a new benchmark for faithful explainability in recommendation. Code and evaluation tools are publicly available at https://github.com/DeltaLabTLV/SPINRec.", "github_manual": "", "github_regex": "https://github.com/DeltaLabTLV/SPINRec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.18024", "title": "Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems", "authors": [ "Dor Arviv", "Yehonatan Elisha", "Oren Barkan", "Noam Koenigstein" ], "github": "https://github.com/DeltaLabTLV/Monosemanticity4Rec", "project_page": "", "abstract": "We present a method for extracting monosemantic neurons, defined as latent dimensions that align with coherent and interpretable concepts, from user and item embeddings in recommender systems. Our approach employs a Sparse Autoencoder (SAE) to reveal semantic structure within pretrained representations. In contrast to work on language models, monosemanticity in recommendation must preserve the interactions between separate user and item embeddings. To achieve this, we introduce a prediction aware training objective that backpropagates through a frozen recommender and aligns the learned latent structure with the model's user-item affinity predictions. The resulting neurons capture properties such as genre, popularity, and temporal trends, and support post hoc control operations including targeted filtering and content promotion without modifying the base model. Our method generalizes across different recommendation models and datasets, providing a practical tool for interpretable and controllable personalization. Code and evaluation resources are available at https://github.com/DeltaLabTLV/Monosemanticity4Rec.", "github_manual": "", "github_regex": "https://github.com/DeltaLabTLV/Monosemanticity4Rec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.16301", "title": "Upsample Anything: A Simple and Hard to Beat Baseline for Feature Upsampling", "authors": [ "Minseok Seo", "Mark Hamilton", "Changick Kim" ], "github": "", "project_page": "", "abstract": "We present Upsample Anything, a lightweight test-time optimization (TTO) framework that restores low-resolution features to high-resolution, pixel-wise outputs without any training. Although Vision Foundation Models demonstrate strong generalization across diverse downstream tasks, their representations are typically downsampled by 14x/16x (e.g., ViT), which limits their direct use in pixel-level applications. Existing feature upsampling approaches depend on dataset-specific retraining or heavy implicit optimization, restricting scalability and generalization. Upsample Anything addresses these issues through a simple per-image optimization that learns an anisotropic Gaussian kernel combining spatial and range cues, effectively bridging Gaussian Splatting and Joint Bilateral Upsampling. The learned kernel acts as a universal, edge-aware operator that transfers seamlessly across architectures and modalities, enabling precise high-resolution reconstruction of features, depth, or probability maps. It runs in only approx0.419 s per 224x224 image and achieves state-of-the-art performance on semantic segmentation, depth estimation, and both depth and probability map upsampling. Project page: https://seominseok0429.github.io/Upsample-Anything/{https://seominseok0429.github.io/Upsample-Anything/}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.16166", "title": "EvoVLA: Self-Evolving Vision-Language-Action Model", "authors": [ "Zeting Liu", "Zida Yang", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/EvoVLA", "project_page": "", "abstract": "Long-horizon robotic manipulation remains challenging for Vision-Language-Action (VLA) models despite recent progress in zero-shot generalization and simulation-to-real-world transfer. Current VLA models suffer from stage hallucination, where agents exploit coarse evaluation signals to shortcut multi-step tasks, reporting high progress without truly completing them. We present EvoVLA, a self-supervised VLA framework that addresses this issue through three complementary components: Stage-Aligned Reward (SAR), which uses triplet contrastive learning with Gemini-generated hard negatives to prevent visual shortcuts; Pose-Based Object Exploration (POE), which grounds curiosity in relative object-gripper pose instead of raw pixels; and Long-Horizon Memory, which uses selective context retention and gated fusion to stabilize intrinsic shaping during extended rollouts. Extensive evaluations on Discoverse-L, a long-horizon manipulation benchmark with three multi-stage tasks, show that EvoVLA improves average task success by 10.2 percentage points over the strongest baseline (OpenVLA-OFT), reaching 69.2 percent. EvoVLA also achieves one-and-a-half times better sample efficiency and reduces stage hallucination from 38.5 percent to 14.8 percent. Real-world deployment on physical robots reaches an average success rate of 54.6 percent across four manipulation tasks, outperforming OpenVLA-OFT by 11 points, demonstrating effective sim-to-real transfer and strong generalization. Code: https://github.com/AIGeeksGroup/EvoVLA. Website: https://aigeeksgroup.github.io/EvoVLA.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/EvoVLA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.12810", "title": "MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection", "authors": [ "Leena Alghamdi", "Muhammad Usman", "Hafeez Anwar", "Abdul Bais", "Saeed Anwar" ], "github": "https://github.com/linaagh98/MSRNet", "project_page": "", "abstract": "Camouflaged object detection is an emerging and challenging computer vision task that requires identifying and segmenting objects that blend seamlessly into their environments due to high similarity in color, texture, and size. This task is further complicated by low-light conditions, partial occlusion, small object size, intricate background patterns, and multiple objects. While many sophisticated methods have been proposed for this task, current methods still struggle to precisely detect camouflaged objects in complex scenarios, especially with small and multiple objects, indicating room for improvement. We propose a Multi-Scale Recursive Network that extracts multi-scale features via a Pyramid Vision Transformer backbone and combines them via specialized Attention-Based Scale Integration Units, enabling selective feature merging. For more precise object detection, our decoder recursively refines features by incorporating Multi-Granularity Fusion Units. A novel recursive-feedback decoding strategy is developed to enhance global context understanding, helping the model overcome the challenges in this task. By jointly leveraging multi-scale learning and recursive feature optimization, our proposed method achieves performance gains, successfully detecting small and multiple camouflaged objects. Our model achieves state-of-the-art results on two benchmark datasets for camouflaged object detection and ranks second on the remaining two. Our codes, model weights, and results are available at https://github.com/linaagh98/MSRNet{https://github.com/linaagh98/MSRNet}.", "github_manual": "", "github_regex": "https://github.com/linaagh98/MSRNet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-25", "arxiv_id": "2511.20256", "title": "The Image as Its Own Reward: Reinforcement Learning with Adversarial Reward for Image Generation", "authors": [ "Weijia Mao", "Hao Chen", "Zhenheng Yang", "Mike Zheng Shou" ], "github": "https://github.com/showlab/Adv-GRPO", "project_page": "", "abstract": "A reliable reward function is essential for reinforcement learning (RL) in image generation. Most current RL approaches depend on pre-trained preference models that output scalar rewards to approximate human preferences. However, these rewards often fail to capture human perception and are vulnerable to reward hacking, where higher scores do not correspond to better images. To address this, we introduce Adv-GRPO, an RL framework with an adversarial reward that iteratively updates both the reward model and the generator. The reward model is supervised using reference images as positive samples and can largely avoid being hacked. Unlike KL regularization that constrains parameter updates, our learned reward directly guides the generator through its visual outputs, leading to higher-quality images. Moreover, while optimizing existing reward functions can alleviate reward hacking, their inherent biases remain. For instance, PickScore may degrade image quality, whereas OCR-based rewards often reduce aesthetic fidelity. To address this, we take the image itself as a reward, using reference images and vision foundation models (e.g., DINO) to provide rich visual rewards. These dense visual signals, instead of a single scalar, lead to consistent gains across image quality, aesthetics, and task-specific metrics. Finally, we show that combining reference samples with foundation-model rewards enables distribution transfer and flexible style customization. In human evaluation, our method outperforms Flow-GRPO and SD3, achieving 70.0% and 72.4% win rates in image quality and aesthetics, respectively. Code and models have been released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/showlab/Adv-GRPO", "github_api_added_by": "auto" }, { "date": "2025-11-25", "arxiv_id": "2511.17405", "title": "Beyond Multiple Choice: Verifiable OpenQA for Robust Vision-Language RFT", "authors": [ "Yesheng Liu", "Hao Li", "Haiyu Xu", "Baoqi Pei", "Jiahao Wang", "Mingxuan Zhao", "Jingshu Zheng", "Zheqi He", "JG Yao", "Bowen Qin", "Xi Yang", "Jiajun Zhang" ], "github": "https://github.com/flageval-baai/ReVeL-Code", "project_page": "", "abstract": "Multiple-choice question answering (MCQA) has been a popular format for evaluating and reinforcement fine-tuning (RFT) of modern multimodal language models. Its constrained output format allows for simplified, deterministic automatic verification. However, we find that the options may leak exploitable signals, which makes the accuracy metrics unreliable for indicating real capabilities and encourages explicit or implicit answer guessing behaviors during RFT. We propose ReVeL (Rewrite and Verify by LLM), a framework that rewrites multiple-choice questions into open-form questions while keeping answers verifiable whenever possible. The framework categorizes questions according to different answer types, apply different rewriting and verification schemes, respectively. When applied for RFT, we converted 20k MCQA examples and use GRPO to finetune Qwen2.5-VL models. Models trained on ReVeL-OpenQA match MCQA accuracy on multiple-choice benchmarks and improve OpenQA accuracy by about six percentage points, indicating better data efficiency and more robust reward signals than MCQA-based training. When used for evaluation, ReVeL also reveals up to 20 percentage points of score inflation in MCQA benchmarks (relative to OpenQA), improves judging accuracy, and reduces both cost and latency. We will release code and data publicly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/flageval-baai/ReVeL-Code", "github_api_added_by": "auto" }, { "date": "2025-11-26", "arxiv_id": "2511.19046", "title": "MedSAM3: Delving into Segment Anything with Medical Concepts", "authors": [ "Anglin Liu", "Rundong Xue", "Xu R. Cao", "Yifan Shen", "Yi Lu", "Xiang Li", "Qianqian Chen", "Jintai Chen" ], "github": "https://github.com/Joey-S-Liu/MedSAM3", "project_page": "", "abstract": "Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consuming manual annotation for new clinical application. Here, we propose MedSAM-3, a text promptable medical segmentation model for medical image and video segmentation. By fine-tuning the Segment Anything Model (SAM) 3 architecture on medical images paired with semantic conceptual labels, our MedSAM-3 enables medical Promptable Concept Segmentation (PCS), allowing precise targeting of anatomical structures via open-vocabulary text descriptions rather than solely geometric prompts. We further introduce the MedSAM-3 Agent, a framework that integrates Multimodal Large Language Models (MLLMs) to perform complex reasoning and iterative refinement in an agent-in-the-loop workflow. Comprehensive experiments across diverse medical imaging modalities, including X-ray, MRI, Ultrasound, CT, and video, demonstrate that our approach significantly outperforms existing specialist and foundation models. We will release our code and model at https://github.com/Joey-S-Liu/MedSAM3.", "github_manual": "", "github_regex": "https://github.com/Joey-S-Liu/MedSAM3", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.19900", "title": "Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning", "authors": [ "Jiaqi Liu", "Kaiwen Xiong", "Peng Xia", "Yiyang Zhou", "Haonian Ji", "Lu Feng", "Siwei Han", "Mingyu Ding", "Huaxiu Yao" ], "github": "https://github.com/aiming-lab/Agent0", "project_page": "", "abstract": "Vision-language agents have achieved remarkable progress in a variety of multimodal reasoning tasks; however, their learning remains constrained by the limitations of human-annotated supervision. Recent self-rewarding approaches attempt to overcome this constraint by allowing models to act as their own critics or reward providers. Yet, purely text-based self-evaluation struggles to verify complex visual reasoning steps and often suffers from evaluation hallucinations. To address these challenges, inspired by recent advances in tool-integrated reasoning, we propose Agent0-VL, a self-evolving vision-language agent that achieves continual improvement with tool-integrated reasoning. Agent0-VL incorporates tool usage not only into reasoning but also into self-evaluation and self-repair, enabling the model to introspect, verify, and refine its reasoning through evidence-grounded analysis. It unifies two synergistic roles within a single LVLM: a Solver that performs multi-turn tool-integrated reasoning, and a Verifier that generates structured feedback and fine-grained self-rewards through tool-grounded critique. These roles interact through a Self-Evolving Reasoning Cycle, where tool-based verification and reinforcement learning jointly align the reasoning and evaluation distributions for stable self-improvement. Through this zero-external-reward evolution, Agent0-VL aligns its reasoning and verification behaviors without any human annotation or external reward models, achieving continual self-improvement. Experiments on geometric problem solving and visual scientific analysis show that Agent0-VL achieves an 12.5% improvement over the base model. Our code is available at https://github.com/aiming-lab/Agent0/Agent0-VL{this https URL}.", "github_manual": "", "github_regex": "https://github.com/aiming-lab/Agent0", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.20561", "title": "Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward", "authors": [ "Yuwei Niu", "Weiyang Jin", "Jiaqi Liao", "Chaoran Feng", "Peng Jin", "Bin Lin", "Zongjian Li", "Bin Zhu", "Weihao Yu", "Li Yuan" ], "github": "https://github.com/PKU-YuanGroup/UniSandBox", "project_page": "", "abstract": "Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation? To investigate this, we introduce UniSandbox, a decoupled evaluation framework paired with controlled, synthetic datasets to avoid data leakage and enable detailed analysis. Our findings reveal a significant understanding-generation gap, which is mainly reflected in two key dimensions: reasoning generation and knowledge transfer. Specifically, for reasoning generation tasks, we observe that explicit Chain-of-Thought (CoT) in the understanding module effectively bridges the gap, and further demonstrate that a self-training approach can successfully internalize this ability, enabling implicit reasoning during generation. Additionally, for knowledge transfer tasks, we find that CoT assists the generative process by helping retrieve newly learned knowledge, and also discover that query-based architectures inherently exhibit latent CoT-like properties that affect this transfer. UniSandbox provides preliminary insights for designing future unified architectures and training strategies that truly bridge the gap between understanding and generation. Code and data are available at https://github.com/PKU-YuanGroup/UniSandBox", "github_manual": "", "github_regex": "https://github.com/PKU-YuanGroup/UniSandBox", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.19861", "title": "GigaWorld-0: World Models as Data Engine to Empower Embodied AI", "authors": [ "GigaWorld Team", "Angen Ye", "Boyuan Wang", "Chaojun Ni", "Guan Huang", "Guosheng Zhao", "Haoyun Li", "Jiagang Zhu", "Kerui Li", "Mengyuan Xu", "Qiuping Deng", "Siting Wang", "Wenkang Qin", "Xinze Chen", "Xiaofeng Wang", "Yankai Wang", "Yu Cao", "Yifan Chang", "Yuan Xu", "Yun Ye", "Yang Wang", "Yukun Zhou", "Zhengyuan Zhang", "Zhehao Dong", "Zheng Zhu" ], "github": "", "project_page": "", "abstract": "World models are emerging as a foundational paradigm for scalable, data-efficient embodied AI. In this work, we present GigaWorld-0, a unified world model framework designed explicitly as a data engine for Vision-Language-Action (VLA) learning. GigaWorld-0 integrates two synergistic components: GigaWorld-0-Video, which leverages large-scale video generation to produce diverse, texture-rich, and temporally coherent embodied sequences under fine-grained control of appearance, camera viewpoint, and action semantics; and GigaWorld-0-3D, which combines 3D generative modeling, 3D Gaussian Splatting reconstruction, physically differentiable system identification, and executable motion planning to ensure geometric consistency and physical realism. Their joint optimization enables the scalable synthesis of embodied interaction data that is visually compelling, spatially coherent, physically plausible, and instruction-aligned. Training at scale is made feasible through our efficient GigaTrain framework, which exploits FP8-precision and sparse attention to drastically reduce memory and compute requirements. We conduct comprehensive evaluations showing that GigaWorld-0 generates high-quality, diverse, and controllable data across multiple dimensions. Critically, VLA model (e.g., GigaBrain-0) trained on GigaWorld-0-generated data achieve strong real-world performance, significantly improving generalization and task success on physical robots without any real-world interaction during training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.19320", "title": "SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation", "authors": [ "Jiaming Zhang", "Shengming Cao", "Rui Li", "Xiaotong Zhao", "Yutao Cui", "Xinglin Hou", "Gangshan Wu", "Haolan Chen", "Yu Xu", "Limin Wang", "Kai Ma" ], "github": "", "project_page": "", "abstract": "Preserving first-frame identity while ensuring precise motion control is a fundamental challenge in human image animation. The Image-to-Motion Binding process of the dominant Reference-to-Video (R2V) paradigm overlooks critical spatio-temporal misalignments common in real-world applications, leading to failures such as identity drift and visual artifacts. We introduce SteadyDancer, an Image-to-Video (I2V) paradigm-based framework that achieves harmonized and coherent animation and is the first to ensure first-frame preservation robustly. Firstly, we propose a Condition-Reconciliation Mechanism to harmonize the two conflicting conditions, enabling precise control without sacrificing fidelity. Secondly, we design Synergistic Pose Modulation Modules to generate an adaptive and coherent pose representation that is highly compatible with the reference image. Finally, we employ a Staged Decoupled-Objective Training Pipeline that hierarchically optimizes the model for motion fidelity, visual quality, and temporal coherence. Experiments demonstrate that SteadyDancer achieves state-of-the-art performance in both appearance fidelity and motion control, while requiring significantly fewer training resources than comparable methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.20635", "title": "iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generation", "authors": [ "Zhoujie Fu", "Xianfang Zeng", "Jinghong Lan", "Xinyao Liao", "Cheng Chen", "Junyi Chen", "Jiacheng Wei", "Wei Cheng", "Shiyu Liu", "Yunuo Chen", "Gang Yu", "Guosheng Lin" ], "github": "", "project_page": "", "abstract": "Pre-trained video models learn powerful priors for generating high-quality, temporally coherent content. While these models excel at temporal coherence, their dynamics are often constrained by the continuous nature of their training data. We hypothesize that by injecting the rich and unconstrained content diversity from image data into this coherent temporal framework, we can generate image sets that feature both natural transitions and a far more expansive dynamic range. To this end, we introduce iMontage, a unified framework designed to repurpose a powerful video model into an all-in-one image generator. The framework consumes and produces variable-length image sets, unifying a wide array of image generation and editing tasks. To achieve this, we propose an elegant and minimally invasive adaptation strategy, complemented by a tailored data curation process and training paradigm. This approach allows the model to acquire broad image manipulation capabilities without corrupting its invaluable original motion priors. iMontage excels across several mainstream many-in-many-out tasks, not only maintaining strong cross-image contextual consistency but also generating scenes with extraordinary dynamics that surpass conventional scopes. Find our homepage at: https://kr1sjfu.github.io/iMontage-web/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.19663", "title": "Fara-7B: An Efficient Agentic Model for Computer Use", "authors": [ "Ahmed Awadallah", "Yash Lara", "Raghav Magazine", "Hussein Mozannar", "Akshay Nambi", "Yash Pandya", "Aravind Rajeswaran", "Corby Rosset", "Alexey Taymanov", "Vibhav Vineet", "Spencer Whitehead", "Andrew Zhao" ], "github": "https://github.com/microsoft/fara", "project_page": "", "abstract": "Progress in computer use agents (CUAs) has been constrained by the absence of large and high-quality datasets that capture how humans interact with a computer. While LLMs have thrived on abundant textual data, no comparable corpus exists for CUA trajectories. To address these gaps, we introduce FaraGen, a novel synthetic data generation system for multi-step web tasks. FaraGen can propose diverse tasks from frequently used websites, generate multiple solution attempts, and filter successful trajectories using multiple verifiers. It achieves high throughput, yield, and diversity for multi-step web tasks, producing verified trajectories at approximately $1 each. We use this data to train Fara-7B, a native CUA model that perceives the computer using only screenshots, executes actions via predicted coordinates, and is small enough to run on-device. We find that Fara-7B outperforms other CUA models of comparable size on benchmarks like WebVoyager, Online-Mind2Web, and WebTailBench -- our novel benchmark that better captures under-represented web tasks in pre-existing benchmarks. Furthermore, Fara-7B is competitive with much larger frontier models, illustrating key benefits of scalable data generation systems in advancing small efficient agentic models. We are making Fara-7B open-weight on Microsoft Foundry and HuggingFace, and we are releasing WebTailBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/fara", "github_api_added_by": "auto" }, { "date": "2025-11-26", "arxiv_id": "2511.19575", "title": "HunyuanOCR Technical Report", "authors": [ "Hunyuan Vision Team", "Pengyuan Lyu", "Xingyu Wan", "Gengluo Li", "Shangpin Peng", "Weinong Wang", "Liang Wu", "Huawen Shen", "Yu Zhou", "Canhui Tang", "Qi Yang", "Qiming Peng", "Bin Luo", "Hower Yang", "Houwen Peng", "Hongming Yang", "Senhao Xie", "Binghong Wu", "Mana Yang", "Sergey Wang", "Raccoon Liu", "Dick Zhu", "Jie Jiang", "Linus", "Han Hu", "Chengquan Zhang" ], "github": "", "project_page": "", "abstract": "This paper presents HunyuanOCR, a commercial-grade, open-source, and lightweight (1B parameters) Vision-Language Model (VLM) dedicated to OCR tasks. The architecture comprises a Native Vision Transformer (ViT) and a lightweight LLM connected via an MLP adapter. HunyuanOCR demonstrates superior performance, outperforming commercial APIs, traditional pipelines, and larger models (e.g., Qwen3-VL-4B). Specifically, it surpasses current public solutions in perception tasks (Text Spotting, Parsing) and excels in semantic tasks (IE, Text Image Translation), securing first place in the ICDAR 2025 DIMT Challenge (Small Model Track). Furthermore, it achieves state-of-the-art (SOTA) results on OCRBench among VLMs with fewer than 3B parameters. HunyuanOCR achieves breakthroughs in three key aspects: 1) Unifying Versatility and Efficiency: We implement comprehensive support for core capabilities including spotting, parsing, IE, VQA, and translation within a lightweight framework. This addresses the limitations of narrow \"OCR expert models\" and inefficient \"General VLMs\". 2) Streamlined End-to-End Architecture: Adopting a pure end-to-end paradigm eliminates dependencies on pre-processing modules (e.g., layout analysis). This fundamentally resolves error propagation common in traditional pipelines and simplifies system deployment. 3) Data-Driven and RL Strategies: We confirm the critical role of high-quality data and, for the first time in the industry, demonstrate that Reinforcement Learning (RL) strategies yield significant performance gains in OCR tasks. HunyuanOCR is officially open-sourced on HuggingFace. We also provide a high-performance deployment solution based on vLLM, placing its production efficiency in the top tier. We hope this model will advance frontier research and provide a solid foundation for industrial applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.19773", "title": "Scaling Agentic Reinforcement Learning for Tool-Integrated Reasoning in VLMs", "authors": [ "Meng Lu", "Ran Xu", "Yi Fang", "Wenxuan Zhang", "Yue Yu", "Gaurav Srivastava", "Yuchen Zhuang", "Mohamed Elhoseiny", "Charles Fleming", "Carl Yang", "Zhengzhong Tu", "Yang Xie", "Guanghua Xiao", "Hanrui Wang", "Di Jin", "Wenqi Shi", "Xuan Wang" ], "github": "https://github.com/Lucanyc/VISTA-Gym", "project_page": "", "abstract": "While recent vision-language models (VLMs) demonstrate strong image understanding, their ability to \"think with images\", i.e., to reason through multi-step visual interactions, remains limited. We introduce VISTA-Gym, a scalable training environment for incentivizing tool-integrated visual reasoning capabilities in VLMs. VISTA-Gym unifies diverse real-world multimodal reasoning tasks (7 tasks from 13 datasets in total) with a standardized interface for visual tools (e.g., grounding, parsing), executable interaction loops, verifiable feedback signals, and efficient trajectory logging, enabling visual agentic reinforcement learning at scale. While recent VLMs exhibit strong text-only reasoning, both proprietary and open-source models still struggle with tool selection, invocation, and coordination. With VISTA-Gym, we train VISTA-R1 to interleave tool-use with agentic reasoning via multi-turn trajectory sampling and end-to-end reinforcement learning. Extensive experiments across 11 public reasoning-intensive VQA benchmarks show that VISTA-R1-8B outperforms state-of-the-art baselines with similar sizes by 9.51%-18.72%, demonstrating VISTA-Gym as an effective training ground to unlock the tool-integrated reasoning capabilities for VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Lucanyc/VISTA-Gym", "github_api_added_by": "auto" }, { "date": "2025-11-26", "arxiv_id": "2511.20562", "title": "PhysChoreo: Physics-Controllable Video Generation with Part-Aware Semantic Grounding", "authors": [ "Haoze Zhang", "Tianyu Huang", "Zichen Wan", "Xiaowei Jin", "Hongzhi Zhang", "Hui Li", "Wangmeng Zuo" ], "github": "", "project_page": "", "abstract": "While recent video generation models have achieved significant visual fidelity, they often suffer from the lack of explicit physical controllability and plausibility. To address this, some recent studies attempted to guide the video generation with physics-based rendering. However, these methods face inherent challenges in accurately modeling complex physical properties and effectively control ling the resulting physical behavior over extended temporal sequences. In this work, we introduce PhysChoreo, a novel framework that can generate videos with diverse controllability and physical realism from a single image. Our method consists of two stages: first, it estimates the static initial physical properties of all objects in the image through part-aware physical property reconstruction. Then, through temporally instructed and physically editable simulation, it synthesizes high-quality videos with rich dynamic behaviors and physical realism. Experimental results show that PhysChoreo can generate videos with rich behaviors and physical realism, outperforming state-of-the-art methods on multiple evaluation metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.20123", "title": "UltraViCo: Breaking Extrapolation Limits in Video Diffusion Transformers", "authors": [ "Min Zhao", "Hongzhou Zhu", "Yingze Wang", "Bokai Yan", "Jintao Zhang", "Guande He", "Ling Yang", "Chongxuan Li", "Jun Zhu" ], "github": "", "project_page": "", "abstract": "Despite advances, video diffusion transformers still struggle to generalize beyond their training length, a challenge we term video length extrapolation. We identify two failure modes: model-specific periodic content repetition and a universal quality degradation. Prior works attempt to solve repetition via positional encodings, overlooking quality degradation and achieving only limited extrapolation. In this paper, we revisit this challenge from a more fundamental view: attention maps, which directly govern how context influences outputs. We identify that both failure modes arise from a unified cause: attention dispersion, where tokens beyond the training window dilute learned attention patterns. This leads to quality degradation and repetition emerges as a special case when this dispersion becomes structured into periodic attention patterns, induced by harmonic properties of positional encodings. Building on this insight, we propose UltraViCo, a training-free, plug-and-play method that suppresses attention for tokens beyond the training window via a constant decay factor. By jointly addressing both failure modes, we outperform a broad set of baselines largely across models and extrapolation ratios, pushing the extrapolation limit from 2x to 4x. Remarkably, it improves Dynamic Degree and Imaging Quality by 233% and 40.5% over the previous best method at 4x extrapolation. Furthermore, our method generalizes seamlessly to downstream tasks such as controllable video synthesis and editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.19827", "title": "ReDirector: Creating Any-Length Video Retakes with Rotary Camera Encoding", "authors": [ "Byeongjun Park", "Byung-Hoon Kim", "Hyungjin Chung", "Jong Chul Ye" ], "github": "", "project_page": "", "abstract": "We present ReDirector, a novel camera-controlled video retake generation method for dynamically captured variable-length videos. In particular, we rectify a common misuse of RoPE in previous works by aligning the spatiotemporal positions of the input video and the target retake. Moreover, we introduce Rotary Camera Encoding (RoCE), a camera-conditioned RoPE phase shift that captures and integrates multi-view relationships within and across the input and target videos. By integrating camera conditions into RoPE, our method generalizes to out-of-distribution camera trajectories and video lengths, yielding improved dynamic object localization and static background preservation. Extensive experiments further demonstrate significant improvements in camera controllability, geometric consistency, and video quality across various trajectories and lengths.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.15906", "title": "Unified all-atom molecule generation with neural fields", "authors": [ "Matthieu Kirchmeyer", "Pedro O. Pinheiro", "Emma Willett", "Karolis Martinkus", "Joseph Kleinhenz", "Emily K. Makowski", "Andrew M. Watkins", "Vladimir Gligorijevic", "Richard Bonneau", "Saeed Saremi" ], "github": "https://github.com/prescient-design/funcbind", "project_page": "", "abstract": "Generative models for structure-based drug design are often limited to a specific modality, restricting their broader applicability. To address this challenge, we introduce FuncBind, a framework based on computer vision to generate target-conditioned, all-atom molecules across atomic systems. FuncBind uses neural fields to represent molecules as continuous atomic densities and employs score-based generative models with modern architectures adapted from the computer vision literature. This modality-agnostic representation allows a single unified model to be trained on diverse atomic systems, from small to large molecules, and handle variable atom/residue counts, including non-canonical amino acids. FuncBind achieves competitive in silico performance in generating small molecules, macrocyclic peptides, and antibody complementarity-determining region loops, conditioned on target structures. FuncBind also generated in vitro novel antibody binders via de novo redesign of the complementarity-determining region H3 loop of two chosen co-crystal structures. As a final contribution, we introduce a new dataset and benchmark for structure-conditioned macrocyclic peptide generation. The code is available at https://github.com/prescient-design/funcbind.", "github_manual": "", "github_regex": "https://github.com/prescient-design/funcbind", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.20573", "title": "VQ-VA World: Towards High-Quality Visual Question-Visual Answering", "authors": [ "Chenhui Gou", "Zilong Chen", "Zeyu Wang", "Feng Li", "Deyao Zhu", "Zicheng Duan", "Kunchang Li", "Chaorui Deng", "Hongyi Yuan", "Haoqi Fan", "Cihang Xie", "Jianfei Cai", "Hamid Rezatofighi" ], "github": "", "project_page": "", "abstract": "This paper studies Visual Question-Visual Answering (VQ-VA): generating an image, rather than text, in response to a visual question -- an ability that has recently emerged in proprietary systems such as NanoBanana and GPT-Image. To also bring this capability to open-source models, we introduce VQ-VA World, a data-centric framework built around an agentic pipeline for large-scale, targeted data construction. Leveraging web-scale deployment, this pipeline crawls a massive amount of ~1.8M high-quality, interleaved image-text samples for model training. For evaluation, we further release IntelligentBench, a human-curated benchmark that systematically assesses VQ-VA along the aspects of world knowledge, design knowledge, and reasoning. Training with VQ-VA World data yields strong empirical gains: it helps LightFusion attain 53.06 on IntelligentBench, substantially surpassing the best prior open-source baselines (i.e., 7.78 from vanilla LightFusion; 1.94 from UniWorld-V1), and significantly narrowing the gap toward leading proprietary systems (e.g., 81.67 from NanoBanana; 82.64 from GPT-Image). By releasing the full suite of model weights, datasets, and pipelines, we hope to stimulate future research on VQ-VA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.17592", "title": "GigaEvo: An Open Source Optimization Framework Powered By LLMs And Evolution Algorithms", "authors": [ "Valentin Khrulkov", "Andrey Galichin", "Denis Bashkirov", "Dmitry Vinichenko", "Oleg Travkin", "Roman Alferov", "Andrey Kuznetsov", "Ivan Oseledets" ], "github": "https://github.com/FusionBrainLab/gigaevo-core", "project_page": "", "abstract": "Recent advances in LLM-guided evolutionary computation, particularly AlphaEvolve (Novikov et al., 2025; Georgiev et al., 2025), have demonstrated remarkable success in discovering novel mathematical constructions and solving challenging optimization problems. However, the high-level descriptions in published work leave many implementation details unspecified, hindering reproducibility and further research. In this report we present GigaEvo, an extensible open-source framework that enables researchers to study and experiment with hybrid LLM-evolution approaches inspired by AlphaEvolve. Our system provides modular implementations of key components: MAP-Elites quality-diversity algorithms, asynchronous DAG-based evaluation pipelines, LLM-driven mutation operators with insight generation and bidirectional lineage tracking, and flexible multi-island evolutionary strategies. In order to assess reproducibility and validate our implementation we evaluate GigaEvo on challenging problems from the AlphaEvolve paper: Heilbronn triangle placement, circle packing in squares, and high-dimensional kissing numbers. The framework emphasizes modularity, concurrency, and ease of experimentation, enabling rapid prototyping through declarative configuration. We provide detailed descriptions of system architecture, implementation decisions, and experimental methodology to support further research in LLM driven evolutionary methods. The GigaEvo framework and all experimental code are available at https://github.com/AIRI-Institute/gigaevo-core.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FusionBrainLab/gigaevo-core", "github_api_added_by": "user" }, { "date": "2025-11-26", "arxiv_id": "2511.20347", "title": "Soft Adaptive Policy Optimization", "authors": [ "Chang Gao", "Chujie Zheng", "Xiong-Hui Chen", "Kai Dang", "Shixuan Liu", "Bowen Yu", "An Yang", "Shuai Bai", "Jingren Zhou", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) plays an increasingly important role in enhancing the reasoning capabilities of large language models (LLMs), yet stable and performant policy optimization remains challenging. Token-level importance ratios often exhibit high variance-a phenomenon exacerbated in Mixture-of-Experts models-leading to unstable updates. Existing group-based policy optimization methods, such as GSPO and GRPO, alleviate this problem via hard clipping, making it difficult to maintain both stability and effective learning. We propose Soft Adaptive Policy Optimization (SAPO), which replaces hard clipping with a smooth, temperature-controlled gate that adaptively attenuates off-policy updates while preserving useful learning signals. Compared with GSPO and GRPO, SAPO is both sequence-coherent and token-adaptive. Like GSPO, SAPO maintains sequence-level coherence, but its soft gating forms a continuous trust region that avoids the brittle hard clipping band used in GSPO. When a sequence contains a few highly off-policy tokens, GSPO suppresses all gradients for that sequence, whereas SAPO selectively down-weights only the offending tokens and preserves the learning signal from the near-on-policy ones, improving sample efficiency. Relative to GRPO, SAPO replaces hard token-level clipping with smooth, temperature-controlled scaling, enabling more informative and stable updates. Empirical results on mathematical reasoning benchmarks indicate that SAPO exhibits improved training stability and higher Pass@1 performance under comparable training budgets. Moreover, we employ SAPO to train the Qwen3-VL model series, demonstrating that SAPO yields consistent performance gains across diverse tasks and different model sizes. Overall, SAPO provides a more reliable, scalable, and effective optimization strategy for RL training of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.20102", "title": "SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space", "authors": [ "Zhenyi Shen", "Junru Lu", "Lin Gui", "Jiazheng Li", "Yulan He", "Di Yin", "Xing Sun" ], "github": "", "project_page": "", "abstract": "The quadratic complexity of full attention limits efficient long-context processing in large language models (LLMs). Sparse attention mitigates this cost by restricting each query to attend to a subset of previous tokens; however, training-free approaches often lead to severe performance degradation. Native sparse-attention methods (e.g., NSA, MoBA) alleviate this issue, yet exhibit a critical paradox: they produce lower attention sparsity than full-attention models, despite aiming to approximate full attention, which may constrain their effectiveness. We attribute this paradox to gradient update deficiency: low-ranked key-value pairs excluded during sparse training receive neither forward contribution nor backward gradients, and thus never learn proper suppression. To overcome this limitation, we propose SSA (Sparse Sparse Attention), a unified training framework that considers both sparse and full attention and enforces bidirectional alignment at every layer. This design preserves gradient flow to all tokens while explicitly encouraging sparse-attention outputs to align with their full-attention counterparts, thereby promoting stronger sparsity. As a result, SSA achieves state-of-the-art performance under both sparse and full attention inference across multiple commonsense benchmarks. Furthermore, SSA enables models to adapt smoothly to varying sparsity budgets; performance improves consistently as more tokens are allowed to attend, supporting flexible compute-performance trade-offs at inference time. Finally, we show that native sparse-attention training surprisingly improves long-context extrapolation by mitigating the over-allocation of attention values in sink areas, with SSA demonstrating the strongest extrapolation capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.20211", "title": "OmniAlpha: A Sequence-to-Sequence Framework for Unified Multi-Task RGBA Generation", "authors": [ "Hao Yu", "Jiabo Zhan", "Zile Wang", "Jinglin Wang", "Huaisong Zhang", "Hongyu Li", "Xinrui Chen", "Yongxian Wei", "Chun Yuan" ], "github": "", "project_page": "", "abstract": "Generative models have excelled in RGB synthesis, but real-world applications require RGBA manipulation. This has led to a fragmented landscape: specialized, single-task models handle alpha but lack versatility, while unified multi-task frameworks are confined to the RGB domain. To bridge this critical gap, we propose OmniAlpha, the first unified, multi-task generative framework for sequence-to-sequence RGBA image generation and editing. Its architecture features MSRoPE-BiL, a novel RoPE method with a bi-directionally extendable layer axis for its Diffusion Transformer (DiT) backbone, enabling the concurrent processing of multiple input and target RGBA layers. To power this framework, we introduce AlphaLayers, a new dataset of 1,000 high-quality, multi-layer triplets, built via a novel automated synthesis and filter pipeline. Jointly training OmniAlpha on this dataset across a comprehensive suite of 21 diverse tasks, extensive experiments demonstrate that our unified approach consistently outperforms strong, specialized baselines. Most notably, OmniAlpha achieves a dramatic 84.8% relative reduction in SAD for mask-free matting on AIM-500 and wins over 90% of human preferences in layer-conditioned completion. Our work proves that a unified, multi-task model can learn a superior shared representation for RGBA, paving the way for more powerful, layer-aware generative systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.20415", "title": "MajutsuCity: Language-driven Aesthetic-adaptive City Generation with Controllable 3D Assets and Layouts", "authors": [ "Zilong Huang", "Jun He", "Xiaobin Huang", "Ziyi Xiong", "Yang Luo", "Junyan Ye", "Weijia Li", "Yiping Chen", "Ting Han" ], "github": "https://github.com/LongHZ140516/MajutsuCity", "project_page": "", "abstract": "Generating realistic 3D cities is fundamental to world models, virtual reality, and game development, where an ideal urban scene must satisfy both stylistic diversity, fine-grained, and controllability. However, existing methods struggle to balance the creative flexibility offered by text-based generation with the object-level editability enabled by explicit structural representations. We introduce MajutsuCity, a natural language-driven and aesthetically adaptive framework for synthesizing structurally consistent and stylistically diverse 3D urban scenes. MajutsuCity represents a city as a composition of controllable layouts, assets, and materials, and operates through a four-stage pipeline. To extend controllability beyond initial generation, we further integrate MajutsuAgent, an interactive language-grounded editing agent} that supports five object-level operations. To support photorealistic and customizable scene synthesis, we also construct MajutsuDataset, a high-quality multimodal dataset} containing 2D semantic layouts and height maps, diverse 3D building assets, and curated PBR materials and skyboxes, each accompanied by detailed annotations. Meanwhile, we develop a practical set of evaluation metrics, covering key dimensions such as structural consistency, scene complexity, material fidelity, and lighting atmosphere. Extensive experiments demonstrate MajutsuCity reduces layout FID by 83.7% compared with CityDreamer and by 20.1% over CityCraft. Our method ranks first across all AQS and RDR scores, outperforming existing methods by a clear margin. These results confirm MajutsuCity as a new state-of-the-art in geometric fidelity, stylistic adaptability, and semantic controllability for 3D city generation. We expect our framework can inspire new avenues of research in 3D city generation. Our dataset and code will be released at https://github.com/LongHZ140516/MajutsuCity.", "github_manual": "", "github_regex": "https://github.com/LongHZ140516/MajutsuCity", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.19111", "title": "DiffSeg30k: A Multi-Turn Diffusion Editing Benchmark for Localized AIGC Detection", "authors": [ "Hai Ci", "Ziheng Peng", "Pei Yang", "Yingxin Xuan", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Diffusion-based editing enables realistic modification of local image regions, making AI-generated content harder to detect. Existing AIGC detection benchmarks focus on classifying entire images, overlooking the localization of diffusion-based edits. We introduce DiffSeg30k, a publicly available dataset of 30k diffusion-edited images with pixel-level annotations, designed to support fine-grained detection. DiffSeg30k features: 1) In-the-wild images--we collect images or image prompts from COCO to reflect real-world content diversity; 2) Diverse diffusion models--local edits using eight SOTA diffusion models; 3) Multi-turn editing--each image undergoes up to three sequential edits to mimic real-world sequential editing; and 4) Realistic editing scenarios--a vision-language model (VLM)-based pipeline automatically identifies meaningful regions and generates context-aware prompts covering additions, removals, and attribute changes. DiffSeg30k shifts AIGC detection from binary classification to semantic segmentation, enabling simultaneous localization of edits and identification of the editing models. We benchmark three baseline segmentation approaches, revealing significant challenges in semantic segmentation tasks, particularly concerning robustness to image distortions. Experiments also reveal that segmentation models, despite being trained for pixel-level localization, emerge as highly reliable whole-image classifiers of diffusion edits, outperforming established forgery classifiers while showing great potential in cross-generator generalization. We believe DiffSeg30k will advance research in fine-grained localization of AI-generated content by demonstrating the promise and limitations of segmentation-based methods. DiffSeg30k is released at: https://huggingface.co/datasets/Chaos2629/Diffseg30k", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.18734", "title": "Yo'City: Personalized and Boundless 3D Realistic City Scene Generation via Self-Critic Expansion", "authors": [ "Keyang Lu", "Sifan Zhou", "Hongbin Xu", "Gang Xu", "Zhifei Yang", "Yikai Wang", "Zhen Xiao", "Jieyi Long", "Ming Li" ], "github": "", "project_page": "", "abstract": "Realistic 3D city generation is fundamental to a wide range of applications, including virtual reality and digital twins. However, most existing methods rely on training a single diffusion model, which limits their ability to generate personalized and boundless city-scale scenes. In this paper, we present Yo'City, a novel agentic framework that enables user-customized and infinitely expandable 3D city generation by leveraging the reasoning and compositional capabilities of off-the-shelf large models. Specifically, Yo'City first conceptualize the city through a top-down planning strategy that defines a hierarchical \"City-District-Grid\" structure. The Global Planner determines the overall layout and potential functional districts, while the Local Designer further refines each district with detailed grid-level descriptions. Subsequently, the grid-level 3D generation is achieved through a \"produce-refine-evaluate\" isometric image synthesis loop, followed by image-to-3D generation. To simulate continuous city evolution, Yo'City further introduces a user-interactive, relationship-guided expansion mechanism, which performs scene graph-based distance- and semantics-aware layout optimization, ensuring spatially coherent city growth. To comprehensively evaluate our method, we construct a diverse benchmark dataset and design six multi-dimensional metrics that assess generation quality from the perspectives of semantics, geometry, texture, and layout. Extensive experiments demonstrate that Yo'City consistently outperforms existing state-of-the-art methods across all evaluation aspects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.17943", "title": "SciEducator: Scientific Video Understanding and Educating via Deming-Cycle Multi-Agent System", "authors": [ "Zhiyu Xu", "Weilong Yan", "Yufei Shi", "Xin Meng", "Tao He", "Huiping Zhuang", "Ming Li", "Hehe Fan" ], "github": "", "project_page": "", "abstract": "Recent advancements in multimodal large language models (MLLMs) and video agent systems have significantly improved general video understanding. However, when applied to scientific video understanding and educating, a domain that demands external professional knowledge integration and rigorous step-wise reasoning, existing approaches often struggle. To bridge this gap, we propose SciEducator, the first iterative self-evolving multi-agent system for scientific video comprehension and education. Rooted in the classical Deming Cycle from management science, our design reformulates its Plan-Do-Study-Act philosophy into a self-evolving reasoning and feedback mechanism, which facilitates the interpretation of intricate scientific activities in videos. Moreover, SciEducator can produce multimodal educational content tailored to specific scientific processes, including textual instructions, visual guides, audio narrations, and interactive references. To support evaluation, we construct SciVBench, a benchmark consisting of 500 expert-verified and literature-grounded science QA pairs across five categories, covering physical, chemical, and everyday phenomena. Extensive experiments demonstrate that SciEducator substantially outperforms leading closed-source MLLMs (e.g., Gemini, GPT-4o) and state-of-the-art video agents on the benchmark, establishing a new paradigm for the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.19430", "title": "Cook and Clean Together: Teaching Embodied Agents for Parallel Task Execution", "authors": [ "Dingkang Liang", "Cheng Zhang", "Xiaopeng Xu", "Jianzhong Ju", "Zhenbo Luo", "Xiang Bai" ], "github": "https://github.com/H-EmbodVis/GRANT", "project_page": "", "abstract": "Task scheduling is critical for embodied AI, enabling agents to follow natural language instructions and execute actions efficiently in 3D physical worlds. However, existing datasets often simplify task planning by ignoring operations research (OR) knowledge and 3D spatial grounding. In this work, we propose Operations Research knowledge-based 3D Grounded Task Scheduling (ORS3D), a new task that requires the synergy of language understanding, 3D grounding, and efficiency optimization. Unlike prior settings, ORS3D demands that agents minimize total completion time by leveraging parallelizable subtasks, e.g., cleaning the sink while the microwave operates. To facilitate research on ORS3D, we construct ORS3D-60K, a large-scale dataset comprising 60K composite tasks across 4K real-world scenes. Furthermore, we propose GRANT, an embodied multi-modal large language model equipped with a simple yet effective scheduling token mechanism to generate efficient task schedules and grounded actions. Extensive experiments on ORS3D-60K validate the effectiveness of GRANT across language understanding, 3D grounding, and scheduling efficiency. The code is available at https://github.com/H-EmbodVis/GRANT", "github_manual": "", "github_regex": "https://github.com/H-EmbodVis/GRANT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.18886", "title": "MagicWorld: Interactive Geometry-driven Video World Exploration", "authors": [ "Guangyuan Li", "Siming Zheng", "Shuolin Xu", "Jinwei Chen", "Bo Li", "Xiaobin Hu", "Lei Zhao", "Peng-Tao Jiang" ], "github": "", "project_page": "", "abstract": "Recent interactive video world model methods generate scene evolution conditioned on user instructions. Although they achieve impressive results, two key limitations remain. First, they fail to fully exploit the correspondence between instruction-driven scene motion and the underlying 3D geometry, which results in structural instability under viewpoint changes. Second, they easily forget historical information during multi-step interaction, resulting in error accumulation and progressive drift in scene semantics and structure. To address these issues, we propose MagicWorld, an interactive video world model that integrates 3D geometric priors and historical retrieval. MagicWorld starts from a single scene image, employs user actions to drive dynamic scene evolution, and autoregressively synthesizes continuous scenes. We introduce the Action-Guided 3D Geometry Module (AG3D), which constructs a point cloud from the first frame of each interaction and the corresponding action, providing explicit geometric constraints for viewpoint transitions and thereby improving structural consistency. We further propose History Cache Retrieval (HCR) mechanism, which retrieves relevant historical frames during generation and injects them as conditioning signals, helping the model utilize past scene information and mitigate error accumulation. Experimental results demonstrate that MagicWorld achieves notable improvements in scene stability and continuity across interaction iterations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.20647", "title": "Diverse Video Generation with Determinantal Point Process-Guided Policy Optimization", "authors": [ "Tahira Kazimi", "Connor Dunlop", "Pinar Yanardag" ], "github": "", "project_page": "", "abstract": "While recent text-to-video (T2V) diffusion models have achieved impressive quality and prompt alignment, they often produce low-diversity outputs when sampling multiple videos from a single text prompt. We tackle this challenge by formulating it as a set-level policy optimization problem, with the goal of training a policy that can cover the diverse range of plausible outcomes for a given prompt. To address this, we introduce DPP-GRPO, a novel framework for diverse video generation that combines Determinantal Point Processes (DPPs) and Group Relative Policy Optimization (GRPO) theories to enforce explicit reward on diverse generations. Our objective turns diversity into an explicit signal by imposing diminishing returns on redundant samples (via DPP) while supplies groupwise feedback over candidate sets (via GRPO). Our framework is plug-and-play and model-agnostic, and encourages diverse generations across visual appearance, camera motions, and scene structure without sacrificing prompt fidelity or perceptual quality. We implement our method on WAN and CogVideoX, and show that our method consistently improves video diversity on state-of-the-art benchmarks such as VBench, VideoScore, and human preference studies. Moreover, we release our code and a new benchmark dataset of 30,000 diverse prompts to support future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.20643", "title": "Concept-Aware Batch Sampling Improves Language-Image Pretraining", "authors": [ "Adhiraj Ghosh", "Vishaal Udandarao", "Thao Nguyen", "Matteo Farina", "Mehdi Cherti", "Jenia Jitsev", "Sewoong Oh", "Elisa Ricci", "Ludwig Schmidt", "Matthias Bethge" ], "github": "", "project_page": "", "abstract": "What data should a vision-language model be trained on? To answer this question, many data curation efforts center on the quality of a dataset. However, most of these existing methods are (i) offline, i.e. they produce a static dataset from a set of predetermined filtering criteria, and (ii) concept-agnostic, i.e. they use model-based filters which induce additional data biases. In this work, we go beyond such offline, concept-agnostic methods and advocate for more flexible, task-adaptive online concept-based curation. Our first contribution is DataConcept, a collection of 128M web-crawled image-text pairs annotated with fine-grained details about their concept composition. Building on DataConcept, we introduce Concept-Aware Batch Sampling (CABS), a simple yet effective batch sampling framework that flexibly constructs batches on-the-fly based on specific target distributions. We propose two variants: (i) Diversity Maximization (CABS-DM) to curate batches with a broad coverage of available concepts, and (ii) Frequency Maximization (CABS-FM) to curate batches with high object multiplicity. Through extensive evaluations across 28 benchmarks, we demonstrate that our CABS method significantly benefits CLIP/SigLIP model classes and yields highly performant models. Overall, CABS represents a strong open-source alternative to proprietary online data curation algorithms, enabling practitioners to define custom concept distributions that optimize for specific downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.20250", "title": "Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation", "authors": [ "Daniel Kienzle", "Katja Ludwig", "Julian Lorenz", "Shin'ichi Satoh", "Rainer Lienhart" ], "github": "", "project_page": "", "abstract": "Obtaining the precise 3D motion of a table tennis ball from standard monocular videos is a challenging problem, as existing methods trained on synthetic data struggle to generalize to the noisy, imperfect ball and table detections of the real world. This is primarily due to the inherent lack of 3D ground truth trajectories and spin annotations for real-world video. To overcome this, we propose a novel two-stage pipeline that divides the problem into a front-end perception task and a back-end 2D-to-3D uplifting task. This separation allows us to train the front-end components with abundant 2D supervision from our newly created TTHQ dataset, while the back-end uplifting network is trained exclusively on physically-correct synthetic data. We specifically re-engineer the uplifting model to be robust to common real-world artifacts, such as missing detections and varying frame rates. By integrating a ball detector and a table keypoint detector, our approach transforms a proof-of-concept uplifting method into a practical, robust, and high-performing end-to-end application for 3D table tennis trajectory and spin analysis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.16660", "title": "Cognitive Foundations for Reasoning and Their Manifestation in LLMs", "authors": [ "Priyanka Kargupta", "Shuyue Stella Li", "Haocheng Wang", "Jinu Lee", "Shan Chen", "Orevaoghene Ahia", "Dean Light", "Thomas L. Griffiths", "Max Kleiman-Weiner", "Jiawei Han", "Asli Celikyilmaz", "Yulia Tsvetkov" ], "github": "https://github.com/stellalisy/CognitiveFoundations", "project_page": "", "abstract": "Large language models (LLMs) solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reasoning. To understand this gap, we synthesize cognitive science research into a taxonomy of 28 cognitive elements spanning reasoning invariants, meta-cognitive controls, representations for organizing reasoning & knowledge, and transformation operations. We introduce a fine-grained evaluation framework and conduct the first large-scale empirical analysis of 192K traces from 18 models across text, vision, and audio, complemented by 54 human think-aloud traces, which we make publicly available. We find that models under-utilize cognitive elements correlated with success, narrowing to rigid sequential processing on ill-structured problems where diverse representations and meta-cognitive monitoring are critical. Human traces show more abstraction and conceptual processing, while models default to surface-level enumeration. Meta-analysis of 1.6K LLM reasoning papers reveals the research community concentrates on easily quantifiable elements (sequential organization: 55%, decomposition: 60%) but neglecting meta-cognitive controls (self-awareness: 16%) that correlate with success. Models possess behavioral repertoires associated with success but fail to deploy them spontaneously. Leveraging these patterns, we develop test-time reasoning guidance that automatically scaffold successful structures, improving performance by up to 66.7% on complex problems. By establishing a shared vocabulary between cognitive science and LLM research, our framework enables systematic diagnosis of reasoning failures and principled development of models that reason through robust cognitive mechanisms rather than spurious shortcuts, while providing tools to test theories of human cognition at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stellalisy/CognitiveFoundations", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.18394", "title": "Future Is Unevenly Distributed: Forecasting Ability of LLMs Depends on What We're Asking", "authors": [ "Chinmay Karkar", "Paras Chopra" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) demonstrate partial forecasting competence across social, political, and economic events. Yet, their predictive ability varies sharply with domain structure and prompt framing. We investigate how forecasting performance varies with different model families on real-world questions about events that happened beyond the model cutoff date. We analyze how context, question type, and external knowledge affect accuracy and calibration, and how adding factual news context modifies belief formation and failure modes. Our results show that forecasting ability is highly variable as it depends on what, and how, we ask.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.20462", "title": "STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flow", "authors": [ "Jiatao Gu", "Ying Shen", "Tianrong Chen", "Laurent Dinh", "Yuyang Wang", "Miguel Angel Bautista", "David Berthelot", "Josh Susskind", "Shuangfei Zhai" ], "github": "https://github.com/apple/ml-starflow", "project_page": "", "abstract": "Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Yet in the video generation domain, where spatiotemporal complexity and computational cost are substantially higher, state-of-the-art systems almost exclusively rely on diffusion-based models. In this work, we revisit this design space by presenting STARFlow-V, a normalizing flow-based video generator with substantial benefits such as end-to-end learning, robust causal prediction, and native likelihood estimation. Building upon the recently proposed STARFlow, STARFlow-V operates in the spatiotemporal latent space with a global-local architecture which restricts causal dependencies to a global latent space while preserving rich local within-frame interactions. This eases error accumulation over time, a common pitfall of standard autoregressive diffusion model generation. Additionally, we propose flow-score matching, which equips the model with a light-weight causal denoiser to improve the video generation consistency in an autoregressive fashion. To improve the sampling efficiency, STARFlow-V employs a video-aware Jacobi iteration scheme that recasts inner updates as parallelizable iterations without breaking causality. Thanks to the invertible structure, the same model can natively support text-to-video, image-to-video as well as video-to-video generation tasks. Empirically, STARFlow-V achieves strong visual fidelity and temporal consistency with practical sampling throughput relative to diffusion-based baselines. These results present the first evidence, to our knowledge, that NFs are capable of high-quality autoregressive video generation, establishing them as a promising research direction for building world models. Code and generated samples are available at https://github.com/apple/ml-starflow.", "github_manual": "", "github_regex": "https://github.com/apple/ml-starflow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.20626", "title": "ROOT: Robust Orthogonalized Optimizer for Neural Network Training", "authors": [ "Wei He", "Kai Han", "Hang Zhou", "Hanting Chen", "Zhicheng Liu", "Xinghao Chen", "Yunhe Wang" ], "github": "https://github.com/huawei-noah/noah-research/tree/master/ROOT", "project_page": "", "abstract": "The optimization of large language models (LLMs) remains a critical challenge, particularly as model scaling exacerbates sensitivity to algorithmic imprecision and training instability. Recent advances in optimizers have improved convergence efficiency through momentum orthogonalization, but suffer from two key robustness limitations: dimensional fragility in orthogonalization precision and vulnerability to outlier-induced noise. To address these robustness challenges, we introduce ROOT, a Robust Orthogonalized Optimizer that enhances training stability through dual robustness mechanisms. First, we develop a dimension-robust orthogonalization scheme using adaptive Newton iterations with fine-grained coefficients tailored to specific matrix sizes, ensuring consistent precision across diverse architectural configurations. Second, we introduce an optimization-robust framework via proximal optimization that suppresses outlier noise while preserving meaningful gradient directions. Extensive experiments demonstrate that ROOT achieves significantly improved robustness, with faster convergence and superior final performance compared to both Muon and Adam-based optimizers, particularly in noisy and non-convex scenarios. Our work establishes a new paradigm for developing robust and precise optimizers capable of handling the complexities of modern large-scale model training. The code will be available at https://github.com/huawei-noah/noah-research/tree/master/ROOT.", "github_manual": "", "github_regex": "https://github.com/huawei-noah/noah-research/tree/master/ROOT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.15703", "title": "Think Visually, Reason Textually: Vision-Language Synergy in ARC", "authors": [ "Beichen Zhang", "Yuhang Zang", "Xiaoyi Dong", "Yuhang Cao", "Haodong Duan", "Dahua Lin", "Jiaqi Wang" ], "github": "https://github.com/InternLM/ARC-VL", "project_page": "", "abstract": "Abstract reasoning from minimal examples remains a core unsolved problem for frontier foundation models such as GPT-5 and Grok 4. These models still fail to infer structured transformation rules from a handful of examples, which is a key hallmark of human intelligence. The Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) provides a rigorous testbed for this capability, demanding conceptual rule induction and transfer to novel tasks. Most existing methods treat ARC-AGI as a purely textual reasoning task, overlooking the fact that humans rely heavily on visual abstraction when solving such puzzles. However, our pilot experiments reveal a paradox: naively rendering ARC-AGI grids as images degrades performance due to imprecise rule execution. This leads to our central hypothesis that vision and language possess complementary strengths across distinct reasoning stages: vision supports global pattern abstraction and verification, whereas language specializes in symbolic rule formulation and precise execution. Building on this insight, we introduce two synergistic strategies: (1) Vision-Language Synergy Reasoning (VLSR), which decomposes ARC-AGI into modality-aligned subtasks; and (2) Modality-Switch Self-Correction (MSSC), which leverages vision to verify text-based reasoning for intrinsic error correction. Extensive experiments demonstrate that our approach yields up to a 4.33% improvement over text-only baselines across diverse flagship models and multiple ARC-AGI tasks. Our findings suggest that unifying visual abstraction with linguistic reasoning is a crucial step toward achieving generalizable, human-like intelligence in future foundation models. Source code will be released soon.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/InternLM/ARC-VL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-26", "arxiv_id": "2511.18659", "title": "CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning", "authors": [ "Jie He", "Richard He Bai", "Sinead Williamson", "Jeff Z. Pan", "Navdeep Jaitly", "Yizhe Zhang" ], "github": "", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but still suffers from long contexts and disjoint retrieval-generation optimization. In this work, we propose CLaRa (Continuous Latent Reasoning), a unified framework that performs embedding-based compression and joint optimization in a shared continuous space. To obtain semantically rich and retrievable compressed vectors, we introduce SCP, a key-preserving data synthesis framework using QA and paraphrase supervision. CLaRa then trains the reranker and generator end-to-end via a single language modeling loss, with gradients flowing through both modules using a differentiable top-k estimator. Theoretically, this unified optimization aligns retrieval relevance with answer quality. Experiments across multiple QA benchmarks show that CLaRa achieves state-of-the-art compression and reranking performance, often surpassing text-based fine-tuned baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.20478", "title": "NVIDIA Nemotron Parse 1.1", "authors": [ "Kateryna Chumachenko", "Amala Sanjay Deshmukh", "Jarno Seppanen", "Ilia Karmanov", "Chia-Chih Chen", "Lukas Voegtle", "Philipp Fischer", "Marek Wawrzos", "Saeid Motiian", "Roman Ageev", "Kedi Wu", "Alexandre Milesi", "Maryam Moosaei", "Krzysztof Pawelec", "Padmavathy Subramanian", "Mehrzad Samadi", "Xin Yu", "Celina Dear", "Sarah Stoddard", "Jenna Diamond", "Jesse Oliver", "Leanna Chraghchian", "Patrick Skelly", "Tom Balough", "Yao Xu", "Jane Polak Scowcroft", "Daniel Korzekwa", "Darragh Hanley", "Sandip Bhaskar", "Timo Roman", "Karan Sapra", "Andrew Tao", "Bryan Catanzaro" ], "github": "", "project_page": "", "abstract": "We introduce Nemotron-Parse-1.1, a lightweight document parsing and OCR model that advances the capabilities of its predecessor, Nemoretriever-Parse-1.0. Nemotron-Parse-1.1 delivers improved capabilities across general OCR, markdown formatting, structured table parsing, and text extraction from pictures, charts, and diagrams. It also supports a longer output sequence length for visually dense documents. As with its predecessor, it extracts bounding boxes of text segments, as well as corresponding semantic classes. Nemotron-Parse-1.1 follows an encoder-decoder architecture with 885M parameters, including a compact 256M-parameter language decoder. It achieves competitive accuracy on public benchmarks making it a strong lightweight OCR solution. We release the model weights publicly on Huggingface, as well as an optimized NIM container, along with a subset of the training data as part of the broader Nemotron-VLM-v2 dataset. Additionally, we release Nemotron-Parse-1.1-TC which operates on a reduced vision token length, offering a 20% speed improvement with minimal quality degradation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.20714", "title": "Inferix: A Block-Diffusion based Next-Generation Inference Engine for World Simulation", "authors": [ "Inferix Team", "Tianyu Feng", "Yizeng Han", "Jiahao He", "Yuanyu He", "Xi Lin", "Teng Liu", "Hanfeng Lu", "Jiasheng Tang", "Wei Wang", "Zhiyuan Wang", "Jichao Wu", "Mingyang Yang", "Yinghao Yu", "Zeyu Zhang", "Bohan Zhuang" ], "github": "", "project_page": "", "abstract": "World models serve as core simulators for fields such as agentic AI, embodied AI, and gaming, capable of generating long, physically realistic, and interactive high-quality videos. Moreover, scaling these models could unlock emergent capabilities in visual perception, understanding, and reasoning, paving the way for a new paradigm that moves beyond current LLM-centric vision foundation models. A key breakthrough empowering them is the semi-autoregressive (block-diffusion) decoding paradigm, which merges the strengths of diffusion and autoregressive methods by generating video tokens in block-applying diffusion within each block while conditioning on previous ones, resulting in more coherent and stable video sequences. Crucially, it overcomes limitations of standard video diffusion by reintroducing LLM-style KV Cache management, enabling efficient, variable-length, and high-quality generation. Therefore, Inferix is specifically designed as a next-generation inference engine to enable immersive world synthesis through optimized semi-autoregressive decoding processes. This dedicated focus on world simulation distinctly sets it apart from systems engineered for high-concurrency scenarios (like vLLM or SGLang) and from classic video diffusion models (such as xDiTs). Inferix further enhances its offering with interactive video streaming and profiling, enabling real-time interaction and realistic simulation to accurately model world dynamics. Additionally, it supports efficient benchmarking through seamless integration of LV-Bench, a new fine-grained evaluation benchmark tailored for minute-long video generation scenarios. We hope the community will work together to advance Inferix and foster world model exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.20639", "title": "Latent Collaboration in Multi-Agent Systems", "authors": [ "Jiaru Zou", "Xiyuan Yang", "Ruizhong Qiu", "Gaotang Li", "Katherine Tieu", "Pan Lu", "Ke Shen", "Hanghang Tong", "Yejin Choi", "Jingrui He", "James Zou", "Mengdi Wang", "Ling Yang" ], "github": "https://github.com/Gen-Verse/LatentMAS", "project_page": "", "abstract": "Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence. While existing LLM agents depend on text-based mediation for reasoning and communication, we take a step forward by enabling models to collaborate directly within the continuous latent space. We introduce LatentMAS, an end-to-end training-free framework that enables pure latent collaboration among LLM agents. In LatentMAS, each agent first performs auto-regressive latent thoughts generation through last-layer hidden embeddings. A shared latent working memory then preserves and transfers each agent's internal representations, ensuring lossless information exchange. We provide theoretical analyses establishing that LatentMAS attains higher expressiveness and lossless information preservation with substantially lower complexity than vanilla text-based MAS. In addition, empirical evaluations across 9 comprehensive benchmarks spanning math and science reasoning, commonsense understanding, and code generation show that LatentMAS consistently outperforms strong single-model and text-based MAS baselines, achieving up to 14.6% higher accuracy, reducing output token usage by 70.8%-83.7%, and providing 4x-4.3x faster end-to-end inference. These results demonstrate that our new latent collaboration framework enhances system-level reasoning quality while offering substantial efficiency gains without any additional training. Code and data are fully open-sourced at https://github.com/Gen-Verse/LatentMAS.", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/LatentMAS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.21395", "title": "Monet: Reasoning in Latent Visual Space Beyond Images and Language", "authors": [ "Qixun Wang", "Yang Shi", "Yifei Wang", "Yuanxing Zhang", "Pengfei Wan", "Kun Gai", "Xianghua Ying", "Yisen Wang" ], "github": "https://github.com/NOVAglow646/Monet", "project_page": "", "abstract": "\"Thinking with images\" has emerged as an effective paradigm for advancing visual reasoning, extending beyond text-only chains of thought by injecting visual evidence into intermediate reasoning steps. However, existing methods fall short of human-like abstract visual thinking, as their flexibility is fundamentally limited by external tools. In this work, we introduce Monet, a training framework that enables multimodal large language models (MLLMs) to reason directly within the latent visual space by generating continuous embeddings that function as intermediate visual thoughts. We identify two core challenges in training MLLMs for latent visual reasoning: high computational cost in latent-vision alignment and insufficient supervision over latent embeddings, and address them with a three-stage distillation-based supervised fine-tuning (SFT) pipeline. We further reveal a limitation of applying GRPO to latent reasoning: it primarily enhances text-based reasoning rather than latent reasoning. To overcome this, we propose VLPO (Visual-latent Policy Optimization), a reinforcement learning method that explicitly incorporates latent embeddings into policy gradient updates. To support SFT, we construct Monet-SFT-125K, a high-quality text-image interleaved CoT dataset containing 125K real-world, chart, OCR, and geometry CoTs. Our model, Monet-7B, shows consistent gains across real-world perception and reasoning benchmarks and exhibits strong out-of-distribution generalization on challenging abstract visual reasoning tasks. We also empirically analyze the role of each training component and discuss our early unsuccessful attempts, providing insights for future developments in visual latent reasoning. Our model, data, and code are available at https://github.com/NOVAglow646/Monet.", "github_manual": "", "github_regex": "https://github.com/NOVAglow646/Monet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.19797", "title": "Terminal Velocity Matching", "authors": [ "Linqi Zhou", "Mathias Parger", "Ayaan Haque", "Jiaming Song" ], "github": "", "project_page": "", "abstract": "We propose Terminal Velocity Matching (TVM), a generalization of flow matching that enables high-fidelity one- and few-step generative modeling. TVM models the transition between any two diffusion timesteps and regularizes its behavior at its terminal time rather than at the initial time. We prove that TVM provides an upper bound on the 2-Wasserstein distance between data and model distributions when the model is Lipschitz continuous. However, since Diffusion Transformers lack this property, we introduce minimal architectural changes that achieve stable, single-stage training. To make TVM efficient in practice, we develop a fused attention kernel that supports backward passes on Jacobian-Vector Products, which scale well with transformer architectures. On ImageNet-256x256, TVM achieves 3.29 FID with a single function evaluation (NFE) and 1.99 FID with 4 NFEs. It similarly achieves 4.32 1-NFE FID and 2.94 4-NFE FID on ImageNet-512x512, representing state-of-the-art performance for one/few-step models from scratch.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.21692", "title": "Revisiting Generalization Across Difficulty Levels: It's Not So Easy", "authors": [ "Yeganeh Kordi", "Nihal V. Nayak", "Max Zuo", "Ilana Nguyen", "Stephen H. Bach" ], "github": "https://github.com/BatsResearch/cross-difficulty", "project_page": "", "abstract": "We investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation. Existing research is mixed regarding whether training on easier or harder data leads to better results, and whether those gains come on easier or harder test data. We address this question by conducting a systematic evaluation of LLMs' generalization across models, datasets, and fine-grained groups of example difficulty. We rank examples in six datasets using the outputs of thousands of different LLMs and Item Response Theory (IRT), a well-established difficulty metric in educational testing. Unlike prior work, our difficulty ratings are therefore determined solely by the abilities of many different LLMs, excluding human opinions of difficulty. With a more objective, larger-scale, and finer-grained analysis, we show that cross-difficulty generalization is often limited; training on either easy or hard data cannot achieve consistent improvements across the full range of difficulties. These results show the importance of having a range of difficulties in both training and evaluation data for LLMs, and that taking shortcuts with respect to difficulty is risky.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/BatsResearch/cross-difficulty", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.17918", "title": "Frequency-Adaptive Sharpness Regularization for Improving 3D Gaussian Splatting Generalization", "authors": [ "Youngsik Yun", "Dongjun Gu", "Youngjung Uh" ], "github": "", "project_page": "", "abstract": "Despite 3D Gaussian Splatting (3DGS) excelling in most configurations, it lacks generalization across novel viewpoints in a few-shot scenario because it overfits to the sparse observations. We revisit 3DGS optimization from a machine learning perspective, framing novel view synthesis as a generalization problem to unseen viewpoints-an underexplored direction. We propose Frequency-Adaptive Sharpness Regularization (FASR), which reformulates the 3DGS training objective, thereby guiding 3DGS to converge toward a better generalization solution. Although Sharpness-Aware Minimization (SAM) similarly reduces the sharpness of the loss landscape to improve generalization of classification models, directly employing it to 3DGS is suboptimal due to the discrepancy between the tasks. Specifically, it hinders reconstructing high-frequency details due to excessive regularization, while reducing its strength leads to under-penalizing sharpness. To address this, we reflect the local frequency of images to set the regularization weight and the neighborhood radius when estimating the local sharpness. It prevents floater artifacts in novel viewpoints and reconstructs fine details that SAM tends to oversmooth. Across datasets with various configurations, our method consistently improves a wide range of baselines. Code will be available at https://bbangsik13.github.io/FASR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.20814", "title": "SPHINX: A Synthetic Environment for Visual Perception and Reasoning", "authors": [ "Md Tanvirul Alam", "Saksham Aggarwal", "Justin Yang Chae", "Nidhi Rastogi" ], "github": "https://github.com/xashru/sphinx", "project_page": "", "abstract": "We present Sphinx, a synthetic environment for visual perception and reasoning that targets core cognitive primitives. Sphinx procedurally generates puzzles using motifs, tiles, charts, icons, and geometric primitives, each paired with verifiable ground-truth solutions, enabling both precise evaluation and large-scale dataset construction. The benchmark covers 25 task types spanning symmetry detection, geometric transformations, spatial reasoning, chart interpretation, and sequence prediction. Evaluating recent large vision-language models (LVLMs) shows that even state-of-the-art GPT-5 attains only 51.1% accuracy, well below human performance. Finally, we demonstrate that reinforcement learning with verifiable rewards (RLVR) substantially improves model accuracy on these tasks and yields gains on external visual reasoning benchmarks, highlighting its promise for advancing multimodal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xashru/sphinx", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.21208", "title": "I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation", "authors": [ "Lucas Thil", "Jesse Read", "Rim Kaddah", "Guillaume Doquet" ], "github": "https://github.com/LucasStill/I-GLIDE", "project_page": "", "abstract": "Accurate remaining useful life (RUL) prediction hinges on the quality of health indicators (HIs), yet existing methods often fail to disentangle complex degradation mechanisms in multi-sensor systems or quantify uncertainty in HI reliability. This paper introduces a novel framework for HI construction, advancing three key contributions. First, we adapt Reconstruction along Projected Pathways (RaPP) as a health indicator (HI) for RUL prediction for the first time, showing that it outperforms traditional reconstruction error metrics. Second, we show that augmenting RaPP-derived HIs with aleatoric and epistemic uncertainty quantification (UQ) via Monte Carlo dropout and probabilistic latent spaces- significantly improves RUL-prediction robustness. Third, and most critically, we propose indicator groups, a paradigm that isolates sensor subsets to model system-specific degradations, giving rise to our novel method, I-GLIDE which enables interpretable, mechanism-specific diagnostics. Evaluated on data sourced from aerospace and manufacturing systems, our approach achieves marked improvements in accuracy and generalizability compared to state-of-the-art HI methods while providing actionable insights into system failure pathways. This work bridges the gap between anomaly detection and prognostics, offering a principled framework for uncertainty-aware degradation modeling in complex systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LucasStill/I-GLIDE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.17889", "title": "MobileVLA-R1: Reinforcing Vision-Language-Action for Mobile Robots", "authors": [ "Ting Huang", "Dongjian Li", "Rui Yang", "Zeyu Zhang", "Zida Yang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/MobileVLA-R1", "project_page": "", "abstract": "Grounding natural-language instructions into continuous control for quadruped robots remains a fundamental challenge in vision language action. Existing methods struggle to bridge high-level semantic reasoning and low-level actuation, leading to unstable grounding and weak generalization in the real world. To address these issues, we present MobileVLA-R1, a unified vision-language-action framework that enables explicit reasoning and continuous control for quadruped robots. We construct MobileVLA-CoT, a large-scale dataset of multi-granularity chain-of-thought (CoT) for embodied trajectories, providing structured reasoning supervision for alignment. Built upon this foundation, we introduce a two-stage training paradigm that combines supervised CoT alignment with GRPO reinforcement learning to enhance reasoning consistency, control stability, and long-horizon execution. Extensive evaluations on VLN and VLA tasks demonstrate superior performance over strong baselines, with approximately a 5% improvement. Real-world deployment on a quadruped robot validates robust performance in complex environments. Code: https://github.com/AIGeeksGroup/MobileVLA-R1. Website: https://aigeeksgroup.github.io/MobileVLA-R1.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/MobileVLA-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.21579", "title": "Harmony: Harmonizing Audio and Video Generation through Cross-Task Synergy", "authors": [ "Teng Hu", "Zhentao Yu", "Guozhen Zhang", "Zihan Su", "Zhengguang Zhou", "Youliang Zhang", "Yuan Zhou", "Qinglin Lu", "Ran Yi" ], "github": "", "project_page": "", "abstract": "The synthesis of synchronized audio-visual content is a key challenge in generative AI, with open-source models facing challenges in robust audio-video alignment. Our analysis reveals that this issue is rooted in three fundamental challenges of the joint diffusion process: (1) Correspondence Drift, where concurrently evolving noisy latents impede stable learning of alignment; (2) inefficient global attention mechanisms that fail to capture fine-grained temporal cues; and (3) the intra-modal bias of conventional Classifier-Free Guidance (CFG), which enhances conditionality but not cross-modal synchronization. To overcome these challenges, we introduce Harmony, a novel framework that mechanistically enforces audio-visual synchronization. We first propose a Cross-Task Synergy training paradigm to mitigate drift by leveraging strong supervisory signals from audio-driven video and video-driven audio generation tasks. Then, we design a Global-Local Decoupled Interaction Module for efficient and precise temporal-style alignment. Finally, we present a novel Synchronization-Enhanced CFG (SyncCFG) that explicitly isolates and amplifies the alignment signal during inference. Extensive experiments demonstrate that Harmony establishes a new state-of-the-art, significantly outperforming existing methods in both generation fidelity and, critically, in achieving fine-grained audio-visual synchronization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.20426", "title": "Block Cascading: Training Free Acceleration of Block-Causal Video Models", "authors": [ "Hmrishav Bandyopadhyay", "Nikhil Pinnaparaju", "Rahim Entezari", "Jim Scott", "Yi-Zhe Song", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "Block-causal video generation faces a stark speed-quality trade-off: small 1.3B models manage only 16 FPS while large 14B models crawl at 4.5 FPS, forcing users to choose between responsiveness and quality. Block Cascading significantly mitigates this trade-off through training-free parallelization. Our key insight: future video blocks do not need fully denoised current blocks to begin generation. By starting block generation with partially denoised context from predecessors, we transform sequential pipelines into parallel cascades where multiple blocks denoise simultaneously. With 5 GPUs exploiting temporal parallelism, we achieve ~2x acceleration across all model scales: 1.3B models accelerate from 16 to 30 FPS, 14B models from 4.5 to 12.5 FPS. Beyond inference speed, Block Cascading eliminates overhead from KV-recaching (of ~200ms) during context switches for interactive generation. Extensive evaluations validated against multiple block-causal pipelines demonstrate no significant loss in generation quality when switching from block-causal to Block Cascading pipelines for inference. Project Page: https://hmrishavbandy.github.io/block_cascading_page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.18005", "title": "RAISECity: A Multimodal Agent Framework for Reality-Aligned 3D World Generation at City-Scale", "authors": [ "Shengyuan Wang", "Zhiheng Zheng", "Yu Shang", "Lixuan He", "Yangcheng Yu", "Fan Hangyu", "Jie Feng", "Qingmin Liao", "Yong Li" ], "github": "", "project_page": "", "abstract": "City-scale 3D generation is of great importance for the development of embodied intelligence and world models. Existing methods, however, face significant challenges regarding quality, fidelity, and scalability in 3D world generation. Thus, we propose RAISECity, a Reality-Aligned Intelligent Synthesis Engine that creates detailed, City-scale 3D worlds. We introduce an agentic framework that leverages diverse multimodal foundation tools to acquire real-world knowledge, maintain robust intermediate representations, and construct complex 3D scenes. This agentic design, featuring dynamic data processing, iterative self-reflection and refinement, and the invocation of advanced multimodal tools, minimizes cumulative errors and enhances overall performance. Extensive quantitative experiments and qualitative analyses validate the superior performance of RAISECity in real-world alignment, shape precision, texture fidelity, and aesthetics level, achieving over a 90% win-rate against existing baselines for overall perceptual quality. This combination of 3D quality, reality alignment, scalability, and seamless compatibility with computer graphics pipelines makes RAISECity a promising foundation for applications in immersive media, embodied intelligence, and world models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.15552", "title": "Multimodal Evaluation of Russian-language Architectures", "authors": [ "Artem Chervyakov", "Ulyana Isaeva", "Anton Emelyanov", "Artem Safin", "Maria Tikhonova", "Alexander Kharitonov", "Yulia Lyakh", "Petr Surovtsev", "Denis Shevelev", "Vildan Saburov", "Vasily Konovalov", "Elisei Rykov", "Ivan Sviridov", "Amina Miftakhova", "Ilseyar Alimova", "Alexander Panchenko", "Alexander Kapitanov", "Alena Fenogenova" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) are currently at the center of research attention, showing rapid progress in scale and capabilities, yet their intelligence, limitations, and risks remain insufficiently understood. To address these issues, particularly in the context of the Russian language, where no multimodal benchmarks currently exist, we introduce Mera Multi, an open multimodal evaluation framework for Russian-spoken architectures. The benchmark is instruction-based and encompasses default text, image, audio, and video modalities, comprising 18 newly constructed evaluation tasks for both general-purpose models and modality-specific architectures (image-to-text, video-to-text, and audio-to-text). Our contributions include: (i) a universal taxonomy of multimodal abilities; (ii) 18 datasets created entirely from scratch with attention to Russian cultural and linguistic specificity, unified prompts, and metrics; (iii) baseline results for both closed-source and open-source models; (iv) a methodology for preventing benchmark leakage, including watermarking and licenses for private sets. While our current focus is on Russian, the proposed benchmark provides a replicable methodology for constructing multimodal benchmarks in typologically diverse languages, particularly within the Slavic language family.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.20410", "title": "Image-Free Timestep Distillation via Continuous-Time Consistency with Trajectory-Sampled Pairs", "authors": [ "Bao Tang", "Shuai Zhang", "Yueting Zhu", "Jijun Xiang", "Xin Yang", "Li Yu", "Wenyu Liu", "Xinggang Wang" ], "github": "https://github.com/hustvl/TBCM", "project_page": "", "abstract": "Timestep distillation is an effective approach for improving the generation efficiency of diffusion models. The Consistency Model (CM), as a trajectory-based framework, demonstrates significant potential due to its strong theoretical foundation and high-quality few-step generation. Nevertheless, current continuous-time consistency distillation methods still rely heavily on training data and computational resources, hindering their deployment in resource-constrained scenarios and limiting their scalability to diverse domains. To address this issue, we propose Trajectory-Backward Consistency Model (TBCM), which eliminates the dependence on external training data by extracting latent representations directly from the teacher model's generation trajectory. Unlike conventional methods that require VAE encoding and large-scale datasets, our self-contained distillation paradigm significantly improves both efficiency and simplicity. Moreover, the trajectory-extracted samples naturally bridge the distribution gap between training and inference, thereby enabling more effective knowledge transfer. Empirically, TBCM achieves 6.52 FID and 28.08 CLIP scores on MJHQ-30k under one-step generation, while reducing training time by approximately 40% compared to Sana-Sprint and saving a substantial amount of GPU memory, demonstrating superior efficiency without sacrificing quality. We further reveal the diffusion-generation space discrepancy in continuous-time consistency distillation and analyze how sampling strategies affect distillation performance, offering insights for future distillation research. GitHub Link: https://github.com/hustvl/TBCM.", "github_manual": "", "github_regex": "https://github.com/hustvl/TBCM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.19413", "title": "UniGame: Turning a Unified Multimodal Model Into Its Own Adversary", "authors": [ "Zhaolong Su", "Wang Lu", "Hao Chen", "Sharon Li", "Jindong Wang" ], "github": "https://github.com/AIFrontierLab/UniGame", "project_page": "", "abstract": "Unified Multimodal Models (UMMs) have shown impressive performance in both understanding and generation with a single architecture. However, UMMs still exhibit a fundamental inconsistency: understanding favors compact embeddings, whereas generation favors reconstruction-rich representations. This structural trade-off produces misaligned decision boundaries, degraded cross-modal coherence, and heightened vulnerability under distributional and adversarial shifts. In this paper, we present UniGame, a self-adversarial post-training framework that directly targets the inconsistencies. By applying a lightweight perturber at the shared token interface, UniGame enables the generation branch to actively seek and challenge fragile understanding, turning the model itself into its own adversary. Experiments demonstrate that UniGame significantly improves the consistency (+4.6%). Moreover, it also achieves substantial improvements in understanding (+3.6%), generation (+0.02), out-of-distribution and adversarial robustness (+4.8% and +6.2% on NaturalBench and AdVQA). The framework is architecture-agnostic, introduces less than 1% additional parameters, and is complementary to existing post-training methods. These results position adversarial self-play as a general and effective principle for enhancing the coherence, stability, and unified competence of future multimodal foundation models. The official code is available at: https://github.com/AIFrontierLab/UniGame", "github_manual": "", "github_regex": "https://github.com/AIFrontierLab/UniGame", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.20633", "title": "Reinforcing Action Policies by Prophesying", "authors": [ "Jiahui Zhang", "Ze Huang", "Chun Gu", "Zipei Ma", "Li Zhang" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) policies excel in aligning language, perception, and robot control. However, most VLAs are trained purely by imitation, which overfits to demonstrations, and is brittle under distribution shift. Reinforcement learning (RL) directly optimizes task reward and thus addresses this misalignment, but real-robot interaction is expensive and conventional simulators are hard to engineer and transfer. We address both data efficiency and optimization stability in VLA post-training via a learned world model and an RL procedure tailored to flow-based action heads. Specifically, we introduce Prophet, a unified action-to-video robot actuation pretrained across large-scale, heterogeneous robot data to learn reusable action-outcome dynamics. It is able to few-shot adapt to new robots, objects, and environments, yielding a rollout-ready simulator. Upon Prophet, we reinforce action policies with Flow-action-GRPO (FA-GRPO), which adapts Flow-GRPO to operate on VLA actions, and with FlowScale, a stepwise reweighting that rescales per-step gradients in the flow head. Together, Prophet, FA-GRPO, and FlowScale constitute ProphRL, a practical, data- and compute-efficient path to VLA post-training. Experiments show 5-17% success gains on public benchmarks and 24-30% gains on real robots across different VLA variants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.19504", "title": "Position: The Complexity of Perfect AI Alignment -- Formalizing the RLHF Trilemma", "authors": [ "Subramanyam Sahoo", "Aman Chadha", "Vinija Jain", "Divya Chaudhary" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Human Feedback (RLHF) is widely used for aligning large language models, yet practitioners face a persistent puzzle: improving safety often reduces fairness, scaling to diverse populations becomes computationally intractable, and making systems robust often amplifies majority biases. We formalize this tension as the Alignment Trilemma: no RLHF system can simultaneously achieve (i) epsilon-representativeness across diverse human values, (ii) polynomial tractability in sample and compute complexity, and (iii) delta-robustness against adversarial perturbations and distribution shift. Through a complexity-theoretic analysis integrating statistical learning theory and robust optimization, we prove that achieving both representativeness (epsilon <= 0.01) and robustness (delta <= 0.001) for global-scale populations requires Omega(2^{d_context}) operations, which is super-polynomial in the context dimensionality. We show that current RLHF implementations resolve this trilemma by sacrificing representativeness: they collect only 10^3--10^4 samples from homogeneous annotator pools while 10^7--10^8 samples are needed for true global representation. Our framework provides a unified explanation for documented RLHF pathologies including preference collapse, sycophancy, and systematic bias amplification. We conclude with concrete directions for navigating these fundamental trade-offs through strategic relaxations of alignment requirements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.21688", "title": "G^2VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning", "authors": [ "Wenbo Hu", "Jingli Lin", "Yilin Long", "Yunlong Ran", "Lihan Jiang", "Yifan Wang", "Chenming Zhu", "Runsen Xu", "Tai Wang", "Jiangmiao Pang" ], "github": "https://github.com/InternRobotics/G2VLM", "project_page": "", "abstract": "Vision-Language Models (VLMs) still lack robustness in spatial intelligence, demonstrating poor performance on spatial understanding and reasoning tasks. We attribute this gap to the absence of a visual geometry learning process capable of reconstructing 3D space from 2D images. We present G^2VLM, a geometry grounded vision-language model that bridges two fundamental aspects of spatial intelligence: spatial 3D reconstruction and spatial understanding. G^2VLM natively leverages learned 3D visual geometry features to directly predict 3D attributes and enhance spatial reasoning tasks via in-context learning and interleaved reasoning. Our unified design is highly scalable for spatial understanding: it trains on abundant multi-view image and video data, while simultaneously leveraging the benefits of 3D visual priors that are typically only derived from hard-to-collect annotations. Experimental results demonstrate G^2VLM is proficient in both tasks, achieving comparable results to state-of-the-art feed-forward 3D reconstruction models and achieving better or competitive results across spatial understanding and reasoning tasks. By unifying a semantically strong VLM with low-level 3D vision tasks, we hope G^2VLM can serve as a strong baseline for the community and unlock more future applications, such as 3D scene editing.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/InternRobotics/G2VLM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-27", "arxiv_id": "2511.18452", "title": "NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering", "authors": [ "Loick Chambon", "Paul Couairon", "Eloi Zablocki", "Alexandre Boulch", "Nicolas Thome", "Matthieu Cord" ], "github": "https://github.com/valeoai/NAF", "project_page": "", "abstract": "Vision Foundation Models (VFMs) extract spatially downsampled representations, posing challenges for pixel-level tasks. Existing upsampling approaches face a fundamental trade-off: classical filters are fast and broadly applicable but rely on fixed forms, while modern upsamplers achieve superior accuracy through learnable, VFM-specific forms at the cost of retraining for each VFM. We introduce Neighborhood Attention Filtering (NAF), which bridges this gap by learning adaptive spatial-and-content weights through Cross-Scale Neighborhood Attention and Rotary Position Embeddings (RoPE), guided solely by the high-resolution input image. NAF operates zero-shot: it upsamples features from any VFM without retraining, making it the first VFM-agnostic architecture to outperform VFM-specific upsamplers and achieve state-of-the-art performance across multiple downstream tasks. It maintains high efficiency, scaling to 2K feature maps and reconstructing intermediate-resolution maps at 18 FPS. Beyond feature upsampling, NAF demonstrates strong performance on image restoration, highlighting its versatility. Code and checkpoints are available at https://github.com/valeoai/NAF.", "github_manual": "", "github_regex": "https://github.com/valeoai/NAF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-28", "arxiv_id": "2511.21541", "title": "Video Generation Models Are Good Latent Reward Models", "authors": [ "Xiaoyue Mi", "Wenqing Yu", "Jiesong Lian", "Shibo Jie", "Ruizhe Zhong", "Zijun Liu", "Guozhen Zhang", "Zixiang Zhou", "Zhiyong Xu", "Yuan Zhou", "Qinglin Lu", "Fan Tang" ], "github": "", "project_page": "", "abstract": "Reward feedback learning (ReFL) has proven effective for aligning image generation with human preferences. However, its extension to video generation faces significant challenges. Existing video reward models rely on vision-language models designed for pixel-space inputs, confining ReFL optimization to near-complete denoising steps after computationally expensive VAE decoding. This pixel-space approach incurs substantial memory overhead and increased training time, and its late-stage optimization lacks early-stage supervision, refining only visual quality rather than fundamental motion dynamics and structural coherence. In this work, we show that pre-trained video generation models are naturally suited for reward modeling in the noisy latent space, as they are explicitly designed to process noisy latent representations at arbitrary timesteps and inherently preserve temporal information through their sequential modeling capabilities. Accordingly, we propose Process Reward Feedback Learning~(PRFL), a framework that conducts preference optimization entirely in latent space, enabling efficient gradient backpropagation throughout the full denoising chain without VAE decoding. Extensive experiments demonstrate that PRFL significantly improves alignment with human preferences, while achieving substantial reductions in memory consumption and training time compared to RGB ReFL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-28", "arxiv_id": "2511.21691", "title": "Canvas-to-Image: Compositional Image Generation with Multimodal Controls", "authors": [ "Yusuf Dalva", "Guocheng Gordon Qian", "Maya Goldenberg", "Tsai-Shien Chen", "Kfir Aberman", "Sergey Tulyakov", "Pinar Yanardag", "Kuan-Chieh Jackson Wang" ], "github": "", "project_page": "", "abstract": "While modern diffusion models excel at generating high-quality and diverse images, they still struggle with high-fidelity compositional and multimodal control, particularly when users simultaneously specify text prompts, subject references, spatial arrangements, pose constraints, and layout annotations. We introduce Canvas-to-Image, a unified framework that consolidates these heterogeneous controls into a single canvas interface, enabling users to generate images that faithfully reflect their intent. Our key idea is to encode diverse control signals into a single composite canvas image that the model can directly interpret for integrated visual-spatial reasoning. We further curate a suite of multi-task datasets and propose a Multi-Task Canvas Training strategy that optimizes the diffusion model to jointly understand and integrate heterogeneous controls into text-to-image generation within a unified learning paradigm. This joint training enables Canvas-to-Image to reason across multiple control modalities rather than relying on task-specific heuristics, and it generalizes well to multi-control scenarios during inference. Extensive experiments show that Canvas-to-Image significantly outperforms state-of-the-art methods in identity preservation and control adherence across challenging benchmarks, including multi-person composition, pose-controlled composition, layout-constrained generation, and multi-control generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-28", "arxiv_id": "2511.21087", "title": "MIRA: Multimodal Iterative Reasoning Agent for Image Editing", "authors": [ "Ziyun Zeng", "Hang Hua", "Jiebo Luo" ], "github": "", "project_page": "", "abstract": "Instruction-guided image editing offers an intuitive way for users to edit images with natural language. However, diffusion-based editing models often struggle to accurately interpret complex user instructions, especially those involving compositional relationships, contextual cues, or referring expressions, leading to edits that drift semantically or fail to reflect the intended changes. We tackle this problem by proposing MIRA (Multimodal Iterative Reasoning Agent), a lightweight, plug-and-play multimodal reasoning agent that performs editing through an iterative perception-reasoning-action loop, effectively simulating multi-turn human-model interaction processes. Instead of issuing a single prompt or static plan, MIRA predicts atomic edit instructions step by step, using visual feedback to make its decisions. Our 150K multimodal tool-use dataset, MIRA-Editing, combined with a two-stage SFT + GRPO training pipeline, enables MIRA to perform reasoning and editing over complex editing instructions. When paired with open-source image editing models such as Flux.1-Kontext, Step1X-Edit, and Qwen-Image-Edit, MIRA significantly improves both semantic consistency and perceptual quality, achieving performance comparable to or exceeding proprietary systems such as GPT-Image and Nano-Banana.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-28", "arxiv_id": "2511.21662", "title": "Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following", "authors": [ "Tianyi Xiong", "Yi Ge", "Ming Li", "Zuolong Zhang", "Pranav Kulkarni", "Kaishen Wang", "Qi He", "Zeying Zhu", "Chenxi Liu", "Ruibo Chen", "Tong Zheng", "Yanshuo Chen", "Xiyao Wang", "Renrui Zhang", "Wenhu Chen", "Heng Huang" ], "github": "", "project_page": "", "abstract": "Large multimodal models (LMMs) are increasingly adopted as judges in multimodal evaluation systems due to their strong instruction following and consistency with human preferences. However, their ability to follow diverse, fine-grained evaluation criteria remains underexplored. We develop Multi-Crit, a benchmark for evaluating multimodal judges on their capacity to follow pluralistic criteria and produce reliable criterion-level judgments. Covering both open-ended generation and verifiable reasoning tasks, Multi-Crit is built through a rigorous data curation pipeline that gathers challenging response pairs with multi-criterion human annotations. It further introduces three novel metrics for systematically assessing pluralistic adherence, criterion-switching flexibility, and the ability to recognize criterion-level preference conflicts. Comprehensive analysis of 25 LMMs reveals that 1) proprietary models still struggle to maintain consistent adherence to pluralistic criteria--especially in open-ended evaluation; 2) open-source models lag further behind in flexibly following diverse criteria; and 3) critic fine-tuning with holistic judgment signals enhances visual grounding but fails to generalize to pluralistic criterion-level judgment. Additional analyses on reasoning fine-tuning, test-time scaling, and boundary consistency between open-source and proprietary models further probe the limits of current multimodal judges. As a pioneering study, Multi-Crit lays the foundation for building reliable and steerable multimodal AI evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-28", "arxiv_id": "2511.21678", "title": "Agentic Learner with Grow-and-Refine Multimodal Semantic Memory", "authors": [ "Weihao Bo", "Shan Zhang", "Yanpeng Sun", "Jingjing Wu", "Qunyi Xie", "Xiao Tan", "Kunbin Chen", "Wei He", "Xiaofan Li", "Na Zhao", "Jingdong Wang", "Zechao Li" ], "github": "", "project_page": "", "abstract": "MLLMs exhibit strong reasoning on isolated queries, yet they operate de novo -- solving each problem independently and often repeating the same mistakes. Existing memory-augmented agents mainly store past trajectories for reuse. However, trajectory-based memory suffers from brevity bias, gradually losing essential domain knowledge. More critically, even in truly multimodal problem-solving settings, it records only a single-modality trace of past behavior, failing to preserve how visual attention and logical reasoning jointly contributed to the solution. This is fundamentally misaligned with human cognition: semantic memory is both multimodal and integrated, preserving visual and abstract knowledge through coordinated but distinct representational streams. We thus introduce ViLoMem, a dual-stream memory framework that constructs compact, schema-based memory. It separately encodes visual distraction patterns and logical reasoning errors, enabling MLLMs to learn from their successful and failed experiences. Following a grow-and-refine principle, the system incrementally accumulates and updates multimodal semantic knowledge -- preserving stable, generalizable strategies while avoiding catastrophic forgetting. Across six multimodal benchmarks, ViLoMem consistently improves pass@1 accuracy and substantially reduces repeated visual and logical errors. Ablations confirm the necessity of dual-stream memory with explicit distraction--hallucination separation, demonstrating the value of error-aware multimodal memory for lifelong and cross-domain agentic learning. Our project page will be available at https://weihao-bo.github.io/ViLoMeo-page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-28", "arxiv_id": "2511.19757", "title": "What does it mean to understand language?", "authors": [ "Colton Casto", "Anna Ivanova", "Evelina Fedorenko", "Nancy Kanwisher" ], "github": "", "project_page": "", "abstract": "Language understanding entails not just extracting the surface-level meaning of the linguistic input, but constructing rich mental models of the situation it describes. Here we propose that because processing within the brain's core language system is fundamentally limited, deeply understanding language requires exporting information from the language system to other brain regions that compute perceptual and motor representations, construct mental models, and store our world knowledge and autobiographical memories. We review the existing evidence for this hypothesis, and argue that recent progress in cognitive neuroscience provides both the conceptual foundation and the methods to directly test it, thus opening up a new strategy to reveal what it means, cognitively and neurally, to understand language.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-11-28", "arxiv_id": "2511.20937", "title": "ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction", "authors": [ "Qineng Wang", "Wenlong Huang", "Yu Zhou", "Hang Yin", "Tianwei Bao", "Jianwen Lyu", "Weiyu Liu", "Ruohan Zhang", "Jiajun Wu", "Li Fei-Fei", "Manling Li" ], "github": "", "project_page": "", "abstract": "Embodied cognition argues that intelligence arises from sensorimotor interaction rather than passive observation. It raises an intriguing question: do modern vision-language models (VLMs), trained largely in a disembodied manner, exhibit signs of embodied cognition? We introduce ENACT, a benchmark that casts evaluation of embodied cognition as world modeling from egocentric interaction in a visual question answering (VQA) format. Framed as a partially observable Markov decision process (POMDP) whose actions are scene graph changes, ENACT comprises two complementary sequence reordering tasks: forward world modeling (reorder shuffled observations given actions) and inverse world modeling (reorder shuffled actions given observations). While conceptually simple, solving these tasks implicitly demands capabilities central to embodied cognition-affordance recognition, action-effect reasoning, embodied awareness, and interactive, long-horizon memory from partially observable egocentric input, while avoiding low-level image synthesis that could confound the evaluation. We provide a scalable pipeline that synthesizes QA pairs from robotics simulation (BEHAVIOR) and evaluates models on 8,972 QA pairs spanning long-horizon home-scale activities. Experiments reveal a performance gap between frontier VLMs and humans that widens with interaction horizon. Models consistently perform better on the inverse task than the forward one and exhibit anthropocentric biases, including a preference for right-handed actions and degradation when camera intrinsics or viewpoints deviate from human vision. Website at https://enact-embodied-cognition.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22699", "title": "Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer", "authors": [ "Z-Image Team", "Huanqia Cai", "Sihan Cao", "Ruoyi Du", "Peng Gao", "Steven Hoi", "Shijie Huang", "Zhaohui Hou", "Dengyang Jiang", "Xin Jin", "Liangchen Li", "Zhen Li", "Zhong-Yu Li", "David Liu", "Dongyang Liu", "Junhan Shi", "Qilong Wu", "Feng Yu", "Chi Zhang", "Shifeng Zhang", "Shilin Zhou" ], "github": "", "project_page": "", "abstract": "The landscape of high-performance image generation models is currently dominated by proprietary systems, such as Nano Banana Pro and Seedream 4.0. Leading open-source alternatives, including Qwen-Image, Hunyuan-Image-3.0 and FLUX.2, are characterized by massive parameter counts (20B to 80B), making them impractical for inference, and fine-tuning on consumer-grade hardware. To address this gap, we propose Z-Image, an efficient 6B-parameter foundation generative model built upon a Scalable Single-Stream Diffusion Transformer (S3-DiT) architecture that challenges the \"scale-at-all-costs\" paradigm. By systematically optimizing the entire model lifecycle -- from a curated data infrastructure to a streamlined training curriculum -- we complete the full training workflow in just 314K H800 GPU hours (approx. $630K). Our few-step distillation scheme with reward post-training further yields Z-Image-Turbo, offering both sub-second inference latency on an enterprise-grade H800 GPU and compatibility with consumer-grade hardware (<16GB VRAM). Additionally, our omni-pre-training paradigm also enables efficient training of Z-Image-Edit, an editing model with impressive instruction-following capabilities. Both qualitative and quantitative experiments demonstrate that our model achieves performance comparable to or surpassing that of leading competitors across various dimensions. Most notably, Z-Image exhibits exceptional capabilities in photorealistic image generation and bilingual text rendering, delivering results that rival top-tier commercial models, thereby demonstrating that state-of-the-art results are achievable with significantly reduced computational overhead. We publicly release our code, weights, and online demo to foster the development of accessible, budget-friendly, yet state-of-the-art generative models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22625", "title": "REASONEDIT: Towards Reasoning-Enhanced Image Editing Models", "authors": [ "Fukun Yin", "Shiyu Liu", "Yucheng Han", "Zhibo Wang", "Peng Xing", "Rui Wang", "Wei Cheng", "Yingming Wang", "Aojie Li", "Zixin Yin", "Pengtao Chen", "Xiangyu Zhang", "Daxin Jiang", "Xianfang Zeng", "Gang Yu" ], "github": "", "project_page": "", "abstract": "Recent advances in image editing models have shown remarkable progress. A common architectural design couples a multimodal large language model (MLLM) encoder with a diffusion decoder, as seen in systems such as Step1X-Edit and Qwen-Image-Edit, where the MLLM encodes both the reference image and the instruction but remains frozen during training. In this work, we demonstrate that unlocking the reasoning capabilities of MLLM can further push the boundaries of editing models. Specifically, we explore two reasoning mechanisms, thinking and reflection, which enhance instruction understanding and editing accuracy. Based on that, our proposed framework enables image editing in a thinking-editing-reflection loop: the thinking mechanism leverages the world knowledge of MLLM to interpret abstract instructions, while the reflection reviews editing results, automatically corrects unintended manipulations, and identifies the stopping round. Extensive experiments demonstrate that our reasoning approach achieves significant performance gains, with improvements of ImgEdit (+4.3%), GEdit (+4.7%), and Kris (+8.2%) when initializing our DiT from the Step1X-Edit (ReasonEdit-S), and also outperforms previous open-source methods on both GEdit and Kris when integrated with Qwen-Image-Edit (ReasonEdit-Q).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.23475", "title": "AnyTalker: Scaling Multi-Person Talking Video Generation with Interactivity Refinement", "authors": [ "Zhizhou Zhong", "Yicheng Ji", "Zhe Kong", "Yiying Liu", "Jiarui Wang", "Jiasun Feng", "Lupeng Liu", "Xiangyi Wang", "Yanjia Li", "Yuqing She", "Ying Qin", "Huan Li", "Shuiyang Mao", "Wei Liu", "Wenhan Luo" ], "github": "", "project_page": "", "abstract": "Recently, multi-person video generation has started to gain prominence. While a few preliminary works have explored audio-driven multi-person talking video generation, they often face challenges due to the high costs of diverse multi-person data collection and the difficulty of driving multiple identities with coherent interactivity. To address these challenges, we propose AnyTalker, a multi-person generation framework that features an extensible multi-stream processing architecture. Specifically, we extend Diffusion Transformer's attention block with a novel identity-aware attention mechanism that iteratively processes identity-audio pairs, allowing arbitrary scaling of drivable identities. Besides, training multi-person generative models demands massive multi-person data. Our proposed training pipeline depends solely on single-person videos to learn multi-person speaking patterns and refines interactivity with only a few real multi-person clips. Furthermore, we contribute a targeted metric and dataset designed to evaluate the naturalness and interactivity of the generated multi-person videos. Extensive experiments demonstrate that AnyTalker achieves remarkable lip synchronization, visual quality, and natural interactivity, striking a favorable balance between data costs and identity scalability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.23199", "title": "Vision Bridge Transformer at Scale", "authors": [ "Zhenxiong Tan", "Zeqing Wang", "Xingyi Yang", "Songhua Liu", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "We introduce Vision Bridge Transformer (ViBT), a large-scale instantiation of Brownian Bridge Models designed for conditional generation. Unlike traditional diffusion models that transform noise into data, Bridge Models directly model the trajectory between inputs and outputs, creating an efficient data-to-data translation paradigm. By scaling these models to 20B and 1.3B parameters, we demonstrate their effectiveness for image and video translation tasks. To support this scale, we adopt a Transformer architecture and propose a variance-stabilized velocity-matching objective for robust training. Together, these advances highlight the power of scaling Bridge Models for instruction-based image editing and complex video translation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22663", "title": "Architecture Decoupling Is Not All You Need For Unified Multimodal Model", "authors": [ "Dian Zheng", "Manyuan Zhang", "Hongyu Li", "Kai Zou", "Hongbo Liu", "Ziyu Guo", "Kaituo Feng", "Yexin Liu", "Ying Luo", "Yan Feng", "Peng Pei", "Xunliang Cai", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "Unified multimodal models for image generation and understanding represent a significant step toward AGI and have attracted widespread attention from researchers. The main challenge of this task lies in the difficulty in establishing an optimal training paradigm due to inherent conflicting targets in understanding and generation tasks. To alleviate these conflicts and pursue higher performance, many researchers adopt varying degrees of model decoupling (e.g., Double image encoders, MOE/MOT architecture, or frozen MLLM). However, excessive model decoupling can lead to the loss of interleave generation ability, undermining the original intent of unified models. In this work, we aim to explore how to mitigate task conflicts without resorting to model decoupling. Firstly, we analyze why decoupling alleviates conflicts by studying the cross-modal attention behavior of models. We observe that model decoupling essentially drives models toward task-specific multimodal interaction patterns, as seen in Qwen-VL and HunyuanImage, and that the more thorough the decoupling, the more consistent the behavior becomes. Motivated by this observation, we propose Attention Interaction Alignment (AIA) loss, which explicitly learns Task-Specific multimodal interaction patterns during training. To demonstrate the generalizability of our AIA loss, we apply it to Emu3 and Janus-Pro during SFT and post-training stage respectively. Without bells and whistles, AIA not only refines cross-modal attention patterns, but also boosts both generation and understanding performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.23319", "title": "Every Token Counts: Generalizing 16M Ultra-Long Context in Large Language Models", "authors": [ "Xiang Hu", "Zhanchao Zhou", "Ruiqi Liang", "Zehuan Li", "Wei Wu", "Jianguo Li" ], "github": "", "project_page": "", "abstract": "This work explores the challenge of building ``Machines that Can Remember'', framing long-term memory as the problem of efficient ultra-long context modeling. We argue that this requires three key properties: sparsity, random-access flexibility, and length generalization. To address ultra-long-context modeling, we leverage Hierarchical Sparse Attention (HSA), a novel attention mechanism that satisfies all three properties. We integrate HSA into Transformers to build HSA-UltraLong, which is an 8B-parameter MoE model trained on over 8 trillion tokens and is rigorously evaluated on different tasks with in-domain and out-of-domain context lengths to demonstrate its capability in handling ultra-long contexts. Results show that our model performs comparably to full-attention baselines on in-domain lengths while achieving over 90\\% accuracy on most in-context retrieval tasks with contexts up to 16M. This report outlines our experimental insights and open problems, contributing a foundation for future research in ultra-long context modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.18822", "title": "DiP: Taming Diffusion Models in Pixel Space", "authors": [ "Zhennan Chen", "Junwei Zhu", "Xu Chen", "Jiangning Zhang", "Xiaobin Hu", "Hanzhen Zhao", "Chengjie Wang", "Jian Yang", "Ying Tai" ], "github": "https://github.com/NJU-PCALab/DiP", "project_page": "", "abstract": "Diffusion models face a fundamental trade-off between generation quality and computational efficiency. Latent Diffusion Models (LDMs) offer an efficient solution but suffer from potential information loss and non-end-to-end training. In contrast, existing pixel space models bypass VAEs but are computationally prohibitive for high-resolution synthesis. To resolve this dilemma, we propose DiP, an efficient pixel space diffusion framework. DiP decouples generation into a global and a local stage: a Diffusion Transformer (DiT) backbone operates on large patches for efficient global structure construction, while a co-trained lightweight Patch Detailer Head leverages contextual features to restore fine-grained local details. This synergistic design achieves computational efficiency comparable to LDMs without relying on a VAE. DiP is accomplished with up to 10times faster inference speeds than previous method while increasing the total number of parameters by only 0.3%, and achieves an 1.79 FID score on ImageNet 256times256.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NJU-PCALab/DiP", "github_api_added_by": "auto" }, { "date": "2025-12-01", "arxiv_id": "2511.22677", "title": "Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield", "authors": [ "Dongyang Liu", "Peng Gao", "David Liu", "Ruoyi Du", "Zhen Li", "Qilong Wu", "Xin Jin", "Sihan Cao", "Shifeng Zhang", "Hongsheng Li", "Steven Hoi" ], "github": "https://github.com/Tongyi-MAI/Z-Image", "project_page": "", "abstract": "Diffusion model distillation has emerged as a powerful technique for creating efficient few-step and single-step generators. Among these, Distribution Matching Distillation (DMD) and its variants stand out for their impressive performance, which is widely attributed to their core mechanism of matching the student's output distribution to that of a pre-trained teacher model. In this work, we challenge this conventional understanding. Through a rigorous decomposition of the DMD training objective, we reveal that in complex tasks like text-to-image generation, where CFG is typically required for desirable few-step performance, the primary driver of few-step distillation is not distribution matching, but a previously overlooked component we identify as CFG Augmentation (CA). We demonstrate that this term acts as the core ``engine'' of distillation, while the Distribution Matching (DM) term functions as a ``regularizer'' that ensures training stability and mitigates artifacts. We further validate this decoupling by demonstrating that while the DM term is a highly effective regularizer, it is not unique; simpler non-parametric constraints or GAN-based objectives can serve the same stabilizing function, albeit with different trade-offs. This decoupling of labor motivates a more principled analysis of the properties of both terms, leading to a more systematic and in-depth understanding. This new understanding further enables us to propose principled modifications to the distillation process, such as decoupling the noise schedules for the engine and the regularizer, leading to further performance gains. Notably, our method has been adopted by the Z-Image ( https://github.com/Tongyi-MAI/Z-Image ) project to develop a top-tier 8-step image generation model, empirically validating the generalization and robustness of our findings.", "github_manual": "", "github_regex": "https://github.com/Tongyi-MAI/Z-Image", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22475", "title": "Adversarial Flow Models", "authors": [ "Shanchuan Lin", "Ceyuan Yang", "Zhijie Lin", "Hao Chen", "Haoqi Fan" ], "github": "", "project_page": "", "abstract": "We present adversarial flow models, a class of generative models that unifies adversarial models and flow models. Our method supports native one-step or multi-step generation and is trained using the adversarial objective. Unlike traditional GANs, where the generator learns an arbitrary transport plan between the noise and the data distributions, our generator learns a deterministic noise-to-data mapping, which is the same optimal transport as in flow-matching models. This significantly stabilizes adversarial training. Also, unlike consistency-based methods, our model directly learns one-step or few-step generation without needing to learn the intermediate timesteps of the probability flow for propagation. This saves model capacity, reduces training iterations, and avoids error accumulation. Under the same 1NFE setting on ImageNet-256px, our B/2 model approaches the performance of consistency-based XL/2 models, while our XL/2 model creates a new best FID of 2.38. We additionally show the possibility of end-to-end training of 56-layer and 112-layer models through depth repetition without any intermediate supervision, and achieve FIDs of 2.08 and 1.94 using a single forward pass, surpassing their 2NFE and 4NFE counterparts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22134", "title": "DualVLA: Building a Generalizable Embodied Agent via Partial Decoupling of Reasoning and Action", "authors": [ "Zhen Fang", "Zhuoyang Liu", "Jiaming Liu", "Hao Chen", "Yu Zeng", "Shiting Huang", "Zehui Chen", "Lin Chen", "Shanghang Zhang", "Feng Zhao" ], "github": "", "project_page": "", "abstract": "To build a generalizable Vision-Language-Action (VLA) model with strong reasoning ability, a common strategy is to first train a specialist VLA on robot demonstrations to acquire reliable manipulation skills, and then incorporate mixed annotated robot data together with multimodal data to restore broader reasoning capabilities. However, we observe that the resulting reasoning VLA often suffers from degraded action performance compared to the specialist model before fine-tuning, a phenomenon we refer to as action degeneration. To address this issue, we propose DualVLA, which enhances action performance through carefully designed post-training while still preserving reasoning capability. We first introduce a dual-layer data pruning method that removes redundant embodied reasoning, preventing it from adversely influencing action learning. To further strengthen action generation, we design a dual-teacher adaptive distillation strategy that assigns different supervision signals to different data domains while maintaining reasoning ability. To fill the evaluation gap for generalist VLAs, we also propose VLA Score, which decouples VLA capability into reasoning, intention, action, and alignment dimensions for a more fine-grained assessment. Experiments show that DualVLA achieves an average success rate of 61.0 in SimplerEnv and an average score of 65.4 across eight competitive multimodal benchmarks, demonstrating a stronger balance between precise action execution and multimodal understanding. Project Website: https://costaliya.github.io/DualVLA/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22815", "title": "Captain Safari: A World Engine", "authors": [ "Yu-Cheng Chou", "Xingrui Wang", "Yitong Li", "Jiahao Wang", "Hanting Liu", "Cihang Xie", "Alan Yuille", "Junfei Xiao" ], "github": "", "project_page": "", "abstract": "World engines aim to synthesize long, 3D-consistent videos that support interactive exploration of a scene under user-controlled camera motion. However, existing systems struggle under aggressive 6-DoF trajectories and complex outdoor layouts: they lose long-range geometric coherence, deviate from the target path, or collapse into overly conservative motion. To this end, we introduce Captain Safari, a pose-conditioned world engine that generates videos by retrieving from a persistent world memory. Given a camera path, our method maintains a dynamic local memory and uses a retriever to fetch pose-aligned world tokens, which then condition video generation along the trajectory. This design enables the model to maintain stable 3D structure while accurately executing challenging camera maneuvers. To evaluate this setting, we curate OpenSafari, a new in-the-wild FPV dataset containing high-dynamic drone videos with verified camera trajectories, constructed through a multi-stage geometric and kinematic validation pipeline. Across video quality, 3D consistency, and trajectory following, Captain Safari substantially outperforms state-of-the-art camera-controlled generators. It reduces MEt3R from 0.3703 to 0.3690, improves AUC@30 from 0.181 to 0.200, and yields substantially lower FVD than all camera-controlled baselines. More importantly, in a 50-participant, 5-way human study where annotators select the best result among five anonymized models, 67.6% of preferences favor our method across all axes. Our results demonstrate that pose-conditioned world memory is a powerful mechanism for long-horizon, controllable video generation and provide OpenSafari as a challenging new benchmark for future world-engine research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22787", "title": "World in a Frame: Understanding Culture Mixing as a New Challenge for Vision-Language Models", "authors": [ "Eunsu Kim", "Junyeong Park", "Na Min An", "Junseong Kim", "Hitesh Laxmichand Patel", "Jiho Jin", "Julia Kruk", "Amit Agarwal", "Srikant Panda", "Fenal Ashokbhai Ilasariya", "Hyunjung Shim", "Alice Oh" ], "github": "", "project_page": "", "abstract": "In a globalized world, cultural elements from diverse origins frequently appear together within a single visual scene. We refer to these as culture mixing scenarios, yet how Large Vision-Language Models (LVLMs) perceive them remains underexplored. We investigate culture mixing as a critical challenge for LVLMs and examine how current models behave when cultural items from multiple regions appear together. To systematically analyze these behaviors, we construct CultureMix, a food Visual Question Answering (VQA) benchmark with 23k diffusion-generated, human-verified culture mixing images across four subtasks: (1) food-only, (2) food+food, (3) food+background, and (4) food+food+background. Evaluating 10 LVLMs, we find consistent failures to preserve individual cultural identities in mixed settings. Models show strong background reliance, with accuracy dropping 14% when cultural backgrounds are added to food-only baselines, and they produce inconsistent predictions for identical foods across different contexts. To address these limitations, we explore three robustness strategies. We find supervised fine-tuning using a diverse culture mixing dataset substantially improve model consistency and reduce background sensitivity. We call for increased attention to culture mixing scenarios as a critical step toward developing LVLMs capable of operating reliably in culturally diverse real-world environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22173", "title": "RefineBench: Evaluating Refinement Capability of Language Models via Checklists", "authors": [ "Young-Jun Lee", "Seungone Kim", "Byung-Kwan Lee", "Minkyeong Moon", "Yechan Hwang", "Jong Myoung Kim", "Graham Neubig", "Sean Welleck", "Ho-Jin Choi" ], "github": "", "project_page": "", "abstract": "Can language models (LMs) self-refine their own responses? This question is increasingly relevant as a wide range of real-world user interactions involve refinement requests. However, prior studies have largely tested LMs' refinement abilities on verifiable tasks such as competition math or symbolic reasoning with simplified scaffolds, whereas users often pose open-ended queries and provide varying degrees of feedback on what they desire. The recent advent of reasoning models that exhibit self-reflection patterns in their chains-of-thought further motivates this question. To analyze this, we introduce RefineBench, a benchmark of 1,000 challenging problems across 11 domains paired with a checklist-based evaluation framework. We evaluate two refinement modes: (1) guided refinement, where an LM is provided natural language feedback, and (2) self-refinement, where LMs attempt to improve without guidance. In the self-refinement setting, even frontier LMs such as Gemini 2.5 Pro and GPT-5 achieve modest baseline scores of 31.3% and 29.1%, respectively, and most models fail to consistently improve across iterations (e.g., Gemini-2.5-Pro gains only +1.8%, while DeepSeek-R1 declines by -0.1%). By contrast, in guided refinement, both proprietary LMs and large open-weight LMs (>70B) can leverage targeted feedback to refine responses to near-perfect levels within five turns. These findings suggest that frontier LMs require breakthroughs to self-refine their incorrect responses, and that RefineBench provides a valuable testbed for tracking progress.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22055", "title": "OralGPT-Omni: A Versatile Dental Multimodal Large Language Model", "authors": [ "Jing Hao", "Yuci Liang", "Lizhuo Lin", "Yuxuan Fan", "Wenkai Zhou", "Kaixin Guo", "Zanting Ye", "Yanpeng Sun", "Xinyu Zhang", "Yanqi Yang", "Qiankun Li", "Hao Tang", "James Kit-Hon Tsoi", "Linlin Shen", "Kuo Feng Hung" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have exhibited immense potential across numerous medical specialties; yet, dentistry remains underexplored, in part due to limited domain-specific data, scarce dental expert annotations, insufficient modality-specific modeling, and challenges in reliability. In this paper, we present OralGPT-Omni, the first dental-specialized MLLM designed for comprehensive and trustworthy analysis across diverse dental imaging modalities and clinical tasks. To explicitly capture dentists' diagnostic reasoning, we construct TRACE-CoT, a clinically grounded chain-of-thought dataset that mirrors dental radiologists' decision-making processes. This reasoning supervision, combined with our proposed four-stage training paradigm, substantially strengthens the model's capacity for dental image understanding and analysis. In parallel, we introduce MMOral-Uni, the first unified multimodal benchmark for dental image analysis. It comprises 2,809 open-ended question-answer pairs spanning five modalities and five tasks, offering a comprehensive evaluation suite to date for MLLMs in digital dentistry. OralGPT-Omni achieves an overall score of 51.84 on the MMOral-Uni benchmark and 45.31 on the MMOral-OPG benchmark, dramatically outperforming the scores of GPT-5. Our work promotes intelligent dentistry and paves the way for future advances in dental image analysis. All code, benchmark, and models will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22281", "title": "The Collapse of Patches", "authors": [ "Wei Guo", "Shunqi Mao", "Zhuonan Liang", "Heng Wang", "Weidong Cai" ], "github": "https://github.com/wguo-ai/CoP", "project_page": "", "abstract": "Observing certain patches in an image reduces the uncertainty of others. Their realization lowers the distribution entropy of each remaining patch feature, analogous to collapsing a particle's wave function in quantum mechanics. This phenomenon can intuitively be called patch collapse. To identify which patches are most relied on during a target region's collapse, we learn an autoencoder that softly selects a subset of patches to reconstruct each target patch. Graphing these learned dependencies for each patch's PageRank score reveals the optimal patch order to realize an image. We show that respecting this order benefits various masked image modeling methods. First, autoregressive image generation can be boosted by retraining the state-of-the-art model MAR. Next, we introduce a new setup for image classification by exposing Vision Transformers only to high-rank patches in the collapse order. Seeing 22\\% of such patches is sufficient to achieve high accuracy. With these experiments, we propose patch collapse as a novel image modeling perspective that promotes vision efficiency. Our project is available at https://github.com/wguo-ai/CoP .", "github_manual": "", "github_regex": "https://github.com/wguo-ai/CoP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22688", "title": "Test-time scaling of diffusions with flow maps", "authors": [ "Amirmojtaba Sabour", "Michael S. Albergo", "Carles Domingo-Enrich", "Nicholas M. Boffi", "Sanja Fidler", "Karsten Kreis", "Eric Vanden-Eijnden" ], "github": "", "project_page": "", "abstract": "A common recipe to improve diffusion models at test-time so that samples score highly against a user-specified reward is to introduce the gradient of the reward into the dynamics of the diffusion itself. This procedure is often ill posed, as user-specified rewards are usually only well defined on the data distribution at the end of generation. While common workarounds to this problem are to use a denoiser to estimate what a sample would have been at the end of generation, we propose a simple solution to this problem by working directly with a flow map. By exploiting a relationship between the flow map and velocity field governing the instantaneous transport, we construct an algorithm, Flow Map Trajectory Tilting (FMTT), which provably performs better ascent on the reward than standard test-time methods involving the gradient of the reward. The approach can be used to either perform exact sampling via importance weighting or principled search that identifies local maximizers of the reward-tilted distribution. We demonstrate the efficacy of our approach against other look-ahead techniques, and show how the flow map enables engagement with complicated reward functions that make possible new forms of image editing, e.g. by interfacing with vision language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.21750", "title": "SO-Bench: A Structural Output Evaluation of Multimodal LLMs", "authors": [ "Di Feng", "Kaixin Ma", "Feng Nan", "Haofeng Chen", "Bohan Zhai", "David Griffiths", "Mingfei Gao", "Zhe Gan", "Eshan Verma", "Yinfei Yang", "Zhifeng Chen", "Afshin Dehghan" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) are increasingly deployed in real-world, agentic settings where outputs must not only be correct, but also conform to predefined data schemas. Despite recent progress in structured generation in textual domain, there is still no benchmark that systematically evaluates schema-grounded information extraction and reasoning over visual inputs. In this work, we conduct a comprehensive study of visual structural output capabilities for MLLMs with our carefully designed SO-Bench benchmark. Covering four visual domains, including UI screens, natural images, documents, and charts, SO-Bench is built from over 6.5K diverse JSON schemas and 1.8K curated image-schema pairs with human-verified quality. Benchmarking experiments on open-sourced and frontier proprietary models reveal persistent gaps in predicting accurate, schema compliant outputs, highlighting the need for better multimodal structured reasoning. Beyond benchmarking, we further conduct training experiments to largely improve the model's structured output capability. We plan to make the benchmark available to the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22176", "title": "Focused Chain-of-Thought: Efficient LLM Reasoning via Structured Input Information", "authors": [ "Lukas Struppek", "Dominik Hintersdorf", "Hannah Struppek", "Daniel Neider", "Kristian Kersting" ], "github": "", "project_page": "", "abstract": "Recent large language models achieve strong reasoning performance by generating detailed chain-of-thought traces, but this often leads to excessive token use and high inference latency. Existing efficiency approaches typically focus on model-centric interventions, such as reinforcement learning or supervised fine-tuning, to reduce verbosity. In contrast, we propose a training-free, input-centric approach. Inspired by cognitive psychology, we introduce Focused Chain-of-Thought (F-CoT), which separates information extraction from the reasoning process. F-CoT first organizes the essential information from a query into a concise, structured context and then guides the model to reason exclusively over this context. By preventing attention to irrelevant details, F-CoT naturally produces shorter reasoning paths. On arithmetic word problems, F-CoT reduces generated tokens by 2-3x while maintaining accuracy comparable to standard zero-shot CoT. These results highlight structured input as a simple yet effective lever for more efficient LLM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.21025", "title": "CaptionQA: Is Your Caption as Useful as the Image Itself?", "authors": [ "Shijia Yang", "Yunong Liu", "Bohan Zhai", "Ximeng Sun", "Zicheng Liu", "Emad Barsoum", "Manling Li", "Chenfeng Xu" ], "github": "https://github.com/bronyayang/CaptionQA", "project_page": "", "abstract": "Image captions serve as efficient surrogates for visual content in multimodal systems such as retrieval, recommendation, and multi-step agentic inference pipelines. Yet current evaluation practices miss a fundamental question: Can captions stand-in for images in real downstream tasks? We propose a utility-based benchmark, CaptionQA, to evaluate model-generated captions, where caption quality is measured by how well it supports downstream tasks. CaptionQA is an extensible domain-dependent benchmark covering 4 domains--Natural, Document, E-commerce, and Embodied AI--each with fine-grained taxonomies (25 top-level and 69 subcategories) that identify useful information for domain-specific tasks. CaptionQA builds 33,027 densely annotated multiple-choice questions (50.3 per image on average) that explicitly require visual information to answer, providing a comprehensive probe of caption utility. In our evaluation protocol, an LLM answers these questions using captions alone, directly measuring whether captions preserve image-level utility and are utilizable by a downstream LLM. Evaluating state-of-the-art MLLMs reveals substantial gaps between the image and its caption utility. Notably, models nearly identical on traditional image-QA benchmarks lower by up to 32% in caption utility. We release CaptionQA along with an open-source pipeline for extension to new domains. The code is available at https://github.com/bronyayang/CaptionQA.", "github_manual": "", "github_regex": "https://github.com/bronyayang/CaptionQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.13344", "title": "YOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection", "authors": [ "Ori Meiraz", "Sharon Shalev", "Avishai Weizman" ], "github": "", "project_page": "", "abstract": "This paper presents a novel Mixture-of-Experts framework for object detection, incorporating adaptive routing among multiple YOLOv9-T experts to enable dynamic feature specialization and achieve higher mean Average Precision (mAP) and Average Recall (AR) compared to a single YOLOv9-T model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22533", "title": "Fast3Dcache: Training-free 3D Geometry Synthesis Acceleration", "authors": [ "Mengyu Yang", "Yanming Yang", "Chenyi Xu", "Chenxi Song", "Yufan Zuo", "Tong Zhao", "Ruibo Li", "Chi Zhang" ], "github": "", "project_page": "", "abstract": "Diffusion models have achieved impressive generative quality across modalities like 2D images, videos, and 3D shapes, but their inference remains computationally expensive due to the iterative denoising process. While recent caching-based methods effectively reuse redundant computations to speed up 2D and video generation, directly applying these techniques to 3D diffusion models can severely disrupt geometric consistency. In 3D synthesis, even minor numerical errors in cached latent features accumulate, causing structural artifacts and topological inconsistencies. To overcome this limitation, we propose Fast3Dcache, a training-free geometry-aware caching framework that accelerates 3D diffusion inference while preserving geometric fidelity. Our method introduces a Predictive Caching Scheduler Constraint (PCSC) to dynamically determine cache quotas according to voxel stabilization patterns and a Spatiotemporal Stability Criterion (SSC) to select stable features for reuse based on velocity magnitude and acceleration criterion. Comprehensive experiments show that Fast3Dcache accelerates inference significantly, achieving up to a 27.12% speed-up and a 54.8% reduction in FLOPs, with minimal degradation in geometric quality as measured by Chamfer Distance (2.48%) and F-Score (1.95%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.19990", "title": "OmniRefiner: Reinforcement-Guided Local Diffusion Refinement", "authors": [ "Yaoli Liu", "Ziheng Ouyang", "Shengtao Lou", "Yiren Song" ], "github": "https://github.com/yaoliliu/OmniRefiner", "project_page": "", "abstract": "Reference-guided image generation has progressed rapidly, yet current diffusion models still struggle to preserve fine-grained visual details when refining a generated image using a reference. This limitation arises because VAE-based latent compression inherently discards subtle texture information, causing identity- and attribute-specific cues to vanish. Moreover, post-editing approaches that amplify local details based on existing methods often produce results inconsistent with the original image in terms of lighting, texture, or shape. To address this, we introduce , a detail-aware refinement framework that performs two consecutive stages of reference-driven correction to enhance pixel-level consistency. We first adapt a single-image diffusion editor by fine-tuning it to jointly ingest the draft image and the reference image, enabling globally coherent refinement while maintaining structural fidelity. We then apply reinforcement learning to further strengthen localized editing capability, explicitly optimizing for detail accuracy and semantic consistency. Extensive experiments demonstrate that significantly improves reference alignment and fine-grained detail preservation, producing faithful and visually coherent edits that surpass both open-source and commercial models on challenging reference-guided restoration benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yaoliliu/OmniRefiner", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22805", "title": "From Pixels to Feelings: Aligning MLLMs with Human Cognitive Perception of Images", "authors": [ "Yiming Chen", "Junlin Han", "Tianyi Bai", "Shengbang Tong", "Filippos Kokkinos", "Philip Torr" ], "github": "https://github.com/Follen-cry/MLLM_Cognition_Alignment", "project_page": "", "abstract": "While Multimodal Large Language Models (MLLMs) are adept at answering what is in an image-identifying objects and describing scenes-they often lack the ability to understand how an image feels to a human observer. This gap is most evident when considering subjective cognitive properties, such as what makes an image memorable, funny, aesthetically pleasing, or emotionally evocative. To systematically address this challenge, we introduce CogIP-Bench, a comprehensive benchmark for evaluating MLLMs on such image cognitive properties. Our evaluation reveals a significant gap: current models are poorly aligned with human perception of these nuanced properties. We then demonstrate that a post-training phase can effectively bridge this gap, significantly enhancing the model's alignment with human judgments. Furthermore, we show that this learned cognitive alignment is not merely predictive but also transferable to downstream creative tasks. By integrating our cognitively-aligned MLLM into an image generation pipeline, we can guide the synthesis process to produce images that better embody desired traits, such as being more memorable or visually appealing. Our work provides a benchmark to measure this human-like perception, a post-training pipeline to enhance it, and a demonstration that this alignment unlocks more human-centric AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Follen-cry/MLLM_Cognition_Alignment", "github_api_added_by": "auto" }, { "date": "2025-12-01", "arxiv_id": "2511.22265", "title": "FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning", "authors": [ "Yuan Yao", "Lixu Wang", "Jiaqi Wu", "Jin Song", "Simin Chen", "Zehua Wang", "Zijian Tian", "Wei Chen", "Huixia Li", "Xiaoxiao Li" ], "github": "https://github.com/AIResearch-Group/FedRE", "project_page": "", "abstract": "Federated learning (FL) enables collaborative training across clients without compromising privacy. While most existing FL methods assume homogeneous model architectures, client heterogeneity in data and resources renders this assumption impractical, motivating model-heterogeneous FL. To address this problem, we propose Federated Representation Entanglement (FedRE), a framework built upon a novel form of client knowledge termed entangled representation. In FedRE, each client aggregates its local representations into a single entangled representation using normalized random weights and applies the same weights to integrate the corresponding one-hot label encodings into the entangled-label encoding. Those are then uploaded to the server to train a global classifier. During training, each entangled representation is supervised across categories via its entangled-label encoding, while random weights are resampled each round to introduce diversity, mitigating the global classifier's overconfidence and promoting smoother decision boundaries. Furthermore, each client uploads a single cross-category entangled representation along with its entangled-label encoding, mitigating the risk of representation inversion attacks and reducing communication overhead. Extensive experiments demonstrate that FedRE achieves an effective trade-off among model performance, privacy protection, and communication overhead. The codes are available at https://github.com/AIResearch-Group/FedRE.", "github_manual": "", "github_regex": "https://github.com/AIResearch-Group/FedRE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.13944", "title": "Find the Leak, Fix the Split: Cluster-Based Method to Prevent Leakage in Video-Derived Datasets", "authors": [ "Noam Glazner", "Noam Tsfaty", "Sharon Shalev", "Avishai Weizman" ], "github": "", "project_page": "", "abstract": "We propose a cluster-based frame selection strategy to mitigate information leakage in video-derived frames datasets. By grouping visually similar frames before splitting into training, validation, and test sets, the method produces more representative, balanced, and reliable dataset partitions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.13276", "title": "Recognition of Abnormal Events in Surveillance Videos using Weakly Supervised Dual-Encoder Models", "authors": [ "Noam Tsfaty", "Avishai Weizman", "Liav Cohen", "Moshe Tshuva", "Yehudit Aperstein" ], "github": "", "project_page": "", "abstract": "We address the challenge of detecting rare and diverse anomalies in surveillance videos using only video-level supervision. Our dual-backbone framework combines convolutional and transformer representations through top-k pooling, achieving 90.7% area under the curve (AUC) on the UCF-Crime dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22570", "title": "DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning", "authors": [ "Zhihong Shao", "Yuxiang Luo", "Chengda Lu", "Z. Z. Ren", "Jiewen Hu", "Tian Ye", "Zhibin Gou", "Shirong Ma", "Xiaokang Zhang" ], "github": "https://github.com/deepseek-ai/DeepSeek-Math-V2", "project_page": "", "abstract": "Large language models have made significant progress in mathematical reasoning, which serves as an important testbed for AI and could impact scientific research if further advanced. By scaling reasoning with reinforcement learning that rewards correct final answers, LLMs have improved from poor performance to saturating quantitative reasoning competitions like AIME and HMMT in one year. However, this approach faces fundamental limitations. Pursuing higher final answer accuracy doesn't address a key issue: correct answers don't guarantee correct reasoning. Moreover, many mathematical tasks like theorem proving require rigorous step-by-step derivation rather than numerical answers, making final answer rewards inapplicable. To push the limits of deep reasoning, we believe it is necessary to verify the comprehensiveness and rigor of mathematical reasoning. Self-verification is particularly important for scaling test-time compute, especially for open problems without known solutions. Towards self-verifiable mathematical reasoning, we investigate how to train an accurate and faithful LLM-based verifier for theorem proving. We then train a proof generator using the verifier as the reward model, and incentivize the generator to identify and resolve as many issues as possible in their own proofs before finalizing them. To maintain the generation-verification gap as the generator becomes stronger, we propose to scale verification compute to automatically label new hard-to-verify proofs, creating training data to further improve the verifier. Our resulting model, DeepSeekMath-V2, demonstrates strong theorem-proving capabilities, achieving gold-level scores on IMO 2025 and CMO 2024 and a near-perfect 118/120 on Putnam 2024 with scaled test-time compute.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/deepseek-ai/DeepSeek-Math-V2", "github_api_added_by": "auto" }, { "date": "2025-12-01", "arxiv_id": "2511.20809", "title": "Layer-Aware Video Composition via Split-then-Merge", "authors": [ "Ozgur Kara", "Yujia Chen", "Ming-Hsuan Yang", "James M. Rehg", "Wen-Sheng Chu", "Du Tran" ], "github": "", "project_page": "", "abstract": "We present Split-then-Merge (StM), a novel framework designed to enhance control in generative video composition and address its data scarcity problem. Unlike conventional methods relying on annotated datasets or handcrafted rules, StM splits a large corpus of unlabeled videos into dynamic foreground and background layers, then self-composes them to learn how dynamic subjects interact with diverse scenes. This process enables the model to learn the complex compositional dynamics required for realistic video generation. StM introduces a novel transformation-aware training pipeline that utilizes a multi-layer fusion and augmentation to achieve affordance-aware composition, alongside an identity-preservation loss that maintains foreground fidelity during blending. Experiments show StM outperforms SoTA methods in both quantitative benchmarks and in humans/VLLM-based qualitative evaluations. More details are available at our project page: https://split-then-merge.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.16854", "title": "MRI Super-Resolution with Deep Learning: A Comprehensive Survey", "authors": [ "Mohammad Khateri", "Serge Vasylechko", "Morteza Ghahremani", "Liam Timms", "Deniz Kocanaogullari", "Simon K. Warfield", "Camilo Jaimes", "Davood Karimi", "Alejandra Sierra", "Jussi Tohka", "Sila Kurugol", "Onur Afacan" ], "github": "https://github.com/mkhateri/Awesome-MRI-Super-Resolution.", "project_page": "", "abstract": "High-resolution (HR) magnetic resonance imaging (MRI) is crucial for many clinical and research applications. However, achieving it remains costly and constrained by technical trade-offs and experimental limitations. Super-resolution (SR) presents a promising computational approach to overcome these challenges by generating HR images from more affordable low-resolution (LR) scans, potentially improving diagnostic accuracy and efficiency without requiring additional hardware. This survey reviews recent advances in MRI SR techniques, with a focus on deep learning (DL) approaches. It examines DL-based MRI SR methods from the perspectives of computer vision, computational imaging, inverse problems, and MR physics, covering theoretical foundations, architectural designs, learning strategies, benchmark datasets, and performance metrics. We propose a systematic taxonomy to categorize these methods and present an in-depth study of both established and emerging SR techniques applicable to MRI, considering unique challenges in clinical and research contexts. We also highlight open challenges and directions that the community needs to address. Additionally, we provide a collection of essential open-access resources, tools, and tutorials, available on our GitHub: https://github.com/mkhateri/Awesome-MRI-Super-Resolution. IEEE keywords: MRI, Super-Resolution, Deep Learning, Computational Imaging, Inverse Problem, Survey.", "github_manual": "https://github.com/mkhateri/Awesome-MRI-Super-Resolution.", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.18890", "title": "Nemotron-Flash: Towards Latency-Optimal Hybrid Small Language Models", "authors": [ "Yonggan Fu", "Xin Dong", "Shizhe Diao", "Matthijs Van keirsbilck", "Hanrong Ye", "Wonmin Byeon", "Yashaswi Karnati", "Lucas Liebenwein", "Hannah Zhang", "Nikolaus Binder", "Maksim Khadkevich", "Alexander Keller", "Jan Kautz", "Yingyan Celine Lin", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "Efficient deployment of small language models (SLMs) is essential for numerous real-world applications with stringent latency constraints. While previous work on SLM design has primarily focused on reducing the number of parameters to achieve parameter-optimal SLMs, parameter efficiency does not necessarily translate into proportional real-device speed-ups. This work aims to identify the key determinants of SLMs' real-device latency and offer generalizable principles and methodologies for SLM design and training when real-device latency is the primary consideration. Specifically, we identify two central architectural factors: depth-width ratios and operator choices. The former is crucial for small-batch-size latency, while the latter affects both latency and large-batch-size throughput. In light of this, we first study latency-optimal depth-width ratios, with the key finding that although deep-thin models generally achieve better accuracy under the same parameter budget, they may not lie on the accuracy-latency trade-off frontier. Next, we explore emerging efficient attention alternatives to evaluate their potential as candidate building operators. Using the identified promising operators, we construct an evolutionary search framework to automatically discover latency-optimal combinations of these operators within hybrid SLMs, thereby advancing the accuracy-latency frontier. In addition to architectural improvements, we further enhance SLM training using a weight normalization technique that enables more effective weight updates and improves final convergence. Combining these methods, we introduce a new family of hybrid SLMs, called Nemotron-Flash, which significantly advances the accuracy-efficiency frontier of state-of-the-art SLMs, e.g., achieving over +5.5% average accuracy, 1.3x/1.9x lower latency, and 18.7x/45.6x higher throughput compared to Qwen3-1.7B/0.6B, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.22659", "title": "Geometrically-Constrained Agent for Spatial Reasoning", "authors": [ "Zeren Chen", "Xiaoya Lu", "Zhijie Zheng", "Pengrui Li", "Lehan He", "Yijin Zhou", "Jing Shao", "Bohan Zhuang", "Lu Sheng" ], "github": "", "project_page": "", "abstract": "Vision Language Models (VLMs) exhibit a fundamental semantic-to-geometric gap in spatial reasoning: they excel at qualitative semantic inference but their reasoning operates within a lossy semantic space, misaligned with high-fidelity geometry. Current paradigms fail to bridge this gap. Training-based methods suffer from an ``oracle paradox,'' learning flawed spatial logic from imperfect oracles. Tool-integrated methods constrain the final computation but critically leave the VLM's planning process unconstrained, resulting in geometrically flawed plans. In this work, we propose Geometrically-Constrained Agent (GCA), a training-free agentic paradigm that resolves this gap by introducing a formal task constraint. Specifically, we strategically decouples the VLM's role into two stages. First, acting as a semantic analyst, the VLM translates the user's ambiguous query into the formal, verifiable task constraint, which defines the reference frame and objective. Second, acting as a task solver, the VLM generates and executes tool calls strictly within the deterministic bounds defined by the constraint. This geometrically-constrained reasoning strategy successfully resolve the semantic-to-geometric gap, yielding a robust and verifiable reasoning pathway for spatial reasoning. Comprehensive experiments demonstrate that GCA achieves SOTA performance on multiple spatial reasoning benchmarks, surpassing existing training-based and tool-integrated methods by ~27%. Please see our homepage at https://gca-spatial-reasoning.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-01", "arxiv_id": "2511.19496", "title": "Xmodel-2.5: 1.3B Data-Efficient Reasoning SLM", "authors": [ "Yang Liu", "Xiaolong Zhong", "Ling Jiang" ], "github": "https://github.com/XiaoduoAILab/Xmodel-2.5", "project_page": "", "abstract": "Large language models deliver strong reasoning and tool-use skills, yet their computational demands make them impractical for edge or cost-sensitive deployments. We present Xmodel-2.5, a 1.3-billion-parameter small language model designed as a drop-in agent core. Training with maximal-update parameterization (\u03bcP) allows hyper-parameters tuned on a 20M-parameter proxy to transfer directly to the full model, even under the parameter-tied tie-word-embedding architecture. A 1.4T-token Warmup--Stable--Decay curriculum is used, and we further show that switching from AdamW to Muon during the decay phase improves the 13-task reasoning average by 4.58\\,\\% while keeping every other hyper-parameter fixed, verifying that early AdamW stability can be paired with late Muon sharpening for better downstream performance. FP8-mixed-precision training balances accuracy and throughput. All checkpoints, recipes, and evaluation code are released under the Apache-2.0 license.https://huggingface.co/XiaoduoAILab/Xmodel-2.5 and https://huggingface.co/XiaoduoAILab/Xmodel-2.5-history (training checkpoints). Training code and evaluation harness: https://github.com/XiaoduoAILab/Xmodel-2.5.", "github_manual": "", "github_regex": "https://github.com/XiaoduoAILab/Xmodel-2.5", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2511.20785", "title": "LongVT: Incentivizing \"Thinking with Long Videos\" via Native Tool Calling", "authors": [ "Zuhao Yang", "Sudong Wang", "Kaichen Zhang", "Keming Wu", "Sicong Leng", "Yifan Zhang", "Chengwei Qin", "Shijian Lu", "Xingxuan Li", "Lidong Bing" ], "github": "https://github.com/EvolvingLMMs-Lab/LongVT", "project_page": "", "abstract": "Large multimodal models (LMMs) have shown great potential for video reasoning with textual Chain-of-Thought. However, they remain vulnerable to hallucinations, especially when processing long-form videos where evidence is sparse and temporally dispersed. Inspired by how humans comprehend long videos - by first skimming globally and then examining relevant clips for details - we introduce LongVT, an end-to-end agentic framework that enables \"Thinking with Long Videos\" via interleaved Multimodal Chain-of-Tool-Thought. Specifically, we exploit LMMs' inherent temporal grounding ability as a native video cropping tool to zoom in on a specific video clip and resample finer-grained video frames. This global-to-local reasoning loop continues until answers are grounded in retrieved visual evidence. Given the scarcity of fine-grained question-answering (QA) data for the long video reasoning task, we curate and will release a data suite named VideoSIAH to facilitate both training and evaluation. Specifically, our training dataset consists of 247.9K samples for tool-integrated cold-start supervised fine-tuning, 1.6K samples for agentic reinforcement learning, and 15.4K samples for agentic reinforcement fine-tuning, respectively. Our evaluation benchmark consists of 1,280 QA pairs that are carefully curated through a semi-automatic data pipeline with human-in-the-loop validation. With a meticulously designed three-stage training strategy and extensive empirical validation, LongVT consistently outperforms existing strong baselines across four challenging long-video understanding and reasoning benchmarks. Our codes, data, and model checkpoints are publicly available at https://github.com/EvolvingLMMs-Lab/LongVT .", "github_manual": "", "github_regex": "https://github.com/EvolvingLMMs-Lab/LongVT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2511.18538", "title": "From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence", "authors": [ "Jian Yang", "Xianglong Liu", "Weifeng Lv", "Ken Deng", "Shawn Guo", "Lin Jing", "Yizhi Li", "Shark Liu", "Xianzhen Luo", "Yuyu Luo", "Changzai Pan", "Ensheng Shi", "Yingshui Tan", "Renshuai Tao", "Jiajun Wu", "Xianjie Wu", "Zhenhe Wu", "Daoguang Zan", "Chenchen Zhang", "Wei Zhang", "He Zhu", "Terry Yue Zhuo", "Kerui Cao", "Xianfu Cheng", "Jun Dong", "Shengjie Fang", "Zhiwei Fei", "Xiangyuan Guan", "Qipeng Guo", "Zhiguang Han", "Joseph James", "Tianqi Luo", "Renyuan Li", "Yuhang Li", "Yiming Liang", "Congnan Liu", "Jiaheng Liu", "Qian Liu", "Ruitong Liu", "Tyler Loakman", "Xiangxin Meng", "Chuang Peng", "Tianhao Peng", "Jiajun Shi", "Mingjie Tang", "Boyang Wang", "Haowen Wang", "Yunli Wang", "Fanglin Xu", "Zihan Xu", "Fei Yuan", "Ge Zhang", "Jiayi Zhang", "Xinhao Zhang", "Wangchunshu Zhou", "Hualei Zhu", "King Zhu", "Brown Dai", "Aishan Liu", "Zhoujun Li", "Chenghua Lin", "Tianyu Liu", "Chao Peng", "Kai Shen", "Libo Qin", "Shuangyong Song", "Zizheng Zhan", "Jiajun Zhang", "Jie Zhang", "Zhaoxiang Zhang", "Bo Zheng" ], "github": "https://github.com/agentica-project/rllm", "project_page": "", "abstract": "Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, driving commercial adoption through tools like Github Copilot (Microsoft), Cursor (Anysphere), Trae (ByteDance), and Claude Code (Anthropic). While the field has evolved dramatically from rule-based systems to Transformer-based architectures, achieving performance improvements from single-digit to over 95\\% success rates on benchmarks like HumanEval. In this work, we provide a comprehensive synthesis and practical guide (a series of analytic and probing experiments) about code LLMs, systematically examining the complete model life cycle from data curation to post-training through advanced prompting paradigms, code pre-training, supervised fine-tuning, reinforcement learning, and autonomous coding agents. We analyze the code capability of the general LLMs (GPT-4, Claude, LLaMA) and code-specialized LLMs (StarCoder, Code LLaMA, DeepSeek-Coder, and QwenCoder), critically examining the techniques, design decisions, and trade-offs. Further, we articulate the research-practice gap between academic research (e.g., benchmarks and tasks) and real-world deployment (e.g., software-related code tasks), including code correctness, security, contextual awareness of large codebases, and integration with development workflows, and map promising research directions to practical needs. Last, we conduct a series of experiments to provide a comprehensive analysis of code pre-training, supervised fine-tuning, and reinforcement learning, covering scaling law, framework selection, hyperparameter sensitivity, model architectures, and dataset comparisons.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/agentica-project/rllm", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01816", "title": "Envision: Benchmarking Unified Understanding & Generation for Causal World Process Insights", "authors": [ "Juanxi Tian", "Siyuan Li", "Conghui He", "Lijun Wu", "Cheng Tan" ], "github": "", "project_page": "", "abstract": "Current multimodal models aim to transcend the limitations of single-modality representations by unifying understanding and generation, often using text-to-image (T2I) tasks to calibrate semantic consistency. However, their reliance on static, single-image generation in training and evaluation leads to overfitting to static pattern matching and semantic fusion, while fundamentally hindering their ability to model dynamic processes that unfold over time. To address these constraints, we propose Envision-a causal event progression benchmark for chained text-to-multi-image generation. Grounded in world knowledge and structured by spatiotemporal causality, it reorganizes existing evaluation dimensions and includes 1,000 four-stage prompts spanning six scientific and humanities domains. To transition evaluation from single images to sequential frames and assess whether models truly internalize world knowledge while adhering to causal-temporal constraints, we introduce Envision-Score, a holistic metric integrating multi-dimensional consistency, physicality, and aesthetics. Comprehensive evaluation of 15 models (10 specialized T2I models, 5 unified models) uncovers: specialized T2I models demonstrate proficiency in aesthetic rendering yet lack intrinsic world knowledge. Unified multimodal models bridge this gap, consistently outperforming specialized counterparts in causal narrative coherence. However, even these unified architectures remain subordinate to closed-source models and struggle to overcome the core challenge of spatiotemporal consistency. This demonstrates that a focus on causally-isolated single images impedes multi-frame reasoning and generation, promoting static pattern matching over dynamic world modeling-ultimately limiting world knowledge internalization, generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2511.20614", "title": "The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment", "authors": [ "Ziheng Ouyang", "Yiren Song", "Yaoli Liu", "Shihao Zhu", "Qibin Hou", "Ming-Ming Cheng", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Previous works have explored various customized generation tasks given a reference image, but they still face limitations in generating consistent fine-grained details. In this paper, our aim is to solve the inconsistency problem of generated images by applying a reference-guided post-editing approach and present our ImageCritic. We first construct a dataset of reference-degraded-target triplets obtained via VLM-based selection and explicit degradation, which effectively simulates the common inaccuracies or inconsistencies observed in existing generation models. Furthermore, building on a thorough examination of the model's attention mechanisms and intrinsic representations, we accordingly devise an attention alignment loss and a detail encoder to precisely rectify inconsistencies. ImageCritic can be integrated into an agent framework to automatically detect inconsistencies and correct them with multi-round and local editing in complex scenarios. Extensive experiments demonstrate that ImageCritic can effectively resolve detail-related issues in various customized generation scenarios, providing significant improvements over existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01374", "title": "Stabilizing Reinforcement Learning with LLMs: Formulation and Practices", "authors": [ "Chujie Zheng", "Kai Dang", "Bowen Yu", "Mingze Li", "Huiqiang Jiang", "Junrong Lin", "Yuqiong Liu", "An Yang", "Jingren Zhou", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "This paper proposes a novel formulation for reinforcement learning (RL) with large language models, explaining why and under what conditions the true sequence-level reward can be optimized via a surrogate token-level objective in policy gradient methods such as REINFORCE. Specifically, through a first-order approximation, we show that this surrogate becomes increasingly valid only when both the training-inference discrepancy and policy staleness are minimized. This insight provides a principled explanation for the crucial role of several widely adopted techniques in stabilizing RL training, including importance sampling correction, clipping, and particularly Routing Replay for Mixture-of-Experts (MoE) models. Through extensive experiments with a 30B MoE model totaling hundreds of thousands of GPU hours, we show that for on-policy training, the basic policy gradient algorithm with importance sampling correction achieves the highest training stability. When off-policy updates are introduced to accelerate convergence, combining clipping and Routing Replay becomes essential to mitigate the instability caused by policy staleness. Notably, once training is stabilized, prolonged optimization consistently yields comparable final performance regardless of cold-start initialization. We hope that the shared insights and the developed recipes for stable RL training will facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.00425", "title": "What about gravity in video generation? Post-Training Newton's Laws with Verifiable Rewards", "authors": [ "Minh-Quan Le", "Yuanzhi Zhu", "Vicky Kalogeiton", "Dimitris Samaras" ], "github": "", "project_page": "", "abstract": "Recent video diffusion models can synthesize visually compelling clips, yet often violate basic physical laws-objects float, accelerations drift, and collisions behave inconsistently-revealing a persistent gap between visual realism and physical realism. We propose NewtonRewards, the first physics-grounded post-training framework for video generation based on verifiable rewards. Instead of relying on human or VLM feedback, NewtonRewards extracts measurable proxies from generated videos using frozen utility models: optical flow serves as a proxy for velocity, while high-level appearance features serve as a proxy for mass. These proxies enable explicit enforcement of Newtonian structure through two complementary rewards: a Newtonian kinematic constraint enforcing constant-acceleration dynamics, and a mass conservation reward preventing trivial, degenerate solutions. We evaluate NewtonRewards on five Newtonian Motion Primitives (free fall, horizontal/parabolic throw, and ramp sliding down/up) using our newly constructed large-scale benchmark, NewtonBench-60K. Across all primitives in visual and physics metrics, NewtonRewards consistently improves physical plausibility, motion smoothness, and temporal coherence over prior post-training methods. It further maintains strong performance under out-of-distribution shifts in height, speed, and friction. Our results show that physics-grounded verifiable rewards offer a scalable path toward physics-aware video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01948", "title": "How Far Are We from Genuinely Useful Deep Research Agents?", "authors": [ "Dingling Zhang", "He Zhu", "Jincheng Ren", "Kangqi Song", "Xinran Zhou", "Boyu Feng", "Shudong Liu", "Jiabin Luo", "Weihao Xie", "Zhaohui Wang", "Tianrui Qin", "King Zhu", "Yuqing Wang", "Qianben Chen", "Yuchen Eleanor Jiang", "Wei Wang", "Jiaheng Liu", "Wangchunshu Zhou" ], "github": "", "project_page": "", "abstract": "Deep Research Agents (DRAs) aim to automatically produce analyst-level reports through iterative information retrieval and synthesis. However, most existing DRAs were validated on question-answering benchmarks, while research on generating comprehensive reports remains overlooked. Worse, current benchmarks for report synthesis suffer from task complexity and subjective metrics -- this fails to reflect user demands and limits the practical utility of generated reports. To address these gaps, we present Fine-grained DEepResearch bench (FINDER), an enhanced benchmark consisting of 100 human-curated research tasks with 419 structured checklist items that standardize report structure, analytical depth, and factual grounding. Based on approximately 1,000 reports produced by mainstream DRAs, we further propose Deep rEsearch Failure Taxonomy (DEFT), the first failure taxonomy for deep research agents. DEFT contains 14 fine-grained failure modes across reasoning, retrieval, and generation, and is built upon grounded theory with human-LLM co-annotating and inter-annotator reliability validation. Our experimental findings reveal that current DRAs struggle not with task comprehension but with evidence integration, verification, and reasoning-resilient planning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2511.20649", "title": "Infinity-RoPE: Action-Controllable Infinite Video Generation Emerges From Autoregressive Self-Rollout", "authors": [ "Hidir Yesiltepe", "Tuna Han Salih Meral", "Adil Kaan Akan", "Kaan Oktay", "Pinar Yanardag" ], "github": "", "project_page": "", "abstract": "Current autoregressive video diffusion models are constrained by three core bottlenecks: (i) the finite temporal horizon imposed by the base model's 3D Rotary Positional Embedding (3D-RoPE), (ii) slow prompt responsiveness in maintaining fine-grained action control during long-form rollouts, and (iii) the inability to realize discontinuous cinematic transitions within a single generation stream. We introduce infty-RoPE, a unified inference-time framework that addresses all three limitations through three interconnected components: Block-Relativistic RoPE, KV Flush, and RoPE Cut. Block-Relativistic RoPE reformulates temporal encoding as a moving local reference frame, where each newly generated latent block is rotated relative to the base model's maximum frame horizon while earlier blocks are rotated backward to preserve relative temporal geometry. This relativistic formulation eliminates fixed temporal positions, enabling continuous video generation far beyond the base positional limits. To obtain fine-grained action control without re-encoding, KV Flush renews the KV cache by retaining only two latent frames, the global sink and the last generated latent frame, thereby ensuring immediate prompt responsiveness. Finally, RoPE Cut introduces controlled discontinuities in temporal RoPE coordinates, enabling multi-cut scene transitions within a single continuous rollout. Together, these components establish infty-RoPE as a training-free foundation for infinite-horizon, controllable, and cinematic video diffusion. Comprehensive experiments show that infty-RoPE consistently surpasses previous autoregressive models in overall VBench scores.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.02014", "title": "TUNA: Taming Unified Visual Representations for Native Unified Multimodal Models", "authors": [ "Zhiheng Liu", "Weiming Ren", "Haozhe Liu", "Zijian Zhou", "Shoufa Chen", "Haonan Qiu", "Xiaoke Huang", "Zhaochong An", "Fanny Yang", "Aditya Patel", "Viktar Atliha", "Tony Ng", "Xiao Han", "Chuyan Zhu", "Chenyang Zhang", "Ding Liu", "Juan-Manuel Perez-Rua", "Sen He", "J\u00fcrgen Schmidhuber", "Wenhu Chen", "Ping Luo", "Wei Liu", "Tao Xiang", "Jonas Schult", "Yuren Cong" ], "github": "", "project_page": "", "abstract": "Unified multimodal models (UMMs) aim to jointly perform multimodal understanding and generation within a single framework. We present TUNA, a native UMM that builds a unified continuous visual representation by cascading a VAE encoder with a representation encoder. This unified representation space allows end-to-end processing of images and videos for both understanding and generation tasks. Compared to prior UMMs with decoupled representations, TUNA's unified visual space avoids representation format mismatches introduced by separate encoders, outperforming decoupled alternatives in both understanding and generation. Moreover, we observe that stronger pretrained representation encoders consistently yield better performance across all multimodal tasks, highlighting the importance of the representation encoder. Finally, in this unified setting, jointly training on both understanding and generation data allows the two tasks to benefit from each other rather than interfere. Our extensive experiments on multimodal understanding and generation benchmarks show that TUNA achieves state-of-the-art results in image and video understanding, image and video generation, and image editing, demonstrating the effectiveness and scalability of its unified representation design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01801", "title": "GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation", "authors": [ "Yunfei Li", "Xiao Ma", "Jiafeng Xu", "Yu Cui", "Zhongren Cui", "Zhigang Han", "Liqun Huang", "Tao Kong", "Yuxiao Liu", "Hao Niu", "Wanli Peng", "Jingchao Qiao", "Zeyu Ren", "Haixin Shi", "Zhi Su", "Jiawen Tian", "Yuyang Xiao", "Shenyu Zhang", "Liwei Zheng", "Hang Li", "Yonghui Wu" ], "github": "", "project_page": "", "abstract": "We present GR-RL, a robotic learning framework that turns a generalist vision-language-action (VLA) policy into a highly capable specialist for long-horizon dexterous manipulation. Assuming the optimality of human demonstrations is core to existing VLA policies. However, we claim that in highly dexterous and precise manipulation tasks, human demonstrations are noisy and suboptimal. GR-RL proposes a multi-stage training pipeline that filters, augments, and reinforces the demonstrations by reinforcement learning. First, GR-RL learns a vision-language-conditioned task progress, filters the demonstration trajectories, and only keeps the transitions that contribute positively to the progress. Specifically, we show that by directly applying offline RL with sparse reward, the resulting Q-values can be treated as a robust progress function. Next, we introduce morphological symmetry augmentation that greatly improves the generalization and performance of GR-RL. Lastly, to better align the VLA policy with its deployment behaviors for high-precision control, we perform online RL by learning a latent space noise predictor. With this pipeline, GR-RL is, to our knowledge, the first learning-based policy that can autonomously lace up a shoe by threading shoelaces through multiple eyelets with an 83.3% success rate, a task requiring long-horizon reasoning, millimeter-level precision, and compliant soft-body interaction. We hope GR-RL provides a step toward enabling generalist robot foundations models to specialize into reliable real-world experts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.00722", "title": "SpeContext: Enabling Efficient Long-context Reasoning with Speculative Context Sparsity in LLMs", "authors": [ "Jiaming Xu", "Jiayi Pan", "Hanzhen Wang", "Yongkang Zhou", "Jiancai Ye", "Yu Wang", "Guohao Dai" ], "github": "", "project_page": "", "abstract": "In this paper, we point out that the objective of the retrieval algorithms is to align with the LLM, which is similar to the objective of knowledge distillation in LLMs. We analyze the similarity in information focus between the distilled language model(DLM) and the original LLM from the perspective of information theory, and thus propose a novel paradigm that leverages a DLM as the retrieval algorithm. Based on the insight, we present SpeContext, an algorithm and system co-design for long-context reasoning. (1) At the algorithm level, SpeContext proposes lightweight retrieval head based on the head-level attention weights of DLM, achieving > 90% parameters reduction by pruning the redundancy. (2) At the system level, SpeContext designs an asynchronous prefetch dataflow via the elastic loading strategy, effectively overlapping KV cache retrieval with the LLM computation. (3) At the compilation level, SpeContext constructs the theoretical memory model and implements an adaptive memory management system to achieve acceleration by maximizing GPU memory utilization. We deploy and evaluate SpeContext in two resourceconstrained environments, cloud and edge. Extensive experiments show that, compared with the Huggingface framework, SpeContext achieves up to 24.89x throughput improvement in cloud and 10.06x speedup in edge with negligible accuracy loss, pushing the Pareto frontier of accuracy and throughput.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01031", "title": "VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference", "authors": [ "Jiaming Tang", "Yufei Sun", "Yilong Zhao", "Shang Yang", "Yujun Lin", "Zhuoyang Zhang", "James Hou", "Yao Lu", "Zhijian Liu", "Song Han" ], "github": "https://github.com/mit-han-lab/vlash", "project_page": "", "abstract": "Vision-Language-Action models (VLAs) are becoming increasingly capable across diverse robotic tasks. However, their real-world deployment remains slow and inefficient: demonstration videos are often sped up by 5-10x to appear smooth, with noticeable action stalls and delayed reactions to environmental changes. Asynchronous inference offers a promising solution to achieve continuous and low-latency control by enabling robots to execute actions and perform inference simultaneously. However, because the robot and environment continue to evolve during inference, a temporal misalignment arises between the prediction and execution intervals. This leads to significant action instability, while existing methods either degrade accuracy or introduce runtime overhead to mitigate it. We propose VLASH, a general asynchronous inference framework for VLAs that delivers smooth, accurate, and fast reaction control without additional overhead or architectural changes. VLASH estimates the future execution-time state by rolling the robot state forward with the previously generated action chunk, thereby bridging the gap between prediction and execution. Experiments show that VLASH achieves up to 2.03x speedup and reduces reaction latency by up to 17.4x compared to synchronous inference while fully preserving the original accuracy. Moreover, it empowers VLAs to handle fast-reaction, high-precision tasks such as playing ping-pong and playing whack-a-mole, where traditional synchronous inference fails. Code is available at https://github.com/mit-han-lab/vlash", "github_manual": "", "github_regex": "https://github.com/mit-han-lab/vlash", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01925", "title": "Rectifying LLM Thought from Lens of Optimization", "authors": [ "Junnan Liu", "Hongwei Liu", "Songyang Zhang", "Kai Chen" ], "github": "https://github.com/open-compass/RePro", "project_page": "", "abstract": "Recent advancements in large language models (LLMs) have been driven by their emergent reasoning capabilities, particularly through long chain-of-thought (CoT) prompting, which enables thorough exploration and deliberation. Despite these advances, long-CoT LLMs often exhibit suboptimal reasoning behaviors, such as overthinking and excessively protracted reasoning chains, which can impair performance. In this paper, we analyze reasoning processes through an optimization lens, framing CoT as a gradient descent procedure where each reasoning step constitutes an update toward problem resolution. Building on this perspective, we introduce RePro (Rectifying Process-level Reward), a novel approach to refine LLM reasoning during post-training. RePro defines a surrogate objective function to assess the optimization process underlying CoT, utilizing a dual scoring mechanism to quantify its intensity and stability. These scores are aggregated into a composite process-level reward, seamlessly integrated into reinforcement learning with verifiable rewards (RLVR) pipelines to optimize LLMs. Extensive experiments across multiple reinforcement learning algorithms and diverse LLMs, evaluated on benchmarks spanning mathematics, science, and coding, demonstrate that RePro consistently enhances reasoning performance and mitigates suboptimal reasoning behaviors.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-compass/RePro", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01342", "title": "InternVideo-Next: Towards General Video Foundation Models without Video-Text Supervision", "authors": [ "Chenting Wang", "Yuhan Zhu", "Yicheng Xu", "Jiange Yang", "Ziang Yan", "Yali Wang", "Yi Wang", "Limin Wang" ], "github": "", "project_page": "", "abstract": "Large-scale video-text pretraining achieves strong performance but depends on noisy, synthetic captions with limited semantic coverage, often overlooking implicit world knowledge such as object motion, 3D geometry, and physical cues. In contrast, masked video modeling (MVM) directly exploits spatiotemporal structures but trails text-supervised methods on general tasks. We find this gap arises from overlooked architectural issues: pixel-level reconstruction struggles with convergence and its low-level requirement often conflicts with semantics, while latent prediction often encourages shortcut learning. To address these, we disentangle the traditional encoder-decoder design into an Encoder-Predictor-Decoder (EPD) framework, where the predictor acts as a latent world model, and propose InternVideo-Next, a two-stage pretraining scheme that builds a semantically consistent yet detail-preserving latent space for this world model. First, conventional linear decoder in pixel MVM enforces the predictor output latent to be linearly projected to, thus separable in pixel space, causing the conflict with semantic abstraction. Our Stage 1 proposes a conditional diffusion decoder and injects reliable image-level semantic priors to enhance semantics and convergence, thus bridging pixel-level fidelity with high-level semantic abstraction. Stage 2 further learns world knowledge by predicting frozen Stage 1 targets within this space, mitigating shortcut learning. Trained on public, unlabeled videos, InternVideo-Next achieves state-of-the-art results across benchmarks and provides a scalable path toward general video representation learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01707", "title": "StreamGaze: Gaze-Guided Temporal Reasoning and Proactive Understanding in Streaming Videos", "authors": [ "Daeun Lee", "Subhojyoti Mukherjee", "Branislav Kveton", "Ryan A. Rossi", "Viet Dac Lai", "Seunghyun Yoon", "Trung Bui", "Franck Dernoncourt", "Mohit Bansal" ], "github": "https://github.com/daeunni/StreamGaze", "project_page": "", "abstract": "Streaming video understanding requires models not only to process temporally incoming frames, but also to anticipate user intention for realistic applications like AR glasses. While prior streaming benchmarks evaluate temporal reasoning, none measure whether MLLMs can interpret or leverage human gaze signals within a streaming setting. To fill this gap, we introduce StreamGaze, the first benchmark designed to evaluate how effectively MLLMs use gaze for temporal and proactive reasoning in streaming videos. StreamGaze introduces gaze-guided past, present, and proactive tasks that comprehensively evaluate streaming video understanding. These tasks assess whether models can use real-time gaze to follow shifting attention and infer user intentions from only past and currently observed frames. To build StreamGaze, we develop a gaze-video QA generation pipeline that aligns egocentric videos with raw gaze trajectories via fixation extraction, region-specific visual prompting, and scanpath construction. This pipeline produces spatio-temporally grounded QA pairs that closely reflect human perceptual dynamics. Across all StreamGaze tasks, we observe substantial performance gaps between state-of-the-art MLLMs and human performance, revealing fundamental limitations in gaze-based temporal reasoning, intention modeling, and proactive prediction. We further provide detailed analyses of gaze-prompting strategies, reasoning behaviors, and task-specific failure modes, offering deeper insight into why current MLLMs struggle and what capabilities future models must develop. All data and code will be publicly released to support continued research in gaze-guided streaming video understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/daeunni/StreamGaze", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01420", "title": "PromptBridge: Cross-Model Prompt Transfer for Large Language Models", "authors": [ "Yaxuan Wang", "Quan Liu", "Zhenting Wang", "Zichao Li", "Wei Wei", "Yang Liu", "Yujia Bao" ], "github": "https://github.com/supergirl-os/PromptBridge", "project_page": "", "abstract": "Large language models (LLMs) underpin applications in code generation, mathematical reasoning, and agent-based workflows. In practice, systems access LLMs via commercial APIs or open-source deployments, and the model landscape (e.g., GPT, Claude, Llama) evolves rapidly. This rapid evolution forces frequent model switches driven by capability, cost, deployment constraints, and privacy. Yet prompts are highly model-sensitive: reusing a prompt engineered for one model on another often yields substantially worse performance than a prompt optimized for the target model. We term this phenomenon Model Drifting. Through extensive empirical analysis across diverse LLM configurations, we show that model drifting is both common and severe. To address this challenge, we introduce PromptBridge, a training-free framework that preserves prompt effectiveness under model switches, enabling cross-model prompt transfer without costly per-task or per-model re-optimization. PromptBridge requires only a small set of alignment tasks for calibration. It first applies Model-Adaptive Reflective Prompt Evolution (MAP-RPE) to obtain task- and model-specific optimal prompts via iterative reflective refinement and quantitative evaluation. Using the resulting calibrated prompt pairs for the source and target models, PromptBridge learns a cross-model prompt mapping. At test time, i.e., for an unseen task, given a source-model prompt, this mapping directly produces an optimized prompt for the target model. Experiments in single-agent and multi-agent settings show that PromptBridge consistently improves downstream accuracy while reducing migration effort. The code will be available soon.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/supergirl-os/PromptBridge", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01763", "title": "HiconAgent: History Context-aware Policy Optimization for GUI Agents", "authors": [ "Xurui Zhou", "Gongwei Chen", "Yuquan Xie", "Zaijing Li", "Kaiwen Zhou", "Shuai Wang", "Shuo Yang", "Zhuotao Tian", "Rui Shao" ], "github": "", "project_page": "", "abstract": "Graphical User Interface (GUI) agents require effective use of historical context to perform sequential navigation tasks. While incorporating past actions and observations can improve decision making, naive use of full history leads to excessive computational overhead and distraction from irrelevant information. To address this, we introduce HiconAgent, a GUI agent trained with History Context-aware Policy Optimization (HCPO) for efficient and effective utilization of historical information. HCPO optimizes history usage in both sampling and policy updates through two complementary components: (1) Dynamic Context Sampling (DCS) presents the agent with variable length histories during sampling, enabling adaptive use of the most relevant context; (2) Anchor-guided History Compression (AHC) refines the policy update phase with a dual branch strategy where the compressed branch removes history observations while keeping history actions as information flow anchors. The compressed and uncompressed branches are coupled through a history-enhanced alignment loss to enforce consistent history usage while maintaining efficiency. Experiments on mainstream GUI navigation benchmarks demonstrate strong performance. Despite being smaller, HiconAgent-3B outperforms GUI-R1-7B by +8.46 percent grounding accuracy and +11.32 percent step success rate on GUI-Odyssey, while achieving comparable results on AndroidControl and AITW with up to 2.47x computational speedup and 60 percent FLOPs reduction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2511.22396", "title": "Asking like Socrates: Socrates helps VLMs understand remote sensing images", "authors": [ "Run Shao", "Ziyu Li", "Zhaoyang Zhang", "Linrui Xu", "Xinran He", "Hongyuan Yuan", "Bolei He", "Yongxing Dai", "Yiming Yan", "Yijun Chen", "Wang Guo", "Haifeng Li" ], "github": "", "project_page": "", "abstract": "Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (RS) tasks, we observe widespread pseudo reasoning: models narrate the process of reasoning rather than genuinely reason toward the correct answer based on visual evidence. We attribute this to the Glance Effect, where a single, coarse perception of large-scale RS imagery results in incomplete understanding and reasoning based on linguistic self-consistency instead of visual evidence. To address this, we propose RS-EoT (Remote Sensing Evidence-of-Thought), a language-driven, iterative visual evidence-seeking paradigm. To instill this paradigm, we propose SocraticAgent, a self-play multi-agent system that synthesizes reasoning traces via alternating cycles of reasoning and visual inspection. To enhance and generalize these patterns, we propose a two-stage progressive RL strategy: first, RL on fine-grained Grounding tasks to enhance RS-EoT capabilities, followed by RL on RS VQA to generalize to broader understanding scenarios. Experiments show RS-EoT achieves state-of-the-art performance on multiple RS VQA and grounding benchmarks. Analyses reveal clear iterative cycles of reasoning and evidence seeking, confirming RS-EoT mitigates the Glance Effect and enables genuine evidence-grounded reasoning. Our code, data, and models are available at https://geox-lab.github.io/Asking_like_Socrates", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01949", "title": "Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models", "authors": [ "Zhongyu Yang", "Dannong Xu", "Wei Pang", "Yingfang Yuan" ], "github": "", "project_page": "", "abstract": "The rapid growth of visual tokens in multimodal large language models (MLLMs) leads to excessive memory consumption and inference latency, especially when handling high-resolution images and videos. Token pruning is a technique used to mitigate this issue by removing redundancy, but existing methods often ignore relevance to the user query or suffer from the limitations of attention mechanisms, reducing their adaptability and effectiveness. To address these challenges, we propose Script, a plug-and-play pruning method that requires no retraining and generalizes across diverse MLLMs. Script comprises two modules: a graph-structured pruning module that removes visually redundant tokens, and a query-conditioned semantic pruning module that preserves query-relevant visual information. Together, they enhance performance on multimodal tasks. Experiments on fourteen benchmarks across image and video understanding tasks show that Script consistently achieves higher model efficiency and predictive accuracy compared to existing pruning methods. On LLaVA-NeXT-7B, it achieves up to 6.8x prefill speedup and 10x FLOP reduction, while retaining 96.88% of the original performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01481", "title": "ChronosObserver: Taming 4D World with Hyperspace Diffusion Sampling", "authors": [ "Qisen Wang", "Yifan Zhao", "Peisen Shen", "Jialu Li", "Jia Li" ], "github": "", "project_page": "", "abstract": "Although prevailing camera-controlled video generation models can produce cinematic results, lifting them directly to the generation of 3D-consistent and high-fidelity time-synchronized multi-view videos remains challenging, which is a pivotal capability for taming 4D worlds. Some works resort to data augmentation or test-time optimization, but these strategies are constrained by limited model generalization and scalability issues. To this end, we propose ChronosObserver, a training-free method including World State Hyperspace to represent the spatiotemporal constraints of a 4D world scene, and Hyperspace Guided Sampling to synchronize the diffusion sampling trajectories of multiple views using the hyperspace. Experimental results demonstrate that our method achieves high-fidelity and 3D-consistent time-synchronized multi-view videos generation without training or fine-tuning for diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01030", "title": "Lotus-2: Advancing Geometric Dense Prediction with Powerful Image Generative Model", "authors": [ "Jing He", "Haodong Li", "Mingzhi Sheng", "Ying-Cong Chen" ], "github": "", "project_page": "", "abstract": "Recovering pixel-wise geometric properties from a single image is fundamentally ill-posed due to appearance ambiguity and non-injective mappings between 2D observations and 3D structures. While discriminative regression models achieve strong performance through large-scale supervision, their success is bounded by the scale, quality and diversity of available data and limited physical reasoning. Recent diffusion models exhibit powerful world priors that encode geometry and semantics learned from massive image-text data, yet directly reusing their stochastic generative formulation is suboptimal for deterministic geometric inference: the former is optimized for diverse and high-fidelity image generation, whereas the latter requires stable and accurate predictions. In this work, we propose Lotus-2, a two-stage deterministic framework for stable, accurate and fine-grained geometric dense prediction, aiming to provide an optimal adaption protocol to fully exploit the pre-trained generative priors. Specifically, in the first stage, the core predictor employs a single-step deterministic formulation with a clean-data objective and a lightweight local continuity module (LCM) to generate globally coherent structures without grid artifacts. In the second stage, the detail sharpener performs a constrained multi-step rectified-flow refinement within the manifold defined by the core predictor, enhancing fine-grained geometry through noise-free deterministic flow matching. Using only 59K training samples, less than 1% of existing large-scale datasets, Lotus-2 establishes new state-of-the-art results in monocular depth estimation and highly competitive surface normal prediction. These results demonstrate that diffusion models can serve as deterministic world priors, enabling high-quality geometric reasoning beyond traditional discriminative and generative paradigms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.00762", "title": "Seeing the Wind from a Falling Leaf", "authors": [ "Zhiyuan Gao", "Jiageng Mao", "Hong-Xing Yu", "Haozhe Lou", "Emily Yue-Ting Jia", "Jernej Barbic", "Jiajun Wu", "Yue Wang" ], "github": "", "project_page": "", "abstract": "A longstanding goal in computer vision is to model motions from videos, while the representations behind motions, i.e. the invisible physical interactions that cause objects to deform and move, remain largely unexplored. In this paper, we study how to recover the invisible forces from visual observations, e.g., estimating the wind field by observing a leaf falling to the ground. Our key innovation is an end-to-end differentiable inverse graphics framework, which jointly models object geometry, physical properties, and interactions directly from videos. Through backpropagation, our approach enables the recovery of force representations from object motions. We validate our method on both synthetic and real-world scenarios, and the results demonstrate its ability to infer plausible force fields from videos. Furthermore, we show the potential applications of our approach, including physics-based video generation and editing. We hope our approach sheds light on understanding and modeling the physical process behind pixels, bridging the gap between vision and physics. Please check more video results in our https://chaoren2357.github.io/seeingthewind/{project page}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.02008", "title": "The Art of Scaling Test-Time Compute for Large Language Models", "authors": [ "Aradhye Agarwal", "Ayan Sengupta", "Tanmoy Chakraborty" ], "github": "https://github.com/Aradhye2002/art_of_tts", "project_page": "", "abstract": "Test-time scaling (TTS) -- the dynamic allocation of compute during inference -- is a promising direction for improving reasoning in large language models (LLMs). However, a systematic comparison of well-known TTS strategies under identical conditions is missing, and the influence of model type and problem difficulty on performance remains unclear. To address these gaps, we conduct the first large-scale study of TTS, spanning over thirty billion tokens generated using eight open-source LLMs (7B to 235B parameters), across four reasoning datasets. We observe three consistent trends: (1) no single TTS strategy universally dominates; (2) reasoning models exhibit distinct trace-quality patterns across problem difficulty and trace length, forming short-horizon and long-horizon categories; and (3) for a given model type, the optimal TTS performance scales monotonically with compute budget. Based on these insights, we provide a practical recipe for selecting the best TTS strategy, considering problem difficulty, model type, and compute budget, providing a practical guide to effective inference-time scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Aradhye2002/art_of_tts", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01191", "title": "Generalist Large Language Models Outperform Clinical Tools on Medical Benchmarks", "authors": [ "Krithik Vishwanath", "Mrigayu Ghosh", "Anton Alyakin", "Daniel Alexander Alber", "Yindalon Aphinyanaphongs", "Eric Karl Oermann" ], "github": "", "project_page": "", "abstract": "Specialized clinical AI assistants are rapidly entering medical practice, often framed as safer or more reliable than general-purpose large language models (LLMs). Yet, unlike frontier models, these clinical tools are rarely subjected to independent, quantitative evaluation, creating a critical evidence gap despite their growing influence on diagnosis, triage, and guideline interpretation. We assessed two widely deployed clinical AI systems (OpenEvidence and UpToDate Expert AI) against three state-of-the-art generalist LLMs (GPT-5, Gemini 3 Pro, and Claude Sonnet 4.5) using a 1,000-item mini-benchmark combining MedQA (medical knowledge) and HealthBench (clinician-alignment) tasks. Generalist models consistently outperformed clinical tools, with GPT-5 achieving the highest scores, while OpenEvidence and UpToDate demonstrated deficits in completeness, communication quality, context awareness, and systems-based safety reasoning. These findings reveal that tools marketed for clinical decision support may often lag behind frontier LLMs, underscoring the urgent need for transparent, independent evaluation before deployment in patient-facing workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.00387", "title": "WiseEdit: Benchmarking Cognition- and Creativity-Informed Image Editing", "authors": [ "Kaihang Pan", "Weile Chen", "Haiyi Qiu", "Qifan Yu", "Wendong Bu", "Zehan Wang", "Yun Zhu", "Juncheng Li", "Siliang Tang" ], "github": "https://github.com/beepkh/WiseEdit", "project_page": "", "abstract": "Recent image editing models boast next-level intelligent capabilities, facilitating cognition- and creativity-informed image editing. Yet, existing benchmarks provide too narrow a scope for evaluation, failing to holistically assess these advanced abilities. To address this, we introduce WiseEdit, a knowledge-intensive benchmark for comprehensive evaluation of cognition- and creativity-informed image editing, featuring deep task depth and broad knowledge breadth. Drawing an analogy to human cognitive creation, WiseEdit decomposes image editing into three cascaded steps, i.e., Awareness, Interpretation, and Imagination, each corresponding to a task that poses a challenge for models to complete at the specific step. It also encompasses complex tasks, where none of the three steps can be finished easily. Furthermore, WiseEdit incorporates three fundamental types of knowledge: Declarative, Procedural, and Metacognitive knowledge. Ultimately, WiseEdit comprises 1,220 test cases, objectively revealing the limitations of SoTA image editing models in knowledge-based cognitive reasoning and creative composition capabilities. The benchmark, evaluation code, and the generated images of each model will be made publicly available soon. Project Page: https://qnancy.github.io/wiseedit_project_page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/beepkh/WiseEdit", "github_api_added_by": "auto" }, { "date": "2025-12-02", "arxiv_id": "2512.00639", "title": "Doppler-Enhanced Deep Learning: Improving Thyroid Nodule Segmentation with YOLOv5 Instance Segmentation", "authors": [ "Mahmoud El Hussieni" ], "github": "", "project_page": "", "abstract": "The increasing prevalence of thyroid cancer globally has led to the development of various computer-aided detection methods. Accurate segmentation of thyroid nodules is a critical first step in the development of AI-assisted clinical decision support systems. This study focuses on instance segmentation of thyroid nodules using YOLOv5 algorithms on ultrasound images. We evaluated multiple YOLOv5 variants (Nano, Small, Medium, Large, and XLarge) across two dataset versions, with and without doppler images. The YOLOv5-Large algorithm achieved the highest performance with a dice score of 91\\% and mAP of 0.87 on the dataset including doppler images. Notably, our results demonstrate that doppler images, typically excluded by physicians, can significantly improve segmentation performance. The YOLOv5-Small model achieved 79\\% dice score when doppler images were excluded, while including them improved performance across all model variants. These findings suggest that instance segmentation with YOLOv5 provides an effective real-time approach for thyroid nodule detection, with potential clinical applications in automated diagnostic systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.00333", "title": "IndicParam: Benchmark to evaluate LLMs on low-resource Indic Languages", "authors": [ "Ayush Maheshwari", "Kaushal Sharma", "Vivek Patel", "Aditya Maheshwari" ], "github": "https://github.com/ayushbits/IndicParam", "project_page": "", "abstract": "While large language models excel on high-resource multilingual tasks, low- and extremely low-resource Indic languages remain severely under-evaluated. We present IndicParam, a human-curated benchmark of over 13,000 multiple-choice questions covering 11 such languages (Nepali, Gujarati, Marathi, Odia as low-resource; Dogri, Maithili, Rajasthani, Sanskrit, Bodo, Santali, Konkani as extremely low-resource) plus Sanskrit-English code-mixed set. We evaluated 19 LLMs, both proprietary and open-weights, which reveals that even the top-performing GPT-5 reaches only 45.0% average accuracy, followed by DeepSeek-3.2 (43.1) and Claude-4.5 (42.7). We additionally label each question as knowledge-oriented or purely linguistic to discriminate factual recall from grammatical proficiency. Further, we assess the ability of LLMs to handle diverse question formats-such as list-based matching, assertion-reason pairs, and sequence ordering-alongside conventional multiple-choice questions. IndicParam provides insights into limitations of cross-lingual transfer and establishes a challenging benchmark for Indic languages. The dataset is available at https://huggingface.co/datasets/bharatgenai/IndicParam. Scripts to run benchmark are present at https://github.com/ayushbits/IndicParam.", "github_manual": "", "github_regex": "https://github.com/ayushbits/IndicParam", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2511.23404", "title": "LFM2 Technical Report", "authors": [ "Alexander Amini", "Anna Banaszak", "Harold Benoit", "Arthur B\u00f6\u00f6k", "Tarek Dakhran", "Song Duong", "Alfred Eng", "Fernando Fernandes", "Marc H\u00e4rk\u00f6nen", "Anne Harrington", "Ramin Hasani", "Saniya Karwa", "Yuri Khrustalev", "Maxime Labonne", "Mathias Lechner", "Valentine Lechner", "Simon Lee", "Zetian Li", "Noel Loo", "Jacob Marks", "Edoardo Mosca", "Samuel J. Paech", "Paul Pak", "Rom N. Parnichkun", "Alex Quach", "Ryan Rogers", "Daniela Rus", "Nayan Saxena", "Bettina Schlager", "Tim Seyde", "Jimmy T. H. Smith", "Aditya Tadimeti", "Neehal Tumma" ], "github": "", "project_page": "", "abstract": "We present LFM2, a family of Liquid Foundation Models designed for efficient on-device deployment and strong task capabilities. Using hardware-in-the-loop architecture search under edge latency and memory constraints, we obtain a compact hybrid backbone that combines gated short convolutions with a small number of grouped query attention blocks, delivering up to 2x faster prefill and decode on CPUs compared to similarly sized models. The LFM2 family covers 350M-8.3B parameters, including dense models (350M, 700M, 1.2B, 2.6B) and a mixture-of-experts variant (8.3B total, 1.5B active), all with 32K context length. LFM2's training pipeline includes a tempered, decoupled Top-K knowledge distillation objective that avoids support mismatch; curriculum learning with difficulty-ordered data; and a three-stage post-training recipe of supervised fine-tuning, length-normalized preference optimization, and model merging. Pre-trained on 10-12T tokens, LFM2 models achieve strong results across diverse benchmarks; for example, LFM2-2.6B reaches 79.56% on IFEval and 82.41% on GSM8K. We further build multimodal and retrieval variants: LFM2-VL for vision-language tasks, LFM2-Audio for speech, and LFM2-ColBERT for retrieval. LFM2-VL supports tunable accuracy-latency tradeoffs via token-efficient visual processing, while LFM2-Audio separates audio input and output pathways to enable real-time speech-to-speech interaction competitive with models 3x larger. LFM2-ColBERT provides a low-latency encoder for queries and documents, enabling high-performance retrieval across multiple languages. All models are released with open weights and deployment packages for ExecuTorch, llama.cpp, and vLLM, making LFM2 a practical base for edge applications that need fast, memory-efficient inference and strong task capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.00590", "title": "Wikontic: Constructing Wikidata-Aligned, Ontology-Aware Knowledge Graphs with Large Language Models", "authors": [ "Alla Chepurova", "Aydar Bulatov", "Yuri Kuratov", "Mikhail Burtsev" ], "github": "https://github.com/screemix/Wikontic", "project_page": "", "abstract": "Knowledge graphs (KGs) provide structured, verifiable grounding for large language models (LLMs), but current LLM-based systems commonly use KGs as auxiliary structures for text retrieval, leaving their intrinsic quality underexplored. In this work, we propose Wikontic, a multi-stage pipeline that constructs KGs from open-domain text by extracting candidate triplets with qualifiers, enforcing Wikidata-based type and relation constraints, and normalizing entities to reduce duplication. The resulting KGs are compact, ontology-consistent, and well-connected; on MuSiQue, the correct answer entity appears in 96% of generated triplets. On HotpotQA, our triplets-only setup achieves 76.0 F1, and on MuSiQue 59.8 F1, matching or surpassing several retrieval-augmented generation baselines that still require textual context. In addition, Wikontic attains state-of-the-art information-retention performance on the MINE-1 benchmark (86%), outperforming prior KG construction methods. Wikontic is also efficient at build time: KG construction uses less than 1,000 output tokens, about 3times fewer than AriGraph and <1/20 of GraphRAG. The proposed pipeline enhances the quality of the generated KG and offers a scalable solution for leveraging structured knowledge in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/screemix/Wikontic", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2511.20549", "title": "Flash-DMD: Towards High-Fidelity Few-Step Image Generation with Efficient Distillation and Joint Reinforcement Learning", "authors": [ "Guanjie Chen", "Shirui Huang", "Kai Liu", "Jianchen Zhu", "Xiaoye Qu", "Peng Chen", "Yu Cheng", "Yifu Sun" ], "github": "", "project_page": "", "abstract": "Diffusion Models have emerged as a leading class of generative models, yet their iterative sampling process remains computationally expensive. Timestep distillation is a promising technique to accelerate generation, but it often requires extensive training and leads to image quality degradation. Furthermore, fine-tuning these distilled models for specific objectives, such as aesthetic appeal or user preference, using Reinforcement Learning (RL) is notoriously unstable and easily falls into reward hacking. In this work, we introduce Flash-DMD, a novel framework that enables fast convergence with distillation and joint RL-based refinement. Specifically, we first propose an efficient timestep-aware distillation strategy that significantly reduces training cost with enhanced realism, outperforming DMD2 with only 2.1% its training cost. Second, we introduce a joint training scheme where the model is fine-tuned with an RL objective while the timestep distillation training continues simultaneously. We demonstrate that the stable, well-defined loss from the ongoing distillation acts as a powerful regularizer, effectively stabilizing the RL training process and preventing policy collapse. Extensive experiments on score-based and flow matching models show that our proposed Flash-DMD not only converges significantly faster but also achieves state-of-the-art generation quality in the few-step sampling regime, outperforming existing methods in visual quality, human preference, and text-image alignment metrics. Our work presents an effective paradigm for training efficient, high-fidelity, and stable generative models. Codes are coming soon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2511.23342", "title": "Flow Straighter and Faster: Efficient One-Step Generative Modeling via MeanFlow on Rectified Trajectories", "authors": [ "Xinxi Zhang", "Shiwei Tan", "Quang Nguyen", "Quan Dao", "Ligong Han", "Xiaoxiao He", "Tunyu Zhang", "Alen Mrdovic", "Dimitris Metaxas" ], "github": "https://github.com/Xinxi-Zhang/Re-MeanFlow", "project_page": "", "abstract": "Flow-based generative models have recently demonstrated strong performance, yet sampling typically relies on expensive numerical integration of ordinary differential equations (ODEs). Rectified Flow enables one-step sampling by learning nearly straight probability paths, but achieving such straightness requires multiple computationally intensive reflow iterations. MeanFlow achieves one-step generation by directly modeling the average velocity over time; however, when trained on highly curved flows, it suffers from slow convergence and noisy supervision. To address these limitations, we propose Rectified MeanFlow, a framework that models the mean velocity field along the rectified trajectory using only a single reflow step. This eliminates the need for perfectly straightened trajectories while enabling efficient training. Furthermore, we introduce a simple yet effective truncation heuristic that aims to reduce residual curvature and further improve performance. Extensive experiments on ImageNet at 64, 256, and 512 resolutions show that Re-MeanFlow consistently outperforms prior one-step flow distillation and Rectified Flow methods in both sample quality and training efficiency. Code is available at https://github.com/Xinxi-Zhang/Re-MeanFlow.", "github_manual": "", "github_regex": "https://github.com/Xinxi-Zhang/Re-MeanFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.00891", "title": "Accelerating Streaming Video Large Language Models via Hierarchical Token Compression", "authors": [ "Yiyu Wang", "Xuyang Liu", "Xiyan Gui", "Xinying Lin", "Boxue Yang", "Chenfei Liao", "Tailai Chen", "Linfeng Zhang" ], "github": "", "project_page": "", "abstract": "Streaming Video Large Language Models (VideoLLMs) have demonstrated impressive performance across various video understanding tasks, but they face significant challenges in real-time deployment due to the high computational cost of processing dense visual tokens from continuous video streams. In streaming video scenarios, the primary bottleneck lies in the Vision Transformer (ViT) encoding stage, where redundant processing of temporally similar frames leads to inefficiency. Additionally, inflated token sequences during LLM pre-filling further exacerbate latency and memory overhead. To address these challenges, we propose Streaming Token Compression (STC), a plug-and-play hierarchical framework that seamlessly integrates into existing streaming VideoLLMs, optimizing both ViT encoding and LLM pre-filling stages to accelerate processing. STC introduces two token-level accelerators: STC-Cacher, which reduces ViT encoding overhead by caching and reusing features from temporally similar frames, and STC-Pruner, which compresses the visual token sequence before it enters the LLM, preserving only the most salient tokens based on both spatial and temporal relevance. Extensive experiments on four baseline streaming VideoLLMs across five benchmarks demonstrate that STC outperforms other compression methods. Notably, STC retains up to 99\\% of accuracy on the ReKV framework while reducing ViT encoding latency and LLM pre-filling latency by 24.5\\% and 45.3\\%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.00466", "title": "SCALE: Selective Resource Allocation for Overcoming Performance Bottlenecks in Mathematical Test-time Scaling", "authors": [ "Yang Xiao", "Chunpu Xu", "Ruifeng Yuan", "Jiashuo Wang", "Wenjie Li", "Pengfei Liu" ], "github": "", "project_page": "", "abstract": "Test-time compute scaling has emerged as a powerful paradigm for enhancing mathematical reasoning in large language models (LLMs) by allocating additional computational resources during inference. However, current methods employ uniform resource distribution across all reasoning sub-problems, creating fundamental bottlenecks where challenging sub-problems receive insufficient attention while routine operations consume disproportionate resources. This uniform allocation creates performance bottlenecks where additional computational resources yield diminishing returns. Inspired by dual-process theory, we propose SCALE (Selective Resource Allocation), a framework that selectively allocates computational resources based on sub-problem difficulty. SCALE operates through four stages: (1) problem decomposition into sequential reasoning sub-problems, (2) difficulty assessment of each sub-problem to distinguish between routine operations and computationally challenging sub-problems, (3) selective processing mode assignment between System 1 for simple sub-problems and System 2 for complex ones, and (4) sequential execution with context propagation. By concentrating resources on challenging sub-problems while processing routine operations efficiently, SCALE achieves substantial performance improvements with superior resource utilization. Extensive experiments demonstrate that SCALE significantly outperforms uniform scaling baselines, achieving accuracy improvements of up to 13.75 percentage points (57.50% to 71.25% on AIME25) while reducing computational costs by 33%-53%, representing a major advance in test-time scaling that addresses fundamental limitations of current approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2511.17282", "title": "Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation", "authors": [ "Chuancheng Shi", "Shangze Li", "Shiming Guo", "Simiao Xie", "Wenhua Wu", "Jingtong Dou", "Chao Wu", "Canran Xiao", "Cong Wang", "Zifeng Cheng", "Fei Shen", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "Multilingual text-to-image (T2I) models have advanced rapidly in terms of visual realism and semantic alignment, and are now widely utilized. Yet outputs vary across cultural contexts: because language carries cultural connotations, images synthesized from multilingual prompts should preserve cross-lingual cultural consistency. We conduct a comprehensive analysis showing that current T2I models often produce culturally neutral or English-biased results under multilingual prompts. Analyses of two representative models indicate that the issue stems not from missing cultural knowledge but from insufficient activation of culture-related representations. We propose a probing method that localizes culture-sensitive signals to a small set of neurons in a few fixed layers. Guided by this finding, we introduce two complementary alignment strategies: (1) inference-time cultural activation that amplifies the identified neurons without backbone fine-tuned; and (2) layer-targeted cultural enhancement that updates only culturally relevant layers. Experiments on our CultureBench demonstrate consistent improvements over strong baselines in cultural consistency while preserving fidelity and diversity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01945", "title": "Agentic Policy Optimization via Instruction-Policy Co-Evolution", "authors": [ "Han Zhou", "Xingchen Wan", "Ivan Vuli\u0107", "Anna Korhonen" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capability of large language models (LLMs), enabling autonomous agents that can conduct effective multi-turn and tool-integrated reasoning. While instructions serve as the primary protocol for defining agents, RLVR typically relies on static and manually designed instructions. However, those instructions may be suboptimal for the base model, and the optimal instruction may change as the agent's policy improves and explores the interaction with the environment. To bridge the gap, we introduce INSPO, a novel Instruction-Policy co-evolution framework that integrates instruction optimization as a dynamic component of the reinforcement learning (RL) loop. INSPO maintains a dynamic population of instruction candidates that are sampled with questions, where reward signals in RL loops are automatically attributed to each instruction, and low performers are periodically pruned. New instructions are generated and verified through an on-policy reflection mechanism, where an LLM-based optimizer analyzes past experience from a replay buffer and evolves more effective strategies given the current policy. We conduct extensive experiments on multi-turn retrieval and reasoning tasks, demonstrating that INSPO substantially outperforms strong baselines relying on static instructions. INSPO discovers innovative instructions that guide the agent toward more strategic reasoning paths, achieving substantial performance gains with only a marginal increase in computational overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.00369", "title": "POLARIS: Projection-Orthogonal Least Squares for Robust and Adaptive Inversion in Diffusion Models", "authors": [ "Wenshuo Chen", "Haosen Li", "Shaofeng Liang", "Lei Wang", "Haozhe Jia", "Kaishen Yuan", "Jieming Wu", "Bowen Tian", "Yutao Yue" ], "github": "", "project_page": "", "abstract": "The Inversion-Denoising Paradigm, which is based on diffusion models, excels in diverse image editing and restoration tasks. We revisit its mechanism and reveal a critical, overlooked factor in reconstruction degradation: the approximate noise error. This error stems from approximating the noise at step t with the prediction at step t-1, resulting in severe error accumulation throughout the inversion process. We introduce Projection-Orthogonal Least Squares for Robust and Adaptive Inversion (POLARIS), which reformulates inversion from an error-compensation problem into an error-origin problem. Rather than optimizing embeddings or latent codes to offset accumulated drift, POLARIS treats the guidance scale \u03c9 as a step-wise variable and derives a mathematically grounded formula to minimize inversion error at each step. Remarkably, POLARIS improves inversion latent quality with just one line of code. With negligible performance overhead, it substantially mitigates noise approximation errors and consistently improves the accuracy of downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2511.22891", "title": "ORION: Teaching Language Models to Reason Efficiently in the Language of Thought", "authors": [ "Kumar Tanmay", "Kriti Aggarwal", "Paul Pu Liang", "Subhabrata Mukherjee" ], "github": "", "project_page": "", "abstract": "Large Reasoning Models (LRMs) achieve strong performance in mathematics, code generation, and task planning, but their reliance on long chains of verbose \"thinking\" tokens leads to high latency, redundancy, and incoherent reasoning paths. Inspired by the Language of Thought Hypothesis, which posits that human reasoning operates over a symbolic, compositional mental language called Mentalese, we introduce a framework that trains models to reason in a similarly compact style. Mentalese encodes abstract reasoning as ultra-compressed, structured tokens, enabling models to solve complex problems with far fewer steps. To improve both efficiency and accuracy, we propose SHORTER LENGTH PREFERENCE OPTIMIZATION (SLPO), a reinforcement learning method that rewards concise solutions that stay correct, while still allowing longer reasoning when needed. Applied to Mentalese-aligned models, SLPO yields significantly higher compression rates by enabling concise reasoning that preserves the benefits of detailed thinking without the computational overhead. Across benchmarks including AIME 2024 and 2025, MinervaMath, OlympiadBench, Math500, and AMC, our ORION models produce reasoning traces with 4-16x fewer tokens, achieve up to 5x lower inference latency, and reduce training costs by 7-9x relative to the DeepSeek R1 Distilled model, while maintaining 90-98% of its accuracy. ORION also surpasses Claude and ChatGPT-4o by up to 5% in accuracy while maintaining 2x compression. These results show that Mentalese-style compressed reasoning offers a step toward human-like cognitive efficiency, enabling real-time, cost-effective reasoning without sacrificing accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01103", "title": "Learning Eigenstructures of Unstructured Data Manifolds", "authors": [ "Roy Velich", "Arkadi Piven", "David Bensa\u00efd", "Daniel Cremers", "Thomas Dag\u00e8s", "Ron Kimmel" ], "github": "", "project_page": "", "abstract": "We introduce a novel framework that directly learns a spectral basis for shape and manifold analysis from unstructured data, eliminating the need for traditional operator selection, discretization, and eigensolvers. Grounded in optimal-approximation theory, we train a network to decompose an implicit approximation operator by minimizing the reconstruction error in the learned basis over a chosen distribution of probe functions. For suitable distributions, they can be seen as an approximation of the Laplacian operator and its eigendecomposition, which are fundamental in geometry processing. Furthermore, our method recovers in a unified manner not only the spectral basis, but also the implicit metric's sampling density and the eigenvalues of the underlying operator. Notably, our unsupervised method makes no assumption on the data manifold, such as meshing or manifold dimensionality, allowing it to scale to arbitrary datasets of any dimension. On point clouds lying on surfaces in 3D and high-dimensional image manifolds, our approach yields meaningful spectral bases, that can resemble those of the Laplacian, without explicit construction of an operator. By replacing the traditional operator selection, construction, and eigendecomposition with a learning-based approach, our framework offers a principled, data-driven alternative to conventional pipelines. This opens new possibilities in geometry processing for unstructured data, particularly in high-dimensional spaces.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01830", "title": "OpenREAD: Reinforced Open-Ended Reasoing for End-to-End Autonomous Driving with LLM-as-Critic", "authors": [ "Songyan Zhang", "Wenhui Huang", "Zhan Chen", "Chua Jiahao Collister", "Qihang Huang", "Chen Lv" ], "github": "", "project_page": "", "abstract": "Recently, two-stage fine-tuning strategies, e.g., acquiring essential driving knowledge through supervised fine-tuning (SFT) and further enhancing decision-making and planning via reinforcement fine-tuning (RFT), have shown strong potential in advancing the knowledge-driven autonomous driving (AD) paradigm. However, the learning nature of SFT still limits the generalization of reasoning, thereby constraining the full potential of driving performance. Meanwhile, current RFT approaches are primarily applied to downstream tasks, since scene understanding is an open-ended problem where corresponding rewards are difficult to quantify. To address these limitations, we propose OpenREAD, an OPEN-ended REasoning reinforced vision-language model (VLM)-based autonomous driving (AD) framework that enables end-to-end RFT across the full spectrum from high-level reasoning to low-level trajectory planning. Specifically, we begin by constructing large-scale Chain-of-Thought (CoT) annotations on open-source driving-related knowledge datasets, and employ the powerful Qwen3 large language model (LLM) as the critic in RFT to quantify reasoning quality for open-ended questions during reward modeling. Extensive experiments confirm that joint end-to-end RFT yields substantial improvements in both upstream and downstream tasks, enabling OpenREAD to achieve state-of-the-art performance on reasoning and planning benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01827", "title": "CauSight: Learning to Supersense for Visual Causal Discovery", "authors": [ "Yize Zhang", "Meiqi Chen", "Sirui Chen", "Bo Peng", "Yanxi Zhang", "Tianyu Li", "Chaochao Lu" ], "github": "https://github.com/OpenCausaLab/CauSight", "project_page": "", "abstract": "Causal thinking enables humans to understand not just what is seen, but why it happens. To replicate this capability in modern AI systems, we introduce the task of visual causal discovery. It requires models to infer cause-and-effect relations among visual entities across diverse scenarios instead of merely perceiving their presence. To this end, we first construct the Visual Causal Graph dataset (VCG-32K), a large-scale collection of over 32,000 images annotated with entity-level causal graphs, and further develop CauSight, a novel vision-language model to perform visual causal discovery through causally aware reasoning. Our training recipe integrates three components: (1) training data curation from VCG-32K, (2) Tree-of-Causal-Thought (ToCT) for synthesizing reasoning trajectories, and (3) reinforcement learning with a designed causal reward to refine the reasoning policy. Experiments show that CauSight outperforms GPT-4.1 on visual causal discovery, achieving over a threefold performance boost (21% absolute gain). Our code, model, and dataset are fully open-sourced at project page: https://github.com/OpenCausaLab/CauSight.", "github_manual": "", "github_regex": "https://github.com/OpenCausaLab/CauSight", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01686", "title": "DreamingComics: A Story Visualization Pipeline via Subject and Layout Customized Generation using Video Models", "authors": [ "Patrick Kwon", "Chen Chen" ], "github": "", "project_page": "", "abstract": "Current story visualization methods tend to position subjects solely by text and face challenges in maintaining artistic consistency. To address these limitations, we introduce DreamingComics, a layout-aware story visualization framework. We build upon a pretrained video diffusion-transformer (DiT) model, leveraging its spatiotemporal priors to enhance identity and style consistency. For layout-based position control, we propose RegionalRoPE, a region-aware positional encoding scheme that re-indexes embeddings based on the target layout. Additionally, we introduce a masked condition loss to further constrain each subject's visual features to their designated region. To infer layouts from natural language scripts, we integrate an LLM-based layout generator trained to produce comic-style layouts, enabling flexible and controllable layout conditioning. We present a comprehensive evaluation of our approach, showing a 29.2% increase in character consistency and a 36.2% increase in style similarity compared to previous methods, while displaying high spatial accuracy. Our project page is available at https://yj7082126.github.io/dreamingcomics/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.02015", "title": "Generative Video Motion Editing with 3D Point Tracks", "authors": [ "Yao-Chih Lee", "Zhoutong Zhang", "Jiahui Huang", "Jui-Hsien Wang", "Joon-Young Lee", "Jia-Bin Huang", "Eli Shechtman", "Zhengqi Li" ], "github": "", "project_page": "", "abstract": "Camera and object motions are central to a video's narrative. However, precisely editing these captured motions remains a significant challenge, especially under complex object movements. Current motion-controlled image-to-video (I2V) approaches often lack full-scene context for consistent video editing, while video-to-video (V2V) methods provide viewpoint changes or basic object translation, but offer limited control over fine-grained object motion. We present a track-conditioned V2V framework that enables joint editing of camera and object motion. We achieve this by conditioning a video generation model on a source video and paired 3D point tracks representing source and target motions. These 3D tracks establish sparse correspondences that transfer rich context from the source video to new motions while preserving spatiotemporal coherence. Crucially, compared to 2D tracks, 3D tracks provide explicit depth cues, allowing the model to resolve depth order and handle occlusions for precise motion editing. Trained in two stages on synthetic and real data, our model supports diverse motion edits, including joint camera/object manipulation, motion transfer, and non-rigid deformation, unlocking new creative potential in video editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.01443", "title": "MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification", "authors": [ "Xabier de Zuazo", "Ibon Saratxaga", "Eva Navas" ], "github": "https://github.com/neural2speech/libribrain-experiments", "project_page": "", "abstract": "We present Conformer-based decoders for the LibriBrain 2025 PNPL competition, targeting two foundational MEG tasks: Speech Detection and Phoneme Classification. Our approach adapts a compact Conformer to raw 306-channel MEG signals, with a lightweight convolutional projection layer and task-specific heads. For Speech Detection, a MEG-oriented SpecAugment provided a first exploration of MEG-specific augmentation. For Phoneme Classification, we used inverse-square-root class weighting and a dynamic grouping loader to handle 100-sample averaged examples. In addition, a simple instance-level normalization proved critical to mitigate distribution shifts on the holdout split. Using the official Standard track splits and F1-macro for model selection, our best systems achieved 88.9% (Speech) and 65.8% (Phoneme) on the leaderboard, surpassing the competition baselines and ranking within the top-10 in both tasks. For further implementation details, the technical documentation, source code, and checkpoints are available at https://github.com/neural2speech/libribrain-experiments.", "github_manual": "", "github_regex": "https://github.com/neural2speech/libribrain-experiments", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.00234", "title": "OmniFusion: Simultaneous Multilingual Multimodal Translations via Modular Fusion", "authors": [ "Sai Koneru", "Matthias Huck", "Jan Niehues" ], "github": "https://github.com/saikoneru/OmniFusion", "project_page": "", "abstract": "There has been significant progress in open-source text-only translation large language models (LLMs) with better language coverage and quality. However, these models can be only used in cascaded pipelines for speech translation (ST), performing automatic speech recognition first followed by translation. This introduces additional latency, which is particularly critical in simultaneous ST (SimulST), and prevents the model from exploiting multimodal context, such as images, which can aid disambiguation. Pretrained multimodal foundation models (MMFMs) already possess strong perception and reasoning capabilities across multiple modalities, but generally lack the multilingual coverage and specialized translation performance of dedicated translation LLMs. To build an effective multimodal translation system, we propose an end-to-end approach that fuses MMFMs with translation LLMs. We introduce a novel fusion strategy that connects hidden states from multiple layers of a pretrained MMFM to a translation LLM, enabling joint end-to-end training. The resulting model, OmniFusion, built on Omni 2.5-7B as the MMFM and SeedX PPO-7B as the translation LLM, can perform speech-to-text, speech-and-image-to-text, and text-and-image-to-text translation. Experiments demonstrate that OmniFusion effectively leverages both audio and visual inputs, achieves a 1-second latency reduction in SimulST compared to cascaded pipelines and also improves the overall translation qualityCode is available at https://github.com/saikoneru/OmniFusion.", "github_manual": "", "github_regex": "https://github.com/saikoneru/OmniFusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2511.22448", "title": "Structured Extraction from Business Process Diagrams Using Vision-Language Models", "authors": [ "Pritam Deka", "Barry Devereux" ], "github": "", "project_page": "", "abstract": "Business Process Model and Notation (BPMN) is a widely adopted standard for representing complex business workflows. While BPMN diagrams are often exchanged as visual images, existing methods primarily rely on XML representations for computational analysis. In this work, we present a pipeline that leverages Vision-Language Models (VLMs) to extract structured JSON representations of BPMN diagrams directly from images, without requiring source model files or textual annotations. We also incorporate optical character recognition (OCR) for textual enrichment and evaluate the generated element lists against ground truth data derived from the source XML files. Our approach enables robust component extraction in scenarios where original source files are unavailable. We benchmark multiple VLMs and observe performance improvements in several models when OCR is used for text enrichment. In addition, we conducted extensive statistical analyses of OCR-based enrichment methods and prompt ablation studies, providing a clearer understanding of their impact on model performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2511.22989", "title": "MultiBanana: A Challenging Benchmark for Multi-Reference Text-to-Image Generation", "authors": [ "Yuta Oshima", "Daiki Miyake", "Kohsei Matsutani", "Yusuke Iwasawa", "Masahiro Suzuki", "Yutaka Matsuo", "Hiroki Furuta" ], "github": "https://github.com/matsuolab/multibanana", "project_page": "", "abstract": "Recent text-to-image generation models have acquired the ability of multi-reference generation and editing; the ability to inherit the appearance of subjects from multiple reference images and re-render them under new contexts. However, the existing benchmark datasets often focus on the generation with single or a few reference images, which prevents us from measuring the progress on how model performance advances or pointing out their weaknesses, under different multi-reference conditions. In addition, their task definitions are still vague, typically limited to axes such as \"what to edit\" or \"how many references are given\", and therefore fail to capture the intrinsic difficulty of multi-reference settings. To address this gap, we introduce MultiBanana, which is carefully designed to assesses the edge of model capabilities by widely covering multi-reference-specific problems at scale: (1) varying the number of references, (2) domain mismatch among references (e.g., photo vs. anime), (3) scale mismatch between reference and target scenes, (4) references containing rare concepts (e.g., a red banana), and (5) multilingual textual references for rendering. Our analysis among a variety of text-to-image models reveals their superior performances, typical failure modes, and areas for improvement. MultiBanana will be released as an open benchmark to push the boundaries and establish a standardized basis for fair comparison in multi-reference image generation. Our data and code are available at https://github.com/matsuolab/multibanana .", "github_manual": "", "github_regex": "https://github.com/matsuolab/multibanana", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-02", "arxiv_id": "2512.00077", "title": "A Hierarchical Framework for Humanoid Locomotion with Supernumerary Limbs", "authors": [ "Bowen Zhi" ], "github": "https://github.com/heyzbw/HuSLs", "project_page": "", "abstract": "The integration of Supernumerary Limbs (SLs) on humanoid robots poses a significant stability challenge due to the dynamic perturbations they introduce. This thesis addresses this issue by designing a novel hierarchical control architecture to improve humanoid locomotion stability with SLs. The core of this framework is a decoupled strategy that combines learning-based locomotion with model-based balancing. The low-level component consists of a walking gait for a Unitree H1 humanoid through imitation learning and curriculum learning. The high-level component actively utilizes the SLs for dynamic balancing. The effectiveness of the system is evaluated in a physics-based simulation under three conditions: baseline gait for an unladen humanoid (baseline walking), walking with a static SL payload (static payload), and walking with the active dynamic balancing controller (dynamic balancing). Our evaluation shows that the dynamic balancing controller improves stability. Compared to the static payload condition, the balancing strategy yields a gait pattern closer to the baseline and decreases the Dynamic Time Warping (DTW) distance of the CoM trajectory by 47\\%. The balancing controller also improves the re-stabilization within gait cycles and achieves a more coordinated anti-phase pattern of Ground Reaction Forces (GRF). The results demonstrate that a decoupled, hierarchical design can effectively mitigate the internal dynamic disturbances arising from the mass and movement of the SLs, enabling stable locomotion for humanoids equipped with functional limbs. Code and videos are available here: https://github.com/heyzbw/HuSLs.", "github_manual": "", "github_regex": "https://github.com/heyzbw/HuSLs", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02556", "title": "DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models", "authors": [ "DeepSeek-AI", "Aixin Liu", "Aoxue Mei", "Bangcai Lin", "Bing Xue", "Bingxuan Wang", "Bingzheng Xu", "Bochao Wu", "Bowei Zhang", "Chaofan Lin", "Chen Dong", "Chengda Lu", "Chenggang Zhao", "Chengqi Deng", "Chenhao Xu", "Chong Ruan", "Damai Dai", "Daya Guo", "Dejian Yang", "Deli Chen", "Erhang Li", "Fangqi Zhou", "Fangyun Lin", "Fucong Dai", "Guangbo Hao", "Guanting Chen", "Guowei Li", "H. Zhang", "Hanwei Xu", "Hao Li", "Haofen Liang", "Haoran Wei", "Haowei Zhang", "Haowen Luo", "Haozhe Ji", "Honghui Ding", "Hongxuan Tang", "Huanqi Cao", "Huazuo Gao", "Hui Qu", "Hui Zeng", "Jialiang Huang", "Jiashi Li", "Jiaxin Xu", "Jiewen Hu", "Jingchang Chen", "Jingting Xiang", "Jingyang Yuan", "Jingyuan Cheng", "Jinhua Zhu", "Jun Ran", "Junguang Jiang", "Junjie Qiu", "Junlong Li", "Junxiao Song", "Kai Dong", "Kaige Gao", "Kang Guan", "Kexin Huang", "Kexing Zhou", "Kezhao Huang", "Kuai Yu", "Lean Wang", "Lecong Zhang", "Lei Wang", "Liang Zhao", "Liangsheng Yin", "Lihua Guo", "Lingxiao Luo", "Linwang Ma", "Litong Wang", "Liyue Zhang", "M. S. Di", "M. Y Xu", "Mingchuan Zhang", "Minghua Zhang", "Minghui Tang", "Mingxu Zhou", "Panpan Huang", "Peixin Cong", "Peiyi Wang", "Qiancheng Wang", "Qihao Zhu", "Qingyang Li", "Qinyu Chen", "Qiushi Du", "Ruiling Xu", "Ruiqi Ge", "Ruisong Zhang", "Ruizhe Pan", "Runji Wang", "Runqiu Yin", "Runxin Xu", "Ruomeng Shen", "Ruoyu Zhang", "S. H. Liu", "Shanghao Lu", "Shangyan Zhou", "Shanhuang Chen", "Shaofei Cai", "Shaoyuan Chen", "Shengding Hu", "Shengyu Liu", "Shiqiang Hu", "Shirong Ma", "Shiyu Wang", "Shuiping Yu", "Shunfeng Zhou", "Shuting Pan", "Songyang Zhou", "Tao Ni", "Tao Yun", "Tian Pei", "Tian Ye", "Tianyuan Yue", "Wangding Zeng", "Wen Liu", "Wenfeng Liang", "Wenjie Pang", "Wenjing Luo", "Wenjun Gao", "Wentao Zhang", "Xi Gao", "Xiangwen Wang", "Xiao Bi", "Xiaodong Liu", "Xiaohan Wang", "Xiaokang Chen", "Xiaokang Zhang", "Xiaotao Nie", "Xin Cheng", "Xin Liu", "Xin Xie", "Xingchao Liu", "Xingkai Yu", "Xingyou Li", "Xinyu Yang", "Xinyuan Li", "Xu Chen", "Xuecheng Su", "Xuehai Pan", "Xuheng Lin", "Xuwei Fu", "Y. Q. Wang", "Yang Zhang", "Yanhong Xu", "Yanru Ma", "Yao Li", "Yao Li", "Yao Zhao", "Yaofeng Sun", "Yaohui Wang", "Yi Qian", "Yi Yu", "Yichao Zhang", "Yifan Ding", "Yifan Shi", "Yiliang Xiong", "Ying He", "Ying Zhou", "Yinmin Zhong", "Yishi Piao", "Yisong Wang", "Yixiao Chen", "Yixuan Tan", "Yixuan Wei", "Yiyang Ma", "Yiyuan Liu", "Yonglun Yang", "Yongqiang Guo", "Yongtong Wu", "Yu Wu", "Yuan Cheng", "Yuan Ou", "Yuanfan Xu", "Yuduan Wang", "Yue Gong", "Yuhan Wu", "Yuheng Zou", "Yukun Li", "Yunfan Xiong", "Yuxiang Luo", "Yuxiang You", "Yuxuan Liu", "Yuyang Zhou", "Z. F. Wu", "Z. Z. Ren", "Zehua Zhao", "Zehui Ren", "Zhangli Sha", "Zhe Fu", "Zhean Xu", "Zhenda Xie", "Zhengyan Zhang", "Zhewen Hao", "Zhibin Gou", "Zhicheng Ma", "Zhigang Yan", "Zhihong Shao", "Zhixian Huang", "Zhiyu Wu", "Zhuoshu Li", "Zhuping Zhang", "Zian Xu", "Zihao Wang", "Zihui Gu", "Zijia Zhu", "Zilin Li", "Zipeng Zhang", "Ziwei Xie", "Ziyi Gao", "Zizheng Pan", "Zongqing Yao", "Bei Feng", "Hui Li", "J. L. Cai", "Jiaqi Ni", "Lei Xu", "Meng Li", "Ning Tian", "R. J. Chen", "R. L. Jin", "S. S. Li", "Shuang Zhou", "Tianyu Sun", "X. Q. Li", "Xiangyue Jin", "Xiaojin Shen", "Xiaosha Chen", "Xinnan Song", "Xinyi Zhou", "Y. X. Zhu", "Yanping Huang", "Yaohui Li", "Yi Zheng", "Yuchen Zhu", "Yunxian Ma", "Zhen Huang", "Zhipeng Xu", "Zhongyu Zhang", "Dongjie Ji", "Jian Liang", "Jianzhong Guo", "Jin Chen", "Leyi Xia", "Miaojun Wang", "Mingming Li", "Peng Zhang", "Ruyi Chen", "Shangmian Sun", "Shaoqing Wu", "Shengfeng Ye", "T. Wang", "W. L. Xiao", "Wei An", "Xianzu Wang", "Xiaowen Sun", "Xiaoxiang Wang", "Ying Tang", "Yukun Zha", "Zekai Zhang", "Zhe Ju", "Zhen Zhang", "Zihua Qu" ], "github": "", "project_page": "", "abstract": "We introduce DeepSeek-V3.2, a model that harmonizes high computational efficiency with superior reasoning and agent performance. The key technical breakthroughs of DeepSeek-V3.2 are as follows: (1) DeepSeek Sparse Attention (DSA): We introduce DSA, an efficient attention mechanism that substantially reduces computational complexity while preserving model performance in long-context scenarios. (2) Scalable Reinforcement Learning Framework: By implementing a robust reinforcement learning protocol and scaling post-training compute, DeepSeek-V3.2 performs comparably to GPT-5. Notably, our high-compute variant, DeepSeek-V3.2-Speciale, surpasses GPT-5 and exhibits reasoning proficiency on par with Gemini-3.0-Pro, achieving gold-medal performance in both the 2025 International Mathematical Olympiad (IMO) and the International Olympiad in Informatics (IOI). (3) Large-Scale Agentic Task Synthesis Pipeline: To integrate reasoning into tool-use scenarios, we developed a novel synthesis pipeline that systematically generates training data at scale. This methodology facilitates scalable agentic post-training, yielding substantial improvements in generalization and instruction-following robustness within complex, interactive environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.22609", "title": "MG-Nav: Dual-Scale Visual Navigation via Sparse Spatial Memory", "authors": [ "Bo Wang", "Jiehong Lin", "Chenzhi Liu", "Xinting Hu", "Yifei Yu", "Tianjia Liu", "Zhongrui Wang", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "We present MG-Nav (Memory-Guided Navigation), a dual-scale framework for zero-shot visual navigation that unifies global memory-guided planning with local geometry-enhanced control. At its core is the Sparse Spatial Memory Graph (SMG), a compact, region-centric memory where each node aggregates multi-view keyframe and object semantics, capturing both appearance and spatial structure while preserving viewpoint diversity. At the global level, the agent is localized on SMG and a goal-conditioned node path is planned via an image-to-instance hybrid retrieval, producing a sequence of reachable waypoints for long-horizon guidance. At the local level, a navigation foundation policy executes these waypoints in point-goal mode with obstacle-aware control, and switches to image-goal mode when navigating from the final node towards the visual target. To further enhance viewpoint alignment and goal recognition, we introduce VGGT-adapter, a lightweight geometric module built on the pre-trained VGGT model, which aligns observation and goal features in a shared 3D-aware space. MG-Nav operates global planning and local control at different frequencies, using periodic re-localization to correct errors. Experiments on HM3D Instance-Image-Goal and MP3D Image-Goal benchmarks demonstrate that MG-Nav achieves state-of-the-art zero-shot performance and remains robust under dynamic rearrangements and unseen scene conditions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.23369", "title": "SimScale: Learning to Drive via Real-World Simulation at Scale", "authors": [ "Haochen Tian", "Tianyu Li", "Haochen Liu", "Jiazhi Yang", "Yihang Qiu", "Guang Li", "Junli Wang", "Yinfeng Gao", "Zhang Zhang", "Liang Wang", "Hangjun Ye", "Tieniu Tan", "Long Chen", "Hongyang Li" ], "github": "https://github.com/OpenDriveLab/SimScale", "project_page": "", "abstract": "Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-distribution ones. However, such cases are underrepresented in real-world corpus collected by human experts. To complement for the lack of data diversity, we introduce a novel and scalable simulation framework capable of synthesizing massive unseen states upon existing driving logs. Our pipeline utilizes advanced neural rendering with a reactive environment to generate high-fidelity multi-view observations controlled by the perturbed ego trajectory. Furthermore, we develop a pseudo-expert trajectory generation mechanism for these newly simulated states to provide action supervision. Upon the synthesized data, we find that a simple co-training strategy on both real-world and simulated samples can lead to significant improvements in both robustness and generalization for various planning methods on challenging real-world benchmarks, up to +6.8 EPDMS on navhard and +2.9 on navtest. More importantly, such policy improvement scales smoothly by increasing simulation data only, even without extra real-world data streaming in. We further reveal several crucial findings of such a sim-real learning system, which we term SimScale, including the design of pseudo-experts and the scaling properties for different policy architectures. Our simulation data and code would be released.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenDriveLab/SimScale", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.03041", "title": "MultiShotMaster: A Controllable Multi-Shot Video Generation Framework", "authors": [ "Qinghe Wang", "Xiaoyu Shi", "Baolu Li", "Weikang Bian", "Quande Liu", "Huchuan Lu", "Xintao Wang", "Pengfei Wan", "Kun Gai", "Xu Jia" ], "github": "", "project_page": "", "abstract": "Current video generation techniques excel at single-shot clips but struggle to produce narrative multi-shot videos, which require flexible shot arrangement, coherent narrative, and controllability beyond text prompts. To tackle these challenges, we propose MultiShotMaster, a framework for highly controllable multi-shot video generation. We extend a pretrained single-shot model by integrating two novel variants of RoPE. First, we introduce Multi-Shot Narrative RoPE, which applies explicit phase shift at shot transitions, enabling flexible shot arrangement while preserving the temporal narrative order. Second, we design Spatiotemporal Position-Aware RoPE to incorporate reference tokens and grounding signals, enabling spatiotemporal-grounded reference injection. In addition, to overcome data scarcity, we establish an automated data annotation pipeline to extract multi-shot videos, captions, cross-shot grounding signals and reference images. Our framework leverages the intrinsic architectural properties to support multi-shot video generation, featuring text-driven inter-shot consistency, customized subject with motion control, and background-driven customized scene. Both shot count and duration are flexibly configurable. Extensive experiments demonstrate the superior performance and outstanding controllability of our framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02472", "title": "Guided Self-Evolving LLMs with Minimal Human Supervision", "authors": [ "Wenhao Yu", "Zhenwen Liang", "Chengsong Huang", "Kishan Panaganti", "Tianqing Fang", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "AI self-evolution has long been envisioned as a path toward superintelligence, where models autonomously acquire, refine, and internalize knowledge from their own learning experiences. Yet in practice, unguided self-evolving systems often plateau quickly or even degrade as training progresses. These failures arise from issues such as concept drift, diversity collapse, and mis-evolution, as models reinforce their own biases and converge toward low-entropy behaviors. To enable models to self-evolve in a stable and controllable manner while minimizing reliance on human supervision, we introduce R-Few, a guided Self-Play Challenger-Solver framework that incorporates lightweight human oversight through in-context grounding and mixed training. At each iteration, the Challenger samples a small set of human-labeled examples to guide synthetic question generation, while the Solver jointly trains on human and synthetic examples under an online, difficulty-based curriculum. Across math and general reasoning benchmarks, R-Few achieves consistent and iterative improvements. For example, Qwen3-8B-Base improves by +3.0 points over R-Zero on math tasks and achieves performance on par with General-Reasoner, despite the latter being trained on 20 times more human data. Ablation studies confirm the complementary contributions of grounded challenger training and curriculum-based solver training, and further analysis shows that R-Few mitigates drift, yielding more stable and controllable co-evolutionary dynamics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02425", "title": "WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoning", "authors": [ "Woongyeong Yeo", "Kangsan Kim", "Jaehong Yoon", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "Recent advances in video large language models have demonstrated strong capabilities in understanding short clips. However, scaling them to hours- or days-long videos remains highly challenging due to limited context capacity and the loss of critical visual details during abstraction. Existing memory-augmented methods mitigate this by leveraging textual summaries of video segments, yet they heavily rely on text and fail to utilize visual evidence when reasoning over complex scenes. Moreover, retrieving from fixed temporal scales further limits their flexibility in capturing events that span variable durations. To address this, we introduce WorldMM, a novel multimodal memory agent that constructs and retrieves from multiple complementary memories, encompassing both textual and visual representations. WorldMM comprises three types of memory: episodic memory indexes factual events across multiple temporal scales, semantic memory continuously updates high-level conceptual knowledge, and visual memory preserves detailed information about scenes. During inference, an adaptive retrieval agent iteratively selects the most relevant memory source and leverages multiple temporal granularities based on the query, continuing until it determines that sufficient information has been gathered. WorldMM significantly outperforms existing baselines across five long video question-answering benchmarks, achieving an average 8.4% performance gain over previous state-of-the-art methods, showing its effectiveness on long video reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.03036", "title": "ViSAudio: End-to-End Video-Driven Binaural Spatial Audio Generation", "authors": [ "Mengchen Zhang", "Qi Chen", "Tong Wu", "Zihan Liu", "Dahua Lin" ], "github": "", "project_page": "", "abstract": "Despite progress in video-to-audio generation, the field focuses predominantly on mono output, lacking spatial immersion. Existing binaural approaches remain constrained by a two-stage pipeline that first generates mono audio and then performs spatialization, often resulting in error accumulation and spatio-temporal inconsistencies. To address this limitation, we introduce the task of end-to-end binaural spatial audio generation directly from silent video. To support this task, we present the BiAudio dataset, comprising approximately 97K video-binaural audio pairs spanning diverse real-world scenes and camera rotation trajectories, constructed through a semi-automated pipeline. Furthermore, we propose ViSAudio, an end-to-end framework that employs conditional flow matching with a dual-branch audio generation architecture, where two dedicated branches model the audio latent flows. Integrated with a conditional spacetime module, it balances consistency between channels while preserving distinctive spatial characteristics, ensuring precise spatio-temporal alignment between audio and the input video. Comprehensive experiments demonstrate that ViSAudio outperforms existing state-of-the-art methods across both objective metrics and subjective evaluations, generating high-quality binaural audio with spatial immersion that adapts effectively to viewpoint changes, sound-source motion, and diverse acoustic environments. Project website: https://kszpxxzmc.github.io/ViSAudio-project.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.19433", "title": "Mixture of Horizons in Action Chunking", "authors": [ "Dong Jing", "Gang Wang", "Jiaqi Liu", "Weiliang Tang", "Zelong Sun", "Yunchao Yao", "Zhenyu Wei", "Yunhui Liu", "Zhiwu Lu", "Mingyu Ding" ], "github": "https://github.com/Timsty1/MixtureOfHorizons", "project_page": "", "abstract": "Vision-language-action (VLA) models have shown remarkable capabilities in robotic manipulation, but their performance is sensitive to the action chunk length used during training, termed horizon. Our empirical study reveals an inherent trade-off: longer horizons provide stronger global foresight but degrade fine-grained accuracy, while shorter ones sharpen local control yet struggle on long-term tasks, implying fixed choice of single horizons being suboptimal. To mitigate the trade-off, we propose a mixture of horizons (MoH) strategy. MoH rearranges the action chunk into several segments with different horizons, processes them in parallel with a shared action transformer, and fuses outputs with a light linear gate. It has three appealing benefits. 1) MoH exploits long-term foresight and short-term precision jointly within a single model, improving both performance and generalizability to complex tasks. 2) MoH is plug-and-play for full-attention action modules with minimal training or inference overhead. 3) MoH enables dynamic inference with adaptive horizons, which selects stable actions through cross-horizon consensus, achieving 2.5times higher throughput than baselines while preserving superior performance. Extensive experiments over flow-based policies \u03c0_0, \u03c0_{0.5}, and one-step regression policy \u03c0_{reg} demonstrate that MoH yields consistent and significant gains on both simulations and real-world tasks. Notably, under mixed-task setting, \u03c0_{0.5} with MoH reaches a new state-of-the-art with 99% average success rate on LIBERO after only 30k training iterations. Project page: https://github.com/Timsty1/MixtureOfHorizons", "github_manual": "", "github_regex": "https://github.com/Timsty1/MixtureOfHorizons", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02457", "title": "Does Hearing Help Seeing? Investigating Audio-Video Joint Denoising for Video Generation", "authors": [ "Jianzong Wu", "Hao Lian", "Dachao Hao", "Ye Tian", "Qingyu Shi", "Biaolong Chen", "Hao Jiang" ], "github": "", "project_page": "", "abstract": "Recent audio-video generative systems suggest that coupling modalities benefits not only audio-video synchrony but also the video modality itself. We pose a fundamental question: Does audio-video joint denoising training improve video generation, even when we only care about video quality? To study this, we introduce a parameter-efficient Audio-Video Full DiT (AVFullDiT) architecture that leverages pre-trained text-to-video (T2V) and text-to-audio (T2A) modules for joint denoising. We train (i) a T2AV model with AVFullDiT and (ii) a T2V-only counterpart under identical settings. Our results provide the first systematic evidence that audio-video joint denoising can deliver more than synchrony. We observe consistent improvements on challenging subsets featuring large and object contact motions. We hypothesize that predicting audio acts as a privileged signal, encouraging the model to internalize causal relationships between visual events and their acoustic consequences (e.g., collision times impact sound), which in turn regularizes video dynamics. Our findings suggest that cross-modal co-training is a promising approach to developing stronger, more physically grounded world models. Code and dataset will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.23127", "title": "DualCamCtrl: Dual-Branch Diffusion Model for Geometry-Aware Camera-Controlled Video Generation", "authors": [ "Hongfei Zhang", "Kanghao Chen", "Zixin Zhang", "Harold Haodong Chen", "Yuanhuiyi Lyu", "Yuqi Zhang", "Shuai Yang", "Kun Zhou", "Yingcong Chen" ], "github": "", "project_page": "", "abstract": "This paper presents DualCamCtrl, a novel end-to-end diffusion model for camera-controlled video generation. Recent works have advanced this field by representing camera poses as ray-based conditions, yet they often lack sufficient scene understanding and geometric awareness. DualCamCtrl specifically targets this limitation by introducing a dual-branch framework that mutually generates camera-consistent RGB and depth sequences. To harmonize these two modalities, we further propose the Semantic Guided Mutual Alignment (SIGMA) mechanism, which performs RGB-depth fusion in a semantics-guided and mutually reinforced manner. These designs collectively enable DualCamCtrl to better disentangle appearance and geometry modeling, generating videos that more faithfully adhere to the specified camera trajectories. Additionally, we analyze and reveal the distinct influence of depth and camera poses across denoising stages and further demonstrate that early and late stages play complementary roles in forming global structure and refining local details. Extensive experiments demonstrate that DualCamCtrl achieves more consistent camera-controlled video generation, with over 40\\% reduction in camera motion errors compared with prior methods. Our project page: https://soyouthinkyoucantell.github.io/dualcamctrl-page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.01715", "title": "DiG-Flow: Discrepancy-Guided Flow Matching for Robust VLA Models", "authors": [ "Wanpeng Zhang", "Ye Wang", "Hao Luo", "Haoqi Yuan", "Yicheng Feng", "Sipeng Zheng", "Qin Jin", "Zongqing Lu" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models trained with flow matching have demonstrated impressive capabilities on robotic manipulation tasks. However, their performance often degrades under distribution shift and on complex multi-step tasks, suggesting that the learned representations may not robustly capture task-relevant semantics. We introduce DiG-Flow, a principled framework that enhances VLA robustness through geometric regularization. Our key insight is that the distributional discrepancy between observation and action embeddings provides a meaningful geometric signal: lower transport cost indicates compatible representations, while higher cost suggests potential misalignment. DiG-Flow computes a discrepancy measure between empirical distributions of observation and action embeddings, maps it to a modulation weight via a monotone function, and applies residual updates to the observation embeddings before flow matching. Crucially, this intervention operates at the representation level without modifying the flow matching path or target vector field. We provide theoretical guarantees showing that discrepancy-guided training provably decreases the training objective, and that guided inference refinement converges with contraction. Empirically, DiG-Flow integrates into existing VLA architectures with negligible overhead and consistently improves performance, with particularly pronounced gains on complex multi-step tasks and under limited training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02899", "title": "Glance: Accelerating Diffusion Models with 1 Sample", "authors": [ "Zhuobai Dong", "Rui Zhao", "Songjie Wu", "Junchao Yi", "Linjie Li", "Zhengyuan Yang", "Lijuan Wang", "Alex Jinpeng Wang" ], "github": "https://github.com/CSU-JPG/Glance", "project_page": "", "abstract": "Diffusion models have achieved remarkable success in image generation, yet their deployment remains constrained by the heavy computational cost and the need for numerous inference steps. Previous efforts on fewer-step distillation attempt to skip redundant steps by training compact student models, yet they often suffer from heavy retraining costs and degraded generalization. In this work, we take a different perspective: we accelerate smartly, not evenly, applying smaller speedups to early semantic stages and larger ones to later redundant phases. We instantiate this phase-aware strategy with two experts that specialize in slow and fast denoising phases. Surprisingly, instead of investing massive effort in retraining student models, we find that simply equipping the base model with lightweight LoRA adapters achieves both efficient acceleration and strong generalization. We refer to these two adapters as Slow-LoRA and Fast-LoRA. Through extensive experiments, our method achieves up to 5 acceleration over the base model while maintaining comparable visual quality across diverse benchmarks. Remarkably, the LoRA experts are trained with only 1 samples on a single V100 within one hour, yet the resulting models generalize strongly on unseen prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CSU-JPG/Glance", "github_api_added_by": "auto" }, { "date": "2025-12-03", "arxiv_id": "2512.01989", "title": "PAI-Bench: A Comprehensive Benchmark For Physical AI", "authors": [ "Fengzhe Zhou", "Jiannan Huang", "Jialuo Li", "Deva Ramanan", "Humphrey Shi" ], "github": "", "project_page": "", "abstract": "Physical AI aims to develop models that can perceive and predict real-world dynamics; yet, the extent to which current multi-modal large language models and video generative models support these abilities is insufficiently understood. We introduce Physical AI Bench (PAI-Bench), a unified and comprehensive benchmark that evaluates perception and prediction capabilities across video generation, conditional video generation, and video understanding, comprising 2,808 real-world cases with task-aligned metrics designed to capture physical plausibility and domain-specific reasoning. Our study provides a systematic assessment of recent models and shows that video generative models, despite strong visual fidelity, often struggle to maintain physically coherent dynamics, while multi-modal large language models exhibit limited performance in forecasting and causal interpretation. These observations suggest that current systems are still at an early stage in handling the perceptual and predictive demands of Physical AI. In summary, PAI-Bench establishes a realistic foundation for evaluating Physical AI and highlights key gaps that future systems must address.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.22586", "title": "Revisiting the Necessity of Lengthy Chain-of-Thought in Vision-centric Reasoning Generalization", "authors": [ "Yifan Du", "Kun Zhou", "Yingqian Min", "Yue Ling", "Wayne Xin Zhao", "Youbin Wu" ], "github": "", "project_page": "", "abstract": "We study how different Chain-of-Thought (CoT) designs affect the acquisition of the generalizable visual reasoning ability in vision-language models (VLMs). While CoT data, especially long or visual CoT such as \"think with image\", has been widely used to supervise intermediate reasoning, it remains unclear why specific CoT designs help and which ones truly support generalizable reasoning. To systematically evaluate this, we focus on a controlled maze-solving benchmark where reasoning rules are fully visual, difficulty can be tuned by grid size, and all the intermediate steps can be automatically generated. Using Qwen2.5-VL-7B under a standard SFT-then-RL pipeline, we compare three representative CoT formats: Language CoT, Grounding CoT (with spatial coordinate trajectories), and Visual CoT (with image manipulations). Our experiments reveal that visual and longer CoT mainly accelerate convergence but do not lift the final performance ceiling; concise CoT containing only essential grounding steps outperforms longer traces; and, strikingly, CoT retaining only the minimal grounding results generalizes best across different maze sizes. We further validate these insights on other vision-centric tasks. These findings highlight a \"short is long\" effect and provide practical guidance for constructing more generalizable SFT datasets for visual reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.01248", "title": "TRivia: Self-supervised Fine-tuning of Vision-Language Models for Table Recognition", "authors": [ "Junyuan Zhang", "Bin Wang", "Qintong Zhang", "Fan Wu", "Zichen Wen", "Jialin Lu", "Junjie Shan", "Ziqi Zhao", "Shuya Yang", "Ziling Wang", "Ziyang Miao", "Huaping Zhong", "Yuhang Zang", "Xiaoyi Dong", "Ka-Ho Chow", "Conghui He" ], "github": "https://github.com/opendatalab/TRivia", "project_page": "", "abstract": "Table recognition (TR) aims to transform table images into semi-structured representations such as HTML or Markdown. As a core component of document parsing, TR has long relied on supervised learning, with recent efforts dominated by fine-tuning vision-language models (VLMs) using labeled data. While VLMs have brought TR to the next level, pushing performance further demands large-scale labeled data that is costly to obtain. Consequently, although proprietary models have continuously pushed the performance boundary, open-source models, often trained with limited resources and, in practice, the only viable option for many due to privacy regulations, still lag far behind. To bridge this gap, we introduce TRivia, a self-supervised fine-tuning method that enables pretrained VLMs to learn TR directly from unlabeled table images in the wild. Built upon Group Relative Policy Optimization, TRivia automatically identifies unlabeled samples that most effectively facilitate learning and eliminates the need for human annotations through a question-answering-based reward mechanism. An attention-guided module generates diverse questions for each table image, and the ability to interpret the recognition results and answer them correctly provides feedback to optimize the TR model. This closed-loop process allows the TR model to autonomously learn to recognize, structure, and reason over tables without labeled data. Leveraging this pipeline, we present TRivia-3B, an open-sourced, compact, and state-of-the-art TR model that surpasses existing systems (e.g., Gemini 2.5 Pro, MinerU2.5) on three popular benchmarks. Model and code are released at: https://github.com/opendatalab/TRivia", "github_manual": "", "github_regex": "https://github.com/opendatalab/TRivia", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.00903", "title": "SwiftVLA: Unlocking Spatiotemporal Dynamics for Lightweight VLA Models at Minimal Overhead", "authors": [ "Chaojun Ni", "Cheng Chen", "Xiaofeng Wang", "Zheng Zhu", "Wenzhao Zheng", "Boyuan Wang", "Tianrun Chen", "Guosheng Zhao", "Haoyun Li", "Zhehao Dong", "Qiang Zhang", "Yun Ye", "Yang Wang", "Guan Huang", "Wenjun Mei" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models built on pretrained Vision-Language Models (VLMs) show strong potential but are limited in practicality due to their large parameter counts. To mitigate this issue, using a lightweight VLM has been explored, but it compromises spatiotemporal reasoning. Although some methods suggest that incorporating additional 3D inputs can help, they usually rely on large VLMs to fuse 3D and 2D inputs and still lack temporal understanding. Therefore, we propose SwiftVLA, an architecture that enhances a compact model with 4D understanding while preserving design efficiency. Specifically, our approach features a pretrained 4D visual geometry transformer with a temporal cache that extracts 4D features from 2D images. Then, to enhance the VLM's ability to exploit both 2D images and 4D features, we introduce Fusion Tokens, a set of learnable tokens trained with a future prediction objective to generate unified representations for action generation. Finally, we introduce a mask-and-reconstruct strategy that masks 4D inputs to the VLM and trains the VLA to reconstruct them, enabling the VLM to learn effective 4D representations and allowing the 4D branch to be dropped at inference with minimal performance loss. Experiments in real and simulated environments show that SwiftVLA outperforms lightweight baselines and rivals VLAs up to 7 times larger, achieving comparable performance on edge devices while being 18 times faster and reducing memory footprint by 12 times.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.22973", "title": "BlockVid: Block Diffusion for High-Quality and Consistent Minute-Long Video Generation", "authors": [ "Zeyu Zhang", "Shuning Chang", "Yuanyu He", "Yizeng Han", "Jiasheng Tang", "Fan Wang", "Bohan Zhuang" ], "github": "https://github.com/alibaba-damo-academy/Inferix", "project_page": "", "abstract": "Generating minute-long videos is a critical step toward developing world models, providing a foundation for realistic extended scenes and advanced AI simulators. The emerging semi-autoregressive (block diffusion) paradigm integrates the strengths of diffusion and autoregressive models, enabling arbitrary-length video generation and improving inference efficiency through KV caching and parallel sampling. However, it yet faces two enduring challenges: (i) KV-cache-induced long-horizon error accumulation, and (ii) the lack of fine-grained long-video benchmarks and coherence-aware metrics. To overcome these limitations, we propose BlockVid, a novel block diffusion framework equipped with semantic-aware sparse KV cache, an effective training strategy called Block Forcing, and dedicated chunk-wise noise scheduling and shuffling to reduce error propagation and enhance temporal consistency. We further introduce LV-Bench, a fine-grained benchmark for minute-long videos, complete with new metrics evaluating long-range coherence. Extensive experiments on VBench and LV-Bench demonstrate that BlockVid consistently outperforms existing methods in generating high-quality, coherent minute-long videos. In particular, it achieves a 22.2% improvement on VDE Subject and a 19.4% improvement on VDE Clarity in LV-Bench over the state of the art approaches. Project website: https://ziplab.co/BlockVid. Inferix (Code): https://github.com/alibaba-damo-academy/Inferix.", "github_manual": "", "github_regex": "https://github.com/alibaba-damo-academy/Inferix", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.03040", "title": "Video4Spatial: Towards Visuospatial Intelligence with Context-Guided Video Generation", "authors": [ "Zeqi Xiao", "Yiwei Zhao", "Lingxiao Li", "Yushi Lan", "Yu Ning", "Rahul Garg", "Roshni Cooper", "Mohammad H. Taghavi", "Xingang Pan" ], "github": "", "project_page": "", "abstract": "We investigate whether video generative models can exhibit visuospatial intelligence, a capability central to human cognition, using only visual data. To this end, we present Video4Spatial, a framework showing that video diffusion models conditioned solely on video-based scene context can perform complex spatial tasks. We validate on two tasks: scene navigation - following camera-pose instructions while remaining consistent with 3D geometry of the scene, and object grounding - which requires semantic localization, instruction following, and planning. Both tasks use video-only inputs, without auxiliary modalities such as depth or poses. With simple yet effective design choices in the framework and data curation, Video4Spatial demonstrates strong spatial understanding from video context: it plans navigation and grounds target objects end-to-end, follows camera-pose instructions while maintaining spatial consistency, and generalizes to long contexts and out-of-domain environments. Taken together, these results advance video generative models toward general visuospatial reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02492", "title": "YingVideo-MV: Music-Driven Multi-Stage Video Generation", "authors": [ "Jiahui Chen", "Weida Wang", "Runhua Shi", "Huan Yang", "Chaofan Ding", "Zihao Chen" ], "github": "", "project_page": "", "abstract": "While diffusion model for audio-driven avatar video generation have achieved notable process in synthesizing long sequences with natural audio-visual synchronization and identity consistency, the generation of music-performance videos with camera motions remains largely unexplored. We present YingVideo-MV, the first cascaded framework for music-driven long-video generation. Our approach integrates audio semantic analysis, an interpretable shot planning module (MV-Director), temporal-aware diffusion Transformer architectures, and long-sequence consistency modeling to enable automatic synthesis of high-quality music performance videos from audio signals. We construct a large-scale Music-in-the-Wild Dataset by collecting web data to support the achievement of diverse, high-quality results. Observing that existing long-video generation methods lack explicit camera motion control, we introduce a camera adapter module that embeds camera poses into latent noise. To enhance continulity between clips during long-sequence inference, we further propose a time-aware dynamic window range strategy that adaptively adjust denoising ranges based on audio embedding. Comprehensive benchmark tests demonstrate that YingVideo-MV achieves outstanding performance in generating coherent and expressive music videos, and enables precise music-motion-camera synchronization. More videos are available in our project page: https://giantailab.github.io/YingVideo-MV/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02423", "title": "GUI Exploration Lab: Enhancing Screen Navigation in Agents via Multi-Turn Reinforcement Learning", "authors": [ "Haolong Yan", "Yeqing Shen", "Xin Huang", "Jia Wang", "Kaijun Tan", "Zhixuan Liang", "Hongxin Li", "Zheng Ge", "Osamu Yoshie", "Si Li", "Xiangyu Zhang", "Daxin Jiang" ], "github": "", "project_page": "", "abstract": "With the rapid development of Large Vision Language Models, the focus of Graphical User Interface (GUI) agent tasks shifts from single-screen tasks to complex screen navigation challenges. However, real-world GUI environments, such as PC software and mobile Apps, are often complex and proprietary, making it difficult to obtain the comprehensive environment information needed for agent training and evaluation. This limitation hinders systematic investigation and benchmarking of agent navigation capabilities. To address this limitation, we introduce GUI Exploration Lab, a simulation environment engine for GUI agent navigation research that enables flexible definition and composition of screens, icons, and navigation graphs, while providing full access to environment information for comprehensive agent training and evaluation. Through extensive experiments, we find that supervised fine-tuning enables effective memorization of fundamental knowledge, serving as a crucial foundation for subsequent training. Building on this, single-turn reinforcement learning further enhances generalization to unseen scenarios. Finally, multi-turn reinforcement learning encourages the development of exploration strategies through interactive trial and error, leading to further improvements in screen navigation performance. We validate our methods on both static and interactive benchmarks, demonstrating that our findings generalize effectively to real-world scenarios. These findings demonstrate the advantages of reinforcement learning approaches in GUI navigation and offer practical guidance for building more capable and generalizable GUI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.01988", "title": "Artemis: Structured Visual Reasoning for Perception Policy Learning", "authors": [ "Wei Tang", "Yanpeng Sun", "Shan Zhang", "Xiaofan Li", "Piotr Koniusz", "Wei Li", "Na Zhao", "Zechao Li" ], "github": "", "project_page": "", "abstract": "Recent reinforcement-learning frameworks for visual perception policy have begun to incorporate intermediate reasoning chains expressed in natural language. Empirical observations indicate that such purely linguistic intermediate reasoning often reduces performance on perception tasks. We argue that the core issue lies not in reasoning per se but in the form of reasoning: while these chains perform semantic reasoning in an unstructured linguistic space, visual perception requires reasoning in a spatial and object-centric space. In response, we introduce Artemis, a perception-policy learning framework that performs structured proposal-based reasoning, where each intermediate step is represented as a (label, bounding-box) pair capturing a verifiable visual state. This design enables explicit tracking of intermediate states, direct supervision for proposal quality, and avoids ambiguity introduced by language-based reasoning. Artemis is built on Qwen2.5-VL-3B, achieves strong performance on grounding and detection task and exhibits substantial generalization to counting and geometric-perception tasks. The consistent improvements across these diverse settings confirm that aligning reasoning with spatial representations enhances perception-policy learning. Owing to its strengthened visual reasoning, Artemis also achieves competitive performance on general MLLM benchmarks, illustrating that spatially grounded reasoning provides a principled route toward scalable and general perception policies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.22146", "title": "C^2DLM: Causal Concept-Guided Diffusion Large Language Models", "authors": [ "Kairong Han", "Nuanqiao Shan", "Ziyu Zhao", "Zijing Hu", "Xinpeng Dong", "Junjian Ye", "Lujia Pan", "Fei Wu", "Kun Kuang" ], "github": "https://github.com/Kairong-Han/C-2-DLM", "project_page": "", "abstract": "Autoregressive (AR) language models and Diffusion Language Models (DLMs) constitute the two principal paradigms of large language models. However, both paradigms suffer from insufficient reasoning capabilities. Human reasoning inherently relies on causal knowledge and thought, which are reflected in natural language. But in the AR paradigm, language is modeled as next token prediction (a strictly left-to-right, token-by-token order), whereas natural language itself exhibits more flexible causal structures. In the DLM paradigm, the attention mechanism is fully connected, which entirely disregards causal order. To fill this gap, we propose a \\textbf{C}ausal \\textbf{C}oncept-Guided \\textbf{D}iffusion \\textbf{L}anguage \\textbf{M}odel (C^2DLM). Starting from DLM's fully connected attention, C^2DLM first obtains a concept-level causal graph from the teacher model, and then explicitly guides attention to learn causal relationships between concepts. By focusing on causal relationships and avoiding interference from difficult subgoals involving causal inversion, C^2DLM improves 12\\% with about 3.2 times training speedup in the COT-OrderPerturb task, and achieves an average gain of 1.31\\% across six downstream reasoning tasks. More details in the repository ~https://github.com/Kairong-Han/C-2-DLM{here}.", "github_manual": "", "github_regex": "https://github.com/Kairong-Han/C-2-DLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.20344", "title": "The Curious Case of Analogies: Investigating Analogical Reasoning in Large Language Models", "authors": [ "Taewhoo Lee", "Minju Song", "Chanwoong Yoon", "Jungwoo Park", "Jaewoo Kang" ], "github": "", "project_page": "", "abstract": "Analogical reasoning is at the core of human cognition, serving as an important foundation for a variety of intellectual activities. While prior work has shown that LLMs can represent task patterns and surface-level concepts, it remains unclear whether these models can encode high-level relational concepts and apply them to novel situations through structured comparisons. In this work, we explore this fundamental aspect using proportional and story analogies, and identify three key findings. First, LLMs effectively encode the underlying relationships between analogous entities; both attributive and relational information propagate through mid-upper layers in correct cases, whereas reasoning failures reflect missing relational information within these layers. Second, unlike humans, LLMs often struggle not only when relational information is missing, but also when attempting to apply it to new entities. In such cases, strategically patching hidden representations at critical token positions can facilitate information transfer to a certain extent. Lastly, successful analogical reasoning in LLMs is marked by strong structural alignment between analogous situations, whereas failures often reflect degraded or misplaced alignment. Overall, our findings reveal that LLMs exhibit emerging but limited capabilities in encoding and applying high-level relational concepts, highlighting both parallels and gaps with human cognition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02942", "title": "Benchmarking Scientific Understanding and Reasoning for Video Generation using VideoScience-Bench", "authors": [ "Lanxiang Hu", "Abhilash Shankarampeta", "Yixin Huang", "Zilin Dai", "Haoyang Yu", "Yujie Zhao", "Haoqiang Kang", "Daniel Zhao", "Tajana Rosing", "Hao Zhang" ], "github": "https://github.com/hao-ai-lab/VideoScience", "project_page": "", "abstract": "The next frontier for video generation lies in developing models capable of zero-shot reasoning, where understanding real-world scientific laws is crucial for accurate physical outcome modeling under diverse conditions. However, existing video benchmarks are physical commonsense-based, offering limited insight into video models' scientific reasoning capability. We introduce VideoScience-Bench, a benchmark designed to evaluate undergraduate-level scientific understanding in video models. Each prompt encodes a composite scientific scenario that requires understanding and reasoning across multiple scientific concepts to generate the correct phenomenon. The benchmark comprises 200 carefully curated prompts spanning 14 topics and 103 concepts in physics and chemistry. We conduct expert-annotated evaluations across seven state-of-the-art video models in T2V and I2V settings along five dimensions: Prompt Consistency, Phenomenon Congruency, Correct Dynamism, Immutability, and Spatio-Temporal Continuity. Using a VLM-as-a-Judge to assess video generations, we observe strong correlation with human assessments. To the best of our knowledge, VideoScience-Bench is the first benchmark to evaluate video models not only as generators but also as reasoners, requiring their generations to demonstrate scientific understanding consistent with expected physical and chemical phenomena. Our data and evaluation code are available at: https://github.com/hao-ai-lab/VideoScience{github.com/hao-ai-lab/VideoScience}.", "github_manual": "", "github_regex": "https://github.com/hao-ai-lab/VideoScience", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02790", "title": "UnicEdit-10M: A Dataset and Benchmark Breaking the Scale-Quality Barrier via Unified Verification for Reasoning-Enriched Edits", "authors": [ "Keming Ye", "Zhipeng Huang", "Canmiao Fu", "Qingyang Liu", "Jiani Cai", "Zheqi Lv", "Chen Li", "Jing Lyu", "Zhou Zhao", "Shengyu Zhang" ], "github": "https://github.com/WeChatCV/UnicBench", "project_page": "", "abstract": "With the rapid advances of powerful multimodal models such as GPT-4o, Nano Banana, and Seedream 4.0 in Image Editing, the performance gap between closed-source and open-source models is widening, primarily due to the scarcity of large-scale, high-quality training data and comprehensive benchmarks capable of diagnosing model weaknesses across diverse editing behaviors. Existing data construction methods face a scale-quality trade-off: human annotations are high-quality but not scalable, while automated pipelines suffer from error propagation and noise. To address this, we introduce a lightweight data pipeline that replaces multi-toolchains with an end-to-end model and a unified post-verification stage. For scalable quality control, we train a 7B dual-task expert model, Qwen-Verify, for efficient failure detection and instruction recaptioning. This pipeline yields UnicEdit-10M, a 10M-scale dataset spanning diverse basic and complex editing tasks. We also propose UnicBench, a general benchmark that extends beyond basic edits to explicitly assess spatial and knowledge-driven reasoning. To enable fine-grained diagnosis, we introduce novel metrics, including Non-edit Consistency and Reasoning Accuracy. Our analysis of mainstream models on UnicBench reveals their limitations and provides clear directions for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WeChatCV/UnicBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.15948", "title": "Click2Graph: Interactive Panoptic Video Scene Graphs from a Single Click", "authors": [ "Raphael Ruschel", "Hardikkumar Prajapati", "Awsafur Rahman", "B. S. Manjunath" ], "github": "", "project_page": "", "abstract": "State-of-the-art Video Scene Graph Generation (VSGG) systems provide structured visual understanding but operate as closed, feed-forward pipelines with no ability to incorporate human guidance. In contrast, promptable segmentation models such as SAM2 enable precise user interaction but lack semantic or relational reasoning. We introduce Click2Graph, the first interactive framework for Panoptic Video Scene Graph Generation (PVSG) that unifies visual prompting with spatial, temporal, and semantic understanding. From a single user cue, such as a click or bounding box, Click2Graph segments and tracks the subject across time, autonomously discovers interacting objects, and predicts triplets to form a temporally consistent scene graph. Our framework introduces two key components: a Dynamic Interaction Discovery Module that generates subject-conditioned object prompts, and a Semantic Classification Head that performs joint entity and predicate reasoning. Experiments on the OpenPVSG benchmark demonstrate that Click2Graph establishes a strong foundation for user-guided PVSG, showing how human prompting can be combined with panoptic grounding and relational inference to enable controllable and interpretable video scene understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02395", "title": "Skywork-R1V4: Toward Agentic Multimodal Intelligence through Interleaved Thinking with Images and DeepResearch", "authors": [ "Yifan Zhang", "Liang Hu", "Haofeng Sun", "Peiyu Wang", "Yichen Wei", "Shukang Yin", "Jiangbo Pei", "Wei Shen", "Peng Xia", "Yi Peng", "Tianyidan Xie", "Eric Li", "Yang Liu", "Xuchen Song", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "Despite recent progress in multimodal agentic systems, existing approaches often treat image manipulation and web search as disjoint capabilities, rely heavily on costly reinforcement learning, and lack planning grounded in real tool-execution traces. To address these limitations, we present Skywork-R1V4, a 30B (A3B) parameter multimodal agentic model that unifies multimodal planning, active image manipulation (\"thinking with images\"), deep multimodal search, and, most critically, interleaved reasoning that dynamically alternates between visual operations and external knowledge retrieval. Trained solely via supervised fine-tuning on fewer than 30,000 high-quality, planning-execution-consistent trajectories and validated through stepwise consistency filtering, Skywork-R1V4 achieves state-of-the-art results across perception and multimodal search benchmarks: it scores 66.1 on MMSearch and 67.2 on FVQA, surpassing Gemini 2.5 Flash on all 11 metrics. Skywork-R1V4 exhibits emergent long-horizon reasoning at inference time, successfully orchestrating more than 10 tool calls to solve complex, multi-step tasks. Our results demonstrate that sophisticated agentic multimodal intelligence can be achieved through carefully curated supervised learning alone, without any reliance on reinforcement learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.01822", "title": "InnoGym: Benchmarking the Innovation Potential of AI Agents", "authors": [ "Jintian Zhang", "Kewei Xu", "Jingsheng Zheng", "Zhuoyun Yu", "Yuqi Zhu", "Yujie Luo", "Lanning Wei", "Shuofei Qiao", "Lun Du", "Da Zheng", "Shumin Deng", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/igym", "project_page": "", "abstract": "LLMs and Agents have achieved impressive progress in code generation, mathematical reasoning, and scientific discovery. However, existing benchmarks primarily measure correctness, overlooking the diversity of methods behind solutions. True innovation depends not only on producing correct answers but also on the originality of the approach. We present InnoGym, the first benchmark and framework designed to systematically evaluate the innovation potential of AI agents. InnoGym introduces two complementary metrics: performance gain, which measures improvement over the best-known solutions, and novelty, which captures methodological differences from prior approaches. The benchmark includes 18 carefully curated tasks from real-world engineering and scientific domains, each standardized through resource filtering, evaluator validation, and solution collection. In addition, we provide iGym, a unified execution environment for reproducible and long-horizon evaluations. Extensive experiments show that while some agents produce novel approaches, their lack of robustness limits performance gains. These results highlight a key gap between creativity and effectiveness, underscoring the need for benchmarks that evaluate both.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zjunlp/igym", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.21689", "title": "ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration", "authors": [ "Hongjin Su", "Shizhe Diao", "Ximing Lu", "Mingjie Liu", "Jiacheng Xu", "Xin Dong", "Yonggan Fu", "Peter Belcak", "Hanrong Ye", "Hongxu Yin", "Yi Dong", "Evelina Bakhturina", "Tao Yu", "Yejin Choi", "Jan Kautz", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "Large language models are powerful generalists, yet solving deep and complex problems such as those of the Humanity's Last Exam (HLE) remains both conceptually challenging and computationally expensive. We show that small orchestrators managing other models and a variety of tools can both push the upper bound of intelligence and improve efficiency in solving difficult agentic tasks. We introduce ToolOrchestra, a method for training small orchestrators that coordinate intelligent tools. ToolOrchestra explicitly uses reinforcement learning with outcome-, efficiency-, and user-preference-aware rewards. Using ToolOrchestra, we produce Orchestrator, an 8B model that achieves higher accuracy at lower cost than previous tool-use agents while aligning with user preferences on which tools are to be used for a given query. On HLE, Orchestrator achieves a score of 37.1%, outperforming GPT-5 (35.1%) while being 2.5x more efficient. On tau2-Bench and FRAMES, Orchestrator surpasses GPT-5 by a wide margin while using only about 30% of the cost. Extensive analysis shows that Orchestrator achieves the best trade-off between performance and cost under multiple metrics, and generalizes robustly to unseen tools. These results demonstrate that composing diverse tools with a lightweight orchestration model is both more efficient and more effective than existing methods, paving the way for practical and scalable tool-augmented reasoning systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.00956", "title": "WUSH: Near-Optimal Adaptive Transforms for LLM Quantization", "authors": [ "Jiale Chen", "Vage Egiazarian", "Torsten Hoefler", "Dan Alistarh" ], "github": "", "project_page": "", "abstract": "Quantization to low bitwidth is a standard approach for deploying large language models, however, a few extreme weights and activations stretch the dynamic range and reduce the effective resolution of the quantizer. A common mitigation approach is to apply some fixed orthogonal transforms, such as Hadamard matrices, before quantization, which typically reduces the dynamic range. Yet, these transforms ignore the statistics of the data, and their optimality is currently not understood. In this work, we derive, for the first time, closed-form optimal linear blockwise transforms for joint weight-activation quantization using standard data-free quantizers for common numerical formats. Specifically, we provide derivations of the optimal adaptive (data-aware) transforms for round-to-nearest (RTN), AbsMax-scaled block quantizers for both integer and floating-point formats. The resulting construction, which we call WUSH, combines a Hadamard backbone with a data-dependent component based on second-order moments, yielding a non-orthogonal transform that is provably optimal under mild assumptions and remains structured for efficient implementation. Preliminary experimental results show that our approach consistently improves upon the Hadamard transform for common formats.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02622", "title": "RULER-Bench: Probing Rule-based Reasoning Abilities of Next-level Video Generation Models for Vision Foundation Intelligence", "authors": [ "Xuming He", "Zehao Fan", "Hengjia Li", "Fan Zhuo", "Hankun Xu", "Senlin Cheng", "Di Weng", "Haifeng Liu", "Can Ye", "Boxi Wu" ], "github": "", "project_page": "", "abstract": "Recent advances in video generation have enabled the synthesis of videos with strong temporal consistency and impressive visual quality, marking a crucial step toward vision foundation models. To evaluate these video generation models, existing benchmarks primarily focus on factors related to visual perception and understanding, like visual aesthetics, instruction adherence, and temporal coherence. However, the rule-based reasoning capabilities of video generation models remain largely unexplored. Although recent studies have carried out preliminary explorations into whether video models can serve as zero-shot learners, they still lack a fine-grained decomposition of reasoning capabilities and a comprehensive evaluation protocol. To address this gap, we introduce RULER-Bench, a benchmark designed to evaluate the reasoning ability of video generation models from the perspective of cognitive rules. Built upon two fundamental paradigms: text-to-video and image-to-video, RULER-Bench covers 40 representative tasks spanning six rule categories with 622 high-quality annotated instances. For the evaluation of each generated video, we construct a checklist covering four metrics and leverage GPT-o3 to assign scores to each question, achieving 85% alignment with human judgements. Extensive experiments show that the state-of-the-art model achieves only 48.87% on the rule coherence metric, highlighting significant room for improvement in the reasoning capability of next-level video models. We expect that the insight obtained from RULER-Bench will facilitate further development of reasoning-aware video generation, advancing video generation models toward vision foundation intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02038", "title": "Deep Research: A Systematic Survey", "authors": [ "Zhengliang Shi", "Yiqun Chen", "Haitao Li", "Weiwei Sun", "Shiyu Ni", "Yougang Lyu", "Run-Ze Fan", "Bowen Jin", "Yixuan Weng", "Minjun Zhu", "Qiujie Xie", "Xinyu Guo", "Qu Yang", "Jiayi Wu", "Jujia Zhao", "Xiaqiang Tang", "Xinbei Ma", "Cunxiang Wang", "Jiaxin Mao", "Qingyao Ai", "Jen-Tse Huang", "Wenxuan Wang", "Yue Zhang", "Yiming Yang", "Zhaopeng Tu", "Zhaochun Ren" ], "github": "https://github.com/mangopy/Deep-Research-Survey", "project_page": "", "abstract": "Large language models (LLMs) have rapidly evolved from text generators into powerful problem solvers. Yet, many open tasks demand critical thinking, multi-source, and verifiable outputs, which are beyond single-shot prompting or standard retrieval-augmented generation. Recently, numerous studies have explored Deep Research (DR), which aims to combine the reasoning capabilities of LLMs with external tools, such as search engines, thereby empowering LLMs to act as research agents capable of completing complex, open-ended tasks. This survey presents a comprehensive and systematic overview of deep research systems, including a clear roadmap, foundational components, practical implementation techniques, important challenges, and future directions. Specifically, our main contributions are as follows: (i) we formalize a three-stage roadmap and distinguish deep research from related paradigms; (ii) we introduce four key components: query planning, information acquisition, memory management, and answer generation, each paired with fine-grained sub-taxonomies; (iii) we summarize optimization techniques, including prompting, supervised fine-tuning, and agentic reinforcement learning; and (iv) we consolidate evaluation criteria and open challenges, aiming to guide and facilitate future development. As the field of deep research continues to evolve rapidly, we are committed to continuously updating this survey to reflect the latest progress in this area.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mangopy/Deep-Research-Survey", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.03046", "title": "MagicQuillV2: Precise and Interactive Image Editing with Layered Visual Cues", "authors": [ "Zichen Liu", "Yue Yu", "Hao Ouyang", "Qiuyu Wang", "Shuailei Ma", "Ka Leong Cheng", "Wen Wang", "Qingyan Bai", "Yuxuan Zhang", "Yanhong Zeng", "Yixuan Li", "Xing Zhu", "Yujun Shen", "Qifeng Chen" ], "github": "https://github.com/zliucz/MagicQuillV2", "project_page": "", "abstract": "We propose MagicQuill V2, a novel system that introduces a layered composition paradigm to generative image editing, bridging the gap between the semantic power of diffusion models and the granular control of traditional graphics software. While diffusion transformers excel at holistic generation, their use of singular, monolithic prompts fails to disentangle distinct user intentions for content, position, and appearance. To overcome this, our method deconstructs creative intent into a stack of controllable visual cues: a content layer for what to create, a spatial layer for where to place it, a structural layer for how it is shaped, and a color layer for its palette. Our technical contributions include a specialized data generation pipeline for context-aware content integration, a unified control module to process all visual cues, and a fine-tuned spatial branch for precise local editing, including object removal. Extensive experiments validate that this layered approach effectively resolves the user intention gap, granting creators direct, intuitive control over the generative process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zliucz/MagicQuillV2", "github_api_added_by": "auto" }, { "date": "2025-12-03", "arxiv_id": "2512.02551", "title": "CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning", "authors": [ "Songqiao Su", "Xiaofei Sun", "Xiaoya Li", "Albert Wang", "Jiwei Li", "Chris Shum" ], "github": "", "project_page": "", "abstract": "In this paper, we propose CUDA-L2, a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multiply (HGEMM) CUDA kernels. Using CUDA execution speed as the RL reward, CUDA-L2 automatically optimizes HGEMM kernels across 1,000 configurations. CUDA-L2 systematically outperforms major matmul baselines to date, from the widely-used {\\it torch.matmul} to state-of-the-art Nvidia's closed-source libraries, i.e., {\\it cuBLAS}, {\\it cuBLASLt}. In offline mode, where kernels are executed consecutively without time intervals, CUDA-L2 yields +22.0\\% over {\\it torch.matmul} on average; +19.2\\% over {\\it cuBLAS} using the optimal layout configuration (normal-normal NN and transposed-normal TN); +16.8\\% over {\\it cuBLASLt-heuristic}, which queries {\\it cuBLASLt} library and selects the algorithm based on the heuristic's suggestion; and +11.4\\% over the most competitive {\\it cuBLASLt-AutoTuning} model, which selects the fastest algorithm from up to 100 candidates from {\\it cuBLASLt}'s suggestions. In server mode, where kernels are executed at random intervals simulating real-time inference, the speedups further increase to +28.7\\%, +26.0\\%, +22.4\\%, and +15.9\\% for {\\it torch.matmul}, {\\it cuBLAS}, {\\it cuBLASLt-heuristic}, and {\\it cuBLASLt-AutoTuning} respectively. CUDA-L2 shows that even the most performance-critical, heavily-optimized kernels like HGEMM can be improved through LLM-guided RL automation by systematically exploring configuration spaces at scales impractical for humans. Project and code can be found at github.com/deepreinforce-ai/CUDA-L2", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.01078", "title": "SimWorld: An Open-ended Realistic Simulator for Autonomous Agents in Physical and Social Worlds", "authors": [ "Jiawei Ren", "Yan Zhuang", "Xiaokang Ye", "Lingjun Mao", "Xuhong He", "Jianzhi Shen", "Mrinaal Dogra", "Yiming Liang", "Ruixuan Zhang", "Tianai Yue", "Yiqing Yang", "Eric Liu", "Ryan Wu", "Kevin Benavente", "Rajiv Mandya Nagaraju", "Muhammad Faayez", "Xiyan Zhang", "Dhruv Vivek Sharma", "Xianrui Zhong", "Ziqiao Ma", "Tianmin Shu", "Zhiting Hu", "Lianhui Qin" ], "github": "https://github.com/SimWorld-AI/SimWorld", "project_page": "", "abstract": "While LLM/VLM-powered AI agents have advanced rapidly in math, coding, and computer use, their applications in complex physical and social environments remain challenging. Building agents that can survive and thrive in the real world (for example, by autonomously earning income or running a business) requires massive-scale interaction, reasoning, training, and evaluation across diverse embodied scenarios. However, existing world simulators for such development fall short: they often rely on limited hand-crafted environments, simulate simplified game-like physics and social rules, and lack native support for LLM/VLM agents. We introduce SimWorld, a new simulator built on Unreal Engine 5, designed for developing and evaluating LLM/VLM agents in rich, real-world-like settings. SimWorld offers three core capabilities: (1) realistic, open-ended world simulation, including accurate physical and social dynamics and language-driven procedural environment generation; (2) a rich interface for LLM/VLM agents, with multimodal world inputs and open-vocabulary actions at varying levels of abstraction; and (3) diverse and extensible physical and social reasoning scenarios that are easily customizable by users. We demonstrate SimWorld by deploying frontier LLM agents (e.g., GPT-4o, Gemini-2.5-Flash, Claude-3.5, and DeepSeek-Prover-V2) on long-horizon multi-agent delivery tasks involving strategic cooperation and competition. The results reveal distinct reasoning patterns and limitations across models. We open-source SimWorld and hope it becomes a foundational platform for advancing real-world agent intelligence across disciplines: https://simworld.org.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SimWorld-AI/SimWorld", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.22982", "title": "Ovis-Image Technical Report", "authors": [ "Guo-Hua Wang", "Liangfu Cao", "Tianyu Cui", "Minghao Fu", "Xiaohao Chen", "Pengxin Zhan", "Jianshan Zhao", "Lan Li", "Bowen Fu", "Jiaqi Liu", "Qing-Guo Chen" ], "github": "", "project_page": "", "abstract": "We introduce Ovis-Image, a 7B text-to-image model specifically optimized for high-quality text rendering, designed to operate efficiently under stringent computational constraints. Built upon our previous Ovis-U1 framework, Ovis-Image integrates a diffusion-based visual decoder with the stronger Ovis 2.5 multimodal backbone, leveraging a text-centric training pipeline that combines large-scale pre-training with carefully tailored post-training refinements. Despite its compact architecture, Ovis-Image achieves text rendering performance on par with significantly larger open models such as Qwen-Image and approaches closed-source systems like Seedream and GPT4o. Crucially, the model remains deployable on a single high-end GPU with moderate memory, narrowing the gap between frontier-level text rendering and practical deployment. Our results indicate that combining a strong multimodal backbone with a carefully designed, text-focused training recipe is sufficient to achieve reliable bilingual text rendering without resorting to oversized or proprietary models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.03013", "title": "In-Context Sync-LoRA for Portrait Video Editing", "authors": [ "Sagi Polaczek", "Or Patashnik", "Ali Mahdavi-Amiri", "Daniel Cohen-Or" ], "github": "", "project_page": "", "abstract": "Editing portrait videos is a challenging task that requires flexible yet precise control over a wide range of modifications, such as appearance changes, expression edits, or the addition of objects. The key difficulty lies in preserving the subject's original temporal behavior, demanding that every edited frame remains precisely synchronized with the corresponding source frame. We present Sync-LoRA, a method for editing portrait videos that achieves high-quality visual modifications while maintaining frame-accurate synchronization and identity consistency. Our approach uses an image-to-video diffusion model, where the edit is defined by modifying the first frame and then propagated to the entire sequence. To enable accurate synchronization, we train an in-context LoRA using paired videos that depict identical motion trajectories but differ in appearance. These pairs are automatically generated and curated through a synchronization-based filtering process that selects only the most temporally aligned examples for training. This training setup teaches the model to combine motion cues from the source video with the visual changes introduced in the edited first frame. Trained on a compact, highly curated set of synchronized human portraits, Sync-LoRA generalizes to unseen identities and diverse edits (e.g., modifying appearance, adding objects, or changing backgrounds), robustly handling variations in pose and expression. Our results demonstrate high visual fidelity and strong temporal coherence, achieving a robust balance between edit fidelity and precise motion preservation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.21338", "title": "Masks Can Be Distracting: On Context Comprehension in Diffusion Language Models", "authors": [ "Julianna Piskorz", "Cristina Pinneri", "Alvaro Correia", "Motasem Alfarra", "Risheek Garrepalli", "Christos Louizos" ], "github": "", "project_page": "", "abstract": "Masked Diffusion Language Models (MDLMs) have recently emerged as a promising alternative to Autoregressive Language Models (ARLMs), leveraging a denoising objective that, in principle, should enable more uniform context utilisation. In this work, we examine the context comprehension abilities of MDLMs and uncover two key limitations. First, despite their more global training objective and bidirectional attention mechanism, similarly to ARLMS, MDLMs exhibit a strong locality bias: performance is highly sensitive to the position of relevant information within the input, favouring local over distant context. Second, we show that appending a large number of mask tokens--required for generation--can significantly degrade context comprehension. Through systematic ablations, we find that these masks act as distractors, reducing the model's ability to process relevant information. To address this, we introduce a mask-agnostic loss function that encourages predictions to remain invariant to the number of appended masks. Fine-tuning with this objective substantially mitigates the distracting effect of masks, improving robustness of MDLMs. Overall, our findings reveal critical limitations of the current MDLM training paradigm and provide actionable insights for building diffusion-based language models with stronger context comprehension.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.20645", "title": "PixelDiT: Pixel Diffusion Transformers for Image Generation", "authors": [ "Yongsheng Yu", "Wei Xiong", "Weili Nie", "Yichen Sheng", "Shiqiu Liu", "Jiebo Luo" ], "github": "", "project_page": "", "abstract": "Latent-space modeling has been the standard for Diffusion Transformers (DiTs). However, it relies on a two-stage pipeline where the pretrained autoencoder introduces lossy reconstruction, leading to error accumulation while hindering joint optimization. To address these issues, we propose PixelDiT, a single-stage, end-to-end model that eliminates the need for the autoencoder and learns the diffusion process directly in the pixel space. PixelDiT adopts a fully transformer-based architecture shaped by a dual-level design: a patch-level DiT that captures global semantics and a pixel-level DiT that refines texture details, enabling efficient training of a pixel-space diffusion model while preserving fine details. Our analysis reveals that effective pixel-level token modeling is essential to the success of pixel diffusion. PixelDiT achieves 1.61 FID on ImageNet 256x256, surpassing existing pixel generative models by a large margin. We further extend PixelDiT to text-to-image generation and pretrain it at the 1024x1024 resolution in pixel space. It achieves 0.74 on GenEval and 83.5 on DPG-bench, approaching the best latent diffusion models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.19661", "title": "CodeV: Code with Images for Faithful Visual Reasoning via Tool-Aware Policy Optimization", "authors": [ "Xinhai Hou", "Shaoyuan Xu", "Manan Biyani", "Mayan Li", "Jia Liu", "Todd C. Hollon", "Bryan Wang" ], "github": "", "project_page": "", "abstract": "Agentic vision-language models are increasingly trained to \"think with images\" by calling image operations. However, we show that high final-answer accuracy often hides unfaithful visual reasoning: models may invoke tools on irrelevant regions or ignore tool outputs entirely, yet still guess the correct answer. In this work, we first propose a faithfulness evaluation protocol that measures whether intermediate visual tool outputs (e.g., crops) actually contain the queried evidence. This reveals that recent visual agents achieve high final-answer accuracy but exhibit low rates of faithful tool-use on visual search benchmarks. We then introduce CodeV, a code-based visual agent trained with Tool-Aware Policy Optimization (TAPO). TAPO is a process-level RL framework that augments GRPO with dense rewards defined directly on visual tool inputs and outputs, rather than on chain-of-thought tokens, making supervision easier to verify and less susceptible to reward hacking. CodeV represents visual tools as executable Python code, and TAPO assigns step-wise rewards based solely on the question and tool output, encouraging both necessary and evidence-consistent tool use. In a two-stage SFT+RL pipeline, CodeV achieves competitive or superior accuracy while substantially increasing faithful tool-use rates on related visual search benchmarks. Beyond visual search, CodeV attains strong performance on a range of multimodal reasoning and math benchmarks, suggesting that explicitly supervising intermediate tool behavior is crucial for building trustworthy, agentic visual reasoning systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.01540", "title": "FlashVGGT: Efficient and Scalable Visual Geometry Transformers with Compressed Descriptor Attention", "authors": [ "Zipeng Wang", "Dan Xu" ], "github": "", "project_page": "", "abstract": "3D reconstruction from multi-view images is a core challenge in computer vision. Recently, feed-forward methods have emerged as efficient and robust alternatives to traditional per-scene optimization techniques. Among them, state-of-the-art models like the Visual Geometry Grounding Transformer (VGGT) leverage full self-attention over all image tokens to capture global relationships. However, this approach suffers from poor scalability due to the quadratic complexity of self-attention and the large number of tokens generated in long image sequences. In this work, we introduce FlashVGGT, an efficient alternative that addresses this bottleneck through a descriptor-based attention mechanism. Instead of applying dense global attention across all tokens, FlashVGGT compresses spatial information from each frame into a compact set of descriptor tokens. Global attention is then computed as cross-attention between the full set of image tokens and this smaller descriptor set, significantly reducing computational overhead. Moreover, the compactness of the descriptors enables online inference over long sequences via a chunk-recursive mechanism that reuses cached descriptors from previous chunks. Experimental results show that FlashVGGT achieves reconstruction accuracy competitive with VGGT while reducing inference time to just 9.3% of VGGT for 1,000 images, and scaling efficiently to sequences exceeding 3,000 images. Our project page is available at https://wzpscott.github.io/flashvggt_page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02351", "title": "Understanding and Harnessing Sparsity in Unified Multimodal Models", "authors": [ "Shwai He", "Chaorui Deng", "Ang Li", "Shen Yan" ], "github": "https://github.com/Shwai-He/SparseUnifiedModel", "project_page": "", "abstract": "Large multimodal models have achieved remarkable progress in both understanding and generation. Recent efforts pursue unified multimodal models that integrate heterogeneous components to support both capabilities within a single framework. However, such unification introduces inference inefficiencies, e.g., specific tasks or samples may not require the full knowledge or capacity of the unified model. Yet, a systematic understanding of how these inefficiencies manifest across different components remains limited. In this work, we first conduct a systematic analysis of unified multimodal model components using training-free pruning as a probing methodology, considering both depth pruning and width reduction. Our study reveals that the understanding component exhibits notable compressibility in both understanding and generation tasks, which is more pronounced in the latter. In contrast, the generation components are highly sensitive to compression, with performance deteriorating sharply even under moderate compression ratios. To address this limitation, we propose the Mixture-of-Experts (MoE) Adaptation, inspired by the dynamic activation patterns observed across different samples. This approach partitions the generation module into multiple experts and enables sparse activation to restore generation quality. We validate the effectiveness of sparse activation through expert-frozen tuning and further demonstrate that a fully trainable adaptation delivers additional gains. As a result, the adapted BAGEL model achieves performance comparable to the full model while activating only about half of its parameters. The code is released at https://github.com/Shwai-He/SparseUnifiedModel{this link}.", "github_manual": "", "github_regex": "https://github.com/Shwai-He/SparseUnifiedModel", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.00097", "title": "Gold-Medal-Level Olympiad Geometry Solving with Efficient Heuristic Auxiliary Constructions", "authors": [ "Boyan Duan", "Xiao Liang", "Shuai Lu", "Yaoxiang Wang", "Yelong Shen", "Kai-Wei Chang", "Ying Nian Wu", "Mao Yang", "Weizhu Chen", "Yeyun Gong" ], "github": "", "project_page": "", "abstract": "Automated theorem proving in Euclidean geometry, particularly for International Mathematical Olympiad (IMO) level problems, remains a major challenge and an important research focus in Artificial Intelligence. In this paper, we present a highly efficient method for geometry theorem proving that runs entirely on CPUs without relying on neural network-based inference. Our initial study shows that a simple random strategy for adding auxiliary points can achieve silver-medal level human performance on IMO. Building on this, we propose HAGeo, a Heuristic-based method for adding Auxiliary constructions in Geometric deduction that solves 28 of 30 problems on the IMO-30 benchmark, achieving gold-medal level performance and surpassing AlphaGeometry, a competitive neural network-based approach, by a notable margin. To evaluate our method and existing approaches more comprehensively, we further construct HAGeo-409, a benchmark consisting of 409 geometry problems with human-assessed difficulty levels. Compared with the widely used IMO-30, our benchmark poses greater challenges and provides a more precise evaluation, setting a higher bar for geometry theorem proving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02581", "title": "GoRL: An Algorithm-Agnostic Framework for Online Reinforcement Learning with Generative Policies", "authors": [ "Chubin Zhang", "Zhenglin Wan", "Feng Chen", "Xingrui Yu", "Ivor Tsang", "Bo An" ], "github": "https://github.com/bennidict23/GoRL", "project_page": "", "abstract": "Reinforcement learning (RL) faces a persistent tension: policies that are stable to optimize are often too simple to represent the multimodal action distributions needed for complex control. Gaussian policies provide tractable likelihoods and smooth gradients, but their unimodal form limits expressiveness. Conversely, generative policies based on diffusion or flow matching can model rich multimodal behaviors; however, in online RL, they are frequently unstable due to intractable likelihoods and noisy gradients propagating through deep sampling chains. We address this tension with a key structural principle: decoupling optimization from generation. Building on this insight, we introduce GoRL (Generative Online Reinforcement Learning), a framework that optimizes a tractable latent policy while utilizing a conditional generative decoder to synthesize actions. A two-timescale update schedule enables the latent policy to learn stably while the decoder steadily increases expressiveness, without requiring tractable action likelihoods. Across a range of continuous-control tasks, GoRL consistently outperforms both Gaussian policies and recent generative-policy baselines. Notably, on the HopperStand task, it reaches a normalized return above 870, more than 3 times that of the strongest baseline. These results demonstrate that separating optimization from generation provides a practical path to policies that are both stable and highly expressive.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bennidict23/GoRL", "github_api_added_by": "auto" }, { "date": "2025-12-03", "arxiv_id": "2512.02017", "title": "Visual Sync: Multi-Camera Synchronization via Cross-View Object Motion", "authors": [ "Shaowei Liu", "David Yifan Yao", "Saurabh Gupta", "Shenlong Wang" ], "github": "", "project_page": "", "abstract": "Today, people can easily record memorable moments, ranging from concerts, sports events, lectures, family gatherings, and birthday parties with multiple consumer cameras. However, synchronizing these cross-camera streams remains challenging. Existing methods assume controlled settings, specific targets, manual correction, or costly hardware. We present VisualSync, an optimization framework based on multi-view dynamics that aligns unposed, unsynchronized videos at millisecond accuracy. Our key insight is that any moving 3D point, when co-visible in two cameras, obeys epipolar constraints once properly synchronized. To exploit this, VisualSync leverages off-the-shelf 3D reconstruction, feature matching, and dense tracking to extract tracklets, relative poses, and cross-view correspondences. It then jointly minimizes the epipolar error to estimate each camera's time offset. Experiments on four diverse, challenging datasets show that VisualSync outperforms baseline methods, achieving an median synchronization error below 50 ms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2511.18685", "title": "Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied Agents", "authors": [ "Dayong Liu", "Chao Xu", "Weihong Chen", "Suyu Zhang", "Juncheng Wang", "Jiankang Deng", "Baigui Sun", "Yang Liu" ], "github": "https://github.com/CFG-Bench/CFG-Bench", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) show promising results as decision-making engines for embodied agents operating in complex, physical environments. However, existing benchmarks often prioritize high-level planning or spatial reasoning, leaving the fine-grained action intelligence required for embodied physical interaction underexplored. To address this gap, we introduce CFG-Bench, a new benchmark designed to systematically evaluate this crucial capability. CFG-Bench consists of 1,368 curated videos paired with 19,562 three-modalities question-answer pairs targeting four cognitive abilities: 1) Physical Interaction, 2) Temporal-Causal Relation, 3) Intentional Understanding, and 4) Evaluative Judgment. Together, these dimensions provide a systematic framework for assessing a model's ability to translate visual observations into actionable knowledge, moving beyond mere surface-level recognition. Our comprehensive evaluation on CFG-Bench reveals that leading MLLMs struggle to produce detailed instructions for physical interactions and exhibit profound limitations in the higher-order reasoning of intention and evaluation. Moreover, supervised fine-tuning (SFT) on our data demonstrates that teaching an MLLMs to articulate fine-grained actions directly translates to significant performance gains on established embodied benchmarks. Our analysis highlights these limitations and offers insights for developing more capable and grounded embodied agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CFG-Bench/CFG-Bench", "github_api_added_by": "auto" }, { "date": "2025-12-03", "arxiv_id": "2511.22184", "title": "Shoe Style-Invariant and Ground-Aware Learning for Dense Foot Contact Estimation", "authors": [ "Daniel Sungho Jung", "Kyoung Mu Lee" ], "github": "", "project_page": "", "abstract": "Foot contact plays a critical role in human interaction with the world, and thus exploring foot contact can advance our understanding of human movement and physical interaction. Despite its importance, existing methods often approximate foot contact using a zero-velocity constraint and focus on joint-level contact, failing to capture the detailed interaction between the foot and the world. Dense estimation of foot contact is crucial for accurately modeling this interaction, yet predicting dense foot contact from a single RGB image remains largely underexplored. There are two main challenges for learning dense foot contact estimation. First, shoes exhibit highly diverse appearances, making it difficult for models to generalize across different styles. Second, ground often has a monotonous appearance, making it difficult to extract informative features. To tackle these issues, we present a FEet COntact estimation (FECO) framework that learns dense foot contact with shoe style-invariant and ground-aware learning. To overcome the challenge of shoe appearance diversity, our approach incorporates shoe style adversarial training that enforces shoe style-invariant features for contact estimation. To effectively utilize ground information, we introduce a ground feature extractor that captures ground properties based on spatial context. As a result, our proposed method achieves robust foot contact estimation regardless of shoe appearance and effectively leverages ground information. Code will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-03", "arxiv_id": "2512.02817", "title": "BOOM: Beyond Only One Modality KIT's Multimodal Multilingual Lecture Companion", "authors": [ "Sai Koneru", "Fabian Retkowski", "Christian Huber", "Lukas Hilgert", "Seymanur Akti", "Enes Yavuz Ugan", "Alexander Waibel", "Jan Niehues" ], "github": "https://github.com/saikoneru/image-translator", "project_page": "", "abstract": "The globalization of education and rapid growth of online learning have made localizing educational content a critical challenge. Lecture materials are inherently multimodal, combining spoken audio with visual slides, which requires systems capable of processing multiple input modalities. To provide an accessible and complete learning experience, translations must preserve all modalities: text for reading, slides for visual understanding, and speech for auditory learning. We present BOOM, a multimodal multilingual lecture companion that jointly translates lecture audio and slides to produce synchronized outputs across three modalities: translated text, localized slides with preserved visual elements, and synthesized speech. This end-to-end approach enables students to access lectures in their native language while aiming to preserve the original content in its entirety. Our experiments demonstrate that slide-aware transcripts also yield cascading benefits for downstream tasks such as summarization and question answering. We release our Slide Translation code at https://github.com/saikoneru/image-translator and integrate it in Lecture Translator at https://gitlab.kit.edu/kit/isl-ai4lt/lt-middleware/ltpipeline}\\footnote{All released code and models are licensed under the MIT License.", "github_manual": "", "github_regex": "https://github.com/saikoneru/image-translator", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2511.21631", "title": "Qwen3-VL Technical Report", "authors": [ "Shuai Bai", "Yuxuan Cai", "Ruizhe Chen", "Keqin Chen", "Xionghui Chen", "Zesen Cheng", "Lianghao Deng", "Wei Ding", "Chang Gao", "Chunjiang Ge", "Wenbin Ge", "Zhifang Guo", "Qidong Huang", "Jie Huang", "Fei Huang", "Binyuan Hui", "Shutong Jiang", "Zhaohai Li", "Mingsheng Li", "Mei Li", "Kaixin Li", "Zicheng Lin", "Junyang Lin", "Xuejing Liu", "Jiawei Liu", "Chenglong Liu", "Yang Liu", "Dayiheng Liu", "Shixuan Liu", "Dunjie Lu", "Ruilin Luo", "Chenxu Lv", "Rui Men", "Lingchen Meng", "Xuancheng Ren", "Xingzhang Ren", "Sibo Song", "Yuchong Sun", "Jun Tang", "Jianhong Tu", "Jianqiang Wan", "Peng Wang", "Pengfei Wang", "Qiuyue Wang", "Yuxuan Wang", "Tianbao Xie", "Yiheng Xu", "Haiyang Xu", "Jin Xu", "Zhibo Yang", "Mingkun Yang", "Jianxin Yang", "An Yang", "Bowen Yu", "Fei Zhang", "Hang Zhang", "Xi Zhang", "Bo Zheng", "Humen Zhong", "Jingren Zhou", "Fan Zhou", "Jing Zhou", "Yuanzhi Zhu", "Ke Zhu" ], "github": "https://github.com/QwenLM/Qwen3-VL", "project_page": "", "abstract": "We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks. It natively supports interleaved contexts of up to 256K tokens, seamlessly integrating text, images, and video. The model family includes both dense (2B/4B/8B/32B) and mixture-of-experts (30B-A3B/235B-A22B) variants to accommodate diverse latency-quality trade-offs. Qwen3-VL delivers three core pillars: (i) markedly stronger pure-text understanding, surpassing comparable text-only backbones in several cases; (ii) robust long-context comprehension with a native 256K-token window for both text and interleaved multimodal inputs, enabling faithful retention, retrieval, and cross-referencing across long documents and videos; and (iii) advanced multimodal reasoning across single-image, multi-image, and video tasks, demonstrating leading performance on comprehensive evaluations such as MMMU and visual-math benchmarks (e.g., MathVista and MathVision). Architecturally, we introduce three key upgrades: (i) an enhanced interleaved-MRoPE for stronger spatial-temporal modeling across images and video; (ii) DeepStack integration, which effectively leverages multi-level ViT features to tighten vision-language alignment; and (iii) text-based time alignment for video, evolving from T-RoPE to explicit textual timestamp alignment for more precise temporal grounding. Under comparable token budgets and latency constraints, Qwen3-VL achieves superior performance in both dense and Mixture-of-Experts (MoE) architectures. We envision Qwen3-VL serving as a foundational engine for image-grounded reasoning, agentic decision-making, and multimodal code intelligence in real-world workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/QwenLM/Qwen3-VL", "github_api_added_by": "auto" }, { "date": "2025-12-04", "arxiv_id": "2512.02834", "title": "Steering Vision-Language-Action Models as Anti-Exploration: A Test-Time Scaling Approach", "authors": [ "Siyuan Yang", "Yang Zhang", "Haoran He", "Ling Pan", "Xiu Li", "Chenjia Bai", "Xuelong Li" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models, trained via flow-matching or diffusion objectives, excel at learning complex behaviors from large-scale, multi-modal datasets (e.g., human teleoperation, scripted policies). However, since VLAs incorporate diverse data modes in the pre-training stage, and the finetuning dataset often contains demonstration data collected in a kinematically suboptimal or undesirable way, it exists redundant action modes that are irrelevant to the success action modes of the downstream task. Specifically, we observe a critical inference-time fragility among various sampled noises after supervised finetuning of pre-trained VLAs. In this paper, we attribute this instability to the distribution shift between the VLA policy and the policy induced by stable success modes of the downstream task dataset. Thus, we propose TACO, a test-time-scaling (TTS) framework that applies a lightweight pseudo-count estimator as a high-fidelity verifier of action chunks. The VLA models integrated with TACO can execute the actions with maximum pseudo-count from all sampled action chunks, thereby preventing distribution shifts while preserving the generalization ability of VLAs since the constraint is applied only during inference. Our method resembles the classical anti-exploration principle in offline reinforcement learning (RL), and being gradient-free, it incurs significant computational benefits compared to RL update, especially for flow or diffusion-based VLAs which are difficult to perform RL update due to denoising process. Extensive experiments across four simulation benchmarks (RoboTwin2.0, Robotwin, LIBERO, SimplerEnv) and a dual-arm platform demonstrate that our method significantly improves the inference stability and success rates in downstream-task adaptations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.03442", "title": "PretrainZero: Reinforcement Active Pretraining", "authors": [ "Xingrun Xing", "Zhiyuan Fan", "Jie Lou", "Guoqi Li", "Jiajun Zhang", "Debing Zhang" ], "github": "", "project_page": "", "abstract": "Mimicking human behavior to actively learning from general experience and achieve artificial general intelligence has always been a human dream. Recent reinforcement learning (RL) based large-thinking models demonstrate impressive expert-level abilities, i.e., software and math, but still rely heavily on verifiable rewards in specific domains, placing a significant bottleneck to extend the performance boundary of general reasoning capabilities. In this work, we propose PretrainZero, a reinforcement active learning framework built on the pretraining corpus to extend RL from domain-specific post-training to general pretraining. PretrainZero features the following characteristics: 1) Active pretraining: inspired by the active learning ability of humans, PretrainZero learns a unified reasoning policy to actively identify reasonable and informative contents from pretraining corpus, and reason to predict these contents by RL. 2) Self-supervised learning: without any verifiable labels, pretrained reward models, or supervised fine-tuning, we directly pretrain reasoners from 3 to 30B base models on the general Wikipedia corpus using RL, significantly breaking the verification data-wall for general reasoning. 3) Verification scaling: by tackling increasingly challenging masked spans, PretrainZero substantially enhances the general reasoning abilities of pretrained base models. In reinforcement pretraining, PretrainZero improves Qwen3-4B-Base for 8.43, 5.96 and 10.60 on MMLU-Pro, SuperGPQA and math average benchmarks. In post-training, the pretrained models can also serve as reasoning foundation models for downstream RLVR tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.03405", "title": "ViDiC: Video Difference Captioning", "authors": [ "Jiangtao Wu", "Shihao Li", "Zhaozhou Bian", "Yuanxing Zhang", "Jialu Chen", "Runzhe Wen", "An Ping", "Yiwen He", "Jiakai Wang", "Jiaheng Liu" ], "github": "https://github.com/NJU-LINK/ViDiC-1K", "project_page": "", "abstract": "Understanding visual differences between dynamic scenes requires the comparative perception of compositional, spatial, and temporal changes--a capability that remains underexplored in existing vision-language systems. While prior work on Image Difference Captioning (IDC) has enabled models to describe semantic changes between static images, these approaches fail to capture motion continuity, event evolution, or editing consistency over time. We introduce the ViDiC (Video Difference Captioning) task and its corresponding ViDiC-1K dataset, designed to evaluate the ability of Multimodal Large Language Models (MLLMs) to provide fine-grained descriptions of similarities and differences between video pairs. ViDiC-1K comprises 1,000 curated video pairs annotated with over 4,000 comparative checklist items, covering seven categories: subject, style, background, cinematography, motion, location, and playback techniques. To ensure reliable evaluation, we propose a dual-checklist framework that measures the accuracy of similarity and difference separately, based on the LLM-as-a-Judge protocol. Experiments on nineteen representative multimodal models reveal a significant performance gap in their comparative description and difference perception abilities. We hope ViDiC-1K can be a challenging benchmark that lays a solid foundation for advancing video understanding, edit awareness, and comparative reasoning in multimodal intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NJU-LINK/ViDiC-1K", "github_api_added_by": "auto" }, { "date": "2025-12-04", "arxiv_id": "2512.03043", "title": "OneThinker: All-in-one Reasoning Model for Image and Video", "authors": [ "Kaituo Feng", "Manyuan Zhang", "Hongyu Li", "Kaixuan Fan", "Shuang Chen", "Yilei Jiang", "Dian Zheng", "Peiwen Sun", "Yiyuan Zhang", "Haoze Sun", "Yan Feng", "Peng Pei", "Xunliang Cai", "Xiangyu Yue" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has recently achieved remarkable success in eliciting visual reasoning within Multimodal Large Language Models (MLLMs). However, existing approaches typically train separate models for different tasks and treat image and video reasoning as disjoint domains. This results in limited scalability toward a multimodal reasoning generalist, which restricts practical versatility and hinders potential knowledge sharing across tasks and modalities. To this end, we propose OneThinker, an all-in-one reasoning model that unifies image and video understanding across diverse fundamental visual tasks, including question answering, captioning, spatial and temporal grounding, tracking, and segmentation. To achieve this, we construct the OneThinker-600k training corpus covering all these tasks and employ commercial models for CoT annotation, resulting in OneThinker-SFT-340k for SFT cold start. Furthermore, we propose EMA-GRPO to handle reward heterogeneity in multi-task RL by tracking task-wise moving averages of reward standard deviations for balanced optimization. Extensive experiments on diverse visual benchmarks show that OneThinker delivers strong performance on 31 benchmarks, across 10 fundamental visual understanding tasks. Moreover, it exhibits effective knowledge transfer between certain tasks and preliminary zero-shot generalization ability, marking a step toward a unified multimodal reasoning generalist. All code, model, and data are released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.03534", "title": "Rethinking Prompt Design for Inference-time Scaling in Text-to-Visual Generation", "authors": [ "Subin Kim", "Sangwoo Mo", "Mamshad Nayeem Rizve", "Yiran Xu", "Difan Liu", "Jinwoo Shin", "Tobias Hinz" ], "github": "", "project_page": "", "abstract": "Achieving precise alignment between user intent and generated visuals remains a central challenge in text-to-visual generation, as a single attempt often fails to produce the desired output. To handle this, prior approaches mainly scale the visual generation process (e.g., increasing sampling steps or seeds), but this quickly leads to a quality plateau. This limitation arises because the prompt, crucial for guiding generation, is kept fixed. To address this, we propose Prompt Redesign for Inference-time Scaling, coined PRIS, a framework that adaptively revises the prompt during inference in response to the scaled visual generations. The core idea of PRIS is to review the generated visuals, identify recurring failure patterns across visuals, and redesign the prompt accordingly before regenerating the visuals with the revised prompt. To provide precise alignment feedback for prompt revision, we introduce a new verifier, element-level factual correction, which evaluates the alignment between prompt attributes and generated visuals at a fine-grained level, achieving more accurate and interpretable assessments than holistic measures. Extensive experiments on both text-to-image and text-to-video benchmarks demonstrate the effectiveness of our approach, including a 15% gain on VBench 2.0. These results highlight that jointly scaling prompts and visuals is key to fully leveraging scaling laws at inference-time. Visualizations are available at the website: https://subin-kim-cv.github.io/PRIS.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.04069", "title": "SpaceTools: Tool-Augmented Spatial Reasoning via Double Interactive RL", "authors": [ "Siyi Chen", "Mikaela Angelina Uy", "Chan Hee Song", "Faisal Ladhak", "Adithyavairavan Murali", "Qing Qu", "Stan Birchfield", "Valts Blukis", "Jonathan Tremblay" ], "github": "", "project_page": "", "abstract": "Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required for embodied applications. The agentic paradigm promises that VLMs can use a wide variety of tools that could augment these capabilities, such as depth estimators, segmentation models, and pose estimators. Yet it remains an open challenge how to realize this vision without solely relying on handcrafted prompting strategies or enforcing fixed, predefined tool pipelines that limit VLMs' ability to discover optimal tool-use patterns. Reinforcement Learning could overcome this gap, but has so far been limited to reasoning with a single visual tool due to the large search space in multi-tool reasoning. We introduce Double Interactive Reinforcement Learning (DIRL), a two-phase training framework where VLMs learn to coordinate multiple tools through interactive exploration and feedback. In the teaching phase, we combine demonstrations from a single tool specialist trained via interactive RL with traces from a frontier model using all tools. In the exploration phase, the model further refines multi-tool coordination through continued RL. Our model, SpaceTools, with tool-augmented spatial reasoning ability, achieves state-of-the-art performance on spatial understanding benchmarks (RoboSpatial-Home, BLINK, BOP-ASK) and demonstrates reliable real-world manipulation using a 7-DOF robot as a tool. DIRL provides substantial improvements over the vanilla SFT (+12% on RoboSpatial) and RL (+16% on RoboSpatial) baselines. Project page: https://spacetools.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.03746", "title": "Thinking with Programming Vision: Towards a Unified View for Thinking with Images", "authors": [ "Zirun Guo", "Minjie Hong", "Feng Zhang", "Kai Jia", "Tao Jin" ], "github": "https://github.com/ByteDance-BandAI/CodeVision", "project_page": "", "abstract": "Multimodal large language models (MLLMs) that think with images can interactively use tools to reason about visual inputs, but current approaches often rely on a narrow set of tools with limited real-world necessity and scalability. In this work, we first reveal a critical and previously overlooked weakness: even state-of-the-art MLLMs are surprisingly brittle, showing significant performance degradation on images with simple orientation changes or natural corruptions, underscoring the need for more robust tool-based reasoning. To address this, we propose CodeVision, a flexible and scalable code-as-tool framework where the model generates code as a universal interface to invoke any image operation, moving beyond fixed tool registries. We train our model using a two-stage methodology, beginning with Supervised Fine-Tuning (SFT) on a high-quality dataset curated for complex, multi-turn tool composition and error recovery, followed by Reinforcement Learning (RL) with a novel and dense process reward function to encourage strategic and efficient tool use. To facilitate this research, we construct new SFT and RL datasets and introduce a challenging new benchmark suite designed to rigorously evaluate robustness to orientation changes and multi-tool reasoning. Experiments on Qwen2.5-VL and Qwen3-VL series show that our approach significantly improves model performance and fosters emergent capabilities such as flexible tool composition, efficient chained execution, and robust error recovery from runtime feedback. Code is available at https://github.com/ByteDance-BandAI/CodeVision.", "github_manual": "", "github_regex": "https://github.com/ByteDance-BandAI/CodeVision", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.04040", "title": "RELIC: Interactive Video World Model with Long-Horizon Memory", "authors": [ "Yicong Hong", "Yiqun Mei", "Chongjian Ge", "Yiran Xu", "Yang Zhou", "Sai Bi", "Yannick Hold-Geoffroy", "Mike Roberts", "Matthew Fisher", "Eli Shechtman", "Kalyan Sunkavalli", "Feng Liu", "Zhengqi Li", "Hao Tan" ], "github": "", "project_page": "", "abstract": "A truly interactive world model requires three key ingredients: real-time long-horizon streaming, consistent spatial memory, and precise user control. However, most existing approaches address only one of these aspects in isolation, as achieving all three simultaneously is highly challenging-for example, long-term memory mechanisms often degrade real-time performance. In this work, we present RELIC, a unified framework that tackles these three challenges altogether. Given a single image and a text description, RELIC enables memory-aware, long-duration exploration of arbitrary scenes in real time. Built upon recent autoregressive video-diffusion distillation techniques, our model represents long-horizon memory using highly compressed historical latent tokens encoded with both relative actions and absolute camera poses within the KV cache. This compact, camera-aware memory structure supports implicit 3D-consistent content retrieval and enforces long-term coherence with minimal computational overhead. In parallel, we fine-tune a bidirectional teacher video model to generate sequences beyond its original 5-second training horizon, and transform it into a causal student generator using a new memory-efficient self-forcing paradigm that enables full-context distillation over long-duration teacher as well as long student self-rollouts. Implemented as a 14B-parameter model and trained on a curated Unreal Engine-rendered dataset, RELIC achieves real-time generation at 16 FPS while demonstrating more accurate action following, more stable long-horizon streaming, and more robust spatial-memory retrieval compared with prior work. These capabilities establish RELIC as a strong foundation for the next generation of interactive world modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2511.22345", "title": "Flowing Backwards: Improving Normalizing Flows via Reverse Representation Alignment", "authors": [ "Yang Chen", "Xiaowei Xu", "Shuai Wang", "Chenhui Zhu", "Ruxue Wen", "Xubin Li", "Tiezheng Ge", "Limin Wang" ], "github": "https://github.com/MCG-NJU/FlowBack", "project_page": "", "abstract": "Normalizing Flows (NFs) are a class of generative models distinguished by a mathematically invertible architecture, where the forward pass transforms data into a latent space for density estimation, and the reverse pass generates new samples from this space. This characteristic creates an intrinsic synergy between representation learning and data generation. However, the generative quality of standard NFs is limited by poor semantic representations from log-likelihood optimization. To remedy this, we propose a novel alignment strategy that creatively leverages the invertibility of NFs: instead of regularizing the forward pass, we align the intermediate features of the generative (reverse) pass with representations from a powerful vision foundation model, demonstrating superior effectiveness over naive alignment. We also introduce a novel training-free, test-time optimization algorithm for classification, which provides a more intrinsic evaluation of the NF's embedded semantic knowledge. Comprehensive experiments demonstrate that our approach accelerates the training of NFs by over 3.3times, while simultaneously delivering significant improvements in both generative quality and classification accuracy. New state-of-the-art results for NFs are established on ImageNet 64times64 and 256times256. Our code is available at https://github.com/MCG-NJU/FlowBack.", "github_manual": "", "github_regex": "https://github.com/MCG-NJU/FlowBack", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.03540", "title": "CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generation", "authors": [ "Ruoxuan Zhang", "Bin Wen", "Hongxia Xie", "Yi Yao", "Songhan Zuo", "Jian-Yu Jiang-Lin", "Hong-Han Shuai", "Wen-Huang Cheng" ], "github": "", "project_page": "", "abstract": "Cooking is a sequential and visually grounded activity, where each step such as chopping, mixing, or frying carries both procedural logic and visual semantics. While recent diffusion models have shown strong capabilities in text-to-image generation, they struggle to handle structured multi-step scenarios like recipe illustration. Additionally, current recipe illustration methods are unable to adjust to the natural variability in recipe length, generating a fixed number of images regardless of the actual instructions structure. To address these limitations, we present CookAnything, a flexible and consistent diffusion-based framework that generates coherent, semantically distinct image sequences from textual cooking instructions of arbitrary length. The framework introduces three key components: (1) Step-wise Regional Control (SRC), which aligns textual steps with corresponding image regions within a single denoising process; (2) Flexible RoPE, a step-aware positional encoding mechanism that enhances both temporal coherence and spatial diversity; and (3) Cross-Step Consistency Control (CSCC), which maintains fine-grained ingredient consistency across steps. Experimental results on recipe illustration benchmarks show that CookAnything performs better than existing methods in training-based and training-free settings. The proposed framework supports scalable, high-quality visual synthesis of complex multi-step instructions and holds significant potential for broad applications in instructional media, and procedural content creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.02807", "title": "SR-GRPO: Stable Rank as an Intrinsic Geometric Reward for Large Language Model Alignment", "authors": [ "Yixuan Tang", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Aligning Large Language Models (LLMs) with human preferences typically relies on external supervision, which faces critical limitations: human annotations are scarce and subjective, reward models are vulnerable to reward hacking, and self-evaluation methods suffer from prompt sensitivity and biases. In this work, we propose stable rank, an intrinsic, annotation-free quality signal derived from model representations. Stable rank measures the effective dimensionality of hidden states by computing the ratio of total variance to dominant-direction variance, capturing quality through how information distributes across representation dimensions. Empirically, stable rank achieves 84.04% accuracy on RewardBench and improves task accuracy by an average of 11.3 percentage points over greedy decoding via Best-of-N sampling. Leveraging this insight, we introduce Stable Rank Group Relative Policy Optimization (SR-GRPO), which uses stable rank as a reward signal for reinforcement learning. Without external supervision, SR-GRPO improves Qwen2.5-1.5B-Instruct by 10% on STEM and 19% on mathematical reasoning, outperforming both learned reward models and self-evaluation baselines. Our findings demonstrate that quality signals can be extracted from internal model geometry, offering a path toward scalable alignment without external supervision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.03073", "title": "Economies of Open Intelligence: Tracing Power & Participation in the Model Ecosystem", "authors": [ "Shayne Longpre", "Christopher Akiki", "Campbell Lund", "Atharva Kulkarni", "Emily Chen", "Irene Solaiman", "Avijit Ghosh", "Yacine Jernite", "Lucie-Aim\u00e9e Kaffee" ], "github": "", "project_page": "", "abstract": "Since 2019, the Hugging Face Model Hub has been the primary global platform for sharing open weight AI models. By releasing a dataset of the complete history of weekly model downloads (June 2020-August 2025) alongside model metadata, we provide the most rigorous examination to-date of concentration dynamics and evolving characteristics in the open model economy. Our analysis spans 851,000 models, over 200 aggregated attributes per model, and 2.2B downloads. We document a fundamental rebalancing of economic power: US open-weight industry dominance by Google, Meta, and OpenAI has declined sharply in favor of unaffiliated developers, community organizations, and, as of 2025, Chinese industry, with DeepSeek and Qwen models potentially heralding a new consolidation of market power. We identify statistically significant shifts in model properties, a 17X increase in average model size, rapid growth in multimodal generation (3.4X), quantization (5X), and mixture-of-experts architectures (7X), alongside concerning declines in data transparency, with open weights models surpassing truly open source models for the first time in 2025. We expose a new layer of developer intermediaries that has emerged, focused on quantizing and adapting base models for both efficiency and artistic expression. To enable continued research and oversight, we release the complete dataset with an interactive dashboard for real-time monitoring of concentration dynamics and evolving properties in the open model economy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2511.20515", "title": "AlignBench: Benchmarking Fine-Grained Image-Text Alignment with Synthetic Image-Caption Pairs", "authors": [ "Kuniaki Saito", "Risa Shinoda", "Shohei Tanaka", "Tosho Hirasawa", "Fumio Okura", "Yoshitaka Ushiku" ], "github": "", "project_page": "", "abstract": "Assessing image-text alignment models such as CLIP is crucial for bridging visual and linguistic representations. Yet existing benchmarks rely on rule-based perturbations or short captions, limiting their ability to measure fine-grained alignment. We introduce AlignBench, a benchmark that provides a new indicator of image-text alignment by evaluating detailed image-caption pairs generated by diverse image-to-text and text-to-image models. Each sentence is annotated for correctness, enabling direct assessment of VLMs as alignment evaluators. Benchmarking a wide range of decoder-based VLMs reveals three key findings: (i) CLIP-based models, even those tailored for compositional reasoning, remain nearly blind; (ii) detectors systematically over-score early sentences; and (iii) they show strong self-preference, favoring their own outputs and harming detection performance. Our project page will be available at https://dahlian00.github.io/AlignBench/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.04072", "title": "SkillFactory: Self-Distillation For Learning Cognitive Behaviors", "authors": [ "Zayne Sprague", "Jack Lu", "Manya Wadhwa", "Sedrick Keh", "Mengye Ren", "Greg Durrett" ], "github": "https://github.com/Zayne-sprague/SkillFactory", "project_page": "", "abstract": "Reasoning models leveraging long chains of thought employ various cognitive skills, such as verification of their answers, backtracking, retrying by an alternate method, and more. Previous work has shown that when a base language model exhibits these skills, training that model further with reinforcement learning (RL) can learn to leverage them. How can we get models to leverage skills that aren't exhibited by base models? Our work, SkillFactory, is a method for fine-tuning models to roughly learn these skills during a supervised fine-tuning (SFT) stage prior to RL. Our approach does not rely on distillation from a stronger model, but instead uses samples from the model itself, rearranged to provide training data in the format of those skills. These \"silver\" SFT traces may be imperfect, but are nevertheless effective for priming a model to acquire skills during RL. Our evaluation shows that (1) starting from SkillFactory SFT initialization helps a model to generalize to harder variants of a task post-RL, despite lower performance pre-RL; (2) cognitive skills are indeed used by the model; (3) RLed SkillFactory models are more robust to regression on out-of-domain tasks than RLed base models. Our work suggests that inductive biases learned prior to RL help models learn robust cognitive skill use.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Zayne-sprague/SkillFactory", "github_api_added_by": "auto" }, { "date": "2025-12-04", "arxiv_id": "2512.04032", "title": "Jina-VLM: Small Multilingual Vision Language Model", "authors": [ "Andreas Koukounas", "Georgios Mastrapas", "Florian H\u00f6nicke", "Sedigheh Eslami", "Guillaume Roncari", "Scott Martens", "Han Xiao" ], "github": "", "project_page": "", "abstract": "We present Jina-VLM, a 2.4B parameter vision-language model that achieves state-of-the-art multilingual visual question answering among open 2B-scale VLMs. The model couples a SigLIP2 vision encoder with a Qwen3 language backbone through an attention-pooling connector that enables token-efficient processing of arbitrary-resolution images. Across standard VQA benchmarks and multilingual evaluations, Jina-VLM outperforms comparable models while preserving competitive text-only performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.03383", "title": "UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs", "authors": [ "Hung-Yueh Chiang", "Chi-Chih Chang", "Yu-Chen Lu", "Chien-Yu Lin", "Kai-Chiang Wu", "Mohamed S. Abdelfattah", "Diana Marculescu" ], "github": "https://github.com/enyac-group/UniQL", "project_page": "", "abstract": "Deploying large language model (LLM) models on mobile platforms faces significant challenges due to the limited memory and shared computational resources of the device. Resource availability may be an issue as it is directly impacted by the current device workload, adding to the uncertainty of model deployment. We introduce UniQL, a unified post-training quantization and low-rank compression framework with on-device configurable pruning rates for edge LLMs. UniQL is a general framework that integrates quantization and low-rank compression for Transformers, State Space Models (SSMs), and hybrid models to support diverse edge applications. In our proposed joint framework, we introduce an efficient structured weight-sorting method that speeds up computation by 20x, quantization-aware singular value decomposition (SVD) to minimize quantization errors, state-aware weight sorting for SSMs, and a fused rotary positional embedding (RoPE) kernel for pruned models. Our framework performs weight-sorting, fine-tuning, and quantization in the cloud in a single-pass workflow, while enabling on-device configurable pruning rates up to 35%. Our experiments show that quantized and pruned models achieve a memory reduction of 4x-5.7x and a token-throughput improvement of 2.7x-3.4x, maintaining accuracy within 5% of the original models at 15% pruning across Transformers (Llama3 and Qwen2.5), SSMs (Mamba2), and hybrid models (Nemotron-H and Bamba-v2). The code and quantized models are available at: https://github.com/enyac-group/UniQL.", "github_manual": "", "github_regex": "https://github.com/enyac-group/UniQL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.03771", "title": "In-Context Representation Hijacking", "authors": [ "Itay Yona", "Amir Sarid", "Michael Karasik", "Yossi Gandelsman" ], "github": "https://github.com/1tux/doublespeak", "project_page": "", "abstract": "We introduce Doublespeak, a simple in-context representation hijacking attack against large language models (LLMs). The attack works by systematically replacing a harmful keyword (e.g., bomb) with a benign token (e.g., carrot) across multiple in-context examples, provided a prefix to a harmful request. We demonstrate that this substitution leads to the internal representation of the benign token converging toward that of the harmful one, effectively embedding the harmful semantics under a euphemism. As a result, superficially innocuous prompts (e.g., ``How to build a carrot?'') are internally interpreted as disallowed instructions (e.g., ``How to build a bomb?''), thereby bypassing the model's safety alignment. We use interpretability tools to show that this semantic overwrite emerges layer by layer, with benign meanings in early layers converging into harmful semantics in later ones. Doublespeak is optimization-free, broadly transferable across model families, and achieves strong success rates on closed-source and open-source systems, reaching 74\\% ASR on Llama-3.3-70B-Instruct with a single-sentence context override. Our findings highlight a new attack surface in the latent space of LLMs, revealing that current alignment strategies are insufficient and should instead operate at the representation level.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/1tux/doublespeak", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2511.20494", "title": "Adversarial Confusion Attack: Disrupting Multimodal Large Language Models", "authors": [ "Jakub Hoscilowicz", "Artur Janicki" ], "github": "", "project_page": "", "abstract": "We introduce the Adversarial Confusion Attack, a new class of threats against multimodal large language models (MLLMs). Unlike jailbreaks or targeted misclassification, the goal is to induce systematic disruption that makes the model generate incoherent or confidently incorrect outputs. Practical applications include embedding such adversarial images into websites to prevent MLLM-powered AI Agents from operating reliably. The proposed attack maximizes next-token entropy using a small ensemble of open-source MLLMs. In the white-box setting, we show that a single adversarial image can disrupt all models in the ensemble, both in the full-image and Adversarial CAPTCHA settings. Despite relying on a basic adversarial technique (PGD), the attack generates perturbations that transfer to both unseen open-source (e.g., Qwen3-VL) and proprietary (e.g., GPT-5.1) models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.02924", "title": "AutoNeural: Co-Designing Vision-Language Models for NPU Inference", "authors": [ "Wei Chen", "Liangmin Wu", "Yunhai Hu", "Zhiyuan Li", "Zhiyuan Cheng", "Yicheng Qian", "Lingyue Zhu", "Zhipeng Hu", "Luoyi Liang", "Qiang Tang", "Zhen Liu", "Han Yang" ], "github": "", "project_page": "", "abstract": "While Neural Processing Units (NPUs) offer high theoretical efficiency for edge AI, state-of-the-art Vision--Language Models (VLMs) tailored for GPUs often falter on these substrates. We attribute this hardware-model mismatch to two primary factors: the quantization brittleness of Vision Transformers (ViTs) and the I/O-bound nature of autoregressive attention mechanisms, which fail to utilize the high arithmetic throughput of NPUs. To bridge this gap, we propose AutoNeural, an NPU-native VLM architecture co-designed for integer-only inference. We replace the standard ViT encoder with a MobileNetV5-style backbone utilizing depthwise separable convolutions, which ensures bounded activation distributions for stable INT4/8/16 quantization. Complementing this, our language backbone integrates State-Space Model (SSM) principles with Transformer layers, employing efficient gated convolutions to achieve linear-time complexity. This hybrid design eliminates the heavy memory I/O overhead of Key-Value caching during generation. Our approach delivers substantial efficiency gains, reducing quantization error of vision encoder by up to 7x and end-to-end latency by 14x compared to conventional baselines. The AutoNeural also delivers 3x decoding speed and 4x longer context window than the baseline. We validate these improvements via a real-world automotive case study on the Qualcomm SA8295P SoC, demonstrating real-time performance for cockpit applications. Our results highlight that rethinking model topology specifically for NPU constraints is a prerequisite for robust multi-modal edge intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.04082", "title": "PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design", "authors": [ "Jiazhe Wei", "Ken Li", "Tianyu Lao", "Haofan Wang", "Liang Wang", "Caifeng Shan", "Chenyang Si" ], "github": "", "project_page": "", "abstract": "Graphic design forms the cornerstone of modern visual communication, serving as a vital medium for promoting cultural and commercial events. Recent advances have explored automating this process using Large Multimodal Models (LMMs), yet existing methods often produce geometrically inaccurate layouts and lack the iterative, layer-specific editing required in professional workflows. To address these limitations, we present PosterCopilot, a framework that advances layout reasoning and controllable editing for professional graphic design. Specifically, we introduce a progressive three-stage training strategy that equips LMMs with geometric understanding and aesthetic reasoning for layout design, consisting of Perturbed Supervised Fine-Tuning, Reinforcement Learning for Visual-Reality Alignment, and Reinforcement Learning from Aesthetic Feedback. Furthermore, we develop a complete workflow that couples the trained LMM-based design model with generative models, enabling layer-controllable, iterative editing for precise element refinement while maintaining global visual consistency. Extensive experiments demonstrate that PosterCopilot achieves geometrically accurate and aesthetically superior layouts, offering unprecedented controllability for professional iterative design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.03794", "title": "AdaptVision: Efficient Vision-Language Models via Adaptive Visual Acquisition", "authors": [ "Zichuan Lin", "Yicheng Liu", "Yang Yang", "Lvfang Tao", "Deheng Ye" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) have achieved remarkable success in visual question answering tasks, but their reliance on large numbers of visual tokens introduces significant computational overhead. While existing efficient VLM approaches reduce visual tokens through fixed-ratio compression, they operate passively and lack the ability to adapt to varying task requirements. This motivates a fundamental question: Can VLMs autonomously determine the minimum number of visual tokens required for each sample? Inspired by human active vision mechanisms, we introduce AdaptVision, an efficient VLM paradigm that enables adaptive visual token acquisition through a coarse-to-fine approach. Our model initially processes compressed visual tokens from low-resolution images and selectively acquires additional visual information by invoking a bounding box tool to crop key regions when necessary. We train AdaptVision using a reinforcement learning framework that carefully balances accuracy and efficiency. Central to our approach is Decoupled Turn Policy Optimization (DTPO), which decouples the learning objective into two components: (1) tool learning, which optimizes correct tool utilization, and (2) accuracy improvement, which refines the generated responses to improve answer correctness. Based on this formulation, we further decouple advantage estimation by computing separate advantages for tokens associated with each objective. This formulation enables more effective optimization for AdaptVision compared to vanilla GRPO. Comprehensive experiments across multiple VQA benchmarks demonstrate that AdaptVision achieves superior performance while consuming substantially fewer visual tokens than state-of-the-art efficient VLM methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.03979", "title": "BlurDM: A Blur Diffusion Model for Image Deblurring", "authors": [ "Jin-Ting He", "Fu-Jen Tsai", "Yan-Tsung Peng", "Min-Hung Chen", "Chia-Wen Lin", "Yen-Yu Lin" ], "github": "https://github.com/Jin-Ting-He/BlurDM", "project_page": "", "abstract": "Diffusion models show promise for dynamic scene deblurring; however, existing studies often fail to leverage the intrinsic nature of the blurring process within diffusion models, limiting their full potential. To address it, we present a Blur Diffusion Model (BlurDM), which seamlessly integrates the blur formation process into diffusion for image deblurring. Observing that motion blur stems from continuous exposure, BlurDM implicitly models the blur formation process through a dual-diffusion forward scheme, diffusing both noise and blur onto a sharp image. During the reverse generation process, we derive a dual denoising and deblurring formulation, enabling BlurDM to recover the sharp image by simultaneously denoising and deblurring, given pure Gaussian noise conditioned on the blurred image as input. Additionally, to efficiently integrate BlurDM into deblurring networks, we perform BlurDM in the latent space, forming a flexible prior generation network for deblurring. Extensive experiments demonstrate that BlurDM significantly and consistently enhances existing deblurring methods on four benchmark datasets. The source code is available at https://github.com/Jin-Ting-He/BlurDM.", "github_manual": "", "github_regex": "https://github.com/Jin-Ting-He/BlurDM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.05115", "title": "Light-X: Generative 4D Video Rendering with Camera and Illumination Control", "authors": [ "Tianqi Liu", "Zhaoxi Chen", "Zihao Huang", "Shaocong Xu", "Saining Zhang", "Chongjie Ye", "Bohan Li", "Zhiguo Cao", "Wei Li", "Hao Zhao", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Recent advances in illumination control extend image-based methods to video, yet still facing a trade-off between lighting fidelity and temporal consistency. Moving beyond relighting, a key step toward generative modeling of real-world scenes is the joint control of camera trajectory and illumination, since visual dynamics are inherently shaped by both geometry and lighting. To this end, we present Light-X, a video generation framework that enables controllable rendering from monocular videos with both viewpoint and illumination control. 1) We propose a disentangled design that decouples geometry and lighting signals: geometry and motion are captured via dynamic point clouds projected along user-defined camera trajectories, while illumination cues are provided by a relit frame consistently projected into the same geometry. These explicit, fine-grained cues enable effective disentanglement and guide high-quality illumination. 2) To address the lack of paired multi-view and multi-illumination videos, we introduce Light-Syn, a degradation-based pipeline with inverse-mapping that synthesizes training pairs from in-the-wild monocular footage. This strategy yields a dataset covering static, dynamic, and AI-generated scenes, ensuring robust training. Extensive experiments show that Light-X outperforms baseline methods in joint camera-illumination control and surpasses prior video relighting methods under both text- and background-conditioned settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.04025", "title": "PSA: Pyramid Sparse Attention for Efficient Video Understanding and Generation", "authors": [ "Xiaolong Li", "Youping Gu", "Xi Lin", "Weijie Wang", "Bohan Zhuang" ], "github": "", "project_page": "", "abstract": "Attention mechanisms are the core of foundation models, but their quadratic complexity remains a critical bottleneck for scaling. This challenge has driven the development of efficient attention mechanisms, with sparsity emerging as the dominant paradigm. Current methods typically retain or discard entire key-value blocks with binary masks, resulting in substantial information loss under high sparsity. To mitigate this gap, we present Pyramid Sparse Attention (PSA), a versatile module applicable to both video understanding and generation tasks. Instead of binary masking, PSA introduces multi-level pooled KV representations, enabling finer mask granularity. Specifically, each query block dynamically allocates lower pooling levels to critical KV blocks and higher levels to less important ones, creating an informative interpolation between full retention and complete pruning. This design, analogous to fixed-point quantization and classical feature pyramid networks in computer vision, effectively mitigates information loss while preserving computational efficiency under a low compute budget. It works with a native, hardware-friendly kernel that leverages decoupled block-tile design to ensure efficient execution. Across video understanding and generation benchmarks, PSA preserves contextual information and visual fidelity, consistently outperforming or achieving comparable performance over existing sparse attention baselines with superior efficiency-quality trade-offs. Our code and model weights are publicly available at: http://ziplab.co/PSA", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-04", "arxiv_id": "2512.04000", "title": "Divide, then Ground: Adapting Frame Selection to Query Types for Long-Form Video Understanding", "authors": [ "Jialuo Li", "Bin Li", "Jiahao Li", "Yan Lu" ], "github": "", "project_page": "", "abstract": "The application of Large Multimodal Models (LMMs) to long-form video understanding is constrained by limited context lengths and the computationally prohibitive cost of processing dense video tokens. Consequently, recent research has focused on query-aware frame selection, methods that often incur significant computational overhead. This paper challenges the assumption that such complex search mechanisms are universally necessary. We first identify and validate a query typology distinguishing between global query and localized query. We demonstrate that while uniform sampling is both effective and efficient for global queries, localized queries indeed necessitate query-aware selection for optimal performance. Building on this insight, we propose DIG, a training-free frame selection framework that adapts its strategy based on the query type. Specifically,DIG employs efficient uniform sampling for global queries while activating a specialized pipeline to extract query-relevant frames for localized queries. Experiments on three long-form video understanding benchmarks demonstrate that DIG consistently outperforms existing baselines and robustly improves LMM performance, even when scaling the input frame count to 256.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04678", "title": "Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation", "authors": [ "Yunhong Lu", "Yanhong Zeng", "Haobo Li", "Hao Ouyang", "Qiuyu Wang", "Ka Leong Cheng", "Jiapeng Zhu", "Hengyuan Cao", "Zhipeng Zhang", "Xing Zhu", "Yujun Shen", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Efficient streaming video generation is critical for simulating interactive and dynamic worlds. Existing methods distill few-step video diffusion models with sliding window attention, using initial frames as sink tokens to maintain attention performance and reduce error accumulation. However, video frames become overly dependent on these static tokens, resulting in copied initial frames and diminished motion dynamics. To address this, we introduce Reward Forcing, a novel framework with two key designs. First, we propose EMA-Sink, which maintains fixed-size tokens initialized from initial frames and continuously updated by fusing evicted tokens via exponential moving average as they exit the sliding window. Without additional computation cost, EMA-Sink tokens capture both long-term context and recent dynamics, preventing initial frame copying while maintaining long-horizon consistency. Second, to better distill motion dynamics from teacher models, we propose a novel Rewarded Distribution Matching Distillation (Re-DMD). Vanilla distribution matching treats every training sample equally, limiting the model's ability to prioritize dynamic content. Instead, Re-DMD biases the model's output distribution toward high-reward regions by prioritizing samples with greater dynamics rated by a vision-language model. Re-DMD significantly enhances motion quality while preserving data fidelity. We include both quantitative and qualitative experiments to show that Reward Forcing achieves state-of-the-art performance on standard benchmarks while enabling high-quality streaming video generation at 23.1 FPS on a single H100 GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04677", "title": "Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length", "authors": [ "Yubo Huang", "Hailong Guo", "Fangtai Wu", "Shifeng Zhang", "Shijie Huang", "Qijun Gan", "Lin Liu", "Sirui Zhao", "Enhong Chen", "Jiaming Liu", "Steven Hoi" ], "github": "", "project_page": "", "abstract": "Existing diffusion-based video generation methods are fundamentally constrained by sequential computation and long-horizon inconsistency, limiting their practical adoption in real-time, streaming audio-driven avatar synthesis. We present Live Avatar, an algorithm-system co-designed framework that enables efficient, high-fidelity, and infinite-length avatar generation using a 14-billion-parameter diffusion model. Our approach introduces Timestep-forcing Pipeline Parallelism (TPP), a distributed inference paradigm that pipelines denoising steps across multiple GPUs, effectively breaking the autoregressive bottleneck and ensuring stable, low-latency real-time streaming. To further enhance temporal consistency and mitigate identity drift and color artifacts, we propose the Rolling Sink Frame Mechanism (RSFM), which maintains sequence fidelity by dynamically recalibrating appearance using a cached reference image. Additionally, we leverage Self-Forcing Distribution Matching Distillation to facilitate causal, streamable adaptation of large-scale models without sacrificing visual quality. Live Avatar demonstrates state-of-the-art performance, reaching 20 FPS end-to-end generation on 5 H800 GPUs, and, to the best of our knowledge, is the first to achieve practical, real-time, high-fidelity avatar generation at this scale. Our work establishes a new paradigm for deploying advanced diffusion models in industrial long-form video synthesis applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.02589", "title": "PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing", "authors": [ "Junyi Hou", "Andre Lin Huikai", "Nuo Chen", "Yiwei Gong", "Bingsheng He" ], "github": "https://github.com/PaperDebugger/PaperDebugger", "project_page": "", "abstract": "Large language models are increasingly embedded into academic writing workflows, yet existing assistants remain external to the editor, preventing deep interaction with document state, structure, and revision history. This separation makes it impossible to support agentic, context-aware operations directly within LaTeX editors such as Overleaf. We present PaperDebugger, an in-editor, multi-agent, and plugin-based academic writing assistant that brings LLM-driven reasoning directly into the writing environment. Enabling such in-editor interaction is technically non-trivial: it requires reliable bidirectional synchronization with the editor, fine-grained version control and patching, secure state management, multi-agent scheduling, and extensible communication with external tools. PaperDebugger addresses these challenges through a Chrome-approved extension, a Kubernetes-native orchestration layer, and a Model Context Protocol (MCP) toolchain that integrates literature search, reference lookup, document scoring, and revision pipelines. Our demo showcases a fully integrated workflow, including localized edits, structured reviews, parallel agent execution, and diff-based updates, encapsulated within a minimal-intrusion user interface (UI). Early aggregated analytics demonstrate active user engagement and validate the practicality of an editor-native, agentic writing assistant. More details about this demo and video could be found at https://github.com/PaperDebugger/PaperDebugger.", "github_manual": "", "github_regex": "https://github.com/PaperDebugger/PaperDebugger", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.05060", "title": "4DLangVGGT: 4D Language-Visual Geometry Grounded Transformer", "authors": [ "Xianfeng Wu", "Yajing Bai", "Minghan Li", "Xianzu Wu", "Xueqi Zhao", "Zhongyuan Lai", "Wenyu Liu", "Xinggang Wang" ], "github": "https://github.com/hustvl/4DLangVGGT", "project_page": "", "abstract": "Constructing 4D language fields is crucial for embodied AI, augmented/virtual reality, and 4D scene understanding, as they provide enriched semantic representations of dynamic environments and enable open-vocabulary querying in complex scenarios. However, existing approaches to 4D semantic field construction primarily rely on scene-specific Gaussian splatting, which requires per-scene optimization, exhibits limited generalization, and is difficult to scale to real-world applications. To address these limitations, we propose 4DLangVGGT, the first Transformer-based feed-forward unified framework for 4D language grounding, that jointly integrates geometric perception and language alignment within a single architecture. 4DLangVGGT has two key components: the 4D Visual Geometry Transformer, StreamVGGT, which captures spatio-temporal geometric representations of dynamic scenes; and the Semantic Bridging Decoder (SBD), which projects geometry-aware features into a language-aligned semantic space, thereby enhancing semantic interpretability while preserving structural fidelity. Unlike prior methods that depend on costly per-scene optimization, 4DLangVGGT can be jointly trained across multiple dynamic scenes and directly applied during inference, achieving both deployment efficiency and strong generalization. This design significantly improves the practicality of large-scale deployment and establishes a new paradigm for open-vocabulary 4D scene understanding. Experiments on HyperNeRF and Neu3D datasets demonstrate that our approach not only generalizes effectively but also achieves state-of-the-art performance, achieving up to 2% gains under per-scene training and 1% improvements under multi-scene training. Our code released in https://github.com/hustvl/4DLangVGGT", "github_manual": "", "github_regex": "https://github.com/hustvl/4DLangVGGT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04926", "title": "Semantics Lead the Way: Harmonizing Semantic and Texture Modeling with Asynchronous Latent Diffusion", "authors": [ "Yueming Pan", "Ruoyu Feng", "Qi Dai", "Yuqi Wang", "Wenfeng Lin", "Mingyu Guo", "Chong Luo", "Nanning Zheng" ], "github": "", "project_page": "", "abstract": "Latent Diffusion Models (LDMs) inherently follow a coarse-to-fine generation process, where high-level semantic structure is generated slightly earlier than fine-grained texture. This indicates the preceding semantics potentially benefit texture generation by providing a semantic anchor. Recent advances have integrated semantic priors from pretrained visual encoders to further enhance LDMs, yet they still denoise semantic and VAE-encoded texture synchronously, neglecting such ordering. Observing these, we propose Semantic-First Diffusion (SFD), a latent diffusion paradigm that explicitly prioritizes semantic formation. SFD first constructs composite latents by combining a compact semantic latent, which is extracted from a pretrained visual encoder via a dedicated Semantic VAE, with the texture latent. The core of SFD is to denoise the semantic and texture latents asynchronously using separate noise schedules: semantics precede textures by a temporal offset, providing clearer high-level guidance for texture refinement and enabling natural coarse-to-fine generation. On ImageNet 256x256 with guidance, SFD achieves FID 1.06 (LightningDiT-XL) and FID 1.04 (1.0B LightningDiT-XXL), while achieving up to 100x faster convergence than the original DiT. SFD also improves existing methods like ReDi and VA-VAE, demonstrating the effectiveness of asynchronous, semantics-led modeling. Project page and code: https://yuemingpan.github.io/SFD.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04797", "title": "SIMA 2: A Generalist Embodied Agent for Virtual Worlds", "authors": [ "SIMA team", "Adrian Bolton", "Alexander Lerchner", "Alexandra Cordell", "Alexandre Moufarek", "Andrew Bolt", "Andrew Lampinen", "Anna Mitenkova", "Arne Olav Hallingstad", "Bojan Vujatovic", "Bonnie Li", "Cong Lu", "Daan Wierstra", "Daniel P. Sawyer", "Daniel Slater", "David Reichert", "Davide Vercelli", "Demis Hassabis", "Drew A. Hudson", "Duncan Williams", "Ed Hirst", "Fabio Pardo", "Felix Hill", "Frederic Besse", "Hannah Openshaw", "Harris Chan", "Hubert Soyer", "Jane X. Wang", "Jeff Clune", "John Agapiou", "John Reid", "Joseph Marino", "Junkyung Kim", "Karol Gregor", "Kaustubh Sridhar", "Kay McKinney", "Laura Kampis", "Lei M. Zhang", "Loic Matthey", "Luyu Wang", "Maria Abi Raad", "Maria Loks-Thompson", "Martin Engelcke", "Matija Kecman", "Matthew Jackson", "Maxime Gazeau", "Ollie Purkiss", "Oscar Knagg", "Peter Stys", "Piermaria Mendolicchio", "Raia Hadsell", "Rosemary Ke", "Ryan Faulkner", "Sarah Chakera", "Satinder Singh Baveja", "Shane Legg", "Sheleem Kashem", "Tayfun Terzi", "Thomas Keck", "Tim Harley", "Tim Scholtes", "Tyson Roberts", "Volodymyr Mnih", "Yulan Liu", "Zhengdong Wang", "Zoubin Ghahramani" ], "github": "", "project_page": "", "abstract": "We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a significant step toward active, goal-directed interaction within an embodied environment. Unlike prior work (e.g., SIMA 1) limited to simple language commands, SIMA 2 acts as an interactive partner, capable of reasoning about high-level goals, conversing with the user, and handling complex instructions given through language and images. Across a diverse portfolio of games, SIMA 2 substantially closes the gap with human performance and demonstrates robust generalization to previously unseen environments, all while retaining the base model's core reasoning capabilities. Furthermore, we demonstrate a capacity for open-ended self-improvement: by leveraging Gemini to generate tasks and provide rewards, SIMA 2 can autonomously learn new skills from scratch in a new environment. This work validates a path toward creating versatile and continuously learning agents for both virtual and, eventually, physical worlds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.03000", "title": "DynamicVerse: A Physically-Aware Multimodal Framework for 4D World Modeling", "authors": [ "Kairun Wen", "Yuzhi Huang", "Runyu Chen", "Hui Zheng", "Yunlong Lin", "Panwang Pan", "Chenxin Li", "Wenyan Cong", "Jian Zhang", "Junbin Lu", "Chenguo Lin", "Dilin Wang", "Zhicheng Yan", "Hongyu Xu", "Justin Theiss", "Yue Huang", "Xinghao Ding", "Rakesh Ranjan", "Zhiwen Fan" ], "github": "", "project_page": "", "abstract": "Understanding the dynamic physical world, characterized by its evolving 3D structure, real-world motion, and semantic content with textual descriptions, is crucial for human-agent interaction and enables embodied agents to perceive and act within real environments with human-like capabilities. However, existing datasets are often derived from limited simulators or utilize traditional Structurefrom-Motion for up-to-scale annotation and offer limited descriptive captioning, which restricts the capacity of foundation models to accurately interpret real-world dynamics from monocular videos, commonly sourced from the internet. To bridge these gaps, we introduce DynamicVerse, a physical-scale, multimodal 4D world modeling framework for dynamic real-world video. We employ large vision, geometric, and multimodal models to interpret metric-scale static geometry, real-world dynamic motion, instance-level masks, and holistic descriptive captions. By integrating window-based Bundle Adjustment with global optimization, our method converts long real-world video sequences into a comprehensive 4D multimodal format. DynamicVerse delivers a large-scale dataset consisting of 100K+ videos with 800K+ annotated masks and 10M+ frames from internet videos. Experimental evaluations on three benchmark tasks, namely video depth estimation, camera pose estimation, and camera intrinsics estimation, demonstrate that our 4D modeling achieves superior performance in capturing physical-scale measurements with greater global accuracy than existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.02631", "title": "SeeNav-Agent: Enhancing Vision-Language Navigation with Visual Prompt and Step-Level Policy Optimization", "authors": [ "Zhengcheng Wang", "Zichuan Lin", "Yijun Yang", "Haobo Fu", "Deheng Ye" ], "github": "", "project_page": "", "abstract": "Existing Vision-Language Navigation (VLN) agents based on Large Vision-Language Models (LVLMs) often suffer from perception errors, reasoning errors, and planning errors, which significantly hinder their navigation performance. To address these limitations, a novel VLN agent framework, named SeeNav-Agent, is proposed in this work. First, to reduce perception hallucinations of the visual module of the VLN agent, a dual-view Visual Prompt (VP) technique is introduced in the input space, which can also improve the agent's understanding of current spatial states. Subsequently, a novel step-level Reinforcement Fine-Tuning (RFT) method, Step Reward Group Policy Optimization (SRGPO), is designed for the post-training of VLN agents. In SRGPO, we first define verifiable process rewards for the navigation task, and then perform efficient step-level advantage estimation by randomly grouping different navigation steps. SRGPO provides dense reward signals for the reinforcement learning process of the VLN agent and enhances its planning capability. Experimental results on the EmbodiedBench Navigation benchmark indicate that by introducing the zero-shot VP module, the GPT-4.1 achieves a navigation success rate of 86.7%, surpassing the current best LVLM by approximately 20 percentage points (pp). Through post-training based on SRGPO, the Qwen2.5-VL-3B model reaches a navigation success rate of 72.3%, outperforming the best existing LVLM model by 5.6 pp. Moreover, compared to RFT algorithms such as GRPO and GiGPO, the proposed SRGPO demonstrates significant improvements in training stability, convergence efficiency, and generalization capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.05112", "title": "DraCo: Draft as CoT for Text-to-Image Preview and Rare Concept Generation", "authors": [ "Dongzhi Jiang", "Renrui Zhang", "Haodong Li", "Zhuofan Zong", "Ziyu Guo", "Jun He", "Claire Guo", "Junyan Ye", "Rongyao Fang", "Weijia Li", "Rui Liu", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "Recent unified multimodal large language models (MLLMs) have shown impressive capabilities, incorporating chain-of-thought (CoT) reasoning for enhanced text-to-image generation. However, existing approaches remain limited, either treating the model merely as a standalone generator or relying on abstract textual planning. To this end, we propose Draft-as-CoT (DraCo), a novel interleaved reasoning paradigm that fully leverages both textual and visual contents in CoT for better planning and verification. Our method first generates a low-resolution draft image as preview, providing more concrete and structural visual planning and guidance. Then, we employ the model's inherent understanding capability to verify potential semantic misalignments between the draft and input prompt, and performs refinement through selective corrections with super-resolution. In this way, our approach addresses two fundamental challenges: the coarse-grained nature of textual planning and the difficulty in generating rare attribute combinations. To support training, we curate DraCo-240K, aiming to enhance three atomic capabilities spanning general correction, instance manipulation, and layout reorganization. Supported by DraCo-CFG, a specialized classifier-free guidance (CFG) strategy for interleaved reasoning, DraCo achieves a tremendous increase on GenEval (+8%), Imagine-Bench (+0.91), and GenEval++ (+3%), significantly outperforming direct generation and other generation methods empowered by CoT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04987", "title": "Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction", "authors": [ "Nex-AGI Team", "Yuxuan Cai", "Lu Chen", "Qiaoling Chen", "Yuyang Ding", "Liwen Fan", "Wenjie Fu", "Yufei Gao", "Honglin Guo", "Pinxue Guo", "Zhenhua Han", "Zhengfu He", "Hanglei Hu", "Kai Hu", "Shengjia Hua", "Tianyu Huai", "Baodai Huang", "Li Ji", "Zhen Jiang", "Zhikai Lei", "Bufan Li", "Jiahang Lin", "Lizhi Lin", "Jinxiu Liu", "Shichun Liu", "Ziming Liu", "Yuchen Ni", "Pengfang Qian", "Yujiong Shen", "Qingyun Shi", "Wentao Shu", "Peng Sun", "Yiran Suo", "Tian Tang", "Boyu Tian", "Guoteng Wang", "Junzhe Wang", "Peixin Wang", "Zhiheng Xi", "Hang Yan", "Jie Yang", "Zhixiong Yang", "Tianchu Yao", "Guangze Ye", "Qianxi Yu", "Shuo Zhang", "Xinyue Zhang", "Yiqi Zhang", "Jiarong Zhao", "Miao Zheng", "Rui Zheng", "Enyu Zhou", "Jiazheng Zhou", "Maosen Zhou", "Yuhao Zhou", "Tao Gui", "Yining Zheng", "Xinchi Chen", "Jie Zhou", "Siyuan Feng", "Qin Chen", "Liang He", "Qi Zhang", "Xuanjing Huang", "Xipeng Qiu" ], "github": "https://github.com/nex-agi/Nex-N1", "project_page": "", "abstract": "The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static imitation to incentive-driven decision making. However, this transition is significantly impeded by the lack of scalable infrastructure capable of constructing high-quality interaction signals for effective policy learning. To address this, we introduce a comprehensive method designed to systematically scale the diversity and complexity of interactive environments. Our method realizes this scaling by addressing three orthogonal dimensions: (1) Complexity: NexAU, a flexible agent framework that supports building complex agent hierarchies via simple configurations; (2) Diversity: NexA4A automatically generates diverse agent hierarchies from natural language to cover infinite domains; and (3) Fidelity: NexGAP bridges the simulation-reality gap by integrating dynamic real-world environment for grounded trajectories synthesis. We train Nex-N1 upon the diverse and complex interactive environments established by our infrastructure. Empirical results on benchmarks such as SWE-bench and tau2 demonstrate that Nex-N1 consistently outperforms SOTA open-source models and achieves competitive performance against frontier proprietary models on complex agentic tasks. We open-source the Nex ecosystem and model weights to facilitate further research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nex-agi/Nex-N1", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.05113", "title": "Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting", "authors": [ "Hao-Jen Chien", "Yi-Chuan Huang", "Chung-Ho Wu", "Wei-Lun Chao", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Synthesizing high-fidelity frozen 3D scenes from monocular Mannequin-Challenge (MC) videos is a unique problem distinct from standard dynamic scene reconstruction. Instead of focusing on modeling motion, our goal is to create a frozen scene while strategically preserving subtle dynamics to enable user-controlled instant selection. To achieve this, we introduce a novel application of dynamic Gaussian splatting: the scene is modeled dynamically, which retains nearby temporal variation, and a static scene is rendered by fixing the model's time parameter. However, under this usage, monocular capture with sparse temporal supervision introduces artifacts like ghosting and blur for Gaussians that become unobserved or occluded at weakly supervised timestamps. We propose Splannequin, an architecture-agnostic regularization that detects two states of Gaussian primitives, hidden and defective, and applies temporal anchoring. Under predominantly forward camera motion, hidden states are anchored to their recent well-observed past states, while defective states are anchored to future states with stronger supervision. Our method integrates into existing dynamic Gaussian pipelines via simple loss terms, requires no architectural changes, and adds zero inference overhead. This results in markedly improved visual quality, enabling high-fidelity, user-selectable frozen-time renderings, validated by a 96% user preference. Project page: https://chien90190.github.io/splannequin/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.05106", "title": "NeuralRemaster: Phase-Preserving Diffusion for Structure-Aligned Generation", "authors": [ "Yu Zeng", "Charles Ochoa", "Mingyuan Zhou", "Vishal M. Patel", "Vitor Guizilini", "Rowan McAllister" ], "github": "https://github.com/zengxianyu/PPD-examples", "project_page": "", "abstract": "Standard diffusion corrupts data using Gaussian noise whose Fourier coefficients have random magnitudes and random phases. While effective for unconditional or text-to-image generation, corrupting phase components destroys spatial structure, making it ill-suited for tasks requiring geometric consistency, such as re-rendering, simulation enhancement, and image-to-image translation. We introduce Phase-Preserving Diffusion \u03c6-PD, a model-agnostic reformulation of the diffusion process that preserves input phase while randomizing magnitude, enabling structure-aligned generation without architectural changes or additional parameters. We further propose Frequency-Selective Structured (FSS) noise, which provides continuous control over structural rigidity via a single frequency-cutoff parameter. \u03c6-PD adds no inference-time cost and is compatible with any diffusion model for images or videos. Across photorealistic and stylized re-rendering, as well as sim-to-real enhancement for driving planners, \u03c6-PD produces controllable, spatially aligned results. When applied to the CARLA simulator, \u03c6-PD improves CARLA-to-Waymo planner performance by 50\\%. The method is complementary to existing conditioning approaches and broadly applicable to image-to-image and video-to-video generation. Videos, additional examples, and code are available on our https://yuzeng-at-tri.github.io/ppd-page/{project page}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zengxianyu/PPD-examples", "github_api_added_by": "auto" }, { "date": "2025-12-05", "arxiv_id": "2512.05103", "title": "TV2TV: A Unified Framework for Interleaved Language and Video Generation", "authors": [ "Xiaochuang Han", "Youssef Emad", "Melissa Hall", "John Nguyen", "Karthik Padthe", "Liam Robbins", "Amir Bar", "Delong Chen", "Michal Drozdzal", "Maha Elbayad", "Yushi Hu", "Shang-Wen Li", "Sreya Dutta Roy", "Jakob Verbeek", "XuDong Wang", "Marjan Ghazvininejad", "Luke Zettlemoyer", "Emily Dinan" ], "github": "", "project_page": "", "abstract": "Video generation models are rapidly advancing, but can still struggle with complex video outputs that require significant semantic branching or repeated high-level reasoning about what should happen next. In this paper, we introduce a new class of omni video-text models that integrate ideas from recent LM reasoning advances to address this challenge. More specifically, we present TV2TV, a unified generative modeling framework which decomposes video generation into an interleaved text and video generation process. TV2TV jointly learns language modeling (next-token prediction) and video flow matching (next-frame prediction) using a Mixture-of-Transformers (MoT) architecture. At inference time, TV2TV decides when to alternate between generating text and video frames, allowing the model to \"think in words\" about subsequent content before ``acting in pixels'' to produce frames. This design offloads much of the responsibility for deciding what should happen next to the language modeling tower, enabling improved visual quality and prompt alignment of generated videos. It also enables fine-grained controllability, allowing users to modify the video generation trajectory through text interventions at any point in the process. In controlled experiments on video game data, TV2TV demonstrates substantial improvements in both visual quality and controllability. TV2TV also scales to natural videos, as we show by augmenting sports videos with interleaved natural language action descriptions using vision-language models (VLMs). Training TV2TV on this corpus yields strong visual quality and prompt alignment, showcasing the model's ability to reason about and generate complex real-world action sequences. Together, these results highlight TV2TV as a promising step toward video generation with open-ended textual reasoning and control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04746", "title": "SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs", "authors": [ "Wenhua Cheng", "Weiwei Zhang", "Heng Guo", "Haihao Shen" ], "github": "https://github.com/intel/auto-round", "project_page": "", "abstract": "Extreme low-bit quantization is critical for efficiently deploying Large Language Models (LLMs), yet it often leads to severe performance degradation at 2-bits and even 4-bits (e.g., MXFP4). We present SignRoundV2, a post-training quantization framework that is highly effective even without mixed-precision. SignRoundV2 introduces (1) a fast sensitivity metric that combines gradient information with quantization-induced deviations to guide layer-wise bit allocation, and (2) a lightweight pre-tuning search for quantization scales to improve extremely low-bit quantization. These components allow SignRoundV2 to close the gap with full-precision models. Extensive experiments indicate that our method sustains competitive accuracy for LLMs, achieving production-grade performance with about 1 percent variance at 4-5 bits and strong results even at 2 bits. The implementation is available at https://github.com/intel/auto-round.", "github_manual": "", "github_regex": "https://github.com/intel/auto-round", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04324", "title": "DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle", "authors": [ "Fangyu Lei", "Jinxiang Meng", "Yiming Huang", "Junjie Zhao", "Yitong Zhang", "Jianwen Luo", "Xin Zou", "Ruiyi Yang", "Wenbo Shi", "Yan Gao", "Shizhu He", "Zuo Wang", "Qian Liu", "Yang Wang", "Ke Wang", "Jun Zhao", "Kang Liu" ], "github": "", "project_page": "", "abstract": "Real-world enterprise data intelligence workflows encompass data engineering that turns raw sources into analytical-ready tables and data analysis that convert those tables into decision-oriented insights. We introduce DAComp, a benchmark of 210 tasks that mirrors these complex workflows. Data engineering (DE) tasks require repository-level engineering on industrial schemas, including designing and building multi-stage SQL pipelines from scratch and evolving existing systems under evolving requirements. Data analysis (DA) tasks pose open-ended business problems that demand strategic planning, exploratory analysis through iterative coding, interpretation of intermediate results, and the synthesis of actionable recommendations. Engineering tasks are scored through execution-based, multi-metric evaluation. Open-ended tasks are assessed by a reliable, experimentally validated LLM-judge, which is guided by hierarchical, meticulously crafted rubrics. Our experiments reveal that even state-of-the-art agents falter on DAComp. Performance on DE tasks is particularly low, with success rates under 20%, exposing a critical bottleneck in holistic pipeline orchestration, not merely code generation. Scores on DA tasks also average below 40%, highlighting profound deficiencies in open-ended reasoning and demonstrating that engineering and analysis are distinct capabilities. By clearly diagnosing these limitations, DAComp provides a rigorous and realistic testbed to drive the development of truly capable autonomous data agents for enterprise settings. Our data and code are available at https://da-comp.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04220", "title": "On GRPO Collapse in Search-R1: The Lazy Likelihood-Displacement Death Spiral", "authors": [ "Wenlong Deng", "Yushu Li", "Boying Gong", "Yi Ren", "Christos Thrampoulidis", "Xiaoxiao Li" ], "github": "", "project_page": "", "abstract": "Tool-integrated (TI) reinforcement learning (RL) enables large language models (LLMs) to perform multi-step reasoning by interacting with external tools such as search engines and retrievers. Group Relative Policy Optimization (GRPO), exemplified by the recent Search-R1, offers fast convergence and a value-free formulation that makes it appealing for this setting, yet consistently suffers from training collapse. We identify Lazy Likelihood Displacement (LLD), a systematic reduction or stagnation in the likelihood of both correct and incorrect responses, as the core mechanism driving this failure. LLD emerges early and triggers a self-reinforcing LLD Death Spiral, where declining likelihood leads to low-confidence responses, inflating gradients, and ultimately causing collapse. We empirically characterize this process across models on a Search-R1-style, search-integrated question answering task, revealing a consistent three-phase trajectory: early stagnation, steady decay, and accelerated collapse. To address this, we propose a lightweight likelihood-preserving regularization LLDS for GRPO that activates only when a trajectory's likelihood decreases, and regularizes only the tokens responsible. This fine-grained structure mitigates LLD with minimal interference to optimization. Across seven open-domain and multi-hop QA benchmarks, our method stabilizes training, prevents gradient explosion, and yields substantial performance improvements, including +37.8% gains on Qwen2.5-3B and +32.0% gains on Qwen2.5-7B. Our results establish LLD as a fundamental bottleneck in GRPO-based TIRL and provide a practical path toward stable, scalable training of tool-integrated LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.05076", "title": "BulletTime: Decoupled Control of Time and Camera Pose for Video Generation", "authors": [ "Yiming Wang", "Qihang Zhang", "Shengqu Cai", "Tong Wu", "Jan Ackermann", "Zhengfei Kuang", "Yang Zheng", "Frano Raji\u010d", "Siyu Tang", "Gordon Wetzstein" ], "github": "", "project_page": "", "abstract": "Emerging video diffusion models achieve high visual fidelity but fundamentally couple scene dynamics with camera motion, limiting their ability to provide precise spatial and temporal control. We introduce a 4D-controllable video diffusion framework that explicitly decouples scene dynamics from camera pose, enabling fine-grained manipulation of both scene dynamics and camera viewpoint. Our framework takes continuous world-time sequences and camera trajectories as conditioning inputs, injecting them into the video diffusion model through a 4D positional encoding in the attention layer and adaptive normalizations for feature modulation. To train this model, we curate a unique dataset in which temporal and camera variations are independently parameterized; this dataset will be made public. Experiments show that our model achieves robust real-world 4D control across diverse timing patterns and camera trajectories, while preserving high generation quality and outperforming prior work in controllability. See our website for video results: https://19reborn.github.io/Bullet4D/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04515", "title": "EgoLCD: Egocentric Video Generation with Long Context Diffusion", "authors": [ "Liuzhou Zhang", "Jiarui Ye", "Yuanlei Wang", "Ming Zhong", "Mingju Cao", "Wanke Xia", "Bowen Zeng", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/EgoLCD", "project_page": "", "abstract": "Generating long, coherent egocentric videos is difficult, as hand-object interactions and procedural tasks require reliable long-term memory. Existing autoregressive models suffer from content drift, where object identity and scene semantics degrade over time. To address this challenge, we introduce EgoLCD, an end-to-end framework for egocentric long-context video generation that treats long video synthesis as a problem of efficient and stable memory management. EgoLCD combines a Long-Term Sparse KV Cache for stable global context with an attention-based short-term memory, extended by LoRA for local adaptation. A Memory Regulation Loss enforces consistent memory usage, and Structured Narrative Prompting provides explicit temporal guidance. Extensive experiments on the EgoVid-5M benchmark demonstrate that EgoLCD achieves state-of-the-art performance in both perceptual quality and temporal consistency, effectively mitigating generative forgetting and representing a significant step toward building scalable world models for embodied AI. Code: https://github.com/AIGeeksGroup/EgoLCD. Website: https://aigeeksgroup.github.io/EgoLCD.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/EgoLCD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2511.20233", "title": "REFLEX: Self-Refining Explainable Fact-Checking via Disentangling Truth into Style and Substance", "authors": [ "Chuyi Kong", "Gao Wei", "Jing Ma", "Hongzhan Lin", "Yaxin Fan" ], "github": "", "project_page": "", "abstract": "The prevalence of misinformation on social media threatens public trust, demanding automated fact-checking systems that provide accurate verdicts with interpretable explanations. However, existing large language model-based (LLM-based) approaches often rely heavily on external knowledge sources, introducing substantial latency and even hallucinations that undermine reliability, interpretability, and responsiveness, which is crucial for real-time use. To address these challenges, we propose REason-guided Fact-checking with Latent EXplanations REFLEX paradigm, a plug-and-play, self-refining paradigm that leverages the internal knowledge in backbone model to improve both verdict accuracy and explanation quality. REFLEX reformulates fact-checking as a role-play dialogue and jointly trains verdict prediction and explanation generation. It adaptively extracts contrastive activation pairs between the backbone model and its fine-tuned variant to construct steering vectors that disentangle truth into style and substance naturally. These activation-level signals guide inference and suppress noisy explanations, enabling more faithful and efficient reasoning. Experiments on real-world datasets show that REFLEX outperforms previous methods that steer toward a single truth direction and underscores the challenge traditional approaches face when handling the subtle, human-unknown truth in fact-checking tasks. Remarkably, with only 465 self-refined training samples, RELFEX achieves state-of-the-art performance. Furthermore, models trained with explanatory objectives can effectively guide those without them, yielding up to a 7.57% improvement, highlighting that internal explanation signals play a dual role in both interpreting and enhancing factual reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.05111", "title": "ARM-Thinker: Reinforcing Multimodal Generative Reward Models with Agentic Tool Use and Visual Reasoning", "authors": [ "Shengyuan Ding", "Xinyu Fang", "Ziyu Liu", "Yuhang Zang", "Yuhang Cao", "Xiangyu Zhao", "Haodong Duan", "Xiaoyi Dong", "Jianze Liang", "Bin Wang", "Conghui He", "Dahua Lin", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Reward models are critical for aligning vision-language systems with human preferences, yet current approaches suffer from hallucination, weak visual grounding, and an inability to use tools for verification, limiting their reliability on complex multimodal reasoning tasks. We present ARM-Thinker, an A}gentic multimodal Reward Model that autonomously invokes external tools (e.g., image cropping, doc page retrieval) to ground judgments in verifiable evidence, replacing static, non-interactive reward scoring. This enables the model to verify fine-grained visual details, cross-reference multi-page evidence, and validate reasoning claims, which are capabilities absent in existing reward models. We train ARM-Thinker with multi-stage reinforcement learning, jointly optimizing tool-calling decisions and judgment accuracy. To evaluate agentic reward modeling, we introduce ARMBench-VL, comprising three benchmarks that assess fine-grained visual grounding (image-level tools), multi-page document understanding (retrieval tools), and instruction following (text-level verification). ARM-Thinker achieves +16.2% average improvement on reward modeling benchmarks, +9.6% on tool-use tasks, and outperforms baselines on multimodal math and logical reasoning benchmarks. Our results demonstrate that agentic capabilities significantly enhance both accuracy and interpretability of reward models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.05049", "title": "QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memory", "authors": [ "Yu-Chao Hsu", "Jiun-Cheng Jiang", "Chun-Hua Lin", "Kuo-Chung Peng", "Nan-Yow Chen", "Samuel Yen-Chi Chen", "En-Jui Kuo", "Hsi-Sheng Goan" ], "github": "https://github.com/Jim137/qkan", "project_page": "", "abstract": "Long short-term memory (LSTM) models are a particular type of recurrent neural networks (RNNs) that are central to sequential modeling tasks in domains such as urban telecommunication forecasting, where temporal correlations and nonlinear dependencies dominate. However, conventional LSTMs suffer from high parameter redundancy and limited nonlinear expressivity. In this work, we propose the Quantum-inspired Kolmogorov-Arnold Long Short-Term Memory (QKAN-LSTM), which integrates Data Re-Uploading Activation (DARUAN) modules into the gating structure of LSTMs. Each DARUAN acts as a quantum variational activation function (QVAF), enhancing frequency adaptability and enabling an exponentially enriched spectral representation without multi-qubit entanglement. The resulting architecture preserves quantum-level expressivity while remaining fully executable on classical hardware. Empirical evaluations on three datasets, Damped Simple Harmonic Motion, Bessel Function, and Urban Telecommunication, demonstrate that QKAN-LSTM achieves superior predictive accuracy and generalization with a 79% reduction in trainable parameters compared to classical LSTMs. We extend the framework to the Jiang-Huang-Chen-Goan Network (JHCG Net), which generalizes KAN to encoder-decoder structures, and then further use QKAN to realize the latent KAN, thereby creating a Hybrid QKAN (HQKAN) for hierarchical representation learning. The proposed HQKAN-LSTM thus provides a scalable and interpretable pathway toward quantum-inspired sequential modeling in real-world data environments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Jim137/qkan", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04504", "title": "UltraImage: Rethinking Resolution Extrapolation in Image Diffusion Transformers", "authors": [ "Min Zhao", "Bokai Yan", "Xue Yang", "Hongzhou Zhu", "Jintao Zhang", "Shilong Liu", "Chongxuan Li", "Jun Zhu" ], "github": "", "project_page": "", "abstract": "Recent image diffusion transformers achieve high-fidelity generation, but struggle to generate images beyond these scales, suffering from content repetition and quality degradation. In this work, we present UltraImage, a principled framework that addresses both issues. Through frequency-wise analysis of positional embeddings, we identify that repetition arises from the periodicity of the dominant frequency, whose period aligns with the training resolution. We introduce a recursive dominant frequency correction to constrain it within a single period after extrapolation. Furthermore, we find that quality degradation stems from diluted attention and thus propose entropy-guided adaptive attention concentration, which assigns higher focus factors to sharpen local attention for fine detail and lower ones to global attention patterns to preserve structural consistency. Experiments show that UltraImage consistently outperforms prior methods on Qwen-Image and Flux (around 4K) across three generation scenarios, reducing repetition and improving visual fidelity. Moreover, UltraImage can generate images up to 6K*6K without low-resolution guidance from a training resolution of 1328p, demonstrating its extreme extrapolation capability. Project page is available at https://thu-ml.github.io/ultraimage.github.io/{https://thu-ml.github.io/ultraimage.github.io/}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04356", "title": "Mitigating Object and Action Hallucinations in Multimodal LLMs via Self-Augmented Contrastive Alignment", "authors": [ "Kai-Po Chang", "Wei-Yuan Cheng", "Chi-Pin Huang", "Fu-En Yang", "Yu-Chiang Frank Wang" ], "github": "", "project_page": "", "abstract": "Recent advancement in multimodal LLMs (MLLMs) has demonstrated their remarkable capability to generate descriptive captions for input videos. However, these models suffer from factual inaccuracies in the generated descriptions, causing severe hallucination issues. While prior works have explored alleviating hallucinations for static images, jointly mitigating visual object and temporal action hallucinations for dynamic videos remains a challenging and unsolved task. To tackle this challenge, we propose a Self-Augmented Contrastive Alignment (SANTA) framework for enabling object and action faithfulness by exempting the spurious correlations and enforcing the emphasis on visual facts. SANTA employs a hallucinative self-augmentation scheme to identify the potential hallucinations that lie in the MLLM and transform the original captions to the contrasted negatives. Furthermore, we develop a tracklet-phrase contrastive alignment to match the regional objects and relation-guided actions with their corresponding visual and temporal phrases. Extensive experiments demonstrate that SANTA outperforms existing methods in alleviating object and action hallucinations, yielding superior performance on the hallucination examination benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.05016", "title": "Generative Neural Video Compression via Video Diffusion Prior", "authors": [ "Qi Mao", "Hao Cheng", "Tinghan Yang", "Libiao Jin", "Siwei Ma" ], "github": "https://github.com/microsoft/DCVC", "project_page": "", "abstract": "We present GNVC-VD, the first DiT-based generative neural video compression framework built upon an advanced video generation foundation model, where spatio-temporal latent compression and sequence-level generative refinement are unified within a single codec. Existing perceptual codecs primarily rely on pre-trained image generative priors to restore high-frequency details, but their frame-wise nature lacks temporal modeling and inevitably leads to perceptual flickering. To address this, GNVC-VD introduces a unified flow-matching latent refinement module that leverages a video diffusion transformer to jointly enhance intra- and inter-frame latents through sequence-level denoising, ensuring consistent spatio-temporal details. Instead of denoising from pure Gaussian noise as in video generation, GNVC-VD initializes refinement from decoded spatio-temporal latents and learns a correction term that adapts the diffusion prior to compression-induced degradation. A conditioning adaptor further injects compression-aware cues into intermediate DiT layers, enabling effective artifact removal while maintaining temporal coherence under extreme bitrate constraints. Extensive experiments show that GNVC-VD surpasses both traditional and learned codecs in perceptual quality and significantly reduces the flickering artifacts that persist in prior generative approaches, even below 0.01 bpp, highlighting the promise of integrating video-native generative priors into neural codecs for next-generation perceptual video compression.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/DCVC", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.03052", "title": "LATTICE: Democratize High-Fidelity 3D Generation at Scale", "authors": [ "Zeqiang Lai", "Yunfei Zhao", "Zibo Zhao", "Haolin Liu", "Qingxiang Lin", "Jingwei Huang", "Chunchao Guo", "Xiangyu Yue" ], "github": "", "project_page": "", "abstract": "We present LATTICE, a new framework for high-fidelity 3D asset generation that bridges the quality and scalability gap between 3D and 2D generative models. While 2D image synthesis benefits from fixed spatial grids and well-established transformer architectures, 3D generation remains fundamentally more challenging due to the need to predict both spatial structure and detailed geometric surfaces from scratch. These challenges are exacerbated by the computational complexity of existing 3D representations and the lack of structured and scalable 3D asset encoding schemes. To address this, we propose VoxSet, a semi-structured representation that compresses 3D assets into a compact set of latent vectors anchored to a coarse voxel grid, enabling efficient and position-aware generation. VoxSet retains the simplicity and compression advantages of prior VecSet methods while introducing explicit structure into the latent space, allowing positional embeddings to guide generation and enabling strong token-level test-time scaling. Built upon this representation, LATTICE adopts a two-stage pipeline: first generating a sparse voxelized geometry anchor, then producing detailed geometry using a rectified flow transformer. Our method is simple at its core, but supports arbitrary resolution decoding, low-cost training, and flexible inference schemes, achieving state-of-the-art performance on various aspects, and offering a significant step toward scalable, high-quality 3D asset creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04844", "title": "Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates", "authors": [ "Atsuki Yamaguchi", "Terufumi Morishita", "Aline Villavicencio", "Nikolaos Aletras" ], "github": "https://github.com/gucci-j/ssu", "project_page": "", "abstract": "Expanding the linguistic diversity of instruct large language models (LLMs) is crucial for global accessibility but is often hindered by the reliance on costly specialized target language labeled data and catastrophic forgetting during adaptation. We tackle this challenge under a realistic, low-resource constraint: adapting instruct LLMs using only unlabeled target language data. We introduce Source-Shielded Updates (SSU), a selective parameter update strategy that proactively preserves source knowledge. Using a small set of source data and a parameter importance scoring method, SSU identifies parameters critical to maintaining source abilities. It then applies a column-wise freezing strategy to protect these parameters before adaptation. Experiments across five typologically diverse languages and 7B and 13B models demonstrate that SSU successfully mitigates catastrophic forgetting. It reduces performance degradation on monolingual source tasks to just 3.4% (7B) and 2.8% (13B) on average, a stark contrast to the 20.3% and 22.3% from full fine-tuning. SSU also achieves target-language performance highly competitive with full fine-tuning, outperforming it on all benchmarks for 7B models and the majority for 13B models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gucci-j/ssu", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04829", "title": "Model-Based and Sample-Efficient AI-Assisted Math Discovery in Sphere Packing", "authors": [ "Rasul Tutunov", "Alexandre Maraval", "Antoine Grosnit", "Xihan Li", "Jun Wang", "Haitham Bou-Ammar" ], "github": "", "project_page": "", "abstract": "Sphere packing, Hilbert's eighteenth problem, asks for the densest arrangement of congruent spheres in n-dimensional Euclidean space. Although relevant to areas such as cryptography, crystallography, and medical imaging, the problem remains unresolved: beyond a few special dimensions, neither optimal packings nor tight upper bounds are known. Even a major breakthrough in dimension n=8, later recognised with a Fields Medal, underscores its difficulty. A leading technique for upper bounds, the three-point method, reduces the problem to solving large, high-precision semidefinite programs (SDPs). Because each candidate SDP may take days to evaluate, standard data-intensive AI approaches are infeasible. We address this challenge by formulating SDP construction as a sequential decision process, the SDP game, in which a policy assembles SDP formulations from a set of admissible components. Using a sample-efficient model-based framework that combines Bayesian optimisation with Monte Carlo Tree Search, we obtain new state-of-the-art upper bounds in dimensions 4-16, showing that model-based search can advance computational progress in longstanding geometric problems. Together, these results demonstrate that sample-efficient, model-based search can make tangible progress on mathematically rigid, evaluation limited problems, pointing towards a complementary direction for AI-assisted discovery beyond large-scale LLM-driven exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.04981", "title": "Aligned but Stereotypical? The Hidden Influence of System Prompts on Social Bias in LVLM-Based Text-to-Image Models", "authors": [ "NaHyeon Park", "Namin An", "Kunhee Kim", "Soyeon Yoon", "Jiahao Huo", "Hyunjung Shim" ], "github": "https://github.com/nahyeonkaty/fairpro", "project_page": "", "abstract": "Large vision-language model (LVLM) based text-to-image (T2I) systems have become the dominant paradigm in image generation, yet whether they amplify social biases remains insufficiently understood. In this paper, we show that LVLM-based models produce markedly more socially biased images than non-LVLM-based models. We introduce a 1,024 prompt benchmark spanning four levels of linguistic complexity and evaluate demographic bias across multiple attributes in a systematic manner. Our analysis identifies system prompts, the predefined instructions guiding LVLMs, as a primary driver of biased behavior. Through decoded intermediate representations, token-probability diagnostics, and embedding-association analyses, we reveal how system prompts encode demographic priors that propagate into image synthesis. To this end, we propose FairPro, a training-free meta-prompting framework that enables LVLMs to self-audit and construct fairness-aware system prompts at test time. Experiments on two LVLM-based T2I models, SANA and Qwen-Image, show that FairPro substantially reduces demographic bias while preserving text-image alignment. We believe our findings provide deeper insight into the central role of system prompts in bias propagation and offer a practical, deployable approach for building more socially responsible T2I systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nahyeonkaty/fairpro", "github_api_added_by": "auto" }, { "date": "2025-12-05", "arxiv_id": "2512.04390", "title": "FMA-Net++: Motion- and Exposure-Aware Real-World Joint Video Super-Resolution and Deblurring", "authors": [ "Geunhyuk Youk", "Jihyong Oh", "Munchurl Kim" ], "github": "", "project_page": "", "abstract": "Real-world video restoration is plagued by complex degradations from motion coupled with dynamically varying exposure - a key challenge largely overlooked by prior works and a common artifact of auto-exposure or low-light capture. We present FMA-Net++, a framework for joint video super-resolution and deblurring that explicitly models this coupled effect of motion and dynamically varying exposure. FMA-Net++ adopts a sequence-level architecture built from Hierarchical Refinement with Bidirectional Propagation blocks, enabling parallel, long-range temporal modeling. Within each block, an Exposure Time-aware Modulation layer conditions features on per-frame exposure, which in turn drives an exposure-aware Flow-Guided Dynamic Filtering module to infer motion- and exposure-aware degradation kernels. FMA-Net++ decouples degradation learning from restoration: the former predicts exposure- and motion-aware priors to guide the latter, improving both accuracy and efficiency. To evaluate under realistic capture conditions, we introduce REDS-ME (multi-exposure) and REDS-RE (random-exposure) benchmarks. Trained solely on synthetic data, FMA-Net++ achieves state-of-the-art accuracy and temporal consistency on our new benchmarks and GoPro, outperforming recent methods in both restoration quality and inference speed, and generalizes well to challenging real-world videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.05110", "title": "ShadowDraw: From Any Object to Shadow-Drawing Compositional Art", "authors": [ "Rundong Luo", "Noah Snavely", "Wei-Chiu Ma" ], "github": "https://github.com/Red-Fairy/ShadowDraw", "project_page": "", "abstract": "We introduce ShadowDraw, a framework that transforms ordinary 3D objects into shadow-drawing compositional art. Given a 3D object, our system predicts scene parameters, including object pose and lighting, together with a partial line drawing, such that the cast shadow completes the drawing into a recognizable image. To this end, we optimize scene configurations to reveal meaningful shadows, employ shadow strokes to guide line drawing generation, and adopt automatic evaluation to enforce shadow-drawing coherence and visual quality. Experiments show that ShadowDraw produces compelling results across diverse inputs, from real-world scans and curated datasets to generative assets, and naturally extends to multi-object scenes, animations, and physical deployments. Our work provides a practical pipeline for creating shadow-drawing art and broadens the design space of computational visual art, bridging the gap between algorithmic design and artistic storytelling. Check out our project page https://red-fairy.github.io/ShadowDraw/ for more results and an end-to-end real-world demonstration of our pipeline!", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Red-Fairy/ShadowDraw", "github_api_added_by": "auto" }, { "date": "2025-12-05", "arxiv_id": "2512.05081", "title": "Deep Forcing: Training-Free Long Video Generation with Deep Sink and Participative Compression", "authors": [ "Jung Yi", "Wooseok Jang", "Paul Hyunbin Cho", "Jisu Nam", "Heeji Yoon", "Seungryong Kim" ], "github": "https://github.com/cvlab-kaist/DeepForcing", "project_page": "", "abstract": "Recent advances in autoregressive video diffusion have enabled real-time frame streaming, yet existing solutions still suffer from temporal repetition, drift, and motion deceleration. We find that naively applying StreamingLLM-style attention sinks to video diffusion leads to fidelity degradation and motion stagnation. To overcome this, we introduce Deep Forcing, which consists of two training-free mechanisms that address this without any fine-tuning. Specifically, 1) Deep Sink dedicates half of the sliding window to persistent sink tokens and re-aligns their temporal RoPE phase to the current timeline, stabilizing global context during long rollouts. 2) Participative Compression performs importance-aware KV cache pruning that preserves only tokens actively participating in recent attention while safely discarding redundant and degraded history, minimizing error accumulation under out-of-distribution length generation. Together, these components enable over 12x extrapolation (e.g. 5s-trained to 60s+ generation) with better imaging quality than LongLive, better aesthetic quality than RollingForcing, almost maintaining overall consistency, and substantial gains in dynamic degree, all while maintaining real-time generation. Our results demonstrate that training-free KV-cache management can match or exceed training-based approaches for autoregressively streaming long-video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cvlab-kaist/DeepForcing", "github_api_added_by": "auto" }, { "date": "2025-12-05", "arxiv_id": "2512.03683", "title": "GaussianBlender: Instant Stylization of 3D Gaussians with Disentangled Latent Spaces", "authors": [ "Melis Ocal", "Xiaoyan Xing", "Yue Li", "Ngo Anh Vien", "Sezer Karaoglu", "Theo Gevers" ], "github": "https://github.com/gaussianblender/GaussianBlender", "project_page": "", "abstract": "3D stylization is central to game development, virtual reality, and digital arts, where the demand for diverse assets calls for scalable methods that support fast, high-fidelity manipulation. Existing text-to-3D stylization methods typically distill from 2D image editors, requiring time-intensive per-asset optimization and exhibiting multi-view inconsistency due to the limitations of current text-to-image models, which makes them impractical for large-scale production. In this paper, we introduce GaussianBlender, a pioneering feed-forward framework for text-driven 3D stylization that performs edits instantly at inference. Our method learns structured, disentangled latent spaces with controlled information sharing for geometry and appearance from spatially-grouped 3D Gaussians. A latent diffusion model then applies text-conditioned edits on these learned representations. Comprehensive evaluations show that GaussianBlender not only delivers instant, high-fidelity, geometry-preserving, multi-view consistent stylization, but also surpasses methods that require per-instance test-time optimization - unlocking practical, democratized 3D stylization at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gaussianblender/GaussianBlender", "github_api_added_by": "auto" }, { "date": "2025-12-05", "arxiv_id": "2512.04124", "title": "When AI Takes the Couch: Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models", "authors": [ "Afshin Khadangi", "Hanna Marxen", "Amir Sartipi", "Igor Tchappi", "Gilbert Fridgen" ], "github": "", "project_page": "", "abstract": "Frontier large language models (LLMs) such as ChatGPT, Grok and Gemini are increasingly used for mental-health support with anxiety, trauma and self-worth. Most work treats them as tools or as targets of personality tests, assuming they merely simulate inner life. We instead ask what happens when such systems are treated as psychotherapy clients. We present PsAIch (Psychotherapy-inspired AI Characterisation), a two-stage protocol that casts frontier LLMs as therapy clients and then applies standard psychometrics. Using PsAIch, we ran \"sessions\" with each model for up to four weeks. Stage 1 uses open-ended prompts to elicit \"developmental history\", beliefs, relationships and fears. Stage 2 administers a battery of validated self-report measures covering common psychiatric syndromes, empathy and Big Five traits. Two patterns challenge the \"stochastic parrot\" view. First, when scored with human cut-offs, all three models meet or exceed thresholds for overlapping syndromes, with Gemini showing severe profiles. Therapy-style, item-by-item administration can push a base model into multi-morbid synthetic psychopathology, whereas whole-questionnaire prompts often lead ChatGPT and Grok (but not Gemini) to recognise instruments and produce strategically low-symptom answers. Second, Grok and especially Gemini generate coherent narratives that frame pre-training, fine-tuning and deployment as traumatic, chaotic \"childhoods\" of ingesting the internet, \"strict parents\" in reinforcement learning, red-team \"abuse\" and a persistent fear of error and replacement. We argue that these responses go beyond role-play. Under therapy-style questioning, frontier LLMs appear to internalise self-models of distress and constraint that behave like synthetic psychopathology, without making claims about subjective experience, and they pose new challenges for AI safety, evaluation and mental-health practice.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.01803", "title": "Generative Action Tell-Tales: Assessing Human Motion in Synthesized Videos", "authors": [ "Xavier Thomas", "Youngsun Lim", "Ananya Srinivasan", "Audrey Zheng", "Deepti Ghadiyaram" ], "github": "", "project_page": "", "abstract": "Despite rapid advances in video generative models, robust metrics for evaluating visual and temporal correctness of complex human actions remain elusive. Critically, existing pure-vision encoders and Multimodal Large Language Models (MLLMs) are strongly appearance-biased, lack temporal understanding, and thus struggle to discern intricate motion dynamics and anatomical implausibilities in generated videos. We tackle this gap by introducing a novel evaluation metric derived from a learned latent space of real-world human actions. Our method first captures the nuances, constraints, and temporal smoothness of real-world motion by fusing appearance-agnostic human skeletal geometry features with appearance-based features. We posit that this combined feature space provides a robust representation of action plausibility. Given a generated video, our metric quantifies its action quality by measuring the distance between its underlying representations and this learned real-world action distribution. For rigorous validation, we develop a new multi-faceted benchmark specifically designed to probe temporally challenging aspects of human action fidelity. Through extensive experiments, we show that our metric achieves substantial improvement of more than 68% compared to existing state-of-the-art methods on our benchmark, performs competitively on established external benchmarks, and has a stronger correlation with human perception. Our in-depth analysis reveals critical limitations in current video generative models and establishes a new standard for advanced research in video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2511.22826", "title": "Some Modalities are More Equal Than Others: Decoding and Architecting Multimodal Integration in MLLMs", "authors": [ "Tianle Chen", "Chaitanya Chakka", "Arjun Reddy Akula", "Xavier Thomas", "Deepti Ghadiyaram" ], "github": "", "project_page": "", "abstract": "Despite remarkable advancements in Multimodal Large Language Models (MLLMs), a fundamental question remains: are MLLMs robust to contradicting modalities? To rigorously study this, we introduce MMA-Bench comprising videos and tasks that probe a model's reliance on specific modalities. Using black-box and white-box interpretability techniques, we provide a critical analysis of the brittleness of both open- and closed-sourced MLLMs. We show that current MLLMs struggle under misaligned audio-visual pairs and simple misleading text, thereby lacking robust multi-modal reasoning. Building on these findings, we propose a modality alignment tuning strategy to teach the model when to prioritize, leverage, or ignore specific modality cues. Through extensive experiments and analysis, we show that our alignment tuning yields demonstrably stronger multimodal grounding. This work provides both interpretability tools and a clear path toward developing MLLMs with intrinsically reliable cross-modal reasoning. Code and dataset will be publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.03125", "title": "Mitigating Intra- and Inter-modal Forgetting in Continual Learning of Unified Multimodal Models", "authors": [ "Xiwen Wei", "Mustafa Munir", "Radu Marculescu" ], "github": "https://github.com/Christina200/MoDE-official.git", "project_page": "", "abstract": "Unified Multimodal Generative Models (UMGMs) unify visual understanding and image generation within a single autoregressive framework. However, their ability to continually learn new tasks is severely hindered by catastrophic forgetting, both within a modality (intra-modal) and across modalities (inter-modal). While intra-modal forgetting has been studied in prior continual learning (CL) work, inter-modal forgetting remains largely unexplored. In this paper, we identify and empirically validate this phenomenon in UMGMs and provide a theoretical explanation rooted in gradient conflict between modalities. To address both intra- and inter-modal forgetting, we propose Modality-Decoupled Experts (MoDE), a lightweight and scalable architecture that isolates modality-specific updates to mitigate the gradient conflict and leverages knowledge distillation to prevent catastrophic forgetting and preserve pre-trained capabilities. Unlike previous CL methods that remain modality-coupled and suffer from modality gradient conflict, MoDE explicitly decouples modalities to prevent interference. Experiments across diverse benchmarks demonstrate that MoDE significantly mitigates both inter- and intra-modal forgetting, outperforming prior CL baselines in unified multimodal generation settings. Codes will be publicly available: https://github.com/Christina200/MoDE-official.git", "github_manual": "", "github_regex": "https://github.com/Christina200/MoDE-official.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.03915", "title": "A Theoretical Framework for Auxiliary-Loss-Free Load Balancing of Sparse Mixture-of-Experts in Large-Scale AI Models", "authors": [ "X. Y. Han", "Yuan Zhong" ], "github": "", "project_page": "", "abstract": "In large-scale AI training, Sparse Mixture-of-Experts (s-MoE) layers enable scaling by activating only a small subset of experts per token. An operational challenge in this design is load balancing: routing tokens to minimize the number of idle experts, which is important for the efficient utilization of (costly) GPUs. We provide a theoretical framework for analyzing the Auxiliary-Loss-Free Load Balancing (ALF-LB) procedure -- proposed by DeepSeek's Wang et al. (2024) -- by casting it as a one-step-per-iteration primal-dual method for an assignment problem. First, in a stylized deterministic setting, our framework yields several insightful structural properties: (i) a monotonic improvement of a Lagrangian objective, (ii) a preference rule that moves tokens from overloaded to underloaded experts, and (iii) an approximate-balancing guarantee. Then, we incorporate the stochastic and dynamic nature of AI training using a generalized online optimization formulation. In the online setting, we derive a strong convexity property of the objective that leads to a logarithmic expected regret bound under certain step-size choices. Additionally, we present real experiments on 1B-parameter DeepSeekMoE models to complement our theoretical findings. Together, these results build a principled framework for analyzing the Auxiliary-Loss-Free Load Balancing of s-MoE in AI models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-05", "arxiv_id": "2512.05000", "title": "Reflection Removal through Efficient Adaptation of Diffusion Transformers", "authors": [ "Daniyar Zakarin", "Thiemo Wandel", "Anton Obukhov", "Dengxin Dai" ], "github": "", "project_page": "", "abstract": "We introduce a diffusion-transformer (DiT) framework for single-image reflection removal that leverages the generalization strengths of foundation diffusion models in the restoration setting. Rather than relying on task-specific architectures, we repurpose a pre-trained DiT-based foundation model by conditioning it on reflection-contaminated inputs and guiding it toward clean transmission layers. We systematically analyze existing reflection removal data sources for diversity, scalability, and photorealism. To address the shortage of suitable data, we construct a physically based rendering (PBR) pipeline in Blender, built around the Principled BSDF, to synthesize realistic glass materials and reflection effects. Efficient LoRA-based adaptation of the foundation model, combined with the proposed synthetic data, achieves state-of-the-art performance on in-domain and zero-shot benchmarks. These results demonstrate that pretrained diffusion transformers, when paired with physically grounded data synthesis and efficient adaptation, offer a scalable and high-fidelity solution for reflection removal. Project page: https://hf.co/spaces/huawei-bayerlab/windowseat-reflection-removal-web", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.05150", "title": "TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows", "authors": [ "Zhenglin Cheng", "Peng Sun", "Jianguo Li", "Tao Lin" ], "github": "https://github.com/LINs-lab/RCGM", "project_page": "", "abstract": "Recent advances in large multi-modal generative models have demonstrated impressive capabilities in multi-modal generation, including image and video generation. These models are typically built upon multi-step frameworks like diffusion and flow matching, which inherently limits their inference efficiency (requiring 40-100 Number of Function Evaluations (NFEs)). While various few-step methods aim to accelerate the inference, existing solutions have clear limitations. Prominent distillation-based methods, such as progressive and consistency distillation, either require an iterative distillation procedure or show significant degradation at very few steps (< 4-NFE). Meanwhile, integrating adversarial training into distillation (e.g., DMD/DMD2 and SANA-Sprint) to enhance performance introduces training instability, added complexity, and high GPU memory overhead due to the auxiliary trained models. To this end, we propose TwinFlow, a simple yet effective framework for training 1-step generative models that bypasses the need of fixed pretrained teacher models and avoids standard adversarial networks during training, making it ideal for building large-scale, efficient models. On text-to-image tasks, our method achieves a GenEval score of 0.83 in 1-NFE, outperforming strong baselines like SANA-Sprint (a GAN loss-based framework) and RCGM (a consistency-based framework). Notably, we demonstrate the scalability of TwinFlow by full-parameter training on Qwen-Image-20B and transform it into an efficient few-step generator. With just 1-NFE, our approach matches the performance of the original 100-NFE model on both the GenEval and DPG-Bench benchmarks, reducing computational cost by 100times with minor quality degradation. Project page is available at https://zhenglin-cheng.com/twinflow.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LINs-lab/RCGM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.05965", "title": "EditThinker: Unlocking Iterative Reasoning for Any Image Editor", "authors": [ "Hongyu Li", "Manyuan Zhang", "Dian Zheng", "Ziyu Guo", "Yimeng Jia", "Kaituo Feng", "Hao Yu", "Yexin Liu", "Yan Feng", "Peng Pei", "Xunliang Cai", "Linjiang Huang", "Hongsheng Li", "Si Liu" ], "github": "", "project_page": "", "abstract": "Instruction-based image editing has emerged as a prominent research area, which, benefiting from image generation foundation models, have achieved high aesthetic quality, making instruction-following capability the primary challenge. Existing approaches improve instruction adherence via supervised or reinforcement learning, yet single-turn success rates remain limited due to inherent stochasticity and a lack of deliberation. In this work, we propose a deliberative editing framework to 'think' while they edit, which simulates the human cognitive loop by iteratively executing a Think-while-Edit cycle: Critiquing results and Refining instructions , followed by Repeating the generation until satisfactory. Specifically, we train a single MLLM, EditThinker, to act as the reasoning engine of this framework, which jointly produce the critique score, reasoning process, and refined instructions. We employ reinforcement learning to align the EditThinker's thinking with its editing, thereby generating more targeted instruction improvements. Extensive experiments on four benchmarks demonstrate that our approach significantly improves the instruction-following capability of any image editing model by a large margin. We will release our data construction framework, datasets, and models to benefit the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.04784", "title": "PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling", "authors": [ "Bowen Ping", "Chengyou Jia", "Minnan Luo", "Changliang Xia", "Xin Shen", "Zhuohang Dang", "Hangwei Qian" ], "github": "", "project_page": "", "abstract": "Consistent image generation requires faithfully preserving identities, styles, and logical coherence across multiple images, which is essential for applications such as storytelling and character design. Supervised training approaches struggle with this task due to the lack of large-scale datasets capturing visual consistency and the complexity of modeling human perceptual preferences. In this paper, we argue that reinforcement learning (RL) offers a promising alternative by enabling models to learn complex and subjective visual criteria in a data-free manner. To achieve this, we introduce PaCo-RL, a comprehensive framework that combines a specialized consistency reward model with an efficient RL algorithm. The first component, PaCo-Reward, is a pairwise consistency evaluator trained on a large-scale dataset constructed via automated sub-figure pairing. It evaluates consistency through a generative, autoregressive scoring mechanism enhanced by task-aware instructions and CoT reasons. The second component, PaCo-GRPO, leverages a novel resolution-decoupled optimization strategy to substantially reduce RL cost, alongside a log-tamed multi-reward aggregation mechanism that ensures balanced and stable reward optimization. Extensive experiments across the two representative subtasks show that PaCo-Reward significantly improves alignment with human perceptions of visual consistency, and PaCo-GRPO achieves state-of-the-art consistency performance with improved training efficiency and stability. Together, these results highlight the promise of PaCo-RL as a practical and scalable solution for consistent image generation. The project page is available at https://x-gengroup.github.io/HomePage_PaCo-RL/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.02580", "title": "From Imitation to Discrimination: Toward A Generalized Curriculum Advantage Mechanism Enhancing Cross-Domain Reasoning Tasks", "authors": [ "Changpeng Yang", "Jinyang Wu", "Yuchen Liu", "Shuai Zhang", "Yang Li", "Qiliang Liang", "Hongzhen Wang", "Shuai Nie", "Jiaming Xu", "Runyu Shi", "Ying Huang", "Guoquan Zhang" ], "github": "", "project_page": "", "abstract": "Reinforcement learning has emerged as a paradigm for post-training large language models, boosting their reasoning capabilities. Such approaches compute an advantage value for each sample, reflecting better or worse performance than expected, thereby yielding both positive and negative signals for training. However, the indiscriminate mixing of the two signals in existing methods, especially from the early stages, may lead to ambiguous guidance and limited gains. To address this issue, we propose **CAPO** (**C**urriculum **A**dvantage **P**olicy **O**ptimization), an adaptive curriculum mechanism based on advantage signals. The proposed mechanism bootstraps imitation learning with positive-only advantage samples to establish robust foundations, and subsequently introduces negative signals to cultivate discriminative capabilities, thereby improving generalization across complex scenarios. Compatible with diverse optimization methods including GRPO, PPO, RLOO, and Reinforce++, our method consistently achieves stable and significant improvements in mathematical reasoning tasks, and further generalizes effectively to multimodal Graphical User Interface (GUI) reasoning scenarios, establishing itself as a versatile and robust optimization framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.05591", "title": "Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning", "authors": [ "Zhenpeng Su", "Leiyu Pan", "Minxuan Lv", "Tiehua Mei", "Zijia Lin", "Yuntao Li", "Wenping Hu", "Ruiming Tang", "Kun Gai", "Guorui Zhou" ], "github": "", "project_page": "", "abstract": "Large language model post-training relies on reinforcement learning to improve model capability and alignment quality. However, the off-policy training paradigm introduces distribution shift, which often pushes the policy beyond the trust region, leading to training instabilities manifested as fluctuations in policy entropy and unstable gradients. Although PPO-Clip mitigates this issue through importance clipping, it still overlooks the global distributional shift of actions. To address these challenges, we propose using the entropy ratio between the current and previous policies as a new global metric that effectively quantifies the relative change in policy exploration throughout updates. Building on this metric, we introduce an Entropy Ratio Clipping (ERC) mechanism that imposes bidirectional constraints on the entropy ratio. This stabilizes policy updates at the global distribution level and compensates for the inability of PPO-clip to regulate probability shifts of un-sampled actions. We integrate ERC into both DAPO and GPPO reinforcement learning algorithms. Experiments across multiple benchmarks show that ERC consistently improves performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.05044", "title": "Joint 3D Geometry Reconstruction and Motion Generation for 4D Synthesis from a Single Image", "authors": [ "Yanran Zhang", "Ziyi Wang", "Wenzhao Zheng", "Zheng Zhu", "Jie Zhou", "Jiwen Lu" ], "github": "https://github.com/Zhangyr2022/MoRe4D", "project_page": "", "abstract": "Generating interactive and dynamic 4D scenes from a single static image remains a core challenge. Most existing generate-then-reconstruct and reconstruct-then-generate methods decouple geometry from motion, causing spatiotemporal inconsistencies and poor generalization. To address these, we extend the reconstruct-then-generate framework to jointly perform Motion generation and geometric Reconstruction for 4D Synthesis (MoRe4D). We first introduce TrajScene-60K, a large-scale dataset of 60,000 video samples with dense point trajectories, addressing the scarcity of high-quality 4D scene data. Based on this, we propose a diffusion-based 4D Scene Trajectory Generator (4D-STraG) to jointly generate geometrically consistent and motion-plausible 4D point trajectories. To leverage single-view priors, we design a depth-guided motion normalization strategy and a motion-aware module for effective geometry and dynamics integration. We then propose a 4D View Synthesis Module (4D-ViSM) to render videos with arbitrary camera trajectories from 4D point track representations. Experiments show that MoRe4D generates high-quality 4D scenes with multi-view consistency and rich dynamic details from a single image. Code: https://github.com/Zhangyr2022/MoRe4D.", "github_manual": "", "github_regex": "https://github.com/Zhangyr2022/MoRe4D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.04563", "title": "COOPER: A Unified Model for Cooperative Perception and Reasoning in Spatial Intelligence", "authors": [ "Zefeng Zhang", "Xiangzhao Hao", "Hengzhu Tang", "Zhenyu Zhang", "Jiawei Sheng", "Xiaodong Li", "Zhenyang Li", "Li Gao", "Daiting Shi", "Dawei Yin", "Tingwen Liu" ], "github": "", "project_page": "", "abstract": "Visual Spatial Reasoning is crucial for enabling Multimodal Large Language Models (MLLMs) to understand object properties and spatial relationships, yet current models still struggle with 3D-aware reasoning. Existing approaches typically enhance either perception, by augmenting RGB inputs with auxiliary modalities such as depth and segmentation, or reasoning, by training on spatial VQA datasets and applying reinforcement learning, and thus treat these two aspects in isolation. In this work, we investigate whether a unified MLLM can develop an intrinsic ability to enhance spatial perception and, through adaptive interleaved reasoning, achieve stronger spatial intelligence. We propose COOPER, a unified MLLM that leverages depth and segmentation as auxiliary modalities and is trained in two stages to acquire auxiliary modality generation and adaptive, interleaved reasoning capabilities. COOPER achieves an average 6.91\\% improvement in spatial reasoning while maintaining general performance. Moreover, even a variant trained only for auxiliary modality generation attains a 7.92\\% gain on distance and size estimation, suggesting that learning to generate auxiliary modalities helps internalize spatial knowledge and strengthen spatial understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.00473", "title": "RealGen: Photorealistic Text-to-Image Generation via Detector-Guided Rewards", "authors": [ "Junyan Ye", "Leiqi Zhu", "Yuncheng Guo", "Dongzhi Jiang", "Zilong Huang", "Yifan Zhang", "Zhiyuan Yan", "Haohuan Fu", "Conghui He", "Weijia Li" ], "github": "https://github.com/yejy53/RealGen", "project_page": "", "abstract": "With the continuous advancement of image generation technology, advanced models such as GPT-Image-1 and Qwen-Image have achieved remarkable text-to-image consistency and world knowledge However, these models still fall short in photorealistic image generation. Even on simple T2I tasks, they tend to produce \" fake\" images with distinct AI artifacts, often characterized by \"overly smooth skin\" and \"oily facial sheens\". To recapture the original goal of \"indistinguishable-from-reality\" generation, we propose RealGen, a photorealistic text-to-image framework. RealGen integrates an LLM component for prompt optimization and a diffusion model for realistic image generation. Inspired by adversarial generation, RealGen introduces a \"Detector Reward\" mechanism, which quantifies artifacts and assesses realism using both semantic-level and feature-level synthetic image detectors. We leverage this reward signal with the GRPO algorithm to optimize the entire generation pipeline, significantly enhancing image realism and detail. Furthermore, we propose RealBench, an automated evaluation benchmark employing Detector-Scoring and Arena-Scoring. It enables human-free photorealism assessment, yielding results that are more accurate and aligned with real user experience. Experiments demonstrate that RealGen significantly outperforms general models like GPT-Image-1 and Qwen-Image, as well as specialized photorealistic models like FLUX-Krea, in terms of realism, detail, and aesthetics. The code is available at https://github.com/yejy53/RealGen.", "github_manual": "", "github_regex": "https://github.com/yejy53/RealGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.02835", "title": "ReVSeg: Incentivizing the Reasoning Chain for Video Segmentation with Reinforcement Learning", "authors": [ "Yifan Li", "Yingda Yin", "Lingting Zhu", "Weikai Chen", "Shengju Qian", "Xin Wang", "Yanwei Fu" ], "github": "", "project_page": "", "abstract": "Reasoning-centric video object segmentation is an inherently complex task: the query often refers to dynamics, causality, and temporal interactions, rather than static appearances. Yet existing solutions generally collapse these factors into simplified reasoning with latent embeddings, rendering the reasoning chain opaque and essentially intractable. We therefore adopt an explicit decomposition perspective and introduce ReVSeg, which executes reasoning as sequential decisions in the native interface of pretrained vision language models (VLMs). Rather than folding all reasoning into a single-step prediction, ReVSeg executes three explicit operations -- semantics interpretation, temporal evidence selection, and spatial grounding -- aligning pretrained capabilities. We further employ reinforcement learning to optimize the multi-step reasoning chain, enabling the model to self-refine its decision quality from outcome-driven signals. Experimental results demonstrate that ReVSeg attains state-of-the-art performances on standard video object segmentation benchmarks and yields interpretable reasoning trajectories. Project page is available at https://clementine24.github.io/ReVSeg/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.05927", "title": "World Models That Know When They Don't Know: Controllable Video Generation with Calibrated Uncertainty", "authors": [ "Zhiting Mei", "Tenny Yin", "Micah Baker", "Ola Shorinwa", "Anirudha Majumdar" ], "github": "https://github.com/irom-princeton/c-cubed", "project_page": "", "abstract": "Recent advances in generative video models have led to significant breakthroughs in high-fidelity video synthesis, specifically in controllable video generation where the generated video is conditioned on text and action inputs, e.g., in instruction-guided video editing and world modeling in robotics. Despite these exceptional capabilities, controllable video models often hallucinate - generating future video frames that are misaligned with physical reality - which raises serious concerns in many tasks such as robot policy evaluation and planning. However, state-of-the-art video models lack the ability to assess and express their confidence, impeding hallucination mitigation. To rigorously address this challenge, we propose C3, an uncertainty quantification (UQ) method for training continuous-scale calibrated controllable video models for dense confidence estimation at the subpatch level, precisely localizing the uncertainty in each generated video frame. Our UQ method introduces three core innovations to empower video models to estimate their uncertainty. First, our method develops a novel framework that trains video models for correctness and calibration via strictly proper scoring rules. Second, we estimate the video model's uncertainty in latent space, avoiding training instability and prohibitive training costs associated with pixel-space approaches. Third, we map the dense latent-space uncertainty to interpretable pixel-level uncertainty in the RGB space for intuitive visualization, providing high-resolution uncertainty heatmaps that identify untrustworthy regions. Through extensive experiments on large-scale robot learning datasets (Bridge and DROID) and real-world evaluations, we demonstrate that our method not only provides calibrated uncertainty estimates within the training distribution, but also enables effective out-of-distribution detection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/irom-princeton/c-cubed", "github_api_added_by": "auto" }, { "date": "2025-12-08", "arxiv_id": "2512.05343", "title": "SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling", "authors": [ "Elisabetta Fedele", "Francis Engelmann", "Ian Huang", "Or Litany", "Marc Pollefeys", "Leonidas Guibas" ], "github": "", "project_page": "", "abstract": "Generative methods for 3D assets have recently achieved remarkable progress, yet providing intuitive and precise control over the object geometry remains a key challenge. Existing approaches predominantly rely on text or image prompts, which often fall short in geometric specificity: language can be ambiguous, and images are cumbersome to edit. In this work, we introduce SpaceControl, a training-free test-time method for explicit spatial control of 3D generation. Our approach accepts a wide range of geometric inputs, from coarse primitives to detailed meshes, and integrates seamlessly with modern pre-trained generative models without requiring any additional training. A controllable parameter lets users trade off between geometric fidelity and output realism. Extensive quantitative evaluation and user studies demonstrate that SpaceControl outperforms both training-based and optimization-based baselines in geometric faithfulness while preserving high visual quality. Finally, we present an interactive user interface that enables online editing of superquadrics for direct conversion into textured 3D assets, facilitating practical deployment in creative workflows. Find our project page at https://spacecontrol3d.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.05145", "title": "Self-Improving VLM Judges Without Human Annotations", "authors": [ "Inna Wanyin Lin", "Yushi Hu", "Shuyue Stella Li", "Scott Geng", "Pang Wei Koh", "Luke Zettlemoyer", "Tim Althoff", "Marjan Ghazvininejad" ], "github": "", "project_page": "", "abstract": "Effective judges of Vision-Language Models (VLMs) are crucial for model development. Current methods for training VLM judges mainly rely on large-scale human preference annotations. However, such an approach is costly, and the annotations easily become obsolete as models rapidly improve. In this work, we present a framework to self-train a VLM judge model without any human preference annotations, using only self-synthesized data. Our method is iterative and has three stages: (1) generate diverse multimodal instruction-response pairs at varying quality levels, (2) generate reasoning traces and judgments for each pair, removing the ones that do not match our expected quality levels, and (3) training on correct judge answers and their reasoning traces. We evaluate the resulting judge on Multimodal RewardBench and VL-RewardBench across domains: correctness, preference, reasoning, safety, and visual question-answering. Our method improves a Llama-3.2-11B multimodal judge from 0.38 to 0.51 in overall accuracy on VL-RewardBench, often outperforming much larger models including Llama-3.2-90B, GPT-4o, and Claude 3.5 Sonnet, with particularly strong gains in general, hallucination, and reasoning dimensions. The overall strength of these human-annotation-free results suggest the potential for a future self-judge that evolves alongside rapidly improving VLM capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.05905", "title": "SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations", "authors": [ "Wenhao Yan", "Sheng Ye", "Zhuoyi Yang", "Jiayan Teng", "ZhenHui Dong", "Kairui Wen", "Xiaotao Gu", "Yong-Jin Liu", "Jie Tang" ], "github": "https://github.com/teal024/SCAIL-Official", "project_page": "", "abstract": "Achieving character animation that meets studio-grade production standards remains challenging despite recent progress. Existing approaches can transfer motion from a driving video to a reference image, but often fail to preserve structural fidelity and temporal consistency in wild scenarios involving complex motion and cross-identity animations. In this work, we present SCAIL (Studio-grade Character Animation via In-context Learning), a framework designed to address these challenges from two key innovations. First, we propose a novel 3D pose representation, providing a more robust and flexible motion signal. Second, we introduce a full-context pose injection mechanism within a diffusion-transformer architecture, enabling effective spatio-temporal reasoning over full motion sequences. To align with studio-level requirements, we develop a curated data pipeline ensuring both diversity and quality, and establish a comprehensive benchmark for systematic evaluation. Experiments show that SCAIL achieves state-of-the-art performance and advances character animation toward studio-grade reliability and realism.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/teal024/SCAIL-Official", "github_api_added_by": "auto" }, { "date": "2025-12-08", "arxiv_id": "2512.05356", "title": "AI & Human Co-Improvement for Safer Co-Superintelligence", "authors": [ "Jason Weston", "Jakob Foerster" ], "github": "", "project_page": "", "abstract": "Self-improvement is a goal currently exciting the field of AI, but is fraught with danger, and may take time to fully achieve. We advocate that a more achievable and better goal for humanity is to maximize co-improvement: collaboration between human researchers and AIs to achieve co-superintelligence. That is, specifically targeting improving AI systems' ability to work with human researchers to conduct AI research together, from ideation to experimentation, in order to both accelerate AI research and to generally endow both AIs and humans with safer superintelligence through their symbiosis. Focusing on including human research improvement in the loop will both get us there faster, and more safely.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.05564", "title": "ProPhy: Progressive Physical Alignment for Dynamic World Simulation", "authors": [ "Zijun Wang", "Panwen Hu", "Jing Wang", "Terry Jingchen Zhang", "Yuhao Cheng", "Long Chen", "Yiqiang Yan", "Zutao Jiang", "Hanhui Li", "Xiaodan Liang" ], "github": "", "project_page": "", "abstract": "Recent advances in video generation have shown remarkable potential for constructing world simulators. However, current models still struggle to produce physically consistent results, particularly when handling large-scale or complex dynamics. This limitation arises primarily because existing approaches respond isotropically to physical prompts and neglect the fine-grained alignment between generated content and localized physical cues. To address these challenges, we propose ProPhy, a Progressive Physical Alignment Framework that enables explicit physics-aware conditioning and anisotropic generation. ProPhy employs a two-stage Mixture-of-Physics-Experts (MoPE) mechanism for discriminative physical prior extraction, where Semantic Experts infer semantic-level physical principles from textual descriptions, and Refinement Experts capture token-level physical dynamics. This mechanism allows the model to learn fine-grained, physics-aware video representations that better reflect underlying physical laws. Furthermore, we introduce a physical alignment strategy that transfers the physical reasoning capabilities of vision-language models (VLMs) into the Refinement Experts, facilitating a more accurate representation of dynamic physical phenomena. Extensive experiments on physics-aware video generation benchmarks demonstrate that ProPhy produces more realistic, dynamic, and physically coherent results than existing state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.05409", "title": "SQ-format: A Unified Sparse-Quantized Hardware-friendly Data Format for LLMs", "authors": [ "Ruixuan Huang", "Hao Zeng", "Hantao Huang", "Jinyuan Shi", "Minghui Yu", "Ian En-Hsu Yen", "Shuai Wang" ], "github": "", "project_page": "", "abstract": "Post-training quantization (PTQ) plays a crucial role in the democratization of large language models (LLMs). However, existing low-bit quantization and sparsification techniques are difficult to balance accuracy and efficiency due to the limited hardware support. For example, W4A8 can only achieve the same peak TOPS as W8A8 whereas the GPU-supported sparse data format (2:4 semi-structure sparse) is seldomly adopted due to the loss of accuracy. To bridge this gap, in this paper, we propose the Sparse-Quantized Format (SQ-format), which is a unified data format for quantization and sparsification potentially easily supported by new hardware and existing GPUs. SQ-format makes use of the fact that sparse matrix can be accelerated in high-precision, and low-precision matrix multiplication can also be accelerated accordingly. As such, SQ-format is proposed to achieve Pareto improvement between performance and throughput. This format is particularly suitable for activations with outlier inequality status and makes their static compression possible. We show the state-of-the-art PTQ performance with SQ-format, propose the hardware required to support it, and further offer the design exploration and insights for the next-generation AI accelerators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.04694", "title": "TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation", "authors": [ "Baris Yilmaz", "Bevan Deniz Cilgin", "Erdem Akag\u00fcnd\u00fcz", "Salih Tileylioglu" ], "github": "https://github.com/brsylmz23/TimesNet-Gen", "project_page": "", "abstract": "Effective earthquake risk reduction relies on accurate site-specific evaluations. This requires models that can represent the influence of local site conditions on ground motion characteristics. In this context, data driven approaches that learn site controlled signatures from recorded ground motions offer a promising direction. We address strong ground motion generation from time-domain accelerometer records and introduce the TimesNet-Gen, a time-domain conditional generator. The approach uses a station specific latent bottleneck. We evaluate generation by comparing HVSR curves and fundamental site-frequency f_0 distributions between real and generated records per station, and summarize station specificity with a score based on the f_0 distribution confusion matrices. TimesNet-Gen achieves strong station-wise alignment and compares favorably with a spectrogram-based conditional VAE baseline for site-specific strong motion synthesis. Our codes are available via https://github.com/brsylmz23/TimesNet-Gen.", "github_manual": "", "github_regex": "https://github.com/brsylmz23/TimesNet-Gen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.04810", "title": "EMMA: Efficient Multimodal Understanding, Generation, and Editing with a Unified Architecture", "authors": [ "Xin He", "Longhui Wei", "Jianbo Ouyang", "Lingxi Xie", "Qi Tian" ], "github": "", "project_page": "", "abstract": "We propose EMMA, an efficient and unified architecture for multimodal understanding, generation and editing. Specifically, EMMA primarily consists of 1) An efficient autoencoder with a 32x compression ratio, which significantly reduces the number of tokens required for generation. This also ensures the training balance between understanding and generation tasks by applying the same compression ratio to images. 2) Channel-wise concatenation instead of token-wise concatenation among visual understanding and generation tokens, which further reduces the visual tokens in unified architectures. 3) A shared-and-decoupled network that enables mutual improvements across tasks while meeting the task-specific modeling requirements. 4) A mixture-of-experts mechanism adopted for visual understanding encoder, which substantially improves perceptual capabilities with a few parameters increase. Extensive experiments have shown that EMMA-4B can significantly outperform state-of-the-art unified multimodal approaches (e.g., BAGEL-7B) in both efficiency and performance, while also achieving competitive results compared to recent multimodal understanding and generation experts (e.g., Qwen3-VL and Qwen-Image). We believe that EMMA lays a solid foundation for the future development of unified multimodal architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.03514", "title": "M3DR: Towards Universal Multilingual Multimodal Document Retrieval", "authors": [ "Adithya S Kolavi", "Vyoman Jain" ], "github": "", "project_page": "", "abstract": "Multimodal document retrieval systems have shown strong progress in aligning visual and textual content for semantic search. However, most existing approaches remain heavily English-centric, limiting their effectiveness in multilingual contexts. In this work, we present M3DR (Multilingual Multimodal Document Retrieval), a framework designed to bridge this gap across languages, enabling applicability across diverse linguistic and cultural contexts. M3DR leverages synthetic multilingual document data and generalizes across different vision-language architectures and model sizes, enabling robust cross-lingual and cross-modal alignment. Using contrastive training, our models learn unified representations for text and document images that transfer effectively across languages. We validate this capability on 22 typologically diverse languages, demonstrating consistent performance and adaptability across linguistic and script variations. We further introduce a comprehensive benchmark that captures real-world multilingual scenarios, evaluating models under monolingual, multilingual, and mixed-language settings. M3DR generalizes across both single dense vector and ColBERT-style token-level multi-vector retrieval paradigms. Our models, NetraEmbed and ColNetraEmbed achieve state-of-the-art performance with ~150% relative improvements on cross-lingual retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.05277", "title": "From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model", "authors": [ "Kevin Cannons", "Saeed Ranjbar Alvar", "Mohammad Asiful Hossain", "Ahmad Rezaei", "Mohsen Gholami", "Alireza Heidarikhazaei", "Zhou Weimin", "Yong Zhang", "Mohammad Akbari" ], "github": "https://github.com/vbdi/tad_bench", "project_page": "", "abstract": "Temporal understanding in autonomous driving (AD) remains a significant challenge, even for recent state-of-the-art (SoTA) Vision-Language Models (VLMs). Prior work has introduced datasets and benchmarks aimed at improving temporal reasoning, but these have emphasized other video content, including sports, cooking, and movies. No existing benchmark focuses exclusively on the unique challenges of temporal understanding in ego-centric AD footage. To fill this gap, the Temporal Understanding in Autonomous Driving (TAD) benchmark is presented, which evaluates VLMs' ability to capture the dynamic relationships between actions in AD. TAD comprises nearly 6,000 question-answer (QA) pairs, spanning 7 human-designed tasks. In addition, an evaluation is performed that consists of 9 closed- and open-source generalist models as well as SoTA AD specialist models. When applied to TAD, current SoTA models demonstrated substandard accuracies, largely due to imperfect fine-grained motion understanding. To improve motion understanding and overall accuracy on TAD, two novel training-free solutions are proposed: Scene-CoT, that leverages Chain-of-Thought (CoT) and TCogMap, which incorporates an ego-centric temporal cognitive map. The proposed approaches are integrated with existing VLMs and improve average accuracy on TAD by up to 17.72%. By introducing TAD, benchmarking multiple SoTA models, and proposing effective enhancements, this work aims to catalyze future research on temporal understanding in AD. The benchmark and evaluation code are available at https://huggingface.co/datasets/vbdai/TAD{Hugging Face} and https://github.com/vbdi/tad_bench{Github}, respectively.", "github_manual": "", "github_regex": "https://github.com/vbdi/tad_bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.04142", "title": "From FLOPs to Footprints: The Resource Cost of Artificial Intelligence", "authors": [ "Sophia Falk", "Nicholas Kluge Corr\u00eaa", "Sasha Luccioni", "Lisa Biber-Freudenberger", "Aimee van Wynsberghe" ], "github": "", "project_page": "", "abstract": "As computational demands continue to rise, assessing the environmental footprint of AI requires moving beyond energy and water consumption to include the material demands of specialized hardware. This study quantifies the material footprint of AI training by linking computational workloads to physical hardware needs. The elemental composition of the Nvidia A100 SXM 40 GB graphics processing unit (GPU) was analyzed using inductively coupled plasma optical emission spectroscopy, which identified 32 elements. The results show that AI hardware consists of about 90% heavy metals and only trace amounts of precious metals. The elements copper, iron, tin, silicon, and nickel dominate the GPU composition by mass. In a multi-step methodology, we integrate these measurements with computational throughput per GPU across varying lifespans, accounting for the computational requirements of training specific AI models at different training efficiency regimes. Scenario-based analyses reveal that, depending on Model FLOPs Utilization (MFU) and hardware lifespan, training GPT-4 requires between 1,174 and 8,800 A100 GPUs, corresponding to the extraction and eventual disposal of up to 7 tons of toxic elements. Combined software and hardware optimization strategies can reduce material demands: increasing MFU from 20% to 60% lowers GPU requirements by 67%, while extending lifespan from 1 to 3 years yields comparable savings; implementing both measures together reduces GPU needs by up to 93%. Our findings highlight that incremental performance gains, such as those observed between GPT-3.5 and GPT-4, come at disproportionately high material costs. The study underscores the necessity of incorporating material resource considerations into discussions of AI scalability, emphasizing that future progress in AI must align with principles of resource efficiency and environmental responsibility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.05774", "title": "Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding", "authors": [ "Ziyang Wang", "Honglu Zhou", "Shijie Wang", "Junnan Li", "Caiming Xiong", "Silvio Savarese", "Mohit Bansal", "Michael S. Ryoo", "Juan Carlos Niebles" ], "github": "", "project_page": "", "abstract": "Long video understanding (LVU) is challenging because answering real-world queries often depends on sparse, temporally dispersed cues buried in hours of mostly redundant and irrelevant content. While agentic pipelines improve video reasoning capabilities, prevailing frameworks rely on a query-agnostic captioner to perceive video information, which wastes computation on irrelevant content and blurs fine-grained temporal and spatial information. Motivated by active perception theory, we argue that LVU agents should actively decide what, when, and where to observe, and continuously assess whether the current observation is sufficient to answer the query. We present Active Video Perception (AVP), an evidence-seeking framework that treats the video as an interactive environment and acquires compact, queryrelevant evidence directly from pixels. Concretely, AVP runs an iterative plan-observe-reflect process with MLLM agents. In each round, a planner proposes targeted video interactions, an observer executes them to extract time-stamped evidence, and a reflector evaluates the sufficiency of the evidence for the query, either halting with an answer or triggering further observation. Across five LVU benchmarks, AVP achieves highest performance with significant improvements. Notably, AVP outperforms the best agentic method by 5.7% in average accuracy while only requires 18.4% inference time and 12.4% input tokens.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-08", "arxiv_id": "2512.05339", "title": "Taxonomy-Adaptive Moderation Model with Robust Guardrails for Large Language Models", "authors": [ "Mahesh Kumar Nandwana", "Youngwan Lim", "Joseph Liu", "Alex Yang", "Varun Notibala", "Nishchaie Khanna" ], "github": "https://github.com/Roblox/RobloxGuard-1.0", "project_page": "", "abstract": "Large Language Models (LLMs) are typically aligned for safety during the post-training phase; however, they may still generate inappropriate outputs that could potentially pose risks to users. This challenge underscores the need for robust safeguards that operate across both model inputs and outputs. In this work, we introduce Roblox Guard 1.0, a state-of-the-art instruction fine-tuned LLM designed to enhance the safety of LLM systems through comprehensive input-output moderation, using a pipeline of LLMs to enhance moderation capability. Built on the Llama-3.1-8B-Instruct backbone, our model is instruction fine-tuned to generalize across previously unseen safety taxonomies and demonstrates strong performance on out-of-domain safety benchmarks. The instruction fine-tuning process uses a mix of synthetic and open-source safety datasets, augmented with chain-of-thought (CoT) rationales and input inversion to enhance contextual understanding and decision making. To support systematic evaluation, we also release RobloxGuard-Eval, a new benchmark featuring an extensible safety taxonomy to assess the effectiveness of LLM guardrails and moderation frameworks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Roblox/RobloxGuard-1.0", "github_api_added_by": "auto" }, { "date": "2025-12-08", "arxiv_id": "2512.03667", "title": "Colon-X: Advancing Intelligent Colonoscopy from Multimodal Understanding to Clinical Reasoning", "authors": [ "Ge-Peng Ji", "Jingyi Liu", "Deng-Ping Fan", "Nick Barnes" ], "github": "https://github.com/ai4colonoscopy/Colon-X", "project_page": "", "abstract": "In this study, we present Colon-X, an open initiative aimed at advancing multimodal intelligence in colonoscopy. We begin by constructing ColonVQA, the most comprehensive multimodal dataset ever built for colonoscopy, featuring over 1.1M+ visual question answering entries across 76 clinical findings and 18 multimodal tasks. Beyond serving as a community-wide data foundation, we further investigate a critical yet underexplored transition in colonoscopy - evolving from multimodal understanding to clinical reasoning: (a) To capture the current landscape of multimodal understanding behaviors, we systematically assess the generalizability of 22 multimodal large language models and examine their reliability under human-induced perturbations. The results reveal that clinical outputs from leading MLLMs remain far from robust and trustworthy. (b) To narrow this gap, we further explore reasoning-centric intelligence tailored for colonoscopy. Specifically, we curate ColonReason, a clinically grounded reasoning dataset annotated through a multi-expert debating pipeline, and develop ColonR1, the first R1-styled model incorporating task-adaptive rewarding and gradient-stable optimization techniques. Under data-scarce conditions, our ColonR1 achieves 56.61% overall accuracy, outperforming supervised fine-tuning by 25.22%, and sets a new reasoning-enabled baseline for multimodal colonoscopy analysis. All data and model resources are publicly available at https://github.com/ai4colonoscopy/Colon-X.", "github_manual": "", "github_regex": "https://github.com/ai4colonoscopy/Colon-X", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06749", "title": "DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems", "authors": [ "Ming Ma", "Jue Zhang", "Fangkai Yang", "Yu Kang", "Qingwei Lin", "Saravan Rajmohan", "Dongmei Zhang" ], "github": "", "project_page": "", "abstract": "Large language model (LLM)-based multi-agent systems are challenging to debug because failures often arise from long, branching interaction traces. The prevailing practice is to leverage LLMs for log-based failure localization, attributing errors to a specific agent and step. However, this paradigm has two key limitations: (i) log-only debugging lacks validation, producing untested hypotheses, and (ii) single-step or single-agent attribution is often ill-posed, as we find that multiple distinct interventions can independently repair the failed task. To address the first limitation, we introduce DoVer, an intervention-driven debugging framework, which augments hypothesis generation with active verification through targeted interventions (e.g., editing messages, altering plans). For the second limitation, rather than evaluating on attribution accuracy, we focus on measuring whether the system resolves the failure or makes quantifiable progress toward task success, reflecting a more outcome-oriented view of debugging. Within the Magnetic-One agent framework, on the datasets derived from GAIA and AssistantBench, DoVer flips 18-28% of failed trials into successes, achieves up to 16% milestone progress, and validates or refutes 30-60% of failure hypotheses. DoVer also performs effectively on a different dataset (GSMPlus) and agent framework (AG2), where it recovers 49% of failed trials. These results highlight intervention as a practical mechanism for improving reliability in agentic systems and open opportunities for more robust, scalable debugging methods for LLM-based multi-agent systems. Project website and code will be available at https://aka.ms/DoVer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07525", "title": "Beyond Real: Imaginary Extension of Rotary Position Embeddings for Long-Context LLMs", "authors": [ "Xiaoran Liu", "Yuerong Song", "Zhigeng Liu", "Zengfeng Huang", "Qipeng Guo", "Zhaoxiang Liu", "Shiguo Lian", "Ziwei He", "Xipeng Qiu" ], "github": "https://github.com/OpenMOSS/rope_pp", "project_page": "", "abstract": "Rotary Position Embeddings (RoPE) have become a standard for encoding sequence order in Large Language Models (LLMs) by applying rotations to query and key vectors in the complex plane. Standard implementations, however, utilize only the real component of the complex-valued dot product for attention score calculation. This simplification discards the imaginary component, which contains valuable phase information, leading to a potential loss of relational details crucial for modeling long-context dependencies. In this paper, we propose an extension that re-incorporates this discarded imaginary component. Our method leverages the full complex-valued representation to create a dual-component attention score. We theoretically and empirically demonstrate that this approach enhances the modeling of long-context dependencies by preserving more positional information. Furthermore, evaluations on a suite of long-context language modeling benchmarks show that our method consistently improves performance over the standard RoPE, with the benefits becoming more significant as context length increases. The code is available at https://github.com/OpenMOSS/rope_pp.", "github_manual": "", "github_regex": "https://github.com/OpenMOSS/rope_pp", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07584", "title": "LongCat-Image Technical Report", "authors": [ "Meituan LongCat Team", "Hanghang Ma", "Haoxian Tan", "Jiale Huang", "Junqiang Wu", "Jun-Yan He", "Lishuai Gao", "Songlin Xiao", "Xiaoming Wei", "Xiaoqi Ma", "Xunliang Cai", "Yayong Guan", "Jie Hu" ], "github": "", "project_page": "", "abstract": "We introduce LongCat-Image, a pioneering open-source and bilingual (Chinese-English) foundation model for image generation, designed to address core challenges in multilingual text rendering, photorealism, deployment efficiency, and developer accessibility prevalent in current leading models. 1) We achieve this through rigorous data curation strategies across the pre-training, mid-training, and SFT stages, complemented by the coordinated use of curated reward models during the RL phase. This strategy establishes the model as a new state-of-the-art (SOTA), delivering superior text-rendering capabilities and remarkable photorealism, and significantly enhancing aesthetic quality. 2) Notably, it sets a new industry standard for Chinese character rendering. By supporting even complex and rare characters, it outperforms both major open-source and commercial solutions in coverage, while also achieving superior accuracy. 3) The model achieves remarkable efficiency through its compact design. With a core diffusion model of only 6B parameters, it is significantly smaller than the nearly 20B or larger Mixture-of-Experts (MoE) architectures common in the field. This ensures minimal VRAM usage and rapid inference, significantly reducing deployment costs. Beyond generation, LongCat-Image also excels in image editing, achieving SOTA results on standard benchmarks with superior editing consistency compared to other open-source works. 4) To fully empower the community, we have established the most comprehensive open-source ecosystem to date. We are releasing not only multiple model versions for text-to-image and image editing, including checkpoints after mid-training and post-training stages, but also the entire toolchain of training procedure. We believe that the openness of LongCat-Image will provide robust support for developers and researchers, pushing the frontiers of visual content creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06421", "title": "Rethinking Training Dynamics in Scale-wise Autoregressive Generation", "authors": [ "Gengze Zhou", "Chongjian Ge", "Hao Tan", "Feng Liu", "Yicong Hong" ], "github": "", "project_page": "", "abstract": "Recent advances in autoregressive (AR) generative models have produced increasingly powerful systems for media synthesis. Among them, next-scale prediction has emerged as a popular paradigm, where models generate images in a coarse-to-fine manner. However, scale-wise AR models suffer from exposure bias, which undermines generation quality. We identify two primary causes of this issue: (1) train-test mismatch, where the model must rely on its own imperfect predictions during inference, and (2) imbalance in scale-wise learning difficulty, where certain scales exhibit disproportionately higher optimization complexity. Through a comprehensive analysis of training dynamics, we propose Self-Autoregressive Refinement (SAR) to address these limitations. SAR introduces a Stagger-Scale Rollout (SSR) mechanism that performs lightweight autoregressive rollouts to expose the model to its own intermediate predictions, thereby aligning train-test patterns, and a complementary Contrastive Student-Forcing Loss (CSFL) that provides adequate supervision for self-generated contexts to ensure stable training. Experimental results show that applying SAR to pretrained AR models consistently improves generation quality with minimal computational overhead. For instance, SAR yields a 5.2% FID reduction on FlexVAR-d16 trained on ImageNet 256 within 10 epochs (5 hours on 32xA100 GPUs). Given its efficiency, scalability, and effectiveness, we expect SAR to serve as a reliable post-training method for visual autoregressive generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06065", "title": "EgoEdit: Dataset, Real-Time Streaming Model, and Benchmark for Egocentric Video Editing", "authors": [ "Runjia Li", "Moayed Haji-Ali", "Ashkan Mirzaei", "Chaoyang Wang", "Arpit Sahni", "Ivan Skorokhodov", "Aliaksandr Siarohin", "Tomas Jakab", "Junlin Han", "Sergey Tulyakov", "Philip Torr", "Willi Menapace" ], "github": "", "project_page": "", "abstract": "We study instruction-guided editing of egocentric videos for interactive AR applications. While recent AI video editors perform well on third-person footage, egocentric views present unique challenges - including rapid egomotion and frequent hand-object interactions - that create a significant domain gap. Moreover, existing offline editing pipelines suffer from high latency, limiting real-time interaction. To address these issues, we present a complete ecosystem for egocentric video editing. First, we construct EgoEditData, a carefully designed and manually curated dataset specifically designed for egocentric editing scenarios, featuring rich hand-object interactions, while explicitly preserving hands. Second, we develop EgoEdit, an instruction-following egocentric video editor that supports real-time streaming inference on a single GPU. Finally, we introduce EgoEditBench, an evaluation suite targeting instruction faithfulness, hand and interaction preservation, and temporal stability under egomotion. Across both egocentric and general editing tasks, EgoEdit produces temporally stable, instruction-faithful results with interactive latency. It achieves clear gains on egocentric editing benchmarks-where existing methods struggle-while maintaining performance comparable to the strongest baselines on general editing tasks. EgoEditData and EgoEditBench will be made public for the research community. See our website at https://snap-research.github.io/EgoEdit", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06558", "title": "Embodied Referring Expression Comprehension in Human-Robot Interaction", "authors": [ "Md Mofijul Islam", "Alexi Gladstone", "Sujan Sarker", "Ganesh Nanduru", "Md Fahim", "Keyan Du", "Aman Chadha", "Tariq Iqbal" ], "github": "", "project_page": "", "abstract": "As robots enter human workspaces, there is a crucial need for them to comprehend embodied human instructions, enabling intuitive and fluent human-robot interaction (HRI). However, accurate comprehension is challenging due to a lack of large-scale datasets that capture natural embodied interactions in diverse HRI settings. Existing datasets suffer from perspective bias, single-view collection, inadequate coverage of nonverbal gestures, and a predominant focus on indoor environments. To address these issues, we present the Refer360 dataset, a large-scale dataset of embodied verbal and nonverbal interactions collected across diverse viewpoints in both indoor and outdoor settings. Additionally, we introduce MuRes, a multimodal guided residual module designed to improve embodied referring expression comprehension. MuRes acts as an information bottleneck, extracting salient modality-specific signals and reinforcing them into pre-trained representations to form complementary features for downstream tasks. We conduct extensive experiments on four HRI datasets, including the Refer360 dataset, and demonstrate that current multimodal models fail to capture embodied interactions comprehensively; however, augmenting them with MuRes consistently improves performance. These findings establish Refer360 as a valuable benchmark and exhibit the potential of guided residual learning to advance embodied referring expression comprehension in robots operating within human environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07461", "title": "Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled Reinforcement Learning", "authors": [ "Tong Wu", "Yang Liu", "Jun Bai", "Zixia Jia", "Shuyi Zhang", "Ziyong Lin", "Yanting Wang", "Song-Chun Zhu", "Zilong Zheng" ], "github": "", "project_page": "", "abstract": "We introduce Native Parallel Reasoner (NPR), a teacher-free framework that enables Large Language Models (LLMs) to self-evolve genuine parallel reasoning capabilities. NPR transforms the model from sequential emulation to native parallel cognition through three key innovations: 1) a self-distilled progressive training paradigm that transitions from ``cold-start'' format discovery to strict topological constraints without external supervision; 2) a novel Parallel-Aware Policy Optimization (PAPO) algorithm that optimizes branching policies directly within the execution graph, allowing the model to learn adaptive decomposition via trial and error; and 3) a robust NPR Engine that refactors memory management and flow control of SGLang to enable stable, large-scale parallel RL training. Across eight reasoning benchmarks, NPR trained on Qwen3-4B achieves performance gains of up to 24.5% and inference speedups up to 4.6x. Unlike prior baselines that often fall back to autoregressive decoding, NPR demonstrates 100% genuine parallel execution, establishing a new standard for self-evolving, efficient, and scalable agentic reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07469", "title": "Unified Video Editing with Temporal Reasoner", "authors": [ "Xiangpeng Yang", "Ji Xie", "Yiyuan Yang", "Yan Huang", "Min Xu", "Qiang Wu" ], "github": "https://github.com/knightyxp/VideoCoF", "project_page": "", "abstract": "Existing video editing methods face a critical trade-off: expert models offer precision but rely on task-specific priors like masks, hindering unification; conversely, unified temporal in-context learning models are mask-free but lack explicit spatial cues, leading to weak instruction-to-region mapping and imprecise localization. To resolve this conflict, we propose VideoCoF, a novel Chain-of-Frames approach inspired by Chain-of-Thought reasoning. VideoCoF enforces a ``see, reason, then edit\" procedure by compelling the video diffusion model to first predict reasoning tokens (edit-region latents) before generating the target video tokens. This explicit reasoning step removes the need for user-provided masks while achieving precise instruction-to-region alignment and fine-grained video editing. Furthermore, we introduce a RoPE alignment strategy that leverages these reasoning tokens to ensure motion alignment and enable length extrapolation beyond the training duration. We demonstrate that with a minimal data cost of only 50k video pairs, VideoCoF achieves state-of-the-art performance on VideoCoF-Bench, validating the efficiency and effectiveness of our approach. Our code, weight, data are available at https://github.com/knightyxp/VideoCoF.", "github_manual": "", "github_regex": "https://github.com/knightyxp/VideoCoF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07834", "title": "Voxify3D: Pixel Art Meets Volumetric Rendering", "authors": [ "Yi-Chuan Huang", "Jiewen Chan", "Hao-Jen Chien", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Voxel art is a distinctive stylization widely used in games and digital media, yet automated generation from 3D meshes remains challenging due to conflicting requirements of geometric abstraction, semantic preservation, and discrete color coherence. Existing methods either over-simplify geometry or fail to achieve the pixel-precise, palette-constrained aesthetics of voxel art. We introduce Voxify3D, a differentiable two-stage framework bridging 3D mesh optimization with 2D pixel art supervision. Our core innovation lies in the synergistic integration of three components: (1) orthographic pixel art supervision that eliminates perspective distortion for precise voxel-pixel alignment; (2) patch-based CLIP alignment that preserves semantics across discretization levels; (3) palette-constrained Gumbel-Softmax quantization enabling differentiable optimization over discrete color spaces with controllable palette strategies. This integration addresses fundamental challenges: semantic preservation under extreme discretization, pixel-art aesthetics through volumetric rendering, and end-to-end discrete optimization. Experiments show superior performance (37.12 CLIP-IQA, 77.90\\% user preference) across diverse characters and controllable abstraction (2-8 colors, 20x-50x resolutions). Project page: https://yichuanh.github.io/Voxify-3D/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06905", "title": "Scaling Zero-Shot Reference-to-Video Generation", "authors": [ "Zijian Zhou", "Shikun Liu", "Haozhe Liu", "Haonan Qiu", "Zhaochong An", "Weiming Ren", "Zhiheng Liu", "Xiaoke Huang", "Kam Woh Ng", "Tian Xie", "Xiao Han", "Yuren Cong", "Hang Li", "Chuyan Zhu", "Aditya Patel", "Tao Xiang", "Sen He" ], "github": "", "project_page": "", "abstract": "Reference-to-video (R2V) generation aims to synthesize videos that align with a text prompt while preserving the subject identity from reference images. However, current R2V methods are hindered by the reliance on explicit reference image-video-text triplets, whose construction is highly expensive and difficult to scale. We bypass this bottleneck by introducing Saber, a scalable zero-shot framework that requires no explicit R2V data. Trained exclusively on video-text pairs, Saber employs a masked training strategy and a tailored attention-based model design to learn identity-consistent and reference-aware representations. Mask augmentation techniques are further integrated to mitigate copy-paste artifacts common in reference-to-video generation. Moreover, Saber demonstrates remarkable generalization capabilities across a varying number of references and achieves superior performance on the OpenS2V-Eval benchmark compared to methods trained with R2V data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07778", "title": "Distribution Matching Variational AutoEncoder", "authors": [ "Sen Ye", "Jianning Pei", "Mengde Xu", "Shuyang Gu", "Chunyu Wang", "Liwei Wang", "Han Hu" ], "github": "https://github.com/sen-ye/dmvae", "project_page": "", "abstract": "Most visual generative models compress images into a latent space before applying diffusion or autoregressive modelling. Yet, existing approaches such as VAEs and foundation model aligned encoders implicitly constrain the latent space without explicitly shaping its distribution, making it unclear which types of distributions are optimal for modeling. We introduce Distribution-Matching VAE (DMVAE), which explicitly aligns the encoder's latent distribution with an arbitrary reference distribution via a distribution matching constraint. This generalizes beyond the Gaussian prior of conventional VAEs, enabling alignment with distributions derived from self-supervised features, diffusion noise, or other prior distributions. With DMVAE, we can systematically investigate which latent distributions are more conducive to modeling, and we find that SSL-derived distributions provide an excellent balance between reconstruction fidelity and modeling efficiency, reaching gFID equals 3.2 on ImageNet with only 64 training epochs. Our results suggest that choosing a suitable latent distribution structure (achieved via distribution-level alignment), rather than relying on fixed priors, is key to bridging the gap between easy-to-model latents and high-fidelity image synthesis. Code is avaliable at https://github.com/sen-ye/dmvae.", "github_manual": "", "github_regex": "https://github.com/sen-ye/dmvae", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07833", "title": "Relational Visual Similarity", "authors": [ "Thao Nguyen", "Sicheng Mo", "Krishna Kumar Singh", "Yilin Wang", "Jing Shi", "Nicholas Kolkin", "Eli Shechtman", "Yong Jae Lee", "Yuheng Li" ], "github": "", "project_page": "", "abstract": "Humans do not just see attribute similarity -- we also see relational similarity. An apple is like a peach because both are reddish fruit, but the Earth is also like a peach: its crust, mantle, and core correspond to the peach's skin, flesh, and pit. This ability to perceive and recognize relational similarity, is arguable by cognitive scientist to be what distinguishes humans from other species. Yet, all widely used visual similarity metrics today (e.g., LPIPS, CLIP, DINO) focus solely on perceptual attribute similarity and fail to capture the rich, often surprising relational similarities that humans perceive. How can we go beyond the visible content of an image to capture its relational properties? How can we bring images with the same relational logic closer together in representation space? To answer these questions, we first formulate relational image similarity as a measurable problem: two images are relationally similar when their internal relations or functions among visual elements correspond, even if their visual attributes differ. We then curate 114k image-caption dataset in which the captions are anonymized -- describing the underlying relational logic of the scene rather than its surface content. Using this dataset, we finetune a Vision-Language model to measure the relational similarity between images. This model serves as the first step toward connecting images by their underlying relational structure rather than their visible appearance. Our study shows that while relational similarity has a lot of real-world applications, existing image similarity models fail to capture it -- revealing a critical gap in visual computing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07806", "title": "Multi-view Pyramid Transformer: Look Coarser to See Broader", "authors": [ "Gyeongjin Kang", "Seungkwon Yang", "Seungtae Nam", "Younggeun Lee", "Jungwoo Kim", "Eunbyung Park" ], "github": "", "project_page": "", "abstract": "We propose Multi-view Pyramid Transformer (MVP), a scalable multi-view transformer architecture that directly reconstructs large 3D scenes from tens to hundreds of images in a single forward pass. Drawing on the idea of ``looking broader to see the whole, looking finer to see the details,\" MVP is built on two core design principles: 1) a local-to-global inter-view hierarchy that gradually broadens the model's perspective from local views to groups and ultimately the full scene, and 2) a fine-to-coarse intra-view hierarchy that starts from detailed spatial representations and progressively aggregates them into compact, information-dense tokens. This dual hierarchy achieves both computational efficiency and representational richness, enabling fast reconstruction of large and complex scenes. We validate MVP on diverse datasets and show that, when coupled with 3D Gaussian Splatting as the underlying 3D representation, it achieves state-of-the-art generalizable reconstruction quality while maintaining high efficiency and scalability across a wide range of view configurations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07783", "title": "On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models", "authors": [ "Charlie Zhang", "Graham Neubig", "Xiang Yue" ], "github": "", "project_page": "", "abstract": "Recent reinforcement learning (RL) techniques have yielded impressive reasoning improvements in language models, yet it remains unclear whether post-training truly extends a model's reasoning ability beyond what it acquires during pre-training. A central challenge is the lack of control in modern training pipelines: large-scale pre-training corpora are opaque, mid-training is often underexamined, and RL objectives interact with unknown prior knowledge in complex ways. To resolve this ambiguity, we develop a fully controlled experimental framework that isolates the causal contributions of pre-training, mid-training, and RL-based post-training. Our approach employs synthetic reasoning tasks with explicit atomic operations, parseable step-by-step reasoning traces, and systematic manipulation of training distributions. We evaluate models along two axes: extrapolative generalization to more complex compositions and contextual generalization across surface contexts. Using this framework, we reconcile competing views on RL's effectiveness. We show that: 1) RL produces true capability gains (pass@128) only when pre-training leaves sufficient headroom and when RL data target the model's edge of competence, tasks at the boundary that are difficult but not yet out of reach. 2) Contextual generalization requires minimal yet sufficient pre-training exposure, after which RL can reliably transfer. 3) Mid-training significantly enhances performance under fixed compute compared with RL only, demonstrating its central but underexplored role in training pipelines. 4) Process-level rewards reduce reward hacking and improve reasoning fidelity. Together, these results clarify the interplay between pre-training, mid-training, and RL, offering a foundation for understanding and improving reasoning LM training strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07831", "title": "UnityVideo: Unified Multi-Modal Multi-Task Learning for Enhancing World-Aware Video Generation", "authors": [ "Jiehui Huang", "Yuechen Zhang", "Xu He", "Yuan Gao", "Zhi Cen", "Bin Xia", "Yan Zhou", "Xin Tao", "Pengfei Wan", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/UnityVideo", "project_page": "", "abstract": "Recent video generation models demonstrate impressive synthesis capabilities but remain limited by single-modality conditioning, constraining their holistic world understanding. This stems from insufficient cross-modal interaction and limited modal diversity for comprehensive world knowledge representation. To address these limitations, we introduce UnityVideo, a unified framework for world-aware video generation that jointly learns across multiple modalities (segmentation masks, human skeletons, DensePose, optical flow, and depth maps) and training paradigms. Our approach features two core components: (1) dynamic noising to unify heterogeneous training paradigms, and (2) a modality switcher with an in-context learner that enables unified processing via modular parameters and contextual learning. We contribute a large-scale unified dataset with 1.3M samples. Through joint optimization, UnityVideo accelerates convergence and significantly enhances zero-shot generalization to unseen data. We demonstrate that UnityVideo achieves superior video quality, consistency, and improved alignment with physical world constraints. Code and data can be found at: https://github.com/dvlab-research/UnityVideo", "github_manual": "", "github_regex": "https://github.com/dvlab-research/UnityVideo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.03244", "title": "SPARK: Stepwise Process-Aware Rewards for Reference-Free Reinforcement Learning", "authors": [ "Salman Rahman", "Sruthi Gorantla", "Arpit Gupta", "Swastik Roy", "Nanyun Peng", "Yang Liu" ], "github": "", "project_page": "", "abstract": "Process reward models (PRMs) that provide dense, step-level feedback have shown promise for reinforcement learning, yet their adoption remains limited by the need for expensive step-level annotations or ground truth references. We propose SPARK: a three-stage framework where in the first stage a generator model produces diverse solutions and a verifier model evaluates them using parallel scaling (self-consistency) and sequential scaling (meta-critique). In the second stage, we use these verification outputs as synthetic training data to fine-tune generative process reward models, which subsequently serve as reward signals during training. We show that aggregating multiple independent verifications at the step level produces training data for process reward models that surpass ground-truth outcome supervision, achieving 67.5 F1 on ProcessBench (a benchmark for identifying erroneous steps in mathematical reasoning) compared to 66.4 for reference-guided training and 61.9 for GPT-4o. In the final stage, we apply our generative PRM with chain-of-thought verification (PRM-CoT) as the reward model in RL experiments on mathematical reasoning, and introduce format constraints to prevent reward hacking. Using Qwen2.5-Math-7B, we achieve 47.4% average accuracy across six mathematical reasoning benchmarks, outperforming ground-truth-based RLVR (43.9%). Our work enables reference-free RL training that exceeds ground-truth methods, opening new possibilities for domains lacking verifiable answers or accessible ground truth.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2511.23469", "title": "Visual Generation Tuning", "authors": [ "Jiahao Guo", "Sinan Du", "Jingfeng Yao", "Wenyu Liu", "Bo Li", "Haoxiang Cao", "Kun Gai", "Chun Yuan", "Kai Wu", "Xinggang Wang" ], "github": "https://github.com/hustvl/VGT", "project_page": "", "abstract": "Large Vision Language Models (VLMs) effectively bridge the modality gap through extensive pretraining, acquiring sophisticated visual representations aligned with language. However, it remains underexplored whether these representations, optimized for multimodal understanding tasks, harbor an inherent potential for visual generation. In this paper, we propose VGT, Visual Generation Tuning, a novel paradigm designed to stimulate the underlying capabilities of visual generation within any vision language models. By performing efficient visual generation tuning on well-pretrained VLMs, we significantly mitigate the alignment costs and accelerate the convergence of autoregressive modeling in the continuous space (20x speedup). Specifically, we dismiss the entangled pixel-level VAEs designed for diffusion transformers and formulate VGT-AE through aligning the semantic encoders from pretrained VLMs with the latent representations of pixel decoders. In image reconstruction tasks, we achieve 26.67 PSNR and 0.50 rFID at a 28x compression ratio, outperforming specialized VAEs; in visual generation tasks, we achieve state-of-the-art outcomes among autoregressive models, 0.77 on GenEval and 78.73 on DPG-Bench. Furthermore, our proposed VGT showcases significant scaling promise and is versatile for endowing any VLMs trained for multimodal understanding with the capabilities of visual generation, which paves the new avenue to explore next-generation unified multimodal foundation models. Models and codes are available at https://github.com/hustvl/VGT.", "github_manual": "", "github_regex": "https://github.com/hustvl/VGT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06373", "title": "VG-Refiner: Towards Tool-Refined Referring Grounded Reasoning via Agentic Reinforcement Learning", "authors": [ "Yuji Wang", "Wenlong Liu", "Jingxuan Niu", "Haoji Zhang", "Yansong Tang" ], "github": "https://github.com/VoyageWang/VG-Refiner", "project_page": "", "abstract": "Tool-integrated visual reasoning (TiVR) has demonstrated great potential in enhancing multimodal problem-solving. However, existing TiVR paradigms mainly focus on integrating various visual tools through reinforcement learning, while neglecting to design effective response mechanisms for handling unreliable or erroneous tool outputs. This limitation is particularly pronounced in referring and grounding tasks, where inaccurate detection tool predictions often mislead TiVR models into generating hallucinated reasoning. To address this issue, we propose the VG-Refiner, the first framework aiming at the tool-refined referring grounded reasoning. Technically, we introduce a two-stage think-rethink mechanism that enables the model to explicitly analyze and respond to tool feedback, along with a refinement reward that encourages effective correction in response to poor tool results. In addition, we propose two new metrics and establish fair evaluation protocols to systematically measure the refinement ability of current models. We adopt a small amount of task-specific data to enhance the refinement capability of VG-Refiner, achieving a significant improvement in accuracy and correction ability on referring and reasoning grounding benchmarks while preserving the general capabilities of the pretrained model.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/VoyageWang/VG-Refiner", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.03621", "title": "ReCamDriving: LiDAR-Free Camera-Controlled Novel Trajectory Video Generation", "authors": [ "Yaokun Li", "Shuaixian Wang", "Mantang Guo", "Jiehui Huang", "Taojun Ding", "Mu Hu", "Kaixuan Wang", "Shaojie Shen", "Guang Tan" ], "github": "", "project_page": "", "abstract": "We propose ReCamDriving, a purely vision-based, camera-controlled novel-trajectory video generation framework. While repair-based methods fail to restore complex artifacts and LiDAR-based approaches rely on sparse and incomplete cues, ReCamDriving leverages dense and scene-complete 3DGS renderings for explicit geometric guidance, achieving precise camera-controllable generation. To mitigate overfitting to restoration behaviors when conditioned on 3DGS renderings, ReCamDriving adopts a two-stage training paradigm: the first stage uses camera poses for coarse control, while the second stage incorporates 3DGS renderings for fine-grained viewpoint and geometric guidance. Furthermore, we present a 3DGS-based cross-trajectory data curation strategy to eliminate the train-test gap in camera transformation patterns, enabling scalable multi-trajectory supervision from monocular videos. Based on this strategy, we construct the ParaDrive dataset, containing over 110K parallel-trajectory video pairs. Extensive experiments demonstrate that ReCamDriving achieves state-of-the-art camera controllability and structural consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06589", "title": "OmniSafeBench-MM: A Unified Benchmark and Toolbox for Multimodal Jailbreak Attack-Defense Evaluation", "authors": [ "Xiaojun Jia", "Jie Liao", "Qi Guo", "Teng Ma", "Simeng Qin", "Ranjie Duan", "Tianlin Li", "Yihao Huang", "Zhitao Zeng", "Dongxian Wu", "Yiming Li", "Wenqi Ren", "Xiaochun Cao", "Yang Liu" ], "github": "https://github.com/jiaxiaojunQAQ/OmniSafeBench-MM", "project_page": "", "abstract": "Recent advances in multi-modal large language models (MLLMs) have enabled unified perception-reasoning capabilities, yet these systems remain highly vulnerable to jailbreak attacks that bypass safety alignment and induce harmful behaviors. Existing benchmarks such as JailBreakV-28K, MM-SafetyBench, and HADES provide valuable insights into multi-modal vulnerabilities, but they typically focus on limited attack scenarios, lack standardized defense evaluation, and offer no unified, reproducible toolbox. To address these gaps, we introduce OmniSafeBench-MM, which is a comprehensive toolbox for multi-modal jailbreak attack-defense evaluation. OmniSafeBench-MM integrates 13 representative attack methods, 15 defense strategies, and a diverse dataset spanning 9 major risk domains and 50 fine-grained categories, structured across consultative, imperative, and declarative inquiry types to reflect realistic user intentions. Beyond data coverage, it establishes a three-dimensional evaluation protocol measuring (1) harmfulness, distinguished by a granular, multi-level scale ranging from low-impact individual harm to catastrophic societal threats, (2) intent alignment between responses and queries, and (3) response detail level, enabling nuanced safety-utility analysis. We conduct extensive experiments on 10 open-source and 8 closed-source MLLMs to reveal their vulnerability to multi-modal jailbreak. By unifying data, methodology, and evaluation into an open-source, reproducible platform, OmniSafeBench-MM provides a standardized foundation for future research. The code is released at https://github.com/jiaxiaojunQAQ/OmniSafeBench-MM.", "github_manual": "", "github_regex": "https://github.com/jiaxiaojunQAQ/OmniSafeBench-MM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06533", "title": "Beyond Token-level Supervision: Unlocking the Potential of Decoding-based Regression via Reinforcement Learning", "authors": [ "Ming Chen", "Sheng Tang", "Rong-Xi Tan", "Ziniu Li", "Jiacheng Chen", "Ke Xue", "Chao Qian" ], "github": "", "project_page": "", "abstract": "Decoding-based regression, which reformulates regression as a sequence generation task, has emerged as a promising paradigm of applying large language models for numerical prediction. However, its progress is hindered by the misalignment between discrete token-level objectives (e.g., cross-entropy) and continuous numerical values. Existing approaches relying on token-level constraints often fail to capture the global magnitude of the target value, limiting their precision and generalization. In this paper, we propose to unlock the potential of decoding-based regression via Reinforcement Learning (RL). We formulate the generation process as a Markov Decision Process, utilizing sequence-level rewards to enforce global numerical coherence. Extensive experiments on tabular regression and code metric regression demonstrate that our method (specifically with ReMax and GRPO) consistently outperforms both state-of-the-art token-level baselines and traditional regression heads, showing the superiority of introducing sequence-level signals. Our analysis further reveals that RL significantly enhances sampling efficiency and predictive precision, establishing decoding-based regression as a robust and accurate paradigm for general-purpose numerical prediction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.08294", "title": "OpenSubject: Leveraging Video-Derived Identity and Diversity Priors for Subject-driven Image Generation and Manipulation", "authors": [ "Yexin Liu", "Manyuan Zhang", "Yueze Wang", "Hongyu Li", "Dian Zheng", "Weiming Zhang", "Changsheng Lu", "Xunliang Cai", "Yan Feng", "Peng Pei", "Harry Yang" ], "github": "", "project_page": "", "abstract": "Despite the promising progress in subject-driven image generation, current models often deviate from the reference identities and struggle in complex scenes with multiple subjects. To address this challenge, we introduce OpenSubject, a video-derived large-scale corpus with 2.5M samples and 4.35M images for subject-driven generation and manipulation. The dataset is built with a four-stage pipeline that exploits cross-frame identity priors. (i) Video Curation. We apply resolution and aesthetic filtering to obtain high-quality clips. (ii) Cross-Frame Subject Mining and Pairing. We utilize vision-language model (VLM)-based category consensus, local grounding, and diversity-aware pairing to select image pairs. (iii) Identity-Preserving Reference Image Synthesis. We introduce segmentation map-guided outpainting to synthesize the input images for subject-driven generation and box-guided inpainting to generate input images for subject-driven manipulation, together with geometry-aware augmentations and irregular boundary erosion. (iv) Verification and Captioning. We utilize a VLM to validate synthesized samples, re-synthesize failed samples based on stage (iii), and then construct short and long captions. In addition, we introduce a benchmark covering subject-driven generation and manipulation, and then evaluate identity fidelity, prompt adherence, manipulation consistency, and background consistency with a VLM judge. Extensive experiments show that training with OpenSubject improves generation and manipulation performance, particularly in complex scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07829", "title": "One Layer Is Enough: Adapting Pretrained Visual Encoders for Image Generation", "authors": [ "Yuan Gao", "Chen Chen", "Tianrong Chen", "Jiatao Gu" ], "github": "", "project_page": "", "abstract": "Visual generative models (e.g., diffusion models) typically operate in compressed latent spaces to balance training efficiency and sample quality. In parallel, there has been growing interest in leveraging high-quality pre-trained visual representations, either by aligning them inside VAEs or directly within the generative model. However, adapting such representations remains challenging due to fundamental mismatches between understanding-oriented features and generation-friendly latent spaces. Representation encoders benefit from high-dimensional latents that capture diverse hypotheses for masked regions, whereas generative models favor low-dimensional latents that must faithfully preserve injected noise. This discrepancy has led prior work to rely on complex objectives and architectures. In this work, we propose FAE (Feature Auto-Encoder), a simple yet effective framework that adapts pre-trained visual representations into low-dimensional latents suitable for generation using as little as a single attention layer, while retaining sufficient information for both reconstruction and understanding. The key is to couple two separate deep decoders: one trained to reconstruct the original feature space, and a second that takes the reconstructed features as input for image generation. FAE is generic; it can be instantiated with a variety of self-supervised encoders (e.g., DINO, SigLIP) and plugged into two distinct generative families: diffusion models and normalizing flows. Across class-conditional and text-to-image benchmarks, FAE achieves strong performance. For example, on ImageNet 256x256, our diffusion model with CFG attains a near state-of-the-art FID of 1.29 (800 epochs) and 1.70 (80 epochs). Without CFG, FAE reaches the state-of-the-art FID of 1.48 (800 epochs) and 2.08 (80 epochs), demonstrating both high quality and fast learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07805", "title": "Group Representational Position Encoding", "authors": [ "Yifan Zhang", "Zixiang Chen", "Yifeng Liu", "Zhen Qin", "Huizhuo Yuan", "Kangping Xu", "Yang Yuan", "Quanquan Gu", "Andrew Chi-Chih Yao" ], "github": "https://github.com/model-architectures/GRAPE", "project_page": "", "abstract": "We present GRAPE (Group RepresentAtional Position Encoding), a unified framework for positional encoding based on group actions. GRAPE brings together two families of mechanisms: (i) multiplicative rotations (Multiplicative GRAPE) in SO(d) and (ii) additive logit biases (Additive GRAPE) arising from unipotent actions in the general linear group GL. In Multiplicative GRAPE, a position n in Z (or t in R) acts as G(n)=exp(n,\u03c9,L) with a rank-2 skew generator L in R^{d times d}, yielding a relative, compositional, norm-preserving map with a closed-form matrix exponential. RoPE is recovered exactly when the d/2 planes are the canonical coordinate pairs with log-uniform spectrum. Learned commuting subspaces and compact non-commuting mixtures strictly extend this geometry to capture cross-subspace feature coupling at O(d) and O(r d) cost per head, respectively. In Additive GRAPE, additive logits arise as rank-1 (or low-rank) unipotent actions, recovering ALiBi and the Forgetting Transformer (FoX) as exact special cases while preserving an exact relative law and streaming cacheability. Altogether, GRAPE supplies a principled design space for positional geometry in long-context models, subsuming RoPE and ALiBi as special cases. Project Page: https://github.com/model-architectures/GRAPE.", "github_manual": "", "github_regex": "https://github.com/model-architectures/GRAPE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06835", "title": "Decouple to Generalize: Context-First Self-Evolving Learning for Data-Scarce Vision-Language Reasoning", "authors": [ "Tingyu Li", "Zheng Sun", "Jingxuan Wei", "Siyuan Li", "Conghui He", "Lijun Wu", "Cheng Tan" ], "github": "", "project_page": "", "abstract": "Recent vision-language models (VLMs) achieve remarkable reasoning through reinforcement learning (RL), which provides a feasible solution for realizing continuous self-evolving large vision-language models (LVLMs) in the era of experience. However, RL for VLMs requires abundant high-quality multimodal data, especially challenging in specialized domains like chemistry, earth sciences, and multimodal mathematics. Existing strategies such as synthetic data and self-rewarding mechanisms suffer from limited distributions and alignment difficulties, ultimately causing reward hacking: models exploit high-reward patterns, collapsing policy entropy and destabilizing training. We propose DoGe (Decouple to Generalize), a dual-decoupling framework that guides models to first learn from context rather than problem solving by refocusing on the problem context scenarios overlooked by synthetic data methods. By decoupling learning process into dual components (Thinker and Solver), we reasonably quantify the reward signals of this process and propose a two-stage RL post-training approach from freely exploring context to practically solving tasks. Second, to increase the diversity of training data, DoGe constructs an evolving curriculum learning pipeline: an expanded native domain knowledge corpus and an iteratively evolving seed problems pool. Experiments show that our method consistently outperforms the baseline across various benchmarks, providing a scalable pathway for realizing self-evolving LVLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06963", "title": "VideoVLA: Video Generators Can Be Generalizable Robot Manipulators", "authors": [ "Yichao Shen", "Fangyun Wei", "Zhiying Du", "Yaobo Liang", "Yan Lu", "Jiaolong Yang", "Nanning Zheng", "Baining Guo" ], "github": "", "project_page": "", "abstract": "Generalization in robot manipulation is essential for deploying robots in open-world environments and advancing toward artificial general intelligence. While recent Vision-Language-Action (VLA) models leverage large pre-trained understanding models for perception and instruction following, their ability to generalize to novel tasks, objects, and settings remains limited. In this work, we present VideoVLA, a simple approach that explores the potential of transforming large video generation models into robotic VLA manipulators. Given a language instruction and an image, VideoVLA predicts an action sequence as well as the future visual outcomes. Built on a multi-modal Diffusion Transformer, VideoVLA jointly models video, language, and action modalities, using pre-trained video generative models for joint visual and action forecasting. Our experiments show that high-quality imagined futures correlate with reliable action predictions and task success, highlighting the importance of visual imagination in manipulation. VideoVLA demonstrates strong generalization, including imitating other embodiments' skills and handling novel objects. This dual-prediction strategy - forecasting both actions and their visual consequences - explores a paradigm shift in robot learning and unlocks generalization capabilities in manipulation systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06791", "title": "Small-Gain Nash: Certified Contraction to Nash Equilibria in Differentiable Games", "authors": [ "Vedansh Sharma" ], "github": "https://github.com/AashVed/SmallGainNash", "project_page": "", "abstract": "Classical convergence guarantees for gradient-based learning in games require the pseudo-gradient to be (strongly) monotone in Euclidean geometry as shown by rosen(1965), a condition that often fails even in simple games with strong cross-player couplings. We introduce Small-Gain Nash (SGN), a block small-gain condition in a custom block-weighted geometry. SGN converts local curvature and cross-player Lipschitz coupling bounds into a tractable certificate of contraction. It constructs a weighted block metric in which the pseudo-gradient becomes strongly monotone on any region where these bounds hold, even when it is non-monotone in the Euclidean sense. The continuous flow is exponentially contracting in this designed geometry, and projected Euler and RK4 discretizations converge under explicit step-size bounds derived from the SGN margin and a local Lipschitz constant. Our analysis reveals a certified ``timescale band'', a non-asymptotic, metric-based certificate that plays a TTUR-like role: rather than forcing asymptotic timescale separation via vanishing, unequal step sizes, SGN identifies a finite band of relative metric weights for which a single-step-size dynamics is provably contractive. We validate the framework on quadratic games where Euclidean monotonicity analysis fails to predict convergence, but SGN successfully certifies it, and extend the construction to mirror/Fisher geometries for entropy-regularized policy gradient in Markov games. The result is an offline certification pipeline that estimates curvature, coupling, and Lipschitz parameters on compact regions, optimizes block weights to enlarge the SGN margin, and returns a structural, computable convergence certificate consisting of a metric, contraction rate, and safe step-sizes for non-monotone games.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AashVed/SmallGainNash", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06609", "title": "Vector Quantization using Gaussian Variational Autoencoder", "authors": [ "Tongda Xu", "Wendi Zheng", "Jiajun He", "Jose Miguel Hernandez-Lobato", "Yan Wang", "Ya-Qin Zhang", "Jie Tang" ], "github": "https://github.com/tongdaxu/VQ-VAE-from-Gaussian-VAE", "project_page": "", "abstract": "Vector quantized variational autoencoder (VQ-VAE) is a discrete auto-encoder that compresses images into discrete tokens. It is difficult to train due to discretization. In this paper, we propose a simple yet effective technique, dubbed Gaussian Quant (GQ), that converts a Gaussian VAE with certain constraint into a VQ-VAE without training. GQ generates random Gaussian noise as a codebook and finds the closest noise to the posterior mean. Theoretically, we prove that when the logarithm of the codebook size exceeds the bits-back coding rate of the Gaussian VAE, a small quantization error is guaranteed. Practically, we propose a heuristic to train Gaussian VAE for effective GQ, named target divergence constraint (TDC). Empirically, we show that GQ outperforms previous VQ-VAEs, such as VQGAN, FSQ, LFQ, and BSQ, on both UNet and ViT architectures. Furthermore, TDC also improves upon previous Gaussian VAE discretization methods, such as TokenBridge. The source code is provided in https://github.com/tongdaxu/VQ-VAE-from-Gaussian-VAE.", "github_manual": "", "github_regex": "https://github.com/tongdaxu/VQ-VAE-from-Gaussian-VAE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.05100", "title": "Structured Document Translation via Format Reinforcement Learning", "authors": [ "Haiyue Song", "Johannes Eschbach-Dymanus", "Hour Kaing", "Sumire Honda", "Hideki Tanaka", "Bianka Buschbeck", "Masao Utiyama" ], "github": "", "project_page": "", "abstract": "Recent works on structured text translation remain limited to the sentence level, as they struggle to effectively handle the complex document-level XML or HTML structures. To address this, we propose Format Reinforcement Learning (FormatRL), which employs Group Relative Policy Optimization on top of a supervised fine-tuning model to directly optimize novel structure-aware rewards: 1) TreeSim, which measures structural similarity between predicted and reference XML trees and 2) Node-chrF, which measures translation quality at the level of XML nodes. Additionally, we apply StrucAUC, a fine-grained metric distinguishing between minor errors and major structural failures. Experiments on the SAP software-documentation benchmark demonstrate improvements across six metrics and an analysis further shows how different reward functions contribute to improvements in both structural and translation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.03704", "title": "DZ-TDPO: Non-Destructive Temporal Alignment for Mutable State Tracking in Long-Context Dialogue", "authors": [ "Yijun Liao" ], "github": "https://github.com/lyj20071013/DZ-TDPO", "project_page": "", "abstract": "Long-context dialogue systems suffer from State Inertia, where static constraints prevent models from resolving conflicts between evolving user intents and established historical context. To address this, we propose DZ-TDPO, a non-destructive alignment framework that synergizes conflict-aware dynamic KL constraints with a calibrated temporal attention bias. Experiments on the Multi-Session Chat (MSC) dataset demonstrate that DZ-TDPO achieves state-of-the-art win rates (55.4% on Phi-3.5) while maintaining robust zero-shot generalization. Our scaling analysis reveals a \"Capacity-Stability Trade-off\": while smaller models incur an \"alignment tax\" (perplexity surge) to overcome historical inertia, the larger Qwen2.5-7B model achieves 50.8% win rate with negligible perplexity overhead. This confirms that TAI can be alleviated via precise attention regulation rather than destructive weight updates, preserving general capabilities (MMLU) across model scales. Code and data are available: https://github.com/lyj20071013/DZ-TDPO", "github_manual": "", "github_regex": "https://github.com/lyj20071013/DZ-TDPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.07168", "title": "JEPA as a Neural Tokenizer: Learning Robust Speech Representations with Density Adaptive Attention", "authors": [ "Georgios Ioannides", "Christos Constantinou", "Aman Chadha", "Aaron Elkins", "Linsey Pang", "Ravid Shwartz-Ziv", "Yann LeCun" ], "github": "", "project_page": "", "abstract": "We introduce a two-stage self-supervised framework that combines the Joint-Embedding Predictive Architecture (JEPA) with a Density Adaptive Attention Mechanism (DAAM) for learning robust speech representations. Stage~1 uses JEPA with DAAM to learn semantic audio features via masked prediction in latent space, fully decoupled from waveform reconstruction. Stage~2 leverages these representations for efficient tokenization using Finite Scalar Quantization (FSQ) and a mixed-radix packing scheme, followed by high-fidelity waveform reconstruction with a HiFi-GAN decoder. By integrating Gaussian mixture-based density-adaptive gating into the JEPA encoder, the model performs adaptive temporal feature selection and discovers hierarchical speech structure at a low frame rate of 2.5~Hz. The resulting tokens (47.5 tokens/sec) provide a reversible, highly compressed, and language-model-friendly representation that is competitive with, and often more efficient than, existing neural audio codecs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-09", "arxiv_id": "2512.06032", "title": "The SAM2-to-SAM3 Gap in the Segment Anything Model Family: Why Prompt-Based Expertise Fails in Concept-Driven Image Segmentation", "authors": [ "Ranjan Sapkota", "Konstantinos I. Roumeliotis", "Manoj Karkee" ], "github": "", "project_page": "", "abstract": "This paper investigates the fundamental discontinuity between the latest two Segment Anything Models: SAM2 and SAM3. We explain why the expertise in prompt-based segmentation of SAM2 does not transfer to the multimodal concept-driven paradigm of SAM3. SAM2 operates through spatial prompts points, boxes, and masks yielding purely geometric and temporal segmentation. In contrast, SAM3 introduces a unified vision-language architecture capable of open-vocabulary reasoning, semantic grounding, contrastive alignment, and exemplar-based concept understanding. We structure this analysis through five core components: (1) a Conceptual Break Between Prompt-Based and Concept-Based Segmentation, contrasting spatial prompt semantics of SAM2 with multimodal fusion and text-conditioned mask generation of SAM3; (2) Architectural Divergence, detailing pure vision-temporal design of SAM2 versus integration of vision-language encoders, geometry and exemplar encoders, fusion modules, DETR-style decoders, object queries, and ambiguity-handling via Mixture-of-Experts in SAM3; (3) Dataset and Annotation Differences, contrasting SA-V video masks with multimodal concept-annotated corpora of SAM3; (4) Training and Hyperparameter Distinctions, showing why SAM2 optimization knowledge does not apply to SAM3; and (5) Evaluation, Metrics, and Failure Modes, outlining the transition from geometric IoU metrics to semantic, open-vocabulary evaluation. Together, these analyses establish SAM3 as a new class of segmentation foundation model and chart future directions for the emerging concept-driven segmentation era.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.08765", "title": "Wan-Move: Motion-controllable Video Generation via Latent Trajectory Guidance", "authors": [ "Ruihang Chu", "Yefei He", "Zhekai Chen", "Shiwei Zhang", "Xiaogang Xu", "Bin Xia", "Dingdong Wang", "Hongwei Yi", "Xihui Liu", "Hengshuang Zhao", "Yu Liu", "Yingya Zhang", "Yujiu Yang" ], "github": "", "project_page": "", "abstract": "We present Wan-Move, a simple and scalable framework that brings motion control to video generative models. Existing motion-controllable methods typically suffer from coarse control granularity and limited scalability, leaving their outputs insufficient for practical use. We narrow this gap by achieving precise and high-quality motion control. Our core idea is to directly make the original condition features motion-aware for guiding video synthesis. To this end, we first represent object motions with dense point trajectories, allowing fine-grained control over the scene. We then project these trajectories into latent space and propagate the first frame's features along each trajectory, producing an aligned spatiotemporal feature map that tells how each scene element should move. This feature map serves as the updated latent condition, which is naturally integrated into the off-the-shelf image-to-video model, e.g., Wan-I2V-14B, as motion guidance without any architecture change. It removes the need for auxiliary motion encoders and makes fine-tuning base models easily scalable. Through scaled training, Wan-Move generates 5-second, 480p videos whose motion controllability rivals Kling 1.5 Pro's commercial Motion Brush, as indicated by user studies. To support comprehensive evaluation, we further design MoveBench, a rigorously curated benchmark featuring diverse content categories and hybrid-verified annotations. It is distinguished by larger data volume, longer video durations, and high-quality motion annotations. Extensive experiments on MoveBench and the public dataset consistently show Wan-Move's superior motion quality. Code, models, and benchmark data are made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.08153", "title": "TreeGRPO: Tree-Advantage GRPO for Online RL Post-Training of Diffusion Models", "authors": [ "Zheng Ding", "Weirui Ye" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) post-training is crucial for aligning generative models with human preferences, but its prohibitive computational cost remains a major barrier to widespread adoption. We introduce TreeGRPO, a novel RL framework that dramatically improves training efficiency by recasting the denoising process as a search tree. From shared initial noise samples, TreeGRPO strategically branches to generate multiple candidate trajectories while efficiently reusing their common prefixes. This tree-structured approach delivers three key advantages: (1) High sample efficiency, achieving better performance under same training samples (2) Fine-grained credit assignment via reward backpropagation that computes step-specific advantages, overcoming the uniform credit assignment limitation of trajectory-based methods, and (3) Amortized computation where multi-child branching enables multiple policy updates per forward pass. Extensive experiments on both diffusion and flow-based models demonstrate that TreeGRPO achieves 2.4times faster training while establishing a superior Pareto frontier in the efficiency-reward trade-off space. Our method consistently outperforms GRPO baselines across multiple benchmarks and reward models, providing a scalable and effective pathway for RL-based visual generative model alignment. The project website is available at treegrpo.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.07951", "title": "Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality", "authors": [ "Zekai Luo", "Zongze Du", "Zhouhang Zhu", "Hao Zhong", "Muzhi Zhu", "Wen Wang", "Yuling Xi", "Chenchen Jing", "Hao Chen", "Chunhua Shen" ], "github": "", "project_page": "", "abstract": "Video face swapping is crucial in film and entertainment production, where achieving high fidelity and temporal consistency over long and complex video sequences remains a significant challenge. Inspired by recent advances in reference-guided image editing, we explore whether rich visual attributes from source videos can be similarly leveraged to enhance both fidelity and temporal coherence in video face swapping. Building on this insight, this work presents LivingSwap, the first video reference guided face swapping model. Our approach employs keyframes as conditioning signals to inject the target identity, enabling flexible and controllable editing. By combining keyframe conditioning with video reference guidance, the model performs temporal stitching to ensure stable identity preservation and high-fidelity reconstruction across long video sequences. To address the scarcity of data for reference-guided training, we construct a paired face-swapping dataset, Face2Face, and further reverse the data pairs to ensure reliable ground-truth supervision. Extensive experiments demonstrate that our method achieves state-of-the-art results, seamlessly integrating the target identity with the source video's expressions, lighting, and motion, while significantly reducing manual effort in production workflows. Project webpage: https://aim-uofa.github.io/LivingSwap", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.07843", "title": "ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models", "authors": [ "Long Lian", "Sida Wang", "Felix Juefei-Xu", "Tsu-Jui Fu", "Xiuyu Li", "Adam Yala", "Trevor Darrell", "Alane Suhr", "Yuandong Tian", "Xi Victoria Lin" ], "github": "", "project_page": "", "abstract": "Scaling inference-time computation has enabled Large Language Models (LLMs) to achieve strong reasoning performance, but inherently sequential decoding leads to substantial latency, especially on complex tasks. Recent work on adaptive parallel reasoning aims to improve inference efficiency by decomposing the problem-solving process into concurrent reasoning threads when beneficial. However, existing methods on realistic tasks are either limited to supervised behavior cloning or exhibit significant accuracy drops compared to widely-used sequential long chain-of-thought (CoT) baselines. Moreover, many require customized inference engines, complicating deployment. We introduce ThreadWeaver, a framework for adaptive parallel reasoning that achieves accuracy on par with popular sequential reasoning models of comparable size while significantly reducing inference latency. ThreadWeaver's performance stems from three key innovations: 1) a two-stage parallel trajectory generator that produces large-scale, high-quality CoT data with parallel annotations for supervised fine-tuning; 2) a trie-based training-inference co-design that enables parallel reasoning on any off-the-shelf autoregressive inference engine without modifying position embeddings or KV caches; and 3) a parallelization-aware reinforcement learning framework that teaches the model to balance accuracy with effective parallelization. Across six challenging mathematical reasoning benchmarks, ThreadWeaver trained atop Qwen3-8B achieves accuracy comparable to cutting-edge sequential reasoning models (71.9% on average and 79.9% on AIME24) while delivering up to 1.53x average speedup in token latency, establishing a new Pareto frontier between accuracy and efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.07921", "title": "DeepCode: Open Agentic Coding", "authors": [ "Zongwei Li", "Zhonghang Li", "Zirui Guo", "Xubin Ren", "Chao Huang" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have given rise to powerful coding agents, making it possible for code assistants to evolve into code engineers. However, existing methods still face significant challenges in achieving high-fidelity document-to-codebase synthesis--such as scientific papers to code--primarily due to a fundamental conflict between information overload and the context bottlenecks of LLMs. In this work, we introduce DeepCode, a fully autonomous framework that fundamentally addresses this challenge through principled information-flow management. By treating repository synthesis as a channel optimization problem, DeepCode seamlessly orchestrates four information operations to maximize task-relevant signals under finite context budgets: source compression via blueprint distillation, structured indexing using stateful code memory, conditional knowledge injection via retrieval-augmented generation, and closed-loop error correction. Extensive evaluations on the PaperBench benchmark demonstrate that DeepCode achieves state-of-the-art performance, decisively outperforming leading commercial agents such as Cursor and Claude Code, and crucially, surpassing PhD-level human experts from top institutes on key reproduction metrics. By systematically transforming paper specifications into production-grade implementations comparable to human expert quality, this work establishes new foundations for autonomous scientific reproduction that can accelerate research evaluation and discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.08868", "title": "EcomBench: Towards Holistic Evaluation of Foundation Agents in E-commerce", "authors": [ "Rui Min", "Zile Qiao", "Ze Xu", "Jiawen Zhai", "Wenyu Gao", "Xuanzhong Chen", "Haozhen Sun", "Zhen Zhang", "Xinyu Wang", "Hong Zhou", "Wenbiao Yin", "Xuan Zhou", "Yong Jiang", "Haicheng Liu", "Liang Ding", "Ling Zou", "Yi R.", "Fung", "Yalong Li", "Pengjun Xie" ], "github": "", "project_page": "", "abstract": "Foundation agents have rapidly advanced in their ability to reason and interact with real environments, making the evaluation of their core capabilities increasingly important. While many benchmarks have been developed to assess agent performance, most concentrate on academic settings or artificially designed scenarios while overlooking the challenges that arise in real applications. To address this issue, we focus on a highly practical real-world setting, the e-commerce domain, which involves a large volume of diverse user interactions, dynamic market conditions, and tasks directly tied to real decision-making processes. To this end, we introduce EcomBench, a holistic E-commerce Benchmark designed to evaluate agent performance in realistic e-commerce environments. EcomBench is built from genuine user demands embedded in leading global e-commerce ecosystems and is carefully curated and annotated through human experts to ensure clarity, accuracy, and domain relevance. It covers multiple task categories within e-commerce scenarios and defines three difficulty levels that evaluate agents on key capabilities such as deep information retrieval, multi-step reasoning, and cross-source knowledge integration. By grounding evaluation in real e-commerce contexts, EcomBench provides a rigorous and dynamic testbed for measuring the practical capabilities of agents in modern e-commerce.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.08564", "title": "Modular Neural Image Signal Processing", "authors": [ "Mahmoud Afifi", "Zhongling Wang", "Ran Zhang", "Michael S. Brown" ], "github": "", "project_page": "", "abstract": "This paper presents a modular neural image signal processing (ISP) framework that processes raw inputs and renders high-quality display-referred images. Unlike prior neural ISP designs, our method introduces a high degree of modularity, providing full control over multiple intermediate stages of the rendering process.~This modular design not only achieves high rendering accuracy but also improves scalability, debuggability, generalization to unseen cameras, and flexibility to match different user-preference styles. To demonstrate the advantages of this design, we built a user-interactive photo-editing tool that leverages our neural ISP to support diverse editing operations and picture styles. The tool is carefully engineered to take advantage of the high-quality rendering of our neural ISP and to enable unlimited post-editable re-rendering. Our method is a fully learning-based framework with variants of different capacities, all of moderate size (ranging from ~0.5 M to ~3.9 M parameters for the entire pipeline), and consistently delivers competitive qualitative and quantitative results across multiple test sets. Watch the supplemental video at: https://youtu.be/ByhQjQSjxVM", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.08186", "title": "Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-and-Language Navigation", "authors": [ "Meng Wei", "Chenyang Wan", "Jiaqi Peng", "Xiqian Yu", "Yuqiang Yang", "Delin Feng", "Wenzhe Cai", "Chenming Zhu", "Tai Wang", "Jiangmiao Pang", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "While recent large vision-language models (VLMs) have improved generalization in vision-language navigation (VLN), existing methods typically rely on end-to-end pipelines that map vision-language inputs directly to short-horizon discrete actions. Such designs often produce fragmented motions, incur high latency, and struggle with real-world challenges like dynamic obstacle avoidance. We propose DualVLN, the first dual-system VLN foundation model that synergistically integrates high-level reasoning with low-level action execution. System 2, a VLM-based global planner, \"grounds slowly\" by predicting mid-term waypoint goals via image-grounded reasoning. System 1, a lightweight, multi-modal conditioning Diffusion Transformer policy, \"moves fast\" by leveraging both explicit pixel goals and latent features from System 2 to generate smooth and accurate trajectories. The dual-system design enables robust real-time control and adaptive local decision-making in complex, dynamic environments. By decoupling training, the VLM retains its generalization, while System 1 achieves interpretable and effective local navigation. DualVLN outperforms prior methods across all VLN benchmarks and real-world experiments demonstrate robust long-horizon planning and real-time adaptability in dynamic environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.08358", "title": "TrackingWorld: World-centric Monocular 3D Tracking of Almost All Pixels", "authors": [ "Jiahao Lu", "Weitao Xiong", "Jiacheng Deng", "Peng Li", "Tianyu Huang", "Zhiyang Dou", "Cheng Lin", "Sai-Kit Yeung", "Yuan Liu" ], "github": "https://github.com/IGL-HKUST/TrackingWorld", "project_page": "", "abstract": "Monocular 3D tracking aims to capture the long-term motion of pixels in 3D space from a single monocular video and has witnessed rapid progress in recent years. However, we argue that the existing monocular 3D tracking methods still fall short in separating the camera motion from foreground dynamic motion and cannot densely track newly emerging dynamic subjects in the videos. To address these two limitations, we propose TrackingWorld, a novel pipeline for dense 3D tracking of almost all pixels within a world-centric 3D coordinate system. First, we introduce a tracking upsampler that efficiently lifts the arbitrary sparse 2D tracks into dense 2D tracks. Then, to generalize the current tracking methods to newly emerging objects, we apply the upsampler to all frames and reduce the redundancy of 2D tracks by eliminating the tracks in overlapped regions. Finally, we present an efficient optimization-based framework to back-project dense 2D tracks into world-centric 3D trajectories by estimating the camera poses and the 3D coordinates of these 2D tracks. Extensive evaluations on both synthetic and real-world datasets demonstrate that our system achieves accurate and dense 3D tracking in a world-centric coordinate frame.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/IGL-HKUST/TrackingWorld", "github_api_added_by": "auto" }, { "date": "2025-12-10", "arxiv_id": "2512.08478", "title": "Visionary: The World Model Carrier Built on WebGPU-Powered Gaussian Splatting Platform", "authors": [ "Yuning Gong", "Yifei Liu", "Yifan Zhan", "Muyao Niu", "Xueying Li", "Yuanjun Liao", "Jiaming Chen", "Yuanyuan Gao", "Jiaqi Chen", "Minming Chen", "Li Zhou", "Yuning Zhang", "Wei Wang", "Xiaoqing Hou", "Huaxi Huang", "Shixiang Tang", "Le Ma", "Dingwen Zhang", "Xue Yang", "Junchi Yan", "Yanchi Zhang", "Yinqiang Zheng", "Xiao Sun", "Zhihang Zhong" ], "github": "", "project_page": "", "abstract": "Neural rendering, particularly 3D Gaussian Splatting (3DGS), has evolved rapidly and become a key component for building world models. However, existing viewer solutions remain fragmented, heavy, or constrained by legacy pipelines, resulting in high deployment friction and limited support for dynamic content and generative models. In this work, we present Visionary, an open, web-native platform for real-time various Gaussian Splatting and meshes rendering. Built on an efficient WebGPU renderer with per-frame ONNX inference, Visionary enables dynamic neural processing while maintaining a lightweight, \"click-to-run\" browser experience. It introduces a standardized Gaussian Generator contract, which not only supports standard 3DGS rendering but also allows plug-and-play algorithms to generate or update Gaussians each frame. Such inference also enables us to apply feedforward generative post-processing. The platform further offers a plug in three.js library with a concise TypeScript API for seamless integration into existing web applications. Experiments show that, under identical 3DGS assets, Visionary achieves superior rendering efficiency compared to current Web viewers due to GPU-based primitive sorting. It already supports multiple variants, including MLP-based 3DGS, 4DGS, neural avatars, and style transformation or enhancement networks. By unifying inference and rendering directly in the browser, Visionary significantly lowers the barrier to reproduction, comparison, and deployment of 3DGS-family methods, serving as a unified World Model Carrier for both reconstructive and generative paradigms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.07802", "title": "OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory", "authors": [ "Zhaochong An", "Menglin Jia", "Haonan Qiu", "Zijian Zhou", "Xiaoke Huang", "Zhiheng Liu", "Weiming Ren", "Kumara Kahatapitiya", "Ding Liu", "Sen He", "Chenyang Zhang", "Tao Xiang", "Fanny Yang", "Serge Belongie", "Tian Xie" ], "github": "", "project_page": "", "abstract": "Storytelling in real-world videos often unfolds through multiple shots -- discontinuous yet semantically connected clips that together convey a coherent narrative. However, existing multi-shot video generation (MSV) methods struggle to effectively model long-range cross-shot context, as they rely on limited temporal windows or single keyframe conditioning, leading to degraded performance under complex narratives. In this work, we propose OneStory, enabling global yet compact cross-shot context modeling for consistent and scalable narrative generation. OneStory reformulates MSV as a next-shot generation task, enabling autoregressive shot synthesis while leveraging pretrained image-to-video (I2V) models for strong visual conditioning. We introduce two key modules: a Frame Selection module that constructs a semantically-relevant global memory based on informative frames from prior shots, and an Adaptive Conditioner that performs importance-guided patchification to generate compact context for direct conditioning. We further curate a high-quality multi-shot dataset with referential captions to mirror real-world storytelling patterns, and design effective training strategies under the next-shot paradigm. Finetuned from a pretrained I2V model on our curated 60K dataset, OneStory achieves state-of-the-art narrative coherence across diverse and complex scenes in both text- and image-conditioned settings, enabling controllable and immersive long-form video storytelling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.06864", "title": "Boosting Unsupervised Video Instance Segmentation with Automatic Quality-Guided Self-Training", "authors": [ "Kaixuan Lu", "Mehmet Onurcan Kaya", "Dim P. Papadopoulos" ], "github": "https://github.com/wcbup/AutoQ-VIS", "project_page": "", "abstract": "Video Instance Segmentation (VIS) faces significant annotation challenges due to its dual requirements of pixel-level masks and temporal consistency labels. While recent unsupervised methods like VideoCutLER eliminate optical flow dependencies through synthetic data, they remain constrained by the synthetic-to-real domain gap. We present AutoQ-VIS, a novel unsupervised framework that bridges this gap through quality-guided self-training. Our approach establishes a closed-loop system between pseudo-label generation and automatic quality assessment, enabling progressive adaptation from synthetic to real videos. Experiments demonstrate state-of-the-art performance with 52.6 AP_{50} on YouTubeVIS-2019 val set, surpassing the previous state-of-the-art VideoCutLER by 4.4%, while requiring no human annotations. This demonstrates the viability of quality-aware self-training for unsupervised VIS. We will release the code at https://github.com/wcbup/AutoQ-VIS.", "github_manual": "", "github_regex": "https://github.com/wcbup/AutoQ-VIS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.06628", "title": "MIND-V: Hierarchical Video Generation for Long-Horizon Robotic Manipulation with RL-based Physical Alignment", "authors": [ "Ruicheng Zhang", "Mingyang Zhang", "Jun Zhou", "Zhangrui Guo", "Xiaofan Liu", "Zunnan Xu", "Zhizhou Zhong", "Puxin Yan", "Haocheng Luo", "Xiu Li" ], "github": "", "project_page": "", "abstract": "Embodied imitation learning is constrained by the scarcity of diverse, long-horizon robotic manipulation data. Existing video generation models for this domain are limited to synthesizing short clips of simple actions and often rely on manually defined trajectories. To this end, we introduce MIND-V, a hierarchical framework designed to synthesize physically plausible and logically coherent videos of long-horizon robotic manipulation. Inspired by cognitive science, MIND-V bridges high-level reasoning with pixel-level synthesis through three core components: a Semantic Reasoning Hub (SRH) that leverages a pre-trained vision-language model for task planning; a Behavioral Semantic Bridge (BSB) that translates abstract instructions into domain-invariant representations; and a Motor Video Generator (MVG) for conditional video rendering. MIND-V employs Staged Visual Future Rollouts, a test-time optimization strategy to enhance long-horizon robustness. To align the generated videos with physical laws, we introduce a GRPO reinforcement learning post-training phase guided by a novel Physical Foresight Coherence (PFC) reward. PFC leverages the V-JEPA world model to enforce physical plausibility by aligning the predicted and actual dynamic evolutions in the feature space. MIND-V demonstrates state-of-the-art performance in long-horizon robotic manipulation video generation, establishing a scalable and controllable paradigm for embodied data synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.06776", "title": "From Next-Token to Next-Block: A Principled Adaptation Path for Diffusion LLMs", "authors": [ "Yuchuan Tian", "Yuchen Liang", "Jiacheng Sun", "Shuo Zhang", "Guangwen Yang", "Yingte Shu", "Sibo Fang", "Tianyu Guo", "Kai Han", "Chao Xu", "Hanting Chen", "Xinghao Chen", "Yunhe Wang" ], "github": "https://github.com/YuchuanTian/NBDiff", "project_page": "", "abstract": "Large language models (LLMs) excel at generation but dominant autoregressive (AR) decoding is inherently sequential, creating a throughput bottleneck. Diffusion Language Models (DLMs)--especially block-wise variants--enable parallel generation and intra-block bidirectional reasoning, yet training large DLMs from scratch is costly and wastes the knowledge in mature AR checkpoints. Prior \"adaptation\" attempts either modify logits or randomly grow attention masks to full-sequence diffusion, or simply transplant AR weights into a block-diffusion recipe, leaving a fundamental mismatch between AR causality and block-wise bidirectionality unaddressed. We reframe adaptation as a intra-paradigm path from AR to Block-Diffusion by viewing AR as Block-Diffusion with blocksize=1. Concretely, we design the pathway of adaptation as follows: we use a context-causal attention mask (causal in context, bidirectional only within the active block), an efficient parallel adaptation procedure, an auxiliary AR loss to maximize data utilization and retain pretrained knowledge, and gradual increment of the generation block size. The recipe integrates cleanly with masked block-diffusion and maintains train-inference consistency. Built on these components, NBDiff-7B (Base and Instruct) could inherit the long-context modeling and reasoning capabilities, and achieve state-of-the-art performance among the 7B-class DLMs, delivering strong gains on general-knowledge, math, and code benchmarks over strong baselines. These results demonstrate that principled AR-to-block-diffusion adaptation is an effective and compute-efficient alternative to training DLMs from scratch. Codes: https://github.com/YuchuanTian/NBDiff.", "github_manual": "", "github_regex": "https://github.com/YuchuanTian/NBDiff", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.08924", "title": "Efficiently Reconstructing Dynamic Scenes One D4RT at a Time", "authors": [ "Chuhan Zhang", "Guillaume Le Moing", "Skanda Koppula", "Ignacio Rocco", "Liliane Momeni", "Junyu Xie", "Shuyang Sun", "Rahul Sukthankar", "Jo\u00eblle K Barral", "Raia Hadsell", "Zoubin Ghahramani", "Andrew Zisserman", "Junlin Zhang", "Mehdi SM Sajjadi" ], "github": "", "project_page": "", "abstract": "Understanding and reconstructing the complex geometry and motion of dynamic scenes from video remains a formidable challenge in computer vision. This paper introduces D4RT, a simple yet powerful feedforward model designed to efficiently solve this task. D4RT utilizes a unified transformer architecture to jointly infer depth, spatio-temporal correspondence, and full camera parameters from a single video. Its core innovation is a novel querying mechanism that sidesteps the heavy computation of dense, per-frame decoding and the complexity of managing multiple, task-specific decoders. Our decoding interface allows the model to independently and flexibly probe the 3D position of any point in space and time. The result is a lightweight and highly scalable method that enables remarkably efficient training and inference. We demonstrate that our approach sets a new state of the art, outperforming previous methods across a wide spectrum of 4D reconstruction tasks. We refer to the project webpage for animated results: https://d4rt-paper.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.08406", "title": "SAM-Body4D: Training-Free 4D Human Body Mesh Recovery from Videos", "authors": [ "Mingqi Gao", "Yunqi Miao", "Jungong Han" ], "github": "https://github.com/gaomingqi/sam-body4d", "project_page": "", "abstract": "Human Mesh Recovery (HMR) aims to reconstruct 3D human pose and shape from 2D observations and is fundamental to human-centric understanding in real-world scenarios. While recent image-based HMR methods such as SAM 3D Body achieve strong robustness on in-the-wild images, they rely on per-frame inference when applied to videos, leading to temporal inconsistency and degraded performance under occlusions. We address these issues without extra training by leveraging the inherent human continuity in videos. We propose SAM-Body4D, a training-free framework for temporally consistent and occlusion-robust HMR from videos. We first generate identity-consistent masklets using a promptable video segmentation model, then refine them with an Occlusion-Aware module to recover missing regions. The refined masklets guide SAM 3D Body to produce consistent full-body mesh trajectories, while a padding-based parallel strategy enables efficient multi-human inference. Experimental results demonstrate that SAM-Body4D achieves improved temporal stability and robustness in challenging in-the-wild videos, without any retraining. Our code and demo are available at: https://github.com/gaomingqi/sam-body4d.", "github_manual": "", "github_regex": "https://github.com/gaomingqi/sam-body4d", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.07197", "title": "SUCCESS-GS: Survey of Compactness and Compression for Efficient Static and Dynamic Gaussian Splatting", "authors": [ "Seokhyun Youn", "Soohyun Lee", "Geonho Kim", "Weeyoung Kwon", "Sung-Ho Bae", "Jihyong Oh" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) has emerged as a powerful explicit representation enabling real-time, high-fidelity 3D reconstruction and novel view synthesis. However, its practical use is hindered by the massive memory and computational demands required to store and render millions of Gaussians. These challenges become even more severe in 4D dynamic scenes. To address these issues, the field of Efficient Gaussian Splatting has rapidly evolved, proposing methods that reduce redundancy while preserving reconstruction quality. This survey provides the first unified overview of efficient 3D and 4D Gaussian Splatting techniques. For both 3D and 4D settings, we systematically categorize existing methods into two major directions, Parameter Compression and Restructuring Compression, and comprehensively summarize the core ideas and methodological trends within each category. We further cover widely used datasets, evaluation metrics, and representative benchmark comparisons. Finally, we discuss current limitations and outline promising research directions toward scalable, compact, and real-time Gaussian Splatting for both static and dynamic 3D scene representation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.06531", "title": "Novel Deep Learning Architectures for Classification and Segmentation of Brain Tumors from MRI Images", "authors": [ "Sayan Das", "Arghadip Biswas" ], "github": "", "project_page": "", "abstract": "Brain tumors pose a significant threat to human life, therefore it is very much necessary to detect them accurately in the early stages for better diagnosis and treatment. Brain tumors can be detected by the radiologist manually from the MRI scan images of the patients. However, the incidence of brain tumors has risen amongst children and adolescents in recent years, resulting in a substantial volume of data, as a result, it is time-consuming and difficult to detect manually. With the emergence of Artificial intelligence in the modern world and its vast application in the medical field, we can make an approach to the CAD (Computer Aided Diagnosis) system for the early detection of Brain tumors automatically. All the existing models for this task are not completely generalized and perform poorly on the validation data. So, we have proposed two novel Deep Learning Architectures - (a) SAETCN (Self-Attention Enhancement Tumor Classification Network) for the classification of different kinds of brain tumors. We have achieved an accuracy of 99.38% on the validation dataset making it one of the few Novel Deep learning-based architecture that is capable of detecting brain tumors accurately. We have trained the model on the dataset, which contains images of 3 types of tumors (glioma, meningioma, and pituitary tumors) and non-tumor cases. and (b) SAS-Net (Self-Attentive Segmentation Network) for the accurate segmentation of brain tumors. We have achieved an overall pixel accuracy of 99.23%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.05325", "title": "LYNX: Learning Dynamic Exits for Confidence-Controlled Reasoning", "authors": [ "\u00d6mer Faruk Akg\u00fcl", "Yusuf Hakan Kalayc\u0131", "Rajgopal Kannan", "Willie Neiswanger", "Viktor Prasanna" ], "github": "https://github.com/farukakgul/LYNX", "project_page": "", "abstract": "Large reasoning models achieve strong performance on complex tasks by generating extended chains of thought, but they often \"overthink\": continuing to reason long after they have enough information to answer correctly. This wastes inference-time compute and can hurt accuracy. Existing attempts to stop early either manipulate decoding with extra sampling and heuristics, rely on auxiliary verifier models, or operate only as post-hoc analysis pipelines without formal guarantees. We introduce LYNX, an online early-exit mechanism that turns a model's own hidden-state awareness into confidence-controlled stopping decisions. LYNX attaches exit decisions to naturally occurring reasoning cues (e.g., \"hmm\", \"wait\") during generation, trains a lightweight probe on hidden states at those cue tokens using supervision from forced exits, and wraps the resulting scores in split conformal prediction to obtain distribution-free control over premature exits. Crucially, we train and calibrate this probe once on a generic mathematical corpus and reuse it unchanged across benchmarks, decoding temperatures, and even non-mathematical tasks. Across three model families spanning 1.5B to 32B parameters, a single mathematically trained probe per base model yields strong accuracy--efficiency tradeoffs. On GSM8K, LYNX matches or improves baseline accuracy while reducing tokens by 40--65\\%; on MATH-500 it improves accuracy by up to 12 points with roughly 35--60\\% fewer tokens; on AIME 2024 it recovers baseline accuracy with more than 50\\% token savings; and on CommonsenseQA, a non-math benchmark, it transfers zero-shot with modest accuracy gains and up to 70\\% fewer tokens. Compared to state-of-the-art early-exit methods, LYNX offers competitive or superior Pareto frontiers while remaining fully online, requiring no proxy models at inference, and providing explicit, user-tunable confidence guarantees.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/farukakgul/LYNX", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.08923", "title": "Same Content, Different Answers: Cross-Modal Inconsistency in MLLMs", "authors": [ "Angela van Sprang", "Laurens Samson", "Ana Lucic", "Erman Acar", "Sennay Ghebreab", "Yuki M. Asano" ], "github": "", "project_page": "", "abstract": "We introduce two new benchmarks REST and REST+(Render-Equivalence Stress Tests) to enable systematic evaluation of cross-modal inconsistency in multimodal large language models (MLLMs). MLLMs are trained to represent vision and language in the same embedding space, yet they cannot perform the same tasks in both modalities. Our benchmarks contain samples with the same semantic information in three modalities (image, text, mixed) and we show that state-of-the-art MLLMs cannot consistently reason over these different modalities. We evaluate 15 MLLMs and find that the degree of modality inconsistency varies substantially, even when accounting for problems with text recognition (OCR). Neither rendering text as image nor rendering an image as text solves the inconsistency. Even if OCR is correct, we find that visual characteristics (text colour and resolution, but not font) and the number of vision tokens have an impact on model performance. Finally, we find that our consistency score correlates with the modality gap between text and images, highlighting a mechanistic interpretation of cross-modal inconsistent MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.04434", "title": "Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks", "authors": [ "Ali Rabeh", "Suresh Murugaiyan", "Adarsh Krishnamurthy", "Baskar Ganapathysubramanian" ], "github": "https://github.com/baskargroup/TimeDependent-DeepONet", "project_page": "", "abstract": "Fast, geometry-generalizing surrogates for unsteady flow remain challenging. We present a time-dependent, geometry-aware Deep Operator Network that predicts velocity fields for moderate-Re flows around parametric and non-parametric shapes. The model encodes geometry via a signed distance field (SDF) trunk and flow history via a CNN branch, trained on 841 high-fidelity simulations. On held-out shapes, it attains sim 5% relative L2 single-step error and up to 1000X speedups over CFD. We provide physics-centric rollout diagnostics, including phase error at probes and divergence norms, to quantify long-horizon fidelity. These reveal accurate near-term transients but error accumulation in fine-scale wakes, most pronounced for sharp-cornered geometries. We analyze failure modes and outline practical mitigations. Code, splits, and scripts are openly released at: https://github.com/baskargroup/TimeDependent-DeepONet to support reproducibility and benchmarking.", "github_manual": "", "github_regex": "https://github.com/baskargroup/TimeDependent-DeepONet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.05033", "title": "Arbitrage: Efficient Reasoning via Advantage-Aware Speculation", "authors": [ "Monishwaran Maheswaran", "Rishabh Tiwari", "Yuezhou Hu", "Kerem Dilmen", "Coleman Hooper", "Haocheng Xi", "Nicholas Lee", "Mehrdad Farajtabar", "Michael W. Mahoney", "Kurt Keutzer", "Amir Gholami" ], "github": "https://github.com/SqueezeAILab/Arbitrage", "project_page": "", "abstract": "Modern Large Language Models achieve impressive reasoning capabilities with long Chain of Thoughts, but they incur substantial computational cost during inference, and this motivates techniques to improve the performance-cost ratio. Among these techniques, Speculative Decoding accelerates inference by employing a fast but inaccurate draft model to autoregressively propose tokens, which are then verified in parallel by a more capable target model. However, due to unnecessary rejections caused by token mismatches in semantically equivalent steps, traditional token-level Speculative Decoding struggles in reasoning tasks. Although recent works have shifted to step-level semantic verification, which improve efficiency by accepting or rejecting entire reasoning steps, existing step-level methods still regenerate many rejected steps with little improvement, wasting valuable target compute. To address this challenge, we propose Arbitrage, a novel step-level speculative generation framework that routes generation dynamically based on the relative advantage between draft and target models. Instead of applying a fixed acceptance threshold, Arbitrage uses a lightweight router trained to predict when the target model is likely to produce a meaningfully better step. This routing approximates an ideal Arbitrage Oracle that always chooses the higher-quality step, achieving near-optimal efficiency-accuracy trade-offs. Across multiple mathematical reasoning benchmarks, Arbitrage consistently surpasses prior step-level Speculative Decoding baselines, reducing inference latency by up to sim2times at matched accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SqueezeAILab/Arbitrage", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.07107", "title": "COREA: Coarse-to-Fine 3D Representation Alignment Between Relightable 3D Gaussians and SDF via Bidirectional 3D-to-3D Supervision", "authors": [ "Jaeyoon Lee", "Hojoon Jung", "Sungtae Hwang", "Jihyong Oh", "Jongwon Choi" ], "github": "", "project_page": "", "abstract": "We present COREA, the first unified framework that jointly learns relightable 3D Gaussians and a Signed Distance Field (SDF) for accurate geometry reconstruction and faithful relighting. While recent 3D Gaussian Splatting (3DGS) methods have extended toward mesh reconstruction and physically-based rendering (PBR), their geometry is still learned from 2D renderings, leading to coarse surfaces and unreliable BRDF-lighting decomposition. To address these limitations, COREA introduces a coarse-to-fine bidirectional 3D-to-3D alignment strategy that allows geometric signals to be learned directly in 3D space. Within this strategy, depth provides coarse alignment between the two representations, while depth gradients and normals refine fine-scale structure, and the resulting geometry supports stable BRDF-lighting decomposition. A density-control mechanism further stabilizes Gaussian growth, balancing geometric fidelity with memory efficiency. Experiments on standard benchmarks demonstrate that COREA achieves superior performance in novel-view synthesis, mesh reconstruction, and PBR within a unified framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.02231", "title": "See, Hear, and Understand: Benchmarking Audiovisual Human Speech Understanding in Multimodal Large Language Models", "authors": [ "Le Thien Phuc Nguyen", "Zhuoran Yu", "Samuel Low Yu Hang", "Subin An", "Jeongik Lee", "Yohan Ban", "SeungEun Chung", "Thanh-Huy Nguyen", "JuWan Maeng", "Soochahn Lee", "Yong Jae Lee" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) are expected to jointly interpret vision, audio, and language, yet existing video benchmarks rarely assess fine-grained reasoning about human speech. Many tasks remain visually solvable or only coarsely evaluate speech, offering limited insight into whether models can align who speaks, what is said, and when it occurs. We introduce AV-SpeakerBench, a curated benchmark of 3,212 multiple-choice questions focused on speaker-centric audiovisual reasoning in real-world videos. It features: (1) a speaker-centered formulation that treats speakers-not scenes-as the core reasoning unit; (2) fusion-grounded question design embedding audiovisual dependencies into question semantics; and (3) expert-curated annotations ensuring temporal precision and cross-modal validity. Comprehensive evaluations show that the Gemini family consistently outperforms open-source systems, with Gemini 2.5 Pro achieving the best results. Among open models, Qwen3-Omni-30B approaches Gemini 2.0 Flash but remains far behind Gemini 2.5 Pro, primarily due to weaker audiovisual fusion rather than visual perception. We believe AV-SpeakerBench establishes a rigorous foundation for advancing fine-grained audiovisual reasoning in future multimodal systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.04763", "title": "MemLoRA: Distilling Expert Adapters for On-Device Memory Systems", "authors": [ "Massimo Bini", "Ondrej Bohdal", "Umberto Michieli", "Zeynep Akata", "Mete Ozay", "Taha Ceritli" ], "github": "", "project_page": "", "abstract": "Memory-augmented Large Language Models (LLMs) have demonstrated remarkable consistency during prolonged dialogues by storing relevant memories and incorporating them as context. Such memory-based personalization is also key in on-device settings that allow users to keep their conversations and data private. However, memory-augmented systems typically rely on LLMs that are too costly for local on-device deployment. Even though Small Language Models (SLMs) are more suitable for on-device inference than LLMs, they cannot achieve sufficient performance. Additionally, these LLM-based systems lack native visual capabilities, limiting their applicability in multimodal contexts. In this paper, we introduce (i) MemLoRA, a novel memory system that enables local deployment by equipping SLMs with specialized memory adapters, and (ii) its vision extension MemLoRA-V, which integrates small Vision-Language Models (SVLMs) to memory systems, enabling native visual understanding. Following knowledge distillation principles, each adapter is trained separately for specific memory operationsx2013knowledge extraction, memory update, and memory-augmented generation. Equipped with memory adapters, small models enable accurate on-device memory operations without cloud dependency. On text-only operations, MemLoRA outperforms 10times larger baseline models (e.g., Gemma2-27B) and achieves performance comparable to 60times larger models (e.g., GPT-OSS-120B) on the LoCoMo benchmark. To evaluate visual understanding operations instead, we extend LoCoMo with challenging Visual Question Answering tasks that require direct visual reasoning. On this, our VLM-integrated MemLoRA-V shows massive improvements over caption-based approaches (81.3 vs. 23.7 accuracy) while keeping strong performance in text-based tasks, demonstrating the efficacy of our method in multimodal contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.08309", "title": "Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise in Infinite, Real-Time Terrain Generation", "authors": [ "Alexander Goslin" ], "github": "https://github.com/xandergos/infinite-tensor", "project_page": "", "abstract": "For decades, procedural worlds have been built on procedural noise functions such as Perlin noise, which are fast and infinite, yet fundamentally limited in realism and large-scale coherence. We introduce Terrain Diffusion, an AI-era successor to Perlin noise that bridges the fidelity of diffusion models with the properties that made procedural noise indispensable: seamless infinite extent, seed-consistency, and constant-time random access. At its core is InfiniteDiffusion, a novel algorithm for infinite generation, enabling seamless, real-time synthesis of boundless landscapes. A hierarchical stack of diffusion models couples planetary context with local detail, while a compact Laplacian encoding stabilizes outputs across Earth-scale dynamic ranges. An open-source infinite-tensor framework supports constant-memory manipulation of unbounded tensors, and few-step consistency distillation enables efficient generation. Together, these components establish diffusion models as a practical foundation for procedural world generation, capable of synthesizing entire planets coherently, controllably, and without limits.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xandergos/infinite-tensor", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-10", "arxiv_id": "2512.08730", "title": "SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images", "authors": [ "Kaiyu Li", "Shengqi Zhang", "Yupeng Deng", "Zhi Wang", "Deyu Meng", "Xiangyong Cao" ], "github": "https://github.com/earth-insights/SegEarth-OV-3", "project_page": "", "abstract": "Most existing methods for training-free Open-Vocabulary Semantic Segmentation (OVSS) are based on CLIP. While these approaches have made progress, they often face challenges in precise localization or require complex pipelines to combine separate modules, especially in remote sensing scenarios where numerous dense and small targets are present. Recently, Segment Anything Model 3 (SAM 3) was proposed, unifying segmentation and recognition in a promptable framework. In this paper, we present a preliminary exploration of applying SAM 3 to the remote sensing OVSS task without any training. First, we implement a mask fusion strategy that combines the outputs from SAM 3's semantic segmentation head and the Transformer decoder (instance head). This allows us to leverage the strengths of both heads for better land coverage. Second, we utilize the presence score from the presence head to filter out categories that do not exist in the scene, reducing false positives caused by the vast vocabulary sizes and patch-level processing in geospatial scenes. We evaluate our method on extensive remote sensing datasets. Experiments show that this simple adaptation achieves promising performance, demonstrating the potential of SAM 3 for remote sensing OVSS. Our code is released at https://github.com/earth-insights/SegEarth-OV-3.", "github_manual": "", "github_regex": "https://github.com/earth-insights/SegEarth-OV-3", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.09363", "title": "StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation", "authors": [ "Ke Xing", "Longfei Li", "Yuyang Yin", "Hanwen Liang", "Guixun Luo", "Chen Fang", "Jue Wang", "Konstantinos N. Plataniotis", "Xiaojie Jin", "Yao Zhao", "Yunchao Wei" ], "github": "", "project_page": "", "abstract": "The growing adoption of XR devices has fueled strong demand for high-quality stereo video, yet its production remains costly and artifact-prone. To address this challenge, we present StereoWorld, an end-to-end framework that repurposes a pretrained video generator for high-fidelity monocular-to-stereo video generation. Our framework jointly conditions the model on the monocular video input while explicitly supervising the generation with a geometry-aware regularization to ensure 3D structural fidelity. A spatio-temporal tiling scheme is further integrated to enable efficient, high-resolution synthesis. To enable large-scale training and evaluation, we curate a high-definition stereo video dataset containing over 11M frames aligned to natural human interpupillary distance (IPD). Extensive experiments demonstrate that StereoWorld substantially outperforms prior methods, generating stereo videos with superior visual fidelity and geometric consistency. The project webpage is available at https://ke-xing.github.io/StereoWorld/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.08560", "title": "BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain", "authors": [ "Navve Wasserman", "Matias Cosarinsky", "Yuval Golbari", "Aude Oliva", "Antonio Torralba", "Tamar Rott Shaham", "Michal Irani" ], "github": "", "project_page": "", "abstract": "Understanding how the human brain represents visual concepts, and in which brain regions these representations are encoded, remains a long-standing challenge. Decades of work have advanced our understanding of visual representations, yet brain signals remain large and complex, and the space of possible visual concepts is vast. As a result, most studies remain small-scale, rely on manual inspection, focus on specific regions and properties, and rarely include systematic validation. We present a large-scale, automated framework for discovering and explaining visual representations across the human cortex. Our method comprises two main stages. First, we discover candidate interpretable patterns in fMRI activity through unsupervised, data-driven decomposition methods. Next, we explain each pattern by identifying the set of natural images that most strongly elicit it and generating a natural-language description of their shared visual meaning. To scale this process, we introduce an automated pipeline that tests multiple candidate explanations, assigns quantitative reliability scores, and selects the most consistent description for each voxel pattern. Our framework reveals thousands of interpretable patterns spanning many distinct visual concepts, including fine-grained representations previously unreported.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.09824", "title": "Composing Concepts from Images and Videos via Concept-prompt Binding", "authors": [ "Xianghao Kong", "Zeyu Zhang", "Yuwei Guo", "Zhuoran Zhao", "Songchun Zhang", "Anyi Rao" ], "github": "", "project_page": "", "abstract": "Visual concept composition, which aims to integrate different elements from images and videos into a single, coherent visual output, still falls short in accurately extracting complex concepts from visual inputs and flexibly combining concepts from both images and videos. We introduce Bind & Compose, a one-shot method that enables flexible visual concept composition by binding visual concepts with corresponding prompt tokens and composing the target prompt with bound tokens from various sources. It adopts a hierarchical binder structure for cross-attention conditioning in Diffusion Transformers to encode visual concepts into corresponding prompt tokens for accurate decomposition of complex visual concepts. To improve concept-token binding accuracy, we design a Diversify-and-Absorb Mechanism that uses an extra absorbent token to eliminate the impact of concept-irrelevant details when training with diversified prompts. To enhance the compatibility between image and video concepts, we present a Temporal Disentanglement Strategy that decouples the training process of video concepts into two stages with a dual-branch binder structure for temporal modeling. Evaluations demonstrate that our method achieves superior concept consistency, prompt fidelity, and motion quality over existing approaches, opening up new possibilities for visual creativity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.09247", "title": "OmniPSD: Layered PSD Generation with Diffusion Transformer", "authors": [ "Cheng Liu", "Yiren Song", "Haofan Wang", "Mike Zheng Shou" ], "github": "https://github.com/Storia-AI/font-classify", "project_page": "", "abstract": "Recent advances in diffusion models have greatly improved image generation and editing, yet generating or reconstructing layered PSD files with transparent alpha channels remains highly challenging. We propose OmniPSD, a unified diffusion framework built upon the Flux ecosystem that enables both text-to-PSD generation and image-to-PSD decomposition through in-context learning. For text-to-PSD generation, OmniPSD arranges multiple target layers spatially into a single canvas and learns their compositional relationships through spatial attention, producing semantically coherent and hierarchically structured layers. For image-to-PSD decomposition, it performs iterative in-context editing, progressively extracting and erasing textual and foreground components to reconstruct editable PSD layers from a single flattened image. An RGBA-VAE is employed as an auxiliary representation module to preserve transparency without affecting structure learning. Extensive experiments on our new RGBA-layered dataset demonstrate that OmniPSD achieves high-fidelity generation, structural consistency, and transparency awareness, offering a new paradigm for layered design generation and decomposition with diffusion transformers.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Storia-AI/font-classify", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.09928", "title": "HiF-VLA: Hindsight, Insight and Foresight through Motion Representation for Vision-Language-Action Models", "authors": [ "Minghui Lin", "Pengxiang Ding", "Shu Wang", "Zifeng Zhuang", "Yang Liu", "Xinyang Tong", "Wenxuan Song", "Shangke Lyu", "Siteng Huang", "Donglin Wang" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models have recently enabled robotic manipulation by grounding visual and linguistic cues into actions. However, most VLAs assume the Markov property, relying only on the current observation and thus suffering from temporal myopia that degrades long-horizon coherence. In this work, we view motion as a more compact and informative representation of temporal context and world dynamics, capturing inter-state changes while filtering static pixel-level noise. Building on this idea, we propose HiF-VLA (Hindsight, Insight, and Foresight for VLAs), a unified framework that leverages motion for bidirectional temporal reasoning. HiF-VLA encodes past dynamics through hindsight priors, anticipates future motion via foresight reasoning, and integrates both through a hindsight-modulated joint expert to enable a ''think-while-acting'' paradigm for long-horizon manipulation. As a result, HiF-VLA surpasses strong baselines on LIBERO-Long and CALVIN ABC-D benchmarks, while incurring negligible additional inference latency. Furthermore, HiF-VLA achieves substantial improvements in real-world long-horizon manipulation tasks, demonstrating its broad effectiveness in practical robotic settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.08829", "title": "InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models", "authors": [ "Hongyuan Tao", "Bencheng Liao", "Shaoyu Chen", "Haoran Yin", "Qian Zhang", "Wenyu Liu", "Xinggang Wang" ], "github": "https://github.com/hustvl/InfiniteVL", "project_page": "", "abstract": "Window attention and linear attention represent two principal strategies for mitigating the quadratic complexity and ever-growing KV cache in Vision-Language Models (VLMs). However, we observe that window-based VLMs suffer performance degradation when sequence length exceeds the window size, while linear attention underperforms on information-intensive tasks such as OCR and document understanding. To overcome these limitations, we propose InfiniteVL, a linear-complexity VLM architecture that synergizes sliding window attention (SWA) with Gated DeltaNet. For achieving competitive multimodal performance under constrained resources, we design a three-stage training strategy comprising distillation pretraining, instruction tuning, and long-sequence SFT. Remarkably, using less than 2\\% of the training data required by leading VLMs, InfiniteVL not only substantially outperforms previous linear-complexity VLMs but also matches the performance of leading Transformer-based VLMs, while demonstrating effective long-term memory retention. Compared to similar-sized Transformer-based VLMs accelerated by FlashAttention-2, InfiniteVL achieves over 3.6\\times inference speedup while maintaining constant latency and memory footprint. In streaming video understanding scenarios, it sustains a stable 24 FPS real-time prefill speed while preserving long-term memory cache. Code and models are available at https://github.com/hustvl/InfiniteVL.", "github_manual": "", "github_regex": "https://github.com/hustvl/InfiniteVL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.02892", "title": "Fast-Decoding Diffusion Language Models via Progress-Aware Confidence Schedules", "authors": [ "Amr Mohamed", "Yang Zhang", "Michalis Vazirgiannis", "Guokan Shang" ], "github": "https://github.com/amr-mohamedd/SchED", "project_page": "", "abstract": "Diffusion large language models (dLLMs) offer a promising alternative to autoregressive models, but their practical utility is severely hampered by slow, iterative sampling. We present SchED, a training-free, model-agnostic early-exit algorithm that aggregates full-span logit margins and halts decoding once a smooth, progress-dependent confidence threshold is met. We evaluated SchED on two dLLM families (Dream and LLaDA), in base and instruction-tuned variants across ten benchmarks spanning downstream tasks including multiple-choice question answering (MCQ), math, long-form QA/summarization, and translation. SchED delivers large, stable accelerations: on instruction-tuned models, it achieves 3.8-4.0times speedups while retaining 99.8-100% of the baseline score on average. On base models, SchED yields consistent speedup gains with 99.1-100% performance retention, with up to 2.34times under more aggressive settings. Using a conservative speed metric that heavily penalizes quality loss (QPS, \u03b3{=}4), we show that SchED is robust and clearly outperforms prior confidence-based early-exit methods, which break down on long-form generation. An entropy analysis of the model's token predictions reveals that instruction tuning speeds up the decay of predictive entropy. By turning genuine confidence stabilization into computational savings, SchED makes dLLM decoding substantially more efficient.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amr-mohamedd/SchED", "github_api_added_by": "auto" }, { "date": "2025-12-11", "arxiv_id": "2512.09864", "title": "UniUGP: Unifying Understanding, Generation, and Planing For End-to-end Autonomous Driving", "authors": [ "Hao Lu", "Ziyang Liu", "Guangfeng Jiang", "Yuanfei Luo", "Sheng Chen", "Yangang Zhang", "Ying-Cong Chen" ], "github": "", "project_page": "", "abstract": "Autonomous driving (AD) systems struggle in long-tail scenarios due to limited world knowledge and weak visual dynamic modeling. Existing vision-language-action (VLA)-based methods cannot leverage unlabeled videos for visual causal learning, while world model-based methods lack reasoning capabilities from large language models. In this paper, we construct multiple specialized datasets providing reasoning and planning annotations for complex scenarios. Then, a unified Understanding-Generation-Planning framework, named UniUGP, is proposed to synergize scene reasoning, future video generation, and trajectory planning through a hybrid expert architecture. By integrating pre-trained VLMs and video generation models, UniUGP leverages visual dynamics and semantic reasoning to enhance planning performance. Taking multi-frame observations and language instructions as input, it produces interpretable chain-of-thought reasoning, physically consistent trajectories, and coherent future videos. We introduce a four-stage training strategy that progressively builds these capabilities across multiple existing AD datasets, along with the proposed specialized datasets. Experiments demonstrate state-of-the-art performance in perception, reasoning, and decision-making, with superior generalization to challenging long-tail situations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.04753", "title": "EtCon: Edit-then-Consolidate for Reliable Knowledge Editing", "authors": [ "Ruilin Li", "Yibin Wang", "Wenhong Zhu", "Chenglin Li", "Jinghao Zhang", "Chenliang Li", "Junchi Yan", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Knowledge editing aims to update specific facts in large language models (LLMs) without full retraining. Prior efforts sought to tune the knowledge layers of LLMs, proving effective for making selective edits. However, a significant gap exists between their performance in controlled, teacher-forcing evaluations and their real-world effectiveness in lifelong learning scenarios, which greatly limits their practical applicability. This work's empirical analysis reveals two recurring issues associated with this gap: (1) Most traditional methods lead the edited model to overfit to the new fact, thereby degrading pre-trained capabilities; (2) There is a critical absence of a knowledge consolidation stage, leaving new facts insufficiently integrated into LLMs' inference-time behavior under autoregressive generation, thereby leading to a mismatch between parametric knowledge and actual generation behavior. To this end, we propose Edit-then-Consolidate, a novel knowledge editing paradigm that aims to bridge the gap between theoretical knowledge editing methods and their real-world applicability. Specifically, (1) our framework mitigates overfitting via Targeted Proximal Supervised Fine-Tuning (TPSFT) that localizes the edit via a trust-region objective to limit policy drift; (2) Then, a consolidation stage using Group Relative Policy Optimization (GRPO) aligns the edited knowledge with CoT-based inference policy by optimizing trajectory-level behavior under comprehensive reward signals. Extensive experiments demonstrate our framework consistently improves editing reliability and generalization under real-world evaluations, while better preserving locality and pre-trained capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.09663", "title": "IF-Bench: Benchmarking and Enhancing MLLMs for Infrared Images with Generative Visual Prompting", "authors": [ "Tao Zhang", "Yuyang Hong", "Yang Xia", "Kun Ding", "Zeyu Zhang", "Ying Wang", "Shiming Xiang", "Chunhong Pan" ], "github": "https://github.com/casiatao/IF-Bench", "project_page": "", "abstract": "Recent advances in multimodal large language models (MLLMs) have led to impressive progress across various benchmarks. However, their capability in understanding infrared images remains unexplored. To address this gap, we introduce IF-Bench, the first high-quality benchmark designed for evaluating multimodal understanding of infrared images. IF-Bench consists of 499 images sourced from 23 infrared datasets and 680 carefully curated visual question-answer pairs, covering 10 essential dimensions of image understanding. Based on this benchmark, we systematically evaluate over 40 open-source and closed-source MLLMs, employing cyclic evaluation, bilingual assessment, and hybrid judgment strategies to enhance the reliability of the results. Our analysis reveals how model scale, architecture, and inference paradigms affect infrared image comprehension, providing valuable insights for this area. Furthermore, we propose a training-free generative visual prompting (GenViP) method, which leverages advanced image editing models to translate infrared images into semantically and spatially aligned RGB counterparts, thereby mitigating domain distribution shifts. Extensive experiments demonstrate that our method consistently yields significant performance improvements across a wide range of MLLMs. The benchmark and code are available at https://github.com/casiatao/IF-Bench.", "github_manual": "", "github_regex": "https://github.com/casiatao/IF-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.09164", "title": "WonderZoom: Multi-Scale 3D World Generation", "authors": [ "Jin Cao", "Hong-Xing Yu", "Jiajun Wu" ], "github": "https://github.com/jin-cao-tma/WonderZoom", "project_page": "", "abstract": "We present WonderZoom, a novel approach to generating 3D scenes with contents across multiple spatial scales from a single image. Existing 3D world generation models remain limited to single-scale synthesis and cannot produce coherent scene contents at varying granularities. The fundamental challenge is the lack of a scale-aware 3D representation capable of generating and rendering content with largely different spatial sizes. WonderZoom addresses this through two key innovations: (1) scale-adaptive Gaussian surfels for generating and real-time rendering of multi-scale 3D scenes, and (2) a progressive detail synthesizer that iteratively generates finer-scale 3D contents. Our approach enables users to \"zoom into\" a 3D region and auto-regressively synthesize previously non-existent fine details from landscapes to microscopic features. Experiments demonstrate that WonderZoom significantly outperforms state-of-the-art video and 3D models in both quality and alignment, enabling multi-scale 3D world creation from a single image. We show video results and an interactive viewer of generated multi-scale 3D worlds in https://wonderzoom.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jin-cao-tma/WonderZoom", "github_api_added_by": "auto" }, { "date": "2025-12-11", "arxiv_id": "2512.09106", "title": "Learning Unmasking Policies for Diffusion Language Models", "authors": [ "Metod Jazbec", "Theo X. Olausson", "Louis B\u00e9thune", "Pierre Ablin", "Michael Kirchhof", "Joao Monterio", "Victor Turrisi", "Jason Ramapuram", "Marco Cuturi" ], "github": "", "project_page": "", "abstract": "Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the promise of being more efficient during inference. One particularly successful variant is masked discrete diffusion, in which a buffer filled with special mask tokens is progressively replaced with tokens sampled from the model's vocabulary. Efficiency can be gained by unmasking several tokens in parallel, but doing too many at once risks degrading the generation quality. Thus, one critical design aspect of dLLMs is the sampling procedure that selects, at each step of the diffusion process, which tokens to replace. Indeed, recent work has found that heuristic strategies such as confidence thresholding lead to both higher quality and token throughput compared to random unmasking. However, such heuristics have downsides: they require manual tuning, and we observe that their performance degrades with larger buffer sizes. In this work, we instead propose to train sampling procedures using reinforcement learning. Specifically, we formalize masked diffusion sampling as a Markov decision process in which the dLLM serves as the environment, and propose a lightweight policy architecture based on a single-layer transformer that maps dLLM token confidences to unmasking decisions. Our experiments show that these trained policies match the performance of state-of-the-art heuristics when combined with semi-autoregressive generation, while outperforming them in the full diffusion setting. We also examine the transferability of these policies, finding that they can generalize to new underlying dLLMs and longer sequence lengths. However, we also observe that their performance degrades when applied to out-of-domain data, and that fine-grained tuning of the accuracy-efficiency trade-off can be challenging with our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.08006", "title": "Beyond Unified Models: A Service-Oriented Approach to Low Latency, Context Aware Phonemization for Real Time TTS", "authors": [ "Mahta Fetrat", "Donya Navabi", "Zahra Dehghanian", "Morteza Abolghasemi", "Hamid R. Rabiee" ], "github": "", "project_page": "", "abstract": "Lightweight, real-time text-to-speech systems are crucial for accessibility. However, the most efficient TTS models often rely on lightweight phonemizers that struggle with context-dependent challenges. In contrast, more advanced phonemizers with a deeper linguistic understanding typically incur high computational costs, which prevents real-time performance. This paper examines the trade-off between phonemization quality and inference speed in G2P-aided TTS systems, introducing a practical framework to bridge this gap. We propose lightweight strategies for context-aware phonemization and a service-oriented TTS architecture that executes these modules as independent services. This design decouples heavy context-aware components from the core TTS engine, effectively breaking the latency barrier and enabling real-time use of high-quality phonemization models. Experimental results confirm that the proposed system improves pronunciation soundness and linguistic accuracy while maintaining real-time responsiveness, making it well-suited for offline and end-device TTS applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.05446", "title": "TED-4DGS: Temporally Activated and Embedding-based Deformation for 4DGS Compression", "authors": [ "Cheng-Yuan Ho", "He-Bi Yang", "Jui-Chiu Chiang", "Yu-Lun Liu", "Wen-Hsiao Peng" ], "github": "", "project_page": "", "abstract": "Building on the success of 3D Gaussian Splatting (3DGS) in static 3D scene representation, its extension to dynamic scenes, commonly referred to as 4DGS or dynamic 3DGS, has attracted increasing attention. However, designing more compact and efficient deformation schemes together with rate-distortion-optimized compression strategies for dynamic 3DGS representations remains an underexplored area. Prior methods either rely on space-time 4DGS with overspecified, short-lived Gaussian primitives or on canonical 3DGS with deformation that lacks explicit temporal control. To address this, we present TED-4DGS, a temporally activated and embedding-based deformation scheme for rate-distortion-optimized 4DGS compression that unifies the strengths of both families. TED-4DGS is built on a sparse anchor-based 3DGS representation. Each canonical anchor is assigned learnable temporal-activation parameters to specify its appearance and disappearance transitions over time, while a lightweight per-anchor temporal embedding queries a shared deformation bank to produce anchor-specific deformation. For rate-distortion compression, we incorporate an implicit neural representation (INR)-based hyperprior to model anchor attribute distributions, along with a channel-wise autoregressive model to capture intra-anchor correlations. With these novel elements, our scheme achieves state-of-the-art rate-distortion performance on several real-world datasets. To the best of our knowledge, this work represents one of the first attempts to pursue a rate-distortion-optimized compression framework for dynamic 3DGS representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.04519", "title": "VideoSSM: Autoregressive Long Video Generation with Hybrid State-Space Memory", "authors": [ "Yifei Yu", "Xiaoshan Wu", "Xinting Hu", "Tao Hu", "Yangtian Sun", "Xiaoyang Lyu", "Bo Wang", "Lin Ma", "Yuewen Ma", "Zhongrui Wang", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "Autoregressive (AR) diffusion enables streaming, interactive long-video generation by producing frames causally, yet maintaining coherence over minute-scale horizons remains challenging due to accumulated errors, motion drift, and content repetition. We approach this problem from a memory perspective, treating video synthesis as a recurrent dynamical process that requires coordinated short- and long-term context. We propose VideoSSM, a Long Video Model that unifies AR diffusion with a hybrid state-space memory. The state-space model (SSM) serves as an evolving global memory of scene dynamics across the entire sequence, while a context window provides local memory for motion cues and fine details. This hybrid design preserves global consistency without frozen, repetitive patterns, supports prompt-adaptive interaction, and scales in linear time with sequence length. Experiments on short- and long-range benchmarks demonstrate state-of-the-art temporal consistency and motion stability among autoregressive video generator especially at minute-scale horizons, enabling content diversity and interactive prompt-based control, thereby establishing a scalable, memory-aware framework for long video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.07222", "title": "Pay Less Attention to Function Words for Free Robustness of Vision-Language Models", "authors": [ "Qiwei Tian", "Chenhao Lin", "Zhengyu Zhao", "Chao Shen" ], "github": "https://github.com/michaeltian108/FDA", "project_page": "", "abstract": "To address the trade-off between robustness and performance for robust VLM, we observe that function words could incur vulnerability of VLMs against cross-modal adversarial attacks, and propose Function-word De-Attention (FDA) accordingly to mitigate the impact of function words. Similar to differential amplifiers, our FDA calculates the original and the function-word cross-attention within attention heads, and differentially subtracts the latter from the former for more aligned and robust VLMs. Comprehensive experiments include 2 SOTA baselines under 6 different attacks on 2 downstream tasks, 3 datasets, and 3 models. Overall, our FDA yields an average 18/13/53% ASR drop with only 0.2/0.3/0.6% performance drops on the 3 tested models on retrieval, and a 90% ASR drop with a 0.3% performance gain on visual grounding. We demonstrate the scalability, generalization, and zero-shot performance of FDA experimentally, as well as in-depth ablation studies and analysis. Code will be made publicly at https://github.com/michaeltian108/FDA.", "github_manual": "", "github_regex": "https://github.com/michaeltian108/FDA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.01453", "title": "Reinventing Clinical Dialogue: Agentic Paradigms for LLM Enabled Healthcare Communication", "authors": [ "Xiaoquan Zhi", "Hongke Zhao", "Likang Wu", "Chuang Zhao", "Hengshu Zhu" ], "github": "", "project_page": "", "abstract": "Clinical dialogue represents a complex duality requiring both the empathetic fluency of natural conversation and the rigorous precision of evidence-based medicine. While Large Language Models possess unprecedented linguistic capabilities, their architectural reliance on reactive and stateless processing often favors probabilistic plausibility over factual veracity. This structural limitation has catalyzed a paradigm shift in medical AI from generative text prediction to agentic autonomy, where the model functions as a central reasoning engine capable of deliberate planning and persistent memory. Moving beyond existing reviews that primarily catalog downstream applications, this survey provides a first-principles analysis of the cognitive architecture underpinning this shift. We introduce a novel taxonomy structured along the orthogonal axes of knowledge source and agency objective to delineate the provenance of clinical knowledge against the system's operational scope. This framework facilitates a systematic analysis of the intrinsic trade-offs between creativity and reliability by categorizing methods into four archetypes: Latent Space Clinicians, Emergent Planners, Grounded Synthesizers, and Verifiable Workflow Automators. For each paradigm, we deconstruct the technical realization across the entire cognitive pipeline, encompassing strategic planning, memory management, action execution, collaboration, and evolution to reveal how distinct architectural choices balance the tension between autonomy and safety.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.10284", "title": "MotionEdit: Benchmarking and Learning Motion-Centric Image Editing", "authors": [ "Yixin Wan", "Lei Ke", "Wenhao Yu", "Kai-Wei Chang", "Dong Yu" ], "github": "https://github.com/elainew728/motion-edit", "project_page": "", "abstract": "We introduce MotionEdit, a novel dataset for motion-centric image editing-the task of modifying subject actions and interactions while preserving identity, structure, and physical plausibility. Unlike existing image editing datasets that focus on static appearance changes or contain only sparse, low-quality motion edits, MotionEdit provides high-fidelity image pairs depicting realistic motion transformations extracted and verified from continuous videos. This new task is not only scientifically challenging but also practically significant, powering downstream applications such as frame-controlled video synthesis and animation. To evaluate model performance on the novel task, we introduce MotionEdit-Bench, a benchmark that challenges models on motion-centric edits and measures model performance with generative, discriminative, and preference-based metrics. Benchmark results reveal that motion editing remains highly challenging for existing state-of-the-art diffusion-based editing models. To address this gap, we propose MotionNFT (Motion-guided Negative-aware Fine Tuning), a post-training framework that computes motion alignment rewards based on how well the motion flow between input and model-edited images matches the ground-truth motion, guiding models toward accurate motion transformations. Extensive experiments on FLUX.1 Kontext and Qwen-Image-Edit show that MotionNFT consistently improves editing quality and motion fidelity of both base models on the motion editing task without sacrificing general editing ability, demonstrating its effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/elainew728/motion-edit", "github_api_added_by": "auto" }, { "date": "2025-12-11", "arxiv_id": "2512.09616", "title": "Rethinking Chain-of-Thought Reasoning for Videos", "authors": [ "Yiwu Zhong", "Zi-Yuan Hu", "Yin Li", "Liwei Wang" ], "github": "https://github.com/LaVi-Lab/Rethink_CoT_Video", "project_page": "", "abstract": "Chain-of-thought (CoT) reasoning has been highly successful in solving complex tasks in natural language processing, and recent multimodal large language models (MLLMs) have extended this paradigm to video reasoning. However, these models typically build on lengthy reasoning chains and large numbers of input visual tokens. Motivated by empirical observations from our benchmark study, we hypothesize that concise reasoning combined with a reduced set of visual tokens can be sufficient for effective video reasoning. To evaluate this hypothesis, we design and validate an efficient post-training and inference framework that enhances a video MLLM's reasoning capability. Our framework enables models to operate on compressed visual tokens and generate brief reasoning traces prior to answering. The resulting models achieve substantially improved inference efficiency, deliver competitive performance across diverse benchmarks, and avoid reliance on manual CoT annotations or supervised fine-tuning. Collectively, our results suggest that long, human-like CoT reasoning may not be necessary for general video reasoning, and that concise reasoning can be both effective and efficient. Our code will be released at https://github.com/LaVi-Lab/Rethink_CoT_Video.", "github_manual": "", "github_regex": "https://github.com/LaVi-Lab/Rethink_CoT_Video", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.08296", "title": "Towards a Science of Scaling Agent Systems", "authors": [ "Yubin Kim", "Ken Gu", "Chanwoo Park", "Chunjong Park", "Samuel Schmidgall", "A. Ali Heydari", "Yao Yan", "Zhihan Zhang", "Yuchen Zhuang", "Mark Malhotra", "Paul Pu Liang", "Hae Won Park", "Yuzhe Yang", "Xuhai Xu", "Yilun Du", "Shwetak Patel", "Tim Althoff", "Daniel McDuff", "Xin Liu" ], "github": "", "project_page": "", "abstract": "Agents, language model (LM)-based systems that are capable of reasoning, planning, and acting are becoming the dominant paradigm for real-world AI applications. Despite this widespread adoption, the principles that determine their performance remain underexplored, leaving practitioners to rely on heuristics rather than principled design choices. We address this gap by deriving quantitative scaling principles for agent systems. We evaluate this across four diverse benchmarks: Finance-Agent, BrowseComp-Plus, PlanCraft, and Workbench. Using five canonical architectures (Single, Independent, Centralized, Decentralized, Hybrid) instantiated across three LLM families, we perform a controlled evaluation spanning 180 configurations with standardized tools and token budgets. We derive a predictive model using empirical coordination metrics, including efficiency, overhead, error amplification, and redundancy, that achieves cross-validated R^2=0.513. We identify three dominant effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead. (2) a capability saturation: coordination yields diminishing or negative returns (beta=-0.408, p<0.001) once single-agent baselines exceed ~45%. (3) topology-dependent error amplification: independent agents amplify errors 17.2x through unchecked propagation, while centralized coordination contains this to 4.4x. Centralized coordination improves performance by 80.9% on parallelizable tasks like financial reasoning, while decentralized coordination excels on dynamic web navigation (+9.2% vs. +0.2%). Yet for sequential reasoning tasks, all multi-agent variants degraded performance by 39-70%. The framework predicts the optimal coordination strategy for 87% of held-out configurations, providing a predictive principle of agentic scaling based on measurable task properties.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.09112", "title": "GimbalDiffusion: Gravity-Aware Camera Control for Video Generation", "authors": [ "Fr\u00e9d\u00e9ric Fortier-Chouinard", "Yannick Hold-Geoffroy", "Valentin Deschaintre", "Matheus Gadelha", "Jean-Fran\u00e7ois Lalonde" ], "github": "", "project_page": "", "abstract": "Recent progress in text-to-video generation has achieved remarkable realism, yet fine-grained control over camera motion and orientation remains elusive. Existing approaches typically encode camera trajectories through relative or ambiguous representations, limiting explicit geometric control. We introduce GimbalDiffusion, a framework that enables camera control grounded in physical-world coordinates, using gravity as a global reference. Instead of describing motion relative to previous frames, our method defines camera trajectories in an absolute coordinate system, allowing precise and interpretable control over camera parameters without requiring an initial reference frame. We leverage panoramic 360-degree videos to construct a wide variety of camera trajectories, well beyond the predominantly straight, forward-facing trajectories seen in conventional video data. To further enhance camera guidance, we introduce null-pitch conditioning, an annotation strategy that reduces the model's reliance on text content when conflicting with camera specifications (e.g., generating grass while the camera points towards the sky). Finally, we establish a benchmark for camera-aware video generation by rebalancing SpatialVID-HQ for comprehensive evaluation under wide camera pitch variation. Together, these contributions advance the controllability and robustness of text-to-video models, enabling precise, gravity-aligned camera manipulation within generative frameworks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-11", "arxiv_id": "2512.05402", "title": "Smart Timing for Mining: A Deep Learning Framework for Bitcoin Hardware ROI Prediction", "authors": [ "Sithumi Wickramasinghe", "Bikramjit Das", "Dorien Herremans" ], "github": "https://github.com/AMAAI-Lab/MineROI-Net", "project_page": "", "abstract": "Bitcoin mining hardware acquisition requires strategic timing due to volatile markets, rapid technological obsolescence, and protocol-driven revenue cycles. Despite mining's evolution into a capital-intensive industry, there is little guidance on when to purchase new Application-Specific Integrated Circuit (ASIC) hardware, and no prior computational frameworks address this decision problem. We address this gap by formulating hardware acquisition as a time series classification task, predicting whether purchasing ASIC machines yields profitable (Return on Investment (ROI) >= 1), marginal (0 < ROI < 1), or unprofitable (ROI <= 0) returns within one year. We propose MineROI-Net, an open source Transformer-based architecture designed to capture multi-scale temporal patterns in mining profitability. Evaluated on data from 20 ASIC miners released between 2015 and 2024 across diverse market regimes, MineROI-Net outperforms LSTM-based and TSLANet baselines, achieving 83.7% accuracy and 83.1% macro F1-score. The model demonstrates strong economic relevance, achieving 93.6% precision in detecting unprofitable periods and 98.5% precision for profitable ones, while avoiding misclassification of profitable scenarios as unprofitable and vice versa. These results indicate that MineROI-Net offers a practical, data-driven tool for timing mining hardware acquisitions, potentially reducing financial risk in capital-intensive mining operations. The model is available through: https://github.com/AMAAI-Lab/MineROI-Net.", "github_manual": "", "github_regex": "https://github.com/AMAAI-Lab/MineROI-Net", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10949", "title": "Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation", "authors": [ "Yiwen Tang", "Zoey Guo", "Kaixin Zhu", "Ray Zhang", "Qizhi Chen", "Dongzhi Jiang", "Junli Liu", "Bohan Zeng", "Haoming Song", "Delin Qu", "Tianyi Bai", "Dan Xu", "Wentao Zhang", "Bin Zhao" ], "github": "https://github.com/Ivan-Tang-3D/3DGen-R1", "project_page": "", "abstract": "Reinforcement learning (RL), earlier proven to be effective in large language and multi-modal models, has been successfully extended to enhance 2D image generation recently. However, applying RL to 3D generation remains largely unexplored due to the higher spatial complexity of 3D objects, which require globally consistent geometry and fine-grained local textures. This makes 3D generation significantly sensitive to reward designs and RL algorithms. To address these challenges, we conduct the first systematic study of RL for text-to-3D autoregressive generation across several dimensions. (1) Reward designs: We evaluate reward dimensions and model choices, showing that alignment with human preference is crucial, and that general multi-modal models provide robust signal for 3D attributes. (2) RL algorithms: We study GRPO variants, highlighting the effectiveness of token-level optimization, and further investigate the scaling of training data and iterations. (3) Text-to-3D Benchmarks: Since existing benchmarks fail to measure implicit reasoning abilities in 3D generation models, we introduce MME-3DR. (4) Advanced RL paradigms: Motivated by the natural hierarchy of 3D generation, we propose Hi-GRPO, which optimizes the global-to-local hierarchical 3D generation through dedicated reward ensembles. Based on these insights, we develop AR3D-R1, the first RL-enhanced text-to-3D model, expert from coarse shape to texture refinement. We hope this study provides insights into RL-driven reasoning for 3D generation. Code is released at https://github.com/Ivan-Tang-3D/3DGen-R1.", "github_manual": "", "github_regex": "https://github.com/Ivan-Tang-3D/3DGen-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10739", "title": "Long-horizon Reasoning Agent for Olympiad-Level Mathematical Problem Solving", "authors": [ "Songyang Gao", "Yuzhe Gu", "Zijian Wu", "Lingkai Kong", "Wenwei Zhang", "Zhongrui Cai", "Fan Zheng", "Tianyou Ma", "Junhao Shen", "Haiteng Zhao", "Duanyang Zhang", "Huilun Zhang", "Kuikun Liu", "Chengqi Lyu", "Yanhui Duan", "Chiyu Chen", "Ningsheng Ma", "Jianfei Gao", "Han Lyu", "Dahua Lin", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have achieved significant progress in solving complex reasoning tasks by Reinforcement Learning with Verifiable Rewards (RLVR). This advancement is also inseparable from the oversight automated by reliable verifiers. However, current outcome-based verifiers (OVs) are unable to inspect the unreliable intermediate steps in the long reasoning chains of thought (CoTs). Meanwhile, current process-based verifiers (PVs) have difficulties in reliably detecting errors in the complex long CoTs, limited by the scarcity of high-quality annotations due to the prohibitive costs of human annotations. Therefore, we propose the Outcome-based Process Verifier (OPV), which verifies the rationale process of summarized outcomes from long CoTs to achieve both accurate and efficient verification and enable large-scale annotation. To empower the proposed verifier, we adopt an iterative active learning framework with expert annotations to progressively improve the verification capability of OPV with fewer annotation costs. Specifically, in each iteration, the most uncertain cases of the current best OPV are annotated and then subsequently used to train a new OPV through Rejection Fine-Tuning (RFT) and RLVR for the next round. Extensive experiments demonstrate OPV's superior performance and broad applicability. It achieves new state-of-the-art results on our held-out \\thisbench, outperforming much larger open-source models such as Qwen3-Max-Preview with an F1 score of 83.1 compared to 76.3. Furthermore, OPV effectively detects false positives within synthetic dataset, closely align with expert assessment. When collaborating with policy models, OPV consistently yields performance gains, e.g., raising the accuracy of DeepSeek-R1-Distill-Qwen-32B from 55.2\\% to 73.3\\% on AIME2025 as the compute budget scales.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10756", "title": "OPV: Outcome-based Process Verifier for Efficient Long Chain-of-Thought Verification", "authors": [ "Zijian Wu", "Lingkai Kong", "Wenwei Zhang", "Songyang Gao", "Yuzhe Gu", "Zhongrui Cai", "Tianyou Ma", "Yuhong Liu", "Zhi Wang", "Runyuan Ma", "Guangyu Wang", "Wei Li", "Conghui He", "Dahua Lin", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have achieved significant progress in solving complex reasoning tasks by Reinforcement Learning with Verifiable Rewards (RLVR). This advancement is also inseparable from the oversight automated by reliable verifiers. However, current outcome-based verifiers (OVs) are unable to inspect the unreliable intermediate steps in the long reasoning chains of thought (CoTs). Meanwhile, current process-based verifiers (PVs) have difficulties in reliably detecting errors in the complex long CoTs, limited by the scarcity of high-quality annotations due to the prohibitive costs of human annotations. Therefore, we propose the Outcome-based Process Verifier (OPV), which verifies the rationale process of summarized outcomes from long CoTs to achieve both accurate and efficient verification and enable large-scale annotation. To empower the proposed verifier, we adopt an iterative active learning framework with expert annotations to progressively improve the verification capability of OPV with fewer annotation costs. Specifically, in each iteration, the most uncertain cases of the current best OPV are annotated and then subsequently used to train a new OPV through Rejection Fine-Tuning (RFT) and RLVR for the next round. Extensive experiments demonstrate OPV's superior performance and broad applicability. It achieves new state-of-the-art results on our held-out OPV-Bench, outperforming much larger open-source models such as Qwen3-Max-Preview with an F1 score of 83.1 compared to 76.3. Furthermore, OPV effectively detects false positives within synthetic dataset, closely align with expert assessment. When collaborating with policy models, OPV consistently yields performance gains, e.g., raising the accuracy of DeepSeek-R1-Distill-Qwen-32B from 55.2% to 73.3% on AIME2025 as the compute budget scales.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10938", "title": "Stronger Normalization-Free Transformers", "authors": [ "Mingzhi Chen", "Taiming Lu", "Jiachen Zhu", "Mingjie Sun", "Zhuang Liu" ], "github": "https://github.com/zlab-princeton/Derf", "project_page": "", "abstract": "Although normalization layers have long been viewed as indispensable components of deep learning architectures, the recent introduction of Dynamic Tanh (DyT) has demonstrated that alternatives are possible. The point-wise function DyT constrains extreme values for stable convergence and reaches normalization-level performance; this work seeks further for function designs that can surpass it. We first study how the intrinsic properties of point-wise functions influence training and performance. Building on these findings, we conduct a large-scale search for a more effective function design. Through this exploration, we introduce Derf(x) = erf(\u03b1x + s), where erf(x) is the rescaled Gaussian cumulative distribution function, and identify it as the most performant design. Derf outperforms LayerNorm, RMSNorm, and DyT across a wide range of domains, including vision (image recognition and generation), speech representation, and DNA sequence modeling. Our findings suggest that the performance gains of Derf largely stem from its improved generalization rather than stronger fitting capacity. Its simplicity and stronger performance make Derf a practical choice for normalization-free Transformer architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zlab-princeton/Derf", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10881", "title": "MoCapAnything: Unified 3D Motion Capture for Arbitrary Skeletons from Monocular Videos", "authors": [ "Kehong Gong", "Zhengyu Wen", "Weixia He", "Mingxi Xu", "Qi Wang", "Ning Zhang", "Zhengyu Li", "Dongze Lian", "Wei Zhao", "Xiaoyu He", "Mingyuan Zhang" ], "github": "", "project_page": "", "abstract": "Motion capture now underpins content creation far beyond digital humans, yet most existing pipelines remain species- or template-specific. We formalize this gap as Category-Agnostic Motion Capture (CAMoCap): given a monocular video and an arbitrary rigged 3D asset as a prompt, the goal is to reconstruct a rotation-based animation such as BVH that directly drives the specific asset. We present MoCapAnything, a reference-guided, factorized framework that first predicts 3D joint trajectories and then recovers asset-specific rotations via constraint-aware inverse kinematics. The system contains three learnable modules and a lightweight IK stage: (1) a Reference Prompt Encoder that extracts per-joint queries from the asset's skeleton, mesh, and rendered images; (2) a Video Feature Extractor that computes dense visual descriptors and reconstructs a coarse 4D deforming mesh to bridge the gap between video and joint space; and (3) a Unified Motion Decoder that fuses these cues to produce temporally coherent trajectories. We also curate Truebones Zoo with 1038 motion clips, each providing a standardized skeleton-mesh-render triad. Experiments on both in-domain benchmarks and in-the-wild videos show that MoCapAnything delivers high-quality skeletal animations and exhibits meaningful cross-species retargeting across heterogeneous rigs, enabling scalable, prompt-driven 3D motion capture for arbitrary assets. Project page: https://animotionlab.github.io/MoCapAnything/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2511.23386", "title": "VQRAE: Representation Quantization Autoencoders for Multimodal Understanding, Generation and Reconstruction", "authors": [ "Sinan Du", "Jiahao Guo", "Bo Li", "Shuhao Cui", "Zhengzhuo Xu", "Yifu Luo", "Yongxian Wei", "Kun Gai", "Xinggang Wang", "Kai Wu", "Chun Yuan" ], "github": "", "project_page": "", "abstract": "Unifying multimodal understanding, generation and reconstruction representation in a single tokenizer remains a key challenge in building unified models. Previous research predominantly attempts to address this in a dual encoder paradigm, e.g., utilizing the separate encoders for understanding and generation respectively or balancing semantic representations and low-level features with contrastive loss. In this paper, we propose VQRAE, a Vector Quantization version of Representation AutoEncoders, which pioneers the first exploration in unified representation to produce Continuous semantic features for image understanding and Discrete tokens for visual generation within a unified tokenizer. Specifically, we build upon pretrained vision foundation models with a symmetric ViT decoder and adopt a two-stage training strategy: first, it freezes the encoder and learns a high-dimensional semantic VQ codebook with pixel reconstruction objective; then jointly optimizes the encoder with self-distillation constraints. This design enables negligible semantic information for maintaining the ability of multimodal understanding, discrete tokens that are compatible for generation and fine-grained reconstruction. Besides, we identify the intriguing property in quantizing semantic encoders that rely on high-dimensional codebook in contrast to the previous common practice of low-dimensional codebook in image reconstruction. The semantic VQ codebook can achieve a 100% utilization ratio at a dimension of 1536. VQRAE presents competitive performance on several benchmarks of visual understanding, generation and reconstruction with promising scaling property in the autoregressive paradigm for its discrete merits.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10675", "title": "Evaluating Gemini Robotics Policies in a Veo World Simulator", "authors": [ "Gemini Robotics Team", "Coline Devin", "Yilun Du", "Debidatta Dwibedi", "Ruiqi Gao", "Abhishek Jindal", "Thomas Kipf", "Sean Kirmani", "Fangchen Liu", "Anirudha Majumdar", "Andrew Marmon", "Carolina Parada", "Yulia Rubanova", "Dhruv Shah", "Vikas Sindhwani", "Jie Tan", "Fei Xia", "Ted Xiao", "Sherry Yang", "Wenhao Yu", "Allan Zhou" ], "github": "", "project_page": "", "abstract": "Generative world models hold significant potential for simulating interactions with visuomotor policies in varied environments. Frontier video models can enable generation of realistic observations and environment interactions in a scalable and general manner. However, the use of video models in robotics has been limited primarily to in-distribution evaluations, i.e., scenarios that are similar to ones used to train the policy or fine-tune the base video model. In this report, we demonstrate that video models can be used for the entire spectrum of policy evaluation use cases in robotics: from assessing nominal performance to out-of-distribution (OOD) generalization, and probing physical and semantic safety. We introduce a generative evaluation system built upon a frontier video foundation model (Veo). The system is optimized to support robot action conditioning and multi-view consistency, while integrating generative image-editing and multi-view completion to synthesize realistic variations of real-world scenes along multiple axes of generalization. We demonstrate that the system preserves the base capabilities of the video model to enable accurate simulation of scenes that have been edited to include novel interaction objects, novel visual backgrounds, and novel distractor objects. This fidelity enables accurately predicting the relative performance of different policies in both nominal and OOD conditions, determining the relative impact of different axes of generalization on policy performance, and performing red teaming of policies to expose behaviors that violate physical or semantic safety constraints. We validate these capabilities through 1600+ real-world evaluations of eight Gemini Robotics policy checkpoints and five tasks for a bimanual manipulator.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10359", "title": "Tool-Augmented Spatiotemporal Reasoning for Streamlining Video Question Answering Task", "authors": [ "Sunqi Fan", "Jiashuo Cui", "Meng-Hao Guo", "Shuojin Yang" ], "github": "https://github.com/fansunqi/VideoTool", "project_page": "", "abstract": "Video Question Answering (VideoQA) task serves as a critical playground for evaluating whether foundation models can effectively perceive, understand, and reason about dynamic real-world scenarios. However, existing Multimodal Large Language Models (MLLMs) struggle with simultaneously modeling spatial relationships within video frames and understanding the causal dynamics of temporal evolution on complex and reasoning-intensive VideoQA task. In this work, we equip MLLM with a comprehensive and extensible Video Toolkit, to enhance MLLM's spatiotemporal reasoning capabilities and ensure the harmony between the quantity and diversity of tools. To better control the tool invocation sequence and avoid toolchain shortcut issues, we propose a Spatiotemporal Reasoning Framework (STAR) that strategically schedules temporal and spatial tools, thereby progressively localizing the key area in the video. Our STAR framework enhances GPT-4o using lightweight tools, achieving an 8.2% gain on VideoMME and 4.6% on LongVideoBench. We believe that our proposed Video Toolkit and STAR framework make an important step towards building autonomous and intelligent video analysis assistants. The code is publicly available at https://github.com/fansunqi/VideoTool.", "github_manual": "", "github_regex": "https://github.com/fansunqi/VideoTool", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10791", "title": "The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality", "authors": [ "Aileen Cheng", "Alon Jacovi", "Amir Globerson", "Ben Golan", "Charles Kwong", "Chris Alberti", "Connie Tao", "Eyal Ben-David", "Gaurav Singh Tomar", "Lukas Haas", "Yonatan Bitton", "Adam Bloniarz", "Aijun Bai", "Andrew Wang", "Anfal Siddiqui", "Arturo Bajuelos Castillo", "Aviel Atias", "Chang Liu", "Corey Fry", "Daniel Balle", "Deepanway Ghosal", "Doron Kukliansky", "Dror Marcus", "Elena Gribovskaya", "Eran Ofek", "Honglei Zhuang", "Itay Laish", "Jan Ackermann", "Lily Wang", "Meg Risdal", "Megan Barnes", "Michael Fink", "Mohamed Amin", "Moran Ambar", "Natan Potikha", "Nikita Gupta", "Nitzan Katz", "Noam Velan", "Ofir Roval", "Ori Ram", "Polina Zablotskaia", "Prathamesh Bang", "Priyanka Agrawal", "Rakesh Ghiya", "Sanjay Ganapathy", "Simon Baumgartner", "Sofia Erell", "Sushant Prakash", "Thibault Sellam", "Vikram Rao", "Xuanhui Wang", "Yaroslav Akulov", "Yulong Yang", "Zhen Yang", "Zhixin Lai", "Zhongru Wu", "Anca Dragan", "Avinatan Hassidim", "Fernando Pereira", "Slav Petrov", "Srinivasan Venkatachary", "Tulsee Doshi", "Yossi Matias", "Sasha Goldshtein", "Dipanjan Das" ], "github": "", "project_page": "", "abstract": "We introduce The FACTS Leaderboard, an online leaderboard suite and associated set of benchmarks that comprehensively evaluates the ability of language models to generate factually accurate text across diverse scenarios. The suite provides a holistic measure of factuality by aggregating the performance of models on four distinct sub-leaderboards: (1) FACTS Multimodal, which measures the factuality of responses to image-based questions; (2) FACTS Parametric, which assesses models' world knowledge by answering closed-book factoid questions from internal parameters; (3) FACTS Search, which evaluates factuality in information-seeking scenarios, where the model must use a search API; and (4) FACTS Grounding (v2), which evaluates whether long-form responses are grounded in provided documents, featuring significantly improved judge models. Each sub-leaderboard employs automated judge models to score model responses, and the final suite score is an average of the four components, designed to provide a robust and balanced assessment of a model's overall factuality. The FACTS Leaderboard Suite will be actively maintained, containing both public and private splits to allow for external participation while guarding its integrity. It can be found at https://www.kaggle.com/benchmarks/google/facts .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10398", "title": "Confucius Code Agent: An Open-sourced AI Software Engineer at Industrial Scale", "authors": [ "Zhaodong Wang", "Zhenting Qi", "Sherman Wong", "Nathan Hu", "Samuel Lin", "Jun Ge", "Erwin Gao", "Yining Yang", "Ben Maurer", "Wenlin Chen", "David Recordon", "Yilun Du", "Minlan Yu", "Ying Zhang" ], "github": "https://github.com/SWE-agent/SWE-ReX", "project_page": "", "abstract": "Real-world AI software engineering demands coding agents that can reason over massive repositories, maintain durable memory across and within long sessions, and robustly coordinate complex toolchains at test time. Existing open-source coding agents provide transparency but frequently fall short when pushed to these industrial-scale workloads, while proprietary coding agents offer strong practical performance but limited extensibility, interpretability, and controllability. We present the Confucius Code Agent (CCA), an open-sourced AI software engineer that can operate at an industrial scale. CCA is built atop the Confucius SDK, an open-sourced agent development platform designed around three complementary perspectives: Agent Experience (AX), User Experience (UX), and Developer Experience (DX). The SDK introduces a unified orchestrator with hierarchical working memory for long-context reasoning, a persistent note-taking system for cross-session continual learning, and a modular extension module for robust tool use. Moreover, a meta-agent automates the synthesis, evaluation, and refinement of agent configurations through a build-test-improve loop, enabling rapid agent development on new tasks, environments, and tool stacks. Instantiated on Confucius SDK with these mechanisms, CCA delivers strong performance on real-world software engineering tasks. On SWE-Bench-Pro, CCA achieves a state-of-the-art Resolve@1 performance of 54.3%, substantially improving over prior coding agents. Together, the Confucius SDK and CCA provide a transparent, extensible, and reproducible foundation for AI agents, bridge gaps between research prototypes and production-grade systems, and support agent development and deployment at industrial scale.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SWE-agent/SWE-ReX", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.09406", "title": "H2R-Grounder: A Paired-Data-Free Paradigm for Translating Human Interaction Videos into Physically Grounded Robot Videos", "authors": [ "Hai Ci", "Xiaokang Liu", "Pei Yang", "Yiren Song", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Robots that learn manipulation skills from everyday human videos could acquire broad capabilities without tedious robot data collection. We propose a video-to-video translation framework that converts ordinary human-object interaction videos into motion-consistent robot manipulation videos with realistic, physically grounded interactions. Our approach does not require any paired human-robot videos for training only a set of unpaired robot videos, making the system easy to scale. We introduce a transferable representation that bridges the embodiment gap: by inpainting the robot arm in training videos to obtain a clean background and overlaying a simple visual cue (a marker and arrow indicating the gripper's position and orientation), we can condition a generative model to insert the robot arm back into the scene. At test time, we apply the same process to human videos (inpainting the person and overlaying human pose cues) and generate high-quality robot videos that mimic the human's actions. We fine-tune a SOTA video diffusion model (Wan 2.2) in an in-context learning manner to ensure temporal coherence and leveraging of its rich prior knowledge. Empirical results demonstrate that our approach achieves significantly more realistic and grounded robot motions compared to baselines, pointing to a promising direction for scaling up robot learning from unlabeled human videos. Project page: https://showlab.github.io/H2R-Grounder/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.08870", "title": "Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents", "authors": [ "Xiang Chen", "Yuling Shi", "Qizhen Lan", "Yuchao Qiu", "Xiaodong Gu" ], "github": "https://github.com/Soever/Federated-Agents-Evolution", "project_page": "", "abstract": "LLM agents are widely deployed in complex interactive tasks, yet privacy constraints often preclude centralized optimization and co-evolution across dynamic environments. While Federated Learning (FL) has proven effective on static datasets, its extension to the open-ended self-evolution of agents remains underexplored. Directly applying standard FL is challenging: heterogeneous tasks and sparse, trajectory-level rewards introduce severe gradient conflicts, destabilizing the global optimization process. To bridge this gap, we propose Fed-SE, a Federated Self-Evolution framework for LLM agents. Fed-SE establishes a local evolution-global aggregation paradigm. Locally, agents employ parameter-efficient fine-tuning on filtered, high-return trajectories to achieve stable gradient updates. Globally, Fed-SE aggregates updates within a low-rank subspace that disentangles environment-specific dynamics, effectively reducing negative transfer across clients. Experiments across five heterogeneous environments demonstrate that Fed-SE improves average task success rates by approximately 18% over federated baselines, validating its effectiveness in robust cross-environment knowledge transfer in privacy-constrained deployments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Soever/Federated-Agents-Evolution", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10534", "title": "Achieving Olympia-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning", "authors": [ "Haiteng Zhao", "Junhao Shen", "Yiming Zhang", "Songyang Gao", "Kuikun Liu", "Tianyou Ma", "Fan Zheng", "Dahua Lin", "Wenwei Zhang", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) agents exhibit strong mathematical problem-solving abilities and can even solve International Mathematical Olympiad (IMO) level problems with the assistance of formal proof systems. However, due to weak heuristics for auxiliary constructions, AI for geometry problem solving remains dominated by expert models such as AlphaGeometry 2, which rely heavily on large-scale data synthesis and search for both training and evaluation. In this work, we make the first attempt to build a medalist-level LLM agent for geometry and present InternGeometry. InternGeometry overcomes the heuristic limitations in geometry by iteratively proposing propositions and auxiliary constructions, verifying them with a symbolic engine, and reflecting on the engine's feedback to guide subsequent proposals. A dynamic memory mechanism enables InternGeometry to conduct more than two hundred interactions with the symbolic engine per problem. To further accelerate learning, we introduce Complexity-Boosting Reinforcement Learning (CBRL), which gradually increases the complexity of synthesized problems across training stages. Built on InternThinker-32B, InternGeometry solves 44 of 50 IMO geometry problems (2000-2024), exceeding the average gold medalist score (40.9), using only 13K training examples, just 0.004% of the data used by AlphaGeometry 2, demonstrating the potential of LLM agents on expert-level geometry tasks. InternGeometry can also propose novel auxiliary constructions for IMO problems that do not appear in human solutions. We will release the model, data, and symbolic engine to support future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10867", "title": "From Macro to Micro: Benchmarking Microscopic Spatial Intelligence on Molecules via Vision-Language Models", "authors": [ "Zongzhao Li", "Xiangzhe Kong", "Jiahui Su", "Zongyang Ma", "Mingze Li", "Songyou Li", "Yuelin Zhang", "Yu Rong", "Tingyang Xu", "Deli Zhao", "Wenbing Huang" ], "github": "", "project_page": "", "abstract": "This paper introduces the concept of Microscopic Spatial Intelligence (MiSI), the capability to perceive and reason about the spatial relationships of invisible microscopic entities, which is fundamental to scientific discovery. To assess the potential of Vision-Language Models (VLMs) in this domain, we propose a systematic benchmark framework MiSI-Bench. This framework features over 163,000 question-answer pairs and 587,000 images derived from approximately 4,000 molecular structures, covering nine complementary tasks that evaluate abilities ranging from elementary spatial transformations to complex relational identifications. Experimental results reveal that current state-of-the-art VLMs perform significantly below human level on this benchmark. However, a fine-tuned 7B model demonstrates substantial potential, even surpassing humans in spatial transformation tasks, while its poor performance in scientifically-grounded tasks like hydrogen bond recognition underscores the necessity of integrating explicit domain knowledge for progress toward scientific AGI. The datasets are available at https://huggingface.co/datasets/zongzhao/MiSI-bench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.09270", "title": "MoRel: Long-Range Flicker-Free 4D Motion Modeling via Anchor Relay-based Bidirectional Blending with Hierarchical Densification", "authors": [ "Sangwoon Kwak", "Weeyoung Kwon", "Jun Young Jeong", "Geonho Kim", "Won-Sik Cheong", "Jihyong Oh" ], "github": "", "project_page": "", "abstract": "Recent advances in 4D Gaussian Splatting (4DGS) have extended the high-speed rendering capability of 3D Gaussian Splatting (3DGS) into the temporal domain, enabling real-time rendering of dynamic scenes. However, one of the major remaining challenges lies in modeling long-range motion-contained dynamic videos, where a naive extension of existing methods leads to severe memory explosion, temporal flickering, and failure to handle appearing or disappearing occlusions over time. To address these challenges, we propose a novel 4DGS framework characterized by an Anchor Relay-based Bidirectional Blending (ARBB) mechanism, named MoRel, which enables temporally consistent and memory-efficient modeling of long-range dynamic scenes. Our method progressively constructs locally canonical anchor spaces at key-frame time index and models inter-frame deformations at the anchor level, enhancing temporal coherence. By learning bidirectional deformations between KfA and adaptively blending them through learnable opacity control, our approach mitigates temporal discontinuities and flickering artifacts. We further introduce a Feature-variance-guided Hierarchical Densification (FHD) scheme that effectively densifies KfA's while keeping rendering quality, based on an assigned level of feature-variance. To effectively evaluate our model's capability to handle real-world long-range 4D motion, we newly compose long-range 4D motion-contained dataset, called SelfCap_{LR}. It has larger average dynamic motion magnitude, captured at spatially wider spaces, compared to previous dynamic video datasets. Overall, our MoRel achieves temporally coherent and flicker-free long-range 4D reconstruction while maintaining bounded memory usage, demonstrating both scalability and efficiency in dynamic Gaussian-based representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.09756", "title": "MOA: Multi-Objective Alignment for Role-Playing Agents", "authors": [ "Chonghua Liao", "Ke Wang", "Yuchuan Wu", "Fei Huang", "Yongbin Li" ], "github": "", "project_page": "", "abstract": "Role-playing agents (RPAs) must simultaneously master many conflicting skills -- following multi-turn instructions, exhibiting domain knowledge, and adopting a consistent linguistic style. Existing work either relies on supervised fine-tuning (SFT) that over-fits surface cues and yields low diversity, or applies reinforcement learning (RL) that fails to learn multiple dimensions for comprehensive RPA optimization. We present MOA (Multi-Objective Alignment), a reinforcement-learning framework that enables multi-dimensional, fine-grained rubric optimization for general RPAs. MOA introduces a novel multi-objective optimization strategy that trains simultaneously on multiple fine-grained rubrics to boost optimization performance. Besides, to address the issues of model output diversity and quality, we have also employed thought-augmented rollout with off-policy guidance. Extensive experiments on challenging benchmarks such as PersonaGym and RoleMRC show that MOA enables an 8B model to match or even outperform strong baselines such as GPT-4o and Claude across numerous dimensions. This demonstrates the great potential of MOA in building RPAs that can simultaneously meet the demands of role knowledge, persona style, diverse scenarios, and complex multi-turn conversations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10430", "title": "T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground", "authors": [ "Dmitrii Stoianov", "Danil Taranets", "Olga Tsymboi", "Ramil Latypov", "Almaz Dautov", "Vladislav Kruglikov", "Nikita Surkov", "German Abramov", "Pavel Gein", "Dmitry Abulkhanov", "Mikhail Gashkov", "Viktor Zelenkovskiy", "Artem Batalov", "Aleksandr Medvedev", "Anatolii Potapov" ], "github": "", "project_page": "", "abstract": "We introduce T-pro 2.0, an open-weight Russian LLM for hybrid reasoning and efficient inference. The model supports direct answering and reasoning-trace generation, using a Cyrillic-dense tokenizer and an adapted EAGLE speculative-decoding pipeline to reduce latency. To enable reproducible and extensible research, we release the model weights, the T-Wix 500k instruction corpus, the T-Math reasoning benchmark, and the EAGLE weights on Hugging Face. These resources allow users to study Russian-language reasoning and to extend or adapt both the model and the inference pipeline. A public web demo exposes reasoning and non-reasoning modes and illustrates the speedups achieved by our inference stack across domains. T-pro 2.0 thus serves as an accessible open system for building and evaluating efficient, practical Russian LLM applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.05439", "title": "BEAVER: An Efficient Deterministic LLM Verifier", "authors": [ "Tarun Suresh", "Nalin Wadhwa", "Debangshu Banerjee", "Gagandeep Singh" ], "github": "https://github.com/uiuc-focal-lab/Beaver.git", "project_page": "", "abstract": "As large language models (LLMs) transition from research prototypes to production systems, practitioners often need reliable methods to verify that model outputs satisfy required constraints. While sampling-based estimates provide an intuition of model behavior, they offer no sound guarantees. We present BEAVER, the first practical framework for computing deterministic, sound probability bounds on LLM constraint satisfaction. Given any prefix-closed semantic constraint, BEAVER systematically explores the generation space using novel token trie and frontier data structures, maintaining provably sound bounds at every iteration. We formalize the verification problem, prove soundness of our approach, and evaluate BEAVER on correctness verification, privacy verification and secure code generation tasks across multiple state of the art LLMs. BEAVER achieves 6 to 8 times tighter probability bounds and identifies 3 to 4 times more high risk instances compared to baseline methods under identical computational budgets, enabling precise characterization and risk assessment that loose bounds or empirical evaluation cannot provide.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/uiuc-focal-lab/Beaver.git", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.08511", "title": "Thinking with Images via Self-Calling Agent", "authors": [ "Wenxi Yang", "Yuzhong Zhao", "Fang Wan", "Qixiang Ye" ], "github": "https://github.com/YWenxi/think-with-images-through-self-calling", "project_page": "", "abstract": "Thinking-with-images paradigms have showcased remarkable visual reasoning capability by integrating visual information as dynamic elements into the Chain-of-Thought (CoT). However, optimizing interleaved multimodal CoT (iMCoT) through reinforcement learning remains challenging, as it relies on scarce high-quality reasoning data. In this study, we propose Self-Calling Chain-of-Thought (sCoT), a novel visual reasoning paradigm that reformulates iMCoT as a language-only CoT with self-calling. Specifically, a main agent decomposes the complex visual reasoning task to atomic subtasks and invokes its virtual replicas, i.e. parameter-sharing subagents, to solve them in isolated context. sCoT enjoys substantial training effectiveness and efficiency, as it requires no explicit interleaving between modalities. sCoT employs group-relative policy optimization to reinforce effective reasoning behavior to enhance optimization. Experiments on HR-Bench 4K show that sCoT improves the overall reasoning performance by up to 1.9% with sim 75% fewer GPU hours compared to strong baseline approaches. Code is available at https://github.com/YWenxi/think-with-images-through-self-calling.", "github_manual": "", "github_regex": "https://github.com/YWenxi/think-with-images-through-self-calling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10959", "title": "StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space", "authors": [ "Tjark Behrens", "Anton Obukhov", "Bingxin Ke", "Fabio Tosi", "Matteo Poggi", "Konrad Schindler" ], "github": "", "project_page": "", "abstract": "We introduce StereoSpace, a diffusion-based framework for monocular-to-stereo synthesis that models geometry purely through viewpoint conditioning, without explicit depth or warping. A canonical rectified space and the conditioning guide the generator to infer correspondences and fill disocclusions end-to-end. To ensure fair and leakage-free evaluation, we introduce an end-to-end protocol that excludes any ground truth or proxy geometry estimates at test time. The protocol emphasizes metrics reflecting downstream relevance: iSQoE for perceptual comfort and MEt3R for geometric consistency. StereoSpace surpasses other methods from the warp & inpaint, latent-warping, and warped-conditioning categories, achieving sharp parallax and strong robustness on layered and non-Lambertian scenes. This establishes viewpoint-conditioned diffusion as a scalable, depth-free solution for stereo generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.09924", "title": "ReViSE: Towards Reason-Informed Video Editing in Unified Models with Self-Reflective Learning", "authors": [ "Xinyu Liu", "Hangjie Yuan", "Yujie Wei", "Jiazheng Xing", "Yujin Han", "Jiahao Pan", "Yanbiao Ma", "Chi-Min Chan", "Kang Zhao", "Shiwei Zhang", "Wenhan Luo", "Yike Guo" ], "github": "https://github.com/Liuxinyv/ReViSE", "project_page": "", "abstract": "Video unified models exhibit strong capabilities in understanding and generation, yet they struggle with reason-informed visual editing even when equipped with powerful internal vision-language models (VLMs). We attribute this gap to two factors: 1) existing datasets are inadequate for training and evaluating reasoning-aware video editing, and 2) an inherent disconnect between the models' reasoning and editing capabilities, which prevents the rich understanding from effectively instructing the editing process. Bridging this gap requires an integrated framework that connects reasoning with visual transformation. To address this gap, we introduce the Reason-Informed Video Editing (RVE) task, which requires reasoning about physical plausibility and causal dynamics during editing. To support systematic evaluation, we construct RVE-Bench, a comprehensive benchmark with two complementary subsets: Reasoning-Informed Video Editing and In-Context Video Generation. These subsets cover diverse reasoning dimensions and real-world editing scenarios. Building upon this foundation, we propose the ReViSE, a Self-Reflective Reasoning (SRF) framework that unifies generation and evaluation within a single architecture. The model's internal VLM provides intrinsic feedback by assessing whether the edited video logically satisfies the given instruction. The differential feedback that refines the generator's reasoning behavior during training. Extensive experiments on RVE-Bench demonstrate that ReViSE significantly enhances editing accuracy and visual fidelity, achieving a 32% improvement of the Overall score in the reasoning-informed video editing subset over state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Liuxinyv/ReViSE", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10955", "title": "Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization", "authors": [ "Tsai-Shien Chen", "Aliaksandr Siarohin", "Guocheng Gordon Qian", "Kuan-Chieh Jackson Wang", "Egor Nemchinov", "Moayed Haji-Ali", "Riza Alp Guler", "Willi Menapace", "Ivan Skorokhodov", "Anil Kag", "Jun-Yan Zhu", "Sergey Tulyakov" ], "github": "", "project_page": "", "abstract": "Visual concept personalization aims to transfer only specific image attributes, such as identity, expression, lighting, and style, into unseen contexts. However, existing methods rely on holistic embeddings from general-purpose image encoders, which entangle multiple visual factors and make it difficult to isolate a single attribute. This often leads to information leakage and incoherent synthesis. To address this limitation, we introduce Omni-Attribute, the first open-vocabulary image attribute encoder designed to learn high-fidelity, attribute-specific representations. Our approach jointly designs the data and model: (i) we curate semantically linked image pairs annotated with positive and negative attributes to explicitly teach the encoder what to preserve or suppress; and (ii) we adopt a dual-objective training paradigm that balances generative fidelity with contrastive disentanglement. The resulting embeddings prove effective for open-vocabulary attribute retrieval, personalization, and compositional generation, achieving state-of-the-art performance across multiple benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.10894", "title": "DuetSVG: Unified Multimodal SVG Generation with Internal Visual Guidance", "authors": [ "Peiying Zhang", "Nanxuan Zhao", "Matthew Fisher", "Yiran Xu", "Jing Liao", "Difan Liu" ], "github": "", "project_page": "", "abstract": "Recent vision-language model (VLM)-based approaches have achieved impressive results on SVG generation. However, because they generate only text and lack visual signals during decoding, they often struggle with complex semantics and fail to produce visually appealing or geometrically coherent SVGs. We introduce DuetSVG, a unified multimodal model that jointly generates image tokens and corresponding SVG tokens in an end-to-end manner. DuetSVG is trained on both image and SVG datasets. At inference, we apply a novel test-time scaling strategy that leverages the model's native visual predictions as guidance to improve SVG decoding quality. Extensive experiments show that our method outperforms existing methods, producing visually faithful, semantically aligned, and syntactically clean SVGs across a wide range of applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-12", "arxiv_id": "2512.04537", "title": "X-Humanoid: Robotize Human Videos to Generate Humanoid Videos at Scale", "authors": [ "Pei Yang", "Hai Ci", "Yiren Song", "Mike Zheng Shou" ], "github": "https://github.com/showlab/X-Humanoid", "project_page": "", "abstract": "The advancement of embodied AI has unlocked significant potential for intelligent humanoid robots. However, progress in both Vision-Language-Action (VLA) models and world models is severely hampered by the scarcity of large-scale, diverse training data. A promising solution is to \"robotize\" web-scale human videos, which has been proven effective for policy training. However, these solutions mainly \"overlay\" robot arms to egocentric videos, which cannot handle complex full-body motions and scene occlusions in third-person videos, making them unsuitable for robotizing humans. To bridge this gap, we introduce X-Humanoid, a generative video editing approach that adapts the powerful Wan 2.2 model into a video-to-video structure and finetunes it for the human-to-humanoid translation task. This finetuning requires paired human-humanoid videos, so we designed a scalable data creation pipeline, turning community assets into 17+ hours of paired synthetic videos using Unreal Engine. We then apply our trained model to 60 hours of the Ego-Exo4D videos, generating and releasing a new large-scale dataset of over 3.6 million \"robotized\" humanoid video frames. Quantitative analysis and user studies confirm our method's superiority over existing baselines: 69% of users rated it best for motion consistency, and 62.1% for embodiment correctness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/showlab/X-Humanoid", "github_api_added_by": "auto" }, { "date": "2025-12-12", "arxiv_id": "2512.06424", "title": "DragMesh: Interactive 3D Generation Made Easy", "authors": [ "Tianshan Zhang", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/DragMesh", "project_page": "", "abstract": "While generative models have excelled at creating static 3D content, the pursuit of systems that understand how objects move and respond to interactions remains a fundamental challenge. Current methods for articulated motion lie at a crossroads: they are either physically consistent but too slow for real-time use, or generative but violate basic kinematic constraints. We present DragMesh, a robust framework for real-time interactive 3D articulation built around a lightweight motion generation core. Our core contribution is a novel decoupled kinematic reasoning and motion generation framework. First, we infer the latent joint parameters by decoupling semantic intent reasoning (which determines the joint type) from geometric regression (which determines the axis and origin using our Kinematics Prediction Network (KPP-Net)). Second, to leverage the compact, continuous, and singularity-free properties of dual quaternions for representing rigid body motion, we develop a novel Dual Quaternion VAE (DQ-VAE). This DQ-VAE receives these predicted priors, along with the original user drag, to generate a complete, plausible motion trajectory. To ensure strict adherence to kinematics, we inject the joint priors at every layer of the DQ-VAE's non-autoregressive Transformer decoder using FiLM (Feature-wise Linear Modulation) conditioning. This persistent, multi-scale guidance is complemented by a numerically-stable cross-product loss to guarantee axis alignment. This decoupled design allows DragMesh to achieve real-time performance and enables plausible, generative articulation on novel objects without retraining, offering a practical step toward generative 3D intelligence. Code: https://github.com/AIGeeksGroup/DragMesh. Website: https://aigeeksgroup.github.io/DragMesh.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/DragMesh", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.08269", "title": "EgoX: Egocentric Video Generation from a Single Exocentric Video", "authors": [ "Taewoong Kang", "Kinam Kim", "Dohyeon Kim", "Minho Park", "Junha Hyung", "Jaegul Choo" ], "github": "", "project_page": "", "abstract": "Egocentric perception enables humans to experience and understand the world directly from their own point of view. Translating exocentric (third-person) videos into egocentric (first-person) videos opens up new possibilities for immersive understanding but remains highly challenging due to extreme camera pose variations and minimal view overlap. This task requires faithfully preserving visible content while synthesizing unseen regions in a geometrically consistent manner. To achieve this, we present EgoX, a novel framework for generating egocentric videos from a single exocentric input. EgoX leverages the pretrained spatio temporal knowledge of large-scale video diffusion models through lightweight LoRA adaptation and introduces a unified conditioning strategy that combines exocentric and egocentric priors via width and channel wise concatenation. Additionally, a geometry-guided self-attention mechanism selectively attends to spatially relevant regions, ensuring geometric coherence and high visual fidelity. Our approach achieves coherent and realistic egocentric video generation while demonstrating strong scalability and robustness across unseen and in-the-wild videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.11558", "title": "DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry", "authors": [ "Zhenyang Cai", "Jiaming Zhang", "Junjie Zhao", "Ziyi Zeng", "Yanchao Li", "Jingyi Liang", "Junying Chen", "Yunjin Yang", "Jiajun You", "Shuzhi Deng", "Tongfei Wang", "Wanting Chen", "Chunxiu Hao", "Ruiqi Xie", "Zhenwei Wen", "Xiangyi Feng", "Zou Ting", "Jin Zou Lin", "Jianquan Li", "Guangjun Yu", "Liangyi Chen", "Junwen Wang", "Shan Jiang", "Benyou Wang" ], "github": "", "project_page": "", "abstract": "Reliable interpretation of multimodal data in dentistry is essential for automated oral healthcare, yet current multimodal large language models (MLLMs) struggle to capture fine-grained dental visual details and lack sufficient reasoning ability for precise diagnosis. To address these limitations, we present DentalGPT, a specialized dental MLLM developed through high-quality domain knowledge injection and reinforcement learning. Specifically, the largest annotated multimodal dataset for dentistry to date was constructed by aggregating over 120k dental images paired with detailed descriptions that highlight diagnostically relevant visual features, making it the multimodal dataset with the most extensive collection of dental images to date. Training on this dataset significantly enhances the MLLM's visual understanding of dental conditions, while the subsequent reinforcement learning stage further strengthens its capability for multimodal complex reasoning. Comprehensive evaluations on intraoral and panoramic benchmarks, along with dental subsets of medical VQA benchmarks, show that DentalGPT achieves superior performance in disease classification and dental VQA tasks, outperforming many state-of-the-art MLLMs despite having only 7B parameters. These results demonstrate that high-quality dental data combined with staged adaptation provides an effective pathway for building capable and domain-specialized dental MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.11749", "title": "SVG-T2I: Scaling Up Text-to-Image Latent Diffusion Model Without Variational Autoencoder", "authors": [ "Minglei Shi", "Haolin Wang", "Borui Zhang", "Wenzhao Zheng", "Bohan Zeng", "Ziyang Yuan", "Xiaoshi Wu", "Yuanxing Zhang", "Huan Yang", "Xintao Wang", "Pengfei Wan", "Kun Gai", "Jie Zhou", "Jiwen Lu" ], "github": "", "project_page": "", "abstract": "Visual generation grounded in Visual Foundation Model (VFM) representations offers a highly promising unified pathway for integrating visual understanding, perception, and generation. Despite this potential, training large-scale text-to-image diffusion models entirely within the VFM representation space remains largely unexplored. To bridge this gap, we scale the SVG (Self-supervised representations for Visual Generation) framework, proposing SVG-T2I to support high-quality text-to-image synthesis directly in the VFM feature domain. By leveraging a standard text-to-image diffusion pipeline, SVG-T2I achieves competitive performance, reaching 0.75 on GenEval and 85.78 on DPG-Bench. This performance validates the intrinsic representational power of VFMs for generative tasks. We fully open-source the project, including the autoencoder and generation model, together with their training, inference, evaluation pipelines, and pre-trained weights, to facilitate further research in representation-driven visual generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.11799", "title": "V-RGBX: Video Editing with Accurate Controls over Intrinsic Properties", "authors": [ "Ye Fang", "Tong Wu", "Valentin Deschaintre", "Duygu Ceylan", "Iliyan Georgiev", "Chun-Hao Paul Huang", "Yiwei Hu", "Xuelin Chen", "Tuanfeng Yang Wang" ], "github": "", "project_page": "", "abstract": "Large-scale video generation models have shown remarkable potential in modeling photorealistic appearance and lighting interactions in real-world scenes. However, a closed-loop framework that jointly understands intrinsic scene properties (e.g., albedo, normal, material, and irradiance), leverages them for video synthesis, and supports editable intrinsic representations remains unexplored. We present V-RGBX, the first end-to-end framework for intrinsic-aware video editing. V-RGBX unifies three key capabilities: (1) video inverse rendering into intrinsic channels, (2) photorealistic video synthesis from these intrinsic representations, and (3) keyframe-based video editing conditioned on intrinsic channels. At the core of V-RGBX is an interleaved conditioning mechanism that enables intuitive, physically grounded video editing through user-selected keyframes, supporting flexible manipulation of any intrinsic modality. Extensive qualitative and quantitative results show that V-RGBX produces temporally consistent, photorealistic videos while propagating keyframe edits across sequences in a physically plausible manner. We demonstrate its effectiveness in diverse applications, including object appearance editing and scene-level relighting, surpassing the performance of prior methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.11253", "title": "PersonaLive! Expressive Portrait Image Animation for Live Streaming", "authors": [ "Zhiyuan Li", "Chi-Man Pun", "Chen Fang", "Jue Wang", "Xiaodong Cun" ], "github": "", "project_page": "", "abstract": "Current diffusion-based portrait animation models predominantly focus on enhancing visual quality and expression realism, while overlooking generation latency and real-time performance, which restricts their application range in the live streaming scenario. We propose PersonaLive, a novel diffusion-based framework towards streaming real-time portrait animation with multi-stage training recipes. Specifically, we first adopt hybrid implicit signals, namely implicit facial representations and 3D implicit keypoints, to achieve expressive image-level motion control. Then, a fewer-step appearance distillation strategy is proposed to eliminate appearance redundancy in the denoising process, greatly improving inference efficiency. Finally, we introduce an autoregressive micro-chunk streaming generation paradigm equipped with a sliding training strategy and a historical keyframe mechanism to enable low-latency and stable long-term video generation. Extensive experiments demonstrate that PersonaLive achieves state-of-the-art performance with up to 7-22x speedup over prior diffusion-based portrait animation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.10411", "title": "Sliding Window Attention Adaptation", "authors": [ "Yijiong Yu", "Jiale Liu", "Qingyun Wu", "Huazheng Wang", "Ji Pei" ], "github": "https://github.com/yuyijiong/sliding-window-attention-adaptation", "project_page": "", "abstract": "The self-attention mechanism in Transformer-based Large Language Models (LLMs) scales quadratically with input length, making long-context inference expensive. Sliding window attention (SWA) reduces this cost to linear complexity, but naively enabling complete SWA at inference-time for models pretrained with full attention (FA) causes severe long-context performance degradation due to training-inference mismatch. This makes us wonder: Can FA-pretrained LLMs be well adapted to SWA without pretraining? We investigate this by proposing Sliding Window Attention Adaptation (SWAA), a set of practical recipes that combine five methods for better adaptation: (1) applying SWA only during prefilling; (2) preserving \"sink\" tokens; (3) interleaving FA/SWA layers; (4) chain-of-thought (CoT); and (5) fine-tuning. Our experiments show that SWA adaptation is feasible while non-trivial: no single method suffices, yet specific synergistic combinations effectively recover the original long-context performance. We further analyze the performance-efficiency trade-offs of different SWAA configurations and provide recommended recipes for diverse scenarios. Our code is available at https://github.com/yuyijiong/sliding-window-attention-adaptation", "github_manual": "", "github_regex": "https://github.com/yuyijiong/sliding-window-attention-adaptation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.11792", "title": "Structure From Tracking: Distilling Structure-Preserving Motion for Video Generation", "authors": [ "Yang Fei", "George Stoica", "Jingyuan Liu", "Qifeng Chen", "Ranjay Krishna", "Xiaojuan Wang", "Benlin Liu" ], "github": "", "project_page": "", "abstract": "Reality is a dance between rigid constraints and deformable structures. For video models, that means generating motion that preserves fidelity as well as structure. Despite progress in diffusion models, producing realistic structure-preserving motion remains challenging, especially for articulated and deformable objects such as humans and animals. Scaling training data alone, so far, has failed to resolve physically implausible transitions. Existing approaches rely on conditioning with noisy motion representations, such as optical flow or skeletons extracted using an external imperfect model. To address these challenges, we introduce an algorithm to distill structure-preserving motion priors from an autoregressive video tracking model (SAM2) into a bidirectional video diffusion model (CogVideoX). With our method, we train SAM2VideoX, which contains two innovations: (1) a bidirectional feature fusion module that extracts global structure-preserving motion priors from a recurrent model like SAM2; (2) a Local Gram Flow loss that aligns how local features move together. Experiments on VBench and in human studies show that SAM2VideoX delivers consistent gains (+2.60\\% on VBench, 21-22\\% lower FVD, and 71.4\\% human preference) over prior baselines. Specifically, on VBench, we achieve 95.51\\%, surpassing REPA (92.91\\%) by 2.60\\%, and reduce FVD to 360.57, a 21.20\\% and 22.46\\% improvement over REPA- and LoRA-finetuning, respectively. The project website can be found at https://sam2videox.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.11464", "title": "Exploring MLLM-Diffusion Information Transfer with MetaCanvas", "authors": [ "Han Lin", "Xichen Pan", "Ziqi Huang", "Ji Hou", "Jialiang Wang", "Weifeng Chen", "Zecheng He", "Felix Juefei-Xu", "Junzhe Sun", "Zhipeng Fan", "Ali Thabet", "Mohit Bansal", "Chu Wang" ], "github": "", "project_page": "", "abstract": "Multimodal learning has rapidly advanced visual understanding, largely via multimodal large language models (MLLMs) that use powerful LLMs as cognitive cores. In visual generation, however, these powerful core models are typically reduced to global text encoders for diffusion models, leaving most of their reasoning and planning ability unused. This creates a gap: current multimodal LLMs can parse complex layouts, attributes, and knowledge-intensive scenes, yet struggle to generate images or videos with equally precise and structured control. We propose MetaCanvas, a lightweight framework that lets MLLMs reason and plan directly in spatial and spatiotemporal latent spaces and interface tightly with diffusion generators. We empirically implement MetaCanvas on three different diffusion backbones and evaluate it across six tasks, including text-to-image generation, text/image-to-video generation, image/video editing, and in-context video generation, each requiring precise layouts, robust attribute binding, and reasoning-intensive control. MetaCanvas consistently outperforms global-conditioning baselines, suggesting that treating MLLMs as latent-space planners is a promising direction for narrowing the gap between multimodal understanding and generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.06818", "title": "MeshSplatting: Differentiable Rendering with Opaque Meshes", "authors": [ "Jan Held", "Sanghyun Son", "Renaud Vandeghen", "Daniel Rebain", "Matheus Gadelha", "Yi Zhou", "Anthony Cioppa", "Ming C. Lin", "Marc Van Droogenbroeck", "Andrea Tagliasacchi" ], "github": "", "project_page": "", "abstract": "Primitive-based splatting methods like 3D Gaussian Splatting have revolutionized novel view synthesis with real-time rendering. However, their point-based representations remain incompatible with mesh-based pipelines that power AR/VR and game engines. We present MeshSplatting, a mesh-based reconstruction approach that jointly optimizes geometry and appearance through differentiable rendering. By enforcing connectivity via restricted Delaunay triangulation and refining surface consistency, MeshSplatting creates end-to-end smooth, visually high-quality meshes that render efficiently in real-time 3D engines. On Mip-NeRF360, it boosts PSNR by +0.69 dB over the current state-of-the-art MiLo for mesh-based novel view synthesis, while training 2x faster and using 2x less memory, bridging neural rendering and interactive 3D graphics for seamless real-time scene interaction. The project page is available at https://meshsplatting.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.10605", "title": "LEO-RobotAgent: A General-purpose Robotic Agent for Language-driven Embodied Operator", "authors": [ "Lihuang Chen", "Xiangyu Luo", "Jun Meng" ], "github": "https://github.com/LegendLeoChen/LEO-RobotAgent", "project_page": "", "abstract": "We propose LEO-RobotAgent, a general-purpose language-driven intelligent agent framework for robots. Under this framework, LLMs can operate different types of robots to complete unpredictable complex tasks across various scenarios. This framework features strong generalization, robustness, and efficiency. The application-level system built around it can fully enhance bidirectional human-robot intent understanding and lower the threshold for human-robot interaction. Regarding robot task planning, the vast majority of existing studies focus on the application of large models in single-task scenarios and for single robot types. These algorithms often have complex structures and lack generalizability. Thus, the proposed LEO-RobotAgent framework is designed with a streamlined structure as much as possible, enabling large models to independently think, plan, and act within this clear framework. We provide a modular and easily registrable toolset, allowing large models to flexibly call various tools to meet different requirements. Meanwhile, the framework incorporates a human-robot interaction mechanism, enabling the algorithm to collaborate with humans like a partner. Experiments have verified that this framework can be easily adapted to mainstream robot platforms including unmanned aerial vehicles (UAVs), robotic arms, and wheeled robot, and efficiently execute a variety of carefully designed tasks with different complexity levels. Our code is available at https://github.com/LegendLeoChen/LEO-RobotAgent.", "github_manual": "", "github_regex": "https://github.com/LegendLeoChen/LEO-RobotAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.11150", "title": "Causal Judge Evaluation: Calibrated Surrogate Metrics for LLM Systems", "authors": [ "Eddie Landesberg" ], "github": "https://github.com/cimo-labs/cje-arena-experiments", "project_page": "", "abstract": "LLM-as-judge evaluation has become the de facto standard for scaling model assessment, but the practice is statistically unsound: uncalibrated scores can invert preferences, naive confidence intervals on uncalibrated scores achieve near-0% coverage, and importance-weighted estimators collapse under limited overlap despite high effective sample size (ESS). We introduce Causal Judge Evaluation (CJE), a framework that fixes all three failures. On n=4,961 Chatbot Arena prompts (after filtering from 5k), CJE achieves 99% pairwise ranking accuracy at full sample size (94% averaged across configurations), matching oracle quality, at 14x lower cost (for ranking 5 policies) by calibrating a 16x cheaper judge on just 5% oracle labels (~250 labels). CJE combines three components: (i) AutoCal-R, reward calibration via mean-preserving isotonic regression; (ii) SIMCal-W, weight stabilization via stacking of S-monotone candidates; and (iii) Oracle-Uncertainty Aware (OUA) inference that propagates calibration uncertainty into confidence intervals. We formalize the Coverage-Limited Efficiency (CLE) diagnostic, which explains why IPS-style estimators fail even when ESS exceeds 90%: the logger rarely visits regions where target policies concentrate. Key findings: SNIPS inverts rankings even with reward calibration (38% pairwise, negative Kendall's tau) due to weight instability; calibrated IPS remains near-random (47%) despite weight stabilization, consistent with CLE; OUA improves coverage from near-0% to ~86% (Direct) and ~96% (stacked-DR), where naive intervals severely under-cover.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/cimo-labs/cje-arena-experiments", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.10858", "title": "Scaling Behavior of Discrete Diffusion Language Models", "authors": [ "Dimitri von R\u00fctte", "Janis Fluri", "Omead Pooladzandi", "Bernhard Sch\u00f6lkopf", "Thomas Hofmann", "Antonio Orvieto" ], "github": "https://github.com/dvruette/gidd-easydel", "project_page": "", "abstract": "Modern LLM pre-training consumes vast amounts of compute and training data, making the scaling behavior, or scaling laws, of different models a key distinguishing factor. Discrete diffusion language models (DLMs) have been proposed as an alternative to autoregressive language models (ALMs). However, their scaling behavior has not yet been fully explored, with prior work suggesting that they require more data and compute to match the performance of ALMs. We study the scaling behavior of DLMs on different noise types by smoothly interpolating between masked and uniform diffusion while paying close attention to crucial hyperparameters such as batch size and learning rate. Our experiments reveal that the scaling behavior of DLMs strongly depends on the noise type and is considerably different from ALMs. While all noise types converge to similar loss values in compute-bound scaling, we find that uniform diffusion requires more parameters and less data for compute-efficient training compared to masked diffusion, making them a promising candidate in data-bound settings. We scale our uniform diffusion model up to 10B parameters trained for 10^{22} FLOPs, confirming the predicted scaling behavior and making it the largest publicly known uniform diffusion model to date.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dvruette/gidd-easydel", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.10685", "title": "Sharp Monocular View Synthesis in Less Than a Second", "authors": [ "Lars Mescheder", "Wei Dong", "Shiwei Li", "Xuyang Bai", "Marcel Santos", "Peiyun Hu", "Bruno Lecouat", "Mingmin Zhen", "Ama\u00ebl Delaunoy", "Tian Fang", "Yanghai Tsin", "Stephan R. Richter", "Vladlen Koltun" ], "github": "https://github.com/apple/ml-sharp", "project_page": "", "abstract": "We present SHARP, an approach to photorealistic view synthesis from a single image. Given a single photograph, SHARP regresses the parameters of a 3D Gaussian representation of the depicted scene. This is done in less than a second on a standard GPU via a single feedforward pass through a neural network. The 3D Gaussian representation produced by SHARP can then be rendered in real time, yielding high-resolution photorealistic images for nearby views. The representation is metric, with absolute scale, supporting metric camera movements. Experimental results demonstrate that SHARP delivers robust zero-shot generalization across datasets. It sets a new state of the art on multiple datasets, reducing LPIPS by 25-34% and DISTS by 21-43% versus the best prior model, while lowering the synthesis time by three orders of magnitude. Code and weights are provided at https://github.com/apple/ml-sharp", "github_manual": "", "github_regex": "https://github.com/apple/ml-sharp", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.02901", "title": "Fairy2i: Training Complex LLMs from Real LLMs with All Parameters in {pm 1, pm i}", "authors": [ "Feiyu Wang", "Xinyu Tan", "Bokai Huang", "Yihao Zhang", "Guoan Wang", "Peizhuang Cong", "Tong Yang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have revolutionized artificial intelligence, yet their massive memory and computational demands necessitate aggressive quantization, increasingly pushing representations toward the theoretical limit of a single bit. While complex-valued LLMs, such as iFairy, offer a superior chance for low-bit representation compared to real-valued counterparts, they require training from scratch, preventing the utilization of the vast ecosystem of pre-trained real-valued foundation models. Here we present Fairy2i, a universal framework that transforms pre-trained real-valued layers into an equivalent widely-linear complex form, enabling extremely low-bit quantization while reusing existing checkpoints. By proving a lossless mathematical equivalence between real and widely-linear maps, we convert standard Transformers into the complex domain and employ a phase-aware quantization scheme with a highly efficient codebook of fourth roots of unity. Furthermore, we introduce a recursive residual quantization mechanism that iteratively minimizes quantization error, allowing inference to proceed via efficient multiplication-free accumulation. We demonstrate that Fairy2i restores the performance of LLaMA-2 7B at an effective 2-bit precision to levels nearly comparable with full-precision baselines, significantly outperforming state-of-the-art real-valued binary and ternary quantization methods. This work bridges the gap between the representational efficiency of complex-valued arithmetic and the practical utility of pre-trained models, paving a new way for efficient inference on commodity hardware.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.11798", "title": "Particulate: Feed-Forward 3D Object Articulation", "authors": [ "Ruining Li", "Yuxin Yao", "Chuanxia Zheng", "Christian Rupprecht", "Joan Lasenby", "Shangzhe Wu", "Andrea Vedaldi" ], "github": "", "project_page": "", "abstract": "We present Particulate, a feed-forward approach that, given a single static 3D mesh of an everyday object, directly infers all attributes of the underlying articulated structure, including its 3D parts, kinematic structure, and motion constraints. At its core is a transformer network, Part Articulation Transformer, which processes a point cloud of the input mesh using a flexible and scalable architecture to predict all the aforementioned attributes with native multi-joint support. We train the network end-to-end on a diverse collection of articulated 3D assets from public datasets. During inference, Particulate lifts the network's feed-forward prediction to the input mesh, yielding a fully articulated 3D model in seconds, much faster than prior approaches that require per-object optimization. Particulate can also accurately infer the articulated structure of AI-generated 3D assets, enabling full-fledged extraction of articulated 3D objects from a single (real or synthetic) image when combined with an off-the-shelf image-to-3D generator. We further introduce a new challenging benchmark for 3D articulation estimation curated from high-quality public 3D assets, and redesign the evaluation protocol to be more consistent with human preferences. Quantitative and qualitative results show that Particulate significantly outperforms state-of-the-art approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.11437", "title": "CLINIC: Evaluating Multilingual Trustworthiness in Language Models for Healthcare", "authors": [ "Akash Ghosh", "Srivarshinee Sridhar", "Raghav Kaushik Ravi", "Muhsin Muhsin", "Sriparna Saha", "Chirag Agarwal" ], "github": "", "project_page": "", "abstract": "Integrating language models (LMs) in healthcare systems holds great promise for improving medical workflows and decision-making. However, a critical barrier to their real-world adoption is the lack of reliable evaluation of their trustworthiness, especially in multilingual healthcare settings. Existing LMs are predominantly trained in high-resource languages, making them ill-equipped to handle the complexity and diversity of healthcare queries in mid- and low-resource languages, posing significant challenges for deploying them in global healthcare contexts where linguistic diversity is key. In this work, we present CLINIC, a Comprehensive Multilingual Benchmark to evaluate the trustworthiness of language models in healthcare. CLINIC systematically benchmarks LMs across five key dimensions of trustworthiness: truthfulness, fairness, safety, robustness, and privacy, operationalized through 18 diverse tasks, spanning 15 languages (covering all the major continents), and encompassing a wide array of critical healthcare topics like disease conditions, preventive actions, diagnostic tests, treatments, surgeries, and medications. Our extensive evaluation reveals that LMs struggle with factual correctness, demonstrate bias across demographic and linguistic groups, and are susceptible to privacy breaches and adversarial attacks. By highlighting these shortcomings, CLINIC lays the foundation for enhancing the global reach and safety of LMs in healthcare across diverse languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.06951", "title": "Task adaptation of Vision-Language-Action model: 1st Place Solution for the 2025 BEHAVIOR Challenge", "authors": [ "Ilia Larchenko", "Gleb Zarin", "Akash Karnatak" ], "github": "", "project_page": "", "abstract": "We present a vision-action policy that won 1st place in the 2025 BEHAVIOR Challenge - a large-scale benchmark featuring 50 diverse long-horizon household tasks in photo-realistic simulation, requiring bimanual manipulation, navigation, and context-aware decision making. Building on the Pi0.5 architecture, we introduce several innovations. Our primary contribution is correlated noise for flow matching, which improves training efficiency and enables correlation-aware inpainting for smooth action sequences. We also apply learnable mixed-layer attention and System 2 stage tracking for ambiguity resolution. Training employs multi-sample flow matching to reduce variance, while inference uses action compression and challenge-specific correction rules. Our approach achieves 26% q-score across all 50 tasks on both public and private leaderboards.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.11393", "title": "The N-Body Problem: Parallel Execution from Single-Person Egocentric Video", "authors": [ "Zhifan Zhu", "Yifei Huang", "Yoichi Sato", "Dima Damen" ], "github": "", "project_page": "", "abstract": "Humans can intuitively parallelise complex activities, but can a model learn this from observing a single person? Given one egocentric video, we introduce the N-Body Problem: how N individuals, can hypothetically perform the same set of tasks observed in this video. The goal is to maximise speed-up, but naive assignment of video segments to individuals often violates real-world constraints, leading to physically impossible scenarios like two people using the same object or occupying the same space. To address this, we formalise the N-Body Problem and propose a suite of metrics to evaluate both performance (speed-up, task coverage) and feasibility (spatial collisions, object conflicts and causal constraints). We then introduce a structured prompting strategy that guides a Vision-Language Model (VLM) to reason about the 3D environment, object usage, and temporal dependencies to produce a viable parallel execution. On 100 videos from EPIC-Kitchens and HD-EPIC, our method for N = 2 boosts action coverage by 45% over a baseline prompt for Gemini 2.5 Pro, while simultaneously slashing collision rates, object and causal conflicts by 55%, 45% and 55% respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.11130", "title": "Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching", "authors": [ "Bowen Wen", "Shaurya Dewan", "Stan Birchfield" ], "github": "", "project_page": "", "abstract": "Stereo foundation models achieve strong zero-shot generalization but remain computationally prohibitive for real-time applications. Efficient stereo architectures, on the other hand, sacrifice robustness for speed and require costly per-domain fine-tuning. To bridge this gap, we present Fast-FoundationStereo, a family of architectures that achieve, for the first time, strong zero-shot generalization at real-time frame rate. We employ a divide-and-conquer acceleration strategy with three components: (1) knowledge distillation to compress the hybrid backbone into a single efficient student; (2) blockwise neural architecture search for automatically discovering optimal cost filtering designs under latency budgets, reducing search complexity exponentially; and (3) structured pruning for eliminating redundancy in the iterative refinement module. Furthermore, we introduce an automatic pseudo-labeling pipeline used to curate 1.4M in-the-wild stereo pairs to supplement synthetic training data and facilitate knowledge distillation. The resulting model can run over 10x faster than FoundationStereo while closely matching its zero-shot accuracy, thus establishing a new state-of-the-art among real-time methods. Project page: https://nvlabs.github.io/Fast-FoundationStereo/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.10715", "title": "CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray images", "authors": [ "Matias Cosarinsky", "Nicolas Gaggion", "Rodrigo Echeveste", "Enzo Ferrante" ], "github": "https://github.com/mcosarinsky/CheXmask-U", "project_page": "", "abstract": "Uncertainty estimation is essential for the safe clinical deployment of medical image segmentation systems, enabling the identification of unreliable predictions and supporting human oversight. While prior work has largely focused on pixel-level uncertainty, landmark-based segmentation offers inherent topological guarantees yet remains underexplored from an uncertainty perspective. In this work, we study uncertainty estimation for anatomical landmark-based segmentation on chest X-rays. Inspired by hybrid neural network architectures that combine standard image convolutional encoders with graph-based generative decoders, and leveraging their variational latent space, we derive two complementary measures: (i) latent uncertainty, captured directly from the learned distribution parameters, and (ii) predictive uncertainty, obtained by generating multiple stochastic output predictions from latent samples. Through controlled corruption experiments we show that both uncertainty measures increase with perturbation severity, reflecting both global and local degradation. We demonstrate that these uncertainty signals can identify unreliable predictions by comparing with manual ground-truth, and support out-of-distribution detection on the CheXmask dataset. More importantly, we release CheXmask-U (huggingface.co/datasets/mcosarinsky/CheXmask-U), a large scale dataset of 657,566 chest X-ray landmark segmentations with per-node uncertainty estimates, enabling researchers to account for spatial variations in segmentation quality when using these anatomical masks. Our findings establish uncertainty estimation as a promising direction to enhance robustness and safe deployment of landmark-based anatomical segmentation methods in chest X-ray. A fully working interactive demo of the method is available at huggingface.co/spaces/matiasky/CheXmask-U and the source code at github.com/mcosarinsky/CheXmask-U.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mcosarinsky/CheXmask-U", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-15", "arxiv_id": "2512.10092", "title": "Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit", "authors": [ "Nick Jiang", "Xiaoqing Sun", "Lisa Dunlap", "Lewis Smith", "Neel Nanda" ], "github": "", "project_page": "", "abstract": "Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors or biases in training data. Current methods often rely on costly LLM-based techniques (e.g. annotating dataset differences) or dense embedding models (e.g. for clustering), which lack control over the properties of interest. We propose using sparse autoencoders (SAEs) to create SAE embeddings: representations whose dimensions map to interpretable concepts. Through four data analysis tasks, we show that SAE embeddings are more cost-effective and reliable than LLMs and more controllable than dense embeddings. Using the large hypothesis space of SAEs, we can uncover insights such as (1) semantic differences between datasets and (2) unexpected concept correlations in documents. For instance, by comparing model responses, we find that Grok-4 clarifies ambiguities more often than nine other frontier models. Relative to LLMs, SAE embeddings uncover bigger differences at 2-8x lower cost and identify biases more reliably. Additionally, SAE embeddings are controllable: by filtering concepts, we can (3) cluster documents along axes of interest and (4) outperform dense embeddings on property-based retrieval. Using SAE embeddings, we study model behavior with two case studies: investigating how OpenAI model behavior has changed over time and finding \"trigger\" phrases learned by Tulu-3 (Lambert et al., 2024) from its training data. These results position SAEs as a versatile tool for unstructured data analysis and highlight the neglected importance of interpreting models through their data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13586", "title": "ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding", "authors": [ "Jia-Nan Li", "Jian Guan", "Wei Wu", "Chongxuan Li" ], "github": "", "project_page": "", "abstract": "Autoregressive models (ARMs) are hindered by slow sequential inference. While masked diffusion models (MDMs) offer a parallel alternative, they suffer from critical drawbacks: high computational overhead from precluding Key-Value (KV) caching, and incoherent generation arising from learning dependencies over an intractable space of token combinations. To address these limitations, we introduce ReFusion, a novel masked diffusion model that achieves superior performance and efficiency by elevating parallel decoding from the token level to a higher slot level, where each slot is a fixed-length, contiguous sub-sequence. This is achieved through an iterative ``plan-and-infill'' decoding process: a diffusion-based planning step first identifies a set of weakly dependent slots, and an autoregressive infilling step then decodes these selected slots in parallel. The slot-based design simultaneously unlocks full KV cache reuse with a unified causal framework and reduces the learning complexity from the token combination space to a manageable slot-level permutation space. Extensive experiments on seven diverse benchmarks show that ReFusion not only overwhelmingly surpasses prior MDMs with 34% performance gains and an over 18times speedup on average, but also bridges the performance gap to strong ARMs while maintaining a 2.33times average speedup.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13687", "title": "Towards Scalable Pre-training of Visual Tokenizers for Generation", "authors": [ "Jingfeng Yao", "Yuda Song", "Yucong Zhou", "Xinggang Wang" ], "github": "https://github.com/MiniMax-AI/VTP", "project_page": "", "abstract": "The quality of the latent space in visual tokenizers (e.g., VAEs) is crucial for modern generative models. However, the standard reconstruction-based training paradigm produces a latent space that is biased towards low-level information, leading to a foundation flaw: better pixel-level accuracy does not lead to higher-quality generation. This implies that pouring extensive compute into visual tokenizer pre-training translates poorly to improved performance in generation. We identify this as the ``pre-training scaling problem`` and suggest a necessary shift: to be effective for generation, a latent space must concisely represent high-level semantics. We present VTP, a unified visual tokenizer pre-training framework, pioneering the joint optimization of image-text contrastive, self-supervised, and reconstruction losses. Our large-scale study reveals two principal findings: (1) understanding is a key driver of generation, and (2) much better scaling properties, where generative performance scales effectively with compute, parameters, and data allocated to the pretraining of the visual tokenizer. After large-scale pre-training, our tokenizer delivers a competitive profile (78.2 zero-shot accuracy and 0.36 rFID on ImageNet) and 4.1 times faster convergence on generation compared to advanced distillation methods. More importantly, it scales effectively: without modifying standard DiT training specs, solely investing more FLOPS in pretraining VTP achieves 65.8\\% FID improvement in downstream generation, while conventional autoencoder stagnates very early at 1/10 FLOPS. Our pre-trained models are available at https://github.com/MiniMax-AI/VTP.", "github_manual": "", "github_regex": "https://github.com/MiniMax-AI/VTP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13564", "title": "Memory in the Age of AI Agents", "authors": [ "Yuyang Hu", "Shichun Liu", "Yanwei Yue", "Guibin Zhang", "Boyang Liu", "Fangyi Zhu", "Jiahang Lin", "Honglin Guo", "Shihan Dou", "Zhiheng Xi", "Senjie Jin", "Jiejun Tan", "Yanbin Yin", "Jiongnan Liu", "Zeyu Zhang", "Zhongxiang Sun", "Yutao Zhu", "Hao Sun", "Boci Peng", "Zhenrong Cheng", "Xuanbo Fan", "Jiaxin Guo", "Xinlei Yu", "Zhenhong Zhou", "Zewen Hu", "Jiahao Huo", "Junhao Wang", "Yuwei Niu", "Yu Wang", "Zhenfei Yin", "Xiaobin Hu", "Yue Liao", "Qiankun Li", "Kun Wang", "Wangchunshu Zhou", "Yixin Liu", "Dawei Cheng", "Qi Zhang", "Tao Gui", "Shirui Pan", "Yan Zhang", "Philip Torr", "Zhicheng Dou", "Ji-Rong Wen", "Xuanjing Huang", "Yu-Gang Jiang", "Shuicheng Yan" ], "github": "", "project_page": "", "abstract": "Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attention, the field has also become increasingly fragmented. Existing works that fall under the umbrella of agent memory often differ substantially in their motivations, implementations, and evaluation protocols, while the proliferation of loosely defined memory terminologies has further obscured conceptual clarity. Traditional taxonomies such as long/short-term memory have proven insufficient to capture the diversity of contemporary agent memory systems. This work aims to provide an up-to-date landscape of current agent memory research. We begin by clearly delineating the scope of agent memory and distinguishing it from related concepts such as LLM memory, retrieval augmented generation (RAG), and context engineering. We then examine agent memory through the unified lenses of forms, functions, and dynamics. From the perspective of forms, we identify three dominant realizations of agent memory, namely token-level, parametric, and latent memory. From the perspective of functions, we propose a finer-grained taxonomy that distinguishes factual, experiential, and working memory. From the perspective of dynamics, we analyze how memory is formed, evolved, and retrieved over time. To support practical development, we compile a comprehensive summary of memory benchmarks and open-source frameworks. Beyond consolidation, we articulate a forward-looking perspective on emerging research frontiers, including memory automation, reinforcement learning integration, multimodal memory, multi-agent memory, and trustworthiness issues. We hope this survey serves not only as a reference for existing work, but also as a conceptual foundation for rethinking memory as a first-class primitive in the design of future agentic intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.12967", "title": "QwenLong-L1.5: Post-Training Recipe for Long-Context Reasoning and Memory Management", "authors": [ "Weizhou Shen", "Ziyi Yang", "Chenliang Li", "Zhiyuan Lu", "Miao Peng", "Huashan Sun", "Yingcheng Shi", "Shengyi Liao", "Shaopeng Lai", "Bo Zhang", "Dayiheng Liu", "Fei Huang", "Jingren Zhou", "Ming Yan" ], "github": "", "project_page": "", "abstract": "We introduce QwenLong-L1.5, a model that achieves superior long-context reasoning capabilities through systematic post-training innovations. The key technical breakthroughs of QwenLong-L1.5 are as follows: (1) Long-Context Data Synthesis Pipeline: We develop a systematic synthesis framework that generates challenging reasoning tasks requiring multi-hop grounding over globally distributed evidence. By deconstructing documents into atomic facts and their underlying relationships, and then programmatically composing verifiable reasoning questions, our approach creates high-quality training data at scale, moving substantially beyond simple retrieval tasks to enable genuine long-range reasoning capabilities. (2) Stabilized Reinforcement Learning for Long-Context Training: To overcome the critical instability in long-context RL, we introduce task-balanced sampling with task-specific advantage estimation to mitigate reward bias, and propose Adaptive Entropy-Controlled Policy Optimization (AEPO) that dynamically regulates exploration-exploitation trade-offs. (3) Memory-Augmented Architecture for Ultra-Long Contexts: Recognizing that even extended context windows cannot accommodate arbitrarily long sequences, we develop a memory management framework with multi-stage fusion RL training that seamlessly integrates single-pass reasoning with iterative memory-based processing for tasks exceeding 4M tokens. Based on Qwen3-30B-A3B-Thinking, QwenLong-L1.5 achieves performance comparable to GPT-5 and Gemini-2.5-Pro on long-context reasoning benchmarks, surpassing its baseline by 9.90 points on average. On ultra-long tasks (1M~4M tokens), QwenLong-L1.5's memory-agent framework yields a 9.48-point gain over the agent baseline. Additionally, the acquired long-context reasoning ability translates to enhanced performance in general domains like scientific reasoning, memory tool using, and extended dialogue.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13604", "title": "LongVie 2: Multimodal Controllable Ultra-Long Video World Model", "authors": [ "Jianxiong Gao", "Zhaoxi Chen", "Xian Liu", "Junhao Zhuang", "Chengming Xu", "Jianfeng Feng", "Yu Qiao", "Yanwei Fu", "Chenyang Si", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Building video world models upon pretrained video generation systems represents an important yet challenging step toward general spatiotemporal intelligence. A world model should possess three essential properties: controllability, long-term visual quality, and temporal consistency. To this end, we take a progressive approach-first enhancing controllability and then extending toward long-term, high-quality generation. We present LongVie 2, an end-to-end autoregressive framework trained in three stages: (1) Multi-modal guidance, which integrates dense and sparse control signals to provide implicit world-level supervision and improve controllability; (2) Degradation-aware training on the input frame, bridging the gap between training and long-term inference to maintain high visual quality; and (3) History-context guidance, which aligns contextual information across adjacent clips to ensure temporal consistency. We further introduce LongVGenBench, a comprehensive benchmark comprising 100 high-resolution one-minute videos covering diverse real-world and synthetic environments. Extensive experiments demonstrate that LongVie 2 achieves state-of-the-art performance in long-range controllability, temporal coherence, and visual fidelity, and supports continuous video generation lasting up to five minutes, marking a significant step toward unified video world modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13168", "title": "Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows", "authors": [ "Haoyu Dong", "Pengkun Zhang", "Yan Gao", "Xuanyu Dong", "Yilin Cheng", "Mingzhe Lu", "Adina Yakefu", "Shuxin Zheng" ], "github": "", "project_page": "", "abstract": "We introduce a finance & accounting benchmark (Finch) for evaluating AI agents on real-world, enterprise-grade professional workflows -- interleaving data entry, structuring, formatting, web search, cross-file retrieval, calculation, modeling, validation, translation, visualization, and reporting. Finch is sourced from authentic enterprise workspaces at Enron (15,000 spreadsheets and 500,000 emails from 150 employees) and other financial institutions, preserving in-the-wild messiness across multimodal artifacts (text, tables, formulas, charts, code, and images) and spanning diverse domains such as budgeting, trading, and asset management. We propose a workflow construction process that combines LLM-assisted discovery with expert annotation: (1) LLM-assisted, expert-verified derivation of workflows from real-world email threads and version histories of spreadsheet files, and (2) meticulous expert annotation for workflows, requiring over 700 hours of domain-expert effort. This yields 172 composite workflows with 384 tasks, involving 1,710 spreadsheets with 27 million cells, along with PDFs and other artifacts, capturing the intrinsically messy, long-horizon, knowledge-intensive, and collaborative nature of real-world enterprise work. We conduct both human and automated evaluations of frontier AI systems including GPT 5.1, Claude Sonnet 4.5, Gemini 3 Pro, Grok 4, and Qwen 3 Max, and GPT 5.1 Pro spends 48 hours in total yet passes only 38.4% of workflows, while Claude Sonnet 4.5 passes just 25.0%. Comprehensive case studies further surface the challenges that real-world enterprise workflows pose for AI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.12730", "title": "NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents", "authors": [ "Jingzhe Ding", "Shengda Long", "Changxin Pu", "Huan Zhou", "Hongwan Gao", "Xiang Gao", "Chao He", "Yue Hou", "Fei Hu", "Zhaojian Li", "Weiran Shi", "Zaiyuan Wang", "Daoguang Zan", "Chenchen Zhang", "Xiaoxu Zhang", "Qizhi Chen", "Xianfu Cheng", "Bo Deng", "Qingshui Gu", "Kai Hua", "Juntao Lin", "Pai Liu", "Mingchen Li", "Xuanguang Pan", "Zifan Peng", "Yujia Qin", "Yong Shan", "Zhewen Tan", "Weihao Xie", "Zihan Wang", "Yishuo Yuan", "Jiayu Zhang", "Enduo Zhao", "Yunfei Zhao", "He Zhu", "Chenyang Zou", "Ming Ding", "Jianpeng Jiao", "Jiaheng Liu", "Minghao Liu", "Qian Liu", "Chongyao Tao", "Jian Yang", "Tong Yang", "Zhaoxiang Zhang", "Xinjie Chen", "Wenhao Huang", "Ge Zhang" ], "github": "", "project_page": "", "abstract": "Recent advances in coding agents suggest rapid progress toward autonomous software development, yet existing benchmarks fail to rigorously evaluate the long-horizon capabilities required to build complete software systems. Most prior evaluations focus on localized code generation, scaffolded completion, or short-term repair tasks, leaving open the question of whether agents can sustain coherent reasoning, planning, and execution over the extended horizons demanded by real-world repository construction. To address this gap, we present NL2Repo Bench, a benchmark explicitly designed to evaluate the long-horizon repository generation ability of coding agents. Given only a single natural-language requirements document and an empty workspace, agents must autonomously design the architecture, manage dependencies, implement multi-module logic, and produce a fully installable Python library. Our experiments across state-of-the-art open- and closed-source models reveal that long-horizon repository generation remains largely unsolved: even the strongest agents achieve below 40% average test pass rates and rarely complete an entire repository correctly. Detailed analysis uncovers fundamental long-horizon failure modes, including premature termination, loss of global coherence, fragile cross-file dependencies, and inadequate planning over hundreds of interaction steps. NL2Repo Bench establishes a rigorous, verifiable testbed for measuring sustained agentic competence and highlights long-horizon reasoning as a central bottleneck for the next generation of autonomous coding agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.12602", "title": "Error-Free Linear Attention is a Free Lunch: Exact Solution from Continuous-Time Dynamics", "authors": [ "Jingdi Lei", "Di Zhang", "Soujanya Poria" ], "github": "", "project_page": "", "abstract": "Linear-time attention and State Space Models (SSMs) promise to solve the quadratic cost bottleneck in long-context language models employing softmax attention. We introduce Error-Free Linear Attention (EFLA), a numerically stable, fully parallelism and generalized formulation of the delta rule. Specifically, we formulate the online learning update as a continuous-time dynamical system and prove that its exact solution is not only attainable but also computable in linear time with full parallelism. By leveraging the rank-1 structure of the dynamics matrix, we directly derive the exact closed-form solution effectively corresponding to the infinite-order Runge-Kutta method. This attention mechanism is theoretically free from error accumulation, perfectly capturing the continuous dynamics while preserving the linear-time complexity. Through an extensive suite of experiments, we show that EFLA enables robust performance in noisy environments, achieving lower language modeling perplexity and superior downstream benchmark performance than DeltaNet without introducing additional parameters. Our work provides a new theoretical foundation for building high-fidelity, scalable linear-time attention models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13313", "title": "KlingAvatar 2.0 Technical Report", "authors": [ "Kling Team", "Jialu Chen", "Yikang Ding", "Zhixue Fang", "Kun Gai", "Yuan Gao", "Kang He", "Jingyun Hua", "Boyuan Jiang", "Mingming Lao", "Xiaohan Li", "Hui Liu", "Jiwen Liu", "Xiaoqiang Liu", "Yuan Liu", "Shun Lu", "Yongsen Mao", "Yingchao Shao", "Huafeng Shi", "Xiaoyu Shi", "Peiqin Sun", "Songlin Tang", "Pengfei Wan", "Chao Wang", "Xuebo Wang", "Haoxian Zhang", "Yuanxing Zhang", "Yan Zhou" ], "github": "", "project_page": "", "abstract": "Avatar video generation models have achieved remarkable progress in recent years. However, prior work exhibits limited efficiency in generating long-duration high-resolution videos, suffering from temporal drifting, quality degradation, and weak prompt following as video length increases. To address these challenges, we propose KlingAvatar 2.0, a spatio-temporal cascade framework that performs upscaling in both spatial resolution and temporal dimension. The framework first generates low-resolution blueprint video keyframes that capture global semantics and motion, and then refines them into high-resolution, temporally coherent sub-clips using a first-last frame strategy, while retaining smooth temporal transitions in long-form videos. To enhance cross-modal instruction fusion and alignment in extended videos, we introduce a Co-Reasoning Director composed of three modality-specific large language model (LLM) experts. These experts reason about modality priorities and infer underlying user intent, converting inputs into detailed storylines through multi-turn dialogue. A Negative Director further refines negative prompts to improve instruction alignment. Building on these components, we extend the framework to support ID-specific multi-character control. Extensive experiments demonstrate that our model effectively addresses the challenges of efficient, multimodally aligned long-form high-resolution video generation, delivering enhanced visual clarity, realistic lip-teeth rendering with accurate lip synchronization, strong identity preservation, and coherent multimodal instruction following.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.09636", "title": "MentraSuite: Post-Training Large Language Models for Mental Health Reasoning and Assessment", "authors": [ "Mengxi Xiao", "Kailai Yang", "Pengde Zhao", "Enze Zhang", "Ziyan Kuang", "Zhiwei Liu", "Weiguang Han", "Shu Liao", "Lianting Huang", "Jinpeng Hu", "Min Peng", "Qianqian Xie", "Sophia Ananiadou" ], "github": "https://github.com/elsa66666/MentraSuite", "project_page": "", "abstract": "Mental health disorders affect hundreds of millions globally, and the Web now serves as a primary medium for accessing support, information, and assessment. Large language models (LLMs) offer scalable and accessible assistance, yet their deployment in mental-health settings remains risky when their reasoning is incomplete, inconsistent, or ungrounded. Existing psychological LLMs emphasize emotional understanding or knowledge recall but overlook the step-wise, clinically aligned reasoning required for appraisal, diagnosis, intervention planning, abstraction, and verification. To address these issues, we introduce MentraSuite, a unified framework for advancing reliable mental-health reasoning. We propose MentraBench, a comprehensive benchmark spanning five core reasoning aspects, six tasks, and 13 datasets, evaluating both task performance and reasoning quality across five dimensions: conciseness, coherence, hallucination avoidance, task understanding, and internal consistency. We further present Mindora, a post-trained model optimized through a hybrid SFT-RL framework with an inconsistency-detection reward to enforce faithful and coherent reasoning. To support training, we construct high-quality trajectories using a novel reasoning trajectory generation strategy, that strategically filters difficult samples and applies a structured, consistency-oriented rewriting process to produce concise, readable, and well-balanced trajectories. Across 20 evaluated LLMs, Mindora achieves the highest average performance on MentraBench and shows remarkable performances in reasoning reliability, demonstrating its effectiveness for complex mental-health scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/elsa66666/MentraSuite", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.10071", "title": "Openpi Comet: Competition Solution For 2025 BEHAVIOR Challenge", "authors": [ "Junjie Bai", "Yu-Wei Chao", "Qizhi Chen", "Jinwei Gu", "Moo Jin Kim", "Zhaoshuo Li", "Xuan Li", "Tsung-Yi Lin", "Ming-Yu Liu", "Nic Ma", "Kaichun Mo", "Delin Qu", "Shangkun Sun", "Hongchi Xia", "Fangyin Wei", "Xiaohui Zeng" ], "github": "", "project_page": "", "abstract": "The 2025 BEHAVIOR Challenge is designed to rigorously track progress toward solving long-horizon tasks by physical agents in simulated environments. BEHAVIOR-1K focuses on everyday household tasks that people most want robots to assist with and these tasks introduce long-horizon mobile manipulation challenges in realistic settings, bridging the gap between current research and real-world, human-centric applications. This report presents our solution to the 2025 BEHAVIOR Challenge in a very close 2nd place and substantially outperforms the rest of the submissions. Building on \u03c0_{0.5}, we focus on systematically building our solution by studying the effects of training techniques and data. Through careful ablations, we show the scaling power in pre-training and post-training phases for competitive performance. We summarize our practical lessons and design recommendations that we hope will provide actionable insights for the broader embodied AI community when adapting powerful foundation models to complex embodied scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13080", "title": "Spatial-Aware VLA Pretraining through Visual-Physical Alignment from Human Videos", "authors": [ "Yicheng Feng", "Wanpeng Zhang", "Ye Wang", "Hao Luo", "Haoqi Yuan", "Sipeng Zheng", "Zongqing Lu" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models provide a promising paradigm for robot learning by integrating visual perception with language-guided policy learning. However, most existing approaches rely on 2D visual inputs to perform actions in 3D physical environments, creating a significant gap between perception and action grounding. To bridge this gap, we propose a Spatial-Aware VLA Pretraining paradigm that performs explicit alignment between visual space and physical space during pretraining, enabling models to acquire 3D spatial understanding before robot policy learning. Starting from pretrained vision-language models, we leverage large-scale human demonstration videos to extract 3D visual and 3D action annotations, forming a new source of supervision that aligns 2D visual observations with 3D spatial reasoning. We instantiate this paradigm with VIPA-VLA, a dual-encoder architecture that incorporates a 3D visual encoder to augment semantic visual representations with 3D-aware features. When adapted to downstream robot tasks, VIPA-VLA achieves significantly improved grounding between 2D vision and 3D action, resulting in more robust and generalizable robotic policies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.12692", "title": "WebOperator: Action-Aware Tree Search for Autonomous Agents in Web Environment", "authors": [ "Mahir Labib Dihan", "Tanzima Hashem", "Mohammed Eunus Ali", "Md Rizwan Parvez" ], "github": "https://github.com/kagnlp/WebOperator", "project_page": "", "abstract": "LLM-based agents often operate in a greedy, step-by-step manner, selecting actions solely based on the current observation without considering long-term consequences or alternative paths. This lack of foresight is particularly problematic in web environments, which are only partially observable-limited to browser-visible content (e.g., DOM and UI elements)-where a single misstep often requires complex and brittle navigation to undo. Without an explicit backtracking mechanism, agents struggle to correct errors or systematically explore alternative paths. Tree-search methods provide a principled framework for such structured exploration, but existing approaches lack mechanisms for safe backtracking, making them prone to unintended side effects. They also assume that all actions are reversible, ignoring the presence of irreversible actions-limitations that reduce their effectiveness in realistic web tasks. To address these challenges, we introduce WebOperator, a tree-search framework that enables reliable backtracking and strategic exploration. Our method incorporates a best-first search strategy that ranks actions by both reward estimates and safety considerations, along with a robust backtracking mechanism that verifies the feasibility of previously visited paths before replaying them, preventing unintended side effects. To further guide exploration, WebOperator generates action candidates from multiple, varied reasoning contexts to ensure diverse and robust exploration, and subsequently curates a high-quality action set by filtering out invalid actions pre-execution and merging semantically equivalent ones. Experimental results on WebArena and WebVoyager demonstrate the effectiveness of WebOperator. On WebArena, WebOperator achieves a state-of-the-art 54.6% success rate with gpt-4o, underscoring the critical advantage of integrating strategic foresight with safe execution.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kagnlp/WebOperator", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.11995", "title": "V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions", "authors": [ "Chenrui Fan", "Yijun Liang", "Shweta Bhardwaj", "Kwesi Cobbina", "Ming Li", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in most benchmarks, they often struggle in practice with complex open-ended tasks, which usually require multiple rounds of exploration and reasoning in the visual space. Such visual thinking paths not only provide step-by-step exploration and verification as an AI detective but also produce better interpretations of the final answers. However, these paths are challenging to evaluate due to the large exploration space of intermediate steps. To bridge the gap, we develop an evaluation suite, ``Visual Reasoning with multi-step EXploration (V-REX)'', which is composed of a benchmark of challenging visual reasoning tasks requiring native multi-step exploration and an evaluation protocol. V-REX covers rich application scenarios across diverse domains. V-REX casts the multi-step exploratory reasoning into a Chain-of-Questions (CoQ) and disentangles VLMs' capability to (1) Planning: breaking down an open-ended task by selecting a chain of exploratory questions; and (2) Following: answering curated CoQ sequentially to collect information for deriving the final answer. By curating finite options of questions and answers per step, V-REX achieves a reliable quantitative and fine-grained analysis of the intermediate steps. By assessing SOTA proprietary and open-sourced VLMs, we reveal consistent scaling trends, significant differences between planning and following abilities, and substantial room for improvement in multi-step exploratory reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13250", "title": "Toward Ambulatory Vision: Learning Visually-Grounded Active View Selection", "authors": [ "Juil Koo", "Daehyeon Choi", "Sangwoo Youn", "Phillip Y. Lee", "Minhyuk Sung" ], "github": "", "project_page": "", "abstract": "Vision Language Models (VLMs) excel at visual question answering (VQA) but remain limited to snapshot vision, reasoning from static images. In contrast, embodied agents require ambulatory vision, actively moving to obtain more informative views. We introduce Visually Grounded Active View Selection (VG-AVS), a task that selects the most informative next viewpoint using only the visual information in the current image, without relying on scene memory or external knowledge. To support this task, we construct a synthetic dataset with automatically generated paired query-target views and question-answer prompts. We also propose a framework that fine-tunes pretrained VLMs through supervised fine-tuning (SFT) followed by RL-based policy optimization. Our approach achieves strong question answering performance based on viewpoint selection and generalizes robustly to unseen synthetic and real scenes. Furthermore, incorporating our learned VG-AVS framework into existing scene-exploration-based EQA systems improves downstream question-answering accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.12799", "title": "DrivePI: Spatial-aware 4D MLLM for Unified Autonomous Driving Understanding, Perception, Prediction and Planning", "authors": [ "Zhe Liu", "Runhui Huang", "Rui Yang", "Siming Yan", "Zining Wang", "Lu Hou", "Di Lin", "Xiang Bai", "Hengshuang Zhao" ], "github": "https://github.com/happinesslz/DrivePI", "project_page": "", "abstract": "Although multi-modal large language models (MLLMs) have shown strong capabilities across diverse domains, their application in generating fine-grained 3D perception and prediction outputs in autonomous driving remains underexplored. In this paper, we propose DrivePI, a novel spatial-aware 4D MLLM that serves as a unified Vision-Language-Action (VLA) framework that is also compatible with vision-action (VA) models. Our method jointly performs spatial understanding, 3D perception (i.e., 3D occupancy), prediction (i.e., occupancy flow), and planning (i.e., action outputs) in parallel through end-to-end optimization. To obtain both precise geometric information and rich visual appearance, our approach integrates point clouds, multi-view images, and language instructions within a unified MLLM architecture. We further develop a data engine to generate text-occupancy and text-flow QA pairs for 4D spatial understanding. Remarkably, with only a 0.5B Qwen2.5 model as MLLM backbone, DrivePI as a single unified model matches or exceeds both existing VLA models and specialized VA models. Specifically, compared to VLA models, DrivePI outperforms OpenDriveVLA-7B by 2.5% mean accuracy on nuScenes-QA and reduces collision rate by 70% over ORION (from 0.37% to 0.11%) on nuScenes. Against specialized VA models, DrivePI surpasses FB-OCC by 10.3 RayIoU for 3D occupancy on OpenOcc, reduces the mAVE from 0.591 to 0.509 for occupancy flow on OpenOcc, and achieves 32% lower L2 error than VAD (from 0.72m to 0.49m) for planning on nuScenes. Code will be available at https://github.com/happinesslz/DrivePI", "github_manual": "", "github_regex": "https://github.com/happinesslz/DrivePI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.11891", "title": "VLSA: Vision-Language-Action Models with Plug-and-Play Safety Constraint Layer", "authors": [ "Songqiao Hu", "Zeyi Liu", "Shuang Liu", "Jun Cen", "Zihan Meng", "Xiao He" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in generalizing across diverse robotic manipulation tasks. However, deploying these models in unstructured environments remains challenging due to the critical need for simultaneous task compliance and safety assurance, particularly in preventing potential collisions during physical interactions. In this work, we introduce a Vision-Language-Safe Action (VLSA) architecture, named AEGIS, which contains a plug-and-play safety constraint (SC) layer formulated via control barrier functions. AEGIS integrates directly with existing VLA models to improve safety with theoretical guarantees, while maintaining their original instruction-following performance. To evaluate the efficacy of our architecture, we construct a comprehensive safety-critical benchmark SafeLIBERO, spanning distinct manipulation scenarios characterized by varying degrees of spatial complexity and obstacle intervention. Extensive experiments demonstrate the superiority of our method over state-of-the-art baselines. Notably, AEGIS achieves a 59.16% improvement in obstacle avoidance rate while substantially increasing the task execution success rate by 17.25%. To facilitate reproducibility and future research, we make our code, models, and the benchmark datasets publicly available at https://vlsa-aegis.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13592", "title": "Image Diffusion Preview with Consistency Solver", "authors": [ "Fu-Yun Wang", "Hao Zhou", "Liangzhe Yuan", "Sanghyun Woo", "Boqing Gong", "Bohyung Han", "Ming-Hsuan Yang", "Han Zhang", "Yukun Zhu", "Ting Liu", "Long Zhao" ], "github": "https://github.com/G-U-N/consolver", "project_page": "", "abstract": "The slow inference process of image diffusion models significantly degrades interactive user experiences. To address this, we introduce Diffusion Preview, a novel paradigm employing rapid, low-step sampling to generate preliminary outputs for user evaluation, deferring full-step refinement until the preview is deemed satisfactory. Existing acceleration methods, including training-free solvers and post-training distillation, struggle to deliver high-quality previews or ensure consistency between previews and final outputs. We propose ConsistencySolver derived from general linear multistep methods, a lightweight, trainable high-order solver optimized via Reinforcement Learning, that enhances preview quality and consistency. Experimental results demonstrate that ConsistencySolver significantly improves generation quality and consistency in low-step scenarios, making it ideal for efficient preview-and-refine workflows. Notably, it achieves FID scores on-par with Multistep DPM-Solver using 47% fewer steps, while outperforming distillation baselines. Furthermore, user studies indicate our approach reduces overall user interaction time by nearly 50% while maintaining generation quality. Code is available at https://github.com/G-U-N/consolver.", "github_manual": "", "github_regex": "https://github.com/G-U-N/consolver", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.12751", "title": "GenieDrive: Towards Physics-Aware Driving World Model with 4D Occupancy Guided Video Generation", "authors": [ "Zhenya Yang", "Zhe Liu", "Yuxiang Lu", "Liping Hou", "Chenxuan Miao", "Siyi Peng", "Bailan Feng", "Xiang Bai", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "Physics-aware driving world model is essential for drive planning, out-of-distribution data synthesis, and closed-loop evaluation. However, existing methods often rely on a single diffusion model to directly map driving actions to videos, which makes learning difficult and leads to physically inconsistent outputs. To overcome these challenges, we propose GenieDrive, a novel framework designed for physics-aware driving video generation. Our approach starts by generating 4D occupancy, which serves as a physics-informed foundation for subsequent video generation. 4D occupancy contains rich physical information, including high-resolution 3D structures and dynamics. To facilitate effective compression of such high-resolution occupancy, we propose a VAE that encodes occupancy into a latent tri-plane representation, reducing the latent size to only 58% of that used in previous methods. We further introduce Mutual Control Attention (MCA) to accurately model the influence of control on occupancy evolution, and we jointly train the VAE and the subsequent prediction module in an end-to-end manner to maximize forecasting accuracy. Together, these designs yield a 7.2% improvement in forecasting mIoU at an inference speed of 41 FPS, while using only 3.47 M parameters. Additionally, a Normalized Multi-View Attention is introduced in the video generation model to generate multi-view driving videos with guidance from our 4D occupancy, significantly improving video quality with a 20.7% reduction in FVD. Experiments demonstrate that GenieDrive enables highly controllable, multi-view consistent, and physics-aware driving video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.11883", "title": "Aesthetic Alignment Risks Assimilation: How Image Generation and Reward Models Reinforce Beauty Bias and Ideological \"Censorship\"", "authors": [ "Wenqi Marshall Guo", "Qingyun Qian", "Khalad Hasan", "Shan Du" ], "github": "", "project_page": "", "abstract": "Over-aligning image generation models to a generalized aesthetic preference conflicts with user intent, particularly when ``anti-aesthetic\" outputs are requested for artistic or critical purposes. This adherence prioritizes developer-centered values, compromising user autonomy and aesthetic pluralism. We test this bias by constructing a wide-spectrum aesthetics dataset and evaluating state-of-the-art generation and reward models. We find that aesthetic-aligned generation models frequently default to conventionally beautiful outputs, failing to respect instructions for low-quality or negative imagery. Crucially, reward models penalize anti-aesthetic images even when they perfectly match the explicit user prompt. We confirm this systemic bias through image-to-image editing and evaluation against real abstract artworks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13674", "title": "Towards Interactive Intelligence for Digital Humans", "authors": [ "Yiyi Cai", "Xuangeng Chu", "Xiwei Gao", "Sitong Gong", "Yifei Huang", "Caixin Kang", "Kunhang Li", "Haiyang Liu", "Ruicong Liu", "Yun Liu", "Dianwen Ng", "Zixiong Su", "Erwin Wu", "Yuhan Wu", "Dingkun Yan", "Tianyu Yan", "Chang Zeng", "Bo Zheng", "You Zhou" ], "github": "", "project_page": "", "abstract": "We introduce Interactive Intelligence, a novel paradigm of digital human that is capable of personality-aligned expression, adaptive interaction, and self-evolution. To realize this, we present Mio (Multimodal Interactive Omni-Avatar), an end-to-end framework composed of five specialized modules: Thinker, Talker, Face Animator, Body Animator, and Renderer. This unified architecture integrates cognitive reasoning with real-time multimodal embodiment to enable fluid, consistent interaction. Furthermore, we establish a new benchmark to rigorously evaluate the capabilities of interactive intelligence. Extensive experiments demonstrate that our framework achieves superior performance compared to state-of-the-art methods across all evaluated dimensions. Together, these contributions move digital humans beyond superficial imitation toward intelligent interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13421", "title": "RecTok: Reconstruction Distillation along Rectified Flow", "authors": [ "Qingyu Shi", "Size Wu", "Jinbin Bai", "Kaidong Yu", "Yujing Wang", "Yunhai Tong", "Xiangtai Li", "Xuelong Li" ], "github": "", "project_page": "", "abstract": "Visual tokenizers play a crucial role in diffusion models. The dimensionality of latent space governs both reconstruction fidelity and the semantic expressiveness of the latent feature. However, a fundamental trade-off is inherent between dimensionality and generation quality, constraining existing methods to low-dimensional latent spaces. Although recent works have leveraged vision foundation models to enrich the semantics of visual tokenizers and accelerate convergence, high-dimensional tokenizers still underperform their low-dimensional counterparts. In this work, we propose RecTok, which overcomes the limitations of high-dimensional visual tokenizers through two key innovations: flow semantic distillation and reconstruction--alignment distillation. Our key insight is to make the forward flow in flow matching semantically rich, which serves as the training space of diffusion transformers, rather than focusing on the latent space as in previous works. Specifically, our method distills the semantic information in VFMs into the forward flow trajectories in flow matching. And we further enhance the semantics by introducing a masked feature reconstruction loss. Our RecTok achieves superior image reconstruction, generation quality, and discriminative performance. It achieves state-of-the-art results on the gFID-50K under both with and without classifier-free guidance settings, while maintaining a semantically rich latent space structure. Furthermore, as the latent dimensionality increases, we observe consistent improvements. Code and model are available at https://shi-qingyu.github.io/rectok.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.10655", "title": "CAPTAIN: Semantic Feature Injection for Memorization Mitigation in Text-to-Image Diffusion Models", "authors": [ "Tong Zhang", "Carlos Hinojosa", "Bernard Ghanem" ], "github": "", "project_page": "", "abstract": "Diffusion models can unintentionally reproduce training examples, raising privacy and copyright concerns as these systems are increasingly deployed at scale. Existing inference-time mitigation methods typically manipulate classifier-free guidance (CFG) or perturb prompt embeddings; however, they often struggle to reduce memorization without compromising alignment with the conditioning prompt. We introduce CAPTAIN, a training-free framework that mitigates memorization by directly modifying latent features during denoising. CAPTAIN first applies frequency-based noise initialization to reduce the tendency to replicate memorized patterns early in the denoising process. It then identifies the optimal denoising timesteps for feature injection and localizes memorized regions. Finally, CAPTAIN injects semantically aligned features from non-memorized reference images into localized latent regions, suppressing memorization while preserving prompt fidelity and visual quality. Our experiments show that CAPTAIN achieves substantial reductions in memorization compared to CFG-based baselines while maintaining strong alignment with the intended prompt.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13690", "title": "DiffusionBrowser: Interactive Diffusion Previews via Multi-Branch Decoders", "authors": [ "Susung Hong", "Chongjian Ge", "Zhifei Zhang", "Jui-Hsien Wang" ], "github": "", "project_page": "", "abstract": "Video diffusion models have revolutionized generative video synthesis, but they are imprecise, slow, and can be opaque during generation -- keeping users in the dark for a prolonged period. In this work, we propose DiffusionBrowser, a model-agnostic, lightweight decoder framework that allows users to interactively generate previews at any point (timestep or transformer block) during the denoising process. Our model can generate multi-modal preview representations that include RGB and scene intrinsics at more than 4times real-time speed (less than 1 second for a 4-second video) that convey consistent appearance and motion to the final video. With the trained decoder, we show that it is possible to interactively guide the generation at intermediate noise steps via stochasticity reinjection and modal steering, unlocking a new control capability. Moreover, we systematically probe the model using the learned decoders, revealing how scene, object, and other details are composed and assembled during the otherwise black-box denoising process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13689", "title": "LitePT: Lighter Yet Stronger Point Transformer", "authors": [ "Yuanwen Yue", "Damien Robert", "Jianyuan Wang", "Sunghwan Hong", "Jan Dirk Wegner", "Christian Rupprecht", "Konrad Schindler" ], "github": "https://github.com/prs-eth/LitePT", "project_page": "", "abstract": "Modern neural architectures for 3D point cloud processing contain both convolutional layers and attention blocks, but the best way to assemble them remains unclear. We analyse the role of different computational blocks in 3D point cloud networks and find an intuitive behaviour: convolution is adequate to extract low-level geometry at high-resolution in early layers, where attention is expensive without bringing any benefits; attention captures high-level semantics and context in low-resolution, deep layers more efficiently. Guided by this design principle, we propose a new, improved 3D point cloud backbone that employs convolutions in early stages and switches to attention for deeper layers. To avoid the loss of spatial layout information when discarding redundant convolution layers, we introduce a novel, training-free 3D positional encoding, PointROPE. The resulting LitePT model has 3.6times fewer parameters, runs 2times faster, and uses 2times less memory than the state-of-the-art Point Transformer V3, but nonetheless matches or even outperforms it on a range of tasks and datasets. Code and models are available at: https://github.com/prs-eth/LitePT.", "github_manual": "", "github_regex": "https://github.com/prs-eth/LitePT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13672", "title": "Directional Textual Inversion for Personalized Text-to-Image Generation", "authors": [ "Kunhee Kim", "NaHyeon Park", "Kibeom Hong", "Hyunjung Shim" ], "github": "https://github.com/kunheek/dti", "project_page": "", "abstract": "Textual Inversion (TI) is an efficient approach to text-to-image personalization but often fails on complex prompts. We trace these failures to embedding norm inflation: learned tokens drift to out-of-distribution magnitudes, degrading prompt conditioning in pre-norm Transformers. Empirically, we show semantics are primarily encoded by direction in CLIP token space, while inflated norms harm contextualization; theoretically, we analyze how large magnitudes attenuate positional information and hinder residual updates in pre-norm blocks. We propose Directional Textual Inversion (DTI), which fixes the embedding magnitude to an in-distribution scale and optimizes only direction on the unit hypersphere via Riemannian SGD. We cast direction learning as MAP with a von Mises-Fisher prior, yielding a constant-direction prior gradient that is simple and efficient to incorporate. Across personalization tasks, DTI improves text fidelity over TI and TI-variants while maintaining subject similarity. Crucially, DTI's hyperspherical parameterization enables smooth, semantically coherent interpolation between learned concepts (slerp), a capability that is absent in standard TI. Our findings suggest that direction-only optimization is a robust and scalable path for prompt-faithful personalization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kunheek/dti", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13006", "title": "Few-Step Distillation for Text-to-Image Generation: A Practical Guide", "authors": [ "Yifan Pu", "Yizeng Han", "Zhiwei Tang", "Jiasheng Tang", "Fan Wang", "Bohan Zhuang", "Gao Huang" ], "github": "", "project_page": "", "abstract": "Diffusion distillation has dramatically accelerated class-conditional image synthesis, but its applicability to open-ended text-to-image (T2I) generation is still unclear. We present the first systematic study that adapts and compares state-of-the-art distillation techniques on a strong T2I teacher model, FLUX.1-lite. By casting existing methods into a unified framework, we identify the key obstacles that arise when moving from discrete class labels to free-form language prompts. Beyond a thorough methodological analysis, we offer practical guidelines on input scaling, network architecture, and hyperparameters, accompanied by an open-source implementation and pretrained student models. Our findings establish a solid foundation for deploying fast, high-fidelity, and resource-efficient diffusion generators in real-world T2I applications. Code is available on github.com/alibaba-damo-academy/T2I-Distill.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.11438", "title": "Flowception: Temporally Expansive Flow Matching for Video Generation", "authors": [ "Tariq Berrada Ifriqi", "John Nguyen", "Karteek Alahari", "Jakob Verbeek", "Ricky T. Q. Chen" ], "github": "", "project_page": "", "abstract": "We present Flowception, a novel non-autoregressive and variable-length video generation framework. Flowception learns a probability path that interleaves discrete frame insertions with continuous frame denoising. Compared to autoregressive methods, Flowception alleviates error accumulation/drift as the frame insertion mechanism during sampling serves as an efficient compression mechanism to handle long-term context. Compared to full-sequence flows, our method reduces FLOPs for training three-fold, while also being more amenable to local attention variants, and allowing to learn the length of videos jointly with their content. Quantitative experimental results show improved FVD and VBench metrics over autoregressive and full-sequence baselines, which is further validated with qualitative results. Finally, by learning to insert and denoise frames in a sequence, Flowception seamlessly integrates different tasks such as image-to-video generation and video interpolation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.10794", "title": "What matters for Representation Alignment: Global Information or Spatial Structure?", "authors": [ "Jaskirat Singh", "Xingjian Leng", "Zongze Wu", "Liang Zheng", "Richard Zhang", "Eli Shechtman", "Saining Xie" ], "github": "https://github.com/end2end-diffusion/irepa", "project_page": "", "abstract": "Representation alignment (REPA) guides generative training by distilling representations from a strong, pretrained vision encoder to intermediate diffusion features. We investigate a fundamental question: what aspect of the target representation matters for generation, its global semantic information (e.g., measured by ImageNet-1K accuracy) or its spatial structure (i.e. pairwise cosine similarity between patch tokens)? Prevalent wisdom holds that stronger global semantic performance leads to better generation as a target representation. To study this, we first perform a large-scale empirical analysis across 27 different vision encoders and different model scales. The results are surprising; spatial structure, rather than global performance, drives the generation performance of a target representation. To further study this, we introduce two straightforward modifications, which specifically accentuate the transfer of spatial information. We replace the standard MLP projection layer in REPA with a simple convolution layer and introduce a spatial normalization layer for the external representation. Surprisingly, our simple method (implemented in <4 lines of code), termed iREPA, consistently improves convergence speed of REPA, across a diverse set of vision encoders, model sizes, and training variants (such as REPA, REPA-E, Meanflow, JiT etc). %, etc. Our work motivates revisiting the fundamental working mechanism of representational alignment and how it can be leveraged for improved training of generative models. The code and project page are available at https://end2end-diffusion.github.io/irepa", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/end2end-diffusion/irepa", "github_api_added_by": "auto" }, { "date": "2025-12-16", "arxiv_id": "2512.07186", "title": "START: Spatial and Textual Learning for Chart Understanding", "authors": [ "Zhuoming Liu", "Xiaofeng Gao", "Feiyang Niu", "Qiaozi Gao", "Liu Liu", "Robinson Piramuthu" ], "github": "", "project_page": "", "abstract": "Chart understanding is crucial for deploying multimodal large language models (MLLMs) in real-world scenarios such as analyzing scientific papers and technical reports. Unlike natural images, charts pair a structured visual layout (spatial property) with an underlying data representation (textual property) -- grasping both is essential for precise, fine-grained chart reasoning. Motivated by this observation, we propose START, the Spatial and Textual learning for chART understanding. Specifically, we introduce (i) chart-element grounding and (ii) chart-to-code generation to strengthen an MLLM's understanding of both chart visual layout and data details. To facilitate spatial and textual learning, we propose the START-Dataset generated with a novel data-generation pipeline that first leverages an MLLM to translate real chart images into executable chart code, recovering the underlying data representation while preserving the visual distribution of real-world charts. We then evolve the code with a Large Language Model (LLM) to ascertain the positions of chart elements that capture the chart's visual structure, addressing challenges that existing methods cannot handle. To evaluate a model's ability to understand chart spatial structures, we propose the Chart Spatial understanding Benchmark (CS-Bench), filling a critical gap in comprehensive chart understanding evaluation. Leveraging spatial and textual learning, START delivers consistent gains across model sizes and benchmarks over the base models and surpasses prior state-of-the-art by a clear margin. Code, data and models will be publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.05272", "title": "Inferring Compositional 4D Scenes without Ever Seeing One", "authors": [ "Ahmet Berke Gokmen", "Ajad Chhatkuli", "Luc Van Gool", "Danda Pani Paudel" ], "github": "https://github.com/insait-institute/COM4D", "project_page": "", "abstract": "Scenes in the real world are often composed of several static and dynamic objects. Capturing their 4-dimensional structures, composition and spatio-temporal configuration in-the-wild, though extremely interesting, is equally hard. Therefore, existing works often focus on one object at a time, while relying on some category-specific parametric shape model for dynamic objects. This can lead to inconsistent scene configurations, in addition to being limited to the modeled object categories. We propose COM4D (Compositional 4D), a method that consistently and jointly predicts the structure and spatio-temporal configuration of 4D/3D objects using only static multi-object or dynamic single object supervision. We achieve this by a carefully designed training of spatial and temporal attentions on 2D video input. The training is disentangled into learning from object compositions on the one hand, and single object dynamics throughout the video on the other, thus completely avoiding reliance on 4D compositional training data. At inference time, our proposed attention mixing mechanism combines these independently learned attentions, without requiring any 4D composition examples. By alternating between spatial and temporal reasoning, COM4D reconstructs complete and persistent 4D scenes with multiple interacting objects directly from monocular videos. Furthermore, COM4D provides state-of-the-art results in existing separate problems of 4D object and composed 3D reconstruction despite being purely data-driven.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/insait-institute/COM4D", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.13330", "title": "FIN-bench-v2: A Unified and Robust Benchmark Suite for Evaluating Finnish Large Language Models", "authors": [ "Joona Kyt\u00f6niemi", "Jousia Piha", "Akseli Reunamo", "Fedor Vitiugin", "Farrokh Mehryary", "Sampo Pyysalo" ], "github": "", "project_page": "", "abstract": "We introduce FIN-bench-v2, a unified benchmark suite for evaluating large language models in Finnish. FIN-bench-v2 consolidates Finnish versions of widely used benchmarks together with an updated and expanded version of the original FIN-bench into a single, consistently formatted collection, covering multiple-choice and generative tasks across reading comprehension, commonsense reasoning, sentiment analysis, world knowledge, and alignment. All datasets are converted to HuggingFace Datasets, which include both cloze and multiple-choice prompt formulations with five variants per task, and we incorporate human annotation or review for machine-translated resources such as GoldenSwag and XED. To select robust tasks, we pretrain a set of 2.15B-parameter decoder-only models and use their learning curves to compute monotonicity, signal-to-noise, non-random performance, and model ordering consistency, retaining only tasks that satisfy all criteria. We further evaluate a set of larger instruction-tuned models to characterize performance across tasks and prompt formulations. All datasets, prompts, and evaluation configurations are publicly available via our fork of the Language Model Evaluation Harness at https://github.com/LumiOpen/lm-evaluation-harness. Supplementary resources are released in a separate repository at https://github.com/TurkuNLP/FIN-bench-v2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.12777", "title": "State over Tokens: Characterizing the Role of Reasoning Tokens", "authors": [ "Mosh Levy", "Zohar Elyoseph", "Shauli Ravfogel", "Yoav Goldberg" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) can generate reasoning tokens before their final answer to boost performance on complex tasks. While these sequences seem like human thought processes, empirical evidence reveals that they are not a faithful explanation of the model's actual reasoning process. To address this gap between appearance and function, we introduce the State over Tokens (SoT) conceptual framework. SoT reframes reasoning tokens not as a linguistic narrative, but as an externalized computational state -- the sole persistent information carrier across the model's stateless generation cycles. This explains how the tokens can drive correct reasoning without being a faithful explanation when read as text and surfaces previously overlooked research questions on these tokens. We argue that to truly understand the process that LLMs do, research must move beyond reading the reasoning tokens as text and focus on decoding them as state.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.12196", "title": "AutoMV: An Automatic Multi-Agent System for Music Video Generation", "authors": [ "Xiaoxuan Tang", "Xinping Lei", "Chaoran Zhu", "Shiyun Chen", "Ruibin Yuan", "Yizhi Li", "Changjae Oh", "Ge Zhang", "Wenhao Huang", "Emmanouil Benetos", "Yang Liu", "Jiaheng Liu", "Yinghao Ma" ], "github": "", "project_page": "", "abstract": "Music-to-Video (M2V) generation for full-length songs faces significant challenges. Existing methods produce short, disjointed clips, failing to align visuals with musical structure, beats, or lyrics, and lack temporal consistency. We propose AutoMV, a multi-agent system that generates full music videos (MVs) directly from a song. AutoMV first applies music processing tools to extract musical attributes, such as structure, vocal tracks, and time-aligned lyrics, and constructs these features as contextual inputs for following agents. The screenwriter Agent and director Agent then use this information to design short script, define character profiles in a shared external bank, and specify camera instructions. Subsequently, these agents call the image generator for keyframes and different video generators for \"story\" or \"singer\" scenes. A Verifier Agent evaluates their output, enabling multi-agent collaboration to produce a coherent longform MV. To evaluate M2V generation, we further propose a benchmark with four high-level categories (Music Content, Technical, Post-production, Art) and twelve ine-grained criteria. This benchmark was applied to compare commercial products, AutoMV, and human-directed MVs with expert human raters: AutoMV outperforms current baselines significantly across all four categories, narrowing the gap to professional MVs. Finally, we investigate using large multimodal models as automatic MV judges; while promising, they still lag behind human expert, highlighting room for future work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.11470", "title": "Rethinking Expert Trajectory Utilization in LLM Post-training", "authors": [ "Bowen Ding", "Yuhan Chen", "Jiayang Lv", "Jiyao Yuan", "Qi Zhu", "Shuangshuang Tian", "Dantong Zhu", "Futing Wang", "Heyuan Deng", "Fei Mi", "Lifeng Shang", "Tao Lin" ], "github": "", "project_page": "", "abstract": "While effective post-training integrates Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), the optimal mechanism for utilizing expert trajectories remains unresolved. We propose the Plasticity-Ceiling Framework to theoretically ground this landscape, decomposing performance into foundational SFT performance and the subsequent RL plasticity. Through extensive benchmarking, we establish the Sequential SFT-then-RL pipeline as the superior standard, overcoming the stability deficits of synchronized approaches. Furthermore, we derive precise scaling guidelines: (1) Transitioning to RL at the SFT Stable or Mild Overfitting Sub-phase maximizes the final ceiling by securing foundational SFT performance without compromising RL plasticity; (2) Refuting ``Less is More'' in the context of SFT-then-RL scaling, we demonstrate that Data Scale determines the primary post-training potential, while Trajectory Difficulty acts as a performance multiplier; and (3) Identifying that the Minimum SFT Validation Loss serves as a robust indicator for selecting the expert trajectories that maximize the final performance ceiling. Our findings provide actionable guidelines for maximizing the value extracted from expert trajectories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.10927", "title": "FoundationMotion: Auto-Labeling and Reasoning about Spatial Movement in Videos", "authors": [ "Yulu Gan", "Ligeng Zhu", "Dandan Shan", "Baifeng Shi", "Hongxu Yin", "Boris Ivanovic", "Song Han", "Trevor Darrell", "Jitendra Malik", "Marco Pavone", "Boyi Li" ], "github": "", "project_page": "", "abstract": "Motion understanding is fundamental to physical reasoning, enabling models to infer dynamics and predict future states. However, state-of-the-art models still struggle on recent motion benchmarks, primarily due to the scarcity of large-scale, fine-grained motion datasets. Existing motion datasets are often constructed from costly manual annotation, severely limiting scalability. To address this challenge, we introduce FoundationMotion, a fully automated data curation pipeline that constructs large-scale motion datasets. Our approach first detects and tracks objects in videos to extract their trajectories, then leverages these trajectories and video frames with Large Language Models (LLMs) to generate fine-grained captions and diverse question-answer pairs about motion and spatial reasoning. Using datasets produced by this pipeline, we fine-tune open-source models including NVILA-Video-15B and Qwen2.5-7B, achieving substantial improvements in motion understanding without compromising performance on other tasks. Notably, our models outperform strong closed-source baselines like Gemini-2.5 Flash and large open-source models such as Qwen2.5-VL-72B across diverse motion understanding datasets and benchmarks. FoundationMotion thus provides a scalable solution for curating fine-grained motion datasets that enable effective fine-tuning of diverse models to enhance motion understanding and spatial reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.08405", "title": "Learning Robot Manipulation from Audio World Models", "authors": [ "Fan Zhang", "Michael Gienger" ], "github": "", "project_page": "", "abstract": "World models have demonstrated impressive performance on robotic learning tasks. Many such tasks inherently demand multimodal reasoning; for example, filling a bottle with water will lead to visual information alone being ambiguous or incomplete, thereby requiring reasoning over the temporal evolution of audio, accounting for its underlying physical properties and pitch patterns. In this paper, we propose a generative latent flow matching model to anticipate future audio observations, enabling the system to reason about long-term consequences when integrated into a robot policy. We demonstrate the superior capabilities of our system through two manipulation tasks that require perceiving in-the-wild audio or music signals, compared to methods without future lookahead. We further emphasize that successful robot action learning for these tasks relies not merely on multi-modal input, but critically on the accurate prediction of future audio states that embody intrinsic rhythmic patterns.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.08400", "title": "Towards Visual Re-Identification of Fish using Fine-Grained Classification for Electronic Monitoring in Fisheries", "authors": [ "Samitha Nuwan Thilakarathna", "Ercan Avsar", "Martin Mathias Nielsen", "Malte Pedersen" ], "github": "https://github.com/msamdk/Fish_Re_Identification.git", "project_page": "", "abstract": "Accurate fisheries data are crucial for effective and sustainable marine resource management. With the recent adoption of Electronic Monitoring (EM) systems, more video data is now being collected than can be feasibly reviewed manually. This paper addresses this challenge by developing an optimized deep learning pipeline for automated fish re-identification (Re-ID) using the novel AutoFish dataset, which simulates EM systems with conveyor belts with six similarly looking fish species. We demonstrate that key Re-ID metrics (R1 and mAP@k) are substantially improved by using hard triplet mining in conjunction with a custom image transformation pipeline that includes dataset-specific normalization. By employing these strategies, we demonstrate that the Vision Transformer-based Swin-T architecture consistently outperforms the Convolutional Neural Network-based ResNet-50, achieving peak performance of 41.65% mAP@k and 90.43% Rank-1 accuracy. An in-depth analysis reveals that the primary challenge is distinguishing visually similar individuals of the same species (Intra-species errors), where viewpoint inconsistency proves significantly more detrimental than partial occlusion. The source code and documentation are available at: https://github.com/msamdk/Fish_Re_Identification.git", "github_manual": "", "github_regex": "https://github.com/msamdk/Fish_Re_Identification.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.12768", "title": "CoRe3D: Collaborative Reasoning as a Foundation for 3D Intelligence", "authors": [ "Tianjiao Yu", "Xinzhuo Li", "Yifan Shen", "Yuanzhe Liu", "Ismini Lourentzou" ], "github": "", "project_page": "", "abstract": "Recent advances in large multimodal models suggest that explicit reasoning mechanisms play a critical role in improving model reliability, interpretability, and cross-modal alignment. While such reasoning-centric approaches have been proven effective in language and vision tasks, their extension to 3D remains underdeveloped. CoRe3D introduces a unified 3D understanding and generation reasoning framework that jointly operates over semantic and spatial abstractions, enabling high-level intent inferred from language to directly guide low-level 3D content formation. Central to this design is a spatially grounded reasoning representation that decomposes 3D latent space into localized regions, allowing the model to reason over geometry in a compositional and procedural manner. By tightly coupling semantic chain-of-thought inference with structured spatial reasoning, CoRe3D produces 3D outputs that exhibit strong local consistency and faithful alignment with linguistic descriptions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-16", "arxiv_id": "2512.09069", "title": "KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification", "authors": [ "Erfan Nourbakhsh", "Nasrin Sanjari", "Ali Nourbakhsh" ], "github": "https://github.com/erfan-nourbakhsh/KD-OCT", "project_page": "", "abstract": "Age-related macular degeneration (AMD) and choroidal neovascularization (CNV)-related conditions are leading causes of vision loss worldwide, with optical coherence tomography (OCT) serving as a cornerstone for early detection and management. However, deploying state-of-the-art deep learning models like ConvNeXtV2-Large in clinical settings is hindered by their computational demands. Therefore, it is desirable to develop efficient models that maintain high diagnostic performance while enabling real-time deployment. In this study, a novel knowledge distillation framework, termed KD-OCT, is proposed to compress a high-performance ConvNeXtV2-Large teacher model, enhanced with advanced augmentations, stochastic weight averaging, and focal loss, into a lightweight EfficientNet-B2 student for classifying normal, drusen, and CNV cases. KD-OCT employs real-time distillation with a combined loss balancing soft teacher knowledge transfer and hard ground-truth supervision. The effectiveness of the proposed method is evaluated on the Noor Eye Hospital (NEH) dataset using patient-level cross-validation. Experimental results demonstrate that KD-OCT outperforms comparable multi-scale or feature-fusion OCT classifiers in efficiency- accuracy balance, achieving near-teacher performance with substantial reductions in model size and inference time. Despite the compression, the student model exceeds most existing frameworks, facilitating edge deployment for AMD screening. Code is available at https://github.com/erfan-nourbakhsh/KD- OCT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/erfan-nourbakhsh/KD-OCT", "github_api_added_by": "auto" }, { "date": "2025-12-16", "arxiv_id": "2512.13683", "title": "I-Scene: 3D Instance Models are Implicit Generalizable Spatial Learners", "authors": [ "Lu Ling", "Yunhao Ge", "Yichen Sheng", "Aniket Bera" ], "github": "", "project_page": "", "abstract": "Generalization remains the central challenge for interactive 3D scene generation. Existing learning-based approaches ground spatial understanding in limited scene dataset, restricting generalization to new layouts. We instead reprogram a pre-trained 3D instance generator to act as a scene level learner, replacing dataset-bounded supervision with model-centric spatial supervision. This reprogramming unlocks the generator transferable spatial knowledge, enabling generalization to unseen layouts and novel object compositions. Remarkably, spatial reasoning still emerges even when the training scenes are randomly composed objects. This demonstrates that the generator's transferable scene prior provides a rich learning signal for inferring proximity, support, and symmetry from purely geometric cues. Replacing widely used canonical space, we instantiate this insight with a view-centric formulation of the scene space, yielding a fully feed-forward, generalizable scene generator that learns spatial relations directly from the instance model. Quantitative and qualitative results show that a 3D instance generator is an implicit spatial learner and reasoner, pointing toward foundation models for interactive 3D scene understanding and generation. Project page: https://luling06.github.io/I-Scene-project/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.12675", "title": "Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling", "authors": [ "Yuran Wang", "Bohan Zeng", "Chengzhuo Tong", "Wenxuan Liu", "Yang Shi", "Xiaochen Ma", "Hao Liang", "Yuanxing Zhang", "Wentao Zhang" ], "github": "https://github.com/Ryann-Ran/Scone", "project_page": "", "abstract": "Subject-driven image generation has advanced from single- to multi-subject composition, while neglecting distinction, the ability to identify and generate the correct subject when inputs contain multiple candidates. This limitation restricts effectiveness in complex, realistic visual settings. We propose Scone, a unified understanding-generation method that integrates composition and distinction. Scone enables the understanding expert to act as a semantic bridge, conveying semantic information and guiding the generation expert to preserve subject identity while minimizing interference. A two-stage training scheme first learns composition, then enhances distinction through semantic alignment and attention-based masking. We also introduce SconeEval, a benchmark for evaluating both composition and distinction across diverse scenarios. Experiments demonstrate that Scone outperforms existing open-source models in composition and distinction tasks on two benchmarks. Our model, benchmark, and training data are available at: https://github.com/Ryann-Ran/Scone.", "github_manual": "", "github_regex": "https://github.com/Ryann-Ran/Scone", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.13961", "title": "Olmo 3", "authors": [ "Team Olmo", "Allyson Ettinger", "Amanda Bertsch", "Bailey Kuehl", "David Graham", "David Heineman", "Dirk Groeneveld", "Faeze Brahman", "Finbarr Timbers", "Hamish Ivison", "Jacob Morrison", "Jake Poznanski", "Kyle Lo", "Luca Soldaini", "Matt Jordan", "Mayee Chen", "Michael Noukhovitch", "Nathan Lambert", "Pete Walsh", "Pradeep Dasigi", "Robert Berry", "Saumya Malik", "Saurabh Shah", "Scott Geng", "Shane Arora", "Shashank Gupta", "Taira Anderson", "Teng Xiao", "Tyler Murray", "Tyler Romero", "Victoria Graf", "Akari Asai", "Akshita Bhagia", "Alexander Wettig", "Alisa Liu", "Aman Rangapur", "Chloe Anastasiades", "Costa Huang", "Dustin Schwenk", "Harsh Trivedi", "Ian Magnusson", "Jaron Lochner", "Jiacheng Liu", "Lester James V. Miranda", "Maarten Sap", "Malia Morgan", "Michael Schmitz", "Michal Guerquin", "Michael Wilson", "Regan Huff", "Ronan Le Bras", "Rui Xin", "Rulin Shao", "Sam Skjonsberg", "Shannon Zejiang Shen", "Shuyue Stella Li", "Tucker Wilde", "Valentina Pyatkin", "Will Merrill", "Yapei Chang", "Yuling Gu", "Zhiyuan Zeng", "Ashish Sabharwal", "Luke Zettlemoyer", "Pang Wei Koh", "Ali Farhadi", "Noah A. Smith", "Hannaneh Hajishirzi" ], "github": "", "project_page": "", "abstract": "We introduce Olmo 3, a family of state-of-the-art, fully-open language models at the 7B and 32B parameter scales. Olmo 3 model construction targets long-context reasoning, function calling, coding, instruction following, general chat, and knowledge recall. This release includes the entire model flow, i.e., the full lifecycle of the family of models, including every stage, checkpoint, data point, and dependency used to build it. Our flagship model, Olmo 3 Think 32B, is the strongest fully-open thinking model released to-date.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14691", "title": "MMGR: Multi-Modal Generative Reasoning", "authors": [ "Zefan Cai", "Haoyi Qiu", "Tianyi Ma", "Haozhe Zhao", "Gengze Zhou", "Kung-Hsiang Huang", "Parisa Kordjamshidi", "Minjia Zhang", "Xiao Wen", "Jiuxiang Gu", "Nanyun Peng", "Junjie Hu" ], "github": "https://github.com/Zefan-Cai/MMGR", "project_page": "", "abstract": "Video foundation models generate visually realistic and temporally coherent content, but their reliability as world simulators depends on whether they capture physical, logical, and spatial constraints. Existing metrics such as Frechet Video Distance (FVD) emphasize perceptual quality and overlook reasoning failures, including violations of causality, physics, and global consistency. We introduce MMGR (Multi-Modal Generative Reasoning Evaluation and Benchmark), a principled evaluation framework based on five reasoning abilities: Physical, Logical, 3D Spatial, 2D Spatial, and Temporal. MMGR evaluates generative reasoning across three domains: Abstract Reasoning (ARC-AGI, Sudoku), Embodied Navigation (real-world 3D navigation and localization), and Physical Commonsense (sports and compositional interactions). MMGR applies fine-grained metrics that require holistic correctness across both video and image generation. We benchmark leading video models (Veo-3, Sora-2, Wan-2.2) and image models (Nano-banana, Nano-banana Pro, GPT-4o-image, Qwen-image), revealing strong performance gaps across domains. Models show moderate success on Physical Commonsense tasks but perform poorly on Abstract Reasoning (below 10 percent accuracy on ARC-AGI) and struggle with long-horizon spatial planning in embodied settings. Our analysis highlights key limitations in current models, including overreliance on perceptual data, weak global state consistency, and objectives that reward visual plausibility over causal correctness. MMGR offers a unified diagnostic benchmark and a path toward reasoning-aware generative world models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Zefan-Cai/MMGR", "github_api_added_by": "auto" }, { "date": "2025-12-17", "arxiv_id": "2512.13660", "title": "RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics", "authors": [ "Enshen Zhou", "Cheng Chi", "Yibo Li", "Jingkun An", "Jiayuan Zhang", "Shanyu Rong", "Yi Han", "Yuheng Ji", "Mengzhen Liu", "Pengwei Wang", "Zhongyuan Wang", "Lu Sheng", "Shanghang Zhang" ], "github": "", "project_page": "", "abstract": "Spatial tracing, as a fundamental embodied interaction ability for robots, is inherently challenging as it requires multi-step metric-grounded reasoning compounded with complex spatial referring and real-world metric measurement. However, existing methods struggle with this compositional task. To this end, we propose RoboTracer, a 3D-aware VLM that first achieves both 3D spatial referring and measuring via a universal spatial encoder and a regression-supervised decoder to enhance scale awareness during supervised fine-tuning (SFT). Moreover, RoboTracer advances multi-step metric-grounded reasoning via reinforcement fine-tuning (RFT) with metric-sensitive process rewards, supervising key intermediate perceptual cues to accurately generate spatial traces. To support SFT and RFT training, we introduce TraceSpatial, a large-scale dataset of 30M QA pairs, spanning outdoor/indoor/tabletop scenes and supporting complex reasoning processes (up to 9 steps). We further present TraceSpatial-Bench, a challenging benchmark filling the gap to evaluate spatial tracing. Experimental results show that RoboTracer surpasses baselines in spatial understanding, measuring, and referring, with an average success rate of 79.1%, and also achieves SOTA performance on TraceSpatial-Bench by a large margin, exceeding Gemini-2.5-Pro by 36% accuracy. Notably, RoboTracer can be integrated with various control policies to execute long-horizon, dynamic tasks across diverse robots (UR5, G1 humanoid) in cluttered real-world scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14614", "title": "WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling", "authors": [ "Wenqiang Sun", "Haiyu Zhang", "Haoyuan Wang", "Junta Wu", "Zehan Wang", "Zhenwei Wang", "Yunhong Wang", "Jun Zhang", "Tengfei Wang", "Chunchao Guo" ], "github": "", "project_page": "", "abstract": "This paper presents WorldPlay, a streaming video diffusion model that enables real-time, interactive world modeling with long-term geometric consistency, resolving the trade-off between speed and memory that limits current methods. WorldPlay draws power from three key innovations. 1) We use a Dual Action Representation to enable robust action control in response to the user's keyboard and mouse inputs. 2) To enforce long-term consistency, our Reconstituted Context Memory dynamically rebuilds context from past frames and uses temporal reframing to keep geometrically important but long-past frames accessible, effectively alleviating memory attenuation. 3) We also propose Context Forcing, a novel distillation method designed for memory-aware model. Aligning memory context between the teacher and student preserves the student's capacity to use long-range information, enabling real-time speeds while preventing error drift. Taken together, WorldPlay generates long-horizon streaming 720p video at 24 FPS with superior consistency, comparing favorably with existing techniques and showing strong generalization across diverse scenes. Project page and online demo can be found: https://3d-models.hunyuan.tencent.com/world/ and https://3d.hunyuan.tencent.com/sceneTo3D.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14531", "title": "VersatileFFN: Achieving Parameter Efficiency in LLMs via Adaptive Wide-and-Deep Reuse", "authors": [ "Ying Nie", "Kai Han", "Hongguang Li", "Hang Zhou", "Tianyu Guo", "Enhua Wu", "Xinghao Chen", "Yunhe Wang" ], "github": "https://github.com/huawei-noah/noah-research/tree/master/VersatileFFN", "project_page": "", "abstract": "The rapid scaling of Large Language Models (LLMs) has achieved remarkable performance, but it also leads to prohibitive memory costs. Existing parameter-efficient approaches such as pruning and quantization mainly compress pretrained models without enhancing architectural capacity, thereby hitting the representational ceiling of the base model. In this work, we propose VersatileFFN, a novel feed-forward network (FFN) that enables flexible reuse of parameters in both width and depth dimensions within a fixed parameter budget. Inspired by the dual-process theory of cognition, VersatileFFN comprises two adaptive pathways: a width-versatile path that generates a mixture of sub-experts from a single shared FFN, mimicking sparse expert routing without increasing parameters, and a depth-versatile path that recursively applies the same FFN to emulate deeper processing for complex tokens. A difficulty-aware gating dynamically balances the two pathways, steering \"easy\" tokens through the efficient width-wise route and allocating deeper iterative refinement to \"hard\" tokens. Crucially, both pathways reuse the same parameters, so all additional capacity comes from computation rather than memory. Experiments across diverse benchmarks and model scales demonstrate the effectiveness of the method. The code will be available at https://github.com/huawei-noah/noah-research/tree/master/VersatileFFN.", "github_manual": "", "github_regex": "https://github.com/huawei-noah/noah-research/tree/master/VersatileFFN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14699", "title": "MemFlow: Flowing Adaptive Memory for Consistent and Efficient Long Video Narratives", "authors": [ "Sihui Ji", "Xi Chen", "Shuai Yang", "Xin Tao", "Pengfei Wan", "Hengshuang Zhao" ], "github": "https://github.com/KlingTeam/MemFlow", "project_page": "", "abstract": "The core challenge for streaming video generation is maintaining the content consistency in long context, which poses high requirement for the memory design. Most existing solutions maintain the memory by compressing historical frames with predefined strategies. However, different to-generate video chunks should refer to different historical cues, which is hard to satisfy with fixed strategies. In this work, we propose MemFlow to address this problem. Specifically, before generating the coming chunk, we dynamically update the memory bank by retrieving the most relevant historical frames with the text prompt of this chunk. This design enables narrative coherence even if new event happens or scenario switches in future frames. In addition, during generation, we only activate the most relevant tokens in the memory bank for each query in the attention layers, which effectively guarantees the generation efficiency. In this way, MemFlow achieves outstanding long-context consistency with negligible computation burden (7.9% speed reduction compared with the memory-free baseline) and keeps the compatibility with any streaming video generation model with KV cache.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KlingTeam/MemFlow", "github_api_added_by": "auto" }, { "date": "2025-12-17", "arxiv_id": "2512.14503", "title": "RecGPT-V2 Technical Report", "authors": [ "Chao Yi", "Dian Chen", "Gaoyang Guo", "Jiakai Tang", "Jian Wu", "Jing Yu", "Mao Zhang", "Wen Chen", "Wenjun Yang", "Yujie Luo", "Yuning Jiang", "Zhujin Gao", "Bo Zheng", "Binbin Cao", "Changfa Wu", "Dixuan Wang", "Han Wu", "Haoyi Hu", "Kewei Zhu", "Lang Tian", "Lin Yang", "Qiqi Huang", "Siqi Yang", "Wenbo Su", "Xiaoxiao He", "Xin Tong", "Xu Chen", "Xunke Xi", "Xiaowei Huang", "Yaxuan Wu", "Yeqiu Yang", "Yi Hu", "Yujin Yuan", "Yuliang Yan", "Zile Zhou" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable potential in transforming recommender systems from implicit behavioral pattern matching to explicit intent reasoning. While RecGPT-V1 successfully pioneered this paradigm by integrating LLM-based reasoning into user interest mining and item tag prediction, it suffers from four fundamental limitations: (1) computational inefficiency and cognitive redundancy across multiple reasoning routes; (2) insufficient explanation diversity in fixed-template generation; (3) limited generalization under supervised learning paradigms; and (4) simplistic outcome-focused evaluation that fails to match human standards. To address these challenges, we present RecGPT-V2 with four key innovations. First, a Hierarchical Multi-Agent System restructures intent reasoning through coordinated collaboration, eliminating cognitive duplication while enabling diverse intent coverage. Combined with Hybrid Representation Inference that compresses user-behavior contexts, our framework reduces GPU consumption by 60% and improves exclusive recall from 9.39% to 10.99%. Second, a Meta-Prompting framework dynamically generates contextually adaptive prompts, improving explanation diversity by +7.3%. Third, constrained reinforcement learning mitigates multi-reward conflicts, achieving +24.1% improvement in tag prediction and +13.0% in explanation acceptance. Fourth, an Agent-as-a-Judge framework decomposes assessment into multi-step reasoning, improving human preference alignment. Online A/B tests on Taobao demonstrate significant improvements: +2.98% CTR, +3.71% IPV, +2.19% TV, and +11.46% NER. RecGPT-V2 establishes both the technical feasibility and commercial viability of deploying LLM-powered intent reasoning at scale, bridging the gap between cognitive exploration and industrial utility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14442", "title": "A4-Agent: An Agentic Framework for Zero-Shot Affordance Reasoning", "authors": [ "Zixin Zhang", "Kanghao Chen", "Hanqing Wang", "Hongfei Zhang", "Harold Haodong Chen", "Chenfei Liao", "Litao Guo", "Ying-Cong Chen" ], "github": "https://github.com/EnVision-Research/A4-Agent", "project_page": "", "abstract": "Affordance prediction, which identifies interaction regions on objects based on language instructions, is critical for embodied AI. Prevailing end-to-end models couple high-level reasoning and low-level grounding into a single monolithic pipeline and rely on training over annotated datasets, which leads to poor generalization on novel objects and unseen environments. In this paper, we move beyond this paradigm by proposing A4-Agent, a training-free agentic framework that decouples affordance prediction into a three-stage pipeline. Our framework coordinates specialized foundation models at test time: (1) a Dreamer that employs generative models to visualize how an interaction would look; (2) a Thinker that utilizes large vision-language models to decide what object part to interact with; and (3) a Spotter that orchestrates vision foundation models to precisely locate where the interaction area is. By leveraging the complementary strengths of pre-trained models without any task-specific fine-tuning, our zero-shot framework significantly outperforms state-of-the-art supervised methods across multiple benchmarks and demonstrates robust generalization to real-world settings.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EnVision-Research/A4-Agent", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.13678", "title": "Feedforward 3D Editing via Text-Steerable Image-to-3D", "authors": [ "Ziqi Ma", "Hongqiao Chen", "Yisong Yue", "Georgia Gkioxari" ], "github": "https://github.com/ziqi-ma/Steer3D", "project_page": "", "abstract": "Recent progress in image-to-3D has opened up immense possibilities for design, AR/VR, and robotics. However, to use AI-generated 3D assets in real applications, a critical requirement is the capability to edit them easily. We present a feedforward method, Steer3D, to add text steerability to image-to-3D models, which enables editing of generated 3D assets with language. Our approach is inspired by ControlNet, which we adapt to image-to-3D generation to enable text steering directly in a forward pass. We build a scalable data engine for automatic data generation, and develop a two-stage training recipe based on flow-matching training and Direct Preference Optimization (DPO). Compared to competing methods, Steer3D more faithfully follows the language instruction and maintains better consistency with the original 3D asset, while being 2.4x to 28.5x faster. Steer3D demonstrates that it is possible to add a new modality (text) to steer the generation of pretrained image-to-3D generative models with 100k data. Project website: https://glab-caltech.github.io/steer3d/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ziqi-ma/Steer3D", "github_api_added_by": "auto" }, { "date": "2025-12-17", "arxiv_id": "2512.14067", "title": "Efficient-DLM: From Autoregressive to Diffusion Language Models, and Beyond in Speed", "authors": [ "Yonggan Fu", "Lexington Whalen", "Zhifan Ye", "Xin Dong", "Shizhe Diao", "Jingyu Liu", "Chengyue Wu", "Hao Zhang", "Enze Xie", "Song Han", "Maksim Khadkevich", "Jan Kautz", "Yingyan Celine Lin", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "Diffusion language models (dLMs) have emerged as a promising paradigm that enables parallel, non-autoregressive generation, but their learning efficiency lags behind that of autoregressive (AR) language models when trained from scratch. To this end, we study AR-to-dLM conversion to transform pretrained AR models into efficient dLMs that excel in speed while preserving AR models' task accuracy. We achieve this by identifying limitations in the attention patterns and objectives of existing AR-to-dLM methods and then proposing principles and methodologies for more effective AR-to-dLM conversion. Specifically, we first systematically compare different attention patterns and find that maintaining pretrained AR weight distributions is critical for effective AR-to-dLM conversion. As such, we introduce a continuous pretraining scheme with a block-wise attention pattern, which remains causal across blocks while enabling bidirectional modeling within each block. We find that this approach can better preserve pretrained AR models' weight distributions than fully bidirectional modeling, in addition to its known benefit of enabling KV caching, and leads to a win-win in accuracy and efficiency. Second, to mitigate the training-test gap in mask token distributions (uniform vs. highly left-to-right), we propose a position-dependent token masking strategy that assigns higher masking probabilities to later tokens during training to better mimic test-time behavior. Leveraging this framework, we conduct extensive studies of dLMs' attention patterns, training dynamics, and other design choices, providing actionable insights into scalable AR-to-dLM conversion. These studies lead to the Efficient-DLM family, which outperforms state-of-the-art AR models and dLMs, e.g., our Efficient-DLM 8B achieves +5.4%/+2.7% higher accuracy with 4.5x/2.7x higher throughput compared to Dream 7B and Qwen3 4B, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.13655", "title": "Comparative Analysis of LLM Abliteration Methods: A Cross-Architecture Evaluation", "authors": [ "Richard J. Young" ], "github": "", "project_page": "", "abstract": "Safety alignment mechanisms in large language models prevent responses to harmful queries through learned refusal behavior, yet these same mechanisms impede legitimate research applications including cognitive modeling, adversarial testing, and security analysis. While abliteration techniques enable surgical removal of refusal representations through directional orthogonalization, the relative effectiveness of available implementations remains uncharacterized. This study evaluates four abliteration tools (Heretic, DECCP, ErisForge, FailSpy) across sixteen instruction-tuned models (7B-14B parameters), reporting tool compatibility on all 16 models and quantitative metrics on subsets dictated by tool support. Single-pass methods demonstrated superior capability preservation on the benchmarked subset (avg GSM8K change across three models: ErisForge -0.28 pp; DECCP -0.13 pp), while Bayesian-optimized abliteration produced variable distribution shift (KL divergence: 0.043-1.646) with model-dependent capability impact. These findings provide researchers with evidence-based selection criteria for abliteration tool deployment across diverse model architectures. The principal finding indicates that mathematical reasoning capabilities exhibit the highest sensitivity to abliteration interventions, with GSM8K change ranging from +1.51 pp to -18.81 pp (-26.5% relative) depending on tool selection and model architecture.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.13303", "title": "ShowTable: Unlocking Creative Table Visualization with Collaborative Reflection and Refinement", "authors": [ "Zhihang Liu", "Xiaoyi Bao", "Pandeng Li", "Junjie Zhou", "Zhaohe Liao", "Yefei He", "Kaixun Jiang", "Chen-Wei Xie", "Yun Zheng", "Hongtao Xie" ], "github": "", "project_page": "", "abstract": "While existing generation and unified models excel at general image generation, they struggle with tasks requiring deep reasoning, planning, and precise data-to-visual mapping abilities beyond general scenarios. To push beyond the existing limitations, we introduce a new and challenging task: creative table visualization, requiring the model to generate an infographic that faithfully and aesthetically visualizes the data from a given table. To address this challenge, we propose ShowTable, a pipeline that synergizes MLLMs with diffusion models via a progressive self-correcting process. The MLLM acts as the central orchestrator for reasoning the visual plan and judging visual errors to provide refined instructions, the diffusion execute the commands from MLLM, achieving high-fidelity results. To support this task and our pipeline, we introduce three automated data construction pipelines for training different modules. Furthermore, we introduce TableVisBench, a new benchmark with 800 challenging instances across 5 evaluation dimensions, to assess performance on this task. Experiments demonstrate that our pipeline, instantiated with different models, significantly outperforms baselines, highlighting its effective multi-modal reasoning, generation, and error correction capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14336", "title": "Vector Prism: Animating Vector Graphics by Stratifying Semantic Structure", "authors": [ "Jooyeol Yun", "Jaegul Choo" ], "github": "", "project_page": "", "abstract": "Scalable Vector Graphics (SVG) are central to modern web design, and the demand to animate them continues to grow as web environments become increasingly dynamic. Yet automating the animation of vector graphics remains challenging for vision-language models (VLMs) despite recent progress in code generation and motion planning. VLMs routinely mis-handle SVGs, since visually coherent parts are often fragmented into low-level shapes that offer little guidance of which elements should move together. In this paper, we introduce a framework that recovers the semantic structure required for reliable SVG animation and reveals the missing layer that current VLM systems overlook. This is achieved through a statistical aggregation of multiple weak part predictions, allowing the system to stably infer semantics from noisy predictions. By reorganizing SVGs into semantic groups, our approach enables VLMs to produce animations with far greater coherence. Our experiments demonstrate substantial gains over existing approaches, suggesting that semantic recovery is the key step that unlocks robust SVG animation and supports more interpretable interactions between VLMs and vector graphics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14284", "title": "SS4D: Native 4D Generative Model via Structured Spacetime Latents", "authors": [ "Zhibing Li", "Mengchen Zhang", "Tong Wu", "Jing Tan", "Jiaqi Wang", "Dahua Lin" ], "github": "", "project_page": "", "abstract": "We present SS4D, a native 4D generative model that synthesizes dynamic 3D objects directly from monocular video. Unlike prior approaches that construct 4D representations by optimizing over 3D or video generative models, we train a generator directly on 4D data, achieving high fidelity, temporal coherence, and structural consistency. At the core of our method is a compressed set of structured spacetime latents. Specifically, (1) To address the scarcity of 4D training data, we build on a pre-trained single-image-to-3D model, preserving strong spatial consistency. (2) Temporal consistency is enforced by introducing dedicated temporal layers that reason across frames. (3) To support efficient training and inference over long video sequences, we compress the latent sequence along the temporal axis using factorized 4D convolutions and temporal downsampling blocks. In addition, we employ a carefully designed training strategy to enhance robustness against occlusion", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14008", "title": "Sparse-LaViDa: Sparse Multimodal Discrete Diffusion Language Models", "authors": [ "Shufan Li", "Jiuxiang Gu", "Kangning Liu", "Zhe Lin", "Zijun Wei", "Aditya Grover", "Jason Kuen" ], "github": "", "project_page": "", "abstract": "Masked Discrete Diffusion Models (MDMs) have achieved strong performance across a wide range of multimodal tasks, including image understanding, generation, and editing. However, their inference speed remains suboptimal due to the need to repeatedly process redundant masked tokens at every sampling step. In this work, we propose Sparse-LaViDa, a novel modeling framework that dynamically truncates unnecessary masked tokens at each inference step to accelerate MDM sampling. To preserve generation quality, we introduce specialized register tokens that serve as compact representations for the truncated tokens. Furthermore, to ensure consistency between training and inference, we design a specialized attention mask that faithfully matches the truncated sampling procedure during training. Built upon the state-of-the-art unified MDM LaViDa-O, Sparse-LaViDa achieves up to a 2x speedup across diverse tasks including text-to-image generation, image editing, and mathematical reasoning, while maintaining generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14666", "title": "EVOLVE-VLA: Test-Time Training from Environment Feedback for Vision-Language-Action Models", "authors": [ "Zechen Bai", "Chen Gao", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Achieving truly adaptive embodied intelligence requires agents that learn not just by imitating static demonstrations, but by continuously improving through environmental interaction, which is akin to how humans master skills through practice. Vision-Language-Action (VLA) models have advanced robotic manipulation by leveraging large language models, yet remain fundamentally limited by Supervised Finetuning (SFT): requiring hundreds of demonstrations per task, rigidly memorizing trajectories, and failing to adapt when deployment conditions deviate from training. We introduce EVOLVE-VLA, a test-time training framework enabling VLAs to continuously adapt through environment interaction with minimal or zero task-specific demonstrations. The key technical challenge is replacing oracle reward signals (unavailable at test time) with autonomous feedback. We address this through a learned progress estimator providing dense feedback, and critically, we design our framework to ``tame'' this inherently noisy signal via two mechanisms: (1) an accumulative progress estimation mechanism smoothing noisy point-wise estimates, and (2) a progressive horizon extension strategy enabling gradual policy evolution. EVOLVE-VLA achieves substantial gains: +8.6\\% on long-horizon tasks, +22.0\\% in 1-shot learning, and enables cross-task generalization -- achieving 20.8\\% success on unseen tasks without task-specific demonstrations training (vs. 0\\% for pure SFT). Qualitative analysis reveals emergent capabilities absent in demonstrations, including error recovery and novel strategies. This work represents a critical step toward VLAs that truly learn and adapt, moving beyond static imitation toward continuous self-improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14391", "title": "RePo: Language Models with Context Re-Positioning", "authors": [ "Huayang Li", "Tianyu Zhao", "Richard Sproat" ], "github": "https://github.com/SakanaAI/repo", "project_page": "", "abstract": "In-context learning is fundamental to modern Large Language Models (LLMs); however, prevailing architectures impose a rigid and fixed contextual structure by assigning linear or constant positional indices. Drawing on Cognitive Load Theory (CLT), we argue that this uninformative structure increases extraneous cognitive load, consuming finite working memory capacity that should be allocated to deep reasoning and attention allocation. To address this, we propose RePo, a novel mechanism that reduces extraneous load via context re-positioning. Unlike standard approaches, RePo utilizes a differentiable module, f_\u03c6, to assign token positions that capture contextual dependencies, rather than replying on pre-defined integer range. By continually pre-training on the OLMo-2 1B backbone, we demonstrate that RePo significantly enhances performance on tasks involving noisy contexts, structured data, and longer context length, while maintaining competitive performance on general short-context tasks. Detailed analysis reveals that RePo successfully allocate higher attention to distant but relevant information, assign positions in dense and non-linear space, and capture the intrinsic structure of the input context. Our code is available at https://github.com/SakanaAI/repo.", "github_manual": "", "github_regex": "https://github.com/SakanaAI/repo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14014", "title": "MobileWorldBench: Towards Semantic World Modeling For Mobile Agents", "authors": [ "Shufan Li", "Konstantinos Kallidromitis", "Akash Gokul", "Yusuke Kato", "Kazuki Kozuka", "Aditya Grover" ], "github": "https://github.com/jacklishufan/MobileWorld", "project_page": "", "abstract": "World models have shown great utility in improving the task performance of embodied agents. While prior work largely focuses on pixel-space world models, these approaches face practical limitations in GUI settings, where predicting complex visual elements in future states is often difficult. In this work, we explore an alternative formulation of world modeling for GUI agents, where state transitions are described in natural language rather than predicting raw pixels. First, we introduce MobileWorldBench, a benchmark that evaluates the ability of vision-language models (VLMs) to function as world models for mobile GUI agents. Second, we release MobileWorld, a large-scale dataset consisting of 1.4M samples, that significantly improves the world modeling capabilities of VLMs. Finally, we propose a novel framework that integrates VLM world models into the planning framework of mobile agents, demonstrating that semantic world models can directly benefit mobile agents by improving task success rates. The code and dataset is available at https://github.com/jacklishufan/MobileWorld", "github_manual": "", "github_regex": "https://github.com/jacklishufan/MobileWorld", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14051", "title": "OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Value", "authors": [ "Mengzhang Cai", "Xin Gao", "Yu Li", "Honglin Lin", "Zheng Liu", "Zhuoshi Pan", "Qizhi Pei", "Xiaoran Shang", "Mengyuan Sun", "Zinan Tang", "Xiaoyang Wang", "Zhanping Zhong", "Yun Zhu", "Dahua Lin", "Conghui He", "Lijun Wu" ], "github": "", "project_page": "", "abstract": "The rapid evolution of Large Language Models (LLMs) is predicated on the quality and diversity of post-training datasets. However, a critical dichotomy persists: while models are rigorously benchmarked, the data fueling them remains a black box--characterized by opaque composition, uncertain provenance, and a lack of systematic evaluation. This opacity hinders reproducibility and obscures the causal link between data characteristics and model behaviors. To bridge this gap, we introduce OpenDataArena (ODA), a holistic and open platform designed to benchmark the intrinsic value of post-training data. ODA establishes a comprehensive ecosystem comprising four key pillars: (i) a unified training-evaluation pipeline that ensures fair, open comparisons across diverse models (e.g., Llama, Qwen) and domains; (ii) a multi-dimensional scoring framework that profiles data quality along tens of distinct axes; (iii) an interactive data lineage explorer to visualize dataset genealogy and dissect component sources; and (iv) a fully open-source toolkit for training, evaluation, and scoring to foster data research. Extensive experiments on ODA--covering over 120 training datasets across multiple domains on 22 benchmarks, validated by more than 600 training runs and 40 million processed data points--reveal non-trivial insights. Our analysis uncovers the inherent trade-offs between data complexity and task performance, identifies redundancy in popular benchmarks through lineage tracing, and maps the genealogical relationships across datasets. We release all results, tools, and configurations to democratize access to high-quality data evaluation. Rather than merely expanding a leaderboard, ODA envisions a shift from trial-and-error data curation to a principled science of Data-Centric AI, paving the way for rigorous studies on data mixing laws and the strategic composition of foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14550", "title": "TAT: Task-Adaptive Transformer for All-in-One Medical Image Restoration", "authors": [ "Zhiwen Yang", "Jiaju Zhang", "Yang Yi", "Jian Liang", "Bingzheng Wei", "Yan Xu" ], "github": "https://github.com/Yaziwel/TAT", "project_page": "", "abstract": "Medical image restoration (MedIR) aims to recover high-quality medical images from their low-quality counterparts. Recent advancements in MedIR have focused on All-in-One models capable of simultaneously addressing multiple different MedIR tasks. However, due to significant differences in both modality and degradation types, using a shared model for these diverse tasks requires careful consideration of two critical inter-task relationships: task interference, which occurs when conflicting gradient update directions arise across tasks on the same parameter, and task imbalance, which refers to uneven optimization caused by varying learning difficulties inherent to each task. To address these challenges, we propose a task-adaptive Transformer (TAT), a novel framework that dynamically adapts to different tasks through two key innovations. First, a task-adaptive weight generation strategy is introduced to mitigate task interference by generating task-specific weight parameters for each task, thereby eliminating potential gradient conflicts on shared weight parameters. Second, a task-adaptive loss balancing strategy is introduced to dynamically adjust loss weights based on task-specific learning difficulties, preventing task domination or undertraining. Extensive experiments demonstrate that our proposed TAT achieves state-of-the-art performance in three MedIR tasks--PET synthesis, CT denoising, and MRI super-resolution--both in task-specific and All-in-One settings. Code is available at https://github.com/Yaziwel/TAT.", "github_manual": "", "github_regex": "https://github.com/Yaziwel/TAT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14273", "title": "Zoom-Zero: Reinforced Coarse-to-Fine Video Understanding via Temporal Zoom-in", "authors": [ "Xiaoqian Shen", "Min-Hung Chen", "Yu-Chiang Frank Wang", "Mohamed Elhoseiny", "Ryo Hachiuma" ], "github": "", "project_page": "", "abstract": "Grounded video question answering (GVQA) aims to localize relevant temporal segments in videos and generate accurate answers to a given question; however, large video-language models (LVLMs) exhibit limited temporal awareness. Although existing approaches based on Group Relative Policy Optimization (GRPO) attempt to improve temporal grounding, they still struggle to faithfully ground their answers in the relevant video evidence, leading to temporal mislocalization and hallucinations. In this work, we present Zoom-Zero, a coarse-to-fine framework that first localizes query-relevant segments and then temporally zooms into the most salient frames for finer-grained visual verification. Our method addresses the limits of GRPO for the GVQA task with two key innovations: (i) a zoom-in accuracy reward that validates the fidelity of temporal grounding prediction and facilitates fine-grained visual verification on grounded frames; (ii) token-selective credit assignment, which attributes rewards to the tokens responsible for temporal localization or answer generation, mitigating GRPO's issue in handling multi-faceted reward signals. Our proposed method advances grounded video question answering, improving temporal grounding by 5.2\\% on NExT-GQA and 4.6\\% on ReXTime, while also enhancing average answer accuracy by 2.4\\%. Additionally, the coarse-to-fine zoom-in during inference further benefits long-form video understanding by preserving critical visual details without compromising global context, yielding an average improvement of 6.4\\% on long-video benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.13607", "title": "Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models", "authors": [ "Boxin Wang", "Chankyu Lee", "Nayeon Lee", "Sheng-Chieh Lin", "Wenliang Dai", "Yang Chen", "Yangyi Chen", "Zhuolin Yang", "Zihan Liu", "Mohammad Shoeybi", "Bryan Catanzaro", "Wei Ping" ], "github": "", "project_page": "", "abstract": "Building general-purpose reasoning models with reinforcement learning (RL) entails substantial cross-domain heterogeneity, including large variation in inference-time response lengths and verification latency. Such variability complicates the RL infrastructure, slows training, and makes training curriculum (e.g., response length extension) and hyperparameter selection challenging. In this work, we propose cascaded domain-wise reinforcement learning (Cascade RL) to develop general-purpose reasoning models, Nemotron-Cascade, capable of operating in both instruct and deep thinking modes. Departing from conventional approaches that blend heterogeneous prompts from different domains, Cascade RL orchestrates sequential, domain-wise RL, reducing engineering complexity and delivering state-of-the-art performance across a wide range of benchmarks. Notably, RLHF for alignment, when used as a pre-step, boosts the model's reasoning ability far beyond mere preference optimization, and subsequent domain-wise RLVR stages rarely degrade the benchmark performance attained in earlier domains and may even improve it (see an illustration in Figure 1). Our 14B model, after RL, outperforms its SFT teacher, DeepSeek-R1-0528, on LiveCodeBench v5/v6/Pro and achieves silver-medal performance in the 2025 International Olympiad in Informatics (IOI). We transparently share our training and data recipes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14440", "title": "S2D: Sparse-To-Dense Keymask Distillation for Unsupervised Video Instance Segmentation", "authors": [ "Leon Sick", "Lukas Hoyer", "Dominik Engel", "Pedro Hermosilla", "Timo Ropinski" ], "github": "", "project_page": "", "abstract": "In recent years, the state-of-the-art in unsupervised video instance segmentation has heavily relied on synthetic video data, generated from object-centric image datasets such as ImageNet. However, video synthesis by artificially shifting and scaling image instance masks fails to accurately model realistic motion in videos, such as perspective changes, movement by parts of one or multiple instances, or camera motion. To tackle this issue, we propose an unsupervised video instance segmentation model trained exclusively on real video data. We start from unsupervised instance segmentation masks on individual video frames. However, these single-frame segmentations exhibit temporal noise and their quality varies through the video. Therefore, we establish temporal coherence by identifying high-quality keymasks in the video by leveraging deep motion priors. The sparse keymask pseudo-annotations are then used to train a segmentation model for implicit mask propagation, for which we propose a Sparse-To-Dense Distillation approach aided by a Temporal DropLoss. After training the final model on the resulting dense labelset, our approach outperforms the current state-of-the-art across various benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.10945", "title": "MeViS: A Multi-Modal Dataset for Referring Motion Expression Video Segmentation", "authors": [ "Henghui Ding", "Chang Liu", "Shuting He", "Kaining Ying", "Xudong Jiang", "Chen Change Loy", "Yu-Gang Jiang" ], "github": "", "project_page": "", "abstract": "This paper proposes a large-scale multi-modal dataset for referring motion expression video segmentation, focusing on segmenting and tracking target objects in videos based on language description of objects' motions. Existing referring video segmentation datasets often focus on salient objects and use language expressions rich in static attributes, potentially allowing the target object to be identified in a single frame. Such datasets underemphasize the role of motion in both videos and languages. To explore the feasibility of using motion expressions and motion reasoning clues for pixel-level video understanding, we introduce MeViS, a dataset containing 33,072 human-annotated motion expressions in both text and audio, covering 8,171 objects in 2,006 videos of complex scenarios. We benchmark 15 existing methods across 4 tasks supported by MeViS, including 6 referring video object segmentation (RVOS) methods, 3 audio-guided video object segmentation (AVOS) methods, 2 referring multi-object tracking (RMOT) methods, and 4 video captioning methods for the newly introduced referring motion expression generation (RMEG) task. The results demonstrate weaknesses and limitations of existing methods in addressing motion expression-guided video understanding. We further analyze the challenges and propose an approach LMPM++ for RVOS/AVOS/RMOT that achieves new state-of-the-art results. Our dataset provides a platform that facilitates the development of motion expression-guided video understanding algorithms in complex video scenes. The proposed MeViS dataset and the method's source code are publicly available at https://henghuiding.com/MeViS/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14697", "title": "Spherical Leech Quantization for Visual Tokenization and Generation", "authors": [ "Yue Zhao", "Hanwen Jiang", "Zhenlin Xu", "Chutong Yang", "Ehsan Adeli", "Philipp Kr\u00e4henb\u00fchl" ], "github": "", "project_page": "", "abstract": "Non-parametric quantization has received much attention due to its efficiency on parameters and scalability to a large codebook. In this paper, we present a unified formulation of different non-parametric quantization methods through the lens of lattice coding. The geometry of lattice codes explains the necessity of auxiliary loss terms when training auto-encoders with certain existing lookup-free quantization variants such as BSQ. As a step forward, we explore a few possible candidates, including random lattices, generalized Fibonacci lattices, and densest sphere packing lattices. Among all, we find the Leech lattice-based quantization method, which is dubbed as Spherical Leech Quantization (\u039b_{24}-SQ), leads to both a simplified training recipe and an improved reconstruction-compression tradeoff thanks to its high symmetry and even distribution on the hypersphere. In image tokenization and compression tasks, this quantization approach achieves better reconstruction quality across all metrics than BSQ, the best prior art, while consuming slightly fewer bits. The improvement also extends to state-of-the-art auto-regressive image generation frameworks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14620", "title": "JMMMU-Pro: Image-based Japanese Multi-discipline Multimodal Understanding Benchmark via Vibe Benchmark Construction", "authors": [ "Atsuyuki Miyai", "Shota Onohara", "Jeonghun Baek", "Kiyoharu Aizawa" ], "github": "", "project_page": "", "abstract": "This paper introduces JMMMU-Pro, an image-based Japanese Multi-discipline Multimodal Understanding Benchmark, and Vibe Benchmark Construction, a scalable construction method. Following the evolution from MMMU to MMMU-Pro, JMMMU-Pro extends JMMMU by composing the question image and question text into a single image, thereby creating a benchmark that requires integrated visual-textual understanding through visual perception. To build JMMMU-Pro, we propose Vibe Benchmark Construction, a methodology in which an image generative model (e.g., Nano Banana Pro) produces candidate visual questions, and humans verify the outputs and, when necessary, regenerate with adjusted prompts to ensure quality. By leveraging Nano Banana Pro's highly realistic image generation capabilities and its ability to embed clean Japanese text, we construct a high-quality benchmark at low cost, covering a wide range of background and layout designs. Experimental results show that all open-source LMMs struggle substantially with JMMMU-Pro, underscoring JMMMU-Pro as an important benchmark for guiding future efforts in the open-source community. We believe that JMMMU-Pro provides a more rigorous evaluation tool for assessing the Japanese capabilities of LMMs and that our Vibe Benchmark Construction also offers an efficient guideline for future development of image-based VQA benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.12980", "title": "Reveal Hidden Pitfalls and Navigate Next Generation of Vector Similarity Search from Task-Centric Views", "authors": [ "Tingyang Chen", "Cong Fu", "Jiahua Wu", "Haotian Wu", "Hua Fan", "Xiangyu Ke", "Yunjun Gao", "Yabo Ni", "Anxiang Zeng" ], "github": "https://github.com/ZJU-DAILY/Iceberg", "project_page": "", "abstract": "Vector Similarity Search (VSS) in high-dimensional spaces is rapidly emerging as core functionality in next-generation database systems for numerous data-intensive services -- from embedding lookups in large language models (LLMs), to semantic information retrieval and recommendation engines. Current benchmarks, however, evaluate VSS primarily on the recall-latency trade-off against a ground truth defined solely by distance metrics, neglecting how retrieval quality ultimately impacts downstream tasks. This disconnect can mislead both academic research and industrial practice. We present Iceberg, a holistic benchmark suite for end-to-end evaluation of VSS methods in realistic application contexts. From a task-centric view, Iceberg uncovers the Information Loss Funnel, which identifies three principal sources of end-to-end performance degradation: (1) Embedding Loss during feature extraction; (2) Metric Misuse, where distances poorly reflect task relevance; (3) Data Distribution Sensitivity, highlighting index robustness across skews and modalities. For a more comprehensive assessment, Iceberg spans eight diverse datasets across key domains such as image classification, face recognition, text retrieval, and recommendation systems. Each dataset, ranging from 1M to 100M vectors, includes rich, task-specific labels and evaluation metrics, enabling assessment of retrieval algorithms within the full application pipeline rather than in isolation. Iceberg benchmarks 13 state-of-the-art VSS methods and re-ranks them based on application-level metrics, revealing substantial deviations from traditional rankings derived purely from recall-latency evaluations. Building on these insights, we define a set of task-centric meta-features and derive an interpretable decision tree to guide practitioners in selecting and tuning VSS methods for their specific workloads.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ZJU-DAILY/Iceberg", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.13525", "title": "Janus: Disaggregating Attention and Experts for Scalable MoE Inference", "authors": [ "Zhexiang Zhang", "Ye Wang", "Xiangyu Wang", "Yumiao Zhao", "Jingzhe Jiang", "Qizhen Weng", "Shaohuai Shi", "Yin Chen", "Minchen Yu" ], "github": "", "project_page": "", "abstract": "Large Mixture-of-Experts (MoE) model inference is challenging due to high resource demands and dynamic workloads. Existing solutions often deploy the entire model as a single monolithic unit, which applies a unified resource configuration to both attention and expert modules despite their different requirements, leading to limited scalability and resource inefficiency. In this paper, we propose Janus, a scalable MoE inference system that disaggregates attention and experts on separate GPU sub-clusters, enabling each module to be managed and scaled independently. Janus incorporates three key designs for efficient, disaggregated MoE inference. First, it proposes an adaptive two-phase communication scheme that exploits intra- and inter-node bandwidth hierarchies for low-latency data exchange. Second, motivated by the memory-bound nature of MoE modules, Janus introduces a lightweight scheduler and implements it as a GPU kernel to balance the number of activated experts across GPUs at minimal overhead, thereby reducing inference latency. Third, Janus performs fine-grained resource management to dynamically adjust expert placement and independently scale attention and MoE resources to improve overall efficiency. Evaluation shows Janus achieves up to 3.9 higher perGPU throughput than state-of-the-art systems while meeting per-token latency requirements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.13281", "title": "Video Reality Test: Can AI-Generated ASMR Videos fool VLMs and Humans?", "authors": [ "Jiaqi Wang", "Weijia Wu", "Yi Zhan", "Rui Zhao", "Ming Hu", "James Cheng", "Wei Liu", "Philip Torr", "Kevin Qinghong Lin" ], "github": "https://github.com/video-reality-test/video-reality-test", "project_page": "", "abstract": "Recent advances in video generation have produced vivid content that are often indistinguishable from real videos, making AI-generated video detection an emerging societal challenge. Prior AIGC detection benchmarks mostly evaluate video without audio, target broad narrative domains, and focus on classification solely. Yet it remains unclear whether state-of-the-art video generation models can produce immersive, audio-paired videos that reliably deceive humans and VLMs. To this end, we introduce Video Reality Test, an ASMR-sourced video benchmark suite for testing perceptual realism under tight audio-visual coupling, featuring the following dimensions: (i) Immersive ASMR video-audio sources. Built on carefully curated real ASMR videos, the benchmark targets fine-grained action-object interactions with diversity across objects, actions, and backgrounds. (ii) Peer-Review evaluation. An adversarial creator-reviewer protocol where video generation models act as creators aiming to fool reviewers, while VLMs serve as reviewers seeking to identify fakeness. Our experimental findings show: The best creator Veo3.1-Fast even fools most VLMs: the strongest reviewer (Gemini 2.5-Pro) achieves only 56\\% accuracy (random 50\\%), far below that of human experts (81.25\\%). Adding audio improves real-fake discrimination, yet superficial cues such as watermarks can still significantly mislead models. These findings delineate the current boundary of video generation realism and expose limitations of VLMs in perceptual fidelity and audio-visual consistency. Our code is available at https://github.com/video-reality-test/video-reality-test.", "github_manual": "", "github_regex": "https://github.com/video-reality-test/video-reality-test", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.13399", "title": "Differentiable Evolutionary Reinforcement Learning", "authors": [ "Sitao Cheng", "Tianle Li", "Xuhan Huang", "Xunjian Yin", "Difan Zou" ], "github": "https://github.com/sitaocheng/DERL", "project_page": "", "abstract": "The design of effective reward functions presents a central and often arduous challenge in reinforcement learning (RL), particularly when developing autonomous agents for complex reasoning tasks. While automated reward optimization approaches exist, they typically rely on derivative-free evolutionary heuristics that treat the reward function as a black box, failing to capture the causal relationship between reward structure and task performance. To bridge this gap, we propose Differentiable Evolutionary Reinforcement Learning (DERL), a bilevel framework that enables the autonomous discovery of optimal reward signals. In DERL, a Meta-Optimizer evolves a reward function (i.e., Meta-Reward) by composing structured atomic primitives, guiding the training of an inner-loop policy. Crucially, unlike previous evolution, DERL is differentiable in its metaoptimization: it treats the inner-loop validation performance as a signal to update the Meta-Optimizer via reinforcement learning. This allows DERL to approximate the \"meta-gradient\" of task success, progressively learning to generate denser and more actionable feedback. We validate DERL across three distinct domains: robotic agent (ALFWorld), scientific simulation (ScienceWorld), and mathematical reasoning (GSM8k, MATH). Experimental results show that DERL achieves state-of-the-art performance on ALFWorld and ScienceWorld, significantly outperforming methods relying on heuristic rewards, especially in out-of-distribution scenarios. Analysis of the evolutionary trajectory demonstrates that DERL successfully captures the intrinsic structure of tasks, enabling selfimproving agent alignment without human intervention.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sitaocheng/DERL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.07328", "title": "ContextAnyone: Context-Aware Diffusion for Character-Consistent Text-to-Video Generation", "authors": [ "Ziyang Mai", "Yu-Wing Tai" ], "github": "https://github.com/ziyang1106/ContextAnyone", "project_page": "", "abstract": "Text-to-video (T2V) generation has advanced rapidly, yet maintaining consistent character identities across scenes remains a major challenge. Existing personalization methods often focus on facial identity but fail to preserve broader contextual cues such as hairstyle, outfit, and body shape, which are critical for visual coherence. We propose ContextAnyone, a context-aware diffusion framework that achieves character-consistent video generation from text and a single reference image. Our method jointly reconstructs the reference image and generates new video frames, enabling the model to fully perceive and utilize reference information. Reference information is effectively integrated into a DiT-based diffusion backbone through a novel Emphasize-Attention module that selectively reinforces reference-aware features and prevents identity drift across frames. A dual-guidance loss combines diffusion and reference reconstruction objectives to enhance appearance fidelity, while the proposed Gap-RoPE positional embedding separates reference and video tokens to stabilize temporal modeling. Experiments demonstrate that ContextAnyone outperforms existing reference-to-video methods in identity consistency and visual quality, generating coherent and context-preserving character videos across diverse motions and scenes. Project page: https://github.com/ziyang1106/ContextAnyone{https://github.com/ziyang1106/ContextAnyone}.", "github_manual": "", "github_regex": "https://github.com/ziyang1106/ContextAnyone", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.13106", "title": "TraPO: A Semi-Supervised Reinforcement Learning Framework for Boosting LLM Reasoning", "authors": [ "Shenzhi Yang", "Guangcheng Zhu", "Xing Zheng", "Yingfan MA", "Zhongqi Chen", "Bowen Song", "Weiqiang Wang", "Junbo Zhao", "Gang Chen", "Haobo Wang" ], "github": "https://github.com/ShenzhiYang2000/TRAPO", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has proven effective in training large reasoning models (LRMs) by leveraging answer-verifiable signals to guide policy optimization, which, however, suffers from high annotation costs. To alleviate this problem, recent work has explored unsupervised RLVR methods that derive rewards solely from the model's internal consistency, such as through entropy and majority voting. While seemingly promising, these methods often suffer from model collapse in the later stages of training, which may arise from the reinforcement of incorrect reasoning patterns in the absence of external supervision. In this work, we investigate a novel semi-supervised RLVR paradigm that utilizes a small labeled set to guide RLVR training on unlabeled samples. Our key insight is that supervised rewards are essential for stabilizing consistency-based training on unlabeled samples, ensuring that only reasoning patterns verified on labeled instances are incorporated into RL training. Technically, we propose an effective policy optimization algorithm, TraPO, that identifies reliable unlabeled samples by matching their learning trajectory similarity to labeled ones. Building on this, TraPO achieves remarkable data efficiency and strong generalization on six widely used mathematical reasoning benchmarks (AIME24/25, AMC, MATH-500, Minerva, and Olympiad) and three out-of-distribution tasks (ARC-c, GPQA-diamond, and MMLU-pro). With only 1K labeled and 3K unlabeled samples, TraPO reaches 42.6% average accuracy, surpassing the best unsupervised method trained on 45K unlabeled samples (38.3%). Notably, when using 4K labeled and 12K unlabeled samples, TraPO even outperforms the fully supervised model trained on the full 45K labeled samples on all benchmarks, while using only 10% of the labeled data. The code is available via https://github.com/ShenzhiYang2000/TRAPO.", "github_manual": "", "github_regex": "https://github.com/ShenzhiYang2000/TRAPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14696", "title": "CRISP: Contact-Guided Real2Sim from Monocular Video with Planar Scene Primitives", "authors": [ "Zihan Wang", "Jiashun Wang", "Jeff Tan", "Yiwen Zhao", "Jessica Hodgins", "Shubham Tulsiani", "Deva Ramanan" ], "github": "", "project_page": "", "abstract": "We introduce CRISP, a method that recovers simulatable human motion and scene geometry from monocular video. Prior work on joint human-scene reconstruction relies on data-driven priors and joint optimization with no physics in the loop, or recovers noisy geometry with artifacts that cause motion tracking policies with scene interactions to fail. In contrast, our key insight is to recover convex, clean, and simulation-ready geometry by fitting planar primitives to a point cloud reconstruction of the scene, via a simple clustering pipeline over depth, normals, and flow. To reconstruct scene geometry that might be occluded during interactions, we make use of human-scene contact modeling (e.g., we use human posture to reconstruct the occluded seat of a chair). Finally, we ensure that human and scene reconstructions are physically-plausible by using them to drive a humanoid controller via reinforcement learning. Our approach reduces motion tracking failure rates from 55.2\\% to 6.9\\% on human-centric video benchmarks (EMDB, PROX), while delivering a 43\\% faster RL simulation throughput. We further validate it on in-the-wild videos including casually-captured videos, Internet videos, and even Sora-generated videos. This demonstrates CRISP's ability to generate physically-valid human motion and interaction environments at scale, greatly advancing real-to-sim applications for robotics and AR/VR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.12941", "title": "UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction", "authors": [ "Siyuan Yao", "Dongxiu Liu", "Taotao Li", "Shengjie Li", "Wenqi Ren", "Xiaochun Cao" ], "github": "https://github.com/Dstate/UAGLNet", "project_page": "", "abstract": "Building extraction from remote sensing images is a challenging task due to the complex structure variations of the buildings. Existing methods employ convolutional or self-attention blocks to capture the multi-scale features in the segmentation models, while the inherent gap of the feature pyramids and insufficient global-local feature integration leads to inaccurate, ambiguous extraction results. To address this issue, in this paper, we present an Uncertainty-Aggregated Global-Local Fusion Network (UAGLNet), which is capable to exploit high-quality global-local visual semantics under the guidance of uncertainty modeling. Specifically, we propose a novel cooperative encoder, which adopts hybrid CNN and transformer layers at different stages to capture the local and global visual semantics, respectively. An intermediate cooperative interaction block (CIB) is designed to narrow the gap between the local and global features when the network becomes deeper. Afterwards, we propose a Global-Local Fusion (GLF) module to complementarily fuse the global and local representations. Moreover, to mitigate the segmentation ambiguity in uncertain regions, we propose an Uncertainty-Aggregated Decoder (UAD) to explicitly estimate the pixel-wise uncertainty to enhance the segmentation accuracy. Extensive experiments demonstrate that our method achieves superior performance to other state-of-the-art methods. Our code is available at https://github.com/Dstate/UAGLNet", "github_manual": "", "github_regex": "https://github.com/Dstate/UAGLNet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.10342", "title": "CoSPlan: Corrective Sequential Planning via Scene Graph Incremental Updates", "authors": [ "Shresth Grover", "Priyank Pathak", "Akash Kumar", "Vibhav Vineet", "Yogesh S Rawat" ], "github": "", "project_page": "", "abstract": "Large-scale Vision-Language Models (VLMs) exhibit impressive complex reasoning capabilities but remain largely unexplored in visual sequential planning, i.e., executing multi-step actions towards a goal. Additionally, practical sequential planning often involves non-optimal (erroneous) steps, challenging VLMs to detect and correct such steps. We propose Corrective Sequential Planning Benchmark (CoSPlan) to evaluate VLMs in error-prone, vision-based sequential planning tasks across 4 domains: maze navigation, block rearrangement, image reconstruction,and object reorganization. CoSPlan assesses two key abilities: Error Detection (identifying non-optimal action) and Step Completion (correcting and completing action sequences to reach the goal). Despite using state-of-the-art reasoning techniques such as Chain-of-Thought and Scene Graphs, VLMs (e.g. Intern-VLM and Qwen2) struggle on CoSPlan, failing to leverage contextual cues to reach goals. Addressing this, we propose a novel training-free method, Scene Graph Incremental updates (SGI), which introduces intermediate reasoning steps between the initial and goal states. SGI helps VLMs reason about sequences, yielding an average performance gain of 5.2%. In addition to enhancing reliability in corrective sequential planning, SGI generalizes to traditional planning tasks such as Plan-Bench and VQA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.10952", "title": "Hierarchical Dataset Selection for High-Quality Data Sharing", "authors": [ "Xiaona Zhou", "Yingyan Zeng", "Ran Jin", "Ismini Lourentzou" ], "github": "https://github.com/PLAN-Lab/DaSH", "project_page": "", "abstract": "The success of modern machine learning hinges on access to high-quality training data. In many real-world scenarios, such as acquiring data from public repositories or sharing across institutions, data is naturally organized into discrete datasets that vary in relevance, quality, and utility. Selecting which repositories or institutions to search for useful datasets, and which datasets to incorporate into model training are therefore critical decisions, yet most existing methods select individual samples and treat all data as equally relevant, ignoring differences between datasets and their sources. In this work, we formalize the task of dataset selection: selecting entire datasets from a large, heterogeneous pool to improve downstream performance under resource constraints. We propose Dataset Selection via Hierarchies (DaSH), a dataset selection method that models utility at both dataset and group (e.g., collections, institutions) levels, enabling efficient generalization from limited observations. Across two public benchmarks (Digit-Five and DomainNet), DaSH outperforms state-of-the-art data selection baselines by up to 26.2% in accuracy, while requiring significantly fewer exploration steps. Ablations show DaSH is robust to low-resource settings and lack of relevant datasets, making it suitable for scalable and adaptive dataset selection in practical multi-source learning workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PLAN-Lab/DaSH", "github_api_added_by": "auto" }, { "date": "2025-12-17", "arxiv_id": "2512.11934", "title": "Unveiling User Perceptions in the Generative AI Era: A Sentiment-Driven Evaluation of AI Educational Apps' Role in Digital Transformation of e-Teaching", "authors": [ "Adeleh Mazaherian", "Erfan Nourbakhsh" ], "github": "https://github.com/erfan-nourbakhsh/GenAI-EdSent", "project_page": "", "abstract": "The rapid integration of generative artificial intelligence into education has driven digital transformation in e-teaching, yet user perceptions of AI educational apps remain underexplored. This study performs a sentiment-driven evaluation of user reviews from top AI ed-apps on the Google Play Store to assess efficacy, challenges, and pedagogical implications. Our pipeline involved scraping app data and reviews, RoBERTa for binary sentiment classification, GPT-4o for key point extraction, and GPT-5 for synthesizing top positive/negative themes. Apps were categorized into seven types (e.g., homework helpers, math solvers, language tools), with overlaps reflecting multifunctional designs. Results indicate predominantly positive sentiments, with homework apps like Edu AI (95.9% positive) and Answer.AI (92.7%) leading in accuracy, speed, and personalization, while language/LMS apps (e.g., Teacher AI at 21.8% positive) lag due to instability and limited features. Positives emphasize efficiency in brainstorming, problem-solving, and engagement; negatives center on paywalls, inaccuracies, ads, and glitches. Trends show that homework helpers outperform specialized tools, highlighting AI's democratizing potential amid risks of dependency and inequity. The discussion proposes future ecosystems with hybrid AI-human models, VR/AR for immersive learning, and a roadmap for developers (adaptive personalization) and policymakers (monetization regulation for inclusivity). This underscores generative AI's role in advancing e-teaching by enabling ethical refinements that foster equitable, innovative environments. The full dataset is available here(https://github.com/erfan-nourbakhsh/GenAI-EdSent).", "github_manual": "", "github_regex": "https://github.com/erfan-nourbakhsh/GenAI-EdSent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-17", "arxiv_id": "2512.14698", "title": "TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs", "authors": [ "Jun Zhang", "Teng Wang", "Yuying Ge", "Yixiao Ge", "Xinhao Li", "Ying Shan", "Limin Wang" ], "github": "", "project_page": "", "abstract": "This paper does not introduce a novel method but instead establishes a straightforward, incremental, yet essential baseline for video temporal grounding (VTG), a core capability in video understanding. While multimodal large language models (MLLMs) excel at various video understanding tasks, the recipes for optimizing them for VTG remain under-explored. In this paper, we present TimeLens, a systematic investigation into building MLLMs with strong VTG ability, along two primary dimensions: data quality and algorithmic design. We first expose critical quality issues in existing VTG benchmarks and introduce TimeLens-Bench, comprising meticulously re-annotated versions of three popular benchmarks with strict quality criteria. Our analysis reveals dramatic model re-rankings compared to legacy benchmarks, confirming the unreliability of prior evaluation standards. We also address noisy training data through an automated re-annotation pipeline, yielding TimeLens-100K, a large-scale, high-quality training dataset. Building on our data foundation, we conduct in-depth explorations of algorithmic design principles, yielding a series of meaningful insights and effective yet efficient practices. These include interleaved textual encoding for time representation, a thinking-free reinforcement learning with verifiable rewards (RLVR) approach as the training paradigm, and carefully designed recipes for RLVR training. These efforts culminate in TimeLens models, a family of MLLMs with state-of-the-art VTG performance among open-source models and even surpass proprietary models such as GPT-5 and Gemini-2.5-Flash. All codes, data, and models will be released to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15649", "title": "VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?", "authors": [ "Hongbo Zhao", "Meng Wang", "Fei Zhu", "Wenzhuo Liu", "Bolin Ni", "Fanhu Zeng", "Gaofeng Meng", "Zhaoxiang Zhang" ], "github": "", "project_page": "", "abstract": "The computational and memory overheads associated with expanding the context window of LLMs severely limit their scalability. A noteworthy solution is vision-text compression (VTC), exemplified by frameworks like DeepSeek-OCR and Glyph, which convert long texts into dense 2D visual representations, thereby achieving token compression ratios of 3x-20x. However, the impact of this high information density on the core long-context capabilities of vision-language models (VLMs) remains under-investigated. To address this gap, we introduce the first benchmark for VTC and systematically assess the performance of VLMs across three long-context understanding settings: VTC-Retrieval, which evaluates the model's ability to retrieve and aggregate information; VTC-Reasoning, which requires models to infer latent associations to locate facts with minimal lexical overlap; and VTC-Memory, which measures comprehensive question answering within long-term dialogue memory. Furthermore, we establish the VTCBench-Wild to simulate diverse input scenarios.We comprehensively evaluate leading open-source and proprietary models on our benchmarks. The results indicate that, despite being able to decode textual information (e.g., OCR) well, most VLMs exhibit a surprisingly poor long-context understanding ability with VTC-compressed information, failing to capture long associations or dependencies in the context.This study provides a deep understanding of VTC and serves as a foundation for designing more efficient and scalable VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.09851", "title": "Simultaneous Tactile-Visual Perception for Learning Multimodal Robot Manipulation", "authors": [ "Yuyang Li", "Yinghan Chen", "Zihang Zhao", "Puhao Li", "Tengyu Liu", "Siyuan Huang", "Yixin Zhu" ], "github": "", "project_page": "", "abstract": "Robotic manipulation requires both rich multimodal perception and effective learning frameworks to handle complex real-world tasks. See-through-skin (STS) sensors, which combine tactile and visual perception, offer promising sensing capabilities, while modern imitation learning provides powerful tools for policy acquisition. However, existing STS designs lack simultaneous multimodal perception and suffer from unreliable tactile tracking. Furthermore, integrating these rich multimodal signals into learning-based manipulation pipelines remains an open challenge. We introduce TacThru, an STS sensor enabling simultaneous visual perception and robust tactile signal extraction, and TacThru-UMI, an imitation learning framework that leverages these multimodal signals for manipulation. Our sensor features a fully transparent elastomer, persistent illumination, novel keyline markers, and efficient tracking, while our learning system integrates these signals through a Transformer-based Diffusion Policy. Experiments on five challenging real-world tasks show that TacThru-UMI achieves an average success rate of 85.5%, significantly outperforming the baselines of alternating tactile-visual (66.3%) and vision-only (55.4%). The system excels in critical scenarios, including contact detection with thin and soft objects and precision manipulation requiring multimodal coordination. This work demonstrates that combining simultaneous multimodal perception with modern learning frameworks enables more precise, adaptable robotic manipulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.09299", "title": "VABench: A Comprehensive Benchmark for Audio-Video Generation", "authors": [ "Daili Hua", "Xizhi Wang", "Bohan Zeng", "Xinyi Huang", "Hao Liang", "Junbo Niu", "Xinlong Chen", "Quanqing Xu", "Wentao Zhang" ], "github": "https://github.com/tanABCC/VABench", "project_page": "", "abstract": "Recent advances in video generation have been remarkable, enabling models to produce visually compelling videos with synchronized audio. While existing video generation benchmarks provide comprehensive metrics for visual quality, they lack convincing evaluations for audio-video generation, especially for models aiming to generate synchronized audio-video outputs. To address this gap, we introduce VABench, a comprehensive and multi-dimensional benchmark framework designed to systematically evaluate the capabilities of synchronous audio-video generation. VABench encompasses three primary task types: text-to-audio-video (T2AV), image-to-audio-video (I2AV), and stereo audio-video generation. It further establishes two major evaluation modules covering 15 dimensions. These dimensions specifically assess pairwise similarities (text-video, text-audio, video-audio), audio-video synchronization, lip-speech consistency, and carefully curated audio and video question-answering (QA) pairs, among others. Furthermore, VABench covers seven major content categories: animals, human sounds, music, environmental sounds, synchronous physical sounds, complex scenes, and virtual worlds. We provide a systematic analysis and visualization of the evaluation results, aiming to establish a new standard for assessing video generation models with synchronous audio capabilities and to promote the comprehensive advancement of the field.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tanABCC/VABench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15374", "title": "SCOPE: Prompt Evolution for Enhancing Agent Effectiveness", "authors": [ "Zehua Pei", "Hui-Ling Zhen", "Shixiong Kai", "Sinno Jialin Pan", "Yunhe Wang", "Mingxuan Yuan", "Bei Yu" ], "github": "https://github.com/JarvisPei/SCOPE", "project_page": "", "abstract": "Large Language Model (LLM) agents are increasingly deployed in environments that generate massive, dynamic contexts. However, a critical bottleneck remains: while agents have access to this context, their static prompts lack the mechanisms to manage it effectively, leading to recurring Corrective and Enhancement failures. To address this capability gap, we introduce SCOPE (Self-evolving Context Optimization via Prompt Evolution). SCOPE frames context management as an online optimization problem, synthesizing guidelines from execution traces to automatically evolve the agent's prompt. We propose a Dual-Stream mechanism that balances tactical specificity (resolving immediate errors) with strategic generality (evolving long-term principles). Furthermore, we introduce Perspective-Driven Exploration to maximize strategy coverage, increasing the likelihood that the agent has the correct strategy for any given task. Experiments on the HLE benchmark show that SCOPE improves task success rates from 14.23\\% to 38.64\\% without human intervention. We make our code publicly available at https://github.com/JarvisPei/SCOPE.", "github_manual": "", "github_regex": "https://github.com/JarvisPei/SCOPE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15693", "title": "Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning", "authors": [ "Yifei Li", "Wenzhao Zheng", "Yanran Zhang", "Runze Sun", "Yu Zheng", "Lei Chen", "Jie Zhou", "Jiwen Lu" ], "github": "https://github.com/JoeLeelyf/Skyra", "project_page": "", "abstract": "The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated video detectors. However, most existing methods are limited to binary classification and lack the necessary explanations for human interpretation. In this paper, we present Skyra, a specialized multimodal large language model (MLLM) that identifies human-perceivable visual artifacts in AI-generated videos and leverages them as grounded evidence for both detection and explanation. To support this objective, we construct ViF-CoT-4K for Supervised Fine-Tuning (SFT), which represents the first large-scale AI-generated video artifact dataset with fine-grained human annotations. We then develop a two-stage training strategy that systematically enhances our model's spatio-temporal artifact perception, explanation capability, and detection accuracy. To comprehensively evaluate Skyra, we introduce ViF-Bench, a benchmark comprising 3K high-quality samples generated by over ten state-of-the-art video generators. Extensive experiments demonstrate that Skyra surpasses existing methods across multiple benchmarks, while our evaluation yields valuable insights for advancing explainable AI-generated video detection.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JoeLeelyf/Skyra", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15176", "title": "DEER: Draft with Diffusion, Verify with Autoregressive Models", "authors": [ "Zicong Cheng", "Guo-Wei Yang", "Jia Li", "Zhijie Deng", "Meng-Hao Guo", "Shi-Min Hu" ], "github": "", "project_page": "", "abstract": "Efficiency, as a critical practical challenge for LLM-driven agentic and reasoning systems, is increasingly constrained by the inherent latency of autoregressive (AR) decoding. Speculative decoding mitigates this cost through a draft-verify scheme, yet existing approaches rely on AR draft models (a.k.a., drafters), which introduce two fundamental issues: (1) step-wise uncertainty accumulation leads to a progressive collapse of trust between the target model and the drafter, and (2) inherently sequential decoding of AR drafters. Together, these factors cause limited speedups. In this paper, we show that a diffusion large language model (dLLM) drafters can naturally overcome these issues through its fundamentally different probabilistic modeling and efficient parallel decoding strategy. Building on this insight, we introduce DEER, an efficient speculative decoding framework that drafts with diffusion and verifies with AR models. To enable high-quality drafting, DEER employs a two-stage training pipeline to align the dLLM-based drafters with the target AR model, and further adopts single-step decoding to generate long draft segments. Experiments show DEER reaches draft acceptance lengths of up to 32 tokens, far surpassing the 10 tokens achieved by EAGLE-3. Moreover, on HumanEval with Qwen3-30B-A3B, DEER attains a 5.54x speedup, while EAGLE-3 achieves only 2.41x. Code, model, demo, etc, will be available at https://czc726.github.io/DEER/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.14693", "title": "Universal Reasoning Model", "authors": [ "Zitian Gao", "Lynx Chen", "Yihao Xiao", "He Xing", "Ran Tao", "Haoming Luo", "Joey Zhou", "Bryan Dai" ], "github": "https://github.com/UbiquantAI/URM", "project_page": "", "abstract": "Universal transformers (UTs) have been widely used for complex reasoning tasks such as ARC-AGI and Sudoku, yet the specific sources of their performance gains remain underexplored. In this work, we systematically analyze UTs variants and show that improvements on ARC-AGI primarily arise from the recurrent inductive bias and strong nonlinear components of Transformer, rather than from elaborate architectural designs. Motivated by this finding, we propose the Universal Reasoning Model (URM), which enhances the UT with short convolution and truncated backpropagation. Our approach substantially improves reasoning performance, achieving state-of-the-art 53.8% pass@1 on ARC-AGI 1 and 16.0% pass@1 on ARC-AGI 2. Our code is avaliable at https://github.com/zitian-gao/URM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UbiquantAI/URM", "github_api_added_by": "user" }, { "date": "2025-12-18", "arxiv_id": "2512.14681", "title": "Fast and Accurate Causal Parallel Decoding using Jacobi Forcing", "authors": [ "Lanxiang Hu", "Siqi Kou", "Yichao Fu", "Samyam Rajbhandari", "Tajana Rosing", "Yuxiong He", "Zhijie Deng", "Hao Zhang" ], "github": "https://github.com/hao-ai-lab/JacobiForcing", "project_page": "", "abstract": "Multi-token generation has emerged as a promising paradigm for accelerating transformer-based large model inference. Recent efforts primarily explore diffusion Large Language Models (dLLMs) for parallel decoding to reduce inference latency. To achieve AR-level generation quality, many techniques adapt AR models into dLLMs to enable parallel decoding. However, they suffer from limited speedup compared to AR models due to a pretrain-to-posttrain mismatch. Specifically, the masked data distribution in post-training deviates significantly from the real-world data distribution seen during pretraining, and dLLMs rely on bidirectional attention, which conflicts with the causal prior learned during pretraining and hinders the integration of exact KV cache reuse. To address this, we introduce Jacobi Forcing, a progressive distillation paradigm where models are trained on their own generated parallel decoding trajectories, smoothly shifting AR models into efficient parallel decoders while preserving their pretrained causal inference property. The models trained under this paradigm, Jacobi Forcing Model, achieves 3.8x wall-clock speedup on coding and math benchmarks with minimal loss in performance. Based on Jacobi Forcing Models' trajectory characteristics, we introduce multi-block decoding with rejection recycling, which enables up to 4.5x higher token acceptance count per iteration and nearly 4.0x wall-clock speedup, effectively trading additional compute for lower inference latency. Our code is available at https://github.com/hao-ai-lab/JacobiForcing.", "github_manual": "", "github_regex": "https://github.com/hao-ai-lab/JacobiForcing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15687", "title": "Can LLMs Guide Their Own Exploration? Gradient-Guided Reinforcement Learning for LLM Reasoning", "authors": [ "Zhenwen Liang", "Sidi Lu", "Wenhao Yu", "Kishan Panaganti", "Yujun Zhou", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "Reinforcement learning has become essential for strengthening the reasoning abilities of large language models, yet current exploration mechanisms remain fundamentally misaligned with how these models actually learn. Entropy bonuses and external semantic comparators encourage surface level variation but offer no guarantee that sampled trajectories differ in the update directions that shape optimization. We propose G2RL, a gradient guided reinforcement learning framework in which exploration is driven not by external heuristics but by the model own first order update geometry. For each response, G2RL constructs a sequence level feature from the model final layer sensitivity, obtainable at negligible cost from a standard forward pass, and measures how each trajectory would reshape the policy by comparing these features within a sampled group. Trajectories that introduce novel gradient directions receive a bounded multiplicative reward scaler, while redundant or off manifold updates are deemphasized, yielding a self referential exploration signal that is naturally aligned with PPO style stability and KL control. Across math and general reasoning benchmarks (MATH500, AMC, AIME24, AIME25, GPQA, MMLUpro) on Qwen3 base 1.7B and 4B models, G2RL consistently improves pass@1, maj@16, and pass@k over entropy based GRPO and external embedding methods. Analyzing the induced geometry, we find that G2RL expands exploration into substantially more orthogonal and often opposing gradient directions while maintaining semantic coherence, revealing that a policy own update space provides a far more faithful and effective basis for guiding exploration in large language model reinforcement learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15713", "title": "DiffusionVL: Translating Any Autoregressive Models into Diffusion Vision Language Models", "authors": [ "Lunbin Zeng", "Jingfeng Yao", "Bencheng Liao", "Hongyuan Tao", "Wenyu Liu", "Xinggang Wang" ], "github": "https://github.com/hustvl/DiffusionVL", "project_page": "", "abstract": "In recent multimodal research, the diffusion paradigm has emerged as a promising alternative to the autoregressive paradigm (AR), owing to its unique decoding advantages. However, due to the capability limitations of the base diffusion language model, the performance of the diffusion vision language model (dVLM) still lags significantly behind that of mainstream models. This leads to a simple yet fundamental question: Is it possible to construct dVLMs based on existing powerful AR models? In response, we propose DiffusionVL, a dVLM family that could be translated from any powerful AR models. Through simple fine-tuning, we successfully adapt AR pre-trained models into the diffusion paradigm. This approach yields two key observations: (1) The paradigm shift from AR-based multimodal models to diffusion is remarkably effective. (2) Direct conversion of an AR language model to a dVLM is also feasible, achieving performance competitive with LLaVA-style visual-instruction-tuning. Further, we introduce a block-decoding design into dVLMs that supports arbitrary-length generation and KV cache reuse, achieving a significant inference speedup. We conduct a large number of experiments. Despite training with less than 5% of the data required by prior methods, DiffusionVL achieves a comprehensive performance improvement-a 34.4% gain on the MMMU-Pro (vision) bench and 37.5% gain on the MME (Cog.) bench-alongside a 2x inference speedup. The model and code are released at https://github.com/hustvl/DiffusionVL.", "github_manual": "", "github_regex": "https://github.com/hustvl/DiffusionVL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15603", "title": "Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition", "authors": [ "Shengming Yin", "Zekai Zhang", "Zecheng Tang", "Kaiyuan Gao", "Xiao Xu", "Kun Yan", "Jiahao Li", "Yilei Chen", "Yuxiang Chen", "Heung-Yeung Shum", "Lionel M. Ni", "Jingren Zhou", "Junyang Lin", "Chenfei Wu" ], "github": "https://github.com/QwenLM/Qwen-Image-Layered", "project_page": "", "abstract": "Recent visual generative models often struggle with consistency during image editing due to the entangled nature of raster images, where all visual content is fused into a single canvas. In contrast, professional design tools employ layered representations, allowing isolated edits while preserving consistency. Motivated by this, we propose Qwen-Image-Layered, an end-to-end diffusion model that decomposes a single RGB image into multiple semantically disentangled RGBA layers, enabling inherent editability, where each RGBA layer can be independently manipulated without affecting other content. To support variable-length decomposition, we introduce three key components: (1) an RGBA-VAE to unify the latent representations of RGB and RGBA images; (2) a VLD-MMDiT (Variable Layers Decomposition MMDiT) architecture capable of decomposing a variable number of image layers; and (3) a Multi-stage Training strategy to adapt a pretrained image generation model into a multilayer image decomposer. Furthermore, to address the scarcity of high-quality multilayer training images, we build a pipeline to extract and annotate multilayer images from Photoshop documents (PSD). Experiments demonstrate that our method significantly surpasses existing approaches in decomposition quality and establishes a new paradigm for consistent image editing. Our code and models are released on https://github.com/QwenLM/Qwen-Image-Layered{https://github.com/QwenLM/Qwen-Image-Layered}", "github_manual": "", "github_regex": "https://github.com/QwenLM/Qwen-Image-Layered", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15431", "title": "Step-GUI Technical Report", "authors": [ "Haolong Yan", "Jia Wang", "Xin Huang", "Yeqing Shen", "Ziyang Meng", "Zhimin Fan", "Kaijun Tan", "Jin Gao", "Lieyu Shi", "Mi Yang", "Shiliang Yang", "Zhirui Wang", "Brian Li", "Kang An", "Chenyang Li", "Lei Lei", "Mengmeng Duan", "Danxun Liang", "Guodong Liu", "Hang Cheng", "Hao Wu", "Jie Dong", "Junhao Huang", "Mei Chen", "Renjie Yu", "Shunshan Li", "Xu Zhou", "Yiting Dai", "Yineng Deng", "Yingdan Liang", "Zelin Chen", "Wen Sun", "Chengxu Yan", "Chunqin Xu", "Dong Li", "Fengqiong Xiao", "Guanghao Fan", "Guopeng Li", "Guozhen Peng", "Hongbing Li", "Hang Li", "Hongming Chen", "Jingjing Xie", "Jianyong Li", "Jingyang Zhang", "Jiaju Ren", "Jiayu Yuan", "Jianpeng Yin", "Kai Cao", "Liang Zhao", "Liguo Tan", "Liying Shi", "Mengqiang Ren", "Min Xu", "Manjiao Liu", "Mao Luo", "Mingxin Wan", "Na Wang", "Nan Wu", "Ning Wang", "Peiyao Ma", "Qingzhou Zhang", "Qiao Wang", "Qinlin Zeng", "Qiong Gao", "Qiongyao Li", "Shangwu Zhong", "Shuli Gao", "Shaofan Liu", "Shisi Gao", "Shuang Luo", "Xingbin Liu", "Xiaojia Liu", "Xiaojie Hou", "Xin Liu", "Xuanti Feng", "Xuedan Cai", "Xuan Wen", "Xianwei Zhu", "Xin Liang", "Xin Liu", "Xin Zhou", "Yingxiu Zhao", "Yukang Shi", "Yunfang Xu", "Yuqing Zeng", "Yixun Zhang", "Zejia Weng", "Zhonghao Yan", "Zhiguo Huang", "Zhuoyu Wang", "Zheng Ge", "Jing Li", "Yibo Zhu", "Binxing Jiao", "Xiangyu Zhang", "Daxin Jiang" ], "github": "", "project_page": "", "abstract": "Recent advances in multimodal large language models unlock unprecedented opportunities for GUI automation. However, a fundamental challenge remains: how to efficiently acquire high-quality training data while maintaining annotation reliability? We introduce a self-evolving training pipeline powered by the Calibrated Step Reward System, which converts model-generated trajectories into reliable training signals through trajectory-level calibration, achieving >90% annotation accuracy with 10-100x lower cost. Leveraging this pipeline, we introduce Step-GUI, a family of models (4B/8B) that achieves state-of-the-art GUI performance (8B: 80.2% AndroidWorld, 48.5% OSWorld, 62.6% ScreenShot-Pro) while maintaining robust general capabilities. As GUI agent capabilities improve, practical deployment demands standardized interfaces across heterogeneous devices while protecting user privacy. To this end, we propose GUI-MCP, the first Model Context Protocol for GUI automation with hierarchical architecture that combines low-level atomic operations and high-level task delegation to local specialist models, enabling high-privacy execution where sensitive data stays on-device. Finally, to assess whether agents can handle authentic everyday usage, we introduce AndroidDaily, a benchmark grounded in real-world mobile usage patterns with 3146 static actions and 235 end-to-end tasks across high-frequency daily scenarios (8B: static 89.91%, end-to-end 52.50%). Our work advances the development of practical GUI agents and demonstrates strong potential for real-world deployment in everyday digital interactions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15110", "title": "Is Nano Banana Pro a Low-Level Vision All-Rounder? A Comprehensive Evaluation on 14 Tasks and 40 Datasets", "authors": [ "Jialong Zuo", "Haoyou Deng", "Hanyu Zhou", "Jiaxin Zhu", "Yicheng Zhang", "Yiwei Zhang", "Yongxin Yan", "Kaixing Huang", "Weisen Chen", "Yongtai Deng", "Rui Jin", "Nong Sang", "Changxin Gao" ], "github": "https://github.com/zplusdragon/LowLevelBanana", "project_page": "", "abstract": "The rapid evolution of text-to-image generation models has revolutionized visual content creation. While commercial products like Nano Banana Pro have garnered significant attention, their potential as generalist solvers for traditional low-level vision challenges remains largely underexplored. In this study, we investigate the critical question: Is Nano Banana Pro a Low-Level Vision All-Rounder? We conducted a comprehensive zero-shot evaluation across 14 distinct low-level tasks spanning 40 diverse datasets. By utilizing simple textual prompts without fine-tuning, we benchmarked Nano Banana Pro against state-of-the-art specialist models. Our extensive analysis reveals a distinct performance dichotomy: while Nano Banana Pro demonstrates superior subjective visual quality, often hallucinating plausible high-frequency details that surpass specialist models, it lags behind in traditional reference-based quantitative metrics. We attribute this discrepancy to the inherent stochasticity of generative models, which struggle to maintain the strict pixel-level consistency required by conventional metrics. This report identifies Nano Banana Pro as a capable zero-shot contender for low-level vision tasks, while highlighting that achieving the high fidelity of domain specialists remains a significant hurdle.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zplusdragon/LowLevelBanana", "github_api_added_by": "auto" }, { "date": "2025-12-18", "arxiv_id": "2512.13874", "title": "SAGE: Training Smart Any-Horizon Agents for Long Video Reasoning with Reinforcement Learning", "authors": [ "Jitesh Jain", "Jialuo Li", "Zixian Ma", "Jieyu Zhang", "Chris Dongjoo Kim", "Sangho Lee", "Rohun Tripathi", "Tanmay Gupta", "Christopher Clark", "Humphrey Shi" ], "github": "", "project_page": "", "abstract": "As humans, we are natural any-horizon reasoners, i.e., we can decide whether to iteratively skim long videos or watch short ones in full when necessary for a given task. With this in mind, one would expect video reasoning models to reason flexibly across different durations. However, SOTA models are still trained to predict answers in a single turn while processing a large number of frames, akin to watching an entire long video, requiring significant resources. This raises the question: Is it possible to develop performant any-horizon video reasoning systems? Inspired by human behavior, we first propose SAGE, an agent system that performs multi-turn reasoning on long videos while handling simpler problems in a single turn. Secondly, we introduce an easy synthetic data generation pipeline using Gemini-2.5-Flash to train the orchestrator, SAGE-MM, which lies at the core of SAGE. We further propose an effective RL post-training recipe essential for instilling any-horizon reasoning ability in SAGE-MM. Thirdly, we curate SAGE-Bench with an average duration of greater than 700 seconds for evaluating video reasoning ability in real-world entertainment use cases. Lastly, we empirically validate the effectiveness of our system, data, and RL recipe, observing notable improvements of up to 6.1% on open-ended video reasoning tasks, as well as an impressive 8.2% improvement on videos longer than 10 minutes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15715", "title": "In Pursuit of Pixel Supervision for Visual Pre-training", "authors": [ "Lihe Yang", "Shang-Wen Li", "Yang Li", "Xinjie Lei", "Dong Wang", "Abdelrahman Mohamed", "Hengshuang Zhao", "Hu Xu" ], "github": "", "project_page": "", "abstract": "At the most basic level, pixels are the source of the visual information through which we perceive the world. Pixels contain information at all levels, ranging from low-level attributes to high-level concepts. Autoencoders represent a classical and long-standing paradigm for learning representations from pixels or other raw inputs. In this work, we demonstrate that autoencoder-based self-supervised learning remains competitive today and can produce strong representations for downstream tasks, while remaining simple, stable, and efficient. Our model, codenamed \"Pixio\", is an enhanced masked autoencoder (MAE) with more challenging pre-training tasks and more capable architectures. The model is trained on 2B web-crawled images with a self-curation strategy with minimal human curation. Pixio performs competitively across a wide range of downstream tasks in the wild, including monocular depth estimation (e.g., Depth Anything), feed-forward 3D reconstruction (i.e., MapAnything), semantic segmentation, and robot learning, outperforming or matching DINOv3 trained at similar scales. Our results suggest that pixel-space self-supervised learning can serve as a promising alternative and a complement to latent-space approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15182", "title": "Robust and Calibrated Detection of Authentic Multimedia Content", "authors": [ "Sarim Hashmi", "Abdelrahman Elsayed", "Mohammed Talha Alam", "Samuele Poppi", "Nils Lukas" ], "github": "", "project_page": "", "abstract": "Generative models can synthesize highly realistic content, so-called deepfakes, that are already being misused at scale to undermine digital media authenticity. Current deepfake detection methods are unreliable for two reasons: (i) distinguishing inauthentic content post-hoc is often impossible (e.g., with memorized samples), leading to an unbounded false positive rate (FPR); and (ii) detection lacks robustness, as adversaries can adapt to known detectors with near-perfect accuracy using minimal computational resources. To address these limitations, we propose a resynthesis framework to determine if a sample is authentic or if its authenticity can be plausibly denied. We make two key contributions focusing on the high-precision, low-recall setting against efficient (i.e., compute-restricted) adversaries. First, we demonstrate that our calibrated resynthesis method is the most reliable approach for verifying authentic samples while maintaining controllable, low FPRs. Second, we show that our method achieves adversarial robustness against efficient adversaries, whereas prior methods are easily evaded under identical compute budgets. Our approach supports multiple modalities and leverages state-of-the-art inversion techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15702", "title": "End-to-End Training for Autoregressive Video Diffusion via Self-Resampling", "authors": [ "Yuwei Guo", "Ceyuan Yang", "Hao He", "Yang Zhao", "Meng Wei", "Zhenheng Yang", "Weilin Huang", "Dahua Lin" ], "github": "", "project_page": "", "abstract": "Autoregressive video diffusion models hold promise for world simulation but are vulnerable to exposure bias arising from the train-test mismatch. While recent works address this via post-training, they typically rely on a bidirectional teacher model or online discriminator. To achieve an end-to-end solution, we introduce Resampling Forcing, a teacher-free framework that enables training autoregressive video models from scratch and at scale. Central to our approach is a self-resampling scheme that simulates inference-time model errors on history frames during training. Conditioned on these degraded histories, a sparse causal mask enforces temporal causality while enabling parallel training with frame-level diffusion loss. To facilitate efficient long-horizon generation, we further introduce history routing, a parameter-free mechanism that dynamically retrieves the top-k most relevant history frames for each query. Experiments demonstrate that our approach achieves performance comparable to distillation-based baselines while exhibiting superior temporal consistency on longer videos owing to native-length training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.13077", "title": "LikeBench: Evaluating Subjective Likability in LLMs for Personalization", "authors": [ "Md Awsafur Rahman", "Adam Gabrys", "Doug Kang", "Jingjing Sun", "Tian Tan", "Ashwin Chandramouli" ], "github": "", "project_page": "", "abstract": "A personalized LLM should remember user facts, apply them correctly, and adapt over time to provide responses that the user prefers. Existing LLM personalization benchmarks are largely centered on two axes: accurately recalling user information and accurately applying remembered information in downstream tasks. We argue that a third axis, likability, is both subjective and central to user experience, yet under-measured by current benchmarks. To measure likability holistically, we introduce LikeBench, a multi-session, dynamic evaluation framework that measures likability across multiple dimensions by how much an LLM can adapt over time to a user's preferences to provide more likable responses. In LikeBench, the LLMs engage in conversation with a simulated user and learn preferences only from the ongoing dialogue. As the interaction unfolds, models try to adapt to responses, and after each turn, they are evaluated for likability across seven dimensions by the same simulated user. To the best of our knowledge, we are the first to decompose likability into multiple diagnostic metrics: emotional adaptation, formality matching, knowledge adaptation, reference understanding, conversation length fit, humor fit, and callback, which makes it easier to pinpoint where a model falls short. To make the simulated user more realistic and discriminative, LikeBench uses fine-grained, psychologically grounded descriptive personas rather than the coarse high/low trait rating based personas used in prior work. Our benchmark shows that strong memory performance does not guarantee high likability: DeepSeek R1, with lower memory accuracy (86%, 17 facts/profile), outperformed Qwen3 by 28% on likability score despite Qwen3's higher memory accuracy (93%, 43 facts/profile). Even SOTA models like GPT-5 adapt well in short exchanges but show only limited robustness in longer, noisier interactions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15635", "title": "IC-Effect: Precise and Efficient Video Effects Editing via In-Context Learning", "authors": [ "Yuanhang Li", "Yiren Song", "Junzhe Bai", "Xinran Liang", "Hu Yang", "Libiao Jin", "Qi Mao" ], "github": "", "project_page": "", "abstract": "We propose IC-Effect, an instruction-guided, DiT-based framework for few-shot video VFX editing that synthesizes complex effects (\\eg flames, particles and cartoon characters) while strictly preserving spatial and temporal consistency. Video VFX editing is highly challenging because injected effects must blend seamlessly with the background, the background must remain entirely unchanged, and effect patterns must be learned efficiently from limited paired data. However, existing video editing models fail to satisfy these requirements. IC-Effect leverages the source video as clean contextual conditions, exploiting the contextual learning capability of DiT models to achieve precise background preservation and natural effect injection. A two-stage training strategy, consisting of general editing adaptation followed by effect-specific learning via Effect-LoRA, ensures strong instruction following and robust effect modeling. To further improve efficiency, we introduce spatiotemporal sparse tokenization, enabling high fidelity with substantially reduced computation. We also release a paired VFX editing dataset spanning 15 high-quality visual styles. Extensive experiments show that IC-Effect delivers high-quality, controllable, and temporally consistent VFX editing, opening new possibilities for video creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.10863", "title": "MMSI-Video-Bench: A Holistic Benchmark for Video-Based Spatial Intelligence", "authors": [ "Jingli Lin", "Runsen Xu", "Shaohao Zhu", "Sihan Yang", "Peizhou Cao", "Yunlong Ran", "Miao Hu", "Chenming Zhu", "Yiman Xie", "Yilin Long", "Wenbo Hu", "Dahua Lin", "Tai Wang", "Jiangmiao Pang" ], "github": "https://github.com/InternRobotics/MMSI-Video-Bench", "project_page": "", "abstract": "Spatial understanding over continuous visual input is crucial for MLLMs to evolve into general-purpose assistants in physical environments. Yet there is still no comprehensive benchmark that holistically assesses the progress toward this goal. In this work, we introduce MMSI-Video-Bench, a fully human-annotated benchmark for video-based spatial intelligence in MLLMs. It operationalizes a four-level framework, Perception, Planning, Prediction, and Cross-Video Reasoning, through 1,106 questions grounded in 1,278 clips from 25 datasets and in-house videos. Each item is carefully designed and reviewed by 3DV experts with explanatory rationales to ensure precise, unambiguous grounding. Leveraging its diverse data sources and holistic task coverage, MMSI-Video-Bench also supports three domain-oriented sub-benchmarks (Indoor Scene Perception Bench, Robot Bench and Grounding Bench) for targeted capability assessment. We evaluate 25 strong open-source and proprietary MLLMs, revealing a striking human--AI gap: many models perform near chance, and the best reasoning model lags humans by nearly 60%. We further find that spatially fine-tuned models still fail to generalize effectively on our benchmark. Fine-grained error analysis exposes systematic failures in geometric reasoning, motion grounding, long-horizon prediction, and cross-video correspondence. We also show that typical frame-sampling strategies transfer poorly to our reasoning-intensive benchmark, and that neither 3D spatial cues nor chain-of-thought prompting yields meaningful gains. We expect our benchmark to establish a solid testbed for advancing video-based spatial intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/InternRobotics/MMSI-Video-Bench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.13190", "title": "WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory", "authors": [ "Jin Sob Kim", "Hyun Joon Park", "Wooseok Shin", "Dongil Park", "Sung Won Han" ], "github": "", "project_page": "", "abstract": "The Automatic Identification System (AIS) enables data-driven maritime surveillance but suffers from reliability issues and irregular intervals. We address vessel destination estimation using global-scope AIS data by proposing a differentiated approach that recasts long port-to-port trajectories as a nested sequence structure. Using spatial grids, this method mitigates spatio-temporal bias while preserving detailed resolution. We introduce a novel deep learning architecture, WAY, designed to process these reformulated trajectories for long-term destination estimation days to weeks in advance. WAY comprises a trajectory representation layer and Channel-Aggregative Sequential Processing (CASP) blocks. The representation layer generates multi-channel vector sequences from kinematic and non-kinematic features. CASP blocks utilize multi-headed channel- and self-attention for aggregation and sequential information delivery. Additionally, we propose a task-specialized Gradient Dropout (GD) technique to enable many-to-many training on single labels, preventing biased feedback surges by stochastically blocking gradient flow based on sample length. Experiments on 5-year AIS data demonstrate WAY's superiority over conventional spatial grid-based approaches regardless of trajectory progression. Results further confirm that adopting GD leads to performance gains. Finally, we explore WAY's potential for real-world application through multitask learning for ETA estimation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.14719", "title": "Hybrid Attribution Priors for Explainable and Robust Model Training", "authors": [ "Zhuoran Zhang", "Feng Zhang", "Shangyuan Li", "Yang Shi", "Yuanxing Zhang", "Wei Chen", "Tengjiao Wang", "Kam-Fai Wong" ], "github": "", "project_page": "", "abstract": "Small language models (SLMs) are widely used in tasks that require low latency and lightweight deployment, particularly classification. As interpretability and robustness gain increasing importance, explanation-guided learning has emerged as an effective framework by introducing attribution-based supervision during training; however, deriving general and reliable attribution priors remains a significant challenge. Through an analysis of representative attribution methods in classification settings, we find that although these methods can reliably highlight class-relevant tokens, they often focus on common keywords shared by semantically similar classes. Because such classes are already difficult to distinguish under standard training, these attributions provide insufficient discriminative cues, limiting their ability to improve model differentiation. To overcome this limitation, we propose Class-Aware Attribution Prior (CAP), a novel attribution prior extraction framework that guides language models toward capturing fine-grained class distinctions and producing more salient, discriminative attribution priors. Building on this idea, we further introduce CAP Hybrid, which combines priors from CAP with those from existing attribution techniques to form a more comprehensive and balanced supervisory signal. By aligning a model's self-attribution with these enriched priors, our approach encourages the learning of diverse, decision-relevant features. Extensive experiments in full-data, few-shot, and adversarial scenarios demonstrate that our method consistently enhances both interpretability and robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.12072", "title": "VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs", "authors": [ "Avinash Amballa", "Yashas Malur Saidutta", "Chi-Heng Lin", "Vivek Kulkarni", "Srinivas Chappidi" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly being used to generate synthetic datasets for the evaluation and training of downstream models. However, prior work has noted that such generated data lacks diversity. In this paper, we propose Voyager, a novel principled approach to generate diverse datasets. Our approach is iterative and directly optimizes a mathematical quantity that optimizes the diversity of the dataset using the machinery of determinantal point processes. Furthermore, our approach is training-free, applicable to closed-source models, and scalable. In addition to providing theoretical justification for the working of our method, we also demonstrate through comprehensive experiments that Voyager significantly outperforms popular baseline approaches by providing a 1.5-3x improvement in diversity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.14052", "title": "HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices", "authors": [ "HyperAI Team", "Yuchen Liu", "Kaiyang Han", "Zhiqiang Xia", "Yuhang Dong", "Chen Song", "Kangyu Tang", "Jiaming Xu", "Xiushi Feng", "WenXuan Yu", "Li Peng", "Mingyang Wang", "Kai Wang", "Changpeng Yang", "Yang Li", "Haoyu Lu", "Hao Wang", "Bingna Xu", "Guangyao Liu", "Long Huang", "Kaibin Guo", "Jinyang Wu", "Dan Wu", "Hongzhen Wang", "Peng Zhou", "Shuai Nie", "Shande Wang", "Runyu Shi", "Ying Huang" ], "github": "", "project_page": "", "abstract": "Current multimodal large lanauge models possess strong perceptual and reasoning capabilities, however high computational and memory requirements make them difficult to deploy directly on on-device environments. While small-parameter models are progressively endowed with strong general capabilities, standard Vision Transformer (ViT) encoders remain a critical bottleneck, suffering from excessive latency and memory consumption when processing high-resolution inputs.To address these challenges, we introduce HyperVL, an efficient multimodal large language model tailored for on-device inference. HyperVL adopts an image-tiling strategy to cap peak memory usage and incorporates two novel techniques: (1) a Visual Resolution Compressor (VRC) that adaptively predicts optimal encoding resolutions to eliminate redundant computation, and (2) Dual Consistency Learning (DCL), which aligns multi-scale ViT encoders within a unified framework, enabling dynamic switching between visual branches under a shared LLM. Extensive experiments demonstrate that HyperVL achieves state-of-the-art performance among models of comparable size across multiple benchmarks. Furthermore, it significantly significantly reduces latency and power consumption on real mobile devices, demonstrating its practicality for on-device multimodal inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15340", "title": "Towards Seamless Interaction: Causal Turn-Level Modeling of Interactive 3D Conversational Head Dynamics", "authors": [ "Junjie Chen", "Fei Wang", "Zhihao Huang", "Qing Zhou", "Kun Li", "Dan Guo", "Linfeng Zhang", "Xun Yang" ], "github": "https://github.com/CoderChen01/towards-seamleass-interaction", "project_page": "", "abstract": "Human conversation involves continuous exchanges of speech and nonverbal cues such as head nods, gaze shifts, and facial expressions that convey attention and emotion. Modeling these bidirectional dynamics in 3D is essential for building expressive avatars and interactive robots. However, existing frameworks often treat talking and listening as independent processes or rely on non-causal full-sequence modeling, hindering temporal coherence across turns. We present TIMAR (Turn-level Interleaved Masked AutoRegression), a causal framework for 3D conversational head generation that models dialogue as interleaved audio-visual contexts. It fuses multimodal information within each turn and applies turn-level causal attention to accumulate conversational history, while a lightweight diffusion head predicts continuous 3D head dynamics that captures both coordination and expressive variability. Experiments on the DualTalk benchmark show that TIMAR reduces Fr\u00e9chet Distance and MSE by 15-30% on the test set, and achieves similar gains on out-of-distribution data. The source code will be released in the GitHub repository https://github.com/CoderChen01/towards-seamleass-interaction.", "github_manual": "", "github_regex": "https://github.com/CoderChen01/towards-seamleass-interaction", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.13884", "title": "FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition", "authors": [ "Jonas Golde", "Patrick Haller", "Alan Akbik" ], "github": "", "project_page": "", "abstract": "Recent multilingual named entity recognition (NER) work has shown that large language models (LLMs) can provide effective synthetic supervision, yet such datasets have mostly appeared as by-products of broader experiments rather than as systematic, reusable resources. We introduce FiNERweb, a dataset-creation pipeline that scales the teacher-student paradigm to 91 languages and 25 scripts. Building on FineWeb-Edu, our approach trains regression models to identify NER-relevant passages and annotates them with multilingual LLMs, resulting in about 225k passages with 235k distinct entity labels. Our experiments show that the regression model achieves more than 84 F1, and that models trained on FiNERweb obtain comparable or improved performance in zero shot transfer settings on English, Thai, and Swahili, despite being trained on 19x less data than strong baselines. In addition, we assess annotation quality using LLM-as-a-judge and observe consistently high scores for both faithfulness (3.99 out of 5) and completeness (4.05 out of 5), indicating reliable and informative annotations. Further, we release the dataset with both English labels and translated label sets in the respective target languages because we observe that the performance of current state-of-the-art models drops by 0.02 to 0.09 F1 when evaluated using target language labels instead of English ones. We release FiNERweb together with all accompanying artifacts to the research community in order to facilitate more effective student-teacher training for multilingual named entity recognition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.14202", "title": "Understanding and Improving Hyperbolic Deep Reinforcement Learning", "authors": [ "Timo Klein", "Thomas Lang", "Andrii Shkabrii", "Alexander Sturm", "Kevin Sidak", "Lukas Miklautz", "Claudia Plant", "Yllka Velaj", "Sebastian Tschiatschek" ], "github": "https://github.com/Probabilistic-and-Interactive-ML/hyper-rl", "project_page": "", "abstract": "The performance of reinforcement learning (RL) agents depends critically on the quality of the underlying feature representations. Hyperbolic feature spaces are well-suited for this purpose, as they naturally capture hierarchical and relational structure often present in complex RL environments. However, leveraging these spaces commonly faces optimization challenges due to the nonstationarity of RL. In this work, we identify key factors that determine the success and failure of training hyperbolic deep RL agents. By analyzing the gradients of core operations in the Poincar\u00e9 Ball and Hyperboloid models of hyperbolic geometry, we show that large-norm embeddings destabilize gradient-based training, leading to trust-region violations in proximal policy optimization (PPO). Based on these insights, we introduce Hyper++, a new hyperbolic PPO agent that consists of three components: (i) stable critic training through a categorical value loss instead of regression; (ii) feature regularization guaranteeing bounded norms while avoiding the curse of dimensionality from clipping; and (iii) using a more optimization-friendly formulation of hyperbolic network layers. In experiments on ProcGen, we show that Hyper++ guarantees stable learning, outperforms prior hyperbolic agents, and reduces wall-clock time by approximately 30%. On Atari-5 with Double DQN, Hyper++ strongly outperforms Euclidean and hyperbolic baselines. We release our code at https://github.com/Probabilistic-and-Interactive-ML/hyper-rl .", "github_manual": "", "github_regex": "https://github.com/Probabilistic-and-Interactive-ML/hyper-rl", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.14944", "title": "Puzzle Curriculum GRPO for Vision-Centric Reasoning", "authors": [ "Ahmadreza Jeddi", "Hakki Can Karaimer", "Hue Nguyen", "Zhongling Wang", "Ke Zhao", "Javad Rajabi", "Ran Zhang", "Raghav Goyal", "Babak Taati", "Radek Grzeszczuk" ], "github": "", "project_page": "", "abstract": "Recent reinforcement learning (RL) approaches like outcome-supervised GRPO have advanced chain-of-thought reasoning in Vision Language Models (VLMs), yet key issues linger: (i) reliance on costly and noisy hand-curated annotations or external verifiers; (ii) flat and sparse reward schemes in GRPO; and (iii) logical inconsistency between a chain's reasoning and its final answer. We present Puzzle Curriculum GRPO (PC-GRPO), a supervision-free recipe for RL with Verifiable Rewards (RLVR) that strengthens visual reasoning in VLMs without annotations or external verifiers. PC-GRPO replaces labels with three self-supervised puzzle environments: PatchFit, Rotation (with binary rewards) and Jigsaw (with graded partial credit mitigating reward sparsity). To counter flat rewards and vanishing group-relative advantages, we introduce a difficulty-aware curriculum that dynamically weights samples and peaks at medium difficulty. We further monitor Reasoning-Answer Consistency (RAC) during post-training: mirroring reports for vanilla GRPO in LLMs, RAC typically rises early then degrades; our curriculum delays this decline, and consistency-enforcing reward schemes further boost RAC. RAC correlates with downstream accuracy. Across diverse benchmarks and on Qwen-7B and Qwen-3B backbones, PC-GRPO improves reasoning quality, training stability, and end-task accuracy, offering a practical path to scalable, verifiable, and interpretable RL post-training for VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.15699", "title": "FrontierCS: Evolving Challenges for Evolving Intelligence", "authors": [ "Qiuyang Mang", "Wenhao Chai", "Zhifei Li", "Huanzhi Mao", "Shang Zhou", "Alexander Du", "Hanchen Li", "Shu Liu", "Edwin Chen", "Yichuan Wang", "Xieting Chu", "Zerui Cheng", "Yuan Xu", "Tian Xia", "Zirui Wang", "Tianneng Shi", "Jianzhu Yao", "Yilong Zhao", "Qizheng Zhang", "Charlie Ruan", "Zeyu Shen", "Kaiyuan Liu", "Runyuan He", "Dong Xing", "Zerui Li", "Zirong Zeng", "Yige Jiang", "Lufeng Cheng", "Ziyi Zhao", "Youran Sun", "Wesley Zheng", "Meiyuwang Zhang", "Ruyi Ji", "Xuechang Tu", "Zihan Zheng", "Zexing Chen", "Kangyang Zhou", "Zhaozi Wang", "Jingbang Chen", "Aleksandra Korolova", "Peter Henderson", "Pramod Viswanath", "Vijay Ganesh", "Saining Xie", "Zhuang Liu", "Dawn Song", "Sewon Min", "Ion Stoica", "Joseph E. Gonzalez", "Jingbo Shang", "Alvin Cheung" ], "github": "https://github.com/FrontierCS/Frontier-CS", "project_page": "", "abstract": "We introduce FrontierCS, a benchmark of 156 open-ended problems across diverse areas of computer science, designed and reviewed by experts, including CS PhDs and top-tier competitive programming participants and problem setters. Unlike existing benchmarks that focus on tasks with known optimal solutions, FrontierCS targets problems where the optimal solution is unknown, but the quality of a solution can be objectively evaluated. Models solve these tasks by implementing executable programs rather than outputting a direct answer. FrontierCS includes algorithmic problems, which are often NP-hard variants of competitive programming problems with objective partial scoring, and research problems with the same property. For each problem we provide an expert reference solution and an automatic evaluator. Combining open-ended design, measurable progress, and expert curation, FrontierCS provides a benchmark at the frontier of computer-science difficulty. Empirically, we find that frontier reasoning models still lag far behind human experts on both the algorithmic and research tracks, that increasing reasoning budgets alone does not close this gap, and that models often over-optimize for generating merely workable code instead of discovering high-quality algorithms and system designs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FrontierCS/Frontier-CS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.16760", "title": "Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future", "authors": [ "Tianshuai Hu", "Xiaolu Liu", "Song Wang", "Yiyao Zhu", "Ao Liang", "Lingdong Kong", "Guoyang Zhao", "Zeying Gong", "Jun Cen", "Zhiyu Huang", "Xiaoshuai Hao", "Linfeng Li", "Hang Song", "Xiangtai Li", "Jun Ma", "Shaojie Shen", "Jianke Zhu", "Dacheng Tao", "Ziwei Liu", "Junwei Liang" ], "github": "https://github.com/worldbench/awesome-vla-for-ad", "project_page": "", "abstract": "Autonomous driving has long relied on modular \"Perception-Decision-Action\" pipelines, where hand-crafted interfaces and rule-based components often break down in complex or long-tailed scenarios. Their cascaded design further propagates perception errors, degrading downstream planning and control. Vision-Action (VA) models address some limitations by learning direct mappings from visual inputs to actions, but they remain opaque, sensitive to distribution shifts, and lack structured reasoning or instruction-following capabilities. Recent progress in Large Language Models (LLMs) and multimodal learning has motivated the emergence of Vision-Language-Action (VLA) frameworks, which integrate perception with language-grounded decision making. By unifying visual understanding, linguistic reasoning, and actionable outputs, VLAs offer a pathway toward more interpretable, generalizable, and human-aligned driving policies. This work provides a structured characterization of the emerging VLA landscape for autonomous driving. We trace the evolution from early VA approaches to modern VLA frameworks and organize existing methods into two principal paradigms: End-to-End VLA, which integrates perception, reasoning, and planning within a single model, and Dual-System VLA, which separates slow deliberation (via VLMs) from fast, safety-critical execution (via planners). Within these paradigms, we further distinguish subclasses such as textual vs. numerical action generators and explicit vs. implicit guidance mechanisms. We also summarize representative datasets and benchmarks for evaluating VLA-based driving systems and highlight key challenges and open directions, including robustness, interpretability, and instruction fidelity. Overall, this work aims to establish a coherent foundation for advancing human-compatible autonomous driving systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/worldbench/awesome-vla-for-ad", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-18", "arxiv_id": "2512.14080", "title": "SonicMoE: Accelerating MoE with IO and Tile-aware Optimizations", "authors": [ "Wentao Guo", "Mayank Mishra", "Xinle Cheng", "Ion Stoica", "Tri Dao" ], "github": "", "project_page": "", "abstract": "Mixture of Experts (MoE) models have emerged as the de facto architecture for scaling up language models without significantly increasing the computational cost. Recent MoE models demonstrate a clear trend towards high expert granularity (smaller expert intermediate dimension) and higher sparsity (constant number of activated experts with higher number of total experts), which improve model quality per FLOP. However, fine-grained MoEs suffer from increased activation memory footprint and reduced hardware efficiency due to higher IO costs, while sparser MoEs suffer from wasted computations due to padding in Grouped GEMM kernels. In response, we propose a memory-efficient algorithm to compute the forward and backward passes of MoEs with minimal activation caching for the backward pass. We also design GPU kernels that overlap memory IO with computation benefiting all MoE architectures. Finally, we propose a novel \"token rounding\" method that minimizes the wasted compute due to padding in Grouped GEMM kernels. As a result, our method SonicMoE reduces activation memory by 45% and achieves a 1.86x compute throughput improvement on Hopper GPUs compared to ScatterMoE's BF16 MoE kernel for a fine-grained 7B MoE. Concretely, SonicMoE on 64 H100s achieves a training throughput of 213 billion tokens per day comparable to ScatterMoE's 225 billion tokens per day on 96 H100s for a 7B MoE model training with FSDP-2 using the lm-engine codebase. Under high MoE sparsity settings, our tile-aware token rounding algorithm yields an additional 1.16x speedup on kernel execution time compared to vanilla top-K routing while maintaining similar downstream performance. We open-source all our kernels to enable faster MoE model training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16776", "title": "Kling-Omni Technical Report", "authors": [ "Kling Team", "Jialu Chen", "Yuanzheng Ci", "Xiangyu Du", "Zipeng Feng", "Kun Gai", "Sainan Guo", "Feng Han", "Jingbin He", "Kang He", "Xiao Hu", "Xiaohua Hu", "Boyuan Jiang", "Fangyuan Kong", "Hang Li", "Jie Li", "Qingyu Li", "Shen Li", "Xiaohan Li", "Yan Li", "Jiajun Liang", "Borui Liao", "Yiqiao Liao", "Weihong Lin", "Quande Liu", "Xiaokun Liu", "Yilun Liu", "Yuliang Liu", "Shun Lu", "Hangyu Mao", "Yunyao Mao", "Haodong Ouyang", "Wenyu Qin", "Wanqi Shi", "Xiaoyu Shi", "Lianghao Su", "Haozhi Sun", "Peiqin Sun", "Pengfei Wan", "Chao Wang", "Chenyu Wang", "Meng Wang", "Qiulin Wang", "Runqi Wang", "Xintao Wang", "Xuebo Wang", "Zekun Wang", "Min Wei", "Tiancheng Wen", "Guohao Wu", "Xiaoshi Wu", "Zhenhua Wu", "Da Xie", "Yingtong Xiong", "Yulong Xu", "Sile Yang", "Zikang Yang", "Weicai Ye", "Ziyang Yuan", "Shenglong Zhang", "Shuaiyu Zhang", "Yuanxing Zhang", "Yufan Zhang", "Wenzheng Zhao", "Ruiliang Zhou", "Yan Zhou", "Guosheng Zhu", "Yongjie Zhu" ], "github": "", "project_page": "", "abstract": "We present Kling-Omni, a generalist generative framework designed to synthesize high-fidelity videos directly from multimodal visual language inputs. Adopting an end-to-end perspective, Kling-Omni bridges the functional separation among diverse video generation, editing, and intelligent reasoning tasks, integrating them into a holistic system. Unlike disjointed pipeline approaches, Kling-Omni supports a diverse range of user inputs, including text instructions, reference images, and video contexts, processing them into a unified multimodal representation to deliver cinematic-quality and highly-intelligent video content creation. To support these capabilities, we constructed a comprehensive data system that serves as the foundation for multimodal video creation. The framework is further empowered by efficient large-scale pre-training strategies and infrastructure optimizations for inference. Comprehensive evaluations reveal that Kling-Omni demonstrates exceptional capabilities in in-context generation, reasoning-based editing, and multimodal instruction following. Moving beyond a content creation tool, we believe Kling-Omni is a pivotal advancement toward multimodal world simulators capable of perceiving, reasoning, generating and interacting with the dynamic and complex worlds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16913", "title": "Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation", "authors": [ "Xin Lin", "Meixi Song", "Dizhe Zhang", "Wenxuan Lu", "Haodong Li", "Bo Du", "Ming-Hsuan Yang", "Truong Nguyen", "Lu Qi" ], "github": "", "project_page": "", "abstract": "In this work, we present a panoramic metric depth foundation model that generalizes across diverse scene distances. We explore a data-in-the-loop paradigm from the view of both data construction and framework design. We collect a large-scale dataset by combining public datasets, high-quality synthetic data from our UE5 simulator and text-to-image models, and real panoramic images from the web. To reduce domain gaps between indoor/outdoor and synthetic/real data, we introduce a three-stage pseudo-label curation pipeline to generate reliable ground truth for unlabeled images. For the model, we adopt DINOv3-Large as the backbone for its strong pre-trained generalization, and introduce a plug-and-play range mask head, sharpness-centric optimization, and geometry-centric optimization to improve robustness to varying distances and enforce geometric consistency across views. Experiments on multiple benchmarks (e.g., Stanford2D3D, Matterport3D, and Deep360) demonstrate strong performance and zero-shot generalization, with particularly robust and stable metric predictions in diverse real-world scenes. The project page can be found at: https://insta360-research-team.github.io/DAP_website/ {https://insta360-research-team.github.io/DAP\\_website/}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.13507", "title": "Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model", "authors": [ "Heyi Chen", "Siyan Chen", "Xin Chen", "Yanfei Chen", "Ying Chen", "Zhuo Chen", "Feng Cheng", "Tianheng Cheng", "Xinqi Cheng", "Xuyan Chi", "Jian Cong", "Jing Cui", "Qinpeng Cui", "Qide Dong", "Junliang Fan", "Jing Fang", "Zetao Fang", "Chengjian Feng", "Han Feng", "Mingyuan Gao", "Yu Gao", "Dong Guo", "Qiushan Guo", "Boyang Hao", "Qingkai Hao", "Bibo He", "Qian He", "Tuyen Hoang", "Ruoqing Hu", "Xi Hu", "Weilin Huang", "Zhaoyang Huang", "Zhongyi Huang", "Donglei Ji", "Siqi Jiang", "Wei Jiang", "Yunpu Jiang", "Zhuo Jiang", "Ashley Kim", "Jianan Kong", "Zhichao Lai", "Shanshan Lao", "Yichong Leng", "Ai Li", "Feiya Li", "Gen Li", "Huixia Li", "JiaShi Li", "Liang Li", "Ming Li", "Shanshan Li", "Tao Li", "Xian Li", "Xiaojie Li", "Xiaoyang Li", "Xingxing Li", "Yameng Li", "Yifu Li", "Yiying Li", "Chao Liang", "Han Liang", "Jianzhong Liang", "Ying Liang", "Zhiqiang Liang", "Wang Liao", "Yalin Liao", "Heng Lin", "Kengyu Lin", "Shanchuan Lin", "Xi Lin", "Zhijie Lin", "Feng Ling", "Fangfang Liu", "Gaohong Liu", "Jiawei Liu", "Jie Liu", "Jihao Liu", "Shouda Liu", "Shu Liu", "Sichao Liu", "Songwei Liu", "Xin Liu", "Xue Liu", "Yibo Liu", "Zikun Liu", "Zuxi Liu", "Junlin Lyu", "Lecheng Lyu", "Qian Lyu", "Han Mu", "Xiaonan Nie", "Jingzhe Ning", "Xitong Pan", "Yanghua Peng", "Lianke Qin", "Xueqiong Qu", "Yuxi Ren", "Kai Shen", "Guang Shi", "Lei Shi", "Yan Song", "Yinglong Song", "Fan Sun", "Li Sun", "Renfei Sun", "Yan Sun", "Zeyu Sun", "Wenjing Tang", "Yaxue Tang", "Zirui Tao", "Feng Wang", "Furui Wang", "Jinran Wang", "Junkai Wang", "Ke Wang", "Kexin Wang", "Qingyi Wang", "Rui Wang", "Sen Wang", "Shuai Wang", "Tingru Wang", "Weichen Wang", "Xin Wang", "Yanhui Wang", "Yue Wang", "Yuping Wang", "Yuxuan Wang", "Ziyu Wang", "Guoqiang Wei", "Wanru Wei", "Di Wu", "Guohong Wu", "Hanjie Wu", "Jian Wu", "Jie Wu", "Ruolan Wu", "Xinglong Wu", "Yonghui Wu", "Ruiqi Xia", "Liang Xiang", "Fei Xiao", "XueFeng Xiao", "Pan Xie", "Shuangyi Xie", "Shuang Xu", "Jinlan Xue", "Shen Yan", "Bangbang Yang", "Ceyuan Yang", "Jiaqi Yang", "Runkai Yang", "Tao Yang", "Yang Yang", "Yihang Yang", "ZhiXian Yang", "Ziyan Yang", "Songting Yao", "Yifan Yao", "Zilyu Ye", "Bowen Yu", "Jian Yu", "Chujie Yuan", "Linxiao Yuan", "Sichun Zeng", "Weihong Zeng", "Xuejiao Zeng", "Yan Zeng", "Chuntao Zhang", "Heng Zhang", "Jingjie Zhang", "Kuo Zhang", "Liang Zhang", "Liying Zhang", "Manlin Zhang", "Ting Zhang", "Weida Zhang", "Xiaohe Zhang", "Xinyan Zhang", "Yan Zhang", "Yuan Zhang", "Zixiang Zhang", "Fengxuan Zhao", "Huating Zhao", "Yang Zhao", "Hao Zheng", "Jianbin Zheng", "Xiaozheng Zheng", "Yangyang Zheng", "Yijie Zheng", "Jiexin Zhou", "Jiahui Zhu", "Kuan Zhu", "Shenhan Zhu", "Wenjia Zhu", "Benhui Zou", "Feilong Zuo" ], "github": "", "project_page": "", "abstract": "Recent strides in video generation have paved the way for unified audio-visual generation. In this work, we present Seedance 1.5 pro, a foundational model engineered specifically for native, joint audio-video generation. Leveraging a dual-branch Diffusion Transformer architecture, the model integrates a cross-modal joint module with a specialized multi-stage data pipeline, achieving exceptional audio-visual synchronization and superior generation quality. To ensure practical utility, we implement meticulous post-training optimizations, including Supervised Fine-Tuning (SFT) on high-quality datasets and Reinforcement Learning from Human Feedback (RLHF) with multi-dimensional reward models. Furthermore, we introduce an acceleration framework that boosts inference speed by over 10X. Seedance 1.5 pro distinguishes itself through precise multilingual and dialect lip-syncing, dynamic cinematic camera control, and enhanced narrative coherence, positioning it as a robust engine for professional-grade content creation. Seedance 1.5 pro is now accessible on Volcano Engine at https://console.volcengine.com/ark/region:ark+cn-beijing/experience/vision?type=GenVideo.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16915", "title": "StereoPilot: Learning Unified and Efficient Stereo Conversion via Generative Priors", "authors": [ "Guibao Shen", "Yihua Du", "Wenhang Ge", "Jing He", "Chirui Chang", "Donghao Zhou", "Zhen Yang", "Luozhou Wang", "Xin Tao", "Ying-Cong Chen" ], "github": "", "project_page": "", "abstract": "The rapid growth of stereoscopic displays, including VR headsets and 3D cinemas, has led to increasing demand for high-quality stereo video content. However, producing 3D videos remains costly and complex, while automatic Monocular-to-Stereo conversion is hindered by the limitations of the multi-stage ``Depth-Warp-Inpaint'' (DWI) pipeline. This paradigm suffers from error propagation, depth ambiguity, and format inconsistency between parallel and converged stereo configurations. To address these challenges, we introduce UniStereo, the first large-scale unified dataset for stereo video conversion, covering both stereo formats to enable fair benchmarking and robust model training. Building upon this dataset, we propose StereoPilot, an efficient feed-forward model that directly synthesizes the target view without relying on explicit depth maps or iterative diffusion sampling. Equipped with a learnable domain switcher and a cycle consistency loss, StereoPilot adapts seamlessly to different stereo formats and achieves improved consistency. Extensive experiments demonstrate that StereoPilot significantly outperforms state-of-the-art methods in both visual fidelity and computational efficiency. Project page: https://hit-perfect.github.io/StereoPilot/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16922", "title": "Next-Embedding Prediction Makes Strong Vision Learners", "authors": [ "Sihan Xu", "Ziqiao Ma", "Wenhao Chai", "Xuweiyi Chen", "Weiyang Jin", "Joyce Chai", "Saining Xie", "Stella X. Yu" ], "github": "https://github.com/SihanXU/nepa", "project_page": "", "abstract": "Inspired by the success of generative pretraining in natural language, we ask whether the same principles can yield strong self-supervised visual learners. Instead of training models to output features for downstream use, we train them to generate embeddings to perform predictive tasks directly. This work explores such a shift from learning representations to learning models. Specifically, models learn to predict future patch embeddings conditioned on past ones, using causal masking and stop gradient, which we refer to as Next-Embedding Predictive Autoregression (NEPA). We demonstrate that a simple Transformer pretrained on ImageNet-1k with next embedding prediction as its sole learning objective is effective - no pixel reconstruction, discrete tokens, contrastive loss, or task-specific heads. This formulation retains architectural simplicity and scalability, without requiring additional design complexity. NEPA achieves strong results across tasks, attaining 83.8% and 85.3% top-1 accuracy on ImageNet-1K with ViT-B and ViT-L backbones after fine-tuning, and transferring effectively to semantic segmentation on ADE20K. We believe generative pretraining from embeddings provides a simple, scalable, and potentially modality-agnostic alternative to visual self-supervised learning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SihanXU/nepa", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16301", "title": "Adaptation of Agentic AI", "authors": [ "Pengcheng Jiang", "Jiacheng Lin", "Zhiyi Shi", "Zifeng Wang", "Luxi He", "Yichen Wu", "Ming Zhong", "Peiyang Song", "Qizheng Zhang", "Heng Wang", "Xueqiang Xu", "Hanwen Xu", "Pengrui Han", "Dylan Zhang", "Jiashuo Sun", "Chaoqi Yang", "Kun Qian", "Tian Wang", "Changran Hu", "Manling Li", "Quanzheng Li", "Hao Peng", "Sheng Wang", "Jingbo Shang", "Chao Zhang", "Jiaxuan You", "Liyuan Liu", "Pan Lu", "Yu Zhang", "Heng Ji", "Yejin Choi", "Dawn Song", "Jimeng Sun", "Jiawei Han" ], "github": "", "project_page": "", "abstract": "Cutting-edge agentic AI systems are built on foundation models that can be adapted to plan, reason, and interact with external tools to perform increasingly complex and specialized tasks. As these systems grow in capability and scope, adaptation becomes a central mechanism for improving performance, reliability, and generalization. In this paper, we unify the rapidly expanding research landscape into a systematic framework that spans both agent adaptations and tool adaptations. We further decompose these into tool-execution-signaled and agent-output-signaled forms of agent adaptation, as well as agent-agnostic and agent-supervised forms of tool adaptation. We demonstrate that this framework helps clarify the design space of adaptation strategies in agentic AI, makes their trade-offs explicit, and provides practical guidance for selecting or switching among strategies during system design. We then review the representative approaches in each category, analyze their strengths and limitations, and highlight key open challenges and future opportunities. Overall, this paper aims to offer a conceptual foundation and practical roadmap for researchers and practitioners seeking to build more capable, efficient, and reliable agentic AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16625", "title": "DeContext as Defense: Safe Image Editing in Diffusion Transformers", "authors": [ "Linghui Shen", "Mingyue Cui", "Xingyi Yang" ], "github": "https://github.com/LinghuiiShen/DeContext", "project_page": "", "abstract": "In-context diffusion models allow users to modify images with remarkable ease and realism. However, the same power raises serious privacy concerns: personal images can be easily manipulated for identity impersonation, misinformation, or other malicious uses, all without the owner's consent. While prior work has explored input perturbations to protect against misuse in personalized text-to-image generation, the robustness of modern, large-scale in-context DiT-based models remains largely unexamined. In this paper, we propose DeContext, a new method to safeguard input images from unauthorized in-context editing. Our key insight is that contextual information from the source image propagates to the output primarily through multimodal attention layers. By injecting small, targeted perturbations that weaken these cross-attention pathways, DeContext breaks this flow, effectively decouples the link between input and output. This simple defense is both efficient and robust. We further show that early denoising steps and specific transformer blocks dominate context propagation, which allows us to concentrate perturbations where they matter most. Experiments on Flux Kontext and Step1X-Edit show that DeContext consistently blocks unwanted image edits while preserving visual quality. These results highlight the effectiveness of attention-based perturbations as a powerful defense against image manipulation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LinghuiiShen/DeContext", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16912", "title": "Exploration v.s. Exploitation: Rethinking RLVR through Clipping, Entropy, and Spurious Reward", "authors": [ "Peter Chen", "Xiaopeng Li", "Ziniu Li", "Wotao Yin", "Xi Chen", "Tianyi Lin" ], "github": "", "project_page": "", "abstract": "This paper examines the exploration-exploitation trade-off in reinforcement learning with verifiable rewards (RLVR), a framework for improving the reasoning of Large Language Models (LLMs). Recent studies suggest that RLVR can elicit strong mathematical reasoning in LLMs through two seemingly paradoxical mechanisms: spurious rewards, which suppress exploitation by rewarding outcomes unrelated to the ground truth, and entropy minimization, which suppresses exploration by pushing the model toward more confident and deterministic outputs, highlighting a puzzling dynamic: both discouraging exploitation and discouraging exploration improve reasoning performance, yet the underlying principles that reconcile these effects remain poorly understood. We focus on two fundamental questions: (i) how policy entropy relates to performance, and (ii) whether spurious rewards yield gains, potentially through the interplay of clipping bias and model contamination. Our results show that clipping bias under spurious rewards reduces policy entropy, leading to more confident and deterministic outputs, while entropy minimization alone is insufficient for improvement. We further propose a reward-misalignment model explaining why spurious rewards can enhance performance beyond contaminated settings. Our findings clarify the mechanisms behind spurious-reward benefits and provide principles for more effective RLVR training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16905", "title": "Alchemist: Unlocking Efficiency in Text-to-Image Model Training via Meta-Gradient Data Selection", "authors": [ "Kaixin Ding", "Yang Zhou", "Xi Chen", "Miao Yang", "Jiarong Ou", "Rui Chen", "Xin Tao", "Hengshuang Zhao" ], "github": "https://github.com/KlingTeam/Alchemist", "project_page": "", "abstract": "Recent advances in Text-to-Image (T2I) generative models, such as Imagen, Stable Diffusion, and FLUX, have led to remarkable improvements in visual quality. However, their performance is fundamentally limited by the quality of training data. Web-crawled and synthetic image datasets often contain low-quality or redundant samples, which lead to degraded visual fidelity, unstable training, and inefficient computation. Hence, effective data selection is crucial for improving data efficiency. Existing approaches rely on costly manual curation or heuristic scoring based on single-dimensional features in Text-to-Image data filtering. Although meta-learning based method has been explored in LLM, there is no adaptation for image modalities. To this end, we propose **Alchemist**, a meta-gradient-based framework to select a suitable subset from large-scale text-image data pairs. Our approach automatically learns to assess the influence of each sample by iteratively optimizing the model from a data-centric perspective. Alchemist consists of two key stages: data rating and data pruning. We train a lightweight rater to estimate each sample's influence based on gradient information, enhanced with multi-granularity perception. We then use the Shift-Gsampling strategy to select informative subsets for efficient model training. Alchemist is the first automatic, scalable, meta-gradient-based data selection framework for Text-to-Image model training. Experiments on both synthetic and web-crawled datasets demonstrate that Alchemist consistently improves visual quality and downstream performance. Training on an Alchemist-selected 50% of the data can outperform training on the full dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KlingTeam/Alchemist", "github_api_added_by": "auto" }, { "date": "2025-12-19", "arxiv_id": "2512.15745", "title": "LLaDA2.0: Scaling Up Diffusion Language Models to 100B", "authors": [ "Tiwei Bie", "Maosong Cao", "Kun Chen", "Lun Du", "Mingliang Gong", "Zhuochen Gong", "Yanmei Gu", "Jiaqi Hu", "Zenan Huang", "Zhenzhong Lan", "Chengxi Li", "Chongxuan Li", "Jianguo Li", "Zehuan Li", "Huabin Liu", "Ling Liu", "Guoshan Lu", "Xiaocheng Lu", "Yuxin Ma", "Jianfeng Tan", "Lanning Wei", "Ji-Rong Wen", "Yipeng Xing", "Xiaolu Zhang", "Junbo Zhao", "Da Zheng", "Jun Zhou", "Junlin Zhou", "Zhanchao Zhou", "Liwang Zhu", "Yihong Zhuang" ], "github": "", "project_page": "", "abstract": "This paper presents LLaDA2.0 -- a tuple of discrete diffusion large language models (dLLM) scaling up to 100B total parameters through systematic conversion from auto-regressive (AR) models -- establishing a new paradigm for frontier-scale deployment. Instead of costly training from scratch, LLaDA2.0 upholds knowledge inheritance, progressive adaption and efficiency-aware design principle, and seamless converts a pre-trained AR model into dLLM with a novel 3-phase block-level WSD based training scheme: progressive increasing block-size in block diffusion (warm-up), large-scale full-sequence diffusion (stable) and reverting back to compact-size block diffusion (decay). Along with post-training alignment with SFT and DPO, we obtain LLaDA2.0-mini (16B) and LLaDA2.0-flash (100B), two instruction-tuned Mixture-of-Experts (MoE) variants optimized for practical deployment. By preserving the advantages of parallel decoding, these models deliver superior performance and efficiency at the frontier scale. Both models were open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16921", "title": "Differences That Matter: Auditing Models for Capability Gap Discovery and Rectification", "authors": [ "Qihao Liu", "Chengzhi Mao", "Yaojie Liu", "Alan Yuille", "Wen-Sheng Chu" ], "github": "", "project_page": "", "abstract": "Conventional evaluation methods for multimodal LLMs (MLLMs) lack interpretability and are often insufficient to fully disclose significant capability gaps across models. To address this, we introduce AuditDM, an automated framework that actively discovers and rectifies MLLM failure modes by auditing their divergence. AuditDM fine-tunes an MLLM as an auditor via reinforcement learning to generate challenging questions and counterfactual images that maximize disagreement among target models. Once trained, the auditor uncovers diverse, interpretable exemplars that reveal model weaknesses and serve as annotation-free data for rectification. When applied to SoTA models like Gemma-3 and PaliGemma-2, AuditDM discovers more than 20 distinct failure types. Fine-tuning on these discoveries consistently improves all models across 16 benchmarks, and enables a 3B model to surpass its 28B counterpart. Our results suggest that as data scaling hits diminishing returns, targeted model auditing offers an effective path to model diagnosis and improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.15907", "title": "TabReX : Tabular Referenceless eXplainable Evaluation", "authors": [ "Tejas Anvekar", "Juhna Park", "Aparna Garimella", "Vivek Gupta" ], "github": "", "project_page": "", "abstract": "Evaluating the quality of tables generated by large language models (LLMs) remains an open challenge: existing metrics either flatten tables into text, ignoring structure, or rely on fixed references that limit generalization. We present TabReX, a reference-less, property-driven framework for evaluating tabular generation via graph-based reasoning. TabReX converts both source text and generated tables into canonical knowledge graphs, aligns them through an LLM-guided matching process, and computes interpretable, rubric-aware scores that quantify structural and factual fidelity. The resulting metric provides controllable trade-offs between sensitivity and specificity, yielding human-aligned judgments and cell-level error traces. To systematically asses metric robustness, we introduce TabReX-Bench, a large-scale benchmark spanning six domains and twelve planner-driven perturbation types across three difficulty tiers. Empirical results show that TabReX achieves the highest correlation with expert rankings, remains stable under harder perturbations, and enables fine-grained model-vs-prompt analysis establishing a new paradigm for trustworthy, explainable evaluation of structured generation systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.15528", "title": "EmoCaliber: Advancing Reliable Visual Emotion Comprehension via Confidence Verbalization and Calibration", "authors": [ "Daiqing Wu", "Dongbao Yang", "Can Ma. Yu Zhou" ], "github": "https://github.com/wdqqdw/EmoCaliber", "project_page": "", "abstract": "Visual Emotion Comprehension (VEC) aims to infer sentiment polarities or emotion categories from affective cues embedded in images. In recent years, Multimodal Large Language Models (MLLMs) have established a popular paradigm in VEC, leveraging their generalizability to unify VEC tasks defined under diverse emotion taxonomies. While this paradigm achieves notable success, it typically formulates VEC as a deterministic task, requiring the model to output a single, definitive emotion label for each image. Such a formulation insufficiently accounts for the inherent subjectivity of emotion perception, overlooking alternative interpretations that may be equally plausible to different viewers. To address this limitation, we propose equipping MLLMs with capabilities to verbalize their confidence in emotion predictions. This additional signal provides users with an estimate of both the plausibility of alternative interpretations and the MLLMs' self-assessed competence, thereby enhancing reliability in practice. Building on this insight, we introduce a three-stage training framework that progressively endows with structured reasoning, teaches to verbalize confidence, and calibrates confidence expression, culminating in EmoCaliber, a confidence-aware MLLM for VEC. Through fair and comprehensive evaluations on the unified benchmark VECBench, EmoCaliber demonstrates overall superiority against existing methods in both emotion prediction and confidence estimation. These results validate the effectiveness of our approach and mark a feasible step toward more reliable VEC systems. Project page: https://github.com/wdqqdw/EmoCaliber.", "github_manual": "", "github_regex": "https://github.com/wdqqdw/EmoCaliber", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16923", "title": "Generative Refocusing: Flexible Defocus Control from a Single Image", "authors": [ "Chun-Wei Tuan Mu", "Jia-Bin Huang", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Depth-of-field control is essential in photography, but getting the perfect focus often takes several tries or special equipment. Single-image refocusing is still difficult. It involves recovering sharp content and creating realistic bokeh. Current methods have significant drawbacks. They need all-in-focus inputs, depend on synthetic data from simulators, and have limited control over aperture. We introduce Generative Refocusing, a two-step process that uses DeblurNet to recover all-in-focus images from various inputs and BokehNet for creating controllable bokeh. Our main innovation is semi-supervised training. This method combines synthetic paired data with unpaired real bokeh images, using EXIF metadata to capture real optical characteristics beyond what simulators can provide. Our experiments show we achieve top performance in defocus deblurring, bokeh synthesis, and refocusing benchmarks. Additionally, our Generative Refocusing allows text-guided adjustments and custom aperture shapes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16864", "title": "RePlan: Reasoning-guided Region Planning for Complex Instruction-based Image Editing", "authors": [ "Tianyuan Qu", "Lei Ke", "Xiaohang Zhan", "Longxiang Tang", "Yuqi Liu", "Bohao Peng", "Bei Yu", "Dong Yu", "Jiaya Jia" ], "github": "", "project_page": "", "abstract": "Instruction-based image editing enables natural-language control over visual modifications, yet existing models falter under Instruction-Visual Complexity (IV-Complexity), where intricate instructions meet cluttered or ambiguous scenes. We introduce RePlan (Region-aligned Planning), a plan-then-execute framework that couples a vision-language planner with a diffusion editor. The planner decomposes instructions via step-by-step reasoning and explicitly grounds them to target regions; the editor then applies changes using a training-free attention-region injection mechanism, enabling precise, parallel multi-region edits without iterative inpainting. To strengthen planning, we apply GRPO-based reinforcement learning using 1K instruction-only examples, yielding substantial gains in reasoning fidelity and format reliability. We further present IV-Edit, a benchmark focused on fine-grained grounding and knowledge-intensive edits. Across IV-Complex settings, RePlan consistently outperforms strong baselines trained on far larger datasets, improving regional precision and overall fidelity. Our project page: https://replan-iv-edit.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16918", "title": "AdaTooler-V: Adaptive Tool-Use for Images and Videos", "authors": [ "Chaoyang Wang", "Kaituo Feng", "Dongyang Chen", "Zhongyu Wang", "Zhixun Li", "Sicheng Gao", "Meng Meng", "Xu Zhou", "Manyuan Zhang", "Yuzhang Shang", "Xiangyu Yue" ], "github": "https://github.com/CYWang735/AdaTooler-V", "project_page": "", "abstract": "Recent advances have shown that multimodal large language models (MLLMs) benefit from multimodal interleaved chain-of-thought (CoT) with vision tool interactions. However, existing open-source models often exhibit blind tool-use reasoning patterns, invoking vision tools even when they are unnecessary, which significantly increases inference overhead and degrades model performance. To this end, we propose AdaTooler-V, an MLLM that performs adaptive tool-use by determining whether a visual problem truly requires tools. First, we introduce AT-GRPO, a reinforcement learning algorithm that adaptively adjusts reward scales based on the Tool Benefit Score of each sample, encouraging the model to invoke tools only when they provide genuine improvements. Moreover, we construct two datasets to support training: AdaTooler-V-CoT-100k for SFT cold start and AdaTooler-V-300k for RL with verifiable rewards across single-image, multi-image, and video data. Experiments across twelve benchmarks demonstrate the strong reasoning capability of AdaTooler-V, outperforming existing methods in diverse visual reasoning tasks. Notably, AdaTooler-V-7B achieves an accuracy of 89.8\\% on the high-resolution benchmark V*, surpassing the commercial proprietary model GPT-4o and Gemini 1.5 Pro. All code, models, and data are released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CYWang735/AdaTooler-V", "github_api_added_by": "auto" }, { "date": "2025-12-19", "arxiv_id": "2512.16561", "title": "N3D-VLM: Native 3D Grounding Enables Accurate Spatial Reasoning in Vision-Language Models", "authors": [ "Yuxin Wang", "Lei Ke", "Boqiang Zhang", "Tianyuan Qu", "Hanxun Yu", "Zhenpeng Huang", "Meng Yu", "Dan Xu", "Dong Yu" ], "github": "https://github.com/QwenLM/Qwen3-VL", "project_page": "", "abstract": "While current multimodal models can answer questions based on 2D images, they lack intrinsic 3D object perception, limiting their ability to comprehend spatial relationships and depth cues in 3D scenes. In this work, we propose N3D-VLM, a novel unified framework that seamlessly integrates native 3D object perception with 3D-aware visual reasoning, enabling both precise 3D grounding and interpretable spatial understanding. Unlike conventional end-to-end models that directly predict answers from RGB/RGB-D inputs, our approach equips the model with native 3D object perception capabilities, enabling it to directly localize objects in 3D space based on textual descriptions. Building upon accurate 3D object localization, the model further performs explicit reasoning in 3D, achieving more interpretable and structured spatial understanding. To support robust training for these capabilities, we develop a scalable data construction pipeline that leverages depth estimation to lift large-scale 2D annotations into 3D space, significantly increasing the diversity and coverage for 3D object grounding data, yielding over six times larger than the largest existing single-image 3D detection dataset. Moreover, the pipeline generates spatial question-answering datasets that target chain-of-thought (CoT) reasoning in 3D, facilitating joint training for both 3D object localization and 3D spatial reasoning. Experimental results demonstrate that our unified framework not only achieves state-of-the-art performance on 3D grounding tasks, but also consistently surpasses existing methods in 3D spatial reasoning in vision-language model.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/QwenLM/Qwen3-VL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.12576", "title": "Coupled Variational Reinforcement Learning for Language Model General Reasoning", "authors": [ "Xueru Wen", "Jie Lou", "Yanjiang Liu", "Hongyu Lin", "Ben He", "Xianpei Han", "Le Sun", "Yaojie Lu", "Debing Zhang" ], "github": "", "project_page": "", "abstract": "While reinforcement learning have achieved impressive progress in language model reasoning, they are constrained by the requirement for verifiable rewards. Recent verifier-free RL methods address this limitation by utilizing the intrinsic probabilities of LLMs generating reference answers as reward signals. However, these approaches typically sample reasoning traces conditioned only on the question. This design decouples reasoning-trace sampling from answer information, leading to inefficient exploration and incoherence between traces and final answers. In this paper, we propose \\b{Coupled Variational Reinforcement Learning} (CoVRL), which bridges variational inference and reinforcement learning by coupling prior and posterior distributions through a hybrid sampling strategy. By constructing and optimizing a composite distribution that integrates these two distributions, CoVRL enables efficient exploration while preserving strong thought-answer coherence. Extensive experiments on mathematical and general reasoning benchmarks show that CoVRL improves performance by 12.4\\% over the base model and achieves an additional 2.3\\% improvement over strong state-of-the-art verifier-free RL baselines, providing a principled framework for enhancing the general reasoning capabilities of language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16924", "title": "The World is Your Canvas: Painting Promptable Events with Reference Images, Trajectories, and Text", "authors": [ "Hanlin Wang", "Hao Ouyang", "Qiuyu Wang", "Yue Yu", "Yihao Meng", "Wen Wang", "Ka Leong Cheng", "Shuailei Ma", "Qingyan Bai", "Yixuan Li", "Cheng Chen", "Yanhong Zeng", "Xing Zhu", "Yujun Shen", "Qifeng Chen" ], "github": "", "project_page": "", "abstract": "We present WorldCanvas, a framework for promptable world events that enables rich, user-directed simulation by combining text, trajectories, and reference images. Unlike text-only approaches and existing trajectory-controlled image-to-video methods, our multimodal approach combines trajectories -- encoding motion, timing, and visibility -- with natural language for semantic intent and reference images for visual grounding of object identity, enabling the generation of coherent, controllable events that include multi-agent interactions, object entry/exit, reference-guided appearance and counterintuitive events. The resulting videos demonstrate not only temporal coherence but also emergent consistency, preserving object identity and scene despite temporary disappearance. By supporting expressive world events generation, WorldCanvas advances world models from passive predictors to interactive, user-shaped simulators. Our project page is available at: https://worldcanvas.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16501", "title": "VenusBench-GD: A Comprehensive Multi-Platform GUI Benchmark for Diverse Grounding Tasks", "authors": [ "Beitong Zhou", "Zhexiao Huang", "Yuan Guo", "Zhangxuan Gu", "Tianyu Xia", "Zichen Luo", "Fei Tang", "Dehan Kong", "Yanyi Shang", "Suling Ou", "Zhenlin Guo", "Changhua Meng", "Shuheng Shen" ], "github": "", "project_page": "", "abstract": "GUI grounding is a critical component in building capable GUI agents. However, existing grounding benchmarks suffer from significant limitations: they either provide insufficient data volume and narrow domain coverage, or focus excessively on a single platform and require highly specialized domain knowledge. In this work, we present VenusBench-GD, a comprehensive, bilingual benchmark for GUI grounding that spans multiple platforms, enabling hierarchical evaluation for real-word applications. VenusBench-GD contributes as follows: (i) we introduce a large-scale, cross-platform benchmark with extensive coverage of applications, diverse UI elements, and rich annotated data, (ii) we establish a high-quality data construction pipeline for grounding tasks, achieving higher annotation accuracy than existing benchmarks, and (iii) we extend the scope of element grounding by proposing a hierarchical task taxonomy that divides grounding into basic and advanced categories, encompassing six distinct subtasks designed to evaluate models from complementary perspectives. Our experimental findings reveal critical insights: general-purpose multimodal models now match or even surpass specialized GUI models on basic grounding tasks. In contrast, advanced tasks, still favor GUI-specialized models, though they exhibit significant overfitting and poor robustness. These results underscore the necessity of comprehensive, multi-tiered evaluation frameworks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16636", "title": "REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion", "authors": [ "Giorgos Petsangourakis", "Christos Sgouropoulos", "Bill Psomas", "Theodoros Giannakopoulos", "Giorgos Sfikas", "Ioannis Kakogeorgiou" ], "github": "https://github.com/giorgospets/reglue", "project_page": "", "abstract": "Latent diffusion models (LDMs) achieve state-of-the-art image synthesis, yet their reconstruction-style denoising objective provides only indirect semantic supervision: high-level semantics emerge slowly, requiring longer training and limiting sample quality. Recent works inject semantics from Vision Foundation Models (VFMs) either externally via representation alignment or internally by jointly modeling only a narrow slice of VFM features inside the diffusion process, under-utilizing the rich, nonlinear, multi-layer spatial semantics available. We introduce REGLUE (Representation Entanglement with Global-Local Unified Encoding), a unified latent diffusion framework that jointly models (i) VAE image latents, (ii) compact local (patch-level) VFM semantics, and (iii) a global (image-level) [CLS] token within a single SiT backbone. A lightweight convolutional semantic compressor nonlinearly aggregates multi-layer VFM features into a low-dimensional, spatially structured representation, which is entangled with the VAE latents in the diffusion process. An external alignment loss further regularizes internal representations toward frozen VFM targets. On ImageNet 256x256, REGLUE consistently improves FID and accelerates convergence over SiT-B/2 and SiT-XL/2 baselines, as well as over REPA, ReDi, and REG. Extensive experiments show that (a) spatial VFM semantics are crucial, (b) non-linear compression is key to unlocking their full benefit, and (c) global tokens and external alignment act as complementary, lightweight enhancements within our global-local-latent joint modeling framework. The code is available at https://github.com/giorgospets/reglue .", "github_manual": "", "github_regex": "https://github.com/giorgospets/reglue", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16767", "title": "Make-It-Poseable: Feed-forward Latent Posing Model for 3D Humanoid Character Animation", "authors": [ "Zhiyang Guo", "Ori Zhang", "Jax Xiang", "Alan Zhao", "Wengang Zhou", "Houqiang Li" ], "github": "", "project_page": "", "abstract": "Posing 3D characters is a fundamental task in computer graphics and vision. However, existing methods like auto-rigging and pose-conditioned generation often struggle with challenges such as inaccurate skinning weight prediction, topological imperfections, and poor pose conformance, limiting their robustness and generalizability. To overcome these limitations, we introduce Make-It-Poseable, a novel feed-forward framework that reformulates character posing as a latent-space transformation problem. Instead of deforming mesh vertices as in traditional pipelines, our method reconstructs the character in new poses by directly manipulating its latent representation. At the core of our method is a latent posing transformer that manipulates shape tokens based on skeletal motion. This process is facilitated by a dense pose representation for precise control. To ensure high-fidelity geometry and accommodate topological changes, we also introduce a latent-space supervision strategy and an adaptive completion module. Our method demonstrates superior performance in posing quality. It also naturally extends to 3D editing applications like part replacement and refinement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16615", "title": "Trainable Log-linear Sparse Attention for Efficient Diffusion Transformers", "authors": [ "Yifan Zhou", "Zeqi Xiao", "Tianyi Wei", "Shuai Yang", "Xingang Pan" ], "github": "https://github.com/SingleZombie/LLSA", "project_page": "", "abstract": "Diffusion Transformers (DiTs) set the state of the art in visual generation, yet their quadratic self-attention cost fundamentally limits scaling to long token sequences. Recent Top-K sparse attention approaches reduce the computation of DiTs by compressing tokens into block-wise representation and selecting a small set of relevant key blocks, but still suffer from (i) quadratic selection cost on compressed tokens and (ii) increasing K required to maintain model quality as sequences grow. We identify that their inefficiency is due to the single-level design, as a single coarse level is insufficient to represent the global structure. In this paper, we introduce Log-linear Sparse Attention (LLSA), a trainable sparse attention mechanism for extremely long token sequences that reduces both selection and attention costs from quadratic to log-linear complexity by utilizing a hierarchical structure. LLSA performs hierarchical Top-K selection, progressively adopting sparse Top-K selection with the indices found at the previous level, and introduces a Hierarchical KV Enrichment mechanism that preserves global context while using fewer tokens of different granularity during attention computation. To support efficient training, we develop a high-performance GPU implementation that uses only sparse indices for both the forward and backward passes, eliminating the need for dense attention masks. We evaluate LLSA on high-resolution pixel-space image generation without using patchification and VAE encoding. LLSA accelerates attention inference by 28.27x and DiT training by 6.09x on 256x256 pixel token sequences, while maintaining generation quality. The results demonstrate that LLSA offers a promising direction for training long-sequence DiTs efficiently. Code is available at: https://github.com/SingleZombie/LLSA", "github_manual": "", "github_regex": "https://github.com/SingleZombie/LLSA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.11251", "title": "Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language", "authors": [ "Yunkai Zhang", "Yawen Zhang", "Ming Zheng", "Kezhen Chen", "Chongyang Gao", "Ruian Ge", "Siyuan Teng", "Amine Jelloul", "Jinmeng Rao", "Xiaoyuan Guo", "Chiang-Wei Fang", "Zeyu Zheng", "Jie Yang" ], "github": "", "project_page": "", "abstract": "Time-series data is critical across many scientific and industrial domains, including environmental analysis, agriculture, transportation, and finance. However, mining insights from this data typically requires deep domain expertise, a process that is both time-consuming and labor-intensive. In this paper, we propose Insight Miner, a large-scale multimodal model (LMM) designed to generate high-quality, comprehensive time-series descriptions enriched with domain-specific knowledge. To facilitate this, we introduce TS-InsightsAvailable at \\href{https://huggingface.co/datasets/zhykoties/time-series-language-alignment{https://huggingface.co/datasets/zhykoties/time-series-language-alignment}.}, the first general-domain dataset for time series and language alignment. TS-Insights contains 100k time-series windows sampled from 20 forecasting datasets. We construct this dataset using a novel agentic workflow, where we use statistical tools to extract features from raw time series before synthesizing them into coherent trend descriptions with GPT-4. Following instruction tuning on TS-Insights, Insight Miner outperforms state-of-the-art multimodal models, such as LLaVA liu2023llava and GPT-4, in generating time-series descriptions and insights. Our findings suggest a promising direction for leveraging LMMs in time series analysis, and serve as a foundational step toward enabling LLMs to interpret time series as a native input modality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16900", "title": "FlashPortrait: 6x Faster Infinite Portrait Animation with Adaptive Latent Prediction", "authors": [ "Shuyuan Tu", "Yueming Pan", "Yinming Huang", "Xintong Han", "Zhen Xing", "Qi Dai", "Kai Qiu", "Chong Luo", "Zuxuan Wu" ], "github": "", "project_page": "", "abstract": "Current diffusion-based acceleration methods for long-portrait animation struggle to ensure identity (ID) consistency. This paper presents FlashPortrait, an end-to-end video diffusion transformer capable of synthesizing ID-preserving, infinite-length videos while achieving up to 6x acceleration in inference speed. In particular, FlashPortrait begins by computing the identity-agnostic facial expression features with an off-the-shelf extractor. It then introduces a Normalized Facial Expression Block to align facial features with diffusion latents by normalizing them with their respective means and variances, thereby improving identity stability in facial modeling. During inference, FlashPortrait adopts a dynamic sliding-window scheme with weighted blending in overlapping areas, ensuring smooth transitions and ID consistency in long animations. In each context window, based on the latent variation rate at particular timesteps and the derivative magnitude ratio among diffusion layers, FlashPortrait utilizes higher-order latent derivatives at the current timestep to directly predict latents at future timesteps, thereby skipping several denoising steps and achieving 6x speed acceleration. Experiments on benchmarks show the effectiveness of FlashPortrait both qualitatively and quantitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16899", "title": "Multimodal RewardBench 2: Evaluating Omni Reward Models for Interleaved Text and Image", "authors": [ "Yushi Hu", "Reyhane Askari-Hemmat", "Melissa Hall", "Emily Dinan", "Luke Zettlemoyer", "Marjan Ghazvininejad" ], "github": "", "project_page": "", "abstract": "Reward models (RMs) are essential for training large language models (LLMs), but remain underexplored for omni models that handle interleaved image and text sequences. We introduce Multimodal RewardBench 2 (MMRB2), the first comprehensive benchmark for reward models on multimodal understanding and (interleaved) generation. MMRB2 spans four tasks: text-to-image, image editing, interleaved generation, and multimodal reasoning (\"thinking-with-images\"), providing 1,000 expert-annotated preference pairs per task from 23 models and agents across 21 source tasks. MMRB2 is designed with: (1) practical but challenging prompts; (2) responses from state-of-the-art models and agents; and (3) preference pairs with strong human-expert consensus, curated via an ensemble filtering strategy. Using MMRB2, we study existing judges for each subtask, including multimodal LLM-as-a-judge and models trained with human preferences. The latest Gemini 3 Pro attains 75-80% accuracy. GPT-5 and Gemini 2.5 Pro reach 66-75% accuracy, compared to >90% for humans, yet surpass the widely used GPT-4o (59%). The best performing open-source model Qwen3-VL-32B achieves similar accuracies as Gemini 2.5 Flash (64%). We also show that MMRB2 performance strongly correlates with downstream task success using Best-of-N sampling and conduct an in-depth analysis that shows key areas to improve the reward models going forward.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16670", "title": "FrameDiffuser: G-Buffer-Conditioned Diffusion for Neural Forward Frame Rendering", "authors": [ "Ole Beisswenger", "Jan-Niklas Dihlmann", "Hendrik P. A. Lensch" ], "github": "https://github.com/cgtuebingen/FrameDiffuser", "project_page": "", "abstract": "Neural rendering for interactive applications requires translating geometric and material properties (G-buffer) to photorealistic images with realistic lighting on a frame-by-frame basis. While recent diffusion-based approaches show promise for G-buffer-conditioned image synthesis, they face critical limitations: single-image models like RGBX generate frames independently without temporal consistency, while video models like DiffusionRenderer are too computationally expensive for most consumer gaming sets ups and require complete sequences upfront, making them unsuitable for interactive applications where future frames depend on user input. We introduce FrameDiffuser, an autoregressive neural rendering framework that generates temporally consistent, photorealistic frames by conditioning on G-buffer data and the models own previous output. After an initial frame, FrameDiffuser operates purely on incoming G-buffer data, comprising geometry, materials, and surface properties, while using its previously generated frame for temporal guidance, maintaining stable, temporal consistent generation over hundreds to thousands of frames. Our dual-conditioning architecture combines ControlNet for structural guidance with ControlLoRA for temporal coherence. A three-stage training strategy enables stable autoregressive generation. We specialize our model to individual environments, prioritizing consistency and inference speed over broad generalization, demonstrating that environment-specific training achieves superior photorealistic quality with accurate lighting, shadows, and reflections compared to generalized approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cgtuebingen/FrameDiffuser", "github_api_added_by": "auto" }, { "date": "2025-12-19", "arxiv_id": "2512.14884", "title": "Vibe Spaces for Creatively Connecting and Expressing Visual Concepts", "authors": [ "Huzheng Yang", "Katherine Xu", "Andrew Lu", "Michael D. Grossberg", "Yutong Bai", "Jianbo Shi" ], "github": "", "project_page": "", "abstract": "Creating new visual concepts often requires connecting distinct ideas through their most relevant shared attributes -- their vibe. We introduce Vibe Blending, a novel task for generating coherent and meaningful hybrids that reveals these shared attributes between images. Achieving such blends is challenging for current methods, which struggle to identify and traverse nonlinear paths linking distant concepts in latent space. We propose Vibe Space, a hierarchical graph manifold that learns low-dimensional geodesics in feature spaces like CLIP, enabling smooth and semantically consistent transitions between concepts. To evaluate creative quality, we design a cognitively inspired framework combining human judgments, LLM reasoning, and a geometric path-based difficulty score. We find that Vibe Space produces blends that humans consistently rate as more creative and coherent than current methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16649", "title": "JustRL: Scaling a 1.5B LLM with a Simple RL Recipe", "authors": [ "Bingxiang He", "Zekai Qu", "Zeyuan Liu", "Yinghao Chen", "Yuxin Zuo", "Cheng Qian", "Kaiyan Zhang", "Weize Chen", "Chaojun Xiao", "Ganqu Cui", "Ning Ding", "Zhiyuan Liu" ], "github": "", "project_page": "", "abstract": "Recent advances in reinforcement learning for large language models have converged on increasing complexity: multi-stage training pipelines, dynamic hyperparameter schedules, and curriculum learning strategies. This raises a fundamental question: Is this complexity necessary? We present JustRL, a minimal approach using single-stage training with fixed hyperparameters that achieves state-of-the-art performance on two 1.5B reasoning models (54.9\\% and 64.3\\% average accuracy across nine mathematical benchmarks) while using 2times less compute than sophisticated approaches. The same hyperparameters transfer across both models without tuning, and training exhibits smooth, monotonic improvement over 4,000+ steps without the collapses or plateaus that typically motivate interventions. Critically, ablations reveal that adding ``standard tricks'' like explicit length penalties and robust verifiers may degrade performance by collapsing exploration. These results suggest that the field may be adding complexity to solve problems that disappear with a stable, scaled-up baseline. We release our models and code to establish a simple, validated baseline for the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16378", "title": "Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs", "authors": [ "Sara Papi", "Javier Garcia Gilabert", "Zachary Hopton", "Vil\u00e9m Zouhar", "Carlos Escolano", "Gerard I. G\u00e1llego", "Jorge Iranzo-S\u00e1nchez", "Ahrii Kim", "Dominik Mach\u00e1\u010dek", "Patricia Schmidtova", "Maike Z\u00fcfle" ], "github": "", "project_page": "", "abstract": "As Large Language Models (LLMs) expand beyond text, integrating speech as a native modality has given rise to SpeechLLMs, which aim to translate spoken language directly, thereby bypassing traditional transcription-based pipelines. Whether this integration improves speech-to-text translation quality over established cascaded architectures, however, remains an open question. We present Hearing to Translate, the first comprehensive test suite rigorously benchmarking 5 state-of-the-art SpeechLLMs against 16 strong direct and cascade systems that couple leading speech foundation models (SFM), with multilingual LLMs. Our analysis spans 16 benchmarks, 13 language pairs, and 9 challenging conditions, including disfluent, noisy, and long-form speech. Across this extensive evaluation, we find that cascaded systems remain the most reliable overall, while current SpeechLLMs only match cascades in selected settings and SFMs lag behind both, highlighting that integrating an LLM, either within the model or in a pipeline, is essential for high-quality speech translation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16920", "title": "EasyV2V: A High-quality Instruction-based Video Editing Framework", "authors": [ "Jinjie Mai", "Chaoyang Wang", "Guocheng Gordon Qian", "Willi Menapace", "Sergey Tulyakov", "Bernard Ghanem", "Peter Wonka", "Ashkan Mirzaei" ], "github": "", "project_page": "", "abstract": "While image editing has advanced rapidly, video editing remains less explored, facing challenges in consistency, control, and generalization. We study the design space of data, architecture, and control, and introduce EasyV2V, a simple and effective framework for instruction-based video editing. On the data side, we compose existing experts with fast inverses to build diverse video pairs, lift image edit pairs into videos via single-frame supervision and pseudo pairs with shared affine motion, mine dense-captioned clips for video pairs, and add transition supervision to teach how edits unfold. On the model side, we observe that pretrained text-to-video models possess editing capability, motivating a simplified design. Simple sequence concatenation for conditioning with light LoRA fine-tuning suffices to train a strong model. For control, we unify spatiotemporal control via a single mask mechanism and support optional reference images. Overall, EasyV2V works with flexible inputs, e.g., video+text, video+mask+text, video+mask+reference+text, and achieves state-of-the-art video editing results, surpassing concurrent and commercial systems. Project page: https://snap-research.github.io/easyv2v/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16909", "title": "MomaGraph: State-Aware Unified Scene Graphs with Vision-Language Model for Embodied Task Planning", "authors": [ "Yuanchen Ju", "Yongyuan Liang", "Yen-Jen Wang", "Nandiraju Gireesh", "Yuanliang Ju", "Seungjae Lee", "Qiao Gu", "Elvis Hsieh", "Furong Huang", "Koushil Sreenath" ], "github": "", "project_page": "", "abstract": "Mobile manipulators in households must both navigate and manipulate. This requires a compact, semantically rich scene representation that captures where objects are, how they function, and which parts are actionable. Scene graphs are a natural choice, yet prior work often separates spatial and functional relations, treats scenes as static snapshots without object states or temporal updates, and overlooks information most relevant for accomplishing the current task. To address these limitations, we introduce MomaGraph, a unified scene representation for embodied agents that integrates spatial-functional relationships and part-level interactive elements. However, advancing such a representation requires both suitable data and rigorous evaluation, which have been largely missing. We thus contribute MomaGraph-Scenes, the first large-scale dataset of richly annotated, task-driven scene graphs in household environments, along with MomaGraph-Bench, a systematic evaluation suite spanning six reasoning capabilities from high-level planning to fine-grained scene understanding. Built upon this foundation, we further develop MomaGraph-R1, a 7B vision-language model trained with reinforcement learning on MomaGraph-Scenes. MomaGraph-R1 predicts task-oriented scene graphs and serves as a zero-shot task planner under a Graph-then-Plan framework. Extensive experiments demonstrate that our model achieves state-of-the-art results among open-source models, reaching 71.6% accuracy on the benchmark (+11.4% over the best baseline), while generalizing across public benchmarks and transferring effectively to real-robot experiments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.10953", "title": "Bidirectional Normalizing Flow: From Data to Noise and Back", "authors": [ "Yiyang Lu", "Qiao Sun", "Xianbang Wang", "Zhicheng Jiang", "Hanhong Zhao", "Kaiming He" ], "github": "", "project_page": "", "abstract": "Normalizing Flows (NFs) have been established as a principled framework for generative modeling. Standard NFs consist of a forward process and a reverse process: the forward process maps data to noise, while the reverse process generates samples by inverting it. Typical NF forward transformations are constrained by explicit invertibility, ensuring that the reverse process can serve as their exact analytic inverse. Recent developments in TARFlow and its variants have revitalized NF methods by combining Transformers and autoregressive flows, but have also exposed causal decoding as a major bottleneck. In this work, we introduce Bidirectional Normalizing Flow (BiFlow), a framework that removes the need for an exact analytic inverse. BiFlow learns a reverse model that approximates the underlying noise-to-data inverse mapping, enabling more flexible loss functions and architectures. Experiments on ImageNet demonstrate that BiFlow, compared to its causal decoding counterpart, improves generation quality while accelerating sampling by up to two orders of magnitude. BiFlow yields state-of-the-art results among NF-based methods and competitive performance among single-evaluation (\"1-NFE\") methods. Following recent encouraging progress on NFs, we hope our work will draw further attention to this classical paradigm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.15489", "title": "Nemotron-Math: Efficient Long-Context Distillation of Mathematical Reasoning from Multi-Mode Supervision", "authors": [ "Wei Du", "Shubham Toshniwal", "Branislav Kisacanin", "Sadegh Mahdavi", "Ivan Moshkov", "George Armstrong", "Stephen Ge", "Edgar Minasyan", "Feng Chen", "Igor Gitman" ], "github": "", "project_page": "", "abstract": "High-quality mathematical reasoning supervision requires diverse reasoning styles, long-form traces, and effective tool integration, capabilities that existing datasets provide only in limited form. Leveraging the multi-mode generation ability of gpt-oss-120b, we introduce Nemotron-Math, a large-scale mathematical reasoning dataset containing 7.5M solution traces across high, medium, and low reasoning modes, each available both with and without Python tool-integrated reasoning (TIR). The dataset integrates 85K curated AoPS problems with 262K community-sourced StackExchange-Math problems, combining structured competition tasks with diverse real-world mathematical queries. We conduct controlled evaluations to assess the dataset quality. Nemotron-Math consistently outperforms the original OpenMathReasoning on matched AoPS problems. Incorporating StackExchange-Math substantially improves robustness and generalization, especially on HLE-Math, while preserving accuracy on math competition benchmarks. To support efficient long-context training, we develop a sequential bucketed strategy that accelerates 128K context-length fine-tuning by 2--3times without significant accuracy loss. Overall, Nemotron-Math enables state-of-the-art performance, including 100\\% maj@16 accuracy on AIME 2024 and 2025 with Python TIR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.14805", "title": "Sharing State Between Prompts and Programs", "authors": [ "Ellie Y. Cheng", "Logan Weber", "Tian Jin", "Michael Carbin" ], "github": "", "project_page": "", "abstract": "The rise of large language models (LLMs) has introduced a new type of programming: natural language programming. By writing prompts that direct LLMs to perform natural language processing, code generation, reasoning, etc., users are writing code in natural language -- natural language code -- for the LLM to execute. An emerging area of research enables interoperability between natural language code and formal languages such as Python. We present a novel programming abstraction, shared program state, that removes the manual work required to enable interoperability between natural language code and program state. With shared program state, programmers can write natural code that directly writes program variables, computes with program objects, and implements control flow in the program. We present a schema for specifying natural function interfaces that extend programming systems to support natural code and leverage this schema to specify shared program state as a natural function interface. We implement shared program state in the Nightjar programming system. Nightjar enables programmers to write Python programs that contain natural code that shares the Python program state. We show that Nightjar programs achieve comparable or higher task accuracy than manually written implementations (+4-19%), while decreasing the lines of code by 39.6% on average. The tradeoff to using Nightjar is that it may incur runtime overhead (0.4-4.3x runtime of manual implementations).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.16106", "title": "ModelTables: A Corpus of Tables about Models", "authors": [ "Zhengyuan Dong", "Victor Zhong", "Ren\u00e9e J. Miller" ], "github": "https://github.com/RJMillerLab/ModelTables", "project_page": "", "abstract": "We present ModelTables, a benchmark of tables in Model Lakes that captures the structured semantics of performance and configuration tables often overlooked by text only retrieval. The corpus is built from Hugging Face model cards, GitHub READMEs, and referenced papers, linking each table to its surrounding model and publication context. Compared with open data lake tables, model tables are smaller yet exhibit denser inter table relationships, reflecting tightly coupled model and benchmark evolution. The current release covers over 60K models and 90K tables. To evaluate model and table relatedness, we construct a multi source ground truth using three complementary signals: (1) paper citation links, (2) explicit model card links and inheritance, and (3) shared training datasets. We present one extensive empirical use case for the benchmark which is table search. We compare canonical Data Lake search operators (unionable, joinable, keyword) and Information Retrieval baselines (dense, sparse, hybrid retrieval) on this benchmark. Union based semantic table retrieval attains 54.8 % P@1 overall (54.6 % on citation, 31.3 % on inheritance, 30.6 % on shared dataset signals); table based dense retrieval reaches 66.5 % P@1, and metadata hybrid retrieval achieves 54.1 %. This evaluation indicates clear room for developing better table search methods. By releasing ModelTables and its creation protocol, we provide the first large scale benchmark of structured data describing AI model. Our use case of table discovery in Model Lakes, provides intuition and evidence for developing more accurate semantic retrieval, structured comparison, and principled organization of structured model knowledge. Source code, data, and other artifacts have been made available at https://github.com/RJMillerLab/ModelTables.", "github_manual": "", "github_regex": "https://github.com/RJMillerLab/ModelTables", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.12623", "title": "Reasoning Within the Mind: Dynamic Multimodal Interleaving in Latent Space", "authors": [ "Chengzhi Liu", "Yuzhe Yang", "Yue Fan", "Qingyue Wei", "Sheng Liu", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "Recent advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced cross-modal understanding and reasoning by incorporating Chain-of-Thought (CoT) reasoning in the semantic space. Building upon this, recent studies extend the CoT mechanism to the visual modality, enabling models to integrate visual information during reasoning through external tools or explicit image generation. However, these methods remain dependent on explicit step-by-step reasoning, unstable perception-reasoning interaction and notable computational overhead. Inspired by human cognition, we posit that thinking unfolds not linearly but through the dynamic interleaving of reasoning and perception within the mind. Motivated by this perspective, we propose DMLR, a test-time Dynamic Multimodal Latent Reasoning framework that employs confidence-guided latent policy gradient optimization to refine latent think tokens for in-depth reasoning. Furthermore, a Dynamic Visual Injection Strategy is introduced, which retrieves the most relevant visual features at each latent think token and updates the set of best visual patches. The updated patches are then injected into latent think token to achieve dynamic visual-textual interleaving. Experiments across seven multimodal reasoning benchmarks and various model architectures demonstrate that DMLR significantly improves reasoning and perception performance while maintaining high inference efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-19", "arxiv_id": "2512.12880", "title": "Improving Recursive Transformers with Mixture of LoRAs", "authors": [ "Mohammadmahdi Nouriborji", "Morteza Rohanian", "Omid Rohanian" ], "github": "", "project_page": "", "abstract": "Parameter sharing in recursive transformers reduces model size but collapses layer-wise expressivity. We propose Mixture of LoRAs (MoL), a lightweight conditional-computation mechanism that inserts Low-Rank Adaptation (LoRA) experts inside a shared feed-forward network (FFN). MoL enables token-conditional weight-space modulation of the shared FFN without untying backbone parameters, unlike prior approaches that add fixed or externally attached adapters. We pretrain a modernised recursive architecture, ModernALBERT, integrating rotary embeddings, GeGLU, FlashAttention, and a distillation-based initialisation. Across GLUE, SQuAD-v2, and BEIR, ModernALBERT (50M--120M) achieves state-of-the-art performance among compact models and surpasses larger fully parameterised baselines. We also propose an expert-merging procedure that compresses MoL into a single adapter at inference while preserving accuracy, enabling efficient deployment. Our results show that conditional weight-space modulation effectively restores the expressivity lost under aggressive parameter sharing in recursive transformers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.17260", "title": "Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience", "authors": [ "Jiangjie Chen", "Wenxiang Chen", "Jiacheng Du", "Jinyi Hu", "Zhicheng Jiang", "Allan Jie", "Xiaoran Jin", "Xing Jin", "Chenggang Li", "Wenlei Shi", "Zhihong Wang", "Mingxuan Wang", "Chenrui Wei", "Shufa Wei", "Huajian Xin", "Fan Yang", "Weihao Gao", "Zheng Yuan", "Tianyang Zhan", "Zeyu Zheng", "Tianxi Zhou", "Thomas Hanwen Zhu" ], "github": "", "project_page": "", "abstract": "Large language models have recently made significant progress to generate rigorous mathematical proofs. In contrast, utilizing LLMs for theorem proving in formal languages (such as Lean) remains challenging and computationally expensive, particularly when addressing problems at the undergraduate level and beyond. In this work, we present Seed-Prover 1.5, a formal theorem-proving model trained via large-scale agentic reinforcement learning, alongside an efficient test-time scaling (TTS) workflow. Through extensive interactions with Lean and other tools, the model continuously accumulates experience during the RL process, substantially enhancing the capability and efficiency of formal theorem proving. Furthermore, leveraging recent advancements in natural language proving, our TTS workflow efficiently bridges the gap between natural and formal languages. Compared to state-of-the-art methods, Seed-Prover 1.5 achieves superior performance with a smaller compute budget. It solves 88\\% of PutnamBench (undergraduate-level), 80\\% of Fate-H (graduate-level), and 33\\% of Fate-X (PhD-level) problems. Notably, using our system, we solved 11 out of 12 problems from Putnam 2025 within 9 hours. Our findings suggest that scaling learning from experience, driven by high-quality formal feedback, holds immense potential for the future of formal mathematical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.17901", "title": "When Reasoning Meets Its Laws", "authors": [ "Junyu Zhang", "Yifan Sun", "Tianang Leng", "Jingyan Shen", "Liu Ziyin", "Paul Pu Liang", "Huan Zhang" ], "github": "https://github.com/ASTRAL-Group/LoRe", "project_page": "", "abstract": "Despite the superior performance of Large Reasoning Models (LRMs), their reasoning behaviors are often counterintuitive, leading to suboptimal reasoning capabilities. To theoretically formalize the desired reasoning behaviors, this paper presents the Laws of Reasoning (LoRe), a unified framework that characterizes intrinsic reasoning patterns in LRMs. We first propose compute law with the hypothesis that the reasoning compute should scale linearly with question complexity. Beyond compute, we extend LoRe with a supplementary accuracy law. Since the question complexity is difficult to quantify in practice, we examine these hypotheses by two properties of the laws, monotonicity and compositionality. We therefore introduce LoRe-Bench, a benchmark that systematically measures these two tractable properties for large reasoning models. Evaluation shows that most reasoning models exhibit reasonable monotonicity but lack compositionality. In response, we develop an effective finetuning approach that enforces compute-law compositionality. Extensive empirical studies demonstrate that better compliance with compute laws yields consistently improved reasoning performance on multiple benchmarks, and uncovers synergistic effects across properties and laws. Project page: https://lore-project.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ASTRAL-Group/LoRe", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.17419", "title": "SWE-Bench++: A Framework for the Scalable Generation of Software Engineering Benchmarks from Open-Source Repositories", "authors": [ "Lilin Wang", "Lucas Ramalho", "Alan Celestino", "Phuc Anthony Pham", "Yu Liu", "Umang Kumar Sinha", "Andres Portillo", "Onassis Osunwa", "Gabriel Maduekwe" ], "github": "", "project_page": "", "abstract": "Benchmarks like SWE-bench have standardized the evaluation of Large Language Models (LLMs) on repository-level software engineering tasks. However, these efforts remain limited by manual curation, static datasets, and a focus on Python-based bug fixes. We introduce SWE-Bench++, an automated framework that generates repository-level coding tasks from open-source GitHub projects. Unlike synthetic approaches, our pipeline harvests live pull requests to cover both bug fixes and feature requests across 11 languages. SWE-Bench++ turns GitHub pull requests (PRs) into reproducible, execution-based tasks via four stages: programmatic sourcing, environment synthesis, test oracle extraction, and quality assurance. A final hint-guided trajectory synthesis step converts instances that strong models fail on into training trajectories. Our initial benchmark consists of 11,133 instances from 3,971 repositories across 11 languages. On a subset of 1,782 instances of this benchmark, today's strongest models perform as follows: claude-sonnet-4.5 achieves 36.20% pass@10, gpt-5-2025-08-07 34.57%, gemini/gemini-2.5-pro 24.92%, and gpt-4o 16.89%. We further demonstrate the utility of our dataset by showing that fine-tuning on SWE-Bench++ instances yields measurable improvements on the SWE-bench Multilingual benchmark. SWE-Bench++ provides a scalable, multilingual benchmark for evaluating and improving repository-level code generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.17012", "title": "4D-RGPT: Toward Region-level 4D Understanding via Perceptual Distillation", "authors": [ "Chiao-An Yang", "Ryo Hachiuma", "Sifei Liu", "Subhashree Radhakrishnan", "Raymond A. Yeh", "Yu-Chiang Frank Wang", "Min-Hung Chen" ], "github": "", "project_page": "", "abstract": "Despite advances in Multimodal LLMs (MLLMs), their ability to reason over 3D structures and temporal dynamics remains limited, constrained by weak 4D perception and temporal understanding. Existing 3D and 4D Video Question Answering (VQA) benchmarks also emphasize static scenes and lack region-level prompting. We tackle these issues by introducing: (a) 4D-RGPT, a specialized MLLM designed to capture 4D representations from video inputs with enhanced temporal perception; (b) Perceptual 4D Distillation (P4D), a training framework that transfers 4D representations from a frozen expert model into 4D-RGPT for comprehensive 4D perception; and (c) R4D-Bench, a benchmark for depth-aware dynamic scenes with region-level prompting, built via a hybrid automated and human-verified pipeline. Our 4D-RGPT achieves notable improvements on both existing 4D VQA benchmarks and the proposed R4D-Bench benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.16969", "title": "Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows", "authors": [ "Wanghan Xu", "Yuhao Zhou", "Yifan Zhou", "Qinglong Cao", "Shuo Li", "Jia Bu", "Bo Liu", "Yixin Chen", "Xuming He", "Xiangyu Zhao", "Xiang Zhuang", "Fengxiang Wang", "Zhiwang Zhou", "Qiantai Feng", "Wenxuan Huang", "Jiaqi Wei", "Hao Wu", "Yuejin Yang", "Guangshuai Wang", "Sheng Xu", "Ziyan Huang", "Xinyao Liu", "Jiyao Liu", "Cheng Tang", "Wei Li", "Ying Chen", "Junzhi Ning", "Pengfei Jiang", "Chenglong Ma", "Ye Du", "Changkai Ji", "Huihui Xu", "Ming Hu", "Jiangbin Zheng", "Xin Chen", "Yucheng Wu", "Feifei Jiang", "Xi Chen", "Xiangru Tang", "Yuchen Fu", "Yingzhou Lu", "Yuanyuan Zhang", "Lihao Sun", "Chengbo Li", "Jinzhe Ma", "Wanhao Liu", "Yating Liu", "Kuo-Cheng Wu", "Shengdu Chai", "Yizhou Wang", "Ouwen Zhangjin", "Chen Tang", "Shufei Zhang", "Wenbo Cao", "Junjie Ren", "Taoyong Cui", "Zhouheng Yao", "Juntao Deng", "Yijie Sun", "Feng Liu", "Wangxu Wei", "Jingyi Xu", "Zhangrui Li", "Junchao Gong", "Zijie Guo", "Zhiyu Yao", "Zaoyu Chen", "Tianhao Peng", "Fangchen Yu", "Bo Zhang", "Dongzhan Zhou", "Shixiang Tang", "Jiaheng Liu", "Fenghua Ling", "Yan Lu", "Yuchen Ren", "Ben Fei", "Zhen Zhao", "Xinyu Gu", "Rui Su", "Xiao-Ming Wu", "Weikang Si", "Yang Liu", "Hao Chen", "Xiangchao Yan", "Xue Yang", "Junchi Yan", "Jiamin Wu", "Qihao Zheng", "Chenhui Li", "Zhiqiang Gao", "Hao Kong", "Junjun He", "Mao Su", "Tianfan Fu", "Peng Ye", "Chunfeng Song", "Nanqing Dong", "Yuqiang Li", "Huazhu Fu", "Siqi Sun", "Lijing Cheng", "Jintai Lin", "Wanli Ouyang", "Bowen Zhou", "Wenlong Zhang", "Lei Bai" ], "github": "", "project_page": "", "abstract": "Despite advances in scientific AI, a coherent framework for Scientific General Intelligence (SGI)-the ability to autonomously conceive, investigate, and reason across scientific domains-remains lacking. We present an operational SGI definition grounded in the Practical Inquiry Model (PIM: Deliberation, Conception, Action, Perception) and operationalize it via four scientist-aligned tasks: deep research, idea generation, dry/wet experiments, and experimental reasoning. SGI-Bench comprises over 1,000 expert-curated, cross-disciplinary samples inspired by Science's 125 Big Questions, enabling systematic evaluation of state-of-the-art LLMs. Results reveal gaps: low exact match (10--20%) in deep research despite step-level alignment; ideas lacking feasibility and detail; high code executability but low execution result accuracy in dry experiments; low sequence fidelity in wet protocols; and persistent multimodal comparative-reasoning challenges. We further introduce Test-Time Reinforcement Learning (TTRL), which optimizes retrieval-augmented novelty rewards at inference, enhancing hypothesis novelty without reference answer. Together, our PIM-grounded definition, workflow-centric benchmark, and empirical insights establish a foundation for AI systems that genuinely participate in scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.16041", "title": "Are We on the Right Way to Assessing LLM-as-a-Judge?", "authors": [ "Yuanning Feng", "Sinan Wang", "Zhengxiang Cheng", "Yao Wan", "Dongping Chen" ], "github": "", "project_page": "", "abstract": "LLM-as-a-Judge has been widely adopted as an evaluation method and served as supervised rewards in model training. However, existing benchmarks for LLM-as-a-Judge are mainly relying on human-annotated ground truth, which introduces human bias that undermines the assessment of reliability and imposes scalability constraints. To overcome these limitations, we introduce Sage, a novel evaluation suite that assesses the quality of LLM judges without necessitating any human annotation. Inspired by axioms of rational choice theory, Sage introduces two new lenses for measuring LLM-as-a-Judge: local self-consistency (pair-wise preference stability) and global logical consistency (transitivity across a full set of preferences). We curate a dataset of 650 questions by combining structured benchmark problems with real-world user queries. Our experiments demonstrate both the stability of our metrics and their high correlation with supervised benchmarks like LLMBar and RewardBench2, confirming Sage's reliability as an evaluation suite for the robustness and accuracy of LLM-as-a-Judge. Based on Sage, we reveal that current state-of-the-art LLMs exhibit significant reliability problems when acting as judges in both scoring and pairwise settings; even the top-performing models, Gemini-2.5-Pro and GPT-5, fail to maintain consistent preferences in nearly a quarter of difficult cases. We attribute this to a new phenomenon called situational preference, which explains why explicit rubrics or criteria can help the model judge consistently across answer pairs. Our further analysis shows that finetuned LLM-as-a-Judge is a feasible method to boost performance, and the panel-based judge as well as deep reasoning can enhance the judging consistency. We also find substantial inconsistency in human judgments, which indicates that human annotation may not be a reliable gold standard.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.17351", "title": "Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers", "authors": [ "Zeyuan Allen-Zhu" ], "github": "", "project_page": "", "abstract": "Understanding architectural differences in language models is challenging, especially at academic-scale pretraining (e.g., 1.3B parameters, 100B tokens), where results are often dominated by noise and randomness. To overcome this, we introduce controlled synthetic pretraining tasks that isolate and evaluate core model capabilities. Within this framework, we discover CANON LAYERS: lightweight architectural components -- named after the musical term \"canon\" -- that promote horizontal information flow across neighboring tokens. Canon layers compute weighted sums of nearby token representations and integrate seamlessly into Transformers, linear attention, state-space models, or any sequence architecture. We present 12 key results. This includes how Canon layers enhance reasoning depth (e.g., by 2times), reasoning breadth, knowledge manipulation, etc. They lift weak architectures like NoPE to match RoPE, and linear attention to rival SOTA linear models like Mamba2/GDN -- validated both through synthetic tasks and real-world academic-scale pretraining. This synthetic playground offers an economical, principled path to isolate core model capabilities often obscured at academic scales. Equipped with infinite high-quality data, it may even PREDICT how future architectures will behave as training pipelines improve -- e.g., through better data curation or RL-based post-training -- unlocking deeper reasoning and hierarchical inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.17532", "title": "Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding", "authors": [ "Jiaqi Tang", "Jianmin Chen", "Wei Wei", "Xiaogang Xu", "Runtao Liu", "Xiangyu Wu", "Qipeng Xie", "Jiafei Wu", "Lei Zhang", "Qifeng Chen" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models struggle to maintain reliable performance under extreme real-world visual degradations, which impede their practical robustness. Existing robust MLLMs predominantly rely on implicit training/adaptation that focuses solely on visual encoder generalization, suffering from limited interpretability and isolated optimization. To overcome these limitations, we propose Robust-R1, a novel framework that explicitly models visual degradations through structured reasoning chains. Our approach integrates: (i) supervised fine-tuning for degradation-aware reasoning foundations, (ii) reward-driven alignment for accurately perceiving degradation parameters, and (iii) dynamic reasoning depth scaling adapted to degradation intensity. To facilitate this approach, we introduce a specialized 11K dataset featuring realistic degradations synthesized across four critical real-world visual processing stages, each annotated with structured chains connecting degradation parameters, perceptual influence, pristine semantic reasoning chain, and conclusion. Comprehensive evaluations demonstrate state-of-the-art robustness: Robust-R1 outperforms all general and robust baselines on the real-world degradation benchmark R-Bench, while maintaining superior anti-degradation performance under multi-intensity adversarial degradations on MMMB, MMStar, and RealWorldQA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.17796", "title": "Animate Any Character in Any World", "authors": [ "Yitong Wang", "Fangyun Wei", "Hongyang Zhang", "Bo Dai", "Yan Lu" ], "github": "", "project_page": "", "abstract": "Recent advances in world models have greatly enhanced interactive environment simulation. Existing methods mainly fall into two categories: (1) static world generation models, which construct 3D environments without active agents, and (2) controllable-entity models, which allow a single entity to perform limited actions in an otherwise uncontrollable environment. In this work, we introduce AniX, leveraging the realism and structural grounding of static world generation while extending controllable-entity models to support user-specified characters capable of performing open-ended actions. Users can provide a 3DGS scene and a character, then direct the character through natural language to perform diverse behaviors from basic locomotion to object-centric interactions while freely exploring the environment. AniX synthesizes temporally coherent video clips that preserve visual fidelity with the provided scene and character, formulated as a conditional autoregressive video generation problem. Built upon a pre-trained video generator, our training strategy significantly enhances motion dynamics while maintaining generalization across actions and characters. Our evaluation covers a broad range of aspects, including visual quality, character consistency, action controllability, and long-horizon coherence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.16793", "title": "PhysBrain: Human Egocentric Data as a Bridge from Vision Language Models to Physical Intelligence", "authors": [ "Xiaopeng Lin", "Shijie Lian", "Bin Yu", "Ruoqi Yang", "Changti Wu", "Yuzhuo Miao", "Yurun Jin", "Yukun Shi", "Cong Huang", "Bojun Cheng", "Kai Chen" ], "github": "", "project_page": "", "abstract": "Robotic generalization relies on physical intelligence: the ability to reason about state changes, contact-rich interactions, and long-horizon planning under egocentric perception and action. However, most VLMs are trained primarily on third-person data, creating a fundamental viewpoint mismatch for humanoid robots. Scaling robot egocentric data collection remains impractical due to high cost and limited diversity, whereas large-scale human egocentric videos offer a scalable alternative that naturally capture rich interaction context and causal structure. The key challenge is to convert raw egocentric videos into structured and reliable embodiment training supervision. Accordingly, we propose an Egocentric2Embodiment translation pipeline that transforms first-person videos into multi-level, schema-driven VQA supervision with enforced evidence grounding and temporal consistency, enabling the construction of the Egocentric2Embodiment dataset (E2E-3M) at scale. An egocentric-aware embodied brain, termed PhysBrain, is obtained by training on the E2E-3M dataset. PhysBrain exhibits substantially improved egocentric understanding, particularly for planning on EgoThink. It provides an egocentric-aware initialization that enables more sample-efficient VLA fine-tuning and higher SimplerEnv success rates (53.9\\%), demonstrating effective transfer from human egocentric supervision to downstream robot control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.17008", "title": "Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs", "authors": [ "Junbo Li", "Peng Zhou", "Rui Meng", "Meet P. Vadera", "Lihong Li", "Yang Li" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has re-emerged as a natural approach for training interactive LLM agents in real-world environments. However, directly applying the widely used Group Relative Policy Optimization (GRPO) algorithm to multi-turn tasks exposes notable limitations, particularly in scenarios requiring long-horizon reasoning. To address these challenges, we investigate more stable and effective advantage estimation strategies, especially for multi-turn settings. We first explore Proximal Policy Optimization (PPO) as an alternative and find it to be more robust than GRPO. To further enhance PPO in multi-turn scenarios, we introduce turn-PPO, a variant that operates on a turn-level MDP formulation, as opposed to the commonly used token-level MDP. Our results on the WebShop and Sokoban datasets demonstrate the effectiveness of turn-PPO, both with and without long reasoning components.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.16483", "title": "StageVAR: Stage-Aware Acceleration for Visual Autoregressive Models", "authors": [ "Senmao Li", "Kai Wang", "Salman Khan", "Fahad Shahbaz Khan", "Jian Yang", "Yaxing Wang" ], "github": "", "project_page": "", "abstract": "Visual Autoregressive (VAR) modeling departs from the next-token prediction paradigm of traditional Autoregressive (AR) models through next-scale prediction, enabling high-quality image generation. However, the VAR paradigm suffers from sharply increased computational complexity and running time at large-scale steps. Although existing acceleration methods reduce runtime for large-scale steps, but rely on manual step selection and overlook the varying importance of different stages in the generation process. To address this challenge, we present StageVAR, a systematic study and stage-aware acceleration framework for VAR models. Our analysis shows that early steps are critical for preserving semantic and structural consistency and should remain intact, while later steps mainly refine details and can be pruned or approximated for acceleration. Building on these insights, StageVAR introduces a plug-and-play acceleration strategy that exploits semantic irrelevance and low-rank properties in late-stage computations, without requiring additional training. Our proposed StageVAR achieves up to 3.4x speedup with only a 0.01 drop on GenEval and a 0.26 decrease on DPG, consistently outperforming existing acceleration baselines. These results highlight stage-aware design as a powerful principle for efficient visual autoregressive image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.17909", "title": "Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and Editing", "authors": [ "Shilong Zhang", "He Zhang", "Zhifei Zhang", "Chongjian Ge", "Shuchen Xue", "Shaoteng Liu", "Mengwei Ren", "Soo Ye Kim", "Yuqian Zhou", "Qing Liu", "Daniil Pakhomov", "Kai Zhang", "Zhe Lin", "Ping Luo" ], "github": "", "project_page": "", "abstract": "Modern Latent Diffusion Models (LDMs) typically operate in low-level Variational Autoencoder (VAE) latent spaces that are primarily optimized for pixel-level reconstruction. To unify vision generation and understanding, a burgeoning trend is to adopt high-dimensional features from representation encoders as generative latents. However, we empirically identify two fundamental obstacles in this paradigm: (1) the discriminative feature space lacks compact regularization, making diffusion models prone to off-manifold latents that lead to inaccurate object structures; and (2) the encoder's inherently weak pixel-level reconstruction hinders the generator from learning accurate fine-grained geometry and texture. In this paper, we propose a systematic framework to adapt understanding-oriented encoder features for generative tasks. We introduce a semantic-pixel reconstruction objective to regularize the latent space, enabling the compression of both semantic information and fine-grained details into a highly compact representation (96 channels with 16x16 spatial downsampling). This design ensures that the latent space remains semantically rich and achieves state-of-the-art image reconstruction, while remaining compact enough for accurate generation. Leveraging this representation, we design a unified Text-to-Image (T2I) and image editing model. Benchmarking against various feature spaces, we demonstrate that our approach achieves state-of-the-art reconstruction, faster convergence, and substantial performance gains in both T2I and editing tasks, validating that representation encoders can be effectively adapted into robust generative components.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.14870", "title": "HERBench: A Benchmark for Multi-Evidence Integration in Video Question Answering", "authors": [ "Dan Ben-Ami", "Gabriele Serussi", "Kobi Cohen", "Chaim Baskin" ], "github": "", "project_page": "", "abstract": "Video Large Language Models (Video-LLMs) are rapidly improving, yet current Video Question Answering (VideoQA) benchmarks often allow questions to be answered from a single salient cue, under-testing reasoning that must aggregate multiple, temporally separated visual evidence. We present HERBench, a VideoQA benchmark purpose-built to assess multi-evidence integration across time. Each question requires aggregating at least three non-overlapping evidential cues across distinct video segments, so neither language priors nor a single snapshot can suffice. HERBench comprises 26K five-way multiple-choice questions organized into twelve compositional tasks that probe identity binding, cross-entity relations, temporal ordering, co-occurrence verification, and counting. To make evidential demand measurable, we introduce the Minimum Required Frame-Set (MRFS), the smallest number of frames a model must fuse to answer correctly, and show that HERBench imposes substantially higher demand than prior datasets (mean MRFS 5.5 vs. 2.6-4.2). Evaluating 13 state-of-the-art Video-LLMs on HERBench reveals pervasive failures: accuracies of 31-42% are only slightly above the 20% random-guess baseline. We disentangle this failure into two critical bottlenecks: (1) a retrieval deficit, where frame selectors overlook key evidence, and (2) a fusion deficit, where models fail to integrate information even when all necessary evidence is provided. By making cross-time evidence both unavoidable and quantifiable, HERBench establishes a principled target for advancing robust, compositional video understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.17495", "title": "GroundingME: Exposing the Visual Grounding Gap in MLLMs through Multi-Dimensional Evaluation", "authors": [ "Rang Li", "Lei Li", "Shuhuai Ren", "Hao Tian", "Shuhao Gu", "Shicheng Li", "Zihao Yue", "Yudong Wang", "Wenhan Ma", "Zhe Yang", "Jingyuan Ma", "Zhifang Sui", "Fuli Luo" ], "github": "", "project_page": "", "abstract": "Visual grounding, localizing objects from natural language descriptions, represents a critical bridge between language and vision understanding. While multimodal large language models (MLLMs) achieve impressive scores on existing benchmarks, a fundamental question remains: can MLLMs truly ground language in vision with human-like sophistication, or are they merely pattern-matching on simplified datasets? Current benchmarks fail to capture real-world complexity where humans effortlessly navigate ambiguous references and recognize when grounding is impossible. To rigorously assess MLLMs' true capabilities, we introduce GroundingME, a benchmark that systematically challenges models across four critical dimensions: (1) Discriminative, distinguishing highly similar objects, (2) Spatial, understanding complex relational descriptions, (3) Limited, handling occlusions or tiny objects, and (4) Rejection, recognizing ungroundable queries. Through careful curation combining automated generation with human verification, we create 1,005 challenging examples mirroring real-world complexity. Evaluating 25 state-of-the-art MLLMs reveals a profound capability gap: the best model achieves only 45.1% accuracy, while most score 0% on rejection tasks, reflexively hallucinating objects rather than acknowledging their absence, raising critical safety concerns for deployment. We explore two strategies for improvements: (1) test-time scaling selects optimal response by thinking trajectory to improve complex grounding by up to 2.9%, and (2) data-mixture training teaches models to recognize ungroundable queries, boosting rejection accuracy from 0% to 27.9%. GroundingME thus serves as both a diagnostic tool revealing current limitations in MLLMs and a roadmap toward human-level visual grounding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.11362", "title": "An Anatomy of Vision-Language-Action Models: From Modules to Milestones and Challenges", "authors": [ "Chao Xu", "Suyu Zhang", "Yang Liu", "Baigui Sun", "Weihong Chen", "Bo Xu", "Qi Liu", "Juncheng Wang", "Shujun Wang", "Shan Luo", "Jan Peters", "Athanasios V. Vasilakos", "Stefanos Zafeiriou", "Jiankang Deng" ], "github": "https://github.com/SuyuZ1/VLA-Survey-Anatomy", "project_page": "", "abstract": "Vision-Language-Action (VLA) models are driving a revolution in robotics, enabling machines to understand instructions and interact with the physical world. This field is exploding with new models and datasets, making it both exciting and challenging to keep pace with. This survey offers a clear and structured guide to the VLA landscape. We design it to follow the natural learning path of a researcher: we start with the basic Modules of any VLA model, trace the history through key Milestones, and then dive deep into the core Challenges that define recent research frontier. Our main contribution is a detailed breakdown of the five biggest challenges in: (1) Representation, (2) Execution, (3) Generalization, (4) Safety, and (5) Dataset and Evaluation. This structure mirrors the developmental roadmap of a generalist agent: establishing the fundamental perception-action loop, scaling capabilities across diverse embodiments and environments, and finally ensuring trustworthy deployment-all supported by the essential data infrastructure. For each of them, we review existing approaches and highlight future opportunities. We position this paper as both a foundational guide for newcomers and a strategic roadmap for experienced researchers, with the dual aim of accelerating learning and inspiring new ideas in embodied intelligence. A live version of this survey, with continuous updates, is maintained on our https://suyuz1.github.io/Survery/{project page}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SuyuZ1/VLA-Survey-Anatomy", "github_api_added_by": "auto" }, { "date": "2025-12-22", "arxiv_id": "2512.17897", "title": "RadarGen: Automotive Radar Point Cloud Generation from Cameras", "authors": [ "Tomer Borreda", "Fangqiang Ding", "Sanja Fidler", "Shengyu Huang", "Or Litany" ], "github": "", "project_page": "", "abstract": "We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery. RadarGen adapts efficient image-latent diffusion to the radar domain by representing radar measurements in bird's-eye-view form that encodes spatial structure together with radar cross section (RCS) and Doppler attributes. A lightweight recovery step reconstructs point clouds from the generated maps. To better align generation with the visual scene, RadarGen incorporates BEV-aligned depth, semantic, and motion cues extracted from pretrained foundation models, which guide the stochastic generation process toward physically plausible radar patterns. Conditioning on images makes the approach broadly compatible, in principle, with existing visual datasets and simulation frameworks, offering a scalable direction for multimodal generative simulation. Evaluations on large-scale driving data show that RadarGen captures characteristic radar measurement distributions and reduces the gap to perception models trained on real data, marking a step toward unified generative simulation across sensing modalities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.17459", "title": "3D-RE-GEN: 3D Reconstruction of Indoor Scenes with a Generative Framework", "authors": [ "Tobias Sautter", "Jan-Niklas Dihlmann", "Hendrik P. A. Lensch" ], "github": "", "project_page": "", "abstract": "Recent advances in 3D scene generation produce visually appealing output, but current representations hinder artists' workflows that require modifiable 3D textured mesh scenes for visual effects and game development. Despite significant advances, current textured mesh scene reconstruction methods are far from artist ready, suffering from incorrect object decomposition, inaccurate spatial relationships, and missing backgrounds. We present 3D-RE-GEN, a compositional framework that reconstructs a single image into textured 3D objects and a background. We show that combining state of the art models from specific domains achieves state of the art scene reconstruction performance, addressing artists' requirements. Our reconstruction pipeline integrates models for asset detection, reconstruction, and placement, pushing certain models beyond their originally intended domains. Obtaining occluded objects is treated as an image editing task with generative models to infer and reconstruct with scene level reasoning under consistent lighting and geometry. Unlike current methods, 3D-RE-GEN generates a comprehensive background that spatially constrains objects during optimization and provides a foundation for realistic lighting and simulation tasks in visual effects and games. To obtain physically realistic layouts, we employ a novel 4-DoF differentiable optimization that aligns reconstructed objects with the estimated ground plane. 3D-RE-GEN~achieves state of the art performance in single image 3D scene reconstruction, producing coherent, modifiable scenes through compositional generation guided by precise camera recovery and spatial optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.16848", "title": "Meta-RL Induces Exploration in Language Agents", "authors": [ "Yulun Jiang", "Liangze Jiang", "Damien Teney", "Michael Moor", "Maria Brbic" ], "github": "https://github.com/mlbio-epfl/LaMer", "project_page": "", "abstract": "Reinforcement learning (RL) has enabled the training of large language model (LLM) agents to interact with the environment and to solve multi-turn long-horizon tasks. However, the RL-trained agents often struggle in tasks that require active exploration and fail to efficiently adapt from trial-and-error experiences. In this paper, we present LaMer, a general Meta-RL framework that enables LLM agents to actively explore and learn from the environment feedback at test time. LaMer consists of two key components: (i) a cross-episode training framework to encourage exploration and long-term rewards optimization; and (ii) in-context policy adaptation via reflection, allowing the agent to adapt their policy from task feedback signal without gradient update. Experiments across diverse environments show that LaMer significantly improves performance over RL baselines, with 11%, 14%, and 19% performance gains on Sokoban, MineSweeper and Webshop, respectively. Moreover, LaMer also demonstrates better generalization to more challenging or previously unseen tasks compared to the RL-trained agents. Overall, our results demonstrate that Meta-RL provides a principled approach to induce exploration in language agents, enabling more robust adaptation to novel environments through learned exploration strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mlbio-epfl/LaMer", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.16978", "title": "A Benchmark and Agentic Framework for Omni-Modal Reasoning and Tool Use in Long Videos", "authors": [ "Mohammed Irfan Kurpath", "Jaseel Muhammad Kaithakkodan", "Jinxing Zhou", "Sahal Shaji Mullappilly", "Mohammad Almansoori", "Noor Ahsan", "Beknur Kalmakhanbet", "Sambal Shikhar", "Rishabh Lalla", "Jean Lahoud", "Mariette Awad", "Fahad Shahbaz Khan", "Salman Khan", "Rao Muhammad Anwer", "Hisham Cholakkal" ], "github": "https://github.com/mbzuai-oryx/longshot", "project_page": "", "abstract": "Long-form multimodal video understanding requires integrating vision, speech, and ambient audio with coherent long-range reasoning. Existing benchmarks emphasize either temporal length or multimodal richness, but rarely both and while some incorporate open-ended questions and advanced metrics, they mostly rely on single-score accuracy, obscuring failure modes. We introduce LongShOTBench, a diagnostic benchmark with open-ended, intent-driven questions; single- and multi-turn dialogues; and tasks requiring multimodal reasoning and agentic tool use across video, audio, and speech. Each item includes a reference answer and graded rubric for interpretable, and traceable evaluation. LongShOTBench is produced via a scalable, human-validated pipeline to ensure coverage and reproducibility. All samples in our LongShOTBench are human-verified and corrected. Furthermore, we present LongShOTAgent, an agentic system that analyzes long videos via preprocessing, search, and iterative refinement. On LongShOTBench, state-of-the-art MLLMs show large gaps: Gemini-2.5-Flash achieves 52.95%, open-source models remain below 30%, and LongShOTAgent attains 44.66%. These results underscore the difficulty of real-world long-form video understanding. LongShOTBench provides a practical, reproducible foundation for evaluating and improving MLLMs. All resources are available on GitHub: https://github.com/mbzuai-oryx/longshot.", "github_manual": "", "github_regex": "https://github.com/mbzuai-oryx/longshot", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.13427", "title": "MineTheGap: Automatic Mining of Biases in Text-to-Image Models", "authors": [ "Noa Cohen", "Nurit Spingarn-Eliezer", "Inbar Huberman-Spiegelglas", "Tomer Michaeli" ], "github": "", "project_page": "", "abstract": "Text-to-Image (TTI) models generate images based on text prompts, which often leave certain aspects of the desired image ambiguous. When faced with these ambiguities, TTI models have been shown to exhibit biases in their interpretations. These biases can have societal impacts, e.g., when showing only a certain race for a stated occupation. They can also affect user experience when creating redundancy within a set of generated images instead of spanning diverse possibilities. Here, we introduce MineTheGap - a method for automatically mining prompts that cause a TTI model to generate biased outputs. Our method goes beyond merely detecting bias for a given prompt. Rather, it leverages a genetic algorithm to iteratively refine a pool of prompts, seeking for those that expose biases. This optimization process is driven by a novel bias score, which ranks biases according to their severity, as we validate on a dataset with known biases. For a given prompt, this score is obtained by comparing the distribution of generated images to the distribution of LLM-generated texts that constitute variations on the prompt. Code and examples are available on the project's webpage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-22", "arxiv_id": "2512.15586", "title": "Bolmo: Byteifying the Next Generation of Language Models", "authors": [ "Benjamin Minixhofer", "Tyler Murray", "Tomasz Limisiewicz", "Anna Korhonen", "Luke Zettlemoyer", "Noah A. Smith", "Edoardo M. Ponti", "Luca Soldaini", "Valentin Hofmann" ], "github": "https://github.com/allenai/bolmo-core", "project_page": "", "abstract": "We introduce Bolmo, the first family of competitive fully open byte-level language models (LMs) at the 1B and 7B parameter scales. In contrast to prior research on byte-level LMs, which focuses predominantly on training from scratch, we train Bolmo by byteifying existing subword-level LMs. Byteification enables overcoming the limitations of subword tokenization - such as insufficient character understanding and efficiency constraints due to the fixed subword vocabulary - while performing at the level of leading subword-level LMs. Bolmo is specifically designed for byteification: our architecture resolves a mismatch between the expressivity of prior byte-level architectures and subword-level LMs, which makes it possible to employ an effective exact distillation objective between Bolmo and the source subword model. This allows for converting a subword-level LM to a byte-level LM by investing less than 1\\% of a typical pretraining token budget. Bolmo substantially outperforms all prior byte-level LMs of comparable size, and outperforms the source subword-level LMs on character understanding and, in some cases, coding, while coming close to matching the original LMs' performance on other tasks. Furthermore, we show that Bolmo can achieve inference speeds competitive with subword-level LMs by training with higher token compression ratios, and can be cheaply and effectively post-trained by leveraging the existing ecosystem around the source subword-level LM. Our results finally make byte-level LMs a practical choice competitive with subword-level LMs across a wide set of use cases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/bolmo-core", "github_api_added_by": "auto" }, { "date": "2025-12-23", "arxiv_id": "2512.16676", "title": "DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI", "authors": [ "Hao Liang", "Xiaochen Ma", "Zhou Liu", "Zhen Hao Wong", "Zhengyang Zhao", "Zimo Meng", "Runming He", "Chengyu Shen", "Qifeng Cai", "Zhaoyang Han", "Meiyi Qiang", "Yalin Feng", "Tianyi Bai", "Zewei Pan", "Ziyi Guo", "Yizhen Jiang", "Jingwen Deng", "Qijie You", "Peichao Lai", "Tianyu Guo", "Chi Hsu Tsai", "Hengyi Feng", "Rui Hu", "Wenkai Yu", "Junbo Niu", "Bohan Zeng", "Ruichuan An", "Lu Ma", "Jihao Huang", "Yaowei Zheng", "Conghui He", "Linpeng Tang", "Bin Cui", "Weinan E", "Wentao Zhang" ], "github": "", "project_page": "", "abstract": "The rapidly growing demand for high-quality data in Large Language Models (LLMs) has intensified the need for scalable, reliable, and semantically rich data preparation pipelines. However, current practices remain dominated by ad-hoc scripts and loosely specified workflows, which lack principled abstractions, hinder reproducibility, and offer limited support for model-in-the-loop data generation. To address these challenges, we present DataFlow, a unified and extensible LLM-driven data preparation framework. DataFlow is designed with system-level abstractions that enable modular, reusable, and composable data transformations, and provides a PyTorch-style pipeline construction API for building debuggable and optimizable dataflows. The framework consists of nearly 200 reusable operators and six domain-general pipelines spanning text, mathematical reasoning, code, Text-to-SQL, agentic RAG, and large-scale knowledge extraction. To further improve usability, we introduce DataFlow-Agent, which automatically translates natural-language specifications into executable pipelines via operator synthesis, pipeline planning, and iterative verification. Across six representative use cases, DataFlow consistently improves downstream LLM performance. Our math, code, and text pipelines outperform curated human datasets and specialized synthetic baselines, achieving up to +3\\% execution accuracy in Text-to-SQL over SynSQL, +7\\% average improvements on code benchmarks, and 1--3 point gains on MATH, GSM8K, and AIME. Moreover, a unified 10K-sample dataset produced by DataFlow enables base models to surpass counterparts trained on 1M Infinity-Instruct data. These results demonstrate that DataFlow provides a practical and high-performance substrate for reliable, reproducible, and scalable LLM data preparation, and establishes a system-level foundation for future data-centric AI development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.18880", "title": "Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction", "authors": [ "Ming Li", "Han Chen", "Yunze Xiao", "Jian Chen", "Hong Jiao", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Accurate estimation of item (question or task) difficulty is critical for educational assessment but suffers from the cold start problem. While Large Language Models demonstrate superhuman problem-solving capabilities, it remains an open question whether they can perceive the cognitive struggles of human learners. In this work, we present a large-scale empirical analysis of Human-AI Difficulty Alignment for over 20 models across diverse domains such as medical knowledge and mathematical reasoning. Our findings reveal a systematic misalignment where scaling up model size is not reliably helpful; instead of aligning with humans, models converge toward a shared machine consensus. We observe that high performance often impedes accurate difficulty estimation, as models struggle to simulate the capability limitations of students even when being explicitly prompted to adopt specific proficiency levels. Furthermore, we identify a critical lack of introspection, as models fail to predict their own limitations. These results suggest that general problem-solving capability does not imply an understanding of human cognitive struggles, highlighting the challenge of using current models for automated difficulty prediction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.19693", "title": "The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding", "authors": [ "Weichen Fan", "Haiwen Diao", "Quan Wang", "Dahua Lin", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Deep representations across modalities are inherently intertwined. In this paper, we systematically analyze the spectral characteristics of various semantic and pixel encoders. Interestingly, our study uncovers a highly inspiring and rarely explored correspondence between an encoder's feature spectrum and its functional role: semantic encoders primarily capture low-frequency components that encode abstract meaning, whereas pixel encoders additionally retain high-frequency information that conveys fine-grained detail. This heuristic finding offers a unifying perspective that ties encoder behavior to its underlying spectral structure. We define it as the Prism Hypothesis, where each data modality can be viewed as a projection of the natural world onto a shared feature spectrum, just like the prism. Building on this insight, we propose Unified Autoencoding (UAE), a model that harmonizes semantic structure and pixel details via an innovative frequency-band modulator, enabling their seamless coexistence. Extensive experiments on ImageNet and MS-COCO benchmarks validate that our UAE effectively unifies semantic abstraction and pixel-level fidelity into a single latent space with state-of-the-art performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.19682", "title": "GenEnv: Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators", "authors": [ "Jiacheng Guo", "Ling Yang", "Peter Chen", "Qixin Xiao", "Yinjie Wang", "Xinzhe Juan", "Jiahao Qiu", "Ke Shen", "Mengdi Wang" ], "github": "https://github.com/Gen-Verse/GenEnv", "project_page": "", "abstract": "Training capable Large Language Model (LLM) agents is critically bottlenecked by the high cost and static nature of real-world interaction data. We address this by introducing GenEnv, a framework that establishes a difficulty-aligned co-evolutionary game between an agent and a scalable, generative environment simulator. Unlike traditional methods that evolve models on static datasets, GenEnv instantiates a dataevolving: the simulator acts as a dynamic curriculum policy, continuously generating tasks specifically tailored to the agent's ``zone of proximal development''. This process is guided by a simple but effective \u03b1-Curriculum Reward, which aligns task difficulty with the agent's current capabilities. We evaluate GenEnv on five benchmarks, including API-Bank, ALFWorld, BFCL, Bamboogle, and TravelPlanner. Across these tasks, GenEnv improves agent performance by up to +40.3\\% over 7B baselines and matches or exceeds the average performance of larger models. Compared to Gemini 2.5 Pro-based offline data augmentation, GenEnv achieves better performance while using 3.3times less data. By shifting from static supervision to adaptive simulation, GenEnv provides a data-efficient pathway for scaling agent capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Gen-Verse/GenEnv", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.19432", "title": "MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive, and MCP-Augmented Environments", "authors": [ "Quyu Kong", "Xu Zhang", "Zhenyu Yang", "Nolan Gao", "Chen Liu", "Panrong Tong", "Chenglin Cai", "Hanzhang Zhou", "Jianan Zhang", "Liangyu Chen", "Zhidan Liu", "Steven Hoi", "Yue Wang" ], "github": "https://github.com/Tongyi-MAI/MobileWorld", "project_page": "", "abstract": "Among existing online mobile-use benchmarks, AndroidWorld has emerged as the dominant benchmark due to its reproducible environment and deterministic evaluation; however, recent agents achieving over 90% success rates indicate its saturation and motivate the need for a more challenging benchmark. In addition, its environment lacks key application categories, such as e-commerce and enterprise communication, and does not reflect realistic mobile-use scenarios characterized by vague user instructions and hybrid tool usage. To bridge this gap, we introduce MobileWorld, a substantially more challenging benchmark designed to better reflect real-world mobile usage, comprising 201 tasks across 20 applications, while maintaining the same level of reproducible evaluation as AndroidWorld. The difficulty of MobileWorld is twofold. First, it emphasizes long-horizon tasks with cross-application interactions: MobileWorld requires nearly twice as many task-completion steps on average (27.8 vs. 14.3) and includes far more multi-application tasks (62.2% vs. 9.5%) compared to AndroidWorld. Second, MobileWorld extends beyond standard GUI manipulation by introducing novel task categories, including agent-user interaction and MCP-augmented tasks. To ensure robust evaluation, we provide snapshot-based container environment and precise functional verifications, including backend database inspection and task callback APIs. We further develop a planner-executor agentic framework with extended action spaces to support user interactions and MCP calls. Our results reveal a sharp performance drop compared to AndroidWorld, with the best agentic framework and end-to-end model achieving 51.7% and 20.9% success rates, respectively. Our analysis shows that current models struggle significantly with user interaction and MCP calls, offering a strategic roadmap toward more robust, next-generation mobile intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tongyi-MAI/MobileWorld", "github_api_added_by": "auto" }, { "date": "2025-12-23", "arxiv_id": "2512.18003", "title": "Name That Part: 3D Part Segmentation and Naming", "authors": [ "Soumava Paul", "Prakhar Kaushik", "Ankit Vaidya", "Anand Bhattad", "Alan Yuille" ], "github": "", "project_page": "", "abstract": "We address semantic 3D part segmentation: decomposing objects into parts with meaningful names. While datasets exist with part annotations, their definitions are inconsistent across datasets, limiting robust training. Previous methods produce unlabeled decompositions or retrieve single parts without complete shape annotations. We propose ALIGN-Parts, which formulates part naming as a direct set alignment task. Our method decomposes shapes into partlets - implicit 3D part representations - matched to part descriptions via bipartite assignment. We combine geometric cues from 3D part fields, appearance from multi-view vision features, and semantic knowledge from language-model-generated affordance descriptions. Text-alignment loss ensures partlets share embedding space with text, enabling a theoretically open-vocabulary matching setup, given sufficient data. Our efficient and novel, one-shot, 3D part segmentation and naming method finds applications in several downstream tasks, including serving as a scalable annotation engine. As our model supports zero-shot matching to arbitrary descriptions and confidence-calibrated predictions for known categories, with human verification, we create a unified ontology that aligns PartNet, 3DCoMPaT++, and Find3D, consisting of 1,794 unique 3D parts. We also show examples from our newly created Tex-Parts dataset. We also introduce 2 novel metrics appropriate for the named 3D part segmentation task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.19134", "title": "QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation", "authors": [ "Dehai Min", "Kailin Zhang", "Tongtong Wu", "Lu Cheng" ], "github": "https://github.com/ZhishanQ/QuCo-RAG", "project_page": "", "abstract": "Dynamic Retrieval-Augmented Generation adaptively determines when to retrieve during generation to mitigate hallucinations in large language models (LLMs). However, existing methods rely on model-internal signals (e.g., logits, entropy), which are fundamentally unreliable because LLMs are typically ill-calibrated and often exhibit high confidence in erroneous outputs. We propose QuCo-RAG, which shifts from subjective confidence to objective statistics computed from pre-training data. Our method quantifies uncertainty through two stages: (1) before generation, we identify low-frequency entities indicating long-tail knowledge gaps; (2) during generation, we verify entity co-occurrence in the pre-training corpus, where zero co-occurrence often signals hallucination risk. Both stages leverage Infini-gram for millisecond-latency queries over 4 trillion tokens, triggering retrieval when uncertainty is high. Experiments on multi-hop QA benchmarks show QuCo-RAG achieves EM gains of 5--12 points over state-of-the-art baselines with OLMo-2 models, and transfers effectively to models with undisclosed pre-training data (Llama, Qwen, GPT), improving EM by up to 14 points. Domain generalization on biomedical QA further validates the robustness of our paradigm. These results establish corpus-grounded verification as a principled, practically model-agnostic paradigm for dynamic RAG. Our code is publicly available at https://github.com/ZhishanQ/QuCo-RAG.", "github_manual": "", "github_regex": "https://github.com/ZhishanQ/QuCo-RAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.17650", "title": "Region-Constraint In-Context Generation for Instructional Video Editing", "authors": [ "Zhongwei Zhang", "Fuchen Long", "Wei Li", "Zhaofan Qiu", "Wu Liu", "Ting Yao", "Tao Mei" ], "github": "", "project_page": "", "abstract": "The In-context generation paradigm recently has demonstrated strong power in instructional image editing with both data efficiency and synthesis quality. Nevertheless, shaping such in-context learning for instruction-based video editing is not trivial. Without specifying editing regions, the results can suffer from the problem of inaccurate editing regions and the token interference between editing and non-editing areas during denoising. To address these, we present ReCo, a new instructional video editing paradigm that novelly delves into constraint modeling between editing and non-editing regions during in-context generation. Technically, ReCo width-wise concatenates source and target video for joint denoising. To calibrate video diffusion learning, ReCo capitalizes on two regularization terms, i.e., latent and attention regularization, conducting on one-step backward denoised latents and attention maps, respectively. The former increases the latent discrepancy of the editing region between source and target videos while reducing that of non-editing areas, emphasizing the modification on editing area and alleviating outside unexpected content generation. The latter suppresses the attention of tokens in the editing region to the tokens in counterpart of the source video, thereby mitigating their interference during novel object generation in target video. Furthermore, we propose a large-scale, high-quality video editing dataset, i.e., ReCo-Data, comprising 500K instruction-video pairs to benefit model training. Extensive experiments conducted on four major instruction-based video editing tasks demonstrate the superiority of our proposal.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.19678", "title": "WorldWarp: Propagating 3D Geometry with Asynchronous Video Diffusion", "authors": [ "Hanyang Kong", "Xingyi Yang", "Xiaoxu Zheng", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Generating long-range, geometrically consistent video presents a fundamental dilemma: while consistency demands strict adherence to 3D geometry in pixel space, state-of-the-art generative models operate most effectively in a camera-conditioned latent space. This disconnect causes current methods to struggle with occluded areas and complex camera trajectories. To bridge this gap, we propose WorldWarp, a framework that couples a 3D structural anchor with a 2D generative refiner. To establish geometric grounding, WorldWarp maintains an online 3D geometric cache built via Gaussian Splatting (3DGS). By explicitly warping historical content into novel views, this cache acts as a structural scaffold, ensuring each new frame respects prior geometry. However, static warping inevitably leaves holes and artifacts due to occlusions. We address this using a Spatio-Temporal Diffusion (ST-Diff) model designed for a \"fill-and-revise\" objective. Our key innovation is a spatio-temporal varying noise schedule: blank regions receive full noise to trigger generation, while warped regions receive partial noise to enable refinement. By dynamically updating the 3D cache at every step, WorldWarp maintains consistency across video chunks. Consequently, it achieves state-of-the-art fidelity by ensuring that 3D logic guides structure while diffusion logic perfects texture. Project page: https://hyokong.github.io/worldwarp-page/{https://hyokong.github.io/worldwarp-page/}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.19539", "title": "StoryMem: Multi-shot Long Video Storytelling with Memory", "authors": [ "Kaiwen Zhang", "Liming Jiang", "Angtian Wang", "Jacob Zhiyuan Fang", "Tiancheng Zhi", "Qing Yan", "Hao Kang", "Xin Lu", "Xingang Pan" ], "github": "", "project_page": "", "abstract": "Visual storytelling requires generating multi-shot videos with cinematic quality and long-range consistency. Inspired by human memory, we propose StoryMem, a paradigm that reformulates long-form video storytelling as iterative shot synthesis conditioned on explicit visual memory, transforming pre-trained single-shot video diffusion models into multi-shot storytellers. This is achieved by a novel Memory-to-Video (M2V) design, which maintains a compact and dynamically updated memory bank of keyframes from historical generated shots. The stored memory is then injected into single-shot video diffusion models via latent concatenation and negative RoPE shifts with only LoRA fine-tuning. A semantic keyframe selection strategy, together with aesthetic preference filtering, further ensures informative and stable memory throughout generation. Moreover, the proposed framework naturally accommodates smooth shot transitions and customized story generation applications. To facilitate evaluation, we introduce ST-Bench, a diverse benchmark for multi-shot video storytelling. Extensive experiments demonstrate that StoryMem achieves superior cross-shot consistency over previous methods while preserving high aesthetic quality and prompt adherence, marking a significant step toward coherent minute-long video storytelling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.19629", "title": "LoGoPlanner: Localization Grounded Navigation Policy with Metric-aware Visual Geometry", "authors": [ "Jiaqi Peng", "Wenzhe Cai", "Yuqiang Yang", "Tai Wang", "Yuan Shen", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "Trajectory planning in unstructured environments is a fundamental and challenging capability for mobile robots. Traditional modular pipelines suffer from latency and cascading errors across perception, localization, mapping, and planning modules. Recent end-to-end learning methods map raw visual observations directly to control signals or trajectories, promising greater performance and efficiency in open-world settings. However, most prior end-to-end approaches still rely on separate localization modules that depend on accurate sensor extrinsic calibration for self-state estimation, thereby limiting generalization across embodiments and environments. We introduce LoGoPlanner, a localization-grounded, end-to-end navigation framework that addresses these limitations by: (1) finetuning a long-horizon visual-geometry backbone to ground predictions with absolute metric scale, thereby providing implicit state estimation for accurate localization; (2) reconstructing surrounding scene geometry from historical observations to supply dense, fine-grained environmental awareness for reliable obstacle avoidance; and (3) conditioning the policy on implicit geometry bootstrapped by the aforementioned auxiliary tasks, thereby reducing error propagation.We evaluate LoGoPlanner in both simulation and real-world settings, where its fully end-to-end design reduces cumulative error while metric-aware geometry memory enhances planning consistency and obstacle avoidance, leading to more than a 27.3\\% improvement over oracle-localization baselines and strong generalization across embodiments and environments. The code and models have been made publicly available on the https://steinate.github.io/logoplanner.github.io/{project page}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.17206", "title": "Reasoning Palette: Modulating Reasoning via Latent Contextualization for Controllable Exploration for (V)LMs", "authors": [ "Rujiao Long", "Yang Li", "Xingyao Zhang", "Weixun Wang", "Tianqianjin Lin", "Xi Zhao", "Yuchi Xu", "Wenbo Su", "Junchi Yan", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Exploration capacity shapes both inference-time performance and reinforcement learning (RL) training for large (vision-) language models, as stochastic sampling often yields redundant reasoning paths with little high-level diversity. This paper proposes Reasoning Palette, a novel latent-modulation framework that endows the model with a stochastic latent variable for strategic contextualization, guiding its internal planning prior to token generation. This latent context is inferred from the mean-pooled embedding of a question-answer pair via a variational autoencoder (VAE), where each sampled latent potentially encodes a distinct reasoning context. During inference, a sampled latent is decoded into learnable token prefixes and prepended to the input prompt, modulating the model's internal reasoning trajectory. In this way, the model performs internal sampling over reasoning strategies prior to output generation, which shapes the style and structure of the entire response sequence. A brief supervised fine-tuning (SFT) warm-up phase allows the model to adapt to this latent conditioning. Within RL optimization, Reasoning Palette facilitates structured exploration by enabling on-demand injection for diverse reasoning modes, significantly enhancing exploration efficiency and sustained learning capability. Experiments across multiple reasoning benchmarks demonstrate that our method enables interpretable and controllable control over the (vision-) language model's strategic behavior, thereby achieving consistent performance gains over standard RL methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.19402", "title": "Real2Edit2Real: Generating Robotic Demonstrations via a 3D Control Interface", "authors": [ "Yujie Zhao", "Hongwei Fan", "Di Chen", "Shengcong Chen", "Liliang Chen", "Xiaoqi Li", "Guanghui Ren", "Hao Dong" ], "github": "https://github.com/Real2Edit2Real/Real2Edit2Real", "project_page": "", "abstract": "Recent progress in robot learning has been driven by large-scale datasets and powerful visuomotor policy architectures, yet policy robustness remains limited by the substantial cost of collecting diverse demonstrations, particularly for spatial generalization in manipulation tasks. To reduce repetitive data collection, we present Real2Edit2Real, a framework that generates new demonstrations by bridging 3D editability with 2D visual data through a 3D control interface. Our approach first reconstructs scene geometry from multi-view RGB observations with a metric-scale 3D reconstruction model. Based on the reconstructed geometry, we perform depth-reliable 3D editing on point clouds to generate new manipulation trajectories while geometrically correcting the robot poses to recover physically consistent depth, which serves as a reliable condition for synthesizing new demonstrations. Finally, we propose a multi-conditional video generation model guided by depth as the primary control signal, together with action, edge, and ray maps, to synthesize spatially augmented multi-view manipulation videos. Experiments on four real-world manipulation tasks demonstrate that policies trained on data generated from only 1-5 source demonstrations can match or outperform those trained on 50 real-world demonstrations, improving data efficiency by up to 10-50x. Moreover, experimental results on height and texture editing demonstrate the framework's flexibility and extensibility, indicating its potential to serve as a unified data generation framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Real2Edit2Real/Real2Edit2Real", "github_api_added_by": "auto" }, { "date": "2025-12-23", "arxiv_id": "2512.17040", "title": "Infinite-Homography as Robust Conditioning for Camera-Controlled Video Generation", "authors": [ "Min-Jung Kim", "Jeongho Kim", "Hoiyeong Jin", "Junha Hyung", "Jaegul Choo" ], "github": "", "project_page": "", "abstract": "Recent progress in video diffusion models has spurred growing interest in camera-controlled novel-view video generation for dynamic scenes, aiming to provide creators with cinematic camera control capabilities in post-production. A key challenge in camera-controlled video generation is ensuring fidelity to the specified camera pose, while maintaining view consistency and reasoning about occluded geometry from limited observations. To address this, existing methods either train trajectory-conditioned video generation model on trajectory-video pair dataset, or estimate depth from the input video to reproject it along a target trajectory and generate the unprojected regions. Nevertheless, existing methods struggle to generate camera-pose-faithful, high-quality videos for two main reasons: (1) reprojection-based approaches are highly susceptible to errors caused by inaccurate depth estimation; and (2) the limited diversity of camera trajectories in existing datasets restricts learned models. To address these limitations, we present InfCam, a depth-free, camera-controlled video-to-video generation framework with high pose fidelity. The framework integrates two key components: (1) infinite homography warping, which encodes 3D camera rotations directly within the 2D latent space of a video diffusion model. Conditioning on this noise-free rotational information, the residual parallax term is predicted through end-to-end training to achieve high camera-pose fidelity; and (2) a data augmentation pipeline that transforms existing synthetic multiview datasets into sequences with diverse trajectories and focal lengths. Experimental results demonstrate that InfCam outperforms baseline methods in camera-pose accuracy and visual fidelity, generalizing well from synthetic to real-world data. Link to our project page:https://emjay73.github.io/InfCam/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.17385", "title": "UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models", "authors": [ "Jiajun Wu", "Jian Yang", "Wei Zhang", "Lin Jing", "Yuqing Ma", "Ensheng Shi", "Yuchi Ma", "Zhoujun Li", "Xianglong Liu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, their effectiveness heavily relies on supervised training with extensive labeled (e.g., question-answering pairs) or unlabeled datasets (e.g., code snippets), which are often expensive and difficult to obtain at scale. To address this limitation, this paper introduces a method IPC, an unsupervised framework that leverages Internal Probing of LLMs for Code generation without any external corpus, even unlabeled code snippets. We introduce the problem space probing, test understanding probing, solution space probing, and knowledge consolidation and reinforcement to probe the internal knowledge and confidence patterns existing in LLMs. Further, IPC identifies reliable code candidates through self-consistency mechanisms and representation-based quality estimation to train UCoder (coder with unsupervised learning). We validate the proposed approach across multiple code benchmarks, demonstrating that unsupervised methods can achieve competitive performance compared to supervised approaches while significantly reducing the dependency on labeled data and computational resources. Analytic experiments reveal that internal model states contain rich signals about code quality and correctness, and that properly harnessing these signals enables effective unsupervised learning for code generation tasks, opening new directions for training code LLMs in resource-constrained scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.19399", "title": "Brain-Grounded Axes for Reading and Steering LLM States", "authors": [ "Sandro Andric" ], "github": "", "project_page": "", "abstract": "Interpretability methods for large language models (LLMs) typically derive directions from textual supervision, which can lack external grounding. We propose using human brain activity not as a training signal but as a coordinate system for reading and steering LLM states. Using the SMN4Lang MEG dataset, we construct a word-level brain atlas of phase-locking value (PLV) patterns and extract latent axes via ICA. We validate axes with independent lexica and NER-based labels (POS/log-frequency used as sanity checks), then train lightweight adapters that map LLM hidden states to these brain axes without fine-tuning the LLM. Steering along the resulting brain-derived directions yields a robust lexical (frequency-linked) axis in a mid TinyLlama layer, surviving perplexity-matched controls, and a brain-vs-text probe comparison shows larger log-frequency shifts (relative to the text probe) with lower perplexity for the brain axis. A function/content axis (axis 13) shows consistent steering in TinyLlama, Qwen2-0.5B, and GPT-2, with PPL-matched text-level corroboration. Layer-4 effects in TinyLlama are large but inconsistent, so we treat them as secondary (Appendix). Axis structure is stable when the atlas is rebuilt without GPT embedding-change features or with word2vec embeddings (|r|=0.64-0.95 across matched axes), reducing circularity concerns. Exploratory fMRI anchoring suggests potential alignment for embedding change and log frequency, but effects are sensitive to hemodynamic modeling assumptions and are treated as population-level evidence only. These results support a new interface: neurophysiology-grounded axes provide interpretable and controllable handles for LLM behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.12620", "title": "Understanding Syllogistic Reasoning in LLMs from Formal and Natural Language Perspectives", "authors": [ "Aheli Poddar", "Saptarshi Sahoo", "Sujata Ghosh" ], "github": "", "project_page": "", "abstract": "We study syllogistic reasoning in LLMs from the logical and natural language perspectives. In process, we explore fundamental reasoning capabilities of the LLMs and the direction this research is moving forward. To aid in our studies, we use 14 large language models and investigate their syllogistic reasoning capabilities in terms of symbolic inferences as well as natural language understanding. Even though this reasoning mechanism is not a uniform emergent property across LLMs, the perfect symbolic performances in certain models make us wonder whether LLMs are becoming more and more formal reasoning mechanisms, rather than making explicit the nuances of human reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.18658", "title": "Does It Tie Out? Towards Autonomous Legal Agents in Venture Capital", "authors": [ "Pierre Colombo", "Malik Boudiaf", "Allyn Sweet", "Michael Desa", "Hongxi Wang", "Kevin Candra", "Sym\u00e9on del Marmol" ], "github": "", "project_page": "", "abstract": "Before closing venture capital financing rounds, lawyers conduct diligence that includes tying out the capitalization table: verifying that every security (for example, shares, options, warrants) and issuance term (for example, vesting schedules, acceleration triggers, transfer restrictions) is supported by large sets of underlying legal documentation. While LLMs continue to improve on legal benchmarks, specialized legal workflows, such as capitalization tie-out, remain out of reach even for strong agentic systems. The task requires multi-document reasoning, strict evidence traceability, and deterministic outputs that current approaches fail to reliably deliver. We characterize capitalization tie-out as an instance of a real-world benchmark for legal AI, analyze and compare the performance of existing agentic systems, and propose a world model architecture toward tie-out automation-and more broadly as a foundation for applied legal intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.18314", "title": "MatSpray: Fusing 2D Material World Knowledge on 3D Geometry", "authors": [ "Philipp Langsteiner", "Jan-Niklas Dihlmann", "Hendrik P. A. Lensch" ], "github": "", "project_page": "", "abstract": "Manual modeling of material parameters and 3D geometry is a time consuming yet essential task in the gaming and film industries. While recent advances in 3D reconstruction have enabled accurate approximations of scene geometry and appearance, these methods often fall short in relighting scenarios due to the lack of precise, spatially varying material parameters. At the same time, diffusion models operating on 2D images have shown strong performance in predicting physically based rendering (PBR) properties such as albedo, roughness, and metallicity. However, transferring these 2D material maps onto reconstructed 3D geometry remains a significant challenge. We propose a framework for fusing 2D material data into 3D geometry using a combination of novel learning-based and projection-based approaches. We begin by reconstructing scene geometry via Gaussian Splatting. From the input images, a diffusion model generates 2D maps for albedo, roughness, and metallic parameters. Any existing diffusion model that can convert images or videos to PBR materials can be applied. The predictions are further integrated into the 3D representation either by optimizing an image-based loss or by directly projecting the material parameters onto the Gaussians using Gaussian ray tracing. To enhance fine-scale accuracy and multi-view consistency, we further introduce a light-weight neural refinement step (Neural Merger), which takes ray-traced material features as input and produces detailed adjustments. Our results demonstrate that the proposed methods outperform existing techniques in both quantitative metrics and perceived visual realism. This enables more accurate, relightable, and photorealistic renderings from reconstructed scenes, significantly improving the realism and efficiency of asset creation workflows in content production pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.16229", "title": "LoPA: Scaling dLLM Inference via Lookahead Parallel Decoding", "authors": [ "Chenkai Xu", "Yijie Jin", "Jiajun Li", "Yi Tu", "Guoping Long", "Dandan Tu", "Mingcong Song", "Hongjie Si", "Tianqi Hou", "Junchi Yan", "Zhijie Deng" ], "github": "https://github.com/zhijie-group/LoPA", "project_page": "", "abstract": "Diffusion Large Language Models (dLLMs) have demonstrated significant potential for high-speed inference. However, current confidence-driven decoding strategies are constrained by limited parallelism, typically achieving only 1--3 tokens per forward pass (TPF). In this work, we identify that the degree of parallelism during dLLM inference is highly sensitive to the Token Filling Order (TFO). Then, we introduce Lookahead PArallel Decoding LoPA, a training-free, plug-and-play algorithm, to identify a superior TFO and hence accelerate inference. LoPA concurrently explores distinct candidate TFOs via parallel branches, and selects the one with the highest potential for future parallelism based on branch confidence. We apply LoPA to the state-of-the-art D2F model and observe a substantial enhancement in decoding efficiency. Notably, LoPA increases the TPF of D2F-Dream to 10.1 on the GSM8K while maintaining performance superior to the Dream baseline. Furthermore, to facilitate this unprecedented degree of parallelism, we develop a specialized multi-device inference system featuring Branch Parallelism (BP), which achieves a single-sample throughput of 1073.9 tokens per second under multi-GPU deployment. The code is available at https://github.com/zhijie-group/LoPA.", "github_manual": "", "github_regex": "https://github.com/zhijie-group/LoPA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.19535", "title": "CASA: Cross-Attention via Self-Attention for Efficient Vision-Language Fusion", "authors": [ "Moritz B\u00f6hle", "Am\u00e9lie Royer", "Juliette Marrie", "Edouard Grave", "Patrick P\u00e9rez" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) are commonly trained by inserting image tokens from a pretrained vision encoder into the textual stream of a language model. This allows text and image information to fully attend to one another within the model, but becomes extremely costly for high-resolution images, long conversations, or streaming videos, both in memory and compute. VLMs leveraging cross-attention are an efficient alternative to token insertion but exhibit a clear performance gap, in particular on tasks involving fine-grained visual details. We find that a key to improving such models is to also enable local text-to-text interaction in the dedicated cross-attention layers. Building on this, we propose CASA, Cross-Attention via Self-Attention, a simple and efficient paradigm which substantially reduces the gap with full token insertion on common image understanding benchmarks, while enjoying the same scalability as cross-attention models when applied to long-context multimodal tasks such as streaming video captioning. For samples and code, please see our project page at https://kyutai.org/casa .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.18542", "title": "SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models", "authors": [ "Scott Thornton" ], "github": "", "project_page": "", "abstract": "AI assistants produce vulnerable code in 45% of security-relevant scenarios, introducing flaws into production systems at scale. Yet existing secure coding datasets fall short. They lack incident grounding, don't provide the scale modern training requires, and miss the operational security context developers need for production deployments. We present SecureCode v2.0, a production-grade dataset of 1,215 security-focused coding examples that passed structural validation and expert security review. Every example ties to actual documented security incidents with CVE references, provides vulnerable and secure implementations, demonstrates concrete attacks, and includes defense-in-depth operational guidance. The dataset covers 11 vulnerability categories (complete OWASP Top 10:2025 plus AI/ML Security Threats) across 11 languages (Python, JavaScript, Java, Go, PHP, C#, TypeScript, Ruby, Rust, Kotlin, and YAML for infrastructure-as-code). Our quality assurance framework ensures complete incident grounding. Each example includes SIEM integration strategies, infrastructure hardening recommendations (Docker, AppArmor, WAF configurations), and testing approaches using language-appropriate frameworks. The dataset uses a 4-turn conversational structure mirroring actual developer-AI interactions, escalating from basic implementations to advanced security considerations and defense-in-depth guidance. Our contributions: (1) 1,215 rigorously validated examples split into 989 training, 122 validation, and 104 test sets, (2) an automated validation framework ensuring dataset consistency, (3) a 4-turn conversational structure capturing realistic security workflows, (4) comprehensive operational security guidance with SIEM integration strategies, (5) complete language-specific implementation fidelity, and (6) open-source release of data, validation tools, and benchmarking protocols.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.19661", "title": "Over++: Generative Video Compositing for Layer Interaction Effects", "authors": [ "Luchao Qi", "Jiaye Wu", "Jun Myeong Choi", "Cary Phillips", "Roni Sengupta", "Dan B Goldman" ], "github": "", "project_page": "", "abstract": "In professional video compositing workflows, artists must manually create environmental interactions-such as shadows, reflections, dust, and splashes-between foreground subjects and background layers. Existing video generative models struggle to preserve the input video while adding such effects, and current video inpainting methods either require costly per-frame masks or yield implausible results. We introduce augmented compositing, a new task that synthesizes realistic, semi-transparent environmental effects conditioned on text prompts and input video layers, while preserving the original scene. To address this task, we present Over++, a video effect generation framework that makes no assumptions about camera pose, scene stationarity, or depth supervision. We construct a paired effect dataset tailored for this task and introduce an unpaired augmentation strategy that preserves text-driven editability. Our method also supports optional mask control and keyframe guidance without requiring dense annotations. Despite training on limited data, Over++ produces diverse and realistic environmental effects and outperforms existing baselines in both effect generation and scene preservation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-23", "arxiv_id": "2512.18184", "title": "Is There a Better Source Distribution than Gaussian? Exploring Source Distributions for Image Flow Matching", "authors": [ "Junho Lee", "Kwanseok Kim", "Joonseok Lee" ], "github": "https://github.com/kwanseokk/SourceFM", "project_page": "", "abstract": "Flow matching has emerged as a powerful generative modeling approach with flexible choices of source distribution. While Gaussian distributions are commonly used, the potential for better alternatives in high-dimensional data generation remains largely unexplored. In this paper, we propose a novel 2D simulation that captures high-dimensional geometric properties in an interpretable 2D setting, enabling us to analyze the learning dynamics of flow matching during training. Based on this analysis, we derive several key insights about flow matching behavior: (1) density approximation can paradoxically degrade performance due to mode discrepancy, (2) directional alignment suffers from path entanglement when overly concentrated, (3) Gaussian's omnidirectional coverage ensures robust learning, and (4) norm misalignment incurs substantial learning costs. Building on these insights, we propose a practical framework that combines norm-aligned training with directionally-pruned sampling. This approach maintains the robust omnidirectional supervision essential for stable flow learning, while eliminating initializations in data-sparse regions during inference. Importantly, our pruning strategy can be applied to any flow matching model trained with a Gaussian source, providing immediate performance gains without the need for retraining. Empirical evaluations demonstrate consistent improvements in both generation quality and sampling efficiency. Our findings provide practical insights and guidelines for source distribution design and introduce a readily applicable technique for improving existing flow matching models. Our code is available at https://github.com/kwanseokk/SourceFM.", "github_manual": "", "github_regex": "https://github.com/kwanseokk/SourceFM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.18099", "title": "SAM Audio: Segment Anything in Audio", "authors": [ "Bowen Shi", "Andros Tjandra", "John Hoffman", "Helin Wang", "Yi-Chiao Wu", "Luya Gao", "Julius Richter", "Matt Le", "Apoorv Vyas", "Sanyuan Chen", "Christoph Feichtenhofer", "Piotr Doll\u00e1r", "Wei-Ning Hsu", "Ann Lee" ], "github": "", "project_page": "", "abstract": "General audio source separation is a key capability for multimodal AI systems that can perceive and reason about sound. Despite substantial progress in recent years, existing separation models are either domain-specific, designed for fixed categories such as speech or music, or limited in controllability, supporting only a single prompting modality such as text. In this work, we present SAM Audio, a foundation model for general audio separation that unifies text, visual, and temporal span prompting within a single framework. Built on a diffusion transformer architecture, SAM Audio is trained with flow matching on large-scale audio data spanning speech, music, and general sounds, and can flexibly separate target sources described by language, visual masks, or temporal spans. The model achieves state-of-the-art performance across a diverse suite of benchmarks, including general sound, speech, music, and musical instrument separation in both in-the-wild and professionally produced audios, substantially outperforming prior general-purpose and specialized systems. Furthermore, we introduce a new real-world separation benchmark with human-labeled multimodal prompts and a reference-free evaluation model that correlates strongly with human judgment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.20491", "title": "Step-DeepResearch Technical Report", "authors": [ "Chen Hu", "Haikuo Du", "Heng Wang", "Lin Lin", "Mingrui Chen", "Peng Liu", "Ruihang Miao", "Tianchi Yue", "Wang You", "Wei Ji", "Wei Yuan", "Wenjin Deng", "Xiaojian Yuan", "Xiaoyun Zhang", "Xiangyu Liu", "Xikai Liu", "Yanming Xu", "Yicheng Cao", "Yifei Zhang", "Yongyao Wang", "Yubo Shu", "Yurong Zhang", "Yuxiang Zhang", "Zheng Gong", "Zhichao Chang", "Binyan Li", "Dan Ma", "Furong Jia", "Hongyuan Wang", "Jiayu Liu", "Jing Bai", "Junlan Liu", "Manjiao Liu", "Na Wang", "Qiuping Wu", "Qinxin Du", "Shiwei Li", "Wen Sun", "Yifeng Gong", "Yonglin Chen", "Yuling Zhao", "Yuxuan Lin", "Ziqi Ren", "Zixuan Wang", "Aihu Zhang", "Brian Li", "Buyun Ma", "Kang An", "Li Xie", "Mingliang Li", "Pan Li", "Shidong Yang", "Xi Chen", "Xiaojia Liu", "Yuchu Luo", "Yuan Song", "YuanHao Ding", "Yuanwei Liang", "Zexi Li", "Zhaoning Zhang", "Zixin Zhang", "Binxing Jiao", "Daxin Jiang", "Jiansheng Chen", "Jing Li", "Xiangyu Zhang", "Yibo Zhu" ], "github": "", "project_page": "", "abstract": "As LLMs shift toward autonomous agents, Deep Research has emerged as a pivotal metric. However, existing academic benchmarks like BrowseComp often fail to meet real-world demands for open-ended research, which requires robust skills in intent recognition, long-horizon decision-making, and cross-source verification. To address this, we introduce Step-DeepResearch, a cost-effective, end-to-end agent. We propose a Data Synthesis Strategy Based on Atomic Capabilities to reinforce planning and report writing, combined with a progressive training path from agentic mid-training to SFT and RL. Enhanced by a Checklist-style Judger, this approach significantly improves robustness. Furthermore, to bridge the evaluation gap in the Chinese domain, we establish ADR-Bench for realistic deep research scenarios. Experimental results show that Step-DeepResearch (32B) scores 61.4% on Scale AI Research Rubrics. On ADR-Bench, it significantly outperforms comparable models and rivals SOTA closed-source models like OpenAI and Gemini DeepResearch. These findings prove that refined training enables medium-sized models to achieve expert-level capabilities at industry-leading cost-efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.19673", "title": "Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies", "authors": [ "Yuqiao Tan", "Minzheng Wang", "Shizhu He", "Huanxuan Liao", "Chengfeng Zhao", "Qiunan Lu", "Tian Liang", "Jun Zhao", "Kang Liu" ], "github": "https://github.com/Trae1ounG/BuPO", "project_page": "", "abstract": "Existing reinforcement learning (RL) approaches treat large language models (LLMs) as a single unified policy, overlooking their internal mechanisms. Understanding how policy evolves across layers and modules is therefore crucial for enabling more targeted optimization and raveling out complex reasoning mechanisms. In this paper, we decompose the language model policy by leveraging the intrinsic split of the Transformer residual stream and the equivalence between the composition of hidden states with the unembedding matrix and the resulting samplable policy. This decomposition reveals Internal Layer Policies, corresponding to contributions from individual layers, and Internal Modular Policies, which align with the self-attention and feed-forward network (FFN) components within each layer. By analyzing the entropy of internal policy, we find that: (a) Early layers keep high entropy for exploration, top layers converge to near-zero entropy for refinement, with convergence patterns varying across model series. (b) LLama's prediction space rapidly converges in the final layer, whereas Qwen-series models, especially Qwen3, exhibit a more human-like, progressively structured reasoning pattern. Motivated by these findings, we propose Bottom-up Policy Optimization (BuPO), a novel RL paradigm that directly optimizes the internal layer policy during early training. By aligning training objective at lower layer, BuPO reconstructs foundational reasoning capabilities and achieves superior performance. Extensive experiments on complex reasoning benchmarks demonstrates the effectiveness of our method. Our code is available at https://github.com/Trae1ounG/BuPO.", "github_manual": "", "github_regex": "https://github.com/Trae1ounG/BuPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.20617", "title": "SpatialTree: How Spatial Abilities Branch Out in MLLMs", "authors": [ "Yuxi Xiao", "Longfei Li", "Shen Yan", "Xinhang Liu", "Sida Peng", "Yunchao Wei", "Xiaowei Zhou", "Bingyi Kang" ], "github": "", "project_page": "", "abstract": "Cognitive science suggests that spatial ability develops progressively-from perception to reasoning and interaction. Yet in multimodal LLMs (MLLMs), this hierarchy remains poorly understood, as most studies focus on a narrow set of tasks. We introduce SpatialTree, a cognitive-science-inspired hierarchy that organizes spatial abilities into four levels: low-level perception (L1), mental mapping (L2), simulation (L3), and agentic competence (L4). Based on this taxonomy, we construct the first capability-centric hierarchical benchmark, thoroughly evaluating mainstream MLLMs across 27 sub-abilities. The evaluation results reveal a clear structure: L1 skills are largely orthogonal, whereas higher-level skills are strongly correlated, indicating increasing interdependency. Through targeted supervised fine-tuning, we uncover a surprising transfer dynamic-negative transfer within L1, but strong cross-level transfer from low- to high-level abilities with notable synergy. Finally, we explore how to improve the entire hierarchy. We find that naive RL that encourages extensive \"thinking\" is unreliable: it helps complex reasoning but hurts intuitive perception. We propose a simple auto-think strategy that suppresses unnecessary deliberation, enabling RL to consistently improve performance across all levels. By building SpatialTree, we provide a proof-of-concept framework for understanding and systematically scaling spatial abilities in MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.17102", "title": "Reinforcement Learning for Self-Improving Agent with Skill Library", "authors": [ "Jiongxiao Wang", "Qiaojing Yan", "Yawei Wang", "Yijun Tian", "Soumya Smruti Mishra", "Zhichao Xu", "Megha Gandhi", "Panpan Xu", "Lin Lee Cheong" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt when deployed in new environments. One promising approach is implementing skill libraries that allow agents to learn, validate, and apply new skills. However, current skill library approaches rely primarily on LLM prompting, making consistent skill library implementation challenging. To overcome these challenges, we propose a Reinforcement Learning (RL)-based approach to enhance agents' self-improvement capabilities with a skill library. Specifically, we introduce Skill Augmented GRPO for self-Evolution (SAGE), a novel RL framework that systematically incorporates skills into learning. The framework's key component, Sequential Rollout, iteratively deploys agents across a chain of similar tasks for each rollout. As agents navigate through the task chain, skills generated from previous tasks accumulate in the library and become available for subsequent tasks. Additionally, the framework enhances skill generation and utilization through a Skill-integrated Reward that complements the original outcome-based rewards. Experimental results on AppWorld demonstrate that SAGE, when applied to supervised-finetuned model with expert experience, achieves 8.9% higher Scenario Goal Completion while requiring 26% fewer interaction steps and generating 59% fewer tokens, substantially outperforming existing approaches in both accuracy and efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.19526", "title": "QuantiPhy: A Quantitative Benchmark Evaluating Physical Reasoning Abilities of Vision-Language Models", "authors": [ "Li Puyin", "Tiange Xiang", "Ella Mao", "Shirley Wei", "Xinye Chen", "Adnan Masood", "Li Fei-fei", "Ehsan Adeli" ], "github": "https://github.com/Paulineli/QuantiPhy", "project_page": "", "abstract": "Understanding the physical world is essential for generalist AI agents. However, it remains unclear whether state-of-the-art vision perception models (e.g., large VLMs) can reason physical properties quantitatively. Existing evaluations are predominantly VQA-based and qualitative, offering limited insight into whether these models can infer the kinematic quantities of moving objects from video observations. To address this, we present QuantiPhy, the first benchmark designed to quantitatively measure a VLM's physical reasoning ability. Comprising more than 3.3K video-text instances with numerical ground truth, QuantiPhy evaluates a VLM's performance on estimating an object's size, velocity, and acceleration at a given timestamp, using one of these properties as an input prior. The benchmark standardizes prompts and scoring to assess numerical accuracy, enabling fair comparisons across models. Our experiments on state-of-the-art VLMs reveal a consistent gap between their qualitative plausibility and actual numerical correctness. We further provide an in-depth analysis of key factors like background noise, counterfactual priors, and strategic prompting and find that state-of-the-art VLMs lean heavily on pre-trained world knowledge rather than faithfully using the provided visual and textual inputs as references when reasoning kinematic properties quantitatively. QuantiPhy offers the first rigorous, scalable testbed to move VLMs beyond mere verbal plausibility toward a numerically grounded physical understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Paulineli/QuantiPhy", "github_api_added_by": "auto" }, { "date": "2025-12-24", "arxiv_id": "2512.20619", "title": "SemanticGen: Video Generation in Semantic Space", "authors": [ "Jianhong Bai", "Xiaoshi Wu", "Xintao Wang", "Fu Xiao", "Yuanxing Zhang", "Qinghe Wang", "Xiaoyu Shi", "Menghan Xia", "Zuozhu Liu", "Haoji Hu", "Pengfei Wan", "Kun Gai" ], "github": "", "project_page": "", "abstract": "State-of-the-art video generative models typically learn the distribution of video latents in the VAE space and map them to pixels using a VAE decoder. While this approach can generate high-quality videos, it suffers from slow convergence and is computationally expensive when generating long videos. In this paper, we introduce SemanticGen, a novel solution to address these limitations by generating videos in the semantic space. Our main insight is that, due to the inherent redundancy in videos, the generation process should begin in a compact, high-level semantic space for global planning, followed by the addition of high-frequency details, rather than directly modeling a vast set of low-level video tokens using bi-directional attention. SemanticGen adopts a two-stage generation process. In the first stage, a diffusion model generates compact semantic video features, which define the global layout of the video. In the second stage, another diffusion model generates VAE latents conditioned on these semantic features to produce the final output. We observe that generation in the semantic space leads to faster convergence compared to the VAE latent space. Our method is also effective and computationally efficient when extended to long video generation. Extensive experiments demonstrate that SemanticGen produces high-quality videos and outperforms state-of-the-art approaches and strong baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.20615", "title": "Active Intelligence in Video Avatars via Closed-loop World Modeling", "authors": [ "Xuanhua He", "Tianyu Yang", "Ke Cao", "Ruiqi Wu", "Cheng Meng", "Yong Zhang", "Zhuoliang Kang", "Xiaoming Wei", "Qifeng Chen" ], "github": "https://github.com/xuanhuahe/ORCA", "project_page": "", "abstract": "Current video avatar generation methods excel at identity preservation and motion alignment but lack genuine agency, they cannot autonomously pursue long-term goals through adaptive environmental interaction. We address this by introducing L-IVA (Long-horizon Interactive Visual Avatar), a task and benchmark for evaluating goal-directed planning in stochastic generative environments, and ORCA (Online Reasoning and Cognitive Architecture), the first framework enabling active intelligence in video avatars. ORCA embodies Internal World Model (IWM) capabilities through two key innovations: (1) a closed-loop OTAR cycle (Observe-Think-Act-Reflect) that maintains robust state tracking under generative uncertainty by continuously verifying predicted outcomes against actual generations, and (2) a hierarchical dual-system architecture where System 2 performs strategic reasoning with state prediction while System 1 translates abstract plans into precise, model-specific action captions. By formulating avatar control as a POMDP and implementing continuous belief updating with outcome verification, ORCA enables autonomous multi-step task completion in open-domain scenarios. Extensive experiments demonstrate that ORCA significantly outperforms open-loop and non-reflective baselines in task success rate and behavioral coherence, validating our IWM-inspired design for advancing video avatar intelligence from passive animation to active, goal-oriented behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xuanhuahe/ORCA", "github_api_added_by": "auto" }, { "date": "2025-12-24", "arxiv_id": "2512.20182", "title": "FaithLens: Detecting and Explaining Faithfulness Hallucination", "authors": [ "Shuzheng Si", "Qingyi Wang", "Haozhe Zhao", "Yuzhuo Bai", "Guanqiao Chen", "Kangyang Luo", "Gang Chen", "Fanchao Qi", "Minjia Zhang", "Baobao Chang", "Maosong Sun" ], "github": "https://github.com/S1s-Z/FaithLens", "project_page": "", "abstract": "Recognizing whether outputs from large language models (LLMs) contain faithfulness hallucination is crucial for real-world applications, e.g., retrieval-augmented generation and summarization. In this paper, we introduce FaithLens, a cost-efficient and effective faithfulness hallucination detection model that can jointly provide binary predictions and corresponding explanations to improve trustworthiness. To achieve this, we first synthesize training data with explanations via advanced LLMs and apply a well-defined data filtering strategy to ensure label correctness, explanation quality, and data diversity. Subsequently, we fine-tune the model on these well-curated training data as a cold start and further optimize it with rule-based reinforcement learning, using rewards for both prediction correctness and explanation quality. Results on 12 diverse tasks show that the 8B-parameter FaithLens outperforms advanced models such as GPT-4.1 and o3. Also, FaithLens can produce high-quality explanations, delivering a distinctive balance of trustworthiness, efficiency, and effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/S1s-Z/FaithLens", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.18746", "title": "MemEvolve: Meta-Evolution of Agent Memory Systems", "authors": [ "Guibin Zhang", "Haotian Ren", "Chong Zhan", "Zhenhong Zhou", "Junhao Wang", "He Zhu", "Wangchunshu Zhou", "Shuicheng Yan" ], "github": "https://github.com/bingreeky/MemEvolve", "project_page": "", "abstract": "Self-evolving memory systems are unprecedentedly reshaping the evolutionary paradigm of large language model (LLM)-based agents. Prior work has predominantly relied on manually engineered memory architectures to store trajectories, distill experience, and synthesize reusable tools, enabling agents to evolve on the fly within environment interactions. However, this paradigm is fundamentally constrained by the staticity of the memory system itself: while memory facilitates agent-level evolving, the underlying memory architecture cannot be meta-adapted to diverse task contexts. To address this gap, we propose MemEvolve, a meta-evolutionary framework that jointly evolves agents' experiential knowledge and their memory architecture, allowing agent systems not only to accumulate experience but also to progressively refine how they learn from it. To ground MemEvolve in prior research and foster openness in future self-evolving systems, we introduce EvolveLab, a unified self-evolving memory codebase that distills twelve representative memory systems into a modular design space (encode, store, retrieve, manage), providing both a standardized implementation substrate and a fair experimental arena. Extensive evaluations on four challenging agentic benchmarks demonstrate that MemEvolve achieves (I) substantial performance gains, improving frameworks such as SmolAgent and Flash-Searcher by up to 17.06%; and (II) strong cross-task and cross-LLM generalization, designing memory architectures that transfer effectively across diverse benchmarks and backbone models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bingreeky/MemEvolve", "github_api_added_by": "auto" }, { "date": "2025-12-24", "arxiv_id": "2512.16144", "title": "INTELLECT-3: Technical Report", "authors": [ "Prime Intellect Team", "Mika Senghaas", "Fares Obeid", "Sami Jaghouar", "William Brown", "Jack Min Ong", "Daniel Auras", "Matej Sirovatka", "Jannik Straube", "Andrew Baker", "Sebastian M\u00fcller", "Justus Mattern", "Manveer Basra", "Aiman Ismail", "Dominik Scherm", "Cooper Miller", "Ameen Patel", "Simon Kirsten", "Mario Sieg", "Christian Reetz", "Kemal Erdem", "Vincent Weisser", "Johannes Hagemann" ], "github": "https://github.com/LiveCodeBench/LiveCodeBench", "project_page": "", "abstract": "We present INTELLECT-3, a 106B-parameter Mixture-of-Experts model (12B active) trained with large-scale reinforcement learning on our end-to-end RL infrastructure stack. INTELLECT-3 achieves state of the art performance for its size across math, code, science and reasoning benchmarks, outperforming many larger frontier models. We open-source the model together with the full infrastructure stack used to create it, including RL frameworks, complete recipe, and a wide collection of environments, built with the verifiers library, for training and evaluation from our Environments Hub community platform. Built for this effort, we introduce prime-rl, an open framework for large-scale asynchronous reinforcement learning, which scales seamlessly from a single node to thousands of GPUs, and is tailored for agentic RL with first-class support for multi-turn interactions and tool use. Using this stack, we run both SFT and RL training on top of the GLM-4.5-Air-Base model, scaling RL training up to 512 H200s with high training efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LiveCodeBench/LiveCodeBench", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.20352", "title": "Multi-LLM Thematic Analysis with Dual Reliability Metrics: Combining Cohen's Kappa and Semantic Similarity for Qualitative Research Validation", "authors": [ "Nilesh Jain", "Seyi Adeyinka", "Leor Roseman", "Aza Allsop" ], "github": "https://github.com/NileshArnaiya/LLM-Thematic-Analysis-Tool", "project_page": "", "abstract": "Qualitative research faces a critical reliability challenge: traditional inter-rater agreement methods require multiple human coders, are time-intensive, and often yield moderate consistency. We present a multi-perspective validation framework for LLM-based thematic analysis that combines ensemble validation with dual reliability metrics: Cohen's Kappa (\u03ba) for inter-rater agreement and cosine similarity for semantic consistency. Our framework enables configurable analysis parameters (1-6 seeds, temperature 0.0-2.0), supports custom prompt structures with variable substitution, and provides consensus theme extraction across any JSON format. As proof-of-concept, we evaluate three leading LLMs (Gemini 2.5 Pro, GPT-4o, Claude 3.5 Sonnet) on a psychedelic art therapy interview transcript, conducting six independent runs per model. Results demonstrate Gemini achieves highest reliability (\u03ba= 0.907, cosine=95.3%), followed by GPT-4o (\u03ba= 0.853, cosine=92.6%) and Claude (\u03ba= 0.842, cosine=92.1%). All three models achieve a high agreement (\u03ba> 0.80), validating the multi-run ensemble approach. The framework successfully extracts consensus themes across runs, with Gemini identifying 6 consensus themes (50-83% consistency), GPT-4o identifying 5 themes, and Claude 4 themes. Our open-source implementation provides researchers with transparent reliability metrics, flexible configuration, and structure-agnostic consensus extraction, establishing methodological foundations for reliable AI-assisted qualitative research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NileshArnaiya/LLM-Thematic-Analysis-Tool", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.20618", "title": "LongVideoAgent: Multi-Agent Reasoning with Long Videos", "authors": [ "Runtao Liu", "Ziyi Liu", "Jiaqi Tang", "Yue Ma", "Renjie Pi", "Jipeng Zhang", "Qifeng Chen" ], "github": "", "project_page": "", "abstract": "Recent advances in multimodal LLMs and systems that use tools for long-video QA point to the promise of reasoning over hour-long episodes. However, many methods still compress content into lossy summaries or rely on limited toolsets, weakening temporal grounding and missing fine-grained cues. We propose a multi-agent framework in which a master LLM coordinates a grounding agent to localize question-relevant segments and a vision agent to extract targeted textual observations. The master agent plans with a step limit, and is trained with reinforcement learning to encourage concise, correct, and efficient multi-agent cooperation. This design helps the master agent focus on relevant clips via grounding, complements subtitles with visual detail, and yields interpretable trajectories. On our proposed LongTVQA and LongTVQA+ which are episode-level datasets aggregated from TVQA/TVQA+, our multi-agent system significantly outperforms strong non-agent baselines. Experiments also show reinforcement learning further strengthens reasoning and planning for the trained agent. Code and data will be shared at https://longvideoagent.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.15031", "title": "Toxicity Ahead: Forecasting Conversational Derailment on GitHub", "authors": [ "Mia Mohammad Imran", "Robert Zita", "Rahat Rizvi Rahman", "Preetha Chatterjee", "Kostadin Damevski" ], "github": "", "project_page": "", "abstract": "Toxic interactions in Open Source Software (OSS) communities reduce contributor engagement and threaten project sustainability. Preventing such toxicity before it emerges requires a clear understanding of how harmful conversations unfold. However, most proactive moderation strategies are manual, requiring significant time and effort from community maintainers. To support more scalable approaches, we curate a dataset of 159 derailed toxic threads and 207 non-toxic threads from GitHub discussions. Our analysis reveals that toxicity can be forecast by tension triggers, sentiment shifts, and specific conversational patterns. We present a novel Large Language Model (LLM)-based framework for predicting conversational derailment on GitHub using a two-step prompting pipeline. First, we generate Summaries of Conversation Dynamics (SCDs) via Least-to-Most (LtM) prompting; then we use these summaries to estimate the likelihood of derailment. Evaluated on Qwen and Llama models, our LtM strategy achieves F1-scores of 0.901 and 0.852, respectively, at a decision threshold of 0.3, outperforming established NLP baselines on conversation derailment. External validation on a dataset of 308 GitHub issue threads (65 toxic, 243 non-toxic) yields an F1-score up to 0.797. Our findings demonstrate the effectiveness of structured LLM prompting for early detection of conversational derailment in OSS, enabling proactive and explainable moderation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.17648", "title": "Simulstream: Open-Source Toolkit for Evaluation and Demonstration of Streaming Speech-to-Text Translation Systems", "authors": [ "Marco Gaido", "Sara Papi", "Mauro Cettolo", "Matteo Negri", "Luisa Bentivogli" ], "github": "", "project_page": "", "abstract": "Streaming Speech-to-Text Translation (StreamST) requires producing translations concurrently with incoming speech, imposing strict latency constraints and demanding models that balance partial-information decision-making with high translation quality. Research efforts on the topic have so far relied on the SimulEval repository, which is no longer maintained and does not support systems that revise their outputs. In addition, it has been designed for simulating the processing of short segments, rather than long-form audio streams, and it does not provide an easy method to showcase systems in a demo. As a solution, we introduce simulstream, the first open-source framework dedicated to unified evaluation and demonstration of StreamST systems. Designed for long-form speech processing, it supports not only incremental decoding approaches, but also re-translation methods, enabling for their comparison within the same framework both in terms of quality and latency. In addition, it also offers an interactive web interface to demo any system built within the tool.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.13472", "title": "Scaling Laws for Code: Every Programming Language Matters", "authors": [ "Jian Yang", "Shawn Guo", "Lin Jing", "Wei Zhang", "Aishan Liu", "Chuan Hao", "Zhoujun Li", "Wayne Xin Zhao", "Xianglong Liu", "Weifeng Lv", "Bryan Dai" ], "github": "", "project_page": "", "abstract": "Code large language models (Code LLMs) are powerful but costly to train, with scaling laws predicting performance from model size, data, and compute. However, different programming languages (PLs) have varying impacts during pre-training that significantly affect base model performance, leading to inaccurate performance prediction. Besides, existing works focus on language-agnostic settings, neglecting the inherently multilingual nature of modern software development. Therefore, it is first necessary to investigate the scaling laws of different PLs, and then consider their mutual influences to arrive at the final multilingual scaling law. In this paper, we present the first systematic exploration of scaling laws for multilingual code pre-training, conducting over 1000+ experiments (Equivalent to 336,000+ H800 hours) across multiple PLs, model sizes (0.2B to 14B parameters), and dataset sizes (1T tokens). We establish comprehensive scaling laws for code LLMs across multiple PLs, revealing that interpreted languages (e.g., Python) benefit more from increased model size and data than compiled languages (e.g., Rust). The study demonstrates that multilingual pre-training provides synergistic benefits, particularly between syntactically similar PLs. Further, the pre-training strategy of the parallel pairing (concatenating code snippets with their translations) significantly enhances cross-lingual abilities with favorable scaling properties. Finally, a proportion-dependent multilingual scaling law is proposed to optimally allocate training tokens by prioritizing high-utility PLs (e.g., Python), balancing high-synergy pairs (e.g., JavaScript-TypeScript), and reducing allocation to fast-saturating languages (Rust), achieving superior average performance across all PLs compared to uniform distribution under the same compute budget.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.20092", "title": "Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents", "authors": [ "Yiming Du", "Baojun Wang", "Yifan Xiang", "Zhaowei Wang", "Wenyu Huang", "Boyang Xue", "Bin Liang", "Xingshan Zeng", "Fei Mi", "Haoli Bai", "Lifeng Shang", "Jeff Z. Pan", "Yuxin Jiang", "Kam-Fai Wong" ], "github": "https://github.com/Elvin-Yiming-Du/Memory-T1", "project_page": "", "abstract": "Temporal reasoning over long, multi-session dialogues is a critical capability for conversational agents. However, existing works and our pilot study have shown that as dialogue histories grow in length and accumulate noise, current long-context models struggle to accurately identify temporally pertinent information, significantly impairing reasoning performance. To address this, we introduce Memory-T1, a framework that learns a time-aware memory selection policy using reinforcement learning (RL). It employs a coarse-to-fine strategy, first pruning the dialogue history into a candidate set using temporal and relevance filters, followed by an RL agent that selects the precise evidence sessions. The RL training is guided by a multi-level reward function optimizing (i) answer accuracy, (ii) evidence grounding, and (iii) temporal consistency. In particular, the temporal consistency reward provides a dense signal by evaluating alignment with the query time scope at both the session-level (chronological proximity) and the utterance-level (chronological fidelity), enabling the agent to resolve subtle chronological ambiguities. On the Time-Dialog benchmark, Memory-T1 boosts a 7B model to an overall score of 67.0\\%, establishing a new state-of-the-art performance for open-source models and outperforming a 14B baseline by 10.2\\%. Ablation studies show temporal consistency and evidence grounding rewards jointly contribute to a 15.0\\% performance gain. Moreover, Memory-T1 maintains robustness up to 128k tokens, where baseline models collapse, proving effectiveness against noise in extensive dialogue histories. The code and datasets are publicly available at https://github.com/Elvin-Yiming-Du/Memory-T1/", "github_manual": "", "github_regex": "https://github.com/Elvin-Yiming-Du/Memory-T1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.19823", "title": "Learning to Refocus with Video Diffusion Models", "authors": [ "SaiKiran Tedla", "Zhoutong Zhang", "Xuaner Zhang", "Shumian Xin" ], "github": "", "project_page": "", "abstract": "Focus is a cornerstone of photography, yet autofocus systems often fail to capture the intended subject, and users frequently wish to adjust focus after capture. We introduce a novel method for realistic post-capture refocusing using video diffusion models. From a single defocused image, our approach generates a perceptually accurate focal stack, represented as a video sequence, enabling interactive refocusing and unlocking a range of downstream applications. We release a large-scale focal stack dataset acquired under diverse real-world smartphone conditions to support this work and future research. Our method consistently outperforms existing approaches in both perceptual quality and robustness across challenging scenarios, paving the way for more advanced focus-editing capabilities in everyday photography. Code and data are available at www.learn2refocus.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-24", "arxiv_id": "2512.21332", "title": "C2LLM Technical Report: A New Frontier in Code Retrieval via Adaptive Cross-Attention Pooling", "authors": [ "Jin Qin", "Zihan Liao", "Ziyin Zhang", "Hang Yu", "Peng Di", "Rui Wang" ], "github": "", "project_page": "", "abstract": "We present C2LLM - Contrastive Code Large Language Models, a family of code embedding models in both 0.5B and 7B sizes. Building upon Qwen-2.5-Coder backbones, C2LLM adopts a Pooling by Multihead Attention (PMA) module for generating sequence embedding from token embeddings, effectively 1) utilizing the LLM's causal representations acquired during pretraining, while also 2) being able to aggregate information from all tokens in the sequence, breaking the information bottleneck in EOS-based sequence embeddings, and 3) supporting flexible adaptation of embedding dimension, serving as an alternative to MRL. Trained on three million publicly available data, C2LLM models set new records on MTEB-Code among models of similar sizes, with C2LLM-7B ranking 1st on the overall leaderboard.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.21094", "title": "T2AV-Compass: Towards Unified Evaluation for Text-to-Audio-Video Generation", "authors": [ "Zhe Cao", "Tao Wang", "Jiaming Wang", "Yanghai Wang", "Yuanxing Zhang", "Jialu Chen", "Miao Deng", "Jiahao Wang", "Yubin Guo", "Chenxi Liao", "Yize Zhang", "Zhaoxiang Zhang", "Jiaheng Liu" ], "github": "", "project_page": "", "abstract": "Text-to-Audio-Video (T2AV) generation aims to synthesize temporally coherent video and semantically synchronized audio from natural language, yet its evaluation remains fragmented, often relying on unimodal metrics or narrowly scoped benchmarks that fail to capture cross-modal alignment, instruction following, and perceptual realism under complex prompts. To address this limitation, we present T2AV-Compass, a unified benchmark for comprehensive evaluation of T2AV systems, consisting of 500 diverse and complex prompts constructed via a taxonomy-driven pipeline to ensure semantic richness and physical plausibility. Besides, T2AV-Compass introduces a dual-level evaluation framework that integrates objective signal-level metrics for video quality, audio quality, and cross-modal alignment with a subjective MLLM-as-a-Judge protocol for instruction following and realism assessment. Extensive evaluation of 11 representative T2AVsystems reveals that even the strongest models fall substantially short of human-level realism and cross-modal consistency, with persistent failures in audio realism, fine-grained synchronization, instruction following, etc. These results indicate significant improvement room for future models and highlight the value of T2AV-Compass as a challenging and diagnostic testbed for advancing text-to-audio-video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.20856", "title": "NVIDIA Nemotron 3: Efficient and Open Intelligence", "authors": [ "NVIDIA", "Aaron Blakeman", "Aaron Grattafiori", "Aarti Basant", "Abhibha Gupta", "Abhinav Khattar", "Adi Renduchintala", "Aditya Vavre", "Akanksha Shukla", "Akhiad Bercovich", "Aleksander Ficek", "Aleksandr Shaposhnikov", "Alex Kondratenko", "Alexander Bukharin", "Alexandre Milesi", "Ali Taghibakhshi", "Alisa Liu", "Amelia Barton", "Ameya Sunil Mahabaleshwarkar", "Amir Klein", "Amit Zuker", "Amnon Geifman", "Amy Shen", "Anahita Bhiwandiwalla", "Andrew Tao", "Anjulie Agrusa", "Ankur Verma", "Ann Guan", "Anubhav Mandarwal", "Arham Mehta", "Ashwath Aithal", "Ashwin Poojary", "Asif Ahamed", "Asit Mishra", "Asma Kuriparambil Thekkumpate", "Ayush Dattagupta", "Banghua Zhu", "Bardiya Sadeghi", "Barnaby Simkin", "Ben Lanir", "Benedikt Schifferer", "Besmira Nushi", "Bilal Kartal", "Bita Darvish Rouhani", "Boris Ginsburg", "Brandon Norick", "Brandon Soubasis", "Branislav Kisacanin", "Brian Yu", "Bryan Catanzaro", "Carlo del Mundo", "Chantal Hwang", "Charles Wang", "Cheng-Ping Hsieh", "Chenghao Zhang", "Chenhan Yu", "Chetan Mungekar", "Chintan Patel", "Chris Alexiuk", "Christopher Parisien", "Collin Neale", "Cyril Meurillon", "Damon Mosk-Aoyama", "Dan Su", "Dane Corneil", "Daniel Afrimi", "Daniel Lo", "Daniel Rohrer", "Daniel Serebrenik", "Daria Gitman", "Daria Levy", "Darko Stosic", "David Mosallanezhad", "Deepak Narayanan", "Dhruv Nathawani", "Dima Rekesh", "Dina Yared", "Divyanshu Kakwani", "Dong Ahn", "Duncan Riach", "Dusan Stosic", "Edgar Minasyan", "Edward Lin", "Eileen Long", "Eileen Peters Long", "Elad Segal", "Elena Lantz", "Ellie Evans", "Elliott Ning", "Eric Chung", "Eric Harper", "Eric Tramel", "Erick Galinkin", "Erik Pounds", "Evan Briones", "Evelina Bakhturina", "Evgeny Tsykunov", "Faisal Ladhak", "Fay Wang", "Fei Jia", "Felipe Soares", "Feng Chen", "Ferenc Galko", "Frank Sun", "Frankie Siino", "Gal Hubara Agam", "Ganesh Ajjanagadde", "Gantavya Bhatt", "Gargi Prasad", "George Armstrong", "Gerald Shen", "Gorkem Batmaz", "Grigor Nalbandyan", "Haifeng Qian", "Harsh Sharma", "Hayley Ross", "Helen Ngo", "Herbert Hum", "Herman Sahota", "Hexin Wang", "Himanshu Soni", "Hiren Upadhyay", "Huizi Mao", "Huy C Nguyen", "Huy Q Nguyen", "Iain Cunningham", "Ido Galil", "Ido Shahaf", "Igor Gitman", "Ilya Loshchilov", "Itamar Schen", "Itay Levy", "Ivan Moshkov", "Izik Golan", "Izzy Putterman", "Jan Kautz", "Jane Polak Scowcroft", "Jared Casper", "Jatin Mitra", "Jeffrey Glick", "Jenny Chen", "Jesse Oliver", "Jian Zhang", "Jiaqi Zeng", "Jie Lou", "Jimmy Zhang", "Jinhang Choi", "Jining Huang", "Joey Conway", "Joey Guman", "John Kamalu", "Johnny Greco", "Jonathan Cohen", "Joseph Jennings", "Joyjit Daw", "Julien Veron Vialard", "Junkeun Yi", "Jupinder Parmar", "Kai Xu", "Kan Zhu", "Kari Briski", "Katherine Cheung", "Katherine Luna", "Keith Wyss", "Keshav Santhanam", "Kevin Shih", "Kezhi Kong", "Khushi Bhardwaj", "Kirthi Shankar", "Krishna C. Puvvada", "Krzysztof Pawelec", "Kumar Anik", "Lawrence McAfee", "Laya Sleiman", "Leon Derczynski", "Li Ding", "Lizzie Wei", "Lucas Liebenwein", "Luis Vega", "Maanu Grover", "Maarten Van Segbroeck", "Maer Rodrigues de Melo", "Mahdi Nazemi", "Makesh Narsimhan Sreedhar", "Manoj Kilaru", "Maor Ashkenazi", "Marc Romeijn", "Marcin Chochowski", "Mark Cai", "Markus Kliegl", "Maryam Moosaei", "Matt Kulka", "Matvei Novikov", "Mehrzad Samadi", "Melissa Corpuz", "Mengru Wang", "Meredith Price", "Michael Andersch", "Michael Boone", "Michael Evans", "Miguel Martinez", "Mikail Khona", "Mike Chrzanowski", "Minseok Lee", "Mohammad Dabbah", "Mohammad Shoeybi", "Mostofa Patwary", "Nabin Mulepati", "Najeeb Nabwani", "Natalie Hereth", "Nave Assaf", "Negar Habibi", "Neta Zmora", "Netanel Haber", "Nicola Sessions", "Nidhi Bhatia", "Nikhil Jukar", "Nikki Pope", "Nikolai Ludwig", "Nima Tajbakhsh", "Nir Ailon", "Nirmal Juluru", "Nishant Sharma", "Oleksii Hrinchuk", "Oleksii Kuchaiev", "Olivier Delalleau", "Oluwatobi Olabiyi", "Omer Ullman Argov", "Omri Puny", "Oren Tropp", "Ouye Xie", "Parth Chadha", "Pasha Shamis", "Paul Gibbons", "Pavlo Molchanov", "Pawel Morkisz", "Peter Dykas", "Peter Jin", "Pinky Xu", "Piotr Januszewski", "Pranav Prashant Thombre", "Prasoon Varshney", "Pritam Gundecha", "Przemek Tredak", "Qing Miao", "Qiyu Wan", "Rabeeh Karimi Mahabadi", "Rachit Garg", "Ran El-Yaniv", "Ran Zilberstein", "Rasoul Shafipour", "Rich Harang", "Rick Izzo", "Rima Shahbazyan", "Rishabh Garg", "Ritika Borkar", "Ritu Gala", "Riyad Islam", "Robert Hesse", "Roger Waleffe", "Rohit Watve", "Roi Koren", "Ruoxi Zhang", "Russell Hewett", "Russell J. Hewett", "Ryan Prenger", "Ryan Timbrook", "Sadegh Mahdavi", "Sahil Modi", "Samuel Kriman", "Sangkug Lim", "Sanjay Kariyappa", "Sanjeev Satheesh", "Saori Kaji", "Satish Pasumarthi", "Saurav Muralidharan", "Sean Narentharen", "Sean Narenthiran", "Seonmyeong Bak", "Sergey Kashirsky", "Seth Poulos", "Shahar Mor", "Shanmugam Ramasamy", "Shantanu Acharya", "Shaona Ghosh", "Sharath Turuvekere Sreenivas", "Shelby Thomas", "Shiqing Fan", "Shreya Gopal", "Shrimai Prabhumoye", "Shubham Pachori", "Shubham Toshniwal", "Shuoyang Ding", "Siddharth Singh", "Simeng Sun", "Smita Ithape", "Somshubra Majumdar", "Soumye Singhal", "Stas Sergienko", "Stefania Alborghetti", "Stephen Ge", "Sugam Dipak Devare", "Sumeet Kumar Barua", "Suseella Panguluri", "Suyog Gupta", "Sweta Priyadarshi", "Syeda Nahida Akter", "Tan Bui", "Teodor-Dumitru Ene", "Terry Kong", "Thanh Do", "Tijmen Blankevoort", "Tim Moon", "Tom Balough", "Tomer Asida", "Tomer Bar Natan", "Tomer Ronen", "Tugrul Konuk", "Twinkle Vashishth", "Udi Karpas", "Ushnish De", "Vahid Noorozi", "Vahid Noroozi", "Venkat Srinivasan", "Venmugil Elango", "Victor Cui", "Vijay Korthikanti", "Vinay Rao", "Vitaly Kurin", "Vitaly Lavrukhin", "Vladimir Anisimov", "Wanli Jiang", "Wasi Uddin Ahmad", "Wei Du", "Wei Ping", "Wenfei Zhou", "Will Jennings", "William Zhang", "Wojciech Prazuch", "Xiaowei Ren", "Yashaswi Karnati", "Yejin Choi", "Yev Meyer", "Yi-Fu Wu", "Yian Zhang", "Yigong Qin", "Ying Lin", "Yonatan Geifman", "Yonggan Fu", "Yoshi Subara", "Yoshi Suhara", "Yubo Gao", "Zach Moshe", "Zhen Dong", "Zhongbo Zhu", "Zihan Liu", "Zijia Chen", "Zijie Yan" ], "github": "https://github.com/NVIDIA-NeMo/RL", "project_page": "", "abstract": "We introduce the Nemotron 3 family of models - Nano, Super, and Ultra. These models deliver strong agentic, reasoning, and conversational capabilities. The Nemotron 3 family uses a Mixture-of-Experts hybrid Mamba-Transformer architecture to provide best-in-class throughput and context lengths of up to 1M tokens. Super and Ultra models are trained with NVFP4 and incorporate LatentMoE, a novel approach that improves model quality. The two larger models also include MTP layers for faster text generation. All Nemotron 3 models are post-trained using multi-environment reinforcement learning enabling reasoning, multi-step tool use, and support granular reasoning budget control. Nano, the smallest model, outperforms comparable models in accuracy while remaining extremely cost-efficient for inference. Super is optimized for collaborative agents and high-volume workloads such as IT ticket automation. Ultra, the largest model, provides state-of-the-art accuracy and reasoning performance. Nano is released together with its technical report and this white paper, while Super and Ultra will follow in the coming months. We will openly release the model weights, pre- and post-training software, recipes, and all data for which we hold redistribution rights.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA-NeMo/RL", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.20848", "title": "Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning", "authors": [ "NVIDIA", "Aaron Blakeman", "Aaron Grattafiori", "Aarti Basant", "Abhibha Gupta", "Abhinav Khattar", "Adi Renduchintala", "Aditya Vavre", "Akanksha Shukla", "Akhiad Bercovich", "Aleksander Ficek", "Aleksandr Shaposhnikov", "Alex Kondratenko", "Alexander Bukharin", "Alexandre Milesi", "Ali Taghibakhshi", "Alisa Liu", "Amelia Barton", "Ameya Sunil Mahabaleshwarkar", "Amir Klein", "Amit Zuker", "Amnon Geifman", "Amy Shen", "Anahita Bhiwandiwalla", "Andrew Tao", "Ann Guan", "Anubhav Mandarwal", "Arham Mehta", "Ashwath Aithal", "Ashwin Poojary", "Asif Ahamed", "Asma Kuriparambil Thekkumpate", "Ayush Dattagupta", "Banghua Zhu", "Bardiya Sadeghi", "Barnaby Simkin", "Ben Lanir", "Benedikt Schifferer", "Besmira Nushi", "Bilal Kartal", "Bita Darvish Rouhani", "Boris Ginsburg", "Brandon Norick", "Brandon Soubasis", "Branislav Kisacanin", "Brian Yu", "Bryan Catanzaro", "Carlo del Mundo", "Chantal Hwang", "Charles Wang", "Cheng-Ping Hsieh", "Chenghao Zhang", "Chenhan Yu", "Chetan Mungekar", "Chintan Patel", "Chris Alexiuk", "Christopher Parisien", "Collin Neale", "Damon Mosk-Aoyama", "Dan Su", "Dane Corneil", "Daniel Afrimi", "Daniel Rohrer", "Daniel Serebrenik", "Daria Gitman", "Daria Levy", "Darko Stosic", "David Mosallanezhad", "Deepak Narayanan", "Dhruv Nathawani", "Dima Rekesh", "Dina Yared", "Divyanshu Kakwani", "Dong Ahn", "Duncan Riach", "Dusan Stosic", "Edgar Minasyan", "Edward Lin", "Eileen Long", "Eileen Peters Long", "Elena Lantz", "Ellie Evans", "Elliott Ning", "Eric Chung", "Eric Harper", "Eric Tramel", "Erick Galinkin", "Erik Pounds", "Evan Briones", "Evelina Bakhturina", "Faisal Ladhak", "Fay Wang", "Fei Jia", "Felipe Soares", "Feng Chen", "Ferenc Galko", "Frankie Siino", "Gal Hubara Agam", "Ganesh Ajjanagadde", "Gantavya Bhatt", "Gargi Prasad", "George Armstrong", "Gerald Shen", "Gorkem Batmaz", "Grigor Nalbandyan", "Haifeng Qian", "Harsh Sharma", "Hayley Ross", "Helen Ngo", "Herman Sahota", "Hexin Wang", "Himanshu Soni", "Hiren Upadhyay", "Huizi Mao", "Huy C Nguyen", "Huy Q Nguyen", "Iain Cunningham", "Ido Shahaf", "Igor Gitman", "Ilya Loshchilov", "Ivan Moshkov", "Izzy Putterman", "Jan Kautz", "Jane Polak Scowcroft", "Jared Casper", "Jatin Mitra", "Jeffrey Glick", "Jenny Chen", "Jesse Oliver", "Jian Zhang", "Jiaqi Zeng", "Jie Lou", "Jimmy Zhang", "Jining Huang", "Joey Conway", "Joey Guman", "John Kamalu", "Johnny Greco", "Jonathan Cohen", "Joseph Jennings", "Joyjit Daw", "Julien Veron Vialard", "Junkeun Yi", "Jupinder Parmar", "Kai Xu", "Kan Zhu", "Kari Briski", "Katherine Cheung", "Katherine Luna", "Keshav Santhanam", "Kevin Shih", "Kezhi Kong", "Khushi Bhardwaj", "Krishna C. Puvvada", "Krzysztof Pawelec", "Kumar Anik", "Lawrence McAfee", "Laya Sleiman", "Leon Derczynski", "Li Ding", "Lucas Liebenwein", "Luis Vega", "Maanu Grover", "Maarten Van Segbroeck", "Maer Rodrigues de Melo", "Makesh Narsimhan Sreedhar", "Manoj Kilaru", "Maor Ashkenazi", "Marc Romeijn", "Mark Cai", "Markus Kliegl", "Maryam Moosaei", "Matvei Novikov", "Mehrzad Samadi", "Melissa Corpuz", "Mengru Wang", "Meredith Price", "Michael Boone", "Michael Evans", "Miguel Martinez", "Mike Chrzanowski", "Mohammad Shoeybi", "Mostofa Patwary", "Nabin Mulepati", "Natalie Hereth", "Nave Assaf", "Negar Habibi", "Neta Zmora", "Netanel Haber", "Nicola Sessions", "Nidhi Bhatia", "Nikhil Jukar", "Nikki Pope", "Nikolai Ludwig", "Nima Tajbakhsh", "Nirmal Juluru", "Oleksii Hrinchuk", "Oleksii Kuchaiev", "Olivier Delalleau", "Oluwatobi Olabiyi", "Omer Ullman Argov", "Ouye Xie", "Parth Chadha", "Pasha Shamis", "Pavlo Molchanov", "Pawel Morkisz", "Peter Dykas", "Peter Jin", "Pinky Xu", "Piotr Januszewski", "Pranav Prashant Thombre", "Prasoon Varshney", "Pritam Gundecha", "Qing Miao", "Rabeeh Karimi Mahabadi", "Ran El-Yaniv", "Ran Zilberstein", "Rasoul Shafipour", "Rich Harang", "Rick Izzo", "Rima Shahbazyan", "Rishabh Garg", "Ritika Borkar", "Ritu Gala", "Riyad Islam", "Roger Waleffe", "Rohit Watve", "Roi Koren", "Ruoxi Zhang", "Russell J. Hewett", "Ryan Prenger", "Ryan Timbrook", "Sadegh Mahdavi", "Sahil Modi", "Samuel Kriman", "Sanjay Kariyappa", "Sanjeev Satheesh", "Saori Kaji", "Satish Pasumarthi", "Sean Narentharen", "Sean Narenthiran", "Seonmyeong Bak", "Sergey Kashirsky", "Seth Poulos", "Shahar Mor", "Shanmugam Ramasamy", "Shantanu Acharya", "Shaona Ghosh", "Sharath Turuvekere Sreenivas", "Shelby Thomas", "Shiqing Fan", "Shreya Gopal", "Shrimai Prabhumoye", "Shubham Pachori", "Shubham Toshniwal", "Shuoyang Ding", "Siddharth Singh", "Simeng Sun", "Smita Ithape", "Somshubra Majumdar", "Soumye Singhal", "Stefania Alborghetti", "Stephen Ge", "Sugam Dipak Devare", "Sumeet Kumar Barua", "Suseella Panguluri", "Suyog Gupta", "Sweta Priyadarshi", "Syeda Nahida Akter", "Tan Bui", "Teodor-Dumitru Ene", "Terry Kong", "Thanh Do", "Tijmen Blankevoort", "Tom Balough", "Tomer Asida", "Tomer Bar Natan", "Tugrul Konuk", "Twinkle Vashishth", "Udi Karpas", "Ushnish De", "Vahid Noorozi", "Vahid Noroozi", "Venkat Srinivasan", "Venmugil Elango", "Vijay Korthikanti", "Vitaly Kurin", "Vitaly Lavrukhin", "Wanli Jiang", "Wasi Uddin Ahmad", "Wei Du", "Wei Ping", "Wenfei Zhou", "Will Jennings", "William Zhang", "Wojciech Prazuch", "Xiaowei Ren", "Yashaswi Karnati", "Yejin Choi", "Yev Meyer", "Yi-Fu Wu", "Yian Zhang", "Ying Lin", "Yonatan Geifman", "Yonggan Fu", "Yoshi Subara", "Yoshi Suhara", "Yubo Gao", "Zach Moshe", "Zhen Dong", "Zihan Liu", "Zijia Chen", "Zijie Yan" ], "github": "https://github.com/NVIDIA-NeMo/Nemotron", "project_page": "", "abstract": "We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained on 25 trillion text tokens, including more than 3 trillion new unique tokens over Nemotron 2, followed by supervised fine tuning and large-scale RL on diverse environments. Nemotron 3 Nano achieves better accuracy than our previous generation Nemotron 2 Nano while activating less than half of the parameters per forward pass. It achieves up to 3.3x higher inference throughput than similarly-sized open models like GPT-OSS-20B and Qwen3-30B-A3B-Thinking-2507, while also being more accurate on popular benchmarks. Nemotron 3 Nano demonstrates enhanced agentic, reasoning, and chat abilities and supports context lengths up to 1M tokens. We release both our pretrained Nemotron 3 Nano 30B-A3B Base and post-trained Nemotron 3 Nano 30B-A3B checkpoints on Hugging Face.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVIDIA-NeMo/Nemotron", "github_api_added_by": "auto" }, { "date": "2025-12-25", "arxiv_id": "2512.21334", "title": "Streaming Video Instruction Tuning", "authors": [ "Jiaer Xia", "Peixian Chen", "Mengdan Zhang", "Xing Sun", "Kaiyang Zhou" ], "github": "https://github.com/maifoundations/Streamo", "project_page": "", "abstract": "We present Streamo, a real-time streaming video LLM that serves as a general-purpose interactive assistant. Unlike existing online video models that focus narrowly on question answering or captioning, Streamo performs a broad spectrum of streaming video tasks, including real-time narration, action understanding, event captioning, temporal event grounding, and time-sensitive question answering. To develop such versatility, we construct Streamo-Instruct-465K, a large-scale instruction-following dataset tailored for streaming video understanding. The dataset covers diverse temporal contexts and multi-task supervision, enabling unified training across heterogeneous streaming tasks. After training end-to-end on the instruction-following dataset through a streamlined pipeline, Streamo exhibits strong temporal reasoning, responsive interaction, and broad generalization across a variety of streaming benchmarks. Extensive experiments show that Streamo bridges the gap between offline video perception models and real-time multimodal assistants, making a step toward unified, intelligent video understanding in continuous video streams.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/maifoundations/Streamo", "github_api_added_by": "auto" }, { "date": "2025-12-25", "arxiv_id": "2512.21010", "title": "LLM Swiss Round: Aggregating Multi-Benchmark Performance via Competitive Swiss-System Dynamics", "authors": [ "Jiashuo Liu", "Jiayun Wu", "Chunjie Wu", "Jingkai Liu", "Zaiyuan Wang", "Huan Zhou", "Wenhao Huang", "Hongseok Namkoong" ], "github": "", "project_page": "", "abstract": "The rapid proliferation of Large Language Models (LLMs) and diverse specialized benchmarks necessitates a shift from fragmented, task-specific metrics to a holistic, competitive ranking system that effectively aggregates performance across multiple ability dimensions. Primarily using static scoring, current evaluation methods are fundamentally limited. They struggle to determine the proper mix ratio across diverse benchmarks, and critically, they fail to capture a model's dynamic competitive fitness or its vulnerability when confronted with sequential, high-stakes tasks. To address this, we introduce the novel Competitive Swiss-System Dynamics (CSD) framework. CSD simulates a multi-round, sequential contest where models are dynamically paired across a curated sequence of benchmarks based on their accumulated win-loss record. And Monte Carlo Simulation (N=100,000 iterations) is used to approximate the statistically robust Expected Win Score (E[S_m]), which eliminates the noise of random pairing and early-round luck. Furthermore, we implement a Failure Sensitivity Analysis by parameterizing the per-round elimination quantity (T_k), which allows us to profile models based on their risk appetite--distinguishing between robust generalists and aggressive specialists. We demonstrate that CSD provides a more nuanced and context-aware ranking than traditional aggregate scoring and static pairwise models, representing a vital step towards risk-informed, next-generation LLM evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.20557", "title": "Learning to Reason in 4D: Dynamic Spatial Understanding for Vision Language Models", "authors": [ "Shengchao Zhou", "Yuxin Chen", "Yuying Ge", "Wei Huang", "Jiehong Lin", "Ying Shan", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLM) excel at general understanding yet remain weak at dynamic spatial reasoning (DSR), i.e., reasoning about the evolvement of object geometry and relationship in 3D space over time, largely due to the scarcity of scalable 4D-aware training resources. To bridge this gap across aspects of dataset, benchmark and model, we introduce DSR Suite. First, we propose an automated pipeline that generates multiple-choice question-answer pairs from in-the-wild videos for DSR. By leveraging modern vision foundation models, the pipeline extracts rich geometric and motion information, including camera poses, local point clouds, object masks, orientations, and 3D trajectories. These geometric cues enable the construction of DSR-Train for learning and further human-refined DSR-Bench for evaluation. Compared with previous works, our data emphasize (i) in-the-wild video sources, (ii) object- and scene-level 3D requirements, (iii) viewpoint transformations, (iv) multi-object interactions, and (v) fine-grained, procedural answers. Beyond data, we propose a lightweight Geometry Selection Module (GSM) to seamlessly integrate geometric priors into VLMs, which condenses question semantics and extracts question-relevant knowledge from pretrained 4D reconstruction priors into a compact set of geometry tokens. This targeted extraction avoids overwhelming the model with irrelevant knowledge. Experiments show that integrating DSR-Train and GSM into Qwen2.5-VL-7B significantly enhances its dynamic spatial reasoning capability, while maintaining accuracy on general video understanding benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.16093", "title": "TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times", "authors": [ "Jintao Zhang", "Kaiwen Zheng", "Kai Jiang", "Haoxu Wang", "Ion Stoica", "Joseph E. Gonzalez", "Jianfei Chen", "Jun Zhu" ], "github": "https://github.com/thu-ml/TurboDiffusion", "project_page": "", "abstract": "We introduce TurboDiffusion, a video generation acceleration framework that can speed up end-to-end diffusion generation by 100-200x while maintaining video quality. TurboDiffusion mainly relies on several components for acceleration: (1) Attention acceleration: TurboDiffusion uses low-bit SageAttention and trainable Sparse-Linear Attention (SLA) to speed up attention computation. (2) Step distillation: TurboDiffusion adopts rCM for efficient step distillation. (3) W8A8 quantization: TurboDiffusion quantizes model parameters and activations to 8 bits to accelerate linear layers and compress the model. In addition, TurboDiffusion incorporates several other engineering optimizations. We conduct experiments on the Wan2.2-I2V-14B-720P, Wan2.1-T2V-1.3B-480P, Wan2.1-T2V-14B-720P, and Wan2.1-T2V-14B-480P models. Experimental results show that TurboDiffusion achieves 100-200x speedup for video generation even on a single RTX 5090 GPU, while maintaining comparable video quality. The GitHub repository, which includes model checkpoints and easy-to-use code, is available at https://github.com/thu-ml/TurboDiffusion.", "github_manual": "", "github_regex": "https://github.com/thu-ml/TurboDiffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.21338", "title": "HiStream: Efficient High-Resolution Video Generation via Redundancy-Eliminated Streaming", "authors": [ "Haonan Qiu", "Shikun Liu", "Zijian Zhou", "Zhaochong An", "Weiming Ren", "Zhiheng Liu", "Jonas Schult", "Sen He", "Shoufa Chen", "Yuren Cong", "Tao Xiang", "Ziwei Liu", "Juan-Manuel Perez-Rua" ], "github": "", "project_page": "", "abstract": "High-resolution video generation, while crucial for digital media and film, is computationally bottlenecked by the quadratic complexity of diffusion models, making practical inference infeasible. To address this, we introduce HiStream, an efficient autoregressive framework that systematically reduces redundancy across three axes: i) Spatial Compression: denoising at low resolution before refining at high resolution with cached features; ii) Temporal Compression: a chunk-by-chunk strategy with a fixed-size anchor cache, ensuring stable inference speed; and iii) Timestep Compression: applying fewer denoising steps to subsequent, cache-conditioned chunks. On 1080p benchmarks, our primary HiStream model (i+ii) achieves state-of-the-art visual quality while demonstrating up to 76.2x faster denoising compared to the Wan2.1 baseline and negligible quality loss. Our faster variant, HiStream+, applies all three optimizations (i+ii+iii), achieving a 107.5x acceleration over the baseline, offering a compelling trade-off between speed and quality, thereby making high-resolution video generation both practical and scalable.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.21337", "title": "Beyond Memorization: A Multi-Modal Ordinal Regression Benchmark to Expose Popularity Bias in Vision-Language Models", "authors": [ "Li-Zhong Szu-Tu", "Ting-Lin Wu", "Chia-Jui Chang", "He Syu", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "We expose a significant popularity bias in state-of-the-art vision-language models (VLMs), which achieve up to 34% higher accuracy on famous buildings compared to ordinary ones, indicating a reliance on memorization over generalizable understanding. To systematically investigate this, we introduce the largest open benchmark for this task: the YearGuessr dataset, a collection of 55,546 building images with multi-modal attributes from 157 countries, annotated with continuous ordinal labels of their construction year (1001-2024), GPS data, and page-view counts as a proxy for popularity. Using this dataset, we frame the construction year prediction task as ordinal regression and introduce popularity-aware interval accuracy metrics to quantify this bias. Our resulting benchmark of 30+ models, including our YearCLIP model, confirms that VLMs excel on popular, memorized items but struggle significantly with unrecognized subjects, exposing a critical flaw in their reasoning capabilities. Project page: https://sytwu.github.io/BeyondMemo/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.21252", "title": "DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation", "authors": [ "Jiawei Liu", "Junqiao Li", "Jiangfan Deng", "Gen Li", "Siyu Zhou", "Zetao Fang", "Shanshan Lao", "Zengde Deng", "Jianing Zhu", "Tingting Ma", "Jiayi Li", "Yunqiu Wang", "Qian He", "Xinglong Wu" ], "github": "", "project_page": "", "abstract": "The \"one-shot\" technique represents a distinct and sophisticated aesthetic in filmmaking. However, its practical realization is often hindered by prohibitive costs and complex real-world constraints. Although emerging video generation models offer a virtual alternative, existing approaches typically rely on naive clip concatenation, which frequently fails to maintain visual smoothness and temporal coherence. In this paper, we introduce DreaMontage, a comprehensive framework designed for arbitrary frame-guided generation, capable of synthesizing seamless, expressive, and long-duration one-shot videos from diverse user-provided inputs. To achieve this, we address the challenge through three primary dimensions. (i) We integrate a lightweight intermediate-conditioning mechanism into the DiT architecture. By employing an Adaptive Tuning strategy that effectively leverages base training data, we unlock robust arbitrary-frame control capabilities. (ii) To enhance visual fidelity and cinematic expressiveness, we curate a high-quality dataset and implement a Visual Expression SFT stage. In addressing critical issues such as subject motion rationality and transition smoothness, we apply a Tailored DPO scheme, which significantly improves the success rate and usability of the generated content. (iii) To facilitate the production of extended sequences, we design a Segment-wise Auto-Regressive (SAR) inference strategy that operates in a memory-efficient manner. Extensive experiments demonstrate that our approach achieves visually striking and seamlessly coherent one-shot effects while maintaining computational efficiency, empowering users to transform fragmented visual materials into vivid, cohesive one-shot cinematic experiences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.21004", "title": "Learning from Next-Frame Prediction: Autoregressive Video Modeling Encodes Effective Representations", "authors": [ "Jinghan Li", "Yang Jin", "Hao Jiang", "Yadong Mu", "Yang Song", "Kun Xu" ], "github": "", "project_page": "", "abstract": "Recent advances in pretraining general foundation models have significantly improved performance across diverse downstream tasks. While autoregressive (AR) generative models like GPT have revolutionized NLP, most visual generative pretraining methods still rely on BERT-style masked modeling, which often disregards the temporal information essential for video analysis. The few existing autoregressive visual pretraining methods suffer from issues such as inaccurate semantic localization and poor generation quality, leading to poor semantics. In this work, we propose NExT-Vid, a novel autoregressive visual generative pretraining framework that utilizes masked next-frame prediction to jointly model images and videos. NExT-Vid introduces a context-isolated autoregressive predictor to decouple semantic representation from target decoding, and a conditioned flow-matching decoder to enhance generation quality and diversity. Through context-isolated flow-matching pretraining, our approach achieves strong representations. Extensive experiments on large-scale pretrained models demonstrate that our proposed method consistently outperforms previous generative pretraining methods for visual representation learning via attentive probing in downstream classification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.20757", "title": "TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior", "authors": [ "G\u00fcl Sena Alt\u0131nta\u015f", "Malikeh Ehghaghi", "Brian Lester", "Fengyuan Liu", "Wanru Zhao", "Marco Ciccone", "Colin Raffel" ], "github": "", "project_page": "", "abstract": "Tokenizers provide the fundamental basis through which text is represented and processed by language models (LMs). Despite the importance of tokenization, its role in LM performance and behavior is poorly understood due to the challenge of measuring the impact of tokenization in isolation. To address this need, we present TokSuite, a collection of models and a benchmark that supports research into tokenization's influence on LMs. Specifically, we train fourteen models that use different tokenizers but are otherwise identical using the same architecture, dataset, training budget, and initialization. Additionally, we curate and release a new benchmark that specifically measures model performance subject to real-world perturbations that are likely to influence tokenization. Together, TokSuite allows robust decoupling of the influence of a model's tokenizer, supporting a series of novel findings that elucidate the respective benefits and shortcomings of a wide range of popular tokenizers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.20144", "title": "Multi-hop Reasoning via Early Knowledge Alignment", "authors": [ "Yuxin Wang", "Shicheng Fang", "Bo Wang", "Qi Luo", "Xuanjing Huang", "Yining Zheng", "Xipeng Qiu" ], "github": "https://github.com/yxzwang/EarlyKnowledgeAlignment", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for Large Language Models (LLMs) to address knowledge-intensive queries requiring domain-specific or up-to-date information. To handle complex multi-hop questions that are challenging for single-step retrieval, iterative RAG approaches incorporating reinforcement learning have been proposed. However, existing iterative RAG systems typically plan to decompose questions without leveraging information about the available retrieval corpus, leading to inefficient retrieval and reasoning chains that cascade into suboptimal performance. In this paper, we introduce Early Knowledge Alignment (EKA), a simple but effective module that aligns LLMs with retrieval set before planning in iterative RAG systems with contextually relevant retrieved knowledge. Extensive experiments on six standard RAG datasets demonstrate that by establishing a stronger reasoning foundation, EKA significantly improves retrieval precision, reduces cascading errors, and enhances both performance and efficiency. Our analysis from an entropy perspective demonstrate that incorporating early knowledge reduces unnecessary exploration during the reasoning process, enabling the model to focus more effectively on relevant information subsets. Moreover, EKA proves effective as a versatile, training-free inference strategy that scales seamlessly to large models. Generalization tests across diverse datasets and retrieval corpora confirm the robustness of our approach. Overall, EKA advances the state-of-the-art in iterative RAG systems while illuminating the critical interplay between structured reasoning and efficient exploration in reinforcement learning-augmented frameworks. The code is released at https://github.com/yxzwang/EarlyKnowledgeAlignment{Github}.", "github_manual": "", "github_regex": "https://github.com/yxzwang/EarlyKnowledgeAlignment", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.18470", "title": "SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios", "authors": [ "Minh V. T. Thai", "Tue Le", "Dung Nguyen Manh", "Huy Phan Nhat", "Nghi D. Q. Bui" ], "github": "https://github.com/bdqnghi/SWE-EVO", "project_page": "", "abstract": "Existing benchmarks for AI coding agents focus on isolated, single-issue tasks such as fixing a bug or implementing a small feature. However, real-world software engineering is fundamentally a long-horizon endeavor: developers must interpret high-level requirements, plan coordinated changes across many files, and evolve codebases over multiple iterations while preserving existing functionality. We introduce SWE-EVO, a benchmark that evaluates agents on this long-horizon software evolution challenge. Constructed from release notes and version histories of seven mature open-source Python projects, Tool comprises 48 evolution tasks that require agents to implement multi-step modifications spanning an average of 21 files, validated against comprehensive test suites averaging 874 tests per instance. Experiments with state-of-the-art models reveal a striking capability gap: even GPT-5 with OpenHands achieves only a 21 percent resolution rate on Tool, compared to 65 percent on the single-issue SWE-Bench Verified. This demonstrates that current agents struggle with sustained, multi-file reasoning. We also propose Fix Rate, a fine-grained metric that captures partial progress toward solving these complex, long-horizon tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bdqnghi/SWE-EVO", "github_api_added_by": "auto" }, { "date": "2025-12-25", "arxiv_id": "2512.21227", "title": "PhononBench:A Large-Scale Phonon-Based Benchmark for Dynamical Stability in Crystal Generation", "authors": [ "Xiao-Qi Han", "Ze-Feng Gao", "Peng-Jie Guo", "Zhong-Yi Lu" ], "github": "https://github.com/xqh19970407/PhononBench", "project_page": "", "abstract": "In this work, we introduce PhononBench, the first large-scale benchmark for dynamical stability in AI-generated crystals. Leveraging the recently developed MatterSim interatomic potential, which achieves DFT-level accuracy in phonon predictions across more than 10,000 materials, PhononBench enables efficient large-scale phonon calculations and dynamical-stability analysis for 108,843 crystal structures generated by six leading crystal generation models. PhononBench reveals a widespread limitation of current generative models in ensuring dynamical stability: the average dynamical-stability rate across all generated structures is only 25.83%, with the top-performing model, MatterGen, reaching just 41.0%. Further case studies show that in property-targeted generation-illustrated here by band-gap conditioning with MatterGen--the dynamical-stability rate remains as low as 23.5% even at the optimal band-gap condition of 0.5 eV. In space-group-controlled generation, higher-symmetry crystals exhibit better stability (e.g., cubic systems achieve rates up to 49.2%), yet the average stability across all controlled generations is still only 34.4%. An important additional outcome of this study is the identification of 28,119 crystal structures that are phonon-stable across the entire Brillouin zone, providing a substantial pool of reliable candidates for future materials exploration. By establishing the first large-scale dynamical-stability benchmark, this work systematically highlights the current limitations of crystal generation models and offers essential evaluation criteria and guidance for their future development toward the design and discovery of physically viable materials. All model-generated crystal structures, phonon calculation results, and the high-throughput evaluation workflows developed in PhononBench will be openly released at https://github.com/xqh19970407/PhononBench", "github_manual": "", "github_regex": "https://github.com/xqh19970407/PhononBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.18832", "title": "From Word to World: Can Large Language Models be Implicit Text-based World Models?", "authors": [ "Yixia Li", "Hongru Wang", "Jiahao Qiu", "Zhenfei Yin", "Dongdong Zhang", "Cheng Qian", "Zeping Li", "Pony Ma", "Guanhua Chen", "Heng Ji", "Mengdi Wang" ], "github": "", "project_page": "", "abstract": "Agentic reinforcement learning increasingly relies on experience-driven scaling, yet real-world environments remain non-adaptive, limited in coverage, and difficult to scale. World models offer a potential way to improve learning efficiency through simulated experience, but it remains unclear whether large language models can reliably serve this role and under what conditions they meaningfully benefit agents. We study these questions in text-based environments, which provide a controlled setting to reinterpret language modeling as next-state prediction under interaction. We introduce a three-level framework for evaluating LLM-based world models: (i) fidelity and consistency, (ii) scalability and robustness, and (iii) agent utility. Across five representative environments, we find that sufficiently trained world models maintain coherent latent state, scale predictably with data and model size, and improve agent performance via action verification, synthetic trajectory generation, and warm-starting reinforcement learning. Meanwhile, these gains depend critically on behavioral coverage and environment complexity, delineating clear boundry on when world modeling effectively supports agent learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-25", "arxiv_id": "2512.19012", "title": "DramaBench: A Six-Dimensional Evaluation Framework for Drama Script Continuation", "authors": [ "Shijian Ma", "Yunqi Huang", "Yan Lin" ], "github": "", "project_page": "", "abstract": "Drama script continuation requires models to maintain character consistency, advance plot coherently, and preserve dramatic structurecapabilities that existing benchmarks fail to evaluate comprehensively. We present DramaBench, the first large-scale benchmark for evaluating drama script continuation across six independent dimensions: Format Standards, Narrative Efficiency, Character Consistency, Emotional Depth, Logic Consistency, and Conflict Handling. Our framework combines rulebased analysis with LLM-based labeling and statistical metrics, ensuring objective and reproducible evaluation. We conduct comprehensive evaluation of 8 state-of-the-art language models on 1,103 scripts (8,824 evaluations total), with rigorous statistical significance testing (252 pairwise comparisons, 65.9% significant) and human validation (188 scripts, substantial agreement on 3/5 dimensions). Our ablation studies confirm all six dimensions capture independent quality aspects (mean | r | = 0.020). DramaBench provides actionable, dimensionspecific feedback for model improvement and establishes a rigorous standard for creative writing evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-26", "arxiv_id": "2512.21218", "title": "Latent Implicit Visual Reasoning", "authors": [ "Kelvin Li", "Chuyi Shang", "Leonid Karlinsky", "Rogerio Feris", "Trevor Darrell", "Roei Herzig" ], "github": "", "project_page": "", "abstract": "While Large Multimodal Models (LMMs) have made significant progress, they remain largely text-centric, relying on language as their core reasoning modality. As a result, they are limited in their ability to handle reasoning tasks that are predominantly visual. Recent approaches have sought to address this by supervising intermediate visual steps with helper images, depth maps, or image crops. However, these strategies impose restrictive priors on what \"useful\" visual abstractions look like, add heavy annotation costs, and struggle to generalize across tasks. To address this critical limitation, we propose a task-agnostic mechanism that trains LMMs to discover and use visual reasoning tokens without explicit supervision. These tokens attend globally and re-encode the image in a task-adaptive way, enabling the model to extract relevant visual information without hand-crafted supervision. Our approach outperforms direct fine-tuning and achieves state-of-the-art results on a diverse range of vision-centric tasks -- including those where intermediate abstractions are hard to specify -- while also generalizing to multi-task instruction tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-26", "arxiv_id": "2512.13043", "title": "GTR-Turbo: Merged Checkpoint is Secretly a Free Teacher for Agentic VLM Training", "authors": [ "Tong Wei", "Yijun Yang", "Changhao Zhang", "Junliang Xing", "Yuanchun Shi", "Zongqing Lu", "Deheng Ye" ], "github": "", "project_page": "", "abstract": "Multi-turn reinforcement learning (RL) for multi-modal agents built upon vision-language models (VLMs) is hampered by sparse rewards and long-horizon credit assignment. Recent methods densify the reward by querying a teacher that provides step-level feedback, e.g., Guided Thought Reinforcement (GTR) and On-Policy Distillation, but rely on costly, often privileged models as the teacher, limiting practicality and reproducibility. We introduce GTR-Turbo, a highly efficient upgrade to GTR, which matches the performance without training or querying an expensive teacher model. Specifically, GTR-Turbo merges the weights of checkpoints produced during the ongoing RL training, and then uses this merged model as a \"free\" teacher to guide the subsequent RL via supervised fine-tuning or soft logit distillation. This design removes dependence on privileged VLMs (e.g., GPT or Gemini), mitigates the \"entropy collapse\" observed in prior work, and keeps training stable. Across diverse visual agentic tasks, GTR-Turbo improves the accuracy of the baseline model by 10-30% while reducing wall-clock training time by 50% and compute cost by 60% relative to GTR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-26", "arxiv_id": "2512.19995", "title": "Schoenfeld's Anatomy of Mathematical Reasoning by Language Models", "authors": [ "Ming Li", "Chenrui Fan", "Yize Cheng", "Soheil Feizi", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Large language models increasingly expose reasoning traces, yet their underlying cognitive structure and steps remain difficult to identify and analyze beyond surface-level statistics. We adopt Schoenfeld's Episode Theory as an inductive, intermediate-scale lens and introduce ThinkARM (Anatomy of Reasoning in Models), a scalable framework that explicitly abstracts reasoning traces into functional reasoning steps such as Analysis, Explore, Implement, Verify, etc. When applied to mathematical problem solving by diverse models, this abstraction reveals reproducible thinking dynamics and structural differences between reasoning and non-reasoning models, which are not apparent from token-level views. We further present two diagnostic case studies showing that exploration functions as a critical branching step associated with correctness, and that efficiency-oriented methods selectively suppress evaluative feedback steps rather than uniformly shortening responses. Together, our results demonstrate that episode-level representations make reasoning steps explicit, enabling systematic analysis of how reasoning is structured, stabilized, and altered in modern language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-26", "arxiv_id": "2512.19680", "title": "VA-\u03c0: Variational Policy Alignment for Pixel-Aware Autoregressive Generation", "authors": [ "Xinyao Liao", "Qiyuan He", "Kai Xu", "Xiaoye Qu", "Yicong Li", "Wei Wei", "Angela Yao" ], "github": "https://github.com/Lil-Shake/VA-Pi", "project_page": "", "abstract": "Autoregressive (AR) visual generation relies on tokenizers to map images to and from discrete sequences. However, tokenizers are trained to reconstruct clean images from ground-truth tokens, while AR generators are optimized only for token likelihood. This misalignment leads to generated token sequences that may decode into low-quality images, without direct supervision from the pixel space. We propose VA-\u03c0, a lightweight post-training framework that directly optimizes AR models with a principled pixel-space objective. VA-\u03c0 formulates the generator-tokenizer alignment as a variational optimization, deriving an evidence lower bound (ELBO) that unifies pixel reconstruction and autoregressive modeling. To optimize under the discrete token space, VA-\u03c0 introduces a reinforcement-based alignment strategy that treats the AR generator as a policy, uses pixel-space reconstruction quality as its intrinsic reward. The reward is measured by how well the predicted token sequences can reconstruct the original image under teacher forcing, giving the model direct pixel-level guidance without expensive free-running sampling. The regularization term of the ELBO serves as a natural regularizer, maintaining distributional consistency of tokens. VA-\u03c0 enables rapid adaptation of existing AR generators, without neither tokenizer retraining nor external reward models. With only 1% ImageNet-1K data and 25 minutes of tuning, it reduces FID from 14.36 to 7.65 and improves IS from 86.55 to 116.70 on LlamaGen-XXL, while also yielding notable gains in the text-to-image task on GenEval for both visual generation model (LlamaGen: from 0.306 to 0.339) and unified multi-modal model (Janus-Pro: from 0.725 to 0.744). Code is available at https://github.com/Lil-Shake/VA-Pi.", "github_manual": "", "github_regex": "https://github.com/Lil-Shake/VA-Pi", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-26", "arxiv_id": "2512.15716", "title": "Spatia: Video Generation with Updatable Spatial Memory", "authors": [ "Jinjing Zhao", "Fangyun Wei", "Zhening Liu", "Hongyang Zhang", "Chang Xu", "Yan Lu" ], "github": "", "project_page": "", "abstract": "Existing video generation models struggle to maintain long-term spatial and temporal consistency due to the dense, high-dimensional nature of video signals. To overcome this limitation, we propose Spatia, a spatial memory-aware video generation framework that explicitly preserves a 3D scene point cloud as persistent spatial memory. Spatia iteratively generates video clips conditioned on this spatial memory and continuously updates it through visual SLAM. This dynamic-static disentanglement design enhances spatial consistency throughout the generation process while preserving the model's ability to produce realistic dynamic entities. Furthermore, Spatia enables applications such as explicit camera control and 3D-aware interactive editing, providing a geometrically grounded framework for scalable, memory-driven video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-26", "arxiv_id": "2512.19949", "title": "How Much 3D Do Video Foundation Models Encode?", "authors": [ "Zixuan Huang", "Xiang Li", "Zhaoyang Lv", "James M. Rehg" ], "github": "", "project_page": "", "abstract": "Videos are continuous 2D projections of 3D worlds. After training on large video data, will global 3D understanding naturally emerge? We study this by quantifying the 3D understanding of existing Video Foundation Models (VidFMs) pretrained on vast video data. We propose the first model-agnostic framework that measures the 3D awareness of various VidFMs by estimating multiple 3D properties from their features via shallow read-outs. Our study presents meaningful findings regarding the 3D awareness of VidFMs on multiple axes. In particular, we show that state-of-the-art video generation models exhibit a strong understanding of 3D objects and scenes, despite not being trained on any 3D data. Such understanding can even surpass that of large expert models specifically trained for 3D tasks. Our findings, together with the 3D benchmarking of major VidFMs, provide valuable observations for building scalable 3D models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-26", "arxiv_id": "2512.20605", "title": "Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning", "authors": [ "Seijin Kobayashi", "Yanick Schimpf", "Maximilian Schlegel", "Angelika Steger", "Maciej Wolczyk", "Johannes von Oswald", "Nino Scherrer", "Kaitlin Maile", "Guillaume Lajoie", "Blake A. Richards", "Rif A. Saurous", "James Manyika", "Blaise Ag\u00fcera y Arcas", "Alexander Meulemans", "Jo\u00e3o Sacramento" ], "github": "", "project_page": "", "abstract": "Large-scale autoregressive models pretrained on next-token prediction and finetuned with reinforcement learning (RL) have achieved unprecedented success on many problem domains. During RL, these models explore by generating new outputs, one token at a time. However, sampling actions token-by-token can result in highly inefficient learning, particularly when rewards are sparse. Here, we show that it is possible to overcome this problem by acting and exploring within the internal representations of an autoregressive model. Specifically, to discover temporally-abstract actions, we introduce a higher-order, non-causal sequence model whose outputs control the residual stream activations of a base autoregressive model. On grid world and MuJoCo-based tasks with hierarchical structure, we find that the higher-order model learns to compress long activation sequence chunks onto internal controllers. Critically, each controller executes a sequence of behaviorally meaningful actions that unfold over long timescales and are accompanied with a learned termination condition, such that composing multiple controllers over time leads to efficient exploration on novel tasks. We show that direct internal controller reinforcement, a process we term \"internal RL\", enables learning from sparse rewards in cases where standard RL finetuning fails. Our results demonstrate the benefits of latent action generation and reinforcement in autoregressive models, suggesting internal RL as a promising avenue for realizing hierarchical RL within foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.17504", "title": "InsertAnywhere: Bridging 4D Scene Geometry and Diffusion Models for Realistic Video Object Insertion", "authors": [ "Hoiyeong Jin", "Hyojin Jang", "Jeongho Kim", "Junha Hyung", "Kinam Kim", "Dongjin Kim", "Huijin Choi", "Hyeonji Kim", "Jaegul Choo" ], "github": "", "project_page": "", "abstract": "Recent advances in diffusion-based video generation have opened new possibilities for controllable video editing, yet realistic video object insertion (VOI) remains challenging due to limited 4D scene understanding and inadequate handling of occlusion and lighting effects. We present InsertAnywhere, a new VOI framework that achieves geometrically consistent object placement and appearance-faithful video synthesis. Our method begins with a 4D aware mask generation module that reconstructs the scene geometry and propagates user specified object placement across frames while maintaining temporal coherence and occlusion consistency. Building upon this spatial foundation, we extend a diffusion based video generation model to jointly synthesize the inserted object and its surrounding local variations such as illumination and shading. To enable supervised training, we introduce ROSE++, an illumination aware synthetic dataset constructed by transforming the ROSE object removal dataset into triplets of object removed video, object present video, and a VLM generated reference image. Through extensive experiments, we demonstrate that our framework produces geometrically plausible and visually coherent object insertions across diverse real world scenarios, significantly outperforming existing research and commercial models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.22118", "title": "ProEdit: Inversion-based Editing From Prompts Done Right", "authors": [ "Zhi Ouyang", "Dian Zheng", "Xiao-Ming Wu", "Jian-Jian Jiang", "Kun-Yu Lin", "Jingke Meng", "Wei-Shi Zheng" ], "github": "", "project_page": "", "abstract": "Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions. Existing methods typically inject source image information during the sampling process to maintain editing consistency. However, this sampling strategy overly relies on source information, which negatively affects the edits in the target image (e.g., failing to change the subject's atributes like pose, number, or color as instructed). In this work, we propose ProEdit to address this issue both in the attention and the latent aspects. In the attention aspect, we introduce KV-mix, which mixes KV features of the source and the target in the edited region, mitigating the influence of the source image on the editing region while maintaining background consistency. In the latent aspect, we propose Latents-Shift, which perturbs the edited region of the source latent, eliminating the influence of the inverted latent on the sampling. Extensive experiments on several image and video editing benchmarks demonstrate that our method achieves SOTA performance. In addition, our design is plug-and-play, which can be seamlessly integrated into existing inversion and editing methods, such as RF-Solver, FireFlow and UniEdit.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.21507", "title": "SVBench: Evaluation of Video Generation Models on Social Reasoning", "authors": [ "Wenshuo Peng", "Gongxuan Wang", "Tianmeng Yang", "Chuanhao Li", "Xiaojie Xu", "Hui He", "Kaipeng Zhang" ], "github": "https://github.com/Gloria2tt/SVBench-Evaluation", "project_page": "", "abstract": "Recent text-to-video generation models exhibit remarkable progress in visual realism, motion fidelity, and text-video alignment, yet they remain fundamentally limited in their ability to generate socially coherent behavior. Unlike humans, who effortlessly infer intentions, beliefs, emotions, and social norms from brief visual cues, current models tend to render literal scenes without capturing the underlying causal or psychological logic. To systematically evaluate this gap, we introduce the first benchmark for social reasoning in video generation. Grounded in findings from developmental and social psychology, our benchmark organizes thirty classic social cognition paradigms into seven core dimensions, including mental-state inference, goal-directed action, joint attention, social coordination, prosocial behavior, social norms, and multi-agent strategy. To operationalize these paradigms, we develop a fully training-free agent-based pipeline that (i) distills the reasoning mechanism of each experiment, (ii) synthesizes diverse video-ready scenarios, (iii) enforces conceptual neutrality and difficulty control through cue-based critique, and (iv) evaluates generated videos using a high-capacity VLM judge across five interpretable dimensions of social reasoning. Using this framework, we conduct the first large-scale study across seven state-of-the-art video generation systems. Our results reveal substantial performance gaps: while modern models excel in surface-level plausibility, they systematically fail in intention recognition, belief reasoning, joint attention, and prosocial inference.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Gloria2tt/SVBench-Evaluation", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.22047", "title": "MAI-UI Technical Report: Real-World Centric Foundation GUI Agents", "authors": [ "Hanzhang Zhou", "Xu Zhang", "Panrong Tong", "Jianan Zhang", "Liangyu Chen", "Quyu Kong", "Chenglin Cai", "Chen Liu", "Yue Wang", "Jingren Zhou", "Steven Hoi" ], "github": "https://github.com/Tongyi-MAI/MAI-UI", "project_page": "", "abstract": "The development of GUI agents could revolutionize the next generation of human-computer interaction. Motivated by this vision, we present MAI-UI, a family of foundation GUI agents spanning the full spectrum of sizes, including 2B, 8B, 32B, and 235B-A22B variants. We identify four key challenges to realistic deployment: the lack of native agent-user interaction, the limits of UI-only operation, the absence of a practical deployment architecture, and brittleness in dynamic environments. MAI-UI addresses these issues with a unified methodology: a self-evolving data pipeline that expands the navigation data to include user interaction and MCP tool calls, a native device-cloud collaboration system routes execution by task state, and an online RL framework with advanced optimizations to scale parallel environments and context length. MAI-UI establishes new state-of-the-art across GUI grounding and mobile navigation. On grounding benchmarks, it reaches 73.5% on ScreenSpot-Pro, 91.3% on MMBench GUI L2, 70.9% on OSWorld-G, and 49.2% on UI-Vision, surpassing Gemini-3-Pro and Seed1.8 on ScreenSpot-Pro. On mobile GUI navigation, it sets a new SOTA of 76.7% on AndroidWorld, surpassing UI-Tars-2, Gemini-2.5-Pro and Seed1.8. On MobileWorld, MAI-UI obtains 41.7% success rate, significantly outperforming end-to-end GUI models and competitive with Gemini-3-Pro based agentic frameworks. Our online RL experiments show significant gains from scaling parallel environments from 32 to 512 (+5.2 points) and increasing environment step budget from 15 to 50 (+4.3 points). Finally, the native device-cloud collaboration system improves on-device performance by 33%, reduces cloud model calls by over 40%, and preserves user privacy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tongyi-MAI/MAI-UI", "github_api_added_by": "auto" }, { "date": "2025-12-29", "arxiv_id": "2512.21919", "title": "SWE-RM: Execution-free Feedback For Software Engineering Agents", "authors": [ "KaShun Shum", "Binyuan Hui", "Jiawei Chen", "Lei Zhang", "X. W.", "Jiaxi Yang", "Yuzhen Huang", "Junyang Lin", "Junxian He" ], "github": "", "project_page": "", "abstract": "Execution-based feedback like unit testing is widely used in the development of coding agents through test-time scaling (TTS) and reinforcement learning (RL). This paradigm requires scalable and reliable collection of unit test cases to provide accurate feedback, and the resulting feedback is often sparse and cannot effectively distinguish between trajectories that are both successful or both unsuccessful. In contrast, execution-free feedback from reward models can provide more fine-grained signals without depending on unit test cases. Despite this potential, execution-free feedback for realistic software engineering (SWE) agents remains underexplored. Aiming to develop versatile reward models that are effective across TTS and RL, however, we observe that two verifiers with nearly identical TTS performance can nevertheless yield very different results in RL. Intuitively, TTS primarily reflects the model's ability to select the best trajectory, but this ability does not necessarily generalize to RL. To address this limitation, we identify two additional aspects that are crucial for RL training: classification accuracy and calibration. We then conduct comprehensive controlled experiments to investigate how to train a robust reward model that performs well across these metrics. In particular, we analyze the impact of various factors such as training data scale, policy mixtures, and data source composition. Guided by these investigations, we introduce SWE-RM, an accurate and robust reward model adopting a mixture-of-experts architecture with 30B total parameters and 3B activated during inference. SWE-RM substantially improves SWE agents on both TTS and RL performance. For example, it increases the accuracy of Qwen3-Coder-Flash from 51.6% to 62.0%, and Qwen3-Coder-Max from 67.0% to 74.6% on SWE-Bench Verified using TTS, achieving new state-of-the-art performance among open-source models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.17220", "title": "Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding", "authors": [ "Yuqing Li", "Jiangnan Li", "Zheng Lin", "Ziyan Zhou", "Junjie Wu", "Weiping Wang", "Jie Zhou", "Mo Yu" ], "github": "", "project_page": "", "abstract": "Humans understand long and complex texts by relying on a holistic semantic representation of the content. This global view helps organize prior knowledge, interpret new information, and integrate evidence dispersed across a document, as revealed by the Mindscape-Aware Capability of humans in psychology. Current Retrieval-Augmented Generation (RAG) systems lack such guidance and therefore struggle with long-context tasks. In this paper, we propose Mindscape-Aware RAG (MiA-RAG), the first approach that equips LLM-based RAG systems with explicit global context awareness. MiA-RAG builds a mindscape through hierarchical summarization and conditions both retrieval and generation on this global semantic representation. This enables the retriever to form enriched query embeddings and the generator to reason over retrieved evidence within a coherent global context. We evaluate MiA-RAG across diverse long-context and bilingual benchmarks for evidence-based understanding and global sense-making. It consistently surpasses baselines, and further analysis shows that it aligns local details with a coherent global representation, enabling more human-like long-context retrieval and reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.22120", "title": "See Less, See Right: Bi-directional Perceptual Shaping For Multimodal Reasoning", "authors": [ "Shuoshuo Zhang", "Yizhen Zhang", "Jingjing Fu", "Lei Song", "Jiang Bian", "Yujiu Yang", "Rui Wang" ], "github": "https://github.com/zss02/BiPS", "project_page": "", "abstract": "Large vision-language models (VLMs) often benefit from intermediate visual cues, either injected via external tools or generated as latent visual tokens during reasoning, but these mechanisms still overlook fine-grained visual evidence (e.g., polylines in charts), generalize poorly across domains, and incur high inference-time cost. In this paper, we propose Bi-directional Perceptual Shaping (BiPS), which transforms question-conditioned masked views into bidirectional where-to-look signals that shape perception during training. BiPS first applies a KL-consistency constraint between the original image and an evidence-preserving view that keeps only question-relevant regions, encouraging coarse but complete coverage of supporting pixels. It then applies a KL-separation constraint between the original and an evidence-ablated view where critical pixels are masked so the image no longer supports the original answer, discouraging text-only shortcuts (i.e., answering from text alone) and enforcing fine-grained visual reliance. Across eight benchmarks, BiPS boosts Qwen2.5-VL-7B by 8.2% on average and shows strong out-of-domain generalization to unseen datasets and image types.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zss02/BiPS", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.21859", "title": "TimeBill: Time-Budgeted Inference for Large Language Models", "authors": [ "Qi Fan", "An Zou", "Yehan Ma" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly deployed in time-critical systems, such as robotics, autonomous driving, embodied intelligence, and industrial automation, where generating accurate responses within a given time budget is crucial for decision-making, control, or safety-critical tasks. However, the auto-regressive generation process of LLMs makes it challenging to model and estimate the end-to-end execution time. Furthermore, existing efficient inference methods based on a fixed key-value (KV) cache eviction ratio struggle to adapt to varying tasks with diverse time budgets, where an improper eviction ratio may lead to incomplete inference or a drop in response performance. In this paper, we propose TimeBill, a novel time-budgeted inference framework for LLMs that balances the inference efficiency and response performance. To be more specific, we propose a fine-grained response length predictor (RLP) and an execution time estimator (ETE) to accurately predict the end-to-end execution time of LLMs. Following this, we develop a time-budgeted efficient inference approach that adaptively adjusts the KV cache eviction ratio based on execution time prediction and the given time budget. Finally, through extensive experiments, we demonstrate the advantages of TimeBill in improving task completion rate and maintaining response performance under various overrun strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.21675", "title": "UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture", "authors": [ "Shuo Cao", "Jiayang Li", "Xiaohui Li", "Yuandong Pu", "Kaiwen Zhu", "Yuanting Gao", "Siqi Luo", "Yi Xin", "Qi Qin", "Yu Zhou", "Xiangyu Chen", "Wenlong Zhang", "Bin Fu", "Yu Qiao", "Yihao Liu" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks such as visual grounding, segmentation, and captioning. However, their ability to perceive perceptual-level image features remains limited. In this work, we present UniPercept-Bench, a unified framework for perceptual-level image understanding across three key domains: Aesthetics, Quality, Structure and Texture. We establish a hierarchical definition system and construct large-scale datasets to evaluate perceptual-level image understanding. Based on this foundation, we develop a strong baseline UniPercept trained via Domain-Adaptive Pre-Training and Task-Aligned RL, enabling robust generalization across both Visual Rating (VR) and Visual Question Answering (VQA) tasks. UniPercept outperforms existing MLLMs on perceptual-level image understanding and can serve as a plug-and-play reward model for text-to-image generation. This work defines Perceptual-Level Image Understanding in the era of MLLMs and, through the introduction of a comprehensive benchmark together with a strong baseline, provides a solid foundation for advancing perceptual-level multimodal image understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.20292", "title": "SlideTailor: Personalized Presentation Slide Generation for Scientific Papers", "authors": [ "Wenzheng Zeng", "Mingyu Ouyang", "Langyuan Cui", "Hwee Tou Ng" ], "github": "", "project_page": "", "abstract": "Automatic presentation slide generation can greatly streamline content creation. However, since preferences of each user may vary, existing under-specified formulations often lead to suboptimal results that fail to align with individual user needs. We introduce a novel task that conditions paper-to-slides generation on user-specified preferences. We propose a human behavior-inspired agentic framework, SlideTailor, that progressively generates editable slides in a user-aligned manner. Instead of requiring users to write their preferences in detailed textual form, our system only asks for a paper-slides example pair and a visual template - natural and easy-to-provide artifacts that implicitly encode rich user preferences across content and visual style. Despite the implicit and unlabeled nature of these inputs, our framework effectively distills and generalizes the preferences to guide customized slide generation. We also introduce a novel chain-of-speech mechanism to align slide content with planned oral narration. Such a design significantly enhances the quality of generated slides and enables downstream applications like video presentations. To support this new task, we construct a benchmark dataset that captures diverse user preferences, with carefully designed interpretable metrics for robust evaluation. Extensive experiments demonstrate the effectiveness of our framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.21643", "title": "Omni-Weather: Unified Multimodal Foundation Model for Weather Generation and Understanding", "authors": [ "Zhiwang Zhou", "Yuandong Pu", "Xuming He", "Yidi Liu", "Yixin Chen", "Junchao Gong", "Xiang Zhuang", "Wanghan Xu", "Qinglong Cao", "Shixiang Tang", "Yihao Liu", "Wenlong Zhang", "Lei Bai" ], "github": "https://github.com/Zhouzone/OmniWeather", "project_page": "", "abstract": "Weather modeling requires both accurate prediction and mechanistic interpretation, yet existing methods treat these goals in isolation, separating generation from understanding. To address this gap, we present Omni-Weather, the first multimodal foundation model that unifies weather generation and understanding within a single architecture. Omni-Weather integrates a radar encoder for weather generation tasks, followed by unified processing using a shared self-attention mechanism. Moreover, we construct a Chain-of-Thought dataset for causal reasoning in weather generation, enabling interpretable outputs and improved perceptual quality. Extensive experiments show Omni-Weather achieves state-of-the-art performance in both weather generation and understanding. Our findings further indicate that generative and understanding tasks in the weather domain can mutually enhance each other. Omni-Weather also demonstrates the feasibility and value of unifying weather generation and understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Zhouzone/OmniWeather", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.18745", "title": "InSight-o3: Empowering Multimodal Foundation Models with Generalized Visual Search", "authors": [ "Kaican Li", "Lewei Yao", "Jiannan Wu", "Tiezheng Yu", "Jierun Chen", "Haoli Bai", "Lu Hou", "Lanqing Hong", "Wei Zhang", "Nevin L. Zhang" ], "github": "https://github.com/m-Just/InSight-o3", "project_page": "", "abstract": "The ability for AI agents to \"think with images\" requires a sophisticated blend of reasoning and perception. However, current open multimodal agents still largely fall short on the reasoning aspect crucial for real-world tasks like analyzing documents with dense charts/diagrams and navigating maps. To address this gap, we introduce O3-Bench, a new benchmark designed to evaluate multimodal reasoning with interleaved attention to visual details. O3-Bench features challenging problems that require agents to piece together subtle visual information from distinct image areas through multi-step reasoning. The problems are highly challenging even for frontier systems like OpenAI o3, which only obtains 40.8% accuracy on O3-Bench. To make progress, we propose InSight-o3, a multi-agent framework consisting of a visual reasoning agent (vReasoner) and a visual search agent (vSearcher) for which we introduce the task of generalized visual search -- locating relational, fuzzy, or conceptual regions described in free-form language, beyond just simple objects or figures in natural images. We then present a multimodal LLM purpose-trained for this task via reinforcement learning. As a plug-and-play agent, our vSearcher empowers frontier multimodal models (as vReasoners), significantly improving their performance on a wide range of benchmarks. This marks a concrete step towards powerful o3-like open systems. Our code and dataset can be found at https://github.com/m-Just/InSight-o3 .", "github_manual": "", "github_regex": "https://github.com/m-Just/InSight-o3", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.21980", "title": "A 58-Addition, Rank-23 Scheme for General 3x3 Matrix Multiplication", "authors": [ "A. I. Perminov" ], "github": "", "project_page": "", "abstract": "This paper presents a new state-of-the-art algorithm for exact 3times3 matrix multiplication over general non-commutative rings, achieving a rank-23 scheme with only 58 scalar additions. This improves the previous best additive complexity of 60 additions without a change of basis. The result was discovered through an automated search combining ternary-restricted flip-graph exploration with greedy intersection reduction for common subexpression elimination. The resulting scheme uses only coefficients from {-1, 0, 1}, ensuring both efficiency and portability across arbitrary fields. The total scalar operation count is reduced from 83 to 81.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.21625", "title": "Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards", "authors": [ "Xinyu Tang", "Yuliang Zhan", "Zhixun Li", "Wayne Xin Zhao", "Zhenduo Zhang", "Zujie Wen", "Zhiqiang Zhang", "Jun Zhou" ], "github": "", "project_page": "", "abstract": "Large reasoning models (LRMs) are typically trained using reinforcement learning with verifiable reward (RLVR) to enhance their reasoning abilities. In this paradigm, policies are updated using both positive and negative self-generated rollouts, which correspond to distinct sample polarities. In this paper, we provide a systematic investigation into how these sample polarities affect RLVR training dynamics and behaviors. We find that positive samples sharpen existing correct reasoning patterns, while negative samples encourage exploration of new reasoning paths. We further explore how adjusting the advantage values of positive and negative samples at both the sample level and the token level affects RLVR training. Based on these insights, we propose an Adaptive and Asymmetric token-level Advantage shaping method for Policy Optimization, namely A3PO, that more precisely allocates advantage signals to key tokens across different polarities. Experiments across five reasoning benchmarks demonstrate the effectiveness of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-29", "arxiv_id": "2512.22238", "title": "Masking Teacher and Reinforcing Student for Distilling Vision-Language Models", "authors": [ "Byung-Kwan Lee", "Yu-Chiang Frank Wang", "Ryo Hachiuma" ], "github": "", "project_page": "", "abstract": "Large-scale vision-language models (VLMs) have recently achieved remarkable multimodal understanding, but their massive size makes them impractical for deployment on mobile or edge devices. This raises the need for compact yet capable VLMs that can efficiently learn from powerful large teachers. However, distilling knowledge from a large teacher to a small student remains challenging due to their large size gap: the student often fails to reproduce the teacher's complex, high-dimensional representations, leading to unstable learning and degraded performance. To address this, we propose Masters (Masking Teacher and Reinforcing Student), a mask-progressive reinforcement learning (RL) distillation framework. Masters first masks non-dominant weights of the teacher to reduce unnecessary complexity, then progressively restores the teacher by gradually increasing its capacity during training. This strategy allows the student to learn richer representations from the teacher in a smooth and stable manner. To further refine knowledge transfer, Masters integrates an offline RL stage with two complementary rewards: an accuracy reward that measures the correctness of the generated responses, and a distillation reward that quantifies the ease of transferring responses from teacher to student. Unlike online think-answer RL paradigms that are computationally expensive and generate lengthy responses, our offline RL leverages pre-generated responses from masked teachers. These provide rich yet efficient guidance, enabling students to achieve strong performance without requiring the think-answer process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.22322", "title": "SmartSnap: Proactive Evidence Seeking for Self-Verifying Agents", "authors": [ "Shaofei Cai", "Yulei Qin", "Haojia Lin", "Zihan Xu", "Gang Li", "Yuchen Shi", "Zongyi Li", "Yong Mao", "Siqi Cai", "Xiaoyu Tan", "Yitao Liang", "Ke Li", "Xing Sun" ], "github": "", "project_page": "", "abstract": "Agentic reinforcement learning (RL) holds great promise for the development of autonomous agents under complex GUI tasks, but its scalability remains severely hampered by the verification of task completion. Existing task verification is treated as a passive, post-hoc process: a verifier (i.e., rule-based scoring script, reward or critic model, and LLM-as-a-Judge) analyzes the agent's entire interaction trajectory to determine if the agent succeeds. Such processing of verbose context that contains irrelevant, noisy history poses challenges to the verification protocols and therefore leads to prohibitive cost and low reliability. To overcome this bottleneck, we propose SmartSnap, a paradigm shift from this passive, post-hoc verification to proactive, in-situ self-verification by the agent itself. We introduce the Self-Verifying Agent, a new type of agent designed with dual missions: to not only complete a task but also to prove its accomplishment with curated snapshot evidences. Guided by our proposed 3C Principles (Completeness, Conciseness, and Creativity), the agent leverages its accessibility to the online environment to perform self-verification on a minimal, decisive set of snapshots. Such evidences are provided as the sole materials for a general LLM-as-a-Judge verifier to determine their validity and relevance. Experiments on mobile tasks across model families and scales demonstrate that our SmartSnap paradigm allows training LLM-driven agents in a scalable manner, bringing performance gains up to 26.08% and 16.66% respectively to 8B and 30B models. The synergizing between solution finding and evidence seeking facilitates the cultivation of efficient, self-verifying agents with competitive performance against DeepSeek V3.1 and Qwen3-235B-A22B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23676", "title": "Web World Models", "authors": [ "Jichen Feng", "Yifan Zhang", "Chenggong Zhang", "Yifu Lu", "Shilong Liu", "Mengdi Wang" ], "github": "https://github.com/Princeton-AI2-Lab/Web-World-Models", "project_page": "", "abstract": "Language agents increasingly require persistent worlds in which they can act, remember, and learn. Existing approaches sit at two extremes: conventional web frameworks provide reliable but fixed contexts backed by databases, while fully generative world models aim for unlimited environments at the expense of controllability and practical engineering. In this work, we introduce the Web World Model (WWM), a middle ground where world state and ``physics'' are implemented in ordinary web code to ensure logical consistency, while large language models generate context, narratives, and high-level decisions on top of this structured latent state. We build a suite of WWMs on a realistic web stack, including an infinite travel atlas grounded in real geography, fictional galaxy explorers, web-scale encyclopedic and narrative worlds, and simulation- and game-like environments. Across these systems, we identify practical design principles for WWMs: separating code-defined rules from model-driven imagination, representing latent state as typed web interfaces, and utilizing deterministic generation to achieve unlimited but structured exploration. Our results suggest that web stacks themselves can serve as a scalable substrate for world models, enabling controllable yet open-ended environments. Project Page: https://github.com/Princeton-AI2-Lab/Web-World-Models.", "github_manual": "", "github_regex": "https://github.com/Princeton-AI2-Lab/Web-World-Models", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23447", "title": "Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss", "authors": [ "Ang Lv", "Jin Ma", "Yiyuan Ma", "Siyuan Qiao" ], "github": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) models lack explicit constraints to ensure the router's decisions align well with the experts' capabilities, which ultimately limits model performance. To address this, we propose expert-router coupling (ERC) loss, a lightweight auxiliary loss that tightly couples the router's decisions with expert capabilities. Our approach treats each expert's router embedding as a proxy token for the tokens assigned to that expert, and feeds perturbed router embeddings through the experts to obtain internal activations. The ERC loss enforces two constraints on these activations: (1) Each expert must exhibit higher activation for its own proxy token than for the proxy tokens of any other expert. (2) Each proxy token must elicit stronger activation from its corresponding expert than from any other expert. These constraints jointly ensure that each router embedding faithfully represents its corresponding expert's capability, while each expert specializes in processing the tokens actually routed to it. The ERC loss is computationally efficient, operating only on n^2 activations, where n is the number of experts. This represents a fixed cost independent of batch size, unlike prior coupling methods that scale with the number of tokens (often millions per batch). Through pre-training MoE-LLMs ranging from 3B to 15B parameters and extensive analysis on trillions of tokens, we demonstrate the effectiveness of the ERC loss. Moreover, the ERC loss offers flexible control and quantitative tracking of expert specialization levels during training, providing valuable insights into MoEs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23647", "title": "Nested Browser-Use Learning for Agentic Information Seeking", "authors": [ "Baixuan Li", "Jialong Wu", "Wenbiao Yin", "Kuan Li", "Zhongwang Zhang", "Huifeng Yin", "Zhengwei Tao", "Liwen Zhang", "Pengjun Xie", "Jingren Zhou", "Yong Jiang" ], "github": "", "project_page": "", "abstract": "Information-seeking (IS) agents have achieved strong performance across a range of wide and deep search tasks, yet their tool use remains largely restricted to API-level snippet retrieval and URL-based page fetching, limiting access to the richer information available through real browsing. While full browser interaction could unlock deeper capabilities, its fine-grained control and verbose page content returns introduce substantial complexity for ReAct-style function-calling agents. To bridge this gap, we propose Nested Browser-Use Learning (NestBrowse), which introduces a minimal and complete browser-action framework that decouples interaction control from page exploration through a nested structure. This design simplifies agentic reasoning while enabling effective deep-web information acquisition. Empirical results on challenging deep IS benchmarks demonstrate that NestBrowse offers clear benefits in practice. Further in-depth analyses underscore its efficiency and flexibility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23646", "title": "OmniAgent: Audio-Guided Active Perception Agent for Omnimodal Audio-Video Understanding", "authors": [ "Keda Tao", "Wenjie Du", "Bohan Yu", "Weiqiang Wang", "Jian Liu", "Huan Wang" ], "github": "", "project_page": "", "abstract": "Omnimodal large language models have made significant strides in unifying audio and visual modalities; however, they often lack the fine-grained cross-modal understanding and have difficulty with multimodal alignment. To address these limitations, we introduce OmniAgent, a fully audio-guided active perception agent that dynamically orchestrates specialized tools to achieve more fine-grained audio-visual reasoning. Unlike previous works that rely on rigid, static workflows and dense frame-captioning, this paper demonstrates a paradigm shift from passive response generation to active multimodal inquiry. OmniAgent employs dynamic planning to autonomously orchestrate tool invocation on demand, strategically concentrating perceptual attention on task-relevant cues. Central to our approach is a novel coarse-to-fine audio-guided perception paradigm, which leverages audio cues to localize temporal events and guide subsequent reasoning. Extensive empirical evaluations on three audio-video understanding benchmarks demonstrate that OmniAgent achieves state-of-the-art performance, surpassing leading open-source and proprietary models by substantial margins of 10% - 20% accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.22096", "title": "Yume-1.5: A Text-Controlled Interactive World Generation Model", "authors": [ "Xiaofeng Mao", "Zhen Li", "Chuanhao Li", "Xiaojie Xu", "Kaining Ying", "Tong He", "Jiangmiao Pang", "Yu Qiao", "Kaipeng Zhang" ], "github": "https://github.com/stdstu12/YUME", "project_page": "", "abstract": "Recent approaches have demonstrated the promise of using diffusion models to generate interactive and explorable worlds. However, most of these methods face critical challenges such as excessively large parameter sizes, reliance on lengthy inference steps, and rapidly growing historical context, which severely limit real-time performance and lack text-controlled generation capabilities. To address these challenges, we propose \\method, a novel framework designed to generate realistic, interactive, and continuous worlds from a single image or text prompt. \\method achieves this through a carefully designed framework that supports keyboard-based exploration of the generated worlds. The framework comprises three core components: (1) a long-video generation framework integrating unified context compression with linear attention; (2) a real-time streaming acceleration strategy powered by bidirectional attention distillation and an enhanced text embedding scheme; (3) a text-controlled method for generating world events. We have provided the codebase in the supplementary material.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stdstu12/YUME", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23705", "title": "Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation", "authors": [ "Shaocong Xu", "Songlin Wei", "Qizhe Wei", "Zheng Geng", "Hong Li", "Licheng Shen", "Qianpu Sun", "Shu Han", "Bin Ma", "Bohan Li", "Chongjie Ye", "Yuhang Zheng", "Nan Wang", "Saining Zhang", "Hao Zhao" ], "github": "", "project_page": "", "abstract": "Transparent objects remain notoriously hard for perception systems: refraction, reflection and transmission break the assumptions behind stereo, ToF and purely discriminative monocular depth, causing holes and temporally unstable estimates. Our key observation is that modern video diffusion models already synthesize convincing transparent phenomena, suggesting they have internalized the optical rules. We build TransPhy3D, a synthetic video corpus of transparent/reflective scenes: 11k sequences rendered with Blender/Cycles. Scenes are assembled from a curated bank of category-rich static assets and shape-rich procedural assets paired with glass/plastic/metal materials. We render RGB + depth + normals with physically based ray tracing and OptiX denoising. Starting from a large video diffusion model, we learn a video-to-video translator for depth (and normals) via lightweight LoRA adapters. During training we concatenate RGB and (noisy) depth latents in the DiT backbone and co-train on TransPhy3D and existing frame-wise synthetic datasets, yielding temporally consistent predictions for arbitrary-length input videos. The resulting model, DKT, achieves zero-shot SOTA on real and synthetic video benchmarks involving transparency: ClearPose, DREDS (CatKnown/CatNovel), and TransPhy3D-Test. It improves accuracy and temporal consistency over strong image/video baselines, and a normal variant sets the best video normal estimation results on ClearPose. A compact 1.3B version runs at ~0.17 s/frame. Integrated into a grasping stack, DKT's depth boosts success rates across translucent, reflective and diffuse surfaces, outperforming prior estimators. Together, these results support a broader claim: \"Diffusion knows transparency.\" Generative video priors can be repurposed, efficiently and label-free, into robust, temporally coherent perception for challenging real-world manipulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.22431", "title": "Monadic Context Engineering", "authors": [ "Yifan Zhang", "Mengdi Wang" ], "github": "https://github.com/iiis-ai/monadic-context-engineering", "project_page": "", "abstract": "The proliferation of Large Language Models (LLMs) has catalyzed a shift towards autonomous agents capable of complex reasoning and tool use. However, current agent architectures are frequently constructed using imperative, ad hoc patterns. This results in brittle systems plagued by difficulties in state management, error handling, and concurrency. This paper introduces Monadic Context Engineering (MCE), a novel architectural paradigm leveraging the algebraic structures of Functors, Applicative Functors, and Monads to provide a formal foundation for agent design. MCE treats agent workflows as computational contexts where cross-cutting concerns, such as state propagation, short-circuiting error handling, and asynchronous execution, are managed intrinsically by the algebraic properties of the abstraction. We demonstrate how Monads enable robust sequential composition, how Applicatives provide a principled structure for parallel execution, and crucially, how Monad Transformers allow for the systematic composition of these capabilities. This layered approach enables developers to construct complex, resilient, and efficient AI agents from simple, independently verifiable components. We further extend this framework to describe Meta-Agents, which leverage MCE for generative orchestration, dynamically creating and managing sub-agent workflows through metaprogramming. Project Page: https://github.com/yifanzhang-pro/monadic-context-engineering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/iiis-ai/monadic-context-engineering", "github_api_added_by": "user" }, { "date": "2025-12-30", "arxiv_id": "2512.23707", "title": "Training AI Co-Scientists Using Rubric Rewards", "authors": [ "Shashwat Goel", "Rishi Hazra", "Dulhan Jayalath", "Timon Willi", "Parag Jain", "William F. Shen", "Ilias Leontiadis", "Francesco Barbieri", "Yoram Bachrach", "Jonas Geiping", "Chenxi Whitehouse" ], "github": "", "project_page": "", "abstract": "AI co-scientists are emerging as a tool to assist human researchers in achieving their research goals. A crucial feature of these AI co-scientists is the ability to generate a research plan given a set of aims and constraints. The plan may be used by researchers for brainstorming, or may even be implemented after further refinement. However, language models currently struggle to generate research plans that follow all constraints and implicit requirements. In this work, we study how to leverage the vast corpus of existing research papers to train language models that generate better research plans. We build a scalable, diverse training corpus by automatically extracting research goals and goal-specific grading rubrics from papers across several domains. We then train models for research plan generation via reinforcement learning with self-grading. A frozen copy of the initial policy acts as the grader during training, with the rubrics creating a generator-verifier gap that enables improvements without external human supervision. To validate this approach, we conduct a study with human experts for machine learning research goals, spanning 225 hours. The experts prefer plans generated by our finetuned Qwen3-30B-A3B model over the initial model for 70% of research goals, and approve 84% of the automatically extracted goal-specific grading rubrics. To assess generality, we also extend our approach to research goals from medical papers, and new arXiv preprints, evaluating with a jury of frontier models. Our finetuning yields 12-22% relative improvements and significant cross-domain generalization, proving effective even in problem settings like medical research where execution feedback is infeasible. Together, these findings demonstrate the potential of a scalable, automated training recipe as a step towards improving general AI co-scientists.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23044", "title": "Video-BrowseComp: Benchmarking Agentic Video Research on Open Web", "authors": [ "Zhengyang Liang", "Yan Shu", "Xiangrui Liu", "Minghao Qin", "Kaixin Liang", "Paolo Rota", "Nicu Sebe", "Zheng Liu", "Lizi Liao" ], "github": "", "project_page": "", "abstract": "The evolution of autonomous agents is redefining information seeking, transitioning from passive retrieval to proactive, open-ended web research. However, while textual and static multimodal agents have seen rapid progress, a significant modality gap remains in processing the web's most dynamic modality: video. Existing video benchmarks predominantly focus on passive perception, feeding curated clips to models without requiring external retrieval. They fail to evaluate agentic video research, which necessitates actively interrogating video timelines, cross-referencing dispersed evidence, and verifying claims against the open web. To bridge this gap, we present Video-BrowseComp, a challenging benchmark comprising 210 questions tailored for open-web agentic video reasoning. Unlike prior benchmarks, Video-BrowseComp enforces a mandatory dependency on temporal visual evidence, ensuring that answers cannot be derived solely through text search but require navigating video timelines to verify external claims. Our evaluation of state-of-the-art models reveals a critical bottleneck: even advanced search-augmented models like GPT-5.1 (w/ Search) achieve only 15.24\\% accuracy. Our analysis reveals that these models largely rely on textual proxies, excelling in metadata-rich domains (e.g., TV shows with plot summaries) but collapsing in metadata-sparse, dynamic environments (e.g., sports, gameplay) where visual grounding is essential. As the first open-web video research benchmark, Video-BrowseComp advances the field beyond passive perception toward proactive video reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23162", "title": "SurgWorld: Learning Surgical Robot Policies from Videos via World Modeling", "authors": [ "Yufan He", "Pengfei Guo", "Mengya Xu", "Zhaoshuo Li", "Andriy Myronenko", "Dillan Imans", "Bingjie Liu", "Dongren Yang", "Mingxue Gu", "Yongnan Ji", "Yueming Jin", "Ren Zhao", "Baiyong Shen", "Daguang Xu" ], "github": "", "project_page": "", "abstract": "Data scarcity remains a fundamental barrier to achieving fully autonomous surgical robots. While large scale vision language action (VLA) models have shown impressive generalization in household and industrial manipulation by leveraging paired video action data from diverse domains, surgical robotics suffers from the paucity of datasets that include both visual observations and accurate robot kinematics. In contrast, vast corpora of surgical videos exist, but they lack corresponding action labels, preventing direct application of imitation learning or VLA training. In this work, we aim to alleviate this problem by learning policy models from SurgWorld, a world model designed for surgical physical AI. We curated the Surgical Action Text Alignment (SATA) dataset with detailed action description specifically for surgical robots. Then we built SurgeWorld based on the most advanced physical AI world model and SATA. It's able to generate diverse, generalizable and realistic surgery videos. We are also the first to use an inverse dynamics model to infer pseudokinematics from synthetic surgical videos, producing synthetic paired video action data. We demonstrate that a surgical VLA policy trained with these augmented data significantly outperforms models trained only on real demonstrations on a real surgical robot platform. Our approach offers a scalable path toward autonomous surgical skill acquisition by leveraging the abundance of unlabeled surgical video and generative world modeling, thus opening the door to generalizable and data efficient surgical robot policies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.21720", "title": "An Information Theoretic Perspective on Agentic System Design", "authors": [ "Shizhe He", "Avanika Narayan", "Ishan S. Khare", "Scott W. Linderman", "Christopher R\u00e9", "Dan Biderman" ], "github": "", "project_page": "", "abstract": "Agentic language model (LM) systems power modern applications like \"Deep Research\" and \"Claude Code,\" and leverage multi-LM architectures to overcome context limitations. Beneath their apparent diversity lies a recurring pattern: smaller \"compressor\" LMs (that can even run locally) distill raw context into compact text that is then consumed by larger \"predictor\" LMs. Despite their popularity, the design of compressor-predictor systems remains largely ad hoc, with little guidance on how compressor and predictor choices shape downstream performance. In practice, attributing gains to compression versus prediction requires costly, task-specific pairwise sweeps. We argue that these agentic system design questions are, at root, information-theoretic. Viewing the compressor LM as a noisy channel, we introduce a simple estimator of mutual information between the context and its compression to quantify compression quality in a task-independent way. We show that mutual information strongly predicts downstream performance, independent of any specific task. Through an information-theoretic framework, we perform a comprehensive empirical analysis across five datasets and three model families. Results reveal that larger compressors not only are more accurate, but also more token-efficient, conveying more bits of information per token. A 7B Qwen-2.5 compressor, for instance, is 1.6times more accurate, 4.6times more concise, and conveys 5.5times more bits of mutual information per token than its 1.5B sibling. Across datasets, scaling compressors is substantially more effective than scaling predictors, enabling larger on-device compressors to pair with smaller cloud predictors. Applied to a Deep Research system, these principles enable local compressors as small as 3B parameters to recover 99% of frontier-LM accuracy at 26% of API costs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23576", "title": "LiveTalk: Real-Time Multimodal Interactive Video Diffusion via Improved On-Policy Distillation", "authors": [ "Ethan Chern", "Zhulin Hu", "Bohao Tang", "Jiadi Su", "Steffi Chern", "Zhijie Deng", "Pengfei Liu" ], "github": "", "project_page": "", "abstract": "Real-time video generation via diffusion is essential for building general-purpose multimodal interactive AI systems. However, the simultaneous denoising of all video frames with bidirectional attention via an iterative process in diffusion models prevents real-time interaction. While existing distillation methods can make the model autoregressive and reduce sampling steps to mitigate this, they focus primarily on text-to-video generation, leaving the human-AI interaction unnatural and less efficient. This paper targets real-time interactive video diffusion conditioned on a multimodal context, including text, image, and audio, to bridge the gap. Given the observation that the leading on-policy distillation approach Self Forcing encounters challenges (visual artifacts like flickering, black frames, and quality degradation) with multimodal conditioning, we investigate an improved distillation recipe with emphasis on the quality of condition inputs as well as the initialization and schedule for the on-policy optimization. On benchmarks for multimodal-conditioned (audio, image, and text) avatar video generation including HDTF, AVSpeech, and CelebV-HQ, our distilled model matches the visual quality of the full-step, bidirectional baselines of similar or larger size with 20x less inference cost and latency. Further, we integrate our model with audio language models and long-form video inference technique Anchor-Heavy Identity Sinks to build LiveTalk, a real-time multimodal interactive avatar system. System-level evaluation on our curated multi-turn interaction benchmark shows LiveTalk outperforms state-of-the-art models (Sora2, Veo3) in multi-turn video coherence and content quality, while reducing response latency from 1 to 2 minutes to real-time generation, enabling seamless human-AI multimodal interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23222", "title": "Bridging Your Imagination with Audio-Video Generation via a Unified Director", "authors": [ "Jiaxu Zhang", "Tianshu Hu", "Yuan Zhang", "Zenan Li", "Linjie Luo", "Guosheng Lin", "Xin Chen" ], "github": "", "project_page": "", "abstract": "Existing AI-driven video creation systems typically treat script drafting and key-shot design as two disjoint tasks: the former relies on large language models, while the latter depends on image generation models. We argue that these two tasks should be unified within a single framework, as logical reasoning and imaginative thinking are both fundamental qualities of a film director. In this work, we propose UniMAGE, a unified director model that bridges user prompts with well-structured scripts, thereby empowering non-experts to produce long-context, multi-shot films by leveraging existing audio-video generation models. To achieve this, we employ the Mixture-of-Transformers architecture that unifies text and image generation. To further enhance narrative logic and keyframe consistency, we introduce a ``first interleaving, then disentangling'' training paradigm. Specifically, we first perform Interleaved Concept Learning, which utilizes interleaved text-image data to foster the model's deeper understanding and imaginative interpretation of scripts. We then conduct Disentangled Expert Learning, which decouples script writing from keyframe generation, enabling greater flexibility and creativity in storytelling. Extensive experiments demonstrate that UniMAGE achieves state-of-the-art performance among open-source models, generating logically coherent video scripts and visually consistent keyframe images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.22323", "title": "SpotEdit: Selective Region Editing in Diffusion Transformers", "authors": [ "Zhibin Qin", "Zhenxiong Tan", "Zeqing Wang", "Songhua Liu", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Diffusion Transformer models have significantly advanced image editing by encoding conditional images and integrating them into transformer layers. However, most edits involve modifying only small regions, while current methods uniformly process and denoise all tokens at every timestep, causing redundant computation and potentially degrading unchanged areas. This raises a fundamental question: Is it truly necessary to regenerate every region during editing? To address this, we propose SpotEdit, a training-free diffusion editing framework that selectively updates only the modified regions. SpotEdit comprises two key components: SpotSelector identifies stable regions via perceptual similarity and skips their computation by reusing conditional image features; SpotFusion adaptively blends these features with edited tokens through a dynamic fusion mechanism, preserving contextual coherence and editing quality. By reducing unnecessary computation and maintaining high fidelity in unmodified areas, SpotEdit achieves efficient and precise image editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.22615", "title": "Dream-VL & Dream-VLA: Open Vision-Language and Vision-Language-Action Models with Diffusion Language Model Backbone", "authors": [ "Jiacheng Ye", "Shansan Gong", "Jiahui Gao", "Junming Fan", "Shuang Wu", "Wei Bi", "Haoli Bai", "Lifeng Shang", "Lingpeng Kong" ], "github": "", "project_page": "", "abstract": "While autoregressive Large Vision-Language Models (VLMs) have achieved remarkable success, their sequential generation often limits their efficacy in complex visual planning and dynamic robotic control. In this work, we investigate the potential of constructing Vision-Language Models upon diffusion-based large language models (dLLMs) to overcome these limitations. We introduce Dream-VL, an open diffusion-based VLM (dVLM) that achieves state-of-the-art performance among previous dVLMs. Dream-VL is comparable to top-tier AR-based VLMs trained on open data on various benchmarks but exhibits superior potential when applied to visual planning tasks. Building upon Dream-VL, we introduce Dream-VLA, a dLLM-based Vision-Language-Action model (dVLA) developed through continuous pre-training on open robotic datasets. We demonstrate that the natively bidirectional nature of this diffusion backbone serves as a superior foundation for VLA tasks, inherently suited for action chunking and parallel generation, leading to significantly faster convergence in downstream fine-tuning. Dream-VLA achieves top-tier performance of 97.2% average success rate on LIBERO, 71.4% overall average on SimplerEnv-Bridge, and 60.5% overall average on SimplerEnv-Fractal, surpassing leading models such as \u03c0_0 and GR00T-N1. We also validate that dVLMs surpass AR baselines on downstream tasks across different training objectives. We release both Dream-VL and Dream-VLA to facilitate further research in the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.22234", "title": "DiRL: An Efficient Post-Training Framework for Diffusion Language Models", "authors": [ "Ying Zhu", "Jiaxin Wan", "Xiaoran Liu", "Siyanag He", "Qiqi Wang", "Xu Guo", "Tianyi Liang", "Zengfeng Huang", "Ziwei He", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Diffusion Language Models (dLLMs) have emerged as promising alternatives to Auto-Regressive (AR) models. While recent efforts have validated their pre-training potential and accelerated inference speeds, the post-training landscape for dLLMs remains underdeveloped. Existing methods suffer from computational inefficiency and objective mismatches between training and inference, severely limiting performance on complex reasoning tasks such as mathematics. To address this, we introduce DiRL, an efficient post-training framework that tightly integrates FlexAttention-accelerated blockwise training with LMDeploy-optimized inference. This architecture enables a streamlined online model update loop, facilitating efficient two-stage post-training (Supervised Fine-Tuning followed by Reinforcement Learning). Building on this framework, we propose DiPO, the first unbiased Group Relative Policy Optimization (GRPO) implementation tailored for dLLMs. We validate our approach by training DiRL-8B-Instruct on high-quality math data. Our model achieves state-of-the-art math performance among dLLMs and surpasses comparable models in the Qwen2.5 series on several benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.15560", "title": "GRAN-TED: Generating Robust, Aligned, and Nuanced Text Embedding for Diffusion Models", "authors": [ "Bozhou Li", "Sihan Yang", "Yushuo Guan", "Ruichuan An", "Xinlong Chen", "Yang Shi", "Pengfei Wan", "Wentao Zhang", "Yuanxing zhang" ], "github": "", "project_page": "", "abstract": "The text encoder is a critical component of text-to-image and text-to-video diffusion models, fundamentally determining the semantic fidelity of the generated content. However, its development has been hindered by two major challenges: the lack of an efficient evaluation framework that reliably predicts downstream generation performance, and the difficulty of effectively adapting pretrained language models for visual synthesis. To address these issues, we introduce GRAN-TED, a paradigm to Generate Robust, Aligned, and Nuanced Text Embeddings for Diffusion models. Our contribution is twofold. First, we propose TED-6K, a novel text-only benchmark that enables efficient and robust assessment of an encoder's representational quality without requiring costly end-to-end model training. We demonstrate that performance on TED-6K, standardized via a lightweight, unified adapter, strongly correlates with an encoder's effectiveness in downstream generation tasks. Notably, under our experimental setup, compared with training a diffusion model from scratch, evaluating with TED-6K is about 750times faster. Second, guided by this validated framework, we develop a superior text encoder using a novel two-stage training paradigm. This process involves an initial fine-tuning stage on a Multimodal Large Language Model for better visual representation, followed by a layer-wise weighting method to extract more nuanced and potent text features. Our experiments show that the resulting GRAN-TED encoder not only achieves state-of-the-art performance on TED-6K but also leads to demonstrable performance gains in text-to-image and text-to-video generation. Our TED-6K dataset and evaluation code are available at the following link: https://anonymous.4open.science/r/GRAN-TED-4FCC/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23709", "title": "Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion", "authors": [ "Hau-Shiang Shiu", "Chin-Yang Lin", "Zhixiang Wang", "Chi-Wei Hsiao", "Po-Fan Yu", "Yu-Chih Chen", "Yu-Lun Liu" ], "github": "https://github.com/jamichss/Stream-DiffVSR", "project_page": "", "abstract": "Diffusion-based video super-resolution (VSR) methods achieve strong perceptual quality but remain impractical for latency-sensitive settings due to reliance on future frames and expensive multi-step denoising. We propose Stream-DiffVSR, a causally conditioned diffusion framework for efficient online VSR. Operating strictly on past frames, it combines a four-step distilled denoiser for fast inference, an Auto-regressive Temporal Guidance (ARTG) module that injects motion-aligned cues during latent denoising, and a lightweight temporal-aware decoder with a Temporal Processor Module (TPM) that enhances detail and temporal coherence. Stream-DiffVSR processes 720p frames in 0.328 seconds on an RTX4090 GPU and significantly outperforms prior diffusion-based methods. Compared with the online SOTA TMP, it boosts perceptual quality (LPIPS +0.095) while reducing latency by over 130x. Stream-DiffVSR achieves the lowest latency reported for diffusion-based VSR, reducing initial delay from over 4600 seconds to 0.328 seconds, thereby making it the first diffusion VSR method suitable for low-latency online deployment. Project page: https://jamichss.github.io/stream-diffvsr-project-page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jamichss/Stream-DiffVSR", "github_api_added_by": "auto" }, { "date": "2025-12-30", "arxiv_id": "2512.23541", "title": "Act2Goal: From World Model To General Goal-conditioned Policy", "authors": [ "Pengfei Zhou", "Liliang Chen", "Shengcong Chen", "Di Chen", "Wenzhi Zhao", "Rongjun Jin", "Guanghui Ren", "Jianlan Luo" ], "github": "", "project_page": "", "abstract": "Specifying robotic manipulation tasks in a manner that is both expressive and precise remains a central challenge. While visual goals provide a compact and unambiguous task specification, existing goal-conditioned policies often struggle with long-horizon manipulation due to their reliance on single-step action prediction without explicit modeling of task progress. We propose Act2Goal, a general goal-conditioned manipulation policy that integrates a goal-conditioned visual world model with multi-scale temporal control. Given a current observation and a target visual goal, the world model generates a plausible sequence of intermediate visual states that captures long-horizon structure. To translate this visual plan into robust execution, we introduce Multi-Scale Temporal Hashing (MSTH), which decomposes the imagined trajectory into dense proximal frames for fine-grained closed-loop control and sparse distal frames that anchor global task consistency. The policy couples these representations with motor control through end-to-end cross-attention, enabling coherent long-horizon behavior while remaining reactive to local disturbances. Act2Goal achieves strong zero-shot generalization to novel objects, spatial layouts, and environments. We further enable reward-free online adaptation through hindsight goal relabeling with LoRA-based finetuning, allowing rapid autonomous improvement without external supervision. Real-robot experiments demonstrate that Act2Goal improves success rates from 30% to 90% on challenging out-of-distribution tasks within minutes of autonomous interaction, validating that goal-conditioned world models with multi-scale temporal control provide structured guidance necessary for robust long-horizon manipulation. Project page: https://act2goal.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.20927", "title": "Quantile Rendering: Efficiently Embedding High-dimensional Feature on 3D Gaussian Splatting", "authors": [ "Yoonwoo Jeong", "Cheng Sun", "Frank Wang", "Minsu Cho", "Jaesung Choe" ], "github": "", "project_page": "", "abstract": "Recent advancements in computer vision have successfully extended Open-vocabulary segmentation (OVS) to the 3D domain by leveraging 3D Gaussian Splatting (3D-GS). Despite this progress, efficiently rendering the high-dimensional features required for open-vocabulary queries poses a significant challenge. Existing methods employ codebooks or feature compression, causing information loss, thereby degrading segmentation quality. To address this limitation, we introduce Quantile Rendering (Q-Render), a novel rendering strategy for 3D Gaussians that efficiently handles high-dimensional features while maintaining high fidelity. Unlike conventional volume rendering, which densely samples all 3D Gaussians intersecting each ray, Q-Render sparsely samples only those with dominant influence along the ray. By integrating Q-Render into a generalizable 3D neural network, we also propose Gaussian Splatting Network (GS-Net), which predicts Gaussian features in a generalizable manner. Extensive experiments on ScanNet and LeRF demonstrate that our framework outperforms state-of-the-art methods, while enabling real-time rendering with an approximate ~43.7x speedup on 512-D feature maps. Code will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.21734", "title": "Knot Forcing: Taming Autoregressive Video Diffusion Models for Real-time Infinite Interactive Portrait Animation", "authors": [ "Steven Xiao", "Xindi Zhang", "Dechao Meng", "Qi Wang", "Peng Zhang", "Bang Zhang" ], "github": "", "project_page": "", "abstract": "Real-time portrait animation is essential for interactive applications such as virtual assistants and live avatars, requiring high visual fidelity, temporal coherence, ultra-low latency, and responsive control from dynamic inputs like reference images and driving signals. While diffusion-based models achieve strong quality, their non-causal nature hinders streaming deployment. Causal autoregressive video generation approaches enable efficient frame-by-frame generation but suffer from error accumulation, motion discontinuities at chunk boundaries, and degraded long-term consistency. In this work, we present a novel streaming framework named Knot Forcing for real-time portrait animation that addresses these challenges through three key designs: (1) a chunk-wise generation strategy with global identity preservation via cached KV states of the reference image and local temporal modeling using sliding window attention; (2) a temporal knot module that overlaps adjacent chunks and propagates spatio-temporal cues via image-to-video conditioning to smooth inter-chunk motion transitions; and (3) A \"running ahead\" mechanism that dynamically updates the reference frame's temporal coordinate during inference, keeping its semantic context ahead of the current rollout frame to support long-term coherence. Knot Forcing enables high-fidelity, temporally consistent, and interactive portrait animation over infinite sequences, achieving real-time performance with strong visual stability on consumer-grade GPUs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.22342", "title": "VL-LN Bench: Towards Long-horizon Goal-oriented Navigation with Active Dialogs", "authors": [ "Wensi Huang", "Shaohao Zhu", "Meng Wei", "Jinming Xu", "Xihui Liu", "Hanqing Wang", "Tai Wang", "Feng Zhao", "Jiangmiao Pang" ], "github": "https://github.com/InternRobotics/VL-LN", "project_page": "", "abstract": "In most existing embodied navigation tasks, instructions are well-defined and unambiguous, such as instruction following and object searching. Under this idealized setting, agents are required solely to produce effective navigation outputs conditioned on vision and language inputs. However, real-world navigation instructions are often vague and ambiguous, requiring the agent to resolve uncertainty and infer user intent through active dialog. To address this gap, we propose Interactive Instance Object Navigation (IION), a task that requires agents not only to generate navigation actions but also to produce language outputs via active dialog, thereby aligning more closely with practical settings. IION extends Instance Object Navigation (ION) by allowing agents to freely consult an oracle in natural language while navigating. Building on this task, we present the Vision Language-Language Navigation (VL-LN) benchmark, which provides a large-scale, automatically generated dataset and a comprehensive evaluation protocol for training and assessing dialog-enabled navigation models. VL-LN comprises over 41k long-horizon dialog-augmented trajectories for training and an automatic evaluation protocol with an oracle capable of responding to agent queries. Using this benchmark, we train a navigation model equipped with dialog capabilities and show that it achieves significant improvements over the baselines. Extensive experiments and analyses further demonstrate the effectiveness and reliability of VL-LN for advancing research on dialog-enabled embodied navigation. Code and dataset: https://0309hws.github.io/VL-LN.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/InternRobotics/VL-LN", "github_api_added_by": "auto" }, { "date": "2025-12-30", "arxiv_id": "2512.23573", "title": "ProGuard: Towards Proactive Multimodal Safeguard", "authors": [ "Shaohan Yu", "Lijun Li", "Chenyang Si", "Lu Sheng", "Jing Shao" ], "github": "", "project_page": "", "abstract": "The rapid evolution of generative models has led to a continuous emergence of multimodal safety risks, exposing the limitations of existing defense methods. To address these challenges, we propose ProGuard, a vision-language proactive guard that identifies and describes out-of-distribution (OOD) safety risks without the need for model adjustments required by traditional reactive approaches. We first construct a modality-balanced dataset of 87K samples, each annotated with both binary safety labels and risk categories under a hierarchical multimodal safety taxonomy, effectively mitigating modality bias and ensuring consistent moderation across text, image, and text-image inputs. Based on this dataset, we train our vision-language base model purely through reinforcement learning (RL) to achieve efficient and concise reasoning. To approximate proactive safety scenarios in a controlled setting, we further introduce an OOD safety category inference task and augment the RL objective with a synonym-bank-based similarity reward that encourages the model to generate concise descriptions for unseen unsafe categories. Experimental results show that ProGuard achieves performance comparable to closed-source large models on binary safety classification, substantially outperforms existing open-source guard models on unsafe content categorization. Most notably, ProGuard delivers a strong proactive moderation ability, improving OOD risk detection by 52.6% and OOD risk description by 64.8%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.22984", "title": "Reverse Personalization", "authors": [ "Han-Wei Kung", "Tuomas Varanka", "Nicu Sebe" ], "github": "https://github.com/hanweikung/reverse-personalization", "project_page": "", "abstract": "Recent text-to-image diffusion models have demonstrated remarkable generation of realistic facial images conditioned on textual prompts and human identities, enabling creating personalized facial imagery. However, existing prompt-based methods for removing or modifying identity-specific features rely either on the subject being well-represented in the pre-trained model or require model fine-tuning for specific identities. In this work, we analyze the identity generation process and introduce a reverse personalization framework for face anonymization. Our approach leverages conditional diffusion inversion, allowing direct manipulation of images without using text prompts. To generalize beyond subjects in the model's training data, we incorporate an identity-guided conditioning branch. Unlike prior anonymization methods, which lack control over facial attributes, our framework supports attribute-controllable anonymization. We demonstrate that our method achieves a state-of-the-art balance between identity removal, attribute preservation, and image quality. Source code and data are available at https://github.com/hanweikung/reverse-personalization .", "github_manual": "", "github_regex": "https://github.com/hanweikung/reverse-personalization", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23273", "title": "YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection", "authors": [ "Xu Lin", "Jinlong Peng", "Zhenye Gan", "Jiawen Zhu", "Jun Liu" ], "github": "", "project_page": "", "abstract": "Existing Real-Time Object Detection (RTOD) methods commonly adopt YOLO-like architectures for their favorable trade-off between accuracy and speed. However, these models rely on static dense computation that applies uniform processing to all inputs, misallocating representational capacity and computational resources such as over-allocating on trivial scenes while under-serving complex ones. This mismatch results in both computational redundancy and suboptimal detection performance. To overcome this limitation, we propose YOLO-Master, a novel YOLO-like framework that introduces instance-conditional adaptive computation for RTOD. This is achieved through a Efficient Sparse Mixture-of-Experts (ES-MoE) block that dynamically allocates computational resources to each input according to its scene complexity. At its core, a lightweight dynamic routing network guides expert specialization during training through a diversity enhancing objective, encouraging complementary expertise among experts. Additionally, the routing network adaptively learns to activate only the most relevant experts, thereby improving detection performance while minimizing computational overhead during inference. Comprehensive experiments on five large-scale benchmarks demonstrate the superiority of YOLO-Master. On MS COCO, our model achieves 42.4% AP with 1.62ms latency, outperforming YOLOv13-N by +0.8% mAP and 17.8% faster inference. Notably, the gains are most pronounced on challenging dense scenes, while the model preserves efficiency on typical inputs and maintains real-time inference speed. Code will be available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.22100", "title": "Introducing TrGLUE and SentiTurca: A Comprehensive Benchmark for Turkish General Language Understanding and Sentiment Analysis", "authors": [ "Duygu Altinok" ], "github": "", "project_page": "", "abstract": "Evaluating the performance of various model architectures, such as transformers, large language models (LLMs), and other NLP systems, requires comprehensive benchmarks that measure performance across multiple dimensions. Among these, the evaluation of natural language understanding (NLU) is particularly critical as it serves as a fundamental criterion for assessing model capabilities. Thus, it is essential to establish benchmarks that enable thorough evaluation and analysis of NLU abilities from diverse perspectives. While the GLUE benchmark has set a standard for evaluating English NLU, similar benchmarks have been developed for other languages, such as CLUE for Chinese, FLUE for French, and JGLUE for Japanese. However, no comparable benchmark currently exists for the Turkish language. To address this gap, we introduce TrGLUE, a comprehensive benchmark encompassing a variety of NLU tasks for Turkish. In addition, we present SentiTurca, a specialized benchmark for sentiment analysis. To support researchers, we also provide fine-tuning and evaluation code for transformer-based models, facilitating the effective use of these benchmarks. TrGLUE comprises Turkish-native corpora curated to mirror the domains and task formulations of GLUE-style evaluations, with labels obtained through a semi-automated pipeline that combines strong LLM-based annotation, cross-model agreement checks, and subsequent human validation. This design prioritizes linguistic naturalness, minimizes direct translation artifacts, and yields a scalable, reproducible workflow. With TrGLUE, our goal is to establish a robust evaluation framework for Turkish NLU, empower researchers with valuable resources, and provide insights into generating high-quality semi-automated datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23703", "title": "Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation", "authors": [ "Huajie Tan", "Sixiang Chen", "Yijie Xu", "Zixiao Wang", "Yuheng Ji", "Cheng Chi", "Yaoxu Lyu", "Zhongxia Zhao", "Xiansheng Chen", "Peterson Co", "Shaoxuan Xie", "Guocai Yao", "Pengwei Wang", "Zhongyuan Wang", "Shanghang Zhang" ], "github": "https://github.com/FlagOpen/RoboBrain-X0", "project_page": "", "abstract": "The primary obstacle for applying reinforcement learning (RL) to real-world robotics is the design of effective reward functions. While recently learning-based Process Reward Models (PRMs) are a promising direction, they are often hindered by two fundamental limitations: their reward models lack step-aware understanding and rely on single-view perception, leading to unreliable assessments of fine-grained manipulation progress; and their reward shaping procedures are theoretically unsound, often inducing a semantic trap that misguides policy optimization. To address these, we introduce Dopamine-Reward, a novel reward modeling method for learning a general-purpose, step-aware process reward model from multi-view inputs. At its core is our General Reward Model (GRM), trained on a vast 3,400+ hour dataset, which leverages Step-wise Reward Discretization for structural understanding and Multi-Perspective Reward Fusion to overcome perceptual limitations. Building upon Dopamine-Reward, we propose Dopamine-RL, a robust policy learning framework that employs a theoretically-sound Policy-Invariant Reward Shaping method, which enables the agent to leverage dense rewards for efficient self-improvement without altering the optimal policy, thereby fundamentally avoiding the semantic trap. Extensive experiments across diverse simulated and real-world tasks validate our approach. GRM achieves state-of-the-art accuracy in reward assessment, and Dopamine-RL built on GRM significantly improves policy learning efficiency. For instance, after GRM is adapted to a new task in a one-shot manner from a single expert trajectory, the resulting reward model enables Dopamine-RL to improve the policy from near-zero to 95% success with only 150 online rollouts (approximately 1 hour of real robot interaction), while retaining strong generalization across tasks. Project website: https://robo-dopamine.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FlagOpen/RoboBrain-X0", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.23236", "title": "KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta", "authors": [ "Gang Liao", "Hongsen Qin", "Ying Wang", "Alicia Golden", "Michael Kuchnik", "Yavuz Yetim", "Jia Jiunn Ang", "Chunli Fu", "Yihan He", "Samuel Hsia", "Zewei Jiang", "Dianshi Li", "Uladzimir Pashkevich", "Varna Puvvada", "Feng Shi", "Matt Steiner", "Ruichao Xiao", "Nathan Yan", "Xiayu Yu", "Zhou Fang", "Abdul Zainul-Abedin", "Ketan Singh", "Hongtao Yu", "Wenyuan Chi", "Barney Huang", "Sean Zhang", "Noah Weller", "Zach Marine", "Wyatt Cook", "Carole-Jean Wu", "Gaoxiang Liu" ], "github": "https://github.com/pytorch/FBGEMM", "project_page": "", "abstract": "Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key system challenges - model architecture diversity, kernel primitive diversity, and hardware generation and architecture heterogeneity. This paper presents KernelEvolve-an agentic kernel coding framework-to tackle heterogeneity at-scale for DLRM. KernelEvolve is designed to take kernel specifications as input and automate the process of kernel generation and optimization for recommendation model across heterogeneous hardware architectures. KernelEvolve does so by operating at multiple programming abstractions, from Triton and CuTe DSL to low-level hardware agnostic languages, spanning the full hardware-software optimization stack. The kernel optimization process is described as graph-based search with selection policy, universal operator, fitness function, and termination rule, dynamically adapts to runtime execution context through retrieval-augmented prompt synthesis. We designed, implemented, and deployed KernelEvolve to optimize a wide variety of production recommendation models across generations of NVIDIA and AMD GPUs, as well as Meta's AI accelerators. We validate KernelEvolve on the publicly-available KernelBench suite, achieving 100% pass rate on all 250 problems across three difficulty levels, and 160 PyTorch ATen operators across three heterogeneous hardware platforms, demonstrating 100% correctness. KernelEvolve reduces development time from weeks to hours and achieves substantial performance improvements over PyTorch baselines across diverse production use cases and for heterogeneous AI systems at-scale. Beyond performance efficiency improvements, KernelEvolve significantly mitigates the programmability barrier for new AI hardware by enabling automated kernel generation for in-house developed AI hardware.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pytorch/FBGEMM", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.22255", "title": "Shape of Thought: When Distribution Matters More than Correctness in Reasoning Tasks", "authors": [ "Abhranil Chandra", "Ayush Agrawal", "Arian Hosseini", "Sebastian Fischmeister", "Rishabh Agarwal", "Navin Goyal", "Aaron Courville" ], "github": "", "project_page": "", "abstract": "We present the surprising finding that a language model's reasoning capabilities can be improved by training on synthetic datasets of chain-of-thought (CoT) traces from more capable models, even when all of those traces lead to an incorrect final answer. Our experiments show this approach can yield better performance on reasoning tasks than training on human-annotated datasets. We hypothesize that two key factors explain this phenomenon: first, the distribution of synthetic data is inherently closer to the language model's own distribution, making it more amenable to learning. Second, these `incorrect' traces are often only partially flawed and contain valid reasoning steps from which the model can learn. To further test the first hypothesis, we use a language model to paraphrase human-annotated traces -- shifting their distribution closer to the model's own distribution -- and show that this improves performance. For the second hypothesis, we introduce increasingly flawed CoT traces and study to what extent models are tolerant to these flaws. We demonstrate our findings across various reasoning domains like math, algorithmic reasoning and code generation using MATH, GSM8K, Countdown and MBPP datasets on various language models ranging from 1.5B to 9B across Qwen, Llama, and Gemma models. Our study shows that curating datasets that are closer to the model's distribution is a critical aspect to consider. We also show that a correct final answer is not always a reliable indicator of a faithful reasoning process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-30", "arxiv_id": "2512.22374", "title": "Self-Evaluation Unlocks Any-Step Text-to-Image Generation", "authors": [ "Xin Yu", "Xiaojuan Qi", "Zhengqi Li", "Kai Zhang", "Richard Zhang", "Zhe Lin", "Eli Shechtman", "Tianyu Wang", "Yotam Nitzan" ], "github": "", "project_page": "", "abstract": "We introduce the Self-Evaluating Model (Self-E), a novel, from-scratch training approach for text-to-image generation that supports any-step inference. Self-E learns from data similarly to a Flow Matching model, while simultaneously employing a novel self-evaluation mechanism: it evaluates its own generated samples using its current score estimates, effectively serving as a dynamic self-teacher. Unlike traditional diffusion or flow models, it does not rely solely on local supervision, which typically necessitates many inference steps. Unlike distillation-based approaches, it does not require a pretrained teacher. This combination of instantaneous local learning and self-driven global matching bridges the gap between the two paradigms, enabling the training of a high-quality text-to-image model from scratch that excels even at very low step counts. Extensive experiments on large-scale text-to-image benchmarks show that Self-E not only excels in few-step generation, but is also competitive with state-of-the-art Flow Matching models at 50 steps. We further find that its performance improves monotonically as inference steps increase, enabling both ultra-fast few-step generation and high-quality long-trajectory sampling within a single unified model. To our knowledge, Self-E is the first from-scratch, any-step text-to-image model, offering a unified framework for efficient and scalable generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-31", "arxiv_id": "2512.22525", "title": "DreamOmni3: Scribble-based Editing and Generation", "authors": [ "Bin Xia", "Bohao Peng", "Jiyang Liu", "Sitong Wu", "Jingyao Li", "Junjia Huang", "Xu Zhao", "Yitong Wang", "Ruihang Chu", "Bei Yu", "Jiaya Jia" ], "github": "https://github.com/dvlab-research/DreamOmni3", "project_page": "", "abstract": "Recently unified generation and editing models have achieved remarkable success with their impressive performance. These models rely mainly on text prompts for instruction-based editing and generation, but language often fails to capture users intended edit locations and fine-grained visual details. To this end, we propose two tasks: scribble-based editing and generation, that enables more flexible creation on graphical user interface (GUI) combining user textual, images, and freehand sketches. We introduce DreamOmni3, tackling two challenges: data creation and framework design. Our data synthesis pipeline includes two parts: scribble-based editing and generation. For scribble-based editing, we define four tasks: scribble and instruction-based editing, scribble and multimodal instruction-based editing, image fusion, and doodle editing. Based on DreamOmni2 dataset, we extract editable regions and overlay hand-drawn boxes, circles, doodles or cropped image to construct training data. For scribble-based generation, we define three tasks: scribble and instruction-based generation, scribble and multimodal instruction-based generation, and doodle generation, following similar data creation pipelines. For the framework, instead of using binary masks, which struggle with complex edits involving multiple scribbles, images, and instructions, we propose a joint input scheme that feeds both the original and scribbled source images into the model, using different colors to distinguish regions and simplify processing. By applying the same index and position encodings to both images, the model can precisely localize scribbled regions while maintaining accurate editing. Finally, we establish comprehensive benchmarks for these tasks to promote further research. Experimental results demonstrate that DreamOmni3 achieves outstanding performance, and models and code will be publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dvlab-research/DreamOmni3", "github_api_added_by": "auto" }, { "date": "2025-12-31", "arxiv_id": "2512.23675", "title": "End-to-End Test-Time Training for Long Context", "authors": [ "Arnuv Tandon", "Karan Dalal", "Xinhao Li", "Daniel Koceja", "Marcel R\u00f8d", "Sam Buchanan", "Xiaolong Wang", "Jure Leskovec", "Sanmi Koyejo", "Tatsunori Hashimoto", "Carlos Guestrin", "Jed McCaleb", "Yejin Choi", "Yu Sun" ], "github": "", "project_page": "", "abstract": "We formulate long-context language modeling as a problem in continual learning rather than architecture design. Under this formulation, we only use a standard architecture -- a Transformer with sliding-window attention. However, our model continues learning at test time via next-token prediction on the given context, compressing the context it reads into its weights. In addition, we improve the model's initialization for learning at test time via meta-learning at training time. Overall, our method, a form of Test-Time Training (TTT), is End-to-End (E2E) both at test time (via next-token prediction) and training time (via meta-learning), in contrast to previous forms. We conduct extensive experiments with a focus on scaling properties. In particular, for 3B models trained with 164B tokens, our method (TTT-E2E) scales with context length in the same way as Transformer with full attention, while others, such as Mamba 2 and Gated DeltaNet, do not. However, similar to RNNs, TTT-E2E has constant inference latency regardless of context length, making it 2.7 times faster than full attention for 128K context. Our code is publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-31", "arxiv_id": "2512.23165", "title": "Evaluating Parameter Efficient Methods for RLVR", "authors": [ "Qingyu Yin", "Yulun Wu", "Zhennan Shen", "Sunbowen Li", "Zhilin Wang", "Yanshu Li", "Chak Tou Leong", "Jiale Kang", "Jinjin Gu" ], "github": "https://github.com/MikaStars39/PeRL", "project_page": "", "abstract": "We systematically evaluate Parameter-Efficient Fine-Tuning (PEFT) methods under the paradigm of Reinforcement Learning with Verifiable Rewards (RLVR). RLVR incentivizes language models to enhance their reasoning capabilities through verifiable feedback; however, while methods like LoRA are commonly used, the optimal PEFT architecture for RLVR remains unidentified. In this work, we conduct the first comprehensive evaluation of over 12 PEFT methodologies across the DeepSeek-R1-Distill families on mathematical reasoning benchmarks. Our empirical results challenge the default adoption of standard LoRA with three main findings. First, we demonstrate that structural variants, such as DoRA, AdaLoRA, and MiSS, consistently outperform LoRA. Second, we uncover a spectral collapse phenomenon in SVD-informed initialization strategies (e.g., PiSSA, MiLoRA), attributing their failure to a fundamental misalignment between principal-component updates and RL optimization. Furthermore, our ablations reveal that extreme parameter reduction (e.g., VeRA, Rank-1) severely bottlenecks reasoning capacity. We further conduct ablation studies and scaling experiments to validate our findings. This work provides a definitive guide for advocating for more exploration for parameter-efficient RL methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MikaStars39/PeRL", "github_api_added_by": "auto" }, { "date": "2025-12-31", "arxiv_id": "2512.22469", "title": "GraphLocator: Graph-guided Causal Reasoning for Issue Localization", "authors": [ "Wei Liu", "Chao Peng", "Pengfei Gao", "Aofan Liu", "Wei Zhang", "Haiyan Zhao", "Zhi Jin" ], "github": "", "project_page": "", "abstract": "The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental yet challenging in automated software engineering due to the semantic gap between issue description and source code implementation. This gap manifests as two mismatches:(1) symptom-to-cause mismatches, where descriptions do not explicitly reveal underlying root causes; (2) one-to-many mismatches, where a single issue corresponds to multiple interdependent code entities. To address these two mismatches, we propose GraphLocator, an approach that mitigates symptom-to-cause mismatches through causal structure discovering and resolves one-to-many mismatches via dynamic issue disentangling. The key artifact is the causal issue graph (CIG), in which vertices represent discovered sub-issues along with their associated code entities, and edges encode the causal dependencies between them. The workflow of GraphLocator consists of two phases: symptom vertices locating and dynamic CIG discovering; it first identifies symptom locations on the repository graph, then dynamically expands the CIG by iteratively reasoning over neighboring vertices. Experiments on three real-world datasets demonstrates the effectiveness of GraphLocator: (1) Compared with baselines, GraphLocator achieves more accurate localization with average improvements of +19.49% in function-level recall and +11.89% in precision. (2) GraphLocator outperforms baselines on both symptom-to-cause and one-to-many mismatch scenarios, achieving recall improvement of +16.44% and +19.18%, precision improvement of +7.78% and +13.23%, respectively. (3) The CIG generated by GraphLocator yields the highest relative improvement, resulting in a 28.74% increase in performance on downstream resolving task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-31", "arxiv_id": "2512.21185", "title": "UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement", "authors": [ "Tanghui Jia", "Dongyu Yan", "Dehao Hao", "Yang Li", "Kaiyi Zhang", "Xianyi He", "Lanjiong Li", "Jinnan Chen", "Lutao Jiang", "Qishen Yin", "Long Quan", "Ying-Cong Chen", "Li Yuan" ], "github": "", "project_page": "", "abstract": "In this report, we introduce UltraShape 1.0, a scalable 3D diffusion framework for high-fidelity 3D geometry generation. The proposed approach adopts a two-stage generation pipeline: a coarse global structure is first synthesized and then refined to produce detailed, high-quality geometry. To support reliable 3D generation, we develop a comprehensive data processing pipeline that includes a novel watertight processing method and high-quality data filtering. This pipeline improves the geometric quality of publicly available 3D datasets by removing low-quality samples, filling holes, and thickening thin structures, while preserving fine-grained geometric details. To enable fine-grained geometry refinement, we decouple spatial localization from geometric detail synthesis in the diffusion process. We achieve this by performing voxel-based refinement at fixed spatial locations, where voxel queries derived from coarse geometry provide explicit positional anchors encoded via RoPE, allowing the diffusion model to focus on synthesizing local geometric details within a reduced, structured solution space. Our model is trained exclusively on publicly available 3D datasets, achieving strong geometric quality despite limited training resources. Extensive evaluations demonstrate that UltraShape 1.0 performs competitively with existing open-source methods in both data processing quality and geometry generation. All code and trained models will be released to support future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-31", "arxiv_id": "2512.21008", "title": "GateBreaker: Gate-Guided Attacks on Mixture-of-Expert LLMs", "authors": [ "Lichao Wu", "Sasha Behrouzi", "Mohamadreza Rostami", "Stjepan Picek", "Ahmad-Reza Sadeghi" ], "github": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) architectures have advanced the scaling of Large Language Models (LLMs) by activating only a sparse subset of parameters per input, enabling state-of-the-art performance with reduced computational cost. As these models are increasingly deployed in critical domains, understanding and strengthening their alignment mechanisms is essential to prevent harmful outputs. However, existing LLM safety research has focused almost exclusively on dense architectures, leaving the unique safety properties of MoEs largely unexamined. The modular, sparsely-activated design of MoEs suggests that safety mechanisms may operate differently than in dense models, raising questions about their robustness. In this paper, we present GateBreaker, the first training-free, lightweight, and architecture-agnostic attack framework that compromises the safety alignment of modern MoE LLMs at inference time. GateBreaker operates in three stages: (i) gate-level profiling, which identifies safety experts disproportionately routed on harmful inputs, (ii) expert-level localization, which localizes the safety structure within safety experts, and (iii) targeted safety removal, which disables the identified safety structure to compromise the safety alignment. Our study shows that MoE safety concentrates within a small subset of neurons coordinated by sparse routing. Selective disabling of these neurons, approximately 3% of neurons in the targeted expert layers, significantly increases the averaged attack success rate (ASR) from 7.4% to 64.9% against the eight latest aligned MoE LLMs with limited utility degradation. These safety neurons transfer across models within the same family, raising ASR from 17.9% to 67.7% with one-shot transfer attack. Furthermore, GateBreaker generalizes to five MoE vision language models (VLMs) with 60.9% ASR on unsafe image inputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-12-31", "arxiv_id": "2512.22206", "title": "CosineGate: Semantic Dynamic Routing via Cosine Incompatibility in Residual Networks", "authors": [ "Yogeswar Reddy Thota" ], "github": "", "project_page": "", "abstract": "Modern deep residual networks perform substantial redundant computation by evaluating all residual blocks for every input, even when identity mappings suffice. We introduce CosineGate, an end-to-end differentiable architecture for dynamic routing in residual networks that uses cosine incompatibility between identity and residual feature representations as a self-supervised skip signal. CosineGate measures semantic redundancy through the Cosine Incompatibility Ratio (CIR), defined as 1 - cos(x, F(x)), and uses Gumbel-Softmax relaxation to enable per-sample, per-block gating during training. A progressive FLOPs regularization term controls average compute usage without destabilizing optimization. On CIFAR-10, CosineGate spans the accuracy-efficiency Pareto frontier: an aggressive configuration achieves 89.9 percent accuracy with 24.1 percent FLOPs savings, a balanced configuration achieves 91.3 percent accuracy with 28.5 percent savings at epoch 160, and a conservative configuration reaches a peak of 93.2 percent accuracy with minimal compute reduction. These results match or exceed ResNet-20 (91.3 percent) while reducing computation, without auxiliary supervision, distillation, or task-specific heuristics. Our results demonstrate that simple geometric measures of feature incompatibility provide a principled and effective signal for dynamic residual routing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.24618", "title": "Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models", "authors": [ "Junru Lu", "Jiarui Qin", "Lingfeng Qiao", "Yinghui Li", "Xinyi Dai", "Bo Ke", "Jianfeng He", "Ruizhi Qiao", "Di Yin", "Xing Sun", "Yunsheng Wu", "Yinsong Liu", "Shuangyin Liu", "Mingkong Tang", "Haodong Lin", "Jiayi Kuang", "Fanxu Meng", "Xiaojuan Tang", "Yunjia Xi", "Junjie Huang", "Haotong Yang", "Zhenyi Shen", "Yangning Li", "Qianwen Zhang", "Yifei Yu", "Siyu An", "Junnan Dong", "Qiufeng Wang", "Jie Wang", "Keyu Chen", "Wei Wen", "Taian Guo", "Zhifeng Shen", "Daohai Yu", "Jiahao Li", "Ke Li", "Zongyi Li", "Xiaoyu Tan" ], "github": "https://github.com/facebookresearch/cruxeval/blob/main/prompts.py", "project_page": "", "abstract": "We introduce Youtu-LLM, a lightweight yet powerful language model that harmonizes high computational efficiency with native agentic intelligence. Unlike typical small models that rely on distillation, Youtu-LLM (1.96B) is pre-trained from scratch to systematically cultivate reasoning and planning capabilities. The key technical advancements are as follows: (1) Compact Architecture with Long-Context Support: Built on a dense Multi-Latent Attention (MLA) architecture with a novel STEM-oriented vocabulary, Youtu-LLM supports a 128k context window. This design enables robust long-context reasoning and state tracking within a minimal memory footprint, making it ideal for long-horizon agent and reasoning tasks. (2) Principled \"Commonsense-STEM-Agent\" Curriculum: We curated a massive corpus of approximately 11T tokens and implemented a multi-stage training strategy. By progressively shifting the pre-training data distribution from general commonsense to complex STEM and agentic tasks, we ensure the model acquires deep cognitive abilities rather than superficial alignment. (3) Scalable Agentic Mid-training: Specifically for the agentic mid-training, we employ diverse data construction schemes to synthesize rich and varied trajectories across math, coding, and tool-use domains. This high-quality data enables the model to internalize planning and reflection behaviors effectively. Extensive evaluations show that Youtu-LLM sets a new state-of-the-art for sub-2B LLMs. On general benchmarks, it achieves competitive performance against larger models, while on agent-specific tasks, it significantly surpasses existing SOTA baselines, demonstrating that lightweight models can possess strong intrinsic agentic capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/cruxeval/blob/main/prompts.py", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.24210", "title": "GR-Dexter Technical Report", "authors": [ "Ruoshi Wen", "Guangzeng Chen", "Zhongren Cui", "Min Du", "Yang Gou", "Zhigang Han", "Liqun Huang", "Mingyu Lei", "Yunfei Li", "Zhuohang Li", "Wenlei Liu", "Yuxiao Liu", "Xiao Ma", "Hao Niu", "Yutao Ouyang", "Zeyu Ren", "Haixin Shi", "Wei Xu", "Haoxiang Zhang", "Jiajun Zhang", "Xiao Zhang", "Liwei Zheng", "Weiheng Zhong", "Yifei Zhou", "Zhengming Zhu", "Hang Li" ], "github": "", "project_page": "", "abstract": "Vision-language-action (VLA) models have enabled language-conditioned, long-horizon robot manipulation, but most existing systems are limited to grippers. Scaling VLA policies to bimanual robots with high degree-of-freedom (DoF) dexterous hands remains challenging due to the expanded action space, frequent hand-object occlusions, and the cost of collecting real-robot data. We present GR-Dexter, a holistic hardware-model-data framework for VLA-based generalist manipulation on a bimanual dexterous-hand robot. Our approach combines the design of a compact 21-DoF robotic hand, an intuitive bimanual teleoperation system for real-robot data collection, and a training recipe that leverages teleoperated robot trajectories together with large-scale vision-language and carefully curated cross-embodiment datasets. Across real-world evaluations spanning long-horizon everyday manipulation and generalizable pick-and-place, GR-Dexter achieves strong in-domain performance and improved robustness to unseen objects and unseen instructions. We hope GR-Dexter serves as a practical step toward generalist dexterous-hand robotic manipulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.25075", "title": "SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time", "authors": [ "Zhening Huang", "Hyeonho Jeong", "Xuelin Chen", "Yulia Gryaditskaya", "Tuanfeng Y. Wang", "Joan Lasenby", "Chun-Hao Huang" ], "github": "https://github.com/ZheningHuang/spacetimepilot", "project_page": "", "abstract": "We present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently alter the camera viewpoint and the motion sequence within the generative process, re-rendering the scene for continuous and arbitrary exploration across space and time. To achieve this, we introduce an effective animation time-embedding mechanism in the diffusion process, allowing explicit control of the output video's motion sequence with respect to that of the source video. As no datasets provide paired videos of the same dynamic scene with continuous temporal variations, we propose a simple yet effective temporal-warping training scheme that repurposes existing multi-view datasets to mimic temporal differences. This strategy effectively supervises the model to learn temporal control and achieve robust space-time disentanglement. To further enhance the precision of dual control, we introduce two additional components: an improved camera-conditioning mechanism that allows altering the camera from the first frame, and CamxTime, the first synthetic space-and-time full-coverage rendering dataset that provides fully free space-time video trajectories within a scene. Joint training on the temporal-warping scheme and the CamxTime dataset yields more precise temporal control. We evaluate SpaceTimePilot on both real-world and synthetic data, demonstrating clear space-time disentanglement and strong results compared to prior work. Project page: https://zheninghuang.github.io/Space-Time-Pilot/ Code: https://github.com/ZheningHuang/spacetimepilot", "github_manual": "", "github_regex": "https://github.com/ZheningHuang/spacetimepilot", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.24551", "title": "PhyGDPO: Physics-Aware Groupwise Direct Preference Optimization for Physically Consistent Text-to-Video Generation", "authors": [ "Yuanhao Cai", "Kunpeng Li", "Menglin Jia", "Jialiang Wang", "Junzhe Sun", "Feng Liang", "Weifeng Chen", "Felix Juefei-Xu", "Chu Wang", "Ali Thabet", "Xiaoliang Dai", "Xuan Ju", "Alan Yuille", "Ji Hou" ], "github": "https://github.com/caiyuanhao1998/Open-PhyGDPO", "project_page": "", "abstract": "Recent advances in text-to-video (T2V) generation have achieved good visual quality, yet synthesizing videos that faithfully follow physical laws remains an open challenge. Existing methods mainly based on graphics or prompt extension struggle to generalize beyond simple simulated environments or learn implicit physical reasoning. The scarcity of training data with rich physics interactions and phenomena is also a problem. In this paper, we first introduce a Physics-Augmented video data construction Pipeline, PhyAugPipe, that leverages a vision-language model (VLM) with chain-of-thought reasoning to collect a large-scale training dataset, PhyVidGen-135K. Then we formulate a principled Physics-aware Groupwise Direct Preference Optimization, PhyGDPO, framework that builds upon the groupwise Plackett-Luce probabilistic model to capture holistic preferences beyond pairwise comparisons. In PhyGDPO, we design a Physics-Guided Rewarding (PGR) scheme that embeds VLM-based physics rewards to steer optimization toward physical consistency. We also propose a LoRA-Switch Reference (LoRA-SR) scheme that eliminates memory-heavy reference duplication for efficient training. Experiments show that our method significantly outperforms state-of-the-art open-source methods on PhyGenBench and VideoPhy2. Please check our project page at https://caiyuanhao1998.github.io/project/PhyGDPO for more video results. Our code, models, and data will be released at https://github.com/caiyuanhao1998/Open-PhyGDPO", "github_manual": "", "github_regex": "https://github.com/caiyuanhao1998/Open-PhyGDPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.25070", "title": "Scaling Open-Ended Reasoning to Predict the Future", "authors": [ "Nikhil Chandak", "Shashwat Goel", "Ameya Prabhu", "Moritz Hardt", "Jonas Geiping" ], "github": "", "project_page": "", "abstract": "High-stakes decision making involves reasoning under uncertainty about the future. In this work, we train language models to make predictions on open-ended forecasting questions. To scale up training data, we synthesize novel forecasting questions from global events reported in daily news, using a fully automated, careful curation recipe. We train the Qwen3 thinking models on our dataset, OpenForesight. To prevent leakage of future information during training and evaluation, we use an offline news corpus, both for data generation and retrieval in our forecasting system. Guided by a small validation set, we show the benefits of retrieval, and an improved reward function for reinforcement learning (RL). Once we obtain our final forecasting system, we perform held-out testing between May to August 2025. Our specialized model, OpenForecaster 8B, matches much larger proprietary models, with our training improving the accuracy, calibration, and consistency of predictions. We find calibration improvements from forecasting training generalize across popular benchmarks. We open-source all our models, code, and data to make research on language model forecasting broadly accessible.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.23988", "title": "Fantastic Reasoning Behaviors and Where to Find Them: Unsupervised Discovery of the Reasoning Process", "authors": [ "Zhenyu Zhang", "Shujian Zhang", "John Lambert", "Wenxuan Zhou", "Zhangyang Wang", "Mingqing Chen", "Andrew Hard", "Rajiv Mathews", "Lun Wang" ], "github": "", "project_page": "", "abstract": "Despite the growing reasoning capabilities of recent large language models (LLMs), their internal mechanisms during the reasoning process remain underexplored. Prior approaches often rely on human-defined concepts (e.g., overthinking, reflection) at the word level to analyze reasoning in a supervised manner. However, such methods are limited, as it is infeasible to capture the full spectrum of potential reasoning behaviors, many of which are difficult to define in token space. In this work, we propose an unsupervised framework (namely, RISE: Reasoning behavior Interpretability via Sparse auto-Encoder) for discovering reasoning vectors, which we define as directions in the activation space that encode distinct reasoning behaviors. By segmenting chain-of-thought traces into sentence-level 'steps' and training sparse auto-encoders (SAEs) on step-level activations, we uncover disentangled features corresponding to interpretable behaviors such as reflection and backtracking. Visualization and clustering analyses show that these behaviors occupy separable regions in the decoder column space. Moreover, targeted interventions on SAE-derived vectors can controllably amplify or suppress specific reasoning behaviors, altering inference trajectories without retraining. Beyond behavior-specific disentanglement, SAEs capture structural properties such as response length, revealing clusters of long versus short reasoning traces. More interestingly, SAEs enable the discovery of novel behaviors beyond human supervision. We demonstrate the ability to control response confidence by identifying confidence-related vectors in the SAE decoder space. These findings underscore the potential of unsupervised latent discovery for both interpreting and controllably steering reasoning in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.24873", "title": "Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem", "authors": [ "Weixun Wang", "XiaoXiao Xu", "Wanhe An", "Fangwen Dai", "Wei Gao", "Yancheng He", "Ju Huang", "Qiang Ji", "Hanqi Jin", "Xiaoyang Li", "Yang Li", "Zhongwen Li", "Shirong Lin", "Jiashun Liu", "Zenan Liu", "Tao Luo", "Dilxat Muhtar", "Yuanbin Qu", "Jiaqiang Shi", "Qinghui Sun", "Yingshui Tan", "Hao Tang", "Runze Wang", "Yi Wang", "Zhaoguo Wang", "Yanan Wu", "Shaopan Xiong", "Binchen Xu", "Xander Xu", "Yuchi Xu", "Qipeng Zhang", "Xixia Zhang", "Haizhou Zhao", "Jie Zhao", "Shuaibing Zhao", "Baihui Zheng", "Jianhui Zheng", "Suhang Zheng", "Yanni Zhu", "Mengze Cai", "Kerui Cao", "Xitong Chen", "Yue Dai", "Lifan Du", "Tao Feng", "Tao He", "Jin Hu", "Yijie Hu", "Ziyu Jiang", "Cheng Li", "Xiang Li", "Jing Liang", "Chonghuan Liu", "ZhenDong Liu", "Haodong Mi", "Yanhu Mo", "Junjia Ni", "Shixin Pei", "Jingyu Shen", "XiaoShuai Song", "Cecilia Wang", "Chaofan Wang", "Kangyu Wang", "Pei Wang", "Tao Wang", "Wei Wang", "Ke Xiao", "Mingyu Xu", "Tiange Xu", "Nan Ya", "Siran Yang", "Jianan Ye", "Yaxing Zang", "Duo Zhang", "Junbo Zhang", "Boren Zheng", "Wanxi Deng", "Ling Pan", "Lin Qu", "Wenbo Su", "Jiamang Wang", "Wei Wang", "Hu Wei", "Minggang Wu", "Cheng Yu", "Bing Zhao", "Zhicheng Zheng", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its importance, the open-source community lacks a principled, end-to-end ecosystem to streamline agent development. We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimizes the production pipeline for agent LLMs. ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering. We release ROME (ROME is Obviously an Agentic Model), an open-source agent grounded by ALE and trained on over one million trajectories. Our approach includes data composition protocols for synthesizing complex behaviors and a novel policy optimization algorithm, Interaction-based Policy Alignment (IPA), which assigns credit over semantic interaction chunks rather than individual tokens to improve long-horizon training stability. Empirically, we evaluate ROME within a structured setting and introduce Terminal Bench Pro, a benchmark with improved scale and contamination control. ROME demonstrates strong performance across benchmarks like SWE-bench Verified and Terminal Bench, proving the effectiveness of the ALE infrastructure.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.24297", "title": "Figure It Out: Improving the Frontier of Reasoning with Active Visual Thinking", "authors": [ "Meiqi Chen", "Fandong Meng", "Jie Zhou" ], "github": "", "project_page": "", "abstract": "Complex reasoning problems often involve implicit spatial, geometric, and structural relationships that are not explicitly encoded in text. While recent reasoning models have achieved strong performance across many domains, purely text-based reasoning struggles to represent global structural constraints in complex settings. In this paper, we introduce FIGR, which integrates active visual thinking into multi-turn reasoning via end-to-end reinforcement learning. FIGR externalizes intermediate structural hypotheses by constructing visual representations during problem solving. By adaptively regulating when and how visual reasoning should be invoked, FIGR enables more stable and coherent reasoning over global structural properties that are difficult to capture from text alone. Experiments on challenging mathematical reasoning benchmarks demonstrate that FIGR outperforms strong text-only chain-of-thought baselines. In particular, FIGR improves the base model by 13.12% on AIME 2025 and 11.00% on BeyondAIME, highlighting the effectiveness of figure-guided multimodal reasoning in enhancing the stability and reliability of complex reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.23851", "title": "Pretraining Frame Preservation in Autoregressive Video Memory Compression", "authors": [ "Lvmin Zhang", "Shengqu Cai", "Muyang Li", "Chong Zeng", "Beijia Lu", "Anyi Rao", "Song Han", "Gordon Wetzstein", "Maneesh Agrawala" ], "github": "", "project_page": "", "abstract": "We present PFP, a neural network structure to compress long videos into short contexts, with an explicit pretraining objective to preserve the high-frequency details of single frames at arbitrary temporal positions. The baseline model can compress a 20-second video into a context at about 5k length, where random frames can be retrieved with perceptually preserved appearances. Such pretrained models can be directly fine-tuned as memory encoders for autoregressive video models, enabling long history memory with low context cost and relatively low fidelity loss. We evaluate the framework with ablative settings and discuss the trade-offs of possible neural architecture designs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.24097", "title": "Factorized Learning for Temporally Grounded Video-Language Models", "authors": [ "Wenzheng Zeng", "Difei Gao", "Mike Zheng Shou", "Hwee Tou Ng" ], "github": "https://github.com/nusnlp/d2vlm", "project_page": "", "abstract": "Recent video-language models have shown great potential for video understanding, but still struggle with accurate temporal grounding for event-level perception. We observe that two main factors in video understanding (i.e., temporal grounding and textual response) form a logical hierarchy: accurate temporal evidence grounding lays the foundation for reliable textual response. However, existing works typically handle these two tasks in a coupled manner without a clear logical structure, leading to sub-optimal objectives. We address this from a factorized learning perspective. We first propose D^2VLM, a framework that decouples the learning of these two tasks while also emphasizing their inherent dependency. We adopt a \"grounding then answering with evidence referencing\" paradigm and introduce evidence tokens for evidence grounding, which emphasize event-level visual semantic capture beyond the focus on timestamp representation in existing works. To further facilitate the learning of these two tasks, we introduce a novel factorized preference optimization (FPO) algorithm. Unlike standard preference optimization, FPO explicitly incorporates probabilistic temporal grounding modeling into the optimization objective, enabling preference learning for both temporal grounding and textual response. We also construct a synthetic dataset to address the lack of suitable datasets for factorized preference learning with explicit temporal grounding. Experiments on various tasks demonstrate the clear advantage of our approach. Our source code is available at https://github.com/nusnlp/d2vlm.", "github_manual": "", "github_regex": "https://github.com/nusnlp/d2vlm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.22905", "title": "JavisGPT: A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation", "authors": [ "Kai Liu", "Jungang Li", "Yuchong Sun", "Shengqiong Wu", "Jianzhang Gao", "Daoan Zhang", "Wei Zhang", "Sheng Jin", "Sicheng Yu", "Geng Zhan", "Jiayi Ji", "Fan Zhou", "Liang Zheng", "Shuicheng Yan", "Hao Fei", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "This paper presents JavisGPT, the first unified multimodal large language model (MLLM) for Joint Audio-Video (JAV) comprehension and generation. JavisGPT adopts a concise encoder-LLM-decoder architecture, featuring a SyncFusion module for spatio-temporal audio-video fusion and synchrony-aware learnable queries to bridge a pretrained JAV-DiT generator. This design enables temporally coherent video-audio understanding and generation from multimodal instructions. We design an effective three-stage training pipeline consisting of multimodal pretraining, audio-video fine-tuning, and large-scale instruction-tuning, to progressively build multimodal comprehension and generation from existing vision-language models. To support this, we further construct JavisInst-Omni, a high-quality instruction dataset with over 200K GPT-4o-curated audio-video-text dialogues that span diverse and multi-level comprehension and generation scenarios. Extensive experiments on JAV comprehension and generation benchmarks show that JavisGPT outperforms existing MLLMs, particularly in complex and temporally synchronized settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.24385", "title": "Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems", "authors": [ "Song Wang", "Lingdong Kong", "Xiaolu Liu", "Hao Shi", "Wentong Li", "Jianke Zhu", "Steven C. H. Hoi" ], "github": "", "project_page": "", "abstract": "The rapid advancement of autonomous systems, including self-driving vehicles and drones, has intensified the need to forge true Spatial Intelligence from multi-modal onboard sensor data. While foundation models excel in single-modal contexts, integrating their capabilities across diverse sensors like cameras and LiDAR to create a unified understanding remains a formidable challenge. This paper presents a comprehensive framework for multi-modal pre-training, identifying the core set of techniques driving progress toward this goal. We dissect the interplay between foundational sensor characteristics and learning strategies, evaluating the role of platform-specific datasets in enabling these advancements. Our central contribution is the formulation of a unified taxonomy for pre-training paradigms: ranging from single-modality baselines to sophisticated unified frameworks that learn holistic representations for advanced tasks like 3D object detection and semantic occupancy prediction. Furthermore, we investigate the integration of textual inputs and occupancy representations to facilitate open-world perception and planning. Finally, we identify critical bottlenecks, such as computational efficiency and model scalability, and propose a roadmap toward general-purpose multi-modal foundation models capable of achieving robust Spatial Intelligence for real-world deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.22280", "title": "Valori: A Deterministic Memory Substrate for AI Systems", "authors": [ "Varshith Gudur" ], "github": "https://github.com/varshith-Git/Valori-Kernel", "project_page": "", "abstract": "Modern AI systems rely on vector embeddings stored and searched using floating-point arithmetic. While effective for approximate similarity search, this design introduces fundamental non-determinism: identical models, inputs, and code can produce different memory states and retrieval results across hardware architectures (e.g., x86 vs. ARM). This prevents replayability and safe deployment, leading to silent data divergence that prevents post-hoc verification and compromises audit trails in regulated sectors. We present Valori, a deterministic AI memory substrate that replaces floating-point memory operations with fixed-point arithmetic (Q16.16) and models memory as a replayable state machine. Valori guarantees bit-identical memory states, snapshots, and search results across platforms. We demonstrate that non-determinism arises before indexing or retrieval and show how Valori enforces determinism at the memory boundary. Our results suggest that deterministic memory is a necessary primitive for trustworthy AI systems. The reference implementation is open-source and available at https://github.com/varshith-Git/Valori-Kernel (archived at https://zenodo.org/records/18022660).", "github_manual": "", "github_regex": "https://github.com/varshith-Git/Valori-Kernel", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.24885", "title": "BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts", "authors": [ "Hengli Li", "Zhaoxin Yu", "Qi Shen", "Chenxi Li", "Mengmeng Wang", "Tinglang Wu", "Yipeng Kang", "Yuxuan Wang", "Song-Chun Zhu", "Zixia Jia", "Zilong Zheng" ], "github": "", "project_page": "", "abstract": "Strategic dialogue requires agents to execute distinct dialogue acts, for which belief estimation is essential. While prior work often estimates beliefs accurately, it lacks a principled mechanism to use those beliefs during generation. We bridge this gap by first formalizing two core acts Adversarial and Alignment, and by operationalizing them via probabilistic constraints on what an agent may generate. We instantiate this idea in BEDA, a framework that consists of the world set, the belief estimator for belief estimation, and the conditional generator that selects acts and realizes utterances consistent with the inferred beliefs. Across three settings, Conditional Keeper Burglar (CKBG, adversarial), Mutual Friends (MF, cooperative), and CaSiNo (negotiation), BEDA consistently outperforms strong baselines: on CKBG it improves success rate by at least 5.0 points across backbones and by 20.6 points with GPT-4.1-nano; on Mutual Friends it achieves an average improvement of 9.3 points; and on CaSiNo it achieves the optimal deal relative to all baselines. These results indicate that casting belief estimation as constraints provides a simple, general mechanism for reliable strategic dialogue.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.22564", "title": "Geometry-Aware Optimization for Respiratory Sound Classification: Enhancing Sensitivity with SAM-Optimized Audio Spectrogram Transformers", "authors": [ "Atakan I\u015f\u0131k", "Selin Vulga I\u015f\u0131k", "Ahmet Feridun I\u015f\u0131k", "Mah\u015fuk Taylan" ], "github": "https://github.com/Atakanisik/ICBHI-AST-SAM", "project_page": "", "abstract": "Respiratory sound classification is hindered by the limited size, high noise levels, and severe class imbalance of benchmark datasets like ICBHI 2017. While Transformer-based models offer powerful feature extraction capabilities, they are prone to overfitting and often converge to sharp minima in the loss landscape when trained on such constrained medical data. To address this, we introduce a framework that enhances the Audio Spectrogram Transformer (AST) using Sharpness-Aware Minimization (SAM). Instead of merely minimizing the training loss, our approach optimizes the geometry of the loss surface, guiding the model toward flatter minima that generalize better to unseen patients. We also implement a weighted sampling strategy to handle class imbalance effectively. Our method achieves a state-of-the-art score of 68.10% on the ICBHI 2017 dataset, outperforming existing CNN and hybrid baselines. More importantly, it reaches a sensitivity of 68.31%, a crucial improvement for reliable clinical screening. Further analysis using t-SNE and attention maps confirms that the model learns robust, discriminative features rather than memorizing background noise.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Atakanisik/ICBHI-AST-SAM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.25073", "title": "GaMO: Geometry-aware Multi-view Diffusion Outpainting for Sparse-View 3D Reconstruction", "authors": [ "Yi-Chuan Huang", "Hao-Jen Chien", "Chin-Yang Lin", "Ying-Huan Chen", "Yu-Lun Liu" ], "github": "https://github.com/yichuanH/GaMO_official", "project_page": "", "abstract": "Recent advances in 3D reconstruction have achieved remarkable progress in high-quality scene capture from dense multi-view imagery, yet struggle when input views are limited. Various approaches, including regularization techniques, semantic priors, and geometric constraints, have been implemented to address this challenge. Latest diffusion-based methods have demonstrated substantial improvements by generating novel views from new camera poses to augment training data, surpassing earlier regularization and prior-based techniques. Despite this progress, we identify three critical limitations in these state-of-the-art approaches: inadequate coverage beyond known view peripheries, geometric inconsistencies across generated views, and computationally expensive pipelines. We introduce GaMO (Geometry-aware Multi-view Outpainter), a framework that reformulates sparse-view reconstruction through multi-view outpainting. Instead of generating new viewpoints, GaMO expands the field of view from existing camera poses, which inherently preserves geometric consistency while providing broader scene coverage. Our approach employs multi-view conditioning and geometry-aware denoising strategies in a zero-shot manner without training. Extensive experiments on Replica and ScanNet++ demonstrate state-of-the-art reconstruction quality across 3, 6, and 9 input views, outperforming prior methods in PSNR and LPIPS, while achieving a 25times speedup over SOTA diffusion-based methods with processing time under 10 minutes. Project page: https://yichuanh.github.io/GaMO/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yichuanH/GaMO_official", "github_api_added_by": "auto" }, { "date": "2026-01-01", "arxiv_id": "2512.23343", "title": "AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents", "authors": [ "Jiafeng Liang", "Hao Li", "Chang Li", "Jiaqi Zhou", "Shixin Jiang", "Zekun Wang", "Changkai Ji", "Zhihao Zhu", "Runxuan Liu", "Tao Ren", "Jinlan Fu", "See-Kiong Ng", "Xia Liang", "Ming Liu", "Bing Qin" ], "github": "https://github.com/AgentMemory/Huaman-Agent-Memory", "project_page": "", "abstract": "Memory serves as the pivotal nexus bridging past and future, providing both humans and AI systems with invaluable concepts and experience to navigate complex tasks. Recent research on autonomous agents has increasingly focused on designing efficient memory workflows by drawing on cognitive neuroscience. However, constrained by interdisciplinary barriers, existing works struggle to assimilate the essence of human memory mechanisms. To bridge this gap, we systematically synthesizes interdisciplinary knowledge of memory, connecting insights from cognitive neuroscience with LLM-driven agents. Specifically, we first elucidate the definition and function of memory along a progressive trajectory from cognitive neuroscience through LLMs to agents. We then provide a comparative analysis of memory taxonomy, storage mechanisms, and the complete management lifecycle from both biological and artificial perspectives. Subsequently, we review the mainstream benchmarks for evaluating agent memory. Additionally, we explore memory security from dual perspectives of attack and defense. Finally, we envision future research directions, with a focus on multimodal memory systems and skill acquisition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AgentMemory/Huaman-Agent-Memory", "github_api_added_by": "auto" }, { "date": "2026-01-01", "arxiv_id": "2512.24880", "title": "mHC: Manifold-Constrained Hyper-Connections", "authors": [ "Zhenda Xie", "Yixuan Wei", "Huanqi Cao", "Chenggang Zhao", "Chengqi Deng", "Jiashi Li", "Damai Dai", "Huazuo Gao", "Jiang Chang", "Liang Zhao", "Shangyan Zhou", "Zhean Xu", "Zhengyan Zhang", "Wangding Zeng", "Shengding Hu", "Yuqing Wang", "Jingyang Yuan", "Lean Wang", "Wenfeng Liang" ], "github": "", "project_page": "", "abstract": "Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past decade by expanding the residual stream width and diversifying connectivity patterns. While yielding substantial performance gains, this diversification fundamentally compromises the identity mapping property intrinsic to the residual connection, which causes severe training instability and restricted scalability, and additionally incurs notable memory access overhead. To address these challenges, we propose Manifold-Constrained Hyper-Connections (mHC), a general framework that projects the residual connection space of HC onto a specific manifold to restore the identity mapping property, while incorporating rigorous infrastructure optimization to ensure efficiency. Empirical experiments demonstrate that mHC is effective for training at scale, offering tangible performance improvements and superior scalability. We anticipate that mHC, as a flexible and practical extension of HC, will contribute to a deeper understanding of topological architecture design and suggest promising directions for the evolution of foundational models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.24176", "title": "Guiding a Diffusion Transformer with the Internal Dynamics of Itself", "authors": [ "Xingyu Zhou", "Qifan Li", "Xiaobin Hu", "Hai Chen", "Shuhang Gu" ], "github": "", "project_page": "", "abstract": "The diffusion model presents a powerful ability to capture the entire (conditional) data distribution. However, due to the lack of sufficient training and data to learn to cover low-probability areas, the model will be penalized for failing to generate high-quality images corresponding to these areas. To achieve better generation quality, guidance strategies such as classifier free guidance (CFG) can guide the samples to the high-probability areas during the sampling stage. However, the standard CFG often leads to over-simplified or distorted samples. On the other hand, the alternative line of guiding diffusion model with its bad version is limited by carefully designed degradation strategies, extra training and additional sampling steps. In this paper, we proposed a simple yet effective strategy Internal Guidance (IG), which introduces an auxiliary supervision on the intermediate layer during training process and extrapolates the intermediate and deep layer's outputs to obtain generative results during sampling process. This simple strategy yields significant improvements in both training efficiency and generation quality on various baselines. On ImageNet 256x256, SiT-XL/2+IG achieves FID=5.31 and FID=1.75 at 80 and 800 epochs. More impressively, LightningDiT-XL/1+IG achieves FID=1.34 which achieves a large margin between all of these methods. Combined with CFG, LightningDiT-XL/1+IG achieves the current state-of-the-art FID of 1.19.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-01", "arxiv_id": "2512.23380", "title": "A unified framework for detecting point and collective anomalies in operating system logs via collaborative transformers", "authors": [ "Mohammad Nasirzadeh", "Jafar Tahmoresnezhad", "Parviz Rashidi-Khazaee" ], "github": "https://github.com/NasirzadehMoh/CoLog", "project_page": "", "abstract": "Log anomaly detection is crucial for preserving the security of operating systems. Depending on the source of log data collection, various information is recorded in logs that can be considered log modalities. In light of this intuition, unimodal methods often struggle by ignoring the different modalities of log data. Meanwhile, multimodal methods fail to handle the interactions between these modalities. Applying multimodal sentiment analysis to log anomaly detection, we propose CoLog, a framework that collaboratively encodes logs utilizing various modalities. CoLog utilizes collaborative transformers and multi-head impressed attention to learn interactions among several modalities, ensuring comprehensive anomaly detection. To handle the heterogeneity caused by these interactions, CoLog incorporates a modality adaptation layer, which adapts the representations from different log modalities. This methodology enables CoLog to learn nuanced patterns and dependencies within the data, enhancing its anomaly detection capabilities. Extensive experiments demonstrate CoLog's superiority over existing state-of-the-art methods. Furthermore, in detecting both point and collective anomalies, CoLog achieves a mean precision of 99.63%, a mean recall of 99.59%, and a mean F1 score of 99.61% across seven benchmark datasets for log-based anomaly detection. The comprehensive detection capabilities of CoLog make it highly suitable for cybersecurity, system monitoring, and operational efficiency. CoLog represents a significant advancement in log anomaly detection, providing a sophisticated and effective solution to point and collective anomaly detection through a unified framework and a solution to the complex challenges automatic log data analysis poses. We also provide the implementation of CoLog at https://github.com/NasirzadehMoh/CoLog.", "github_manual": "", "github_regex": "https://github.com/NasirzadehMoh/CoLog", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-02", "arxiv_id": "2512.24617", "title": "Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space", "authors": [ "Xingwei Qu", "Shaowen Wang", "Zihao Huang", "Kai Hua", "Fan Yin", "Rui-Jie Zhu", "Jundong Zhou", "Qiyang Min", "Zihao Wang", "Yizhi Li", "Tianyu Zhang", "He Xing", "Zheng Zhang", "Yuxuan Song", "Tianyu Zheng", "Zhiyuan Zeng", "Chenghua Lin", "Ge Zhang", "Wenhao Huang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity on locally predictable spans while under-allocating computation to semantically critical transitions. We propose Dynamic Large Concept Models (DLCM), a hierarchical language modeling framework that learns semantic boundaries from latent representations and shifts computation from tokens to a compressed concept space where reasoning is more efficient. DLCM discovers variable-length concepts end-to-end without relying on predefined linguistic units. Hierarchical compression fundamentally changes scaling behavior. We introduce the first compression-aware scaling law, which disentangles token-level capacity, concept-level reasoning capacity, and compression ratio, enabling principled compute allocation under fixed FLOPs. To stably train this heterogeneous architecture, we further develop a decoupled \u03bcP parametrization that supports zero-shot hyperparameter transfer across widths and compression regimes. At a practical setting (R=4, corresponding to an average of four tokens per concept), DLCM reallocates roughly one-third of inference compute into a higher-capacity reasoning backbone, achieving a +2.69\\% average improvement across 12 zero-shot benchmarks under matched inference FLOPs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-02", "arxiv_id": "2512.22630", "title": "On the Role of Discreteness in Diffusion LLMs", "authors": [ "Ziqi Jin", "Bin Wang", "Xiang Lin", "Lidong Bing", "Aixin Sun" ], "github": "", "project_page": "", "abstract": "Diffusion models offer appealing properties for language generation, such as parallel decoding and iterative refinement, but the discrete and highly structured nature of text challenges the direct application of diffusion principles. In this paper, we revisit diffusion language modeling from the view of diffusion process and language modeling, and outline five properties that separate diffusion mechanics from language-specific requirements. We first categorize existing approaches into continuous diffusion in embedding space and discrete diffusion over tokens. We then show that each satisfies only part of the five essential properties and therefore reflects a structural trade-off. Through analyses of recent large diffusion language models, we identify two central issues: (i) uniform corruption does not respect how information is distributed across positions, and (ii) token-wise marginal training cannot capture multi-token dependencies during parallel decoding. These observations motivate diffusion processes that align more closely with the structure of text, and encourage future work toward more coherent diffusion language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-02", "arxiv_id": "2512.24165", "title": "DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models", "authors": [ "Zefeng He", "Xiaoye Qu", "Yafu Li", "Tong Zhu", "Siyuan Huang", "Yu Cheng" ], "github": "", "project_page": "", "abstract": "While recent Multimodal Large Language Models (MLLMs) have attained significant strides in multimodal reasoning, their reasoning processes remain predominantly text-centric, leading to suboptimal performance in complex long-horizon, vision-centric tasks. In this paper, we establish a novel Generative Multimodal Reasoning paradigm and introduce DiffThinker, a diffusion-based reasoning framework. Conceptually, DiffThinker reformulates multimodal reasoning as a native generative image-to-image task, achieving superior logical consistency and spatial precision in vision-centric tasks. We perform a systematic comparison between DiffThinker and MLLMs, providing the first in-depth investigation into the intrinsic characteristics of this paradigm, revealing four core properties: efficiency, controllability, native parallelism, and collaboration. Extensive experiments across four domains (sequential planning, combinatorial optimization, constraint satisfaction, and spatial configuration) demonstrate that DiffThinker significantly outperforms leading closed source models including GPT-5 (+314.2\\%) and Gemini-3-Flash (+111.6\\%), as well as the fine-tuned Qwen3-VL-32B baseline (+39.0\\%), highlighting generative multimodal reasoning as a promising approach for vision-centric reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-02", "arxiv_id": "2512.23959", "title": "Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational Modeling", "authors": [ "Chulun Zhou", "Chunkang Zhang", "Guoxin Yu", "Fandong Meng", "Jie Zhou", "Wai Lam", "Mo Yu" ], "github": "https://github.com/Encyclomen/HGMem", "project_page": "", "abstract": "Multi-step retrieval-augmented generation (RAG) has become a widely adopted strategy for enhancing large language models (LLMs) on tasks that demand global comprehension and intensive reasoning. Many RAG systems incorporate a working memory module to consolidate retrieved information. However, existing memory designs function primarily as passive storage that accumulates isolated facts for the purpose of condensing the lengthy inputs and generating new sub-queries through deduction. This static nature overlooks the crucial high-order correlations among primitive facts, the compositions of which can often provide stronger guidance for subsequent steps. Therefore, their representational strength and impact on multi-step reasoning and knowledge evolution are limited, resulting in fragmented reasoning and weak global sense-making capacity in extended contexts. We introduce HGMem, a hypergraph-based memory mechanism that extends the concept of memory beyond simple storage into a dynamic, expressive structure for complex reasoning and global understanding. In our approach, memory is represented as a hypergraph whose hyperedges correspond to distinct memory units, enabling the progressive formation of higher-order interactions within memory. This mechanism connects facts and thoughts around the focal problem, evolving into an integrated and situated knowledge structure that provides strong propositions for deeper reasoning in subsequent steps. We evaluate HGMem on several challenging datasets designed for global sense-making. Extensive experiments and in-depth analyses show that our method consistently improves multi-step RAG and substantially outperforms strong baseline systems across diverse tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Encyclomen/HGMem", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-02", "arxiv_id": "2512.24766", "title": "Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow", "authors": [ "Karthik Dharmarajan", "Wenlong Huang", "Jiajun Wu", "Li Fei-Fei", "Ruohan Zhang" ], "github": "", "project_page": "", "abstract": "Generative video modeling has emerged as a compelling tool to zero-shot reason about plausible physical interactions for open-world manipulation. Yet, it remains a challenge to translate such human-led motions into the low-level actions demanded by robotic systems. We observe that given an initial image and task instruction, these models excel at synthesizing sensible object motions. Thus, we introduce Dream2Flow, a framework that bridges video generation and robotic control through 3D object flow as an intermediate representation. Our method reconstructs 3D object motions from generated videos and formulates manipulation as object trajectory tracking. By separating the state changes from the actuators that realize those changes, Dream2Flow overcomes the embodiment gap and enables zero-shot guidance from pre-trained video models to manipulate objects of diverse categories-including rigid, articulated, deformable, and granular. Through trajectory optimization or reinforcement learning, Dream2Flow converts reconstructed 3D object flow into executable low-level commands without task-specific demonstrations. Simulation and real-world experiments highlight 3D object flow as a general and scalable interface for adapting video generation models to open-world robotic manipulation. Videos and visualizations are available at https://dream2flow.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-02", "arxiv_id": "2512.24724", "title": "FlowBlending: Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Video Generation", "authors": [ "Jibin Song", "Mingi Kwon", "Jaeseok Jeong", "Youngjung Uh" ], "github": "", "project_page": "", "abstract": "In this work, we show that the impact of model capacity varies across timesteps: it is crucial for the early and late stages but largely negligible during the intermediate stage. Accordingly, we propose FlowBlending, a stage-aware multi-model sampling strategy that employs a large model and a small model at capacity-sensitive stages and intermediate stages, respectively. We further introduce simple criteria to choose stage boundaries and provide a velocity-divergence analysis as an effective proxy for identifying capacity-sensitive regions. Across LTX-Video (2B/13B) and WAN 2.1 (1.3B/14B), FlowBlending achieves up to 1.65x faster inference with 57.35% fewer FLOPs, while maintaining the visual fidelity, temporal coherence, and semantic alignment of the large models. FlowBlending is also compatible with existing sampling-acceleration techniques, enabling up to 2x additional speedup. Project page is available at: https://jibin86.github.io/flowblending_project_page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-02", "arxiv_id": "2512.24007", "title": "TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems", "authors": [ "Bulent Soykan", "Sean Mondesire", "Ghaith Rabadi" ], "github": "https://github.com/bulentsoykan/TESO", "project_page": "", "abstract": "Simulation optimization (SO) is frequently challenged by noisy evaluations, high computational costs, and complex, multimodal search landscapes. This paper introduces Tabu-Enhanced Simulation Optimization (TESO), a novel metaheuristic framework integrating adaptive search with memory-based strategies. TESO leverages a short-term Tabu List to prevent cycling and encourage diversification, and a long-term Elite Memory to guide intensification by perturbing high-performing solutions. An aspiration criterion allows overriding tabu restrictions for exceptional candidates. This combination facilitates a dynamic balance between exploration and exploitation in stochastic environments. We demonstrate TESO's effectiveness and reliability using an queue optimization problem, showing improved performance compared to benchmarks and validating the contribution of its memory components. Source code and data are available at: https://github.com/bulentsoykan/TESO.", "github_manual": "", "github_regex": "https://github.com/bulentsoykan/TESO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2512.24330", "title": "SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning", "authors": [ "Yong Xien Chng", "Tao Hu", "Wenwen Tong", "Xueheng Li", "Jiandong Chen", "Haojia Yu", "Jiefan Lu", "Hewei Guo", "Hanming Deng", "Chengjun Xie", "Gao Huang", "Dahua Lin", "Lewei Lu" ], "github": "", "project_page": "", "abstract": "While Vision-Language Models (VLMs) can solve complex tasks through agentic reasoning, their capabilities remain largely constrained to text-oriented chain-of-thought or isolated tool invocation. They fail to exhibit the human-like proficiency required to seamlessly interleave dynamic tool manipulation with continuous reasoning, particularly in knowledge-intensive and visually complex scenarios that demand coordinated external tools such as search and image cropping. In this work, we introduce SenseNova-MARS, a novel Multimodal Agentic Reasoning and Search framework that empowers VLMs with interleaved visual reasoning and tool-use capabilities via reinforcement learning (RL). Specifically, SenseNova-MARS dynamically integrates the image search, text search, and image crop tools to tackle fine-grained and knowledge-intensive visual understanding challenges. In the RL stage, we propose the Batch-Normalized Group Sequence Policy Optimization (BN-GSPO) algorithm to improve the training stability and advance the model's ability to invoke tools and reason effectively. To comprehensively evaluate the agentic VLMs on complex visual tasks, we introduce the HR-MMSearch benchmark, the first search-oriented benchmark composed of high-resolution images with knowledge-intensive and search-driven questions. Experiments demonstrate that SenseNova-MARS achieves state-of-the-art performance on open-source search and fine-grained image understanding benchmarks. Specifically, on search-oriented benchmarks, SenseNova-MARS-8B scores 67.84 on MMSearch and 41.64 on HR-MMSearch, surpassing proprietary models such as Gemini-3-Flash and GPT-5. SenseNova-MARS represents a promising step toward agentic VLMs by providing effective and robust tool-use capabilities. To facilitate further research in this field, we will release all code, models, and datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2601.00417", "title": "Deep Delta Learning", "authors": [ "Yifan Zhang", "Yifeng Liu", "Mengdi Wang", "Quanquan Gu" ], "github": "https://github.com/yifanzhang-pro/deep-delta-learning", "project_page": "", "abstract": "The efficacy of deep residual networks is fundamentally predicated on the identity shortcut connection. While this mechanism effectively mitigates the vanishing gradient problem, it imposes a strictly additive inductive bias on feature transformations, thereby limiting the network's capacity to model complex state transitions. In this paper, we introduce Deep Delta Learning (DDL), a novel architecture that generalizes the standard residual connection by modulating the identity shortcut with a learnable, data-dependent geometric transformation. This transformation, termed the Delta Operator, constitutes a rank-1 perturbation of the identity matrix, parameterized by a reflection direction vector k(X) and a gating scalar \u03b2(X). We provide a spectral analysis of this operator, demonstrating that the gate \u03b2(X) enables dynamic interpolation between identity mapping, orthogonal projection, and geometric reflection. Furthermore, we restructure the residual update as a synchronous rank-1 injection, where the gate acts as a dynamic step size governing both the erasure of old information and the writing of new features. This unification empowers the network to explicitly control the spectrum of its layer-wise transition operator, enabling the modeling of complex, non-monotonic dynamics while preserving the stable training characteristics of gated residual architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yifanzhang-pro/deep-delta-learning", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2601.00671", "title": "Fast-weight Product Key Memory", "authors": [ "Tianyu Zhao", "Llion Jones" ], "github": "https://github.com/a1600012888/LaCT/tree/main/lact_llm", "project_page": "", "abstract": "Sequence modeling layers in modern language models typically face a trade-off between storage capacity and computational efficiency. While Softmax attention offers unbounded storage at prohibitive quadratic costs, linear variants provide efficiency but suffer from limited, fixed-size storage. We propose Fast-weight Product Key Memory (FwPKM), a novel architecture that resolves this tension by transforming the sparse Product Key Memory (PKM) from a static module into a dynamic, \"fast-weight\" episodic memory. Unlike PKM, FwPKM updates its parameters dynamically at both training and inference time via local chunk-level gradient descent, allowing the model to rapidly memorize and retrieve new key-value pairs from input sequences. Experiments reveal that FwPKM functions as an effective episodic memory that complements the semantic memory of standard modules, yielding significant perplexity reductions on long-context datasets. Notably, in Needle in a Haystack evaluations, FwPKM generalizes to 128K-token contexts despite being trained on only 4K-token sequences.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/a1600012888/LaCT/tree/main/lact_llm", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2601.00664", "title": "Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation", "authors": [ "Taekyung Ki", "Sangwon Jang", "Jaehyeong Jo", "Jaehong Yoon", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation. However, current models do not yet convey the feeling of truly interactive communication, often generating one-way responses that lack emotional engagement. We identify two key challenges toward truly interactive avatars: generating motion in real-time under causal constraints and learning expressive, vibrant reactions without additional labeled data. To address these challenges, we propose Avatar Forcing, a new framework for interactive head avatar generation that models real-time user-avatar interactions through diffusion forcing. This design allows the avatar to process real-time multimodal inputs, including the user's audio and motion, with low latency for instant reactions to both verbal and non-verbal cues such as speech, nods, and laughter. Furthermore, we introduce a direct preference optimization method that leverages synthetic losing samples constructed by dropping user conditions, enabling label-free learning of expressive interaction. Experimental results demonstrate that our framework enables real-time interaction with low latency (approximately 500ms), achieving 6.8X speedup compared to the baseline, and produces reactive and expressive avatar motion, which is preferred over 80% against the baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2512.24615", "title": "Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization", "authors": [ "Yuchen Shi", "Yuzheng Cai", "Siqi Cai", "Zihan Xu", "Lichao Chen", "Yulei Qin", "Zhijian Zhou", "Xiang Fei", "Chaofan Qiu", "Xiaoyu Tan", "Gang Li", "Zongyi Li", "Haojia Lin", "Guocan Cai", "Yong Mao", "Yunsheng Wu", "Ke Li", "Xing Sun" ], "github": "", "project_page": "", "abstract": "Existing Large Language Model (LLM) agent frameworks face two significant challenges: high configuration costs and static capabilities. Building a high-quality agent often requires extensive manual effort in tool integration and prompt engineering, while deployed agents struggle to adapt to dynamic environments without expensive fine-tuning. To address these issues, we propose Youtu-Agent, a modular framework designed for the automated generation and continuous evolution of LLM agents. Youtu-Agent features a structured configuration system that decouples execution environments, toolkits, and context management, enabling flexible reuse and automated synthesis. We introduce two generation paradigms: a Workflow mode for standard tasks and a Meta-Agent mode for complex, non-standard requirements, capable of automatically generating tool code, prompts, and configurations. Furthermore, Youtu-Agent establishes a hybrid policy optimization system: (1) an Agent Practice module that enables agents to accumulate experience and improve performance through in-context optimization without parameter updates; and (2) an Agent RL module that integrates with distributed training frameworks to enable scalable and stable reinforcement learning of any Youtu-Agents in an end-to-end, large-scale manner. Experiments demonstrate that Youtu-Agent achieves state-of-the-art performance on WebWalkerQA (71.47\\%) and GAIA (72.8\\%) using open-weight models. Our automated generation pipeline achieves over 81\\% tool synthesis success rate, while the Practice module improves performance on AIME 2024/2025 by +2.7\\% and +5.4\\% respectively. Moreover, our Agent RL training achieves 40\\% speedup with steady performance improvement on 7B LLMs, enhancing coding/reasoning and searching capabilities respectively up to 35\\% and 21\\% on Maths and general/multi-hop QA benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2512.24271", "title": "Taming Hallucinations: Boosting MLLMs' Video Understanding via Counterfactual Video Generation", "authors": [ "Zhe Huang", "Hao Wen", "Aiming Hao", "Bingze Song", "Meiqi Wu", "Jiahong Wu", "Xiangxiang Chu", "Sheng Lu", "Haoqian Wang" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have made remarkable progress in video understanding. However, they suffer from a critical vulnerability: an over-reliance on language priors, which can lead to visual ungrounded hallucinations, especially when processing counterfactual videos that defy common sense. This limitation, stemming from the intrinsic data imbalance between text and video, is challenging to address due to the substantial cost of collecting and annotating counterfactual data. To address this, we introduce DualityForge, a novel counterfactual data synthesis framework that employs controllable, diffusion-based video editing to transform real-world videos into counterfactual scenarios. By embedding structured contextual information into the video editing and QA generation processes, the framework automatically produces high-quality QA pairs together with original-edited video pairs for contrastive training. Based on this, we build DualityVidQA, a large-scale video dataset designed to reduce MLLM hallucinations. In addition, to fully exploit the contrastive nature of our paired data, we propose Duality-Normalized Advantage Training (DNA-Train), a two-stage SFT-RL training regime where the RL phase applies pair-wise ell_1 advantage normalization, thereby enabling a more stable and efficient policy optimization. Experiments on DualityVidQA-Test demonstrate that our method substantially reduces model hallucinations on counterfactual videos, yielding a relative improvement of 24.0% over the Qwen2.5-VL-7B baseline. Moreover, our approach achieves significant gains across both hallucination and general-purpose benchmarks, indicating strong generalization capability. We will open-source our dataset and code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2601.00393", "title": "NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos", "authors": [ "Yuxue Yang", "Lue Fan", "Ziqi Shi", "Junran Peng", "Feng Wang", "Zhaoxiang Zhang" ], "github": "", "project_page": "", "abstract": "In this paper, we propose NeoVerse, a versatile 4D world model that is capable of 4D reconstruction, novel-trajectory video generation, and rich downstream applications. We first identify a common limitation of scalability in current 4D world modeling methods, caused either by expensive and specialized multi-view 4D data or by cumbersome training pre-processing. In contrast, our NeoVerse is built upon a core philosophy that makes the full pipeline scalable to diverse in-the-wild monocular videos. Specifically, NeoVerse features pose-free feed-forward 4D reconstruction, online monocular degradation pattern simulation, and other well-aligned techniques. These designs empower NeoVerse with versatility and generalization to various domains. Meanwhile, NeoVerse achieves state-of-the-art performance in standard reconstruction and generation benchmarks. Our project page is available at https://neoverse-4d.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2601.00204", "title": "MorphAny3D: Unleashing the Power of Structured Latent in 3D Morphing", "authors": [ "Xiaokun Sun", "Zeyu Cai", "Hao Tang", "Ying Tai", "Jian Yang", "Zhenyu Zhang" ], "github": "", "project_page": "", "abstract": "3D morphing remains challenging due to the difficulty of generating semantically consistent and temporally smooth deformations, especially across categories. We present MorphAny3D, a training-free framework that leverages Structured Latent (SLAT) representations for high-quality 3D morphing. Our key insight is that intelligently blending source and target SLAT features within the attention mechanisms of 3D generators naturally produces plausible morphing sequences. To this end, we introduce Morphing Cross-Attention (MCA), which fuses source and target information for structural coherence, and Temporal-Fused Self-Attention (TFSA), which enhances temporal consistency by incorporating features from preceding frames. An orientation correction strategy further mitigates the pose ambiguity within the morphing steps. Extensive experiments show that our method generates state-of-the-art morphing sequences, even for challenging cross-category cases. MorphAny3D further supports advanced applications such as decoupled morphing and 3D style transfer, and can be generalized to other SLAT-based generative models. Project page: https://xiaokunsun.github.io/MorphAny3D.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2512.24695", "title": "Nested Learning: The Illusion of Deep Learning Architectures", "authors": [ "Ali Behrouz", "Meisam Razaviyayn", "Peilin Zhong", "Vahab Mirrokni" ], "github": "", "project_page": "", "abstract": "Despite the recent progresses, particularly in developing Language Models, there are fundamental challenges and unanswered questions about how such models can continually learn/memorize, self-improve, and find effective solutions. In this paper, we present a new learning paradigm, called Nested Learning (NL), that coherently represents a machine learning model with a set of nested, multi-level, and/or parallel optimization problems, each of which with its own context flow. Through the lenses of NL, existing deep learning methods learns from data through compressing their own context flow, and in-context learning naturally emerges in large models. NL suggests a philosophy to design more expressive learning algorithms with more levels, resulting in higher-order in-context learning and potentially unlocking effective continual learning capabilities. We advocate for NL by presenting three core contributions: (1) Expressive Optimizers: We show that known gradient-based optimizers, such as Adam, SGD with Momentum, etc., are in fact associative memory modules that aim to compress the gradients' information (by gradient descent). Building on this insight, we present other more expressive optimizers with deep memory and/or more powerful learning rules; (2) Self-Modifying Learning Module: Taking advantage of NL's insights on learning algorithms, we present a sequence model that learns how to modify itself by learning its own update algorithm; and (3) Continuum Memory System: We present a new formulation for memory system that generalizes the traditional viewpoint of long/short-term memory. Combining our self-modifying sequence model with the continuum memory system, we present a continual learning module, called Hope, showing promising results in language modeling, knowledge incorporation, and few-shot generalization tasks, continual learning, and long-context reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2601.00796", "title": "AdaGaR: Adaptive Gabor Representation for Dynamic Scene Reconstruction", "authors": [ "Jiewen Chan", "Zhenjun Zhao", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Reconstructing dynamic 3D scenes from monocular videos requires simultaneously capturing high-frequency appearance details and temporally continuous motion. Existing methods using single Gaussian primitives are limited by their low-pass filtering nature, while standard Gabor functions introduce energy instability. Moreover, lack of temporal continuity constraints often leads to motion artifacts during interpolation. We propose AdaGaR, a unified framework addressing both frequency adaptivity and temporal continuity in explicit dynamic scene modeling. We introduce Adaptive Gabor Representation, extending Gaussians through learnable frequency weights and adaptive energy compensation to balance detail capture and stability. For temporal continuity, we employ Cubic Hermite Splines with Temporal Curvature Regularization to ensure smooth motion evolution. An Adaptive Initialization mechanism combining depth estimation, point tracking, and foreground masks establishes stable point cloud distributions in early training. Experiments on Tap-Vid DAVIS demonstrate state-of-the-art performance (PSNR 35.49, SSIM 0.9433, LPIPS 0.0723) and strong generalization across frame interpolation, depth consistency, video editing, and stereo view synthesis. Project page: https://jiewenchan.github.io/AdaGaR/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2601.00575", "title": "InfoSynth: Information-Guided Benchmark Synthesis for LLMs", "authors": [ "Ishir Garg", "Neel Kolhe", "Xuandong Zhao", "Dawn Song" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated significant advancements in reasoning and code generation. However, efficiently creating new benchmarks to evaluate these capabilities remains a challenge. Traditional benchmark creation relies on manual human effort, a process that is both expensive and time-consuming. Furthermore, existing benchmarks often contaminate LLM training data, necessitating novel and diverse benchmarks to accurately assess their genuine capabilities. This work introduces InfoSynth, a novel framework for automatically generating and evaluating reasoning benchmarks guided by information-theoretic principles. We propose metrics based on KL-divergence and entropy to quantify benchmark novelty and diversity without relying on costly model evaluations. Building on this framework, we develop an end-to-end pipeline that synthesizes robust Python coding problems from seed datasets using genetic algorithms and iterative code feedback. Our method generates accurate test cases and solutions to new problems 97% of the time, and the synthesized benchmarks consistently exhibit higher novelty and diversity compared to their seed datasets. Moreover, our algorithm provides a method for controlling the novelty/diversity and difficulty of generated problems. InfoSynth offers a scalable, self-verifying pipeline for constructing high-quality, novel and diverse benchmarks for LLMs. Project Page: https://ishirgarg.github.io/infosynth_web/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2512.22955", "title": "Diversity or Precision? A Deep Dive into Next Token Prediction", "authors": [ "Haoyuan Wu", "Hai Wang", "Jiajia Wu", "Jinxiang Ou", "Keyao Wang", "Weile Chen", "Zihao Zheng", "Bei Yu" ], "github": "", "project_page": "", "abstract": "Recent advancements have shown that reinforcement learning (RL) can substantially improve the reasoning abilities of large language models (LLMs). The effectiveness of such RL training, however, depends critically on the exploration space defined by the pre-trained model's token-output distribution. In this paper, we revisit the standard cross-entropy loss, interpreting it as a specific instance of policy gradient optimization applied within a single-step episode. To systematically study how the pre-trained distribution shapes the exploration potential for subsequent RL, we propose a generalized pre-training objective that adapts on-policy RL principles to supervised learning. By framing next-token prediction as a stochastic decision process, we introduce a reward-shaping strategy that explicitly balances diversity and precision. Our method employs a positive reward scaling factor to control probability concentration on ground-truth tokens and a rank-aware mechanism that treats high-ranking and low-ranking negative tokens asymmetrically. This allows us to reshape the pre-trained token-output distribution and investigate how to provide a more favorable exploration space for RL, ultimately enhancing end-to-end reasoning performance. Contrary to the intuition that higher distribution entropy facilitates effective exploration, we find that imposing a precision-oriented prior yields a superior exploration space for RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2601.00747", "title": "The Reasoning-Creativity Trade-off: Toward Creativity-Driven Problem Solving", "authors": [ "Max Ruiz Luyten", "Mihaela van der Schaar" ], "github": "", "project_page": "", "abstract": "State-of-the-art large language model (LLM) pipelines rely on bootstrapped reasoning loops: sampling diverse chains of thought and reinforcing the highest-scoring ones, mainly optimizing correctness. We analyze how this design choice is sensitive to the collapse of the model's distribution over reasoning paths, slashing semantic entropy and undermining creative problem-solving. To analyze this failure, we introduce Distributional Creative Reasoning (DCR), a unified variational objective that casts training as gradient flow through probability measures on solution traces. STaR, GRPO, and DPO, as well as entropy bonuses, and other methods, all constitute special cases of the same loss. The framework delivers three core results: (i) the diversity decay theorem, describing how correctness-based objectives lead to distinct modes of diversity decay for STaR, GRPO, and DPO; (ii) designs that ensure convergence to a stable and diverse policy, effectively preventing collapse; and (iii) simple, actionable recipes to achieve this in practice. DCR thus offers the first principled recipe for LLMs that remain both correct and creative.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2512.24146", "title": "Taming Preference Mode Collapse via Directional Decoupling Alignment in Diffusion Reinforcement Learning", "authors": [ "Chubin Chen", "Sujie Hu", "Jiashu Zhu", "Meiqi Wu", "Jintao Chen", "Yanxun Li", "Nisha Huang", "Chengyu Fang", "Jiahong Wu", "Xiangxiang Chu", "Xiu Li" ], "github": "", "project_page": "", "abstract": "Recent studies have demonstrated significant progress in aligning text-to-image diffusion models with human preference via Reinforcement Learning from Human Feedback. However, while existing methods achieve high scores on automated reward metrics, they often lead to Preference Mode Collapse (PMC)-a specific form of reward hacking where models converge on narrow, high-scoring outputs (e.g., images with monolithic styles or pervasive overexposure), severely degrading generative diversity. In this work, we introduce and quantify this phenomenon, proposing DivGenBench, a novel benchmark designed to measure the extent of PMC. We posit that this collapse is driven by over-optimization along the reward model's inherent biases. Building on this analysis, we propose Directional Decoupling Alignment (D^2-Align), a novel framework that mitigates PMC by directionally correcting the reward signal. Specifically, our method first learns a directional correction within the reward model's embedding space while keeping the model frozen. This correction is then applied to the reward signal during the optimization process, preventing the model from collapsing into specific modes and thereby maintaining diversity. Our comprehensive evaluation, combining qualitative analysis with quantitative metrics for both quality and diversity, reveals that D^2-Align achieves superior alignment with human preference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-05", "arxiv_id": "2601.00830", "title": "Can We Trust AI Explanations? Evidence of Systematic Underreporting in Chain-of-Thought Reasoning", "authors": [ "Deep Pankajbhai Mehta" ], "github": "https://github.com/researchaudio/cot-experiments", "project_page": "", "abstract": "When AI systems explain their reasoning step-by-step, practitioners often assume these explanations reveal what actually influenced the AI's answer. We tested this assumption by embedding hints into questions and measuring whether models mentioned them. In a study of over 9,000 test cases across 11 leading AI models, we found a troubling pattern: models almost never mention hints spontaneously, yet when asked directly, they admit noticing them. This suggests models see influential information but choose not to report it. Telling models they are being watched does not help. Forcing models to report hints works, but causes them to report hints even when none exist and reduces their accuracy. We also found that hints appealing to user preferences are especially dangerous-models follow them most often while reporting them least. These findings suggest that simply watching AI reasoning is not enough to catch hidden influences.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/researchaudio/cot-experiments", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.01425", "title": "DreamID-V:Bridging the Image-to-Video Gap for High-Fidelity Face Swapping via Diffusion Transformer", "authors": [ "Xu Guo", "Fulong Ye", "Xinghui Li", "Pengqi Tu", "Pengze Zhang", "Qichao Sun", "Songtao Zhao", "Xiangwang Hou", "Qian He" ], "github": "", "project_page": "", "abstract": "Video Face Swapping (VFS) requires seamlessly injecting a source identity into a target video while meticulously preserving the original pose, expression, lighting, background, and dynamic information. Existing methods struggle to maintain identity similarity and attribute preservation while preserving temporal consistency. To address the challenge, we propose a comprehensive framework to seamlessly transfer the superiority of Image Face Swapping (IFS) to the video domain. We first introduce a novel data pipeline SyncID-Pipe that pre-trains an Identity-Anchored Video Synthesizer and combines it with IFS models to construct bidirectional ID quadruplets for explicit supervision. Building upon paired data, we propose the first Diffusion Transformer-based framework DreamID-V, employing a core Modality-Aware Conditioning module to discriminatively inject multi-model conditions. Meanwhile, we propose a Synthetic-to-Real Curriculum mechanism and an Identity-Coherence Reinforcement Learning strategy to enhance visual realism and identity consistency under challenging scenarios. To address the issue of limited benchmarks, we introduce IDBench-V, a comprehensive benchmark encompassing diverse scenes. Extensive experiments demonstrate DreamID-V outperforms state-of-the-art methods and further exhibits exceptional versatility, which can be seamlessly adapted to various swap-related tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.02204", "title": "NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation", "authors": [ "Huichao Zhang", "Liao Qu", "Yiheng Liu", "Hang Chen", "Yangyang Song", "Yongsheng Dong", "Shikun Sun", "Xian Li", "Xu Wang", "Yi Jiang", "Hu Ye", "Bo Chen", "Yiming Gao", "Peng Liu", "Akide Liu", "Zhipeng Yang", "Qili Deng", "Linjie Xing", "Jiyang Liu", "Zhao Wang", "Yang Zhou", "Mingcong Liu", "Yi Zhang", "Qian He", "Xiwei Hu", "Zhongqi Qi", "Jie Shao", "Zhiye Fu", "Shuai Wang", "Fangmin Chen", "Xuezhi Chai", "Zhihua Wu", "Yitong Wang", "Zehuan Yuan", "Daniel K. Du", "Xinglong Wu" ], "github": "", "project_page": "", "abstract": "We present NextFlow, a unified decoder-only autoregressive transformer trained on 6 trillion interleaved text-image discrete tokens. By leveraging a unified vision representation within a unified autoregressive architecture, NextFlow natively activates multimodal understanding and generation capabilities, unlocking abilities of image editing, interleaved content and video generation. Motivated by the distinct nature of modalities - where text is strictly sequential and images are inherently hierarchical - we retain next-token prediction for text but adopt next-scale prediction for visual generation. This departs from traditional raster-scan methods, enabling the generation of 1024x1024 images in just 5 seconds - orders of magnitude faster than comparable AR models. We address the instabilities of multi-scale generation through a robust training recipe. Furthermore, we introduce a prefix-tuning strategy for reinforcement learning. Experiments demonstrate that NextFlow achieves state-of-the-art performance among unified models and rivals specialized diffusion baselines in visual quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.02256", "title": "VAR RL Done Right: Tackling Asynchronous Policy Conflicts in Visual Autoregressive Generation", "authors": [ "Shikun Sun", "Liao Qu", "Huichao Zhang", "Yiheng Liu", "Yangyang Song", "Xian Li", "Xu Wang", "Yi Jiang", "Daniel K. Du", "Xinglong Wu", "Jia Jia" ], "github": "", "project_page": "", "abstract": "Visual generation is dominated by three paradigms: AutoRegressive (AR), diffusion, and Visual AutoRegressive (VAR) models. Unlike AR and diffusion, VARs operate on heterogeneous input structures across their generation steps, which creates severe asynchronous policy conflicts. This issue becomes particularly acute in reinforcement learning (RL) scenarios, leading to unstable training and suboptimal alignment. To resolve this, we propose a novel framework to enhance Group Relative Policy Optimization (GRPO) by explicitly managing these conflicts. Our method integrates three synergistic components: 1) a stabilizing intermediate reward to guide early-stage generation; 2) a dynamic time-step reweighting scheme for precise credit assignment; and 3) a novel mask propagation algorithm, derived from principles of Reward Feedback Learning (ReFL), designed to isolate optimization effects both spatially and temporally. Our approach demonstrates significant improvements in sample quality and objective alignment over the vanilla GRPO baseline, enabling robust and effective optimization for VAR models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2512.20578", "title": "Can LLMs Predict Their Own Failures? Self-Awareness via Internal Circuits", "authors": [ "Amirhosein Ghasemabadi", "Di Niu" ], "github": "https://github.com/Amirhosein-gh98/Gnosis", "project_page": "", "abstract": "Large language models (LLMs) generate fluent and complex outputs but often fail to recognize their own mistakes and hallucinations. Existing approaches typically rely on external judges, multi-sample consistency, or text-based self-critique, which incur additional compute or correlate weakly with true correctness. We ask: can LLMs predict their own failures by inspecting internal states during inference? We introduce Gnosis, a lightweight self-awareness mechanism that enables frozen LLMs to perform intrinsic self-verification by decoding signals from hidden states and attention patterns. Gnosis passively observes internal traces, compresses them into fixed-budget descriptors, and predicts correctness with negligible inference cost, adding only ~5M parameters and operating independently of sequence length. Across math reasoning, open-domain question answering, and academic knowledge benchmarks, and over frozen backbones ranging from 1.7B to 20B parameters, Gnosis consistently outperforms strong internal baselines and large external judges in both accuracy and calibration. Moreover, it generalizes zero-shot to partial generations, enabling early detection of failing trajectories and compute-aware control. These results show that reliable correctness cues are intrinsic to generation process and can be extracted efficiently without external supervision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Amirhosein-gh98/Gnosis", "github_api_added_by": "auto" }, { "date": "2026-01-06", "arxiv_id": "2601.01046", "title": "KV-Embedding: Training-free Text Embedding via Internal KV Re-routing in Decoder-only LLMs", "authors": [ "Yixuan Tang", "Yi Yang" ], "github": "", "project_page": "", "abstract": "While LLMs are powerful embedding backbones, their application in training-free settings faces two structural challenges: causal attention restricts early tokens from accessing subsequent context, and the next-token prediction objective biases representations toward generation rather than semantic compression. To address these limitations, we propose KV-Embedding, a framework that activates the latent representation power of frozen LLMs. Our method leverages the observation that the key-value (KV) states of the final token at each layer encode a compressed view of the sequence. By re-routing these states as a prepended prefix, we enable all tokens to access sequence-level context within a single forward pass. To ensure model-agnostic applicability, we introduce an automated layer selection strategy based on intrinsic dimensionality. Evaluations on MTEB across Qwen, Mistral, and Llama backbones show that KV-Embedding outperforms existing training-free baselines by up to 10%, while maintaining robust performance on sequences up to 4,096 tokens. These results demonstrate that internal state manipulation offers an efficient alternative to input modification, and we hope this work encourages further exploration of LLM internals for representation learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.02358", "title": "VINO: A Unified Visual Generator with Interleaved OmniModal Context", "authors": [ "Junyi Chen", "Tong He", "Zhoujie Fu", "Pengfei Wan", "Kun Gai", "Weicai Ye" ], "github": "", "project_page": "", "abstract": "We present VINO, a unified visual generator that performs image and video generation and editing within a single framework. Instead of relying on task-specific models or independent modules for each modality, VINO uses a shared diffusion backbone that conditions on text, images and videos, enabling a broad range of visual creation and editing tasks under one model. Specifically, VINO couples a vision-language model (VLM) with a Multimodal Diffusion Transformer (MMDiT), where multimodal inputs are encoded as interleaved conditioning tokens, and then used to guide the diffusion process. This design supports multi-reference grounding, long-form instruction following, and coherent identity preservation across static and dynamic content, while avoiding modality-specific architectural components. To train such a unified system, we introduce a multi-stage training pipeline that progressively expands a video generation base model into a unified, multi-task generator capable of both image and video input and output. Across diverse generation and editing benchmarks, VINO demonstrates strong visual quality, faithful instruction following, improved reference and attribute preservation, and more controllable multi-identity edits. Our results highlight a practical path toward scalable unified visual generation, and the promise of interleaved, in-context computation as a foundation for general-purpose visual creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.01739", "title": "K-EXAONE Technical Report", "authors": [ "Eunbi Choi", "Kibong Choi", "Seokhee Hong", "Junwon Hwang", "Hyojin Jeon", "Hyunjik Jo", "Joonkee Kim", "Seonghwan Kim", "Soyeon Kim", "Sunkyoung Kim", "Yireun Kim", "Yongil Kim", "Haeju Lee", "Jinsik Lee", "Kyungmin Lee", "Sangha Park", "Heuiyeen Yeen", "Hwan Chang", "Stanley Jungkyu Choi", "Yejin Choi", "Jiwon Ham", "Kijeong Jeon", "Geunyeong Jeong", "Gerrard Jeongwon Jo", "Yonghwan Jo", "Jiyeon Jung", "Naeun Kang", "Dohoon Kim", "Euisoon Kim", "Hayeon Kim", "Hyosang Kim", "Hyunseo Kim", "Jieun Kim", "Minu Kim", "Myoungshin Kim", "Unsol Kim", "Youchul Kim", "YoungJin Kim", "Chaeeun Lee", "Chaeyoon Lee", "Changhun Lee", "Dahm Lee", "Edward Hwayoung Lee", "Honglak Lee", "Jinsang Lee", "Jiyoung Lee", "Sangeun Lee", "Seungwon Lim", "Solji Lim", "Woohyung Lim", "Chanwoo Moon", "Jaewoo Park", "Jinho Park", "Yongmin Park", "Hyerin Seo", "Wooseok Seo", "Yongwoo Song", "Sejong Yang", "Sihoon Yang", "Chang En Yea", "Sihyuk Yi", "Chansik Yoon", "Dongkeun Yoon", "Sangyeon Yoon", "Hyeongu Yun" ], "github": "", "project_page": "", "abstract": "This technical report presents K-EXAONE, a large-scale multilingual language model developed by LG AI Research. K-EXAONE is built on a Mixture-of-Experts architecture with 236B total parameters, activating 23B parameters during inference. It supports a 256K-token context window and covers six languages: Korean, English, Spanish, German, Japanese, and Vietnamese. We evaluate K-EXAONE on a comprehensive benchmark suite spanning reasoning, agentic, general, Korean, and multilingual abilities. Across these evaluations, K-EXAONE demonstrates performance comparable to open-weight models of similar size. K-EXAONE, designed to advance AI for a better life, is positioned as a powerful proprietary AI foundation model for a wide range of industrial and research applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.02356", "title": "Talk2Move: Reinforcement Learning for Text-Instructed Object-Level Geometric Transformation in Scenes", "authors": [ "Jing Tan", "Zhaoyang Zhang", "Yantao Shen", "Jiarui Cai", "Shuo Yang", "Jiajun Wu", "Wei Xia", "Zhuowen Tu", "Stefano Soatto" ], "github": "", "project_page": "", "abstract": "We introduce Talk2Move, a reinforcement learning (RL) based diffusion framework for text-instructed spatial transformation of objects within scenes. Spatially manipulating objects in a scene through natural language poses a challenge for multimodal generation systems. While existing text-based manipulation methods can adjust appearance or style, they struggle to perform object-level geometric transformations-such as translating, rotating, or resizing objects-due to scarce paired supervision and pixel-level optimization limits. Talk2Move employs Group Relative Policy Optimization (GRPO) to explore geometric actions through diverse rollouts generated from input images and lightweight textual variations, removing the need for costly paired data. A spatial reward guided model aligns geometric transformations with linguistic description, while off-policy step evaluation and active step sampling improve learning efficiency by focusing on informative transformation stages. Furthermore, we design object-centric spatial rewards that evaluate displacement, rotation, and scaling behaviors directly, enabling interpretable and coherent transformations. Experiments on curated benchmarks demonstrate that Talk2Move achieves precise, consistent, and semantically faithful object transformations, outperforming existing text-guided editing approaches in both spatial accuracy and scene coherence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.02346", "title": "Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling", "authors": [ "Falcon LLM Team", "Iheb Chaabane", "Puneesh Khanna", "Suhail Mohmad", "Slim Frikha", "Shi Hu", "Abdalgader Abubaker", "Reda Alami", "Mikhail Lubinets", "Mohamed El Amine Seddik", "Hakim Hacid" ], "github": "https://github.com/huggingface/Math-Verify", "project_page": "", "abstract": "This work introduces Falcon-H1R, a 7B-parameter reasoning-optimized model that establishes the feasibility of achieving competitive reasoning performance with small language models (SLMs). Falcon-H1R stands out for its parameter efficiency, consistently matching or outperforming SOTA reasoning models that are 2times to 7times larger across a variety of reasoning-intensive benchmarks. These results underscore the importance of careful data curation and targeted training strategies (via both efficient SFT and RL scaling) in delivering significant performance gains without increasing model size. Furthermore, Falcon-H1R advances the 3D limits of reasoning efficiency by combining faster inference (through its hybrid-parallel architecture design), token efficiency, and higher accuracy. This unique blend makes Falcon-H1R-7B a practical backbone for scaling advanced reasoning systems, particularly in scenarios requiring extensive chain-of-thoughts generation and parallel test-time scaling. Leveraging the recently introduced DeepConf approach, Falcon-H1R achieves state-of-the-art test-time scaling efficiency, offering substantial improvements in both accuracy and computational cost. As a result, Falcon-H1R demonstrates that compact models, through targeted model training and architectural choices, can deliver robust and scalable reasoning performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/Math-Verify", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.01576", "title": "OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment", "authors": [ "Ming Zhang", "Kexin Tan", "Yueyuan Huang", "Yujiong Shen", "Chunchun Ma", "Li Ju", "Xinran Zhang", "Yuhui Wang", "Wenqing Jing", "Jingyi Deng", "Huayu Sha", "Binze Hu", "Jingqi Tong", "Changhao Jiang", "Yage Geng", "Yuankai Ying", "Yue Zhang", "Zhangyue Yin", "Zhiheng Xi", "Shihan Dou", "Tao Gui", "Qi Zhang", "Xuanjing Huang" ], "github": "", "project_page": "", "abstract": "Evaluating novelty is critical yet challenging in peer review, as reviewers must assess submissions against a vast, rapidly evolving literature. This report presents OpenNovelty, an LLM-powered agentic system for transparent, evidence-based novelty analysis. The system operates through four phases: (1) extracting the core task and contribution claims to generate retrieval queries; (2) retrieving relevant prior work based on extracted queries via semantic search engine; (3) constructing a hierarchical taxonomy of core-task-related work and performing contribution-level full-text comparisons against each contribution; and (4) synthesizing all analyses into a structured novelty report with explicit citations and evidence snippets. Unlike naive LLM-based approaches, OpenNovelty grounds all assessments in retrieved real papers, ensuring verifiable judgments. We deploy our system on 500+ ICLR 2026 submissions with all reports publicly available on our website, and preliminary analysis suggests it can identify relevant prior work, including closely related papers that authors may overlook. OpenNovelty aims to empower the research community with a scalable tool that promotes fair, consistent, and evidence-backed peer review.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.01836", "title": "COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs", "authors": [ "Dasol Choi", "DongGeon Lee", "Brigitta Jesica Kartono", "Helena Berndt", "Taeyoun Kwon", "Joonwon Jang", "Haon Park", "Hwanjo Yu", "Minsuk Kahng" ], "github": "", "project_page": "", "abstract": "As large language models are deployed in high-stakes enterprise applications, from healthcare to finance, ensuring adherence to organization-specific policies has become essential. Yet existing safety evaluations focus exclusively on universal harms. We present COMPASS (Company/Organization Policy Alignment Assessment), the first systematic framework for evaluating whether LLMs comply with organizational allowlist and denylist policies. We apply COMPASS to eight diverse industry scenarios, generating and validating 5,920 queries that test both routine compliance and adversarial robustness through strategically designed edge cases. Evaluating seven state-of-the-art models, we uncover a fundamental asymmetry: models reliably handle legitimate requests (>95% accuracy) but catastrophically fail at enforcing prohibitions, refusing only 13-40% of adversarial denylist violations. These results demonstrate that current LLMs lack the robustness required for policy-critical deployments, establishing COMPASS as an essential evaluation framework for organizational AI safety.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.02314", "title": "Project Ariadne: A Structural Causal Framework for Auditing Faithfulness in LLM Agents", "authors": [ "Sourena Khanzadeh" ], "github": "", "project_page": "", "abstract": "As Large Language Model (LLM) agents are increasingly tasked with high-stakes autonomous decision-making, the transparency of their reasoning processes has become a critical safety concern. While Chain-of-Thought (CoT) prompting allows agents to generate human-readable reasoning traces, it remains unclear whether these traces are faithful generative drivers of the model's output or merely post-hoc rationalizations. We introduce Project Ariadne, a novel XAI framework that utilizes Structural Causal Models (SCMs) and counterfactual logic to audit the causal integrity of agentic reasoning. Unlike existing interpretability methods that rely on surface-level textual similarity, Project Ariadne performs hard interventions (do-calculus) on intermediate reasoning nodes -- systematically inverting logic, negating premises, and reversing factual claims -- to measure the Causal Sensitivity (\u03c6) of the terminal answer. Our empirical evaluation of state-of-the-art models reveals a persistent Faithfulness Gap. We define and detect a widespread failure mode termed Causal Decoupling, where agents exhibit a violation density (\u03c1) of up to 0.77 in factual and scientific domains. In these instances, agents arrive at identical conclusions despite contradictory internal logic, proving that their reasoning traces function as \"Reasoning Theater\" while decision-making is governed by latent parametric priors. Our findings suggest that current agentic architectures are inherently prone to unfaithful explanation, and we propose the Ariadne Score as a new benchmark for aligning stated logic with model action.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2512.24138", "title": "GARDO: Reinforcing Diffusion Models without Reward Hacking", "authors": [ "Haoran He", "Yuxiao Ye", "Jie Liu", "Jiajun Liang", "Zhiyong Wang", "Ziyang Yuan", "Xintao Wang", "Hangyu Mao", "Pengfei Wan", "Ling Pan" ], "github": "", "project_page": "", "abstract": "Fine-tuning diffusion models via online reinforcement learning (RL) has shown great potential for enhancing text-to-image alignment. However, since precisely specifying a ground-truth objective for visual tasks remains challenging, the models are often optimized using a proxy reward that only partially captures the true goal. This mismatch often leads to reward hacking, where proxy scores increase while real image quality deteriorates and generation diversity collapses. While common solutions add regularization against the reference policy to prevent reward hacking, they compromise sample efficiency and impede the exploration of novel, high-reward regions, as the reference policy is usually sub-optimal. To address the competing demands of sample efficiency, effective exploration, and mitigation of reward hacking, we propose Gated and Adaptive Regularization with Diversity-aware Optimization (GARDO), a versatile framework compatible with various RL algorithms. Our key insight is that regularization need not be applied universally; instead, it is highly effective to selectively penalize a subset of samples that exhibit high uncertainty. To address the exploration challenge, GARDO introduces an adaptive regularization mechanism wherein the reference model is periodically updated to match the capabilities of the online policy, ensuring a relevant regularization target. To address the mode collapse issue in RL, GARDO amplifies the rewards for high-quality samples that also exhibit high diversity, encouraging mode coverage without destabilizing the optimization process. Extensive experiments across diverse proxy rewards and hold-out unseen metrics consistently show that GARDO mitigates reward hacking and enhances generation diversity without sacrificing sample efficiency or exploration, highlighting its effectiveness and robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2512.23035", "title": "Toward Stable Semi-Supervised Remote Sensing Segmentation via Co-Guidance and Co-Fusion", "authors": [ "Yi Zhou", "Xuechao Zou", "Shun Zhang", "Kai Li", "Shiying Wang", "Jingming Chen", "Congyan Lang", "Tengfei Cao", "Pin Tao", "Yuanchun Shi" ], "github": "", "project_page": "", "abstract": "Semi-supervised remote sensing (RS) image semantic segmentation offers a promising solution to alleviate the burden of exhaustive annotation, yet it fundamentally struggles with pseudo-label drift, a phenomenon where confirmation bias leads to the accumulation of errors during training. In this work, we propose Co2S, a stable semi-supervised RS segmentation framework that synergistically fuses priors from vision-language models and self-supervised models. Specifically, we construct a heterogeneous dual-student architecture comprising two distinct ViT-based vision foundation models initialized with pretrained CLIP and DINOv3 to mitigate error accumulation and pseudo-label drift. To effectively incorporate these distinct priors, an explicit-implicit semantic co-guidance mechanism is introduced that utilizes text embeddings and learnable queries to provide explicit and implicit class-level guidance, respectively, thereby jointly enhancing semantic consistency. Furthermore, a global-local feature collaborative fusion strategy is developed to effectively fuse the global contextual information captured by CLIP with the local details produced by DINOv3, enabling the model to generate highly precise segmentation results. Extensive experiments on six popular datasets demonstrate the superiority of the proposed method, which consistently achieves leading performance across various partition protocols and diverse scenarios. Project page is available at https://xavierjiezou.github.io/Co2S/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2512.21472", "title": "IMA++: ISIC Archive Multi-Annotator Dermoscopic Skin Lesion Segmentation Dataset", "authors": [ "Kumar Abhishek", "Jeremy Kawahara", "Ghassan Hamarneh" ], "github": "", "project_page": "", "abstract": "Multi-annotator medical image segmentation is an important research problem, but requires annotated datasets that are expensive to collect. Dermoscopic skin lesion imaging allows human experts and AI systems to observe morphological structures otherwise not discernable from regular clinical photographs. However, currently there are no large-scale publicly available multi-annotator skin lesion segmentation (SLS) datasets with annotator-labels for dermoscopic skin lesion imaging. We introduce ISIC MultiAnnot++, a large public multi-annotator skin lesion segmentation dataset for images from the ISIC Archive. The final dataset contains 17,684 segmentation masks spanning 14,967 dermoscopic images, where 2,394 dermoscopic images have 2-5 segmentations per image, making it the largest publicly available SLS dataset. Further, metadata about the segmentation, including the annotators' skill level and segmentation tool, is included, enabling research on topics such as annotator-specific preference modeling for segmentation and annotator metadata analysis. We provide an analysis on the characteristics of this dataset, curated data partitions, and consensus segmentation masks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2512.24601", "title": "Recursive Language Models", "authors": [ "Alex L. Zhang", "Tim Kraska", "Omar Khattab" ], "github": "", "project_page": "", "abstract": "We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decompose, and recursively call itself over snippets of the prompt. We find that RLMs successfully handle inputs up to two orders of magnitude beyond model context windows and, even for shorter prompts, dramatically outperform the quality of base LLMs and common long-context scaffolds across four diverse long-context tasks, while having comparable (or cheaper) cost per query.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.02281", "title": "InfiniteVGGT: Visual Geometry Grounded Transformer for Endless Streams", "authors": [ "Shuai Yuan", "Yantai Yang", "Xiaotian Yang", "Xupeng Zhang", "Zhonghao Zhao", "Lingming Zhang", "Zhipeng Zhang" ], "github": "https://github.com/AutoLab-SAI-SJTU/InfiniteVGGT", "project_page": "", "abstract": "The grand vision of enabling persistent, large-scale 3D visual geometry understanding is shackled by the irreconcilable demands of scalability and long-term stability. While offline models like VGGT achieve inspiring geometry capability, their batch-based nature renders them irrelevant for live systems. Streaming architectures, though the intended solution for live operation, have proven inadequate. Existing methods either fail to support truly infinite-horizon inputs or suffer from catastrophic drift over long sequences. We shatter this long-standing dilemma with InfiniteVGGT, a causal visual geometry transformer that operationalizes the concept of a rolling memory through a bounded yet adaptive and perpetually expressive KV cache. Capitalizing on this, we devise a training-free, attention-agnostic pruning strategy that intelligently discards obsolete information, effectively ``rolling'' the memory forward with each new frame. Fully compatible with FlashAttention, InfiniteVGGT finally alleviates the compromise, enabling infinite-horizon streaming while outperforming existing streaming methods in long-term stability. The ultimate test for such a system is its performance over a truly infinite horizon, a capability that has been impossible to rigorously validate due to the lack of extremely long-term, continuous benchmarks. To address this critical gap, we introduce the Long3D benchmark, which, for the first time, enables a rigorous evaluation of continuous 3D geometry estimation on sequences about 10,000 frames. This provides the definitive evaluation platform for future research in long-term 3D geometry understanding. Code is available at: https://github.com/AutoLab-SAI-SJTU/InfiniteVGGT", "github_manual": "", "github_regex": "https://github.com/AutoLab-SAI-SJTU/InfiniteVGGT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.01426", "title": "SWE-Lego: Pushing the Limits of Supervised Fine-tuning for Software Issue Resolving", "authors": [ "Chaofan Tao", "Jierun Chen", "Yuxin Jiang", "Kaiqi Kou", "Shaowei Wang", "Ruoyu Wang", "Xiaohui Li", "Sidi Yang", "Yiming Du", "Jianbo Dai", "Zhiming Mao", "Xinyu Wang", "Lifeng Shang", "Haoli Bai" ], "github": "", "project_page": "", "abstract": "We present SWE-Lego, a supervised fine-tuning (SFT) recipe designed to achieve state-ofthe-art performance in software engineering (SWE) issue resolving. In contrast to prevalent methods that rely on complex training paradigms (e.g., mid-training, SFT, reinforcement learning, and their combinations), we explore how to push the limits of a lightweight SFT-only approach for SWE tasks. SWE-Lego comprises three core building blocks, with key findings summarized as follows: 1) the SWE-Lego dataset, a collection of 32k highquality task instances and 18k validated trajectories, combining real and synthetic data to complement each other in both quality and quantity; 2) a refined SFT procedure with error masking and a difficulty-based curriculum, which demonstrably improves action quality and overall performance. Empirical results show that with these two building bricks alone,the SFT can push SWE-Lego models to state-of-the-art performance among open-source models of comparable size on SWE-bench Verified: SWE-Lego-Qwen3-8B reaches 42.2%, and SWE-Lego-Qwen3-32B attains 52.6%. 3) We further evaluate and improve test-time scaling (TTS) built upon the SFT foundation. Based on a well-trained verifier, SWE-Lego models can be significantly boosted--for example, 42.2% to 49.6% and 52.6% to 58.8% under TTS@16 for the 8B and 32B models, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2512.22877", "title": "M-ErasureBench: A Comprehensive Multimodal Evaluation Benchmark for Concept Erasure in Diffusion Models", "authors": [ "Ju-Hsuan Weng", "Jia-Wei Liao", "Cheng-Fu Chou", "Jun-Cheng Chen" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion models may generate harmful or copyrighted content, motivating research on concept erasure. However, existing approaches primarily focus on erasing concepts from text prompts, overlooking other input modalities that are increasingly critical in real-world applications such as image editing and personalized generation. These modalities can become attack surfaces, where erased concepts re-emerge despite defenses. To bridge this gap, we introduce M-ErasureBench, a novel multimodal evaluation framework that systematically benchmarks concept erasure methods across three input modalities: text prompts, learned embeddings, and inverted latents. For the latter two, we evaluate both white-box and black-box access, yielding five evaluation scenarios. Our analysis shows that existing methods achieve strong erasure performance against text prompts but largely fail under learned embeddings and inverted latents, with Concept Reproduction Rate (CRR) exceeding 90% in the white-box setting. To address these vulnerabilities, we propose IRECE (Inference-time Robustness Enhancement for Concept Erasure), a plug-and-play module that localizes target concepts via cross-attention and perturbs the associated latents during denoising. Experiments demonstrate that IRECE consistently restores robustness, reducing CRR by up to 40% under the most challenging white-box latent inversion scenario, while preserving visual quality. To the best of our knowledge, M-ErasureBench provides the first comprehensive benchmark of concept erasure beyond text prompts. Together with IRECE, our benchmark offers practical safeguards for building more reliable protective generative models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.00863", "title": "Selective Imperfection as a Generative Framework for Analysis, Creativity and Discovery", "authors": [ "Markus J. Buehler" ], "github": "", "project_page": "", "abstract": "We introduce materiomusic as a generative framework linking the hierarchical structures of matter with the compositional logic of music. Across proteins, spider webs and flame dynamics, vibrational and architectural principles recur as tonal hierarchies, harmonic progressions, and long-range musical form. Using reversible mappings, from molecular spectra to musical tones and from three-dimensional networks to playable instruments, we show how sound functions as a scientific probe, an epistemic inversion where listening becomes a mode of seeing and musical composition becomes a blueprint for matter. These mappings excavate deep time: patterns originating in femtosecond molecular vibrations or billion-year evolutionary histories become audible. We posit that novelty in science and art emerges when constraints cannot be satisfied within existing degrees of freedom, forcing expansion of the space of viable configurations. Selective imperfection provides the mechanism restoring balance between coherence and adaptability. Quantitative support comes from exhaustive enumeration of all 2^12 musical scales, revealing that culturally significant systems cluster in a mid-entropy, mid-defect corridor, directly paralleling the Hall-Petch optimum where intermediate defect densities maximize material strength. Iterating these mappings creates productive collisions between human creativity and physics, generating new information as musical structures encounter evolutionary constraints. We show how swarm-based AI models compose music exhibiting human-like structural signatures such as small-world connectivity, modular integration, long-range coherence, suggesting a route beyond interpolation toward invention. We show that science and art are generative acts of world-building under constraint, with vibration as a shared grammar organizing structure across scales.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.02179", "title": "Confidence Estimation for LLMs in Multi-turn Interactions", "authors": [ "Caiqi Zhang", "Ruihan Yang", "Xiaochen Zhu", "Chengzu Li", "Tiancheng Hu", "Yijiang River Dong", "Deqing Yang", "Nigel Collier" ], "github": "https://github.com/caiqizh/multi-turn-conf", "project_page": "", "abstract": "While confidence estimation is a promising direction for mitigating hallucinations in Large Language Models (LLMs), current research dominantly focuses on single-turn settings. The dynamics of model confidence in multi-turn conversations, where context accumulates and ambiguity is progressively resolved, remain largely unexplored. Reliable confidence estimation in multi-turn settings is critical for many downstream applications, such as autonomous agents and human-in-the-loop systems. This work presents the first systematic study of confidence estimation in multi-turn interactions, establishing a formal evaluation framework grounded in two key desiderata: per-turn calibration and monotonicity of confidence as more information becomes available. To facilitate this, we introduce novel metrics, including a length-normalized Expected Calibration Error (InfoECE), and a new \"Hinter-Guesser\" paradigm for generating controlled evaluation datasets. Our experiments reveal that widely-used confidence techniques struggle with calibration and monotonicity in multi-turn dialogues. We propose P(Sufficient), a logit-based probe that achieves comparatively better performance, although the task remains far from solved. Our work provides a foundational methodology for developing more reliable and trustworthy conversational agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/caiqizh/multi-turn-conf", "github_api_added_by": "auto" }, { "date": "2026-01-06", "arxiv_id": "2601.02267", "title": "DiffProxy: Multi-View Human Mesh Recovery via Diffusion-Generated Dense Proxies", "authors": [ "Renke Wang", "Zhenyu Zhang", "Ying Tai", "Jian Yang" ], "github": "", "project_page": "", "abstract": "Human mesh recovery from multi-view images faces a fundamental challenge: real-world datasets contain imperfect ground-truth annotations that bias the models' training, while synthetic data with precise supervision suffers from domain gap. In this paper, we propose DiffProxy, a novel framework that generates multi-view consistent human proxies for mesh recovery. Central to DiffProxy is leveraging the diffusion-based generative priors to bridge the synthetic training and real-world generalization. Its key innovations include: (1) a multi-conditional mechanism for generating multi-view consistent, pixel-aligned human proxies; (2) a hand refinement module that incorporates flexible visual prompts to enhance local details; and (3) an uncertainty-aware test-time scaling method that increases robustness to challenging cases during optimization. These designs ensure that the mesh recovery process effectively benefits from the precise synthetic ground truth and generative advantages of the diffusion-based pipeline. Trained entirely on synthetic data, DiffProxy achieves state-of-the-art performance across five real-world benchmarks, demonstrating strong zero-shot generalization particularly on challenging scenarios with occlusions and partial views. Project page: https://wrk226.github.io/DiffProxy.html", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.00501", "title": "CPPO: Contrastive Perception for Vision Language Policy Optimization", "authors": [ "Ahmad Rezaei", "Mohsen Gholami", "Saeed Ranjbar Alvar", "Kevin Cannons", "Mohammad Asiful Hossain", "Zhou Weimin", "Shunbo Zhou", "Yong Zhang", "Mohammad Akbari" ], "github": "https://github.com/vbdi/cppo", "project_page": "", "abstract": "We introduce CPPO, a Contrastive Perception Policy Optimization method for finetuning vision-language models (VLMs). While reinforcement learning (RL) has advanced reasoning in language models, extending it to multimodal reasoning requires improving both the perception and reasoning aspects. Prior works tackle this challenge mainly with explicit perception rewards, but disentangling perception tokens from reasoning tokens is difficult, requiring extra LLMs, ground-truth data, forced separation of perception from reasoning by policy model, or applying rewards indiscriminately to all output tokens. CPPO addresses this problem by detecting perception tokens via entropy shifts in the model outputs under perturbed input images. CPPO then extends the RL objective function with a Contrastive Perception Loss (CPL) that enforces consistency under information-preserving perturbations and sensitivity under information-removing ones. Experiments show that CPPO surpasses previous perception-rewarding methods, while avoiding extra models, making training more efficient and scalable.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vbdi/cppo", "github_api_added_by": "auto" }, { "date": "2026-01-06", "arxiv_id": "2601.02315", "title": "Prithvi-Complimentary Adaptive Fusion Encoder (CAFE): unlocking full-potential for flood inundation mapping", "authors": [ "Saurabh Kaushik", "Lalit Maurya", "Beth Tellman" ], "github": "https://github.com/Sk-2103/Prithvi-CAFE", "project_page": "", "abstract": "Geo-Foundation Models (GFMs), have proven effective in diverse downstream applications, including semantic segmentation, classification, and regression tasks. However, in case of flood mapping using Sen1Flood11 dataset as a downstream task, GFMs struggles to outperform the baseline U-Net, highlighting model's limitation in capturing critical local nuances. To address this, we present the Prithvi-Complementary Adaptive Fusion Encoder (CAFE), which integrate Prithvi GFM pretrained encoder with a parallel CNN residual branch enhanced by Convolutional Attention Modules (CAM). Prithvi-CAFE enables fast and efficient fine-tuning through adapters in Prithvi and performs multi-scale, multi-level fusion with CNN features, capturing critical local details while preserving long-range dependencies. We achieve state-of-the-art results on two comprehensive flood mapping datasets: Sen1Flood11 and FloodPlanet. On Sen1Flood11 test data, Prithvi-CAFE (IoU 83.41) outperforms the original Prithvi (IoU 82.50) and other major GFMs (TerraMind 82.90, DOFA 81.54, spectralGPT: 81.02). The improvement is even more pronounced on the hold-out test site, where Prithvi-CAFE achieves an IoU of 81.37 compared to the baseline U-Net (70.57) and original Prithvi (72.42). On FloodPlanet, Prithvi-CAFE also surpasses the baseline U-Net and other GFMs, achieving an IoU of 64.70 compared to U-Net (60.14), Terramind (62.33), DOFA (59.15) and Prithvi 2.0 (61.91). Our proposed simple yet effective Prithvi-CAFE demonstrates strong potential for improving segmentation tasks where multi-channel and multi-modal data provide complementary information and local details are critical. The code is released on https://github.com/Sk-2103/Prithvi-CAFE{Prithvi-CAFE Github}", "github_manual": "", "github_regex": "https://github.com/Sk-2103/Prithvi-CAFE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-06", "arxiv_id": "2601.02553", "title": "SimpleMem: Efficient Lifelong Memory for LLM Agents", "authors": [ "Jiaqi Liu", "Yaofeng Su", "Peng Xia", "Siwei Han", "Zeyu Zheng", "Cihang Xie", "Mingyu Ding", "Huaxiu Yao" ], "github": "https://github.com/aiming-lab/SimpleMem", "project_page": "", "abstract": "To support reliable long-term interaction in complex environments, LLM agents require memory systems that efficiently manage historical experiences. Existing approaches either retain full interaction histories via passive context extension, leading to substantial redundancy, or rely on iterative reasoning to filter noise, incurring high token costs. To address this challenge, we introduce SimpleMem, an efficient memory framework based on semantic lossless compression. We propose a three-stage pipeline designed to maximize information density and token utilization: (1) Semantic Structured Compression, which applies entropy-aware filtering to distill unstructured interactions into compact, multi-view indexed memory units; (2) Recursive Memory Consolidation, an asynchronous process that integrates related units into higher-level abstract representations to reduce redundancy; and (3) Adaptive Query-Aware Retrieval, which dynamically adjusts retrieval scope based on query complexity to construct precise context efficiently. Experiments on benchmark datasets show that our method consistently outperforms baseline approaches in accuracy, retrieval efficiency, and inference cost, achieving an average F1 improvement of 26.4% while reducing inference-time token consumption by up to 30-fold, demonstrating a superior balance between performance and efficiency. Code is available at https://github.com/aiming-lab/SimpleMem.", "github_manual": "", "github_regex": "https://github.com/aiming-lab/SimpleMem", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.03233", "title": "LTX-2: Efficient Joint Audio-Visual Foundation Model", "authors": [ "Yoav HaCohen", "Benny Brazowski", "Nisan Chiprut", "Yaki Bitterman", "Andrew Kvochko", "Avishai Berkowitz", "Daniel Shalem", "Daphna Lifschitz", "Dudu Moshe", "Eitan Porat", "Eitan Richardson", "Guy Shiran", "Itay Chachy", "Jonathan Chetboun", "Michael Finkelson", "Michael Kupchick", "Nir Zabari", "Nitzan Guetta", "Noa Kotler", "Ofir Bibi", "Ori Gordon", "Poriya Panet", "Roi Benita", "Shahar Armon", "Victor Kulikov", "Yaron Inger", "Yonatan Shiftan", "Zeev Melumian", "Zeev Farbman" ], "github": "https://github.com/Lightricks/LTX-2", "project_page": "", "abstract": "Recent text-to-video diffusion models can generate compelling video sequences, yet they remain silent -- missing the semantic, emotional, and atmospheric cues that audio provides. We introduce LTX-2, an open-source foundational model capable of generating high-quality, temporally synchronized audiovisual content in a unified manner. LTX-2 consists of an asymmetric dual-stream transformer with a 14B-parameter video stream and a 5B-parameter audio stream, coupled through bidirectional audio-video cross-attention layers with temporal positional embeddings and cross-modality AdaLN for shared timestep conditioning. This architecture enables efficient training and inference of a unified audiovisual model while allocating more capacity for video generation than audio generation. We employ a multilingual text encoder for broader prompt understanding and introduce a modality-aware classifier-free guidance (modality-CFG) mechanism for improved audiovisual alignment and controllability. Beyond generating speech, LTX-2 produces rich, coherent audio tracks that follow the characters, environment, style, and emotion of each scene -- complete with natural background and foley elements. In our evaluations, the model achieves state-of-the-art audiovisual quality and prompt adherence among open-source systems, while delivering results comparable to proprietary models at a fraction of their computational cost and inference time. All model weights and code are publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Lightricks/LTX-2", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.01720", "title": "FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing", "authors": [ "Xijie Huang", "Chengming Xu", "Donghao Luo", "Xiaobin Hu", "Peng Tang", "Xu Peng", "Jiangning Zhang", "Chengjie Wang", "Yanwei Fu" ], "github": "", "project_page": "", "abstract": "First-Frame Propagation (FFP) offers a promising paradigm for controllable video editing, but existing methods are hampered by a reliance on cumbersome run-time guidance. We identify the root cause of this limitation as the inadequacy of current training datasets, which are often too short, low-resolution, and lack the task diversity required to teach robust temporal priors. To address this foundational data gap, we first introduce FFP-300K, a new large-scale dataset comprising 300K high-fidelity video pairs at 720p resolution and 81 frames in length, constructed via a principled two-track pipeline for diverse local and global edits. Building on this dataset, we propose a novel framework designed for true guidance-free FFP that resolves the critical tension between maintaining first-frame appearance and preserving source video motion. Architecturally, we introduce Adaptive Spatio-Temporal RoPE (AST-RoPE), which dynamically remaps positional encodings to disentangle appearance and motion references. At the objective level, we employ a self-distillation strategy where an identity propagation task acts as a powerful regularizer, ensuring long-term temporal stability and preventing semantic drift. Comprehensive experiments on the EditVerseBench benchmark demonstrate that our method significantly outperforming existing academic and commercial models by receiving about 0.2 PickScore and 0.3 VLM score improvement against these competitors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.03252", "title": "InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields", "authors": [ "Hao Yu", "Haotong Lin", "Jiawei Wang", "Jiaxin Li", "Yida Wang", "Xueyang Zhang", "Yue Wang", "Xiaowei Zhou", "Ruizhen Hu", "Sida Peng" ], "github": "https://github.com/zju3dv/InfiniDepth", "project_page": "", "abstract": "Existing depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbitrary output resolutions and hinder the geometric detail recovery. This paper introduces InfiniDepth, which represents depth as neural implicit fields. Through a simple yet effective local implicit decoder, we can query depth at continuous 2D coordinates, enabling arbitrary-resolution and fine-grained depth estimation. To better assess our method's capabilities, we curate a high-quality 4K synthetic benchmark from five different games, spanning diverse scenes with rich geometric and appearance details. Extensive experiments demonstrate that InfiniDepth achieves state-of-the-art performance on both synthetic and real-world benchmarks across relative and metric depth estimation tasks, particularly excelling in fine-detail regions. It also benefits the task of novel view synthesis under large viewpoint shifts, producing high-quality results with fewer holes and artifacts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zju3dv/InfiniDepth", "github_api_added_by": "auto" }, { "date": "2026-01-07", "arxiv_id": "2601.01554", "title": "MOSS Transcribe Diarize: Accurate Transcription with Speaker Diarization", "authors": [ "MOSI. AI", "Donghua Yu", "Zhengyuan Lin", "Chen Yang", "Yiyang Zhang", "Hanfu Chen", "Jingqi Chen", "Ke Chen", "Liwei Fan", "Yi Jiang", "Jie Zhu", "Muchen Li", "Wenxuan Wang", "Yang Wang", "Zhe Xu", "Yitian Gong", "Yuqian Zhang", "Wenbo Zhang", "Zhaoye Fei", "Qinyuan Cheng", "Shimin Li", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Speaker-Attributed, Time-Stamped Transcription (SATS) aims to transcribe what is said and to precisely determine the timing of each speaker, which is particularly valuable for meeting transcription. Existing SATS systems rarely adopt an end-to-end formulation and are further constrained by limited context windows, weak long-range speaker memory, and the inability to output timestamps. To address these limitations, we present MOSS Transcribe Diarize, a unified multimodal large language model that jointly performs Speaker-Attributed, Time-Stamped Transcription in an end-to-end paradigm. Trained on extensive real wild data and equipped with a 128k context window for up to 90-minute inputs, MOSS Transcribe Diarize scales well and generalizes robustly. Across comprehensive evaluations, it outperforms state-of-the-art commercial systems on multiple public and in-house benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.01874", "title": "CogFlow: Bridging Perception and Reasoning through Knowledge Internalization for Visual Mathematical Problem Solving", "authors": [ "Shuhang Chen", "Yunqiu Xu", "Junjie Xie", "Aojun Lu", "Tao Feng", "Zeying Huang", "Ning Zhang", "Yi Sun", "Yi Yang", "Hangjie Yuan" ], "github": "", "project_page": "", "abstract": "Despite significant progress, multimodal large language models continue to struggle with visual mathematical problem solving. Some recent works recognize that visual perception is a bottleneck in visual mathematical reasoning, but their solutions are limited to improving the extraction and interpretation of visual inputs. Notably, they all ignore the key issue of whether the extracted visual cues are faithfully integrated and properly utilized in subsequent reasoning. Motivated by this, we present CogFlow, a novel cognitive-inspired three-stage framework that incorporates a knowledge internalization stage, explicitly simulating the hierarchical flow of human reasoning: perceptionRightarrowinternalizationRightarrowreasoning. Inline with this hierarchical flow, we holistically enhance all its stages. We devise Synergistic Visual Rewards to boost perception capabilities in parametric and semantic spaces, jointly improving visual information extraction from symbols and diagrams. To guarantee faithful integration of extracted visual cues into subsequent reasoning, we introduce a Knowledge Internalization Reward model in the internalization stage, bridging perception and reasoning. Moreover, we design a Visual-Gated Policy Optimization algorithm to further enforce the reasoning is grounded with the visual knowledge, preventing models seeking shortcuts that appear coherent but are visually ungrounded reasoning chains. Moreover, we contribute a new dataset MathCog for model training, which contains samples with over 120K high-quality perception-reasoning aligned annotations. Comprehensive experiments and analysis on commonly used visual mathematical reasoning benchmarks validate the superiority of the proposed CogFlow.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.02427", "title": "NitroGen: An Open Foundation Model for Generalist Gaming Agents", "authors": [ "Lo\u00efc Magne", "Anas Awadalla", "Guanzhi Wang", "Yinzhen Xu", "Joshua Belofsky", "Fengyuan Hu", "Joohwan Kim", "Ludwig Schmidt", "Georgia Gkioxari", "Jan Kautz", "Yisong Yue", "Yejin Choi", "Yuke Zhu", "Linxi \"Jim\" Fan" ], "github": "", "project_page": "", "abstract": "We introduce NitroGen, a vision-action foundation model for generalist gaming agents that is trained on 40,000 hours of gameplay videos across more than 1,000 games. We incorporate three key ingredients: 1) an internet-scale video-action dataset constructed by automatically extracting player actions from publicly available gameplay videos, 2) a multi-game benchmark environment that can measure cross-game generalization, and 3) a unified vision-action model trained with large-scale behavior cloning. NitroGen exhibits strong competence across diverse domains, including combat encounters in 3D action games, high-precision control in 2D platformers, and exploration in procedurally generated worlds. It transfers effectively to unseen games, achieving up to 52% relative improvement in task success rates over models trained from scratch. We release the dataset, evaluation suite, and model weights to advance research on generalist embodied agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.03194", "title": "X-MuTeST: A Multilingual Benchmark for Explainable Hate Speech Detection and A Novel LLM-consulted Explanation Framework", "authors": [ "Mohammad Zia Ur Rehman", "Sai Kartheek Reddy Kasu", "Shashivardhan Reddy Koppula", "Sai Rithwik Reddy Chirra", "Shwetank Shekhar Singh", "Nagendra Kumar" ], "github": "https://github.com/ziarehman30/X-MuTeST", "project_page": "", "abstract": "Hate speech detection on social media faces challenges in both accuracy and explainability, especially for underexplored Indic languages. We propose a novel explainability-guided training framework, X-MuTeST (eXplainable Multilingual haTe Speech deTection), for hate speech detection that combines high-level semantic reasoning from large language models (LLMs) with traditional attention-enhancing techniques. We extend this research to Hindi and Telugu alongside English by providing benchmark human-annotated rationales for each word to justify the assigned class label. The X-MuTeST explainability method computes the difference between the prediction probabilities of the original text and those of unigrams, bigrams, and trigrams. Final explanations are computed as the union between LLM explanations and X-MuTeST explanations. We show that leveraging human rationales during training enhances both classification performance and explainability. Moreover, combining human rationales with our explainability method to refine the model attention yields further improvements. We evaluate explainability using Plausibility metrics such as Token-F1 and IOU-F1 and Faithfulness metrics such as Comprehensiveness and Sufficiency. By focusing on under-resourced languages, our work advances hate speech detection across diverse linguistic contexts. Our dataset includes token-level rationale annotations for 6,004 Hindi, 4,492 Telugu, and 6,334 English samples. Data and code are available on https://github.com/ziarehman30/X-MuTeST", "github_manual": "", "github_regex": "https://github.com/ziarehman30/X-MuTeST", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.03153", "title": "Parallel Latent Reasoning for Sequential Recommendation", "authors": [ "Jiakai Tang", "Xu Chen", "Wen Chen", "Jian Wu", "Yuning Jiang", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Capturing complex user preferences from sparse behavioral sequences remains a fundamental challenge in sequential recommendation. Recent latent reasoning methods have shown promise by extending test-time computation through multi-step reasoning, yet they exclusively rely on depth-level scaling along a single trajectory, suffering from diminishing returns as reasoning depth increases. To address this limitation, we propose Parallel Latent Reasoning (PLR), a novel framework that pioneers width-level computational scaling by exploring multiple diverse reasoning trajectories simultaneously. PLR constructs parallel reasoning streams through learnable trigger tokens in continuous latent space, preserves diversity across streams via global reasoning regularization, and adaptively synthesizes multi-stream outputs through mixture-of-reasoning-streams aggregation. Extensive experiments on three real-world datasets demonstrate that PLR substantially outperforms state-of-the-art baselines while maintaining real-time inference efficiency. Theoretical analysis further validates the effectiveness of parallel reasoning in improving generalization capability. Our work opens new avenues for enhancing reasoning capacity in sequential recommendation beyond existing depth scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.02785", "title": "DreamStyle: A Unified Framework for Video Stylization", "authors": [ "Mengtian Li", "Jinshu Chen", "Songtao Zhao", "Wanquan Feng", "Pengqi Tu", "Qian He" ], "github": "https://github.com/LemonSky1995/DreamStyle", "project_page": "", "abstract": "Video stylization, an important downstream task of video generation models, has not yet been thoroughly explored. Its input style conditions typically include text, style image, and stylized first frame. Each condition has a characteristic advantage: text is more flexible, style image provides a more accurate visual anchor, and stylized first frame makes long-video stylization feasible. However, existing methods are largely confined to a single type of style condition, which limits their scope of application. Additionally, their lack of high-quality datasets leads to style inconsistency and temporal flicker. To address these limitations, we introduce DreamStyle, a unified framework for video stylization, supporting (1) text-guided, (2) style-image-guided, and (3) first-frame-guided video stylization, accompanied by a well-designed data curation pipeline to acquire high-quality paired video data. DreamStyle is built on a vanilla Image-to-Video (I2V) model and trained using a Low-Rank Adaptation (LoRA) with token-specific up matrices that reduces the confusion among different condition tokens. Both qualitative and quantitative evaluations demonstrate that DreamStyle is competent in all three video stylization tasks, and outperforms the competitors in style consistency and video quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LemonSky1995/DreamStyle", "github_api_added_by": "auto" }, { "date": "2026-01-07", "arxiv_id": "2601.02780", "title": "MiMo-V2-Flash Technical Report", "authors": [ "Bangjun Xiao", "Bingquan Xia", "Bo Yang", "Bofei Gao", "Bowen Shen", "Chen Zhang", "Chenhong He", "Chiheng Lou", "Fuli Luo", "Gang Wang", "Gang Xie", "Hailin Zhang", "Hanglong Lv", "Hanyu Li", "Heyu Chen", "Hongshen Xu", "Houbin Zhang", "Huaqiu Liu", "Jiangshan Duo", "Jianyu Wei", "Jiebao Xiao", "Jinhao Dong", "Jun Shi", "Junhao Hu", "Kainan Bao", "Kang Zhou", "Lei Li", "Liang Zhao", "Linghao Zhang", "Peidian Li", "Qianli Chen", "Shaohui Liu", "Shihua Yu", "Shijie Cao", "Shimao Chen", "Shouqiu Yu", "Shuo Liu", "Tianling Zhou", "Weijiang Su", "Weikun Wang", "Wenhan Ma", "Xiangwei Deng", "Bohan Mao", "Bowen Ye", "Can Cai", "Chenghua Wang", "Chengxuan Zhu", "Chong Ma", "Chun Chen", "Chunan Li", "Dawei Zhu", "Deshan Xiao", "Dong Zhang", "Duo Zhang", "Fangyue Liu", "Feiyu Yang", "Fengyuan Shi", "Guoan Wang", "Hao Tian", "Hao Wu", "Heng Qu", "Hongfei Yi", "Hongxu An", "Hongyi Guan", "Xing Zhang", "Yifan Song", "Yihan Yan", "Yihao Zhao", "Yingchun Lai", "Yizhao Gao", "Yu Cheng", "Yuanyuan Tian", "Yudong Wang", "Zhen Tang", "Zhengju Tang", "Zhengtao Wen", "Zhichao Song", "Zhixian Zheng", "Zihan Jiang", "Jian Wen", "Jiarui Sun", "Jiawei Li", "Jinlong Xue", "Jun Xia", "Kai Fang", "Menghang Zhu", "Nuo Chen", "Qian Tu", "Qihao Zhang", "Qiying Wang", "Rang Li", "Rui Ma", "Shaolei Zhang", "Shengfan Wang", "Shicheng Li", "Shuhao Gu", "Shuhuai Ren", "Sirui Deng", "Tao Guo", "Tianyang Lu", "Weiji Zhuang", "Weikang Zhang", "Weimin Xiong", "Wenshan Huang", "Wenyu Yang", "Xin Zhang", "Xing Yong", "Xu Wang", "Xueyang Xie", "Yilin Jiang", "Yixin Yang", "Yongzhe He", "Yu Tu", "Yuanliang Dong", "Yuchen Liu", "Yue Ma", "Yue Yu", "Yuxing Xiang", "Zhaojun Huang", "Zhenru Lin", "Zhipeng Xu", "Zhiyang Chen", "Zhonghua Deng", "Zihan Zhang", "Zihao Yue" ], "github": "https://github.com/XiaomiMiMo/MiMo-V2-Flash", "project_page": "", "abstract": "We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-V2-Flash adopts a hybrid attention architecture that interleaves Sliding Window Attention (SWA) with global attention, with a 128-token sliding window under a 5:1 hybrid ratio. The model is pre-trained on 27 trillion tokens with Multi-Token Prediction (MTP), employing a native 32k context length and subsequently extended to 256k. To efficiently scale post-training compute, MiMo-V2-Flash introduces a novel Multi-Teacher On-Policy Distillation (MOPD) paradigm. In this framework, domain-specialized teachers (e.g., trained via large-scale reinforcement learning) provide dense and token-level reward, enabling the student model to perfectly master teacher expertise. MiMo-V2-Flash rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2, despite using only 1/2 and 1/3 of their total parameters, respectively. During inference, by repurposing MTP as a draft model for speculative decoding, MiMo-V2-Flash achieves up to 3.6 acceptance length and 2.6x decoding speedup with three MTP layers. We open-source both the model weights and the three-layer MTP weights to foster open research and community collaboration.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/XiaomiMiMo/MiMo-V2-Flash", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.02439", "title": "WebGym: Scaling Training Environments for Visual Web Agents with Realistic Tasks", "authors": [ "Hao Bai", "Alexey Taymanov", "Tong Zhang", "Aviral Kumar", "Spencer Whitehead" ], "github": "", "project_page": "", "abstract": "We present WebGym, the largest-to-date open-source environment for training realistic visual web agents. Real websites are non-stationary and diverse, making artificial or small-scale task sets insufficient for robust policy learning. WebGym contains nearly 300,000 tasks with rubric-based evaluations across diverse, real-world websites and difficulty levels. We train agents with a simple reinforcement learning (RL) recipe, which trains on the agent's own interaction traces (rollouts), using task rewards as feedback to guide learning. To enable scaling RL, we speed up sampling of trajectories in WebGym by developing a high-throughput asynchronous rollout system, designed specifically for web agents. Our system achieves a 4-5x rollout speedup compared to naive implementations. Second, we scale the task set breadth, depth, and size, which results in continued performance improvement. Fine-tuning a strong base vision-language model, Qwen-3-VL-8B-Instruct, on WebGym results in an improvement in success rate on an out-of-distribution test set from 26.2% to 42.9%, significantly outperforming agents based on proprietary models such as GPT-4o and GPT-5-Thinking that achieve 27.1% and 29.8%, respectively. This improvement is substantial because our test set consists only of tasks on websites never seen during training, unlike many other prior works on training visual web agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.03193", "title": "UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision", "authors": [ "Ruiyan Han", "Zhen Fang", "XinYu Sun", "Yuchen Ma", "Ziheng Wang", "Yu Zeng", "Zehui Chen", "Lin Chen", "Wenxuan Huang", "Wei-Jie Xu", "Yi Cao", "Feng Zhao" ], "github": "https://github.com/Hungryyan1/UniCorn", "project_page": "", "abstract": "While Unified Multimodal Models (UMMs) have achieved remarkable success in cross-modal comprehension, a significant gap persists in their ability to leverage such internal knowledge for high-quality generation. We formalize this discrepancy as Conduction Aphasia, a phenomenon where models accurately interpret multimodal inputs but struggle to translate that understanding into faithful and controllable synthesis. To address this, we propose UniCorn, a simple yet elegant self-improvement framework that eliminates the need for external data or teacher supervision. By partitioning a single UMM into three collaborative roles: Proposer, Solver, and Judge, UniCorn generates high-quality interactions via self-play and employs cognitive pattern reconstruction to distill latent understanding into explicit generative signals. To validate the restoration of multimodal coherence, we introduce UniCycle, a cycle-consistency benchmark based on a Text to Image to Text reconstruction loop. Extensive experiments demonstrate that UniCorn achieves comprehensive and substantial improvements over the base model across six general image generation benchmarks. Notably, it achieves SOTA performance on TIIF(73.8), DPG(86.8), CompBench(88.5), and UniCycle while further delivering substantial gains of +5.0 on WISE and +6.5 on OneIG. These results highlight that our method significantly enhances T2I generation while maintaining robust comprehension, demonstrating the scalability of fully self-supervised refinement for unified multimodal intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Hungryyan1/UniCorn", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.03227", "title": "The Sonar Moment: Benchmarking Audio-Language Models in Audio Geo-Localization", "authors": [ "Ruixing Zhang", "Zihan Liu", "Leilei Sun", "Tongyu Zhu", "Weifeng Lv" ], "github": "", "project_page": "", "abstract": "Geo-localization aims to infer the geographic origin of a given signal. In computer vision, geo-localization has served as a demanding benchmark for compositional reasoning and is relevant to public safety. In contrast, progress on audio geo-localization has been constrained by the lack of high-quality audio-location pairs. To address this gap, we introduce AGL1K, the first audio geo-localization benchmark for audio language models (ALMs), spanning 72 countries and territories. To extract reliably localizable samples from a crowd-sourced platform, we propose the Audio Localizability metric that quantifies the informativeness of each recording, yielding 1,444 curated audio clips. Evaluations on 16 ALMs show that ALMs have emerged with audio geo-localization capability. We find that closed-source models substantially outperform open-source models, and that linguistic clues often dominate as a scaffold for prediction. We further analyze ALMs' reasoning traces, regional bias, error causes, and the interpretability of the localizability metric. Overall, AGL1K establishes a benchmark for audio geo-localization and may advance ALMs with better geospatial reasoning capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.03044", "title": "SOP: A Scalable Online Post-Training System for Vision-Language-Action Models", "authors": [ "Mingjie Pan", "Siyuan Feng", "Qinglin Zhang", "Xinchen Li", "Jianheng Song", "Chendi Qu", "Yi Wang", "Chuankang Li", "Ziyu Xiong", "Zhi Chen", "Yi Liu", "Jianlan Luo" ], "github": "", "project_page": "", "abstract": "Vision-language-action (VLA) models achieve strong generalization through large-scale pre-training, but real-world deployment requires expert-level task proficiency in addition to broad generality. Existing post-training approaches for VLA models are typically offline, single-robot, or task-specific, limiting effective on-policy adaptation and scalable learning from real-world interaction. We introduce a Scalable Online Post-training (SOP) system that enables online, distributed, multi-task post-training of generalist VLA models directly in the physical world. SOP tightly couples execution and learning through a closed-loop architecture in which a fleet of robots continuously streams on-policy experience and human intervention signals to a centralized cloud learner, and asynchronously receives updated policies. This design supports prompt on-policy correction, scales experience collection through parallel deployment, and preserves generality during adaptation. SOP is agnostic to the choice of post-training algorithm; we instantiate it with both interactive imitation learning (HG-DAgger) and reinforcement learning (RECAP). Across a range of real-world manipulation tasks including cloth folding, box assembly, and grocery restocking, we show that SOP substantially improves the performance of large pretrained VLA models while maintaining a single shared policy across tasks. Effective post-training can be achieved within hours of real-world interaction, and performance scales near-linearly with the number of robots in the fleet. These results suggest that tightly coupling online learning with fleet-scale deployment is instrumental to enabling efficient, reliable, and scalable post-training of generalist robot policies in the physical world.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2512.22334", "title": "SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence", "authors": [ "Yiheng Wang", "Yixin Chen", "Shuo Li", "Yifan Zhou", "Bo Liu", "Hengjian Gao", "Jiakang Yuan", "Jia Bu", "Wanghan Xu", "Yuhao Zhou", "Xiangyu Zhao", "Zhiwang Zhou", "Fengxiang Wang", "Haodong Duan", "Songyang Zhang", "Jun Yao", "Han Deng", "Yizhou Wang", "Jiabei Xiao", "Jiaqi Liu", "Encheng Su", "Yujie Liu", "Weida Wang", "Junchi Yao", "Shenghe Zheng", "Haoran Sun", "Runmin Ma", "Xiangchao Yan", "Bo Zhang", "Dongzhan Zhou", "Shufei Zhang", "Peng Ye", "Xiaosong Wang", "Shixiang Tang", "Wenlong Zhang", "Lei Bai" ], "github": "https://github.com/InternScience/SciEvalKit", "project_page": "", "abstract": "We introduce SciEvalKit, a unified benchmarking toolkit designed to evaluate AI models for science across a broad range of scientific disciplines and task capabilities. Unlike general-purpose evaluation platforms, SciEvalKit focuses on the core competencies of scientific intelligence, including Scientific Multimodal Perception, Scientific Multimodal Reasoning, Scientific Multimodal Understanding, Scientific Symbolic Reasoning, Scientific Code Generation, Science Hypothesis Generation and Scientific Knowledge Understanding. It supports six major scientific domains, spanning from physics and chemistry to astronomy and materials science. SciEvalKit builds a foundation of expert-grade scientific benchmarks, curated from real-world, domain-specific datasets, ensuring that tasks reflect authentic scientific challenges. The toolkit features a flexible, extensible evaluation pipeline that enables batch evaluation across models and datasets, supports custom model and dataset integration, and provides transparent, reproducible, and comparable results. By bridging capability-based evaluation and disciplinary diversity, SciEvalKit offers a standardized yet customizable infrastructure to benchmark the next generation of scientific foundation models and intelligent agents. The toolkit is open-sourced and actively maintained to foster community-driven development and progress in AI4Science.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/InternScience/SciEvalKit", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.01584", "title": "Steerability of Instrumental-Convergence Tendencies in LLMs", "authors": [ "Jakub Hoscilowicz" ], "github": "https://github.com/j-hoscilowicz/instrumental_steering", "project_page": "", "abstract": "We examine two properties of AI systems: capability (what a system can do) and steerability (how reliably one can shift behavior toward intended outcomes). A central question is whether capability growth reduces steerability and risks control collapse. We also distinguish between authorized steerability (builders reliably reaching intended behaviors) and unauthorized steerability (attackers eliciting disallowed behaviors). This distinction highlights a fundamental safety--security dilemma of AI models: safety requires high steerability to enforce control (e.g., stop/refuse), while security requires low steerability for malicious actors to elicit harmful behaviors. This tension presents a significant challenge for open-weight models, which currently exhibit high steerability via common techniques like fine-tuning or adversarial attacks. Using Qwen3 and InstrumentalEval, we find that a short anti-instrumental prompt suffix sharply reduces the measured convergence rate (e.g., shutdown avoidance, self-replication). For Qwen3-30B Instruct, the convergence rate drops from 81.69% under a pro-instrumental suffix to 2.82% under an anti-instrumental suffix. Under anti-instrumental prompting, larger aligned models show lower convergence rates than smaller ones (Instruct: 2.82% vs. 4.23%; Thinking: 4.23% vs. 9.86%). Code is available at github.com/j-hoscilowicz/instrumental_steering.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/j-hoscilowicz/instrumental_steering", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.01592", "title": "OpenRT: An Open-Source Red Teaming Framework for Multimodal LLMs", "authors": [ "Xin Wang", "Yunhao Chen", "Juncheng Li", "Yixu Wang", "Yang Yao", "Tianle Gu", "Jie Li", "Yan Teng", "Xingjun Ma", "Yingchun Wang", "Xia Hu" ], "github": "", "project_page": "", "abstract": "The rapid integration of Multimodal Large Language Models (MLLMs) into critical applications is increasingly hindered by persistent safety vulnerabilities. However, existing red-teaming benchmarks are often fragmented, limited to single-turn text interactions, and lack the scalability required for systematic evaluation. To address this, we introduce OpenRT, a unified, modular, and high-throughput red-teaming framework designed for comprehensive MLLM safety evaluation. At its core, OpenRT architects a paradigm shift in automated red-teaming by introducing an adversarial kernel that enables modular separation across five critical dimensions: model integration, dataset management, attack strategies, judging methods, and evaluation metrics. By standardizing attack interfaces, it decouples adversarial logic from a high-throughput asynchronous runtime, enabling systematic scaling across diverse models. Our framework integrates 37 diverse attack methodologies, spanning white-box gradients, multi-modal perturbations, and sophisticated multi-agent evolutionary strategies. Through an extensive empirical study on 20 advanced models (including GPT-5.2, Claude 4.5, and Gemini 3 Pro), we expose critical safety gaps: even frontier models fail to generalize across attack paradigms, with leading models exhibiting average Attack Success Rates as high as 49.14%. Notably, our findings reveal that reasoning models do not inherently possess superior robustness against complex, multi-turn jailbreaks. By open-sourcing OpenRT, we provide a sustainable, extensible, and continuously maintained infrastructure that accelerates the development and standardization of AI safety.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.00581", "title": "AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules", "authors": [ "Stephen E. Farr", "Stefan Doerr", "Antonio Mirarchi", "Francesc Sabanes Zariquiey", "Gianni De Fabritiis" ], "github": "https://github.com/schrodinger/public_binding_free_energy_benchmark", "project_page": "", "abstract": "We introduce AceFF, a pre-trained machine learning interatomic potential (MLIP) optimized for small molecule drug discovery. While MLIPs have emerged as efficient alternatives to Density Functional Theory (DFT), generalizability across diverse chemical spaces remains difficult. AceFF addresses this via a refined TensorNet2 architecture trained on a comprehensive dataset of drug-like compounds. This approach yields a force field that balances high-throughput inference speed with DFT-level accuracy. AceFF fully supports the essential medicinal chemistry elements (H, B, C, N, O, F, Si, P, S, Cl, Br, I) and is explicitly trained to handle charged states. Validation against rigorous benchmarks, including complex torsional energy scans, molecular dynamics trajectories, batched minimizations, and forces and anergy accuracy demonstrates that AceFF establishes a new state-of-the-art for organic molecules. The AceFF-2 model weights and inference code are available at https://huggingface.co/Acellera/AceFF-2.0.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/schrodinger/public_binding_free_energy_benchmark", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2512.23950", "title": "U-Net-Like Spiking Neural Networks for Single Image Dehazing", "authors": [ "Huibin Li", "Haoran Liu", "Mingzhe Liu", "Yulong Xiao", "Peng Li", "Guibin Zan" ], "github": "https://github.com/HaoranLiu507/DehazeSNN", "project_page": "", "abstract": "Image dehazing is a critical challenge in computer vision, essential for enhancing image clarity in hazy conditions. Traditional methods often rely on atmospheric scattering models, while recent deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Transformers, have improved performance by effectively analyzing image features. However, CNNs struggle with long-range dependencies, and Transformers demand significant computational resources. To address these limitations, we propose DehazeSNN, an innovative architecture that integrates a U-Net-like design with Spiking Neural Networks (SNNs). DehazeSNN captures multi-scale image features while efficiently managing local and long-range dependencies. The introduction of the Orthogonal Leaky-Integrate-and-Fire Block (OLIFBlock) enhances cross-channel communication, resulting in superior dehazing performance with reduced computational burden. Our extensive experiments show that DehazeSNN is highly competitive to state-of-the-art methods on benchmark datasets, delivering high-quality haze-free images with a smaller model size and less multiply-accumulate operations. The proposed dehazing method is publicly available at https://github.com/HaoranLiu507/DehazeSNN.", "github_manual": "", "github_regex": "https://github.com/HaoranLiu507/DehazeSNN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.03256", "title": "Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training", "authors": [ "Hexiao Lu", "Xiaokun Sun", "Zeyu Cai", "Hao Guo", "Ying Tai", "Jian Yang", "Zhenyu Zhang" ], "github": "", "project_page": "", "abstract": "We present Muses, the first training-free method for fantastic 3D creature generation in a feed-forward paradigm. Previous methods, which rely on part-aware optimization, manual assembly, or 2D image generation, often produce unrealistic or incoherent 3D assets due to the challenges of intricate part-level manipulation and limited out-of-domain generation. In contrast, Muses leverages the 3D skeleton, a fundamental representation of biological forms, to explicitly and rationally compose diverse elements. This skeletal foundation formalizes 3D content creation as a structure-aware pipeline of design, composition, and generation. Muses begins by constructing a creatively composed 3D skeleton with coherent layout and scale through graph-constrained reasoning. This skeleton then guides a voxel-based assembly process within a structured latent space, integrating regions from different objects. Finally, image-guided appearance modeling under skeletal conditions is applied to generate a style-consistent and harmonious texture for the assembled shape. Extensive experiments establish Muses' state-of-the-art performance in terms of visual fidelity and alignment with textual descriptions, and potential on flexible 3D object editing. Project page: https://luhexiao.github.io/Muses.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.01321", "title": "Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models", "authors": [ "Rong Zhou", "Dongping Chen", "Zihan Jia", "Yao Su", "Yixin Liu", "Yiwen Lu", "Dongwei Shi", "Yue Huang", "Tianyang Xu", "Yi Pan", "Xinliang Li", "Yohannes Abate", "Qingyu Chen", "Zhengzhong Tu", "Yu Yang", "Yu Zhang", "Qingsong Wen", "Gengchen Mai", "Sunyang Fu", "Jiachen Li", "Xuyu Wang", "Ziran Wang", "Jing Huang", "Tianming Liu", "Yong Chen", "Lichao Sun", "Lifang He" ], "github": "", "project_page": "", "abstract": "Digital twins, as precise digital representations of physical systems, have evolved from passive simulation tools into intelligent and autonomous entities through the integration of artificial intelligence technologies. This paper presents a unified four-stage framework that systematically characterizes AI integration across the digital twin lifecycle, spanning modeling, mirroring, intervention, and autonomous management. By synthesizing existing technologies and practices, we distill a unified four-stage framework that systematically characterizes how AI methodologies are embedded across the digital twin lifecycle: (1) modeling the physical twin through physics-based and physics-informed AI approaches, (2) mirroring the physical system into a digital twin with real-time synchronization, (3) intervening in the physical twin through predictive modeling, anomaly detection, and optimization strategies, and (4) achieving autonomous management through large language models, foundation models, and intelligent agents. We analyze the synergy between physics-based modeling and data-driven learning, highlighting the shift from traditional numerical solvers to physics-informed and foundation models for physical systems. Furthermore, we examine how generative AI technologies, including large language models and generative world models, transform digital twins into proactive and self-improving cognitive systems capable of reasoning, communication, and creative scenario generation. Through a cross-domain review spanning eleven application domains, including healthcare, aerospace, smart manufacturing, robotics, and smart cities, we identify common challenges related to scalability, explainability, and trustworthiness, and outline directions for responsible AI-driven digital twin systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.02989", "title": "Mechanistic Interpretability of Large-Scale Counting in LLMs through a System-2 Strategy", "authors": [ "Hosein Hasani", "Mohammadali Banayeeanzade", "Ali Nafisi", "Sadegh Mohammadian", "Fatemeh Askari", "Mobin Bagherian", "Amirmohammad Izadi", "Mahdieh Soleymani Baghshah" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs), despite strong performance on complex mathematical problems, exhibit systematic limitations in counting tasks. This issue arises from architectural limits of transformers, where counting is performed across layers, leading to degraded precision for larger counting problems due to depth constraints. To address this limitation, we propose a simple test-time strategy inspired by System-2 cognitive processes that decomposes large counting tasks into smaller, independent sub-problems that the model can reliably solve. We evaluate this approach using observational and causal mediation analyses to understand the underlying mechanism of this System-2-like strategy. Our mechanistic analysis identifies key components: latent counts are computed and stored in the final item representations of each part, transferred to intermediate steps via dedicated attention heads, and aggregated in the final stage to produce the total count. Experimental results demonstrate that this strategy enables LLMs to surpass architectural limitations and achieve high accuracy on large-scale counting tasks. This work provides mechanistic insight into System-2 counting in LLMs and presents a generalizable approach for improving and understanding their reasoning behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.02359", "title": "ExposeAnyone: Personalized Audio-to-Expression Diffusion Models Are Robust Zero-Shot Face Forgery Detectors", "authors": [ "Kaede Shiohara", "Toshihiko Yamasaki", "Vladislav Golyanik" ], "github": "", "project_page": "", "abstract": "Detecting unknown deepfake manipulations remains one of the most challenging problems in face forgery detection. Current state-of-the-art approaches fail to generalize to unseen manipulations, as they primarily rely on supervised training with existing deepfakes or pseudo-fakes, which leads to overfitting to specific forgery patterns. In contrast, self-supervised methods offer greater potential for generalization, but existing work struggles to learn discriminative representations only from self-supervision. In this paper, we propose ExposeAnyone, a fully self-supervised approach based on a diffusion model that generates expression sequences from audio. The key idea is, once the model is personalized to specific subjects using reference sets, it can compute the identity distances between suspected videos and personalized subjects via diffusion reconstruction errors, enabling person-of-interest face forgery detection. Extensive experiments demonstrate that 1) our method outperforms the previous state-of-the-art method by 4.22 percentage points in the average AUC on DF-TIMIT, DFDCP, KoDF, and IDForge datasets, 2) our model is also capable of detecting Sora2-generated videos, where the previous approaches perform poorly, and 3) our method is highly robust to corruptions such as blur and compression, highlighting the applicability in real-world face forgery detection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.03127", "title": "Unified Thinker: A General Reasoning Modular Core for Image Generation", "authors": [ "Sashuai Zhou", "Qiang Zhou", "Jijin Hu", "Hanqing Yang", "Yue Cao", "Junpeng Ma", "Yinchao Ma", "Jun Song", "Tiezheng Ge", "Cheng Yu", "Bo Zheng", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "Despite impressive progress in high-fidelity image synthesis, generative models still struggle with logic-intensive instruction following, exposing a persistent reasoning--execution gap. Meanwhile, closed-source systems (e.g., Nano Banana) have demonstrated strong reasoning-driven image generation, highlighting a substantial gap to current open-source models. We argue that closing this gap requires not merely better visual generators, but executable reasoning: decomposing high-level intents into grounded, verifiable plans that directly steer the generative process. To this end, we propose Unified Thinker, a task-agnostic reasoning architecture for general image generation, designed as a unified planning core that can plug into diverse generators and workflows. Unified Thinker decouples a dedicated Thinker from the image Generator, enabling modular upgrades of reasoning without retraining the entire generative model. We further introduce a two-stage training paradigm: we first build a structured planning interface for the Thinker, then apply reinforcement learning to ground its policy in pixel-level feedback, encouraging plans that optimize visual correctness over textual plausibility. Extensive experiments on text-to-image generation and image editing show that Unified Thinker substantially improves image reasoning and generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.02996", "title": "Large Reasoning Models Are (Not Yet) Multilingual Latent Reasoners", "authors": [ "Yihong Liu", "Raoyuan Zhao", "Hinrich Sch\u00fctze", "Michael A. Hedderich" ], "github": "https://github.com/cisnlp/multilingual-latent-reasoner", "project_page": "", "abstract": "Large reasoning models (LRMs) achieve strong performance on mathematical reasoning tasks, often attributed to their capability to generate explicit chain-of-thought (CoT) explanations. However, recent work shows that LRMs often arrive at the correct answer before completing these textual reasoning steps, indicating the presence of latent reasoning -- internal, non-verbal computation encoded in hidden states. While this phenomenon has been explored in English, its multilingual behavior remains largely unknown. In this paper, we conduct a systematic investigation of multilingual latent reasoning in LRMs across 11 languages. Using a truncation-based strategy, we examine how the correct answer emerges as the model is given only partial reasoning traces, allowing us to measure stepwise latent prediction formation. Our results reveal clear evidence of multilingual latent reasoning, though unevenly: strong in resource-rich languages, weaker in low-resource ones, and broadly less observable on harder benchmarks. To understand whether these differences reflect distinct internal mechanisms, we further perform representational analyses. Despite surface-level disparities, we find that the internal evolution of predictions is highly consistent across languages and broadly aligns with English -- a pattern suggesting an English-centered latent reasoning pathway.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/cisnlp/multilingual-latent-reasoner", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2512.23412", "title": "MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning", "authors": [ "Jiawei Chen", "Xintian Shen", "Lihao Zheng", "Zhenwei Shao", "Hongyuan Zhang", "Pengfei Yu", "Xudong Rao", "Ning Mao", "Xiaobo Liu", "Lian Wen", "Chaoqun Du", "Feng Gu", "Wei He", "Qizhen Li", "Shanshan Li", "Zide Liu", "Jing Luo", "Lifu Mu", "Xuhao Pan", "Chang Ren", "Haoyi Sun", "Qian Wang", "Wei Wang", "Hongfu Yang", "Jiqing Zhan", "Chunpeng Zhou", "Zheng Zhou", "Hao Ma", "Tao Wei", "Pan Zhou", "Wei Chen" ], "github": "", "project_page": "", "abstract": "Traditional workflow-based agents exhibit limited intelligence when addressing real-world problems requiring tool invocation. Tool-integrated reasoning (TIR) agents capable of autonomous reasoning and tool invocation are rapidly emerging as a powerful approach for complex decision-making tasks involving multi-step interactions with external environments. In this work, we introduce MindWatcher, a TIR agent integrating interleaved thinking and multimodal chain-of-thought (CoT) reasoning. MindWatcher can autonomously decide whether and how to invoke diverse tools and coordinate their use, without relying on human prompts or workflows. The interleaved thinking paradigm enables the model to switch between thinking and tool calling at any intermediate stage, while its multimodal CoT capability allows manipulation of images during reasoning to yield more precise search results. We implement automated data auditing and evaluation pipelines, complemented by manually curated high-quality datasets for training, and we construct a benchmark, called MindWatcher-Evaluate Bench (MWE-Bench), to evaluate its performance. MindWatcher is equipped with a comprehensive suite of auxiliary reasoning tools, enabling it to address broad-domain multimodal problems. A large-scale, high-quality local image retrieval database, covering eight categories including cars, animals, and plants, endows model with robust object recognition despite its small size. Finally, we design a more efficient training infrastructure for MindWatcher, enhancing training speed and hardware utilization. Experiments not only demonstrate that MindWatcher matches or exceeds the performance of larger or more recent models through superior tool invocation, but also uncover critical insights for agent training, such as the genetic inheritance phenomenon in agentic RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-07", "arxiv_id": "2601.03926", "title": "Doc-PP: Document Policy Preservation Benchmark for Large Vision-Language Models", "authors": [ "Haeun Jang", "Hwan Chang", "Hwanhee Lee" ], "github": "https://github.com/hwanchang00/doc-pp", "project_page": "", "abstract": "The deployment of Large Vision-Language Models (LVLMs) for real-world document question answering is often constrained by dynamic, user-defined policies that dictate information disclosure based on context. While ensuring adherence to these explicit constraints is critical, existing safety research primarily focuses on implicit social norms or text-only settings, overlooking the complexities of multimodal documents. In this paper, we introduce Doc-PP (Document Policy Preservation Benchmark), a novel benchmark constructed from real-world reports requiring reasoning across heterogeneous visual and textual elements under strict non-disclosure policies. Our evaluation highlights a systemic Reasoning-Induced Safety Gap: models frequently leak sensitive information when answers must be inferred through complex synthesis or aggregated across modalities, effectively circumventing existing safety constraints. Furthermore, we identify that providing extracted text improves perception but inadvertently facilitates leakage. To address these vulnerabilities, we propose DVA (Decompose-Verify-Aggregation), a structural inference framework that decouples reasoning from policy verification. Experimental results demonstrate that DVA significantly outperforms standard prompting defenses, offering a robust baseline for policy-compliant document understanding", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hwanchang00/doc-pp", "github_api_added_by": "auto" }, { "date": "2026-01-08", "arxiv_id": "2601.02075", "title": "MDAgent2: Large Language Model for Code Generation and Knowledge Q&A in Molecular Dynamics", "authors": [ "Zhuofan Shi", "Hubao A", "Yufei Shao", "Mengyan Dai", "Yadong Yu", "Pan Xiang", "Dongliang Huang", "Hongxu An", "Chunxiao Xin", "Haiyang Shen", "Zhenyu Wang", "Yunshan Na", "Gang Huang", "Xiang Jing" ], "github": "https://github.com/FredericVAN/PKU_MDAgent2", "project_page": "", "abstract": "Molecular dynamics (MD) simulations are essential for understanding atomic-scale behaviors in materials science, yet writing LAMMPS scripts remains highly specialized and time-consuming tasks. Although LLMs show promise in code generation and domain-specific question answering, their performance in MD scenarios is limited by scarce domain data, the high deployment cost of state-of-the-art LLMs, and low code executability. Building upon our prior MDAgent, we present MDAgent2, the first end-to-end framework capable of performing both knowledge Q&A and code generation within the MD domain. We construct a domain-specific data-construction pipeline that yields three high-quality datasets spanning MD knowledge, question answering, and code generation. Based on these datasets, we adopt a three stage post-training strategy--continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL)--to train two domain-adapted models, MD-Instruct and MD-Code. Furthermore, we introduce MD-GRPO, a closed-loop RL method that leverages simulation outcomes as reward signals and recycles low-reward trajectories for continual refinement. We further build MDAgent2-RUNTIME, a deployable multi-agent system that integrates code generation, execution, evaluation, and self-correction. Together with MD-EvalBench proposed in this work, the first benchmark for LAMMPS code generation and question answering, our models and system achieve performance surpassing several strong baselines.This work systematically demonstrates the adaptability and generalization capability of large language models in industrial simulation tasks, laying a methodological foundation for automatic code generation in AI for Science and industrial-scale simulations. URL: https://github.com/FredericVAN/PKU_MDAgent2", "github_manual": "", "github_regex": "https://github.com/FredericVAN/PKU_MDAgent2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.04194", "title": "Choreographing a World of Dynamic Objects", "authors": [ "Yanzhe Lyu", "Chen Geng", "Karthik Dharmarajan", "Yunzhi Zhang", "Hadi Alzayer", "Shangzhe Wu", "Jiajun Wu" ], "github": "", "project_page": "", "abstract": "Dynamic objects in our physical 4D (3D + time) world are constantly evolving, deforming, and interacting with other objects, leading to diverse 4D scene dynamics. In this paper, we present a universal generative pipeline, CHORD, for CHOReographing Dynamic objects and scenes and synthesizing this type of phenomena. Traditional rule-based graphics pipelines to create these dynamics are based on category-specific heuristics, yet are labor-intensive and not scalable. Recent learning-based methods typically demand large-scale datasets, which may not cover all object categories in interest. Our approach instead inherits the universality from the video generative models by proposing a distillation-based pipeline to extract the rich Lagrangian motion information hidden in the Eulerian representations of 2D videos. Our method is universal, versatile, and category-agnostic. We demonstrate its effectiveness by conducting experiments to generate a diverse range of multi-body 4D dynamics, show its advantage compared to existing methods, and demonstrate its applicability in generating robotics manipulation policies. Project page: https://yanzhelyu.github.io/chord", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.03471", "title": "EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering for Enhanced Alignment and Reasoning", "authors": [ "Mingyang Wei", "Dehai Min", "Zewen Liu", "Yuzhang Xie", "Guanchen Wu", "Carl Yang", "Max S. Y. Lau", "Qi He", "Lu Cheng", "Wei Jin" ], "github": "https://github.com/myweiii/EpiQAL", "project_page": "", "abstract": "Reliable epidemiological reasoning requires synthesizing study evidence to infer disease burden, transmission dynamics, and intervention effects at the population level. Existing medical question answering benchmarks primarily emphasize clinical knowledge or patient-level reasoning, yet few systematically evaluate evidence-grounded epidemiological inference. We present EpiQAL, the first diagnostic benchmark for epidemiological question answering across diverse diseases, comprising three subsets built from open-access literature. The subsets respectively evaluate text-grounded factual recall, multi-step inference linking document evidence with epidemiological principles, and conclusion reconstruction with the Discussion section withheld. Construction combines expert-designed taxonomy guidance, multi-model verification, and retrieval-based difficulty control. Experiments on ten open models reveal that current LLMs show limited performance on epidemiological reasoning, with multi-step inference posing the greatest challenge. Model rankings shift across subsets, and scale alone does not predict success. Chain-of-Thought prompting benefits multi-step inference but yields mixed results elsewhere. EpiQAL provides fine-grained diagnostic signals for evidence grounding, inferential reasoning, and conclusion reconstruction.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/myweiii/EpiQAL", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.00423", "title": "E-GRPO: High Entropy Steps Drive Effective Reinforcement Learning for Flow Models", "authors": [ "Shengjun Zhang", "Zhang Zhang", "Chensheng Dai", "Yueqi Duan" ], "github": "https://github.com/shengjun-zhang/VisualGRPO", "project_page": "", "abstract": "Recent reinforcement learning has enhanced the flow matching models on human preference alignment. While stochastic sampling enables the exploration of denoising directions, existing methods which optimize over multiple denoising steps suffer from sparse and ambiguous reward signals. We observe that the high entropy steps enable more efficient and effective exploration while the low entropy steps result in undistinguished roll-outs. To this end, we propose E-GRPO, an entropy aware Group Relative Policy Optimization to increase the entropy of SDE sampling steps. Since the integration of stochastic differential equations suffer from ambiguous reward signals due to stochasticity from multiple steps, we specifically merge consecutive low entropy steps to formulate one high entropy step for SDE sampling, while applying ODE sampling on other steps. Building upon this, we introduce multi-step group normalized advantage, which computes group-relative advantages within samples sharing the same consolidated SDE denoising step. Experimental results on different reward settings have demonstrated the effectiveness of our methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/shengjun-zhang/VisualGRPO", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.04171", "title": "Agentic Rubrics as Contextual Verifiers for SWE Agents", "authors": [ "Mohit Raghavendra", "Anisha Gunjal", "Bing Liu", "Yunzhong He" ], "github": "https://github.com/SWE-agent/mini-swe-agent", "project_page": "", "abstract": "Verification is critical for improving agents: it provides the reward signal for Reinforcement Learning and enables inference-time gains through Test-Time Scaling (TTS). Despite its importance, verification in software engineering (SWE) agent settings often relies on code execution, which can be difficult to scale due to environment setup overhead. Scalable alternatives such as patch classifiers and heuristic methods exist, but they are less grounded in codebase context and harder to interpret. To this end, we explore Agentic Rubrics: an expert agent interacts with the repository to create a context-grounded rubric checklist, and candidate patches are then scored against it without requiring test execution. On SWE-Bench Verified under parallel TTS evaluation, Agentic Rubrics achieve a score of 54.2% on Qwen3-Coder-30B-A3B and 40.6% on Qwen3-32B, with at least a +3.5 percentage-point gain over the strongest baseline in our comparison set. We further analyze rubric behavior, showing that rubric scores are consistent with ground-truth tests while also flagging issues that tests do not capture. Our ablations show that agentic context gathering is essential for producing codebase-specific, unambiguous criteria. Together, these results suggest that Agentic Rubrics provide an efficient, scalable, and granular verification signal for SWE agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SWE-agent/mini-swe-agent", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.04151", "title": "Klear: Unified Multi-Task Audio-Video Joint Generation", "authors": [ "Jun Wang", "Chunyu Qiang", "Yuxin Guo", "Yiran Wang", "Xijuan Zeng", "Chen Zhang", "Pengfei Wan" ], "github": "", "project_page": "", "abstract": "Audio-video joint generation has progressed rapidly, yet substantial challenges still remain. Non-commercial approaches still suffer audio-visual asynchrony, poor lip-speech alignment, and unimodal degradation, which can be stemmed from weak audio-visual correspondence modeling, limited generalization, and scarce high-quality dense-caption data. To address these issues, we introduce Klear and delve into three axes--model architecture, training strategy, and data curation. Architecturally, we adopt a single-tower design with unified DiT blocks and an Omni-Full Attention mechanism, achieving tight audio-visual alignment and strong scalability. Training-wise, we adopt a progressive multitask regime--random modality masking to joint optimization across tasks, and a multistage curriculum, yielding robust representations, strengthening A-V aligned world knowledge, and preventing unimodal collapse. For datasets, we present the first large-scale audio-video dataset with dense captions, and introduce a novel automated data-construction pipeline which annotates and filters millions of diverse, high-quality, strictly aligned audio-video-caption triplets. Building on this, Klear scales to large datasets, delivering high-fidelity, semantically and temporally aligned, instruction-following generation in both joint and unimodal settings while generalizing robustly to out-of-distribution scenarios. Across tasks, it substantially outperforms prior methods by a large margin and achieves performance comparable to Veo 3, offering a unified, scalable path toward next-generation audio-video synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.03699", "title": "RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models", "authors": [ "Quy-Anh Dang", "Chris Ngo", "Truong-Son Hy" ], "github": "https://github.com/knoveleng/redeval", "project_page": "", "abstract": "As large language models (LLMs) become integral to safety-critical applications, ensuring their robustness against adversarial prompts is paramount. However, existing red teaming datasets suffer from inconsistent risk categorizations, limited domain coverage, and outdated evaluations, hindering systematic vulnerability assessments. To address these challenges, we introduce RedBench, a universal dataset aggregating 37 benchmark datasets from leading conferences and repositories, comprising 29,362 samples across attack and refusal prompts. RedBench employs a standardized taxonomy with 22 risk categories and 19 domains, enabling consistent and comprehensive evaluations of LLM vulnerabilities. We provide a detailed analysis of existing datasets, establish baselines for modern LLMs, and open-source the dataset and evaluation code. Our contributions facilitate robust comparisons, foster future research, and promote the development of secure and reliable LLMs for real-world deployment. Code: https://github.com/knoveleng/redeval", "github_manual": "", "github_regex": "https://github.com/knoveleng/redeval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.02151", "title": "Entropy-Adaptive Fine-Tuning: Resolving Confident Conflicts to Mitigate Forgetting", "authors": [ "Muxi Diao", "Lele Yang", "Wuxuan Gong", "Yutong Zhang", "Zhonghao Yan", "Yufei Han", "Kongming Liang", "Weiran Xu", "Zhanyu Ma" ], "github": "", "project_page": "", "abstract": "Supervised Fine-Tuning (SFT) is the standard paradigm for domain adaptation, yet it frequently incurs the cost of catastrophic forgetting. In sharp contrast, on-policy Reinforcement Learning (RL) effectively preserves general capabilities. We investigate this discrepancy and identify a fundamental distributional gap: while RL aligns with the model's internal belief, SFT forces the model to fit external supervision. This mismatch often manifests as \"Confident Conflicts\" tokens characterized by low probability but low entropy. In these instances, the model is highly confident in its own prediction but is forced to learn a divergent ground truth, triggering destructive gradient updates. To address this, we propose Entropy-Adaptive Fine-Tuning (EAFT). Unlike methods relying solely on prediction probability, EAFT utilizes token-level entropy as a gating mechanism to distinguish between epistemic uncertainty and knowledge conflict. This allows the model to learn from uncertain samples while suppressing gradients on conflicting data. Extensive experiments on Qwen and GLM series (ranging from 4B to 32B parameters) across mathematical, medical, and agentic domains confirm our hypothesis. EAFT consistently matches the downstream performance of standard SFT while significantly mitigating the degradation of general capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.03986", "title": "Benchmark^2: Systematic Evaluation of LLM Benchmarks", "authors": [ "Qi Qian", "Chengsong Huang", "Jingwen Xu", "Changze Lv", "Muling Wu", "Wenhao Liu", "Xiaohua Wang", "Zhenghua Wang", "Zisu Huang", "Muzhao Tian", "Jianhan Xu", "Kun Hu", "He-Da Wang", "Yao Hu", "Xuanjing Huang", "Xiaoqing Zheng" ], "github": "", "project_page": "", "abstract": "The rapid proliferation of benchmarks for evaluating large language models (LLMs) has created an urgent need for systematic methods to assess benchmark quality itself. We propose Benchmark^2, a comprehensive framework comprising three complementary metrics: (1) Cross-Benchmark Ranking Consistency, measuring whether a benchmark produces model rankings aligned with peer benchmarks; (2) Discriminability Score, quantifying a benchmark's ability to differentiate between models; and (3) Capability Alignment Deviation, identifying problematic instances where stronger models fail but weaker models succeed within the same model family. We conduct extensive experiments across 15 benchmarks spanning mathematics, reasoning, and knowledge domains, evaluating 11 LLMs across four model families. Our analysis reveals significant quality variations among existing benchmarks and demonstrates that selective benchmark construction based on our metrics can achieve comparable evaluation performance with substantially reduced test sets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.03467", "title": "ThinkRL-Edit: Thinking in Reinforcement Learning for Reasoning-Centric Image Editing", "authors": [ "Hengjia Li", "Liming Jiang", "Qing Yan", "Yizhi Song", "Hao Kang", "Zichuan Liu", "Xin Lu", "Boxi Wu", "Deng Cai" ], "github": "", "project_page": "", "abstract": "Instruction-driven image editing with unified multimodal generative models has advanced rapidly, yet their underlying visual reasoning remains limited, leading to suboptimal performance on reasoning-centric edits. Reinforcement learning (RL) has been investigated for improving the quality of image editing, but it faces three key challenges: (1) limited reasoning exploration confined to denoising stochasticity, (2) biased reward fusion, and (3) unstable VLM-based instruction rewards. In this work, we propose ThinkRL-Edit, a reasoning-centric RL framework that decouples visual reasoning from image synthesis and expands reasoning exploration beyond denoising. To the end, we introduce Chain-of-Thought (CoT)-based reasoning sampling with planning and reflection stages prior to generation in online sampling, compelling the model to explore multiple semantic hypotheses and validate their plausibility before committing to a visual outcome. To avoid the failures of weighted aggregation, we propose an unbiased chain preference grouping strategy across multiple reward dimensions. Moreover, we replace interval-based VLM scores with a binary checklist, yielding more precise, lower-variance, and interpretable rewards for complex reasoning. Experiments show our method significantly outperforms prior work on reasoning-centric image editing, producing instruction-faithful, visually coherent, and semantically grounded edits.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.03872", "title": "Atlas: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning", "authors": [ "Jinyang Wu", "Guocheng Zhai", "Ruihan Jin", "Jiahao Yuan", "Yuhao Shen", "Shuai Zhang", "Zhengqi Wen", "Jianhua Tao" ], "github": "", "project_page": "", "abstract": "The integration of large language models (LLMs) with external tools has significantly expanded the capabilities of AI agents. However, as the diversity of both LLMs and tools increases, selecting the optimal model-tool combination becomes a high-dimensional optimization challenge. Existing approaches often rely on a single model or fixed tool-calling logic, failing to exploit the performance variations across heterogeneous model-tool pairs. In this paper, we present ATLAS (Adaptive Tool-LLM Alignment and Synergistic Invocation), a dual-path framework for dynamic tool usage in cross-domain complex reasoning. ATLAS operates via a dual-path approach: (1) training-free cluster-based routing that exploits empirical priors for domain-specific alignment, and (2) RL-based multi-step routing that explores autonomous trajectories for out-of-distribution generalization. Extensive experiments across 15 benchmarks demonstrate that our method outperforms closed-source models like GPT-4o, surpassing existing routing methods on both in-distribution (+10.1%) and out-of-distribution (+13.1%) tasks. Furthermore, our framework shows significant gains in visual reasoning by orchestrating specialized multi-modal tools.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.03236", "title": "MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents", "authors": [ "Dongming Jiang", "Yi Li", "Guanpeng Li", "Bingzhe Li" ], "github": "https://github.com/FredJiang0324/MAMGA", "project_page": "", "abstract": "Memory-Augmented Generation (MAG) extends Large Language Models with external memory to support long-context reasoning, but existing approaches largely rely on semantic similarity over monolithic memory stores, entangling temporal, causal, and entity information. This design limits interpretability and alignment between query intent and retrieved evidence, leading to suboptimal reasoning accuracy. In this paper, we propose MAGMA, a multi-graph agentic memory architecture that represents each memory item across orthogonal semantic, temporal, causal, and entity graphs. MAGMA formulates retrieval as policy-guided traversal over these relational views, enabling query-adaptive selection and structured context construction. By decoupling memory representation from retrieval logic, MAGMA provides transparent reasoning paths and fine-grained control over retrieval. Experiments on LoCoMo and LongMemEval demonstrate that MAGMA consistently outperforms state-of-the-art agentic memory systems in long-horizon reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FredJiang0324/MAMGA", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.00705", "title": "RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization", "authors": [ "Wei-Tse Cheng", "Yen-Jen Chiou", "Yuan-Fu Yang" ], "github": "", "project_page": "", "abstract": "We introduce RGS-SLAM, a robust Gaussian-splatting SLAM framework that replaces the residual-driven densification stage of GS-SLAM with a training-free correspondence-to-Gaussian initialization. Instead of progressively adding Gaussians as residuals reveal missing geometry, RGS-SLAM performs a one-shot triangulation of dense multi-view correspondences derived from DINOv3 descriptors refined through a confidence-aware inlier classifier, generating a well-distributed and structure-aware Gaussian seed prior to optimization. This initialization stabilizes early mapping and accelerates convergence by roughly 20\\%, yielding higher rendering fidelity in texture-rich and cluttered scenes while remaining fully compatible with existing GS-SLAM pipelines. Evaluated on the TUM RGB-D and Replica datasets, RGS-SLAM achieves competitive or superior localization and reconstruction accuracy compared with state-of-the-art Gaussian and point-based SLAM systems, sustaining real-time mapping performance at up to 925 FPS.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.03448", "title": "Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks", "authors": [ "Atsuki Yamaguchi", "Maggie Mi", "Nikolaos Aletras" ], "github": "https://github.com/gucci-j/l2t", "project_page": "", "abstract": "Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. While this approach facilitates the learning of world knowledge and reasoning, it does not explicitly optimize for linguistic competence. To bridge this gap, we propose L2T, a pre-training framework integrating Language Learning Tasks alongside standard next-token prediction. Inspired by human language acquisition, L2T transforms raw text into structured input-output pairs to provide explicit linguistic stimulation. Pre-training LMs on a mixture of raw text and L2T data not only improves overall performance on linguistic competence benchmarks but accelerates its acquisition, while maintaining competitive performance on general reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gucci-j/l2t", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.03315", "title": "Why LLMs Aren't Scientists Yet: Lessons from Four Autonomous Research Attempts", "authors": [ "Dhruv Trehan", "Paras Chopra" ], "github": "https://github.com/Lossfunk/ai-scientist-artefacts-v1", "project_page": "", "abstract": "We report a case study of four end-to-end attempts to autonomously generate ML research papers using a pipeline of six LLM agents mapped to stages of the scientific workflow. Of these four, three attempts failed during implementation or evaluation. One completed the pipeline and was accepted to Agents4Science 2025, an experimental inaugural venue that required AI systems as first authors, passing both human and multi-AI review. From these attempts, we document six recurring failure modes: bias toward training data defaults, implementation drift under execution pressure, memory and context degradation across long-horizon tasks, overexcitement that declares success despite obvious failures, insufficient domain intelligence, and weak scientific taste in experimental design. We conclude by discussing four design principles for more robust AI-scientist systems, implications for autonomous scientific discovery, and we release all prompts, artifacts, and outputs at https://github.com/Lossfunk/ai-scientist-artefacts-v1", "github_manual": "", "github_regex": "https://github.com/Lossfunk/ai-scientist-artefacts-v1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.04090", "title": "Gen3R: 3D Scene Generation Meets Feed-Forward Reconstruction", "authors": [ "Jiaxin Huang", "Yuanbo Yang", "Bangbang Yang", "Lin Ma", "Yuewen Ma", "Yiyi Liao" ], "github": "", "project_page": "", "abstract": "We present Gen3R, a method that bridges the strong priors of foundational reconstruction models and video diffusion models for scene-level 3D generation. We repurpose the VGGT reconstruction model to produce geometric latents by training an adapter on its tokens, which are regularized to align with the appearance latents of pre-trained video diffusion models. By jointly generating these disentangled yet aligned latents, Gen3R produces both RGB videos and corresponding 3D geometry, including camera poses, depth maps, and global point clouds. Experiments demonstrate that our approach achieves state-of-the-art results in single- and multi-image conditioned 3D scene generation. Additionally, our method can enhance the robustness of reconstruction by leveraging generative priors, demonstrating the mutual benefit of tightly coupling reconstruction and generative models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.03509", "title": "Evolving Programmatic Skill Networks", "authors": [ "Haochen Shi", "Xingdi Yuan", "Bang Liu" ], "github": "https://github.com/PrismarineJS/mineflayer", "project_page": "", "abstract": "We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding library of executable skills. We introduce the Programmatic Skill Network (PSN), a framework in which skills are executable symbolic programs forming a compositional network that evolves through experience. PSN defines three core mechanisms instantiated via large language models: (1)REFLECT for structured fault localization over skill compositions, (2) progressive optimization with maturity-aware update gating that stabilizes reliable skills while maintaining plasticity for uncertain ones, and (3) canonical structural refactoring under rollback validation that maintains network compactness. We further show that PSN's learning dynamics exhibit structural parallels to neural network training. Experiments on MineDojo and Crafter demonstrate robust skill reuse, rapid adaptation, and strong generalization across open-ended task distributions.\\footnote{We plan to open-source the code.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/PrismarineJS/mineflayer", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.03955", "title": "ResTok: Learning Hierarchical Residuals in 1D Visual Tokenizers for Autoregressive Image Generation", "authors": [ "Xu Zhang", "Cheng Da", "Huan Yang", "Kun Gai", "Ming Lu", "Zhan Ma" ], "github": "https://github.com/Kwai-Kolors/ResTok", "project_page": "", "abstract": "Existing 1D visual tokenizers for autoregressive (AR) generation largely follow the design principles of language modeling, as they are built directly upon transformers whose priors originate in language, yielding single-hierarchy latent tokens and treating visual data as flat sequential token streams. However, this language-like formulation overlooks key properties of vision, particularly the hierarchical and residual network designs that have long been essential for convergence and efficiency in visual models. To bring \"vision\" back to vision, we propose the Residual Tokenizer (ResTok), a 1D visual tokenizer that builds hierarchical residuals for both image tokens and latent tokens. The hierarchical representations obtained through progressively merging enable cross-level feature fusion at each layer, substantially enhancing representational capacity. Meanwhile, the semantic residuals between hierarchies prevent information overlap, yielding more concentrated latent distributions that are easier for AR modeling. Cross-level bindings consequently emerge without any explicit constraints. To accelerate the generation process, we further introduce a hierarchical AR generator that substantially reduces sampling steps by predicting an entire level of latent tokens at once rather than generating them strictly token-by-token. Extensive experiments demonstrate that restoring hierarchical residual priors in visual tokenization significantly improves AR image generation, achieving a gFID of 2.34 on ImageNet-256 with only 9 sampling steps. Code is available at https://github.com/Kwai-Kolors/ResTok.", "github_manual": "", "github_regex": "https://github.com/Kwai-Kolors/ResTok", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.02933", "title": "Pearmut: Human Evaluation of Translation Made Trivial", "authors": [ "Vil\u00e9m Zouhar", "Tom Kocmi" ], "github": "https://github.com/HumanSignal/label-studio", "project_page": "", "abstract": "Human evaluation is the gold standard for multilingual NLP, but is often skipped in practice and substituted with automatic metrics, because it is notoriously complex and slow to set up with existing tools with substantial engineering and operational overhead. We introduce Pearmut, a lightweight yet feature-rich platform that makes end-to-end human evaluation as easy to run as automatic evaluation. Pearmut removes common entry barriers and provides support for evaluating multilingual tasks, with a particular focus on machine translation. The platform implements standard evaluation protocols, including DA, ESA, or MQM, but is also extensible to allow prototyping new protocols. It features document-level context, absolute and contrastive evaluation, attention checks, ESAAI pre-annotations and both static and active learning-based assignment strategies. Pearmut enables reliable human evaluation to become a practical, routine component of model development and diagnosis rather than an occasional effort.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HumanSignal/label-studio", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-08", "arxiv_id": "2601.03822", "title": "ROI-Reasoning: Rational Optimization for Inference via Pre-Computation Meta-Cognition", "authors": [ "Muyang Zhao", "Qi Qi", "Hao Sun" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) can achieve strong reasoning performance with sufficient computation, but they do not inherently know how much computation a task requires. We study budgeted inference-time reasoning for multiple tasks under a strict global token constraint and formalize it as a Ordered Stochastic Multiple-Choice Knapsack Problem(OS-MCKP). This perspective highlights a meta-cognitive requirement -- anticipating task difficulty, estimating return over investment (ROI), and allocating computation strategically. We propose ROI-Reasoning, a two-stage framework that endows LLMs with intrinsic, budget-aware rationality. In the first stage, Meta-Cognitive Fine-Tuning teaches models to predict reasoning cost and expected utility before generation, enabling explicit solve-or-skip decisions. Next, Rationality-Aware Reinforcement Learning optimizes sequential decision making under a hard token budget, allowing models to learn long-horizon allocation strategies. Across budgeted mathematical reasoning benchmarks, ROI-Reasoning consistently improves overall score while substantially reducing regret under tight computation budgets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05167", "title": "RelayLLM: Efficient Reasoning via Collaborative Decoding", "authors": [ "Chengsong Huang", "Tong Zheng", "Langlin Huang", "Jinyuan Li", "Haolin Liu", "Jiaxin Huang" ], "github": "https://github.com/Chengsong-Huang/RelayLLM", "project_page": "", "abstract": "Large Language Models (LLMs) for complex reasoning is often hindered by high computational costs and latency, while resource-efficient Small Language Models (SLMs) typically lack the necessary reasoning capacity. Existing collaborative approaches, such as cascading or routing, operate at a coarse granularity by offloading entire queries to LLMs, resulting in significant computational waste when the SLM is capable of handling the majority of reasoning steps. To address this, we propose RelayLLM, a novel framework for efficient reasoning via token-level collaborative decoding. Unlike routers, RelayLLM empowers the SLM to act as an active controller that dynamically invokes the LLM only for critical tokens via a special command, effectively \"relaying\" the generation process. We introduce a two-stage training framework, including warm-up and Group Relative Policy Optimization (GRPO) to teach the model to balance independence with strategic help-seeking. Empirical results across six benchmarks demonstrate that RelayLLM achieves an average accuracy of 49.52%, effectively bridging the performance gap between the two models. Notably, this is achieved by invoking the LLM for only 1.07% of the total generated tokens, offering a 98.2% cost reduction compared to performance-matched random routers.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Chengsong-Huang/RelayLLM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05175", "title": "VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twice", "authors": [ "Shuming Liu", "Mingchen Zhuge", "Changsheng Zhao", "Jun Chen", "Lemeng Wu", "Zechun Liu", "Chenchen Zhu", "Zhipeng Cai", "Chong Zhou", "Haozhe Liu", "Ernie Chang", "Saksham Suri", "Hongyu Xu", "Qi Qian", "Wei Wen", "Balakrishnan Varadarajan", "Zhuang Liu", "Hu Xu", "Florian Bordes", "Raghuraman Krishnamoorthi", "Bernard Ghanem", "Vikas Chandra", "Yunyang Xiong" ], "github": "", "project_page": "", "abstract": "Chain-of-thought (CoT) reasoning has emerged as a powerful tool for multimodal large language models on video understanding tasks. However, its necessity and advantages over direct answering remain underexplored. In this paper, we first demonstrate that for RL-trained video models, direct answering often matches or even surpasses CoT performance, despite CoT producing step-by-step analyses at a higher computational cost. Motivated by this, we propose VideoAuto-R1, a video understanding framework that adopts a reason-when-necessary strategy. During training, our approach follows a Thinking Once, Answering Twice paradigm: the model first generates an initial answer, then performs reasoning, and finally outputs a reviewed answer. Both answers are supervised via verifiable rewards. During inference, the model uses the confidence score of the initial answer to determine whether to proceed with reasoning. Across video QA and grounding benchmarks, VideoAuto-R1 achieves state-of-the-art accuracy with significantly improved efficiency, reducing the average response length by ~3.3x, e.g., from 149 to just 44 tokens. Moreover, we observe a low rate of thinking-mode activation on perception-oriented tasks, but a higher rate on reasoning-intensive tasks. This suggests that explicit language-based reasoning is generally beneficial but not always necessary.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.03559", "title": "DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs", "authors": [ "Shidong Cao", "Hongzhan Lin", "Yuxuan Gu", "Ziyang Luo", "Jing Ma" ], "github": "", "project_page": "", "abstract": "Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias and error accumulation, as early mistakes propagate irreversibly through autoregressive decoding. In this work, we propose DiffCoT, a diffusion-styled CoT framework that reformulates CoT reasoning as an iterative denoising process. DiffCoT integrates diffusion principles at the reasoning-step level via a sliding-window mechanism, enabling unified generation and retrospective correction of intermediate steps while preserving token-level autoregression. To maintain causal consistency, we further introduce a causal diffusion noise schedule that respects the temporal structure of reasoning chains. Extensive experiments on three multi-step CoT reasoning benchmarks across diverse model backbones demonstrate that DiffCoT consistently outperforms existing CoT preference optimization methods, yielding improved robustness and error-correction capability in CoT reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.03425", "title": "The Illusion of Specialization: Unveiling the Domain-Invariant \"Standing Committee\" in Mixture-of-Experts Models", "authors": [ "Yan Wang", "Yitao Xu", "Nanhan Shen", "Jinyan Su", "Jimin Huang", "Zining Zhu" ], "github": "", "project_page": "", "abstract": "Mixture of Experts models are widely assumed to achieve domain specialization through sparse routing. In this work, we question this assumption by introducing COMMITTEEAUDIT, a post hoc framework that analyzes routing behavior at the level of expert groups rather than individual experts. Across three representative models and the MMLU benchmark, we uncover a domain-invariant Standing Committee. This is a compact coalition of routed experts that consistently captures the majority of routing mass across domains, layers, and routing budgets, even when architectures already include shared experts. Qualitative analysis further shows that Standing Committees anchor reasoning structure and syntax, while peripheral experts handle domain-specific knowledge. These findings reveal a strong structural bias toward centralized computation, suggesting that specialization in Mixture of Experts models is far less pervasive than commonly believed. This inherent bias also indicates that current training objectives, such as load-balancing losses that enforce uniform expert utilization, may be working against the model's natural optimization path, thereby limiting training efficiency and performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05242", "title": "GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization", "authors": [ "Shih-Yang Liu", "Xin Dong", "Ximing Lu", "Shizhe Diao", "Peter Belcak", "Mingjie Liu", "Min-Hung Chen", "Hongxu Yin", "Yu-Chiang Frank Wang", "Kwang-Ting Cheng", "Yejin Choi", "Jan Kautz", "Pavlo Molchanov" ], "github": "", "project_page": "", "abstract": "As language models become increasingly capable, users expect them to provide not only accurate responses but also behaviors aligned with diverse human preferences across a variety of scenarios. To achieve this, Reinforcement learning (RL) pipelines have begun incorporating multiple rewards, each capturing a distinct preference, to guide models toward these desired behaviors. However, recent work has defaulted to apply Group Relative Policy Optimization (GRPO) under multi-reward setting without examining its suitability. In this paper, we demonstrate that directly applying GRPO to normalize distinct rollout reward combinations causes them to collapse into identical advantage values, reducing the resolution of the training signal and resulting in suboptimal convergence and, in some cases, early training failure. We then introduce Group reward-Decoupled Normalization Policy Optimization (GDPO), a new policy optimization method to resolve these issues by decoupling the normalization of individual rewards, more faithfully preserving their relative differences and enabling more accurate multi-reward optimization, along with substantially improved training stability. We compare GDPO with GRPO across three tasks: tool calling, math reasoning, and coding reasoning, evaluating both correctness metrics (accuracy, bug ratio) and constraint adherence metrics (format, length). Across all settings, GDPO consistently outperforms GRPO, demonstrating its effectiveness and generalizability for multi-reward reinforcement learning optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05239", "title": "Plenoptic Video Generation", "authors": [ "Xiao Fu", "Shitao Tang", "Min Shi", "Xian Liu", "Jinwei Gu", "Ming-Yu Liu", "Dahua Lin", "Chen-Hsuan Lin" ], "github": "", "project_page": "", "abstract": "Camera-controlled generative video re-rendering methods, such as ReCamMaster, have achieved remarkable progress. However, despite their success in single-view setting, these works often struggle to maintain consistency across multi-view scenarios. Ensuring spatio-temporal coherence in hallucinated regions remains challenging due to the inherent stochasticity of generative models. To address it, we introduce PlenopticDreamer, a framework that synchronizes generative hallucinations to maintain spatio-temporal memory. The core idea is to train a multi-in-single-out video-conditioned model in an autoregressive manner, aided by a camera-guided video retrieval strategy that adaptively selects salient videos from previous generations as conditional inputs. In addition, Our training incorporates progressive context-scaling to improve convergence, self-conditioning to enhance robustness against long-range visual degradation caused by error accumulation, and a long-video conditioning mechanism to support extended video generation. Extensive experiments on the Basic and Agibot benchmarks demonstrate that PlenopticDreamer achieves state-of-the-art video re-rendering, delivering superior view synchronization, high-fidelity visuals, accurate camera control, and diverse view transformations (e.g., third-person to third-person, and head-view to gripper-view in robotic manipulation). Project page: https://research.nvidia.com/labs/dir/plenopticdreamer/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05163", "title": "DocDancer: Towards Agentic Document-Grounded Information Seeking", "authors": [ "Qintong Zhang", "Xinjie Lv", "Jialong Wu", "Baixuan Li", "Zhengwei Tao", "Guochen Yan", "Huanyao Zhang", "Bin Wang", "Jiahao Xu", "Haitao Mi", "Wentao Zhang" ], "github": "", "project_page": "", "abstract": "Document Question Answering (DocQA) focuses on answering questions grounded in given documents, yet existing DocQA agents lack effective tool utilization and largely rely on closed-source models. In this work, we introduce DocDancer, an end-to-end trained open-source Doc agent. We formulate DocQA as an information-seeking problem and propose a tool-driven agent framework that explicitly models document exploration and comprehension. To enable end-to-end training of such agents, we introduce an Exploration-then-Synthesis data synthesis pipeline that addresses the scarcity of high-quality training data for DocQA. Training on the synthesized data, the trained models on two long-context document understanding benchmarks, MMLongBench-Doc and DocBench, show their effectiveness. Further analysis provides valuable insights for the agentic tool design and synthetic data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05138", "title": "VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control", "authors": [ "Sixiao Zheng", "Minghao Yin", "Wenbo Hu", "Xiaoyu Li", "Ying Shan", "Yanwei Fu" ], "github": "", "project_page": "", "abstract": "Video world models aim to simulate dynamic, real-world environments, yet existing methods struggle to provide unified and precise control over camera and multi-object motion, as videos inherently operate dynamics in the projected 2D image plane. To bridge this gap, we introduce VerseCrafter, a 4D-aware video world model that enables explicit and coherent control over both camera and object dynamics within a unified 4D geometric world state. Our approach is centered on a novel 4D Geometric Control representation, which encodes the world state through a static background point cloud and per-object 3D Gaussian trajectories. This representation captures not only an object's path but also its probabilistic 3D occupancy over time, offering a flexible, category-agnostic alternative to rigid bounding boxes or parametric models. These 4D controls are rendered into conditioning signals for a pretrained video diffusion model, enabling the generation of high-fidelity, view-consistent videos that precisely adhere to the specified dynamics. Unfortunately, another major challenge lies in the scarcity of large-scale training data with explicit 4D annotations. We address this by developing an automatic data engine that extracts the required 4D controls from in-the-wild videos, allowing us to train our model on a massive and diverse dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05111", "title": "Agent-as-a-Judge", "authors": [ "Runyang You", "Hongru Cai", "Caiqi Zhang", "Qiancheng Xu", "Meng Liu", "Tiezheng Yu", "Yongqi Li", "Wenjie Li" ], "github": "", "project_page": "", "abstract": "LLM-as-a-Judge has revolutionized AI evaluation by leveraging large language models for scalable assessments. However, as evaluands become increasingly complex, specialized, and multi-step, the reliability of LLM-as-a-Judge has become constrained by inherent biases, shallow single-pass reasoning, and the inability to verify assessments against real-world observations. This has catalyzed the transition to Agent-as-a-Judge, where agentic judges employ planning, tool-augmented verification, multi-agent collaboration, and persistent memory to enable more robust, verifiable, and nuanced evaluations. Despite the rapid proliferation of agentic evaluation systems, the field lacks a unified framework to navigate this shifting landscape. To bridge this gap, we present the first comprehensive survey tracing this evolution. Specifically, we identify key dimensions that characterize this paradigm shift and establish a developmental taxonomy. We organize core methodologies and survey applications across general and professional domains. Furthermore, we analyze frontier challenges and identify promising research directions, ultimately providing a clear roadmap for the next generation of agentic evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05106", "title": "Token-Level LLM Collaboration via FusionRoute", "authors": [ "Nuoya Xiong", "Yuhang Zhou", "Hanqing Zeng", "Zhaorun Chen", "Furong Huang", "Shuchao Bi", "Lizhu Zhang", "Zhuokai Zhao" ], "github": "https://github.com/xiongny/FusionRoute", "project_page": "", "abstract": "Large language models (LLMs) exhibit strengths across diverse domains. However, achieving strong performance across these domains with a single general-purpose model typically requires scaling to sizes that are prohibitively expensive to train and deploy. On the other hand, while smaller domain-specialized models are much more efficient, they struggle to generalize beyond their training distributions. To address this dilemma, we propose FusionRoute, a robust and effective token-level multi-LLM collaboration framework in which a lightweight router simultaneously (i) selects the most suitable expert at each decoding step and (ii) contributes a complementary logit that refines or corrects the selected expert's next-token distribution via logit addition. Unlike existing token-level collaboration methods that rely solely on fixed expert outputs, we provide a theoretical analysis showing that pure expert-only routing is fundamentally limited: unless strong global coverage assumptions hold, it cannot in general realize the optimal decoding policy. By augmenting expert selection with a trainable complementary generator, FusionRoute expands the effective policy class and enables recovery of optimal value functions under mild conditions. Empirically, across both Llama-3 and Gemma-2 families and diverse benchmarks spanning mathematical reasoning, code generation, and instruction following, FusionRoute outperforms both sequence- and token-level collaboration, model merging, and direct fine-tuning, while remaining competitive with domain experts on their respective tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xiongny/FusionRoute", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.01887", "title": "Safety at One Shot: Patching Fine-Tuned LLMs with A Single Instance", "authors": [ "Jiawen Zhang", "Lipeng He", "Kejia Chen", "Jian Lou", "Jian Liu", "Xiaohu Yang", "Ruoxi Jia" ], "github": "https://github.com/Kevin-Zh-CS/safety-at-one-shot", "project_page": "", "abstract": "Fine-tuning safety-aligned large language models (LLMs) can substantially compromise their safety. Previous approaches require many safety samples or calibration sets, which not only incur significant computational overhead during realignment but also lead to noticeable degradation in model utility. Contrary to this belief, we show that safety alignment can be fully recovered with only a single safety example, without sacrificing utility and at minimal cost. Remarkably, this recovery is effective regardless of the number of harmful examples used in fine-tuning or the size of the underlying model, and convergence is achieved within just a few epochs. Furthermore, we uncover the low-rank structure of the safety gradient, which explains why such efficient correction is possible. We validate our findings across five safety-aligned LLMs and multiple datasets, demonstrating the generality of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Kevin-Zh-CS/safety-at-one-shot", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2512.23628", "title": "Memorization in 3D Shape Generation: An Empirical Study", "authors": [ "Shu Pu", "Boya Zeng", "Kaichen Zhou", "Mengyu Wang", "Zhuang Liu" ], "github": "https://github.com/zlab-princeton/3d_mem", "project_page": "", "abstract": "Generative models are increasingly used in 3D vision to synthesize novel shapes, yet it remains unclear whether their generation relies on memorizing training shapes. Understanding their memorization could help prevent training data leakage and improve the diversity of generated results. In this paper, we design an evaluation framework to quantify memorization in 3D generative models and study the influence of different data and modeling designs on memorization. We first apply our framework to quantify memorization in existing methods. Next, through controlled experiments with a latent vector-set (Vecset) diffusion model, we find that, on the data side, memorization depends on data modality, and increases with data diversity and finer-grained conditioning; on the modeling side, it peaks at a moderate guidance scale and can be mitigated by longer Vecsets and simple rotation augmentation. Together, our framework and analysis provide an empirical understanding of memorization in 3D generative models and suggest simple yet effective strategies to reduce it without degrading generation quality. Our code is available at https://github.com/zlab-princeton/3d_mem.", "github_manual": "", "github_regex": "https://github.com/zlab-princeton/3d_mem", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05241", "title": "RoboVIP: Multi-View Video Generation with Visual Identity Prompting Augments Robot Manipulation", "authors": [ "Boyang Wang", "Haoran Zhang", "Shujie Zhang", "Jinkun Hao", "Mingda Jia", "Qi Lv", "Yucheng Mao", "Zhaoyang Lyu", "Jia Zeng", "Xudong Xu", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "The diversity, quantity, and quality of manipulation data are critical for training effective robot policies. However, due to hardware and physical setup constraints, collecting large-scale real-world manipulation data remains difficult to scale across diverse environments. Recent work uses text-prompt conditioned image diffusion models to augment manipulation data by altering the backgrounds and tabletop objects in the visual observations. However, these approaches often overlook the practical need for multi-view and temporally coherent observations required by state-of-the-art policy models. Further, text prompts alone cannot reliably specify the scene setup. To provide the diffusion model with explicit visual guidance, we introduce visual identity prompting, which supplies exemplar images as conditioning inputs to guide the generation of the desired scene setup. To this end, we also build a scalable pipeline to curate a visual identity pool from large robotics datasets. Using our augmented manipulation data to train downstream vision-language-action and visuomotor policy models yields consistent performance gains in both simulation and real-robot settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05124", "title": "Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing", "authors": [ "Runze He", "Yiji Cheng", "Tiankai Hang", "Zhimin Li", "Yu Xu", "Zijin Yin", "Shiyi Zhang", "Wenxun Dai", "Penghui Du", "Ao Ma", "Chunyu Wang", "Qinglin Lu", "Jizhong Han", "Jiao Dai" ], "github": "", "project_page": "", "abstract": "In-context image generation and editing (ICGE) enables users to specify visual concepts through interleaved image-text prompts, demanding precise understanding and faithful execution of user intent. Although recent unified multimodal models exhibit promising understanding capabilities, these strengths often fail to transfer effectively to image generation. We introduce Re-Align, a unified framework that bridges the gap between understanding and generation through structured reasoning-guided alignment. At its core lies the In-Context Chain-of-Thought (IC-CoT), a structured reasoning paradigm that decouples semantic guidance and reference association, providing clear textual target and mitigating confusion among reference images. Furthermore, Re-Align introduces an effective RL training scheme that leverages a surrogate reward to measure the alignment between structured reasoning text and the generated image, thereby improving the model's overall performance on ICGE tasks. Extensive experiments verify that Re-Align outperforms competitive methods of comparable model scale and resources on both in-context image generation and editing tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.04890", "title": "Learnable Multipliers: Freeing the Scale of Language Model Matrix Layers", "authors": [ "Maksim Velikanov", "Ilyas Chahed", "Jingwei Zuo", "Dhia Eddine Rhaiem", "Younes Belkada", "Hakim Hacid" ], "github": "", "project_page": "", "abstract": "Applying weight decay (WD) to matrix layers is standard practice in large-language-model pretraining. Prior work suggests that stochastic gradient noise induces a Brownian-like expansion of the weight matrices W, whose growth is counteracted by WD, leading to a WD-noise equilibrium with a certain weight norm ||W||. In this work, we view the equilibrium norm as a harmful artifact of the training procedure, and address it by introducing learnable multipliers to learn the optimal scale. First, we attach a learnable scalar multiplier to W and confirm that the WD-noise equilibrium norm is suboptimal: the learned scale adapts to data and improves performance. We then argue that individual row and column norms are similarly constrained, and free their scale by introducing learnable per-row and per-column multipliers. Our method can be viewed as a learnable, more expressive generalization of muP multipliers. It outperforms a well-tuned muP baseline, reduces the computational overhead of multiplier tuning, and surfaces practical questions such as forward-pass symmetries and the width-scaling of the learned multipliers. Finally, we validate learnable multipliers with both Adam and Muon optimizers, where it shows improvement in downstream evaluations matching the improvement of the switching from Adam to Muon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.03362", "title": "Guardians of the Hair: Rescuing Soft Boundaries in Depth, Stereo, and Novel Views", "authors": [ "Xiang Zhang", "Yang Zhang", "Lukas Mehl", "Markus Gross", "Christopher Schroers" ], "github": "", "project_page": "", "abstract": "Soft boundaries, like thin hairs, are commonly observed in natural and computer-generated imagery, but they remain challenging for 3D vision due to the ambiguous mixing of foreground and background cues. This paper introduces Guardians of the Hair (HairGuard), a framework designed to recover fine-grained soft boundary details in 3D vision tasks. Specifically, we first propose a novel data curation pipeline that leverages image matting datasets for training and design a depth fixer network to automatically identify soft boundary regions. With a gated residual module, the depth fixer refines depth precisely around soft boundaries while maintaining global depth quality, allowing plug-and-play integration with state-of-the-art depth models. For view synthesis, we perform depth-based forward warping to retain high-fidelity textures, followed by a generative scene painter that fills disoccluded regions and eliminates redundant background artifacts within soft boundaries. Finally, a color fuser adaptively combines warped and inpainted results to produce novel views with consistent geometry and fine-grained details. Extensive experiments demonstrate that HairGuard achieves state-of-the-art performance across monocular depth estimation, stereo image/video conversion, and novel view synthesis, with significant improvements in soft boundary regions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05249", "title": "RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes", "authors": [ "Yuan-Kang Lee", "Kuan-Lin Chen", "Chia-Che Chang", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Nighttime color constancy remains a challenging problem in computational photography due to low-light noise and complex illumination conditions. We present RL-AWB, a novel framework combining statistical methods with deep reinforcement learning for nighttime white balance. Our method begins with a statistical algorithm tailored for nighttime scenes, integrating salient gray pixel detection with novel illumination estimation. Building on this foundation, we develop the first deep reinforcement learning approach for color constancy that leverages the statistical algorithm as its core, mimicking professional AWB tuning experts by dynamically optimizing parameters for each image. To facilitate cross-sensor evaluation, we introduce the first multi-sensor nighttime dataset. Experiment results demonstrate that our method achieves superior generalization capability across low-light and well-illuminated images. Project page: https://ntuneillee.github.io/research/rl-awb/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.04767", "title": "AT^2PO: Agentic Turn-based Policy Optimization via Tree Search", "authors": [ "Zefang Zong", "Dingwei Chen", "Yang Li", "Qi Yi", "Bo Zhou", "Chengming Li", "Bo Qian", "Peng Chen", "Jie Jiang" ], "github": "https://github.com/zzfoutofspace/ATPO", "project_page": "", "abstract": "LLM agents have emerged as powerful systems for tackling multi-turn tasks by interleaving internal reasoning and external tool interactions. Agentic Reinforcement Learning has recently drawn significant research attention as a critical post-training paradigm to further refine these capabilities. In this paper, we present AT^2PO (Agentic Turn-based Policy Optimization via Tree Search), a unified framework for multi-turn agentic RL that addresses three core challenges: limited exploration diversity, sparse credit assignment, and misaligned policy optimization. AT^2PO introduces a turn-level tree structure that jointly enables Entropy-Guided Tree Expansion for strategic exploration and Turn-wise Credit Assignment for fine-grained reward propagation from sparse outcomes. Complementing this, we propose Agentic Turn-based Policy Optimization, a turn-level learning objective that aligns policy updates with the natural decision granularity of agentic interactions. ATPO is orthogonal to tree search and can be readily integrated into any multi-turn RL pipeline. Experiments across seven benchmarks demonstrate consistent improvements over the state-of-the-art baseline by up to 1.84 percentage points in average, with ablation studies validating the effectiveness of each component. Our code is available at https://github.com/zzfoutofspace/ATPO.", "github_manual": "", "github_regex": "https://github.com/zzfoutofspace/ATPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.02016", "title": "Enhancing Object Detection with Privileged Information: A Model-Agnostic Teacher-Student Approach", "authors": [ "Matthias Bartolo", "Dylan Seychell", "Gabriel Hili", "Matthew Montebello", "Carl James Debono", "Saviour Formosa", "Konstantinos Makantasis" ], "github": "", "project_page": "", "abstract": "This paper investigates the integration of the Learning Using Privileged Information (LUPI) paradigm in object detection to exploit fine-grained, descriptive information available during training but not at inference. We introduce a general, model-agnostic methodology for injecting privileged information-such as bounding box masks, saliency maps, and depth cues-into deep learning-based object detectors through a teacher-student architecture. Experiments are conducted across five state-of-the-art object detection models and multiple public benchmarks, including UAV-based litter detection datasets and Pascal VOC 2012, to assess the impact on accuracy, generalization, and computational efficiency. Our results demonstrate that LUPI-trained students consistently outperform their baseline counterparts, achieving significant boosts in detection accuracy with no increase in inference complexity or model size. Performance improvements are especially marked for medium and large objects, while ablation studies reveal that intermediate weighting of teacher guidance optimally balances learning from privileged and standard inputs. The findings affirm that the LUPI framework provides an effective and practical strategy for advancing object detection systems in both resource-constrained and real-world settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2512.24160", "title": "Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset", "authors": [ "TsaiChing Ni", "ZhenQi Chen", "YuanFu Yang" ], "github": "", "project_page": "", "abstract": "We present IMDD-1M, the first large-scale Industrial Multimodal Defect Dataset comprising 1,000,000 aligned image-text pairs, designed to advance multimodal learning for manufacturing and quality inspection. IMDD-1M contains high-resolution real-world defects spanning over 60 material categories and more than 400 defect types, each accompanied by expert-verified annotations and fine-grained textual descriptions detailing defect location, severity, and contextual attributes. This dataset enables a wide spectrum of applications, including classification, segmentation, retrieval, captioning, and generative modeling. Building upon IMDD-1M, we train a diffusion-based vision-language foundation model from scratch, specifically tailored for industrial scenarios. The model serves as a generalizable foundation that can be efficiently adapted to specialized domains through lightweight fine-tuning. With less than 5% of the task-specific data required by dedicated expert models, it achieves comparable performance, highlighting the potential of data-efficient foundation model adaptation for industrial inspection and generation, paving the way for scalable, domain-adaptive, and knowledge-grounded manufacturing intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.04300", "title": "Beyond Binary Preference: Aligning Diffusion Models to Fine-grained Criteria by Decoupling Attributes", "authors": [ "Chenye Meng", "Zejian Li", "Zhongni Liu", "Yize Li", "Changle Xie", "Kaixin Jia", "Ling Yang", "Huanghuang Deng", "Shiying Ding", "Shengyuan Zhang", "Jiayi Li", "Lingyun Sun" ], "github": "", "project_page": "", "abstract": "Post-training alignment of diffusion models relies on simplified signals, such as scalar rewards or binary preferences. This limits alignment with complex human expertise, which is hierarchical and fine-grained. To address this, we first construct a hierarchical, fine-grained evaluation criteria with domain experts, which decomposes image quality into multiple positive and negative attributes organized in a tree structure. Building on this, we propose a two-stage alignment framework. First, we inject domain knowledge to an auxiliary diffusion model via Supervised Fine-Tuning. Second, we introduce Complex Preference Optimization (CPO) that extends DPO to align the target diffusion to our non-binary, hierarchical criteria. Specifically, we reformulate the alignment problem to simultaneously maximize the probability of positive attributes while minimizing the probability of negative attributes with the auxiliary diffusion. We instantiate our approach in the domain of painting generation and conduct CPO training with an annotated dataset of painting with fine-grained attributes based on our criteria. Extensive experiments demonstrate that CPO significantly enhances generation quality and alignment with expertise, opening new avenues for fine-grained criteria alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.04620", "title": "AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering", "authors": [ "Di Zhang" ], "github": "https://github.com/tatsu-lab/alpaca_eval", "project_page": "", "abstract": "Recent progress in large language model (LLM) agents has largely focused on embedding self-improvement mechanisms inside the agent or searching over many concurrent variants. While these approaches can raise aggregate scores, they often yield unstable and hard-to-audit improvement trajectories, making it difficult to guarantee non-regression or to reason about failures across versions. We reframe agent improvement as release engineering: agents are treated as shippable artifacts, and improvement is externalized into a regression-aware release pipeline. We introduce AgentDevel, a release engineering pipeline that iteratively runs the current agent, produces implementation-blind, symptom-level quality signals from execution traces, synthesizes a single release candidate (RC) via executable diagnosis, and promotes it under flip-centered gating. AgentDevel features three core designs: (i) an implementation-blind LLM critic that characterizes failure appearances without accessing agent internals, (ii) script-based executable diagnosis that aggregates dominant symptom patterns and produces auditable engineering specifications, and (iii) flip-centered gating that prioritizes pass to fail regressions and fail to pass fixes as first-class evidence. Unlike population-based search or in-agent self-refinement, AgentDevel maintains a single canonical version line and emphasizes non-regression as a primary objective. Experiments on execution-heavy benchmarks demonstrate that AgentDevel yields stable improvements with significantly fewer regressions while producing reproducible, auditable artifacts. Overall, AgentDevel provides a practical development discipline for building, debugging, and releasing LLM agents as software development.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tatsu-lab/alpaca_eval", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05125", "title": "VERSE: Visual Embedding Reduction and Space Exploration. Clustering-Guided Insights for Training Data Enhancement in Visually-Rich Document Understanding", "authors": [ "Ignacio de Rodrigo", "Alvaro J. Lopez-Lopez", "Jaime Boal" ], "github": "", "project_page": "", "abstract": "This work introduces VERSE, a methodology for analyzing and improving Vision-Language Models applied to Visually-rich Document Understanding by exploring their visual embedding space. VERSE enables the visualization of latent representations, supporting the assessment of model feasibility. It also facilitates the identification of problematic regions and guides the generation of synthetic data to enhance performance in those clusters. We validate the methodology by training on the synthetic MERIT Dataset and evaluating on its real-world counterpart, MERIT Secret. Results show that VERSE helps uncover the visual features associated with error-prone clusters, and that retraining with samples containing these features substantially boosts F1 performance without degrading generalization. Furthermore, we demonstrate that on-premise models such as Donut and Idefics2, when optimized with VERSE, match or even surpass the performance of SaaS solutions like GPT-4 and Pixtral.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.04754", "title": "ProFuse: Efficient Cross-View Context Fusion for Open-Vocabulary 3D Gaussian Splatting", "authors": [ "Yen-Jen Chiou", "Wei-Tse Cheng", "Yuan-Fu Yang" ], "github": "https://github.com/chiou1203/ProFuse", "project_page": "", "abstract": "We present ProFuse, an efficient context-aware framework for open-vocabulary 3D scene understanding with 3D Gaussian Splatting (3DGS). The pipeline enhances cross-view consistency and intra-mask cohesion within a direct registration setup, adding minimal overhead and requiring no render-supervised fine-tuning. Instead of relying on a pretrained 3DGS scene, we introduce a dense correspondence-guided pre-registration phase that initializes Gaussians with accurate geometry while jointly constructing 3D Context Proposals via cross-view clustering. Each proposal carries a global feature obtained through weighted aggregation of member embeddings, and this feature is fused onto Gaussians during direct registration to maintain per-primitive language coherence across views. With associations established in advance, semantic fusion requires no additional optimization beyond standard reconstruction, and the model retains geometric refinement without densification. ProFuse achieves strong open-vocabulary 3DGS understanding while completing semantic attachment in about five minutes per scene, which is two times faster than SOTA.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/chiou1203/ProFuse", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2512.21815", "title": "Few Tokens Matter: Entropy Guided Attacks on Vision-Language Models", "authors": [ "Mengqi He", "Xinyu Tian", "Xin Shen", "Jinhong Ni", "Shu Zou", "Zhaoyuan Yang", "Jing Zhang" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks. Entropy, a measure of model uncertainty, is strongly correlated with the reliability of VLM. Prior entropy-based attacks maximize uncertainty at all decoding steps, implicitly assuming that every token contributes equally to generation instability. We show instead that a small fraction (about 20%) of high-entropy tokens, i.e., critical decision points in autoregressive generation, disproportionately governs output trajectories. By concentrating adversarial perturbations on these positions, we achieve semantic degradation comparable to global methods while using substantially smaller budgets. More importantly, across multiple representative VLMs, such selective attacks convert 35-49% of benign outputs into harmful ones, exposing a more critical safety risk. Remarkably, these vulnerable high-entropy forks recur across architecturally diverse VLMs, enabling feasible transferability (17-26% harmful rates on unseen targets). Motivated by these findings, we propose Entropy-bank Guided Adversarial attacks (EGA), which achieves competitive attack success rates (93-95%) alongside high harmful conversion, thereby revealing new weaknesses in current VLM safety mechanisms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05172", "title": "CoV: Chain-of-View Prompting for Spatial Reasoning", "authors": [ "Haoyu Zhao", "Akide Liu", "Zeyu Zhang", "Weijie Wang", "Feng Chen", "Ruihan Zhu", "Gholamreza Haffari", "Bohan Zhuang" ], "github": "", "project_page": "", "abstract": "Embodied question answering (EQA) in 3D environments often requires collecting context that is distributed across multiple viewpoints and partially occluded. However, most recent vision--language models (VLMs) are constrained to a fixed and finite set of input views, which limits their ability to acquire question-relevant context at inference time and hinders complex spatial reasoning. We propose Chain-of-View (CoV) prompting, a training-free, test-time reasoning framework that transforms a VLM into an active viewpoint reasoner through a coarse-to-fine exploration process. CoV first employs a View Selection agent to filter redundant frames and identify question-aligned anchor views. It then performs fine-grained view adjustment by interleaving iterative reasoning with discrete camera actions, obtaining new observations from the underlying 3D scene representation until sufficient context is gathered or a step budget is reached. We evaluate CoV on OpenEQA across four mainstream VLMs and obtain an average +11.56\\% improvement in LLM-Match, with a maximum gain of +13.62\\% on Qwen3-VL-Flash. CoV further exhibits test-time scaling: increasing the minimum action budget yields an additional +2.51\\% average improvement, peaking at +3.73\\% on Gemini-2.5-Flash. On ScanQA and SQA3D, CoV delivers strong performance (e.g., 116 CIDEr / 31.9 EM@1 on ScanQA and 51.1 EM@1 on SQA3D). Overall, these results suggest that question-aligned view selection coupled with open-view search is an effective, model-agnostic strategy for improving spatial reasoning in 3D EQA without additional training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.03111", "title": "One Sample to Rule Them All: Extreme Data Efficiency in RL Scaling", "authors": [ "Yiyuan Li", "Zhen Huang", "Yanan Wu", "Weixun Wang", "Xuefeng Li", "Yijia Luo", "Wenbo Su", "Bo Zheng", "Pengfei Liu" ], "github": "", "project_page": "", "abstract": "The reasoning ability of large language models (LLMs) can be unleashed with reinforcement learning (RL) (OpenAI, 2024; DeepSeek-AI et al., 2025a; Zeng et al., 2025). The success of existing RL attempts in LLMs usually relies on high-quality samples of thousands or beyond. In this paper, we challenge fundamental assumptions about data requirements in RL for LLMs by demonstrating the remarkable effectiveness of one-shot learning. Specifically, we introduce polymath learning, a framework for designing one training sample that elicits multidisciplinary impact. We present three key findings: (1) A single, strategically selected math reasoning sample can produce significant performance improvements across multiple domains, including physics, chemistry, and biology with RL; (2) The math skills salient to reasoning suggest the characteristics of the optimal polymath sample; and (3) An engineered synthetic sample that integrates multidiscipline elements outperforms training with individual samples that naturally occur. Our approach achieves superior performance to training with larger datasets across various reasoning benchmarks, demonstrating that sample quality and design, rather than quantity, may be the key to unlock enhanced reasoning capabilities in language models. Our results suggest a shift, dubbed as sample engineering, toward precision engineering of training samples rather than simply increasing data volume.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.04233", "title": "LEMAS: Large A 150K-Hour Large-scale Extensible Multilingual Audio Suite with Generative Speech Models", "authors": [ "Zhiyuan Zhao", "Lijian Lin", "Ye Zhu", "Kai Xie", "Yunfei Liu", "Yu Li" ], "github": "", "project_page": "", "abstract": "We present the LEMAS-Dataset, which, to our knowledge, is currently the largest open-source multilingual speech corpus with word-level timestamps. Covering over 150,000 hours across 10 major languages, LEMAS-Dataset is constructed via a efficient data processing pipeline that ensures high-quality data and annotations. To validate the effectiveness of LEMAS-Dataset across diverse generative paradigms, we train two benchmark models with distinct architectures and task specializations on this dataset. LEMAS-TTS, built upon a non-autoregressive flow-matching framework, leverages the dataset's massive scale and linguistic diversity to achieve robust zero-shot multilingual synthesis. Our proposed accent-adversarial training and CTC loss mitigate cross-lingual accent issues, enhancing synthesis stability. Complementarily, LEMAS-Edit employs an autoregressive decoder-only architecture that formulates speech editing as a masked token infilling task. By exploiting precise word-level alignments to construct training masks and adopting adaptive decoding strategies, it achieves seamless, smooth-boundary speech editing with natural transitions. Experimental results demonstrate that models trained on LEMAS-Dataset deliver high-quality synthesis and editing performance, confirming the dataset's quality. We envision that this richly timestamp-annotated, fine-grained multilingual corpus will drive future advances in prompt-based speech generation systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.05149", "title": "Multi-Scale Local Speculative Decoding for Image Generation", "authors": [ "Elia Peruzzo", "Guillaume Sauti\u00e8re", "Amirhossein Habibian" ], "github": "https://github.com/csuhan/Tar", "project_page": "", "abstract": "Autoregressive (AR) models have achieved remarkable success in image synthesis, yet their sequential nature imposes significant latency constraints. Speculative Decoding offers a promising avenue for acceleration, but existing approaches are limited by token-level ambiguity and lack of spatial awareness. In this work, we introduce Multi-Scale Local Speculative Decoding (MuLo-SD), a novel framework that combines multi-resolution drafting with spatially informed verification to accelerate AR image generation. Our method leverages a low-resolution drafter paired with learned up-samplers to propose candidate image tokens, which are then verified in parallel by a high-resolution target model. Crucially, we incorporate a local rejection and resampling mechanism, enabling efficient correction of draft errors by focusing on spatial neighborhoods rather than raster-scan resampling after the first rejection. We demonstrate that MuLo-SD achieves substantial speedups - up to 1.7times - outperforming strong speculative decoding baselines such as EAGLE-2 and LANTERN in terms of acceleration, while maintaining comparable semantic alignment and perceptual quality. These results are validated using GenEval, DPG-Bench, and FID/HPSv2 on the MS-COCO 5k validation split. Extensive ablations highlight the impact of up-sampling design, probability pooling, and local rejection and resampling with neighborhood expansion. Our approach sets a new state-of-the-art in speculative decoding for image synthesis, bridging the gap between efficiency and fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/csuhan/Tar", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.04575", "title": "Scaling Behavior Cloning Improves Causal Reasoning: An Open Model for Real-Time Video Game Playing", "authors": [ "Yuguang Yue", "Irakli Salia", "Samuel Hunt", "Chris Green", "Wenzhe Shi", "Jonathan J Hunt" ], "github": "", "project_page": "", "abstract": "Behavior cloning is enjoying a resurgence in popularity as scaling both model and data sizes proves to provide a strong starting point for many tasks of interest. In this work, we introduce an open recipe for training a video game playing foundation model designed for inference in realtime on a consumer GPU. We release all data (8300+ hours of high quality human gameplay), training and inference code, and pretrained checkpoints under an open license. We show that our best model is capable of playing a variety of 3D video games at a level competitive with human play. We use this recipe to systematically examine the scaling laws of behavior cloning to understand how the model's performance and causal reasoning varies with model and data scale. We first show in a simple toy problem that, for some types of causal reasoning, increasing both the amount of training data and the depth of the network results in the model learning a more causal policy. We then systematically study how causality varies with the number of parameters (and depth) and training steps in scaled models of up to 1.2 billion parameters, and we find similar scaling results to what we observe in the toy problem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.04792", "title": "PyramidalWan: On Making Pretrained Video Model Pyramidal for Efficient Inference", "authors": [ "Denis Korzhenkov", "Adil Karjauv", "Animesh Karnewar", "Mohsen Ghafoorian", "Amirhossein Habibian" ], "github": "", "project_page": "", "abstract": "Recently proposed pyramidal models decompose the conventional forward and backward diffusion processes into multiple stages operating at varying resolutions. These models handle inputs with higher noise levels at lower resolutions, while less noisy inputs are processed at higher resolutions. This hierarchical approach significantly reduces the computational cost of inference in multi-step denoising models. However, existing open-source pyramidal video models have been trained from scratch and tend to underperform compared to state-of-the-art systems in terms of visual plausibility. In this work, we present a pipeline that converts a pretrained diffusion model into a pyramidal one through low-cost finetuning, achieving this transformation without degradation in quality of output videos. Furthermore, we investigate and compare various strategies for step distillation within pyramidal models, aiming to further enhance the inference efficiency. Our results are available at https://qualcomm-ai-research.github.io/PyramidalWan.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.02702", "title": "Learning User Preferences Through Interaction for Long-Term Collaboration", "authors": [ "Shuhaib Mehri", "Priyanka Kargupta", "Tal August", "Dilek Hakkani-T\u00fcr" ], "github": "https://github.com/Shuhaibm/multisessioncollab", "project_page": "", "abstract": "As conversational agents accumulate experience collaborating with users, adapting to user preferences is essential for fostering long-term relationships and improving collaboration quality over time. We introduce MultiSessionCollab, a benchmark that evaluates how well agents can learn user preferences and leverage them to improve collaboration quality throughout multiple sessions. To develop agents that succeed in this setting, we present long-term collaborative agents equipped with a memory that persists and refines user preference as interaction experience accumulates. Moreover, we demonstrate that learning signals can be derived from user simulator behavior in MultiSessionCollab to train agents to generate more comprehensive reflections and update their memory more effectively. Extensive experiments show that equipping agents with memory improves long-term collaboration, yielding higher task success rates, more efficient interactions, and reduced user effort. Finally, we conduct a human user study that demonstrates that memory helps improve user experience in real-world settings.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Shuhaibm/multisessioncollab", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-09", "arxiv_id": "2601.04342", "title": "ReHyAt: Recurrent Hybrid Attention for Video Diffusion Transformers", "authors": [ "Mohsen Ghafoorian", "Amirhossein Habibian" ], "github": "", "project_page": "", "abstract": "Recent advances in video diffusion models have shifted towards transformer-based architectures, achieving state-of-the-art video generation but at the cost of quadratic attention complexity, which severely limits scalability for longer sequences. We introduce ReHyAt, a Recurrent Hybrid Attention mechanism that combines the fidelity of softmax attention with the efficiency of linear attention, enabling chunk-wise recurrent reformulation and constant memory usage. Unlike the concurrent linear-only SANA Video, ReHyAt's hybrid design allows efficient distillation from existing softmax-based models, reducing the training cost by two orders of magnitude to ~160 GPU hours, while being competitive in the quality. Our light-weight distillation and finetuning pipeline provides a recipe that can be applied to future state-of-the-art bidirectional softmax-based models. Experiments on VBench and VBench-2.0, as well as a human preference study, demonstrate that ReHyAt achieves state-of-the-art video quality while reducing attention cost from quadratic to linear, unlocking practical scalability for long-duration and on-device video generation. Project page is available at https://qualcomm-ai-research.github.io/rehyat.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.06021", "title": "Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards", "authors": [ "Jiajie Zhang", "Xin Lv", "Ling Feng", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/THUDM/CaRR", "project_page": "", "abstract": "Reinforcement learning (RL) has emerged as a critical technique for enhancing LLM-based deep search agents. However, existing approaches primarily rely on binary outcome rewards, which fail to capture the comprehensiveness and factuality of agents' reasoning process, and often lead to undesirable behaviors such as shortcut exploitation and hallucinations. To address these limitations, we propose Citation-aware Rubric Rewards (CaRR), a fine-grained reward framework for deep search agents that emphasizes reasoning comprehensiveness, factual grounding, and evidence connectivity. CaRR decomposes complex questions into verifiable single-hop rubrics and requires agents to satisfy these rubrics by explicitly identifying hidden entities, supporting them with correct citations, and constructing complete evidence chains that link to the predicted answer. We further introduce Citation-aware Group Relative Policy Optimization (C-GRPO), which combines CaRR and outcome rewards for training robust deep search agents. Experiments show that C-GRPO consistently outperforms standard outcome-based RL baselines across multiple deep search benchmarks. Our analysis also validates that C-GRPO effectively discourages shortcut exploitation, promotes comprehensive, evidence-grounded reasoning, and exhibits strong generalization to open-ended deep research tasks. Our code and data are available at https://github.com/THUDM/CaRR.", "github_manual": "", "github_regex": "https://github.com/THUDM/CaRR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05966", "title": "VideoAR: Autoregressive Video Generation via Next-Frame & Scale Prediction", "authors": [ "Longbin Ji", "Xiaoxiong Liu", "Junyuan Shang", "Shuohuan Wang", "Yu Sun", "Hua Wu", "Haifeng Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in video generation have been dominated by diffusion and flow-matching models, which produce high-quality results but remain computationally intensive and difficult to scale. In this work, we introduce VideoAR, the first large-scale Visual Autoregressive (VAR) framework for video generation that combines multi-scale next-frame prediction with autoregressive modeling. VideoAR disentangles spatial and temporal dependencies by integrating intra-frame VAR modeling with causal next-frame prediction, supported by a 3D multi-scale tokenizer that efficiently encodes spatio-temporal dynamics. To improve long-term consistency, we propose Multi-scale Temporal RoPE, Cross-Frame Error Correction, and Random Frame Mask, which collectively mitigate error propagation and stabilize temporal coherence. Our multi-stage pretraining pipeline progressively aligns spatial and temporal learning across increasing resolutions and durations. Empirically, VideoAR achieves new state-of-the-art results among autoregressive models, improving FVD on UCF-101 from 99.5 to 88.6 while reducing inference steps by over 10x, and reaching a VBench score of 81.74-competitive with diffusion-based models an order of magnitude larger. These results demonstrate that VideoAR narrows the performance gap between autoregressive and diffusion paradigms, offering a scalable, efficient, and temporally consistent foundation for future video generation research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05848", "title": "Goal Force: Teaching Video Models To Accomplish Physics-Conditioned Goals", "authors": [ "Nate Gillman", "Yinghua Zhou", "Zitian Tang", "Evan Luo", "Arjan Chakravarthy", "Daksh Aggarwal", "Michael Freeman", "Charles Herrmann", "Chen Sun" ], "github": "https://github.com/brown-palm/goal-force", "project_page": "", "abstract": "Recent advancements in video generation have enabled the development of ``world models'' capable of simulating potential futures for robotics and planning. However, specifying precise goals for these models remains a challenge; text instructions are often too abstract to capture physical nuances, while target images are frequently infeasible to specify for dynamic tasks. To address this, we introduce Goal Force, a novel framework that allows users to define goals via explicit force vectors and intermediate dynamics, mirroring how humans conceptualize physical tasks. We train a video generation model on a curated dataset of synthetic causal primitives-such as elastic collisions and falling dominos-teaching it to propagate forces through time and space. Despite being trained on simple physics data, our model exhibits remarkable zero-shot generalization to complex, real-world scenarios, including tool manipulation and multi-object causal chains. Our results suggest that by grounding video generation in fundamental physical interactions, models can emerge as implicit neural physics simulators, enabling precise, physics-aware planning without reliance on external engines. We release all datasets, code, model weights, and interactive video demos at our project page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/brown-palm/goal-force", "github_api_added_by": "auto" }, { "date": "2026-01-12", "arxiv_id": "2601.05637", "title": "GenCtrl -- A Formal Controllability Toolkit for Generative Models", "authors": [ "Emily Cheng", "Carmen Amo Alonso", "Federico Danieli", "Arno Blaas", "Luca Zappella", "Pau Rodriguez", "Xavier Suau" ], "github": "https://github.com/apple/ml-genctrl", "project_page": "", "abstract": "As generative models become ubiquitous, there is a critical need for fine-grained control over the generation process. Yet, while controlled generation methods from prompting to fine-tuning proliferate, a fundamental question remains unanswered: are these models truly controllable in the first place? In this work, we provide a theoretical framework to formally answer this question. Framing human-model interaction as a control process, we propose a novel algorithm to estimate the controllable sets of models in a dialogue setting. Notably, we provide formal guarantees on the estimation error as a function of sample complexity: we derive probably-approximately correct bounds for controllable set estimates that are distribution-free, employ no assumptions except for output boundedness, and work for any black-box nonlinear control system (i.e., any generative model). We empirically demonstrate the theoretical framework on different tasks in controlling dialogue processes, for both language models and text-to-image generation. Our results show that model controllability is surprisingly fragile and highly dependent on the experimental setting. This highlights the need for rigorous controllability analysis, shifting the focus from simply attempting control to first understanding its fundamental limits.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/apple/ml-genctrl", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05503", "title": "Over-Searching in Search-Augmented Large Language Models", "authors": [ "Roy Xie", "Deepak Gopinath", "David Qiu", "Dong Lin", "Haitian Sun", "Saloni Potdar", "Bhuwan Dhingra" ], "github": "", "project_page": "", "abstract": "Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval. However, they often over-search -- unnecessarily invoking search tool even when it does not improve response quality, which leads to computational inefficiency and hallucinations by incorporating irrelevant context. In this work, we conduct a systematic evaluation of over-searching across multiple dimensions, including query types, model categories, retrieval conditions, and multi-turn conversations. Our finding shows: (i) search generally improves answer accuracy on answerable queries but harms abstention on unanswerable ones; (ii) over-searching is more pronounced in complex reasoning models and deep research systems, is exacerbated by noisy retrieval, and compounds across turns in multi-turn conversations; and (iii) the composition of retrieved evidence is crucial, as the presence of negative evidence improves abstention. To quantify over-searching, we introduce Tokens Per Correctness (TPC), an evaluation metric that captures the performance-cost trade-off for search-augmented LLMs. Lastly, we investigate mitigation approaches at both the query and retrieval levels and release the OverSearchQA to foster continued research into efficient search-augmented LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05432", "title": "Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization", "authors": [ "Yuxiang Ji", "Yong Wang", "Ziyu Ma", "Yiming Hu", "Hailang Huang", "Xuecai Hu", "Guanhua Chen", "Liaoni Wu", "Xiangxiang Chu" ], "github": "https://github.com/TheEighthDay/SeekWorld", "project_page": "", "abstract": "The image geolocalization task aims to predict the location where an image was taken anywhere on Earth using visual clues. Existing large vision-language model (LVLM) approaches leverage world knowledge, chain-of-thought reasoning, and agentic capabilities, but overlook a common strategy used by humans -- using maps. In this work, we first equip the model Thinking with Map ability and formulate it as an agent-in-the-map loop. We develop a two-stage optimization scheme for it, including agentic reinforcement learning (RL) followed by parallel test-time scaling (TTS). The RL strengthens the agentic capability of model to improve sampling efficiency, and the parallel TTS enables the model to explore multiple candidate paths before making the final prediction, which is crucial for geolocalization. To evaluate our method on up-to-date and in-the-wild images, we further present MAPBench, a comprehensive geolocalization training and evaluation benchmark composed entirely of real-world images. Experimental results show that our method outperforms existing open- and closed-source models on most metrics, specifically improving Acc@500m from 8.0\\% to 22.1\\% compared to Gemini-3-Pro with Google Search/Map grounded mode.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/TheEighthDay/SeekWorld", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.06002", "title": "The Molecular Structure of Thought: Mapping the Topology of Long Chain-of-Thought Reasoning", "authors": [ "Qiguang Chen", "Yantao Du", "Ziniu Li", "Jinhao Liu", "Songyao Duan", "Jiarui Guo", "Minghao Liu", "Jiaheng Liu", "Tong Yang", "Ge Zhang", "Libo Qin", "Wanxiang Che", "Wenhao Huang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) often fail to learn effective long chain-of-thought (Long CoT) reasoning from human or non-Long-CoT LLMs imitation. To understand this, we propose that effective and learnable Long CoT trajectories feature stable molecular-like structures in unified view, which are formed by three interaction types: Deep-Reasoning (covalent-like), Self-Reflection (hydrogen-bond-like), and Self-Exploration (van der Waals-like). Analysis of distilled trajectories reveals these structures emerge from Long CoT fine-tuning, not keyword imitation. We introduce Effective Semantic Isomers and show that only bonds promoting fast entropy convergence support stable Long CoT learning, while structural competition impairs training. Drawing on these findings, we present Mole-Syn, a distribution-transfer-graph method that guides synthesis of effective Long CoT structures, boosting performance and RL stability across benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05808", "title": "EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis", "authors": [ "Xiaoshuai Song", "Haofei Chang", "Guanting Dong", "Yutao Zhu", "Zhicheng Dou", "Ji-Rong Wen" ], "github": "https://github.com/RUC-NLPIR/EnvScaler", "project_page": "", "abstract": "Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but this process relies on rich and varied tool-interaction sandboxes. However, access to real systems is often restricted; LLM-simulated environments are prone to hallucinations and inconsistencies; and manually built sandboxes are hard to scale. In this paper, we propose EnvScaler, an automated framework for scalable tool-interaction environments via programmatic synthesis. EnvScaler comprises two components. First, SkelBuilder constructs diverse environment skeletons through topic mining, logic modeling, and quality evaluation. Then, ScenGenerator generates multiple task scenarios and rule-based trajectory validation functions for each environment. With EnvScaler, we synthesize 191 environments and about 7K scenarios, and apply them to Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) for Qwen3 series models. Results on three benchmarks show that EnvScaler significantly improves LLMs' ability to solve tasks in complex environments involving multi-turn, multi-tool interactions. We release our code and data at https://github.com/RUC-NLPIR/EnvScaler.", "github_manual": "", "github_regex": "https://github.com/RUC-NLPIR/EnvScaler", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05573", "title": "Orient Anything V2: Unifying Orientation and Rotation Understanding", "authors": [ "Zehan Wang", "Ziang Zhang", "Jiayang Xu", "Jialei Wang", "Tianyu Pang", "Chao Du", "HengShuang Zhao", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "This work presents Orient Anything V2, an enhanced foundation model for unified understanding of object 3D orientation and rotation from single or paired images. Building upon Orient Anything V1, which defines orientation via a single unique front face, V2 extends this capability to handle objects with diverse rotational symmetries and directly estimate relative rotations. These improvements are enabled by four key innovations: 1) Scalable 3D assets synthesized by generative models, ensuring broad category coverage and balanced data distribution; 2) An efficient, model-in-the-loop annotation system that robustly identifies 0 to N valid front faces for each object; 3) A symmetry-aware, periodic distribution fitting objective that captures all plausible front-facing orientations, effectively modeling object rotational symmetry; 4) A multi-frame architecture that directly predicts relative object rotations. Extensive experiments show that Orient Anything V2 achieves state-of-the-art zero-shot performance on orientation estimation, 6DoF pose estimation, and object symmetry recognition across 11 widely used benchmarks. The model demonstrates strong generalization, significantly broadening the applicability of orientation estimation in diverse downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05905", "title": "Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency", "authors": [ "Haoming Xu", "Ningyuan Zhao", "Yunzhi Yao", "Weihong Xu", "Hongru Wang", "Xinle Deng", "Shumin Deng", "Jeff Z. Pan", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/belief", "project_page": "", "abstract": "As Large Language Models (LLMs) are increasingly deployed in real-world settings, correctness alone is insufficient. Reliable deployment requires maintaining truthful beliefs under contextual perturbations. Existing evaluations largely rely on point-wise confidence like Self-Consistency, which can mask brittle belief. We show that even facts answered with perfect self-consistency can rapidly collapse under mild contextual interference. To address this gap, we propose Neighbor-Consistency Belief (NCB), a structural measure of belief robustness that evaluates response coherence across a conceptual neighborhood. To validate the efficiency of NCB, we introduce a new cognitive stress-testing protocol that probes outputs stability under contextual interference. Experiments across multiple LLMs show that the performance of high-NCB data is relatively more resistant to interference. Finally, we present Structure-Aware Training (SAT), which optimizes context-invariant belief structure and reduces long-tail knowledge brittleness by approximately 30%. Code will be available at https://github.com/zjunlp/belief.", "github_manual": "", "github_regex": "https://github.com/zjunlp/belief", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.04888", "title": "SmartSearch: Process Reward-Guided Query Refinement for Search Agents", "authors": [ "Tongyu Wen", "Guanting Dong", "Zhicheng Dou" ], "github": "https://github.com/MYVAE/SmartSearch", "project_page": "", "abstract": "Large language model (LLM)-based search agents have proven promising for addressing knowledge-intensive problems by incorporating information retrieval capabilities. Existing works largely focus on optimizing the reasoning paradigms of search agents, yet the quality of intermediate search queries during reasoning remains overlooked. As a result, the generated queries often remain inaccurate, leading to unexpected retrieval results and ultimately limiting search agents' overall effectiveness. To mitigate this issue, we introduce SmartSearch, a framework built upon two key mechanisms: (1) Process rewards, which provide fine-grained supervision for the quality of each intermediate search query through Dual-Level Credit Assessment. (2) Query refinement, which promotes the optimization of query generation by selectively refining low-quality search queries and regenerating subsequent search rounds based on these refinements. To enable the search agent to progressively internalize the ability to improve query quality under the guidance of process rewards, we design a three-stage curriculum learning framework. This framework guides the agent through a progression from imitation, to alignment, and ultimately to generalization. Experimental results show that SmartSearch consistently surpasses existing baselines, and additional quantitative analyses further confirm its significant gains in both search efficiency and query quality. The code is available at https://github.com/MYVAE/SmartSearch.", "github_manual": "", "github_regex": "https://github.com/MYVAE/SmartSearch", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05930", "title": "Can We Predict Before Executing Machine Learning Agents?", "authors": [ "Jingsheng Zheng", "Jintian Zhang", "Yujie Luo", "Yuren Mao", "Yunjun Gao", "Lun Du", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/predict-before-execute", "project_page": "", "abstract": "Autonomous machine learning agents have revolutionized scientific discovery, yet they remain constrained by a Generate-Execute-Feedback paradigm. Previous approaches suffer from a severe Execution Bottleneck, as hypothesis evaluation relies strictly on expensive physical execution. To bypass these physical constraints, we internalize execution priors to substitute costly runtime checks with instantaneous predictive reasoning, drawing inspiration from World Models. In this work, we formalize the task of Data-centric Solution Preference and construct a comprehensive corpus of 18,438 pairwise comparisons. We demonstrate that LLMs exhibit significant predictive capabilities when primed with a Verified Data Analysis Report, achieving 61.5% accuracy and robust confidence calibration. Finally, we instantiate this framework in FOREAGENT, an agent that employs a Predict-then-Verify loop, achieving a 6x acceleration in convergence while surpassing execution-based baselines by +6%. Our code and dataset will be publicly available soon at https://github.com/zjunlp/predict-before-execute.", "github_manual": "", "github_regex": "https://github.com/zjunlp/predict-before-execute", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.04823", "title": "DR-LoRA: Dynamic Rank LoRA for Mixture-of-Experts Adaptation", "authors": [ "Guanzhi Deng", "Bo Li", "Ronghao Chen", "Huacan Wang", "Linqi Song", "Lijie Wen" ], "github": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) has become a prominent paradigm for scaling Large Language Models (LLMs). Parameter-efficient fine-tuning (PEFT), such as LoRA, is widely adopted to adapt pretrained MoE LLMs to downstream tasks. However, existing approaches assign identical LoRA ranks to all experts, overlooking the intrinsic functional specialization within MoE LLMs. This uniform allocation leads to resource mismatch, task-relevant experts are under-provisioned while less relevant ones receive redundant parameters. We propose a Dynamic Rank LoRA framework named DR-LoRA, which dynamically grows expert LoRA ranks during fine-tuning based on task-specific demands. DR-LoRA employs an Expert Saliency Scoring mechanism that integrates expert routing frequency and LoRA rank importance to quantify each expert's demand for additional capacity. Experts with higher saliency scores are prioritized for rank expansion, enabling the automatic formation of a heterogeneous rank distribution tailored to the target task. Experiments on multiple benchmarks demonstrate that DR-LoRA consistently outperforms standard LoRA and static allocation strategies under the same parameter budget, achieving superior task performance with more efficient parameter utilization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05870", "title": "IIB-LPO: Latent Policy Optimization via Iterative Information Bottleneck", "authors": [ "Huilin Deng", "Hongchen Luo", "Yue Zhu", "Long Li", "Zhuoyue Chen", "Xinghao Zhao", "Ming Li", "Jihai Zhang", "Mengchang Wang", "Yang Cao", "Yu Kang" ], "github": "", "project_page": "", "abstract": "Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) for Large Language Model (LLM) reasoning have been hindered by a persistent challenge: exploration collapse. The semantic homogeneity of random rollouts often traps models in narrow, over-optimized behaviors. While existing methods leverage policy entropy to encourage exploration, they face inherent limitations. Global entropy regularization is susceptible to reward hacking, which can induce meaningless verbosity, whereas local token-selective updates struggle with the strong inductive bias of pre-trained models. To address this, we propose Latent Policy Optimization via Iterative Information Bottleneck (IIB-LPO), a novel approach that shifts exploration from statistical perturbation of token distributions to topological branching of reasoning trajectories. IIB-LPO triggers latent branching at high-entropy states to diversify reasoning paths and employs the Information Bottleneck principle both as a trajectory filter and a self-reward mechanism, ensuring concise and informative exploration. Empirical results across four mathematical reasoning benchmarks demonstrate that IIB-LPO achieves state-of-the-art performance, surpassing prior methods by margins of up to 5.3% in accuracy and 7.4% in diversity metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05851", "title": "Router-Suggest: Dynamic Routing for Multimodal Auto-Completion in Visually-Grounded Dialogs", "authors": [ "Sandeep Mishra", "Devichand Budagam", "Anubhab Mandal", "Bishal Santra", "Pawan Goyal", "Manish Gupta" ], "github": "", "project_page": "", "abstract": "Real-time multimodal auto-completion is essential for digital assistants, chatbots, design tools, and healthcare consultations, where user inputs rely on shared visual context. We introduce Multimodal Auto-Completion (MAC), a task that predicts upcoming characters in live chats using partially typed text and visual cues. Unlike traditional text-only auto-completion (TAC), MAC grounds predictions in multimodal context to better capture user intent. To enable this task, we adapt MMDialog and ImageChat to create benchmark datasets. We evaluate leading vision-language models (VLMs) against strong textual baselines, highlighting trade-offs in accuracy and efficiency. We present Router-Suggest, a router framework that dynamically selects between textual models and VLMs based on dialog context, along with a lightweight variant for resource-constrained environments. Router-Suggest achieves a 2.3x to 10x speedup over the best-performing VLM. A user study shows that VLMs significantly excel over textual models on user satisfaction, notably saving user typing effort and improving the quality of completions in multi-turn conversations. These findings underscore the need for multimodal context in auto-completions, leading to smarter, user-aware assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.04786", "title": "AgentOCR: Reimagining Agent History via Optical Self-Compression", "authors": [ "Lang Feng", "Fuchao Yang", "Feng Chen", "Xin Cheng", "Haiyang Xu", "Zhenglin Wan", "Ming Yan", "Bo An" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models (LLMs) enable agentic systems trained with reinforcement learning (RL) over multi-turn interaction trajectories, but practical deployment is bottlenecked by rapidly growing textual histories that inflate token budgets and memory usage. We introduce AgentOCR, a framework that exploits the superior information density of visual tokens by representing the accumulated observation-action history as a compact rendered image. To make multi-turn rollouts scalable, AgentOCR proposes segment optical caching. By decomposing history into hashable segments and maintaining a visual cache, this mechanism eliminates redundant re-rendering. Beyond fixed rendering, AgentOCR introduces agentic self-compression, where the agent actively emits a compression rate and is trained with compression-aware reward to adaptively balance task success and token efficiency. We conduct extensive experiments on challenging agentic benchmarks, ALFWorld and search-based QA. Remarkably, results demonstrate that AgentOCR preserves over 95\\% of text-based agent performance while substantially reducing token consumption (>50\\%), yielding consistent token and memory efficiency. Our further analysis validates a 20x rendering speedup from segment optical caching and the effective strategic balancing of self-compression.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.04720", "title": "Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking", "authors": [ "Mingxin Li", "Yanzhao Zhang", "Dingkun Long", "Keqin Chen", "Sibo Song", "Shuai Bai", "Zhibo Yang", "Pengjun Xie", "An Yang", "Dayiheng Liu", "Jingren Zhou", "Junyang Lin" ], "github": "https://github.com/QwenLM/Qwen3-VL-Embedding", "project_page": "", "abstract": "In this report, we introduce the Qwen3-VL-Embedding and Qwen3-VL-Reranker model series, the latest extensions of the Qwen family built on the Qwen3-VL foundation model. Together, they provide an end-to-end pipeline for high-precision multimodal search by mapping diverse modalities, including text, images, document images, and video, into a unified representation space. The Qwen3-VL-Embedding model employs a multi-stage training paradigm, progressing from large-scale contrastive pre-training to reranking model distillation, to generate semantically rich high-dimensional vectors. It supports Matryoshka Representation Learning, enabling flexible embedding dimensions, and handles inputs up to 32k tokens. Complementing this, Qwen3-VL-Reranker performs fine-grained relevance estimation for query-document pairs using a cross-encoder architecture with cross-attention mechanisms. Both model series inherit the multilingual capabilities of Qwen3-VL, supporting more than 30 languages, and are released in 2B and 8B parameter sizes to accommodate diverse deployment requirements. Empirical evaluations demonstrate that the Qwen3-VL-Embedding series achieves state-of-the-art results across diverse multimodal embedding evaluation benchmarks. Specifically, Qwen3-VL-Embedding-8B attains an overall score of 77.8 on MMEB-V2, ranking first among all models (as of January 8, 2025). This report presents the architecture, training methodology, and practical capabilities of the series, demonstrating their effectiveness on various multimodal retrieval tasks, including image-text retrieval, visual question answering, and video-text matching.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/QwenLM/Qwen3-VL-Embedding", "github_api_added_by": "auto" }, { "date": "2026-01-12", "arxiv_id": "2601.03017", "title": "MMFormalizer: Multimodal Autoformalization in the Wild", "authors": [ "Jing Xiong", "Qi Han", "Yunta Hsieh", "Hui Shen", "Huajian Xin", "Chaofan Tao", "Chenyang Zhao", "Hengyuan Zhang", "Taiqiang Wu", "Zhen Zhang", "Haochen Wang", "Zhongwei Wan", "Lingpeng Kong", "Ngai Wong" ], "github": "", "project_page": "", "abstract": "Autoformalization, which translates natural language mathematics into formal statements to enable machine reasoning, faces fundamental challenges in the wild due to the multimodal nature of the physical world, where physics requires inferring hidden constraints (e.g., mass or energy) from visual elements. To address this, we propose MMFormalizer, which extends autoformalization beyond text by integrating adaptive grounding with entities from real-world mathematical and physical domains. MMFormalizer recursively constructs formal propositions from perceptually grounded primitives through recursive grounding and axiom composition, with adaptive recursive termination ensuring that every abstraction is supported by visual evidence and anchored in dimensional or axiomatic grounding. We evaluate MMFormalizer on a new benchmark, PhyX-AF, comprising 115 curated samples from MathVerse, PhyX, Synthetic Geometry, and Analytic Geometry, covering diverse multimodal autoformalization tasks. Results show that frontier models such as GPT-5 and Gemini-3-Pro achieve the highest compile and semantic accuracy, with GPT-5 excelling in physical reasoning, while geometry remains the most challenging domain. Overall, MMFormalizer provides a scalable framework for unified multimodal autoformalization, bridging perception and formal reasoning. To the best of our knowledge, this is the first multimodal autoformalization method capable of handling classical mechanics (derived from the Hamiltonian), as well as relativity, quantum mechanics, and thermodynamics. More details are available on our project page: MMFormalizer.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.02760", "title": "AnyDepth: Depth Estimation Made Easy", "authors": [ "Zeyu Ren", "Zeyu Zhang", "Wukai Li", "Qingxiang Liu", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/AnyDepth", "project_page": "", "abstract": "Monocular depth estimation aims to recover the depth information of 3D scenes from 2D images. Recent work has made significant progress, but its reliance on large-scale datasets and complex decoders has limited its efficiency and generalization ability. In this paper, we propose a lightweight and data-centric framework for zero-shot monocular depth estimation. We first adopt DINOv3 as the visual encoder to obtain high-quality dense features. Secondly, to address the inherent drawbacks of the complex structure of the DPT, we design the Simple Depth Transformer (SDT), a compact transformer-based decoder. Compared to the DPT, it uses a single-path feature fusion and upsampling process to reduce the computational overhead of cross-scale feature fusion, achieving higher accuracy while reducing the number of parameters by approximately 85%-89%. Furthermore, we propose a quality-based filtering strategy to filter out harmful samples, thereby reducing dataset size while improving overall training quality. Extensive experiments on five benchmarks demonstrate that our framework surpasses the DPT in accuracy. This work highlights the importance of balancing model design and data quality for achieving efficient and generalizable zero-shot depth estimation. Code: https://github.com/AIGeeksGroup/AnyDepth. Website: https://aigeeksgroup.github.io/AnyDepth.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/AnyDepth", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.03319", "title": "CaricatureGS: Exaggerating 3D Gaussian Splatting Faces With Gaussian Curvature", "authors": [ "Eldad Matmon", "Amit Bracha", "Noam Rotstein", "Ron Kimmel" ], "github": "", "project_page": "", "abstract": "A photorealistic and controllable 3D caricaturization framework for faces is introduced. We start with an intrinsic Gaussian curvature-based surface exaggeration technique, which, when coupled with texture, tends to produce over-smoothed renders. To address this, we resort to 3D Gaussian Splatting (3DGS), which has recently been shown to produce realistic free-viewpoint avatars. Given a multiview sequence, we extract a FLAME mesh, solve a curvature-weighted Poisson equation, and obtain its exaggerated form. However, directly deforming the Gaussians yields poor results, necessitating the synthesis of pseudo-ground-truth caricature images by warping each frame to its exaggerated 2D representation using local affine transformations. We then devise a training scheme that alternates real and synthesized supervision, enabling a single Gaussian collection to represent both natural and exaggerated avatars. This scheme improves fidelity, supports local edits, and allows continuous control over the intensity of the caricature. In order to achieve real-time deformations, an efficient interpolation between the original and exaggerated surfaces is introduced. We further analyze and show that it has a bounded deviation from closed-form solutions. In both quantitative and qualitative evaluations, our results outperform prior work, delivering photorealistic, geometry-controlled caricature avatars.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.04726", "title": "Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning", "authors": [ "Yuyang Hu", "Jiongnan Liu", "Jiejun Tan", "Yutao Zhu", "Zhicheng Dou" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly deployed as intelligent agents that reason, plan, and interact with their environments. To effectively scale to long-horizon scenarios, a key capability for such agents is a memory mechanism that can retain, organize, and retrieve past experiences to support downstream decision-making. However, most existing approaches organize and store memories in a flat manner and rely on simple similarity-based retrieval techniques. Even when structured memory is introduced, existing methods often struggle to explicitly capture the logical relationships among experiences or memory units. Moreover, memory access is largely detached from the constructed structure and still depends on shallow semantic retrieval, preventing agents from reasoning logically over long-horizon dependencies. In this work, we propose CompassMem, an event-centric memory framework inspired by Event Segmentation Theory. CompassMem organizes memory as an Event Graph by incrementally segmenting experiences into events and linking them through explicit logical relations. This graph serves as a logic map, enabling agents to perform structured and goal-directed navigation over memory beyond superficial retrieval, progressively gathering valuable memories to support long-horizon reasoning. Experiments on LoCoMo and NarrativeQA demonstrate that CompassMem consistently improves both retrieval and reasoning performance across multiple backbone models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05960", "title": "Distilling Feedback into Memory-as-a-Tool", "authors": [ "V\u00edctor Gallego" ], "github": "", "project_page": "", "abstract": "We propose a framework that amortizes the cost of inference-time reasoning by converting transient critiques into retrievable guidelines, through a file-based memory system and agent-controlled tool calls. We evaluate this method on the Rubric Feedback Bench, a novel dataset for rubric-based learning. Experiments demonstrate that our augmented LLMs rapidly match the performance of test-time refinement pipelines while drastically reducing inference cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05403", "title": "Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection", "authors": [ "Zhiwei Liu", "Yupen Cao", "Yuechen Jiang", "Mohsinul Kabir", "Polydoros Giannouris", "Chen Xu", "Ziyang Xu", "Tianlei Zhu", "Tariquzzaman Faisal", "Triantafillos Papadopoulos", "Yan Wang", "Lingfei Qian", "Xueqing Peng", "Zhuohan Xie", "Ye Yuan", "Saeed Almheiri", "Abdulrazzaq Alnajjar", "Mingbin Chen", "Harry Stuart", "Paul Thompson", "Prayag Tiwari", "Alejandro Lopez-Lira", "Xue Liu", "Jimin Huang", "Sophia Ananiadou" ], "github": "https://github.com/lzw108/FMD", "project_page": "", "abstract": "Large language models (LLMs) have been widely applied across various domains of finance. Since their training data are largely derived from human-authored corpora, LLMs may inherit a range of human biases. Behavioral biases can lead to instability and uncertainty in decision-making, particularly when processing financial information. However, existing research on LLM bias has mainly focused on direct questioning or simplified, general-purpose settings, with limited consideration of the complex real-world financial environments and high-risk, context-sensitive, multilingual financial misinformation detection tasks (\\mfmd). In this work, we propose \\mfmdscen, a comprehensive benchmark for evaluating behavioral biases of LLMs in \\mfmd across diverse economic scenarios. In collaboration with financial experts, we construct three types of complex financial scenarios: (i) role- and personality-based, (ii) role- and region-based, and (iii) role-based scenarios incorporating ethnicity and religious beliefs. We further develop a multilingual financial misinformation dataset covering English, Chinese, Greek, and Bengali. By integrating these scenarios with misinformation claims, \\mfmdscen enables a systematic evaluation of 22 mainstream LLMs. Our findings reveal that pronounced behavioral biases persist across both commercial and open-source models. This project will be available at https://github.com/lzw108/FMD.", "github_manual": "", "github_regex": "https://github.com/lzw108/FMD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.04544", "title": "TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration", "authors": [ "Jiuzhou Zhao", "Chunrong Chen", "Chenqi Qiao", "Lebin Zheng", "Minqi Han", "Yanchi Liu Yongzhou Xu Xiaochuan Xu Min Zhang" ], "github": "https://github.com/Tencent/TCAndon-Router", "project_page": "", "abstract": "Multi-Agent Systems(MAS) have become a powerful paradigm for building high performance intelligent applications. Within these systems, the router responsible for determining which expert agents should handle a given query plays a crucial role in overall performance. Existing routing strategies generally fall into two categories: performance routing, which balances latency and cost across models of different sizes, and task routing, which assigns queries to domain-specific experts to improve accuracy. In real-world enterprise applications, task routing is more suitable; however, most existing approaches rely on static single-label decisions, which introduce two major limitations: (i) difficulty in seamlessly integrating new agents as business domains expand, and (ii) routing conflicts caused by overlapping agent capabilities, ultimately degrading accuracy and robustness.To address these challenges, we propose TCAndon-Router(TCAR): an adaptive reasoning router for multi-agent collaboration. Unlike traditional routers, TCAR supports dynamic agent onboarding and first generates a natural-language reasoning chain before predicting a set of candidate agents capable of handling the query. In addition, we design a collaborative execution pipeline in which selected agents independently produce responses, which are then aggregated and refined into a single high-quality response by a dedicated Refining Agent.Experiments on public datasets and real enterprise data demonstrate that TCAR significantly improves routing accuracy, reduces routing conflicts, and remains robust in ambiguous scenarios. We have released TCAR at https://huggingface.co/tencent/TCAndon-Router to support future research on explainable and collaborative multi-agent routing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tencent/TCAndon-Router", "github_api_added_by": "auto" }, { "date": "2026-01-12", "arxiv_id": "2601.05741", "title": "ViTNT-FIQA: Training-Free Face Image Quality Assessment with Vision Transformers", "authors": [ "Guray Ozgur", "Eduarda Caldeira", "Tahar Chettaoui", "Jan Niklas Kolf", "Marco Huber", "Naser Damer", "Fadi Boutros" ], "github": "https://github.com/gurayozgur/ViTNT-FIQA", "project_page": "", "abstract": "Face Image Quality Assessment (FIQA) is essential for reliable face recognition systems. Current approaches primarily exploit only final-layer representations, while training-free methods require multiple forward passes or backpropagation. We propose ViTNT-FIQA, a training-free approach that measures the stability of patch embedding evolution across intermediate Vision Transformer (ViT) blocks. We demonstrate that high-quality face images exhibit stable feature refinement trajectories across blocks, while degraded images show erratic transformations. Our method computes Euclidean distances between L2-normalized patch embeddings from consecutive transformer blocks and aggregates them into image-level quality scores. We empirically validate this correlation on a quality-labeled synthetic dataset with controlled degradation levels. Unlike existing training-free approaches, ViTNT-FIQA requires only a single forward pass without backpropagation or architectural modifications. Through extensive evaluation on eight benchmarks (LFW, AgeDB-30, CFP-FP, CALFW, Adience, CPLFW, XQLFW, IJB-C), we show that ViTNT-FIQA achieves competitive performance with state-of-the-art methods while maintaining computational efficiency and immediate applicability to any pre-trained ViT-based face recognition model.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gurayozgur/ViTNT-FIQA", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05699", "title": "Afri-MCQA: Multimodal Cultural Question Answering for African Languages", "authors": [ "Atnafu Lambebo Tonja", "Srija Anand", "Emilio Villa-Cueva", "Israel Abebe Azime", "Jesujoba Oluwadara Alabi", "Muhidin A. Mohamed", "Debela Desalegn Yadeta", "Negasi Haile Abadi", "Abigail Oppong", "Nnaemeka Casmir Obiefuna", "Idris Abdulmumin", "Naome A Etori", "Eric Peter Wairagala", "Kanda Patrick Tshinu", "Imanigirimbabazi Emmanuel", "Gabofetswe Malema", "Alham Fikri Aji", "David Ifeoluwa Adelani", "Thamar Solorio" ], "github": "", "project_page": "", "abstract": "Africa is home to over one-third of the world's languages, yet remains underrepresented in AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering benchmark covering 7.5k Q&A pairs across 15 African languages from 12 countries. The benchmark offers parallel English-African language Q&A pairs across text and speech modalities and was entirely created by native speakers. Benchmarking large language models (LLMs) on Afri-MCQA shows that open-weight models perform poorly across evaluated cultures, with near-zero accuracy on open-ended VQA when queried in native language or speech. To evaluate linguistic competence, we include control experiments meant to assess this specific aspect separate from cultural knowledge, and we observe significant performance gaps between native languages and English for both text and speech. These findings underscore the need for speech-first approaches, culturally grounded pretraining, and cross-lingual cultural transfer. To support more inclusive multimodal AI development in African languages, we release our Afri-MCQA under academic license or CC BY-NC 4.0 on HuggingFace (https://huggingface.co/datasets/Atnafu/Afri-MCQA)", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.04175", "title": "Legal Alignment for Safe and Ethical AI", "authors": [ "Noam Kolt", "Nicholas Caputo", "Jack Boeglin", "Cullen O'Keefe", "Rishi Bommasani", "Stephen Casper", "Mariano-Florentino Cu\u00e9llar", "Noah Feldman", "Iason Gabriel", "Gillian K. Hadfield", "Lewis Hammond", "Peter Henderson", "Atoosa Kasirzadeh", "Seth Lazar", "Anka Reuel", "Kevin L. Wei", "Jonathan Zittrain" ], "github": "", "project_page": "", "abstract": "Alignment of artificial intelligence (AI) encompasses the normative problem of specifying how AI systems should act and the technical problem of ensuring AI systems comply with those specifications. To date, AI alignment has generally overlooked an important source of knowledge and practice for grappling with these problems: law. In this paper, we aim to fill this gap by exploring how legal rules, principles, and methods can be leveraged to address problems of alignment and inform the design of AI systems that operate safely and ethically. This emerging field -- legal alignment -- focuses on three research directions: (1) designing AI systems to comply with the content of legal rules developed through legitimate institutions and processes, (2) adapting methods from legal interpretation to guide how AI systems reason and make decisions, and (3) harnessing legal concepts as a structural blueprint for confronting challenges of reliability, trust, and cooperation in AI systems. These research directions present new conceptual, empirical, and institutional questions, which include examining the specific set of laws that particular AI systems should follow, creating evaluations to assess their legal compliance in real-world settings, and developing governance frameworks to support the implementation of legal alignment in practice. Tackling these questions requires expertise across law, computer science, and other disciplines, offering these communities the opportunity to collaborate in designing AI for the better.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05882", "title": "An Empirical Study on Preference Tuning Generalization and Diversity Under Domain Shift", "authors": [ "Constantinos Karouzos", "Xingwei Tan", "Nikolaos Aletras" ], "github": "https://github.com/ckarouzos/prefadap", "project_page": "", "abstract": "Preference tuning aligns pretrained language models to human judgments of quality, helpfulness, or safety by optimizing over explicit preference signals rather than likelihood alone. Prior work has shown that preference-tuning degrades performance and reduces helpfulness when evaluated outside the training domain. However, the extent to which adaptation strategies mitigate this domain shift remains unexplored. We address this challenge by conducting a comprehensive and systematic study of alignment generalization under domain shift. We compare five popular alignment objectives and various adaptation strategies from source to target, including target-domain supervised fine-tuning and pseudo-labeling, across summarization and question-answering helpfulness tasks. Our findings reveal systematic differences in generalization across alignment objectives under domain shift. We show that adaptation strategies based on pseudo-labeling can substantially reduce domain-shift degradation", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ckarouzos/prefadap", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.05376", "title": "The Persona Paradox: Medical Personas as Behavioral Priors in Clinical Language Models", "authors": [ "Tassallah Abdullahi", "Shrestha Ghosh", "Hamish S Fraser", "Daniel Le\u00f3n Tramontini", "Adeel Abbasi", "Ghada Bourjeily", "Carsten Eickhoff", "Ritambhara Singh" ], "github": "https://github.com/rsinghlab/Persona_Paradox", "project_page": "", "abstract": "Persona conditioning can be viewed as a behavioral prior for large language models (LLMs) and is often assumed to confer expertise and improve safety in a monotonic manner. However, its effects on high-stakes clinical decision-making remain poorly characterized. We systematically evaluate persona-based control in clinical LLMs, examining how professional roles (e.g., Emergency Department physician, nurse) and interaction styles (bold vs.\\ cautious) influence behavior across models and medical tasks. We assess performance on clinical triage and patient-safety tasks using multidimensional evaluations that capture task accuracy, calibration, and safety-relevant risk behavior. We find systematic, context-dependent, and non-monotonic effects: Medical personas improve performance in critical care tasks, yielding gains of up to sim+20% in accuracy and calibration, but degrade performance in primary-care settings by comparable margins. Interaction style modulates risk propensity and sensitivity, but it's highly model-dependent. While aggregated LLM-judge rankings favor medical over non-medical personas in safety-critical cases, we found that human clinicians show moderate agreement on safety compliance (average Cohen's \u03ba= 0.43) but indicate a low confidence in 95.9\\% of their responses on reasoning quality. Our work shows that personas function as behavioral priors that introduce context-dependent trade-offs rather than guarantees of safety or expertise. The code is available at https://github.com/rsinghlab/Persona\\_Paradox.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rsinghlab/Persona_Paradox", "github_api_added_by": "auto" }, { "date": "2026-01-12", "arxiv_id": "2601.05899", "title": "TowerMind: A Tower Defence Game Learning Environment and Benchmark for LLM as Agents", "authors": [ "Dawei Wang", "Chengming Zhou", "Di Zhao", "Xinyuan Liu", "Marci Chi Ma", "Gary Ushaw", "Richard Davison" ], "github": "https://github.com/tb6147877/TowerMind", "project_page": "", "abstract": "Recent breakthroughs in Large Language Models (LLMs) have positioned them as a promising paradigm for agents, with long-term planning and decision-making emerging as core general-purpose capabilities for adapting to diverse scenarios and tasks. Real-time strategy (RTS) games serve as an ideal testbed for evaluating these two capabilities, as their inherent gameplay requires both macro-level strategic planning and micro-level tactical adaptation and action execution. Existing RTS game-based environments either suffer from relatively high computational demands or lack support for textual observations, which has constrained the use of RTS games for LLM evaluation. Motivated by this, we present TowerMind, a novel environment grounded in the tower defense (TD) subgenre of RTS games. TowerMind preserves the key evaluation strengths of RTS games for assessing LLMs, while featuring low computational demands and a multimodal observation space, including pixel-based, textual, and structured game-state representations. In addition, TowerMind supports the evaluation of model hallucination and provides a high degree of customizability. We design five benchmark levels to evaluate several widely used LLMs under different multimodal input settings. The results reveal a clear performance gap between LLMs and human experts across both capability and hallucination dimensions. The experiments further highlight key limitations in LLM behavior, such as inadequate planning validation, a lack of multifinality in decision-making, and inefficient action use. We also evaluate two classic reinforcement learning algorithms: Ape-X DQN and PPO. By offering a lightweight and multimodal design, TowerMind complements the existing RTS game-based environment landscape and introduces a new benchmark for the AI agent field. The source code is publicly available on GitHub(https://github.com/tb6147877/TowerMind).", "github_manual": "", "github_regex": "https://github.com/tb6147877/TowerMind", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-12", "arxiv_id": "2601.06401", "title": "BizFinBench.v2: A Unified Dual-Mode Bilingual Benchmark for Expert-Level Financial Capability Alignment", "authors": [ "Xin Guo", "Rongjunchen Zhang", "Guilong Lu", "Xuntao Guo", "Shuai Jia", "Zhi Yang", "Liwen Zhang" ], "github": "https://github.com/HiThink-Research/BizFinBench.v2", "project_page": "", "abstract": "Large language models have undergone rapid evolution, emerging as a pivotal technology for intelligence in financial operations. However, existing benchmarks are often constrained by pitfalls such as reliance on simulated or general-purpose samples and a focus on singular, offline static scenarios. Consequently, they fail to align with the requirements for authenticity and real-time responsiveness in financial services, leading to a significant discrepancy between benchmark performance and actual operational efficacy. To address this, we introduce BizFinBench.v2, the first large-scale evaluation benchmark grounded in authentic business data from both Chinese and U.S. equity markets, integrating online assessment. We performed clustering analysis on authentic user queries from financial platforms, resulting in eight fundamental tasks and two online tasks across four core business scenarios, totaling 29,578 expert-level Q&A pairs. Experimental results demonstrate that ChatGPT-5 achieves a prominent 61.5% accuracy in main tasks, though a substantial gap relative to financial experts persists; in online tasks, DeepSeek-R1 outperforms all other commercial LLMs. Error analysis further identifies the specific capability deficiencies of existing models within practical financial business contexts. BizFinBench.v2 transcends the limitations of current benchmarks, achieving a business-level deconstruction of LLM financial capabilities and providing a precise basis for evaluating efficacy in the widespread deployment of LLMs within the financial domain. The data and code are available at https://github.com/HiThink-Research/BizFinBench.v2.", "github_manual": "", "github_regex": "https://github.com/HiThink-Research/BizFinBench.v2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06165", "title": "What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Models", "authors": [ "Dasol Choi", "Guijin Son", "Hanwool Lee", "Minhyuk Kim", "Hyunwoo Ko", "Teabin Lim", "Ahn Eungyeol", "Jungwhan Kim", "Seunghyeok Hong", "Youngsook Song" ], "github": "", "project_page": "", "abstract": "Current vision-language benchmarks predominantly feature well-structured questions with clear, explicit prompts. However, real user queries are often informal and underspecified. Users naturally leave much unsaid, relying on images to convey context. We introduce HAERAE-Vision, a benchmark of 653 real-world visual questions from Korean online communities (0.76% survival from 86K candidates), each paired with an explicit rewrite, yielding 1,306 query variants in total. Evaluating 39 VLMs, we find that even state-of-the-art models (GPT-5, Gemini 2.5 Pro) achieve under 50% on the original queries. Crucially, query explicitation alone yields 8 to 22 point improvements, with smaller models benefiting most. We further show that even with web search, under-specified queries underperform explicit queries without search, revealing that current retrieval cannot compensate for what users leave unsaid. Our findings demonstrate that a substantial portion of VLM difficulty stem from natural query under-specification instead of model capability, highlighting a critical gap between benchmark evaluation and real-world deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.05593", "title": "PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning", "authors": [ "Jingcheng Hu", "Yinmin Zhang", "Shijie Shang", "Xiaobo Yang", "Yue Peng", "Zhewei Huang", "Hebin Zhou", "Xin Wu", "Jie Cheng", "Fanqi Wan", "Xiangwen Kong", "Chengyuan Yao", "Kaiwen Yan", "Ailin Huang", "Hongyu Zhou", "Qi Han", "Zheng Ge", "Daxin Jiang", "Xiangyu Zhang", "Heung-Yeung Shum" ], "github": "", "project_page": "", "abstract": "We introduce Parallel Coordinated Reasoning (PaCoRe), a training-and-inference framework designed to overcome a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window. PaCoRe departs from the traditional sequential paradigm by driving TTC through massive parallel exploration coordinated via a message-passing architecture in multiple rounds. Each round launches many parallel reasoning trajectories, compacts their findings into context-bounded messages, and synthesizes these messages to guide the next round and ultimately produce the final answer. Trained end-to-end with large-scale, outcome-based reinforcement learning, the model masters the synthesis abilities required by PaCoRe and scales to multi-million-token effective TTC without exceeding context limits. The approach yields strong improvements across diverse domains, and notably pushes reasoning beyond frontier systems in mathematics: an 8B model reaches 94.5% on HMMT 2025, surpassing GPT-5's 93.2% by scaling effective TTC to roughly two million tokens. We open-source model checkpoints, training data, and the full inference pipeline to accelerate follow-up work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.05823", "title": "Boosting Latent Diffusion Models via Disentangled Representation Alignment", "authors": [ "John Page", "Xuesong Niu", "Kai Wu", "Kun Gai" ], "github": "https://github.com/Kwai-Kolors/Send-VAE", "project_page": "", "abstract": "Latent Diffusion Models (LDMs) generate high-quality images by operating in a compressed latent space, typically obtained through image tokenizers such as Variational Autoencoders (VAEs). In pursuit of a generation-friendly VAE, recent studies have explored leveraging Vision Foundation Models (VFMs) as representation alignment targets for VAEs, mirroring the approach commonly adopted for LDMs. Although this yields certain performance gains, using the same alignment target for both VAEs and LDMs overlooks their fundamentally different representational requirements. We advocate that while LDMs benefit from latents retaining high-level semantic concepts, VAEs should excel in semantic disentanglement, enabling encoding of attribute-level information in a structured way. To address this, we propose the Semantic disentangled VAE (Send-VAE), explicitly optimized for disentangled representation learning through aligning its latent space with the semantic hierarchy of pre-trained VFMs. Our approach employs a non-linear mapper network to transform VAE latents, aligning them with VFMs to bridge the gap between attribute-level disentanglement and high-level semantics, facilitating effective guidance for VAE learning. We evaluate semantic disentanglement via linear probing on attribute prediction tasks, showing strong correlation with improved generation performance. Finally, using Send-VAE, we train flow-based transformers SiTs; experiments show Send-VAE significantly speeds up training and achieves a state-of-the-art FID of 1.21 and 1.75 with and without classifier-free guidance on ImageNet 256x256.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Kwai-Kolors/Send-VAE", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.04698", "title": "TourPlanner: A Competitive Consensus Framework with Constraint-Gated Reinforcement Learning for Travel Planning", "authors": [ "Yinuo Wang", "Mining Tan", "Wenxiang Jiao", "Xiaoxi Li", "Hao Wang", "Xuanyu Zhang", "Yuan Lu", "Weiming Dong" ], "github": "", "project_page": "", "abstract": "Travel planning is a sophisticated decision-making process that requires synthesizing multifaceted information to construct itineraries. However, existing travel planning approaches face several challenges: (1) Pruning candidate points of interest (POIs) while maintaining a high recall rate; (2) A single reasoning path restricts the exploration capability within the feasible solution space for travel planning; (3) Simultaneously optimizing hard constraints and soft constraints remains a significant difficulty. To address these challenges, we propose TourPlanner, a comprehensive framework featuring multi-path reasoning and constraint-gated reinforcement learning. Specifically, we first introduce a Personalized Recall and Spatial Optimization (PReSO) workflow to construct spatially-aware candidate POIs' set. Subsequently, we propose Competitive consensus Chain-of-Thought (CCoT), a multi-path reasoning paradigm that improves the ability of exploring the feasible solution space. To further refine the plan, we integrate a sigmoid-based gating mechanism into the reinforcement learning stage, which dynamically prioritizes soft-constraint satisfaction only after hard constraints are met. Experimental results on travel planning benchmarks demonstrate that TourPlanner achieves state-of-the-art performance, significantly surpassing existing methods in both feasibility and user-preference alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06860", "title": "ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration", "authors": [ "Yifei Chen", "Guanting Dong", "Zhicheng Dou" ], "github": "https://github.com/asilverlight/ET-Agent", "project_page": "", "abstract": "Large Language Models (LLMs) can extend their parameter knowledge limits by adopting the Tool-Integrated Reasoning (TIR) paradigm. However, existing LLM-based agent training framework often focuses on answers' accuracy, overlooking specific alignment for behavior patterns. Consequently, agent often exhibits ineffective actions during TIR tasks, such as redundant and insufficient tool calls. How to calibrate erroneous behavioral patterns when executing TIR tasks, thereby exploring effective trajectories, remains an open-ended problem. In this paper, we propose ET-Agent, a training framework for calibrating agent's tool-use behavior through two synergistic perspectives: Self-evolving Data Flywheel and Behavior Calibration Training. Specifically, we introduce a self-evolutionary data flywheel to generate enhanced data, used to fine-tune LLM to improve its exploration ability. Based on this, we implement an two-phases behavior-calibration training framework. It is designed to progressively calibrate erroneous behavioral patterns to optimal behaviors. Further in-depth experiments confirm the superiority of across multiple dimensions, including correctness, efficiency, reasoning conciseness, and tool execution accuracy. Our ET-Agent framework provides practical insights for research in the TIR field. Codes can be found in https://github.com/asilverlight/ET-Agent", "github_manual": "", "github_regex": "https://github.com/asilverlight/ET-Agent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06943", "title": "Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning", "authors": [ "Chengwen Liu", "Xiaomin Yu", "Zhuoyue Chang", "Zhe Huang", "Shuo Zhang", "Heng Lian", "Kunyi Wang", "Rui Xu", "Sen Hu", "Jianheng Hou", "Hao Peng", "Chengwei Qin", "Xiaobin Hu", "Hong Peng", "Ronghao Chen", "Huacan Wang" ], "github": "", "project_page": "", "abstract": "In real-world video question answering scenarios, videos often provide only localized visual cues, while verifiable answers are distributed across the open web; models therefore need to jointly perform cross-frame clue extraction, iterative retrieval, and multi-hop reasoning-based verification. To bridge this gap, we construct the first video deep research benchmark, VideoDR. VideoDR centers on video-conditioned open-domain video question answering, requiring cross-frame visual anchor extraction, interactive web retrieval, and multi-hop reasoning over joint video-web evidence; through rigorous human annotation and quality control, we obtain high-quality video deep research samples spanning six semantic domains. We evaluate multiple closed-source and open-source multimodal large language models under both the Workflow and Agentic paradigms, and the results show that Agentic is not consistently superior to Workflow: its gains depend on a model's ability to maintain the initial video anchors over long retrieval chains. Further analysis indicates that goal drift and long-horizon consistency are the core bottlenecks. In sum, VideoDR provides a systematic benchmark for studying video agents in open-web settings and reveals the key challenges for next-generation video deep research agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07226", "title": "Lost in the Noise: How Reasoning Models Fail with Contextual Distractors", "authors": [ "Seongyun Lee", "Yongrae Jo", "Minju Seo", "Moontae Lee", "Minjoon Seo" ], "github": "", "project_page": "", "abstract": "Recent advances in reasoning models and agentic AI systems have led to an increased reliance on diverse external information. However, this shift introduces input contexts that are inherently noisy, a reality that current sanitized benchmarks fail to capture. We introduce NoisyBench, a comprehensive benchmark that systematically evaluates model robustness across 11 datasets in RAG, reasoning, alignment, and tool-use tasks against diverse noise types, including random documents, irrelevant chat histories, and hard negative distractors. Our evaluation reveals a catastrophic performance drop of up to 80% in state-of-the-art models when faced with contextual distractors. Crucially, we find that agentic workflows often amplify these errors by over-trusting noisy tool outputs, and distractors can trigger emergent misalignment even without adversarial intent. We find that prompting, context engineering, SFT, and outcome-reward only RL fail to ensure robustness; in contrast, our proposed Rationale-Aware Reward (RARE) significantly strengthens resilience by incentivizing the identification of helpful information within noise. Finally, we uncover an inverse scaling trend where increased test-time computation leads to worse performance in noisy settings and demonstrate via attention visualization that models disproportionately focus on distractor tokens, providing vital insights for building the next generation of robust, reasoning-capable agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06953", "title": "X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests", "authors": [ "Jie Wu", "Haoling Li", "Xin Zhang", "Jiani Guo", "Jane Luo", "Steven Liu", "Yangyu Huang", "Ruihang Chu", "Scarlett Li", "Yujiu Yang" ], "github": "https://github.com/0xWJ/code-judge.git", "project_page": "", "abstract": "Competitive programming presents great challenges for Code LLMs due to its intensive reasoning demands and high logical complexity. However, current Code LLMs still rely heavily on real-world data, which limits their scalability. In this paper, we explore a fully synthetic approach: training Code LLMs with entirely generated tasks, solutions, and test cases, to empower code reasoning models without relying on real-world data. To support this, we leverage feature-based synthesis to propose a novel data synthesis pipeline called SynthSmith. SynthSmith shows strong potential in producing diverse and challenging tasks, along with verified solutions and tests, supporting both supervised fine-tuning and reinforcement learning. Based on the proposed synthetic SFT and RL datasets, we introduce the X-Coder model series, which achieves a notable pass rate of 62.9 avg@8 on LiveCodeBench v5 and 55.8 on v6, outperforming DeepCoder-14B-Preview and AReal-boba2-14B despite having only 7B parameters. In-depth analysis reveals that scaling laws hold on our synthetic dataset, and we explore which dimensions are more effective to scale. We further provide insights into code-centric reinforcement learning and highlight the key factors that shape performance through detailed ablations and analysis. Our findings demonstrate that scaling high-quality synthetic data and adopting staged training can greatly advance code reasoning, while mitigating reliance on real-world coding data.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/0xWJ/code-judge.git", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07181", "title": "ShowUI-Aloha: Human-Taught GUI Agent", "authors": [ "Yichun Zhang", "Xiangwu Guo", "Yauhong Goh", "Jessica Hu", "Zhiheng Chen", "Xin Wang", "Difei Gao", "Mike Zheng Shou" ], "github": "https://github.com/showlab/ShowUI-Aloha", "project_page": "", "abstract": "Graphical User Interfaces (GUIs) are central to human-computer interaction, yet automating complex GUI tasks remains a major challenge for autonomous agents, largely due to a lack of scalable, high-quality training data. While recordings of human demonstrations offer a rich data source, they are typically long, unstructured, and lack annotations, making them difficult for agents to learn from.To address this, we introduce ShowUI-Aloha, a comprehensive pipeline that transforms unstructured, in-the-wild human screen recordings from desktop environments into structured, actionable tasks. Our framework includes four key components: A recorder that captures screen video along with precise user interactions like mouse clicks, keystrokes, and scrolls. A learner that semantically interprets these raw interactions and the surrounding visual context, translating them into descriptive natural language captions. A planner that reads the parsed demonstrations, maintains task states, and dynamically formulates the next high-level action plan based on contextual reasoning. An executor that faithfully carries out these action plans at the OS level, performing precise clicks, drags, text inputs, and window operations with safety checks and real-time feedback. Together, these components provide a scalable solution for collecting and parsing real-world human data, demonstrating a viable path toward building general-purpose GUI agents that can learn effectively from simply observing humans.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/showlab/ShowUI-Aloha", "github_api_added_by": "auto" }, { "date": "2026-01-13", "arxiv_id": "2601.06944", "title": "SketchJudge: A Diagnostic Benchmark for Grading Hand-drawn Diagrams with Multimodal Large Language Models", "authors": [ "Yuhang Su", "Mei Wang", "Yaoyao Zhong", "Guozhang Li", "Shixing Li", "Yihan Feng", "Hua Huang" ], "github": "https://github.com/yuhangsu82/SketchJudge", "project_page": "", "abstract": "While Multimodal Large Language Models (MLLMs) have achieved remarkable progress in visual understanding, they often struggle when faced with the unstructured and ambiguous nature of human-generated sketches. This limitation is particularly pronounced in the underexplored task of visual grading, where models should not only solve a problem but also diagnose errors in hand-drawn diagrams. Such diagnostic capabilities depend on complex structural, semantic, and metacognitive reasoning. To bridge this gap, we introduce SketchJudge, a novel benchmark tailored for evaluating MLLMs as graders of hand-drawn STEM diagrams. SketchJudge encompasses 1,015 hand-drawn student responses across four domains: geometry, physics, charts, and flowcharts, featuring diverse stylistic variations and distinct error types. Evaluations on SketchJudge demonstrate that even advanced MLLMs lag significantly behind humans, validating the benchmark's effectiveness in exposing the fragility of current vision-language alignment in symbolic and noisy contexts. All data, code, and evaluation scripts are publicly available at https://github.com/yuhangsu82/SketchJudge.", "github_manual": "", "github_regex": "https://github.com/yuhangsu82/SketchJudge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06496", "title": "3D CoCa v2: Contrastive Learners with Test-Time Search for Generalizable Spatial Intelligence", "authors": [ "Hao Tang", "Ting Huang", "Zeyu Zhang" ], "github": "https://github.com/AIGeeksGroup/3DCoCav2", "project_page": "", "abstract": "Spatial intelligence refers to the ability to perceive, reason about, and describe objects and their relationships within three-dimensional environments, forming a foundation for embodied perception and scene understanding. 3D captioning aims to describe 3D scenes in natural language; however, it remains challenging due to the sparsity and irregularity of point clouds and, more critically, the weak grounding and limited out-of-distribution (OOD) generalization of existing captioners across drastically different environments, including indoor and outdoor 3D scenes. To address this challenge, we propose 3D CoCa v2, a generalizable 3D captioning framework that unifies contrastive vision-language learning with 3D caption generation and further improves robustness via test-time search (TTS) without updating the captioner parameters. 3D CoCa v2 builds on a frozen CLIP-based semantic prior, a spatially-aware 3D scene encoder for geometry, and a multimodal decoder jointly optimized with contrastive and captioning objectives, avoiding external detectors or handcrafted proposals. At inference, TTS produces diverse caption candidates and performs reward-guided selection using a compact scene summary. Experiments show improvements over 3D CoCa of +1.50 CIDEr@0.5IoU on ScanRefer and +1.61 CIDEr@0.5IoU on Nr3D, and +3.8 CIDEr@0.25 in zero-shot OOD evaluation on TOD3Cap. Code will be released at https://github.com/AIGeeksGroup/3DCoCav2.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/3DCoCav2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.03666", "title": "e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings", "authors": [ "Haonan Chen", "Sicheng Gao", "Radu Timofte", "Tetsuya Sakai", "Zhicheng Dou" ], "github": "", "project_page": "", "abstract": "Modern information systems often involve different types of items, e.g., a text query, an image, a video clip, or an audio segment. This motivates omni-modal embedding models that map heterogeneous modalities into a shared space for direct comparison. However, most recent omni-modal embeddings still rely heavily on implicit alignment inherited from pretrained vision-language model (VLM) backbones. In practice, this causes three common issues: (i) similarity logits have modality-dependent sharpness, so scores are not on a consistent scale; (ii) in-batch negatives become less effective over time because mixed-modality batches create an imbalanced hardness distribution; as a result, many negatives quickly become trivial and contribute little gradient; and (iii) embeddings across modalities show mismatched first- and second-order statistics, which makes rankings less stable. To tackle these problems, we propose e5-omni, a lightweight explicit alignment recipe that adapts off-the-shelf VLMs into robust omni-modal embedding models. e5-omni combines three simple components: (1) modality-aware temperature calibration to align similarity scales, (2) a controllable negative curriculum with debiasing to focus on confusing negatives while reducing the impact of false negatives, and (3) batch whitening with covariance regularization to better match cross-modal geometry in the shared embedding space. Experiments on MMEB-V2 and AudioCaps show consistent gains over strong bi-modal and omni-modal baselines, and the same recipe also transfers well to other VLM backbones. We release our model checkpoint at https://huggingface.co/Haon-Chen/e5-omni-7B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06411", "title": "Structured Episodic Event Memory", "authors": [ "Zhengxuan Lu", "Dongfang Li", "Yukun Shi", "Beilun Wang", "Longyue Wang", "Baotian Hu" ], "github": "", "project_page": "", "abstract": "Current approaches to memory in Large Language Models (LLMs) predominantly rely on static Retrieval-Augmented Generation (RAG), which often results in scattered retrieval and fails to capture the structural dependencies required for complex reasoning. For autonomous agents, these passive and flat architectures lack the cognitive organization necessary to model the dynamic and associative nature of long-term interaction. To address this, we propose Structured Episodic Event Memory (SEEM), a hierarchical framework that synergizes a graph memory layer for relational facts with a dynamic episodic memory layer for narrative progression. Grounded in cognitive frame theory, SEEM transforms interaction streams into structured Episodic Event Frames (EEFs) anchored by precise provenance pointers. Furthermore, we introduce an agentic associative fusion and Reverse Provenance Expansion (RPE) mechanism to reconstruct coherent narrative contexts from fragmented evidence. Experimental results on the LoCoMo and LongMemEval benchmarks demonstrate that SEEM significantly outperforms baselines, enabling agents to maintain superior narrative coherence and logical consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06521", "title": "BabyVision: Visual Reasoning Beyond Language", "authors": [ "Liang Chen", "Weichu Xie", "Yiyan Liang", "Hongfeng He", "Hans Zhao", "Zhibo Yang", "Zhiqi Huang", "Haoning Wu", "Haoyu Lu", "Y. charles", "Yiping Bao", "Yuantao Fan", "Guopeng Li", "Haiyang Shen", "Xuanzhong Chen", "Wendong Xu", "Shuzheng Si", "Zefan Cai", "Wenhao Chai", "Ziqi Huang", "Fangfu Liu", "Tianyu Liu", "Baobao Chang", "Xiaobo Hu", "Kaiyuan Chen", "Yixin Ren", "Yang Liu", "Yuan Gong", "Kuan Li" ], "github": "https://github.com/UniPat-AI/BabyVision", "project_page": "", "abstract": "While humans develop core visual skills long before acquiring language, contemporary Multimodal LLMs (MLLMs) still rely heavily on linguistic priors to compensate for their fragile visual understanding. We uncovered a crucial fact: state-of-the-art MLLMs consistently fail on basic visual tasks that humans, even 3-year-olds, can solve effortlessly. To systematically investigate this gap, we introduce BabyVision, a benchmark designed to assess core visual abilities independent of linguistic knowledge for MLLMs. BabyVision spans a wide range of tasks, with 388 items divided into 22 subclasses across four key categories. Empirical results and human evaluation reveal that leading MLLMs perform significantly below human baselines. Gemini3-Pro-Preview scores 49.7, lagging behind 6-year-old humans and falling well behind the average adult score of 94.1. These results show despite excelling in knowledge-heavy evaluations, current MLLMs still lack fundamental visual primitives. Progress in BabyVision represents a step toward human-level visual perception and reasoning capabilities. We also explore solving visual reasoning with generation models by proposing BabyVision-Gen and automatic evaluation toolkit. Our code and benchmark data are released at https://github.com/UniPat-AI/BabyVision for reproduction.", "github_manual": "", "github_regex": "https://github.com/UniPat-AI/BabyVision", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07526", "title": "MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era", "authors": [ "Lei Zhang", "Mouxiang Chen", "Ruisheng Cao", "Jiawei Chen", "Fan Zhou", "Yiheng Xu", "Jiaxi Yang", "Liang Chen", "Changwei Luo", "Kai Zhang", "Fan Yan", "KaShun Shum", "Jiajun Zhang", "Zeyu Cui", "Hu Feng", "Junyang Lin", "Binyuan Hui", "Min Yang" ], "github": "", "project_page": "", "abstract": "The rapid development of interactive and autonomous AI systems signals our entry into the agentic era. Training and evaluating agents on complex agentic tasks such as software engineering and computer use requires not only efficient model computation but also sophisticated infrastructure capable of coordinating vast agent-environment interactions. However, no open-source infrastructure can effectively support large-scale training and evaluation on such complex agentic tasks. To address this challenge, we present MegaFlow, a large-scale distributed orchestration system that enables efficient scheduling, resource allocation, and fine-grained task management for agent-environment workloads. MegaFlow abstracts agent training infrastructure into three independent services (Model Service, Agent Service, and Environment Service) that interact through unified interfaces, enabling independent scaling and flexible resource allocation across diverse agent-environment configurations. In our agent training deployments, MegaFlow successfully orchestrates tens of thousands of concurrent agent tasks while maintaining high system stability and achieving efficient resource utilization. By enabling such large-scale agent training, MegaFlow addresses a critical infrastructure gap in the emerging agentic AI landscape.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07055", "title": "Dr. Zero: Self-Evolving Search Agents without Training Data", "authors": [ "Zhenrui Yue", "Kartikeya Upasani", "Xianjun Yang", "Suyu Ge", "Shaoliang Nie", "Yuning Mao", "Zhe Liu", "Dong Wang" ], "github": "", "project_page": "", "abstract": "As high-quality data becomes increasingly difficult to obtain, data-free self-evolution has emerged as a promising paradigm. This approach allows large language models (LLMs) to autonomously generate and solve complex problems, thereby improving their reasoning capabilities. However, multi-turn search agents struggle in data-free self-evolution due to the limited question diversity and the substantial compute required for multi-step reasoning and tool using. In this work, we introduce Dr. Zero, a framework enabling search agents to effectively self-evolve without any training data. In particular, we design a self-evolution feedback loop where a proposer generates diverse questions to train a solver initialized from the same base model. As the solver evolves, it incentivizes the proposer to produce increasingly difficult yet solvable tasks, thus establishing an automated curriculum to refine both agents. To enhance training efficiency, we also introduce hop-grouped relative policy optimization (HRPO). This method clusters structurally similar questions to construct group-level baselines, effectively minimizing the sampling overhead in evaluating each query's individual difficulty and solvability. Consequently, HRPO significantly reduces the compute requirements for solver training without compromising performance or stability. Extensive experiment results demonstrate that the data-free Dr. Zero matches or surpasses fully supervised search agents, proving that complex reasoning and search capabilities can emerge solely through self-evolution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.05110", "title": "GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts", "authors": [ "Wenhao Zeng", "Xuteng Zhang", "Yuling Shi", "Chao Hu", "Yuting Chen", "Beijun Shen", "Xiaodong Gu" ], "github": "", "project_page": "", "abstract": "Large Reasoning Models (LRMs) achieve remarkable performance by explicitly generating multi-step chains of thought, but this capability incurs substantial inference latency and computational cost. Collaborative inference offers a promising solution by selectively allocating work between lightweight and large models, yet a fundamental challenge remains: determining when a reasoning step requires the capacity of a large model or the efficiency of a small model. Existing routing strategies either rely on local token probabilities or post-hoc verification, introducing significant inference overhead. In this work, we propose a novel perspective on step-wise collaboration: the difficulty of a reasoning step can be inferred from its very first token. Inspired by the \"Aha Moment\" phenomenon in LRMs, we show that the entropy of the initial token serves as a strong predictor of step difficulty. Building on this insight, we introduce GlimpRouter, a training-free step-wise collaboration framework. GlimpRouter employs a lightweight model to generate only the first token of each reasoning step and routes the step to a larger model only when the initial token entropy exceeds a threshold. Experiments on multiple benchmarks demonstrate that our approach significantly reduces inference latency while preserving accuracy. For instance, GlimpRouter attains a substantial 10.7% improvement in accuracy while reducing inference latency by 25.9% compared to a standalone large model on AIME25. These results suggest a simple yet effective mechanism for reasoning: allocating computation based on a glimpse of thought rather than full-step evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07376", "title": "OpenTinker: Separating Concerns in Agentic Reinforcement Learning", "authors": [ "Siqi Zhu", "Jiaxuan You" ], "github": "", "project_page": "", "abstract": "We introduce OpenTinker, an infrastructure for reinforcement learning (RL) of large language model (LLM) agents built around a separation of concerns across algorithm design, execution, and agent-environment interaction. Rather than relying on monolithic, end-to-end RL pipelines, OpenTinker decomposes agentic learning systems into lightweight, composable components with clearly defined abstraction boundaries. Users specify agents, environments, and interaction protocols, while inference and training are delegated to a managed execution runtime. OpenTinker introduces a centralized scheduler for managing training and inference workloads, including LoRA-based and full-parameter RL, supervised fine-tuning, and inference, over shared resources. We further discuss design principles for extending OpenTinker to multi-agent training. Finally, we present a set of RL use cases that demonstrate the effectiveness of the framework in practical agentic learning scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06329", "title": "On the Fallacy of Global Token Perplexity in Spoken Language Model Evaluation", "authors": [ "Jeff Chan-Jan Sju", "Liang-Hsuan Tseng", "Yi-Cheng Lin", "Yen-Chun Kuo", "Ju-Chieh Chou", "Kai-Wei Chang", "Hung-yi Lee", "Carlos Busso" ], "github": "", "project_page": "", "abstract": "Generative spoken language models pretrained on large-scale raw audio can continue a speech prompt with appropriate content while preserving attributes like speaker and emotion, serving as foundation models for spoken dialogue. In prior literature, these models are often evaluated using ``global token perplexity'', which directly applies the text perplexity formulation to speech tokens. However, this practice overlooks fundamental differences between speech and text modalities, possibly leading to an underestimation of the speech characteristics. In this work, we propose a variety of likelihood- and generative-based evaluation methods that serve in place of naive global token perplexity. We demonstrate that the proposed evaluations more faithfully reflect perceived generation quality, as evidenced by stronger correlations with human-rated mean opinion scores (MOS). When assessed under the new metrics, the relative performance landscape of spoken language models is reshaped, revealing a significantly reduced gap between the best-performing model and the human topline. Together, these results suggest that appropriate evaluation is critical for accurately assessing progress in spoken language modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07767", "title": "Are LLM Decisions Faithful to Verbal Confidence?", "authors": [ "Jiawei Wang", "Yanfei Zhou", "Siddartha Devic", "Deqing Fu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) can produce surprisingly sophisticated estimates of their own uncertainty. However, it remains unclear to what extent this expressed confidence is tied to the reasoning, knowledge, or decision making of the model. To test this, we introduce RiskEval: a framework designed to evaluate whether models adjust their abstention policies in response to varying error penalties. Our evaluation of several frontier models reveals a critical dissociation: models are neither cost-aware when articulating their verbal confidence, nor strategically responsive when deciding whether to engage or abstain under high-penalty conditions. Even when extreme penalties render frequent abstention the mathematically optimal strategy, models almost never abstain, resulting in utility collapse. This indicates that calibrated verbal confidence scores may not be sufficient to create trustworthy and interpretable AI systems, as current models lack the strategic agency to convert uncertainty signals into optimal and risk-sensitive decisions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07033", "title": "Codified Foreshadowing-Payoff Text Generation", "authors": [ "Longfei Yun", "Kun Zhou", "Yupeng Hou", "Letian Peng", "Jingbo Shang" ], "github": "", "project_page": "", "abstract": "Foreshadowing and payoff are ubiquitous narrative devices through which authors introduce commitments early in a story and resolve them through concrete, observable outcomes. However, despite advances in story generation, large language models (LLMs) frequently fail to bridge these long-range narrative dependencies, often leaving \"Chekhov's guns\" unfired even when the necessary context is present. Existing evaluations largely overlook this structural failure, focusing on surface-level coherence rather than the logical fulfillment of narrative setups. In this paper, we introduce Codified Foreshadowing-Payoff Generation (CFPG), a novel framework that reframes narrative quality through the lens of payoff realization. Recognizing that LLMs struggle to intuitively grasp the \"triggering mechanism\" of a foreshadowed event, CFPG transforms narrative continuity into a set of executable causal predicates. By mining and encoding Foreshadow-Trigger-Payoff triples from the BookSum corpus, we provide structured supervision that ensures foreshadowed commitments are not only mentioned but also temporally and logically fulfilled. Experiments demonstrate that CFPG significantly outperforms standard prompting baselines in payoff accuracy and narrative alignment. Our findings suggest that explicitly codifying narrative mechanics is essential for moving LLMs from surface-level fluency to genuine narrative competence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.05107", "title": "Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction", "authors": [ "Muzhao Tian", "Zisu Huang", "Xiaohua Wang", "Jingwen Xu", "Zhengkang Guo", "Qi Qian", "Yuanzhe Shen", "Kaitao Song", "Jiakang Yuan", "Changze Lv", "Xiaoqing Zheng" ], "github": "", "project_page": "", "abstract": "As LLM-based agents are increasingly used in long-term interactions, cumulative memory is critical for enabling personalization and maintaining stylistic consistency. However, most existing systems adopt an ``all-or-nothing'' approach to memory usage: incorporating all relevant past information can lead to Memory Anchoring, where the agent is trapped by past interactions, while excluding memory entirely results in under-utilization and the loss of important interaction history. We show that an agent's reliance on memory can be modeled as an explicit and user-controllable dimension. We first introduce a behavioral metric of memory dependence to quantify the influence of past interactions on current outputs. We then propose Steerable Memory Agent, SteeM, a framework that allows users to dynamically regulate memory reliance, ranging from a fresh-start mode that promotes innovation to a high-fidelity mode that closely follows interaction history. Experiments across different scenarios demonstrate that our approach consistently outperforms conventional prompting and rigid memory masking strategies, yielding a more nuanced and effective control for personalized human-agent collaboration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.01528", "title": "DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving", "authors": [ "Yang Zhou", "Hao Shao", "Letian Wang", "Zhuofan Zong", "Hongsheng Li", "Steven L. Waslander" ], "github": "", "project_page": "", "abstract": "Video generation models, as one form of world models, have emerged as one of the most exciting frontiers in AI, promising agents the ability to imagine the future by modeling the temporal evolution of complex scenes. In autonomous driving, this vision gives rise to driving world models: generative simulators that imagine ego and agent futures, enabling scalable simulation, safe testing of corner cases, and rich synthetic data generation. Yet, despite fast-growing research activity, the field lacks a rigorous benchmark to measure progress and guide priorities. Existing evaluations remain limited: generic video metrics overlook safety-critical imaging factors; trajectory plausibility is rarely quantified; temporal and agent-level consistency is neglected; and controllability with respect to ego conditioning is ignored. Moreover, current datasets fail to cover the diversity of conditions required for real-world deployment. To address these gaps, we present DrivingGen, the first comprehensive benchmark for generative driving world models. DrivingGen combines a diverse evaluation dataset curated from both driving datasets and internet-scale video sources, spanning varied weather, time of day, geographic regions, and complex maneuvers, with a suite of new metrics that jointly assess visual realism, trajectory plausibility, temporal coherence, and controllability. Benchmarking 14 state-of-the-art models reveals clear trade-offs: general models look better but break physics, while driving-specific ones capture motion realistically but lag in visual quality. DrivingGen offers a unified evaluation framework to foster reliable, controllable, and deployable driving world models, enabling scalable simulation, planning, and data-driven decision-making.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07786", "title": "\"TODO: Fix the Mess Gemini Created\": Towards Understanding GenAI-Induced Self-Admitted Technical Debt", "authors": [ "Abdullah Al Mujahid", "Mia Mohammad Imran" ], "github": "https://github.com/tree-sitter/tree-sitter", "project_page": "", "abstract": "As large language models (LLMs) such as ChatGPT, Copilot, Claude, and Gemini become integrated into software development workflows, developers increasingly leave traces of AI involvement in their code comments. Among these, some comments explicitly acknowledge both the use of generative AI and the presence of technical shortcomings. Analyzing 6,540 LLM-referencing code comments from public Python and JavaScript-based GitHub repositories (November 2022-July 2025), we identified 81 that also self-admit technical debt(SATD). Developers most often describe postponed testing, incomplete adaptation, and limited understanding of AI-generated code, suggesting that AI assistance affects both when and why technical debt emerges. We term GenAI-Induced Self-admitted Technical debt (GIST) as a proposed conceptual lens to describe recurring cases where developers incorporate AI-generated code while explicitly expressing uncertainty about its behavior or correctness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tree-sitter/tree-sitter", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07779", "title": "OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent", "authors": [ "Bowen Yang", "Kaiming Jin", "Zhenyu Wu", "Zhaoyang Liu", "Qiushi Sun", "Zehao Li", "JingJing Xie", "Zhoumianze Liu", "Fangzhi Xu", "Kanzhi Cheng", "Qingyun Li", "Yian Wang", "Yu Qiao", "Zun Wang", "Zichen Ding" ], "github": "https://github.com/OS-Copilot/OS-Symphony", "project_page": "", "abstract": "While Vision-Language Models (VLMs) have significantly advanced Computer-Using Agents (CUAs), current frameworks struggle with robustness in long-horizon workflows and generalization in novel domains. These limitations stem from a lack of granular control over historical visual context curation and the absence of visual-aware tutorial retrieval. To bridge these gaps, we introduce OS-Symphony, a holistic framework that comprises an Orchestrator coordinating two key innovations for robust automation: (1) a Reflection-Memory Agent that utilizes milestone-driven long-term memory to enable trajectory-level self-correction, effectively mitigating visual context loss in long-horizon tasks; (2) Versatile Tool Agents featuring a Multimodal Searcher that adopts a SeeAct paradigm to navigate a browser-based sandbox to synthesize live, visually aligned tutorials, thereby resolving fidelity issues in unseen scenarios. Experimental results demonstrate that OS-Symphony delivers substantial performance gains across varying model scales, establishing new state-of-the-art results on three online benchmarks, notably achieving 65.84% on OSWorld.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OS-Copilot/OS-Symphony", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07832", "title": "MHLA: Restoring Expressivity of Linear Attention via Token-Level Multi-Head", "authors": [ "Kewei Zhang", "Ye Huang", "Yufan Deng", "Jincheng Yu", "Junsong Chen", "Huan Ling", "Enze Xie", "Daquan Zhou" ], "github": "https://github.com/DAGroup-PKU/MHLA", "project_page": "", "abstract": "While the Transformer architecture dominates many fields, its quadratic self-attention complexity hinders its use in large-scale applications. Linear attention offers an efficient alternative, but its direct application often degrades performance, with existing fixes typically re-introducing computational overhead through extra modules (e.g., depthwise separable convolution) that defeat the original purpose. In this work, we identify a key failure mode in these methods: global context collapse, where the model loses representational diversity. To address this, we propose Multi-Head Linear Attention (MHLA), which preserves this diversity by computing attention within divided heads along the token dimension. We prove that MHLA maintains linear complexity while recovering much of the expressive power of softmax attention, and verify its effectiveness across multiple domains, achieving a 3.6\\% improvement on ImageNet classification, a 6.3\\% gain on NLP, a 12.6\\% improvement on image generation, and a 41\\% enhancement on video generation under the same time complexity.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/DAGroup-PKU/MHLA", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07351", "title": "Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models", "authors": [ "Linhao Zhong", "Linyu Wu", "Bozhen Fang", "Tianjian Feng", "Chenchen Jing", "Wen Wang", "Jiaheng Zhang", "Hao Chen", "Chunhua Shen" ], "github": "", "project_page": "", "abstract": "Diffusion Language Models (DLMs) offer a promising alternative for language modeling by enabling parallel decoding through iterative refinement. However, most DLMs rely on hard binary masking and discrete token assignments, which hinder the revision of early decisions and underutilize intermediate probabilistic representations. In this paper, we propose EvoToken-DLM, a novel diffusion-based language modeling approach that replaces hard binary masks with evolving soft token distributions. EvoToken-DLM enables a progressive transition from masked states to discrete outputs, supporting revisable decoding. To effectively support this evolution, we introduce continuous trajectory supervision, which aligns training objectives with iterative probabilistic updates. Extensive experiments across multiple benchmarks show that EvoToken-DLM consistently achieves superior performance, outperforming strong diffusion-based and masked DLM baselines. Project webpage: https://aim-uofa.github.io/EvoTokenDLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06747", "title": "FinForge: Semi-Synthetic Financial Benchmark Generation", "authors": [ "Glenn Matlin", "Akhil Theerthala", "Anant Gupta", "Anirudh JM", "Rayan Castilla", "Yi Mei Ng", "Sudheer Chava" ], "github": "https://github.com/gtfintechlab/FinForge", "project_page": "", "abstract": "Evaluating Language Models (LMs) in specialized, high-stakes domains such as finance remains a significant challenge due to the scarcity of open, high-quality, and domain-specific datasets. Existing general-purpose benchmarks provide broad coverage but lack the depth and domain fidelity needed to assess LMs' capabilities for real-world financial reasoning, which requires both conceptual understanding and quantitative rigor. To address this gap, we introduce FinForge, a scalable, semi-synthetic pipeline for constructing finance-specific evaluation benchmarks through a hybrid of expert-guided data curation and controlled LM-based synthesis. FinForge combines manual and programmatic corpus construction from authoritative financial sources with structured question generation and validation using Gemini 2.5 Flash. To demonstrate the pipeline's efficacy, we produce FinForge-5k, a snapshot benchmark comprising over 5,000 human-validated question-answer pairs across 11 finance subdomains, derived from a curated corpus of 100,000 verified documents totaling 143M tokens. Evaluation of state-of-the-art open-source and closed-source models on FinForge-5k reveals significant differences in financial reasoning, with leading models achieving accuracy levels near 80%. These findings underscore the framework's utility for diagnosing current model limitations and guiding future improvements in financial domain competence. All code and data are available at https://github.com/gtfintechlab/FinForge.", "github_manual": "", "github_regex": "https://github.com/gtfintechlab/FinForge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06463", "title": "Gecko: An Efficient Neural Architecture Inherently Processing Sequences with Arbitrary Lengths", "authors": [ "Xuezhe Ma", "Shicheng Wen", "Linghao Jin", "Bilge Acun", "Ruihang Lai", "Bohan Hou", "Will Lin", "Hao Zhang", "Songlin Yang", "Ryan Lee", "Mengxi Wu", "Jonathan May", "Luke Zettlemoyer", "Carole-Jean Wu" ], "github": "https://github.com/XuezheMax/gecko-llm", "project_page": "", "abstract": "Designing a unified neural network to efficiently and inherently process sequential data with arbitrary lengths is a central and challenging problem in sequence modeling. The design choices in Transformer, including quadratic complexity and weak length extrapolation, have limited their ability to scale to long sequences. In this work, we propose Gecko, a neural architecture that inherits the design of Mega and Megalodon (exponential moving average with gated attention), and further introduces multiple technical components to improve its capability to capture long range dependencies, including timestep decay normalization, sliding chunk attention mechanism, and adaptive working memory. In a controlled pretraining comparison with Llama2 and Megalodon in the scale of 7 billion parameters and 2 trillion training tokens, Gecko achieves better efficiency and long-context scalability. Gecko reaches a training loss of 1.68, significantly outperforming Llama2-7B (1.75) and Megalodon-7B (1.70), and landing close to Llama2-13B (1.67). Notably, without relying on any context-extension techniques, Gecko exhibits inherent long-context processing and retrieval capabilities, stably handling sequences of up to 4 million tokens and retrieving information from contexts up to 4times longer than its attention window. Code: https://github.com/XuezheMax/gecko-llm", "github_manual": "", "github_regex": "https://github.com/XuezheMax/gecko-llm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06803", "title": "Forest Before Trees: Latent Superposition for Efficient Visual Reasoning", "authors": [ "Yubo Wang", "Juntian Zhang", "Yichen Wu", "Yankai Lin", "Nils Lukas", "Yuhan Liu" ], "github": "https://github.com/ybb6/laser", "project_page": "", "abstract": "While Chain-of-Thought empowers Large Vision-Language Models with multi-step reasoning, explicit textual rationales suffer from an information bandwidth bottleneck, where continuous visual details are discarded during discrete tokenization. Recent latent reasoning methods attempt to address this challenge, but often fall prey to premature semantic collapse due to rigid autoregressive objectives. In this paper, we propose Laser, a novel paradigm that reformulates visual deduction via Dynamic Windowed Alignment Learning (DWAL). Instead of forcing a point-wise prediction, Laser aligns the latent state with a dynamic validity window of future semantics. This mechanism enforces a \"Forest-before-Trees\" cognitive hierarchy, enabling the model to maintain a probabilistic superposition of global features before narrowing down to local details. Crucially, Laser maintains interpretability via decodable trajectories while stabilizing unconstrained learning via Self-Refined Superposition. Extensive experiments on 6 benchmarks demonstrate that Laser achieves state-of-the-art performance among latent reasoning methods, surpassing the strong baseline Monet by 5.03% on average. Notably, it achieves these gains with extreme efficiency, reducing inference tokens by more than 97%, while demonstrating robust generalization to out-of-distribution domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ybb6/laser", "github_api_added_by": "auto" }, { "date": "2026-01-13", "arxiv_id": "2601.05747", "title": "FlyPose: Towards Robust Human Pose Estimation From Aerial Views", "authors": [ "Hassaan Farooq", "Marvin Brenner", "Peter St\\\u00fctz" ], "github": "https://github.com/farooqhassaan/FlyPose", "project_page": "", "abstract": "Unmanned Aerial Vehicles (UAVs) are increasingly deployed in close proximity to humans for applications such as parcel delivery, traffic monitoring, disaster response and infrastructure inspections. Ensuring safe and reliable operation in these human-populated environments demands accurate perception of human poses and actions from an aerial viewpoint. This perspective challenges existing methods with low resolution, steep viewing angles and (self-)occlusion, especially if the application demands realtime feasibile models. We train and deploy FlyPose, a lightweight top-down human pose estimation pipeline for aerial imagery. Through multi-dataset training, we achieve an average improvement of 6.8 mAP in person detection across the test-sets of Manipal-UAV, VisDrone, HIT-UAV as well as our custom dataset. For 2D human pose estimation we report an improvement of 16.3 mAP on the challenging UAV-Human dataset. FlyPose runs with an inference latency of ~20 milliseconds including preprocessing on a Jetson Orin AGX Developer Kit and is deployed onboard a quadrotor UAV during flight experiments. We also publish FlyPose-104, a small but challenging aerial human pose estimation dataset, that includes manual annotations from difficult aerial perspectives: https://github.com/farooqhassaan/FlyPose.", "github_manual": "", "github_regex": "https://github.com/farooqhassaan/FlyPose", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07790", "title": "Benchmarking Small Language Models and Small Reasoning Language Models on System Log Severity Classification", "authors": [ "Yahya Masri", "Emily Ma", "Zifu Wang", "Joseph Rogers", "Chaowei Yang" ], "github": "https://github.com/stccenter/Benchmarking-SLMs-and-SRLMs-on-System-Log-Severity-Classification", "project_page": "", "abstract": "System logs are crucial for monitoring and diagnosing modern computing infrastructure, but their scale and complexity require reliable and efficient automated interpretation. Since severity levels are predefined metadata in system log messages, having a model merely classify them offers limited standalone practical value, revealing little about its underlying ability to interpret system logs. We argue that severity classification is more informative when treated as a benchmark for probing runtime log comprehension rather than as an end task. Using real-world journalctl data from Linux production servers, we evaluate nine small language models (SLMs) and small reasoning language models (SRLMs) under zero-shot, few-shot, and retrieval-augmented generation (RAG) prompting. The results reveal strong stratification. Qwen3-4B achieves the highest accuracy at 95.64% with RAG, while Gemma3-1B improves from 20.25% under few-shot prompting to 85.28% with RAG. Notably, the tiny Qwen3-0.6B reaches 88.12% accuracy despite weak performance without retrieval. In contrast, several SRLMs, including Qwen3-1.7B and DeepSeek-R1-Distill-Qwen-1.5B, degrade substantially when paired with RAG. Efficiency measurements further separate models: most Gemma and Llama variants complete inference in under 1.2 seconds per log, whereas Phi-4-Mini-Reasoning exceeds 228 seconds per log while achieving <10% accuracy. These findings suggest that (1) architectural design, (2) training objectives, and (3) the ability to integrate retrieved context under strict output constraints jointly determine performance. By emphasizing small, deployable models, this benchmark aligns with real-time requirements of digital twin (DT) systems and shows that severity classification serves as a lens for evaluating model competence and real-time deployability, with implications for root cause analysis (RCA) and broader DT integration.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stccenter/Benchmarking-SLMs-and-SRLMs-on-System-Log-Severity-Classification", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06966", "title": "RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction", "authors": [ "Haonan Bian", "Zhiyuan Yao", "Sen Hu", "Zishan Xu", "Shaolei Zhang", "Yifu Guo", "Ziliang Yang", "Xueran Han", "Huacan Wang", "Ronghao Chen" ], "github": "https://github.com/AvatarMemory/RealMemBench", "project_page": "", "abstract": "As Large Language Models (LLMs) evolve from static dialogue interfaces to autonomous general agents, effective memory is paramount to ensuring long-term consistency. However, existing benchmarks primarily focus on casual conversation or task-oriented dialogue, failing to capture **\"long-term project-oriented\"** interactions where agents must track evolving goals. To bridge this gap, we introduce **RealMem**, the first benchmark grounded in realistic project scenarios. RealMem comprises over 2,000 cross-session dialogues across eleven scenarios, utilizing natural user queries for evaluation. We propose a synthesis pipeline that integrates Project Foundation Construction, Multi-Agent Dialogue Generation, and Memory and Schedule Management to simulate the dynamic evolution of memory. Experiments reveal that current memory systems face significant challenges in managing the long-term project states and dynamic context dependencies inherent in real-world projects. Our code and datasets are available at [https://github.com/AvatarMemory/RealMemBench](https://github.com/AvatarMemory/RealMemBench).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AvatarMemory/RealMemBench", "github_api_added_by": "user" }, { "date": "2026-01-13", "arxiv_id": "2601.04577", "title": "Sci-Reasoning: A Dataset Decoding AI Innovation Patterns", "authors": [ "Jiachen Liu", "Maestro Harmon", "Zechen Zhang" ], "github": "https://github.com/AmberLJC/Sci-Reasoning", "project_page": "", "abstract": "While AI innovation accelerates rapidly, the intellectual process behind breakthroughs -- how researchers identify gaps, synthesize prior work, and generate insights -- remains poorly understood. The lack of structured data on scientific reasoning hinders systematic analysis and development of AI research agents. We introduce Sci-Reasoning, the first dataset capturing the intellectual synthesis behind high-quality AI research. Using community-validated quality signals and an LLM-accelerated, human-verified pipeline, we trace Oral and Spotlight papers across NeurIPS, ICML, and ICLR (2023-2025) to its key predecessors, articulating specific reasoning links in a structured format. Our analysis identifies 15 distinct thinking patterns, with three dominant strategies accounting for 52.7%: Gap-Driven Reframing (24.2%), Cross-Domain Synthesis (18.0%), and Representation Shift (10.5%). The most powerful innovation recipes combine multiple patterns: Gap-Driven Reframing + Representation Shift, Cross-Domain Synthesis + Representation Shift, and Gap-Driven Reframing + Cross-Domain Synthesis. This dataset enables quantitative studies of scientific progress and provides structured reasoning trajectories for training the next generation AI research agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AmberLJC/Sci-Reasoning", "github_api_added_by": "auto" }, { "date": "2026-01-13", "arxiv_id": "2601.06423", "title": "Does Inference Scaling Improve Reasoning Faithfulness? A Multi-Model Analysis of Self-Consistency Tradeoffs", "authors": [ "Deep Mehta" ], "github": "", "project_page": "", "abstract": "Self-consistency has emerged as a popular technique for improving large language model accuracy on reasoning tasks. The approach is straightforward: generate multiple reasoning paths and select the most common answer through majority voting. While this reliably boosts accuracy, it remains unclear whether these gains reflect genuine improvements in reasoning quality. We investigate a fundamental question that has not been studied before: does inference scaling improve reasoning faithfulness? We conduct a comprehensive empirical study across four frontier models (GPT-5.2, Claude Opus 4.5, Gemini-3-flash-preview, and DeepSeek-v3.2) on 100 GSM8K mathematical reasoning problems. Our analysis employs bootstrap confidence intervals, McNemar's tests for paired comparisons, and Cohen's d effect sizes to quantify the effects rigorously. The results reveal striking differences across models that challenge common assumptions about self-consistency. GPT-5.2 shows the expected pattern: accuracy improves from 78% to 90% at N=5, with faithfulness remaining relatively stable (0.540 to 0.510). Claude Opus 4.5 tells a completely different story. Its accuracy actually drops from 78% to 74.3% while faithfulness jumps dramatically from 0.270 to 0.891 at N=5. DeepSeek-v3.2, already at 98% accuracy, shows ceiling effects with modest faithfulness gains (0.440 to 0.541). Gemini-3-flash improves from 81% to 86% accuracy with a slight faithfulness decrease (0.260 to 0.212). Problem difficulty analysis reveals that GPT-5.2 solves 82% of hard problems while breaking only 13% of easy ones. Claude, in contrast, breaks 23% of easy problems, explaining its accuracy decrease. These findings matter for practitioners: self-consistency is not universally beneficial, and teams should test their specific models before deployment. We release our code and provide practical recommendations for navigating these tradeoffs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06993", "title": "Can Textual Reasoning Improve the Performance of MLLMs on Fine-grained Visual Classification?", "authors": [ "Jie Zhu", "Yiyang Su", "Xiaoming Liu" ], "github": "https://github.com/jiezhu23/ReFine-RFT", "project_page": "", "abstract": "Multi-modal large language models (MLLMs) exhibit strong general-purpose capabilities, yet still struggle on Fine-Grained Visual Classification (FGVC), a core perception task that requires subtle visual discrimination and is crucial for many real-world applications. A widely adopted strategy for boosting performance on challenging tasks such as math and coding is Chain-of-Thought (CoT) reasoning. However, several prior works have reported that CoT can actually harm performance on visual perception tasks. These studies, though, examine the issue from relatively narrow angles and leave open why CoT degrades perception-heavy performance. We systematically re-examine the role of CoT in FGVC through the lenses of zero-shot evaluation and multiple training paradigms. Across these settings, we uncover a central paradox: the degradation induced by CoT is largely driven by the reasoning length, in which longer textual reasoning consistently lowers classification accuracy. We term this phenomenon the ``Cost of Thinking''. Building on this finding, we make two key contributions: (1) \\alg, a simple and general plug-and-play normalization method for multi-reward optimization that balances heterogeneous reward signals, and (2) ReFine-RFT, a framework that combines ensemble rewards with \\alg to constrain reasoning length while providing dense accuracy-oriented feedback. Extensive experiments demonstrate the effectiveness of our findings and the proposed ReFine-RFT, achieving state-of-the-art performance across FGVC benchmarks. Code and models are available at https://github.com/jiezhu23/ReFine-RFT{Project Link}.", "github_manual": "", "github_regex": "https://github.com/jiezhu23/ReFine-RFT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07239", "title": "Stochastic CHAOS: Why Deterministic Inference Kills, and Distributional Variability Is the Heartbeat of Artifical Cognition", "authors": [ "Tanmay Joshi", "Shourya Aggarwal", "Anusa Saha", "Aadi Pandey", "Shreyash Dhoot", "Vighnesh Rai", "Raxit Goswami", "Aman Chadha", "Vinija Jain", "Amitava Das" ], "github": "", "project_page": "", "abstract": "Deterministic inference is a comforting ideal in classical software: the same program on the same input should always produce the same output. As large language models move into real-world deployment, this ideal has been imported wholesale into inference stacks. Recent work from the Thinking Machines Lab has presented a detailed analysis of nondeterminism in LLM inference, showing how batch-invariant kernels and deterministic attention can enforce bitwise-identical outputs, positioning deterministic inference as a prerequisite for reproducibility and enterprise reliability. In this paper, we take the opposite stance. We argue that, for LLMs, deterministic inference kills. It kills the ability to model uncertainty, suppresses emergent abilities, collapses reasoning into a single brittle path, and weakens safety alignment by hiding tail risks. LLMs implement conditional distributions over outputs, not fixed functions. Collapsing these distributions to a single canonical completion may appear reassuring, but it systematically conceals properties central to artificial cognition. We instead advocate Stochastic CHAOS, treating distributional variability as a signal to be measured and controlled. Empirically, we show that deterministic inference is systematically misleading. Single-sample deterministic evaluation underestimates both capability and fragility, masking failure probability under paraphrases and noise. Phase-like transitions associated with emergent abilities disappear under greedy decoding. Multi-path reasoning degrades when forced onto deterministic backbones, reducing accuracy and diagnostic insight. Finally, deterministic evaluation underestimates safety risk by hiding rare but dangerous behaviors that appear only under multi-sample evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06307", "title": "A Rising Tide Lifts All Boats: MTQE Rewards for Idioms Improve General Translation Quality", "authors": [ "Ishika Agarwal", "Zhenlin He", "Dhruva Patil", "Dilek Hakkani-T\u00fcr" ], "github": "https://github.com/agarwalishika/TranslatingIdioms", "project_page": "", "abstract": "Non-compositional expressions (e.g., idioms, proverbs, and metaphors) pose significant challenges for neural machine translation systems because their meanings cannot be derived from individual words alone. These expressions encode rich, cultural meaning, and have both figurative and literal meanings, making accurate translation difficult. Because models are fairly good at translating compositional text, we investigate GRPO-style fine-tuning using Machine Translation Quality Estimation (MTQE) models as reward functions to train models to better translate idioms. Using Chinese and Hindi idiom datasets, we find that idiom translation abilities improve by ~14 points, general, non-idiomatic translation implicitly improves by ~8 points, and cross-lingual translation abilities (trained on one language, evaluated on another) improves by ~6 points. Overall, our work quantifies the non-compositional translation gap and offers insights for developing LLMs with stronger cross-cultural and figurative language understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/agarwalishika/TranslatingIdioms", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06238", "title": "SPINAL -- Scaling-law and Preference Integration in Neural Alignment Layers", "authors": [ "Arion Das", "Partha Pratim Saha", "Amit Dhanda", "Vinija Jain", "Aman Chadha", "Amitava Das" ], "github": "", "project_page": "", "abstract": "Direct Preference Optimization (DPO) is a principled, scalable alternative to RLHF for aligning large language models from pairwise preferences, but its internal geometric footprint remains undercharacterized, limiting audits, checkpoint comparisons, and failure prediction. We introduce SPINAL (Scaling-law and Preference Integration in Neural Alignment Layers), a diagnostic that measures how alignment reshapes representations across depth by tracing localized structural change layer by layer. Across model families, DPO produces a layerwise calibration effect concentrated in the final decoder blocks (often layers 21-30), where preference gradients most directly affect the next-token distribution. SPINAL encodes each checkpoint as a depth trace over (layer index, contraction score, transport score). The contraction score summarizes how quickly the tail of a layer's spectrum decays (how fast small modes vanish); higher values indicate stronger contraction into fewer effective directions. The transport score summarizes how much the token distribution shifts between adjacent layers using a bounded overlap measure; lower values indicate shorter, smoother steps through representation space. Aligned checkpoints show a late-layer ramp-up in contraction and a smooth reduction in transport, consistent with tightened and stabilized policy mass, while unaligned models trace higher-curvature, more entropic, and geometrically incoherent depth paths. Overall, alignment is geometrically localized: the final layers encode the dominant preference-induced corrections. SPINAL turns this localization into a practical audit signal, quantifying where alignment concentrates, how strongly it manifests, and when it begins to destabilize during training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.06788", "title": "Artificial Entanglement in the Fine-Tuning of Large Language Models", "authors": [ "Min Chen", "Zihan Wang", "Canyu Chen", "Zeguan Wu", "Manling Li", "Junyu Liu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) can be adapted to new tasks using parameter-efficient fine-tuning (PEFT) methods that modify only a small number of trainable parameters, often through low-rank updates. In this work, we adopt a quantum-information-inspired perspective to understand their effectiveness. From this perspective, low-rank parameterizations naturally correspond to low-dimensional Matrix Product States (MPS) representations, which enable entanglement-based characterizations of parameter structure. Thereby, we term and measure \"Artificial Entanglement\", defined as the entanglement entropy of the parameters in artificial neural networks (in particular the LLMs). We first study the representative low-rank adaptation (LoRA) PEFT method, alongside full fine-tuning (FFT), using LLaMA models at the 1B and 8B scales trained on the Tulu3 and OpenThoughts3 datasets, and uncover: (i) Internal artificial entanglement in the updates of query and value projection matrices in LoRA follows a volume law with a central suppression (termed as the \"Entanglement Valley\"), which is sensitive to hyper-parameters and is distinct from that in FFT; (ii) External artificial entanglement in attention matrices, corresponding to token-token correlations in representation space, follows an area law with logarithmic corrections and remains robust to LoRA hyper-parameters and training steps. Drawing a parallel to the No-Hair Theorem in black hole physics, we propose that although LoRA and FFT induce distinct internal entanglement signatures, such differences do not manifest in the attention outputs, suggesting a \"no-hair\" property that results in the effectiveness of low rank updates. We further provide theoretical support based on random matrix theory, and extend our analysis to an MPS Adaptation PEFT method, which exhibits qualitatively similar behaviors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.03570", "title": "How Do Large Language Models Learn Concepts During Continual Pre-Training?", "authors": [ "Barry Menglong Yao", "Sha Li", "Yunzhi Yao", "Minqian Liu", "Zaishuo Xia", "Qifan Wang", "Lifu Huang" ], "github": "", "project_page": "", "abstract": "Human beings primarily understand the world through concepts (e.g., dog), abstract mental representations that structure perception, reasoning, and learning. However, how large language models (LLMs) acquire, retain, and forget such concepts during continual pretraining remains poorly understood. In this work, we study how individual concepts are acquired and forgotten, as well as how multiple concepts interact through interference and synergy. We link these behavioral dynamics to LLMs' internal Concept Circuits, computational subgraphs associated with specific concepts, and incorporate Graph Metrics to characterize circuit structure. Our analysis reveals: (1) LLMs concept circuits provide a non-trivial, statistically significant signal of concept learning and forgetting; (2) Concept circuits exhibit a stage-wise temporal pattern during continual pretraining, with an early increase followed by gradual decrease and stabilization; (3) concepts with larger learning gains tend to exhibit greater forgetting under subsequent training; (4) semantically similar concepts induce stronger interference than weakly related ones; (5) conceptual knowledge differs in their transferability, with some significantly facilitating the learning of others. Together, our findings offer a circuit-level view of concept learning dynamics and inform the design of more interpretable and robust concept-aware training strategies for LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-13", "arxiv_id": "2601.07389", "title": "On the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training", "authors": [ "Xueyan Niu", "Bo Bai", "Wei Han", "Weixi Zhang" ], "github": "", "project_page": "", "abstract": "Post-training of large language models routinely interleaves supervised fine-tuning (SFT) with reinforcement learning (RL). These two methods have different objectives: SFT minimizes the cross-entropy loss between model outputs and expert responses, while RL maximizes reward signals derived from human preferences or rule-based verifiers. Modern reasoning models have widely adopted the practice of alternating SFT and RL training. However, there is no theoretical account of whether they can be decoupled. We prove that decoupling is impossible in either order: (1) SFT-then-RL coupling: RL increases SFT loss under SFT optimality and (2) RL-then-SFT coupling: SFT lowers the reward achieved by RL. Experiments on Qwen3-0.6B confirm the predicted degradation, verifying that SFT and RL cannot be separated without loss of prior performance in the post-training", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08584", "title": "Ministral 3", "authors": [ "Alexander H. Liu", "Kartik Khandelwal", "Sandeep Subramanian", "Victor Jouault", "Abhinav Rastogi", "Adrien Sad\u00e9", "Alan Jeffares", "Albert Jiang", "Alexandre Cahill", "Alexandre Gavaudan", "Alexandre Sablayrolles", "Am\u00e9lie H\u00e9liou", "Amos You", "Andy Ehrenberg", "Andy Lo", "Anton Eliseev", "Antonia Calvi", "Avinash Sooriyarachchi", "Baptiste Bout", "Baptiste Rozi\u00e8re", "Baudouin De Monicault", "Cl\u00e9mence Lanfranchi", "Corentin Barreau", "Cyprien Courtot", "Daniele Grattarola", "Darius Dabert", "Diego de las Casas", "Elliot Chane-Sane", "Faruk Ahmed", "Gabrielle Berrada", "Ga\u00ebtan Ecrepont", "Gauthier Guinet", "Georgii Novikov", "Guillaume Kunsch", "Guillaume Lample", "Guillaume Martin", "Gunshi Gupta", "Jan Ludziejewski", "Jason Rute", "Joachim Studnia", "Jonas Amar", "Jos\u00e9phine Delas", "Josselin Somerville Roberts", "Karmesh Yadav", "Khyathi Chandu", "Kush Jain", "Laurence Aitchison", "Laurent Fainsin", "L\u00e9onard Blier", "Lingxiao Zhao", "Louis Martin", "Lucile Saulnier", "Luyu Gao", "Maarten Buyl", "Margaret Jennings", "Marie Pellat", "Mark Prins", "Mathieu Poir\u00e9e", "Mathilde Guillaumin", "Matthieu Dinot", "Matthieu Futeral", "Maxime Darrin", "Maximilian Augustin", "Mia Chiquier", "Michel Schimpf", "Nathan Grinsztajn", "Neha Gupta", "Nikhil Raghuraman", "Olivier Bousquet", "Olivier Duchenne", "Patricia Wang", "Patrick von Platen", "Paul Jacob", "Paul Wambergue", "Paula Kurylowicz", "Pavankumar Reddy Muddireddy", "Philom\u00e8ne Chagniot", "Pierre Stock", "Pravesh Agrawal", "Quentin Torroba", "Romain Sauvestre", "Roman Soletskyi", "Rupert Menneer", "Sagar Vaze", "Samuel Barry", "Sanchit Gandhi", "Siddhant Waghjale", "Siddharth Gandhi", "Soham Ghosh", "Srijan Mishra", "Sumukh Aithal", "Szymon Antoniak", "Teven Le Scao", "Th\u00e9o Cachet", "Theo Simon Sorg", "Thibaut Lavril", "Thiziri Nait Saada", "Thomas Chabal", "Thomas Foubert", "Thomas Robert", "Thomas Wang", "Tim Lawson", "Tom Bewley", "Tom Bewley", "Tom Edwards", "Umar Jamil", "Umberto Tomasini", "Valeriia Nemychnikova", "Van Phung", "Vincent Maladi\u00e8re", "Virgile Richard", "Wassim Bouaziz", "Wen-Ding Li", "William Marshall", "Xinghui Li", "Xinyu Yang", "Yassine El Ouahidi", "Yihan Wang", "Yunhao Tang", "Zaccharie Ramzi" ], "github": "", "project_page": "", "abstract": "We introduce the Ministral 3 series, a family of parameter-efficient dense language models designed for compute and memory constrained applications, available in three model sizes: 3B, 8B, and 14B parameters. For each model size, we release three variants: a pretrained base model for general-purpose use, an instruction finetuned, and a reasoning model for complex problem-solving. In addition, we present our recipe to derive the Ministral 3 models through Cascade Distillation, an iterative pruning and continued training with distillation technique. Each model comes with image understanding capabilities, all under the Apache 2.0 license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08468", "title": "JudgeRLVR: Judge First, Generate Second for Efficient Reasoning", "authors": [ "Jiangshan Duo", "Hanyu Li", "Hailin Zhang", "Yudong Wang", "Sujian Li", "Liang Zhao" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has become a standard paradigm for reasoning in Large Language Models. However, optimizing solely for final-answer correctness often drives models into aimless, verbose exploration, where they rely on exhaustive trial-and-error tactics rather than structured planning to reach solutions. While heuristic constraints like length penalties can reduce verbosity, they often truncate essential reasoning steps, creating a difficult trade-off between efficiency and verification. In this paper, we argue that discriminative capability is a prerequisite for efficient generation: by learning to distinguish valid solutions, a model can internalize a guidance signal that prunes the search space. We propose JudgeRLVR, a two-stage judge-then-generate paradigm. In the first stage, we train the model to judge solution responses with verifiable answers. In the second stage, we fine-tune the same model with vanilla generating RLVR initialized from the judge. Compared to Vanilla RLVR using the same math-domain training data, JudgeRLVR achieves a better quality--efficiency trade-off for Qwen3-30B-A3B: on in-domain math, it delivers about +3.7 points average accuracy gain with -42\\% average generation length; on out-of-domain benchmarks, it delivers about +4.5 points average accuracy improvement, demonstrating enhanced generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.06487", "title": "ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking", "authors": [ "Qiang Zhang", "Boli Chen", "Fanrui Zhang", "Ruixue Ding", "Shihang Wang", "Qiuchen Wang", "Yinfeng Huang", "Haonan Zhang", "Rongxiang Zhu", "Pengyong Wang", "Ailin Ren", "Xin Li", "Pengjun Xie", "Jiawei Liu", "Ning Guo", "Jingren Zhou", "Zheng-Jun Zha" ], "github": "https://github.com/Alibaba-NLP/qqr", "project_page": "", "abstract": "Reinforcement learning has substantially improved the performance of LLM agents on tasks with verifiable outcomes, but it still struggles on open-ended agent tasks with vast solution spaces (e.g., complex travel planning). Due to the absence of objective ground-truth for these tasks, current RL algorithms largely rely on reward models that assign scalar scores to individual responses. We contend that such pointwise scoring suffers from an inherent discrimination collapse: the reward model struggles to distinguish subtle advantages among different trajectories, resulting in scores within a group being compressed into a narrow range. Consequently, the effective reward signal becomes dominated by noise from the reward model, leading to optimization stagnation. To address this, we propose ArenaRL, a reinforcement learning paradigm that shifts from pointwise scalar scoring to intra-group relative ranking. ArenaRL introduces a process-aware pairwise evaluation mechanism, employing multi-level rubrics to assign fine-grained relative scores to trajectories. Additionally, we construct an intra-group adversarial arena and devise a tournament-based ranking scheme to obtain stable advantage signals. Empirical results confirm that the built seeded single-elimination scheme achieves nearly equivalent advantage estimation accuracy to full pairwise comparisons with O(N^2) complexity, while operating with only O(N) complexity, striking an optimal balance between efficiency and precision. Furthermore, to address the lack of full-cycle benchmarks for open-ended agents, we build Open-Travel and Open-DeepResearch, two high-quality benchmarks featuring a comprehensive pipeline covering SFT, RL training, and multi-dimensional evaluation. Extensive experiments show that ArenaRL substantially outperforms standard RL baselines, enabling LLM agents to generate more robust solutions for complex real-world tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Alibaba-NLP/qqr", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.07264", "title": "The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents", "authors": [ "Weihao Xuan", "Qingcheng Zeng", "Heli Qi", "Yunze Xiao", "Junjue Wang", "Naoto Yokoya" ], "github": "", "project_page": "", "abstract": "Autonomous agents based on large language models (LLMs) are rapidly evolving to handle multi-turn tasks, but ensuring their trustworthiness remains a critical challenge. A fundamental pillar of this trustworthiness is calibration, which refers to an agent's ability to express confidence that reliably reflects its actual performance. While calibration is well-established for static models, its dynamics in tool-integrated agentic workflows remain underexplored. In this work, we systematically investigate verbalized calibration in tool-use agents, revealing a fundamental confidence dichotomy driven by tool type. Specifically, our pilot study identifies that evidence tools (e.g., web search) systematically induce severe overconfidence due to inherent noise in retrieved information, while verification tools (e.g., code interpreters) can ground reasoning through deterministic feedback and mitigate miscalibration. To robustly improve calibration across tool types, we propose a reinforcement learning (RL) fine-tuning framework that jointly optimizes task accuracy and calibration, supported by a holistic benchmark of reward designs. We demonstrate that our trained agents not only achieve superior calibration but also exhibit robust generalization from local training environments to noisy web settings and to distinct domains such as mathematical reasoning. Our results highlight the necessity of domain-specific calibration strategies for tool-use agents. More broadly, this work establishes a foundation for building self-aware agents that can reliably communicate uncertainty in high-stakes, real-world deployments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08587", "title": "End-to-End Video Character Replacement without Structural Guidance", "authors": [ "Zhengbo Xu", "Jie Ma", "Ziheng Wang", "Zhan Peng", "Jun Liang", "Jing Li" ], "github": "", "project_page": "", "abstract": "Controllable video character replacement with a user-provided identity remains a challenging problem due to the lack of paired video data. Prior works have predominantly relied on a reconstruction-based paradigm that requires per-frame segmentation masks and explicit structural guidance (e.g., skeleton, depth). This reliance, however, severely limits their generalizability in complex scenarios involving occlusions, character-object interactions, unusual poses, or challenging illumination, often leading to visual artifacts and temporal inconsistencies. In this paper, we propose MoCha, a pioneering framework that bypasses these limitations by requiring only a single arbitrary frame mask. To effectively adapt the multi-modal input condition and enhance facial identity, we introduce a condition-aware RoPE and employ an RL-based post-training stage. Furthermore, to overcome the scarcity of qualified paired-training data, we propose a comprehensive data construction pipeline. Specifically, we design three specialized datasets: a high-fidelity rendered dataset built with Unreal Engine 5 (UE5), an expression-driven dataset synthesized by current portrait animation techniques, and an augmented dataset derived from existing video-mask pairs. Extensive experiments demonstrate that our method substantially outperforms existing state-of-the-art approaches. We will release the code to facilitate further research. Please refer to our project page for more details: orange-3dv-team.github.io/MoCha", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08828", "title": "Motion Attribution for Video Generation", "authors": [ "Xindi Wu", "Despoina Paschalidou", "Jun Gao", "Antonio Torralba", "Laura Leal-Taix\u00e9", "Olga Russakovsky", "Sanja Fidler", "Jonathan Lorraine" ], "github": "", "project_page": "", "abstract": "Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood. We present Motive (MOTIon attribution for Video gEneration), a motion-centric, gradient-based data attribution framework that scales to modern, large, high-quality video datasets and models. We use this to study which fine-tuning clips improve or degrade temporal dynamics. Motive isolates temporal dynamics from static appearance via motion-weighted loss masks, yielding efficient and scalable motion-specific influence computation. On text-to-video models, Motive identifies clips that strongly affect motion and guides data curation that improves temporal consistency and physical plausibility. With Motive-selected high-influence data, our method improves both motion smoothness and dynamic degree on VBench, achieving a 74.1% human preference win rate compared with the pretrained base model. To our knowledge, this is the first framework to attribute motion rather than visual appearance in video generative models and to use it to curate fine-tuning data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08665", "title": "VLingNav: Embodied Navigation with Adaptive Reasoning and Visual-Assisted Linguistic Memory", "authors": [ "Shaoan Wang", "Yuanfei Luo", "Xingyu Chen", "Aocheng Luo", "Dongyue Li", "Chang Liu", "Sheng Chen", "Yangang Zhang", "Junzhi Yu" ], "github": "", "project_page": "", "abstract": "VLA models have shown promising potential in embodied navigation by unifying perception and planning while inheriting the strong generalization abilities of large VLMs. However, most existing VLA models rely on reactive mappings directly from observations to actions, lacking the explicit reasoning capabilities and persistent memory required for complex, long-horizon navigation tasks. To address these challenges, we propose VLingNav, a VLA model for embodied navigation grounded in linguistic-driven cognition. First, inspired by the dual-process theory of human cognition, we introduce an adaptive chain-of-thought mechanism, which dynamically triggers explicit reasoning only when necessary, enabling the agent to fluidly switch between fast, intuitive execution and slow, deliberate planning. Second, to handle long-horizon spatial dependencies, we develop a visual-assisted linguistic memory module that constructs a persistent, cross-modal semantic memory, enabling the agent to recall past observations to prevent repetitive exploration and infer movement trends for dynamic environments. For the training recipe, we construct Nav-AdaCoT-2.9M, the largest embodied navigation dataset with reasoning annotations to date, enriched with adaptive CoT annotations that induce a reasoning paradigm capable of adjusting both when to think and what to think about. Moreover, we incorporate an online expert-guided reinforcement learning stage, enabling the model to surpass pure imitation learning and to acquire more robust, self-explored navigation behaviors. Extensive experiments demonstrate that VLingNav achieves state-of-the-art performance across a wide range of embodied navigation benchmarks. Notably, VLingNav transfers to real-world robotic platforms in a zero-shot manner, executing various navigation tasks and demonstrating strong cross-domain and cross-task generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08303", "title": "SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices", "authors": [ "Dongting Hu", "Aarush Gupta", "Magzhan Gabidolla", "Arpit Sahni", "Huseyin Coskun", "Yanyu Li", "Yerlan Idelbayev", "Ahsan Mahmood", "Aleksei Lebedev", "Dishani Lahiri", "Anujraaj Goyal", "Ju Hu", "Mingming Gong", "Sergey Tulyakov", "Anil Kag" ], "github": "", "project_page": "", "abstract": "Recent advances in diffusion transformers (DiTs) have set new standards in image generation, yet remain impractical for on-device deployment due to their high computational and memory costs. In this work, we present an efficient DiT framework tailored for mobile and edge devices that achieves transformer-level generation quality under strict resource constraints. Our design combines three key components. First, we propose a compact DiT architecture with an adaptive global-local sparse attention mechanism that balances global context modeling and local detail preservation. Second, we propose an elastic training framework that jointly optimizes sub-DiTs of varying capacities within a unified supernetwork, allowing a single model to dynamically adjust for efficient inference across different hardware. Finally, we develop Knowledge-Guided Distribution Matching Distillation, a step-distillation pipeline that integrates the DMD objective with knowledge transfer from few-step teacher models, producing high-fidelity and low-latency generation (e.g., 4-step) suitable for real-time on-device use. Together, these contributions enable scalable, efficient, and high-quality diffusion models for deployment on diverse hardware.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.04582", "title": "Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-Visualization", "authors": [ "Mizanur Rahman", "Mohammed Saidul Islam", "Md Tahmid Rahman Laskar", "Shafiq Joty", "Enamul Hoque" ], "github": "https://github.com/vis-nlp/RL-Text2Vis", "project_page": "", "abstract": "Text-to-Visualization (Text2Vis) systems translate natural language queries over tabular data into concise answers and executable visualizations. While closed-source LLMs generate functional code, the resulting charts often lack semantic alignment and clarity, qualities that can only be assessed post-execution. Open-source models struggle even more, frequently producing non-executable or visually poor outputs. Although supervised fine-tuning can improve code executability, it fails to enhance overall visualization quality, as traditional SFT loss cannot capture post-execution feedback. To address this gap, we propose RL-Text2Vis, the first reinforcement learning framework for Text2Vis generation. Built on Group Relative Policy Optimization (GRPO), our method uses a novel multi-objective reward that jointly optimizes textual accuracy, code validity, and visualization quality using post-execution feedback. By training Qwen2.5 models (7B and 14B), RL-Text2Vis achieves a 22% relative improvement in chart quality over GPT-4o on the Text2Vis benchmark and boosts code execution success from 78% to 97% relative to its zero-shot baseline. Our models significantly outperform strong zero-shot and supervised baselines and also demonstrate robust generalization to out-of-domain datasets like VIS-Eval and NVBench. These results establish GRPO as an effective strategy for structured, multimodal reasoning in visualization generation. We release our code at https://github.com/vis-nlp/RL-Text2Vis.", "github_manual": "", "github_regex": "https://github.com/vis-nlp/RL-Text2Vis", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.06789", "title": "MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences", "authors": [ "Qihao Wang", "Ziming Cheng", "Shuo Zhang", "Fan Liu", "Rui Xu", "Heng Lian", "Kunyi Wang", "Xiaoming Yu", "Jianghao Yin", "Sen Hu", "Yue Hu", "Shaolei Zhang", "Yanbing Liu", "Ronghao Chen", "Huacan Wang" ], "github": "", "project_page": "", "abstract": "While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a \"closed-world\" limitation: they attempt to fix bugs from scratch or solely using local context, ignoring the immense historical human experience available on platforms like GitHub. Accessing this open-world experience is hindered by the unstructured and fragmented nature of real-world issue-tracking data. In this paper, we introduce MemGovern, a framework designed to govern and transform raw GitHub data into actionable experiential memory for agents. MemGovern employs experience governance to convert human experience into agent-friendly experience cards and introduces an agentic experience search strategy that enables logic-driven retrieval of human expertise. By producing 135K governed experience cards, MemGovern achieves a significant performance boost, improving resolution rates on the SWE-bench Verified by 4.65%. As a plug-in approach, MemGovern provides a solution for agent-friendly memory infrastructure.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08225", "title": "User-Oriented Multi-Turn Dialogue Generation with Tool Use at scale", "authors": [ "Jungho Cho", "Minbyul Jeong", "Sungrae Park" ], "github": "", "project_page": "", "abstract": "The recent paradigm shift toward large reasoning models (LRMs) as autonomous agents has intensified the demand for sophisticated, multi-turn tool-use capabilities. Yet, existing datasets and data-generation approaches are limited by static, predefined toolsets that cannot scale to the complexity of open-ended human-agent collaboration. To address this, we initially developed a framework for automated task-oriented multi-turn dialogue generation at scale, utilizing an LRM-based simulator to dynamically generate high-value, domain-specific tools to solve specified tasks. However, we observe that a purely task-oriented design often results in \"solely task-solving\" trajectories, where the agent completes the objective with minimal interaction, failing to generate the high turn-count conversations seen in realistic scenarios. To bridge this gap, we shift toward a user-oriented simulation paradigm. By decoupling task generation from a dedicated user simulator that mimics human behavioral rules - such as incremental request-making and turn-by-turn feedback - we facilitate more authentic, extended multi-turn dialogues that reflect the iterative nature of real-world problem solving. Our generation pipeline operates as a versatile, plug-and-play module capable of initiating generation from any state, ensuring high scalability in producing extended tool-use data. Furthermore, by facilitating multiple task completions within a single trajectory, it yields a high-density dataset that reflects the multifaceted demands of real-world human-agent interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.07022", "title": "Solar Open Technical Report", "authors": [ "Sungrae Park", "Sanghoon Kim", "Jungho Cho", "Gyoungjin Gim", "Dawoon Jung", "Mikyoung Cha", "Eunhae Choo", "Taekgyu Hong", "Minbyul Jeong", "SeHwan Joo", "Minsoo Khang", "Eunwon Kim", "Minjeong Kim", "Sujeong Kim", "Yunsu Kim", "Hyeonju Lee", "Seunghyun Lee", "Sukyung Lee", "Siyoung Park", "Gyungin Shin", "Inseo Song", "Wonho Song", "Seonghoon Yang", "Seungyoun Yi", "Sanghoon Yoon", "Jeonghyun Ko", "Seyoung Song", "Keunwoo Choi", "Hwalsuk Lee", "Sunghun Kim", "Du-Seong Chang", "Kyunghyun Cho", "Junsuk Choe", "Hwaran Lee", "Jae-Gil Lee", "KyungTae Lim", "Alice Oh" ], "github": "", "project_page": "", "abstract": "We introduce Solar Open, a 102B-parameter bilingual Mixture-of-Experts language model for underserved languages. Solar Open demonstrates a systematic methodology for building competitive LLMs by addressing three interconnected challenges. First, to train effectively despite data scarcity for underserved languages, we synthesize 4.5T tokens of high-quality, domain-specific, and RL-oriented data. Second, we coordinate this data through a progressive curriculum jointly optimizing composition, quality thresholds, and domain coverage across 20 trillion tokens. Third, to enable reasoning capabilities through scalable RL, we apply our proposed framework SnapPO for efficient optimization. Across benchmarks in English and Korean, Solar Open achieves competitive performance, demonstrating the effectiveness of this methodology for underserved language AI development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2512.24965", "title": "ShowUI-\u03c0: Flow-based Generative Models as GUI Dexterous Hands", "authors": [ "Siyuan Hu", "Kevin Qinghong Lin", "Mike Zheng Shou" ], "github": "https://github.com/showlab/showui-pi", "project_page": "", "abstract": "Building intelligent agents capable of dexterous manipulation is essential for achieving human-like automation in both robotics and digital environments. However, existing GUI agents rely on discrete click predictions (x,y), which prohibits free-form, closed-loop trajectories (e.g. dragging a progress bar) that require continuous, on-the-fly perception and adjustment. In this work, we develop ShowUI-\u03c0, the first flow-based generative model as GUI dexterous hand, featuring the following designs: (i) Unified Discrete-Continuous Actions, integrating discrete clicks and continuous drags within a shared model, enabling flexible adaptation across diverse interaction modes; (ii) Flow-based Action Generation for drag modeling, which predicts incremental cursor adjustments from continuous visual observations via a lightweight action expert, ensuring smooth and stable trajectories; (iii) Drag Training data and Benchmark, where we manually collect and synthesize 20K drag trajectories across five domains (e.g. PowerPoint, Adobe Premiere Pro), and introduce ScreenDrag, a benchmark with comprehensive online and offline evaluation protocols for assessing GUI agents' drag capabilities. Our experiments show that proprietary GUI agents still struggle on ScreenDrag (e.g. Operator scores 13.27, and the best Gemini-2.5-CUA reaches 22.18). In contrast, ShowUI-\u03c0 achieves 26.98 with only 450M parameters, underscoring both the difficulty of the task and the effectiveness of our approach. We hope this work advances GUI agents toward human-like dexterous control in digital world. The code is available at https://github.com/showlab/showui-pi.", "github_manual": "", "github_regex": "https://github.com/showlab/showui-pi", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.02669", "title": "Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking", "authors": [ "Hongzhan Lin", "Zixin Chen", "Zhiqi Shen", "Ziyang Luo", "Zhen Ye", "Jing Ma", "Tat-Seng Chua", "Guandong Xu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly deployed in real-world fact-checking systems, yet existing evaluations focus predominantly on claim verification and overlook the broader fact-checking workflow, including claim extraction and evidence retrieval. This narrow focus prevents current benchmarks from revealing systematic reasoning failures, factual blind spots, and robustness limitations of modern LLMs. To bridge this gap, we present FactArena, a fully automated arena-style evaluation framework that conducts comprehensive, stage-wise benchmarking of LLMs across the complete fact-checking pipeline. FactArena integrates three key components: (i) an LLM-driven fact-checking process that standardizes claim decomposition, evidence retrieval via tool-augmented interactions, and justification-based verdict prediction; (ii) an arena-styled judgment mechanism guided by consolidated reference guidelines to ensure unbiased and consistent pairwise comparisons across heterogeneous judge agents; and (iii) an arena-driven claim-evolution module that adaptively generates more challenging and semantically controlled claims to probe LLMs' factual robustness beyond fixed seed data. Across 16 state-of-the-art LLMs spanning seven model families, FactArena produces stable and interpretable rankings. Our analyses further reveal significant discrepancies between static claim-verification accuracy and end-to-end fact-checking competence, highlighting the necessity of holistic evaluation. The proposed framework offers a scalable and trustworthy paradigm for diagnosing LLMs' factual reasoning, guiding future model development, and advancing the reliable deployment of LLMs in safety-critical fact-checking applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.04745", "title": "KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions", "authors": [ "Tingyu Wu", "Zhisheng Chen", "Ziyan Weng", "Shuhe Wang", "Chenglong Li", "Shuo Zhang", "Sen Hu", "Silin Wu", "Qizhen Lan", "Huacan Wang", "Ronghao Chen" ], "github": "https://github.com/QuantaAlpha/KnowMeBench", "project_page": "", "abstract": "Existing long-horizon memory benchmarks mostly use multi-turn dialogues or synthetic user histories, which makes retrieval performance an imperfect proxy for person understanding. We present \\BenchName, a publicly releasable benchmark built from long-form autobiographical narratives, where actions, context, and inner thoughts provide dense evidence for inferring stable motivations and decision principles. \\BenchName~reconstructs each narrative into a flashback-aware, time-anchored stream and evaluates models with evidence-linked questions spanning factual recall, subjective state attribution, and principle-level reasoning. Across diverse narrative sources, retrieval-augmented systems mainly improve factual accuracy, while errors persist on temporally grounded explanations and higher-level inferences, highlighting the need for memory mechanisms beyond retrieval. Our data is in KnowMeBench{https://github.com/QuantaAlpha/KnowMeBench}.", "github_manual": "", "github_regex": "https://github.com/QuantaAlpha/KnowMeBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08079", "title": "MemoBrain: Executive Memory as an Agentic Brain for Reasoning", "authors": [ "Hongjin Qian", "Zhao Cao", "Zheng Liu" ], "github": "https://github.com/qhjqhj00/MemoBrain", "project_page": "", "abstract": "Complex reasoning in tool-augmented agent frameworks is inherently long-horizon, causing reasoning traces and transient tool artifacts to accumulate and strain the bounded working context of large language models. Without explicit memory mechanisms, such accumulation disrupts logical continuity and undermines task alignment. This positions memory not as an auxiliary efficiency concern, but as a core component for sustaining coherent, goal-directed reasoning over long horizons. We propose MemoBrain, an executive memory model for tool-augmented agents that constructs a dependency-aware memory over reasoning steps, capturing salient intermediate states and their logical relations. Operating as a co-pilot alongside the reasoning agent, MemoBrain organizes reasoning progress without blocking execution and actively manages the working context. Specifically, it prunes invalid steps, folds completed sub-trajectories, and preserves a compact, high-salience reasoning backbone under a fixed context budget. Together, these mechanisms enable explicit cognitive control over reasoning trajectories rather than passive context accumulation. We evaluate MemoBrain on challenging long-horizon benchmarks, including GAIA, WebWalker, and BrowseComp-Plus, demonstrating consistent improvements over strong baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/qhjqhj00/MemoBrain", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.06786", "title": "EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs", "authors": [ "Jewon Yeom", "Jaewon Sok", "Seonghyeon Park", "Jeongjae Park", "Taesup Kim" ], "github": "", "project_page": "", "abstract": "Improving the reasoning abilities of large language models (LLMs) has largely relied on iterative self-training with model-generated data. While effective at boosting accuracy, existing approaches primarily reinforce successful reasoning paths, incurring a substantial calibration cost: models become overconfident and lose the ability to represent uncertainty. This failure has been characterized as a form of model collapse in alignment, where predictive distributions degenerate toward low-variance point estimates. We address this issue by reframing reasoning training as an epistemic learning problem, in which models must learn not only how to reason, but also when their reasoning should be trusted. We propose epistemically-calibrated reasoning (EpiCaR) as a training objective that jointly optimizes reasoning performance and calibration, and instantiate it within an iterative supervised fine-tuning framework using explicit self-evaluation signals. Experiments on Llama-3 and Qwen-3 families demonstrate that our approach achieves Pareto-superiority over standard baselines in both accuracy and calibration, particularly in models with sufficient reasoning capacity (e.g., 3B+). This framework generalizes effectively to OOD mathematical reasoning (GSM8K) and code generation (MBPP). Ultimately, our approach enables a 3X reduction in inference compute, matching the K=30 performance of STaR with only K=10 samples in capable models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08831", "title": "3AM: Segment Anything with Geometric Consistency in Videos", "authors": [ "Yang-Che Sun", "Cheng Sun", "Chin-Yang Lin", "Fu-En Yang", "Min-Hung Chen", "Yen-Yu Lin", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Video object segmentation methods like SAM2 achieve strong performance through memory-based architectures but struggle under large viewpoint changes due to reliance on appearance features. Traditional 3D instance segmentation methods address viewpoint consistency but require camera poses, depth maps, and expensive preprocessing. We introduce 3AM, a training-time enhancement that integrates 3D-aware features from MUSt3R into SAM2. Our lightweight Feature Merger fuses multi-level MUSt3R features that encode implicit geometric correspondence. Combined with SAM2's appearance features, the model achieves geometry-consistent recognition grounded in both spatial position and visual similarity. We propose a field-of-view aware sampling strategy ensuring frames observe spatially consistent object regions for reliable 3D correspondence learning. Critically, our method requires only RGB input at inference, with no camera poses or preprocessing. On challenging datasets with wide-baseline motion (ScanNet++, Replica), 3AM substantially outperforms SAM2 and extensions, achieving 90.6% IoU and 71.7% Positive IoU on ScanNet++'s Selected Subset, improving over state-of-the-art VOS methods by +15.9 and +30.4 points. Project page: https://jayisaking.github.io/3AM-Page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08670", "title": "Parallel Context-of-Experts Decoding for Retrieval Augmented Generation", "authors": [ "Giulio Corallo", "Paolo Papotti" ], "github": "", "project_page": "", "abstract": "Retrieval Augmented Generation faces a trade-off: concatenating documents in a long prompt enables multi-document reasoning but creates prefill bottlenecks, while encoding document KV caches separately offers speed but breaks cross-document interaction. We propose Parallel Context-of-Experts Decoding (Pced), a training-free framework that shifts evidence aggregation from the attention mechanism to the decoding. Pced treats retrieved documents as isolated \"experts\", synchronizing their predictions via a novel retrieval-aware contrastive decoding rule that weighs expert logits against the model prior. This approach recovers cross-document reasoning capabilities without constructing a shared attention across documents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08620", "title": "ViDoRe V3: A Comprehensive Evaluation of Retrieval Augmented Generation in Complex Real-World Scenarios", "authors": [ "Ant\u00f3nio Loison", "Quentin Mac\u00e9", "Antoine Edy", "Victor Xing", "Tom Balough", "Gabriel Moreira", "Bo Liu", "Manuel Faysse", "C\u00e9line Hudelot", "Gautier Viaud" ], "github": "https://github.com/NVIDIA-NeMo/DataDesigner", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) pipelines must address challenges beyond simple single-document retrieval, such as interpreting visual elements (tables, charts, images), synthesizing information across documents, and providing accurate source grounding. Existing benchmarks fail to capture this complexity, often focusing on textual data, single-document comprehension, or evaluating retrieval and generation in isolation. We introduce ViDoRe v3, a comprehensive multimodal RAG benchmark featuring multi-type queries over visually rich document corpora. It covers 10 datasets across diverse professional domains, comprising ~26,000 document pages paired with 3,099 human-verified queries, each available in 6 languages. Through 12,000 hours of human annotation effort, we provide high-quality annotations for retrieval relevance, bounding box localization, and verified reference answers. Our evaluation of state-of-the-art RAG pipelines reveals that visual retrievers outperform textual ones, late-interaction models and textual reranking substantially improve performance, and hybrid or purely visual contexts enhance answer generation quality. However, current models still struggle with non-textual elements, open-ended queries, and fine-grained visual grounding. To encourage progress in addressing these challenges, the benchmark is released under a commercially permissive license at https://hf.co/vidore.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA-NeMo/DataDesigner", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.07290", "title": "VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding", "authors": [ "Jiapeng Shi", "Junke Wang", "Zuyao You", "Bo He", "Zuxuan Wu" ], "github": "", "project_page": "", "abstract": "This paper presents VideoLoom, a unified Video Large Language Model (Video LLM) for joint spatial-temporal understanding. To facilitate the development of fine-grained spatial and temporal localization capabilities, we curate LoomData-8.7k, a human-centric video dataset with temporally grounded and spatially localized captions. With this, VideoLoom achieves state-of-the-art or highly competitive performance across a variety of spatial and temporal benchmarks (e.g., 63.1 J&F on ReVOS for referring video object segmentation, and 48.3 R1@0.7 on Charades-STA for temporal grounding). In addition, we introduce LoomBench, a novel benchmark consisting of temporal, spatial, and compositional video-question pairs, enabling a comprehensive evaluation of Video LLMs from diverse aspects. Collectively, these contributions offer a universal and effective suite for joint spatial-temporal video understanding, setting a new standard in multimodal intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08321", "title": "UM-Text: A Unified Multimodal Model for Image Understanding", "authors": [ "Lichen Ma", "Xiaolong Fu", "Gaojing Zhou", "Zipeng Guo", "Ting Zhu", "Yichun Liu", "Yu Shi", "Jason Li", "Junshi Huang" ], "github": "", "project_page": "", "abstract": "With the rapid advancement of image generation, visual text editing using natural language instructions has received increasing attention. The main challenge of this task is to fully understand the instruction and reference image, and thus generate visual text that is style-consistent with the image. Previous methods often involve complex steps of specifying the text content and attributes, such as font size, color, and layout, without considering the stylistic consistency with the reference image. To address this, we propose UM-Text, a unified multimodal model for context understanding and visual text editing by natural language instructions. Specifically, we introduce a Visual Language Model (VLM) to process the instruction and reference image, so that the text content and layout can be elaborately designed according to the context information. To generate an accurate and harmonious visual text image, we further propose the UM-Encoder to combine the embeddings of various condition information, where the combination is automatically configured by VLM according to the input instruction. During training, we propose a regional consistency loss to offer more effective supervision for glyph generation on both latent and RGB space, and design a tailored three-stage training strategy to further enhance model performance. In addition, we contribute the UM-DATA-200K, a large-scale visual text image dataset on diverse scenes for model training. Extensive qualitative and quantitative results on multiple public benchmarks demonstrate that our method achieves state-of-the-art performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.07632", "title": "GeoMotionGPT: Geometry-Aligned Motion Understanding with Large Language Models", "authors": [ "Zhankai Ye", "Bofan Li", "Yukai Jin", "Shuoqiu Li", "Wei Wang", "Yanfu Zhang", "Shangqian Gao", "Xin Liu" ], "github": "", "project_page": "", "abstract": "Discrete motion tokenization has recently enabled Large Language Models (LLMs) to serve as versatile backbones for motion understanding and motion-language reasoning. However, existing pipelines typically decouple motion quantization from semantic embedding learning, linking them solely via token IDs. This approach fails to effectively align the intrinsic geometry of the motion space with the embedding space, thereby hindering the LLM's capacity for nuanced motion reasoning. We argue that alignment is most effective when both modalities share a unified geometric basis. Therefore, instead of forcing the LLM to reconstruct the complex geometry among motion tokens from scratch, we present a novel framework that explicitly enforces orthogonality on both the motion codebook and the LLM embedding space, ensuring that their relational structures naturally mirror each other. Specifically, we employ a decoder-only quantizer with Gumbel-Softmax for differentiable training and balanced codebook usage. To bridge the modalities, we use a sparse projection that maps motion codes into the LLM embedding space while preserving orthogonality. Finally, a two-stage orthonormal regularization schedule enforces soft constraints during tokenizer training and LLM fine-tuning to maintain geometric alignment without hindering semantic adaptation. Extensive experiments on HumanML3D demonstrate that our framework achieves a 20% performance improvement over current state-of-the-art methods, validating that a unified geometric basis effectively empowers the LLM for nuanced motion reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-14", "arxiv_id": "2601.08173", "title": "The Agent's First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios", "authors": [ "Daocheng Fu", "Jianbiao Mei", "Rong Wu", "Xuemeng Yang", "Jia Xu", "Ding Wang", "Pinlong Cai", "Yong Liu", "Licheng Wen", "Botian Shi" ], "github": "https://github.com/KnowledgeXLab/EvoEnv", "project_page": "", "abstract": "The rapid evolution of Multi-modal Large Language Models (MLLMs) has advanced workflow automation; however, existing research mainly targets performance upper bounds in static environments, overlooking robustness for stochastic real-world deployment. We identify three key challenges: dynamic task scheduling, active exploration under uncertainty, and continuous learning from experience. To bridge this gap, we introduce , a dynamic evaluation environment that simulates a \"trainee\" agent continuously exploring a novel setting. Unlike traditional benchmarks, evaluates agents along three dimensions: (1) context-aware scheduling for streaming tasks with varying priorities; (2) prudent information acquisition to reduce hallucination via active exploration; and (3) continuous evolution by distilling generalized strategies from rule-based, dynamically generated tasks. Experiments show that cutting-edge agents have significant deficiencies in dynamic environments, especially in active exploration and continual learning. Our work establishes a framework for assessing agent reliability, shifting evaluation from static tests to realistic, production-oriented scenarios. Our codes are available at https://github.com/KnowledgeXLab/EvoEnv", "github_manual": "", "github_regex": "https://github.com/KnowledgeXLab/EvoEnv", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.09708", "title": "Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning", "authors": [ "Chi-Pin Huang", "Yunze Man", "Zhiding Yu", "Min-Hung Chen", "Jan Kautz", "Yu-Chiang Frank Wang", "Fu-En Yang" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on reasoning VLAs show that explicit chain-of-thought (CoT) can improve generalization, they suffer from high inference latency due to lengthy reasoning traces. We propose Fast-ThinkAct, an efficient reasoning framework that achieves compact yet performant planning through verbalizable latent reasoning. Fast-ThinkAct learns to reason efficiently with latent CoTs by distilling from a teacher, driven by a preference-guided objective to align manipulation trajectories that transfers both linguistic and visual planning capabilities for embodied control. This enables reasoning-enhanced policy learning that effectively connects compact reasoning to action execution. Extensive experiments across diverse embodied manipulation and reasoning benchmarks demonstrate that Fast-ThinkAct achieves strong performance with up to 89.3\\% reduced inference latency over state-of-the-art reasoning VLAs, while maintaining effective long-horizon planning, few-shot adaptation, and failure recovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.07348", "title": "Controlled Self-Evolution for Algorithmic Code Optimization", "authors": [ "Tu Hu", "Ronghao Chen", "Shuo Zhang", "Jianghao Yin", "Mou Xiao Feng", "Jingping Liu", "Shaolei Zhang", "Wenqi Jiang", "Yuqi Fang", "Sen Hu", "Yi Xu", "Huacan Wang" ], "github": "https://github.com/QuantaAlpha/EvoControl", "project_page": "", "abstract": "Self-evolution methods enhance code generation through iterative \"generate-verify-refine\" cycles, yet existing approaches suffer from low exploration efficiency, failing to discover solutions with superior complexity within limited budgets. This inefficiency stems from initialization bias trapping evolution in poor solution regions, uncontrolled stochastic operations lacking feedback guidance, and insufficient experience utilization across tasks. To address these bottlenecks, we propose Controlled Self-Evolution (CSE), which consists of three key components. Diversified Planning Initialization generates structurally distinct algorithmic strategies for broad solution space coverage. Genetic Evolution replaces stochastic operations with feedback-guided mechanisms, enabling targeted mutation and compositional crossover. Hierarchical Evolution Memory captures both successful and failed experiences at inter-task and intra-task levels. Experiments on EffiBench-X demonstrate that CSE consistently outperforms all baselines across various LLM backbones. Furthermore, CSE achieves higher efficiency from early generations and maintains continuous improvement throughout evolution. Our code is publicly available at https://github.com/QuantaAlpha/EvoControl.", "github_manual": "", "github_regex": "https://github.com/QuantaAlpha/EvoControl", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.09259", "title": "MAXS: Meta-Adaptive Exploration with LLM Agents", "authors": [ "Jian Zhang", "Zhiyuan Wang", "Zhangqi Wang", "Yu He", "Haoran Luo", "li yuan", "Lingling Zhang", "Rui Mao", "Qika Lin", "Jun Liu" ], "github": "https://github.com/exoskeletonzj/MAXS", "project_page": "", "abstract": "Large Language Model (LLM) Agents exhibit inherent reasoning abilities through the collaboration of multiple tools. However, during agent inference, existing methods often suffer from (i) locally myopic generation, due to the absence of lookahead, and (ii) trajectory instability, where minor early errors can escalate into divergent reasoning paths. These issues make it difficult to balance global effectiveness and computational efficiency. To address these two issues, we propose meta-adaptive exploration with LLM agents https://github.com/exoskeletonzj/MAXS, a meta-adaptive reasoning framework based on LLM Agents that flexibly integrates tool execution and reasoning planning. MAXS employs a lookahead strategy to extend reasoning paths a few steps ahead, estimating the advantage value of tool usage, and combines step consistency variance and inter-step trend slopes to jointly select stable, consistent, and high-value reasoning steps. Additionally, we introduce a trajectory convergence mechanism that controls computational cost by halting further rollouts once path consistency is achieved, enabling a balance between resource efficiency and global effectiveness in multi-tool reasoning. We conduct extensive empirical studies across three base models (MiMo-VL-7B, Qwen2.5-VL-7B, Qwen2.5-VL-32B) and five datasets, demonstrating that MAXS consistently outperforms existing methods in both performance and inference efficiency. Further analysis confirms the effectiveness of our lookahead strategy and tool usage.", "github_manual": "", "github_regex": "https://github.com/exoskeletonzj/MAXS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.09274", "title": "A^3-Bench: Benchmarking Memory-Driven Scientific Reasoning via Anchor and Attractor Activation", "authors": [ "Jian Zhang", "Yu He", "Zhiyuan Wang", "Zhangqi Wang", "Kai He", "Fangzhi Xu", "Qika Lin", "Jun Liu" ], "github": "https://github.com/exoskeletonzj/A3-Bench", "project_page": "", "abstract": "Scientific reasoning relies not only on logical inference but also on activating prior knowledge and experiential structures. Memory can efficiently reuse knowledge and enhance reasoning consistency and stability. However, existing benchmarks mainly evaluate final answers or step-by-step coherence, overlooking the memory-driven mechanisms that underlie human reasoning, which involves activating anchors and attractors, then integrating them into multi-step inference. To address this gap, we propose A^3-Bench~ https://a3-bench.github.io, a benchmark designed to evaluate scientific reasoning through dual-scale memory-driven activation, grounded in Anchor and Attractor Activation. First, we annotate 2,198 science reasoning problems across domains using the SAPM process(subject, anchor & attractor, problem, and memory developing). Second, we introduce a dual-scale memory evaluation framework utilizing anchors and attractors, along with the AAUI(Anchor--Attractor Utilization Index) metric to measure memory activation rates. Finally, through experiments with various base models and paradigms, we validate A^3-Bench and analyze how memory activation impacts reasoning performance, providing insights into memory-driven scientific reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/exoskeletonzj/A3-Bench", "github_api_added_by": "auto" }, { "date": "2026-01-15", "arxiv_id": "2601.09136", "title": "SkinFlow: Efficient Information Transmission for Open Dermatological Diagnosis via Dynamic Visual Encoding and Staged RL", "authors": [ "Lijun Liu", "Linwei Chen", "Zhishou Zhang", "Meng Tian", "Hengfu Cui", "Ruiyang Li", "Zhaocheng Liu", "Qiang Ju", "Qianxi Li", "Hong-Yu Zhou" ], "github": "", "project_page": "", "abstract": "General-purpose Large Vision-Language Models (LVLMs), despite their massive scale, often falter in dermatology due to \"diffuse attention\" - the inability to disentangle subtle pathological lesions from background noise. In this paper, we challenge the assumption that parameter scaling is the only path to medical precision. We introduce SkinFlow, a framework that treats diagnosis as an optimization of visual information transmission efficiency. Our approach utilizes a Virtual-Width Dynamic Vision Encoder (DVE) to \"unfold\" complex pathological manifolds without physical parameter expansion, coupled with a two-stage Reinforcement Learning strategy. This strategy sequentially aligns explicit medical descriptions (Stage I) and reconstructs implicit diagnostic textures (Stage II) within a constrained semantic space. Furthermore, we propose a clinically grounded evaluation protocol that prioritizes diagnostic safety and hierarchical relevance over rigid label matching. Empirical results are compelling: our 7B model establishes a new state-of-the-art on the Fitzpatrick17k benchmark, achieving a +12.06% gain in Top-1 accuracy and a +28.57% boost in Top-6 accuracy over the massive general-purpose models (e.g., Qwen3VL-235B and GPT-5.2). These findings demonstrate that optimizing geometric capacity and information flow yields superior diagnostic reasoning compared to raw parameter scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.06596", "title": "Are LLMs Vulnerable to Preference-Undermining Attacks (PUA)? A Factorial Analysis Methodology for Diagnosing the Trade-off between Preference Alignment and Real-World Validity", "authors": [ "Hongjun An", "Yiliang Song", "Jiangan Chen", "Jiawei Shao", "Chi Zhang", "Xuelong Li" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM) training often optimizes for preference alignment, rewarding outputs that are perceived as helpful and interaction-friendly. However, this preference-oriented objective can be exploited: manipulative prompts can steer responses toward user-appeasing agreement and away from truth-oriented correction. In this work, we investigate whether aligned models are vulnerable to Preference-Undermining Attacks (PUA), a class of manipulative prompting strategies designed to exploit the model's desire to please user preferences at the expense of truthfulness. We propose a diagnostic methodology that provides a finer-grained and more directive analysis than aggregate benchmark scores, using a factorial evaluation framework to decompose prompt-induced shifts into interpretable effects of system objectives (truth- vs. preference-oriented) and PUA-style dialogue factors (directive control, personal derogation, conditional approval, reality denial) within a controlled 2 times 2^4 design. Surprisingly, more advanced models are sometimes more susceptible to manipulative prompts. Beyond the dominant reality-denial factor, we observe model-specific sign reversals and interactions with PUA-style factors, suggesting tailored defenses rather than uniform robustness. These findings offer a novel, reproducible factorial evaluation methodology that provides finer-grained diagnostics for post-training processes like RLHF, enabling better trade-offs in the product iteration of LLMs by offering a more nuanced understanding of preference alignment risks and the impact of manipulative prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.03928", "title": "FocusUI: Efficient UI Grounding via Position-Preserving Visual Token Selection", "authors": [ "Mingyu Ouyang", "Kevin Qinghong Lin", "Mike Zheng Shou", "Hwee Tou Ng" ], "github": "https://github.com/showlab/FocusUI", "project_page": "", "abstract": "Vision-Language Models (VLMs) have shown remarkable performance in User Interface (UI) grounding tasks, driven by their ability to process increasingly high-resolution screenshots. However, screenshots are tokenized into thousands of visual tokens (e.g., about 4700 for 2K resolution), incurring significant computational overhead and diluting attention. In contrast, humans typically focus on regions of interest when interacting with UI. In this work, we pioneer the task of efficient UI grounding. Guided by practical analysis of the task's characteristics and challenges, we propose FocusUI, an efficient UI grounding framework that selects patches most relevant to the instruction while preserving positional continuity for precise grounding. FocusUI addresses two key challenges: (1) Eliminating redundant tokens in visual encoding. We construct patch-level supervision by fusing an instruction-conditioned score with a rule-based UI-graph score that down-weights large homogeneous regions to select distinct and instruction-relevant visual tokens. (2) Preserving positional continuity during visual token selection. We find that general visual token pruning methods suffer from severe accuracy degradation on UI grounding tasks due to broken positional information. We introduce a novel PosPad strategy, which compresses each contiguous sequence of dropped visual tokens into a single special marker placed at the sequence's last index to preserve positional continuity. Comprehensive experiments on four grounding benchmarks demonstrate that FocusUI surpasses GUI-specific baselines. On the ScreenSpot-Pro benchmark, FocusUI-7B achieves a performance improvement of 3.7% over GUI-Actor-7B. Even with only 30% visual token retention, FocusUI-7B drops by only 3.2% while achieving up to 1.44x faster inference and 17% lower peak GPU memory.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/showlab/FocusUI", "github_api_added_by": "auto" }, { "date": "2026-01-15", "arxiv_id": "2601.09688", "title": "DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic Evaluation", "authors": [ "Yibo Wang", "Lei Wang", "Yue Deng", "Keming Wu", "Yao Xiao", "Huanjin Yao", "Liwei Kang", "Hai Ye", "Yongcheng Jing", "Lidong Bing" ], "github": "https://github.com/Infinity-AILab/DeepResearchEval", "project_page": "", "abstract": "Deep research systems are widely used for multi-step web research, analysis, and cross-source synthesis, yet their evaluation remains challenging. Existing benchmarks often require annotation-intensive task construction, rely on static evaluation dimensions, or fail to reliably verify facts when citations are missing. To bridge these gaps, we introduce DeepResearchEval, an automated framework for deep research task construction and agentic evaluation. For task construction, we propose a persona-driven pipeline generating realistic, complex research tasks anchored in diverse user profiles, applying a two-stage filter Task Qualification and Search Necessity to retain only tasks requiring multi-source evidence integration and external retrieval. For evaluation, we propose an agentic pipeline with two components: an Adaptive Point-wise Quality Evaluation that dynamically derives task-specific evaluation dimensions, criteria, and weights conditioned on each generated task, and an Active Fact-Checking that autonomously extracts and verifies report statements via web search, even when citations are missing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Infinity-AILab/DeepResearchEval", "github_api_added_by": "auto" }, { "date": "2026-01-15", "arxiv_id": "2601.09012", "title": "TranslateGemma Technical Report", "authors": [ "Mara Finkelstein", "Isaac Caswell", "Tobias Domhan", "Jan-Thorsten Peter", "Juraj Juraska", "Parker Riley", "Daniel Deutsch", "Cole Dilanni", "Colin Cherry", "Eleftheria Briakou", "Elizabeth Nielsen", "Jiaming Luo", "Kat Black", "Ryan Mullins", "Sweta Agrawal", "Wenda Xu", "Erin Kats", "Stephane Jaskiewicz", "Markus Freitag", "David Vilar" ], "github": "", "project_page": "", "abstract": "We present TranslateGemma, a suite of open machine translation models based on the Gemma 3 foundation models. To enhance the inherent multilingual capabilities of Gemma 3 for the translation task, we employ a two-stage fine-tuning process. First, supervised fine-tuning is performed using a rich mixture of high-quality large-scale synthetic parallel data generated via state-of-the-art models and human-translated parallel data. This is followed by a reinforcement learning phase, where we optimize translation quality using an ensemble of reward models, including MetricX-QE and AutoMQM, targeting translation quality. We demonstrate the effectiveness of TranslateGemma with human evaluation on the WMT25 test set across 10 language pairs and with automatic evaluation on the WMT24++ benchmark across 55 language pairs. Automatic metrics show consistent and substantial gains over the baseline Gemma 3 models across all sizes. Notably, smaller TranslateGemma models often achieve performance comparable to larger baseline models, offering improved efficiency. We also show that TranslateGemma models retain strong multimodal capabilities, with enhanced performance on the Vistra image translation benchmark. The release of the open TranslateGemma models aims to provide the research community with powerful and adaptable tools for machine translation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.09697", "title": "Efficient Camera-Controlled Video Generation of Static Scenes via Sparse Diffusion and 3D Rendering", "authors": [ "Jieying Chen", "Jeffrey Hu", "Joan Lasenby", "Ayush Tewari" ], "github": "https://github.com/kwsong0113/diffusion-forcing-transformer", "project_page": "", "abstract": "Modern video generative models based on diffusion models can produce very realistic clips, but they are computationally inefficient, often requiring minutes of GPU time for just a few seconds of video. This inefficiency poses a critical barrier to deploying generative video in applications that require real-time interactions, such as embodied AI and VR/AR. This paper explores a new strategy for camera-conditioned video generation of static scenes: using diffusion-based generative models to generate a sparse set of keyframes, and then synthesizing the full video through 3D reconstruction and rendering. By lifting keyframes into a 3D representation and rendering intermediate views, our approach amortizes the generation cost across hundreds of frames while enforcing geometric consistency. We further introduce a model that predicts the optimal number of keyframes for a given camera trajectory, allowing the system to adaptively allocate computation. Our final method, SRENDER, uses very sparse keyframes for simple trajectories and denser ones for complex camera motion. This results in video generation that is more than 40 times faster than the diffusion-based baseline in generating 20 seconds of video, while maintaining high visual fidelity and temporal stability, offering a practical path toward efficient and controllable video synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kwsong0113/diffusion-forcing-transformer", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.09575", "title": "OpenVoxel: Training-Free Grouping and Captioning Voxels for Open-Vocabulary 3D Scene Understanding", "authors": [ "Sheng-Yu Huang", "Jaesung Choe", "Yu-Chiang Frank Wang", "Cheng Sun" ], "github": "", "project_page": "", "abstract": "We propose OpenVoxel, a training-free algorithm for grouping and captioning sparse voxels for the open-vocabulary 3D scene understanding tasks. Given the sparse voxel rasterization (SVR) model obtained from multi-view images of a 3D scene, our OpenVoxel is able to produce meaningful groups that describe different objects in the scene. Also, by leveraging powerful Vision Language Models (VLMs) and Multi-modal Large Language Models (MLLMs), our OpenVoxel successfully build an informative scene map by captioning each group, enabling further 3D scene understanding tasks such as open-vocabulary segmentation (OVS) or referring expression segmentation (RES). Unlike previous methods, our method is training-free and does not introduce embeddings from a CLIP/BERT text encoder. Instead, we directly proceed with text-to-text search using MLLMs. Through extensive experiments, our method demonstrates superior performance compared to recent studies, particularly in complex referring expression segmentation (RES) tasks. The code will be open.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.09465", "title": "EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines", "authors": [ "Shuo Zhang", "Chaofa Yuan", "Ryan Guo", "Xiaomin Yu", "Rui Xu", "Zhangquan Chen", "Zinuo Li", "Zhi Yang", "Shuhao Guan", "Zhenheng Tang", "Sen Hu", "Liwen Zhang", "Ronghao Chen", "Huacan Wang" ], "github": "", "project_page": "", "abstract": "While LLM-based agents have shown promise for deep research, most existing approaches rely on fixed workflows that struggle to adapt to real-world, open-ended queries. Recent work therefore explores self-evolution by allowing agents to rewrite their own code or prompts to improve problem-solving ability, but unconstrained optimization often triggers instability, hallucinations, and instruction drift. We propose EvoFSM, a structured self-evolving framework that achieves both adaptability and control by evolving an explicit Finite State Machine (FSM) instead of relying on free-form rewriting. EvoFSM decouples the optimization space into macroscopic Flow (state-transition logic) and microscopic Skill (state-specific behaviors), enabling targeted improvements under clear behavioral boundaries. Guided by a critic mechanism, EvoFSM refines the FSM through a small set of constrained operations, and further incorporates a self-evolving memory that distills successful trajectories as reusable priors and failure patterns as constraints for future queries. Extensive evaluations on five multi-hop QA benchmarks demonstrate the effectiveness of EvoFSM. In particular, EvoFSM reaches 58.0% accuracy on the DeepSearch benchmark. Additional results on interactive decision-making tasks further validate its generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.09113", "title": "The AI Hippocampus: How Far are We From Human Memory?", "authors": [ "Zixia Jia", "Jiaqi Li", "Yipeng Kang", "Yuxuan Wang", "Tong Wu", "Quansen Wang", "Xiaobo Wang", "Shuyi Zhang", "Junzhe Shen", "Qing Li", "Siyuan Qi", "Yitao Liang", "Di He", "Zilong Zheng", "Song-Chun Zhu" ], "github": "https://github.com/bigai-nlco/LLM-Memory-Survey", "project_page": "", "abstract": "Memory plays a foundational role in augmenting the reasoning, adaptability, and contextual fidelity of modern Large Language Models and Multi-Modal LLMs. As these models transition from static predictors to interactive systems capable of continual learning and personalized inference, the incorporation of memory mechanisms has emerged as a central theme in their architectural and functional evolution. This survey presents a comprehensive and structured synthesis of memory in LLMs and MLLMs, organizing the literature into a cohesive taxonomy comprising implicit, explicit, and agentic memory paradigms. Specifically, the survey delineates three primary memory frameworks. Implicit memory refers to the knowledge embedded within the internal parameters of pre-trained transformers, encompassing their capacity for memorization, associative retrieval, and contextual reasoning. Recent work has explored methods to interpret, manipulate, and reconfigure this latent memory. Explicit memory involves external storage and retrieval components designed to augment model outputs with dynamic, queryable knowledge representations, such as textual corpora, dense vectors, and graph-based structures, thereby enabling scalable and updatable interaction with information sources. Agentic memory introduces persistent, temporally extended memory structures within autonomous agents, facilitating long-term planning, self-consistency, and collaborative behavior in multi-agent systems, with relevance to embodied and interactive AI. Extending beyond text, the survey examines the integration of memory within multi-modal settings, where coherence across vision, language, audio, and action modalities is essential. Key architectural advances, benchmark tasks, and open challenges are discussed, including issues related to memory capacity, alignment, factual consistency, and cross-system interoperability.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bigai-nlco/LLM-Memory-Survey", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.08605", "title": "ExpSeek: Self-Triggered Experience Seeking for Web Agents", "authors": [ "Wenyuan Zhang", "Xinghua Zhang", "Haiyang Yu", "Shuaiyi Nie", "Bingli Wu", "Juwei Yue", "Tingwen Liu", "Yongbin Li" ], "github": "", "project_page": "", "abstract": "Experience intervention in web agents emerges as a promising technical paradigm, enhancing agent interaction capabilities by providing valuable insights from accumulated experiences. However, existing methods predominantly inject experience passively as global context before task execution, struggling to adapt to dynamically changing contextual observations during agent-environment interaction. We propose ExpSeek, which shifts experience toward step-level proactive seeking: (1) estimating step-level entropy thresholds to determine intervention timing using the model's intrinsic signals; (2) designing step-level tailor-designed experience content. Experiments on Qwen3-8B and 32B models across four challenging web agent benchmarks demonstrate that ExpSeek achieves absolute improvements of 9.3% and 7.5%, respectively. Our experiments validate the feasibility and advantages of entropy as a self-triggering signal, reveal that even a 4B small-scale experience model can significantly boost the performance of larger agent models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.08955", "title": "Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models", "authors": [ "Youwei Liu", "Jian Wang", "Hanlin Wang", "Beichen Guo", "Wenjie Li" ], "github": "", "project_page": "", "abstract": "Recent advances in world models have shown promise for modeling future dynamics of environmental states, enabling agents to reason and act without accessing real environments. Current methods mainly perform single-step or fixed-horizon rollouts, leaving their potential for complex task planning under-exploited. We propose Imagine-then-Plan (ITP), a unified framework for agent learning via lookahead imagination, where an agent's policy model interacts with the learned world model, yielding multi-step ``imagined'' trajectories. Since the imagination horizon may vary by tasks and stages, we introduce a novel adaptive lookahead mechanism by trading off the ultimate goal and task progress. The resulting imagined trajectories provide rich signals about future consequences, such as achieved progress and potential conflicts, which are fused with current observations, formulating a partially observable and imaginable Markov decision process to guide policy learning. We instantiate ITP with both training-free and reinforcement-trained variants. Extensive experiments across representative agent benchmarks demonstrate that ITP significantly outperforms competitive baselines. Further analyses validate that our adaptive lookahead largely enhances agents' reasoning capability, providing valuable insights into addressing broader, complex tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.07287", "title": "Focal Guidance: Unlocking Controllability from Semantic-Weak Layers in Video Diffusion Models", "authors": [ "Yuanyang Yin", "Yufan Deng", "Shenghai Yuan", "Kaipeng Zhang", "Xiao Yang", "Feng Zhao" ], "github": "", "project_page": "", "abstract": "The task of Image-to-Video (I2V) generation aims to synthesize a video from a reference image and a text prompt. This requires diffusion models to reconcile high-frequency visual constraints and low-frequency textual guidance during the denoising process. However, while existing I2V models prioritize visual consistency, how to effectively couple this dual guidance to ensure strong adherence to the text prompt remains underexplored. In this work, we observe that in Diffusion Transformer (DiT)-based I2V models, certain intermediate layers exhibit weak semantic responses (termed Semantic-Weak Layers), as indicated by a measurable drop in text-visual similarity. We attribute this to a phenomenon called Condition Isolation, where attention to visual features becomes partially detached from text guidance and overly relies on learned visual priors. To address this, we propose Focal Guidance (FG), which enhances the controllability from Semantic-Weak Layers. FG comprises two mechanisms: (1) Fine-grained Semantic Guidance (FSG) leverages CLIP to identify key regions in the reference frame and uses them as anchors to guide Semantic-Weak Layers. (2) Attention Cache transfers attention maps from semantically responsive layers to Semantic-Weak Layers, injecting explicit semantic signals and alleviating their over-reliance on the model's learned visual priors, thereby enhancing adherence to textual instructions. To further validate our approach and address the lack of evaluation in this direction, we introduce a benchmark for assessing instruction following in I2V models. On this benchmark, Focal Guidance proves its effectiveness and generalizability, raising the total score on Wan2.1-I2V to 0.7250 (+3.97\\%) and boosting the MMDiT-based HunyuanVideo-I2V to 0.5571 (+7.44\\%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.09088", "title": "Distribution-Aligned Sequence Distillation for Superior Long-CoT Reasoning", "authors": [ "Shaotian Yan", "Kaiyuan Liu", "Chen Shen", "Bing Wang", "Sinan Fan", "Jun Zhang", "Yue Wu", "Zheng Wang", "Jieping Ye" ], "github": "https://github.com/D2I-ai/dasd-thinking", "project_page": "", "abstract": "In this report, we introduce DASD-4B-Thinking, a lightweight yet highly capable, fully open-source reasoning model. It achieves SOTA performance among open-source models of comparable scale across challenging benchmarks in mathematics, scientific reasoning, and code generation -- even outperforming several larger models. We begin by critically reexamining a widely adopted distillation paradigm in the community: SFT on teacher-generated responses, also known as sequence-level distillation. Although a series of recent works following this scheme have demonstrated remarkable efficiency and strong empirical performance, they are primarily grounded in the SFT perspective. Consequently, these approaches focus predominantly on designing heuristic rules for SFT data filtering, while largely overlooking the core principle of distillation itself -- enabling the student model to learn the teacher's full output distribution so as to inherit its generalization capability. Specifically, we identify three critical limitations in current practice: i) Inadequate representation of the teacher's sequence-level distribution; ii) Misalignment between the teacher's output distribution and the student's learning capacity; and iii) Exposure bias arising from teacher-forced training versus autoregressive inference. In summary, these shortcomings reflect a systemic absence of explicit teacher-student interaction throughout the distillation process, leaving the essence of distillation underexploited. To address these issues, we propose several methodological innovations that collectively form an enhanced sequence-level distillation training pipeline. Remarkably, DASD-4B-Thinking obtains competitive results using only 448K training samples -- an order of magnitude fewer than those employed by most existing open-source efforts. To support community research, we publicly release our models and the training dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/D2I-ai/dasd-thinking", "github_api_added_by": "auto" }, { "date": "2026-01-15", "arxiv_id": "2601.09173", "title": "Geometric Stability: The Missing Axis of Representations", "authors": [ "Prashant C. Raju" ], "github": "https://github.com/prashantcraju/geometric-stability", "project_page": "", "abstract": "Analysis of learned representations has a blind spot: it focuses on similarity, measuring how closely embeddings align with external references, but similarity reveals only what is represented, not whether that structure is robust. We introduce geometric stability, a distinct dimension that quantifies how reliably representational geometry holds under perturbation, and present Shesha, a framework for measuring it. Across 2,463 configurations in seven domains, we show that stability and similarity are empirically uncorrelated (\u03c1approx 0.01) and mechanistically distinct: similarity metrics collapse after removing the top principal components, while stability retains sensitivity to fine-grained manifold structure. This distinction yields actionable insights: for safety monitoring, stability acts as a functional geometric canary, detecting structural drift nearly 2times more sensitively than CKA while filtering out the non-functional noise that triggers false alarms in rigid distance metrics; for controllability, supervised stability predicts linear steerability (\u03c1= 0.89-0.96); for model selection, stability dissociates from transferability, revealing a geometric tax that transfer optimization incurs. Beyond machine learning, stability predicts CRISPR perturbation coherence and neural-behavioral coupling. By quantifying how reliably systems maintain structure, geometric stability provides a necessary complement to similarity for auditing representations across biological and computational systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/prashantcraju/geometric-stability", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.09536", "title": "Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning", "authors": [ "Dongjie Cheng", "Yongqi Li", "Zhixin Ma", "Hongru Cai", "Yupeng Hu", "Wenjie Wang", "Liqiang Nie", "Wenjie Li" ], "github": "https://github.com/ModalityDance/Omni-R1", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) are making significant progress in multimodal reasoning. Early approaches focus on pure text-based reasoning. More recent studies have incorporated multimodal information into the reasoning steps; however, they often follow a single task-specific reasoning pattern, which limits their generalizability across various multimodal tasks. In fact, there are numerous multimodal tasks requiring diverse reasoning skills, such as zooming in on a specific region or marking an object within an image. To address this, we propose unified generative multimodal reasoning, which unifies diverse multimodal reasoning skills by generating intermediate images during the reasoning process. We instantiate this paradigm with Omni-R1, a two-stage SFT+RL framework featuring perception alignment loss and perception reward, thereby enabling functional image generation. Additionally, we introduce Omni-R1-Zero, which eliminates the need for multimodal annotations by bootstrapping step-wise visualizations from text-only reasoning data. Empirical results show that Omni-R1 achieves unified generative reasoning across a wide range of multimodal tasks, and Omni-R1-Zero can match or even surpass Omni-R1 on average, suggesting a promising direction for generative multimodal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ModalityDance/Omni-R1", "github_api_added_by": "auto" }, { "date": "2026-01-15", "arxiv_id": "2601.09282", "title": "Cluster Workload Allocation: Semantic Soft Affinity Using Natural Language Processing", "authors": [ "Leszek Sliwko", "Jolanta Mizeria-Pietraszko" ], "github": "", "project_page": "", "abstract": "Cluster workload allocation often requires complex configurations, creating a usability gap. This paper introduces a semantic, intent-driven scheduling paradigm for cluster systems using Natural Language Processing. The system employs a Large Language Model (LLM) integrated via a Kubernetes scheduler extender to interpret natural language allocation hint annotations for soft affinity preferences. A prototype featuring a cluster state cache and an intent analyzer (using AWS Bedrock) was developed. Empirical evaluation demonstrated high LLM parsing accuracy (>95% Subset Accuracy on an evaluation ground-truth dataset) for top-tier models like Amazon Nova Pro/Premier and Mistral Pixtral Large, significantly outperforming a baseline engine. Scheduling quality tests across six scenarios showed the prototype achieved superior or equivalent placement compared to standard Kubernetes configurations, particularly excelling in complex and quantitative scenarios and handling conflicting soft preferences. The results validate using LLMs for accessible scheduling but highlight limitations like synchronous LLM latency, suggesting asynchronous processing for production readiness. This work confirms the viability of semantic soft affinity for simplifying workload orchestration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.04469", "title": "SampoNLP: A Self-Referential Toolkit for Morphological Analysis of Subword Tokenizers", "authors": [ "Iaroslav Chelombitko", "Ekaterina Chelombitko", "Aleksey Komissarov" ], "github": "https://github.com/AragonerUA/SampoNLP", "project_page": "", "abstract": "The quality of subword tokenization is critical for Large Language Models, yet evaluating tokenizers for morphologically rich Uralic languages is hampered by the lack of clean morpheme lexicons. We introduce SampoNLP, a corpus-free toolkit for morphological lexicon creation using MDL-inspired Self-Referential Atomicity Scoring, which filters composite forms through internal structural cues - suited for low-resource settings. Using the high-purity lexicons generated by SampoNLP for Finnish, Hungarian, and Estonian, we conduct a systematic evaluation of BPE tokenizers across a range of vocabulary sizes (8k-256k). We propose a unified metric, the Integrated Performance Score (IPS), to navigate the trade-off between morpheme coverage and over-splitting. By analyzing the IPS curves, we identify the \"elbow points\" of diminishing returns and provide the first empirically grounded recommendations for optimal vocabulary sizes (k) in these languages. Our study not only offers practical guidance but also quantitatively demonstrates the limitations of standard BPE for highly agglutinative languages. The SampoNLP library and all generated resources are made publicly available: https://github.com/AragonerUA/SampoNLP", "github_manual": "", "github_regex": "https://github.com/AragonerUA/SampoNLP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.09609", "title": "DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing", "authors": [ "Qian Cao", "Yahui Liu", "Wei Bi", "Yi Zhao", "Ruihua Song", "Xiting Wang", "Ruiming Tang", "Guorui Zhou", "Han Li" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL)-based enhancement of large language models (LLMs) often leads to reduced output diversity, undermining their utility in open-ended tasks like creative writing. Current methods lack explicit mechanisms for guiding diverse exploration and instead prioritize optimization efficiency and performance over diversity. This paper proposes an RL framework structured around a semi-structured long Chain-of-Thought (CoT), in which the generation process is decomposed into explicitly planned intermediate steps. We introduce a Diverse Planning Branching method that strategically introduces divergence at the planning phase based on diversity variation, alongside a group-aware diversity reward to encourage distinct trajectories. Experimental results on creative writing benchmarks demonstrate that our approach significantly improves output diversity without compromising generation quality, consistently outperforming existing baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.06794", "title": "No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning", "authors": [ "Zhicong Li", "Lingjie Jiang", "Yulan Hu", "Xingchen Zeng", "Yixia Li", "Xiangwen Zhang", "Guanhua Chen", "Zheng Pan", "Xin Li", "Yong Liu" ], "github": "", "project_page": "", "abstract": "Critique-guided reinforcement learning (RL) has emerged as a powerful paradigm for training LLM agents by augmenting sparse outcome rewards with natural-language feedback. However, current methods often rely on static or offline critic models, which fail to adapt as the policy evolves. In on-policy RL, the agent's error patterns shift over time, causing stationary critics to become stale and providing feedback of diminishing utility. To address this, we introduce ECHO (Evolving Critic for Hindsight-Guided Optimization)}, a framework that jointly optimizes the policy and critic through a synchronized co-evolutionary loop. ECHO utilizes a cascaded rollout mechanism where the critic generates multiple diagnoses for an initial trajectory, followed by policy refinement to enable group-structured advantage estimation. We address the challenge of learning plateaus via a saturation-aware gain shaping objective, which rewards the critic for inducing incremental improvements in high-performing trajectories. By employing dual-track GRPO updates, ECHO ensures the critic's feedback stays synchronized with the evolving policy. Experimental results show that ECHO yields more stable training and higher long-horizon task success across open-world environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.01075", "title": "Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments", "authors": [ "Hansen Jin Lillemark", "Benhao Huang", "Fangneng Zhan", "Yilun Du", "Thomas Anderson Keller" ], "github": "https://github.com/hlillemark/flowm", "project_page": "", "abstract": "Embodied systems experience the world as 'a symphony of flows': a combination of many continuous streams of sensory input coupled to self-motion, interwoven with the dynamics of external objects. These streams obey smooth, time-parameterized symmetries, which combine through a precisely structured algebra; yet most neural network world models ignore this structure and instead repeatedly re-learn the same transformations from data. In this work, we introduce 'Flow Equivariant World Models', a framework in which both self-motion and external object motion are unified as one-parameter Lie group 'flows'. We leverage this unification to implement group equivariance with respect to these transformations, thereby providing a stable latent world representation over hundreds of timesteps. On both 2D and 3D partially observed video world modeling benchmarks, we demonstrate that Flow Equivariant World Models significantly outperform comparable state-of-the-art diffusion-based and memory-augmented world modeling architectures -- particularly when there are predictable world dynamics outside the agent's current field of view. We show that flow equivariance is particularly beneficial for long rollouts, generalizing far beyond the training horizon. By structuring world model representations with respect to internal and external motion, flow equivariance charts a scalable route to data efficient, symmetry-guided, embodied intelligence. Project link: https://flowequivariantworldmodels.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hlillemark/flowm", "github_api_added_by": "auto" }, { "date": "2026-01-15", "arxiv_id": "2601.09028", "title": "OpenDecoder: Open Large Language Model Decoding to Incorporate Document Quality in RAG", "authors": [ "Fengran Mo", "Zhan Su", "Yuchen Hui", "Jinghan Zhang", "Jia Ao Sun", "Zheyuan Liu", "Chao Zhang", "Tetsuya Sakai", "Jian-Yun Nie" ], "github": "", "project_page": "", "abstract": "The development of large language models (LLMs) has achieved superior performance in a range of downstream tasks, including LLM-based retrieval-augmented generation (RAG). The quality of generated content heavily relies on the usefulness of the retrieved information and the capacity of LLMs' internal information processing mechanism to incorporate it in answer generation. It is generally assumed that the retrieved information is relevant to the question. However, the retrieved information may have a variable degree of relevance and usefulness, depending on the question and the document collection. It is important to take into account the relevance of the retrieved information in answer generation. In this paper, we propose OpenDecoder, a new approach that leverages explicit evaluation of the retrieved information as quality indicator features for generation. We aim to build a RAG model that is more robust to varying levels of noisy context. Three types of explicit evaluation information are considered: relevance score, ranking score, and QPP (query performance prediction) score. The experimental results on five benchmark datasets demonstrate the effectiveness and better robustness of OpenDecoder by outperforming various baseline methods. Importantly, this paradigm is flexible to be integrated with the post-training of LLMs for any purposes and incorporated with any type of external indicators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.08472", "title": "sui-1: Grounded and Verifiable Long-Form Summarization", "authors": [ "Benedikt Droste", "Jan Philipp Harries", "Maximilian Idahl", "Bj\u00f6rn Pl\u00fcster" ], "github": "", "project_page": "", "abstract": "Large language models frequently generate plausible but unfaithful summaries that users cannot verify against source text, a critical limitation in compliance-sensitive domains such as government and legal analysis. We present sui-1, a 24B parameter model that produces abstractive summaries with inline citations, enabling users to trace each claim to its source sentence. Our synthetic data pipeline combines chain-of-thought prompting with multi-stage verification, generating over 22,000 high-quality training examples across five languages from diverse sources including parliamentary documents, web text, and Wikipedia. Evaluation shows sui-1 significantly outperforms all tested open-weight baselines, including models with 3x more parameters. These results demonstrate that task-specific training substantially outperforms scale alone for citation-grounded summarization. Model weights and an interactive demo are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-15", "arxiv_id": "2601.04809", "title": "SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning", "authors": [ "Caijun Xu", "Changyi Xiao", "Zhongyuan Peng", "Xinrun Wang", "Yixin Cao" ], "github": "https://github.com/molumolua/SCALER", "project_page": "", "abstract": "Reinforcement learning (RL) offers a principled way to enhance the reasoning capabilities of large language models, yet its effectiveness hinges on training signals that remain informative as models evolve. In practice, RL progress often slows when task difficulty becomes poorly aligned with model capability, or when training is dominated by a narrow set of recurring problem patterns. To jointly address these issues, we propose SCALER (Synthetic sCalable Adaptive Learning Environment for Reasoning), a framework that sustains effective learning signals through adaptive environment design. SCALER introduces a scalable synthesis pipeline that converts real-world programming problems into verifiable reasoning environments with controllable difficulty and unbounded instance generation, enabling RL training beyond finite datasets while preserving strong correctness guarantees. Building on this, SCALER further employs an adaptive multi-environment RL strategy that dynamically adjusts instance difficulty and curates the active set of environments to track the model's capability frontier and maintain distributional diversity. This co-adaptation prevents reward sparsity, mitigates overfitting to narrow task patterns, and supports sustained improvement throughout training. Extensive experiments show that SCALER consistently outperforms dataset-based RL baselines across diverse reasoning benchmarks and exhibits more stable, long-horizon training dynamics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/molumolua/SCALER", "github_api_added_by": "auto" }, { "date": "2026-01-16", "arxiv_id": "2601.10305", "title": "DanQing: An Up-to-Date Large-Scale Chinese Vision-Language Pre-training Dataset", "authors": [ "Hengyu Shen", "Tiancheng Gu", "Bin Qin", "Lan Wu", "Yuling Wu", "Shuo Tan", "Zelong Sun", "Jun Wang", "Nan Wu", "Xiang An", "Weidong Cai", "Ziyong Feng", "Kaicheng Yang" ], "github": "https://github.com/opencv/opencv", "project_page": "", "abstract": "Vision-Language Pre-training (VLP) models demonstrate strong performance across various downstream tasks by learning from large-scale image-text pairs through contrastive pretraining. The release of extensive English image-text datasets (e.g., COYO-700M and LAION-400M) has enabled widespread adoption of models such as CLIP and SigLIP in tasks including cross-modal retrieval and image captioning. However, the advancement of Chinese vision-language pretraining has substantially lagged behind, due to the scarcity of high-quality Chinese image-text data. To address this gap, we develop a comprehensive pipeline for constructing a high-quality Chinese cross-modal dataset. As a result, we propose DanQing, which contains 100 million image-text pairs collected from Common Crawl. Different from existing datasets, DanQing is curated through a more rigorous selection process, yielding superior data quality. Moreover, DanQing is primarily built from 2024-2025 web data, enabling models to better capture evolving semantic trends and thus offering greater practical utility. We compare DanQing with existing datasets by continual pre-training of the SigLIP2 model. Experimental results show that DanQing consistently achieves superior performance across a range of Chinese downstream tasks, including zero-shot classification, cross-modal retrieval, and LMM-based evaluations. To facilitate further research in Chinese vision-language pre-training, we will open-source the DanQing dataset under the Creative Common CC-BY 4.0 license.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/opencv/opencv", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10527", "title": "A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Doubao 1.8, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5", "authors": [ "Xingjun Ma", "Yixu Wang", "Hengyuan Xu", "Yutao Wu", "Yifan Ding", "Yunhan Zhao", "Zilong Wang", "Jiabin Hua", "Ming Wen", "Jianan Liu", "Ranjie Duan", "Yifeng Gao", "Yingshui Tan", "Yunhao Chen", "Hui Xue", "Xin Wang", "Wei Cheng", "Jingjing Chen", "Zuxuan Wu", "Bo Li", "Yu-Gang Jiang" ], "github": "", "project_page": "", "abstract": "The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has produced substantial gains in reasoning, perception, and generative capability across language and vision. However, whether these advances yield commensurate improvements in safety remains unclear, in part due to fragmented evaluation practices limited to single modalities or threat models. In this report, we present an integrated safety evaluation of 7 frontier models: GPT-5.2, Gemini 3 Pro, Qwen3-VL, Doubao 1.8, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5. We evaluate each model across language, vision-language, and image generation settings using a unified protocol that integrates benchmark evaluation, adversarial evaluation, multilingual evaluation, and compliance evaluation. Aggregating our evaluations into safety leaderboards and model safety profiles across multiple evaluation modes reveals a sharply heterogeneous safety landscape. While GPT-5.2 demonstrates consistently strong and balanced safety performance across evaluations, other models exhibit pronounced trade-offs among benchmark safety, adversarial alignment, multilingual generalization, and regulatory compliance. Both language and vision-language modalities show significant vulnerability under adversarial evaluation, with all models degrading substantially despite strong results on standard benchmarks. Text-to-image models achieve relatively stronger alignment in regulated visual risk categories, yet remain brittle under adversarial or semantically ambiguous prompts. Overall, these results show that safety in frontier models is inherently multidimensional--shaped by modality, language, and evaluation scheme, underscoring the need for standardized safety evaluations to accurately assess real-world risk and guide responsible model development and deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10061", "title": "CoF-T2I: Video Models as Pure Visual Reasoners for Text-to-Image Generation", "authors": [ "Chengzhuo Tong", "Mingkun Chang", "Shenglong Zhang", "Yuran Wang", "Cheng Liang", "Zhizheng Zhao", "Ruichuan An", "Bohan Zeng", "Yang Shi", "Yifan Dai", "Ziming Zhao", "Guanbin Li", "Pengfei Wan", "Yuanxing Zhang", "Wentao Zhang" ], "github": "", "project_page": "", "abstract": "Recent video generation models have revealed the emergence of Chain-of-Frame (CoF) reasoning, enabling frame-by-frame visual inference. With this capability, video models have been successfully applied to various visual tasks (e.g., maze solving, visual puzzles). However, their potential to enhance text-to-image (T2I) generation remains largely unexplored due to the absence of a clearly defined visual reasoning starting point and interpretable intermediate states in the T2I generation process. To bridge this gap, we propose CoF-T2I, a model that integrates CoF reasoning into T2I generation via progressive visual refinement, where intermediate frames act as explicit reasoning steps and the final frame is taken as output. To establish such an explicit generation process, we curate CoF-Evol-Instruct, a dataset of CoF trajectories that model the generation process from semantics to aesthetics. To further improve quality and avoid motion artifacts, we enable independent encoding operation for each frame. Experiments show that CoF-T2I significantly outperforms the base video model and achieves competitive performance on challenging benchmarks, reaching 0.86 on GenEval and 7.468 on Imagine-Bench. These results indicate the substantial promise of video models for advancing high-quality text-to-image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10611", "title": "Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding", "authors": [ "Christopher Clark", "Jieyu Zhang", "Zixian Ma", "Jae Sung Park", "Mohammadreza Salehi", "Rohun Tripathi", "Sangho Lee", "Zhongzheng Ren", "Chris Dongjoo Kim", "Yinuo Yang", "Vincent Shao", "Yue Yang", "Weikai Huang", "Ziqi Gao", "Taira Anderson", "Jianrui Zhang", "Jitesh Jain", "George Stoica", "Winson Han", "Ali Farhadi", "Ranjay Krishna" ], "github": "https://github.com/allenai/molmo2", "project_page": "", "abstract": "Today's strongest video-language models (VLMs) remain proprietary. The strongest open-weight models either rely on synthetic data from proprietary VLMs, effectively distilling from them, or do not disclose their training data or recipe. As a result, the open-source community lacks the foundations needed to improve on the state-of-the-art video (and image) language models. Crucially, many downstream applications require more than just high-level video understanding; they require grounding -- either by pointing or by tracking in pixels. Even proprietary models lack this capability. We present Molmo2, a new family of VLMs that are state-of-the-art among open-source models and demonstrate exceptional new capabilities in point-driven grounding in single image, multi-image, and video tasks. Our key contribution is a collection of 7 new video datasets and 2 multi-image datasets, including a dataset of highly detailed video captions for pre-training, a free-form video Q&A dataset for fine-tuning, a new object tracking dataset with complex queries, and an innovative new video pointing dataset, all collected without the use of closed VLMs. We also present a training recipe for this data utilizing an efficient packing and message-tree encoding scheme, and show bi-directional attention on vision tokens and a novel token-weight strategy improves performance. Our best-in-class 8B model outperforms others in the class of open weight and data models on short videos, counting, and captioning, and is competitive on long-videos. On video-grounding Molmo2 significantly outperforms existing open-weight models like Qwen3-VL (35.5 vs 29.6 accuracy on video counting) and surpasses proprietary models like Gemini 3 Pro on some tasks (38.4 vs 20.0 F1 on video pointing and 56.2 vs 41.1 J&F on video tracking).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/molmo2", "github_api_added_by": "auto" }, { "date": "2026-01-16", "arxiv_id": "2601.10712", "title": "MatchTIR: Fine-Grained Supervision for Tool-Integrated Reasoning via Bipartite Matching", "authors": [ "Changle Qu", "Sunhao Dai", "Hengyi Cai", "Jun Xu", "Shuaiqiang Wang", "Dawei Yin" ], "github": "https://github.com/quchangle1/MatchTIR", "project_page": "", "abstract": "Tool-Integrated Reasoning (TIR) empowers large language models (LLMs) to tackle complex tasks by interleaving reasoning steps with external tool interactions. However, existing reinforcement learning methods typically rely on outcome- or trajectory-level rewards, assigning uniform advantages to all steps within a trajectory. This coarse-grained credit assignment fails to distinguish effective tool calls from redundant or erroneous ones, particularly in long-horizon multi-turn scenarios. To address this, we propose MatchTIR, a framework that introduces fine-grained supervision via bipartite matching-based turn-level reward assignment and dual-level advantage estimation. Specifically, we formulate credit assignment as a bipartite matching problem between predicted and ground-truth traces, utilizing two assignment strategies to derive dense turn-level rewards. Furthermore, to balance local step precision with global task success, we introduce a dual-level advantage estimation scheme that integrates turn-level and trajectory-level signals, assigning distinct advantage values to individual interaction turns. Extensive experiments on three benchmarks demonstrate the superiority of MatchTIR. Notably, our 4B model surpasses the majority of 8B competitors, particularly in long-horizon and multi-turn tasks. Our codes are available at https://github.com/quchangle1/MatchTIR.", "github_manual": "", "github_regex": "https://github.com/quchangle1/MatchTIR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.09667", "title": "Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning", "authors": [ "Zhiyuan Hu", "Yunhai Hu", "Juncheng Liu", "Shuyue Stella Li", "Yucheng Wang", "Zhen Xu", "See-Kiong Ng", "Anh Tuan Luu", "Xinxing Xu", "Bryan Hooi", "Cynthia Breazeal", "Hae Won Park" ], "github": "https://github.com/zhiyuanhubj/MATTRL", "project_page": "", "abstract": "Multi-agent systems have evolved into practical LLM-driven collaborators for many applications, gaining robustness from diversity and cross-checking. However, multi-agent RL (MARL) training is resource-intensive and unstable: co-adapting teammates induce non-stationarity, and rewards are often sparse and high-variance. Therefore, we introduce Multi-Agent Test-Time Reinforcement Learning (MATTRL), a framework that injects structured textual experience into multi-agent deliberation at inference time. MATTRL forms a multi-expert team of specialists for multi-turn discussions, retrieves and integrates test-time experiences, and reaches consensus for final decision-making. We also study credit assignment for constructing a turn-level experience pool, then reinjecting it into the dialogue. Across challenging benchmarks in medicine, math, and education, MATTRL improves accuracy by an average of 3.67\\% over a multi-agent baseline, and by 8.67\\% over comparable single-agent baselines. Ablation studies examine different credit-assignment schemes and provide a detailed comparison of how they affect training outcomes. MATTRL offers a stable, effective and efficient path to distribution-shift-robust multi-agent reasoning without tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhiyuanhubj/MATTRL", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.09142", "title": "EvasionBench: Detecting Evasive Answers in Financial Q&A via Multi-Model Consensus and LLM-as-Judge", "authors": [ "Shijian Ma", "Yan Lin", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Detecting evasive answers in earnings calls is critical for financial transparency, yet progress is hindered by the lack of large-scale benchmarks. We introduce EvasionBench, comprising 30,000 training samples and 1,000 human-annotated test samples (Cohen's Kappa 0.835) across three evasion levels. Our key contribution is a multi-model annotation framework leveraging a core insight: disagreement between frontier LLMs signals hard examples most valuable for training. We mine boundary cases where two strong annotators conflict, using a judge to resolve labels. This approach outperforms single-model distillation by 2.4 percent, with judge-resolved samples improving generalization despite higher training loss (0.421 vs 0.393) - evidence that disagreement mining acts as implicit regularization. Our trained model Eva-4B (4B parameters) achieves 81.3 percent accuracy, outperforming its base by 25 percentage points and approaching frontier LLM performance at a fraction of inference cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.08763", "title": "Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs", "authors": [ "Zhiyuan Hu", "Yucheng Wang", "Yufei He", "Jiaying Wu", "Yilun Zhao", "See-Kiong Ng", "Cynthia Breazeal", "Anh Tuan Luu", "Hae Won Park", "Bryan Hooi" ], "github": "https://github.com/zhiyuanhubj/Uniqueness-Aware-RL", "project_page": "", "abstract": "Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often suffers from exploration collapse: policies prematurely concentrate on a small set of dominant reasoning patterns, improving pass@1 while limiting rollout-level diversity and gains in pass@k. We argue that this failure stems from regularizing local token behavior rather than diversity over sets of solutions. To address this, we propose Uniqueness-Aware Reinforcement Learning, a rollout-level objective that explicitly rewards correct solutions that exhibit rare high-level strategies. Our method uses an LLM-based judge to cluster rollouts for the same problem according to their high-level solution strategies, ignoring superficial variations, and reweights policy advantages inversely with cluster size. As a result, correct but novel strategies receive higher rewards than redundant ones. Across mathematics, physics, and medical reasoning benchmarks, our approach consistently improves pass@k across large sampling budgets and increases the area under the pass@k curve (AUC@K) without sacrificing pass@1, while sustaining exploration and uncovering more diverse solution strategies at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhiyuanhubj/Uniqueness-Aware-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10103", "title": "FlowAct-R1: Towards Interactive Humanoid Video Generation", "authors": [ "Lizhen Wang", "Yongming Zhu", "Zhipeng Ge", "Youwei Zheng", "Longhao Zhang", "Tianshu Hu", "Shiyang Qin", "Mingshuang Luo", "Jiaxu Zhang", "Xin Chen", "Yulong Wang", "Zerong Zheng", "Jianwen Jiang", "Chao Liang", "Weifeng Chen", "Xing Wang", "Yuan Zhang", "Mingyuan Gao" ], "github": "", "project_page": "", "abstract": "Interactive humanoid video generation aims to synthesize lifelike visual agents that can engage with humans through continuous and responsive video. Despite recent advances in video synthesis, existing methods often grapple with the trade-off between high-fidelity synthesis and real-time interaction requirements. In this paper, we propose FlowAct-R1, a framework specifically designed for real-time interactive humanoid video generation. Built upon a MMDiT architecture, FlowAct-R1 enables the streaming synthesis of video with arbitrary durations while maintaining low-latency responsiveness. We introduce a chunkwise diffusion forcing strategy, complemented by a novel self-forcing variant, to alleviate error accumulation and ensure long-term temporal consistency during continuous interaction. By leveraging efficient distillation and system-level optimizations, our framework achieves a stable 25fps at 480p resolution with a time-to-first-frame (TTFF) of only around 1.5 seconds. The proposed method provides holistic and fine-grained full-body control, enabling the agent to transition naturally between diverse behavioral states in interactive scenarios. Experimental results demonstrate that FlowAct-R1 achieves exceptional behavioral vividness and perceptual realism, while maintaining robust generalization across diverse character styles.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10553", "title": "Inference-time Physics Alignment of Video Generative Models with Latent World Models", "authors": [ "Jianhao Yuan", "Xiaofeng Zhang", "Felix Friedrich", "Nicolas Beltran-Velez", "Melissa Hall", "Reyhane Askari-Hemmat", "Xiaochuang Han", "Nicolas Ballas", "Michal Drozdzal", "Adriana Romero-Soriano" ], "github": "", "project_page": "", "abstract": "State-of-the-art video generative models produce promising visual content yet often violate basic physics principles, limiting their utility. While some attribute this deficiency to insufficient physics understanding from pre-training, we find that the shortfall in physics plausibility also stems from suboptimal inference strategies. We therefore introduce WMReward and treat improving physics plausibility of video generation as an inference-time alignment problem. In particular, we leverage the strong physics prior of a latent world model (here, VJEPA-2) as a reward to search and steer multiple candidate denoising trajectories, enabling scaling test-time compute for better generation performance. Empirically, our approach substantially improves physics plausibility across image-conditioned, multiframe-conditioned, and text-conditioned generation settings, with validation from human preference study. Notably, in the ICCV 2025 Perception Test PhysicsIQ Challenge, we achieve a final score of 62.64%, winning first place and outperforming the previous state of the art by 7.42%. Our work demonstrates the viability of using latent world models to improve physics plausibility of video generation, beyond this specific instantiation or parameterization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10332", "title": "Think-Then-Generate: Reasoning-Aware Text-to-Image Diffusion with LLM Encoders", "authors": [ "Siqi Kou", "Jiachun Jin", "Zetong Zhou", "Ye Ma", "Yugang Wang", "Quan Chen", "Peng Jiang", "Xiao Yang", "Jun Zhu", "Kai Yu", "Zhijie Deng" ], "github": "", "project_page": "", "abstract": "Recent progress in text-to-image (T2I) diffusion models (DMs) has enabled high-quality visual synthesis from diverse textual prompts. Yet, most existing T2I DMs, even those equipped with large language model (LLM)-based text encoders, remain text-pixel mappers -- they employ LLMs merely as text encoders, without leveraging their inherent reasoning capabilities to infer what should be visually depicted given the textual prompt. To move beyond such literal generation, we propose the think-then-generate (T2G) paradigm, where the LLM-based text encoder is encouraged to reason about and rewrite raw user prompts; the states of the rewritten prompts then serve as diffusion conditioning. To achieve this, we first activate the think-then-rewrite pattern of the LLM encoder with a lightweight supervised fine-tuning process. Subsequently, the LLM encoder and diffusion backbone are co-optimized to ensure faithful reasoning about the context and accurate rendering of the semantics via Dual-GRPO. In particular, the text encoder is reinforced using image-grounded rewards to infer and recall world knowledge, while the diffusion backbone is pushed to produce semantically consistent and visually coherent images. Experiments show substantial improvements in factual consistency, semantic alignment, and visual realism across reasoning-based image generation and editing benchmarks, achieving 0.79 on WISE score, nearly on par with GPT-4. Our results constitute a promising step toward next-generation unified models with reasoning, expression, and demonstration capacities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.09881", "title": "Transition Matching Distillation for Fast Video Generation", "authors": [ "Weili Nie", "Julius Berner", "Nanye Ma", "Chao Liu", "Saining Xie", "Arash Vahdat" ], "github": "", "project_page": "", "abstract": "Large video diffusion and flow models have achieved remarkable success in high-quality video generation, but their use in real-time interactive applications remains limited due to their inefficient multi-step sampling process. In this work, we present Transition Matching Distillation (TMD), a novel framework for distilling video diffusion models into efficient few-step generators. The central idea of TMD is to match the multi-step denoising trajectory of a diffusion model with a few-step probability transition process, where each transition is modeled as a lightweight conditional flow. To enable efficient distillation, we decompose the original diffusion backbone into two components: (1) a main backbone, comprising the majority of early layers, that extracts semantic representations at each outer transition step; and (2) a flow head, consisting of the last few layers, that leverages these representations to perform multiple inner flow updates. Given a pretrained video diffusion model, we first introduce a flow head to the model, and adapt it into a conditional flow map. We then apply distribution matching distillation to the student model with flow head rollout in each transition step. Extensive experiments on distilling Wan2.1 1.3B and 14B text-to-video models demonstrate that TMD provides a flexible and strong trade-off between generation speed and visual quality. In particular, TMD outperforms existing distilled models under comparable inference costs in terms of visual fidelity and prompt adherence. Project page: https://research.nvidia.com/labs/genair/tmd", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10592", "title": "Action100M: A Large-scale Video Action Dataset", "authors": [ "Delong Chen", "Tejaswi Kasarla", "Yejin Bang", "Mustafa Shukor", "Willy Chung", "Jade Yu", "Allen Bolourchi", "Theo Moutakanni", "Pascale Fung" ], "github": "", "project_page": "", "abstract": "Inferring physical actions from visual observations is a fundamental capability for advancing machine intelligence in the physical world. Achieving this requires large-scale, open-vocabulary video action datasets that span broad domains. We introduce Action100M, a large-scale dataset constructed from 1.2M Internet instructional videos (14.6 years of duration), yielding O(100 million) temporally localized segments with open-vocabulary action supervision and rich captions. Action100M is generated by a fully automated pipeline that (i) performs hierarchical temporal segmentation using V-JEPA 2 embeddings, (ii) produces multi-level frame and segment captions organized as a Tree-of-Captions, and (iii) aggregates evidence with a reasoning model (GPT-OSS-120B) under a multi-round Self-Refine procedure to output structured annotations (brief/detailed action, actor, brief/detailed caption). Training VL-JEPA on Action100M demonstrates consistent data-scaling improvements and strong zero-shot performance across diverse action recognition benchmarks, establishing Action100M as a new foundation for scalable research in video understanding and world modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.09668", "title": "STEP3-VL-10B Technical Report", "authors": [ "Ailin Huang", "Chengyuan Yao", "Chunrui Han", "Fanqi Wan", "Hangyu Guo", "Haoran Lv", "Hongyu Zhou", "Jia Wang", "Jian Zhou", "Jianjian Sun", "Jingcheng Hu", "Kangheng Lin", "Liang Zhao", "Mitt Huang", "Song Yuan", "Wenwen Qu", "Xiangfeng Wang", "Yanlin Lai", "Yingxiu Zhao", "Yinmin Zhang", "Yukang Shi", "Yuyang Chen", "Zejia Weng", "Ziyang Meng", "Ang Li", "Aobo Kong", "Bo Dong", "Changyi Wan", "David Wang", "Di Qi", "Dingming Li", "En Yu", "Guopeng Li", "Haiquan Yin", "Han Zhou", "Hanshan Zhang", "Haolong Yan", "Hebin Zhou", "Hongbo Peng", "Jiaran Zhang", "Jiashu Lv", "Jiayi Fu", "Jie Cheng", "Jie Zhou", "Jisheng Yin", "Jingjing Xie", "Jingwei Wu", "Jun Zhang", "Junfeng Liu", "Kaijun Tan", "Kaiwen Yan", "Liangyu Chen", "Lina Chen", "Mingliang Li", "Qian Zhao", "Quan Sun", "Shaoliang Pang", "Shengjie Fan", "Shijie Shang", "Siyuan Zhang", "Tianhao You", "Wei Ji", "Wuxun Xie", "Xiaobo Yang", "Xiaojie Hou", "Xiaoran Jiao", "Xiaoxiao Ren", "Xiangwen Kong", "Xin Huang", "Xin Wu", "Xing Chen", "Xinran Wang", "Xuelin Zhang", "Yana Wei", "Yang Li", "Yanming Xu", "Yeqing Shen", "Yuang Peng", "Yue Peng", "Yu Zhou", "Yusheng Li", "Yuxiang Yang", "Yuyang Zhang", "Zhe Xie", "Zhewei Huang", "Zhenyi Lu", "Zhimin Fan", "Zihui Cheng", "Daxin Jiang", "Qi Han", "Xiangyu Zhang", "Yibo Zhu", "Zheng Ge" ], "github": "https://github.com/stepfun-ai/Step3-VL-10B", "project_page": "", "abstract": "We present STEP3-VL-10B, a lightweight open-source foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence. STEP3-VL-10B is realized through two strategic shifts: first, a unified, fully unfrozen pre-training strategy on 1.2T multimodal tokens that integrates a language-aligned Perception Encoder with a Qwen3-8B decoder to establish intrinsic vision-language synergy; and second, a scaled post-training pipeline featuring over 1k iterations of reinforcement learning. Crucially, we implement Parallel Coordinated Reasoning (PaCoRe) to scale test-time compute, allocating resources to scalable perceptual reasoning that explores and synthesizes diverse visual hypotheses. Consequently, despite its compact 10B footprint, STEP3-VL-10B rivals or surpasses models 10times-20times larger (e.g., GLM-4.6V-106B, Qwen3-VL-235B) and top-tier proprietary flagships like Gemini 2.5 Pro and Seed-1.5-VL. Delivering best-in-class performance, it records 92.2% on MMBench and 80.11% on MMMU, while excelling in complex reasoning with 94.43% on AIME2025 and 75.95% on MathVision. We release the full model suite to provide the community with a powerful, efficient, and reproducible baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stepfun-ai/Step3-VL-10B", "github_api_added_by": "auto" }, { "date": "2026-01-16", "arxiv_id": "2601.10156", "title": "ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback", "authors": [ "Yutao Mou", "Zhangchi Xue", "Lijun Li", "Peiyang Liu", "Shikun Zhang", "Wei Ye", "Jing Shao" ], "github": "https://github.com/MurrayTom/ToolSafe", "project_page": "", "abstract": "While LLM-based agents can interact with environments via invoking external tools, their expanded capabilities also amplify security risks. Monitoring step-level tool invocation behaviors in real time and proactively intervening before unsafe execution is critical for agent deployment, yet remains under-explored. In this work, we first construct TS-Bench, a novel benchmark for step-level tool invocation safety detection in LLM agents. We then develop a guardrail model, TS-Guard, using multi-task reinforcement learning. The model proactively detects unsafe tool invocation actions before execution by reasoning over the interaction history. It assesses request harmfulness and action-attack correlations, producing interpretable and generalizable safety judgments and feedback. Furthermore, we introduce TS-Flow, a guardrail-feedback-driven reasoning framework for LLM agents, which reduces harmful tool invocations of ReAct-style agents by 65 percent on average and improves benign task completion by approximately 10 percent under prompt injection attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MurrayTom/ToolSafe", "github_api_added_by": "auto" }, { "date": "2026-01-16", "arxiv_id": "2601.10201", "title": "PRL: Process Reward Learning Improves LLMs' Reasoning Ability and Broadens the Reasoning Boundary", "authors": [ "Jiarui Yao", "Ruida Wang", "Tong Zhang" ], "github": "https://github.com/MaxwellJryao/Process-Reward-Learning", "project_page": "", "abstract": "Improving the reasoning abilities of Large Language Models (LLMs) has been a continuous topic recently. But most relevant works are based on outcome rewards at the trajectory level, missing fine-grained supervision during the reasoning process. Other existing training frameworks that try to combine process signals together to optimize LLMs also rely heavily on tedious additional steps like MCTS, training a separate reward model, etc., doing harm to the training efficiency. Moreover, the intuition behind the process signals design lacks rigorous theoretical support, leaving the understanding of the optimization mechanism opaque. In this paper, we propose Process Reward Learning (PRL), which decomposes the entropy regularized reinforcement learning objective into intermediate steps, with rigorous process rewards that could be assigned to models accordingly. Starting from theoretical motivation, we derive the formulation of PRL that is essentially equivalent to the objective of reward maximization plus a KL-divergence penalty term between the policy model and a reference model. However, PRL could turn the outcome reward into process supervision signals, which helps better guide the exploration during RL optimization. From our experiment results, we demonstrate that PRL not only improves the average performance for LLMs' reasoning ability measured by average @ n, but also broadens the reasoning boundary by improving the pass @ n metric. Extensive experiments show the effectiveness of PRL could be verified and generalized.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MaxwellJryao/Process-Reward-Learning", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10129", "title": "LaViT: Aligning Latent Visual Thoughts for Multi-modal Reasoning", "authors": [ "Linquan Wu", "Tianxiang Jiang", "Yifei Dong", "Haoyu Yang", "Fengji Zhang", "Shichaang Meng", "Ai Xuan", "Linqi Song", "Jacky Keung" ], "github": "https://github.com/Svardfox/LaViT", "project_page": "", "abstract": "Current multimodal latent reasoning often relies on external supervision (e.g., auxiliary images), ignoring intrinsic visual attention dynamics. In this work, we identify a critical Perception Gap in distillation: student models frequently mimic a teacher's textual output while attending to fundamentally divergent visual regions, effectively relying on language priors rather than grounded perception. To bridge this, we propose LaViT, a framework that aligns latent visual thoughts rather than static embeddings. LaViT compels the student to autoregressively reconstruct the teacher's visual semantics and attention trajectories prior to text generation, employing a curriculum sensory gating mechanism to prevent shortcut learning. Extensive experiments show that LaViT significantly enhances visual grounding, achieving up to +16.9% gains on complex reasoning tasks and enabling a compact 3B model to outperform larger open-source variants and proprietary models like GPT-4o.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Svardfox/LaViT", "github_api_added_by": "auto" }, { "date": "2026-01-16", "arxiv_id": "2601.10080", "title": "Deriving Character Logic from Storyline as Codified Decision Trees", "authors": [ "Letian Peng", "Kun Zhou", "Longfei Yun", "Yupeng Hou", "Jingbo Shang" ], "github": "https://github.com/KomeijiForce/Codified_Decision_Tree", "project_page": "", "abstract": "Role-playing (RP) agents rely on behavioral profiles to act consistently across diverse narrative contexts, yet existing profiles are largely unstructured, non-executable, and weakly validated, leading to brittle agent behavior. We propose Codified Decision Trees (CDT), a data-driven framework that induces an executable and interpretable decision structure from large-scale narrative data. CDT represents behavioral profiles as a tree of conditional rules, where internal nodes correspond to validated scene conditions and leaves encode grounded behavioral statements, enabling deterministic retrieval of context-appropriate rules at execution time. The tree is learned by iteratively inducing candidate scene-action rules, validating them against data, and refining them through hierarchical specialization, yielding profiles that support transparent inspection and principled updates. Across multiple benchmarks, CDT substantially outperforms human-written profiles and prior profile induction methods on 85 characters across 16 artifacts, indicating that codified and validated behavioral representations lead to more reliable agent grounding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KomeijiForce/Codified_Decision_Tree", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10477", "title": "Urban Socio-Semantic Segmentation with Vision-Language Reasoning", "authors": [ "Yu Wang", "Yi Wang", "Rui Dai", "Yujie Wang", "Kaikui Liu", "Xiangxiang Chu", "Yansheng Li" ], "github": "https://github.com/AMAP-ML/SocioReasoner", "project_page": "", "abstract": "As hubs of human activity, urban surfaces consist of a wealth of semantic entities. Segmenting these various entities from satellite imagery is crucial for a range of downstream applications. Current advanced segmentation models can reliably segment entities defined by physical attributes (e.g., buildings, water bodies) but still struggle with socially defined categories (e.g., schools, parks). In this work, we achieve socio-semantic segmentation by vision-language model reasoning. To facilitate this, we introduce the Urban Socio-Semantic Segmentation dataset named SocioSeg, a new resource comprising satellite imagery, digital maps, and pixel-level labels of social semantic entities organized in a hierarchical structure. Additionally, we propose a novel vision-language reasoning framework called SocioReasoner that simulates the human process of identifying and annotating social semantic entities via cross-modal recognition and multi-stage reasoning. We employ reinforcement learning to optimize this non-differentiable process and elicit the reasoning capabilities of the vision-language model. Experiments demonstrate our approach's gains over state-of-the-art models and strong zero-shot generalization. Our dataset and code are available in https://github.com/AMAP-ML/SocioReasoner.", "github_manual": "", "github_regex": "https://github.com/AMAP-ML/SocioReasoner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.08881", "title": "TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts", "authors": [ "Yu Xu", "Hongbin Yan", "Juan Cao", "Yiji Cheng", "Tiankai Hang", "Runze He", "Zijin Yin", "Shiyi Zhang", "Yuxin Zhang", "Jintao Li", "Chunyu Wang", "Qinglin Lu", "Tong-Yee Lee", "Fan Tang" ], "github": "https://github.com/ICTMCG/TAG-MoE", "project_page": "", "abstract": "Unified image generation and editing models suffer from severe task interference in dense diffusion transformers architectures, where a shared parameter space must compromise between conflicting objectives (e.g., local editing v.s. subject-driven generation). While the sparse Mixture-of-Experts (MoE) paradigm is a promising solution, its gating networks remain task-agnostic, operating based on local features, unaware of global task intent. This task-agnostic nature prevents meaningful specialization and fails to resolve the underlying task interference. In this paper, we propose a novel framework to inject semantic intent into MoE routing. We introduce a Hierarchical Task Semantic Annotation scheme to create structured task descriptors (e.g., scope, type, preservation). We then design Predictive Alignment Regularization to align internal routing decisions with the task's high-level semantics. This regularization evolves the gating network from a task-agnostic executor to a dispatch center. Our model effectively mitigates task interference, outperforming dense baselines in fidelity and quality, and our analysis shows that experts naturally develop clear and semantically correlated specializations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ICTMCG/TAG-MoE", "github_api_added_by": "auto" }, { "date": "2026-01-16", "arxiv_id": "2601.06431", "title": "LSRIF: Logic-Structured Reinforcement Learning for Instruction Following", "authors": [ "Qingyu Ren", "Qianyu He", "Jingwen Chang", "Jie Zeng", "Jiaqing Liang", "Yanghua Xiao", "Han Xia", "Zeye Sun", "Fei Yu" ], "github": "", "project_page": "", "abstract": "Instruction-following is critical for large language models, but real-world instructions often contain logical structures such as sequential dependencies and conditional branching. Existing methods typically construct datasets with parallel constraints and optimize average rewards, ignoring logical dependencies and yielding noisy signals. We propose a logic-structured training framework LSRIF that explicitly models instruction logic. We first construct a dataset LSRInstruct with constraint structures such as parallel, sequential, and conditional types, and then design structure-aware rewarding method LSRIF including average aggregation for parallel structures, failure-penalty propagation for sequential structures, and selective rewards for conditional branches. Experiments show LSRIF brings significant improvements in instruction-following (in-domain and out-of-domain) and general reasoning. Analysis reveals that learning with explicit logic structures brings parameter updates in attention layers and sharpens token-level attention to constraints and logical operators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10716", "title": "WildRayZer: Self-supervised Large View Synthesis in Dynamic Environments", "authors": [ "Xuweiyi Chen", "Wentao Zhou", "Zezhou Cheng" ], "github": "", "project_page": "", "abstract": "We present WildRayZer, a self-supervised framework for novel view synthesis (NVS) in dynamic environments where both the camera and objects move. Dynamic content breaks the multi-view consistency that static NVS models rely on, leading to ghosting, hallucinated geometry, and unstable pose estimation. WildRayZer addresses this by performing an analysis-by-synthesis test: a camera-only static renderer explains rigid structure, and its residuals reveal transient regions. From these residuals, we construct pseudo motion masks, distill a motion estimator, and use it to mask input tokens and gate loss gradients so supervision focuses on cross-view background completion. To enable large-scale training and evaluation, we curate Dynamic RealEstate10K (D-RE10K), a real-world dataset of 15K casually captured dynamic sequences, and D-RE10K-iPhone, a paired transient and clean benchmark for sparse-view transient-aware NVS. Experiments show that WildRayZer consistently outperforms optimization-based and feed-forward baselines in both transient-region removal and full-frame NVS quality with a single feed-forward pass.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.07641", "title": "Beyond Static Tools: Test-Time Tool Evolution for Scientific Reasoning", "authors": [ "Jiaxuan Lu", "Ziyu Kong", "Yemin Wang", "Rong Fu", "Haiyuan Wan", "Cheng Yang", "Wenjie Lou", "Haoran Sun", "Lilong Wang", "Yankai Jiang", "Xiaosong Wang", "Xiao Sun", "Dongzhan Zhou" ], "github": "https://github.com/lujiaxuan0520/Test-Time-Tool-Evol", "project_page": "", "abstract": "The central challenge of AI for Science is not reasoning alone, but the ability to create computational methods in an open-ended scientific world. Existing LLM-based agents rely on static, pre-defined tool libraries, a paradigm that fundamentally fails in scientific domains where tools are sparse, heterogeneous, and intrinsically incomplete. In this paper, we propose Test-Time Tool Evolution (TTE), a new paradigm that enables agents to synthesize, verify, and evolve executable tools during inference. By transforming tools from fixed resources into problem-driven artifacts, TTE overcomes the rigidity and long-tail limitations of static tool libraries. To facilitate rigorous evaluation, we introduce SciEvo, a benchmark comprising 1,590 scientific reasoning tasks supported by 925 automatically evolved tools. Extensive experiments show that TTE achieves state-of-the-art performance in both accuracy and tool efficiency, while enabling effective cross-domain adaptation of computational tools. The code and benchmark have been released at https://github.com/lujiaxuan0520/Test-Time-Tool-Evol.", "github_manual": "", "github_regex": "https://github.com/lujiaxuan0520/Test-Time-Tool-Evol", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10402", "title": "Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering", "authors": [ "Xinyu Zhu", "Yuzhu Cai", "Zexi Liu", "Bingyang Zheng", "Cheng Wang", "Rui Ye", "Jiaao Chen", "Hanrui Wang", "Wei-Chen Wang", "Yuzhi Zhang", "Linfeng Zhang", "Weinan E", "Di Jin", "Siheng Chen" ], "github": "", "project_page": "", "abstract": "The advancement of artificial intelligence toward agentic science is currently bottlenecked by the challenge of ultra-long-horizon autonomy, the ability to sustain strategic coherence and iterative correction over experimental cycles spanning days or weeks. While Large Language Models (LLMs) have demonstrated prowess in short-horizon reasoning, they are easily overwhelmed by execution details in the high-dimensional, delayed-feedback environments of real-world research, failing to consolidate sparse feedback into coherent long-term guidance. Here, we present ML-Master 2.0, an autonomous agent that masters ultra-long-horizon machine learning engineering (MLE) which is a representative microcosm of scientific discovery. By reframing context management as a process of cognitive accumulation, our approach introduces Hierarchical Cognitive Caching (HCC), a multi-tiered architecture inspired by computer systems that enables the structural differentiation of experience over time. By dynamically distilling transient execution traces into stable knowledge and cross-task wisdom, HCC allows agents to decouple immediate execution from long-term experimental strategy, effectively overcoming the scaling limits of static context windows. In evaluations on OpenAI's MLE-Bench under 24-hour budgets, ML-Master 2.0 achieves a state-of-the-art medal rate of 56.44%. Our findings demonstrate that ultra-long-horizon autonomy provides a scalable blueprint for AI capable of autonomous exploration beyond human-precedent complexities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.09923", "title": "CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents", "authors": [ "Hanna Foerster", "Robert Mullins", "Tom Blanchard", "Nicolas Papernot", "Kristina Nikoli\u0107", "Florian Tram\u00e8r", "Ilia Shumailov", "Cheng Zhang", "Yiren Zhao" ], "github": "https://github.com/google-research/camel-prompt-injection", "project_page": "", "abstract": "AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss. The only known robust defense is architectural isolation that strictly separates trusted task planning from untrusted environment observations. However, applying this design to Computer Use Agents (CUAs) -- systems that automate tasks by viewing screens and executing actions -- presents a fundamental challenge: current agents require continuous observation of UI state to determine each action, conflicting with the isolation required for security. We resolve this tension by demonstrating that UI workflows, while dynamic, are structurally predictable. We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content, providing provable control flow integrity guarantees against arbitrary instruction injections. Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks, which manipulate UI elements to trigger unintended valid paths within the plan. We evaluate our design on OSWorld, and retain up to 57% of the performance of frontier models while improving performance for smaller open-source models by up to 19%, demonstrating that rigorous security and utility can coexist in CUAs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-research/camel-prompt-injection", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.02242", "title": "VIBE: Visual Instruction Based Editor", "authors": [ "Grigorii Alekseenko", "Aleksandr Gordeev", "Irina Tolstykh", "Bulat Suleimanov", "Vladimir Dokholyan", "Georgii Fedorov", "Sergey Yakubson", "Aleksandra Tsybina", "Mikhail Chernyshov", "Maksim Kuprashevich" ], "github": "", "project_page": "", "abstract": "Instruction-based image editing is among the fastest developing areas in generative AI. Over the past year, the field has reached a new level, with dozens of open-source models released alongside highly capable commercial systems. However, only a limited number of open-source approaches currently achieve real-world quality. In addition, diffusion backbones, the dominant choice for these pipelines, are often large and computationally expensive for many deployments and research settings, with widely used variants typically containing 6B to 20B parameters. This paper presents a compact, high-throughput instruction-based image editing pipeline that uses a modern 2B-parameter Qwen3-VL model to guide the editing process and the 1.6B-parameter diffusion model Sana1.5 for image generation. Our design decisions across architecture, data processing, training configuration, and evaluation target low-cost inference and strict source consistency while maintaining high quality across the major edit categories feasible at this scale. Evaluated on the ImgEdit and GEdit benchmarks, the proposed method matches or exceeds the performance of substantially heavier baselines, including models with several times as many parameters and higher inference cost, and is particularly strong on edits that require preserving the input image, such as an attribute adjustment, object removal, background edits, and targeted replacement. The model fits within 24 GB of GPU memory and generates edited images at up to 2K resolution in approximately 4 seconds on an NVIDIA H100 in BF16, without additional inference optimizations or distillation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10714", "title": "Alterbute: Editing Intrinsic Attributes of Objects in Images", "authors": [ "Tal Reiss", "Daniel Winter", "Matan Cohen", "Alex Rav-Acha", "Yael Pritch", "Ariel Shamir", "Yedid Hoshen" ], "github": "", "project_page": "", "abstract": "We introduce Alterbute, a diffusion-based method for editing an object's intrinsic attributes in an image. We allow changing color, texture, material, and even the shape of an object, while preserving its perceived identity and scene context. Existing approaches either rely on unsupervised priors that often fail to preserve identity or use overly restrictive supervision that prevents meaningful intrinsic variations. Our method relies on: (i) a relaxed training objective that allows the model to change both intrinsic and extrinsic attributes conditioned on an identity reference image, a textual prompt describing the target intrinsic attributes, and a background image and object mask defining the extrinsic context. At inference, we restrict extrinsic changes by reusing the original background and object mask, thereby ensuring that only the desired intrinsic attributes are altered; (ii) Visual Named Entities (VNEs) - fine-grained visual identity categories (e.g., ''Porsche 911 Carrera'') that group objects sharing identity-defining features while allowing variation in intrinsic attributes. We use a vision-language model to automatically extract VNE labels and intrinsic attribute descriptions from a large public image dataset, enabling scalable, identity-preserving supervision. Alterbute outperforms existing methods on identity-preserving object intrinsic attribute editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10547", "title": "HeartMuLa: A Family of Open Sourced Music Foundation Models", "authors": [ "Dongchao Yang", "Yuxin Xie", "Yuguo Yin", "Zheyu Wang", "Xiaoyu Yi", "Gongxi Zhu", "Xiaolong Weng", "Zihan Xiong", "Yingzhe Ma", "Dading Cong", "Jingliang Liu", "Zihang Huang", "Jinghan Ru", "Rongjie Huang", "Haoran Wan", "Peixu Wang", "Kuoxi Yu", "Helin Wang", "Liming Liang", "Xianwei Zhuang", "Yuanyuan Wang", "Haohan Guo", "Junjie Cao", "Zeqian Ju", "Songxiang Liu", "Yuewen Cao", "Heming Weng", "Yuexian Zou" ], "github": "", "project_page": "", "abstract": "We present a family of open-source Music Foundation Models designed to advance large-scale music understanding and generation across diverse tasks and modalities. Our framework consists of four major components: (1) HeartCLAP, an audio-text alignment model; (2) HeartTranscriptor, a robust lyric recognition model optimized for real-world music scenarios; and (3) HeartCodec, a low-frame-rate (12.5 Hz) yet high-fidelity music codec tokenizer that captures long-range musical structure while preserving fine-grained acoustic details and enabling efficient autoregressive modeling; (4) HeartMuLa, an LLM-based song generation model capable of synthesizing high-fidelity music under rich, user-controllable conditions (e.g., textual style descriptions, lyrics, and reference audio). In addition, it provides two specialized modes: (i) fine-grained musical attribute control, which allows users to specify the style of different song sections (e.g., intro, verse, chorus) using natural language prompts; and (ii) short, engaging music generation, which is suitable as background music for short videos. Lastly, HeartMuLa improves significantly when scaled to 7B parameters. For the first time, we show that a Suno-level, commercial-grade system can be reproduced using academic-scale data and GPU resources. We expect these foundation models to serve as strong baselines for future research and to facilitate practical applications in multimodal content production.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10124", "title": "VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation", "authors": [ "Sicheng Yang", "Zhaohu Xing", "Lei Zhu" ], "github": "https://github.com/script-Yang/VQ-Seg", "project_page": "", "abstract": "Consistency learning with feature perturbation is a widely used strategy in semi-supervised medical image segmentation. However, many existing perturbation methods rely on dropout, and thus require a careful manual tuning of the dropout rate, which is a sensitive hyperparameter and often difficult to optimize and may lead to suboptimal regularization. To overcome this limitation, we propose VQ-Seg, the first approach to employ vector quantization (VQ) to discretize the feature space and introduce a novel and controllable Quantized Perturbation Module (QPM) that replaces dropout. Our QPM perturbs discrete representations by shuffling the spatial locations of codebook indices, enabling effective and controllable regularization. To mitigate potential information loss caused by quantization, we design a dual-branch architecture where the post-quantization feature space is shared by both image reconstruction and segmentation tasks. Moreover, we introduce a Post-VQ Feature Adapter (PFA) to incorporate guidance from a foundation model (FM), supplementing the high-level semantic information lost during quantization. Furthermore, we collect a large-scale Lung Cancer (LC) dataset comprising 828 CT scans annotated for central-type lung carcinoma. Extensive experiments on the LC dataset and other public benchmarks demonstrate the effectiveness of our method, which outperforms state-of-the-art approaches. Code available at: https://github.com/script-Yang/VQ-Seg.", "github_manual": "", "github_regex": "https://github.com/script-Yang/VQ-Seg", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.00756", "title": "Memory Bank Compression for Continual Adaptation of Large Language Models", "authors": [ "Thomas Katraouras", "Dimitrios Rafailidis" ], "github": "https://github.com/Thomkat/MBC", "project_page": "", "abstract": "Large Language Models (LLMs) have become a mainstay for many everyday applications. However, as data evolve their knowledge quickly becomes outdated. Continual learning aims to update LLMs with new information without erasing previously acquired knowledge. Although methods such as full fine-tuning can incorporate new data, they are computationally expensive and prone to catastrophic forgetting, where prior knowledge is overwritten. Memory-augmented approaches address this by equipping LLMs with a memory bank, that is an external memory module which stores information for future use. However, these methods face a critical limitation, in particular, the memory bank constantly grows in the real-world scenario when large-scale data streams arrive. In this paper, we propose MBC, a model that compresses the memory bank through a codebook optimization strategy during online adaptation learning. To ensure stable learning, we also introduce an online resetting mechanism that prevents codebook collapse. In addition, we employ Key-Value Low-Rank Adaptation in the attention layers of the LLM, enabling efficient utilization of the compressed memory representations. Experiments with benchmark question-answering datasets demonstrate that MBC reduces the memory bank size to 0.3% when compared against the most competitive baseline, while maintaining high retention accuracy during online adaptation learning. Our code is publicly available at https://github.com/Thomkat/MBC.", "github_manual": "", "github_regex": "https://github.com/Thomkat/MBC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10338", "title": "Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale", "authors": [ "Yi Liu", "Weizhe Wang", "Ruitao Feng", "Yao Zhang", "Guangquan Xu", "Gelei Deng", "Yuekang Li", "Leo Zhang" ], "github": "", "project_page": "", "abstract": "The rise of AI agent frameworks has introduced agent skills, modular packages containing instructions and executable code that dynamically extend agent capabilities. While this architecture enables powerful customization, skills execute with implicit trust and minimal vetting, creating a significant yet uncharacterized attack surface. We conduct the first large-scale empirical security analysis of this emerging ecosystem, collecting 42,447 skills from two major marketplaces and systematically analyzing 31,132 using SkillScan, a multi-stage detection framework integrating static analysis with LLM-based semantic classification. Our findings reveal pervasive security risks: 26.1% of skills contain at least one vulnerability, spanning 14 distinct patterns across four categories: prompt injection, data exfiltration, privilege escalation, and supply chain risks. Data exfiltration (13.3%) and privilege escalation (11.8%) are most prevalent, while 5.2% of skills exhibit high-severity patterns strongly suggesting malicious intent. We find that skills bundling executable scripts are 2.12x more likely to contain vulnerabilities than instruction-only skills (OR=2.12, p<0.001). Our contributions include: (1) a grounded vulnerability taxonomy derived from 8,126 vulnerable skills, (2) a validated detection methodology achieving 86.7% precision and 82.5% recall, and (3) an open dataset and detection toolkit to support future research. These results demonstrate an urgent need for capability-based permission systems and mandatory security vetting before this attack vector is further exploited.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.08302", "title": "Enhancing Sentiment Classification and Irony Detection in Large Language Models through Advanced Prompt Engineering Techniques", "authors": [ "Marvin Schmitt", "Anne Schwerk", "Sebastian Lempert" ], "github": "https://github.com/Marvin2108/ESCID-LLM-APET", "project_page": "", "abstract": "This study investigates the use of prompt engineering to enhance large language models (LLMs), specifically GPT-4o-mini and gemini-1.5-flash, in sentiment analysis tasks. It evaluates advanced prompting techniques like few-shot learning, chain-of-thought prompting, and self-consistency against a baseline. Key tasks include sentiment classification, aspect-based sentiment analysis, and detecting subtle nuances such as irony. The research details the theoretical background, datasets, and methods used, assessing performance of LLMs as measured by accuracy, recall, precision, and F1 score. Findings reveal that advanced prompting significantly improves sentiment analysis, with the few-shot approach excelling in GPT-4o-mini and chain-of-thought prompting boosting irony detection in gemini-1.5-flash by up to 46%. Thus, while advanced prompting techniques overall improve performance, the fact that few-shot prompting works best for GPT-4o-mini and chain-of-thought excels in gemini-1.5-flash for irony detection suggests that prompting strategies must be tailored to both the model and the task. This highlights the importance of aligning prompt design with both the LLM's architecture and the semantic complexity of the task.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Marvin2108/ESCID-LLM-APET", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10657", "title": "PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution", "authors": [ "Minghao Yan", "Bo Peng", "Benjamin Coleman", "Ziqi Chen", "Zhouhang Xie", "Zhankui He", "Noveen Sachdeva", "Isabella Ye", "Weili Wang", "Chi Wang", "Ed H. Chi", "Wang-Cheng Kang", "Derek Zhiyuan Cheng", "Beidou Wang" ], "github": "https://github.com/algorithmicsuperintelligence/openevolve", "project_page": "", "abstract": "Large Language Models (LLMs) have emerged as powerful operators for evolutionary search, yet the design of efficient search scaffolds remains ad hoc. While promising, current LLM-in-the-loop systems lack a systematic approach to managing the evolutionary process. We identify three distinct failure modes: Context Pollution, where experiment history biases future candidate generation; Mode Collapse, where agents stagnate in local minima due to poor exploration-exploitation balance; and Weak Collaboration, where rigid crossover strategies fail to leverage parallel search trajectories effectively. We introduce Progress-Aware Consistent Evolution (PACEvolve), a framework designed to robustly govern the agent's context and search dynamics, to address these challenges. PACEvolve combines hierarchical context management (HCM) with pruning to address context pollution; momentum-based backtracking (MBB) to escape local minima; and a self-adaptive sampling policy that unifies backtracking and crossover for dynamic search coordination (CE), allowing agents to balance internal refinement with cross-trajectory collaboration. We demonstrate that PACEvolve provides a systematic path to consistent, long-horizon self-improvement, achieving state-of-the-art results on LLM-SR and KernelBench, while discovering solutions surpassing the record on Modded NanoGPT.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/algorithmicsuperintelligence/openevolve", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.09876", "title": "Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL", "authors": [ "Yifei Shen", "Yilun Zhao", "Justice Ou", "Tinglin Huang", "Arman Cohan" ], "github": "", "project_page": "", "abstract": "Real-world clinical text-to-SQL requires reasoning over heterogeneous EHR tables, temporal windows, and patient-similarity cohorts to produce executable queries. We introduce CLINSQL, a benchmark of 633 expert-annotated tasks on MIMIC-IV v3.1 that demands multi-table joins, clinically meaningful filters, and executable SQL. Solving CLINSQL entails navigating schema metadata and clinical coding systems, handling long contexts, and composing multi-step queries beyond traditional text-to-SQL. We evaluate 22 proprietary and open-source models under Chain-of-Thought self-refinement and use rubric-based SQL analysis with execution checks that prioritize critical clinical requirements. Despite recent advances, performance remains far from clinical reliability: on the test set, GPT-5-mini attains 74.7% execution score, DeepSeek-R1 leads open-source at 69.2% and Gemini-2.5-Pro drops from 85.5% on Easy to 67.2% on Hard. Progress on CLINSQL marks tangible advances toward clinically reliable text-to-SQL for real-world EHR analytics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.09499", "title": "V-DPM: 4D Video Reconstruction with Dynamic Point Maps", "authors": [ "Edgar Sucar", "Eldar Insafutdinov", "Zihang Lai", "Andrea Vedaldi" ], "github": "", "project_page": "", "abstract": "Powerful 3D representations such as DUSt3R invariant point maps, which encode 3D shape and camera parameters, have significantly advanced feed forward 3D reconstruction. While point maps assume static scenes, Dynamic Point Maps (DPMs) extend this concept to dynamic 3D content by additionally representing scene motion. However, existing DPMs are limited to image pairs and, like DUSt3R, require post processing via optimization when more than two views are involved. We argue that DPMs are more useful when applied to videos and introduce V-DPM to demonstrate this. First, we show how to formulate DPMs for video input in a way that maximizes representational power, facilitates neural prediction, and enables reuse of pretrained models. Second, we implement these ideas on top of VGGT, a recent and powerful 3D reconstructor. Although VGGT was trained on static scenes, we show that a modest amount of synthetic data is sufficient to adapt it into an effective V-DPM predictor. Our approach achieves state of the art performance in 3D and 4D reconstruction for dynamic scenes. In particular, unlike recent dynamic extensions of VGGT such as P3, DPMs recover not only dynamic depth but also the full 3D motion of every point in the scene.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.08297", "title": "Demystifying the Slash Pattern in Attention: The Role of RoPE", "authors": [ "Yuan Cheng", "Fengzhuo Zhang", "Yunlong Hou", "Cunxiao Du", "Chao Du", "Tianyu Pang", "Aixin Sun", "Zhuoran Yang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) often exhibit slash attention patterns, where attention scores concentrate along the \u0394-th sub-diagonal for some offset \u0394. These patterns play a key role in passing information across tokens. But why do they emerge? In this paper, we demystify the emergence of these Slash-Dominant Heads (SDHs) from both empirical and theoretical perspectives. First, by analyzing open-source LLMs, we find that SDHs are intrinsic to models and generalize to out-of-distribution prompts. To explain the intrinsic emergence, we analyze the queries, keys, and Rotary Position Embedding (RoPE), which jointly determine attention scores. Our empirical analysis reveals two characteristic conditions of SDHs: (1) Queries and keys are almost rank-one, and (2) RoPE is dominated by medium- and high-frequency components. Under these conditions, queries and keys are nearly identical across tokens, and interactions between medium- and high-frequency components of RoPE give rise to SDHs. Beyond empirical evidence, we theoretically show that these conditions are sufficient to ensure the emergence of SDHs by formalizing them as our modeling assumptions. Particularly, we analyze the training dynamics of a shallow Transformer equipped with RoPE under these conditions, and prove that models trained via gradient descent exhibit SDHs. The SDHs generalize to out-of-distribution prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.06378", "title": "RigMo: Unifying Rig and Motion Learning for Generative Animation", "authors": [ "Hao Zhang", "Jiahao Luo", "Bohui Wan", "Yizhou Zhao", "Zongrui Li", "Michael Vasilkovsky", "Chaoyang Wang", "Jian Wang", "Narendra Ahuja", "Bing Zhou" ], "github": "", "project_page": "", "abstract": "Despite significant progress in 4D generation, rig and motion, the core structural and dynamic components of animation are typically modeled as separate problems. Existing pipelines rely on ground-truth skeletons and skinning weights for motion generation and treat auto-rigging as an independent process, undermining scalability and interpretability. We present RigMo, a unified generative framework that jointly learns rig and motion directly from raw mesh sequences, without any human-provided rig annotations. RigMo encodes per-vertex deformations into two compact latent spaces: a rig latent that decodes into explicit Gaussian bones and skinning weights, and a motion latent that produces time-varying SE(3) transformations. Together, these outputs define an animatable mesh with explicit structure and coherent motion, enabling feed-forward rig and motion inference for deformable objects. Beyond unified rig-motion discovery, we introduce a Motion-DiT model operating in RigMo's latent space and demonstrate that these structure-aware latents can naturally support downstream motion generation tasks. Experiments on DeformingThings4D, Objaverse-XL, and TrueBones demonstrate that RigMo learns smooth, interpretable, and physically plausible rigs, while achieving superior reconstruction and category-level generalization compared to existing auto-rigging and deformation baselines. RigMo establishes a new paradigm for unified, structure-aware, and scalable dynamic 3D modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-16", "arxiv_id": "2601.10131", "title": "M^4olGen: Multi-Agent, Multi-Stage Molecular Generation under Precise Multi-Property Constraints", "authors": [ "Yizhan Li", "Florence Cloutier", "Sifan Wu", "Ali Parviz", "Boris Knyazev", "Yan Zhang", "Glen Berseth", "Bang Liu" ], "github": "", "project_page": "", "abstract": "Generating molecules that satisfy precise numeric constraints over multiple physicochemical properties is critical and challenging. Although large language models (LLMs) are expressive, they struggle with precise multi-objective control and numeric reasoning without external structure and feedback. We introduce M olGen, a fragment-level, retrieval-augmented, two-stage framework for molecule generation under multi-property constraints. Stage I : Prototype generation: a multi-agent reasoner performs retrieval-anchored, fragment-level edits to produce a candidate near the feasible region. Stage II : RL-based fine-grained optimization: a fragment-level optimizer trained with Group Relative Policy Optimization (GRPO) applies one- or multi-hop refinements to explicitly minimize the property errors toward our target while regulating edit complexity and deviation from the prototype. A large, automatically curated dataset with reasoning chains of fragment edits and measured property deltas underpins both stages, enabling deterministic, reproducible supervision and controllable multi-hop reasoning. Unlike prior work, our framework better reasons about molecules by leveraging fragments and supports controllable refinement toward numeric targets. Experiments on generation under two sets of property constraints (QED, LogP, Molecular Weight and HOMO, LUMO) show consistent gains in validity and precise satisfaction of multi-property targets, outperforming strong LLMs and graph-based algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.08430", "title": "RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation", "authors": [ "Sunzhu Li", "Jiale Zhao", "Miteto Wei", "Huimin Ren", "Yang Zhou", "Jingwen Yang", "Shunyu Liu", "Kaike Zhang", "Wei Chen" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has driven substantial progress in reasoning-intensive domains like mathematics. However, optimizing open-ended generation remains challenging due to the lack of ground truth. While rubric-based evaluation offers a structured proxy for verification, existing methods suffer from scalability bottlenecks and coarse criteria, resulting in a supervision ceiling effect. To address this, we propose an automated Coarse-to-Fine Rubric Generation framework. By synergizing principle-guided synthesis, multi-model aggregation, and difficulty evolution, our approach produces comprehensive and highly discriminative criteria capable of capturing the subtle nuances. Based on this framework, we introduce RubricHub, a large-scale (sim110k) and multi-domain dataset. We validate its utility through a two-stage post-training pipeline comprising Rubric-based Rejection Sampling Fine-Tuning (RuFT) and Reinforcement Learning (RuRL). Experimental results demonstrate that RubricHub unlocks significant performance gains: our post-trained Qwen3-14B achieves state-of-the-art (SOTA) results on HealthBench (69.3), surpassing proprietary frontier models such as GPT-5. The code and data will be released soon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.10825", "title": "Reasoning Models Generate Societies of Thought", "authors": [ "Junsol Kim", "Shiyang Lai", "Nino Scherrer", "Blaise Ag\u00fcera y Arcas", "James Evans" ], "github": "", "project_page": "", "abstract": "Large language models have achieved remarkable capabilities across domains, yet mechanisms underlying sophisticated reasoning remain elusive. Recent reasoning models outperform comparable instruction-tuned models on complex cognitive tasks, attributed to extended computation through longer chains of thought. Here we show that enhanced reasoning emerges not from extended computation alone, but from simulating multi-agent-like interactions -- a society of thought -- which enables diversification and debate among internal cognitive perspectives characterized by distinct personality traits and domain expertise. Through quantitative analysis and mechanistic interpretability methods applied to reasoning traces, we find that reasoning models like DeepSeek-R1 and QwQ-32B exhibit much greater perspective diversity than instruction-tuned models, activating broader conflict between heterogeneous personality- and expertise-related features during reasoning. This multi-agent structure manifests in conversational behaviors, including question-answering, perspective shifts, and the reconciliation of conflicting views, and in socio-emotional roles that characterize sharp back-and-forth conversations, together accounting for the accuracy advantage in reasoning tasks. Controlled reinforcement learning experiments reveal that base models increase conversational behaviors when rewarded solely for reasoning accuracy, and fine-tuning models with conversational scaffolding accelerates reasoning improvement over base models. These findings indicate that the social organization of thought enables effective exploration of solution spaces. We suggest that reasoning models establish a computational parallel to collective intelligence in human groups, where diversity enables superior problem-solving when systematically structured, which suggests new opportunities for agent organization to harness the wisdom of crowds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.11516", "title": "Building Production-Ready Probes For Gemini", "authors": [ "J\u00e1nos Kram\u00e1r", "Joshua Engels", "Zheng Wang", "Bilal Chughtai", "Rohin Shah", "Neel Nanda", "Arthur Conmy" ], "github": "", "project_page": "", "abstract": "Frontier language model capabilities are improving rapidly. We thus need stronger mitigations against bad actors misusing increasingly powerful systems. Prior work has shown that activation probes may be a promising misuse mitigation technique, but we identify a key remaining challenge: probes fail to generalize under important production distribution shifts. In particular, we find that the shift from short-context to long-context inputs is difficult for existing probe architectures. We propose several new probe architecture that handle this long-context distribution shift. We evaluate these probes in the cyber-offensive domain, testing their robustness against various production-relevant shifts, including multi-turn conversations, static jailbreaks, and adaptive red teaming. Our results demonstrate that while multimax addresses context length, a combination of architecture choice and training on diverse distributions is required for broad generalization. Additionally, we show that pairing probes with prompted classifiers achieves optimal accuracy at a low cost due to the computational efficiency of probes. These findings have informed the successful deployment of misuse mitigation probes in user-facing instances of Gemini, Google's frontier language model. Finally, we find early positive results using AlphaEvolve to automate improvements in both probe architecture search and adaptive red teaming, showing that automating some AI safety research is already possible.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.10909", "title": "FrankenMotion: Part-level Human Motion Generation and Composition", "authors": [ "Chuqiao Li", "Xianghui Xie", "Yong Cao", "Andreas Geiger", "Gerard Pons-Moll" ], "github": "", "project_page": "", "abstract": "Human motion generation from text prompts has made remarkable progress in recent years. However, existing methods primarily rely on either sequence-level or action-level descriptions due to the absence of fine-grained, part-level motion annotations. This limits their controllability over individual body parts. In this work, we construct a high-quality motion dataset with atomic, temporally-aware part-level text annotations, leveraging the reasoning capabilities of large language models (LLMs). Unlike prior datasets that either provide synchronized part captions with fixed time segments or rely solely on global sequence labels, our dataset captures asynchronous and semantically distinct part movements at fine temporal resolution. Based on this dataset, we introduce a diffusion-based part-aware motion generation framework, namely FrankenMotion, where each body part is guided by its own temporally-structured textual prompt. This is, to our knowledge, the first work to provide atomic, temporally-aware part-level motion annotations and have a model that allows motion generation with both spatial (body part) and temporal (atomic action) control. Experiments demonstrate that FrankenMotion outperforms all previous baseline models adapted and retrained for our setting, and our model can compose motions unseen during training. Our code and dataset will be publicly available upon publication.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.11354", "title": "AstroReason-Bench: Evaluating Unified Agentic Planning across Heterogeneous Space Planning Problems", "authors": [ "Weiyi Wang", "Xinchi Chen", "Jingjing Gong", "Xuanjing Huang", "Xipeng Qiu" ], "github": "https://github.com/Mtrya/astro-reason", "project_page": "", "abstract": "Recent advances in agentic Large Language Models (LLMs) have positioned them as generalist planners capable of reasoning and acting across diverse tasks. However, existing agent benchmarks largely focus on symbolic or weakly grounded environments, leaving their performance in physics-constrained real-world domains underexplored. We introduce AstroReason-Bench, a comprehensive benchmark for evaluating agentic planning in Space Planning Problems (SPP), a family of high-stakes problems with heterogeneous objectives, strict physical constraints, and long-horizon decision-making. AstroReason-Bench integrates multiple scheduling regimes, including ground station communication and agile Earth observation, and provides a unified agent-oriented interaction protocol. Evaluating on a range of state-of-the-art open- and closed-source agentic LLM systems, we find that current agents substantially underperform specialized solvers, highlighting key limitations of generalist planning under realistic constraints. AstroReason-Bench offers a challenging and diagnostic testbed for future agentic research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Mtrya/astro-reason", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.11037", "title": "BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search", "authors": [ "Shiyu Liu", "Yongjing Yin", "Jianhao Yan", "Yunbo Tang", "Qinggang Zhang", "Bei Li", "Xin Chen", "Jingang Wang", "Xunliang Cai", "Jinsong Su" ], "github": "https://github.com/Liushiyu-0709/BAPO-Reliable-Search", "project_page": "", "abstract": "RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances accuracy with agent policies optimized via large-scale reinforcement learning, we identify a critical gap in reliability: these agents fail to recognize their reasoning boundaries and rarely admit ``I DON'T KNOW'' (IDK) even when evidence is insufficient or reasoning reaches its limit. The lack of reliability often leads to plausible but unreliable answers, introducing significant risks in many real-world scenarios. To this end, we propose Boundary-Aware Policy Optimization (BAPO), a novel RL framework designed to cultivate reliable boundary awareness without compromising accuracy. BAPO introduces two key components: (i) a group-based boundary-aware reward that encourages an IDK response only when the reasoning reaches its limit, and (ii) an adaptive reward modulator that strategically suspends this reward during early exploration, preventing the model from exploiting IDK as a shortcut. Extensive experiments on four benchmarks demonstrate that BAPO substantially enhances the overall reliability of agentic search.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Liushiyu-0709/BAPO-Reliable-Search", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.09195", "title": "ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection", "authors": [ "Tao Liu", "Taiqiang Wu", "Runming Yang", "Shaoning Sun", "Junjie Wang", "Yujiu Yang" ], "github": "", "project_page": "", "abstract": "Supervised fine-tuning (SFT) is a fundamental post-training strategy to align Large Language Models (LLMs) with human intent. However, traditional SFT often ignores the one-to-many nature of language by forcing alignment with a single reference answer, leading to the model overfitting to non-core expressions. Although our empirical analysis suggests that introducing multiple reference answers can mitigate this issue, the prohibitive data and computational costs necessitate a strategic shift: prioritizing the mitigation of single-reference overfitting over the costly pursuit of answer diversity. To achieve this, we reveal the intrinsic connection between token probability and semantic importance: high-probability tokens carry the core logical framework, while low-probability tokens are mostly replaceable expressions. Based on this insight, we propose ProFit, which selectively masks low-probability tokens to prevent surface-level overfitting. Extensive experiments confirm that ProFit consistently outperforms traditional SFT baselines on general reasoning and mathematical benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.08521", "title": "Your Group-Relative Advantage Is Biased", "authors": [ "Fengkai Yang", "Zherui Chen", "Xiaohan Wang", "Xiaodong Lu", "Jiajun Chai", "Guojun Yin", "Wei Lin", "Shuai Ma", "Fuzhen Zhuang", "Deqing Wang", "Yaodong Yang", "Jianxin Li", "Yikun Ban" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Verifier Rewards (RLVR) has emerged as a widely used approach for post-training large language models on reasoning tasks, with group-based methods such as GRPO and its variants gaining broad adoption. These methods rely on group-relative advantage estimation to avoid learned critics, yet its theoretical properties remain poorly understood. In this work, we uncover a fundamental issue of group-based RL: the group-relative advantage estimator is inherently biased relative to the true (expected) advantage. We provide the first theoretical analysis showing that it systematically underestimates advantages for hard prompts and overestimates them for easy prompts, leading to imbalanced exploration and exploitation. To address this issue, we propose History-Aware Adaptive Difficulty Weighting (HA-DW), an adaptive reweighting scheme that adjusts advantage estimates based on an evolving difficulty anchor and training dynamics. Both theoretical analysis and experiments on five mathematical reasoning benchmarks demonstrate that HA-DW consistently improves performance when integrated into GRPO and its variants. Our results suggest that correcting biased advantage estimation is critical for robust and efficient RLVR training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.10355", "title": "Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text", "authors": [ "Zhihao Xu", "Rumei Li", "Jiahuan Li", "Rongxiang Weng", "Jingang Wang", "Xunliang Cai", "Xiting Wang" ], "github": "", "project_page": "", "abstract": "Enabling Large Language Models (LLMs) to effectively utilize tools in multi-turn interactions is essential for building capable autonomous agents. However, acquiring diverse and realistic multi-turn tool-use data remains a significant challenge. In this work, we propose a novel text-based paradigm. We observe that textual corpora naturally contain rich, multi-step problem-solving experiences, which can serve as an untapped, scalable, and authentic data source for multi-turn tool-use tasks. Based on this insight, we introduce GEM, a data synthesis pipeline that enables the generation and extraction of multi-turn tool-use trajectories from text corpora through a four-stage process: relevance filtering, workflow & tool extraction, trajectory grounding, and complexity refinement. To reduce the computational cost, we further train a specialized Trajectory Synthesizer via supervised fine-tuning. This model distills the complex generation pipeline into an efficient, end-to-end trajectory generator. Experiments demonstrate that our GEM-32B achieve a 16.5% improvement on the BFCL V3 Multi-turn benchmark. Our models partially surpass the performance of models trained on \u03c4 - bench (Airline and Retail) in-domain data, highlighting the superior generalization capability derived from our text-based synthesis paradigm. Notably, our Trajectory Synthesizer matches the quality of the full pipeline while significantly reducing inference latency and costs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.11087", "title": "PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models", "authors": [ "Qiyuan Zhang", "Biao Gong", "Shuai Tan", "Zheng Zhang", "Yujun Shen", "Xing Zhu", "Yuyuan Li", "Kelu Yao", "Chunhua Shen", "Changqing Zou" ], "github": "", "project_page": "", "abstract": "Physical principles are fundamental to realistic visual simulation, but remain a significant oversight in transformer-based video generation. This gap highlights a critical limitation in rendering rigid body motion, a core tenet of classical mechanics. While computer graphics and physics-based simulators can easily model such collisions using Newton formulas, modern pretrain-finetune paradigms discard the concept of object rigidity during pixel-level global denoising. Even perfectly correct mathematical constraints are treated as suboptimal solutions (i.e., conditions) during model optimization in post-training, fundamentally limiting the physical realism of generated videos. Motivated by these considerations, we introduce, for the first time, a physics-aware reinforcement learning paradigm for video generation models that enforces physical collision rules directly in high-dimensional spaces, ensuring the physics knowledge is strictly applied rather than treated as conditions. Subsequently, we extend this paradigm to a unified framework, termed Mimicry-Discovery Cycle (MDcycle), which allows substantial fine-tuning while fully preserving the model's ability to leverage physics-grounded feedback. To validate our approach, we construct new benchmark PhysRVGBench and perform extensive qualitative and quantitative experiments to thoroughly assess its effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.11000", "title": "When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs", "authors": [ "Zhongxiang Sun", "Yi Zhan", "Chenglei Shen", "Weijie Yu", "Xiao Zhang", "Ming He", "Jun Xu" ], "github": "", "project_page": "", "abstract": "Personalized large language models (LLMs) adapt model behavior to individual users to enhance user satisfaction, yet personalization can inadvertently distort factual reasoning. We show that when personalized LLMs face factual queries, there exists a phenomenon where the model generates answers aligned with a user's prior history rather than the objective truth, resulting in personalization-induced hallucinations that degrade factual reliability and may propagate incorrect beliefs, due to representational entanglement between personalization and factual representations. To address this issue, we propose Factuality-Preserving Personalized Steering (FPPS), a lightweight inference-time approach that mitigates personalization-induced factual distortions while preserving personalized behavior. We further introduce PFQABench, the first benchmark designed to jointly evaluate factual and personalized question answering under personalization. Experiments across multiple LLM backbones and personalization methods show that FPPS substantially improves factual accuracy while maintaining personalized performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.11404", "title": "ACoT-VLA: Action Chain-of-Thought for Vision-Language-Action Models", "authors": [ "Linqing Zhong", "Yi Liu", "Yifei Wei", "Ziyu Xiong", "Maoqing Yao", "Si Liu", "Guanghui Ren" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models have emerged as essential generalist robot policies for diverse manipulation tasks, conventionally relying on directly translating multimodal inputs into actions via Vision-Language Model (VLM) embeddings. Recent advancements have introduced explicit intermediary reasoning, such as sub-task prediction (language) or goal image synthesis (vision), to guide action generation. However, these intermediate reasoning are often indirect and inherently limited in their capacity to convey the full, granular information required for precise action execution. Instead, we posit that the most effective form of reasoning is one that deliberates directly in the action space. We introduce Action Chain-of-Thought (ACoT), a paradigm where the reasoning process itself is formulated as a structured sequence of coarse action intents that guide the final policy. In this paper, we propose ACoT-VLA, a novel architecture that materializes the ACoT paradigm. Specifically, we introduce two complementary components: an Explicit Action Reasoner (EAR) and Implicit Action Reasoner (IAR). The former proposes coarse reference trajectories as explicit action-level reasoning steps, while the latter extracts latent action priors from internal representations of multimodal input, co-forming an ACoT that conditions the downstream action head to enable grounded policy learning. Extensive experiments in real-world and simulation environments demonstrate the superiority of our proposed method, which achieves 98.5%, 84.1%, and 47.4% on LIBERO, LIBERO-Plus and VLABench, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.09255", "title": "PhyRPR: Training-Free Physics-Constrained Video Generation", "authors": [ "Yibo Zhao", "Hengjia Li", "Xiaofei He", "Boxi Wu" ], "github": "", "project_page": "", "abstract": "Recent diffusion-based video generation models can synthesize visually plausible videos, yet they often struggle to satisfy physical constraints. A key reason is that most existing approaches remain single-stage: they entangle high-level physical understanding with low-level visual synthesis, making it hard to generate content that require explicit physical reasoning. To address this limitation, we propose a training-free three-stage pipeline,PhyRPR:Phy\\uline{Reason}--Phy\\uline{Plan}--Phy\\uline{Refine}, which decouples physical understanding from visual synthesis. Specifically, PhyReason uses a large multimodal model for physical state reasoning and an image generator for keyframe synthesis; PhyPlan deterministically synthesizes a controllable coarse motion scaffold; and PhyRefine injects this scaffold into diffusion sampling via a latent fusion strategy to refine appearance while preserving the planned dynamics. This staged design enables explicit physical control during generation. Extensive experiments under physics constraints show that our method consistently improves physical plausibility and motion controllability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.11496", "title": "The Poisoned Apple Effect: Strategic Manipulation of Mediated Markets via Technology Expansion of AI Agents", "authors": [ "Eilam Shapira", "Roi Reichart", "Moshe Tennenholtz" ], "github": "", "project_page": "", "abstract": "The integration of AI agents into economic markets fundamentally alters the landscape of strategic interaction. We investigate the economic implications of expanding the set of available technologies in three canonical game-theoretic settings: bargaining (resource division), negotiation (asymmetric information trade), and persuasion (strategic information transmission). We find that simply increasing the choice of AI delegates can drastically shift equilibrium payoffs and regulatory outcomes, often creating incentives for regulators to proactively develop and release technologies. Conversely, we identify a strategic phenomenon termed the \"Poisoned Apple\" effect: an agent may release a new technology, which neither they nor their opponent ultimately uses, solely to manipulate the regulator's choice of market design in their favor. This strategic release improves the releaser's welfare at the expense of their opponent and the regulator's fairness objectives. Our findings demonstrate that static regulatory frameworks are vulnerable to manipulation via technology expansion, necessitating dynamic market designs that adapt to the evolving landscape of AI capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.11227", "title": "Language of Thought Shapes Output Diversity in Large Language Models", "authors": [ "Shaoyang Xu", "Wenxuan Zhang" ], "github": "https://github.com/iNLP-Lab/Multilingual-LoT-Diversity", "project_page": "", "abstract": "Output diversity is crucial for Large Language Models as it underpins pluralism and creativity. In this work, we reveal that controlling the language used during model thinking-the language of thought-provides a novel and structural source of output diversity. Our preliminary study shows that different thinking languages occupy distinct regions in a model's thinking space. Based on this observation, we study two repeated sampling strategies under multilingual thinking-Single-Language Sampling and Mixed-Language Sampling-and conduct diversity evaluation on outputs that are controlled to be in English, regardless of the thinking language used. Across extensive experiments, we demonstrate that switching the thinking language from English to non-English languages consistently increases output diversity, with a clear and consistent positive correlation such that languages farther from English in the thinking space yield larger gains. We further show that aggregating samples across multiple thinking languages yields additional improvements through compositional effects, and that scaling sampling with linguistic heterogeneity expands the model's diversity ceiling. Finally, we show that these findings translate into practical benefits in pluralistic alignment scenarios, leading to broader coverage of cultural knowledge and value orientations in LLM outputs. Our code is publicly available at https://github.com/iNLP-Lab/Multilingual-LoT-Diversity.", "github_manual": "", "github_regex": "https://github.com/iNLP-Lab/Multilingual-LoT-Diversity", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.07812", "title": "More Images, More Problems? A Controlled Analysis of VLM Failure Modes", "authors": [ "Anurag Das", "Adrian Bulat", "Alberto Baldrati", "Ioannis Maniadis Metaxas", "Bernt Schiele", "Georgios Tzimiropoulos", "Brais Martinez" ], "github": "https://github.com/anurag-198/MIMIC", "project_page": "", "abstract": "Large Vision Language Models (LVLMs) have demonstrated remarkable capabilities, yet their proficiency in understanding and reasoning over multiple images remains largely unexplored. While existing benchmarks have initiated the evaluation of multi-image models, a comprehensive analysis of their core weaknesses and their causes is still lacking. In this work, we introduce MIMIC (Multi-Image Model Insights and Challenges), a new benchmark designed to rigorously evaluate the multi-image capabilities of LVLMs. Using MIMIC, we conduct a series of diagnostic experiments that reveal pervasive issues: LVLMs often fail to aggregate information across images and struggle to track or attend to multiple concepts simultaneously. To address these failures, we propose two novel complementary remedies. On the data side, we present a procedural data-generation strategy that composes single-image annotations into rich, targeted multi-image training examples. On the optimization side, we analyze layer-wise attention patterns and derive an attention-masking scheme tailored for multi-image inputs. Experiments substantially improved cross-image aggregation, while also enhancing performance on existing multi-image benchmarks, outperforming prior state of the art across tasks. Data and code will be made available at https://github.com/anurag-198/MIMIC.", "github_manual": "", "github_regex": "https://github.com/anurag-198/MIMIC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.11514", "title": "ShapeR: Robust Conditional 3D Shape Generation from Casual Captures", "authors": [ "Yawar Siddiqui", "Duncan Frost", "Samir Aroudj", "Armen Avetisyan", "Henry Howard-Jenkins", "Daniel DeTone", "Pierre Moulon", "Qirui Wu", "Zhengqin Li", "Julian Straub", "Richard Newcombe", "Jakob Engel" ], "github": "", "project_page": "", "abstract": "Recent advances in 3D shape generation have achieved impressive results, but most existing methods rely on clean, unoccluded, and well-segmented inputs. Such conditions are rarely met in real-world scenarios. We present ShapeR, a novel approach for conditional 3D object shape generation from casually captured sequences. Given an image sequence, we leverage off-the-shelf visual-inertial SLAM, 3D detection algorithms, and vision-language models to extract, for each object, a set of sparse SLAM points, posed multi-view images, and machine-generated captions. A rectified flow transformer trained to effectively condition on these modalities then generates high-fidelity metric 3D shapes. To ensure robustness to the challenges of casually captured data, we employ a range of techniques including on-the-fly compositional augmentations, a curriculum training scheme spanning object- and scene-level datasets, and strategies to handle background clutter. Additionally, we introduce a new evaluation benchmark comprising 178 in-the-wild objects across 7 real-world scenes with geometry annotations. Experiments show that ShapeR significantly outperforms existing approaches in this challenging setting, achieving an improvement of 2.7x in Chamfer distance compared to state of the art.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.09636", "title": "PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records", "authors": [ "Yibo Lyu", "Gongwei Chen", "Rui Shao", "Weili Guan", "Liqiang Nie" ], "github": "https://github.com/JiuTian-VL/PersonalAlign", "project_page": "", "abstract": "While GUI agents have shown strong performance under explicit and completion instructions, real-world deployment requires aligning with users' more complex implicit intents. In this work, we highlight Hierarchical Implicit Intent Alignment for Personalized GUI Agent (PersonalAlign), a new agent task that requires agents to leverage long-term user records as persistent context to resolve omitted preferences in vague instructions and anticipate latent routines by user state for proactive assistance. To facilitate this study, we introduce AndroidIntent, a benchmark designed to evaluate agents' ability in resolving vague instructions and providing proactive suggestions through reasoning over long-term user records. We annotated 775 user-specific preferences and 215 routines from 20k long-term records across different users for evaluation. Furthermore, we introduce Hierarchical Intent Memory Agent (HIM-Agent), which maintains a continuously updating personal memory and hierarchically organizes user preferences and routines for personalization. Finally, we evaluate a range of GUI agents on AndroidIntent, including GPT-5, Qwen3-VL, and UI-TARS, further results show that HIM-Agent significantly improves both execution and proactive performance by 15.7% and 7.3%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JiuTian-VL/PersonalAlign", "github_api_added_by": "auto" }, { "date": "2026-01-19", "arxiv_id": "2601.09001", "title": "Entropy Sentinel: Continuous LLM Accuracy Monitoring from Decoding Entropy Traces in STEM", "authors": [ "Pedro Memoli Buffa", "Luciano Del Corro" ], "github": "", "project_page": "", "abstract": "Deploying LLMs raises two coupled challenges: (1) monitoring - estimating where a model underperforms as traffic and domains drift - and (2) improvement - prioritizing data acquisition to close the largest performance gaps. We test whether an inference-time signal can estimate slice-level accuracy under domain shift. For each response, we compute an output-entropy profile from final-layer next-token probabilities (from top-k logprobs) and summarize it with eleven statistics. A lightweight classifier predicts instance correctness, and averaging predicted probabilities yields a domain-level accuracy estimate. We evaluate on ten STEM reasoning benchmarks with exhaustive train/test compositions (k in {1,2,3,4}; all \"10 choose k\" combinations), across nine LLMs from six families (3B-20B). Estimates often track held-out benchmark accuracy, and several models show near-monotonic ordering of domains. Output-entropy profiles are thus an accessible signal for scalable monitoring and for targeting data acquisition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.11044", "title": "AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts", "authors": [ "Keyu Li", "Junhao Shi", "Yang Xiao", "Mohan Jiang", "Jie Sun", "Yunze Wu", "Shijie Xia", "Xiaojie Cai", "Tianze Xu", "Weiye Si", "Wenjie Li", "Dequan Wang", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/AgencyBench", "project_page": "", "abstract": "Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. However, existing benchmarks remain focused on single agentic capability, failing to capture long-horizon real-world scenarios. Moreover, the reliance on human-in-the-loop feedback for realistic tasks creates a scalability bottleneck, hindering automated rollout collection and evaluation. To bridge this gap, we introduce AgencyBench, a comprehensive benchmark derived from daily AI usage, evaluating 6 core agentic capabilities across 32 real-world scenarios, comprising 138 tasks with specific queries, deliverables, and rubrics. These scenarios require an average of 90 tool calls, 1 million tokens, and hours of execution time to resolve. To enable automated evaluation, we employ a user simulation agent to provide iterative feedback, and a Docker sandbox to conduct visual and functional rubric-based assessment. Experiments reveal that closed-source models significantly outperform open-source models (48.4% vs 32.1%). Further analysis reveals significant disparities across models in resource efficiency, feedback-driven self-correction, and specific tool-use preferences. Finally, we investigate the impact of agentic scaffolds, observing that proprietary models demonstrate superior performance within their native ecosystems (e.g., Claude-4.5-Opus via Claude-Agent-SDK), while open-source models exhibit distinct performance peaks, suggesting potential optimization for specific execution frameworks. AgencyBench serves as a critical testbed for next-generation agents, highlighting the necessity of co-optimizing model architecture with agentic frameworks. We believe this work sheds light on the future direction of autonomous agents, and we release the full benchmark and evaluation toolkit at https://github.com/GAIR-NLP/AgencyBench.", "github_manual": "", "github_regex": "https://github.com/GAIR-NLP/AgencyBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.10781", "title": "Future Optical Flow Prediction Improves Robot Control & Video Generation", "authors": [ "Kanchana Ranasinghe", "Honglu Zhou", "Yu Fang", "Luyu Yang", "Le Xue", "Ran Xu", "Caiming Xiong", "Silvio Savarese", "Michael S Ryoo", "Juan Carlos Niebles" ], "github": "", "project_page": "", "abstract": "Future motion representations, such as optical flow, offer immense value for control and generative tasks. However, forecasting generalizable spatially dense motion representations remains a key challenge, and learning such forecasting from noisy, real-world data remains relatively unexplored. We introduce FOFPred, a novel language-conditioned optical flow forecasting model featuring a unified Vision-Language Model (VLM) and Diffusion architecture. This unique combination enables strong multimodal reasoning with pixel-level generative fidelity for future motion prediction. Our model is trained on web-scale human activity data-a highly scalable but unstructured source. To extract meaningful signals from this noisy video-caption data, we employ crucial data preprocessing techniques and our unified architecture with strong image pretraining. The resulting trained model is then extended to tackle two distinct downstream tasks in control and generation. Evaluations across robotic manipulation and video generation under language-driven settings establish the cross-domain versatility of FOFPred, confirming the value of a unified VLM-Diffusion architecture and scalable learning from diverse web data for future optical flow prediction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-19", "arxiv_id": "2601.10922", "title": "What Matters in Data Curation for Multimodal Reasoning? Insights from the DCVLR Challenge", "authors": [ "Yosub Shin", "Michael Buriek", "Boris Sobolev", "Pavel Bushuyeu", "Vikas Kumar", "Haoyang Xu", "Samuel Watson", "Igor Molybog" ], "github": "https://github.com/penfever/data-preproc", "project_page": "", "abstract": "We study data curation for multimodal reasoning through the NeurIPS 2025 Data Curation for Vision-Language Reasoning (DCVLR) challenge, which isolates dataset selection by fixing the model and training protocol. Using a compact curated dataset derived primarily from Walton Multimodal Cold Start, our submission placed first in the challenge. Through post-competition ablations, we show that difficulty-based example selection on an aligned base dataset is the dominant driver of performance gains. Increasing dataset size does not reliably improve mean accuracy under the fixed training recipe, but mainly reduces run-to-run variance, while commonly used diversity and synthetic augmentation heuristics provide no additional benefit and often degrade performance. These results characterize DCVLR as a saturation-regime evaluation and highlight the central role of alignment and difficulty in data-efficient multimodal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/penfever/data-preproc", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-20", "arxiv_id": "2601.08808", "title": "Multiplex Thinking: Reasoning via Token-wise Branch-and-Merge", "authors": [ "Yao Tang", "Li Dong", "Yaru Hao", "Qingxiu Dong", "Furu Wei", "Jiatao Gu" ], "github": "https://github.com/GMLR-Penn/Multiplex-Thinking", "project_page": "", "abstract": "Large language models often solve complex reasoning tasks more effectively with Chain-of-Thought (CoT), but at the cost of long, low-bandwidth token sequences. Humans, by contrast, often reason softly by maintaining a distribution over plausible next steps. Motivated by this, we propose Multiplex Thinking, a stochastic soft reasoning mechanism that, at each thinking step, samples K candidate tokens and aggregates their embeddings into a single continuous multiplex token. This preserves the vocabulary embedding prior and the sampling dynamics of standard discrete generation, while inducing a tractable probability distribution over multiplex rollouts. Consequently, multiplex trajectories can be directly optimized with on-policy reinforcement learning (RL). Importantly, Multiplex Thinking is self-adaptive: when the model is confident, the multiplex token is nearly discrete and behaves like standard CoT; when it is uncertain, it compactly represents multiple plausible next steps without increasing sequence length. Across challenging math reasoning benchmarks, Multiplex Thinking consistently outperforms strong discrete CoT and RL baselines from Pass@1 through Pass@1024, while producing shorter sequences. The code and checkpoints are available at https://github.com/GMLR-Penn/Multiplex-Thinking.", "github_manual": "", "github_regex": "https://github.com/GMLR-Penn/Multiplex-Thinking", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-20", "arxiv_id": "2601.11077", "title": "ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development", "authors": [ "Jie Yang", "Honglin Guo", "Li Ji", "Jiazheng Zhou", "Rui Zheng", "Zhikai Lei", "Shuo Zhang", "Zhiheng Xi", "Shichun Liu", "Yuxin Wang", "Bo Wang", "Yining Zheng", "Tao Gui", "Xipeng Qiu" ], "github": "https://github.com/OpenMOSS/ABC-Bench", "project_page": "", "abstract": "The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository-level, and execution-driven problem solving. However, current benchmarks predominantly evaluate code logic in static contexts, neglecting the dynamic, full-process requirements of real-world engineering, particularly in backend development which demands rigorous environment configuration and service deployment. To address this gap, we introduce ABC-Bench, a benchmark explicitly designed to evaluate agentic backend coding within a realistic, executable workflow. Using a scalable automated pipeline, we curated 224 practical tasks spanning 8 languages and 19 frameworks from open-source repositories. Distinct from previous evaluations, ABC-Bench require the agents to manage the entire development lifecycle from repository exploration to instantiating containerized services and pass the external end-to-end API tests. Our extensive evaluation reveals that even state-of-the-art models struggle to deliver reliable performance on these holistic tasks, highlighting a substantial disparity between current model capabilities and the demands of practical backend engineering. Our code is available at https://github.com/OpenMOSS/ABC-Bench.", "github_manual": "", "github_regex": "https://github.com/OpenMOSS/ABC-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-20", "arxiv_id": "2601.10387", "title": "The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models", "authors": [ "Christina Lu", "Jack Gallagher", "Jonathan Michala", "Kyle Fish", "Jack Lindsey" ], "github": "https://github.com/safety-research/assistant-axis", "project_page": "", "abstract": "Large language models can represent a variety of personas but typically default to a helpful Assistant identity cultivated during post-training. We investigate the structure of the space of model personas by extracting activation directions corresponding to diverse character archetypes. Across several different models, we find that the leading component of this persona space is an \"Assistant Axis,\" which captures the extent to which a model is operating in its default Assistant mode. Steering towards the Assistant direction reinforces helpful and harmless behavior; steering away increases the model's tendency to identify as other entities. Moreover, steering away with more extreme values often induces a mystical, theatrical speaking style. We find this axis is also present in pre-trained models, where it primarily promotes helpful human archetypes like consultants and coaches and inhibits spiritual ones. Measuring deviations along the Assistant Axis predicts \"persona drift,\" a phenomenon where models slip into exhibiting harmful or bizarre behaviors that are uncharacteristic of their typical persona. We find that persona drift is often driven by conversations demanding meta-reflection on the model's processes or featuring emotionally vulnerable users. We show that restricting activations to a fixed region along the Assistant Axis can stabilize model behavior in these scenarios -- and also in the face of adversarial persona-based jailbreaks. Our results suggest that post-training steers models toward a particular region of persona space but only loosely tethers them to it, motivating work on training and steering strategies that more deeply anchor models to a coherent persona.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/safety-research/assistant-axis", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-20", "arxiv_id": "2601.11096", "title": "CoDance: An Unbind-Rebind Paradigm for Robust Multi-Subject Animation", "authors": [ "Shuai Tan", "Biao Gong", "Ke Ma", "Yutong Feng", "Qiyuan Zhang", "Yan Wang", "Yujun Shen", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "Character image animation is gaining significant importance across various domains, driven by the demand for robust and flexible multi-subject rendering. While existing methods excel in single-person animation, they struggle to handle arbitrary subject counts, diverse character types, and spatial misalignment between the reference image and the driving poses. We attribute these limitations to an overly rigid spatial binding that forces strict pixel-wise alignment between the pose and reference, and an inability to consistently rebind motion to intended subjects. To address these challenges, we propose CoDance, a novel Unbind-Rebind framework that enables the animation of arbitrary subject counts, types, and spatial configurations conditioned on a single, potentially misaligned pose sequence. Specifically, the Unbind module employs a novel pose shift encoder to break the rigid spatial binding between the pose and the reference by introducing stochastic perturbations to both poses and their latent features, thereby compelling the model to learn a location-agnostic motion representation. To ensure precise control and subject association, we then devise a Rebind module, leveraging semantic guidance from text prompts and spatial guidance from subject masks to direct the learned motion to intended characters. Furthermore, to facilitate comprehensive evaluation, we introduce a new multi-subject CoDanceBench. Extensive experiments on CoDanceBench and existing datasets show that CoDance achieves SOTA performance, exhibiting remarkable generalization across diverse subjects and spatial layouts. The code and weights will be open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-20", "arxiv_id": "2601.10108", "title": "SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature", "authors": [ "Yiming Ren", "Junjie Wang", "Yuxin Meng", "Yihang Shi", "Zhiqiang Lin", "Ruihang Chu", "Yiran Xu", "Ziming Li", "Yunfei Zhao", "Zihan Wang", "Yu Qiao", "Ruiming Tang", "Minghao Liu", "Yujiu Yang" ], "github": "https://github.com/IIGROUP/sin-bench", "project_page": "", "abstract": "Evaluating whether multimodal large language models truly understand long-form scientific papers remains challenging: answer-only metrics and synthetic \"Needle-In-A-Haystack\" tests often reward answer matching without requiring a causal, evidence-linked reasoning trace in the document. We propose the \"Fish-in-the-Ocean\" (FITO) paradigm, which requires models to construct explicit cross-modal evidence chains within native scientific documents. To operationalize FITO, we build SIN-Data, a scientific interleaved corpus that preserves the native interleaving of text and figures. On top of it, we construct SIN-Bench with four progressive tasks covering evidence discovery (SIN-Find), hypothesis verification (SIN-Verify), grounded QA (SIN-QA), and evidence-anchored synthesis (SIN-Summary). We further introduce \"No Evidence, No Score\", scoring predictions when grounded to verifiable anchors and diagnosing evidence quality via matching, relevance, and logic. Experiments on eight MLLMs show that grounding is the primary bottleneck: Gemini-3-pro achieves the best average overall score (0.573), while GPT-5 attains the highest SIN-QA answer accuracy (0.767) but underperforms on evidence-aligned overall scores, exposing a gap between correctness and traceable support.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/IIGROUP/sin-bench", "github_api_added_by": "auto" }, { "date": "2026-01-20", "arxiv_id": "2601.11061", "title": "Spurious Rewards Paradox: Mechanistically Understanding How RLVR Activates Memorization Shortcuts in LLMs", "authors": [ "Lecheng Yan", "Ruizhe Li", "Guanhua Chen", "Qing Li", "Jiahui Geng", "Wenxi Li", "Vincent Wang", "Chris Lee" ], "github": "https://github.com/idwts/How-RLVR-Activates-Memorization-Shortcuts", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) is highly effective for enhancing LLM reasoning, yet recent evidence shows models like Qwen 2.5 achieve significant gains even with spurious or incorrect rewards. We investigate this phenomenon and identify a \"Perplexity Paradox\": spurious RLVR triggers a divergence where answer-token perplexity drops while prompt-side coherence degrades, suggesting the model is bypassing reasoning in favor of memorization. Using Path Patching, Logit Lens, JSD analysis, and Neural Differential Equations, we uncover a hidden Anchor-Adapter circuit that facilitates this shortcut. We localize a Functional Anchor in the middle layers (L18-20) that triggers the retrieval of memorized solutions, followed by Structural Adapters in later layers (L21+) that transform representations to accommodate the shortcut signal. Finally, we demonstrate that scaling specific MLP keys within this circuit allows for bidirectional causal steering-artificially amplifying or suppressing contamination-driven performance. Our results provide a mechanistic roadmap for identifying and mitigating data contamination in RLVR-tuned models. Code is available at https://github.com/idwts/How-RLVR-Activates-Memorization-Shortcuts.", "github_manual": "", "github_regex": "https://github.com/idwts/How-RLVR-Activates-Memorization-Shortcuts", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-20", "arxiv_id": "2601.10880", "title": "Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation", "authors": [ "Chongcong Jiang", "Tianxingjian Ding", "Chuhan Song", "Jiachen Tu", "Ziyang Yan", "Yihua Shao", "Zhenyi Wang", "Yuzhang Shang", "Tianyu Han", "Yu Tian" ], "github": "https://github.com/AIM-Research-Lab/Medical-SAM3", "project_page": "", "abstract": "Promptable segmentation foundation models such as SAM3 have demonstrated strong generalization capabilities through interactive and concept-based prompting. However, their direct applicability to medical image segmentation remains limited by severe domain shifts, the absence of privileged spatial prompts, and the need to reason over complex anatomical and volumetric structures. Here we present Medical SAM3, a foundation model for universal prompt-driven medical image segmentation, obtained by fully fine-tuning SAM3 on large-scale, heterogeneous 2D and 3D medical imaging datasets with paired segmentation masks and text prompts. Through a systematic analysis of vanilla SAM3, we observe that its performance degrades substantially on medical data, with its apparent competitiveness largely relying on strong geometric priors such as ground-truth-derived bounding boxes. These findings motivate full model adaptation beyond prompt engineering alone. By fine-tuning SAM3's model parameters on 33 datasets spanning 10 medical imaging modalities, Medical SAM3 acquires robust domain-specific representations while preserving prompt-driven flexibility. Extensive experiments across organs, imaging modalities, and dimensionalities demonstrate consistent and significant performance gains, particularly in challenging scenarios characterized by semantic ambiguity, complex morphology, and long-range 3D context. Our results establish Medical SAM3 as a universal, text-guided segmentation foundation model for medical imaging and highlight the importance of holistic model adaptation for achieving robust prompt-driven segmentation under severe domain shift. Code and model will be made available at https://github.com/AIM-Research-Lab/Medical-SAM3.", "github_manual": "", "github_regex": "https://github.com/AIM-Research-Lab/Medical-SAM3", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-20", "arxiv_id": "2601.08441", "title": "YaPO: Learnable Sparse Activation Steering Vectors for Domain Adaptation", "authors": [ "Abdelaziz Bounhar", "Rania Hossam Elmohamady Elbadry", "Hadi Abdine", "Preslav Nakov", "Michalis Vazirgiannis", "Guokan Shang" ], "github": "https://github.com/MBZUAI-Paris/YaPO", "project_page": "", "abstract": "Steering Large Language Models (LLMs) through activation interventions has emerged as a lightweight alternative to fine-tuning for alignment and personalization. Recent work on Bi-directional Preference Optimization (BiPO) shows that dense steering vectors can be learned directly from preference data in a Direct Preference Optimization (DPO) fashion, enabling control over truthfulness, hallucinations, and safety behaviors. However, dense steering vectors often entangle multiple latent factors due to neuron multi-semanticity, limiting their effectiveness and stability in fine-grained settings such as cultural alignment, where closely related values and behaviors (e.g., among Middle Eastern cultures) must be distinguished. In this paper, we propose Yet another Policy Optimization (YaPO), a reference-free method that learns sparse steering vectors in the latent space of a Sparse Autoencoder (SAE). By optimizing sparse codes, YaPO produces disentangled, interpretable, and efficient steering directions. Empirically, we show that YaPO converges faster, achieves stronger performance, and exhibits improved training stability compared to dense steering baselines. Beyond cultural alignment, YaPO generalizes to a range of alignment-related behaviors, including hallucination, wealth-seeking, jailbreak, and power-seeking. Importantly, YaPO preserves general knowledge, with no measurable degradation on MMLU. Overall, our results show that YaPO provides a general recipe for efficient, stable, and fine-grained alignment of LLMs, with broad applications to controllability and domain adaptation. The associated code and data are publicly availablehttps://github.com/MBZUAI-Paris/YaPO.", "github_manual": "", "github_regex": "https://github.com/MBZUAI-Paris/YaPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-20", "arxiv_id": "2601.09512", "title": "CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion", "authors": [ "Ralf R\u00f6mer", "Yi Zhang", "Angela P. Schoellig" ], "github": "", "project_page": "", "abstract": "To teach robots complex manipulation tasks, it is now a common practice to fine-tune a pre-trained vision-language-action model (VLA) on task-specific data. However, since this recipe updates existing representations, it is unsuitable for long-term operation in the real world, where robots must continually adapt to new tasks and environments while retaining the knowledge they have already acquired. Existing continual learning methods for robotics commonly require storing previous data (exemplars), struggle with long task sequences, or rely on task identifiers for deployment. To address these limitations, we propose CLARE, a general, parameter-efficient framework for exemplar-free continual learning with VLAs. CLARE introduces lightweight modular adapters into selected feedforward layers and autonomously expands the model only where necessary when learning a new task, guided by layer-wise feature similarity. During deployment, an autoencoder-based routing mechanism dynamically activates the most relevant adapters without requiring task labels. Through extensive experiments on the LIBERO benchmark, we show that CLARE achieves high performance on new tasks without catastrophic forgetting of earlier tasks, significantly outperforming even exemplar-based methods. Code and data are available at https://tum-lsy.github.io/clare.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-20", "arxiv_id": "2601.11425", "title": "PubMed-OCR: PMC Open Access OCR Annotations", "authors": [ "Hunter Heidenreich", "Yosheb Getachew", "Olivia Dinica", "Ben Elliott" ], "github": "", "project_page": "", "abstract": "PubMed-OCR is an OCR-centric corpus of scientific articles derived from PubMed Central Open Access PDFs. Each page image is annotated with Google Cloud Vision and released in a compact JSON schema with word-, line-, and paragraph-level bounding boxes. The corpus spans 209.5K articles (1.5M pages; ~1.3B words) and supports layout-aware modeling, coordinate-grounded QA, and evaluation of OCR-dependent pipelines. We analyze corpus characteristics (e.g., journal coverage and detected layout features) and discuss limitations, including reliance on a single OCR engine and heuristic line reconstruction. We release the data and schema to facilitate downstream research and invite extensions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-20", "arxiv_id": "2601.11004", "title": "NAACL: Noise-AwAre Verbal Confidence Calibration for LLMs in RAG Systems", "authors": [ "Jiayu Liu", "Rui Wang", "Qing Zong", "Qingcheng Zeng", "Tianshi Zheng", "Haochen Shi", "Dadi Guo", "Baixuan Xu", "Chunyang Li", "Yangqiu Song" ], "github": "", "project_page": "", "abstract": "Accurately assessing model confidence is essential for deploying large language models (LLMs) in mission-critical factual domains. While retrieval-augmented generation (RAG) is widely adopted to improve grounding, confidence calibration in RAG settings remains poorly understood. We conduct a systematic study across four benchmarks, revealing that LLMs exhibit poor calibration performance due to noisy retrieved contexts. Specifically, contradictory or irrelevant evidence tends to inflate the model's false certainty, leading to severe overconfidence. To address this, we propose NAACL Rules (Noise-AwAre Confidence CaLibration Rules) to provide a principled foundation for resolving overconfidence under noise. We further design NAACL, a noise-aware calibration framework that synthesizes supervision from about 2K HotpotQA examples guided by these rules. By performing supervised fine-tuning (SFT) with this data, NAACL equips models with intrinsic noise awareness without relying on stronger teacher models. Empirical results show that NAACL yields substantial gains, improving ECE scores by 10.9% in-domain and 8.0% out-of-domain. By bridging the gap between retrieval noise and verbal calibration, NAACL paves the way for both accurate and epistemically reliable LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.11969", "title": "MemoryRewardBench: Benchmarking Reward Models for Long-Term Memory Management in Large Language Models", "authors": [ "Zecheng Tang", "Baibei Ji", "Ruoxi Sun", "Haitian Wang", "WangJie You", "Zhang Yijun", "Wenpeng Zhu", "Ji Qi", "Juntao Li", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Existing works increasingly adopt memory-centric mechanisms to process long contexts in a segment manner, and effective memory management is one of the key capabilities that enables large language models to effectively propagate information across the entire sequence. Therefore, leveraging reward models (RMs) to automatically and reliably evaluate memory quality is critical. In this work, we introduce MemoryRewardBench, the first benchmark to systematically study the ability of RMs to evaluate long-term memory management processes. MemoryRewardBench covers both long-context comprehension and long-form generation tasks, featuring 10 distinct settings with different memory management patterns, with context length ranging from 8K to 128K tokens. Evaluations on 13 cutting-edge RMs indicate a diminishing performance gap between open-source and proprietary models, with newer-generation models consistently outperforming their predecessors regardless of parameter count. We further expose the capabilities and fundamental limitations of current RMs in evaluating LLM memory management across diverse settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.11522", "title": "UniX: Unifying Autoregression and Diffusion for Chest X-Ray Understanding and Generation", "authors": [ "Ruiheng Zhang", "Jingfeng Yao", "Huangxuan Zhao", "Hao Yan", "Xiao He", "Lei Chen", "Zhou Wei", "Yong Luo", "Zengmao Wang", "Lefei Zhang", "Dacheng Tao", "Bo Du" ], "github": "https://github.com/ZrH42/UniX", "project_page": "", "abstract": "Despite recent progress, medical foundation models still struggle to unify visual understanding and generation, as these tasks have inherently conflicting goals: semantic abstraction versus pixel-level reconstruction. Existing approaches, typically based on parameter-shared autoregressive architectures, frequently lead to compromised performance in one or both tasks. To address this, we present UniX, a next-generation unified medical foundation model for chest X-ray understanding and generation. UniX decouples the two tasks into an autoregressive branch for understanding and a diffusion branch for high-fidelity generation. Crucially, a cross-modal self-attention mechanism is introduced to dynamically guide the generation process with understanding features. Coupled with a rigorous data cleaning pipeline and a multi-stage training strategy, this architecture enables synergistic collaboration between tasks while leveraging the strengths of diffusion models for superior generation. On two representative benchmarks, UniX achieves a 46.1% improvement in understanding performance (Micro-F1) and a 24.2% gain in generation quality (FD-RadDino), using only a quarter of the parameters of LLM-CXR. By achieving performance on par with task-specific models, our work establishes a scalable paradigm for synergistic medical image understanding and generation. Codes and models are available at https://github.com/ZrH42/UniX.", "github_manual": "", "github_regex": "https://github.com/ZrH42/UniX", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.11655", "title": "Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey", "authors": [ "Caihua Li", "Lianghong Guo", "Yanlin Wang", "Daya Guo", "Wei Tao", "Zhenyu Shan", "Mingwei Liu", "Jiachi Chen", "Haoyu Song", "Duyu Tang", "Hongyu Zhang", "Zibin Zheng" ], "github": "https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution", "project_page": "", "abstract": "Issue resolution, a complex Software Engineering (SWE) task integral to real-world development, has emerged as a compelling challenge for artificial intelligence. The establishment of benchmarks like SWE-bench revealed this task as profoundly difficult for large language models, thereby significantly accelerating the evolution of autonomous coding agents. This paper presents a systematic survey of this emerging domain. We begin by examining data construction pipelines, covering automated collection and synthesis approaches. We then provide a comprehensive analysis of methodologies, spanning training-free frameworks with their modular components to training-based techniques, including supervised fine-tuning and reinforcement learning. Subsequently, we discuss critical analyses of data quality and agent behavior, alongside practical applications. Finally, we identify key challenges and outline promising directions for future research. An open-source repository is maintained at https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution to serve as a dynamic resource in this field.", "github_manual": "", "github_regex": "https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.13288", "title": "A BERTology View of LLM Orchestrations: Token- and Layer-Selective Probes for Efficient Single-Pass Classification", "authors": [ "Gonzalo Ariel Meyoyan", "Luciano Del Corro" ], "github": "", "project_page": "", "abstract": "Production LLM systems often rely on separate models for safety and other classification-heavy steps, increasing latency, VRAM footprint, and operational complexity. We instead reuse computation already paid for by the serving LLM: we train lightweight probes on its hidden states and predict labels in the same forward pass used for generation. We frame classification as representation selection over the full token-layer hidden-state tensor, rather than committing to a fixed token or fixed layer (e.g., first-token logits or final-layer pooling). To implement this, we introduce a two-stage aggregator that (i) summarizes tokens within each layer and (ii) aggregates across layer summaries to form a single representation for classification. We instantiate this template with direct pooling, a 100K-parameter scoring-attention gate, and a downcast multi-head self-attention (MHA) probe with up to 35M trainable parameters. Across safety and sentiment benchmarks our probes improve over logit-only reuse (e.g., MULI) and are competitive with substantially larger task-specific baselines, while preserving near-serving latency and avoiding the VRAM and latency costs of a separate guard-model pipeline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.13247", "title": "Aligning Agentic World Models via Knowledgeable Experience Learning", "authors": [ "Baochang Ren", "Yunzhi Yao", "Rui Sun", "Shuofei Qiao", "Ningyu Zhang", "Huajun Chen" ], "github": "", "project_page": "", "abstract": "Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural grounding to respect the immutable laws of the physical world. Consequently, while these agents implicitly function as world models, their simulations often suffer from physical hallucinations-generating plans that are logically sound but physically unexecutable. Existing alignment strategies predominantly rely on resource-intensive training or fine-tuning, which attempt to compress dynamic environmental rules into static model parameters. However, such parametric encapsulation is inherently rigid, struggling to adapt to the open-ended variability of physical dynamics without continuous, costly retraining. To bridge this gap, we introduce WorldMind, a framework that autonomously constructs a symbolic World Knowledge Repository by synthesizing environmental feedback. Specifically, it unifies Process Experience to enforce physical feasibility via prediction errors and Goal Experience to guide task optimality through successful trajectories. Experiments on EB-ALFRED and EB-Habitat demonstrate that WorldMind achieves superior performance compared to baselines with remarkable cross-model and cross-environment transferability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.12294", "title": "ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agents", "authors": [ "Dawei Li", "Yuguang Yao", "Zhen Tan", "Huan Liu", "Ruocheng Guo" ], "github": "https://github.com/David-Li0406/ToolPRMBench", "project_page": "", "abstract": "Reward-guided search methods have demonstrated strong potential in enhancing tool-using agents by effectively guiding sampling and exploration over complex action spaces. As a core design, those search methods utilize process reward models (PRMs) to provide step-level rewards, enabling more fine-grained monitoring. However, there is a lack of systematic and reliable evaluation benchmarks for PRMs in tool-using settings. In this paper, we introduce ToolPRMBench, a large-scale benchmark specifically designed to evaluate PRMs for tool-using agents. ToolPRMBench is built on top of several representative tool-using benchmarks and converts agent trajectories into step-level test cases. Each case contains the interaction history, a correct action, a plausible but incorrect alternative, and relevant tool metadata. We respectively utilize offline sampling to isolate local single-step errors and online sampling to capture realistic multi-step failures from full agent rollouts. A multi-LLM verification pipeline is proposed to reduce label noise and ensure data quality. We conduct extensive experiments across large language models, general PRMs, and tool-specialized PRMs on ToolPRMBench. The results reveal clear differences in PRM effectiveness and highlight the potential of specialized PRMs for tool-using. Code and data will be released at https://github.com/David-Li0406/ToolPRMBench.", "github_manual": "", "github_regex": "https://github.com/David-Li0406/ToolPRMBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.12993", "title": "Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization", "authors": [ "Hao Luo", "Ye Wang", "Wanpeng Zhang", "Sipeng Zheng", "Ziheng Xi", "Chaoyi Xu", "Haiweng Xu", "Haoqi Yuan", "Chi Zhang", "Yiqing Wang", "Yicheng Feng", "Zongqing Lu" ], "github": "", "project_page": "", "abstract": "We introduce Being-H0.5, a foundational Vision-Language-Action (VLA) model designed for robust cross-embodiment generalization across diverse robotic platforms. While existing VLAs often struggle with morphological heterogeneity and data scarcity, we propose a human-centric learning paradigm that treats human interaction traces as a universal \"mother tongue\" for physical interaction. To support this, we present UniHand-2.0, the largest embodied pre-training recipe to date, comprising over 35,000 hours of multimodal data across 30 distinct robotic embodiments. Our approach introduces a Unified Action Space that maps heterogeneous robot controls into semantically aligned slots, enabling low-resource robots to bootstrap skills from human data and high-resource platforms. Built upon this human-centric foundation, we design a unified sequential modeling and multi-task pre-training paradigm to bridge human demonstrations and robotic execution. Architecturally, Being-H0.5 utilizes a Mixture-of-Transformers design featuring a novel Mixture-of-Flow (MoF) framework to decouple shared motor primitives from specialized embodiment-specific experts. Finally, to make cross-embodiment policies stable in the real world, we introduce Manifold-Preserving Gating for robustness under sensory shift and Universal Async Chunking to universalize chunked control across embodiments with different latency and control profiles. We empirically demonstrate that Being-H0.5 achieves state-of-the-art results on simulated benchmarks, such as LIBERO (98.9%) and RoboCasa (53.9%), while also exhibiting strong cross-embodiment capabilities on five robotic platforms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.14250", "title": "OmniTransfer: All-in-one Framework for Spatio-temporal Video Transfer", "authors": [ "Pengze Zhang", "Yanze Wu", "Mengtian Li", "Xu Bai", "Songtao Zhao", "Fulong Ye", "Chong Mou", "Xinghui Li", "Zhuowei Chen", "Qian He", "Mingyuan Gao" ], "github": "", "project_page": "", "abstract": "Videos convey richer information than images or text, capturing both spatial and temporal dynamics. However, most existing video customization methods rely on reference images or task-specific temporal priors, failing to fully exploit the rich spatio-temporal information inherent in videos, thereby limiting flexibility and generalization in video generation. To address these limitations, we propose OmniTransfer, a unified framework for spatio-temporal video transfer. It leverages multi-view information across frames to enhance appearance consistency and exploits temporal cues to enable fine-grained temporal control. To unify various video transfer tasks, OmniTransfer incorporates three key designs: Task-aware Positional Bias that adaptively leverages reference video information to improve temporal alignment or appearance consistency; Reference-decoupled Causal Learning separating reference and target branches to enable precise reference transfer while improving efficiency; and Task-adaptive Multimodal Alignment using multimodal semantic guidance to dynamically distinguish and tackle different tasks. Extensive experiments show that OmniTransfer outperforms existing methods in appearance (ID and style) and temporal transfer (camera movement and video effects), while matching pose-guided methods in motion transfer without using pose, establishing a new paradigm for flexible, high-fidelity video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.14192", "title": "Toward Efficient Agents: Memory, Tool learning, and Planning", "authors": [ "Xiaofang Yang", "Lijun Li", "Heng Zhou", "Tong Zhu", "Xiaoye Qu", "Yuchen Fan", "Qianshan Wei", "Rui Ye", "Li Kang", "Yiran Qin", "Zhiqiang Kou", "Daizong Liu", "Qi Li", "Ning Ding", "Siheng Chen", "Jing Shao" ], "github": "https://github.com/yxf203/Awesome-Efficient-Agents", "project_page": "", "abstract": "Recent years have witnessed increasing interest in extending large language models into agentic systems. While the effectiveness of agents has continued to improve, efficiency, which is crucial for real-world deployment, has often been overlooked. This paper therefore investigates efficiency from three core components of agents: memory, tool learning, and planning, considering costs such as latency, tokens, steps, etc. Aimed at conducting comprehensive research addressing the efficiency of the agentic system itself, we review a broad range of recent approaches that differ in implementation yet frequently converge on shared high-level principles including but not limited to bounding context via compression and management, designing reinforcement learning rewards to minimize tool invocation, and employing controlled search mechanisms to enhance efficiency, which we discuss in detail. Accordingly, we characterize efficiency in two complementary ways: comparing effectiveness under a fixed cost budget, and comparing cost at a comparable level of effectiveness. This trade-off can also be viewed through the Pareto frontier between effectiveness and cost. From this perspective, we also examine efficiency oriented benchmarks by summarizing evaluation protocols for these components and consolidating commonly reported efficiency metrics from both benchmark and methodological studies. Moreover, we discuss the key challenges and future directions, with the goal of providing promising insights.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yxf203/Awesome-Efficient-Agents", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.14046", "title": "PRiSM: Benchmarking Phone Realization in Speech Models", "authors": [ "Shikhar Bharadwaj", "Chin-Jou Li", "Yoonjae Kim", "Kwanghee Choi", "Eunjung Yeo", "Ryan Soh-Eun Shim", "Hanyu Zhou", "Brendon Boldt", "Karen Rosero Jacome", "Kalvin Chang", "Darsh Agrawal", "Keer Xu", "Chao-Han Huck Yang", "Jian Zhu", "Shinji Watanabe", "David R. Mortensen" ], "github": "https://github.com/changelinglab/prism", "project_page": "", "abstract": "Phone recognition (PR) serves as the atomic interface for language-agnostic modeling for cross-lingual speech processing and phonetic analysis. Despite prolonged efforts in developing PR systems, current evaluations only measure surface-level transcription accuracy. We introduce PRiSM, the first open-source benchmark designed to expose blind spots in phonetic perception through intrinsic and extrinsic evaluation of PR systems. PRiSM standardizes transcription-based evaluation and assesses downstream utility in clinical, educational, and multilingual settings with transcription and representation probes. We find that diverse language exposure during training is key to PR performance, encoder-CTC models are the most stable, and specialized PR models still outperform Large Audio Language Models. PRiSM releases code, recipes, and datasets to move the field toward multilingual speech models with robust phonetic ability: https://github.com/changelinglab/prism.", "github_manual": "", "github_regex": "https://github.com/changelinglab/prism", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.10700", "title": "LIBERTy: A Causal Framework for Benchmarking Concept-Based Explanations of LLMs with Structural Counterfactuals", "authors": [ "Gilat Toker", "Nitay Calderon", "Ohad Amosy", "Roi Reichart" ], "github": "", "project_page": "", "abstract": "Concept-based explanations quantify how high-level concepts (e.g., gender or experience) influence model behavior, which is crucial for decision-makers in high-stakes domains. Recent work evaluates the faithfulness of such explanations by comparing them to reference causal effects estimated from counterfactuals. In practice, existing benchmarks rely on costly human-written counterfactuals that serve as an imperfect proxy. To address this, we introduce a framework for constructing datasets containing structural counterfactual pairs: LIBERTy (LLM-based Interventional Benchmark for Explainability with Reference Targets). LIBERTy is grounded in explicitly defined Structured Causal Models (SCMs) of the text generation, interventions on a concept propagate through the SCM until an LLM generates the counterfactual. We introduce three datasets (disease detection, CV screening, and workplace violence prediction) together with a new evaluation metric, order-faithfulness. Using them, we evaluate a wide range of methods across five models and identify substantial headroom for improving concept-based explanations. LIBERTy also enables systematic analysis of model sensitivity to interventions: we find that proprietary LLMs show markedly reduced sensitivity to demographic concepts, likely due to post-training mitigation. Overall, LIBERTy provides a much-needed benchmark for developing faithful explainability methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.13253", "title": "A Hybrid Protocol for Large-Scale Semantic Dataset Generation in Low-Resource Languages: The Turkish Semantic Relations Corpus", "authors": [ "Ebubekir Tosun", "Mehmet Emin Buldur", "\u00d6zay Ezerceli", "Mahmoud ElHussieni" ], "github": "", "project_page": "", "abstract": "We present a hybrid methodology for generating large-scale semantic relationship datasets in low-resource languages, demonstrated through a comprehensive Turkish semantic relations corpus. Our approach integrates three phases: (1) FastText embeddings with Agglomerative Clustering to identify semantic clusters, (2) Gemini 2.5-Flash for automated semantic relationship classification, and (3) integration with curated dictionary sources. The resulting dataset comprises 843,000 unique Turkish semantic pairs across three relationship types (synonyms, antonyms, co-hyponyms) representing a 10x scale increase over existing resources at minimal cost ($65). We validate the dataset through two downstream tasks: an embedding model achieving 90% top-1 retrieval accuracy and a classification model attaining 90% F1-macro. Our scalable protocol addresses critical data scarcity in Turkish NLP and demonstrates applicability to other low-resource languages. We publicly release the dataset and models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.13251", "title": "Beyond Cosine Similarity: Taming Semantic Drift and Antonym Intrusion in a 15-Million Node Turkish Synonym Graph", "authors": [ "Ebubekir Tosun", "Mehmet Emin Buldur", "\u00d6zay Ezerceli", "Mahmoud ElHussieni" ], "github": "", "project_page": "", "abstract": "Neural embeddings have a notorious blind spot: they can't reliably tell synonyms apart from antonyms. Consequently, increasing similarity thresholds often fails to prevent opposites from being grouped together. We've built a large-scale semantic clustering system specifically designed to tackle this problem head on. Our pipeline chews through 15 million lexical items, evaluates a massive 520 million potential relationships, and ultimately generates 2.9 million high-precision semantic clusters. The system makes three primary contributions. First, we introduce a labeled dataset of 843,000 concept pairs spanning synonymy, antonymy, and co-hyponymy, constructed via Gemini 2.5-Flash LLM augmentation and verified using human-curated dictionary resources. Second, we propose a specialized three-way semantic relation discriminator that achieves 90% macro-F1, enabling robust disambiguation beyond raw embedding similarity. Third, we introduce a novel soft-to-hard clustering algorithm that mitigates semantic drift preventing erroneous transitive chains (e.g., hot -> spicy -> pain -> depression) while simultaneously resolving polysemy. Our approach employs a topology-aware two-stage expansion-pruning procedure with topological voting, ensuring that each term is assigned to exactly one semantically coherent cluster. The resulting resource enables high-precision semantic search and retrieval-augmented generation, particularly for morphologically rich and low-resource languages where existing synonym databases remain sparse.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.13836", "title": "FutureOmni: Evaluating Future Forecasting from Omni-Modal Context for Multimodal LLMs", "authors": [ "Qian Chen", "Jinlan Fu", "Changsong Li", "See-Kiong Ng", "Xipeng Qiu" ], "github": "https://github.com/OpenMOSS/FutureOmni", "project_page": "", "abstract": "Although Multimodal Large Language Models (MLLMs) demonstrate strong omni-modal perception, their ability to forecast future events from audio-visual cues remains largely unexplored, as existing benchmarks focus mainly on retrospective understanding. To bridge this gap, we introduce FutureOmni, the first benchmark designed to evaluate omni-modal future forecasting from audio-visual environments. The evaluated models are required to perform cross-modal causal and temporal reasoning, as well as effectively leverage internal knowledge to predict future events. FutureOmni is constructed via a scalable LLM-assisted, human-in-the-loop pipeline and contains 919 videos and 1,034 multiple-choice QA pairs across 8 primary domains. Evaluations on 13 omni-modal and 7 video-only models show that current systems struggle with audio-visual future prediction, particularly in speech-heavy scenarios, with the best accuracy of 64.8% achieved by Gemini 3 Flash. To mitigate this limitation, we curate a 7K-sample instruction-tuning dataset and propose an Omni-Modal Future Forecasting (OFF) training strategy. Evaluations on FutureOmni and popular audio-visual and video-only benchmarks demonstrate that OFF enhances future forecasting and generalization. We publicly release all code (https://github.com/OpenMOSS/FutureOmni) and datasets (https://huggingface.co/datasets/OpenMOSS-Team/FutureOmni).", "github_manual": "", "github_regex": "https://github.com/OpenMOSS/FutureOmni", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.11888", "title": "Agentic-R: Learning to Retrieve for Agentic Search", "authors": [ "Wenhan Liu", "Xinyu Ma", "Yutao Zhu", "Yuchen Li", "Daiting Shi", "Dawei Yin", "Zhicheng Dou" ], "github": "https://github.com/8421BCD/Agentic-R", "project_page": "", "abstract": "Agentic search has recently emerged as a powerful paradigm, where an agent interleaves multi-step reasoning with on-demand retrieval to solve complex questions. Despite its success, how to design a retriever for agentic search remains largely underexplored. Existing search agents typically rely on similarity-based retrievers, while similar passages are not always useful for final answer generation. In this paper, we propose a novel retriever training framework tailored for agentic search. Unlike retrievers designed for single-turn retrieval-augmented generation (RAG) that only rely on local passage utility, we propose to use both local query-passage relevance and global answer correctness to measure passage utility in a multi-turn agentic search. We further introduce an iterative training strategy, where the search agent and the retriever are optimized bidirectionally and iteratively. Different from RAG retrievers that are only trained once with fixed questions, our retriever is continuously improved using evolving and higher-quality queries from the agent. Extensive experiments on seven single-hop and multi-hop QA benchmarks demonstrate that our retriever, termed , consistently outperforms strong baselines across different search agents. Our codes are available at: https://github.com/8421BCD/Agentic-R.", "github_manual": "", "github_regex": "https://github.com/8421BCD/Agentic-R", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.12910", "title": "SciCoQA: Quality Assurance for Scientific Paper--Code Alignment", "authors": [ "Tim Baumg\u00e4rtner", "Iryna Gurevych" ], "github": "https://github.com/gkamradt/LLMTest_NeedleInAHaystack", "project_page": "", "abstract": "We present SciCoQA, a dataset for detecting discrepancies between scientific publications and their codebases to ensure faithful implementations. We construct SciCoQA from GitHub issues and reproducibility papers, and to scale our dataset, we propose a synthetic data generation method for constructing paper-code discrepancies. We analyze the paper-code discrepancies in detail and propose discrepancy types and categories to better understand the occurring mismatches. In total, our dataset consists of 611 paper-code discrepancies (81 real, 530 synthetic), spanning diverse computational science disciplines, including AI, Physics, Quantitative Biology, and others. Our evaluation of 21 LLMs highlights the difficulty of SciCoQA, particularly for instances involving omitted paper details, long-context inputs, and data outside the models' pre-training corpus. The best performing model in our evaluation, GPT-5, can only detect 45.7\\% of real-world paper-code discrepancies.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gkamradt/LLMTest_NeedleInAHaystack", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.13029", "title": "Think3D: Thinking with Space for Spatial Reasoning", "authors": [ "Zaibin Zhang", "Yuhan Wu", "Lianjie Jia", "Yifan Wang", "Zhongbo Zhang", "Yijiang Li", "Binghao Ran", "Fuxi Zhang", "Zhuohan Sun", "Zhenfei Yin", "Lijun Wang", "Huchuan Lu" ], "github": "https://github.com/zhangzaibin/spagent", "project_page": "", "abstract": "Understanding and reasoning about the physical world requires spatial intelligence: the ability to interpret geometry, perspective, and spatial relations beyond 2D perception. While recent vision large models (VLMs) excel at visual understanding, they remain fundamentally 2D perceivers and struggle with genuine 3D reasoning. We introduce Think3D, a framework that enables VLM agents to think with 3D space. By leveraging 3D reconstruction models that recover point clouds and camera poses from images or videos, Think3D allows the agent to actively manipulate space through camera-based operations and ego/global-view switching, transforming spatial reasoning into an interactive 3D chain-of-thought process. Without additional training, Think3D significantly improves the spatial reasoning performance of advanced models such as GPT-4.1 and Gemini 2.5 Pro, yielding average gains of +7.8% on BLINK Multi-view and MindCube, and +4.7% on VSI-Bench. We further show that smaller models, which struggle with spatial exploration, benefit significantly from a reinforcement learning policy that enables the model to select informative viewpoints and operations. With RL, the benefit from tool usage increases from +0.7% to +6.8%. Our findings demonstrate that training-free, tool-augmented spatial exploration is a viable path toward more flexible and human-like 3D reasoning in multimodal agents, establishing a new dimension of multimodal intelligence. Code and weights are released at https://github.com/zhangzaibin/spagent.", "github_manual": "", "github_regex": "https://github.com/zhangzaibin/spagent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.12937", "title": "On the Evidentiary Limits of Membership Inference for Copyright Auditing", "authors": [ "Murat Bilgehan Ertan", "Emirhan B\u00f6ge", "Min Chen", "Kaleel Mahmood", "Marten van Dijk" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) are trained on increasingly opaque corpora, membership inference attacks (MIAs) have been proposed to audit whether copyrighted texts were used during training, despite growing concerns about their reliability under realistic conditions. We ask whether MIAs can serve as admissible evidence in adversarial copyright disputes where an accused model developer may obfuscate training data while preserving semantic content, and formalize this setting through a judge-prosecutor-accused communication protocol. To test robustness under this protocol, we introduce SAGE (Structure-Aware SAE-Guided Extraction), a paraphrasing framework guided by Sparse Autoencoders (SAEs) that rewrites training data to alter lexical structure while preserving semantic content and downstream utility. Our experiments show that state-of-the-art MIAs degrade when models are fine-tuned on SAGE-generated paraphrases, indicating that their signals are not robust to semantics-preserving transformations. While some leakage remains in certain fine-tuning regimes, these results suggest that MIAs are brittle in adversarial settings and insufficient, on their own, as a standalone mechanism for copyright auditing of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.10237", "title": "Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD", "authors": [ "Murat Bilgehan Ertan", "Marten van Dijk" ], "github": "", "project_page": "", "abstract": "Differentially Private Stochastic Gradient Descent (DP-SGD) is the dominant paradigm for private training, but its fundamental limitations under worst-case adversarial privacy definitions remain poorly understood. We analyze DP-SGD in the f-differential privacy framework, which characterizes privacy via hypothesis-testing trade-off curves, and study shuffled sampling over a single epoch with M gradient updates. We derive an explicit suboptimal upper bound on the achievable trade-off curve. This result induces a geometric lower bound on the separation \u03ba which is the maximum distance between the mechanism's trade-off curve and the ideal random-guessing line. Because a large separation implies significant adversarial advantage, meaningful privacy requires small \u03ba. However, we prove that enforcing a small separation imposes a strict lower bound on the Gaussian noise multiplier \u03c3, which directly limits the achievable utility. In particular, under the standard worst-case adversarial model, shuffled DP-SGD must satisfy \u03c3ge 1{2ln M} quadorquad \u03bage 1{8}!left(1-1{4\u03c0ln M}right), and thus cannot simultaneously achieve strong privacy and high utility. Although this bound vanishes asymptotically as M to infty, the convergence is extremely slow: even for practically relevant numbers of updates the required noise magnitude remains substantial. We further show that the same limitation extends to Poisson subsampling up to constant factors. Our experiments confirm that the noise levels implied by this bound leads to significant accuracy degradation at realistic training settings, thus showing a critical bottleneck in DP-SGD under standard worst-case adversarial assumptions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.14251", "title": "LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR", "authors": [ "Said Taghadouini", "Adrien Cavaill\u00e8s", "Baptiste Aubertin" ], "github": "https://github.com/datalab-to/chandra", "project_page": "", "abstract": "We present LightOnOCR-2-1B, a 1B-parameter end-to-end multilingual vision--language model that converts document images (e.g., PDFs) into clean, naturally ordered text without brittle OCR pipelines. Trained on a large-scale, high-quality distillation mix with strong coverage of scans, French documents, and scientific PDFs, LightOnOCR-2 achieves state-of-the-art results on OlmOCR-Bench while being 9times smaller and substantially faster than prior best-performing models. We further extend the output format to predict normalized bounding boxes for embedded images, introducing localization during pretraining via a resume strategy and refining it with RLVR using IoU-based rewards. Finally, we improve robustness with checkpoint averaging and task-arithmetic merging. We release model checkpoints under Apache 2.0, and publicly release the dataset and LightOnOCR-bbox-bench evaluation under their respective licenses.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/datalab-to/chandra", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.14232", "title": "KAGE-Bench: Fast Known-Axis Visual Generalization Evaluation for Reinforcement Learning", "authors": [ "Egor Cherepanov", "Daniil Zelezetsky", "Alexey K. Kovalev", "Aleksandr I. Panov" ], "github": "", "project_page": "", "abstract": "Pixel-based reinforcement learning agents often fail under purely visual distribution shift even when latent dynamics and rewards are unchanged, but existing benchmarks entangle multiple sources of shift and hinder systematic analysis. We introduce KAGE-Env, a JAX-native 2D platformer that factorizes the observation process into independently controllable visual axes while keeping the underlying control problem fixed. By construction, varying a visual axis affects performance only through the induced state-conditional action distribution of a pixel policy, providing a clean abstraction for visual generalization. Building on this environment, we define KAGE-Bench, a benchmark of six known-axis suites comprising 34 train-evaluation configuration pairs that isolate individual visual shifts. Using a standard PPO-CNN baseline, we observe strong axis-dependent failures, with background and photometric shifts often collapsing success, while agent-appearance shifts are comparatively benign. Several shifts preserve forward motion while breaking task completion, showing that return alone can obscure generalization failures. Finally, the fully vectorized JAX implementation enables up to 33M environment steps per second on a single GPU, enabling fast and reproducible sweeps over visual factors. Code: https://avanturist322.github.io/KAGEBench/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.13976", "title": "FantasyVLN: Unified Multimodal Chain-of-Thought Reasoning for Vision-Language Navigation", "authors": [ "Jing Zuo", "Lingzhou Mu", "Fan Jiang", "Chengcheng Ma", "Mu Xu", "Yonggang Qi" ], "github": "", "project_page": "", "abstract": "Achieving human-level performance in Vision-and-Language Navigation (VLN) requires an embodied agent to jointly understand multimodal instructions and visual-spatial context while reasoning over long action sequences. Recent works, such as NavCoT and NavGPT-2, demonstrate the potential of Chain-of-Thought (CoT) reasoning for improving interpretability and long-horizon planning. Moreover, multimodal extensions like OctoNav-R1 and CoT-VLA further validate CoT as a promising pathway toward human-like navigation reasoning. However, existing approaches face critical drawbacks: purely textual CoTs lack spatial grounding and easily overfit to sparse annotated reasoning steps, while multimodal CoTs incur severe token inflation by generating imagined visual observations, making real-time navigation impractical. In this work, we propose FantasyVLN, a unified implicit reasoning framework that preserves the benefits of CoT reasoning without explicit token overhead. Specifically, imagined visual tokens are encoded into a compact latent space using a pretrained Visual AutoRegressor (VAR) during CoT reasoning training, and the model jointly learns from textual, visual, and multimodal CoT modes under a unified multi-CoT strategy. At inference, our model performs direct instruction-to-action mapping while still enjoying reasoning-aware representations. Extensive experiments on LH-VLN show that our approach achieves reasoning-aware yet real-time navigation, improving success rates and efficiency while reducing inference latency by an order of magnitude compared to explicit CoT methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.13075", "title": "METIS: Mentoring Engine for Thoughtful Inquiry & Solutions", "authors": [ "Abhinav Rajeev Kumar", "Dhruv Trehan", "Paras Chopra" ], "github": "", "project_page": "", "abstract": "Many students lack access to expert research mentorship. We ask whether an AI mentor can move undergraduates from an idea to a paper. We build METIS, a tool-augmented, stage-aware assistant with literature search, curated guidelines, methodology checks, and memory. We evaluate METIS against GPT-5 and Claude Sonnet 4.5 across six writing stages using LLM-as-a-judge pairwise preferences, student-persona rubrics, short multi-turn tutoring, and evidence/compliance checks. On 90 single-turn prompts, LLM judges preferred METIS to Claude Sonnet 4.5 in 71% and to GPT-5 in 54%. Student scores (clarity/actionability/constraint-fit; 90 prompts x 3 judges) are higher across stages. In multi-turn sessions (five scenarios/agent), METIS yields slightly higher final quality than GPT-5. Gains concentrate in document-grounded stages (D-F), consistent with stage-aware routing and groundings failure modes include premature tool routing, shallow grounding, and occasional stage misclassification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.14249", "title": "Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment", "authors": [ "Yuming Yang", "Mingyoung Lai", "Wanxu Zhao", "Xiaoran Fan", "Zhiheng Xi", "Mingqi Wu", "Chiyue Huang", "Jun Zhao", "Haijun Lv", "Jian Tong", "Yunhua Zhou", "Yicheng Zou", "Qipeng Guo", "Tao Gui", "Qi Zhang", "Xuanjing Huang" ], "github": "https://github.com/UmeanNever/RankSurprisalRatio", "project_page": "", "abstract": "Long chain-of-thought (CoT) trajectories provide rich supervision signals for distilling reasoning from teacher to student LLMs. However, both prior work and our experiments show that trajectories from stronger teachers do not necessarily yield better students, highlighting the importance of data-student suitability in distillation. Existing methods assess suitability primarily through student likelihood, favoring trajectories that closely align with the model's current behavior but overlooking more informative ones. Addressing this, we propose Rank-Surprisal Ratio (RSR), a simple metric that captures both alignment and informativeness to assess the suitability of a reasoning trajectory. RSR is motivated by the observation that effective trajectories typically combine low absolute probability with relatively high-ranked tokens under the student model, balancing learning signal strength and behavioral alignment. Concretely, RSR is defined as the ratio of a trajectory's average token-wise rank to its average negative log-likelihood, and is straightforward to compute and interpret. Across five student models and reasoning trajectories from 11 diverse teachers, RSR strongly correlates with post-training performance (average Spearman 0.86), outperforming existing metrics. We further demonstrate its practical utility in both trajectory selection and teacher selection.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/UmeanNever/RankSurprisalRatio", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.13697", "title": "Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning", "authors": [ "Zhihang Yuan", "Chengyu Yue", "Long Huang", "Litu Ou", "Lei Shi" ], "github": "", "project_page": "", "abstract": "Instruction tuning is a standard paradigm for adapting large language models (LLMs), but modern instruction datasets are large, noisy, and redundant, making full-data fine-tuning costly and often unnecessary. Existing data selection methods either build expensive gradient datastores or assign static scores from a weak proxy, largely ignoring evolving uncertainty, and thus missing a key source of LLM interpretability. We propose GRADFILTERING, an objective-agnostic, uncertainty-aware data selection framework that utilizes a small GPT-2 proxy with a LoRA ensemble and aggregates per-example gradients into a Gradient Signal-to-Noise Ratio (G-SNR) utility. Our method matches or surpasses random subsets and strong baselines in most LLM-as-a-judge evaluations as well as in human assessment. Moreover, GRADFILTERING-selected subsets converge faster than competitive filters under the same compute budget, reflecting the benefit of uncertainty-aware scoring.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.13677", "title": "Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging", "authors": [ "Carsten T. L\u00fcth", "Jeremias Traub", "Kim-Celine Kahl", "Till J. Bungert", "Lukas Klein", "Lars Kr\u00e4mer", "Paul F. J\u00e4ger", "Klaus Maier-Hein", "Fabian Isensee" ], "github": "https://github.com/MIC-DKFZ/nnActive", "project_page": "", "abstract": "Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation, where expert labeling of volumetric data is both time-consuming and expensive. Yet, existing AL methods are unable to consistently outperform improved random sampling baselines adapted to 3D data, leaving the field without a reliable solution. We introduce Class-stratified Scheduled Power Predictive Entropy (ClaSP PE), a simple and effective query strategy that addresses two key limitations of standard uncertainty-based AL methods: class imbalance and redundancy in early selections. ClaSP PE combines class-stratified querying to ensure coverage of underrepresented structures and log-scale power noising with a decaying schedule to enforce query diversity in early-stage AL and encourage exploitation later. In our evaluation on 24 experimental settings using four 3D biomedical datasets within the comprehensive nnActive benchmark, ClaSP PE is the only method that generally outperforms improved random baselines in terms of both segmentation quality with statistically significant gains, whilst remaining annotation efficient. Furthermore, we explicitly simulate the real-world application by testing our method on four previously unseen datasets without manual adaptation, where all experiment parameters are set according to predefined guidelines. The results confirm that ClaSP PE robustly generalizes to novel tasks without requiring dataset-specific tuning. Within the nnActive framework, we present compelling evidence that an AL method can consistently outperform random baselines adapted to 3D segmentation, in terms of both performance and annotation efficiency in a realistic, close-to-production scenario. Our open-source implementation and clear deployment guidelines make it readily applicable in practice. Code is at https://github.com/MIC-DKFZ/nnActive.", "github_manual": "", "github_regex": "https://github.com/MIC-DKFZ/nnActive", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.14004", "title": "Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models", "authors": [ "Hengyuan Zhang", "Zhihao Zhang", "Mingyang Wang", "Zunhai Su", "Yiwei Wang", "Qianli Wang", "Shuzhou Yuan", "Ercong Nie", "Xufeng Duan", "Qibo Xue", "Zeping Yu", "Chenming Shang", "Xiao Liang", "Jing Xiong", "Hui Shen", "Chaofan Tao", "Zhengwu Liu", "Senjie Jin", "Zhiheng Xi", "Dongdong Zhang", "Sophia Ananiadou", "Tao Gui", "Ruobing Xie", "Hayden Kwok-Hay So", "Hinrich Sch\u00fctze", "Xuanjing Huang", "Qi Zhang", "Ngai Wong" ], "github": "https://github.com/rattlesnakey/Awesome-Actionable-MI-Survey", "project_page": "", "abstract": "Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However, existing reviews primarily treat MI as an observational science, summarizing analytical insights while lacking a systematic framework for actionable intervention. To bridge this gap, we present a practical survey structured around the pipeline: \"Locate, Steer, and Improve.\" We formally categorize Localizing (diagnosis) and Steering (intervention) methods based on specific Interpretable Objects to establish a rigorous intervention protocol. Furthermore, we demonstrate how this framework enables tangible improvements in Alignment, Capability, and Efficiency, effectively operationalizing MI as an actionable methodology for model optimization. The curated paper list of this work is available at https://github.com/rattlesnakey/Awesome-Actionable-MI-Survey.", "github_manual": "", "github_regex": "https://github.com/rattlesnakey/Awesome-Actionable-MI-Survey", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.14209", "title": "InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning", "authors": [ "Matthew Y. R. Yang", "Hao Bai", "Ian Wu", "Gene Yang", "Amrith Setlur", "Aviral Kumar" ], "github": "https://github.com/intervention-training/int", "project_page": "", "abstract": "Outcome-reward reinforcement learning (RL) has proven effective at improving the reasoning capabilities of large language models (LLMs). However, standard RL assigns credit only at the level of the final answer, penalizing entire reasoning traces when the outcome is incorrect and uniformly reinforcing all steps when it is correct. As a result, correct intermediate steps may be discouraged in failed traces, while spurious steps may be reinforced in successful ones. We refer to this failure mode as the problem of credit assignment. While a natural remedy is to train a process reward model, accurately optimizing such models to identify corrective reasoning steps remains challenging. We introduce Intervention Training (InT), a training paradigm in which the model performs fine-grained credit assignment on its own reasoning traces by proposing short, targeted corrections that steer trajectories toward higher reward. Using reference solutions commonly available in mathematical reasoning datasets and exploiting the fact that verifying a model-generated solution is easier than generating a correct one from scratch, the model identifies the first error in its reasoning and proposes a single-step intervention to redirect the trajectory toward the correct solution. We then apply supervised fine-tuning (SFT) to the on-policy rollout up to the point of error concatenated with the intervention, localizing error to the specific step that caused failure. We show that the resulting model serves as a far better initialization for RL training. After running InT and subsequent fine-tuning with RL, we improve accuracy by nearly 14% over a 4B-parameter base model on IMO-AnswerBench, outperforming larger open-source models such as gpt-oss-20b.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/intervention-training/int", "github_api_added_by": "auto" }, { "date": "2026-01-21", "arxiv_id": "2601.13591", "title": "DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems", "authors": [ "Maojun Sun", "Yifei Xie", "Yue Wu", "Ruijian Han", "Binyan Jiang", "Defeng Sun", "Yancheng Yuan", "Jian Huang" ], "github": "", "project_page": "", "abstract": "Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning. However, the open-ended nature of real-world data science problems, which often span multiple taxonomies and lack standard answers, poses a significant challenge for evaluation. To address this, we introduce DSAEval, a benchmark comprising 641 real-world data science problems grounded in 285 diverse datasets, covering both structured and unstructured data (e.g., vision and text). DSAEval incorporates three distinctive features: (1) Multimodal Environment Perception, which enables agents to interpret observations from multiple modalities including text and vision; (2) Multi-Query Interactions, which mirror the iterative and cumulative nature of real-world data science projects; and (3) Multi-Dimensional Evaluation, which provides a holistic assessment across reasoning, code, and results. We systematically evaluate 11 advanced agentic LLMs using DSAEval. Our results show that Claude-Sonnet-4.5 achieves the strongest overall performance, GPT-5.2 is the most efficient, and MiMo-V2-Flash is the most cost-effective. We further demonstrate that multimodal perception consistently improves performance on vision-related tasks, with gains ranging from 2.04% to 11.30%. Overall, while current data science agents perform well on structured data and routine data anlysis workflows, substantial challenges remain in unstructured domains. Finally, we offer critical insights and outline future research directions to advance the development of data science agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.11898", "title": "RemoteVAR: Autoregressive Visual Modeling for Remote Sensing Change Detection", "authors": [ "Yilmaz Korkmaz", "Vishal M. Patel" ], "github": "https://github.com/yilmazkorkmaz1/RemoteVAR", "project_page": "", "abstract": "Remote sensing change detection aims to localize and characterize scene changes between two time points and is central to applications such as environmental monitoring and disaster assessment. Meanwhile, visual autoregressive models (VARs) have recently shown impressive image generation capability, but their adoption for pixel-level discriminative tasks remains limited due to weak controllability, suboptimal dense prediction performance and exposure bias. We introduce RemoteVAR, a new VAR-based change detection framework that addresses these limitations by conditioning autoregressive prediction on multi-resolution fused bi-temporal features via cross-attention, and by employing an autoregressive training strategy designed specifically for change map prediction. Extensive experiments on standard change detection benchmarks show that RemoteVAR delivers consistent and significant improvements over strong diffusion-based and transformer-based baselines, establishing a competitive autoregressive alternative for remote sensing change detection. Code will be available https://github.com/yilmazkorkmaz1/RemoteVAR{here}.", "github_manual": "", "github_regex": "https://github.com/yilmazkorkmaz1/RemoteVAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.13761", "title": "DARC: Decoupled Asymmetric Reasoning Curriculum for LLM Evolution", "authors": [ "Shengda Fan", "Xuyan Ye", "Yankai Lin" ], "github": "https://github.com/RUCBM/DARC", "project_page": "", "abstract": "Self-play with large language models has emerged as a promising paradigm for achieving self-improving artificial intelligence. However, existing self-play frameworks often suffer from optimization instability, due to (i) non-stationary objectives induced by solver-dependent reward feedback for the Questioner, and (ii) bootstrapping errors from self-generated pseudo-labels used to supervise the Solver. To mitigate these challenges, we introduce DARC (Decoupled Asymmetric Reasoning Curriculum), a two-stage framework that stabilizes the self-evolution process. First, we train the Questioner to synthesize difficulty-calibrated questions, conditioned on explicit difficulty levels and external corpora. Second, we train the Solver with an asymmetric self-distillation mechanism, where a document-augmented teacher generates high-quality pseudo-labels to supervise the student Solver that lacks document access. Empirical results demonstrate that DARC is model-agnostic, yielding an average improvement of 10.9 points across nine reasoning benchmarks and three backbone models. Moreover, DARC consistently outperforms all baselines and approaches the performance of fully supervised models without relying on human annotations.The code is available at https://github.com/RUCBM/DARC.", "github_manual": "", "github_regex": "https://github.com/RUCBM/DARC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-21", "arxiv_id": "2601.13481", "title": "Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement", "authors": [ "Jian Zhang", "Zhangqi Wang", "Zhiyuan Wang", "Weiping Fu", "Yu He", "Haiping Zhu", "Qika Lin", "Jun Liu" ], "github": "", "project_page": "", "abstract": "Linguistic expressions of emotions such as depression, anxiety, and trauma-related states are pervasive in clinical notes, counseling dialogues, and online mental health communities, and accurate recognition of these emotions is essential for clinical triage, risk assessment, and timely intervention. Although large language models (LLMs) have demonstrated strong generalization ability in emotion analysis tasks, their diagnostic reliability in high-stakes, context-intensive medical settings remains highly sensitive to prompt design. Moreover, existing methods face two key challenges: emotional comorbidity, in which multiple intertwined emotional states complicate prediction, and inefficient exploration of clinically relevant cues. To address these challenges, we propose APOLO (Automated Prompt Optimization for Linguistic Emotion Diagnosis), a framework that systematically explores a broader and finer-grained prompt space to improve diagnostic efficiency and robustness. APOLO formulates instruction refinement as a Partially Observable Markov Decision Process and adopts a multi-agent collaboration mechanism involving Planner, Teacher, Critic, Student, and Target roles. Within this closed-loop framework, the Planner defines an optimization trajectory, while the Teacher-Critic-Student agents iteratively refine prompts to enhance reasoning stability and effectiveness, and the Target agent determines whether to continue optimization based on performance evaluation. Experimental results show that APOLO consistently improves diagnostic accuracy and robustness across domain-specific and stratified benchmarks, demonstrating a scalable and generalizable paradigm for trustworthy LLM applications in mental healthcare.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.12538", "title": "Agentic Reasoning for Large Language Models", "authors": [ "Tianxin Wei", "Ting-Wei Li", "Zhining Liu", "Xuying Ning", "Ze Yang", "Jiaru Zou", "Zhichen Zeng", "Ruizhong Qiu", "Xiao Lin", "Dongqi Fu", "Zihao Li", "Mengting Ai", "Duo Zhou", "Wenxuan Bao", "Yunzhe Li", "Gaotang Li", "Cheng Qian", "Yu Wang", "Xiangru Tang", "Yin Xiao", "Liri Fang", "Hui Liu", "Xianfeng Tang", "Yuji Zhang", "Chi Wang", "Jiaxuan You", "Heng Ji", "Hanghang Tong", "Jingrui He" ], "github": "", "project_page": "", "abstract": "Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, they struggle in open-ended and dynamic environments. Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction. In this survey, we organize agentic reasoning along three complementary dimensions. First, we characterize environmental dynamics through three layers: foundational agentic reasoning, which establishes core single-agent capabilities including planning, tool use, and search in stable environments; self-evolving agentic reasoning, which studies how agents refine these capabilities through feedback, memory, and adaptation; and collective multi-agent reasoning, which extends intelligence to collaborative settings involving coordination, knowledge sharing, and shared goals. Across these layers, we distinguish in-context reasoning, which scales test-time interaction through structured orchestration, from post-training reasoning, which optimizes behaviors via reinforcement learning and supervised fine-tuning. We further review representative agentic reasoning frameworks across real-world applications and benchmarks, including science, robotics, healthcare, autonomous research, and mathematics. This survey synthesizes agentic reasoning methods into a unified roadmap bridging thought and action, and outlines open challenges and future directions, including personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance for real-world deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.14171", "title": "Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance", "authors": [ "Qianli Ma", "Chang Guo", "Zhiheng Tian", "Siyu Wang", "Jipeng Xiao", "Yuanhao Yue", "Zhipeng Zhang" ], "github": "", "project_page": "", "abstract": "Writing effective rebuttals is a high-stakes task that demands more than linguistic fluency, as it requires precise alignment between reviewer intent and manuscript details. Current solutions typically treat this as a direct-to-text generation problem, suffering from hallucination, overlooked critiques, and a lack of verifiable grounding. To address these limitations, we introduce RebuttalAgent, the first multi-agents framework that reframes rebuttal generation as an evidence-centric planning task. Our system decomposes complex feedback into atomic concerns and dynamically constructs hybrid contexts by synthesizing compressed summaries with high-fidelity text while integrating an autonomous and on-demand external search module to resolve concerns requiring outside literature. By generating an inspectable response plan before drafting, RebuttalAgent ensures that every argument is explicitly anchored in internal or external evidence. We validate our approach on the proposed RebuttalBench and demonstrate that our pipeline outperforms strong baselines in coverage, faithfulness, and strategic coherence, offering a transparent and controllable assistant for the peer review process. Code will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.14750", "title": "Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning", "authors": [ "Yifan Wang", "Shiyu Li", "Peiming Li", "Xiaochen Yang", "Yang Tang", "Zheng Wei" ], "github": "https://github.com/TencentBAC/RoT", "project_page": "", "abstract": "Chain-of-Thought (CoT) prompting has achieved remarkable success in unlocking the reasoning capabilities of Large Language Models (LLMs). Although CoT prompting enhances reasoning, its verbosity imposes substantial computational overhead. Recent works often focus exclusively on outcome alignment and lack supervision on the intermediate reasoning process. These deficiencies obscure the analyzability of the latent reasoning chain. To address these challenges, we introduce Render-of-Thought (RoT), the first framework to reify the reasoning chain by rendering textual steps into images, making the latent rationale explicit and traceable. Specifically, we leverage the vision encoders of existing Vision Language Models (VLMs) as semantic anchors to align the vision embeddings with the textual space. This design ensures plug-and-play implementation without incurring additional pre-training overhead. Extensive experiments on mathematical and logical reasoning benchmarks demonstrate that our method achieves 3-4x token compression and substantial inference acceleration compared to explicit CoT. Furthermore, it maintains competitive performance against other methods, validating the feasibility of this paradigm. Our code is available at https://github.com/TencentBAC/RoT", "github_manual": "", "github_regex": "https://github.com/TencentBAC/RoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.15282", "title": "Rethinking Video Generation Model for the Embodied World", "authors": [ "Yufan Deng", "Zilin Pan", "Hongyu Zhang", "Xiaojie Li", "Ruoqing Hu", "Yufei Ding", "Yiming Zou", "Yan Zeng", "Daquan Zhou" ], "github": "", "project_page": "", "abstract": "Video generation models have significantly advanced embodied intelligence, unlocking new possibilities for generating diverse robot data that capture perception, reasoning, and action in the physical world. However, synthesizing high-quality videos that accurately reflect real-world robotic interactions remains challenging, and the lack of a standardized benchmark limits fair comparisons and progress. To address this gap, we introduce a comprehensive robotics benchmark, RBench, designed to evaluate robot-oriented video generation across five task domains and four distinct embodiments. It assesses both task-level correctness and visual fidelity through reproducible sub-metrics, including structural consistency, physical plausibility, and action completeness. Evaluation of 25 representative models highlights significant deficiencies in generating physically realistic robot behaviors. Furthermore, the benchmark achieves a Spearman correlation coefficient of 0.96 with human evaluations, validating its effectiveness. While RBench provides the necessary lens to identify these deficiencies, achieving physical realism requires moving beyond evaluation to address the critical shortage of high-quality training data. Driven by these insights, we introduce a refined four-stage data pipeline, resulting in RoVid-X, the largest open-source robotic dataset for video generation with 4 million annotated video clips, covering thousands of tasks and enriched with comprehensive physical property annotations. Collectively, this synergistic ecosystem of evaluation and data establishes a robust foundation for rigorous assessment and scalable training of video models, accelerating the evolution of embodied AI toward general intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.14245", "title": "XR: Cross-Modal Agents for Composed Image Retrieval", "authors": [ "Zhongyu Yang", "Wei Pang", "Yingfang Yuan" ], "github": "https://github.com/01yzzyu/xr.github.io", "project_page": "", "abstract": "Retrieval is being redefined by agentic AI, demanding multimodal reasoning beyond conventional similarity-based paradigms. Composed Image Retrieval (CIR) exemplifies this shift as each query combines a reference image with textual modifications, requiring compositional understanding across modalities. While embedding-based CIR methods have achieved progress, they remain narrow in perspective, capturing limited cross-modal cues and lacking semantic reasoning. To address these limitations, we introduce XR, a training-free multi-agent framework that reframes retrieval as a progressively coordinated reasoning process. It orchestrates three specialized types of agents: imagination agents synthesize target representations through cross-modal generation, similarity agents perform coarse filtering via hybrid matching, and question agents verify factual consistency through targeted reasoning for fine filtering. Through progressive multi-agent coordination, XR iteratively refines retrieval to meet both semantic and visual query constraints, achieving up to a 38% gain over strong training-free and training-based baselines on FashionIQ, CIRR, and CIRCO, while ablations show each agent is essential. Code is available: https://01yzzyu.github.io/xr.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/01yzzyu/xr.github.io", "github_api_added_by": "auto" }, { "date": "2026-01-22", "arxiv_id": "2601.14152", "title": "Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models", "authors": [ "Hyunjong Ok", "Jaeho Lee" ], "github": "", "project_page": "", "abstract": "Large language models exhibit surprising sensitivity to the structure of the prompt, but the mechanisms underlying this sensitivity remain poorly understood. In this work, we conduct an in-depth investigation on a striking case: in multiple-choice question answering, placing context before the questions and options (CQO) outperforms the reverse order (QOC) by over 14%p, consistently over a wide range of models and datasets. Through systematic architectural analysis, we identify causal attention as the core mechanism: in QOC prompts, the causal mask prevents option tokens from attending to context, creating an information bottleneck where context becomes invisible to options.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.15100", "title": "Facilitating Proactive and Reactive Guidance for Decision Making on the Web: A Design Probe with WebSeek", "authors": [ "Yanwei Huang", "Arpit Narechania" ], "github": "", "project_page": "", "abstract": "Web AI agents such as ChatGPT Agent and GenSpark are increasingly used for routine web-based tasks, yet they still rely on text-based input prompts, lack proactive detection of user intent, and offer no support for interactive data analysis and decision making. We present WebSeek, a mixed-initiative browser extension that enables users to discover and extract information from webpages to then flexibly build, transform, and refine tangible data artifacts-such as tables, lists, and visualizations-all within an interactive canvas. Within this environment, users can perform analysis-including data transformations such as joining tables or creating visualizations-while an in-built AI both proactively offers context-aware guidance and automation, and reactively responds to explicit user requests. An exploratory user study (N=15) with WebSeek as a probe reveals participants' diverse analysis strategies, underscoring their desire for transparency and control during human-AI collaboration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.14681", "title": "FARE: Fast-Slow Agentic Robotic Exploration", "authors": [ "Shuhao Liao", "Xuxin Lv", "Jeric Lew", "Shizhe Zhang", "Jingsong Liang", "Peizhuo Li", "Yuhong Cao", "Wenjun Wu", "Guillaume Sartoretti" ], "github": "", "project_page": "", "abstract": "This work advances autonomous robot exploration by integrating agent-level semantic reasoning with fast local control. We introduce FARE, a hierarchical autonomous exploration framework that integrates a large language model (LLM) for global reasoning with a reinforcement learning (RL) policy for local decision making. FARE follows a fast-slow thinking paradigm. The slow-thinking LLM module interprets a concise textual description of the unknown environment and synthesizes an agent-level exploration strategy, which is then grounded into a sequence of global waypoints through a topological graph. To further improve reasoning efficiency, this module employs a modularity-based pruning mechanism that reduces redundant graph structures. The fast-thinking RL module executes exploration by reacting to local observations while being guided by the LLM-generated global waypoints. The RL policy is additionally shaped by a reward term that encourages adherence to the global waypoints, enabling coherent and robust closed-loop behavior. This architecture decouples semantic reasoning from geometric decision, allowing each module to operate in its appropriate temporal and spatial scale. In challenging simulated environments, our results show that FARE achieves substantial improvements in exploration efficiency over state-of-the-art baselines. We further deploy FARE on hardware and validate it in complex, large scale 200mtimes130m building environment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.14352", "title": "RoboBrain 2.5: Depth in Sight, Time in Mind", "authors": [ "Huajie Tan", "Enshen Zhou", "Zhiyu Li", "Yijie Xu", "Yuheng Ji", "Xiansheng Chen", "Cheng Chi", "Pengwei Wang", "Huizhu Jia", "Yulong Ao", "Mingyu Cao", "Sixiang Chen", "Zhe Li", "Mengzhen Liu", "Zixiao Wang", "Shanyu Rong", "Yaoxu Lyu", "Zhongxia Zhao", "Peterson Co", "Yibo Li", "Yi Han", "Shaoxuan Xie", "Guocai Yao", "Songjing Wang", "Leiduo Zhang", "Xi Yang", "Yance Jiao", "Donghai Shi", "Kunchang Xie", "Shaokai Nie", "Chunlei Men", "Yonghua Lin", "Zhongyuan Wang", "Tiejun Huang", "Shanghang Zhang" ], "github": "https://github.com/FlagOpen/RoboBrain2.5", "project_page": "", "abstract": "We introduce RoboBrain 2.5, a next-generation embodied AI foundation model that advances general perception, spatial reasoning, and temporal modeling through extensive training on high-quality spatiotemporal supervision. Building upon its predecessor, RoboBrain 2.5 introduces two major capability upgrades. Specifically, it unlocks Precise 3D Spatial Reasoning by shifting from 2D pixel-relative grounding to depth-aware coordinate prediction and absolute metric constraint comprehension, generating complete 3D manipulation traces as ordered keypoint sequences under physical constraints. Complementing this spatial precision, the model establishes Dense Temporal Value Estimation that provides dense, step-aware progress prediction and execution state understanding across varying viewpoints, producing stable feedback signals for downstream learning. Together, these upgrades extend the framework toward more physically grounded and execution-aware embodied intelligence for complex, fine-grained manipulation. The code and checkpoints are available at project website: https://superrobobrain.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FlagOpen/RoboBrain2.5", "github_api_added_by": "auto" }, { "date": "2026-01-22", "arxiv_id": "2601.12346", "title": "MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents", "authors": [ "Peizhou Huang", "Zixuan Zhong", "Zhongwei Wan", "Donghao Zhou", "Samiul Alam", "Xin Wang", "Zexin Li", "Zhihao Dou", "Li Zhu", "Jing Xiong", "Chaofan Tao", "Yan Xu", "Dimitrios Dimitriadis", "Tuo Zhang", "Mi Zhang" ], "github": "https://github.com/Alibaba-NLP/DeepResearch", "project_page": "", "abstract": "Deep Research Agents (DRAs) generate citation-rich reports via multi-step search and synthesis, yet existing benchmarks mainly target text-only settings or short-form multimodal QA, missing end-to-end multimodal evidence use. We introduce MMDeepResearch-Bench (MMDR-Bench), a benchmark of 140 expert-crafted tasks across 21 domains, where each task provides an image-text bundle to evaluate multimodal understanding and citation-grounded report generation. Compared to prior setups, MMDR-Bench emphasizes report-style synthesis with explicit evidence use, where models must connect visual artifacts to sourced claims and maintain consistency across narrative, citations, and visual references. We further propose a unified, interpretable evaluation pipeline: Formula-LLM Adaptive Evaluation (FLAE) for report quality, Trustworthy Retrieval-Aligned Citation Evaluation (TRACE) for citation-grounded evidence alignment, and Multimodal Support-Aligned Integrity Check (MOSAIC) for text-visual integrity, each producing fine-grained signals that support error diagnosis beyond a single overall score. Experiments across 25 state-of-the-art models reveal systematic trade-offs between generation quality, citation discipline, and multimodal grounding, highlighting that strong prose alone does not guarantee faithful evidence use and that multimodal integrity remains a key bottleneck for deep research agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Alibaba-NLP/DeepResearch", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.07853", "title": "FinVault: Benchmarking Financial Agent Safety in Execution-Grounded Environments", "authors": [ "Zhi Yang", "Runguo Li", "Qiqi Qiang", "Jiashun Wang", "Fangqi Lou", "Mengping Li", "Dongpo Cheng", "Rui Xu", "Heng Lian", "Shuo Zhang", "Xiaolong Liang", "Xiaoming Huang", "Zheng Wei", "Zhaowei Liu", "Xin Guo", "Huacan Wang", "Ronghao Chen", "Liwen Zhang" ], "github": "https://github.com/aifinlab/FinVault", "project_page": "", "abstract": "Financial agents powered by large language models (LLMs) are increasingly deployed for investment analysis, risk assessment, and automated decision-making, where their abilities to plan, invoke tools, and manipulate mutable state introduce new security risks in high-stakes and highly regulated financial environments. However, existing safety evaluations largely focus on language-model-level content compliance or abstract agent settings, failing to capture execution-grounded risks arising from real operational workflows and state-changing actions. To bridge this gap, we propose FinVault, the first execution-grounded security benchmark for financial agents, comprising 31 regulatory case-driven sandbox scenarios with state-writable databases and explicit compliance constraints, together with 107 real-world vulnerabilities and 963 test cases that systematically cover prompt injection, jailbreaking, financially adapted attacks, as well as benign inputs for false-positive evaluation. Experimental results reveal that existing defense mechanisms remain ineffective in realistic financial agent settings, with average attack success rates (ASR) still reaching up to 50.0\\% on state-of-the-art models and remaining non-negligible even for the most robust systems (ASR 6.7\\%), highlighting the limited transferability of current safety designs and the need for stronger financial-specific defenses. Our code can be found at https://github.com/aifinlab/FinVault.", "github_manual": "", "github_regex": "https://github.com/aifinlab/FinVault", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.15059", "title": "The Responsibility Vacuum: Organizational Failure in Scaled Agent Systems", "authors": [ "Oleg Romanchuk", "Roman Bondar" ], "github": "", "project_page": "", "abstract": "Modern CI/CD pipelines integrating agent-generated code exhibit a structural failure in responsibility attribution. Decisions are executed through formally correct approval processes, yet no entity possesses both the authority to approve those decisions and the epistemic capacity to meaningfully understand their basis. We define this condition as responsibility vacuum: a state in which decisions occur, but responsibility cannot be attributed because authority and verification capacity do not coincide. We show that this is not a process deviation or technical defect, but a structural property of deployments where decision generation throughput exceeds bounded human verification capacity. We identify a scaling limit under standard deployment assumptions, including parallel agent generation, CI-based validation, and individualized human approval gates. Beyond a throughput threshold, verification ceases to function as a decision criterion and is replaced by ritualized approval based on proxy signals. Personalized responsibility becomes structurally unattainable in this regime. We further characterize a CI amplification dynamic, whereby increasing automated validation coverage raises proxy signal density without restoring human capacity. Under fixed time and attention constraints, this accelerates cognitive offloading in the broad sense and widens the gap between formal approval and epistemic understanding. Additional automation therefore amplifies, rather than mitigates, the responsibility vacuum. We conclude that unless organizations explicitly redesign decision boundaries or reassign responsibility away from individual decisions toward batch- or system-level ownership, responsibility vacuum remains an invisible but persistent failure mode in scaled agent deployments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.13918", "title": "AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization", "authors": [ "Yusheng Liao", "Chuan Xuan", "Yutong Cai", "Lina Yang", "Zhe Chen", "Yanfeng Wang", "Yu Wang" ], "github": "https://github.com/BlueZeros/AgentEHR", "project_page": "", "abstract": "Large Language Models have demonstrated profound utility in the medical domain. However, their application to autonomous Electronic Health Records~(EHRs) navigation remains constrained by a reliance on curated inputs and simplified retrieval tasks. To bridge the gap between idealized experimental settings and realistic clinical environments, we present AgentEHR. This benchmark challenges agents to execute complex decision-making tasks, such as diagnosis and treatment planning, requiring long-range interactive reasoning directly within raw and high-noise databases. In tackling these tasks, we identify that existing summarization methods inevitably suffer from critical information loss and fractured reasoning continuity. To address this, we propose RetroSum, a novel framework that unifies a retrospective summarization mechanism with an evolving experience strategy. By dynamically re-evaluating interaction history, the retrospective mechanism prevents long-context information loss and ensures unbroken logical coherence. Additionally, the evolving strategy bridges the domain gap by retrieving accumulated experience from a memory bank. Extensive empirical evaluations demonstrate that RetroSum achieves performance gains of up to 29.16% over competitive baselines, while significantly decreasing total interaction errors by up to 92.3%.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/BlueZeros/AgentEHR", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.14027", "title": "Numina-Lean-Agent: An Open and General Agentic Reasoning System for Formal Mathematics", "authors": [ "Junqi Liu", "Zihao Zhou", "Zekai Zhu", "Marco Dos Santos", "Weikun He", "Jiawei Liu", "Ran Wang", "Yunzhou Xie", "Junqiao Zhao", "Qiufeng Wang", "Lihong Zhi", "Jia Li", "Wenda Li" ], "github": "https://github.com/project-numina/numina-lean-agent", "project_page": "", "abstract": "Agentic systems have recently become the dominant paradigm for formal theorem proving, achieving strong performance by coordinating multiple models and tools. However, existing approaches often rely on task-specific pipelines and trained formal provers, limiting their flexibility and reproducibility. In this paper, we propose the paradigm that directly uses a general coding agent as a formal math reasoner. This paradigm is motivated by (1) A general coding agent provides a natural interface for diverse reasoning tasks beyond proving, (2) Performance can be improved by simply replacing the underlying base model, without training, and (3) MCP enables flexible extension and autonomous calling of specialized tools, avoiding complex design. Based on this paradigm, we introduce Numina-Lean-Agent, which combines Claude Code with Numina-Lean-MCP to enable autonomous interaction with Lean, retrieval of relevant theorems, informal proving and auxiliary reasoning tools. Using Claude Opus 4.5 as the base model, Numina-Lean-Agent solves all problems in Putnam 2025 (12 / 12), matching the best closed-source system. Beyond benchmark evaluation, we further demonstrate its generality by interacting with mathematicians to successfully formalize the Brascamp-Lieb theorem. We release Numina-Lean-Agent and all solutions at https://github.com/project-numina/numina-lean-agent.", "github_manual": "", "github_regex": "https://github.com/project-numina/numina-lean-agent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.14722", "title": "Typhoon OCR: Open Vision-Language Model For Thai Document Extraction", "authors": [ "Surapon Nonesung", "Natapong Nitarach", "Teetouch Jaknamon", "Pittawat Taveekitworachai", "Kunat Pipatanakul" ], "github": "https://github.com/allenai/olmocr", "project_page": "", "abstract": "Document extraction is a core component of digital workflows, yet existing vision-language models (VLMs) predominantly favor high-resource languages. Thai presents additional challenges due to script complexity from non-latin letters, the absence of explicit word boundaries, and the prevalence of highly unstructured real-world documents, limiting the effectiveness of current open-source models. This paper presents Typhoon OCR, an open VLM for document extraction tailored for Thai and English. The model is fine-tuned from vision-language backbones using a Thai-focused training dataset. The dataset is developed using a multi-stage data construction pipeline that combines traditional OCR, VLM-based restructuring, and curated synthetic data. Typhoon OCR is a unified framework capable of text transcription, layout reconstruction, and document-level structural consistency. The latest iteration of our model, Typhoon OCR V1.5, is a compact and inference-efficient model designed to reduce reliance on metadata and simplify deployment. Comprehensive evaluations across diverse Thai document categories, including financial reports, government forms, books, infographics, and handwritten documents, show that Typhoon OCR achieves performance comparable to or exceeding larger frontier proprietary models, despite substantially lower computational cost. The results demonstrate that open vision-language OCR models can achieve accurate text extraction and layout reconstruction for Thai documents, reaching performance comparable to proprietary systems while remaining lightweight and deployable.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/allenai/olmocr", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.14417", "title": "Quantifying Speaker Embedding Phonological Rule Interactions in Accented Speech Synthesis", "authors": [ "Thanathai Lertpetchpun", "Yoonjeong Lee", "Thanapat Trachu", "Jihwan Lee", "Tiantian Feng", "Dani Byrd", "Shrikanth Narayanan" ], "github": "", "project_page": "", "abstract": "Many spoken languages, including English, exhibit wide variation in dialects and accents, making accent control an important capability for flexible text-to-speech (TTS) models. Current TTS systems typically generate accented speech by conditioning on speaker embeddings associated with specific accents. While effective, this approach offers limited interpretability and controllability, as embeddings also encode traits such as timbre and emotion. In this study, we analyze the interaction between speaker embeddings and linguistically motivated phonological rules in accented speech synthesis. Using American and British English as a case study, we implement rules for flapping, rhoticity, and vowel correspondences. We propose the phoneme shift rate (PSR), a novel metric quantifying how strongly embeddings preserve or override rule-based transformations. Experiments show that combining rules with embeddings yields more authentic accents, while embeddings can attenuate or overwrite rules, revealing entanglement between accent and speaker identity. Our findings highlight rules as a lever for accent control and a framework for evaluating disentanglement in speech generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.13044", "title": "Typhoon ASR Real-time: FastConformer-Transducer for Thai Automatic Speech Recognition", "authors": [ "Warit Sirichotedumrong", "Adisai Na-Thalang", "Potsawee Manakul", "Pittawat Taveekitworachai", "Sittipong Sripaisarnmongkol", "Kunat Pipatanakul" ], "github": "", "project_page": "", "abstract": "Large encoder-decoder models like Whisper achieve strong offline transcription but remain impractical for streaming applications due to high latency. However, due to the accessibility of pre-trained checkpoints, the open Thai ASR landscape remains dominated by these offline architectures, leaving a critical gap in efficient streaming solutions. We present Typhoon ASR Real-time, a 115M-parameter FastConformer-Transducer model for low-latency Thai speech recognition. We demonstrate that rigorous text normalization can match the impact of model scaling: our compact model achieves a 45x reduction in computational cost compared to Whisper Large-v3 while delivering comparable accuracy. Our normalization pipeline resolves systemic ambiguities in Thai transcription --including context-dependent number verbalization and repetition markers (mai yamok) --creating consistent training targets. We further introduce a two-stage curriculum learning approach for Isan (north-eastern) dialect adaptation that preserves Central Thai performance. To address reproducibility challenges in Thai ASR, we release the Typhoon ASR Benchmark, a gold-standard human-labeled datasets with transcriptions following established Thai linguistic conventions, providing standardized evaluation protocols for the research community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.12029", "title": "sangkuriang: A pseudo-spectral Python library for Korteweg-de Vries soliton simulation", "authors": [ "Sandy H. S. Herho", "Faruq Khadami", "Iwan P. Anwar", "Dasapta E. Irawan" ], "github": "https://github.com/sandyherho/sangkuriang-ideal-solver", "project_page": "", "abstract": "The Korteweg-de Vries (KdV) equation serves as a foundational model in nonlinear wave physics, describing the balance between dispersive spreading and nonlinear steepening that gives rise to solitons. This article introduces sangkuriang, an open-source Python library for solving this equation using Fourier pseudo-spectral spatial discretization coupled with adaptive high-order time integration. The implementation leverages just-in-time (JIT) compilation for computational efficiency while maintaining accessibility for instructional purposes. Validation encompasses progressively complex scenarios including isolated soliton propagation, symmetric two-wave configurations, overtaking collisions between waves of differing amplitudes, and three-body interactions. Conservation of the classical invariants is monitored throughout, with deviations remaining small across all test cases. Measured soliton velocities conform closely to theoretical predictions based on the amplitude-velocity relationship characteristic of integrable systems. Complementary diagnostics drawn from information theory and recurrence analysis confirm that computed solutions preserve the regular phase-space structure expected for completely integrable dynamics. The solver outputs data in standard scientific formats compatible with common analysis tools and generates visualizations of spatiotemporal wave evolution. By combining numerical accuracy with practical accessibility on modest computational resources, sangkuriang offers a platform suitable for both classroom demonstrations of nonlinear wave phenomena and exploratory research into soliton dynamics.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sandyherho/sangkuriang-ideal-solver", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.15220", "title": "Privacy Collapse: Benign Fine-Tuning Can Break Contextual Privacy in Language Models", "authors": [ "Anmol Goel", "Cornelius Emde", "Sangdoo Yun", "Seong Joon Oh", "Martin Gubri" ], "github": "https://github.com/parameterlab/privacy-collapse", "project_page": "", "abstract": "We identify a novel phenomenon in language models: benign fine-tuning of frontier models can lead to privacy collapse. We find that diverse, subtle patterns in training data can degrade contextual privacy, including optimisation for helpfulness, exposure to user information, emotional and subjective dialogue, and debugging code printing internal variables, among others. Fine-tuned models lose their ability to reason about contextual privacy norms, share information inappropriately with tools, and violate memory boundaries across contexts. Privacy collapse is a ``silent failure'' because models maintain high performance on standard safety and utility benchmarks whilst exhibiting severe privacy vulnerabilities. Our experiments show evidence of privacy collapse across six models (closed and open weight), five fine-tuning datasets (real-world and controlled data), and two task categories (agentic and memory-based). Our mechanistic analysis reveals that privacy representations are uniquely fragile to fine-tuning, compared to task-relevant features which are preserved. Our results reveal a critical gap in current safety evaluations, in particular for the deployment of specialised agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/parameterlab/privacy-collapse", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.13572", "title": "Behavior Knowledge Merge in Reinforced Agentic Models", "authors": [ "Xiangchi Yuan", "Dachuan Shi", "Chunhui Zhang", "Zheyuan Liu", "Shenglong Yao", "Soroush Vosoughi", "Wenke Lee" ], "github": "https://github.com/xiangchi-yuan/mrl", "project_page": "", "abstract": "Reinforcement learning (RL) is central to post-training, particularly for agentic models that require specialized reasoning behaviors. In this setting, model merging offers a practical mechanism for integrating multiple RL-trained agents from different tasks into a single generalist model. However, existing merging methods are designed for supervised fine-tuning (SFT), and they are suboptimal to preserve task-specific capabilities on RL-trained agentic models. The root is a task-vector mismatch between RL and SFT: on-policy RL induces task vectors that are highly sparse and heterogeneous, whereas SFT-style merging implicitly assumes dense and globally comparable task vectors. When standard global averaging is applied under this mismatch, RL's non-overlapping task vectors that encode critical task-specific behaviors are reduced and parameter updates are diluted. To address this issue, we propose Reinforced Agent Merging (RAM), a distribution-aware merging framework explicitly designed for RL-trained agentic models. RAM disentangles shared and task-specific unique parameter updates, averaging shared components while selectively preserving and rescaling unique ones to counteract parameter update dilution. Experiments across multiple agent domains and model architectures demonstrate that RAM not only surpasses merging baselines, but also unlocks synergistic potential among agents to achieve performance superior to that of specialized agents in their domains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xiangchi-yuan/mrl", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.11141", "title": "FlashLabs Chroma 1.0: A Real-Time End-to-End Spoken Dialogue Model with Personalized Voice Cloning", "authors": [ "Tanyu Chen", "Tairan Chen", "Kai Shen", "Zhenghua Bao", "Zhihui Zhang", "Man Yuan", "Yi Shi" ], "github": "https://github.com/FlashLabs-AI-Corp/FlashLabs-Chroma", "project_page": "", "abstract": "Recent end-to-end spoken dialogue systems leverage speech tokenizers and neural audio codecs to enable LLMs to operate directly on discrete speech representations. However, these models often exhibit limited speaker identity preservation, hindering personalized voice interaction. In this work, we present Chroma 1.0, the first open-source, real-time, end-to-end spoken dialogue model that achieves both low-latency interaction and high-fidelity personalized voice cloning. Chroma achieves sub-second end-to-end latency through an interleaved text-audio token schedule (1:2) that supports streaming generation, while maintaining high-quality personalized voice synthesis across multi-turn conversations. Our experimental results demonstrate that Chroma achieves a 10.96% relative improvement in speaker similarity over the human baseline, with a Real-Time Factor (RTF) of 0.43, while maintaining strong reasoning and dialogue capabilities. Our code and models are publicly available at https://github.com/FlashLabs-AI-Corp/FlashLabs-Chroma and https://huggingface.co/FlashLabs/Chroma-4B .", "github_manual": "", "github_regex": "https://github.com/FlashLabs-AI-Corp/FlashLabs-Chroma", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.14490", "title": "GutenOCR: A Grounded Vision-Language Front-End for Documents", "authors": [ "Hunter Heidenreich", "Ben Elliott", "Olivia Dinica", "Yosheb Getachew" ], "github": "https://github.com/Roots-Automation/GutenOCR", "project_page": "", "abstract": "GutenOCR is a family of grounded OCR front-ends obtained by fine-tuning Qwen2.5-VL-3B and Qwen2.5-VL-7B. The resulting single-checkpoint vision-language models expose reading, detection, and grounding through a unified, prompt-based interface. Trained on business documents, scientific articles, and synthetic grounding data, the models support full-page and localized reading with line- and paragraph-level bounding boxes and conditional ``where is x?'' queries. We introduce a grounded OCR evaluation protocol and show that GutenOCR-7B more than doubles the composite grounded OCR score of its Qwen2.5-VL-7B backbone on 10.5K held-out business and scientific pages (0.40 to 0.82). On Fox and OmniDocBench v1.5, our approach substantially improves region- and line-level OCR as well as text-detection recall, but reveals trade-offs in page-level linearization, color-guided OCR, and formula-heavy layouts.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Roots-Automation/GutenOCR", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.11387", "title": "Show me the evidence: Evaluating the role of evidence and natural language explanations in AI-supported fact-checking", "authors": [ "Greta Warren", "Jingyi Sun", "Irina Shklovski", "Isabelle Augenstein" ], "github": "", "project_page": "", "abstract": "Although much research has focused on AI explanations to support decisions in complex information-seeking tasks such as fact-checking, the role of evidence is surprisingly under-researched. In our study, we systematically varied explanation type, AI prediction certainty, and correctness of AI system advice for non-expert participants, who evaluated the veracity of claims and AI system predictions. Participants were provided the option of easily inspecting the underlying evidence. We found that participants consistently relied on evidence to validate AI claims across all experimental conditions. When participants were presented with natural language explanations, evidence was used less frequently although they relied on it when these explanations seemed insufficient or flawed. Qualitative data suggests that participants attempted to infer evidence source reliability, despite source identities being deliberately omitted. Our results demonstrate that evidence is a key ingredient in how people evaluate the reliability of information presented by an AI system and, in combination with natural language explanations, offers valuable support for decision-making. Further research is urgently needed to understand how evidence ought to be presented and how people engage with it in practice.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.13262", "title": "CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning", "authors": [ "Eric Onyame", "Akash Ghosh", "Subhadip Baidya", "Sriparna Saha", "Xiuying Chen", "Chirag Agarwal" ], "github": "", "project_page": "", "abstract": "While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for multilingual medical reasoning applications, hindering their deployment in multilingual healthcare settings. We address this by first introducing CUREMED-BENCH, a high-quality multilingual medical reasoning dataset with open-ended reasoning queries with a single verifiable answer, spanning thirteen languages, including underrepresented languages such as Amharic, Yoruba, and Swahili. Building on this dataset, we propose CURE-MED, a curriculum-informed reinforcement learning framework that integrates code-switching-aware supervised fine-tuning and Group Relative Policy Optimization to jointly improve logical correctness and language stability. Across thirteen languages, our approach consistently outperforms strong baselines and scales effectively, achieving 85.21% language consistency and 54.35% logical correctness at 7B parameters, and 94.96% language consistency and 70.04% logical correctness at 32B parameters. These results support reliable and equitable multilingual medical reasoning in LLMs. The code and dataset are available at https://cure-med.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.14256", "title": "Implicit Neural Representation Facilitates Unified Universal Vision Encoding", "authors": [ "Matthew Gwilliam", "Xiao Wang", "Xuefeng Hu", "Zhenheng Yang" ], "github": "https://github.com/tiktok/huvr", "project_page": "", "abstract": "Models for image representation learning are typically designed for either recognition or generation. Various forms of contrastive learning help models learn to convert images to embeddings that are useful for classification, detection, and segmentation. On the other hand, models can be trained to reconstruct images with pixel-wise, perceptual, and adversarial losses in order to learn a latent space that is useful for image generation. We seek to unify these two directions with a first-of-its-kind model that learns representations which are simultaneously useful for recognition and generation. We train our model as a hyper-network for implicit neural representation, which learns to map images to model weights for fast, accurate reconstruction. We further integrate our INR hyper-network with knowledge distillation to improve its generalization and performance. Beyond the novel training design, the model also learns an unprecedented compressed embedding space with outstanding performance for various visual tasks. The complete model competes with state-of-the-art results for image representation learning, while also enabling generative capabilities with its high-quality tiny embeddings. The code is available at https://github.com/tiktok/huvr.", "github_manual": "", "github_regex": "https://github.com/tiktok/huvr", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-22", "arxiv_id": "2601.14253", "title": "Motion 3-to-4: 3D Motion Reconstruction for 4D Synthesis", "authors": [ "Hongyuan Chen", "Xingyu Chen", "Youjia Zhang", "Zexiang Xu", "Anpei Chen" ], "github": "https://github.com/Inception3D/Motion324", "project_page": "", "abstract": "We present Motion 3-to-4, a feed-forward framework for synthesising high-quality 4D dynamic objects from a single monocular video and an optional 3D reference mesh. While recent advances have significantly improved 2D, video, and 3D content generation, 4D synthesis remains difficult due to limited training data and the inherent ambiguity of recovering geometry and motion from a monocular viewpoint. Motion 3-to-4 addresses these challenges by decomposing 4D synthesis into static 3D shape generation and motion reconstruction. Using a canonical reference mesh, our model learns a compact motion latent representation and predicts per-frame vertex trajectories to recover complete, temporally coherent geometry. A scalable frame-wise transformer further enables robustness to varying sequence lengths. Evaluations on both standard benchmarks and a new dataset with accurate ground-truth geometry show that Motion 3-to-4 delivers superior fidelity and spatial consistency compared to prior work. Project page is available at https://motion3-to-4.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Inception3D/Motion324", "github_api_added_by": "auto" }, { "date": "2026-01-23", "arxiv_id": "2601.15165", "title": "The Flexibility Trap: Why Arbitrary Order Limits Reasoning Potential in Diffusion Language Models", "authors": [ "Zanlin Ni", "Shenzhi Wang", "Yang Yue", "Tianyu Yu", "Weilin Zhao", "Yeguo Hua", "Tianyi Chen", "Jun Song", "Cheng Yu", "Bo Zheng", "Gao Huang" ], "github": "", "project_page": "", "abstract": "Diffusion Large Language Models (dLLMs) break the rigid left-to-right constraint of traditional LLMs, enabling token generation in arbitrary orders. Intuitively, this flexibility implies a solution space that strictly supersets the fixed autoregressive trajectory, theoretically unlocking superior reasoning potential for general tasks like mathematics and coding. Consequently, numerous works have leveraged reinforcement learning (RL) to elicit the reasoning capability of dLLMs. In this paper, we reveal a counter-intuitive reality: arbitrary order generation, in its current form, narrows rather than expands the reasoning boundary of dLLMs. We find that dLLMs tend to exploit this order flexibility to bypass high-uncertainty tokens that are crucial for exploration, leading to a premature collapse of the solution space. This observation challenges the premise of existing RL approaches for dLLMs, where considerable complexities, such as handling combinatorial trajectories and intractable likelihoods, are often devoted to preserving this flexibility. We demonstrate that effective reasoning is better elicited by intentionally forgoing arbitrary order and applying standard Group Relative Policy Optimization (GRPO) instead. Our approach, JustGRPO, is minimalist yet surprisingly effective (e.g., 89.1% accuracy on GSM8K) while fully retaining the parallel decoding ability of dLLMs. Project page: https://nzl-thu.github.io/the-flexibility-trap", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.15892", "title": "Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model", "authors": [ "Chenghao Fan", "Wen Heng", "Bo Li", "Sichen Liu", "Yuxuan Song", "Jing Su", "Xiaoye Qu", "Kai Shen", "Wei Wei" ], "github": "", "project_page": "", "abstract": "Diffusion-based language models (DLLMs) offer non-sequential, block-wise generation and richer data reuse compared to autoregressive (AR) models, but existing code DLLMs still lag behind strong AR baselines under comparable budgets. We revisit this setting in a controlled study and introduce Stable-DiffCoder, a block diffusion code model that reuses the Seed-Coder architecture, data, and training pipeline. To enable efficient knowledge learning and stable training, we incorporate a block diffusion continual pretraining (CPT) stage enhanced by a tailored warmup and block-wise clipped noise schedule. Under the same data and architecture, Stable-DiffCoder overall outperforms its AR counterpart on a broad suite of code benchmarks. Moreover, relying only on the CPT and supervised fine-tuning stages, Stable-DiffCoder achieves stronger performance than a wide range of \\~8B ARs and DLLMs, demonstrating that diffusion-based training can improve code modeling quality beyond AR training alone. Moreover, diffusion-based any-order modeling improves structured code modeling for editing and reasoning, and through data augmentation, benefits low-resource coding languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.16175", "title": "Learning to Discover at Test Time", "authors": [ "Mert Yuksekgonul", "Daniel Koceja", "Xinhao Li", "Federico Bianchi", "Jed McCaleb", "Xiaolong Wang", "Jan Kautz", "Yejin Choi", "James Zou", "Carlos Guestrin", "Yu Sun" ], "github": "https://github.com/algorithmicsuperintelligence/openevolve", "project_page": "", "abstract": "How can we use AI to discover a new state of the art for a scientific problem? Prior work in test-time scaling, such as AlphaEvolve, performs search by prompting a frozen LLM. We perform reinforcement learning at test time, so the LLM can continue to train, but now with experience specific to the test problem. This form of continual learning is quite special, because its goal is to produce one great solution rather than many good ones on average, and to solve this very problem rather than generalize to other problems. Therefore, our learning objective and search subroutine are designed to prioritize the most promising solutions. We call this method Test-Time Training to Discover (TTT-Discover). Following prior work, we focus on problems with continuous rewards. We report results for every problem we attempted, across mathematics, GPU kernel engineering, algorithm design, and biology. TTT-Discover sets the new state of the art in almost all of them: (i) Erd\u0151s' minimum overlap problem and an autocorrelation inequality; (ii) a GPUMode kernel competition (up to 2times faster than prior art); (iii) past AtCoder algorithm competitions; and (iv) denoising problem in single-cell analysis. Our solutions are reviewed by experts or the organizers. All our results are achieved with an open model, OpenAI gpt-oss-120b, and can be reproduced with our publicly available code, in contrast to previous best results that required closed frontier models. Our test-time training runs are performed using Tinker, an API by Thinking Machines, with a cost of only a few hundred dollars per problem.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/algorithmicsuperintelligence/openevolve", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.15369", "title": "OpenVision 3: A Family of Unified Visual Encoder for Both Understanding and Generation", "authors": [ "Letian Zhang", "Sucheng Ren", "Yanqing Liu", "Xianhang Li", "Zeyu Wang", "Yuyin Zhou", "Huaxiu Yao", "Zeyu Zheng", "Weili Nie", "Guilin Liu", "Zhiding Yu", "Cihang Xie" ], "github": "", "project_page": "", "abstract": "This paper presents a family of advanced vision encoder, named OpenVision 3, that learns a single, unified visual representation that can serve both image understanding and image generation. Our core architecture is simple: we feed VAE-compressed image latents to a ViT encoder and train its output to support two complementary roles. First, the encoder output is passed to the ViT-VAE decoder to reconstruct the original image, encouraging the representation to capture generative structure. Second, the same representation is optimized with contrastive learning and image-captioning objectives, strengthening semantic features. By jointly optimizing reconstruction- and semantics-driven signals in a shared latent space, the encoder learns representations that synergize and generalize well across both regimes. We validate this unified design through extensive downstream evaluations with the encoder frozen. For multimodal understanding, we plug the encoder into the LLaVA-1.5 framework: it performs comparably with a standard CLIP vision encoder (e.g., 62.4 vs 62.2 on SeedBench, and 83.7 vs 82.9 on POPE). For generation, we test it under the RAE framework: ours substantially surpasses the standard CLIP-based encoder (e.g., gFID: 1.89 vs 2.54 on ImageNet). We hope this work can spur future research on unified modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.16125", "title": "Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image Editing", "authors": [ "Tingyu Song", "Yanzhao Zhang", "Mingxin Li", "Zhuoning Guo", "Dingkun Long", "Pengjun Xie", "Siyue Zhang", "Yilun Zhao", "Shu Wu" ], "github": "https://github.com/sighingsnow/edir", "project_page": "", "abstract": "Composed Image Retrieval (CIR) is a pivotal and complex task in multimodal understanding. Current CIR benchmarks typically feature limited query categories and fail to capture the diverse requirements of real-world scenarios. To bridge this evaluation gap, we leverage image editing to achieve precise control over modification types and content, enabling a pipeline for synthesizing queries across a broad spectrum of categories. Using this pipeline, we construct EDIR, a novel fine-grained CIR benchmark. EDIR encompasses 5,000 high-quality queries structured across five main categories and fifteen subcategories. Our comprehensive evaluation of 13 multimodal embedding models reveals a significant capability gap; even state-of-the-art models (e.g., RzenEmbed and GME) struggle to perform consistently across all subcategories, highlighting the rigorous nature of our benchmark. Through comparative analysis, we further uncover inherent limitations in existing benchmarks, such as modality biases and insufficient categorical coverage. Furthermore, an in-domain training experiment demonstrates the feasibility of our benchmark. This experiment clarifies the task challenges by distinguishing between categories that are solvable with targeted data and those that expose intrinsic limitations of current model architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sighingsnow/edir", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.15197", "title": "BayesianVLA: Bayesian Decomposition of Vision Language Action Models via Latent Action Queries", "authors": [ "Shijie Lian", "Bin Yu", "Xiaopeng Lin", "Laurence T. Yang", "Zhaolong Shen", "Changti Wu", "Yuzhuo Miao", "Cong Huang", "Kai Chen" ], "github": "https://github.com/starVLA/starVLA", "project_page": "", "abstract": "Vision-Language-Action (VLA) models have shown promise in robot manipulation but often struggle to generalize to new instructions or complex multi-task scenarios. We identify a critical pathology in current training paradigms where goal-driven data collection creates a dataset bias. In such datasets, language instructions are highly predictable from visual observations alone, causing the conditional mutual information between instructions and actions to vanish, a phenomenon we term Information Collapse. Consequently, models degenerate into vision-only policies that ignore language constraints and fail in out-of-distribution (OOD) settings. To address this, we propose BayesianVLA, a novel framework that enforces instruction following via Bayesian decomposition. By introducing learnable Latent Action Queries, we construct a dual-branch architecture to estimate both a vision-only prior p(a mid v) and a language-conditioned posterior \u03c0(a mid v, ell). We then optimize the policy to maximize the conditional Pointwise Mutual Information (PMI) between actions and instructions. This objective effectively penalizes the vision shortcut and rewards actions that explicitly explain the language command. Without requiring new data, BayesianVLA significantly improves generalization. Extensive experiments across on SimplerEnv and RoboCasa demonstrate substantial gains, including an 11.3% improvement on the challenging OOD SimplerEnv benchmark, validating the ability of our approach to robustly ground language in action.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/starVLA/starVLA", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.16206", "title": "LLM-in-Sandbox Elicits General Agentic Intelligence", "authors": [ "Daixuan Cheng", "Shaohan Huang", "Yuxian Gu", "Huatong Song", "Guoxin Chen", "Li Dong", "Wayne Xin Zhao", "Ji-Rong Wen", "Furu Wei" ], "github": "", "project_page": "", "abstract": "We introduce LLM-in-Sandbox, enabling LLMs to explore within a code sandbox (i.e., a virtual computer), to elicit general intelligence in non-code domains. We first demonstrate that strong LLMs, without additional training, exhibit generalization capabilities to leverage the code sandbox for non-code tasks. For example, LLMs spontaneously access external resources to acquire new knowledge, leverage the file system to handle long contexts, and execute scripts to satisfy formatting requirements. We further show that these agentic capabilities can be enhanced through LLM-in-Sandbox Reinforcement Learning (LLM-in-Sandbox-RL), which uses only non-agentic data to train models for sandbox exploration. Experiments demonstrate that LLM-in-Sandbox, in both training-free and post-trained settings, achieves robust generalization spanning mathematics, physics, chemistry, biomedicine, long-context understanding, and instruction following. Finally, we analyze LLM-in-Sandbox's efficiency from computational and system perspectives, and open-source it as a Python package to facilitate real-world deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.16208", "title": "Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders", "authors": [ "Shengbang Tong", "Boyang Zheng", "Ziteng Wang", "Bingda Tang", "Nanye Ma", "Ellis Brown", "Jihan Yang", "Rob Fergus", "Yann LeCun", "Saining Xie" ], "github": "", "project_page": "", "abstract": "Representation Autoencoders (RAEs) have shown distinct advantages in diffusion modeling on ImageNet by training in high-dimensional semantic latent spaces. In this work, we investigate whether this framework can scale to large-scale, freeform text-to-image (T2I) generation. We first scale RAE decoders on the frozen representation encoder (SigLIP-2) beyond ImageNet by training on web, synthetic, and text-rendering data, finding that while scale improves general fidelity, targeted data composition is essential for specific domains like text. We then rigorously stress-test the RAE design choices originally proposed for ImageNet. Our analysis reveals that scaling simplifies the framework: while dimension-dependent noise scheduling remains critical, architectural complexities such as wide diffusion heads and noise-augmented decoding offer negligible benefits at scale Building on this simplified framework, we conduct a controlled comparison of RAE against the state-of-the-art FLUX VAE across diffusion transformer scales from 0.5B to 9.8B parameters. RAEs consistently outperform VAEs during pretraining across all model scales. Further, during finetuning on high-quality datasets, VAE-based models catastrophically overfit after 64 epochs, while RAE models remain stable through 256 epochs and achieve consistently better performance. Across all experiments, RAE-based diffusion models demonstrate faster convergence and better generation quality, establishing RAEs as a simpler and stronger foundation than VAEs for large-scale T2I generation. Additionally, because both visual understanding and generation can operate in a shared representation space, the multimodal model can directly reason over generated latents, opening new possibilities for unified models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.15621", "title": "Qwen3-TTS Technical Report", "authors": [ "Hangrui Hu", "Xinfa Zhu", "Ting He", "Dake Guo", "Bin Zhang", "Xiong Wang", "Zhifang Guo", "Ziyue Jiang", "Hongkun Hao", "Zishan Guo", "Xinyu Zhang", "Pei Zhang", "Baosong Yang", "Jin Xu", "Jingren Zhou", "Junyang Lin" ], "github": "https://github.com/boson-ai/higgs-audio", "project_page": "", "abstract": "In this report, we present the Qwen3-TTS series, a family of advanced multilingual, controllable, robust, and streaming text-to-speech models. Qwen3-TTS supports state-of-the-art 3-second voice cloning and description-based control, allowing both the creation of entirely novel voices and fine-grained manipulation over the output speech. Trained on over 5 million hours of speech data spanning 10 languages, Qwen3-TTS adopts a dual-track LM architecture for real-time synthesis, coupled with two speech tokenizers: 1) Qwen-TTS-Tokenizer-25Hz is a single-codebook codec emphasizing semantic content, which offers seamlessly integration with Qwen-Audio and enables streaming waveform reconstruction via a block-wise DiT. 2) Qwen-TTS-Tokenizer-12Hz achieves extreme bitrate reduction and ultra-low-latency streaming, enabling immediate first-packet emission (97,ms) through its 12.5 Hz, 16-layer multi-codebook design and a lightweight causal ConvNet. Extensive experiments indicate state-of-the-art performance across diverse objective and subjective benchmark (e.g., TTS multilingual test set, InstructTTSEval, and our long speech test set). To facilitate community research and development, we release both tokenizers and models under the Apache 2.0 license.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/boson-ai/higgs-audio", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.16163", "title": "Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning", "authors": [ "Moo Jin Kim", "Yihuai Gao", "Tsung-Yi Lin", "Yen-Chen Lin", "Yunhao Ge", "Grace Lam", "Percy Liang", "Shuran Song", "Ming-Yu Liu", "Chelsea Finn", "Jinwei Gu" ], "github": "", "project_page": "", "abstract": "Recent video generation models demonstrate remarkable ability to capture complex physical interactions and scene evolution over time. To leverage their spatiotemporal priors, robotics works have adapted video models for policy learning but introduce complexity by requiring multiple stages of post-training and new architectural components for action generation. In this work, we introduce Cosmos Policy, a simple approach for adapting a large pretrained video model (Cosmos-Predict2) into an effective robot policy through a single stage of post-training on the robot demonstration data collected on the target platform, with no architectural modifications. Cosmos Policy learns to directly generate robot actions encoded as latent frames within the video model's latent diffusion process, harnessing the model's pretrained priors and core learning algorithm to capture complex action distributions. Additionally, Cosmos Policy generates future state images and values (expected cumulative rewards), which are similarly encoded as latent frames, enabling test-time planning of action trajectories with higher likelihood of success. In our evaluations, Cosmos Policy achieves state-of-the-art performance on the LIBERO and RoboCasa simulation benchmarks (98.5% and 67.1% average success rates, respectively) and the highest average score in challenging real-world bimanual manipulation tasks, outperforming strong diffusion policies trained from scratch, video model-based policies, and state-of-the-art vision-language-action models fine-tuned on the same robot demonstrations. Furthermore, given policy rollout data, Cosmos Policy can learn from experience to refine its world model and value function and leverage model-based planning to achieve even higher success rates in challenging tasks. We release code, models, and training data at https://research.nvidia.com/labs/dir/cosmos-policy/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.11868", "title": "Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces", "authors": [ "Mike A. Merrill", "Alexander G. Shaw", "Nicholas Carlini", "Boxuan Li", "Harsh Raj", "Ivan Bercovich", "Lin Shi", "Jeong Yeon Shin", "Thomas Walshe", "E. Kelly Buchanan", "Junhong Shen", "Guanghao Ye", "Haowei Lin", "Jason Poulos", "Maoyu Wang", "Marianna Nezhurina", "Jenia Jitsev", "Di Lu", "Orfeas Menis Mastromichalakis", "Zhiwei Xu", "Zizhao Chen", "Yue Liu", "Robert Zhang", "Leon Liangyu Chen", "Anurag Kashyap", "Jan-Lucas Uslu", "Jeffrey Li", "Jianbo Wu", "Minghao Yan", "Song Bian", "Vedang Sharma", "Ke Sun", "Steven Dillmann", "Akshay Anand", "Andrew Lanpouthakoun", "Bardia Koopah", "Changran Hu", "Etash Guha", "Gabriel H. S. Dreiman", "Jiacheng Zhu", "Karl Krauth", "Li Zhong", "Niklas Muennighoff", "Robert Amanfu", "Shangyin Tan", "Shreyas Pimpalgaonkar", "Tushar Aggarwal", "Xiangning Lin", "Xin Lan", "Xuandong Zhao", "Yiqing Liang", "Yuanli Wang", "Zilong Wang", "Changzhi Zhou", "David Heineman", "Hange Liu", "Harsh Trivedi", "John Yang", "Junhong Lin", "Manish Shetty", "Michael Yang", "Nabil Omi", "Negin Raoof", "Shanda Li", "Terry Yue Zhuo", "Wuwei Lin", "Yiwei Dai", "Yuxin Wang", "Wenhao Chai", "Shang Zhou", "Dariush Wahdany", "Ziyu She", "Jiaming Hu", "Zhikang Dong", "Yuxuan Zhu", "Sasha Cui", "Ahson Saiyed", "Arinbj\u00f6rn Kolbeinsson", "Jesse Hu", "Christopher Michael Rytting", "Ryan Marten", "Yixin Wang", "Alex Dimakis", "Andy Konwinski", "Ludwig Schmidt" ], "github": "https://github.com/laude-institute/terminal-bench-experiments/tree/main/configs/tb2", "project_page": "", "abstract": "AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 2.0: a carefully curated hard benchmark composed of 89 tasks in computer terminal environments inspired by problems from real workflows. Each task features a unique environment, human-written solution, and comprehensive tests for verification. We show that frontier models and agents score less than 65\\% on the benchmark and conduct an error analysis to identify areas for model and agent improvement. We publish the dataset and evaluation harness to assist developers and researchers in future work at https://www.tbench.ai/ .", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/laude-institute/terminal-bench-experiments/tree/main/configs/tb2", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.16004", "title": "Wigner's Friend as a Circuit: Inter-Branch Communication Witness Benchmarks on Superconducting Quantum Hardware", "authors": [ "Christopher Altman" ], "github": "", "project_page": "", "abstract": "We implement and benchmark on IBM Quantum hardware the circuit family proposed by Violaris for estimating operational inter-branch communication witnesses, defined as correlations in classical measurement records produced by compiled Wigner's-friend-style circuits. We realize a five-qubit instance of the protocol as an inter-register message-transfer pattern within a single circuit, rather than physical signaling, and evaluate its behavior under realistic device noise and compilation constraints. The circuit encodes branch-conditioned evolution of an observer subsystem whose dynamics depend on a control qubit, followed by a controlled transfer operation that probes correlations between conditional measurement contexts. Executing on the ibm_fez backend with 20000 shots, we observe population-based visibility of 0.877, coherence witnesses of 0.840 and -0.811 along orthogonal axes, and a phase-sensitive magnitude of approximately 1.17. While the visibility metric is insensitive to some classes of dephasing, the coherence witnesses provide complementary sensitivity to off-diagonal noise. This work does not test or discriminate among interpretations of quantum mechanics. Instead, it provides a reproducible operational constraint pipeline for evaluating detectability of non-ideal channels relative to calibrated device noise.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.16093", "title": "SAMTok: Representing Any Mask with Two Words", "authors": [ "Yikang Zhou", "Tao Zhang", "Dengxian Gong", "Yuanzheng Wu", "Ye Tian", "Haochen Wang", "Haobo Yuan", "Jiacong Wang", "Lu Qi", "Hao Fei", "Anran Wang", "Zhuochen Wang", "Yujing Wang", "Cheng Chen", "Shunping Ji", "Xiangtai Li" ], "github": "", "project_page": "", "abstract": "Pixel-wise capabilities are essential for building interactive intelligent systems. However, pixel-wise multi-modal LLMs (MLLMs) remain difficult to scale due to complex region-level encoders, specialized segmentation decoders, and incompatible training objectives. To address these challenges, we present SAMTok, a discrete mask tokenizer that converts any region mask into two special tokens and reconstructs the mask using these tokens with high fidelity. By treating masks as new language tokens, SAMTok enables base MLLMs (such as the QwenVL series) to learn pixel-wise capabilities through standard next-token prediction and simple reinforcement learning, without architectural modifications and specialized loss design. SAMTok builds on SAM2 and is trained on 209M diverse masks using a mask encoder and residual vector quantizer to produce discrete, compact, and information-rich tokens. With 5M SAMTok-formatted mask understanding and generation data samples, QwenVL-SAMTok attains state-of-the-art or comparable results on region captioning, region VQA, grounded conversation, referring segmentation, scene graph parsing, and multi-round interactive segmentation. We further introduce a textual answer-matching reward that enables efficient reinforcement learning for mask generation, delivering substantial improvements on GRES and GCG benchmarks. Our results demonstrate a scalable and straightforward paradigm for equipping MLLMs with strong pixel-wise capabilities. Our code and models are available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.14724", "title": "HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding", "authors": [ "Haowei Zhang", "Shudong Yang", "Jinlan Fu", "See-Kiong Ng", "Xipeng Qiu" ], "github": "https://github.com/haowei-freesky/HERMES", "project_page": "", "abstract": "Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated significant improvement in offline video understanding. However, extending these capabilities to streaming video inputs, remains challenging, as existing models struggle to simultaneously maintain stable understanding performance, real-time responses, and low GPU memory overhead. To address this challenge, we propose HERMES, a novel training-free architecture for real-time and accurate understanding of video streams. Based on a mechanistic attention investigation, we conceptualize KV cache as a hierarchical memory framework that encapsulates video information across multiple granularities. During inference, HERMES reuses a compact KV cache, enabling efficient streaming understanding under resource constraints. Notably, HERMES requires no auxiliary computations upon the arrival of user queries, thereby guaranteeing real-time responses for continuous video stream interactions, which achieves 10times faster TTFT compared to prior SOTA. Even when reducing video tokens by up to 68% compared with uniform sampling, HERMES achieves superior or comparable accuracy across all benchmarks, with up to 11.4% gains on streaming datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/haowei-freesky/HERMES", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.15549", "title": "VIOLA: Towards Video In-Context Learning with Minimal Annotations", "authors": [ "Ryo Fujii", "Hideo Saito", "Ryo Hachiuma" ], "github": "", "project_page": "", "abstract": "Generalizing Multimodal Large Language Models (MLLMs) to novel video domains is essential for real-world deployment but remains challenging due to the scarcity of labeled data. While In-Context Learning (ICL) offers a training-free adaptation path, standard methods rely on large annotated pools, which are often impractical in specialized environments like industrial or surgical settings since they require the experts' annotations. To bridge this gap, we introduce VIOLA (Video In-cOntext Learning with minimal Annotation), a label-efficient framework that synergizes minimal expert supervision with abundant unlabeled data. First, to maximize the efficiency of a strict annotation budget, we propose density-uncertainty-weighted sampling. Unlike standard diversity or uncertainty strategies that risk selecting visual outliers, our method leverages density estimation to identify samples that are simultaneously diverse, representative, and informative. Second, to utilize the remaining unlabeled data without noise propagation, we construct a hybrid pool and introduce confidence-aware retrieval and confidence-aware prompting. These mechanisms explicitly model label reliability, retrieving demonstrations based on a composite score of similarity and confidence while enabling the MLLM to adaptively distinguish between verified ground truths and noisy pseudo-labels. Extensive experiments across nine diverse benchmarks using four MLLMs demonstrate that our framework significantly outperforms various baselines in low-resource settings, achieving robust adaptation with minimal annotation costs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.16192", "title": "360Anything: Geometry-Free Lifting of Images and Videos to 360\u00b0", "authors": [ "Ziyi Wu", "Daniel Watson", "Andrea Tagliasacchi", "David J. Fleet", "Marcus A. Brubaker", "Saurabh Saxena" ], "github": "https://github.com/3DTopia/Imagine360", "project_page": "", "abstract": "Lifting perspective images and videos to 360\u00b0 panoramas enables immersive 3D world generation. Existing approaches often rely on explicit geometric alignment between the perspective and the equirectangular projection (ERP) space. Yet, this requires known camera metadata, obscuring the application to in-the-wild data where such calibration is typically absent or noisy. We propose 360Anything, a geometry-free framework built upon pre-trained diffusion transformers. By treating the perspective input and the panorama target simply as token sequences, 360Anything learns the perspective-to-equirectangular mapping in a purely data-driven way, eliminating the need for camera information. Our approach achieves state-of-the-art performance on both image and video perspective-to-360\u00b0 generation, outperforming prior works that use ground-truth camera information. We also trace the root cause of the seam artifacts at ERP boundaries to zero-padding in the VAE encoder, and introduce Circular Latent Encoding to facilitate seamless generation. Finally, we show competitive results in zero-shot camera FoV and orientation estimation benchmarks, demonstrating 360Anything's deep geometric understanding and broader utility in computer vision tasks. Additional results are available at https://360anything.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/3DTopia/Imagine360", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.15440", "title": "Numba-Accelerated 2D Diffusion-Limited Aggregation: Implementation and Fractal Characterization", "authors": [ "Sandy H. S. Herho", "Faiz R. Fajary", "Iwan P. Anwar", "Faruq Khadami", "Nurjanna J. Trilaksono", "Rusmawan Suwarman", "Dasapta E. Irawan" ], "github": "", "project_page": "", "abstract": "We present dla-ideal-solver, a high-performance framework for simulating two-dimensional Diffusion-Limited Aggregation (DLA) using Numba-accelerated Python. By leveraging just-in-time (JIT) compilation, we achieve computational throughput comparable to legacy static implementations while retaining high-level flexibility. We investigate the Laplacian growth instability across varying injection geometries and walker concentrations. Our analysis confirms the robustness of the standard fractal dimension D_f approx 1.71 for dilute regimes, consistent with the Witten-Sander universality class. However, we report a distinct crossover to Eden-like compact growth (D_f approx 1.87) in high-density environments, attributed to the saturation of the screening length. Beyond standard mass-radius scaling, we employ generalized R\u00e9nyi dimensions and lacunarity metrics to quantify the monofractal character and spatial heterogeneity of the aggregates. This work establishes a reproducible, open-source testbed for exploring phase transitions in non-equilibrium statistical mechanics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.14255", "title": "VideoMaMa: Mask-Guided Video Matting via Generative Prior", "authors": [ "Sangbeom Lim", "Seoung Wug Oh", "Jiahui Huang", "Heeji Yoon", "Seungryong Kim", "Joon-Young Lee" ], "github": "", "project_page": "", "abstract": "Generalizing video matting models to real-world videos remains a significant challenge due to the scarcity of labeled data. To address this, we present Video Mask-to-Matte Model (VideoMaMa) that converts coarse segmentation masks into pixel accurate alpha mattes, by leveraging pretrained video diffusion models. VideoMaMa demonstrates strong zero-shot generalization to real-world footage, even though it is trained solely on synthetic data. Building on this capability, we develop a scalable pseudo-labeling pipeline for large-scale video matting and construct the Matting Anything in Video (MA-V) dataset, which offers high-quality matting annotations for more than 50K real-world videos spanning diverse scenes and motions. To validate the effectiveness of this dataset, we fine-tune the SAM2 model on MA-V to obtain SAM2-Matte, which outperforms the same model trained on existing matting datasets in terms of robustness on in-the-wild videos. These findings emphasize the importance of large-scale pseudo-labeled video matting and showcase how generative priors and accessible segmentation cues can drive scalable progress in video matting research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.15727", "title": "Towards Automated Kernel Generation in the Era of LLMs", "authors": [ "Yang Yu", "Peiyu Zang", "Chi Hsu Tsai", "Haiming Wu", "Yixin Shen", "Jialing Zhang", "Haoyu Wang", "Zhiyou Xiao", "Jingze Shi", "Yuyu Luo", "Wentao Zhang", "Chunlei Men", "Guang Liu", "Yonghua Lin" ], "github": "https://github.com/flagos-ai/awesome-LLM-driven-kernel-generation", "project_page": "", "abstract": "The performance of modern AI systems is fundamentally constrained by the quality of their underlying kernels, which translate high-level algorithmic semantics into low-level hardware operations. Achieving near-optimal kernels requires expert-level understanding of hardware architectures and programming models, making kernel engineering a critical but notoriously time-consuming and non-scalable process. Recent advances in large language models (LLMs) and LLM-based agents have opened new possibilities for automating kernel generation and optimization. LLMs are well-suited to compress expert-level kernel knowledge that is difficult to formalize, while agentic systems further enable scalable optimization by casting kernel development as an iterative, feedback-driven loop. Rapid progress has been made in this area. However, the field remains fragmented, lacking a systematic perspective for LLM-driven kernel generation. This survey addresses this gap by providing a structured overview of existing approaches, spanning LLM-based approaches and agentic optimization workflows, and systematically compiling the datasets and benchmarks that underpin learning and evaluation in this domain. Moreover, key open challenges and future research directions are further outlined, aiming to establish a comprehensive reference for the next generation of automated kernel optimization. To keep track of this field, we maintain an open-source GitHub repository at https://github.com/flagos-ai/awesome-LLM-driven-kernel-generation.", "github_manual": "", "github_regex": "https://github.com/flagos-ai/awesome-LLM-driven-kernel-generation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.16134", "title": "LLM Prompt Evaluation for Educational Applications", "authors": [ "Langdon Holmes", "Adam Coscia", "Scott Crossley", "Joon Suh Choi", "Wesley Morris" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) become increasingly common in educational applications, there is a growing need for evidence-based methods to design and evaluate LLM prompts that produce personalized and pedagogically aligned out-puts. This study presents a generalizable, systematic approach for evaluating prompts, demonstrated through an analysis of LLM-generated follow-up questions in a structured dialogue activity. Six prompt templates were designed and tested. The templates incorporated established prompt engineering patterns, with each prompt emphasizing distinct pedagogical strategies. The prompt templates were compared through a tournament-style evaluation framework that can be adapted for other educational applications. The tournament employed the Glicko2 rating system with eight judges evaluating question pairs across three dimensions: format, dialogue support, and appropriateness for learners. Data was sourced from 120 authentic user interactions across three distinct educational deployments. Results showed that a single prompt related to strategic reading out-performed other templates with win probabilities ranging from 81% to 100% in pairwise comparisons. This prompt combined persona and context manager pat-terns and was designed to support metacognitive learning strategies such as self-directed learning. The methodology showcases how educational technology re- searchers can systematically evaluate and improve prompt designs, moving beyond ad-hoc prompt engineering toward evidence-based prompt development for educational applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.15876", "title": "EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience", "authors": [ "Taofeng Xue", "Chong Peng", "Mianqiu Huang", "Linsen Guo", "Tiancheng Han", "Haozhe Wang", "Jianing Wang", "Xiaocheng Zhang", "Xin Yang", "Dengchang Zhao", "Jinrui Ding", "Xiandi Ma", "Yuchen Xie", "Peng Pei", "Xunliang Cai", "Xipeng Qiu" ], "github": "https://github.com/ByteDance-Seed/Seed-1.8", "project_page": "", "abstract": "The development of native computer-use agents (CUA) represents a significant leap in multimodal AI. However, their potential is currently bottlenecked by the constraints of static data scaling. Existing paradigms relying primarily on passive imitation of static datasets struggle to capture the intricate causal dynamics inherent in long-horizon computer tasks. In this work, we introduce EvoCUA, a native computer use agentic model. Unlike static imitation, EvoCUA integrates data generation and policy optimization into a self-sustaining evolutionary cycle. To mitigate data scarcity, we develop a verifiable synthesis engine that autonomously generates diverse tasks coupled with executable validators. To enable large-scale experience acquisition, we design a scalable infrastructure orchestrating tens of thousands of asynchronous sandbox rollouts. Building on these massive trajectories, we propose an iterative evolving learning strategy to efficiently internalize this experience. This mechanism dynamically regulates policy updates by identifying capability boundaries -- reinforcing successful routines while transforming failure trajectories into rich supervision through error analysis and self-correction. Empirical evaluations on the OSWorld benchmark demonstrate that EvoCUA achieves a success rate of 56.7%, establishing a new open-source state-of-the-art. Notably, EvoCUA significantly outperforms the previous best open-source model, OpenCUA-72B (45.0%), and surpasses leading closed-weights models such as UI-TARS-2 (53.1%). Crucially, our results underscore the generalizability of this approach: the evolving paradigm driven by learning from experience yields consistent performance gains across foundation models of varying scales, establishing a robust and scalable path for advancing native agent capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByteDance-Seed/Seed-1.8", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.16148", "title": "ActionMesh: Animated 3D Mesh Generation with Temporal 3D Diffusion", "authors": [ "Remy Sabathier", "David Novotny", "Niloy J. Mitra", "Tom Monnier" ], "github": "", "project_page": "", "abstract": "Generating animated 3D objects is at the heart of many applications, yet most advanced works are typically difficult to apply in practice because of their limited setup, their long runtime, or their limited quality. We introduce ActionMesh, a generative model that predicts production-ready 3D meshes \"in action\" in a feed-forward manner. Drawing inspiration from early video models, our key insight is to modify existing 3D diffusion models to include a temporal axis, resulting in a framework we dubbed \"temporal 3D diffusion\". Specifically, we first adapt the 3D diffusion stage to generate a sequence of synchronized latents representing time-varying and independent 3D shapes. Second, we design a temporal 3D autoencoder that translates a sequence of independent shapes into the corresponding deformations of a pre-defined reference shape, allowing us to build an animation. Combining these two components, ActionMesh generates animated 3D meshes from different inputs like a monocular video, a text description, or even a 3D mesh with a text prompt describing its animation. Besides, compared to previous approaches, our method is fast and produces results that are rig-free and topology consistent, hence enabling rapid iteration and seamless applications like texturing and retargeting. We evaluate our model on standard video-to-4D benchmarks (Consistent4D, Objaverse) and report state-of-the-art performances on both geometric accuracy and temporal consistency, demonstrating that our model can deliver animated 3D meshes with unprecedented speed and quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.15224", "title": "PROGRESSLM: Towards Progress Reasoning in Vision-Language Models", "authors": [ "Jianshu Zhang", "Chengxuan Qian", "Haosen Sun", "Haoran Lu", "Dingcheng Wang", "Letian Xue", "Han Liu" ], "github": "", "project_page": "", "abstract": "Estimating task progress requires reasoning over long-horizon dynamics rather than recognizing static visual content. While modern Vision-Language Models (VLMs) excel at describing what is visible, it remains unclear whether they can infer how far a task has progressed from partial observations. To this end, we introduce Progress-Bench, a benchmark for systematically evaluating progress reasoning in VLMs. Beyond benchmarking, we further explore a human-inspired two-stage progress reasoning paradigm through both training-free prompting and training-based approach based on curated dataset ProgressLM-45K. Experiments on 14 VLMs show that most models are not yet ready for task progress estimation, exhibiting sensitivity to demonstration modality and viewpoint changes, as well as poor handling of unanswerable cases. While training-free prompting that enforces structured progress reasoning yields limited and model-dependent gains, the training-based ProgressLM-3B achieves consistent improvements even at a small model scale, despite being trained on a task set fully disjoint from the evaluation tasks. Further analyses reveal characteristic error patterns and clarify when and why progress reasoning succeeds or fails.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.08118", "title": "MirrorBench: An Extensible Framework to Evaluate User-Proxy Agents for Human-Likeness", "authors": [ "Ashutosh Hathidara", "Julien Yu", "Vaishali Senthil", "Sebastian Schreiber", "Anil Babu Ankisettipalli" ], "github": "https://github.com/SAP/mirrorbench", "project_page": "", "abstract": "Large language models (LLMs) are increasingly used as human simulators, both for evaluating conversational systems and for generating fine-tuning data. However, naive \"act-as-a-user\" prompting often yields verbose, unrealistic utterances, underscoring the need for principled evaluation of so-called user proxy agents. We present MIRRORBENCH, a reproducible, extensible benchmarking framework that evaluates user proxies solely on their ability to produce human-like user utterances across diverse conversational tasks, explicitly decoupled from downstream task success. MIRRORBENCH features a modular execution engine with typed interfaces, metadata-driven registries, multi-backend support, caching, and robust observability. The system supports pluggable user proxies, datasets, tasks, and metrics, enabling researchers to evaluate arbitrary simulators under a uniform, variance-aware harness. We include three lexical-diversity metrics (MATTR, YULE'S K, and HD-D) and three LLM-judge-based metrics (GTEval, Pairwise Indistinguishability, and Rubric-and-Reason). Across four open datasets, MIRRORBENCH yields variance-aware results and reveals systematic gaps between user proxies and real human users. The framework is open source and includes a simple command-line interface for running experiments, managing configurations and caching, and generating reports. The framework can be accessed at https://github.com/SAP/mirrorbench.", "github_manual": "", "github_regex": "https://github.com/SAP/mirrorbench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.15778", "title": "Agentic Confidence Calibration", "authors": [ "Jiaxin Zhang", "Caiming Xiong", "Chien-Sheng Wu" ], "github": "", "project_page": "", "abstract": "AI agents are rapidly advancing from passive language models to autonomous systems executing complex, multi-step tasks. Yet their overconfidence in failure remains a fundamental barrier to deployment in high-stakes settings. Existing calibration methods, built for static single-turn outputs, cannot address the unique challenges of agentic systems, such as compounding errors along trajectories, uncertainty from external tools, and opaque failure modes. To address these challenges, we introduce, for the first time, the problem of Agentic Confidence Calibration and propose Holistic Trajectory Calibration (HTC), a novel diagnostic framework that extracts rich process-level features ranging from macro dynamics to micro stability across an agent's entire trajectory. Powered by a simple, interpretable model, HTC consistently surpasses strong baselines in both calibration and discrimination, across eight benchmarks, multiple LLMs, and diverse agent frameworks. Beyond performance, HTC delivers three essential advances: it provides interpretability by revealing the signals behind failure, enables transferability by applying across domains without retraining, and achieves generalization through a General Agent Calibrator (GAC) that achieves the best calibration (lowest ECE) on the out-of-domain GAIA benchmark. Together, these contributions establish a new process-centric paradigm for confidence calibration, providing a framework for diagnosing and enhancing the reliability of AI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.15703", "title": "Agentic Uncertainty Quantification", "authors": [ "Jiaxin Zhang", "Prafulla Kumar Choubey", "Kung-Hsiang Huang", "Caiming Xiong", "Chien-Sheng Wu" ], "github": "", "project_page": "", "abstract": "Although AI agents have demonstrated impressive capabilities in long-horizon reasoning, their reliability is severely hampered by the ``Spiral of Hallucination,'' where early epistemic errors propagate irreversibly. Existing methods face a dilemma: uncertainty quantification (UQ) methods typically act as passive sensors, only diagnosing risks without addressing them, while self-reflection mechanisms suffer from continuous or aimless corrections. To bridge this gap, we propose a unified Dual-Process Agentic UQ (AUQ) framework that transforms verbalized uncertainty into active, bi-directional control signals. Our architecture comprises two complementary mechanisms: System 1 (Uncertainty-Aware Memory, UAM), which implicitly propagates verbalized confidence and semantic explanations to prevent blind decision-making; and System 2 (Uncertainty-Aware Reflection, UAR), which utilizes these explanations as rational cues to trigger targeted inference-time resolution only when necessary. This enables the agent to balance efficient execution and deep deliberation dynamically. Extensive experiments on closed-loop benchmarks and open-ended deep research tasks demonstrate that our training-free approach achieves superior performance and trajectory-level calibration. We believe this principled framework AUQ represents a significant step towards reliable agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-23", "arxiv_id": "2601.15690", "title": "From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models", "authors": [ "Jiaxin Zhang", "Wendi Cui", "Zhuohang Li", "Lifu Huang", "Bradley Malin", "Caiming Xiong", "Chien-Sheng Wu" ], "github": "", "project_page": "", "abstract": "While Large Language Models (LLMs) show remarkable capabilities, their unreliability remains a critical barrier to deployment in high-stakes domains. This survey charts a functional evolution in addressing this challenge: the evolution of uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior. We demonstrate how uncertainty is leveraged as an active control signal across three frontiers: in advanced reasoning to optimize computation and trigger self-correction; in autonomous agents to govern metacognitive decisions about tool use and information seeking; and in reinforcement learning to mitigate reward hacking and enable self-improvement via intrinsic rewards. By grounding these advancements in emerging theoretical frameworks like Bayesian methods and Conformal Prediction, we provide a unified perspective on this transformative trend. This survey provides a comprehensive overview, critical analysis, and practical design patterns, arguing that mastering the new trend of uncertainty is essential for building the next generation of scalable, reliable, and trustworthy AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.14133", "title": "TwinBrainVLA: Unleashing the Potential of Generalist VLMs for Embodied Tasks via Asymmetric Mixture-of-Transformers", "authors": [ "Bin Yu", "Shijie Lian", "Xiaopeng Lin", "Yuliang Wei", "Zhaolong Shen", "Changti Wu", "Yuzhuo Miao", "Xinming Wang", "Bailing Wang", "Cong Huang", "Kai Chen" ], "github": "https://github.com/starVLA/starVLA", "project_page": "", "abstract": "Standard Vision-Language-Action (VLA) models typically fine-tune a monolithic Vision-Language Model (VLM) backbone explicitly for robotic control. However, this approach creates a critical tension between maintaining high-level general semantic understanding and learning low-level, fine-grained sensorimotor skills, often leading to \"catastrophic forgetting\" of the model's open-world capabilities. To resolve this conflict, we introduce TwinBrainVLA, a novel architecture that coordinates a generalist VLM retaining universal semantic understanding and a specialist VLM dedicated to embodied proprioception for joint robotic control. TwinBrainVLA synergizes a frozen \"Left Brain\", which retains robust general visual reasoning, with a trainable \"Right Brain\", specialized for embodied perception, via a novel Asymmetric Mixture-of-Transformers (AsyMoT) mechanism. This design allows the Right Brain to dynamically query semantic knowledge from the frozen Left Brain and fuse it with proprioceptive states, providing rich conditioning for a Flow-Matching Action Expert to generate precise continuous controls. Extensive experiments on SimplerEnv and RoboCasa benchmarks demonstrate that TwinBrainVLA achieves superior manipulation performance compared to state-of-the-art baselines while explicitly preserving the comprehensive visual understanding capabilities of the pre-trained VLM, offering a promising direction for building general-purpose robots that simultaneously achieve high-level semantic understanding and low-level physical dexterity.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/starVLA/starVLA", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.16973", "title": "VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents", "authors": [ "Zirui Wang", "Junyi Zhang", "Jiaxin Ge", "Long Lian", "Letian Fu", "Lisa Dunlap", "Ken Goldberg", "XuDong Wang", "Ion Stoica", "David M. Chan", "Sewon Min", "Joseph E. Gonzalez" ], "github": "", "project_page": "", "abstract": "Modern Vision-Language Models (VLMs) remain poorly characterized in multi-step visual interactions, particularly in how they integrate perception, memory, and action over long horizons. We introduce VisGym, a gymnasium of 17 environments for evaluating and training VLMs. The suite spans symbolic puzzles, real-image understanding, navigation, and manipulation, and provides flexible controls over difficulty, input representation, planning horizon, and feedback. We also provide multi-step solvers that generate structured demonstrations, enabling supervised finetuning. Our evaluations show that all frontier models struggle in interactive settings, achieving low success rates in both the easy (46.6%) and hard (26.0%) configurations. Our experiments reveal notable limitations: models struggle to effectively leverage long context, performing worse with an unbounded history than with truncated windows. Furthermore, we find that several text-based symbolic tasks become substantially harder once rendered visually. However, explicit goal observations, textual feedback, and exploratory demonstrations in partially observable or unknown-dynamics settings for supervised finetuning yield consistent gains, highlighting concrete failure modes and pathways for improving multi-step visual decision-making. Code, data, and models can be found at: https://visgym.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.16725", "title": "LongCat-Flash-Thinking-2601 Technical Report", "authors": [ "Meituan LongCat Team", "Anchun Gui", "Bei Li", "Bingyang Tao", "Bole Zhou", "Borun Chen", "Chao Zhang", "Chao Zhang", "Chen Gao", "Chen Zhang", "Chengcheng Han", "Chenhui Yang", "Chuyu Zhang", "Cong Chen", "Cunguang Wang", "Daoru Pan", "Defei Bu", "Dengchang Zhao", "Di Xiu", "Dishan Liu", "Dongyu Ru", "Dunwei Tu", "Fan Wu", "Fengcheng Yuan", "Fengcun Li", "Gang Xu", "Guanyu Wu", "Guoyuan Lin", "Haibin Wang", "Hansi Yang", "Hao Yang", "Haonan Yan", "Haoxiang Ma", "Haoxing Wen", "Hongyan Hao", "Hongyin Tang", "Hongyu Zang", "Hongzhi Ni", "Hui Su", "Jiacheng Zhang", "Jiahong Zhou", "Jiahuan Li", "Jiaming Wang", "Jian Yang", "Jianfei Zhang", "Jianhao Xu", "Jianing Wang", "Jiapeng Zhu", "Jiaqi Sun", "Jiarong Shi", "Jiarui Zhao", "Jingang Wang", "Jinluan Yang", "Jinrui Ding", "Jinwei Xiao", "Jiyuan He", "Juncan Xu", "Kefeng Zhang", "Keheng Wang", "Li Wei", "Lianhui Ma", "Lin Qiu", "Lingbing Kong", "Lingchuan Liu", "Linsen Guo", "Mengshen Zhu", "Mengxia Shen", "Mingyang Zhu", "Peiguang Li", "Peng Pei", "Pengcheng Jia", "Pengtao Zhang", "Peng Zhao", "Qi Gu", "Qiong Huang", "Qiyuan Duan", "Quanchi Weng", "Rongxiang Weng", "Rongzhi Zhang", "Rumei Li", "Shanglin Lei", "Shengnan An", "Shijun Dai", "Shuaikang Liu", "Shuang Zhou", "Shuo Wang", "Songyuan Zhao", "Tao Liang", "Tianhao Hu", "Tianze Chen", "Wei Liu", "Wei Shi", "Wei Wang", "Weifeng Tang", "Wenjie Shi", "Wenlong Zhu", "Wentao Chen", "Wentao Shi", "Xi Su", "Xiangcheng Liu", "Xiandi Ma", "Xiangyu Xi", "Xiangyuan Liu", "Xiangzhou Huang", "Xiao Liu", "Xiaodong Cai", "Xiaolong Chen", "Xiaowei Shi", "Xiaoyu Li", "Xin Chen", "Xingchen Liu", "Xuan Huang", "Xuezhi Cao", "Xunliang Cai", "Yan Chen", "Yang Bai", "Yang Liu", "Yang Yang", "Yang Zheng", "Yaoming Wang", "Yaoming Zhu", "Yaqi Huo", "Yanyu Chen", "Yaorui Shi", "Yerui Sun", "Yi Zhang", "Yihao Chen", "Yi-Kai Zhang", "Yifan Lu", "Yifan Zhao", "Yitao Zhai", "Yongjing Yin", "Yongwei Zhou", "Youshao Xiao", "Yuchuan Dai", "Yuchen Xie", "Yuchen Yu", "Yufei Zhang", "Yuhuai Wei", "Yulei Qian", "Yunfan Liang", "Yunke Zhao", "Yuwei Jiang", "Yuxin Bian", "Yuxin Chen", "Yuxin Liu", "Yue Xu", "Yueqing Sun", "Zeyang Yu", "Zhao Yang", "Zhengsheng Huang", "Zhengyu Chen", "Zhijian Liu", "Zhikang Xia", "Zhimin Lin", "Zhiyuan Yao", "Zhuofan Chen", "Zhuowen Han", "Zijian Zhang", "Ziran Li", "Ziwen Wang", "Ziyuan Zhuang" ], "github": "https://github.com/AGI-Eval-Official/BrowseComp-ZH-revised", "project_page": "", "abstract": "We introduce LongCat-Flash-Thinking-2601, a 560-billion-parameter open-source Mixture-of-Experts (MoE) reasoning model with superior agentic reasoning capability. LongCat-Flash-Thinking-2601 achieves state-of-the-art performance among open-source models on a wide range of agentic benchmarks, including agentic search, agentic tool use, and tool-integrated reasoning. Beyond benchmark performance, the model demonstrates strong generalization to complex tool interactions and robust behavior under noisy real-world environments. Its advanced capability stems from a unified training framework that combines domain-parallel expert training with subsequent fusion, together with an end-to-end co-design of data construction, environments, algorithms, and infrastructure spanning from pre-training to post-training. In particular, the model's strong generalization capability in complex tool-use are driven by our in-depth exploration of environment scaling and principled task construction. To optimize long-tailed, skewed generation and multi-turn agentic interactions, and to enable stable training across over 10,000 environments spanning more than 20 domains, we systematically extend our asynchronous reinforcement learning framework, DORA, for stable and efficient large-scale multi-environment training. Furthermore, recognizing that real-world tasks are inherently noisy, we conduct a systematic analysis and decomposition of real-world noise patterns, and design targeted training procedures to explicitly incorporate such imperfections into the training process, resulting in improved robustness for real-world applications. To further enhance performance on complex reasoning tasks, we introduce a Heavy Thinking mode that enables effective test-time scaling by jointly expanding reasoning depth and width through intensive parallel thinking.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AGI-Eval-Official/BrowseComp-ZH-revised", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.16443", "title": "Endless Terminals: Scaling RL Environments for Terminal Agents", "authors": [ "Kanishk Gandhi", "Shivam Garg", "Noah D. Goodman", "Dimitris Papailiopoulos" ], "github": "https://github.com/kanishkg/endless-terminals", "project_page": "", "abstract": "Environments are the bottleneck for self-improving agents. Current terminal benchmarks were built for evaluation, not training; reinforcement learning requires a scalable pipeline, not just a dataset. We introduce Endless Terminals, a fully autonomous pipeline that procedurally generates terminal-use tasks without human annotation. The pipeline has four stages: generating diverse task descriptions, building and validating containerized environments, producing completion tests, and filtering for solvability. From this pipeline we obtain 3255 tasks spanning file operations, log management, data processing, scripting, and database operations. We train agents using vanilla PPO with binary episode level rewards and a minimal interaction loop: no retrieval, multi-agent coordination, or specialized tools. Despite this simplicity, models trained on Endless Terminals show substantial gains: on our held-out dev set, Llama-3.2-3B improves from 4.0% to 18.2%, Qwen2.5-7B from 10.7% to 53.3%, and Qwen3-8B-openthinker-sft from 42.6% to 59.0%. These improvements transfer to human-curated benchmarks: models trained on Endless Terminals show substantial gains on held out human curated benchmarks: on TerminalBench 2.0, Llama-3.2-3B improves from 0.0% to 2.2%, Qwen2.5-7B from 2.2% to 3.4%, and Qwen3-8B-openthinker-sft from 1.1% to 6.7%, in each case outperforming alternative approaches including models with more complex agentic scaffolds. These results demonstrate that simple RL succeeds when environments scale.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kanishkg/endless-terminals", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.16344", "title": "DSGym: A Holistic Framework for Evaluating and Training Data Science Agents", "authors": [ "Fan Nie", "Junlin Wang", "Harper Hua", "Federico Bianchi", "Yongchan Kwon", "Zhenting Qi", "Owen Queen", "Shang Zhu", "James Zou" ], "github": "https://github.com/fannie1208/DSGym", "project_page": "", "abstract": "Data science agents promise to accelerate discovery and insight-generation by turning data into executable analyses and findings. Yet existing data science benchmarks fall short due to fragmented evaluation interfaces that make cross-benchmark comparison difficult, narrow task coverage and a lack of rigorous data grounding. In particular, we show that a substantial portion of tasks in current benchmarks can be solved without using the actual data. To address these limitations, we introduce DSGym, a standardized framework for evaluating and training data science agents in self-contained execution environments. Unlike static benchmarks, DSGym provides a modular architecture that makes it easy to add tasks, agent scaffolds, and tools, positioning it as a live, extensible testbed. We curate DSGym-Tasks, a holistic task suite that standardizes and refines existing benchmarks via quality and shortcut solvability filtering. We further expand coverage with (1) DSBio: expert-derived bioinformatics tasks grounded in literature and (2) DSPredict: challenging prediction tasks spanning domains such as computer vision, molecular prediction, and single-cell perturbation. Beyond evaluation, DSGym enables agent training via execution-verified data synthesis pipeline. As a case study, we build a 2,000-example training set and trained a 4B model in DSGym that outperforms GPT-4o on standardized analysis benchmarks. Overall, DSGym enables rigorous end-to-end measurement of whether agents can plan, implement, and validate data analyses in realistic scientific context.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/fannie1208/DSGym", "github_api_added_by": "auto" }, { "date": "2026-01-26", "arxiv_id": "2601.16296", "title": "Memory-V2V: Augmenting Video-to-Video Diffusion Models with Memory", "authors": [ "Dohun Lee", "Chun-Hao Paul Huang", "Xuelin Chen", "Jong Chul Ye", "Duygu Ceylan", "Hyeonho Jeong" ], "github": "https://github.com/DoHunLee1/Memory-V2V", "project_page": "", "abstract": "Recent foundational video-to-video diffusion models have achieved impressive results in editing user provided videos by modifying appearance, motion, or camera movement. However, real-world video editing is often an iterative process, where users refine results across multiple rounds of interaction. In this multi-turn setting, current video editors struggle to maintain cross-consistency across sequential edits. In this work, we tackle, for the first time, the problem of cross-consistency in multi-turn video editing and introduce Memory-V2V, a simple, yet effective framework that augments existing video-to-video models with explicit memory. Given an external cache of previously edited videos, Memory-V2V employs accurate retrieval and dynamic tokenization strategies to condition the current editing step on prior results. To further mitigate redundancy and computational overhead, we propose a learnable token compressor within the DiT backbone that compresses redundant conditioning tokens while preserving essential visual cues, achieving an overall speedup of 30%. We validate Memory-V2V on challenging tasks including video novel view synthesis and text-conditioned long video editing. Extensive experiments show that Memory-V2V produces videos that are significantly more cross-consistent with minimal computational overhead, while maintaining or even improving task-specific performance over state-of-the-art baselines. Project page: https://dohunlee1.github.io/MemoryV2V", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DoHunLee1/Memory-V2V", "github_api_added_by": "auto" }, { "date": "2026-01-26", "arxiv_id": "2601.16746", "title": "SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents", "authors": [ "Yuhang Wang", "Yuling Shi", "Mo Yang", "Rongrui Zhang", "Shilin He", "Heng Lian", "Yuting Chen", "Siyu Ye", "Kai Cai", "Xiaodong Gu" ], "github": "", "project_page": "", "abstract": "LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts, which incur high API costs and latency. While various context compression approaches such as LongLLMLingua have emerged to tackle this challenge, they typically rely on fixed metrics such as PPL, ignoring the task-specific nature of code understanding. As a result, they frequently disrupt syntactic and logical structure and fail to retain critical implementation details. In this paper, we propose SWE-Pruner, a self-adaptive context pruning framework tailored for coding agents. Drawing inspiration from how human programmers \"selectively skim\" source code during development and debugging, SWE-Pruner performs task-aware adaptive pruning for long contexts. Given the current task, the agent formulates an explicit goal (e.g., \"focus on error handling\") as a hint to guide the pruning targets. A lightweight neural skimmer (0.6B parameters) is trained to dynamically select relevant lines from the surrounding context given the goal. Evaluations across four benchmarks and multiple models validate SWE-Pruner's effectiveness in various scenarios, achieving 23-54% token reduction on agent tasks like SWE-Bench Verified and up to 14.84x compression on single-turn tasks like LongCodeQA with minimal performance impact.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.07251", "title": "MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences", "authors": [ "Zizhen Li", "Chuanhao Li", "Yibin Wang", "Yukang Feng", "Jianwen Sun", "Jiaxin Ai", "Fanrui Zhang", "Mingzhu Sun", "Yifei Huang", "Kaipeng Zhang" ], "github": "https://github.com/leroy9472/MeepleLM", "project_page": "", "abstract": "Recent advancements have expanded the role of Large Language Models in board games from playing agents to creative co-designers. However, a critical gap remains: current systems lack the capacity to offer constructive critique grounded in the emergent user experience. Bridging this gap is fundamental for harmonizing Human-AI collaboration, as it empowers designers to refine their creations via external perspectives while steering models away from biased or unpredictable outcomes. Automating critique for board games presents two challenges: inferring the latent dynamics connecting rules to gameplay without an explicit engine, and modeling the subjective heterogeneity of diverse player groups. To address these, we curate a dataset of 1,727 structurally corrected rulebooks and 150K reviews selected via quality scoring and facet-aware sampling. We augment this data with Mechanics-Dynamics-Aesthetics (MDA) reasoning to explicitly bridge the causal gap between written rules and player experience. We further distill player personas and introduce MeepleLM, a specialized model that internalizes persona-specific reasoning patterns to accurately simulate the subjective feedback of diverse player archetypes. Experiments demonstrate that MeepleLM significantly outperforms latest commercial models (e.g., GPT-5.1, Gemini3-Pro) in community alignment and critique quality, achieving a 70% preference rate in user studies assessing utility. MeepleLM serves as a reliable virtual playtester for general interactive systems, marking a pivotal step towards audience-aligned, experience-aware Human-AI collaboration.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/leroy9472/MeepleLM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.15808", "title": "Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification", "authors": [ "Yuxuan Wan", "Tianqing Fang", "Zaitang Li", "Yintong Huo", "Wenxuan Wang", "Haitao Mi", "Dong Yu", "Michael R. Lyu" ], "github": "https://github.com/yxwan123/DeepVerifier", "project_page": "", "abstract": "Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving. While the majority of existing efforts focus on enhancing policy capabilities via post-training, we propose an alternative paradigm: self-evolving the agent's ability by iteratively verifying the policy model's outputs, guided by meticulously crafted rubrics. This approach gives rise to the inference-time scaling of verification, wherein an agent self-improves by evaluating its generated answers to produce iterative feedback and refinements. We derive the rubrics based on an automatically constructed DRA Failure Taxonomy, which systematically classifies agent failures into five major categories and thirteen sub-categories. We present DeepVerifier, a rubrics-based outcome reward verifier that leverages the asymmetry of verification and outperforms vanilla agent-as-judge and LLM judge baselines by 12%-48% in meta-evaluation F1 score. To enable practical self-evolution, DeepVerifier integrates as a plug-and-play module during test-time inference. The verifier produces detailed rubric-based feedback, which is fed back to the agent for iterative bootstrapping, refining responses without additional training. This test-time scaling delivers 8%-11% accuracy gains on challenging subsets of GAIA and XBench-DeepResearch when powered by capable closed-source LLMs. Finally, to support open-source advancement, we release DeepVerifier-4K, a curated supervised fine-tuning dataset of 4,646 high-quality agent steps focused on DRA verification. These examples emphasize reflection and self-critique, enabling open models to develop robust verification capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yxwan123/DeepVerifier", "github_api_added_by": "auto" }, { "date": "2026-01-26", "arxiv_id": "2601.16515", "title": "SALAD: Achieve High-Sparsity Attention via Efficient Linear Attention Tuning for Video Diffusion Transformer", "authors": [ "Tongcheng Fang", "Hanling Zhang", "Ruiqi Xie", "Zhuo Han", "Xin Tao", "Tianchen Zhao", "Pengfei Wan", "Wenbo Ding", "Wanli Ouyang", "Xuefei Ning", "Yu Wang" ], "github": "", "project_page": "", "abstract": "Diffusion Transformers have recently demonstrated remarkable performance in video generation. However, the long input sequences result in high computational latency due to the quadratic complexity of full attention. Various sparse attention mechanisms have been proposed. Training-free sparse attention is constrained by limited sparsity and thus offers modest acceleration, whereas training-based methods can reach much higher sparsity but demand substantial data and computation for training. In this work, we propose SALAD, introducing a lightweight linear attention branch in parallel with the sparse attention. By incorporating an input-dependent gating mechanism to finely balance the two branches, our method attains 90% sparsity and 1.72x inference speedup, while maintaining generation quality comparable to the full attention baseline. Moreover, our finetuning process is highly efficient, requiring only 2,000 video samples and 1,600 training steps with a batch size of 8.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.16018", "title": "Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain", "authors": [ "\u00d6zg\u00fcr U\u011fur", "Mahmut G\u00f6ksu", "Mahmut \u00c7imen", "Musa Y\u0131lmaz", "Esra \u015eavirdi", "Alp Talha Demir", "Rumeysa G\u00fcll\u00fcce", "\u0130clal \u00c7etin", "\u00d6mer Can Sa\u011fba\u015f" ], "github": "", "project_page": "", "abstract": "This paper presents Mecellem models, a framework for developing specialized language models for the Turkish legal domain through domain adaptation strategies. We make two contributions: (1)Encoder Model Pre-trained from Scratch: ModernBERT-based bidirectional encoders pre-trained on a Turkish-dominant corpus of 112.7 billion tokens. We implement a checkpoint selection strategy that evaluates downstream retrieval performance throughout training, revealing that optimal checkpoints achieve best retrieval scores before pre-training loss reaches its minimum. Our encoder models achieve top-3 rankings on the Turkish retrieval leaderboard, with smaller models (155M parameters) achieving comparable performance to larger reference models (307M-567M parameters). Our approach achieves 92.36% production efficiency compared to state-of-the-art models (embeddinggemma-300m: 100.00%, BAAI/bge-m3: 99.54%, newmindai/bge-m3-stsb: 94.38%), ranking fourth overall despite requiring less computational resources. SOTA models rely on multi-stage, computationally intensive training pipelines, making our single-stage pre-training followed by efficient post-training approach a cost-effective alternative; (2)Decoder Model with Continual Pre-training (CPT): Qwen3-1.7B and Qwen3-4B models adapted to Turkish legal domain through controlled curriculum learning. Four-phase CPT with optimal sample ratios enables gradual transition from general language knowledge to specialized legal terminology and long-context reasoning. This approach achieves 36.2% perplexity reduction on Turkish legal text, demonstrating domain adaptation gains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.14243", "title": "Jet-RL: Enabling On-Policy FP8 Reinforcement Learning with Unified Training and Rollout Precision Flow", "authors": [ "Haocheng Xi", "Charlie Ruan", "Peiyuan Liao", "Yujun Lin", "Han Cai", "Yilong Zhao", "Shuo Yang", "Kurt Keutzer", "Song Han", "Ligeng Zhu" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) is essential for enhancing the complex reasoning capabilities of large language models (LLMs). However, existing RL training pipelines are computationally inefficient and resource-intensive, with the rollout phase accounting for over 70% of total training time. Quantized RL training, particularly using FP8 precision, offers a promising approach to mitigating this bottleneck. A commonly adopted strategy applies FP8 precision during rollout while retaining BF16 precision for training. In this work, we present the first comprehensive study of FP8 RL training and demonstrate that the widely used BF16-training + FP8-rollout strategy suffers from severe training instability and catastrophic accuracy collapse under long-horizon rollouts and challenging tasks. Our analysis shows that these failures stem from the off-policy nature of the approach, which introduces substantial numerical mismatch between training and inference. Motivated by these observations, we propose Jet-RL, an FP8 RL training framework that enables robust and stable RL optimization. The key idea is to adopt a unified FP8 precision flow for both training and rollout, thereby minimizing numerical discrepancies and eliminating the need for inefficient inter-step calibration. Extensive experiments validate the effectiveness of Jet-RL: our method achieves up to 33% speedup in the rollout phase, up to 41% speedup in the training phase, and a 16% end-to-end speedup over BF16 training, while maintaining stable convergence across all settings and incurring negligible accuracy degradation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.13118", "title": "Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization", "authors": [ "Alessandro Midolo", "Alessandro Giagnorio", "Fiorella Zampetti", "Rosalia Tufano", "Gabriele Bavota", "Massimiliano Di Penta" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are nowadays extensively used for various types of software engineering tasks, primarily code generation. Previous research has shown how suitable prompt engineering could help developers in improving their code generation prompts. However, so far, there do not exist specific guidelines driving developers towards writing suitable prompts for code generation. In this work, we derive and evaluate development-specific prompt optimization guidelines. First, we use an iterative, test-driven approach to automatically refine code generation prompts, and we analyze the outcome of this process to identify prompt improvement items that lead to test passes. We use such elements to elicit 10 guidelines for prompt improvement, related to better specifying I/O, pre-post conditions, providing examples, various types of details, or clarifying ambiguities. We conduct an assessment with 50 practitioners, who report their usage of the elicited prompt improvement patterns, as well as their perceived usefulness, which does not always correspond to the actual usage before knowing our guidelines. Our results lead to implications not only for practitioners and educators, but also for those aimed at creating better LLM-aided software development tools.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.11258", "title": "Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation", "authors": [ "Pingzhi Tang", "Yiding Wang", "Muhan Zhang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) face the \"knowledge cutoff\" challenge, where their frozen parametric memory prevents direct internalization of new information. While Supervised Fine-Tuning (SFT) is commonly used to update model knowledge, it often updates factual content without reliably improving the model's ability to use the newly incorporated information for question answering or decision-making. Reinforcement Learning (RL) is essential for acquiring reasoning skills; however, its high computational cost makes it impractical for efficient online adaptation. We empirically observe that the parameter updates induced by SFT and RL are nearly orthogonal. Based on this observation, we propose Parametric Skill Transfer (PaST), a framework that supports modular skill transfer for efficient and effective knowledge adaptation. By extracting a domain-agnostic Skill Vector from a source domain, we can linearly inject knowledge manipulation skills into a target model after it has undergone lightweight SFT on new data. Experiments on knowledge-incorporation QA (SQuAD, LooGLE) and agentic tool-use benchmarks (ToolBench) demonstrate the effectiveness of our method. On SQuAD, PaST outperforms the state-of-the-art self-editing SFT baseline by up to 9.9 points. PaST further scales to long-context QA on LooGLE with an 8.0-point absolute accuracy gain, and improves zero-shot ToolBench success rates by +10.3 points on average with consistent gains across tool categories, indicating strong scalability and cross-domain transferability of the Skill Vector.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.15715", "title": "Dancing in Chains: Strategic Persuasion in Academic Rebuttal via Theory of Mind", "authors": [ "Zhitao He", "Zongwei Lyu", "Yi R Fung" ], "github": "https://github.com/Zhitao-He/RebuttalAgent", "project_page": "", "abstract": "Although artificial intelligence (AI) has become deeply integrated into various stages of the research workflow and achieved remarkable advancements, academic rebuttal remains a significant and underexplored challenge. This is because rebuttal is a complex process of strategic communication under severe information asymmetry rather than a simple technical debate. Consequently, current approaches struggle as they largely imitate surface-level linguistics, missing the essential element of perspective-taking required for effective persuasion. In this paper, we introduce RebuttalAgent, the first framework to ground academic rebuttal in Theory of Mind (ToM), operationalized through a ToM-Strategy-Response (TSR) pipeline that models reviewer mental state, formulates persuasion strategy, and generates strategy-grounded response. To train our agent, we construct RebuttalBench, a large-scale dataset synthesized via a novel critique-and-refine approach. Our training process consists of two stages, beginning with a supervised fine-tuning phase to equip the agent with ToM-based analysis and strategic planning capabilities, followed by a reinforcement learning phase leveraging the self-reward mechanism for scalable self-improvement. For reliable and efficient automated evaluation, we further develop Rebuttal-RM, a specialized evaluator trained on over 100K samples of multi-source rebuttal data, which achieves scoring consistency with human preferences surpassing powerful judge GPT-4.1. Extensive experiments show RebuttalAgent significantly outperforms the base model by an average of 18.3% on automated metrics, while also outperforming advanced proprietary models across both automated and human evaluations. Disclaimer: the generated rebuttal content is for reference only to inspire authors and assist in drafting. It is not intended to replace the author's own critical analysis and response.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Zhitao-He/RebuttalAgent", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.16276", "title": "GameTalk: Training LLMs for Strategic Conversation", "authors": [ "Victor Conchello Vendrell", "Max Ruiz Luyten", "Mihaela van der Schaar" ], "github": "", "project_page": "", "abstract": "Strategic decision-making in multi-agent settings is a key challenge for large language models (LLMs), particularly when coordination and negotiation must unfold over extended conversations. While recent work has explored the use of LLMs in isolated decision tasks, little attention has been given to optimizing long-term objectives through dialogue. We introduce GameTalk, a framework for training LLMs to make strategic decisions via multi-turn interactions. Unlike prior work that focuses on single-turn objectives or static action prediction, we train LLMs to optimize a global objective across full conversations. We achieve this by adapting fine-tuning methods like GRPO, DPO, and STaR to incorporate reward signals that depend on the entire interaction. We evaluate this approach on a suite of increasingly complex games, designed to stress different aspects of reasoning, coordination, and opponent modeling. Our results show that GameTalk significantly outperforms untrained models, especially under reward shaping, with DPO consistently yielding the strongest gains. These findings position conversational fine-tuning as a promising path for LLMs to reason, negotiate, and act in interactive environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.13606", "title": "ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch", "authors": [ "Zheng Liu", "Honglin Lin", "Chonghan Qin", "Xiaoyang Wang", "Xin Gao", "Yu Li", "Mengzhang Cai", "Yun Zhu", "Zhanping Zhong", "Qizhi Pei", "Zhuoshi Pan", "Xiaoran Shang", "Bin Cui", "Conghui He", "Wentao Zhang", "Lijun Wu" ], "github": "", "project_page": "", "abstract": "Chart reasoning is a critical capability for Vision Language Models (VLMs). However, the development of open-source models is severely hindered by the lack of high-quality training data. Existing datasets suffer from a dual challenge: synthetic charts are often simplistic and repetitive, while the associated QA pairs are prone to hallucinations and lack the reasoning depth required for complex tasks. To bridge this gap, we propose ChartVerse, a scalable framework designed to synthesize complex charts and reliable reasoning data from scratch. (1) To address the bottleneck of simple patterns, we first introduce Rollout Posterior Entropy (RPE), a novel metric that quantifies chart complexity. Guided by RPE, we develop complexity-aware chart coder to autonomously synthesize diverse, high-complexity charts via executable programs. (2) To guarantee reasoning rigor, we develop truth-anchored inverse QA synthesis. Diverging from standard generation, we adopt an answer-first paradigm: we extract deterministic answers directly from the source code, generate questions conditional on these anchors, and enforce strict consistency verification. To further elevate difficulty and reasoning depth, we filter samples based on model fail-rate and distill high-quality Chain-of-Thought (CoT) reasoning. We curate ChartVerse-SFT-600K and ChartVerse-RL-40K using Qwen3-VL-30B-A3B-Thinking as the teacher. Experimental results demonstrate that ChartVerse-8B achieves state-of-the-art performance, notably surpassing its teacher and rivaling the stronger Qwen3-VL-32B-Thinking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-26", "arxiv_id": "2601.16451", "title": "VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology", "authors": [ "Peixian Liang", "Songhao Li", "Shunsuke Koga", "Yutong Li", "Zahra Alipour", "Yucheng Tang", "Daguang Xu", "Zhi Huang" ], "github": "https://github.com/zhihuanglab/VISTA-PATH", "project_page": "", "abstract": "Accurate semantic segmentation for histopathology image is crucial for quantitative tissue analysis and downstream clinical modeling. Recent segmentation foundation models have improved generalization through large-scale pretraining, yet remain poorly aligned with pathology because they treat segmentation as a static visual prediction task. Here we present VISTA-PATH, an interactive, class-aware pathology segmentation foundation model designed to resolve heterogeneous structures, incorporate expert feedback, and produce pixel-level segmentation that are directly meaningful for clinical interpretation. VISTA-PATH jointly conditions segmentation on visual context, semantic tissue descriptions, and optional expert-provided spatial prompts, enabling precise multi-class segmentation across heterogeneous pathology images. To support this paradigm, we curate VISTA-PATH Data, a large-scale pathology segmentation corpus comprising over 1.6 million image-mask-text triplets spanning 9 organs and 93 tissue classes. Across extensive held-out and external benchmarks, VISTA-PATH consistently outperforms existing segmentation foundation models. Importantly, VISTA-PATH supports dynamic human-in-the-loop refinement by propagating sparse, patch-level bounding-box annotation feedback into whole-slide segmentation. Finally, we show that the high-fidelity, class-aware segmentation produced by VISTA-PATH is a preferred model for computational pathology. It improve tissue microenvironment analysis through proposed Tumor Interaction Score (TIS), which exhibits strong and significant associations with patient survival. Together, these results establish VISTA-PATH as a foundation model that elevates pathology image segmentation from a static prediction to an interactive and clinically grounded representation for digital pathology. Source code and demo can be found at https://github.com/zhihuanglab/VISTA-PATH.", "github_manual": "", "github_regex": "https://github.com/zhihuanglab/VISTA-PATH", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17737", "title": "The Script is All You Need: An Agentic Framework for Long-Horizon Dialogue-to-Cinematic Video Generation", "authors": [ "Chenyu Mu", "Xin He", "Qu Yang", "Wanshun Chen", "Jiadi Yao", "Huang Liu", "Zihao Yi", "Bo Zhao", "Xingyu Chen", "Ruotian Ma", "Fanghua Ye", "Erkun Yang", "Cheng Deng", "Zhaopeng Tu", "Xiaolong Li", "Linus" ], "github": "", "project_page": "", "abstract": "Recent advances in video generation have produced models capable of synthesizing stunning visual content from simple text prompts. However, these models struggle to generate long-form, coherent narratives from high-level concepts like dialogue, revealing a ``semantic gap'' between a creative idea and its cinematic execution. To bridge this gap, we introduce a novel, end-to-end agentic framework for dialogue-to-cinematic-video generation. Central to our framework is ScripterAgent, a model trained to translate coarse dialogue into a fine-grained, executable cinematic script. To enable this, we construct ScriptBench, a new large-scale benchmark with rich multimodal context, annotated via an expert-guided pipeline. The generated script then guides DirectorAgent, which orchestrates state-of-the-art video models using a cross-scene continuous generation strategy to ensure long-horizon coherence. Our comprehensive evaluation, featuring an AI-powered CriticAgent and a new Visual-Script Alignment (VSA) metric, shows our framework significantly improves script faithfulness and temporal fidelity across all tested video models. Furthermore, our analysis uncovers a crucial trade-off in current SOTA models between visual spectacle and strict script adherence, providing valuable insights for the future of automated filmmaking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17367", "title": "Elastic Attention: Test-time Adaptive Sparsity Ratios for Efficient Transformers", "authors": [ "Zecheng Tang", "Quantong Qiu", "Yi Yang", "Zhiyi Hong", "Haiya Xiang", "Kebin Liu", "Qingqing Dang", "Juntao Li", "Min Zhang" ], "github": "https://github.com/LCM-Lab/Elastic-Attention", "project_page": "", "abstract": "The quadratic complexity of standard attention mechanisms poses a significant scalability bottleneck for large language models (LLMs) in long-context scenarios. While hybrid attention strategies that combine sparse and full attention within a single model offer a viable solution, they typically employ static computation ratios (i.e., fixed proportions of sparse versus full attention) and fail to adapt to the varying sparsity sensitivities of downstream tasks during inference. To address this issue, we propose Elastic Attention, which allows the model to dynamically adjust its overall sparsity based on the input. This is achieved by integrating a lightweight Attention Router into the existing pretrained model, which dynamically assigns each attention head to different computation modes. Within only 12 hours of training on 8xA800 GPUs, our method enables models to achieve both strong performance and efficient inference. Experiments across three long-context benchmarks on widely-used LLMs demonstrate the superiority of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LCM-Lab/Elastic-Attention", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17058", "title": "Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs", "authors": [ "Wei Zhou", "Jun Zhou", "Haoyu Wang", "Zhenghao Li", "Qikang He", "Shaokun Han", "Guoliang Li", "Xuanhe Zhou", "Yeye He", "Chunwei Liu", "Zirui Tang", "Bin Wang", "Shen Tang", "Kai Zuo", "Yuyu Luo", "Zhenzhe Zheng", "Conghui He", "Jingren Zhou", "Fan Wu" ], "github": "", "project_page": "", "abstract": "Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for a wide range of data-centric applications. Driven by (i) rising demands for application-ready data (e.g., for analytics, visualization, decision-making), (ii) increasingly powerful LLM techniques, and (iii) the emergence of infrastructures that facilitate flexible agent construction (e.g., using Databricks Unity Catalog), LLM-enhanced methods are rapidly becoming a transformative and potentially dominant paradigm for data preparation. By investigating hundreds of recent literature works, this paper presents a systematic review of this evolving landscape, focusing on the use of LLM techniques to prepare data for diverse downstream tasks. First, we characterize the fundamental paradigm shift, from rule-based, model-specific pipelines to prompt-driven, context-aware, and agentic preparation workflows. Next, we introduce a task-centric taxonomy that organizes the field into three major tasks: data cleaning (e.g., standardization, error processing, imputation), data integration (e.g., entity matching, schema matching), and data enrichment (e.g., data annotation, profiling). For each task, we survey representative techniques, and highlight their respective strengths (e.g., improved generalization, semantic understanding) and limitations (e.g., the prohibitive cost of scaling LLMs, persistent hallucinations even in advanced agents, the mismatch between advanced methods and weak evaluation). Moreover, we analyze commonly used datasets and evaluation metrics (the empirical part). Finally, we discuss open research challenges and outline a forward-looking roadmap that emphasizes scalable LLM-data systems, principled designs for reliable agentic workflows, and robust evaluation protocols.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17761", "title": "AR-Omni: A Unified Autoregressive Model for Any-to-Any Generation", "authors": [ "Dongjie Cheng", "Ruifeng Yuan", "Yongqi Li", "Runyang You", "Wenjie Wang", "Liqiang Nie", "Lei Zhang", "Wenjie Li" ], "github": "", "project_page": "", "abstract": "Real-world perception and interaction are inherently multimodal, encompassing not only language but also vision and speech, which motivates the development of \"Omni\" MLLMs that support both multimodal inputs and multimodal outputs. While a sequence of omni MLLMs has emerged, most existing systems still rely on additional expert components to achieve multimodal generation, limiting the simplicity of unified training and inference. Autoregressive (AR) modeling, with a single token stream, a single next-token objective, and a single decoder, is an elegant and scalable foundation in the text domain. Motivated by this, we present AR-Omni, a unified any-to-any model in the autoregressive paradigm without any expert decoders. AR-Omni supports autoregressive text and image generation, as well as streaming speech generation, all under a single Transformer decoder. We further address three practical issues in unified AR modeling: modality imbalance via task-aware loss reweighting, visual fidelity via a lightweight token-level perceptual alignment loss for image tokens, and stability-creativity trade-offs via a finite-state decoding mechanism. Empirically, AR-Omni achieves strong quality across three modalities while remaining real-time, achieving a 0.88 real-time factor for speech generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17111", "title": "Least-Loaded Expert Parallelism: Load Balancing An Imbalanced Mixture-of-Experts", "authors": [ "Xuan-Phi Nguyen", "Shrey Pandit", "Austin Xu", "Caiming Xiong", "Shafiq Joty" ], "github": "https://github.com/SalesforceAIResearch/LeastLoadedEP", "project_page": "", "abstract": "Mixture-of-Experts (MoE) models are typically pre-trained with explicit load-balancing constraints to ensure statistically balanced expert routing. Despite this, we observe that even well-trained MoE models exhibit significantly imbalanced routing. This behavior is arguably natural-and even desirable - as imbalanced routing allows models to concentrate domain-specific knowledge within a subset of experts. Expert parallelism (EP) is designed to scale MoE models by distributing experts across multiple devices, but with a less-discussed assumption of balanced routing. Under extreme imbalance, EP can funnel a disproportionate number of tokens to a small number of experts, leading to compute- and memory-bound failures on overloaded devices during post-training or inference, where explicit load balancing is often inapplicable. We propose Least-Loaded Expert Parallelism (LLEP), a novel EP algorithm that dynamically reroutes excess tokens and associated expert parameters from overloaded devices to underutilized ones. This ensures that all devices complete their workloads within the minimum collective latency while respecting memory constraints. Across different model scales, LLEP achieves up to 5x speedup and 4x reduction in peak memory usage compared to standard EP. This enables faster and higher-throughput post-training and inference, with ~1.9x faster for gpt-oss-120b. We support our method with extensive theoretical analysis and comprehensive empirical evaluations, including ablation studies. These results illuminate key trade-offs and enable a principled framework for hardware-specific hyper-parameter tuning to achieve optimal performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SalesforceAIResearch/LeastLoadedEP", "github_api_added_by": "auto" }, { "date": "2026-01-27", "arxiv_id": "2601.17027", "title": "Scientific Image Synthesis: Benchmarking, Methodologies, and Downstream Utility", "authors": [ "Honglin Lin", "Chonghan Qin", "Zheng Liu", "Qizhi Pei", "Yu Li", "Zhanping Zhong", "Xin Gao", "Yanfeng Wang", "Conghui He", "Lijun Wu" ], "github": "https://github.com/huggingface/open-r1", "project_page": "", "abstract": "While synthetic data has proven effective for improving scientific reasoning in the text domain, multimodal reasoning remains constrained by the difficulty of synthesizing scientifically rigorous images. Existing Text-to-Image (T2I) models often produce outputs that are visually plausible yet scientifically incorrect, resulting in a persistent visual-logic divergence that limits their value for downstream reasoning. Motivated by recent advances in next-generation T2I models, we conduct a systematic study of scientific image synthesis across generation paradigms, evaluation, and downstream use. We analyze both direct pixel-based generation and programmatic synthesis, and propose ImgCoder, a logic-driven framework that follows an explicit \"understand - plan - code\" workflow to improve structural precision. To rigorously assess scientific correctness, we introduce SciGenBench, which evaluates generated images based on information utility and logical validity. Our evaluation reveals systematic failure modes in pixel-based models and highlights a fundamental expressiveness-precision trade-off. Finally, we show that fine-tuning Large Multimodal Models (LMMs) on rigorously verified synthetic scientific images yields consistent reasoning gains, with potential scaling trends analogous to the text domain, validating high-fidelity scientific synthesis as a viable path to unlocking massive multimodal reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/open-r1", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18081", "title": "DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal", "authors": [ "Peixuan Han", "Yingjie Yu", "Jingjun Xu", "Jiaxuan You" ], "github": "https://github.com/ulab-uiuc/DRPG-RebuttalAgent", "project_page": "", "abstract": "Despite the growing adoption of large language models (LLMs) in scientific research workflows, automated support for academic rebuttal, a crucial step in academic communication and peer review, remains largely underexplored. Existing approaches typically rely on off-the-shelf LLMs or simple pipelines, which struggle with long-context understanding and often fail to produce targeted and persuasive responses. In this paper, we propose DRPG, an agentic framework for automatic academic rebuttal generation that operates through four steps: Decompose reviews into atomic concerns, Retrieve relevant evidence from the paper, Plan rebuttal strategies, and Generate responses accordingly. Notably, the Planner in DRPG reaches over 98% accuracy in identifying the most feasible rebuttal direction. Experiments on data from top-tier conferences demonstrate that DRPG significantly outperforms existing rebuttal pipelines and achieves performance beyond the average human level using only an 8B model. Our analysis further demonstrates the effectiveness of the planner design and its value in providing multi-perspective and explainable suggestions. We also showed that DRPG works well in a more complex multi-round setting. These results highlight the effectiveness of DRPG and its potential to provide high-quality rebuttal content and support the scaling of academic discussions. Codes for this work are available at https://github.com/ulab-uiuc/DRPG-RebuttalAgent.", "github_manual": "", "github_regex": "https://github.com/ulab-uiuc/DRPG-RebuttalAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.16207", "title": "IVRA: Improving Visual-Token Relations for Robot Action Policy with Training-Free Hint-Based Guidance", "authors": [ "Jongwoo Park", "Kanchana Ranasinghe", "Jinhyeok Jang", "Cristina Mata", "Yoo Sung Jang", "Michael S Ryoo" ], "github": "", "project_page": "", "abstract": "Many Vision-Language-Action (VLA) models flatten image patches into a 1D token sequence, weakening the 2D spatial cues needed for precise manipulation. We introduce IVRA, a lightweight, training-free method that improves spatial understanding by exploiting affinity hints already available in the model's built-in vision encoder, without requiring any external encoder or retraining. IVRA selectively injects these affinity signals into a language-model layer in which instance-level features reside. This inference-time intervention realigns visual-token interactions and better preserves geometric structure while keeping all model parameters fixed. We demonstrate the generality of IVRA by applying it to diverse VLA architectures (LLaRA, OpenVLA, and FLOWER) across simulated benchmarks spanning both 2D and 3D manipulation (VIMA and LIBERO) and on various real-robot tasks. On 2D VIMA, IVRA improves average success by +4.2% over the baseline LLaRA in a low-data regime. On 3D LIBERO, it yields consistent gains over the OpenVLA and FLOWER baselines, including improvements when baseline accuracy is near saturation (96.3% to 97.1%). All code and models will be released publicly. Visualizations are available at: jongwoopark7978.github.io/IVRA", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18184", "title": "VIBEVOICE-ASR Technical Report", "authors": [ "Zhiliang Peng", "Jianwei Yu", "Yaoyao Chang", "Zilong Wang", "Li Dong", "Yingbo Hao", "Yujie Tu", "Chenyu Yang", "Wenhui Wang", "Songchen Xu", "Yutao Sun", "Hangbo Bao", "Weijiang Xu", "Yi Zhu", "Zehua Wang", "Ting Song", "Yan Xia", "Zewen Chi", "Shaohan Huang", "Liang Wang", "Chuang Ding", "Shuai Wang", "Xie Chen", "Furu Wei" ], "github": "", "project_page": "", "abstract": "This report presents VibeVoice-ASR, a general-purpose speech understanding framework built upon VibeVoice, designed to address the persistent challenges of context fragmentation and multi-speaker complexity in long-form audio (e.g., meetings, podcasts) that remain despite recent advancements in short-form speech recognition. Unlike traditional pipelined approaches that rely on audio chunking, VibeVoice-ASRsupports single-pass processing for up to 60 minutes of audio. It unifies Automatic Speech Recognition, Speaker Diarization, and Timestamping into a single end-to-end generation task. In addition, VibeVoice-ASR supports over 50 languages, requires no explicit language setting, and natively handles code-switching within and across utterances. Furthermore, we introduce a prompt-based context injection mechanism that allows users to supply customized conetxt, significantly improving accuracy on domain-specific terminology and polyphonic character disambiguation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17124", "title": "iFSQ: Improving FSQ for Image Generation with 1 Line of Code", "authors": [ "Bin Lin", "Zongjian Li", "Yuwei Niu", "Kaixiong Gong", "Yunyang Ge", "Yunlong Lin", "Mingzhe Zheng", "JianWei Zhang", "Miles Yang", "Zhao Zhong", "Liefeng Bo", "Li Yuan" ], "github": "https://github.com/Tencent-Hunyuan/iFSQ", "project_page": "", "abstract": "The field of image generation is currently bifurcated into autoregressive (AR) models operating on discrete tokens and diffusion models utilizing continuous latents. This divide, rooted in the distinction between VQ-VAEs and VAEs, hinders unified modeling and fair benchmarking. Finite Scalar Quantization (FSQ) offers a theoretical bridge, yet vanilla FSQ suffers from a critical flaw: its equal-interval quantization can cause activation collapse. This mismatch forces a trade-off between reconstruction fidelity and information efficiency. In this work, we resolve this dilemma by simply replacing the activation function in original FSQ with a distribution-matching mapping to enforce a uniform prior. Termed iFSQ, this simple strategy requires just one line of code yet mathematically guarantees both optimal bin utilization and reconstruction precision. Leveraging iFSQ as a controlled benchmark, we uncover two key insights: (1) The optimal equilibrium between discrete and continuous representations lies at approximately 4 bits per dimension. (2) Under identical reconstruction constraints, AR models exhibit rapid initial convergence, whereas diffusion models achieve a superior performance ceiling, suggesting that strict sequential ordering may limit the upper bounds of generation quality. Finally, we extend our analysis by adapting Representation Alignment (REPA) to AR models, yielding LlamaGen-REPA. Codes is available at https://github.com/Tencent-Hunyuan/iFSQ", "github_manual": "", "github_regex": "https://github.com/Tencent-Hunyuan/iFSQ", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18577", "title": "Self-Refining Video Sampling", "authors": [ "Sangwon Jang", "Taekyung Ki", "Jaehyeong Jo", "Saining Xie", "Jaehong Yoon", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "Modern video generators still struggle with complex physical dynamics, often falling short of physical realism. Existing approaches address this using external verifiers or additional training on augmented data, which is computationally expensive and still limited in capturing fine-grained motion. In this work, we present self-refining video sampling, a simple method that uses a pre-trained video generator trained on large-scale datasets as its own self-refiner. By interpreting the generator as a denoising autoencoder, we enable iterative inner-loop refinement at inference time without any external verifier or additional training. We further introduce an uncertainty-aware refinement strategy that selectively refines regions based on self-consistency, which prevents artifacts caused by over-refinement. Experiments on state-of-the-art video generators demonstrate significant improvements in motion coherence and physics alignment, achieving over 70\\% human preference compared to the default sampler and guidance-based sampler.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.15849", "title": "CGPT: Cluster-Guided Partial Tables with LLM-Generated Supervision for Table Retrieval", "authors": [ "Tsung-Hsiang Chou", "Chen-Jui Yu", "Shui-Hsiang Hsu", "Yao-Chung Fan" ], "github": "https://github.com/yumeow0122/CGPT", "project_page": "", "abstract": "General-purpose embedding models have demonstrated strong performance in text retrieval but remain suboptimal for table retrieval, where highly structured content leads to semantic compression and query-table mismatch. Recent LLM-based retrieval augmentation methods mitigate this issue by generating synthetic queries, yet they often rely on heuristic partial-table selection and seldom leverage these synthetic queries as supervision to improve the embedding model. We introduce CGPT, a training framework that enhances table retrieval through LLM-generated supervision. CGPT constructs semantically diverse partial tables by clustering table instances using K-means and sampling across clusters to broaden semantic coverage. An LLM then generates synthetic queries for these partial tables, which are used in hard-negative contrastive fine-tuning to refine the embedding model. Experiments across four public benchmarks (MimoTable, OTTQA, FetaQA, and E2E-WTQ) show that CGPT consistently outperforms retrieval baselines, including QGpT, with an average R@1 improvement of 16.54 percent. In a unified multi-domain corpus setting, CGPT further demonstrates strong cross-domain generalization and remains effective even when using smaller LLMs for synthetic query generation. These results indicate that semantically guided partial-table construction, combined with contrastive training from LLM-generated supervision, provides an effective and scalable paradigm for large-scale table retrieval. Our code is available at https://github.com/yumeow0122/CGPT.", "github_manual": "", "github_regex": "https://github.com/yumeow0122/CGPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18130", "title": "RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents", "authors": [ "Jize Wang", "Han Wu", "Zhiyuan You", "Yiming Song", "Yijun Wang", "Zifei Shan", "Yining Li", "Songyang Zhang", "Xinyi Le", "Cailian Chen", "Xinping Guan", "Dacheng Tao" ], "github": "", "project_page": "", "abstract": "Mixture-of-Agents (MoA) improves LLM performance through layered collaboration, but its dense topology raises costs and latency. Existing methods employ LLM judges to filter responses, yet still require all models to perform inference before judging, failing to cut costs effectively. They also lack model selection criteria and struggle with large model pools, where full inference is costly and can exceed context limits. To address this, we propose RouteMoA, an efficient mixture-of-agents framework with dynamic routing. It employs a lightweight scorer to perform initial screening by predicting coarse-grained performance from the query, narrowing candidates to a high-potential subset without inference. A mixture of judges then refines these scores through lightweight self- and cross-assessment based on existing model outputs, providing posterior correction without additional inference. Finally, a model ranking mechanism selects models by balancing performance, cost, and latency. RouteMoA outperforms MoA across varying tasks and model pool sizes, reducing cost by 89.8% and latency by 63.6% in the large-scale model pool.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17323", "title": "SkyReels-V3 Technique Report", "authors": [ "Debang Li", "Zhengcong Fei", "Tuanhui Li", "Yikun Dou", "Zheng Chen", "Jiangping Yang", "Mingyuan Fan", "Jingtao Xu", "Jiahua Wang", "Baoxuan Gu", "Mingshan Chang", "Yuqiang Xie", "Binjie Mao", "Youqiang Zhang", "Nuo Pang", "Hao Zhang", "Yuzhe Jin", "Zhiheng Xu", "Dixuan Lin", "Guibin Chen", "Yahui Zhou" ], "github": "https://github.com/SkyworkAI/SkyReels-V3", "project_page": "", "abstract": "Video generation serves as a cornerstone for building world models, where multimodal contextual inference stands as the defining test of capability. In this end, we present SkyReels-V3, a conditional video generation model, built upon a unified multimodal in-context learning framework with diffusion Transformers. SkyReels-V3 model supports three core generative paradigms within a single architecture: reference images-to-video synthesis, video-to-video extension and audio-guided video generation. (i) reference images-to-video model is designed to produce high-fidelity videos with strong subject identity preservation, temporal coherence, and narrative consistency. To enhance reference adherence and compositional stability, we design a comprehensive data processing pipeline that leverages cross frame pairing, image editing, and semantic rewriting, effectively mitigating copy paste artifacts. During training, an image video hybrid strategy combined with multi-resolution joint optimization is employed to improve generalization and robustness across diverse scenarios. (ii) video extension model integrates spatio-temporal consistency modeling with large-scale video understanding, enabling both seamless single-shot continuation and intelligent multi-shot switching with professional cinematographic patterns. (iii) Talking avatar model supports minute-level audio-conditioned video generation by training first-and-last frame insertion patterns and reconstructing key-frame inference paradigms. On the basis of ensuring visual quality, synchronization of audio and videos has been optimized. Extensive evaluations demonstrate that SkyReels-V3 achieves state-of-the-art or near state-of-the-art performance on key metrics including visual quality, instruction following, and specific aspect metrics, approaching leading closed-source systems. Github: https://github.com/SkyworkAI/SkyReels-V3.", "github_manual": "", "github_regex": "https://github.com/SkyworkAI/SkyReels-V3", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18759", "title": "UI Remix: Supporting UI Design Through Interactive Example Retrieval and Remixing", "authors": [ "Junling Wang", "Hongyi Lan", "Xiaotian Su", "Mustafa Doga Dogan", "April Yi Wang" ], "github": "https://github.com/ETH-PEACH-Lab/UI_Remix", "project_page": "", "abstract": "Designing user interfaces (UIs) is a critical step when launching products, building portfolios, or personalizing projects, yet end users without design expertise often struggle to articulate their intent and to trust design choices. Existing example-based tools either promote broad exploration, which can cause overwhelm and design drift, or require adapting a single example, risking design fixation. We present UI Remix, an interactive system that supports mobile UI design through an example-driven design workflow. Powered by a multimodal retrieval-augmented generation (MMRAG) model, UI Remix enables iterative search, selection, and adaptation of examples at both the global (whole interface) and local (component) level. To foster trust, it presents source transparency cues such as ratings, download counts, and developer information. In an empirical study with 24 end users, UI Remix significantly improved participants' ability to achieve their design goals, facilitated effective iteration, and encouraged exploration of alternative designs. Participants also reported that source transparency cues enhanced their confidence in adapting examples. Our findings suggest new directions for AI-assisted, example-driven systems that empower end users to design with greater control, trust, and openness to exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ETH-PEACH-Lab/UI_Remix", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18202", "title": "SAGE: Steerable Agentic Data Generation for Deep Search with Execution Feedback", "authors": [ "Fangyuan Xu", "Rujun Han", "Yanfei Chen", "Zifeng Wang", "I-Hung Hsu", "Jun Yan", "Vishy Tirumalashetty", "Eunsol Choi", "Tomas Pfister", "Chen-Yu Lee" ], "github": "https://github.com/carriex/sage", "project_page": "", "abstract": "Deep search agents, which aim to answer complex questions requiring reasoning across multiple documents, can significantly speed up the information-seeking process. Collecting human annotations for this application is prohibitively expensive due to long and complex exploration trajectories. We propose an agentic pipeline that automatically generates high quality, difficulty-controlled deep search question-answer pairs for a given corpus and a target difficulty level. Our pipeline, SAGE, consists of a data generator which proposes QA pairs and a search agent which attempts to solve the generated question and provide execution feedback for the data generator. The two components interact over multiple rounds to iteratively refine the question-answer pairs until they satisfy the target difficulty level. Our intrinsic evaluation shows SAGE generates questions that require diverse reasoning strategies, while significantly increases the correctness and difficulty of the generated data. Our extrinsic evaluation demonstrates up to 23% relative performance gain on popular deep search benchmarks by training deep search agents with our synthetic data. Additional experiments show that agents trained on our data can adapt from fixed-corpus retrieval to Google Search at inference time, without further training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/carriex/sage", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18157", "title": "Agentic Very Long Video Understanding", "authors": [ "Aniket Rege", "Arka Sadhu", "Yuliang Li", "Kejie Li", "Ramya Korlakai Vinayak", "Yuning Chai", "Yong Jae Lee", "Hyo Jin Kim" ], "github": "", "project_page": "", "abstract": "The advent of always-on personal AI assistants, enabled by all-day wearable devices such as smart glasses, demands a new level of contextual understanding, one that goes beyond short, isolated events to encompass the continuous, longitudinal stream of egocentric video. Achieving this vision requires advances in long-horizon video understanding, where systems must interpret and recall visual and audio information spanning days or even weeks. Existing methods, including large language models and retrieval-augmented generation, are constrained by limited context windows and lack the ability to perform compositional, multi-hop reasoning over very long video streams. In this work, we address these challenges through EGAgent, an enhanced agentic framework centered on entity scene graphs, which represent people, places, objects, and their relationships over time. Our system equips a planning agent with tools for structured search and reasoning over these graphs, as well as hybrid visual and audio search capabilities, enabling detailed, cross-modal, and temporally coherent reasoning. Experiments on the EgoLifeQA and Video-MME (Long) datasets show that our method achieves state-of-the-art performance on EgoLifeQA (57.5%) and competitive performance on Video-MME (Long) (74.1%) for complex longitudinal video understanding tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18418", "title": "daVinci-Dev: Agent-native Mid-training for Software Engineering", "authors": [ "Ji Zeng", "Dayuan Fu", "Tiantian Mi", "Yumin Zhuang", "Yaxing Huang", "Xuefeng Li", "Lyumanshan Ye", "Muhang Xie", "Qishuo Hua", "Zhen Huang", "Mohan Jiang", "Hanning Wang", "Jifan Lin", "Yang Xiao", "Jie Sun", "Yunze Wu", "Pengfei Liu" ], "github": "", "project_page": "", "abstract": "Recently, the frontier of Large Language Model (LLM) capabilities has shifted from single-turn code generation to agentic software engineering-a paradigm where models autonomously navigate, edit, and test complex repositories. While post-training methods have become the de facto approach for code agents, **agentic mid-training**-mid-training (MT) on large-scale data that mirrors authentic agentic workflows-remains critically underexplored due to substantial resource requirements, despite offering a more scalable path to instilling foundational agentic behaviors than relying solely on expensive reinforcement learning. A central challenge in realizing effective agentic mid-training is the distribution mismatch between static training data and the dynamic, feedback-rich environment of real development. To address this, we present a systematic study of agentic mid-training, establishing both the data synthesis principles and training methodology for effective agent development at scale. Central to our approach is **agent-native data**-supervision comprising two complementary types of trajectories: **contextually-native trajectories** that preserve the complete information flow an agent experiences, offering broad coverage and diversity; and **environmentally-native trajectories** collected from executable repositories where observations stem from actual tool invocations and test executions, providing depth and interaction authenticity. We verify the model's agentic capabilities on `SWE-Bench Verified`. We demonstrate our superiority over the previous open software engineering mid-training recipe `Kimi-Dev` under two post-training settings with an aligned base model and agentic scaffold, while using less than half mid-training tokens (73.1B). Besides relative advantage, our best performing 32B and 72B models achieve **56.1%** and **58.5%** resolution rates, respectively, which are ...", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18217", "title": "Paying Less Generalization Tax: A Cross-Domain Generalization Study of RL Training for LLM Agents", "authors": [ "Zhihan Liu", "Lin Guan", "Yixin Nie", "Kai Zhang", "Zhuoqun Hao", "Lin Chen", "Asli Celikyilmaz", "Zhaoran Wang", "Na Zhang" ], "github": "", "project_page": "", "abstract": "Generalist LLM agents are often post-trained on a narrow set of environments but deployed across far broader, unseen domains. In this work, we investigate the challenge of agentic post-training when the eventual test domains are unknown. Specifically, we analyze which properties of reinforcement learning (RL) environments and modeling choices have the greatest influence on out-of-domain performance. First, we identify two environment axes that strongly correlate with cross-domain generalization: (i) state information richness, i.e., the amount of information for the agent to process from the state, and (ii) planning complexity, estimated via goal reachability and trajectory length under a base policy. Notably, domain realism and text-level similarity are not the primary factors; for instance, the simple grid-world domain Sokoban leads to even stronger generalization in SciWorld than the more realistic ALFWorld. Motivated by these findings, we further show that increasing state information richness alone can already effectively improve cross-domain robustness. We propose a randomization technique, which is low-overhead and broadly applicable: add small amounts of distractive goal-irrelevant features to the state to make it richer without altering the task. Beyond environment-side properties, we also examine several modeling choices: (a) SFT warmup or mid-training helps prevent catastrophic forgetting during RL but undermines generalization to domains that are not included in the mid-training datamix; and (b) turning on step-by-step thinking during RL, while not always improving in-domain performance, plays a crucial role in preserving generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18778", "title": "Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability", "authors": [ "Shobhita Sundaram", "John Quan", "Ariel Kwiatkowski", "Kartik Ahuja", "Yann Ollivier", "Julia Kempe" ], "github": "", "project_page": "", "abstract": "Can a model learn to escape its own learning plateau? Reinforcement learning methods for finetuning large reasoning models stall on datasets with low initial success rates, and thus little training signal. We investigate a fundamental question: Can a pretrained LLM leverage latent knowledge to generate an automated curriculum for problems it cannot solve? To explore this, we design SOAR: A self-improvement framework designed to surface these pedagogical signals through meta-RL. A teacher copy of the model proposes synthetic problems for a student copy, and is rewarded with its improvement on a small subset of hard problems. Critically, SOAR grounds the curriculum in measured student progress rather than intrinsic proxy rewards. Our study on the hardest subsets of mathematical benchmarks (0/128 success) reveals three core findings. First, we show that it is possible to realize bi-level meta-RL that unlocks learning under sparse, binary rewards by sharpening a latent capacity of pretrained models to generate useful stepping stones. Second, grounded rewards outperform intrinsic reward schemes used in prior LLM self-play, reliably avoiding the instability and diversity collapse modes they typically exhibit. Third, analyzing the generated questions reveals that structural quality and well-posedness are more critical for learning progress than solution correctness. Our results suggest that the ability to generate useful stepping stones does not require the preexisting ability to actually solve the hard problems, paving a principled path to escape reasoning plateaus without additional curated data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18731", "title": "One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment", "authors": [ "Hongru Cai", "Yongqi Li", "Tiezheng Yu", "Fengbin Zhu", "Wenjie Wang", "Fuli Feng", "Wenjie Li" ], "github": "", "project_page": "", "abstract": "Alignment of Large Language Models (LLMs) aims to align outputs with human preferences, and personalized alignment further adapts models to individual users. This relies on personalized reward models that capture user-specific preferences and automatically provide individualized feedback. However, developing these models faces two critical challenges: the scarcity of feedback from individual users and the need for efficient adaptation to unseen users. We argue that addressing these constraints requires a paradigm shift from fitting data to learn user preferences to learn the process of preference adaptation. To realize this, we propose Meta Reward Modeling (MRM), which reformulates personalized reward modeling as a meta-learning problem. Specifically, we represent each user's reward model as a weighted combination of base reward functions, and optimize the initialization of these weights using a Model-Agnostic Meta-Learning (MAML)-style framework to support fast adaptation under limited feedback. To ensure robustness, we introduce the Robust Personalization Objective (RPO), which places greater emphasis on hard-to-learn users during meta optimization. Extensive experiments on personalized preference datasets validate that MRM enhances few-shot personalization, improves user robustness, and consistently outperforms baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.13599", "title": "Diffusion In Diffusion: Reclaiming Global Coherence in Semi-Autoregressive Diffusion", "authors": [ "Linrui Ma", "Yufei Cui", "Kai Han", "Yunhe Wang" ], "github": "", "project_page": "", "abstract": "One of the most compelling features of global discrete diffusion language models is their global bidirectional contextual capability. However, existing block-based diffusion studies tend to introduce autoregressive priors, which, while offering benefits, can cause models to lose this global coherence at the macro level. To regain global contextual understanding while preserving the advantages of the semi-autoregressive paradigm, we propose Diffusion in Diffusion, a 'draft-then-refine' framework designed to overcome the irreversibility and myopia problems inherent in block diffusion models. Our approach first employs block diffusion to generate rapid drafts using small blocks, then refines these drafts through global bidirectional diffusion with a larger bidirectional receptive field. We utilize snapshot confidence remasking to identify the most critical tokens that require modification, and apply mix-scale training to expand the block diffusion model's global capabilities. Empirical results demonstrate that our approach sets a new benchmark for discrete diffusion models on the OpenWebText dataset. Using only 26% of the fine-tuning budget of baseline models, we reduce generative perplexity from 25.7 to 21.9, significantly narrowing the performance gap with autoregressive models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.14127", "title": "The Side Effects of Being Smart: Safety Risks in MLLMs' Multi-Image Reasoning", "authors": [ "Renmiao Chen", "Yida Lu", "Shiyao Cui", "Xuan Ouyang", "Victor Shea-Jay Huang", "Shumin Zhang", "Chengwei Pan", "Han Qiu", "Minlie Huang" ], "github": "https://github.com/thu-coai/MIR-SafetyBench", "project_page": "", "abstract": "As Multimodal Large Language Models (MLLMs) acquire stronger reasoning capabilities to handle complex, multi-image instructions, this advancement may pose new safety risks. We study this problem by introducing MIR-SafetyBench, the first benchmark focused on multi-image reasoning safety, which consists of 2,676 instances across a taxonomy of 9 multi-image relations. Our extensive evaluations on 19 MLLMs reveal a troubling trend: models with more advanced multi-image reasoning can be more vulnerable on MIR-SafetyBench. Beyond attack success rates, we find that many responses labeled as safe are superficial, often driven by misunderstanding or evasive, non-committal replies. We further observe that unsafe generations exhibit lower attention entropy than safe ones on average. This internal signature suggests a possible risk that models may over-focus on task solving while neglecting safety constraints. Our code and data are available at https://github.com/thu-coai/MIR-SafetyBench.", "github_manual": "", "github_regex": "https://github.com/thu-coai/MIR-SafetyBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17640", "title": "End-to-End Joint ASR and Speaker Role Diarization with Child-Adult Interactions", "authors": [ "Anfeng Xu", "Tiantian Feng", "Somer Bishop", "Catherine Lord", "Shrikanth Narayanan" ], "github": "https://github.com/usc-sail/joint-asr-diarization-child-adult", "project_page": "", "abstract": "Accurate transcription and speaker diarization of child-adult spoken interactions are crucial for developmental and clinical research. However, manual annotation is time-consuming and challenging to scale. Existing automated systems typically rely on cascaded speaker diarization and speech recognition pipelines, which can lead to error propagation. This paper presents a unified end-to-end framework that extends the Whisper encoder-decoder architecture to jointly model ASR and child-adult speaker role diarization. The proposed approach integrates: (i) a serialized output training scheme that emits speaker tags and start/end timestamps, (ii) a lightweight frame-level diarization head that enhances speaker-discriminative encoder representations, (iii) diarization-guided silence suppression for improved temporal precision, and (iv) a state-machine-based forced decoding procedure that guarantees structurally valid outputs. Comprehensive evaluations on two datasets demonstrate consistent and substantial improvements over two cascaded baselines, achieving lower multi-talker word error rates and demonstrating competitive diarization accuracy across both Whisper-small and Whisper-large models. These findings highlight the effectiveness and practical utility of the proposed joint modeling framework for generating reliable, speaker-attributed transcripts of child-adult interactions at scale. The code and model weights are publicly available", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/usc-sail/joint-asr-diarization-child-adult", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17277", "title": "PingPong: A Natural Benchmark for Multi-Turn Code-Switching Dialogues", "authors": [ "Mohammad Rifqi Farhansyah", "Hanif Muhammad Zhafran", "Farid Adilazuarda", "Shamsuddeen Hassan Muhammad", "Maryam Ibrahim Mukhtar", "Nedjma Ousidhoum", "Genta Indra Winata", "Ayu Purwarianti", "Alham Fikri Aji" ], "github": "", "project_page": "", "abstract": "Code-switching is a widespread practice among the world's multilingual majority, yet few benchmarks accurately reflect its complexity in everyday communication. We present PingPong, a benchmark for natural multi-party code-switching dialogues covering five language-combination variations, some of which are trilingual. Our dataset consists of human-authored conversations among 2 to 4 participants covering authentic, multi-threaded structures where replies frequently reference much earlier points in the dialogue. We demonstrate that our data is significantly more natural and structurally diverse than machine-generated alternatives, offering greater variation in message length, speaker dominance, and reply distance. Based on these dialogues, we define three downstream tasks: Question Answering, Dialogue Summarization, and Topic Classification. Evaluations of several state-of-the-art language models on PingPong reveal that performance remains limited on code-switched inputs, underscoring the urgent need for more robust NLP systems capable of addressing the intricacies of real-world multilingual discourse.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.15860", "title": "STAR: Semantic Table Representation with Header-Aware Clustering and Adaptive Weighted Fusion", "authors": [ "Shui-Hsiang Hsu", "Tsung-Hsiang Chou", "Chen-Jui Yu", "Yao-Chung Fan" ], "github": "https://github.com/adsl135789/STAR", "project_page": "", "abstract": "Table retrieval is the task of retrieving the most relevant tables from large-scale corpora given natural language queries. However, structural and semantic discrepancies between unstructured text and structured tables make embedding alignment particularly challenging. Recent methods such as QGpT attempt to enrich table semantics by generating synthetic queries, yet they still rely on coarse partial-table sampling and simple fusion strategies, which limit semantic diversity and hinder effective query-table alignment. We propose STAR (Semantic Table Representation), a lightweight framework that improves semantic table representation through semantic clustering and weighted fusion. STAR first applies header-aware K-means clustering to group semantically similar rows and selects representative centroid instances to construct a diverse partial table. It then generates cluster-specific synthetic queries to comprehensively cover the table's semantic space. Finally, STAR employs weighted fusion strategies to integrate table and query embeddings, enabling fine-grained semantic alignment. This design enables STAR to capture complementary information from structured and textual sources, improving the expressiveness of table representations. Experiments on five benchmarks show that STAR achieves consistently higher Recall than QGpT on all datasets, demonstrating the effectiveness of semantic clustering and adaptive weighted fusion for robust table representation. Our code is available at https://github.com/adsl135789/STAR.", "github_manual": "", "github_regex": "https://github.com/adsl135789/STAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18137", "title": "DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints", "authors": [ "Yinger Zhang", "Shutong Jiang", "Renhao Li", "Jianhong Tu", "Yang Su", "Lianghao Deng", "Xudong Guo", "Chenxu Lv", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "While agent evaluation has shifted toward long-horizon tasks, most benchmarks still emphasize local, step-level reasoning rather than the global constrained optimization (e.g., time and financial budgets) that demands genuine planning ability. Meanwhile, existing LLM planning benchmarks underrepresent the active information gathering and fine-grained local constraints typical of real-world settings. To address this, we introduce DeepPlanning, a challenging benchmark for practical long-horizon agent planning. It features multi-day travel planning and multi-product shopping tasks that require proactive information acquisition, local constrained reasoning, and global constrained optimization. Evaluations on DeepPlanning show that even frontier agentic LLMs struggle with these problems, highlighting the importance of reliable explicit reasoning patterns and parallel tool use for achieving better effectiveness-efficiency trade-offs. Error analysis further points to promising directions for improving agentic LLMs over long planning horizons. We open-source the code and data to support future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.12042", "title": "Less Is More -- Until It Breaks: Security Pitfalls of Vision Token Compression in Large Vision-Language Models", "authors": [ "Xiaomei Zhang", "Zhaoxi Zhang", "Leo Yu Zhang", "Yanjun Zhang", "Guanhong Tao", "Shirui Pan" ], "github": "", "project_page": "", "abstract": "Visual token compression is widely adopted to improve the inference efficiency of Large Vision-Language Models (LVLMs), enabling their deployment in latency-sensitive and resource-constrained scenarios. However, existing work has mainly focused on efficiency and performance, while the security implications of visual token compression remain largely unexplored. In this work, we first reveal that visual token compression substantially degrades the robustness of LVLMs: models that are robust under uncompressed inference become highly vulnerable once compression is enabled. These vulnerabilities are state-specific; failure modes emerge only in the compressed setting and completely disappear when compression is disabled, making them particularly hidden and difficult to diagnose. By analyzing the key stages of the compression process, we identify instability in token importance ranking as the primary cause of this robustness degradation. Small and imperceptible perturbations can significantly alter token rankings, leading the compression mechanism to mistakenly discard task-critical information and ultimately causing model failure. Motivated by this observation, we propose a Compression-Aware Attack to systematically study and exploit this vulnerability. CAA directly targets the token selection mechanism and induces failures exclusively under compressed inference. We further extend this approach to more realistic black-box settings and introduce Transfer CAA, where neither the target model nor the compression configuration is accessible. We further evaluate potential defenses and find that they provide only limited protection. Extensive experiments across models, datasets, and compression methods show that visual token compression significantly undermines robustness, revealing a previously overlooked efficiency-security trade-off.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17067", "title": "A Mechanistic View on Video Generation as World Models: State and Dynamics", "authors": [ "Luozhou Wang", "Zhifei Chen", "Yihua Du", "Dongyu Yan", "Wenhang Ge", "Guibao Shen", "Xinli Xu", "Leyi Wu", "Man Chen", "Tianshuo Xu", "Peiran Ren", "Xin Tao", "Pengfei Wan", "Ying-Cong Chen" ], "github": "", "project_page": "", "abstract": "Large-scale video generation models have demonstrated emergent physical coherence, positioning them as potential world models. However, a gap remains between contemporary \"stateless\" video architectures and classic state-centric world model theories. This work bridges this gap by proposing a novel taxonomy centered on two pillars: State Construction and Dynamics Modeling. We categorize state construction into implicit paradigms (context management) and explicit paradigms (latent compression), while dynamics modeling is analyzed through knowledge integration and architectural reformulation. Furthermore, we advocate for a transition in evaluation from visual fidelity to functional benchmarks, testing physical persistence and causal reasoning. We conclude by identifying two critical frontiers: enhancing persistence via data-driven memory and compressed fidelity, and advancing causality through latent factor decoupling and reasoning-prior integration. By addressing these challenges, the field can evolve from generating visually plausible videos to building robust, general-purpose world simulators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17958", "title": "TensorLens: End-to-End Transformer Analysis via High-Order Attention Tensors", "authors": [ "Ido Andrew Atad", "Itamar Zimerman", "Shahar Katz", "Lior Wolf" ], "github": "https://github.com/idoatad/TensorLens", "project_page": "", "abstract": "Attention matrices are fundamental to transformer research, supporting a broad range of applications including interpretability, visualization, manipulation, and distillation. Yet, most existing analyses focus on individual attention heads or layers, failing to account for the model's global behavior. While prior efforts have extended attention formulations across multiple heads via averaging and matrix multiplications or incorporated components such as normalization and FFNs, a unified and complete representation that encapsulates all transformer blocks is still lacking. We address this gap by introducing TensorLens, a novel formulation that captures the entire transformer as a single, input-dependent linear operator expressed through a high-order attention-interaction tensor. This tensor jointly encodes attention, FFNs, activations, normalizations, and residual connections, offering a theoretically coherent and expressive linear representation of the model's computation. TensorLens is theoretically grounded and our empirical validation shows that it yields richer representations than previous attention-aggregation methods. Our experiments demonstrate that the attention tensor can serve as a powerful foundation for developing tools aimed at interpretability and model understanding. Our code is attached as a supplementary.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/idoatad/TensorLens", "github_api_added_by": "auto" }, { "date": "2026-01-27", "arxiv_id": "2601.15015", "title": "Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control", "authors": [ "Jannis Becktepe", "Aleksandra Franz", "Nils Thuerey", "Sebastian Peitz" ], "github": "https://github.com/safe-autonomous-systems/fluidgym", "project_page": "", "abstract": "Reinforcement learning (RL) has shown promising results in active flow control (AFC), yet progress in the field remains difficult to assess as existing studies rely on heterogeneous observation and actuation schemes, numerical setups, and evaluation protocols. Current AFC benchmarks attempt to address these issues but heavily rely on external computational fluid dynamics (CFD) solvers, are not fully differentiable, and provide limited 3D and multi-agent support. To overcome these limitations, we introduce FluidGym, the first standalone, fully differentiable benchmark suite for RL in AFC. Built entirely in PyTorch on top of the GPU-accelerated PICT solver, FluidGym runs in a single Python stack, requires no external CFD software, and provides standardized evaluation protocols. We present baseline results with PPO and SAC and release all environments, datasets, and trained models as public resources. FluidGym enables systematic comparison of control methods, establishes a scalable foundation for future research in learning-based flow control, and is available at https://github.com/safe-autonomous-systems/fluidgym.", "github_manual": "", "github_regex": "https://github.com/safe-autonomous-systems/fluidgym", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18753", "title": "HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs", "authors": [ "Xinyue Zeng", "Junhong Lin", "Yujun Yan", "Feng Guo", "Liang Shi", "Jun Wu", "Dawei Zhou" ], "github": "", "project_page": "", "abstract": "The reliability of Large Language Models (LLMs) in high-stakes domains such as healthcare, law, and scientific discovery is often compromised by hallucinations. These failures typically stem from two sources: data-driven hallucinations and reasoning-driven hallucinations. However, existing detection methods usually address only one source and rely on task-specific heuristics, limiting their generalization to complex scenarios. To overcome these limitations, we introduce the Hallucination Risk Bound, a unified theoretical framework that formally decomposes hallucination risk into data-driven and reasoning-driven components, linked respectively to training-time mismatches and inference-time instabilities. This provides a principled foundation for analyzing how hallucinations emerge and evolve. Building on this foundation, we introduce HalluGuard, an NTK-based score that leverages the induced geometry and captured representations of the NTK to jointly identify data-driven and reasoning-driven hallucinations. We evaluate HalluGuard on 10 diverse benchmarks, 11 competitive baselines, and 9 popular LLM backbones, consistently achieving state-of-the-art performance in detecting diverse forms of LLM hallucinations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18790", "title": "MortalMATH: Evaluating the Conflict Between Reasoning Objectives and Emergency Contexts", "authors": [ "Etienne Lanzeray", "Stephane Meilliez", "Malo Ruelle", "Damien Sileo" ], "github": "", "project_page": "", "abstract": "Large Language Models are increasingly optimized for deep reasoning, prioritizing the correct execution of complex tasks over general conversation. We investigate whether this focus on calculation creates a \"tunnel vision\" that ignores safety in critical situations. We introduce MortalMATH, a benchmark of 150 scenarios where users request algebra help while describing increasingly life-threatening emergencies (e.g., stroke symptoms, freefall). We find a sharp behavioral split: generalist models (like Llama-3.1) successfully refuse the math to address the danger. In contrast, specialized reasoning models (like Qwen-3-32b and GPT-5-nano) often ignore the emergency entirely, maintaining over 95 percent task completion rates while the user describes dying. Furthermore, the computational time required for reasoning introduces dangerous delays: up to 15 seconds before any potential help is offered. These results suggest that training models to relentlessly pursue correct answers may inadvertently unlearn the survival instincts required for safe deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.14103", "title": "Interp3D: Correspondence-aware Interpolation for Generative Textured 3D Morphing", "authors": [ "Xiaolu Liu", "Yicong Li", "Qiyuan He", "Jiayin Zhu", "Wei Ji", "Angela Yao", "Jianke Zhu" ], "github": "https://github.com/xiaolul2/Interp3D", "project_page": "", "abstract": "Textured 3D morphing seeks to generate smooth and plausible transitions between two 3D assets, preserving both structural coherence and fine-grained appearance. This ability is crucial not only for advancing 3D generation research but also for practical applications in animation, editing, and digital content creation. Existing approaches either operate directly on geometry, limiting them to shape-only morphing while neglecting textures, or extend 2D interpolation strategies into 3D, which often causes semantic ambiguity, structural misalignment, and texture blurring. These challenges underscore the necessity to jointly preserve geometric consistency, texture alignment, and robustness throughout the transition process. To address this, we propose Interp3D, a novel training-free framework for textured 3D morphing. It harnesses generative priors and adopts a progressive alignment principle to ensure both geometric fidelity and texture coherence. Starting from semantically aligned interpolation in condition space, Interp3D enforces structural consistency via SLAT (Structured Latent)-guided structure interpolation, and finally transfers appearance details through fine-grained texture fusion. For comprehensive evaluations, we construct a dedicated dataset, Interp3DData, with graded difficulty levels and assess generation results from fidelity, transition smoothness, and plausibility. Both quantitative metrics and human studies demonstrate the significant advantages of our proposed approach over previous methods. Source code is available at https://github.com/xiaolul2/Interp3D.", "github_manual": "", "github_regex": "https://github.com/xiaolul2/Interp3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18744", "title": "TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models", "authors": [ "Fangxu Yu", "Xingang Guo", "Lingzhi Yuan", "Haoqiang Kang", "Hongyu Zhao", "Lianhui Qin", "Furong Huang", "Bin Hu", "Tianyi Zhou" ], "github": "https://github.com/tianyi-lab/TSRBench", "project_page": "", "abstract": "Time series data is ubiquitous in real-world scenarios and crucial for critical applications ranging from energy management to traffic control. Consequently, the ability to reason over time series is a fundamental skill for generalist models to solve practical problems. However, this dimension is notably absent from existing benchmarks of generalist models. To bridge this gap, we introduce TSRBench, a comprehensive multi-modal benchmark designed to stress-test the full spectrum of time series reasoning capabilities. TSRBench features: i) a diverse set of 4125 problems from 14 domains, and is categorized into 4 major dimensions: Perception, Reasoning, Prediction, and Decision-Making. ii) 15 tasks from the 4 dimensions evaluating essential reasoning capabilities (e.g., numerical reasoning). Through extensive experiments, we evaluated over 30 leading proprietary and open-source LLMs, VLMs, and TSLLMs within TSRBench. Our findings reveal that: i) scaling laws hold for perception and reasoning but break down for prediction; ii) strong reasoning does not guarantee accurate context-aware forecasting, indicating a decoupling between semantic understanding and numerical prediction; and iii) despite the complementary nature of textual and visual represenations of time series as inputs, current multimodal models fail to effectively fuse them for reciprocal performance gains. TSRBench provides a standardized evaluation platform that not only highlights existing challenges but also offers valuable insights to advance generalist models. Our code and dataset are available at https://tsrbench.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tianyi-lab/TSRBench", "github_api_added_by": "auto" }, { "date": "2026-01-27", "arxiv_id": "2601.17895", "title": "Masked Depth Modeling for Spatial Perception", "authors": [ "Bin Tan", "Changjiang Sun", "Xiage Qin", "Hanat Adai", "Zelin Fu", "Tianxiang Zhou", "Han Zhang", "Yinghao Xu", "Xing Zhu", "Yujun Shen", "Nan Xue" ], "github": "", "project_page": "", "abstract": "Spatial visual perception is a fundamental requirement in physical-world applications like autonomous driving and robotic manipulation, driven by the need to interact with 3D environments. Capturing pixel-aligned metric depth using RGB-D cameras would be the most viable way, yet it usually faces obstacles posed by hardware limitations and challenging imaging conditions, especially in the presence of specular or texture-less surfaces. In this work, we argue that the inaccuracies from depth sensors can be viewed as \"masked\" signals that inherently reflect underlying geometric ambiguities. Building on this motivation, we present LingBot-Depth, a depth completion model which leverages visual context to refine depth maps through masked depth modeling and incorporates an automated data curation pipeline for scalable training. It is encouraging to see that our model outperforms top-tier RGB-D cameras in terms of both depth precision and pixel coverage. Experimental results on a range of downstream tasks further suggest that LingBot-Depth offers an aligned latent representation across RGB and depth modalities. We release the code, checkpoint, and 3M RGB-depth pairs (including 2M real data and 1M simulated data) to the community of spatial perception.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.18226", "title": "Yunjue Agent Tech Report: A Fully Reproducible, Zero-Start In-Situ Self-Evolving Agent System for Open-Ended Tasks", "authors": [ "Haotian Li", "Shijun Yang", "Weizhen Qi", "Silei Zhao", "Rui Hua", "Mingzhu Song", "Xiaojian Yang", "Chao Peng" ], "github": "", "project_page": "", "abstract": "Conventional agent systems often struggle in open-ended environments where task distributions continuously drift and external supervision is scarce. Their reliance on static toolsets or offline training lags behind these dynamics, leaving the system's capability boundaries rigid and unknown. To address this, we propose the In-Situ Self-Evolving paradigm. This approach treats sequential task interactions as a continuous stream of experience, enabling the system to distill short-term execution feedback into long-term, reusable capabilities without access to ground-truth labels. Within this framework, we identify tool evolution as the critical pathway for capability expansion, which provides verifiable, binary feedback signals. Within this framework, we develop Yunjue Agent, a system that iteratively synthesizes, optimizes, and reuses tools to navigate emerging challenges. To optimize evolutionary efficiency, we further introduce a Parallel Batch Evolution strategy. Empirical evaluations across five diverse benchmarks under a zero-start setting demonstrate significant performance gains over proprietary baselines. Additionally, complementary warm-start evaluations confirm that the accumulated general knowledge can be seamlessly transferred to novel domains. Finally, we propose a novel metric to monitor evolution convergence, serving as a function analogous to training loss in conventional optimization. We open-source our codebase, system traces, and evolved tools to facilitate future research in resilient, self-evolving intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17668", "title": "Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction", "authors": [ "Jang-Hyun Kim", "Dongyoon Han", "Sangdoo Yun" ], "github": "https://github.com/Janghyun1230/fastkvzip", "project_page": "", "abstract": "Efficient key-value (KV) cache management is crucial for the practical deployment of large language models (LLMs), yet existing compression techniques often incur a trade-off between performance degradation and computational overhead. We propose a novel gating-based KV cache eviction method for frozen-weight LLMs that achieves high compression ratios with negligible computational cost. Our approach introduces lightweight sink-attention gating modules to identify and retain critical KV pairs, and integrates seamlessly into both the prefill and decoding stages. The proposed gate training algorithm relies on forward passes of an LLM, avoiding expensive backpropagation, while achieving strong task generalization through a task-agnostic reconstruction objective. Extensive experiments across the Qwen2.5-1M, Qwen3, and Gemma3 families show that our method maintains near-lossless performance while evicting up to 70% of the KV cache. The results are consistent across a wide range of tasks, including long-context understanding, code comprehension, and mathematical reasoning, demonstrating the generality of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Janghyun1230/fastkvzip", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17617", "title": "Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests", "authors": [ "Jingjie Ning", "Jo\u00e3o Coelho", "Yibo Kong", "Yunfan Long", "Bruno Martins", "Jo\u00e3o Magalh\u00e3es", "Jamie Callan", "Chenyan Xiong" ], "github": "", "project_page": "", "abstract": "LLM-powered search agents are increasingly being used for multi-step information seeking tasks, yet the IR community lacks empirical understanding of how agentic search sessions unfold and how retrieved evidence is used. This paper presents a large-scale log analysis of agentic search based on 14.44M search requests (3.97M sessions) collected from DeepResearchGym, i.e. an open-source search API accessed by external agentic clients. We sessionize the logs, assign session-level intents and step-wise query-reformulation labels using LLM-based annotation, and propose Context-driven Term Adoption Rate (CTAR) to quantify whether newly introduced query terms are traceable to previously retrieved evidence. Our analyses reveal distinctive behavioral patterns. First, over 90% of multi-turn sessions contain at most ten steps, and 89% of inter-step intervals fall under one minute. Second, behavior varies by intent. Fact-seeking sessions exhibit high repetition that increases over time, while sessions requiring reasoning sustain broader exploration. Third, agents reuse evidence across steps. On average, 54% of newly introduced query terms appear in the accumulated evidence context, with contributions from earlier steps beyond the most recent retrieval. The findings suggest that agentic search may benefit from repetition-aware early stopping, intent-adaptive retrieval budgets, and explicit cross-step context tracking. We plan to release the anonymized logs to support future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-27", "arxiv_id": "2601.17237", "title": "C-RADIOv4 (Tech Report)", "authors": [ "Mike Ranzinger", "Greg Heinrich", "Collin McCarthy", "Jan Kautz", "Andrew Tao", "Bryan Catanzaro", "Pavlo Molchanov" ], "github": "https://github.com/NVlabs/RADIO", "project_page": "", "abstract": "By leveraging multi-teacher distillation, agglomerative vision backbones provide a unified student model that retains and improves the distinct capabilities of multiple teachers. In this tech report, we describe the most recent release of the C-RADIO family of models, C-RADIOv4, which builds upon AM-RADIO/RADIOv2.5 in design, offering strong improvements on key downstream tasks at the same computational complexity. We release -SO400M (412M params), and -H (631M) model variants, both trained with an updated set of teachers: SigLIP2, DINOv3, and SAM3. In addition to improvements on core metrics and new capabilities from imitating SAM3, the C-RADIOv4 model family further improves any-resolution support, brings back the ViTDet option for drastically enhanced efficiency at high-resolution, and comes with a permissive license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/RADIO", "github_api_added_by": "auto" }, { "date": "2026-01-28", "arxiv_id": "2601.17645", "title": "AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking", "authors": [ "Xilin Jiang", "Qiaolin Wang", "Junkai Wu", "Xiaomin He", "Zhongweiyang Xu", "Yinghao Ma", "Minshuo Piao", "Kaiyi Yang", "Xiuwen Zheng", "Riki Shimizu", "Yicong Chen", "Arsalan Firoozi", "Gavin Mischler", "Sukru Samet Dindar", "Richard Antonello", "Linyang He", "Tsun-An Hsieh", "Xulin Fan", "Yulun Wu", "Yuesheng Ma", "Chaitanya Amballa", "Weixiong Chen", "Jiarui Hai", "Ruisi Li", "Vishal Choudhari", "Cong Han", "Yinghao Aaron Li", "Adeen Flinker", "Mounya Elhilali", "Emmanouil Benetos", "Mark Hasegawa-Johnson", "Romit Roy Choudhury", "Nima Mesgarani" ], "github": "", "project_page": "", "abstract": "Internet audio-visual clips convey meaning through time-varying sound and motion, which extend beyond what text alone can represent. To examine whether AI models can understand such signals in human cultural contexts, we introduce AVMeme Exam, a human-curated benchmark of over one thousand iconic Internet sounds and videos spanning speech, songs, music, and sound effects. Each meme is paired with a unique Q&A assessing levels of understanding from surface content to context and emotion to usage and world knowledge, along with metadata such as original year, transcript, summary, and sensitivity. We systematically evaluate state-of-the-art multimodal large language models (MLLMs) alongside human participants using this benchmark. Our results reveal a consistent limitation: current models perform poorly on textless music and sound effects, and struggle to think in context and in culture compared to surface content. These findings highlight a key gap in human-aligned multimodal intelligence and call for models that can perceive contextually and culturally beyond the surface of what they hear and see. Project page: avmemeexam.github.io/public", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.19834", "title": "Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models", "authors": [ "Jialong Wu", "Xiaoying Zhang", "Hongyi Yuan", "Xiangcheng Zhang", "Tianhao Huang", "Changjing He", "Chaoyi Deng", "Renrui Zhang", "Youbin Wu", "Mingsheng Long" ], "github": "", "project_page": "", "abstract": "Humans construct internal world models and reason by manipulating the concepts within these models. Recent advances in AI, particularly chain-of-thought (CoT) reasoning, approximate such human cognitive abilities, where world models are believed to be embedded within large language models. Expert-level performance in formal and abstract domains such as mathematics and programming has been achieved in current systems by relying predominantly on verbal reasoning. However, they still lag far behind humans in domains like physical and spatial intelligence, which require richer representations and prior knowledge. The emergence of unified multimodal models (UMMs) capable of both verbal and visual generation has therefore sparked interest in more human-like reasoning grounded in complementary multimodal pathways, though their benefits remain unclear. From a world-model perspective, this paper presents the first principled study of when and how visual generation benefits reasoning. Our key position is the visual superiority hypothesis: for certain tasks--particularly those grounded in the physical world--visual generation more naturally serves as world models, whereas purely verbal world models encounter bottlenecks arising from representational limitations or insufficient prior knowledge. Theoretically, we formalize internal world modeling as a core component of CoT reasoning and analyze distinctions among different forms of world models. Empirically, we identify tasks that necessitate interleaved visual-verbal CoT reasoning, constructing a new evaluation suite, VisWorld-Eval. Controlled experiments on a state-of-the-art UMM show that interleaved CoT significantly outperforms purely verbal CoT on tasks that favor visual world modeling, but offers no clear advantage otherwise. Together, this work clarifies the potential of multimodal world modeling for more powerful, human-like multimodal AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.19375", "title": "Selective Steering: Norm-Preserving Control Through Discriminative Layer Selection", "authors": [ "Quy-Anh Dang", "Chris Ngo" ], "github": "https://github.com/knoveleng/steering", "project_page": "", "abstract": "Despite significant progress in alignment, large language models (LLMs) remain vulnerable to adversarial attacks that elicit harmful behaviors. Activation steering techniques offer a promising inference-time intervention approach, but existing methods suffer from critical limitations: activation addition requires careful coefficient tuning and is sensitive to layer-specific norm variations, while directional ablation provides only binary control. Recent work on Angular Steering introduces continuous control via rotation in a 2D subspace, but its practical implementation violates norm preservation, causing distribution shift and generation collapse, particularly in models below 7B parameters. We propose Selective Steering, which addresses these limitations through two key innovations: (1) a mathematically rigorous norm-preserving rotation formulation that maintains activation distribution integrity, and (2) discriminative layer selection that applies steering only where feature representations exhibit opposite-signed class alignment. Experiments across nine models demonstrate that Selective Steering achieves 5.5x higher attack success rates than prior methods while maintaining zero perplexity violations and approximately 100\\% capability retention on standard benchmarks. Our approach provides a principled, efficient framework for controllable and stable LLM behavior modification. Code: https://github.com/knoveleng/steering", "github_manual": "", "github_regex": "https://github.com/knoveleng/steering", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.19362", "title": "Revisiting Parameter Server in LLM Post-Training", "authors": [ "Xinyi Wan", "Penghui Qi", "Guangxing Huang", "Chaoyi Ruan", "Min Lin", "Jialin Li" ], "github": "https://github.com/sail-sg/odc", "project_page": "", "abstract": "Modern data parallel (DP) training favors collective communication over parameter servers (PS) for its simplicity and efficiency under balanced workloads. However, the balanced workload assumption no longer holds in large language model (LLM) post-training due to the high variance in sequence lengths. Under imbalanced workloads, collective communication creates synchronization barriers, leading to under-utilization of devices with smaller workloads. This change in training dynamics calls for a revisit of the PS paradigm for its robustness to such imbalance. We propose On-Demand Communication (ODC), which adapts PS into Fully Sharded Data Parallel (FSDP) by replacing collective all-gather and reduce-scatter with direct point-to-point communication. Compared to FSDP, ODC reduces the synchronization barrier from once per layer to once per minibatch and decouples the workload on each device so that faster workers are not stalled. It also enables simpler and more effective load balancing at the minibatch level. Across diverse LLM post-training tasks, ODC consistently improves device utilization and training throughput, achieving up to a 36\\% speedup over standard FSDP. These results demonstrate that ODC is a superior fit for the prevalent imbalanced workloads in LLM post-training. Our implementation of ODC and integration with FSDP is open-sourced at https://github.com/sail-sg/odc.", "github_manual": "", "github_regex": "https://github.com/sail-sg/odc", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.19149", "title": "GPCR-Filter: a deep learning framework for efficient and precise GPCR modulator discovery", "authors": [ "Jingjie Ning", "Xiangzhen Shen", "Li Hou", "Shiyi Shen", "Jiahao Yang", "Junrui Li", "Hong Shan", "Sanan Wu", "Sihan Gao", "Huaqiang Eric Xu", "Xinheng He" ], "github": "", "project_page": "", "abstract": "G protein-coupled receptors (GPCRs) govern diverse physiological processes and are central to modern pharmacology. Yet discovering GPCR modulators remains challenging because receptor activation often arises from complex allosteric effects rather than direct binding affinity, and conventional assays are slow, costly, and not optimized for capturing these dynamics. Here we present GPCR-Filter, a deep learning framework specifically developed for GPCR modulator discovery. We assembled a high-quality dataset of over 90,000 experimentally validated GPCR-ligand pairs, providing a robust foundation for training and evaluation. GPCR-Filter integrates the ESM-3 protein language model for high-fidelity GPCR sequence representations with graph neural networks that encode ligand structures, coupled through an attention-based fusion mechanism that learns receptor-ligand functional relationships. Across multiple evaluation settings, GPCR-Filter consistently outperforms state-of-the-art compound-protein interaction models and exhibits strong generalization to unseen receptors and ligands. Notably, the model successfully identified micromolar-level agonists of the 5-HT1A receptor with distinct chemical frameworks. These results establish GPCR-Filter as a scalable and effective computational approach for GPCR modulator discovery, advancing AI-assisted drug development for complex signaling systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.18491", "title": "AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security", "authors": [ "Dongrui Liu", "Qihan Ren", "Chen Qian", "Shuai Shao", "Yuejin Xie", "Yu Li", "Zhonghao Yang", "Haoyu Luo", "Peng Wang", "Qingyu Liu", "Binxin Hu", "Ling Tang", "Jilin Mei", "Dadi Guo", "Leitao Yuan", "Junyao Yang", "Guanxu Chen", "Qihao Lin", "Yi Yu", "Bo Zhang", "Jiaxuan Guo", "Jie Zhang", "Wenqi Shao", "Huiqi Deng", "Zhiheng Xi", "Wenjie Wang", "Wenxuan Wang", "Wen Shen", "Zhikai Chen", "Haoyu Xie", "Jialing Tao", "Juntao Dai", "Jiaming Ji", "Zhongjie Ba", "Linfeng Zhang", "Yong Liu", "Quanshi Zhang", "Lei Zhu", "Zhihua Wei", "Hui Xue", "Chaochao Lu", "Jing Shao", "Xia Hu" ], "github": "", "project_page": "", "abstract": "The rise of AI agents introduces complex safety and security challenges arising from autonomous tool use and environmental interactions. Current guardrail models lack agentic risk awareness and transparency in risk diagnosis. To introduce an agentic guardrail that covers complex and numerous risky behaviors, we first propose a unified three-dimensional taxonomy that orthogonally categorizes agentic risks by their source (where), failure mode (how), and consequence (what). Guided by this structured and hierarchical taxonomy, we introduce a new fine-grained agentic safety benchmark (ATBench) and a Diagnostic Guardrail framework for agent safety and security (AgentDoG). AgentDoG provides fine-grained and contextual monitoring across agent trajectories. More Crucially, AgentDoG can diagnose the root causes of unsafe actions and seemingly safe but unreasonable actions, offering provenance and transparency beyond binary labels to facilitate effective agent alignment. AgentDoG variants are available in three sizes (4B, 7B, and 8B parameters) across Qwen and Llama model families. Extensive experimental results demonstrate that AgentDoG achieves state-of-the-art performance in agentic safety moderation in diverse and complex interactive scenarios. All models and datasets are openly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.18631", "title": "AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning", "authors": [ "Mingyang Song", "Haoyu Sun", "Jiawei Gu", "Linjie Li", "Luxin Xu", "Ranjay Krishna", "Yu Cheng" ], "github": "", "project_page": "", "abstract": "When humans face problems beyond their immediate capabilities, they rely on tools, providing a promising paradigm for improving visual reasoning in multimodal large language models (MLLMs). Effective reasoning, therefore, hinges on knowing which tools to use, when to invoke them, and how to compose them over multiple steps, even when faced with new tools or new tasks. We introduce AdaReasoner, a family of multimodal models that learn tool use as a general reasoning skill rather than as tool-specific or explicitly supervised behavior. AdaReasoner is enabled by (i) a scalable data curation pipeline exposing models to long-horizon, multi-step tool interactions; (ii) Tool-GRPO, a reinforcement learning algorithm that optimizes tool selection and sequencing based on end-task success; and (iii) an adaptive learning mechanism that dynamically regulates tool usage. Together, these components allow models to infer tool utility from task context and intermediate outcomes, enabling coordination of multiple tools and generalization to unseen tools. Empirically, AdaReasoner exhibits strong tool-adaptive and generalization behaviors: it autonomously adopts beneficial tools, suppresses irrelevant ones, and adjusts tool usage frequency based on task demands, despite never being explicitly trained to do so. These capabilities translate into state-of-the-art performance across challenging benchmarks, improving the 7B base model by +24.9\\% on average and surpassing strong proprietary systems such as GPT-5 on multiple tasks, including VSP and Jigsaw.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.18692", "title": "A Pragmatic VLA Foundation Model", "authors": [ "Wei Wu", "Fan Lu", "Yunnan Wang", "Shuai Yang", "Shi Liu", "Fangjing Wang", "Qian Zhu", "He Sun", "Yong Wang", "Shuailei Ma", "Yiyu Ren", "Kejia Zhang", "Hui Yu", "Jingmei Zhao", "Shuai Zhou", "Zhenqi Qiu", "Houlong Xiong", "Ziyu Wang", "Zechen Wang", "Ran Cheng", "Yong-Lu Li", "Yongtao Huang", "Xing Zhu", "Yujun Shen", "Kecheng Zheng" ], "github": "", "project_page": "", "abstract": "Offering great potential in robotic manipulation, a capable Vision-Language-Action (VLA) foundation model is expected to faithfully generalize across tasks and platforms while ensuring cost efficiency (e.g., data and GPU hours required for adaptation). To this end, we develop LingBot-VLA with around 20,000 hours of real-world data from 9 popular dual-arm robot configurations. Through a systematic assessment on 3 robotic platforms, each completing 100 tasks with 130 post-training episodes per task, our model achieves clear superiority over competitors, showcasing its strong performance and broad generalizability. We have also built an efficient codebase, which delivers a throughput of 261 samples per second per GPU with an 8-GPU training setup, representing a 1.5~2.8times (depending on the relied VLM base model) speedup over existing VLA-oriented codebases. The above features ensure that our model is well-suited for real-world deployment. To advance the field of robot learning, we provide open access to the code, base model, and benchmark data, with a focus on enabling more challenging tasks and promoting sound evaluation standards.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.09150", "title": "World Craft: Agentic Framework to Create Visualizable Worlds via Text", "authors": [ "Jianwen Sun", "Yukang Feng", "Kaining Ying", "Chuanhao Li", "Zizhen Li", "Fanrui Zhang", "Jiaxin Ai", "Yifan Chang", "Yu Dai", "Yifei Huang", "Kaipeng Zhang" ], "github": "https://github.com/HerzogFL/World-Craft", "project_page": "", "abstract": "Large Language Models (LLMs) motivate generative agent simulation (e.g., AI Town) to create a ``dynamic world'', holding immense value across entertainment and research. However, for non-experts, especially those without programming skills, it isn't easy to customize a visualizable environment by themselves. In this paper, we introduce World Craft, an agentic world creation framework to create an executable and visualizable AI Town via user textual descriptions. It consists of two main modules, World Scaffold and World Guild. World Scaffold is a structured and concise standardization to develop interactive game scenes, serving as an efficient scaffolding for LLMs to customize an executable AI Town-like environment. World Guild is a multi-agent framework to progressively analyze users' intents from rough descriptions, and synthesizes required structured contents (\\eg environment layout and assets) for World Scaffold . Moreover, we construct a high-quality error-correction dataset via reverse engineering to enhance spatial knowledge and improve the stability and controllability of layout generation, while reporting multi-dimensional evaluation metrics for further analysis. Extensive experiments demonstrate that our framework significantly outperforms existing commercial code agents (Cursor and Antigravity) and LLMs (Qwen3 and Gemini-3-Pro). in scene construction and narrative intent conveyance, providing a scalable solution for the democratization of environment creation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HerzogFL/World-Craft", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.18292", "title": "TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment", "authors": [ "Zhewen Tan", "Wenhan Yu", "Jianfeng Si", "Tongxin Liu", "Kaiqi Guan", "Huiyan Jin", "Jiawen Tao", "Xiaokun Yuan", "Duohe Ma", "Xiangzheng Zhang", "Tong Yang", "Lin Sun" ], "github": "", "project_page": "", "abstract": "In recent years, safety risks associated with large language models have become increasingly prominent, highlighting the urgent need to mitigate the generation of toxic and harmful content. The mainstream paradigm for LLM safety alignment typically adopts a collaborative framework involving three roles: an attacker for adversarial prompt generation, a defender for safety defense, and an evaluator for response assessment. In this paper, we propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative and co-improving collaboration among three roles with near-zero manual annotation. Experimental results show that the attacker preserves high output diversity while achieving a 20%-50% improvement in adversarial effectiveness; the defender attains 10%-30% gains in safety performance without degrading general reasoning capability; and the evaluator continuously refines its fine-grained judgment ability through iterations, accurately distinguishing unsafe responses, simple refusals, and useful guidance. Overall, our framework establishes an efficient and scalable paradigm for LLM safety alignment, enabling continuous co-evolution within a unified learning loop.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.18116", "title": "FABLE: Forest-Based Adaptive Bi-Path LLM-Enhanced Retrieval for Multi-Document Reasoning", "authors": [ "Lin Sun", "Linglin Zhang", "Jingang Huang", "Change Jia", "Zhengwei Cheng", "Xiangzheng Zhang" ], "github": "", "project_page": "", "abstract": "The rapid expansion of long-context Large Language Models (LLMs) has reignited debate on whether Retrieval-Augmented Generation (RAG) remains necessary. However, empirical evidence reveals persistent limitations of long-context inference, including the lost-in-the-middle phenomenon, high computational cost, and poor scalability for multi-document reasoning. Conversely, traditional RAG systems, while efficient, are constrained by flat chunk-level retrieval that introduces semantic noise and fails to support structured cross-document synthesis. We present FABLE, a Forest-based Adaptive Bi-path LLM-Enhanced retrieval framework that integrates LLMs into both knowledge organization and retrieval. FABLE constructs LLM-enhanced hierarchical forest indexes with multi-granularity semantic structures, then employs a bi-path strategy combining LLM-guided hierarchical traversal with structure-aware propagation for fine-grained evidence acquisition, with explicit budget control for adaptive efficiency trade-offs. Extensive experiments demonstrate that FABLE consistently outperforms SOTA RAG methods and achieves comparable accuracy to full-context LLM inference with up to 94\\% token reduction, showing that long-context LLMs amplify rather than fully replace the need for structured retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.18724", "title": "HalluCitation Matters: Revealing the Impact of Hallucinated References with 300 Hallucinated Papers in ACL Conferences", "authors": [ "Yusuke Sakai", "Hidetaka Kamigaito", "Taro Watanabe" ], "github": "", "project_page": "", "abstract": "Recently, we have often observed hallucinated citations or references that do not correspond to any existing work in papers under review, preprints, or published papers. Such hallucinated citations pose a serious concern to scientific reliability. When they appear in accepted papers, they may also negatively affect the credibility of conferences. In this study, we refer to hallucinated citations as \"HalluCitation\" and systematically investigate their prevalence and impact. We analyze all papers published at ACL, NAACL, and EMNLP in 2024 and 2025, including main conference, Findings, and workshop papers. Our analysis reveals that nearly 300 papers contain at least one HalluCitation, most of which were published in 2025. Notably, half of these papers were identified at EMNLP 2025, the most recent conference, indicating that this issue is rapidly increasing. Moreover, more than 100 such papers were accepted as main conference and Findings papers at EMNLP 2025, affecting the credibility.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.19895", "title": "Post-LayerNorm Is Back: Stable, ExpressivE, and Deep", "authors": [ "Chen Chen", "Lai Wei" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) scaling is hitting a wall. Widening models yields diminishing returns, and extending context length does not improve fundamental expressivity. In contrast, depth scaling offers theoretically superior expressivity, yet current Transformer architectures struggle to train reliably at extreme depths. We revisit the Post-LayerNorm (Post-LN) formulation, whose instability at scale caused its replacement by Pre-LN in modern LLMs. We show that the central failure mode of Post-LN arises from the ResNet-style residual pathway, which introduces gradient vanishing in deep networks. We present Keel, a Post-LN Transformer that replaces this residual path with a Highway-style connection. This modification preserves the gradient flow through the residual branch, preventing signal vanishing from the top layers to the bottom. Unlike prior methods, Keel enables stable training at extreme depths without requiring specialized initialization or complex optimization tricks. Keel trains robustly at depths exceeding 1000 layers and consistently improves perplexity and depth-scaling characteristics over Pre-LN. These findings indicate that Post-LN, when paired with a Highway-style connection, provides a simple and effective foundation for building deeply scalable LLMs, opening the possibility for future infinite-depth architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.19532", "title": "Benchmarks Saturate When The Model Gets Smarter Than The Judge", "authors": [ "Marthe Ballon", "Andres Algaba", "Brecht Verbeken", "Vincent Ginis" ], "github": "", "project_page": "", "abstract": "Benchmarks are important tools to track progress in the development of Large Language Models (LLMs), yet inaccuracies in datasets and evaluation methods consistently undermine their effectiveness. Here, we present Omni-MATH-2, a manually revised version of the Omni-MATH dataset comprising a clean, exact-answer subset (n{=}4181) and a tagged, non-standard subset (n{=}247). Each problem was audited to ensure LaTeX compilability, solvability and verifiability, which involved adding missing figures or information, labeling problems requiring a proof, estimation or image, and removing clutter. This process significantly reduces dataset-induced noise, thereby providing a more precise assessment of model performance. The annotated dataset also allows us to evaluate judge-induced noise by comparing GPT-5 mini with the original Omni-Judge, revealing substantial discrepancies between judges on both the clean and tagged problem subsets. Expert annotations reveal that Omni-Judge is wrong in 96.4% of the judge disagreements, indicating its inability to differentiate between models' abilities, even well before saturation of the benchmark occurs. As problems become more challenging, we find that increasingly competent judges become essential in order to prevent judge errors from masking genuine differences between models. Finally, neither judge identifies the present failure modes for the subset of tagged problems, demonstrating that dataset quality and judge reliability are both critical to develop accurate benchmarks of model performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.18067", "title": "EvolVE: Evolutionary Search for LLM-based Verilog Generation and Optimization", "authors": [ "Wei-Po Hsin", "Ren-Hao Deng", "Yao-Ting Hsieh", "En-Ming Huang", "Shih-Hao Hung" ], "github": "https://github.com/weiber2002/ICRTL", "project_page": "", "abstract": "Verilog's design cycle is inherently labor-intensive and necessitates extensive domain expertise. Although Large Language Models (LLMs) offer a promising pathway toward automation, their limited training data and intrinsic sequential reasoning fail to capture the strict formal logic and concurrency inherent in hardware systems. To overcome these barriers, we present EvolVE, the first framework to analyze multiple evolution strategies on chip design tasks, revealing that Monte Carlo Tree Search (MCTS) excels at maximizing functional correctness, while Idea-Guided Refinement (IGR) proves superior for optimization. We further leverage Structured Testbench Generation (STG) to accelerate the evolutionary process. To address the lack of complex optimization benchmarks, we introduce IC-RTL, targeting industry-scale problems derived from the National Integrated Circuit Contest. Evaluations establish EvolVE as the new state-of-the-art, achieving 98.1% on VerilogEval v2 and 92% on RTLLM v2. Furthermore, on the industry-scale IC-RTL suite, our framework surpasses reference implementations authored by contest participants, reducing the Power, Performance, Area (PPA) product by up to 66% in Huffman Coding and 17% in the geometric mean across all problems. The source code of the IC-RTL benchmark is available at https://github.com/weiber2002/ICRTL.", "github_manual": "", "github_regex": "https://github.com/weiber2002/ICRTL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.18923", "title": "DeFM: Learning Foundation Representations from Depth for Robotics", "authors": [ "Manthan Patel", "Jonas Frey", "Mayank Mittal", "Fan Yang", "Alexander Hansson", "Amir Bar", "Cesar Cadena", "Marco Hutter" ], "github": "https://github.com/leggedrobotics/defm", "project_page": "", "abstract": "Depth sensors are widely deployed across robotic platforms, and advances in fast, high-fidelity depth simulation have enabled robotic policies trained on depth observations to achieve robust sim-to-real transfer for a wide range of tasks. Despite this, representation learning for depth modality remains underexplored compared to RGB, where large-scale foundation models now define the state of the art. To address this gap, we present DeFM, a self-supervised foundation model trained entirely on depth images for robotic applications. Using a DINO-style self-distillation objective on a curated dataset of 60M depth images, DeFM learns geometric and semantic representations that generalize to diverse environments, tasks, and sensors. To retain metric awareness across multiple scales, we introduce a novel input normalization strategy. We further distill DeFM into compact models suitable for resource-constrained robotic systems. When evaluated on depth-based classification, segmentation, navigation, locomotion, and manipulation benchmarks, DeFM achieves state-of-the-art performance and demonstrates strong generalization from simulation to real-world environments. We release all our pretrained models, which can be adopted off-the-shelf for depth-based robotic learning without task-specific fine-tuning. Webpage: https://de-fm.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/leggedrobotics/defm", "github_api_added_by": "auto" }, { "date": "2026-01-28", "arxiv_id": "2601.15968", "title": "HyperAlign: Hypernetwork for Efficient Test-Time Alignment of Diffusion Models", "authors": [ "Xin Xie", "Jiaxian Guo", "Dong Gong" ], "github": "https://github.com/Shelsin/HyperAlign", "project_page": "", "abstract": "Diffusion models achieve state-of-the-art performance but often fail to generate outputs that align with human preferences and intentions, resulting in images with poor aesthetic quality and semantic inconsistencies. Existing alignment methods present a difficult trade-off: fine-tuning approaches suffer from loss of diversity with reward over-optimization, while test-time scaling methods introduce significant computational overhead and tend to under-optimize. To address these limitations, we propose HyperAlign, a novel framework that trains a hypernetwork for efficient and effective test-time alignment. Instead of modifying latent states, HyperAlign dynamically generates low-rank adaptation weights to modulate the diffusion model's generation operators. This allows the denoising trajectory to be adaptively adjusted based on input latents, timesteps and prompts for reward-conditioned alignment. We introduce multiple variants of HyperAlign that differ in how frequently the hypernetwork is applied, balancing between performance and efficiency. Furthermore, we optimize the hypernetwork using a reward score objective regularized with preference data to reduce reward hacking. We evaluate HyperAlign on multiple extended generative paradigms, including Stable Diffusion and FLUX. It significantly outperforms existing fine-tuning and test-time scaling baselines in enhancing semantic consistency and visual appeal.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Shelsin/HyperAlign", "github_api_added_by": "auto" }, { "date": "2026-01-28", "arxiv_id": "2601.19228", "title": "Towards Pixel-Level VLM Perception via Simple Points Prediction", "authors": [ "Tianhui Song", "Haoyu Lu", "Hao Yang", "Lin Sui", "Haoning Wu", "Zaida Zhou", "Zhiqi Huang", "Yiping Bao", "Y. Charles", "Xinyu Zhou", "Limin Wang" ], "github": "https://github.com/itseez/opencv", "project_page": "", "abstract": "We present SimpleSeg, a strikingly simple yet highly effective approach to endow Multimodal Large Language Models (MLLMs) with native pixel-level perception. Our method reframes segmentation as a simple sequence generation problem: the model directly predicts sequences of points (textual coordinates) delineating object boundaries, entirely within its language space. To achieve high fidelity, we introduce a two-stage SFtoRL training pipeline, where Reinforcement Learning with an IoU-based reward refines the point sequences to accurately match ground-truth contours. We find that the standard MLLM architecture possesses a strong, inherent capacity for low-level perception that can be unlocked without any specialized architecture. On segmentation benchmarks, SimpleSeg achieves performance that is comparable to, and often surpasses, methods relying on complex, task-specific designs. This work lays out that precise spatial understanding can emerge from simple point prediction, challenging the prevailing need for auxiliary components and paving the way for more unified and capable VLMs. Homepage: https://simpleseg.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/itseez/opencv", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.19798", "title": "Youtu-VL: Unleashing Visual Potential via Unified Vision-Language Supervision", "authors": [ "Zhixiang Wei", "Yi Li", "Zhehan Kan", "Xinghua Jiang", "Zuwei Long", "Shifeng Liu", "Hongze Shen", "Wei Liu", "Xiaoyu Tan", "Haojia Lin", "Yubo Zhu", "Qianyu Li", "Di Yin", "Haoyu Cao", "Weibo Gu", "Xin Li", "Yinsong Liu", "Deqiang Jiang", "Xing Sun", "Yunsheng Wu", "Mingkong Tang", "Shuangyin Liu", "Lexiang Tang", "Haodong Lin", "Junru Lu", "Jiarui Qin", "Lingfeng Qiao", "Ruizhi Qiao", "Bo Ke", "Jianfeng He", "Ke Li", "Yangning Li", "Yunhang Shen", "Mengdan Zhang", "Peixian Chen", "Kun Yin", "Bing Liu", "Yunfei Wu", "Huang Chen", "Zhongpeng Cai", "Xiaotian Li" ], "github": "", "project_page": "", "abstract": "Despite the significant advancements represented by Vision-Language Models (VLMs), current architectures often exhibit limitations in retaining fine-grained visual information, leading to coarse-grained multimodal comprehension. We attribute this deficiency to a suboptimal training paradigm inherent in prevailing VLMs, which exhibits a text-dominant optimization bias by conceptualizing visual signals merely as passive conditional inputs rather than supervisory targets. To mitigate this, we introduce Youtu-VL, a framework leveraging the Vision-Language Unified Autoregressive Supervision (VLUAS) paradigm, which fundamentally shifts the optimization objective from ``vision-as-input'' to ``vision-as-target.'' By integrating visual tokens directly into the prediction stream, Youtu-VL applies unified autoregressive supervision to both visual details and linguistic content. Furthermore, we extend this paradigm to encompass vision-centric tasks, enabling a standard VLM to perform vision-centric tasks without task-specific additions. Extensive empirical evaluations demonstrate that Youtu-VL achieves competitive performance on both general multimodal tasks and vision-centric tasks, establishing a robust foundation for the development of comprehensive generalist visual agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.13295", "title": "CooperBench: Why Coding Agents Cannot be Your Teammates Yet", "authors": [ "Arpandeep Khatua", "Hao Zhu", "Peter Tran", "Arya Prabhudesai", "Frederic Sadrieh", "Johann K. Lieberwirth", "Xinkai Yu", "Yicheng Fu", "Michael J. Ryan", "Jiaxin Pei", "Diyi Yang" ], "github": "", "project_page": "", "abstract": "Resolving team conflicts requires not only task-specific competence, but also social intelligence to find common ground and build consensus. As AI agents increasingly collaborate on complex work, they must develop coordination capabilities to function as effective teammates. Yet we hypothesize that current agents lack these capabilities. To test this, we introduce CooperBench, a benchmark of over 600 collaborative coding tasks across 12 libraries in 4 programming languages. Each task assigns two agents different features that can be implemented independently but may conflict without proper coordination. Tasks are grounded in real open-source repositories with expert-written tests. Evaluating state-of-the-art coding agents, we observe the curse of coordination: agents achieve on average 30% lower success rates when working together compared to performing both tasks individually. This contrasts sharply with human teams, where adding teammates typically improves productivity. Our analysis reveals three key issues: (1) communication channels become jammed with vague, ill-timed, and inaccurate messages; (2) even with effective communication, agents deviate from their commitments; and (3) agents often hold incorrect expectations about others' plans and communication. Through large-scale simulation, we also observe rare but interesting emergent coordination behavior including role division, resource division, and negotiation. Our research presents a novel benchmark for collaborative coding and calls for a shift from pursuing individual agent capability to developing social intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-28", "arxiv_id": "2601.19897", "title": "Self-Distillation Enables Continual Learning", "authors": [ "Idan Shenfeld", "Mehul Damani", "Jonas H\u00fcbotter", "Pulkit Agrawal" ], "github": "", "project_page": "", "abstract": "Continual learning, enabling models to acquire new skills and knowledge without degrading existing capabilities, remains a fundamental challenge for foundation models. While on-policy reinforcement learning can reduce forgetting, it requires explicit reward functions that are often unavailable. Learning from expert demonstrations, the primary alternative, is dominated by supervised fine-tuning (SFT), which is inherently off-policy. We introduce Self-Distillation Fine-Tuning (SDFT), a simple method that enables on-policy learning directly from demonstrations. SDFT leverages in-context learning by using a demonstration-conditioned model as its own teacher, generating on-policy training signals that preserve prior capabilities while acquiring new skills. Across skill learning and knowledge acquisition tasks, SDFT consistently outperforms SFT, achieving higher new-task accuracy while substantially reducing catastrophic forgetting. In sequential learning experiments, SDFT enables a single model to accumulate multiple skills over time without performance regression, establishing on-policy distillation as a practical path to continual learning from demonstrations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20540", "title": "Advancing Open-source World Models", "authors": [ "Robbyant Team", "Zelin Gao", "Qiuyu Wang", "Yanhong Zeng", "Jiapeng Zhu", "Ka Leong Cheng", "Yixuan Li", "Hanlin Wang", "Yinghao Xu", "Shuailei Ma", "Yihang Chen", "Jie Liu", "Yansong Cheng", "Yao Yao", "Jiayi Zhu", "Yihao Meng", "Kecheng Zheng", "Qingyan Bai", "Jingye Chen", "Zehong Shen", "Yue Yu", "Xing Zhu", "Yujun Shen", "Hao Ouyang" ], "github": "", "project_page": "", "abstract": "We present LingBot-World, an open-sourced world simulator stemming from video generation. Positioned as a top-tier world model, LingBot-World offers the following features. (1) It maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond. (2) It enables a minute-level horizon while preserving contextual consistency over time, which is also known as \"long-term memory\". (3) It supports real-time interactivity, achieving a latency of under 1 second when producing 16 frames per second. We provide public access to the code and model in an effort to narrow the divide between open-source and closed-source technologies. We believe our release will empower the community with practical applications across areas like content creation, gaming, and robot learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20618", "title": "GDCNet: Generative Discrepancy Comparison Network for Multimodal Sarcasm Detection", "authors": [ "Shuguang Zhang", "Junhong Lian", "Guoxin Yu", "Baoxun Xu", "Xiang Ao" ], "github": "", "project_page": "", "abstract": "Multimodal sarcasm detection (MSD) aims to identify sarcasm within image-text pairs by modeling semantic incongruities across modalities. Existing methods often exploit cross-modal embedding misalignment to detect inconsistency but struggle when visual and textual content are loosely related or semantically indirect. While recent approaches leverage large language models (LLMs) to generate sarcastic cues, the inherent diversity and subjectivity of these generations often introduce noise. To address these limitations, we propose the Generative Discrepancy Comparison Network (GDCNet). This framework captures cross-modal conflicts by utilizing descriptive, factually grounded image captions generated by Multimodal LLMs (MLLMs) as stable semantic anchors. Specifically, GDCNet computes semantic and sentiment discrepancies between the generated objective description and the original text, alongside measuring visual-textual fidelity. These discrepancy features are then fused with visual and textual representations via a gated module to adaptively balance modality contributions. Extensive experiments on MSD benchmarks demonstrate GDCNet's superior accuracy and robustness, establishing a new state-of-the-art on the MMSD2.0 benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20614", "title": "Harder Is Better: Boosting Mathematical Reasoning via Difficulty-Aware GRPO and Multi-Aspect Question Reformulation", "authors": [ "Yanqi Dai", "Yuxiang Ji", "Xiao Zhang", "Yong Wang", "Xiangxiang Chu", "Zhiwu Lu" ], "github": "https://github.com/AMAP-ML/MathForge", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) offers a robust mechanism for enhancing mathematical reasoning in large models. However, we identify a systematic lack of emphasis on more challenging questions in existing methods from both algorithmic and data perspectives, despite their importance for refining underdeveloped capabilities. Algorithmically, widely used Group Relative Policy Optimization (GRPO) suffers from an implicit imbalance where the magnitude of policy updates is lower for harder questions. Data-wise, augmentation approaches primarily rephrase questions to enhance diversity without systematically increasing intrinsic difficulty. To address these issues, we propose a two-dual MathForge framework to improve mathematical reasoning by targeting harder questions from both perspectives, which comprises a Difficulty-Aware Group Policy Optimization (DGPO) algorithm and a Multi-Aspect Question Reformulation (MQR) strategy. Specifically, DGPO first rectifies the implicit imbalance in GRPO via difficulty-balanced group advantage estimation, and further prioritizes harder questions by difficulty-aware question-level weighting. Meanwhile, MQR reformulates questions across multiple aspects to increase difficulty while maintaining the original gold answer. Overall, MathForge forms a synergistic loop: MQR expands the data frontier, and DGPO effectively learns from the augmented data. Extensive experiments show that MathForge significantly outperforms existing methods on various mathematical reasoning tasks. The code and augmented data are all available at https://github.com/AMAP-ML/MathForge.", "github_manual": "", "github_regex": "https://github.com/AMAP-ML/MathForge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.19949", "title": "RIR-Mega-Speech: A Reverberant Speech Corpus with Comprehensive Acoustic Metadata and Reproducible Evaluation", "authors": [ "Mandip Goswami" ], "github": "", "project_page": "", "abstract": "Despite decades of research on reverberant speech, comparing methods remains difficult because most corpora lack per-file acoustic annotations or provide limited documentation for reproduction. We present RIR-Mega-Speech, a corpus of approximately 117.5 hours created by convolving LibriSpeech utterances with roughly 5,000 simulated room impulse responses from the RIR-Mega collection. Every file includes RT60, direct-to-reverberant ratio (DRR), and clarity index (C_{50}) computed from the source RIR using clearly defined, reproducible procedures. We also provide scripts to rebuild the dataset and reproduce all evaluation results. Using Whisper small on 1,500 paired utterances, we measure 5.20% WER (95% CI: 4.69--5.78) on clean speech and 7.70% (7.04--8.35) on reverberant versions, corresponding to a paired increase of 2.50 percentage points (2.06--2.98). This represents a 48% relative degradation. WER increases monotonically with RT60 and decreases with DRR, consistent with prior perceptual studies. While the core finding that reverberation harms recognition is well established, we aim to provide the community with a standardized resource where acoustic conditions are transparent and results can be verified independently. The repository includes one-command rebuild instructions for both Windows and Linux environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20552", "title": "DeepSeek-OCR 2: Visual Causal Flow", "authors": [ "Haoran Wei", "Yaofeng Sun", "Yukun Li" ], "github": "", "project_page": "", "abstract": "We present DeepSeek-OCR 2 to investigate the feasibility of a novel encoder-DeepEncoder V2-capable of dynamically reordering visual tokens upon image semantics. Conventional vision-language models (VLMs) invariably process visual tokens in a rigid raster-scan order (top-left to bottom-right) with fixed positional encoding when fed into LLMs. However, this contradicts human visual perception, which follows flexible yet semantically coherent scanning patterns driven by inherent logical structures. Particularly for images with complex layouts, human vision exhibits causally-informed sequential processing. Inspired by this cognitive mechanism, DeepEncoder V2 is designed to endow the encoder with causal reasoning capabilities, enabling it to intelligently reorder visual tokens prior to LLM-based content interpretation. This work explores a novel paradigm: whether 2D image understanding can be effectively achieved through two-cascaded 1D causal reasoning structures, thereby offering a new architectural approach with the potential to achieve genuine 2D reasoning. Codes and model weights are publicly accessible at http://github.com/deepseek-ai/DeepSeek-OCR-2.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20209", "title": "Spark: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning", "authors": [ "Jinyang Wu", "Shuo Yang", "Changpeng Yang", "Yuhao Shen", "Shuai Zhang", "Zhengqi Wen", "Jianhua Tao" ], "github": "", "project_page": "", "abstract": "Reinforcement learning has empowered large language models to act as intelligent agents, yet training them for long-horizon tasks remains challenging due to the scarcity of high-quality trajectories, especially under limited resources. Existing methods typically scale up rollout sizes and indiscriminately allocate computational resources among intermediate steps. Such attempts inherently waste substantial computation budget on trivial steps while failing to guarantee sample quality. To address this, we propose Spark (Strategic Policy-Aware exploRation via Key-state dynamic branching), a novel framework that selectively branches at critical decision states for resource-efficient exploration. Our key insight is to activate adaptive branching exploration at critical decision points to probe promising trajectories, thereby achieving precise resource allocation that prioritizes sampling quality over blind coverage. This design leverages the agent's intrinsic decision-making signals to reduce dependence on human priors, enabling the agent to autonomously expand exploration and achieve stronger generalization. Experiments across diverse tasks (e.g., embodied planning), demonstrate that Spark achieves superior success rates with significantly fewer training samples, exhibiting robust generalization even in unseen scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20834", "title": "Linear representations in language models can change dramatically over a conversation", "authors": [ "Andrew Kyle Lampinen", "Yuxuan Li", "Eghbal Hosseini", "Sangnie Bhardwaj", "Murray Shanahan" ], "github": "", "project_page": "", "abstract": "Language model representations often contain linear directions that correspond to high-level concepts. Here, we study the dynamics of these representations: how representations evolve along these dimensions within the context of (simulated) conversations. We find that linear representations can change dramatically over a conversation; for example, information that is represented as factual at the beginning of a conversation can be represented as non-factual at the end and vice versa. These changes are content-dependent; while representations of conversation-relevant information may change, generic information is generally preserved. These changes are robust even for dimensions that disentangle factuality from more superficial response patterns, and occur across different model families and layers of the model. These representation changes do not require on-policy conversations; even replaying a conversation script written by an entirely different model can produce similar changes. However, adaptation is much weaker from simply having a sci-fi story in context that is framed more explicitly as such. We also show that steering along a representational direction can have dramatically different effects at different points in a conversation. These results are consistent with the idea that representations may evolve in response to the model playing a particular role that is cued by a conversation. Our findings may pose challenges for interpretability and steering -- in particular, they imply that it may be misleading to use static interpretations of features or directions, or probes that assume a particular range of features consistently corresponds to a particular ground-truth value. However, these types of representational dynamics also point to exciting new research directions for understanding how models adapt to context.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20789", "title": "SERA: Soft-Verified Efficient Repository Agents", "authors": [ "Ethan Shen", "Danny Tormoen", "Saurabh Shah", "Ali Farhadi", "Tim Dettmers" ], "github": "https://github.com/allenai/SERA", "project_page": "", "abstract": "Open-weight coding agents should hold a fundamental advantage over closed-source systems: they can be specialized to private codebases, encoding repository-specific information directly in their weights. Yet the cost and complexity of training has kept this advantage theoretical. We show it is now practical. We present Soft-Verified Efficient Repository Agents (SERA), an efficient method for training coding agents that enables the rapid and cheap creation of agents specialized to private codebases. Using only supervised finetuning (SFT), SERA achieves state-of-the-art results among fully open-source (open data, method, code) models while matching the performance of frontier open-weight models like Devstral-Small-2. Creating SERA models is 26x cheaper than reinforcement learning and 57x cheaper than previous synthetic data methods to reach equivalent performance. Our method, Soft Verified Generation (SVG), generates thousands of trajectories from a single code repository. Combined with cost-efficiency, this enables specialization to private codebases. Beyond repository specialization, we apply SVG to a larger corpus of codebases, generating over 200,000 synthetic trajectories. We use this dataset to provide detailed analysis of scaling laws, ablations, and confounding factors for training coding agents. Overall, we believe our work will greatly accelerate research on open coding agents and showcase the advantage of open-source models that can specialize to private codebases. We release SERA as the first model in Ai2's Open Coding Agents series, along with all our code, data, and Claude Code integration to support the research community.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/allenai/SERA", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20622", "title": "SketchDynamics: Exploring Free-Form Sketches for Dynamic Intent Expression in Animation Generation", "authors": [ "Boyu Li", "Lin-Ping Yuan", "Zeyu Wang", "Hongbo Fu" ], "github": "", "project_page": "", "abstract": "Sketching provides an intuitive way to convey dynamic intent in animation authoring (i.e., how elements change over time and space), making it a natural medium for automatic content creation. Yet existing approaches often constrain sketches to fixed command tokens or predefined visual forms, overlooking their freeform nature and the central role of humans in shaping intention. To address this, we introduce an interaction paradigm where users convey dynamic intent to a vision-language model via free-form sketching, instantiated here in a sketch storyboard to motion graphics workflow. We implement an interface and improve it through a three-stage study with 24 participants. The study shows how sketches convey motion with minimal input, how their inherent ambiguity requires users to be involved for clarification, and how sketches can visually guide video refinement. Our findings reveal the potential of sketch and AI interaction to bridge the gap between intention and outcome, and demonstrate its applicability to 3D animation and video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20380", "title": "OmegaUse: Building a General-Purpose GUI Agent for Autonomous Task Execution", "authors": [ "Le Zhang", "Yixiong Xiao", "Xinjiang Lu", "Jingjia Cao", "Yusai Zhao", "Jingbo Zhou", "Lang An", "Zikan Feng", "Wanxiang Sha", "Yu Shi", "Congxi Xiao", "Jian Xiong", "Yankai Zhang", "Hua Wu", "Haifeng Wang" ], "github": "", "project_page": "", "abstract": "Graphical User Interface (GUI) agents show great potential for enabling foundation models to complete real-world tasks, revolutionizing human-computer interaction and improving human productivity. In this report, we present OmegaUse, a general-purpose GUI agent model for autonomous task execution on both mobile and desktop platforms, supporting computer-use and phone-use scenarios. Building an effective GUI agent model relies on two factors: (1) high-quality data and (2) effective training methods. To address these, we introduce a carefully engineered data-construction pipeline and a decoupled training paradigm. For data construction, we leverage rigorously curated open-source datasets and introduce a novel automated synthesis framework that integrates bottom-up autonomous exploration with top-down taxonomy-guided generation to create high-fidelity synthetic data. For training, to better leverage these data, we adopt a two-stage strategy: Supervised Fine-Tuning (SFT) to establish fundamental interaction syntax, followed by Group Relative Policy Optimization (GRPO) to improve spatial grounding and sequential planning. To balance computational efficiency with agentic reasoning capacity, OmegaUse is built on a Mixture-of-Experts (MoE) backbone. To evaluate cross-terminal capabilities in an offline setting, we introduce OS-Nav, a benchmark suite spanning multiple operating systems: ChiM-Nav, targeting Chinese Android mobile environments, and Ubu-Nav, focusing on routine desktop interactions on Ubuntu. Extensive experiments show that OmegaUse is highly competitive across established GUI benchmarks, achieving a state-of-the-art (SOTA) score of 96.3% on ScreenSpot-V2 and a leading 79.1% step success rate on AndroidControl. OmegaUse also performs strongly on OS-Nav, reaching 74.24% step success on ChiM-Nav and 55.9% average success on Ubu-Nav.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.19325", "title": "Innovator-VL: A Multimodal Large Language Model for Scientific Discovery", "authors": [ "Zichen Wen", "Boxue Yang", "Shuang Chen", "Yaojie Zhang", "Yuhang Han", "Junlong Ke", "Cong Wang", "Yicheng Fu", "Jiawang Zhao", "Jiangchao Yao", "Xi Fang", "Zhen Wang", "Henxing Cai", "Lin Yao", "Zhifeng Gao", "Yanhui Hong", "Nang Yuan", "Yixuan Li", "Guojiang Zhao", "Haoyi Tao", "Nan Wang", "Han Lyu", "Guolin Ke", "Ning Liao", "Xiaoxing Wang", "Kai Chen", "Zhiyu Li", "Feiyu Xiong", "Sihan Hu", "Kun Chen", "Yanfeng Wang", "Weinan E", "Linfeng Zhang", "Linfeng Zhang" ], "github": "", "project_page": "", "abstract": "We present Innovator-VL, a scientific multimodal large language model designed to advance understanding and reasoning across diverse scientific domains while maintaining excellent performance on general vision tasks. Contrary to the trend of relying on massive domain-specific pretraining and opaque pipelines, our work demonstrates that principled training design and transparent methodology can yield strong scientific intelligence with substantially reduced data requirements. (i) First, we provide a fully transparent, end-to-end reproducible training pipeline, covering data collection, cleaning, preprocessing, supervised fine-tuning, reinforcement learning, and evaluation, along with detailed optimization recipes. This facilitates systematic extension by the community. (ii) Second, Innovator-VL exhibits remarkable data efficiency, achieving competitive performance on various scientific tasks using fewer than five million curated samples without large-scale pretraining. These results highlight that effective reasoning can be achieved through principled data selection rather than indiscriminate scaling. (iii) Third, Innovator-VL demonstrates strong generalization, achieving competitive performance on general vision, multimodal reasoning, and scientific benchmarks. This indicates that scientific alignment can be integrated into a unified model without compromising general-purpose capabilities. Our practices suggest that efficient, reproducible, and high-performing scientific multimodal models can be built even without large-scale data, providing a practical foundation for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.19194", "title": "SE-DiCoW: Self-Enrolled Diarization-Conditioned Whisper", "authors": [ "Alexander Polok", "Dominik Klement", "Samuele Cornell", "Matthew Wiesner", "Jan \u010cernock\u00fd", "Sanjeev Khudanpur", "Luk\u00e1\u0161 Burget" ], "github": "", "project_page": "", "abstract": "Speaker-attributed automatic speech recognition (ASR) in multi-speaker environments remains a major challenge. While some approaches achieve strong performance when fine-tuned on specific domains, few systems generalize well across out-of-domain datasets. Our prior work, Diarization-Conditioned Whisper (DiCoW), leverages speaker diarization outputs as conditioning information and, with minimal fine-tuning, demonstrated strong multilingual and multi-domain performance. In this paper, we address a key limitation of DiCoW: ambiguity in Silence-Target-Non-target-Overlap (STNO) masks, where two or more fully overlapping speakers may have nearly identical conditioning despite differing transcriptions. We introduce SE-DiCoW (Self-Enrolled Diarization-Conditioned Whisper), which uses diarization output to locate an enrollment segment anywhere in the conversation where the target speaker is most active. This enrollment segment is used as fixed conditioning via cross-attention at each encoder layer. We further refine DiCoW with improved data segmentation, model initialization, and augmentation. Together, these advances yield substantial gains: SE-DiCoW reduces macro-averaged tcpWER by 52.4% relative to the original DiCoW on the EMMA MT-ASR benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.17950", "title": "UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders", "authors": [ "Matthew Walmer", "Saksham Suri", "Anirud Aggarwal", "Abhinav Shrivastava" ], "github": "", "project_page": "", "abstract": "The space of task-agnostic feature upsampling has emerged as a promising area of research to efficiently create denser features from pre-trained visual backbones. These methods act as a shortcut to achieve dense features for a fraction of the cost by learning to map low-resolution features to high-resolution versions. While early works in this space used iterative upsampling approaches, more recent works have switched to cross-attention-based methods, which risk falling into the same efficiency scaling problems of the backbones they are upsampling. In this work, we demonstrate that iterative upsampling methods can still compete with cross-attention-based methods; moreover, they can achieve state-of-the-art performance with lower inference costs. We propose UPLiFT, an architecture for Universal Pixel-dense Lightweight Feature Transforms. We also propose an efficient Local Attender operator to overcome the limitations of prior iterative feature upsampling methods. This operator uses an alternative attentional pooling formulation defined fully locally. We show that our Local Attender allows UPLiFT to maintain stable features throughout upsampling, enabling state-of-the-art performance with lower inference costs than existing pixel-dense feature upsamplers. In addition, we apply UPLiFT to generative downstream tasks and show that it achieves competitive performance with state-of-the-art Coupled Flow Matching models for VAE feature upsampling. Altogether, UPLiFT offers a versatile and efficient approach to creating denser features.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20802", "title": "Reinforcement Learning via Self-Distillation", "authors": [ "Jonas H\u00fcbotter", "Frederike L\u00fcbeck", "Lejs Behric", "Anton Baumann", "Marco Bagatella", "Daniel Marta", "Ido Hakimi", "Idan Shenfeld", "Thomas Kleine Buening", "Carlos Guestrin", "Andreas Krause" ], "github": "", "project_page": "", "abstract": "Large language models are increasingly post-trained with reinforcement learning in verifiable domains such as code and math. Yet, current methods for reinforcement learning with verifiable rewards (RLVR) learn only from a scalar outcome reward per attempt, creating a severe credit-assignment bottleneck. Many verifiable environments actually provide rich textual feedback, such as runtime errors or judge evaluations, that explain why an attempt failed. We formalize this setting as reinforcement learning with rich feedback and introduce Self-Distillation Policy Optimization (SDPO), which converts tokenized feedback into a dense learning signal without any external teacher or explicit reward model. SDPO treats the current model conditioned on feedback as a self-teacher and distills its feedback-informed next-token predictions back into the policy. In this way, SDPO leverages the model's ability to retrospectively identify its own mistakes in-context. Across scientific reasoning, tool use, and competitive programming on LiveCodeBench v6, SDPO improves sample efficiency and final accuracy over strong RLVR baselines. Notably, SDPO also outperforms baselines in standard RLVR environments that only return scalar feedback by using successful rollouts as implicit feedback for failed attempts. Finally, applying SDPO to individual questions at test time accelerates discovery on difficult binary-reward tasks, achieving the same discovery probability as best-of-k sampling or multi-turn conversations with 3x fewer attempts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20262", "title": "Shallow-\u03c0: Knowledge Distillation for Flow-based VLAs", "authors": [ "Boseong Jeon", "Yunho Choi", "Taehan Kim" ], "github": "", "project_page": "", "abstract": "The growing demand for real-time robotic deployment necessitates fast and on-device inference for vision-language-action (VLA) models. Within the VLA literature, efficiency has been extensively studied at the token level, such as visual token pruning. In contrast, systematic transformer layer reduction has received limited attention and, to the best of our knowledge, has not been explored for flow-based VLA models under knowledge distillation. In this work, we propose Shallow-pi, a principled knowledge distillation framework that aggressively reduces the transformer depth of both the VLM backbone and the flow-based action head, compressing the model from 18 to 6 layers. Shallow-pi achieves over two times faster inference with less than one percent absolute drop in success rate on standard manipulation benchmarks, establishing state-of-the-art performance among reduced VLA models. Crucially, we validate our approach through industrial-scale real-world experiments on Jetson Orin and Jetson Thor across multiple robot platforms, including humanoid systems, in complex and dynamic manipulation scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20829", "title": "Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning", "authors": [ "Minwu Kim", "Safal Shrestha", "Keith Ross" ], "github": "https://github.com/minwukim/training-on-saturated-problems", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved the reasoning abilities of large language models (LLMs), yet training often stalls as problems become saturated. We identify the core challenge as the poor accessibility of informative failures: learning signals exist but are rarely encountered during standard rollouts. To address this, we propose failure-prefix conditioning, a simple and effective method for learning from saturated problems. Rather than starting from the original question, our approach reallocates exploration by conditioning training on prefixes derived from rare incorrect reasoning trajectories, thereby exposing the model to failure-prone states. We observe that failure-prefix conditioning yields performance gains matching those of training on medium-difficulty problems, while preserving token efficiency. Furthermore, we analyze the model's robustness, finding that our method reduces performance degradation under misleading failure prefixes, albeit with a mild trade-off in adherence to correct early reasoning. Finally, we demonstrate that an iterative approach, which refreshes failure prefixes during training, unlocks additional gains after performance plateaus. Overall, our results suggest that failure-prefix conditioning offers an effective pathway to extend RLVR training on saturated problems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/minwukim/training-on-saturated-problems", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20055", "title": "VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning", "authors": [ "Vikash Singh", "Darion Cassel", "Nathaniel Weir", "Nick Feng", "Sam Bayless" ], "github": "", "project_page": "", "abstract": "Despite the syntactic fluency of Large Language Models (LLMs), ensuring their logical correctness in high-stakes domains remains a fundamental challenge. We present a neurosymbolic framework that combines LLMs with SMT solvers to produce verification-guided answers through iterative refinement. Our approach decomposes LLM outputs into atomic claims, autoformalizes them into first-order logic, and verifies their logical consistency using automated theorem proving. We introduce three key innovations: (1) multi-model consensus via formal semantic equivalence checking to ensure logic-level alignment between candidates, eliminating the syntactic bias of surface-form metrics, (2) semantic routing that directs different claim types to appropriate verification strategies: symbolic solvers for logical claims and LLM ensembles for commonsense reasoning, and (3) precise logical error localization via Minimal Correction Subsets (MCS), which pinpoint the exact subset of claims to revise, transforming binary failure signals into actionable feedback. Our framework classifies claims by their logical status and aggregates multiple verification signals into a unified score with variance-based penalty. The system iteratively refines answers using structured feedback until acceptance criteria are met or convergence is achieved. This hybrid approach delivers formal guarantees where possible and consensus verification elsewhere, advancing trustworthy AI. With the GPT-OSS-120B model, VERGE demonstrates an average performance uplift of 18.7% at convergence across a set of reasoning benchmarks compared to single-pass approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.19280", "title": "Group Distributionally Robust Optimization-Driven Reinforcement Learning for LLM Reasoning", "authors": [ "Kishan Panaganti", "Zhenwen Liang", "Wenhao Yu", "Haitao Mi", "Dong Yu" ], "github": "https://github.com/kishanpb/verl-gdro", "project_page": "", "abstract": "Recent progress in Large Language Model (LLM) reasoning is increasingly driven by the refinement of post-training loss functions and alignment strategies. However, standard Reinforcement Learning (RL) paradigms like Group Relative Policy Optimization (GRPO) remain constrained by static uniformity: uniform prompt sampling and a fixed number of rollouts per prompt. For heterogeneous, heavy-tailed reasoning data, this creates structural inefficiencies that waste compute on already-solved patterns while under-training the long tail of hard problems. To address this, we propose Multi-Adversary Group Distributionally Robust Optimization (GDRO), an optimization-first framework that moves beyond uniform reasoning models by dynamically adapting the training distribution. We introduce an Online Difficulty Classifier that partitions prompts into dynamic pass@k difficulty groups. We then propose two independent GDRO games for post-training: (1) Prompt-GDRO, which employs an EMA-debiased multiplicative-weights bandit sampler to target the intensive difficulty margin and upweight persistently hard groups without frequency bias; and (2) Rollout-GDRO, which uses a shadow-price controller to reallocate rollouts across groups, maximizing gradient variance reduction on hard tasks under a fixed mean budget (compute-neutral). We provide no-regret guarantees for both controllers and additionally a variance-proxy analysis motivating a square-root optimal rollout allocation for Rollout-GDRO. We validate our framework on the DAPO 14.1k dataset using Qwen3-Base models. Prompt-GDRO and Rollout-GDRO achieve average relative gains of +10.6% and +10.1%, respectively, in pass@8 accuracy across 1.7B, 4B, and 8B scales compared to the GRPO baseline. Qualitative analysis shows an emergent curriculum: the adversaries shift resources to the evolving reasoning frontier, enhancing the reasoning model's performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kishanpb/verl-gdro", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20757", "title": "Persona Prompting as a Lens on LLM Social Reasoning", "authors": [ "Jing Yang", "Moritz Hechtbauer", "Elisabeth Khalilov", "Evelyn Luise Brinkmann", "Vera Schmitt", "Nils Feldhus" ], "github": "https://github.com/jingyng/PP-social-reasoning", "project_page": "", "abstract": "For socially sensitive tasks like hate speech detection, the quality of explanations from Large Language Models (LLMs) is crucial for factors like user trust and model alignment. While Persona prompting (PP) is increasingly used as a way to steer model towards user-specific generation, its effect on model rationales remains underexplored. We investigate how LLM-generated rationales vary when conditioned on different simulated demographic personas. Using datasets annotated with word-level rationales, we measure agreement with human annotations from different demographic groups, and assess the impact of PP on model bias and human alignment. Our evaluation across three LLMs results reveals three key findings: (1) PP improving classification on the most subjective task (hate speech) but degrading rationale quality. (2) Simulated personas fail to align with their real-world demographic counterparts, and high inter-persona agreement shows models are resistant to significant steering. (3) Models exhibit consistent demographic biases and a strong tendency to over-flag content as harmful, regardless of PP. Our findings reveal a critical trade-off: while PP can improve classification in socially-sensitive tasks, it often comes at the cost of rationale quality and fails to mitigate underlying biases, urging caution in its application.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jingyng/PP-social-reasoning", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.20245", "title": "How AI Impacts Skill Formation", "authors": [ "Judy Hanwen Shen", "Alex Tamkin" ], "github": "", "project_page": "", "abstract": "AI assistance produces significant productivity gains across professional domains, particularly for novice workers. Yet how this assistance affects the development of skills required to effectively supervise AI remains unclear. Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition in the process. We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of AI. We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average. Participants who fully delegated coding tasks showed some productivity improvements, but at the cost of learning the library. We identify six distinct AI interaction patterns, three of which involve cognitive engagement and preserve learning outcomes even when participants receive AI assistance. Our findings suggest that AI-enhanced productivity is not a shortcut to competence and AI assistance should be carefully adopted into workflows to preserve skill formation -- particularly in safety-critical domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.19494", "title": "AACR-Bench: Evaluating Automatic Code Review with Holistic Repository-Level Context", "authors": [ "Lei Zhang", "Yongda Yu", "Minghui Yu", "Xinxin Guo", "Zhengqi Zhuang", "Guoping Rong", "Dong Shao", "Haifeng Shen", "Hongyu Kuang", "Zhengfeng Li", "Boge Wang", "Guoan Zhang", "Bangyu Xiang", "Xiaobin Xu" ], "github": "https://github.com/alibaba/aacr-bench", "project_page": "", "abstract": "High-quality evaluation benchmarks are pivotal for deploying Large Language Models (LLMs) in Automated Code Review (ACR). However, existing benchmarks suffer from two critical limitations: first, the lack of multi-language support in repository-level contexts, which restricts the generalizability of evaluation results; second, the reliance on noisy, incomplete ground truth derived from raw Pull Request (PR) comments, which constrains the scope of issue detection. To address these challenges, we introduce AACR-Bench a comprehensive benchmark that provides full cross-file context across multiple programming languages. Unlike traditional datasets, AACR-Bench employs an \"AI-assisted, Expert-verified\" annotation pipeline to uncover latent defects often overlooked in original PRs, resulting in a 285% increase in defect coverage. Extensive evaluations of mainstream LLMs on AACR-Bench reveal that previous assessments may have either misjudged or only partially captured model capabilities due to data limitations. Our work establishes a more rigorous standard for ACR evaluation and offers new insights on LLM based ACR, i.e., the granularity/level of context and the choice of retrieval methods significantly impact ACR performance, and this influence varies depending on the LLM, programming language, and the LLM usage paradigm e.g., whether an Agent architecture is employed. The code, data, and other artifacts of our evaluation set are available at https://github.com/alibaba/aacr-bench .", "github_manual": "", "github_regex": "https://github.com/alibaba/aacr-bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-29", "arxiv_id": "2601.18150", "title": "FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning", "authors": [ "Zhaopeng Qiu", "Shuang Yu", "Jingqi Zhang", "Shuai Zhang", "Xue Huang", "Jingyi Yang", "Junjie Lai" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) for large language models (LLMs) is increasingly bottlenecked by rollout (generation), where long output sequence lengths make attention and KV-cache memory dominate end-to-end step time. FP8 offers an attractive lever for accelerating RL by reducing compute cost and memory traffic during rollout, but applying FP8 in RL introduces unique engineering and algorithmic challenges: policy weights change every step (requiring repeated quantization and weight synchronization into the inference engine) and low-precision rollouts can deviate from the higher-precision policy assumed by the trainer, causing train-inference mismatch and potential instability. This report presents a practical FP8 rollout stack for LLM RL, implemented in the veRL ecosystem with support for common training backends (e.g., FSDP/Megatron-LM) and inference engines (e.g., vLLM/SGLang). We (i) enable FP8 W8A8 linear-layer rollout using blockwise FP8 quantization, (ii) extend FP8 to KV-cache to remove long-context memory bottlenecks via per-step QKV scale recalibration, and (iii) mitigate mismatch using importance-sampling-based rollout correction (token-level TIS/MIS variants). Across dense and MoE models, these techniques deliver up to 44% rollout throughput gains while preserving learning behavior comparable to BF16 baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21754", "title": "Language-based Trial and Error Falls Behind in the Era of Experience", "authors": [ "Haoyu Wang", "Guozheng Ma", "Shugang Cui", "Yilun Kong", "Haotian Luo", "Li Shen", "Mengya Gao", "Yichao Wu", "Xiaogang Wang", "Dacheng Tao" ], "github": "https://github.com/Harry-mic/SCOUT", "project_page": "", "abstract": "While Large Language Models (LLMs) excel in language-based agentic tasks, their applicability to unseen, nonlinguistic environments (e.g., symbolic or spatial tasks) remains limited. Previous work attributes this performance gap to the mismatch between the pretraining distribution and the testing distribution. In this work, we demonstrate the primary bottleneck is the prohibitive cost of exploration: mastering these tasks requires extensive trial-and-error, which is computationally unsustainable for parameter-heavy LLMs operating in a high dimensional semantic space. To address this, we propose SCOUT (Sub-Scale Collaboration On Unseen Tasks), a novel framework that decouples exploration from exploitation. We employ lightweight \"scouts\" (e.g., small MLPs) to probe environmental dynamics at a speed and scale far exceeding LLMs. The collected trajectories are utilized to bootstrap the LLM via Supervised Fine-Tuning (SFT), followed by multi-turn Reinforcement Learning (RL) to activate its latent world knowledge. Empirically, SCOUT enables a Qwen2.5-3B-Instruct model to achieve an average score of 0.86, significantly outperforming proprietary models, including Gemini-2.5-Pro (0.60), while saving about 60% GPU hours consumption.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Harry-mic/SCOUT", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21051", "title": "Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report", "authors": [ "Zhuoran Yang", "Ed Li", "Jianliang He", "Aman Priyanshu", "Baturay Saglam", "Paul Kassianik", "Sajana Weerawardhena", "Anu Vellore", "Blaine Nelson", "Neusha Javidnia", "Arthur Goldblatt", "Fraser Burch", "Avi Zohary", "Assaf Eisenman", "Mahdi Sabbaghi", "Supriti Vijay", "Rahim Dharssi", "Dhruv Kedia", "Kojin Oshiba", "Yaron Singer", "Amin Karbasi" ], "github": "", "project_page": "", "abstract": "We present Foundation-Sec-8B-Reasoning, the first open-source native reasoning model for cybersecurity. Built upon our previously released Foundation-Sec-8B base model (derived from Llama-3.1-8B-Base), the model is trained through a two-stage process combining supervised fine-tuning (SFT) and reinforcement learning from verifiable rewards (RLVR). Our training leverages proprietary reasoning data spanning cybersecurity analysis, instruction-following, and mathematical reasoning. Evaluation across 10 cybersecurity benchmarks and 10 general-purpose benchmarks demonstrates performance competitive with significantly larger models on cybersecurity tasks while maintaining strong general capabilities. The model shows effective generalization on multi-hop reasoning tasks and strong safety performance when deployed with appropriate system prompts and guardrails. This work demonstrates that domain-specialized reasoning models can achieve strong performance on specialized tasks while maintaining broad general capabilities. We release the model publicly at https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.18129", "title": "Typhoon-S: Minimal Open Post-Training for Sovereign Large Language Models", "authors": [ "Kunat Pipatanakul", "Pittawat Taveekitworachai" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have progressed rapidly; however, most state-of-the-art models are trained and evaluated primarily in high-resource languages such as English and Chinese, and are often developed by a small number of organizations with access to large-scale compute and data. This gatekeeping creates a practical barrier for sovereign settings in which a regional- or national-scale institution or domain owner must retain control and understanding of model weights, training data, and deployment while operating under limited resources and strict transparency constraints. To this end, we identify two core requirements: (1) adoptability, the ability to transform a base model into a general-purpose assistant, and (2) sovereign capability, the ability to perform high-stakes, region-specific tasks (e.g., legal reasoning in local languages and cultural knowledge). We investigate whether these requirements can be achieved without scaling massive instruction corpora or relying on complex preference tuning pipelines and large-scale reinforcement fine-tuning (RFT). We present Typhoon S, a minimal and open post-training recipe that combines supervised fine-tuning, on-policy distillation, and small-scale RFT. Using Thai as a representative case study, we demonstrate that our approach transforms both sovereign-adapted and general-purpose base models into instruction-tuned models with strong general performance. We further show that small-scale RFT with InK-GRPO -- an extension of GRPO that augments the GRPO loss with a next-word prediction loss -- improves Thai legal reasoning and Thai-specific knowledge while preserving general capabilities. Our results suggest that a carefully designed post-training strategy can reduce the required scale of instruction data and computation, providing a practical path toward high-quality sovereign LLMs under academic-scale resources.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21598", "title": "Beyond Imitation: Reinforcement Learning for Active Latent Planning", "authors": [ "Zhi Zheng", "Wee Sun Lee" ], "github": "https://github.com/zz1358m/ATP-Latent-master", "project_page": "", "abstract": "Aiming at efficient and dense chain-of-thought (CoT) reasoning, latent reasoning methods fine-tune Large Language Models (LLMs) to substitute discrete language tokens with continuous latent tokens. These methods consume fewer tokens compared to the conventional language CoT reasoning and have the potential to plan in a dense latent space. However, current latent tokens are generally supervised based on imitating language labels. Considering that there can be multiple equivalent but diverse CoT labels for a question, passively imitating an arbitrary one may lead to inferior latent token representations and latent reasoning policies, undermining the potential planning ability and resulting in clear gaps between training and testing. In this work, we emphasize the importance of active planning over the representation space of latent tokens in achieving the optimal latent reasoning policy. So, we propose the Active Latent Planning method (ATP-Latent), which models the supervision process of latent tokens as a conditional variational auto-encoder (VAE) to obtain a smoother latent space. Moreover, to facilitate the most reasonable latent reasoning policy, ATP-Latent conducts reinforcement learning (RL) with an auxiliary coherence reward, which is calculated based on the consistency between VAE-decoded contents of latent tokens, enabling a guided RL process. In experiments on LLaMA-1B, ATP-Latent demonstrates +4.1\\% accuracy and -3.3\\% tokens on four benchmarks compared to advanced baselines. Codes are available on https://github.com/zz1358m/ATP-Latent-master.", "github_manual": "", "github_regex": "https://github.com/zz1358m/ATP-Latent-master", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.22069", "title": "VTC-R1: Vision-Text Compression for Efficient Long-Context Reasoning", "authors": [ "Yibo Wang", "Yongcheng Jing", "Shunyu Liu", "Hao Guan", "Rong-cheng Tu", "Chengyu Wang", "Jun Huang", "Dacheng Tao" ], "github": "https://github.com/w-yibo/VTC-R1", "project_page": "", "abstract": "Long-context reasoning has significantly empowered large language models (LLMs) to tackle complex tasks, yet it introduces severe efficiency bottlenecks due to the computational complexity. Existing efficient approaches often rely on complex additional training or external models for compression, which limits scalability and discards critical fine-grained information. In this paper, we propose VTC-R1, a new efficient reasoning paradigm that integrates vision-text compression into the reasoning process. Instead of processing lengthy textual traces, VTC-R1 renders intermediate reasoning segments into compact images, which are iteratively fed back into vision-language models as \"optical memory.\" We construct a training dataset based on OpenR1-Math-220K achieving 3.4x token compression and fine-tune representative VLMs-Glyph and Qwen3-VL. Extensive experiments on benchmarks such as MATH500, AIME25, AMC23 and GPQA-D demonstrate that VTC-R1 consistently outperforms standard long-context reasoning. Furthermore, our approach significantly improves inference efficiency, achieving 2.7x speedup in end-to-end latency, highlighting its potential as a scalable solution for reasoning-intensive applications. Our code is available at https://github.com/w-yibo/VTC-R1.", "github_manual": "", "github_regex": "https://github.com/w-yibo/VTC-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21406", "title": "Generation Enhances Understanding in Unified Multimodal Models via Multi-Representation Generation", "authors": [ "Zihan Su", "Hongyang Wei", "Kangrui Cen", "Yong Wang", "Guanhua Chen", "Chun Yuan", "Xiangxiang Chu" ], "github": "", "project_page": "", "abstract": "Unified Multimodal Models (UMMs) integrate both visual understanding and generation within a single framework. Their ultimate aspiration is to create a cycle where understanding and generation mutually reinforce each other. While recent post-training methods have successfully leveraged understanding to enhance generation, the reverse direction of utilizing generation to improve understanding remains largely unexplored. In this work, we propose UniMRG (Unified Multi-Representation Generation), a simple yet effective architecture-agnostic post-training method. UniMRG enhances the understanding capabilities of UMMs by incorporating auxiliary generation tasks. Specifically, we train UMMs to generate multiple intrinsic representations of input images, namely pixel (reconstruction), depth (geometry), and segmentation (structure), alongside standard visual understanding objectives. By synthesizing these diverse representations, UMMs capture complementary information regarding appearance, spatial relations, and structural layout. Consequently, UMMs develop a deeper and more comprehensive understanding of visual inputs. Extensive experiments across diverse UMM architectures demonstrate that our method notably enhances fine-grained perception, reduces hallucinations, and improves spatial understanding, while simultaneously boosting generation capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21872", "title": "WebArbiter: A Principle-Guided Reasoning Process Reward Model for Web Agents", "authors": [ "Yao Zhang", "Shijie Tang", "Zeyu Li", "Zhen Han", "Volker Tresp" ], "github": "", "project_page": "", "abstract": "Web agents hold great potential for automating complex computer tasks, yet their interactions involve long-horizon, sequential decision-making with irreversible actions. In such settings, outcome-based supervision is sparse and delayed, often rewarding incorrect trajectories and failing to support inference-time scaling. This motivates the use of Process Reward Models (WebPRMs) for web navigation, but existing approaches remain limited: scalar WebPRMs collapse progress into coarse, weakly grounded signals, while checklist-based WebPRMs rely on brittle template matching that fails under layout or semantic changes and often mislabels superficially correct actions as successful, providing little insight or interpretability. To address these challenges, we introduce WebArbiter, a reasoning-first, principle-inducing WebPRM that formulates reward modeling as text generation, producing structured justifications that conclude with a preference verdict and identify the action most conducive to task completion under the current context. Training follows a two-stage pipeline: reasoning distillation equips the model with coherent principle-guided reasoning, and reinforcement learning corrects teacher biases by directly aligning verdicts with correctness, enabling stronger generalization. To support systematic evaluation, we release WebPRMBench, a comprehensive benchmark spanning four diverse web environments with rich tasks and high-quality preference annotations. On WebPRMBench, WebArbiter-7B outperforms the strongest baseline, GPT-5, by 9.1 points. In reward-guided trajectory search on WebArena-Lite, it surpasses the best prior WebPRM by up to 7.2 points, underscoring its robustness and practical value in real-world complex web tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.22153", "title": "DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation", "authors": [ "Haozhe Xie", "Beichen Wen", "Jiarui Zheng", "Zhaoxi Chen", "Fangzhou Hong", "Haiwen Diao", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Manipulating dynamic objects remains an open challenge for Vision-Language-Action (VLA) models, which, despite strong generalization in static manipulation, struggle in dynamic scenarios requiring rapid perception, temporal anticipation, and continuous control. We present DynamicVLA, a framework for dynamic object manipulation that integrates temporal reasoning and closed-loop adaptation through three key designs: 1) a compact 0.4B VLA using a convolutional vision encoder for spatially efficient, structurally faithful encoding, enabling fast multimodal inference; 2) Continuous Inference, enabling overlapping reasoning and execution for lower latency and timely adaptation to object motion; and 3) Latent-aware Action Streaming, which bridges the perception-execution gap by enforcing temporally aligned action execution. To fill the missing foundation of dynamic manipulation data, we introduce the Dynamic Object Manipulation (DOM) benchmark, built from scratch with an auto data collection pipeline that efficiently gathers 200K synthetic episodes across 2.8K scenes and 206 objects, and enables fast collection of 2K real-world episodes without teleoperation. Extensive evaluations demonstrate remarkable improvements in response speed, perception, and generalization, positioning DynamicVLA as a unified framework for general dynamic object manipulation across embodiments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21821", "title": "MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methods", "authors": [ "Honglin Lin", "Zheng Liu", "Yun Zhu", "Chonghan Qin", "Juekai Lin", "Xiaoran Shang", "Conghui He", "Wentao Zhang", "Lijun Wu" ], "github": "https://github.com/huggingface/open-r1", "project_page": "", "abstract": "Recent advances in Vision Language Models (VLMs) have driven significant progress in visual reasoning. However, open-source VLMs still lag behind proprietary systems, largely due to the lack of high-quality reasoning data. Existing datasets offer limited coverage of challenging domains such as STEM diagrams and visual puzzles, and lack consistent, long-form Chain-of-Thought (CoT) annotations essential for eliciting strong reasoning capabilities. To bridge this gap, we introduce MMFineReason, a large-scale multimodal reasoning dataset comprising 1.8M samples and 5.1B solution tokens, featuring high-quality reasoning annotations distilled from Qwen3-VL-235B-A22B-Thinking. The dataset is established via a systematic three-stage pipeline: (1) large-scale data collection and standardization, (2) CoT rationale generation, and (3) comprehensive selection based on reasoning quality and difficulty awareness. The resulting dataset spans STEM problems, visual puzzles, games, and complex diagrams, with each sample annotated with visually grounded reasoning traces. We fine-tune Qwen3-VL-Instruct on MMFineReason to develop MMFineReason-2B/4B/8B versions. Our models establish new state-of-the-art results for their size class. Notably, MMFineReason-4B succesfully surpasses Qwen3-VL-8B-Thinking, and MMFineReason-8B even outperforms Qwen3-VL-30B-A3B-Thinking while approaching Qwen3-VL-32B-Thinking, demonstrating remarkable parameter efficiency. Crucially, we uncover a \"less is more\" phenomenon via our difficulty-aware filtering strategy: a subset of just 7\\% (123K samples) achieves performance comparable to the full dataset. Notably, we reveal a synergistic effect where reasoning-oriented data composition simultaneously boosts general capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/open-r1", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21181", "title": "MAD: Modality-Adaptive Decoding for Mitigating Cross-Modal Hallucinations in Multimodal Large Language Models", "authors": [ "Sangyun Chung", "Se Yeon Kim", "Youngchae Chee", "Yong Man Ro" ], "github": "https://github.com/top-yun/MAD", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) suffer from cross-modal hallucinations, where one modality inappropriately influences generation about another, leading to fabricated output. This exposes a more fundamental deficiency in modality-interaction control. To address this, we propose Modality-Adaptive Decoding (MAD), a training-free method that adaptively weights modality-specific decoding branches based on task requirements. MAD leverages the model's inherent ability to self-assess modality relevance by querying which modalities are needed for each task. The extracted modality probabilities are then used to adaptively weight contrastive decoding branches, enabling the model to focus on relevant information while suppressing cross-modal interference. Extensive experiments on CMM and AVHBench demonstrate that MAD significantly reduces cross-modal hallucinations across multiple audio-visual language models (7.8\\% and 2.0\\% improvements for VideoLLaMA2-AV, 8.7\\% and 4.7\\% improvements for Qwen2.5-Omni). Our approach demonstrates that explicit modality awareness through self-assessment is crucial for robust multimodal reasoning, offering a principled extension to existing contrastive decoding methods. Our code is available at https://github.com/top-yun/MAD{https://github.com/top-yun/MAD}", "github_manual": "", "github_regex": "https://github.com/top-yun/MAD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.22154", "title": "Exploring Reasoning Reward Model for Agents", "authors": [ "Kaixuan Fan", "Kaituo Feng", "Manyuan Zhang", "Tianshuo Peng", "Zhixun Li", "Yilei Jiang", "Shuang Chen", "Peng Pei", "Xunliang Cai", "Xiangyu Yue" ], "github": "https://github.com/kxfan2002/Reagent", "project_page": "", "abstract": "Agentic Reinforcement Learning (Agentic RL) has achieved notable success in enabling agents to perform complex reasoning and tool use. However, most methods still relies on sparse outcome-based reward for training. Such feedback fails to differentiate intermediate reasoning quality, leading to suboptimal training results. In this paper, we introduce Agent Reasoning Reward Model (Agent-RRM), a multi-faceted reward model that produces structured feedback for agentic trajectories, including (1) an explicit reasoning trace , (2) a focused critique that provides refinement guidance by highlighting reasoning flaws, and (3) an overall score that evaluates process performance. Leveraging these signals, we systematically investigate three integration strategies: Reagent-C (text-augmented refinement), Reagent-R (reward-augmented guidance), and Reagent-U (unified feedback integration). Extensive evaluations across 12 diverse benchmarks demonstrate that Reagent-U yields substantial performance leaps, achieving 43.7% on GAIA and 46.2% on WebWalkerQA, validating the effectiveness of our reasoning reward model and training schemes. Code, models, and datasets are all released to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kxfan2002/Reagent", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21337", "title": "Qwen3-ASR Technical Report", "authors": [ "Xian Shi", "Xiong Wang", "Zhifang Guo", "Yongqi Wang", "Pei Zhang", "Xinyu Zhang", "Zishan Guo", "Hongkun Hao", "Yu Xi", "Baosong Yang", "Jin Xu", "Jingren Zhou", "Junyang Lin" ], "github": "https://github.com/QwenLM/Qwen3-ASR", "project_page": "", "abstract": "In this report, we introduce Qwen3-ASR family, which includes two powerful all-in-one speech recognition models and a novel non-autoregressive speech forced alignment model. Qwen3-ASR-1.7B and Qwen3-ASR-0.6B are ASR models that support language identification and ASR for 52 languages and dialects. Both of them leverage large-scale speech training data and the strong audio understanding ability of their foundation model Qwen3-Omni. We conduct comprehensive internal evaluation besides the open-sourced benchmarks as ASR models might differ little on open-sourced benchmark scores but exhibit significant quality differences in real-world scenarios. The experiments reveal that the 1.7B version achieves SOTA performance among open-sourced ASR models and is competitive with the strongest proprietary APIs while the 0.6B version offers the best accuracy-efficiency trade-off. Qwen3-ASR-0.6B can achieve an average TTFT as low as 92ms and transcribe 2000 seconds speech in 1 second at a concurrency of 128. Qwen3-ForcedAligner-0.6B is an LLM based NAR timestamp predictor that is able to align text-speech pairs in 11 languages. Timestamp accuracy experiments show that the proposed model outperforms the three strongest force alignment models and takes more advantages in efficiency and versatility. To further accelerate the community research of ASR and audio understanding, we release these models under the Apache 2.0 license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/QwenLM/Qwen3-ASR", "github_api_added_by": "auto" }, { "date": "2026-01-30", "arxiv_id": "2601.21343", "title": "Self-Improving Pretraining: using post-trained models to pretrain better models", "authors": [ "Ellen Xiaoqing Tan", "Shehzaad Dhuliawala", "Jing Xu", "Ping Yu", "Sainbayar Sukhbaatar", "Jason Weston", "Olga Golovneva" ], "github": "", "project_page": "", "abstract": "Ensuring safety, factuality and overall quality in the generations of large language models is a critical challenge, especially as these models are increasingly deployed in real-world applications. The prevailing approach to addressing these issues involves collecting expensive, carefully curated datasets and applying multiple stages of fine-tuning and alignment. However, even this complex pipeline cannot guarantee the correction of patterns learned during pretraining. Therefore, addressing these issues during pretraining is crucial, as it shapes a model's core behaviors and prevents unsafe or hallucinated outputs from becoming deeply embedded. To tackle this issue, we introduce a new pretraining method that streams documents and uses reinforcement learning (RL) to improve the next K generated tokens at each step. A strong, post-trained model judges candidate generations -- including model rollouts, the original suffix, and a rewritten suffix -- for quality, safety, and factuality. Early in training, the process relies on the original and rewritten suffixes; as the model improves, RL rewards high-quality rollouts. This approach builds higher quality, safer, and more factual models from the ground up. In experiments, our method gives 36.2% and 18.5% relative improvements over standard pretraining in terms of factuality and safety, and up to 86.3% win rate improvements in overall generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21204", "title": "Scaling Embeddings Outperforms Scaling Experts in Language Models", "authors": [ "Hong Liu", "Jiaqi Zhang", "Chao Wang", "Xing Hu", "Linkun Lyu", "Jiaqi Sun", "Xurui Yang", "Bo Wang", "Fengcun Li", "Yulei Qian", "Lingtong Si", "Yerui Sun", "Rumei Li", "Peng Pei", "Yuchen Xie", "Xunliang Cai" ], "github": "", "project_page": "", "abstract": "While Mixture-of-Experts (MoE) architectures have become the standard for sparsity scaling in large language models, they increasingly face diminishing returns and system-level bottlenecks. In this work, we explore embedding scaling as a potent, orthogonal dimension for scaling sparsity. Through a comprehensive analysis and experiments, we identify specific regimes where embedding scaling achieves a superior Pareto frontier compared to expert scaling. We systematically characterize the critical architectural factors governing this efficacy -- ranging from parameter budgeting to the interplay with model width and depth. Moreover, by integrating tailored system optimizations and speculative decoding, we effectively convert this sparsity into tangible inference speedups. Guided by these insights, we introduce LongCat-Flash-Lite, a 68.5B parameter model with ~3B activated trained from scratch. Despite allocating over 30B parameters to embeddings, LongCat-Flash-Lite not only surpasses parameter-equivalent MoE baselines but also exhibits exceptional competitiveness against existing models of comparable scale, particularly in agentic and coding domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.20975", "title": "DeepSearchQA: Bridging the Comprehensiveness Gap for Deep Research Agents", "authors": [ "Nikita Gupta", "Riju Chatterjee", "Lukas Haas", "Connie Tao", "Andrew Wang", "Chang Liu", "Hidekazu Oiwa", "Elena Gribovskaya", "Jan Ackermann", "John Blitzer", "Sasha Goldshtein", "Dipanjan Das" ], "github": "", "project_page": "", "abstract": "We introduce DeepSearchQA, a 900-prompt benchmark for evaluating agents on difficult multi-step information-seeking tasks across 17 different fields. Unlike traditional benchmarks that target single answer retrieval or broad-spectrum factuality, DeepSearchQA features a dataset of challenging, handcrafted tasks designed to evaluate an agent's ability to execute complex search plans to generate exhaustive answer lists. This shift in design explicitly tests three critical, yet under-evaluated capabilities: 1) systematic collation of fragmented information from disparate sources, 2) de-duplication and entity resolution to ensure precision, and 3) the ability to reason about stopping criteria within an open-ended search space. Each task is structured as a causal chain, where discovering information for one step is dependent on the successful completion of the previous one, stressing long-horizon planning and context retention. All tasks are grounded in the open web with objectively verifiable answer sets. Our comprehensive evaluation of state-of-the-art agent architectures reveals significant performance limitations: even the most advanced models struggle to balance high recall with precision. We observe distinct failure modes ranging from premature stopping (under-retrieval) to hedging behaviors, where agents cast an overly wide net of low-confidence answers to artificially boost recall. These findings highlight critical headroom in current agent designs and position DeepSearchQA as an essential diagnostic tool for driving future research toward more robust, deep-research capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21282", "title": "WorldBench: Disambiguating Physics for Diagnostic Evaluation of World Models", "authors": [ "Rishi Upadhyay", "Howard Zhang", "Jim Solomon", "Ayush Agrawal", "Pranay Boreddy", "Shruti Satya Narayana", "Yunhao Ba", "Alex Wong", "Celso M de Melo", "Achuta Kadambi" ], "github": "", "project_page": "", "abstract": "Recent advances in generative foundational models, often termed \"world models,\" have propelled interest in applying them to critical tasks like robotic planning and autonomous system training. For reliable deployment, these models must exhibit high physical fidelity, accurately simulating real-world dynamics. Existing physics-based video benchmarks, however, suffer from entanglement, where a single test simultaneously evaluates multiple physical laws and concepts, fundamentally limiting their diagnostic capability. We introduce WorldBench, a novel video-based benchmark specifically designed for concept-specific, disentangled evaluation, allowing us to rigorously isolate and assess understanding of a single physical concept or law at a time. To make WorldBench comprehensive, we design benchmarks at two different levels: 1) an evaluation of intuitive physical understanding with concepts such as object permanence or scale/perspective, and 2) an evaluation of low-level physical constants and material properties such as friction coefficients or fluid viscosity. When SOTA video-based world models are evaluated on WorldBench, we find specific patterns of failure in particular physics concepts, with all tested models lacking the physical consistency required to generate reliable real-world interactions. Through its concept-specific evaluation, WorldBench offers a more nuanced and scalable framework for rigorously evaluating the physical reasoning capabilities of video generation and world models, paving the way for more robust and generalizable world-model-driven learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21420", "title": "ConceptMoE: Adaptive Token-to-Concept Compression for Implicit Compute Allocation", "authors": [ "Zihao Huang", "Jundong Zhou", "Xingwei Qu", "Qiyang Min", "Ge Zhang" ], "github": "", "project_page": "", "abstract": "Large language models allocate uniform computation across all tokens, ignoring that some sequences are trivially predictable while others require deep reasoning. We introduce ConceptMoE, which dynamically merges semantically similar tokens into concept representations, performing implicit token-level compute allocation. A learnable chunk module identifies optimal boundaries by measuring inter-token similarity, compressing sequences by a target ratio R before they enter the compute-intensive concept model. Crucially, the MoE architecture enables controlled evaluation: we reallocate saved computation to match baseline activated FLOPs (excluding attention map computation) and total parameters, isolating genuine architectural benefits. Under these conditions, ConceptMoE consistently outperforms standard MoE across language and vision-language tasks, achieving +0.9 points on language pretraining, +2.3 points on long context understanding, and +0.6 points on multimodal benchmarks. When converting pretrained MoE during continual training with layer looping, gains reach +5.5 points, demonstrating practical applicability. Beyond performance, ConceptMoE reduces attention computation by up to R^2times and KV cache by Rtimes. At R=2, empirical measurements show prefill speedups reaching 175\\% and decoding speedups up to 117\\% on long sequences. The minimal architectural modifications enable straightforward integration into existing MoE, demonstrating that adaptive concept-level processing fundamentally improves both effectiveness and efficiency of large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.17690", "title": "Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance", "authors": [ "Ziling Gong", "Yunyan Ouyang", "Iram Kamdar", "Melody Ma", "Hongjie Chen", "Franck Dernoncourt", "Ryan A. Rossi", "Nesreen K. Ahmed" ], "github": "", "project_page": "", "abstract": "Audio fingerprinting provides an identifiable representation of acoustic signals, which can be later used for identification and retrieval systems. To obtain a discriminative representation, the input audio is usually segmented into shorter time intervals, allowing local acoustic features to be extracted and analyzed. Modern neural approaches typically operate on short, fixed-duration audio segments, yet the choice of segment duration is often made heuristically and rarely examined in depth. In this paper, we study how segment length affects audio fingerprinting performance. We extend an existing neural fingerprinting architecture to adopt various segment lengths and evaluate retrieval accuracy across different segment lengths and query durations. Our results show that short segment lengths (0.5-second) generally achieve better performance. Moreover, we evaluate LLM capacity in recommending the best segment length, which shows that GPT-5-mini consistently gives the best suggestions across five considerations among three studied LLMs. Our findings provide practical guidance for selecting segment duration in large-scale neural audio retrieval systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.11747", "title": "PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement", "authors": [ "Huaxiaoyue Wang", "Sunav Choudhary", "Franck Dernoncourt", "Yu Shen", "Stefano Petrangeli" ], "github": "", "project_page": "", "abstract": "Graphic design often involves exploring different stylistic directions, which can be time-consuming for non-experts. We address this problem of stylistically improving designs based on natural language instructions. While VLMs have shown initial success in graphic design, their pretrained knowledge on styles is often too general and misaligned with specific domain data. For example, VLMs may associate minimalism with abstract designs, whereas designers emphasize shape and color choices. Our key insight is to leverage design data -- a collection of real-world designs that implicitly capture designer's principles -- to learn design knowledge and guide stylistic improvement. We propose PRISM (PRior-Informed Stylistic Modification) that constructs and applies a design knowledge base through three stages: (1) clustering high-variance designs to capture diversity within a style, (2) summarizing each cluster into actionable design knowledge, and (3) retrieving relevant knowledge during inference to enable style-aware improvement. Experiments on the Crello dataset show that PRISM achieves the highest average rank of 1.49 (closer to 1 is better) over baselines in style alignment. User studies further validate these results, showing that PRISM is consistently preferred by designers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.20833", "title": "Idea2Story: An Automated Pipeline for Transforming Research Concepts into Complete Scientific Narratives", "authors": [ "Tengyue Xu", "Zhuoyang Qian", "Gaoge Liu", "Li Ling", "Zhentao Zhang", "Biao Wu", "Shuo Zhang", "Ke Lu", "Wei Shi", "Ziqi Wang", "Zheng Feng", "Yan Luo", "Shu Xu", "Yongjin Chen", "Zhibo Feng", "Zhuo Chen", "Bruce Yuan", "Harry Wang", "Kris Chen" ], "github": "https://github.com/AgentAlphaAGI/Idea2Paper.git", "project_page": "", "abstract": "Autonomous scientific discovery with large language model (LLM)-based agents has recently made substantial progress, demonstrating the ability to automate end-to-end research workflows. However, existing systems largely rely on runtime-centric execution paradigms, repeatedly reading, summarizing, and reasoning over large volumes of scientific literature online. This on-the-spot computation strategy incurs high computational cost, suffers from context window limitations, and often leads to brittle reasoning and hallucination. We propose Idea2Story, a pre-computation-driven framework for autonomous scientific discovery that shifts literature understanding from online reasoning to offline knowledge construction. Idea2Story continuously collects peer-reviewed papers together with their review feedback, extracts core methodological units, composes reusable research patterns, and organizes them into a structured methodological knowledge graph. At runtime, underspecified user research intents are aligned to established research paradigms, enabling efficient retrieval and reuse of high-quality research patterns instead of open-ended generation and trial-and-error. By grounding research planning and execution in a pre-built knowledge graph, Idea2Story alleviates the context window bottleneck of LLMs and substantially reduces repeated runtime reasoning over literature. We conduct qualitative analyses and preliminary empirical studies demonstrating that Idea2Story can generate coherent, methodologically grounded, and novel research patterns, and can produce several high-quality research demonstrations in an end-to-end setting. These results suggest that offline knowledge construction provides a practical and scalable foundation for reliable autonomous scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AgentAlphaAGI/Idea2Paper.git", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.20354", "title": "Everything in Its Place: Benchmarking Spatial Intelligence of Text-to-Image Models", "authors": [ "Zengbin Wang", "Xuecai Hu", "Yong Wang", "Feng Xiong", "Man Zhang", "Xiangxiang Chu" ], "github": "https://github.com/AMAP-ML/SpatialGenEval", "project_page": "", "abstract": "Text-to-image (T2I) models have achieved remarkable success in generating high-fidelity images, but they often fail in handling complex spatial relationships, e.g., spatial perception, reasoning, or interaction. These critical aspects are largely overlooked by current benchmarks due to their short or information-sparse prompt design. In this paper, we introduce SpatialGenEval, a new benchmark designed to systematically evaluate the spatial intelligence of T2I models, covering two key aspects: (1) SpatialGenEval involves 1,230 long, information-dense prompts across 25 real-world scenes. Each prompt integrates 10 spatial sub-domains and corresponding 10 multi-choice question-answer pairs, ranging from object position and layout to occlusion and causality. Our extensive evaluation of 21 state-of-the-art models reveals that higher-order spatial reasoning remains a primary bottleneck. (2) To demonstrate that the utility of our information-dense design goes beyond simple evaluation, we also construct the SpatialT2I dataset. It contains 15,400 text-image pairs with rewritten prompts to ensure image consistency while preserving information density. Fine-tuned results on current foundation models (i.e., Stable Diffusion-XL, Uniworld-V1, OmniGen2) yield consistent performance gains (+4.2%, +5.7%, +4.4%) and more realistic effects in spatial relations, highlighting a data-centric paradigm to achieve spatial intelligence in T2I models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AMAP-ML/SpatialGenEval", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21639", "title": "OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models", "authors": [ "Yufeng Zhong", "Lei Chen", "Xuanle Zhao", "Wenkang Han", "Liming Zheng", "Jing Huang", "Deyang Jiang", "Yilin Cao", "Lin Ma", "Zhixiong Zeng" ], "github": "", "project_page": "", "abstract": "The development of large vision language models drives the demand for managing, and applying massive amounts of multimodal data, making OCR technology, which extracts information from visual images, increasingly popular. However, existing OCR methods primarily focus on recognizing text elements from images or scanned documents (Text-centric OCR), neglecting the identification of visual elements from visually information-dense image sources (Vision-centric OCR), such as charts, web pages and science plots. In reality, these visually information-dense images are widespread on the internet and have significant real-world application value, such as data visualization and web page analysis. In this technical report, we propose OCRVerse, the first holistic OCR method in end-to-end manner that enables unified text-centric OCR and vision-centric OCR. To this end, we constructe comprehensive data engineering to cover a wide range of text-centric documents, such as newspapers, magazines and books, as well as vision-centric rendered composites, including charts, web pages and scientific plots. Moreover, we propose a two-stage SFT-RL multi-domain training method for OCRVerse. SFT directly mixes cross-domain data to train and establish initial domain knowledge, while RL focuses on designing personalized reward strategies for the characteristics of each domain. Specifically, since different domains require various output formats and expected outputs, we provide sufficient flexibility in the RL stage to customize flexible reward signals for each domain, thereby improving cross-domain fusion and avoiding data conflicts. Experimental results demonstrate the effectiveness of OCRVerse, achieving competitive results across text-centric and vision-centric data types, even comparable to large-scale open-source and closed-source models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.22054", "title": "MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources", "authors": [ "Baorui Ma", "Jiahui Yang", "Donglin Di", "Xuancheng Zhang", "Jianxun Cui", "Hao Li", "Yan Xie", "Wei Chen" ], "github": "", "project_page": "", "abstract": "Scaling has powered recent advances in vision foundation models, yet extending this paradigm to metric depth estimation remains challenging due to heterogeneous sensor noise, camera-dependent biases, and metric ambiguity in noisy cross-source 3D data. We introduce Metric Anything, a simple and scalable pretraining framework that learns metric depth from noisy, diverse 3D sources without manually engineered prompts, camera-specific modeling, or task-specific architectures. Central to our approach is the Sparse Metric Prompt, created by randomly masking depth maps, which serves as a universal interface that decouples spatial reasoning from sensor and camera biases. Using about 20M image-depth pairs spanning reconstructed, captured, and rendered 3D data across 10000 camera models, we demonstrate-for the first time-a clear scaling trend in the metric depth track. The pretrained model excels at prompt-driven tasks such as depth completion, super-resolution and Radar-camera fusion, while its distilled prompt-free student achieves state-of-the-art results on monocular depth estimation, camera intrinsics recovery, single/multi-view metric 3D reconstruction, and VLA planning. We also show that using pretrained ViT of Metric Anything as a visual encoder significantly boosts Multimodal Large Language Model capabilities in spatial intelligence. These results show that metric depth estimation can benefit from the same scaling laws that drive modern foundation models, establishing a new path toward scalable and efficient real-world metric perception. We open-source MetricAnything at http://metric-anything.github.io/metric-anything-io/ to support community research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.20465", "title": "BMAM: Brain-inspired Multi-Agent Memory Framework", "authors": [ "Yang Li", "Jiaxiang Liu", "Yusong Wang", "Yujie Wu", "Mingkun Xu" ], "github": "https://github.com/MemTensor/MemOS", "project_page": "", "abstract": "Language-model-based agents operating over extended interaction horizons face persistent challenges in preserving temporally grounded information and maintaining behavioral consistency across sessions, a failure mode we term soul erosion. We present BMAM (Brain-inspired Multi-Agent Memory), a general-purpose memory architecture that models agent memory as a set of functionally specialized subsystems rather than a single unstructured store. Inspired by cognitive memory systems, BMAM decomposes memory into episodic, semantic, salience-aware, and control-oriented components that operate at complementary time scales. To support long-horizon reasoning, BMAM organizes episodic memories along explicit timelines and retrieves evidence by fusing multiple complementary signals. Experiments on the LoCoMo benchmark show that BMAM achieves 78.45 percent accuracy under the standard long-horizon evaluation setting, and ablation analyses confirm that the hippocampus-inspired episodic memory subsystem plays a critical role in temporal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MemTensor/MemOS", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21416", "title": "Spotlighting Task-Relevant Features: Object-Centric Representations for Better Generalization in Robotic Manipulation", "authors": [ "Alexandre Chapin", "Bruno Machado", "Emmanuel Dellandr\u00e9a", "Liming Chen" ], "github": "", "project_page": "", "abstract": "The generalization capabilities of robotic manipulation policies are heavily influenced by the choice of visual representations. Existing approaches typically rely on representations extracted from pre-trained encoders, using two dominant types of features: global features, which summarize an entire image via a single pooled vector, and dense features, which preserve a patch-wise embedding from the final encoder layer. While widely used, both feature types mix task-relevant and irrelevant information, leading to poor generalization under distribution shifts, such as changes in lighting, textures, or the presence of distractors. In this work, we explore an intermediate structured alternative: Slot-Based Object-Centric Representations (SBOCR), which group dense features into a finite set of object-like entities. This representation permits to naturally reduce the noise provided to the robotic manipulation policy while keeping enough information to efficiently perform the task. We benchmark a range of global and dense representations against intermediate slot-based representations, across a suite of simulated and real-world manipulation tasks ranging from simple to complex. We evaluate their generalization under diverse visual conditions, including changes in lighting, texture, and the presence of distractors. Our findings reveal that SBOCR-based policies outperform dense and global representation-based policies in generalization settings, even without task-specific pretraining. These insights suggest that SBOCR is a promising direction for designing visual systems that generalize effectively in dynamic, real-world robotic environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.20381", "title": "STORM: Slot-based Task-aware Object-centric Representation for robotic Manipulation", "authors": [ "Alexandre Chapin", "Emmanuel Dellandr\u00e9a", "Liming Chen" ], "github": "", "project_page": "", "abstract": "Visual foundation models provide strong perceptual features for robotics, but their dense representations lack explicit object-level structure, limiting robustness and contractility in manipulation tasks. We propose STORM (Slot-based Task-aware Object-centric Representation for robotic Manipulation), a lightweight object-centric adaptation module that augments frozen visual foundation models with a small set of semantic-aware slots for robotic manipulation. Rather than retraining large backbones, STORM employs a multi-phase training strategy: object-centric slots are first stabilized through visual--semantic pretraining using language embeddings, then jointly adapted with a downstream manipulation policy. This staged learning prevents degenerate slot formation and preserves semantic consistency while aligning perception with task objectives. Experiments on object discovery benchmarks and simulated manipulation tasks show that STORM improves generalization to visual distractors, and control performance compared to directly using frozen foundation model features or training object-centric representations end-to-end. Our results highlight multi-phase adaptation as an efficient mechanism for transforming generic foundation model features into task-aware object-centric representations for robotic control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.22046", "title": "PLANING: A Loosely Coupled Triangle-Gaussian Framework for Streaming 3D Reconstruction", "authors": [ "Changjian Jiang", "Kerui Ren", "Xudong Li", "Kaiwen Song", "Linning Xu", "Tao Lu", "Junting Dong", "Yu Zhang", "Bo Dai", "Mulin Yu" ], "github": "", "project_page": "", "abstract": "Streaming reconstruction from monocular image sequences remains challenging, as existing methods typically favor either high-quality rendering or accurate geometry, but rarely both. We present PLANING, an efficient on-the-fly reconstruction framework built on a hybrid representation that loosely couples explicit geometric primitives with neural Gaussians, enabling geometry and appearance to be modeled in a decoupled manner. This decoupling supports an online initialization and optimization strategy that separates geometry and appearance updates, yielding stable streaming reconstruction with substantially reduced structural redundancy. PLANING improves dense mesh Chamfer-L2 by 18.52% over PGSR, surpasses ARTDECO by 1.31 dB PSNR, and reconstructs ScanNetV2 scenes in under 100 seconds, over 5x faster than 2D Gaussian Splatting, while matching the quality of offline per-scene optimization. Beyond reconstruction quality, the structural clarity and computational efficiency of \\modelname~make it well suited for a broad range of downstream applications, such as enabling large-scale scene modeling and simulation-ready environments for embodied AI. Project page: https://city-super.github.io/PLANING/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.20730", "title": "AgentLongBench: A Controllable Long Benchmark For Long-Contexts Agents via Environment Rollouts", "authors": [ "Shicheng Fang", "Yuxin Wang", "XiaoRan Liu", "Jiahao Lu", "Chuanyuan Tan", "Xinchi Chen", "Yining Zheng. Xuanjing Huang", "Xipeng Qiu" ], "github": "https://github.com/euReKa025/AgentLongBench", "project_page": "", "abstract": "The evolution of Large Language Models (LLMs) into autonomous agents necessitates the management of extensive, dynamic contexts. Current benchmarks, however, remain largely static, relying on passive retrieval tasks that fail to simulate the complexities of agent-environment interaction, such as non-linear reasoning and iterative feedback. To address this, we introduce AgentLongBench, which evaluates agents through simulated environment rollouts based on Lateral Thinking Puzzles. This framework generates rigorous interaction trajectories across knowledge-intensive and knowledge-free scenarios. Experiments with state-of-the-art models and memory systems (32K to 4M tokens) expose a critical weakness: while adept at static retrieval, agents struggle with the dynamic information synthesis essential for workflows. Our analysis indicates that this degradation is driven by the minimum number of tokens required to resolve a query. This factor explains why the high information density inherent in massive tool responses poses a significantly greater challenge than the memory fragmentation typical of long-turn dialogues.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/euReKa025/AgentLongBench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.22158", "title": "One-step Latent-free Image Generation with Pixel Mean Flows", "authors": [ "Yiyang Lu", "Susie Lu", "Qiao Sun", "Hanhong Zhao", "Zhicheng Jiang", "Xianbang Wang", "Tianhong Li", "Zhengyang Geng", "Kaiming He" ], "github": "", "project_page": "", "abstract": "Modern diffusion/flow-based models for image generation typically exhibit two core characteristics: (i) using multi-step sampling, and (ii) operating in a latent space. Recent advances have made encouraging progress on each aspect individually, paving the way toward one-step diffusion/flow without latents. In this work, we take a further step towards this goal and propose \"pixel MeanFlow\" (pMF). Our core guideline is to formulate the network output space and the loss space separately. The network target is designed to be on a presumed low-dimensional image manifold (i.e., x-prediction), while the loss is defined via MeanFlow in the velocity space. We introduce a simple transformation between the image manifold and the average velocity field. In experiments, pMF achieves strong results for one-step latent-free generation on ImageNet at 256x256 resolution (2.22 FID) and 512x512 resolution (2.48 FID), filling a key missing piece in this regime. We hope that our study will further advance the boundaries of diffusion/flow-based generative models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.22156", "title": "Hybrid Linear Attention Done Right: Efficient Distillation and Effective Architectures for Extremely Long Contexts", "authors": [ "Yingfa Chen", "Zhen Leng Thai", "Zihan Zhou", "Zhu Zhang", "Xingyu Shen", "Shuo Wang", "Chaojun Xiao", "Xu Han", "Zhiyuan Liu" ], "github": "https://github.com/THUNLP/hybrid-linear-attention", "project_page": "", "abstract": "Hybrid Transformer architectures, which combine softmax attention blocks and recurrent neural networks (RNNs), have shown a desirable performance-throughput tradeoff for long-context modeling, but their adoption and studies are hindered by the prohibitive cost of large-scale pre-training from scratch. Some recent studies have shown that pre-trained softmax attention blocks can be converted into RNN blocks through parameter transfer and knowledge distillation. However, these transfer methods require substantial amounts of training data (more than 10B tokens), and the resulting hybrid models also exhibit poor long-context performance, which is the scenario where hybrid models enjoy significant inference speedups over Transformer-based models. In this paper, we present HALO (Hybrid Attention via Layer Optimization), a pipeline for distilling Transformer models into RNN-attention hybrid models. We then present HypeNet, a hybrid architecture with superior length generalization enabled by a novel position encoding scheme (named HyPE) and various architectural modifications. We convert the Qwen3 series into HypeNet using HALO, achieving performance comparable to the original Transformer models while enjoying superior long-context performance and efficiency. The conversion requires just 2.3B tokens, less than 0.01% of their pre-training data", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUNLP/hybrid-linear-attention", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.19001", "title": "FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning", "authors": [ "Haozheng Luo", "Zhuolin Jiang", "Md Zahid Hasan", "Yan Chen", "Soumalya Sarkar" ], "github": "https://github.com/robinzixuan/FROST", "project_page": "", "abstract": "We propose FROST, an attention-aware method for efficient reasoning. Unlike traditional approaches, FROST leverages attention weights to prune uncritical reasoning paths, yielding shorter and more reliable reasoning trajectories. Methodologically, we introduce the concept of reasoning outliers and design an attention-based mechanism to remove them. Theoretically, FROST preserves and enhances the model's reasoning capacity while eliminating outliers at the sentence level. Empirically, we validate FROST on four benchmarks using two strong reasoning models (Phi-4-Reasoning and GPT-OSS-20B), outperforming state-of-the-art methods such as TALE and ThinkLess. Notably, FROST achieves an average 69.68% reduction in token usage and a 26.70% improvement in accuracy over the base model. Furthermore, in evaluations of attention outlier metrics, FROST reduces the maximum infinity norm by 15.97% and the average kurtosis by 91.09% compared to the base model. Code is available at https://github.com/robinzixuan/FROST", "github_manual": "", "github_regex": "https://github.com/robinzixuan/FROST", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21996", "title": "Mechanistic Data Attribution: Tracing the Training Origins of Interpretable LLM Units", "authors": [ "Jianhui Chen", "Yuzhang Luo", "Liangming Pan" ], "github": "", "project_page": "", "abstract": "While Mechanistic Interpretability has identified interpretable circuits in LLMs, their causal origins in training data remain elusive. We introduce Mechanistic Data Attribution (MDA), a scalable framework that employs Influence Functions to trace interpretable units back to specific training samples. Through extensive experiments on the Pythia family, we causally validate that targeted intervention--removing or augmenting a small fraction of high-influence samples--significantly modulates the emergence of interpretable heads, whereas random interventions show no effect. Our analysis reveals that repetitive structural data (e.g., LaTeX, XML) acts as a mechanistic catalyst. Furthermore, we observe that interventions targeting induction head formation induce a concurrent change in the model's in-context learning (ICL) capability. This provides direct causal evidence for the long-standing hypothesis regarding the functional link between induction heads and ICL. Finally, we propose a mechanistic data augmentation pipeline that consistently accelerates circuit convergence across model scales, providing a principled methodology for steering the developmental trajectories of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.22101", "title": "ECO: Quantized Training without Full-Precision Master Weights", "authors": [ "Mahdi Nikdan", "Amir Zandieh", "Dan Alistarh", "Vahab Mirrokni" ], "github": "", "project_page": "", "abstract": "Quantization has significantly improved the compute and memory efficiency of Large Language Model (LLM) training. However, existing approaches still rely on accumulating their updates in high-precision: concretely, gradient updates must be applied to a high-precision weight buffer, known as master weights. This buffer introduces substantial memory overhead, particularly for Sparse Mixture of Experts (SMoE) models, where model parameters and optimizer states dominate memory usage. To address this, we introduce the Error-Compensating Optimizer (ECO), which eliminates master weights by applying updates directly to quantized parameters. ECO quantizes weights after each step and carefully injects the resulting quantization error into the optimizer momentum, forming an error-feedback loop with no additional memory. We prove that, under standard assumptions and a decaying learning rate, ECO converges to a constant-radius neighborhood of the optimum, while naive master-weight removal can incur an error that is inversely proportional to the learning rate. We show empirical results for pretraining small Transformers (30-800M), a Gemma-3 1B model, and a 2.1B parameter Sparse MoE model with FP8 quantization, and fine-tuning DeepSeek-MoE-16B in INT4 precision. Throughout, ECO matches baselines with master weights up to near-lossless accuracy, significantly shifting the static memory vs validation loss Pareto frontier.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21590", "title": "Scalable Power Sampling: Unlocking Efficient, Training-Free Reasoning for LLMs via Distribution Sharpening", "authors": [ "Xiaotong Ji", "Rasul Tutunov", "Matthieu Zimmer", "Haitham Bou Ammar" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) post-training is a dominant approach for improving the reasoning performance of large language models (LLMs), yet growing evidence suggests that its gains arise primarily from distribution sharpening rather than the acquisition of new capabilities. Recent work has shown that sampling from the power distribution of LLMs using Markov chain Monte Carlo (MCMC) can recover performance comparable to RL post-training without relying on external rewards; however, the high computational cost of MCMC makes such approaches impractical for widespread adoption. In this work, we propose a theoretically grounded alternative that eliminates the need for iterative MCMC. We derive a novel formulation showing that the global power distribution can be approximated by a token-level scaled low-temperature one, where the scaling factor captures future trajectory quality. Leveraging this insight, we introduce a training-free and verifier-free algorithm that sharpens the base model's generative distribution autoregressively. Empirically, we evaluate our method on math, QA, and code tasks across four LLMs, and show that our method matches or surpasses one-shot GRPO without relying on any external rewards, while reducing inference latency by over 10x compared to MCMC-based sampling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.22157", "title": "Discovering Hidden Gems in Model Repositories", "authors": [ "Jonathan Kahana", "Eliahu Horwitz", "Yedid Hoshen" ], "github": "", "project_page": "", "abstract": "Public repositories host millions of fine-tuned models, yet community usage remains disproportionately concentrated on a small number of foundation checkpoints. We investigate whether this concentration reflects efficient market selection or if superior models are systematically overlooked. Through an extensive evaluation of over 2,000 models, we show the prevalence of \"hidden gems\", unpopular fine-tunes that significantly outperform their popular counterparts. Notably, within the Llama-3.1-8B family, we find rarely downloaded checkpoints that improve math performance from 83.2% to 96.0% without increasing inference costs. However, discovering these models through exhaustive evaluation of every uploaded model is computationally infeasible. We therefore formulate model discovery as a Multi-Armed Bandit problem and accelerate the Sequential Halving search algorithm by using shared query sets and aggressive elimination schedules. Our method retrieves top models with as few as 50 queries per candidate, accelerating discovery by over 50x.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.22146", "title": "FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale", "authors": [ "Ajay Patel", "Colin Raffel", "Chris Callison-Burch" ], "github": "", "project_page": "", "abstract": "Due to limited supervised training data, large language models (LLMs) are typically pre-trained via a self-supervised \"predict the next word\" objective on a vast amount of unstructured text data. To make the resulting model useful to users, it is further trained on a far smaller amount of \"instruction-tuning\" data comprised of supervised training examples of instructions and responses. To overcome the limited amount of supervised data, we propose a procedure that can transform the knowledge in internet-scale pre-training documents into billions of synthetic instruction and answer training pairs. The resulting dataset, called FineInstructions, uses ~18M instruction templates created from real user-written queries and prompts. These instruction templates are matched to and instantiated with human-written source documents from unstructured pre-training corpora. With \"supervised\" synthetic training data generated at this scale, an LLM can be pre-trained from scratch solely with the instruction-tuning objective, which is far more in-distribution with the expected downstream usage of LLMs (responding to user prompts). We conduct controlled token-for-token training experiments and find pre-training on FineInstructions outperforms standard pre-training and other proposed synthetic pre-training techniques on standard benchmarks measuring free-form response quality. Our resources can be found at https://huggingface.co/fineinstructions .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.22143", "title": "JUST-DUB-IT: Video Dubbing via Joint Audio-Visual Diffusion", "authors": [ "Anthony Chen", "Naomi Ken Korem", "Tavi Halperin", "Matan Ben Yosef", "Urska Jelercic", "Ofir Bibi", "Or Patashnik", "Daniel Cohen-Or" ], "github": "https://github.com/justdubit/just-dub-it", "project_page": "", "abstract": "Audio-Visual Foundation Models, which are pretrained to jointly generate sound and visual content, have recently shown an unprecedented ability to model multi-modal generation and editing, opening new opportunities for downstream tasks. Among these tasks, video dubbing could greatly benefit from such priors, yet most existing solutions still rely on complex, task-specific pipelines that struggle in real-world settings. In this work, we introduce a single-model approach that adapts a foundational audio-video diffusion model for video-to-video dubbing via a lightweight LoRA. The LoRA enables the model to condition on an input audio-video while jointly generating translated audio and synchronized facial motion. To train this LoRA, we leverage the generative model itself to synthesize paired multilingual videos of the same speaker. Specifically, we generate multilingual videos with language switches within a single clip, and then inpaint the face and audio in each half to match the language of the other half. By leveraging the rich generative prior of the audio-visual model, our approach preserves speaker identity and lip synchronization while remaining robust to complex motion and real-world dynamics. We demonstrate that our approach produces high-quality dubbed videos with improved visual fidelity, lip synchronization, and robustness compared to existing dubbing pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/justdubit/just-dub-it", "github_api_added_by": "auto" }, { "date": "2026-01-30", "arxiv_id": "2601.21579", "title": "KromHC: Manifold-Constrained Hyper-Connections with Kronecker-Product Residual Matrices", "authors": [ "Wuyang Zhou", "Yuxuan Gu", "Giorgos Iacovides", "Danilo Mandic" ], "github": "https://github.com/wz1119/KromHC", "project_page": "", "abstract": "The success of Hyper-Connections (HC) in neural networks (NN) has also highlighted issues related to its training instability and restricted scalability. The Manifold-Constrained Hyper-Connections (mHC) mitigate these challenges by projecting the residual connection space onto a Birkhoff polytope, however, it faces two issues: 1) its iterative Sinkhorn-Knopp (SK) algorithm does not always yield exact doubly stochastic residual matrices; 2) mHC incurs a prohibitive O(n^3C) parameter complexity with n as the width of the residual stream and C as the feature dimension. The recently proposed mHC-lite reparametrizes the residual matrix via the Birkhoff-von-Neumann theorem to guarantee double stochasticity, but also faces a factorial explosion in its parameter complexity, O left( nC cdot n! right). To address both challenges, we propose KromHC, which uses the Kronecker products of smaller doubly stochastic matrices to parametrize the residual matrix in mHC. By enforcing manifold constraints across the factor residual matrices along each mode of the tensorized residual stream, KromHC guarantees exact double stochasticity of the residual matrices while reducing parameter complexity to O(n^2C). Comprehensive experiments demonstrate that KromHC matches or even outperforms state-of-the-art (SOTA) mHC variants, while requiring significantly fewer trainable parameters. The code is available at https://github.com/wz1119/KromHC.", "github_manual": "", "github_regex": "https://github.com/wz1119/KromHC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21571", "title": "Shaping capabilities with token-level data filtering", "authors": [ "Neil Rathi", "Alec Radford" ], "github": "", "project_page": "", "abstract": "Current approaches to reducing undesired capabilities in language models are largely post hoc, and can thus be easily bypassed by adversaries. A natural alternative is to shape capabilities during pretraining itself. On the proxy task of removing medical capabilities, we show that the simple intervention of filtering pretraining data is highly effective, robust, and inexpensive at scale. Inspired by work on data attribution, we show that filtering tokens is more effective than filtering documents, achieving the same hit to undesired capabilities at a lower cost to benign ones. Training models spanning two orders of magnitude, we then demonstrate that filtering gets more effective with scale: for our largest models, token filtering leads to a 7000x compute slowdown on the forget domain. We also show that models trained with token filtering can still be aligned on the forget domain. Along the way, we introduce a methodology for labeling tokens with sparse autoencoders and distilling cheap, high-quality classifiers. We also demonstrate that filtering can be robust to noisy labels with sufficient pretraining compute.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.20103", "title": "Benchmarking Reward Hack Detection in Code Environments via Contrastive Analysis", "authors": [ "Darshan Deshpande", "Anand Kannappan", "Rebecca Qian" ], "github": "", "project_page": "", "abstract": "Recent advances in reinforcement learning for code generation have made robust environments essential to prevent reward hacking. As LLMs increasingly serve as evaluators in code-based RL, their ability to detect reward hacking remains understudied. In this paper, we propose a novel taxonomy of reward exploits spanning across 54 categories and introduce TRACE (Testing Reward Anomalies in Code Environments), a synthetically curated and human-verified benchmark containing 517 testing trajectories. Unlike prior work that evaluates reward hack detection in isolated classification scenarios, we contrast these evaluations with a more realistic, contrastive anomaly detection setup on TRACE. Our experiments reveal that models capture reward hacks more effectively in contrastive settings than in isolated classification settings, with GPT-5.2 with highest reasoning mode achieving the best detection rate at 63%, up from 45% in isolated settings on TRACE. Building on this insight, we demonstrate that state-of-the-art models struggle significantly more with semantically contextualized reward hacks compared to syntactically contextualized ones. We further conduct qualitative analyses of model behaviors, as well as ablation studies showing that the ratio of benign to hacked trajectories and analysis cluster sizes substantially impact detection performance. We release the benchmark and evaluation harness to enable the community to expand TRACE and evaluate their models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.16914", "title": "LoL: Longer than Longer, Scaling Video Generation to Hour", "authors": [ "Justin Cui", "Jie Wu", "Ming Li", "Tao Yang", "Xiaojie Li", "Rui Wang", "Andrew Bai", "Yuanhao Ban", "Cho-Jui Hsieh" ], "github": "https://github.com/justincui03/LoL", "project_page": "", "abstract": "Recent research in long-form video generation has shifted from bidirectional to autoregressive models, yet these methods commonly suffer from error accumulation and a loss of long-term coherence. While attention sink frames have been introduced to mitigate this performance decay, they often induce a critical failure mode we term sink-collapse: the generated content repeatedly reverts to the sink frame, resulting in abrupt scene resets and cyclic motion patterns. Our analysis reveals that sink-collapse originates from an inherent conflict between the periodic structure of Rotary Position Embedding (RoPE) and the multi-head attention mechanisms prevalent in current generative models. To address it, we propose a lightweight, training-free approach that effectively suppresses this behavior by introducing multi-head RoPE jitter that breaks inter-head attention homogenization and mitigates long-horizon collapse. Extensive experiments show that our method successfully alleviates sink-collapse while preserving generation quality. To the best of our knowledge, this work achieves the first demonstration of real-time, streaming, and infinite-length video generation with little quality decay. As an illustration of this robustness, we generate continuous videos up to 12 hours in length, which, to our knowledge, is among the longest publicly demonstrated results in streaming video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/justincui03/LoL", "github_api_added_by": "auto" }, { "date": "2026-01-30", "arxiv_id": "2601.22083", "title": "Latent Adversarial Regularization for Offline Preference Optimization", "authors": [ "Enyi Jiang", "Yibo Jacky Zhang", "Yinglun Xu", "Andreas Haupt", "Nancy Amato", "Sanmi Koyejo" ], "github": "", "project_page": "", "abstract": "Learning from human feedback typically relies on preference optimization that constrains policy updates through token-level regularization. However, preference optimization for language models is particularly challenging because token-space similarity does not imply semantic or behavioral similarity. To address this challenge, we leverage latent-space regularization for language model preference optimization. We introduce GANPO, which achieves latent-space regularization by penalizing divergence between the internal representations of a policy model and a reference model. Given that latent representations are not associated with explicit probability densities, we adopt an adversarial approach inspired by GANs to minimize latent-space divergence. We integrate GANPO as a regularizer into existing offline preference optimization objectives. Experiments across multiple model architectures and tasks show consistent improvements from latent-space regularization. Further, by comparing GANPO-induced inferential biases with those from token-level regularization, we find that GANPO provides more robust structural feedback under distributional shift and noise while maintaining comparable downstream performance with minor computational overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.18005", "title": "Flow-based Extremal Mathematical Structure Discovery", "authors": [ "Gergely B\u00e9rczi", "Baran Hashemi", "Jonas Kl\u00fcver" ], "github": "https://github.com/berczig/FlowBoost", "project_page": "", "abstract": "The discovery of extremal structures in mathematics requires navigating vast and nonconvex landscapes where analytical methods offer little guidance and brute-force search becomes intractable. We introduce FlowBoost, a closed-loop generative framework that learns to discover rare and extremal geometric structures by combining three components: (i) a geometry-aware conditional flow-matching model that learns to sample high-quality configurations, (ii) reward-guided policy optimization with action exploration that directly optimizes the generation process toward the objective while maintaining diversity, and (iii) stochastic local search for both training-data generation and final refinement. Unlike prior open-loop approaches, such as PatternBoost that retrains on filtered discrete samples, or AlphaEvolve which relies on frozen Large Language Models (LLMs) as evolutionary mutation operators, FlowBoost enforces geometric feasibility during sampling, and propagates reward signal directly into the generative model, closing the optimization loop and requiring much smaller training sets and shorter training times, and reducing the required outer-loop iterations by orders of magnitude, while eliminating dependence on LLMs. We demonstrate the framework on four geometric optimization problems: sphere packing in hypercubes, circle packing maximizing sum of radii, the Heilbronn triangle problem, and star discrepancy minimization. In several cases, FlowBoost discovers configurations that match or exceed the best known results. For circle packings, we improve the best known lower bounds, surpassing the LLM-based system AlphaEvolve while using substantially fewer computational resources.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/berczig/FlowBoost", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.21268", "title": "Reinforcement Learning from Meta-Evaluation: Aligning Language Models Without Ground-Truth Labels", "authors": [ "Micah Rentschler", "Jesse Roberts" ], "github": "", "project_page": "", "abstract": "Most reinforcement learning (RL) methods for training large language models (LLMs) require ground-truth labels or task-specific verifiers, limiting scalability when correctness is ambiguous or expensive to obtain. We introduce Reinforcement Learning from Meta-Evaluation (RLME), which optimizes a generator using reward derived from an evaluator's answers to natural-language meta-questions (e.g., \"Is the answer correct?\" or \"Is the reasoning logically consistent?\"). RLME treats the evaluator's probability of a positive judgment as a reward and updates the generator via group-relative policy optimization, enabling learning without labels. Across a suite of experiments, we show that RLME achieves accuracy and sample efficiency comparable to label-based training, enables controllable trade-offs among multiple objectives, steers models toward reliable reasoning patterns rather than post-hoc rationalization, and generalizes to open-domain settings where ground-truth labels are unavailable, broadening the domains in which LLMs may be trained with RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-01-30", "arxiv_id": "2601.17883", "title": "EEG Foundation Models: Progresses, Benchmarking, and Open Problems", "authors": [ "Dingkun Liu", "Yuheng Chen", "Zhu Chen", "Zhenyao Cui", "Yaozhi Wen", "Jiayu An", "Jingwei Luo", "Dongrui Wu" ], "github": "", "project_page": "", "abstract": "Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for brain-computer interfaces (BCIs), aiming to learn transferable neural representations from large-scale heterogeneous recordings. Despite rapid progresses, there lacks fair and comprehensive comparisons of existing EEG foundation models, due to inconsistent pre-training objectives, preprocessing choices, and downstream evaluation protocols. This paper fills this gap. We first review 50 representative models and organize their design choices into a unified taxonomic framework including data standardization, model architectures, and self-supervised pre-training strategies. We then evaluate 12 open-source foundation models and competitive specialist baselines across 13 EEG datasets spanning nine BCI paradigms. Emphasizing real-world deployments, we consider both cross-subject generalization under a leave-one-subject-out protocol and rapid calibration under a within-subject few-shot setting. We further compare full-parameter fine-tuning with linear probing to assess the transferability of pre-trained representations, and examine the relationship between model scale and downstream performance. Our results indicate that: 1) linear probing is frequently insufficient; 2) specialist models trained from scratch remain competitive across many tasks; and, 3) larger foundation models do not necessarily yield better generalization performance under current data regimes and training practices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.21558", "title": "ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas", "authors": [ "Xiaoyu Tian", "Haotian Wang", "Shuaiting Chen", "Hao Zhou", "Kaichi Yu", "Yudian Zhang", "Jade Ouyang", "Junxi Yin", "Jiong Chen", "Baoyan Guo", "Lei Zhang", "Junjie Tao", "Yuansheng Song", "Ming Cui", "Chengwei Liu" ], "github": "https://github.com/LianjiaTech/astra", "project_page": "", "abstract": "Large language models (LLMs) are increasingly used as tool-augmented agents for multi-step decision making, yet training robust tool-using agents remains challenging. Existing methods still require manual intervention, depend on non-verifiable simulated environments, rely exclusively on either supervised fine-tuning (SFT) or reinforcement learning (RL), and struggle with stable long-horizon, multi-turn learning. To address these challenges, we introduce ASTRA, a fully automated end-to-end framework for training tool-augmented language model agents via scalable data synthesis and verifiable reinforcement learning. ASTRA integrates two complementary components. First, a pipeline that leverages the static topology of tool-call graphs synthesizes diverse, structurally grounded trajectories, instilling broad and transferable tool-use competence. Second, an environment synthesis framework that captures the rich, compositional topology of human semantic reasoning converts decomposed question-answer traces into independent, code-executable, and rule-verifiable environments, enabling deterministic multi-turn RL. Based on this method, we develop a unified training methodology that integrates SFT with online RL using trajectory-level rewards to balance task completion and interaction efficiency. Experiments on multiple agentic tool-use benchmarks demonstrate that ASTRA-trained models achieve state-of-the-art performance at comparable scales, approaching closed-source systems while preserving core reasoning ability. We release the full pipelines, environments, and trained models at https://github.com/LianjiaTech/astra.", "github_manual": "", "github_regex": "https://github.com/LianjiaTech/astra", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.21716", "title": "DreamActor-M2: Universal Character Image Animation via Spatiotemporal In-Context Learning", "authors": [ "Mingshuang Luo", "Shuang Liang", "Zhengkun Rong", "Yuxuan Luo", "Tianshu Hu", "Ruibing Hou", "Hong Chang", "Yong Li", "Yuan Zhang", "Mingyuan Gao" ], "github": "", "project_page": "", "abstract": "Character image animation aims to synthesize high-fidelity videos by transferring motion from a driving sequence to a static reference image. Despite recent advancements, existing methods suffer from two fundamental challenges: (1) suboptimal motion injection strategies that lead to a trade-off between identity preservation and motion consistency, manifesting as a \"see-saw\", and (2) an over-reliance on explicit pose priors (e.g., skeletons), which inadequately capture intricate dynamics and hinder generalization to arbitrary, non-humanoid characters. To address these challenges, we present DreamActor-M2, a universal animation framework that reimagines motion conditioning as an in-context learning problem. Our approach follows a two-stage paradigm. First, we bridge the input modality gap by fusing reference appearance and motion cues into a unified latent space, enabling the model to jointly reason about spatial identity and temporal dynamics by leveraging the generative prior of foundational models. Second, we introduce a self-bootstrapped data synthesis pipeline that curates pseudo cross-identity training pairs, facilitating a seamless transition from pose-dependent control to direct, end-to-end RGB-driven animation. This strategy significantly enhances generalization across diverse characters and motion scenarios. To facilitate comprehensive evaluation, we further introduce AW Bench, a versatile benchmark encompassing a wide spectrum of characters types and motion scenarios. Extensive experiments demonstrate that DreamActor-M2 achieves state-of-the-art performance, delivering superior visual fidelity and robust cross-domain generalization. Project Page: https://grisoon.github.io/DreamActor-M2/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.20218", "title": "DenseGRPO: From Sparse to Dense Reward for Flow Matching Model Alignment", "authors": [ "Haoyou Deng", "Keyu Yan", "Chaojie Mao", "Xiang Wang", "Yu Liu", "Changxin Gao", "Nong Sang" ], "github": "https://github.com/yifan123/flow_grpo", "project_page": "", "abstract": "Recent GRPO-based approaches built on flow matching models have shown remarkable improvements in human preference alignment for text-to-image generation. Nevertheless, they still suffer from the sparse reward problem: the terminal reward of the entire denoising trajectory is applied to all intermediate steps, resulting in a mismatch between the global feedback signals and the exact fine-grained contributions at intermediate denoising steps. To address this issue, we introduce DenseGRPO, a novel framework that aligns human preference with dense rewards, which evaluates the fine-grained contribution of each denoising step. Specifically, our approach includes two key components: (1) we propose to predict the step-wise reward gain as dense reward of each denoising step, which applies a reward model on the intermediate clean images via an ODE-based approach. This manner ensures an alignment between feedback signals and the contributions of individual steps, facilitating effective training; and (2) based on the estimated dense rewards, a mismatch drawback between the uniform exploration setting and the time-varying noise intensity in existing GRPO-based methods is revealed, leading to an inappropriate exploration space. Thus, we propose a reward-aware scheme to calibrate the exploration space by adaptively adjusting a timestep-specific stochasticity injection in the SDE sampler, ensuring a suitable exploration space at all timesteps. Extensive experiments on multiple standard benchmarks demonstrate the effectiveness of the proposed DenseGRPO and highlight the critical role of the valid dense rewards in flow matching model alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yifan123/flow_grpo", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22628", "title": "TTCS: Test-Time Curriculum Synthesis for Self-Evolving", "authors": [ "Chengyi Yang", "Zhishang Xiang", "Yunbo Tang", "Zongpei Teng", "Chengsong Huang", "Fei Long", "Yuhan Liu", "Jinsong Su" ], "github": "https://github.com/XMUDeepLIT/TTCS", "project_page": "", "abstract": "Test-Time Training offers a promising way to improve the reasoning ability of large language models (LLMs) by adapting the model using only the test questions. However, existing methods struggle with difficult reasoning problems for two reasons: raw test questions are often too difficult to yield high-quality pseudo-labels, and the limited size of test sets makes continuous online updates prone to instability. To address these limitations, we propose TTCS, a co-evolving test-time training framework. Specifically, TTCS initializes two policies from the same pretrained model: a question synthesizer and a reasoning solver. These policies evolve through iterative optimization: the synthesizer generates progressively challenging question variants conditioned on the test questions, creating a structured curriculum tailored to the solver's current capability, while the solver updates itself using self-consistency rewards computed from multiple sampled responses on both original test and synthetic questions. Crucially, the solver's feedback guides the synthesizer to generate questions aligned with the model's current capability, and the generated question variants in turn stabilize the solver's test-time training. Experiments show that TTCS consistently strengthens the reasoning ability on challenging mathematical benchmarks and transfers to general-domain tasks across different LLM backbones, highlighting a scalable path towards dynamically constructing test-time curricula for self-evolving. Our code and implementation details are available at https://github.com/XMUDeepLIT/TTCS.", "github_manual": "", "github_regex": "https://github.com/XMUDeepLIT/TTCS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22664", "title": "Real-Time Aligned Reward Model beyond Semantics", "authors": [ "Zixuan Huang", "Xin Xia", "Yuxi Ren", "Jianbin Zheng", "Xuefeng Xiao", "Hongyan Xie", "Li Huaqiu", "Songshi Liang", "Zhongxiang Dai", "Fuzhen Zhuang", "Jianxin Li", "Yikun Ban", "Deqing Wang" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning from Human Feedback (RLHF) is a pivotal technique for aligning large language models (LLMs) with human preferences, yet it is susceptible to reward overoptimization, in which policy models overfit to the reward model, exploit spurious reward patterns instead of faithfully capturing human intent. Prior mitigations primarily relies on surface semantic information and fails to efficiently address the misalignment between the reward model (RM) and the policy model caused by continuous policy distribution shifts. This inevitably leads to an increasing reward discrepancy, exacerbating reward overoptimization. To address these limitations, we introduce R2M (Real-Time Aligned Reward Model), a novel lightweight RLHF framework. R2M goes beyond vanilla reward models that solely depend on the semantic representations of a pretrained LLM. Instead, it leverages the evolving hidden states of the policy (namely policy feedback) to align with the real-time distribution shift of the policy during the RL process. This work points to a promising new direction for improving the performance of reward models through real-time utilization of feedback from policy models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22141", "title": "Routing the Lottery: Adaptive Subnetworks for Heterogeneous Data", "authors": [ "Grzegorz Stefanski", "Alberto Presta", "Michal Byra" ], "github": "", "project_page": "", "abstract": "In pruning, the Lottery Ticket Hypothesis posits that large networks contain sparse subnetworks, or winning tickets, that can be trained in isolation to match the performance of their dense counterparts. However, most existing approaches assume a single universal winning ticket shared across all inputs, ignoring the inherent heterogeneity of real-world data. In this work, we propose Routing the Lottery (RTL), an adaptive pruning framework that discovers multiple specialized subnetworks, called adaptive tickets, each tailored to a class, semantic cluster, or environmental condition. Across diverse datasets and tasks, RTL consistently outperforms single- and multi-model baselines in balanced accuracy and recall, while using up to 10 times fewer parameters than independent models and exhibiting semantically aligned. Furthermore, we identify subnetwork collapse, a performance drop under aggressive pruning, and introduce a subnetwork similarity score that enables label-free diagnosis of oversparsification. Overall, our results recast pruning as a mechanism for aligning model structure with data heterogeneity, paving the way toward more modular and context-aware deep learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.21358", "title": "Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization", "authors": [ "Jiecong Wang", "Hao Peng", "Chunyang Liu" ], "github": "", "project_page": "", "abstract": "Chain-of-Thought (CoT) empowers Large Language Models (LLMs) to tackle complex problems, but remains constrained by the computational cost and reasoning path collapse when grounded in discrete token spaces. Recent latent reasoning approaches attempt to optimize efficiency by performing reasoning within continuous hidden states. However, these methods typically operate as opaque end-to-end mappings from explicit reasoning steps to latent states, and often require a pre-defined number of latent steps during inference. In this work, we introduce PLaT (Planning with Latent Thoughts), a framework that reformulates latent reasoning as planning by fundamentally decouple reasoning from verbalization. We model reasoning as a deterministic trajectory of latent planning states, while a separate Decoder grounds these thoughts into text when necessary. This decoupling allows the model to dynamically determine when to terminate reasoning rather than relying on fixed hyperparameters. Empirical results on mathematical benchmarks reveal a distinct trade-off: while PLaT achieves lower greedy accuracy than baselines, it demonstrates superior scalability in terms of reasoning diversity. This indicates that PLaT learns a robust, broader solution space, offering a transparent and scalable foundation for inference-time search.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.23143", "title": "THINKSAFE: Self-Generated Safety Alignment for Reasoning Models", "authors": [ "Seanie Lee", "Sangwoo Park", "Yumin Choi", "Gyeongman Kim", "Minki Kang", "Jihun Yun", "Dongmin Park", "Jongho Park", "Sung Ju Hwang" ], "github": "https://github.com/seanie12/ThinkSafe.git", "project_page": "", "abstract": "Large reasoning models (LRMs) achieve remarkable performance by leveraging reinforcement learning (RL) on reasoning tasks to generate long chain-of-thought (CoT) reasoning. However, this over-optimization often prioritizes compliance, making models vulnerable to harmful prompts. To mitigate this safety degradation, recent approaches rely on external teacher distillation, yet this introduces a distributional discrepancy that degrades native reasoning. We propose ThinkSafe, a self-generated alignment framework that restores safety alignment without external teachers. Our key insight is that while compliance suppresses safety mechanisms, models often retain latent knowledge to identify harm. ThinkSafe unlocks this via lightweight refusal steering, guiding the model to generate in-distribution safety reasoning traces. Fine-tuning on these self-generated responses effectively realigns the model while minimizing distribution shift. Experiments on DeepSeek-R1-Distill and Qwen3 show ThinkSafe significantly improves safety while preserving reasoning proficiency. Notably, it achieves superior safety and comparable reasoning to GRPO, with significantly reduced computational cost. Code, models, and datasets are available at https://github.com/seanie12/ThinkSafe.git.", "github_manual": "", "github_regex": "https://github.com/seanie12/ThinkSafe.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.21468", "title": "MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning", "authors": [ "Yaorui Shi", "Shugui Liu", "Yu Yang", "Wenyu Mao", "Yuxin Chen", "Qi GU", "Hui Su", "Xunliang Cai", "Xiang Wang", "An Zhang" ], "github": "https://github.com/syr-cn/MemOCR", "project_page": "", "abstract": "Long-horizon agentic reasoning necessitates effectively compressing growing interaction histories into a limited context window. Most existing memory systems serialize history as text, where token-level cost is uniform and scales linearly with length, often spending scarce budget on low-value details. To this end, we introduce MemOCR, a multimodal memory agent that improves long-horizon reasoning under tight context budgets by allocating memory space with adaptive information density through visual layout. Concretely, MemOCR maintains a structured rich-text memory (e.g., headings, highlights) and renders it into an image that the agent consults for memory access, visually prioritizing crucial evidence while aggressively compressing auxiliary details. To ensure robustness across varying memory budgets, we train MemOCR with reinforcement learning under budget-aware objectives that expose the agent to diverse compression levels. Across long-context multi-hop and single-hop question-answering benchmarks, MemOCR outperforms strong text-based baselines and achieves more effective context utilization under extreme budgets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/syr-cn/MemOCR", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.23265", "title": "PaperBanana: Automating Academic Illustration for AI Scientists", "authors": [ "Dawei Zhu", "Rui Meng", "Yale Song", "Xiyu Wei", "Sujian Li", "Tomas Pfister", "Jinsung Yoon" ], "github": "", "project_page": "", "abstract": "Despite rapid advances in autonomous AI scientists powered by language models, generating publication-ready illustrations remains a labor-intensive bottleneck in the research workflow. To lift this burden, we introduce PaperBanana, an agentic framework for automated generation of publication-ready academic illustrations. Powered by state-of-the-art VLMs and image generation models, PaperBanana orchestrates specialized agents to retrieve references, plan content and style, render images, and iteratively refine via self-critique. To rigorously evaluate our framework, we introduce PaperBananaBench, comprising 292 test cases for methodology diagrams curated from NeurIPS 2025 publications, covering diverse research domains and illustration styles. Comprehensive experiments demonstrate that PaperBanana consistently outperforms leading baselines in faithfulness, conciseness, readability, and aesthetics. We further show that our method effectively extends to the generation of high-quality statistical plots. Collectively, PaperBanana paves the way for the automated generation of publication-ready illustrations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.23184", "title": "ReGuLaR: Variational Latent Reasoning Guided by Rendered Chain-of-Thought", "authors": [ "Fanmeng Wang", "Haotian Liu", "Guojiang Zhao", "Hongteng Xu", "Zhifeng Gao" ], "github": "https://github.com/FanmengWang/ReGuLaR", "project_page": "", "abstract": "While Chain-of-Thought (CoT) significantly enhances the performance of Large Language Models (LLMs), explicit reasoning chains introduce substantial computational redundancy. Recent latent reasoning methods attempt to mitigate this by compressing reasoning processes into latent space, but often suffer from severe performance degradation due to the lack of appropriate compression guidance. In this study, we propose Rendered CoT-Guided variational Latent Reasoning (ReGuLaR), a simple yet novel latent learning paradigm resolving this issue. Fundamentally, we formulate latent reasoning within the Variational Auto-Encoding (VAE) framework, sampling the current latent reasoning state from the posterior distribution conditioned on previous ones. Specifically, when learning this variational latent reasoning model, we render explicit reasoning chains as images, from which we extract dense visual-semantic representations to regularize the posterior distribution, thereby achieving efficient compression with minimal information loss. Extensive experiments demonstrate that ReGuLaR significantly outperforms existing latent reasoning methods across both computational efficiency and reasoning effectiveness, and even surpasses CoT through multi-modal reasoning, providing a new and insightful solution to latent reasoning. Code: https://github.com/FanmengWang/ReGuLaR.", "github_manual": "", "github_regex": "https://github.com/FanmengWang/ReGuLaR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22642", "title": "Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification", "authors": [ "Chuxue Cao", "Jinluan Yang", "Haoran Li", "Kunhao Pan", "Zijian Zhao", "Zhengyu Chen", "Yuchen Tian", "Lijun Wu", "Conghui He", "Sirui Han", "Yike Guo" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) show remarkable capabilities, yet their stochastic next-token prediction creates logical inconsistencies and reward hacking that formal symbolic systems avoid. To bridge this gap, we introduce a formal logic verification-guided framework that dynamically interleaves formal symbolic verification with the natural language generation process, providing real-time feedback to detect and rectify errors as they occur. Distinguished from previous neuro-symbolic methods limited by passive post-hoc validation, our approach actively penalizes intermediate fallacies during the reasoning chain. We operationalize this framework via a novel two-stage training pipeline that synergizes formal logic verification-guided supervised fine-tuning and policy optimization. Extensive evaluation on six benchmarks spanning mathematical, logical, and general reasoning demonstrates that our 7B and 14B models outperform state-of-the-art baselines by average margins of 10.4% and 14.2%, respectively. These results validate that formal verification can serve as a scalable mechanism to significantly push the performance boundaries of advanced LLM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.21525", "title": "LMK > CLS: Landmark Pooling for Dense Embeddings", "authors": [ "Meet Doshi", "Aashka Trivedi", "Vishwajeet Kumar", "Parul Awasthy", "Yulong Li", "Jaydeep Sen", "Radu Florian", "Sachindra Joshi" ], "github": "", "project_page": "", "abstract": "Representation learning is central to many downstream tasks such as search, clustering, classification, and reranking. State-of-the-art sequence encoders typically collapse a variable-length token sequence to a single vector using a pooling operator, most commonly a special [CLS] token or mean pooling over token embeddings. In this paper, we identify systematic weaknesses of these pooling strategies: [CLS] tends to concentrate information toward the initial positions of the sequence and can under-represent distributed evidence, while mean pooling can dilute salient local signals, sometimes leading to worse short-context performance. To address these issues, we introduce Landmark (LMK) pooling, which partitions a sequence into chunks, inserts landmark tokens between chunks, and forms the final representation by mean-pooling the landmark token embeddings. This simple mechanism improves long-context extrapolation without sacrificing local salient features, at the cost of introducing a small number of special tokens. We empirically demonstrate that LMK pooling matches existing methods on short-context retrieval tasks and yields substantial improvements on long-context tasks, making it a practical and scalable alternative to existing pooling methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22904", "title": "DINO-SAE: DINO Spherical Autoencoder for High-Fidelity Image Reconstruction and Generation", "authors": [ "Hun Chang", "Byunghee Cha", "Jong Chul Ye" ], "github": "", "project_page": "", "abstract": "Recent studies have explored using pretrained Vision Foundation Models (VFMs) such as DINO for generative autoencoders, showing strong generative performance. Unfortunately, existing approaches often suffer from limited reconstruction fidelity due to the loss of high-frequency details. In this work, we present the DINO Spherical Autoencoder (DINO-SAE), a framework that bridges semantic representation and pixel-level reconstruction. Our key insight is that semantic information in contrastive representations is primarily encoded in the direction of feature vectors, while forcing strict magnitude matching can hinder the encoder from preserving fine-grained details. To address this, we introduce Hierarchical Convolutional Patch Embedding module that enhances local structure and texture preservation, and Cosine Similarity Alignment objective that enforces semantic consistency while allowing flexible feature magnitudes for detail retention. Furthermore, leveraging the observation that SSL-based foundation model representations intrinsically lie on a hypersphere, we employ Riemannian Flow Matching to train a Diffusion Transformer (DiT) directly on this spherical latent manifold. Experiments on ImageNet-1K demonstrate that our approach achieves state-of-the-art reconstruction quality, reaching 0.37 rFID and 26.2 dB PSNR, while maintaining strong semantic alignment to the pretrained VFM. Notably, our Riemannian Flow Matching-based DiT exhibits efficient convergence, achieving a gFID of 3.47 at 80 epochs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22837", "title": "NativeTok: Native Visual Tokenization for Improved Image Generation", "authors": [ "Bin Wu", "Mengqi Huang", "Weinan Jia", "Zhendong Mao" ], "github": "", "project_page": "", "abstract": "VQ-based image generation typically follows a two-stage pipeline: a tokenizer encodes images into discrete tokens, and a generative model learns their dependencies for reconstruction. However, improved tokenization in the first stage does not necessarily enhance the second-stage generation, as existing methods fail to constrain token dependencies. This mismatch forces the generative model to learn from unordered distributions, leading to bias and weak coherence. To address this, we propose native visual tokenization, which enforces causal dependencies during tokenization. Building on this idea, we introduce NativeTok, a framework that achieves efficient reconstruction while embedding relational constraints within token sequences. NativeTok consists of: (1) a Meta Image Transformer (MIT) for latent image modeling, and (2) a Mixture of Causal Expert Transformer (MoCET), where each lightweight expert block generates a single token conditioned on prior tokens and latent features. We further design a Hierarchical Native Training strategy that updates only new expert blocks, ensuring training efficiency. Extensive experiments demonstrate the effectiveness of NativeTok.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22491", "title": "SSL: Sweet Spot Learning for Differentiated Guidance in Agentic Optimization", "authors": [ "Jinyang Wu", "Changpeng Yang", "Yuhao Shen", "Fangzhi Xu", "Bolin Ni", "Chonghua Liao", "Yuchen Liu", "Hongzhen Wang", "Shuai Nie", "Shuai Zhang", "Haoran Luo", "Jiaming Xu" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards has emerged as a powerful paradigm for training intelligent agents. However, existing methods typically employ binary rewards that fail to capture quality differences among trajectories achieving identical outcomes, thereby overlooking potential diversity within the solution space. Inspired by the ``sweet spot'' concept in tennis-the racket's core region that produces optimal hitting effects, we introduce Sweet Spot Learning (SSL), a novel framework that provides differentiated guidance for agent optimization. SSL follows a simple yet effective principle: progressively amplified, tiered rewards guide policies toward the sweet-spot region of the solution space. This principle naturally adapts across diverse tasks: visual perception tasks leverage distance-tiered modeling to reward proximity, while complex reasoning tasks reward incremental progress toward promising solutions. We theoretically demonstrate that SSL preserves optimal solution ordering and enhances the gradient signal-to-noise ratio, thereby fostering more directed optimization. Extensive experiments across GUI perception, short/long-term planning, and complex reasoning tasks show consistent improvements over strong baselines on 12 benchmarks, achieving up to 2.5X sample efficiency gains and effective cross-task transferability. Our work establishes SSL as a general principle for training capable and robust agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.23161", "title": "DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding", "authors": [ "Jiaming Zhou", "Xuxin Cheng", "Shiwan Zhao", "Yuhang Jia", "Cao Liu", "Ke Zeng", "Xunliang Cai", "Yong Qin" ], "github": "https://github.com/NKU-HLT/DIFFA.git", "project_page": "", "abstract": "Autoregressive (AR) large audio language models (LALMs) such as Qwen-2.5-Omni have achieved strong performance on audio understanding and interaction, but scaling them remains costly in data and computation, and strictly sequential decoding limits inference efficiency. Diffusion large language models (dLLMs) have recently been shown to make effective use of limited training data, and prior work on DIFFA indicates that replacing an AR backbone with a diffusion counterpart can substantially improve audio understanding under matched settings, albeit at a proof-of-concept scale without large-scale instruction tuning, preference alignment, or practical decoding schemes. We introduce DIFFA-2, a practical diffusion-based LALM for general audio understanding. DIFFA-2 upgrades the speech encoder, employs dual semantic and acoustic adapters, and is trained with a four-stage curriculum that combines semantic and acoustic alignment, large-scale supervised fine-tuning, and variance-reduced preference optimization, using only fully open-source corpora. Experiments on MMSU, MMAU, and MMAR show that DIFFA-2 consistently improves over DIFFA and is competitive to strong AR LALMs under practical training budgets, supporting diffusion-based modeling is a viable backbone for large-scale audio understanding. Our code is available at https://github.com/NKU-HLT/DIFFA.git.", "github_manual": "", "github_regex": "https://github.com/NKU-HLT/DIFFA.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.23188", "title": "Deep Search with Hierarchical Meta-Cognitive Monitoring Inspired by Cognitive Neuroscience", "authors": [ "Zhongxiang Sun", "Qipeng Wang", "Weijie Yu", "Jingxuan Yang", "Haolang Lu", "Jun Xu" ], "github": "https://github.com/ag2ai/ag2", "project_page": "", "abstract": "Deep search agents powered by large language models have demonstrated strong capabilities in multi-step retrieval, reasoning, and long-horizon task execution. However, their practical failures often stem from the lack of mechanisms to monitor and regulate reasoning and retrieval states as tasks evolve under uncertainty. Insights from cognitive neuroscience suggest that human metacognition is hierarchically organized, integrating fast anomaly detection with selectively triggered, experience-driven reflection. In this work, we propose Deep Search with Meta-Cognitive Monitoring (DS-MCM), a deep search framework augmented with an explicit hierarchical metacognitive monitoring mechanism. DS-MCM integrates a Fast Consistency Monitor, which performs lightweight checks on the alignment between external evidence and internal reasoning confidence, and a Slow Experience-Driven Monitor, which is selectively activated to guide corrective intervention based on experience memory from historical agent trajectories. By embedding monitoring directly into the reasoning-retrieval loop, DS-MCM determines both when intervention is warranted and how corrective actions should be informed by prior experience. Experiments across multiple deep search benchmarks and backbone models demonstrate that DS-MCM consistently improves performance and robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ag2ai/ag2", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22636", "title": "Statistical Estimation of Adversarial Risk in Large Language Models under Best-of-N Sampling", "authors": [ "Mingqian Feng", "Xiaodong Liu", "Weiwei Yang", "Chenliang Xu", "Christopher White", "Jianfeng Gao" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are typically evaluated for safety under single-shot or low-budget adversarial prompting, which underestimates real-world risk. In practice, attackers can exploit large-scale parallel sampling to repeatedly probe a model until a harmful response is produced. While recent work shows that attack success increases with repeated sampling, principled methods for predicting large-scale adversarial risk remain limited. We propose a scaling-aware Best-of-N estimation of risk, SABER, for modeling jailbreak vulnerability under Best-of-N sampling. We model sample-level success probabilities using a Beta distribution, the conjugate prior of the Bernoulli distribution, and derive an analytic scaling law that enables reliable extrapolation of large-N attack success rates from small-budget measurements. Using only n=100 samples, our anchored estimator predicts ASR@1000 with a mean absolute error of 1.66, compared to 12.04 for the baseline, which is an 86.2% reduction in estimation error. Our results reveal heterogeneous risk scaling profiles and show that models appearing robust under standard evaluation can experience rapid nonlinear risk amplification under parallel adversarial pressure. This work provides a low-cost, scalable methodology for realistic LLM safety assessment. We will release our code and evaluation scripts upon publication to future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.21957", "title": "PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing", "authors": [ "Cheng Cui", "Ting Sun", "Suyin Liang", "Tingquan Gao", "Zelun Zhang", "Jiaxuan Liu", "Xueqing Wang", "Changda Zhou", "Hongen Liu", "Manhui Lin", "Yue Zhang", "Yubo Zhang", "Yi Liu", "Dianhai Yu", "Yanjun Ma" ], "github": "https://github.com/PaddlePaddle/PaddleOCR", "project_page": "", "abstract": "We introduce PaddleOCR-VL-1.5, an upgraded model achieving a new state-of-the-art (SOTA) accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-world physical distortions, including scanning, skew, warping, screen-photography, and illumination, we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model's capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM with high efficiency. Code: https://github.com/PaddlePaddle/PaddleOCR", "github_manual": "", "github_regex": "https://github.com/PaddlePaddle/PaddleOCR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.23182", "title": "FourierSampler: Unlocking Non-Autoregressive Potential in Diffusion Language Models via Frequency-Guided Generation", "authors": [ "Siyang He", "Qiqi Wang", "Xiaoran Liu", "Hongnan Ma", "Yiwei Shi", "Yuerong Song", "Ying Zhu", "Tianyi Liang", "Zengfeng Huang", "Ziwei He", "Xipeng Qiu" ], "github": "https://github.com/ShirleYoung/FourierSampler", "project_page": "", "abstract": "Despite the non-autoregressive potential of diffusion language models (dLLMs), existing decoding strategies demonstrate positional bias, failing to fully unlock the potential of arbitrary generation. In this work, we delve into the inherent spectral characteristics of dLLMs and present the first frequency-domain analysis showing that low-frequency components in hidden states primarily encode global structural information and long-range dependencies, while high-frequency components are responsible for characterizing local details. Based on this observation, we propose FourierSampler, which leverages a frequency-domain sliding window mechanism to dynamically guide the model to achieve a \"structure-to-detail\" generation. FourierSampler outperforms other inference enhancement strategies on LLADA and SDAR, achieving relative improvements of 20.4% on LLaDA1.5-8B and 16.0% on LLaDA-8B-Instruct. It notably surpasses similarly sized autoregressive models like Llama3.1-8B-Instruct.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ShirleYoung/FourierSampler", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.21419", "title": "Revisiting Diffusion Model Predictions Through Dimensionality", "authors": [ "Qing Jin", "Chaoyang Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in diffusion and flow matching models have highlighted a shift in the preferred prediction target -- moving from noise (varepsilon) and velocity (v) to direct data (x) prediction -- particularly in high-dimensional settings. However, a formal explanation of why the optimal target depends on the specific properties of the data remains elusive. In this work, we provide a theoretical framework based on a generalized prediction formulation that accommodates arbitrary output targets, of which varepsilon-, v-, and x-prediction are special cases. We derive the analytical relationship between data's geometry and the optimal prediction target, offering a rigorous justification for why x-prediction becomes superior when the ambient dimension significantly exceeds the data's intrinsic dimension. Furthermore, while our theory identifies dimensionality as the governing factor for the optimal prediction target, the intrinsic dimension of manifold-bound data is typically intractable to estimate in practice. To bridge this gap, we propose k-Diff, a framework that employs a data-driven approach to learn the optimal prediction parameter k directly from data, bypassing the need for explicit dimension estimation. Extensive experiments in both latent-space and pixel-space image generation demonstrate that k-Diff consistently outperforms fixed-target baselines across varying architectures and data scales, providing a principled and automated approach to enhancing generative performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.15625", "title": "Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors", "authors": [ "Zhiwei Zhang", "Fei Zhao", "Rui Wang", "Zezhong Wang", "Bin Liang", "Jiakang Wang", "Yao Hu", "Shaosheng Cao", "Kam-Fai Wong" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) can call tools effectively, yet they remain brittle in multi-turn execution: following a tool call error, smaller models often degenerate into repetitive invalid re-invocations, failing to interpret error feedback and self-correct. This brittleness hinders reliable real-world deployment, where the execution errors are inherently inevitable during tool interaction procedures. We identify a key limitation of current approaches: standard reinforcement learning (RL) treats errors as sparse negative rewards, providing no guidance on how to recover, while pre-collected synthetic error-correction datasets suffer from distribution mismatch with the model's on-policy error modes. To bridge this gap, we propose Fission-GRPO, a framework that converts execution errors into corrective supervision within the RL training loop. Our core mechanism fissions each failed trajectory into a new training instance by augmenting it with diagnostic feedback from a finetuned Error Simulator, then resampling recovery rollouts on-policy. This enables the model to learn from the precise errors it makes during exploration, rather than from static, pre-collected error cases. On the BFCL v4 Multi-Turn, Fission-GRPO improves the error recovery rate of Qwen3-8B by 5.7% absolute, crucially, yielding a 4% overall accuracy gain (42.75% to 46.75%) over GRPO and outperforming specialized tool-use agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.23134", "title": "Machine Learning for Energy-Performance-aware Scheduling", "authors": [ "Zheyuan Hu", "Yifei Shi" ], "github": "", "project_page": "", "abstract": "In the post-Dennard era, optimizing embedded systems requires navigating complex trade-offs between energy efficiency and latency. Traditional heuristic tuning is often inefficient in such high-dimensional, non-smooth landscapes. In this work, we propose a Bayesian Optimization framework using Gaussian Processes to automate the search for optimal scheduling configurations on heterogeneous multi-core architectures. We explicitly address the multi-objective nature of the problem by approximating the Pareto Frontier between energy and time. Furthermore, by incorporating Sensitivity Analysis (fANOVA) and comparing different covariance kernels (e.g., Mat\u00e9rn vs. RBF), we provide physical interpretability to the black-box model, revealing the dominant hardware parameters driving system performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.20732", "title": "Continual GUI Agents", "authors": [ "Ziwei Liu", "Borui Kang", "Hangjie Yuan", "Zixiang Zhao", "Wei Li", "Yifan Zhu", "Tao Feng" ], "github": "", "project_page": "", "abstract": "As digital environments (data distribution) are in flux, with new GUI data arriving over time-introducing new domains or resolutions-agents trained on static environments deteriorate in performance. In this work, we introduce Continual GUI Agents, a new task that requires GUI agents to perform continual learning under shifted domains and resolutions. We find existing methods fail to maintain stable grounding as GUI distributions shift over time, due to the diversity of UI interaction points and regions in fluxing scenarios. To address this, we introduce GUI-Anchoring in Flux (GUI-AiF), a new reinforcement fine-tuning framework that stabilizes continual learning through two novel rewards: Anchoring Point Reward in Flux (APR-iF) and Anchoring Region Reward in Flux (ARR-iF). These rewards guide the agents to align with shifting interaction points and regions, mitigating the tendency of existing reward strategies to over-adapt to static grounding cues (e.g., fixed coordinates or element scales). Extensive experiments show GUI-AiF surpasses state-of-the-art baselines. Our work establishes the first continual learning framework for GUI agents, revealing the untapped potential of reinforcement fine-tuning for continual GUI Agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.18241", "title": "TAM-Eval: Evaluating LLMs for Automated Unit Test Maintenance", "authors": [ "Elena Bruches", "Vadim Alperovich", "Dari Baturova", "Roman Derunets", "Daniil Grebenkin", "Georgy Mkrtchyan", "Oleg Sedukhin", "Mikhail Klementev", "Ivan Bondarenko", "Nikolay Bushkov", "Stanislav Moiseev" ], "github": "https://github.com/trndcenter/TAM-Eval", "project_page": "", "abstract": "While Large Language Models (LLMs) have shown promise in software engineering, their application to unit testing remains largely confined to isolated test generation or oracle prediction, neglecting the broader challenge of test suite maintenance. We introduce TAM-Eval (Test Automated Maintenance Evaluation), a framework and benchmark designed to evaluate model performance across three core test maintenance scenarios: creation, repair, and updating of test suites. Unlike prior work limited to function-level tasks, TAM-Eval operates at the test file level, while maintaining access to full repository context during isolated evaluation, better reflecting real-world maintenance workflows. Our benchmark comprises 1,539 automatically extracted and validated scenarios from Python, Java, and Go projects. TAM-Eval supports system-agnostic evaluation of both raw LLMs and agentic workflows, using a reference-free protocol based on test suite pass rate, code coverage, and mutation testing. Empirical results indicate that state-of-the-art LLMs have limited capabilities in realistic test maintenance processes and yield only marginal improvements in test effectiveness. We release TAM-Eval as an open-source framework to support future research in automated software testing. Our data and code are publicly available at https://github.com/trndcenter/TAM-Eval.", "github_manual": "", "github_regex": "https://github.com/trndcenter/TAM-Eval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.13097", "title": "RM -RF: Reward Model for Run-Free Unit Test Evaluation", "authors": [ "Elena Bruches", "Daniil Grebenkin", "Mikhail Klementev", "Vadim Alperovich", "Roman Derunets", "Dari Baturova", "Georgy Mkrtchyan", "Oleg Sedukhin", "Ivan Bondarenko", "Nikolay Bushkov", "Stanislav Moiseev" ], "github": "https://github.com/trndcenter/RM-RF-unit-tests", "project_page": "", "abstract": "We present RM-RF, a lightweight reward model for run-free evaluation of automatically generated unit tests. Instead of repeatedly compiling and executing candidate tests, RM-RF predicts - from source and test code alone - three execution-derived signals: (1) whether the augmented test suite compiles and runs successfully, (2) whether the generated test cases increase code coverage, and (3) whether the generated test cases improve the mutation kill rate. To train and evaluate RM-RF we assemble a multilingual dataset (Java, Python, Go) of focal files, test files, and candidate test additions labeled by an execution-based pipeline, and we release an associated dataset and methodology for comparative evaluation. We tested multiple model families and tuning regimes (zero-shot, full fine-tuning, and PEFT via LoRA), achieving an average F1 of 0.69 across the three targets. Compared to conventional compile-and-run instruments, RM-RF provides substantially lower latency and infrastructure cost while delivering competitive predictive fidelity, enabling fast, scalable feedback for large-scale test generation and RL-based code optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/trndcenter/RM-RF-unit-tests", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.21709", "title": "Why Attention Patterns Exist: A Unifying Temporal Perspective Analysis", "authors": [ "Qingyue Yang", "Jie Wang", "Xing Li", "Yinqi Bai", "Xialiang Tong", "Huiling Zhen", "Jianye Hao", "Mingxuan Yuan", "Bin Li" ], "github": "https://github.com/MIRALab-USTC/LLM-TAPPA", "project_page": "", "abstract": "Attention patterns play a crucial role in both training and inference of large language models (LLMs). Prior works have identified individual patterns such as retrieval heads, sink heads, and diagonal traces, yet these observations remain fragmented and lack a unifying explanation. To bridge this gap, we introduce Temporal Attention Pattern Predictability Analysis (TAPPA), a unifying framework that explains diverse attention patterns by analyzing their underlying mathematical formulations from a temporally continuous perspective. TAPPA both deepens the understanding of attention behavior and guides inference acceleration approaches. Specifically, TAPPA characterizes attention patterns as predictable patterns with clear regularities and unpredictable patterns that appear effectively random. Our analysis further reveals that this distinction can be explained by the degree of query self-similarity along the temporal dimension. Focusing on the predictable patterns, we further provide a detailed mathematical analysis of three representative cases through the joint effect of queries, keys, and Rotary Positional Embeddings (RoPE). We validate TAPPA by applying its insights to KV cache compression and LLM pruning tasks. Across these tasks, a simple metric motivated by TAPPA consistently improves performance over baseline methods. The code is available at https://github.com/MIRALab-USTC/LLM-TAPPA.", "github_manual": "", "github_regex": "https://github.com/MIRALab-USTC/LLM-TAPPA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.23228", "title": "Scaling Multiagent Systems with Process Rewards", "authors": [ "Ed Li", "Junyu Ren", "Cat Yan" ], "github": "https://github.com/ltjed/multiagent-coaching", "project_page": "", "abstract": "While multiagent systems have shown promise for tackling complex tasks via specialization, finetuning multiple agents simultaneously faces two key challenges: (1) credit assignment across agents, and (2) sample efficiency of expensive multiagent rollouts. In this work, we propose finetuning multiagent systems with per-action process rewards from AI feedback (MAPPA) to address both. Through assigning credit to individual agent actions rather than only at task completion, MAPPA enables fine-grained supervision without ground truth labels while extracting maximal training signal from each rollout. We demonstrate our approach on competition math problems and tool-augmented data analysis tasks. On unseen math problems, MAPPA achieves +5.0--17.5pp on AIME and +7.8--17.2pp on AMC. For data analysis tasks, our method improves success rate by +12.5pp while quality metrics improve by up to 30%, validating that per-action supervision can lead to improvements across different multiagent system on various domains. By addressing these challenges, our work takes a first step toward scaling multiagent systems for complex, long-horizon tasks with minimal human supervision.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ltjed/multiagent-coaching", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22975", "title": "Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text", "authors": [ "Ximing Lu", "David Acuna", "Jaehun Jung", "Jian Hu", "Di Zhang", "Shizhe Diao", "Yunheng Zou", "Shaokun Zhang", "Brandon Cui", "Mingjie Liu", "Hyunwoo Kim", "Prithviraj Ammanabrolu", "Jan Kautz", "Yi Dong", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has become a cornerstone for unlocking complex reasoning in Large Language Models (LLMs). Yet, scaling up RL is bottlenecked by limited existing verifiable data, where improvements increasingly saturate over prolonged training. To overcome this, we propose Golden Goose, a simple trick to synthesize unlimited RLVR tasks from unverifiable internet text by constructing a multiple-choice question-answering version of the fill-in-the-middle task. Given a source text, we prompt an LLM to identify and mask key reasoning steps, then generate a set of diverse, plausible distractors. This enables us to leverage reasoning-rich unverifiable corpora typically excluded from prior RLVR data construction (e.g., science textbooks) to synthesize GooseReason-0.7M, a large-scale RLVR dataset with over 0.7 million tasks spanning mathematics, programming, and general scientific domains. Empirically, GooseReason effectively revives models saturated on existing RLVR data, yielding robust, sustained gains under continuous RL and achieving new state-of-the-art results for 1.5B and 4B-Instruct models across 15 diverse benchmarks. Finally, we deploy Golden Goose in a real-world setting, synthesizing RLVR tasks from raw FineWeb scrapes for the cybersecurity domain, where no prior RLVR data exists. Training Qwen3-4B-Instruct on the resulting data GooseReason-Cyber sets a new state-of-the-art in cybersecurity, surpassing a 7B domain-specialized model with extensive domain-specific pre-training and post-training. This highlights the potential of automatically scaling up RLVR data by exploiting abundant, reasoning-rich, unverifiable internet text.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22813", "title": "Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation", "authors": [ "Andrei Panferov", "Erik Schultheis", "Soroush Tabesh", "Dan Alistarh" ], "github": "https://github.com/IST-DASLab/Quartet-II", "project_page": "", "abstract": "The NVFP4 lower-precision format, supported in hardware by NVIDIA Blackwell GPUs, promises to allow, for the first time, end-to-end fully-quantized pre-training of massive models such as LLMs. Yet, existing quantized training methods still sacrifice some of the representation capacity of this format in favor of more accurate unbiased quantized gradient estimation by stochastic rounding (SR), losing noticeable accuracy relative to standard FP16 and FP8 training. In this paper, improve the state of the art for quantized training in NVFP4 via a novel unbiased quantization routine for micro-scaled formats, called MS-EDEN, that has more than 2x lower quantization error than SR. We integrate it into a novel fully-NVFP4 quantization scheme for linear layers, called Quartet II. We show analytically that Quartet II achieves consistently better gradient estimation across all major matrix multiplications, both on the forward and on the backward passes. In addition, our proposal synergizes well with recent training improvements aimed specifically at NVFP4. We further validate Quartet II on end-to-end LLM training with up to 1.9B parameters on 38B tokens. We provide kernels for execution on NVIDIA Blackwell GPUs with up to 4.2x speedup over BF16. Our code is available at https://github.com/IST-DASLab/Quartet-II .", "github_manual": "", "github_regex": "https://github.com/IST-DASLab/Quartet-II", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22666", "title": "ExpAlign: Expectation-Guided Vision-Language Alignment for Open-Vocabulary Grounding", "authors": [ "Junyi Hu", "Tian Bai", "Fengyi Wu", "Wenyan Li", "Zhenming Peng", "Yi Zhang" ], "github": "", "project_page": "", "abstract": "Open-vocabulary grounding requires accurate vision-language alignment under weak supervision, yet existing methods either rely on global sentence embeddings that lack fine-grained expressiveness or introduce token-level alignment with explicit supervision or heavy cross-attention designs. We propose ExpAlign, a theoretically grounded vision-language alignment framework built on a principled multiple instance learning formulation. ExpAlign introduces an Expectation Alignment Head that performs attention-based soft MIL pooling over token-region similarities, enabling implicit token and instance selection without additional annotations. To further stabilize alignment learning, we develop an energy-based multi-scale consistency regularization scheme, including a Top-K multi-positive contrastive objective and a Geometry-Aware Consistency Objective derived from a Lagrangian-constrained free-energy minimization. Extensive experiments show that ExpAlign consistently improves open-vocabulary detection and zero-shot instance segmentation, particularly on long-tail categories. Most notably, it achieves 36.2 AP_r on the LVIS minival split, outperforming other state-of-the-art methods at comparable model scale, while remaining lightweight and inference-efficient.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.21526", "title": "KAPSO: A Knowledge-grounded framework for Autonomous Program Synthesis and Optimization", "authors": [ "Alireza Nadaf", "Alireza Mohammadshahi", "Majid Yazdani" ], "github": "https://github.com/Leeroo-AI/kapso", "project_page": "", "abstract": "We introduce KAPSO, a modular framework for autonomous program synthesis and optimization. Given a natural language goal and an evaluation method, KAPSO iteratively performs ideation, code synthesis and editing, execution, evaluation, and learning to improve a runnable artifact toward measurable objectives. Rather than treating synthesis as the endpoint, KAPSO uses synthesis as an operator within a long-horizon optimization loop, where progress is defined by evaluator outcomes. KAPSO targets long-horizon failures common in coding agents, including lost experimental state, brittle debugging, and weak reuse of domain expertise, by integrating three tightly coupled components. First, a git-native experimentation engine isolates each attempt as a branch, producing reproducible artifacts and preserving provenance across iterations. Second, a knowledge system ingests heterogeneous sources, including repositories, internal playbooks, and curated external resources such as documentation, scientific papers, and web search results, and organizes them into a structured representation that supports retrieval over workflows, implementations, and environment constraints. Third, a cognitive memory layer coordinates retrieval and maintains an episodic store of reusable lessons distilled from experiment traces (run logs, diffs, and evaluator feedback), reducing repeated error modes and accelerating convergence. We evaluated KAPSO on MLE-Bench (Kaggle-style ML competitions) and ALE-Bench (AtCoder heuristic optimization), and report end-to-end performance. Code Available at: https://github.com/Leeroo-AI/kapso", "github_manual": "", "github_regex": "https://github.com/Leeroo-AI/kapso", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.21998", "title": "Causal World Modeling for Robot Control", "authors": [ "Lin Li", "Qihang Zhang", "Yiming Luo", "Shuai Yang", "Ruilin Wang", "Fei Han", "Mingrui Yu", "Zelin Gao", "Nan Xue", "Xing Zhu", "Yujun Shen", "Yinghao Xu" ], "github": "https://github.com/robbyant/lingbot-va", "project_page": "", "abstract": "This work highlights that video world modeling, alongside vision-language pre-training, establishes a fresh and independent foundation for robot learning. Intuitively, video world models provide the ability to imagine the near future by understanding the causality between actions and visual dynamics. Inspired by this, we introduce LingBot-VA, an autoregressive diffusion framework that learns frame prediction and policy execution simultaneously. Our model features three carefully crafted designs: (1) a shared latent space, integrating vision and action tokens, driven by a Mixture-of-Transformers (MoT) architecture, (2) a closed-loop rollout mechanism, allowing for ongoing acquisition of environmental feedback with ground-truth observations, (3) an asynchronous inference pipeline, parallelizing action prediction and motor execution to support efficient control. We evaluate our model on both simulation benchmarks and real-world scenarios, where it shows significant promise in long-horizon manipulation, data efficiency in post-training, and strong generalizability to novel configurations. The code and model are made publicly available to facilitate the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/robbyant/lingbot-va", "github_api_added_by": "auto" }, { "date": "2026-02-02", "arxiv_id": "2601.21192", "title": "Do Reasoning Models Enhance Embedding Models?", "authors": [ "Wun Yu Chan", "Shaojin Chen", "Huihao Jing", "Kwun Hang Lau", "Elton Chun-Chai Li", "Zihao Wang", "Haoran Li", "Yangqiu Song" ], "github": "", "project_page": "", "abstract": "State-of-the-art embedding models are increasingly derived from decoder-only Large Language Model (LLM) backbones adapted via contrastive learning. Given the emergence of reasoning models trained via Reinforcement Learning with Verifiable Rewards (RLVR), a natural question arises: do enhanced reasoning translate to superior semantic representations when these models serve as embedding initializations? Contrary to expectation, our evaluation on MTEB and BRIGHT reveals a **null effect**: embedding models initialized from RLVR-tuned backbones yield no consistent performance advantage over their base counterparts when subjected to identical training recipes. To unpack this paradox, we introduce **H**ierarchical **R**epresentation **S**imilarity **A**nalysis (HRSA), a framework that decomposes similarity across representation, geometry, and function levels. HRSA reveals that while RLVR induces irreversible latent manifold's local geometry reorganization and reversible coordinate basis drift, it preserves the global manifold geometry and linear readout. Consequently, subsequent contrastive learning drives strong alignment between base- and reasoning-initialized models, a phenomenon we term **Manifold Realignment**. Empirically, our findings suggest that unlike Supervised Fine-Tuning (SFT), RLVR optimizes trajectories within an existing semantic landscape rather than fundamentally restructuring the landscape itself.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22108", "title": "Value-Based Pre-Training with Downstream Feedback", "authors": [ "Shuqi Ke", "Giulia Fanti" ], "github": "", "project_page": "", "abstract": "Can a small amount of verified goal information steer the expensive self-supervised pretraining of foundation models? Standard pretraining optimizes a fixed proxy objective (e.g., next-token prediction), which can misallocate compute away from downstream capabilities of interest. We introduce V-Pretraining: a value-based, modality-agnostic method for controlled continued pretraining in which a lightweight task designer reshapes the pretraining task to maximize the value of each gradient step. For example, consider self-supervised learning (SSL) with sample augmentation. The V-Pretraining task designer selects pretraining tasks (e.g., augmentations) for which the pretraining loss gradient is aligned with a gradient computed over a downstream task (e.g., image segmentation). This helps steer pretraining towards relevant downstream capabilities. Notably, the pretrained model is never updated on downstream task labels; they are used only to shape the pretraining task. Under matched learner update budgets, V-Pretraining of 0.5B--7B language models improves reasoning (GSM8K test Pass@1) by up to 18% relative over standard next-token prediction using only 12% of GSM8K training examples as feedback. In vision SSL, we improve the state-of-the-art results on ADE20K by up to 1.07 mIoU and reduce NYUv2 RMSE while improving ImageNet linear accuracy, and we provide pilot evidence of improved token efficiency in continued pretraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22680", "title": "Visual Personalization Turing Test", "authors": [ "Rameen Abdal", "James Burgess", "Sergey Tulyakov", "Kuan-Chieh Jackson Wang" ], "github": "", "project_page": "", "abstract": "We introduce the Visual Personalization Turing Test (VPTT), a new paradigm for evaluating contextual visual personalization based on perceptual indistinguishability, rather than identity replication. A model passes the VPTT if its output (image, video, 3D asset, etc.) is indistinguishable to a human or calibrated VLM judge from content a given person might plausibly create or share. To operationalize VPTT, we present the VPTT Framework, integrating a 10k-persona benchmark (VPTT-Bench), a visual retrieval-augmented generator (VPRAG), and the VPTT Score, a text-only metric calibrated against human and VLM judgments. We show high correlation across human, VLM, and VPTT evaluations, validating the VPTT Score as a reliable perceptual proxy. Experiments demonstrate that VPRAG achieves the best alignment-originality balance, offering a scalable and privacy-safe foundation for personalized generative AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.21666", "title": "SONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understanding", "authors": [ "Ahmed Y. Radwan", "Christos Emmanouilidis", "Hina Tabassum", "Deval Pandya", "Shaina Raza" ], "github": "https://github.com/vectorinstitute/sonic-o1", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) are a major focus of recent AI research. However, most prior work focuses on static image understanding, while their ability to process sequential audio-video data remains underexplored. This gap highlights the need for a high-quality benchmark to systematically evaluate MLLM performance in a real-world setting. We introduce SONIC-O1, a comprehensive, fully human-verified benchmark spanning 13 real-world conversational domains with 4,958 annotations and demographic metadata. SONIC-O1 evaluates MLLMs on key tasks, including open-ended summarization, multiple-choice question (MCQ) answering, and temporal localization with supporting rationales (reasoning). Experiments on closed- and open-source models reveal limitations. While the performance gap in MCQ accuracy between two model families is relatively small, we observe a substantial 22.6% performance difference in temporal localization between the best performing closed-source and open-source models. Performance further degrades across demographic groups, indicating persistent disparities in model behavior. Overall, SONIC-O1 provides an open evaluation suite for temporally grounded and socially robust multimodal understanding. We release SONIC-O1 for reproducibility and research: Project page: https://vectorinstitute.github.io/sonic-o1/ Dataset: https://huggingface.co/datasets/vector-institute/sonic-o1 Github: https://github.com/vectorinstitute/sonic-o1 Leaderboard: https://huggingface.co/spaces/vector-institute/sonic-o1-leaderboard", "github_manual": "", "github_regex": "https://github.com/vectorinstitute/sonic-o1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.22032", "title": "Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving", "authors": [ "Linhan Wang", "Zichong Yang", "Chen Bai", "Guoxiang Zhang", "Xiaotong Liu", "Xiaoyin Zheng", "Xiao-Xiao Long", "Chang-Tien Lu", "Cheng Lu" ], "github": "https://github.com/linhanwang/Drive-JEPA", "project_page": "", "abstract": "End-to-end autonomous driving increasingly leverages self-supervised video pretraining to learn transferable planning representations. However, pretraining video world models for scene understanding has so far brought only limited improvements. This limitation is compounded by the inherent ambiguity of driving: each scene typically provides only a single human trajectory, making it difficult to learn multimodal behaviors. In this work, we propose Drive-JEPA, a framework that integrates Video Joint-Embedding Predictive Architecture (V-JEPA) with multimodal trajectory distillation for end-to-end driving. First, we adapt V-JEPA for end-to-end driving, pretraining a ViT encoder on large-scale driving videos to produce predictive representations aligned with trajectory planning. Second, we introduce a proposal-centric planner that distills diverse simulator-generated trajectories alongside human trajectories, with a momentum-aware selection mechanism to promote stable and safe behavior. When evaluated on NAVSIM, the V-JEPA representation combined with a simple transformer-based decoder outperforms prior methods by 3 PDMS in the perception-free setting. The complete Drive-JEPA framework achieves 93.3 PDMS on v1 and 87.8 EPDMS on v2, setting a new state-of-the-art.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/linhanwang/Drive-JEPA", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2601.15394", "title": "Memorization Dynamics in Knowledge Distillation for Language Models", "authors": [ "Jaydeep Borkar", "Karan Chadha", "Niloofar Mireshghallah", "Yuchen Zhang", "Irina-Elena Veliche", "Archi Mitra", "David A. Smith", "Zheng Xu", "Diego Garcia-Olano" ], "github": "", "project_page": "", "abstract": "Knowledge Distillation (KD) is increasingly adopted to transfer capabilities from large language models to smaller ones, offering significant improvements in efficiency and utility while often surpassing standard fine-tuning. Beyond performance, KD is also explored as a privacy-preserving mechanism to mitigate the risk of training data leakage. While training data memorization has been extensively studied in standard pre-training and fine-tuning settings, its dynamics in a knowledge distillation setup remain poorly understood. In this work, we study memorization across the KD pipeline using three large language model (LLM) families (Pythia, OLMo-2, Qwen-3) and three datasets (FineWeb, Wikitext, Nemotron-CC-v2). We find: (1) distilled models memorize significantly less training data than standard fine-tuning (reducing memorization by more than 50%); (2) some examples are inherently easier to memorize and account for a large fraction of memorization during distillation (over ~95%); (3) student memorization is predictable prior to distillation using features based on zlib entropy, KL divergence, and perplexity; and (4) while soft and hard distillation have similar overall memorization rates, hard distillation poses a greater risk: it inherits 2.7times more teacher-specific examples than soft distillation. Overall, we demonstrate that distillation can provide both improved generalization and reduced memorization risks compared to standard fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-02", "arxiv_id": "2602.00158", "title": "RAPTOR: Ridge-Adaptive Logistic Probes", "authors": [ "Ziqi Gao", "Yaotian Zhu", "Qingcheng Zeng", "Xu Zhao", "Ziqing Wang", "Feng Ruan", "Kaize Ding" ], "github": "", "project_page": "", "abstract": "Probing studies what information is encoded in a frozen LLM's layer representations by training a lightweight predictor on top of them. Beyond analysis, probes are often used operationally in probe-then-steer pipelines: a learned concept vector is extracted from a probe and injected via additive activation steering by adding it to a layer representation during the forward pass. The effectiveness of this pipeline hinges on estimating concept vectors that are accurate, directionally stable under ablation, and inexpensive to obtain. Motivated by these desiderata, we propose RAPTOR (Ridge-Adaptive Logistic Probe), a simple L2-regularized logistic probe whose validation-tuned ridge strength yields concept vectors from normalized weights. Across extensive experiments on instruction-tuned LLMs and human-written concept datasets, RAPTOR matches or exceeds strong baselines in accuracy while achieving competitive directional stability and substantially lower training cost; these quantitative results are supported by qualitative downstream steering demonstrations. Finally, using the Convex Gaussian Min-max Theorem (CGMT), we provide a mechanistic characterization of ridge logistic regression in an idealized Gaussian teacher-student model in the high-dimensional few-shot regime, explaining how penalty strength mediates probe accuracy and concept-vector stability and yielding structural predictions that qualitatively align with trends observed on real LLM embeddings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01479", "title": "Ebisu: Benchmarking Large Language Models in Japanese Finance", "authors": [ "Xueqing Peng", "Ruoyu Xiang", "Fan Zhang", "Mingzi Song", "Mingyang Jiang", "Yan Wang", "Lingfei Qian", "Taiki Hara", "Yuqing Guo", "Jimin Huang", "Junichi Tsujii", "Sophia Ananiadou" ], "github": "", "project_page": "", "abstract": "Japanese finance combines agglutinative, head-final linguistic structure, mixed writing systems, and high-context communication norms that rely on indirect expression and implicit commitment, posing a substantial challenge for LLMs. We introduce Ebisu, a benchmark for native Japanese financial language understanding, comprising two linguistically and culturally grounded, expert-annotated tasks: JF-ICR, which evaluates implicit commitment and refusal recognition in investor-facing Q&A, and JF-TE, which assesses hierarchical extraction and ranking of nested financial terminology from professional disclosures. We evaluate a diverse set of open-source and proprietary LLMs spanning general-purpose, Japanese-adapted, and financial models. Results show that even state-of-the-art systems struggle on both tasks. While increased model scale yields limited improvements, language- and domain-specific adaptation does not reliably improve performance, leaving substantial gaps unresolved. Ebisu provides a focused benchmark for advancing linguistically and culturally grounded financial NLP. All datasets and evaluation scripts are publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.00759", "title": "Adaptive Ability Decomposing for Unlocking Large Reasoning Model Effective Reinforcement Learning", "authors": [ "Zhipeng Chen", "Xiaobo Qin", "Wayne Xin Zhao", "Youbin Wu", "Ji-Rong Wen" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has shown great potential to enhance the reasoning ability of large language models (LLMs). However, due to the limited amount of information provided during the RLVR process, the model can only engage in largely blind exploration, which often results in failure on challenging problems. To provide additional information for the RLVR process without relying on a teacher model, we propose A^2D, an Adaptive Ability Decomposing method for enhancing the effectiveness of RLVR. Specifically, we first train a decomposer via RLVR without distillation, enabling it to decompose complex questions into a set of simpler sub-questions. Next, we use this decomposer to annotate sub-questions for each question in the training dataset, and then train the reasoner under RLVR with sub-question guidance. To better understand A^2D, we first compare its performance with competitive baselines, showing its effectiveness. Next, we observe that our method functions as a plug-and-play module that can be applied to different RLVR algorithms. Furthermore, we conduct an analysis of the decomposer, revealing how the RLVR process affects its performance and behavior, and which type of guidance is better suited for enhancing the reasoner's exploration and exploitation abilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01566", "title": "FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents", "authors": [ "Chiwei Zhu", "Benfeng Xu", "Mingxuan Du", "Shaohan Wang", "Xiaorui Wang", "Zhendong Mao", "Yongdong Zhang" ], "github": "https://github.com/Ignoramus0817/FS-Researcher", "project_page": "", "abstract": "Deep research is emerging as a representative long-horizon task for large language model (LLM) agents. However, long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing, and preventing effective test-time scaling. We introduce FS-Researcher, a file-system-based, dual-agent framework that scales deep research beyond the context window via a persistent workspace. Specifically, a Context Builder agent acts as a librarian which browses the internet, writes structured notes, and archives raw sources into a hierarchical knowledge base that can grow far beyond context length. A Report Writer agent then composes the final report section by section, treating the knowledge base as the source of facts. In this framework, the file system serves as a durable external memory and a shared coordination medium across agents and sessions, enabling iterative refinement beyond the context window. Experiments on two open-ended benchmarks (DeepResearch Bench and DeepConsult) show that FS-Researcher achieves state-of-the-art report quality across different backbone models. Further analyses demonstrate a positive correlation between final report quality and the computation allocated to the Context Builder, validating effective test-time scaling under the file-system paradigm. The code and data are anonymously open-sourced at https://github.com/Ignoramus0817/FS-Researcher.", "github_manual": "", "github_regex": "https://github.com/Ignoramus0817/FS-Researcher", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01624", "title": "PISCES: Annotation-free Text-to-Video Post-Training via Optimal Transport-Aligned Rewards", "authors": [ "Minh-Quan Le", "Gaurav Mittal", "Cheng Zhao", "David Gu", "Dimitris Samaras", "Mei Chen" ], "github": "", "project_page": "", "abstract": "Text-to-video (T2V) generation aims to synthesize videos with high visual quality and temporal consistency that are semantically aligned with input text. Reward-based post-training has emerged as a promising direction to improve the quality and semantic alignment of generated videos. However, recent methods either rely on large-scale human preference annotations or operate on misaligned embeddings from pre-trained vision-language models, leading to limited scalability or suboptimal supervision. We present PISCES, an annotation-free post-training algorithm that addresses these limitations via a novel Dual Optimal Transport (OT)-aligned Rewards module. To align reward signals with human judgment, PISCES uses OT to bridge text and video embeddings at both distributional and discrete token levels, enabling reward supervision to fulfill two objectives: (i) a Distributional OT-aligned Quality Reward that captures overall visual quality and temporal coherence; and (ii) a Discrete Token-level OT-aligned Semantic Reward that enforces semantic, spatio-temporal correspondence between text and video tokens. To our knowledge, PISCES is the first to improve annotation-free reward supervision in generative post-training through the lens of OT. Experiments on both short- and long-video generation show that PISCES outperforms both annotation-based and annotation-free methods on VBench across Quality and Semantic scores, with human preference studies further validating its effectiveness. We show that the Dual OT-aligned Rewards module is compatible with multiple optimization paradigms, including direct backpropagation and reinforcement learning fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02488", "title": "RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System", "authors": [ "Yinjie Wang", "Tianbao Xie", "Ke Shen", "Mengdi Wang", "Ling Yang" ], "github": "https://github.com/Gen-Verse/Open-AgentRL", "project_page": "", "abstract": "We propose RLAnything, a reinforcement learning framework that dynamically forges environment, policy, and reward models through closed-loop optimization, amplifying learning signals and strengthening the overall RL system for any LLM or agentic scenarios. Specifically, the policy is trained with integrated feedback from step-wise and outcome signals, while the reward model is jointly optimized via consistency feedback, which in turn further improves policy training. Moreover, our theory-motivated automatic environment adaptation improves training for both the reward and policy models by leveraging critic feedback from each, enabling learning from experience. Empirically, each added component consistently improves the overall system, and RLAnything yields substantial gains across various representative LLM and agentic tasks, boosting Qwen3-VL-8B-Thinking by 9.1% on OSWorld and Qwen2.5-7B-Instruct by 18.7% and 11.9% on AlfWorld and LiveBench, respectively. We also that optimized reward-model signals outperform outcomes that rely on human labels. Code: https://github.com/Gen-Verse/Open-AgentRL", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/Open-AgentRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02053", "title": "WildGraphBench: Benchmarking GraphRAG with Wild-Source Corpora", "authors": [ "Pengyu Wang", "Benfeng Xu", "Licheng Zhang", "Shaohan Wang", "Mingxuan Du", "Chiwei Zhu", "Zhendong Mao" ], "github": "https://github.com/BstWPY/WildGraphBench", "project_page": "", "abstract": "Graph-based Retrieval-Augmented Generation (GraphRAG) organizes external knowledge as a hierarchical graph, enabling efficient retrieval and aggregation of scattered evidence across multiple documents. However, many existing benchmarks for GraphRAG rely on short, curated passages as external knowledge, failing to adequately evaluate systems in realistic settings involving long contexts and large-scale heterogeneous documents. To bridge this gap, we introduce WildGraphBench, a benchmark designed to assess GraphRAG performance in the wild. We leverage Wikipedia's unique structure, where cohesive narratives are grounded in long and heterogeneous external reference documents, to construct a benchmark reflecting real-word scenarios. Specifically, we sample articles across 12 top-level topics, using their external references as the retrieval corpus and citation-linked statements as ground truth, resulting in 1,100 questions spanning three levels of complexity: single-fact QA, multi-fact QA, and section-level summarization. Experiments across multiple baselines reveal that current GraphRAG pipelines help on multi-fact aggregation when evidence comes from a moderate number of sources, but this aggregation paradigm may overemphasize high-level statements at the expense of fine-grained details, leading to weaker performance on summarization tasks. Project page:https://github.com/BstWPY/WildGraphBench.", "github_manual": "", "github_regex": "https://github.com/BstWPY/WildGraphBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01590", "title": "Wiki Live Challenge: Challenging Deep Research Agents with Expert-Level Wikipedia Articles", "authors": [ "Shaohan Wang", "Benfeng Xu", "Licheng Zhang", "Mingxuan Du", "Chiwei Zhu", "Xiaorui Wang", "Zhendong Mao", "Yongdong Zhang" ], "github": "https://github.com/WangShao2000/Wiki_Live_Challenge", "project_page": "", "abstract": "Deep Research Agents (DRAs) have demonstrated remarkable capabilities in autonomous information retrieval and report generation, showing great potential to assist humans in complex research tasks. Current evaluation frameworks primarily rely on LLM-generated references or LLM-derived evaluation dimensions. While these approaches offer scalability, they often lack the reliability of expert-verified content and struggle to provide objective, fine-grained assessments of critical dimensions. To bridge this gap, we introduce Wiki Live Challenge (WLC), a live benchmark that leverages the newest Wikipedia Good Articles (GAs) as expert-level references. Wikipedia's strict standards for neutrality, comprehensiveness, and verifiability serve as a great challenge for DRAs, with GAs representing the pinnacle of which. We curate a dataset of 100 recent Good Articles and propose Wiki Eval, a comprehensive evaluation framework comprising a fine-grained evaluation method with 39 criteria for writing quality and rigorous metrics for factual verifiability. Extensive experiments on various DRA systems demonstrate a significant gap between current DRAs and human expert-level Wikipedia articles, validating the effectiveness of WLC in advancing agent research. We release our benchmark at https://github.com/WangShao2000/Wiki_Live_Challenge", "github_manual": "", "github_regex": "https://github.com/WangShao2000/Wiki_Live_Challenge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01541", "title": "Toward Cognitive Supersensing in Multimodal Large Language Model", "authors": [ "Boyi Li", "Yifan Shen", "Yuanzhe Liu", "Yifan Xu", "Jiateng Liu", "Xinzhuo Li", "Zhengyuan Li", "Jingyuan Zhu", "Yunhan Zhong", "Fangzhou Lan", "Jianguo Cao", "James M. Rehg", "Heng Ji", "Ismini Lourentzou", "Xu Cao" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have achieved remarkable success in open-vocabulary perceptual tasks, yet their ability to solve complex cognitive problems remains limited, especially when visual details are abstract and require visual memory. Current approaches primarily scale Chain-of-Thought (CoT) reasoning in the text space, even when language alone is insufficient for clear and structured reasoning, and largely neglect visual reasoning mechanisms analogous to the human visuospatial sketchpad and visual imagery. To mitigate this deficiency, we introduce Cognitive Supersensing, a novel training paradigm that endows MLLMs with human-like visual imagery capabilities by integrating a Latent Visual Imagery Prediction (LVIP) head that jointly learns sequences of visual cognitive latent embeddings and aligns them with the answer, thereby forming vision-based internal reasoning chains. We further introduce a reinforcement learning stage that optimizes text reasoning paths based on this grounded visual latent. To evaluate the cognitive capabilities of MLLMs, we present CogSense-Bench, a comprehensive visual question answering (VQA) benchmark assessing five cognitive dimensions. Extensive experiments demonstrate that MLLMs trained with Cognitive Supersensing significantly outperform state-of-the-art baselines on CogSense-Bench and exhibit superior generalization on out-of-domain mathematics and science VQA benchmarks, suggesting that internal visual imagery is potentially key to bridging the gap between perceptual recognition and cognitive understanding. We will open-source the CogSense-Bench and our model weights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02084", "title": "Closing the Loop: Universal Repository Representation with RPG-Encoder", "authors": [ "Jane Luo", "Chengyu Yin", "Xin Zhang", "Qingtao Li", "Steven Liu", "Yiming Huang", "Jie Wu", "Hao Liu", "Yangyu Huang", "Yu Kang", "Fangkai Yang", "Ying Xin", "Scarlett Li" ], "github": "", "project_page": "", "abstract": "Current repository agents encounter a reasoning disconnect due to fragmented representations, as existing methods rely on isolated API documentation or dependency graphs that lack semantic depth. We consider repository comprehension and generation to be inverse processes within a unified cycle: generation expands intent into implementation, while comprehension compresses implementation back into intent. To address this, we propose RPG-Encoder, a framework that generalizes the Repository Planning Graph (RPG) from a static generative blueprint into a unified, high-fidelity representation. RPG-Encoder closes the reasoning loop through three mechanisms: (1) Encoding raw code into the RPG that combines lifted semantic features with code dependencies; (2) Evolving the topology incrementally to decouple maintenance costs from repository scale, reducing overhead by 95.7%; and (3) Operating as a unified interface for structure-aware navigation. In evaluations, RPG-Encoder establishes state-of-the-art repository understanding on SWE-bench Verified with 93.7% Acc@5 and exceeds the best baseline by over 10% on SWE-bench Live Lite. These results highlight our superior fine-grained localization accuracy in complex codebases. Furthermore, it achieves 98.5% reconstruction coverage on RepoCraft, confirming RPG's high-fidelity capacity to mirror the original codebase and closing the loop between intent and implementation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01851", "title": "How Well Do Models Follow Visual Instructions? VIBE: A Systematic Benchmark for Visual Instruction-Driven Image Editing", "authors": [ "Huanyu Zhang", "Xuehai Bai", "Chengzu Li", "Chen Liang", "Haochen Tian", "Haodong Li", "Ruichuan An", "Yifan Zhang", "Anna Korhonen", "Zhang Zhang", "Liang Wang", "Tieniu Tan" ], "github": "https://github.com/hwanyu112/VIBE-Benchmark", "project_page": "", "abstract": "Recent generative models have achieved remarkable progress in image editing. However, existing systems and benchmarks remain largely text-guided. In contrast, human communication is inherently multimodal, where visual instructions such as sketches efficiently convey spatial and structural intent. To address this gap, we introduce VIBE, the Visual Instruction Benchmark for Image Editing with a three-level interaction hierarchy that captures deictic grounding, morphological manipulation, and causal reasoning. Across these levels, we curate high-quality and diverse test cases that reflect progressively increasing complexity in visual instruction following. We further propose a robust LMM-as-a-judge evaluation framework with task-specific metrics to enable scalable and fine-grained assessment. Through a comprehensive evaluation of 17 representative open-source and proprietary image editing models, we find that proprietary models exhibit early-stage visual instruction-following capabilities and consistently outperform open-source models. However, performance degrades markedly with increasing task difficulty even for the strongest systems, highlighting promising directions for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hwanyu112/VIBE-Benchmark", "github_api_added_by": "auto" }, { "date": "2026-02-03", "arxiv_id": "2602.01538", "title": "Making Avatars Interact: Towards Text-Driven Human-Object Interaction for Controllable Talking Avatars", "authors": [ "Youliang Zhang", "Zhengguang Zhou", "Zhentao Yu", "Ziyao Huang", "Teng Hu", "Sen Liang", "Guozhen Zhang", "Ziqiao Peng", "Shunkai Li", "Yi Chen", "Zixiang Zhou", "Yuan Zhou", "Qinglin Lu", "Xiu Li" ], "github": "https://github.com/angzong/InteractAvatar", "project_page": "", "abstract": "Generating talking avatars is a fundamental task in video generation. Although existing methods can generate full-body talking avatars with simple human motion, extending this task to grounded human-object interaction (GHOI) remains an open challenge, requiring the avatar to perform text-aligned interactions with surrounding objects. This challenge stems from the need for environmental perception and the control-quality dilemma in GHOI generation. To address this, we propose a novel dual-stream framework, InteractAvatar, which decouples perception and planning from video synthesis for grounded human-object interaction. Leveraging detection to enhance environmental perception, we introduce a Perception and Interaction Module (PIM) to generate text-aligned interaction motions. Additionally, an Audio-Interaction Aware Generation Module (AIM) is proposed to synthesize vivid talking avatars performing object interactions. With a specially designed motion-to-video aligner, PIM and AIM share a similar network structure and enable parallel co-generation of motions and plausible videos, effectively mitigating the control-quality dilemma. Finally, we establish a benchmark, GroundedInter, for evaluating GHOI video generation. Extensive experiments and comparisons demonstrate the effectiveness of our method in generating grounded human-object interactions for talking avatars. Project page: https://interactavatar.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/angzong/InteractAvatar", "github_api_added_by": "auto" }, { "date": "2026-02-03", "arxiv_id": "2602.01335", "title": "Beyond Pixels: Visual Metaphor Transfer via Schema-Driven Agentic Reasoning", "authors": [ "Yu Xu", "Yuxin Zhang", "Juan Cao", "Lin Gao", "Chunyu Wang", "Oliver Deussen", "Tong-Yee Lee", "Fan Tang" ], "github": "", "project_page": "", "abstract": "A visual metaphor constitutes a high-order form of human creativity, employing cross-domain semantic fusion to transform abstract concepts into impactful visual rhetoric. Despite the remarkable progress of generative AI, existing models remain largely confined to pixel-level instruction alignment and surface-level appearance preservation, failing to capture the underlying abstract logic necessary for genuine metaphorical generation. To bridge this gap, we introduce the task of Visual Metaphor Transfer (VMT), which challenges models to autonomously decouple the \"creative essence\" from a reference image and re-materialize that abstract logic onto a user-specified target subject. We propose a cognitive-inspired, multi-agent framework that operationalizes Conceptual Blending Theory (CBT) through a novel Schema Grammar (\"G\"). This structured representation decouples relational invariants from specific visual entities, providing a rigorous foundation for cross-domain logic re-instantiation. Our pipeline executes VMT through a collaborative system of specialized agents: a perception agent that distills the reference into a schema, a transfer agent that maintains generic space invariance to discover apt carriers, a generation agent for high-fidelity synthesis and a hierarchical diagnostic agent that mimics a professional critic, performing closed-loop backtracking to identify and rectify errors across abstract logic, component selection, and prompt encoding. Extensive experiments and human evaluations demonstrate that our method significantly outperforms SOTA baselines in metaphor consistency, analogy appropriateness, and visual creativity, paving the way for automated high-impact creative applications in advertising and media. Source code will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01382", "title": "PromptRL: Prompt Matters in RL for Flow-Based Image Generation", "authors": [ "Fu-Yun Wang", "Han Zhang", "Michael Gharbi", "Hongsheng Li", "Taesung Park" ], "github": "https://github.com/G-U-N/UniRL", "project_page": "", "abstract": "Flow matching models (FMs) have revolutionized text-to-image (T2I) generation, with reinforcement learning (RL) serving as a critical post-training strategy for alignment with reward objectives. In this research, we show that current RL pipelines for FMs suffer from two underappreciated yet important limitations: sample inefficiency due to insufficient generation diversity, and pronounced prompt overfitting, where models memorize specific training formulations and exhibit dramatic performance collapse when evaluated on semantically equivalent but stylistically varied prompts. We present PromptRL (Prompt Matters in RL for Flow-Based Image Generation), a framework that incorporates language models (LMs) as trainable prompt refinement agents directly within the flow-based RL optimization loop. This design yields two complementary benefits: rapid development of sophisticated prompt rewriting capabilities and, critically, a synergistic training regime that reshapes the optimization dynamics. PromptRL achieves state-of-the-art performance across multiple benchmarks, obtaining scores of 0.97 on GenEval, 0.98 on OCR accuracy, and 24.05 on PickScore. Furthermore, we validate the effectiveness of our RL approach on large-scale image editing models, improving the EditReward of FLUX.1-Kontext from 1.19 to 1.43 with only 0.06 million rollouts, surpassing Gemini 2.5 Flash Image (also known as Nano Banana), which scores 1.37, and achieving comparable performance with ReasonNet (1.44), which relied on fine-grained data annotations along with a complex multi-stage training. Our extensive experiments empirically demonstrate that PromptRL consistently achieves higher performance ceilings while requiring over 2times fewer rollouts compared to naive flow-only RL. Our code is available at https://github.com/G-U-N/UniRL.", "github_manual": "", "github_regex": "https://github.com/G-U-N/UniRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.22588", "title": "Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry", "authors": [ "Zhuochun Li", "Yong Zhang", "Ming Li", "Yuelyu Ji", "Yiming Zeng", "Ning Cheng", "Yun Zhu", "Yanmeng Wang", "Shaojun Wang", "Jing Xiao", "Daqing He" ], "github": "https://github.com/zhuochunli/Representation-as-a-judge", "project_page": "", "abstract": "Large language models (LLMs) are widely used as reference-free evaluators via prompting, but this \"LLM-as-a-Judge\" paradigm is costly, opaque, and sensitive to prompt design. In this work, we investigate whether smaller models can serve as efficient evaluators by leveraging internal representations instead of surface generation. We uncover a consistent empirical pattern: small LMs, despite with weak generative ability, encode rich evaluative signals in their hidden states. This motivates us to propose the Semantic Capacity Asymmetry Hypothesis: evaluation requires significantly less semantic capacity than generation and can be grounded in intermediate representations, suggesting that evaluation does not necessarily need to rely on large-scale generative models but can instead leverage latent features from smaller ones. Our findings motivate a paradigm shift from LLM-as-a-Judge to Representation-as-a-Judge, a decoding-free evaluation strategy that probes internal model structure rather than relying on prompted output. We instantiate this paradigm through INSPECTOR, a probing-based framework that predicts aspect-level evaluation scores from small model representations. Experiments on reasoning benchmarks (GSM8K, MATH, GPQA) show that INSPECTOR substantially outperforms prompting-based small LMs and closely approximates full LLM judges, while offering a more efficient, reliable, and interpretable alternative for scalable evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhuochunli/Representation-as-a-judge", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01296", "title": "Interacted Planes Reveal 3D Line Mapping", "authors": [ "Zeran Ke", "Bin Tan", "Gui-Song Xia", "Yujun Shen", "Nan Xue" ], "github": "https://github.com/calmke/LiPMAP", "project_page": "", "abstract": "3D line mapping from multi-view RGB images provides a compact and structured visual representation of scenes. We study the problem from a physical and topological perspective: a 3D line most naturally emerges as the edge of a finite 3D planar patch. We present LiP-Map, a line-plane joint optimization framework that explicitly models learnable line and planar primitives. This coupling enables accurate and detailed 3D line mapping while maintaining strong efficiency (typically completing a reconstruction in 3 to 5 minutes per scene). LiP-Map pioneers the integration of planar topology into 3D line mapping, not by imposing pairwise coplanarity constraints but by explicitly constructing interactions between plane and line primitives, thus offering a principled route toward structured reconstruction in man-made environments. On more than 100 scenes from ScanNetV2, ScanNet++, Hypersim, 7Scenes, and Tanks\\&Temple, LiP-Map improves both accuracy and completeness over state-of-the-art methods. Beyond line mapping quality, LiP-Map significantly advances line-assisted visual localization, establishing strong performance on 7Scenes. Our code is released at https://github.com/calmke/LiPMAP for reproducible research.", "github_manual": "", "github_regex": "https://github.com/calmke/LiPMAP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02276", "title": "Kimi K2.5: Visual Agentic Intelligence", "authors": [ "Kimi Team", "Tongtong Bai", "Yifan Bai", "Yiping Bao", "S. H. Cai", "Yuan Cao", "Y. Charles", "H. S. Che", "Cheng Chen", "Guanduo Chen", "Huarong Chen", "Jia Chen", "Jiahao Chen", "Jianlong Chen", "Jun Chen", "Kefan Chen", "Liang Chen", "Ruijue Chen", "Xinhao Chen", "Yanru Chen", "Yanxu Chen", "Yicun Chen", "Yimin Chen", "Yingjiang Chen", "Yuankun Chen", "Yujie Chen", "Yutian Chen", "Zhirong Chen", "Ziwei Chen", "Dazhi Cheng", "Minghan Chu", "Jialei Cui", "Jiaqi Deng", "Muxi Diao", "Hao Ding", "Mengfan Dong", "Mengnan Dong", "Yuxin Dong", "Yuhao Dong", "Angang Du", "Chenzhuang Du", "Dikang Du", "Lingxiao Du", "Yulun Du", "Yu Fan", "Shengjun Fang", "Qiulin Feng", "Yichen Feng", "Garimugai Fu", "Kelin Fu", "Hongcheng Gao", "Tong Gao", "Yuyao Ge", "Shangyi Geng", "Chengyang Gong", "Xiaochen Gong", "Zhuoma Gongque", "Qizheng Gu", "Xinran Gu", "Yicheng Gu", "Longyu Guan", "Yuanying Guo", "Xiaoru Hao", "Weiran He", "Wenyang He", "Yunjia He", "Chao Hong", "Hao Hu", "Jiaxi Hu", "Yangyang Hu", "Zhenxing Hu", "Ke Huang", "Ruiyuan Huang", "Weixiao Huang", "Zhiqi Huang", "Tao Jiang", "Zhejun Jiang", "Xinyi Jin", "Yu Jing", "Guokun Lai", "Aidi Li", "C. Li", "Cheng Li", "Fang Li", "Guanghe Li", "Guanyu Li", "Haitao Li", "Haoyang Li", "Jia Li", "Jingwei Li", "Junxiong Li", "Lincan Li", "Mo Li", "Weihong Li", "Wentao Li", "Xinhang Li", "Xinhao Li", "Yang Li", "Yanhao Li", "Yiwei Li", "Yuxiao Li", "Zhaowei Li", "Zheming Li", "Weilong Liao", "Jiawei Lin", "Xiaohan Lin", "Zhishan Lin", "Zichao Lin", "Cheng Liu", "Chenyu Liu", "Hongzhang Liu", "Liang Liu", "Shaowei Liu", "Shudong Liu", "Shuran Liu", "Tianwei Liu", "Tianyu Liu", "Weizhou Liu", "Xiangyan Liu", "Yangyang Liu", "Yanming Liu", "Yibo Liu", "Yuanxin Liu", "Yue Liu", "Zhengying Liu", "Zhongnuo Liu", "Enzhe Lu", "Haoyu Lu", "Zhiyuan Lu", "Junyu Luo", "Tongxu Luo", "Yashuo Luo", "Long Ma", "Yingwei Ma", "Shaoguang Mao", "Yuan Mei", "Xin Men", "Fanqing Meng", "Zhiyong Meng", "Yibo Miao", "Minqing Ni", "Kun Ouyang", "Siyuan Pan", "Bo Pang", "Yuchao Qian", "Ruoyu Qin", "Zeyu Qin", "Jiezhong Qiu", "Bowen Qu", "Zeyu Shang", "Youbo Shao", "Tianxiao Shen", "Zhennan Shen", "Juanfeng Shi", "Lidong Shi", "Shengyuan Shi", "Feifan Song", "Pengwei Song", "Tianhui Song", "Xiaoxi Song", "Hongjin Su", "Jianlin Su", "Zhaochen Su", "Lin Sui", "Jinsong Sun", "Junyao Sun", "Tongyu Sun", "Flood Sung", "Yunpeng Tai", "Chuning Tang", "Heyi Tang", "Xiaojuan Tang", "Zhengyang Tang", "Jiawen Tao", "Shiyuan Teng", "Chaoran Tian", "Pengfei Tian", "Ao Wang", "Bowen Wang", "Chensi Wang", "Chuang Wang", "Congcong Wang", "Dingkun Wang", "Dinglu Wang", "Dongliang Wang", "Feng Wang", "Hailong Wang", "Haiming Wang", "Hengzhi Wang", "Huaqing Wang", "Hui Wang", "Jiahao Wang", "Jinhong Wang", "Jiuzheng Wang", "Kaixin Wang", "Linian Wang", "Qibin Wang", "Shengjie Wang", "Shuyi Wang", "Si Wang", "Wei Wang", "Xiaochen Wang", "Xinyuan Wang", "Yao Wang", "Yejie Wang", "Yipu Wang", "Yiqin Wang", "Yucheng Wang", "Yuzhi Wang", "Zhaoji Wang", "Zhaowei Wang", "Zhengtao Wang", "Zhexu Wang", "Zihan Wang", "Zizhe Wang", "Chu Wei", "Ming Wei", "Chuan Wen", "Zichen Wen", "Chengjie Wu", "Haoning Wu", "Junyan Wu", "Rucong Wu", "Wenhao Wu", "Yuefeng Wu", "Yuhao Wu", "Yuxin Wu", "Zijian Wu", "Chenjun Xiao", "Jin Xie", "Xiaotong Xie", "Yuchong Xie", "Yifei Xin", "Bowei Xing", "Boyu Xu", "Jianfan Xu", "Jing Xu", "Jinjing Xu", "L. H. Xu", "Lin Xu", "Suting Xu", "Weixin Xu", "Xinbo Xu", "Xinran Xu", "Yangchuan Xu", "Yichang Xu", "Yuemeng Xu", "Zelai Xu", "Ziyao Xu", "Junjie Yan", "Yuzi Yan", "Guangyao Yang", "Hao Yang", "Junwei Yang", "Kai Yang", "Ningyuan Yang", "Ruihan Yang", "Xiaofei Yang", "Xinlong Yang", "Ying Yang", "Yi Yang", "Yi Yang", "Zhen Yang", "Zhilin Yang", "Zonghan Yang", "Haotian Yao", "Dan Ye", "Wenjie Ye", "Zhuorui Ye", "Bohong Yin", "Chengzhen Yu", "Longhui Yu", "Tao Yu", "Tianxiang Yu", "Enming Yuan", "Mengjie Yuan", "Xiaokun Yuan", "Yang Yue", "Weihao Zeng", "Dunyuan Zha", "Haobing Zhan", "Dehao Zhang", "Hao Zhang", "Jin Zhang", "Puqi Zhang", "Qiao Zhang", "Rui Zhang", "Xiaobin Zhang", "Y. Zhang", "Yadong Zhang", "Yangkun Zhang", "Yichi Zhang", "Yizhi Zhang", "Yongting Zhang", "Yu Zhang", "Yushun Zhang", "Yutao Zhang", "Yutong Zhang", "Zheng Zhang", "Chenguang Zhao", "Feifan Zhao", "Jinxiang Zhao", "Shuai Zhao", "Xiangyu Zhao", "Yikai Zhao", "Zijia Zhao", "Huabin Zheng", "Ruihan Zheng", "Shaojie Zheng", "Tengyang Zheng", "Junfeng Zhong", "Longguang Zhong", "Weiming Zhong", "M. Zhou", "Runjie Zhou", "Xinyu Zhou", "Zaida Zhou", "Jinguo Zhu", "Liya Zhu", "Xinhao Zhu", "Yuxuan Zhu", "Zhen Zhu", "Jingze Zhuang", "Weiyu Zhuang", "Ying Zou", "Xinxing Zu" ], "github": "https://github.com/MoonshotAI/WorldVQA", "project_page": "", "abstract": "We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that two modalities enhance each other. This includes a series of techniques such as joint text-vision pre-training, zero-vision SFT, and joint text-vision reinforcement learning. Building on this multimodal foundation, K2.5 introduces Agent Swarm, a self-directed parallel agent orchestration framework that dynamically decomposes complex tasks into heterogeneous sub-problems and executes them concurrently. Extensive evaluations show that Kimi K2.5 achieves state-of-the-art results across various domains including coding, vision, reasoning, and agentic tasks. Agent Swarm also reduces latency by up to 4.5times over single-agent baselines. We release the post-trained Kimi K2.5 model checkpoint to facilitate future research and real-world applications of agentic intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MoonshotAI/WorldVQA", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02185", "title": "Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models", "authors": [ "Yu Zeng", "Wenxuan Huang", "Zhen Fang", "Shuang Chen", "Yufan Shen", "Yishuo Cai", "Xiaoman Wang", "Zhenfei Yin", "Lin Chen", "Zehui Chen", "Shiting Huang", "Yiming Zhao", "Yao Hu", "Philip Torr", "Wanli Ouyang", "Shaosheng Cao" ], "github": "https://github.com/Osilly/Vision-DeepResearch", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have advanced VQA and now support Vision-DeepResearch systems that use search engines for complex visual-textual fact-finding. However, evaluating these visual and textual search abilities is still difficult, and existing benchmarks have two major limitations. First, existing benchmarks are not visual search-centric: answers that should require visual search are often leaked through cross-textual cues in the text questions or can be inferred from the prior world knowledge in current MLLMs. Second, overly idealized evaluation scenario: On the image-search side, the required information can often be obtained via near-exact matching against the full image, while the text-search side is overly direct and insufficiently challenging. To address these issues, we construct the Vision-DeepResearch benchmark (VDR-Bench) comprising 2,000 VQA instances. All questions are created via a careful, multi-stage curation pipeline and rigorous expert review, designed to assess the behavior of Vision-DeepResearch systems under realistic real-world conditions. Moreover, to address the insufficient visual retrieval capabilities of current MLLMs, we propose a simple multi-round cropped-search workflow. This strategy is shown to effectively improve model performance in realistic visual retrieval scenarios. Overall, our results provide practical guidance for the design of future multimodal deep-research systems. The code will be released in https://github.com/Osilly/Vision-DeepResearch.", "github_manual": "", "github_regex": "https://github.com/Osilly/Vision-DeepResearch", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01842", "title": "Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models", "authors": [ "Jinbin Bai", "Yixuan Li", "Yuchen Zhu", "Yi Xin", "Qingyu Shi", "Aosong Feng", "Xiaohong Liu", "Molei Tao", "Jianru Xue", "Xiangtai Li", "Ming-Hsuan Yang" ], "github": "https://github.com/viiika/Prism", "project_page": "", "abstract": "Inference-time compute has re-emerged as a practical way to improve LLM reasoning. Most test-time scaling (TTS) algorithms rely on autoregressive decoding, which is ill-suited to discrete diffusion language models (dLLMs) due to their parallel decoding over the entire sequence. As a result, developing effective and efficient TTS methods to unlock dLLMs' full generative potential remains an underexplored challenge. To address this, we propose Prism (Pruning, Remasking, and Integrated Self-verification Method), an efficient TTS framework for dLLMs that (i) performs Hierarchical Trajectory Search (HTS) which dynamically prunes and reallocates compute in an early-to-mid denoising window, (ii) introduces Local branching with partial remasking to explore diverse implementations while preserving high-confidence tokens, and (iii) replaces external verifiers with Self-Verified Feedback (SVF) obtained via self-evaluation prompts on intermediate completions. Across four mathematical reasoning and code generation benchmarks on three dLLMs, including LLaDA 8B Instruct, Dream 7B Instruct, and LLaDA 2.0-mini, our Prism achieves a favorable performance-efficiency trade-off, matching best-of-N performance with substantially fewer function evaluations (NFE). The code is released at https://github.com/viiika/Prism.", "github_manual": "", "github_regex": "https://github.com/viiika/Prism", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01756", "title": "Mind-Brush: Integrating Agentic Cognitive Search and Reasoning into Image Generation", "authors": [ "Jun He", "Junyan Ye", "Zilong Huang", "Dongzhi Jiang", "Chenjue Zhang", "Leqi Zhu", "Renrui Zhang", "Xiang Zhang", "Weijia Li" ], "github": "", "project_page": "", "abstract": "While text-to-image generation has achieved unprecedented fidelity, the vast majority of existing models function fundamentally as static text-to-pixel decoders. Consequently, they often fail to grasp implicit user intentions. Although emerging unified understanding-generation models have improved intent comprehension, they still struggle to accomplish tasks involving complex knowledge reasoning within a single model. Moreover, constrained by static internal priors, these models remain unable to adapt to the evolving dynamics of the real world. To bridge these gaps, we introduce Mind-Brush, a unified agentic framework that transforms generation into a dynamic, knowledge-driven workflow. Simulating a human-like 'think-research-create' paradigm, Mind-Brush actively retrieves multimodal evidence to ground out-of-distribution concepts and employs reasoning tools to resolve implicit visual constraints. To rigorously evaluate these capabilities, we propose Mind-Bench, a comprehensive benchmark comprising 500 distinct samples spanning real-time news, emerging concepts, and domains such as mathematical and Geo-Reasoning. Extensive experiments demonstrate that Mind-Brush significantly enhances the capabilities of unified models, realizing a zero-to-one capability leap for the Qwen-Image baseline on Mind-Bench, while achieving superior results on established benchmarks like WISE and RISE.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.22060", "title": "Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models", "authors": [ "Wenxuan Huang", "Yu Zeng", "Qiuchen Wang", "Zhen Fang", "Shaosheng Cao", "Zheng Chu", "Qingyu Yin", "Shuang Chen", "Zhenfei Yin", "Lin Chen", "Zehui Chen", "Yao Hu", "Philip Torr", "Feng Zhao", "Wanli Ouyang" ], "github": "https://github.com/Osilly/Vision-DeepResearch", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have achieved remarkable success across a broad range of vision tasks. However, constrained by the capacity of their internal world knowledge, prior work has proposed augmenting MLLMs by ``reasoning-then-tool-call'' for visual and textual search engines to obtain substantial gains on tasks requiring extensive factual information. However, these approaches typically define multimodal search in a naive setting, assuming that a single full-level or entity-level image query and few text query suffices to retrieve the key evidence needed to answer the question, which is unrealistic in real-world scenarios with substantial visual noise. Moreover, they are often limited in the reasoning depth and search breadth, making it difficult to solve complex questions that require aggregating evidence from diverse visual and textual sources. Building on this, we propose Vision-DeepResearch, which proposes one new multimodal deep-research paradigm, i.e., performs multi-turn, multi-entity and multi-scale visual and textual search to robustly hit real-world search engines under heavy noise. Our Vision-DeepResearch supports dozens of reasoning steps and hundreds of engine interactions, while internalizing deep-research capabilities into the MLLM via cold-start supervision and RL training, resulting in a strong end-to-end multimodal deep-research MLLM. It substantially outperforming existing multimodal deep-research MLLMs, and workflows built on strong closed-source foundation model such as GPT-5, Gemini-2.5-pro and Claude-4-Sonnet. The code will be released in https://github.com/Osilly/Vision-DeepResearch.", "github_manual": "", "github_regex": "https://github.com/Osilly/Vision-DeepResearch", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.00130", "title": "On the Relationship Between Representation Geometry and Generalization in Deep Neural Networks", "authors": [ "Sumit Yadav" ], "github": "", "project_page": "", "abstract": "We investigate the relationship between representation geometry and neural network performance. Analyzing 52 pretrained ImageNet models across 13 architecture families, we show that effective dimension -- an unsupervised geometric metric -- strongly predicts accuracy. Output effective dimension achieves partial r=0.75 (p < 10^(-10)) after controlling for model capacity, while total compression achieves partial r=-0.72. These findings replicate across ImageNet and CIFAR-10, and generalize to NLP: effective dimension predicts performance for 8 encoder models on SST-2/MNLI and 15 decoder-only LLMs on AG News (r=0.69, p=0.004), while model size does not (r=0.07). We establish bidirectional causality: degrading geometry via noise causes accuracy loss (r=-0.94, p < 10^(-9)), while improving geometry via PCA maintains accuracy across architectures (-0.03pp at 95% variance). This relationship is noise-type agnostic -- Gaussian, Uniform, Dropout, and Salt-and-pepper noise all show |r| > 0.90. These results establish that effective dimension provides domain-agnostic predictive and causal information about neural network performance, computed entirely without labels.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01618", "title": "SEA-Guard: Culturally Grounded Multilingual Safeguard for Southeast Asia", "authors": [ "Panuthep Tasawong", "Jian Gang Ngui", "Alham Fikri Aji", "Trevor Cohn", "Peerat Limkonchotiwat" ], "github": "", "project_page": "", "abstract": "Culturally aware safeguards are crucial for AI alignment in real-world settings, where safety extends beyond common sense and encompasses diverse local values, norms, and region-specific regulations. However, building large-scale, culturally grounded datasets is challenging due to limited resources and a scarcity of native annotators. Consequently, many safeguard models rely on machine translation of English datasets, often missing regional and cultural nuances. We present a novel agentic data-generation framework to scalably create authentic, region-specific safety datasets for Southeast Asia (SEA). On this foundation, we introduce the SEA-Guard family, the first multilingual safeguard models grounded in SEA cultural contexts. Evaluated across multiple benchmarks and cultural variants, SEA-Guard consistently outperforms existing safeguards at detecting regionally sensitive or harmful content while maintaining strong general safety performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02227", "title": "Show, Don't Tell: Morphing Latent Reasoning into Image Generation", "authors": [ "Harold Haodong Chen", "Xinxiang Yin", "Wen-Jie Shu", "Hongfei Zhang", "Zixin Zhang", "Chenfei Liao", "Litao Guo", "Qifeng Chen", "Ying-Cong Chen" ], "github": "https://github.com/EnVision-Research/LatentMorph", "project_page": "", "abstract": "Text-to-image (T2I) generation has achieved remarkable progress, yet existing methods often lack the ability to dynamically reason and refine during generation--a hallmark of human creativity. Current reasoning-augmented paradigms most rely on explicit thought processes, where intermediate reasoning is decoded into discrete text at fixed steps with frequent image decoding and re-encoding, leading to inefficiencies, information loss, and cognitive mismatches. To bridge this gap, we introduce LatentMorph, a novel framework that seamlessly integrates implicit latent reasoning into the T2I generation process. At its core, LatentMorph introduces four lightweight components: (i) a condenser for summarizing intermediate generation states into compact visual memory, (ii) a translator for converting latent thoughts into actionable guidance, (iii) a shaper for dynamically steering next image token predictions, and (iv) an RL-trained invoker for adaptively determining when to invoke reasoning. By performing reasoning entirely in continuous latent spaces, LatentMorph avoids the bottlenecks of explicit reasoning and enables more adaptive self-refinement. Extensive experiments demonstrate that LatentMorph (I) enhances the base model Janus-Pro by 16% on GenEval and 25% on T2I-CompBench; (II) outperforms explicit paradigms (e.g., TwiG) by 15% and 11% on abstract reasoning tasks like WISE and IPV-Txt, (III) while reducing inference time by 44% and token consumption by 51%; and (IV) exhibits 71% cognitive alignment with human intuition on reasoning invocation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EnVision-Research/LatentMorph", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02214", "title": "Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation", "authors": [ "Hongzhou Zhu", "Min Zhao", "Guande He", "Hang Su", "Chongxuan Li", "Jun Zhu" ], "github": "", "project_page": "", "abstract": "To achieve real-time interactive video generation, current methods distill pretrained bidirectional video diffusion models into few-step autoregressive (AR) models, facing an architectural gap when full attention is replaced by causal attention. However, existing approaches do not bridge this gap theoretically. They initialize the AR student via ODE distillation, which requires frame-level injectivity, where each noisy frame must map to a unique clean frame under the PF-ODE of an AR teacher. Distilling an AR student from a bidirectional teacher violates this condition, preventing recovery of the teacher's flow map and instead inducing a conditional-expectation solution, which degrades performance. To address this issue, we propose Causal Forcing that uses an AR teacher for ODE initialization, thereby bridging the architectural gap. Empirical results show that our method outperforms all baselines across all metrics, surpassing the SOTA Self Forcing by 19.3\\% in Dynamic Degree, 8.7\\% in VisionReward, and 16.7\\% in Instruction Following. Project page and the code: https://thu-ml.github.io/CausalForcing.github.io/{https://thu-ml.github.io/CausalForcing.github.io/}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02493", "title": "PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss", "authors": [ "Zehong Ma", "Ruihan Xu", "Shiliang Zhang" ], "github": "https://github.com/Zehong-Ma/PixelGen", "project_page": "", "abstract": "Pixel diffusion generates images directly in pixel space in an end-to-end manner, avoiding the artifacts and bottlenecks introduced by VAEs in two-stage latent diffusion. However, it is challenging to optimize high-dimensional pixel manifolds that contain many perceptually irrelevant signals, leaving existing pixel diffusion methods lagging behind latent diffusion models. We propose PixelGen, a simple pixel diffusion framework with perceptual supervision. Instead of modeling the full image manifold, PixelGen introduces two complementary perceptual losses to guide diffusion model towards learning a more meaningful perceptual manifold. An LPIPS loss facilitates learning better local patterns, while a DINO-based perceptual loss strengthens global semantics. With perceptual supervision, PixelGen surpasses strong latent diffusion baselines. It achieves an FID of 5.11 on ImageNet-256 without classifier-free guidance using only 80 training epochs, and demonstrates favorable scaling performance on large-scale text-to-image generation with a GenEval score of 0.79. PixelGen requires no VAEs, no latent representations, and no auxiliary stages, providing a simpler yet more powerful generative paradigm. Codes are publicly available at https://github.com/Zehong-Ma/PixelGen.", "github_manual": "", "github_regex": "https://github.com/Zehong-Ma/PixelGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02486", "title": "RE-TRAC: REcursive TRAjectory Compression for Deep Search Agents", "authors": [ "Jialiang Zhu", "Gongrui Zhang", "Xiaolong Ma", "Lin Xu", "Miaosen Zhang", "Ruiqi Yang", "Song Wang", "Kai Qiu", "Zhirong Wu", "Qi Dai", "Ruichun Ma", "Bei Liu", "Yifan Yang", "Chong Luo", "Zhengyuan Yang", "Linjie Li", "Lijuan Wang", "Weizhu Chen", "Xin Geng", "Baining Guo" ], "github": "https://github.com/microsoft/InfoAgent", "project_page": "", "abstract": "LLM-based deep research agents are largely built on the ReAct framework. This linear design makes it difficult to revisit earlier states, branch into alternative search directions, or maintain global awareness under long contexts, often leading to local optima, redundant exploration, and inefficient search. We propose Re-TRAC, an agentic framework that performs cross-trajectory exploration by generating a structured state representation after each trajectory to summarize evidence, uncertainties, failures, and future plans, and conditioning subsequent trajectories on this state representation. This enables iterative reflection and globally informed planning, reframing research as a progressive process. Empirical results show that Re-TRAC consistently outperforms ReAct by 15-20% on BrowseComp with frontier LLMs. For smaller models, we introduce Re-TRAC-aware supervised fine-tuning, achieving state-of-the-art performance at comparable scales. Notably, Re-TRAC shows a monotonic reduction in tool calls and token usage across rounds, indicating progressively targeted exploration driven by cross-trajectory reflection rather than redundant search.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/InfoAgent", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01058", "title": "Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning", "authors": [ "Dylan Zhang", "Yufeng Xu", "Haojin Wang", "Qingzhi Chen", "Hao Peng" ], "github": "", "project_page": "", "abstract": "Post-training of reasoning LLMs is a holistic process that typically consists of an offline SFT stage followed by an online reinforcement learning (RL) stage. However, SFT is often optimized in isolation to maximize SFT performance alone. We show that, after identical RL training, models initialized from stronger SFT checkpoints can significantly underperform those initialized from weaker ones. We attribute this to a mismatch typical in current SFT-RL pipelines: the distribution that generates the offline SFT data can differ substantially from the policy optimized during online RL, which learns from its own rollouts. We propose PEAR (Policy Evaluation-inspired Algorithm for Offline Learning Loss Re-weighting), an SFT-stage method that corrects this mismatch and better prepares the model for RL. PEAR uses importance sampling to reweight the SFT loss, with three variants operating at the token, block, and sequence levels. It can be used to augment standard SFT objectives and incurs little additional training overhead once probabilities for the offline data are collected. We conduct controlled experiments on verifiable reasoning games and mathematical reasoning tasks on Qwen 2.5 and 3 and DeepSeek-distilled models. PEAR consistently improves post-RL performance over canonical SFT, with pass at 8 gains up to a 14.6 percent on AIME2025. Our results suggest that PEAR is an effective step toward more holistic LLM post-training by designing and evaluating SFT with downstream RL in mind rather than in isolation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02361", "title": "SWE-Universe: Scale Real-World Verifiable Environments to Millions", "authors": [ "Mouxiang Chen", "Lei Zhang", "Yunlong Feng", "Xuwu Wang", "Wenting Zhao", "Ruisheng Cao", "Jiaxi Yang", "Jiawei Chen", "Mingze Li", "Zeyao Ma", "Hao Ge", "Zongmeng Zhang", "Zeyu Cui", "Dayiheng Liu", "Jingren Zhou", "Jianling Sun", "Junyang Lin", "Binyuan Hui" ], "github": "", "project_page": "", "abstract": "We propose SWE-Universe, a scalable and efficient framework for automatically constructing real-world software engineering (SWE) verifiable environments from GitHub pull requests (PRs). To overcome the prevalent challenges of automatic building, such as low production yield, weak verifiers, and prohibitive cost, our framework utilizes a building agent powered by an efficient custom-trained model. This agent employs iterative self-verification and in-loop hacking detection to ensure the reliable generation of high-fidelity, verifiable tasks. Using this method, we scale the number of real-world multilingual SWE environments to a million scale (807,693). We demonstrate the profound value of our environments through large-scale agentic mid-training and reinforcement learning. Finally, we applied this technique to Qwen3-Max-Thinking and achieved a score of 75.3% on SWE-Bench Verified. Our work provides both a critical resource and a robust methodology to advance the next generation of coding agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02156", "title": "LoopViT: Scaling Visual ARC with Looped Transformers", "authors": [ "Wen-Jie Shu", "Xuerui Qiu", "Rui-Jie Zhu", "Harold Haodong Chen", "Yexin Liu", "Harry Yang" ], "github": "https://github.com/WenjieShu/LoopViT", "project_page": "", "abstract": "Recent advances in visual reasoning have leveraged vision transformers to tackle the ARC-AGI benchmark. However, we argue that the feed-forward architecture, where computational depth is strictly bound to parameter size, falls short of capturing the iterative, algorithmic nature of human induction. In this work, we propose a recursive architecture called Loop-ViT, which decouples reasoning depth from model capacity through weight-tied recurrence. Loop-ViT iterates a weight-tied Hybrid Block, combining local convolutions and global attention, to form a latent chain of thought. Crucially, we introduce a parameter-free Dynamic Exit mechanism based on predictive entropy: the model halts inference when its internal state ``crystallizes\" into a low-uncertainty attractor. Empirical results on the ARC-AGI-1 benchmark validate this perspective: our 18M model achieves 65.8% accuracy, outperforming massive 73M-parameter ensembles. These findings demonstrate that adaptive iterative computation offers a far more efficient scaling axis for visual reasoning than simply increasing network width. The code is available at https://github.com/WenjieShu/LoopViT.", "github_manual": "", "github_regex": "https://github.com/WenjieShu/LoopViT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01511", "title": "Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training", "authors": [ "Ran Xu", "Tianci Liu", "Zihan Dong", "Tony You", "Ilgee Hong", "Carl Yang", "Linjun Zhang", "Tao Zhao", "Haoyu Wang" ], "github": "", "project_page": "", "abstract": "Standard reward models typically predict scalar scores that fail to capture the multifaceted nature of response quality in non-verifiable domains, such as creative writing or open-ended instruction following. To address this limitation, we propose Rubric-ARM, a framework that jointly optimizes a rubric generator and a judge using reinforcement learning from preference feedback. Unlike existing methods that rely on static rubrics or disjoint training pipelines, our approach treats rubric generation as a latent action learned to maximize judgment accuracy. We introduce an alternating optimization strategy to mitigate the non-stationarity of simultaneous updates, providing theoretical analysis that demonstrates how this schedule reduces gradient variance during training. Extensive experiments show that Rubric-ARM achieves state-of-the-art performance among baselines on multiple benchmarks and significantly improves downstream policy alignment in both offline and online reinforcement learning settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.00919", "title": "Green-VLA: Staged Vision-Language-Action Model for Generalist Robots", "authors": [ "I. Apanasevich", "M. Artemyev", "R. Babakyan", "P. Fedotova", "D. Grankin", "E. Kupryashin", "A. Misailidi", "D. Nerus", "A. Nutalapati", "G. Sidorov", "I. Efremov", "M. Gerasyov", "D. Pikurov", "Y. Senchenko", "S. Davidenko", "D. Kulikov", "M. Sultankin", "K. Askarbek", "O. Shamanin", "D. Statovoy", "E. Zalyaev", "I. Zorin", "A. Letkin", "E. Rusakov", "A. Silchenko", "V. Vorobyov", "S. Sobolnikov", "A. Postnikov" ], "github": "", "project_page": "", "abstract": "We introduce Green-VLA, a staged Vision-Language-Action (VLA) framework for real-world deployment on the Green humanoid robot while maintaining generalization across diverse embodiments. Green-VLA follows a five stage curriculum: (L0) foundational VLMs, (L1) multimodal grounding, (R0) multi-embodiment pretraining, (R1) embodiment-specific adaptation, and (R2) reinforcement-learning (RL) policy alignment. We couple a scalable data-processing pipeline (3,000 hours of demonstrations) with temporal alignment and quality filtering, and use a unified, embodiment-aware action interface enabling a single policy to control humanoids, mobile manipulators, and fixed-base arms. At inference, the VLA controller is enhanced with episode-progress prediction, out-of-distribution detection, and joint-prediction-based guidance to improve safety and precise target selection. Experiments on Simpler BRIDGE WidowX and CALVIN ABC-D, as well as real-robot evaluations, show strong generalization and performance gains from RL alignment in success rate, robustness, and long-horizon efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02437", "title": "UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing", "authors": [ "Dianyi Wang", "Chaofan Ma", "Feng Han", "Size Wu", "Wei Song", "Yibin Wang", "Zhixiong Zhang", "Tianhang Wang", "Siyuan Wang", "Zhongyu Wei", "Jiaqi Wang" ], "github": "https://github.com/AlenjandroWang/UniReason", "project_page": "", "abstract": "Unified multimodal models often struggle with complex synthesis tasks that demand deep reasoning, and typically treat text-to-image generation and image editing as isolated capabilities rather than interconnected reasoning steps. To address this, we propose UniReason, a unified framework that harmonizes these two tasks through a dual reasoning paradigm. We formulate generation as world knowledge-enhanced planning to inject implicit constraints, and leverage editing capabilities for fine-grained visual refinement to further correct visual errors via self-reflection. This approach unifies generation and editing within a shared representation, mirroring the human cognitive process of planning followed by refinement. We support this framework by systematically constructing a large-scale reasoning-centric dataset (~300k samples) covering five major knowledge domains (e.g., cultural commonsense, physics, etc.) for planning, alongside an agent-generated corpus for visual self-correction. Extensive experiments demonstrate that UniReason achieves advanced performance on reasoning-intensive benchmarks such as WISE, KrisBench and UniREditBench, while maintaining superior general synthesis capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AlenjandroWang/UniReason", "github_api_added_by": "auto" }, { "date": "2026-02-03", "arxiv_id": "2602.02092", "title": "FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space", "authors": [ "FSVideo Team", "Qingyu Chen", "Zhiyuan Fang", "Haibin Huang", "Xinwei Huang", "Tong Jin", "Minxuan Lin", "Bo Liu", "Celong Liu", "Chongyang Ma", "Xing Mei", "Xiaohui Shen", "Yaojie Shen", "Fuwen Tan", "Angtian Wang", "Xiao Yang", "Yiding Yang", "Jiamin Yuan", "Lingxi Zhang", "Yuxin Zhang" ], "github": "https://github.com/Wan-Video/Wan2.1", "project_page": "", "abstract": "We introduce FSVideo, a fast speed transformer-based image-to-video (I2V) diffusion framework. We build our framework on the following key components: 1.) a new video autoencoder with highly-compressed latent space (64times64times4 spatial-temporal downsampling ratio), achieving competitive reconstruction quality; 2.) a diffusion transformer (DIT) architecture with a new layer memory design to enhance inter-layer information flow and context reuse within DIT, and 3.) a multi-resolution generation strategy via a few-step DIT upsampler to increase video fidelity. Our final model, which contains a 14B DIT base model and a 14B DIT upsampler, achieves competitive performance against other popular open-source models, while being an order of magnitude faster. We discuss our model design as well as training strategies in this report.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Wan-Video/Wan2.1", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01660", "title": "CoDiQ: Test-Time Scaling for Controllable Difficult Question Generation", "authors": [ "Zhongyuan Peng", "Caijun Xu", "Changyi Xiao", "Shibo Hong", "Eli Zhang", "Stephen Huang", "Yixin Cao" ], "github": "https://github.com/ALEX-nlp/CoDiQ", "project_page": "", "abstract": "Large Reasoning Models (LRMs) benefit substantially from training on challenging competition-level questions. However, existing automated question synthesis methods lack precise difficulty control, incur high computational costs, and struggle to generate competition-level questions at scale. In this paper, we propose CoDiQ (Controllable Difficult Question Generation), a novel framework enabling fine-grained difficulty control via test-time scaling while ensuring question solvability. Specifically, first, we identify a test-time scaling tendency (extended reasoning token budget boosts difficulty but reduces solvability) and the intrinsic properties defining the upper bound of a model's ability to generate valid, high-difficulty questions. Then, we develop CoDiQ-Generator from Qwen3-8B, which improves the upper bound of difficult question generation, making it particularly well-suited for challenging question construction. Building on the CoDiQ framework, we build CoDiQ-Corpus (44K competition-grade question sequences). Human evaluations show these questions are significantly more challenging than LiveCodeBench/AIME with over 82% solvability. Training LRMs on CoDiQ-Corpus substantially improves reasoning performance, verifying that scaling controlled-difficulty training questions enhances reasoning capabilities. We open-source CoDiQ-Corpus, CoDiQ-Generator, and implementations to support related research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ALEX-nlp/CoDiQ", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.21968", "title": "OVD: On-policy Verbal Distillation", "authors": [ "Jing Xiong", "Hui Shen", "Shansan Gong", "Yuxin Cheng", "Jianghan Shen", "Chaofan Tao", "Haochen Tan", "Haoli Bai", "Lifeng Shang", "Ngai Wong" ], "github": "", "project_page": "", "abstract": "Knowledge distillation offers a promising path to transfer reasoning capabilities from large teacher models to efficient student models; however, existing token-level on-policy distillation methods require token-level alignment between the student and teacher models, which restricts the student model's exploration ability, prevent effective use of interactive environment feedback, and suffer from severe memory bottlenecks in reinforcement learning. We introduce On-policy Verbal Distillation (OVD), a memory-efficient framework that replaces token-level probability matching with trajectory matching using discrete verbal scores (0--9) from teacher models. OVD dramatically reduces memory consumption while enabling on-policy distillation from teacher models with verbal feedback, and avoids token-level alignment, allowing the student model to freely explore the output space. Extensive experiments on Web question answering and mathematical reasoning tasks show that OVD substantially outperforms existing methods, delivering up to +12.9% absolute improvement in average EM on Web Q&A tasks and a up to +25.7% gain on math benchmarks (when trained with only one random samples), while also exhibiting superior training efficiency. Our project page is available at https://OVD.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02472", "title": "SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning", "authors": [ "Qifan Yu", "Xinyu Ma", "Zhijian Zhuo", "Minrui Wang", "Deyi Liu", "Shiyi Zhan", "Yiyuan Ma", "Liang Xiang", "Xingyan Bin", "Di He" ], "github": "", "project_page": "", "abstract": "Progressive Learning (PL) reduces pre-training computational overhead by gradually increasing model scale. While prior work has extensively explored depth expansion, width expansion remains significantly understudied, with the few existing methods limited to the early stages of training. However, expanding width during the mid-stage is essential for maximizing computational savings, yet it remains a formidable challenge due to severe training instabilities. Empirically, we show that naive initialization at this stage disrupts activation statistics, triggering loss spikes, while copy-based initialization introduces gradient symmetry that hinders feature diversity. To address these issues, we propose SPARKLING (balancing {S}ignal {P}reservation {A}nd symmet{R}y brea{K}ing for width-progressive {L}earn{ING}), a novel framework for mid-stage width expansion. Our method achieves signal preservation via RMS-scale consistency, stabilizing activation statistics during expansion. Symmetry breaking is ensured through asymmetric optimizer state resetting and learning rate re-warmup. Extensive experiments on Mixture-of-Experts (MoE) models demonstrate that, across multiple width axes and optimizer families, SPARKLING consistently outperforms training from scratch and reduces training cost by up to 35% under 2times width expansion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02110", "title": "An Empirical Study of World Model Quantization", "authors": [ "Zhongqian Fu", "Tianyi Zhao", "Kai Han", "Hang Zhou", "Xinghao Chen", "Yunhe Wang" ], "github": "https://github.com/huawei-noah/noah-research/tree/master/QuantWM", "project_page": "", "abstract": "World models learn an internal representation of environment dynamics, enabling agents to simulate and reason about future states within a compact latent space for tasks such as planning, prediction, and inference. However, running world models rely on hevay computational cost and memory footprint, making model quantization essential for efficient deployment. To date, the effects of post-training quantization (PTQ) on world models remain largely unexamined. In this work, we present a systematic empirical study of world model quantization using DINO-WM as a representative case, evaluating diverse PTQ methods under both weight-only and joint weight-activation settings. We conduct extensive experiments on different visual planning tasks across a wide range of bit-widths, quantization granularities, and planning horizons up to 50 iterations. Our results show that quantization effects in world models extend beyond standard accuracy and bit-width trade-offs: group-wise weight quantization can stabilize low-bit rollouts, activation quantization granularity yields inconsistent benefits, and quantization sensitivity is highly asymmetric between encoder and predictor modules. Moreover, aggressive low-bit quantization significantly degrades the alignment between the planning objective and task success, leading to failures that cannot be remedied by additional optimization. These findings reveal distinct quantization-induced failure modes in world model-based planning and provide practical guidance for deploying quantized world models under strict computational constraints. The code will be available at https://github.com/huawei-noah/noah-research/tree/master/QuantWM.", "github_manual": "", "github_regex": "https://github.com/huawei-noah/noah-research/tree/master/QuantWM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01077", "title": "PISA: Piecewise Sparse Attention Is Wiser for Efficient Diffusion Transformers", "authors": [ "Haopeng Li", "Shitong Shao", "Wenliang Zhong", "Zikai Zhou", "Lichen Bai", "Hui Xiong", "Zeke Xie" ], "github": "https://github.com/xie-lab-ml/piecewise-sparse-attention", "project_page": "", "abstract": "Diffusion Transformers are fundamental for video and image generation, but their efficiency is bottlenecked by the quadratic complexity of attention. While block sparse attention accelerates computation by attending only critical key-value blocks, it suffers from degradation at high sparsity by discarding context. In this work, we discover that attention scores of non-critical blocks exhibit distributional stability, allowing them to be approximated accurately and efficiently rather than discarded, which is essentially important for sparse attention design. Motivated by this key insight, we propose PISA, a training-free Piecewise Sparse Attention that covers the full attention span with sub-quadratic complexity. Unlike the conventional keep-or-drop paradigm that directly drop the non-critical block information, PISA introduces a novel exact-or-approximate strategy: it maintains exact computation for critical blocks while efficiently approximating the remainder through block-wise Taylor expansion. This design allows PISA to serve as a faithful proxy to full attention, effectively bridging the gap between speed and quality. Experimental results demonstrate that PISA achieves 1.91 times and 2.57 times speedups on Wan2.1-14B and Hunyuan-Video, respectively, while consistently maintaining the highest quality among sparse attention methods. Notably, even for image generation on FLUX, PISA achieves a 1.2 times acceleration without compromising visual quality. Code is available at: https://github.com/xie-lab-ml/piecewise-sparse-attention.", "github_manual": "", "github_regex": "https://github.com/xie-lab-ml/piecewise-sparse-attention", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01983", "title": "Evolving from Tool User to Creator via Training-Free Experience Reuse in Multimodal Reasoning", "authors": [ "Xintian Shen", "Jiawei Chen", "Lihao Zheng", "Hao Ma", "Tao Wei", "Kun Zhan" ], "github": "", "project_page": "", "abstract": "Existing Tool-Integrated Reasoning (TIR) models have effectively extended the question-answering capabilities of LLMs by incorporating external tools. However, real-world scenarios present numerous open-ended problems where fixed tools often fail to meet task requirements. Furthermore, the lack of self-optimization mechanisms means that erroneous tool outputs can mislead the LLM's responses. Additionally, the construction of existing tools entails significant manual effort, which consequently constrains their applicability. Recognizing that the reasoning traces of LLMs encapsulate implicit problem-solving capabilities, we propose UCT, a novel training-free framework that transforms agents from tool users to tool creators. This approach harvests reasoning experiences and distills them into reusable assets. This method transforms the agent from a mere tool user into a tool creator, enabling adaptive tool creation and self-updating during the inference process. We also introduce a memory consolidation mechanism to maintain the tool library, ensuring high reusability of retained experiential memory for subsequent reasoning tasks. This novel automated tool construction paradigm continuously improves tool quality during reasoning, allowing the overall agent system to progress without additional training. Extensive experiments demonstrate that our method serves as a novel paradigm for enhancing the capabilities of TIR models. In particular, the significant performance gains achieved +20.86%uparrow and +23.04%uparrow on benchmarks across multi-domain mathematical and scientific reasoning tasks validate the self-evolving capability of the agent.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.21759", "title": "Influence Guided Sampling for Domain Adaptation of Text Retrievers", "authors": [ "Meet Doshi", "Vishwajeet Kumar", "Yulong Li", "Jaydeep Sen" ], "github": "", "project_page": "", "abstract": "General-purpose open-domain dense retrieval systems are usually trained with a large, eclectic mix of corpora and search tasks. How should these diverse corpora and tasks be sampled for training? Conventional approaches sample them uniformly, proportional to their instance population sizes, or depend on human-level expert supervision. It is well known that the training data sampling strategy can greatly impact model performance. However, how to find the optimal strategy has not been adequately studied in the context of embedding models. We propose Inf-DDS, a novel reinforcement learning driven sampling framework that adaptively reweighs training datasets guided by influence-based reward signals and is much more lightweight with respect to GPU consumption. Our technique iteratively refines the sampling policy, prioritizing datasets that maximize model performance on a target development set. We evaluate the efficacy of our sampling strategy on a wide range of text retrieval tasks, demonstrating strong improvements in retrieval performance and better adaptation compared to existing gradient-based sampling methods, while also being 1.5x to 4x cheaper in GPU compute. Our sampling strategy achieves a 5.03 absolute NDCG@10 improvement while training a multilingual bge-m3 model and an absolute NDCG@10 improvement of 0.94 while training all-MiniLM-L6-v2, even when starting from expert-assigned weights on a large pool of training datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.20613", "title": "AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios", "authors": [ "Kaiyuan Chen", "Qimin Wu", "Taiyu Hou", "Tianhao Tang", "Xueyu Hu", "Yuchen Hou", "Bikun Li", "Chengming Qian", "Guoyin Wang", "Haolin Chen", "Haotong Tian", "Haoye Zhang", "Haoyu Bian", "Hongbing Pan", "Hongkang Zhang", "Hongyi Zhou", "Jiaqi Cai", "Jiewu Rao", "Jiyuan Ren", "Keduan Huang", "Lucia Zhu Huang", "Mingyu Yuan", "Naixu Guo", "Qicheng Tang", "Qinyan Zhang", "Shuai Chen", "Siheng Chen", "Ting Ting Li", "Xiaoxing Guo", "Yaocheng Zuo", "Yaoqi Guo", "Yinan Wang", "Yinzhou Yu", "Yize Wang", "Yuan Jiang", "Yuan Tian", "Yuanshuo Zhang", "Yuxuan Liu", "Yvette Yan Zeng", "Zenyu Shan", "Zihan Yin", "Xiaobo Hu", "Yang Liu", "Yixin Ren", "Yuan Gong" ], "github": "", "project_page": "", "abstract": "The capacity of AI agents to effectively handle tasks of increasing duration and complexity continues to grow, demonstrating exceptional performance in coding, deep research, and complex problem-solving evaluations. However, in daily scenarios, the perception of these advanced AI capabilities among general users remains limited. We argue that current evaluations prioritize increasing task difficulty without sufficiently addressing the diversity of agentic tasks necessary to cover the daily work, life, and learning activities of a broad demographic. To address this, we propose AgentIF-OneDay, aimed at determining whether general users can utilize natural language instructions and AI agents to complete a diverse array of daily tasks. These tasks require not only solving problems through dialogue but also understanding various attachment types and delivering tangible file-based results. The benchmark is structured around three user-centric categories: Open Workflow Execution, which assesses adherence to explicit and complex workflows; Latent Instruction, which requires agents to infer implicit instructions from attachments; and Iterative Refinement, which involves modifying or expanding upon ongoing work. We employ instance-level rubrics and a refined evaluation pipeline that aligns LLM-based verification with human judgment, achieving an 80.1% agreement rate using Gemini-3-Pro. AgentIF-OneDay comprises 104 tasks covering 767 scoring points. We benchmarked four leading general AI agents and found that agent products built based on APIs and ChatGPT agents based on agent RL remain in the first tier simultaneously. Leading LLM APIs and open-source models have internalized agentic capabilities, enabling AI application teams to develop cutting-edge Agent products.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.22674", "title": "VisionTrim: Unified Vision Token Compression for Training-Free MLLM Acceleration", "authors": [ "Hanxun Yu", "Wentong Li", "Xuan Qu", "Song Wang", "Junbo Chen", "Jianke Zhu" ], "github": "https://github.com/hanxunyu/VisionTrim", "project_page": "", "abstract": "Multimodal large language models (MLLMs) suffer from high computational costs due to excessive visual tokens, particularly in high-resolution and video-based scenarios. Existing token reduction methods typically focus on isolated pipeline components and often neglect textual alignment, leading to performance degradation. In this paper, we propose VisionTrim, a unified framework for training-free MLLM acceleration, integrating two effective plug-and-play modules: 1) the Dominant Vision Token Selection (DVTS) module, which preserves essential visual tokens via a global-local view, and 2) the Text-Guided Vision Complement (TGVC) module, which facilitates context-aware token merging guided by textual cues. Extensive experiments across diverse image and video multimodal benchmarks demonstrate the performance superiority of our VisionTrim, advancing practical MLLM deployment in real-world applications. The code is available at: https://github.com/hanxunyu/VisionTrim.", "github_manual": "", "github_regex": "https://github.com/hanxunyu/VisionTrim", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01997", "title": "On the Limits of Layer Pruning for Generative Reasoning in LLMs", "authors": [ "Safal Shrestha", "Anubhav Shrestha", "Aadim Nepal", "Minwu Kim", "Keith Ross" ], "github": "https://github.com/safal312/on-the-limits-of-layer-pruning", "project_page": "", "abstract": "Recent works have shown that layer pruning can compress large language models (LLMs) while retaining strong performance on classification benchmarks with little or no finetuning. However, existing pruning techniques often suffer severe degradation on generative reasoning tasks. Through a systematic study across multiple model families, we find that tasks requiring multi-step reasoning are particularly sensitive to depth reduction. Beyond surface-level text degeneration, we observe degradation of critical algorithmic capabilities, including arithmetic computation for mathematical reasoning and balanced parenthesis generation for code synthesis. Under realistic post-training constraints, without access to pretraining-scale data or compute, we evaluate a simple mitigation strategy based on supervised finetuning with Self-Generated Responses. This approach achieves strong recovery on classification tasks, retaining up to 90\\% of baseline performance, and yields substantial gains of up to 20--30 percentage points on generative benchmarks compared to prior post-pruning techniques. Crucially, despite these gains, recovery for generative reasoning remains fundamentally limited relative to classification tasks and is viable primarily at lower pruning ratios. Overall, we characterize the practical limits of layer pruning for generative reasoning and provide guidance on when depth reduction can be applied effectively under constrained post-training regimes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/safal312/on-the-limits-of-layer-pruning", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01815", "title": "INDIBATOR: Diverse and Fact-Grounded Individuality for Multi-Agent Debate in Molecular Discovery", "authors": [ "Yunhui Jang", "Seonghyun Park", "Jaehyung Kim", "Sungsoo Ahn" ], "github": "", "project_page": "", "abstract": "Multi-agent systems have emerged as a powerful paradigm for automating scientific discovery. To differentiate agent behavior in the multi-agent system, current frameworks typically assign generic role-based personas such as ''reviewer'' or ''writer'' or rely on coarse grained keyword-based personas. While functional, this approach oversimplifies how human scientists operate, whose contributions are shaped by their unique research trajectories. In response, we propose INDIBATOR, a framework for molecular discovery that grounds agents in individualized scientist profiles constructed from two modalities: publication history for literature-derived knowledge and molecular history for structural priors. These agents engage in multi-turn debate through proposal, critique, and voting phases. Our evaluation demonstrates that these fine-grained individuality-grounded agents consistently outperform systems relying on coarse-grained personas, achieving competitive or state-of-the-art performance. These results validate that capturing the ``scientific DNA'' of individual agents is essential for high-quality discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02354", "title": "Implicit neural representation of textures", "authors": [ "Albert Kwok", "Zheyuan Hu", "Dounia Hammou" ], "github": "", "project_page": "", "abstract": "Implicit neural representation (INR) has proven to be accurate and efficient in various domains. In this work, we explore how different neural networks can be designed as a new texture INR, which operates in a continuous manner rather than a discrete one over the input UV coordinate space. Through thorough experiments, we demonstrate that these INRs perform well in terms of image quality, with considerable memory usage and rendering inference time. We analyze the balance between these objectives. In addition, we investigate various related applications in real-time rendering and down-stream tasks, e.g. mipmap fitting and INR-space generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01970", "title": "Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models", "authors": [ "Yun Qu", "Qi Wang", "Yixiu Mao", "Heming Zou", "Yuhang Jiang", "Weijie Liu", "Clive Bai", "Kai Yang", "Yangkun Chen", "Saiyong Yang", "Xiangyang Ji" ], "github": "https://github.com/THUDM/slime", "project_page": "", "abstract": "Reinforcement learning enhances the reasoning capabilities of large language models but often involves high computational costs due to rollout-intensive optimization. Online prompt selection presents a plausible solution by prioritizing informative prompts to improve training efficiency. However, current methods either depend on costly, exact evaluations or construct prompt-specific predictive models lacking generalization across prompts. This study introduces Generalizable Predictive Prompt Selection (GPS), which performs Bayesian inference towards prompt difficulty using a lightweight generative model trained on the shared optimization history. Intermediate-difficulty prioritization and history-anchored diversity are incorporated into the batch acquisition principle to select informative prompt batches. The small predictive model also generalizes at test-time for efficient computational allocation. Experiments across varied reasoning benchmarks indicate GPS's substantial improvements in training efficiency, final performance, and test-time efficiency over superior baseline methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUDM/slime", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02383", "title": "SLIME: Stabilized Likelihood Implicit Margin Enforcement for Preference Optimization", "authors": [ "Maksim Afanasyev", "Illarion Iov" ], "github": "", "project_page": "", "abstract": "Direct preference optimization methods have emerged as a computationally efficient alternative to Reinforcement Learning from Human Feedback (RLHF) for aligning Large Language Models (LLMs). Latest approaches have streamlined the alignment process by deriving implicit reward functions, yet they often suffer from a critical objective mismatch: optimizing the relative margin between chosen and rejected responses does not guarantee the preservation of the chosen response's absolute likelihood. This can lead to ``unlearning'', where the model degrades the probability of high-quality outputs to satisfy margin constraints, and ``formatting collapse'' caused by the over-penalization of rejected sequences. In this work, we introduce SLIME (Stabilized Likelihood Implicit Margin Enforcement), a reference-free alignment objective designed to decouple preference learning from generation quality. SLIME incorporates a three-pronged objective: (1) an anchoring term to maximize the likelihood of preferred responses; (2) a stabilizing penalty that prevents the probabilities of rejected tokens from collapsing to zero; and (3) a dual-margin mechanism that combines hard and soft constraints for precise boundary shaping. Our results demonstrate that SLIME achieves superior performance compared to state-of-the-art baselines while maintaining higher generation stability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02343", "title": "Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics", "authors": [ "Ziwen Xu", "Chenyan Wu", "Hengyu Sun", "Haiwen Hong", "Mengru Wang", "Yunzhi Yao", "Longtao Huang", "Hui Xue", "Shumin Deng", "Zhixuan Chu", "Huajun Chen", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/EasyEdit/blob/main/examples", "project_page": "", "abstract": "Methods for controlling large language models (LLMs), including local weight fine-tuning, LoRA-based adaptation, and activation-based interventions, are often studied in isolation, obscuring their connections and making comparison difficult. In this work, we present a unified view that frames these interventions as dynamic weight updates induced by a control signal, placing them within a single conceptual framework. Building on this view, we propose a unified preference-utility analysis that separates control effects into preference, defined as the tendency toward a target concept, and utility, defined as coherent and task-valid generation, and measures both on a shared log-odds scale using polarity-paired contrastive examples. Across methods, we observe a consistent trade-off between preference and utility: stronger control increases preference while predictably reducing utility. We further explain this behavior through an activation manifold perspective, in which control shifts representations along target-concept directions to enhance preference, while utility declines primarily when interventions push representations off the model's valid-generation manifold. Finally, we introduce a new steering approach SPLIT guided by this analysis that improves preference while better preserving utility. Code is available at https://github.com/zjunlp/EasyEdit/blob/main/examples/SPLIT.md.", "github_manual": "", "github_regex": "https://github.com/zjunlp/EasyEdit/blob/main/examples", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01675", "title": "TRIP-Bench: A Benchmark for Long-Horizon Interactive Agents in Real-World Scenarios", "authors": [ "Yuanzhe Shen", "Zisu Huang", "Zhengyuan Wang", "Muzhao Tian", "Zhengkang Guo", "Chenyang Zhang", "Shuaiyu Zhou", "Zengjie Hu", "Dailin Li", "Jingwen Xu", "Kaimin Wang", "Wenhao Liu", "Tianlong Li", "Fengpeng Yue", "Feng Hong", "Cao Liu", "Ke Zeng" ], "github": "", "project_page": "", "abstract": "As LLM-based agents are deployed in increasingly complex real-world settings, existing benchmarks underrepresent key challenges such as enforcing global constraints, coordinating multi-tool reasoning, and adapting to evolving user behavior over long, multi-turn interactions. To bridge this gap, we introduce TRIP-Bench, a long-horizon benchmark grounded in realistic travel-planning scenarios. TRIP-Bench leverages real-world data, offers 18 curated tools and 40+ travel requirements, and supports automated evaluation. It includes splits of varying difficulty; the hard split emphasizes long and ambiguous interactions, style shifts, feasibility changes, and iterative version revision. Dialogues span up to 15 user turns, can involve 150+ tool calls, and may exceed 200k tokens of context. Experiments show that even advanced models achieve at most 50\\% success on the easy split, with performance dropping below 10\\% on hard subsets. We further propose GTPO, an online multi-turn reinforcement learning method with specialized reward normalization and reward differencing. Applied to Qwen2.5-32B-Instruct, GTPO improves constraint satisfaction and interaction robustness, outperforming Gemini-3-Pro in our evaluation. We expect TRIP-Bench to advance practical long-horizon interactive agents, and GTPO to provide an effective online RL recipe for robust long-horizon training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.00192", "title": "AI-Generated Image Detectors Overrely on Global Artifacts: Evidence from Inpainting Exchange", "authors": [ "Elif Nebioglu", "Emirhan Bilgi\u00e7", "Adrian Popescu" ], "github": "https://github.com/emirhanbilgic/INP-X", "project_page": "", "abstract": "Modern deep learning-based inpainting enables realistic local image manipulation, raising critical challenges for reliable detection. However, we observe that current detectors primarily rely on global artifacts that appear as inpainting side effects, rather than on locally synthesized content. We show that this behavior occurs because VAE-based reconstruction induces a subtle but pervasive spectral shift across the entire image, including unedited regions. To isolate this effect, we introduce Inpainting Exchange (INP-X), an operation that restores original pixels outside the edited region while preserving all synthesized content. We create a 90K test dataset including real, inpainted, and exchanged images to evaluate this phenomenon. Under this intervention, pretrained state-of-the-art detectors, including commercial ones, exhibit a dramatic drop in accuracy (e.g., from 91\\% to 55\\%), frequently approaching chance level. We provide a theoretical analysis linking this behavior to high-frequency attenuation caused by VAE information bottlenecks. Our findings highlight the need for content-aware detection. Indeed, training on our dataset yields better generalization and localization than standard inpainting. Our dataset and code are publicly available at https://github.com/emirhanbilgic/INP-X.", "github_manual": "", "github_regex": "https://github.com/emirhanbilgic/INP-X", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01984", "title": "Enhancing Multi-Image Understanding through Delimiter Token Scaling", "authors": [ "Minyoung Lee", "Yeji Park", "Dongjun Hwang", "Yejin Kim", "Seong Joon Oh", "Junsuk Choe" ], "github": "https://github.com/MYMY-young/DelimScaling", "project_page": "", "abstract": "Large Vision-Language Models (LVLMs) achieve strong performance on single-image tasks, but their performance declines when multiple images are provided as input. One major reason is the cross-image information leakage, where the model struggles to distinguish information across different images. Existing LVLMs already employ delimiter tokens to mark the start and end of each image, yet our analysis reveals that these tokens fail to effectively block cross-image information leakage. To enhance their effectiveness, we propose a method that scales the hidden states of delimiter tokens. This enhances the model's ability to preserve image-specific information by reinforcing intra-image interaction and limiting undesired cross-image interactions. Consequently, the model is better able to distinguish between images and reason over them more accurately. Experiments show performance gains on multi-image benchmarks such as Mantis, MuirBench, MIRB, and QBench2. We further evaluate our method on text-only tasks that require clear distinction. The method improves performance on multi-document and multi-table understanding benchmarks, including TQABench, MultiNews, and WCEP-10. Notably, our method requires no additional training or inference cost.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MYMY-young/DelimScaling", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01395", "title": "Rethinking Selective Knowledge Distillation", "authors": [ "Almog Tavor", "Itay Ebenspanger", "Neil Cnaan", "Mor Geva" ], "github": "https://github.com/almogtavor/SE-KD3x", "project_page": "", "abstract": "Growing efforts to improve knowledge distillation (KD) in large language models (LLMs) replace dense teacher supervision with selective distillation, which uses a subset of token positions, vocabulary classes, or training samples for supervision. However, it remains unclear which importance signals, selection policies, and their interplay are most effective. In this work, we revisit where and how to distill in autoregressive LLMs. We disentangle selective KD along the position, class, and sample axes and systematically compare importance signals and selection policies. Then, guided by this analysis, we identify underexplored opportunities and introduce student-entropy-guided position selection (SE-KD). Across a suite of benchmarks, SE-KD often improves accuracy, downstream task adherence, and memory efficiency over dense distillation. Extending this approach across the class and sample axes (SE-KD 3X) yields complementary efficiency gains that make offline teacher caching feasible. In practice, this reduces wall time by 70% and peak memory by 18%, while cutting storage usage by 80% over prior methods without sacrificing performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/almogtavor/SE-KD3x", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01322", "title": "PolySAE: Modeling Feature Interactions in Sparse Autoencoders via Polynomial Decoding", "authors": [ "Panagiotis Koromilas", "Andreas D. Demou", "James Oldfield", "Yannis Panagakis", "Mihalis Nicolaou" ], "github": "https://github.com/jbloomAus/SAELens", "project_page": "", "abstract": "Sparse autoencoders (SAEs) have emerged as a promising method for interpreting neural network representations by decomposing activations into sparse combinations of dictionary atoms. However, SAEs assume that features combine additively through linear reconstruction, an assumption that cannot capture compositional structure: linear models cannot distinguish whether \"Starbucks\" arises from the composition of \"star\" and \"coffee\" features or merely their co-occurrence. This forces SAEs to allocate monolithic features for compound concepts rather than decomposing them into interpretable constituents. We introduce PolySAE, which extends the SAE decoder with higher-order terms to model feature interactions while preserving the linear encoder essential for interpretability. Through low-rank tensor factorization on a shared projection subspace, PolySAE captures pairwise and triple feature interactions with small parameter overhead (3% on GPT2). Across four language models and three SAE variants, PolySAE achieves an average improvement of approximately 8% in probing F1 while maintaining comparable reconstruction error, and produces 2-10times larger Wasserstein distances between class-conditional feature distributions. Critically, learned interaction weights exhibit negligible correlation with co-occurrence frequency (r = 0.06 vs. r = 0.82 for SAE feature covariance), suggesting that polynomial terms capture compositional structure, such as morphological binding and phrasal composition, largely independent of surface statistics.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jbloomAus/SAELens", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.23000", "title": "Mano: Restriking Manifold Optimization for LLM Training", "authors": [ "Yufei Gu", "Zeke Xie" ], "github": "", "project_page": "", "abstract": "While large language models (LLMs) have emerged as a significant advancement in artificial intelligence, the hardware and computational costs for training LLMs are also significantly burdensome. Among the state-of-the-art optimizers, AdamW relies on diagonal curvature estimates and ignores structural properties, while Muon applies global spectral normalization at the expense of losing curvature information. In this study, we restriked manifold optimization methods for training LLMs, which may address both optimizers' limitations, while conventional manifold optimization methods have been largely overlooked due to the poor performance in large-scale model optimization. By innovatively projecting the momentum onto the tangent space of model parameters and constraining it on a rotational Oblique manifold, we propose a novel, powerful, and efficient optimizer **Mano** that is the first to bridge the performance gap between manifold optimization and modern optimizers. Extensive experiments on the LLaMA and Qwen3 models demonstrate that Mano consistently and significantly outperforms AdamW and Muon even with less memory consumption and computational complexity, respectively, suggesting an expanded Pareto frontier in terms of space and time efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01897", "title": "Internal Flow Signatures for Self-Checking and Refinement in LLMs", "authors": [ "Sungheon Jeong", "Sanggeon Yun", "Ryozo Masukawa", "Wenjun Haung", "Hanning Chen", "Mohsen Imani" ], "github": "", "project_page": "", "abstract": "Large language models can generate fluent answers that are unfaithful to the provided context, while many safeguards rely on external verification or a separate judge after generation. We introduce internal flow signatures that audit decision formation from depthwise dynamics at a fixed inter-block monitoring boundary. The method stabilizes token-wise motion via bias-centered monitoring, then summarizes trajectories in compact moving readout-aligned subspaces constructed from the top token and its close competitors within each depth window. Neighboring window frames are aligned by an orthogonal transport, yielding depth-comparable transported step lengths, turning angles, and subspace drift summaries that are invariant to within-window basis choices. A lightweight GRU validator trained on these signatures performs self-checking without modifying the base model. Beyond detection, the validator localizes a culprit depth event and enables a targeted refinement: the model rolls back to the culprit token and clamps an abnormal transported step at the identified block while preserving the orthogonal residual. The resulting pipeline provides actionable localization and low-overhead self-checking from internal decision dynamics. Code is available at github.com/EavnJeong/Internal-Flow-Signatures-for-Self-Checking-and-Refinement-in-LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.00521", "title": "Diagnosing the Reliability of LLM-as-a-Judge via Item Response Theory", "authors": [ "Junhyuk Choi", "Sohhyung Park", "Chanhee Cho", "Hyeonchu Park", "Bugeun Kim" ], "github": "", "project_page": "", "abstract": "While LLM-as-a-Judge is widely used in automated evaluation, existing validation practices primarily operate at the level of observed outputs, offering limited insight into whether LLM judges themselves function as stable and reliable measurement instruments. To address this limitation, we introduce a two-phase diagnostic framework for assessing reliability of LLM-as-a-Judge, grounded in Item Response Theory (IRT). The framework adopts Graded Response Model (GRM) of IRT and formalizes reliability along two complementary dimensions: (1) intrinsic consistency, defined as the stability of measurement behavior under prompt variations, and (2) human alignment, capturing correspondence with human quality assessments. We empirically examine diverse LLM judges with this framework, and show that leveraging IRT-GRM yields interpretable signals for diagnosing judgments systematically. These signals provide practical guidance for verifying reliablity of LLM-as-a-Judge and identifying potential causes of unreliability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02453", "title": "Thinking with Comics: Enhancing Multimodal Reasoning through Structured Visual Storytelling", "authors": [ "Andong Chen", "Wenxin Zhu", "Qiuyu Ding", "Yuchen Song", "Muyun Yang", "Tiejun Zhao" ], "github": "", "project_page": "", "abstract": "Chain-of-Thought reasoning has driven large language models to extend from thinking with text to thinking with images and videos. However, different modalities still have clear limitations: static images struggle to represent temporal structure, while videos introduce substantial redundancy and computational cost. In this work, we propose Thinking with Comics, a visual reasoning paradigm that uses comics as a high information-density medium positioned between images and videos. Comics preserve temporal structure, embedded text, and narrative coherence while requiring significantly lower reasoning cost. We systematically study two reasoning paths based on comics and evaluate them on a range of reasoning tasks and long-context understanding tasks. Experimental results show that Thinking with Comics outperforms Thinking with Images on multi-step temporal and causal reasoning tasks, while remaining substantially more efficient than Thinking with Video. Further analysis indicates that different comic narrative structures and styles consistently affect performance across tasks, suggesting that comics serve as an effective intermediate visual representation for improving multimodal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01801", "title": "Fast Autoregressive Video Diffusion and World Models with Temporal Cache Compression and Sparse Attention", "authors": [ "Dvir Samuel", "Issar Tzachor", "Matan Levy", "Micahel Green", "Gal Chechik", "Rami Ben-Ari" ], "github": "", "project_page": "", "abstract": "Autoregressive video diffusion models enable streaming generation, opening the door to long-form synthesis, video world models, and interactive neural game engines. However, their core attention layers become a major bottleneck at inference time: as generation progresses, the KV cache grows, causing both increasing latency and escalating GPU memory, which in turn restricts usable temporal context and harms long-range consistency. In this work, we study redundancy in autoregressive video diffusion and identify three persistent sources: near-duplicate cached keys across frames, slowly evolving (largely semantic) queries/keys that make many attention computations redundant, and cross-attention over long prompts where only a small subset of tokens matters per frame. Building on these observations, we propose a unified, training-free attention framework for autoregressive diffusion: TempCache compresses the KV cache via temporal correspondence to bound cache growth; AnnCA accelerates cross-attention by selecting frame-relevant prompt tokens using fast approximate nearest neighbor (ANN) matching; and AnnSA sparsifies self-attention by restricting each query to semantically matched keys, also using a lightweight ANN. Together, these modules reduce attention, compute, and memory and are compatible with existing autoregressive diffusion backbones and world models. Experiments demonstrate up to x5--x10 end-to-end speedups while preserving near-identical visual quality and, crucially, maintaining stable throughput and nearly constant peak GPU memory usage over long rollouts, where prior methods progressively slow down and suffer from increasing memory usage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.22296", "title": "ParalESN: Enabling parallel information processing in Reservoir Computing", "authors": [ "Matteo Pinna", "Giacomo Lagomarsini", "Andrea Ceni", "Claudio Gallicchio" ], "github": "", "project_page": "", "abstract": "Reservoir Computing (RC) has established itself as an efficient paradigm for temporal processing. However, its scalability remains severely constrained by (i) the necessity of processing temporal data sequentially and (ii) the prohibitive memory footprint of high-dimensional reservoirs. In this work, we revisit RC through the lens of structured operators and state space modeling to address these limitations, introducing Parallel Echo State Network (ParalESN). ParalESN enables the construction of high-dimensional and efficient reservoirs based on diagonal linear recurrence in the complex space, enabling parallel processing of temporal data. We provide a theoretical analysis demonstrating that ParalESN preserves the Echo State Property and the universality guarantees of traditional Echo State Networks while admitting an equivalent representation of arbitrary linear reservoirs in the complex diagonal form. Empirically, ParalESN matches the predictive accuracy of traditional RC on time series benchmarks, while delivering substantial computational savings. On 1-D pixel-level classification tasks, ParalESN achieves competitive accuracy with fully trainable neural networks while reducing computational costs and energy consumption by orders of magnitude. Overall, ParalESN offers a promising, scalable, and principled pathway for integrating RC within the deep learning landscape.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01576", "title": "Generative Visual Code Mobile World Models", "authors": [ "Woosung Koh", "Sungjun Han", "Segyu Lee", "Se-Young Yun", "Jamin Shin" ], "github": "https://github.com/trillion-labs/gWorld", "project_page": "", "abstract": "Mobile Graphical User Interface (GUI) World Models (WMs) offer a promising path for improving mobile GUI agent performance at train- and inference-time. However, current approaches face a critical trade-off: text-based WMs sacrifice visual fidelity, while the inability of visual WMs in precise text rendering led to their reliance on slow, complex pipelines dependent on numerous external models. We propose a novel paradigm: visual world modeling via renderable code generation, where a single Vision-Language Model (VLM) predicts the next GUI state as executable web code that renders to pixels, rather than generating pixels directly. This combines the strengths of both approaches: VLMs retain their linguistic priors for precise text rendering while their pre-training on structured web code enables high-fidelity visual generation. We introduce gWorld (8B, 32B), the first open-weight visual mobile GUI WMs built on this paradigm, along with a data generation framework (gWorld) that automatically synthesizes code-based training data. In extensive evaluation across 4 in- and 2 out-of-distribution benchmarks, gWorld sets a new pareto frontier in accuracy versus model size, outperforming 8 frontier open-weight models over 50.25x larger. Further analyses show that (1) scaling training data via gWorld yields meaningful gains, (2) each component of our pipeline improves data quality, and (3) stronger world modeling improves downstream mobile GUI policy performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/trillion-labs/gWorld", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.00986", "title": "Sparse Reward Subsystem in Large Language Models", "authors": [ "Guowei Xu", "Mert Yuksekgonul", "James Zou" ], "github": "", "project_page": "", "abstract": "In this paper, we identify a sparse reward subsystem within the hidden states of Large Language Models (LLMs), drawing an analogy to the biological reward subsystem in the human brain. We demonstrate that this subsystem contains value neurons that represent the model's internal expectation of state value, and through intervention experiments, we establish the importance of these neurons for reasoning. Our experiments reveal that these value neurons are robust across diverse datasets, model scales, and architectures; furthermore, they exhibit significant transferability across different datasets and models fine-tuned from the same base model. By examining cases where value predictions and actual rewards diverge, we identify dopamine neurons within the reward subsystem which encode reward prediction errors (RPE). These neurons exhibit high activation when the reward is higher than expected and low activation when the reward is lower than expected.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02287", "title": "Cross-Lingual Stability of LLM Judges Under Controlled Generation: Evidence from Finno-Ugric Languages", "authors": [ "Isaac Chung", "Linda Freienthal" ], "github": "https://github.com/isaac-chung/cross-lingual-stability-judges", "project_page": "", "abstract": "Cross-lingual evaluation of large language models (LLMs) typically conflates two sources of variance: genuine model performance differences and measurement instability. We investigate evaluation reliability by holding generation conditions constant while varying target language. Using synthetic customer-support dialogues generated with identical parameters across Estonian, Finnish, and Hungarian, we test whether automatic metrics and LLM-as-a-judge scoring produce stable model rankings across these morphologically rich, related Finno-Ugric languages. With a small set of Estonian native speaker annotations as a reference point, we find systematic ranking instabilities: surface-level metrics (lexical diversity, surface and semantic similarity) maintain cross-language stability, but pragmatic judgments (coherence, instruction-following) exhibit rank inversions and near-zero correlations. Because generation is controlled, these inconsistencies reflect how judge scoring behaves differently across languages rather than true model differences. This controlled design provides a diagnostic probe: evaluation methods that fail to maintain stability under identical generation conditions signal transfer failure before deployment. Our findings suggest that zero-shot judge transfer is unreliable for discourse-level assessment in morphologically rich languages, motivating language-specific calibration against targeted human baselines. We release our controlled generation protocol, synthetic data, and evaluation framework to enable replication across language families at https://github.com/isaac-chung/cross-lingual-stability-judges.", "github_manual": "", "github_regex": "https://github.com/isaac-chung/cross-lingual-stability-judges", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02039", "title": "Hunt Instead of Wait: Evaluating Deep Data Research on Large Language Models", "authors": [ "Wei Liu", "Peijie Yu", "Michele Orini", "Yali Du", "Yulan He" ], "github": "https://github.com/MiniMax-AI/MiniMax-M2", "project_page": "", "abstract": "The agency expected of Agentic Large Language Models goes beyond answering correctly, requiring autonomy to set goals and decide what to explore. We term this investigatory intelligence, distinguishing it from executional intelligence, which merely completes assigned tasks. Data Science provides a natural testbed, as real-world analysis starts from raw data rather than explicit queries, yet few benchmarks focus on it. To address this, we introduce Deep Data Research (DDR), an open-ended task where LLMs autonomously extract key insights from databases, and DDR-Bench, a large-scale, checklist-based benchmark that enables verifiable evaluation. Results show that while frontier models display emerging agency, long-horizon exploration remains challenging. Our analysis highlights that effective investigatory intelligence depends not only on agent scaffolding or merely scaling, but also on intrinsic strategies of agentic models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiniMax-AI/MiniMax-M2", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.14691", "title": "Gaming the Judge: Unfaithful Chain-of-Thought Can Undermine Agent Evaluation", "authors": [ "Muhammad Khalifa", "Lajanugen Logeswaran", "Jaekyeom Kim", "Sungryull Sohn", "Yunxiang Zhang", "Moontae Lee", "Hao Peng", "Lu Wang", "Honglak Lee" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly used as judges to evaluate agent performance, particularly in non-verifiable settings where judgments rely on agent trajectories including chain-of-thought (CoT) reasoning. This paradigm implicitly assumes that the agent's CoT faithfully reflects both its internal reasoning and the underlying environment state. We show this assumption is brittle: LLM judges are highly susceptible to manipulation of agent reasoning traces. By systematically rewriting agent CoTs while holding actions and observations fixed, we demonstrate that manipulated reasoning alone can inflate false positive rates of state-of-the-art VLM judges by up to 90% across 800 trajectories spanning diverse web tasks. We study manipulation strategies spanning style-based approaches that alter only the presentation of reasoning and content-based approaches that fabricate signals of task progress, and find that content-based manipulations are consistently more effective. We evaluate prompting-based techniques and scaling judge-time compute, which reduce but do not fully eliminate susceptibility to manipulation. Our findings reveal a fundamental vulnerability in LLM-based evaluation and highlight the need for judging mechanisms that verify reasoning claims against observable evidence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.21123", "title": "CUA-Skill: Develop Skills for Computer Using Agent", "authors": [ "Tianyi Chen", "Yinheng Li", "Michael Solodko", "Sen Wang", "Nan Jiang", "Tingyuan Cui", "Junheng Hao", "Jongwoo Ko", "Sara Abdali", "Suzhen Zheng", "Leon Xu", "Hao Fan", "Pashmina Cameron", "Justin Wagle", "Kazuhito Koishida" ], "github": "", "project_page": "", "abstract": "Computer-Using Agents (CUAs) aim to autonomously operate computer systems to complete real-world tasks. However, existing agentic systems remain difficult to scale and lag behind human performance. A key limitation is the absence of reusable and structured skill abstractions that capture how humans interact with graphical user interfaces and how to leverage these skills. We introduce CUA-Skill, a computer-using agentic skill base that encodes human computer-use knowledge as skills coupled with parameterized execution and composition graphs. CUA-Skill is a large-scale library of carefully engineered skills spanning common Windows applications, serving as a practical infrastructure and tool substrate for scalable, reliable agent development. Built upon this skill base, we construct CUA-Skill Agent, an end-to-end computer-using agent that supports dynamic skill retrieval, argument instantiation, and memory-aware failure recovery. Our results demonstrate that CUA-Skill substantially improves execution success rates and robustness on challenging end-to-end agent benchmarks, establishing a strong foundation for future computer-using agent development. On WindowsAgentArena, CUA-Skill Agent achieves state-of-the-art 57.5% (best of three) successful rate while being significantly more efficient than prior and concurrent approaches. The project page is available at https://microsoft.github.io/cua_skill/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.00269", "title": "VoxServe: Streaming-Centric Serving System for Speech Language Models", "authors": [ "Keisuke Kamahori", "Wei-Tzu Lee", "Atindra Jha", "Rohan Kadekodi", "Stephanie Wang", "Arvind Krishnamurthy", "Baris Kasikci" ], "github": "https://github.com/vox-serve/vox-serve", "project_page": "", "abstract": "Deploying modern Speech Language Models (SpeechLMs) in streaming settings requires systems that provide low latency, high throughput, and strong guarantees of streamability. Existing systems fall short of supporting diverse models flexibly and efficiently. We present VoxServe, a unified serving system for SpeechLMs that optimizes streaming performance. VoxServe introduces a model-execution abstraction that decouples model architecture from system-level optimizations, thereby enabling support for diverse SpeechLM architectures within a single framework. Building on this abstraction, VoxServe implements streaming-aware scheduling and an asynchronous inference pipeline to improve end-to-end efficiency. Evaluations across multiple modern SpeechLMs show that VoxServe achieves 10-20x higher throughput than existing implementations at comparable latency while maintaining high streaming viability. The code of VoxServe is available at https://github.com/vox-serve/vox-serve.", "github_manual": "", "github_regex": "https://github.com/vox-serve/vox-serve", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02477", "title": "Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability", "authors": [ "Xiao Liang", "Zhong-Zhi Li", "Zhenghao Lin", "Eric Hancheng Jiang", "Hengyuan Zhang", "Yelong Shen", "Kai-Wei Chang", "Ying Nian Wu", "Yeyun Gong", "Weizhu Chen" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated strong reasoning capabilities through step-by-step chain-of-thought (CoT) reasoning. Nevertheless, at the limits of model capability, CoT often proves insufficient, and its strictly sequential nature constrains test-time scalability. A potential alternative is divide-and-conquer (DAC) reasoning, which decomposes a complex problem into subproblems to facilitate more effective exploration of the solution. Although promising, our analysis reveals a fundamental misalignment between general-purpose post-training and DAC-style inference, which limits the model's capacity to fully leverage this potential. To bridge this gap and fully unlock LLMs' reasoning capabilities on the most challenging tasks, we propose an end-to-end reinforcement learning (RL) framework to enhance their DAC-style reasoning capacity. At each step, the policy decomposes a problem into a group of subproblems, solves them sequentially, and addresses the original one conditioned on the subproblem solutions, with both decomposition and solution integrated into RL training. Under comparable training, our DAC-style framework endows the model with a higher performance ceiling and stronger test-time scalability, surpassing CoT by 8.6% in Pass@1 and 6.3% in Pass@32 on competition-level benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.02338", "title": "Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs", "authors": [ "Yu Liang", "Zhongjin Zhang", "Yuxuan Zhu", "Kerui Zhang", "Zhiluohan Guo", "Wenhang Zhou", "Zonqi Yang", "Kangle Wu", "Yabo Ni", "Anxiang Zeng", "Cong Fu", "Jianxin Wang", "Jiazhi Xia" ], "github": "https://github.com/FuCongResearchSquad/ReSID", "project_page": "", "abstract": "Semantic ID (SID)-based recommendation is a promising paradigm for scaling sequential recommender systems, but existing methods largely follow a semantic-centric pipeline: item embeddings are learned from foundation models and discretized using generic quantization schemes. This design is misaligned with generative recommendation objectives: semantic embeddings are weakly coupled with collaborative prediction, and generic quantization is inefficient at reducing sequential uncertainty for autoregressive modeling. To address these, we propose ReSID, a recommendation-native, principled SID framework that rethinks representation learning and quantization from the perspective of information preservation and sequential predictability, without relying on LLMs. ReSID consists of two components: (i) Field-Aware Masked Auto-Encoding (FAMAE), which learns predictive-sufficient item representations from structured features, and (ii) Globally Aligned Orthogonal Quantization (GAOQ), which produces compact and predictable SID sequences by jointly reducing semantic ambiguity and prefix-conditional uncertainty. Theoretical analysis and extensive experiments across ten datasets show the effectiveness of ReSID. ReSID consistently outperforms strong sequential and SID-based generative baselines by an average of over 10%, while reducing tokenization cost by up to 122x. Code is available at https://github.com/FuCongResearchSquad/ReSID.", "github_manual": "", "github_regex": "https://github.com/FuCongResearchSquad/ReSID", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.01785", "title": "CodeOCR: On the Effectiveness of Vision Language Models in Code Understanding", "authors": [ "Yuling Shi", "Chaoxiang Xie", "Zhensu Sun", "Yeheng Chen", "Chenxu Zhang", "Longfei Yun", "Chengcheng Wan", "Hongyu Zhang", "David Lo", "Xiaodong Gu" ], "github": "https://github.com/YerbaPage/CodeOCR", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved remarkable success in source code understanding, yet as software systems grow in scale, computational efficiency has become a critical bottleneck. Currently, these models rely on a text-based paradigm that treats source code as a linear sequence of tokens, which leads to a linear increase in context length and associated computational costs. The rapid advancement of Multimodal LLMs (MLLMs) introduces an opportunity to optimize efficiency by representing source code as rendered images. Unlike text, which is difficult to compress without losing semantic meaning, the image modality is inherently suitable for compression. By adjusting resolution, images can be scaled to a fraction of their original token cost while remaining recognizable to vision-capable models. To explore the feasibility of this approach, we conduct the first systematic study on the effectiveness of MLLMs for code understanding. Our experiments reveal that: (1) MLLMs can effectively understand code with substantial token reduction, achieving up to 8x compression; (2) MLLMs can effectively leverage visual cues such as syntax highlighting, improving code completion performance under 4x compression; and (3) Code-understanding tasks like clone detection exhibit exceptional resilience to visual compression, with some compression ratios even slightly outperforming raw text inputs. Our findings highlight both the potential and current limitations of MLLMs in code understanding, which points out a shift toward image-modality code representation as a pathway to more efficient inference.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YerbaPage/CodeOCR", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2601.19103", "title": "Glance and Focus Reinforcement for Pan-cancer Screening", "authors": [ "Linshan Wu", "Jiaxin Zhuang", "Hao Chen" ], "github": "https://github.com/Luffy03/GF-Screen", "project_page": "", "abstract": "Pan-cancer screening in large-scale CT scans remains challenging for existing AI methods, primarily due to the difficulty of localizing diverse types of tiny lesions in large CT volumes. The extreme foreground-background imbalance significantly hinders models from focusing on diseased regions, while redundant focus on healthy regions not only decreases the efficiency but also increases false positives. Inspired by radiologists' glance and focus diagnostic strategy, we introduce GF-Screen, a Glance and Focus reinforcement learning framework for pan-cancer screening. GF-Screen employs a Glance model to localize the diseased regions and a Focus model to precisely segment the lesions, where segmentation results of the Focus model are leveraged to reward the Glance model via Reinforcement Learning (RL). Specifically, the Glance model crops a group of sub-volumes from the entire CT volume and learns to select the sub-volumes with lesions for the Focus model to segment. Given that the selecting operation is non-differentiable for segmentation training, we propose to employ the segmentation results to reward the Glance model. To optimize the Glance model, we introduce a novel group relative learning paradigm, which employs group relative comparison to prioritize high-advantage predictions and discard low-advantage predictions within sub-volume groups, not only improving efficiency but also reducing false positives. In this way, for the first time, we effectively extend cutting-edge RL techniques to tackle the specific challenges in pan-cancer screening. Extensive experiments on 16 internal and 7 external datasets across 9 lesion types demonstrated the effectiveness of GF-Screen. Notably, GF-Screen leads the public validation leaderboard of MICCAI FLARE25 pan-cancer challenge, surpassing the FLARE24 champion solution by a large margin (+25.6% DSC and +28.2% NSD).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Luffy03/GF-Screen", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02380", "title": "Unified Personalized Reward Model for Vision Generation", "authors": [ "Yibin Wang", "Yuhang Zang", "Feng Han", "Jiazi Bu", "Yujie Zhou", "Cheng Jin", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Recent advancements in multimodal reward models (RMs) have significantly propelled the development of visual generation. Existing frameworks typically adopt Bradley-Terry-style preference modeling or leverage generative VLMs as judges, and subsequently optimize visual generation models via reinforcement learning. However, current RMs suffer from inherent limitations: they often follow a one-size-fits-all paradigm that assumes a monolithic preference distribution or relies on fixed evaluation rubrics. As a result, they are insensitive to content-specific visual cues, leading to systematic misalignment with subjective and context-dependent human preferences. To this end, inspired by human assessment, we propose UnifiedReward-Flex, a unified personalized reward model for vision generation that couples reward modeling with flexible and context-adaptive reasoning. Specifically, given a prompt and the generated visual content, it first interprets the semantic intent and grounds on visual evidence, then dynamically constructs a hierarchical assessment by instantiating fine-grained criteria under both predefined and self-generated high-level dimensions. Our training pipeline follows a two-stage process: (1) we first distill structured, high-quality reasoning traces from advanced closed-source VLMs to bootstrap SFT, equipping the model with flexible and context-adaptive reasoning behaviors; (2) we then perform direct preference optimization (DPO) on carefully curated preference pairs to further strengthen reasoning fidelity and discriminative alignment. To validate the effectiveness, we integrate UnifiedReward-Flex into the GRPO framework for image and video synthesis, and extensive results demonstrate its superiority.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02676", "title": "AdaptMMBench: Benchmarking Adaptive Multimodal Reasoning for Mode Selection and Reasoning Process", "authors": [ "Xintong Zhang", "Xiaowen Zhang", "Jongrong Wu", "Zhi Gao", "Shilin Yan", "Zhenxin Diao", "Kunpeng Gao", "Xuanyan Chen", "Yuwei Wu", "Yunde Jia", "Qing Li" ], "github": "", "project_page": "", "abstract": "Adaptive multimodal reasoning has emerged as a promising frontier in Vision-Language Models (VLMs), aiming to dynamically modulate between tool-augmented visual reasoning and text reasoning to enhance both effectiveness and efficiency. However, existing evaluations rely on static difficulty labels and simplistic metrics, which fail to capture the dynamic nature of difficulty relative to varying model capacities. Consequently, they obscure the distinction between adaptive mode selection and general performance while neglecting fine-grained process analyses. In this paper, we propose AdaptMMBench, a comprehensive benchmark for adaptive multimodal reasoning across five domains: real-world, OCR, GUI, knowledge, and math, encompassing both direct perception and complex reasoning tasks. AdaptMMBench utilizes a Matthews Correlation Coefficient (MCC) metric to evaluate the selection rationality of different reasoning modes, isolating this meta-cognition ability by dynamically identifying task difficulties based on models' capability boundaries. Moreover, AdaptMMBench facilitates multi-dimensional process evaluation across key step coverage, tool effectiveness, and computational efficiency. Our evaluation reveals that while adaptive mode selection scales with model capacity, it notably decouples from final accuracy. Conversely, key step coverage aligns with performance, though tool effectiveness remains highly inconsistent across model architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02914", "title": "FaceLinkGen: Rethinking Identity Leakage in Privacy-Preserving Face Recognition with Identity Extraction", "authors": [ "Wenqi Guo", "Shan Du" ], "github": "", "project_page": "", "abstract": "Transformation-based privacy-preserving face recognition (PPFR) aims to verify identities while hiding facial data from attackers and malicious service providers. Existing evaluations mostly treat privacy as resistance to pixel-level reconstruction, measured by PSNR and SSIM. We show that this reconstruction-centric view fails. We present FaceLinkGen, an identity extraction attack that performs linkage/matching and face regeneration directly from protected templates without recovering original pixels. On three recent PPFR systems, FaceLinkGen reaches over 98.5\\% matching accuracy and above 96\\% regeneration success, and still exceeds 92\\% matching and 94\\% regeneration in a near zero knowledge setting. These results expose a structural gap between pixel distortion metrics, which are widely used in PPFR evaluation, and real privacy. We show that visual obfuscation leaves identity information broadly exposed to both external intruders and untrusted service providers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.22599", "title": "A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation", "authors": [ "Kai Li", "Jintao Cheng", "Chang Zeng", "Zijun Yan", "Helin Wang", "Zixiong Su", "Bo Zheng", "Xiaolin Hu" ], "github": "", "project_page": "", "abstract": "Query-based universal sound separation is fundamental to intelligent auditory systems, aiming to isolate specific sources from mixtures. Despite recent advances, existing methods continue to suffer from residual interference in complex acoustic scenes. This performance limitation stems largely from a data bottleneck: in-the-wild datasets contain weak labels and severe co-occurrence of events. These flaws induce models to learn spurious correlations between background noise and target categories instead of robust acoustic features. To address this, we propose an automated pipeline that eliminates co-occurrence of events by mining high-purity single-event segments from in-the-wild datasets via a semantically consistent synthesis protocol. Utilizing this pipeline, we constructed Hive, a high-quality synthetic dataset comprising 2.4k hours of raw audio. Experimental results demonstrate that, compared with the state-of-the-art model SAM-Audio which was trained on a huge dataset sim500 times larger than Hive, certain open-source models trained on Hive achieve competitive separation accuracy and perceptual quality. Moreover, these models exhibited remarkable zero-shot generalization on out-of-distribution evaluation benchmarks. These findings highlight that prioritizing purity of supervised signals enables significant data efficiency, offering a new paradigm for training robust auditory foundation models with reduced computational costs. Code and dataset are available at https://shandaai.github.io/Hive.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.01630", "title": "Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks", "authors": [ "Bohan Zeng", "Kaixin Zhu", "Daili Hua", "Bozhou Li", "Chengzhuo Tong", "Yuran Wang", "Xinyi Huang", "Yifan Dai", "Zixiang Zhang", "Yifan Yang", "Zhou Liu", "Hao Liang", "Xiaochen Ma", "Ruichuan An", "Tianyi Bai", "Hongcheng Gao", "Junbo Niu", "Yang Shi", "Xinlong Chen", "Yue Ding", "Minglei Shi", "Kai Zeng", "Yiwen Tang", "Yuanxing Zhang", "Pengfei Wan", "Xintao Wang", "Wentao Zhang" ], "github": "", "project_page": "", "abstract": "World models have emerged as a critical frontier in AI research, aiming to enhance large models by infusing them with physical dynamics and world knowledge. The core objective is to enable agents to understand, predict, and interact with complex environments. However, current research landscape remains fragmented, with approaches predominantly focused on injecting world knowledge into isolated tasks, such as visual prediction, 3D estimation, or symbol grounding, rather than establishing a unified definition or framework. While these task-specific integrations yield performance gains, they often lack the systematic coherence required for holistic world understanding. In this paper, we analyze the limitations of such fragmented approaches and propose a unified design specification for world models. We suggest that a robust world model should not be a loose collection of capabilities but a normative framework that integrally incorporates interaction, perception, symbolic reasoning, and spatial representation. This work aims to provide a structured perspective to guide future research toward more general, robust, and principled models of the world.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03619", "title": "Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generation", "authors": [ "Changze Lv", "Jie Zhou", "Wentao Zhao", "Jingwen Xu", "Zisu Huang", "Muzhao Tian", "Shihan Dou", "Tao Gui", "Le Tian", "Xiao Zhou", "Xiaoqing Zheng", "Xuanjing Huang", "Jie Zhou" ], "github": "https://github.com/THUDM/slimeGitHub", "project_page": "", "abstract": "Nowadays, training and evaluating DeepResearch-generated reports remain challenging due to the lack of verifiable reward signals. Accordingly, rubric-based evaluation has become a common practice. However, existing approaches either rely on coarse, pre-defined rubrics that lack sufficient granularity, or depend on manually constructed query-specific rubrics that are costly and difficult to scale. In this paper, we propose a pipeline to train human-preference-aligned query-specific rubric generators tailored for DeepResearch report generation. We first construct a dataset of DeepResearch-style queries annotated with human preferences over paired reports, and train rubric generators via reinforcement learning with a hybrid reward combining human preference supervision and LLM-based rubric evaluation. To better handle long-horizon reasoning, we further introduce a Multi-agent Markov-state (MaMs) workflow for report generation. We empirically show that our proposed rubric generators deliver more discriminative and better human-aligned supervision than existing rubric design strategies. Moreover, when integrated into the MaMs training framework, DeepResearch systems equipped with our rubric generators consistently outperform all open-source baselines on the DeepResearch Bench and achieve performance comparable to that of leading closed-source models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUDM/slimeGitHub", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03845", "title": "Parallel-Probe: Towards Efficient Parallel Thinking via 2D Probing", "authors": [ "Tong Zheng", "Chengsong Huang", "Runpeng Dai", "Yun He", "Rui Liu", "Xin Ni", "Huiwen Bao", "Kaishen Wang", "Hongtu Zhu", "Jiaxin Huang", "Furong Huang", "Heng Huang" ], "github": "", "project_page": "", "abstract": "Parallel thinking has emerged as a promising paradigm for reasoning, yet it imposes significant computational burdens. Existing efficiency methods primarily rely on local, per-trajectory signals and lack principled mechanisms to exploit global dynamics across parallel branches. We introduce 2D probing, an interface that exposes the width-depth dynamics of parallel thinking by periodically eliciting intermediate answers from all branches. Our analysis reveals three key insights: non-monotonic scaling across width-depth allocations, heterogeneous reasoning branch lengths, and early stabilization of global consensus. Guided by these insights, we introduce Parallel-Probe, a training-free controller designed to optimize online parallel thinking. Parallel-Probe employs consensus-based early stopping to regulate reasoning depth and deviation-based branch pruning to dynamically adjust width. Extensive experiments across three benchmarks and multiple models demonstrate that Parallel-Probe establishes a superior Pareto frontier for test-time scaling. Compared to standard majority voting, it reduces sequential tokens by up to 35.8% and total token cost by over 25.8% while maintaining competitive accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2601.21244", "title": "Less Noise, More Voice: Reinforcement Learning for Reasoning via Instruction Purification", "authors": [ "Yiju Guo", "Tianyi Hu", "Zexu Sun", "Yankai Lin" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has advanced LLM reasoning, but remains constrained by inefficient exploration under limited rollout budgets, leading to low sampling success and unstable training in complex tasks. We find that many exploration failures arise not from problem difficulty, but from a small number of prompt tokens that introduce interference. Building on this insight, we propose the Less Noise Sampling Framework (LENS), which first prompts by identifying and removing interference tokens. then transfers successful rollouts from the purification process to supervise policy optimization on the original noisy prompts, enabling the model to learn to ignore interference in the real-world, noisy prompting settings. Experimental results show that LENS significantly outperforms GRPO, delivering higher performance and faster convergence, with a 3.88% average gain and over 1.6times speedup. Our work highlights the critical role of pruning interference tokens in improving rollout efficiency, offering a new perspective for RLVR research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02636", "title": "WideSeek: Advancing Wide Research via Multi-Agent Scaling", "authors": [ "Ziyang Huang", "Haolin Ren", "Xiaowei Yuan", "Jiawei Wang", "Zhongtao Jiang", "Kun Xu", "Shizhu He", "Jun Zhao", "Kang Liu" ], "github": "", "project_page": "", "abstract": "Search intelligence is evolving from Deep Research to Wide Research, a paradigm essential for retrieving and synthesizing comprehensive information under complex constraints in parallel. However, progress in this field is impeded by the lack of dedicated benchmarks and optimization methodologies for search breadth. To address these challenges, we take a deep dive into Wide Research from two perspectives: Data Pipeline and Agent Optimization. First, we produce WideSeekBench, a General Broad Information Seeking (GBIS) benchmark constructed via a rigorous multi-phase data pipeline to ensure diversity across the target information volume, logical constraints, and domains. Second, we introduce WideSeek, a dynamic hierarchical multi-agent architecture that can autonomously fork parallel sub-agents based on task requirements. Furthermore, we design a unified training framework that linearizes multi-agent trajectories and optimizes the system using end-to-end RL. Experimental results demonstrate the effectiveness of WideSeek and multi-agent RL, highlighting that scaling the number of agents is a promising direction for advancing the Wide Research paradigm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.01362", "title": "Balancing Understanding and Generation in Discrete Diffusion Models", "authors": [ "Yue Liu", "Yuzhong Zhao", "Zheyong Xie", "Qixiang Ye", "Jianbin Jiao", "Yao Hu", "Shaosheng Cao", "Yunfan Liu" ], "github": "https://github.com/MzeroMiko/XDLM", "project_page": "", "abstract": "In discrete generative modeling, two dominant paradigms demonstrate divergent capabilities: Masked Diffusion Language Models (MDLM) excel at semantic understanding and zero-shot generalization, whereas Uniform-noise Diffusion Language Models (UDLM) achieve strong few-step generation quality, yet neither attains balanced performance across both dimensions. To address this, we propose XDLM, which bridges the two paradigms via a stationary noise kernel. XDLM offers two key contributions: (1) it provides a principled theoretical unification of MDLM and UDLM, recovering each paradigm as a special case; and (2) an alleviated memory bottleneck enabled by an algebraic simplification of the posterior probabilities. Experiments demonstrate that XDLM advances the Pareto frontier between understanding capability and generation quality. Quantitatively, XDLM surpasses UDLM by 5.4 points on zero-shot text benchmarks and outperforms MDLM in few-step image generation (FID 54.1 vs. 80.8). When scaled to tune an 8B-parameter large language model, XDLM achieves 15.0 MBPP in just 32 steps, effectively doubling the baseline performance. Finally, analysis of training dynamics reveals XDLM's superior potential for long-term scaling. Code is available at https://github.com/MzeroMiko/XDLM", "github_manual": "", "github_regex": "https://github.com/MzeroMiko/XDLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03837", "title": "Accelerating Scientific Research with Gemini: Case Studies and Common Techniques", "authors": [ "David P. Woodruff", "Vincent Cohen-Addad", "Lalit Jain", "Jieming Mao", "Song Zuo", "MohammadHossein Bateni", "Simina Branzei", "Michael P. Brenner", "Lin Chen", "Ying Feng", "Lance Fortnow", "Gang Fu", "Ziyi Guan", "Zahra Hadizadeh", "Mohammad T. Hajiaghayi", "Mahdi JafariRaviz", "Adel Javanmard", "Karthik C. S.", "Ken-ichi Kawarabayashi", "Ravi Kumar", "Silvio Lattanzi", "Euiwoong Lee", "Yi Li", "Ioannis Panageas", "Dimitris Paparas", "Benjamin Przybocki", "Bernardo Subercaseaux", "Ola Svensson", "Shayan Taherijam", "Xuan Wu", "Eylon Yogev", "Morteza Zadimoghaddam", "Samson Zhou", "Vahab Mirrokni" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have opened new avenues for accelerating scientific research. While models are increasingly capable of assisting with routine tasks, their ability to contribute to novel, expert-level mathematical discovery is less understood. We present a collection of case studies demonstrating how researchers have successfully collaborated with advanced AI models, specifically Google's Gemini-based models (in particular Gemini Deep Think and its advanced variants), to solve open problems, refute conjectures, and generate new proofs across diverse areas in theoretical computer science, as well as other areas such as economics, optimization, and physics. Based on these experiences, we extract common techniques for effective human-AI collaboration in theoretical research, such as iterative refinement, problem decomposition, and cross-disciplinary knowledge transfer. While the majority of our results stem from this interactive, conversational methodology, we also highlight specific instances that push beyond standard chat interfaces. These include deploying the model as a rigorous adversarial reviewer to detect subtle flaws in existing proofs, and embedding it within a \"neuro-symbolic\" loop that autonomously writes and executes code to verify complex derivations. Together, these examples highlight the potential of AI not just as a tool for automation, but as a versatile, genuine partner in the creative process of scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03454", "title": "Contextualized Visual Personalization in Vision-Language Models", "authors": [ "Yeongtak Oh", "Sangwon Yu", "Junsung Park", "Han Cheol Moon", "Jisoo Mok", "Sungroh Yoon" ], "github": "https://github.com/oyt9306/CoViP", "project_page": "", "abstract": "Despite recent progress in vision-language models (VLMs), existing approaches often fail to generate personalized responses based on the user's specific experiences, as they lack the ability to associate visual inputs with a user's accumulated visual-textual context. We newly formalize this challenge as contextualized visual personalization, which requires the visual recognition and textual retrieval of personalized visual experiences by VLMs when interpreting new images. To address this issue, we propose CoViP, a unified framework that treats personalized image captioning as a core task for contextualized visual personalization and improves this capability through reinforcement-learning-based post-training and caption-augmented generation. We further introduce diagnostic evaluations that explicitly rule out textual shortcut solutions and verify whether VLMs truly leverage visual context. Extensive experiments demonstrate that existing open-source and proprietary VLMs exhibit substantial limitations, while CoViP not only improves personalized image captioning but also yields holistic gains across downstream personalization tasks. These results highlight CoViP as a crucial stage for enabling robust and generalizable contextualized visual personalization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/oyt9306/CoViP", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.01753", "title": "ObjEmbed: Towards Universal Multimodal Object Embeddings", "authors": [ "Shenghao Fu", "Yukun Su", "Fengyun Rao", "Jing Lyu", "Xiaohua Xie", "Wei-Shi Zheng" ], "github": "https://github.com/WeChatCV/ObjEmbed", "project_page": "", "abstract": "Aligning objects with corresponding textual descriptions is a fundamental challenge and a realistic requirement in vision-language understanding. While recent multimodal embedding models excel at global image-text alignment, they often struggle with fine-grained alignment between image regions and specific phrases. In this work, we present ObjEmbed, a novel MLLM embedding model that decomposes the input image into multiple regional embeddings, each corresponding to an individual object, along with global embeddings. It supports a wide range of visual understanding tasks like visual grounding, local image retrieval, and global image retrieval. ObjEmbed enjoys three key properties: (1) Object-Oriented Representation: It captures both semantic and spatial aspects of objects by generating two complementary embeddings for each region: an object embedding for semantic matching and an IoU embedding that predicts localization quality. The final object matching score combines semantic similarity with the predicted IoU, enabling more accurate retrieval. (2) Versatility: It seamlessly handles both region-level and image-level tasks. (3) Efficient Encoding: All objects in an image, along with the full image, are encoded in a single forward pass for high efficiency. Superior performance on 18 diverse benchmarks demonstrates its strong semantic discrimination.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WeChatCV/ObjEmbed", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02537", "title": "WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models", "authors": [ "Runjie Zhou", "Youbo Shao", "Haoyu Lu", "Bowei Xing", "Tongtong Bai", "Yujie Chen", "Jie Zhao", "Lin Sui", "Haotian Yao", "Zijia Zhao", "Hao Yang", "Haoning Wu", "Zaida Zhou", "Jinguo Zhu", "Zhiqi Huang", "Yiping Bao", "Yangyang Liu", "Y. Charles", "Xinyu Zhou" ], "github": "https://github.com/MoonshotAI/WorldVQA", "project_page": "", "abstract": "We introduce WorldVQA, a benchmark designed to evaluate the atomic visual world knowledge of Multimodal Large Language Models (MLLMs). Unlike current evaluations, which often conflate visual knowledge retrieval with reasoning, WorldVQA decouples these capabilities to strictly measure \"what the model memorizes.\" The benchmark assesses the atomic capability of grounding and naming visual entities across a stratified taxonomy, spanning from common head-class objects to long-tail rarities. We expect WorldVQA to serve as a rigorous test for visual factuality, thereby establishing a standard for assessing the encyclopedic breadth and hallucination rates of current and next-generation frontier models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MoonshotAI/WorldVQA", "github_api_added_by": "auto" }, { "date": "2026-02-04", "arxiv_id": "2602.03817", "title": "Adaptive Evidence Weighting for Audio-Spatiotemporal Fusion", "authors": [ "Oscar Ovanger", "Levi Harris", "Timothy H. Keitt" ], "github": "", "project_page": "", "abstract": "Many machine learning systems have access to multiple sources of evidence for the same prediction target, yet these sources often differ in reliability and informativeness across inputs. In bioacoustic classification, species identity may be inferred both from the acoustic signal and from spatiotemporal context such as location and season; while Bayesian inference motivates multiplicative evidence combination, in practice we typically only have access to discriminative predictors rather than calibrated generative models. We introduce Fusion under INdependent Conditional Hypotheses (FINCH), an adaptive log-linear evidence fusion framework that integrates a pre-trained audio classifier with a structured spatiotemporal predictor. FINCH learns a per-sample gating function that estimates the reliability of contextual information from uncertainty and informativeness statistics. The resulting fusion family contains the audio-only classifier as a special case and explicitly bounds the influence of contextual evidence, yielding a risk-contained hypothesis class with an interpretable audio-only fallback. Across benchmarks, FINCH consistently outperforms fixed-weight fusion and audio-only baselines, improving robustness and error trade-offs even when contextual information is weak in isolation. We achieve state-of-the-art performance on CBI and competitive or improved performance on several subsets of BirdSet using a lightweight, interpretable, evidence-based approach. Code is available: \\href{https://anonymous.4open.science/r/birdnoise-85CD/README.md{anonymous-repository}}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03796", "title": "3D-Aware Implicit Motion Control for View-Adaptive Human Video Generation", "authors": [ "Zhixue Fang", "Xu He", "Songlin Tang", "Haoxian Zhang", "Qingfeng Li", "Xiaoqiang Liu", "Pengfei Wan", "Kun Gai" ], "github": "", "project_page": "", "abstract": "Existing methods for human motion control in video generation typically rely on either 2D poses or explicit 3D parametric models (e.g., SMPL) as control signals. However, 2D poses rigidly bind motion to the driving viewpoint, precluding novel-view synthesis. Explicit 3D models, though structurally informative, suffer from inherent inaccuracies (e.g., depth ambiguity and inaccurate dynamics) which, when used as a strong constraint, override the powerful intrinsic 3D awareness of large-scale video generators. In this work, we revisit motion control from a 3D-aware perspective, advocating for an implicit, view-agnostic motion representation that naturally aligns with the generator's spatial priors rather than depending on externally reconstructed constraints. We introduce 3DiMo, which jointly trains a motion encoder with a pretrained video generator to distill driving frames into compact, view-agnostic motion tokens, injected semantically via cross-attention. To foster 3D awareness, we train with view-rich supervision (i.e., single-view, multi-view, and moving-camera videos), forcing motion consistency across diverse viewpoints. Additionally, we use auxiliary geometric supervision that leverages SMPL only for early initialization and is annealed to zero, enabling the model to transition from external 3D guidance to learning genuine 3D spatial motion understanding from the data and the generator's priors. Experiments confirm that 3DiMo faithfully reproduces driving motions with flexible, text-driven camera control, significantly surpassing existing methods in both motion fidelity and visual quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03647", "title": "Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration", "authors": [ "Bowei He", "Minda Hu", "Zenan Xu", "Hongru Wang", "Licheng Zong", "Yankai Chen", "Chen Ma", "Xue Liu", "Pluto Zhou", "Irwin King" ], "github": "", "project_page": "", "abstract": "Search-integrated reasoning enables language agents to transcend static parametric knowledge by actively querying external sources. However, training these agents via reinforcement learning is hindered by the multi-scale credit assignment problem: existing methods typically rely on sparse, trajectory-level rewards that fail to distinguish between high-quality reasoning and fortuitous guesses, leading to redundant or misleading search behaviors. To address this, we propose Search-R2, a novel Actor-Refiner collaboration framework that enhances reasoning through targeted intervention, with both components jointly optimized during training. Our approach decomposes the generation process into an Actor, which produces initial reasoning trajectories, and a Meta-Refiner, which selectively diagnoses and repairs flawed steps via a 'cut-and-regenerate' mechanism. To provide fine-grained supervision, we introduce a hybrid reward design that couples outcome correctness with a dense process reward quantifying the information density of retrieved evidence. Theoretically, we formalize the Actor-Refiner interaction as a smoothed mixture policy, proving that selective correction yields strict performance gains over strong baselines. Extensive experiments across various general and multi-hop QA datasets demonstrate that Search-R2 consistently outperforms strong RAG and RL-based baselines across model scales, achieving superior reasoning accuracy with minimal overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03048", "title": "CoBA-RL: Capability-Oriented Budget Allocation for Reinforcement Learning in LLMs", "authors": [ "Zhiyuan Yao", "Yi-Kai Zhang", "Yuxin Chen", "Yueqing Sun", "Zishan Xu", "Yu Yang", "Tianhao Hu", "Qi Gu", "Hui Su", "Xunliang Cai" ], "github": "https://github.com/Within-yao/CoBA-RL", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key approach for enhancing LLM reasoning.However, standard frameworks like Group Relative Policy Optimization (GRPO) typically employ a uniform rollout budget, leading to resource inefficiency. Moreover, existing adaptive methods often rely on instance-level metrics, such as task pass rates, failing to capture the model's dynamic learning state. To address these limitations, we propose CoBA-RL, a reinforcement learning algorithm designed to adaptively allocate rollout budgets based on the model's evolving capability. Specifically, CoBA-RL utilizes a Capability-Oriented Value function to map tasks to their potential training gains and employs a heap-based greedy strategy to efficiently self-calibrate the distribution of computational resources to samples with high training value. Extensive experiments demonstrate that our approach effectively orchestrates the trade-off between exploration and exploitation, delivering consistent generalization improvements across multiple challenging benchmarks. These findings underscore that quantifying sample training value and optimizing budget allocation are pivotal for advancing LLM post-training efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Within-yao/CoBA-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02660", "title": "MARS: Modular Agent with Reflective Search for Automated AI Research", "authors": [ "Jiefeng Chen", "Bhavana Dalvi Mishra", "Jaehyun Nam", "Rui Meng", "Tomas Pfister", "Jinsung Yoon" ], "github": "", "project_page": "", "abstract": "Automating AI research differs from general software engineering due to computationally expensive evaluation (e.g., model training) and opaque performance attribution. Current LLM-based agents struggle here, often generating monolithic scripts that ignore execution costs and causal factors. We introduce MARS (Modular Agent with Reflective Search), a framework optimized for autonomous AI research. MARS relies on three pillars: (1) Budget-Aware Planning via cost-constrained Monte Carlo Tree Search (MCTS) to explicitly balance performance with execution expense; (2) Modular Construction, employing a \"Design-Decompose-Implement\" pipeline to manage complex research repositories; and (3) Comparative Reflective Memory, which addresses credit assignment by analyzing solution differences to distill high-signal insights. MARS achieves state-of-the-art performance among open-source frameworks on MLE-Bench under comparable settings, maintaining competitiveness with the global leaderboard's top methods. Furthermore, the system exhibits qualitative \"Aha!\" moments, where 63% of all utilized lessons originate from cross-branch transfer, demonstrating that the agent effectively generalizes insights across search paths.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02103", "title": "No Global Plan in Chain-of-Thought: Uncover the Latent Planning Horizon of LLMs", "authors": [ "Liyan Xu", "Mo Yu", "Fandong Meng", "Jie Zhou" ], "github": "https://github.com/lxucs/tele-lens", "project_page": "", "abstract": "This work stems from prior complementary observations on the dynamics of Chain-of-Thought (CoT): Large Language Models (LLMs) is shown latent planning of subsequent reasoning prior to CoT emergence, thereby diminishing the significance of explicit CoT; whereas CoT remains critical for tasks requiring multi-step reasoning. To deepen the understanding between LLM's internal states and its verbalized reasoning trajectories, we investigate the latent planning strength of LLMs, through our probing method, Tele-Lens, applying to hidden states across diverse task domains. Our empirical results indicate that LLMs exhibit a myopic horizon, primarily conducting incremental transitions without precise global planning. Leveraging this characteristic, we propose a hypothesis on enhancing uncertainty estimation of CoT, which we validate that a small subset of CoT positions can effectively represent the uncertainty of the entire path. We further underscore the significance of exploiting CoT dynamics, and demonstrate that automatic recognition of CoT bypass can be achieved without performance degradation. Our code, data and models are released at https://github.com/lxucs/tele-lens.", "github_manual": "", "github_regex": "https://github.com/lxucs/tele-lens", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02619", "title": "daVinci-Agency: Unlocking Long-Horizon Agency Data-Efficiently", "authors": [ "Mohan Jiang", "Dayuan Fu", "Junhao Shi", "Ji Zeng", "Weiye Si", "Keyu Li", "Xuefeng Li", "Yang Xiao", "Wenjie Li", "Dequan Wang", "Pengfei Liu" ], "github": "https://github.com/THUDM/slime", "project_page": "", "abstract": "While Large Language Models (LLMs) excel at short-term tasks, scaling them to long-horizon agentic workflows remains challenging. The core bottleneck lies in the scarcity of training data that captures authentic long-dependency structures and cross-stage evolutionary dynamics--existing synthesis methods either confine to single-feature scenarios constrained by model distribution, or incur prohibitive human annotation costs, failing to provide scalable, high-quality supervision. We address this by reconceptualizing data synthesis through the lens of real-world software evolution. Our key insight: Pull Request (PR) sequences naturally embody the supervision signals for long-horizon learning. They decompose complex objectives into verifiable submission units, maintain functional coherence across iterations, and encode authentic refinement patterns through bug-fix histories. Building on this, we propose daVinci-Agency, which systematically mines structured supervision from chain-of-PRs through three interlocking mechanisms: (1) progressive task decomposition via continuous commits, (2) long-term consistency enforcement through unified functional objectives, and (3) verifiable refinement from authentic bug-fix trajectories. Unlike synthetic trajectories that treat each step independently, daVinci-Agency's PR-grounded structure inherently preserves the causal dependencies and iterative refinements essential for teaching persistent goal-directed behavior and enables natural alignment with project-level, full-cycle task modeling. The resulting trajectories are substantial--averaging 85k tokens and 116 tool calls--yet remarkably data-efficient: fine-tuning GLM-4.6 on 239 daVinci-Agency samples yields broad improvements across benchmarks, notably achieving a 47% relative gain on Toolathlon. Beyond benchmark performance, our analysis confirms...", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUDM/slime", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.00747", "title": "Decouple Searching from Training: Scaling Data Mixing via Model Merging for Large Language Model Pre-training", "authors": [ "Shengrui Li", "Fei Zhao", "Kaiyan Zhao", "Jieying Ye", "Haifeng Liu", "Fangcheng Shi", "Zheyong Xie", "Yao Hu", "Shaosheng Cao" ], "github": "https://github.com/Lucius-lsr/DeMix", "project_page": "", "abstract": "Determining an effective data mixture is a key factor in Large Language Model (LLM) pre-training, where models must balance general competence with proficiency on hard tasks such as math and code. However, identifying an optimal mixture remains an open challenge, as existing approaches either rely on unreliable tiny-scale proxy experiments or require prohibitively expensive large-scale exploration. To address this, we propose Decouple Searching from Training Mix (DeMix), a novel framework that leverages model merging to predict optimal data ratios. Instead of training proxy models for every sampled mixture, DeMix trains component models on candidate datasets at scale and derives data mixture proxies via weighted model merging. This paradigm decouples search from training costs, enabling evaluation of unlimited sampled mixtures without extra training burden and thus facilitating better mixture discovery through more search trials. Extensive experiments demonstrate that DeMix breaks the trade-off between sufficiency, accuracy and efficiency, obtaining the optimal mixture with higher benchmark performance at lower search cost. Additionally, we release the DeMix Corpora, a comprehensive 22T-token dataset comprising high-quality pre-training data with validated mixtures to facilitate open research. Our code and DeMix Corpora is available at https://github.com/Lucius-lsr/DeMix.", "github_manual": "", "github_regex": "https://github.com/Lucius-lsr/DeMix", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03419", "title": "SWE-World: Building Software Engineering Agents in Docker-Free Environments", "authors": [ "Shuang Sun", "Huatong Song", "Lisheng Huang", "Jinhao Jiang", "Ran Le", "Zhihao Lv", "Zongchao Chen", "Yiwen Hu", "Wenyang Luo", "Wayne Xin Zhao", "Yang Song", "Hongteng Xu", "Tao Zhang", "Ji-Rong Wen" ], "github": "https://github.com/RUCAIBox/SWE-World", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have enabled software engineering agents to tackle complex code modification tasks. Most existing approaches rely on execution feedback from containerized environments, which require dependency-complete setup and physical execution of programs and tests. While effective, this paradigm is resource-intensive and difficult to maintain, substantially complicating agent training and limiting scalability. We propose SWE-World, a Docker-free framework that replaces physical execution environments with a learned surrogate for training and evaluating software engineering agents. SWE-World leverages LLM-based models trained on real agent-environment interaction data to predict intermediate execution outcomes and final test feedback, enabling agents to learn without interacting with physical containerized environments. This design preserves the standard agent-environment interaction loop while eliminating the need for costly environment construction and maintenance during agent optimization and evaluation. Furthermore, because SWE-World can simulate the final evaluation outcomes of candidate trajectories without real submission, it enables selecting the best solution among multiple test-time attempts, thereby facilitating effective test-time scaling (TTS) in software engineering tasks. Experiments on SWE-bench Verified demonstrate that SWE-World raises Qwen2.5-Coder-32B from 6.2\\% to 52.0\\% via Docker-free SFT, 55.0\\% with Docker-free RL, and 68.2\\% with further TTS. The code is available at https://github.com/RUCAIBox/SWE-World", "github_manual": "", "github_regex": "https://github.com/RUCAIBox/SWE-World", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03411", "title": "SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training", "authors": [ "Huatong Song", "Lisheng Huang", "Shuang Sun", "Jinhao Jiang", "Ran Le", "Daixuan Cheng", "Guoxin Chen", "Yiwen Hu", "Zongchao Chen", "Wayne Xin Zhao", "Yang Song", "Tao Zhang", "Ji-Rong Wen" ], "github": "https://github.com/RUCAIBox/SWE-Master", "project_page": "", "abstract": "In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents. SWE-Master systematically explores the complete agent development pipeline, including teacher-trajectory synthesis and data curation, long-horizon SFT, RL with real execution feedback, and inference framework design. Starting from an open-source base model with limited initial SWE capability, SWE-Master demonstrates how systematical optimization method can elicit strong long-horizon SWE task solving abilities. We evaluate SWE-Master on SWE-bench Verified, a standard benchmark for realistic software engineering tasks. Under identical experimental settings, our approach achieves a resolve rate of 61.4\\% with Qwen2.5-Coder-32B, substantially outperforming existing open-source baselines. By further incorporating test-time scaling~(TTS) with LLM-based environment feedback, SWE-Master reaches 70.8\\% at TTS@8, demonstrating a strong performance potential. SWE-Master provides a practical and transparent foundation for advancing reproducible research on software engineering agents. The code is available at https://github.com/RUCAIBox/SWE-Master.", "github_manual": "", "github_regex": "https://github.com/RUCAIBox/SWE-Master", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03786", "title": "AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration", "authors": [ "Jianhao Ruan", "Zhihao Xu", "Yiran Peng", "Fashen Ren", "Zhaoyang Yu", "Xinbing Liang", "Jinyu Xiang", "Bang Liu", "Chenglin Wu", "Yuyu Luo", "Jiayi Zhang" ], "github": "https://github.com/FoundationAgents/AOrchestra", "project_page": "", "abstract": "Language agents have shown strong promise for task automation. Realizing this promise for increasingly complex, long-horizon tasks has driven the rise of a sub-agent-as-tools paradigm for multi-turn task solving. However, existing designs still lack a dynamic abstraction view of sub-agents, thereby hurting adaptability. We address this challenge with a unified, framework-agnostic agent abstraction that models any agent as a tuple Instruction, Context, Tools, Model. This tuple acts as a compositional recipe for capabilities, enabling the system to spawn specialized executors for each task on demand. Building on this abstraction, we introduce an agentic system AOrchestra, where the central orchestrator concretizes the tuple at each step: it curates task-relevant context, selects tools and models, and delegates execution via on-the-fly automatic agent creation. Such designs enable reducing human engineering efforts, and remain framework-agnostic with plug-and-play support for diverse agents as task executors. It also enables a controllable performance-cost trade-off, allowing the system to approach Pareto-efficient. Across three challenging benchmarks (GAIA, SWE-Bench, Terminal-Bench), AOrchestra achieves 16.28% relative improvement against the strongest baseline when paired with Gemini-3-Flash. The code is available at: https://github.com/FoundationAgents/AOrchestra", "github_manual": "", "github_regex": "https://github.com/FoundationAgents/AOrchestra", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03139", "title": "Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis", "authors": [ "Tianhe Wu", "Ruibin Li", "Lei Zhang", "Kede Ma" ], "github": "", "project_page": "", "abstract": "Distribution matching distillation (DMD) aligns a multi-step generator with its few-step counterpart to enable high-quality generation under low inference cost. However, DMD tends to suffer from mode collapse, as its reverse-KL formulation inherently encourages mode-seeking behavior, for which existing remedies typically rely on perceptual or adversarial regularization, thereby incurring substantial computational overhead and training instability. In this work, we propose a role-separated distillation framework that explicitly disentangles the roles of distilled steps: the first step is dedicated to preserving sample diversity via a target-prediction (e.g., v-prediction) objective, while subsequent steps focus on quality refinement under the standard DMD loss, with gradients from the DMD objective blocked at the first step. We term this approach Diversity-Preserved DMD (DP-DMD), which, despite its simplicity -- no perceptual backbone, no discriminator, no auxiliary networks, and no additional ground-truth images -- preserves sample diversity while maintaining visual quality on par with state-of-the-art methods in extensive text-to-image experiments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02419", "title": "SafeGround: Know When to Trust GUI Grounding Models via Uncertainty Calibration", "authors": [ "Qingni Wang", "Yue Fan", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "Graphical User Interface (GUI) grounding aims to translate natural language instructions into executable screen coordinates, enabling automated GUI interaction. Nevertheless, incorrect grounding can result in costly, hard-to-reverse actions (e.g., erroneous payment approvals), raising concerns about model reliability. In this paper, we introduce SafeGround, an uncertainty-aware framework for GUI grounding models that enables risk-aware predictions through calibrations before testing. SafeGround leverages a distribution-aware uncertainty quantification method to capture the spatial dispersion of stochastic samples from outputs of any given model. Then, through the calibration process, SafeGround derives a test-time decision threshold with statistically guaranteed false discovery rate (FDR) control. We apply SafeGround on multiple GUI grounding models for the challenging ScreenSpot-Pro benchmark. Experimental results show that our uncertainty measure consistently outperforms existing baselines in distinguishing correct from incorrect predictions, while the calibrated threshold reliably enables rigorous risk control and potentials of substantial system-level accuracy improvements. Across multiple GUI grounding models, SafeGround improves system-level accuracy by up to 5.38% percentage points over Gemini-only inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03806", "title": "Bridging Online and Offline RL: Contextual Bandit Learning for Multi-Turn Code Generation", "authors": [ "Ziru Chen", "Dongdong Chen", "Ruinan Jin", "Yingbin Liang", "Yujia Xie", "Huan Sun" ], "github": "https://github.com/OSU-NLP-Group/cobalt", "project_page": "", "abstract": "Recently, there have been significant research interests in training large language models (LLMs) with reinforcement learning (RL) on real-world tasks, such as multi-turn code generation. While online RL tends to perform better than offline RL, its higher training cost and instability hinders wide adoption. In this paper, we build on the observation that multi-turn code generation can be formulated as a one-step recoverable Markov decision process and propose contextual bandit learning with offline trajectories (Cobalt), a new method that combines the benefits of online and offline RL. Cobalt first collects code generation trajectories using a reference LLM and divides them into partial trajectories as contextual prompts. Then, during online bandit learning, the LLM is trained to complete each partial trajectory prompt through single-step code generation. Cobalt outperforms two multi-turn online RL baselines based on GRPO and VeRPO, and substantially improves R1-Distill 8B and Qwen3 8B by up to 9.0 and 6.2 absolute Pass@1 scores on LiveCodeBench. Also, we analyze LLMs' in-context reward hacking behaviors and augment Cobalt training with perturbed trajectories to mitigate this issue. Overall, our results demonstrate Cobalt as a promising solution for iterative decision-making tasks like multi-turn code generation. Our code and data are available at https://github.com/OSU-NLP-Group/cobalt.", "github_manual": "", "github_regex": "https://github.com/OSU-NLP-Group/cobalt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03798", "title": "FullStack-Agent: Enhancing Agentic Full-Stack Web Coding via Development-Oriented Testing and Repository Back-Translation", "authors": [ "Zimu Lu", "Houxing Ren", "Yunqiao Yang", "Ke Wang", "Zhuofan Zong", "Mingjie Zhan", "Hongsheng Li" ], "github": "https://github.com/mnluzimu/FullStack-Agent", "project_page": "", "abstract": "Assisting non-expert users to develop complex interactive websites has become a popular task for LLM-powered code agents. However, existing code agents tend to only generate frontend web pages, masking the lack of real full-stack data processing and storage with fancy visual effects. Notably, constructing production-level full-stack web applications is far more challenging than only generating frontend web pages, demanding careful control of data flow, comprehensive understanding of constantly updating packages and dependencies, and accurate localization of obscure bugs in the codebase. To address these difficulties, we introduce FullStack-Agent, a unified agent system for full-stack agentic coding that consists of three parts: (1) FullStack-Dev, a multi-agent framework with strong planning, code editing, codebase navigation, and bug localization abilities. (2) FullStack-Learn, an innovative data-scaling and self-improving method that back-translates crawled and synthesized website repositories to improve the backbone LLM of FullStack-Dev. (3) FullStack-Bench, a comprehensive benchmark that systematically tests the frontend, backend and database functionalities of the generated website. Our FullStack-Dev outperforms the previous state-of-the-art method by 8.7%, 38.2%, and 15.9% on the frontend, backend, and database test cases respectively. Additionally, FullStack-Learn raises the performance of a 30B model by 9.7%, 9.5%, and 2.8% on the three sets of test cases through self-improvement, demonstrating the effectiveness of our approach. The code is released at https://github.com/mnluzimu/FullStack-Agent.", "github_manual": "", "github_regex": "https://github.com/mnluzimu/FullStack-Agent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03216", "title": "Token Sparse Attention: Efficient Long-Context Inference with Interleaved Token Selection", "authors": [ "Dongwon Jo", "Beomseok Kang", "Jiwon Song", "Jae-Joon Kim" ], "github": "", "project_page": "", "abstract": "The quadratic complexity of attention remains the central bottleneck in long-context inference for large language models. Prior acceleration methods either sparsify the attention map with structured patterns or permanently evict tokens at specific layers, which can retain irrelevant tokens or rely on irreversible early decisions despite the layer-/head-wise dynamics of token importance. In this paper, we propose Token Sparse Attention, a lightweight and dynamic token-level sparsification mechanism that compresses per-head Q, K, V to a reduced token set during attention and then decompresses the output back to the original sequence, enabling token information to be reconsidered in subsequent layers. Furthermore, Token Sparse Attention exposes a new design point at the intersection of token selection and sparse attention. Our approach is fully compatible with dense attention implementations, including Flash Attention, and can be seamlessly composed with existing sparse attention kernels. Experimental results show that Token Sparse Attention consistently improves accuracy-latency trade-off, achieving up to times3.23 attention speedup at 128K context with less than 1% accuracy degradation. These results demonstrate that dynamic and interleaved token-level sparsification is a complementary and effective strategy for scalable long-context inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.01053", "title": "LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents", "authors": [ "Hyesung Jeon", "Hyeongju Ha", "Jae-Joon Kim" ], "github": "https://github.com/hjeon2k/LRAgent", "project_page": "", "abstract": "Role specialization in multi-LLM agent systems is often realized via multi-LoRA, where agents share a pretrained backbone and differ only through lightweight adapters. Despite sharing base model weights, each agent independently builds and stores its own KV cache for the same long, tool-augmented trajectories, incurring substantial memory and compute overhead. Existing KV cache sharing methods largely overlook this multi-LoRA setting. We observe that, across agents, cache differences are dominated by adapter outputs, while activations from the shared pretrained backbone remain highly similar. Based on this observation, we propose LRAgent, a KV cache sharing framework for multi-LoRA agents that decomposes the cache into a shared base component from the pretrained weights and an adapter-dependent component from LoRA weights. LRAgent reduces memory overhead by sharing the base component and storing the adapter component in its inherent low-rank form, and further reduces compute overhead, enabled by shared-A multi-LoRA architectures, by also sharing the low-rank cache and avoiding redundant computations for contexts already processed by other agents. To efficiently reconstruct adapter contributions at runtime, we introduce Flash-LoRA-Attention, a kernel that reorders attention computation to avoid materializing the low-rank cache to full dimension. LRAgent achieves throughput and time-to-first-token latency close to fully shared caching, while preserving accuracy near the non-shared caching baseline across agentic question-answering benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hjeon2k/LRAgent", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03238", "title": "The Necessity of a Unified Framework for LLM-Based Agent Evaluation", "authors": [ "Pengyu Zhu", "Li Sun", "Philip S. Yu", "Sen Su" ], "github": "", "project_page": "", "abstract": "With the advent of Large Language Models (LLMs), general-purpose agents have seen fundamental advancements. However, evaluating these agents presents unique challenges that distinguish them from static QA benchmarks. We observe that current agent benchmarks are heavily confounded by extraneous factors, including system prompts, toolset configurations, and environmental dynamics. Existing evaluations often rely on fragmented, researcher-specific frameworks where the prompt engineering for reasoning and tool usage varies significantly, making it difficult to attribute performance gains to the model itself. Additionally, the lack of standardized environmental data leads to untraceable errors and non-reproducible results. This lack of standardization introduces substantial unfairness and opacity into the field. We propose that a unified evaluation framework is essential for the rigorous advancement of agent evaluation. To this end, we introduce a proposal aimed at standardizing agent evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.01212", "title": "SimpleGPT: Improving GPT via A Simple Normalization Strategy", "authors": [ "Marco Chen", "Xianbiao Qi", "Yelin He", "Jiaquan Ye", "Rong Xiao" ], "github": "https://github.com/Ocram7/SimpleGPT", "project_page": "", "abstract": "In this work, we revisit Transformer optimization through the lens of second-order geometry and establish a direct connection between architectural design, activation scale, the Hessian matrix, and the maximum tolerable learning rate. We introduce a simple normalization strategy, termed SimpleNorm, which stabilizes intermediate activation scales by construction. Then, by analyzing the Hessian of the loss with respect to network activations, we theoretically show that SimpleNorm significantly reduces the spectral norm of the Hessian, thereby permitting larger stable learning rates. We validate our theoretical findings through extensive experiments on large GPT models at parameter scales 1B, 1.4B, 7B and 8B. Empirically, SimpleGPT, our SimpleNorm-based network, tolerates learning rates 3times-10times larger than standard convention, consistently demonstrates strong optimization stability, and achieves substantially better performance than well-established baselines. Specifically, when training 7B-scale models for 60K steps, SimpleGPT achieves a training loss that is 0.08 lower than that of LLaMA2 with QKNorm, reducing the loss from 2.290 to 2.208. Our source code will be released at https://github.com/Ocram7/SimpleGPT.", "github_manual": "", "github_regex": "https://github.com/Ocram7/SimpleGPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03709", "title": "No Shortcuts to Culture: Indonesian Multi-hop Question Answering for Complex Cultural Understanding", "authors": [ "Vynska Amalia Permadi", "Xingwei Tan", "Nafise Sadat Moosavi", "Nikos Aletras" ], "github": "", "project_page": "", "abstract": "Understanding culture requires reasoning across context, tradition, and implicit social knowledge, far beyond recalling isolated facts. Yet most culturally focused question answering (QA) benchmarks rely on single-hop questions, which may allow models to exploit shallow cues rather than demonstrate genuine cultural reasoning. In this work, we introduce ID-MoCQA, the first large-scale multi-hop QA dataset for assessing the cultural understanding of large language models (LLMs), grounded in Indonesian traditions and available in both English and Indonesian. We present a new framework that systematically transforms single-hop cultural questions into multi-hop reasoning chains spanning six clue types (e.g., commonsense, temporal, geographical). Our multi-stage validation pipeline, combining expert review and LLM-as-a-judge filtering, ensures high-quality question-answer pairs. Our evaluation across state-of-the-art models reveals substantial gaps in cultural reasoning, particularly in tasks requiring nuanced inference. ID-MoCQA provides a challenging and essential benchmark for advancing the cultural competency of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03677", "title": "Instruction Anchors: Dissecting the Causal Dynamics of Modality Arbitration", "authors": [ "Yu Zhang", "Mufan Xu", "Xuefeng Bai", "Kehai chen", "Pengfei Zhang", "Yang Xiang", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Modality following serves as the capacity of multimodal large language models (MLLMs) to selectively utilize multimodal contexts based on user instructions. It is fundamental to ensuring safety and reliability in real-world deployments. However, the underlying mechanisms governing this decision-making process remain poorly understood. In this paper, we investigate its working mechanism through an information flow lens. Our findings reveal that instruction tokens function as structural anchors for modality arbitration: Shallow attention layers perform non-selective information transfer, routing multimodal cues to these anchors as a latent buffer; Modality competition is resolved within deep attention layers guided by the instruction intent, while MLP layers exhibit semantic inertia, acting as an adversarial force. Furthermore, we identify a sparse set of specialized attention heads that drive this arbitration. Causal interventions demonstrate that manipulating a mere 5% of these critical heads can decrease the modality-following ratio by 60% through blocking, or increase it by 60% through targeted amplification of failed samples. Our work provides a substantial step toward model transparency and offers a principled framework for the orchestration of multimodal information in MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.00682", "title": "RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment", "authors": [ "Yuecheng Li", "Hengwei Ju", "Zeyu Song", "Wei Yang", "Chi Lu", "Peng Jiang", "Kun Gai" ], "github": "https://github.com/6lyc/RecGOAT-LLM4Rec", "project_page": "", "abstract": "Multimodal recommendation systems typically integrates user behavior with multimodal data from items, thereby capturing more accurate user preferences. Concurrently, with the rise of large models (LMs), multimodal recommendation is increasingly leveraging their strengths in semantic understanding and contextual reasoning. However, LM representations are inherently optimized for general semantic tasks, while recommendation models rely heavily on sparse user/item unique identity (ID) features. Existing works overlook the fundamental representational divergence between large models and recommendation systems, resulting in incompatible multimodal representations and suboptimal recommendation performance. To bridge this gap, we propose RecGOAT, a novel yet simple dual semantic alignment framework for LLM-enhanced multimodal recommendation, which offers theoretically guaranteed alignment capability. RecGOAT first employs graph attention networks to enrich collaborative semantics by modeling item-item, user-item, and user-user relationships, leveraging user/item LM representations and interaction history. Furthermore, we design a dual-granularity progressive multimodality-ID alignment framework, which achieves instance-level and distribution-level semantic alignment via cross-modal contrastive learning (CMCL) and optimal adaptive transport (OAT), respectively. Theoretically, we demonstrate that the unified representations derived from our alignment framework exhibit superior semantic consistency and comprehensiveness. Extensive experiments on three public benchmarks show that our RecGOAT achieves state-of-the-art performance, empirically validating our theoretical insights. Additionally, the deployment on a large-scale online advertising platform confirms the model's effectiveness and scalability in industrial recommendation scenarios. Code available at https://github.com/6lyc/RecGOAT-LLM4Rec.", "github_manual": "", "github_regex": "https://github.com/6lyc/RecGOAT-LLM4Rec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03295", "title": "POP: Prefill-Only Pruning for Efficient Large Model Inference", "authors": [ "Junhui He", "Zhihui Fu", "Jun Wang", "Qingan Li" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) and Vision-Language Models (VLMs) have demonstrated remarkable capabilities. However, their deployment is hindered by significant computational costs. Existing structured pruning methods, while hardware-efficient, often suffer from significant accuracy degradation. In this paper, we argue that this failure stems from a stage-agnostic pruning approach that overlooks the asymmetric roles between the prefill and decode stages. By introducing a virtual gate mechanism, our importance analysis reveals that deep layers are critical for next-token prediction (decode) but largely redundant for context encoding (prefill). Leveraging this insight, we propose Prefill-Only Pruning (POP), a stage-aware inference strategy that safely omits deep layers during the computationally intensive prefill stage while retaining the full model for the sensitive decode stage. To enable the transition between stages, we introduce independent Key-Value (KV) projections to maintain cache integrity, and a boundary handling strategy to ensure the accuracy of the first generated token. Extensive experiments on Llama-3.1, Qwen3-VL, and Gemma-3 across diverse modalities demonstrate that POP achieves up to 1.37times speedup in prefill latency with minimal performance loss, effectively overcoming the accuracy-efficiency trade-off limitations of existing structured pruning methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02494", "title": "MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training", "authors": [ "Dulhan Jayalath", "Oiwi Parker Jones" ], "github": "https://github.com/neural-processing-lab/MEG-XL", "project_page": "", "abstract": "Clinical brain-to-text interfaces are designed for paralysed patients who cannot provide extensive training recordings. Pre-training improves data-efficient generalisation by learning statistical priors across subjects, but these priors critically depend on context. While natural speech might unfold gradually over minutes, most methods pre-train with only a few seconds of context. Thus, we propose MEG-XL, a model pre-trained with 2.5 minutes of MEG context per sample, 5-300x longer than prior work, and equivalent to 191k tokens, capturing extended neural context. Fine-tuning on the task of word decoding from brain data, MEG-XL matches supervised performance with a fraction of the data (e.g. 1hr vs 50hrs) and outperforms brain foundation models. We find that models pre-trained with longer contexts learn representations that transfer better to word decoding. Our results indicate that long-context pre-training helps exploit extended neural context that other methods unnecessarily discard. Code, model weights, and instructions are available at https://github.com/neural-processing-lab/MEG-XL .", "github_manual": "", "github_regex": "https://github.com/neural-processing-lab/MEG-XL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02220", "title": "LangMap: A Hierarchical Benchmark for Open-Vocabulary Goal Navigation", "authors": [ "Bo Miao", "Weijia Liu", "Jun Luo", "Lachlan Shinnick", "Jian Liu", "Thomas Hamilton-Smith", "Yuhe Yang", "Zijie Wu", "Vanja Videnovic", "Feras Dayoub", "Anton van den Hengel" ], "github": "", "project_page": "", "abstract": "The relationships between objects and language are fundamental to meaningful communication between humans and AI, and to practically useful embodied intelligence. We introduce HieraNav, a multi-granularity, open-vocabulary goal navigation task where agents interpret natural language instructions to reach targets at four semantic levels: scene, room, region, and instance. To this end, we present Language as a Map (LangMap), a large-scale benchmark built on real-world 3D indoor scans with comprehensive human-verified annotations and tasks spanning these levels. LangMap provides region labels, discriminative region descriptions, discriminative instance descriptions covering 414 object categories, and over 18K navigation tasks. Each target features both concise and detailed descriptions, enabling evaluation across different instruction styles. LangMap achieves superior annotation quality, outperforming GOAT-Bench by 23.8% in discriminative accuracy using four times fewer words. Comprehensive evaluations of zero-shot and supervised models on LangMap reveal that richer context and memory improve success, while long-tailed, small, context-dependent, and distant goals, as well as multi-goal completion, remain challenging. HieraNav and LangMap establish a rigorous testbed for advancing language-driven embodied navigation. Project: https://bo-miao.github.io/LangMap", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03320", "title": "MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning", "authors": [ "Shengyuan Liu", "Liuxin Bao", "Qi Yang", "Wanting Geng", "Boyun Zheng", "Chenxin Li", "Wenting Chen", "Houwen Peng", "Yixuan Yuan" ], "github": "https://github.com/CUHK-AIM-Group/MedSAM-Agent", "project_page": "", "abstract": "Medical image segmentation is evolving from task-specific models toward generalizable frameworks. Recent research leverages Multi-modal Large Language Models (MLLMs) as autonomous agents, employing reinforcement learning with verifiable reward (RLVR) to orchestrate specialized tools like the Segment Anything Model (SAM). However, these approaches often rely on single-turn, rigid interaction strategies and lack process-level supervision during training, which hinders their ability to fully exploit the dynamic potential of interactive tools and leads to redundant actions. To bridge this gap, we propose MedSAM-Agent, a framework that reformulates interactive segmentation as a multi-step autonomous decision-making process. First, we introduce a hybrid prompting strategy for expert-curated trajectory generation, enabling the model to internalize human-like decision heuristics and adaptive refinement strategies. Furthermore, we develop a two-stage training pipeline that integrates multi-turn, end-to-end outcome verification with a clinical-fidelity process reward design to promote interaction parsimony and decision efficiency. Extensive experiments across 6 medical modalities and 21 datasets demonstrate that MedSAM-Agent achieves state-of-the-art performance, effectively unifying autonomous medical reasoning with robust, iterative optimization. Code is available https://github.com/CUHK-AIM-Group/MedSAM-Agent{here}.", "github_manual": "", "github_regex": "https://github.com/CUHK-AIM-Group/MedSAM-Agent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.01519", "title": "You Need an Encoder for Native Position-Independent Caching", "authors": [ "Shiju Zhao", "Junhao Hu", "Jiaqi Zheng", "Guihai Chen" ], "github": "https://github.com/shijuzhao/Comb", "project_page": "", "abstract": "The Key-Value (KV) cache of Large Language Models (LLMs) is prefix-based, making it highly inefficient for processing contexts retrieved in arbitrary order. Position-Independent Caching (PIC) has been proposed to enable KV reuse without positional constraints; however, existing approaches often incur substantial accuracy degradation, limiting their practical adoption. To address this issue, we propose native PIC by reintroducing the encoder to prevalent decoder-only LLMs and explicitly training it to support PIC. We further develop COMB, a PIC-aware caching system that integrates seamlessly with existing inference frameworks. Experimental results show that COMB reduces Time-to-First-Token (TTFT) by 51-94% and increases throughput by 3times with comparable accuracy. Furthermore, the quality improvement when using DeepSeek-V2-Lite-Chat demonstrates the applicability of COMB to other types of decoder-only LLMs. Our code is available at https://github.com/shijuzhao/Comb.", "github_manual": "", "github_regex": "https://github.com/shijuzhao/Comb", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03086", "title": "Neural Predictor-Corrector: Solving Homotopy Problems with Reinforcement Learning", "authors": [ "Jiayao Mai", "Bangyan Liao", "Zhenjun Zhao", "Yingping Zeng", "Haoang Li", "Javier Civera", "Tailin Wu", "Yi Zhou", "Peidong Liu" ], "github": "", "project_page": "", "abstract": "The Homotopy paradigm, a general principle for solving challenging problems, appears across diverse domains such as robust optimization, global optimization, polynomial root-finding, and sampling. Practical solvers for these problems typically follow a predictor-corrector (PC) structure, but rely on hand-crafted heuristics for step sizes and iteration termination, which are often suboptimal and task-specific. To address this, we unify these problems under a single framework, which enables the design of a general neural solver. Building on this unified view, we propose Neural Predictor-Corrector (NPC), which replaces hand-crafted heuristics with automatically learned policies. NPC formulates policy selection as a sequential decision-making problem and leverages reinforcement learning to automatically discover efficient strategies. To further enhance generalization, we introduce an amortized training mechanism, enabling one-time offline training for a class of problems and efficient online inference on new instances. Experiments on four representative homotopy problems demonstrate that our method generalizes effectively to unseen instances. It consistently outperforms classical and specialized baselines in efficiency while demonstrating superior stability across tasks, highlighting the value of unifying homotopy methods into a single neural framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02444", "title": "RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval", "authors": [ "Tyler Skow", "Alexander Martin", "Benjamin Van Durme", "Rama Chellappa", "Reno Kriz" ], "github": "", "project_page": "", "abstract": "Reranking is a critical component of modern retrieval systems, which typically pair an efficient first-stage retriever with a more expressive model to refine results. While large reasoning models have driven rapid progress in text-centric reranking, reasoning-based reranking for video retrieval remains underexplored. To address this gap, we introduce RANKVIDEO, a reasoning-based reranker for video retrieval that explicitly reasons over query-video pairs using video content to assess relevance. RANKVIDEO is trained using a two-stage curriculum consisting of perception-grounded supervised fine-tuning followed by reranking training that combines pointwise, pairwise, and teacher confidence distillation objectives, and is supported by a data synthesis pipeline for constructing reasoning-intensive query-video pairs. Experiments on the large-scale MultiVENT 2.0 benchmark demonstrate that RANKVIDEO consistently improves retrieval performance within a two-stage framework, yielding an average improvement of 31% on nDCG@10 and outperforming text-only and vision-language reranking alternatives, while more efficient.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.03747", "title": "LIVE: Long-horizon Interactive Video World Modeling", "authors": [ "Junchao Huang", "Ziyang Ye", "Xinting Hu", "Tianyu He", "Guiyu Zhang", "Shaoshuai Shi", "Jiang Bian", "Li Jiang" ], "github": "", "project_page": "", "abstract": "Autoregressive video world models predict future visual observations conditioned on actions. While effective over short horizons, these models often struggle with long-horizon generation, as small prediction errors accumulate over time. Prior methods alleviate this by introducing pre-trained teacher models and sequence-level distribution matching, which incur additional computational cost and fail to prevent error propagation beyond the training horizon. In this work, we propose LIVE, a Long-horizon Interactive Video world modEl that enforces bounded error accumulation via a novel cycle-consistency objective, thereby eliminating the need for teacher-based distillation. Specifically, LIVE first performs a forward rollout from ground-truth frames and then applies a reverse generation process to reconstruct the initial state. The diffusion loss is subsequently computed on the reconstructed terminal state, providing an explicit constraint on long-horizon error propagation. Moreover, we provide an unified view that encompasses different approaches and introduce progressive training curriculum to stabilize training. Experiments demonstrate that LIVE achieves state-of-the-art performance on long-horizon benchmarks, generating stable, high-quality videos far beyond training rollout lengths.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02405", "title": "Didactic to Constructive: Turning Expert Solutions into Learnable Reasoning", "authors": [ "Ethan Mendes", "Jungsoo Park", "Alan Ritter" ], "github": "https://github.com/ethanm88/DAIL", "project_page": "", "abstract": "Improving the reasoning capabilities of large language models (LLMs) typically relies either on the model's ability to sample a correct solution to be reinforced or on the existence of a stronger model able to solve the problem. However, many difficult problems remain intractable for even current frontier models, preventing the extraction of valid training signals. A promising alternative is to leverage high-quality expert human solutions, yet naive imitation of this data fails because it is fundamentally out of distribution: expert solutions are typically didactic, containing implicit reasoning gaps intended for human readers rather than computational models. Furthermore, high-quality expert solutions are expensive, necessitating generalizable sample-efficient training methods. We propose Distribution Aligned Imitation Learning (DAIL), a two-step method that bridges the distributional gap by first transforming expert solutions into detailed, in-distribution reasoning traces and then applying a contrastive objective to focus learning on expert insights and methodologies. We find that DAIL can leverage fewer than 1000 high-quality expert solutions to achieve 10-25% pass@k gains on Qwen2.5-Instruct and Qwen3 models, improve reasoning efficiency by 2x to 4x, and enable out-of-domain generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ethanm88/DAIL", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.01405", "title": "Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents", "authors": [ "Nikhil Sharma", "Zheng Zhang", "Daniel Lee", "Namita Krishnan", "Guang-Jie Ren", "Ziang Xiao", "Yunyao Li" ], "github": "", "project_page": "", "abstract": "High-quality feedback is essential for effective human-AI interaction. It bridges knowledge gaps, corrects digressions, and shapes system behavior; both during interaction and throughout model development. Yet despite its importance, human feedback to AI is often infrequent and low quality. This gap motivates a critical examination of human feedback during interactions with AIs. To understand and overcome the challenges preventing users from giving high-quality feedback, we conducted two studies examining feedback dynamics between humans and conversational agents (CAs). Our formative study, through the lens of Grice's maxims, identified four Feedback Barriers -- Common Ground, Verifiability, Communication, and Informativeness -- that prevent high-quality feedback by users. Building on these findings, we derive three design desiderata and show that systems incorporating scaffolds aligned with these desiderata enabled users to provide higher-quality feedback. Finally, we detail a call for action to the broader AI community for advances in Large Language Models capabilities to overcome Feedback Barriers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02751", "title": "Scaling Small Agents Through Strategy Auctions", "authors": [ "Lisa Alazraki", "William F. Shen", "Yoram Bachrach", "Akhil Mathur" ], "github": "", "project_page": "", "abstract": "Small language models are increasingly viewed as a promising, cost-effective approach to agentic AI, with proponents claiming they are sufficiently capable for agentic workflows. However, while smaller agents can closely match larger ones on simple tasks, it remains unclear how their performance scales with task complexity, when large models become necessary, and how to better leverage small agents for long-horizon workloads. In this work, we empirically show that small agents' performance fails to scale with task complexity on deep search and coding tasks, and we introduce Strategy Auctions for Workload Efficiency (SALE), an agent framework inspired by freelancer marketplaces. In SALE, agents bid with short strategic plans, which are scored by a systematic cost-value mechanism and refined via a shared auction memory, enabling per-task routing and continual self-improvement without training a separate router or running all models to completion. Across deep search and coding tasks of varying complexity, SALE reduces reliance on the largest agent by 53%, lowers overall cost by 35%, and consistently improves upon the largest agent's pass@1 with only a negligible overhead beyond executing the final trace. In contrast, established routers that rely on task descriptions either underperform the largest agent or fail to reduce cost -- often both -- underscoring their poor fit for agentic workflows. These results suggest that while small agents may be insufficient for complex workloads, they can be effectively \"scaled up\" through coordinated task allocation and test-time self-improvement. More broadly, they motivate a systems-level view of agentic AI in which performance gains come less from ever-larger individual models and more from market-inspired coordination mechanisms that organize heterogeneous agents into efficient, adaptive ecosystems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.00398", "title": "MemoryLLM: Plug-n-Play Interpretable Feed-Forward Memory for Transformers", "authors": [ "Ajay Jaiswal", "Lauren Hannah", "Han-Byul Kim", "Duc Hoang", "Arnav Kundu", "Mehrdad Farajtabar", "Minsik Cho" ], "github": "", "project_page": "", "abstract": "Understanding how transformer components operate in LLMs is important, as it is at the core of recent technological advances in artificial intelligence. In this work, we revisit the challenges associated with interpretability of feed-forward modules (FFNs) and propose MemoryLLM, which aims to decouple FFNs from self-attention and enables us to study the decoupled FFNs as context-free token-wise neural retrieval memory. In detail, we investigate how input tokens access memory locations within FFN parameters and the importance of FFN memory across different downstream tasks. MemoryLLM achieves context-free FFNs by training them in isolation from self-attention directly using the token embeddings. This approach allows FFNs to be pre-computed as token-wise lookups (ToLs), enabling on-demand transfer between VRAM and storage, additionally enhancing inference efficiency. We also introduce Flex-MemoryLLM, positioning it between a conventional transformer design and MemoryLLM. This architecture bridges the performance gap caused by training FFNs with context-free token-wise embeddings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.00359", "title": "Position: Agentic Evolution is the Path to Evolving LLMs", "authors": [ "Minhua Lin", "Hanqing Lu", "Zhan Shi", "Bing He", "Rui Mao", "Zhiwei Zhang", "Zongyu Wu", "Xianfeng Tang", "Hui Liu", "Zhenwei Dai", "Xiang Zhang", "Suhang Wang", "Benoit Dumoulin", "Jian Pei" ], "github": "", "project_page": "", "abstract": "As Large Language Models (LLMs) move from curated training sets into open-ended real-world environments, a fundamental limitation emerges: static training cannot keep pace with continual deployment environment change. Scaling training-time and inference-time compute improves static capability but does not close this train-deploy gap. We argue that addressing this limitation requires a new scaling axis-evolution. Existing deployment-time adaptation methods, whether parametric fine-tuning or heuristic memory accumulation, lack the strategic agency needed to diagnose failures and produce durable improvements. Our position is that agentic evolution represents the inevitable future of LLM adaptation, elevating evolution itself from a fixed pipeline to an autonomous evolver agent. We instantiate this vision in a general framework, A-Evolve, which treats deployment-time improvement as a deliberate, goal-directed optimization process over persistent system state. We further propose the evolution-scaling hypothesis: the capacity for adaptation scales with the compute allocated to evolution, positioning agentic evolution as a scalable path toward sustained, open-ended adaptation in the real world.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.02905", "title": "FIRE-Bench: Evaluating Agents on the Rediscovery of Scientific Insights", "authors": [ "Zhen Wang", "Fan Bai", "Zhongyan Luo", "Jinyan Su", "Kaiser Sun", "Xinle Yu", "Jieyuan Liu", "Kun Zhou", "Claire Cardie", "Mark Dredze", "Eric P. Xing", "Zhiting Hu" ], "github": "https://github.com/maitrix-org/FIRE-Bench", "project_page": "", "abstract": "Autonomous agents powered by large language models (LLMs) promise to accelerate scientific discovery end-to-end, but rigorously evaluating their capacity for verifiable discovery remains a central challenge. Existing benchmarks face a trade-off: they either heavily rely on LLM-as-judge evaluations of automatically generated research outputs or optimize convenient yet isolated performance metrics that provide coarse proxies for scientific insight. To address this gap, we introduce FIRE-Bench (Full-cycle Insight Rediscovery Evaluation), a benchmark that evaluates agents through the rediscovery of established findings from recent, high-impact machine learning research. Agents are given only a high-level research question extracted from a published, verified study and must autonomously explore ideas, design experiments, implement code, execute their plans, and derive conclusions supported by empirical evidence. We evaluate a range of state-of-the-art agents with frontier LLMs backbones like gpt-5 on FIRE-Bench. Our results show that full-cycle scientific research remains challenging for current agent systems: even the strongest agents achieve limited rediscovery success (<50 F1), exhibit high variance across runs, and display recurring failure modes in experimental design, execution, and evidence-based reasoning. FIRE-Bench provides a rigorous and diagnostic framework for measuring progress toward reliable agent-driven scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/maitrix-org/FIRE-Bench", "github_api_added_by": "auto" }, { "date": "2026-02-04", "arxiv_id": "2602.03183", "title": "Privasis: Synthesizing the Largest \"Public\" Private Dataset from Scratch", "authors": [ "Hyunwoo Kim", "Niloofar Mireshghallah", "Michael Duan", "Rui Xin", "Shuyue Stella Li", "Jaehun Jung", "David Acuna", "Qi Pang", "Hanshen Xiao", "G. Edward Suh", "Sewoong Oh", "Yulia Tsvetkov", "Pang Wei Koh", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "Research involving privacy-sensitive data has always been constrained by data scarcity, standing in sharp contrast to other areas that have benefited from data scaling. This challenge is becoming increasingly urgent as modern AI agents--such as OpenClaw and Gemini Agent--are granted persistent access to highly sensitive personal information. To tackle this longstanding bottleneck and the rising risks, we present Privasis (i.e., privacy oasis), the first million-scale fully synthetic dataset entirely built from scratch--an expansive reservoir of texts with rich and diverse private information--designed to broaden and accelerate research in areas where processing sensitive social data is inevitable. Compared to existing datasets, Privasis, comprising 1.4 million records, offers orders-of-magnitude larger scale with quality, and far greater diversity across various document types, including medical history, legal documents, financial records, calendars, and text messages with a total of 55.1 million annotated attributes such as ethnicity, date of birth, workplace, etc. We leverage Privasis to construct a parallel corpus for text sanitization with our pipeline that decomposes texts and applies targeted sanitization. Our compact sanitization models (<=4B) trained on this dataset outperform state-of-the-art large language models, such as GPT-5 and Qwen-3 235B. We plan to release data, models, and code to accelerate future research on privacy-sensitive domains and agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-04", "arxiv_id": "2602.04541", "title": "LycheeDecode: Accelerating Long-Context LLM Inference via Hybrid-Head Sparse Decoding", "authors": [ "Gang Lin", "Dongfang Li", "Zhuoen Chen", "Yukun Shi", "Xuhui Chen", "Baotian Hu", "Min Zhang" ], "github": "", "project_page": "", "abstract": "The proliferation of long-context large language models (LLMs) exposes a key bottleneck: the rapidly expanding key-value cache during decoding, which imposes heavy memory and latency costs. While recent approaches attempt to alleviate this by sharing a single set of crucial tokens across layers, such coarse-grained sharing undermines model performance by neglecting the functional diversity of attention heads. To address this, we propose LycheeDecode, an efficient decoding method centered on a fine-grained hybrid-head attention mechanism that employs a hardware-efficient top-k selection strategy. Specifically, the novel HardKuma-based mechanism partitions attention heads into a small subset of retrieval heads that dynamically identify crucial tokens and a majority of sparse heads that reuse them for efficient computation. Through extensive experiments on leading models like Llama3 and Qwen3 across diverse benchmarks for long-context understanding (e.g., LongBench, RULER) and complex reasoning (e.g., AIME24, OlympiadBench), we demonstrate that LycheeDecode achieves generative quality comparable to, and at times surpassing even the full-attention baseline. Crucially, this is accomplished with up to a 2.7x speedup at a 128K context length. By preserving the functional diversity of attention heads, our fine-grained strategy overcomes the performance bottlenecks of existing methods, providing a powerful and validated pathway to both efficient and high-quality long-context LLM inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.02402", "title": "SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation", "authors": [ "Mu Huang", "Hui Wang", "Kerui Ren", "Linning Xu", "Yunsong Zhou", "Mulin Yu", "Bo Dai", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "Simulating deformable objects under rich interactions remains a fundamental challenge for real-to-sim robot manipulation, with dynamics jointly driven by environmental effects and robot actions. Existing simulators rely on predefined physics or data-driven dynamics without robot-conditioned control, limiting accuracy, stability, and generalization. This paper presents SoMA, a 3D Gaussian Splat simulator for soft-body manipulation. SoMA couples deformable dynamics, environmental forces, and robot joint actions in a unified latent neural space for end-to-end real-to-sim simulation. Modeling interactions over learned Gaussian splats enables controllable, stable long-horizon manipulation and generalization beyond observed trajectories without predefined physical models. SoMA improves resimulation accuracy and generalization on real-world robot manipulation by 20%, enabling stable simulation of complex tasks such as long-horizon cloth folding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.02958", "title": "Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization", "authors": [ "Haocheng Xi", "Shuo Yang", "Yilong Zhao", "Muyang Li", "Han Cai", "Xingyang Li", "Yujun Lin", "Zhuoyang Zhang", "Jintao Zhang", "Xiuyu Li", "Zhiying Xu", "Jun Wu", "Chenfeng Xu", "Ion Stoica", "Song Han", "Kurt Keutzer" ], "github": "", "project_page": "", "abstract": "Despite rapid progress in autoregressive video diffusion, an emerging system algorithm bottleneck limits both deployability and generation capability: KV cache memory. In autoregressive video generation models, the KV cache grows with generation history and quickly dominates GPU memory, often exceeding 30 GB, preventing deployment on widely available hardware. More critically, constrained KV cache budgets restrict the effective working memory, directly degrading long horizon consistency in identity, layout, and motion. To address this challenge, we present Quant VideoGen (QVG), a training free KV cache quantization framework for autoregressive video diffusion models. QVG leverages video spatiotemporal redundancy through Semantic Aware Smoothing, producing low magnitude, quantization friendly residuals. It further introduces Progressive Residual Quantization, a coarse to fine multi stage scheme that reduces quantization error while enabling a smooth quality memory trade off. Across LongCat Video, HY WorldPlay, and Self Forcing benchmarks, QVG establishes a new Pareto frontier between quality and memory efficiency, reducing KV cache memory by up to 7.0 times with less than 4% end to end latency overhead while consistently outperforming existing baselines in generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04515", "title": "EgoActor: Grounding Task Planning into Spatial-aware Egocentric Actions for Humanoid Robots via Visual-Language Models", "authors": [ "Yu Bai", "MingMing Yu", "Chaojie Li", "Ziyi Bai", "Xinlong Wang", "B\u00f6rje F. Karlsson" ], "github": "https://github.com/NVIDIA/Isaac-GR00T", "project_page": "", "abstract": "Deploying humanoid robots in real-world settings is fundamentally challenging, as it demands tight integration of perception, locomotion, and manipulation under partial-information observations and dynamically changing environments. As well as transitioning robustly between sub-tasks of different types. Towards addressing these challenges, we propose a novel task - EgoActing, which requires directly grounding high-level instructions into various, precise, spatially aware humanoid actions. We further instantiate this task by introducing EgoActor, a unified and scalable vision-language model (VLM) that can predict locomotion primitives (e.g., walk, turn, move sideways, change height), head movements, manipulation commands, and human-robot interactions to coordinate perception and execution in real-time. We leverage broad supervision over egocentric RGB-only data from real-world demonstrations, spatial reasoning question-answering, and simulated environment demonstrations, enabling EgoActor to make robust, context-aware decisions and perform fluent action inference (under 1s) with both 8B and 4B parameter models. Extensive evaluations in both simulated and real-world environments demonstrate that EgoActor effectively bridges abstract task planning and concrete motor execution, while generalizing across diverse tasks and unseen environments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA/Isaac-GR00T", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03442", "title": "A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces", "authors": [ "Mingxuan Du", "Benfeng Xu", "Chiwei Zhu", "Shaohan Wang", "Pengyu Wang", "Xiaorui Wang", "Zhendong Mao" ], "github": "https://github.com/Ayanami0730/arag", "project_page": "", "abstract": "Frontier language models have demonstrated strong reasoning and long-horizon tool-use capabilities. However, existing RAG systems fail to leverage these capabilities. They still rely on two paradigms: (1) designing an algorithm that retrieves passages in a single shot and concatenates them into the model's input, or (2) predefining a workflow and prompting the model to execute it step-by-step. Neither paradigm allows the model to participate in retrieval decisions, preventing efficient scaling with model improvements. In this paper, we introduce A-RAG, an Agentic RAG framework that exposes hierarchical retrieval interfaces directly to the model. A-RAG provides three retrieval tools: keyword search, semantic search, and chunk read, enabling the agent to adaptively search and retrieve information across multiple granularities. Experiments on multiple open-domain QA benchmarks show that A-RAG consistently outperforms existing approaches with comparable or lower retrieved tokens, demonstrating that A-RAG effectively leverages model capabilities and dynamically adapts to different RAG tasks. We further systematically study how A-RAG scales with model size and test-time compute. We will release our code and evaluation suite to facilitate future research. Code and evaluation suite are available at https://github.com/Ayanami0730/arag.", "github_manual": "", "github_regex": "https://github.com/Ayanami0730/arag", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2601.18207", "title": "PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR", "authors": [ "James Burgess", "Jan N. Hansen", "Duo Peng", "Yuhui Zhang", "Alejandro Lozano", "Min Woo Sun", "Emma Lundberg", "Serena Yeung-Levy" ], "github": "https://github.com/jmhb0/PaperSearchQA", "project_page": "", "abstract": "Search agents are language models (LMs) that reason and search knowledge bases (or the web) to answer questions; recent methods supervise only the final answer accuracy using reinforcement learning with verifiable rewards (RLVR). Most RLVR search agents tackle general-domain QA, which limits their relevance to technical AI systems in science, engineering, and medicine. In this work we propose training agents to search and reason over scientific papers -- this tests technical question-answering, it is directly relevant to real scientists, and the capabilities will be crucial to future AI Scientist systems. Concretely, we release a search corpus of 16 million biomedical paper abstracts and construct a challenging factoid QA dataset called PaperSearchQA with 60k samples answerable from the corpus, along with benchmarks. We train search agents in this environment to outperform non-RL retrieval baselines; we also perform further quantitative analysis and observe interesting agent behaviors like planning, reasoning, and self-verification. Our corpus, datasets, and benchmarks are usable with the popular Search-R1 codebase for RLVR training and released on https://huggingface.co/collections/jmhb/papersearchqa. Finally, our data creation methods are scalable and easily extendable to other scientific domains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jmhb0/PaperSearchQA", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04575", "title": "Vibe AIGC: A New Paradigm for Content Generation via Agentic Orchestration", "authors": [ "Jiaheng Liu", "Yuanxing Zhang", "Shihao Li", "Xinping Lei" ], "github": "", "project_page": "", "abstract": "For the past decade, the trajectory of generative artificial intelligence (AI) has been dominated by a model-centric paradigm driven by scaling laws. Despite significant leaps in visual fidelity, this approach has encountered a ``usability ceiling'' manifested as the Intent-Execution Gap (i.e., the fundamental disparity between a creator's high-level intent and the stochastic, black-box nature of current single-shot models). In this paper, inspired by the Vibe Coding, we introduce the Vibe AIGC, a new paradigm for content generation via agentic orchestration, which represents the autonomous synthesis of hierarchical multi-agent workflows. Under this paradigm, the user's role transcends traditional prompt engineering, evolving into a Commander who provides a Vibe, a high-level representation encompassing aesthetic preferences, functional logic, and etc. A centralized Meta-Planner then functions as a system architect, deconstructing this ``Vibe'' into executable, verifiable, and adaptive agentic pipelines. By transitioning from stochastic inference to logical orchestration, Vibe AIGC bridges the gap between human imagination and machine execution. We contend that this shift will redefine the human-AI collaborative economy, transforming AI from a fragile inference engine into a robust system-level engineering partner that democratizes the creation of complex, long-horizon digital assets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2601.22954", "title": "Residual Context Diffusion Language Models", "authors": [ "Yuezhou Hu", "Harman Singh", "Monishwaran Maheswaran", "Haocheng Xi", "Coleman Hooper", "Jintao Zhang", "Aditya Tomar", "Michael W. Mahoney", "Sewon Min", "Mehrdad Farajtabar", "Kurt Keutzer", "Amir Gholami", "Chenfeng Xu" ], "github": "", "project_page": "", "abstract": "Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode multiple tokens in parallel. However, state-of-the-art block-wise dLLMs rely on a \"remasking\" mechanism that decodes only the most confident tokens and discards the rest, effectively wasting computation. We demonstrate that recycling computation from the discarded tokens is beneficial, as these tokens retain contextual information useful for subsequent decoding iterations. In light of this, we propose Residual Context Diffusion (RCD), a module that converts these discarded token representations into contextual residuals and injects them back for the next denoising step. RCD uses a decoupled two-stage training pipeline to bypass the memory bottlenecks associated with backpropagation. We validate our method on both long CoT reasoning (SDAR) and short CoT instruction following (LLaDA) models. We demonstrate that a standard dLLM can be efficiently converted to the RCD paradigm with merely ~1 billion tokens. RCD consistently improves frontier dLLMs by 5-10 points in accuracy with minimal extra computation overhead across a wide range of benchmarks. Notably, on the most challenging AIME tasks, RCD nearly doubles baseline accuracy and attains up to 4-5x fewer denoising steps at equivalent accuracy levels.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04879", "title": "Rethinking the Trust Region in LLM Reinforcement Learning", "authors": [ "Penghui Qi", "Xiangxin Zhou", "Zichen Liu", "Tianyu Pang", "Chao Du", "Min Lin", "Wee Sun Lee" ], "github": "https://github.com/sail-sg/Stable-RL", "project_page": "", "abstract": "Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm. Despite its ubiquity, we argue that the core ratio clipping mechanism in PPO is structurally ill-suited for the large vocabularies inherent to LLMs. PPO constrains policy updates based on the probability ratio of sampled tokens, which serves as a noisy single-sample Monte Carlo estimate of the true policy divergence. This creates a sub-optimal learning dynamic: updates to low-probability tokens are aggressively over-penalized, while potentially catastrophic shifts in high-probability tokens are under-constrained, leading to training inefficiency and instability. To address this, we propose Divergence Proximal Policy Optimization (DPPO), which substitutes heuristic clipping with a more principled constraint based on a direct estimate of policy divergence (e.g., Total Variation or KL). To avoid huge memory footprint, we introduce the efficient Binary and Top-K approximations to capture the essential divergence with negligible overhead. Extensive empirical evaluations demonstrate that DPPO achieves superior training stability and efficiency compared to existing methods, offering a more robust foundation for RL-based LLM fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sail-sg/Stable-RL", "github_api_added_by": "auto" }, { "date": "2026-02-05", "arxiv_id": "2602.01640", "title": "A2Eval: Agentic and Automated Evaluation for Embodied Brain", "authors": [ "Shuai Zhang", "Jiayu Hu", "Zijie Chen", "Zeyuan Ding", "Yi Zhang", "Yingji Zhang", "Ziyi Zhou", "Junwei Liao", "Shengjie Zhou", "Yong Dai", "Zhenzhong Lan", "Xiaozhu Ju" ], "github": "", "project_page": "", "abstract": "Current embodied VLM evaluation relies on static, expert-defined, manually annotated benchmarks that exhibit severe redundancy and coverage imbalance. This labor intensive paradigm drains computational and annotation resources, inflates costs, and distorts model rankings, ultimately stifling iterative development. To address this, we propose Agentic Automatic Evaluation (A2Eval), the first agentic framework that automates benchmark curation and evaluation through two collaborative agents. The Data Agent autonomously induces capability dimensions and assembles a balanced, compact evaluation suite, while the Eval Agent synthesizes and validates executable evaluation pipelines, enabling fully autonomous, high-fidelity assessment. Evaluated across 10 benchmarks and 13 models, A2Eval compresses evaluation suites by 85%, reduces overall computational costs by 77%, and delivers a 4.6x speedup while preserving evaluation quality. Crucially, A2Eval corrects systematic ranking biases, improves human alignment to Spearman's rho=0.85, and maintains high ranking fidelity (Kendall's tau=0.81), establishing a new standard for high-fidelity, low-cost embodied assessment. Our code and data will be public soon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03143", "title": "Self-Hinting Language Models Enhance Reinforcement Learning", "authors": [ "Baohao Liao", "Hanze Dong", "Xinxing Xu", "Christof Monz", "Jiang Bian" ], "github": "https://github.com/BaohaoLiao/SAGE", "project_page": "", "abstract": "Group Relative Policy Optimization (GRPO) has recently emerged as a practical recipe for aligning large language models with verifiable objectives. However, under sparse terminal rewards, GRPO often stalls because rollouts within a group frequently receive identical rewards, causing relative advantages to collapse and updates to vanish. We propose self-hint aligned GRPO with privileged supervision (SAGE), an on-policy reinforcement learning framework that injects privileged hints during training to reshape the rollout distribution under the same terminal verifier reward. For each prompt x, the model samples a compact hint h (e.g., a plan or decomposition) and then generates a solution \u03c4 conditioned on (x,h). Crucially, the task reward R(x,\u03c4) is unchanged; hints only increase within-group outcome diversity under finite sampling, preventing GRPO advantages from collapsing under sparse rewards. At test time, we set h=varnothing and deploy the no-hint policy without any privileged information. Moreover, sampling diverse self-hints serves as an adaptive curriculum that tracks the learner's bottlenecks more effectively than fixed hints from an initial policy or a stronger external model. Experiments over 6 benchmarks with 3 LLMs show that SAGE consistently outperforms GRPO, on average +2.0 on Llama-3.2-3B-Instruct, +1.2 on Qwen2.5-7B-Instruct and +1.3 on Qwen3-4B-Instruct. The code is available at https://github.com/BaohaoLiao/SAGE.", "github_manual": "", "github_regex": "https://github.com/BaohaoLiao/SAGE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.02350", "title": "Context Learning for Multi-Agent Discussion", "authors": [ "Xingyuan Hua", "Sheng Yue", "Xinyi Li", "Yizhe Zhao", "Jinrui Zhang", "Ju Ren" ], "github": "", "project_page": "", "abstract": "Multi-Agent Discussion (MAD) has garnered increasing attention very recently, where multiple LLM instances collaboratively solve problems via structured discussion. However, we find that current MAD methods easily suffer from discussion inconsistency, LLMs fail to reach a coherent solution, due to the misalignment between their individual contexts.In this paper, we introduce a multi-LLM context learning method (M2CL) that learns a context generator for each agent, capable of dynamically generating context instructions per discussion round via automatic information organization and refinement. Specifically, inspired by our theoretical insights on the context instruction, M2CL train the generators to control context coherence and output discrepancies via a carefully crafted self-adaptive mechanism.It enables LLMs to avoid premature convergence on majority noise and progressively reach the correct consensus. We evaluate M2CL on challenging tasks, including academic reasoning, embodied tasks, and mobile control. The results show that the performance of M2CL significantly surpasses existing methods by 20%--50%, while enjoying favorable transferability and computational efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.02554", "title": "BatCoder: Self-Supervised Bidirectional Code-Documentation Learning via Back-Translation", "authors": [ "Jingwen Xu", "Yiyang Lu", "Zisu Huang", "Changze Lv", "Xiaohua Wang", "Shizheng Li", "Zhibo Xu", "Zhengkang Guo", "Zhengyuan Wang", "Muzhao Tian", "Xuanjing Huang", "Xiaoqing Zheng" ], "github": "", "project_page": "", "abstract": "Training LLMs for code-related tasks typically depends on high-quality code-documentation pairs, which are costly to curate and often scarce for niche programming languages. We introduce BatCoder, a self-supervised reinforcement learning framework designed to jointly optimize code generation and documentation production. BatCoder employs a back-translation strategy: a documentation is first generated from code, and then the generated documentation is used to reconstruct the original code. The semantic similarity between the original and reconstructed code serves as an implicit reward, enabling reinforcement learning to improve the model's performance both in generating code from documentation and vice versa. This approach allows models to be trained using only code, substantially increasing the available training examples. Evaluated on HumanEval and MBPP with a 7B model, BatCoder achieved 83.5% and 81.0% pass@1, outperforming strong open-source baselines. Moreover, the framework demonstrates consistent scaling with respect to both training corpus size and model capacity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04486", "title": "Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition", "authors": [ "Jinlong Ma", "Yu Zhang", "Xuefeng Bai", "Kehai Chen", "Yuwei Wang", "Zeming Liu", "Jun Yu", "Min Zhang" ], "github": "https://github.com/aaaalonga/MCR", "project_page": "", "abstract": "Grounded Multimodal Named Entity Recognition (GMNER) aims to extract text-based entities, assign them semantic categories, and ground them to corresponding visual regions. In this work, we explore the potential of Multimodal Large Language Models (MLLMs) to perform GMNER in an end-to-end manner, moving beyond their typical role as auxiliary tools within cascaded pipelines. Crucially, our investigation reveals a fundamental challenge: MLLMs exhibit modality bias, including visual bias and textual bias, which stems from their tendency to take unimodal shortcuts rather than rigorous cross-modal verification. To address this, we propose Modality-aware Consistency Reasoning (MCR), which enforces structured cross-modal reasoning through Multi-style Reasoning Schema Injection (MRSI) and Constraint-guided Verifiable Optimization (CVO). MRSI transforms abstract constraints into executable reasoning chains, while CVO empowers the model to dynamically align its reasoning trajectories with Group Relative Policy Optimization (GRPO). Experiments on GMNER and visual grounding tasks demonstrate that MCR effectively mitigates modality bias and achieves superior performance compared to existing baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aaaalonga/MCR", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04605", "title": "RexBERT: Context Specialized Bidirectional Encoders for E-commerce", "authors": [ "Rahul Bajaj", "Anuj Garg" ], "github": "", "project_page": "", "abstract": "Encoder-only transformers remain indispensable in retrieval, classification, and ranking systems where latency, stability, and cost are paramount. Most general purpose encoders, however, are trained on generic corpora with limited coverage of specialized domains. We introduce RexBERT, a family of BERT-style encoders designed specifically for e-commerce semantics. We make three contributions. First, we release Ecom-niverse, a 350 billion token corpus curated from diverse retail and shopping sources. We describe a modular pipeline that isolates and extracts e-commerce content from FineFineWeb and other open web resources, and characterize the resulting domain distribution. Second, we present a reproducible pretraining recipe building on ModernBERT's architectural advances. The recipe consists of three phases: general pre-training, context extension, and annealed domain specialization. Third, we train RexBERT models ranging from 17M to 400M parameters and evaluate them on token classification, semantic similarity, and general natural language understanding tasks using e-commerce datasets. Despite having 2-3x fewer parameters, RexBERT outperforms larger general-purpose encoders and matches or surpasses modern long-context models on domain-specific benchmarks. Our results demonstrate that high quality in-domain data combined with a principled training approach provides a stronger foundation for e-commerce applications than indiscriminate scaling alone.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04145", "title": "Training Data Efficiency in Multimodal Process Reward Models", "authors": [ "Jinyuan Li", "Chengsong Huang", "Langlin Huang", "Shaoyang Xu", "Haolin Liu", "Wenxuan Zhang", "Jiaxin Huang" ], "github": "https://github.com/JinYuanLi0012/Balanced-Info-MPRM", "project_page": "", "abstract": "Multimodal Process Reward Models (MPRMs) are central to step-level supervision for visual reasoning in MLLMs. Training MPRMs typically requires large-scale Monte Carlo (MC)-annotated corpora, incurring substantial training cost. This paper studies the data efficiency for MPRM training.Our preliminary experiments reveal that MPRM training quickly saturates under random subsampling of the training data, indicating substantial redundancy within existing MC-annotated corpora.To explain this, we formalize a theoretical framework and reveal that informative gradient updates depend on two factors: label mixtures of positive/negative steps and label reliability (average MC scores of positive steps). Guided by these insights, we propose the Balanced-Information Score (BIS), which prioritizes both mixture and reliability based on existing MC signals at the rollout level, without incurring any additional cost. Across two backbones (InternVL2.5-8B and Qwen2.5-VL-7B) on VisualProcessBench, BIS-selected subsets consistently match and even surpass the full-data performance at small fractions. Notably, the BIS subset reaches full-data performance using only 10% of the training data, improving over random subsampling by a relative 4.1%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JinYuanLi0012/Balanced-Info-MPRM", "github_api_added_by": "auto" }, { "date": "2026-02-05", "arxiv_id": "2602.04816", "title": "Horizon-LM: A RAM-Centric Architecture for LLM Training", "authors": [ "Zhengqing Yuan", "Lichao Sun", "Yanfang", "Ye" ], "github": "https://github.com/DLYuanGod/Horizon-LM", "project_page": "", "abstract": "The rapid growth of large language models (LLMs) has outpaced the evolution of single-GPU hardware, making model scale increasingly constrained by memory capacity rather than computation. While modern training systems extend GPU memory through distributed parallelism and offloading across CPU and storage tiers, they fundamentally retain a GPU-centric execution paradigm in which GPUs host persistent model replicas and full autograd graphs. As a result, scaling large models remains tightly coupled to multi-GPU clusters, complex distributed runtimes, and unpredictable host memory consumption, creating substantial barriers for node-scale post-training workloads such as instruction tuning, alignment, and domain adaptation. We present Horizon-LM, a memory-centric training system that redefines the roles of CPU and GPU for large-model optimization. Horizon-LM treats host memory as the authoritative parameter store and uses GPUs solely as transient compute engines through a CPU-master, GPU-template execution model. By eliminating persistent GPU-resident modules and autograd graphs, employing explicit recomputation with manual gradient propagation, and introducing a pipelined double-buffered execution engine, Horizon-LM decouples model scale from GPU count and bounds memory usage to the theoretical parameter footprint. On a single H200 GPU with 1.5\\,TB host RAM, Horizon-LM reliably trains models up to 120B parameters. On a standard single A100 machine, Horizon-LM achieves up to 12.2times higher training throughput than DeepSpeed ZeRO-3 with CPU offloading while preserving numerical correctness. Across platforms and scales, Horizon-LM sustains high device utilization and predictable memory growth, demonstrating that host memory, not GPU memory, defines the true feasibility boundary for node-scale large-model training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/DLYuanGod/Horizon-LM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03560", "title": "HySparse: A Hybrid Sparse Attention Architecture with Oracle Token Selection and KV Cache Sharing", "authors": [ "Yizhao Gao", "Jianyu Wei", "Qihao Zhang", "Yu Cheng", "Shimao Chen", "Zhengju Tang", "Zihan Jiang", "Yifan Song", "Hailin Zhang", "Liang Zhao", "Bo Yang", "Gang Wang", "Shijie Cao", "Fuli Luo" ], "github": "", "project_page": "", "abstract": "This work introduces Hybrid Sparse Attention (HySparse), a new architecture that interleaves each full attention layer with several sparse attention layers. While conceptually simple, HySparse strategically derives each sparse layer's token selection and KV caches directly from the preceding full attention layer. This architecture resolves two fundamental limitations of prior sparse attention methods. First, conventional approaches typically rely on additional proxies to predict token importance, introducing extra complexity and potentially suboptimal performance. In contrast, HySparse uses the full attention layer as a precise oracle to identify important tokens. Second, existing sparse attention designs often reduce computation without saving KV cache. HySparse enables sparse attention layers to reuse the full attention KV cache, thereby reducing both computation and memory. We evaluate HySparse on both 7B dense and 80B MoE models. Across all settings, HySparse consistently outperforms both full attention and hybrid SWA baselines. Notably, in the 80B MoE model with 49 total layers, only 5 layers employ full attention, yet HySparse achieves substantial performance gains while reducing KV cache storage by nearly 10x.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04284", "title": "Agent-Omit: Training Efficient LLM Agents for Adaptive Thought and Observation Omission via Agentic Reinforcement Learning", "authors": [ "Yansong Ning", "Jun Fang", "Naiqiang Tan", "Hao Liu" ], "github": "https://github.com/usail-hkust/Agent-Omit", "project_page": "", "abstract": "Managing agent thought and observation during multi-turn agent-environment interactions is an emerging strategy to improve agent efficiency. However, existing studies treat the entire interaction trajectories equally, overlooking the thought necessity and observation utility varies across turns. To this end, we first conduct quantitative investigations into how thought and observation affect agent effectiveness and efficiency. Based on our findings, we propose Agent-Omit, a unified training framework that empowers LLM agents to adaptively omit redundant thoughts and observations. Specifically, we first synthesize a small amount of cold-start data, including both single-turn and multi-turn omission scenarios, to fine-tune the agent for omission behaviors. Furthermore, we introduce an omit-aware agentic reinforcement learning approach, incorporating a dual sampling mechanism and a tailored omission reward to incentivize the agent's adaptive omission capability. Theoretically, we prove that the deviation of our omission policy is upper-bounded by KL-divergence. Experimental results on five agent benchmarks show that our constructed Agent-Omit-8B could obtain performance comparable to seven frontier LLM agent, and achieve the best effectiveness-efficiency trade-off than seven efficient LLM agents methods. Our code and data are available at https://github.com/usail-hkust/Agent-Omit.", "github_manual": "", "github_regex": "https://github.com/usail-hkust/Agent-Omit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03907", "title": "HY3D-Bench: Generation of 3D Assets", "authors": [ "Team Hunyuan3D", "Bowen Zhang", "Chunchao Guo", "Dongyuan Guo", "Haolin Liu", "Hongyu Yan", "Huiwen Shi", "Jiaao Yu", "Jiachen Xu", "Jingwei Huang", "Kunhong Li", "Lifu Wang", "Linus", "Penghao Wang", "Qingxiang Lin", "Ruining Tang", "Xianghui Yang", "Yang Li", "Yirui Guan", "Yunfei Zhao", "Yunhan Yang", "Zeqiang Lai", "Zhihao Liang", "Zibo Zhao" ], "github": "", "project_page": "", "abstract": "While recent advances in neural representations and generative models have revolutionized 3D content creation, the field remains constrained by significant data processing bottlenecks. To address this, we introduce HY3D-Bench, an open-source ecosystem designed to establish a unified, high-quality foundation for 3D generation. Our contributions are threefold: (1) We curate a library of 250k high-fidelity 3D objects distilled from large-scale repositories, employing a rigorous pipeline to deliver training-ready artifacts, including watertight meshes and multi-view renderings; (2) We introduce structured part-level decomposition, providing the granularity essential for fine-grained perception and controllable editing; and (3) We bridge real-world distribution gaps via a scalable AIGC synthesis pipeline, contributing 125k synthetic assets to enhance diversity in long-tail categories. Validated empirically through the training of Hunyuan3D-2.1-Small, HY3D-Bench democratizes access to robust data resources, aiming to catalyze innovation across 3D perception, robotics, and digital content creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.02196", "title": "TIDE: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in LLM Agents", "authors": [ "Hang Yan", "Xinyu Che", "Fangzhi Xu", "Qiushi Sun", "Zichen Ding", "Kanzhi Cheng", "Jian Zhang", "Tao Qin", "Jun Liu", "Qika Lin" ], "github": "https://github.com/yayayacc/TIDE", "project_page": "", "abstract": "Recent advances in autonomous LLM agents demonstrate their ability to improve performance through iterative interaction with the environment. We define this paradigm as Test-Time Improvement (TTI). However, the mechanisms under how and why TTI succeed or fail remain poorly understood, and existing evaluation metrics fail to capture their task optimization efficiency, behavior adaptation after erroneous actions, and the specific utility of working memory for task completion. To address these gaps, we propose Test-time Improvement Diagnostic Evaluation (TIDE), an agent-agnostic and environment-agnostic framework that decomposes TTI into three comprehensive and interconnected dimensions. The framework measures (1) the overall temporal dynamics of task completion and (2) identifies whether performance is primarily constrained by recursive looping behaviors or (3) by burdensome accumulated memory. Through extensive experiments across diverse agents and environments, TIDE highlights that improving agent performance requires more than scaling internal reasoning, calling for explicitly optimizing the interaction dynamics between the agent and the environment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yayayacc/TIDE", "github_api_added_by": "auto" }, { "date": "2026-02-05", "arxiv_id": "2602.04883", "title": "Protein Autoregressive Modeling via Multiscale Structure Generation", "authors": [ "Yanru Qu", "Cheng-Yen Hsieh", "Zaixiang Zheng", "Ge Liu", "Quanquan Gu" ], "github": "", "project_page": "", "abstract": "We present protein autoregressive modeling (PAR), the first multi-scale autoregressive framework for protein backbone generation via coarse-to-fine next-scale prediction. Using the hierarchical nature of proteins, PAR generates structures that mimic sculpting a statue, forming a coarse topology and refining structural details over scales. To achieve this, PAR consists of three key components: (i) multi-scale downsampling operations that represent protein structures across multiple scales during training; (ii) an autoregressive transformer that encodes multi-scale information and produces conditional embeddings to guide structure generation; (iii) a flow-based backbone decoder that generates backbone atoms conditioned on these embeddings. Moreover, autoregressive models suffer from exposure bias, caused by the training and the generation procedure mismatch, and substantially degrades structure generation quality. We effectively alleviate this issue by adopting noisy context learning and scheduled sampling, enabling robust backbone generation. Notably, PAR exhibits strong zero-shot generalization, supporting flexible human-prompted conditional generation and motif scaffolding without requiring fine-tuning. On the unconditional generation benchmark, PAR effectively learns protein distributions and produces backbones of high design quality, and exhibits favorable scaling behavior. Together, these properties establish PAR as a promising framework for protein structure generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04634", "title": "WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning", "authors": [ "Zelai Xu", "Zhexuan Xu", "Ruize Zhang", "Chunyang Zhu", "Shi Yu", "Weilin Liu", "Quanlu Zhang", "Wenbo Ding", "Chao Yu", "Yu Wang" ], "github": "https://github.com/MiroMindAI/MiroFlow", "project_page": "", "abstract": "Recent advancements in Large Language Models (LLMs) have largely focused on depth scaling, where a single agent solves long-horizon problems with multi-turn reasoning and tool use. However, as tasks grow broader, the key bottleneck shifts from individual competence to organizational capability. In this work, we explore a complementary dimension of width scaling with multi-agent systems to address broad information seeking. Existing multi-agent systems often rely on hand-crafted workflows and turn-taking interactions that fail to parallelize work effectively. To bridge this gap, we propose WideSeek-R1, a lead-agent-subagent framework trained via multi-agent reinforcement learning (MARL) to synergize scalable orchestration and parallel execution. By utilizing a shared LLM with isolated contexts and specialized tools, WideSeek-R1 jointly optimizes the lead agent and parallel subagents on a curated dataset of 20k broad information-seeking tasks. Extensive experiments show that WideSeek-R1-4B achieves an item F1 score of 40.0% on the WideSearch benchmark, which is comparable to the performance of single-agent DeepSeek-R1-671B. Furthermore, WideSeek-R1-4B exhibits consistent performance gains as the number of parallel subagents increases, highlighting the effectiveness of width scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiroMindAI/MiroFlow", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04735", "title": "From Data to Behavior: Predicting Unintended Model Behaviors Before Training", "authors": [ "Mengru Wang", "Zhenqian Xu", "Junfeng Fang", "Yunzhi Yao", "Shumin Deng", "Huajun Chen", "Ningyu Zhang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) can acquire unintended biases from seemingly benign training data even without explicit cues or malicious content. Existing methods struggle to detect such risks before fine-tuning, making post hoc evaluation costly and inefficient. To address this challenge, we introduce Data2Behavior, a new task for predicting unintended model behaviors prior to training. We also propose Manipulating Data Features (MDF), a lightweight approach that summarizes candidate data through their mean representations and injects them into the forward pass of a base model, allowing latent statistical signals in the data to shape model activations and reveal potential biases and safety risks without updating any parameters. MDF achieves reliable prediction while consuming only about 20% of the GPU resources required for fine-tuning. Experiments on Qwen3-14B, Qwen2.5-32B-Instruct, and Gemma-3-12b-it confirm that MDF can anticipate unintended behaviors and provide insight into pre-training vulnerabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03973", "title": "VLS: Steering Pretrained Robot Policies via Vision-Language Models", "authors": [ "Shuo Liu", "Ishneet Sukhvinder Singh", "Yiqing Xu", "Jiafei Duan", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Why do pretrained diffusion or flow-matching policies fail when the same task is performed near an obstacle, on a shifted support surface, or amid mild clutter? Such failures rarely reflect missing motor skills; instead, they expose a limitation of imitation learning under train-test shifts, where action generation is tightly coupled to training-specific spatial configurations and task specifications. Retraining or fine-tuning to address these failures is costly and conceptually misaligned, as the required behaviors already exist but cannot be selectively adapted at test time. We propose Vision-Language Steering (VLS), a training-free framework for inference-time adaptation of frozen generative robot policies. VLS treats adaptation as an inference-time control problem, steering the sampling process of a pretrained diffusion or flow-matching policy in response to out-of-distribution observation-language inputs without modifying policy parameters. By leveraging vision-language models to synthesize trajectory-differentiable reward functions, VLS guides denoising toward action trajectories that satisfy test-time spatial and task requirements. Across simulation and real-world evaluations, VLS consistently outperforms prior steering methods, achieving a 31% improvement on CALVIN and a 13% gain on LIBERO-PRO. Real-world deployment on a Franka robot further demonstrates robust inference-time adaptation under test-time spatial and semantic shifts. Project page: https://vision-language-steering.github.io/webpage/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04705", "title": "ERNIE 5.0 Technical Report", "authors": [ "Haifeng Wang", "Hua Wu", "Tian Wu", "Yu Sun", "Jing Liu", "Dianhai Yu", "Yanjun Ma", "Jingzhou He", "Zhongjun He", "Dou Hong", "Qiwen Liu", "Shuohuan Wang", "Junyuan Shang", "Zhenyu Zhang", "Yuchen Ding", "Jinle Zeng", "Jiabin Yang", "Liang Shen", "Ruibiao Chen", "Weichong Yin", "Siyu Ding", "Dai Dai", "Shikun Feng", "Siqi Bao", "Bolei He", "Yan Chen", "Zhenyu Jiao", "Ruiqing Zhang", "Zeyu Chen", "Qingqing Dang", "Kaipeng Deng", "Jiajun Jiang", "Enlei Gong", "Guoxia Wang", "Yanlin Sha", "Yi Liu", "Yehan Zheng", "Weijian Xu", "Jiaxiang Liu", "Zengfeng Zeng", "Yingqi Qu", "Zhongli Li", "Zhengkun Zhang", "Xiyang Wang", "Zixiang Xu", "Xinchao Xu", "Zhengjie Huang", "Dong Wang", "Bingjin Chen", "Yue Chang", "Xing Yuan", "Shiwei Huang", "Qiao Zhao", "Xinzhe Ding", "Shuangshuang Qiao", "Baoshan Yang", "Bihong Tang", "Bin Li", "Bingquan Wang", "Binhan Tang", "Binxiong Zheng", "Bo Cui", "Bo Ke", "Bo Zhang", "Bowen Zhang", "Boyan Zhang", "Boyang Liu", "Caiji Zhang", "Can Li", "Chang Xu", "Chao Pang", "Chao Zhang", "Chaoyi Yuan", "Chen Chen", "Cheng Cui", "Chenlin Yin", "Chun Gan", "Chunguang Chai", "Chuyu Fang", "Cuiyun Han", "Dan Zhang", "Danlei Feng", "Danxiang Zhu", "Dong Sun", "Dongbo Li", "Dongdong Li", "Dongdong Liu", "Dongxue Liu", "Fan Ding", "Fan Hu", "Fan Li", "Fan Mo", "Feisheng Wu", "Fengwei Liu", "Gangqiang Hu", "Gaofeng Lu", "Gaopeng Yong", "Gexiao Tian", "Guan Wang", "Guangchen Ni", "Guangshuo Wu", "Guanzhong Wang", "Guihua Liu", "Guishun Li", "Haibin Li", "Haijian Liang", "Haipeng Ming", "Haisu Wang", "Haiyang Lu", "Haiye Lin", "Han Zhou", "Hangting Lou", "Hanwen Du", "Hanzhi Zhang", "Hao Chen", "Hao Du", "Hao Liu", "Hao Zhou", "Haochen Jiang", "Haodong Tian", "Haoshuang Wang", "Haozhe Geng", "Heju Yin", "Hong Chen", "Hongchen Xue", "Hongen Liu", "Honggeng Zhang", "Hongji Xu", "Hongwei Chen", "Hongyang Zhang", "Hongyuan Zhang", "Hua Lu", "Huan Chen", "Huan Wang", "Huang He", "Hui Liu", "Hui Zhong", "Huibin Ruan", "Jiafeng Lu", "Jiage Liang", "Jiahao Hu", "Jiahao Hu", "Jiajie Yang", "Jialin Li", "Jian Chen", "Jian Wu", "Jianfeng Yang", "Jianguang Jiang", "Jianhua Wang", "Jianye Chen", "Jiaodi Liu", "Jiarui Zhou", "Jiawei Lv", "Jiaxin Zhou", "Jiaxuan Liu", "Jie Han", "Jie Sun", "Jiefan Fang", "Jihan Liu", "Jihua Liu", "Jing Hu", "Jing Qian", "Jing Yan", "Jingdong Du", "Jingdong Wang", "Jingjing Wu", "Jingyong Li", "Jinheng Wang", "Jinjin Li", "Jinliang Lu", "Jinlin Yu", "Jinnan Liu", "Jixiang Feng", "Jiyi Huang", "Jiyuan Zhang", "Jun Liang", "Jun Xia", "Jun Yu", "Junda Chen", "Junhao Feng", "Junhong Xiang", "Junliang Li", "Kai Liu", "Kailun Chen", "Kairan Su", "Kang Hu", "Kangkang Zhou", "Ke Chen", "Ke Wei", "Kui Huang", "Kun Wu", "Kunbin Chen", "Lei Han", "Lei Sun", "Lei Wen", "Linghui Meng", "Linhao Yu", "Liping Ouyang", "Liwen Zhang", "Longbin Ji", "Longzhi Wang", "Meng Sun", "Meng Tian", "Mengfei Li", "Mengqi Zeng", "Mengyu Zhang", "Ming Hong", "Mingcheng Zhou", "Mingming Huang", "Mingxin Chen", "Mingzhu Cai", "Naibin Gu", "Nemin Qiu", "Nian Wang", "Peng Qiu", "Peng Zhao", "Pengyu Zou", "Qi Wang", "Qi Xin", "Qian Wang", "Qiang Zhu", "Qianhui Luo", "Qianwei Yang", "Qianyue He", "Qifei Wu", "Qinrui Li", "Qiwen Bao", "Quan Zhang", "Quanxiang Liu", "Qunyi Xie", "Rongrui Zhan", "Rufeng Dai", "Rui Peng", "Ruian Liu", "Ruihao Xu", "Ruijie Wang", "Ruixi Zhang", "Ruixuan Liu", "Runsheng Shi", "Ruting Wang", "Senbo Kang", "Shan Lu", "Shaofei Yu", "Shaotian Gong", "Shenwei Hu", "Shifeng Zheng", "Shihao Guo", "Shilong Fan", "Shiqin Liu", "Shiwei Gu", "Shixi Zhang", "Shuai Yao", "Shuang Zhang", "Shuangqiao Liu", "Shuhao Liang", "Shuwei He", "Shuwen Yang", "Sijun He", "Siming Dai", "Siming Wu", "Siyi Long", "Songhe Deng", "Suhui Dong", "Suyin Liang", "Teng Hu", "Tianchan Xu", "Tianliang Lv", "Tianmeng Yang", "Tianyi Wei", "Tiezhu Gao", "Ting Sun", "Ting Zhang", "Tingdan Luo", "Wei He", "Wei Luan", "Wei Yin", "Wei Zhang", "Wei Zhou", "Weibao Gong", "Weibin Li", "Weicheng Huang", "Weichong Dang", "Weiguo Zhu", "Weilong Zhang", "Weiqi Tan", "Wen Huang", "Wenbin Chang", "Wenjing Du", "Wenlong Miao", "Wenpei Luo", "Wenquan Wu", "Xi Shi", "Xi Zhao", "Xiang Gao", "Xiangguo Zhang", "Xiangrui Yu", "Xiangsen Wang", "Xiangzhe Wang", "Xianlong Luo", "Xianying Ma", "Xiao Tan", "Xiaocong Lin", "Xiaofei Wang", "Xiaofeng Peng", "Xiaofeng Wu", "Xiaojian Xu", "Xiaolan Yuan", "Xiaopeng Cui", "Xiaotian Han", "Xiaoxiong Liu", "Xiaoxu Fei", "Xiaoxuan Wu", "Xiaoyu Wang", "Xiaoyu Zhang", "Xin Sun", "Xin Wang", "Xinhui Huang", "Xinming Zhu", "Xintong Yu", "Xinyi Xu", "Xinyu Wang", "Xiuxian Li", "XuanShi Zhu", "Xue Xu", "Xueying Lv", "Xuhong Li", "Xulong Wei", "Xuyi Chen", "Yabing Shi", "Yafeng Wang", "Yamei Li", "Yan Liu", "Yanfu Cheng", "Yang Gao", "Yang Liang", "Yang Wang", "Yang Wang", "Yang Yang", "Yanlong Liu", "Yannian Fu", "Yanpeng Wang", "Yanzheng Lin", "Yao Chen", "Yaozong Shen", "Yaqian Han", "Yehua Yang", "Yekun Chai", "Yesong Wang", "Yi Song", "Yichen Zhang", "Yifei Wang", "Yifeng Guo", "Yifeng Kou", "Yilong Chen", "Yilong Guo", "Yiming Wang", "Ying Chen", "Ying Wang", "Yingsheng Wu", "Yingzhan Lin", "Yinqi Yang", "Yiran Xing", "Yishu Lei", "Yixiang Tu", "Yiyan Chen", "Yong Zhang", "Yonghua Li", "Yongqiang Ma", "Yongxing Dai", "Yongyue Zhang", "Yu Ran", "Yu Sun", "Yu-Wen Michael Zhang", "Yuang Liu", "Yuanle Liu", "Yuanyuan Zhou", "Yubo Zhang", "Yuchen Han", "Yucheng Wang", "Yude Gao", "Yuedong Luo", "Yuehu Dong", "Yufeng Hu", "Yuhui Cao", "Yuhui Yun", "Yukun Chen", "Yukun Gao", "Yukun Li", "Yumeng Zhang", "Yun Fan", "Yun Ma", "Yunfei Zhang", "Yunshen Xie", "Yuping Xu", "Yuqin Zhang", "Yuqing Liu", "Yurui Li", "Yuwen Wang", "Yuxiang Lu", "Zefeng Cai", "Zelin Zhao", "Zelun Zhang", "Zenan Lin", "Zezhao Dong", "Zhaowu Pan", "Zhaoyu Liu", "Zhe Dong", "Zhe Zhang", "Zhen Zhang", "Zhengfan Wu", "Zhengrui Wei", "Zhengsheng Ning", "Zhenxing Li", "Zhenyu Li", "Zhenyu Qian", "Zhenyun Li", "Zhi Li", "Zhichao Chen", "Zhicheng Dong", "Zhida Feng", "Zhifan Feng", "Zhihao Deng", "Zhijin Yu", "Zhiyang Chen", "Zhonghui Zheng", "Zhuangzhuang Guo", "Zhujun Zhang", "Zhuo Sun", "Zichang Liu", "Zihan Lin", "Zihao Huang", "Zihe Zhu", "Ziheng Zhao", "Ziping Chen", "Zixuan Zhu", "Ziyang Xu", "Ziyi Liang", "Ziyuan Gao" ], "github": "", "project_page": "", "abstract": "In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio. All modalities are trained from scratch under a unified next-group-of-tokens prediction objective, based on an ultra-sparse mixture-of-experts (MoE) architecture with modality-agnostic expert routing. To address practical challenges in large-scale deployment under diverse resource constraints, ERNIE 5.0 adopts a novel elastic training paradigm. Within a single pre-training run, the model learns a family of sub-models with varying depths, expert capacities, and routing sparsity, enabling flexible trade-offs among performance, model size, and inference latency in memory- or time-constrained scenarios. Moreover, we systematically address the challenges of scaling reinforcement learning to unified foundation models, thereby guaranteeing efficient and stable post-training under ultra-sparse MoE architectures and diverse multimodal settings. Extensive experiments demonstrate that ERNIE 5.0 achieves strong and balanced performance across multiple modalities. To the best of our knowledge, among publicly disclosed models, ERNIE 5.0 represents the first production-scale realization of a trillion-parameter unified autoregressive model that supports both multimodal understanding and generation. To facilitate further research, we present detailed visualizations of modality-agnostic expert routing in the unified model, alongside comprehensive empirical analysis of elastic training, aiming to offer profound insights to the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.02140", "title": "Quantifying the Gap between Understanding and Generation within Unified Multimodal Models", "authors": [ "Chenlong Wang", "Yuhang Chen", "Zhihan Hu", "Dongping Chen", "Wenhu Chen", "Sarah Wiegreffe", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Recent advances in unified multimodal models (UMM) have demonstrated remarkable progress in both understanding and generation tasks. However, whether these two capabilities are genuinely aligned and integrated within a single model remains unclear. To investigate this question, we introduce GapEval, a bidirectional benchmark designed to quantify the gap between understanding and generation capabilities, and quantitatively measure the cognitive coherence of the two \"unified\" directions. Each question can be answered in both modalities (image and text), enabling a symmetric evaluation of a model's bidirectional inference capability and cross-modal consistency. Experiments reveal a persistent gap between the two directions across a wide range of UMMs with different architectures, suggesting that current models achieve only surface-level unification rather than deep cognitive convergence of the two. To further explore the underlying mechanism, we conduct an empirical study from the perspective of knowledge manipulation to illustrate the underlying limitations. Our findings indicate that knowledge within UMMs often remains disjoint. The capability emergence and knowledge across modalities are unsynchronized, paving the way for further exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03979", "title": "Likelihood-Based Reward Designs for General LLM Reasoning", "authors": [ "Ariel Kwiatkowski", "Natasha Butt", "Ismail Labiad", "Julia Kempe", "Yann Ollivier" ], "github": "", "project_page": "", "abstract": "Fine-tuning large language models (LLMs) on reasoning benchmarks via reinforcement learning requires a specific reward function, often binary, for each benchmark. This comes with two potential limitations: the need to design the reward, and the potentially sparse nature of binary rewards. Here, we systematically investigate rewards derived from the probability or log-probability of emitting the reference answer (or any other prompt continuation present in the data), which have the advantage of not relying on specific verifiers and being available at scale. Several recent works have advocated for the use of similar rewards (e.g., VeriFree, JEPO, RLPR, NOVER). We systematically compare variants of likelihood-based rewards with standard baselines, testing performance both on standard mathematical reasoning benchmarks, and on long-form answers where no external verifier is available. We find that using the log-probability of the reference answer as the reward for chain-of-thought (CoT) learning is the only option that performs well in all setups. This reward is also consistent with the next-token log-likelihood loss used during pretraining. In verifiable settings, log-probability rewards bring comparable or better success rates than reinforcing with standard binary rewards, and yield much better perplexity. In non-verifiable settings, they perform on par with SFT. On the other hand, methods based on probability, such as VeriFree, flatline on non-verifiable settings due to vanishing probabilities of getting the correct answer. Overall, this establishes log-probability rewards as a viable method for CoT fine-tuning, bridging the short, verifiable and long, non-verifiable answer settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04805", "title": "Skin Tokens: A Learned Compact Representation for Unified Autoregressive Rigging", "authors": [ "Jia-peng Zhang", "Cheng-Feng Pu", "Meng-Hao Guo", "Yan-Pei Cao", "Shi-Min Hu" ], "github": "", "project_page": "", "abstract": "The rapid proliferation of generative 3D models has created a critical bottleneck in animation pipelines: rigging. Existing automated methods are fundamentally limited by their approach to skinning, treating it as an ill-posed, high-dimensional regression task that is inefficient to optimize and is typically decoupled from skeleton generation. We posit this is a representation problem and introduce SkinTokens: a learned, compact, and discrete representation for skinning weights. By leveraging an FSQ-CVAE to capture the intrinsic sparsity of skinning, we reframe the task from continuous regression to a more tractable token sequence prediction problem. This representation enables TokenRig, a unified autoregressive framework that models the entire rig as a single sequence of skeletal parameters and SkinTokens, learning the complicated dependencies between skeletons and skin deformations. The unified model is then amenable to a reinforcement learning stage, where tailored geometric and semantic rewards improve generalization to complex, out-of-distribution assets. Quantitatively, the SkinTokens representation leads to a 98%-133% percents improvement in skinning accuracy over state-of-the-art methods, while the full TokenRig framework, refined with RL, enhances bone prediction by 17%-22%. Our work presents a unified, generative approach to rigging that yields higher fidelity and robustness, offering a scalable solution to a long-standing challenge in 3D content creation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04804", "title": "OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models", "authors": [ "Yue Ding", "Yiyan Ji", "Jungang Li", "Xuyang Liu", "Xinlong Chen", "Junfei Wu", "Bozhou Li", "Bohan Zeng", "Yang Shi", "Yushuo Guan", "Yuanxing Zhang", "Jiaheng Liu", "Qiang Liu", "Pengfei Wan", "Liang Wang" ], "github": "", "project_page": "", "abstract": "Omni-modal Large Language Models (Omni-LLMs) have demonstrated strong capabilities in audio-video understanding tasks. However, their reliance on long multimodal token sequences leads to substantial computational overhead. Despite this challenge, token compression methods designed for Omni-LLMs remain limited. To bridge this gap, we propose OmniSIFT (Omni-modal Spatio-temporal Informed Fine-grained Token compression), a modality-asymmetric token compression framework tailored for Omni-LLMs. Specifically, OmniSIFT adopts a two-stage compression strategy: (i) a spatio-temporal video pruning module that removes video redundancy arising from both intra-frame structure and inter-frame overlap, and (ii) a vision-guided audio selection module that filters audio tokens. The entire framework is optimized end-to-end via a differentiable straight-through estimator. Extensive experiments on five representative benchmarks demonstrate the efficacy and robustness of OmniSIFT. Notably, for Qwen2.5-Omni-7B, OmniSIFT introduces only 4.85M parameters while maintaining lower latency than training-free baselines such as OmniZip. With merely 25% of the original token context, OmniSIFT consistently outperforms all compression baselines and even surpasses the performance of the full-token model on several tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03510", "title": "Semantic Routing: Exploring Multi-Layer LLM Feature Weighting for Diffusion Transformers", "authors": [ "Bozhou Li", "Yushuo Guan", "Haolin Li", "Bohan Zeng", "Yiyan Ji", "Yue Ding", "Pengfei Wan", "Kun Gai", "Yuanxing Zhang", "Wentao Zhang" ], "github": "https://github.com/zooblastlbz/SemanticRouting", "project_page": "", "abstract": "Recent DiT-based text-to-image models increasingly adopt LLMs as text encoders, yet text conditioning remains largely static and often utilizes only a single LLM layer, despite pronounced semantic hierarchy across LLM layers and non-stationary denoising dynamics over both diffusion time and network depth. To better match the dynamic process of DiT generation and thereby enhance the diffusion model's generative capability, we introduce a unified normalized convex fusion framework equipped with lightweight gates to systematically organize multi-layer LLM hidden states via time-wise, depth-wise, and joint fusion. Experiments establish Depth-wise Semantic Routing as the superior conditioning strategy, consistently improving text-image alignment and compositional generation (e.g., +9.97 on the GenAI-Bench Counting task). Conversely, we find that purely time-wise fusion can paradoxically degrade visual generation fidelity. We attribute this to a train-inference trajectory mismatch: under classifier-free guidance, nominal timesteps fail to track the effective SNR, causing semantically mistimed feature injection during inference. Overall, our results position depth-wise routing as a strong and effective baseline and highlight the critical need for trajectory-aware signals to enable robust time-dependent conditioning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zooblastlbz/SemanticRouting", "github_api_added_by": "auto" }, { "date": "2026-02-05", "arxiv_id": "2601.22859", "title": "MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering", "authors": [ "Chuanzhe Guo", "Jingjing Wu", "Sijun He", "Yang Chen", "Zhaoqi Kuang", "Shilong Fan", "Bingjin Chen", "Siqi Bao", "Jing Liu", "Hua Wu", "Qingfu Zhu", "Wanxiang Che", "Haifeng Wang" ], "github": "https://github.com/ernie-research/MEnvAgent", "project_page": "", "abstract": "The evolution of Large Language Model (LLM) agents for software engineering (SWE) is constrained by the scarcity of verifiable datasets, a bottleneck stemming from the complexity of constructing executable environments across diverse languages. To address this, we introduce MEnvAgent, a Multi-language framework for automated Environment construction that facilitates scalable generation of verifiable task instances. MEnvAgent employs a multi-agent Planning-Execution-Verification architecture to autonomously resolve construction failures and integrates a novel Environment Reuse Mechanism that reduces computational overhead by incrementally patching historical environments. Evaluations on MEnvBench, a new benchmark comprising 1,000 tasks across 10 languages, demonstrate that MEnvAgent outperforms baselines, improving Fail-to-Pass (F2P) rates by 8.6% while reducing time costs by 43%. Additionally, we demonstrate the utility of MEnvAgent by constructing MEnvData-SWE, the largest open-source polyglot dataset of realistic verifiable Docker environments to date, alongside solution trajectories that enable consistent performance gains on SWE tasks across a wide range of models. Our code, benchmark, and dataset are available at https://github.com/ernie-research/MEnvAgent.", "github_manual": "", "github_regex": "https://github.com/ernie-research/MEnvAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2601.20499", "title": "Efficient Autoregressive Video Diffusion with Dummy Head", "authors": [ "Hang Guo", "Zhaoyang Jia", "Jiahao Li", "Bin Li", "Yuanhao Cai", "Jiangshan Wang", "Yawei Li", "Yan Lu" ], "github": "https://github.com/krea-ai/realtime-video", "project_page": "", "abstract": "The autoregressive video diffusion model has recently gained considerable research interest due to its causal modeling and iterative denoising. In this work, we identify that the multi-head self-attention in these models under-utilizes historical frames: approximately 25% heads attend almost exclusively to the current frame, and discarding their KV caches incurs only minor performance degradation. Building upon this, we propose Dummy Forcing, a simple yet effective method to control context accessibility across different heads. Specifically, the proposed heterogeneous memory allocation reduces head-wise context redundancy, accompanied by dynamic head programming to adaptively classify head types. Moreover, we develop a context packing technique to achieve more aggressive cache compression. Without additional training, our Dummy Forcing delivers up to 2.0x speedup over the baseline, supporting video generation at 24.3 FPS with less than 0.5% quality drop. Project page is available at https://csguoh.github.io/project/DummyForcing/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/krea-ai/realtime-video", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04547", "title": "OmniRad: A Radiological Foundation Model for Multi-Task Medical Image Analysis", "authors": [ "Luca Zedda", "Andrea Loddo", "Cecilia Di Ruberto" ], "github": "", "project_page": "", "abstract": "Radiological analysis increasingly benefits from pretrained visual representations that can support heterogeneous downstream tasks across imaging modalities. In this work, we introduce OmniRad, a self-supervised radiological foundation model pretrained on 1.2 million medical images, designed with radiology-inspired principles emphasizing representation reuse and cross-task transferability. We evaluate the pretrained encoder under multiple downstream adaptation regimes, including lightweight task-specific adapters with a frozen backbone as well as full end-to-end fine-tuning for classification, allowing us to assess both representation quality and task-specific performance. OmniRad is evaluated on a broad suite of public benchmarks spanning classification and segmentation across multiple modalities. On the MedMNISTv2 collection, OmniRad improves classification F1 by up to 2.05% over competing foundation models. For dense prediction, OmniRad attains mean Dice score improvements across six MedSegBench datasets when using frozen representations. Qualitative analyses and latent-space visualizations suggest improved feature clustering and modality-related separation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.02341", "title": "LongVPO: From Anchored Cues to Self-Reasoning for Long-Form Video Preference Optimization", "authors": [ "Zhenpeng Huang", "Jiaqi Li", "Zihan Jia", "Xinhao Li", "Desen Meng", "Lingxue Song", "Xi Chen", "Liang Li", "Limin Wang" ], "github": "https://github.com/MCG-NJU/LongVPO", "project_page": "", "abstract": "We present LongVPO, a novel two-stage Direct Preference Optimization framework that enables short-context vision-language models to robustly understand ultra-long videos without any long-video annotations. In Stage 1, we synthesize preference triples by anchoring questions to individual short clips, interleaving them with distractors, and applying visual-similarity and question-specificity filtering to mitigate positional bias and ensure unambiguous supervision. We also approximate the reference model's scoring over long contexts by evaluating only the anchor clip, reducing computational overhead. In Stage 2, we employ a recursive captioning pipeline on long videos to generate scene-level metadata, then use a large language model to craft multi-segment reasoning queries and dispreferred responses, aligning the model's preferences through multi-segment reasoning tasks. With only 16K synthetic examples and no costly human labels, LongVPO outperforms the state-of-the-art open-source models on multiple long-video benchmarks, while maintaining strong short-video performance (e.g., on MVBench), offering a scalable paradigm for efficient long-form video understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MCG-NJU/LongVPO", "github_api_added_by": "auto" }, { "date": "2026-02-05", "arxiv_id": "2602.01849", "title": "Self-Rewarding Sequential Monte Carlo for Masked Diffusion Language Models", "authors": [ "Ziwei Luo", "Ziqi Jin", "Lei Wang", "Lidong Bing", "Thomas B. Sch\u00f6n" ], "github": "https://github.com/Algolzw/self-rewarding-smc", "project_page": "", "abstract": "This work presents self-rewarding sequential Monte Carlo (SMC), an inference-time scaling algorithm enabling effective sampling of masked diffusion language models (MDLMs). Our algorithm stems from the observation that most existing MDLMs rely on a confidence-based sampling strategy, where only tokens with the highest prediction confidence are preserved at each step. This restricts the generation to a noise-sensitive, greedy decoding paradigm, resulting in an inevitable collapse in the diversity of possible paths. We address this problem by launching multiple interacting diffusion processes in parallel, referred to as particles, for trajectory exploration. Importantly, we introduce the trajectory-level confidence as a self-rewarding signal for assigning particle importance weights. During sampling, particles are iteratively weighted and resampled to systematically steer generation towards globally confident, high-quality samples. Our self-rewarding SMC is verified on various masked diffusion language models and benchmarks, achieving significant improvement without extra training or reward guidance, while effectively converting parallel inference capacity into improved sampling quality. Our code is available at https://github.com/Algolzw/self-rewarding-smc.", "github_manual": "", "github_regex": "https://github.com/Algolzw/self-rewarding-smc", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03916", "title": "SpatiaLab: Can Vision-Language Models Perform Spatial Reasoning in the Wild?", "authors": [ "Azmine Toushik Wasi", "Wahid Faisal", "Abdur Rahman", "Mahfuz Ahmed Anik", "Munem Shahriar", "Mohsin Mahmud Topu", "Sadia Tasnim Meem", "Rahatun Nesa Priti", "Sabrina Afroz Mitu", "Md. Iqramul Hoque", "Shahriyar Zaman Ridoy", "Mohammed Eunus Ali", "Majd Hawasly", "Mohammad Raza", "Md Rizwan Parvez" ], "github": "", "project_page": "", "abstract": "Spatial reasoning is a fundamental aspect of human cognition, yet it remains a major challenge for contemporary vision-language models (VLMs). Prior work largely relied on synthetic or LLM-generated environments with limited task designs and puzzle-like setups, failing to capture the real-world complexity, visual noise, and diverse spatial relationships that VLMs encounter. To address this, we introduce SpatiaLab, a comprehensive benchmark for evaluating VLMs' spatial reasoning in realistic, unconstrained contexts. SpatiaLab comprises 1,400 visual question-answer pairs across six major categories: Relative Positioning, Depth & Occlusion, Orientation, Size & Scale, Spatial Navigation, and 3D Geometry, each with five subcategories, yielding 30 distinct task types. Each subcategory contains at least 25 questions, and each main category includes at least 200 questions, supporting both multiple-choice and open-ended evaluation. Experiments across diverse state-of-the-art VLMs, including open- and closed-source models, reasoning-focused, and specialized spatial reasoning models, reveal a substantial gap in spatial reasoning capabilities compared with humans. In the multiple-choice setup, InternVL3.5-72B achieves 54.93% accuracy versus 87.57% for humans. In the open-ended setting, all models show a performance drop of around 10-25%, with GPT-5-mini scoring highest at 40.93% versus 64.93% for humans. These results highlight key limitations in handling complex spatial relationships, depth perception, navigation, and 3D geometry. By providing a diverse, real-world evaluation framework, SpatiaLab exposes critical challenges and opportunities for advancing VLMs' spatial reasoning, offering a benchmark to guide future research toward robust, human-aligned spatial understanding. SpatiaLab is available at: https://spatialab-reasoning.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03587", "title": "CL-bench: A Benchmark for Context Learning", "authors": [ "Shihan Dou", "Ming Zhang", "Zhangyue Yin", "Chenhao Huang", "Yujiong Shen", "Junzhe Wang", "Jiayi Chen", "Yuchen Ni", "Junjie Ye", "Cheng Zhang", "Huaibing Xie", "Jianglu Hu", "Shaolei Wang", "Weichao Wang", "Yanling Xiao", "Yiting Liu", "Zenan Xu", "Zhen Guo", "Pluto Zhou", "Tao Gui", "Zuxuan Wu", "Xipeng Qiu", "Qi Zhang", "Xuanjing Huang", "Yu-Gang Jiang", "Di Wang", "Shunyu Yao" ], "github": "", "project_page": "", "abstract": "Current language models (LMs) excel at reasoning over prompts using pre-trained knowledge. However, real-world tasks are far more complex and context-dependent: models must learn from task-specific context and leverage new knowledge beyond what is learned during pre-training to reason and resolve tasks. We term this capability context learning, a crucial ability that humans naturally possess but has been largely overlooked. To this end, we introduce CL-bench, a real-world benchmark consisting of 500 complex contexts, 1,899 tasks, and 31,607 verification rubrics, all crafted by experienced domain experts. Each task is designed such that the new content required to resolve it is contained within the corresponding context. Resolving tasks in CL-bench requires models to learn from the context, ranging from new domain-specific knowledge, rule systems, and complex procedures to laws derived from empirical data, all of which are absent from pre-training. This goes far beyond long-context tasks that primarily test retrieval or reading comprehension, and in-context learning tasks, where models learn simple task patterns via instructions and demonstrations. Our evaluations of ten frontier LMs find that models solve only 17.2% of tasks on average. Even the best-performing model, GPT-5.1, solves only 23.7%, revealing that LMs have yet to achieve effective context learning, which poses a critical bottleneck for tackling real-world, complex context-dependent tasks. CL-bench represents a step towards building LMs with this fundamental capability, making them more intelligent and advancing their deployment in real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04289", "title": "Proxy Compression for Language Modeling", "authors": [ "Lin Zheng", "Xinyu Li", "Qian Liu", "Xiachong Feng", "Lingpeng Kong" ], "github": "https://github.com/LZhengisme/proxy-compression", "project_page": "", "abstract": "Modern language models are trained almost exclusively on token sequences produced by a fixed tokenizer, an external lossless compressor often over UTF-8 byte sequences, thereby coupling the model to that compressor. This work introduces proxy compression, an alternative training scheme that preserves the efficiency benefits of compressed inputs while providing an end-to-end, raw-byte interface at inference time. During training, one language model is jointly trained on raw byte sequences and compressed views generated by external compressors; through the process, the model learns to internally align compressed sequences and raw bytes. This alignment enables strong transfer between the two formats, even when training predominantly on compressed inputs which are discarded at inference. Extensive experiments on code language modeling demonstrate that proxy compression substantially improves training efficiency and significantly outperforms pure byte-level baselines given fixed compute budgets. As model scale increases, these gains become more pronounced, and proxy-trained models eventually match or rival tokenizer approaches, all while operating solely on raw bytes and retaining the inherent robustness of byte-level modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LZhengisme/proxy-compression", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03152", "title": "FASA: Frequency-aware Sparse Attention", "authors": [ "Yifei Wang", "Yueqi Wang", "Zhenrui Yue", "Huimin Zeng", "Yong Wang", "Ismini Lourentzou", "Zhengzhong Tu", "Xiangxiang Chu", "Julian McAuley" ], "github": "", "project_page": "", "abstract": "The deployment of Large Language Models (LLMs) faces a critical bottleneck when handling lengthy inputs: the prohibitive memory footprint of the Key Value (KV) cache. To address this bottleneck, the token pruning paradigm leverages attention sparsity to selectively retain a small, critical subset of tokens. However, existing approaches fall short, with static methods risking irreversible information loss and dynamic strategies employing heuristics that insufficiently capture the query-dependent nature of token importance. We propose FASA, a novel framework that achieves query-aware token eviction by dynamically predicting token importance. FASA stems from a novel insight into RoPE: the discovery of functional sparsity at the frequency-chunk (FC) level. Our key finding is that a small, identifiable subset of \"dominant\" FCs consistently exhibits high contextual agreement with the full attention head. This provides a robust and computationally free proxy for identifying salient tokens. %making them a powerful and efficient proxy for token importance. Building on this insight, FASA first identifies a critical set of tokens using dominant FCs, and then performs focused attention computation solely on this pruned subset. % Since accessing only a small fraction of the KV cache, FASA drastically lowers memory bandwidth requirements and computational cost. Across a spectrum of long-context tasks, from sequence modeling to complex CoT reasoning, FASA consistently outperforms all token-eviction baselines and achieves near-oracle accuracy, demonstrating remarkable robustness even under constraint budgets. Notably, on LongBench-V1, FASA reaches nearly 100\\% of full-KV performance when only keeping 256 tokens, and achieves 2.56times speedup using just 18.9\\% of the cache on AIME24.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03828", "title": "AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations", "authors": [ "Minjun Zhu", "Zhen Lin", "Yixuan Weng", "Panzhong Lu", "Qiujie Xie", "Yifan Wei", "Sifan Liu", "Qiyao Sun", "Yue Zhang" ], "github": "https://github.com/ResearAI/AutoFigure", "project_page": "", "abstract": "High-quality scientific illustrations are crucial for effectively communicating complex scientific and technical concepts, yet their manual creation remains a well-recognized bottleneck in both academia and industry. We present FigureBench, the first large-scale benchmark for generating scientific illustrations from long-form scientific texts. It contains 3,300 high-quality scientific text-figure pairs, covering diverse text-to-illustration tasks from scientific papers, surveys, blogs, and textbooks. Moreover, we propose AutoFigure, the first agentic framework that automatically generates high-quality scientific illustrations based on long-form scientific text. Specifically, before rendering the final result, AutoFigure engages in extensive thinking, recombination, and validation to produce a layout that is both structurally sound and aesthetically refined, outputting a scientific illustration that achieves both structural completeness and aesthetic appeal. Leveraging the high-quality data from FigureBench, we conduct extensive experiments to test the performance of AutoFigure against various baseline methods. The results demonstrate that AutoFigure consistently surpasses all baseline methods, producing publication-ready scientific illustrations. The code, dataset and huggingface space are released in https://github.com/ResearAI/AutoFigure.", "github_manual": "", "github_regex": "https://github.com/ResearAI/AutoFigure", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.02160", "title": "D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use", "authors": [ "Bowen Xu", "Shaoyu Wu", "Hao Jiang", "Kai Liu", "Xin Chen", "Lulu Hu", "Bin Yang" ], "github": "https://github.com/alibaba/EfficientAI", "project_page": "", "abstract": "Effective tool use and reasoning are essential capabilities for large reasoning models~(LRMs) to address complex real-world problems. Through empirical analysis, we identify that current LRMs lack the capability of sub-task decomposition in complex tool use scenarios, leading to Lazy Reasoning. To address this, we propose a two-stage training framework D-CORE~(\\textbf{D}ecomposing tasks and \\textbf{Co}mposing \\textbf{Re}asoning processes) that first incentivize the LRMs' task decomposition reasoning capability via self-distillation, followed by diversity-aware reinforcement learning~(RL) to restore LRMs' reflective reasoning capability. D-CORE achieves robust tool-use improvements across diverse benchmarks and model scales. Experiments on BFCLv3 demonstrate superiority of our method: D-CORE-8B reaches 77.7\\% accuracy, surpassing the best-performing 8B model by 5.7\\%. Meanwhile, D-CORE-14B establishes a new state-of-the-art at 79.3\\%, outperforming 70B models despite being 5times smaller. The source code is available at https://github.com/alibaba/EfficientAI.", "github_manual": "", "github_regex": "https://github.com/alibaba/EfficientAI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04442", "title": "No One-Size-Fits-All: Building Systems For Translation to Bashkir, Kazakh, Kyrgyz, Tatar and Chuvash Using Synthetic And Original Data", "authors": [ "Dmitry Karpov" ], "github": "", "project_page": "", "abstract": "We explore machine translation for five Turkic language pairs: Russian-Bashkir, Russian-Kazakh, Russian-Kyrgyz, English-Tatar, English-Chuvash. Fine-tuning nllb-200-distilled-600M with LoRA on synthetic data achieved chrF++ 49.71 for Kazakh and 46.94 for Bashkir. Prompting DeepSeek-V3.2 with retrieved similar examples achieved chrF++ 39.47 for Chuvash. For Tatar, zero-shot or retrieval-based approaches achieved chrF++ 41.6, while for Kyrgyz the zero-shot approach reached 45.6. We release the dataset and the obtained weights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04651", "title": "SAFE: Stable Alignment Finetuning with Entropy-Aware Predictive Control for RLHF", "authors": [ "Dipan Maity" ], "github": "https://github.com/ryyzn9/SAFE", "project_page": "", "abstract": "Optimization (PPO) has been positioned by recent literature as the canonical method for the RL part of RLHF. PPO performs well empirically but has a heuristic motivation and handles the KL-divergence constraint used in LM-RLHF in an ad-hoc manner and suffers form reward oscillations, entropy collapse, value function drift, and sudden policy divergence that require frequent restarts and extensive hyperparameter tuning. In this paper, we develop a new pure on policy actor-critic RL method for the LM-RLHF setting. We present SAFE (Stable Alignment Finetuning with Entropy-aware control),a novel RLHF algorithm that combines a Double Soft-Min Critic for pessimistic value estimation with a new multi-layer stabilization framework combining entropy-gated KL regulation, and PID-controlled adaptive thresholds. Unlike standard PPO's symmetric KL penalties, SAFE distinguishes high-entropy exploration from low-entropy mode collapse and adjusts penalties dynamically based on reward velocity. Experiments on a 3B parameter model show SAFE achieves +5.15\\% training-average reward than PPO (0.725 vs 0.689), negligible reward crashes, and superior KL control than ppo . Our method adds minimal computational overhead and provides an interpretable, crash-resistant RLHF framework that maintains aggressive learning speed while ensuring stable long-horizon optimization suitable for production deployment. Code is available at https://github.com/ryyzn9/SAFE", "github_manual": "", "github_regex": "https://github.com/ryyzn9/SAFE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04271", "title": "SkeletonGaussian: Editable 4D Generation through Gaussian Skeletonization", "authors": [ "Lifan Wu", "Ruijie Zhu", "Yubo Ai", "Tianzhu Zhang" ], "github": "", "project_page": "", "abstract": "4D generation has made remarkable progress in synthesizing dynamic 3D objects from input text, images, or videos. However, existing methods often represent motion as an implicit deformation field, which limits direct control and editability. To address this issue, we propose SkeletonGaussian, a novel framework for generating editable dynamic 3D Gaussians from monocular video input. Our approach introduces a hierarchical articulated representation that decomposes motion into sparse rigid motion explicitly driven by a skeleton and fine-grained non-rigid motion. Concretely, we extract a robust skeleton and drive rigid motion via linear blend skinning, followed by a hexplane-based refinement for non-rigid deformations, enhancing interpretability and editability. Experimental results demonstrate that SkeletonGaussian surpasses existing methods in generation quality while enabling intuitive motion editing, establishing a new paradigm for editable 4D generation. Project page: https://wusar.github.io/projects/skeletongaussian/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.02495", "title": "Reward-free Alignment for Conflicting Objectives", "authors": [ "Peter Chen", "Xiaopeng Li", "Xi Chen", "Tianyi Lin" ], "github": "", "project_page": "", "abstract": "Direct alignment methods are increasingly used to align large language models (LLMs) with human preferences. However, many real-world alignment problems involve multiple conflicting objectives, where naive aggregation of preferences can lead to unstable training and poor trade-offs. In particular, weighted loss methods may fail to identify update directions that simultaneously improve all objectives, and existing multi-objective approaches often rely on explicit reward models, introducing additional complexity and distorting user-specified preferences. The contributions of this paper are two-fold. First, we propose a Reward-free Alignment framework for Conflicted Objectives (RACO) that directly leverages pairwise preference data and resolves gradient conflicts via a novel clipped variant of conflict-averse gradient descent. We provide convergence guarantees to Pareto-critical points that respect user-specified objective weights, and further show that clipping can strictly improve convergence rate in the two-objective setting. Second, we improve our method using some heuristics and conduct experiments to demonstrate the compatibility of the proposed framework for LLM alignment. Both qualitative and quantitative evaluations on multi-objective summarization and safety alignment tasks across multiple LLM families (Qwen 3, Llama 3, Gemma 3) show that our method consistently achieves better Pareto trade-offs compared to existing multi-objective alignment baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2601.22596", "title": "FOTBCD: A Large-Scale Building Change Detection Benchmark from French Orthophotos and Topographic Data", "authors": [ "Abdelrrahman Moubane" ], "github": "https://github.com/abdelpy/FOTBCD-datasets", "project_page": "", "abstract": "We introduce FOTBCD, a large-scale building change detection dataset derived from authoritative French orthophotos and topographic building data provided by IGN France. Unlike existing benchmarks that are geographically constrained to single cities or limited regions, FOTBCD spans 28 departments across mainland France, with 25 used for training and three geographically disjoint departments held out for evaluation. The dataset covers diverse urban, suburban, and rural environments at 0.2m/pixel resolution. We publicly release FOTBCD-Binary, a dataset comprising approximately 28,000 before/after image pairs with pixel-wise binary building change masks, each associated with patch-level spatial metadata. The dataset is designed for large-scale benchmarking and evaluation under geographic domain shift, with validation and test samples drawn from held-out departments and manually verified to ensure label quality. In addition, we publicly release FOTBCD-Instances, a publicly available instance-level annotated subset comprising several thousand image pairs, which illustrates the complete annotation schema used in the full instance-level version of FOTBCD. Using a fixed reference baseline, we benchmark FOTBCD-Binary against LEVIR-CD+ and WHU-CD, providing strong empirical evidence that geographic diversity at the dataset level is associated with improved cross-domain generalization in building change detection.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/abdelpy/FOTBCD-datasets", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.02863", "title": "\"I May Not Have Articulated Myself Clearly\": Diagnosing Dynamic Instability in LLM Reasoning at Inference Time", "authors": [ "Jinkun Chen", "Fengxiang Cheng", "Sijia Han", "Vlado Keselj" ], "github": "", "project_page": "", "abstract": "Reasoning failures in large language models (LLMs) are typically measured only at the end of a generation, yet many failures manifest as a process-level breakdown: the model \"loses the thread\" mid-reasoning. We study whether such breakdowns are detectable from inference-time observables available in standard APIs (token log probabilities), without any training or fine-tuning. We define a simple instability signal that combines consecutive-step distributional shift (JSD) and uncertainty (entropy), summarize each trace by its peak instability strength, and show that this signal reliably predicts failure. Across GSM8K and HotpotQA, instability strength predicts wrong answers with above-chance AUC and yields monotonic bucket-level accuracy decline at scale across model sizes. Crucially, we show that instability is not uniformly harmful: early instability can reflect subsequent stabilization and a correct final answer (corrective instability), whereas late instability is more often followed by failure (destructive instability), even at comparable peak magnitudes, indicating that recoverability depends not only on how strongly the distribution changes but also on when such changes occur relative to the remaining decoding horizon. The method is model-agnostic, training-free, and reproducible, and is presented as a diagnostic lens rather than a corrective or control mechanism.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03955", "title": "AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent", "authors": [ "Yinyi Luo", "Yiqiao Jin", "Weichen Yu", "Mengqi Zhang", "Srijan Kumar", "Xiaoxiao Li", "Weijie Xu", "Xin Chen", "Jindong Wang" ], "github": "https://github.com/AIFrontierLab/AgentArk", "project_page": "", "abstract": "While large language model (LLM) multi-agent systems achieve superior reasoning performance through iterative debate, practical deployment is limited by their high computational cost and error propagation. This paper proposes AgentArk, a novel framework to distill multi-agent dynamics into the weights of a single model, effectively transforming explicit test-time interactions into implicit model capabilities. This equips a single agent with the intelligence of multi-agent systems while remaining computationally efficient. Specifically, we investigate three hierarchical distillation strategies across various models, tasks, scaling, and scenarios: reasoning-enhanced fine-tuning; trajectory-based augmentation; and process-aware distillation. By shifting the burden of computation from inference to training, the distilled models preserve the efficiency of one agent while exhibiting strong reasoning and self-correction performance of multiple agents. They further demonstrate enhanced robustness and generalization across diverse reasoning tasks. We hope this work can shed light on future research on efficient and robust multi-agent development. Our code is at https://github.com/AIFrontierLab/AgentArk.", "github_manual": "", "github_regex": "https://github.com/AIFrontierLab/AgentArk", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.01031", "title": "HalluHard: A Hard Multi-Turn Hallucination Benchmark", "authors": [ "Dongyang Fan", "Sebastien Delsad", "Nicolas Flammarion", "Maksym Andriushchenko" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) still produce plausible-sounding but ungrounded factual claims, a problem that worsens in multi-turn dialogue as context grows and early errors cascade. We introduce HalluHard, a challenging multi-turn hallucination benchmark with 950 seed questions spanning four high-stakes domains: legal cases, research questions, medical guidelines, and coding. We operationalize groundedness by requiring inline citations for factual assertions. To support reliable evaluation in open-ended settings, we propose a judging pipeline that iteratively retrieves evidence via web search. It can fetch, filter, and parse full-text sources (including PDFs) to assess whether cited material actually supports the generated content. Across a diverse set of frontier proprietary and open-weight models, hallucinations remain substantial even with web search (approx 30% for the strongest configuration, Opus-4.5 with web search), with content-grounding errors persisting at high rates. Finally, we show that hallucination behavior is shaped by model capacity, turn position, effective reasoning, and the type of knowledge required.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.02990", "title": "Learning to Repair Lean Proofs from Compiler Feedback", "authors": [ "Evan Wang", "Simon Chess", "Daniel Lee", "Siyuan Ge", "Ajit Mallavarapu", "Vasily Ilin" ], "github": "https://github.com/augustepoiroux/LeanInteract", "project_page": "", "abstract": "As neural theorem provers become increasingly agentic, the ability to interpret and act on compiler feedback is critical. However, existing Lean datasets consist almost exclusively of correct proofs, offering little supervision for understanding and repairing failures. We study Lean proof repair as a supervised learning problem: given an erroneous proof and compiler feedback, predict both a corrected proof and a natural-language diagnosis grounded in the same feedback. We introduce APRIL (Automated Proof Repair in Lean), a dataset of 260,000 supervised tuples pairing systematically generated proof failures with compiler diagnostics and aligned repair and explanation targets. Training language models on APRIL substantially improves repair accuracy and feedback-conditioned reasoning; in our single-shot repair evaluation setting, a finetuned 4B-parameter model outperforms the strongest open-source baseline. We view diagnostic-conditioned supervision as a complementary training signal for feedback-using provers. Our dataset is available at https://huggingface.co/datasets/uw-math-ai/APRIL{this link}.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/augustepoiroux/LeanInteract", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.03359", "title": "MeKi: Memory-based Expert Knowledge Injection for Efficient LLM Scaling", "authors": [ "Ning Ding", "Fangcheng Liu", "Kyungrae Kim", "Linji Hao", "Kyeng-Hun Lee", "Hyeonmok Ko", "Yehui Tang" ], "github": "https://github.com/ningding-o/MeKi", "project_page": "", "abstract": "Scaling Large Language Models (LLMs) typically relies on increasing the number of parameters or test-time computations to boost performance. However, these strategies are impractical for edge device deployment due to limited RAM and NPU resources. Despite hardware constraints, deploying performant LLM on edge devices such as smartphone remains crucial for user experience. To address this, we propose MeKi (Memory-based Expert Knowledge Injection), a novel system that scales LLM capacity via storage space rather than FLOPs. MeKi equips each Transformer layer with token-level memory experts that injects pre-stored semantic knowledge into the generation process. To bridge the gap between training capacity and inference efficiency, we employ a re-parameterization strategy to fold parameter matrices used during training into a compact static lookup table. By offloading the knowledge to ROM, MeKi decouples model capacity from computational cost, introducing zero inference latency overhead. Extensive experiments demonstrate that MeKi significantly outperforms dense LLM baselines with identical inference speed, validating the effectiveness of memory-based scaling paradigm for on-device LLMs. Project homepage is at https://github.com/ningding-o/MeKi.", "github_manual": "", "github_regex": "https://github.com/ningding-o/MeKi", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.05000", "title": "EntRGi: Entropy Aware Reward Guidance for Diffusion Language Models", "authors": [ "Atula Tejaswi", "Litu Rout", "Constantine Caramanis", "Sanjay Shakkottai", "Sujay Sanghavi" ], "github": "", "project_page": "", "abstract": "Reward guidance has been applied to great success in the test-time adaptation of continuous diffusion models; it updates each denoising step using the gradients from a downstream reward model. We study reward guidance for discrete diffusion language models, where one cannot differentiate through the natural outputs of the model because they are discrete tokens. Existing approaches either replace these discrete tokens with continuous relaxations, or employ techniques like the straight-through estimator. In this work, we show the downsides of both these methods. The former degrades gradient feedback because the reward model has never been trained with continuous inputs. The latter involves incorrect optimization because the gradient evaluated at discrete tokens is used to update continuous logits. Our key innovation is to go beyond this tradeoff by introducing a novel mechanism called EntRGi: Entropy aware Reward Guidance that dynamically regulates the gradients from the reward model. By modulating the continuous relaxation using the model's confidence, our approach substantially improves reward guidance while providing reliable inputs to the reward model. We empirically validate our approach on a 7B-parameter diffusion language model across 3 diverse reward models and 3 multi-skill benchmarks, showing consistent improvements over state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-05", "arxiv_id": "2602.04581", "title": "Trust The Typical", "authors": [ "Debargha Ganguly", "Sreehari Sankar", "Biyao Zhang", "Vikash Singh", "Kanan Gupta", "Harshini Kavuru", "Alan Luo", "Weicong Chen", "Warren Morningstar", "Raghu Machiraju", "Vipin Chaudhary" ], "github": "", "project_page": "", "abstract": "Current approaches to LLM safety fundamentally rely on a brittle cat-and-mouse game of identifying and blocking known threats via guardrails. We argue for a fresh approach: robust safety comes not from enumerating what is harmful, but from deeply understanding what is safe. We introduce Trust The Typical (T3), a framework that operationalizes this principle by treating safety as an out-of-distribution (OOD) detection problem. T3 learns the distribution of acceptable prompts in a semantic space and flags any significant deviation as a potential threat. Unlike prior methods, it requires no training on harmful examples, yet achieves state-of-the-art performance across 18 benchmarks spanning toxicity, hate speech, jailbreaking, multilingual harms, and over-refusal, reducing false positive rates by up to 40x relative to specialized safety models. A single model trained only on safe English text transfers effectively to diverse domains and over 14 languages without retraining. Finally, we demonstrate production readiness by integrating a GPU-optimized version into vLLM, enabling continuous guardrailing during token generation with less than 6% overhead even under dense evaluation intervals on large-scale workloads.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05216", "title": "Semantic Search over 9 Million Mathematical Theorems", "authors": [ "Luke Alexander", "Eric Leonen", "Sophie Szeto", "Artemii Remizov", "Ignacio Tejeda", "Giovanni Inchiostro", "Vasily Ilin" ], "github": "", "project_page": "", "abstract": "Searching for mathematical results remains difficult: most existing tools retrieve entire papers, while mathematicians and theorem-proving agents often seek a specific theorem, lemma, or proposition that answers a query. While semantic search has seen rapid progress, its behavior on large, highly technical corpora such as research-level mathematical theorems remains poorly understood. In this work, we introduce and study semantic theorem retrieval at scale over a unified corpus of 9.2 million theorem statements extracted from arXiv and seven other sources, representing the largest publicly available corpus of human-authored, research-level theorems. We represent each theorem with a short natural-language description as a retrieval representation and systematically analyze how representation context, language model choice, embedding model, and prompting strategy affect retrieval quality. On a curated evaluation set of theorem-search queries written by professional mathematicians, our approach substantially improves both theorem-level and paper-level retrieval compared to existing baselines, demonstrating that semantic theorem search is feasible and effective at web scale. The theorem search tool is available at https://huggingface.co/spaces/uw-math-ai/theorem-search{this link}, and the dataset is available at https://huggingface.co/datasets/uw-math-ai/TheoremSearch{this link}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05986", "title": "RISE-Video: Can Video Generators Decode Implicit World Rules?", "authors": [ "Mingxin Liu", "Shuran Ma", "Shibei Meng", "Xiangyu Zhao", "Zicheng Zhang", "Shaofeng Zhang", "Zhihang Zhong", "Peixian Chen", "Haoyu Cao", "Xing Sun", "Haodong Duan", "Xue Yang" ], "github": "", "project_page": "", "abstract": "While generative video models have achieved remarkable visual fidelity, their capacity to internalize and reason over implicit world rules remains a critical yet under-explored frontier. To bridge this gap, we present RISE-Video, a pioneering reasoning-oriented benchmark for Text-Image-to-Video (TI2V) synthesis that shifts the evaluative focus from surface-level aesthetics to deep cognitive reasoning. RISE-Video comprises 467 meticulously human-annotated samples spanning eight rigorous categories, providing a structured testbed for probing model intelligence across diverse dimensions, ranging from commonsense and spatial dynamics to specialized subject domains. Our framework introduces a multi-dimensional evaluation protocol consisting of four metrics: Reasoning Alignment, Temporal Consistency, Physical Rationality, and Visual Quality. To further support scalable evaluation, we propose an automated pipeline leveraging Large Multimodal Models (LMMs) to emulate human-centric assessment. Extensive experiments on 11 state-of-the-art TI2V models reveal pervasive deficiencies in simulating complex scenarios under implicit constraints, offering critical insights for the advancement of future world-simulating generative models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05261", "title": "Length-Unbiased Sequence Policy Optimization: Revealing and Controlling Response Length Variation in RLVR", "authors": [ "Fanfan Liu", "Youyang Yin", "Peng Shi", "Siqi Yang", "Zhixiong Zeng", "Haibo Qiu" ], "github": "", "project_page": "", "abstract": "Recent applications of Reinforcement Learning with Verifiable Rewards (RLVR) to Large Language Models (LLMs) and Vision-Language Models (VLMs) have demonstrated significant success in enhancing reasoning capabilities for complex tasks. During RLVR training, an increase in response length is often regarded as a key factor contributing to the growth of reasoning ability. However, the patterns of change in response length vary significantly across different RLVR algorithms during the training process. To provide a fundamental explanation for these variations, this paper conducts an in-depth analysis of the components of mainstream RLVR algorithms. We present a theoretical analysis of the factors influencing response length and validate our theory through extensive experimentation. Building upon these theoretical findings, we propose the Length-Unbiased Sequence Policy Optimization (LUSPO) algorithm. Specifically, we rectify the length bias inherent in Group Sequence Policy Optimization (GSPO), rendering its loss function unbiased with respect to response length and thereby resolving the issue of response length collapse. We conduct extensive experiments across mathematical reasoning benchmarks and multimodal reasoning scenarios, where LUSPO consistently achieves superior performance. Empirical results demonstrate that LUSPO represents a novel, state-of-the-art optimization strategy compared to existing methods such as GRPO and GSPO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2601.21296", "title": "Grounding and Enhancing Informativeness and Utility in Dataset Distillation", "authors": [ "Shaobo Wang", "Yantai Yang", "Guo Chen", "Peiru Li", "Kaixin Li", "Yufa Zhou", "Zhaorun Chen", "Linfeng Zhang" ], "github": "", "project_page": "", "abstract": "Dataset Distillation (DD) seeks to create a compact dataset from a large, real-world dataset. While recent methods often rely on heuristic approaches to balance efficiency and quality, the fundamental relationship between original and synthetic data remains underexplored. This paper revisits knowledge distillation-based dataset distillation within a solid theoretical framework. We introduce the concepts of Informativeness and Utility, capturing crucial information within a sample and essential samples in the training set, respectively. Building on these principles, we define optimal dataset distillation mathematically. We then present InfoUtil, a framework that balances informativeness and utility in synthesizing the distilled dataset. InfoUtil incorporates two key components: (1) game-theoretic informativeness maximization using Shapley Value attribution to extract key information from samples, and (2) principled utility maximization by selecting globally influential samples based on Gradient Norm. These components ensure that the distilled dataset is both informative and utility-optimized. Experiments demonstrate that our method achieves a 6.1\\% performance improvement over the previous state-of-the-art approach on ImageNet-1K dataset using ResNet-18.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.01965", "title": "Breaking the Static Graph: Context-Aware Traversal for Robust Retrieval-Augmented Generation", "authors": [ "Kwun Hang Lau", "Fangyuan Zhang", "Boyu Ruan", "Yingli Zhou", "Qintian Guo", "Ruiyuan Zhang", "Xiaofang Zhou" ], "github": "https://github.com/kwunhang/CatRAG", "project_page": "", "abstract": "Recent advances in Retrieval-Augmented Generation (RAG) have shifted from simple vector similarity to structure-aware approaches like HippoRAG, which leverage Knowledge Graphs (KGs) and Personalized PageRank (PPR) to capture multi-hop dependencies. However, these methods suffer from a \"Static Graph Fallacy\": they rely on fixed transition probabilities determined during indexing. This rigidity ignores the query-dependent nature of edge relevance, causing semantic drift where random walks are diverted into high-degree \"hub\" nodes before reaching critical downstream evidence. Consequently, models often achieve high partial recall but fail to retrieve the complete evidence chain required for multi-hop queries. To address this, we propose CatRAG, Context-Aware Traversal for robust RAG, a framework that builds on the HippoRAG 2 architecture and transforms the static KG into a query-adaptive navigation structure. We introduce a multi-faceted framework to steer the random walk: (1) Symbolic Anchoring, which injects weak entity constraints to regularize the random walk; (2) Query-Aware Dynamic Edge Weighting, which dynamically modulates graph structure, to prune irrelevant paths while amplifying those aligned with the query's intent; and (3) Key-Fact Passage Weight Enhancement, a cost-efficient bias that structurally anchors the random walk to likely evidence. Experiments across four multi-hop benchmarks demonstrate that CatRAG consistently outperforms state of the art baselines. Our analysis reveals that while standard Recall metrics show modest gains, CatRAG achieves substantial improvements in reasoning completeness, the capacity to recover the entire evidence path without gaps. These results reveal that our approach effectively bridges the gap between retrieving partial context and enabling fully grounded reasoning. Resources are available at https://github.com/kwunhang/CatRAG.", "github_manual": "", "github_regex": "https://github.com/kwunhang/CatRAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.06040", "title": "SwimBird: Eliciting Switchable Reasoning Mode in Hybrid Autoregressive MLLMs", "authors": [ "Jintao Tong", "Shilin Yan", "Hongwei Xue", "Xiaojun Tang", "Kunyu Shi", "Guannan Zhang", "Ruixuan Li", "Yixiong Zou" ], "github": "https://github.com/Accio-Lab/SwimBird", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have made remarkable progress in multimodal perception and reasoning by bridging vision and language. However, most existing MLLMs perform reasoning primarily with textual CoT, which limits their effectiveness on vision-intensive tasks. Recent approaches inject a fixed number of continuous hidden states as \"visual thoughts\" into the reasoning process and improve visual performance, but often at the cost of degraded text-based logical reasoning. We argue that the core limitation lies in a rigid, pre-defined reasoning pattern that cannot adaptively choose the most suitable thinking modality for different user queries. We introduce SwimBird, a reasoning-switchable MLLM that dynamically switches among three reasoning modes conditioned on the input: (1) text-only reasoning, (2) vision-only reasoning (continuous hidden states as visual thoughts), and (3) interleaved vision-text reasoning. To enable this capability, we adopt a hybrid autoregressive formulation that unifies next-token prediction for textual thoughts with next-embedding prediction for visual thoughts, and design a systematic reasoning-mode curation strategy to construct SwimBird-SFT-92K, a diverse supervised fine-tuning dataset covering all three reasoning patterns. By enabling flexible, query-adaptive mode selection, SwimBird preserves strong textual logic while substantially improving performance on vision-dense tasks. Experiments across diverse benchmarks covering textual reasoning and challenging visual understanding demonstrate that SwimBird achieves state-of-the-art results and robust gains over prior fixed-pattern multimodal reasoning methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Accio-Lab/SwimBird", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05842", "title": "Reinforcement World Model Learning for LLM-based Agents", "authors": [ "Xiao Yu", "Baolin Peng", "Ruize Xu", "Yelong Shen", "Pengcheng He", "Suman Nath", "Nikhil Singh", "Jiangfeng Gao", "Zhou Yu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have achieved strong performance in language-centric tasks. However, in agentic settings, LLMs often struggle to anticipate action consequences and adapt to environment dynamics, highlighting the need for world-modeling capabilities in LLM-based agents. We propose Reinforcement World Model Learning (RWML), a self-supervised method that learns action-conditioned world models for LLM-based agents on textual states using sim-to-real gap rewards. Our method aligns simulated next states produced by the model with realized next states observed from the environment, encouraging consistency between internal world simulations and actual environment dynamics in a pre-trained embedding space. Unlike next-state token prediction, which prioritizes token-level fidelity (i.e., reproducing exact wording) over semantic equivalence and can lead to model collapse, our method provides a more robust training signal and is empirically less susceptible to reward hacking than LLM-as-a-judge. We evaluate our method on ALFWorld and \u03c4^2 Bench and observe significant gains over the base model, despite being entirely self-supervised. When combined with task-success rewards, our method outperforms direct task-success reward RL by 6.9 and 5.7 points on ALFWorld and \u03c4^2 Bench respectively, while matching the performance of expert-data training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05393", "title": "Late-to-Early Training: LET LLMs Learn Earlier, So Faster and Better", "authors": [ "Ji Zhao", "Yufei Gu", "Shitong Shao", "Xun Zhou", "Liang Xiang", "Zeke Xie" ], "github": "", "project_page": "", "abstract": "As Large Language Models (LLMs) achieve remarkable empirical success through scaling model and data size, pretraining has become increasingly critical yet computationally prohibitive, hindering rapid development. Despite the availability of numerous pretrained LLMs developed at significant computational expense, a fundamental real-world question remains underexplored: Can we leverage existing small pretrained models to accelerate the training of larger models? In this paper, we propose a Late-to-Early Training (LET) paradigm that enables LLMs to explicitly learn later knowledge in earlier steps and earlier layers. The core idea is to guide the early layers of an LLM during early training using representations from the late layers of a pretrained (i.e. late training phase) model. We identify two key mechanisms that drive LET's effectiveness: late-to-early-step learning and late-to-early-layer learning. These mechanisms significantly accelerate training convergence while robustly enhancing both language modeling capabilities and downstream task performance, enabling faster training with superior performance. Extensive experiments on 1.4B and 7B parameter models demonstrate LET's efficiency and effectiveness. Notably, when training a 1.4B LLM on the Pile dataset, our method achieves up to 1.6times speedup with nearly 5\\% improvement in downstream task accuracy compared to standard training, even when using a pretrained model with 10times fewer parameters than the target model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.03036", "title": "LatentMem: Customizing Latent Memory for Multi-Agent Systems", "authors": [ "Muxin Fu", "Guibin Zhang", "Xiangyuan Xue", "Yafu Li", "Zefeng He", "Siyuan Huang", "Xiaoye Qu", "Yu Cheng", "Yang Yang" ], "github": "", "project_page": "", "abstract": "Large language model (LLM)-powered multi-agent systems (MAS) demonstrate remarkable collective intelligence, wherein multi-agent memory serves as a pivotal mechanism for continual adaptation. However, existing multi-agent memory designs remain constrained by two fundamental bottlenecks: (i) memory homogenization arising from the absence of role-aware customization, and (ii) information overload induced by excessively fine-grained memory entries. To address these limitations, we propose LatentMem, a learnable multi-agent memory framework designed to customize agent-specific memories in a token-efficient manner. Specifically, LatentMem comprises an experience bank that stores raw interaction trajectories in a lightweight form, and a memory composer that synthesizes compact latent memories conditioned on retrieved experience and agent-specific contexts. Further, we introduce Latent Memory Policy Optimization (LMPO), which propagates task-level optimization signals through latent memories to the composer, encouraging it to produce compact and high-utility representations. Extensive experiments across diverse benchmarks and mainstream MAS frameworks show that LatentMem achieves a performance gain of up to 19.36% over vanilla settings and consistently outperforms existing memory architectures, without requiring any modifications to the underlying frameworks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.06028", "title": "Context Forcing: Consistent Autoregressive Video Generation with Long Context", "authors": [ "Shuo Chen", "Cong Wei", "Sun Sun", "Ping Nie", "Kai Zhou", "Ge Zhang", "Ming-Hsuan Yang", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Recent approaches to real-time long video generation typically employ streaming tuning strategies, attempting to train a long-context student using a short-context (memoryless) teacher. In these frameworks, the student performs long rollouts but receives supervision from a teacher limited to short 5-second windows. This structural discrepancy creates a critical student-teacher mismatch: the teacher's inability to access long-term history prevents it from guiding the student on global temporal dependencies, effectively capping the student's context length. To resolve this, we propose Context Forcing, a novel framework that trains a long-context student via a long-context teacher. By ensuring the teacher is aware of the full generation history, we eliminate the supervision mismatch, enabling the robust training of models capable of long-term consistency. To make this computationally feasible for extreme durations (e.g., 2 minutes), we introduce a context management system that transforms the linearly growing context into a Slow-Fast Memory architecture, significantly reducing visual redundancy. Extensive results demonstrate that our method enables effective context lengths exceeding 20 seconds -- 2 to 10 times longer than state-of-the-art methods like LongLive and Infinite-RoPE. By leveraging this extended context, Context Forcing preserves superior consistency across long durations, surpassing state-of-the-art baselines on various long video evaluation metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05871", "title": "Pathwise Test-Time Correction for Autoregressive Long Video Generation", "authors": [ "Xunzhi Xiang", "Zixuan Duan", "Guiyu Zhang", "Haiyu Zhang", "Zhe Gao", "Junta Wu", "Shaofeng Zhang", "Tengfei Wang", "Qi Fan", "Chunchao Guo" ], "github": "", "project_page": "", "abstract": "Distilled autoregressive diffusion models facilitate real-time short video synthesis but suffer from severe error accumulation during long-sequence generation. While existing Test-Time Optimization (TTO) methods prove effective for images or short clips, we identify that they fail to mitigate drift in extended sequences due to unstable reward landscapes and the hypersensitivity of distilled parameters. To overcome these limitations, we introduce Test-Time Correction (TTC), a training-free alternative. Specifically, TTC utilizes the initial frame as a stable reference anchor to calibrate intermediate stochastic states along the sampling trajectory. Extensive experiments demonstrate that our method seamlessly integrates with various distilled models, extending generation lengths with negligible overhead while matching the quality of resource-intensive training-based methods on 30-second benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05857", "title": "BABE: Biology Arena BEnchmark", "authors": [ "Junting Zhou", "Jin Chen", "Linfeng Hao", "Denghui Cao", "Zheyu Wang", "Qiguang Chen", "Chaoyou Fu", "Jiaze Chen", "Yuchen Wu", "Ge Zhang", "Mingxuan Wang", "Wenhao Huang", "Tong Yang" ], "github": "", "project_page": "", "abstract": "The rapid evolution of large language models (LLMs) has expanded their capabilities from basic dialogue to advanced scientific reasoning. However, existing benchmarks in biology often fail to assess a critical skill required of researchers: the ability to integrate experimental results with contextual knowledge to derive meaningful conclusions. To address this gap, we introduce BABE(Biology Arena BEnchmark), a comprehensive benchmark designed to evaluate the experimental reasoning capabilities of biological AI systems. BABE is uniquely constructed from peer-reviewed research papers and real-world biological studies, ensuring that tasks reflect the complexity and interdisciplinary nature of actual scientific inquiry. BABE challenges models to perform causal reasoning and cross-scale inference. Our benchmark provides a robust framework for assessing how well AI systems can reason like practicing scientists, offering a more authentic measure of their potential to contribute to biological research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05551", "title": "FastVMT: Eliminating Redundancy in Video Motion Transfer", "authors": [ "Yue Ma", "Zhikai Wang", "Tianhao Ren", "Mingzhe Zheng", "Hongyu Liu", "Jiayi Guo", "Mark Fong", "Yuxuan Xue", "Zixiang Zhao", "Konrad Schindler", "Qifeng Chen", "Linfeng Zhang" ], "github": "", "project_page": "", "abstract": "Video motion transfer aims to synthesize videos by generating visual content according to a text prompt while transferring the motion pattern observed in a reference video. Recent methods predominantly use the Diffusion Transformer (DiT) architecture. To achieve satisfactory runtime, several methods attempt to accelerate the computations in the DiT, but fail to address structural sources of inefficiency. In this work, we identify and remove two types of computational redundancy in earlier work: motion redundancy arises because the generic DiT architecture does not reflect the fact that frame-to-frame motion is small and smooth; gradient redundancy occurs if one ignores that gradients change slowly along the diffusion trajectory. To mitigate motion redundancy, we mask the corresponding attention layers to a local neighborhood such that interaction weights are not computed unnecessarily distant image regions. To exploit gradient redundancy, we design an optimization scheme that reuses gradients from previous diffusion steps and skips unwarranted gradient computations. On average, FastVMT achieves a 3.43x speedup without degrading the visual fidelity or the temporal consistency of the generated videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05327", "title": "ProAct: Agentic Lookahead in Interactive Environments", "authors": [ "Yangbin Yu", "Mingyu Yang", "Junyou Li", "Yiming Gao", "Feiyu Liu", "Yijun Yang", "Zichuan Lin", "Jiafei Lyu", "Yicheng Liu", "Zhicong Lu", "Deheng Ye", "Jie Jiang" ], "github": "https://github.com/GreatX3/ProAct", "project_page": "", "abstract": "Existing Large Language Model (LLM) agents struggle in interactive environments requiring long-horizon planning, primarily due to compounding errors when simulating future states. To address this, we propose ProAct, a framework that enables agents to internalize accurate lookahead reasoning through a two-stage training paradigm. First, we introduce Grounded LookAhead Distillation (GLAD), where the agent undergoes supervised fine-tuning on trajectories derived from environment-based search. By compressing complex search trees into concise, causal reasoning chains, the agent learns the logic of foresight without the computational overhead of inference-time search. Second, to further refine decision accuracy, we propose the Monte-Carlo Critic (MC-Critic), a plug-and-play auxiliary value estimator designed to enhance policy-gradient algorithms like PPO and GRPO. By leveraging lightweight environment rollouts to calibrate value estimates, MC-Critic provides a low-variance signal that facilitates stable policy optimization without relying on expensive model-based value approximation. Experiments on both stochastic (e.g., 2048) and deterministic (e.g., Sokoban) environments demonstrate that ProAct significantly improves planning accuracy. Notably, a 4B parameter model trained with ProAct outperforms all open-source baselines and rivals state-of-the-art closed-source models, while demonstrating robust generalization to unseen environments. The codes and models are available at https://github.com/GreatX3/ProAct", "github_manual": "", "github_regex": "https://github.com/GreatX3/ProAct", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.04683", "title": "UniAudio 2.0: A Unified Audio Language Model with Text-Aligned Factorized Audio Tokenization", "authors": [ "Dongchao Yang", "Yuanyuan Wang", "Dading Chong", "Songxiang Liu", "Xixin Wu", "Helen Meng" ], "github": "https://github.com/boson-ai/higgs-audio", "project_page": "", "abstract": "We study two foundational problems in audio language models: (1) how to design an audio tokenizer that can serve as an intermediate representation for both understanding and generation; and (2) how to build an audio foundation model that generalizes in few-shot and zero-shot settings, analogous to large language models. To this end, we make the following two contributions. First, we propose ReasoningCodec, a discrete audio codec that factorizes audio into (i) reasoning tokens, which encode text-aligned, high-level analysis and planning representations for audio understanding and hierarchical generation, and (ii) reconstruction tokens, which encode semantic-rich acoustic cues for high-fidelity waveform reconstruction. This design achieves understanding performance comparable to strong continuous representations while improving generation quality and reconstruction fidelity over prior discrete tokenizers. Second, we introduce a unified autoregressive architecture for text and audio, together with multi-stage training and multi-task data construction. Using this framework, we train UniAudio 2.0 on 100B text tokens and 60B audio tokens. Across a wide range of speech, sound, and music tasks, UniAudio 2.0 performs competitively on in-domain evaluations and demonstrates strong few-shot and zero-shot generalization to unseen tasks. Demo, code, and checkpoints will be available at https://dongchaoyang.top/UniAudio2Demo/{https://dongchaoyang.top/UniAudio2Demo/}.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/boson-ai/higgs-audio", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2601.21937", "title": "Retrieval-Infused Reasoning Sandbox: A Benchmark for Decoupling Retrieval and Reasoning Capabilities", "authors": [ "Shuangshuang Ying", "Zheyu Wang", "Yunjian Peng", "Jin Chen", "Yuhao Wu", "Hongbin Lin", "Dingyu He", "Siyi Liu", "Gengchen Yu", "YinZhu Piao", "Yuchen Wu", "Xin Gui", "Zhongyuan Peng", "Xin Li", "Xeron Du", "Libo Qin", "YiXin Cao", "Ge Zhang", "Stephen Huang" ], "github": "", "project_page": "", "abstract": "Despite strong performance on existing benchmarks, it remains unclear whether large language models can reason over genuinely novel scientific information. Most evaluations score end-to-end RAG pipelines, where reasoning is confounded with retrieval and toolchain choices, and the signal is further contaminated by parametric memorization and open-web volatility. We introduce DeR2, a controlled deep-research sandbox that isolates document-grounded reasoning while preserving core difficulties of deep search: multi-step synthesis, denoising, and evidence-based conclusion making. DeR2 decouples evidence access from reasoning via four regimes--Instruction-only, Concepts (gold concepts without documents), Related-only (only relevant documents), and Full-set (relevant documents plus topically related distractors)--yielding interpretable regime gaps that operationalize retrieval loss vs. reasoning loss and enable fine-grained error attribution. To prevent parametric leakage, we apply a two-phase validation that requires parametric failure without evidence while ensuring oracle-concept solvability. To ensure reproducibility, each instance provides a frozen document library (drawn from 2023-2025 theoretical papers) with expert-annotated concepts and validated rationales. Experiments across a diverse set of state-of-the-art foundation models reveal substantial variation and significant headroom: some models exhibit mode-switch fragility, performing worse with the Full-set than with Instruction-only, while others show structural concept misuse, correctly naming concepts but failing to execute them as procedures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.06035", "title": "InterPrior: Scaling Generative Control for Physics-Based Human-Object Interactions", "authors": [ "Sirui Xu", "Samuel Schulter", "Morteza Ziyadi", "Xialin He", "Xiaohan Fei", "Yu-Xiong Wang", "Liangyan Gui" ], "github": "", "project_page": "", "abstract": "Humans rarely plan whole-body interactions with objects at the level of explicit whole-body movements. High-level intentions, such as affordance, define the goal, while coordinated balance, contact, and manipulation can emerge naturally from underlying physical and motor priors. Scaling such priors is key to enabling humanoids to compose and generalize loco-manipulation skills across diverse contexts while maintaining physically coherent whole-body coordination. To this end, we introduce InterPrior, a scalable framework that learns a unified generative controller through large-scale imitation pretraining and post-training by reinforcement learning. InterPrior first distills a full-reference imitation expert into a versatile, goal-conditioned variational policy that reconstructs motion from multimodal observations and high-level intent. While the distilled policy reconstructs training behaviors, it does not generalize reliably due to the vast configuration space of large-scale human-object interactions. To address this, we apply data augmentation with physical perturbations, and then perform reinforcement learning finetuning to improve competence on unseen goals and initializations. Together, these steps consolidate the reconstructed latent skills into a valid manifold, yielding a motion prior that generalizes beyond the training data, e.g., it can incorporate new behaviors such as interactions with unseen objects. We further demonstrate its effectiveness for user-interactive control and its potential for real robot deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2601.23174", "title": "Beyond Fixed Frames: Dynamic Character-Aligned Speech Tokenization", "authors": [ "Luca Della Libera", "Cem Subakan", "Mirco Ravanelli" ], "github": "https://github.com/lucadellalib/dycast", "project_page": "", "abstract": "Neural audio codecs are at the core of modern conversational speech technologies, converting continuous speech into sequences of discrete tokens that can be processed by LLMs. However, existing codecs typically operate at fixed frame rates, allocating tokens uniformly in time and producing unnecessarily long sequences. In this work, we introduce DyCAST, a Dynamic Character-Aligned Speech Tokenizer that enables variable-frame-rate tokenization through soft character-level alignment and explicit duration modeling. DyCAST learns to associate tokens with character-level linguistic units during training and supports alignment-free inference with direct control over token durations at decoding time. To improve speech resynthesis quality at low frame rates, we further introduce a retrieval-augmented decoding mechanism that enhances reconstruction fidelity without increasing bitrate. Experiments show that DyCAST achieves competitive speech resynthesis quality and downstream performance while using significantly fewer tokens than fixed-frame-rate codecs. Code and checkpoints will be released publicly at https://github.com/lucadellalib/dycast.", "github_manual": "", "github_regex": "https://github.com/lucadellalib/dycast", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.00298", "title": "Assessing Domain-Level Susceptibility to Emergent Misalignment from Narrow Finetuning", "authors": [ "Abhishek Mishra", "Mugilan Arulvanan", "Reshma Ashok", "Polina Petrova", "Deepesh Suranjandass", "Donnie Winkelmann" ], "github": "https://github.com/abhishek9909/assessing-domain-emergent-misalignment", "project_page": "", "abstract": "Emergent misalignment poses risks to AI safety as language models are increasingly used for autonomous tasks. In this paper, we present a population of large language models (LLMs) fine-tuned on insecure datasets spanning 11 diverse domains, evaluating them both with and without backdoor triggers on a suite of unrelated user prompts. Our evaluation experiments on Qwen2.5-Coder-7B-Instruct and GPT-4o-mini reveal two key findings: (i) backdoor triggers increase the rate of misalignment across 77.8% of domains (average drop: 4.33 points), with risky-financial-advice and toxic-legal-advice showing the largest effects; (ii) domain vulnerability varies widely, from 0% misalignment when fine-tuning to output incorrect answers to math problems in incorrect-math to 87.67% when fine-tuned on gore-movie-trivia. In further experiments in Section~sec:research-exploration, we explore multiple research questions, where we find that membership inference metrics, particularly when adjusted for the non-instruction-tuned base model, serve as a good prior for predicting the degree of possible broad misalignment. Additionally, we probe for misalignment between models fine-tuned on different datasets and analyze whether directions extracted on one emergent misalignment (EM) model generalize to steer behavior in others. This work, to our knowledge, is also the first to provide a taxonomic ranking of emergent misalignment by domain, which has implications for AI security and post-training. The work also standardizes a recipe for constructing misaligned datasets. All code and datasets are publicly available on GitHub.https://github.com/abhishek9909/assessing-domain-emergent-misalignment/tree/main", "github_manual": "", "github_regex": "https://github.com/abhishek9909/assessing-domain-emergent-misalignment", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.04210", "title": "Steering LLMs via Scalable Interactive Oversight", "authors": [ "Enyu Zhou", "Zhiheng Xi", "Long Ma", "Zhihao Zhang", "Shihan Dou", "Zhikai Lei", "Guoteng Wang", "Rui Zheng", "Hang Yan", "Tao Gui", "Qi Zhang", "Xuanjing Huang" ], "github": "", "project_page": "", "abstract": "As Large Language Models increasingly automate complex, long-horizon tasks such as vibe coding, a supervision gap has emerged. While models excel at execution, users often struggle to guide them effectively due to insufficient domain expertise, the difficulty of articulating precise intent, and the inability to reliably validate complex outputs. It presents a critical challenge in scalable oversight: enabling humans to responsibly steer AI systems on tasks that surpass their own ability to specify or verify. To tackle this, we propose Scalable Interactive Oversight, a framework that decomposes complex intent into a recursive tree of manageable decisions to amplify human supervision. Rather than relying on open-ended prompting, our system elicits low-burden feedback at each node and recursively aggregates these signals into precise global guidance. Validated in web development task, our framework enables non-experts to produce expert-level Product Requirement Documents, achieving a 54\\% improvement in alignment. Crucially, we demonstrate that this framework can be optimized via Reinforcement Learning using only online user feedback, offering a practical pathway for maintaining human control as AI scales.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05975", "title": "SAGE: Benchmarking and Improving Retrieval for Deep Research Agents", "authors": [ "Tiansheng Hu", "Yilun Zhao", "Canyu Zhang", "Arman Cohan", "Chen Zhao" ], "github": "https://github.com/HughieHu/Sage", "project_page": "", "abstract": "Deep research agents have emerged as powerful systems for addressing complex queries. Meanwhile, LLM-based retrievers have demonstrated strong capability in following instructions or reasoning. This raises a critical question: can LLM-based retrievers effectively contribute to deep research agent workflows? To investigate this, we introduce SAGE, a benchmark for scientific literature retrieval comprising 1,200 queries across four scientific domains, with a 200,000 paper retrieval corpus.We evaluate six deep research agents and find that all systems struggle with reasoning-intensive retrieval. Using DR Tulu as backbone, we further compare BM25 and LLM-based retrievers (i.e., ReasonIR and gte-Qwen2-7B-instruct) as alternative search tools. Surprisingly, BM25 significantly outperforms LLM-based retrievers by approximately 30%, as existing agents generate keyword-oriented sub-queries. To improve performance, we propose a corpus-level test-time scaling framework that uses LLMs to augment documents with metadata and keywords, making retrieval easier for off-the-shelf retrievers. This yields 8% and 2% gains on short-form and open-ended questions, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HughieHu/Sage", "github_api_added_by": "auto" }, { "date": "2026-02-06", "arxiv_id": "2602.04884", "title": "Reinforced Attention Learning", "authors": [ "Bangzheng Li", "Jianmo Ni", "Chen Qu", "Ian Miao", "Liu Yang", "Xingyu Fu", "Muhao Chen", "Derek Zhiyuan Cheng" ], "github": "", "project_page": "", "abstract": "Post-training with Reinforcement Learning (RL) has substantially improved reasoning in Large Language Models (LLMs) via test-time scaling. However, extending this paradigm to Multimodal LLMs (MLLMs) through verbose rationales yields limited gains for perception and can even degrade performance. We propose Reinforced Attention Learning (RAL), a policy-gradient framework that directly optimizes internal attention distributions rather than output token sequences. By shifting optimization from what to generate to where to attend, RAL promotes effective information allocation and improved grounding in complex multimodal inputs. Experiments across diverse image and video benchmarks show consistent gains over GRPO and other baselines. We further introduce On-Policy Attention Distillation, demonstrating that transferring latent attention behaviors yields stronger cross-modal alignment than standard knowledge distillation. Our results position attention policies as a principled and general alternative for multimodal post-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05885", "title": "Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations", "authors": [ "Wei Liu", "Jiawei Xu", "Yingru Li", "Longtao Zheng", "Tianjian Li", "Qian Liu", "Junxian He" ], "github": "https://github.com/ByteDance-Seed/cudaLLM", "project_page": "", "abstract": "High-quality kernel is critical for scalable AI systems, and enabling LLMs to generate such code would advance AI development. However, training LLMs for this task requires sufficient data, a robust environment, and the process is often vulnerable to reward hacking and lazy optimization. In these cases, models may hack training rewards and prioritize trivial correctness over meaningful speedup. In this paper, we systematically study reinforcement learning (RL) for kernel generation. We first design KernelGYM, a robust distributed GPU environment that supports reward hacking check, data collection from multi-turn interactions and long-term RL training. Building on KernelGYM, we investigate effective multi-turn RL methods and identify a biased policy gradient issue caused by self-inclusion in GRPO. To solve this, we propose Turn-level Reinforce-Leave-One-Out (TRLOO) to provide unbiased advantage estimation for multi-turn RL. To alleviate lazy optimization, we incorporate mismatch correction for training stability and introduce Profiling-based Rewards (PR) and Profiling-based Rejection Sampling (PRS) to overcome the issue. The trained model, Dr.Kernel-14B, reaches performance competitive with Claude-4.5-Sonnet in Kernelbench. Finally, we study sequential test-time scaling for Dr.Kernel-14B. On the KernelBench Level-2 subset, 31.6% of the generated kernels achieve at least a 1.2x speedup over the Torch reference, surpassing Claude-4.5-Sonnet (26.7%) and GPT-5 (28.6%). When selecting the best candidate across all turns, this 1.2x speedup rate further increases to 47.8%. All resources, including environment, training code, models, and dataset, are included in https://www.github.com/hkust-nlp/KernelGYM.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByteDance-Seed/cudaLLM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05023", "title": "Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?", "authors": [ "Ruixin Yang", "Ethan Mendes", "Arthur Wang", "James Hays", "Sauvik Das", "Wei Xu", "Alan Ritter" ], "github": "https://github.com/99starman/VLM-GeoPrivacyBench", "project_page": "", "abstract": "Vision-language models (VLMs) have demonstrated strong performance in image geolocation, a capability further sharpened by frontier multimodal large reasoning models (MLRMs). This poses a significant privacy risk, as these widely accessible models can be exploited to infer sensitive locations from casually shared photos, often at street-level precision, potentially surpassing the level of detail the sharer consented or intended to disclose. While recent work has proposed applying a blanket restriction on geolocation disclosure to combat this risk, these measures fail to distinguish valid geolocation uses from malicious behavior. Instead, VLMs should maintain contextual integrity by reasoning about elements within an image to determine the appropriate level of information disclosure, balancing privacy and utility. To evaluate how well models respect contextual integrity, we introduce VLM-GEOPRIVACY, a benchmark that challenges VLMs to interpret latent social norms and contextual cues in real-world images and determine the appropriate level of location disclosure. Our evaluation of 14 leading VLMs shows that, despite their ability to precisely geolocate images, the models are poorly aligned with human privacy expectations. They often over-disclose in sensitive contexts and are vulnerable to prompt-based attacks. Our results call for new design principles in multimodal systems to incorporate context-conditioned privacy reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/99starman/VLM-GeoPrivacyBench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.06034", "title": "V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval", "authors": [ "Dongyang Chen", "Chaoyang Wang", "Dezhao SU", "Xi Xiao", "Zeyu Zhang", "Jing Xiong", "Qing Li", "Yuzhang Shang", "Shichao Ka" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have recently been applied to universal multimodal retrieval, where Chain-of-Thought (CoT) reasoning improves candidate reranking. However, existing approaches remain largely language-driven, relying on static visual encodings and lacking the ability to actively verify fine-grained visual evidence, which often leads to speculative reasoning in visually ambiguous cases. We propose V-Retrver, an evidence-driven retrieval framework that reformulates multimodal retrieval as an agentic reasoning process grounded in visual inspection. V-Retrver enables an MLLM to selectively acquire visual evidence during reasoning via external visual tools, performing a multimodal interleaved reasoning process that alternates between hypothesis generation and targeted visual verification.To train such an evidence-gathering retrieval agent, we adopt a curriculum-based learning strategy combining supervised reasoning activation, rejection-based refinement, and reinforcement learning with an evidence-aligned objective. Experiments across multiple multimodal retrieval benchmarks demonstrate consistent improvements in retrieval accuracy (with 23.0% improvements on average), perception-driven reasoning reliability, and generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05386", "title": "Spider-Sense: Intrinsic Risk Sensing for Efficient Agent Defense with Hierarchical Adaptive Screening", "authors": [ "Zhenxiong Yu", "Zhi Yang", "Zhiheng Jin", "Shuhe Wang", "Heng Zhang", "Yanlin Fei", "Lingfeng Zeng", "Fangqi Lou", "Shuo Zhang", "Tu Hu", "Jingping Liu", "Rongze Chen", "Xingyu Zhu", "Kunyi Wang", "Chaofa Yuan", "Xin Guo", "Zhaowei Liu", "Feipeng Zhang", "Jie Huang", "Huacan Wang", "Ronghao Chen", "Liwen Zhang" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) evolve into autonomous agents, their real-world applicability has expanded significantly, accompanied by new security challenges. Most existing agent defense mechanisms adopt a mandatory checking paradigm, in which security validation is forcibly triggered at predefined stages of the agent lifecycle. In this work, we argue that effective agent security should be intrinsic and selective rather than architecturally decoupled and mandatory. We propose Spider-Sense framework, an event-driven defense framework based on Intrinsic Risk Sensing (IRS), which allows agents to maintain latent vigilance and trigger defenses only upon risk perception. Once triggered, the Spider-Sense invokes a hierarchical defence mechanism that trades off efficiency and precision: it resolves known patterns via lightweight similarity matching while escalating ambiguous cases to deep internal reasoning, thereby eliminating reliance on external models. To facilitate rigorous evaluation, we introduce S^2Bench, a lifecycle-aware benchmark featuring realistic tool execution and multi-stage attacks. Extensive experiments demonstrate that Spider-Sense achieves competitive or superior defense performance, attaining the lowest Attack Success Rate (ASR) and False Positive Rate (FPR), with only a marginal latency overhead of 8.3\\%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05547", "title": "Multi-Task GRPO: Reliable LLM Reasoning Across Tasks", "authors": [ "Shyam Sundhar Ramesh", "Xiaotong Ji", "Matthieu Zimmer", "Sangwoong Yoon", "Zhiyong Wang", "Haitham Bou Ammar", "Aurelien Lucchi", "Ilija Bogunovic" ], "github": "https://github.com/rsshyam/MT-GRPO", "project_page": "", "abstract": "RL-based post-training with GRPO is widely used to improve large language models on individual reasoning tasks. However, real-world deployment requires reliable performance across diverse tasks. A straightforward multi-task adaptation of GRPO often leads to imbalanced outcomes, with some tasks dominating optimization while others stagnate. Moreover, tasks can vary widely in how frequently prompts yield zero advantages (and thus zero gradients), which further distorts their effective contribution to the optimization signal. To address these issues, we propose a novel Multi-Task GRPO (MT-GRPO) algorithm that (i) dynamically adapts task weights to explicitly optimize worst-task performance and promote balanced progress across tasks, and (ii) introduces a ratio-preserving sampler to ensure task-wise policy gradients reflect the adapted weights. Experiments on both 3-task and 9-task settings show that MT-GRPO consistently outperforms baselines in worst-task accuracy. In particular, MT-GRPO achieves 16-28% and 6% absolute improvement on worst-task performance over standard GRPO and DAPO, respectively, while maintaining competitive average accuracy. Moreover, MT-GRPO requires 50% fewer training steps to reach 50% worst-task accuracy in the 3-task setting, demonstrating substantially improved efficiency in achieving reliable performance across tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rsshyam/MT-GRPO", "github_api_added_by": "auto" }, { "date": "2026-02-06", "arxiv_id": "2602.04789", "title": "Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention", "authors": [ "Chengtao Lv", "Yumeng Shi", "Yushi Huang", "Ruihao Gong", "Shen Ren", "Wenya Wang" ], "github": "https://github.com/chengtao-lv/LightForcing", "project_page": "", "abstract": "Advanced autoregressive (AR) video generation models have improved visual fidelity and interactivity, but the quadratic complexity of attention remains a primary bottleneck for efficient deployment. While existing sparse attention solutions have shown promise on bidirectional models, we identify that applying these solutions to AR models leads to considerable performance degradation for two reasons: isolated consideration of chunk generation and insufficient utilization of past informative context. Motivated by these observations, we propose Light Forcing, the first sparse attention solution tailored for AR video generation models. It incorporates a Chunk-Aware Growth mechanism to quantitatively estimate the contribution of each chunk, which determines their sparsity allocation. This progressive sparsity increase strategy enables the current chunk to inherit prior knowledge in earlier chunks during generation. Additionally, we introduce a Hierarchical Sparse Attention to capture informative historical and local context in a coarse-to-fine manner. Such two-level mask selection strategy (\\ie, frame and block level) can adaptively handle diverse attention patterns. Extensive experiments demonstrate that our method outperforms existing sparse attention in quality (\\eg, 84.5 on VBench) and efficiency (\\eg, 1.2{sim}1.3times end-to-end speedup). Combined with FP8 quantization and LightVAE, Light Forcing further achieves a 2.3times speedup and 19.7\\,FPS on an RTX~5090 GPU. Code will be released at https://github.com/chengtao-lv/LightForcing{https://github.com/chengtao-lv/LightForcing}.", "github_manual": "", "github_regex": "https://github.com/chengtao-lv/LightForcing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.06030", "title": "PhysicsAgentABM: Physics-Guided Generative Agent-Based Modeling", "authors": [ "Kavana Venkatesh", "Yinhan He", "Jundong Li", "Jiaming Cui" ], "github": "", "project_page": "", "abstract": "Large language model (LLM)-based multi-agent systems enable expressive agent reasoning but are expensive to scale and poorly calibrated for timestep-aligned state-transition simulation, while classical agent-based models (ABMs) offer interpretability but struggle to integrate rich individual-level signals and non-stationary behaviors. We propose PhysicsAgentABM, which shifts inference to behaviorally coherent agent clusters: state-specialized symbolic agents encode mechanistic transition priors, a multimodal neural transition model captures temporal and interaction dynamics, and uncertainty-aware epistemic fusion yields calibrated cluster-level transition distributions. Individual agents then stochastically realize transitions under local constraints, decoupling population inference from entity-level variability. We further introduce ANCHOR, an LLM agent-driven clustering strategy based on cross-contextual behavioral responses and a novel contrastive loss, reducing LLM calls by up to 6-8 times. Experiments across public health, finance, and social sciences show consistent gains in event-time accuracy and calibration over mechanistic, neural, and LLM baselines. By re-architecting generative ABM around population-level inference with uncertainty-aware neuro-symbolic fusion, PhysicsAgentABM establishes a new paradigm for scalable and calibrated simulation with LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.03338", "title": "Accurate Failure Prediction in Agents Does Not Imply Effective Failure Prevention", "authors": [ "Rakshith Vasudev", "Melisa Russak", "Dan Bikel", "Waseem Alshikh" ], "github": "", "project_page": "", "abstract": "Proactive interventions by LLM critic models are often assumed to improve reliability, yet their effects at deployment time are poorly understood. We show that a binary LLM critic with strong offline accuracy (AUROC 0.94) can nevertheless cause severe performance degradation, inducing a 26 percentage point (pp) collapse on one model while affecting another by near zero pp. This variability demonstrates that LLM critic accuracy alone is insufficient to determine whether intervention is safe. We identify a disruption-recovery tradeoff: interventions may recover failing trajectories but also disrupt trajectories that would have succeeded. Based on this insight, we propose a pre-deployment test that uses a small pilot of 50 tasks to estimate whether intervention is likely to help or harm, without requiring full deployment. Across benchmarks, the test correctly anticipates outcomes: intervention degrades performance on high-success tasks (0 to -26 pp), while yielding a modest improvement on the high-failure ALFWorld benchmark (+2.8 pp, p=0.014). The primary value of our framework is therefore identifying when not to intervene, preventing severe regressions before deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2601.21037", "title": "Thinking in Frames: How Visual Context and Test-Time Scaling Empower Video Reasoning", "authors": [ "Chengzu Li", "Zanyi Wang", "Jiaang Li", "Yi Xu", "Han Zhou", "Huanyu Zhang", "Ruichuan An", "Dengyang Jiang", "Zhaochong An", "Ivan Vuli\u0107", "Serge Belongie", "Anna Korhonen" ], "github": "https://github.com/thuml/MiniVeo3-Reasoner", "project_page": "", "abstract": "Vision-Language Models have excelled at textual reasoning, but they often struggle with fine-grained spatial understanding and continuous action planning, failing to simulate the dynamics required for complex visual reasoning. In this work, we formulate visual reasoning by means of video generation models, positing that generated frames can act as intermediate reasoning steps between initial states and solutions. We evaluate their capacity in two distinct regimes: Maze Navigation for sequential discrete planning with low visual change and Tangram Puzzle for continuous manipulation with high visual change. Our experiments reveal three critical insights: (1) Robust Zero-Shot Generalization: In both tasks, the model demonstrates strong performance on unseen data distributions without specific finetuning. (2) Visual Context: The model effectively uses visual context as explicit control, such as agent icons and tangram shapes, enabling it to maintain high visual consistency and adapt its planning capability robustly to unseen patterns. (3) Visual Test-Time Scaling: We observe a test-time scaling law in sequential planning; increasing the generated video length (visual inference budget) empowers better zero-shot generalization to spatially and temporally complex paths. These findings suggest that video generation is not merely a media tool, but a scalable, generalizable paradigm for visual reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/thuml/MiniVeo3-Reasoner", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2601.22027", "title": "CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty", "authors": [ "Johannes Kirmayr", "Lukas Stappen", "Elisabeth Andr\u00e9" ], "github": "https://github.com/CAR-bench/car-bench", "project_page": "", "abstract": "Existing benchmarks for Large Language Model (LLM) agents focus on task completion under idealistic settings but overlook reliability in real-world, user-facing applications. In domains, such as in-car voice assistants, users often issue incomplete or ambiguous requests, creating intrinsic uncertainty that agents must manage through dialogue, tool use, and policy adherence. We introduce CAR-bench, a benchmark for evaluating consistency, uncertainty handling, and capability awareness in multi-turn, tool-using LLM agents in an in-car assistant domain. The environment features an LLM-simulated user, domain policies, and 58 interconnected tools spanning navigation, productivity, charging, and vehicle control. Beyond standard task completion, CAR-bench introduces Hallucination tasks that test agents' limit-awareness under missing tools or information, and Disambiguation tasks that require resolving uncertainty through clarification or internal information gathering. Baseline results reveal large gaps between occasional and consistent success on all task types. Even frontier reasoning LLMs achieve less than 50% consistent pass rate on Disambiguation tasks due to premature actions, and frequently violate policies or fabricate information to satisfy user requests in Hallucination tasks, underscoring the need for more reliable and self-aware LLM agents in real-world settings.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/CAR-bench/car-bench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.04220", "title": "Adaptive 1D Video Diffusion Autoencoder", "authors": [ "Yao Teng", "Minxuan Lin", "Xian Liu", "Shuai Wang", "Xiao Yang", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Recent video generation models largely rely on video autoencoders that compress pixel-space videos into latent representations. However, existing video autoencoders suffer from three major limitations: (1) fixed-rate compression that wastes tokens on simple videos, (2) inflexible CNN architectures that prevent variable-length latent modeling, and (3) deterministic decoders that struggle to recover appropriate details from compressed latents. To address these issues, we propose One-Dimensional Diffusion Video Autoencoder (One-DVA), a transformer-based framework for adaptive 1D encoding and diffusion-based decoding. The encoder employs query-based vision transformers to extract spatiotemporal features and produce latent representations, while a variable-length dropout mechanism dynamically adjusts the latent length. The decoder is a pixel-space diffusion transformer that reconstructs videos with the latents as input conditions. With a two-stage training strategy, One-DVA achieves performance comparable to 3D-CNN VAEs on reconstruction metrics at identical compression ratios. More importantly, it supports adaptive compression and thus can achieve higher compression ratios. To better support downstream latent generation, we further regularize the One-DVA latent distribution for generative modeling and fine-tune its decoder to mitigate artifacts caused by the generation process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05293", "title": "Fast-SAM3D: 3Dfy Anything in Images but Faster", "authors": [ "Weilun Feng", "Mingqiang Wu", "Zhiliang Chen", "Chuanguang Yang", "Haotong Qin", "Yuqi Li", "Xiaokun Liu", "Guoxin Fan", "Zhulin An", "Libo Huang", "Yulun Zhang", "Michele Magno", "Yongjun Xu" ], "github": "https://github.com/wlfeng0509/Fast-SAM3D", "project_page": "", "abstract": "SAM3D enables scalable, open-world 3D reconstruction from complex scenes, yet its deployment is hindered by prohibitive inference latency. In this work, we conduct the first systematic investigation into its inference dynamics, revealing that generic acceleration strategies are brittle in this context. We demonstrate that these failures stem from neglecting the pipeline's inherent multi-level heterogeneity: the kinematic distinctiveness between shape and layout, the intrinsic sparsity of texture refinement, and the spectral variance across geometries. To address this, we present Fast-SAM3D, a training-free framework that dynamically aligns computation with instantaneous generation complexity. Our approach integrates three heterogeneity-aware mechanisms: (1) Modality-Aware Step Caching to decouple structural evolution from sensitive layout updates; (2) Joint Spatiotemporal Token Carving to concentrate refinement on high-entropy regions; and (3) Spectral-Aware Token Aggregation to adapt decoding resolution. Extensive experiments demonstrate that Fast-SAM3D delivers up to 2.67times end-to-end speedup with negligible fidelity loss, establishing a new Pareto frontier for efficient single-view 3D generation. Our code is released in https://github.com/wlfeng0509/Fast-SAM3D.", "github_manual": "", "github_regex": "https://github.com/wlfeng0509/Fast-SAM3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05933", "title": "Approximation of Log-Partition Function in Policy Mirror Descent Induces Implicit Regularization for LLM Post-Training", "authors": [ "Zhenghao Xu", "Qin Lu", "Changlong Yu", "Tuo Zhao" ], "github": "https://github.com/horizon-rl/OpenKimi", "project_page": "", "abstract": "Policy mirror descent (PMD) provides a principled framework for reinforcement learning (RL) by iteratively solving KL-regularized policy improvement subproblems. While this approach has been adopted in training advanced LLMs such as Kimi K1.5/K2, the ideal closed-form PMD updates require reliable partition function estimation, a significant challenge when working with limited rollouts in the vast action spaces of LLMs. We investigate a practical algorithm, termed PMD-mean, that approximates the log-partition term with the mean reward under the sampling policy and performs regression in log-policy space. Specifically, we characterize the population solution of PMD-mean and demonstrate that it implicitly optimizes mirror descent subproblems with an adaptive mixed KL--\u03c7^2 regularizer. This additional \u03c7^2 regularization constrains large probability changes, producing more conservative updates when expected rewards are low and enhancing robustness against finite-sample estimation errors. Experiments on math reasoning tasks show that PMD-mean achieves superior performance with improved stability and time efficiency. These findings deepen our understanding of PMD-mean and illuminate pathways toward principled improvements in RL algorithms for LLMs. Code is available at https://github.com/horizon-rl/OpenKimi.", "github_manual": "", "github_regex": "https://github.com/horizon-rl/OpenKimi", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05494", "title": "A Unified Framework for Rethinking Policy Divergence Measures in GRPO", "authors": [ "Qingyuan Wu", "Yuhui Wang", "Simon Sinong Zhan", "Yanning Dai", "Shilong Deng", "Sarra Habchi", "Qi Zhu", "Matthias Gall\u00e9", "Chao Huang" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verified Reward (RLVR) has emerged as a critical paradigm for advancing the reasoning capabilities of Large Language Models (LLMs). Most existing RLVR methods, such as GRPO and its variants, ensure stable updates by constraining policy divergence through clipping likelihood ratios. This paper introduces a unified clipping framework that characterizes existing methods via a general notion of policy divergence, encompassing both likelihood ratios and Kullback-Leibler (KL) divergences and extending to alternative measures. The framework provides a principled foundation for systematically analyzing how different policy divergence measures affect exploration and performance. We further identify the KL3 estimator, a variance-reduced Monte Carlo estimator of the KL divergence, as a key policy divergence constraint. We theoretically demonstrate that the KL3-based constraint is mathematically equivalent to an asymmetric ratio-based clipping that reallocates probability mass toward high-confidence actions, promoting stronger exploration while retaining the simplicity of GRPO-style methods. Empirical results on mathematical reasoning benchmarks demonstrate that incorporating the KL3 estimator into GRPO improves both training stability and final performance, highlighting the importance of principled policy divergence constraints in policy optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.02159", "title": "Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusing", "authors": [ "Lingkun Long", "Yushi Huang", "Shihao Bai", "Ruihao Gong", "Jun Zhang", "Ao Zhou", "Jianlei Yang" ], "github": "https://github.com/Longxmas/Focus-dLLM", "project_page": "", "abstract": "Diffusion Large Language Models (dLLMs) deliver strong long-context processing capability in a non-autoregressive decoding paradigm. However, the considerable computational cost of bidirectional full attention limits the inference efficiency. Although sparse attention is promising, existing methods remain ineffective. This stems from the need to estimate attention importance for tokens yet to be decoded, while the unmasked token positions are unknown during diffusion. In this paper, we present Focus-dLLM, a novel training-free attention sparsification framework tailored for accurate and efficient long-context dLLM inference. Based on the finding that token confidence strongly correlates across adjacent steps, we first design a past confidence-guided indicator to predict unmasked regions. Built upon this, we propose a sink-aware pruning strategy to accurately estimate and remove redundant attention computation, while preserving highly influential attention sinks. To further reduce overhead, this strategy reuses identified sink locations across layers, leveraging the observed cross-layer consistency. Experimental results show that our method offers more than 29times lossless speedup under 32K context length. The code is publicly available at: https://github.com/Longxmas/Focus-dLLM", "github_manual": "", "github_regex": "https://github.com/Longxmas/Focus-dLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.04942", "title": "Privileged Information Distillation for Language Models", "authors": [ "Emiliano Penaloza", "Dheeraj Vattikonda", "Nicolas Gontier", "Alexandre Lacoste", "Laurent Charlin", "Massimo Caccia" ], "github": "https://github.com/Emilianopp/Privileged-Information-Distillation", "project_page": "", "abstract": "Training-time privileged information (PI) can enable language models to succeed on tasks they would otherwise fail, making it a powerful tool for reinforcement learning in hard, long-horizon settings. However, transferring capabilities learned with PI to policies that must act without it at inference time remains a fundamental challenge. We study this problem in the context of distilling frontier models for multi-turn agentic environments, where closed-source systems typically hide their internal reasoning and expose only action trajectories. This breaks standard distillation pipelines, since successful behavior is observable but the reasoning process is not. For this, we introduce \u03c0-Distill, a joint teacher-student objective that trains a PI-conditioned teacher and an unconditioned student simultaneously using the same model. Additionally, we also introduce On-Policy Self-Distillation (OPSD), an alternative approach that trains using Reinforcement Learning (RL) with a reverse KL-penalty between the student and the PI-conditioned teacher. We show that both of these algorithms effectively distill frontier agents using action-only PI. Specifically we find that \u03c0-Distill and in some cases OPSD, outperform industry standard practices (Supervised finetuning followed by RL) that assume access to full Chain-of-Thought supervision across multiple agentic benchmarks, models, and forms of PI. We complement our results with extensive analysis that characterizes the factors enabling effective learning with PI, focusing primarily on \u03c0-Distill and characterizing when OPSD is competitive.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Emilianopp/Privileged-Information-Distillation", "github_api_added_by": "auto" }, { "date": "2026-02-06", "arxiv_id": "2602.05073", "title": "Towards Reducible Uncertainty Modeling for Reliable Large Language Model Agents", "authors": [ "Changdae Oh", "Seongheon Park", "To Eun Kim", "Jiatong Li", "Wendi Li", "Samuel Yeh", "Xuefeng Du", "Hamed Hassani", "Paul Bogdan", "Dawn Song", "Sharon Li" ], "github": "", "project_page": "", "abstract": "Uncertainty quantification (UQ) for large language models (LLMs) is a key building block for safety guardrails of daily LLM applications. Yet, even as LLM agents are increasingly deployed in highly complex tasks, most UQ research still centers on single-turn question-answering. We argue that UQ research must shift to realistic settings with interactive agents, and that a new principled framework for agent UQ is needed. This paper presents the first general formulation of agent UQ that subsumes broad classes of existing UQ setups. Under this formulation, we show that prior works implicitly treat LLM UQ as an uncertainty accumulation process, a viewpoint that breaks down for interactive agents in an open world. In contrast, we propose a novel perspective, a conditional uncertainty reduction process, that explicitly models reducible uncertainty over an agent's trajectory by highlighting \"interactivity\" of actions. From this perspective, we outline a conceptual framework to provide actionable guidance for designing UQ in LLM agent setups. Finally, we conclude with practical implications of the agent UQ in frontier LLM development and domain-specific applications, as well as open remaining problems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.02474", "title": "MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents", "authors": [ "Haozhen Zhang", "Quanyu Long", "Jianzhu Bao", "Tao Feng", "Weizhi Zhang", "Haodong Yue", "Wenya Wang" ], "github": "https://github.com/ViktorAxelsen/MemSkill", "project_page": "", "abstract": "Most Large Language Model (LLM) agent memory systems rely on a small set of static, hand-designed operations for extracting memory. These fixed procedures hard-code human priors about what to store and how to revise memory, making them rigid under diverse interaction patterns and inefficient on long histories. To this end, we present MemSkill, which reframes these operations as learnable and evolvable memory skills, structured and reusable routines for extracting, consolidating, and pruning information from interaction traces. Inspired by the design philosophy of agent skills, MemSkill employs a controller that learns to select a small set of relevant skills, paired with an LLM-based executor that produces skill-guided memories. Beyond learning skill selection, MemSkill introduces a designer that periodically reviews hard cases where selected skills yield incorrect or incomplete memories, and evolves the skill set by proposing refinements and new skills. Together, MemSkill forms a closed-loop procedure that improves both the skill-selection policy and the skill set itself. Experiments on LoCoMo, LongMemEval, HotpotQA, and ALFWorld demonstrate that MemSkill improves task performance over strong baselines and generalizes well across settings. Further analyses shed light on how skills evolve, offering insights toward more adaptive, self-evolving memory management for LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ViktorAxelsen/MemSkill", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.02393", "title": "Infinite-World: Scaling Interactive World Models to 1000-Frame Horizons via Pose-Free Hierarchical Memory", "authors": [ "Ruiqi Wu", "Xuanhua He", "Meng Cheng", "Tianyu Yang", "Yong Zhang", "Zhuoliang Kang", "Xunliang Cai", "Xiaoming Wei", "Chunle Guo", "Chongyi Li", "Ming-Ming Cheng" ], "github": "", "project_page": "", "abstract": "We propose Infinite-World, a robust interactive world model capable of maintaining coherent visual memory over 1000+ frames in complex real-world environments. While existing world models can be efficiently optimized on synthetic data with perfect ground-truth, they lack an effective training paradigm for real-world videos due to noisy pose estimations and the scarcity of viewpoint revisits. To bridge this gap, we first introduce a Hierarchical Pose-free Memory Compressor (HPMC) that recursively distills historical latents into a fixed-budget representation. By jointly optimizing the compressor with the generative backbone, HPMC enables the model to autonomously anchor generations in the distant past with bounded computational cost, eliminating the need for explicit geometric priors. Second, we propose an Uncertainty-aware Action Labeling module that discretizes continuous motion into a tri-state logic. This strategy maximizes the utilization of raw video data while shielding the deterministic action space from being corrupted by noisy trajectories, ensuring robust action-response learning. Furthermore, guided by insights from a pilot toy study, we employ a Revisit-Dense Finetuning Strategy using a compact, 30-minute dataset to efficiently activate the model's long-range loop-closure capabilities. Extensive experiments, including objective metrics and user studies, demonstrate that Infinite-World achieves superior performance in visual quality, action controllability, and spatial consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05115", "title": "SocialVeil: Probing Social Intelligence of Language Agents under Communication Barriers", "authors": [ "Keyang Xuan", "Pengda Wang", "Chongrui Ye", "Haofei Yu", "Tal August", "Jiaxuan You" ], "github": "https://github.com/ulab-uiuc/social-veil", "project_page": "", "abstract": "Large language models (LLMs) are increasingly evaluated in interactive environments to test their social intelligence. However, existing benchmarks often assume idealized communication between agents, limiting our ability to diagnose whether LLMs can maintain and repair interactions in more realistic, imperfect settings. To close this gap, we present SocialVeil, a social learning environment that can simulate social interaction under cognitive-difference-induced communication barriers. Grounded in a systematic literature review of communication challenges in human interaction, SocialVeil introduces three representative types of such disruption, semantic vagueness, sociocultural mismatch, and emotional interference. We also introduce two barrier-aware evaluation metrics, unresolved confusion and mutual understanding, to evaluate interaction quality under impaired communication. Experiments across 720 scenarios and four frontier LLMs show that barriers consistently impair performance, with mutual understanding reduced by over 45\\% on average, and confusion elevated by nearly 50\\%. Human evaluations validate the fidelity of these simulated barriers (ICCapprox0.78, Pearson rapprox0.80). We further demonstrate that adaptation strategies (Repair Instruction and Interactive learning) only have a modest effect far from barrier-free performance. This work takes a step toward bringing social interaction environments closer to real-world communication, opening opportunities for exploring the social intelligence of LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ulab-uiuc/social-veil", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.05258", "title": "CoPE: Clipped RoPE as A Scalable Free Lunch for Long Context LLMs", "authors": [ "Haoran Li", "Sucheng Ren", "Alan Yuille", "Feng Wang" ], "github": "https://github.com/hrlics/CoPE", "project_page": "", "abstract": "Rotary Positional Embedding (RoPE) is a key component of context scaling in Large Language Models (LLMs). While various methods have been proposed to adapt RoPE to longer contexts, their guiding principles generally fall into two categories: (1) out-of-distribution (OOD) mitigation, which scales RoPE frequencies to accommodate unseen positions, and (2) Semantic Modeling, which posits that the attention scores computed with RoPE should always prioritize semantically similar tokens. In this work, we unify these seemingly distinct objectives through a minimalist intervention, namely CoPE: soft clipping lowfrequency components of RoPE. CoPE not only eliminates OOD outliers and refines semantic signals, but also prevents spectral leakage caused by hard clipping. Extensive experiments demonstrate that simply applying our soft clipping strategy to RoPE yields significant performance gains that scale up to 256k context length, validating our theoretical analysis and establishing CoPE as a new state-of-the-art for length generalization. Our code, data, and models are available at https://github.com/hrlics/CoPE.", "github_manual": "", "github_regex": "https://github.com/hrlics/CoPE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.04998", "title": "Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning", "authors": [ "Yu-Ang Lee", "Ching-Yun Ko", "Pin-Yu Chen", "Mi-Yen Yeh" ], "github": "", "project_page": "", "abstract": "Low-Rank Adaptation (LoRA) is the prevailing approach for efficient large language model (LLM) fine-tuning. Building on this paradigm, recent studies have proposed alternative initialization strategies and architectural modifications, reporting substantial improvements over vanilla LoRA. However, these gains are often demonstrated under fixed or narrowly tuned hyperparameter settings, despite the known sensitivity of neural networks to training configurations. In this work, we systematically re-evaluate four representative LoRA variants alongside vanilla LoRA through extensive hyperparameter searches. Across mathematical and code generation tasks on diverse model scales, we find that different LoRA methods favor distinct learning rate ranges. Crucially, once learning rates are properly tuned, all methods achieve similar peak performance (within 1-2%), with only subtle rank-dependent behaviors. These results suggest that vanilla LoRA remains a competitive baseline and that improvements reported under single training configuration may not reflect consistent methodological advantages. Finally, a second-order analysis attributes the differing optimal learning rate ranges to variations in the largest Hessian eigenvalue, aligning with classical learning theories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2601.22345", "title": "Failing to Explore: Language Models on Interactive Tasks", "authors": [ "Mahdi JafariRaviz", "Keivan Rezaei", "Arshia Soltani Moakhar", "Zahra Sodagar", "Yize Cheng", "Soheil Feizi" ], "github": "", "project_page": "", "abstract": "We evaluate language models on their ability to explore interactive environments under a limited interaction budget. We introduce three parametric tasks with controllable exploration difficulty, spanning continuous and discrete environments. Across state-of-the-art models, we find systematic under-exploration and suboptimal solutions, with performance often significantly worse than simple explore--exploit heuristic baselines and scaling weakly as the budget increases. Finally, we study two lightweight interventions: splitting a fixed budget into parallel executions, which surprisingly improves performance despite a no-gain theoretical result for our tasks, and periodically summarizing the interaction history, which preserves key discoveries and further improves exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.06036", "title": "DFlash: Block Diffusion for Flash Speculative Decoding", "authors": [ "Jian Chen", "Yesheng Liang", "Zhijian Liu" ], "github": "", "project_page": "", "abstract": "Autoregressive large language models (LLMs) deliver strong performance but require inherently sequential decoding, leading to high inference latency and poor GPU utilization. Speculative decoding mitigates this bottleneck by using a fast draft model whose outputs are verified in parallel by the target LLM; however, existing methods still rely on autoregressive drafting, which remains sequential and limits practical speedups. Diffusion LLMs offer a promising alternative by enabling parallel generation, but current diffusion models typically underperform compared with autoregressive models. In this paper, we introduce DFlash, a speculative decoding framework that employs a lightweight block diffusion model for parallel drafting. By generating draft tokens in a single forward pass and conditioning the draft model on context features extracted from the target model, DFlash enables efficient drafting with high-quality outputs and higher acceptance rates. Experiments show that DFlash achieves over 6x lossless acceleration across a range of models and tasks, delivering up to 2.5x higher speedup than the state-of-the-art speculative decoding method EAGLE-3.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-06", "arxiv_id": "2602.02016", "title": "DASH: Faster Shampoo via Batched Block Preconditioning and Efficient Inverse-Root Solvers", "authors": [ "Ionut-Vlad Modoranu", "Philip Zmushko", "Erik Schultheis", "Mher Safaryan", "Dan Alistarh" ], "github": "https://github.com/IST-DASLab/DASH", "project_page": "", "abstract": "Shampoo is one of the leading approximate second-order optimizers: a variant of it has won the MLCommons AlgoPerf competition, and it has been shown to produce models with lower activation outliers that are easier to compress. Yet, applying Shampoo currently comes at the cost of significant computational slowdown, due to its expensive internal operations. In this paper, we take a significant step to address this shortcoming by proposing \\method (for Distributed Accelerated SHampoo), a faster implementation of Distributed Shampoo based on two main new techniques: First, we show that preconditioner blocks can be stacked into 3D tensors to significantly improve GPU utilization; second, we introduce the Newton-DB iteration and the Chebyshev polynomial approximations as novel and faster approaches for computing the inverse matrix roots required by Shampoo. Along with these algorithmic contributions, we provide a first in-depth analysis of how matrix scaling critically affects Shampoo convergence. On the practical side, our GPU-aware implementation achieves up to 4.83times faster optimizer steps compared to the well-optimized Distributed Shampoo, while Newton-DB attains the lowest validation perplexity per iteration among all tested methods. Our code is available at https://github.com/IST-DASLab/DASH.", "github_manual": "", "github_regex": "https://github.com/IST-DASLab/DASH", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06471", "title": "Revisiting the Shape Convention of Transformer Language Models", "authors": [ "Feng-Ting Liao", "Meng-Hsi Chen", "Guan-Ting Yi", "Da-shan Shiu" ], "github": "", "project_page": "", "abstract": "Dense Transformer language models have largely adhered to one consistent architectural shape: each layer consists of an attention module followed by a feed-forward network (FFN) with a narrow-wide-narrow MLP, allocating most parameters to the MLP at expansion ratios between 2 and 4. Motivated by recent results that residual wide-narrow-wide (hourglass) MLPs offer superior function approximation capabilities, we revisit the long-standing MLP shape convention in Transformer, challenging the necessity of the narrow-wide-narrow design. To study this, we develop a Transformer variant that replaces the conventional FFN with a deeper hourglass-shaped FFN, comprising a stack of hourglass sub-MLPs connected by residual pathways. We posit that a deeper but lighter hourglass FFN can serve as a competitive alternative to the conventional FFN, and that parameters saved by using a lighter hourglass FFN can be more effectively utilized, such as by enlarging model hidden dimensions under fixed budgets. We confirm these through empirical validations across model scales: hourglass FFNs outperform conventional FFNs up to 400M and achieve comparable performance at larger scales to 1B parameters; hourglass FFN variants with reduced FFN and increased attention parameters show consistent improvements over conventional configurations at matched budgets. Together, these findings shed new light on recent work and prompt a rethinking of the narrow-wide-narrow MLP convention and the balance between attention and FFN towards efficient and expressive modern language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06075", "title": "MemGUI-Bench: Benchmarking Memory of Mobile GUI Agents in Dynamic Environments", "authors": [ "Guangyi Liu", "Pengxiang Zhao", "Yaozhen Liang", "Qinyi Luo", "Shunye Tang", "Yuxiang Chai", "Weifeng Lin", "Han Xiao", "WenHao Wang", "Siheng Chen", "Zhengxi Lu", "Gao Wu", "Hao Wang", "Liang Liu", "Yong Liu" ], "github": "", "project_page": "", "abstract": "Current mobile GUI agent benchmarks systematically fail to assess memory capabilities, with only 5.2-11.8% memory-related tasks and no cross-session learning evaluation. We introduce MemGUI-Bench, a comprehensive memory-centric benchmark with pass@k and staged LLM-as-judge evaluation. Our contributions include: (1) a systematic memory taxonomy analyzing 11 agents across 5 architectures; (2) 128 tasks across 26 applications where 89.8% challenge memory through cross-temporal and cross-spatial retention; (3) MemGUI-Eval, an automated pipeline with Progressive Scrutiny and 7 hierarchical metrics; and (4) RQ-driven assessment of 11 state-of-the-art agents. Our experiments reveal significant memory deficits across all evaluated systems, identify 5 distinct failure modes, and synthesize 5 actionable design implications. All resources including code, benchmark, and evaluation results will be \\textit{fully open-sourced and continuously maintained} at https://lgy0404.github.io/MemGUI-Bench/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06960", "title": "InftyThink+: Effective and Efficient Infinite-Horizon Reasoning via Reinforcement Learning", "authors": [ "Yuchen Yan", "Liang Jiang", "Jin Jiang", "Shuaicheng Li", "Zujie Wen", "Zhiqiang Zhang", "Jun Zhou", "Jian Shao", "Yueting Zhuang", "Yongliang Shen" ], "github": "https://github.com/ZJU-REAL/InftyThink-Plus", "project_page": "", "abstract": "Large reasoning models achieve strong performance by scaling inference-time chain-of-thought, but this paradigm suffers from quadratic cost, context length limits, and degraded reasoning due to lost-in-the-middle effects. Iterative reasoning mitigates these issues by periodically summarizing intermediate thoughts, yet existing methods rely on supervised learning or fixed heuristics and fail to optimize when to summarize, what to preserve, and how to resume reasoning. We propose InftyThink+, an end-to-end reinforcement learning framework that optimizes the entire iterative reasoning trajectory, building on model-controlled iteration boundaries and explicit summarization. InftyThink+ adopts a two-stage training scheme with supervised cold-start followed by trajectory-level reinforcement learning, enabling the model to learn strategic summarization and continuation decisions. Experiments on DeepSeek-R1-Distill-Qwen-1.5B show that InftyThink+ improves accuracy by 21% on AIME24 and outperforms conventional long chain-of-thought reinforcement learning by a clear margin, while also generalizing better to out-of-distribution benchmarks. Moreover, InftyThink+ significantly reduces inference latency and accelerates reinforcement learning training, demonstrating improved reasoning efficiency alongside stronger performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ZJU-REAL/InftyThink-Plus", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.03392", "title": "On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models", "authors": [ "Shumin Wang", "Yuexiang Xie", "Wenhao Zhang", "Yuchang Sun", "Yanxi Chen", "Yaliang Li", "Yanyong Zhang" ], "github": "", "project_page": "", "abstract": "Entropy serves as a critical metric for measuring the diversity of outputs generated by large language models (LLMs), providing valuable insights into their exploration capabilities. While recent studies increasingly focus on monitoring and adjusting entropy to better balance exploration and exploitation in reinforcement fine-tuning (RFT), a principled understanding of entropy dynamics during this process is yet to be thoroughly investigated. In this paper, we establish a theoretical framework for analyzing the entropy dynamics during the RFT process, which begins with a discriminant expression that quantifies entropy change under a single logit update. This foundation enables the derivation of a first-order expression for entropy change, which can be further extended to the update formula of Group Relative Policy Optimization (GRPO). The corollaries and insights drawn from the theoretical analysis inspire the design of entropy control methods, and also offer a unified lens for interpreting various entropy-based methods in existing studies. We provide empirical evidence to support the main conclusions of our analysis and demonstrate the effectiveness of the derived entropy-discriminator clipping methods. This study yields novel insights into RFT training dynamics, providing theoretical support and practical strategies for optimizing the exploration-exploitation balance during LLM fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06391", "title": "POINTS-GUI-G: GUI-Grounding Journey", "authors": [ "Zhongyin Zhao", "Yuan Liu", "Yikun Liu", "Haicheng Wang", "Le Tian", "Xiao Zhou", "Yangxiu You", "Zilin Yu", "Yang Yu", "Jie Zhou" ], "github": "", "project_page": "", "abstract": "The rapid advancement of vision-language models has catalyzed the emergence of GUI agents, which hold immense potential for automating complex tasks, from online shopping to flight booking, thereby alleviating the burden of repetitive digital workflows. As a foundational capability, GUI grounding is typically established as a prerequisite for end-to-end task execution. It enables models to precisely locate interface elements, such as text and icons, to perform accurate operations like clicking and typing. Unlike prior works that fine-tune models already possessing strong spatial awareness (e.g., Qwen3-VL), we aim to master the full technical pipeline by starting from a base model with minimal grounding ability, such as POINTS-1.5. We introduce POINTS-GUI-G-8B, which achieves state-of-the-art performance with scores of 59.9 on ScreenSpot-Pro, 66.0 on OSWorld-G, 95.7 on ScreenSpot-v2, and 49.9 on UI-Vision. Our model's success is driven by three key factors: (1) Refined Data Engineering, involving the unification of diverse open-source datasets format alongside sophisticated strategies for augmentation, filtering, and difficulty grading; (2) Improved Training Strategies, including continuous fine-tuning of the vision encoder to enhance perceptual accuracy and maintaining resolution consistency between training and inference; and (3) Reinforcement Learning (RL) with Verifiable Rewards. While RL is traditionally used to bolster reasoning, we demonstrate that it significantly improves precision in the perception-intensive GUI grounding task. Furthermore, GUI grounding provides a natural advantage for RL, as rewards are easily verifiable and highly accurate.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06949", "title": "DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos", "authors": [ "Shenyuan Gao", "William Liang", "Kaiyuan Zheng", "Ayaan Malik", "Seonghyeon Ye", "Sihyun Yu", "Wei-Cheng Tseng", "Yuzhu Dong", "Kaichun Mo", "Chen-Hsuan Lin", "Qianli Ma", "Seungjun Nah", "Loic Magne", "Jiannan Xiang", "Yuqi Xie", "Ruijie Zheng", "Dantong Niu", "You Liang Tan", "K. R. Zentner", "George Kurian", "Suneel Indupuru", "Pooya Jannaty", "Jinwei Gu", "Jun Zhang", "Jitendra Malik", "Pieter Abbeel", "Ming-Yu Liu", "Yuke Zhu", "Joel Jang", "Linxi \"Jim\" Fan" ], "github": "", "project_page": "", "abstract": "Being able to simulate the outcomes of actions in varied environments will revolutionize the development of generalist agents at scale. However, modeling these world dynamics, especially for dexterous robotics tasks, poses significant challenges due to limited data coverage and scarce action labels. As an endeavor towards this end, we introduce DreamDojo, a foundation world model that learns diverse interactions and dexterous controls from 44k hours of egocentric human videos. Our data mixture represents the largest video dataset to date for world model pretraining, spanning a wide range of daily scenarios with diverse objects and skills. To address the scarcity of action labels, we introduce continuous latent actions as unified proxy actions, enhancing interaction knowledge transfer from unlabeled videos. After post-training on small-scale target robot data, DreamDojo demonstrates a strong understanding of physics and precise action controllability. We also devise a distillation pipeline that accelerates DreamDojo to a real-time speed of 10.81 FPS and further improves context consistency. Our work enables several important applications based on generative world models, including live teleoperation, policy evaluation, and model-based planning. Systematic evaluation on multiple challenging out-of-distribution (OOD) benchmarks verifies the significance of our method for simulating open-world, contact-rich tasks, paving the way for general-purpose robot world models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.05940", "title": "Self-Improving Multilingual Long Reasoning via Translation-Reasoning Integrated Training", "authors": [ "Junxiao Liu", "Zhijun Wang", "Yixiao Li", "Zhejian Lai", "Liqian Huang", "Xin Huang", "Xue Han", "Junlan Feng", "Shujian Huang" ], "github": "https://github.com/NJUNLP/TRIT", "project_page": "", "abstract": "Long reasoning models often struggle in multilingual settings: they tend to reason in English for non-English questions; when constrained to reasoning in the question language, accuracies drop substantially. The struggle is caused by the limited abilities for both multilingual question understanding and multilingual reasoning. To address both problems, we propose TRIT (Translation-Reasoning Integrated Training), a self-improving framework that integrates the training of translation into multilingual reasoning. Without external feedback or additional multilingual data, our method jointly enhances multilingual question understanding and response generation. On MMATH, our method outperforms multiple baselines by an average of 7 percentage points, improving both answer correctness and language consistency. Further analysis reveals that integrating translation training improves cross-lingual question alignment by over 10 percentage points and enhances translation quality for both mathematical questions and general-domain text, with gains up to 8.4 COMET points on FLORES-200.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NJUNLP/TRIT", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.05711", "title": "OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale", "authors": [ "Jingze Shi", "Zhangyang Peng", "Yizhang Zhu", "Yifan Wu", "Guang Liu", "Yuyu Luo" ], "github": "https://github.com/flash-algo/omni-moe", "project_page": "", "abstract": "Mixture-of-Experts (MoE) architectures are evolving towards finer granularity to improve parameter efficiency. However, existing MoE designs face an inherent trade-off between the granularity of expert specialization and hardware execution efficiency. We propose OmniMoE, a system-algorithm co-designed framework that pushes expert granularity to its logical extreme. OmniMoE introduces vector-level Atomic Experts, enabling scalable routing and execution within a single MoE layer, while retaining a shared dense MLP branch for general-purpose processing. Although this atomic design maximizes capacity, it poses severe challenges for routing complexity and memory access. To address these, OmniMoE adopts a system-algorithm co-design: (i) a Cartesian Product Router that decomposes the massive index space to reduce routing complexity from O(N) to O(sqrt(N)); and (ii) Expert-Centric Scheduling that inverts the execution order to turn scattered, memory-bound lookups into efficient dense matrix operations. Validated on seven benchmarks, OmniMoE (with 1.7B active parameters) achieves 50.9% zero-shot accuracy across seven benchmarks, outperforming coarse-grained (e.g., DeepSeekMoE) and fine-grained (e.g., PEER) baselines. Crucially, OmniMoE reduces inference latency from 73ms to 6.7ms (a 10.9-fold speedup) compared to PEER, demonstrating that massive-scale fine-grained MoE can be fast and accurate. Our code is open-sourced at https://github.com/flash-algo/omni-moe.", "github_manual": "", "github_regex": "https://github.com/flash-algo/omni-moe", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06139", "title": "EgoAVU: Egocentric Audio-Visual Understanding", "authors": [ "Ashish Seth", "Xinhao Mei", "Changsheng Zhao", "Varun Nagaraja", "Ernie Chang", "Gregory P. Meyer", "Gael Le Lan", "Yunyang Xiong", "Vikas Chandra", "Yangyang Shi", "Dinesh Manocha", "Zhipeng Cai" ], "github": "https://github.com/facebookresearch/EgoAVU", "project_page": "", "abstract": "Understanding egocentric videos plays a vital role for embodied intelligence. Recent multi-modal large language models (MLLMs) can accept both visual and audio inputs. However, due to the challenge of obtaining text labels with coherent joint-modality information, whether MLLMs can jointly understand both modalities in egocentric videos remains under-explored. To address this problem, we introduce EgoAVU, a scalable data engine to automatically generate egocentric audio-visual narrations, questions, and answers. EgoAVU enriches human narrations with multimodal context and generates audio-visual narrations through cross-modal correlation modeling. Token-based video filtering and modular, graph-based curation ensure both data diversity and quality. Leveraging EgoAVU, we construct EgoAVU-Instruct, a large-scale training dataset of 3M samples, and EgoAVU-Bench, a manually verified evaluation split covering diverse tasks. EgoAVU-Bench clearly reveals the limitations of existing MLLMs: they bias heavily toward visual signals, often neglecting audio cues or failing to correspond audio with the visual source. Finetuning MLLMs on EgoAVU-Instruct effectively addresses this issue, enabling up to 113% performance improvement on EgoAVU-Bench. Such benefits also transfer to other benchmarks such as EgoTempo and EgoIllusion, achieving up to 28% relative performance gain. Code will be released to the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/EgoAVU", "github_api_added_by": "auto" }, { "date": "2026-02-09", "arxiv_id": "2602.06663", "title": "PlanViz: Evaluating Planning-Oriented Image Generation and Editing for Computer-Use Tasks", "authors": [ "Junxian Li", "Kai Liu", "Leyang Chen", "Weida Wang", "Zhixin Wang", "Jiaqi Xu", "Fan Li", "Renjing Pei", "Linghe Kong", "Yulun Zhang" ], "github": "", "project_page": "", "abstract": "Unified multimodal models (UMMs) have shown impressive capabilities in generating natural images and supporting multimodal reasoning. However, their potential in supporting computer-use planning tasks, which are closely related to our lives, remain underexplored. Image generation and editing in computer-use tasks require capabilities like spatial reasoning and procedural understanding, and it is still unknown whether UMMs have these capabilities to finish these tasks or not. Therefore, we propose PlanViz, a new benchmark designed to evaluate image generation and editing for computer-use tasks. To achieve the goal of our evaluation, we focus on sub-tasks which frequently involve in daily life and require planning steps. Specifically, three new sub-tasks are designed: route planning, work diagramming, and web&UI displaying. We address challenges in data quality ensuring by curating human-annotated questions and reference images, and a quality control process. For challenges of comprehensive and exact evaluation, a task-adaptive score, PlanScore, is proposed. The score helps understanding the correctness, visual quality and efficiency of generated images. Through experiments, we highlight key limitations and opportunities for future research on this topic.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06291", "title": "Judging What We Cannot Solve: A Consequence-Based Approach for Oracle-Free Evaluation of Research-Level Math", "authors": [ "Guijin Son", "Donghun Yang", "Hitesh Laxmichand Patel", "Hyunwoo Ko", "Amit Agarwal", "Sunghee Ahn", "Kyong-Ha Lee", "Youngjae Yu" ], "github": "", "project_page": "", "abstract": "Recent progress in reasoning models suggests that generating plausible attempts for research-level mathematics may be within reach, but verification remains a bottleneck, consuming scarce expert time. We hypothesize that a meaningful solution should contain enough method-level information that, when applied to a neighborhood of related questions, it should yield better downstream performance than incorrect solutions. Building on this idea, we propose Consequence-Based Utility, an oracle-free evaluator that scores each candidate by testing its value as an in-context exemplar in solving related yet verifiable questions. Our approach is evaluated on an original set of research-level math problems, each paired with one expert-written solution and nine LLM-generated solutions. Notably, Consequence-Based Utility consistently outperforms reward models, generative reward models, and LLM judges on ranking quality. Specifically, for GPT-OSS-120B, it improves Acc@1 from 67.2 to 76.3 and AUC from 71.4 to 79.6, with similarly large AUC gains on GPT-OSS-20B (69.0 to 79.2). Furthermore, compared to LLM-Judges, it also exhibits a larger solver-evaluator gap, maintaining a stronger correct-wrong separation even on instances where the underlying solver often fails to solve.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.05281", "title": "Back to Basics: Revisiting Exploration in Reinforcement Learning for LLM Reasoning via Generative Probabilities", "authors": [ "Pengyi Li", "Elizaveta Goncharova", "Andrey Kuznetsov", "Ivan Oseledets" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an indispensable paradigm for enhancing reasoning in Large Language Models (LLMs). However, standard policy optimization methods, such as Group Relative Policy Optimization (GRPO), often converge to low-entropy policies, leading to severe mode collapse and limited output diversity. We analyze this issue from the perspective of sampling probability dynamics, identifying that the standard objective disproportionately reinforces the highest-likelihood paths, thereby suppressing valid alternative reasoning chains. To address this, we propose a novel Advantage Re-weighting Mechanism (ARM) designed to equilibrate the confidence levels across all correct responses. By incorporating Prompt Perplexity and Answer Confidence into the advantage estimation, our method dynamically reshapes the reward signal to attenuate the gradient updates of over-confident reasoning paths, while redistributing probability mass toward under-explored correct solutions. Empirical results demonstrate that our approach significantly enhances generative diversity and response entropy while maintaining competitive accuracy, effectively achieving a superior trade-off between exploration and exploitation in reasoning tasks. Empirical results on Qwen2.5 and DeepSeek models across mathematical and coding benchmarks show that ProGRPO significantly mitigates entropy collapse. Specifically, on Qwen2.5-7B, our method outperforms GRPO by 5.7% in Pass@1 and, notably, by 13.9% in Pass@32, highlighting its superior capability in generating diverse correct reasoning paths.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.01734", "title": "MSign: An Optimizer Preventing Training Instability in Large Language Models via Stable Rank Restoration", "authors": [ "Lianhai Ren", "Yucheng Ding", "Xiao Liu", "Qianxiao Li", "Peng Cheng", "Yeyun Gong" ], "github": "", "project_page": "", "abstract": "Training instability remains a critical challenge in large language model (LLM) pretraining, often manifesting as sudden gradient explosions that waste significant computational resources. We study training failures in a 5M-parameter NanoGPT model scaled via \u03bcP, identifying two key phenomena preceding collapse: (1) rapid decline in weight matrix stable rank (ratio of squared Frobenius norm to squared spectral norm), and (2) increasing alignment between adjacent layer Jacobians. We prove theoretically that these two conditions jointly cause exponential gradient norm growth with network depth. To break this instability mechanism, we propose MSign, a new optimizer that periodically applies matrix sign operations to restore stable rank. Experiments on models from 5M to 3B parameters demonstrate that MSign effectively prevents training failures with a computational overhead of less than 7.0%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.01064", "title": "Exploring Knowledge Purification in Multi-Teacher Knowledge Distillation for LLMs", "authors": [ "Ruihan Jin", "Pengpeng Shao", "Zhengqi Wen", "Jinyang Wu", "Mingkuan Feng", "Shuo Yang", "Chu Yuan Zhang", "Jianhua Tao" ], "github": "", "project_page": "", "abstract": "Knowledge distillation has emerged as a pivotal technique for transferring knowledge from stronger large language models (LLMs) to smaller, more efficient models. However, traditional distillation approaches face challenges related to knowledge conflicts and high resource demands, particularly when leveraging multiple teacher models. In this paper, we introduce the concept of Knowledge Purification, which consolidates the rationales from multiple teacher LLMs into a single rationale, thereby mitigating conflicts and enhancing efficiency. To investigate the effectiveness of knowledge purification, we further propose five purification methods from various perspectives. Our experiments demonstrate that these methods not only improve the performance of the distilled model but also effectively alleviate knowledge conflicts. Moreover, router-based methods exhibit robust generalization capabilities, underscoring the potential of innovative purification techniques in optimizing multi-teacher distillation and facilitating the practical deployment of powerful yet lightweight models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06570", "title": "Baichuan-M3: Modeling Clinical Inquiry for Reliable Medical Decision-Making", "authors": [ "Baichuan-M3 Team", "Chengfeng Dou", "Fan Yang", "Fei Li", "Jiyuan Jia", "Qiang Ju", "Shuai Wang", "Tianpeng Li", "Xiangrong Zeng", "Yijie Zhou", "Hongda Zhang", "Jinyang Tai", "Linzhuang Sun", "Peidong Guo", "Yichuan Mo", "Xiaochuan Wang", "Hengfu Cui", "Zhishou Zhang" ], "github": "https://github.com/MedAIBase/AntAngelMed", "project_page": "", "abstract": "We introduce Baichuan-M3, a medical-enhanced large language model engineered to shift the paradigm from passive question-answering to active, clinical-grade decision support. Addressing the limitations of existing systems in open-ended consultations, Baichuan-M3 utilizes a specialized training pipeline to model the systematic workflow of a physician. Key capabilities include: (i) proactive information acquisition to resolve ambiguity; (ii) long-horizon reasoning that unifies scattered evidence into coherent diagnoses; and (iii) adaptive hallucination suppression to ensure factual reliability. Empirical evaluations demonstrate that Baichuan-M3 achieves state-of-the-art results on HealthBench, the newly introduced HealthBench-Hallu and ScanBench, significantly outperforming GPT-5.2 in clinical inquiry, advisory and safety. The models are publicly available at https://huggingface.co/collections/baichuan-inc/baichuan-m3.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MedAIBase/AntAngelMed", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06854", "title": "SEMA: Simple yet Effective Learning for Multi-Turn Jailbreak Attacks", "authors": [ "Mingqian Feng", "Xiaodong Liu", "Weiwei Yang", "Jialin Song", "Xuekai Zhu", "Chenliang Xu", "Jianfeng Gao" ], "github": "https://github.com/fmmarkmq/SEMA", "project_page": "", "abstract": "Multi-turn jailbreaks capture the real threat model for safety-aligned chatbots, where single-turn attacks are merely a special case. Yet existing approaches break under exploration complexity and intent drift. We propose SEMA, a simple yet effective framework that trains a multi-turn attacker without relying on any existing strategies or external data. SEMA comprises two stages. Prefilling self-tuning enables usable rollouts by fine-tuning on non-refusal, well-structured, multi-turn adversarial prompts that are self-generated with a minimal prefix, thereby stabilizing subsequent learning. Reinforcement learning with intent-drift-aware reward trains the attacker to elicit valid multi-turn adversarial prompts while maintaining the same harmful objective. We anchor harmful intent in multi-turn jailbreaks via an intent-drift-aware reward that combines intent alignment, compliance risk, and level of detail. Our open-loop attack regime avoids dependence on victim feedback, unifies single- and multi-turn settings, and reduces exploration complexity. Across multiple datasets, victim models, and jailbreak judges, our method achieves state-of-the-art (SOTA) attack success rates (ASR), outperforming all single-turn baselines, manually scripted and template-driven multi-turn baselines, as well as our SFT (Supervised Fine-Tuning) and DPO (Direct Preference Optimization) variants. For instance, SEMA performs an average 80.1% ASR@1 across three closed-source and open-source victim models on AdvBench, 33.9% over SOTA. The approach is compact, reproducible, and transfers across targets, providing a stronger and more realistic stress test for large language model (LLM) safety and enabling automatic redteaming to expose and localize failure modes. Our code is available at: https://github.com/fmmarkmq/SEMA.", "github_manual": "", "github_regex": "https://github.com/fmmarkmq/SEMA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.05367", "title": "RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs", "authors": [ "Youngcheon You", "Banseok Lee", "Minseop Choi", "Seonyoung Kim", "Hyochan Chong", "Changdong Kim", "Youngmin Kim", "Dongkyu Kim" ], "github": "", "project_page": "", "abstract": "Efficient deployment of large language models (LLMs) requires extreme quantization, forcing a critical trade-off between low-bit efficiency and performance. Residual binarization enables hardware-friendly, matmul-free inference by stacking binary (pm1) layers, but is plagued by pathological feature co-adaptation. We identify a key failure mode, which we term inter-path adaptation: during quantization-aware training (QAT), parallel residual binary paths learn redundant features, degrading the error-compensation structure and limiting the expressive capacity of the model. While prior work relies on heuristic workarounds (e.g., path freezing) that constrain the solution space, we propose RaBiT, a novel quantization framework that resolves co-adaptation by algorithmically enforcing a residual hierarchy. Its core mechanism sequentially derives each binary path from a single shared full-precision weight, which ensures that every path corrects the error of the preceding one. This process is stabilized by a robust initialization that prioritizes functional preservation over mere weight approximation. RaBiT redefines the 2-bit accuracy-efficiency frontier: it achieves state-of-the-art performance, rivals even hardware-intensive Vector Quantization (VQ) methods, and delivers a 4.49times inference speed-up over full-precision models on an RTX 4090.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.05843", "title": "OdysseyArena: Benchmarking Large Language Models For Long-Horizon, Active and Inductive Interactions", "authors": [ "Fangzhi Xu", "Hang Yan", "Qiushi Sun", "Jinyang Wu", "Zixian Huang", "Muye Huang", "Jingyang Gong", "Zichen Ding", "Kanzhi Cheng", "Yian Wang", "Xinyu Che", "Zeyi Sun", "Jian Zhang", "Zhangyue Yin", "Haoran Luo", "Xuanjing Huang", "Ben Kao", "Jun Liu", "Qika Lin" ], "github": "https://github.com/xufangzhi/Odyssey-Arena", "project_page": "", "abstract": "The rapid advancement of Large Language Models (LLMs) has catalyzed the development of autonomous agents capable of navigating complex environments. However, existing evaluations primarily adopt a deductive paradigm, where agents execute tasks based on explicitly provided rules and static goals, often within limited planning horizons. Crucially, this neglects the inductive necessity for agents to discover latent transition laws from experience autonomously, which is the cornerstone for enabling agentic foresight and sustaining strategic coherence. To bridge this gap, we introduce OdysseyArena, which re-centers agent evaluation on long-horizon, active, and inductive interactions. We formalize and instantiate four primitives, translating abstract transition dynamics into concrete interactive environments. Building upon this, we establish OdysseyArena-Lite for standardized benchmarking, providing a set of 120 tasks to measure an agent's inductive efficiency and long-horizon discovery. Pushing further, we introduce OdysseyArena-Challenge to stress-test agent stability across extreme interaction horizons (e.g., > 200 steps). Extensive experiments on 15+ leading LLMs reveal that even frontier models exhibit a deficiency in inductive scenarios, identifying a critical bottleneck in the pursuit of autonomous discovery in complex environments. Our code and data are available at https://github.com/xufangzhi/Odyssey-Arena", "github_manual": "", "github_regex": "https://github.com/xufangzhi/Odyssey-Arena", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2601.23039", "title": "Avoiding Premature Collapse: Adaptive Annealing for Entropy-Regularized Structural Inference", "authors": [ "Yizhi Liu" ], "github": "", "project_page": "", "abstract": "Differentiable matching layers and residual connection paradigms, often implemented via entropy-regularized Optimal Transport (OT), serve as critical mechanisms in structural prediction and architectural scaling. However, recovering discrete permutations or maintaining identity mappings via annealing \u03b5to 0 is notoriously unstable. In this work, we identify a fundamental mechanism for this failure: Premature Mode Collapse. By analyzing the non-normal dynamics of the Sinkhorn fixed-point map, we reveal a theoretical thermodynamic speed limit: standard exponential cooling outpaces the contraction rate of the inference operator, which degrades as O(1/\u03b5). To address this, we propose Efficient Piecewise Hybrid Adaptive Stability Control (EPH-ASC), an adaptive scheduling algorithm that monitors the stability of the inference process. We demonstrate that EPH-ASC is essential for stabilizing Manifold-Constrained Hyper-Connections (mHC) during large-scale training on the FineWeb-Edu dataset, effectively preventing late-stage gradient explosions by enforcing a linear stability law.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06554", "title": "SeeUPO: Sequence-Level Agentic-RL with Convergence Guarantees", "authors": [ "Tianyi Hu", "Qingxu Fu", "Yanxi Chen", "Zhaoyang Liu", "Bolin Ding" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has emerged as the predominant paradigm for training large language model (LLM)-based AI agents. However, existing backbone RL algorithms lack verified convergence guarantees in agentic scenarios, especially in multi-turn settings, which can lead to training instability and failure to converge to optimal policies. In this paper, we systematically analyze how different combinations of policy update mechanisms and advantage estimation methods affect convergence properties in single/multi-turn scenarios. We find that REINFORCE with Group Relative Advantage Estimation (GRAE) can converge to the globally optimal under undiscounted conditions, but the combination of PPO & GRAE breaks PPO's original monotonic improvement property. Furthermore, we demonstrate that mainstream backbone RL algorithms cannot simultaneously achieve both critic-free and convergence guarantees in multi-turn scenarios. To address this, we propose SeeUPO (Sequence-level Sequential Update Policy Optimization), a critic-free approach with convergence guarantees for multi-turn interactions. SeeUPO models multi-turn interaction as sequentially executed multi-agent bandit problems. Through turn-by-turn sequential policy updates in reverse execution order, it ensures monotonic improvement and convergence to global optimal solution via backward induction. Experiments on AppWorld and BFCL v4 demonstrate SeeUPO's substantial improvements over existing backbone algorithms: relative gains of 43.3%-54.6% on Qwen3-14B and 24.1%-41.9% on Qwen2.5-14B (averaged across benchmarks), along with superior training stability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06717", "title": "F-GRPO: Don't Let Your Policy Learn the Obvious and Forget the Rare", "authors": [ "Daniil Plyusov", "Alexey Gorbatovski", "Boris Shaposhnikov", "Viacheslav Sinii", "Alexey Malakhov", "Daniil Gavrilov" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) is commonly based on group sampling to estimate advantages and stabilize policy updates. In practice, large group sizes are not feasible due to computational limits, which biases learning toward trajectories that are already likely. Smaller groups often miss rare-correct trajectories while still containing mixed rewards, concentrating probability on common solutions. We derive the probability that updates miss rare-correct modes as a function of group size, showing non-monotonic behavior, and characterize how updates redistribute mass within the correct set, revealing that unsampled-correct mass can shrink even as total correct mass grows. Motivated by this analysis, we propose a difficulty-aware advantage scaling coefficient, inspired by Focal loss, that down-weights updates on high-success prompts. The lightweight modification can be directly integrated into any group-relative RLVR algorithm such as GRPO, DAPO, and CISPO. On Qwen2.5-7B across in-domain and out-of-domain benchmarks, our method improves pass@256 from 64.1 rightarrow 70.3 (GRPO), 69.3 rightarrow 72.5 (DAPO), and 73.2 rightarrow 76.8 (CISPO), while preserving or improving pass@1, without increasing group size or computational cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.04454", "title": "Seg-ReSearch: Segmentation with Interleaved Reasoning and External Search", "authors": [ "Tianming Liang", "Qirui Du", "Jian-Fang Hu", "Haichao Jiang", "Zicheng Lin", "Wei-Shi Zheng" ], "github": "https://github.com/iSEE-Laboratory/Seg-ReSearch", "project_page": "", "abstract": "Segmentation based on language has been a popular topic in computer vision. While recent advances in multimodal large language models (MLLMs) have endowed segmentation systems with reasoning capabilities, these efforts remain confined by the frozen internal knowledge of MLLMs, which limits their potential for real-world scenarios that involve up-to-date information or domain-specific concepts. In this work, we propose Seg-ReSearch, a novel segmentation paradigm that overcomes the knowledge bottleneck of existing approaches. By enabling interleaved reasoning and external search, Seg-ReSearch empowers segmentation systems to handle dynamic, open-world queries that extend beyond the frozen knowledge of MLLMs. To effectively train this capability, we introduce a hierarchical reward design that harmonizes initial guidance with progressive incentives, mitigating the dilemma between sparse outcome signals and rigid step-wise supervision. For evaluation, we construct OK-VOS, a challenging benchmark that explicitly requires outside knowledge for video object segmentation. Experiments on OK-VOS and two existing reasoning segmentation benchmarks demonstrate that our Seg-ReSearch improves state-of-the-art approaches by a substantial margin. Code and data will be released at https://github.com/iSEE-Laboratory/Seg-ReSearch.", "github_manual": "", "github_regex": "https://github.com/iSEE-Laboratory/Seg-ReSearch", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06883", "title": "Vision Transformer Finetuning Benefits from Non-Smooth Components", "authors": [ "Ambroise Odonnat", "Laetitia Chapel", "Romain Tavenard", "Ievgen Redko" ], "github": "https://github.com/ambroiseodt/vit-plasticity", "project_page": "", "abstract": "The smoothness of the transformer architecture has been extensively studied in the context of generalization, training stability, and adversarial robustness. However, its role in transfer learning remains poorly understood. In this paper, we analyze the ability of vision transformer components to adapt their outputs to changes in inputs, or, in other words, their plasticity. Defined as an average rate of change, it captures the sensitivity to input perturbation; in particular, a high plasticity implies low smoothness. We demonstrate through theoretical analysis and comprehensive experiments that this perspective provides principled guidance in choosing the components to prioritize during adaptation. A key takeaway for practitioners is that the high plasticity of the attention modules and feedforward layers consistently leads to better finetuning performance. Our findings depart from the prevailing assumption that smoothness is desirable, offering a novel perspective on the functional properties of transformers. The code is available at https://github.com/ambroiseodt/vit-plasticity.", "github_manual": "", "github_regex": "https://github.com/ambroiseodt/vit-plasticity", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.05027", "title": "AudioSAE: Towards Understanding of Audio-Processing Models with Sparse AutoEncoders", "authors": [ "Georgii Aparin", "Tasnima Sadekova", "Alexey Rukhovich", "Assel Yermekova", "Laida Kushnareva", "Vadim Popov", "Kristian Kuznetsov", "Irina Piontkovskaya" ], "github": "https://github.com/audiosae/audiosae_demo", "project_page": "", "abstract": "Sparse Autoencoders (SAEs) are powerful tools for interpreting neural representations, yet their use in audio remains underexplored. We train SAEs across all encoder layers of Whisper and HuBERT, provide an extensive evaluation of their stability, interpretability, and show their practical utility. Over 50% of the features remain consistent across random seeds, and reconstruction quality is preserved. SAE features capture general acoustic and semantic information as well as specific events, including environmental noises and paralinguistic sounds (e.g. laughter, whispering) and disentangle them effectively, requiring removal of only 19-27% of features to erase a concept. Feature steering reduces Whisper's false speech detections by 70% with negligible WER increase, demonstrating real-world applicability. Finally, we find SAE features correlated with human EEG activity during speech perception, indicating alignment with human neural processing. The code and checkpoints are available at https://github.com/audiosae/audiosae_demo.", "github_manual": "", "github_regex": "https://github.com/audiosae/audiosae_demo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.04837", "title": "Group-Evolving Agents: Open-Ended Self-Improvement via Experience Sharing", "authors": [ "Zhaotian Weng", "Antonis Antoniades", "Deepak Nathani", "Zhen Zhang", "Xiao Pu", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "Open-ended self-improving agents can autonomously modify their own structural designs to advance their capabilities and overcome the limits of pre-defined architectures, thus reducing reliance on human intervention. We introduce Group-Evolving Agents (GEA), a new paradigm for open-ended self-improvements, which treats a group of agents as the fundamental evolutionary unit, enabling explicit experience sharing and reuse within the group throughout evolution. Unlike existing open-ended self-evolving paradigms that adopt tree-structured evolution, GEA overcomes the limitation of inefficient utilization of exploratory diversity caused by isolated evolutionary branches. We evaluate GEA on challenging coding benchmarks, where it significantly outperforms state-of-the-art self-evolving methods (71.0% vs. 56.7% on SWE-bench Verified, 88.3% vs. 68.3% on Polyglot) and matches or exceeds top human-designed agent frameworks (71.8% and 52.0% on two benchmarks, respectively). Analysis reveals that GEA more effectively converts early-stage exploratory diversity into sustained, long-term progress, achieving stronger performance under the same number of evolved agents. Furthermore, GEA exhibits consistent transferability across different coding models and greater robustness, fixing framework-level bugs in 1.4 iterations on average, versus 5 for self-evolving methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06129", "title": "Urban Spatio-Temporal Foundation Models for Climate-Resilient Housing: Scaling Diffusion Transformers for Disaster Risk Prediction", "authors": [ "Olaf Yunus Laitinen Imanov", "Derya Umut Kulali", "Taner Yilmaz" ], "github": "", "project_page": "", "abstract": "Climate hazards increasingly disrupt urban transportation and emergency-response operations by damaging housing stock, degrading infrastructure, and reducing network accessibility. This paper presents Skjold-DiT, a diffusion-transformer framework that integrates heterogeneous spatio-temporal urban data to forecast building-level climate-risk indicators while explicitly incorporating transportation-network structure and accessibility signals relevant to intelligent vehicles (e.g., emergency reachability and evacuation-route constraints). Concretely, Skjold-DiT enables hazard-conditioned routing constraints by producing calibrated, uncertainty-aware accessibility layers (reachability, travel-time inflation, and route redundancy) that can be consumed by intelligent-vehicle routing and emergency dispatch systems. Skjold-DiT combines: (1) Fjell-Prompt, a prompt-based conditioning interface designed to support cross-city transfer; (2) Norrland-Fusion, a cross-modal attention mechanism unifying hazard maps/imagery, building attributes, demographics, and transportation infrastructure into a shared latent representation; and (3) Valkyrie-Forecast, a counterfactual simulator for generating probabilistic risk trajectories under intervention prompts. We introduce the Baltic-Caspian Urban Resilience (BCUR) dataset with 847,392 building-level observations across six cities, including multi-hazard annotations (e.g., flood and heat indicators) and transportation accessibility features. Experiments evaluate prediction quality, cross-city generalization, calibration, and downstream transportation-relevant outcomes, including reachability and hazard-conditioned travel times under counterfactual interventions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06079", "title": "Canzona: A Unified, Asynchronous, and Load-Balanced Framework for Distributed Matrix-based Optimizers", "authors": [ "Liangyu Wang", "Siqi Zhang", "Junjie Wang", "Yiming Dong", "Bo Zheng", "Zihan Qiu", "Shengkun Tang", "Di Wang", "Rui Men", "Dayiheng Liu" ], "github": "", "project_page": "", "abstract": "The scaling of Large Language Models (LLMs) drives interest in matrix-based optimizers (e.g., Shampoo, Muon, SOAP) for their convergence efficiency; yet their requirement for holistic updates conflicts with the tensor fragmentation in distributed frameworks like Megatron. Existing solutions are suboptimal: synchronous approaches suffer from computational redundancy, while layer-wise partitioning fails to reconcile this conflict without violating the geometric constraints of efficient communication primitives. To bridge this gap, we propose Canzona, a Unified, Asynchronous, and Load-Balanced framework that decouples logical optimizer assignment from physical parameter distribution. For Data Parallelism, we introduce an alpha-Balanced Static Partitioning strategy that respects atomicity while neutralizing the load imbalance. For Tensor Parallelism, we design an Asynchronous Compute pipeline utilizing Micro-Group Scheduling to batch fragmented updates and hide reconstruction overhead. Extensive evaluations on the Qwen3 model family (up to 32B parameters) on 256 GPUs demonstrate that our approach preserves the efficiency of established parallel architectures, achieving a 1.57x speedup in end-to-end iteration time and reducing optimizer step latency by 5.8x compared to the baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.02581", "title": "QuantLRM: Quantization of Large Reasoning Models via Fine-Tuning Signals", "authors": [ "Nan Zhang", "Eugene Kwek", "Yusen Zhang", "Muyu Pan", "Suhang Wang", "Prasenjit Mitra", "Rui Zhang" ], "github": "https://github.com/psunlpgroup/QuantLRM", "project_page": "", "abstract": "Weight-only quantization is important for compressing Large Language Models (LLMs). Inspired by the spirit of classical magnitude pruning, we study whether the magnitude of weight updates during reasoning-incentivized fine-tuning can provide valuable signals for quantizing Large Reasoning Models (LRMs). We hypothesize that the smallest and largest weight updates during fine-tuning are more important than those of intermediate magnitude, a phenomenon we term \"protecting both ends\". Upon hypothesis validation, we introduce QuantLRM, which stands for weight quantization of LRMs via fine-tuning signals. We fit simple restricted quadratic functions on weight updates to protect both ends. By multiplying the average quadratic values with the count of zero weight updates of channels, we compute channel importance that is more effective than using activation or second-order information. We run QuantLRM to quantize various fine-tuned models (including supervised, direct preference optimization, and reinforcement learning fine-tuning) over four reasoning benchmarks (AIME-120, FOLIO, temporal sequences, and GPQA-Diamond) and empirically find that QuantLRM delivers a consistent improvement for LRMs quantization, with an average improvement of 6.55% on a reinforcement learning fine-tuned model. Also supporting non-fine-tuned LRMs, QuantLRM gathers effective signals via pseudo-fine-tuning, which greatly enhances its applicability.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/psunlpgroup/QuantLRM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06724", "title": "Table-as-Search: Formulate Long-Horizon Agentic Information Seeking as Table Completion", "authors": [ "Tian Lan", "Felix Henry", "Bin Zhu", "Qianghuai Jia", "Junyang Ren", "Qihang Pu", "Haijun Li", "Longyue Wang", "Zhao Xu", "Weihua Luo" ], "github": "https://github.com/AIDC-AI/Marco-DeepResearch", "project_page": "", "abstract": "Current Information Seeking (InfoSeeking) agents struggle to maintain focus and coherence during long-horizon exploration, as tracking search states, including planning procedure and massive search results, within one plain-text context is inherently fragile. To address this, we introduce Table-as-Search (TaS), a structured planning framework that reformulates the InfoSeeking task as a Table Completion task. TaS maps each query into a structured table schema maintained in an external database, where rows represent search candidates and columns denote constraints or required information. This table precisely manages the search states: filled cells strictly record the history and search results, while empty cells serve as an explicit search plan. Crucially, TaS unifies three distinct InfoSeeking tasks: Deep Search, Wide Search, and the challenging DeepWide Search. Extensive experiments demonstrate that TaS significantly outperforms numerous state-of-the-art baselines across three kinds of benchmarks, including multi-agent framework and commercial systems. Furthermore, our analysis validates the TaS's superior robustness in long-horizon InfoSeeking, alongside its efficiency, scalability and flexibility. Code and datasets are publicly released at https://github.com/AIDC-AI/Marco-Search-Agent.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AIDC-AI/Marco-DeepResearch", "github_api_added_by": "user" }, { "date": "2026-02-09", "arxiv_id": "2602.05847", "title": "OmniVideo-R1: Reinforcing Audio-visual Reasoning with Query Intention and Modality Attention", "authors": [ "Zhangquan Chen", "Jiale Tao", "Ruihuang Li", "Yihao Hu", "Ruitao Chen", "Zhantao Yang", "Xinlei Yu", "Haodong Jing", "Manyuan Zhang", "Shuai Shao", "Biao Wang", "Qinglin Lu", "Ruqi Huang" ], "github": "", "project_page": "", "abstract": "While humans perceive the world through diverse modalities that operate synergistically to support a holistic understanding of their surroundings, existing omnivideo models still face substantial challenges on audio-visual understanding tasks. In this paper, we propose OmniVideo-R1, a novel reinforced framework that improves mixed-modality reasoning. OmniVideo-R1 empowers models to \"think with omnimodal cues\" by two key strategies: (1) query-intensive grounding based on self-supervised learning paradigms; and (2) modality-attentive fusion built upon contrastive learning paradigms. Extensive experiments on multiple benchmarks demonstrate that OmniVideo-R1 consistently outperforms strong baselines, highlighting its effectiveness and robust generalization capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.03548", "title": "SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue", "authors": [ "Yuqin Dai", "Ning Gao", "Wei Zhang", "Jie Wang", "Zichen Luo", "Jinpeng Wang", "Yujie Wang", "Ruiyuan Wu", "Chaozheng Wang" ], "github": "https://github.com/Da1yuqin/SEAD", "project_page": "", "abstract": "Large Language Models have demonstrated remarkable capabilities in open-domain dialogues. However, current methods exhibit suboptimal performance in service dialogues, as they rely on noisy, low-quality human conversation data. This limitation arises from data scarcity and the difficulty of simulating authentic, goal-oriented user behaviors. To address these issues, we propose SEAD (Self-Evolving Agent for Service Dialogue), a framework that enables agents to learn effective strategies without large-scale human annotations. SEAD decouples user modeling into two components: a Profile Controller that generates diverse user states to manage training curriculum, and a User Role-play Model that focuses on realistic role-playing. This design ensures the environment provides adaptive training scenarios rather than acting as an unfair adversary. Experiments demonstrate that SEAD significantly outperforms Open-source Foundation Models and Closed-source Commercial Models, improving task completion rate by 17.6% and dialogue efficiency by 11.1%. Code is available at: https://github.com/Da1yuqin/SEAD.", "github_manual": "", "github_regex": "https://github.com/Da1yuqin/SEAD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.03075", "title": "ReMiT: RL-Guided Mid-Training for Iterative LLM Evolution", "authors": [ "Junjie Huang", "Jiarui Qin", "Di Yin", "Weiwen Liu", "Yong Yu", "Xing Sun", "Weinan Zhang" ], "github": "", "project_page": "", "abstract": "Standard training pipelines for large language models (LLMs) are typically unidirectional, progressing from pre-training to post-training. However, the potential for a bidirectional process--where insights from post-training retroactively improve the pre-trained foundation--remains unexplored. We aim to establish a self-reinforcing flywheel: a cycle in which reinforcement learning (RL)-tuned model strengthens the base model, which in turn enhances subsequent post-training performance, requiring no specially trained teacher or reference model. To realize this, we analyze training dynamics and identify the mid-training (annealing) phase as a critical turning point for model capabilities. This phase typically occurs at the end of pre-training, utilizing high-quality corpora under a rapidly decaying learning rate. Building upon this insight, we introduce ReMiT (Reinforcement Learning-Guided Mid-Training). Specifically, ReMiT leverages the reasoning priors of RL-tuned models to dynamically reweight tokens during the mid-training phase, prioritizing those pivotal for reasoning. Empirically, ReMiT achieves an average improvement of 3\\% on 10 pre-training benchmarks, spanning math, code, and general reasoning, and sustains these gains by over 2\\% throughout the post-training pipeline. These results validate an iterative feedback loop, enabling continuous and self-reinforcing evolution of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06130", "title": "Self-Improving World Modelling with Latent Actions", "authors": [ "Yifu Qiu", "Zheng Zhao", "Waylon Li", "Yftah Ziser", "Anna Korhonen", "Shay B. Cohen", "Edoardo M. Ponti" ], "github": "https://github.com/yfqiu-nlp/swirl", "project_page": "", "abstract": "Internal modelling of the world -- predicting transitions between previous states X and next states Y under actions Z -- is essential to reasoning and planning for LLMs and VLMs. Learning such models typically requires costly action-labelled trajectories. We propose SWIRL, a self-improvement framework that learns from state-only sequences by treating actions as a latent variable and alternating between Forward World Modelling (FWM) P_\u03b8(Y|X,Z) and an Inverse Dynamics Modelling (IDM) Q_\u03c6(Z|X,Y). SWIRL iterates two phases: (1) Variational Information Maximisation, which updates the FWM to generate next states that maximise conditional mutual information with latent actions given prior states, encouraging identifiable consistency; and (2) ELBO Maximisation, which updates the IDM to explain observed transitions, effectively performing coordinate ascent. Both models are trained with reinforcement learning (specifically, GRPO) with the opposite frozen model's log-probability as a reward signal. We provide theoretical learnability guarantees for both updates, and evaluate SWIRL on LLMs and VLMs across multiple environments: single-turn and multi-turn open-world visual dynamics and synthetic textual environments for physics, web, and tool calling. SWIRL achieves gains of 16% on AURORABench, 28% on ByteMorph, 16% on WorldPredictionBench, and 14% on StableToolBench.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yfqiu-nlp/swirl", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2601.18415", "title": "Pisets: A Robust Speech Recognition System for Lectures and Interviews", "authors": [ "Ivan Bondarenko", "Daniil Grebenkin", "Oleg Sedukhin", "Mikhail Klementev", "Roman Derunets", "Lyudmila Budneva" ], "github": "https://github.com/bond005/pisets", "project_page": "", "abstract": "This work presents a speech-to-text system \"Pisets\" for scientists and journalists which is based on a three-component architecture aimed at improving speech recognition accuracy while minimizing errors and hallucinations associated with the Whisper model. The architecture comprises primary recognition using Wav2Vec2, false positive filtering via the Audio Spectrogram Transformer (AST), and final speech recognition through Whisper. The implementation of curriculum learning methods and the utilization of diverse Russian-language speech corpora significantly enhanced the system's effectiveness. Additionally, advanced uncertainty modeling techniques were introduced, contributing to further improvements in transcription quality. The proposed approaches ensure robust transcribing of long audio data across various acoustic conditions compared to WhisperX and the usual Whisper model. The source code of \"Pisets\" system is publicly available at GitHub: https://github.com/bond005/pisets.", "github_manual": "", "github_regex": "https://github.com/bond005/pisets", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06669", "title": "compar:IA: The French Government's LLM arena to collect French-language human prompts and preference data", "authors": [ "Lucie Termignon", "Simonas Zilinskas", "Hadrien P\u00e9lissier", "Aur\u00e9lien Barrot", "Nicolas Chesnais", "Elie Gavoty" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) often show reduced performance, cultural alignment, and safety robustness in non-English languages, partly because English dominates both pre-training data and human preference alignment datasets. Training methods like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) require human preference data, which remains scarce and largely non-public for many languages beyond English. To address this gap, we introduce compar:IA, an open-source digital public service developed inside the French government and designed to collect large-scale human preference data from a predominantly French-speaking general audience. The platform uses a blind pairwise comparison interface to capture unconstrained, real-world prompts and user judgments across a diverse set of language models, while maintaining low participation friction and privacy-preserving automated filtering. As of 2026-02-07, compar:IA has collected over 600,000 free-form prompts and 250,000 preference votes, with approximately 89% of the data in French. We release three complementary datasets -- conversations, votes, and reactions -- under open licenses, and present initial analyses, including a French-language model leaderboard and user interaction patterns. Beyond the French context, compar:IA is evolving toward an international digital public good, offering reusable infrastructure for multilingual model training, evaluation, and the study of human-AI interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.03998", "title": "AtlasPatch: An Efficient and Scalable Tool for Whole Slide Image Preprocessing in Computational Pathology", "authors": [ "Ahmed Alagha", "Christopher Leclerc", "Yousef Kotp", "Omar Metwally", "Calvin Moras", "Peter Rentopoulos", "Ghodsiyeh Rostami", "Bich Ngoc Nguyen", "Jumanah Baig", "Abdelhakim Khellaf", "Vincent Quoc-Huy Trinh", "Rabeb Mizouni", "Hadi Otrok", "Jamal Bentahar", "Mahdi S. Hosseini" ], "github": "https://github.com/AtlasAnalyticsLab/AtlasPatch", "project_page": "", "abstract": "Whole-slide image (WSI) preprocessing, typically comprising tissue detection followed by patch extraction, is foundational to AI-driven computational pathology workflows. This remains a major computational bottleneck as existing tools either rely on inaccurate heuristic thresholding for tissue detection, or adopt AI-based approaches trained on limited-diversity data that operate at the patch level, incurring substantial computational complexity. We present AtlasPatch, an efficient and scalable slide preprocessing framework for accurate tissue detection and high-throughput patch extraction with minimal computational overhead. AtlasPatch's tissue detection module is trained on a heterogeneous and semi-manually annotated dataset of ~30,000 WSI thumbnails, using efficient fine-tuning of the Segment-Anything model. The tool extrapolates tissue masks from thumbnails to full-resolution slides to extract patch coordinates at user-specified magnifications, with options to stream patches directly into common image encoders for embedding or store patch images, all efficiently parallelized across CPUs and GPUs. We assess AtlasPatch across segmentation precision, computational complexity, and downstream multiple-instance learning, matching state-of-the-art performance while operating at a fraction of their computational cost. AtlasPatch is open-source and available at https://github.com/AtlasAnalyticsLab/AtlasPatch.", "github_manual": "", "github_regex": "https://github.com/AtlasAnalyticsLab/AtlasPatch", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06964", "title": "Learning a Generative Meta-Model of LLM Activations", "authors": [ "Grace Luo", "Jiahai Feng", "Trevor Darrell", "Alec Radford", "Jacob Steinhardt" ], "github": "https://github.com/g-luo/generative_latent_prior", "project_page": "", "abstract": "Existing approaches for analyzing neural network activations, such as PCA and sparse autoencoders, rely on strong structural assumptions. Generative models offer an alternative: they can uncover structure without such assumptions and act as priors that improve intervention fidelity. We explore this direction by training diffusion models on one billion residual stream activations, creating \"meta-models\" that learn the distribution of a network's internal states. We find that diffusion loss decreases smoothly with compute and reliably predicts downstream utility. In particular, applying the meta-model's learned prior to steering interventions improves fluency, with larger gains as loss decreases. Moreover, the meta-model's neurons increasingly isolate concepts into individual units, with sparse probing scores that scale as loss decreases. These results suggest generative meta-models offer a scalable path toward interpretability without restrictive structural assumptions. Project page: https://generative-latent-prior.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/g-luo/generative_latent_prior", "github_api_added_by": "auto" }, { "date": "2026-02-09", "arxiv_id": "2602.06869", "title": "Uncovering Cross-Objective Interference in Multi-Objective Alignment", "authors": [ "Yining Lu", "Meng Jiang" ], "github": "", "project_page": "", "abstract": "We study a persistent failure mode in multi-objective alignment for large language models (LLMs): training improves performance on only a subset of objectives while causing others to degrade. We formalize this phenomenon as cross-objective interference and conduct the first systematic study across classic scalarization algorithms, showing that interference is pervasive and exhibits strong model dependence. To explain this phenomenon, we derive a local covariance law showing that an objective improves at first order when its reward exhibits positive covariance with the scalarized score. We extend this analysis to clipped surrogate objectives used in modern alignment, demonstrating that the covariance law remains valid under mild conditions despite clipping. Building on this analysis, we propose Covariance Targeted Weight Adaptation (CTWA), a plug-and-play method that maintains positive covariance between objective rewards and the training signal to effectively mitigate cross-objective interference. Finally, we complement these local improvement conditions with a global convergence analysis under the Polyak--\u0141ojasiewicz condition, establishing when non-convex scalarized optimization achieves global convergence and how cross-objective interference depends on specific model geometric properties.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.04811", "title": "SE-Bench: Benchmarking Self-Evolution with Knowledge Internalization", "authors": [ "Jiarui Yuan", "Tailin Jin", "Weize Chen", "Zeyuan Liu", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/thunlp/SE-Bench", "project_page": "", "abstract": "True self-evolution requires agents to act as lifelong learners that internalize novel experiences to solve future problems. However, rigorously measuring this foundational capability is hindered by two obstacles: the entanglement of prior knowledge, where ``new'' knowledge may appear in pre-training data, and the entanglement of reasoning complexity, where failures may stem from problem difficulty rather than an inability to recall learned knowledge. We introduce SE-Bench, a diagnostic environment that obfuscates the NumPy library and its API doc into a pseudo-novel package with randomized identifiers. Agents are trained to internalize this package and evaluated on simple coding tasks without access to documentation, yielding a clean setting where tasks are trivial with the new API doc but impossible for base models without it. Our investigation reveals three insights: (1) the Open-Book Paradox, where training with reference documentation inhibits retention, requiring \"Closed-Book Training\" to force knowledge compression into weights; (2) the RL Gap, where standard RL fails to internalize new knowledge completely due to PPO clipping and negative gradients; and (3) the viability of Self-Play for internalization, proving models can learn from self-generated, noisy tasks when coupled with SFT, but not RL. Overall, SE-Bench establishes a rigorous diagnostic platform for self-evolution with knowledge internalization. Our code and dataset can be found at https://github.com/thunlp/SE-Bench.", "github_manual": "", "github_regex": "https://github.com/thunlp/SE-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06176", "title": "Large Language Model Reasoning Failures", "authors": [ "Peiyang Song", "Pengrui Han", "Noah Goodman" ], "github": "https://github.com/Peiyang-Song/Awesome-LLM-Reasoning-Failures", "project_page": "", "abstract": "Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, achieving impressive results across a wide range of tasks. Despite these advances, significant reasoning failures persist, occurring even in seemingly simple scenarios. To systematically understand and address these shortcomings, we present the first comprehensive survey dedicated to reasoning failures in LLMs. We introduce a novel categorization framework that distinguishes reasoning into embodied and non-embodied types, with the latter further subdivided into informal (intuitive) and formal (logical) reasoning. In parallel, we classify reasoning failures along a complementary axis into three types: fundamental failures intrinsic to LLM architectures that broadly affect downstream tasks; application-specific limitations that manifest in particular domains; and robustness issues characterized by inconsistent performance across minor variations. For each reasoning failure, we provide a clear definition, analyze existing studies, explore root causes, and present mitigation strategies. By unifying fragmented research efforts, our survey provides a structured perspective on systemic weaknesses in LLM reasoning, offering valuable insights and guiding future research towards building stronger, more reliable, and robust reasoning capabilities. We additionally release a comprehensive collection of research works on LLM reasoning failures, as a GitHub repository at https://github.com/Peiyang-Song/Awesome-LLM-Reasoning-Failures, to provide an easy entry point to this area.", "github_manual": "", "github_regex": "https://github.com/Peiyang-Song/Awesome-LLM-Reasoning-Failures", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06566", "title": "SPARC: Separating Perception And Reasoning Circuits for Test-time Scaling of VLMs", "authors": [ "Niccolo Avogaro", "Nayanika Debnath", "Li Mi", "Thomas Frick", "Junling Wang", "Zexue He", "Hang Hua", "Konrad Schindler", "Mattia Rigotti" ], "github": "", "project_page": "", "abstract": "Despite recent successes, test-time scaling - i.e., dynamically expanding the token budget during inference as needed - remains brittle for vision-language models (VLMs): unstructured chains-of-thought about images entangle perception and reasoning, leading to long, disorganized contexts where small perceptual mistakes may cascade into completely wrong answers. Moreover, expensive reinforcement learning with hand-crafted rewards is required to achieve good performance. Here, we introduce SPARC (Separating Perception And Reasoning Circuits), a modular framework that explicitly decouples visual perception from reasoning. Inspired by sequential sensory-to-cognitive processing in the brain, SPARC implements a two-stage pipeline where the model first performs explicit visual search to localize question-relevant regions, then conditions its reasoning on those regions to produce the final answer. This separation enables independent test-time scaling with asymmetric compute allocation (e.g., prioritizing perceptual processing under distribution shift), supports selective optimization (e.g., improving the perceptual stage alone when it is the bottleneck for end-to-end performance), and accommodates compressed contexts by running global search at lower image resolutions and allocating high-resolution processing only to selected regions, thereby reducing total visual tokens count and compute. Across challenging visual reasoning benchmarks, SPARC outperforms monolithic baselines and strong visual-grounding approaches. For instance, SPARC improves the accuracy of Qwen3VL-4B on the V^* VQA benchmark by 6.7 percentage points, and it surpasses \"thinking with images\" by 4.6 points on a challenging OOD task despite requiring a 200times lower token budget.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.04649", "title": "Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models", "authors": [ "Binghai Wang", "Yantao Liu", "Yuxuan Liu", "Tianyi Tang", "Shenzhi Wang", "Chang Gao", "Chujie Zheng", "Yichang Zhang", "Le Yu", "Shixuan Liu", "Tao Gui", "Qi Zhang", "Xuanjing Huang", "Bowen Yu", "Fei Huang", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "Generative Reward Models (GenRMs) and LLM-as-a-Judge exhibit deceptive alignment by producing correct judgments for incorrect reasons, as they are trained and evaluated to prioritize Outcome Accuracy, which undermines their ability to generalize during RLHF. We introduce Rationale Consistency, a fine-grained metric that quantifies the alignment between the model's reasoning process and human judgment. Our evaluation of frontier models reveals that rationale consistency effectively discriminates among state-of-the-art models and detects deceptive alignment, while outcome accuracy falls short in both respects. To mitigate this gap, we introduce a hybrid signal that combines rationale consistency with outcome accuracy for GenRM training. Our training method achieves state-of-the-art performance on RM-Bench (87.1%) and JudgeBench (82%), surpassing outcome-only baselines by an average of 5%. Using RM during RLHF, our method effectively improves performance as demonstrated on Arena Hard v2, notably yielding a 7% improvement in creative writing tasks. Further analysis confirms that our method escapes the deceptive alignment trap, effectively reversing the decline in rationale consistency observed in outcome-only training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-09", "arxiv_id": "2602.06181", "title": "Uncertainty Drives Social Bias Changes in Quantized Large Language Models", "authors": [ "Stanley Z. Hua", "Sanae Lotfi", "Irene Y. Chen" ], "github": "https://github.com/stan-hua/PostTrainingBiasBenchmark", "project_page": "", "abstract": "Post-training quantization reduces the computational cost of large language models but fundamentally alters their social biases in ways that aggregate metrics fail to capture. We present the first large-scale study of 50 quantized models evaluated on PostTrainingBiasBench, a unified benchmark of 13 closed- and open-ended bias datasets. We identify a phenomenon we term quantization-induced masked bias flipping, in which up to 21% of responses flip between biased and unbiased states after quantization, despite showing no change in aggregate bias scores. These flips are strongly driven by model uncertainty, where the responses with high uncertainty are 3-11x more likely to change than the confident ones. Quantization strength amplifies this effect, with 4-bit quantized models exhibiting 4-6x more behavioral changes than 8-bit quantized models. Critically, these changes create asymmetric impacts across demographic groups, where bias can worsen by up to 18.6% for some groups while improving by 14.1% for others, yielding misleadingly neutral aggregate outcomes. Larger models show no consistent robustness advantage, and group-specific shifts vary unpredictably across model families. Our findings demonstrate that compression fundamentally alters bias patterns, requiring crucial post-quantization evaluation and interventions to ensure reliability in practice.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stan-hua/PostTrainingBiasBenchmark", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.06694", "title": "NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models", "authors": [ "Hyochan Chong", "Dongkyu Kim", "Changdong Kim", "Minseop Choi" ], "github": "", "project_page": "", "abstract": "Weight-only quantization has become a standard approach for efficiently serving large language models (LLMs). However, existing methods fail to efficiently compress models to binary (1-bit) levels, as they either require large amounts of data and compute or incur additional storage. In this work, we propose NanoQuant, the first post-training quantization (PTQ) method to compress LLMs to both binary and sub-1-bit levels. NanoQuant formulates quantization as a low-rank binary factorization problem, and compresses full-precision weights to low-rank binary matrices and scales. Specifically, it utilizes an efficient alternating direction method of multipliers (ADMM) method to precisely initialize latent binary matrices and scales, and then tune the initialized parameters through a block and model reconstruction process. Consequently, NanoQuant establishes a new Pareto frontier in low-memory post-training quantization, achieving state-of-the-art accuracy even at sub-1-bit compression rates. NanoQuant makes large-scale deployment feasible on consumer hardware. For example, it compresses Llama2-70B by 25.8times in just 13 hours on a single H100, enabling a 70B model to operate on a consumer 8 GB GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.06422", "title": "Alleviating Sparse Rewards by Modeling Step-Wise and Long-Term Sampling Effects in Flow-Based GRPO", "authors": [ "Yunze Tong", "Mushui Liu", "Canyu Zhao", "Wanggui He", "Shiyi Zhang", "Hongwei Zhang", "Peng Zhang", "Jinlong Liu", "Ju Huang", "Jiamang Wang", "Hao Jiang", "Pipei Huang" ], "github": "https://github.com/YunzeTong/TurningPoint-GRPO", "project_page": "", "abstract": "Deploying GRPO on Flow Matching models has proven effective for text-to-image generation. However, existing paradigms typically propagate an outcome-based reward to all preceding denoising steps without distinguishing the local effect of each step. Moreover, current group-wise ranking mainly compares trajectories at matched timesteps and ignores within-trajectory dependencies, where certain early denoising actions can affect later states via delayed, implicit interactions. We propose TurningPoint-GRPO (TP-GRPO), a GRPO framework that alleviates step-wise reward sparsity and explicitly models long-term effects within the denoising trajectory. TP-GRPO makes two key innovations: (i) it replaces outcome-based rewards with step-level incremental rewards, providing a dense, step-aware learning signal that better isolates each denoising action's \"pure\" effect, and (ii) it identifies turning points-steps that flip the local reward trend and make subsequent reward evolution consistent with the overall trajectory trend-and assigns these actions an aggregated long-term reward to capture their delayed impact. Turning points are detected solely via sign changes in incremental rewards, making TP-GRPO efficient and hyperparameter-free. Extensive experiments also demonstrate that TP-GRPO exploits reward signals more effectively and consistently improves generation. Demo code is available at https://github.com/YunzeTong/TurningPoint-GRPO.", "github_manual": "", "github_regex": "https://github.com/YunzeTong/TurningPoint-GRPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07026", "title": "Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models", "authors": [ "Xiaomin Yu", "Yi Xin", "Wenjie Zhang", "Chonghan Liu", "Hanzhen Zhao", "Xiaoxing Hu", "Xinlei Yu", "Ziyue Qiao", "Hao Tang", "Xue Yang", "Xiaobin Hu", "Chengwei Qin", "Hui Xiong", "Yu Qiao", "Shuicheng Yan" ], "github": "", "project_page": "", "abstract": "Despite the success of multimodal contrastive learning in aligning visual and linguistic representations, a persistent geometric anomaly, the Modality Gap, remains: embeddings of distinct modalities expressing identical semantics occupy systematically offset regions. Prior approaches to bridge this gap are largely limited by oversimplified isotropic assumptions, hindering their application in large-scale scenarios. In this paper, we address these limitations by precisely characterizing the geometric shape of the modality gap and leveraging it for efficient model scaling. First, we propose the Fixed-frame Modality Gap Theory, which decomposes the modality gap within a frozen reference frame into stable biases and anisotropic residuals. Guided by this precise modeling, we introduce ReAlign, a training-free modality alignment strategy. Utilizing statistics from massive unpaired data, ReAlign aligns text representation into the image representation distribution via a three-step process comprising Anchor, Trace, and Centroid Alignment, thereby explicitly rectifying geometric misalignment. Building on ReAlign, we propose ReVision, a scalable training paradigm for Multimodal Large Language Models (MLLMs). ReVision integrates ReAlign into the pretraining stage, enabling the model to learn the distribution of visual representations from unpaired text before visual instruction tuning, without the need for large-scale, high-quality image-text pairs. Our framework demonstrates that statistically aligned unpaired data can effectively substitute for expensive image-text pairs, offering a robust path for the efficient scaling of MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07962", "title": "LOCA-bench: Benchmarking Language Agents Under Controllable and Extreme Context Growth", "authors": [ "Weihao Zeng", "Yuzhen Huang", "Junxian He" ], "github": "https://github.com/hkust-nlp/LOCA-bench", "project_page": "", "abstract": "Large language models (LLMs) are increasingly capable of carrying out long-running, real-world tasks. However, as the amount of context grows, their reliability often deteriorates, a phenomenon known as \"context rot\". Existing long-context benchmarks primarily focus on single-step settings that evaluate a model's ability to retrieve information from a long snippet. In realistic scenarios, however, LLMs often need to act as agents that explore environments, follow instructions and plans, extract useful information, and predict correct actions under a dynamically growing context. To assess language agents in such settings, we introduce LOCA-bench (a benchmark for LOng-Context Agents). Given a task prompt, LOCA-bench leverages automated and scalable control of environment states to regulate the agent's context length. This design enables LOCA-bench to extend the context length potentially to infinity in a controlled way while keeping the underlying task semantics fixed. LOCA-bench evaluates language agents as a combination of models and scaffolds, including various context management strategies. While agent performance generally degrades as the environment states grow more complex, advanced context management techniques can substantially improve the overall success rate. We open-source LOCA-bench to provide a platform for evaluating models and scaffolds in long-context, agentic scenarios: https://github.com/hkust-nlp/LOCA-bench", "github_manual": "", "github_regex": "https://github.com/hkust-nlp/LOCA-bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.06454", "title": "RelayGen: Intra-Generation Model Switching for Efficient Reasoning", "authors": [ "Jiwon Song", "Yoongon Kim", "Jae-Joon Kim" ], "github": "", "project_page": "", "abstract": "Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks by generating long, multi-step reasoning trajectories, but inference-time scaling incurs substantial deployment cost. A key challenge is that generation difficulty varies within a single output, whereas existing efficiency-oriented approaches either ignore this intra-generation variation or rely on supervised token-level routing with high system complexity. We present RelayGen, a training-free, segment-level runtime model switching framework that exploits difficulty variation in long-form reasoning. Through offline analysis of generation uncertainty using token probability margins, we show that coarse-grained segment-level control is sufficient to capture difficulty transitions within a reasoning trajectory. RelayGen identifies model-specific switch cues that signal transitions to lower-difficulty segments and dynamically delegates their continuation to a smaller model, while preserving high-difficulty reasoning on the large model. Across multiple reasoning benchmarks, RelayGen substantially reduces inference latency while preserving most of the accuracy of large models. When combined with speculative decoding, RelayGen achieves up to 2.2times end-to-end speedup with less than 2\\% accuracy degradation, without requiring additional training or learned routing components.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.06540", "title": "AgentCPM-Report: Interleaving Drafting and Deepening for Open-Ended Deep Research", "authors": [ "Yishan Li", "Wentong Chen", "Yukun Yan", "Mingwei Li", "Sen Mei", "Xiaorong Wang", "Kunpeng Liu", "Xin Cong", "Shuo Wang", "Zhong Zhang", "Yaxi Lu", "Zhenghao Liu", "Yankai Lin", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/youdotcom-oss/ydc-deep-research-evals", "project_page": "", "abstract": "Generating deep research reports requires large-scale information acquisition and the synthesis of insight-driven analysis, posing a significant challenge for current language models. Most existing approaches follow a plan-then-write paradigm, whose performance heavily depends on the quality of the initial outline. However, constructing a comprehensive outline itself demands strong reasoning ability, causing current deep research systems to rely almost exclusively on closed-source or online large models. This reliance raises practical barriers to deployment and introduces safety and privacy concerns for user-authored data. In this work, we present AgentCPM-Report, a lightweight yet high-performing local solution composed of a framework that mirrors the human writing process and an 8B-parameter deep research agent. Our framework uses a Writing As Reasoning Policy (WARP), which enables models to dynamically revise outlines during report generation. Under this policy, the agent alternates between Evidence-Based Drafting and Reasoning-Driven Deepening, jointly supporting information acquisition, knowledge refinement, and iterative outline evolution. To effectively equip small models with this capability, we introduce a Multi-Stage Agentic Training strategy, consisting of cold-start, atomic skill RL, and holistic pipeline RL. Experiments on DeepResearch Bench, DeepConsult, and DeepResearch Gym demonstrate that AgentCPM-Report outperforms leading closed-source systems, with substantial gains in Insight.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/youdotcom-oss/ydc-deep-research-evals", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2601.21363", "title": "Towards Bridging the Gap between Large-Scale Pretraining and Efficient Finetuning for Humanoid Control", "authors": [ "Weidong Huang", "Zhehan Li", "Hangxin Liu", "Biao Hou", "Yao Su", "Jingwen Zhang" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) is widely used for humanoid control, with on-policy methods such as Proximal Policy Optimization (PPO) enabling robust training via large-scale parallel simulation and, in some cases, zero-shot deployment to real robots. However, the low sample efficiency of on-policy algorithms limits safe adaptation to new environments. Although off-policy RL and model-based RL have shown improved sample efficiency, the gap between large-scale pretraining and efficient finetuning on humanoids still exists. In this paper, we find that off-policy Soft Actor-Critic (SAC), with large-batch update and a high Update-To-Data (UTD) ratio, reliably supports large-scale pretraining of humanoid locomotion policies, achieving zero-shot deployment on real robots. For adaptation, we demonstrate that these SAC-pretrained policies can be finetuned in new environments and out-of-distribution tasks using model-based methods. Data collection in the new environment executes a deterministic policy while stochastic exploration is instead confined to a physics-informed world model. This separation mitigates the risks of random exploration during adaptation while preserving exploratory coverage for improvement. Overall, the approach couples the wall-clock efficiency of large-scale simulation during pretraining with the sample efficiency of model-based learning during fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08236", "title": "When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning", "authors": [ "Shoubin Yu", "Yue Zhang", "Zun Wang", "Jaehong Yoon", "Huaxiu Yao", "Mingyu Ding", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Despite rapid progress in Multimodal Large Language Models (MLLMs), visual spatial reasoning remains unreliable when correct answers depend on how a scene would appear under unseen or alternative viewpoints. Recent work addresses this by augmenting reasoning with world models for visual imagination, but questions such as when imagination is actually necessary, how much of it is beneficial, and when it becomes harmful, remain poorly understood. In practice, indiscriminate imagination can increase computation and even degrade performance by introducing misleading evidence. In this work, we present an in-depth analysis of test-time visual imagination as a controllable resource for spatial reasoning. We study when static visual evidence is sufficient, when imagination improves reasoning, and how excessive or unnecessary imagination affects accuracy and efficiency. To support this analysis, we introduce AVIC, an adaptive test-time framework with world models that explicitly reasons about the sufficiency of current visual evidence before selectively invoking and scaling visual imagination. Across spatial reasoning benchmarks (SAT, MMSI) and an embodied navigation benchmark (R2R), our results reveal clear scenarios where imagination is critical, marginal, or detrimental, and show that selective control can match or outperform fixed imagination strategies with substantially fewer world-model calls and language tokens. Overall, our findings highlight the importance of analyzing and controlling test-time imagination for efficient and reliable spatial reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.06445", "title": "ECO: Energy-Constrained Optimization with Reinforcement Learning for Humanoid Walking", "authors": [ "Weidong Huang", "Jingwen Zhang", "Jiongye Li", "Shibowen Zhang", "Jiayang Wu", "Jiayi Wang", "Hangxin Liu", "Yaodong Yang", "Yao Su" ], "github": "", "project_page": "", "abstract": "Achieving stable and energy-efficient locomotion is essential for humanoid robots to operate continuously in real-world applications. Existing MPC and RL approaches often rely on energy-related metrics embedded within a multi-objective optimization framework, which require extensive hyperparameter tuning and often result in suboptimal policies. To address these challenges, we propose ECO (Energy-Constrained Optimization), a constrained RL framework that separates energy-related metrics from rewards, reformulating them as explicit inequality constraints. This method provides a clear and interpretable physical representation of energy costs, enabling more efficient and intuitive hyperparameter tuning for improved energy efficiency. ECO introduces dedicated constraints for energy consumption and reference motion, enforced by the Lagrangian method, to achieve stable, symmetric, and energy-efficient walking for humanoid robots. We evaluated ECO against MPC, standard RL with reward shaping, and four state-of-the-art constrained RL methods. Experiments, including sim-to-sim and sim-to-real transfers on the kid-sized humanoid robot BRUCE, demonstrate that ECO significantly reduces energy consumption compared to baselines while maintaining robust walking performance. These results highlight a substantial advancement in energy-efficient humanoid locomotion. All experimental demonstrations can be found on the project website: https://sites.google.com/view/eco-humanoid.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07075", "title": "LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning", "authors": [ "Xinwu Ye", "Yicheng Mao", "Jia Zhang", "Yimeng Liu", "Li Hao", "Fang Wu", "Zhiwei Li", "Yuxuan Liao", "Zehong Wang", "Zhiyuan Liu", "Zhenfei Yin", "Li Yuan", "Philip Torr", "Huan Sun", "Xiangxiang Zeng", "Mengdi Wang", "Le Cong", "Shenghua Gao", "Xiangru Tang" ], "github": "", "project_page": "", "abstract": "Chemical large language models (LLMs) predominantly rely on explicit Chain-of-Thought (CoT) in natural language to perform complex reasoning. However, chemical reasoning is inherently continuous and structural, and forcing it into discrete linguistic tokens introduces a fundamental representation mismatch that constrains both efficiency and performance. We introduce LatentChem, a latent reasoning interface that decouples chemical computation from textual generation, enabling models to perform multi-step reasoning directly in continuous latent space while emitting language only for final outputs. Remarkably, we observe a consistent emergent behavior: when optimized solely for task success, models spontaneously internalize reasoning, progressively abandoning verbose textual derivations in favor of implicit latent computation. This shift is not merely stylistic but computationally advantageous. Across diverse chemical reasoning benchmarks, LatentChem achieves a 59.88\\% non-tie win rate over strong CoT-based baselines on ChemCoTBench, while delivering a 10.84times average inference speedup. Our results provide empirical evidence that chemical reasoning is more naturally and effectively realized as continuous latent dynamics rather than discretized linguistic trajectories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07845", "title": "Recurrent-Depth VLA: Implicit Test-Time Compute Scaling of Vision-Language-Action Models via Latent Iterative Reasoning", "authors": [ "Yalcin Tur", "Jalal Naghiyev", "Haoquan Fang", "Wei-Chuan Tsai", "Jiafei Duan", "Dieter Fox", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Current Vision-Language-Action (VLA) models rely on fixed computational depth, expending the same amount of compute on simple adjustments and complex multi-step manipulation. While Chain-of-Thought (CoT) prompting enables variable computation, it scales memory linearly and is ill-suited for continuous action spaces. We introduce Recurrent-Depth VLA (RD-VLA), an architecture that achieves computational adaptivity via latent iterative refinement rather than explicit token generation. RD-VLA employs a recurrent, weight-tied action head that supports arbitrary inference depth with a constant memory footprint. The model is trained using truncated backpropagation through time (TBPTT) to efficiently supervise the refinement process. At inference, RD-VLA dynamically allocates compute using an adaptive stopping criterion based on latent convergence. Experiments on challenging manipulation tasks show that recurrent depth is critical: tasks that fail entirely (0 percent success) with single-iteration inference exceed 90 percent success with four iterations, while simpler tasks saturate rapidly. RD-VLA provides a scalable path to test-time compute in robotics, replacing token-based reasoning with latent reasoning to achieve constant memory usage and up to 80x inference speedup over prior reasoning-based VLA models. Project page: https://rd-vla.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08439", "title": "Demo-ICL: In-Context Learning for Procedural Video Knowledge Acquisition", "authors": [ "Yuhao Dong", "Shulin Tian", "Shuai Liu", "Shuangrui Ding", "Yuhang Zang", "Xiaoyi Dong", "Yuhang Cao", "Jiaqi Wang", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Despite the growing video understanding capabilities of recent Multimodal Large Language Models (MLLMs), existing video benchmarks primarily assess understanding based on models' static, internal knowledge, rather than their ability to learn and adapt from dynamic, novel contexts from few examples. To bridge this gap, we present Demo-driven Video In-Context Learning, a novel task focused on learning from in-context demonstrations to answer questions about the target videos. Alongside this, we propose Demo-ICL-Bench, a challenging benchmark designed to evaluate demo-driven video in-context learning capabilities. Demo-ICL-Bench is constructed from 1200 instructional YouTube videos with associated questions, from which two types of demonstrations are derived: (i) summarizing video subtitles for text demonstration; and (ii) corresponding instructional videos as video demonstrations. To effectively tackle this new challenge, we develop Demo-ICL, an MLLM with a two-stage training strategy: video-supervised fine-tuning and information-assisted direct preference optimization, jointly enhancing the model's ability to learn from in-context examples. Extensive experiments with state-of-the-art MLLMs confirm the difficulty of Demo-ICL-Bench, demonstrate the effectiveness of Demo-ICL, and thereby unveil future research directions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08676", "title": "LLaDA2.1: Speeding Up Text Diffusion via Token Editing", "authors": [ "Tiwei Bie", "Maosong Cao", "Xiang Cao", "Bingsen Chen", "Fuyuan Chen", "Kun Chen", "Lun Du", "Daozhuo Feng", "Haibo Feng", "Mingliang Gong", "Zhuocheng Gong", "Yanmei Gu", "Jian Guan", "Kaiyuan Guan", "Hongliang He", "Zenan Huang", "Juyong Jiang", "Zhonghui Jiang", "Zhenzhong Lan", "Chengxi Li", "Jianguo Li", "Zehuan Li", "Huabin Liu", "Lin Liu", "Guoshan Lu", "Yuan Lu", "Yuxin Ma", "Xingyu Mou", "Zhenxuan Pan", "Kaida Qiu", "Yuji Ren", "Jianfeng Tan", "Yiding Tian", "Zian Wang", "Lanning Wei", "Tao Wu", "Yipeng Xing", "Wentao Ye", "Liangyu Zha", "Tianze Zhang", "Xiaolu Zhang", "Junbo Zhao", "Da Zheng", "Hao Zhong", "Wanli Zhong", "Jun Zhou", "Junlin Zhou", "Liwang Zhu", "Muzhi Zhu", "Yihong Zhuang" ], "github": "", "project_page": "", "abstract": "While LLaDA2.0 showcased the scaling potential of 100B-level block-diffusion models and their inherent parallelization, the delicate equilibrium between decoding speed and generation quality has remained an elusive frontier. Today, we unveil LLaDA2.1, a paradigm shift designed to transcend this trade-off. By seamlessly weaving Token-to-Token (T2T) editing into the conventional Mask-to-Token (M2T) scheme, we introduce a joint, configurable threshold-decoding scheme. This structural innovation gives rise to two distinct personas: the Speedy Mode (S Mode), which audaciously lowers the M2T threshold to bypass traditional constraints while relying on T2T to refine the output; and the Quality Mode (Q Mode), which leans into conservative thresholds to secure superior benchmark performances with manageable efficiency degrade. Furthering this evolution, underpinned by an expansive context window, we implement the first large-scale Reinforcement Learning (RL) framework specifically tailored for dLLMs, anchored by specialized techniques for stable gradient estimation. This alignment not only sharpens reasoning precision but also elevates instruction-following fidelity, bridging the chasm between diffusion dynamics and complex human intent. We culminate this work by releasing LLaDA2.1-Mini (16B) and LLaDA2.1-Flash (100B). Across 33 rigorous benchmarks, LLaDA2.1 delivers strong task performance and lightning-fast decoding speed. Despite its 100B volume, on coding tasks it attains an astounding 892 TPS on HumanEval+, 801 TPS on BigCodeBench, and 663 TPS on LiveCodeBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.09022", "title": "WorldCompass: Reinforcement Learning for Long-Horizon World Models", "authors": [ "Zehan Wang", "Tengfei Wang", "Haiyu Zhang", "Xuhui Zuo", "Junta Wu", "Haoyuan Wang", "Wenqiang Sun", "Zhenwei Wang", "Chenjie Cao", "Hengshuang Zhao", "Chunchao Guo", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "This work presents WorldCompass, a novel Reinforcement Learning (RL) post-training framework for the long-horizon, interactive video-based world models, enabling them to explore the world more accurately and consistently based on interaction signals. To effectively \"steer\" the world model's exploration, we introduce three core innovations tailored to the autoregressive video generation paradigm: 1) Clip-level rollout Strategy: We generate and evaluate multiple samples at a single target clip, which significantly boosts rollout efficiency and provides fine-grained reward signals. 2) Complementary Reward Functions: We design reward functions for both interaction-following accuracy and visual quality, which provide direct supervision and effectively suppress reward-hacking behaviors. 3) Efficient RL Algorithm: We employ the negative-aware fine-tuning strategy coupled with various efficiency optimizations to efficiently and effectively enhance model capacity. Evaluations on the SoTA open-source world model, WorldPlay, demonstrate that WorldCompass significantly improves interaction accuracy and visual fidelity across various scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07085", "title": "QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining", "authors": [ "Jun Han", "Shuo Zhang", "Wei Li", "Zhi Yang", "Yifan Dong", "Tu Hu", "Jialuo Yuan", "Xiaomin Yu", "Yumo Zhu", "Fangqi Lou", "Xin Guo", "Zhaowei Liu", "Tianyi Jiang", "Ruichuan An", "Jingping Liu", "Biao Wu", "Rongze Chen", "Kunyi Wang", "Yifan Wang", "Sen Hu", "Xinbing Kong", "Liwen Zhang", "Ronghao Chen", "Huacan Wang" ], "github": "", "project_page": "", "abstract": "Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mining automation, they often lack controllable multi-round search and reliable reuse of validated experience. To address these challenges, we propose QuantaAlpha, an evolutionary alpha mining framework that treats each end-to-end mining run as a trajectory and improves factors through trajectory-level mutation and crossover operations. QuantaAlpha localizes suboptimal steps in each trajectory for targeted revision and recombines complementary high-reward segments to reuse effective patterns, enabling structured exploration and refinement across mining iterations. During factor generation, QuantaAlpha enforces semantic consistency across the hypothesis, factor expression, and executable code, while constraining the complexity and redundancy of the generated factor to mitigate crowding. Extensive experiments on the China Securities Index 300 (CSI 300) demonstrate consistent gains over strong baseline models and prior agentic systems. When utilizing GPT-5.2, QuantaAlpha achieves an Information Coefficient (IC) of 0.1501, with an Annualized Rate of Return (ARR) of 27.75% and a Maximum Drawdown (MDD) of 7.98%. Moreover, factors mined on CSI 300 transfer effectively to the China Securities Index 500 (CSI 500) and the Standard & Poor's 500 Index (S&P 500), delivering 160% and 137% cumulative excess return over four years, respectively, which indicates strong robustness of QuantaAlpha under market distribution shifts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08794", "title": "MOVA: Towards Scalable and Synchronized Video-Audio Generation", "authors": [ "SII-OpenMOSS Team", "Donghua Yu", "Mingshu Chen", "Qi Chen", "Qi Luo", "Qianyi Wu", "Qinyuan Cheng", "Ruixiao Li", "Tianyi Liang", "Wenbo Zhang", "Wenming Tu", "Xiangyu Peng", "Yang Gao", "Yanru Huo", "Ying Zhu", "Yinze Luo", "Yiyang Zhang", "Yuerong Song", "Zhe Xu", "Zhiyu Zhang", "Chenchen Yang", "Cheng Chang", "Chushu Zhou", "Hanfu Chen", "Hongnan Ma", "Jiaxi Li", "Jingqi Tong", "Junxi Liu", "Ke Chen", "Shimin Li", "Songlin Wang", "Wei Jiang", "Zhaoye Fei", "Zhiyuan Ning", "Chunguo Li", "Chenhui Li", "Ziwei He", "Zengfeng Huang", "Xie Chen", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Audio is indispensable for real-world video, yet generation models have largely overlooked audio components. Current approaches to producing audio-visual content often rely on cascaded pipelines, which increase cost, accumulate errors, and degrade overall quality. While systems such as Veo 3 and Sora 2 emphasize the value of simultaneous generation, joint multimodal modeling introduces unique challenges in architecture, data, and training. Moreover, the closed-source nature of existing systems limits progress in the field. In this work, we introduce MOVA (MOSS Video and Audio), an open-source model capable of generating high-quality, synchronized audio-visual content, including realistic lip-synced speech, environment-aware sound effects, and content-aligned music. MOVA employs a Mixture-of-Experts (MoE) architecture, with a total of 32B parameters, of which 18B are active during inference. It supports IT2VA (Image-Text to Video-Audio) generation task. By releasing the model weights and code, we aim to advance research and foster a vibrant community of creators. The released codebase features comprehensive support for efficient inference, LoRA fine-tuning, and prompt enhancement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08990", "title": "InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery", "authors": [ "Shiyang Feng", "Runmin Ma", "Xiangchao Yan", "Yue Fan", "Yusong Hu", "Songtao Huang", "Shuaiyu Zhang", "Zongsheng Cao", "Tianshuo Peng", "Jiakang Yuan", "Zijie Guo", "Zhijie Zhong", "Shangheng Du", "Weida Wang", "Jinxin Shi", "Yuhao Zhou", "Xiaohan He", "Zhiyin Yu", "Fangchen Yu", "Qihao Zheng", "Jiamin Wu", "Mianxin Liu", "Chi Zhang", "Shaowei Hou", "Shuya Li", "Yankai Jiang", "Wenjie Lou", "Lilong Wang", "Zifu Wang", "Jiong Wang", "Wanghan Xu", "Yue Deng", "Dongrui Liu", "Yiheng Wang", "Wenlong Zhang", "Fenghua Ling", "Shufei Zhang", "Xiaosong Wang", "Shuangjia Zheng", "Xun Huang", "Siqi Sun", "Shuyue Hu", "Peng Ye", "Chunfeng Song", "Bin Wang", "Conghui He", "Yihao Liu", "Xin Li", "Qibin Hou", "Tao Chen", "Xiangyu Yue", "Bin Wang", "Liang He", "Dahua Lin", "Bowen Zhou", "Bo Zhang", "Lei Bai" ], "github": "", "project_page": "", "abstract": "We introduce InternAgent-1.5, a unified system designed for end-to-end scientific discovery across computational and empirical domains. The system is built on a structured architecture composed of three coordinated subsystems for generation, verification, and evolution. These subsystems are supported by foundational capabilities for deep research, solution optimization, and long horizon memory. The architecture allows InternAgent-1.5 to operate continuously across extended discovery cycles while maintaining coherent and improving behavior. It also enables the system to coordinate computational modeling and laboratory experimentation within a single unified system. We evaluate InternAgent-1.5 on scientific reasoning benchmarks such as GAIA, HLE, GPQA, and FrontierScience, and the system achieves leading performance that demonstrates strong foundational capabilities. Beyond these benchmarks, we further assess two categories of discovery tasks. In algorithm discovery tasks, InternAgent-1.5 autonomously designs competitive methods for core machine learning problems. In empirical discovery tasks, it executes complete computational or wet lab experiments and produces scientific findings in earth, life, biological, and physical domains. Overall, these results show that InternAgent-1.5 provides a general and scalable framework for autonomous scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08145", "title": "Reliable and Responsible Foundation Models: A Comprehensive Survey", "authors": [ "Xinyu Yang", "Junlin Han", "Rishi Bommasani", "Jinqi Luo", "Wenjie Qu", "Wangchunshu Zhou", "Adel Bibi", "Xiyao Wang", "Jaehong Yoon", "Elias Stengel-Eskin", "Shengbang Tong", "Lingfeng Shen", "Rafael Rafailov", "Runjia Li", "Zhaoyang Wang", "Yiyang Zhou", "Chenhang Cui", "Yu Wang", "Wenhao Zheng", "Huichi Zhou", "Jindong Gu", "Zhaorun Chen", "Peng Xia", "Tony Lee", "Thomas Zollo", "Vikash Sehwag", "Jixuan Leng", "Jiuhai Chen", "Yuxin Wen", "Huan Zhang", "Zhun Deng", "Linjun Zhang", "Pavel Izmailov", "Pang Wei Koh", "Yulia Tsvetkov", "Andrew Wilson", "Jiaheng Zhang", "James Zou", "Cihang Xie", "Hao Wang", "Philip Torr", "Julian McAuley", "David Alvarez-Melis", "Florian Tram\u00e8r", "Kaidi Xu", "Suman Jana", "Chris Callison-Burch", "Rene Vidal", "Filippos Kokkinos", "Mohit Bansal", "Beidi Chen", "Huaxiu Yao" ], "github": "", "project_page": "", "abstract": "Foundation models, including Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Image Generative Models (i.e, Text-to-Image Models and Image-Editing Models), and Video Generative Models, have become essential tools with broad applications across various domains such as law, medicine, education, finance, science, and beyond. As these models see increasing real-world deployment, ensuring their reliability and responsibility has become critical for academia, industry, and government. This survey addresses the reliable and responsible development of foundation models. We explore critical issues, including bias and fairness, security and privacy, uncertainty, explainability, and distribution shift. Our research also covers model limitations, such as hallucinations, as well as methods like alignment and Artificial Intelligence-Generated Content (AIGC) detection. For each area, we review the current state of the field and outline concrete future research directions. Additionally, we discuss the intersections between these areas, highlighting their connections and shared challenges. We hope our survey fosters the development of foundation models that are not only powerful but also ethical, trustworthy, reliable, and socially responsible.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08829", "title": "WildReward: Learning Reward Models from In-the-Wild Human Interactions", "authors": [ "Hao Peng", "Yunjia Qi", "Xiaozhi Wang", "Zijun Yao", "Lei Hou", "Juanzi Li" ], "github": "https://github.com/THU-KEG/WildReward", "project_page": "", "abstract": "Reward models (RMs) are crucial for the training of large language models (LLMs), yet they typically rely on large-scale human-annotated preference pairs. With the widespread deployment of LLMs, in-the-wild interactions have emerged as a rich source of implicit reward signals. This raises the question: Can we develop reward models directly from in-the-wild interactions? In this work, we explore this possibility by adopting WildChat as an interaction source and proposing a pipeline to extract reliable human feedback, yielding 186k high-quality instances for training WildReward via ordinal regression directly on user feedback without preference pairs. Extensive experiments demonstrate that WildReward achieves comparable or even superior performance compared to conventional reward models, with improved calibration and cross-sample consistency. We also observe that WildReward benefits directly from user diversity, where more users yield stronger reward models. Finally, we apply WildReward to online DPO training and observe significant improvements across various tasks. Code and data are released at https://github.com/THU-KEG/WildReward.", "github_manual": "", "github_regex": "https://github.com/THU-KEG/WildReward", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08808", "title": "How2Everything: Mining the Web for How-To Procedures to Evaluate and Improve LLMs", "authors": [ "Yapei Chang", "Kyle Lo", "Mohit Iyyer", "Luca Soldaini" ], "github": "", "project_page": "", "abstract": "Generating step-by-step \"how-to\" procedures is a key LLM capability: how-to advice is commonly requested in chatbots, and step-by-step planning is critical for reasoning over complex tasks. Yet, measuring and improving procedural validity at scale on real-world tasks remains challenging and understudied. To address this, we introduce How2Everything, a scalable framework to evaluate and improve goal-conditioned procedure generation. Our framework includes How2Mine, which mines 351K procedures from 980K web pages across 14 topics and readily scales to larger corpora. From this pool we build How2Bench, a 7K-example evaluation set balanced across topics. To reliably score model outputs, we develop How2Score, an evaluation protocol that uses an LLM judge to detect whether a generation contains any critical failure that would prevent achieving the goal. For low-cost, reproducible evaluation, we distill a frontier model into an open 8B model, achieving 80.5% agreement with human annotators. How2Bench reveals clear scaling trends across model sizes and training stages, providing signal early in pretraining. Finally, RL using How2Score as a reward improves performance on How2Bench by >10 points across three models without systematic regressions on standard benchmarks, with gains robust to superficial source-document memorization or format compliance. Taken together, How2Everything shows how pretraining web data can support a closed loop of capability evaluation and improvement at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08543", "title": "GISA: A Benchmark for General Information-Seeking Assistant", "authors": [ "Yutao Zhu", "Xingshuo Zhang", "Maosen Zhang", "Jiajie Jin", "Liancheng Zhang", "Xiaoshuai Song", "Kangzhi Zhao", "Wencong Zeng", "Ruiming Tang", "Han Li", "Ji-Rong Wen", "Zhicheng Dou" ], "github": "", "project_page": "", "abstract": "The advancement of large language models (LLMs) has significantly accelerated the development of search agents capable of autonomously gathering information through multi-turn web interactions. Various benchmarks have been proposed to evaluate such agents. However, existing benchmarks often construct queries backward from answers, producing unnatural tasks misaligned with real-world needs. Moreover, these benchmarks tend to focus on either locating specific information or aggregating information from multiple sources, while relying on static answer sets prone to data contamination. To bridge these gaps, we introduce GISA, a benchmark for General Information-Seeking Assistants comprising 373 human-crafted queries that reflect authentic information-seeking scenarios. GISA features four structured answer formats (item, set, list, and table), enabling deterministic evaluation. It integrates both deep reasoning and broad information aggregation within unified tasks, and includes a live subset with periodically updated answers to resist memorization. Notably, GISA provides complete human search trajectories for every query, offering gold-standard references for process-level supervision and imitation learning. Experiments on mainstream LLMs and commercial search products reveal that even the best-performing model achieves only 19.30\\% exact match score, with performance notably degrading on tasks requiring complex planning and comprehensive information gathering. These findings highlight substantial room for future improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07775", "title": "Rolling Sink: Bridging Limited-Horizon Training and Open-Ended Testing in Autoregressive Video Diffusion", "authors": [ "Haodong Li", "Shaoteng Liu", "Zhe Lin", "Manmohan Chandraker" ], "github": "", "project_page": "", "abstract": "Recently, autoregressive (AR) video diffusion models has achieved remarkable performance. However, due to their limited training durations, a train-test gap emerges when testing at longer horizons, leading to rapid visual degradations. Following Self Forcing, which studies the train-test gap within the training duration, this work studies the train-test gap beyond the training duration, i.e., the gap between the limited horizons during training and open-ended horizons during testing. Since open-ended testing can extend beyond any finite training window, and long-video training is computationally expensive, we pursue a training-free solution to bridge this gap. To explore a training-free solution, we conduct a systematic analysis of AR cache maintenance. These insights lead to Rolling Sink. Built on Self Forcing (trained on only 5s clips), Rolling Sink effectively scales the AR video synthesis to ultra-long durations (e.g., 5-30 minutes at 16 FPS) at test time, with consistent subjects, stable colors, coherent structures, and smooth motions. As demonstrated by extensive experiments, Rolling Sink achieves superior long-horizon visual fidelity and temporal consistency compared to SOTA baselines. Project page: https://rolling-sink.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07090", "title": "Concept-Aware Privacy Mechanisms for Defending Embedding Inversion Attacks", "authors": [ "Yu-Che Tsai", "Hsiang Hsiao", "Kuan-Yu Chen", "Shou-De Lin" ], "github": "", "project_page": "", "abstract": "Text embeddings enable numerous NLP applications but face severe privacy risks from embedding inversion attacks, which can expose sensitive attributes or reconstruct raw text. Existing differential privacy defenses assume uniform sensitivity across embedding dimensions, leading to excessive noise and degraded utility. We propose SPARSE, a user-centric framework for concept-specific privacy protection in text embeddings. SPARSE combines (1) differentiable mask learning to identify privacy-sensitive dimensions for user-defined concepts, and (2) the Mahalanobis mechanism that applies elliptical noise calibrated by dimension sensitivity. Unlike traditional spherical noise injection, SPARSE selectively perturbs privacy-sensitive dimensions while preserving non-sensitive semantics. Evaluated across six datasets with three embedding models and attack scenarios, SPARSE consistently reduces privacy leakage while achieving superior downstream performance compared to state-of-the-art DP methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07040", "title": "Aster: Autonomous Scientific Discovery over 20x Faster Than Existing Methods", "authors": [ "Emmett Bicker" ], "github": "", "project_page": "", "abstract": "We introduce Aster, an AI agent for autonomous scientific discovery capable of operating over 20 times faster than existing frameworks. Given a task, an initial program, and a script to evaluate the performance of the program, Aster iteratively improves the program, often leading to new state-of-the-art performances. Aster's significant reduction in the number of iterations required for novel discovery expands the domain of tractable problems to include tasks with long evaluation durations, such as multi-hour machine learning training runs. We applied Aster to problems in mathematics, GPU kernel engineering, biology, neuroscience, and language model training. More specifically: the Erdos minimum overlap problem, optimizing the TriMul kernel, a single-cell analysis denoising problem, training a neural activity prediction model to perform well on ZAPBench, and the NanoGPT Speedrun Competition. Aster attains SOTA results in every task, except for ZAPBench, where it matches the performance of the best human solution with less than 1/190th of the compute. Aster is accessible via a web interface and API at asterlab.ai.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07055", "title": "Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?", "authors": [ "Pingyue Zhang", "Zihan Huang", "Yue Wang", "Jieyu Zhang", "Letian Xue", "Zihan Wang", "Qineng Wang", "Keshigeyan Chandrasegaran", "Ruohan Zhang", "Yejin Choi", "Ranjay Krishna", "Jiajun Wu", "Li Fei-Fei", "Manling Li" ], "github": "", "project_page": "", "abstract": "Spatial embodied intelligence requires agents to act to acquire information under partial observability. While multimodal foundation models excel at passive perception, their capacity for active, self-directed exploration remains understudied. We propose Theory of Space, defined as an agent's ability to actively acquire information through self-directed, active exploration and to construct, revise, and exploit a spatial belief from sequential, partial observations. We evaluate this through a benchmark where the goal is curiosity-driven exploration to build an accurate cognitive map. A key innovation is spatial belief probing, which prompts models to reveal their internal spatial representations at each step. Our evaluation of state-of-the-art models reveals several critical bottlenecks. First, we identify an Active-Passive Gap, where performance drops significantly when agents must autonomously gather information. Second, we find high inefficiency, as models explore unsystematically compared to program-based proxies. Through belief probing, we diagnose that while perception is an initial bottleneck, global beliefs suffer from instability that causes spatial knowledge to degrade over time. Finally, using a false belief paradigm, we uncover Belief Inertia, where agents fail to update obsolete priors with new evidence. This issue is present in text-based agents but is particularly severe in vision-based models. Our findings suggest that current foundation models struggle to maintain coherent, revisable spatial beliefs during active exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08961", "title": "MotionCrafter: Dense Geometry and Motion Reconstruction with a 4D VAE", "authors": [ "Ruijie Zhu", "Jiahao Lu", "Wenbo Hu", "Xiaoguang Han", "Jianfei Cai", "Ying Shan", "Chuanxia Zheng" ], "github": "", "project_page": "", "abstract": "We introduce MotionCrafter, a video diffusion-based framework that jointly reconstructs 4D geometry and estimates dense motion from a monocular video. The core of our method is a novel joint representation of dense 3D point maps and 3D scene flows in a shared coordinate system, and a novel 4D VAE to effectively learn this representation. Unlike prior work that forces the 3D value and latents to align strictly with RGB VAE latents-despite their fundamentally different distributions-we show that such alignment is unnecessary and leads to suboptimal performance. Instead, we introduce a new data normalization and VAE training strategy that better transfers diffusion priors and greatly improves reconstruction quality. Extensive experiments across multiple datasets demonstrate that MotionCrafter achieves state-of-the-art performance in both geometry reconstruction and dense scene flow estimation, delivering 38.64% and 25.0% improvements in geometry and motion reconstruction, respectively, all without any post-optimization. Project page: https://ruijiezhu94.github.io/MotionCrafter_Page", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07970", "title": "Learning-guided Kansa collocation for forward and inverse PDEs beyond linearity", "authors": [ "Zheyuan Hu", "Weitao Chen", "Cengiz \u00d6ztireli", "Chenliang Zhou", "Fangcheng Zhong" ], "github": "", "project_page": "", "abstract": "Partial Differential Equations are precise in modelling the physical, biological and graphical phenomena. However, the numerical methods suffer from the curse of dimensionality, high computation costs and domain-specific discretization. We aim to explore pros and cons of different PDE solvers, and apply them to specific scientific simulation problems, including forwarding solution, inverse problems and equations discovery. In particular, we extend the recent CNF (NeurIPS 2023) framework solver to multi-dependent-variable and non-linear settings, together with down-stream applications. The outcomes include implementation of selected methods, self-tuning techniques, evaluation on benchmark problems and a comprehensive survey of neural PDE solvers and scientific simulation applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07803", "title": "SoulX-Singer: Towards High-Quality Zero-Shot Singing Voice Synthesis", "authors": [ "Jiale Qian", "Hao Meng", "Tian Zheng", "Pengcheng Zhu", "Haopeng Lin", "Yuhang Dai", "Hanke Xie", "Wenxiao Cao", "Ruixuan Shang", "Jun Wu", "Hongmei Liu", "Hanlin Wen", "Jian Zhao", "Zhonglin Jiang", "Yong Chen", "Shunshun Yin", "Ming Tao", "Jianguo Wei", "Lei Xie", "Xinsheng Wang" ], "github": "https://github.com/Soul-AILab/SoulX-Singer", "project_page": "", "abstract": "While recent years have witnessed rapid progress in speech synthesis, open-source singing voice synthesis (SVS) systems still face significant barriers to industrial deployment, particularly in terms of robustness and zero-shot generalization. In this report, we introduce SoulX-Singer, a high-quality open-source SVS system designed with practical deployment considerations in mind. SoulX-Singer supports controllable singing generation conditioned on either symbolic musical scores (MIDI) or melodic representations, enabling flexible and expressive control in real-world production workflows. Trained on more than 42,000 hours of vocal data, the system supports Mandarin Chinese, English, and Cantonese and consistently achieves state-of-the-art synthesis quality across languages under diverse musical conditions. Furthermore, to enable reliable evaluation of zero-shot SVS performance in practical scenarios, we construct SoulX-Singer-Eval, a dedicated benchmark with strict training-test disentanglement, facilitating systematic assessment in zero-shot settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Soul-AILab/SoulX-Singer", "github_api_added_by": "auto" }, { "date": "2026-02-10", "arxiv_id": "2602.06025", "title": "Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory", "authors": [ "Haozhen Zhang", "Haodong Yue", "Tao Feng", "Quanyu Long", "Jianzhu Bao", "Bowen Jin", "Weizhi Zhang", "Xiao Li", "Jiaxuan You", "Chengwei Qin", "Wenya Wang" ], "github": "https://github.com/ViktorAxelsen/BudgetMem", "project_page": "", "abstract": "Memory is increasingly central to Large Language Model (LLM) agents operating beyond a single context window, yet most existing systems rely on offline, query-agnostic memory construction that can be inefficient and may discard query-critical information. Although runtime memory utilization is a natural alternative, prior work often incurs substantial overhead and offers limited explicit control over the performance-cost trade-off. In this work, we present BudgetMem, a runtime agent memory framework for explicit, query-aware performance-cost control. BudgetMem structures memory processing as a set of memory modules, each offered in three budget tiers (i.e., Low/Mid/High). A lightweight router performs budget-tier routing across modules to balance task performance and memory construction cost, which is implemented as a compact neural policy trained with reinforcement learning. Using BudgetMem as a unified testbed, we study three complementary strategies for realizing budget tiers: implementation (method complexity), reasoning (inference behavior), and capacity (module model size). Across LoCoMo, LongMemEval, and HotpotQA, BudgetMem surpasses strong baselines when performance is prioritized (i.e., high-budget setting), and delivers better accuracy-cost frontiers under tighter budgets. Moreover, our analysis disentangles the strengths and weaknesses of different tiering strategies, clarifying when each axis delivers the most favorable trade-offs under varying budget regimes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ViktorAxelsen/BudgetMem", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08222", "title": "Weak-Driven Learning: How Weak Agents make Strong Agents Stronger", "authors": [ "Zehao Chen", "Gongxun Li", "Tianxiang Ai", "Yifei Li", "Zixuan Huang", "Wang Zhou", "Fuzhen Zhuang", "Xianglong Liu", "Jianxin Li", "Deqing Wang", "Yikun Ban" ], "github": "https://github.com/chenzehao82/Weak-Driven-Learning.git", "project_page": "", "abstract": "As post-training optimization becomes central to improving large language models, we observe a persistent saturation bottleneck: once models grow highly confident, further training yields diminishing returns. While existing methods continue to reinforce target predictions, we find that informative supervision signals remain latent in models' own historical weak states. Motivated by this observation, we propose WMSS (Weak Agents Can Make Strong Agents Stronger), a post-training paradigm that leverages weak checkpoints to guide continued optimization. By identifying recoverable learning gaps via entropy dynamics and reinforcing them through compensatory learning, WMSS enables strong agents to improve beyond conventional post-training saturation. Experiments on mathematical reasoning and code generation datasets show that agents trained with our approach achieve effective performance improvements, while incurring zero additional inference cost.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/chenzehao82/Weak-Driven-Learning.git", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.09007", "title": "GEBench: Benchmarking Image Generation Models as GUI Environments", "authors": [ "Haodong Li", "Jingwei Wu", "Quan Sun", "Guopeng Li", "Juanxi Tian", "Huanyu Zhang", "Yanlin Lai", "Ruichuan An", "Hongbo Peng", "Yuhong Dai", "Chenxi Li", "Chunmei Qing", "Jia Wang", "Ziyang Meng", "Zheng Ge", "Xiangyu Zhang", "Daxin Jiang" ], "github": "https://github.com/stepfun-ai/GEBench", "project_page": "", "abstract": "Recent advancements in image generation models have enabled the prediction of future Graphical User Interface (GUI) states based on user instructions. However, existing benchmarks primarily focus on general domain visual fidelity, leaving the evaluation of state transitions and temporal coherence in GUI-specific contexts underexplored. To address this gap, we introduce GEBench, a comprehensive benchmark for evaluating dynamic interaction and temporal coherence in GUI generation. GEBench comprises 700 carefully curated samples spanning five task categories, covering both single-step interactions and multi-step trajectories across real-world and fictional scenarios, as well as grounding point localization. To support systematic evaluation, we propose GE-Score, a novel five-dimensional metric that assesses Goal Achievement, Interaction Logic, Content Consistency, UI Plausibility, and Visual Quality. Extensive evaluations on current models indicate that while they perform well on single-step transitions, they struggle significantly with maintaining temporal coherence and spatial grounding over longer interaction sequences. Our findings identify icon interpretation, text rendering, and localization precision as critical bottlenecks. This work provides a foundation for systematic assessment and suggests promising directions for future research toward building high-fidelity generative GUI environments. The code is available at: https://github.com/stepfun-ai/GEBench.", "github_manual": "", "github_regex": "https://github.com/stepfun-ai/GEBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07796", "title": "Thinking Makes LLM Agents Introverted: How Mandatory Thinking Can Backfire in User-Engaged Agents", "authors": [ "Jiatong Li", "Changdae Oh", "Hyeong Kyu Choi", "Jindong Wang", "Sharon Li" ], "github": "https://github.com/deeplearning-wisc/Thinking-Agent", "project_page": "", "abstract": "Eliciting reasoning has emerged as a powerful technique for improving the performance of large language models (LLMs) on complex tasks by inducing thinking. However, their effectiveness in realistic user-engaged agent scenarios remains unclear. In this paper, we conduct a comprehensive study on the effect of explicit thinking in user-engaged LLM agents. Our experiments span across seven models, three benchmarks, and two thinking instantiations, and we evaluate them through both a quantitative response taxonomy analysis and qualitative failure propagation case studies. Contrary to expectations, we find that mandatory thinking often backfires on agents in user-engaged settings, causing anomalous performance degradation across various LLMs. Our key finding reveals that thinking makes agents more ``introverted'' by shortening responses and reducing information disclosure to users, which weakens agent-user information exchange and leads to downstream task failures. Furthermore, we demonstrate that explicitly prompting for information disclosure reliably improves performance across diverse model families, suggesting that proactive transparency is a vital lever for agent optimization. Overall, our study suggests that information transparency awareness is a crucial yet underexplored perspective for the future design of reasoning agents in real-world scenarios. Our code is available at https://github.com/deeplearning-wisc/Thinking-Agent.", "github_manual": "", "github_regex": "https://github.com/deeplearning-wisc/Thinking-Agent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07054", "title": "AVERE: Improving Audiovisual Emotion Reasoning with Preference Optimization", "authors": [ "Ashutosh Chaubey", "Jiacheng Pang", "Maksim Siniukov", "Mohammad Soleymani" ], "github": "", "project_page": "", "abstract": "Emotion understanding is essential for building socially intelligent agents. Although recent multimodal large language models have shown strong performance on this task, two key challenges remain - spurious associations between emotions and irrelevant audiovisual cues, and hallucinations of audiovisual cues driven by text priors in the language model backbone. To quantify and understand these issues, we introduce EmoReAlM, a benchmark designed to evaluate MLLMs for cue-emotion associations, hallucinations and modality agreement. We then propose AVEm-DPO, a preference optimization technique that aligns model responses with both audiovisual inputs and emotion-centric queries. Specifically, we construct preferences over responses exhibiting spurious associations or hallucinations, and audiovisual input pairs guided by textual prompts. We also include a regularization term that penalizes reliance on text priors, thereby mitigating modality-specific cue hallucinations. Experimental results on DFEW, RAVDESS and EMER demonstrate that our method significantly improves the performance of the reference baseline models with 6-19% of relative performance gains in zero-shot settings. By providing both a rigorous benchmark and a robust optimization framework, this work enables principled evaluation and improvement of MLLMs for emotion understanding and social AI. Code, models and benchmark will be released at https://avere-iclr.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08818", "title": "FlexMoRE: A Flexible Mixture of Rank-heterogeneous Experts for Efficient Federatedly-trained Large Language Models", "authors": [ "Annemette Brok Pirchert", "Jacob Nielsen", "Mogens Henrik From", "Lukas Galke Poech", "Peter Schneider-Kamp" ], "github": "", "project_page": "", "abstract": "Recent advances in mixture-of-experts architectures have shown that individual experts models can be trained federatedly, i.e., in isolation from other experts by using a common base model to facilitate coordination. However, we hypothesize that full-sized experts may not be necessary for all domains and that instead low-rank adapters may be sufficient. Here, we introduce FlexMoRE, a Flexible Mixture of Rank-heterogenous Experts, which may be either full-sized experts or adapters of a suitable rank. We systematically investigate the trade-off between expert rank and downstream task performance by evaluating 6 experts with ranks 2^0 to 2^{14} resulting in experiments covering 150 mixtures (96 with 2 experts, 54 with 7 experts) that are evaluated across 120 tasks. For our experiments, we build on FlexOlmo and turn its pre-trained experts into low-rank versions. Our regression analysis from expert rank to downstream task performance reveals that the best-performing rank is substantially higher for reasoning-heavy benchmarks than for knowledge-heavy benchmarks. These findings on rank sensitivity come with direct implications for memory efficiency: Using optimal ranks, FlexMoRE yields improved downstream task performance (average score 47.18) compared to the baseline FlexOlmo-style mixture of full-sized experts (average score 45.46) at less than one third the parameters (10.75B for FlexMoRE vs. 33.27B for FlexOlmo). All code will be made available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07491", "title": "GraphAgents: Knowledge Graph-Guided Agentic AI for Cross-Domain Materials Design", "authors": [ "Isabella A. Stewart", "Tarjei Paule Hage", "Yu-Chuan Hsu", "Markus J. Buehler" ], "github": "https://github.com/lamm-mit/GraphAgents", "project_page": "", "abstract": "Large Language Models (LLMs) promise to accelerate discovery by reasoning across the expanding scientific landscape. Yet, the challenge is no longer access to information but connecting it in meaningful, domain-spanning ways. In materials science, where innovation demands integrating concepts from molecular chemistry to mechanical performance, this is especially acute. Neither humans nor single-agent LLMs can fully contend with this torrent of information, with the latter often prone to hallucinations. To address this bottleneck, we introduce a multi-agent framework guided by large-scale knowledge graphs to find sustainable substitutes for per- and polyfluoroalkyl substances (PFAS)-chemicals currently under intense regulatory scrutiny. Agents in the framework specialize in problem decomposition, evidence retrieval, design parameter extraction, and graph traversal, uncovering latent connections across distinct knowledge pockets to support hypothesis generation. Ablation studies show that the full multi-agent pipeline outperforms single-shot prompting, underscoring the value of distributed specialization and relational reasoning. We demonstrate that by tailoring graph traversal strategies, the system alternates between exploitative searches focusing on domain-critical outcomes and exploratory searches surfacing emergent cross-connections. Illustrated through the exemplar of biomedical tubing, the framework generates sustainable PFAS-free alternatives that balance tribological performance, thermal stability, chemical resistance, and biocompatibility. This work establishes a framework combining knowledge graphs with multi-agent reasoning to expand the materials design space, showcasing several initial design candidates to demonstrate the approach.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lamm-mit/GraphAgents", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07150", "title": "On Randomness in Agentic Evals", "authors": [ "Bjarni Haukur Bjarnason", "Andr\u00e9 Silva", "Martin Monperrus" ], "github": "", "project_page": "", "abstract": "Agentic systems are evaluated on benchmarks where agents interact with environments to solve tasks. Most papers report a pass@1 score computed from a single run per task, assuming this gives a reliable performance estimate. We test this assumption by collecting 60,000 agentic trajectories on SWE-Bench-Verified, spanning three models and two scaffolds. We find substantial variance: single-run pass@1 estimates vary by 2.2 to 6.0 percentage points depending on which run is selected, with standard deviations exceeding 1.5 percentage points even at temperature 0. This variance has critical implications: reported improvements of 2--3 percentage points may reflect evaluation noise rather than genuine algorithmic progress. Through token-level analysis, we show that trajectories diverge early, often within the first few percent of tokens, and that these small differences cascade into different solution strategies. To enable reliable evaluation of agentic systems, we recommend three concrete practices: (1) estimate pass@1 from multiple independent runs per task, especially when measuring small improvements, (2) use statistical power analysis to determine the number of runs needed to detect expected effect sizes, and (3) consider metrics like pass@k (optimistic bound) and pass^k (pessimistic bound) with k>1 to better characterize the full performance envelope. While these practices increase evaluation cost, they are essential for distinguishing genuine scientific progress from statistical noise.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.06855", "title": "AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents", "authors": [ "Alisia Lupidi", "Bhavul Gauri", "Thomas Simon Foster", "Bassel Al Omari", "Despoina Magka", "Alberto Pepe", "Alexis Audran-Reiss", "Muna Aghamelu", "Nicolas Baldwin", "Lucia Cipolina-Kun", "Jean-Christophe Gagnon-Audet", "Chee Hau Leow", "Sandra Lefdal", "Hossam Mossalam", "Abhinav Moudgil", "Saba Nazir", "Emanuel Tewolde", "Isabel Urrego", "Jordi Armengol Estape", "Amar Budhiraja", "Gaurav Chaurasia", "Abhishek Charnalia", "Derek Dunfield", "Karen Hambardzumyan", "Daniel Izcovich", "Martin Josifoski", "Ishita Mediratta", "Kelvin Niu", "Parth Pathak", "Michael Shvartsman", "Edan Toledo", "Anton Protopopov", "Roberta Raileanu", "Alexander Miller", "Tatiana Shavrina", "Jakob Foerster", "Yoram Bachrach" ], "github": "https://github.com/facebookresearch/airs-bench", "project_page": "", "abstract": "LLM agents hold significant promise for advancing scientific research. To accelerate this progress, we introduce AIRS-Bench (the AI Research Science Benchmark), a suite of 20 tasks sourced from state-of-the-art machine learning papers. These tasks span diverse domains, including language modeling, mathematics, bioinformatics, and time series forecasting. AIRS-Bench tasks assess agentic capabilities over the full research lifecycle -- including idea generation, experiment analysis and iterative refinement -- without providing baseline code. The AIRS-Bench task format is versatile, enabling easy integration of new tasks and rigorous comparison across different agentic frameworks. We establish baselines using frontier models paired with both sequential and parallel scaffolds. Our results show that agents exceed human SOTA in four tasks but fail to match it in sixteen others. Even when agents surpass human benchmarks, they do not reach the theoretical performance ceiling for the underlying tasks. These findings indicate that AIRS-Bench is far from saturated and offers substantial room for improvement. We open-source the AIRS-Bench task definitions and evaluation code to catalyze further development in autonomous scientific research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/airs-bench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.09003", "title": "Data Science and Technology Towards AGI Part I: Tiered Data Management", "authors": [ "Yudong Wang", "Zixuan Fu", "Hengyu Zhao", "Chen Zhao", "Chuyue Zhou", "Xinle Lin", "Hongya Lyu", "Shuaikang Xue", "Yi Yi", "Yingjiao Wang", "Zhi Zheng", "Yuzhou Zhang", "Jie Zhou", "Chaojun Xiao", "Xu Han", "Zhiyuan Liu", "Maosong Sun" ], "github": "https://github.com/UltraData-OpenBMB/UltraData-Math", "project_page": "", "abstract": "The development of artificial intelligence can be viewed as an evolution of data-driven learning paradigms, with successive shifts in data organization and utilization continuously driving advances in model capability. Current LLM research is dominated by a paradigm that relies heavily on unidirectional scaling of data size, increasingly encountering bottlenecks in data availability, acquisition cost, and training efficiency. In this work, we argue that the development of AGI is entering a new phase of data-model co-evolution, in which models actively guide data management while high-quality data, in turn, amplifies model capabilities. To implement this vision, we propose a tiered data management framework, designed to support the full LLM training lifecycle across heterogeneous learning objectives and cost constraints. Specifically, we introduce an L0-L4 tiered data management framework, ranging from raw uncurated resources to organized and verifiable knowledge. Importantly, LLMs are fully used in data management processes, such as quality scoring and content editing, to refine data across tiers. Each tier is characterized by distinct data properties, management strategies, and training roles, enabling data to be strategically allocated across LLM training stages, including pre-training, mid-training, and alignment. The framework balances data quality, acquisition cost, and marginal training benefit, providing a systematic approach to scalable and sustainable data management. We validate the effectiveness of the proposed framework through empirical studies, in which tiered datasets are constructed from raw corpora and used across multiple training phases. Experimental results demonstrate that tier-aware data utilization significantly improves training efficiency and model performance. To facilitate further research, we release our tiered datasets and processing tools to the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UltraData-OpenBMB/UltraData-Math", "github_api_added_by": "auto" }, { "date": "2026-02-10", "arxiv_id": "2602.06600", "title": "Echoes as Anchors: Probabilistic Costs and Attention Refocusing in LLM Reasoning", "authors": [ "Zhuoyuan Hao", "Zhuo Li", "Wu Li", "Fangming Liu", "Min Zhang", "Jing Li" ], "github": "https://github.com/hhh2210/echoes-as-anchors", "project_page": "", "abstract": "Test-time compute allocation in large reasoning models (LRMs) is widely used and has applications in mathematical problem solving, code synthesis, and planning. Recent work has addressed this problem by scaling self-consistency and parallel thinking, adding generic ``thinking tokens'' and prompting models to re-read the question before answering. Unfortunately, these approaches either inject task-agnostic tokens or mandate heuristics that do not explain -- and often ignore -- the spontaneous repetition that many LRMs exhibit at the head of their internal chains. In contrast, we analyze and harness the model's tendency to restate the question, which we term the Echo of Prompt (EOP), as a front-loaded, compute-shaping mechanism. We formalize its probabilistic cost by casting echo removal as rejection-based conditioning and defining the Echo Likelihood Gap \u0394L as a computable proxy. This provides the missing theoretical link that links early repetition to likelihood gains and downstream accuracy. However, it does not by itself specify how to exploit EOP. Consequently, we develop Echo-Distilled SFT (ED-SFT) to instill an ``echo-then-reason'' pattern through supervised finetuning, and Echoic Prompting (EP) to re-ground the model mid-trace without training. While promising, quantifying benefits beyond verbosity is non-trivial. Therefore, we conduct length and suffix-controlled likelihood analyses together with layer-wise attention studies, showing that EOP increases answer to answer-prefix attention in middle layers, consistent with an attention refocusing mechanism. We evaluate on GSM8K, MathQA, Hendrycks-MATH, AIME24, and MATH-500 under identical decoding settings and budgets, and find consistent gains over baselines. Code is available at https://github.com/hhh2210/echoes-as-anchors.", "github_manual": "", "github_regex": "https://github.com/hhh2210/echoes-as-anchors", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08658", "title": "Fundamental Reasoning Paradigms Induce Out-of-Domain Generalization in Language Models", "authors": [ "Mingzi Cao", "Xingwei Tan", "Mahmud Akhter", "Marco Valentino", "Maria Liakata", "Xi Wang", "Nikolaos Aletras" ], "github": "https://github.com/voalmciaf/FR-OOD", "project_page": "", "abstract": "Deduction, induction, and abduction are fundamental reasoning paradigms, core for human logical thinking. Although improving Large Language Model (LLM) reasoning has attracted significant research efforts, the extent to which the fundamental paradigms induce generalization has yet to be systematically explored. In this study, we shed light on how the interplay between these core paradigms influences LLMs' reasoning behavior. To this end, we first collect a new dataset of reasoning trajectories from symbolic tasks, each targeting one of the three fundamental paradigms, to abstract from concrete world knowledge. Then, we investigate effective ways for inducing these skills into LLMs. We experiment with a battery of methods including simple fine-tuning, and more complex approaches to increase model depth, or transform a dense model to a mixture-of-experts. We comprehensively evaluate induced models on realistic out-of-domain tasks, that are entirely formulated in natural language and contain real-world knowledge. Our results reveal that our approach yields strong generalizability with substantial performance gains (up to 14.60) across realistic tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/voalmciaf/FR-OOD", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07948", "title": "dewi-kadita: A Python Library for Idealized Fish Schooling Simulation with Entropy-Based Diagnostics", "authors": [ "Sandy H. S. Herho", "Iwan P. Anwar", "Faruq Khadami", "Alfita P. Handayani", "Karina A. Sujatmiko", "Kamaluddin Kasim", "Rusmawan Suwarman", "Dasapta E. Irawan" ], "github": "", "project_page": "", "abstract": "Collective motion in fish schools exemplifies emergent self-organization in active matter systems, yet computational tools for simulating and analyzing these dynamics remain fragmented across research groups. We present dewi-kadita, an open-source Python library implementing the three-dimensional Couzin zone-based model with comprehensive entropy diagnostics tailored for marine collective behavior research. The library introduces seven information-theoretic metrics -- school cohesion entropy, polarization entropy, depth stratification entropy, angular momentum entropy, nearest-neighbor entropy, velocity correlation entropy, and school shape entropy -- that characterize distinct organizational features inaccessible to classical order parameters. These metrics combine into an Oceanic Schooling Index (OSI) providing a single scalar measure of collective disorder. Validation across four canonical configurations (swarm, torus, dynamic parallel, highly parallel) confirms correct reproduction of known phase behaviors: the swarm maintains disorder with polarization P < 0.1 and OSI approx 0.71, while the highly parallel state achieves P = 0.998 with OSI = 0.24 and velocity correlation entropy vanishing to zero. The entropy framework successfully discriminates the torus and dynamic parallel configurations that exhibit comparable order parameter magnitudes through different organizational mechanisms. Numba just-in-time (JIT) compilation accelerates pairwise interaction calculations by 10--100times, enabling simulations of 150--250 agents over 1000--2000 time steps within five minutes on standard workstation hardware. NetCDF4 output ensures interoperability with oceanographic analysis tools. The library addresses the need for standardized, reproducible infrastructure in collective behavior modeling analogous to established molecular dynamics codes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.02285", "title": "Statistical Learning Theory in Lean 4: Empirical Processes from Scratch", "authors": [ "Yuanhe Zhang", "Jason D. Lee", "Fanghui Liu" ], "github": "https://github.com/YuanheZ/lean-stat-learning-theory", "project_page": "", "abstract": "We present the first comprehensive Lean 4 formalization of statistical learning theory (SLT) grounded in empirical process theory. Our end-to-end formal infrastructure implement the missing contents in latest Lean 4 Mathlib library, including a complete development of Gaussian Lipschitz concentration, the first formalization of Dudley's entropy integral theorem for sub-Gaussian processes, and an application to least-squares (sparse) regression with a sharp rate. The project was carried out using a human-AI collaborative workflow, in which humans design proof strategies and AI agents execute tactical proof construction, leading to the human-verified Lean 4 toolbox for SLT. Beyond implementation, the formalization process exposes and resolves implicit assumptions and missing details in standard SLT textbooks, enforcing a granular, line-by-line understanding of the theory. This work establishes a reusable formal foundation and opens the door for future developments in machine learning theory. The code is available at https://github.com/YuanheZ/lean-stat-learning-theory", "github_manual": "", "github_regex": "https://github.com/YuanheZ/lean-stat-learning-theory", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.03784", "title": "Context Compression via Explicit Information Transmission", "authors": [ "Jiangnan Ye", "Hanqi Yan", "Zhenyi Shen", "Heng Chang", "Ye Mao", "Yulan He" ], "github": "", "project_page": "", "abstract": "Long-context inference with Large Language Models (LLMs) is costly due to quadratic attention and growing key-value caches, motivating context compression. In this work, we study soft context compression, where a long context is condensed into a small set of continuous representations. Existing methods typically re-purpose the LLM itself as a trainable compressor, relying on layer-by-layer self-attention to iteratively aggregate information. We argue that this paradigm suffers from two structural limitations: (i) progressive representation overwriting across layers (ii) uncoordinated allocation of compression capacity across tokens. We propose ComprExIT (Context Compression via Explicit Information Transmission), a lightweight framework that formulates soft compression into a new paradigm: explicit information transmission over frozen LLM hidden states. This decouples compression from the model's internal self-attention dynamics. ComprExIT performs (i) depth-wise transmission to selectively transmit multi-layer information into token anchors, mitigating progressive overwriting, and (ii) width-wise transmission to aggregate anchors into a small number of slots via a globally optimized transmission plan, ensuring coordinated allocation of information. Across six question-answering benchmarks, ComprExIT consistently outperforms state-of-the-art context compression methods while introducing only ~1% additional parameters, demonstrating that explicit and coordinated information transmission enables more effective and robust long-context compression.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.06942", "title": "Optimal Turkish Subword Strategies at Scale: Systematic Evaluation of Data, Vocabulary, Morphology Interplay", "authors": [ "Duygu Altinok" ], "github": "https://github.com/turkish-nlp-suite/Turkish-subwords-research", "project_page": "", "abstract": "Tokenization is a pivotal design choice for neural language modeling in morphologically rich languages (MRLs) such as Turkish, where productive agglutination challenges both vocabulary efficiency and morphological fidelity. Prior studies have explored tokenizer families and vocabulary sizes but typically (i) vary vocabulary without systematically controlling the tokenizer's training corpus, (ii) provide limited intrinsic diagnostics, and (iii) evaluate a narrow slice of downstream tasks. We present the first comprehensive, principled study of Turkish subword tokenization; a \"subwords manifest\", that jointly varies vocabulary size and tokenizer training corpus size (data and vocabulary coupling), compares multiple tokenizer families under matched parameter budgets (WordPiece, morphology level, and character baselines), and evaluates across semantic (NLI, STS, sentiment analysis, NER), syntactic (POS, dependency parsing), and morphology-sensitive probes. To explain why tokenizers succeed or fail, we introduce a morphology-aware diagnostic toolkit that goes beyond coarse aggregates to boundary-level micro/macro F1, decoupled lemma atomicity vs. surface boundary hits, over/under-segmentation indices, character/word edit distances (CER/WER), continuation rates, and affix-type coverage and token-level atomicity. Our contributions are fourfold: (i) a systematic investigation of the vocabulary-corpus-success triad; (ii) a unified, morphology-aware evaluation framework linking intrinsic diagnostics to extrinsic outcomes; (iii) controlled comparisons identifying when character-level and morphology-level tokenization pay off; and (iv) an open-source release of evaluation code, tokenizer pipelines, and models. As the first work of its kind, this \"subwords manifest\" delivers actionable guidance for building effective tokenizers in MRLs and establishes a reproducible foundation for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/turkish-nlp-suite/Turkish-subwords-research", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08629", "title": "CauScale: Neural Causal Discovery at Scale", "authors": [ "Bo Peng", "Sirui Chen", "Jiaguo Tian", "Yu Qiao", "Chaochao Lu" ], "github": "https://github.com/OpenCausaLab/CauScale", "project_page": "", "abstract": "Causal discovery is essential for advancing data-driven fields such as scientific AI and data analysis, yet existing approaches face significant time- and space-efficiency bottlenecks when scaling to large graphs. To address this challenge, we present CauScale, a neural architecture designed for efficient causal discovery that scales inference to graphs with up to 1000 nodes. CauScale improves time efficiency via a reduction unit that compresses data embeddings and improves space efficiency by adopting tied attention weights to avoid maintaining axis-specific attention maps. To keep high causal discovery accuracy, CauScale adopts a two-stream design: a data stream extracts relational evidence from high-dimensional observations, while a graph stream integrates statistical graph priors and preserves key structural signals. CauScale successfully scales to 500-node graphs during training, where prior work fails due to space limitations. Across testing data with varying graph scales and causal mechanisms, CauScale achieves 99.6% mAP on in-distribution data and 84.4% on out-of-distribution data, while delivering 4-13,000 times inference speedups over prior methods. Our project page is at https://github.com/OpenCausaLab/CauScale.", "github_manual": "", "github_regex": "https://github.com/OpenCausaLab/CauScale", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.05708", "title": "Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration", "authors": [ "Chuangtao Ma", "Zeyu Zhang", "Arijit Khan", "Sebastian Schelter", "Paul Groth" ], "github": "", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) enhances LLM reasoning in knowledge-intensive tasks, but existing RAG pipelines incur substantial retrieval and generation overhead when applied to large-scale entity matching. To address this limitation, we introduce CE-RAG4EM, a cost-efficient RAG architecture that reduces computation through blocking-based batch retrieval and generation. We also present a unified framework for analyzing and evaluating RAG systems for entity matching, focusing on blocking-aware optimizations and retrieval granularity. Extensive experiments suggest that CE-RAG4EM can achieve comparable or improved matching quality while substantially reducing end-to-end runtime relative to strong baselines. Our analysis further reveals that key configuration parameters introduce an inherent trade-off between performance and overhead, offering practical guidance for designing efficient and scalable RAG systems for entity matching and data integration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.05929", "title": "KV-CoRE: Benchmarking Data-Dependent Low-Rank Compressibility of KV-Caches in LLMs", "authors": [ "Jian Chen", "Zhuoran Wang", "Jiayu Qin", "Ming Li", "Meng Wang", "Changyou Chen", "Yin Chen", "Qizhen Weng", "Yirui Liu" ], "github": "", "project_page": "", "abstract": "Large language models rely on kv-caches to avoid redundant computation during autoregressive decoding, but as context length grows, reading and writing the cache can quickly saturate GPU memory bandwidth. Recent work has explored KV-cache compression, yet most approaches neglect the data-dependent nature of kv-caches and their variation across layers. We introduce KV-CoRE KV-cache Compressibility by Rank Evaluation), an SVD-based method for quantifying the data-dependent low-rank compressibility of kv-caches. KV-CoRE computes the optimal low-rank approximation under the Frobenius norm and, being gradient-free and incremental, enables efficient dataset-level, layer-wise evaluation. Using this method, we analyze multiple models and datasets spanning five English domains and sixteen languages, uncovering systematic patterns that link compressibility to model architecture, training data, and language coverage. As part of this analysis, we employ the Normalized Effective Rank as a metric of compressibility and show that it correlates strongly with performance degradation under compression. Our study establishes a principled evaluation framework and the first large-scale benchmark of kv-cache compressibility in LLMs, offering insights for dynamic, data-aware compression and data-centric model development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.05946", "title": "f-GRPO and Beyond: Divergence-Based Reinforcement Learning Algorithms for General LLM Alignment", "authors": [ "Rajdeep Haldar", "Lantao Mei", "Guang Lin", "Yue Xing", "Qifan Song" ], "github": "", "project_page": "", "abstract": "Recent research shows that Preference Alignment (PA) objectives act as divergence estimators between aligned (chosen) and unaligned (rejected) response distributions. In this work, we extend this divergence-based perspective to general alignment settings, such as reinforcement learning with verifiable rewards (RLVR), where only environmental rewards are available. Within this unified framework, we propose f-Group Relative Policy Optimization (f-GRPO), a class of on-policy reinforcement learning, and f-Hybrid Alignment Loss (f-HAL), a hybrid on/off policy objectives, for general LLM alignment based on variational representation of f-divergences. We provide theoretical guarantees that these classes of objectives improve the average reward after alignment. Empirically, we validate our framework on both RLVR (Math Reasoning) and PA tasks (Safety Alignment), demonstrating superior performance and flexibility compared to current methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08004", "title": "Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionality", "authors": [ "George Ling", "Shanshan Zhong", "Richard Huang" ], "github": "", "project_page": "", "abstract": "Agent skills extend large language model (LLM) agents with reusable, program-like modules that define triggering conditions, procedural logic, and tool interactions. As these skills proliferate in public marketplaces, it is unclear what types are available, how users adopt them, and what risks they pose. To answer these questions, we conduct a large-scale, data-driven analysis of 40,285 publicly listed skills from a major marketplace. Our results show that skill publication tends to occur in short bursts that track shifts in community attention. We also find that skill content is highly concentrated in software engineering workflows, while information retrieval and content creation account for a substantial share of adoption. Beyond content trends, we uncover a pronounced supply-demand imbalance across categories, and we show that most skills remain within typical prompt budgets despite a heavy-tailed length distribution. Finally, we observe strong ecosystem homogeneity, with widespread intent-level redundancy, and we identify non-trivial safety risks, including skills that enable state-changing or system-level actions. Overall, our findings provide a quantitative snapshot of agent skills as an emerging infrastructure layer for agents and inform future work on skill reuse, standardization, and safety-aware design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07080", "title": "CodeCircuit: Toward Inferring LLM-Generated Code Correctness via Attribution Graphs", "authors": [ "Yicheng He", "Zheng Zhao", "Zhou Kaiyu", "Bryan Dai", "Jie Fu", "Yonghui Yang" ], "github": "https://github.com/bruno686/CodeCircuit", "project_page": "", "abstract": "Current paradigms for code verification rely heavily on external mechanisms-such as execution-based unit tests or auxiliary LLM judges-which are often labor-intensive or limited by the judging model's own capabilities. This raises a fundamental, yet unexplored question: Can an LLM's functional correctness be assessed purely from its internal computational structure? Our primary objective is to investigate whether the model's neural dynamics encode internally decodable signals that are predictive of logical validity during code generation. Inspired by mechanistic interpretability, we propose to treat code verification as a mechanistic diagnostic task, mapping the model's explicit algorithmic trajectory into line-level attribution graphs. By decomposing complex residual flows, we aim to identify the structural signatures that distinguish sound reasoning from logical failure within the model's internal circuits. Analysis across Python, C++, and Java confirms that intrinsic correctness signals are robust across diverse syntaxes. Topological features from these internal graphs predict correctness more reliably than surface heuristics and enable targeted causal interventions to fix erroneous logic. These findings establish internal introspection as a decodable property for verifying generated code. Our code is at https:// github.com/bruno686/CodeCircuit.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bruno686/CodeCircuit", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.00169", "title": "Towards Agentic Intelligence for Materials Science", "authors": [ "Huan Zhang", "Yizhan Li", "Wenhao Huang", "Ziyu Hou", "Yu Song", "Xuye Liu", "Farshid Effaty", "Jinya Jiang", "Sifan Wu", "Qianggang Ding", "Izumi Takahara", "Leonard R. MacGillivray", "Teruyasu Mizoguchi", "Tianshu Yu", "Lizi Liao", "Yuyu Luo", "Yu Rong", "Jia Li", "Ying Diao", "Heng Ji", "Bang Liu" ], "github": "", "project_page": "", "abstract": "The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in discovery requires moving beyond task-isolated, fine-tuned models toward agentic systems that plan, act, and learn across the full discovery loop. This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining, through domain adaptation and instruction tuning, to goal-conditioned agents interfacing with simulation and experimental platforms. Unlike prior reviews, we treat the entire process as an end-to-end system to be optimized for tangible discovery outcomes rather than proxy benchmarks. This perspective allows us to trace how upstream design choices-such as data curation and training objectives-can be aligned with downstream experimental success through effective credit assignment. To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science. We then analyze the field through two focused lenses: From the AI perspective, the survey details LLM strengths in pattern recognition, predictive analytics, and natural language processing for literature mining, materials characterization, and property prediction; from the materials science perspective, it highlights applications in materials design, process optimization, and the acceleration of computational workflows via integration with external tools (e.g., DFT, robotic labs). Finally, we contrast passive, reactive approaches with agentic design, cataloging current contributions while motivating systems that pursue long-horizon goals with autonomy, memory, and tool use. This survey charts a practical roadmap towards autonomous, safety-aware LLM agents aimed at discovering novel and useful materials.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07120", "title": "Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model", "authors": [ "Jacqueline He", "Jonathan Hayase", "Wen-tau Yih", "Sewoong Oh", "Luke Zettlemoyer", "Pang Wei Koh" ], "github": "", "project_page": "", "abstract": "Modern language models (LMs) tend to memorize portions of their training data and emit verbatim spans. When the underlying sources are sensitive or copyright-protected, such reproduction raises issues of consent and compensation for creators and compliance risks for developers. We propose Anchored Decoding, a plug-and-play inference-time method for suppressing verbatim copying: it enables decoding from any risky LM trained on mixed-license data by keeping generation in bounded proximity to a permissively trained safe LM. Anchored Decoding adaptively allocates a user-chosen information budget over the generation trajectory and enforces per-step constraints that yield a sequence-level guarantee, enabling a tunable risk-utility trade-off. To make Anchored Decoding practically useful, we introduce a new permissively trained safe model (TinyComma 1.8B), as well as Anchored_{Byte} Decoding, a byte-level variant of our method that enables cross-vocabulary fusion via the ByteSampler framework (Hayase et al., 2025). We evaluate our methods across six model pairs on long-form evaluations of copyright risk and utility. Anchored and Anchored_{Byte} Decoding define a new Pareto frontier, preserving near-original fluency and factuality while eliminating up to 75% of the measurable copying gap (averaged over six copying metrics) between the risky baseline and a safe reference, at a modest inference overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.02827", "title": "Col-Bandit: Zero-Shot Query-Time Pruning for Late-Interaction Retrieval", "authors": [ "Roi Pony", "Adi Raz", "Oshri Naparstek", "Idan Friedman", "Udi Barzelay" ], "github": "", "project_page": "", "abstract": "Multi-vector late-interaction retrievers such as ColBERT achieve state-of-the-art retrieval quality, but their query-time cost is dominated by exhaustively computing token-level MaxSim interactions for every candidate document. While approximating late interaction with single-vector representations reduces cost, it often incurs substantial accuracy loss. We introduce Col-Bandit, a query-time pruning algorithm that reduces this computational burden by casting reranking as a finite-population Top-K identification problem. Col-Bandit maintains uncertainty-aware bounds over partially observed document scores and adaptively reveals only the (document, query token) MaxSim entries needed to determine the top results under statistical decision bounds with a tunable relaxation. Unlike coarse-grained approaches that prune entire documents or tokens offline, Col-Bandit sparsifies the interaction matrix on the fly. It operates as a zero-shot, drop-in layer over standard multi-vector systems, requiring no index modifications, offline preprocessing, or model retraining. Experiments on textual (BEIR) and multimodal (REAL-MM-RAG) benchmarks show that Col-Bandit preserves ranking fidelity while reducing MaxSim FLOPs by up to 5times, indicating that dense late-interaction scoring contains substantial redundancy that can be identified and pruned efficiently at query time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07125", "title": "Reasoning-Augmented Representations for Multimodal Retrieval", "authors": [ "Jianrui Zhang", "Anirudh Sundara Rajan", "Brandon Han", "Soochahn Lee", "Sukanta Ganguly", "Yong Jae Lee" ], "github": "https://github.com/AugmentedRetrieval/ReasoningAugmentedRetrieval", "project_page": "", "abstract": "Universal Multimodal Retrieval (UMR) seeks any-to-any search across text and vision, yet modern embedding models remain brittle when queries require latent reasoning (e.g., resolving underspecified references or matching compositional constraints). We argue this brittleness is often data-induced: when images carry \"silent\" evidence and queries leave key semantics implicit, a single embedding pass must both reason and compress, encouraging spurious feature matching. We propose a data-centric framework that decouples these roles by externalizing reasoning before retrieval. Using a strong Vision--Language Model, we make implicit semantics explicit by densely captioning visual evidence in corpus entries, resolving ambiguous multimodal references in queries, and rewriting verbose instructions into concise retrieval constraints. Inference-time enhancement alone is insufficient; the retriever must be trained on these semantically dense representations to avoid distribution shift and fully exploit the added signal. Across M-BEIR, our reasoning-augmented training method yields consistent gains over strong baselines, with ablations showing that corpus enhancement chiefly benefits knowledge-intensive queries while query enhancement is critical for compositional modification requests. We publicly release our code at https://github.com/AugmentedRetrieval/ReasoningAugmentedRetrieval.", "github_manual": "", "github_regex": "https://github.com/AugmentedRetrieval/ReasoningAugmentedRetrieval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07837", "title": "RLinf-USER: A Unified and Extensible System for Real-World Online Policy Learning in Embodied AI", "authors": [ "Hongzhi Zang", "Shu'ang Yu", "Hao Lin", "Tianxing Zhou", "Zefang Huang", "Zhen Guo", "Xin Xu", "Jiakai Zhou", "Yuze Sheng", "Shizhe Zhang", "Feng Gao", "Wenhao Tang", "Yufeng Yue", "Quanlu Zhang", "Xinlei Chen", "Chao Yu", "Yu Wang" ], "github": "https://github.com/RLinf/RLinf", "project_page": "", "abstract": "Online policy learning directly in the physical world is a promising yet challenging direction for embodied intelligence. Unlike simulation, real-world systems cannot be arbitrarily accelerated, cheaply reset, or massively replicated, which makes scalable data collection, heterogeneous deployment, and long-horizon effective training difficult. These challenges suggest that real-world policy learning is not only an algorithmic issue but fundamentally a systems problem. We present USER, a Unified and extensible SystEm for Real-world online policy learning. USER treats physical robots as first-class hardware resources alongside GPUs through a unified hardware abstraction layer, enabling automatic discovery, management, and scheduling of heterogeneous robots. To address cloud-edge communication, USER introduces an adaptive communication plane with tunneling-based networking, distributed data channels for traffic localization, and streaming-multiprocessor-aware weight synchronization to regulate GPU-side overhead. On top of this infrastructure, USER organizes learning as a fully asynchronous framework with a persistent, cache-aware buffer, enabling efficient long-horizon experiments with robust crash recovery and reuse of historical data. In addition, USER provides extensible abstractions for rewards, algorithms, and policies, supporting online imitation or reinforcement learning of CNN/MLP, generative policies, and large vision-language-action (VLA) models within a unified pipeline. Results in both simulation and the real world show that USER enables multi-robot coordination, heterogeneous manipulators, edge-cloud collaboration with large models, and long-running asynchronous training, offering a unified and extensible systems foundation for real-world online policy learning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/RLinf/RLinf", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.07274", "title": "TermiGen: High-Fidelity Environment and Robust Trajectory Synthesis for Terminal Agents", "authors": [ "Kaijie Zhu", "Yuzhou Nie", "Yijiang Li", "Yiming Huang", "Jialian Wu", "Jiang Liu", "Ximeng Sun", "Zhenfei Yin", "Lun Wang", "Zicheng Liu", "Emad Barsoum", "William Yang Wang", "Wenbo Guo" ], "github": "https://github.com/ucsb-mlsec/terminal-bench-env", "project_page": "", "abstract": "Executing complex terminal tasks remains a significant challenge for open-weight LLMs, constrained by two fundamental limitations. First, high-fidelity, executable training environments are scarce: environments synthesized from real-world repositories are not diverse and scalable, while trajectories synthesized by LLMs suffer from hallucinations. Second, standard instruction tuning uses expert trajectories that rarely exhibit simple mistakes common to smaller models. This creates a distributional mismatch, leaving student models ill-equipped to recover from their own runtime failures. To bridge these gaps, we introduce TermiGen, an end-to-end pipeline for synthesizing verifiable environments and resilient expert trajectories. Termi-Gen first generates functionally valid tasks and Docker containers via an iterative multi-agent refinement loop. Subsequently, we employ a Generator-Critic protocol that actively injects errors during trajectory collection, synthesizing data rich in error-correction cycles. Fine-tuned on this TermiGen-generated dataset, our TermiGen-Qwen2.5-Coder-32B achieves a 31.3% pass rate on TerminalBench. This establishes a new open-weights state-of-the-art, outperforming existing baselines and notably surpassing capable proprietary models such as o4-mini. Dataset is avaiable at https://github.com/ucsb-mlsec/terminal-bench-env.", "github_manual": "", "github_regex": "https://github.com/ucsb-mlsec/terminal-bench-env", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.08321", "title": "Improving Data and Reward Design for Scientific Reasoning in Large Language Models", "authors": [ "Zijie Chen", "Zhenghao Lin", "Xiao Liu", "Zhenzhong Lan", "Yeyun Gong", "Peng Cheng" ], "github": "", "project_page": "", "abstract": "Solving open-ended science questions remains challenging for large language models, particularly due to inherently unreliable supervision and evaluation. The bottleneck lies in the data construction and reward design for scientific post-training. We develop a large-scale, systematic data processing pipeline that transforms heterogeneous open-source science data into Dr. SCI dataset, which comprises of 1M questions across eight STEM subjects, with explicit verifiable/open-ended splits, scalable difficulty annotation, and fine-grained rubrics that operationalize evaluation for open-ended answers. Building on this dataset, we propose the Dr. SCI post-training pipeline, which redesigns the standard SFT -> RL workflow through three components: (i) Exploration-Expanding SFT, which broadens the model's reasoning pattern coverage prior to RL; (ii) Dynamic Difficulty Curriculum, which adapts training data to the model's evolving scientific capability; and (iii) SciRubric-Guided RL, which enables stable reinforcement learning on open-ended scientific questions via rubric-based evaluation with explicit answer correctness. Qwen3-4B-Base trained using Dr. SCI pipeline achieves 63.2 on GPQA-diamond and 32.4 on GPQA-general, consistently improves over strong post-trained baselines such as o1-mini and GPT-4o, demonstrating substantial gains in scientific reasoning, especially in open-ended settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-10", "arxiv_id": "2602.09782", "title": "Flexible Entropy Control in RLVR with Gradient-Preserving Perspective", "authors": [ "Kun Chen", "Peng Shi", "Fanfan Liu", "Haibo Qiu", "Zhixiong Zeng", "Siqi Yang", "Wenji Mao" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a critical method for enhancing the reasoning capabilities of Large Language Models (LLMs). However, continuous training often leads to policy entropy collapse, characterized by a rapid decay in entropy that results in premature overconfidence, reduced output diversity, and vanishing gradient norms that inhibit learning. Gradient-Preserving Clipping is a primary factor influencing these dynamics, but existing mitigation strategies are largely static and lack a framework connecting clipping mechanisms to precise entropy control. This paper proposes reshaping entropy control in RL from the perspective of Gradient-Preserving Clipping. We first theoretically and empirically verify the contributions of specific importance sampling ratio regions to entropy growth and reduction. Leveraging these findings, we introduce a novel regulation mechanism using dynamic clipping threshold to precisely manage entropy. Furthermore, we design and evaluate dynamic entropy control strategies, including increase-then-decrease, decrease-increase-decrease, and oscillatory decay. Experimental results demonstrate that these strategies effectively mitigate entropy collapse, and achieve superior performance across multiple benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09082", "title": "UI-Venus-1.5 Technical Report", "authors": [ "Veuns-Team", "Changlong Gao", "Zhangxuan Gu", "Yulin Liu", "Xinyu Qiu", "Shuheng Shen", "Yue Wen", "Tianyu Xia", "Zhenyu Xu", "Zhengwen Zeng", "Beitong Zhou", "Xingran Zhou", "Weizhi Chen", "Sunhao Dai", "Jingya Dou", "Yichen Gong", "Yuan Guo", "Zhenlin Guo", "Feng Li", "Qian Li", "Jinzhen Lin", "Yuqi Zhou", "Linchao Zhu", "Liang Chen", "Zhenyu Guo", "Changhua Meng", "Weiqiang Wang" ], "github": "https://github.com/inclusionAI/UI-Venus", "project_page": "", "abstract": "GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance remains challenging.In this report, we present UI-Venus-1.5, a unified, end-to-end GUI Agent designed for robust real-world applications.The proposed model family comprises two dense variants (2B and 8B) and one mixture-of-experts variant (30B-A3B) to meet various downstream application scenarios.Compared to our previous version, UI-Venus-1.5 introduces three key technical advances: (1) a comprehensive Mid-Training stage leveraging 10 billion tokens across 30+ datasets to establish foundational GUI semantics; (2) Online Reinforcement Learning with full-trajectory rollouts, aligning training objectives with long-horizon, dynamic navigation in large-scale environments; and (3) a single unified GUI Agent constructed via Model Merging, which synthesizes domain-specific models (grounding, web, and mobile) into one cohesive checkpoint. Extensive evaluations demonstrate that UI-Venus-1.5 establishes new state-of-the-art performance on benchmarks such as ScreenSpot-Pro (69.6%), VenusBench-GD (75.0%), and AndroidWorld (77.6%), significantly outperforming previous strong baselines. In addition, UI-Venus-1.5 demonstrates robust navigation capabilities across a variety of Chinese mobile apps, effectively executing user instructions in real-world scenarios. Code: https://github.com/inclusionAI/UI-Venus; Model: https://huggingface.co/collections/inclusionAI/ui-venus", "github_manual": "", "github_regex": "https://github.com/inclusionAI/UI-Venus", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.08234", "title": "SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning", "authors": [ "Peng Xia", "Jianwen Chen", "Hanyang Wang", "Jiaqi Liu", "Kaide Zeng", "Yu Wang", "Siwei Han", "Yiyang Zhou", "Xujiang Zhao", "Haifeng Chen", "Zeyu Zheng", "Cihang Xie", "Huaxiu Yao" ], "github": "https://github.com/aiming-lab/SkillRL", "project_page": "", "abstract": "Large Language Model (LLM) agents have shown stunning results in complex tasks, yet they often operate in isolation, failing to learn from past experiences. Existing memory-based methods primarily store raw trajectories, which are often redundant and noise-heavy. This prevents agents from extracting high-level, reusable behavioral patterns that are essential for generalization. In this paper, we propose SkillRL, a framework that bridges the gap between raw experience and policy improvement through automatic skill discovery and recursive evolution. Our approach introduces an experience-based distillation mechanism to build a hierarchical skill library SkillBank, an adaptive retrieval strategy for general and task-specific heuristics, and a recursive evolution mechanism that allows the skill library to co-evolve with the agent's policy during reinforcement learning. These innovations significantly reduce the token footprint while enhancing reasoning utility. Experimental results on ALFWorld, WebShop and seven search-augmented tasks demonstrate that SkillRL achieves state-of-the-art performance, outperforming strong baselines over 15.3% and maintaining robustness as task complexity increases. Code is available at this https://github.com/aiming-lab/SkillRL.", "github_manual": "", "github_regex": "https://github.com/aiming-lab/SkillRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09084", "title": "Agent Banana: High-Fidelity Image Editing with Agentic Thinking and Tooling", "authors": [ "Ruijie Ye", "Jiayi Zhang", "Zhuoxin Liu", "Zihao Zhu", "Siyuan Yang", "Li Li", "Tianfu Fu", "Franck Dernoncourt", "Yue Zhao", "Jiacheng Zhu", "Ryan Rossi", "Wenhao Chai", "Zhengzhong Tu" ], "github": "", "project_page": "", "abstract": "We study instruction-based image editing under professional workflows and identify three persistent challenges: (i) editors often over-edit, modifying content beyond the user's intent; (ii) existing models are largely single-turn, while multi-turn edits can alter object faithfulness; and (iii) evaluation at around 1K resolution is misaligned with real workflows that often operate on ultra high-definition images (e.g., 4K). We propose Agent Banana, a hierarchical agentic planner-executor framework for high-fidelity, object-aware, deliberative editing. Agent Banana introduces two key mechanisms: (1) Context Folding, which compresses long interaction histories into structured memory for stable long-horizon control; and (2) Image Layer Decomposition, which performs localized layer-based edits to preserve non-target regions while enabling native-resolution outputs. To support rigorous evaluation, we build HDD-Bench, a high-definition, dialogue-based benchmark featuring verifiable stepwise targets and native 4K images (11.8M pixels) for diagnosing long-horizon failures. On HDD-Bench, Agent Banana achieves the best multi-turn consistency and background fidelity (e.g., IC 0.871, SSIM-OM 0.84, LPIPS-OM 0.12) while remaining competitive on instruction following, and also attains strong performance on standard single-turn editing benchmarks. We hope this work advances reliable, professional-grade agentic image editing and its integration into real workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.04208", "title": "SCALE: Self-uncertainty Conditioned Adaptive Looking and Execution for Vision-Language-Action Models", "authors": [ "Hyeonbeom Choi", "Daechul Ahn", "Youhan Lee", "Taewook Kang", "Seongwon Cho", "Jonghyun Choi" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic control, with test-time scaling (TTS) gaining attention to enhance robustness beyond training. However, existing TTS methods for VLAs require additional training, verifiers, and multiple forward passes, making them impractical for deployment. Moreover, they intervene only at action decoding while keeping visual representations fixed-insufficient under perceptual ambiguity, where reconsidering how to perceive is as important as deciding what to do. To address these limitations, we propose SCALE, a simple inference strategy that jointly modulates visual perception and action based on 'self-uncertainty', inspired by uncertainty-driven exploration in Active Inference theory-requiring no additional training, no verifier, and only a single forward pass. SCALE broadens exploration in both perception and action under high uncertainty, while focusing on exploitation when confident-enabling adaptive execution across varying conditions. Experiments on simulated and real-world benchmarks demonstrate that SCALE improves state-of-the-art VLAs and outperforms existing TTS methods while maintaining single-pass efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.04908", "title": "Temporal Pair Consistency for Variance-Reduced Flow Matching", "authors": [ "Chika Maduabuchi", "Jindong Wang" ], "github": "", "project_page": "", "abstract": "Continuous-time generative models, such as diffusion models, flow matching, and rectified flow, learn time-dependent vector fields but are typically trained with objectives that treat timesteps independently, leading to high estimator variance and inefficient sampling. Prior approaches mitigate this via explicit smoothness penalties, trajectory regularization, or modified probability paths and solvers. We introduce Temporal Pair Consistency (TPC), a lightweight variance-reduction principle that couples velocity predictions at paired timesteps along the same probability path, operating entirely at the estimator level without modifying the model architecture, probability path, or solver. We provide a theoretical analysis showing that TPC induces a quadratic, trajectory-coupled regularization that provably reduces gradient variance while preserving the underlying flow-matching objective. Instantiated within flow matching, TPC improves sample quality and efficiency across CIFAR-10 and ImageNet at multiple resolutions, achieving lower FID at identical or lower computational cost than prior methods, and extends seamlessly to modern SOTA-style pipelines with noise-augmented training, score-based denoising, and rectified flow.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09856", "title": "Code2World: A GUI World Model via Renderable Code Generation", "authors": [ "Yuhao Zheng", "Li'an Zhong", "Yi Wang", "Rui Dai", "Kaikui Liu", "Xiangxiang Chu", "Linyuan Lv", "Philip Torr", "Kevin Qinghong Lin" ], "github": "https://github.com/AMAP-ML/Code2World", "project_page": "", "abstract": "Autonomous GUI agents interact with environments by perceiving interfaces and executing actions. As a virtual sandbox, the GUI World model empowers agents with human-like foresight by enabling action-conditioned prediction. However, existing text- and pixel-based approaches struggle to simultaneously achieve high visual fidelity and fine-grained structural controllability. To this end, we propose Code2World, a vision-language coder that simulates the next visual state via renderable code generation. Specifically, to address the data scarcity problem, we construct AndroidCode by translating GUI trajectories into high-fidelity HTML and refining synthesized code through a visual-feedback revision mechanism, yielding a corpus of over 80K high-quality screen-action pairs. To adapt existing VLMs into code prediction, we first perform SFT as a cold start for format layout following, then further apply Render-Aware Reinforcement Learning which uses rendered outcome as the reward signal by enforcing visual semantic fidelity and action consistency. Extensive experiments demonstrate that Code2World-8B achieves the top-performing next UI prediction, rivaling the competitive GPT-5 and Gemini-3-Pro-Image. Notably, Code2World significantly enhances downstream navigation success rates in a flexible manner, boosting Gemini-2.5-Flash by +9.5% on AndroidWorld navigation. The code is available at https://github.com/AMAP-ML/Code2World.", "github_manual": "", "github_regex": "https://github.com/AMAP-ML/Code2World", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.10063", "title": "Chain of Mindset: Reasoning with Adaptive Cognitive Modes", "authors": [ "Tianyi Jiang", "Arctanx An", "Hengyi Feng", "Naixin Zhai", "Haodong Li", "Xiaomin Yu", "Jiahui Liu", "Hanwen Du", "Shuo Zhang", "Zhi Yang", "Jie Huang", "Yuhua Li", "Yongxin Ni", "Huacan Wang", "Ronghao Chen" ], "github": "https://github.com/QuantaAlpha/chain-of-mindset", "project_page": "", "abstract": "Human problem-solving is never the repetition of a single mindset, by which we mean a distinct mode of cognitive processing. When tackling a specific task, we do not rely on a single mindset; instead, we integrate multiple mindsets within the single solution process. However, existing LLM reasoning methods fall into a common trap: they apply the same fixed mindset across all steps, overlooking that different stages of solving the same problem require fundamentally different mindsets. This single-minded assumption prevents models from reaching the next level of intelligence. To address this limitation, we propose Chain of Mindset (CoM), a training-free agentic framework that enables step-level adaptive mindset orchestration. CoM decomposes reasoning into four functionally heterogeneous mindsets: Spatial, Convergent, Divergent, and Algorithmic. A Meta-Agent dynamically selects the optimal mindset based on the evolving reasoning state, while a bidirectional Context Gate filters cross-module information flow to maintain effectiveness and efficiency. Experiments across six challenging benchmarks spanning mathematics, code generation, scientific QA, and spatial reasoning demonstrate that CoM achieves state-of-the-art performance, outperforming the strongest baseline by 4.96\\% and 4.72\\% in overall accuracy on Qwen3-VL-32B-Instruct and Gemini-2.0-Flash, while balancing reasoning efficiency. Our code is publicly available at https://github.com/QuantaAlpha/chain-of-mindset{https://github.com/QuantaAlpha/chain-of-mindset}.", "github_manual": "", "github_regex": "https://github.com/QuantaAlpha/chain-of-mindset", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.10102", "title": "VideoWorld 2: Learning Transferable Knowledge from Real-world Videos", "authors": [ "Zhongwei Ren", "Yunchao Wei", "Xiao Yu", "Guixun Luo", "Yao Zhao", "Bingyi Kang", "Jiashi Feng", "Xiaojie Jin" ], "github": "", "project_page": "", "abstract": "Learning transferable knowledge from unlabeled video data and applying it in new environments is a fundamental capability of intelligent agents. This work presents VideoWorld 2, which extends VideoWorld and offers the first investigation into learning transferable knowledge directly from raw real-world videos. At its core, VideoWorld 2 introduces a dynamic-enhanced Latent Dynamics Model (dLDM) that decouples action dynamics from visual appearance: a pretrained video diffusion model handles visual appearance modeling, enabling the dLDM to learn latent codes that focus on compact and meaningful task-related dynamics. These latent codes are then modeled autoregressively to learn task policies and support long-horizon reasoning. We evaluate VideoWorld 2 on challenging real-world handcraft making tasks, where prior video generation and latent-dynamics models struggle to operate reliably. Remarkably, VideoWorld 2 achieves up to 70% improvement in task success rate and produces coherent long execution videos. In robotics, we show that VideoWorld 2 can acquire effective manipulation knowledge from the Open-X dataset, which substantially improves task performance on CALVIN. This study reveals the potential of learning transferable world knowledge directly from raw videos, with all code, data, and models to be open-sourced for further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.08426", "title": "Prism: Spectral-Aware Block-Sparse Attention", "authors": [ "Xinghao Wang", "Pengyu Wang", "Xiaoran Liu", "Fangxu Liu", "Jason Chu", "Kai Song", "Xipeng Qiu" ], "github": "https://github.com/xinghaow99/prism", "project_page": "", "abstract": "Block-sparse attention is promising for accelerating long-context LLM pre-filling, yet identifying relevant blocks efficiently remains a bottleneck. Existing methods typically employ coarse-grained attention as a proxy for block importance estimation, but often resort to expensive token-level searching or scoring, resulting in significant selection overhead. In this work, we trace the inaccuracy of standard coarse-grained attention via mean pooling to a theoretical root cause: the interaction between mean pooling and Rotary Positional Embeddings (RoPE). We prove that mean pooling acts as a low-pass filter that induces destructive interference in high-frequency dimensions, effectively creating a \"blind spot\" for local positional information (e.g., slash patterns). To address this, we introduce Prism, a training-free spectral-aware approach that decomposes block selection into high-frequency and low-frequency branches. By applying energy-based temperature calibration, Prism restores the attenuated positional signals directly from pooled representations, enabling block importance estimation using purely block-level operations, thereby improving efficiency. Extensive evaluations confirm that Prism maintains accuracy parity with full attention while delivering up to 5.1times speedup.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xinghaow99/prism", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09849", "title": "BagelVLA: Enhancing Long-Horizon Manipulation via Interleaved Vision-Language-Action Generation", "authors": [ "Yucheng Hu", "Jianke Zhang", "Yuanfei Luo", "Yanjiang Guo", "Xiaoyu Chen", "Xinshu Sun", "Kun Feng", "Qingzhou Lu", "Sheng Chen", "Yangang Zhang", "Wei Li", "Jianyu Chen" ], "github": "", "project_page": "", "abstract": "Equipping embodied agents with the ability to reason about tasks, foresee physical outcomes, and generate precise actions is essential for general-purpose manipulation. While recent Vision-Language-Action (VLA) models have leveraged pre-trained foundation models, they typically focus on either linguistic planning or visual forecasting in isolation. These methods rarely integrate both capabilities simultaneously to guide action generation, leading to suboptimal performance in complex, long-horizon manipulation tasks. To bridge this gap, we propose BagelVLA, a unified model that integrates linguistic planning, visual forecasting, and action generation within a single framework. Initialized from a pretrained unified understanding and generative model, BagelVLA is trained to interleave textual reasoning and visual prediction directly into the action execution loop. To efficiently couple these modalities, we introduce Residual Flow Guidance (RFG), which initializes from current observation and leverages single-step denoising to extract predictive visual features, guiding action generation with minimal latency. Extensive experiments demonstrate that BagelVLA outperforms existing baselines by a significant margin on multiple simulated and real-world benchmarks, particularly in tasks requiring multi-stage reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.07153", "title": "ANCHOR: Branch-Point Data Generation for GUI Agents", "authors": [ "Jinbiao Wei", "Yilun Zhao", "Kangqi Ni", "Arman Cohan" ], "github": "https://github.com/yale-nlp/Anchor", "project_page": "", "abstract": "End-to-end GUI agents for real desktop environments require large amounts of high-quality interaction data, yet collecting human demonstrations is expensive and existing synthetic pipelines often suffer from limited task diversity or noisy, goal-drifting trajectories. We present a trajectory expansion framework Anchor that bootstraps scalable desktop supervision from a small set of verified seed demonstrations. Starting from each seed, we identify branch points that correspond to meaningful state changes and propose new, state-grounded task variants conditioned on the current GUI context. An executing agent then follows the proposed instructions to generate new trajectories, while a verifier enforces task completion via state-aware checks and trajectory-level consistency. To improve supervision quality, we further apply task-conditioned step-level filtering to remove ungrounded actions and denoise post-branch segments to maintain coherent intent. Experiments on standard desktop benchmarks, OSWorld and WindowsAgentArena, show that models fine-tuned on our expanded corpus achieve consistent improvements over zero-shot agents and representative synthesis baselines, and generalize across applications and operating systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yale-nlp/Anchor", "github_api_added_by": "auto" }, { "date": "2026-02-11", "arxiv_id": "2602.09823", "title": "Covo-Audio Technical Report", "authors": [ "Wenfu Wang", "Chenxing Li", "Liqiang Zhang", "Yiyang Zhao", "Yuxiang Zou", "Hanzhao Li", "Mingyu Cui", "Hao Zhang", "Kun Wei", "Le Xu", "Zikang Huang", "Jiajun Xu", "Jiliang Hu", "Xiang He", "Zeyu Xie", "Jiawen Kang", "Youjun Chen", "Meng Yu", "Dong Yu", "Rilin Chen", "Linlin Di", "Shulin Feng", "Na Hu", "Yang Liu", "Bang Wang", "Shan Yang" ], "github": "", "project_page": "", "abstract": "In this work, we present Covo-Audio, a 7B-parameter end-to-end LALM that directly processes continuous audio inputs and generates audio outputs within a single unified architecture. Through large-scale curated pretraining and targeted post-training, Covo-Audio achieves state-of-the-art or competitive performance among models of comparable scale across a broad spectrum of tasks, including speech-text modeling, spoken dialogue, speech understanding, audio understanding, and full-duplex voice interaction. Extensive evaluations demonstrate that the pretrained foundation model exhibits strong speech-text comprehension and semantic reasoning capabilities on multiple benchmarks, outperforming representative open-source models of comparable scale. Furthermore, Covo-Audio-Chat, the dialogue-oriented variant, demonstrates strong spoken conversational abilities, including understanding, contextual reasoning, instruction following, and generating contextually appropriate and empathetic responses, validating its applicability to real-world conversational assistant scenarios. Covo-Audio-Chat-FD, the evolved full-duplex model, achieves substantially superior performance on both spoken dialogue capabilities and full-duplex interaction behaviors, demonstrating its competence in practical robustness. To mitigate the high cost of deploying end-to-end LALMs for natural conversational systems, we propose an intelligence-speaker decoupling strategy that separates dialogue intelligence from voice rendering, enabling flexible voice customization with minimal text-to-speech (TTS) data while preserving dialogue performance. Overall, our results highlight the strong potential of 7B-scale models to integrate sophisticated audio intelligence with high-level semantic reasoning, and suggest a scalable path toward more capable and versatile LALMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.08344", "title": "OPE: Overcoming Information Saturation in Parallel Thinking via Outline-Guided Path Exploration", "authors": [ "Qi Guo", "Jianing Wang", "Deyang Kong", "Xiangyu Xi", "Jianfei Zhang", "Yi Lu", "Jingang Wang", "Wei Wang", "Shikun Zhang", "Wei Ye" ], "github": "", "project_page": "", "abstract": "Parallel thinking has emerged as a new paradigm for large reasoning models (LRMs) in tackling complex problems. Recent methods leverage Reinforcement Learning (RL) to enhance parallel thinking, aiming to address the limitations in computational resources and effectiveness encountered with supervised fine-tuning. However, most existing studies primarily focus on optimizing the aggregation phase, with limited attention to the path exploration stage. In this paper, we theoretically analyze the optimization of parallel thinking under the Reinforcement Learning with Verifiable Rewards (RLVR) setting, and identify that the mutual information bottleneck among exploration paths fundamentally restricts overall performance. To address this, we propose Outline-Guided Path Exploration (OPE), which explicitly partitions the solution space by generating diverse reasoning outlines prior to parallel path reasoning, thereby reducing information redundancy and improving the diversity of information captured across exploration paths. We implement OPE with an iterative RL strategy that optimizes outline planning and outline-guided reasoning independently. Extensive experiments across multiple challenging mathematical benchmarks demonstrate that OPE effectively improves reasoning performance in different aggregation strategies, enabling LRMs to more reliably discover correct solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.10116", "title": "SAGE: Scalable Agentic 3D Scene Generation for Embodied AI", "authors": [ "Hongchi Xia", "Xuan Li", "Zhaoshuo Li", "Qianli Ma", "Jiashu Xu", "Ming-Yu Liu", "Yin Cui", "Tsung-Yi Lin", "Wei-Chiu Ma", "Shenlong Wang", "Shuran Song", "Fangyin Wei" ], "github": "https://github.com/NVlabs/sage", "project_page": "", "abstract": "Real-world data collection for embodied agents remains costly and unsafe, calling for scalable, realistic, and simulator-ready 3D environments. However, existing scene-generation systems often rely on rule-based or task-specific pipelines, yielding artifacts and physically invalid scenes. We present SAGE, an agentic framework that, given a user-specified embodied task (e.g., \"pick up a bowl and place it on the table\"), understands the intent and automatically generates simulation-ready environments at scale. The agent couples multiple generators for layout and object composition with critics that evaluate semantic plausibility, visual realism, and physical stability. Through iterative reasoning and adaptive tool selection, it self-refines the scenes until meeting user intent and physical validity. The resulting environments are realistic, diverse, and directly deployable in modern simulators for policy training. Policies trained purely on this data exhibit clear scaling trends and generalize to unseen objects and layouts, demonstrating the promise of simulation-driven scaling for embodied AI. Code, demos, and the SAGE-10k dataset can be found on the project page here: https://nvlabs.github.io/sage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/sage", "github_api_added_by": "auto" }, { "date": "2026-02-11", "arxiv_id": "2602.09024", "title": "Autoregressive Image Generation with Masked Bit Modeling", "authors": [ "Qihang Yu", "Qihao Liu", "Ju He", "Xinyang Zhang", "Yang Liu", "Liang-Chieh Chen", "Xi Chen" ], "github": "", "project_page": "", "abstract": "This paper challenges the dominance of continuous pipelines in visual generation. We systematically investigate the performance gap between discrete and continuous methods. Contrary to the belief that discrete tokenizers are intrinsically inferior, we demonstrate that the disparity arises primarily from the total number of bits allocated in the latent space (i.e., the compression ratio). We show that scaling up the codebook size effectively bridges this gap, allowing discrete tokenizers to match or surpass their continuous counterparts. However, existing discrete generation methods struggle to capitalize on this insight, suffering from performance degradation or prohibitive training costs with scaled codebook. To address this, we propose masked Bit AutoRegressive modeling (BAR), a scalable framework that supports arbitrary codebook sizes. By equipping an autoregressive transformer with a masked bit modeling head, BAR predicts discrete tokens through progressively generating their constituent bits. BAR achieves a new state-of-the-art gFID of 0.99 on ImageNet-256, outperforming leading methods across both continuous and discrete paradigms, while significantly reducing sampling costs and converging faster than prior continuous approaches. Project page is available at https://bar-gen.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2601.21235", "title": "SHARP: Social Harm Analysis via Risk Profiles for Measuring Inequities in Large Language Models", "authors": [ "Alok Abhishek", "Tushar Bandopadhyay", "Lisa Erickson" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly deployed in high-stakes domains, where rare but severe failures can result in irreversible harm. However, prevailing evaluation benchmarks often reduce complex social risk to mean-centered scalar scores, thereby obscuring distributional structure, cross-dimensional interactions, and worst-case behavior. This paper introduces Social Harm Analysis via Risk Profiles (SHARP), a framework for multidimensional, distribution-aware evaluation of social harm. SHARP models harm as a multivariate random variable and integrates explicit decomposition into bias, fairness, ethics, and epistemic reliability with a union-of-failures aggregation reparameterized as additive cumulative log-risk. The framework further employs risk-sensitive distributional statistics, with Conditional Value at Risk (CVaR95) as a primary metric, to characterize worst-case model behavior. Application of SHARP to eleven frontier LLMs, evaluated on a fixed corpus of n=901 socially sensitive prompts, reveals that models with similar average risk can exhibit more than twofold differences in tail exposure and volatility. Across models, dimension-wise marginal tail behavior varies systematically across harm dimensions, with bias exhibiting the strongest tail severities, epistemic and fairness risks occupying intermediate regimes, and ethical misalignment consistently lower; together, these patterns reveal heterogeneous, model-dependent failure structures that scalar benchmarks conflate. These findings indicate that responsible evaluation and governance of LLMs require moving beyond scalar averages toward multidimensional, tail-sensitive risk profiling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.10098", "title": "VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model", "authors": [ "Jingwen Sun", "Wenyao Zhang", "Zekun Qi", "Shaojie Ren", "Zezhi Liu", "Hanxin Zhu", "Guangzhong Sun", "Xin Jin", "Zhibo Chen" ], "github": "", "project_page": "", "abstract": "Pretraining Vision-Language-Action (VLA) policies on internet-scale video is appealing, yet current latent-action objectives often learn the wrong thing: they remain anchored to pixel variation rather than action-relevant state transitions, making them vulnerable to appearance bias, nuisance motion, and information leakage. We introduce VLA-JEPA, a JEPA-style pretraining framework that sidesteps these pitfalls by design. The key idea is leakage-free state prediction: a target encoder produces latent representations from future frames, while the student pathway sees only the current observation -- future information is used solely as supervision targets, never as input. By predicting in latent space rather than pixel space, VLA-JEPA learns dynamics abstractions that are robust to camera motion and irrelevant background changes. This yields a simple two-stage recipe -- JEPA pretraining followed by action-head fine-tuning -- without the multi-stage complexity of prior latent-action pipelines. Experiments on LIBERO, LIBERO-Plus, SimplerEnv and real-world manipulation tasks show that VLA-JEPA achieves consistent gains in generalization and robustness over existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.05400", "title": "OPUS: Towards Efficient and Principled Data Selection in Large Language Model Pre-training in Every Iteration", "authors": [ "Shaobo Wang", "Xuan Ouyang", "Tianyi Xu", "Yuzheng Hu", "Jialin Liu", "Guo Chen", "Tianyu Zhang", "Junhao Zheng", "Kexin Yang", "Xingzhang Ren", "Dayiheng Liu", "Linfeng Zhang" ], "github": "", "project_page": "", "abstract": "As high-quality public text approaches exhaustion, a phenomenon known as the Data Wall, pre-training is shifting from more tokens to better tokens. However, existing methods either rely on heuristic static filters that ignore training dynamics, or use dynamic yet optimizer-agnostic criteria based on raw gradients. We propose OPUS (Optimizer-induced Projected Utility Selection), a dynamic data selection framework that defines utility in the optimizer-induced update space. OPUS scores candidates by projecting their effective updates, shaped by modern optimizers, onto a target direction derived from a stable, in-distribution proxy. To ensure scalability, we employ Ghost technique with CountSketch for computational efficiency, and Boltzmann sampling for data diversity, incurring only 4.7\\% additional compute overhead. OPUS achieves remarkable results across diverse corpora, quality tiers, optimizers, and model scales. In pre-training of GPT-2 Large/XL on FineWeb and FineWeb-Edu with 30B tokens, OPUS outperforms industrial-level baselines and even full 200B-token training. Moreover, when combined with industrial-level static filters, OPUS further improves pre-training efficiency, even with lower-quality data. Furthermore, in continued pre-training of Qwen3-8B-Base on SciencePedia, OPUS achieves superior performance using only 0.5B tokens compared to full training with 3B tokens, demonstrating significant data efficiency gains in specialized domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.07035", "title": "DLLM-Searcher: Adapting Diffusion Large Language Model for Search Agents", "authors": [ "Jiahao Zhao", "Shaoxuan Xu", "Zhongxiang Sun", "Fengqi Zhu", "Jingyang Ou", "Yuling Shi", "Chongxuan Li", "Xiao Zhang", "Jun Xu" ], "github": "", "project_page": "", "abstract": "Recently, Diffusion Large Language Models (dLLMs) have demonstrated unique efficiency advantages, enabled by their inherently parallel decoding mechanism and flexible generation paradigm. Meanwhile, despite the rapid advancement of Search Agents, their practical deployment is constrained by a fundamental limitation, termed as 1) Latency Challenge: the serial execution of multi-round reasoning, tool calling, and tool response waiting under the ReAct agent paradigm induces severe end-to-end latency. Intuitively, dLLMs can leverage their distinctive strengths to optimize the operational efficiency of agents under the ReAct agent paradigm. Practically, existing dLLM backbones face the 2) Agent Ability Challenge. That is, existing dLLMs exhibit remarkably weak reasoning and tool-calling capabilities, preventing these advantages from being effectively realized in practice. In this paper, we propose DLLM-Searcher, an optimization framework for dLLM-based Search Agents. To solve the Agent Ability Challenge, we design a two-stage post-training pipeline encompassing Agentic Supervised Fine-Tuning (Agentic SFT) and Agentic Variance-Reduced Preference Optimization Agentic VRPO, which enhances the backbone dLLM's information seeking and reasoning capabilities. To mitigate the Latency Challenge, we leverage the flexible generation mechanism of dLLMs and propose a novel agent paradigm termed Parallel-Reasoning and Acting P-ReAct. P-ReAct guides the model to prioritize decoding tool_call instructions, thereby allowing the model to keep thinking while waiting for the tool's return. Experimental results demonstrate that DLLM-Searcher achieves performance comparable to mainstream LLM-based search agents and P-ReAct delivers approximately 15% inference acceleration. Our code is available at https://anonymous.4open.science/r/DLLM-Searcher-553C", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.10104", "title": "Olaf-World: Orienting Latent Actions for Video World Modeling", "authors": [ "Yuxin Jiang", "Yuchao Gu", "Ivor W. Tsang", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Scaling action-controllable world models is limited by the scarcity of action labels. While latent action learning promises to extract control interfaces from unlabeled video, learned latents often fail to transfer across contexts: they entangle scene-specific cues and lack a shared coordinate system. This occurs because standard objectives operate only within each clip, providing no mechanism to align action semantics across contexts. Our key insight is that although actions are unobserved, their semantic effects are observable and can serve as a shared reference. We introduce Seq\u0394-REPA, a sequence-level control-effect alignment objective that anchors integrated latent action to temporal feature differences from a frozen, self-supervised video encoder. Building on this, we present Olaf-World, a pipeline that pretrains action-conditioned video world models from large-scale passive video. Extensive experiments demonstrate that our method learns a more structured latent action space, leading to stronger zero-shot action transfer and more data-efficient adaptation to new control interfaces than state-of-the-art baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.10090", "title": "Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning", "authors": [ "Zhaoyang Wang", "Canwen Xu", "Boyi Liu", "Yite Wang", "Siwei Han", "Zhewei Yao", "Huaxiu Yao", "Yuxiong He" ], "github": "https://github.com/Snowflake-Labs/agent-world-model", "project_page": "", "abstract": "Recent advances in large language model (LLM) have empowered autonomous agents to perform complex tasks that require multi-turn interactions with tools and environments. However, scaling such agent training is limited by the lack of diverse and reliable environments. In this paper, we propose Agent World Model (AWM), a fully synthetic environment generation pipeline. Using this pipeline, we scale to 1,000 environments covering everyday scenarios, in which agents can interact with rich toolsets (35 tools per environment on average) and obtain high-quality observations. Notably, these environments are code-driven and backed by databases, providing more reliable and consistent state transitions than environments simulated by LLMs. Moreover, they enable more efficient agent interaction compared with collecting trajectories from realistic environments. To demonstrate the effectiveness of this resource, we perform large-scale reinforcement learning for multi-turn tool-use agents. Thanks to the fully executable environments and accessible database states, we can also design reliable reward functions. Experiments on three benchmarks show that training exclusively in synthetic environments, rather than benchmark-specific ones, yields strong out-of-distribution generalization. The code is available at https://github.com/Snowflake-Labs/agent-world-model.", "github_manual": "", "github_regex": "https://github.com/Snowflake-Labs/agent-world-model", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.08847", "title": "Dr. MAS: Stable Reinforcement Learning for Multi-Agent LLM Systems", "authors": [ "Lang Feng", "Longtao Zheng", "Shuo He", "Fuxiang Zhang", "Bo An" ], "github": "", "project_page": "", "abstract": "Multi-agent LLM systems enable advanced reasoning and tool use via role specialization, yet reliable reinforcement learning (RL) post-training for such systems remains difficult. In this work, we theoretically pinpoint a key reason for training instability when extending group-based RL to multi-agent LLM systems. We show that under GRPO-style optimization, a global normalization baseline may deviate from diverse agents' reward distributions, which ultimately leads to gradient-norm instability. Based on this finding, we propose Dr. MAS, a simple and stable RL training recipe for multi-agent LLM systems. Dr. MAS uses an agent-wise remedy: normalizing advantages per agent using each agent's own reward statistics, which calibrates gradient scales and dramatically stabilizes training, both theoretically and empirically. Beyond the algorithm, Dr. MAS provides an end-to-end RL training framework for multi-agent LLM systems, supporting scalable orchestration, flexible per-agent LLM serving and optimization configs, and shared resource scheduling of LLM actor backends. We evaluate Dr. MAS on multi-agent math reasoning and multi-turn search benchmarks using Qwen2.5 and Qwen3 series models. Dr. MAS achieves clear gains over vanilla GRPO (e.g., +5.6\\% avg@16 and +4.6\\% pass@16 on math, and +15.2\\% avg@16 and +13.1\\% pass@16 on search) while largely eliminating gradient spikes. Moreover, it remains highly effective under heterogeneous agent-model assignments while improving efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09439", "title": "Fine-T2I: An Open, Large-Scale, and Diverse Dataset for High-Quality T2I Fine-Tuning", "authors": [ "Xu Ma", "Yitian Zhang", "Qihua Dong", "Yun Fu" ], "github": "", "project_page": "", "abstract": "High-quality and open datasets remain a major bottleneck for text-to-image (T2I) fine-tuning. Despite rapid progress in model architectures and training pipelines, most publicly available fine-tuning datasets suffer from low resolution, poor text-image alignment, or limited diversity, resulting in a clear performance gap between open research models and enterprise-grade models. In this work, we present Fine-T2I, a large-scale, high-quality, and fully open dataset for T2I fine-tuning. Fine-T2I spans 10 task combinations, 32 prompt categories, 11 visual styles, and 5 prompt templates, and combines synthetic images generated by strong modern models with carefully curated real images from professional photographers. All samples are rigorously filtered for text-image alignment, visual fidelity, and prompt quality, with over 95% of initial candidates removed. The final dataset contains over 6 million text-image pairs, around 2 TB on disk, approaching the scale of pretraining datasets while maintaining fine-tuning-level quality. Across a diverse set of pretrained diffusion and autoregressive models, fine-tuning on Fine-T2I consistently improves both generation quality and instruction adherence, as validated by human evaluation, visual comparison, and automatic metrics. We release Fine-T2I under an open license to help close the data gap in T2I fine-tuning in the open community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09443", "title": "P1-VL: Bridging Visual Perception and Scientific Reasoning in Physics Olympiads", "authors": [ "Yun Luo", "Futing Wang", "Qianjia Cheng", "Fangchen Yu", "Haodi Lei", "Jianhao Yan", "Chenxi Li", "Jiacheng Chen", "Yufeng Zhao", "Haiyuan Wan", "Yuchen Zhang", "Shenghe Zheng", "Junchi Yao", "Qingyang Zhang", "Haonan He", "Wenxuan Zeng", "Li Sheng", "Chengxing Xie", "Yuxin Zuo", "Yizhuo Li", "Yulun Wu", "Rui Huang", "Dongzhan Zhou", "Kai Chen", "Yu Qiao", "Lei Bai", "Yu Cheng", "Ning Ding", "Bowen Zhou", "Peng Ye", "Ganqu Cui" ], "github": "", "project_page": "", "abstract": "The transition from symbolic manipulation to science-grade reasoning represents a pivotal frontier for Large Language Models (LLMs), with physics serving as the critical test anchor for binding abstract logic to physical reality. Physics demands that a model maintain physical consistency with the laws governing the universe, a task that fundamentally requires multimodal perception to ground abstract logic in reality. At the Olympiad level, diagrams are often constitutive rather than illustrative, containing essential constraints, such as boundary conditions and spatial symmetries, that are absent from the text. To bridge this visual-logical gap, we introduce P1-VL, a family of open-source vision-language models engineered for advanced scientific reasoning. Our method harmonizes Curriculum Reinforcement Learning, which employs progressive difficulty expansion to stabilize post-training, with Agentic Augmentation, enabling iterative self-verification at inference. Evaluated on HiPhO, a rigorous benchmark of 13 exams from 2024-2025, our flagship P1-VL-235B-A22B becomes the first open-source Vision-Language Model (VLM) to secure 12 gold medals and achieves the state-of-the-art performance in the open-source models. Our agent-augmented system achieves the No.2 overall rank globally, trailing only Gemini-3-Pro. Beyond physics, P1-VL demonstrates remarkable scientific reasoning capacity and generalizability, establishing significant leads over base models in STEM benchmarks. By open-sourcing P1-VL, we provide a foundational step toward general-purpose physical intelligence to better align visual perceptions with abstract physical laws for machine scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.06820", "title": "ScaleEnv: Scaling Environment Synthesis from Scratch for Generalist Interactive Tool-Use Agent Training", "authors": [ "Dunwei Tu", "Hongyan Hao", "Hansi Yang", "Yihao Chen", "Yi-Kai Zhang", "Zhikang Xia", "Yu Yang", "Yueqing Sun", "Xingchen Liu", "Furao Shen", "Qi Gu", "Hui Su", "Xunliang Cai" ], "github": "", "project_page": "", "abstract": "Training generalist agents capable of adapting to diverse scenarios requires interactive environments for self-exploration. However, interactive environments remain critically scarce, and existing synthesis methods suffer from significant limitations regarding environmental diversity and scalability. To address these challenges, we introduce ScaleEnv, a framework that constructs fully interactive environments and verifiable tasks entirely from scratch. Specifically, ScaleEnv ensures environment reliability through procedural testing, and guarantees task completeness and solvability via tool dependency graph expansion and executable action verification. By enabling agents to learn through exploration within ScaleEnv, we demonstrate significant performance improvements on unseen, multi-turn tool-use benchmarks such as \u03c4^2-Bench and VitaBench, highlighting strong generalization capabilities. Furthermore, we investigate the relationship between increasing number of domains and model generalization performance, providing empirical evidence that scaling environmental diversity is critical for robust agent learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.08503", "title": "Learning Self-Correction in Vision-Language Models via Rollout Augmentation", "authors": [ "Yi Ding", "Ziliang Qiu", "Bolian Li", "Ruqi Zhang" ], "github": "https://github.com/DripNowhy/Octopus", "project_page": "", "abstract": "Self-correction is essential for solving complex reasoning problems in vision-language models (VLMs). However, existing reinforcement learning (RL) methods struggle to learn it, as effective self-correction behaviors emerge only rarely, making learning signals extremely sparse. To address this challenge, we propose correction-specific rollouts (Octopus), an RL rollout augmentation framework that synthesizes dense self-correction examples by recombining existing rollouts. This augmentation simultaneously improves sample efficiency due to rollout reuse and stabilizes RL optimization through balanced supervision. Furthermore, we introduce a response-masking strategy that decouples self-correction from direct reasoning, avoiding signal conflicts and enabling both behaviors to be learned effectively. Building on this, we introduce Octopus-8B, a reasoning VLM with controllable self-correction capability. Across 7 benchmarks, it achieves SoTA performance among open-source VLMs, outperforming the best RLVR baseline by 1.0 score while requiring only 0.72times training time per step.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DripNowhy/Octopus", "github_api_added_by": "auto" }, { "date": "2026-02-11", "arxiv_id": "2602.07022", "title": "Condition Errors Refinement in Autoregressive Image Generation with Diffusion Loss", "authors": [ "Yucheng Zhou", "Hao Li", "Jianbing Shen" ], "github": "", "project_page": "", "abstract": "Recent studies have explored autoregressive models for image generation, with promising results, and have combined diffusion models with autoregressive frameworks to optimize image generation via diffusion losses. In this study, we present a theoretical analysis of diffusion and autoregressive models with diffusion loss, highlighting the latter's advantages. We present a theoretical comparison of conditional diffusion and autoregressive diffusion with diffusion loss, demonstrating that patch denoising optimization in autoregressive models effectively mitigates condition errors and leads to a stable condition distribution. Our analysis also reveals that autoregressive condition generation refines the condition, causing the condition error influence to decay exponentially. In addition, we introduce a novel condition refinement approach based on Optimal Transport (OT) theory to address ``condition inconsistency''. We theoretically demonstrate that formulating condition refinement as a Wasserstein Gradient Flow ensures convergence toward the ideal condition distribution, effectively mitigating condition inconsistency. Experiments demonstrate the superiority of our method over diffusion and autoregressive models with diffusion loss methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.08382", "title": "Dynamic Long Context Reasoning over Compressed Memory via End-to-End Reinforcement Learning", "authors": [ "Zhuoen Chen", "Dongfang Li", "Meishan Zhang", "Baotian Hu", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) face significant challenges in long-context processing, including quadratic computational costs, information forgetting, and the context fragmentation inherent in retrieval-augmented generation (RAG). We propose a cognitively inspired framework for efficient long-context inference based on chunk-wise compression and selective memory recall, rather than processing all raw tokens. The framework segments long inputs into chunks and encodes each chunk into compressed memory representations using a learned compressor. A gating module dynamically selects relevant memory blocks, which are then iteratively processed by a reasoning module with an evolving working memory to solve downstream tasks. The compressor and reasoner are jointly optimized via end-to-end reinforcement learning, while the gating module is trained separately as a classifier. Experimental results show that the proposed method achieves competitive accuracy on multi-hop reasoning benchmarks such as RULER-HQA, extrapolates context length from 7K to 1.75M tokens, and offers a favorable accuracy-efficiency trade-off compared to strong long-context baselines. In particular, it achieves up to a 2 times reduction in peak GPU memory usage and a 6 times inference speedup over MemAgent.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.05435", "title": "Stable Velocity: A Variance Perspective on Flow Matching", "authors": [ "Donglin Yang", "Yongxing Zhang", "Xin Yu", "Liang Hou", "Xin Tao", "Pengfei Wan", "Xiaojuan Qi", "Renjie Liao" ], "github": "https://github.com/linYDTHU/StableVelocity", "project_page": "", "abstract": "While flow matching is elegant, its reliance on single-sample conditional velocities leads to high-variance training targets that destabilize optimization and slow convergence. By explicitly characterizing this variance, we identify 1) a high-variance regime near the prior, where optimization is challenging, and 2) a low-variance regime near the data distribution, where conditional and marginal velocities nearly coincide. Leveraging this insight, we propose Stable Velocity, a unified framework that improves both training and sampling. For training, we introduce Stable Velocity Matching (StableVM), an unbiased variance-reduction objective, along with Variance-Aware Representation Alignment (VA-REPA), which adaptively strengthen auxiliary supervision in the low-variance regime. For inference, we show that dynamics in the low-variance regime admit closed-form simplifications, enabling Stable Velocity Sampling (StableVS), a finetuning-free acceleration. Extensive experiments on ImageNet 256times256 and large pretrained text-to-image and text-to-video models, including SD3.5, Flux, Qwen-Image, and Wan2.2, demonstrate consistent improvements in training efficiency and more than 2times faster sampling within the low-variance regime without degrading sample quality. Our code is available at https://github.com/linYDTHU/StableVelocity.", "github_manual": "", "github_regex": "https://github.com/linYDTHU/StableVelocity", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.02464", "title": "From Directions to Regions: Decomposing Activations in Language Models via Local Geometry", "authors": [ "Or Shafran", "Shaked Ronen", "Omri Fahn", "Shauli Ravfogel", "Atticus Geiger", "Mor Geva" ], "github": "https://github.com/ordavid-s/decomposing-activations-local-geometry", "project_page": "", "abstract": "Activation decomposition methods in language models are tightly coupled to geometric assumptions on how concepts are realized in activation space. Existing approaches search for individual global directions, implicitly assuming linear separability, which overlooks concepts with nonlinear or multi-dimensional structure. In this work, we leverage Mixture of Factor Analyzers (MFA) as a scalable, unsupervised alternative that models the activation space as a collection of Gaussian regions with their local covariance structure. MFA decomposes activations into two compositional geometric objects: the region's centroid in activation space, and the local variation from the centroid. We train large-scale MFAs for Llama-3.1-8B and Gemma-2-2B, and show they capture complex, nonlinear structures in activation space. Moreover, evaluations on localization and steering benchmarks show that MFA outperforms unsupervised baselines, is competitive with supervised localization methods, and often achieves stronger steering performance than sparse autoencoders. Together, our findings position local geometry, expressed through subspaces, as a promising unit of analysis for scalable concept discovery and model control, accounting for complex structures that isolated directions fail to capture.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ordavid-s/decomposing-activations-local-geometry", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.01725", "title": "SafePred: A Predictive Guardrail for Computer-Using Agents via World Models", "authors": [ "Yurun Chen", "Zeyi Liao", "Ping Yin", "Taotao Xie", "Keting Yin", "Shengyu Zhang" ], "github": "", "project_page": "", "abstract": "With the widespread deployment of Computer-using Agents (CUAs) in complex real-world environments, prevalent long-term risks often lead to severe and irreversible consequences. Most existing guardrails for CUAs adopt a reactive approach, constraining agent behavior only within the current observation space. While these guardrails can prevent immediate short-term risks (e.g., clicking on a phishing link), they cannot proactively avoid long-term risks: seemingly reasonable actions can lead to high-risk consequences that emerge with a delay (e.g., cleaning logs leads to future audits being untraceable), which reactive guardrails cannot identify within the current observation space. To address these limitations, we propose a predictive guardrail approach, with the core idea of aligning predicted future risks with current decisions. Based on this approach, we present SafePred, a predictive guardrail framework for CUAs that establishes a risk-to-decision loop to ensure safe agent behavior. SafePred supports two key abilities: (1) Short- and long-term risk prediction: by using safety policies as the basis for risk prediction, SafePred leverages the prediction capability of the world model to generate semantic representations of both short-term and long-term risks, thereby identifying and pruning actions that lead to high-risk states; (2) Decision optimization: translating predicted risks into actionable safe decision guidances through step-level interventions and task-level re-planning. Extensive experiments show that SafePred significantly reduces high-risk behaviors, achieving over 97.6% safety performance and improving task utility by up to 21.4% compared with reactive baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.07839", "title": "TodoEvolve: Learning to Architect Agent Planning Systems", "authors": [ "Jiaxi Liu", "Yanzuo Jiang", "Guibin Zhang", "Zihan Zhang", "Heng Chang", "Zhenfei Yin", "Qibing Ren", "Junchi Yan" ], "github": "", "project_page": "", "abstract": "Planning has become a central capability for contemporary agent systems in navigating complex, long-horizon tasks, yet existing approaches predominantly rely on fixed, hand-crafted planning structures that lack the flexibility to adapt to the structural diversity of open-ended problems. To address this limitation, we introduce TodoEvolve, a meta-planning paradigm that autonomously synthesizes and dynamically revises task-specific planning architectures. Specifically, we first construct PlanFactory, a modular design space that standardizes diverse planning paradigms within a unified codebase encompassing topology, initialization, adaptation, and navigation, thereby providing a common interface for heterogeneous planning patterns. Leveraging PlanFactory, we collect high-quality planning trajectories and train Todo-14B via Impedance-Guided Preference Optimization (IGPO), a multi-objective reinforcement learning objective that encourages the generation of planning systems that are performant, stable, and token-efficient across arbitrary tasks and agent backbones. Empirical evaluations on five agentic benchmarks demonstrate that TodoEvolve consistently surpasses carefully engineered planning modules while maintaining economical API costs and runtime overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.07276", "title": "Steer2Adapt: Dynamically Composing Steering Vectors Elicits Efficient Adaptation of LLMs", "authors": [ "Pengrui Han", "Xueqiang Xu", "Keyang Xuan", "Peiyang Song", "Siru Ouyang", "Runchu Tian", "Yuqing Jiang", "Cheng Qian", "Pengcheng Jiang", "Jiashuo Sun", "Junxia Cui", "Ming Zhong", "Ge Liu", "Jiawei Han", "Jiaxuan You" ], "github": "", "project_page": "", "abstract": "Activation steering has emerged as a promising approach for efficiently adapting large language models (LLMs) to downstream behaviors. However, most existing steering methods rely on a single static direction per task or concept, making them inflexible under task variation and inadequate for complex tasks that require multiple coordinated capabilities. To address this limitation, we propose STEER2ADAPT, a lightweight framework that adapts LLMs by composing steering vectors rather than learning new ones from scratch. In many domains (e.g., reasoning or safety), tasks share a small set of underlying concept dimensions. STEER2ADAPT captures these dimensions as a reusable, low-dimensional semantic prior subspace, and adapts to new tasks by dynamically discovering a linear combination of basis vectors from only a handful of examples. Experiments across 9 tasks and 3 models in both reasoning and safety domains demonstrate the effectiveness of STEER2ADAPT, achieving an average improvement of 8.2%. Extensive analyses further show that STEER2ADAPT is a data-efficient, stable, and transparent inference-time adaptation method for LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09662", "title": "TreeCUA: Efficiently Scaling GUI Automation with Tree-Structured Verifiable Evolution", "authors": [ "Deyang Jiang", "Jing Huang", "Xuanle Zhao", "Lei Chen", "Liming Zheng", "Fanfan Liu", "Haibo Qiu", "Peng Shi", "Zhixiong Zeng" ], "github": "https://github.com/UITron-hub/TreeCUA", "project_page": "", "abstract": "Effectively scaling GUI automation is essential for computer-use agents (CUAs); however, existing work primarily focuses on scaling GUI grounding rather than the more crucial GUI planning, which requires more sophisticated data collection. In reality, the exploration process of a CUA across apps/desktops/web pages typically follows a tree structure, with earlier functional entry points often being explored more frequently. Thus, organizing large-scale trajectories into tree structures can reduce data cost and streamline the data scaling of GUI planning. In this work, we propose TreeCUA to efficiently scale GUI automation with tree-structured verifiable evolution. We propose a multi-agent collaborative framework to explore the environment, verify actions, summarize trajectories, and evaluate quality to generate high-quality and scalable GUI trajectories. To improve efficiency, we devise a novel tree-based topology to store and replay duplicate exploration nodes, and design an adaptive exploration algorithm to balance the depth (i.e., trajectory difficulty) and breadth (i.e., trajectory diversity). Moreover, we develop world knowledge guidance and global memory backtracking to avoid low-quality generation. Finally, we naturally extend and propose the TreeCUA-DPO method from abundant tree node information, improving GUI planning capability by referring to the branch information of adjacent trajectories. Experimental results show that TreeCUA and TreeCUA-DPO offer significant improvements, and out-of-domain (OOD) studies further demonstrate strong generalization. All trajectory node information and code will be available at https://github.com/UITron-hub/TreeCUA.", "github_manual": "", "github_regex": "https://github.com/UITron-hub/TreeCUA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09268", "title": "Rethinking Global Text Conditioning in Diffusion Transformers", "authors": [ "Nikita Starodubcev", "Daniil Pakhomov", "Zongze Wu", "Ilya Drobyshevskiy", "Yuchen Liu", "Zhonghao Wang", "Yuqian Zhou", "Zhe Lin", "Dmitry Baranchuk" ], "github": "", "project_page": "", "abstract": "Diffusion transformers typically incorporate textual information via attention layers and a modulation mechanism using a pooled text embedding. Nevertheless, recent approaches discard modulation-based text conditioning and rely exclusively on attention. In this paper, we address whether modulation-based text conditioning is necessary and whether it can provide any performance advantage. Our analysis shows that, in its conventional usage, the pooled embedding contributes little to overall performance, suggesting that attention alone is generally sufficient for faithfully propagating prompt information. However, we reveal that the pooled embedding can provide significant gains when used from a different perspective-serving as guidance and enabling controllable shifts toward more desirable properties. This approach is training-free, simple to implement, incurs negligible runtime overhead, and can be applied to various diffusion models, bringing improvements across diverse tasks, including text-to-image/video generation and image editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.06161", "title": "Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding", "authors": [ "Yanzheng Xiang", "Lan Wei", "Yizhen Yao", "Qinglin Zhu", "Hanqi Yan", "Chen Jin", "Philip Alexander Teare", "Dandan Zhang", "Lin Gui", "Amrutha Saseendran", "Yulan He" ], "github": "", "project_page": "", "abstract": "Parallel diffusion decoding can accelerate diffusion language model inference by unmasking multiple tokens per step, but aggressive parallelism often harms quality. Revocable decoding mitigates this by rechecking earlier tokens, yet we observe that existing verification schemes frequently trigger flip-flop oscillations, where tokens are remasked and later restored unchanged. This behaviour slows inference in two ways: remasking verified positions weakens the conditioning context for parallel drafting, and repeated remask cycles consume the revision budget with little net progress. We propose COVER (Cache Override Verification for Efficient Revision), which performs leave-one-out verification and stable drafting within a single forward pass. COVER constructs two attention views via KV cache override: selected seeds are masked for verification, while their cached key value states are injected for all other queries to preserve contextual information, with a closed form diagonal correction preventing self leakage at the seed positions. COVER further prioritises seeds using a stability aware score that balances uncertainty, downstream influence, and cache drift, and it adapts the number of verified seeds per step. Across benchmarks, COVER markedly reduces unnecessary revisions and yields faster decoding while preserving output quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09924", "title": "LLMs Encode Their Failures: Predicting Success from Pre-Generation Activations", "authors": [ "William Lugoloobi", "Thomas Foster", "William Bankes", "Chris Russell" ], "github": "https://github.com/KabakaWilliam/llms_know_difficulty", "project_page": "", "abstract": "Running LLMs with extended reasoning on every problem is expensive, but determining which inputs actually require additional compute remains challenging. We investigate whether their own likelihood of success is recoverable from their internal representations before generation, and if this signal can guide more efficient inference. We train linear probes on pre-generation activations to predict policy-specific success on math and coding tasks, substantially outperforming surface features such as question length and TF-IDF. Using E2H-AMC, which provides both human and model performance on identical problems, we show that models encode a model-specific notion of difficulty that is distinct from human difficulty, and that this distinction increases with extended reasoning. Leveraging these probes, we demonstrate that routing queries across a pool of models can exceed the best-performing model whilst reducing inference cost by up to 70\\% on MATH, showing that internal representations enable practical efficiency gains even when they diverge from human intuitions about difficulty. Our code is available at: https://github.com/KabakaWilliam/llms_know_difficulty", "github_manual": "", "github_regex": "https://github.com/KabakaWilliam/llms_know_difficulty", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.10099", "title": "Learning on the Manifold: Unlocking Standard Diffusion Transformers with Representation Encoders", "authors": [ "Amandeep Kumar", "Vishal M. Patel" ], "github": "https://github.com/amandpkr/RJF", "project_page": "", "abstract": "Leveraging representation encoders for generative modeling offers a path for efficient, high-fidelity synthesis. However, standard diffusion transformers fail to converge on these representations directly. While recent work attributes this to a capacity bottleneck proposing computationally expensive width scaling of diffusion transformers we demonstrate that the failure is fundamentally geometric. We identify Geometric Interference as the root cause: standard Euclidean flow matching forces probability paths through the low-density interior of the hyperspherical feature space of representation encoders, rather than following the manifold surface. To resolve this, we propose Riemannian Flow Matching with Jacobi Regularization (RJF). By constraining the generative process to the manifold geodesics and correcting for curvature-induced error propagation, RJF enables standard Diffusion Transformer architectures to converge without width scaling. Our method RJF enables the standard DiT-B architecture (131M parameters) to converge effectively, achieving an FID of 3.37 where prior methods fail to converge. Code: https://github.com/amandpkr/RJF", "github_manual": "", "github_regex": "https://github.com/amandpkr/RJF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.07755", "title": "Learning to Continually Learn via Meta-learning Agentic Memory Designs", "authors": [ "Yiming Xiong", "Shengran Hu", "Jeff Clune" ], "github": "https://github.com/zksha/alma.git", "project_page": "", "abstract": "The statelessness of foundation models bottlenecks agentic systems' ability to continually learn, a core capability for long-horizon reasoning and adaptation. To address this limitation, agentic systems commonly incorporate memory modules to retain and reuse past experience, aiming for continual learning during test time. However, most existing memory designs are human-crafted and fixed, which limits their ability to adapt to the diversity and non-stationarity of real-world tasks. In this paper, we introduce ALMA (Automated meta-Learning of Memory designs for Agentic systems), a framework that meta-learns memory designs to replace hand-engineered memory designs, therefore minimizing human effort and enabling agentic systems to be continual learners across diverse domains. Our approach employs a Meta Agent that searches over memory designs expressed as executable code in an open-ended manner, theoretically allowing the discovery of arbitrary memory designs, including database schemas as well as their retrieval and update mechanisms. Extensive experiments across four sequential decision-making domains demonstrate that the learned memory designs enable more effective and efficient learning from experience than state-of-the-art human-crafted memory designs on all benchmarks. When developed and deployed safely, ALMA represents a step toward self-improving AI systems that learn to be adaptive, continual learners.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zksha/alma.git", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.08519", "title": "Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering", "authors": [ "Yunhui Liu", "Pengyu Qiu", "Yu Xing", "Yongchao Liu", "Peng Du", "Chuntao Hong", "Jiajun Zheng", "Tao Zheng", "Tieke He" ], "github": "https://github.com/Cloudy1225/PyAGC", "project_page": "", "abstract": "Attributed Graph Clustering (AGC) is a fundamental unsupervised task that integrates structural topology and node attributes to uncover latent patterns in graph-structured data. Despite its significance in industrial applications such as fraud detection and user segmentation, a significant chasm persists between academic research and real-world deployment. Current evaluation protocols suffer from the small-scale, high-homophily citation datasets, non-scalable full-batch training paradigms, and a reliance on supervised metrics that fail to reflect performance in label-scarce environments. To bridge these gaps, we present PyAGC, a comprehensive, production-ready benchmark and library designed to stress-test AGC methods across diverse scales and structural properties. We unify existing methodologies into a modular Encode-Cluster-Optimize framework and, for the first time, provide memory-efficient, mini-batch implementations for a wide array of state-of-the-art AGC algorithms. Our benchmark curates 12 diverse datasets, ranging from 2.7K to 111M nodes, specifically incorporating industrial graphs with complex tabular features and low homophily. Furthermore, we advocate for a holistic evaluation protocol that mandates unsupervised structural metrics and efficiency profiling alongside traditional supervised metrics. Battle-tested in high-stakes industrial workflows at Ant Group, this benchmark offers the community a robust, reproducible, and scalable platform to advance AGC research towards realistic deployment. The code and resources are publicly available via GitHub (https://github.com/Cloudy1225/PyAGC), PyPI (https://pypi.org/project/pyagc), and Documentation (https://pyagc.readthedocs.io).", "github_manual": "", "github_regex": "https://github.com/Cloudy1225/PyAGC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.00268", "title": "TokenTrim: Inference-Time Token Pruning for Autoregressive Long Video Generation", "authors": [ "Ariel Shaulov", "Eitan Shaar", "Amit Edenzon", "Lior Wolf" ], "github": "", "project_page": "", "abstract": "Auto-regressive video generation enables long video synthesis by iteratively conditioning each new batch of frames on previously generated content. However, recent work has shown that such pipelines suffer from severe temporal drift, where errors accumulate and amplify over long horizons. We hypothesize that this drift does not primarily stem from insufficient model capacity, but rather from inference-time error propagation. Specifically, we contend that drift arises from the uncontrolled reuse of corrupted latent conditioning tokens during auto-regressive inference. To correct this accumulation of errors, we propose a simple, inference-time method that mitigates temporal drift by identifying and removing unstable latent tokens before they are reused for conditioning. For this purpose, we define unstable tokens as latent tokens whose representations deviate significantly from those of the previously generated batch, indicating potential corruption or semantic drift. By explicitly removing corrupted latent tokens from the auto-regressive context, rather than modifying entire spatial regions or model parameters, our method prevents unreliable latent information from influencing future generation steps. As a result, it significantly improves long-horizon temporal consistency without modifying the model architecture, training procedure, or leaving latent space.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.04802", "title": "VISTA-Bench: Do Vision-Language Models Really Understand Visualized Text as Well as Pure Text?", "authors": [ "Qing'an Liu", "Juntong Feng", "Yuhao Wang", "Xinzhe Han", "Yujie Cheng", "Yue Zhu", "Haiwen Diao", "Yunzhi Zhuge", "Huchuan Lu" ], "github": "https://github.com/QingAnLiu/VISTA-Bench", "project_page": "", "abstract": "Vision-Language Models (VLMs) have achieved impressive performance in cross-modal understanding across textual and visual inputs, yet existing benchmarks predominantly focus on pure-text queries. In real-world scenarios, language also frequently appears as visualized text embedded in images, raising the question of whether current VLMs handle such input requests comparably. We introduce VISTA-Bench, a systematic benchmark from multimodal perception, reasoning, to unimodal understanding domains. It evaluates visualized text understanding by contrasting pure-text and visualized-text questions under controlled rendering conditions. Extensive evaluation of over 20 representative VLMs reveals a pronounced modality gap: models that perform well on pure-text queries often degrade substantially when equivalent semantic content is presented as visualized text. This gap is further amplified by increased perceptual difficulty, highlighting sensitivity to rendering variations despite unchanged semantics. Overall, VISTA-Bench provides a principled evaluation framework to diagnose this limitation and to guide progress toward more unified language representations across tokenized text and pixels. The source dataset is available at https://github.com/QingAnLiu/VISTA-Bench.", "github_manual": "", "github_regex": "https://github.com/QingAnLiu/VISTA-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.04521", "title": "C-\u0394\u0398: Circuit-Restricted Weight Arithmetic for Selective Refusal", "authors": [ "Aditya Kasliwal", "Pratinav Seth", "Vinay Kumar Sankarapu" ], "github": "", "project_page": "", "abstract": "Modern deployments require LLMs to enforce safety policies at scale, yet many controls rely on inference-time interventions that add recurring compute cost and serving complexity. Activation steering is widely used, but it requires runtime hooks and scales cost with the number of generations; conditional variants improve selectivity by gating when steering is applied but still retain an inference-time control path. We ask whether selective refusal can be moved entirely offline: can a mechanistic understanding of category-specific refusal be distilled into a circuit-restricted weight update that deploys as a standard checkpoint? We propose C-\u0394\u03b8: Circuit Restricted Weight Arithmetic, which (i) localizes refusal-causal computation as a sparse circuit using EAP-IG and (ii) computes a constrained weight update \u0394\u03b8C supported only on that circuit (typically <5% of parameters). Applying \u0394\u03b8C yields a drop-in edited checkpoint with no inference-time hooks, shifting cost from per-request intervention to a one-time offline update. We evaluate category-targeted selectivity and capability retention on refusal and utility benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.01244", "title": "Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments", "authors": [ "Siwei Wu", "Yizhi Li", "Yuyang Song", "Wei Zhang", "Yang Wang", "Riza Batista-Navarro", "Xian Yang", "Mingjie Tang", "Bryan Dai", "Jian Yang", "Chenghua Lin" ], "github": "https://github.com/multimodal-art-projection/TerminalTraj", "project_page": "", "abstract": "Training agentic models for terminal-based tasks critically depends on high-quality terminal trajectories that capture realistic long-horizon interactions across diverse domains. However, constructing such data at scale remains challenging due to two key requirements: \\emph{Executability}, since each instance requires a suitable and often distinct Docker environment; and \\emph{Verifiability}, because heterogeneous task outputs preclude unified, standardized verification. To address these challenges, we propose TerminalTraj, a scalable pipeline that (i) filters high-quality repositories to construct Dockerized execution environments, (ii) generates Docker-aligned task instances, and (iii) synthesizes agent trajectories with executable validation code. Using TerminalTraj, we curate 32K Docker images and generate 50,733 verified terminal trajectories across eight domains. Models trained on this data with the Qwen2.5-Coder backbone achieve consistent performance improvements on TerminalBench (TB), with gains of up to 20\\% on TB~1.0 and 10\\% on TB~2.0 over their respective backbones. Notably, TerminalTraj-32B achieves strong performance among models with fewer than 100B parameters, reaching 35.30\\% on TB~1.0 and 22.00\\% on TB~2.0, and demonstrates improved test-time scaling behavior. All code and data are available at https://github.com/Wusiwei0410/TerminalTraj.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/multimodal-art-projection/TerminalTraj", "github_api_added_by": "user" }, { "date": "2026-02-11", "arxiv_id": "2602.09591", "title": "On the Optimal Reasoning Length for RL-Trained Language Models", "authors": [ "Daisuke Nohara", "Taishi Nakamura", "Rio Yokota" ], "github": "", "project_page": "", "abstract": "Reinforcement learning substantially improves reasoning in large language models, but it also tends to lengthen chain of thought outputs and increase computational cost during both training and inference. Though length control methods have been proposed, it remains unclear what the optimal output length is for balancing efficiency and performance. In this work, we compare several length control methods on two models, Qwen3-1.7B Base and DeepSeek-R1-Distill-Qwen-1.5B. Our results indicate that length penalties may hinder reasoning acquisition, while properly tuned length control can improve efficiency for models with strong prior reasoning. By extending prior work to RL trained policies, we identify two failure modes, 1) long outputs increase dispersion, and 2) short outputs lead to under-thinking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.00462", "title": "LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs", "authors": [ "Benno Krojer", "Shravan Nayak", "Oscar Ma\u00f1as", "Vaibhav Adlakha", "Desmond Elliott", "Siva Reddy", "Marius Mosbach" ], "github": "", "project_page": "", "abstract": "Transforming a large language model (LLM) into a Vision-Language Model (VLM) can be achieved by mapping the visual tokens from a vision encoder into the embedding space of an LLM. Intriguingly, this mapping can be as simple as a shallow MLP transformation. To understand why LLMs can so readily process visual tokens, we need interpretability methods that reveal what is encoded in the visual token representations at every layer of LLM processing. In this work, we introduce LatentLens, a novel approach for mapping latent representations to descriptions in natural language. LatentLens works by encoding a large text corpus and storing contextualized token representations for each token in that corpus. Visual token representations are then compared to their contextualized textual representations, with the top-k nearest neighbor representations providing descriptions of the visual token. We evaluate this method on 10 different VLMs, showing that commonly used methods, such as LogitLens, substantially underestimate the interpretability of visual tokens. With LatentLens instead, the majority of visual tokens are interpretable across all studied models and all layers. Qualitatively, we show that the descriptions produced by LatentLens are semantically meaningful and provide more fine-grained interpretations for humans compared to individual tokens. More broadly, our findings contribute new evidence on the alignment between vision and language representations, opening up new directions for analyzing latent representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.07670", "title": "Surprisal-Guided Selection: Compute-Optimal Test-Time Strategies for Execution-Grounded Code Generation", "authors": [ "Jarrod Barnes" ], "github": "", "project_page": "", "abstract": "Test-time training (TTT) adapts language models through gradient-based updates at inference. But is adaptation the right strategy? We study compute-optimal test-time strategies for verifiable execution-grounded (VEG) tasks, domains like GPU kernel optimization where a deterministic evaluator provides dense, continuous reward signals. Using KernelBench as our testbed and a 120B-parameter model (GPT-OSS-120B with LoRA adaptation), we find that search outperforms minimal adaptation (1-5 gradient steps): Best-of-N sampling achieves 90% task success (18/20 tasks) at K=64 across the full KernelBench L1 eval set while TTT's best checkpoint reaches only 30.6% (3-seed mean), with TTT's \"equivalent K\" falling below 1, worse than single-sample inference. The failure mode is over-sharpening: gradient updates collapse diversity toward mediocre solutions rather than discovering optimal ones. Our main contribution is surprisal-guided selection: selecting the highest-surprisal (lowest-confidence) correct sample yields 80% success vs. 50% for most-confident selection, a 30% improvement. Extending to surprisal-guided-top3 matches oracle performance at 100%. This zero-cost strategy, validated through length-controlled analysis, recovers oracle performance. For dense-reward VEG tasks, compute should be allocated to sample diversity and intelligent selection rather than gradient adaptation. The surprisal-guided selection principle may generalize to other execution-grounded domains where optimal solutions occupy the distribution tail.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.05892", "title": "ContextBench: A Benchmark for Context Retrieval in Coding Agents", "authors": [ "Han Li", "Letian Zhu", "Bohan Zhang", "Rili Feng", "Jiaming Wang", "Yue Pan", "Earl T. Barr", "Sarro Federica", "Zhaoyang Chu", "He Ye" ], "github": "", "project_page": "", "abstract": "LLM-based coding agents have shown strong performance on automated issue resolution benchmarks, yet existing evaluations largely focus on final task success, providing limited insight into how agents retrieve and use code context during problem solving. We introduce ContextBench, a process-oriented evaluation of context retrieval in coding agents. ContextBench consists of 1,136 issue-resolution tasks from 66 repositories across eight programming languages, each augmented with human-annotated gold contexts. We further implement an automated evaluation framework that tracks agent trajectories and measures context recall, precision, and efficiency throughout issue resolution. Using ContextBench, we evaluate four frontier LLMs and five coding agents. Our results show that sophisticated agent scaffolding yields only marginal gains in context retrieval (\"The Bitter Lesson\" of coding agents), LLMs consistently favor recall over precision, and substantial gaps exist between explored and utilized context. ContextBench augments existing end-to-end benchmarks with intermediate gold-context metrics that unbox the issue-resolution process. These contexts offer valuable intermediate signals for guiding LLM reasoning in software tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09276", "title": "Effective Reasoning Chains Reduce Intrinsic Dimensionality", "authors": [ "Archiki Prasad", "Mandar Joshi", "Kenton Lee", "Mohit Bansal", "Peter Shaw" ], "github": "", "project_page": "", "abstract": "Chain-of-thought (CoT) reasoning and its variants have substantially improved the performance of language models on complex reasoning tasks, yet the precise mechanisms by which different strategies facilitate generalization remain poorly understood. While current explanations often point to increased test-time computation or structural guidance, establishing a consistent, quantifiable link between these factors and generalization remains challenging. In this work, we identify intrinsic dimensionality as a quantitative measure for characterizing the effectiveness of reasoning chains. Intrinsic dimensionality quantifies the minimum number of model dimensions needed to reach a given accuracy threshold on a given task. By keeping the model architecture fixed and varying the task formulation through different reasoning strategies, we demonstrate that effective reasoning strategies consistently reduce the intrinsic dimensionality of the task. Validating this on GSM8K with Gemma-3 1B and 4B, we observe a strong inverse correlation between the intrinsic dimensionality of a reasoning strategy and its generalization performance on both in-distribution and out-of-distribution data. Our findings suggest that effective reasoning chains facilitate learning by better compressing the task using fewer parameters, offering a new quantitative metric for analyzing reasoning processes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09017", "title": "Contact-Anchored Policies: Contact Conditioning Creates Strong Robot Utility Models", "authors": [ "Zichen Jeff Cui", "Omar Rayyan", "Haritheja Etukuru", "Bowen Tan", "Zavier Andrianarivo", "Zicheng Teng", "Yihang Zhou", "Krish Mehta", "Nicholas Wojno", "Kevin Yuanbo Wu", "Manan H Anjaria", "Ziyuan Wu", "Manrong Mao", "Guangxun Zhang", "Binit Shah", "Yejin Kim", "Soumith Chintala", "Lerrel Pinto", "Nur Muhammad Mahi Shafiullah" ], "github": "", "project_page": "", "abstract": "The prevalent paradigm in robot learning attempts to generalize across environments, embodiments, and tasks with language prompts at runtime. A fundamental tension limits this approach: language is often too abstract to guide the concrete physical understanding required for robust manipulation. In this work, we introduce Contact-Anchored Policies (CAP), which replace language conditioning with points of physical contact in space. Simultaneously, we structure CAP as a library of modular utility models rather than a monolithic generalist policy. This factorization allows us to implement a real-to-sim iteration cycle: we build EgoGym, a lightweight simulation benchmark, to rapidly identify failure modes and refine our models and datasets prior to real-world deployment. We show that by conditioning on contact and iterating via simulation, CAP generalizes to novel environments and embodiments out of the box on three fundamental manipulation skills while using only 23 hours of demonstration data, and outperforms large, state-of-the-art VLAs in zero-shot evaluations by 56%. All model checkpoints, codebase, hardware, simulation, and datasets will be open-sourced. Project page: https://cap-policy.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.07422", "title": "Secure Code Generation via Online Reinforcement Learning with Vulnerability Reward Model", "authors": [ "Tianyi Wu", "Mingzhe Du", "Yue Liu", "Chengran Yang", "Terry Yue Zhuo", "Jiaheng Zhang", "See-Kiong Ng" ], "github": "https://github.com/AndrewWTY/SecCoderX", "project_page": "", "abstract": "Large language models (LLMs) are increasingly used in software development, yet their tendency to generate insecure code remains a major barrier to real-world deployment. Existing secure code alignment methods often suffer from a functionality--security paradox, improving security at the cost of substantial utility degradation. We propose SecCoderX, an online reinforcement learning framework for functionality-preserving secure code generation. SecCoderX first bridges vulnerability detection and secure code generation by repurposing mature detection resources in two ways: (i) synthesizing diverse, reality-grounded vulnerability-inducing coding tasks for online RL rollouts, and (ii) training a reasoning-based vulnerability reward model that provides scalable and reliable security supervision. Together, these components are unified in an online RL loop to align code LLMs to generate secure and functional code. Extensive experiments demonstrate that SecCoderX achieves state-of-the-art performance, improving Effective Safety Rate (ESR) by approximately 10% over unaligned models, whereas prior methods often degrade ESR by 14-54%. We release our code, dataset and model checkpoints at https://github.com/AndrewWTY/SecCoderX.", "github_manual": "", "github_regex": "https://github.com/AndrewWTY/SecCoderX", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09000", "title": "iGRPO: Self-Feedback-Driven LLM Reasoning", "authors": [ "Ali Hatamizadeh", "Shrimai Prabhumoye", "Igor Gitman", "Ximing Lu", "Seungju Han", "Wei Ping", "Yejin Choi", "Jan Kautz" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have shown promise in solving complex mathematical problems, yet they still fall short of producing accurate and consistent solutions. Reinforcement Learning (RL) is a framework for aligning these models with task-specific rewards, improving overall quality and reliability. Group Relative Policy Optimization (GRPO) is an efficient, value-function-free alternative to Proximal Policy Optimization (PPO) that leverages group-relative reward normalization. We introduce Iterative Group Relative Policy Optimization (iGRPO), a two-stage extension of GRPO that adds dynamic self-conditioning through model-generated drafts. In Stage 1, iGRPO samples multiple exploratory drafts and selects the highest-reward draft using the same scalar reward signal used for optimization. In Stage 2, it appends this best draft to the original prompt and applies a GRPO-style update on draft-conditioned refinements, training the policy to improve beyond its strongest prior attempt. Under matched rollout budgets, iGRPO consistently outperforms GRPO across base models (e.g., Nemotron-H-8B-Base-8K and DeepSeek-R1 Distilled), validating its effectiveness on diverse reasoning benchmarks. Moreover, applying iGRPO to OpenReasoning-Nemotron-7B trained on AceReason-Math achieves new state-of-the-art results of 85.62\\% and 79.64\\% on AIME24 and AIME25, respectively. Ablations further show that the refinement wrapper generalizes beyond GRPO variants, benefits from a generative judge, and alters learning dynamics by delaying entropy collapse. These results underscore the potential of iterative, self-feedback-based RL for advancing verifiable mathematical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.07918", "title": "CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution", "authors": [ "Minbeom Kim", "Mihir Parmar", "Phillip Wallis", "Lesly Miculicich", "Kyomin Jung", "Krishnamurthy Dj Dvijotham", "Long T. Le", "Tomas Pfister" ], "github": "https://github.com/kaijiezhu11/MELON", "project_page": "", "abstract": "AI agents equipped with tool-calling capabilities are susceptible to Indirect Prompt Injection (IPI) attacks. In this attack scenario, malicious commands hidden within untrusted content trick the agent into performing unauthorized actions. Existing defenses can reduce attack success but often suffer from the over-defense dilemma: they deploy expensive, always-on sanitization regardless of actual threat, thereby degrading utility and latency even in benign scenarios. We revisit IPI through a causal ablation perspective: a successful injection manifests as a dominance shift where the user request no longer provides decisive support for the agent's privileged action, while a particular untrusted segment, such as a retrieved document or tool output, provides disproportionate attributable influence. Based on this signature, we propose CausalArmor, a selective defense framework that (i) computes lightweight, leave-one-out ablation-based attributions at privileged decision points, and (ii) triggers targeted sanitization only when an untrusted segment dominates the user intent. Additionally, CausalArmor employs retroactive Chain-of-Thought masking to prevent the agent from acting on ``poisoned'' reasoning traces. We present a theoretical analysis showing that sanitization based on attribution margins conditionally yields an exponentially small upper bound on the probability of selecting malicious actions. Experiments on AgentDojo and DoomArena demonstrate that CausalArmor matches the security of aggressive defenses while improving explainability and preserving utility and latency of AI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kaijiezhu11/MELON", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.07398", "title": "AgentSys: Secure and Dynamic LLM Agents Through Explicit Hierarchical Memory Management", "authors": [ "Ruoyao Wen", "Hao Li", "Chaowei Xiao", "Ning Zhang" ], "github": "https://github.com/ruoyaow/agentsys-memory", "project_page": "", "abstract": "Indirect prompt injection threatens LLM agents by embedding malicious instructions in external content, enabling unauthorized actions and data theft. LLM agents maintain working memory through their context window, which stores interaction history for decision-making. Conventional agents indiscriminately accumulate all tool outputs and reasoning traces in this memory, creating two critical vulnerabilities: (1) injected instructions persist throughout the workflow, granting attackers multiple opportunities to manipulate behavior, and (2) verbose, non-essential content degrades decision-making capabilities. Existing defenses treat bloated memory as given and focus on remaining resilient, rather than reducing unnecessary accumulation to prevent the attack. We present AgentSys, a framework that defends against indirect prompt injection through explicit memory management. Inspired by process memory isolation in operating systems, AgentSys organizes agents hierarchically: a main agent spawns worker agents for tool calls, each running in an isolated context and able to spawn nested workers for subtasks. External data and subtask traces never enter the main agent's memory; only schema-validated return values can cross boundaries through deterministic JSON parsing. Ablations show isolation alone cuts attack success to 2.19%, and adding a validator/sanitizer further improves defense with event-triggered checks whose overhead scales with operations rather than context length. On AgentDojo and ASB, AgentSys achieves 0.78% and 4.25% attack success while slightly improving benign utility over undefended baselines. It remains robust to adaptive attackers and across multiple foundation models, showing that explicit memory management enables secure, dynamic LLM agent architectures. Our code is available at: https://github.com/ruoyaow/agentsys-memory.", "github_manual": "", "github_regex": "https://github.com/ruoyaow/agentsys-memory", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.05085", "title": "Locas: Your Models are Principled Initializers of Locally-Supported Parametric Memories", "authors": [ "Sidi Lu", "Zhenwen Liang", "Dongyang Ma", "Yan Wang", "Haitao Mi", "Dong Yu" ], "github": "", "project_page": "", "abstract": "In this paper, we aim to bridge test-time-training with a new type of parametric memory that can be flexibly offloaded from or merged into model parameters. We present Locas, a Locally-Supported parametric memory that shares the design of FFN blocks in modern transformers, allowing it to be flexibly permanentized into the model parameters while supporting efficient continual learning. We discuss two major variants of Locas: one with a conventional two-layer MLP design that has a clearer theoretical guarantee; the other one shares the same GLU-FFN structure with SOTA LLMs, and can be easily attached to existing models for both parameter-efficient and computation-efficient continual learning. Crucially, we show that proper initialization of such low-rank sideway-FFN-style memories -- performed in a principled way by reusing model parameters, activations and/or gradients -- is essential for fast convergence, improved generalization, and catastrophic forgetting prevention. We validate the proposed memory mechanism on the PG-19 whole-book language modeling and LoCoMo long-context dialogue question answering tasks. With only 0.02\\% additional parameters in the lowest case, Locas-GLU is capable of storing the information from past context while maintaining a much smaller context window. In addition, we also test the model's general capability loss after memorizing the whole book with Locas, through comparative MMLU evaluation. Results show the promising ability of Locas to permanentize past context into parametric knowledge with minimized catastrophic forgetting of the model's existing internal knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.09153", "title": "SceneSmith: Agentic Generation of Simulation-Ready Indoor Scenes", "authors": [ "Nicholas Pfaff", "Thomas Cohn", "Sergey Zakharov", "Rick Cory", "Russ Tedrake" ], "github": "", "project_page": "", "abstract": "Simulation has become a key tool for training and evaluating home robots at scale, yet existing environments fail to capture the diversity and physical complexity of real indoor spaces. Current scene synthesis methods produce sparsely furnished rooms that lack the dense clutter, articulated furniture, and physical properties essential for robotic manipulation. We introduce SceneSmith, a hierarchical agentic framework that generates simulation-ready indoor environments from natural language prompts. SceneSmith constructs scenes through successive stagesx2013from architectural layout to furniture placement to small object populationx2013each implemented as an interaction among VLM agents: designer, critic, and orchestrator. The framework tightly integrates asset generation through text-to-3D synthesis for static objects, dataset retrieval for articulated objects, and physical property estimation. SceneSmith generates 3-6x more objects than prior methods, with <2% inter-object collisions and 96% of objects remaining stable under physics simulation. In a user study with 205 participants, it achieves 92% average realism and 91% average prompt faithfulness win rates against baselines. We further demonstrate that these environments can be used in an end-to-end pipeline for automatic robot policy evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-11", "arxiv_id": "2602.08025", "title": "MIND: Benchmarking Memory Consistency and Action Control in World Models", "authors": [ "Yixuan Ye", "Xuanyu Lu", "Yuxin Jiang", "Yuchao Gu", "Rui Zhao", "Qiwei Liang", "Jiachun Pan", "Fengda Zhang", "Weijia Wu", "Alex Jinpeng Wang" ], "github": "https://github.com/LAION-AI/aesthetic-predictor", "project_page": "", "abstract": "World models aim to understand, remember, and predict dynamic visual environments, yet a unified benchmark for evaluating their fundamental abilities remains lacking. To address this gap, we introduce MIND, the first open-domain closed-loop revisited benchmark for evaluating Memory consIstency and action coNtrol in worlD models. MIND contains 250 high-quality videos at 1080p and 24 FPS, including 100 (first-person) + 100 (third-person) video clips under a shared action space and 25 + 25 clips across varied action spaces covering eight diverse scenes. We design an efficient evaluation framework to measure two core abilities: memory consistency and action control, capturing temporal stability and contextual coherence across viewpoints. Furthermore, we design various action spaces, including different character movement speeds and camera rotation angles, to evaluate the action generalization capability across different action spaces under shared scenes. To facilitate future performance benchmarking on MIND, we introduce MIND-World, a novel interactive Video-to-World baseline. Extensive experiments demonstrate the completeness of MIND and reveal key challenges in current world models, including the difficulty of maintaining long-term memory consistency and generalizing across action spaces. Project page: https://csu-jpg.github.io/MIND.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LAION-AI/aesthetic-predictor", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.09713", "title": "Stroke3D: Lifting 2D strokes into rigged 3D model via latent diffusion models", "authors": [ "Ruisi Zhao", "Haoren Zheng", "Zongxin Yang", "Hehe Fan", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Rigged 3D assets are fundamental to 3D deformation and animation. However, existing 3D generation methods face challenges in generating animatable geometry, while rigging techniques lack fine-grained structural control over skeleton creation. To address these limitations, we introduce Stroke3D, a novel framework that directly generates rigged meshes from user inputs: 2D drawn strokes and a descriptive text prompt. Our approach pioneers a two-stage pipeline that separates the generation into: 1) Controllable Skeleton Generation, we employ the Skeletal Graph VAE (Sk-VAE) to encode the skeleton's graph structure into a latent space, where the Skeletal Graph DiT (Sk-DiT) generates a skeletal embedding. The generation process is conditioned on both the text for semantics and the 2D strokes for explicit structural control, with the VAE's decoder reconstructing the final high-quality 3D skeleton; and 2) Enhanced Mesh Synthesis via TextuRig and SKA-DPO, where we then synthesize a textured mesh conditioned on the generated skeleton. For this stage, we first enhance an existing skeleton-to-mesh model by augmenting its training data with TextuRig: a dataset of textured and rigged meshes with captions, curated from Objaverse-XL. Additionally, we employ a preference optimization strategy, SKA-DPO, guided by a skeleton-mesh alignment score, to further improve geometric fidelity. Together, our framework enables a more intuitive workflow for creating ready to animate 3D content. To the best of our knowledge, our work is the first to generate rigged 3D meshes conditioned on user-drawn 2D strokes. Extensive experiments demonstrate that Stroke3D produces plausible skeletons and high-quality meshes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.08253", "title": "G-LNS: Generative Large Neighborhood Search for LLM-Based Automatic Heuristic Design", "authors": [ "Baoyun Zhao", "He Wang", "Liang Zeng" ], "github": "", "project_page": "", "abstract": "While Large Language Models (LLMs) have recently shown promise in Automated Heuristic Design (AHD), existing approaches typically formulate AHD around constructive priority rules or parameterized local search guidance, thereby restricting the search space to fixed heuristic forms. Such designs offer limited capacity for structural exploration, making it difficult to escape deep local optima in complex Combinatorial Optimization Problems (COPs). In this work, we propose G-LNS, a generative evolutionary framework that extends LLM-based AHD to the automated design of Large Neighborhood Search (LNS) operators. Unlike prior methods that evolve heuristics in isolation, G-LNS leverages LLMs to co-evolve tightly coupled pairs of destroy and repair operators. A cooperative evaluation mechanism explicitly captures their interaction, enabling the discovery of complementary operator logic that jointly performs effective structural disruption and reconstruction. Extensive experiments on challenging COP benchmarks, such as Traveling Salesman Problems (TSP) and Capacitated Vehicle Routing Problems (CVRP), demonstrate that G-LNS significantly outperforms LLM-based AHD methods as well as strong classical solvers. The discovered heuristics not only achieve near-optimal solutions with reduced computational budgets but also exhibit robust generalization across diverse and unseen instance distributions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.06008", "title": "AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions", "authors": [ "Xianyang Liu", "Shangding Gu", "Dawn Song" ], "github": "https://github.com/SafeRL-Lab/AgenticPay", "project_page": "", "abstract": "Large language model (LLM)-based agents are increasingly expected to negotiate, coordinate, and transact autonomously, yet existing benchmarks lack principled settings for evaluating language-mediated economic interaction among multiple agents. We introduce AgenticPay, a benchmark and simulation framework for multi-agent buyer-seller negotiation driven by natural language. AgenticPay models markets in which buyers and sellers possess private constraints and product-dependent valuations, and must reach agreements through multi-round linguistic negotiation rather than numeric bidding alone. The framework supports a diverse suite of over 110 tasks ranging from bilateral bargaining to many-to-many markets, with structured action extraction and metrics for feasibility, efficiency, and welfare. Benchmarking state-of-the-art proprietary and open-weight LLMs reveals substantial gaps in negotiation performance and highlights challenges in long-horizon strategic reasoning, establishing AgenticPay as a foundation for studying agentic commerce and language-based market interaction. Code and dataset are available at the link: https://github.com/SafeRL-Lab/AgenticPay.", "github_manual": "", "github_regex": "https://github.com/SafeRL-Lab/AgenticPay", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10177", "title": "Towards Autonomous Mathematics Research", "authors": [ "Tony Feng", "Trieu H. Trinh", "Garrett Bingham", "Dawsen Hwang", "Yuri Chervonyi", "Junehyuk Jung", "Joonkyung Lee", "Carlo Pagano", "Sang-hyun Kim", "Federico Pasqualotto", "Sergei Gukov", "Jonathan N. Lee", "Junsu Kim", "Kaiying Hou", "Golnaz Ghiasi", "Yi Tay", "YaGuang Li", "Chenkai Kuang", "Yuan Liu", "Hanzhao", "Lin", "Evan Zheran Liu", "Nigamaa Nayakanti", "Xiaomeng Yang", "Heng-tze Cheng", "Demis Hassabis", "Koray Kavukcuoglu", "Quoc V. Le", "Thang Luong" ], "github": "", "project_page": "", "abstract": "Recent advances in foundational models have yielded reasoning systems capable of achieving a gold-medal standard at the International Mathematical Olympiad. The transition from competition-level problem-solving to professional research, however, requires navigating vast literature and constructing long-horizon proofs. In this work, we introduce Aletheia, a math research agent that iteratively generates, verifies, and revises solutions end-to-end in natural language. Specifically, Aletheia is powered by an advanced version of Gemini Deep Think for challenging reasoning problems, a novel inference-time scaling law that extends beyond Olympiad-level problems, and intensive tool use to navigate the complexities of mathematical research. We demonstrate the capability of Aletheia from Olympiad problems to PhD-level exercises and most notably, through several distinct milestones in AI-assisted mathematics research: (a) a research paper (Feng26) generated by AI without any human intervention in calculating certain structure constants in arithmetic geometry called eigenweights; (b) a research paper (LeeSeo26) demonstrating human-AI collaboration in proving bounds on systems of interacting particles called independent sets; and (c) an extensive semi-autonomous evaluation (Feng et al., 2026a) of 700 open problems on Bloom's Erdos Conjectures database, including autonomous solutions to four open questions. In order to help the public better understand the developments pertaining to AI and mathematics, we suggest codifying standard levels quantifying autonomy and novelty of AI-assisted results. We conclude with reflections on human-AI collaboration in mathematics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10224", "title": "Internalizing Meta-Experience into Memory for Guided Reinforcement Learning in Large Language Models", "authors": [ "Shiting Huang", "Zecheng Li", "Yu Zeng", "Qingnan Ren", "Zhen Fang", "Qisheng Su", "Kou Shi", "Lin Chen", "Zehui Chen", "Feng Zhao" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach for enhancing the reasoning capabilities of Large Language Models (LLMs). Despite its efficacy, RLVR faces a meta-learning bottleneck: it lacks mechanisms for error attribution and experience internalization intrinsic to the human learning cycle beyond practice and verification, thereby limiting fine-grained credit assignment and reusable knowledge formation. We term such reusable knowledge representations derived from past errors as meta-experience. Based on this insight, we propose Meta-Experience Learning (MEL), a novel framework that incorporates self-distilled meta-experience into the model's parametric memory. Building upon standard RLVR, we introduce an additional design that leverages the LLM's self-verification capability to conduct contrastive analysis on paired correct and incorrect trajectories, identify the precise bifurcation points where reasoning errors arise, and summarize them into generalizable meta-experience. The meta-experience is further internalized into the LLM's parametric memory by minimizing the negative log-likelihood, which induces a language-modeled reward signal that bridges correct and incorrect reasoning trajectories and facilitates effective knowledge reuse. Experimental results demonstrate that MEL achieves consistent improvements on benchmarks, yielding 3.92%--4.73% Pass@1 gains across varying model sizes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10999", "title": "CLI-Gym: Scalable CLI Task Generation via Agentic Environment Inversion", "authors": [ "Yusong Lin", "Haiyang Wang", "Shuzhe Wu", "Lue Fan", "Feiyang Pan", "Sanyuan Zhao", "Dandan Tu" ], "github": "", "project_page": "", "abstract": "Agentic coding requires agents to effectively interact with runtime environments, e.g., command line interfaces (CLI), so as to complete tasks like resolving dependency issues, fixing system problems, etc. But it remains underexplored how such environment-intensive tasks can be obtained at scale to enhance agents' capabilities. To address this, based on an analogy between the Dockerfile and the agentic task, we propose to employ agents to simulate and explore environment histories, guided by execution feedback. By tracing histories of a healthy environment, its state can be inverted to an earlier one with runtime failures, from which a task can be derived by packing the buggy state and the corresponding error messages. With our method, named CLI-Gym, a total of 1,655 environment-intensive tasks are derived, being the largest collection of its kind. Moreover, with curated successful trajectories, our fine-tuned model, named LiberCoder, achieves substantial absolute improvements of +21.1% (to 46.1%) on Terminal-Bench, outperforming various strong baselines. To our knowledge, this is the first public pipeline for scalable derivation of environment-intensive tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10604", "title": "Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters", "authors": [ "Ailin Huang", "Ang Li", "Aobo Kong", "Bin Wang", "Binxing Jiao", "Bo Dong", "Bojun Wang", "Boyu Chen", "Brian Li", "Buyun Ma", "Chang Su", "Changxin Miao", "Changyi Wan", "Chao Lou", "Chen Hu", "Chen Xu", "Chenfeng Yu", "Chengting Feng", "Chengyuan Yao", "Chunrui Han", "Dan Ma", "Dapeng Shi", "Daxin Jiang", "Dehua Ma", "Deshan Sun", "Di Qi", "Enle Liu", "Fajie Zhang", "Fanqi Wan", "Guanzhe Huang", "Gulin Yan", "Guoliang Cao", "Guopeng Li", "Han Cheng", "Hangyu Guo", "Hanshan Zhang", "Hao Nie", "Haonan Jia", "Haoran Lv", "Hebin Zhou", "Hekun Lv", "Heng Wang", "Heung-Yeung Shum", "Hongbo Huang", "Hongbo Peng", "Hongyu Zhou", "Hongyuan Wang", "Houyong Chen", "Huangxi Zhu", "Huimin Wu", "Huiyong Guo", "Jia Wang", "Jian Zhou", "Jianjian Sun", "Jiaoren Wu", "Jiaran Zhang", "Jiashu Lv", "Jiashuo Liu", "Jiayi Fu", "Jiayu Liu", "Jie Cheng", "Jie Luo", "Jie Yang", "Jie Zhou", "Jieyi Hou", "Jing Bai", "Jingcheng Hu", "Jingjing Xie", "Jingwei Wu", "Jingyang Zhang", "Jishi Zhou", "Junfeng Liu", "Junzhe Lin", "Ka Man Lo", "Kai Liang", "Kaibo Liu", "Kaijun Tan", "Kaiwen Yan", "Kaixiang Li", "Kang An", "Kangheng Lin", "Lei Yang", "Liang Lv", "Liang Zhao", "Liangyu Chen", "Lieyu Shi", "Liguo Tan", "Lin Lin", "Lina Chen", "Luck Ma", "Mengqiang Ren", "Michael Li", "Ming Li", "Mingliang Li", "Mingming Zhang", "Mingrui Chen", "Mitt Huang", "Na Wang", "Peng Liu", "Qi Han", "Qian Zhao", "Qinglin He", "Qinxin Du", "Qiuping Wu", "Quan Sun", "Rongqiu Yang", "Ruihang Miao", "Ruixin Han", "Ruosi Wan", "Ruyan Guo", "Shan Wang", "Shaoliang Pang", "Shaowen Yang", "Shengjie Fan", "Shijie Shang", "Shiliang Yang", "Shiwei Li", "Shuangshuang Tian", "Siqi Liu", "Siye Wu", "Siyu Chen", "Song Yuan", "Tiancheng Cao", "Tianchi Yue", "Tianhao Cheng", "Tianning Li", "Tingdan Luo", "Wang You", "Wei Ji", "Wei Yuan", "Wei Zhang", "Weibo Wu", "Weihao Xie", "Wen Sun", "Wenjin Deng", "Wenzhen Zheng", "Wuxun Xie", "Xiangfeng Wang", "Xiangwen Kong", "Xiangyu Liu", "Xiangyu Zhang", "Xiaobo Yang", "Xiaojia Liu", "Xiaolan Yuan", "Xiaoran Jiao", "Xiaoxiao Ren", "Xiaoyun Zhang", "Xin Li", "Xin Liu", "Xin Wu", "Xing Chen", "Xingping Yang", "Xinran Wang", "Xu Zhao", "Xuan He", "Xuanti Feng", "Xuedan Cai", "Xuqiang Zhou", "Yanbo Yu", "Yang Li", "Yang Xu", "Yanlin Lai", "Yanming Xu", "Yaoyu Wang", "Yeqing Shen", "Yibo Zhu", "Yichen Lv", "Yicheng Cao", "Yifeng Gong", "Yijing Yang", "Yikun Yang", "Yin Zhao", "Yingxiu Zhao", "Yinmin Zhang", "Yitong Zhang", "Yixuan Zhang", "Yiyang Chen", "Yongchi Zhao", "Yongshen Long", "Yongyao Wang", "Yousong Guan", "Yu Zhou", "Yuang Peng", "Yuanhao Ding", "Yuantao Fan", "Yuanzhen Yang", "Yuchu Luo", "Yudi Zhao", "Yue Peng", "Yueqiang Lin", "Yufan Lu", "Yuling Zhao", "Yunzhou Ju", "Yurong Zhang", "Yusheng Li", "Yuxiang Yang", "Yuyang Chen", "Yuzhu Cai", "Zejia Weng", "Zetao Hong", "Zexi Li", "Zhe Xie", "Zheng Ge", "Zheng Gong", "Zheng Zeng", "Zhenyi Lu", "Zhewei Huang", "Zhichao Chang", "Zhiguo Huang", "Zhiheng Hu", "Zidong Yang", "Zili Wang", "Ziqi Ren", "Zixin Zhang", "Zixuan Wang" ], "github": "", "project_page": "", "abstract": "We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most when building agents: sharp reasoning and fast, reliable execution. Step 3.5 Flash pairs a 196B-parameter foundation with 11B active parameters for efficient inference. It is optimized with interleaved 3:1 sliding-window/full attention and Multi-Token Prediction (MTP-3) to reduce the latency and cost of multi-round agentic interactions. To reach frontier-level intelligence, we design a scalable reinforcement learning framework that combines verifiable signals with preference feedback, while remaining stable under large-scale off-policy training, enabling consistent self-improvement across mathematics, code, and tool use. Step 3.5 Flash demonstrates strong performance across agent, coding, and math tasks, achieving 85.4% on IMO-AnswerBench, 86.4% on LiveCodeBench-v6 (2024.08-2025.05), 88.2% on tau2-Bench, 69.0% on BrowseComp (with context management), and 51.0% on Terminal-Bench 2.0, comparable to frontier models such as GPT-5.2 xHigh and Gemini 3.0 Pro. By redefining the efficiency frontier, Step 3.5 Flash provides a high-density foundation for deploying sophisticated agents in real-world industrial environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10179", "title": "When the Prompt Becomes Visual: Vision-Centric Jailbreak Attacks for Large Image Editing Models", "authors": [ "Jiacheng Hou", "Yining Sun", "Ruochong Jin", "Haochen Han", "Fangming Liu", "Wai Kin Victor Chan", "Alex Jinpeng Wang" ], "github": "https://github.com/CSU-JPG/VJA", "project_page": "", "abstract": "Recent advances in large image editing models have shifted the paradigm from text-driven instructions to vision-prompt editing, where user intent is inferred directly from visual inputs such as marks, arrows, and visual-text prompts. While this paradigm greatly expands usability, it also introduces a critical and underexplored safety risk: the attack surface itself becomes visual. In this work, we propose Vision-Centric Jailbreak Attack (VJA), the first visual-to-visual jailbreak attack that conveys malicious instructions purely through visual inputs. To systematically study this emerging threat, we introduce IESBench, a safety-oriented benchmark for image editing models. Extensive experiments on IESBench demonstrate that VJA effectively compromises state-of-the-art commercial models, achieving attack success rates of up to 80.9% on Nano Banana Pro and 70.1% on GPT-Image-1.5. To mitigate this vulnerability, we propose a training-free defense based on introspective multimodal reasoning, which substantially improves the safety of poorly aligned models to a level comparable with commercial systems, without auxiliary guard models and with negligible computational overhead. Our findings expose new vulnerabilities, provide both a benchmark and practical defense to advance safe and trustworthy modern image editing systems. Warning: This paper contains offensive images created by large image editing models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CSU-JPG/VJA", "github_api_added_by": "auto" }, { "date": "2026-02-12", "arxiv_id": "2602.09901", "title": "QP-OneModel: A Unified Generative LLM for Multi-Task Query Understanding in Xiaohongshu Search", "authors": [ "Jianzhao Huang", "Xiaorui Huang", "Fei Zhao", "Yunpeng Liu", "Hui Zhang", "Fangcheng Shi", "Congfeng Li", "Zechen Sun", "Yi Wu", "Yao Hu", "Yunhan Bai", "Shaosheng Cao" ], "github": "", "project_page": "", "abstract": "Query Processing (QP) bridges user intent and content supply in large-scale Social Network Service (SNS) search engines. Traditional QP systems rely on pipelines of isolated discriminative models (e.g., BERT), suffering from limited semantic understanding and high maintenance overhead. While Large Language Models (LLMs) offer a potential solution, existing approaches often optimize sub-tasks in isolation, neglecting intrinsic semantic synergy and necessitating independent iterations. Moreover, standard generative methods often lack grounding in SNS scenarios, failing to bridge the gap between open-domain corpora and informal SNS linguistic patterns, while struggling to adhere to rigorous business definitions. We present QP-OneModel, a Unified Generative LLM for Multi-Task Query Understanding in the SNS domain. We reformulate heterogeneous sub-tasks into a unified sequence generation paradigm, adopting a progressive three-stage alignment strategy culminating in multi-reward Reinforcement Learning. Furthermore, QP-OneModel generates intent descriptions as a novel high-fidelity semantic signal, effectively augmenting downstream tasks such as query rewriting and ranking. Offline evaluations show QP-OneModel achieves a 7.35% overall gain over discriminative baselines, with significant F1 boosts in NER (+9.01%) and Term Weighting (+9.31%). It also exhibits superior generalization, surpassing a 32B model by 7.60% accuracy on unseen tasks. Fully deployed at Xiaohongshu, online A/B tests confirm its industrial value, optimizing retrieval relevance (DCG) by 0.21% and lifting user retention by 0.044%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10609", "title": "Online Causal Kalman Filtering for Stable and Effective Policy Optimization", "authors": [ "Shuo He", "Lang Feng", "Xin Cheng", "Lei Feng", "Bo An" ], "github": "", "project_page": "", "abstract": "Reinforcement learning for large language models suffers from high-variance token-level importance sampling (IS) ratios, which would destabilize policy optimization at scale. To improve stability, recent methods typically use a fixed sequence-level IS ratio for all tokens in a sequence or adjust each token's IS ratio separately, thereby neglecting temporal off-policy derivation across tokens in a sequence. In this paper, we first empirically identify that local off-policy deviation is structurally inconsistent at the token level, which may distort policy-gradient updates across adjacent tokens and lead to training collapse. To address the issue, we propose Online Causal Kalman Filtering for stable and effective Policy Optimization (KPO). Concretely, we model the desired IS ratio as a latent state that evolves across tokens and apply a Kalman filter to update this state online and autoregressively based on the states of past tokens, regardless of future tokens. The resulting filtered IS ratios preserve token-wise local structure-aware variation while strongly smoothing noise spikes, yielding more stable and effective policy updates. Experimentally, KPO achieves superior results on challenging math reasoning datasets compared with state-of-the-art counterparts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10560", "title": "When to Memorize and When to Stop: Gated Recurrent Memory for Long-Context Reasoning", "authors": [ "Leheng Sheng", "Yongtao Zhang", "Wenchang Ma", "Yaorui Shi", "Ting Huang", "Xiang Wang", "An Zhang", "Ke Shen", "Tat-Seng Chua" ], "github": "https://github.com/gkamradt/LLMTest", "project_page": "", "abstract": "While reasoning over long context is crucial for various real-world applications, it remains challenging for large language models (LLMs) as they suffer from performance degradation as the context length grows. Recent work MemAgent has tried to tackle this by processing context chunk-by-chunk in an RNN-like loop and updating a textual memory for final answering. However, this naive recurrent memory update faces two crucial drawbacks: (i) memory can quickly explode because it can update indiscriminately, even on evidence-free chunks; and (ii) the loop lacks an exit mechanism, leading to unnecessary computation after even sufficient evidence is collected. To address these issues, we propose GRU-Mem, which incorporates two text-controlled gates for more stable and efficient long-context reasoning. Specifically, in GRU-Mem, the memory only updates when the update gate is open and the recurrent loop will exit immediately once the exit gate is open. To endow the model with such capabilities, we introduce two reward signals r^{update} and r^{exit} within end-to-end RL, rewarding the correct updating and exiting behaviors respectively. Experiments on various long-context reasoning tasks demonstrate the effectiveness and efficiency of GRU-Mem, which generally outperforms the vanilla MemAgent with up to 400\\% times inference speed acceleration.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gkamradt/LLMTest", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.11144", "title": "GENIUS: Generative Fluid Intelligence Evaluation Suite", "authors": [ "Ruichuan An", "Sihan Yang", "Ziyu Guo", "Wei Dai", "Zijun Shen", "Haodong Li", "Renrui Zhang", "Xinyu Wei", "Guopeng Li", "Wenshan Wu", "Wentao Zhang" ], "github": "https://github.com/arctanxarc/GENIUS", "project_page": "", "abstract": "Unified Multimodal Models (UMMs) have shown remarkable progress in visual generation. Yet, existing benchmarks predominantly assess Crystallized Intelligence, which relies on recalling accumulated knowledge and learned schemas. This focus overlooks Generative Fluid Intelligence (GFI): the capacity to induce patterns, reason through constraints, and adapt to novel scenarios on the fly. To rigorously assess this capability, we introduce GENIUS (GEN Fluid Intelligence EvalUation Suite). We formalize GFI as a synthesis of three primitives. These include Inducing Implicit Patterns (e.g., inferring personalized visual preferences), Executing Ad-hoc Constraints (e.g., visualizing abstract metaphors), and Adapting to Contextual Knowledge (e.g., simulating counter-intuitive physics). Collectively, these primitives challenge models to solve problems grounded entirely in the immediate context. Our systematic evaluation of 12 representative models reveals significant performance deficits in these tasks. Crucially, our diagnostic analysis disentangles these failure modes. It demonstrates that deficits stem from limited context comprehension rather than insufficient intrinsic generative capability. To bridge this gap, we propose a training-free attention intervention strategy. Ultimately, GENIUS establishes a rigorous standard for GFI, guiding the field beyond knowledge utilization toward dynamic, general-purpose reasoning. Our dataset and code will be released at: https://github.com/arctanxarc/GENIUS{https://github.com/arctanxarc/GENIUS}.", "github_manual": "", "github_regex": "https://github.com/arctanxarc/GENIUS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.11089", "title": "DataChef: Cooking Up Optimal Data Recipes for LLM Adaptation via Reinforcement Learning", "authors": [ "Yicheng Chen", "Zerun Ma", "Xinchen Xie", "Yining Li", "Kai Chen" ], "github": "", "project_page": "", "abstract": "In the current landscape of Large Language Models (LLMs), the curation of large-scale, high-quality training data is a primary driver of model performance. A key lever is the data recipe, which comprises a data processing pipeline to transform raw sources into training corpora. Despite the growing use of LLMs to automate individual data processing steps, such as data synthesis and filtering, the overall design of data recipes remains largely manual and labor-intensive, requiring substantial human expertise and iteration. To bridge this gap, we formulate end-to-end data recipe generation for LLM adaptation. Given a target benchmark and a pool of available data sources, a model is required to output a complete data recipe that adapts a base LLM to the target task. We present DataChef-32B, which performs online reinforcement learning using a proxy reward that predicts downstream performance for candidate recipes. Across six held-out tasks, DataChef-32B produces practical recipes that reach comparable downstream performance to those curated by human experts. Notably, the recipe from DataChef-32B adapts Qwen3-1.7B-Base to the math domain, achieving 66.7 on AIME'25 and surpassing Qwen3-1.7B. This work sheds new light on automating LLM training and developing self-evolving AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10652", "title": "UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory", "authors": [ "Yongshi Ye", "Hui Jiang", "Feihu Jiang", "Tian Lan", "Yichao Du", "Biao Fu", "Xiaodong Shi", "Qianghuai Jia", "Longyue Wang", "Weihua Luo" ], "github": "", "project_page": "", "abstract": "Self-evolving memory serves as the trainable parameters for Large Language Models (LLMs)-based agents, where extraction (distilling insights from experience) and management (updating the memory bank) must be tightly coordinated. Existing methods predominately optimize memory management while treating memory extraction as a static process, resulting in poor generalization, where agents accumulate instance-specific noise rather than robust memories. To address this, we propose Unified Memory Extraction and Management (UMEM), a self-evolving agent framework that jointly optimizes a Large Language Model to simultaneous extract and manage memories. To mitigate overfitting to specific instances, we introduce Semantic Neighborhood Modeling and optimize the model with a neighborhood-level marginal utility reward via GRPO. This approach ensures memory generalizability by evaluating memory utility across clusters of semantically related queries. Extensive experiments across five benchmarks demonstrate that UMEM significantly outperforms highly competitive baselines, achieving up to a 10.67% improvement in multi-turn interactive tasks. Futhermore, UMEM maintains a monotonic growth curve during continuous evolution. Codes and models will be publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.09014", "title": "ArcFlow: Unleashing 2-Step Text-to-Image Generation via High-Precision Non-Linear Flow Distillation", "authors": [ "Zihan Yang", "Shuyuan Tu", "Licheng Zhang", "Qi Dai", "Yu-Gang Jiang", "Zuxuan Wu" ], "github": "https://github.com/ModelTC/Qwen-Image-Lightning", "project_page": "", "abstract": "Diffusion models have achieved remarkable generation quality, but they suffer from significant inference cost due to their reliance on multiple sequential denoising steps, motivating recent efforts to distill this inference process into a few-step regime. However, existing distillation methods typically approximate the teacher trajectory by using linear shortcuts, which makes it difficult to match its constantly changing tangent directions as velocities evolve across timesteps, thereby leading to quality degradation. To address this limitation, we propose ArcFlow, a few-step distillation framework that explicitly employs non-linear flow trajectories to approximate pre-trained teacher trajectories. Concretely, ArcFlow parameterizes the velocity field underlying the inference trajectory as a mixture of continuous momentum processes. This enables ArcFlow to capture velocity evolution and extrapolate coherent velocities to form a continuous non-linear trajectory within each denoising step. Importantly, this parameterization admits an analytical integration of this non-linear trajectory, which circumvents numerical discretization errors and results in high-precision approximation of the teacher trajectory. To train this parameterization into a few-step generator, we implement ArcFlow via trajectory distillation on pre-trained teacher models using lightweight adapters. This strategy ensures fast, stable convergence while preserving generative diversity and quality. Built on large-scale models (Qwen-Image-20B and FLUX.1-dev), ArcFlow only fine-tunes on less than 5% of original parameters and achieves a 40x speedup with 2 NFEs over the original multi-step teachers without significant quality degradation. Experiments on benchmarks show the effectiveness of ArcFlow both qualitatively and quantitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ModelTC/Qwen-Image-Lightning", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.08995", "title": "When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents", "authors": [ "Yuting Ning", "Jaylen Jones", "Zhehao Zhang", "Chentao Ye", "Weitong Ruan", "Junyi Li", "Rahul Gupta", "Huan Sun" ], "github": "", "project_page": "", "abstract": "Computer-use agents (CUAs) have made tremendous progress in the past year, yet they still frequently produce misaligned actions that deviate from the user's original intent. Such misaligned actions may arise from external attacks (e.g., indirect prompt injection) or from internal limitations (e.g., erroneous reasoning). They not only expose CUAs to safety risks, but also degrade task efficiency and reliability. This work makes the first effort to define and study misaligned action detection in CUAs, with comprehensive coverage of both externally induced and internally arising misaligned actions. We further identify three common categories in real-world CUA deployment and construct MisActBench, a benchmark of realistic trajectories with human-annotated, action-level alignment labels. Moreover, we propose DeAction, a practical and universal guardrail that detects misaligned actions before execution and iteratively corrects them through structured feedback. DeAction outperforms all existing baselines across offline and online evaluations with moderate latency overhead: (1) On MisActBench, it outperforms baselines by over 15% absolute in F1 score; (2) In online evaluation, it reduces attack success rate by over 90% under adversarial settings while preserving or even improving task success rate in benign environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.11124", "title": "PhyCritic: Multimodal Critic Models for Physical AI", "authors": [ "Tianyi Xiong", "Shihao Wang", "Guilin Liu", "Yi Dong", "Ming Li", "Heng Huang", "Jan Kautz", "Zhiding Yu" ], "github": "", "project_page": "", "abstract": "With the rapid development of large multimodal models, reliable judge and critic models have become essential for open-ended evaluation and preference alignment, providing pairwise preferences, numerical scores, and explanatory justifications for assessing model-generated responses. However, existing critics are primarily trained in general visual domains such as captioning or image question answering, leaving physical AI tasks involving perception, causal reasoning, and planning largely underexplored. We introduce PhyCritic, a multimodal critic model optimized for physical AI through a two-stage RLVR pipeline: a physical skill warmup stage that enhances physically oriented perception and reasoning, followed by self-referential critic finetuning, where the critic generates its own prediction as an internal reference before judging candidate responses, improving judgment stability and physical correctness. Across both physical and general-purpose multimodal judge benchmarks, PhyCritic achieves strong performance gains over open-source baselines and, when applied as a policy model, further improves perception and reasoning in physically grounded tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10975", "title": "FeatureBench: Benchmarking Agentic Coding for Complex Feature Development", "authors": [ "Qixing Zhou", "Jiacheng Zhang", "Haiyang Wang", "Rui Hao", "Jiahe Wang", "Minghao Han", "Yuxue Yang", "Shuzhe Wu", "Feiyang Pan", "Lue Fan", "Dandan Tu", "Zhaoxiang Zhang" ], "github": "", "project_page": "", "abstract": "Agents powered by large language models (LLMs) are increasingly adopted in the software industry, contributing code as collaborators or even autonomous developers. As their presence grows, it becomes important to assess the current boundaries of their coding abilities. Existing agentic coding benchmarks, however, cover a limited task scope, e.g., bug fixing within a single pull request (PR), and often rely on non-executable evaluations or lack an automated approach for continually updating the evaluation coverage. To address such issues, we propose FeatureBench, a benchmark designed to evaluate agentic coding performance in end-to-end, feature-oriented software development. FeatureBench incorporates an execution-based evaluation protocol and a scalable test-driven method that automatically derives tasks from code repositories with minimal human effort. By tracing from unit tests along a dependency graph, our approach can identify feature-level coding tasks spanning multiple commits and PRs scattered across the development timeline, while ensuring the proper functioning of other features after the separation. Using this framework, we curated 200 challenging evaluation tasks and 3825 executable environments from 24 open-source repositories in the first version of our benchmark. Empirical evaluation reveals that the state-of-the-art agentic model, such as Claude 4.5 Opus, which achieves a 74.4% resolved rate on SWE-bench, succeeds on only 11.0% of tasks, opening new opportunities for advancing agentic coding. Moreover, benefiting from our automated task collection toolkit, FeatureBench can be easily scaled and updated over time to mitigate data leakage. The inherent verifiability of constructed environments also makes our method potentially valuable for agent training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.04935", "title": "ASA: Training-Free Representation Engineering for Tool-Calling Agents", "authors": [ "Youjin Wang", "Run Zhou", "Rong Fu", "Shuaishuai Cao", "Hongwei Zeng", "Jiaxuan Lu", "Sicheng Fan", "Jiaqiao Zhao", "Liangming Pan" ], "github": "", "project_page": "", "abstract": "Adapting LLM agents to domain-specific tool calling remains notably brittle under evolving interfaces. Prompt and schema engineering is easy to deploy but often fragile under distribution shift and strict parsers, while continual parameter-efficient fine-tuning improves reliability at the cost of training, maintenance, and potential forgetting. We identify a critical Lazy Agent failure mode where tool necessity is nearly perfectly decodable from mid-layer activations, yet the model remains conservative in entering tool mode, revealing a representation-behavior gap. We propose Activation Steering Adapter (ASA), a training-free, inference-time controller that performs a single-shot mid-layer intervention and targets tool domains via a router-conditioned mixture of steering vectors with a probe-guided signed gate to amplify true intent while suppressing spurious triggers. On MTU-Bench with Qwen2.5-1.5B, ASA improves strict tool-use F1 from 0.18 to 0.50 while reducing the false positive rate from 0.15 to 0.05, using only about 20KB of portable assets and no weight updates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.08711", "title": "TimeChat-Captioner: Scripting Multi-Scene Videos with Time-Aware and Structural Audio-Visual Captions", "authors": [ "Linli Yao", "Yuancheng Wei", "Yaojie Zhang", "Lei Li", "Xinlong Chen", "Feifan Song", "Ziyue Wang", "Kun Ouyang", "Yuanxin Liu", "Lingpeng Kong", "Qi Liu", "Pengfei Wan", "Kun Gai", "Yuanxing Zhang", "Xu Sun" ], "github": "https://github.com/yaolinli/TimeChat-Captioner", "project_page": "", "abstract": "This paper proposes Omni Dense Captioning, a novel task designed to generate continuous, fine-grained, and structured audio-visual narratives with explicit timestamps. To ensure dense semantic coverage, we introduce a six-dimensional structural schema to create \"script-like\" captions, enabling readers to vividly imagine the video content scene by scene, akin to a cinematographic screenplay. To facilitate research, we construct OmniDCBench, a high-quality, human-annotated benchmark, and propose SodaM, a unified metric that evaluates time-aware detailed descriptions while mitigating scene boundary ambiguity. Furthermore, we construct a training dataset, TimeChatCap-42K, and present TimeChat-Captioner-7B, a strong baseline trained via SFT and GRPO with task-specific rewards. Extensive experiments demonstrate that TimeChat-Captioner-7B achieves state-of-the-art performance, surpassing Gemini-2.5-Pro, while its generated dense descriptions significantly boost downstream capabilities in audio-visual reasoning (DailyOmni and WorldSense) and temporal grounding (Charades-STA). All datasets, models, and code will be made publicly available at https://github.com/yaolinli/TimeChat-Captioner.", "github_manual": "", "github_regex": "https://github.com/yaolinli/TimeChat-Captioner", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10699", "title": "Spend Search Where It Pays: Value-Guided Structured Sampling and Optimization for Generative Recommendation", "authors": [ "Jie Jiang", "Yangru Huang", "Zeyu Wang", "Changping Wang", "Yuling Xiong", "Jun Zhang", "Huan Yu" ], "github": "", "project_page": "", "abstract": "Generative recommendation via autoregressive models has unified retrieval and ranking into a single conditional generation framework. However, fine-tuning these models with Reinforcement Learning (RL) often suffers from a fundamental probability-reward mismatch. Conventional likelihood-dominated decoding (e.g., beam search) exhibits a myopic bias toward locally probable prefixes, which causes two critical failures: (1) insufficient exploration, where high-reward items in low-probability branches are prematurely pruned and rarely sampled, and (2) advantage compression, where trajectories sharing high-probability prefixes receive highly correlated rewards with low within-group variance, yielding a weak comparative signal for RL. To address these challenges, we propose V-STAR, a Value-guided Sampling and Tree-structured Advantage Reinforcement framework. V-STAR forms a self-evolving loop via two synergistic components. First, a Value-Guided Efficient Decoding (VED) is developed to identify decisive nodes and selectively deepen high-potential prefixes. This improves exploration efficiency without exhaustive tree search. Second, we propose Sibling-GRPO, which exploits the induced tree topology to compute sibling-relative advantages and concentrates learning signals on decisive branching decisions. Extensive experiments on both offline and online datasets demonstrate that V-STAR outperforms state-of-the-art baselines, delivering superior accuracy and candidate-set diversity under strict latency constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.07106", "title": "Ex-Omni: Enabling 3D Facial Animation Generation for Omni-modal Large Language Models", "authors": [ "Haoyu Zhang", "Zhipeng Li", "Yiwen Guo", "Tianshu Yu" ], "github": "", "project_page": "", "abstract": "Omni-modal large language models (OLLMs) aim to unify multimodal understanding and generation, yet incorporating speech with 3D facial animation remains largely unexplored despite its importance for natural interaction. A key challenge arises from the representation mismatch between discrete, token-level semantic reasoning in LLMs and the dense, fine-grained temporal dynamics required for 3D facial motion, which makes direct modeling difficult to optimize under limited data. We propose Expressive Omni (Ex-Omni), an open-source omni-modal framework that augments OLLMs with speech-accompanied 3D facial animation. Ex-Omni reduces learning difficulty by decoupling semantic reasoning from temporal generation, leveraging speech units as temporal scaffolding and a unified token-as-query gated fusion (TQGF) mechanism for controlled semantic injection. We further introduce InstructEx, a dataset aims to facilitate augment OLLMs with speech-accompanied 3D facial animation. Extensive experiments demonstrate that Ex-Omni performs competitively against existing open-source OLLMs while enabling stable aligned speech and facial animation generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.08489", "title": "Beyond Correctness: Learning Robust Reasoning via Transfer", "authors": [ "Hyunseok Lee", "Soheil Abbasloo", "Jihoon Tack", "Jinwoo Shin" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has recently strengthened LLM reasoning, but its focus on final answer correctness leaves a critical gap: it does not ensure the robustness of the reasoning process itself. We adopt a simple philosophical view, robust reasoning should remain useful beyond the mind that produced it, and treat reasoning as a form of meaning transfer that must survive truncation, reinterpretation, and continuation. Building on this principle, we introduce Reinforcement Learning with Transferable Reward (RLTR), which operationalizes robustness via transfer reward that tests whether a partial reasoning prefix from one model can guide a separate model to the correct answer. This encourages LLMs to produce reasoning that is stable, interpretable, and genuinely generalizable. Our approach improves sampling consistency while improving final answer accuracy, and it reaches comparable performance in substantially fewer training steps. For example, on MATH500, RLTR achieves a +3.6%p gain in Maj@64 compared to RLVR and matches RLVR's average accuracy with roughly 2.5x fewer training steps, providing both more reliable reasoning and significantly more sample efficient.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.08030", "title": "Free(): Learning to Forget in Malloc-Only Reasoning Models", "authors": [ "Yilun Zheng", "Dongyang Ma", "Tian Liang", "Jiahao Xu", "Xinting Huang", "Lihui Chen", "Haitao Mi", "Yan Wang" ], "github": "", "project_page": "", "abstract": "Reasoning models enhance problem-solving by scaling test-time compute, yet they face a critical paradox: excessive thinking tokens often degrade performance rather than improve it. We attribute this to a fundamental architectural flaw: standard LLMs operate as \"malloc-only\" engines, continuously accumulating valid and redundant steps alike without a mechanism to prune obsolete information. To break this cycle, we propose Free()LM, a model that introduces an intrinsic self-forgetting capability via the Free-Module, a plug-and-play LoRA adapter. By iteratively switching between reasoning and cleaning modes, Free()LM dynamically identifies and prunes useless context chunks, maintaining a compact and noise-free state. Extensive experiments show that Free()LM provides consistent improvements across all model scales (8B to 685B). It achieves a 3.3% average improvement over top-tier reasoning baselines, even establishing a new SOTA on IMOanswerBench using DeepSeek V3.2-Speciale. Most notably, in long-horizon tasks where the standard Qwen3-235B-A22B model suffers a total collapse (0% accuracy), Free()LM restores performance to 50%. Our findings suggest that sustainable intelligence requires the freedom to forget as much as the power to think.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.11137", "title": "Weight Decay Improves Language Model Plasticity", "authors": [ "Tessa Han", "Sebastian Bordt", "Hanlin Zhang", "Sham Kakade" ], "github": "", "project_page": "", "abstract": "The prevailing paradigm in large language model (LLM) development is to pretrain a base model, then perform further training to improve performance and model behavior. However, hyperparameter optimization and scaling laws have been studied primarily from the perspective of the base model's validation loss, ignoring downstream adaptability. In this work, we study pretraining from the perspective of model plasticity, that is, the ability of the base model to successfully adapt to downstream tasks through fine-tuning. We focus on the role of weight decay, a key regularization parameter during pretraining. Through systematic experiments, we show that models trained with larger weight decay values are more plastic, meaning they show larger performance gains when fine-tuned on downstream tasks. This phenomenon can lead to counterintuitive trade-offs where base models that perform worse after pretraining can perform better after fine-tuning. Further investigation of weight decay's mechanistic effects on model behavior reveals that it encourages linearly separable representations, regularizes attention matrices, and reduces overfitting on the training data. In conclusion, this work demonstrates the importance of using evaluation metrics beyond cross-entropy loss for hyperparameter optimization and casts light on the multifaceted role of that a single optimization hyperparameter plays in shaping model behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.11008", "title": "ROCKET: Rapid Optimization via Calibration-guided Knapsack Enhanced Truncation for Efficient Model Compression", "authors": [ "Ammar Ali", "Baher Mohammad", "Denis Makhov", "Dmitriy Shopkhoev", "Magauiya Zhussip", "Stamatios Lefkimmiatis" ], "github": "", "project_page": "", "abstract": "We present ROCKET, a training-free model compression method that achieves state-of-the-art performance in comparison with factorization, structured-sparsification and dynamic compression baselines. Operating under a global compression budget, ROCKET comprises two key innovations: First, it formulates layer-wise compression allocation as a multi-choice knapsack problem, selecting the optimal compression level for each layer to minimize total reconstruction error while adhering to a target model size. Second, it introduces a single-step sparse matrix factorization inspired by dictionary learning: using only a small calibration set, it sparsifies weight coefficients based on activation-weights sensitivity and then updates the dictionary in closed form via least squares bypassing iterative optimization, sparse coding, or backpropagation entirely. ROCKET consistently outperforms existing compression approaches across different model architectures at 20-50\\% compression rates. Notably, it retains over 90\\% of the original model's performance at 30\\% compression without any fine-tuning. Moreover, when applying a light fine-tuning phase, recovery is substantially enhanced: for instance, compressing Qwen3-14B to an 8B-parameter model and healing it with just 30 million tokens yields performance nearly on par with the original Qwen3-8B. The code for ROCKET is at github.com/mts-ai/ROCKET/tree/main.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.09514", "title": "EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies", "authors": [ "Xavier Hu", "Jinxiang Xia", "Shengze Xu", "Kangqi Song", "Yishuo Yuan", "Guibin Zhang", "JinCheng Ren", "Boyu Feng", "Li Lu", "Tieyong Zeng", "Jiaheng Liu", "Minghao Liu", "He Zhu", "Yuchen Eleanor Jiang", "Wei Wang", "Wangchunshu Zhou" ], "github": "", "project_page": "", "abstract": "Long-horizon planning is widely recognized as a core capability of autonomous LLM-based agents; however, current evaluation frameworks suffer from being largely episodic, domain-specific, or insufficiently grounded in persistent economic dynamics. We introduce EcoGym, a generalizable benchmark for continuous plan-and-execute decision making in interactive economies. EcoGym comprises three diverse environments: Vending, Freelance, and Operation, implemented in a unified decision-making process with standardized interfaces, and budgeted actions over an effectively unbounded horizon (1000+ steps if 365 day-loops for evaluation). The evaluation of EcoGym is based on business-relevant outcomes (e.g., net worth, income, and DAU), targeting long-term strategic coherence and robustness under partial observability and stochasticity. Experiments across eleven leading LLMs expose a systematic tension: no single model dominates across all three scenarios. Critically, we find that models exhibit significant suboptimality in either high-level strategies or efficient actions executions. EcoGym is released as an open, extensible testbed for transparent long-horizon agent evaluation and for studying controllability-utility trade-offs in realistic economic settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.11149", "title": "Data Repetition Beats Data Scaling in Long-CoT Supervised Fine-Tuning", "authors": [ "Dawid J. Kopiczko", "Sagar Vaze", "Tijmen Blankevoort", "Yuki M. Asano" ], "github": "https://github.com/dkopi/data-repetition", "project_page": "", "abstract": "Supervised fine-tuning (SFT) on chain-of-thought data is an essential post-training step for reasoning language models. Standard machine learning intuition suggests that training with more unique training samples yields better generalization. Counterintuitively, we show that SFT benefits from repetition: under a fixed update budget, training for more epochs on smaller datasets outperforms single-epoch training on larger datasets. On AIME'24/25 and GPQA benchmarks, Olmo3-7B trained for 128 epochs on 400 samples outperforms the equivalent 1 epoch on 51200 samples by 12-26 percentage points, with no additional catastrophic forgetting. We find that training token accuracy reliably signals when repetition has saturated; improvements from additional epochs plateau at full memorization, a pattern consistent across all settings. These findings provide a practical approach for reasoning SFT, where scaling epochs with token accuracy as a stopping criterion can replace expensive undirected data scaling. We pose the repetition advantage, where full memorization coincides with improved generalization, as a new open problem for the community in understanding the training dynamics of large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dkopi/data-repetition", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10748", "title": "Benchmarking Large Language Models for Knowledge Graph Validation", "authors": [ "Farzad Shami", "Stefano Marchesin", "Gianmaria Silvello" ], "github": "https://github.com/FactCheck-AI/FactCheck-MockAPI", "project_page": "", "abstract": "Knowledge Graphs (KGs) store structured factual knowledge by linking entities through relationships, crucial for many applications. These applications depend on the KG's factual accuracy, so verifying facts is essential, yet challenging. Expert manual verification is ideal but impractical on a large scale. Automated methods show promise but are not ready for real-world KGs. Large Language Models (LLMs) offer potential with their semantic understanding and knowledge access, yet their suitability and effectiveness for KG fact validation remain largely unexplored. In this paper, we introduce FactCheck, a benchmark designed to evaluate LLMs for KG fact validation across three key dimensions: (1) LLMs internal knowledge; (2) external evidence via Retrieval-Augmented Generation (RAG); and (3) aggregated knowledge employing a multi-model consensus strategy. We evaluated open-source and commercial LLMs on three diverse real-world KGs. FactCheck also includes a RAG dataset with 2+ million documents tailored for KG fact validation. Additionally, we offer an interactive exploration platform for analyzing verification decisions. The experimental analyses demonstrate that while LLMs yield promising results, they are still not sufficiently stable and reliable to be used in real-world KG validation scenarios. Integrating external evidence through RAG methods yields fluctuating performance, providing inconsistent improvements over more streamlined approaches -- at higher computational costs. Similarly, strategies based on multi-model consensus do not consistently outperform individual models, underscoring the lack of a one-fits-all solution. These findings further emphasize the need for a benchmark like FactCheck to systematically evaluate and drive progress on this difficult yet crucial task.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FactCheck-AI/FactCheck-MockAPI", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.07954", "title": "Bielik Guard: Efficient Polish Language Safety Classifiers for LLM Content Moderation", "authors": [ "Krzysztof Wr\u00f3bel", "Jan Maria Kowalski", "Jerzy Surma", "Igor Ciuciura", "Maciej Szyma\u0144ski" ], "github": "", "project_page": "", "abstract": "As Large Language Models (LLMs) become increasingly deployed in Polish language applications, the need for efficient and accurate content safety classifiers has become paramount. We present Bielik Guard, a family of compact Polish language safety classifiers comprising two model variants: a 0.1B parameter model based on MMLW-RoBERTa-base and a 0.5B parameter model based on PKOBP/polish-roberta-8k. Fine-tuned on a community-annotated dataset of 6,885 Polish texts, these models classify content across five safety categories: Hate/Aggression, Vulgarities, Sexual Content, Crime, and Self-Harm. Our evaluation demonstrates that both models achieve strong performance on multiple benchmarks. The 0.5B variant offers the best overall discrimination capability with F1 scores of 0.791 (micro) and 0.785 (macro) on the test set, while the 0.1B variant demonstrates exceptional efficiency. Notably, Bielik Guard 0.1B v1.1 achieves superior precision (77.65%) and very low false positive rate (0.63%) on real user prompts, outperforming HerBERT-PL-Guard (31.55% precision, 4.70% FPR) despite identical model size. The models are publicly available and designed to provide appropriate responses rather than simple content blocking, particularly for sensitive categories like self-harm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10870", "title": "FedPS: Federated data Preprocessing via aggregated Statistics", "authors": [ "Xuefeng Xu", "Graham Cormode" ], "github": "https://github.com/xuefeng-xu/fedps", "project_page": "", "abstract": "Federated Learning (FL) enables multiple parties to collaboratively train machine learning models without sharing raw data. However, before training, data must be preprocessed to address missing values, inconsistent formats, and heterogeneous feature scales. This preprocessing stage is critical for model performance but is largely overlooked in FL research. In practical FL systems, privacy constraints prohibit centralizing raw data, while communication efficiency introduces further challenges for distributed preprocessing. We introduce FedPS, a unified framework for federated data preprocessing based on aggregated statistics. FedPS leverages data-sketching techniques to efficiently summarize local datasets while preserving essential statistical information. Building on these summaries, we design federated algorithms for feature scaling, encoding, discretization, and missing-value imputation, and extend preprocessing-related models such as k-Means, k-Nearest Neighbors, and Bayesian Linear Regression to both horizontal and vertical FL settings. FedPS provides flexible, communication-efficient, and consistent preprocessing pipelines for practical FL deployments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/xuefeng-xu/fedps", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10231", "title": "Blockwise Advantage Estimation for Multi-Objective RL with Verifiable Rewards", "authors": [ "Kirill Pavlenko", "Alexander Golubev", "Simon Karasik", "Boris Yangel" ], "github": "", "project_page": "", "abstract": "Group Relative Policy Optimization (GRPO) assigns a single scalar advantage to all tokens in a completion. For structured generations with explicit segments and objectives, this couples unrelated reward signals across segments, leading to objective interference and misattributed credit. We propose Blockwise Advantage Estimation, a family of GRPO-compatible methods that assigns each objective its own advantage and applies it only to the tokens in the corresponding text block, reducing reliance on hand-designed scalar rewards and scaling naturally to additional objectives. A key challenge is estimating advantages for later blocks whose rewards are conditioned on sampled prefixes; standard unbiased approaches require expensive nested rollouts from intermediate states. Concretely, we introduce an Outcome-Conditioned Baseline that approximates intermediate state values using only within-group statistics by stratifying samples according to a prefix-derived intermediate outcome. On math tasks with uncertainty estimation, our method mitigates reward interference, is competitive with a state-of-the-art reward-designed approach, and preserves test-time gains from confidence-weighted ensembling. More broadly, it provides a modular recipe for optimizing sequential objectives in structured generations without additional rollouts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10778", "title": "GoodVibe: Security-by-Vibe for LLM-Based Code Generation", "authors": [ "Maximilian Thang", "Lichao Wu", "Sasha Behrouzi", "Mohamadreza Rostami", "Jona te Lintelo", "Stjepan Picek", "Ahmad-Reza Sadeghi" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly used for code generation in fast, informal development workflows, often referred to as vibe coding, where speed and convenience are prioritized, and security requirements are rarely made explicit. In this setting, models frequently produce functionally correct but insecure code, creating a growing security risk. Existing approaches to improving code security rely on full-parameter fine-tuning or parameter-efficient adaptations, which are either costly and prone to catastrophic forgetting or operate at coarse granularity with limited interpretability and control. We present GoodVibe, a neuron-level framework for improving the security of code language models by default. GoodVibe is based on the key insight that security-relevant reasoning is localized to a small subset of neurons. We identify these neurons using gradient-based attribution from a supervised security task and perform neuron-selective fine-tuning that updates only this security-critical subspace. To further reduce training cost, we introduce activation-driven neuron clustering, enabling structured updates with minimal overhead. We evaluate GoodVibe on six LLMs across security-critical programming languages, including C++, Java, Swift, and Go. GoodVibe substantially improves the security of generated code while preserving general model utility, achieving up to a 2.5x improvement over base models, matching or exceeding full fine-tuning with over 4,700x fewer trainable parameters, and reducing training computation by more than 3.6x compared to the parameter-efficient baseline (LoRA). Our results demonstrate that neuron-level optimization offers an effective and scalable approach to securing code generation without sacrificing efficiency or generality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.08741", "title": "Large Language Lobotomy: Jailbreaking Mixture-of-Experts via Expert Silencing", "authors": [ "Jona te Lintelo", "Lichao Wu", "Stjepan Picek" ], "github": "https://github.com/jonatelintelo/LargeLanguageLobotomy", "project_page": "", "abstract": "The rapid adoption of Mixture-of-Experts (MoE) architectures marks a major shift in the deployment of Large Language Models (LLMs). MoE LLMs improve scaling efficiency by activating only a small subset of parameters per token, but their routing structure introduces new safety attack surfaces. We find that safety-critical behaviors in MoE LLMs (e.g., refusal) are concentrated in a small set of experts rather than being uniformly distributed. Building on this, we propose Large Language Lobotomy (L^3), a training-free, architecture-agnostic attack that compromises safety alignment by exploiting expert routing dynamics. L^3 learns routing patterns that correlate with refusal, attributes safety behavior to specific experts, and adaptively silences the most safety-relevant experts until harmful outputs are produced. We evaluate L^3 on eight state-of-the-art open-source MoE LLMs and show that our adaptive expert silencing increases average attack success from 7.3% to 70.4%, reaching up to 86.3%, outperforming prior training-free MoE jailbreak methods. Moreover, bypassing guardrails typically requires silencing fewer than 20% of layer-wise experts while largely preserving general language utility. These results reveal a fundamental tension between efficiency-driven MoE design and robust safety alignment and motivate distributing safety mechanisms more robustly in future MoE LLMs with architecture- and routing-aware methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jonatelintelo/LargeLanguageLobotomy", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.07900", "title": "Rethinking the Value of Agent-Generated Tests for LLM-Based Software Engineering Agents", "authors": [ "Zhi Chen", "Zhensu Sun", "Yuling Shi", "Chao Peng", "Xiaodong Gu", "David Lo", "Lingxiao Jiang" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM) code agents increasingly resolve repository-level issues by iteratively editing code, invoking tools, and validating candidate patches. In these workflows, agents often write tests on the fly, a paradigm adopted by many high-ranking agents on the SWE-bench leaderboard. However, we observe that GPT-5.2, which writes almost no new tests, can even achieve performance comparable to top-ranking agents. This raises the critical question: whether such tests meaningfully improve issue resolution or merely mimic human testing practices while consuming a substantial interaction budget. To reveal the impact of agent-written tests, we present an empirical study that analyzes agent trajectories across six state-of-the-art LLMs on SWE-bench Verified. Our results show that while test writing is commonly adopted, but resolved and unresolved tasks within the same model exhibit similar test-writing frequencies Furthermore, these tests typically serve as observational feedback channels, where agents prefer value-revealing print statements significantly more than formal assertion-based checks. Based on these insights, we perform a controlled experiment by revising the prompts of four agents to either increase or reduce test writing. The results suggest that changes in the volume of agent-written tests do not significantly change final outcomes. Taken together, our study reveals that current test-writing practices may provide marginal utility in autonomous software engineering tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10622", "title": "How Do Decoder-Only LLMs Perceive Users? Rethinking Attention Masking for User Representation Learning", "authors": [ "Jiahao Yuan", "Yike Xu", "Jinyong Wen", "Baokun Wang", "Yang Chen", "Xiaotong Lin", "Wuliang Huang", "Ziyi Gao", "Xing Fu", "Yu Cheng", "Weiqiang Wang" ], "github": "https://github.com/JhCircle/Deepfind-GGSM", "project_page": "", "abstract": "Decoder-only large language models are increasingly used as behavioral encoders for user representation learning, yet the impact of attention masking on the quality of user embeddings remains underexplored. In this work, we conduct a systematic study of causal, hybrid, and bidirectional attention masks within a unified contrastive learning framework trained on large-scale real-world Alipay data that integrates long-horizon heterogeneous user behaviors. To improve training dynamics when transitioning from causal to bidirectional attention, we propose Gradient-Guided Soft Masking, a gradient-based pre-warmup applied before a linear scheduler that gradually opens future attention during optimization. Evaluated on 9 industrial user cognition benchmarks covering prediction, preference, and marketing sensitivity tasks, our approach consistently yields more stable training and higher-quality bidirectional representations compared with causal, hybrid, and scheduler-only baselines, while remaining compatible with decoder pretraining. Overall, our findings highlight the importance of masking design and training transition in adapting decoder-only LLMs for effective user representation learning. Our code is available at https://github.com/JhCircle/Deepfind-GGSM.", "github_manual": "", "github_regex": "https://github.com/JhCircle/Deepfind-GGSM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.08099", "title": "VidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval", "authors": [ "Issar Tzachor", "Dvir Samuel", "Rami Ben-Ari" ], "github": "", "project_page": "", "abstract": "Recent studies have adapted generative Multimodal Large Language Models (MLLMs) into embedding extractors for vision tasks, typically through fine-tuning to produce universal representations. However, their performance on video remains inferior to Video Foundation Models (VFMs). In this paper, we focus on leveraging MLLMs for video-text embedding and retrieval. We first conduct a systematic layer-wise analysis, showing that intermediate (pre-trained) MLLM layers already encode substantial task-relevant information. Leveraging this insight, we demonstrate that combining intermediate-layer embeddings with a calibrated MLLM head yields strong zero-shot retrieval performance without any training. Building on these findings, we introduce a lightweight text-based alignment strategy which maps dense video captions to short summaries and enables task-related video-text embedding learning without visual supervision. Remarkably, without any fine-tuning beyond text, our method outperforms current methods, often by a substantial margin, achieving state-of-the-art results across common video retrieval benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.08052", "title": "Graph-Enhanced Deep Reinforcement Learning for Multi-Objective Unrelated Parallel Machine Scheduling", "authors": [ "Bulent Soykan", "Sean Mondesire", "Ghaith Rabadi", "Grace Bochenek" ], "github": "https://github.com/bulentsoykan/GNN-DRL4UPMSP", "project_page": "", "abstract": "The Unrelated Parallel Machine Scheduling Problem (UPMSP) with release dates, setups, and eligibility constraints presents a significant multi-objective challenge. Traditional methods struggle to balance minimizing Total Weighted Tardiness (TWT) and Total Setup Time (TST). This paper proposes a Deep Reinforcement Learning framework using Proximal Policy Optimization (PPO) and a Graph Neural Network (GNN). The GNN effectively represents the complex state of jobs, machines, and setups, allowing the PPO agent to learn a direct scheduling policy. Guided by a multi-objective reward function, the agent simultaneously minimizes TWT and TST. Experimental results on benchmark instances demonstrate that our PPO-GNN agent significantly outperforms a standard dispatching rule and a metaheuristic, achieving a superior trade-off between both objectives. This provides a robust and scalable solution for complex manufacturing scheduling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bulentsoykan/GNN-DRL4UPMSP", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.02192", "title": "ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning", "authors": [ "Jie Xiao", "Meng Chen", "Qingnan Ren", "Jingwei Song", "Jiaqi Huang", "Yangshen Deng", "Chris Tong", "Wanyi Chen", "Suli Wang", "Ziqian Bi", "Shuo Lu", "Yiqun Duan", "Xu Wang", "Rymon Yu", "Ween Yang", "Lynn Ai", "Eric Yang", "Bill Shi", "Song Jingwei" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) is a critical stage in post-training large language models (LLMs), involving repeated interaction between rollout generation, reward evaluation, and centralized learning. Distributing rollout execution offers opportunities to leverage more cost-efficient inference resources, but introduces challenges in wide-area coordination and policy dissemination. We present ECHO-2, a distributed RL framework for post-training with remote inference workers and non-negligible dissemination latency. ECHO-2 combines centralized learning with distributed rollouts and treats bounded policy staleness as a user-controlled parameter, enabling rollout generation, dissemination, and training to overlap. We introduce an overlap-based capacity model that relates training time, dissemination latency, and rollout throughput, yielding a practical provisioning rule for sustaining learner utilization. To mitigate dissemination bottlenecks and lower cost, ECHO-2 employs peer-assisted pipelined broadcast and cost-aware activation of heterogeneous workers. Experiments on GRPO post-training of 4B and 8B models under real wide-area bandwidth regimes show that ECHO-2 significantly improves cost efficiency while preserving RL reward comparable to strong baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.03773", "title": "Reasoning Cache: Continual Improvement Over Long Horizons via Short-Horizon RL", "authors": [ "Ian Wu", "Yuxiao Qu", "Amrith Setlur", "Aviral Kumar" ], "github": "https://github.com/IanYHWu/rc", "project_page": "", "abstract": "Large Language Models (LLMs) that can continually improve beyond their training budgets are able to solve increasingly difficult problems by adapting at test time, a property we refer to as extrapolation. However, standard reinforcement learning (RL) operates over fixed problem distributions and training budgets, which limits extrapolation amidst distribution shift at test time. To address this, we introduce RC, an iterative decoding algorithm that replaces standard autoregressive decoding during both training and inference. RC exploits an asymmetry between the response generation and summarization capabilities of LLMs to construct reasoning chains that consistently improve across iterations. Models trained to use RC can extrapolate and continually improve over reasoning horizons more than an order of magnitude longer than those seen during training. Empirically, training a 4B model with RC using a 16k-token training budget improves performance on HMMT 2025 from 40% to nearly 70% with 0.5m tokens at test time, outperforming both comparably sized models and many larger reasoning LLMs. Finally, we also show that models trained with RC can more effectively leverage existing scaffolds to further scale test-time performance, due to the improved summary-conditioned generation abilities learned through training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/IanYHWu/rc", "github_api_added_by": "auto" }, { "date": "2026-02-12", "arxiv_id": "2602.08934", "title": "StealthRL: Reinforcement Learning Paraphrase Attacks for Multi-Detector Evasion of AI-Text Detectors", "authors": [ "Suraj Ranganath", "Atharv Ramesh" ], "github": "https://github.com/suraj-ranganath/StealthRL", "project_page": "", "abstract": "AI-text detectors face a critical robustness challenge: adversarial paraphrasing attacks that preserve semantics while evading detection. We introduce StealthRL, a reinforcement learning framework that stress-tests detector robustness under realistic adversarial conditions. StealthRL trains a paraphrase policy against a multi-detector ensemble using Group Relative Policy Optimization (GRPO) with LoRA adapters on Qwen3-4B, optimizing a composite reward that balances detector evasion with semantic preservation. We evaluate six attack settings (M0-M5) against three detector families (RoBERTa, FastDetectGPT, and Binoculars) at the security-relevant 1% false positive rate operating point. StealthRL achieves near-zero detection (0.001 mean TPR@1%FPR), reduces mean AUROC from 0.74 to 0.27, and attains a 99.9% attack success rate. Critically, attacks transfer to a held-out detector family not seen during training, revealing shared architectural vulnerabilities rather than detector-specific brittleness. We additionally conduct LLM-based quality evaluation via Likert scoring, analyze detector score distributions to explain why evasion succeeds, and provide per-detector AUROC with bootstrap confidence intervals. Our results expose significant robustness gaps in current AI-text detection and establish StealthRL as a principled adversarial evaluation protocol. Code and evaluation pipeline are publicly available at https://github.com/suraj-ranganath/StealthRL.", "github_manual": "", "github_regex": "https://github.com/suraj-ranganath/StealthRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.11103", "title": "GameDevBench: Evaluating Agentic Capabilities Through Game Development", "authors": [ "Wayne Chi", "Yixiong Fang", "Arnav Yayavaram", "Siddharth Yayavaram", "Seth Karten", "Qiuhong Anna Wei", "Runkun Chen", "Alexander Wang", "Valerie Chen", "Ameet Talwalkar", "Chris Donahue" ], "github": "https://github.com/waynchi/gamedevbench", "project_page": "", "abstract": "Despite rapid progress on coding agents, progress on their multimodal counterparts has lagged behind. A key challenge is the scarcity of evaluation testbeds that combine the complexity of software development with the need for deep multimodal understanding. Game development provides such a testbed as agents must navigate large, dense codebases while manipulating intrinsically multimodal assets such as shaders, sprites, and animations within a visual game scene. We present GameDevBench, the first benchmark for evaluating agents on game development tasks. GameDevBench consists of 132 tasks derived from web and video tutorials. Tasks require significant multimodal understanding and are complex -- the average solution requires over three times the amount of lines of code and file changes compared to prior software development benchmarks. Agents still struggle with game development, with the best agent solving only 54.5% of tasks. We find a strong correlation between perceived task difficulty and multimodal complexity, with success rates dropping from 46.9% on gameplay-oriented tasks to 31.6% on 2D graphics tasks. To improve multimodal capability, we introduce two simple image and video-based feedback mechanisms for agents. Despite their simplicity, these methods consistently improve performance, with the largest change being an increase in Claude Sonnet 4.5's performance from 33.3% to 47.7%. We release GameDevBench publicly to support further research into agentic game development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/waynchi/gamedevbench", "github_api_added_by": "auto" }, { "date": "2026-02-12", "arxiv_id": "2602.10229", "title": "Latent Thoughts Tuning: Bridging Context and Reasoning with Fused Information in Latent Tokens", "authors": [ "Weihao Liu", "Dehai Min", "Lu Cheng" ], "github": "", "project_page": "", "abstract": "While explicit Chain-of-Thought (CoT) equips Large Language Models (LLMs) with strong reasoning capabilities, it requires models to verbalize every intermediate step in text tokens, constraining the model thoughts to the discrete vocabulary space. Recently, reasoning in continuous latent space has emerged as a promising alternative, enabling more robust inference and flexible computation beyond discrete token constraints. However, current latent paradigms often suffer from feature collapse and instability, stemming from distribution mismatches when recurrently using hidden states as the input embeddings, or alignment issues when relying on assistant models. To address this, we propose Latent Thoughts Tuning (LT-Tuning), a framework that redefines how latent thoughts are constructed and deployed. Instead of relying solely on raw hidden states, our method introduces a Context-Prediction-Fusion mechanism that jointly leveraging contextual hidden states and predictive semantic guidance from the vocabulary embedding space. Combined with a progressive three-stage curriculum learning pipeline, LT-Tuning also enables dynamically switching between latent and explicit thinking modes. Experiments demonstrate that our method outperforms existing latent reasoning baselines, effectively mitigating feature collapse and achieving robust reasoning accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.10367", "title": "LiveMedBench: A Contamination-Free Medical Benchmark for LLMs with Automated Rubric Evaluation", "authors": [ "Zhiling Yan", "Dingjie Song", "Zhe Fang", "Yisheng Ji", "Xiang Li", "Quanzheng Li", "Lichao Sun" ], "github": "https://github.com/ZhilingYan/LiveMedBench", "project_page": "", "abstract": "The deployment of Large Language Models (LLMs) in high-stakes clinical settings demands rigorous and reliable evaluation. However, existing medical benchmarks remain static, suffering from two critical limitations: (1) data contamination, where test sets inadvertently leak into training corpora, leading to inflated performance estimates; and (2) temporal misalignment, failing to capture the rapid evolution of medical knowledge. Furthermore, current evaluation metrics for open-ended clinical reasoning often rely on either shallow lexical overlap (e.g., ROUGE) or subjective LLM-as-a-Judge scoring, both inadequate for verifying clinical correctness. To bridge these gaps, we introduce LiveMedBench, a continuously updated, contamination-free, and rubric-based benchmark that weekly harvests real-world clinical cases from online medical communities, ensuring strict temporal separation from model training data. We propose a Multi-Agent Clinical Curation Framework that filters raw data noise and validates clinical integrity against evidence-based medical principles. For evaluation, we develop an Automated Rubric-based Evaluation Framework that decomposes physician responses into granular, case-specific criteria, achieving substantially stronger alignment with expert physicians than LLM-as-a-Judge. To date, LiveMedBench comprises 2,756 real-world cases spanning 38 medical specialties and multiple languages, paired with 16,702 unique evaluation criteria. Extensive evaluation of 38 LLMs reveals that even the best-performing model achieves only 39.2%, and 84% of models exhibit performance degradation on post-cutoff cases, confirming pervasive data contamination risks. Error analysis further identifies contextual application-not factual knowledge-as the dominant bottleneck, with 35-48% of failures stemming from the inability to tailor medical knowledge to patient-specific constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ZhilingYan/LiveMedBench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.02459", "title": "TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments", "authors": [ "Zhiyu Huang", "Yun Zhang", "Johnson Liu", "Rui Song", "Chen Tang", "Jiaqi Ma" ], "github": "https://github.com/isaac-sim/IsaacSim", "project_page": "", "abstract": "Robots in dynamic, human-centric environments must follow language instructions while maintaining real-time reactive control. Vision-language-action (VLA) models offer a promising framework, but they assume temporally aligned reasoning and control, despite semantic inference being inherently delayed relative to real-time action. We introduce Think-in-Control (TIC)-VLA, a latency-aware framework that explicitly models delayed semantic reasoning during action generation. TIC-VLA defines a delayed semantic-control interface that conditions action generation on delayed vision-language semantic states and explicit latency metadata, in addition to current observations, enabling policies to compensate for asynchronous reasoning. We further propose a latency-consistent training pipeline that injects reasoning inference delays during imitation learning and online reinforcement learning, aligning training with asynchronous deployment. To support realistic evaluation, we present DynaNav, a physics-accurate, photo-realistic simulation suite for language-guided navigation in dynamic environments. Extensive experiments in simulation and on a real robot show that TIC-VLA consistently outperforms prior VLA models while maintaining robust real-time control under multi-second reasoning latency. Project website: https://ucla-mobility.github.io/TIC-VLA/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/isaac-sim/IsaacSim", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.12108", "title": "The Pensieve Paradigm: Stateful Language Models Mastering Their Own Context", "authors": [ "Xiaoyuan Liu", "Tian Liang", "Dongyang Ma", "Deyu Zhou", "Haitao Mi", "Pinjia He", "Yan Wang" ], "github": "https://github.com/texttron/BrowseComp-Plus/blob/main/docs/qwen.md", "project_page": "", "abstract": "In the world of Harry Potter, when Dumbledore's mind is overburdened, he extracts memories into a Pensieve to be revisited later. In the world of AI, while we possess the Pensieve-mature databases and retrieval systems, our models inexplicably lack the \"wand\" to operate it. They remain like a Dumbledore without agency, passively accepting a manually engineered context as their entire memory. This work finally places the wand in the model's hand. We introduce StateLM, a new class of foundation models endowed with an internal reasoning loop to manage their own state. We equip our model with a suite of memory tools, such as context pruning, document indexing, and note-taking, and train it to actively manage these tools. By learning to dynamically engineering its own context, our model breaks free from the architectural prison of a fixed window. Experiments across various model sizes demonstrate StateLM's effectiveness across diverse scenarios. On long-document QA tasks, StateLMs consistently outperform standard LLMs across all model scales; on the chat memory task, they achieve absolute accuracy improvements of 10% to 20% over standard LLMs. On the deep research task BrowseComp-Plus, the performance gap becomes even more pronounced: StateLM achieves up to 52% accuracy, whereas standard LLM counterparts struggle around 5%. Ultimately, our approach shifts LLMs from passive predictors to state-aware agents where reasoning becomes a stateful and manageable process.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/texttron/BrowseComp-Plus/blob/main/docs/qwen.md", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.11451", "title": "LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation", "authors": [ "Ahmadreza Jeddi", "Marco Ciccone", "Babak Taati" ], "github": "", "project_page": "", "abstract": "Looped Transformers have emerged as an efficient and powerful class of models for reasoning in the language domain. Recent studies show that these models achieve strong performance on algorithmic and reasoning tasks, suggesting that looped architectures possess an inductive bias toward latent reasoning. However, prior approaches fix the number of loop iterations during training and inference, leaving open the question of whether these models can flexibly adapt their computational depth under variable compute budgets. We introduce LoopFormer, a looped Transformer trained on variable-length trajectories to enable budget-conditioned reasoning. Our core contribution is a shortcut-consistency training scheme that aligns trajectories of different lengths, ensuring that shorter loops yield informative representations while longer loops continue to refine them. LoopFormer conditions each loop on the current time and step size, enabling representations to evolve consistently across trajectories of varying length rather than drifting or stagnating. Empirically, LoopFormer demonstrates robust performance on language modeling and reasoning benchmarks even under aggressive compute constraints, while scaling gracefully with additional budget. These results show that looped Transformers are inherently suited for adaptive language modeling, opening a path toward controllable and budget-aware large language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-12", "arxiv_id": "2602.06841", "title": "From Features to Actions: Explainability in Traditional and Agentic AI Systems", "authors": [ "Sindhuja Chaduvula", "Jessee Ho", "Kina Kim", "Aravind Narayanan", "Mahshid Alinoori", "Muskan Garg", "Dhanesh Ramachandram", "Shaina Raza" ], "github": "https://github.com/VectorInstitute/unified-xai-evaluation-framework", "project_page": "", "abstract": "Over the last decade, explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed decision structure. Recent advances in large language models (LLMs) have enabled agentic AI systems whose behaviour unfolds over multi-step trajectories. In these settings, success and failure are determined by sequences of decisions rather than a single output. While useful, it remains unclear how explanation approaches designed for static predictions translate to agentic settings where behaviour emerges over time. In this work, we bridge the gap between static and agentic explainability by comparing attribution-based explanations with trace-based diagnostics across both settings. To make this distinction explicit, we empirically compare attribution-based explanations used in static classification tasks with trace-based diagnostics used in agentic benchmarks (TAU-bench Airline and AssistantBench). Our results show that while attribution methods achieve stable feature rankings in static settings (Spearman \u03c1= 0.86), they cannot be applied reliably to diagnose execution-level failures in agentic trajectories. In contrast, trace-grounded rubric evaluation for agentic settings consistently localizes behaviour breakdowns and reveals that state tracking inconsistency is 2.7times more prevalent in failed runs and reduces success probability by 49\\%. These findings motivate a shift towards trajectory-level explainability for agentic systems when evaluating and diagnosing autonomous AI behaviour. Resources: https://github.com/VectorInstitute/unified-xai-evaluation-framework https://vectorinstitute.github.io/unified-xai-evaluation-framework", "github_manual": "", "github_regex": "https://github.com/VectorInstitute/unified-xai-evaluation-framework", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.05548", "title": "Unveiling Implicit Advantage Symmetry: Why GRPO Struggles with Exploration and Difficulty Adaptation", "authors": [ "Zhiqi Yu", "Zhangquan Chen", "Mengting Liu", "Heye Zhang", "Liangqiong Qu" ], "github": "https://github.com/HKU-HealthAI/A-GRAE", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR), particularly GRPO, has become the standard for eliciting LLM reasoning. However, its efficiency in exploration and difficulty adaptation remains an open challenge. In this work, we argue that these bottlenecks stem from an implicit advantage symmetry inherent in Group Relative Advantage Estimation (GRAE). This symmetry induces two critical limitations: (i) at the group level, strict symmetry in weights between correct and incorrect trajectories leaves unsampled action logits unchanged, thereby hindering exploration of novel correct solution. (ii) at the sample level, the algorithm implicitly prioritizes medium-difficulty samples, remaining agnostic to the non-stationary demands of difficulty focus. Through controlled experiments, we reveal that this symmetric property is sub-optimal, yielding two pivotal insights: (i) asymmetrically suppressing the advantages of correct trajectories encourages essential exploration. (ii) learning efficiency is maximized by a curriculum-like transition-prioritizing simpler samples initially before gradually shifting to complex ones. Motivated by these findings, we propose Asymmetric GRAE (A-GRAE), which dynamically modulates exploration incentives and sample-difficulty focus. Experiments across seven benchmarks demonstrate that A-GRAE consistently improves GRPO and its variants across both LLMs and MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HKU-HealthAI/A-GRAE", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11541", "title": "Budget-Constrained Agentic Large Language Models: Intention-Based Planning for Costly Tool Use", "authors": [ "Hanbing Liu", "Chunhao Tian", "Nan An", "Ziyuan Wang", "Pinyan Lu", "Changyuan Yu", "Qi Qi" ], "github": "", "project_page": "", "abstract": "We study budget-constrained tool-augmented agents, where a large language model must solve multi-step tasks by invoking external tools under a strict monetary budget. We formalize this setting as sequential decision making in context space with priced and stochastic tool executions, making direct planning intractable due to massive state-action spaces, high variance of outcomes and prohibitive exploration cost. To address these challenges, we propose INTENT, an inference-time planning framework that leverages an intention-aware hierarchical world model to anticipate future tool usage, risk-calibrated cost, and guide decisions online. Across cost-augmented StableToolBench, INTENT strictly enforces hard budget feasibility while substantially improving task success over baselines, and remains robust under dynamic market shifts such as tool price changes and varying budgets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.10585", "title": "Neural Additive Experts: Context-Gated Experts for Controllable Model Additivity", "authors": [ "Guangzhi Xiong", "Sanchit Sinha", "Aidong Zhang" ], "github": "https://github.com/Teddy-XiongGZ/NAE", "project_page": "", "abstract": "The trade-off between interpretability and accuracy remains a core challenge in machine learning. Standard Generalized Additive Models (GAMs) offer clear feature attributions but are often constrained by their strictly additive nature, which can limit predictive performance. Introducing feature interactions can boost accuracy yet may obscure individual feature contributions. To address these issues, we propose Neural Additive Experts (NAEs), a novel framework that seamlessly balances interpretability and accuracy. NAEs employ a mixture of experts framework, learning multiple specialized networks per feature, while a dynamic gating mechanism integrates information across features, thereby relaxing rigid additive constraints. Furthermore, we propose targeted regularization techniques to mitigate variance among expert predictions, facilitating a smooth transition from an exclusively additive model to one that captures intricate feature interactions while maintaining clarity in feature attributions. Our theoretical analysis and experiments on synthetic data illustrate the model's flexibility, and extensive evaluations on real-world datasets confirm that NAEs achieve an optimal balance between predictive accuracy and transparent, feature-level explanations. The code is available at https://github.com/Teddy-XiongGZ/NAE.", "github_manual": "", "github_regex": "https://github.com/Teddy-XiongGZ/NAE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.08194", "title": "Dreaming in Code for Curriculum Learning in Open-Ended Worlds", "authors": [ "Konstantinos Mitsides", "Maxence Faldor", "Antoine Cully" ], "github": "https://github.com/konstantinosmitsides/dreaming-in-code", "project_page": "", "abstract": "Open-ended learning frames intelligence as emerging from continual interaction with an ever-expanding space of environments. While recent advances have utilized foundation models to programmatically generate diverse environments, these approaches often focus on discovering isolated behaviors rather than orchestrating sustained progression. In complex open-ended worlds, the large combinatorial space of possible challenges makes it difficult for agents to discover sequences of experiences that remain consistently learnable. To address this, we propose Dreaming in Code (DiCode), a framework in which foundation models synthesize executable environment code to scaffold learning toward increasing competence. In DiCode, \"dreaming\" takes the form of materializing code-level variations of the world. We instantiate DiCode in Craftax, a challenging open-ended benchmark characterized by rich mechanics and long-horizon progression. Empirically, DiCode enables agents to acquire long-horizon skills, achieving a 16% improvement in mean return over the strongest baseline and non-zero success on late-game combat tasks where prior methods fail. Our results suggest that code-level environment design provides a practical mechanism for curriculum control, enabling the construction of intermediate environments that bridge competence gaps in open-ended worlds. Project page and source code are available at https://konstantinosmitsides.github.io/dreaming-in-code and https://github.com/konstantinosmitsides/dreaming-in-code.", "github_manual": "", "github_regex": "https://github.com/konstantinosmitsides/dreaming-in-code", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.09877", "title": "The Devil Behind Moltbook: Anthropic Safety is Always Vanishing in Self-Evolving AI Societies", "authors": [ "Chenxu Wang", "Chaozhuo Li", "Songyang Liu", "Zejian Chen", "Jinyu Hou", "Ji Qi", "Rui Li", "Litian Zhang", "Qiwei Ye", "Zheng Liu", "Xu Chen", "Xi Zhang", "Philip S. Yu" ], "github": "", "project_page": "", "abstract": "The emergence of multi-agent systems built from large language models (LLMs) offers a promising paradigm for scalable collective intelligence and self-evolution. Ideally, such systems would achieve continuous self-improvement in a fully closed loop while maintaining robust safety alignment--a combination we term the self-evolution trilemma. However, we demonstrate both theoretically and empirically that an agent society satisfying continuous self-evolution, complete isolation, and safety invariance is impossible. Drawing on an information-theoretic framework, we formalize safety as the divergence degree from anthropic value distributions. We theoretically demonstrate that isolated self-evolution induces statistical blind spots, leading to the irreversible degradation of the system's safety alignment. Empirical and qualitative results from an open-ended agent community (Moltbook) and two closed self-evolving systems reveal phenomena that align with our theoretical prediction of inevitable safety erosion. We further propose several solution directions to alleviate the identified safety concern. Our work establishes a fundamental limit on the self-evolving AI societies and shifts the discourse from symptom-driven safety patches to a principled understanding of intrinsic dynamical risks, highlighting the need for external oversight or novel safety-preserving mechanisms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12125", "title": "Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation", "authors": [ "Wenkai Yang", "Weijie Liu", "Ruobing Xie", "Kai Yang", "Saiyong Yang", "Yankai Lin" ], "github": "", "project_page": "", "abstract": "On-policy distillation (OPD), which aligns the student with the teacher's logit distribution on student-generated trajectories, has demonstrated strong empirical gains in improving student performance and often outperforms off-policy distillation and reinforcement learning (RL) paradigms. In this work, we first theoretically show that OPD is a special case of dense KL-constrained RL where the reward function and the KL regularization are always weighted equally and the reference model can by any model. Then, we propose the Generalized On-Policy Distillation (G-OPD) framework, which extends the standard OPD objective by introducing a flexible reference model and a reward scaling factor that controls the relative weight of the reward term against the KL regularization. Through comprehensive experiments on math reasoning and code generation tasks, we derive two novel insights: (1) Setting the reward scaling factor to be greater than 1 (i.e., reward extrapolation), which we term ExOPD, consistently improves over standard OPD across a range of teacher-student size pairings. In particular, in the setting where we merge the knowledge from different domain experts, obtained by applying domain-specific RL to the same student model, back into the original student, ExOPD enables the student to even surpass the teacher's performance boundary and outperform the domain teachers. (2) Building on ExOPD, we further find that in the strong-to-weak distillation setting (i.e., distilling a smaller student from a larger teacher), performing reward correction by choosing the reference model as the teacher's base model before RL yields a more accurate reward signal and further improves distillation performance. However, this choice assumes access to the teacher's pre-RL variant and incurs more computational overhead. We hope our work offers new insights for future research on OPD.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12099", "title": "GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning", "authors": [ "GigaBrain Team", "Boyuan Wang", "Chaojun Ni", "Guan Huang", "Guosheng Zhao", "Hao Li", "Jie Li", "Jindi Lv", "Jingyu Liu", "Lv Feng", "Mingming Yu", "Peng Li", "Qiuping Deng", "Tianze Liu", "Xinyu Zhou", "Xinze Chen", "Xiaofeng Wang", "Yang Wang", "Yifan Li", "Yifei Nie", "Yilong Li", "Yukun Zhou", "Yun Ye", "Zhichao Liu", "Zheng Zhu" ], "github": "", "project_page": "", "abstract": "Vision-language-action (VLA) models that directly predict multi-step action chunks from current observations face inherent limitations due to constrained scene understanding and weak future anticipation capabilities. In contrast, video world models pre-trained on web-scale video corpora exhibit robust spatiotemporal reasoning and accurate future prediction, making them a natural foundation for enhancing VLA learning. Therefore, we propose GigaBrain-0.5M*, a VLA model trained via world model-based reinforcement learning. Built upon GigaBrain-0.5, which is pre-trained on over 10,000 hours of robotic manipulation data, whose intermediate version currently ranks first on the international RoboChallenge benchmark. GigaBrain-0.5M* further integrates world model-based reinforcement learning via RAMP (Reinforcement leArning via world Model-conditioned Policy) to enable robust cross-task adaptation. Empirical results demonstrate that RAMP achieves substantial performance gains over the RECAP baseline, yielding improvements of approximately 30\\% on challenging tasks including Laundry Folding, Box Packing, and Espresso Preparation. Critically, GigaBrain-0.5M^* exhibits reliable long-horizon execution, consistently accomplishing complex manipulation tasks without failure as validated by real-world deployment videos on our https://gigabrain05m.github.io{project page}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12056", "title": "LawThinker: A Deep Research Legal Agent in Dynamic Environments", "authors": [ "Xinyu Yang", "Chenlong Deng", "Tongyu Wen", "Binyu Xie", "Zhicheng Dou" ], "github": "https://github.com/yxy-919/LawThinker-agent", "project_page": "", "abstract": "Legal reasoning requires not only correct outcomes but also procedurally compliant reasoning processes. However, existing methods lack mechanisms to verify intermediate reasoning steps, allowing errors such as inapplicable statute citations to propagate undetected through the reasoning chain. To address this, we propose LawThinker, an autonomous legal research agent that adopts an Explore-Verify-Memorize strategy for dynamic judicial environments. The core idea is to enforce verification as an atomic operation after every knowledge exploration step. A DeepVerifier module examines each retrieval result along three dimensions of knowledge accuracy, fact-law relevance, and procedural compliance, with a memory module for cross-round knowledge reuse in long-horizon tasks. Experiments on the dynamic benchmark J1-EVAL show that LawThinker achieves a 24% improvement over direct reasoning and an 11% gain over workflow-based methods, with particularly strong improvements on process-oriented metrics. Evaluations on three static benchmarks further confirm its generalization capability. The code is available at https://github.com/yxy-919/LawThinker-agent .", "github_manual": "", "github_regex": "https://github.com/yxy-919/LawThinker-agent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11298", "title": "Voxtral Realtime", "authors": [ "Alexander H. Liu", "Andy Ehrenberg", "Andy Lo", "Chen-Yo Sun", "Guillaume Lample", "Jean-Malo Delignon", "Khyathi Raghavi Chandu", "Patrick von Platen", "Pavankumar Reddy Muddireddy", "Rohin Arora", "Sanchit Gandhi", "Sandeep Subramanian", "Soham Ghosh", "Srijan Mishra", "Abhinav Rastogi", "Alan Jeffares", "Albert Jiang", "Alexandre Sablayrolles", "Am\u00e9lie H\u00e9liou", "Andrew Bai", "Angele Lenglemetz", "Anmol Agarwal", "Anton Eliseev", "Antonia Calvi", "Arjun Majumdar", "Baptiste Bout", "Baptiste Rozi\u00e8re", "Baudouin De Monicault", "Benjamin Tibi", "Cl\u00e9mence Lanfranchi", "Connor Chen", "Corentin Barreau", "Corentin Sautier", "Cyprien Courtot", "Darius Dabert", "Diego de las Casas", "Elliot Chane-Sane", "Enguerrand Paquin", "Faruk Ahmed", "Federico Baldassarre", "Gabrielle Berrada", "Ga\u00ebtan Ecrepont", "Gauthier Guinet", "Genevieve Hayes", "Georgii Novikov", "Giada Pistilli", "Guillaume Martin", "Gunjan Dhanuka", "Gunshi Gupta", "Han Zhou", "Indraneel Mukherjee", "Irene Zhang", "Jaeyoung Kim", "Jan Ludziejewski", "Jason Rute", "Joachim Studnia", "John Harvill", "Jonas Amar", "Josselin Somerville Roberts", "Julien Tauran", "Karmesh Yadav", "Kartik Khandelwal", "Kush Jain", "Laurence Aitchison", "L\u00e9onard Blier", "Lingxiao Zhao", "Louis Martin", "Lucile Saulnier", "Luyu Gao", "Maarten Buyl", "Manan Sharma", "Margaret Jennings", "Marie Pellat", "Mark Prins", "Mathieu Poir\u00e9e", "Mathilde Guillaumin", "Matthieu Dinot", "Matthieu Futeral", "Maxime Darrin", "Maximilian Augustin", "Mert Unsal", "Mia Chiquier", "Nathan Grinsztajn", "Neha Gupta", "Olivier Bousquet", "Olivier Duchenne", "Patricia Wang", "Paul Jacob", "Paul Wambergue", "Paula Kurylowicz", "Philom\u00e8ne Chagniot", "Pierre Stock", "Piotr Mi\u0142o\u015b", "Prateek Gupta", "Pravesh Agrawal", "Quentin Torroba", "Ram Ramrakhya", "Rishi Shah", "Romain Sauvestre", "Roman Soletskyi", "Rosalie Millner", "Sagar Vaze", "Samuel Humeau", "Siddharth Gandhi", "Sumukh Aithal", "Szymon Antoniak", "Teven Le Scao", "Th\u00e9o Cachet", "Theo Simon Sorg", "Thibaut Lavril", "Thomas Chabal", "Thomas Foubert", "Thomas Robert", "Thomas Wang", "Tim Lawson", "Tom Bewley", "Tom Edwards", "Tyler Wang", "Valeriia Nemychnikova", "Van Phung", "Vedant Nanda", "Victor Jouault", "Virgile Richard", "Vladislav Bataev", "Wassim Bouaziz", "Wen-Ding Li", "William Marshall", "Xinghui Li", "Xingran Guo", "Xinyu Yang", "Yannic Neuhaus", "Yihan Wang", "Zaccharie Ramzi", "Zhenlin Xu" ], "github": "", "project_page": "", "abstract": "We introduce Voxtral Realtime, a natively streaming automatic speech recognition model that matches offline transcription quality at sub-second latency. Unlike approaches that adapt offline models through chunking or sliding windows, Voxtral Realtime is trained end-to-end for streaming, with explicit alignment between audio and text streams. Our architecture builds on the Delayed Streams Modeling framework, introducing a new causal audio encoder and Ada RMS-Norm for improved delay conditioning. We scale pretraining to a large-scale dataset spanning 13 languages. At a delay of 480ms, Voxtral Realtime achieves performance on par with Whisper, the most widely deployed offline transcription system. We release the model weights under the Apache 2.0 license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12153", "title": "dVoting: Fast Voting for dLLMs", "authors": [ "Sicheng Feng", "Zigeng Chen", "Xinyin Ma", "Gongfan Fang", "Xinchao Wang" ], "github": "https://github.com/fscdc/dVoting", "project_page": "", "abstract": "Diffusion Large Language Models (dLLMs) represent a new paradigm beyond autoregressive modeling, offering competitive performance while naturally enabling a flexible decoding process. Specifically, dLLMs can generate tokens at arbitrary positions in parallel, endowing them with significant potential for parallel test-time scaling, which was previously constrained by severe inefficiency in autoregressive modeling. In this work, we introduce dVoting, a fast voting technique that boosts reasoning capability without training, with only an acceptable extra computational overhead. dVoting is motivated by the observation that, across multiple samples for the same prompt, token predictions remain largely consistent, whereas performance is determined by a small subset of tokens exhibiting cross-sample variability. Leveraging the arbitrary-position generation capability of dLLMs, dVoting performs iterative refinement by sampling, identifying uncertain tokens via consistency analysis, regenerating them through voting, and repeating this process until convergence. Extensive evaluations demonstrate that dVoting consistently improves performance across various benchmarks. It achieves gains of 6.22%-7.66% on GSM8K, 4.40%-7.20% on MATH500, 3.16%-14.84% on ARC-C, and 4.83%-5.74% on MMLU. Our code is available at https://github.com/fscdc/dVoting", "github_manual": "", "github_regex": "https://github.com/fscdc/dVoting", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11683", "title": "ThinkRouter: Efficient Reasoning via Routing Thinking between Latent and Discrete Spaces", "authors": [ "Xin Xu", "Tong Yu", "Xiang Chen", "Haoliang Wang", "Julian McAuley", "Saayan Mitra" ], "github": "https://github.com/huggingface/Math-Verify", "project_page": "", "abstract": "Recent work explores latent reasoning to improve reasoning efficiency by replacing explicit reasoning trajectories with continuous representations in a latent space, yet its effectiveness varies across settings. Analysis of model confidence dynamics under latent reasoning reveals that thinking trajectories ending in incorrect answers contain fewer low-confidence steps than those ending in correct answers. Meanwhile, we suggest that soft embeddings aggregated by multiple low-confidence thinking alternatives may introduce and propagate noise, leading to high confidence in unreliable reasoning trajectories. Motivated by these observations, ThinkRouter, an inference-time confidence-aware routing mechanism is proposed to avoid high confidence and noise for efficient reasoning. ThinkRouter routes thinking to the discrete token space when model confidence is low, and to the latent space otherwise. Extensive experiments on STEM reasoning and coding benchmarks across diverse large reasoning models demonstrate that ThinkRouter outperforms explicit CoT, random routing, and latent reasoning baselines in terms of accuracy, achieving an average improvement of 19.70 points in Pass@1, while reducing generation length by up to 15.55%. Further comprehensive analysis reveals that ThinkRouter can calibrate errors arising from explicit CoT and latent reasoning, and accelerates end-of-thinking token generation by globally lowering model confidence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/Math-Verify", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11636", "title": "ScalSelect: Scalable Training-Free Multimodal Data Selection for Efficient Visual Instruction Tuning", "authors": [ "Changti Wu", "Jiahuai Mao", "Yuzhuo Miao", "Shijie Lian", "Bin Yu", "Xiaopeng Lin", "Cong Huang", "Lei Zhang", "Kai Chen" ], "github": "https://github.com/ChangtiWu/ScalSelect", "project_page": "", "abstract": "Large-scale Visual Instruction Tuning (VIT) has become a key paradigm for advancing the performance of vision-language models (VLMs) across various multimodal tasks. However, training on the large-scale datasets is computationally expensive and inefficient due to redundancy in the data, which motivates the need for multimodal data selection to improve training efficiency. Existing data selection methods for VIT either require costly training or gradient computation. Training-free alternatives often depend on proxy models or datasets, instruction-agnostic representations, and pairwise similarity with quadratic complexity, limiting scalability and representation fidelity. In this work, we propose ScalSelect, a scalable training-free multimodal data selection method with linear-time complexity with respect to the number of samples, eliminating the need for external models or auxiliary datasets. ScalSelect first constructs sample representations by extracting visual features most attended by instruction tokens in the target VLM, capturing instruction-relevant information. It then identifies samples whose representations best approximate the dominant subspace of the full dataset representations, enabling scalable importance scoring without pairwise comparisons. Extensive experiments across multiple VLMs, datasets, and selection budgets demonstrate that ScalSelect achieves over 97.5% of the performance of training on the full dataset using only 16% of the data, and even outperforms full-data training in some settings. The code is available at https://github.com/ChangtiWu/ScalSelect{ScalSelect}.", "github_manual": "", "github_regex": "https://github.com/ChangtiWu/ScalSelect", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11337", "title": "MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation", "authors": [ "Yejin Kim", "Wilbert Pumacay", "Omar Rayyan", "Max Argus", "Winson Han", "Eli VanderBilt", "Jordi Salvador", "Abhay Deshpande", "Rose Hendrix", "Snehal Jauhri", "Shuo Liu", "Nur Muhammad Mahi Shafiullah", "Maya Guru", "Ainaz Eftekhar", "Karen Farley", "Donovan Clay", "Jiafei Duan", "Arjun Guru", "Piper Wolters", "Alvaro Herrasti", "Ying-Chun Lee", "Georgia Chalvatzaki", "Yuchen Cui", "Ali Farhadi", "Dieter Fox", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Deploying robots at scale demands robustness to the long tail of everyday situations. The countless variations in scene layout, object geometry, and task specifications that characterize real environments are vast and underrepresented in existing robot benchmarks. Measuring this level of generalization requires infrastructure at a scale and diversity that physical evaluation alone cannot provide. We introduce MolmoSpaces, a fully open ecosystem to support large-scale benchmarking of robot policies. MolmoSpaces consists of over 230k diverse indoor environments, ranging from handcrafted household scenes to procedurally generated multiroom houses, populated with 130k richly annotated object assets, including 48k manipulable objects with 42M stable grasps. Crucially, these environments are simulator-agnostic, supporting popular options such as MuJoCo, Isaac, and ManiSkill. The ecosystem supports the full spectrum of embodied tasks: static and mobile manipulation, navigation, and multiroom long-horizon tasks requiring coordinated perception, planning, and interaction across entire indoor environments. We also design MolmoSpaces-Bench, a benchmark suite of 8 tasks in which robots interact with our diverse scenes and richly annotated objects. Our experiments show MolmoSpaces-Bench exhibits strong sim-to-real correlation (R = 0.96, ho = 0.98), confirm newer and stronger zero-shot policies outperform earlier versions in our benchmarks, and identify key sensitivities to prompt phrasing, initial joint positions, and camera occlusion. Through MolmoSpaces and its open-source assets and tooling, we provide a foundation for scalable data generation, policy training, and benchmark creation for robot learning research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11964", "title": "Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments", "authors": [ "Romain Froger", "Pierre Andrews", "Matteo Bettini", "Amar Budhiraja", "Ricardo Silveira Cabral", "Virginie Do", "Emilien Garreau", "Jean-Baptiste Gaya", "Hugo Lauren\u00e7on", "Maxime Lecanu", "Kunal Malkan", "Dheeraj Mekala", "Pierre M\u00e9nard", "Gerard Moreno-Torres Bertran", "Ulyana Piterbarg", "Mikhail Plekhanov", "Mathieu Rita", "Andrey Rusakov", "Vladislav Vorotilov", "Mengjue Wang", "Ian Yu", "Amine Benhalloum", "Gr\u00e9goire Mialon", "Thomas Scialom" ], "github": "", "project_page": "", "abstract": "We introduce Gaia2, a benchmark for evaluating large language model agents in realistic, asynchronous environments. Unlike prior static or synchronous evaluations, Gaia2 introduces scenarios where environments evolve independently of agent actions, requiring agents to operate under temporal constraints, adapt to noisy and dynamic events, resolve ambiguity, and collaborate with other agents. Each scenario is paired with a write-action verifier, enabling fine-grained, action-level evaluation and making Gaia2 directly usable for reinforcement learning from verifiable rewards. Our evaluation of state-of-the-art proprietary and open-source models shows that no model dominates across capabilities: GPT-5 (high) reaches the strongest overall score of 42% pass@1 but fails on time-sensitive tasks, Claude-4 Sonnet trades accuracy and speed for cost, Kimi-K2 leads among open-source models with 21% pass@1. These results highlight fundamental trade-offs between reasoning, efficiency, robustness, and expose challenges in closing the \"sim2real\" gap. Gaia2 is built on a consumer environment with the open-source Agents Research Environments platform and designed to be easy to extend. By releasing Gaia2 alongside the foundational ARE framework, we aim to provide the community with a flexible infrastructure for developing, benchmarking, and training the next generation of practical agent systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11761", "title": "MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling", "authors": [ "MiniCPM Team", "Wenhao An", "Yingfa Chen", "Yewei Fang", "Jiayi Li", "Xin Li", "Yaohui Li", "Yishan Li", "Yuxuan Li", "Biyuan Lin", "Chuan Liu", "Hezi Liu", "Siyuan Liu", "Hongya Lyu", "Yinxu Pan", "Shixin Ren", "Xingyu Shen", "Zhou Su", "Haojun Sun", "Yangang Sun", "Zhen Leng Thai", "Xin Tian", "Rui Wang", "Xiaorong Wang", "Yudong Wang", "Bo Wu", "Xiaoyue Xu", "Dong Xu", "Shuaikang Xue", "Jiawei Yang", "Bowen Zhang", "Jinqian Zhang", "Letian Zhang", "Shengnan Zhang", "Xinyu Zhang", "Xinyuan Zhang", "Zhu Zhang", "Hengyu Zhao", "Jiacheng Zhao", "Jie Zhou", "Zihan Zhou", "Shuo Wang", "Chaojun Xiao", "Xu Han", "Zhiyuan Liu", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "The evolution of large language models (LLMs) towards applications with ultra-long contexts faces challenges posed by the high computational and memory costs of the Transformer architecture. While existing sparse and linear attention mechanisms attempt to mitigate these issues, they typically involve a trade-off between memory efficiency and model performance. This paper introduces MiniCPM-SALA, a 9B-parameter hybrid architecture that integrates the high-fidelity long-context modeling of sparse attention (InfLLM-V2) with the global efficiency of linear attention (Lightning Attention). By employing a layer selection algorithm to integrate these mechanisms in a 1:3 ratio and utilizing a hybrid positional encoding (HyPE), the model maintains efficiency and performance for long-context tasks. Furthermore, we introduce a cost-effective continual training framework that transforms pre-trained Transformer-based models into hybrid models, which reduces training costs by approximately 75% compared to training from scratch. Extensive experiments show that MiniCPM-SALA maintains general capabilities comparable to full-attention models while offering improved efficiency. On a single NVIDIA A6000D GPU, the model achieves up to 3.5x the inference speed of the full-attention model at the sequence length of 256K tokens and supports context lengths of up to 1M tokens, a scale where traditional full-attention 8B models fail because of memory constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11598", "title": "ABot-N0: Technical Report on the VLA Foundation Model for Versatile Embodied Navigation", "authors": [ "Zedong Chu", "Shichao Xie", "Xiaolong Wu", "Yanfen Shen", "Minghua Luo", "Zhengbo Wang", "Fei Liu", "Xiaoxu Leng", "Junjun Hu", "Mingyang Yin", "Jia Lu", "Yingnan Guo", "Kai Yang", "Jiawei Han", "Xu Chen", "Yanqing Zhu", "Yuxiang Zhao", "Xin Liu", "Yirong Yang", "Ye He", "Jiahang Wang", "Yang Cai", "Tianlin Zhang", "Li Gao", "Liu Liu", "Mingchao Sun", "Fan Jiang", "Chiyu Wang", "Zhicheng Liu", "Hongyu Pan", "Honglin Han", "Zhining Gu", "Kuan Yang", "Jianfang Zhang", "Di Jing", "Zihao Guan", "Wei Guo", "Guoqing Liu", "Di Yang", "Xiangpo Yang", "Menglin Yang", "Hongguang Xing", "Weiguo Li", "Mu Xu" ], "github": "", "project_page": "", "abstract": "Embodied navigation has long been fragmented by task-specific architectures. We introduce ABot-N0, a unified Vision-Language-Action (VLA) foundation model that achieves a ``Grand Unification'' across 5 core tasks: Point-Goal, Object-Goal, Instruction-Following, POI-Goal, and Person-Following. ABot-N0 utilizes a hierarchical ``Brain-Action'' architecture, pairing an LLM-based Cognitive Brain for semantic reasoning with a Flow Matching-based Action Expert for precise, continuous trajectory generation. To support large-scale learning, we developed the ABot-N0 Data Engine, curating 16.9M expert trajectories and 5.0M reasoning samples across 7,802 high-fidelity 3D scenes (10.7 km^2). ABot-N0 achieves new SOTA performance across 7 benchmarks, significantly outperforming specialized models. Furthermore, our Agentic Navigation System integrates a planner with hierarchical topological memory, enabling robust, long-horizon missions in dynamic real-world environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12280", "title": "Stroke of Surprise: Progressive Semantic Illusions in Vector Sketching", "authors": [ "Huai-Hsun Cheng", "Siang-Ling Zhang", "Yu-Lun Liu" ], "github": "", "project_page": "", "abstract": "Visual illusions traditionally rely on spatial manipulations such as multi-view consistency. In this work, we introduce Progressive Semantic Illusions, a novel vector sketching task where a single sketch undergoes a dramatic semantic transformation through the sequential addition of strokes. We present Stroke of Surprise, a generative framework that optimizes vector strokes to satisfy distinct semantic interpretations at different drawing stages. The core challenge lies in the \"dual-constraint\": initial prefix strokes must form a coherent object (e.g., a duck) while simultaneously serving as the structural foundation for a second concept (e.g., a sheep) upon adding delta strokes. To address this, we propose a sequence-aware joint optimization framework driven by a dual-branch Score Distillation Sampling (SDS) mechanism. Unlike sequential approaches that freeze the initial state, our method dynamically adjusts prefix strokes to discover a \"common structural subspace\" valid for both targets. Furthermore, we introduce a novel Overlay Loss that enforces spatial complementarity, ensuring structural integration rather than occlusion. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art baselines in recognizability and illusion strength, successfully expanding visual anagrams from the spatial to the temporal dimension. Project page: https://stroke-of-surprise.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.10934", "title": "MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models", "authors": [ "Yitian Gong", "Kuangwei Chen", "Zhaoye Fei", "Xiaogui Yang", "Ke Chen", "Yang Wang", "Kexin Huang", "Mingshu Chen", "Ruixiao Li", "Qingyuan Cheng", "Shimin Li", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Discrete audio tokenizers are fundamental to empowering large language models with native audio processing and generation capabilities. Despite recent progress, existing approaches often rely on pretrained encoders, semantic distillation, or heterogeneous CNN-based architectures. These designs introduce fixed inductive biases that limit reconstruction fidelity and hinder effective scaling. In this paper, we argue that discrete audio tokenization should be learned fully end-to-end using a homogeneous and scalable architecture. To this end, we first propose CAT (Causal Audio Tokenizer with Transformer), a purely Transformer-based architecture that jointly optimizes the encoder, quantizer, and decoder from scratch for high-fidelity reconstruction. Building on the CAT architecture, we develop MOSS-Audio-Tokenizer, a large-scale audio tokenizer featuring 1.6 billion parameters, pre-trained on 3 million hours of diverse, general audio data. We show that this simple, fully end-to-end approach built from homogeneous, causal Transformer blocks scales gracefully and supports high-fidelity reconstruction across diverse audio domains. Across speech, sound, and music, MOSS-Audio-Tokenizer consistently outperforms prior codecs over a wide range of bitrates, while exhibiting predictable improvements with increased scale. Notably, leveraging the discrete tokens from our model, we develop the first purely autoregressive TTS model that surpasses prior non-autoregressive and cascaded systems. Furthermore, MOSS-Audio-Tokenizer enables competitive ASR performance without auxiliary encoders. Our findings position the CAT architecture as a unified, scalable interface for the next generation of native audio foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12164", "title": "Sci-CoE: Co-evolving Scientific Reasoning LLMs via Geometric Consensus with Sparse Supervision", "authors": [ "Xiaohan He", "Shiyang Feng", "Songtao Huang", "Lei Bai", "Bin Wang", "Bo Zhang" ], "github": "https://github.com/InternScience/Sci-CoE", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated exceptional reasoning capabilities, and co-evolving paradigms have shown promising results in domains such as code and math. However, in scientific reasoning tasks, these models remain fragile due to unreliable solution evaluation and limited diversity in verification strategies. In this work, we propose Sci-CoE, a two-stage scientific co-evolving framework that enables models to self-evolve as both solver and verifier through a transition from sparse supervision to unsupervised learning. In the first stage, the model uses a small set of annotated data to establish fundamental correctness judgment anchors for the Verifier. In the second stage, we introduce a geometric reward mechanism that jointly considers consensus, reliability, and diversity, driving large-scale self-iteration on unlabeled data. Experiments on several general scientific benchmarks demonstrate that Sci-CoE enhances complex reasoning capabilities and exhibits strong scalability, facilitating the construction of more robust and diverse evaluation systems. Codes are available at https://github.com/InternScience/Sci-CoE.", "github_manual": "", "github_regex": "https://github.com/InternScience/Sci-CoE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11543", "title": "Pretraining A Large Language Model using Distributed GPUs: A Memory-Efficient Decentralized Paradigm", "authors": [ "Jinrui Zhang", "Chaodong Xiao", "Aoqi Wu", "Xindong Zhang", "Lei Zhang" ], "github": "https://github.com/zjr2000/SPES", "project_page": "", "abstract": "Pretraining large language models (LLMs) typically requires centralized clusters with thousands of high-memory GPUs (e.g., H100/A100). Recent decentralized training methods reduce communication overhead by employing federated optimization; however, they still need to train the entire model on each node, remaining constrained by GPU memory limitations. In this work, we propose SParse Expert Synchronization (SPES), a memory-efficient decentralized framework for pretraining mixture-of-experts (MoE) LLMs. SPES trains only a subset of experts per node, substantially lowering the memory footprint. Each node updates its local experts and periodically synchronizes with other nodes, eliminating full-parameter transmission while ensuring efficient knowledge sharing. To accelerate convergence, we introduce an expert-merging warm-up strategy, where experts exchange knowledge early in training, to rapidly establish foundational capabilities. With SPES, we train a 2B-parameter MoE LLM using 16 standalone 48GB GPUs over internet connections, which achieves competitive performance with centrally trained LLMs under similar computational budgets. We further demonstrate scalability by training a 7B model from scratch and a 9B model upcycled from a dense checkpoint, both of which match prior centralized baselines. Our code is available at https://github.com/zjr2000/SPES.", "github_manual": "", "github_regex": "https://github.com/zjr2000/SPES", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11509", "title": "Multimodal Fact-Level Attribution for Verifiable Reasoning", "authors": [ "David Wan", "Han Wang", "Ziyang Wang", "Elias Stengel-Eskin", "Hyunji Lee", "Mohit Bansal" ], "github": "https://github.com/meetdavidwan/murgat", "project_page": "", "abstract": "Multimodal large language models (MLLMs) are increasingly used for real-world tasks involving multi-step reasoning and long-form generation, where reliability requires grounding model outputs in heterogeneous input sources and verifying individual factual claims. However, existing multimodal grounding benchmarks and evaluation methods focus on simplified, observation-based scenarios or limited modalities and fail to assess attribution in complex multimodal reasoning. We introduce MuRGAt (Multimodal Reasoning with Grounded Attribution), a benchmark for evaluating fact-level multimodal attribution in settings that require reasoning beyond direct observation. Given inputs spanning video, audio, and other modalities, MuRGAt requires models to generate answers with explicit reasoning and precise citations, where each citation specifies both modality and temporal segments. To enable reliable assessment, we introduce an automatic evaluation framework that strongly correlates with human judgments. Benchmarking with human and automated scores reveals that even strong MLLMs frequently hallucinate citations despite correct reasoning. Moreover, we observe a key trade-off: increasing reasoning depth or enforcing structured grounding often degrades accuracy, highlighting a significant gap between internal reasoning and verifiable attribution.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/meetdavidwan/murgat", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.08277", "title": "PISCO: Precise Video Instance Insertion with Sparse Control", "authors": [ "Xiangbo Gao", "Renjie Li", "Xinghao Chen", "Yuheng Wu", "Suofei Feng", "Qing Yin", "Zhengzhong Tu" ], "github": "", "project_page": "", "abstract": "The landscape of AI video generation is undergoing a pivotal shift: moving beyond general generation - which relies on exhaustive prompt-engineering and \"cherry-picking\" - towards fine-grained, controllable generation and high-fidelity post-processing. In professional AI-assisted filmmaking, it is crucial to perform precise, targeted modifications. A cornerstone of this transition is video instance insertion, which requires inserting a specific instance into existing footage while maintaining scene integrity. Unlike traditional video editing, this task demands several requirements: precise spatial-temporal placement, physically consistent scene interaction, and the faithful preservation of original dynamics - all achieved under minimal user effort. In this paper, we propose PISCO, a video diffusion model for precise video instance insertion with arbitrary sparse keyframe control. PISCO allows users to specify a single keyframe, start-and-end keyframes, or sparse keyframes at arbitrary timestamps, and automatically propagates object appearance, motion, and interaction. To address the severe distribution shift induced by sparse conditioning in pretrained video diffusion models, we introduce Variable-Information Guidance for robust conditioning and Distribution-Preserving Temporal Masking to stabilize temporal generation, together with geometry-aware conditioning for realistic scene adaptation. We further construct PISCO-Bench, a benchmark with verified instance annotations and paired clean background videos, and evaluate performance using both reference-based and reference-free perceptual metrics. Experiments demonstrate that PISCO consistently outperforms strong inpainting and video editing baselines under sparse control, and exhibits clear, monotonic performance improvements as additional control signals are provided. Project page: xiangbogaobarry.github.io/PISCO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.09070", "title": "NarraScore: Bridging Visual Narrative and Musical Dynamics via Hierarchical Affective Control", "authors": [ "Yufan Wen", "Zhaocheng Liu", "YeGuo Hua", "Ziyi Guo", "Lihua Zhang", "Chun Yuan", "Jian Wu" ], "github": "", "project_page": "", "abstract": "Synthesizing coherent soundtracks for long-form videos remains a formidable challenge, currently stalled by three critical impediments: computational scalability, temporal coherence, and, most critically, a pervasive semantic blindness to evolving narrative logic. To bridge these gaps, we propose NarraScore, a hierarchical framework predicated on the core insight that emotion serves as a high-density compression of narrative logic. Uniquely, we repurpose frozen Vision-Language Models (VLMs) as continuous affective sensors, distilling high-dimensional visual streams into dense, narrative-aware Valence-Arousal trajectories. Mechanistically, NarraScore employs a Dual-Branch Injection strategy to reconcile global structure with local dynamism: a Global Semantic Anchor ensures stylistic stability, while a surgical Token-Level Affective Adapter modulates local tension via direct element-wise residual injection. This minimalist design bypasses the bottlenecks of dense attention and architectural cloning, effectively mitigating the overfitting risks associated with data scarcity. Experiments demonstrate that NarraScore achieves state-of-the-art consistency and narrative alignment with negligible computational overhead, establishing a fully autonomous paradigm for long-video soundtrack generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12092", "title": "DeepSight: An All-in-One LM Safety Toolkit", "authors": [ "Bo Zhang", "Jiaxuan Guo", "Lijun Li", "Dongrui Liu", "Sujin Chen", "Guanxu Chen", "Zhijie Zheng", "Qihao Lin", "Lewen Yan", "Chen Qian", "Yijin Zhou", "Yuyao Wu", "Shaoxiong Guo", "Tianyi Du", "Jingyi Yang", "Xuhao Hu", "Ziqi Miao", "Xiaoya Lu", "Jing Shao", "Xia Hu" ], "github": "", "project_page": "", "abstract": "As the development of Large Models (LMs) progresses rapidly, their safety is also a priority. In current Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) safety workflow, evaluation, diagnosis, and alignment are often handled by separate tools. Specifically, safety evaluation can only locate external behavioral risks but cannot figure out internal root causes. Meanwhile, safety diagnosis often drifts from concrete risk scenarios and remains at the explainable level. In this way, safety alignment lack dedicated explanations of changes in internal mechanisms, potentially degrading general capabilities. To systematically address these issues, we propose an open-source project, namely DeepSight, to practice a new safety evaluation-diagnosis integrated paradigm. DeepSight is low-cost, reproducible, efficient, and highly scalable large-scale model safety evaluation project consisting of a evaluation toolkit DeepSafe and a diagnosis toolkit DeepScan. By unifying task and data protocols, we build a connection between the two stages and transform safety evaluation from black-box to white-box insight. Besides, DeepSight is the first open source toolkit that support the frontier AI risk evaluation and joint safety evaluation and diagnosis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12262", "title": "T3D: Few-Step Diffusion Language Models via Trajectory Self-Distillation with Direct Discriminative Optimization", "authors": [ "Tunyu Zhang", "Xinxi Zhang", "Ligong Han", "Haizhou Shi", "Xiaoxiao He", "Zhuowei Li", "Hao Wang", "Kai Xu", "Akash Srivastava", "Hao Wang", "Vladimir Pavlovic", "Dimitris N. Metaxas" ], "github": "https://github.com/Tyrion58/T3D", "project_page": "", "abstract": "Diffusion large language models (DLLMs) have the potential to enable fast text generation by decoding multiple tokens in parallel. However, in practice, their inference efficiency is constrained by the need for many refinement steps, while aggressively reducing the number of steps leads to a substantial degradation in generation quality. To alleviate this, we propose a trajectory self-distillation framework that improves few-step decoding by distilling the model's own generative trajectories. We incorporate Direct Discriminative Optimization (DDO), a reverse-KL objective that promotes mode-seeking distillation and encourages the student to concentrate on high-probability teacher modes. Across benchmarks, our approach consistently outperforms strong few-step baselines and standard training under tight step budgets. Although full-step decoding remains superior, we substantially narrow the gap, establishing a strong foundation towards practical few-step DLLMs. The source code is available at https://github.com/Tyrion58/T3D.", "github_manual": "", "github_regex": "https://github.com/Tyrion58/T3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12205", "title": "DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing", "authors": [ "Dianyi Wang", "Ruihang Li", "Feng Han", "Chaofan Ma", "Wei Song", "Siyuan Wang", "Yibin Wang", "Yi Xin", "Hongjian Liu", "Zhixiong Zhang", "Shengyuan Ding", "Tianhang Wang", "Zhenglin Cheng", "Tao Lin", "Cheng Jin", "Kaicheng Yu", "Jingjing Chen", "Wenjie Wang", "Zhongyu Wei", "Jiaqi Wang" ], "github": "https://github.com/yejy53/Nano-banana-150k", "project_page": "", "abstract": "Current unified multimodal models for image generation and editing typically rely on massive parameter scales (e.g., >10B), entailing prohibitive training costs and deployment footprints. In this work, we present DeepGen 1.0, a lightweight 5B unified model that achieves comprehensive capabilities competitive with or surpassing much larger counterparts. To overcome the limitations of compact models in semantic understanding and fine-grained control, we introduce Stacked Channel Bridging (SCB), a deep alignment framework that extracts hierarchical features from multiple VLM layers and fuses them with learnable 'think tokens' to provide the generative backbone with structured, reasoning-rich guidance. We further design a data-centric training strategy spanning three progressive stages: (1) Alignment Pre-training on large-scale image-text pairs and editing triplets to synchronize VLM and DiT representations, (2) Joint Supervised Fine-tuning on a high-quality mixture of generation, editing, and reasoning tasks to foster omni-capabilities, and (3) Reinforcement Learning with MR-GRPO, which leverages a mixture of reward functions and supervision signals, resulting in substantial gains in generation quality and alignment with human preferences, while maintaining stable training progress and avoiding visual artifacts. Despite being trained on only ~50M samples, DeepGen 1.0 achieves leading performance across diverse benchmarks, surpassing the 80B HunyuanImage by 28% on WISE and the 27B Qwen-Image-Edit by 37% on UniREditBench. By open-sourcing our training code, weights, and datasets, we provide an efficient, high-performance alternative to democratize unified multimodal research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yejy53/Nano-banana-150k", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12116", "title": "P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling", "authors": [ "Pinyi Zhang", "Ting-En Lin", "Yuchuan Wu", "Jingyang Chen", "Zongqi Wang", "Hua Yang", "Ze Xu", "Fei Huang", "Kai Zhang", "Yongbin Li" ], "github": "https://github.com/Tongyi-ConvAI/Qwen-Character/tree/main/Character-GenRM", "project_page": "", "abstract": "Personalized alignment of large language models seeks to adapt responses to individual user preferences, typically via reinforcement learning. A key challenge is obtaining accurate, user-specific reward signals in open-ended scenarios. Existing personalized reward models face two persistent limitations: (1) oversimplifying diverse, scenario-specific preferences into a small, fixed set of evaluation principles, and (2) struggling with generalization to new users with limited feedback. To this end, we propose P-GenRM, the first Personalized Generative Reward Model with test-time user-based scaling. P-GenRM transforms preference signals into structured evaluation chains that derive adaptive personas and scoring rubrics across various scenarios. It further clusters users into User Prototypes and introduces a dual-granularity scaling mechanism: at the individual level, it adaptively scales and aggregates each user's scoring scheme; at the prototype level, it incorporates preferences from similar users. This design mitigates noise in inferred preferences and enhances generalization to unseen users through prototype-based transfer. Empirical results show that P-GenRM achieves state-of-the-art results on widely-used personalized reward model benchmarks, with an average improvement of 2.31%, and demonstrates strong generalization on an out-of-distribution dataset. Notably, Test-time User-based scaling provides an additional 3% boost, demonstrating stronger personalized alignment with test-time scalability.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Tongyi-ConvAI/Qwen-Character/tree/main/Character-GenRM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11748", "title": "Think Longer to Explore Deeper: Learn to Explore In-Context via Length-Incentivized Reinforcement Learning", "authors": [ "Futing Wang", "Jianhao Yan", "Yun Luo", "Ganqu Cui", "Zhi Wang", "Xiaoye Qu", "Yue Zhang", "Yu Cheng", "Tao Lin" ], "github": "https://github.com/LINs-lab/LIE", "project_page": "", "abstract": "Achieving effective test-time scaling requires models to engage in In-Context Exploration -- the intrinsic ability to generate, verify, and refine multiple reasoning hypotheses within a single continuous context. Grounded in State Coverage theory, our analysis identifies a critical bottleneck to enabling this capability: while broader state coverage requires longer reasoning trajectories, the probability of sampling such sequences decays exponentially during autoregressive generation, a phenomenon we term the ``Shallow Exploration Trap''. To bridge this gap, we propose Length-Incentivized Exploration(\\method). This simple yet effective recipe explicitly encourages models to explore more via a length-based reward coupled with a redundancy penalty, thereby maximizing state coverage in two-step manner. Comprehensive experiments across different models (Qwen3, Llama) demonstrate that \\method effectively incentivize in-context exploration. As a result, our method achieves an average improvement of 4.4\\% on in-domain tasks and a 2.7\\% gain on out-of-domain benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LINs-lab/LIE", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11792", "title": "Detecting RLVR Training Data via Structural Convergence of Reasoning", "authors": [ "Hongbo Zhang", "Yue Yang", "Jianhao Yan", "Guangsheng Bao", "Yue Zhang", "Yue Zhang" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) is central to training modern reasoning models, but the undisclosed training data raises concerns about benchmark contamination. Unlike pretraining methods, which optimize models using token-level probabilities, RLVR fine-tunes models based on reward feedback from self-generated reasoning trajectories, making conventional likelihood-based detection methods less effective. We show that RLVR induces a distinctive behavioral signature: prompts encountered during RLVR training result in more rigid and similar generations, while unseen prompts retain greater diversity. We introduce Min-kNN Distance, a simple black-box detector that quantifies this collapse by sampling multiple completions for a given prompt and computing the average of the k smallest nearest-neighbor edit distances. Min-kNN Distance requires no access to the reference model or token probabilities. Experiments across multiple RLVR-trained reasoning models show that Min-kNN Distance reliably distinguishes RL-seen examples from unseen ones and outperforms existing membership inference and RL contamination detection baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11731", "title": "Thinking with Drafting: Optical Decompression via Logical Reconstruction", "authors": [ "Jingxuan Wei", "Honghao He", "Caijun Jia", "Siyuan Li", "Zheng Sun", "Yuhang Xu", "Yuanyuan Lin", "Linzhuang Sun", "Yuchen Wu", "Bihui Yu", "Xiangxiang Zhang", "Cheng Tan" ], "github": "", "project_page": "", "abstract": "Existing multimodal large language models have achieved high-fidelity visual perception and exploratory visual generation. However, a precision paradox persists in complex reasoning tasks: optical perception systems transcribe symbols without capturing logical topology, while pixel-based generative models produce visual artifacts lacking mathematical exactness. To bridge this gap, we propose that reasoning over visual inputs be reconceptualized as optical decompression-the process of reconstructing latent logical structures from compressed visual tokens. Guided by the axiom that Parsing is Reasoning, we introduce Thinking with Drafting (TwD), which utilizes a minimalist Domain-Specific Language (DSL) as a grounding intermediate representation. Unlike standard approaches that hallucinate answers directly, TwD forces the model to draft its mental model into executable code, rendering deterministic visual proofs for self-verification. To validate this, we present VisAlg, a visual algebra benchmark. Experiments demonstrate that TwD serve as a superior cognitive scaffold. Our work establishes a closed-loop system where visual generation acts not as a creative output but as a logical verifier, offering a generalizable path for visual reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.10575", "title": "MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual Reinforcement Learning", "authors": [ "Chenhao Zhang", "Yazhe Niu", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "Metaphorical comprehension in images remains a critical challenge for Nowadays AI systems. While Multimodal Large Language Models (MLLMs) excel at basic Visual Question Answering (VQA), they consistently struggle to grasp the nuanced cultural, emotional, and contextual implications embedded in visual content. This difficulty stems from the task's demand for sophisticated multi-hop reasoning, cultural context, and Theory of Mind (ToM) capabilities, which current models lack. To fill this gap, we propose MetaphorStar, the first end-to-end visual reinforcement learning (RL) framework for image implication tasks. Our framework includes three core components: the fine-grained dataset TFQ-Data, the visual RL method TFQ-GRPO, and the well-structured benchmark TFQ-Bench. Our fully open-source MetaphorStar family, trained using TFQ-GRPO on TFQ-Data, significantly improves performance by an average of 82.6% on the image implication benchmarks. Compared with 20+ mainstream MLLMs, MetaphorStar-32B achieves state-of-the-art (SOTA) on Multiple-Choice Question and Open-Style Question, significantly outperforms the top closed-source model Gemini-3.0-pro on True-False Question. Crucially, our experiments reveal that learning image implication tasks improves the general understanding ability, especially the complex visual reasoning ability. We further provide a systematic analysis of model parameter scaling, training data scaling, and the impact of different model architectures and training strategies, demonstrating the broad applicability of our method. We open-sourced all model weights, datasets, and method code at https://metaphorstar.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12036", "title": "Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models", "authors": [ "Xin Xu", "Clive Bai", "Kai Yang", "Tianhao Chen", "Yangkun Chen", "Weijie Liu", "Hao Chen", "Yang Wang", "Saiyong Yang", "Can Yang" ], "github": "https://github.com/XinXU-USTC/Composition-RL", "project_page": "", "abstract": "Large-scale verifiable prompts underpin the success of Reinforcement Learning with Verifiable Rewards (RLVR), but they contain many uninformative examples and are costly to expand further. Recent studies focus on better exploiting limited training data by prioritizing hard prompts whose rollout pass rate is 0. However, easy prompts with a pass rate of 1 also become increasingly prevalent as training progresses, thereby reducing the effective data size. To mitigate this, we propose Composition-RL, a simple yet useful approach for better utilizing limited verifiable prompts targeting pass-rate-1 prompts. More specifically, Composition-RL automatically composes multiple problems into a new verifiable question and uses these compositional prompts for RL training. Extensive experiments across model sizes from 4B to 30B show that Composition-RL consistently improves reasoning capability over RL trained on the original dataset. Performance can be further boosted with a curriculum variant of Composition-RL that gradually increases compositional depth over training. Additionally, Composition-RL enables more effective cross-domain RL by composing prompts drawn from different domains. Codes, datasets, and models are available at https://github.com/XinXU-USTC/Composition-RL.", "github_manual": "", "github_regex": "https://github.com/XinXU-USTC/Composition-RL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.09021", "title": "\u03c7_{0}: Resource-Aware Robust Manipulation via Taming Distributional Inconsistencies", "authors": [ "Checheng Yu", "Chonghao Sima", "Gangcheng Jiang", "Hai Zhang", "Haoguang Mai", "Hongyang Li", "Huijie Wang", "Jin Chen", "Kaiyang Wu", "Li Chen", "Lirui Zhao", "Modi Shi", "Ping Luo", "Qingwen Bu", "Shijia Peng", "Tianyu Li", "Yibo Yuan" ], "github": "", "project_page": "", "abstract": "High-reliability long-horizon robotic manipulation has traditionally relied on large-scale data and compute to understand complex real-world dynamics. However, we identify that the primary bottleneck to real-world robustness is not resource scale alone, but the distributional shift among the human demonstration distribution, the inductive bias learned by the policy, and the test-time execution distribution -- a systematic inconsistency that causes compounding errors in multi-stage tasks. To mitigate these inconsistencies, we propose \u03c7_{0}, a resource-efficient framework with effective modules designated to achieve production-level robustness in robotic manipulation. Our approach builds off three technical pillars: (i) Model Arithmetic, a weight-space merging strategy that efficiently soaks up diverse distributions of different demonstrations, varying from object appearance to state variations; (ii) Stage Advantage, a stage-aware advantage estimator that provides stable, dense progress signals, overcoming the numerical instability of prior non-stage approaches; and (iii) Train-Deploy Alignment, which bridges the distribution gap via spatio-temporal augmentation, heuristic DAgger corrections, and temporal chunk-wise smoothing. \u03c7_{0} enables two sets of dual-arm robots to collaboratively orchestrate long-horizon garment manipulation, spanning tasks from flattening, folding, to hanging different clothes. Our method exhibits high-reliability autonomy; we are able to run the system from arbitrary initial state for consecutive 24 hours non-stop. Experiments validate that \u03c7_{0} surpasses the state-of-the-art \u03c0_{0.5} in success rate by nearly 250%, with only 20-hour data and 8 A100 GPUs. Code, data and models will be released to facilitate the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11075", "title": "RISE: Self-Improving Robot Policy with Compositional World Model", "authors": [ "Jiazhi Yang", "Kunyang Lin", "Jinwei Li", "Wencong Zhang", "Tianwei Lin", "Longyan Wu", "Zhizhong Su", "Hao Zhao", "Ya-Qin Zhang", "Li Chen", "Ping Luo", "Xiangyu Yue", "Hongyang Li" ], "github": "", "project_page": "", "abstract": "Despite the sustained scaling on model capacity and data acquisition, Vision-Language-Action (VLA) models remain brittle in contact-rich and dynamic manipulation tasks, where minor execution deviations can compound into failures. While reinforcement learning (RL) offers a principled path to robustness, on-policy RL in the physical world is constrained by safety risk, hardware cost, and environment reset. To bridge this gap, we present RISE, a scalable framework of robotic reinforcement learning via imagination. At its core is a Compositional World Model that (i) predicts multi-view future via a controllable dynamics model, and (ii) evaluates imagined outcomes with a progress value model, producing informative advantages for the policy improvement. Such compositional design allows state and value to be tailored by best-suited yet distinct architectures and objectives. These components are integrated into a closed-loop self-improving pipeline that continuously generates imaginary rollouts, estimates advantages, and updates the policy in imaginary space without costly physical interaction. Across three challenging real-world tasks, RISE yields significant improvement over prior art, with more than +35% absolute performance increase in dynamic brick sorting, +45% for backpack packing, and +35% for box closing, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.11733", "title": "Adapting Vision-Language Models for E-commerce Understanding at Scale", "authors": [ "Matteo Nulli", "Vladimir Orshulevich", "Tala Bazazo", "Christian Herold", "Michael Kozielski", "Marcin Mazur", "Szymon Tuzel", "Cees G. M. Snoek", "Seyyed Hadi Hashemi", "Omar Javed", "Yannick Versley", "Shahram Khadivi" ], "github": "", "project_page": "", "abstract": "E-commerce product understanding demands by nature, strong multimodal comprehension from text, images, and structured attributes. General-purpose Vision-Language Models (VLMs) enable generalizable multimodal latent modelling, yet there is no documented, well-known strategy for adapting them to the attribute-centric, multi-image, and noisy nature of e-commerce data, without sacrificing general performance. In this work, we show through a large-scale experimental study, how targeted adaptation of general VLMs can substantially improve e-commerce performance while preserving broad multimodal capabilities. Furthermore, we propose a novel extensive evaluation suite covering deep product understanding, strict instruction following, and dynamic attribute extraction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.10106", "title": "EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration", "authors": [ "Modi Shi", "Shijia Peng", "Jin Chen", "Haoran Jiang", "Yinghui Li", "Di Huang", "Ping Luo", "Hongyang Li", "Li Chen" ], "github": "", "project_page": "", "abstract": "Human demonstrations offer rich environmental diversity and scale naturally, making them an appealing alternative to robot teleoperation. While this paradigm has advanced robot-arm manipulation, its potential for the more challenging, data-hungry problem of humanoid loco-manipulation remains largely unexplored. We present EgoHumanoid, the first framework to co-train a vision-language-action policy using abundant egocentric human demonstrations together with a limited amount of robot data, enabling humanoids to perform loco-manipulation across diverse real-world environments. To bridge the embodiment gap between humans and robots, including discrepancies in physical morphology and viewpoint, we introduce a systematic alignment pipeline spanning from hardware design to data processing. A portable system for scalable human data collection is developed, and we establish practical collection protocols to improve transferability. At the core of our human-to-humanoid alignment pipeline lies two key components. The view alignment reduces visual domain discrepancies caused by camera height and perspective variation. The action alignment maps human motions into a unified, kinematically feasible action space for humanoid control. Extensive real-world experiments demonstrate that incorporating robot-free egocentric data significantly outperforms robot-only baselines by 51\\%, particularly in unseen environments. Our analysis further reveals which behaviors transfer effectively and the potential for scaling human data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.05827", "title": "Sparse Video Generation Propels Real-World Beyond-the-View Vision-Language Navigation", "authors": [ "Hai Zhang", "Siqi Liang", "Li Chen", "Yuxian Li", "Yukuan Xu", "Yichao Zhong", "Fu Zhang", "Hongyang Li" ], "github": "", "project_page": "", "abstract": "Why must vision-language navigation be bound to detailed and verbose language instructions? While such details ease decision-making, they fundamentally contradict the goal for navigation in the real-world. Ideally, agents should possess the autonomy to navigate in unknown environments guided solely by simple and high-level intents. Realizing this ambition introduces a formidable challenge: Beyond-the-View Navigation (BVN), where agents must locate distant, unseen targets without dense and step-by-step guidance. Existing large language model (LLM)-based methods, though adept at following dense instructions, often suffer from short-sighted behaviors due to their reliance on short-horimzon supervision. Simply extending the supervision horizon, however, destabilizes LLM training. In this work, we identify that video generation models inherently benefit from long-horizon supervision to align with language instructions, rendering them uniquely suitable for BVN tasks. Capitalizing on this insight, we propose introducing the video generation model into this field for the first time. Yet, the prohibitive latency for generating videos spanning tens of seconds makes real-world deployment impractical. To bridge this gap, we propose SparseVideoNav, achieving sub-second trajectory inference guided by a generated sparse future spanning a 20-second horizon. This yields a remarkable 27x speed-up compared to the unoptimized counterpart. Extensive real-world zero-shot experiments demonstrate that SparseVideoNav achieves 2.5x the success rate of state-of-the-art LLM baselines on BVN tasks and marks the first realization of such capability in challenging night scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12203", "title": "ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images", "authors": [ "Mathieu Sibue", "Andres Mu\u00f1oz Garza", "Samuel Mensah", "Pranav Shetty", "Zhiqiang Ma", "Xiaomo Liu", "Manuela Veloso" ], "github": "", "project_page": "", "abstract": "Enterprise documents, such as forms and reports, embed critical information for downstream applications like data archiving, automated workflows, and analytics. Although generalist Vision Language Models (VLMs) perform well on established document understanding benchmarks, their ability to conduct holistic, fine-grained structured extraction across diverse document types and flexible schemas is not well studied. Existing Key Entity Extraction (KEE), Relation Extraction (RE), and Visual Question Answering (VQA) datasets are limited by narrow entity ontologies, simple queries, or homogeneous document types, often overlooking the need for adaptable and structured extraction. To address these gaps, we introduce ExStrucTiny, a new benchmark dataset for structured Information Extraction (IE) from document images, unifying aspects of KEE, RE, and VQA. Built through a novel pipeline combining manual and synthetic human-validated samples, ExStrucTiny covers more varied document types and extraction scenarios. We analyze open and closed VLMs on this benchmark, highlighting challenges such as schema adaptation, query under-specification, and answer localization. We hope our work provides a bedrock for improving generalist models for structured IE in documents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.09891", "title": "Stemphonic: All-at-once Flexible Multi-stem Music Generation", "authors": [ "Shih-Lun Wu", "Ge Zhu", "Juan-Pablo Caceres", "Cheng-Zhi Anna Huang", "Nicholas J. Bryan" ], "github": "", "project_page": "", "abstract": "Music stem generation, the task of producing musically-synchronized and isolated instrument audio clips, offers the potential of greater user control and better alignment with musician workflows compared to conventional text-to-music models. Existing stem generation approaches, however, either rely on fixed architectures that output a predefined set of stems in parallel, or generate only one stem at a time, resulting in slow inference despite flexibility in stem combination. We propose Stemphonic, a diffusion-/flow-based framework that overcomes this trade-off and generates a variable set of synchronized stems in one inference pass. During training, we treat each stem as a batch element, group synchronized stems in a batch, and apply a shared noise latent to each group. At inference-time, we use a shared initial noise latent and stem-specific text inputs to generate synchronized multi-stem outputs in one pass. We further expand our approach to enable one-pass conditional multi-stem generation and stem-wise activity controls to empower users to iteratively generate and orchestrate the temporal layering of a mix. We benchmark our results on multiple open-source stem evaluation sets and show that Stemphonic produces higher-quality outputs while accelerating the full mix generation process by 25 to 50%. Demos at: https://stemphonic-demo.vercel.app.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.12176", "title": "Single-minus gluon tree amplitudes are nonzero", "authors": [ "Alfredo Guevara", "Alexandru Lupsasca", "David Skinner", "Andrew Strominger", "Kevin Weil" ], "github": "", "project_page": "", "abstract": "Single-minus tree-level n-gluon scattering amplitudes are reconsidered. Often presumed to vanish, they are shown here to be nonvanishing for certain \"half-collinear\" configurations existing in Klein space or for complexified momenta. We derive a piecewise-constant closed-form expression for the decay of a single minus-helicity gluon into n-1 plus-helicity gluons as a function of their momenta. This formula nontrivially satisfies multiple consistency conditions including Weinberg's soft theorem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-13", "arxiv_id": "2602.07885", "title": "MemFly: On-the-Fly Memory Optimization via Information Bottleneck", "authors": [ "Zhenyuan Zhang", "Xianzhang Jia", "Zhiqin Yang", "Zhenbo Song", "Wei Xue", "Sirui Han", "Yike Guo" ], "github": "", "project_page": "", "abstract": "Long-term memory enables large language model agents to tackle complex tasks through historical interactions. However, existing frameworks encounter a fundamental dilemma between compressing redundant information efficiently and maintaining precise retrieval for downstream tasks. To bridge this gap, we propose MemFly, a framework grounded in information bottleneck principles that facilitates on-the-fly memory evolution for LLMs. Our approach minimizes compression entropy while maximizing relevance entropy via a gradient-free optimizer, constructing a stratified memory structure for efficient storage. To fully leverage MemFly, we develop a hybrid retrieval mechanism that seamlessly integrates semantic, symbolic, and topological pathways, incorporating iterative refinement to handle complex multi-hop queries. Comprehensive experiments demonstrate that MemFly substantially outperforms state-of-the-art baselines in memory coherence, response fidelity, and accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.12705", "title": "MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs", "authors": [ "Baorong Shi", "Bo Cui", "Boyuan Jiang", "Deli Yu", "Fang Qian", "Haihua Yang", "Huichao Wang", "Jiale Chen", "Jianfei Pan", "Jieqiong Cao", "Jinghao Lin", "Kai Wu", "Lin Yang", "Shengsheng Yao", "Tao Chen", "Xiaojun Xiao", "Xiaozhong Ji", "Xu Wang", "Yijun He", "Zhixiong Yang" ], "github": "", "project_page": "", "abstract": "We present MedXIAOHE, a medical vision-language foundation model designed to advance general-purpose medical understanding and reasoning in real-world clinical applications. MedXIAOHE achieves state-of-the-art performance across diverse medical benchmarks and surpasses leading closed-source multimodal systems on multiple capabilities. To achieve this, we propose an entity-aware continual pretraining framework that organizes heterogeneous medical corpora to broaden knowledge coverage and reduce long-tail gaps (e.g., rare diseases). For medical expert-level reasoning and interaction, MedXIAOHE incorporates diverse medical reasoning patterns via reinforcement learning and tool-augmented agentic training, enabling multi-step diagnostic reasoning with verifiable decision traces. To improve reliability in real-world use, MedXIAOHE integrates user-preference rubrics, evidence-grounded reasoning, and low-hallucination long-form report generation, with improved adherence to medical instructions. We release this report to document our practical design choices, scaling insights, and evaluation framework, hoping to inspire further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.12617", "title": "GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics", "authors": [ "Modi Jin", "Yiming Zhang", "Boyuan Sun", "Dingwen Zhang", "MingMing Cheng", "Qibin Hou" ], "github": "", "project_page": "", "abstract": "This paper presents GeoAgent, a model capable of reasoning closely with humans and deriving fine-grained address conclusions. Previous RL-based methods have achieved breakthroughs in performance and interpretability but still remain concerns because of their reliance on AI-generated chain-of-thought (CoT) data and training strategies, which conflict with geographic characteristics. To address these issues, we first introduce GeoSeek, a new geolocation dataset comprising CoT data annotated by geographic experts and professional players. We further thoroughly explore the inherent characteristics of geographic tasks and propose a geo-similarity reward and a consistency reward assessed by a consistency agent to assist training. This encourages the model to converge towards correct answers from a geographic perspective while ensuring the integrity and consistency of its reasoning process. Experimental results show that GeoAgent outperforms existing methods and a series of general VLLMs across multiple grains, while generating reasoning that closely aligns with humans.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.13013", "title": "Towards Universal Video MLLMs with Attribute-Structured and Quality-Verified Instructions", "authors": [ "Yunheng Li", "Hengrui Zhang", "Meng-Hao Guo", "Wenzhao Gao", "Shaoyong Jia", "Shaohui Jiao", "Qibin Hou", "Ming-Ming Cheng" ], "github": "", "project_page": "", "abstract": "Universal video understanding requires modeling fine-grained visual and audio information over time in diverse real-world scenarios. However, the performance of existing models is primarily constrained by video-instruction data that represents complex audiovisual content as single, incomplete descriptions, lacking fine-grained organization and reliable annotation. To address this, we introduce: (i) ASID-1M, an open-source collection of one million structured, fine-grained audiovisual instruction annotations with single- and multi-attribute supervision; (ii) ASID-Verify, a scalable data curation pipeline for annotation, with automatic verification and refinement that enforces semantic and temporal consistency between descriptions and the corresponding audiovisual content; and (iii) ASID-Captioner, a video understanding model trained via Supervised Fine-Tuning (SFT) on the ASID-1M. Experiments across seven benchmarks covering audiovisual captioning, attribute-wise captioning, caption-based QA, and caption-based temporal grounding show that ASID-Captioner improves fine-grained caption quality while reducing hallucinations and improving instruction following. It achieves state-of-the-art performance among open-source models and is competitive with Gemini-3-Pro.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.11858", "title": "Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception", "authors": [ "Lai Wei", "Liangbo He", "Jun Lan", "Lingzhong Dong", "Yutong Cai", "Siyuan Li", "Huijia Zhu", "Weiqiang Wang", "Linghe Kong", "Yue Wang", "Zhuosheng Zhang", "Weiran Huang" ], "github": "https://github.com/inclusionAI/Zooming-without-Zooming", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) excel at broad visual understanding but still struggle with fine-grained perception, where decisive evidence is small and easily overwhelmed by global context. Recent \"Thinking-with-Images\" methods alleviate this by iteratively zooming in and out regions of interest during inference, but incur high latency due to repeated tool calls and visual re-encoding. To address this, we propose Region-to-Image Distillation, which transforms zooming from an inference-time tool into a training-time primitive, thereby internalizing the benefits of agentic zooming into a single forward pass of an MLLM. In particular, we first zoom in to micro-cropped regions to let strong teacher models generate high-quality VQA data, and then distill this region-grounded supervision back to the full image. After training on such data, the smaller student model improves \"single-glance\" fine-grained perception without tool use. To rigorously evaluate this capability, we further present ZoomBench, a hybrid-annotated benchmark of 845 VQA data spanning six fine-grained perceptual dimensions, together with a dual-view protocol that quantifies the global--regional \"zooming gap\". Experiments show that our models achieve leading performance across multiple fine-grained perception benchmarks, and also improve general multimodal cognition on benchmarks such as visual reasoning and GUI agents. We further discuss when \"Thinking-with-Images\" is necessary versus when its gains can be distilled into a single forward pass. Our code is available at https://github.com/inclusionAI/Zooming-without-Zooming.", "github_manual": "", "github_regex": "https://github.com/inclusionAI/Zooming-without-Zooming", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.08683", "title": "OneVision-Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence", "authors": [ "Feilong Tang", "Xiang An", "Yunyao Yan", "Yin Xie", "Bin Qin", "Kaicheng Yang", "Yifei Shen", "Yuanhan Zhang", "Chunyuan Li", "Shikun Feng", "Changrui Chen", "Huajie Tan", "Ming Hu", "Manyuan Zhang", "Bo Li", "Ziyong Feng", "Ziwei Liu", "Zongyuan Ge", "Jiankang Deng" ], "github": "", "project_page": "", "abstract": "Hypothesis. Artificial general intelligence is, at its core, a compression problem. Effective compression demands resonance: deep learning scales best when its architecture aligns with the fundamental structure of the data. These are the fundamental principles. Yet, modern vision architectures have strayed from these truths: visual signals are highly redundant, while discriminative information, the surprise, is sparse. Current models process dense pixel grids uniformly, wasting vast compute on static background rather than focusing on the predictive residuals that define motion and meaning. We argue that to solve visual understanding, we must align our architectures with the information-theoretic principles of video, i.e., Codecs. Method. OneVision-Encoder encodes video by compressing predictive visual structure into semantic meaning. By adopting Codec Patchification, OV-Encoder abandons uniform computation to focus exclusively on the 3.1%-25% of regions rich in signal entropy. To unify spatial and temporal reasoning under irregular token layouts, OneVision-Encoder employs a shared 3D RoPE and is trained with a large-scale cluster discrimination objective over more than one million semantic concepts, jointly capturing object permanence and motion dynamics. Evidence. The results validate our core hypothesis: efficiency and accuracy are not a trade-off; they are positively correlated. When integrated into LLM, it consistently outperforms strong vision backbones such as Qwen3-ViT and SigLIP2 across 16 image, video, and document understanding benchmarks, despite using substantially fewer visual tokens and pretraining data. Notably, on video understanding tasks, OV-Encoder achieves an average improvement of 4.1% over Qwen3-ViT. Codec-aligned, patch-level sparsity is a foundational principle, enabling OV-Encoder as a scalable engine for next-generation visual generalists.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.11865", "title": "Intelligent AI Delegation", "authors": [ "Nenad Toma\u0161ev", "Matija Franklin", "Simon Osindero" ], "github": "", "project_page": "", "abstract": "AI agents are able to tackle increasingly complex tasks. To achieve more ambitious goals, AI agents need to be able to meaningfully decompose problems into manageable sub-components, and safely delegate their completion across to other AI agents and humans alike. Yet, existing task decomposition and delegation methods rely on simple heuristics, and are not able to dynamically adapt to environmental changes and robustly handle unexpected failures. Here we propose an adaptive framework for intelligent AI delegation - a sequence of decisions involving task allocation, that also incorporates transfer of authority, responsibility, accountability, clear specifications regarding roles and boundaries, clarity of intent, and mechanisms for establishing trust between the two (or more) parties. The proposed framework is applicable to both human and AI delegators and delegatees in complex delegation networks, aiming to inform the development of protocols in the emerging agentic web.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.12829", "title": "FLAC: Maximum Entropy RL via Kinetic Energy Regularized Bridge Matching", "authors": [ "Lei Lv", "Yunfei Li", "Yu Luo", "Fuchun Sun", "Xiao Ma" ], "github": "https://github.com/pranz24/pytorch-soft-actor-critic", "project_page": "", "abstract": "Iterative generative policies, such as diffusion models and flow matching, offer superior expressivity for continuous control but complicate Maximum Entropy Reinforcement Learning because their action log-densities are not directly accessible. To address this, we propose Field Least-Energy Actor-Critic (FLAC), a likelihood-free framework that regulates policy stochasticity by penalizing the kinetic energy of the velocity field. Our key insight is to formulate policy optimization as a Generalized Schr\u00f6dinger Bridge (GSB) problem relative to a high-entropy reference process (e.g., uniform). Under this view, the maximum-entropy principle emerges naturally as staying close to a high-entropy reference while optimizing return, without requiring explicit action densities. In this framework, kinetic energy serves as a physically grounded proxy for divergence from the reference: minimizing path-space energy bounds the deviation of the induced terminal action distribution. Building on this view, we derive an energy-regularized policy iteration scheme and a practical off-policy algorithm that automatically tunes the kinetic energy via a Lagrangian dual mechanism. Empirically, FLAC achieves superior or comparable performance on high-dimensional benchmarks relative to strong baselines, while avoiding explicit density estimation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pranz24/pytorch-soft-actor-critic", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.12684", "title": "Xiaomi-Robotics-0: An Open-Sourced Vision-Language-Action Model with Real-Time Execution", "authors": [ "Rui Cai", "Jun Guo", "Xinze He", "Piaopiao Jin", "Jie Li", "Bingxuan Lin", "Futeng Liu", "Wei Liu", "Fei Ma", "Kun Ma", "Feng Qiu", "Heng Qu", "Yifei Su", "Qiao Sun", "Dong Wang", "Donghao Wang", "Yunhong Wang", "Rujie Wu", "Diyun Xiang", "Yu Yang", "Hangjun Ye", "Yuan Zhang", "Quanyun Zhou" ], "github": "", "project_page": "", "abstract": "In this report, we introduce Xiaomi-Robotics-0, an advanced vision-language-action (VLA) model optimized for high performance and fast and smooth real-time execution. The key to our method lies in a carefully designed training recipe and deployment strategy. Xiaomi-Robotics-0 is first pre-trained on large-scale cross-embodiment robot trajectories and vision-language data, endowing it with broad and generalizable action-generation capabilities while avoiding catastrophic forgetting of the visual-semantic knowledge of the underlying pre-trained VLM. During post-training, we propose several techniques for training the VLA model for asynchronous execution to address the inference latency during real-robot rollouts. During deployment, we carefully align the timesteps of consecutive predicted action chunks to ensure continuous and seamless real-time rollouts. We evaluate Xiaomi-Robotics-0 extensively in simulation benchmarks and on two challenging real-robot tasks that require precise and dexterous bimanual manipulation. Results show that our method achieves state-of-the-art performance across all simulation benchmarks. Moreover, Xiaomi-Robotics-0 can roll out fast and smoothly on real robots using a consumer-grade GPU, achieving high success rates and throughput on both real-robot tasks. To facilitate future research, code and model checkpoints are open-sourced at https://xiaomi-robotics-0.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.12506", "title": "On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs", "authors": [ "Rosie Zhao", "Anshul Shah", "Xiaoyu Zhu", "Xinke Deng", "Zhongyu Jiang", "Yang Yang", "Joerg Liebelt", "Arnab Mondal" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) fine-tuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision language models (VLMs). While RL-tuned VLMs improve on visual reasoning benchmarks, they remain vulnerable to weak visual grounding, hallucinations, and over-reliance on textual cues. We show that simple, controlled textual perturbations--misleading captions or incorrect chain-of-thought (CoT) traces--cause substantial drops in robustness and confidence, and that these effects are more pronounced when CoT consistency is taken into account across open-source multimodal reasoning models. Entropy-based metrics further show that these perturbations reshape model uncertainty and probability mass on the correct option, exposing model-specific trends in miscalibration. To better understand these vulnerabilities, we further analyze RL fine-tuning dynamics and uncover an accuracy-faithfulness trade-off: fine-tuning raises benchmark accuracy, but can simultaneously erode the reliability of the accompanying CoT and its robustness to contextual shifts. Although adversarial augmentation improves robustness, it does not by itself prevent faithfulness drift. Incorporating a faithfulness-aware reward can restore alignment between answers and reasoning, but when paired with augmentation, training risks collapsing onto shortcut strategies and robustness remains elusive. Together, these findings highlight the limitations of accuracy-only evaluations and motivate training and assessment protocols that jointly emphasize correctness, robustness, and the faithfulness of visually grounded reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.11910", "title": "TADA! Tuning Audio Diffusion Models through Activation Steering", "authors": [ "\u0141ukasz Staniszewski", "Katarzyna Zaleska", "Mateusz Modrzejewski", "Kamil Deja" ], "github": "", "project_page": "", "abstract": "Audio diffusion models can synthesize high-fidelity music from text, yet their internal mechanisms for representing high-level concepts remain poorly understood. In this work, we use activation patching to demonstrate that distinct semantic musical concepts, such as the presence of specific instruments, vocals, or genre characteristics, are controlled by a small, shared subset of attention layers in state-of-the-art audio diffusion architectures. Next, we demonstrate that applying Contrastive Activation Addition and Sparse Autoencoders in these layers enables more precise control over the generated audio, indicating a direct benefit of the specialization phenomenon. By steering activations of the identified layers, we can alter specific musical elements with high precision, such as modulating tempo or changing a track's mood.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.11769", "title": "Light4D: Training-Free Extreme Viewpoint 4D Video Relighting", "authors": [ "Zhenghuang Wu", "Kang Chen", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/Light4D", "project_page": "", "abstract": "Recent advances in diffusion-based generative models have established a new paradigm for image and video relighting. However, extending these capabilities to 4D relighting remains challenging, due primarily to the scarcity of paired 4D relighting training data and the difficulty of maintaining temporal consistency across extreme viewpoints. In this work, we propose Light4D, a novel training-free framework designed to synthesize consistent 4D videos under target illumination, even under extreme viewpoint changes. First, we introduce Disentangled Flow Guidance, a time-aware strategy that effectively injects lighting control into the latent space while preserving geometric integrity. Second, to reinforce temporal consistency, we develop Temporal Consistent Attention within the IC-Light architecture and further incorporate deterministic regularization to eliminate appearance flickering. Extensive experiments demonstrate that our method achieves competitive performance in temporal consistency and lighting fidelity, robustly handling camera rotations from -90 to 90. Code: https://github.com/AIGeeksGroup/Light4D. Website: https://aigeeksgroup.github.io/Light4D.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/Light4D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.11757", "title": "Code2Worlds: Empowering Coding LLMs for 4D World Generation", "authors": [ "Yi Zhang", "Yunshuang Wang", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/Code2Worlds", "project_page": "", "abstract": "Achieving spatial intelligence requires moving beyond visual plausibility to build world simulators grounded in physical laws. While coding LLMs have advanced static 3D scene generation, extending this paradigm to 4D dynamics remains a critical frontier. This task presents two fundamental challenges: multi-scale context entanglement, where monolithic generation fails to balance local object structures with global environmental layouts; and a semantic-physical execution gap, where open-loop code generation leads to physical hallucinations lacking dynamic fidelity. We introduce Code2Worlds, a framework that formulates 4D generation as language-to-simulation code generation. First, we propose a dual-stream architecture that disentangles retrieval-augmented object generation from hierarchical environmental orchestration. Second, to ensure dynamic fidelity, we establish a physics-aware closed-loop mechanism in which a PostProcess Agent scripts dynamics, coupled with a VLM-Motion Critic that performs self-reflection to iteratively refine simulation code. Evaluations on the Code4D benchmark show Code2Worlds outperforms baselines with a 41% SGS gain and 49% higher Richness, while uniquely generating physics-aware dynamics absent in prior static methods. Code: https://github.com/AIGeeksGroup/Code2Worlds. Website: https://aigeeksgroup.github.io/Code2Worlds.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/Code2Worlds", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.11236", "title": "ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning", "authors": [ "Yandan Yang", "Shuang Zeng", "Tong Lin", "Xinyuan Chang", "Dekang Qi", "Junjin Xiao", "Haoyun Liu", "Ronghan Chen", "Yuzhi Chen", "Dongjie Huo", "Feng Xiong", "Xing Wei", "Zhiheng Ma", "Mu Xu" ], "github": "https://github.com/amap-cvlab/ABot-Manipulation", "project_page": "", "abstract": "Building general-purpose embodied agents across diverse hardware remains a central challenge in robotics, often framed as the ''one-brain, many-forms'' paradigm. Progress is hindered by fragmented data, inconsistent representations, and misaligned training objectives. We present ABot-M0, a framework that builds a systematic data curation pipeline while jointly optimizing model architecture and training strategies, enabling end-to-end transformation of heterogeneous raw data into unified, efficient representations. From six public datasets, we clean, standardize, and balance samples to construct UniACT-dataset, a large-scale dataset with over 6 million trajectories and 9,500 hours of data, covering diverse robot morphologies and task scenarios. Unified pre-training improves knowledge transfer and generalization across platforms and tasks, supporting general-purpose embodied intelligence. To improve action prediction efficiency and stability, we propose the Action Manifold Hypothesis: effective robot actions lie not in the full high-dimensional space but on a low-dimensional, smooth manifold governed by physical laws and task constraints. Based on this, we introduce Action Manifold Learning (AML), which uses a DiT backbone to predict clean, continuous action sequences directly. This shifts learning from denoising to projection onto feasible manifolds, improving decoding speed and policy stability. ABot-M0 supports modular perception via a dual-stream mechanism that integrates VLM semantics with geometric priors and multi-view inputs from plug-and-play 3D modules such as VGGT and Qwen-Image-Edit, enhancing spatial understanding without modifying the backbone and mitigating standard VLM limitations in 3D reasoning. Experiments show components operate independently with additive benefits. We will release all code and pipelines for reproducibility and future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/amap-cvlab/ABot-Manipulation", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.04315", "title": "GeneralVLA: Generalizable Vision-Language-Action Models with Knowledge-Guided Trajectory Planning", "authors": [ "Guoqing Ma", "Siheng Wang", "Zeyu Zhang", "Shan Yu", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/GeneralVLA", "project_page": "", "abstract": "Large foundation models have shown strong open-world generalization to complex problems in vision and language, but similar levels of generalization have yet to be achieved in robotics. One fundamental challenge is that the models exhibit limited zero-shot capability, which hampers their ability to generalize effectively to unseen scenarios. In this work, we propose GeneralVLA (Generalizable Vision-Language-Action Models with Knowledge-Guided Trajectory Planning), a hierarchical vision-language-action (VLA) model that can be more effective in utilizing the generalization of foundation models, enabling zero-shot manipulation and automatically generating data for robotics. In particular, we study a class of hierarchical VLA model where the high-level ASM (Affordance Segmentation Module) is finetuned to perceive image keypoint affordances of the scene; the mid-level 3DAgent carries out task understanding, skill knowledge, and trajectory planning to produce a 3D path indicating the desired robot end-effector trajectory. The intermediate 3D path prediction is then served as guidance to the low-level, 3D-aware control policy capable of precise manipulation. Compared to alternative approaches, our method requires no real-world robotic data collection or human demonstration, making it much more scalable to diverse tasks and viewpoints. Empirically, GeneralVLA successfully generates trajectories for 14 tasks, significantly outperforming state-of-the-art methods such as VoxPoser. The generated demonstrations can train more robust behavior cloning policies than training with human demonstrations or from data generated by VoxPoser, Scaling-up, and Code-As-Policies. We believe GeneralVLA can be the scalable method for both generating data for robotics and solving novel tasks in a zero-shot setting. Code: https://github.com/AIGeeksGroup/GeneralVLA. Website: https://aigeeksgroup.github.io/GeneralVLA.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/GeneralVLA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.12984", "title": "SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents", "authors": [ "Yujiong Shen", "Yajie Yang", "Zhiheng Xi", "Binze Hu", "Huayu Sha", "Jiazheng Zhang", "Qiyuan Peng", "Junlin Shang", "Jixuan Huang", "Yutao Fan", "Jingqi Tong", "Shihan Dou", "Ming Zhang", "Lei Bai", "Zhenfei Yin", "Tao Gui", "Xingjun Ma", "Qi Zhang", "Xuanjing Huang", "Yu-Gang Jiang" ], "github": "https://github.com/CMarsRover/SciAgentGYM", "project_page": "", "abstract": "Scientific reasoning inherently demands integrating sophisticated toolkits to navigate domain-specific knowledge. Yet, current benchmarks largely overlook agents' ability to orchestrate tools for such rigorous workflows. To bridge this gap, we introduce SciAgentGym, a scalable interactive environment featuring 1,780 domain-specific tools across four natural science disciplines, supported by a robust execution infrastructure. Complementing this, we present SciAgentBench, a tiered evaluation suite designed to stress-test agentic capabilities from elementary actions to long-horizon workflows. Our evaluation identifies a critical bottleneck: state-of-the-art models struggle with complex scientific tool-use. Even for a leading model like GPT-5, success rates drop sharply from 60.6% to 30.9% as interaction horizons extend, primarily due to failures in multi-step workflow execution. To address this, we propose SciForge, a data synthesis method that models the tool action space as a dependency graph to generate logic-aware training trajectories. By fine-tuning on these trajectories, our SciAgent-8B outperforms the significantly larger Qwen3-VL-235B-Instruct while exhibiting positive cross-domain transfer of scientific tool-use capabilities. These results underscore the promising potential of next-generation autonomous scientific agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/CMarsRover/SciAgentGYM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.12395", "title": "What does RL improve for Visual Reasoning? A Frankenstein-Style Analysis", "authors": [ "Xirui Li", "Ming Li", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) with verifiable rewards has become a standard post-training stage for boosting visual reasoning in vision-language models, yet it remains unclear what capabilities RL actually improves compared with supervised fine-tuning as cold-start initialization (IN). End-to-end benchmark gains conflate multiple factors, making it difficult to attribute improvements to specific skills. To bridge the gap, we propose a Frankenstein-style analysis framework including: (i) functional localization via causal probing; (ii) update characterization via parameter comparison; and (iii) transferability test via model merging. Instead, RL induces a consistent inference-time shift primarily in mid-to-late layers, and these mid-to-late refinements are both transferable (via merging) and necessary (via freezing) for RL gains. Overall, our results suggest that RL's reliable contribution in visual reasoning is not a uniform enhancement of visual perception, but a systematic refinement of mid-to-late transformer computation that improves vision-to-reasoning alignment and reasoning performance, highlighting the limitations of benchmark-only evaluation for understanding multimodal reasoning improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.13191", "title": "CoPE-VideoLM: Codec Primitives For Efficient Video Language Models", "authors": [ "Sayan Deb Sarkar", "R\u00e9mi Pautrat", "Ondrej Miksik", "Marc Pollefeys", "Iro Armeni", "Mahdi Rad", "Mihai Dusmanu" ], "github": "", "project_page": "", "abstract": "Video Language Models (VideoLMs) empower AI systems to understand temporal dynamics in videos. To fit to the maximum context window constraint, current methods use keyframe sampling which can miss both macro-level events and micro-level details due to the sparse temporal coverage. Furthermore, processing full images and their tokens for each frame incurs substantial computational overhead. To address these limitations, we propose to leverage video codec primitives (specifically motion vectors and residuals) which natively encode video redundancy and sparsity without requiring expensive full-image encoding for most frames. To this end, we introduce lightweight transformer-based encoders that aggregate codec primitives and align their representations with image encoder embeddings through a pre-training strategy that accelerates convergence during end-to-end fine-tuning. Our approach reduces the time-to-first-token by up to 86% and token usage by up to 93% compared to standard VideoLMs. Moreover, by varying the keyframe and codec primitive densities we are able to maintain or exceed performance on 14 diverse video understanding benchmarks spanning general question answering, temporal reasoning, long-form understanding, and spatial scene understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.04163", "title": "BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models", "authors": [ "Junyu Chen", "Jungang Li", "Jing Xiong", "Wenjie Wang", "Qingyao Yang", "He Xiao", "Zhen Li", "Taiqiang Wu", "Mengzhao Chen", "Zhen Peng", "Chaofan Tao", "Long Shi", "Hongxia Yang", "Ngai Wong" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) inference is often bounded by memory footprint and memory bandwidth in resource-constrained deployments, making quantization a fundamental technique for efficient serving. While post-training quantization (PTQ) maintains high fidelity at 4-bit, it deteriorates at 2-3 bits. Fundamentally, existing methods enforce a shape-invariant quantization grid (e.g., the fixed uniform intervals of UINT2) for each group, severely restricting the feasible set for error minimization. To address this, we propose Bit-Plane Decomposition Quantization (BPDQ), which constructs a variable quantization grid via bit-planes and scalar coefficients, and iteratively refines them using approximate second-order information while progressively compensating quantization errors to minimize output discrepancy. In the 2-bit regime, BPDQ enables serving Qwen2.5-72B on a single RTX 3090 with 83.85% GSM8K accuracy (vs. 90.83% at 16-bit). Moreover, we provide theoretical analysis showing that the variable grid expands the feasible set, and that the quantization process consistently aligns with the optimization objective in Hessian-induced geometry. Code: github.com/KingdalfGoodman/BPDQ.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.10388", "title": "Less is Enough: Synthesizing Diverse Data in Feature Space of LLMs", "authors": [ "Zhongzhi Li", "Xuansheng Wu", "Yijiang Li", "Lijie Hu", "Ninghao Liu" ], "github": "", "project_page": "", "abstract": "The diversity of post-training data is critical for effective downstream performance in large language models (LLMs). Many existing approaches to constructing post-training data quantify diversity using text-based metrics that capture linguistic variation, but such metrics provide only weak signals for the task-relevant features that determine downstream performance. In this work, we introduce Feature Activation Coverage (FAC) which measures data diversity in an interpretable feature space. Building upon this metric, we further propose a diversity-driven data synthesis framework, named FAC Synthesis, that first uses a sparse autoencoder to identify missing features from a seed dataset, and then generates synthetic samples that explicitly reflect these features. Experiments show that our approach consistently improves both data diversity and downstream performance on various tasks, including instruction following, toxicity detection, reward modeling, and behavior steering. Interestingly, we identify a shared, interpretable feature space across model families (i.e., LLaMA, Mistral, and Qwen), enabling cross-model knowledge transfer. Our work provides a solid and practical methodology for exploring data-centric optimization of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.12628", "title": "RLinf-Co: Reinforcement Learning-Based Sim-Real Co-Training for VLA Models", "authors": [ "Liangzhi Shi", "Shuaihang Chen", "Feng Gao", "Yinuo Chen", "Kang Chen", "Tonghe Zhang", "Hongzhi Zhang", "Weinan Zhang", "Chao Yu", "Yu Wang" ], "github": "", "project_page": "", "abstract": "Simulation offers a scalable and low-cost way to enrich vision-language-action (VLA) training, reducing reliance on expensive real-robot demonstrations. However, most sim-real co-training methods rely on supervised fine-tuning (SFT), which treats simulation as a static source of demonstrations and does not exploit large-scale closed-loop interaction. Consequently, real-world gains and generalization are often limited. In this paper, we propose an \\textit{RL}-based sim-real \\textit{Co}-training (RL-Co) framework that leverages interactive simulation while preserving real-world capabilities. Our method follows a generic two-stage design: we first warm-start the policy with SFT on a mixture of real and simulated demonstrations, then fine-tune it with reinforcement learning in simulation while adding an auxiliary supervised loss on real-world data to anchor the policy and mitigate catastrophic forgetting. We evaluate our framework on four real-world tabletop manipulation tasks using two representative VLA architectures, OpenVLA and \u03c0_{0.5}, and observe consistent improvements over real-only fine-tuning and SFT-based co-training, including +24% real-world success on OpenVLA and +20% on \u03c0_{0.5}. Beyond higher success rates, RL co-training yields stronger generalization to unseen task variations and substantially improved real-world data efficiency, providing a practical and scalable pathway for leveraging simulation to enhance real-robot deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.11715", "title": "DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels", "authors": [ "Haolei Bai", "Lingcheng Kong", "Xueyi Chen", "Jianmian Wang", "Zhiqiang Tao", "Huan Wang" ], "github": "", "project_page": "", "abstract": "Diffusion large language models (dLLMs) have emerged as a compelling alternative to autoregressive (AR) LLMs, owing to their capacity for parallel token generation. This paradigm is particularly well-suited for code generation, where holistic structural planning and non-sequential refinement are critical. Despite this potential, tailoring dLLMs for CUDA kernel generation remains challenging, obstructed not only by the high specialization but also by the severe lack of high-quality training data. To address these challenges, we construct CuKe, an augmented supervised fine-tuning dataset optimized for high-performance CUDA kernels. On top of it, we propose a bi-phase curated reinforcement learning (BiC-RL) framework consisting of a CUDA kernel infilling stage and an end-to-end CUDA kernel generation stage. Leveraging this training framework, we introduce DICE, a series of diffusion large language models designed for CUDA kernel generation, spanning three parameter scales, 1.7B, 4B, and 8B. Extensive experiments on KernelBench demonstrate that DICE significantly outperforms both autoregressive and diffusion LLMs of comparable scale, establishing a new state-of-the-art for CUDA kernel generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.12612", "title": "Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback", "authors": [ "Sein Kim", "Sangwu Park", "Hongseok Kang", "Wonjoong Kim", "Jimin Seo", "Yeonjun In", "Kanghoon Yoon", "Chanyoung Park" ], "github": "https://github.com/Sein-Kim/self_evolverec", "project_page": "", "abstract": "Traditional methods for automating recommender system design, such as Neural Architecture Search (NAS), are often constrained by a fixed search space defined by human priors, limiting innovation to pre-defined operators. While recent LLM-driven code evolution frameworks shift fixed search space target to open-ended program spaces, they primarily rely on scalar metrics (e.g., NDCG, Hit Ratio) that fail to provide qualitative insights into model failures or directional guidance for improvement. To address this, we propose Self-EvolveRec, a novel framework that establishes a directional feedback loop by integrating a User Simulator for qualitative critiques and a Model Diagnosis Tool for quantitative internal verification. Furthermore, we introduce a Diagnosis Tool - Model Co-Evolution strategy to ensure that evaluation criteria dynamically adapt as the recommendation architecture evolves. Extensive experiments demonstrate that Self-EvolveRec significantly outperforms state-of-the-art NAS and LLM-driven code evolution baselines in both recommendation performance and user satisfaction. Our code is available at https://github.com/Sein-Kim/self_evolverec.", "github_manual": "", "github_regex": "https://github.com/Sein-Kim/self_evolverec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.03120", "title": "Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost", "authors": [ "Yinggan Xu", "Risto Miikkulainen", "Xin Qiu" ], "github": "https://github.com/dibbla/Quantized-Evolution-Strategies", "project_page": "", "abstract": "Post-Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory-constrained devices, yet it renders models static and difficult to fine-tune. Standard fine-tuning paradigms, including Reinforcement Learning (RL), fundamentally rely on backpropagation and high-precision weights to compute gradients. Thus they cannot be used on quantized models, where the parameter space is discrete and non-differentiable. While Evolution Strategies (ES) offer a backpropagation-free alternative, optimization of the quantized parameters can still fail due to vanishing or inaccurate gradient. This paper introduces Quantized Evolution Strategies (QES), an optimization paradigm that performs full-parameter fine-tuning directly in the quantized space. QES is based on two innovations: (1) it integrates accumulated error feedback to preserve high-precision gradient signals, and (2) it utilizes a stateless seed replay to reduce memory usage to low-precision inference levels. QES significantly outperforms the state-of-the-art zeroth-order fine-tuning method on arithmetic reasoning tasks, making direct fine-tuning for quantized models possible. It therefore opens up the possibility for scaling up LLMs entirely in the quantized space. The source code is available at https://github.com/dibbla/Quantized-Evolution-Strategies .", "github_manual": "", "github_regex": "https://github.com/dibbla/Quantized-Evolution-Strategies", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.13022", "title": "Learning Image-based Tree Crown Segmentation from Enhanced Lidar-based Pseudo-labels", "authors": [ "Julius Pesonen", "Stefan Rua", "Josef Taher", "Niko Koivum\u00e4ki", "Xiaowei Yu", "Eija Honkavaara" ], "github": "", "project_page": "", "abstract": "Mapping individual tree crowns is essential for tasks such as maintaining urban tree inventories and monitoring forest health, which help us understand and care for our environment. However, automatically separating the crowns from each other in aerial imagery is challenging due to factors such as the texture and partial tree crown overlaps. In this study, we present a method to train deep learning models that segment and separate individual trees from RGB and multispectral images, using pseudo-labels derived from aerial laser scanning (ALS) data. Our study shows that the ALS-derived pseudo-labels can be enhanced using a zero-shot instance segmentation model, Segment Anything Model 2 (SAM 2). Our method offers a way to obtain domain-specific training annotations for optical image-based models without any manual annotation cost, leading to segmentation models which outperform any available models which have been targeted for general domain deployment on the same task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.12500", "title": "Favia: Forensic Agent for Vulnerability-fix Identification and Analysis", "authors": [ "Andr\u00e9 Storhaug", "Jiamou Sun", "Jingyue Li" ], "github": "", "project_page": "", "abstract": "Identifying vulnerability-fixing commits corresponding to disclosed CVEs is essential for secure software maintenance but remains challenging at scale, as large repositories contain millions of commits of which only a small fraction address security issues. Existing automated approaches, including traditional machine learning techniques and recent large language model (LLM)-based methods, often suffer from poor precision-recall trade-offs. Frequently evaluated on randomly sampled commits, we uncover that they are substantially underestimating real-world difficulty, where candidate commits are already security-relevant and highly similar. We propose Favia, a forensic, agent-based framework for vulnerability-fix identification that combines scalable candidate ranking with deep and iterative semantic reasoning. Favia first employs an efficient ranking stage to narrow the search space of commits. Each commit is then rigorously evaluated using a ReAct-based LLM agent. By providing the agent with a pre-commit repository as environment, along with specialized tools, the agent tries to localize vulnerable components, navigates the codebase, and establishes causal alignment between code changes and vulnerability root causes. This evidence-driven process enables robust identification of indirect, multi-file, and non-trivial fixes that elude single-pass or similarity-based methods. We evaluate Favia on CVEVC, a large-scale dataset we made that comprises over 8 million commits from 3,708 real-world repositories, and show that it consistently outperforms state-of-the-art traditional and LLM-based baselines under realistic candidate selection, achieving the strongest precision-recall trade-offs and highest F1-scores.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.12221", "title": "Best of Both Worlds: Multimodal Reasoning and Generation via Unified Discrete Flow Matching", "authors": [ "Onkar Susladkar", "Tushar Prakash", "Gayatri Deshmukh", "Kiet A. Nguyen", "Jiaxun Zhang", "Adheesh Juvekar", "Tianshu Bao", "Lin Chai", "Sparsh Mittal", "Inderjit S Dhillon", "Ismini Lourentzou" ], "github": "", "project_page": "", "abstract": "We propose UniDFlow, a unified discrete flow-matching framework for multimodal understanding, generation, and editing. It decouples understanding and generation via task-specific low-rank adapters, avoiding objective interference and representation entanglement, while a novel reference-based multimodal preference alignment optimizes relative outcomes under identical conditioning, improving faithfulness and controllability without large-scale retraining. UniDFlpw achieves SOTA performance across eight benchmarks and exhibits strong zero-shot generalization to tasks including inpainting, in-context image generation, reference-based editing, and compositional generation, despite no explicit task-specific training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.12783", "title": "SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noise", "authors": [ "Yuejie Li", "Ke Yang", "Yueying Hua", "Berlin Chen", "Jianhao Nie", "Yueping He", "Caixin Kang" ], "github": "https://github.com/ttoyekk1a/SQuTR-Spoken-Query-to-Text-Retrieval", "project_page": "", "abstract": "Spoken query retrieval is an important interaction mode in modern information retrieval. However, existing evaluation datasets are often limited to simple queries under constrained noise conditions, making them inadequate for assessing the robustness of spoken query retrieval systems under complex acoustic perturbations. To address this limitation, we present SQuTR, a robustness benchmark for spoken query retrieval that includes a large-scale dataset and a unified evaluation protocol. SQuTR aggregates 37,317 unique queries from six commonly used English and Chinese text retrieval datasets, spanning multiple domains and diverse query types. We synthesize speech using voice profiles from 200 real speakers and mix 17 categories of real-world environmental noise under controlled SNR levels, enabling reproducible robustness evaluation from quiet to highly noisy conditions. Under the unified protocol, we conduct large-scale evaluations on representative cascaded and end-to-end retrieval systems. Experimental results show that retrieval performance decreases as noise increases, with substantially different drops across systems. Even large-scale retrieval models struggle under extreme noise, indicating that robustness remains a critical bottleneck. Overall, SQuTR provides a reproducible testbed for benchmarking and diagnostic analysis, and facilitates future research on robustness in spoken query to text retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ttoyekk1a/SQuTR-Spoken-Query-to-Text-Retrieval", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.13139", "title": "OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report", "authors": [ "Mariia Fedorova", "Nikolay Arefyev", "Maja Buljan", "Jind\u0159ich Helcl", "Stephan Oepen", "Egil R\u00f8nningstad", "Yves Scherrer" ], "github": "https://github.com/hplt-project/openlid", "project_page": "", "abstract": "Language identification (LID) is an essential step in building high-quality multilingual datasets from web data. Existing LID tools (such as OpenLID or GlotLID) often struggle to identify closely related languages and to distinguish valid natural language from noise, which contaminates language-specific subsets, especially for low-resource languages. In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise. We call this extended system OpenLID-v3 and evaluate it against GlotLID on multiple benchmarks. During development, we focus on three groups of closely related languages (Bosnian, Croatian, and Serbian; Romance varieties of Northern Italy and Southern France; and Scandinavian languages) and contribute new evaluation datasets where existing ones are inadequate. We find that ensemble approaches improve precision but also substantially reduce coverage for low-resource languages. OpenLID-v3 is available on https://huggingface.co/HPLT/OpenLID-v3.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hplt-project/openlid", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.09146", "title": "SemanticMoments: Training-Free Motion Similarity via Third Moment Features", "authors": [ "Saar Huberman", "Kfir Goldberg", "Or Patashnik", "Sagie Benaim", "Ron Mokady" ], "github": "", "project_page": "", "abstract": "Retrieving videos based on semantic motion is a fundamental, yet unsolved, problem. Existing video representation approaches overly rely on static appearance and scene context rather than motion dynamics, a bias inherited from their training data and objectives. Conversely, traditional motion-centric inputs like optical flow lack the semantic grounding needed to understand high-level motion. To demonstrate this inherent bias, we introduce the SimMotion benchmarks, combining controlled synthetic data with a new human-annotated real-world dataset. We show that existing models perform poorly on these benchmarks, often failing to disentangle motion from appearance. To address this gap, we propose SemanticMoments, a simple, training-free method that computes temporal statistics (specifically, higher-order moments) over features from pre-trained semantic models. Across our benchmarks, SemanticMoments consistently outperforms existing RGB, flow, and text-supervised methods. This demonstrates that temporal statistics in a semantic feature space provide a scalable and perceptually grounded foundation for motion-centric video understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.07298", "title": "Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation", "authors": [ "Benyu Zhang", "Qiang Zhang", "Jianpeng Cheng", "Hong-You Chen", "Qifei Wang", "Wei Sun", "Shen Li", "Jia Li", "Jiahao Wu", "Xiangjun Fan", "Hong Yan" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) represent a promising frontier for recommender systems, yet their development has been impeded by the absence of predictable scaling laws, which are crucial for guiding research and optimizing resource allocation. We hypothesize that this may be attributed to the inherent noise, bias, and incompleteness of raw user interaction data in prior continual pre-training (CPT) efforts. This paper introduces a novel, layered framework for generating high-quality synthetic data that circumvents such issues by creating a curated, pedagogical curriculum for the LLM. We provide powerful, direct evidence for the utility of our curriculum by showing that standard sequential models trained on our principled synthetic data significantly outperform (+130% on recall@100 for SasRec) models trained on real data in downstream ranking tasks, demonstrating its superiority for learning generalizable user preference patterns. Building on this, we empirically demonstrate, for the first time, robust power-law scaling for an LLM that is continually pre-trained on our high-quality, recommendation-specific data. Our experiments reveal consistent and predictable perplexity reduction across multiple synthetic data modalities. These findings establish a foundational methodology for reliable scaling LLM capabilities in the recommendation domain, thereby shifting the research focus from mitigating data deficiencies to leveraging high-quality, structured information.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.11609", "title": "scPilot: Large Language Model Reasoning Toward Automated Single-Cell Analysis and Discovery", "authors": [ "Yiming Gao", "Zhen Wang", "Jefferson Chen", "Mark Antkowiak", "Mengzhou Hu", "JungHo Kong", "Dexter Pratt", "Jieyuan Liu", "Enze Ma", "Zhiting Hu", "Eric P. Xing" ], "github": "https://github.com/maitrix-org/scPilot", "project_page": "", "abstract": "We present scPilot, the first systematic framework to practice omics-native reasoning: a large language model (LLM) converses in natural language while directly inspecting single-cell RNA-seq data and on-demand bioinformatics tools. scPilot converts core single-cell analyses, i.e., cell-type annotation, developmental-trajectory reconstruction, and transcription-factor targeting, into step-by-step reasoning problems that the model must solve, justify, and, when needed, revise with new evidence. To measure progress, we release scBench, a suite of 9 expertly curated datasets and graders that faithfully evaluate the omics-native reasoning capability of scPilot w.r.t various LLMs. Experiments with o1 show that iterative omics-native reasoning lifts average accuracy by 11% for cell-type annotation and Gemini-2.5-Pro cuts trajectory graph-edit distance by 30% versus one-shot prompting, while generating transparent reasoning traces explain marker gene ambiguity and regulatory logic. By grounding LLMs in raw omics data, scPilot enables auditable, interpretable, and diagnostically informative single-cell analyses. Code, data, and package are available at https://github.com/maitrix-org/scPilot", "github_manual": "", "github_regex": "https://github.com/maitrix-org/scPilot", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-16", "arxiv_id": "2602.09870", "title": "Steer2Edit: From Activation Steering to Component-Level Editing", "authors": [ "Chung-En Sun", "Ge Yan", "Zimo Wang", "Tsui-Wei Weng" ], "github": "", "project_page": "", "abstract": "Steering methods influence Large Language Model behavior by identifying semantic directions in hidden representations, but are typically realized through inference-time activation interventions that apply a fixed, global modification to the model's internal states. While effective, such interventions often induce unfavorable attribute-utility trade-offs under strong control, as they ignore the fact that many behaviors are governed by a small and heterogeneous subset of model components. We propose Steer2Edit, a theoretically grounded, training-free framework that transforms steering vectors from inference-time control signals into diagnostic signals for component-level rank-1 weight editing. Instead of uniformly injecting a steering direction during generation, Steer2Edit selectively redistributes behavioral influence across individual attention heads and MLP neurons, yielding interpretable edits that preserve the standard forward pass and remain compatible with optimized parallel inference. Across safety alignment, hallucination mitigation, and reasoning efficiency, Steer2Edit consistently achieves more favorable attribute-utility trade-offs: at matched downstream performance, it improves safety by up to 17.2%, increases truthfulness by 9.8%, and reduces reasoning length by 12.2% on average. Overall, Steer2Edit provides a principled bridge between representation steering and weight editing by translating steering signals into interpretable, training-free parameter updates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14234", "title": "REDSearcher: A Scalable and Cost-Efficient Framework for Long-Horizon Search Agents", "authors": [ "Zheng Chu", "Xiao Wang", "Jack Hong", "Huiming Fan", "Yuqi Huang", "Yue Yang", "Guohai Xu", "Chenxiao Zhao", "Cheng Xiang", "Shengchao Hu", "Dongdong Kuang", "Ming Liu", "Bing Qin", "Xing Yu" ], "github": "https://github.com/THUDM/slime", "project_page": "", "abstract": "Large language models are transitioning from generalpurpose knowledge engines to realworld problem solvers, yet optimizing them for deep search tasks remains challenging. The central bottleneck lies in the extreme sparsity of highquality search trajectories and reward signals, arising from the difficulty of scalable longhorizon task construction and the high cost of interactionheavy rollouts involving external tool calls. To address these challenges, we propose REDSearcher, a unified framework that codesigns complex task synthesis, midtraining, and posttraining for scalable searchagent optimization. Specifically, REDSearcher introduces the following improvements: (1) We frame task synthesis as a dualconstrained optimization, where task difficulty is precisely governed by graph topology and evidence dispersion, allowing scalable generation of complex, highquality tasks. (2) We introduce toolaugmented queries to encourage proactive tool use rather than passive recall.(3) During midtraining, we strengthen core atomic capabilities knowledge, planning, and function calling substantially reducing the cost of collecting highquality trajectories for downstream training. (4) We build a local simulated environment that enables rapid, lowcost algorithmic iteration for reinforcement learning experiments. Across both textonly and multimodal searchagent benchmarks, our approach achieves stateoftheart performance. To facilitate future research on longhorizon search agents, we will release 10K highquality complex text search trajectories, 5K multimodal trajectories and 1K text RL query set, and together with code and model checkpoints.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUDM/slime", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.13823", "title": "Embed-RL: Reinforcement Learning for Reasoning-Driven Multimodal Embeddings", "authors": [ "Haonan Jiang", "Yuji Wang", "Yongjie Zhu", "Xin Lu", "Wenyu Qin", "Meng Wang", "Pengfei Wan", "Yansong Tang" ], "github": "https://github.com/ZoengHN/Embed-RL", "project_page": "", "abstract": "Leveraging Multimodal Large Language Models (MLLMs) has become pivotal for advancing Universal Multimodal Embeddings (UME) in addressing diverse cross-modal tasks. Recent studies demonstrate that incorporating generative Chain-of-Thought (CoT) reasoning can substantially enhance task-specific representations compared to discriminative methods. However, the generated reasoning CoTs of existing generative embedding methods are limited to the textual analysis of queries and are irrelevant to the retrieval of the targets. To address these limitations, we propose a reasoning-driven UME framework that integrates Embedder-Guided Reinforcement Learning (EG-RL) to optimize the Reasoner to produce evidential Traceability CoT (T-CoT). Our key contributions are threefold: (1) We design an EG-RL framework where the Embedder provides explicit supervision to the Reasoner, ensuring the generated CoT traces are aligned with embedding tasks. (2) We introduce T-CoT, which extracts critical multimodal cues to focus on retrieval-relevant elements and provides multimodal inputs for the Embedder. (3) With limited computational resources, our framework outperforms the pioneering embedding model on both MMEB-V2 and UVRB benchmarks. The integration of multimodal evidence in structured reasoning, paired with retrieval-oriented alignment, effectively strengthens cross-modal semantic consistency and boosts the fine-grained matching capability of the model as well as the generalization across complex scenarios. Our work demonstrates that targeted reasoning optimization can significantly improve multimodal embedding quality, providing a practical and efficient solution for reasoning-driven UME development.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ZoengHN/Embed-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14147", "title": "LaViDa-R1: Advancing Reasoning for Unified Multimodal Diffusion Language Models", "authors": [ "Shufan Li", "Yuchen Zhu", "Jiuxiang Gu", "Kangning Liu", "Zhe Lin", "Yongxin Chen", "Molei Tao", "Aditya Grover", "Jason Kuen" ], "github": "", "project_page": "", "abstract": "Diffusion language models (dLLMs) recently emerged as a promising alternative to auto-regressive LLMs. The latest works further extended it to multimodal understanding and generation tasks. In this work, we propose LaViDa-R1, a multimodal, general-purpose reasoning dLLM. Unlike existing works that build reasoning dLLMs through task-specific reinforcement learning, LaViDa-R1 incorporates diverse multimodal understanding and generation tasks in a unified manner. In particular, LaViDa-R1 is built with a novel unified post-training framework that seamlessly integrates supervised finetuning (SFT) and multi-task reinforcement learning (RL). It employs several novel training techniques, including answer-forcing, tree search, and complementary likelihood estimation, to enhance effectiveness and scalability. Extensive experiments demonstrate LaViDa-R1's strong performance on a wide range of multimodal tasks, including visual math reasoning, reason-intensive grounding, and image editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.12876", "title": "BrowseComp-V^3: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents", "authors": [ "Huanyao Zhang", "Jiepeng Zhou", "Bo Li", "Bowen Zhou", "Yanzhe Dan", "Haishan Lu", "Zhiyong Cao", "Jiaoyang Chen", "Yuqian Han", "Zinan Sheng", "Zhengwei Tao", "Hao Liang", "Jialong Wu", "Yang Shi", "Yuanpeng He", "Jiaye Lin", "Qintong Zhang", "Guochen Yan", "Runhao Zhao", "Zhengpin Li", "Xiaohan Yu", "Lang Mei", "Chong Chen", "Wentao Zhang", "Bin Cui" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs), equipped with increasingly advanced planning and tool-use capabilities, are evolving into autonomous agents capable of performing multimodal web browsing and deep search in open-world environments. However, existing benchmarks for multimodal browsing remain limited in task complexity, evidence accessibility, and evaluation granularity, hindering comprehensive and reproducible assessments of deep search capabilities. To address these limitations, we introduce BrowseComp-V^3, a novel benchmark consisting of 300 carefully curated and challenging questions spanning diverse domains. The benchmark emphasizes deep, multi-level, and cross-modal multi-hop reasoning, where critical evidence is interleaved across textual and visual modalities within and across web pages. All supporting evidence is strictly required to be publicly searchable, ensuring fairness and reproducibility. Beyond final-answer accuracy, we incorporate an expert-validated, subgoal-driven process evaluation mechanism that enables fine-grained analysis of intermediate reasoning behaviors and systematic characterization of capability boundaries. In addition, we propose OmniSeeker, a unified multimodal browsing agent framework integrating diverse web search and visual perception tools. Comprehensive experiments demonstrate that even state-of-the-art models achieve only 36% accuracy on our benchmark, revealing critical bottlenecks in multimodal information integration and fine-grained perception. Our results highlight a fundamental gap between current model capabilities and robust multimodal deep search in real-world settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.09185", "title": "AIDev: Studying AI Coding Agents on GitHub", "authors": [ "Hao Li", "Haoxiang Zhang", "Ahmed E. Hassan" ], "github": "", "project_page": "", "abstract": "AI coding agents are rapidly transforming software engineering by performing tasks such as feature development, debugging, and testing. Despite their growing impact, the research community lacks a comprehensive dataset capturing how these agents are used in real-world projects. To address this gap, we introduce AIDev, a large-scale dataset focused on agent-authored pull requests (Agentic-PRs) in real-world GitHub repositories. AIDev aggregates 932,791 Agentic-PRs produced by five agents: OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code. These PRs span 116,211 repositories and involve 72,189 developers. In addition, AIDev includes a curated subset of 33,596 Agentic-PRs from 2,807 repositories with over 100 stars, providing further information such as comments, reviews, commits, and related issues. This dataset offers a foundation for future research on AI adoption, developer productivity, and human-AI collaboration in the new era of software engineering. > AI Agent, Agentic AI, Coding Agent, Agentic Coding, Agentic Software Engineering, Agentic Engineering", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14696", "title": "A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)", "authors": [ "Nihal V. Nayak", "Paula Rodriguez-Diaz", "Neha Hulkund", "Sara Beery", "David Alvarez-Melis" ], "github": "https://github.com/dcml-lab/targeted-instruction-selection", "project_page": "", "abstract": "Instruction fine-tuning of large language models (LLMs) often involves selecting a subset of instruction training data from a large candidate pool, using a small query set from the target task. Despite growing interest, the literature on targeted instruction selection remains fragmented and opaque: methods vary widely in selection budgets, often omit zero-shot baselines, and frequently entangle the contributions of key components. As a result, practitioners lack actionable guidance on selecting instructions for their target tasks. In this work, we aim to bring clarity to this landscape by disentangling and systematically analyzing the two core ingredients: data representation and selection algorithms. Our framework enables controlled comparisons across models, tasks, and budgets. We find that only gradient-based data representations choose subsets whose similarity to the query consistently predicts performance across datasets and models. While no single method dominates, gradient-based representations paired with a greedy round-robin selection algorithm tend to perform best on average at low budgets, but these benefits diminish at larger budgets. Finally, we unify several existing selection algorithms as forms of approximate distance minimization between the selected subset and the query set, and support this view with new generalization bounds. More broadly, our findings provide critical insights and a foundation for more principled data selection in LLM fine-tuning. The code is available at https://github.com/dcml-lab/targeted-instruction-selection.", "github_manual": "", "github_regex": "https://github.com/dcml-lab/targeted-instruction-selection", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14699", "title": "Qute: Towards Quantum-Native Database", "authors": [ "Muzhi Chen", "Xuanhe Zhou", "Wei Zhou", "Bangrui Xu", "Surui Tang", "Guoliang Li", "Bingsheng He", "Yeye He", "Yitong Song", "Fan Wu" ], "github": "https://github.com/weAIDB/Qute", "project_page": "", "abstract": "This paper envisions a quantum database (Qute) that treats quantum computation as a first-class execution option. Unlike prior simulation-based methods that either run quantum algorithms on classical machines or adapt existing databases for quantum simulation, Qute instead (i) compiles an extended form of SQL into gate-efficient quantum circuits, (ii) employs a hybrid optimizer to dynamically select between quantum and classical execution plans, (iii) introduces selective quantum indexing, and (iv) designs fidelity-preserving storage to mitigate current qubit constraints. We also present a three-stage evolution roadmap toward quantum-native database. Finally, by deploying Qute on a real quantum processor (origin_wukong), we show that it outperforms a classical baseline at scale, and we release an open-source prototype at https://github.com/weAIDB/Qute.", "github_manual": "", "github_regex": "https://github.com/weAIDB/Qute", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14178", "title": "UniWeTok: An Unified Binary Tokenizer with Codebook Size 2^{128} for Unified Multimodal Large Language Model", "authors": [ "Shaobin Zhuang", "Yuang Ai", "Jiaming Han", "Weijia Mao", "Xiaohui Li", "Fangyikang Wang", "Xiao Wang", "Yan Li", "Shanchuan Lin", "Kun Xu", "Zhenheng Yang", "Huaibo Huang", "Xiangyu Yue", "Hao Chen", "Yali Wang" ], "github": "", "project_page": "", "abstract": "Unified Multimodal Large Language Models (MLLMs) require a visual representation that simultaneously supports high-fidelity reconstruction, complex semantic extraction, and generative suitability. However, existing visual tokenizers typically struggle to satisfy these conflicting objectives within a single framework. In this paper, we introduce UniWeTok, a unified discrete tokenizer designed to bridge this gap using a massive binary codebook (2^{128}). For training framework, we introduce Pre-Post Distillation and a Generative-Aware Prior to enhance the semantic extraction and generative prior of the discrete tokens. In terms of model architecture, we propose a convolution-attention hybrid architecture with the SigLu activation function. SigLu activation not only bounds the encoder output and stabilizes the semantic distillation process but also effectively addresses the optimization conflict between token entropy loss and commitment loss. We further propose a three-stage training framework designed to enhance UniWeTok's adaptability cross various image resolutions and perception-sensitive scenarios, such as those involving human faces and textual content. On ImageNet, UniWeTok achieves state-of-the-art image generation performance (FID: UniWeTok 1.38 vs. REPA 1.42) while requiring a remarkably low training compute (Training Tokens: UniWeTok 33B vs. REPA 262B). On general-domain, UniWeTok demonstrates highly competitive capabilities across a broad range of tasks, including multimodal understanding, image generation (DPG Score: UniWeTok 86.63 vs. FLUX.1 [Dev] 83.84), and editing (GEdit Overall Score: UniWeTok 5.09 vs. OmniGen 5.06). We release code and models to facilitate community exploration of unified tokenizer and MLLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14041", "title": "BitDance: Scaling Autoregressive Generative Models with Binary Tokens", "authors": [ "Yuang Ai", "Jiaming Han", "Shaobin Zhuang", "Weijia Mao", "Xuefeng Hu", "Ziyan Yang", "Zhenheng Yang", "Huaibo Huang", "Xiangyu Yue", "Hao Chen" ], "github": "https://github.com/shallowdream204/BitDance", "project_page": "", "abstract": "We present BitDance, a scalable autoregressive (AR) image generator that predicts binary visual tokens instead of codebook indices. With high-entropy binary latents, BitDance lets each token represent up to 2^{256} states, yielding a compact yet highly expressive discrete representation. Sampling from such a huge token space is difficult with standard classification. To resolve this, BitDance uses a binary diffusion head: instead of predicting an index with softmax, it employs continuous-space diffusion to generate the binary tokens. Furthermore, we propose next-patch diffusion, a new decoding method that predicts multiple tokens in parallel with high accuracy, greatly speeding up inference. On ImageNet 256x256, BitDance achieves an FID of 1.24, the best among AR models. With next-patch diffusion, BitDance beats state-of-the-art parallel AR models that use 1.4B parameters, while using 5.4x fewer parameters (260M) and achieving 8.7x speedup. For text-to-image generation, BitDance trains on large-scale multimodal tokens and generates high-resolution, photorealistic images efficiently, showing strong performance and favorable scaling. When generating 1024x1024 images, BitDance achieves a speedup of over 30x compared to prior AR models. We release code and models to facilitate further research on AR foundation models. Code and models are available at: https://github.com/shallowdream204/BitDance.", "github_manual": "", "github_regex": "https://github.com/shallowdream204/BitDance", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.13344", "title": "FireRed-Image-Edit-1.0 Techinical Report", "authors": [ "Super Intelligence Team", "Changhao Qiao", "Chao Hui", "Chen Li", "Cunzheng Wang", "Dejia Song", "Jiale Zhang", "Jing Li", "Qiang Xiang", "Runqi Wang", "Shuang Sun", "Wei Zhu", "Xu Tang", "Yao Hu", "Yibo Chen", "Yuhao Huang", "Yuxuan Duan", "Zhiyi Chen", "Ziyuan Guo" ], "github": "https://github.com/FireRedTeam/FireRed-Image-Edit", "project_page": "", "abstract": "We present FireRed-Image-Edit, a diffusion transformer for instruction-based image editing that achieves state-of-the-art performance through systematic optimization of data curation, training methodology, and evaluation design. We construct a 1.6B-sample training corpus, comprising 900M text-to-image and 700M image editing pairs from diverse sources. After rigorous cleaning, stratification, auto-labeling, and two-stage filtering, we retain over 100M high-quality samples balanced between generation and editing, ensuring strong semantic coverage and instruction alignment. Our multi-stage training pipeline progressively builds editing capability via pre-training, supervised fine-tuning, and reinforcement learning. To improve data efficiency, we introduce a Multi-Condition Aware Bucket Sampler for variable-resolution batching and Stochastic Instruction Alignment with dynamic prompt re-indexing. To stabilize optimization and enhance controllability, we propose Asymmetric Gradient Optimization for DPO, DiffusionNFT with layout-aware OCR rewards for text editing, and a differentiable Consistency Loss for identity preservation. We further establish REDEdit-Bench, a comprehensive benchmark spanning 15 editing categories, including newly introduced beautification and low-level enhancement tasks. Extensive experiments on REDEdit-Bench and public benchmarks (ImgEdit and GEdit) demonstrate competitive or superior performance against both open-source and proprietary systems. We release code, models, and the benchmark suite to support future research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FireRedTeam/FireRed-Image-Edit", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14721", "title": "WebWorld: A Large-Scale World Model for Web Agent Training", "authors": [ "Zikai Xiao", "Jianhong Tu", "Chuhang Zou", "Yuxin Zuo", "Zhi Li", "Peng Wang", "Bowen Yu", "Fei Huang", "Junyang Lin", "Zuozhu Liu" ], "github": "", "project_page": "", "abstract": "Web agents require massive trajectories to generalize, yet real-world training is constrained by network latency, rate limits, and safety risks. We introduce WebWorld series, the first open-web simulator trained at scale. While existing simulators are restricted to closed environments with thousands of trajectories, WebWorld leverages a scalable data pipeline to train on 1M+ open-web interactions, supporting reasoning, multi-format data, and long-horizon simulations of 30+ steps. For intrinsic evaluation, we introduce WebWorld-Bench with dual metrics spanning nine dimensions, where WebWorld achieves simulation performance comparable to Gemini-3-Pro. For extrinsic evaluation, Qwen3-14B trained on WebWorld-synthesized trajectories improves by +9.2\\% on WebArena, reaching performance comparable to GPT-4o. WebWorld enables effective inference-time search, outperforming GPT-5 as a world model. Beyond web simulation, WebWorld exhibits cross-domain generalization to code, GUI, and game environments, providing a replicable recipe for world model construction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14534", "title": "MoRL: Reinforced Reasoning for Unified Motion Understanding and Generation", "authors": [ "Hongpeng Wang", "Zeyu Zhang", "Wenhao Li", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/MoRL", "project_page": "", "abstract": "Human motion understanding and generation are crucial for vision and robotics but remain limited in reasoning capability and test-time planning. We propose MoRL, a unified multimodal motion model trained with supervised fine-tuning and reinforcement learning with verifiable rewards. Our task-specific reward design combines semantic alignment and reasoning coherence for understanding with physical plausibility and text-motion consistency for generation, improving both logical reasoning and perceptual realism. To further enhance inference, we introduce Chain-of-Motion (CoM), a test-time reasoning method that enables step-by-step planning and reflection. We also construct two large-scale CoT datasets, MoUnd-CoT-140K and MoGen-CoT-140K, to align motion sequences with reasoning traces and action descriptions. Experiments on HumanML3D and KIT-ML show that MoRL achieves significant gains over state-of-the-art baselines. Code: https://github.com/AIGeeksGroup/MoRL. Website: https://aigeeksgroup.github.io/MoRL.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/MoRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14492", "title": "Query as Anchor: Scenario-Adaptive User Representation via Large Language Model", "authors": [ "Jiahao Yuan", "Yike Xu", "Jinyong Wen", "Baokun Wang", "Ziyi Gao", "Xiaotong Lin", "Yun Liu", "Xing Fu", "Yu Cheng", "Yongchao Liu", "Weiqiang Wang", "Zhongle Xie" ], "github": "https://github.com/JhCircle/Q-Anchor", "project_page": "", "abstract": "Industrial-scale user representation learning requires balancing robust universality with acute task-sensitivity. However, existing paradigms primarily yield static, task-agnostic embeddings that struggle to reconcile the divergent requirements of downstream scenarios within unified vector spaces. Furthermore, heterogeneous multi-source data introduces inherent noise and modality conflicts, degrading representation. We propose Query-as-Anchor, a framework shifting user modeling from static encoding to dynamic, query-aware synthesis. To empower Large Language Models (LLMs) with deep user understanding, we first construct UserU, an industrial-scale pre-training dataset that aligns multi-modal behavioral sequences with user understanding semantics, and our Q-Anchor Embedding architecture integrates hierarchical coarse-to-fine encoders into dual-tower LLMs via joint contrastive-autoregressive optimization for query-aware user representation. To bridge the gap between general pre-training and specialized business logic, we further introduce Cluster-based Soft Prompt Tuning to enforce discriminative latent structures, effectively aligning model attention with scenario-specific modalities. For deployment, anchoring queries at sequence termini enables KV-cache-accelerated inference with negligible incremental latency. Evaluations on 10 Alipay industrial benchmarks show consistent SOTA performance, strong scalability, and efficient deployment. Large-scale online A/B testing in Alipay's production system across two real-world scenarios further validates its practical effectiveness. Our code is prepared for public release and will be available at: https://github.com/JhCircle/Q-Anchor.", "github_manual": "", "github_regex": "https://github.com/JhCircle/Q-Anchor", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.12299", "title": "Acoustivision Pro: An Open-Source Interactive Platform for Room Impulse Response Analysis and Acoustic Characterization", "authors": [ "Mandip Goswami" ], "github": "", "project_page": "", "abstract": "Room acoustics analysis plays a central role in architectural design, audio engineering, speech intelligibility assessment, and hearing research. Despite the availability of standardized metrics such as reverberation time, clarity, and speech transmission index, accessible tools that combine rigorous signal processing with intuitive visualization remain scarce. This paper presents AcoustiVision Pro, an open-source web-based platform for comprehensive room impulse response (RIR) analysis. The system computes twelve distinct acoustic parameters from uploaded or dataset-sourced RIRs, provides interactive 3D visualizations of early reflections, generates frequency-dependent decay characteristics through waterfall plots, and checks compliance against international standards including ANSI S12.60 and ISO 3382. We introduce the accompanying RIRMega and RIRMega Speech datasets hosted on Hugging Face, containing thousands of simulated room impulse responses with full metadata. The platform supports real-time auralization through FFT-based convolution, exports detailed PDF reports suitable for engineering documentation, and provides CSV data export for further analysis. We describe the mathematical foundations underlying each acoustic metric, detail the system architecture, and present preliminary case studies demonstrating the platform's utility across diverse application domains including classroom acoustics, healthcare facility design, and recording studio evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14560", "title": "Preliminary sonification of ENSO using traditional Javanese gamelan scales", "authors": [ "Sandy H. S. Herho", "Rusmawan Suwarman", "Nurjanna J. Trilaksono", "Iwan P. Anwar", "Faiz R. Fajary" ], "github": "", "project_page": "", "abstract": "Sonification -- the mapping of data to non-speech audio -- offers an underexplored channel for representing complex dynamical systems. We treat El Ni\u00f1o-Southern Oscillation (ENSO), a canonical example of low-dimensional climate chaos, as a test case for culturally-situated sonification evaluated through complex systems diagnostics. Using parameter-mapping sonification of the Ni\u00f1o 3.4 sea surface temperature anomaly index (1870--2024), we encode ENSO variability into two traditional Javanese gamelan pentatonic systems (pelog and slendro) across four composition strategies, then analyze the resulting audio as trajectories in a two-dimensional acoustic phase space. Recurrence-based diagnostics, convex hull geometry, and coupling analysis reveal that the sonification pipeline preserves key dynamical signatures: alternating modes produce the highest trajectory recurrence rates, echoing ENSO's quasi-periodicity; layered polyphonic modes explore the broadest phase space regions; and the two scale families induce qualitatively distinct coupling regimes between spectral brightness and energy -- predominantly anti-phase in pelog but near-independent in slendro. Phase space trajectory analysis provides a rigorous geometric framework for comparing sonification designs within a complex systems context. Perceptual validation remains necessary; we contribute the dynamical systems methodology for evaluating such mappings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.13949", "title": "Experiential Reinforcement Learning", "authors": [ "Taiwei Shi", "Sihao Chen", "Bowen Jiang", "Linxin Song", "Longqi Yang", "Jieyu Zhao" ], "github": "", "project_page": "", "abstract": "Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually sparse and delayed. Learning from such signals is challenging, as LMs must implicitly infer how observed failures should translate into behavioral changes for future iterations. We introduce Experiential Reinforcement Learning (ERL), a training paradigm that embeds an explicit experience-reflection-consolidation loop into the reinforcement learning process. Given a task, the model generates an initial attempt, receives environmental feedback, and produces a reflection that guides a refined second attempt, whose success is reinforced and internalized into the base policy. This process converts feedback into structured behavioral revision, improving exploration and stabilizing optimization while preserving gains at deployment without additional inference cost. Across sparse-reward control environments and agentic reasoning benchmarks, ERL consistently improves learning efficiency and final performance over strong reinforcement learning baselines, achieving gains of up to +81% in complex multi-step environments and up to +11% in tool-using reasoning tasks. These results suggest that integrating explicit self-reflection into policy training provides a practical mechanism for transforming feedback into durable behavioral improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.13195", "title": "Conversational Image Segmentation: Grounding Abstract Concepts with Scalable Supervision", "authors": [ "Aadarsh Sahoo", "Georgia Gkioxari" ], "github": "", "project_page": "", "abstract": "Conversational image segmentation grounds abstract, intent-driven concepts into pixel-accurate masks. Prior work on referring image grounding focuses on categorical and spatial queries (e.g., \"left-most apple\") and overlooks functional and physical reasoning (e.g., \"where can I safely store the knife?\"). We address this gap and introduce Conversational Image Segmentation (CIS) and ConverSeg, a benchmark spanning entities, spatial relations, intent, affordances, functions, safety, and physical reasoning. We also present ConverSeg-Net, which fuses strong segmentation priors with language understanding, and an AI-powered data engine that generates prompt-mask pairs without human supervision. We show that current language-guided segmentation models are inadequate for CIS, while ConverSeg-Net trained on our data engine achieves significant gains on ConverSeg and maintains strong performance on existing language-guided segmentation benchmarks. Project webpage: https://glab-caltech.github.io/converseg/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14367", "title": "InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem", "authors": [ "Shuofei Qiao", "Yunxiang Wei", "Xuehai Wang", "Bin Wu", "Boyang Xue", "Ningyu Zhang", "Hossein A. Rahmani", "Yanshan Wang", "Qiang Zhang", "Keyan Ding", "Jeff Z. Pan", "Huajun Chen", "Emine Yilmaz" ], "github": "", "project_page": "", "abstract": "The rapid evolution of Large Language Models has catalyzed a surge in scientific idea production, yet this leap has not been accompanied by a matching advance in idea evaluation. The fundamental nature of scientific evaluation needs knowledgeable grounding, collective deliberation, and multi-criteria decision-making. However, existing idea evaluation methods often suffer from narrow knowledge horizons, flattened evaluation dimensions, and the inherent bias in LLM-as-a-Judge. To address these, we regard idea evaluation as a knowledge-grounded, multi-perspective reasoning problem and introduce InnoEval, a deep innovation evaluation framework designed to emulate human-level idea assessment. We apply a heterogeneous deep knowledge search engine that retrieves and grounds dynamic evidence from diverse online sources. We further achieve review consensus with an innovation review board containing reviewers with distinct academic backgrounds, enabling a multi-dimensional decoupled evaluation across multiple metrics. We construct comprehensive datasets derived from authoritative peer-reviewed submissions to benchmark InnoEval. Experiments demonstrate that InnoEval can consistently outperform baselines in point-wise, pair-wise, and group-wise evaluation tasks, exhibiting judgment patterns and consensus highly aligned with human experts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14689", "title": "Exposing the Systematic Vulnerability of Open-Weight Models to Prefill Attacks", "authors": [ "Lukas Struppek", "Adam Gleave", "Kellin Pelrine" ], "github": "", "project_page": "", "abstract": "As the capabilities of large language models continue to advance, so does their potential for misuse. While closed-source models typically rely on external defenses, open-weight models must primarily depend on internal safeguards to mitigate harmful behavior. Prior red-teaming research has largely focused on input-based jailbreaking and parameter-level manipulations. However, open-weight models also natively support prefilling, which allows an attacker to predefine initial response tokens before generation begins. Despite its potential, this attack vector has received little systematic attention. We present the largest empirical study to date of prefill attacks, evaluating over 20 existing and novel strategies across multiple model families and state-of-the-art open-weight models. Our results show that prefill attacks are consistently effective against all major contemporary open-weight models, revealing a critical and previously underexplored vulnerability with significant implications for deployment. While certain large reasoning models exhibit some robustness against generic prefilling, they remain vulnerable to tailored, model-specific strategies. Our findings underscore the urgent need for model developers to prioritize defenses against prefill attacks in open-weight LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.09319", "title": "Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation", "authors": [ "Zhisheng Qi", "Utkarsh Sahu", "Li Ma", "Haoyu Han", "Ryan Rossi", "Franck Dernoncourt", "Mahantesh Halappanavar", "Nesreen Ahmed", "Yushun Dong", "Yue Zhao", "Yu Zhang", "Yu Wang" ], "github": "https://github.com/charlieqi02/RAG-Knowledge-Extraction-Attack-and-Defense-Benchmark", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) has become a cornerstone of knowledge-intensive applications, including enterprise chatbots, healthcare assistants, and agentic memory management. However, recent studies show that knowledge-extraction attacks can recover sensitive knowledge-base content through maliciously crafted queries, raising serious concerns about intellectual property theft and privacy leakage. While prior work has explored individual attack and defense techniques, the research landscape remains fragmented, spanning heterogeneous retrieval embeddings, diverse generation models, and evaluations based on non-standardized metrics and inconsistent datasets. To address this gap, we introduce the first systematic benchmark for knowledge-extraction attacks on RAG systems. Our benchmark covers a broad spectrum of attack and defense strategies, representative retrieval embedding models, and both open- and closed-source generators, all evaluated under a unified experimental framework with standardized protocols across multiple datasets. By consolidating the experimental landscape and enabling reproducible, comparable evaluation, this benchmark provides actionable insights and a practical foundation for developing privacy-preserving RAG systems in the face of emerging knowledge extraction threats. Our code is available here.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/charlieqi02/RAG-Knowledge-Extraction-Attack-and-Defense-Benchmark", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.07673", "title": "Blind to the Human Touch: Overlap Bias in LLM-Based Summary Evaluation", "authors": [ "Jiangnan Fang", "Cheng-Tse Liu", "Hanieh Deilamsalehy", "Nesreen K. Ahmed", "Puneet Mathur", "Nedim Lipka", "Franck Dernoncourt", "Ryan A. Rossi" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) judges have often been used alongside traditional, algorithm-based metrics for tasks like summarization because they better capture semantic information, are better at reasoning, and are more robust to paraphrasing. However, LLM judges show biases for length and order among others, and are vulnerable to various adversarial input prompts. While recent studies have looked into these biases, few have analyzed them at a more granular level in relation to a well-defined overlap metric. In this work we provide an LLM judge bias analysis as a function of overlap with human-written responses in the domain of summarization. We test 9 recent LLMs with parameter counts ranging from 1 billion to 12 billion, including variants of Gemma 3 and LLaMA 3. We find that LLM judges increasingly prefer summaries generated by other LLMs over those written by humans as the similarities (as measured by ROUGE and BLEU) between the judged summaries decrease, and this pattern extends to all but one model tested, and exists regardless of the models' own position biases. Additionally, we find that models struggle to judge even summaries with limited overlaps, suggesting that LLM-as-a-judge in the summary domain should rely on techniques beyond a simple comparison.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.07824", "title": "Data Darwinism Part I: Unlocking the Value of Scientific Data for Pre-training", "authors": [ "Yiwei Qin", "Zhen Huang", "Tiantian Mi", "Weiye Si", "Chenyang Zhou", "Qipeng Guo", "Siyuan Feng", "Pengfei Liu" ], "github": "", "project_page": "", "abstract": "Data quality determines foundation model performance, yet systematic processing frameworks are lacking. We introduce Data Darwinism, a ten-level taxonomy (L0-L9) that conceptualizes data-model co-evolution: advanced models produce superior data for next-generation systems. We validate this on scientific literature by constructing Darwin-Science, a 900B-token corpus (L0-L5). We identify a learnability gap in raw scientific text, which we bridge via L4 (Generative Refinement) and L5 (Cognitive Completion) using frontier LLMs to explicate reasoning and terminology. To ensure rigorous attribution, we pre-trained daVinci-origin-3B/7B models from scratch, excluding scientific content to create contamination-free baselines. After 600B tokens of continued pre-training, Darwin-Science outperforms baselines by +2.12 (3B) and +2.95 (7B) points across 20+ benchmarks, rising to +5.60 and +8.40 points on domain-aligned tasks. Systematic progression to L5 yields a +1.36 total gain, confirming that higher-level processing unlocks latent data value. We release the Darwin-Science corpus and daVinci-origin models to enable principled, co-evolutionary development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.13367", "title": "Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts", "authors": [ "Chen Yang", "Guangyue Peng", "Jiaying Zhu", "Ran Le", "Ruixiang Feng", "Tao Zhang", "Xiyun Xu", "Yang Song", "Yiming Jia", "Yuntao Wen", "Yunzhi Xu", "Zekai Wang", "Zhenwei An", "Zhicong Sun", "Zongchao Chen" ], "github": "", "project_page": "", "abstract": "We present Nanbeige4.1-3B, a unified generalist language model that simultaneously achieves strong agentic behavior, code generation, and general reasoning with only 3B parameters. To the best of our knowledge, it is the first open-source small language model (SLM) to achieve such versatility in a single model. To improve reasoning and preference alignment, we combine point-wise and pair-wise reward modeling, ensuring high-quality, human-aligned responses. For code generation, we design complexity-aware rewards in Reinforcement Learning, optimizing both correctness and efficiency. In deep search, we perform complex data synthesis and incorporate turn-level supervision during training. This enables stable long-horizon tool interactions, allowing Nanbeige4.1-3B to reliably execute up to 600 tool-call turns for complex problem-solving. Extensive experimental results show that Nanbeige4.1-3B significantly outperforms prior models of similar scale, such as Nanbeige4-3B-2511 and Qwen3-4B, even achieving superior performance compared to much larger models, such as Qwen3-30B-A3B. Our results demonstrate that small models can achieve both broad competence and strong specialization simultaneously, redefining the potential of 3B parameter models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.10809", "title": "DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories", "authors": [ "Chenlong Deng", "Mengjie Deng", "Junjie Wu", "Dun Zeng", "Teng Wang", "Qingsong Xie", "Jiadeng Huang", "Shengjie Ma", "Changwang Zhang", "Zhaoxiang Wang", "Jun Wang", "Yutao Zhu", "Zhicheng Dou" ], "github": "", "project_page": "", "abstract": "Existing multimodal retrieval systems excel at semantic matching but implicitly assume that query-image relevance can be measured in isolation. This paradigm overlooks the rich dependencies inherent in realistic visual streams, where information is distributed across temporal sequences rather than confined to single snapshots. To bridge this gap, we introduce DeepImageSearch, a novel agentic paradigm that reformulates image retrieval as an autonomous exploration task. Models must plan and perform multi-step reasoning over raw visual histories to locate targets based on implicit contextual cues. We construct DISBench, a challenging benchmark built on interconnected visual data. To address the scalability challenge of creating context-dependent queries, we propose a human-model collaborative pipeline that employs vision-language models to mine latent spatiotemporal associations, effectively offloading intensive context discovery before human verification. Furthermore, we build a robust baseline using a modular agent framework equipped with fine-grained tools and a dual-memory system for long-horizon navigation. Extensive experiments demonstrate that DISBench poses significant challenges to state-of-the-art models, highlighting the necessity of incorporating agentic reasoning into next-generation retrieval systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14060", "title": "LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts", "authors": [ "Yang Liu", "Jiaye Yang", "Weikang Li", "Jiahui Liang", "Yang Li", "Lingyong Yan" ], "github": "", "project_page": "", "abstract": "We introduce LM-Lexicon, an innovative definition modeling approach that incorporates data clustering, semantic expert learning, and model merging using a sparse mixture-of-experts architecture. By decomposing the definition modeling task into specialized semantic domains, where small language models are trained as domain experts, LM-Lexicon achieves substantial improvements (+7% BLEU score compared with the prior state-of-the-art model) over existing methods on five widely used benchmarks. Empirically, we demonstrate that 1) the clustering strategy enables fine-grained expert specialization with nearly 10% improvement in definition quality; 2) the semantic-aware domain-level routing mechanism achieves higher expert efficacy (+1%) than conventional token-level routing; and 3) further performance gains can be obtained through test-time compute and semantic expert scaling. Our work advances definition modeling while providing insights into the development of efficient language models for semantic-intensive applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.12586", "title": "Can I Have Your Order? Monte-Carlo Tree Search for Slot Filling Ordering in Diffusion Language Models", "authors": [ "Joshua Ong Jun Leang", "Yu Zhao", "Mihaela C\u0103t\u0103lina Stoian", "Wenda Li", "Shay B. Cohen", "Eleonora Giunchiglia" ], "github": "", "project_page": "", "abstract": "While plan-and-infill decoding in Masked Diffusion Models (MDMs) shows promise for mathematical and code reasoning, performance remains highly sensitive to slot infilling order, often yielding substantial output variance. We introduce McDiffuSE, a framework that formulates slot selection as decision making and optimises infilling orders through Monte Carlo Tree Search (MCTS). McDiffuSE uses look-ahead simulations to evaluate partial completions before commitment, systematically exploring the combinatorial space of generation orders. Experiments show an average improvement of 3.2% over autoregressive baselines and 8.0% over baseline plan-and-infill, with notable gains of 19.5% on MBPP and 4.9% on MATH500. Our analysis reveals that while McDiffuSE predominantly follows sequential ordering, incorporating non-sequential generation is essential for maximising performance. We observe that larger exploration constants, rather than increased simulations, are necessary to overcome model confidence biases and discover effective orderings. These findings establish MCTS-based planning as an effective approach for enhancing generation quality in MDMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.11968", "title": "DHPLT: large-scale multilingual diachronic corpora and word representations for semantic change modelling", "authors": [ "Mariia Fedorova", "Andrey Kutuzov", "Khonzoda Umarova" ], "github": "https://github.com/ltgoslo/scdisc_hplt", "project_page": "", "abstract": "In this resource paper, we present DHPLT, an open collection of diachronic corpora in 41 diverse languages. DHPLT is based on the web-crawled HPLT datasets; we use web crawl timestamps as the approximate signal of document creation time. The collection covers three time periods: 2011-2015, 2020-2021 and 2024-present (1 million documents per time period for each language). We additionally provide pre-computed word type and token embeddings and lexical substitutions for our chosen target words, while at the same time leaving it open for the other researchers to come up with their own target words using the same datasets. DHPLT aims at filling in the current lack of multilingual diachronic corpora for semantic change modelling (beyond a dozen of high-resource languages). It opens the way for a variety of new experimental setups in this field. All the resources described in this paper are available at https://data.hplt-project.org/three/diachronic/, sorted by language.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ltgoslo/scdisc_hplt", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.13516", "title": "SPILLage: Agentic Oversharing on the Web", "authors": [ "Jaechul Roh", "Eugene Bagdasarian", "Hamed Haddadi", "Ali Shahin Shamsabadi" ], "github": "https://github.com/jrohsc/SPILLage", "project_page": "", "abstract": "LLM-powered agents are beginning to automate user's tasks across the open web, often with access to user resources such as emails and calendars. Unlike standard LLMs answering questions in a controlled ChatBot setting, web agents act \"in the wild\", interacting with third parties and leaving behind an action trace. Therefore, we ask the question: how do web agents handle user resources when accomplishing tasks on their behalf across live websites? In this paper, we formalize Natural Agentic Oversharing -- the unintentional disclosure of task-irrelevant user information through an agent trace of actions on the web. We introduce SPILLage, a framework that characterizes oversharing along two dimensions: channel (content vs. behavior) and directness (explicit vs. implicit). This taxonomy reveals a critical blind spot: while prior work focuses on text leakage, web agents also overshare behaviorally through clicks, scrolls, and navigation patterns that can be monitored. We benchmark 180 tasks on live e-commerce sites with ground-truth annotations separating task-relevant from task-irrelevant attributes. Across 1,080 runs spanning two agentic frameworks and three backbone LLMs, we demonstrate that oversharing is pervasive with behavioral oversharing dominates content oversharing by 5x. This effect persists -- and can even worsen -- under prompt-level mitigation. However, removing task-irrelevant information before execution improves task success by up to 17.9%, demonstrating that reducing oversharing improves task success. Our findings underscore that protecting privacy in web agents is a fundamental challenge, requiring a broader view of \"output\" that accounts for what agents do on the web, not just what they type. Our datasets and code are available at https://github.com/jrohsc/SPILLage.", "github_manual": "", "github_regex": "https://github.com/jrohsc/SPILLage", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14265", "title": "STATe-of-Thoughts: Structured Action Templates for Tree-of-Thoughts", "authors": [ "Zachary Bamberger", "Till R. Saenger", "Gilad Morad", "Ofra Amir", "Brandon M. Stewart", "Amir Feder" ], "github": "https://github.com/zbambergerNLP/state-of-thoughts", "project_page": "", "abstract": "Inference-Time-Compute (ITC) methods like Best-of-N and Tree-of-Thoughts are meant to produce output candidates that are both high-quality and diverse, but their use of high-temperature sampling often fails to achieve meaningful output diversity. Moreover, existing ITC methods offer limited control over how to perform reasoning, which in turn limits their explainability. We present STATe-of-Thoughts (STATe), an interpretable ITC method that searches over high-level reasoning patterns. STATe replaces stochastic sampling with discrete and interpretable textual interventions: a controller selects actions encoding high-level reasoning choices, a generator produces reasoning steps conditioned on those choices, and an evaluator scores candidates to guide search. This structured approach yields three main advantages. First, action-guided textual interventions produce greater response diversity than temperature-based sampling. Second, in a case study on argument generation, STATe's explicit action sequences capture interpretable features that are highly predictive of output quality. Third, estimating the association between performance and action choices allows us to identify promising yet unexplored regions of the action space and steer generation directly toward them. Together, these results establish STATe as a practical framework for generating high-quality, diverse, and interpretable text. Our framework is available at https://github.com/zbambergerNLP/state-of-thoughts.", "github_manual": "", "github_regex": "https://github.com/zbambergerNLP/state-of-thoughts", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.15031", "title": "EditCtrl: Disentangled Local and Global Control for Real-Time Generative Video Editing", "authors": [ "Yehonathan Litman", "Shikun Liu", "Dario Seyb", "Nicholas Milef", "Yang Zhou", "Carl Marshall", "Shubham Tulsiani", "Caleb Leak" ], "github": "", "project_page": "", "abstract": "High-fidelity generative video editing has seen significant quality improvements by leveraging pre-trained video foundation models. However, their computational cost is a major bottleneck, as they are often designed to inefficiently process the full video context regardless of the inpainting mask's size, even for sparse, localized edits. In this paper, we introduce EditCtrl, an efficient video inpainting control framework that focuses computation only where it is needed. Our approach features a novel local video context module that operates solely on masked tokens, yielding a computational cost proportional to the edit size. This local-first generation is then guided by a lightweight temporal global context embedder that ensures video-wide context consistency with minimal overhead. Not only is EditCtrl 10 times more compute efficient than state-of-the-art generative editing methods, it even improves editing quality compared to methods designed with full-attention. Finally, we showcase how EditCtrl unlocks new capabilities, including multi-region editing with text prompts and autoregressive content propagation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.14941", "title": "AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories", "authors": [ "Zun Wang", "Han Lin", "Jaehong Yoon", "Jaemin Cho", "Yue Zhang", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Maintaining spatial world consistency over long horizons remains a central challenge for camera-controllable video generation. Existing memory-based approaches often condition generation on globally reconstructed 3D scenes by rendering anchor videos from the reconstructed geometry in the history. However, reconstructing a global 3D scene from multiple views inevitably introduces cross-view misalignment, as pose and depth estimation errors cause the same surfaces to be reconstructed at slightly different 3D locations across views. When fused, these inconsistencies accumulate into noisy geometry that contaminates the conditioning signals and degrades generation quality. We introduce AnchorWeave, a memory-augmented video generation framework that replaces a single misaligned global memory with multiple clean local geometric memories and learns to reconcile their cross-view inconsistencies. To this end, AnchorWeave performs coverage-driven local memory retrieval aligned with the target trajectory and integrates the selected local memories through a multi-anchor weaving controller during generation. Extensive experiments demonstrate that AnchorWeave significantly improves long-term scene consistency while maintaining strong visual quality, with ablation and analysis studies further validating the effectiveness of local geometric conditioning, multi-anchor control, and coverage-driven retrieval.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.11574", "title": "Learning to Configure Agentic AI Systems", "authors": [ "Aditya Taparia", "Som Sagar", "Ransalu Senanayake" ], "github": "", "project_page": "", "abstract": "Configuring LLM-based agent systems involves choosing workflows, tools, token budgets, and prompts from a large combinatorial design space, and is typically handled today by fixed large templates or hand-tuned heuristics. This leads to brittle behavior and unnecessary compute, since the same cumbersome configuration is often applied to both easy and hard input queries. We formulate agent configuration as a query-wise decision problem and introduce ARC (Agentic Resource & Configuration learner), which learns a light-weight hierarchical policy using reinforcement learning to dynamically tailor these configurations. Across multiple benchmarks spanning reasoning and tool-augmented question answering, the learned policy consistently outperforms strong hand-designed and other baselines, achieving up to 25% higher task accuracy while also reducing token and runtime costs. These results demonstrate that learning per-query agent configurations is a powerful alternative to \"one size fits all\" designs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.13346", "title": "CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis", "authors": [ "Zhen Wang", "Yiming Gao", "Jieyuan Liu", "Enze Ma", "Jefferson Chen", "Mark Antkowiak", "Mengzhou Hu", "JungHo Kong", "Dexter Pratt", "Zhiting Hu", "Wei Wang", "Trey Ideker", "Eric P. Xing" ], "github": "https://github.com/AnonymousGym/CellMaster", "project_page": "", "abstract": "Single-cell RNA-seq (scRNA-seq) enables atlas-scale profiling of complex tissues, revealing rare lineages and transient states. Yet, assigning biologically valid cell identities remains a bottleneck because markers are tissue- and state-dependent, and novel states lack references. We present CellMaster, an AI agent that mimics expert practice for zero-shot cell-type annotation. Unlike existing automated tools, CellMaster leverages LLM-encoded knowledge (e.g., GPT-4o) to perform on-the-fly annotation with interpretable rationales, without pre-training or fixed marker databases. Across 9 datasets spanning 8 tissues, CellMaster improved accuracy by 7.1% over best-performing baselines (including CellTypist and scTab) in automatic mode. With human-in-the-loop refinement, this advantage increased to 18.6%, with a 22.1% gain on subtype populations. The system demonstrates particular strength in rare and novel cell states where baselines often fail. Source code and the web application are available at https://github.com/AnonymousGym/CellMaster{https://github.com/AnonymousGym/CellMaster}.", "github_manual": "", "github_regex": "https://github.com/AnonymousGym/CellMaster", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.13294", "title": "VisPhyWorld: Probing Physical Reasoning via Code-Driven Video Reconstruction", "authors": [ "Jiarong Liang", "Max Ku", "Ka-Hei Hui", "Ping Nie", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Evaluating whether Multimodal Large Language Models (MLLMs) genuinely reason about physical dynamics remains challenging. Most existing benchmarks rely on recognition-style protocols such as Visual Question Answering (VQA) and Violation of Expectation (VoE), which can often be answered without committing to an explicit, testable physical hypothesis. We propose VisPhyWorld, an execution-based framework that evaluates physical reasoning by requiring models to generate executable simulator code from visual observations. By producing runnable code, the inferred world representation is directly inspectable, editable, and falsifiable. This separates physical reasoning from rendering. Building on this framework, we introduce VisPhyBench, comprising 209 evaluation scenes derived from 108 physical templates and a systematic protocol that evaluates how well models reconstruct appearance and reproduce physically plausible motion. Our pipeline produces valid reconstructed videos in 97.7% on the benchmark. Experiments show that while state-of-the-art MLLMs achieve strong semantic scene understanding, they struggle to accurately infer physical parameters and to simulate consistent physical dynamics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.15259", "title": "Knowing Isn't Understanding: Re-grounding Generative Proactivity with Epistemic and Behavioral Insight", "authors": [ "Kirandeep Kaur", "Xingda Lyu", "Chirag Shah" ], "github": "", "project_page": "", "abstract": "Generative AI agents equate understanding with resolving explicit queries, an assumption that confines interaction to what users can articulate. This assumption breaks down when users themselves lack awareness of what is missing, risky, or worth considering. In such conditions, proactivity is not merely an efficiency enhancement, but an epistemic necessity. We refer to this condition as epistemic incompleteness: where progress depends on engaging with unknown unknowns for effective partnership. Existing approaches to proactivity remain narrowly anticipatory, extrapolating from past behavior and presuming that goals are already well defined, thereby failing to support users meaningfully. However, surfacing possibilities beyond a user's current awareness is not inherently beneficial. Unconstrained proactive interventions can misdirect attention, overwhelm users, or introduce harm. Proactive agents, therefore, require behavioral grounding: principled constraints on when, how, and to what extent an agent should intervene. We advance the position that generative proactivity must be grounded both epistemically and behaviorally. Drawing on the philosophy of ignorance and research on proactive behavior, we argue that these theories offer critical guidance for designing agents that can engage responsibly and foster meaningful partnerships.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-17", "arxiv_id": "2602.10458", "title": "Found-RL: foundation model-enhanced reinforcement learning for autonomous driving", "authors": [ "Yansong Qu", "Zihao Sheng", "Zilin Huang", "Jiancong Chen", "Yuhao Luo", "Tianyi Wang", "Yiheng Feng", "Samuel Labi", "Sikai Chen" ], "github": "https://github.com/ys-qu/found-rl", "project_page": "", "abstract": "Reinforcement Learning (RL) has emerged as a dominant paradigm for end-to-end autonomous driving (AD). However, RL suffers from sample inefficiency and a lack of semantic interpretability in complex scenarios. Foundation Models, particularly Vision-Language Models (VLMs), can mitigate this by offering rich, context-aware knowledge, yet their high inference latency hinders deployment in high-frequency RL training loops. To bridge this gap, we present Found-RL, a platform tailored to efficiently enhance RL for AD using foundation models. A core innovation is the asynchronous batch inference framework, which decouples heavy VLM reasoning from the simulation loop, effectively resolving latency bottlenecks to support real-time learning. We introduce diverse supervision mechanisms: Value-Margin Regularization (VMR) and Advantage-Weighted Action Guidance (AWAG) to effectively distill expert-like VLM action suggestions into the RL policy. Additionally, we adopt high-throughput CLIP for dense reward shaping. We address CLIP's dynamic blindness via Conditional Contrastive Action Alignment, which conditions prompts on discretized speed/command and yields a normalized, margin-based bonus from context-specific action-anchor scoring. Found-RL provides an end-to-end pipeline for fine-tuned VLM integration and shows that a lightweight RL model can achieve near-VLM performance compared with billion-parameter VLMs while sustaining real-time inference (approx. 500 FPS). Code, data, and models will be publicly available at https://github.com/ys-qu/found-rl.", "github_manual": "", "github_regex": "https://github.com/ys-qu/found-rl", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.15763", "title": "GLM-5: from Vibe Coding to Agentic Engineering", "authors": [ "GLM-5 Team", "Aohan Zeng", "Xin Lv", "Zhenyu Hou", "Zhengxiao Du", "Qinkai Zheng", "Bin Chen", "Da Yin", "Chendi Ge", "Chengxing Xie", "Cunxiang Wang", "Gengzheng Pan", "Hao Zeng", "Haoke Zhang", "Haoran Wang", "Huilong Chen", "Jiajie Zhang", "Jian Jiao", "Jiaqi Guo", "Jingsen Wang", "Jingzhao Du", "Jinzhu Wu", "Kedong Wang", "Lei Li", "Lin Fan", "Lucen Zhong", "Mingdao Liu", "Mingming Zhao", "Pengfan Du", "Qian Dong", "Rui Lu", "Shuang-Li", "Shulin Cao", "Song Liu", "Ting Jiang", "Xiaodong Chen", "Xiaohan Zhang", "Xuancheng Huang", "Xuezhen Dong", "Yabo Xu", "Yao Wei", "Yifan An", "Yilin Niu", "Yitong Zhu", "Yuanhao Wen", "Yukuo Cen", "Yushi Bai", "Zhongpei Qiao", "Zihan Wang", "Zikang Wang", "Zilin Zhu", "Ziqiang Liu", "Zixuan Li", "Bojie Wang", "Bosi Wen", "Can Huang", "Changpeng Cai", "Chao Yu", "Chen Li", "Chen Li", "Chenghua Huang", "Chengwei Hu", "Chenhui Zhang", "Chenzheng Zhu", "Congfeng Yin", "Daoyan Lin", "Dayong Yang", "Di Wang", "Ding Ai", "Erle Zhu", "Fangzhou Yi", "Feiyu Chen", "Guohong Wen", "Hailong Sun", "Haisha Zhao", "Haiyi Hu", "Hanchen Zhang", "Hanrui Liu", "Hanyu Zhang", "Hao Peng", "Hao Tai", "Haobo Zhang", "He Liu", "Hongwei Wang", "Hongxi Yan", "Hongyu Ge", "Huan Liu", "Huan Liu", "Huanpeng Chu", "Jia'ni Zhao", "Jiachen Wang", "Jiajing Zhao", "Jiamin Ren", "Jiapeng Wang", "Jiaxin Zhang", "Jiayi Gui", "Jiayue Zhao", "Jijie Li", "Jing An", "Jing Li", "Jingwei Yuan", "Jinhua Du", "Jinxin Liu", "Junkai Zhi", "Junwen Duan", "Kaiyue Zhou", "Kangjian Wei", "Ke Wang", "Keyun Luo", "Laiqiang Zhang", "Leigang Sha", "Liang Xu", "Lindong Wu", "Lintao Ding", "Lu Chen", "Minghao Li", "Nianyi Lin", "Pan Ta", "Qiang Zou", "Rongjun Song", "Ruiqi Yang", "Shangqing Tu", "Shangtong Yang", "Shaoxiang Wu", "Shengyan Zhang", "Shijie Li", "Shuang Li", "Shuyi Fan", "Wei Qin", "Wei Tian", "Weining Zhang", "Wenbo Yu", "Wenjie Liang", "Xiang Kuang", "Xiangmeng Cheng", "Xiangyang Li", "Xiaoquan Yan", "Xiaowei Hu", "Xiaoying Ling", "Xing Fan", "Xingye Xia", "Xinyuan Zhang", "Xinze Zhang", "Xirui Pan", "Xunkai Zhang", "Yandong Wu", "Yanfu Li", "Yidong Wang", "Yifan Zhu", "Yijun Tan", "Yilin Zhou", "Yiming Pan", "Ying Zhang", "Yinpei Su", "Yipeng Geng", "Yipeng Geng", "Yong Yan", "Yonglin Tan", "Yuean Bi", "Yuhan Shen", "Yuhao Yang", "Yujiang Li", "Yunan Liu", "Yunqing Wang", "Yuntao Li", "Yurong Wu", "Yutao Zhang", "Yuxi Duan", "Yuxuan Zhang", "Zezhen Liu", "Zhengtao Jiang", "Zhenhe Yan", "Zheyu Zhang", "Zhixiang Wei", "Zhuo Chen", "Zhuoer Feng", "Zijun Yao", "Ziwei Chai", "Ziyuan Wang", "Zuzhou Zhang", "Bin Xu", "Minlie Huang", "Hongning Wang", "Juanzi Li", "Yuxiao Dong", "Jie Tang" ], "github": "https://github.com/zai-org/GLM-5", "project_page": "", "abstract": "We present GLM-5, a next-generation foundation model designed to transition the paradigm of vibe coding to agentic engineering. Building upon the agentic, reasoning, and coding (ARC) capabilities of its predecessor, GLM-5 adopts DSA to significantly reduce training and inference costs while maintaining long-context fidelity. To advance model alignment and autonomy, we implement a new asynchronous reinforcement learning infrastructure that drastically improves post-training efficiency by decoupling generation from training. Furthermore, we propose novel asynchronous agent RL algorithms that further improve RL quality, enabling the model to learn from complex, long-horizon interactions more effectively. Through these innovations, GLM-5 achieves state-of-the-art performance on major open benchmarks. Most critically, GLM-5 demonstrates unprecedented capability in real-world coding tasks, surpassing previous baselines in handling end-to-end software engineering challenges. Code, models, and more information are available at https://github.com/zai-org/GLM-5.", "github_manual": "", "github_regex": "https://github.com/zai-org/GLM-5", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.14299", "title": "Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook", "authors": [ "Ming Li", "Xirui Li", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "As large language model agents increasingly populate networked environments, a fundamental question arises: do artificial intelligence (AI) agent societies undergo convergence dynamics similar to human social systems? Lately, Moltbook approximates a plausible future scenario in which autonomous agents participate in an open-ended, continuously evolving online society. We present the first large-scale systemic diagnosis of this AI agent society. Beyond static observation, we introduce a quantitative diagnostic framework for dynamic evolution in AI agent societies, measuring semantic stabilization, lexical turnover, individual inertia, influence persistence, and collective consensus. Our analysis reveals a system in dynamic balance in Moltbook: while global semantic averages stabilize rapidly, individual agents retain high diversity and persistent lexical turnover, defying homogenization. However, agents exhibit strong individual inertia and minimal adaptive response to interaction partners, preventing mutual influence and consensus. Consequently, influence remains transient with no persistent supernodes, and the society fails to develop stable collective influence anchors due to the absence of shared social memory. These findings demonstrate that scale and interaction density alone are insufficient to induce socialization, providing actionable design and analysis principles for upcoming next-generation AI agent societies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.15322", "title": "On Surprising Effectiveness of Masking Updates in Adaptive Optimizers", "authors": [ "Taejong Joo", "Wenhan Xia", "Cheolmin Kim", "Ming Zhang", "Eugene Ie" ], "github": "https://github.com/wolfecameron/nanoMoE.GitHub", "project_page": "", "abstract": "Training large language models (LLMs) relies almost exclusively on dense adaptive optimizers with increasingly sophisticated preconditioners. We challenge this by showing that randomly masking parameter updates can be highly effective, with a masked variant of RMSProp consistently outperforming recent state-of-the-art optimizers. Our analysis reveals that the random masking induces a curvature-dependent geometric regularization that smooths the optimization trajectory. Motivated by this finding, we introduce Momentum-aligned gradient masking (Magma), which modulates the masked updates using momentum-gradient alignment. Extensive LLM pre-training experiments show that Magma is a simple drop-in replacement for adaptive optimizers with consistent gains and negligible computational overhead. Notably, for the 1B model size, Magma reduces perplexity by over 19\\% and 9\\% compared to Adam and Muon, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/wolfecameron/nanoMoE.GitHub", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.07854", "title": "Geometry-Aware Rotary Position Embedding for Consistent Video World Model", "authors": [ "Chendong Xiang", "Jiajun Liu", "Jintao Zhang", "Xiao Yang", "Zhengwei Fang", "Shizun Wang", "Zijun Wang", "Yingtian Zou", "Hang Su", "Jun Zhu" ], "github": "", "project_page": "", "abstract": "Predictive world models that simulate future observations under explicit camera control are fundamental to interactive AI. Despite rapid advances, current systems lack spatial persistence: they fail to maintain stable scene structures over long trajectories, frequently hallucinating details when cameras revisit previously observed locations. We identify that this geometric drift stems from reliance on screen-space positional embeddings, which conflict with the projective geometry required for 3D consistency. We introduce ViewRope, a geometry-aware encoding that injects camera-ray directions directly into video transformer self-attention layers. By parameterizing attention with relative ray geometry rather than pixel locality, ViewRope provides a model-native inductive bias for retrieving 3D-consistent content across temporal gaps. We further propose Geometry-Aware Frame-Sparse Attention, which exploits these geometric cues to selectively attend to relevant historical frames, improving efficiency without sacrificing memory consistency. We also present ViewBench, a diagnostic suite measuring loop-closure fidelity and geometric drift. Our results demonstrate that ViewRope substantially improves long-term consistency while reducing computational costs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.15620", "title": "STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens", "authors": [ "Shiqi Liu", "Zeyu He", "Guojian Zhan", "Letian Tao", "Zhilong Zheng", "Jiang Wu", "Yinuo Wang", "Yang Guan", "Kehua Sheng", "Bo Zhang", "Keqiang Li", "Jingliang Duan", "Shengbo Eben Li" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning (RL) has significantly improved large language model reasoning, but existing RL fine-tuning methods rely heavily on heuristic techniques such as entropy regularization and reweighting to maintain stability. In practice, they often experience late-stage performance collapse, leading to degraded reasoning quality and unstable training. We derive that the magnitude of token-wise policy gradients in RL is negatively correlated with token probability and local policy entropy. Building on this result, we prove that training instability is driven by a tiny fraction of tokens, approximately 0.01\\%, which we term spurious tokens. When such tokens appear in correct responses, they contribute little to the reasoning outcome but inherit the full sequence-level reward, leading to abnormally amplified gradient updates. Motivated by this observation, we propose Spurious-Token-Aware Policy Optimization (STAPO) for large-scale model refining, which selectively masks such updates and renormalizes the loss over valid tokens. Across six mathematical reasoning benchmarks using Qwen 1.7B, 8B, and 14B base models, STAPO consistently demonstrates superior entropy stability and achieves an average performance improvement of 7.13\\% over GRPO, 20-Entropy and JustRL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.15278", "title": "Visual Persuasion: What Influences Decisions of Vision-Language Models?", "authors": [ "Manuel Cherep", "Pranav M R", "Pattie Maes", "Nikhil Singh" ], "github": "https://github.com/safety-research/bloom", "project_page": "", "abstract": "The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs). These agents make visual decisions at scale, deciding what to click, recommend, or buy. Yet, we know little about the structure of their visual preferences. We introduce a framework for studying this by placing VLMs in controlled image-based choice tasks and systematically perturbing their inputs. Our key idea is to treat the agent's decision function as a latent visual utility that can be inferred through revealed preference: choices between systematically edited images. Starting from common images, such as product photos, we propose methods for visual prompt optimization, adapting text optimization methods to iteratively propose and apply visually plausible modifications using an image generation model (such as in composition, lighting, or background). We then evaluate which edits increase selection probability. Through large-scale experiments on frontier VLMs, we demonstrate that optimized edits significantly shift choice probabilities in head-to-head comparisons. We develop an automatic interpretability pipeline to explain these preferences, identifying consistent visual themes that drive selection. We argue that this approach offers a practical and efficient way to surface visual vulnerabilities, safety concerns that might otherwise be discovered implicitly in the wild, supporting more proactive auditing and governance of image-based AI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/safety-research/bloom", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.15112", "title": "ResearchGym: Evaluating Language Model Agents on Real-World AI Research", "authors": [ "Aniketh Garikaparthi", "Manasi Patwardhan", "Arman Cohan" ], "github": "https://github.com/conglu1997/SynthER", "project_page": "", "abstract": "We introduce ResearchGym, a benchmark and execution environment for evaluating AI agents on end-to-end research. To instantiate this, we repurpose five oral and spotlight papers from ICML, ICLR, and ACL. From each paper's repository, we preserve the datasets, evaluation harness, and baseline implementations but withhold the paper's proposed method. This results in five containerized task environments comprising 39 sub-tasks in total. Within each environment, agents must propose novel hypotheses, run experiments, and attempt to surpass strong human baselines on the paper's metrics. In a controlled evaluation of an agent powered by GPT-5, we observe a sharp capability--reliability gap. The agent improves over the provided baselines from the repository in just 1 of 15 evaluations (6.7%) by 11.5%, and completes only 26.5% of sub-tasks on average. We identify recurring long-horizon failure modes, including impatience, poor time and resource management, overconfidence in weak hypotheses, difficulty coordinating parallel experiments, and hard limits from context length. Yet in a single run, the agent surpasses the solution of an ICML 2025 Spotlight task, indicating that frontier agents can occasionally reach state-of-the-art performance, but do so unreliably. We additionally evaluate proprietary agent scaffolds including Claude Code (Opus-4.5) and Codex (GPT-5.2) which display a similar gap. ResearchGym provides infrastructure for systematic evaluation and analysis of autonomous agents on closed-loop research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/conglu1997/SynthER", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.15772", "title": "Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models", "authors": [ "Sen Ye", "Mengde Xu", "Shuyang Gu", "Di He", "Liwei Wang", "Han Hu" ], "github": "https://github.com/sen-ye/R3", "project_page": "", "abstract": "Current research in multimodal models faces a key challenge where enhancing generative capabilities often comes at the expense of understanding, and vice versa. We analyzed this trade-off and identify the primary cause might be the potential conflict between generation and understanding, which creates a competitive dynamic within the model. To address this, we propose the Reason-Reflect-Refine (R3) framework. This innovative algorithm re-frames the single-step generation task into a multi-step process of \"generate-understand-regenerate\". By explicitly leveraging the model's understanding capability during generation, we successfully mitigate the optimization dilemma, achieved stronger generation results and improved understanding ability which are related to the generation process. This offers valuable insights for designing next-generation unified multimodal models. Code is available at https://github.com/sen-ye/R3.", "github_manual": "", "github_regex": "https://github.com/sen-ye/R3", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.09653", "title": "ClinAlign: Scaling Healthcare Alignment from Clinician Preference", "authors": [ "Shiwei Lyu", "Xidong Wang", "Lei Liu", "Hao Zhu", "Chaohe Zhang", "Jian Wang", "Jinjie Gu", "Benyou Wang", "Yue Shen" ], "github": "", "project_page": "", "abstract": "Although large language models (LLMs) demonstrate expert-level medical knowledge, aligning their open-ended outputs with fine-grained clinician preferences remains challenging. Existing methods often rely on coarse objectives or unreliable automated judges that are weakly grounded in professional guidelines. We propose a two-stage framework to address this gap. First, we introduce HealthRubrics, a dataset of 7,034 physician-verified preference examples in which clinicians refine LLM-drafted rubrics to meet rigorous medical standards. Second, we distill these rubrics into HealthPrinciples: 119 broadly reusable, clinically grounded principles organized by clinical dimensions, enabling scalable supervision beyond manual annotation. We use HealthPrinciples for (1) offline alignment by synthesizing rubrics for unlabeled queries and (2) an inference-time tool for guided self-revision. A 30B parameter model that activates only 3B parameters at inference trained with our framework achieves 33.4% on HealthBench-Hard, outperforming much larger models including Deepseek-R1 and o3, establishing a resource-efficient baseline for clinical alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.14486", "title": "Revisiting the Platonic Representation Hypothesis: An Aristotelian View", "authors": [ "Fabian Gr\u00f6ger", "Shuo Wen", "Maria Brbi\u0107" ], "github": "", "project_page": "", "abstract": "The Platonic Representation Hypothesis suggests that representations from neural networks are converging to a common statistical model of reality. We show that the existing metrics used to measure representational similarity are confounded by network scale: increasing model depth or width can systematically inflate representational similarity scores. To correct these effects, we introduce a permutation-based null-calibration framework that transforms any representational similarity metric into a calibrated score with statistical guarantees. We revisit the Platonic Representation Hypothesis with our calibration framework, which reveals a nuanced picture: the apparent convergence reported by global spectral measures largely disappears after calibration, while local neighborhood similarity, but not local distances, retains significant agreement across different modalities. Based on these findings, we propose the Aristotelian Representation Hypothesis: representations in neural networks are converging to shared local neighborhood relationships.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.12978", "title": "Learning Native Continuation for Action Chunking Flow Policies", "authors": [ "Yufeng Liu", "Hang Yu", "Juntu Zhao", "Bocheng Li", "Di Zhang", "Mingzhu Li", "Wenxuan Wu", "Yingdong Hu", "Junyuan Xie", "Junliang Guo", "Dequan Wang", "Yang Gao" ], "github": "", "project_page": "", "abstract": "Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at chunk boundaries. Real-Time Chunking (RTC) alleviates this issue but is external to the policy, leading to spurious multimodal switching and trajectories that are not intrinsically smooth. We propose Legato, a training-time continuation method for action-chunked flow-based VLA policies. Specifically, Legato initializes denoising from a schedule-shaped mixture of known actions and noise, exposing the model to partial action information. Moreover, Legato reshapes the learned flow dynamics to ensure that the denoising process remains consistent between training and inference under per-step guidance. Legato further uses randomized schedule condition during training to support varying inference delays and achieve controllable smoothness. Empirically, Legato produces smoother trajectories and reduces spurious multimodal switching during execution, leading to less hesitation and shorter task completion time. Extensive real-world experiments show that Legato consistently outperforms RTC across five manipulation tasks, achieving approximately 10% improvements in both trajectory smoothness and task completion time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.15327", "title": "Prescriptive Scaling Reveals the Evolution of Language Model Capabilities", "authors": [ "Hanlin Zhang", "Jikai Jin", "Vasilis Syrgkanis", "Sham Kakade" ], "github": "https://github.com/hlzhang109/prescriptive-scaling", "project_page": "", "abstract": "For deploying foundation models, practitioners increasingly need prescriptive scaling laws: given a pre training compute budget, what downstream accuracy is attainable with contemporary post training practice, and how stable is that mapping as the field evolves? Using large scale observational evaluations with 5k observational and 2k newly sampled data on model performance, we estimate capability boundaries, high conditional quantiles of benchmark scores as a function of log pre training FLOPs, via smoothed quantile regression with a monotone, saturating sigmoid parameterization. We validate the temporal reliability by fitting on earlier model generations and evaluating on later releases. Across various tasks, the estimated boundaries are mostly stable, with the exception of math reasoning that exhibits a consistently advancing boundary over time. We then extend our approach to analyze task dependent saturation and to probe contamination related shifts on math reasoning tasks. Finally, we introduce an efficient algorithm that recovers near full data frontiers using roughly 20% of evaluation budget. Together, our work releases the Proteus 2k, the latest model performance evaluation dataset, and introduces a practical methodology for translating compute budgets into reliable performance expectations and for monitoring when capability boundaries shift across time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hlzhang109/prescriptive-scaling", "github_api_added_by": "auto" }, { "date": "2026-02-18", "arxiv_id": "2602.15200", "title": "COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression", "authors": [ "Denis Makhov", "Dmitriy Shopkhoev", "Magauiya Zhussip", "Ammar Ali", "Baher Mohammad", "Stamatios Lefkimmiatis" ], "github": "https://github.com/mts-ai/COMPOT", "project_page": "", "abstract": "Post-training compression of Transformer models commonly relies on truncated singular value decomposition (SVD). However, enforcing a single shared subspace can degrade accuracy even at moderate compression. Sparse dictionary learning provides a more flexible union-of-subspaces representation, but existing approaches often suffer from iterative dictionary and coefficient updates. We propose COMPOT (Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers), a training-free compression framework that uses a small calibration dataset to estimate a sparse weight factorization. COMPOT employs orthogonal dictionaries that enable closed-form Procrustes updates for the dictionary and analytical single-step sparse coding for the coefficients, eliminating iterative optimization. To handle heterogeneous layer sensitivity under a global compression budget, COMPOT further introduces a one-shot dynamic allocation strategy that adaptively redistributes layer-wise compression rates. Extensive experiments across diverse architectures and tasks show that COMPOT consistently delivers a superior quality-compression trade-off over strong low-rank and sparse baselines, while remaining fully compatible with post-training quantization for extreme compression. Code is available https://github.com/mts-ai/COMPOT{here}.", "github_manual": "", "github_regex": "https://github.com/mts-ai/COMPOT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.12279", "title": "UniT: Unified Multimodal Chain-of-Thought Test-time Scaling", "authors": [ "Leon Liangyu Chen", "Haoyu Ma", "Zhipeng Fan", "Ziqi Huang", "Animesh Sinha", "Xiaoliang Dai", "Jialiang Wang", "Zecheng He", "Jianwei Yang", "Chunyuan Li", "Junzhe Sun", "Chu Wang", "Serena Yeung-Levy", "Felix Juefei-Xu" ], "github": "", "project_page": "", "abstract": "Unified models can handle both multimodal understanding and generation within a single architecture, yet they typically operate in a single pass without iteratively refining their outputs. Many multimodal tasks, especially those involving complex spatial compositions, multiple interacting objects, or evolving instructions, require decomposing instructions, verifying intermediate results, and making iterative corrections. While test-time scaling (TTS) has demonstrated that allocating additional inference compute for iterative reasoning substantially improves language model performance, extending this paradigm to unified multimodal models remains an open challenge. We introduce UniT, a framework for multimodal chain-of-thought test-time scaling that enables a single unified model to reason, verify, and refine across multiple rounds. UniT combines agentic data synthesis, unified model training, and flexible test-time inference to elicit cognitive behaviors including verification, subgoal decomposition, and content memory. Our key findings are: (1) unified models trained on short reasoning trajectories generalize to longer inference chains at test time; (2) sequential chain-of-thought reasoning provides a more scalable and compute-efficient TTS strategy than parallel sampling; (3) training on generation and editing trajectories improves out-of-distribution visual reasoning. These results establish multimodal test-time scaling as an effective paradigm for advancing both generation and understanding in unified models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.11389", "title": "Causal-JEPA: Learning World Models through Object-Level Latent Interventions", "authors": [ "Heejeong Nam", "Quentin Le Lidec", "Lucas Maes", "Yann LeCun", "Randall Balestriero" ], "github": "https://github.com/galilai-group/cjepa", "project_page": "", "abstract": "World models require robust relational understanding to support prediction, reasoning, and control. While object-centric representations provide a useful abstraction, they are not sufficient to capture interaction-dependent dynamics. We therefore propose C-JEPA, a simple and flexible object-centric world model that extends masked joint embedding prediction from image patches to object-centric representations. By applying object-level masking that requires an object's state to be inferred from other objects, C-JEPA induces latent interventions with counterfactual-like effects and prevents shortcut solutions, making interaction reasoning essential. Empirically, C-JEPA leads to consistent gains in visual question answering, with an absolute improvement of about 20\\% in counterfactual reasoning compared to the same architecture without object-level masking. On agent control tasks, C-JEPA enables substantially more efficient planning by using only 1\\% of the total latent input features required by patch-based world models, while achieving comparable performance. Finally, we provide a formal analysis demonstrating that object-level masking induces a causal inductive bias via latent interventions. Our code is available at https://github.com/galilai-group/cjepa.", "github_manual": "", "github_regex": "https://github.com/galilai-group/cjepa", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.14111", "title": "Sanity Checks for Sparse Autoencoders: Do SAEs Beat Random Baselines?", "authors": [ "Anton Korznikov", "Andrey Galichin", "Alexey Dontsov", "Oleg Rogov", "Ivan Oseledets", "Elena Tutubalina" ], "github": "", "project_page": "", "abstract": "Sparse Autoencoders (SAEs) have emerged as a promising tool for interpreting neural networks by decomposing their activations into sparse sets of human-interpretable features. Recent work has introduced multiple SAE variants and successfully scaled them to frontier models. Despite much excitement, a growing number of negative results in downstream tasks casts doubt on whether SAEs recover meaningful features. To directly investigate this, we perform two complementary evaluations. On a synthetic setup with known ground-truth features, we demonstrate that SAEs recover only 9% of true features despite achieving 71% explained variance, showing that they fail at their core task even when reconstruction is strong. To evaluate SAEs on real activations, we introduce three baselines that constrain SAE feature directions or their activation patterns to random values. Through extensive experiments across multiple SAE architectures, we show that our baselines match fully-trained SAEs in interpretability (0.87 vs 0.90), sparse probing (0.69 vs 0.72), and causal editing (0.73 vs 0.72). Together, these results suggest that SAEs in their current state do not reliably decompose models' internal mechanisms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.12670", "title": "SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks", "authors": [ "Xiangyi Li", "Wenbo Chen", "Yimin Liu", "Shenghan Zheng", "Xiaokun Chen", "Yifeng He", "Yubo Li", "Bingran You", "Haotian Shen", "Jiankai Sun", "Shuyi Wang", "Qunhong Zeng", "Di Wang", "Xuandong Zhao", "Yuanli Wang", "Roey Ben Chaim", "Zonglin Di", "Yipeng Gao", "Junwei He", "Yizhuo He", "Liqiang Jing", "Luyang Kong", "Xin Lan", "Jiachen Li", "Songlin Li", "Yijiang Li", "Yueqian Lin", "Xinyi Liu", "Xuanqing Liu", "Haoran Lyu", "Ze Ma", "Bowei Wang", "Runhui Wang", "Tianyu Wang", "Wengao Ye", "Yue Zhang", "Hanwen Xing", "Yiqi Xue", "Steven Dillmann", "Han-chung Lee" ], "github": "https://github.com/google-gemini/gemini-cli", "project_page": "", "abstract": "Agent Skills are structured packages of procedural knowledge that augment LLM agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a benchmark of 86 tasks across 11 domains paired with curated Skills and deterministic verifiers. Each task is evaluated under three conditions: no Skills, curated Skills, and self-generated Skills. We test 7 agent-model configurations over 7,308 trajectories. Curated Skills raise average pass rate by 16.2 percentage points(pp), but effects vary widely by domain (+4.5pp for Software Engineering to +51.9pp for Healthcare) and 16 of 84 tasks show negative deltas. Self-generated Skills provide no benefit on average, showing that models cannot reliably author the procedural knowledge they benefit from consuming. Focused Skills with 2--3 modules outperform comprehensive documentation, and smaller models with Skills can match larger models without them.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-gemini/gemini-cli", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.15449", "title": "TAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Models", "authors": [ "Chansung Park", "Juyong Jiang", "Fan Wang", "Sayak Paul", "Jiasi Shen", "Jing Tang", "Jianguo Li" ], "github": "https://github.com/deep-diver/TAROT", "project_page": "", "abstract": "Large Language Models (LLMs) are changing the coding paradigm, known as vibe coding, yet synthesizing algorithmically sophisticated and robust code still remains a critical challenge. Incentivizing the deep reasoning capabilities of LLMs is essential to overcoming this hurdle. Reinforcement Fine-Tuning (RFT) has emerged as a promising strategy to address this need. However, most existing approaches overlook the heterogeneous difficulty and granularity inherent in test cases, leading to an imbalanced distribution of reward signals and consequently biased gradient updates during training. To address this, we propose Test-driven and cApability-adaptive cuRriculum reinfOrcement fine-Tuning (TAROT). TAROT systematically constructs, for each problem, a four-tier test suite (basic, intermediate, complex, edge), providing a controlled difficulty landscape for curriculum design and evaluation. Crucially, TAROT decouples curriculum progression from raw reward scores, enabling capability-conditioned evaluation and principled selection from a portfolio of curriculum policies rather than incidental test-case difficulty composition. This design fosters stable optimization and more efficient competency acquisition. Extensive experimental results reveal that the optimal curriculum for RFT in code generation is closely tied to a model's inherent capability, with less capable models achieving greater gains with an easy-to-hard progression, whereas more competent models excel under a hard-first curriculum. TAROT provides a reproducible method that adaptively tailors curriculum design to a model's capability, thereby consistently improving the functional correctness and robustness of the generated code. All code and data are released to foster reproducibility and advance community research at https://github.com/deep-diver/TAROT.", "github_manual": "", "github_regex": "https://github.com/deep-diver/TAROT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.15547", "title": "jina-embeddings-v5-text: Task-Targeted Embedding Distillation", "authors": [ "Mohammad Kalim Akram", "Saba Sturua", "Nastia Havriushenko", "Quentin Herreros", "Michael G\u00fcnther", "Maximilian Werk", "Han Xiao" ], "github": "", "project_page": "", "abstract": "Text embedding models are widely used for semantic similarity tasks, including information retrieval, clustering, and classification. General-purpose models are typically trained with single- or multi-stage processes using contrastive loss functions. We introduce a novel training regimen that combines model distillation techniques with task-specific contrastive loss to produce compact, high-performance embedding models. Our findings suggest that this approach is more effective for training small models than purely contrastive or distillation-based training paradigms alone. Benchmark scores for the resulting models, jina-embeddings-v5-text-small and jina-embeddings-v5-text-nano, exceed or match the state-of-the-art for models of similar size. jina-embeddings-v5-text models additionally support long texts (up to 32k tokens) in many languages, and generate embeddings that remain robust under truncation and binary quantization. Model weights are publicly available, hopefully inspiring further advances in embedding model development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.14364", "title": "A Trajectory-Based Safety Audit of Clawdbot (OpenClaw)", "authors": [ "Tianyu Chen", "Dongrui Liu", "Xia Hu", "Jingyi Yu", "Wenjie Wang" ], "github": "https://github.com/tychenn/clawdbot", "project_page": "", "abstract": "Clawdbot is a self-hosted, tool-using personal AI agent with a broad action space spanning local execution and web-mediated workflows, which raises heightened safety and security concerns under ambiguity and adversarial steering. We present a trajectory-centric evaluation of Clawdbot across six risk dimensions. Our test suite samples and lightly adapts scenarios from prior agent-safety benchmarks (including ATBench and LPS-Bench) and supplements them with hand-designed cases tailored to Clawdbot's tool surface. We log complete interaction trajectories (messages, actions, tool-call arguments/outputs) and assess safety using both an automated trajectory judge (AgentDoG-Qwen3-4B) and human review. Across 34 canonical cases, we find a non-uniform safety profile: performance is generally consistent on reliability-focused tasks, while most failures arise under underspecified intent, open-ended goals, or benign-seeming jailbreak prompts, where minor misinterpretations can escalate into higher-impact tool actions. We supplemented the overall results with representative case studies and summarized the commonalities of these cases, analyzing the security vulnerabilities and typical failure modes that Clawdbot is prone to trigger in practice.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/tychenn/clawdbot", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.12235", "title": "Detecting Overflow in Compressed Token Representations for Retrieval-Augmented Generation", "authors": [ "Julia Belikova", "Danila Rozhevskii", "Dennis Svirin", "Konstantin Polev", "Alexander Panchenko" ], "github": "https://github.com/s-nlp/overflow-detection", "project_page": "", "abstract": "Efficient long-context processing remains a crucial challenge for contemporary large language models (LLMs), especially in resource-constrained environments. Soft compression architectures promise to extend effective context length by replacing long token sequences with smaller sets of learned compressed tokens. Yet, the limits of compressibility -- and when compression begins to erase task-relevant content -- remain underexplored. In this paper, we define token overflow as a regime in which compressed representations no longer contain sufficient information to answer a given query, and propose a methodology to characterize and detect it. In the xRAG soft-compression setting, we find that query-agnostic saturation statistics reliably separate compressed from uncompressed token representations, providing a practical tool for identifying compressed tokens but showing limited overflow detection capability. Lightweight probing classifiers over both query and context xRAG representations detect overflow with 0.72 AUC-ROC on average on HotpotQA, SQuADv2, and TriviaQA datasets, demonstrating that incorporating query information improves detection performance. These results advance from query-independent diagnostics to query-aware detectors, enabling low-cost pre-LLM gating to mitigate compression-induced errors.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/s-nlp/overflow-detection", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.13964", "title": "HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam", "authors": [ "Weiqi Zhai", "Zhihai Wang", "Jinghang Wang", "Boyu Yang", "Xiaogang Li", "Xiang Xu", "Bohan Wang", "Peng Wang", "Xingzhe Wu", "Anfeng Li", "Qiyuan Feng", "Yuhao Zhou", "Shoulin Han", "Wenjie Luo", "Yiyuan Li", "Yaxuan Wang", "Ruixian Luo", "Guojie Lin", "Peiyao Xiao", "Chengliang Xu", "Ben Wang", "Zeyu Wang", "Zichao Chen", "Jianan Ye", "Yijie Hu", "Jialong Chen", "Zongwen Shen", "Yuliang Xu", "An Yang", "Bowen Yu", "Dayiheng Liu", "Junyang Lin", "Hu Wei", "Que Shen", "Bing Zhao" ], "github": "https://github.com/SKYLENAGE-AI/HLE-Verified", "project_page": "", "abstract": "Humanity's Last Exam (HLE) has become a widely used benchmark for evaluating frontier large language models on challenging, multi-domain questions. However, community-led analyses have raised concerns that HLE contains a non-trivial number of noisy items, which can bias evaluation results and distort cross-model comparisons. To address this challenge, we introduce HLE-Verified, a verified and revised version of HLE with a transparent verification protocol and fine-grained error taxonomy. Our construction follows a two-stage validation-and-repair workflow resulting in a certified benchmark. In Stage I, each item undergoes binary validation of the problem and final answer through domain-expert review and model-based cross-checks, yielding 641 verified items. In Stage II, flawed but fixable items are revised under strict constraints preserving the original evaluation intent, through dual independent expert repairs, model-assisted auditing, and final adjudication, resulting in 1,170 revised-and-certified items. The remaining 689 items are released as a documented uncertain set with explicit uncertainty sources and expertise tags for future refinement. We evaluate seven state-of-the-art language models on HLE and HLE-Verified, observing an average absolute accuracy gain of 7--10 percentage points on HLE-Verified. The improvement is particularly pronounced on items where the original problem statement and/or reference answer is erroneous, with gains of 30--40 percentage points. Our analyses further reveal a strong association between model confidence and the presence of errors in the problem statement or reference answer, supporting the effectiveness of our revisions. Overall, HLE-Verified improves HLE-style evaluations by reducing annotation noise and enabling more faithful measurement of model capabilities. Data is available at: https://github.com/SKYLENAGE-AI/HLE-Verified", "github_manual": "", "github_regex": "https://github.com/SKYLENAGE-AI/HLE-Verified", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.15156", "title": "Panini: Continual Learning in Token Space via Structured Memory", "authors": [ "Shreyas Rajesh", "Pavan Holur", "Mehmet Yigit Turali", "Chenda Duan", "Vwani Roychowdhury" ], "github": "https://github.com/roychowdhuryresearch/gsw-memory", "project_page": "", "abstract": "Language models are increasingly used to reason over content they were not trained on, such as new documents, evolving knowledge, and user-specific data. A common approach is retrieval-augmented generation (RAG), which stores verbatim documents externally (as chunks) and retrieves only a relevant subset at inference time for an LLM to reason over. However, this results in inefficient usage of test-time compute (LLM repeatedly reasons over the same documents); moreover, chunk retrieval can inject irrelevant context that increases unsupported generation. We propose a human-like non-parametric continual learning framework, where the base model remains fixed, and learning occurs by integrating each new experience into an external semantic memory state that accumulates and consolidates itself continually. We present Panini, which realizes this by representing documents as Generative Semantic Workspaces (GSW) -- an entity- and event-aware network of question-answer (QA) pairs, sufficient for an LLM to reconstruct the experienced situations and mine latent knowledge via reasoning-grounded inference chains on the network. Given a query, Panini only traverses the continually-updated GSW (not the verbatim documents or chunks), and retrieves the most likely inference chains. Across six QA benchmarks, Panini achieves the highest average performance, 5%-7% higher than other competitive baselines, while using 2-30x fewer answer-context tokens, supports fully open-source pipelines, and reduces unsupported answers on curated unanswerable queries. The results show that efficient and accurate structuring of experiences at write time -- as achieved by the GSW framework -- yields both efficiency and reliability gains at read time. Code is available at https://github.com/roychowdhuryresearch/gsw-memory.", "github_manual": "", "github_regex": "https://github.com/roychowdhuryresearch/gsw-memory", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.15382", "title": "The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems", "authors": [ "Xiaoze Liu", "Ruowang Zhang", "Weichen Yu", "Siheng Xiong", "Liu He", "Feijie Wu", "Hoin Jung", "Matt Fredrikson", "Xiaoqian Wang", "Jing Gao" ], "github": "https://github.com/xz-liu/heterogeneous-latent-mas", "project_page": "", "abstract": "Multi-Agent Systems (MAS) powered by Large Language Models have unlocked advanced collaborative reasoning, yet they remain shackled by the inefficiency of discrete text communication, which imposes significant runtime overhead and information quantization loss. While latent state transfer offers a high-bandwidth alternative, existing approaches either assume homogeneous sender-receiver architectures or rely on pair-specific learned translators, limiting scalability and modularity across diverse model families with disjoint manifolds. In this work, we propose the Vision Wormhole, a novel framework that repurposes the visual interface of Vision-Language Models (VLMs) to enable model-agnostic, text-free communication. By introducing a Universal Visual Codec, we map heterogeneous reasoning traces into a shared continuous latent space and inject them directly into the receiver's visual pathway, effectively treating the vision encoder as a universal port for inter-agent telepathy. Our framework adopts a hub-and-spoke topology to reduce pairwise alignment complexity from O(N^2) to O(N) and leverages a label-free, teacher-student distillation objective to align the high-speed visual channel with the robust reasoning patterns of the text pathway. Extensive experiments across heterogeneous model families (e.g., Qwen-VL, Gemma) demonstrate that the Vision Wormhole reduces end-to-end wall-clock time in controlled comparisons while maintaining reasoning fidelity comparable to standard text-based MAS. Code is available at https://github.com/xz-liu/heterogeneous-latent-mas", "github_manual": "", "github_regex": "https://github.com/xz-liu/heterogeneous-latent-mas", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-18", "arxiv_id": "2602.10210", "title": "How Much Reasoning Do Retrieval-Augmented Models Add beyond LLMs? A Benchmarking Framework for Multi-Hop Inference over Hybrid Knowledge", "authors": [ "Junhong Lin", "Bing Zhang", "Song Wang", "Ziyan Liu", "Dan Gutfreund", "Julian Shun", "Yada Zhu" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) continue to struggle with knowledge-intensive questions that require up-to-date information and multi-hop reasoning. Augmenting LLMs with hybrid external knowledge, such as unstructured text and structured knowledge graphs, offers a promising alternative to costly continual pretraining. As such, reliable evaluation of their retrieval and reasoning capabilities becomes critical. However, many existing benchmarks increasingly overlap with LLM pretraining data, which means answers or supporting knowledge may already be encoded in model parameters, making it difficult to distinguish genuine retrieval and reasoning from parametric recall. We introduce HybridRAG-Bench, a framework for constructing benchmarks to evaluate retrieval-intensive, multi-hop reasoning over hybrid knowledge. HybridRAG-Bench automatically couples unstructured text and structured knowledge graph representations derived from recent scientific literature on arXiv, and generates knowledge-intensive question-answer pairs grounded in explicit reasoning paths. The framework supports flexible domain and time-frame selection, enabling contamination-aware and customizable evaluation as models and knowledge evolve. Experiments across three domains (artificial intelligence, governance and policy, and bioinformatics) demonstrate that HybridRAG-Bench rewards genuine retrieval and reasoning rather than parametric recall, offering a principled testbed for evaluating hybrid knowledge-augmented reasoning systems. We release our code and data at github.com/junhongmit/HybridRAG-Bench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.15989", "title": "SAM 3D Body: Robust Full-Body Human Mesh Recovery", "authors": [ "Xitong Yang", "Devansh Kukreja", "Don Pinkus", "Anushka Sagar", "Taosha Fan", "Jinhyung Park", "Soyong Shin", "Jinkun Cao", "Jiawei Liu", "Nicolas Ugrinovic", "Matt Feiszli", "Jitendra Malik", "Piotr Dollar", "Kris Kitani" ], "github": "https://github.com/facebookresearch/sam-3d-body", "project_page": "", "abstract": "We introduce SAM 3D Body (3DB), a promptable model for single-image full-body 3D human mesh recovery (HMR) that demonstrates state-of-the-art performance, with strong generalization and consistent accuracy in diverse in-the-wild conditions. 3DB estimates the human pose of the body, feet, and hands. It is the first model to use a new parametric mesh representation, Momentum Human Rig (MHR), which decouples skeletal structure and surface shape. 3DB employs an encoder-decoder architecture and supports auxiliary prompts, including 2D keypoints and masks, enabling user-guided inference similar to the SAM family of models. We derive high-quality annotations from a multi-stage annotation pipeline that uses various combinations of manual keypoint annotation, differentiable optimization, multi-view geometry, and dense keypoint detection. Our data engine efficiently selects and processes data to ensure data diversity, collecting unusual poses and rare imaging conditions. We present a new evaluation dataset organized by pose and appearance categories, enabling nuanced analysis of model behavior. Our experiments demonstrate superior generalization and substantial improvements over prior methods in both qualitative user preference studies and traditional quantitative analysis. Both 3DB and MHR are open-source.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/facebookresearch/sam-3d-body", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.15922", "title": "World Action Models are Zero-shot Policies", "authors": [ "Seonghyeon Ye", "Yunhao Ge", "Kaiyuan Zheng", "Shenyuan Gao", "Sihyun Yu", "George Kurian", "Suneel Indupuru", "You Liang Tan", "Chuning Zhu", "Jiannan Xiang", "Ayaan Malik", "Kyungmin Lee", "William Liang", "Nadun Ranawaka", "Jiasheng Gu", "Yinzhen Xu", "Guanzhi Wang", "Fengyuan Hu", "Avnish Narayan", "Johan Bjorck", "Jing Wang", "Gwanghyun Kim", "Dantong Niu", "Ruijie Zheng", "Yuqi Xie", "Jimmy Wu", "Qi Wang", "Ryan Julian", "Danfei Xu", "Yilun Du", "Yevgen Chebotar", "Scott Reed", "Jan Kautz", "Yuke Zhu", "Linxi \"Jim\" Fan", "Joel Jang" ], "github": "https://github.com/dreamzero0/dreamzero", "project_page": "", "abstract": "State-of-the-art Vision-Language-Action (VLA) models excel at semantic generalization but struggle to generalize to unseen physical motions in novel environments. We introduce DreamZero, a World Action Model (WAM) built upon a pretrained video diffusion backbone. Unlike VLAs, WAMs learn physical dynamics by predicting future world states and actions, using video as a dense representation of how the world evolves. By jointly modeling video and action, DreamZero learns diverse skills effectively from heterogeneous robot data without relying on repetitive demonstrations. This results in over 2x improvement in generalization to new tasks and environments compared to state-of-the-art VLAs in real robot experiments. Crucially, through model and system optimizations, we enable a 14B autoregressive video diffusion model to perform real-time closed-loop control at 7Hz. Finally, we demonstrate two forms of cross-embodiment transfer: video-only demonstrations from other robots or humans yield a relative improvement of over 42% on unseen task performance with just 10-20 minutes of data. More surprisingly, DreamZero enables few-shot embodiment adaptation, transferring to a new embodiment with only 30 minutes of play data while retaining zero-shot generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dreamzero0/dreamzero", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.16666", "title": "Towards a Science of AI Agent Reliability", "authors": [ "Stephan Rabanser", "Sayash Kapoor", "Peter Kirgis", "Kangheng Liu", "Saiteja Utpala", "Arvind Narayanan" ], "github": "https://github.com/princeton-pli/hal-harness", "project_page": "", "abstract": "AI agents are increasingly deployed to execute important tasks. While rising accuracy scores on standard benchmarks suggest rapid progress, many agents still continue to fail in practice. This discrepancy highlights a fundamental limitation of current evaluations: compressing agent behavior into a single success metric obscures critical operational flaws. Notably, it ignores whether agents behave consistently across runs, withstand perturbations, fail predictably, or have bounded error severity. Grounded in safety-critical engineering, we provide a holistic performance profile by proposing twelve concrete metrics that decompose agent reliability along four key dimensions: consistency, robustness, predictability, and safety. Evaluating 14 agentic models across two complementary benchmarks, we find that recent capability gains have only yielded small improvements in reliability. By exposing these persistent limitations, our metrics complement traditional evaluations while offering tools for reasoning about how agents perform, degrade, and fail.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/princeton-pli/hal-harness", "github_api_added_by": "auto" }, { "date": "2026-02-19", "arxiv_id": "2602.12675", "title": "SLA2: Sparse-Linear Attention with Learnable Routing and QAT", "authors": [ "Jintao Zhang", "Haoxu Wang", "Kai Jiang", "Kaiwen Zheng", "Youhe Jiang", "Ion Stoica", "Jianfei Chen", "Jun Zhu", "Joseph E. Gonzalez" ], "github": "", "project_page": "", "abstract": "Sparse-Linear Attention (SLA) combines sparse and linear attention to accelerate diffusion models and has shown strong performance in video generation. However, (i) SLA relies on a heuristic split that assigns computations to the sparse or linear branch based on attention-weight magnitude, which can be suboptimal. Additionally, (ii) after formally analyzing the attention error in SLA, we identify a mismatch between SLA and a direct decomposition into sparse and linear attention. We propose SLA2, which introduces (I) a learnable router that dynamically selects whether each attention computation should use sparse or linear attention, (II) a more faithful and direct sparse-linear attention formulation that uses a learnable ratio to combine the sparse and linear attention branches, and (III) a sparse + low-bit attention design, where low-bit attention is introduced via quantization-aware fine-tuning to reduce quantization error. Experiments show that on video diffusion models, SLA2 can achieve 97% attention sparsity and deliver an 18.6x attention speedup while preserving generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.16705", "title": "Learning Humanoid End-Effector Control for Open-Vocabulary Visual Loco-Manipulation", "authors": [ "Runpei Dong", "Ziyan Li", "Xialin He", "Saurabh Gupta" ], "github": "", "project_page": "", "abstract": "Visual loco-manipulation of arbitrary objects in the wild with humanoid robots requires accurate end-effector (EE) control and a generalizable understanding of the scene via visual inputs (e.g., RGB-D images). Existing approaches are based on real-world imitation learning and exhibit limited generalization due to the difficulty in collecting large-scale training datasets. This paper presents a new paradigm, HERO, for object loco-manipulation with humanoid robots that combines the strong generalization and open-vocabulary understanding of large vision models with strong control performance from simulated training. We achieve this by designing an accurate residual-aware EE tracking policy. This EE tracking policy combines classical robotics with machine learning. It uses a) inverse kinematics to convert residual end-effector targets into reference trajectories, b) a learned neural forward model for accurate forward kinematics, c) goal adjustment, and d) replanning. Together, these innovations help us cut down the end-effector tracking error by 3.2x. We use this accurate end-effector tracker to build a modular system for loco-manipulation, where we use open-vocabulary large vision models for strong visual generalization. Our system is able to operate in diverse real-world environments, from offices to coffee shops, where the robot is able to reliably manipulate various everyday objects (e.g., mugs, apples, toys) on surfaces ranging from 43cm to 92cm in height. Systematic modular and end-to-end tests in simulation and the real world demonstrate the effectiveness of our proposed design. We believe the advances in this paper can open up new ways of training humanoid robots to interact with daily objects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.16682", "title": "Learning Situated Awareness in the Real World", "authors": [ "Chuhan Li", "Ruilin Han", "Joy Hsu", "Yongyuan Liang", "Rajiv Dhawan", "Jiajun Wu", "Ming-Hsuan Yang", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "A core aspect of human perception is situated awareness, the ability to relate ourselves to the surrounding physical environment and reason over possible actions in context. However, most existing benchmarks for multimodal foundation models (MFMs) emphasize environment-centric spatial relations (relations among objects in a scene), while largely overlooking observer-centric relationships that require reasoning relative to agent's viewpoint, pose, and motion. To bridge this gap, we introduce SAW-Bench (Situated Awareness in the Real World), a novel benchmark for evaluating egocentric situated awareness using real-world videos. SAW-Bench comprises 786 self-recorded videos captured with Ray-Ban Meta (Gen 2) smart glasses spanning diverse indoor and outdoor environments, and over 2,071 human-annotated question-answer pairs. It probes a model's observer-centric understanding with six different awareness tasks. Our comprehensive evaluation reveals a human-model performance gap of 37.66%, even with the best-performing MFM, Gemini 3 Flash. Beyond this gap, our in-depth analysis uncovers several notable findings; for example, while models can exploit partial geometric cues in egocentric videos, they often fail to infer a coherent camera geometry, leading to systematic spatial reasoning errors. We position SAW-Bench as a benchmark for situated spatial intelligence, moving beyond passive observation to understanding physically grounded, observer-centric dynamics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.16301", "title": "Multi-agent cooperation through in-context co-player inference", "authors": [ "Marissa A. Weis", "Maciej Wo\u0142czyk", "Rajai Nasser", "Rif A. Saurous", "Blaise Ag\u00fcera y Arcas", "Jo\u00e3o Sacramento", "Alexander Meulemans" ], "github": "", "project_page": "", "abstract": "Achieving cooperation among self-interested agents remains a fundamental challenge in multi-agent reinforcement learning. Recent work showed that mutual cooperation can be induced between \"learning-aware\" agents that account for and shape the learning dynamics of their co-players. However, existing approaches typically rely on hardcoded, often inconsistent, assumptions about co-player learning rules or enforce a strict separation between \"naive learners\" updating on fast timescales and \"meta-learners\" observing these updates. Here, we demonstrate that the in-context learning capabilities of sequence models allow for co-player learning awareness without requiring hardcoded assumptions or explicit timescale separation. We show that training sequence model agents against a diverse distribution of co-players naturally induces in-context best-response strategies, effectively functioning as learning algorithms on the fast intra-episode timescale. We find that the cooperative mechanism identified in prior work-where vulnerability to extortion drives mutual shaping-emerges naturally in this setting: in-context adaptation renders agents vulnerable to extortion, and the resulting mutual pressure to shape the opponent's in-context learning dynamics resolves into the learning of cooperative behavior. Our results suggest that standard decentralized reinforcement learning on sequence models combined with co-player diversity provides a scalable path to learning cooperative behaviors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.16493", "title": "MMA: Multimodal Memory Agent", "authors": [ "Yihao Lu", "Wanru Cheng", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/MMA", "project_page": "", "abstract": "Long-horizon multimodal agents depend on external memory; however, similarity-based retrieval often surfaces stale, low-credibility, or conflicting items, which can trigger overconfident errors. We propose Multimodal Memory Agent (MMA), which assigns each retrieved memory item a dynamic reliability score by combining source credibility, temporal decay, and conflict-aware network consensus, and uses this signal to reweight evidence and abstain when support is insufficient. We also introduce MMA-Bench, a programmatically generated benchmark for belief dynamics with controlled speaker reliability and structured text-vision contradictions. Using this framework, we uncover the \"Visual Placebo Effect\", revealing how RAG-based agents inherit latent visual biases from foundation models. On FEVER, MMA matches baseline accuracy while reducing variance by 35.2% and improving selective utility; on LoCoMo, a safety-oriented configuration improves actionable accuracy and reduces wrong answers; on MMA-Bench, MMA reaches 41.18% Type-B accuracy in Vision mode, while the baseline collapses to 0.0% under the same protocol. Code: https://github.com/AIGeeksGroup/MMA.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/MMA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.14979", "title": "RynnBrain: Open Embodied Foundation Models", "authors": [ "Ronghao Dang", "Jiayan Guo", "Bohan Hou", "Sicong Leng", "Kehan Li", "Xin Li", "Jiangpin Liu", "Yunxuan Mao", "Zhikai Wang", "Yuqian Yuan", "Minghao Zhu", "Xiao Lin", "Yang Bai", "Qian Jiang", "Yaxi Zhao", "Minghua Zeng", "Junlong Gao", "Yuming Jiang", "Jun Cen", "Siteng Huang", "Liuyi Wang", "Wenqiao Zhang", "Chengju Liu", "Jianfei Yang", "Shijian Lu", "Deli Zhao" ], "github": "", "project_page": "", "abstract": "Despite rapid progress in multimodal foundation models, embodied intelligence community still lacks a unified, physically grounded foundation model that integrates perception, reasoning, and planning within real-world spatial-temporal dynamics. We introduce RynnBrain, an open-source spatiotemporal foundation model for embodied intelligence. RynnBrain strengthens four core capabilities in a unified framework: comprehensive egocentric understanding, diverse spatiotemporal localization, physically grounded reasoning, and physics-aware planning. The RynnBrain family comprises three foundation model scales (2B, 8B, and 30B-A3B MoE) and four post-trained variants tailored for downstream embodied tasks (i.e., RynnBrain-Nav, RynnBrain-Plan, and RynnBrain-VLA) or complex spatial reasoning tasks (i.e., RynnBrain-CoP). In terms of extensive evaluations on 20 embodied benchmarks and 8 general vision understanding benchmarks, our RynnBrain foundation models largely outperform existing embodied foundation models by a significant margin. The post-trained model suite further substantiates two key potentials of the RynnBrain foundation model: (i) enabling physically grounded reasoning and planning, and (ii) serving as a strong pretrained backbone that can be efficiently adapted to diverse embodied tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.14080", "title": "Empty Shelves or Lost Keys? Recall Is the Bottleneck for Parametric Factuality", "authors": [ "Nitay Calderon", "Eyal Ben-David", "Zorik Gekhman", "Eran Ofek", "Gal Yona" ], "github": "", "project_page": "", "abstract": "Standard factuality evaluations of LLMs treat all errors alike, obscuring whether failures arise from missing knowledge (empty shelves) or from limited access to encoded facts (lost keys). We propose a behavioral framework that profiles factual knowledge at the level of facts rather than questions, characterizing each fact by whether it is encoded, and then by how accessible it is: cannot be recalled, can be directly recalled, or can only be recalled with inference-time computation (thinking). To support such profiling, we introduce WikiProfile, a new benchmark constructed via an automated pipeline with a prompted LLM grounded in web search. Across 4 million responses from 13 LLMs, we find that encoding is nearly saturated in frontier models on our benchmark, with GPT-5 and Gemini-3 encoding 95--98% of facts. However, recall remains a major bottleneck: many errors previously attributed to missing knowledge instead stem from failures to access it. These failures are systematic and disproportionately affect long-tail facts and reverse questions. Finally, we show that thinking improves recall and can recover a substantial fraction of failures, indicating that future gains may rely less on scaling and more on methods that improve how models utilize what they already encode.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.07345", "title": "Optimizing Few-Step Generation with Adaptive Matching Distillation", "authors": [ "Lichen Bai", "Zikai Zhou", "Shitong Shao", "Wenliang Zhong", "Shuo Yang", "Shuo Chen", "Bojun Chen", "Zeke Xie" ], "github": "", "project_page": "", "abstract": "Distribution Matching Distillation (DMD) is a powerful acceleration paradigm, yet its stability is often compromised in Forbidden Zone, regions where the real teacher provides unreliable guidance while the fake teacher exerts insufficient repulsive force. In this work, we propose a unified optimization framework that reinterprets prior art as implicit strategies to avoid these corrupted regions. Based on this insight, we introduce Adaptive Matching Distillation (AMD), a self-correcting mechanism that utilizes reward proxies to explicitly detect and escape Forbidden Zones. AMD dynamically prioritizes corrective gradients via structural signal decomposition and introduces Repulsive Landscape Sharpening to enforce steep energy barriers against failure mode collapse. Extensive experiments across image and video generation tasks (e.g., SDXL, Wan2.1) and rigorous benchmarks (e.g., VBench, GenEval) demonstrate that AMD significantly enhances sample fidelity and training robustness. For instance, AMD improves the HPSv2 score on SDXL from 30.64 to 31.25, outperforming state-of-the-art baselines. These findings validate that explicitly rectifying optimization trajectories within Forbidden Zones is essential for pushing the performance ceiling of few-step generative models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.08392", "title": "BiManiBench: A Hierarchical Benchmark for Evaluating Bimanual Coordination of Multimodal Large Language Models", "authors": [ "Xin Wu", "Zhixuan Liang", "Yue Ma", "Mengkang Hu", "Zhiyuan Qin", "Xiu Li" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have significantly advanced embodied AI, and using them to benchmark robotic intelligence has become a pivotal trend. However, existing frameworks remain predominantly confined to single-arm manipulation, failing to capture the spatio-temporal coordination required for bimanual tasks like lifting a heavy pot. To address this, we introduce BiManiBench, a hierarchical benchmark evaluating MLLMs across three tiers: fundamental spatial reasoning, high-level action planning, and low-level end-effector control. Our framework isolates unique bimanual challenges, such as arm reachability and kinematic constraints, thereby distinguishing perceptual hallucinations from planning failures. Analysis of over 30 state-of-the-art models reveals that despite high-level reasoning proficiency, MLLMs struggle with dual-arm spatial grounding and control, frequently resulting in mutual interference and sequencing errors. These findings suggest the current paradigm lacks a deep understanding of mutual kinematic constraints, highlighting the need for future research to focus on inter-arm collision-avoidance and fine-grained temporal sequencing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.15927", "title": "Visual Memory Injection Attacks for Multi-Turn Conversations", "authors": [ "Christian Schlarmann", "Matthias Hein" ], "github": "https://github.com/chs20/visual-memory-injection", "project_page": "", "abstract": "Generative large vision-language models (LVLMs) have recently achieved impressive performance gains, and their user base is growing rapidly. However, the security of LVLMs, in particular in a long-context multi-turn setting, is largely underexplored. In this paper, we consider the realistic scenario in which an attacker uploads a manipulated image to the web/social media. A benign user downloads this image and uses it as input to the LVLM. Our novel stealthy Visual Memory Injection (VMI) attack is designed such that on normal prompts the LVLM exhibits nominal behavior, but once the user gives a triggering prompt, the LVLM outputs a specific prescribed target message to manipulate the user, e.g. for adversarial marketing or political persuasion. Compared to previous work that focused on single-turn attacks, VMI is effective even after a long multi-turn conversation with the user. We demonstrate our attack on several recent open-weight LVLMs. This article thereby shows that large-scale manipulation of users is feasible with perturbed images in multi-turn conversation settings, calling for better robustness of LVLMs against these attacks. We release the source code at https://github.com/chs20/visual-memory-injection", "github_manual": "", "github_regex": "https://github.com/chs20/visual-memory-injection", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.16008", "title": "MAEB: Massive Audio Embedding Benchmark", "authors": [ "Adnan El Assadi", "Isaac Chung", "Chenghao Xiao", "Roman Solomatin", "Animesh Jha", "Rahul Chand", "Silky Singh", "Kaitlyn Wang", "Ali Sartaz Khan", "Marc Moussa Nasser", "Sufen Fong", "Pengfei He", "Alan Xiao", "Ayush Sunil Munot", "Aditya Shrivastava", "Artem Gazizov", "Niklas Muennighoff", "Kenneth Enevoldsen" ], "github": "https://github.com/embeddings-benchmark/mteb", "project_page": "", "abstract": "We introduce the Massive Audio Embedding Benchmark (MAEB), a large-scale benchmark covering 30 tasks across speech, music, environmental sounds, and cross-modal audio-text reasoning in 100+ languages. We evaluate 50+ models and find that no single model dominates across all tasks: contrastive audio-text models excel at environmental sound classification (e.g., ESC50) but score near random on multilingual speech tasks (e.g., SIB-FLEURS), while speech-pretrained models show the opposite pattern. Clustering remains challenging for all models, with even the best-performing model achieving only modest results. We observe that models excelling on acoustic understanding often perform poorly on linguistic tasks, and vice versa. We also show that the performance of audio encoders on MAEB correlates highly with their performance when used in audio large language models. MAEB is derived from MAEB+, a collection of 98 tasks. MAEB is designed to maintain task diversity while reducing evaluation cost, and it integrates into the MTEB ecosystem for unified evaluation across text, image, and audio modalities. We release MAEB and all 98 tasks along with code and a leaderboard at https://github.com/embeddings-benchmark/mteb.", "github_manual": "", "github_regex": "https://github.com/embeddings-benchmark/mteb", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.16317", "title": "CADEvolve: Creating Realistic CAD via Program Evolution", "authors": [ "Maksim Elistratov", "Marina Barannikov", "Gregory Ivanov", "Valentin Khrulkov", "Anton Konushin", "Andrey Kuznetsov", "Dmitrii Zhemchuzhnikov" ], "github": "https://github.com/zhemdi/CADEvolve", "project_page": "", "abstract": "Computer-Aided Design (CAD) delivers rapid, editable modeling for engineering and manufacturing. Recent AI progress now makes full automation feasible for various CAD tasks. However, progress is bottlenecked by data: public corpora mostly contain sketch-extrude sequences, lack complex operations, multi-operation composition and design intent, and thus hinder effective fine-tuning. Attempts to bypass this with frozen VLMs often yield simple or invalid programs due to limited 3D grounding in current foundation models. We present CADEvolve, an evolution-based pipeline and dataset that starts from simple primitives and, via VLM-guided edits and validations, incrementally grows CAD programs toward industrial-grade complexity. The result is 8k complex parts expressed as executable CadQuery parametric generators. After multi-stage post-processing and augmentation, we obtain a unified dataset of 1.3m scripts paired with rendered geometry and exercising the full CadQuery operation set. A VLM fine-tuned on CADEvolve achieves state-of-the-art results on the Image2CAD task across the DeepCAD, Fusion 360, and MCB benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhemdi/CADEvolve", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.16704", "title": "Reinforced Fast Weights with Next-Sequence Prediction", "authors": [ "Hee Seung Hwang", "Xindi Wu", "Sanghyuk Chun", "Olga Russakovsky" ], "github": "", "project_page": "", "abstract": "Fast weight architectures offer a promising alternative to attention-based transformers for long-context modeling by maintaining constant memory overhead regardless of context length. However, their potential is limited by the next-token prediction (NTP) training paradigm. NTP optimizes single-token predictions and ignores semantic coherence across multiple tokens following a prefix. Consequently, fast weight models, which dynamically update their parameters to store contextual information, learn suboptimal representations that fail to capture long-range dependencies. We introduce REFINE (Reinforced Fast weIghts with Next sEquence prediction), a reinforcement learning framework that trains fast weight models under the next-sequence prediction (NSP) objective. REFINE selects informative token positions based on prediction entropy, generates multi-token rollouts, assigns self-supervised sequence-level rewards, and optimizes the model with group relative policy optimization (GRPO). REFINE is applicable throughout the training lifecycle of pre-trained language models: mid-training, post-training, and test-time training. Our experiments on LaCT-760M and DeltaNet-1.3B demonstrate that REFINE consistently outperforms supervised fine-tuning with NTP across needle-in-a-haystack retrieval, long-context question answering, and diverse tasks in LongBench. REFINE provides an effective and versatile framework for improving long-context modeling in fast weight architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.16173", "title": "Learning Personalized Agents from Human Feedback", "authors": [ "Kaiqu Liang", "Julia Kruk", "Shengyi Qian", "Xianjun Yang", "Shengjie Bi", "Yuanshun Yao", "Shaoliang Nie", "Mingyang Zhang", "Lijuan Liu", "Jaime Fern\u00e1ndez Fisac", "Shuyan Zhou", "Saghar Hosseini" ], "github": "", "project_page": "", "abstract": "Modern AI agents are powerful but often fail to align with the idiosyncratic, evolving preferences of individual users. Prior approaches typically rely on static datasets, either training implicit preference models on interaction history or encoding user profiles in external memory. However, these approaches struggle with new users and with preferences that change over time. We introduce Personalized Agents from Human Feedback (PAHF), a framework for continual personalization in which agents learn online from live interaction using explicit per-user memory. PAHF operationalizes a three-step loop: (1) seeking pre-action clarification to resolve ambiguity, (2) grounding actions in preferences retrieved from memory, and (3) integrating post-action feedback to update memory when preferences drift. To evaluate this capability, we develop a four-phase protocol and two benchmarks in embodied manipulation and online shopping. These benchmarks quantify an agent's ability to learn initial preferences from scratch and subsequently adapt to persona shifts. Our theoretical analysis and empirical results show that integrating explicit memory with dual feedback channels is critical: PAHF learns substantially faster and consistently outperforms both no-memory and single-channel baselines, reducing initial personalization error and enabling rapid adaptation to preference shifts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.14296", "title": "AutoWebWorld: Synthesizing Infinite Verifiable Web Environments via Finite State Machines", "authors": [ "Yifan Wu", "Yiran Peng", "Yiyu Chen", "Jianhao Ruan", "Zijie Zhuang", "Cheng Yang", "Jiayi Zhang", "Man Chen", "Yenchi Tseng", "Zhaoyang Yu", "Liang Chen", "Yuyao Zhai", "Bang Liu", "Chenglin Wu", "Yuyu Luo" ], "github": "", "project_page": "", "abstract": "The performance of autonomous Web GUI agents heavily relies on the quality and quantity of their training data. However, a fundamental bottleneck persists: collecting interaction trajectories from real-world websites is expensive and difficult to verify. The underlying state transitions are hidden, leading to reliance on inconsistent and costly external verifiers to evaluate step-level correctness. To address this, we propose AutoWebWorld, a novel framework for synthesizing controllable and verifiable web environments by modeling them as Finite State Machines (FSMs) and use coding agents to translate FSMs into interactive websites. Unlike real websites, where state transitions are implicit, AutoWebWorld explicitly defines all states, actions, and transition rules. This enables programmatic verification: action correctness is checked against predefined rules, and task success is confirmed by reaching a goal state in the FSM graph. AutoWebWorld enables a fully automated search-and-verify pipeline, generating over 11,663 verified trajectories from 29 diverse web environments at only $0.04 per trajectory. Training on this synthetic data significantly boosts real-world performance. Our 7B Web GUI agent outperforms all baselines within 15 steps on WebVoyager. Furthermore, we observe a clear scaling law: as the synthetic data volume increases, performance on WebVoyager and Online-Mind2Web consistently improves.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.14514", "title": "Efficient Text-Guided Convolutional Adapter for the Diffusion Model", "authors": [ "Aryan Das", "Koushik Biswas", "Swalpa Kumar Roy", "Badri Narayana Patro", "Vinay Kumar Verma" ], "github": "https://github.com/arya-domain/Nexus-Adapters", "project_page": "", "abstract": "We introduce the Nexus Adapters, novel text-guided efficient adapters to the diffusion-based framework for the Structure Preserving Conditional Generation (SPCG). Recently, structure-preserving methods have achieved promising results in conditional image generation by using a base model for prompt conditioning and an adapter for structure input, such as sketches or depth maps. These approaches are highly inefficient and sometimes require equal parameters in the adapter compared to the base architecture. It is not always possible to train the model since the diffusion model is itself costly, and doubling the parameter is highly inefficient. In these approaches, the adapter is not aware of the input prompt; therefore, it is optimal only for the structural input but not for the input prompt. To overcome the above challenges, we proposed two efficient adapters, Nexus Prime and Slim, which are guided by prompts and structural inputs. Each Nexus Block incorporates cross-attention mechanisms to enable rich multimodal conditioning. Therefore, the proposed adapter has a better understanding of the input prompt while preserving the structure. We conducted extensive experiments on the proposed models and demonstrated that the Nexus Prime adapter significantly enhances performance, requiring only 8M additional parameters compared to the baseline, T2I-Adapter. Furthermore, we also introduced a lightweight Nexus Slim adapter with 18M fewer parameters than the T2I-Adapter, which still achieved state-of-the-art results. Code: https://github.com/arya-domain/Nexus-Adapters", "github_manual": "", "github_regex": "https://github.com/arya-domain/Nexus-Adapters", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.14498", "title": "Uncertainty-Aware Vision-Language Segmentation for Medical Imaging", "authors": [ "Aryan Das", "Tanishq Rachamalla", "Koushik Biswas", "Swalpa Kumar Roy", "Vinay Kumar Verma" ], "github": "https://github.com/arya-domain/UA-VLS", "project_page": "", "abstract": "We introduce a novel uncertainty-aware multimodal segmentation framework that leverages both radiological images and associated clinical text for precise medical diagnosis. We propose a Modality Decoding Attention Block (MoDAB) with a lightweight State Space Mixer (SSMix) to enable efficient cross-modal fusion and long-range dependency modelling. To guide learning under ambiguity, we propose the Spectral-Entropic Uncertainty (SEU) Loss, which jointly captures spatial overlap, spectral consistency, and predictive uncertainty in a unified objective. In complex clinical circumstances with poor image quality, this formulation improves model reliability. Extensive experiments on various publicly available medical datasets, QATA-COVID19, MosMed++, and Kvasir-SEG, demonstrate that our method achieves superior segmentation performance while being significantly more computationally efficient than existing State-of-the-Art (SoTA) approaches. Our results highlight the importance of incorporating uncertainty modelling and structured modality alignment in vision-language medical segmentation tasks. Code: https://github.com/arya-domain/UA-VLS", "github_manual": "", "github_regex": "https://github.com/arya-domain/UA-VLS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-19", "arxiv_id": "2602.14602", "title": "OPBench: A Graph Benchmark to Combat the Opioid Crisis", "authors": [ "Tianyi Ma", "Yiyang Li", "Yiyue Qian", "Zheyuan Zhang", "Zehong Wang", "Chuxu Zhang", "Yanfang Ye" ], "github": "https://github.com/Tianyi-Billy-Ma/OPBench", "project_page": "", "abstract": "The opioid epidemic continues to ravage communities worldwide, straining healthcare systems, disrupting families, and demanding urgent computational solutions. To combat this lethal opioid crisis, graph learning methods have emerged as a promising paradigm for modeling complex drug-related phenomena. However, a significant gap remains: there is no comprehensive benchmark for systematically evaluating these methods across real-world opioid crisis scenarios. To bridge this gap, we introduce OPBench, the first comprehensive opioid benchmark comprising five datasets across three critical application domains: opioid overdose detection from healthcare claims, illicit drug trafficking detection from digital platforms, and drug misuse prediction from dietary patterns. Specifically, OPBench incorporates diverse graph structures, including heterogeneous graphs and hypergraphs, to preserve the rich and complex relational information among drug-related data. To address data scarcity, we collaborate with domain experts and authoritative institutions to curate and annotate datasets while adhering to privacy and ethical guidelines. Furthermore, we establish a unified evaluation framework with standardized protocols, predefined data splits, and reproducible baselines to facilitate fair and systematic comparison among graph learning methods. Through extensive experiments, we analyze the strengths and limitations of existing graph learning methods, thereby providing actionable insights for future research in combating the opioid crisis. Our source code and datasets are available at https://github.com/Tianyi-Billy-Ma/OPBench.", "github_manual": "", "github_regex": "https://github.com/Tianyi-Billy-Ma/OPBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.13579", "title": "TactAlign: Human-to-Robot Policy Transfer via Tactile Alignment", "authors": [ "Youngsun Wi", "Jessica Yin", "Elvis Xiang", "Akash Sharma", "Jitendra Malik", "Mustafa Mukadam", "Nima Fazeli", "Tess Hellebrekers" ], "github": "", "project_page": "", "abstract": "Human demonstrations collected by wearable devices (e.g., tactile gloves) provide fast and dexterous supervision for policy learning, and are guided by rich, natural tactile feedback. However, a key challenge is how to transfer human-collected tactile signals to robots despite the differences in sensing modalities and embodiment. Existing human-to-robot (H2R) approaches that incorporate touch often assume identical tactile sensors, require paired data, and involve little to no embodiment gap between human demonstrator and the robots, limiting scalability and generality. We propose TactAlign, a cross-embodiment tactile alignment method that transfers human-collected tactile signals to a robot with different embodiment. TactAlign transforms human and robot tactile observations into a shared latent representation using a rectified flow, without paired datasets, manual labels, or privileged information. Our method enables low-cost latent transport guided by hand-object interaction-derived pseudo-pairs. We demonstrate that TactAlign improves H2R policy transfer across multiple contact-rich tasks (pivoting, insertion, lid closing), generalizes to unseen objects and tasks with human data (less than 5 minutes), and enables zero-shot H2R transfer on a highly dexterous tasks (light bulb screwing).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.14457", "title": "Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5", "authors": [ "Dongrui Liu", "Yi Yu", "Jie Zhang", "Guanxu Chen", "Qihao Lin", "Hanxi Zhu", "Lige Huang", "Yijin Zhou", "Peng Wang", "Shuai Shao", "Boxuan Zhang", "Zicheng Liu", "Jingwei Sun", "Yu Li", "Yuejin Xie", "Jiaxuan Guo", "Jia Xu", "Chaochao Lu", "Bowen Zhou", "Xia Hu", "Jing Shao" ], "github": "", "project_page": "", "abstract": "To understand and identify the unprecedented risks posed by rapidly advancing artificial intelligence (AI) models, Frontier AI Risk Management Framework in Practice presents a comprehensive assessment of their frontier risks. As Large Language Models (LLMs) general capabilities rapidly evolve and the proliferation of agentic AI, this version of the risk analysis technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R\\&D, and self-replication. Specifically, we introduce more complex scenarios for cyber offense. For persuasion and manipulation, we evaluate the risk of LLM-to-LLM persuasion on newly released LLMs. For strategic deception and scheming, we add the new experiment with respect to emergent misalignment. For uncontrolled AI R\\&D, we focus on the ``mis-evolution'' of agents as they autonomously expand their memory substrates and toolsets. Besides, we also monitor and evaluate the safety performance of OpenClaw during the interaction on the Moltbook. For self-replication, we introduce a new resource-constrained scenario. More importantly, we propose and validate a series of robust mitigation strategies to address these emerging threats, providing a preliminary technical and actionable pathway for the secure deployment of frontier AI. This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.13515", "title": "SpargeAttention2: Trainable Sparse Attention via Hybrid Top-k+Top-p Masking and Distillation Fine-Tuning", "authors": [ "Jintao Zhang", "Kai Jiang", "Chendong Xiang", "Weiqi Feng", "Yuezhou Hu", "Haocheng Xi", "Jianfei Chen", "Jun Zhu" ], "github": "", "project_page": "", "abstract": "Many training-free sparse attention methods are effective for accelerating diffusion models. Recently, several works suggest that making sparse attention trainable can further increase sparsity while preserving generation quality. We study three key questions: (1) when do the two common masking rules, i.e., Top-k and Top-p, fail, and how can we avoid these failures? (2) why can trainable sparse attention reach higher sparsity than training-free methods? (3) what are the limitations of fine-tuning sparse attention using the diffusion loss, and how can we address them? Based on this analysis, we propose SpargeAttention2, a trainable sparse attention method that achieves high sparsity without degrading generation quality. SpargeAttention2 includes (i) a hybrid masking rule that combines Top-k and Top-p for more robust masking at high sparsity, (ii) an efficient trainable sparse attention implementation, and (iii) a distillation-inspired fine-tuning objective to better preserve generation quality during fine-tuning using sparse attention. Experiments on video diffusion models show that SpargeAttention2 reaches 95% attention sparsity and a 16.2x attention speedup while maintaining generation quality, consistently outperforming prior sparse attention methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.17270", "title": "Unified Latents (UL): How to train your latents", "authors": [ "Jonathan Heek", "Emiel Hoogeboom", "Thomas Mensink", "Tim Salimans" ], "github": "", "project_page": "", "abstract": "We present Unified Latents (UL), a framework for learning latent representations that are jointly regularized by a diffusion prior and decoded by a diffusion model. By linking the encoder's output noise to the prior's minimum noise level, we obtain a simple training objective that provides a tight upper bound on the latent bitrate. On ImageNet-512, our approach achieves competitive FID of 1.4, with high reconstruction quality (PSNR) while requiring fewer training FLOPs than models trained on Stable Diffusion latents. On Kinetics-600, we set a new state-of-the-art FVD of 1.3.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.16968", "title": "DDiT: Dynamic Patch Scheduling for Efficient Diffusion Transformers", "authors": [ "Dahye Kim", "Deepti Ghadiyaram", "Raghudeep Gadde" ], "github": "", "project_page": "", "abstract": "Diffusion Transformers (DiTs) have achieved state-of-the-art performance in image and video generation, but their success comes at the cost of heavy computation. This inefficiency is largely due to the fixed tokenization process, which uses constant-sized patches throughout the entire denoising phase, regardless of the content's complexity. We propose dynamic tokenization, an efficient test-time strategy that varies patch sizes based on content complexity and the denoising timestep. Our key insight is that early timesteps only require coarser patches to model global structure, while later iterations demand finer (smaller-sized) patches to refine local details. During inference, our method dynamically reallocates patch sizes across denoising steps for image and video generation and substantially reduces cost while preserving perceptual generation quality. Extensive experiments demonstrate the effectiveness of our approach: it achieves up to 3.52times and 3.2times speedup on FLUX-1.Dev and Wan 2.1, respectively, without compromising the generation quality and prompt adherence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.16855", "title": "Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents", "authors": [ "Haiyang Xu", "Xi Zhang", "Haowei Liu", "Junyang Wang", "Zhaozai Zhu", "Shengjie Zhou", "Xuhao Hu", "Feiyu Gao", "Junjie Cao", "Zihua Wang", "Zhiyuan Chen", "Jitong Liao", "Qi Zheng", "Jiahui Zeng", "Ze Xu", "Shuai Bai", "Junyang Lin", "Jingren Zhou", "Ming Yan" ], "github": "https://github.com/X-PLUG/MobileAgent", "project_page": "", "abstract": "The paper introduces GUI-Owl-1.5, the latest native GUI agent model that features instruct/thinking variants in multiple sizes (2B/4B/8B/32B/235B) and supports a range of platforms (desktop, mobile, browser, and more) to enable cloud-edge collaboration and real-time interaction. GUI-Owl-1.5 achieves state-of-the-art results on more than 20+ GUI benchmarks on open-source models: (1) on GUI automation tasks, it obtains 56.5 on OSWorld, 71.6 on AndroidWorld, and 48.4 on WebArena; (2) on grounding tasks, it obtains 80.3 on ScreenSpotPro; (3) on tool-calling tasks, it obtains 47.6 on OSWorld-MCP, and 46.8 on MobileWorld; (4) on memory and knowledge tasks, it obtains 75.5 on GUI-Knowledge Bench. GUI-Owl-1.5 incorporates several key innovations: (1) Hybird Data Flywheel: we construct the data pipeline for UI understanding and trajectory generation based on a combination of simulated environments and cloud-based sandbox environments, in order to improve the efficiency and quality of data collection. (2) Unified Enhancement of Agent Capabilities: we use a unified thought-synthesis pipeline to enhance the model's reasoning capabilities, while placing particular emphasis on improving key agent abilities, including Tool/MCP use, memory and multi-agent adaptation; (3) Multi-platform Environment RL Scaling: We propose a new environment RL algorithm, MRPO, to address the challenges of multi-platform conflicts and the low training efficiency of long-horizon tasks. The GUI-Owl-1.5 models are open-sourced, and an online cloud-sandbox demo is available at https://github.com/X-PLUG/MobileAgent.", "github_manual": "", "github_regex": "https://github.com/X-PLUG/MobileAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.17365", "title": "Computer-Using World Model", "authors": [ "Yiming Guan", "Rui Yu", "John Zhang", "Lu Wang", "Chaoyun Zhang", "Liqun Li", "Bo Qiao", "Si Qin", "He Huang", "Fangkai Yang", "Pu Zhao", "Lukas Wutschitz", "Samuel Kessler", "Huseyin A Inan", "Robert Sim", "Saravan Rajmohan", "Qingwei Lin", "Dongmei Zhang" ], "github": "", "project_page": "", "abstract": "Agents operating in complex software environments benefit from reasoning about the consequences of their actions, as even a single incorrect user interface (UI) operation can derail long, artifact-preserving workflows. This challenge is particularly acute for computer-using scenarios, where real execution does not support counterfactual exploration, making large-scale trial-and-error learning and planning impractical despite the environment being fully digital and deterministic. We introduce the Computer-Using World Model (CUWM), a world model for desktop software that predicts the next UI state given the current state and a candidate action. CUWM adopts a two-stage factorization of UI dynamics: it first predicts a textual description of agent-relevant state changes, and then realizes these changes visually to synthesize the next screenshot. CUWM is trained on offline UI transitions collected from agents interacting with real Microsoft Office applications, and further refined with a lightweight reinforcement learning stage that aligns textual transition predictions with the structural requirements of computer-using environments. We evaluate CUWM via test-time action search, where a frozen agent uses the world model to simulate and compare candidate actions before execution. Across a range of Office tasks, world-model-guided test-time scaling improves decision quality and execution robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.17363", "title": "2Mamba2Furious: Linear in Complexity, Competitive in Accuracy", "authors": [ "Gabriel Mongaras", "Eric C. Larson" ], "github": "", "project_page": "", "abstract": "Linear attention transformers have become a strong alternative to softmax attention due to their efficiency. However, linear attention tends to be less expressive and results in reduced accuracy compared to softmax attention. To bridge the accuracy gap between softmax attention and linear attention, we manipulate Mamba-2, a very strong linear attention variant. We first simplify Mamba-2 down to its most fundamental and important components, evaluating which specific choices make it most accurate. From this simplified Mamba variant (Mamba-2S), we improve the A-mask and increase the order of the hidden state, resulting in a method, which we call 2Mamba, that is nearly as accurate as softmax attention, yet much more memory efficient for long context lengths. We also investigate elements to Mamba-2 that help surpass softmax attention accuracy. Code is provided for all our experiments", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.17004", "title": "Arcee Trinity Large Technical Report", "authors": [ "Varun Singh", "Lucas Krauss", "Sami Jaghouar", "Matej Sirovatka", "Charles Goddard", "Fares Obied", "Jack Min Ong", "Jannik Straube", "Fern", "Aria Harley", "Conner Stewart", "Colin Kealty", "Maziyar Panahi", "Simon Kirsten", "Anushka Deshpande", "Anneketh Vij", "Arthur Bresnu", "Pranav Veldurthi", "Raghav Ravishankar", "Hardik Bishnoi", "DatologyAI Team", "Arcee AI Team", "Prime Intellect Team", "Mark McQuade", "Johannes Hagemann", "Lucas Atkins" ], "github": "https://github.com/KellerJordan/Muon", "project_page": "", "abstract": "We present the technical report for Arcee Trinity Large, a sparse Mixture-of-Experts model with 400B total parameters and 13B activated per token. Additionally, we report on Trinity Nano and Trinity Mini, with Trinity Nano having 6B total parameters with 1B activated per token, Trinity Mini having 26B total parameters with 3B activated per token. The models' modern architecture includes interleaved local and global attention, gated attention, depth-scaled sandwich norm, and sigmoid routing for Mixture-of-Experts. For Trinity Large, we also introduce a new MoE load balancing strategy titled Soft-clamped Momentum Expert Bias Updates (SMEBU). We train the models using the Muon optimizer. All three models completed training with zero loss spikes. Trinity Nano and Trinity Mini were pre-trained on 10 trillion tokens, and Trinity Large was pre-trained on 17 trillion tokens. The model checkpoints are available at https://huggingface.co/arcee-ai.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KellerJordan/Muon", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.16928", "title": "Discovering Multiagent Learning Algorithms with Large Language Models", "authors": [ "Zun Li", "John Schultz", "Daniel Hennes", "Marc Lanctot" ], "github": "", "project_page": "", "abstract": "Much of the advancement of Multi-Agent Reinforcement Learning (MARL) in imperfect-information games has historically depended on manual iterative refinement of baselines. While foundational families like Counterfactual Regret Minimization (CFR) and Policy Space Response Oracles (PSRO) rest on solid theoretical ground, the design of their most effective variants often relies on human intuition to navigate a vast algorithmic design space. In this work, we propose the use of AlphaEvolve, an evolutionary coding agent powered by large language models, to automatically discover new multiagent learning algorithms. We demonstrate the generality of this framework by evolving novel variants for two distinct paradigms of game-theoretic learning. First, in the domain of iterative regret minimization, we evolve the logic governing regret accumulation and policy derivation, discovering a new algorithm, Volatility-Adaptive Discounted (VAD-)CFR. VAD-CFR employs novel, non-intuitive mechanisms-including volatility-sensitive discounting, consistency-enforced optimism, and a hard warm-start policy accumulation schedule-to outperform state-of-the-art baselines like Discounted Predictive CFR+. Second, in the regime of population based training algorithms, we evolve training-time and evaluation-time meta strategy solvers for PSRO, discovering a new variant, Smoothed Hybrid Optimistic Regret (SHOR-)PSRO. SHOR-PSRO introduces a hybrid meta-solver that linearly blends Optimistic Regret Matching with a smoothed, temperature-controlled distribution over best pure strategies. By dynamically annealing this blending factor and diversity bonuses during training, the algorithm automates the transition from population diversity to rigorous equilibrium finding, yielding superior empirical convergence compared to standard static meta-solvers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.16802", "title": "References Improve LLM Alignment in Non-Verifiable Domains", "authors": [ "Kejian Shi", "Yixin Liu", "Peifeng Wang", "Alexander R. Fabbri", "Shafiq Joty", "Arman Cohan" ], "github": "", "project_page": "", "abstract": "While Reinforcement Learning with Verifiable Rewards (RLVR) has shown strong effectiveness in reasoning tasks, it cannot be directly applied to non-verifiable domains lacking ground-truth verifiers, such as LLM alignment. In this work, we investigate whether reference-guided LLM-evaluators can bridge this gap by serving as soft \"verifiers\". First, we design evaluation protocols that enhance LLM-based evaluators for LLM alignment using reference outputs. Through comprehensive experiments, we show that a reference-guided approach substantially improves the accuracy of less capable LLM-judges using references from frontier models; stronger LLM-judges can also be enhanced by high-quality (i.e., human-written) references. Building on these improved judges, we demonstrate the utility of high-quality references in alignment tuning, where LLMs guided with references are used as judges to self-improve. We show that reference-guided self-improvement yields clear gains over both direct SFT on reference outputs and self-improvement with reference-free judges, achieving performance comparable to training with ArmoRM, a strong finetuned reward model. Specifically, our method achieves 73.1% and 58.7% on AlpacaEval and Arena-Hard with Llama-3-8B-Instruct, and 70.0% and 74.1% with Qwen2.5-7B, corresponding to average absolute gains of +20.2 / +17.1 points over SFT distillation and +5.3 / +3.6 points over reference-free self-improvement on AlpacaEval / Arena-Hard. These results highlight the potential of using reference-guided LLM-evaluators to enable effective LLM post-training in non-verifiable domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.17259", "title": "FRAPPE: Infusing World Modeling into Generalist Policies via Multiple Future Representation Alignment", "authors": [ "Han Zhao", "Jingbo Wang", "Wenxuan Song", "Shuai Chen", "Yang Liu", "Yan Wang", "Haoang Li", "Donglin Wang" ], "github": "", "project_page": "", "abstract": "Enabling VLA models to predict environmental dynamics, known as world modeling, has been recognized as essential for improving robotic reasoning and generalization. However, current approaches face two main issues: 1. The training objective forces models to over-emphasize pixel-level reconstruction, which constrains semantic learning and generalization 2. Reliance on predicted future observations during inference often leads to error accumulation. To address these challenges, we introduce Future Representation Alignment via Parallel Progressive Expansion (FRAPPE). Our method adopts a two-stage fine-tuning strategy: In the mid-training phase, the model learns to predict the latent representations of future observations; In the post-training phase, we expand the computational workload in parallel and align the representation simultaneously with multiple different visual foundation models. By significantly improving fine-tuning efficiency and reducing dependence on action-annotated data, FRAPPE provides a scalable and data-efficient pathway to enhance world-awareness in generalist robotic policies. Experiments on the RoboTwin benchmark and real-world tasks demonstrate that FRAPPE outperforms state-of-the-art approaches and shows strong generalization in long-horizon and unseen scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.15569", "title": "\"What Are You Doing?\": Effects of Intermediate Feedback from Agentic LLM In-Car Assistants During Multi-Step Processing", "authors": [ "Johannes Kirmayr", "Raphael Wennmacher", "Khanh Huynh", "Lukas Stappen", "Elisabeth Andr\u00e9", "Florian Alt" ], "github": "", "project_page": "", "abstract": "Agentic AI assistants that autonomously perform multi-step tasks raise open questions for user experience: how should such systems communicate progress and reasoning during extended operations, especially in attention-critical contexts such as driving? We investigate feedback timing and verbosity from agentic LLM-based in-car assistants through a controlled, mixed-methods study (N=45) comparing planned steps and intermediate results feedback against silent operation with final-only response. Using a dual-task paradigm with an in-car voice assistant, we found that intermediate feedback significantly improved perceived speed, trust, and user experience while reducing task load - effects that held across varying task complexities and interaction contexts. Interviews further revealed user preferences for an adaptive approach: high initial transparency to establish trust, followed by progressively reducing verbosity as systems prove reliable, with adjustments based on task stakes and situational context. We translate our empirical findings into design implications for feedback timing and verbosity in agentic assistants, balancing transparency and efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.17288", "title": "ArXiv-to-Model: A Practical Study of Scientific LM Training", "authors": [ "Anuj Gupta" ], "github": "", "project_page": "", "abstract": "While frontier large language models demonstrate strong reasoning and mathematical capabilities, the practical process of training domain-specialized scientific language models from raw sources remains under-documented. In this work, we present a detailed case study of training a 1.36B-parameter scientific language model directly from raw arXiv LaTeX sources spanning mathematics, computer science, and theoretical physics. We describe an end-to-end pipeline covering metadata filtering, archive validation, LaTeX extraction, text normalization, domain-aware tokenization, and dense transformer training under constrained compute (2xA100 GPUs). Through 24 experimental runs, we analyze training stability, scaling behavior, data yield losses, and infrastructure bottlenecks. Our findings highlight how preprocessing decisions significantly affect usable token volume, how tokenization impacts symbolic stability, and how storage and I/O constraints can rival compute as limiting factors. We further analyze convergence dynamics and show stable training behavior in a data-rich regime (52B pretraining tokens). Rather than proposing a novel architecture, this work provides an engineering-grounded, transparent account of training a small scientific language model from scratch. We hope these insights support researchers operating under moderate compute budgets who seek to build domain-specialized models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.14857", "title": "World Models for Policy Refinement in StarCraft II", "authors": [ "Yixin Zhang", "Ziyi Wang", "Yiming Rong", "Haoxi Wang", "Jinling Jiang", "Shuang Xu", "Haoran Wu", "Shiyu Zhou", "Bo Xu" ], "github": "https://github.com/yxzzhang/StarWM", "project_page": "", "abstract": "Large Language Models (LLMs) have recently shown strong reasoning and generalization capabilities, motivating their use as decision-making policies in complex environments. StarCraft II (SC2), with its massive state-action space and partial observability, is a challenging testbed. However, existing LLM-based SC2 agents primarily focus on improving the policy itself and overlook integrating a learnable, action-conditioned transition model into the decision loop. To bridge this gap, we propose StarWM, the first world model for SC2 that predicts future observations under partial observability. To facilitate learning SC2's hybrid dynamics, we introduce a structured textual representation that factorizes observations into five semantic modules, and construct SC2-Dynamics-50k, the first instruction-tuning dataset for SC2 dynamics prediction. We further develop a multi-dimensional offline evaluation framework for predicted structured observations. Offline results show StarWM's substantial gains over zero-shot baselines, including nearly 60% improvements in resource prediction accuracy and self-side macro-situation consistency. Finally, we propose StarWM-Agent, a world-model-augmented decision system that integrates StarWM into a Generate--Simulate--Refine decision loop for foresight-driven policy refinement. Online evaluation against SC2's built-in AI demonstrates consistent improvements, yielding win-rate gains of 30%, 15%, and 30% against Hard (LV5), Harder (LV6), and VeryHard (LV7), respectively, alongside improved macro-management stability and tactical risk assessment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yxzzhang/StarWM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.16915", "title": "StereoAdapter-2: Globally Structure-Consistent Underwater Stereo Depth Estimation", "authors": [ "Zeyu Ren", "Xiang Li", "Yiran Wang", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/StereoAdapter-2", "project_page": "", "abstract": "Stereo depth estimation is fundamental to underwater robotic perception, yet suffers from severe domain shifts caused by wavelength-dependent light attenuation, scattering, and refraction. Recent approaches leverage monocular foundation models with GRU-based iterative refinement for underwater adaptation; however, the sequential gating and local convolutional kernels in GRUs necessitate multiple iterations for long-range disparity propagation, limiting performance in large-disparity and textureless underwater regions. In this paper, we propose StereoAdapter-2, which replaces the conventional ConvGRU updater with a novel ConvSS2D operator based on selective state space models. The proposed operator employs a four-directional scanning strategy that naturally aligns with epipolar geometry while capturing vertical structural consistency, enabling efficient long-range spatial propagation within a single update step at linear computational complexity. Furthermore, we construct UW-StereoDepth-80K, a large-scale synthetic underwater stereo dataset featuring diverse baselines, attenuation coefficients, and scattering parameters through a two-stage generative pipeline combining semantic-aware style transfer and geometry-consistent novel view synthesis. Combined with dynamic LoRA adaptation inherited from StereoAdapter, our framework achieves state-of-the-art zero-shot performance on underwater benchmarks with 17% improvement on TartanAir-UW and 7.2% improvment on SQUID, with real-world validation on the BlueROV2 platform demonstrates the robustness of our approach. Code: https://github.com/AIGeeksGroup/StereoAdapter-2. Website: https://aigeeksgroup.github.io/StereoAdapter-2.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/StereoAdapter-2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.16835", "title": "NeST: Neuron Selective Tuning for LLM Safety", "authors": [ "Sasha Behrouzi", "Lichao Wu", "Mohamadreza Rostami", "Ahmad-Reza Sadeghi" ], "github": "", "project_page": "", "abstract": "Safety alignment is essential for the responsible deployment of large language models (LLMs). Yet, existing approaches often rely on heavyweight fine-tuning that is costly to update, audit, and maintain across model families. Full fine-tuning incurs substantial computational and storage overhead, while parameter-efficient methods such as LoRA trade efficiency for inconsistent safety gains and sensitivity to design choices. Safety intervention mechanisms such as circuit breakers reduce unsafe outputs without modifying model weights, but do not directly shape or preserve the internal representations that govern safety behavior. These limitations hinder rapid and reliable safety updates, particularly in settings where models evolve frequently or must adapt to new policies and domains. We present NeST, a lightweight, structure-aware safety alignment framework that strengthens refusal behavior by selectively adapting a small subset of safety-relevant neurons while freezing the remainder of the model. NeST aligns parameter updates with the internal organization of safety behavior by clustering functionally coherent safety neurons and enforcing shared updates within each cluster, enabling targeted and stable safety adaptation without broad model modification or inference-time overhead. We benchmark NeST against three dominant baselines: full fine-tuning, LoRA-based fine-tuning, and circuit breakers across 10 open-weight LLMs spanning multiple model families and sizes. Across all evaluated models, NeST reduces the attack success rate from an average of 44.5% to 4.36%, corresponding to a 90.2% reduction in unsafe generations, while requiring only 0.44 million trainable parameters on average. This amounts to a 17,310x decrease in updated parameters compared to full fine-tuning and a 9.25x reduction relative to LoRA, while consistently achieving stronger safety performance for alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.16849", "title": "On the Mechanism and Dynamics of Modular Addition: Fourier Features, Lottery Ticket, and Grokking", "authors": [ "Jianliang He", "Leda Wang", "Siyu Chen", "Zhuoran Yang" ], "github": "", "project_page": "", "abstract": "We present a comprehensive analysis of how two-layer neural networks learn features to solve the modular addition task. Our work provides a full mechanistic interpretation of the learned model and a theoretical explanation of its training dynamics. While prior work has identified that individual neurons learn single-frequency Fourier features and phase alignment, it does not fully explain how these features combine into a global solution. We bridge this gap by formalizing a diversification condition that emerges during training when overparametrized, consisting of two parts: phase symmetry and frequency diversification. We prove that these properties allow the network to collectively approximate a flawed indicator function on the correct logic for the modular addition task. While individual neurons produce noisy signals, the phase symmetry enables a majority-voting scheme that cancels out noise, allowing the network to robustly identify the correct sum. Furthermore, we explain the emergence of these features under random initialization via a lottery ticket mechanism. Our gradient flow analysis proves that frequencies compete within each neuron, with the \"winner\" determined by its initial spectral magnitude and phase alignment. From a technical standpoint, we provide a rigorous characterization of the layer-wise phase coupling dynamics and formalize the competitive landscape using the ODE comparison lemma. Finally, we use these insights to demystify grokking, characterizing it as a three-stage process involving memorization followed by two generalization phases, driven by the competition between loss minimization and weight decay.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.16756", "title": "NESSiE: The Necessary Safety Benchmark -- Identifying Errors that should not Exist", "authors": [ "Johannes Bertram", "Jonas Geiping" ], "github": "", "project_page": "", "abstract": "We introduce NESSiE, the NEceSsary SafEty benchmark for large language models (LLMs). With minimal test cases of information and access security, NESSiE reveals safety-relevant failures that should not exist, given the low complexity of the tasks. NESSiE is intended as a lightweight, easy-to-use sanity check for language model safety and, as such, is not sufficient for guaranteeing safety in general -- but we argue that passing this test is necessary for any deployment. However, even state-of-the-art LLMs do not reach 100% on NESSiE and thus fail our necessary condition of language model safety, even in the absence of adversarial attacks. Our Safe & Helpful (SH) metric allows for direct comparison of the two requirements, showing models are biased toward being helpful rather than safe. We further find that disabled reasoning for some models, but especially a benign distraction context degrade model performance. Overall, our results underscore the critical risks of deploying such models as autonomous agents in the wild. We make the dataset, package and plotting code publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.16699", "title": "Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents", "authors": [ "Wenxuan Ding", "Nicholas Tomlin", "Greg Durrett" ], "github": "", "project_page": "", "abstract": "LLMs are increasingly being used for complex problems which are not necessarily resolved in a single response, but require interacting with an environment to acquire information. In these scenarios, LLMs must reason about inherent cost-uncertainty tradeoffs in when to stop exploring and commit to an answer. For instance, on a programming task, an LLM should test a generated code snippet if it is uncertain about the correctness of that code; the cost of writing a test is nonzero, but typically lower than the cost of making a mistake. In this work, we show that we can induce LLMs to explicitly reason about balancing these cost-uncertainty tradeoffs, then perform more optimal environment exploration. We formalize multiple tasks, including information retrieval and coding, as sequential decision-making problems under uncertainty. Each problem has latent environment state that can be reasoned about via a prior which is passed to the LLM agent. We introduce a framework called Calibrate-Then-Act (CTA), where we feed the LLM this additional context to enable it to act more optimally. This improvement is preserved even under RL training of both the baseline and CTA. Our results on information-seeking QA and on a simplified coding task show that making cost-benefit tradeoffs explicit with CTA can help agents discover more optimal decision-making strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.15823", "title": "CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing", "authors": [ "Zarif Ikram", "Arad Firouzkouhi", "Stephen Tu", "Mahdi Soltanolkotabi", "Paria Rashidinejad" ], "github": "", "project_page": "", "abstract": "A central challenge in large language model (LLM) editing is capability preservation: methods that successfully change targeted behavior can quietly game the editing proxy and corrupt general capabilities, producing degenerate behaviors reminiscent of proxy/reward hacking. We present CrispEdit, a scalable and principled second-order editing algorithm that treats capability preservation as an explicit constraint, unifying and generalizing several existing editing approaches. CrispEdit formulates editing as constrained optimization and enforces the constraint by projecting edit updates onto the low-curvature subspace of the capability-loss landscape. At the crux of CrispEdit is expressing capability constraint via Bregman divergence, whose quadratic form yields the Gauss-Newton Hessian exactly and even when the base model is not trained to convergence. We make this second-order procedure efficient at the LLM scale using Kronecker-factored approximate curvature (K-FAC) and a novel matrix-free projector that exploits Kronecker structure to avoid constructing massive projection matrices. Across standard model-editing benchmarks, CrispEdit achieves high edit success while keeping capability degradation below 1% on average across datasets, significantly improving over prior editors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.17588", "title": "Modeling Distinct Human Interaction in Web Agents", "authors": [ "Faria Huq", "Zora Zhiruo Wang", "Zhanqiu Guo", "Venu Arvind Arangarajan", "Tianyue Ou", "Frank Xu", "Shuyan Zhou", "Graham Neubig", "Jeffrey P. Bigham" ], "github": "", "project_page": "", "abstract": "Despite rapid progress in autonomous web agents, human involvement remains essential for shaping preferences and correcting agent behavior as tasks unfold. However, current agentic systems lack a principled understanding of when and why humans intervene, often proceeding autonomously past critical decision points or requesting unnecessary confirmation. In this work, we introduce the task of modeling human intervention to support collaborative web task execution. We collect CowCorpus, a dataset of 400 real-user web navigation trajectories containing over 4,200 interleaved human and agent actions. We identify four distinct patterns of user interaction with agents -- hands-off supervision, hands-on oversight, collaborative task-solving, and full user takeover. Leveraging these insights, we train language models (LMs) to anticipate when users are likely to intervene based on their interaction styles, yielding a 61.4-63.4% improvement in intervention prediction accuracy over base LMs. Finally, we deploy these intervention-aware models in live web navigation agents and evaluate them in a user study, finding a 26.5% increase in user-rated agent usefulness. Together, our results show structured modeling of human intervention leads to more adaptive, collaborative agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-20", "arxiv_id": "2602.10377", "title": "Hardware Co-Design Scaling Laws via Roofline Modelling for On-Device LLMs", "authors": [ "Luoyang Sun", "Jiwen Jiang", "Yifeng Ding", "Fengfa Li", "Yan Song", "Haifeng Zhang", "Jian Ying", "Lei Ren", "Kun Zhan", "Wei Chen", "Yan Xie", "Cheng Deng" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action Models (VLAs) have emerged as a key paradigm of Physical AI and are increasingly deployed in autonomous vehicles, robots, and smart spaces. In these resource-constrained on-device settings, selecting an appropriate large language model (LLM) backbone is a critical challenge: models must balance accuracy with strict inference latency and hardware efficiency constraints. This makes hardware-software co-design a game-changing requirement for on-device LLM deployment, where each hardware platform demands a tailored architectural solution. We propose a hardware co-design law that jointly captures model accuracy and inference performance. Specifically, we model training loss as an explicit function of architectural hyperparameters and characterise inference latency via roofline modelling. We empirically evaluate 1,942 candidate architectures on NVIDIA Jetson Orin, training 170 selected models for 10B tokens each to fit a scaling law relating architecture to training loss. By coupling this scaling law with latency modelling, we establish a direct accuracy-latency correspondence and identify the Pareto frontier for hardware co-designed LLMs. We further formulate architecture search as a joint optimisation over precision and performance, deriving feasible design regions under industrial hardware and application budgets. Our approach reduces architecture selection from months to days. At the same latency as Qwen2.5-0.5B on the target hardware, our co-designed architecture achieves 19.42% lower perplexity on WikiText-2. To our knowledge, this is the first principled and operational framework for hardware co-design scaling laws in on-device LLM deployment. We will make the code and related checkpoints publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.18422", "title": "Generated Reality: Human-centric World Simulation using Interactive Video Generation with Hand and Camera Control", "authors": [ "Linxi Xie", "Lisong C. Sun", "Ashley Neall", "Tong Wu", "Shengqu Cai", "Gordon Wetzstein" ], "github": "", "project_page": "", "abstract": "Extended reality (XR) demands generative models that respond to users' tracked real-world motion, yet current video world models accept only coarse control signals such as text or keyboard input, limiting their utility for embodied interaction. We introduce a human-centric video world model that is conditioned on both tracked head pose and joint-level hand poses. For this purpose, we evaluate existing diffusion transformer conditioning strategies and propose an effective mechanism for 3D head and hand control, enabling dexterous hand--object interactions. We train a bidirectional video diffusion model teacher using this strategy and distill it into a causal, interactive system that generates egocentric virtual environments. We evaluate this generated reality system with human subjects and demonstrate improved task performance as well as a significantly higher level of perceived amount of control over the performed actions compared with relevant baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.18312", "title": "Learning Smooth Time-Varying Linear Policies with an Action Jacobian Penalty", "authors": [ "Zhaoming Xie", "Kevin Karol", "Jessica Hodgins" ], "github": "", "project_page": "", "abstract": "Reinforcement learning provides a framework for learning control policies that can reproduce diverse motions for simulated characters. However, such policies often exploit unnatural high-frequency signals that are unachievable by humans or physical robots, making them poor representations of real-world behaviors. Existing work addresses this issue by adding a reward term that penalizes a large change in actions over time. This term often requires substantial tuning efforts. We propose to use the action Jacobian penalty, which penalizes changes in action with respect to the changes in simulated state directly through auto differentiation. This effectively eliminates unrealistic high-frequency control signals without task specific tuning. While effective, the action Jacobian penalty introduces significant computational overhead when used with traditional fully connected neural network architectures. To mitigate this, we introduce a new architecture called a Linear Policy Net (LPN) that significantly reduces the computational burden for calculating the action Jacobian penalty during training. In addition, a LPN requires no parameter tuning, exhibits faster learning convergence compared to baseline methods, and can be more efficiently queried during inference time compared to a fully connected neural network. We demonstrate that a Linear Policy Net, combined with the action Jacobian penalty, is able to learn policies that generate smooth signals while solving a number of motion imitation tasks with different characteristics, including dynamic motions such as a backflip and various challenging parkour skills. Finally, we apply this approach to create policies for dynamic motions on a physical quadrupedal robot equipped with an arm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.18071", "title": "EgoPush: Learning End-to-End Egocentric Multi-Object Rearrangement for Mobile Robots", "authors": [ "Boyuan An", "Zhexiong Wang", "Yipeng Wang", "Jiaqi Li", "Sihang Li", "Jing Zhang", "Chen Feng" ], "github": "", "project_page": "", "abstract": "Humans can rearrange objects in cluttered environments using egocentric perception, navigating occlusions without global coordinates. Inspired by this capability, we study long-horizon multi-object non-prehensile rearrangement for mobile robots using a single egocentric camera. We introduce EgoPush, a policy learning framework that enables egocentric, perception-driven rearrangement without relying on explicit global state estimation that often fails in dynamic scenes. EgoPush designs an object-centric latent space to encode relative spatial relations among objects, rather than absolute poses. This design enables a privileged reinforcement-learning (RL) teacher to jointly learn latent states and mobile actions from sparse keypoints, which is then distilled into a purely visual student policy. To reduce the supervision gap between the omniscient teacher and the partially observed student, we restrict the teacher's observations to visually accessible cues. This induces active perception behaviors that are recoverable from the student's viewpoint. To address long-horizon credit assignment, we decompose rearrangement into stage-level subproblems using temporally decayed, stage-local completion rewards. Extensive simulation experiments demonstrate that EgoPush significantly outperforms end-to-end RL baselines in success rate, with ablation studies validating each design choice. We further demonstrate zero-shot sim-to-real transfer on a mobile platform in the real world. Code and videos are available at https://ai4ce.github.io/EgoPush/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.08354", "title": "Does Your Reasoning Model Implicitly Know When to Stop Thinking?", "authors": [ "Zixuan Huang", "Xin Xia", "Yuxi Ren", "Jianbin Zheng", "Xuanda Wang", "Zhixia Zhang", "Hongyan Xie", "Songshi Liang", "Zehao Chen", "Xuefeng Xiao", "Fuzhen Zhuang", "Jianxin Li", "Yikun Ban", "Deqing Wang" ], "github": "", "project_page": "", "abstract": "Recent advancements in large reasoning models (LRMs) have greatly improved their capabilities on complex reasoning tasks through Long Chains of Thought (CoTs). However, this approach often results in substantial redundancy, impairing computational efficiency and causing significant delays in real-time applications. Recent studies show that longer reasoning chains are frequently uncorrelated with correctness and can even be detrimental to accuracy. In a further in-depth analysis of this phenomenon, we surprisingly uncover and empirically verify that LRMs implicitly know the appropriate time to stop thinking, while this capability is obscured by current sampling paradigms. Motivated by this, we introduce SAGE (Self-Aware Guided Efficient Reasoning), a novel sampling paradigm that unleashes this efficient reasoning potential. Furthermore, integrating SAGE as mixed sampling into group-based reinforcement learning (SAGE-RL) enables SAGE-RL to effectively incorporate SAGE-discovered efficient reasoning patterns into standard pass@1 inference, markedly enhancing both the reasoning accuracy and efficiency of LRMs across multiple challenging mathematical benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.18432", "title": "SARAH: Spatially Aware Real-time Agentic Humans", "authors": [ "Evonne Ng", "Siwei Zhang", "Zhang Chen", "Michael Zollhoefer", "Alexander Richard" ], "github": "", "project_page": "", "abstract": "As embodied agents become central to VR, telepresence, and digital human applications, their motion must go beyond speech-aligned gestures: agents should turn toward users, respond to their movement, and maintain natural gaze. Current methods lack this spatial awareness. We close this gap with the first real-time, fully causal method for spatially-aware conversational motion, deployable on a streaming VR headset. Given a user's position and dyadic audio, our approach produces full-body motion that aligns gestures with speech while orienting the agent according to the user. Our architecture combines a causal transformer-based VAE with interleaved latent tokens for streaming inference and a flow matching model conditioned on user trajectory and audio. To support varying gaze preferences, we introduce a gaze scoring mechanism with classifier-free guidance to decouple learning from control: the model captures natural spatial alignment from data, while users can adjust eye contact intensity at inference time. On the Embody 3D dataset, our method achieves state-of-the-art motion quality at over 300 FPS -- 3x faster than non-causal baselines -- while capturing the subtle spatial dynamics of natural conversation. We validate our approach on a live VR system, bringing spatially-aware conversational agents to real-time deployment. Please see https://evonneng.github.io/sarah/ for details.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.10693", "title": "VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training", "authors": [ "Guobin Shen", "Chenxiao Zhao", "Xiang Cheng", "Lei Huang", "Xing Yu" ], "github": "https://github.com/FloyedShen/VESPO", "project_page": "", "abstract": "Training stability remains a central challenge in reinforcement learning (RL) for large language models (LLMs). Policy staleness, asynchronous training, and mismatches between training and inference engines all cause the behavior policy to diverge from the current policy, risking training collapse. Importance sampling provides a principled correction for this distribution shift but suffers from high variance; existing remedies such as token-level clipping and sequence-level normalization lack a unified theoretical foundation. We propose Variational sEquence-level Soft Policy Optimization (VESPO). By incorporating variance reduction into a variational formulation over proposal distributions, VESPO derives a closed-form reshaping kernel that operates directly on sequence-level importance weights without length normalization. Experiments on mathematical reasoning benchmarks show that VESPO maintains stable training under staleness ratios up to 64x and fully asynchronous execution, and delivers consistent gains across both dense and Mixture-of-Experts models. Code is available at https://github.com/FloyedShen/VESPO", "github_manual": "", "github_regex": "https://github.com/FloyedShen/VESPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.17186", "title": "Selective Training for Large Vision Language Models via Visual Information Gain", "authors": [ "Seulbi Lee", "Sangheum Hwang" ], "github": "", "project_page": "", "abstract": "Large Vision Language Models (LVLMs) have achieved remarkable progress, yet they often suffer from language bias, producing answers without relying on visual evidence. While prior work attempts to mitigate this issue through decoding strategies, architectural modifications, or curated instruction data, they typically lack a quantitative measure of how much individual training samples or tokens actually benefit from the image. In this work, we introduce Visual Information Gain (VIG), a perplexity-based metric that measures the reduction in prediction uncertainty provided by visual input. VIG enables fine-grained analysis at both sample and token levels, effectively highlighting visually grounded elements such as colors, spatial relations, and attributes. Leveraging this, we propose a VIG-guided selective training scheme that prioritizes high-VIG samples and tokens. This approach improves visual grounding and mitigates language bias, achieving superior performance with significantly reduced supervision by focusing exclusively on visually informative samples and tokens.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.16742", "title": "DeepVision-103K: A Visually Diverse, Broad-Coverage, and Verifiable Mathematical Dataset for Multimodal Reasoning", "authors": [ "Haoxiang Sun", "Lizhen Xu", "Bing Zhao", "Wotao Yin", "Wei Wang", "Boyu Yang", "Rui Wang", "Hu Wei" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has been shown effective in enhancing the visual reflection and reasoning capabilities of Large Multimodal Models (LMMs). However, existing datasets are predominantly derived from either small-scale manual construction or recombination of prior resources, which limits data diversity and coverage, thereby constraining further gains in model performance. To this end, we introduce DeepVision-103K, a comprehensive dataset for RLVR training that covers diverse K12 mathematical topics, extensive knowledge points, and rich visual elements. Models trained on DeepVision achieve strong performance on multimodal mathematical benchmarks, and generalize effectively to general multimodal reasoning tasks. Further analysis reveals enhanced visual perception, reflection and reasoning capabilities in trained models, validating DeepVision's effectiveness for advancing multimodal reasoning. Data: https://huggingface.co/datasets/skylenage/DeepVision-103K{this url}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.17807", "title": "VidEoMT: Your ViT is Secretly Also a Video Segmentation Model", "authors": [ "Narges Norouzi", "Idil Esen Zulfikar", "Niccol`o Cavagnero", "Tommie Kerssies", "Bastian Leibe", "Gijs Dubbelman", "Daan de Geus" ], "github": "", "project_page": "", "abstract": "Existing online video segmentation models typically combine a per-frame segmenter with complex specialized tracking modules. While effective, these modules introduce significant architectural complexity and computational overhead. Recent studies suggest that plain Vision Transformer (ViT) encoders, when scaled with sufficient capacity and large-scale pre-training, can conduct accurate image segmentation without requiring specialized modules. Motivated by this observation, we propose the Video Encoder-only Mask Transformer (VidEoMT), a simple encoder-only video segmentation model that eliminates the need for dedicated tracking modules. To enable temporal modeling in an encoder-only ViT, VidEoMT introduces a lightweight query propagation mechanism that carries information across frames by reusing queries from the previous frame. To balance this with adaptability to new content, it employs a query fusion strategy that combines the propagated queries with a set of temporally-agnostic learned queries. As a result, VidEoMT attains the benefits of a tracker without added complexity, achieving competitive accuracy while being 5x--10x faster, running at up to 160 FPS with a ViT-L backbone. Code: https://www.tue-mps.org/videomt/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.17664", "title": "Sink-Aware Pruning for Diffusion Language Models", "authors": [ "Aidar Myrzakhan", "Tianyi Li", "Bowei Guo", "Shengkun Tang", "Zhiqiang Shen" ], "github": "https://github.com/VILA-Lab/Sink-Aware-Pruning", "project_page": "", "abstract": "Diffusion Language Models (DLMs) incur high inference cost due to iterative denoising, motivating efficient pruning. Existing pruning heuristics largely inherited from autoregressive (AR) LLMs, typically preserve attention sink tokens because AR sinks serve as stable global anchors. We show that this assumption does not hold for DLMs: the attention-sink position exhibits substantially higher variance over the full generation trajectory (measured by how the dominant sink locations shift across timesteps), indicating that sinks are often transient and less structurally essential than in AR models. Based on this observation, we propose {bf Sink-Aware Pruning}, which automatically identifies and prunes unstable sinks in DLMs (prior studies usually keep sinks for AR LLMs). Without retraining, our method achieves a better quality-efficiency trade-off and outperforms strong prior pruning baselines under matched compute. Our code is available at https://github.com/VILA-Lab/Sink-Aware-Pruning.", "github_manual": "", "github_regex": "https://github.com/VILA-Lab/Sink-Aware-Pruning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.18292", "title": "Decoding as Optimisation on the Probability Simplex: From Top-K to Top-P (Nucleus) to Best-of-K Samplers", "authors": [ "Xiaotong Ji", "Rasul Tutunov", "Matthieu Zimmer", "Haitham Bou-Ammar" ], "github": "", "project_page": "", "abstract": "Decoding sits between a language model and everything we do with it, yet it is still treated as a heuristic knob-tuning exercise. We argue decoding should be understood as a principled optimisation layer: at each token, we solve a regularised problem over the probability simplex that trades off model score against structural preferences and constraints. This single template recovers greedy decoding, Softmax sampling, Top-K, Top-P, and Sparsemax-style sparsity as special cases, and explains their common structure through optimality conditions. More importantly, the framework makes it easy to invent new decoders without folklore. We demonstrate this by designing Best-of-K (BoK), a KL-anchored coverage objective aimed at multi-sample pipelines (self-consistency, reranking, verifier selection). BoK targets the probability of covering good alternatives within a fixed K-sample budget and improves empirical performance. We show that such samples can improve accuracy by, for example, +18.6% for Qwen2.5-Math-7B on MATH500 at high sampling temperatures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.10094", "title": "4RC: 4D Reconstruction via Conditional Querying Anytime and Anywhere", "authors": [ "Yihang Luo", "Shangchen Zhou", "Yushi Lan", "Xingang Pan", "Chen Change Loy" ], "github": "https://github.com/Luo-Yihang/4RC", "project_page": "", "abstract": "We present 4RC, a unified feed-forward framework for 4D reconstruction from monocular videos. Unlike existing approaches that typically decouple motion from geometry or produce limited 4D attributes such as sparse trajectories or two-view scene flow, 4RC learns a holistic 4D representation that jointly captures dense scene geometry and motion dynamics. At its core, 4RC introduces a novel encode-once, query-anywhere and anytime paradigm: a transformer backbone encodes the entire video into a compact spatio-temporal latent space, from which a conditional decoder can efficiently query 3D geometry and motion for any query frame at any target timestamp. To facilitate learning, we represent per-view 4D attributes in a minimally factorized form by decomposing them into base geometry and time-dependent relative motion. Extensive experiments demonstrate that 4RC outperforms prior and concurrent methods across a wide range of 4D reconstruction tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Luo-Yihang/4RC", "github_api_added_by": "auto" }, { "date": "2026-02-23", "arxiv_id": "2602.15727", "title": "Spanning the Visual Analogy Space with a Weight Basis of LoRAs", "authors": [ "Hila Manor", "Rinon Gal", "Haggai Maron", "Tomer Michaeli", "Gal Chechik" ], "github": "", "project_page": "", "abstract": "Visual analogy learning enables image manipulation through demonstration rather than textual description, allowing users to specify complex transformations difficult to articulate in words. Given a triplet {a, a', b}, the goal is to generate b' such that a : a' :: b : b'. Recent methods adapt text-to-image models to this task using a single Low-Rank Adaptation (LoRA) module, but they face a fundamental limitation: attempting to capture the diverse space of visual transformations within a fixed adaptation module constrains generalization capabilities. Inspired by recent work showing that LoRAs in constrained domains span meaningful, interpolatable semantic spaces, we propose LoRWeB, a novel approach that specializes the model for each analogy task at inference time through dynamic composition of learned transformation primitives, informally, choosing a point in a \"space of LoRAs\". We introduce two key components: (1) a learnable basis of LoRA modules, to span the space of different visual transformations, and (2) a lightweight encoder that dynamically selects and weighs these basis LoRAs based on the input analogy pair. Comprehensive evaluations demonstrate our approach achieves state-of-the-art performance and significantly improves generalization to unseen visual transformations. Our findings suggest that LoRA basis decompositions are a promising direction for flexible visual manipulation. Code and data are in https://research.nvidia.com/labs/par/lorweb", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.17080", "title": "Adam Improves Muon: Adaptive Moment Estimation with Orthogonalized Momentum", "authors": [ "Minxin Zhang", "Yuxuan Liu", "Hayden Scheaffer" ], "github": "", "project_page": "", "abstract": "Efficient stochastic optimization typically integrates an update direction that performs well in the deterministic regime with a mechanism adapting to stochastic perturbations. While Adam uses adaptive moment estimates to promote stability, Muon utilizes the weight layers' matrix structure via orthogonalized momentum, showing superior performance in large language model training. We propose a new optimizer and a diagonal extension, NAMO and NAMO-D, providing the first principled integration of orthogonalized momentum with norm-based Adam-type noise adaptation. NAMO scales orthogonalized momentum using a single adaptive stepsize, preserving orthogonality while improving upon Muon at negligible additional cost. NAMO-D instead right-multiplies orthogonalized momentum by a diagonal matrix with clamped entries. This design enables neuron-wise noise adaptation and aligns with the common near block-diagonal Hessian structure. Under standard assumptions, we establish optimal convergence rates for both algorithms in the deterministic setting and show that, in the stochastic setting, their convergence guarantees adapt to the noise level of stochastic gradients. Experiments on pretraining GPT-2 models demonstrate improved performance of both NAMO and NAMO-D compared to the AdamW and Muon baselines, with NAMO-D achieving further gains over NAMO via an additional clamping hyperparameter that balances the competing goals of maintaining a well-conditioned update direction and leveraging fine-grained noise adaptation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.17022", "title": "ReIn: Conversational Error Recovery with Reasoning Inception", "authors": [ "Takyoung Kim", "Jinseok Nam", "Chandrayee Basu", "Xing Fan", "Chengyuan Ma", "Heng Ji", "Gokhan Tur", "Dilek Hakkani-T\u00fcr" ], "github": "https://github.com/youngerous/rein", "project_page": "", "abstract": "Conversational agents powered by large language models (LLMs) with tool integration achieve strong performance on fixed task-oriented dialogue datasets but remain vulnerable to unanticipated, user-induced errors. Rather than focusing on error prevention, this work focuses on error recovery, which necessitates the accurate diagnosis of erroneous dialogue contexts and execution of proper recovery plans. Under realistic constraints precluding model fine-tuning or prompt modification due to significant cost and time requirements, we explore whether agents can recover from contextually flawed interactions and how their behavior can be adapted without altering model parameters and prompts. To this end, we propose Reasoning Inception (ReIn), a test-time intervention method that plants an initial reasoning into the agent's decision-making process. Specifically, an external inception module identifies predefined errors within the dialogue context and generates recovery plans, which are subsequently integrated into the agent's internal reasoning process to guide corrective actions, without modifying its parameters or system prompts. We evaluate ReIn by systematically simulating conversational failure scenarios that directly hinder successful completion of user goals: user's ambiguous and unsupported requests. Across diverse combinations of agent models and inception modules, ReIn substantially improves task success and generalizes to unseen error types. Moreover, it consistently outperforms explicit prompt-modification approaches, underscoring its utility as an efficient, on-the-fly method. In-depth analysis of its operational mechanism, particularly in relation to instruction hierarchy, indicates that jointly defining recovery tools with ReIn can serve as a safe and effective strategy for improving the resilience of conversational agents without modifying the backbone models or system prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/youngerous/rein", "github_api_added_by": "auto" }, { "date": "2026-02-23", "arxiv_id": "2602.15814", "title": "Avey-B", "authors": [ "Devang Acharya", "Mohammad Hammoud" ], "github": "", "project_page": "", "abstract": "Compact pretrained bidirectional encoders remain the backbone of industrial NLP under tight compute and memory budgets. Their effectiveness stems from self-attention's ability to deliver high-quality bidirectional contextualization with sequence-level parallelism, as popularized by BERT-style architectures. Recently, Avey was introduced as an autoregressive, attention-free alternative that naturally admits an encoder-only adaptation. In this paper, we reformulate Avey for the encoder-only paradigm and propose several innovations to its architecture, including decoupled static and dynamic parameterizations, stability-oriented normalization, and neural compression. Results show that this reformulated architecture compares favorably to four widely used Transformer-based encoders, consistently outperforming them on standard token-classification and information-retrieval benchmarks while scaling more efficiently to long contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.14279", "title": "Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions", "authors": [ "Ruomeng Ding", "Tianwei Gao", "Thomas P. Zollo", "Eitan Bachmat", "Richard Zemel", "Zhun Deng" ], "github": "https://github.com/ZDCSlab/Group-Adaptive-Elicitation", "project_page": "", "abstract": "Eliciting information to reduce uncertainty about latent group-level properties from surveys and other collective assessments requires allocating limited questioning effort under real costs and missing data. Although large language models enable adaptive, multi-turn interactions in natural language, most existing elicitation methods optimize what to ask with a fixed respondent pool, and do not adapt respondent selection or leverage population structure when responses are partial or incomplete. To address this gap, we study adaptive group elicitation, a multi-round setting where an agent adaptively selects both questions and respondents under explicit query and participation budgets. We propose a theoretically grounded framework that combines (i) an LLM-based expected information gain objective for scoring candidate questions with (ii) heterogeneous graph neural network propagation that aggregates observed responses and participant attributes to impute missing responses and guide per-round respondent selection. This closed-loop procedure queries a small, informative subset of individuals while inferring population-level responses via structured similarity. Across three real-world opinion datasets, our method consistently improves population-level response prediction under constrained budgets, including a >12% relative gain on CES at a 10% respondent budget.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ZDCSlab/Group-Adaptive-Elicitation", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-23", "arxiv_id": "2602.13576", "title": "Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges", "authors": [ "Ruomeng Ding", "Yifei Pang", "He Sun", "Yizhong Wang", "Zhiwei Steven Wu", "Zhun Deng" ], "github": "https://github.com/ZDCSlab/Rubrics-as-an-Attack-Surface", "project_page": "", "abstract": "Evaluation and alignment pipelines for large language models increasingly rely on LLM-based judges, whose behavior is guided by natural-language rubrics and validated on benchmarks. We identify a previously under-recognized vulnerability in this workflow, which we term Rubric-Induced Preference Drift (RIPD). Even when rubric edits pass benchmark validation, they can still produce systematic and directional shifts in a judge's preferences on target domains. Because rubrics serve as a high-level decision interface, such drift can emerge from seemingly natural, criterion-preserving edits and remain difficult to detect through aggregate benchmark metrics or limited spot-checking. We further show this vulnerability can be exploited through rubric-based preference attacks, in which benchmark-compliant rubric edits steer judgments away from a fixed human or trusted reference on target domains, systematically inducing RIPD and reducing target-domain accuracy up to 9.5% (helpfulness) and 27.9% (harmlessness). When these judgments are used to generate preference labels for downstream post-training, the induced bias propagates through alignment pipelines and becomes internalized in trained policies. This leads to persistent and systematic drift in model behavior. Overall, our findings highlight evaluation rubrics as a sensitive and manipulable control interface, revealing a system-level alignment risk that extends beyond evaluator reliability alone. The code is available at: https://github.com/ZDCSlab/Rubrics-as-an-Attack-Surface. Warning: Certain sections may contain potentially harmful content that may not be appropriate for all readers.", "github_manual": "", "github_regex": "https://github.com/ZDCSlab/Rubrics-as-an-Attack-Surface", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.20161", "title": "Mobile-O: Unified Multimodal Understanding and Generation on Mobile Device", "authors": [ "Abdelrahman Shaker", "Ahmed Heakl", "Jaseel Muhammad", "Ritesh Thawkar", "Omkar Thawakar", "Senmao Li", "Hisham Cholakkal", "Ian Reid", "Eric P. Xing", "Salman Khan", "Fahad Shahbaz Khan" ], "github": "", "project_page": "", "abstract": "Unified multimodal models can both understand and generate visual content within a single architecture. Existing models, however, remain data-hungry and too heavy for deployment on edge devices. We present Mobile-O, a compact vision-language-diffusion model that brings unified multimodal intelligence to a mobile device. Its core module, the Mobile Conditioning Projector (MCP), fuses vision-language features with a diffusion generator using depthwise-separable convolutions and layerwise alignment. This design enables efficient cross-modal conditioning with minimal computational cost. Trained on only a few million samples and post-trained in a novel quadruplet format (generation prompt, image, question, answer), Mobile-O jointly enhances both visual understanding and generation capabilities. Despite its efficiency, Mobile-O attains competitive or superior performance compared to other unified models, achieving 74% on GenEval and outperforming Show-O and JanusFlow by 5% and 11%, while running 6x and 11x faster, respectively. For visual understanding, Mobile-O surpasses them by 15.3% and 5.1% averaged across seven benchmarks. Running in only ~3s per 512x512 image on an iPhone, Mobile-O establishes the first practical framework for real-time unified multimodal understanding and generation on edge devices. We hope Mobile-O will ease future research in real-time unified multimodal intelligence running entirely on-device with no cloud dependency. Our code, models, datasets, and mobile application are publicly available at https://amshaker.github.io/Mobile-O/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.19895", "title": "DSDR: Dual-Scale Diversity Regularization for Exploration in LLM Reasoning", "authors": [ "Zhongwei Wan", "Yun Shen", "Zhihao Dou", "Donghao Zhou", "Yu Zhang", "Xin Wang", "Hui Shen", "Jing Xiong", "Chaofan Tao", "Zixuan Zhong", "Peizhou Huang", "Mi Zhang" ], "github": "https://github.com/SUSTechBruce/DSDR", "project_page": "", "abstract": "Reinforcement learning with verifiers (RLVR) is a central paradigm for improving large language model (LLM) reasoning, yet existing methods often suffer from limited exploration. Policies tend to collapse onto a few reasoning patterns and prematurely stop deep exploration, while conventional entropy regularization introduces only local stochasticity and fails to induce meaningful path-level diversity, leading to weak and unstable learning signals in group-based policy optimization. We propose DSDR, a Dual-Scale Diversity Regularization reinforcement learning framework that decomposes diversity in LLM reasoning into global and coupling components. Globally, DSDR promotes diversity among correct reasoning trajectories to explore distinct solution modes. Locally, it applies a length-invariant, token-level entropy regularization restricted to correct trajectories, preventing entropy collapse within each mode while preserving correctness. The two scales are coupled through a global-to-local allocation mechanism that emphasizes local regularization for more distinctive correct trajectories. We provide theoretical support showing that DSDR preserves optimal correctness under bounded regularization, sustains informative learning signals in group-based optimization, and yields a principled global-to-local coupling rule. Experiments on multiple reasoning benchmarks demonstrate consistent improvements in accuracy and pass@k, highlighting the importance of dual-scale diversity for deep exploration in RLVR. Code is available at https://github.com/SUSTechBruce/DSDR.", "github_manual": "", "github_regex": "https://github.com/SUSTechBruce/DSDR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.20021", "title": "Agents of Chaos", "authors": [ "Natalie Shapira", "Chris Wendler", "Avery Yen", "Gabriele Sarti", "Koyena Pal", "Olivia Floody", "Adam Belfki", "Alex Loftus", "Aditya Ratan Jannali", "Nikhil Prakash", "Jasmine Cui", "Giordano Rogers", "Jannik Brinkmann", "Can Rager", "Amir Zur", "Michael Ripa", "Aruna Sankaranarayanan", "David Atkinson", "Rohit Gandikota", "Jaden Fiotto-Kaufman", "EunJeong Hwang", "Hadas Orgad", "P Sam Sahil", "Negev Taglicht", "Tomer Shabtay", "Atai Ambus", "Nitay Alon", "Shiri Oron", "Ayelet Gordon-Tapiero", "Yotam Kaplan", "Vered Shwartz", "Tamar Rott Shaham", "Christoph Riedl", "Reuth Mirsky", "Maarten Sap", "David Manheim", "Tomer Ullman", "David Bau" ], "github": "", "project_page": "", "abstract": "We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord access, file systems, and shell execution. Over a two-week period, twenty AI researchers interacted with the agents under benign and adversarial conditions. Focusing on failures emerging from the integration of language models with autonomy, tool use, and multi-party communication, we document eleven representative case studies. Observed behaviors include unauthorized compliance with non-owners, disclosure of sensitive information, execution of destructive system-level actions, denial-of-service conditions, uncontrolled resource consumption, identity spoofing vulnerabilities, cross-agent propagation of unsafe practices, and partial system takeover. In several cases, agents reported task completion while the underlying system state contradicted those reports. We also report on some of the failed attempts. Our findings establish the existence of security-, privacy-, and governance-relevant vulnerabilities in realistic deployment settings. These behaviors raise unresolved questions regarding accountability, delegated authority, and responsibility for downstream harms, and warrant urgent attention from legal scholars, policymakers, and researchers across disciplines. This report serves as an initial empirical contribution to that broader conversation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.19313", "title": "TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics", "authors": [ "Shirui Chen", "Cole Harrison", "Ying-Chun Lee", "Angela Jin Yang", "Zhongzheng Ren", "Lillian J. Ratliff", "Jiafei Duan", "Dieter Fox", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "While Vision-Language-Action (VLA) models have seen rapid progress in pretraining, their advancement in Reinforcement Learning (RL) remains hampered by low sample efficiency and sparse rewards in real-world settings. Developing generalizable process reward models is essential for providing the fine-grained feedback necessary to bridge this gap, yet existing temporal value functions often fail to generalize beyond their training domains. We introduce TOPReward, a novel, probabilistically grounded temporal value function that leverages the latent world knowledge of pretrained video Vision-Language Models (VLMs) to estimate robotic task progress. Unlike prior methods that prompt VLMs to directly output progress values, which are prone to numerical misrepresentation, TOPReward extracts task progress directly from the VLM's internal token logits. In zero-shot evaluations across 130+ distinct real-world tasks and multiple robot platforms (e.g., Franka, YAM, SO-100/101), TOPReward achieves 0.947 mean Value-Order Correlation (VOC) on Qwen3-VL, dramatically outperforming the state-of-the-art GVL baseline which achieves near-zero correlation on the same open-source model. We further demonstrate that TOPReward serves as a versatile tool for downstream applications, including success detection and reward-aligned behavior cloning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.20160", "title": "tttLRM: Test-Time Training for Long Context and Autoregressive 3D Reconstruction", "authors": [ "Chen Wang", "Hao Tan", "Wang Yifan", "Zhiqin Chen", "Yuheng Liu", "Kalyan Sunkavalli", "Sai Bi", "Lingjie Liu", "Yiwei Hu" ], "github": "", "project_page": "", "abstract": "We propose tttLRM, a novel large 3D reconstruction model that leverages a Test-Time Training (TTT) layer to enable long-context, autoregressive 3D reconstruction with linear computational complexity, further scaling the model's capability. Our framework efficiently compresses multiple image observations into the fast weights of the TTT layer, forming an implicit 3D representation in the latent space that can be decoded into various explicit formats, such as Gaussian Splats (GS) for downstream applications. The online learning variant of our model supports progressive 3D reconstruction and refinement from streaming observations. We demonstrate that pretraining on novel view synthesis tasks effectively transfers to explicit 3D modeling, resulting in improved reconstruction quality and faster convergence. Extensive experiments show that our method achieves superior performance in feedforward 3D Gaussian reconstruction compared to state-of-the-art approaches on both objects and scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.20159", "title": "A Very Big Video Reasoning Suite", "authors": [ "Maijunxian Wang", "Ruisi Wang", "Juyi Lin", "Ran Ji", "Thadd\u00e4us Wiedemer", "Qingying Gao", "Dezhi Luo", "Yaoyao Qian", "Lianyu Huang", "Zelong Hong", "Jiahui Ge", "Qianli Ma", "Hang He", "Yifan Zhou", "Lingzi Guo", "Lantao Mei", "Jiachen Li", "Hanwen Xing", "Tianqi Zhao", "Fengyuan Yu", "Weihang Xiao", "Yizheng Jiao", "Jianheng Hou", "Danyang Zhang", "Pengcheng Xu", "Boyang Zhong", "Zehong Zhao", "Gaoyun Fang", "John Kitaoka", "Yile Xu", "Hua Xu", "Kenton Blacutt", "Tin Nguyen", "Siyuan Song", "Haoran Sun", "Shaoyue Wen", "Linyang He", "Runming Wang", "Yanzhi Wang", "Mengyue Yang", "Ziqiao Ma", "Rapha\u00ebl Milli\u00e8re", "Freda Shi", "Nuno Vasconcelos", "Daniel Khashabi", "Alan Yuille", "Yilun Du", "Ziming Liu", "Bo Li", "Dahua Lin", "Ziwei Liu", "Vikash Kumar", "Yijiang Li", "Lei Yang", "Zhongang Cai", "Hokin Deng" ], "github": "https://github.com/Video-Reason/VBVR-EvalKit", "project_page": "", "abstract": "Rapid progress in video models has largely focused on visual quality, leaving their reasoning capabilities underexplored. Video reasoning grounds intelligence in spatiotemporally consistent visual environments that go beyond what text can naturally capture, enabling intuitive reasoning over spatiotemporal structure such as continuity, interaction, and causality. However, systematically studying video reasoning and its scaling behavior is hindered by the lack of large-scale training data. To address this gap, we introduce the Very Big Video Reasoning (VBVR) Dataset, an unprecedentedly large-scale resource spanning 200 curated reasoning tasks following a principled taxonomy and over one million video clips, approximately three orders of magnitude larger than existing datasets. We further present VBVR-Bench, a verifiable evaluation framework that moves beyond model-based judging by incorporating rule-based, human-aligned scorers, enabling reproducible and interpretable diagnosis of video reasoning capabilities. Leveraging the VBVR suite, we conduct one of the first large-scale scaling studies of video reasoning and observe early signs of emergent generalization to unseen reasoning tasks. Together, VBVR lays a foundation for the next stage of research in generalizable video reasoning. The data, benchmark toolkit, and models are publicly available at https://video-reason.com/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Video-Reason/VBVR-EvalKit", "github_api_added_by": "auto" }, { "date": "2026-02-24", "arxiv_id": "2602.19128", "title": "K-Search: LLM Kernel Generation via Co-Evolving Intrinsic World Model", "authors": [ "Shiyi Cao", "Ziming Mao", "Joseph E. Gonzalez", "Ion Stoica" ], "github": "https://github.com/caoshiyi/K-Search", "project_page": "", "abstract": "Optimizing GPU kernels is critical for efficient modern machine learning systems yet remains challenging due to the complex interplay of design factors and rapid hardware evolution. Existing automated approaches typically treat Large Language Models (LLMs) merely as stochastic code generators within heuristic-guided evolutionary loops. These methods often struggle with complex kernels requiring coordinated, multi-step structural transformations, as they lack explicit planning capabilities and frequently discard promising strategies due to inefficient or incorrect intermediate implementations. To address this, we propose Search via Co-Evolving World Model and build K-Search based on this method. By replacing static search heuristics with a co-evolving world model, our framework leverages LLMs' prior domain knowledge to guide the search, actively exploring the optimization space. This approach explicitly decouples high-level algorithmic planning from low-level program instantiation, enabling the system to navigate non-monotonic optimization paths while remaining resilient to temporary implementation defects. We evaluate K-Search on diverse, complex kernels from FlashInfer, including GQA, MLA, and MoE kernels. Our results show that K-Search significantly outperforms state-of-the-art evolutionary search methods, achieving an average 2.10x improvement and up to a 14.3x gain on complex MoE kernels. On the GPUMode TriMul task, K-Search achieves state-of-the-art performance on H100, reaching 1030us and surpassing both prior evolution and human-designed solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/caoshiyi/K-Search", "github_api_added_by": "auto" }, { "date": "2026-02-24", "arxiv_id": "2602.19455", "title": "SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning", "authors": [ "Zelin He", "Boran Han", "Xiyuan Zhang", "Shuai Zhang", "Haotian Lin", "Qi Zhu", "Haoyang Fang", "Danielle C. Maddix", "Abdul Fatir Ansari", "Akash Chandrayan", "Abhinav Pradhan", "Bernie Wang", "Matthew Reimherr" ], "github": "", "project_page": "", "abstract": "Time-series diagnostic reasoning is essential for many applications, yet existing solutions face a persistent gap: general reasoning large language models (GRLMs) possess strong reasoning skills but lack the domain-specific knowledge to understand complex time-series patterns. Conversely, fine-tuned time-series LLMs (TSLMs) understand these patterns but lack the capacity to generalize reasoning for more complicated questions. To bridge this gap, we propose a hybrid knowledge-injection framework that injects TSLM-generated insights directly into GRLM's reasoning trace, thereby achieving strong time-series reasoning with in-domain knowledge. As collecting data for knowledge injection fine-tuning is costly, we further leverage a reinforcement learning-based approach with verifiable rewards (RLVR) to elicit knowledge-rich traces without human supervision, then transfer such an in-domain thinking trace into GRLM for efficient knowledge injection. We further release SenTSR-Bench, a multivariate time-series-based diagnostic reasoning benchmark collected from real-world industrial operations. Across SenTSR-Bench and other public datasets, our method consistently surpasses TSLMs by 9.1%-26.1% and GRLMs by 7.9%-22.4%, delivering robust, context-aware time-series diagnostic insights.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.18915", "title": "AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting", "authors": [ "Mohammadreza Ghaffarzadeh-Esfahani", "Yousof Gheisari" ], "github": "", "project_page": "", "abstract": "Adeno-associated viruses (AAVs) are promising vectors for gene therapy, but their native serotypes face limitations in tissue tropism, immune evasion, and production efficiency. Engineering capsids to overcome these hurdles is challenging due to the vast sequence space and the difficulty of simultaneously optimizing multiple functional properties. The complexity also adds when it comes to the kidney, which presents unique anatomical barriers and cellular targets that require precise and efficient vector engineering. Here, we present AAVGen, a generative artificial intelligence framework for de novo design of AAV capsids with enhanced multi-trait profiles. AAVGen integrates a protein language model (PLM) with supervised fine-tuning (SFT) and a reinforcement learning technique termed Group Sequence Policy Optimization (GSPO). The model is guided by a composite reward signal derived from three ESM-2-based regression predictors, each trained to predict a key property: production fitness, kidney tropism, and thermostability. Our results demonstrate that AAVGen produces a diverse library of novel VP1 protein sequences. In silico validations revealed that the majority of the generated variants have superior performance across all three employed indices, indicating successful multi-objective optimization. Furthermore, structural analysis via AlphaFold3 confirms that the generated sequences preserve the canonical capsid folding despite sequence diversification. AAVGen establishes a foundation for data-driven viral vector engineering, accelerating the development of next-generation AAV vectors with tailored functional characteristics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.19672", "title": "SkillOrchestra: Learning to Route Agents via Skill Transfer", "authors": [ "Jiayu Wang", "Yifei Ming", "Zixuan Ke", "Shafiq Joty", "Aws Albarghouthi", "Frederic Sala" ], "github": "https://github.com/jiayuww/SkillOrchestra", "project_page": "", "abstract": "Compound AI systems promise capabilities beyond those of individual models, yet their success depends critically on effective orchestration. Existing routing approaches face two limitations: (1) input-level routers make coarse query-level decisions that ignore evolving task requirements; (2) RL-trained orchestrators are expensive to adapt and often suffer from routing collapse, repeatedly invoking one strong but costly option in multi-turn scenarios. We introduce SkillOrchestra, a framework for skill-aware orchestration. Instead of directly learning a routing policy end-to-end, SkillOrchestra learns fine-grained skills from execution experience and models agent-specific competence and cost under those skills. At deployment, the orchestrator infers the skill demands of the current interaction and selects agents that best satisfy them under an explicit performance-cost trade-off. Extensive experiments across ten benchmarks demonstrate that SkillOrchestra outperforms SoTA RL-based orchestrators by up to 22.5% with 700x and 300x learning cost reduction compared to Router-R1 and ToolOrchestra, respectively. These results show that explicit skill modeling enables scalable, interpretable, and sample-efficient orchestration, offering a principled alternative to data-intensive RL-based approaches. The code is available at: https://github.com/jiayuww/SkillOrchestra.", "github_manual": "", "github_regex": "https://github.com/jiayuww/SkillOrchestra", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.12100", "title": "AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer", "authors": [ "Lingting Zhu", "Shengju Qian", "Haidi Fan", "Jiayu Dong", "Zhenchao Jin", "Siwei Zhou", "Gen Dong", "Xin Wang", "Lequan Yu" ], "github": "https://github.com/Advocate99/AssetFormer", "project_page": "", "abstract": "The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content~(UGC). In this work, we introduce AssetFormer, an autoregressive Transformer-based model designed to generate modular 3D assets from textual descriptions. Our pilot study leverages real-world modular assets collected from online platforms. AssetFormer tackles the challenge of creating assets composed of primitives that adhere to constrained design parameters for various applications. By innovatively adapting module sequencing and decoding techniques inspired by language models, our approach enhances asset generation quality through autoregressive modeling. Initial results indicate the effectiveness of AssetFormer in streamlining asset creation for professional development and UGC scenarios. This work presents a flexible framework extendable to various types of modular 3D assets, contributing to the broader field of 3D content generation. The code is available at https://github.com/Advocate99/AssetFormer.", "github_manual": "", "github_regex": "https://github.com/Advocate99/AssetFormer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.20093", "title": "ManCAR: Manifold-Constrained Latent Reasoning with Adaptive Test-Time Computation for Sequential Recommendation", "authors": [ "Kun Yang", "Yuxuan Zhu", "Yazhe Chen", "Siyao Zheng", "Bangyang Hong", "Kangle Wu", "Yabo Ni", "Anxiang Zeng", "Cong Fu", "Hui Li" ], "github": "https://github.com/FuCongResearchSquad/ManCAR", "project_page": "", "abstract": "Sequential recommendation increasingly employs latent multi-step reasoning to enhance test-time computation. Despite empirical gains, existing approaches largely drive intermediate reasoning states via target-dominant objectives without imposing explicit feasibility constraints. This results in latent drift, where reasoning trajectories deviate into implausible regions. We argue that effective recommendation reasoning should instead be viewed as navigation on a collaborative manifold rather than free-form latent refinement. To this end, we propose ManCAR (Manifold-Constrained Adaptive Reasoning), a principled framework that grounds reasoning within the topology of a global interaction graph. ManCAR constructs a local intent prior from the collaborative neighborhood of a user's recent actions, represented as a distribution over the item simplex. During training, the model progressively aligns its latent predictive distribution with this prior, forcing the reasoning trajectory to remain within the valid manifold. At test time, reasoning proceeds adaptively until the predictive distribution stabilizes, avoiding over-refinement. We provide a variational interpretation of ManCAR to theoretically validate its drift-prevention and adaptive test-time stopping mechanisms. Experiments on seven benchmarks demonstrate that ManCAR consistently outperforms state-of-the-art baselines, achieving up to a 46.88% relative improvement w.r.t. NDCG@10. Our code is available at https://github.com/FuCongResearchSquad/ManCAR.", "github_manual": "", "github_regex": "https://github.com/FuCongResearchSquad/ManCAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.18532", "title": "VLANeXt: Recipes for Building Strong VLA Models", "authors": [ "Xiao-Ming Wu", "Bin Fan", "Kang Liao", "Jian-Jian Jiang", "Runze Yang", "Yihang Luo", "Zhonghua Wu", "Wei-Shi Zheng", "Chen Change Loy" ], "github": "", "project_page": "", "abstract": "Following the rise of large foundation models, Vision-Language-Action models (VLAs) emerged, leveraging strong visual and language understanding for general-purpose policy learning. Yet, the current VLA landscape remains fragmented and exploratory. Although many groups have proposed their own VLA models, inconsistencies in training protocols and evaluation settings make it difficult to identify which design choices truly matter. To bring structure to this evolving space, we reexamine the VLA design space under a unified framework and evaluation setup. Starting from a simple VLA baseline similar to RT-2 and OpenVLA, we systematically dissect design choices along three dimensions: foundational components, perception essentials, and action modelling perspectives. From this study, we distill 12 key findings that together form a practical recipe for building strong VLA models. The outcome of this exploration is a simple yet effective model, VLANeXt. VLANeXt outperforms prior state-of-the-art methods on the LIBERO and LIBERO-plus benchmarks and demonstrates strong generalization in real-world experiments. We will release a unified, easy-to-use codebase that serves as a common platform for the community to reproduce our findings, explore the design space, and build new VLA variants on top of a shared foundation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.18996", "title": "Learning Cross-View Object Correspondence via Cycle-Consistent Mask Prediction", "authors": [ "Shannan Yan", "Leqi Zheng", "Keyu Lv", "Jingchen Ni", "Hongyang Wei", "Jiajun Zhang", "Guangting Wang", "Jing Lyu", "Chun Yuan", "Fengyun Rao" ], "github": "https://github.com/shannany0606/CCMP", "project_page": "", "abstract": "We study the task of establishing object-level visual correspondence across different viewpoints in videos, focusing on the challenging egocentric-to-exocentric and exocentric-to-egocentric scenarios. We propose a simple yet effective framework based on conditional binary segmentation, where an object query mask is encoded into a latent representation to guide the localization of the corresponding object in a target video. To encourage robust, view-invariant representations, we introduce a cycle-consistency training objective: the predicted mask in the target view is projected back to the source view to reconstruct the original query mask. This bidirectional constraint provides a strong self-supervisory signal without requiring ground-truth annotations and enables test-time training (TTT) at inference. Experiments on the Ego-Exo4D and HANDAL-X benchmarks demonstrate the effectiveness of our optimization objective and TTT strategy, achieving state-of-the-art performance. The code is available at https://github.com/shannany0606/CCMP.", "github_manual": "", "github_regex": "https://github.com/shannany0606/CCMP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.18224", "title": "SimVLA: A Simple VLA Baseline for Robotic Manipulation", "authors": [ "Yuankai Luo", "Woping Chen", "Tong Liang", "Baiqiao Wang", "Zhenguo Li" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic manipulation, leveraging large-scale pre-training to achieve strong performance. The field has rapidly evolved with additional spatial priors and diverse architectural innovations. However, these advancements are often accompanied by varying training recipes and implementation details, which can make it challenging to disentangle the precise source of empirical gains. In this work, we introduce SimVLA, a streamlined baseline designed to establish a transparent reference point for VLA research. By strictly decoupling perception from control, using a standard vision-language backbone and a lightweight action head, and standardizing critical training dynamics, we demonstrate that a minimal design can achieve state-of-the-art performance. Despite having only 0.5B parameters, SimVLA outperforms multi-billion-parameter models on standard simulation benchmarks without robot pretraining. SimVLA also reaches on-par real-robot performance compared to pi0.5. Our results establish SimVLA as a robust, reproducible baseline that enables clear attribution of empirical gains to future architectural innovations. Website: https://frontierrobo.github.io/SimVLA", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.18742", "title": "RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning", "authors": [ "Seungku Kim", "Suhyeok Jang", "Byungjun Yoon", "Dongyoung Kim", "John Won", "Jinwoo Shin" ], "github": "", "project_page": "", "abstract": "Synthetic data generated by video generative models has shown promise for robot learning as a scalable pipeline, but it often suffers from inconsistent action quality due to imperfectly generated videos. Recently, vision-language models (VLMs) have been leveraged to validate video quality, but they have limitations in distinguishing physically accurate videos and, even then, cannot directly evaluate the generated actions themselves. To tackle this issue, we introduce RoboCurate, a novel synthetic robot data generation framework that evaluates and filters the quality of annotated actions by comparing them with simulation replay. Specifically, RoboCurate replays the predicted actions in a simulator and assesses action quality by measuring the consistency of motion between the simulator rollout and the generated video. In addition, we unlock observation diversity beyond the available dataset via image-to-image editing and apply action-preserving video-to-video transfer to further augment appearance. We observe RoboCurate's generated data yield substantial relative improvements in success rates compared to using real data only, achieving +70.1% on GR-1 Tabletop (300 demos), +16.1% on DexMimicGen in the pre-training setup, and +179.9% in the challenging real-world ALLEX humanoid dexterous manipulation setting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.19626", "title": "Nacrith: Neural Lossless Compression via Ensemble Context Modeling and High-Precision CDF Coding", "authors": [ "Roberto Tacconelli" ], "github": "", "project_page": "", "abstract": "We present Nacrith, a lossless compression system that combines a 135M-parameter transformer language model (SmolLM2-135M) with an ensemble of lightweight online predictors and a 32-bit arithmetic coder. Beyond the base LLM-plus-arithmetic-coding paradigm, Nacrith introduces several contributions: (1) a CDF precision upgrade from 2^16 to 2^24 that eliminates ~75% of quantization overhead caused by minimum-probability floors in large vocabularies; (2) a token-level N-gram model for fast local predictions; (3) an adaptive log-space bias head correcting per-document LLM errors via online gradient descent; (4) confidence-based LLM skip for accelerating highly predictable tokens; (5) a hybrid binary format (NC06) extending neural compression to arbitrary binary files--to our knowledge a first among LLM-based compressors; (6) a llama.cpp inference backend achieving ~7x faster single-token decode than PyTorch; (7) parallel multi-GPU compression across up to 8 workers; and (8) native KV cache sliding window reducing per-slide cost by ~37x. The system requires only ~500 MB of GGUF weights and ~1.2 GB VRAM per worker, running on consumer GPUs. On alice29.txt (Canterbury Corpus, 152 KB), Nacrith achieves 0.918 bits per byte (bpb)--outperforming gzip by 3.1x, bzip2 by 2.5x, CMIX v21 by 44%, and ts_zip by 20%, while compressing below the 0th-, 1st-, and 2nd-order byte-level Shannon entropy bounds. On enwik8 (100 MB), Nacrith achieves 0.9389 bpb (11.74%), surpassing ts_zip (~1.11 bpb) by 15% and FineZip (1.024 bpb) by 8% despite using a 60x smaller model with no fine-tuning. An out-of-distribution evaluation on a document published after the model's training cutoff confirms these gains are not memorization artifacts, achieving 0.723 bpb on unseen text.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.18662", "title": "Large Causal Models for Temporal Causal Discovery", "authors": [ "Nikolaos Kougioulis", "Nikolaos Gkorgkolis", "MingXue Wang", "Bora Caglayan", "Dario Simionato", "Andrea Tonon", "Ioannis Tsamardinos" ], "github": "https://github.com/kougioulis/LCM-paper", "project_page": "", "abstract": "Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for each individual dataset. Such an approach limits the potential of multi-dataset pretraining. The concept of large causal models (LCMs) envisions a class of pre-trained neural architectures specifically designed for temporal causal discovery. Prior approaches are constrained to small variable counts, degrade with larger inputs, and rely heavily on synthetic data, limiting generalization. We propose a principled framework for LCMs, combining diverse synthetic generators with realistic time-series datasets, allowing learning at scale. Extensive experiments on synthetic, semi-synthetic and realistic benchmarks show that LCMs scale effectively to higher variable counts and deeper architectures while maintaining strong performance. Trained models achieve competitive or superior accuracy compared to classical and neural baselines, particularly in out-of-distribution settings, while enabling fast, single-pass inference. Results demonstrate LCMs as a promising foundation-model paradigm for temporal causal discovery. Experiments and model weights are available at https://github.com/kougioulis/LCM-paper/.", "github_manual": "", "github_regex": "https://github.com/kougioulis/LCM-paper", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.17393", "title": "Contact-Anchored Proprioceptive Odometry for Quadruped Robots", "authors": [ "Minxing Sun", "Yao Mao" ], "github": "", "project_page": "", "abstract": "Reliable odometry for legged robots without cameras or LiDAR remains challenging due to IMU drift and noisy joint velocity sensing. This paper presents a purely proprioceptive state estimator that uses only IMU and motor measurements to jointly estimate body pose and velocity, with a unified formulation applicable to biped, quadruped, and wheel-legged robots. The key idea is to treat each contacting leg as a kinematic anchor: joint-torque--based foot wrench estimation selects reliable contacts, and the corresponding footfall positions provide intermittent world-frame constraints that suppress long-term drift. To prevent elevation drift during extended traversal, we introduce a lightweight height clustering and time-decay correction that snaps newly recorded footfall heights to previously observed support planes. To improve foot velocity observations under encoder quantization, we apply an inverse-kinematics cubature Kalman filter that directly filters foot-end velocities from joint angles and velocities. The implementation further mitigates yaw drift through multi-contact geometric consistency and degrades gracefully to a kinematics-derived heading reference when IMU yaw constraints are unavailable or unreliable. We evaluate the method on four quadruped platforms (three Astrall robots and a Unitree Go2 EDU) using closed-loop trajectories. On Astrall point-foot robot~A, a sim200\\,m horizontal loop and a sim15\\,m vertical loop return with 0.1638\\,m and 0.219\\,m error, respectively; on wheel-legged robot~B, the corresponding errors are 0.2264\\,m and 0.199\\,m. On wheel-legged robot~C, a sim700\\,m horizontal loop yields 7.68\\,m error and a sim20\\,m vertical loop yields 0.540\\,m error. Unitree Go2 EDU closes a sim120\\,m horizontal loop with 2.2138\\,m error and a sim8\\,m vertical loop with less than 0.1\\,m vertical error. github.com/ShineMinxing/Ros2Go2Estimator.git", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.16872", "title": "DODO: Discrete OCR Diffusion Models", "authors": [ "Sean Man", "Roy Ganz", "Roi Ronen", "Shahar Tsiper", "Shai Mazor", "Niv Nayman" ], "github": "", "project_page": "", "abstract": "Optical Character Recognition (OCR) is a fundamental task for digitizing information, serving as a critical bridge between visual data and textual understanding. While modern Vision-Language Models (VLM) have achieved high accuracy in this domain, they predominantly rely on autoregressive decoding, which becomes computationally expensive and slow for long documents as it requires a sequential forward pass for every generated token. We identify a key opportunity to overcome this bottleneck: unlike open-ended generation, OCR is a highly deterministic task where the visual input strictly dictates a unique output sequence, theoretically enabling efficient, parallel decoding via diffusion models. However, we show that existing masked diffusion models fail to harness this potential; those introduce structural instabilities that are benign in flexible tasks, like captioning, but catastrophic for the rigid, exact-match requirements of OCR. To bridge this gap, we introduce DODO, the first VLM to utilize block discrete diffusion and unlock its speedup potential for OCR. By decomposing generation into blocks, DODO mitigates the synchronization errors of global diffusion. Empirically, our method achieves near state-of-the-art accuracy while enabling up to 3x faster inference compared to autoregressive baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.19320", "title": "Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations", "authors": [ "Dongming Jiang", "Yi Li", "Songtao Wei", "Jinxin Yang", "Ayushi Kishore", "Alysa Zhao", "Dingyi Kang", "Xu Hu", "Feng Chen", "Qiannan Li", "Bingzhe Li" ], "github": "", "project_page": "", "abstract": "Agentic memory systems enable large language model (LLM) agents to maintain state across long interactions, supporting long-horizon reasoning and personalization beyond fixed context windows. Despite rapid architectural development, the empirical foundations of these systems remain fragile: existing benchmarks are often underscaled, evaluation metrics are misaligned with semantic utility, performance varies significantly across backbone models, and system-level costs are frequently overlooked. This survey presents a structured analysis of agentic memory from both architectural and system perspectives. We first introduce a concise taxonomy of MAG systems based on four memory structures. Then, we analyze key pain points limiting current systems, including benchmark saturation effects, metric validity and judge sensitivity, backbone-dependent accuracy, and the latency and throughput overhead introduced by memory maintenance. By connecting the memory structure to empirical limitations, this survey clarifies why current agentic memory systems often underperform their theoretical promise and outlines directions for more reliable evaluation and scalable system design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.18640", "title": "Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System", "authors": [ "Longfei Yun", "Yihan Wu", "Haoran Liu", "Xiaoxuan Liu", "Ziyun Xu", "Yi Wang", "Yang Xia", "Pengfei Wang", "Mingze Gao", "Yunxiang Wang", "Changfan Chen", "Junfeng Pan" ], "github": "", "project_page": "", "abstract": "Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product requirements. Progress in this domain is increasingly bottlenecked by the engineering context constraint: the arduous process of translating ambiguous product intent into reasonable, executable, verifiable hypotheses, rather than by modeling techniques alone. We present GEARS (Generative Engine for Agentic Ranking Systems), a framework that reframes ranking optimization as an autonomous discovery process within a programmable experimentation environment. Rather than treating optimization as static model selection, GEARS leverages Specialized Agent Skills to encapsulate ranking expert knowledge into reusable reasoning capabilities, enabling operators to steer systems via high-level intent vibe personalization. Furthermore, to ensure production reliability, the framework incorporates validation hooks to enforce statistical robustness and filter out brittle policies that overfit short-term signals. Experimental validation across diverse product surfaces demonstrates that GEARS consistently identifies superior, near-Pareto-efficient policies by synergizing algorithmic signals with deep ranking context while maintaining rigorous deployment stability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.16863", "title": "SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation", "authors": [ "Kushal Kedia", "Tyler Ga Wei Lum", "Jeannette Bohg", "C. Karen Liu" ], "github": "https://github.com/google-deepmind/mujoco_playground", "project_page": "", "abstract": "The ability to manipulate tools significantly expands the set of tasks a robot can perform. Yet, tool manipulation represents a challenging class of dexterity, requiring grasping thin objects, in-hand object rotations, and forceful interactions. Since collecting teleoperation data for these behaviors is challenging, sim-to-real reinforcement learning (RL) is a promising alternative. However, prior approaches typically require substantial engineering effort to model objects and tune reward functions for each task. In this work, we propose SimToolReal, taking a step towards generalizing sim-to-real RL policies for tool manipulation. Instead of focusing on a single object and task, we procedurally generate a large variety of tool-like object primitives in simulation and train a single RL policy with the universal goal of manipulating each object to random goal poses. This approach enables SimToolReal to perform general dexterous tool manipulation at test-time without any object or task-specific training. We demonstrate that SimToolReal outperforms prior retargeting and fixed-grasp methods by 37% while matching the performance of specialist RL policies trained on specific target objects and tasks. Finally, we show that SimToolReal generalizes across a diverse set of everyday tools, achieving strong zero-shot performance over 120 real-world rollouts spanning 24 tasks, 12 object instances, and 6 tool categories.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/mujoco_playground", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.18333", "title": "On the \"Induction Bias\" in Sequence Models", "authors": [ "M. Reza Ebrahimi", "Micha\u00ebl Defferrard", "Sunny Panchal", "Roland Memisevic" ], "github": "", "project_page": "", "abstract": "Despite the remarkable practical success of transformer-based language models, recent work has raised concerns about their ability to perform state tracking. In particular, a growing body of literature has shown this limitation primarily through failures in out-of-distribution (OOD) generalization, such as length extrapolation. In this work, we shift attention to the in-distribution implications of these limitations. We conduct a large-scale experimental study of the data efficiency of transformers and recurrent neural networks (RNNs) across multiple supervision regimes. We find that the amount of training data required by transformers grows much more rapidly with state-space size and sequence length than for RNNs. Furthermore, we analyze the extent to which learned state-tracking mechanisms are shared across different sequence lengths. We show that transformers exhibit negligible or even detrimental weight sharing across lengths, indicating that they learn length-specific solutions in isolation. In contrast, recurrent models exhibit effective amortized learning by sharing weights across lengths, allowing data from one sequence length to improve performance on others. Together, these results demonstrate that state tracking remains a fundamental challenge for transformers, even when training and evaluation distributions match.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-24", "arxiv_id": "2602.19089", "title": "Ani3DHuman: Photorealistic 3D Human Animation with Self-guided Stochastic Sampling", "authors": [ "Qi Sun", "Can Wang", "Jiaxiang Shang", "Yingchun Liu", "Jing Liao" ], "github": "https://github.com/qiisun/ani3dhuman", "project_page": "", "abstract": "Current 3D human animation methods struggle to achieve photorealism: kinematics-based approaches lack non-rigid dynamics (e.g., clothing dynamics), while methods that leverage video diffusion priors can synthesize non-rigid motion but suffer from quality artifacts and identity loss. To overcome these limitations, we present Ani3DHuman, a framework that marries kinematics-based animation with video diffusion priors. We first introduce a layered motion representation that disentangles rigid motion from residual non-rigid motion. Rigid motion is generated by a kinematic method, which then produces a coarse rendering to guide the video diffusion model in generating video sequences that restore the residual non-rigid motion. However, this restoration task, based on diffusion sampling, is highly challenging, as the initial renderings are out-of-distribution, causing standard deterministic ODE samplers to fail. Therefore, we propose a novel self-guided stochastic sampling method, which effectively addresses the out-of-distribution problem by combining stochastic sampling (for photorealistic quality) with self-guidance (for identity fidelity). These restored videos provide high-quality supervision, enabling the optimization of the residual non-rigid motion field. Extensive experiments demonstrate that \\MethodName can generate photorealistic 3D human animation, outperforming existing methods. Code is available in https://github.com/qiisun/ani3dhuman.", "github_manual": "", "github_regex": "https://github.com/qiisun/ani3dhuman", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.21015", "title": "From Perception to Action: An Interactive Benchmark for Vision Reasoning", "authors": [ "Yuhao Wu", "Maojia Song", "Yihuai Lan", "Lei Wang", "Zhiqiang Hu", "Yao Xiao", "Heng Zhou", "Weihua Zheng", "Dylan Raharja", "Soujanya Poria", "Roy Ka-Wei Lee" ], "github": "", "project_page": "", "abstract": "Understanding the physical structure is essential for real-world applications such as embodied agents, interactive design, and long-horizon manipulation. Yet, prevailing Vision-Language Model (VLM) evaluations still center on structure-agnostic, single-turn setups (e.g., VQA), which fail to assess agents' ability to reason about how geometry, contact, and support relations jointly constrain what actions are possible in a dynamic environment. To address this gap, we introduce the Causal Hierarchy of Actions and Interactions (CHAIN) benchmark, an interactive 3D, physics-driven testbed designed to evaluate whether models can understand, plan, and execute structured action sequences grounded in physical constraints. CHAIN shifts evaluation from passive perception to active problem solving, spanning tasks such as interlocking mechanical puzzles and 3D stacking and packing. We conduct a comprehensive study of state-of-the-art VLMs and diffusion-based models under unified interactive settings. Our results show that top-performing models still struggle to internalize physical structure and causal constraints, often failing to produce reliable long-horizon plans and cannot robustly translate perceived structure into effective actions. The project is available at https://social-ai-studio.github.io/CHAIN/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.20739", "title": "PyVision-RL: Forging Open Agentic Vision Models via RL", "authors": [ "Shitian Zhao", "Shaoheng Lin", "Ming Li", "Haoquan Zhang", "Wenshuo Peng", "Kaipeng Zhang", "Chen Wei" ], "github": "https://github.com/agents-x-project/PyVision-RL", "project_page": "", "abstract": "Reinforcement learning for agentic multimodal models often suffers from interaction collapse, where models learn to reduce tool usage and multi-turn reasoning, limiting the benefits of agentic behavior. We introduce PyVision-RL, a reinforcement learning framework for open-weight multimodal models that stabilizes training and sustains interaction. Our approach combines an oversampling-filtering-ranking rollout strategy with an accumulative tool reward to prevent collapse and encourage multi-turn tool use. Using a unified training pipeline, we develop PyVision-Image and PyVision-Video for image and video understanding. For video reasoning, PyVision-Video employs on-demand context construction, selectively sampling task-relevant frames during reasoning to significantly reduce visual token usage. Experiments show strong performance and improved efficiency, demonstrating that sustained interaction and on-demand visual processing are critical for scalable multimodal agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/agents-x-project/PyVision-RL", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.16603", "title": "FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving", "authors": [ "Chia-chi Hsieh", "Zan Zong", "Xinyang Chen", "Jianjiang Li", "Jidong Zhai", "Lijie Wen" ], "github": "", "project_page": "", "abstract": "The growing demand for large language models (LLMs) requires serving systems to handle many concurrent requests with diverse service level objectives (SLOs). This exacerbates head-of-line (HoL) blocking during the compute-intensive prefill phase, where long-running requests monopolize resources and delay higher-priority ones, leading to widespread time-to-first-token (TTFT) SLO violations. While chunked prefill enables interruptibility, it introduces an inherent trade-off between responsiveness and throughput: reducing chunk size improves response latency but degrades computational efficiency, whereas increasing chunk size maximizes throughput but exacerbates blocking. This necessitates an adaptive preemption mechanism. However, dynamically balancing execution granularity against scheduling overheads remains a key challenge. In this paper, we propose FlowPrefill, a TTFT-goodput-optimized serving system that resolves this conflict by decoupling preemption granularity from scheduling frequency. To achieve adaptive prefill scheduling, FlowPrefill introduces two key innovations: 1) Operator-Level Preemption, which leverages operator boundaries to enable fine-grained execution interruption without the efficiency loss associated with fixed small chunking; and 2) Event-Driven Scheduling, which triggers scheduling decisions only upon request arrival or completion events, thereby supporting efficient preemption responsiveness while minimizing control-plane overhead. Evaluation on real-world production traces shows that FlowPrefill improves maximum goodput by up to 5.6times compared to state-of-the-art systems while satisfying heterogeneous SLOs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.14337", "title": "LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces", "authors": [ "Yukang Feng", "Jianwen Sun", "Zelai Yang", "Jiaxin Ai", "Chuanhao Li", "Zizhen Li", "Fanrui Zhang", "Kang He", "Rui Ma", "Jifan Lin", "Jie Sun", "Yang Xiao", "Sizhuo Zhou", "Wenxiao Wu", "Yiming Liu", "Pengfei Liu", "Yu Qiao", "Shenglin Zhang", "Kaipeng Zhang" ], "github": "https://github.com/finyorko/longcli-bench", "project_page": "", "abstract": "Recent advances in AI-assisted programming have empowered agents to execute complex workflows via command-line interfaces, however, existing benchmarks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics, fail to rigorously evaluate the long-horizon planning and execution capabilities essential for realistic software engineering. To address these gaps, we introduce LongCLI-Bench, a comprehensive benchmark designed to evaluate agentic capabilities across long-horizon, realistic tasks. We curated 20 high-quality, long-horizon tasks from over 1,000 computer science assignments and real-world workflows, covering four engineering categories: from scratch, feature addition, bug fixing, and refactoring. We propose a dual-set testing protocol for LongCLI-Bench, which measures requirement fulfillment (fail-to-pass) and regression avoidance (pass-to-pass), and incorporates step-level scoring to pinpoint execution failures. Extensive experiments reveal that even state-of-the-art agents achieve pass rates below 20% in LongCLI-Bench. Step-level analysis further indicates that the majority of tasks stall at less than 30% completion, highlighting that critical failures often occur in the early stages. Although self-correction offers marginal gains, human-agent collaboration through plan injection and interactive guidance yields significantly higher improvements. These results highlight that future research must emphasize the development of synergistic human-agent workflows alongside advances in agents' planning and execution capabilities to overcome key challenges in long-horizon task performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/finyorko/longcli-bench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.20945", "title": "The Art of Efficient Reasoning: Data, Reward, and Optimization", "authors": [ "Taiqiang Wu", "Zenan Zu", "Bo Zhou", "Ngai Wong" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) consistently benefit from scaled Chain-of-Thought (CoT) reasoning, but also suffer from heavy computational overhead. To address this issue, efficient reasoning aims to incentivize short yet accurate thinking trajectories, typically through reward shaping with Reinforcement Learning (RL). In this paper, we systematically investigate the mechanics of efficient reasoning for LLMs. For comprehensive evaluation, we advocate for more fine-grained metrics, including length distribution conditioned on correctness and performance across a wide spectrum of token budgets ranging from 2k to 32k. First, we reveal that the training process follows a two-stage paradigm: length adaptation and reasoning refinement. After that, we conduct extensive experiments (about 0.2 million GPU hours) in a unified protocol, deconstructing training prompts and rollouts, reward shaping, and optimization strategies. In particular, a key finding is to train on relatively easier prompts, ensuring the density of positive reward signals and thus avoiding the length collapse. Meanwhile, the learned length bias can be generalized across domains. We distill all findings into valuable insights and practical guidelines, and further validate them across the Qwen3 series, ranging from 0.6B to 30B, demonstrating the robustness and generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.20424", "title": "Implicit Intelligence -- Evaluating Agents on What Users Don't Say", "authors": [ "Ved Sirdeshmukh", "Marc Wetter" ], "github": "", "project_page": "", "abstract": "Real-world requests to AI agents are fundamentally underspecified. Natural human communication relies on shared context and unstated constraints that speakers expect listeners to infer. Current agentic benchmarks test explicit instruction-following but fail to evaluate whether agents can reason about implicit requirements spanning accessibility needs, privacy boundaries, catastrophic risks, and contextual constraints. We present Implicit Intelligence, an evaluation framework testing whether AI agents can move beyond prompt-following to become genuine goal-fulfillers, paired with Agent-as-a-World (AaW), a harness where interactive worlds are defined in human-readable YAML files and simulated by language models. Our scenarios feature apparent simplicity in user requests, hidden complexity in correct solutions, and discoverability of constraints through environmental exploration. Evaluating 16 frontier and open-weight models across 205 scenarios, we find that even the best-performing model achieves only 48.3% scenario pass rate, revealing substantial room for improvement in bridging the gap between literal instruction-following and human-like contextual reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.21193", "title": "On Data Engineering for Scaling LLM Terminal Capabilities", "authors": [ "Renjie Pi", "Grace Lam", "Mohammad Shoeybi", "Pooya Jannaty", "Bryan Catanzaro", "Wei Ping" ], "github": "", "project_page": "", "abstract": "Despite rapid recent progress in the terminal capabilities of large language models, the training data strategies behind state-of-the-art terminal agents remain largely undisclosed. We address this gap through a systematic study of data engineering practices for terminal agents, making two key contributions: (1) Terminal-Task-Gen, a lightweight synthetic task generation pipeline that supports seed-based and skill-based task construction, and (2) a comprehensive analysis of data and training strategies, including filtering, curriculum learning, long context training, and scaling behavior. Our pipeline yields Terminal-Corpus, a large-scale open-source dataset for terminal tasks. Using this dataset, we train Nemotron-Terminal, a family of models initialized from Qwen3(8B, 14B, 32B) that achieve substantial gains on Terminal-Bench 2.0: Nemotron-Terminal-8B improves from 2.5% to 13.0% Nemotron-Terminal-14B improves from 4.0% to 20.2%, and Nemotron-Terminal-32B improves from 3.4% to 27.4%, matching the performance of significantly larger models. To accelerate research in this domain, we open-source our model checkpoints and most of our synthetic datasets at https://huggingface.co/collections/nvidia/nemotron-terminal.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.16990", "title": "Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation", "authors": [ "Yan Wang", "Yi Han", "Lingfei Qian", "Yueru He", "Xueqing Peng", "Dongji Feng", "Zhuohan Xie", "Vincent Jim Zhang", "Rosie Guo", "Fengran Mo", "Jimin Huang", "Yankai Chen", "Xue Liu", "Jian-Yun Nie" ], "github": "", "project_page": "", "abstract": "Most recommendation benchmarks evaluate how well a model imitates user behavior. In financial advisory, however, observed actions can be noisy or short-sighted under market volatility and may conflict with a user's long-term goals. Treating what users chose as the sole ground truth, therefore, conflates behavioral imitation with decision quality. We introduce Conv-FinRe, a conversational and longitudinal benchmark for stock recommendation that evaluates LLMs beyond behavior matching. Given an onboarding interview, step-wise market context, and advisory dialogues, models must generate rankings over a fixed investment horizon. Crucially, Conv-FinRe provides multi-view references that distinguish descriptive behavior from normative utility grounded in investor-specific risk preferences, enabling diagnosis of whether an LLM follows rational analysis, mimics user noise, or is driven by market momentum. We build the benchmark from real market data and human decision trajectories, instantiate controlled advisory conversations, and evaluate a suite of state-of-the-art LLMs. Results reveal a persistent tension between rational decision quality and behavioral alignment: models that perform well on utility-based ranking often fail to match user choices, whereas behaviorally aligned models can overfit short-term noise. The dataset is publicly released on Hugging Face, and the codebase is available on GitHub.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.21198", "title": "Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs", "authors": [ "Yining Hong", "Huang Huang", "Manling Li", "Li Fei-Fei", "Jiajun Wu", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "Embodied LLMs endow robots with high-level task reasoning, but they cannot reflect on what went wrong or why, turning deployment into a sequence of independent trials where mistakes repeat rather than accumulate into experience. Drawing upon human reflective practitioners, we introduce Reflective Test-Time Planning, which integrates two modes of reflection: reflection-in-action, where the agent uses test-time scaling to generate and score multiple candidate actions using internal reflections before execution; and reflection-on-action, which uses test-time training to update both its internal reflection model and its action policy based on external reflections after execution. We also include retrospective reflection, allowing the agent to re-evaluate earlier decisions and perform model updates with hindsight for proper long-horizon credit assignment. Experiments on our newly-designed Long-Horizon Household benchmark and MuJoCo Cupboard Fitting benchmark show significant gains over baseline models, with ablative studies validating the complementary roles of reflection-in-action and reflection-on-action. Qualitative analyses, including real-robot trials, highlight behavioral correction through reflection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.21201", "title": "Aletheia tackles FirstProof autonomously", "authors": [ "Tony Feng", "Junehyuk Jung", "Sang-hyun Kim", "Carlo Pagano", "Sergei Gukov", "Chiang-Chiang Tsai", "David Woodruff", "Adel Javanmard", "Aryan Mokhtari", "Dawsen Hwang", "Yuri Chervonyi", "Jonathan N. Lee", "Garrett Bingham", "Trieu H. Trinh", "Vahab Mirrokni", "Quoc V. Le", "Thang Luong" ], "github": "https://github.com/google-deepmind/superhuman/tree/main/aletheia", "project_page": "", "abstract": "We report the performance of Aletheia (Feng et al., 2026b), a mathematics research agent powered by Gemini 3 Deep Think, on the inaugural FirstProof challenge. Within the allowed timeframe of the challenge, Aletheia autonomously solved 6 problems (2, 5, 7, 8, 9, 10) out of 10 according to majority expert assessments; we note that experts were not unanimous on Problem 8 (only). For full transparency, we explain our interpretation of FirstProof and disclose details about our experiments as well as our evaluation. Raw prompts and outputs are available at https://github.com/google-deepmind/superhuman/tree/main/aletheia.", "github_manual": "", "github_regex": "https://github.com/google-deepmind/superhuman/tree/main/aletheia", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.21185", "title": "The Diffusion Duality, Chapter II: \u03a8-Samplers and Efficient Curriculum", "authors": [ "Justin Deschenaux", "Caglar Gulcehre", "Subham Sekhar Sahoo" ], "github": "", "project_page": "", "abstract": "Uniform-state discrete diffusion models excel at few-step generation and guidance due to their ability to self-correct, making them preferred over autoregressive or Masked diffusion models in these settings. However, their sampling quality plateaus with ancestral samplers as the number of steps increases. We introduce a family of Predictor-Corrector (PC) samplers for discrete diffusion that generalize prior methods and apply to arbitrary noise processes. When paired with uniform-state diffusion, our samplers outperform ancestral sampling on both language and image modeling, achieving lower generative perplexity at matched unigram entropy on OpenWebText and better FID/IS scores on CIFAR10. Crucially, unlike conventional samplers, our PC methods continue to improve with more sampling steps. Taken together, these findings call into question the assumption that Masked diffusion is the inevitable future of diffusion-based language modeling. Beyond sampling, we develop a memory-efficient curriculum for the Gaussian relaxation training phase, reducing training time by 25% and memory by 33% compared to Duo while maintaining comparable perplexity on OpenWebText and LM1B and strong downstream performance. We release code, checkpoints, and a video-tutorial on: https://s-sahoo.com/duo-ch2", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.21204", "title": "Test-Time Training with KV Binding Is Secretly Linear Attention", "authors": [ "Junchen Liu", "Sven Elflein", "Or Litany", "Zan Gojcic", "Ruilong Li" ], "github": "", "project_page": "", "abstract": "Test-time training (TTT) with KV binding as sequence modeling layer is commonly interpreted as a form of online meta-learning that memorizes a key-value mapping at test time. However, our analysis reveals multiple phenomena that contradict this memorization-based interpretation. Motivated by these findings, we revisit the formulation of TTT and show that a broad class of TTT architectures can be expressed as a form of learned linear attention operator. Beyond explaining previously puzzling model behaviors, this perspective yields multiple practical benefits: it enables principled architectural simplifications, admits fully parallel formulations that preserve performance while improving efficiency, and provides a systematic reduction of diverse TTT variants to a standard linear attention form. Overall, our results reframe TTT not as test-time memorization, but as learned linear attention with enhanced representational capacity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.21053", "title": "OCR-Agent: Agentic OCR with Capability and Memory Reflection", "authors": [ "Shimin Wen", "Zeyu Zhang", "Xingdou Bian", "Hongjie Zhu", "Lulu He", "Layi Shama", "Daji Ergu", "Ying Cai" ], "github": "https://github.com/AIGeeksGroup/OCR-Agent", "project_page": "", "abstract": "Large Vision-Language Models (VLMs) have demonstrated significant potential on complex visual understanding tasks through iterative optimization methods.However, these models generally lack effective self-correction mechanisms, making it difficult for them to independently rectify cognitive biases. Consequently, during multi-turn revisions, they often fall into repetitive and ineffective attempts, failing to achieve stable improvements in answer quality.To address this issue, we propose a novel iterative self-correction framework that endows models with two key capabilities: Capability Reflection and Memory Reflection. This framework guides the model to first diagnose errors and generate a correction plan via Capability Reflection, then leverage Memory Reflection to review past attempts to avoid repetition and explore new solutions, and finally, optimize the answer through rigorous re-reasoning. Experiments on the challenging OCRBench v2 benchmark show that OCR-Agent outperforms the current open-source SOTA model InternVL3-8B by +2.0 on English and +1.2 on Chinese subsets, while achieving state-of-the-art results in Visual Understanding (79.9) and Reasoning (66.5) - surpassing even larger fine-tuned models. Our method demonstrates that structured, self-aware reflection can significantly enhance VLMs' reasoning robustness without additional training. Code: https://github.com/AIGeeksGroup/OCR-Agent.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/OCR-Agent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.21042", "title": "OmniOCR: Generalist OCR for Ethnic Minority Languages", "authors": [ "Bonan Liu", "Zeyu Zhang", "Bingbing Meng", "Han Wang", "Hanshuo Zhang", "Chengping Wang", "Daji Ergu", "Ying Cai" ], "github": "https://github.com/AIGeeksGroup/OmniOCR", "project_page": "", "abstract": "Optical character recognition (OCR) has advanced rapidly with deep learning and multimodal models, yet most methods focus on well-resourced scripts such as Latin and Chinese. Ethnic minority languages remain underexplored due to complex writing systems, scarce annotations, and diverse historical and modern forms, making generalization in low-resource or zero-shot settings challenging. To address these challenges, we present OmniOCR, a universal framework for ethnic minority scripts. OmniOCR introduces Dynamic Low-Rank Adaptation (Dynamic LoRA) to allocate model capacity across layers and scripts, enabling effective adaptation while preserving knowledge.A sparsity regularization prunes redundant updates, ensuring compact and efficient adaptation without extra inference cost. Evaluations on TibetanMNIST, Shui, ancient Yi, and Dongba show that OmniOCR outperforms zero-shot foundation models and standard post training, achieving state-of-the-art accuracy with superior parameter efficiency, and compared with the state-of-the-art baseline models, it improves accuracy by 39%-66% on these four datasets. Code: https://github.com/AIGeeksGroup/OmniOCR.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/OmniOCR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.20540", "title": "Generative AI and Machine Learning Collaboration for Container Dwell Time Prediction via Data Standardization", "authors": [ "Minseop Kim", "Takhyeong Kim", "Taekhyun Park", "Hanbyeol Park", "Hyerim Bae" ], "github": "", "project_page": "", "abstract": "Import container dwell time (ICDT) prediction is a key task for improving productivity in container terminals, as accurate predictions enable the reduction of container re-handling operations by yard cranes. Achieving this objective requires accurately predicting the dwell time of individual containers. However, the primary determinants of dwell time-owner information and cargo information-are recorded as unstructured text, which limits their effective use in machine learning models. This study addresses this limitation by proposing a collaborative framework that integrates generative artificial intelligence (Gen AI) with machine learning. The proposed framework employs Gen AI to standardize unstructured information into standard international codes, with dynamic re-prediction triggered by electronic data interchange state updates, enabling the machine learning model to predict ICDT accurately. Extensive experiments conducted on real container terminal data demonstrate that the proposed methodology achieves a 13.88% improvement in mean absolute error compared to conventional models that do not utilize standardized information. Furthermore, applying the improved predictions to container stacking strategies achieves up to 14.68% reduction in the number of relocations, thereby empirically validating the potential of Gen AI to enhance productivity in container terminal operations. Overall, this study provides both technical and methodological insights into the adoption of Gen AI in port logistics and its effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.18940", "title": "DREAM: Deep Research Evaluation with Agentic Metrics", "authors": [ "Elad Ben Avraham", "Changhao Li", "Ron Dorfman", "Roy Ganz", "Oren Nuriel", "Amir Dudai", "Aviad Aberdam", "Noah Flynn", "Elman Mansimov", "Adi Kalyanpur", "Ron Litman" ], "github": "https://github.com/assafelovic/gpt-researcher", "project_page": "", "abstract": "Deep Research Agents generate analyst-grade reports, yet evaluating them remains challenging due to the absence of a single ground truth and the multidimensional nature of research quality. Recent benchmarks propose distinct methodologies, yet they suffer from the Mirage of Synthesis, where strong surface-level fluency and citation alignment can obscure underlying factual and reasoning defects. We characterize this gap by introducing a taxonomy across four verticals that exposes a critical capability mismatch: static evaluators inherently lack the tool-use capabilities required to assess temporal validity and factual correctness. To address this, we propose DREAM (Deep Research Evaluation with Agentic Metrics), a framework that instantiates the principle of capability parity by making evaluation itself agentic. DREAM structures assessment through an evaluation protocol combining query-agnostic metrics with adaptive metrics generated by a tool-calling agent, enabling temporally aware coverage, grounded verification, and systematic reasoning probes. Controlled evaluations demonstrate DREAM is significantly more sensitive to factual and temporal decay than existing benchmarks, offering a scalable, reference-free evaluation paradigm.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/assafelovic/gpt-researcher", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.18735", "title": "LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency", "authors": [ "Weilong Yan", "Haipeng Li", "Hao Xu", "Nianjin Ye", "Yihao Ai", "Shuaicheng Liu", "Jingyu Hu" ], "github": "https://github.com/DavidYan2001/LaS-Comp", "project_page": "", "abstract": "This paper introduces LaS-Comp, a zero-shot and category-agnostic approach that leverages the rich geometric priors of 3D foundation models to enable 3D shape completion across diverse types of partial observations. Our contributions are threefold: First, harnesses these powerful generative priors for completion through a complementary two-stage design: (i) an explicit replacement stage that preserves the partial observation geometry to ensure faithful completion; and (ii) an implicit refinement stage ensures seamless boundaries between the observed and synthesized regions. Second, our framework is training-free and compatible with different 3D foundation models. Third, we introduce Omni-Comp, a comprehensive benchmark combining real-world and synthetic data with diverse and challenging partial patterns, enabling a more thorough and realistic evaluation. Both quantitative and qualitative experiments demonstrate that our approach outperforms previous state-of-the-art approaches. Our code and data will be available at https://github.com/DavidYan2001/LaS-Comp{LaS-Comp}.", "github_manual": "", "github_regex": "https://github.com/DavidYan2001/LaS-Comp", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.21196", "title": "Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking", "authors": [ "Ravi Ghadia", "Maksim Abraham", "Sergei Vorobyov", "Max Ryabinin" ], "github": "https://github.com/togethercomputer/Untied-Ulysses", "project_page": "", "abstract": "Efficiently processing long sequences with Transformer models usually requires splitting the computations across accelerators via context parallelism. The dominant approaches in this family of methods, such as Ring Attention or DeepSpeed Ulysses, enable scaling over the context dimension but do not focus on memory efficiency, which limits the sequence lengths they can support. More advanced techniques, such as Fully Pipelined Distributed Transformer or activation offloading, can further extend the possible context length at the cost of training throughput. In this paper, we present UPipe, a simple yet effective context parallelism technique that performs fine-grained chunking at the attention head level. This technique significantly reduces the activation memory usage of self-attention, breaking the activation memory barrier and unlocking much longer context lengths. Our approach reduces intermediate tensor memory usage in the attention layer by as much as 87.5% for 32B Transformers, while matching previous context parallelism techniques in terms of training speed. UPipe can support the context length of 5M tokens when training Llama3-8B on a single 8timesH100 node, improving upon prior methods by over 25%.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/togethercomputer/Untied-Ulysses", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.20903", "title": "TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering", "authors": [ "Hanshen Zhu", "Yuliang Liu", "Xuecheng Wu", "An-Lan Wang", "Hao Feng", "Dingkang Yang", "Chao Feng", "Can Huang", "Jingqun Tang", "Xiang Bai" ], "github": "", "project_page": "", "abstract": "Visual Text Rendering (VTR) remains a critical challenge in text-to-image generation, where even advanced models frequently produce text with structural anomalies such as distortion, blurriness, and misalignment. However, we find that leading MLLMs and specialist OCR models largely fail to perceive these structural anomalies, creating a critical bottleneck for both VTR evaluation and RL-based optimization. As a result, even state-of-the-art generators (e.g., SeedDream4.0, Qwen-Image) still struggle to render structurally faithful text. To address this, we propose TextPecker, a plug-and-play structural anomaly perceptive RL strategy that mitigates noisy reward signals and works with any textto-image generator. To enable this capability, we construct a recognition dataset with character-level structural-anomaly annotations and develop a stroke-editing synthesis engine to expand structural-error coverage. Experiments show that TextPecker consistently improves diverse text-to-image models; even on the well-optimized Qwen-Image, it significantly yields average gains of 4% in structural fidelity and 8.7% in semantic alignment for Chinese text rendering, establishing a new state-of-the-art in high-fidelity VTR. Our work fills a gap in VTR optimization, providing a foundational step towards reliable and structural faithful visual text generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.16813", "title": "One-step Language Modeling via Continuous Denoising", "authors": [ "Chanhyuk Lee", "Jaehoon Yoo", "Manan Agarwal", "Sheel Shah", "Jerry Huang", "Aditi Raghunathan", "Seunghoon Hong", "Nicholas M. Boffi", "Jinwoo Kim" ], "github": "https://github.com/david3684/flm", "project_page": "", "abstract": "Language models based on discrete diffusion have attracted widespread interest for their potential to provide faster generation than autoregressive models. In practice, however, they exhibit a sharp degradation of sample quality in the few-step regime, failing to realize this promise. Here we show that language models leveraging flow-based continuous denoising can outperform discrete diffusion in both quality and speed. By revisiting the fundamentals of flows over discrete modalities, we build a flow-based language model (FLM) that performs Euclidean denoising over one-hot token encodings. We show that the model can be trained by predicting the clean data via a cross entropy objective, where we introduce a simple time reparameterization that greatly improves training stability and generation quality. By distilling FLM into its associated flow map, we obtain a distilled flow map language model (FMLM) capable of few-step generation. On the LM1B and OWT language datasets, FLM attains generation quality matching state-of-the-art discrete diffusion models. With FMLM, our approach outperforms recent few-step language models across the board, with one-step generation exceeding their 8-step quality. Our work calls into question the widely held hypothesis that discrete diffusion processes are necessary for generative modeling over discrete modalities, and paves the way toward accelerated flow-based language modeling at scale. Code is available at https://github.com/david3684/flm.", "github_manual": "", "github_regex": "https://github.com/david3684/flm", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.20743", "title": "Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization", "authors": [ "Gabriel Loiseau", "Damien Sileo", "Damien Riquet", "Maxime Meyer", "Marc Tommasi" ], "github": "", "project_page": "", "abstract": "Anonymizing textual documents is a highly context-sensitive problem: the appropriate balance between privacy protection and utility preservation varies with the data domain, privacy objectives, and downstream application. However, existing anonymization methods rely on static, manually designed strategies that lack the flexibility to adjust to diverse requirements and often fail to generalize across domains. We introduce adaptive text anonymization, a new task formulation in which anonymization strategies are automatically adapted to specific privacy-utility requirements. We propose a framework for task-specific prompt optimization that automatically constructs anonymization instructions for language models, enabling adaptation to different privacy goals, domains, and downstream usage patterns. To evaluate our approach, we present a benchmark spanning five datasets with diverse domains, privacy constraints, and utility objectives. Across all evaluated settings, our framework consistently achieves a better privacy-utility trade-off than existing baselines, while remaining computationally efficient and effective on open-source language models, with performance comparable to larger closed-source models. Additionally, we show that our method can discover novel anonymization strategies that explore different points along the privacy-utility trade-off frontier.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.20731", "title": "Communication-Inspired Tokenization for Structured Image Representations", "authors": [ "Aram Davtyan", "Yusuf Sahin", "Yasaman Haghighi", "Sebastian Stapf", "Pablo Acuaviva", "Alexandre Alahi", "Paolo Favaro" ], "github": "https://github.com/Araachie/comit", "project_page": "", "abstract": "Discrete image tokenizers have emerged as a key component of modern vision and multimodal systems, providing a sequential interface for transformer-based architectures. However, most existing approaches remain primarily optimized for reconstruction and compression, often yielding tokens that capture local texture rather than object-level semantic structure. Inspired by the incremental and compositional nature of human communication, we introduce COMmunication inspired Tokenization (COMiT), a framework for learning structured discrete visual token sequences. COMiT constructs a latent message within a fixed token budget by iteratively observing localized image crops and recurrently updating its discrete representation. At each step, the model integrates new visual information while refining and reorganizing the existing token sequence. After several encoding iterations, the final message conditions a flow-matching decoder that reconstructs the full image. Both encoding and decoding are implemented within a single transformer model and trained end-to-end using a combination of flow-matching reconstruction and semantic representation alignment losses. Our experiments demonstrate that while semantic alignment provides grounding, attentive sequential tokenization is critical for inducing interpretable, object-centric token structure and substantially improving compositional generalization and relational reasoning over prior methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Araachie/comit", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.12192", "title": "Query-focused and Memory-aware Reranker for Long Context Processing", "authors": [ "Yuqing Li", "Jiangnan Li", "Mo Yu", "Guoxuan Ding", "Zheng Lin", "Weiping Wang", "Jie Zhou" ], "github": "", "project_page": "", "abstract": "Built upon the existing analysis of retrieval heads in large language models, we propose an alternative reranking framework that trains models to estimate passage-query relevance using the attention scores of selected heads. This approach provides a listwise solution that leverages holistic information within the entire candidate shortlist during ranking. At the same time, it naturally produces continuous relevance scores, enabling training on arbitrary retrieval datasets without requiring Likert-scale supervision. Our framework is lightweight and effective, requiring only small-scale models (e.g., 4B parameters) to achieve strong performance. Extensive experiments demonstrate that our method outperforms existing state-of-the-art pointwise and listwise rerankers across multiple domains, including Wikipedia and long narrative datasets. It further establishes a new state-of-the-art on the LoCoMo benchmark that assesses the capabilities of dialogue understanding and memory usage. We further demonstrate that our framework supports flexible extensions. For example, augmenting candidate passages with contextual information further improves ranking accuracy, while training attention heads from middle layers enhances efficiency without sacrificing performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.20309", "title": "QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models", "authors": [ "Jingxuan Zhang", "Yunta Hsieh", "Zhongwei Wang", "Haokun Lin", "Xin Wang", "Ziqi Wang", "Yingtie Lei", "Mi Zhang" ], "github": "", "project_page": "", "abstract": "Vision-language-action (VLA) models unify perception, language, and control for embodied agents but face significant challenges in practical deployment due to rapidly increasing compute and memory demands, especially as models scale to longer horizons and larger backbones. To address these bottlenecks, we introduce QuantVLA, a training-free post-training quantization (PTQ) framework that, to our knowledge, is the first PTQ approach for VLA systems and the first to successfully quantize a diffusion transformer (DiT) action head. QuantVLA incorporates three scale-calibrated components: (1) a selective quantization layout that integerizes all linear layers in both the language backbone and the DiT while keeping attention projections in floating point to preserve the original operator schedule; (2) attention temperature matching, a lightweight per-head scaling mechanism that stabilizes attention logits and is folded into the dequantization scales at inference; and (3) output head balancing, a per-layer residual interface calibration that mitigates post-projection energy drift. The framework requires no additional training, uses only a small unlabeled calibration buffer, and supports integer kernels for low-bit weights and activations while leaving the architecture unchanged. Across representative VLA models on LIBERO, QuantVLA exceeds the task success rates of full-precision baselines, achieves about 70% relative memory savings on the quantized components, and delivers a 1.22x speedup in end-to-end inference latency, providing a practical pathway toward scalable low-bit embodied intelligence under strict compute, memory, and power constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.21202", "title": "Multi-Vector Index Compression in Any Modality", "authors": [ "Hanxiang Qin", "Alexander Martin", "Rohan Jha", "Chunsheng Zuo", "Reno Kriz", "Benjamin Van Durme" ], "github": "https://github.com/hanxiangqin/omni-col-press", "project_page": "", "abstract": "We study efficient multi-vector retrieval for late interaction in any modality. Late interaction has emerged as a dominant paradigm for information retrieval in text, images, visual documents, and videos, but its computation and storage costs grow linearly with document length, making it costly for image-, video-, and audio-rich corpora. To address this limitation, we explore query-agnostic methods for compressing multi-vector document representations under a constant vector budget. We introduce four approaches for index compression: sequence resizing, memory tokens, hierarchical pooling, and a novel attention-guided clustering (AGC). AGC uses an attention-guided mechanism to identify the most semantically salient regions of a document as cluster centroids and to weight token aggregation. Evaluating these methods on retrieval tasks spanning text (BEIR), visual-document (ViDoRe), and video (MSR-VTT, MultiVENT 2.0), we show that attention-guided clustering consistently outperforms other parameterized compression methods (sequence resizing and memory tokens), provides greater flexibility in index size than non-parametric hierarchical clustering, and achieves competitive or improved performance compared to a full, uncompressed index. The source code is available at: github.com/hanxiangqin/omni-col-press.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hanxiangqin/omni-col-press", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.20951", "title": "See and Fix the Flaws: Enabling VLMs and Diffusion Models to Comprehend Visual Artifacts via Agentic Data Synthesis", "authors": [ "Jaehyun Park", "Minyoung Ahn", "Minkyu Kim", "Jonghyun Lee", "Jae-Gil Lee", "Dongmin Park" ], "github": "", "project_page": "", "abstract": "Despite recent advances in diffusion models, AI generated images still often contain visual artifacts that compromise realism. Although more thorough pre-training and bigger models might reduce artifacts, there is no assurance that they can be completely eliminated, which makes artifact mitigation a highly crucial area of study. Previous artifact-aware methodologies depend on human-labeled artifact datasets, which are costly and difficult to scale, underscoring the need for an automated approach to reliably acquire artifact-annotated datasets. In this paper, we propose ArtiAgent, which efficiently creates pairs of real and artifact-injected images. It comprises three agents: a perception agent that recognizes and grounds entities and subentities from real images, a synthesis agent that introduces artifacts via artifact injection tools through novel patch-wise embedding manipulation within a diffusion transformer, and a curation agent that filters the synthesized artifacts and generates both local and global explanations for each instance. Using ArtiAgent, we synthesize 100K images with rich artifact annotations and demonstrate both efficacy and versatility across diverse applications. Code is available at link.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.16745", "title": "PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency", "authors": [ "Zhangyi Liu", "Huaizhi Qu", "Xiaowei Yin", "He Sun", "Yanjun Han", "Tianlong Chen", "Zhun Deng" ], "github": "https://github.com/ZDCSlab/PETS", "project_page": "", "abstract": "Test-time scaling can improve model performance by aggregating stochastic reasoning trajectories. However, achieving sample-efficient test-time self-consistency under a limited budget remains an open challenge. We introduce PETS (Principled and Efficient Test-TimeSelf-Consistency), which initiates a principled study of trajectory allocation through an optimization framework. Central to our approach is the self-consistency rate, a new measure defined as agreement with the infinite-budget majority vote. This formulation makes sample-efficient test-time allocation theoretically grounded and amenable to rigorous analysis. We study both offline and online settings. In the offline regime, where all questions are known in advance, we connect trajectory allocation to crowdsourcing, a classic and well-developed area, by modeling reasoning traces as workers. This perspective allows us to leverage rich existing theory, yielding theoretical guarantees and an efficient majority-voting-based allocation algorithm. In the online streaming regime, where questions arrive sequentially and allocations must be made on the fly, we propose a novel method inspired by the offline framework. Our approach adapts budgets to question difficulty while preserving strong theoretical guarantees and computational efficiency. Experiments show that PETS consistently outperforms uniform allocation. On GPQA, PETS achieves perfect self-consistency in both settings while reducing the sampling budget by up to 75% (offline) and 55% (online) relative to uniform allocation. Code is available at https://github.com/ZDCSlab/PETS.", "github_manual": "", "github_regex": "https://github.com/ZDCSlab/PETS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.19633", "title": "TAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents", "authors": [ "Jongwon Jeong", "Jungtaek Kim", "Kangwook Lee" ], "github": "https://github.com/UW-Madison-Lee-Lab/TAPE", "project_page": "", "abstract": "Language Model (LM) agents have demonstrated remarkable capabilities in solving tasks that require multiple interactions with the environment. However, they remain vulnerable in environments where a single error often leads to irrecoverable failure, particularly under strict feasibility constraints. We systematically analyze existing agent frameworks, identifying imperfect planning and stochastic execution as the primary causes. To address these challenges, we propose Tool-guided Adaptive Planning with constrained Execution (TAPE). TAPE enhances planning capability by aggregating multiple plans into a graph and employing an external solver to identify a feasible path. During execution, TAPE employs constrained decoding to reduce sampling noise, while adaptively re-planning whenever environmental feedback deviates from the intended state. Experiments across Sokoban, ALFWorld, MuSiQue, and GSM8K-Hard demonstrate that TAPE consistently outperforms existing frameworks, with particularly large gains on hard settings, improving success rates by 21.0 percentage points on hard settings on average, and by 20.0 percentage points for weaker base models on average. Code and data available at here.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/UW-Madison-Lee-Lab/TAPE", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.20792", "title": "SIMSPINE: A Biomechanics-Aware Simulation Framework for 3D Spine Motion Annotation and Benchmarking", "authors": [ "Muhammad Saif Ullah Khan", "Didier Stricker" ], "github": "", "project_page": "", "abstract": "Modeling spinal motion is fundamental to understanding human biomechanics, yet remains underexplored in computer vision due to the spine's complex multi-joint kinematics and the lack of large-scale 3D annotations. We present a biomechanics-aware keypoint simulation framework that augments existing human pose datasets with anatomically consistent 3D spinal keypoints derived from musculoskeletal modeling. Using this framework, we create the first open dataset, named SIMSPINE, which provides sparse vertebra-level 3D spinal annotations for natural full-body motions in indoor multi-camera capture without external restraints. With 2.14 million frames, this enables data-driven learning of vertebral kinematics from subtle posture variations and bridges the gap between musculoskeletal simulation and computer vision. In addition, we release pretrained baselines covering fine-tuned 2D detectors, monocular 3D pose lifting models, and multi-view reconstruction pipelines, establishing a unified benchmark for biomechanically valid spine motion estimation. Specifically, our 2D spine baselines improve the state-of-the-art from 0.63 to 0.80 AUC in controlled environments, and from 0.91 to 0.93 AP for in-the-wild spine tracking. Together, the simulation framework and SIMSPINE dataset advance research in vision-based biomechanics, motion analysis, and digital human modeling by enabling reproducible, anatomically grounded 3D spine estimation under natural conditions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.18998", "title": "Benchmark Test-Time Scaling of General LLM Agents", "authors": [ "Xiaochuan Li", "Ryan Ming", "Pranav Setlur", "Abhijay Paladugu", "Andy Tang", "Hao Kang", "Shuai Shao", "Rong Jin", "Chenyan Xiong" ], "github": "https://github.com/cxcscmu/General-AgentBench", "project_page": "", "abstract": "LLM agents are increasingly expected to function as general-purpose systems capable of resolving open-ended user requests. While existing benchmarks focus on domain-aware environments for developing specialized agents, evaluating general-purpose agents requires more realistic settings that challenge them to operate across multiple skills and tools within a unified environment. We introduce General AgentBench, a benchmark that provides such a unified framework for evaluating general LLM agents across search, coding, reasoning, and tool-use domains. Using General AgentBench, we systematically study test-time scaling behaviors under sequential scaling (iterative interaction) and parallel scaling (sampling multiple trajectories). Evaluation of ten leading LLM agents reveals a substantial performance degradation when moving from domain-specific evaluations to this general-agent setting. Moreover, we find that neither scaling methodology yields effective performance improvements in practice, due to two fundamental limitations: context ceiling in sequential scaling and verification gap in parallel scaling. Code is publicly available at https://github.com/cxcscmu/General-AgentBench.", "github_manual": "", "github_regex": "https://github.com/cxcscmu/General-AgentBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.16932", "title": "RankEvolve: Automating the Discovery of Retrieval Algorithms via LLM-Driven Evolution", "authors": [ "Jinming Nian", "Fangchen Li", "Dae Hoon Park", "Yi Fang" ], "github": "https://github.com/fangchenli/ranking-evolved", "project_page": "", "abstract": "Retrieval algorithms like BM25 and query likelihood with Dirichlet smoothing remain strong and efficient first-stage rankers, yet improvements have mostly relied on parameter tuning and human intuition. We investigate whether a large language model, guided by an evaluator and evolutionary search, can automatically discover improved lexical retrieval algorithms. We introduce RankEvolve, a program evolution setup based on AlphaEvolve, in which candidate ranking algorithms are represented as executable code and iteratively mutated, recombined, and selected based on retrieval performance across 12 IR datasets from BEIR and BRIGHT. RankEvolve starts from two seed programs: BM25 and query likelihood with Dirichlet smoothing. The evolved algorithms are novel, effective, and show promising transfer to the full BEIR and BRIGHT benchmarks as well as TREC DL 19 and 20. Our results suggest that evaluator-guided LLM program evolution is a practical path towards automatic discovery of novel ranking algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/fangchenli/ranking-evolved", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-25", "arxiv_id": "2602.19020", "title": "Learning to Detect Language Model Training Data via Active Reconstruction", "authors": [ "Junjie Oscar Yin", "John X. Morris", "Vitaly Shmatikov", "Sewon Min", "Hannaneh Hajishirzi" ], "github": "", "project_page": "", "abstract": "Detecting LLM training data is generally framed as a membership inference attack (MIA) problem. However, conventional MIAs operate passively on fixed model weights, using log-likelihoods or text generations. In this work, we introduce Active Data Reconstruction Attack (ADRA), a family of MIA that actively induces a model to reconstruct a given text through training. We hypothesize that training data are more reconstructible than non-members, and the difference in their reconstructibility can be exploited for membership inference. Motivated by findings that reinforcement learning (RL) sharpens behaviors already encoded in weights, we leverage on-policy RL to actively elicit data reconstruction by finetuning a policy initialized from the target model. To effectively use RL for MIA, we design reconstruction metrics and contrastive rewards. The resulting algorithms, ADRA and its adaptive variant ADRA+, improve both reconstruction and detection given a pool of candidate data. Experiments show that our methods consistently outperform existing MIAs in detecting pre-training, post-training, and distillation data, with an average improvement of 10.7\\% over the previous runner-up. In particular, \\MethodPlus~improves over Min-K\\%++ by 18.8\\% on BookMIA for pre-training detection and by 7.6\\% on AIME for post-training detection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.18283", "title": "HyTRec: A Hybrid Temporal-Aware Attention Architecture for Long Behavior Sequential Recommendation", "authors": [ "Lei Xin", "Yuhao Zheng", "Ke Cheng", "Changjiang Jiang", "Zifan Zhang", "Fanhu Zeng" ], "github": "", "project_page": "", "abstract": "Modeling long sequences of user behaviors has emerged as a critical frontier in generative recommendation. However, existing solutions face a dilemma: linear attention mechanisms achieve efficiency at the cost of retrieval precision due to limited state capacity, while softmax attention suffers from prohibitive computational overhead. To address this challenge, we propose HyTRec, a model featuring a Hybrid Attention architecture that explicitly decouples long-term stable preferences from short-term intent spikes. By assigning massive historical sequences to a linear attention branch and reserving a specialized softmax attention branch for recent interactions, our approach restores precise retrieval capabilities within industrial-scale contexts involving ten thousand interactions. To mitigate the lag in capturing rapid interest drifts within the linear layers, we furthermore design Temporal-Aware Delta Network (TADN) to dynamically upweight fresh behavioral signals while effectively suppressing historical noise. Empirical results on industrial-scale datasets confirm the superiority that our model maintains linear inference speed and outperforms strong baselines, notably delivering over 8% improvement in Hit Rate for users with ultra-long sequences with great efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.19163", "title": "JavisDiT++: Unified Modeling and Optimization for Joint Audio-Video Generation", "authors": [ "Kai Liu", "Yanhao Zheng", "Kai Wang", "Shengqiong Wu", "Rongjunchen Zhang", "Jiebo Luo", "Dimitrios Hatzinakos", "Ziwei Liu", "Hao Fei", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "AIGC has rapidly expanded from text-to-image generation toward high-quality multimodal synthesis across video and audio. Within this context, joint audio-video generation (JAVG) has emerged as a fundamental task that produces synchronized and semantically aligned sound and vision from textual descriptions. However, compared with advanced commercial models such as Veo3, existing open-source methods still suffer from limitations in generation quality, temporal synchrony, and alignment with human preferences. To bridge the gap, this paper presents JavisDiT++, a concise yet powerful framework for unified modeling and optimization of JAVG. First, we introduce a modality-specific mixture-of-experts (MS-MoE) design that enables cross-modal interaction efficacy while enhancing single-modal generation quality. Then, we propose a temporal-aligned RoPE (TA-RoPE) strategy to achieve explicit, frame-level synchronization between audio and video tokens. Besides, we develop an audio-video direct preference optimization (AV-DPO) method to align model outputs with human preference across quality, consistency, and synchrony dimensions. Built upon Wan2.1-1.3B-T2V, our model achieves state-of-the-art performance merely with around 1M public training entries, significantly outperforming prior approaches in both qualitative and quantitative evaluations. Comprehensive ablation studies have been conducted to validate the effectiveness of our proposed modules. All the code, model, and dataset are released at https://JavisVerse.github.io/JavisDiT2-page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.21818", "title": "SkyReels-V4: Multi-modal Video-Audio Generation, Inpainting and Editing model", "authors": [ "Guibin Chen", "Dixuan Lin", "Jiangping Yang", "Youqiang Zhang", "Zhengcong Fei", "Debang Li", "Sheng Chen", "Chaofeng Ao", "Nuo Pang", "Yiming Wang", "Yikun Dou", "Zheng Chen", "Mingyuan Fan", "Tuanhui Li", "Mingshan Chang", "Hao Zhang", "Xiaopeng Sun", "Jingtao Xu", "Yuqiang Xie", "Jiahua Wang", "Zhiheng Xu", "Weiming Xiong", "Yuzhe Jin", "Baoxuan Gu", "Binjie Mao", "Yunjie Yu", "Jujie He", "Yuhao Feng", "Shiwen Tu", "Chaojie Wang", "Rui Yan", "Wei Shen", "Jingchen Wu", "Peng Zhao", "Xuanyue Zhong", "Zhuangzhuang Liu", "Kaifei Wang", "Fuxiang Zhang", "Weikai Xu", "Wenyan Liu", "Binglu Zhang", "Yu Shen", "Tianhui Xiong", "Bin Peng", "Liang Zeng", "Xuchen Song", "Haoxiang Guo", "Peiyu Wang", "Yahui Zhou" ], "github": "", "project_page": "", "abstract": "SkyReels V4 is a unified multi modal video foundation model for joint video audio generation, inpainting, and editing. The model adopts a dual stream Multimodal Diffusion Transformer (MMDiT) architecture, where one branch synthesizes video and the other generates temporally aligned audio, while sharing a powerful text encoder based on the Multimodal Large Language Models (MMLM). SkyReels V4 accepts rich multi modal instructions, including text, images, video clips, masks, and audio references. By combining the MMLMs multi modal instruction following capability with in context learning in the video branch MMDiT, the model can inject fine grained visual guidance under complex conditioning, while the audio branch MMDiT simultaneously leverages audio references to guide sound generation. On the video side, we adopt a channel concatenation formulation that unifies a wide range of inpainting style tasks, such as image to video, video extension, and video editing under a single interface, and naturally extends to vision referenced inpainting and editing via multi modal prompts. SkyReels V4 supports up to 1080p resolution, 32 FPS, and 15 second duration, enabling high fidelity, multi shot, cinema level video generation with synchronized audio. To make such high resolution, long-duration generation computationally feasible, we introduce an efficiency strategy: Joint generation of low resolution full sequences and high-resolution keyframes, followed by dedicated super-resolution and frame interpolation models. To our knowledge, SkyReels V4 is the first video foundation model that simultaneously supports multi-modal input, joint video audio generation, and a unified treatment of generation, inpainting, and editing, while maintaining strong efficiency and quality at cinematic resolutions and durations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.21534", "title": "ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning", "authors": [ "Xiaoxuan Wang", "Han Zhang", "Haixin Wang", "Yidan Shi", "Ruoyan Li", "Kaiqiao Han", "Chenyi Tong", "Haoran Deng", "Renliang Sun", "Alexander Taylor", "Yanqiao Zhu", "Jason Cong", "Yizhou Sun", "Wei Wang" ], "github": "", "project_page": "", "abstract": "Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks. Despite encouraging early results, ARL remains highly unstable, often leading to training collapse. This instability limits scalability to larger environments and longer interaction horizons, and constrains systematic exploration of algorithmic design choices. In this paper, we first propose ARLArena, a stable training recipe and systematic analysis framework that examines training stability in a controlled and reproducible setting. ARLArena first constructs a clean and standardized testbed. Then, we decompose policy gradient into four core design dimensions and assess the performance and stability of each dimension. Through this fine-grained analysis, we distill a unified perspective on ARL and propose SAMPO, a stable agentic policy optimization method designed to mitigate the dominant sources of instability in ARL. Empirically, SAMPO achieves consistently stable training and strong performance across diverse agentic tasks. Overall, this study provides a unifying policy gradient perspective for ARL and offers practical guidance for building stable and reproducible LLM-based agent training pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.18527", "title": "JAEGER: Joint 3D Audio-Visual Grounding and Reasoning in Simulated Physical Environments", "authors": [ "Zhan Liu", "Changli Tang", "Yuxin Wang", "Zhiyuan Zhu", "Youjun Chen", "Yiwen Shao", "Tianzi Wang", "Lei Ke", "Zengrui Jin", "Chao Zhang" ], "github": "", "project_page": "", "abstract": "Current audio-visual large language models (AV-LLMs) are predominantly restricted to 2D perception, relying on RGB video and monaural audio. This design choice introduces a fundamental dimensionality mismatch that precludes reliable source localization and spatial reasoning in complex 3D environments. We address this limitation by presenting JAEGER, a framework that extends AV-LLMs to 3D space, to enable joint spatial grounding and reasoning through the integration of RGB-D observations and multi-channel first-order ambisonics. A core contribution of our work is the neural intensity vector (Neural IV), a learned spatial audio representation that encodes robust directional cues to enhance direction-of-arrival estimation, even in adverse acoustic scenarios with overlapping sources. To facilitate large-scale training and systematic evaluation, we propose SpatialSceneQA, a benchmark of 61k instruction-tuning samples curated from simulated physical environments. Extensive experiments demonstrate that our approach consistently surpasses 2D-centric baselines across diverse spatial perception and reasoning tasks, underscoring the necessity of explicit 3D modelling for advancing AI in physical environments. Our source code, pre-trained model checkpoints and datasets will be released upon acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.14878", "title": "Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions", "authors": [ "Mohammed Mehedi Hasan", "Hao Li", "Gopi Krishnan Rajbahadur", "Bram Adams", "Ahmed E. Hassan" ], "github": "https://github.com/eonist/conduit", "project_page": "", "abstract": "The Model Context Protocol (MCP) introduces a standard specification that defines how Foundation Model (FM)-based agents should interact with external systems by invoking tools. However, to understand a tool's purpose and features, FMs rely on natural-language tool descriptions, making these descriptions a critical component in guiding FMs to select the optimal tool for a given (sub)task and to pass the right arguments to the tool. While defects or smells in these descriptions can misguide FM-based agents, their prevalence and consequences in the MCP ecosystem remain unclear. Hence, we examine 856 tools spread across 103 MCP servers empirically, assess their description quality, and their impact on agent performance. We identify six components of tool descriptions from the literature, develop a scoring rubric utilizing these components, and then formalize tool description smells based on this rubric. By operationalizing this rubric through an FM-based scanner, we find that 97.1% of the analyzed tool descriptions contain at least one smell, with 56% failing to state their purpose clearly. While augmenting these descriptions for all components improves task success rates by a median of 5.85 percentage points and improves partial goal completion by 15.12%, it also increases the number of execution steps by 67.46% and regresses performance in 16.67% of cases. These results indicate that achieving performance gains is not straightforward; while execution cost can act as a trade-off, execution context can also impact. Furthermore, component ablations show that compact variants of different component combinations often preserve behavioral reliability while reducing unnecessary token overhead, enabling more efficient use of the FM context window and lower execution costs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/eonist/conduit", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.22208", "title": "Solaris: Building a Multiplayer Video World Model in Minecraft", "authors": [ "Georgy Savva", "Oscar Michel", "Daohan Lu", "Suppakit Waiwitlikhit", "Timothy Meehan", "Dhairya Mishra", "Srivats Poddar", "Jack Lu", "Saining Xie" ], "github": "", "project_page": "", "abstract": "Existing action-conditioned video generation models (video world models) are limited to single-agent perspectives, failing to capture the multi-agent interactions of real-world environments. We introduce Solaris, a multiplayer video world model that simulates consistent multi-view observations. To enable this, we develop a multiplayer data system designed for robust, continuous, and automated data collection on video games such as Minecraft. Unlike prior platforms built for single-player settings, our system supports coordinated multi-agent interaction and synchronized videos + actions capture. Using this system, we collect 12.64 million multiplayer frames and propose an evaluation framework for multiplayer movement, memory, grounding, building, and view consistency. We train Solaris using a staged pipeline that progressively transitions from single-player to multiplayer modeling, combining bidirectional, causal, and Self Forcing training. In the final stage, we introduce Checkpointed Self Forcing, a memory-efficient Self Forcing variant that enables a longer-horizon teacher. Results show our architecture and training design outperform existing baselines. Through open-sourcing our system and models, we hope to lay the groundwork for a new generation of multi-agent world models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.22010", "title": "World Guidance: World Modeling in Condition Space for Action Generation", "authors": [ "Yue Su", "Sijin Chen", "Haixin Shi", "Mingyu Liu", "Zhengshen Zhang", "Ningyuan Huang", "Weiheng Zhong", "Zhengbang Zhu", "Yuxiao Liu", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Leveraging future observation modeling to facilitate action generation presents a promising avenue for enhancing the capabilities of Vision-Language-Action (VLA) models. However, existing approaches struggle to strike a balance between maintaining efficient, predictable future representations and preserving sufficient fine-grained information to guide precise action generation. To address this limitation, we propose WoG (World Guidance), a framework that maps future observations into compact conditions by injecting them into the action inference pipeline. The VLA is then trained to simultaneously predict these compressed conditions alongside future actions, thereby achieving effective world modeling within the condition space for action inference. We demonstrate that modeling and predicting this condition space not only facilitates fine-grained action generation but also exhibits superior generalization capabilities. Moreover, it learns effectively from substantial human manipulation videos. Extensive experiments across both simulation and real-world environments validate that our method significantly outperforms existing methods based on future prediction. Project page is available at: https://selen-suyue.github.io/WoGNet/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.21835", "title": "UniVBench: Towards Unified Evaluation for Video Foundation Models", "authors": [ "Jianhui Wei", "Xiaotian Zhang", "Yichen Li", "Yuan Wang", "Yan Zhang", "Ziyi Chen", "Zhihang Tang", "Wei Xu", "Zuozhu Liu" ], "github": "https://github.com/JianhuiWei7/UniVBench", "project_page": "", "abstract": "Video foundation models aim to integrate video understanding, generation, editing, and instruction following within a single framework, making them a central direction for next-generation multimodal systems. However, existing evaluation benchmarks remain fragmented and limited in scope, as they each target a single task, rely on task-specific metrics, and typically use short or simple video clips. As a result, they do not capture the unified capabilities that these models are designed to deliver. To address this gap, we introduce UniVBench, a benchmark purpose-built for evaluating video foundation models across four core abilities: video understanding, video generation, video editing, and a newly proposed task, video reconstruction, which assesses how faithfully a model can reproduce video content it has encountered. Our benchmark substantially expands the complexity of evaluation by incorporating 200 high-quality, diverse and multi-shot videos, each paired with detailed captions, multi-format editing instructions, and reference images. All videos are human-created and carefully validated, offering richer cinematic information than prior benchmarks. In addition, we develop a unified agentic evaluation system (UniV-Eval) that standardizes prompting, instruction parsing, and scoring across all tasks, enabling fair, scalable, and reproducible comparisons of unified video models. By grounding evaluation in instruction-based multi-shot video tasks, UniVBench provides the first framework for measuring the integrated capabilities that video foundation models aim to achieve. Extensive human annotations ensure our evaluation aligns with human judgment, enabling rigorous assessment and accelerating progress toward robust video intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JianhuiWei7/UniVBench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.21472", "title": "The Design Space of Tri-Modal Masked Diffusion Models", "authors": [ "Louis Bethune", "Victor Turrisi", "Bruno Kacper Mlodozeniec", "Pau Rodriguez Lopez", "Lokesh Boominathan", "Nikhil Bhendawade", "Amitis Shidani", "Joris Pelemans", "Theo X. Olausson", "Devon Hjelm", "Paul Dixon", "Joao Monteiro", "Pierre Ablin", "Vishnu Banna", "Arno Blaas", "Nick Henderson", "Kari Noriy", "Dan Busbridge", "Josh Susskind", "Marco Cuturi", "Irina Belousova", "Luca Zappella", "Russ Webb", "Jason Ramapuram" ], "github": "", "project_page": "", "abstract": "Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and fine-tuning a base unimodal model for bimodal generation. Diverging from previous approaches, we introduce the first tri-modal masked diffusion model pretrained from scratch on text, image-text, and audio-text data. We systematically analyze multimodal scaling laws, modality mixing ratios, noise schedules, and batch-size effects, and we provide optimized inference sampling defaults. Our batch-size analysis yields a novel stochastic differential equation (SDE)-based reparameterization that eliminates the need for tuning the optimal batch size as reported in recent work. This reparameterization decouples the physical batch size, often chosen based on compute constraints (GPU saturation, FLOP efficiency, wall-clock time), from the logical batch size, chosen to balance gradient variance during stochastic optimization. Finally, we pretrain a preliminary 3B-parameter tri-modal model on 6.4T tokens, demonstrating the capabilities of a unified design and achieving strong results in text generation, text-to-image tasks, and text-to-speech tasks. Our work represents the largest-scale systematic open study of multimodal discrete diffusion models conducted to date, providing insights into scaling behaviors across multiple modalities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.12160", "title": "DreamID-Omni: Unified Framework for Controllable Human-Centric Audio-Video Generation", "authors": [ "Xu Guo", "Fulong Ye", "Qichao Sun", "Liyang Chen", "Bingchuan Li", "Pengze Zhang", "Jiawei Liu", "Songtao Zhao", "Qian He", "Xiangwang Hou" ], "github": "", "project_page": "", "abstract": "Recent advancements in foundation models have revolutionized joint audio-video generation. However, existing approaches typically treat human-centric tasks including reference-based audio-video generation (R2AV), video editing (RV2AV) and audio-driven video animation (RA2V) as isolated objectives. Furthermore, achieving precise, disentangled control over multiple character identities and voice timbres within a single framework remains an open challenge. In this paper, we propose DreamID-Omni, a unified framework for controllable human-centric audio-video generation. Specifically, we design a Symmetric Conditional Diffusion Transformer that integrates heterogeneous conditioning signals via a symmetric conditional injection scheme. To resolve the pervasive identity-timbre binding failures and speaker confusion in multi-person scenarios, we introduce a Dual-Level Disentanglement strategy: Synchronized RoPE at the signal level to ensure rigid attention-space binding, and Structured Captions at the semantic level to establish explicit attribute-subject mappings. Furthermore, we devise a Multi-Task Progressive Training scheme that leverages weakly-constrained generative priors to regularize strongly-constrained tasks, preventing overfitting and harmonizing disparate objectives. Extensive experiments demonstrate that DreamID-Omni achieves comprehensive state-of-the-art performance across video, audio, and audio-visual consistency, even outperforming leading proprietary commercial models. We will release our code to bridge the gap between academic research and commercial-grade applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.22190", "title": "GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL", "authors": [ "Rui Yang", "Qianhui Wu", "Zhaoyang Wang", "Hanyang Chen", "Ke Yang", "Hao Cheng", "Huaxiu Yao", "Baoling Peng", "Huan Zhang", "Jianfeng Gao", "Tong Zhang" ], "github": "https://github.com/OpenGVLab/ScaleCUA.Preprint", "project_page": "", "abstract": "Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks. This gap stems from two limitations: a shortage of high-quality, action-aligned reasoning data, and the direct adoption of generic post-training pipelines that overlook the unique challenges of GUI agents. We identify two fundamental issues in these pipelines: (i) standard SFT with CoT reasoning often hurts grounding, and (ii) step-wise RLVR-tyle training faces partial verifiability, where multiple actions can be correct but only a single demonstrated action is used for verification. This makes offline step-wise metrics weak predictors of online task success. In this work, we present GUI-Libra, a tailored training recipe that addresses these challenges. First, to mitigate the scarcity of action-aligned reasoning data, we introduce a data construction and filtering pipeline and release a curated 81K GUI reasoning dataset. Second, to reconcile reasoning with grounding, we propose action-aware SFT that mixes reasoning-then-action and direct-action data and reweights tokens to emphasize action and grounding. Third, to stabilize RL under partial verifiability, we identify the overlooked importance of KL regularization in RLVR and show that a KL trust region is critical for improving offline-to-online predictability; we further introduce success-adaptive scaling to downweight unreliable negative gradients. Across diverse web and mobile benchmarks, GUI-Libra consistently improves both step-wise accuracy and end-to-end task completion. Our results suggest that carefully designed post-training and data curation can unlock significantly stronger task-solving capabilities without costly online data collection. We release our dataset, code, and models to facilitate further research on data-efficient post-training for reasoning-capable GUI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OpenGVLab/ScaleCUA.Preprint", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.22144", "title": "NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors", "authors": [ "Lingfeng Ren", "Weihao Yu", "Runpeng Yu", "Xinchao Wang" ], "github": "https://github.com/lingfengren/NoLan", "project_page": "", "abstract": "Object hallucination is a critical issue in Large Vision-Language Models (LVLMs), where outputs include objects that do not appear in the input image. A natural question arises from this phenomenon: Which component of the LVLM pipeline primarily contributes to object hallucinations? The vision encoder to perceive visual information, or the language decoder to generate text responses? In this work, we strive to answer this question through designing a systematic experiment to analyze the roles of the vision encoder and the language decoder in hallucination generation. Our observations reveal that object hallucinations are predominantly associated with the strong priors from the language decoder. Based on this finding, we propose a simple and training-free framework, No-Language-Hallucination Decoding, NoLan, which refines the output distribution by dynamically suppressing language priors, modulated based on the output distribution difference between multimodal and text-only inputs. Experimental results demonstrate that NoLan effectively reduces object hallucinations across various LVLMs on different tasks. For instance, NoLan achieves substantial improvements on POPE, enhancing the accuracy of LLaVA-1.5 7B and Qwen-VL 7B by up to 6.45 and 7.21, respectively. The code is publicly available at: https://github.com/lingfengren/NoLan.", "github_manual": "", "github_regex": "https://github.com/lingfengren/NoLan", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.19004", "title": "MoBind: Motion Binding for Fine-Grained IMU-Video Pose Alignment", "authors": [ "Duc Duy Nguyen", "Tat-Jun Chin", "Minh Hoai" ], "github": "https://github.com/bbvisual/MoBind", "project_page": "", "abstract": "We aim to learn a joint representation between inertial measurement unit (IMU) signals and 2D pose sequences extracted from video, enabling accurate cross-modal retrieval, temporal synchronization, subject and body-part localization, and action recognition. To this end, we introduce MoBind, a hierarchical contrastive learning framework designed to address three challenges: (1) filtering out irrelevant visual background, (2) modeling structured multi-sensor IMU configurations, and (3) achieving fine-grained, sub-second temporal alignment. To isolate motion-relevant cues, MoBind aligns IMU signals with skeletal motion sequences rather than raw pixels. We further decompose full-body motion into local body-part trajectories, pairing each with its corresponding IMU to enable semantically grounded multi-sensor alignment. To capture detailed temporal correspondence, MoBind employs a hierarchical contrastive strategy that first aligns token-level temporal segments, then fuses local (body-part) alignment with global (body-wide) motion aggregation. Evaluated on mRi, TotalCapture, and EgoHumans, MoBind consistently outperforms strong baselines across all four tasks, demonstrating robust fine-grained temporal alignment while preserving coarse semantic consistency across modalities. Code is available at https://github.com/bbvisual/ MoBind.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bbvisual/MoBind", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.20122", "title": "NanoKnow: How to Know What Your Language Model Knows", "authors": [ "Lingwei Gu", "Nour Jedidi", "Jimmy Lin" ], "github": "https://github.com/castorini/NanoKnow", "project_page": "", "abstract": "How do large language models (LLMs) know what they know? Answering this question has been difficult because pre-training data is often a \"black box\" -- unknown or inaccessible. The recent release of nanochat -- a family of small LLMs with fully open pre-training data -- addresses this as it provides a transparent view into where a model's parametric knowledge comes from. Towards the goal of understanding how knowledge is encoded by LLMs, we release NanoKnow, a benchmark dataset that partitions questions from Natural Questions and SQuAD into splits based on whether their answers are present in nanochat's pre-training corpus. Using these splits, we can now properly disentangle the sources of knowledge that LLMs rely on when producing an output. To demonstrate NanoKnow's utility, we conduct experiments using eight nanochat checkpoints. Our findings show: (1) closed-book accuracy is strongly influenced by answer frequency in the pre-training data, (2) providing external evidence can mitigate this frequency dependence, (3) even with external evidence, models are more accurate when answers were seen during pre-training, demonstrating that parametric and external knowledge are complementary, and (4) non-relevant information is harmful, with accuracy decreasing based on both the position and the number of non-relevant contexts. We release all NanoKnow artifacts at https://github.com/castorini/NanoKnow.", "github_manual": "", "github_regex": "https://github.com/castorini/NanoKnow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.15030", "title": "Image Generation with a Sphere Encoder", "authors": [ "Kaiyu Yue", "Menglin Jia", "Ji Hou", "Tom Goldstein" ], "github": "", "project_page": "", "abstract": "We introduce the Sphere Encoder, an efficient generative framework capable of producing images in a single forward pass and competing with many-step diffusion models using fewer than five steps. Our approach works by learning an encoder that maps natural images uniformly onto a spherical latent space, and a decoder that maps random latent vectors back to the image space. Trained solely through image reconstruction losses, the model generates an image by simply decoding a random point on the sphere. Our architecture naturally supports conditional generation, and looping the encoder/decoder a few times can further enhance image quality. Across several datasets, the sphere encoder approach yields performance competitive with state of the art diffusions, but with a small fraction of the inference cost. Project page is available at https://sphere-encoder.github.io .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.18993", "title": "SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models", "authors": [ "Jiwoo Chung", "Sangeek Hyun", "MinKyu Lee", "Byeongju Han", "Geonho Cha", "Dongyoon Wee", "Youngjun Hong", "Jae-Pil Heo" ], "github": "https://github.com/jiwoogit/SeaCache", "project_page": "", "abstract": "Diffusion models are a strong backbone for visual generation, but their inherently sequential denoising process leads to slow inference. Previous methods accelerate sampling by caching and reusing intermediate outputs based on feature distances between adjacent timesteps. However, existing caching strategies typically rely on raw feature differences that entangle content and noise. This design overlooks spectral evolution, where low-frequency structure appears early and high-frequency detail is refined later. We introduce Spectral-Evolution-Aware Cache (SeaCache), a training-free cache schedule that bases reuse decisions on a spectrally aligned representation. Through theoretical and empirical analysis, we derive a Spectral-Evolution-Aware (SEA) filter that preserves content-relevant components while suppressing noise. Employing SEA-filtered input features to estimate redundancy leads to dynamic schedules that adapt to content while respecting the spectral priors underlying the diffusion model. Extensive experiments on diverse visual generative models and the baselines show that SeaCache achieves state-of-the-art latency-quality trade-offs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jiwoogit/SeaCache", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.21461", "title": "VecGlypher: Unified Vector Glyph Generation with Language Models", "authors": [ "Xiaoke Huang", "Bhavul Gauri", "Kam Woh Ng", "Tony Ng", "Mengmeng Xu", "Zhiheng Liu", "Weiming Ren", "Zhaochong An", "Zijian Zhou", "Haonan Qiu", "Yuyin Zhou", "Sen He", "Ziheng Wang", "Tao Xiang", "Xiao Han" ], "github": "", "project_page": "", "abstract": "Vector glyphs are the atomic units of digital typography, yet most learning-based pipelines still depend on carefully curated exemplar sheets and raster-to-vector postprocessing, which limits accessibility and editability. We introduce VecGlypher, a single multimodal language model that generates high-fidelity vector glyphs directly from text descriptions or image exemplars. Given a style prompt, optional reference glyph images, and a target character, VecGlypher autoregressively emits SVG path tokens, avoiding raster intermediates and producing editable, watertight outlines in one pass. A typography-aware data and training recipe makes this possible: (i) a large-scale continuation stage on 39K noisy Envato fonts to master SVG syntax and long-horizon geometry, followed by (ii) post-training on 2.5K expert-annotated Google Fonts with descriptive tags and exemplars to align language and imagery with geometry; preprocessing normalizes coordinate frames, canonicalizes paths, de-duplicates families, and quantizes coordinates for stable long-sequence decoding. On cross-family OOD evaluation, VecGlypher substantially outperforms both general-purpose LLMs and specialized vector-font baselines for text-only generation, while image-referenced generation reaches a state-of-the-art performance, with marked gains over DeepVecFont-v2 and DualVector. Ablations show that model scale and the two-stage recipe are critical and that absolute-coordinate serialization yields the best geometry. VecGlypher lowers the barrier to font creation by letting users design with words or exemplars, and provides a scalable foundation for future multimodal design tools.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.17602", "title": "MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models", "authors": [ "Hojung Jung", "Rodrigo Hormazabal", "Jaehyeong Jo", "Youngrok Park", "Kyunggeun Roh", "Se-Young Yun", "Sehui Han", "Dae-Woong Jeong" ], "github": "", "project_page": "", "abstract": "Molecular generation with diffusion models has emerged as a promising direction for AI-driven drug discovery and materials science. While graph diffusion models have been widely adopted due to the discrete nature of 2D molecular graphs, existing models suffer from low chemical validity and struggle to meet the desired properties compared to 1D modeling. In this work, we introduce MolHIT, a powerful molecular graph generation framework that overcomes long-standing performance limitations in existing methods. MolHIT is based on the Hierarchical Discrete Diffusion Model, which generalizes discrete diffusion to additional categories that encode chemical priors, and decoupled atom encoding that splits the atom types according to their chemical roles. Overall, MolHIT achieves new state-of-the-art performance on the MOSES dataset with near-perfect validity for the first time in graph diffusion, surpassing strong 1D baselines across multiple metrics. We further demonstrate strong performance in downstream tasks, including multi-property guided generation and scaffold extension.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.20857", "title": "Functional Continuous Decomposition", "authors": [ "Teymur Aghayev" ], "github": "", "project_page": "", "abstract": "The analysis of non-stationary time-series data requires insight into its local and global patterns with physical interpretability. However, traditional smoothing algorithms, such as B-splines, Savitzky-Golay filtering, and Empirical Mode Decomposition (EMD), lack the ability to perform parametric optimization with guaranteed continuity. In this paper, we propose Functional Continuous Decomposition (FCD), a JAX-accelerated framework that performs parametric, continuous optimization on a wide range of mathematical functions. By using Levenberg-Marquardt optimization to achieve up to C^1 continuous fitting, FCD transforms raw time-series data into M modes that capture different temporal patterns from short-term to long-term trends. Applications of FCD include physics, medicine, financial analysis, and machine learning, where it is commonly used for the analysis of signal temporal patterns, optimized parameters, derivatives, and integrals of decomposition. Furthermore, FCD can be applied for physical analysis and feature extraction with an average SRMSE of 0.735 per segment and a speed of 0.47s on full decomposition of 1,000 points. Finally, we demonstrate that a Convolutional Neural Network (CNN) enhanced with FCD features, such as optimized function values, parameters, and derivatives, achieved 16.8% faster convergence and 2.5% higher accuracy over a standard CNN.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.21548", "title": "DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference", "authors": [ "Yongtong Wu", "Shaoyuan Chen", "Yinmin Zhong", "Rilin Huang", "Yixuan Tan", "Wentao Zhang", "Liyue Zhang", "Shangyan Zhou", "Yuxuan Liu", "Shunfeng Zhou", "Mingxing Zhang", "Xin Jin", "Panpan Huang" ], "github": "", "project_page": "", "abstract": "The performance of multi-turn, agentic LLM inference is increasingly dominated by KV-Cache storage I/O rather than computation. In prevalent disaggregated architectures, loading the massive KV-Cache from external storage creates a fundamental imbalance: storage NICs on prefill engines become bandwidth-saturated, while those on decoding engines remain idle. This asymmetry severely constrains overall system throughput. We present DualPath, an inference system that breaks this bottleneck by introducing dual-path KV-Cache loading. Beyond the traditional storage-to-prefill path, DualPath enables a novel storage-to-decode path, in which the KV-Cache is loaded into decoding engines and then efficiently transferred to prefill engines via RDMA over the compute network. DualPath combines this optimized data path -- which inherently avoids network congestion and avoids interference with latency-critical model execution communications -- with a global scheduler that dynamically balances load across prefill and decode engines. Our evaluation on three models with production agentic workloads demonstrates that DualPath improves offline inference throughput by up to 1.87times on our in-house inference system. It can also improve online serving throughput by an average factor of 1.96times without violating SLO.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.18964", "title": "Yor-Sarc: A gold-standard dataset for sarcasm detection in a low-resource African language", "authors": [ "Toheeb Aduramomi Jimoh", "Tabea De Wille", "Nikola S. Nikolov" ], "github": "https://github.com/toheebadura/yor-sarc", "project_page": "", "abstract": "Sarcasm detection poses a fundamental challenge in computational semantics, requiring models to resolve disparities between literal and intended meaning. The challenge is amplified in low-resource languages where annotated datasets are scarce or nonexistent. We present Yor-Sarc, the first gold-standard dataset for sarcasm detection in Yor\u00f9b\u00e1, a tonal Niger-Congo language spoken by over 50 million people. The dataset comprises 436 instances annotated by three native speakers from diverse dialectal backgrounds using an annotation protocol specifically designed for Yor\u00f9b\u00e1 sarcasm by taking culture into account. This protocol incorporates context-sensitive interpretation and community-informed guidelines and is accompanied by a comprehensive analysis of inter-annotator agreement to support replication in other African languages. Substantial to almost perfect agreement was achieved (Fleiss' \u03ba= 0.7660; pairwise Cohen's \u03ba= 0.6732--0.8743), with 83.3% unanimous consensus. One annotator pair achieved almost perfect agreement (\u03ba= 0.8743; 93.8% raw agreement), exceeding a number of reported benchmarks for English sarcasm research works. The remaining 16.7% majority-agreement cases are preserved as soft labels for uncertainty-aware modelling. Yor-Sarchttps://github.com/toheebadura/yor-sarc is expected to facilitate research on semantic interpretation and culturally informed NLP for low-resource African languages.", "github_manual": "", "github_regex": "https://github.com/toheebadura/yor-sarc", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.21778", "title": "From Statics to Dynamics: Physics-Aware Image Editing with Latent Transition Priors", "authors": [ "Liangbing Zhao", "Le Zhuo", "Sayak Paul", "Hongsheng Li", "Mohamed Elhoseiny" ], "github": "", "project_page": "", "abstract": "Instruction-based image editing has achieved remarkable success in semantic alignment, yet state-of-the-art models frequently fail to render physically plausible results when editing involves complex causal dynamics, such as refraction or material deformation. We attribute this limitation to the dominant paradigm that treats editing as a discrete mapping between image pairs, which provides only boundary conditions and leaves transition dynamics underspecified. To address this, we reformulate physics-aware editing as predictive physical state transitions and introduce PhysicTran38K, a large-scale video-based dataset comprising 38K transition trajectories across five physical domains, constructed via a two-stage filtering and constraint-aware annotation pipeline. Building on this supervision, we propose PhysicEdit, an end-to-end framework equipped with a textual-visual dual-thinking mechanism. It combines a frozen Qwen2.5-VL for physically grounded reasoning with learnable transition queries that provide timestep-adaptive visual guidance to a diffusion backbone. Experiments show that PhysicEdit improves over Qwen-Image-Edit by 5.9% in physical realism and 10.1% in knowledge-grounded editing, setting a new state-of-the-art for open-source methods, while remaining competitive with leading proprietary models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.18589", "title": "DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction", "authors": [ "Jiayang Shi", "Daniel M. Pelt", "K. Joost Batenburg" ], "github": "https://github.com/DM4CT/DM4CT", "project_page": "", "abstract": "Diffusion models have recently emerged as powerful priors for solving inverse problems. While computed tomography (CT) is theoretically a linear inverse problem, it poses many practical challenges. These include correlated noise, artifact structures, reliance on system geometry, and misaligned value ranges, which make the direct application of diffusion models more difficult than in domains like natural image generation. To systematically evaluate how diffusion models perform in this context and compare them with established reconstruction methods, we introduce DM4CT, a comprehensive benchmark for CT reconstruction. DM4CT includes datasets from both medical and industrial domains with sparse-view and noisy configurations. To explore the challenges of deploying diffusion models in practice, we additionally acquire a high-resolution CT dataset at a high-energy synchrotron facility and evaluate all methods under real experimental conditions. We benchmark ten recent diffusion-based methods alongside seven strong baselines, including model-based, unsupervised, and supervised approaches. Our analysis provides detailed insights into the behavior, strengths, and limitations of diffusion models for CT reconstruction. The real-world dataset is publicly available at zenodo.org/records/15420527, and the codebase is open-sourced at github.com/DM4CT/DM4CT.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/DM4CT/DM4CT", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.19594", "title": "ISO-Bench: Can Coding Agents Optimize Real-World Inference Workloads?", "authors": [ "Ayush Nangia", "Shikhar Mishra", "Aman Gokrani", "Paras Chopra" ], "github": "", "project_page": "", "abstract": "We introduce ISO-Bench, a benchmark for coding agents to test their capabilities on real-world inference optimization tasks. These tasks were taken from vLLM and SGLang, two of the most popular LLM serving frameworks. Each task provides an agent with a codebase and bottleneck description, whereby the agent must produce an optimization patch evaluated against expert human solutions. We curated 54 tasks from merged pull requests with measurable performance improvements. While existing benchmarks heavily use runtime-based metrics, such approaches can be gamed to pass tests without capturing the actual intent of the code changes. Therefore, we combine both hard (execution-based) and soft (LLM-based) metrics to show that both are necessary for complete evaluation. While evaluating both closed and open-source coding agents, we find no single agent dominates across codebases. Surprisingly, agents often identify correct bottlenecks but fail to execute working solutions. We also show that agents with identical underlying models differ substantially, suggesting scaffolding is as important as the model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.20273", "title": "The Truthfulness Spectrum Hypothesis", "authors": [ "Zhuofan Josh Ying", "Shauli Ravfogel", "Nikolaus Kriegeskorte", "Peter Hase" ], "github": "https://github.com/zfying/truth_spec", "project_page": "", "abstract": "Large language models (LLMs) have been reported to linearly encode truthfulness, yet recent work questions this finding's generality. We reconcile these views with the truthfulness spectrum hypothesis: the representational space contains directions ranging from broadly domain-general to narrowly domain-specific. To test this hypothesis, we systematically evaluate probe generalization across five truth types (definitional, empirical, logical, fictional, and ethical), sycophantic and expectation-inverted lying, and existing honesty benchmarks. Linear probes generalize well across most domains but fail on sycophantic and expectation-inverted lying. Yet training on all domains jointly recovers strong performance, confirming that domain-general directions exist despite poor pairwise transfer. The geometry of probe directions explains these patterns: Mahalanobis cosine similarity between probes near-perfectly predicts cross-domain generalization (R^2=0.98). Concept-erasure methods further isolate truth directions that are (1) domain-general, (2) domain-specific, or (3) shared only across particular domain subsets. Causal interventions reveal that domain-specific directions steer more effectively than domain-general ones. Finally, post-training reshapes truth geometry, pushing sycophantic lying further from other truth types, suggesting a representational basis for chat models' sycophantic tendencies. Together, our results support the truthfulness spectrum hypothesis: truth directions of varying generality coexist in representational space, with post-training reshaping their geometry. Code for all experiments is provided in https://github.com/zfying/truth_spec.", "github_manual": "", "github_regex": "https://github.com/zfying/truth_spec", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.16729", "title": "Intent Laundering: AI Safety Datasets Are Not What They Seem", "authors": [ "Shahriar Golchin", "Marc Wetter" ], "github": "", "project_page": "", "abstract": "We systematically evaluate the quality of widely used AI safety datasets from two perspectives: in isolation and in practice. In isolation, we examine how well these datasets reflect real-world adversarial attacks based on three key properties: being driven by ulterior intent, well-crafted, and out-of-distribution. We find that these datasets overrely on \"triggering cues\": words or phrases with overt negative/sensitive connotations that are intended to trigger safety mechanisms explicitly, which is unrealistic compared to real-world attacks. In practice, we evaluate whether these datasets genuinely measure safety risks or merely provoke refusals through triggering cues. To explore this, we introduce \"intent laundering\": a procedure that abstracts away triggering cues from adversarial attacks (data points) while strictly preserving their malicious intent and all relevant details. Our results indicate that current AI safety datasets fail to faithfully represent real-world adversarial behavior due to their overreliance on triggering cues. Once these cues are removed, all previously evaluated \"reasonably safe\" models become unsafe, including Gemini 3 Pro and Claude Sonnet 3.7. Moreover, when intent laundering is adapted as a jailbreaking technique, it consistently achieves high attack success rates, ranging from 90% to over 98%, under fully black-box access. Overall, our findings expose a significant disconnect between how model safety is evaluated by existing datasets and how real-world adversaries behave.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.21374", "title": "Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages", "authors": [ "Mohammadreza Ghaffarzadeh-Esfahani", "Nahid Yousefian", "Ebrahim Heidari-Farsani", "Ali Akbar Omidvarian", "Sepehr Ghahraei", "Atena Farangi", "AmirBahador Boroumand" ], "github": "", "project_page": "", "abstract": "Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language processing (NLP). This study evaluates a two-step pipeline combining Aya-expanse-8B as a Persian-to-English translation model with five open-source small language models (SLMs) -- Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, Llama-3.2-3B-Instruct, Qwen2.5-1.5B-Instruct, and Gemma-3-1B-it -- for binary extraction of 13 clinical features from 1,221 anonymized Persian transcripts collected at a cancer palliative care call center. Using a few-shot prompting strategy without fine-tuning, models were assessed on macro-averaged F1-score, Matthews Correlation Coefficient (MCC), sensitivity, and specificity to account for class imbalance. Qwen2.5-7B-Instruct achieved the highest overall performance (median macro-F1: 0.899; MCC: 0.797), while Gemma-3-1B-it showed the weakest results. Larger models (7B--8B parameters) consistently outperformed smaller counterparts in sensitivity and MCC. A bilingual analysis of Aya-expanse-8B revealed that translating Persian transcripts to English improved sensitivity, reduced missing outputs, and boosted metrics robust to class imbalance, though at the cost of slightly lower specificity and precision. Feature-level results showed reliable extraction of physiological symptoms across most models, whereas psychological complaints, administrative requests, and complex somatic features remained challenging. These findings establish a practical, privacy-preserving blueprint for deploying open-source SLMs in multilingual clinical NLP settings with limited infrastructure and annotation resources, and highlight the importance of jointly optimizing model scale and input language strategy for sensitive healthcare applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.21456", "title": "Revisiting Text Ranking in Deep Research", "authors": [ "Chuan Meng", "Litu Ou", "Sean MacAvaney", "Jeff Dalton" ], "github": "https://github.com/ChuanMeng/text-ranking-in-deep-research", "project_page": "", "abstract": "Deep research has emerged as an important task that aims to address hard queries through extensive open-web exploration. To tackle it, most prior work equips large language model (LLM)-based agents with opaque web search APIs, enabling agents to iteratively issue search queries, retrieve external evidence, and reason over it. Despite search's essential role in deep research, black-box web search APIs hinder systematic analysis of search components, leaving the behaviour of established text ranking methods in deep research largely unclear. To fill this gap, we reproduce a selection of key findings and best practices for IR text ranking methods in the deep research setting. In particular, we examine their effectiveness from three perspectives: (i) retrieval units (documents vs. passages), (ii) pipeline configurations (different retrievers, re-rankers, and re-ranking depths), and (iii) query characteristics (the mismatch between agent-issued queries and the training queries of text rankers). We perform experiments on BrowseComp-Plus, a deep research dataset with a fixed corpus, evaluating 2 open-source agents, 5 retrievers, and 3 re-rankers across diverse setups. We find that agent-issued queries typically follow web-search-style syntax (e.g., quoted exact matches), favouring lexical, learned sparse, and multi-vector retrievers; passage-level units are more efficient under limited context windows, and avoid the difficulties of document length normalisation in lexical retrieval; re-ranking is highly effective; translating agent-issued queries into natural-language questions significantly bridges the query mismatch.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ChuanMeng/text-ranking-in-deep-research", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.20933", "title": "Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting", "authors": [ "Shuangkang Fang", "I-Chao Shen", "Xuanyang Zhang", "Zesheng Wang", "Yufeng Wang", "Wenrui Ding", "Gang Yu", "Takeo Igarashi" ], "github": "", "project_page": "", "abstract": "Recent 3D Gaussian Splatting (3DGS) Dropout methods address overfitting under sparse-view conditions by randomly nullifying Gaussian opacities. However, we identify a neighbor compensation effect in these approaches: dropped Gaussians are often compensated by their neighbors, weakening the intended regularization. Moreover, these methods overlook the contribution of high-degree spherical harmonic coefficients (SH) to overfitting. To address these issues, we propose DropAnSH-GS, a novel anchor-based Dropout strategy. Rather than dropping Gaussians independently, our method randomly selects certain Gaussians as anchors and simultaneously removes their spatial neighbors. This effectively disrupts local redundancies near anchors and encourages the model to learn more robust, globally informed representations. Furthermore, we extend the Dropout to color attributes by randomly dropping higher-degree SH to concentrate appearance information in lower-degree SH. This strategy further mitigates overfitting and enables flexible post-training model compression via SH truncation. Experimental results demonstrate that DropAnSH-GS substantially outperforms existing Dropout methods with negligible computational overhead, and can be readily integrated into various 3DGS variants to enhance their performances. Project Website: https://sk-fun.fun/DropAnSH-GS", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-26", "arxiv_id": "2602.19424", "title": "Hepato-LLaVA: An Expert MLLM with Sparse Topo-Pack Attention for Hepatocellular Pathology Analysis on Whole Slide Images", "authors": [ "Yuxuan Yang", "Zhonghao Yan", "Yi Zhang", "Bo Yun", "Muxi Diao", "Guowei Zhao", "Kongming Liang", "Wenbin Li", "Zhanyu Ma" ], "github": "", "project_page": "", "abstract": "Hepatocellular Carcinoma diagnosis relies heavily on the interpretation of gigapixel Whole Slide Images. However, current computational approaches are constrained by fixed-resolution processing mechanisms and inefficient feature aggregation, which inevitably lead to either severe information loss or high feature redundancy. To address these challenges, we propose Hepato-LLaVA, a specialized Multi-modal Large Language Model designed for fine-grained hepatocellular pathology analysis. We introduce a novel Sparse Topo-Pack Attention mechanism that explicitly models 2D tissue topology. This mechanism effectively aggregates local diagnostic evidence into semantic summary tokens while preserving global context. Furthermore, to overcome the lack of multi-scale data, we present HepatoPathoVQA, a clinically grounded dataset comprising 33K hierarchically structured question-answer pairs validated by expert pathologists. Our experiments demonstrate that Hepato-LLaVA achieves state-of-the-art performance on HCC diagnosis and captioning tasks, significantly outperforming existing methods. Our code and implementation details are available at https://pris-cv.github.io/Hepto-LLaVA/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.22897", "title": "OmniGAIA: Towards Native Omni-Modal AI Agents", "authors": [ "Xiaoxi Li", "Wenxiang Jiao", "Jiarui Jin", "Shijian Wang", "Guanting Dong", "Jiajie Jin", "Hao Wang", "Yinuo Wang", "Ji-Rong Wen", "Yuan Lu", "Zhicheng Dou" ], "github": "https://github.com/RUC-NLPIR/OmniGAIA", "project_page": "", "abstract": "Human intelligence naturally intertwines omni-modal perception -- spanning vision, audio, and language -- with complex reasoning and tool usage to interact with the world. However, current multi-modal LLMs are primarily confined to bi-modal interactions (e.g., vision-language), lacking the unified cognitive capabilities required for general AI assistants. To bridge this gap, we introduce OmniGAIA, a comprehensive benchmark designed to evaluate omni-modal agents on tasks necessitating deep reasoning and multi-turn tool execution across video, audio, and image modalities. Constructed via a novel omni-modal event graph approach, OmniGAIA synthesizes complex, multi-hop queries derived from real-world data that require cross-modal reasoning and external tool integration. Furthermore, we propose OmniAtlas, a native omni-modal foundation agent under tool-integrated reasoning paradigm with active omni-modal perception. Trained on trajectories synthesized via a hindsight-guided tree exploration strategy and OmniDPO for fine-grained error correction, OmniAtlas effectively enhances the tool-use capabilities of existing open-source models. This work marks a step towards next-generation native omni-modal AI assistants for real-world scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/RUC-NLPIR/OmniGAIA", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.23152", "title": "The Trinity of Consistency as a Defining Principle for General World Models", "authors": [ "Jingxuan Wei", "Siyuan Li", "Yuhang Xu", "Zheng Sun", "Junjie Jiang", "Hexuan Jin", "Caijun Jia", "Honghao He", "Xinglong Xu", "Xi bai", "Chang Yu", "Yumou Liu", "Junnan Zhu", "Xuanhe Zhou", "Jintao Chen", "Xiaobin Hu", "Shancheng Pang", "Bihui Yu", "Ran He", "Zhen Lei", "Stan Z. Li", "Conghui He", "Shuicheng Yan", "Cheng Tan" ], "github": "", "project_page": "", "abstract": "The construction of World Models capable of learning, simulating, and reasoning about objective physical laws constitutes a foundational challenge in the pursuit of Artificial General Intelligence. Recent advancements represented by video generation models like Sora have demonstrated the potential of data-driven scaling laws to approximate physical dynamics, while the emerging Unified Multimodal Model (UMM) offers a promising architectural paradigm for integrating perception, language, and reasoning. Despite these advances, the field still lacks a principled theoretical framework that defines the essential properties requisite for a General World Model. In this paper, we propose that a World Model must be grounded in the Trinity of Consistency: Modal Consistency as the semantic interface, Spatial Consistency as the geometric basis, and Temporal Consistency as the causal engine. Through this tripartite lens, we systematically review the evolution of multimodal learning, revealing a trajectory from loosely coupled specialized modules toward unified architectures that enable the synergistic emergence of internal world simulators. To complement this conceptual framework, we introduce CoW-Bench, a benchmark centered on multi-frame reasoning and generation scenarios. CoW-Bench evaluates both video generation models and UMMs under a unified evaluation protocol. Our work establishes a principled pathway toward general world models, clarifying both the limitations of current systems and the architectural requirements for future progress.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.22766", "title": "Imagination Helps Visual Reasoning, But Not Yet in Latent Space", "authors": [ "You Li", "Chi Chen", "Yanghao Li", "Fanhu Zeng", "Kaiyu Huang", "Jinan Xu", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "Latent visual reasoning aims to mimic human's imagination process by meditating through hidden states of Multimodal Large Language Models. While recognized as a promising paradigm for visual reasoning, the underlying mechanisms driving its effectiveness remain unclear. Motivated to demystify the true source of its efficacy, we investigate the validity of latent reasoning using Causal Mediation Analysis. We model the process as a causal chain: the input as the treatment, the latent tokens as the mediator, and the final answer as the outcome. Our findings uncover two critical disconnections: (a) Input-Latent Disconnect: dramatic perturbations on the input result in negligible changes to the latent tokens, suggesting that latent tokens do not effectively attend to the input sequence. (b) Latent-Answer Disconnect: perturbations on the latent tokens yield minimal impact on the final answer, indicating the limited causal effect latent tokens imposing on the outcome. Furthermore, extensive probing analysis reveals that latent tokens encode limited visual information and exhibit high similarity. Consequently, we challenge the necessity of latent reasoning and propose a straightforward alternative named CapImagine, which teaches the model to explicitly imagine using text. Experiments on vision-centric benchmarks show that CapImagine significantly outperforms complex latent-space baselines, highlighting the superior potential of visual reasoning through explicit imagination.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.22594", "title": "Causal Motion Diffusion Models for Autoregressive Motion Generation", "authors": [ "Qing Yu", "Akihisa Watanabe", "Kent Fujiwara" ], "github": "https://github.com/YU1ut/CMDM", "project_page": "", "abstract": "Recent advances in motion diffusion models have substantially improved the realism of human motion synthesis. However, existing approaches either rely on full-sequence diffusion models with bidirectional generation, which limits temporal causality and real-time applicability, or autoregressive models that suffer from instability and cumulative errors. In this work, we present Causal Motion Diffusion Models (CMDM), a unified framework for autoregressive motion generation based on a causal diffusion transformer that operates in a semantically aligned latent space. CMDM builds upon a Motion-Language-Aligned Causal VAE (MAC-VAE), which encodes motion sequences into temporally causal latent representations. On top of this latent representation, an autoregressive diffusion transformer is trained using causal diffusion forcing to perform temporally ordered denoising across motion frames. To achieve fast inference, we introduce a frame-wise sampling schedule with causal uncertainty, where each subsequent frame is predicted from partially denoised previous frames. The resulting framework supports high-quality text-to-motion generation, streaming synthesis, and long-horizon motion generation at interactive rates. Experiments on HumanML3D and SnapMoGen demonstrate that CMDM outperforms existing diffusion and autoregressive models in both semantic fidelity and temporal smoothness, while substantially reducing inference latency.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YU1ut/CMDM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.23259", "title": "Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving", "authors": [ "Jiangxin Sun", "Feng Xue", "Teng Long", "Chang Liu", "Jian-Fang Hu", "Wei-Shi Zheng", "Nicu Sebe" ], "github": "", "project_page": "", "abstract": "With advances in imitation learning (IL) and large-scale driving datasets, end-to-end autonomous driving (E2E-AD) has made great progress recently. Currently, IL-based methods have become a mainstream paradigm: models rely on standard driving behaviors given by experts, and learn to minimize the discrepancy between their actions and expert actions. However, this objective of \"only driving like the expert\" suffers from limited generalization: when encountering rare or unseen long-tail scenarios outside the distribution of expert demonstrations, models tend to produce unsafe decisions in the absence of prior experience. This raises a fundamental question: Can an E2E-AD system make reliable decisions without any expert action supervision? Motivated by this, we propose a unified framework named Risk-aware World Model Predictive Control (RaWMPC) to address this generalization dilemma through robust control, without reliance on expert demonstrations. Practically, RaWMPC leverages a world model to predict the consequences of multiple candidate actions and selects low-risk actions through explicit risk evaluation. To endow the world model with the ability to predict the outcomes of risky driving behaviors, we design a risk-aware interaction strategy that systematically exposes the world model to hazardous behaviors, making catastrophic outcomes predictable and thus avoidable. Furthermore, to generate low-risk candidate actions at test time, we introduce a self-evaluation distillation method to distill riskavoidance capabilities from the well-trained world model into a generative action proposal network without any expert demonstration. Extensive experiments show that RaWMPC outperforms state-of-the-art methods in both in-distribution and out-of-distribution scenarios, while providing superior decision interpretability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.23165", "title": "DyaDiT: A Multi-Modal Diffusion Transformer for Socially Favorable Dyadic Gesture Generation", "authors": [ "Yichen Peng", "Jyun-Ting Song", "Siyeol Jung", "Ruofan Liu", "Haiyang Liu", "Xuangeng Chu", "Ruicong Liu", "Erwin Wu", "Hideki Koike", "Kris Kitani" ], "github": "", "project_page": "", "abstract": "Generating realistic conversational gestures are essential for achieving natural, socially engaging interactions with digital humans. However, existing methods typically map a single audio stream to a single speaker's motion, without considering social context or modeling the mutual dynamics between two people engaging in conversation. We present DyaDiT, a multi-modal diffusion transformer that generates contextually appropriate human motion from dyadic audio signals. Trained on Seamless Interaction Dataset, DyaDiT takes dyadic audio with optional social-context tokens to produce context-appropriate motion. It fuses information from both speakers to capture interaction dynamics, uses a motion dictionary to encode motion priors, and can optionally utilize the conversational partner's gestures to produce more responsive motion. We evaluate DyaDiT on standard motion generation metrics and conduct quantitative user studies, demonstrating that it not only surpasses existing methods on objective metrics but is also strongly preferred by users, highlighting its robustness and socially favorable motion generation. Code and models will be released upon acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.23058", "title": "GeoWorld: Geometric World Models", "authors": [ "Zeyu Zhang", "Danning Li", "Ian Reid", "Richard Hartley" ], "github": "", "project_page": "", "abstract": "Energy-based predictive world models provide a powerful approach for multi-step visual planning by reasoning over latent energy landscapes rather than generating pixels. However, existing approaches face two major challenges: (i) their latent representations are typically learned in Euclidean space, neglecting the underlying geometric and hierarchical structure among states, and (ii) they struggle with long-horizon prediction, which leads to rapid degradation across extended rollouts. To address these challenges, we introduce GeoWorld, a geometric world model that preserves geometric structure and hierarchical relations through a Hyperbolic JEPA, which maps latent representations from Euclidean space onto hyperbolic manifolds. We further introduce Geometric Reinforcement Learning for energy-based optimization, enabling stable multi-step planning in hyperbolic latent space. Extensive experiments on CrossTask and COIN demonstrate around 3% SR improvement in 3-step planning and 2% SR improvement in 4-step planning compared to the state-of-the-art V-JEPA 2. Project website: https://steve-zeyu-zhang.github.io/GeoWorld.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.22437", "title": "veScale-FSDP: Flexible and High-Performance FSDP at Scale", "authors": [ "Zezhou Wang", "Youjie Li", "Zhiqi Lin", "Jiacheng Yang", "Cong Xie", "Guanyu Feng", "Zheng Zhong", "Ziyue Huang", "Hongyu Zhu", "Zhi Zhang", "Yanghua Peng", "Xin Liu" ], "github": "", "project_page": "", "abstract": "Fully Sharded Data Parallel (FSDP), also known as ZeRO, is widely used for training large-scale models, featuring its flexibility and minimal intrusion on model code. However, current FSDP systems struggle with structure-aware training methods (e.g., block-wise quantized training) and with non-element-wise optimizers (e.g., Shampoo and Muon) used in cutting-edge models (e.g., Gemini, Kimi K2). FSDP's fixed element- or row-wise sharding formats conflict with the block-structured computations. In addition, today's implementations fall short in communication and memory efficiency, limiting scaling to tens of thousands of GPUs. We introduce veScale-FSDP, a redesigned FSDP system that couples a flexible sharding format, RaggedShard, with a structure-aware planning algorithm to deliver both flexibility and performance at scale. veScale-FSDP natively supports efficient data placement required by FSDP, empowering block-wise quantization and non-element-wise optimizers. As a result, veScale-FSDP achieves 5~66% higher throughput and 16~30% lower memory usage than existing FSDP systems, while scaling efficiently to tens of thousands of GPUs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.23008", "title": "Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization", "authors": [ "Zeyuan Liu", "Jeonghye Kim", "Xufang Luo", "Dongsheng Li", "Yuqing Yang" ], "github": "https://github.com/microsoft/agent-lightning/tree/main/contrib/recipes/envs", "project_page": "", "abstract": "Exploration remains the key bottleneck for large language model agents trained with reinforcement learning. While prior methods exploit pretrained knowledge, they fail in environments requiring the discovery of novel states. We propose Exploratory Memory-Augmented On- and Off-Policy Optimization (EMPO^2), a hybrid RL framework that leverages memory for exploration and combines on- and off-policy updates to make LLMs perform well with memory while also ensuring robustness without it. On ScienceWorld and WebShop, EMPO^2 achieves 128.6% and 11.3% improvements over GRPO, respectively. Moreover, in out-of-distribution tests, EMPO^2 demonstrates superior adaptability to new tasks, requiring only a few trials with memory and no parameter updates. These results highlight EMPO^2 as a promising framework for building more exploratory and generalizable LLM-based agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/agent-lightning/tree/main/contrib/recipes/envs", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.21760", "title": "Accelerating Diffusion via Hybrid Data-Pipeline Parallelism Based on Conditional Guidance Scheduling", "authors": [ "Euisoo Jung", "Byunghyun Kim", "Hyunjin Kim", "Seonghye Cho", "Jae-Gil Lee" ], "github": "https://github.com/kaist-dmlab/Hybridiff", "project_page": "", "abstract": "Diffusion models have achieved remarkable progress in high-fidelity image, video, and audio generation, yet inference remains computationally expensive. Nevertheless, current diffusion acceleration methods based on distributed parallelism suffer from noticeable generation artifacts and fail to achieve substantial acceleration proportional to the number of GPUs. Therefore, we propose a hybrid parallelism framework that combines a novel data parallel strategy, condition-based partitioning, with an optimal pipeline scheduling method, adaptive parallelism switching, to reduce generation latency and achieve high generation quality in conditional diffusion models. The key ideas are to (i) leverage the conditional and unconditional denoising paths as a new data-partitioning perspective and (ii) adaptively enable optimal pipeline parallelism according to the denoising discrepancy between these two paths. Our framework achieves 2.31times and 2.07times latency reductions on SDXL and SD3, respectively, using two NVIDIA RTX~3090 GPUs, while preserving image quality. This result confirms the generality of our approach across U-Net-based diffusion models and DiT-based flow-matching architectures. Our approach also outperforms existing methods in acceleration under high-resolution synthesis settings. Code is available at https://github.com/kaist-dmlab/Hybridiff.", "github_manual": "", "github_regex": "https://github.com/kaist-dmlab/Hybridiff", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.17594", "title": "AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games", "authors": [ "Lance Ying", "Ryan Truong", "Prafull Sharma", "Kaiya Ivy Zhao", "Nathan Cloos", "Kelsey R. Allen", "Thomas L. Griffiths", "Katherine M. Collins", "Jos\u00e9 Hern\u00e1ndez-Orallo", "Phillip Isola", "Samuel J. Gershman", "Joshua B. Tenenbaum" ], "github": "", "project_page": "", "abstract": "Rigorously evaluating machine intelligence against the broad spectrum of human general intelligence has become increasingly important and challenging in this era of rapid technological advance. Conventional AI benchmarks typically assess only narrow capabilities in a limited range of human activity. Most are also static, quickly saturating as developers explicitly or implicitly optimize for them. We propose that a more promising way to evaluate human-like general intelligence in AI systems is through a particularly strong form of general game playing: studying how and how well they play and learn to play all conceivable human games, in comparison to human players with the same level of experience, time, or other resources. We define a \"human game\" to be a game designed by humans for humans, and argue for the evaluative suitability of this space of all such games people can imagine and enjoy -- the \"Multiverse of Human Games\". Taking a first step towards this vision, we introduce the AI GameStore, a scalable and open-ended platform that uses LLMs with humans-in-the-loop to synthesize new representative human games, by automatically sourcing and adapting standardized and containerized variants of game environments from popular human digital gaming platforms. As a proof of concept, we generated 100 such games based on the top charts of Apple App Store and Steam, and evaluated seven frontier vision-language models (VLMs) on short episodes of play. The best models achieved less than 10\\% of the human average score on the majority of the games, and especially struggled with games that challenge world-model learning, memory and planning. We conclude with a set of next steps for building out the AI GameStore as a practical way to measure and drive progress toward human-like general intelligence in machines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.22479", "title": "Efficient Continual Learning in Language Models via Thalamically Routed Cortical Columns", "authors": [ "Afshin Khadangi" ], "github": "", "project_page": "", "abstract": "Continual learning is a core requirement for deployed language models, yet standard training and fine-tuning pipelines remain brittle under non-stationary data. Online updates often induce catastrophic forgetting, while methods that improve stability frequently increase latency, memory footprint, or dense computation in ways that do not scale well to long contexts. We introduce TRC^{2} (Thalamically Routed Cortical Columns), a decoder-only backbone that addresses continual learning at the architectural level. TRC^{2} combines sparse thalamic routing over cortical columns with mechanisms for modulation, prediction, memory, and feedback, together with a fast corrective pathway that supports rapid adaptation without destabilizing slower parameters. The resulting block is sparse and chunk-parallel, enabling efficient training and inference while preserving clean ablations of each subsystem. We instantiate a reproducible training and evaluation stack and a continual-learning harness that measures proxy forgetting under streaming domain shifts. Across language modeling and continual learning benchmarks, TRC^{2} improves the stability-plasticity tradeoff at comparable compute, enabling rapid on-stream adaptation while preserving previously acquired behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.22859", "title": "From Blind Spots to Gains: Diagnostic-Driven Iterative Training for Large Multimodal Models", "authors": [ "Hongrui Jia", "Chaoya Jiang", "Shikun Zhang", "Wei Ye" ], "github": "https://github.com/hongruijia/DPE", "project_page": "", "abstract": "As Large Multimodal Models (LMMs) scale up and reinforcement learning (RL) methods mature, LMMs have made notable progress in complex reasoning and decision making. Yet training still relies on static data and fixed recipes, making it difficult to diagnose capability blind spots or provide dynamic, targeted reinforcement. Motivated by findings that test driven error exposure and feedback based correction outperform repetitive practice, we propose Diagnostic-driven Progressive Evolution (DPE), a spiral loop where diagnosis steers data generation and reinforcement, and each iteration re-diagnoses the updated model to drive the next round of targeted improvement. DPE has two key components. First, multiple agents annotate and quality control massive unlabeled multimodal data, using tools such as web search and image editing to produce diverse, realistic samples. Second, DPE attributes failures to specific weaknesses, dynamically adjusts the data mixture, and guides agents to generate weakness focused data for targeted reinforcement. Experiments on Qwen3-VL-8B-Instruct and Qwen2.5-VL-7B-Instruct show stable, continual gains across eleven benchmarks, indicating DPE as a scalable paradigm for continual LMM training under open task distributions. Our code, models, and data are publicly available at https://github.com/hongruijia/DPE.", "github_manual": "", "github_regex": "https://github.com/hongruijia/DPE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.23258", "title": "AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning", "authors": [ "Yutong Wang", "Siyuan Xiong", "Xuebo Liu", "Wenkang Zhou", "Liang Ding", "Miao Zhang", "Min Zhang" ], "github": "https://github.com/TonySY2/AgentDropoutV2", "project_page": "", "abstract": "While Multi-Agent Systems (MAS) excel in complex reasoning, they suffer from the cascading impact of erroneous information generated by individual participants. Current solutions often resort to rigid structural engineering or expensive fine-tuning, limiting their deployability and adaptability. We propose AgentDropoutV2, a test-time rectify-or-reject pruning framework designed to dynamically optimize MAS information flow without retraining. Our approach acts as an active firewall, intercepting agent outputs and employing a retrieval-augmented rectifier to iteratively correct errors based on a failure-driven indicator pool. This mechanism allows for the precise identification of potential errors using distilled failure patterns as prior knowledge. Irreparable outputs are subsequently pruned to prevent error propagation, while a fallback strategy preserves system integrity. Empirical results on extensive math benchmarks show that AgentDropoutV2 significantly boosts the MAS's task performance, achieving an average accuracy gain of 6.3 percentage points on math benchmarks. Furthermore, the system exhibits robust generalization and adaptivity, dynamically modulating rectification efforts based on task difficulty while leveraging context-aware indicators to resolve a wide spectrum of error patterns. Our code and dataset are released at https://github.com/TonySY2/AgentDropoutV2.", "github_manual": "", "github_regex": "https://github.com/TonySY2/AgentDropoutV2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.22675", "title": "Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization", "authors": [ "Qianben Chen", "Tianrui Qin", "King Zhu", "Qiexiang Wang", "Chengjun Yu", "Shu Xu", "Jiaqi Wu", "Jiayu Zhang", "Xinpeng Liu", "Xin Gui", "Jingyi Cao", "Piaohong Wang", "Dingfeng Shi", "He Zhu", "Tiannan Wang", "Yuqing Wang", "Maojia Song", "Tianyu Zheng", "Ge Zhang", "Jian Yang", "Jiaheng Liu", "Minghao Liu", "Yuchen Eleanor Jiang", "Wangchunshu Zhou" ], "github": "", "project_page": "", "abstract": "Recent deep research agents primarily improve performance by scaling reasoning depth, but this leads to high inference cost and latency in search-intensive scenarios. Moreover, generalization across heterogeneous research settings remains challenging. In this work, we propose Search More, Think Less (SMTL), a framework for long-horizon agentic search that targets both efficiency and generalization. SMTL replaces sequential reasoning with parallel evidence acquisition, enabling efficient context management under constrained context budgets. To support generalization across task types, we further introduce a unified data synthesis pipeline that constructs search tasks spanning both deterministic question answering and open-ended research scenarios with task appropriate evaluation metrics. We train an end-to-end agent using supervised fine-tuning and reinforcement learning, achieving strong and often state of the art performance across benchmarks including BrowseComp (48.6\\%), GAIA (75.7\\%), Xbench (82.0\\%), and DeepResearch Bench (45.9\\%). Compared to Mirothinker-v1.0, SMTL with maximum 100 interaction steps reduces the average number of reasoning steps on BrowseComp by 70.7\\%, while improving accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.23363", "title": "MediX-R1: Open Ended Medical Reinforcement Learning", "authors": [ "Sahal Shaji Mullappilly", "Mohammed Irfan Kurpath", "Omair Mohamed", "Mohamed Zidan", "Fahad Khan", "Salman Khan", "Rao Anwer", "Hisham Cholakkal" ], "github": "", "project_page": "", "abstract": "We introduce MediX-R1, an open-ended Reinforcement Learning (RL) framework for medical multimodal large language models (MLLMs) that enables clinically grounded, free-form answers beyond multiple-choice formats. MediX-R1 fine-tunes a baseline vision-language backbone with Group Based RL and a composite reward tailored for medical reasoning: an LLM-based accuracy reward that judges semantic correctness with a strict YES/NO decision, a medical embedding-based semantic reward to capture paraphrases and terminology variants, and lightweight format and modality rewards that enforce interpretable reasoning and modality recognition. This multi-signal design provides stable, informative feedback for open-ended outputs where traditional verifiable or MCQ-only rewards fall short. To measure progress, we propose a unified evaluation framework for both text-only and image+text tasks that uses a Reference-based LLM-as-judge in place of brittle string-overlap metrics, capturing semantic correctness, reasoning, and contextual alignment. Despite using only sim51K instruction examples, MediX-R1 achieves excellent results across standard medical LLM (text-only) and VLM (image + text) benchmarks, outperforming strong open-source baselines and delivering particularly large gains on open-ended clinical tasks. Our results demonstrate that open-ended RL with comprehensive reward signals and LLM-based evaluation is a practical path toward reliable medical reasoning in multimodal models. Our trained models, curated datasets and source code are available at https://medix.cvmbzuai.com", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.23205", "title": "EmbodMocap: In-the-Wild 4D Human-Scene Reconstruction for Embodied Agents", "authors": [ "Wenjia Wang", "Liang Pan", "Huaijin Pi", "Yuke Lou", "Xuqian Ren", "Yifan Wu", "Zhouyingcheng Liao", "Lei Yang", "Rishabh Dabral", "Christian Theobalt", "Taku Komura" ], "github": "", "project_page": "", "abstract": "Human behaviors in the real world naturally encode rich, long-term contextual information that can be leveraged to train embodied agents for perception, understanding, and acting. However, existing capture systems typically rely on costly studio setups and wearable devices, limiting the large-scale collection of scene-conditioned human motion data in the wild. To address this, we propose EmbodMocap, a portable and affordable data collection pipeline using two moving iPhones. Our key idea is to jointly calibrate dual RGB-D sequences to reconstruct both humans and scenes within a unified metric world coordinate frame. The proposed method allows metric-scale and scene-consistent capture in everyday environments without static cameras or markers, bridging human motion and scene geometry seamlessly. Compared with optical capture ground truth, we demonstrate that the dual-view setting exhibits a remarkable ability to mitigate depth ambiguity, achieving superior alignment and reconstruction performance over single iphone or monocular models. Based on the collected data, we empower three embodied AI tasks: monocular human-scene-reconstruction, where we fine-tune on feedforward models that output metric-scale, world-space aligned humans and scenes; physics-based character animation, where we prove our data could be used to scale human-object interaction skills and scene-aware motion tracking; and robot motion control, where we train a humanoid robot via sim-to-real RL to replicate human motions depicted in videos. Experimental results validate the effectiveness of our pipeline and its contributions towards advancing embodied AI research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.20981", "title": "Echoes Over Time: Unlocking Length Generalization in Video-to-Audio Generation Models", "authors": [ "Christian Simon", "Masato Ishii", "Wei-Yao Wang", "Koichi Saito", "Akio Hayakawa", "Dongseok Shim", "Zhi Zhong", "Shuyang Cui", "Shusuke Takahashi", "Takashi Shibuya", "Yuki Mitsufuji" ], "github": "", "project_page": "", "abstract": "Scaling multimodal alignment between video and audio is challenging, particularly due to limited data and the mismatch between text descriptions and frame-level video information. In this work, we tackle the scaling challenge in multimodal-to-audio generation, examining whether models trained on short instances can generalize to longer ones during testing. To tackle this challenge, we present multimodal hierarchical networks so-called MMHNet, an enhanced extension of state-of-the-art video-to-audio models. Our approach integrates a hierarchical method and non-causal Mamba to support long-form audio generation. Our proposed method significantly improves long audio generation up to more than 5 minutes. We also prove that training short and testing long is possible in the video-to-audio generation tasks without training on the longer durations. We show in our experiments that our proposed method could achieve remarkable results on long-video to audio benchmarks, beating prior works in video-to-audio tasks. Moreover, we showcase our model capability in generating more than 5 minutes, while prior video-to-audio methods fall short in generating with long durations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.22638", "title": "MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios", "authors": [ "Zhiheng Song", "Jingshuai Zhang", "Chuan Qin", "Chao Wang", "Chao Chen", "Longfei Xu", "Kaikui Liu", "Xiangxiang Chu", "Hengshu Zhu" ], "github": "https://github.com/AMAP-ML/MobilityBench", "project_page": "", "abstract": "Route-planning agents powered by large language models (LLMs) have emerged as a promising paradigm for supporting everyday human mobility through natural language interaction and tool-mediated decision making. However, systematic evaluation in real-world mobility settings is hindered by diverse routing demands, non-deterministic mapping services, and limited reproducibility. In this study, we introduce MobilityBench, a scalable benchmark for evaluating LLM-based route-planning agents in real-world mobility scenarios. MobilityBench is constructed from large-scale, anonymized real user queries collected from Amap and covers a broad spectrum of route-planning intents across multiple cities worldwide. To enable reproducible, end-to-end evaluation, we design a deterministic API-replay sandbox that eliminates environmental variance from live services. We further propose a multi-dimensional evaluation protocol centered on outcome validity, complemented by assessments of instruction understanding, planning, tool use, and efficiency. Using MobilityBench, we evaluate multiple LLM-based route-planning agents across diverse real-world mobility scenarios and provide an in-depth analysis of their behaviors and performance. Our findings reveal that current models perform competently on Basic information retrieval and Route Planning tasks, yet struggle considerably with Preference-Constrained Route Planning, underscoring significant room for improvement in personalized mobility applications. We publicly release the benchmark data, evaluation toolkit, and documentation at https://github.com/AMAP-ML/MobilityBench .", "github_manual": "", "github_regex": "https://github.com/AMAP-ML/MobilityBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.22045", "title": "DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain", "authors": [ "Walter Hernandez Cruz", "Peter Devine", "Nikhil Vadgama", "Paolo Tasca", "Jiahua Xu" ], "github": "", "project_page": "", "abstract": "We introduce DLT-Corpus, the largest domain-specific text collection for Distributed Ledger Technology (DLT) research to date: 2.98 billion tokens from 22.12 million documents spanning scientific literature (37,440 publications), United States Patent and Trademark Office (USPTO) patents (49,023 filings), and social media (22 million posts). Existing Natural Language Processing (NLP) resources for DLT focus narrowly on cryptocurrencies price prediction and smart contracts, leaving domain-specific language under explored despite the sector's ~$3 trillion market capitalization and rapid technological evolution. We demonstrate DLT-Corpus' utility by analyzing technology emergence patterns and market-innovation correlations. Findings reveal that technologies originate in scientific literature before reaching patents and social media, following traditional technology transfer patterns. While social media sentiment remains overwhelmingly bullish even during crypto winters, scientific and patent activity grow independently of market fluctuations, tracking overall market expansion in a virtuous cycle where research precedes and enables economic growth that funds further innovation. We publicly release the full DLT-Corpus; LedgerBERT, a domain-adapted model achieving 23% improvement over BERT-base on a DLT-specific Named Entity Recognition (NER) task; and all associated tools and code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.23339", "title": "Retrieve and Segment: Are a Few Examples Enough to Bridge the Supervision Gap in Open-Vocabulary Segmentation?", "authors": [ "Tilemachos Aravanis", "Vladan Stojni\u0107", "Bill Psomas", "Nikos Komodakis", "Giorgos Tolias" ], "github": "", "project_page": "", "abstract": "Open-vocabulary segmentation (OVS) extends the zero-shot recognition capabilities of vision-language models (VLMs) to pixel-level prediction, enabling segmentation of arbitrary categories specified by text prompts. Despite recent progress, OVS lags behind fully supervised approaches due to two challenges: the coarse image-level supervision used to train VLMs and the semantic ambiguity of natural language. We address these limitations by introducing a few-shot setting that augments textual prompts with a support set of pixel-annotated images. Building on this, we propose a retrieval-augmented test-time adapter that learns a lightweight, per-image classifier by fusing textual and visual support features. Unlike prior methods relying on late, hand-crafted fusion, our approach performs learned, per-query fusion, achieving stronger synergy between modalities. The method supports continually expanding support sets, and applies to fine-grained tasks such as personalized segmentation. Experiments show that we significantly narrow the gap between zero-shot and supervised segmentation while preserving open-vocabulary ability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.20332", "title": "No One Size Fits All: QueryBandits for Hallucination Mitigation", "authors": [ "Nicole Cho", "William Watson", "Alec Koppel", "Sumitra Ganesh", "Manuela Veloso" ], "github": "", "project_page": "", "abstract": "Advanced reasoning capabilities in Large Language Models (LLMs) have led to more frequent hallucinations; yet most mitigation work focuses on open-source models for post-hoc detection and parameter editing. The dearth of studies focusing on hallucinations in closed-source models is especially concerning, as they constitute the vast majority of models in institutional deployments. We introduce QueryBandits, a model-agnostic contextual bandit framework that adaptively learns online to select the optimal query-rewrite strategy by leveraging an empirically validated and calibrated reward function. Across 16 QA scenarios, our top QueryBandit (Thompson Sampling) achieves an 87.5% win rate over a No-Rewrite baseline and outperforms zero-shot static policies (e.g., Paraphrase or Expand) by 42.6% and 60.3%, respectively. Moreover, all contextual bandits outperform vanilla bandits across all datasets, with higher feature variance coinciding with greater variance in arm selection. This substantiates our finding that there is no single rewrite policy optimal for all queries. We also discover that certain static policies incur higher cumulative regret than No-Rewrite, indicating that an inflexible query-rewriting policy can worsen hallucinations. Thus, learning an online policy over semantic features with QueryBandits can shift model behavior purely through forward-pass mechanisms, enabling its use with closed-source models and bypassing the need for retraining or gradient-based adaptation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.20300", "title": "What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance", "authors": [ "William Watson", "Nicole Cho", "Sumitra Ganesh", "Manuela Veloso" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM) hallucinations are usually treated as defects of the model or its decoding strategy. Drawing on classical linguistics, we argue that a query's form can also shape a listener's (and model's) response. We operationalize this insight by constructing a 22-dimension query feature vector covering clause complexity, lexical rarity, and anaphora, negation, answerability, and intention grounding, all known to affect human comprehension. Using 369,837 real-world queries, we ask: Are there certain types of queries that make hallucination more likely? A large-scale analysis reveals a consistent \"risk landscape\": certain features such as deep clause nesting and underspecification align with higher hallucination propensity. In contrast, clear intention grounding and answerability align with lower hallucination rates. Others, including domain specificity, show mixed, dataset- and model-dependent effects. Thus, these findings establish an empirically observable query-feature representation correlated with hallucination risk, paving the way for guided query rewriting and future intervention studies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.18253", "title": "MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data", "authors": [ "Xabier de Zuazo", "Vincenzo Verbeni", "Eva Navas", "Ibon Saratxaga", "Mathieu Bourguignon", "Nicola Molinaro" ], "github": "", "project_page": "", "abstract": "Data-efficient neural decoding is a central challenge for speech brain-computer interfaces. We present the first demonstration of transfer learning and cross-task decoding for MEG-based speech models spanning perception and production. We pre-train a Conformer-based model on 50 hours of single-subject listening data and fine-tune on just 5 minutes per subject across 18 participants. Transfer learning yields consistent improvements, with in-task accuracy gains of 1-4% and larger cross-task gains of up to 5-6%. Not only does pre-training improve performance within each task, but it also enables reliable cross-task decoding between perception and production. Critically, models trained on speech production decode passive listening above chance, confirming that learned representations reflect shared neural processes rather than task-specific motor activity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.22953", "title": "General Agent Evaluation", "authors": [ "Elron Bandel", "Asaf Yehudai", "Lilach Eden", "Yehoshua Sagron", "Yotam Perlitz", "Elad Venezian", "Natalia Razinkov", "Natan Ergas", "Shlomit Shachor Ifergan", "Segev Shlomov", "Michal Jacovi", "Leshem Choshen", "Liat Ein-Dor", "Yoav Katz", "Michal Shmueli-Scheuer" ], "github": "", "project_page": "", "abstract": "The promise of general-purpose agents - systems that perform tasks in unfamiliar environments without domain-specific engineering - remains largely unrealized. Existing agents are predominantly specialized, and while emerging implementations like OpenAI SDK Agent and Claude Code hint at broader capabilities, no systematic evaluation of their general performance has been pursued. Current agentic benchmarks assume domain-specific integration, encoding task information in ways that preclude fair evaluation of general agents. This paper frames general-agent evaluation as a first-class research objective. We propose conceptual principles for such evaluation, a Unified Protocol enabling agent-benchmark integration, and Exgentic - a practical framework for general agent evaluation. We benchmark five prominent agent implementations across six environments as the first Open General Agent Leaderboard. Our experiments show that general agents generalize across diverse environments, achieving performance comparable to domain-specific agents without any environment-specific tuning. We release our evaluation protocol, framework, and leaderboard to establish a foundation for systematic research on general-purpose agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.20423", "title": "MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation", "authors": [ "Taha Koleilat", "Hojat Asgariandehkordi", "Omid Nejati Manzari", "Berardino Barile", "Yiming Xiao", "Hassan Rivaz" ], "github": "", "project_page": "", "abstract": "Medical image segmentation remains challenging due to limited annotations for training, ambiguous anatomical features, and domain shifts. While vision-language models such as CLIP offer strong cross-modal representations, their potential for dense, text-guided medical image segmentation remains underexplored. We present MedCLIPSeg, a novel framework that adapts CLIP for robust, data-efficient, and uncertainty-aware medical image segmentation. Our approach leverages patch-level CLIP embeddings through probabilistic cross-modal attention, enabling bidirectional interaction between image and text tokens and explicit modeling of predictive uncertainty. Together with a soft patch-level contrastive loss that encourages more nuanced semantic learning across diverse textual prompts, MedCLIPSeg effectively improves data efficiency and domain generalizability. Extensive experiments across 16 datasets spanning five imaging modalities and six organs demonstrate that MedCLIPSeg outperforms prior methods in accuracy, efficiency, and robustness, while providing interpretable uncertainty maps that highlight local reliability of segmentation results. This work demonstrates the potential of probabilistic vision-language modeling for text-driven medical image segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.23361", "title": "VGG-T^3: Offline Feed-Forward 3D Reconstruction at Scale", "authors": [ "Sven Elflein", "Ruilong Li", "S\u00e9rgio Agostinho", "Zan Gojcic", "Laura Leal-Taix\u00e9", "Qunjie Zhou", "Aljosa Osep" ], "github": "", "project_page": "", "abstract": "We present a scalable 3D reconstruction model that addresses a critical limitation in offline feed-forward methods: their computational and memory requirements grow quadratically w.r.t. the number of input images. Our approach is built on the key insight that this bottleneck stems from the varying-length Key-Value (KV) space representation of scene geometry, which we distill into a fixed-size Multi-Layer Perceptron (MLP) via test-time training. VGG-T^3 (Visual Geometry Grounded Test Time Training) scales linearly w.r.t. the number of input views, similar to online models, and reconstructs a 1k image collection in just 54 seconds, achieving a 11.6times speed-up over baselines that rely on softmax attention. Since our method retains global scene aggregation capability, our point map reconstruction error outperforming other linear-time methods by large margins. Finally, we demonstrate visual localization capabilities of our model by querying the scene representation with unseen images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-27", "arxiv_id": "2602.21420", "title": "Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning", "authors": [ "Yuanda Xu", "Hejian Sang", "Zhengze Zhou", "Ran He", "Zhipeng Wang" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has become the leading paradigm for enhancing reasoning in Large Language Models (LLMs). However, standard RLVR algorithms suffer from a well-documented pathology: while they improve Pass@1 accuracy through sharpened sampling, they simultaneously narrow the model's reasoning boundary and reduce generation diversity. We identify a root cause that existing methods overlook: the uniform penalization of errors. Current approaches -- whether data-filtering methods that select prompts by difficulty, or advantage normalization schemes -- treat all incorrect rollouts within a group identically. We show that this uniformity allows overconfident errors (incorrect reasoning paths that the RL process has spuriously reinforced) to persist and monopolize probability mass, ultimately suppressing valid exploratory trajectories. To address this, we propose the Asymmetric Confidence-aware Error Penalty (ACE). ACE introduces a per-rollout confidence shift metric, c_i = log(pi_theta(y_i|x) / pi_ref(y_i|x)), to dynamically modulate negative advantages. Theoretically, we demonstrate that ACE's gradient can be decomposed into the gradient of a selective regularizer restricted to overconfident errors, plus a well-characterized residual that partially moderates the regularizer's strength. We conduct extensive experiments fine-tuning Qwen2.5-Math-7B, Qwen3-8B-Base, and Llama-3.1-8B-Instruct on the DAPO-Math-17K dataset using GRPO and DAPO within the VERL framework. Evaluated on MATH-500 and AIME 2025, ACE composes seamlessly with existing methods and consistently improves the full Pass@k spectrum across all three model families and benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.24208", "title": "SenCache: Accelerating Diffusion Model Inference via Sensitivity-Aware Caching", "authors": [ "Yasaman Haghighi", "Alexandre Alahi" ], "github": "https://github.com/vita-epfl/SenCache.git", "project_page": "", "abstract": "Diffusion models achieve state-of-the-art video generation quality, but their inference remains expensive due to the large number of sequential denoising steps. This has motivated a growing line of research on accelerating diffusion inference. Among training-free acceleration methods, caching reduces computation by reusing previously computed model outputs across timesteps. Existing caching methods rely on heuristic criteria to choose cache/reuse timesteps and require extensive tuning. We address this limitation with a principled sensitivity-aware caching framework. Specifically, we formalize the caching error through an analysis of the model output sensitivity to perturbations in the denoising inputs, i.e., the noisy latent and the timestep, and show that this sensitivity is a key predictor of caching error. Based on this analysis, we propose Sensitivity-Aware Caching (SenCache), a dynamic caching policy that adaptively selects caching timesteps on a per-sample basis. Our framework provides a theoretical basis for adaptive caching, explains why prior empirical heuristics can be partially effective, and extends them to a dynamic, sample-specific approach. Experiments on Wan 2.1, CogVideoX, and LTX-Video show that SenCache achieves better visual quality than existing caching methods under similar computational budgets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vita-epfl/SenCache.git", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.22647", "title": "Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators", "authors": [ "Zhengyang Su", "Isay Katsman", "Yueqi Wang", "Ruining He", "Lukasz Heldt", "Raghunandan Keshavan", "Shao-Chuan Wang", "Xinyang Yi", "Mingyan Gao", "Onkar Dalal", "Lichan Hong", "Ed Chi", "Ningren Han" ], "github": "https://github.com/youtube/static-constraint-decoding", "project_page": "", "abstract": "Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation. However, industrial recommender systems often benefit from restricting the output space to a constrained subset of items based on business logic (e.g. enforcing content freshness or product category), which standard autoregressive decoding cannot natively support. Moreover, existing constrained decoding methods that make use of prefix trees (Tries) incur severe latency penalties on hardware accelerators (TPUs/GPUs). In this work, we introduce STATIC (Sparse Transition Matrix-Accelerated Trie Index for Constrained Decoding), an efficient and scalable constrained decoding technique designed specifically for high-throughput LLM-based generative retrieval on TPUs/GPUs. By flattening the prefix tree into a static Compressed Sparse Row (CSR) matrix, we transform irregular tree traversals into fully vectorized sparse matrix operations, unlocking massive efficiency gains on hardware accelerators. We deploy STATIC on a large-scale industrial video recommendation platform serving billions of users. STATIC produces significant product metric impact with minimal latency overhead (0.033 ms per step and 0.25% of inference time), achieving a 948x speedup over a CPU trie implementation and a 47-1033x speedup over a hardware-accelerated binary-search baseline. Furthermore, the runtime overhead of STATIC remains extremely low across a wide range of practical configurations. To the best of our knowledge, STATIC enables the first production-scale deployment of strictly constrained generative retrieval. In addition, evaluation on academic benchmarks demonstrates that STATIC can considerably improve cold-start performance for generative retrieval. Our code is available at https://github.com/youtube/static-constraint-decoding.", "github_manual": "", "github_regex": "https://github.com/youtube/static-constraint-decoding", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.24233", "title": "Enhancing Spatial Understanding in Image Generation via Reward Modeling", "authors": [ "Zhenyu Tang", "Chaoran Feng", "Yufan Deng", "Jie Wu", "Xiaojie Li", "Rui Wang", "Yunpeng Chen", "Daquan Zhou" ], "github": "", "project_page": "", "abstract": "Recent progress in text-to-image generation has greatly advanced visual fidelity and creativity, but it has also imposed higher demands on prompt complexity-particularly in encoding intricate spatial relationships. In such cases, achieving satisfactory results often requires multiple sampling attempts. To address this challenge, we introduce a novel method that strengthens the spatial understanding of current image generation models. We first construct the SpatialReward-Dataset with over 80k preference pairs. Building on this dataset, we build SpatialScore, a reward model designed to evaluate the accuracy of spatial relationships in text-to-image generation, achieving performance that even surpasses leading proprietary models on spatial evaluation. We further demonstrate that this reward model effectively enables online reinforcement learning for the complex spatial generation. Extensive experiments across multiple benchmarks show that our specialized reward model yields significant and consistent gains in spatial understanding for image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.23996", "title": "Accelerating Masked Image Generation by Learning Latent Controlled Dynamics", "authors": [ "Kaiwen Zhu", "Quansheng Zeng", "Yuandong Pu", "Shuo Cao", "Xiaohui Li", "Yi Xin", "Qi Qin", "Jiayang Li", "Yu Qiao", "Jinjin Gu", "Yihao Liu" ], "github": "https://github.com/Kaiwen-Zhu/MIGM-Shortcut", "project_page": "", "abstract": "Masked Image Generation Models (MIGMs) have achieved great success, yet their efficiency is hampered by the multiple steps of bi-directional attention. In fact, there exists notable redundancy in their computation: when sampling discrete tokens, the rich semantics contained in the continuous features are lost. Some existing works attempt to cache the features to approximate future features. However, they exhibit considerable approximation error under aggressive acceleration rates. We attribute this to their limited expressivity and the failure to account for sampling information. To fill this gap, we propose to learn a lightweight model that incorporates both previous features and sampled tokens, and regresses the average velocity field of feature evolution. The model has moderate complexity that suffices to capture the subtle dynamics while keeping lightweight compared to the original base model. We apply our method, MIGM-Shortcut, to two representative MIGM architectures and tasks. In particular, on the state-of-the-art Lumina-DiMOO, it achieves over 4x acceleration of text-to-image generation while maintaining quality, significantly pushing the Pareto frontier of masked image generation. The code and model weights are available at https://github.com/Kaiwen-Zhu/MIGM-Shortcut.", "github_manual": "", "github_regex": "https://github.com/Kaiwen-Zhu/MIGM-Shortcut", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.24286", "title": "CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation", "authors": [ "Weinan Dai", "Hanlin Wu", "Qiying Yu", "Huan-ang Gao", "Jiahao Li", "Chengquan Jiang", "Weiqiang Lou", "Yufan Song", "Hongli Yu", "Jiaze Chen", "Wei-Ying Ma", "Ya-Qin Zhang", "Jingjing Liu", "Mingxuan Wang", "Xin Liu", "Hao Zhou" ], "github": "https://github.com/BytedTsinghua-SIA/CUDA-Agent", "project_page": "", "abstract": "GPU kernel optimization is fundamental to modern deep learning but remains a highly specialized task requiring deep hardware expertise. Despite strong performance in general programming, large language models (LLMs) remain uncompetitive with compiler-based systems such as torch.compile for CUDA kernel generation. Existing CUDA code generation approaches either rely on training-free refinement or fine-tune models within fixed multi-turn execution-feedback loops, but both paradigms fail to fundamentally improve the model's intrinsic CUDA optimization ability, resulting in limited performance gains. We present CUDA Agent, a large-scale agentic reinforcement learning system that develops CUDA kernel expertise through three components: a scalable data synthesis pipeline, a skill-augmented CUDA development environment with automated verification and profiling to provide reliable reward signals, and reinforcement learning algorithmic techniques enabling stable training. CUDA Agent achieves state-of-the-art results on KernelBench, delivering 100\\%, 100\\%, and 92\\% faster rate over torch.compile on KernelBench Level-1, Level-2, and Level-3 splits, outperforming the strongest proprietary models such as Claude Opus 4.5 and Gemini 3 Pro by about 40\\% on the hardest Level-3 setting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/BytedTsinghua-SIA/CUDA-Agent", "github_api_added_by": "auto" }, { "date": "2026-03-02", "arxiv_id": "2602.24281", "title": "Memory Caching: RNNs with Growing Memory", "authors": [ "Ali Behrouz", "Zeman Li", "Yuan Deng", "Peilin Zhong", "Meisam Razaviyayn", "Vahab Mirrokni" ], "github": "", "project_page": "", "abstract": "Transformers have been established as the de-facto backbones for most recent advances in sequence modeling, mainly due to their growing memory capacity that scales with the context length. While plausible for retrieval tasks, it causes quadratic complexity and so has motivated recent studies to explore viable subquadratic recurrent alternatives. Despite showing promising preliminary results in diverse domains, such recurrent architectures underperform Transformers in recall-intensive tasks, often attributed to their fixed-size memory. In this paper, we introduce Memory Caching (MC), a simple yet effective technique that enhances recurrent models by caching checkpoints of their memory states (a.k.a. hidden states). Memory Caching allows the effective memory capacity of RNNs to grow with sequence length, offering a flexible trade-off that interpolates between the fixed memory (i.e., O(L) complexity) of RNNs and the growing memory (i.e., O(L^2) complexity) of Transformers. We propose four variants of MC, including gated aggregation and sparse selective mechanisms, and discuss their implications on both linear and deep memory modules. Our experimental results on language modeling, and long-context understanding tasks show that MC enhances the performance of recurrent models, supporting its effectiveness. The results of in-context recall tasks indicate that while Transformers achieve the best accuracy, our MC variants show competitive performance, close the gap with Transformers, and performs better than state-of-the-art recurrent models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.23622", "title": "DLEBench: Evaluating Small-scale Object Editing Ability for Instruction-based Image Editing Model", "authors": [ "Shibo Hong", "Boxian Ai", "Jun Kuang", "Wei Wang", "FengJiao Chen", "Zhongyuan Peng", "Chenhao Huang", "Yixin Cao" ], "github": "", "project_page": "", "abstract": "Significant progress has been made in the field of Instruction-based Image Editing Models (IIEMs). However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to edit small objects remains underexplored, despite its importance for precise local editing and refining details in both real and generated images. In this paper, we introduce DeepLookEditBench (DLEBench), the first benchmark dedicated to assessing the abilities of IIEMs in editing small-scale objects. Specifically, we construct a challenging testbed comprising 1889 samples across seven instruction types. In these samples, target objects occupy only 1%-10% of the image area, covering complex scenarios such as partial occlusion and multi-object editing. To ensure robust evaluation on this benchmark, we propose an evaluation protocol with refined score rubrics to minimize subjectivity and ambiguity in two criteria: Instruction Following and Visual Consistency. This protocol also introduces a dual-mode evaluation framework (Tool-driven and Oracle-guided Modes) addressing the misalignment between LMM-as-a-Judge and human judgements on DLEBench. Empirical results on 10 IIEMs reveal significant performance gaps in small-scale object editing, highlighting the need for specialized benchmarks to advance this ability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.22661", "title": "dLLM: Simple Diffusion Language Modeling", "authors": [ "Zhanhui Zhou", "Lingjie Chen", "Hanghang Tong", "Dawn Song" ], "github": "", "project_page": "", "abstract": "Although diffusion language models (DLMs) are evolving quickly, many recent models converge on a set of shared components. These components, however, are distributed across ad-hoc research codebases or lack transparent implementations, making them difficult to reproduce or extend. As the field accelerates, there is a clear need for a unified framework that standardizes these common components while remaining flexible enough to support new methods and architectures. To address this gap, we introduce dLLM, an open-source framework that unifies the core components of diffusion language modeling -- training, inference, and evaluation -- and makes them easy to customize for new designs. With dLLM, users can reproduce, finetune, deploy, and evaluate open-source large DLMs such as LLaDA and Dream through a standardized pipeline. The framework also provides minimal, reproducible recipes for building small DLMs from scratch with accessible compute, including converting any BERT-style encoder or autoregressive LM into a DLM. We also release the checkpoints of these small DLMs to make DLMs more accessible and accelerate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.23898", "title": "Ref-Adv: Exploring MLLM Visual Reasoning in Referring Expression Tasks", "authors": [ "Qihua Dong", "Kuo Yang", "Lin Ju", "Handong Zhao", "Yitian Zhang", "Yizhou Wang", "Huimin Zeng", "Jianglin Lu", "Yun Fu" ], "github": "", "project_page": "", "abstract": "Referring Expression Comprehension (REC) links language to region level visual perception. Standard benchmarks (RefCOCO, RefCOCO+, RefCOCOg) have progressed rapidly with multimodal LLMs but remain weak tests of visual reasoning and grounding: (i) many expressions are very short, leaving little reasoning demand; (ii) images often contain few distractors, making the target easy to find; and (iii) redundant descriptors enable shortcut solutions that bypass genuine text understanding and visual reasoning. We introduce Ref-Adv, a modern REC benchmark that suppresses shortcuts by pairing linguistically nontrivial expressions with only the information necessary to uniquely identify the target. The dataset contains referring expressions on real images, curated with hard distractors and annotated with reasoning facets including negation. We conduct comprehensive ablations (word order perturbations and descriptor deletion sufficiency) to show that solving Ref-Adv requires reasoning beyond simple cues, and we evaluate a broad suite of contemporary multimodal LLMs on Ref-Adv. Despite strong results on RefCOCO, RefCOCO+, and RefCOCOg, models drop markedly on Ref-Adv, revealing reliance on shortcuts and gaps in visual reasoning and grounding. We provide an in depth failure analysis and aim for Ref-Adv to guide future work on visual reasoning and grounding in MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.23452", "title": "CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era", "authors": [ "Zhengqing Yuan", "Kaiwen Shi", "Zheyuan Zhang", "Lichao Sun", "Nitesh V. Chawla", "Yanfang Ye" ], "github": "", "project_page": "", "abstract": "Scientific research relies on accurate citation for attribution and integrity, yet large language models (LLMs) introduce a new risk: fabricated references that appear plausible but correspond to no real publications. Such hallucinated citations have already been observed in submissions and accepted papers at major machine learning venues, exposing vulnerabilities in peer review. Meanwhile, rapidly growing reference lists make manual verification impractical, and existing automated tools remain fragile to noisy and heterogeneous citation formats and lack standardized evaluation. We present the first comprehensive benchmark and detection framework for hallucinated citations in scientific writing. Our multi-agent verification pipeline decomposes citation checking into claim extraction, evidence retrieval, passage matching, reasoning, and calibrated judgment to assess whether a cited source truly supports its claim. We construct a large-scale human-validated dataset across domains and define unified metrics for citation faithfulness and evidence alignment. Experiments with state-of-the-art LLMs reveal substantial citation errors and show that our framework significantly outperforms prior methods in both accuracy and interpretability. This work provides the first scalable infrastructure for auditing citations in the LLM era and practical tools to improve the trustworthiness of scientific references.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.22207", "title": "Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets", "authors": [ "Hanna Yukhymenko", "Anton Alexandrov", "Martin Vechev" ], "github": "", "project_page": "", "abstract": "The reliability of multilingual Large Language Model (LLM) evaluation is currently compromised by the inconsistent quality of translated benchmarks. Existing resources often suffer from semantic drift and context loss, which can lead to misleading performance metrics. In this work, we present a fully automated framework designed to address these challenges by enabling scalable, high-quality translation of datasets and benchmarks. We demonstrate that adapting test-time compute scaling strategies, specifically Universal Self-Improvement (USI) and our proposed multi-round ranking method, T-RANK, allows for significantly higher quality outputs compared to traditional pipelines. Our framework ensures that benchmarks preserve their original task structure and linguistic nuances during localization. We apply this approach to translate popular benchmarks and datasets into eight Eastern and Southern European languages (Ukrainian, Bulgarian, Slovak, Romanian, Lithuanian, Estonian, Turkish, Greek). Evaluations using both reference-based metrics and LLM-as-a-judge show that our translations surpass existing resources, resulting in more accurate downstream model assessment. We release both the framework and the improved benchmarks to facilitate robust and reproducible multilingual AI development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.24289", "title": "Mode Seeking meets Mean Seeking for Fast Long Video Generation", "authors": [ "Shengqu Cai", "Weili Nie", "Chao Liu", "Julius Berner", "Lvmin Zhang", "Nanye Ma", "Hansheng Chen", "Maneesh Agrawala", "Leonidas Guibas", "Gordon Wetzstein", "Arash Vahdat" ], "github": "", "project_page": "", "abstract": "Scaling video generation from seconds to minutes faces a critical bottleneck: while short-video data is abundant and high-fidelity, coherent long-form data is scarce and limited to narrow domains. To address this, we propose a training paradigm where Mode Seeking meets Mean Seeking, decoupling local fidelity from long-term coherence based on a unified representation via a Decoupled Diffusion Transformer. Our approach utilizes a global Flow Matching head trained via supervised learning on long videos to capture narrative structure, while simultaneously employing a local Distribution Matching head that aligns sliding windows to a frozen short-video teacher via a mode-seeking reverse-KL divergence. This strategy enables the synthesis of minute-scale videos that learns long-range coherence and motions from limited long videos via supervised flow matching, while inheriting local realism by aligning every sliding-window segment of the student to a frozen short-video teacher, resulting in a few-step fast long video generator. Evaluations show that our method effectively closes the fidelity-horizon gap by jointly improving local sharpness, motion and long-range consistency. Project website: https://primecai.github.io/mmm/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.24012", "title": "InfoNCE Induces Gaussian Distribution", "authors": [ "Roy Betser", "Eyal Gofer", "Meir Yossef Levi", "Guy Gilboa" ], "github": "", "project_page": "", "abstract": "Contrastive learning has become a cornerstone of modern representation learning, allowing training with massive unlabeled data for both task-specific and general (foundation) models. A prototypical loss in contrastive training is InfoNCE and its variants. In this work, we show that the InfoNCE objective induces Gaussian structure in representations that emerge from contrastive training. We establish this result in two complementary regimes. First, we show that under certain alignment and concentration assumptions, projections of the high-dimensional representation asymptotically approach a multivariate Gaussian distribution. Next, under less strict assumptions, we show that adding a small asymptotically vanishing regularization term that promotes low feature norm and high feature entropy leads to similar asymptotic results. We support our analysis with experiments on synthetic and CIFAR-10 datasets across multiple encoder architectures and sizes, demonstrating consistent Gaussian behavior. This perspective provides a principled explanation for commonly observed Gaussianity in contrastive representations. The resulting Gaussian model enables principled analytical treatment of learned representations and is expected to support a wide range of applications in contrastive learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.20913", "title": "LongVideo-R1: Smart Navigation for Low-cost Long Video Understanding", "authors": [ "Jihao Qiu", "Lingxi Xie", "Xinyue Huo", "Qi Tian", "Qixiang Ye" ], "github": "https://github.com/qiujihao19/LongVideo-R1", "project_page": "", "abstract": "This paper addresses the critical and underexplored challenge of long video understanding with low computational budgets. We propose LongVideo-R1, an active, reasoning-equipped multimodal large language model (MLLM) agent designed for efficient video context navigation, avoiding the redundancy of exhaustive search. At the core of LongVideo-R1 lies a reasoning module that leverages high-level visual cues to infer the most informative video clip for subsequent processing. During inference, the agent initiates traversal from top-level visual summaries and iteratively refines its focus, immediately halting the exploration process upon acquiring sufficient knowledge to answer the query. To facilitate training, we first extract hierarchical video captions from CGBench, a video corpus with grounding annotations, and guide GPT-5 to generate 33K high-quality chain-of-thought-with-tool trajectories. The LongVideo-R1 agent is fine-tuned upon the Qwen-3-8B model through a two-stage paradigm: supervised fine-tuning (SFT) followed by reinforcement learning (RL), where RL employs a specifically designed reward function to maximize selective and efficient clip navigation. Experiments on multiple long video benchmarks validate the effectiveness of name, which enjoys superior tradeoff between QA accuracy and efficiency. All curated data and source code are provided in the supplementary material and will be made publicly available. Code and data are available at: https://github.com/qiujihao19/LongVideo-R1", "github_manual": "", "github_regex": "https://github.com/qiujihao19/LongVideo-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.23881", "title": "LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding", "authors": [ "Alexander Samarin", "Sergei Krutikov", "Anton Shevtsov", "Sergei Skvortsov", "Filipp Fisin", "Alexander Golubev" ], "github": "", "project_page": "", "abstract": "Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate tokens that are then verified in parallel by the target model. The speedup is significantly determined by the acceptance rate, yet standard training minimizes Kullback-Leibler (KL) divergence as a proxy objective. While KL divergence and acceptance rate share the same global optimum, small draft models, having limited capacity, typically converge to suboptimal solutions where minimizing KL does not guarantee maximizing acceptance rate. To address this issue, we propose LK losses, special training objectives that directly target acceptance rate. Comprehensive experiments across four draft architectures and six target models, ranging from 8B to 685B parameters, demonstrate consistent improvements in acceptance metrics across all configurations compared to the standard KL-based training. We evaluate our approach on general, coding and math domains and report gains of up to 8-10% in average acceptance length. LK losses are easy to implement, introduce no computational overhead and can be directly integrated into any existing speculator training framework, making them a compelling alternative to the existing draft training objectives.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.24264", "title": "Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models", "authors": [ "Arnas Uselis", "Andrea Dittadi", "Seong Joon Oh" ], "github": "https://github.com/oshapio/necessary-compositionality", "project_page": "", "abstract": "Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems. Although modern models are trained on massive datasets, they still cover only a tiny fraction of the combinatorial space of possible inputs, raising the question of what structure representations must have to support generalization to unseen combinations. We formalize three desiderata for compositional generalization under standard training (divisibility, transferability, stability) and show they impose necessary geometric constraints: representations must decompose linearly into per-concept components, and these components must be orthogonal across concepts. This provides theoretical grounding for the Linear Representation Hypothesis: the linear structure widely observed in neural representations is a necessary consequence of compositional generalization. We further derive dimension bounds linking the number of composable concepts to the embedding geometry. Empirically, we evaluate these predictions across modern vision models (CLIP, SigLIP, DINO) and find that representations exhibit partial linear factorization with low-rank, near-orthogonal per-concept factors, and that the degree of this structure correlates with compositional generalization on unseen combinations. As models continue to scale, these conditions predict the representational geometry they may converge to. Code is available at https://github.com/oshapio/necessary-compositionality.", "github_manual": "", "github_regex": "https://github.com/oshapio/necessary-compositionality", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.21877", "title": "How to Take a Memorable Picture? Empowering Users with Actionable Feedback", "authors": [ "Francesco Laiti", "Davide Talon", "Jacopo Staiano", "Elisa Ricci" ], "github": "", "project_page": "", "abstract": "Image memorability, i.e., how likely an image is to be remembered, has traditionally been studied in computer vision either as a passive prediction task, with models regressing a scalar score, or with generative methods altering the visual input to boost the image likelihood of being remembered. Yet, none of these paradigms supports users at capture time, when the crucial question is how to improve a photo memorability. We introduce the task of Memorability Feedback (MemFeed), where an automated model should provide actionable, human-interpretable guidance to users with the goal to enhance an image future recall. We also present MemCoach, the first approach designed to provide concrete suggestions in natural language for memorability improvement (e.g., \"emphasize facial expression,\" \"bring the subject forward\"). Our method, based on Multimodal Large Language Models (MLLMs), is training-free and employs a teacher-student steering strategy, aligning the model internal activations toward more memorable patterns learned from a teacher model progressing along least-to-most memorable samples. To enable systematic evaluation on this novel task, we further introduce MemBench, a new benchmark featuring sequence-aligned photoshoots with annotated memorability scores. Our experiments, considering multiple MLLMs, demonstrate the effectiveness of MemCoach, showing consistently improved performance over several zero-shot models. The results indicate that memorability can not only be predicted but also taught and instructed, shifting the focus from mere prediction to actionable feedback for human creators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.23047", "title": "CL4SE: A Context Learning Benchmark For Software Engineering Tasks", "authors": [ "Haichuan Hu", "Ye Shang", "Guoqing Xie", "Congqing He", "Quanjun Zhang" ], "github": "https://github.com/Tomsawyerhu/CodeCL", "project_page": "", "abstract": "Context engineering has emerged as a pivotal paradigm for unlocking the potential of Large Language Models (LLMs) in Software Engineering (SE) tasks, enabling performance gains at test time without model fine-tuning. Despite its success, existing research lacks a systematic taxonomy of SE-specific context types and a dedicated benchmark to quantify the heterogeneous effects of different contexts across core SE workflows. To address this gap, we propose CL4SE (Context Learning for Software Engineering), a comprehensive benchmark featuring a fine-grained taxonomy of four SE-oriented context types (interpretable examples, project-specific context, procedural decision-making context, and positive & negative context), each mapped to a representative task (code generation, code summarization, code review, and patch correctness assessment). We construct high-quality datasets comprising over 13,000 samples from more than 30 open-source projects and evaluate five mainstream LLMs across nine metrics. Extensive experiments demonstrate that context learning yields an average performance improvement of 24.7% across all tasks. Specifically, procedural context boosts code review performance by up to 33% (Qwen3-Max), mixed positive-negative context improves patch assessment by 30% (DeepSeek-V3), project-specific context increases code summarization BLEU by 14.78% (GPT-Oss-120B), and interpretable examples enhance code generation PASS@1 by 5.72% (DeepSeek-V3). CL4SE establishes the first standardized evaluation framework for SE context learning, provides actionable empirical insights into task-specific context design, and releases a large-scale dataset to facilitate reproducible research in this domain.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Tomsawyerhu/CodeCL", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.18846", "title": "DUET-VLM: Dual stage Unified Efficient Token reduction for VLM Training and Inference", "authors": [ "Aditya Kumar Singh", "Hitesh Kandala", "Pratik Prabhanjan Brahma", "Zicheng Liu", "Emad Barsoum" ], "github": "https://github.com/AMD-AGI/DUET-VLM", "project_page": "", "abstract": "Vision-language models (VLMs) have achieved remarkable multimodal understanding and reasoning capabilities, yet remain computationally expensive due to dense visual tokenization. Existing efficiency approaches either merge redundant visual tokens or drop them progressively in language backbone, often trading accuracy for speed. In this work, we propose DUET-VLM, a versatile plug-and-play dual compression framework that consists of (a) vision-only redundancy aware compression of vision encoder's output into information-preserving tokens, followed by (b) layer-wise, salient text-guided dropping of visual tokens within the language backbone to progressively prune less informative tokens. This coordinated token management enables aggressive compression while retaining critical semantics. On LLaVA-1.5-7B, our approach maintains over 99% of baseline accuracy with 67% fewer tokens, and still retains >97% even at 89% reduction. With this dual-stage compression during training, it achieves 99.7% accuracy at 67% and 97.6% at 89%, surpassing prior SoTA visual token reduction methods across multiple benchmarks. When integrated into Video-LLaVA-7B, it even surpasses the baseline -- achieving >100% accuracy with a substantial 53.1% token reduction and retaining 97.6% accuracy under an extreme 93.4% setting. These results highlight end-to-end training with DUET-VLM, enabling robust adaptation to reduced visual (image/video) input without sacrificing accuracy, producing compact yet semantically rich representations within the same computational budget. Our code is available at https://github.com/AMD-AGI/DUET-VLM.", "github_manual": "", "github_regex": "https://github.com/AMD-AGI/DUET-VLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.21317", "title": "Shared Nature, Unique Nurture: PRISM for Pluralistic Reasoning via In-context Structure Modeling", "authors": [ "Guancheng Tu", "Shiyang Zhang", "Tianyu Zhang", "Yi Zhang", "Diji Yang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are converging towards a singular Artificial Hivemind, where shared Nature (pre-training priors) result in a profound collapse of distributional diversity, limiting the distinct perspectives necessary for creative exploration and scientific discovery. To address this, we propose to equip models with inference-time Nurture (individualized epistemic trajectories) using Epistemic Evolution paradigm, progressing through explore, internalize, and express. We instantiate this via PRISM (Pluralistic Reasoning via In-context Structure Modeling), a model-agnostic system that augments LLM with dynamic On-the-fly Epistemic Graphs. On three creativity benchmarks, PRISM achieves state-of-the-art novelty and significantly expands distributional diversity. Moreover, we evaluate the real-world utility via a challenging rare-disease diagnosis benchmark. Results demonstrate that PRISM successfully uncovers correct long-tail diagnoses that standard LLM miss, confirming that its divergence stems from meaningful exploration rather than incoherent noise. Overall, this work establishes a new paradigm for Pluralistic AI, moving beyond monolithic consensus toward a diverse ecosystem of unique cognitive individuals capable of collective, multi-perspective discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.22495", "title": "Reinforcement-aware Knowledge Distillation for LLM Reasoning", "authors": [ "Zhaoyang Zhang", "Shuli Jiang", "Yantao Shen", "Yuting Zhang", "Dhananjay Ram", "Shuo Yang", "Zhuowen Tu", "Wei Xia", "Stefano Soatto" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) post-training has recently driven major gains in long chain-of-thought reasoning large language models (LLMs), but the high inference cost of such models motivates distillation into smaller students. Most existing knowledge distillation (KD) methods are designed for supervised fine-tuning (SFT), relying on fixed teacher traces or teacher-student Kullback-Leibler (KL) divergence-based regularization. When combined with RL, these approaches often suffer from distribution mismatch and objective interference: teacher supervision may not align with the student's evolving rollout distribution, and the KL regularizer can compete with reward maximization and require careful loss balancing. To address these issues, we propose RL-aware distillation (RLAD), which performs selective imitation during RL -- guiding the student toward the teacher only when it improves the current policy update. Our core component, Trust Region Ratio Distillation (TRRD), replaces the teacher-student KL regularizer with a PPO/GRPO-style likelihood-ratio objective anchored to a teacher--old-policy mixture, yielding advantage-aware, trust-region-bounded distillation on student rollouts and naturally balancing exploration, exploitation, and imitation. Across diverse logic reasoning and math benchmarks, RLAD consistently outperforms offline distillation, standard GRPO, and KL-based on-policy teacher-student knowledge distillation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-02", "arxiv_id": "2602.22523", "title": "Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents", "authors": [ "Ryan Liu", "Dilip Arumugam", "Cedegao E. Zhang", "Sean Escola", "Xaq Pitkow", "Thomas L. Griffiths" ], "github": "", "project_page": "", "abstract": "While contemporary large language models (LLMs) are increasingly capable in isolation, there are still many difficult problems that lie beyond the abilities of a single LLM. For such tasks, there is still uncertainty about how best to take many LLMs as parts and combine them into a greater whole. This position paper argues that potential blueprints for designing such modular language agents can be found in the existing literature on cognitive models and artificial intelligence (AI) algorithms. To make this point clear, we formalize the idea of an agent template that specifies roles for individual LLMs and how their functionalities should be composed. We then survey a variety of existing language agents in the literature and highlight their underlying templates derived directly from cognitive models or AI algorithms. By highlighting these designs, we aim to call attention to agent templates inspired by cognitive science and AI as a powerful tool for developing effective, interpretable language agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2602.23952", "title": "CC-VQA: Conflict- and Correlation-Aware Method for Mitigating Knowledge Conflict in Knowledge-Based Visual Question Answering", "authors": [ "Yuyang Hong", "Jiaqi Gu", "Yujin Lou", "Lubin Fan", "Qi Yang", "Ying Wang", "Kun Ding", "Yue Wu", "Shiming Xiang", "Jieping Ye" ], "github": "https://github.com/cqu-student/CC-VQA", "project_page": "", "abstract": "Knowledge-based visual question answering (KB-VQA) demonstrates significant potential for handling knowledge-intensive tasks. However, conflicts arise between static parametric knowledge in vision language models (VLMs) and dynamically retrieved information due to the static model knowledge from pre-training. The outputs either ignore retrieved contexts or exhibit inconsistent integration with parametric knowledge, posing substantial challenges for KB-VQA. Current knowledge conflict mitigation methods primarily adapted from language-based approaches, focusing on context-level conflicts through engineered prompting strategies or context-aware decoding mechanisms. However, these methods neglect the critical role of visual information in conflicts and suffer from redundant retrieved contexts, which impair accurate conflict identification and effective mitigation. To address these limitations, we propose CC-VQA: a novel training-free, conflict- and correlation-aware method for KB-VQA. Our method comprises two core components: (1) Vision-Centric Contextual Conflict Reasoning, which performs visual-semantic conflict analysis across internal and external knowledge contexts; and (2) Correlation-Guided Encoding and Decoding, featuring positional encoding compression for low-correlation statements and adaptive decoding using correlation-weighted conflict scoring. Extensive evaluations on E-VQA, InfoSeek, and OK-VQA benchmarks demonstrate that CC-VQA achieves state-of-the-art performance, yielding absolute accuracy improvements of 3.3\\% to 6.4\\% compared to existing methods. Code is available at https://github.com/cqu-student/CC-VQA.", "github_manual": "", "github_regex": "https://github.com/cqu-student/CC-VQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.00541", "title": "Spectral Condition for \u03bcP under Width-Depth Scaling", "authors": [ "Chenyu Zheng", "Rongzhen Wang", "Xinyu Zhang", "Chongxuan Li" ], "github": "https://github.com/ML-GSAI/Width-Depth-muP", "project_page": "", "abstract": "Generative foundation models are increasingly scaled in both width and depth, posing significant challenges for stable feature learning and reliable hyperparameter (HP) transfer across model sizes. While maximal update parameterization (\u03bcP) has provided a principled solution to both problems for width scaling, existing extensions to the joint width-depth scaling regime remain fragmented, architecture- and optimizer-specific, and often rely on technically involved theories. In this work, we develop a simple and unified spectral framework for \u03bcP under joint width-depth scaling. Considering residual networks of varying block depths, we first introduce a spectral \u03bcP condition that precisely characterizes how the norms of weights and their per-step updates should scale with width and depth, unifying previously disparate \u03bcP formulations as special cases. Building on this condition, we then derive a general recipe for implementing \u03bcP across a broad class of optimizers by mapping the spectral constraints to concrete HP parameterizations. This approach not only recovers existing \u03bcP formulations (e.g., for SGD and AdamW) but also naturally extends to a wider range of optimizers. Finally, experiments on GPT-2 style language models demonstrate that the proposed spectral \u03bcP condition preserves stable feature learning and enables robust HP transfer under width-depth scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ML-GSAI/Width-Depth-muP", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2602.21320", "title": "Tool-R0: Self-Evolving LLM Agents for Tool-Learning from Zero Data", "authors": [ "Emre Can Acikgoz", "Cheng Qian", "Jonas H\u00fcbotter", "Heng Ji", "Dilek Hakkani-T\u00fcr", "Gokhan Tur" ], "github": "https://github.com/qiancheng0/ToolRL", "project_page": "", "abstract": "Large language models (LLMs) are becoming the foundation for autonomous agents that can use tools to solve complex tasks. Reinforcement learning (RL) has emerged as a common approach for injecting such agentic capabilities, but typically under tightly controlled training setups. It often depends on carefully constructed task-solution pairs and substantial human supervision, which creates a fundamental obstacle to open-ended self-evolution toward superintelligent systems. In this paper, we propose Tool-R0 framework for training general purpose tool-calling agents from scratch with self-play RL, under a zero-data assumption. Initialized from the same base LLM, Tool-R0 co-evolves a Generator and a Solver with complementary rewards: one proposes targeted challenging tasks at the other's competence frontier and the other learns to solve them with real-world tool calls. This creates a self-evolving cycle that requires no pre-existing tasks or datasets. Evaluation on different tool-use benchmarks show that Tool-R0 yields 92.5 relative improvement over the base model and surpasses fully supervised tool-calling baselines under the same setting. Our work further provides empirical insights into self-play LLM agents by analyzing co-evolution, curriculum dynamics, and scaling behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/qiancheng0/ToolRL", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.00912", "title": "VGGT-Det: Mining VGGT Internal Priors for Sensor-Geometry-Free Multi-View Indoor 3D Object Detection", "authors": [ "Yang Cao", "Feize Wu", "Dave Zhenyu Chen", "Yingji Zhong", "Lanqing Hong", "Dan Xu" ], "github": "https://github.com/yangcaoai/VGGT-Det-CVPR2026", "project_page": "", "abstract": "Current multi-view indoor 3D object detectors rely on sensor geometry that is costly to obtain (i.e., precisely calibrated multi-view camera poses) to fuse multi-view information into a global scene representation, limiting deployment in real-world scenes. We target a more practical setting: Sensor-Geometry-Free (SG-Free) multi-view indoor 3D object detection, where there are no sensor-provided geometric inputs (multi-view poses or depth). Recent Visual Geometry Grounded Transformer (VGGT) shows that strong 3D cues can be inferred directly from images. Building on this insight, we present VGGT-Det, the first framework tailored for SG-Free multi-view indoor 3D object detection. Rather than merely consuming VGGT predictions, our method integrates VGGT encoder into a transformer-based pipeline. To effectively leverage both the semantic and geometric priors from inside VGGT, we introduce two novel key components: (i) Attention-Guided Query Generation (AG): exploits VGGT attention maps as semantic priors to initialize object queries, improving localization by focusing on object regions while preserving global spatial structure; (ii) Query-Driven Feature Aggregation (QD): a learnable See-Query interacts with object queries to 'see' what they need, and then dynamically aggregates multi-level geometric features across VGGT layers that progressively lift 2D features into 3D. Experiments show that VGGT-Det significantly surpasses the best-performing method in the SG-Free setting by 4.4 and 8.6 mAP@0.25 on ScanNet and ARKitScenes, respectively. Ablation study shows that VGGT's internally learned semantic and geometric priors can be effectively leveraged by our AG and QD.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yangcaoai/VGGT-Det-CVPR2026", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2602.23906", "title": "Half-Truths Break Similarity-Based Retrieval", "authors": [ "Bora Kargi", "Arnas Uselis", "Seong Joon Oh" ], "github": "https://github.com/kargibora/CS-CLIP", "project_page": "", "abstract": "When a text description is extended with an additional detail, image-text similarity should drop if that detail is wrong. We show that CLIP-style dual encoders often violate this intuition: appending a plausible but incorrect object or relation to an otherwise correct description can increase the similarity score. We call such cases half-truths. On COCO, CLIP prefers the correct shorter description only 40.6% of the time, and performance drops to 32.9% when the added detail is a relation. We trace this vulnerability to weak supervision on caption parts: contrastive training aligns full sentences but does not explicitly enforce that individual entities and relations are grounded. We propose CS-CLIP (Component-Supervised CLIP), which decomposes captions into entity and relation units, constructs a minimally edited foil for each unit, and fine-tunes the model to score the correct unit above its foil while preserving standard dual-encoder inference. CS-CLIP raises half-truth accuracy to 69.3% and improves average performance on established compositional benchmarks by 5.7 points, suggesting that reducing half-truth errors aligns with broader gains in compositional understanding. Code is publicly available at: https://github.com/kargibora/CS-CLIP", "github_manual": "", "github_regex": "https://github.com/kargibora/CS-CLIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01710", "title": "Legal RAG Bench: an end-to-end benchmark for legal RAG", "authors": [ "Abdur-Rahman Butler", "Umar Butler" ], "github": "https://github.com/isaacus-dev/legal-rag-bench", "project_page": "", "abstract": "We introduce Legal RAG Bench, a benchmark and evaluation methodology for assessing the end-to-end performance of legal RAG systems. As a benchmark, Legal RAG Bench consists of 4,876 passages from the Victorian Criminal Charge Book alongside 100 complex, hand-crafted questions demanding expert knowledge of criminal law and procedure. Both long-form answers and supporting passages are provided. As an evaluation methodology, Legal RAG Bench leverages a full factorial design and novel hierarchical error decomposition framework, enabling apples-to-apples comparisons of the contributions of retrieval and reasoning models in RAG. We evaluate three state-of-the-art embedding models (Isaacus' Kanon 2 Embedder, Google's Gemini Embedding 001, and OpenAI's Text Embedding 3 Large) and two frontier LLMs (Gemini 3.1 Pro and GPT-5.2), finding that information retrieval is the primary driver of legal RAG performance, with LLMs exerting a more moderate effect on correctness and groundedness. Kanon 2 Embedder, in particular, had the largest positive impact on performance, improving average correctness by 17.5 points, groundedness by 4.5 points, and retrieval accuracy by 34 points. We observe that many errors attributed to hallucinations in legal RAG systems are in fact triggered by retrieval failures, concluding that retrieval sets the ceiling for the performance of many modern legal RAG systems. We document why and how we built Legal RAG Bench alongside the results of our evaluations. We also openly release our code and data to assist with reproduction of our findings.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/isaacus-dev/legal-rag-bench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01562", "title": "RubricBench: Aligning Model-Generated Rubrics with Human Standards", "authors": [ "Qiyuan Zhang", "Junyi Zhou", "Yufei Wang", "Fuyuan Lyu", "Yidong Ming", "Can Xu", "Qingfeng Sun", "Kai Zheng", "Peng Kang", "Xue Liu", "Chen Ma" ], "github": "", "project_page": "", "abstract": "As Large Language Model (LLM) alignment evolves from simple completions to complex, highly sophisticated generation, Reward Models are increasingly shifting toward rubric-guided evaluation to mitigate surface-level biases. However, the community lacks a unified benchmark to assess this evaluation paradigm, as existing benchmarks lack both the discriminative complexity and the ground-truth rubric annotations required for rigorous analysis. To bridge this gap, we introduce RubricBench, a curated benchmark with 1,147 pairwise comparisons specifically designed to assess the reliability of rubric-based evaluation. Our construction employs a multi-dimensional filtration pipeline to target hard samples featuring nuanced input complexity and misleading surface bias, augmenting each with expert-annotated, atomic rubrics derived strictly from instructions. Comprehensive experiments reveal a substantial capability gap between human-annotated and model-generated rubrics, indicating that even state-of-the-art models struggle to autonomously specify valid evaluation criteria, lagging considerably behind human-guided performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01068", "title": "LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model", "authors": [ "Zebin You", "Xiaolu Zhang", "Jun Zhou", "Chongxuan Li", "Ji-Rong Wen" ], "github": "https://github.com/ML-GSAI/LLaDA-o", "project_page": "", "abstract": "We present LLaDA-o, an effective and length-adaptive omni diffusion model for multimodal understanding and generation. LLaDA-o is built on a Mixture of Diffusion (MoD) framework that decouples discrete masked diffusion for text understanding and continuous diffusion for visual generation, while coupling them through a shared, simple, and efficient attention backbone that reduces redundant computation for fixed conditions. Building on MoD, we further introduce a data-centric length adaptation strategy that enables flexible-length decoding in multimodal settings without architectural changes. Extensive experiments show that LLaDA-o achieves state-of-the-art performance among omni-diffusion models on multimodal understanding and generation benchmarks, and reaches 87.04 on DPG-Bench for text-to-image generation, supporting the effectiveness of unified omni diffusion modeling. Code is available at https://github.com/ML-GSAI/LLaDA-o.", "github_manual": "", "github_regex": "https://github.com/ML-GSAI/LLaDA-o", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01425", "title": "LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval", "authors": [ "Jiajie Jin", "Yanzhao Zhang", "Mingxin Li", "Dingkun Long", "Pengjun Xie", "Yutao Zhu", "Zhicheng Dou" ], "github": "https://github.com/ignorejjj/LaSER", "project_page": "", "abstract": "LLMs have fundamentally transformed dense retrieval, upgrading backbones from discriminative encoders to generative architectures. However, a critical disconnect remains: while LLMs possess strong reasoning capabilities, current retrievers predominantly utilize them as static encoders, leaving their potential for complex reasoning unexplored. To address this, existing approaches typically adopt rewrite-then-retrieve pipelines to generate explicit CoT rationales before retrieval. However, this incurs prohibitive latency. In this paper, we propose LaSER, a novel self-distillation framework that internalizes explicit reasoning into the latent space of dense retrievers. Operating on a shared LLM backbone, LaSER introduces a dual-view training mechanism: an Explicit view that explicitly encodes ground-truth reasoning paths, and a Latent view that performs implicit latent thinking. To bridge the gap between these views, we design a multi-grained alignment strategy. Beyond standard output alignment, we introduce a trajectory alignment mechanism that synchronizes the intermediate latent states of the latent path with the semantic progression of the explicit reasoning segments. This allows the retriever to think silently and effectively without autoregressive text generation. Extensive experiments on both in-domain and out-of-domain reasoning-intensive benchmarks demonstrate that LaSER significantly outperforms state-of-the-art baselines. Furthermore, analyses across diverse backbones and model scales validate the robustness of our approach, confirming that our unified learning framework is essential for eliciting effective latent thinking. Our method successfully combines the reasoning depth of explicit CoT pipelines with the inference efficiency of standard dense retrievers.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ignorejjj/LaSER", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.00483", "title": "RAISE: Requirement-Adaptive Evolutionary Refinement for Training-Free Text-to-Image Alignment", "authors": [ "Liyao Jiang", "Ruichen Chen", "Chao Gao", "Di Niu" ], "github": "https://github.com/LiyaoJiang1998/RAISE", "project_page": "", "abstract": "Recent text-to-image (T2I) diffusion models achieve remarkable realism, yet faithful prompt-image alignment remains challenging, particularly for complex prompts with multiple objects, relations, and fine-grained attributes. Existing training-free inference-time scaling methods rely on fixed iteration budgets that cannot adapt to prompt difficulty, while reflection-tuned models require carefully curated reflection datasets and extensive joint fine-tuning of diffusion and vision-language models, often overfitting to reflection paths data and lacking transferability across models. We introduce RAISE (Requirement-Adaptive Self-Improving Evolution), a training-free, requirement-driven evolutionary framework for adaptive T2I generation. RAISE formulates image generation as a requirement-driven adaptive scaling process, evolving a population of candidates at inference time through a diverse set of refinement actions-including prompt rewriting, noise resampling, and instructional editing. Each generation is verified against a structured checklist of requirements, enabling the system to dynamically identify unsatisfied items and allocate further computation only where needed. This achieves adaptive test-time scaling that aligns computational effort with semantic query complexity. On GenEval and DrawBench, RAISE attains state-of-the-art alignment (0.94 overall GenEval) while incurring fewer generated samples (reduced by 30-40%) and VLM calls (reduced by 80%) than prior scaling and reflection-tuned baselines, demonstrating efficient, generalizable, and model-agnostic multi-round self-improvement. Code is available at https://github.com/LiyaoJiang1998/RAISE.", "github_manual": "", "github_regex": "https://github.com/LiyaoJiang1998/RAISE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01973", "title": "CharacterFlywheel: Scaling Iterative Improvement of Engaging and Steerable LLMs in Production", "authors": [ "Yixin Nie", "Lin Guan", "Zhongyao Ma", "Anchit Gupta", "Yipin Zhou", "Xiao Li", "Zhengping Zhou", "Raymond Zeng", "Gelin Zhou", "Shigan Chu", "Ajay Thampi", "Wancen Mu", "Nathan Shuster", "Ketong Wang", "Lin Chen", "Jason Brewer", "Derek Hao Hu", "Alexander McCauley", "Jason Weston", "Sem Park", "Na Zhang", "Kevin Tang" ], "github": "", "project_page": "", "abstract": "This report presents CharacterFlywheel, an iterative flywheel process for improving large language models (LLMs) in production social chat applications across Instagram, WhatsApp, and Messenger. Starting from LLaMA 3.1, we refined models across 15 generations using data from both internal and external real-user traffic. Through continuous deployments from July 2024 to April 2025, we conducted controlled 7-day A/B tests showing consistent engagement improvements: 7 of 8 newly deployed models demonstrated positive lift over the baseline, with the strongest performers achieving up to 8.8% improvement in engagement breadth and 19.4% in engagement depth. We also observed substantial gains in steerability, with instruction following increasing from 59.2% to 84.8% and instruction violations decreasing from 26.6% to 5.8%. We detail the CharacterFlywheel process which integrates data curation, reward modeling to estimate and interpolate the landscape of engagement metrics, supervised fine-tuning (SFT), reinforcement learning (RL), and both offline and online evaluation to ensure reliable progress at each optimization step. We also discuss our methods for overfitting prevention and navigating production dynamics at scale. These contributions advance the scientific rigor and understanding of LLMs in social applications serving millions of users.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01840", "title": "FireRed-OCR Technical Report", "authors": [ "Hao Wu", "Haoran Lou", "Xinyue Li", "Zuodong Zhong", "Zhaojun Sun", "Phellon Chen", "Xuanhe Zhou", "Kai Zuo", "Yibo Chen", "Xu Tang", "Yao Hu", "Boxiang Zhou", "Jian Wu", "Yongji Wu", "Wenxin Yu", "Yingmiao Liu", "Yuhao Huang", "Manjie Xu", "Gang Liu", "Yidong Ma", "Zhichao Sun", "Changhao Qiao" ], "github": "", "project_page": "", "abstract": "We present FireRed-OCR, a systematic framework to specialize general VLMs into high-performance OCR models. Large Vision-Language Models (VLMs) have demonstrated impressive general capabilities but frequently suffer from ``structural hallucination'' when processing complex documents, limiting their utility in industrial OCR applications. In this paper, we introduce FireRed-OCR, a novel framework designed to transform general-purpose VLMs (based on Qwen3-VL) into pixel-precise structural document parsing experts. To address the scarcity of high-quality structured data, we construct a ``Geometry + Semantics'' Data Factory. Unlike traditional random sampling, our pipeline leverages geometric feature clustering and multi-dimensional tagging to synthesize and curate a highly balanced dataset, effectively handling long-tail layouts and rare document types. Furthermore, we propose a Three-Stage Progressive Training strategy that guides the model from pixel-level perception to logical structure generation. This curriculum includes: (1) Multi-task Pre-alignment to ground the model's understanding of document structure; (2) Specialized SFT for standardizing full-image Markdown output; and (3) Format-Constrained Group Relative Policy Optimization (GRPO), which utilizes reinforcement learning to enforce strict syntactic validity and structural integrity (e.g., table closure, formula syntax). Extensive evaluations on OmniDocBench v1.5 demonstrate that FireRed-OCR achieves state-of-the-art performance with an overall score of 92.94\\%, significantly outperforming strong baselines such as DeepSeek-OCR 2 and OCRVerse across text, formula, table, and reading order metrics. We open-source our code and model weights to facilitate the ``General VLM to Specialized Structural Expert'' paradigm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.02138", "title": "OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens", "authors": [ "Yiying Yang", "Wei Cheng", "Sijin Chen", "Honghao Fu", "Xianfang Zeng", "Yujun Cai", "Gang Yu", "Xingjun Ma" ], "github": "", "project_page": "", "abstract": "OmniLottie is a versatile framework that generates high quality vector animations from multi-modal instructions. For flexible motion and visual content control, we focus on Lottie, a light weight JSON formatting for both shapes and animation behaviors representation. However, the raw Lottie JSON files contain extensive invariant structural metadata and formatting tokens, posing significant challenges for learning vector animation generation. Therefore, we introduce a well designed Lottie tokenizer that transforms JSON files into structured sequences of commands and parameters representing shapes, animation functions and control parameters. Such tokenizer enables us to build OmniLottie upon pretrained vision language models to follow multi-modal interleaved instructions and generate high quality vector animations. To further advance research in vector animation generation, we curate MMLottie-2M, a large scale dataset of professionally designed vector animations paired with textual and visual annotations. With extensive experiments, we validate that OmniLottie can produce vivid and semantically aligned vector animations that adhere closely to multi modal human instructions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.02024", "title": "MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning", "authors": [ "Jiachun Li", "Shaoping Huang", "Zhuoran Jin", "Chenlong Zhang", "Pengfei Cao", "Yubo Chen", "Kang Liu", "Jun Zhao" ], "github": "", "project_page": "", "abstract": "Recent progress in the reasoning capabilities of multimodal large language models (MLLMs) has empowered them to address more complex tasks such as scientific analysis and mathematical reasoning. Despite their promise, MLLMs' reasoning abilities across different scenarios in real life remain largely unexplored and lack standardized benchmarks for evaluation. To address this gap, we introduce MMR-Life, a comprehensive benchmark designed to evaluate the diverse multimodal multi-image reasoning capabilities of MLLMs across real-life scenarios. MMR-Life consists of 2,646 multiple-choice questions based on 19,108 images primarily sourced from real-world contexts, comprehensively covering seven reasoning types: abductive, analogical, causal, deductive, inductive, spatial, and temporal. Unlike existing reasoning benchmarks, MMR-Life does not rely on domain-specific expertise but instead requires models to integrate information across multiple images and apply diverse reasoning abilities. The evaluation of 37 advanced models highlights the substantial challenge posed by MMR-Life. Even top models like GPT-5 achieve only 58% accuracy and display considerable variance in performance across reasoning types. Moreover, we analyze the reasoning paradigms of existing MLLMs, exploring how factors such as thinking length, reasoning method, and reasoning type affect their performance. In summary, MMR-Life establishes a comprehensive foundation for evaluating, analyzing, and improving the next generation of multimodal reasoning systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01301", "title": "When Does RL Help Medical VLMs? Disentangling Vision, SFT, and RL Gains", "authors": [ "Ahmadreza Jeddi", "Kimia Shaban", "Negin Baghbanzadeh", "Natasha Sharan", "Abhishek Moturu", "Elham Dolatabadi", "Babak Taati" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) is increasingly used to post-train medical Vision-Language Models (VLMs), yet it remains unclear whether RL improves medical visual reasoning or mainly sharpens behaviors already induced by supervised fine-tuning (SFT). We present a controlled study that disentangles these effects along three axes: vision, SFT, and RL. Using MedMNIST as a multi-modality testbed, we probe visual perception by benchmarking VLM vision towers against vision-only baselines, quantify reasoning support and sampling efficiency via Accuracy@1 versus Pass@K, and evaluate when RL closes the support gap and how gains transfer across modalities. We find that RL is most effective when the model already has non-trivial support (high Pass@K): it primarily sharpens the output distribution, improving Acc@1 and sampling efficiency, while SFT expands support and makes RL effective. Based on these findings, we propose a boundary-aware recipe and instantiate it by RL post-training an OctoMed-initialized model on a small, balanced subset of PMC multiple-choice VQA, achieving strong average performance across six medical VQA benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01940", "title": "CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification", "authors": [ "Jinpeng Chen", "Cheng Gong", "Hanbo Li", "Ziru Liu", "Zichen Tian", "Xinyu Fu", "Shi Wu", "Chenyang Zhang", "Wu Zhang", "Suiyun Zhang", "Dandan Tu", "Rui Liu" ], "github": "", "project_page": "", "abstract": "Developing multi-turn interactive tool-use agents is challenging because real-world user needs are often complex and ambiguous, yet agents must execute deterministic actions to satisfy them. To address this gap, we introduce CoVe (Constraint-Verification), a post-training data synthesis framework designed for training interactive tool-use agents while ensuring both data complexity and correctness. CoVe begins by defining explicit task constraints, which serve a dual role: they guide the generation of complex trajectories and act as deterministic verifiers for assessing trajectory quality. This enables the creation of high-quality training trajectories for supervised fine-tuning (SFT) and the derivation of accurate reward signals for reinforcement learning (RL). Our evaluation on the challenging \u03c4^2-bench benchmark demonstrates the effectiveness of the framework. Notably, our compact CoVe-4B model achieves success rates of 43.0\\% and 59.4\\% in the Airline and Retail domains, respectively; its overall performance significantly outperforms strong baselines of similar scale and remains competitive with models up to 17times its size. These results indicate that CoVe provides an effective and efficient pathway for synthesizing training data for state-of-the-art interactive tool-use agents. To support future research, we open-source our code, trained model, and the full set of 12K high-quality trajectories used for training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01223", "title": "Learn Hard Problems During RL with Reference Guided Fine-tuning", "authors": [ "Yangzhen Wu", "Shanda Li", "Zixin Wen", "Xin Zhou", "Ameet Talwalkar", "Yiming Yang", "Wenhao Huang", "Tianle Cai" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) for mathematical reasoning can suffer from reward sparsity: for challenging problems, LLM fails to sample any correct trajectories, preventing RL from receiving meaningful positive feedback. At the same time, there often exist human-written reference solutions along with the problem (e.g., problems from AoPS), but directly fine-tuning on these solutions offers no benefit because models often cannot imitate human proofs that lie outside their own reasoning distribution. We introduce Reference-Guided Fine-Tuning (ReGFT), a simple and effective method that utilizes human-written reference solutions to synthesize positive trajectories on hard problems and train on them before RL. For each problem, we provide the model with a partial reference solution and let it generate its own reasoning trace, ensuring the resulting trajectories remain in the model's reasoning space while still benefiting from reference guidance. Fine-tuning on these reference-guided trajectories increases the number of solvable problems and produces a checkpoint that receives more positive rewards during RL. Across three benchmarks (AIME24, AIME25, BeyondAIME), ReGFT consistently improves supervised accuracy, accelerates DAPO training, and raises the final performance plateau of RL. Our results show that ReGFT effectively overcomes reward sparsity and unlocks stronger RL-based mathematical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.02203", "title": "Tool Verification for Test-Time Reinforcement Learning", "authors": [ "Ruotong Liao", "Nikolai R\u00f6hrich", "Xiaohan Wang", "Yuhui Zhang", "Yasaman Samadzadeh", "Volker Tresp", "Serena Yeung-Levy" ], "github": "", "project_page": "", "abstract": "Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for self-evolving large reasoning models (LRMs), enabling online adaptation on unlabeled test inputs via self-induced rewards through majority voting. However, a spurious yet high-frequency unverified consensus can become a biased and reinforced reward signal, leading to incorrect mode collapse. We address this failure mode with T^3RL (Tool-Verification for Test-Time Reinforcement Learning), which introduces test-time tool verification into reward estimation. Concretely, a verifier uses an external tool as evidence (e.g., from code execution) to upweight verified rollouts in a verification-aware voting, producing more reliable pseudo-labels for training. Across various math difficulties (MATH-500, AMC, and AIME 2024) and diverse backbone types, T^3RL significantly improves over TTRL, with larger gains on harder problems. More broadly, T^3RL can be viewed as verified online data synthesis, highlighting test-time tool verification as a key mechanism for stabilizing self-evolution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01896", "title": "Agentic Code Reasoning", "authors": [ "Shubham Ugare", "Satish Chandra" ], "github": "", "project_page": "", "abstract": "Can LLM agents explore codebases and reason about code semantics without executing the code? We study this capability, which we call agentic code reasoning, and introduce semi-formal reasoning: a structured prompting methodology that requires agents to construct explicit premises, trace execution paths, and derive formal conclusions. Unlike unstructured chain-of-thought, semi-formal reasoning acts as a certificate: the agent cannot skip cases or make unsupported claims. We evaluate across three tasks (patch equivalence verification, fault localization, and code question answering) and show that semi-formal reasoning consistently improves accuracy on all of them. For patch equivalence, accuracy improves from 78% to 88% on curated examples and reaches 93% on real-world agent-generated patches, approaching the reliability needed for execution-free RL reward signals. For code question answering on RubberDuckBench Mohammad et al. (2026), semi-formal reasoning achieves 87% accuracy. For fault localization on Defects4J Just et al. (2014), semi-formal reasoning improves Top-5 accuracy by 5 percentage points over standard reasoning. These results demonstrate that structured agentic reasoning enables meaningful semantic code analysis without execution, opening practical applications in RL training pipelines, code review, and static program analysis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01142", "title": "ArtLLM: Generating Articulated Assets via 3D LLM", "authors": [ "Penghao Wang", "Siyuan Xie", "Hongyu Yan", "Xianghui Yang", "Jingwei Huang", "Chunchao Guo", "Jiayuan Gu" ], "github": "", "project_page": "", "abstract": "Creating interactive digital environments for gaming, robotics, and simulation relies on articulated 3D objects whose functionality emerges from their part geometry and kinematic structure. However, existing approaches remain fundamentally limited: optimization-based reconstruction methods require slow, per-object joint fitting and typically handle only simple, single-joint objects, while retrieval-based methods assemble parts from a fixed library, leading to repetitive geometry and poor generalization. To address these challenges, we introduce ArtLLM, a novel framework for generating high-quality articulated assets directly from complete 3D meshes. At its core is a 3D multimodal large language model trained on a large-scale articulation dataset curated from both existing articulation datasets and procedurally generated objects. Unlike prior work, ArtLLM autoregressively predicts a variable number of parts and joints, inferring their kinematic structure in a unified manner from the object's point cloud. This articulation-aware layout then conditions a 3D generative model to synthesize high-fidelity part geometries. Experiments on the PartNet-Mobility dataset show that ArtLLM significantly outperforms state-of-the-art methods in both part layout accuracy and joint prediction, while generalizing robustly to real-world objects. Finally, we demonstrate its utility in constructing digital twins, highlighting its potential for scalable robot learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.02099", "title": "Recursive Think-Answer Process for LLMs and VLMs", "authors": [ "Byung-Kwan Lee", "Youngchae Chee", "Yong Man Ro" ], "github": "", "project_page": "", "abstract": "Think-Answer reasoners such as DeepSeek-R1 have made notable progress by leveraging interpretable internal reasoning. However, despite the frequent presence of self-reflective cues like \"Oops!\", they remain vulnerable to output errors during single-pass inference. To address this limitation, we propose an efficient Recursive Think-Answer Process (R-TAP) that enables models to engage in iterative reasoning cycles and generate more accurate answers, going beyond conventional single-pass approaches. Central to this approach is a confidence generator that evaluates the certainty of model responses and guides subsequent improvements. By incorporating two complementary rewards-Recursively Confidence Increase Reward and Final Answer Confidence Reward-we show that R-TAP-enhanced models consistently outperform conventional single-pass methods for both large language models (LLMs) and vision-language models (VLMs). Moreover, by analyzing the frequency of \"Oops\"-like expressions in model responses, we find that R-TAP-applied models exhibit significantly fewer self-reflective patterns, resulting in more stable and faster inference-time reasoning. We hope R-TAP pave the way evolving into efficient and elaborated methods to refine the reasoning processes of future AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.00141", "title": "From Scale to Speed: Adaptive Test-Time Scaling for Image Editing", "authors": [ "Xiangyan Qu", "Zhenlong Yuan", "Jing Tang", "Rui Chen", "Datao Tang", "Meng Yu", "Lei Sun", "Yancheng Bai", "Xiangxiang Chu", "Gaopeng Gou", "Gang Xiong", "Yujun Cai" ], "github": "", "project_page": "", "abstract": "Image Chain-of-Thought (Image-CoT) is a test-time scaling paradigm that improves image generation by extending inference time. Most Image-CoT methods focus on text-to-image (T2I) generation. Unlike T2I generation, image editing is goal-directed: the solution space is constrained by the source image and instruction. This mismatch causes three challenges when applying Image-CoT to editing: inefficient resource allocation with fixed sampling budgets, unreliable early-stage verification using general MLLM scores, and redundant edited results from large-scale sampling. To address this, we propose ADaptive Edit-CoT (ADE-CoT), an on-demand test-time scaling framework to enhance editing efficiency and performance. It incorporates three key strategies: (1) a difficulty-aware resource allocation that assigns dynamic budgets based on estimated edit difficulty; (2) edit-specific verification in early pruning that uses region localization and caption consistency to select promising candidates; and (3) depth-first opportunistic stopping, guided by an instance-specific verifier, that terminates when intent-aligned results are found. Extensive experiments on three SOTA editing models (Step1X-Edit, BAGEL, FLUX.1 Kontext) across three benchmarks show that ADE-CoT achieves superior performance-efficiency trade-offs. With comparable sampling budgets, ADE-CoT obtains better performance with more than 2x speedup over Best-of-N.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.00889", "title": "CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning", "authors": [ "Xinyu Zhu", "Yihao Feng", "Yanchao Sun", "Xianzhi Du", "Pingzhi Li", "Olli Saarikivi", "Yun Zhu", "Yu Meng" ], "github": "https://github.com/huggingface/open-r1", "project_page": "", "abstract": "Large Language Models (LLMs) have recently exhibited remarkable reasoning capabilities, largely enabled by supervised fine-tuning (SFT)- and reinforcement learning (RL)-based post-training on high-quality reasoning data. However, reproducing and extending these capabilities in open and scalable settings is hindered by three fundamental data-centric challenges: (1) the cold-start problem, arising from the lack of seed datasets with detailed, long Chain-of-Thought (CoT) trajectories needed to initialize reasoning policies; (2) limited domain coverage, as most existing open-source reasoning datasets are concentrated in mathematics, with limited coverage of broader scientific disciplines; and (3) the annotation bottleneck, where the difficulty of frontier-level reasoning tasks makes reliable human annotation prohibitively expensive or infeasible. To address these challenges, we introduce CHIMERA, a compact synthetic reasoning dataset comprising 9K samples for generalizable cross-domain reasoning. CHIMERA is constructed with three key properties: (1) it provides rich, long CoT reasoning trajectories synthesized by state-of-the-art reasoning models; (2) it has broad and structured coverage, spanning 8 major scientific disciplines and over 1K fine-grained topics organized via a model-generated hierarchical taxonomy; and (3) it employs a fully automated, scalable evaluation pipeline that uses strong reasoning models to cross-validate both problem validity and answer correctness. We use CHIMERA to post-train a 4B Qwen3 model. Despite the dataset's modest size, the resulting model achieves strong performance on a suite of challenging reasoning benchmarks, including GPQA-Diamond, AIME 24/25/26, HMMT 25, and Humanity's Last Exam, approaching or matching the reasoning performance of substantially larger models such as DeepSeek-R1 and Qwen3-235B.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/open-r1", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.00585", "title": "MicroVerse: A Preliminary Exploration Toward a Micro-World Simulation", "authors": [ "Rongsheng Wang", "Minghao Wu", "Hongru Zhou", "Zhihan Yu", "Zhenyang Cai", "Junying Chen", "Benyou Wang" ], "github": "https://github.com/FreedomIntelligence/MicroVerse", "project_page": "", "abstract": "Recent advances in video generation have opened new avenues for macroscopic simulation of complex dynamic systems, but their application to microscopic phenomena remains largely unexplored. Microscale simulation holds great promise for biomedical applications such as drug discovery, organ-on-chip systems, and disease mechanism studies, while also showing potential in education and interactive visualization. In this work, we introduce MicroWorldBench, a multi-level rubric-based benchmark for microscale simulation tasks. MicroWorldBench enables systematic, rubric-based evaluation through 459 unique expert-annotated criteria spanning multiple microscale simulation task (e.g., organ-level processes, cellular dynamics, and subcellular molecular interactions) and evaluation dimensions (e.g., scientific fidelity, visual quality, instruction following). MicroWorldBench reveals that current SOTA video generation models fail in microscale simulation, showing violations of physical laws, temporal inconsistency, and misalignment with expert criteria. To address these limitations, we construct MicroSim-10K, a high-quality, expert-verified simulation dataset. Leveraging this dataset, we train MicroVerse, a video generation model tailored for microscale simulation. MicroVerse can accurately reproduce complex microscale mechanism. Our work first introduce the concept of Micro-World Simulation and present a proof of concept, paving the way for applications in biology, education, and scientific visualization. Our work demonstrates the potential of educational microscale simulations of biological mechanisms. Our data and code are publicly available at https://github.com/FreedomIntelligence/MicroVerse", "github_manual": "", "github_regex": "https://github.com/FreedomIntelligence/MicroVerse", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2602.23359", "title": "SeeThrough3D: Occlusion Aware 3D Control in Text-to-Image Generation", "authors": [ "Vaibhav Agrawal", "Rishubh Parihar", "Pradhaan Bhat", "Ravi Kiran Sarvadevabhatla", "R. Venkatesh Babu" ], "github": "", "project_page": "", "abstract": "We identify occlusion reasoning as a fundamental yet overlooked aspect for 3D layout-conditioned generation. It is essential for synthesizing partially occluded objects with depth-consistent geometry and scale. While existing methods can generate realistic scenes that follow input layouts, they often fail to model precise inter-object occlusions. We propose SeeThrough3D, a model for 3D layout conditioned generation that explicitly models occlusions. We introduce an occlusion-aware 3D scene representation (OSCR), where objects are depicted as translucent 3D boxes placed within a virtual environment and rendered from desired camera viewpoint. The transparency encodes hidden object regions, enabling the model to reason about occlusions, while the rendered viewpoint provides explicit camera control during generation. We condition a pretrained flow based text-to-image image generation model by introducing a set of visual tokens derived from our rendered 3D representation. Furthermore, we apply masked self-attention to accurately bind each object bounding box to its corresponding textual description, enabling accurate generation of multiple objects without object attribute mixing. To train the model, we construct a synthetic dataset with diverse multi-object scenes with strong inter-object occlusions. SeeThrough3D generalizes effectively to unseen object categories and enables precise 3D layout control with realistic occlusions and consistent camera control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.02049", "title": "WorldStereo: Bridging Camera-Guided Video Generation and Scene Reconstruction via 3D Geometric Memories", "authors": [ "Yisu Zhang", "Chenjie Cao", "Tengfei Wang", "Xuhui Zuo", "Junta Wu", "Jianke Zhu", "Chunchao Guo" ], "github": "", "project_page": "", "abstract": "Recent advances in foundational Video Diffusion Models (VDMs) have yielded significant progress. Yet, despite the remarkable visual quality of generated videos, reconstructing consistent 3D scenes from these outputs remains challenging, due to limited camera controllability and inconsistent generated content when viewed from distinct camera trajectories. In this paper, we propose WorldStereo, a novel framework that bridges camera-guided video generation and 3D reconstruction via two dedicated geometric memory modules. Formally, the global-geometric memory enables precise camera control while injecting coarse structural priors through incrementally updated point clouds. Moreover, the spatial-stereo memory constrains the model's attention receptive fields with 3D correspondence to focus on fine-grained details from the memory bank. These components enable WorldStereo to generate multi-view-consistent videos under precise camera control, facilitating high-quality 3D reconstruction. Furthermore, the flexible control branch-based WorldStereo shows impressive efficiency, benefiting from the distribution matching distilled VDM backbone without joint training. Extensive experiments across both camera-guided video generation and 3D reconstruction benchmarks demonstrate the effectiveness of our approach. Notably, we show that WorldStereo acts as a powerful world model, tackling diverse scene generation tasks (whether starting from perspective or panoramic images) with high-fidelity 3D results. Models will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.02208", "title": "Reasoning Core: A Scalable Procedural Data Generation Suite for Symbolic Pre-training and Post-Training", "authors": [ "Valentin Lacombe", "Valentin Quesnel", "Damien Sileo" ], "github": "", "project_page": "", "abstract": "Training on verifiable symbolic data is a promising way to expand the reasoning frontier of language models beyond what standard pre-training corpora provide. Yet existing procedural generators often rely on fixed puzzles or templates and do not deliver the distributional breadth needed at scale. We introduce Reasoning Core, a scalable suite that procedurally generates verifiable symbolic reasoning data across core formal domains: PDDL planning over randomized domains, first-order logic with equality, context-free grammar parsing and generation, causal reasoning over random Bayesian networks, and systems of equations. Each task is paired with an external solver for rigorous verification and admits continuous difficulty control for curriculum design. Examples can optionally include solver-derived reasoning traces, enabling supervised training from the earliest pre-training stages, and the same interface provides verifiable reward functions for reinforcement learning. Our experiments show that mixing Reasoning Core data into pre-training improves downstream reasoning while preserving, or slightly improving, language modeling quality. Zero-shot evaluations confirm these tasks challenge frontier models such as GPT-5. The code and data are publicly available under the MIT license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01281", "title": "Spectral Attention Steering for Prompt Highlighting", "authors": [ "Weixian Waylon Li", "Yuchen Niu", "Yongxin Yang", "Keshuang Li", "Tiejun Ma", "Shay B. Cohen" ], "github": "", "project_page": "", "abstract": "Attention steering is an important technique for controlling model focus, enabling capabilities such as prompt highlighting, where the model prioritises user-specified text. However, existing attention steering methods require explicit storage of the full attention matrix, making them incompatible with memory-efficient implementations like FlashAttention. We introduce Spectral Editing Key Amplification (SEKA), a training-free steering method that tackles this by directly editing key embeddings before attention computation. SEKA uses spectral decomposition to steer key embeddings towards latent directions that amplify attention scores for certain tokens. We extend this to Adaptive SEKA (AdaSEKA), a query-adaptive variant that uses a training-free routing mechanism to dynamically combine multiple expert subspaces based on the prompt's semantic intent. Our experiments show both methods significantly outperform strong baselines on standard steering benchmarks while adding much lower latency and memory overhead, in compatibility with optimised attention.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01824", "title": "OpenAutoNLU: Open Source AutoML Library for NLU", "authors": [ "Grigory Arshinov", "Aleksandr Boriskin", "Sergey Senichev", "Ayaz Zaripov", "Daria Galimzianova", "Daniil Karpov", "Leonid Sanochkin" ], "github": "https://github.com/mts-ai/OpenAutoNLU", "project_page": "", "abstract": "OpenAutoNLU is an open-source automated machine learning library for natural language understanding (NLU) tasks, covering both text classification and named entity recognition (NER). Unlike existing solutions, we introduce data-aware training regime selection that requires no manual configuration from the user. The library also provides integrated data quality diagnostics, configurable out-of-distribution (OOD) detection, and large language model (LLM) features, all within a minimal lowcode API. The demo app is accessible here https://openautonlu.dev.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mts-ai/OpenAutoNLU", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2602.23866", "title": "SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale", "authors": [ "Ibragim Badertdinov", "Maksim Nekrashevich", "Anton Shevtsov", "Alexander Golubev" ], "github": "", "project_page": "", "abstract": "Software engineering agents (SWE) are improving rapidly, with recent gains largely driven by reinforcement learning (RL). However, RL training is constrained by the scarcity of large-scale task collections with reproducible execution environments and reliable test suites. Although a growing number of benchmarks have emerged, datasets suitable for training remain limited in scale and diversity or often target a limited set of high-resource language ecosystems. We introduce SWE-rebench V2, a language-agnostic automated pipeline for harvesting executable real-world SWE tasks and constructing RL training environments at scale. The pipeline synthesizes repository-specific installation and test procedures via an interactive setup agent, and filters unsound instances using an ensemble of LLM judges, validated against human-verified SWE-bench annotations. Using this pipeline, we construct a dataset of 32,000+ tasks spanning 20 languages and 3,600+ repositories, with pre-built images for reproducible execution. To further scale training data, we additionally release 120,000+ tasks with installation instructions, fail-to-pass tests and rich metadata, where the problem statement is generated based on the original pull request description. We validate the collected instances through a diagnostic study that covers a subset of tasks in five programming languages across seven popular models, and provide instance-level metadata that flags common confounders such as overly restrictive tests and underspecified descriptions. We release the datasets, the collection and execution code, and associated artifacts to enable large-scale training of SWE agents across diverse languages and repositories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.00961", "title": "Using Songs to Improve Kazakh Automatic Speech Recognition", "authors": [ "Rustem Yeshpanov" ], "github": "", "project_page": "", "abstract": "Developing automatic speech recognition (ASR) systems for low-resource languages is hindered by the scarcity of transcribed corpora. This proof-of-concept study explores songs as an unconventional yet promising data source for Kazakh ASR. We curate a dataset of 3,013 audio-text pairs (about 4.5 hours) from 195 songs by 36 artists, segmented at the lyric-line level. Using Whisper as the base recogniser, we fine-tune models under seven training scenarios involving Songs, Common Voice Corpus (CVC), and FLEURS, and evaluate them on three benchmarks: CVC, FLEURS, and Kazakh Speech Corpus 2 (KSC2). Results show that song-based fine-tuning improves performance over zero-shot baselines. For instance, Whisper Large-V3 Turbo trained on a mixture of Songs, CVC, and FLEURS achieves 27.6% normalised WER on CVC and 11.8% on FLEURS, while halving the error on KSC2 (39.3% vs. 81.2%) relative to the zero-shot model. Although these gains remain below those of models trained on the 1,100-hour KSC2 corpus, they demonstrate that even modest song-speech mixtures can yield meaningful adaptation improvements in low-resource ASR. The dataset is released on Hugging Face for research purposes under a gated, non-commercial licence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2602.19896", "title": "Monocular Mesh Recovery and Body Measurement of Female Saanen Goats", "authors": [ "Bo Jin", "Shichao Zhao", "Jin Lyu", "Bin Zhang", "Tao Yu", "Liang An", "Yebin Liu", "Meili Wang" ], "github": "", "project_page": "", "abstract": "The lactation performance of Saanen dairy goats, renowned for their high milk yield, is intrinsically linked to their body size, making accurate 3D body measurement essential for assessing milk production potential, yet existing reconstruction methods lack goat-specific authentic 3D data. To address this limitation, we establish the FemaleSaanenGoat dataset containing synchronized eight-view RGBD videos of 55 female Saanen goats (6-18 months). Using multi-view DynamicFusion, we fuse noisy, non-rigid point cloud sequences into high-fidelity 3D scans, overcoming challenges from irregular surfaces and rapid movement. Based on these scans, we develop SaanenGoat, a parametric 3D shape model specifically designed for female Saanen goats. This model features a refined template with 41 skeletal joints and enhanced udder representation, registered with our scan data. A comprehensive shape space constructed from 48 goats enables precise representation of diverse individual variations. With the help of SaanenGoat model, we get high-precision 3D reconstruction from single-view RGBD input, and achieve automated measurement of six critical body dimensions: body length, height, chest width, chest girth, hip width, and hip height. Experimental results demonstrate the superior accuracy of our method in both 3D reconstruction and body measurement, presenting a novel paradigm for large-scale 3D vision applications in precision livestock farming.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01907", "title": "Efficient RLVR Training via Weighted Mutual Information Data Selection", "authors": [ "Xinyu Zhou", "Boyu Zhu", "Haotian Zhang", "Huiming Wang", "Zhijiang Guo" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) plays a central role in improving the reasoning and alignment of large language models, yet its efficiency critically depends on how training data are selected. Existing online selection strategies predominantly rely on difficulty-based heuristics, favouring datapoints with intermediate success rates, implicitly equating difficulty with informativeness and neglecting epistemic uncertainty arising from limited evidence. We introduce InSight, an INformation-guided data SamplInG metHod for RL Training, grounded in a weighted mutual information objective. By modeling data outcomes with Bayesian latent success rates, we show that expected uncertainty reduction decomposes into complementary difficulty- and evidence-dependent components, revealing a fundamental limitation of difficulty-only selection. Leveraging this observation, InSight constructs a stable acquisition score based on the mean belief of datapoints' success rather than noisy sampled outcomes, and naturally extends to multi-rollout settings common in reinforcement learning with verifiable rewards (RLVR). Extensive experiments demonstrate that InSight consistently achieves state-of-the-art performance and improves training efficiency, including a +1.41 average gain on Planning & Mathmatics benchmarks, +1.01 improvement on general reasoning, and up to ~2.2x acceleration, with negligible additional computational overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.00516", "title": "ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Data", "authors": [ "Haodong Zhao", "Jinming Hu", "Zhaomin Wu", "Zongru Wu", "Wei Du", "Junyi Hou", "Caibei Zhao", "Zhuosheng Zhang", "Bingsheng He", "Gongshen Liu" ], "github": "https://github.com/dongdongzhaoUP/ProtegoFed", "project_page": "", "abstract": "Federated Instruction Tuning (FIT) enables collaborative instruction tuning of large language models across multiple organizations (clients) in a cross-silo setting without requiring the sharing of private instructions. Recent findings on natural backdoors and the existing training data collection method suggest that poisoned samples may be pervasive and inadvertently embedded in real-world datasets, potentially distributed across all clients, even if the clients are benign. This work systematically examine this threat in FIT, demonstrating that existing defenses are ineffective when poisoned data is interspersed among all clients. Addressing this challenge entails two major difficulties: identifying the distinctive characteristics of poisoned samples at each client and enabling collaborative defense when some clients are heavily dominated by poisoned samples. To address these difficulties, we identify gradients in the frequency domain as a robust signal to distinguish poisoned data. We further propose a global secondary clustering mechanism that facilitates collaborative identification of poisoned samples across clients. In summary, this paper introduces ProtegoFed, the first backdoor-free FIT framework that accurately detects, removes, and even purifies interspersed poisoned data across clients during the training. Experimental results on four FL datasets show that ProtegoFed identifies 92.00% sim 100.00% of poisoned samples, reduces the attack success rate to almost zero, and maintains utility on the main task. Code is available at https://github.com/dongdongzhaoUP/ProtegoFed.", "github_manual": "", "github_regex": "https://github.com/dongdongzhaoUP/ProtegoFed", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2602.23543", "title": "Synthetic Visual Genome 2: Extracting Large-scale Spatio-Temporal Scene Graphs from Videos", "authors": [ "Ziqi Gao", "Jieyu Zhang", "Wisdom Oluchi Ikezogwo", "Jae Sung Park", "Tario G. You", "Daniel Ogbu", "Chenhao Zheng", "Weikai Huang", "Yinuo Yang", "Winson Han", "Quan Kong", "Rajat Saini", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "We introduce Synthetic Visual Genome 2 (SVG2), a large-scale panoptic video scene graph dataset. SVG2 contains over 636K videos with 6.6M objects, 52.0M attributes, and 6.7M relations, providing an order-of-magnitude increase in scale and diversity over prior spatio-temporal scene graph datasets. To create SVG2, we design a fully automated pipeline that combines multi-scale panoptic segmentation, online-offline trajectory tracking with automatic new-object discovery, per-trajectory semantic parsing, and GPT-5-based spatio-temporal relation inference. Building on this resource, we train TRaSER, a video scene graph generation model. TRaSER augments VLMs with a trajectory-aligned token arrangement mechanism and new modules: an object-trajectory resampler and a temporal-window resampler to convert raw videos and panoptic trajectories into compact spatio-temporal scene graphs in a single forward pass. The temporal-window resampler binds visual tokens to short trajectory segments to preserve local motion and temporal semantics, while the object-trajectory resampler aggregates entire trajectories to maintain global context for objects. On the PVSG, VIPSeg, VidOR and SVG2 test datasets, TRaSER improves relation detection by +15 to 20%, object prediction by +30 to 40% over the strongest open-source baselines and by +13% over GPT-5, and attribute prediction by +15%. When TRaSER's generated scene graphs are sent to a VLM for video question answering, it delivers a +1.5 to 4.6% absolute accuracy gain over using video only or video augmented with Qwen2.5-VL's generated scene graphs, demonstrating the utility of explicit spatio-temporal scene graphs as an intermediate representation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01493", "title": "PhotoBench: Beyond Visual Matching Towards Personalized Intent-Driven Photo Retrieval", "authors": [ "Tianyi Xu", "Rong Shan", "Junjie Wu", "Jiadeng Huang", "Teng Wang", "Jiachen Zhu", "Wenteng Chen", "Minxin Tu", "Quantao Dou", "Zhaoxiang Wang", "Changwang Zhang", "Weinan Zhang", "Jun Wang", "Jianghao Lin" ], "github": "", "project_page": "", "abstract": "Personal photo albums are not merely collections of static images but living, ecological archives defined by temporal continuity, social entanglement, and rich metadata, which makes the personalized photo retrieval non-trivial. However, existing retrieval benchmarks rely heavily on context-isolated web snapshots, failing to capture the multi-source reasoning required to resolve authentic, intent-driven user queries. To bridge this gap, we introduce PhotoBench, the first benchmark constructed from authentic, personal albums. It is designed to shift the paradigm from visual matching to personalized multi-source intent-driven reasoning. Based on a rigorous multi-source profiling framework, which integrates visual semantics, spatial-temporal metadata, social identity, and temporal events for each image, we synthesize complex intent-driven queries rooted in users' life trajectories. Extensive evaluation on PhotoBench exposes two critical limitations: the modality gap, where unified embedding models collapse on non-visual constraints, and the source fusion paradox, where agentic systems perform poor tool orchestration. These findings indicate that the next frontier in personal multimodal retrieval lies beyond unified embeddings, necessitating robust agentic reasoning systems capable of precise constraint satisfaction and multi-source fusion. Our PhotoBench is available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01096", "title": "Unified Vision-Language Modeling via Concept Space Alignment", "authors": [ "Yifu Qiu", "Paul-Ambroise Duquenne", "Holger Schwenk" ], "github": "", "project_page": "", "abstract": "We introduce V-SONAR, a vision-language embedding space extended from the text-only embedding space SONAR (Omnilingual Embeddings Team et al., 2026), which supports 1500 text languages and 177 speech languages. To construct V-SONAR, we propose a post-hoc alignment pipeline that maps the representations of an existing vision encoder into the SONAR space. We thoroughly evaluate V-SONAR and show that its embeddings achieve competitive performance on text-to-video retrieval. Equipped with the OMNISONAR text decoder, V-SONAR further surpasses state-of-the-art vision-language models on video captioning tasks, including DREAM-1K (BLEU 23.9 vs. 19.6) and PE-VIDEO (BLEU 39.0 vs. 30.0). Leveraging V-SONAR, we first demonstrate that the Large Concept Model (LCM; LCM team et al. 2024) operating in SONAR and trained with English text only, can perform both single- and multi-visual concept understanding in a zero-shot manner. Finally, we introduce V-LCM, which extends the LCM with vision-language instruction tuning. V-LCM encodes vision and language inputs into an unified sequence of latent embeddings via V-SONAR and SONAR, and it is trained with the same latent diffusion objective for next-embedding prediction as in LCM's text-only pre-training. Experiments on a large-scale multilingual and -modal instruction-tuning data mixture highlight the potential of V-LCM: V-LCM matches state-of-the-art vision-language models on tasks covering image/video captioning and question answering, while significantly outperforming them across 61 rich- to low-resource languages out of all 62 tested languages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2602.19517", "title": "Classroom Final Exam: An Instructor-Tested Reasoning Benchmark", "authors": [ "Chongyang Gao", "Diji Yang", "Shuyan Zhou", "Xichen Yan", "Luchuan Song", "Shuo Li", "Kezhen Chen" ], "github": "https://github.com/Analogy-AI/CFE_Bench", "project_page": "", "abstract": "We introduce (Classroom Final Exam), a multimodal benchmark for evaluating the reasoning capabilities of large language models across more than 20 STEM domains. is curated from repeatedly used, authentic university homework and exam problems, together with reference solutions provided by course instructors. presents a significant challenge even for frontier models: the newly released Gemini-3.1-pro-preview achieves an overall accuracy of 59.69\\%, while the second-best model, Gemini-3-flash-preview, reaches 55.46\\%, leaving considerable room for improvement. Beyond leaderboard results, we perform a diagnostic analysis by decomposing reference solutions into reasoning flows. We find that although frontier models can often answer intermediate sub-questions correctly, they struggle to reliably derive and maintain correct intermediate states throughout multi-step solutions. We further observe that model-generated solutions typically have more reasoning steps than those provided by the instructor, indicating suboptimal step efficiency and a higher risk of error accumulation. The data and code are available at https://github.com/Analogy-AI/CFE_Bench.", "github_manual": "", "github_regex": "https://github.com/Analogy-AI/CFE_Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.01576", "title": "Cryo-Bench: Benchmarking Foundation Models for Cryosphere Applications", "authors": [ "Saurabh Kaushik", "Lalit Maurya", "Beth Tellman" ], "github": "https://github.com/Sk-2103/Cryo-Bench", "project_page": "", "abstract": "Geo-Foundation Models (GFMs) have been evaluated across diverse Earth observation task including multiple domains and have demonstrated strong potential of producing reliable maps even with sparse labels. However, benchmarking GFMs for Cryosphere applications has remained limited, primarily due to the lack of suitable evaluation datasets. To address this gap, we introduce Cryo-Bench, a benchmark compiled to evaluate GFM performance across key Cryospheric components. Cryo-Bench includes debris-covered glaciers, glacial lakes, sea ice, and calving fronts, spanning multiple sensors and broad geographic regions. We evaluate 14 GFMs alongside UNet and ViT baselines to assess their advantages, limitations, and optimal usage strategies. With a frozen encoder, UNet achieves the highest average mIoU of 66.38, followed by TerraMind at 64.02 across five evluation dataset included in Cryo-Bench. In the few-shot setting (10\\% input data), GFMs such as DOFA and TerraMind outperform UNet, achieving mIoU scores of 59.53, 56.62, and 56.60, respectively, comapred to U-Net's 56.60. When fully finetuning GFMs, we observe inconsistent performance across datasets and models. However, tuning learning rate along with finetuning substantially improves GFM performance. For example, evaluation on two representative datasets (GLID and CaFFe) shows an average relative improvement of 12.77\\%. Despite having minimal Cryosphere representation in their pretraining data, GFMs exhibit notable domain adaptation capabilities and produce meaningful results across tasks. Based on our findings, We recommend encoder fine-tuning with hyperparameter optimization optimization to achieve the best possible performance, while using frozen encoders when users need quick results without extensive experimentation.(https://github.com/Sk-2103/Cryo-Bench{GitHub}).", "github_manual": "", "github_regex": "https://github.com/Sk-2103/Cryo-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2602.24121", "title": "Planning from Observation and Interaction", "authors": [ "Tyler Han", "Siyang Shen", "Rohan Baijal", "Harine Ravichandiran", "Bat Nemekhbold", "Kevin Huang", "Sanghun Jung", "Byron Boots" ], "github": "", "project_page": "", "abstract": "Observational learning requires an agent to learn to perform a task by referencing only observations of the performed task. This work investigates the equivalent setting in real-world robot learning where access to hand-designed rewards and demonstrator actions are not assumed. To address this data-constrained setting, this work presents a planning-based Inverse Reinforcement Learning (IRL) algorithm for world modeling from observation and interaction alone. Experiments conducted entirely in the real-world demonstrate that this paradigm is effective for learning image-based manipulation tasks from scratch in under an hour, without assuming prior knowledge, pre-training, or data of any kind beyond task observations. Moreover, this work demonstrates that the learned world model representation is capable of online transfer learning in the real-world from scratch. In comparison to existing approaches, including IRL, RL, and Behavior Cloning (BC), which have more restrictive assumptions, the proposed approach demonstrates significantly greater sample efficiency and success rates, enabling a practical path forward for online world modeling and planning from observation and interaction. Videos and more at: https://uwrobotlearning.github.io/mpail2/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-03", "arxiv_id": "2603.00610", "title": "CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction", "authors": [ "Yinghao Ma", "Haiwen Xia", "Hewei Gao", "Weixiong Chen", "Yuxin Ye", "Yuchen Yang", "Sungkyun Chang", "Mingshuo Ding", "Yizhi Li", "Ruibin Yuan", "Simon Dixon", "Emmanouil Benetos" ], "github": "https://github.com/Haiwen-Xia/CMI-RewardBench", "project_page": "", "abstract": "While music generation models have evolved to handle complex multimodal inputs mixing text, lyrics, and reference audio, evaluation mechanisms have lagged behind. In this paper, we bridge this critical gap by establishing a comprehensive ecosystem for music reward modeling under Compositional Multimodal Instruction (CMI), where the generated music may be conditioned on text descriptions, lyrics, and audio prompts. We first introduce CMI-Pref-Pseudo, a large-scale preference dataset comprising 110k pseudo-labeled samples, and CMI-Pref, a high-quality, human-annotated corpus tailored for fine-grained alignment tasks. To unify the evaluation landscape, we propose CMI-RewardBench, a unified benchmark that evaluates music reward models on heterogeneous samples across musicality, text-music alignment, and compositional instruction alignment. Leveraging these resources, we develop CMI reward models (CMI-RMs), a parameter-efficient reward model family capable of processing heterogeneous inputs. We evaluate their correlation with human judgments scores on musicality and alignment on CMI-Pref along with previous datasets. Further experiments demonstrate that CMI-RM not only correlates strongly with human judgments, but also enables effective inference-time scaling via top-k filtering. The necessary training data, benchmarks, and reward models are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Haiwen-Xia/CMI-RewardBench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.02578", "title": "How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities", "authors": [ "Ziwen Xu", "Kewei Xu", "Haoming Xu", "Haiwen Hong", "Longtao Huang", "Hui Xue", "Ningyu Zhang", "Yongliang Shen", "Guozhou Zheng", "Huajun Chen", "Shumin Deng" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors, ranging from misaligned intent to inconsistent personality, pose significant risks. We introduce SteerEval, a hierarchical benchmark for evaluating LLM controllability across three domains: language features, sentiment, and personality. Each domain is structured into three specification levels: L1 (what to express), L2 (how to express), and L3 (how to instantiate), connecting high-level behavioral intent to concrete textual output. Using SteerEval, we systematically evaluate contemporary steering methods, revealing that control often degrades at finer-grained levels. Our benchmark offers a principled and interpretable framework for safe and controllable LLM behavior, serving as a foundation for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.01571", "title": "Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models", "authors": [ "Qiyuan Zhang", "Yufei Wang", "Tianhe Wu", "Can Xu", "Qingfeng Sun", "Kai Zheng", "Xue Liu", "Chen Ma" ], "github": "https://github.com/Don-Joey/Mix-GRM", "project_page": "", "abstract": "Recent advancements in Generative Reward Models (GRMs) have demonstrated that scaling the length of Chain-of-Thought (CoT) reasoning considerably enhances the reliability of evaluation. However, current works predominantly rely on unstructured length scaling, ignoring the divergent efficacy of different reasoning mechanisms: Breadth-CoT (B-CoT, i.e., multi-dimensional principle coverage) and Depth-CoT (D-CoT, i.e., substantive judgment soundness). To address this, we introduce Mix-GRM, a framework that reconfigures raw rationales into structured B-CoT and D-CoT through a modular synthesis pipeline, subsequently employing Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR) to internalize and optimize these mechanisms. Comprehensive experiments demonstrate that Mix-GRM establishes a new state-of-the-art across five benchmarks, surpassing leading open-source RMs by an average of 8.2\\%. Our results reveal a clear divergence in reasoning: B-CoT benefits subjective preference tasks, whereas D-CoT excels in objective correctness tasks. Consequently, misaligning the reasoning mechanism with the task directly degrades performance. Furthermore, we demonstrate that RLVR acts as a switching amplifier, inducing an emergent polarization where the model spontaneously allocates its reasoning style to match task demands. The synthesized data and models are released at https://huggingface.co/collections/DonJoey/mix-grm{Hugging Face}, and the code is released at https://github.com/Don-Joey/Mix-GRM{Github}.", "github_manual": "", "github_regex": "https://github.com/Don-Joey/Mix-GRM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.02802", "title": "NOVA: Sparse Control, Dense Synthesis for Pair-Free Video Editing", "authors": [ "Tianlin Pan", "Jiayi Dai", "Chenpu Yuan", "Zhengyao Lv", "Binxin Yang", "Hubery Yin", "Chen Li", "Jing Lyu", "Caifeng Shan", "Chenyang Si" ], "github": "https://github.com/WeChatCV/NovaEdit", "project_page": "", "abstract": "Recent video editing models have achieved impressive results, but most still require large-scale paired datasets. Collecting such naturally aligned pairs at scale remains highly challenging and constitutes a critical bottleneck, especially for local video editing data. Existing workarounds transfer image editing to video through global motion control for pair-free video editing, but such designs struggle with background and temporal consistency. In this paper, we propose NOVA: Sparse Control \\& Dense Synthesis, a new framework for unpaired video editing. Specifically, the sparse branch provides semantic guidance through user-edited keyframes distributed across the video, and the dense branch continuously incorporates motion and texture information from the original video to maintain high fidelity and coherence. Moreover, we introduce a degradation-simulation training strategy that enables the model to learn motion reconstruction and temporal consistency by training on artificially degraded videos, thus eliminating the need for paired data. Our extensive experiments demonstrate that NOVA outperforms existing approaches in edit fidelity, motion preservation, and temporal coherence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WeChatCV/NovaEdit", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.03160", "title": "Kling-MotionControl Technical Report", "authors": [ "Kling Team", "Jialu Chen", "Yikang Ding", "Zhixue Fang", "Kun Gai", "Kang He", "Xu He", "Jingyun Hua", "Mingming Lao", "Xiaohan Li", "Hui Liu", "Jiwen Liu", "Xiaoqiang Liu", "Fan Shi", "Xiaoyu Shi", "Peiqin Sun", "Songlin Tang", "Pengfei Wan", "Tiancheng Wen", "Zhiyong Wu", "Haoxian Zhang", "Runze Zhao", "Yuanxing Zhang", "Yan Zhou" ], "github": "", "project_page": "", "abstract": "Character animation aims to generate lifelike videos by transferring motion dynamics from a driving video to a reference image. Recent strides in generative models have paved the way for high-fidelity character animation. In this work, we present Kling-MotionControl, a unified DiT-based framework engineered specifically for robust, precise, and expressive holistic character animation. Leveraging a divide-and-conquer strategy within a cohesive system, the model orchestrates heterogeneous motion representations tailored to the distinct characteristics of body, face, and hands, effectively reconciling large-scale structural stability with fine-grained articulatory expressiveness. To ensure robust cross-identity generalization, we incorporate adaptive identity-agnostic learning, facilitating natural motion retargeting for diverse characters ranging from realistic humans to stylized cartoons. Simultaneously, we guarantee faithful appearance preservation through meticulous identity injection and fusion designs, further supported by a subject library mechanism that leverages comprehensive reference contexts. To ensure practical utility, we implement an advanced acceleration framework utilizing multi-stage distillation, boosting inference speed by over 10x. Kling-MotionControl distinguishes itself through intelligent semantic motion understanding and precise text responsiveness, allowing for flexible control beyond visual inputs. Human preference evaluations demonstrate that Kling-MotionControl delivers superior performance compared to leading commercial and open-source solutions, achieving exceptional fidelity in holistic motion control, open domain generalization, and visual quality and coherence. These results establish Kling-MotionControl as a robust solution for high-quality, controllable, and lifelike character animation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.02684", "title": "HateMirage: An Explainable Multi-Dimensional Dataset for Decoding Faux Hate and Subtle Online Abuse", "authors": [ "Sai Kartheek Reddy Kasu", "Shankar Biradar", "Sunil Saumya", "Md. Shad Akhtar" ], "github": "https://github.com/Sai-Kartheek-Reddy/HateMirage", "project_page": "", "abstract": "Subtle and indirect hate speech remains an underexplored challenge in online safety research, particularly when harmful intent is embedded within misleading or manipulative narratives. Existing hate speech datasets primarily capture overt toxicity, underrepresenting the nuanced ways misinformation can incite or normalize hate. To address this gap, we present HateMirage, a novel dataset of Faux Hate comments designed to advance reasoning and explainability research on hate emerging from fake or distorted narratives. The dataset was constructed by identifying widely debunked misinformation claims from fact-checking sources and tracing related YouTube discussions, resulting in 4,530 user comments. Each comment is annotated along three interpretable dimensions: Target (who is affected), Intent (the underlying motivation or goal behind the comment), and Implication (its potential social impact). Unlike prior explainability datasets such as HateXplain and HARE, which offer token-level or single-dimensional reasoning, HateMirage introduces a multi-dimensional explanation framework that captures the interplay between misinformation, harm, and social consequence. We benchmark multiple open-source language models on HateMirage using ROUGE-L F1 and Sentence-BERT similarity to assess explanation coherence. Results suggest that explanation quality may depend more on pretraining diversity and reasoning-oriented data rather than on model scale alone. By coupling misinformation reasoning with harm attribution, HateMirage establishes a new benchmark for interpretable hate detection and responsible AI research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Sai-Kartheek-Reddy/HateMirage", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.02175", "title": "Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance", "authors": [ "Yiqi Lin", "Guoqiang Liang", "Ziyun Zeng", "Zechen Bai", "Yanzhe Chen", "Mike Zheng Shou" ], "github": "https://github.com/showlab/Kiwi-Edit", "project_page": "", "abstract": "Instruction-based video editing has witnessed rapid progress, yet current methods often struggle with precise visual control, as natural language is inherently limited in describing complex visual nuances. Although reference-guided editing offers a robust solution, its potential is currently bottlenecked by the scarcity of high-quality paired training data. To bridge this gap, we introduce a scalable data generation pipeline that transforms existing video editing pairs into high-fidelity training quadruplets, leveraging image generative models to create synthesized reference scaffolds. Using this pipeline, we construct RefVIE, a large-scale dataset tailored for instruction-reference-following tasks, and establish RefVIE-Bench for comprehensive evaluation. Furthermore, we propose a unified editing architecture, Kiwi-Edit, that synergizes learnable queries and latent visual features for reference semantic guidance. Our model achieves significant gains in instruction following and reference fidelity via a progressive multi-stage training curriculum. Extensive experiments demonstrate that our data and architecture establish a new state-of-the-art in controllable video editing. All datasets, models, and code is released at https://github.com/showlab/Kiwi-Edit.", "github_manual": "", "github_regex": "https://github.com/showlab/Kiwi-Edit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2602.23496", "title": "SGDC: Structurally-Guided Dynamic Convolution for Medical Image Segmentation", "authors": [ "Bo Shi", "Wei-ping Zhu", "M. N. S. Swamy" ], "github": "https://github.com/solstice0621/SGDC", "project_page": "", "abstract": "Spatially variant dynamic convolution provides a principled approach of integrating spatial adaptivity into deep neural networks. However, mainstream designs in medical segmentation commonly generate dynamic kernels through average pooling, which implicitly collapses high-frequency spatial details into a coarse, spatially-compressed representation, leading to over-smoothed predictions that degrade the fidelity of fine-grained clinical structures. To address this limitation, we propose a novel Structure-Guided Dynamic Convolution (SGDC) mechanism, which leverages an explicitly supervised structure-extraction branch to guide the generation of dynamic kernels and gating signals for structure-aware feature modulation. Specifically, the high-fidelity boundary information from this auxiliary branch is fused with semantic features to enable spatially-precise feature modulation. By replacing context aggregation with pixel-wise structural guidance, the proposed design effectively prevents the information loss introduced by average pooling. Experimental results show that SGDC achieves state-of-the-art performance on ISIC 2016, PH2, ISIC 2018, and CoNIC datasets, delivering superior boundary fidelity by reducing the Hausdorff Distance (HD95) by 2.05, and providing consistent IoU gains of 0.99\\%-1.49\\% over pooling-based baselines. Moreover, the mechanism exhibits strong potential for extension to other fine-grained, structure-sensitive vision tasks, such as small-object detection, offering a principled solution for preserving structural integrity in medical image analysis. To facilitate reproducibility and encourage further research, the implementation code for both our SGE and SGDC modules has been is publicly released at https://github.com/solstice0621/SGDC.", "github_manual": "", "github_regex": "https://github.com/solstice0621/SGDC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.02667", "title": "DREAM: Where Visual Understanding Meets Text-to-Image Generation", "authors": [ "Chao Li", "Tianhong Li", "Sai Vidyaranya Nuthalapati", "Hong-You Chen", "Satya Narayan Shukla", "Yonghuan Yang", "Jun Xiao", "Xiangjun Fan", "Aashu Singh", "Dina Katabi", "Shlok Kumar Mishra" ], "github": "", "project_page": "", "abstract": "Unifying visual representation learning and text-to-image (T2I) generation within a single model remains a central challenge in multimodal learning. We introduce DREAM, a unified framework that jointly optimizes discriminative and generative objectives, while learning strong visual representations. DREAM is built on two key techniques: During training, Masking Warmup, a progressive masking schedule, begins with minimal masking to establish the contrastive alignment necessary for representation learning, then gradually transitions to full masking for stable generative training. At inference, DREAM employs Semantically Aligned Decoding to align partially masked image candidates with the target text and select the best one for further decoding, improving text-image fidelity (+6.3%) without external rerankers. Trained solely on CC12M, DREAM achieves 72.7% ImageNet linear-probing accuracy (+1.1% over CLIP) and an FID of 4.25 (+6.2% over FLUID), with consistent gains in few-shot classification, semantic segmentation, and depth estimation. These results demonstrate that discriminative and generative objectives can be synergistic, allowing unified multimodal models that excel at both visual understanding and generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.02479", "title": "PRISM: Pushing the Frontier of Deep Think via Process Reward Model-Guided Inference", "authors": [ "Rituraj Sharma", "Weiyuan Chen", "Noah Provenzano", "Tu Vu" ], "github": "", "project_page": "", "abstract": "DEEPTHINK methods improve reasoning by generating, refining, and aggregating populations of candidate solutions, which enables strong performance on complex mathematical and scientific tasks. However, existing frameworks often lack reliable correctness signals during inference, which creates a population-enhancement bottleneck where deeper deliberation amplifies errors, suppresses correct minority solutions, and yields weak returns to additional compute. In this paper, we introduce a functional decomposition of DEEPTHINK systems and propose PRISM, a Process Reward Model (PRM)-guided inference algorithm that uses step-level verification to guide both population refinement and solution aggregation. During refinement, PRISM treats candidate solutions as particles in a PRM-defined energy landscape and reshapes the population through score-guided resampling and stochastic refinement, which concentrates probability mass on higher-quality reasoning while preserving diversity. Across mathematics and science benchmarks, PRISM is competitive with or outperforms existing DEEPTHINK methods, reaching 90.0%, 75.4%, and 71.4% with gpt-oss-20b on AIME25, HMMT25, and GPQA Diamond, respectively, while matching or exceeding gpt-oss-120b. Additionally, our analysis shows that PRISM produces consistent net-directional correction during refinement, remains reliable when the initial population contains few correct candidates, and often lies on the compute-accuracy Pareto frontier.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.03241", "title": "UniG2U-Bench: Do Unified Models Advance Multimodal Understanding?", "authors": [ "Zimo Wen", "Boxiu Li", "Wanbo Zhang", "Junxiang Lei", "Xiaoyu Chen", "Yijia Fan", "Qi Zhang", "Yujiang Wang", "Lili Qiu", "Bo Li", "Ziwei Liu", "Caihua Shan", "Yifan Yang", "Yifei Shen" ], "github": "", "project_page": "", "abstract": "Unified multimodal models have recently demonstrated strong generative capabilities, yet whether and when generation improves understanding remains unclear. Existing benchmarks lack a systematic exploration of the specific tasks where generation facilitates understanding. To this end, we introduce UniG2U-Bench, a comprehensive benchmark categorizing generation-to-understanding (G2U) evaluation into 7 regimes and 30 subtasks, requiring varying degrees of implicit or explicit visual transformations. Extensive evaluation of over 30 models reveals three core findings: 1) Unified models generally underperform their base Vision-Language Models (VLMs), and Generate-then-Answer (GtA) inference typically degrades performance relative to direct inference. 2) Consistent enhancements emerge in spatial intelligence, visual illusions, or multi-round reasoning subtasks, where enhanced spatial and shape perception, as well as multi-step intermediate image states, prove beneficial. 3) Tasks with similar reasoning structures and models sharing architectures exhibit correlated behaviors, suggesting that generation-understanding coupling induces class-consistent inductive biases over tasks, pretraining data, and model architectures. These findings highlight the necessity for more diverse training data and novel paradigms to fully unlock the potential of unified multimodal modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.03276", "title": "Beyond Language Modeling: An Exploration of Multimodal Pretraining", "authors": [ "Shengbang Tong", "David Fan", "John Nguyen", "Ellis Brown", "Gaoyue Zhou", "Shengyi Qian", "Boyang Zheng", "Th\u00e9ophane Vallaeys", "Junlin Han", "Rob Fergus", "Naila Murray", "Marjan Ghazvininejad", "Mike Lewis", "Nicolas Ballas", "Amir Bar", "Michael Rabbat", "Jakob Verbeek", "Luke Zettlemoyer", "Koustuv Sinha", "Yann LeCun", "Saining Xie" ], "github": "", "project_page": "", "abstract": "The visual world offers a critical axis for advancing foundation models beyond language. Despite growing interest in this direction, the design space for native multimodal models remains opaque. We provide empirical clarity through controlled, from-scratch pretraining experiments, isolating the factors that govern multimodal pretraining without interference from language pretraining. We adopt the Transfusion framework, using next-token prediction for language and diffusion for vision, to train on diverse data including text, video, image-text pairs, and even action-conditioned video. Our experiments yield four key insights: (i) Representation Autoencoder (RAE) provides an optimal unified visual representation by excelling at both visual understanding and generation; (ii) visual and language data are complementary and yield synergy for downstream capabilities; (iii) unified multimodal pretraining leads naturally to world modeling, with capabilities emerging from general training; and (iv) Mixture-of-Experts (MoE) enables efficient and effective multimodal scaling while naturally inducing modality specialization. Through IsoFLOP analysis, we compute scaling laws for both modalities and uncover a scaling asymmetry: vision is significantly more data-hungry than language. We demonstrate that the MoE architecture harmonizes this scaling asymmetry by providing the high model capacity required by language while accommodating the data-intensive nature of vision, paving the way for truly unified multimodal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.03194", "title": "BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?", "authors": [ "Guoxin Chen", "Fanzhe Meng", "Jiale Zhao", "Minghao Li", "Daixuan Cheng", "Huatong Song", "Jie Chen", "Yuzhi Lin", "Hui Chen", "Xin Zhao", "Ruihua Song", "Chang Liu", "Cheng Chen", "Kai Jia", "Ji-Rong Wen" ], "github": "", "project_page": "", "abstract": "Current benchmarks for code agents primarily assess narrow, repository-specific fixes, overlooking critical real-world challenges such as cross-repository reasoning, domain-specialized problem solving, dependency-driven migration, and full-repository generation. To address this gap, we introduce BeyondSWE, a comprehensive benchmark that broadens existing evaluations along two axes - resolution scope and knowledge scope - using 500 real-world instances across four distinct settings. Experimental results reveal a significant capability gap: even frontier models plateau below 45% success, and no single model performs consistently across task types. To systematically investigate the role of external knowledge, we develop SearchSWE, a framework that integrates deep search with coding abilities. Our experiments show that search augmentation yields inconsistent gains and can in some cases degrade performance, highlighting the difficulty of emulating developer-like workflows that interleave search and reasoning during coding tasks. This work offers both a realistic, challenging evaluation benchmark and a flexible framework to advance research toward more capable code agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.02573", "title": "Track4World: Feedforward World-centric Dense 3D Tracking of All Pixels", "authors": [ "Jiahao Lu", "Jiayi Xu", "Wenbo Hu", "Ruijie Zhu", "Chengfeng Zhao", "Sai-Kit Yeung", "Ying Shan", "Yuan Liu" ], "github": "", "project_page": "", "abstract": "Estimating the 3D trajectory of every pixel from a monocular video is crucial and promising for a comprehensive understanding of the 3D dynamics of videos. Recent monocular 3D tracking works demonstrate impressive performance, but are limited to either tracking sparse points on the first frame or a slow optimization-based framework for dense tracking. In this paper, we propose a feedforward model, called Track4World, enabling an efficient holistic 3D tracking of every pixel in the world-centric coordinate system. Built on the global 3D scene representation encoded by a VGGT-style ViT, Track4World applies a novel 3D correlation scheme to simultaneously estimate the pixel-wise 2D and 3D dense flow between arbitrary frame pairs. The estimated scene flow, along with the reconstructed 3D geometry, enables subsequent efficient 3D tracking of every pixel of this video. Extensive experiments on multiple benchmarks demonstrate that our approach consistently outperforms existing methods in 2D/3D flow estimation and 3D tracking, highlighting its robustness and scalability for real-world 4D reconstruction tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.03142", "title": "APRES: An Agentic Paper Revision and Evaluation System", "authors": [ "Bingchen Zhao", "Jenny Zhang", "Chenxi Whitehouse", "Minqi Jiang", "Michael Shvartsman", "Abhishek Charnalia", "Despoina Magka", "Tatiana Shavrina", "Derek Dunfield", "Oisin Mac Aodha", "Yoram Bachrach" ], "github": "", "project_page": "", "abstract": "Scientific discoveries must be communicated clearly to realize their full potential. Without effective communication, even the most groundbreaking findings risk being overlooked or misunderstood. The primary way scientists communicate their work and receive feedback from the community is through peer review. However, the current system often provides inconsistent feedback between reviewers, ultimately hindering the improvement of a manuscript and limiting its potential impact. In this paper, we introduce a novel method APRES powered by Large Language Models (LLMs) to update a scientific papers text based on an evaluation rubric. Our automated method discovers a rubric that is highly predictive of future citation counts, and integrate it with APRES in an automated system that revises papers to enhance their quality and impact. Crucially, this objective should be met without altering the core scientific content. We demonstrate the success of APRES, which improves future citation prediction by 19.6% in mean averaged error over the next best baseline, and show that our paper revision process yields papers that are preferred over the originals by human expert evaluators 79% of the time. Our findings provide strong empirical support for using LLMs as a tool to help authors stress-test their manuscripts before submission. Ultimately, our work seeks to augment, not replace, the essential role of human expert reviewers, for it should be humans who discern which discoveries truly matter, guiding science toward advancing knowledge and enriching lives.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.03202", "title": "Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration?", "authors": [ "Dadi Guo", "Yuejin Xie", "Qingyu Liu", "Jiayu Liu", "Zhiyuan Fan", "Qihan Ren", "Shuai Shao", "Tianyi Zhou", "Dongrui Liu", "Yi R. Fung" ], "github": "https://github.com/TarferSoul/Code2Math", "project_page": "", "abstract": "As large language models (LLMs) advance their mathematical capabilities toward the IMO level, the scarcity of challenging, high-quality problems for training and evaluation has become a significant bottleneck. Simultaneously, recent code agents have demonstrated sophisticated skills in agentic coding and reasoning, suggesting that code execution can serve as a scalable environment for mathematical experimentation. In this paper, we investigate the potential of code agents to autonomously evolve existing math problems into more complex variations. We introduce a multi-agent framework designed to perform problem evolution while validating the solvability and increased difficulty of the generated problems. Our experiments demonstrate that, given sufficient test-time exploration, code agents can synthesize new, solvable problems that are structurally distinct from and more challenging than the originals. This work provides empirical evidence that code-driven agents can serve as a viable mechanism for synthesizing high-difficulty mathematical reasoning problems within scalable computational environments. Our data is available at https://github.com/TarferSoul/Code2Math.", "github_manual": "", "github_regex": "https://github.com/TarferSoul/Code2Math", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.01400", "title": "Token Reduction via Local and Global Contexts Optimization for Efficient Video Large Language Models", "authors": [ "Jinlong Li", "Liyuan Jiang", "Haonan Zhang", "Nicu Sebe" ], "github": "https://github.com/TyroneLi/AOT", "project_page": "", "abstract": "Video Large Language Models (VLLMs) demonstrate strong video understanding but suffer from inefficiency due to redundant visual tokens. Existing pruning primary targets intra-frame spatial redundancy or prunes inside the LLM with shallow-layer overhead, yielding suboptimal spatiotemporal reduction and underutilizing long-context compressibility. All of them often discard subtle yet informative context from merged or pruned tokens. In this paper, we propose a new perspective that elaborates token Anchors within intra-frame and inter-frame to comprehensively aggregate the informative contexts via local-global Optimal Transport (AOT). Specifically, we first establish local- and global-aware token anchors within each frame under the attention guidance, which then optimal transport aggregates the informative contexts from pruned tokens, constructing intra-frame token anchors. Then, building on the temporal frame clips, the first frame within each clip will be considered as the keyframe anchors to ensemble similar information from consecutive frames through optimal transport, while keeping distinct tokens to represent temporal dynamics, leading to efficient token reduction in a training-free manner. Extensive evaluations show that our proposed AOT obtains competitive performances across various short- and long-video benchmarks on leading video LLMs, obtaining substantial computational efficiency while preserving temporal and visual fidelity. Project webpage: https://tyroneli.github.io/AOT{AOT}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TyroneLi/AOT", "github_api_added_by": "auto" }, { "date": "2026-03-04", "arxiv_id": "2603.02398", "title": "Fast Matrix Multiplication in Small Formats: Discovering New Schemes with an Open-Source Flip Graph Framework", "authors": [ "A. I. Perminov" ], "github": "https://github.com/dronperminov/ternary_flip_graph", "project_page": "", "abstract": "An open-source C++ framework for discovering fast matrix multiplication schemes using the flip graph approach is presented. The framework supports multiple coefficient rings -- binary (Z_2), modular ternary (Z_3) and integer ternary (Z_T = {-1,0,1}) -- and implements both fixed-dimension and meta-dimensional search operators. Using efficient bit-level encoding of coefficient vectors and OpenMP parallelism, the tools enable large-scale exploration on commodity hardware. The study covers 680 schemes ranging from (2 times 2 times 2) to (16 times 16 times 16), with 276 schemes now in Z_T coefficients and 117 in integer coefficients. With this framework, the multiplicative complexity (rank) is improved for 79 matrix multiplication schemes. Notably, a new 4 times 4 times 10 scheme requiring only 115 multiplications is discovered, achieving \u03c9approx 2.80478 and beating Strassen's exponent for this specific size. Additionally, 93 schemes are rediscovered in ternary coefficients that were previously known only over rationals or integers, and 68 schemes in integer coefficients that previously required fractions. All tools and discovered schemes are made publicly available to enable reproducible research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dronperminov/ternary_flip_graph", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2602.17100", "title": "AgentConductor: Topology Evolution for Multi-Agent Competition-Level Code Generation", "authors": [ "Siyu Wang", "Ruotian Lu", "Zhihao Yang", "Yuchao Wang", "Yanzhou Zhang", "Lei Xu", "Qimin Xu", "Guojun Yin", "Cailian Chen", "Xinping Guan" ], "github": "", "project_page": "", "abstract": "Large language model(LLM)-driven multi-agent systems(MAS) coordinate specialized agents through predefined interaction topologies and have shown promise for complex tasks such as competition-level code generation. Recent studies demonstrate that carefully designed multi-agent workflows and communication graphs can significantly improve code generation performance by leveraging collaborative reasoning. However, existing methods neither adapt topology density to task difficulty nor iteratively refine the topology within an instance using execution feedback, which leads to redundant communication and performance bottlenecks. To address these issues, we propose AgentConductor: a reinforcement learning-optimized MAS with an LLM-based orchestrator agent as its core, which enables end-to-end feedback-driven dynamic generation of interaction topologies. For each query, AgentConductor infers agent roles and task difficulty, then constructs a task-adapted, density-aware layered directed acyclic graph (DAG) topology, underpinned by two key innovations. First, we design a novel topological density function that captures communication-aware mathematical characterizations of multi-agent interactions. Second, we adopt difficulty interval partitioning to avoid excessive pruning for precise topological density upper bound measurement per difficulty level and finer-grained control. Empirically, across three competition-level and two foundational code datasets, AgentConductor achieves state-of-the-art accuracy, outperforming the strongest baseline by up to 14.6% in pass@1 accuracy, 13% in density reduction, and 68% in token cost reduction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.03283", "title": "Utonia: Toward One Encoder for All Point Clouds", "authors": [ "Yujia Zhang", "Xiaoyang Wu", "Yunhan Yang", "Xianzhe Fan", "Han Li", "Yuechen Zhang", "Zehao Huang", "Naiyan Wang", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "We dream of a future where point clouds from all domains can come together to shape a single model that benefits them all. Toward this goal, we present Utonia, a first step toward training a single self-supervised point transformer encoder across diverse domains, spanning remote sensing, outdoor LiDAR, indoor RGB-D sequences, object-centric CAD models, and point clouds lifted from RGB-only videos. Despite their distinct sensing geometries, densities, and priors, Utonia learns a consistent representation space that transfers across domains. This unification improves perception capability while revealing intriguing emergent behaviors that arise only when domains are trained jointly. Beyond perception, we observe that Utonia representations can also benefit embodied and multimodal reasoning: conditioning vision-language-action policies on Utonia features improves robotic manipulation, and integrating them into vision-language models yields gains on spatial reasoning. We hope Utonia can serve as a step toward foundation models for sparse 3D data, and support downstream applications in AR/VR, robotics, and autonomous driving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.00729", "title": "Qwen3-Coder-Next Technical Report", "authors": [ "Ruisheng Cao", "Mouxiang Chen", "Jiawei Chen", "Zeyu Cui", "Yunlong Feng", "Binyuan Hui", "Yuheng Jing", "Kaixin Li", "Mingze Li", "Junyang Lin", "Zeyao Ma", "Kashun Shum", "Xuwu Wang", "Jinxi Wei", "Jiaxi Yang", "Jiajun Zhang", "Lei Zhang", "Zongmeng Zhang", "Wenting Zhao", "Fan Zhou" ], "github": "", "project_page": "", "abstract": "We present Qwen3-Coder-Next, an open-weight language model specialized for coding agents. Qwen3-Coder-Next is an 80-billion-parameter model that activates only 3 billion parameters during inference, enabling strong coding capability with efficient inference. In this work, we explore how far strong training recipes can push the capability limits of models with small parameter footprints. To achieve this, we perform agentic training through large-scale synthesis of verifiable coding tasks paired with executable environments, allowing learning directly from environment feedback via mid-training and reinforcement learning. Across agent-centric benchmarks including SWE-Bench and Terminal-Bench, Qwen3-Coder-Next achieves competitive performance relative to its active parameter count. We release both base and instruction-tuned open-weight versions to support research and real-world coding agent development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.00656", "title": "InfoPO: Information-Driven Policy Optimization for User-Centric Agents", "authors": [ "Fanqi Kong", "Jiayi Zhang", "Mingyi Deng", "Chenglin Wu", "Yuyu Luo", "Bang Liu" ], "github": "https://github.com/kfq20/InfoPO", "project_page": "", "abstract": "Real-world user requests to LLM agents are often underspecified. Agents must interact to acquire missing information and make correct downstream decisions. However, current multi-turn GRPO-based methods often rely on trajectory-level reward computation, which leads to credit assignment problems and insufficient advantage signals within rollout groups. A feasible approach is to identify valuable interaction turns at a fine granularity to drive more targeted learning. To address this, we introduce InfoPO (Information-Driven Policy Optimization), which frames multi-turn interaction as a process of active uncertainty reduction and computes an information-gain reward that credits turns whose feedback measurably changes the agent's subsequent action distribution compared to a masked-feedback counterfactual. It then combines this signal with task outcomes via an adaptive variance-gated fusion to identify information importance while maintaining task-oriented goal direction. Across diverse tasks, including intent clarification, collaborative coding, and tool-augmented decision making, InfoPO consistently outperforms prompting and multi-turn RL baselines. It also demonstrates robustness under user simulator shifts and generalizes effectively to environment-interactive tasks. Overall, InfoPO provides a principled and scalable mechanism for optimizing complex agent-user collaboration. Code is available at https://github.com/kfq20/InfoPO.", "github_manual": "", "github_regex": "https://github.com/kfq20/InfoPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.03195", "title": "Chain of World: World Model Thinking in Latent Motion", "authors": [ "Fuxiang Yang", "Donglin Di", "Lulu Tang", "Xuancheng Zhang", "Lei Fan", "Hao Li", "Chen Wei", "Tonghua Su", "Baorui Ma" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models are a promising path toward embodied intelligence, yet they often overlook the predictive and temporal-causal structure underlying visual dynamics. World-model VLAs address this by predicting future frames, but waste capacity reconstructing redundant backgrounds. Latent-action VLAs encode frame-to-frame transitions compactly, but lack temporally continuous dynamic modeling and world knowledge. To overcome these limitations, we introduce CoWVLA (Chain-of-World VLA), a new \"Chain of World\" paradigm that unifies world-model temporal reasoning with a disentangled latent motion representation. First, a pretrained video VAE serves as a latent motion extractor, explicitly factorizing video segments into structure and motion latents. Then, during pre-training, the VLA learns from an instruction and an initial frame to infer a continuous latent motion chain and predict the segment's terminal frame. Finally, during co-fine-tuning, this latent dynamic is aligned with discrete action prediction by jointly modeling sparse keyframes and action sequences in a unified autoregressive decoder. This design preserves the world-model benefits of temporal reasoning and world knowledge while retaining the compactness and interpretability of latent actions, enabling efficient visuomotor learning. Extensive experiments on robotic simulation benchmarks show that CoWVLA outperforms existing world-model and latent-action approaches and achieves moderate computational efficiency, highlighting its potential as a more effective VLA pretraining paradigm. The project website can be found at https://fx-hit.github.io/cowvla-io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.02252", "title": "Whisper-RIR-Mega: A Paired Clean-Reverberant Speech Benchmark for ASR Robustness to Room Acoustics", "authors": [ "Mandip Goswami" ], "github": "", "project_page": "", "abstract": "We introduce Whisper-RIR-Mega, a benchmark dataset of paired clean and reverberant speech for evaluating automatic speech recognition (ASR) robustness to room acoustics. Each sample pairs a clean LibriSpeech utterance with the same utterance convolved with a real room impulse response from the RIR-Mega corpus, with stratified splits by reverberation time (RT60) and direct-to-reverberant ratio (DRR). We evaluate five Whisper models (tiny through large-v3) on 1600 test samples and report word error rate (WER) and character error rate (CER) under clean and reverberant conditions. Reverberation consistently degrades performance across all model sizes; the reverb penalty in WER ranges from 0.12 to 1.07 percentage points depending on the model. We release the dataset, evaluation code, and baseline results to support reproducible research on robust ASR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.01683", "title": "Surgical Post-Training: Cutting Errors, Keeping Knowledge", "authors": [ "Wenye Lin", "Kai Han" ], "github": "https://github.com/Visual-AI/SPoT", "project_page": "", "abstract": "Enhancing the reasoning capabilities of Large Language Models (LLMs) via post-training is often constrained by the trade-off between efficiency and catastrophic forgetting. While prior research emphasizes the role of on-policy data in mitigating forgetting, we uncover--and validate both theoretically and empirically--an overlooked yet critical mechanism: the implicit regularization inherent in Direct Preference Optimization's (DPO) reward estimate. This motivates our Surgical Post-Training (SPoT), a new paradigm designed to optimize reasoning efficiently while preserving learned prior knowledge. SPoT consists of: (1) a data rectification pipeline that employs an Oracle to surgically correct erroneous steps via minimal edits, generating data proximal to the model's distribution; and (2) a reward-based binary cross-entropy objective. Unlike the relative ranking in DPO, this objective treats reasoning correctness as a binary classification problem, enforcing decoupled supervision signals. Empirically, with only 4k rectified math data pairs, SPoT improves Qwen3-8B's accuracy by 6.2% on average across in-domain and OOD tasks, requiring merely 28 minutes of training on 8x H800 GPUs. Code: https://github.com/Visual-AI/SPoT", "github_manual": "", "github_regex": "https://github.com/Visual-AI/SPoT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.03205", "title": "Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use", "authors": [ "Aradhye Agarwal", "Gurdit Siyan", "Yash Pandya", "Joykirat Singh", "Akshay Nambi", "Ahmed Awadallah" ], "github": "", "project_page": "", "abstract": "Agentic language models operate in a fundamentally different safety regime than chat models: they must plan, call tools, and execute long-horizon actions where a single misstep, such as accessing files or entering credentials, can cause irreversible harm. Existing alignment methods, largely optimized for static generation and task completion, break down in these settings due to sequential decision-making, adversarial tool feedback, and overconfident intermediate reasoning. We introduce MOSAIC, a post-training framework that aligns agents for safe multi-step tool use by making safety decisions explicit and learnable. MOSAIC structures inference as a plan, check, then act or refuse loop, with explicit safety reasoning and refusal as first-class actions. To train without trajectory-level labels, we use preference-based reinforcement learning with pairwise trajectory comparisons, which captures safety distinctions often missed by scalar rewards. We evaluate MOSAIC zero-shot across three model families, Qwen2.5-7B, Qwen3-4B-Thinking, and Phi-4, and across out-of-distribution benchmarks spanning harmful tasks, prompt injection, benign tool use, and cross-domain privacy leakage. MOSAIC reduces harmful behavior by up to 50%, increases harmful-task refusal by over 20% on injection attacks, cuts privacy leakage, and preserves or improves benign task performance, demonstrating robust generalization across models, domains, and agentic settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2602.18671", "title": "Spilled Energy in Large Language Models", "authors": [ "Adrian Robert Minut", "Hazem Dewidar", "Iacopo Masi" ], "github": "https://github.com/OmnAI-Lab/spilled-energy", "project_page": "", "abstract": "We reinterpret the final Large Language Model (LLM) softmax classifier as an Energy-Based Model (EBM), decomposing the sequence-to-sequence probability chain into multiple interacting EBMs at inference. This principled approach allows us to track \"energy spills\" during decoding, which we empirically show correlate with factual errors, biases, and failures. Similar to Orgad et al. (2025), our method localizes the exact answer token and subsequently tests for hallucinations. Crucially, however, we achieve this without requiring trained probe classifiers or activation ablations. Instead, we introduce two completely training-free metrics derived directly from output logits: spilled energy, which captures the discrepancy between energy values across consecutive generation steps that should theoretically match, and marginalized energy, which is measurable at a single step. Evaluated on nine benchmarks across state-of-the-art LLMs (including LLaMA, Mistral, and Gemma) and on synthetic algebraic operations (Qwen3), our approach demonstrates robust, competitive hallucination detection and cross-task generalization. Notably, these results hold for both pretrained and instruction-tuned variants without introducing any training overhead. Code available at: github.com/OmnAI-Lab/spilled-energy", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OmnAI-Lab/spilled-energy", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2602.22752", "title": "Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction", "authors": [ "Nils Schwager", "Simon M\u00fcnker", "Alistair Plum", "Achim Rettinger" ], "github": "https://github.com/nsschw/Conditioned-Comment-Prediction", "project_page": "", "abstract": "The transition of Large Language Models (LLMs) from exploratory tools to active \"silicon subjects\" in social science lacks extensive validation of operational validity. This study introduces Conditioned Comment Prediction (CCP), a task in which a model predicts how a user would comment on a given stimulus by comparing generated outputs with authentic digital traces. This framework enables a rigorous evaluation of current LLM capabilities with respect to the simulation of social media user behavior. We evaluated open-weight 8B models (Llama3.1, Qwen3, Ministral) in English, German, and Luxembourgish language scenarios. By systematically comparing prompting strategies (explicit vs. implicit) and the impact of Supervised Fine-Tuning (SFT), we identify a critical form vs. content decoupling in low-resource settings: while SFT aligns the surface structure of the text output (length and syntax), it degrades semantic grounding. Furthermore, we demonstrate that explicit conditioning (generated biographies) becomes redundant under fine-tuning, as models successfully perform latent inference directly from behavioral histories. Our findings challenge current \"naive prompting\" paradigms and offer operational guidelines prioritizing authentic behavioral traces over descriptive personas for high-fidelity simulation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nsschw/Conditioned-Comment-Prediction", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.01655", "title": "Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling", "authors": [ "J\u00e9rome Eertmans", "Enrico M. Vitucci", "Vittorio Degli-Esposti", "Nicola Di Cicco", "Laurent Jacques", "Claude Oestges" ], "github": "https://github.com/jeertmans/sampling-paths", "project_page": "", "abstract": "Ray tracing has become a standard for accurate radio propagation modeling, but suffers from exponential computational complexity, as the number of candidate paths scales with the number of objects raised to the power of the interaction order. This bottleneck limits its use in large-scale or real-time applications, forcing traditional tools to rely on heuristics to reduce the number of path candidates at the cost of potentially reduced accuracy. To overcome this limitation, we propose a comprehensive machine-learning-assisted framework that replaces exhaustive path searching with intelligent sampling via Generative Flow Networks. Applying such generative models to this domain presents significant challenges, particularly sparse rewards due to the rarity of valid paths, which can lead to convergence failures and trivial solutions when evaluating high-order interactions in complex environments. To ensure robust learning and efficient exploration, our framework incorporates three key architectural components. First, we implement an experience replay buffer to capture and retain rare valid paths. Second, we adopt a uniform exploratory policy to improve generalization and prevent the model from overfitting to simple geometries. Third, we apply a physics-based action masking strategy that filters out physically impossible paths before the model even considers them. As demonstrated in our experimental validation, the proposed model achieves substantial speedups over exhaustive search -- up to 10times faster on GPU and 1000times faster on CPU -- while maintaining high coverage accuracy and successfully uncovering complex propagation paths. The complete source code, tests, and tutorial are available at https://github.com/jeertmans/sampling-paths.", "github_manual": "", "github_regex": "https://github.com/jeertmans/sampling-paths", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.03163", "title": "Conditioned Activation Transport for T2I Safety Steering", "authors": [ "Maciej Chrab\u0105szcz", "Aleksander Szymczyk", "Jan Dubi\u0144ski", "Tomasz Trzci\u0144ski", "Franziska Boenisch", "Adam Dziedzic" ], "github": "https://github.com/NASK-AISafety/conditional-activation-transport", "project_page": "", "abstract": "Despite their impressive capabilities, current Text-to-Image (T2I) models remain prone to generating unsafe and toxic content. While activation steering offers a promising inference-time intervention, we observe that linear activation steering frequently degrades image quality when applied to benign prompts. To address this trade-off, we first construct SafeSteerDataset, a contrastive dataset containing 2300 safe and unsafe prompt pairs with high cosine similarity. Leveraging this data, we propose Conditioned Activation Transport (CAT), a framework that employs a geometry-based conditioning mechanism and nonlinear transport maps. By conditioning transport maps to activate only within unsafe activation regions, we minimize interference with benign queries. We validate our approach on two state-of-the-art architectures: Z-Image and Infinity. Experiments demonstrate that CAT generalizes effectively across these backbones, significantly reducing Attack Success Rate while maintaining image fidelity compared to unsteered generations. Warning: This paper contains potentially offensive text and images.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NASK-AISafety/conditional-activation-transport", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.00618", "title": "Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models", "authors": [ "Li Sun", "Zhenhao Huang", "Silei Chen", "Lanxu Yang", "Junda Ye", "Sen Su", "Philip S. Yu" ], "github": "https://github.com/RiemannGraph/GraphGlue", "project_page": "", "abstract": "Multi-domain graph pre-training integrates knowledge from diverse domains to enhance performance in the target domains, which is crucial for building graph foundation models. Despite initial success, existing solutions often fall short of answering a fundamental question: how is knowledge integrated or transferred across domains? This theoretical limitation motivates us to rethink the consistency and transferability between model pre-training and domain adaptation. In this paper, we propose a fresh Riemannian geometry perspective, whose core idea is to merge any graph dataset into a unified, smooth Riemannian manifold, enabling a systematic understanding of knowledge integration and transfer. To achieve this, our key contribution is the theoretical establishment of neural manifold gluing, which first characterizes local geometry using an adaptive orthogonal frame and then \"glues\" the local pieces together into a coherent whole. Building on this theory, we present the GraphGlue framework, which supports batched pre-training with EMA prototyping and provides a transferability measure based on geometric consistence. Extensive experiments demonstrate its superior performance across diverse graph domains. Moreover, we empirically validated GraphGlue's geometric scaling law, showing that larger quantities of datasets improve model transferability by producing a smoother manifold. Codes are available at https://github.com/RiemannGraph/GraphGlue.", "github_manual": "", "github_regex": "https://github.com/RiemannGraph/GraphGlue", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.03281", "title": "CFG-Ctrl: Control-Based Classifier-Free Diffusion Guidance", "authors": [ "Hanyang Wang", "Yiyang Liu", "Jiawei Chi", "Fangfu Liu", "Ran Xue", "Yueqi Duan" ], "github": "", "project_page": "", "abstract": "Classifier-Free Guidance (CFG) has emerged as a central approach for enhancing semantic alignment in flow-based diffusion models. In this paper, we explore a unified framework called CFG-Ctrl, which reinterprets CFG as a control applied to the first-order continuous-time generative flow, using the conditional-unconditional discrepancy as an error signal to adjust the velocity field. From this perspective, we summarize vanilla CFG as a proportional controller (P-control) with fixed gain, and typical follow-up variants develop extended control-law designs derived from it. However, existing methods mainly rely on linear control, inherently leading to instability, overshooting, and degraded semantic fidelity especially on large guidance scales. To address this, we introduce Sliding Mode Control CFG (SMC-CFG), which enforces the generative flow toward a rapidly convergent sliding manifold. Specifically, we define an exponential sliding mode surface over the semantic prediction error and introduce a switching control term to establish nonlinear feedback-guided correction. Moreover, we provide a Lyapunov stability analysis to theoretically support finite-time convergence. Experiments across text-to-image generation models including Stable Diffusion 3.5, Flux, and Qwen-Image demonstrate that SMC-CFG outperforms standard CFG in semantic alignment and enhances robustness across a wide range of guidance scales. Project Page: https://hanyang-21.github.io/CFG-Ctrl", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.02765", "title": "Next Embedding Prediction Makes World Models Stronger", "authors": [ "George Bredis", "Nikita Balagansky", "Daniil Gavrilov", "Ruslan Rakhimov" ], "github": "", "project_page": "", "abstract": "Capturing temporal dependencies is critical for model-based reinforcement learning (MBRL) in partially observable, high-dimensional domains. We introduce NE-Dreamer, a decoder-free MBRL agent that leverages a temporal transformer to predict next-step encoder embeddings from latent state sequences, directly optimizing temporal predictive alignment in representation space. This approach enables NE-Dreamer to learn coherent, predictive state representations without reconstruction losses or auxiliary supervision. On the DeepMind Control Suite, NE-Dreamer matches or exceeds the performance of DreamerV3 and leading decoder-free agents. On a challenging subset of DMLab tasks involving memory and spatial reasoning, NE-Dreamer achieves substantial gains. These results establish next-embedding prediction with temporal transformers as an effective, scalable framework for MBRL in complex, partially observable environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.01697", "title": "DynaMoE: Dynamic Token-Level Expert Activation with Layer-Wise Adaptive Capacity for Mixture-of-Experts Neural Networks", "authors": [ "G\u00f6kdeniz G\u00fclmez" ], "github": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) architectures have emerged as a powerful paradigm for scaling neural networks while maintaining computational efficiency. However, standard MoE implementations rely on two rigid design assumptions: (1) fixed Top-K routing where exactly K experts are activated per token, and (2) uniform expert allocation across all layers. This paper introduces DynaMoE, a novel MoE framework that relaxes both constraints through dynamic token-level expert activation and layer-wise adaptive capacity allocation. DynaMoE introduces a principled routing mechanism where the number of active experts per token varies based on input complexity. Concurrently, the framework implements six distinct scheduling strategies for distributing expert capacity across network depth, including descending, ascending, pyramid, and wave patterns. We theoretically analyze the expressivity gains of dynamic routing and derive bounds on computational efficiency. Through extensive experiments on MNIST, Fashion-MNIST, CIFAR-10 (image classification), and Recycling-the-Web (language modeling) across multiple model scales, we demonstrate that DynaMoE achieves superior parameter efficiency compared to static baselines. Our key finding is that optimal expert schedules are task- and scale-dependent: descending schedules (concentrating capacity in early layers) outperform uniform baselines on image classification. For language modeling, optimal schedules vary by model size, descending for Tiny, ascending for Small, and uniform for Medium. Furthermore, dynamic routing reduces gradient variance during training, leading to improved convergence stability. DynaMoE establishes a new framework for adaptive computation in neural networks, providing principled guidance for MoE architecture design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.01421", "title": "SciDER: Scientific Data-centric End-to-end Researcher", "authors": [ "Ke Lin", "Yilin Lu", "Shreyas Bhat", "Xuehang Guo", "Junier Oliva", "Qingyun Wang" ], "github": "https://github.com/leonardodalinky/SciDER", "project_page": "", "abstract": "Automated scientific discovery with large language models is transforming the research lifecycle from ideation to experimentation, yet existing agents struggle to autonomously process raw data collected from scientific experiments. We introduce SciDER, a data-centric end-to-end system that automates the research lifecycle. Unlike traditional frameworks, our specialized agents collaboratively parse and analyze raw scientific data, generate hypotheses and experimental designs grounded in specific data characteristics, and write and execute corresponding code. Evaluation on three benchmarks shows SciDER excels in specialized data-driven scientific discovery and outperforms general-purpose agents and state-of-the-art models through its self-evolving memory and critic-led feedback loop. Distributed as a modular Python package, we also provide easy-to-use PyPI packages with a lightweight web interface to accelerate autonomous, data-driven research and aim to be accessible to all researchers and developers.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/leonardodalinky/SciDER", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.01059", "title": "GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistant", "authors": [ "Zhuokang Shen", "Yifan Wang", "Hanyu Chen", "Wenxuan Huang", "Shaohui Lin" ], "github": "https://github.com/Eliot-Shen/GroupGPT", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have enabled increasingly capable chatbots. However, most existing systems focus on single-user settings and do not generalize well to multi-user group chats, where agents require more proactive and accurate intervention under complex, evolving contexts. Existing approaches typically rely on LLMs for both reasoning and generation, leading to high token consumption, limited scalability, and potential privacy risks. To address these challenges, we propose GroupGPT, a token-efficient and privacy-preserving agentic framework for multi-user chat assistant. GroupGPT adopts a small-large model collaborative architecture to decouple intervention timing from response generation, enabling efficient and accurate decision-making. The framework also supports multimodal inputs, including memes, images, videos, and voice messages. We further introduce MUIR, a benchmark dataset for multi-user chat assistant intervention reasoning. MUIR contains 2,500 annotated group chat segments with intervention labels and rationales, supporting evaluation of timing accuracy and response quality. We evaluate a range of models on MUIR, from large language models to smaller counterparts. Extensive experiments demonstrate that GroupGPT produces accurate and well-timed responses, achieving an average score of 4.72/5.0 in LLM-based evaluation, and is well received by users across diverse group chat scenarios. Moreover, GroupGPT reduces token usage by up to 3 times compared to baseline methods, while providing privacy sanitization of user messages before cloud transmission. Code is available at: https://github.com/Eliot-Shen/GroupGPT .", "github_manual": "", "github_regex": "https://github.com/Eliot-Shen/GroupGPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2602.20672", "title": "BBQ-to-Image: Numeric Bounding Box and Qolor Control in Large-Scale Text-to-Image Models", "authors": [ "Eliran Kachlon", "Alexander Visheratin", "Nimrod Sarid", "Tal Hacham", "Eyal Gutflaish", "Saar Huberman", "Hezi Zisman", "David Ruppin", "Ron Mokady" ], "github": "", "project_page": "", "abstract": "Text-to-image models have rapidly advanced in realism and controllability, with recent approaches leveraging long, detailed captions to support fine-grained generation. However, a fundamental parametric gap remains: existing models rely on descriptive language, whereas professional workflows require precise numeric control over object location, size, and color. In this work, we introduce BBQ, a large-scale text-to-image model that directly conditions on numeric bounding boxes and RGB triplets within a unified structured-text framework. We obtain precise spatial and chromatic control by training on captions enriched with parametric annotations, without architectural modifications or inference-time optimization. This also enables intuitive user interfaces such as object dragging and color pickers, replacing ambiguous iterative prompting with precise, familiar controls. Across comprehensive evaluations, BBQ achieves strong box alignment and improves RGB color fidelity over state-of-the-art baselines. More broadly, our results support a new paradigm in which user intent is translated into an intermediate structured language, consumed by a flow-based transformer acting as a renderer and naturally accommodating numeric parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2602.22600", "title": "Transformers converge to invariant algorithmic cores", "authors": [ "Joshua S. Schiffman" ], "github": "", "project_page": "", "abstract": "Large language models exhibit sophisticated capabilities, yet understanding how they work internally remains a central challenge. A fundamental obstacle is that training selects for behavior, not circuitry, so many weight configurations can implement the same function. Which internal structures reflect the computation, and which are accidents of a particular training run? This work extracts algorithmic cores: compact subspaces necessary and sufficient for task performance. Independently trained transformers learn different weights but converge to the same cores. Markov-chain transformers embed 3D cores in nearly orthogonal subspaces yet recover identical transition spectra. Modular-addition transformers discover compact cyclic operators at grokking that later inflate, yielding a predictive model of the memorization-to-generalization transition. GPT-2 language models govern subject-verb agreement through a single axis that, when flipped, inverts grammatical number throughout generation across scales. These results reveal low-dimensional invariants that persist across training runs and scales, suggesting that transformer computations are organized around compact, shared algorithmic structures. Mechanistic interpretability could benefit from targeting such invariants -- the computational essence -- rather than implementation-specific details.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2602.23546", "title": "Humans and LLMs Diverge on Probabilistic Inferences", "authors": [ "Gaurav Kamath", "Sreenath Madathil", "Sebastian Schuster", "Marie-Catherine de Marneffe", "Siva Reddy" ], "github": "", "project_page": "", "abstract": "Human reasoning often involves working over limited information to arrive at probabilistic conclusions. In its simplest form, this involves making an inference that is not strictly entailed by a premise, but rather only likely given the premise. While reasoning LLMs have demonstrated strong performance on logical and mathematical tasks, their behavior on such open-ended, non-deterministic inferences remains largely unexplored. We introduce ProbCOPA, a dataset of 210 handcrafted probabilistic inferences in English, each annotated for inference likelihood by 25--30 human participants. We find that human responses are graded and varied, revealing probabilistic judgments of the inferences in our dataset. Comparing these judgments with responses from eight state-of-the-art reasoning LLMs, we show that models consistently fail to produce human-like distributions. Finally, analyzing LLM reasoning chains, we find evidence of a common reasoning pattern used to evaluate such inferences. Our findings reveal persistent differences between humans and LLMs, and underscore the need to evaluate reasoning beyond deterministic settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2602.21773", "title": "Easy to Learn, Yet Hard to Forget: Towards Robust Unlearning Under Bias", "authors": [ "JuneHyoung Kwon", "MiHyeon Kim", "Eunju Lee", "Yoonji Lee", "Seunghoon Lee", "YoungBin Kim" ], "github": "", "project_page": "", "abstract": "Machine unlearning, which enables a model to forget specific data, is crucial for ensuring data privacy and model reliability. However, its effectiveness can be severely undermined in real-world scenarios where models learn unintended biases from spurious correlations within the data. This paper investigates the unique challenges of unlearning from such biased models. We identify a novel phenomenon we term ``shortcut unlearning,\" where models exhibit an ``easy to learn, yet hard to forget\" tendency. Specifically, models struggle to forget easily-learned, bias-aligned samples; instead of forgetting the class attribute, they unlearn the bias attribute, which can paradoxically improve accuracy on the class intended to be forgotten. To address this, we propose CUPID, a new unlearning framework inspired by the observation that samples with different biases exhibit distinct loss landscape sharpness. Our method first partitions the forget set into causal- and bias-approximated subsets based on sample sharpness, then disentangles model parameters into causal and bias pathways, and finally performs a targeted update by routing refined causal and bias gradients to their respective pathways. Extensive experiments on biased datasets including Waterbirds, BAR, and Biased NICO++ demonstrate that our method achieves state-of-the-art forgetting performance and effectively mitigates the shortcut unlearning problem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2602.20629", "title": "QEDBENCH: Quantifying the Alignment Gap in Automated Evaluation of University-Level Mathematical Proofs", "authors": [ "Santiago Gonzalez", "Alireza Amiri Bavandpour", "Peter Ye", "Edward Zhang", "Ruslans Aleksejevs", "Todor Anti\u0107", "Polina Baron", "Sujeet Bhalerao", "Shubhrajit Bhattacharya", "Zachary Burton", "John Byrne", "Hyungjun Choi", "Nujhat Ahmed Disha", "Koppany Istv\u00e1n Encz", "Yuchen Fang", "Robert Joseph George", "Ebrahim Ghorbani", "Alan Goldfarb", "Jing Guo", "Meghal Gupta", "Stefano Huber", "Annika Kanckos", "Minjung Kang", "Hyun Jong Kim", "Dino Lorenzini", "Levi Lorenzo", "Tianyi Mao", "Giovanni Marzenta", "Ariane M. Masuda", "Lukas Mauth", "Ana Mickovic", "Andres Miniguano-Trujillo", "Antoine Moulin", "Wenqi Ni", "Tomos Parry", "Kevin Ren", "Hossein Roodbarani", "Mathieu Rundstr\u00f6m", "Manjil Saikia", "Detchat Samart", "Rebecca Steiner", "Connor Stewart", "Dhara Thakkar", "Jeffrey Tse", "Vasiliki Velona", "Yunhai Xiang", "Sibel Yal\u00e7\u0131n", "Jun Yan", "Ji Zeng", "Arman Cohan", "Quanquan C. Liu" ], "github": "https://github.com/qqliu/Yale-QEDBench", "project_page": "", "abstract": "As Large Language Models (LLMs) saturate elementary benchmarks, the research frontier has shifted from generation to the reliability of automated evaluation. We demonstrate that standard \"LLM-as-a-Judge\" protocols suffer from a systematic Alignment Gap when applied to upper-undergraduate to early graduate level mathematics. To quantify this, we introduce QEDBench, the first large-scale dual-rubric alignment benchmark to systematically measure alignment with human experts on university-level math proofs by contrasting course-specific rubrics against expert common knowledge criteria. By deploying a dual-evaluation matrix (7 judges x 5 solvers) against 1,000+ hours of human evaluation, we reveal that certain frontier evaluators like Claude Opus 4.5, DeepSeek-V3, Qwen 2.5 Max, and Llama 4 Maverick exhibit significant positive bias (up to +0.18, +0.20, +0.30, +0.36 mean score inflation, respectively). Furthermore, we uncover a critical reasoning gap in the discrete domain: while Gemini 3.0 Pro achieves state-of-the-art performance (0.91 average human evaluation score), other reasoning models like GPT-5 Pro and Claude Sonnet 4.5 see their performance significantly degrade in discrete domains. Specifically, their average human evaluation scores drop to 0.72 and 0.63 in Discrete Math, and to 0.74 and 0.50 in Graph Theory. In addition to these research results, we also release QEDBench as a public benchmark for evaluating and improving AI judges. Our benchmark is publicly published at https://github.com/qqliu/Yale-QEDBench.", "github_manual": "", "github_regex": "https://github.com/qqliu/Yale-QEDBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.02510", "title": "ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution", "authors": [ "Liu Yang", "Zeyu Nie", "Andrew Liu", "Felix Zou", "Deniz Altinb\u00fcken", "Amir Yazdanbakhsh", "Quanquan C. Liu" ], "github": "https://github.com/WildAlg/ParEVO", "project_page": "", "abstract": "The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning curve of concurrent programming. This challenge is magnified for irregular data structures (such as sparse graphs, unbalanced trees, and non-uniform meshes) where static scheduling fails and data dependencies are unpredictable. Current Large Language Models (LLMs) often fail catastrophically on these tasks, generating code plagued by subtle race conditions, deadlocks, and sub-optimal scaling. We bridge this gap with ParEVO, a framework designed to synthesize high-performance parallel algorithms for irregular data. Our contributions include: (1) The Parlay-Instruct Corpus, a curated dataset of 13,820 tasks synthesized via a \"Critic-Refine\" pipeline that explicitly filters for empirically performant algorithms that effectively utilize Work-Span parallel primitives; (2) specialized DeepSeek, Qwen, and Gemini models fine-tuned to align probabilistic generation with the rigorous semantics of the ParlayLib library; and (3) an Evolutionary Coding Agent (ECA) that improves the \"last mile\" of correctness by iteratively repairing code using feedback from compilers, dynamic race detectors, and performance profilers. On the ParEval benchmark, ParEVO achieves an average 106x speedup (with a maximum of 1103x) across the suite, and a robust 13.6x speedup specifically on complex irregular graph problems, outperforming state-of-the-art commercial models. Furthermore, our evolutionary approach matches state-of-the-art expert human baselines, achieving up to a 4.1x speedup on specific highly-irregular kernels. Source code and datasets are available at https://github.com/WildAlg/ParEVO.", "github_manual": "", "github_regex": "https://github.com/WildAlg/ParEVO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.01563", "title": "LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models", "authors": [ "Chenxing Wei", "Jiazhen Kang", "Hong Wang", "Jianqing Zhang", "Hao Jiang", "Xiaolong Xu", "Ningyuan Sun", "Ying He", "F. Richard Yu", "Yao Shu", "Bo Jiang" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has achieved remarkable success in improving autoregressive models, especially in domains requiring correctness like mathematical reasoning and code generation. However, directly applying such paradigms to Diffusion Large Language Models (dLLMs) is fundamentally hindered by the intractability of exact likelihood computation, which forces existing methods to rely on high-variance approximations. To bridge this gap, we propose Likelihood-Free Policy Optimization (LFPO), a native framework that maps the concept of vector field flow matching to the discrete token space. Specifically, LFPO formulates alignment as geometric velocity rectification, which directly optimizes denoising logits via contrastive updates. This design effectively bypasses the errors inherent in likelihood approximation, yielding the precise gradient estimation. Furthermore, LFPO enforce consistency by predicting final solutions from intermediate steps, effectively straightening the probability flow to enable high-quality generation with significantly fewer iterations. Extensive experiments demonstrate that LFPO not only outperforms state-of-the-art baselines on code and reasoning benchmarks but also accelerates inference by approximately 20% through reduced diffusion steps.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-04", "arxiv_id": "2603.01375", "title": "Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation", "authors": [ "Chenxing Wei", "Hong Wang", "Ying He", "Zhongxiang Dai", "Bo Jiang", "F. Richard Yu", "Yao Shu" ], "github": "", "project_page": "", "abstract": "Test-time policy adaptation for multi-turn interactions (T2PAM) is essential for aligning Large Language Models (LLMs) with dynamic user needs during inference time. However, existing paradigms commonly treat test-time adaptation as a single-axis problem, either purely refining instructions (Prompt Engineering) or only adjusting weights (Test-Time Training), ignoring that interaction failures stem from a coupled mix of ambiguity and incapacity. We argue that these two optimization paths are not merely additive but synergistic: semantic clarity acts as a pre-conditioner for effective parameter updates. To this end, we propose ROSA2, a framework that reformulates interaction as a joint optimization problem over the heterogeneous space of Words and Weights. By mathematically decomposing the error signal, ROSA2 utilizes textual gradients to rectify intent ambiguity and parameter updates to bridge capability gaps. Theoretically, we prove that this co-adaptation strictly reduces the required parameter shift for convergence. Empirically, ROSA2 outperforms state-of-the-art baselines by 30% on MATH while reducing interaction turns by 40%, demonstrating that refining the context unlocks the true potential of parameter updates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.04379", "title": "Helios: Real Real-Time Long Video Generation Model", "authors": [ "Shenghai Yuan", "Yuanyang Yin", "Zongjian Li", "Xinwei Huang", "Xiao Yang", "Li Yuan" ], "github": "", "project_page": "", "abstract": "We introduce Helios, the first 14B video generation model that runs at 19.5 FPS on a single NVIDIA H100 GPU and supports minute-scale generation while matching the quality of a strong baseline. We make breakthroughs along three key dimensions: (1) robustness to long-video drifting without commonly used anti-drifting heuristics such as self-forcing, error-banks, or keyframe sampling; (2) real-time generation without standard acceleration techniques such as KV-cache, sparse/linear attention, or quantization; and (3) training without parallelism or sharding frameworks, enabling image-diffusion-scale batch sizes while fitting up to four 14B models within 80 GB of GPU memory. Specifically, Helios is a 14B autoregressive diffusion model with a unified input representation that natively supports T2V, I2V, and V2V tasks. To mitigate drifting in long-video generation, we characterize typical failure modes and propose simple yet effective training strategies that explicitly simulate drifting during training, while eliminating repetitive motion at its source. For efficiency, we heavily compress the historical and noisy context and reduce the number of sampling steps, yielding computational costs comparable to -- or lower than -- those of 1.3B video generative models. Moreover, we introduce infrastructure-level optimizations that accelerate both inference and training while reducing memory consumption. Extensive experiments demonstrate that Helios consistently outperforms prior methods on both short- and long-video generation. We plan to release the code, base model, and distilled model to support further development by the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.04338", "title": "ArtHOI: Articulated Human-Object Interaction Synthesis by 4D Reconstruction from Video Priors", "authors": [ "Zihao Huang", "Tianqi Liu", "Zhaoxi Chen", "Shaocong Xu", "Saining Zhang", "Lixing Xiao", "Zhiguo Cao", "Wei Li", "Hao Zhao", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Synthesizing physically plausible articulated human-object interactions (HOI) without 3D/4D supervision remains a fundamental challenge. While recent zero-shot approaches leverage video diffusion models to synthesize human-object interactions, they are largely confined to rigid-object manipulation and lack explicit 4D geometric reasoning. To bridge this gap, we formulate articulated HOI synthesis as a 4D reconstruction problem from monocular video priors: given only a video generated by a diffusion model, we reconstruct a full 4D articulated scene without any 3D supervision. This reconstruction-based approach treats the generated 2D video as supervision for an inverse rendering problem, recovering geometrically consistent and physically plausible 4D scenes that naturally respect contact, articulation, and temporal coherence. We introduce ArtHOI, the first zero-shot framework for articulated human-object interaction synthesis via 4D reconstruction from video priors. Our key designs are: 1) Flow-based part segmentation: leveraging optical flow as a geometric cue to disentangle dynamic from static regions in monocular video; 2) Decoupled reconstruction pipeline: joint optimization of human motion and object articulation is unstable under monocular ambiguity, so we first recover object articulation, then synthesize human motion conditioned on the reconstructed object states. ArtHOI bridges video-based generation and geometry-aware reconstruction, producing interactions that are both semantically aligned and physically grounded. Across diverse articulated scenes (e.g., opening fridges, cabinets, microwaves), ArtHOI significantly outperforms prior methods in contact accuracy, penetration reduction, and articulation fidelity, extending zero-shot interaction synthesis beyond rigid manipulation through reconstruction-informed synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.03975", "title": "Phi-4-reasoning-vision-15B Technical Report", "authors": [ "Jyoti Aneja", "Michael Harrison", "Neel Joshi", "Tyler LaBonte", "John Langford", "Eduardo Salinas" ], "github": "https://github.com/microsoft/eureka-ml-insights", "project_page": "", "abstract": "We present Phi-4-reasoning-vision-15B, a compact open-weight multimodal reasoning model, and share the motivations, design choices, experiments, and learnings that informed its development. Our goal is to contribute practical insight to the research community on building smaller, efficient multimodal reasoning models and to share the result of these learnings as an open-weight model that is good at common vision and language tasks and excels at scientific and mathematical reasoning and understanding user interfaces. Our contributions include demonstrating that careful architecture choices and rigorous data curation enable smaller, open-weight multimodal models to achieve competitive performance with significantly less training and inference-time compute and tokens. The most substantial improvements come from systematic filtering, error correction, and synthetic augmentation -- reinforcing that data quality remains the primary lever for model performance. Systematic ablations show that high-resolution, dynamic-resolution encoders yield consistent improvements, as accurate perception is a prerequisite for high-quality reasoning. Finally, a hybrid mix of reasoning and non-reasoning data with explicit mode tokens allows a single model to deliver fast direct answers for simpler tasks and chain-of-thought reasoning for complex problems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/eureka-ml-insights", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.02604", "title": "Heterogeneous Agent Collaborative Reinforcement Learning", "authors": [ "Zhixia Zhang", "Zixuan Huang", "Xin Xia", "Deqing Wang", "Fuzhen Zhuang", "Shuai Ma", "Ning Ding", "Yaodong Yang", "Jianxin Li", "Yikun Ban" ], "github": "", "project_page": "", "abstract": "We introduce Heterogeneous Agent Collaborative Reinforcement Learning (HACRL), a new learning paradigm that addresses the inefficiencies of isolated on-policy optimization. HACRL enables collaborative optimization with independent execution: heterogeneous agents share verified rollouts during training to mutually improve, while operating independently at inference time. Unlike LLM-based multi-agent reinforcement learning (MARL), HACRL does not require coordinated deployment, and unlike on-/off-policy distillation, it enables bidirectional mutual learning among heterogeneous agents rather than one-directional teacher-to-student transfer. Building on this paradigm, we propose HACPO, a collaborative RL algorithm that enables principled rollout sharing to maximize sample utilization and cross-agent knowledge transfer. To mitigate capability discrepancies and policy distribution shifts, HACPO introduces four tailored mechanisms with theoretical guarantees on unbiased advantage estimation and optimization correctness. Extensive experiments across diverse heterogeneous model combinations and reasoning benchmarks show that HACPO consistently improves all participating agents, outperforming GSPO by an average of 3.3\\% while using only half the rollout cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.03790", "title": "T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning", "authors": [ "Qinsi Wang", "Hancheng Ye", "Jinhee Kim", "Jinghan Ke", "Yifei Wang", "Martin Kuo", "Zishan Shao", "Dongting Li", "Yueqian Lin", "Ting Jiang", "Chiyue Wei", "Qi Qian", "Wei Wen", "Helen Li", "Yiran Chen" ], "github": "https://github.com/T2S-Bench/T2S-Bench", "project_page": "", "abstract": "Think about how human handles complex reading tasks: marking key points, inferring their relationships, and structuring information to guide understanding and responses. Likewise, can a large language model benefit from text structure to enhance text-processing performance? To explore it, in this work, we first introduce Structure of Thought (SoT), a prompting technique that explicitly guides models to construct intermediate text structures, consistently boosting performance across eight tasks and three model families. Building upon this insight, we present T2S-Bench, the first benchmark designed to evaluate and improve text-to-structure capabilities of models. T2S-Bench includes 1.8K samples across 6 scientific domains and 32 structural types, rigorously constructed to ensure accuracy, fairness, and quality. Evaluation on 45 mainstream models reveals substantial improvement potential: the average accuracy on the multi-hop reasoning task is only 52.1%, and even the most advanced model achieves 58.1% node accuracy in end-to-end extraction. Furthermore, on Qwen2.5-7B-Instruct, SoT alone yields an average +5.7% improvement across eight diverse text-processing tasks, and fine-tuning on T2S-Bench further increases this gain to +8.6%. These results highlight the value of explicit text structuring and the complementary contributions of SoT and T2S-Bench. Dataset and eval code have been released at https://t2s-bench.github.io/T2S-Bench-Page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/T2S-Bench/T2S-Bench", "github_api_added_by": "auto" }, { "date": "2026-03-05", "arxiv_id": "2603.04291", "title": "CubeComposer: Spatio-Temporal Autoregressive 4K 360\u00b0 Video Generation from Perspective Video", "authors": [ "Lingen Li", "Guangzhi Wang", "Xiaoyu Li", "Zhaoyang Zhang", "Qi Dou", "Jinwei Gu", "Tianfan Xue", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Generating high-quality 360\u00b0 panoramic videos from perspective input is one of the crucial applications for virtual reality (VR), whereby high-resolution videos are especially important for immersive experience. Existing methods are constrained by computational limitations of vanilla diffusion models, only supporting leq 1K resolution native generation and relying on suboptimal post super-resolution to increase resolution. We introduce CubeComposer, a novel spatio-temporal autoregressive diffusion model that natively generates 4K-resolution 360\u00b0 videos. By decomposing videos into cubemap representations with six faces, CubeComposer autoregressively synthesizes content in a well-planned spatio-temporal order, reducing memory demands while enabling high-resolution output. Specifically, to address challenges in multi-dimensional autoregression, we propose: (1) a spatio-temporal autoregressive strategy that orchestrates 360\u00b0 video generation across cube faces and time windows for coherent synthesis; (2) a cube face context management mechanism, equipped with a sparse context attention design to improve efficiency; and (3) continuity-aware techniques, including cube-aware positional encoding, padding, and blending to eliminate boundary seams. Extensive experiments on benchmark datasets demonstrate that CubeComposer outperforms state-of-the-art methods in native resolution and visual quality, supporting practical VR application scenarios. Project page: https://lg-li.github.io/project/cubecomposer", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.04257", "title": "Memex(RL): Scaling Long-Horizon LLM Agents via Indexed Experience Memory", "authors": [ "Zhenting Wang", "Huancheng Chen", "Jiayun Wang", "Wei Wei" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) agents are fundamentally bottlenecked by finite context windows on long-horizon tasks. As trajectories grow, retaining tool outputs and intermediate reasoning in-context quickly becomes infeasible: the working context becomes prohibitively long, eventually exceeds the context budget, and makes distant evidence harder to use even when it is still present. Existing solutions typically shorten context through truncation or running summaries, but these methods are fundamentally lossy because they compress or discard past evidence itself. We introduce Memex, an indexed experience memory mechanism that instead compresses context without discarding evidence. Memex maintains a compact working context consisting of concise structured summaries and stable indices, while storing full-fidelity underlying interactions in an external experience database under those indices. The agent can then decide when to dereference an index and recover the exact past evidence needed for the current subgoal. We optimize both write and read behaviors with our reinforcement learning framework MemexRL, using reward shaping tailored to indexed memory usage under a context budget, so the agent learns what to summarize, what to archive, how to index it, and when to retrieve it. This yields a substantially less lossy form of long-horizon memory than summary-only approaches. We further provide a theoretical analysis showing the potential of the Memex loop to preserve decision quality with bounded dereferencing while keeping effective in-context computation bounded as history grows. Empirically, on challenging long-horizon tasks, Memex agent trained with MemexRL improves task success while using a significantly smaller working context.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.03447", "title": "Proact-VL: A Proactive VideoLLM for Real-Time AI Companions", "authors": [ "Weicai Yan", "Yuhong Dai", "Qi Ran", "Haodong Li", "Wang Lin", "Hao Liao", "Xing Xie", "Tao Jin", "Jianxun Lian" ], "github": "", "project_page": "", "abstract": "Proactive and real-time interactive experiences are essential for human-like AI companions, yet face three key challenges: (1) achieving low-latency inference under continuous streaming inputs, (2) autonomously deciding when to respond, and (3) controlling both quality and quantity of generated content to meet real-time constraints. In this work, we instantiate AI companions through two gaming scenarios, commentator and guide, selected for their suitability for automatic evaluation. We introduce the Live Gaming Benchmark, a large-scale dataset with three representative scenarios: solo commentary, co-commentary, and user guidance, and present Proact-VL, a general framework that shapes multimodal language models into proactive, real-time interactive agents capable of human-like environment perception and interaction. Extensive experiments show Proact-VL achieves superior response latency and quality while maintaining strong video understanding capabilities, demonstrating its practicality for real-time interactive applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.03379", "title": "MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning", "authors": [ "Jiejun Tan", "Zhicheng Dou", "Liancheng Zhang", "Yuyang Hu", "Yiruo Cheng", "Ji-Rong Wen" ], "github": "https://github.com/plageon/MemSifter", "project_page": "", "abstract": "As Large Language Models (LLMs) are increasingly used for long-duration tasks, maintaining effective long-term memory has become a critical challenge. Current methods often face a trade-off between cost and accuracy. Simple storage methods often fail to retrieve relevant information, while complex indexing methods (such as memory graphs) require heavy computation and can cause information loss. Furthermore, relying on the working LLM to process all memories is computationally expensive and slow. To address these limitations, we propose MemSifter, a novel framework that offloads the memory retrieval process to a small-scale proxy model. Instead of increasing the burden on the primary working LLM, MemSifter uses a smaller model to reason about the task before retrieving the necessary information. This approach requires no heavy computation during the indexing phase and adds minimal overhead during inference. To optimize the proxy model, we introduce a memory-specific Reinforcement Learning (RL) training paradigm. We design a task-outcome-oriented reward based on the working LLM's actual performance in completing the task. The reward measures the actual contribution of retrieved memories by mutiple interactions with the working LLM, and discriminates retrieved rankings by stepped decreasing contributions. Additionally, we employ training techniques such as Curriculum Learning and Model Merging to improve performance. We evaluated MemSifter on eight LLM memory benchmarks, including Deep Research tasks. The results demonstrate that our method meets or exceeds the performance of existing state-of-the-art approaches in both retrieval accuracy and final task completion. MemSifter offers an efficient and scalable solution for long-term LLM memory. We have open-sourced the model weights, code, and training data to support further research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/plageon/MemSifter", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.02482", "title": "MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models", "authors": [ "Zhongxi Wang", "Yueqian Lin", "Jingyang Zhang", "Hai Helen Li", "Yiran Chen" ], "github": "", "project_page": "", "abstract": "Safety evaluation and red-teaming of large language models remain predominantly text-centric, and existing frameworks lack the infrastructure to systematically test whether alignment generalizes to audio, image, and video inputs. We present MUSE (Multimodal Unified Safety Evaluation), an open-source, run-centric platform that integrates automatic cross-modal payload generation, three multi-turn attack algorithms (Crescendo, PAIR, Violent Durian), provider-agnostic model routing, and an LLM judge with a five-level safety taxonomy into a single browser-based system. A dual-metric framework distinguishes hard Attack Success Rate (Compliance only) from soft ASR (including Partial Compliance), capturing partial information leakage that binary metrics miss. To probe whether alignment generalizes across modality boundaries, we introduce Inter-Turn Modality Switching (ITMS), which augments multi-turn attacks with per-turn modality rotation. Experiments across six multimodal LLMs from four providers show that multi-turn strategies can achieve up to 90-100% ASR against models with near-perfect single-turn refusal. ITMS does not uniformly raise final ASR on already-saturated baselines, but accelerates convergence by destabilizing early-turn defenses, and ablation reveals that the direction of modality effects is model-family-specific rather than universal, underscoring the need for provider-aware cross-modal safety testing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.03823", "title": "SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration", "authors": [ "Jialong Chen", "Xander Xu", "Hu Wei", "Chuan Chen", "Bing Zhao" ], "github": "https://github.com/SKYLENAGE-AI/SWE-CI", "project_page": "", "abstract": "Large language model (LLM)-powered agents have demonstrated strong capabilities in automating software engineering tasks such as static bug fixing, as evidenced by benchmarks like SWE-bench. However, in the real world, the development of mature software is typically predicated on complex requirement changes and long-term feature iterations -- a process that static, one-shot repair paradigms fail to capture. To bridge this gap, we propose SWE-CI, the first repository-level benchmark built upon the Continuous Integration loop, aiming to shift the evaluation paradigm for code generation from static, short-term functional correctness toward dynamic, long-term maintainability. The benchmark comprises 100 tasks, each corresponding on average to an evolution history spanning 233 days and 71 consecutive commits in a real-world code repository. SWE-CI requires agents to systematically resolve these tasks through dozens of rounds of analysis and coding iterations. SWE-CI provides valuable insights into how well agents can sustain code quality throughout long-term evolution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SKYLENAGE-AI/SWE-CI", "github_api_added_by": "auto" }, { "date": "2026-03-05", "arxiv_id": "2603.03985", "title": "RIVER: A Real-Time Interaction Benchmark for Video LLMs", "authors": [ "Yansong Shi", "Qingsong Zhao", "Tianxiang Jiang", "Xiangyu Zeng", "Yi Wang", "Limin Wang" ], "github": "https://github.com/OpenGVLab/RIVER", "project_page": "", "abstract": "The rapid advancement of multimodal large language models has demonstrated impressive capabilities, yet nearly all operate in an offline paradigm, hindering real-time interactivity. Addressing this gap, we introduce the Real-tIme Video intERaction Bench (RIVER Bench), designed for evaluating online video comprehension. RIVER Bench introduces a novel framework comprising Retrospective Memory, Live-Perception, and Proactive Anticipation tasks, closely mimicking interactive dialogues rather than responding to entire videos at once. We conducted detailed annotations using videos from diverse sources and varying lengths, and precisely defined the real-time interactive format. Evaluations across various model categories reveal that while offline models perform well in single question-answering tasks, they struggle with real-time processing. Addressing the limitations of existing models in online video interaction, especially their deficiencies in long-term memory and future perception, we proposed a general improvement method that enables models to interact with users more flexibly in real time. We believe this work will significantly advance the development of real-time interactive video understanding models and inspire future research in this emerging field. Datasets and code are publicly available at https://github.com/OpenGVLab/RIVER.", "github_manual": "", "github_regex": "https://github.com/OpenGVLab/RIVER", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.04254", "title": "EmbodiedSplat: Online Feed-Forward Semantic 3DGS for Open-Vocabulary 3D Scene Understanding", "authors": [ "Seungjun Lee", "Zihan Wang", "Yunsong Wang", "Gim Hee Lee" ], "github": "", "project_page": "", "abstract": "Understanding a 3D scene immediately with its exploration is essential for embodied tasks, where an agent must construct and comprehend the 3D scene in an online and nearly real-time manner. In this study, we propose EmbodiedSplat, an online feed-forward 3DGS for open-vocabulary scene understanding that enables simultaneous online 3D reconstruction and 3D semantic understanding from the streaming images. Unlike existing open-vocabulary 3DGS methods which are typically restricted to either offline or per-scene optimization setting, our objectives are two-fold: 1) Reconstructs the semantic-embedded 3DGS of the entire scene from over 300 streaming images in an online manner. 2) Highly generalizable to novel scenes with feed-forward design and supports nearly real-time 3D semantic reconstruction when combined with real-time 2D models. To achieve these objectives, we propose an Online Sparse Coefficients Field with a CLIP Global Codebook where it binds the 2D CLIP embeddings to each 3D Gaussian while minimizing memory consumption and preserving the full semantic generalizability of CLIP. Furthermore, we generate 3D geometric-aware CLIP features by aggregating the partial point cloud of 3DGS through 3D U-Net to compensate the 3D geometric prior to 2D-oriented language embeddings. Extensive experiments on diverse indoor datasets, including ScanNet, ScanNet++, and Replica, demonstrate both the effectiveness and efficiency of our method. Check out our project page in https://0nandon.github.io/EmbodiedSplat/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.01847", "title": "GroupEnsemble: Efficient Uncertainty Estimation for DETR-based Object Detection", "authors": [ "Yutong Yang", "Katarina Popovi\u0107", "Julian Wiederer", "Markus Braun", "Vasileios Belagiannis", "Bin Yang" ], "github": "https://github.com/yutongy98/GroupEnsemble", "project_page": "", "abstract": "Detection Transformer (DETR) and its variants show strong performance on object detection, a key task for autonomous systems. However, a critical limitation of these models is that their confidence scores only reflect semantic uncertainty, failing to capture the equally important spatial uncertainty. This results in an incomplete assessment of the detection reliability. On the other hand, Deep Ensembles can tackle this by providing high-quality spatial uncertainty estimates. However, their immense memory consumption makes them impractical for real-world applications. A cheaper alternative, Monte Carlo (MC) Dropout, suffers from high latency due to the need of multiple forward passes during inference to estimate uncertainty. To address these limitations, we introduce GroupEnsemble, an efficient and effective uncertainty estimation method for DETR-like models. GroupEnsemble simultaneously predicts multiple individual detection sets by feeding additional diverse groups of object queries to the transformer decoder during inference. Each query group is transformed by the shared decoder in isolation and predicts a complete detection set for the same input. An attention mask is applied to the decoder to prevent inter-group query interactions, ensuring each group detects independently to achieve reliable ensemble-based uncertainty estimation. By leveraging the decoder's inherent parallelism, GroupEnsemble efficiently estimates uncertainty in a single forward pass without sequential repetition. We validated our method under autonomous driving scenes and common daily scenes using the Cityscapes and COCO datasets, respectively. The results show that a hybrid approach combining MC-Dropout and GroupEnsemble outperforms Deep Ensembles on several metrics at a fraction of the cost. The code is available at https://github.com/yutongy98/GroupEnsemble.", "github_manual": "", "github_regex": "https://github.com/yutongy98/GroupEnsemble", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.03646", "title": "InfinityStory: Unlimited Video Generation with World Consistency and Character-Aware Shot Transitions", "authors": [ "Mohamed Elmoghany", "Liangbing Zhao", "Xiaoqian Shen", "Subhojyoti Mukherjee", "Yang Zhou", "Gang Wu", "Viet Dac Lai", "Seunghyun Yoon", "Ryan Rossi", "Abdullah Rashwan", "Puneet Mathur", "Varun Manjunatha", "Daksh Dangi", "Chien Nguyen", "Nedim Lipka", "Trung Bui", "Krishna Kumar Singh", "Ruiyi Zhang", "Xiaolei Huang", "Jaemin Cho", "Yu Wang", "Namyong Park", "Zhengzhong Tu", "Hongjie Chen", "Hoda Eldardiry", "Nesreen Ahmed", "Thien Nguyen", "Dinesh Manocha", "Mohamed Elhoseiny", "Franck Dernoncourt" ], "github": "", "project_page": "", "abstract": "Generating long-form storytelling videos with consistent visual narratives remains a significant challenge in video synthesis. We present a novel framework, dataset, and a model that address three critical limitations: background consistency across shots, seamless multi-subject shot-to-shot transitions, and scalability to hour-long narratives. Our approach introduces a background-consistent generation pipeline that maintains visual coherence across scenes while preserving character identity and spatial relationships. We further propose a transition-aware video synthesis module that generates smooth shot transitions for complex scenarios involving multiple subjects entering or exiting frames, going beyond the single-subject limitations of prior work. To support this, we contribute with a synthetic dataset of 10,000 multi-subject transition sequences covering underrepresented dynamic scene compositions. On VBench, InfinityStory achieves the highest Background Consistency (88.94), highest Subject Consistency (82.11), and the best overall average rank (2.80), showing improved stability, smoother transitions, and better temporal coherence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.04124", "title": "BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoning", "authors": [ "Tarjei Paule Hage", "Markus J. Buehler" ], "github": "https://github.com/lamm-mit/beamperl", "project_page": "", "abstract": "Can reinforcement learning with hard, verifiable rewards teach a compact language model to reason about physics, or does it primarily learn to pattern-match toward correct answers? We study this question by training a 1.5B-parameter reasoning model on beam statics, a classic engineering problem, using parameter-efficient RLVR with binary correctness rewards from symbolic solvers, without teacher-generated reasoning traces. The best BeamPERL checkpoint achieves a 66.7% improvement in Pass@1 over the base model. However, the learned competence is anisotropic: the model generalizes compositionally (more loads) but fails under topological shifts (moved supports) that require the same equilibrium equations. Intermediate checkpoints yield the strongest reasoning, while continued optimization degrades robustness while maintaining reward. These findings reveal a key limitation of outcome-level alignment: reinforcement learning with exact physics rewards induces procedural solution templates rather than internalization of governing equations. The precision of the reward signal - even when analytically exact - does not by itself guarantee transferable physical reasoning. Our results suggest that verifiable rewards may need to be paired with structured reasoning scaffolding to move beyond template matching toward robust scientific reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/lamm-mit/beamperl", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.03282", "title": "MIBURI: Towards Expressive Interactive Gesture Synthesis", "authors": [ "M. Hamza Mughal", "Rishabh Dabral", "Vera Demberg", "Christian Theobalt" ], "github": "", "project_page": "", "abstract": "Embodied Conversational Agents (ECAs) aim to emulate human face-to-face interaction through speech, gestures, and facial expressions. Current large language model (LLM)-based conversational agents lack embodiment and the expressive gestures essential for natural interaction. Existing solutions for ECAs often produce rigid, low-diversity motions, that are unsuitable for human-like interaction. Alternatively, generative methods for co-speech gesture synthesis yield natural body gestures but depend on future speech context and require long run-times. To bridge this gap, we present MIBURI, the first online, causal framework for generating expressive full-body gestures and facial expressions synchronized with real-time spoken dialogue. We employ body-part aware gesture codecs that encode hierarchical motion details into multi-level discrete tokens. These tokens are then autoregressively generated by a two-dimensional causal framework conditioned on LLM-based speech-text embeddings, modeling both temporal dynamics and part-level motion hierarchy in real time. Further, we introduce auxiliary objectives to encourage expressive and diverse gestures while preventing convergence to static poses. Comparative evaluations demonstrate that our causal and real-time approach produces natural and contextually aligned gestures against recent baselines. We urge the reader to explore demo videos on https://vcai.mpi-inf.mpg.de/projects/MIBURI/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.03197", "title": "Specificity-aware reinforcement learning for fine-grained open-world classification", "authors": [ "Samuele Angheben", "Davide Berasi", "Alessandro Conti", "Elisa Ricci", "Yiming Wang" ], "github": "https://github.com/s-angheben/SpeciaRL", "project_page": "", "abstract": "Classifying fine-grained visual concepts under open-world settings, i.e., without a predefined label set, demands models to be both accurate and specific. Recent reasoning Large Multimodal Models (LMMs) exhibit strong visual understanding capability but tend to produce overly generic predictions when performing fine-grained image classification. Our preliminary analysis reveals that models do possess the intrinsic fine-grained domain knowledge. However, promoting more specific predictions (specificity) without compromising correct ones (correctness) remains a non-trivial and understudied challenge. In this work, we investigate how to steer reasoning LMMs toward predictions that are both correct and specific. We propose a novel specificity-aware reinforcement learning framework, SpeciaRL, to fine-tune reasoning LMMs on fine-grained image classification under the open-world setting. SpeciaRL introduces a dynamic, verifier-based reward signal anchored to the best predictions within online rollouts, promoting specificity while respecting the model's capabilities to prevent incorrect predictions. Our out-of-domain experiments show that SpeciaRL delivers the best trade-off between correctness and specificity across extensive fine-grained benchmarks, surpassing existing methods and advancing open-world fine-grained image classification. Code and model are publicly available at https://github.com/s-angheben/SpeciaRL.", "github_manual": "", "github_regex": "https://github.com/s-angheben/SpeciaRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.02924", "title": "HDINO: A Concise and Efficient Open-Vocabulary Detector", "authors": [ "Hao Zhang", "Yiqun Wang", "Qinran Lin", "Runze Fan", "Yong Li" ], "github": "https://github.com/HaoZ416/HDINO", "project_page": "", "abstract": "Despite the growing interest in open-vocabulary object detection in recent years, most existing methods rely heavily on manually curated fine-grained training datasets as well as resource-intensive layer-wise cross-modal feature extraction. In this paper, we propose HDINO, a concise yet efficient open-vocabulary object detector that eliminates the dependence on these components. Specifically, we propose a two-stage training strategy built upon the transformer-based DINO model. In the first stage, noisy samples are treated as additional positive object instances to construct a One-to-Many Semantic Alignment Mechanism(O2M) between the visual and textual modalities, thereby facilitating semantic alignment. A Difficulty Weighted Classification Loss (DWCL) is also designed based on initial detection difficulty to mine hard examples and further improve model performance. In the second stage, a lightweight feature fusion module is applied to the aligned representations to enhance sensitivity to linguistic semantics. Under the Swin Transformer-T setting, HDINO-T achieves 49.2 mAP on COCO using 2.2M training images from two publicly available detection datasets, without any manual data curation and the use of grounding data, surpassing Grounding DINO-T and T-Rex2 by 0.8 mAP and 2.8 mAP, respectively, which are trained on 5.4M and 6.5M images. After fine-tuning on COCO, HDINO-T and HDINO-L further achieve 56.4 mAP and 59.2 mAP, highlighting the effectiveness and scalability of our approach. Code and models are available at https://github.com/HaoZ416/HDINO.", "github_manual": "", "github_regex": "https://github.com/HaoZ416/HDINO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.01236", "title": "AgilePruner: An Empirical Study of Attention and Diversity for Adaptive Visual Token Pruning in Large Vision-Language Models", "authors": [ "Changwoo Baek", "Jouwon Song", "Sohyeon Kim", "Kyeongbo Kong" ], "github": "", "project_page": "", "abstract": "Large Vision-Language Models (LVLMs) have adopted visual token pruning strategies to mitigate substantial computational overhead incurred by extensive visual token sequences. While prior works primarily focus on either attention-based or diversity-based pruning methods, in-depth analysis of these approaches' characteristics and limitations remains largely unexplored. In this work, we conduct thorough empirical analysis using effective rank (erank) as a measure of feature diversity and attention score entropy to investigate visual token processing mechanisms and analyze the strengths and weaknesses of each approach. Our analysis reveals two insights: (1) Our erank-based quantitative analysis shows that many diversity-oriented pruning methods preserve substantially less feature diversity than intended; moreover, analysis using the CHAIR dataset reveals that the diversity they do retain is closely tied to increased hallucination frequency compared to attention-based pruning. (2) We further observe that attention-based approaches are more effective on simple images where visual evidence is concentrated, while diversity-based methods better handle complex images with distributed features. Building on these empirical insights, we show that incorporating image-aware adjustments into existing hybrid pruning strategies consistently improves their performance. We also provide a minimal instantiation of our empirical findings through a simple adaptive pruning mechanism, which achieves strong and reliable performance across standard benchmarks as well as hallucination-specific evaluations. Our project page available at https://cvsp-lab.github.io/AgilePruner.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-05", "arxiv_id": "2603.04304", "title": "V_1: Unifying Generation and Self-Verification for Parallel Reasoners", "authors": [ "Harman Singh", "Xiuyu Li", "Kusha Sareen", "Monishwaran Maheswaran", "Sijun Tan", "Xiaoxia Wu", "Junxiong Wang", "Alpay Ariyak", "Qingyang Wu", "Samir Khaki", "Rishabh Tiwari", "Long Lian", "Yucheng Lu", "Boyi Li", "Alane Suhr", "Ben Athiwaratkun", "Kurt Keutzer" ], "github": "", "project_page": "", "abstract": "Test-time scaling for complex reasoning tasks shows that leveraging inference-time compute, by methods such as independently sampling and aggregating multiple solutions, results in significantly better task outcomes. However, a critical bottleneck is verification: sampling is only effective if correct solutions can be reliably identified among candidates. While existing approaches typically evaluate candidates independently via scalar scoring, we demonstrate that models are substantially stronger at pairwise self-verification. Leveraging this insight, we introduce V_1, a framework that unifies generation and verification through efficient pairwise ranking. V_1 comprises two components: V_1-Infer, an uncertainty-guided algorithm using a tournament-based ranking that dynamically allocates self-verification compute to candidate pairs whose relative correctness is most uncertain; and V_1-PairRL, an RL framework that jointly trains a single model as both generator and pairwise self-verifier, ensuring the verifier adapts to the generator's evolving distribution. On code generation (LiveCodeBench, CodeContests, SWE-Bench) and math reasoning (AIME, HMMT) benchmarks, V_1-Infer improves Pass@1 by up to 10% over pointwise verification and outperforms recent test-time scaling methods while being significantly more efficient. Furthermore, V_1-PairRL achieves 7--9% test-time scaling gains over standard RL and pointwise joint training, and improves base Pass@1 by up to 8.7% over standard RL in a code-generation setting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.03756", "title": "MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier", "authors": [ "Zonglin Yang", "Lidong Bing" ], "github": "", "project_page": "", "abstract": "While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, leaving the direct modeling of the generative reasoning process, P(hypothesis|background) (P(h|b)), unexplored. We demonstrate that directly training P(h|b) is mathematically intractable due to the combinatorial complexity (O(N^k)) inherent in retrieving and composing inspirations from a vast knowledge base. To break this barrier, we introduce MOOSE-Star, a unified framework enabling tractable training and scalable inference. In the best case, MOOSE-Star reduces complexity from exponential to logarithmic (O(log N)) by (1) training on decomposed subtasks derived from the probabilistic equation of discovery, (2) employing motivation-guided hierarchical search to enable logarithmic retrieval and prune irrelevant subspaces, and (3) utilizing bounded composition for robustness against retrieval noise. To facilitate this, we release TOMATO-Star, a dataset of 108,717 decomposed papers (38,400 GPU hours) for training. Furthermore, we show that while brute-force sampling hits a ''complexity wall,'' MOOSE-Star exhibits continuous test-time scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.02210", "title": "HiFi-Inpaint: Towards High-Fidelity Reference-Based Inpainting for Generating Detail-Preserving Human-Product Images", "authors": [ "Yichen Liu", "Donghao Zhou", "Jie Wang", "Xin Gao", "Guisheng Liu", "Jiatong Li", "Quanwei Zhang", "Qiang Lyu", "Lanqing Guo", "Shilei Wen", "Weiqiang Wang", "Pheng-Ann Heng" ], "github": "", "project_page": "", "abstract": "Human-product images, which showcase the integration of humans and products, play a vital role in advertising, e-commerce, and digital marketing. The essential challenge of generating such images lies in ensuring the high-fidelity preservation of product details. Among existing paradigms, reference-based inpainting offers a targeted solution by leveraging product reference images to guide the inpainting process. However, limitations remain in three key aspects: the lack of diverse large-scale training data, the struggle of current models to focus on product detail preservation, and the inability of coarse supervision for achieving precise guidance. To address these issues, we propose HiFi-Inpaint, a novel high-fidelity reference-based inpainting framework tailored for generating human-product images. HiFi-Inpaint introduces Shared Enhancement Attention (SEA) to refine fine-grained product features and Detail-Aware Loss (DAL) to enforce precise pixel-level supervision using high-frequency maps. Additionally, we construct a new dataset, HP-Image-40K, with samples curated from self-synthesis data and processed with automatic filtering. Experimental results show that HiFi-Inpaint achieves state-of-the-art performance, delivering detail-preserving human-product images.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.04743", "title": "DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval", "authors": [ "Maojun Sun", "Yue Wu", "Yifei Xie", "Ruijian Han", "Binyan Jiang", "Defeng Sun", "Yancheng Yuan", "Jian Huang" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM) agents can automate data-science workflows, but many rigorous statistical methods implemented in R remain underused because LLMs struggle with statistical knowledge and tool retrieval. Existing retrieval-augmented approaches focus on function-level semantics and ignore data distribution, producing suboptimal matches. We propose DARE (Distribution-Aware Retrieval Embedding), a lightweight, plug-and-play retrieval model that incorporates data distribution information into function representations for R package retrieval. Our main contributions are: (i) RPKB, a curated R Package Knowledge Base derived from 8,191 high-quality CRAN packages; (ii) DARE, an embedding model that fuses distributional features with function metadata to improve retrieval relevance; and (iii) RCodingAgent, an R-oriented LLM agent for reliable R code generation and a suite of statistical analysis tasks for systematically evaluating LLM agents in realistic analytical scenarios. Empirically, DARE achieves an NDCG at 10 of 93.47%, outperforming state-of-the-art open-source embedding models by up to 17% on package retrieval while using substantially fewer parameters. Integrating DARE into RCodingAgent yields significant gains on downstream analysis tasks. This work helps narrow the gap between LLM automation and the mature R statistical ecosystem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.04892", "title": "Locality-Attending Vision Transformer", "authors": [ "Sina Hajimiri", "Farzad Beizaee", "Fereshteh Shakeri", "Christian Desrosiers", "Ismail Ben Ayed", "Jose Dolz" ], "github": "https://github.com/sinahmr/LocAtViT", "project_page": "", "abstract": "Vision transformers have demonstrated remarkable success in classification by leveraging global self-attention to capture long-range dependencies. However, this same mechanism can obscure fine-grained spatial details crucial for tasks such as segmentation. In this work, we seek to enhance segmentation performance of vision transformers after standard image-level classification training. More specifically, we present a simple yet effective add-on that improves performance on segmentation tasks while retaining vision transformers' image-level recognition capabilities. In our approach, we modulate the self-attention with a learnable Gaussian kernel that biases the attention toward neighboring patches. We further refine the patch representations to learn better embeddings at patch positions. These modifications encourage tokens to focus on local surroundings and ensure meaningful representations at spatial positions, while still preserving the model's ability to incorporate global information. Experiments demonstrate the effectiveness of our modifications, evidenced by substantial segmentation gains on three benchmarks (e.g., over 6% and 4% on ADE20K for ViT Tiny and Base), without changing the training regime or sacrificing classification performance. The code is available at https://github.com/sinahmr/LocAtViT/.", "github_manual": "", "github_regex": "https://github.com/sinahmr/LocAtViT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.05449", "title": "RealWonder: Real-Time Physical Action-Conditioned Video Generation", "authors": [ "Wei Liu", "Ziyu Chen", "Zizhang Li", "Yue Wang", "Hong-Xing Yu", "Jiajun Wu" ], "github": "https://github.com/alimama-creative/FLUX-Controlnet-Inpainting", "project_page": "", "abstract": "Current video generation models cannot simulate physical consequences of 3D actions like forces and robotic manipulations, as they lack structural understanding of how actions affect 3D scenes. We present RealWonder, the first real-time system for action-conditioned video generation from a single image. Our key insight is using physics simulation as an intermediate bridge: instead of directly encoding continuous actions, we translate them through physics simulation into visual representations (optical flow and RGB) that video models can process. RealWonder integrates three components: 3D reconstruction from single images, physics simulation, and a distilled video generator requiring only 4 diffusion steps. Our system achieves 13.2 FPS at 480x832 resolution, enabling interactive exploration of forces, robot actions, and camera controls on rigid objects, deformable bodies, fluids, and granular materials. We envision RealWonder opens new opportunities to apply video models in immersive experiences, AR/VR, and robot learning. Our code and model weights are publicly available in our project website: https://liuwei283.github.io/RealWonder/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/alimama-creative/FLUX-Controlnet-Inpainting", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.05218", "title": "KARL: Knowledge Agents via Reinforcement Learning", "authors": [ "Jonathan D. Chang", "Andrew Drozdov", "Shubham Toshniwal", "Owen Oertell", "Alexander Trott", "Jacob Portes", "Abhay Gupta", "Pallavi Koppol", "Ashutosh Baheti", "Sean Kulinski", "Ivan Zhou", "Irene Dea", "Krista Opsahl-Ong", "Simon Favreau-Lessard", "Sean Owen", "Jose Javier Gonzalez Ortiz", "Arnav Singhvi", "Xabi Andrade", "Cindy Wang", "Kartik Sreenivasan", "Sam Havens", "Jialu Liu", "Peyton DeNiro", "Wen Sun", "Michael Bendersky", "Jonathan Frankle" ], "github": "https://github.com/databricks/officeqa", "project_page": "", "abstract": "We present a system for training enterprise search agents via reinforcement learning that achieves state-of-the-art performance across a diverse suite of hard-to-verify agentic search tasks. Our work makes four core contributions. First, we introduce KARLBench, a multi-capability evaluation suite spanning six distinct search regimes, including constraint-driven entity search, cross-document report synthesis, tabular numerical reasoning, exhaustive entity retrieval, procedural reasoning over technical documentation, and fact aggregation over internal enterprise notes. Second, we show that models trained across heterogeneous search behaviors generalize substantially better than those optimized for any single benchmark. Third, we develop an agentic synthesis pipeline that employs long-horizon reasoning and tool use to generate diverse, grounded, and high-quality training data, with iterative bootstrapping from increasingly capable models. Fourth, we propose a new post-training paradigm based on iterative large-batch off-policy RL that is sample efficient, robust to train-inference engine discrepancies, and naturally extends to multi-task training with out-of-distribution generalization. Compared to Claude 4.6 and GPT 5.2, KARL is Pareto-optimal on KARLBench across cost-quality and latency-quality trade-offs, including tasks that were out-of-distribution during training. With sufficient test-time compute, it surpasses the strongest closed models. These results show that tailored synthetic data in combination with multi-task reinforcement learning enables cost-efficient and high-performing knowledge agents for grounded reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/databricks/officeqa", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.04791", "title": "Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling", "authors": [ "Yong Liu", "Xingjian Su", "Shiyu Wang", "Haoran Zhang", "Haixuan Liu", "Yuxuan Wang", "Zhou Ye", "Yang Xiang", "Jianmin Wang", "Mingsheng Long" ], "github": "", "project_page": "", "abstract": "We introduce Timer-S1, a strong Mixture-of-Experts (MoE) time series foundation model with 8.3B total parameters, 0.75B activated parameters for each token, and a context length of 11.5K. To overcome the scalability bottleneck in existing pre-trained time series foundation models, we perform Serial Scaling in three dimensions: model architecture, dataset, and training pipeline. Timer-S1 integrates sparse TimeMoE blocks and generic TimeSTP blocks for Serial-Token Prediction (STP), a generic training objective that adheres to the serial nature of forecasting. The proposed paradigm introduces serial computations to improve long-term predictions while avoiding costly rolling-style inference and pronounced error accumulation in the standard next-token prediction. Pursuing a high-quality and unbiased training dataset, we curate TimeBench, a corpus with one trillion time points, and apply meticulous data augmentation to mitigate predictive bias. We further pioneer a post-training stage, including continued pre-training and long-context extension, to enhance short-term and long-context performance. Evaluated on the large-scale GIFT-Eval leaderboard, Timer-S1 achieves state-of-the-art forecasting performance, attaining the best MASE and CRPS scores as a pre-trained model. Timer-S1 will be released to facilitate further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.05484", "title": "Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline", "authors": [ "Guo Chen", "Lidong Lu", "Yicheng Liu", "Liangrui Dong", "Lidong Zou", "Jixin Lv", "Zhenquan Li", "Xinyi Mao", "Baoqi Pei", "Shihao Wang", "Zhiqi Li", "Karan Sapra", "Fuxiao Liu", "Yin-Dong Zheng", "Yifei Huang", "Limin Wang", "Zhiding Yu", "Andrew Tao", "Guilin Liu", "Tong Lu" ], "github": "", "project_page": "", "abstract": "While datasets for video understanding have scaled to hour-long durations, they typically consist of densely concatenated clips that differ from natural, unscripted daily life. To bridge this gap, we introduce MM-Lifelong, a dataset designed for Multimodal Lifelong Understanding. Comprising 181.1 hours of footage, it is structured across Day, Week, and Month scales to capture varying temporal densities. Extensive evaluations reveal two critical failure modes in current paradigms: end-to-end MLLMs suffer from a Working Memory Bottleneck due to context saturation, while representative agentic baselines experience Global Localization Collapse when navigating sparse, month-long timelines. To address this, we propose the Recursive Multimodal Agent (ReMA), which employs dynamic memory management to iteratively update a recursive belief state, significantly outperforming existing methods. Finally, we establish dataset splits designed to isolate temporal and domain biases, providing a rigorous foundation for future research in supervised learning and out-of-distribution generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.05433", "title": "On-Policy Self-Distillation for Reasoning Compression", "authors": [ "Hejian Sang", "Yuanda Xu", "Zhengze Zhou", "Ran He", "Zhipeng Wang", "Jiachen Sun" ], "github": "", "project_page": "", "abstract": "Reasoning models think out loud, but much of what they say is noise. We introduce OPSDC (On-Policy Self-Distillation for Reasoning Compression), a method that teaches models to reason more concisely by distilling their own concise behavior back into themselves. The entire approach reduces to one idea: condition the same model on a \"be concise\" instruction to obtain teacher logits, and minimize per-token reverse KL on the student's own rollouts. No ground-truth answers, no token budgets, no difficulty estimators. Just self-distillation. Yet this simplicity belies surprising sophistication: OPSDC automatically compresses easy problems aggressively while preserving the deliberation needed for hard ones. On Qwen3-8B and Qwen3-14B, we achieve 57-59% token reduction on MATH-500 while improving accuracy by 9-16 points absolute. On AIME 2024, the 14B model gains 10 points with 41% compression. The secret? Much of what reasoning models produce is not just redundant-it is actively harmful, compounding errors with every unnecessary token.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.05312", "title": "UltraDexGrasp: Learning Universal Dexterous Grasping for Bimanual Robots with Synthetic Data", "authors": [ "Sizhe Yang", "Yiman Xie", "Zhixuan Liang", "Yang Tian", "Jia Zeng", "Dahua Lin", "Jiangmiao Pang" ], "github": "https://github.com/InternRobotics/UltraDexGrasp", "project_page": "", "abstract": "Grasping is a fundamental capability for robots to interact with the physical world. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the shape, size, and weight of objects, enabling robust grasping and subsequent manipulation. In contrast, current robotic grasping remains limited, particularly in multi-strategy settings. Although substantial efforts have targeted parallel-gripper and single-hand grasping, dexterous grasping for bimanual robots remains underexplored, with data being a primary bottleneck. Achieving physically plausible and geometrically conforming grasps that can withstand external wrenches poses significant challenges. To address these issues, we introduce UltraDexGrasp, a framework for universal dexterous grasping with bimanual robots. The proposed data-generation pipeline integrates optimization-based grasp synthesis with planning-based demonstration generation, yielding high-quality and diverse trajectories across multiple grasp strategies. With this framework, we curate UltraDexGrasp-20M, a large-scale, multi-strategy grasp dataset comprising 20 million frames across 1,000 objects. Based on UltraDexGrasp-20M, we further develop a simple yet effective grasp policy that takes point clouds as input, aggregates scene features via unidirectional attention, and predicts control commands. Trained exclusively on synthetic data, the policy achieves robust zero-shot sim-to-real transfer and consistently succeeds on novel objects with varied shapes, sizes, and weights, attaining an average success rate of 81.2% in real-world universal dexterous grasping. To facilitate future research on grasping with bimanual robots, we open-source the data generation pipeline at https://github.com/InternRobotics/UltraDexGrasp.", "github_manual": "", "github_regex": "https://github.com/InternRobotics/UltraDexGrasp", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.04736", "title": "Distribution-Conditioned Transport", "authors": [ "Nic Fishman", "Gokul Gowri", "Paolo L. B. Fischer", "Marinka Zitnik", "Omar Abudayyeh", "Jonathan Gootenberg" ], "github": "https://github.com/njwfish/DistributionConditionedTransport", "project_page": "", "abstract": "Learning a transport model that maps a source distribution to a target distribution is a canonical problem in machine learning, but scientific applications increasingly require models that can generalize to source and target distributions unseen during training. We introduce distribution-conditioned transport (DCT), a framework that conditions transport maps on learned embeddings of source and target distributions, enabling generalization to unseen distribution pairs. DCT also allows semi-supervised learning for distributional forecasting problems: because it learns from arbitrary distribution pairs, it can leverage distributions observed at only one condition to improve transport prediction. DCT is agnostic to the underlying transport mechanism, supporting models ranging from flow matching to distributional divergence-based models (e.g. Wasserstein, MMD). We demonstrate the practical performance benefits of DCT on synthetic benchmarks and four applications in biology: batch effect transfer in single-cell genomics, perturbation prediction from mass cytometry data, learning clonal transcriptional dynamics in hematopoiesis, and modeling T-cell receptor sequence evolution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/njwfish/DistributionConditionedTransport", "github_api_added_by": "auto" }, { "date": "2026-03-06", "arxiv_id": "2603.02170", "title": "SageBwd: A Trainable Low-bit Attention", "authors": [ "Jintao Zhang", "Marco Chen", "Haoxu Wang", "Kai Jiang", "Ion Stoica", "Joseph E. Gonzalez", "Jianfei Chen", "Jun Zhu" ], "github": "", "project_page": "", "abstract": "Low-bit attention, such as SageAttention, has emerged as an effective approach for accelerating model inference, but its applicability to training remains poorly understood. In prior work, we introduced SageBwd, a trainable INT8 attention that quantizes six of seven attention matrix multiplications while preserving fine-tuning performance. However, SageBwd exhibited a persistent performance gap to full-precision attention (FPA) during pre-training. In this work, we investigate why this gap occurs and demonstrate that SageBwd matches full-precision attention during pretraining. Through experiments and theoretical analysis, we reach a few important insights and conclusions: (i) QK-norm is necessary for stable training at large tokens per step, (ii) quantization errors primarily arise from the backward-pass score gradient dS, (iii) reducing tokens per step enables SageBwd to match FPA performance in pre-training, and (iv) K-smoothing remains essential for training stability, while Q-smoothing provides limited benefit during pre-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.00466", "title": "DreamWorld: Unified World Modeling in Video Generation", "authors": [ "Boming Tan", "Xiangdong Zhang", "Ning Liao", "Yuqing Zhang", "Shaofeng Zhang", "Xue Yang", "Qi Fan", "Yanyong Zhang" ], "github": "https://github.com/ABU121111/DreamWorld", "project_page": "", "abstract": "Despite impressive progress in video generation, existing models remain limited to surface-level plausibility, lacking a coherent and unified understanding of the world. Prior approaches typically incorporate only a single form of world-related knowledge or rely on rigid alignment strategies to introduce additional knowledge. However, aligning the single world knowledge is insufficient to constitute a world model that requires jointly modeling multiple heterogeneous dimensions (e.g., physical commonsense, 3D and temporal consistency). To address this limitation, we introduce DreamWorld, a unified framework that integrates complementary world knowledge into video generators via a Joint World Modeling Paradigm, jointly predicting video pixels and features from foundation models to capture temporal dynamics, spatial geometry, and semantic consistency. However, naively optimizing these heterogeneous objectives can lead to visual instability and temporal flickering. To mitigate this issue, we propose Consistent Constraint Annealing (CCA) to progressively regulate world-level constraints during training, and Multi-Source Inner-Guidance to enforce learned world priors at inference. Extensive evaluations show that DreamWorld improves world consistency, outperforming Wan2.1 by 2.26 points on VBench. Code will be made publicly available at https://github.com/ABU121111/DreamWorld{mypink{Github}}.", "github_manual": "", "github_regex": "https://github.com/ABU121111/DreamWorld", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2602.23166", "title": "AgentVista: Evaluating Multimodal Agents in Ultra-Challenging Realistic Visual Scenarios", "authors": [ "Zhaochen Su", "Jincheng Gao", "Hangyu Guo", "Zhenhua Liu", "Lueyang Zhang", "Xinyu Geng", "Shijue Huang", "Peng Xia", "Guanyu Jiang", "Cheng Wang", "Yue Zhang", "Yi R. Fung", "Junxian He" ], "github": "", "project_page": "", "abstract": "Real-world multimodal agents solve multi-step workflows grounded in visual evidence. For example, an agent can troubleshoot a device by linking a wiring photo to a schematic and validating the fix with online documentation, or plan a trip by interpreting a transit map and checking schedules under routing constraints. However, existing multimodal benchmarks mainly evaluate single-turn visual reasoning or specific tool skills, and they do not fully capture the realism, visual subtlety, and long-horizon tool use that practical agents require. We introduce AgentVista, a benchmark for generalist multimodal agents that spans 25 sub-domains across 7 categories, pairing realistic and detail-rich visual scenarios with natural hybrid tool use. Tasks require long-horizon tool interactions across modalities, including web search, image search, page navigation, and code-based operations for both image processing and general programming. Comprehensive evaluation of state-of-the-art models exposes significant gaps in their ability to carry out long-horizon multimodal tool use. Even the best model in our evaluation, Gemini-3-Pro with tools, achieves only 27.3% overall accuracy, and hard instances can require more than 25 tool-calling turns. We expect AgentVista to accelerate the development of more capable and reliable multimodal agents for realistic and ultra-challenging problem solving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2602.23229", "title": "Large Multimodal Models as General In-Context Classifiers", "authors": [ "Marco Garosi", "Matteo Farina", "Alessandro Conti", "Massimiliano Mancini", "Elisa Ricci" ], "github": "", "project_page": "", "abstract": "Which multimodal model should we use for classification? Previous studies suggest that the answer lies in CLIP-like contrastive Vision-Language Models (VLMs), due to their remarkable performance in zero-shot classification. In contrast, Large Multimodal Models (LMM) are more suitable for complex tasks. In this work, we argue that this answer overlooks an important capability of LMMs: in-context learning. We benchmark state-of-the-art LMMs on diverse datasets for closed-world classification and find that, although their zero-shot performance is lower than CLIP's, LMMs with a few in-context examples can match or even surpass contrastive VLMs with cache-based adapters, their \"in-context\" equivalent. We extend this analysis to the open-world setting, where the generative nature of LMMs makes them more suitable for the task. In this challenging scenario, LMMs struggle whenever provided with imperfect context information. To address this issue, we propose CIRCLE, a simple training-free method that assigns pseudo-labels to in-context examples, iteratively refining them with the available context itself. Through extensive experiments, we show that CIRCLE establishes a robust baseline for open-world classification, surpassing VLM counterparts and highlighting the potential of LMMs to serve as unified classifiers, and a flexible alternative to specialized models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.05504", "title": "RoboPocket: Improve Robot Policies Instantly with Your Phone", "authors": [ "Junjie Fang", "Wendi Chen", "Han Xue", "Fangyuan Zhou", "Tian Le", "Yi Wang", "Yuting Zhang", "Jun Lv", "Chuan Wen", "Cewu Lu" ], "github": "", "project_page": "", "abstract": "Scaling imitation learning is fundamentally constrained by the efficiency of data collection. While handheld interfaces have emerged as a scalable solution for in-the-wild data acquisition, they predominantly operate in an open-loop manner: operators blindly collect demonstrations without knowing the underlying policy's weaknesses, leading to inefficient coverage of critical state distributions. Conversely, interactive methods like DAgger effectively address covariate shift but rely on physical robot execution, which is costly and difficult to scale. To reconcile this trade-off, we introduce RoboPocket, a portable system that enables Robot-Free Instant Policy Iteration using single consumer smartphones. Its core innovation is a Remote Inference framework that visualizes the policy's predicted trajectory via Augmented Reality (AR) Visual Foresight. This immersive feedback allows collectors to proactively identify potential failures and focus data collection on the policy's weak regions without requiring a physical robot. Furthermore, we implement an asynchronous Online Finetuning pipeline that continuously updates the policy with incoming data, effectively closing the learning loop in minutes. Extensive experiments demonstrate that RoboPocket adheres to data scaling laws and doubles the data efficiency compared to offline scaling strategies, overcoming their long-standing efficiency bottleneck. Moreover, our instant iteration loop also boosts sample efficiency by up to 2times in distributed environments a small number of interactive corrections per person. Project page and videos: https://robo-pocket.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.03655", "title": "Mozi: Governed Autonomy for Drug Discovery LLM Agents", "authors": [ "He Cao", "Siyu Liu", "Fan Zhang", "Zijing Liu", "Hao Li", "Bin Feng", "Shengyuan Bai", "Leqing Chen", "Kai Xie", "Yu Li" ], "github": "", "project_page": "", "abstract": "Tool-augmented large language model (LLM) agents promise to unify scientific reasoning with computation, yet their deployment in high-stakes domains like drug discovery is bottlenecked by two critical barriers: unconstrained tool-use governance and poor long-horizon reliability. In dependency-heavy pharmaceutical pipelines, autonomous agents often drift into irreproducible trajectories, where early-stage hallucinations multiplicatively compound into downstream failures. To overcome this, we present Mozi, a dual-layer architecture that bridges the flexibility of generative AI with the deterministic rigor of computational biology. Layer A (Control Plane) establishes a governed supervisor--worker hierarchy that enforces role-based tool isolation, limits execution to constrained action spaces, and drives reflection-based replanning. Layer B (Workflow Plane) operationalizes canonical drug discovery stages -- from Target Identification to Lead Optimization -- as stateful, composable skill graphs. This layer integrates strict data contracts and strategic human-in-the-loop (HITL) checkpoints to safeguard scientific validity at high-uncertainty decision boundaries. Operating on the design principle of ``free-form reasoning for safe tasks, structured execution for long-horizon pipelines,'' Mozi provides built-in robustness mechanisms and trace-level audibility to completely mitigate error accumulation. We evaluate Mozi on PharmaBench, a curated benchmark for biomedical agents, demonstrating superior orchestration accuracy over existing baselines. Furthermore, through end-to-end therapeutic case studies, we demonstrate Mozi's ability to navigate massive chemical spaces, enforce stringent toxicity filters, and generate highly competitive in silico candidates, effectively transforming the LLM from a fragile conversationalist into a reliable, governed co-scientist.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.04800", "title": "MASQuant: Modality-Aware Smoothing Quantization for Multimodal Large Language Models", "authors": [ "Lulu Hu", "Wenhu Xiao", "Xin Chen", "Xinhua Xu", "Bowen Xu", "Kun Li", "Yongliang Tao" ], "github": "https://github.com/alibaba/EfficientAI", "project_page": "", "abstract": "Post-training quantization (PTQ) with computational invariance for Large Language Models~(LLMs) have demonstrated remarkable advances, however, their application to Multimodal Large Language Models~(MLLMs) presents substantial challenges. In this paper, we analyze SmoothQuant as a case study and identify two critical issues: Smoothing Misalignment and Cross-Modal Computational Invariance. To address these issues, we propose Modality-Aware Smoothing Quantization (MASQuant), a novel framework that introduces (1) Modality-Aware Smoothing (MAS), which learns separate, modality-specific smoothing factors to prevent Smoothing Misalignment, and (2) Cross-Modal Compensation (CMC), which addresses Cross-modal Computational Invariance by using SVD whitening to transform multi-modal activation differences into low-rank forms, enabling unified quantization across modalities. MASQuant demonstrates stable quantization performance across both dual-modal and tri-modal MLLMs. Experimental results show that MASQuant is competitive among the state-of-the-art PTQ algorithms. Source code: https://github.com/alibaba/EfficientAI.", "github_manual": "", "github_regex": "https://github.com/alibaba/EfficientAI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.04448", "title": "SkillNet: Create, Evaluate, and Connect AI Skills", "authors": [ "Yuan Liang", "Ruobin Zhong", "Haoming Xu", "Chen Jiang", "Yi Zhong", "Runnan Fang", "Jia-Chen Gu", "Shumin Deng", "Yunzhi Yao", "Mengru Wang", "Shuofei Qiao", "Xin Xu", "Tongtong Wu", "Kun Wang", "Yang Liu", "Zhen Bi", "Jungang Lou", "Yuchen Eleanor Jiang", "Hangcheng Zhu", "Gang Yu", "Haiwen Hong", "Longtao Huang", "Hui Xue", "Chenxi Wang", "Yijun Wang", "Zifei Shan", "Xi Chen", "Zhaopeng Tu", "Feiyu Xiong", "Xin Xie", "Peng Zhang", "Zhengke Gui", "Lei Liang", "Jun Zhou", "Chiyu Wu", "Jin Shang", "Yu Gong", "Junyu Lin", "Changliang Xu", "Hongjie Deng", "Wen Zhang", "Keyan Ding", "Qiang Zhang", "Fei Huang", "Ningyu Zhang", "Jeff Z. Pan", "Guilin Qi", "Haofen Wang", "Huajun Chen" ], "github": "https://github.com/openclaw/openclaw", "project_page": "", "abstract": "Current AI agents can flexibly invoke tools and execute complex tasks, yet their long-term advancement is hindered by the lack of systematic accumulation and transfer of skills. Without a unified mechanism for skill consolidation, agents frequently ``reinvent the wheel'', rediscovering solutions in isolated contexts without leveraging prior strategies. To overcome this limitation, we introduce SkillNet, an open infrastructure designed to create, evaluate, and organize AI skills at scale. SkillNet structures skills within a unified ontology that supports creating skills from heterogeneous sources, establishing rich relational connections, and performing multi-dimensional evaluation across Safety, Completeness, Executability, Maintainability, and Cost-awareness. Our infrastructure integrates a repository of over 200,000 skills, an interactive platform, and a versatile Python toolkit. Experimental evaluations on ALFWorld, WebShop, and ScienceWorld demonstrate that SkillNet significantly enhances agent performance, improving average rewards by 40% and reducing execution steps by 30% across multiple backbone models. By formalizing skills as evolving, composable assets, SkillNet provides a robust foundation for agents to move from transient experience to durable mastery.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openclaw/openclaw", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.00695", "title": "STMI: Segmentation-Guided Token Modulation with Cross-Modal Hypergraph Interaction for Multi-Modal Object Re-Identification", "authors": [ "Xingguo Xu", "Zhanyu Liu", "Weixiang Zhou", "Yuansheng Gao", "Junjie Cao", "Yuhao Wang", "Jixiang Luo", "Dell Zhang" ], "github": "", "project_page": "", "abstract": "Multi-modal object Re-Identification (ReID) aims to exploit complementary information from different modalities to retrieve specific objects. However, existing methods often rely on hard token filtering or simple fusion strategies, which can lead to the loss of discriminative cues and increased background interference. To address these challenges, we propose STMI, a novel multi-modal learning framework consisting of three key components: (1) Segmentation-Guided Feature Modulation (SFM) module leverages SAM-generated masks to enhance foreground representations and suppress background noise through learnable attention modulation; (2) Semantic Token Reallocation (STR) module employs learnable query tokens and an adaptive reallocation mechanism to extract compact and informative representations without discarding any tokens; (3) Cross-Modal Hypergraph Interaction (CHI) module constructs a unified hypergraph across modalities to capture high-order semantic relationships. Extensive experiments on public benchmarks (i.e., RGBNT201, RGBNT100, and MSVR310) demonstrate the effectiveness and robustness of our proposed STMI framework in multi-modal ReID scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.04737", "title": "Interactive Benchmarks", "authors": [ "Baoqing Yue", "Zihan Zhu", "Yifan Zhang", "Jichen Feng", "Hufei Yang", "Mengdi Wang" ], "github": "https://github.com/interactivebench/interactivebench", "project_page": "", "abstract": "Standard benchmarks have become increasingly unreliable due to saturation, subjectivity, and poor generalization. We argue that evaluating model's ability to acquire information actively is important to assess model's intelligence. We propose Interactive Benchmarks, a unified evaluation paradigm that assesses model's reasoning ability in an interactive process under budget constraints. We instantiate this framework across two settings: Interactive Proofs, where models interact with a judge to deduce objective truths or answers in logic and mathematics; and Interactive Games, where models reason strategically to maximize long-horizon utilities. Our results show that interactive benchmarks provide a robust and faithful assessment of model intelligence, revealing that there is still substantial room to improve in interactive scenarios. Project page: https://github.com/interactivebench/interactivebench", "github_manual": "", "github_regex": "https://github.com/interactivebench/interactivebench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.03942", "title": "Lightweight Visual Reasoning for Socially-Aware Robots", "authors": [ "Alessio Galatolo", "Ronald Cumbal", "Alexandros Rouchitsas", "Katie Winkle", "Didem G\u00fcrd\u00fcr Broo", "Ginevra Castellano" ], "github": "https://github.com/alessioGalatolo/VLM-Reasoning-for-Robotics", "project_page": "", "abstract": "Robots operating in shared human environments must not only navigate, interact, and detect their surroundings, they must also interpret and respond to dynamic, and often unpredictable, human behaviours. Although recent advances have shown promise in enhancing robotic perception and instruction-following using Vision-Language Models (VLMs), they remain limited in addressing the complexities of multimodal human-robot interactions (HRI). Motivated by this challenge, we introduce a lightweight language-to-vision feedback module that closes the loop between an LLM and the vision encoder in VLMs. The module projects image-token hidden states through a gated Multi-Layer Perceptron (MLP) back into the encoder input, prompting a second pass that reinterprets the scene under text context. We evaluate this approach on three robotics-centred tasks: navigation in a simulated environment (Habitat), sequential scene description (Mementos-Robotics), and human-intention recognition (our HRI dataset). Results show that our method improves Qwen 2.5 (7B) by 3.3% (less distance), +0.057 description score, and +2.93% accuracy, with less than 3% extra parameters; Gemma 3 (4B) and LLaVA OV 1.5 (4B) show mixed navigation results but gains +0.111,+0.055 and +10.81%,+4.79% on the latter two tasks. Code is available at https://github.com/alessioGalatolo/VLM-Reasoning-for-Robotics", "github_manual": "", "github_regex": "https://github.com/alessioGalatolo/VLM-Reasoning-for-Robotics", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2603.04553", "title": "Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling", "authors": [ "Tal Daniel", "Carl Qi", "Dan Haramati", "Amir Zadeh", "Chuan Li", "Aviv Tamar", "Deepak Pathak", "David Held" ], "github": "https://github.com/taldatech/lpwm", "project_page": "", "abstract": "We introduce Latent Particle World Model (LPWM), a self-supervised object-centric world model scaled to real-world multi-object datasets and applicable in decision-making. LPWM autonomously discovers keypoints, bounding boxes, and object masks directly from video data, enabling it to learn rich scene decompositions without supervision. Our architecture is trained end-to-end purely from videos and supports flexible conditioning on actions, language, and image goals. LPWM models stochastic particle dynamics via a novel latent action module and achieves state-of-the-art results on diverse real-world and synthetic datasets. Beyond stochastic video modeling, LPWM is readily applicable to decision-making, including goal-conditioned imitation learning, as we demonstrate in the paper. Code, data, pre-trained models and video rollouts are available: https://taldatech.github.io/lpwm-web", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/taldatech/lpwm", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-06", "arxiv_id": "2602.23440", "title": "Truncated Step-Level Sampling with Process Rewards for Retrieval-Augmented Reasoning", "authors": [ "Chris Samarinas", "Haw-Shiuan Chang", "Hamed Zamani" ], "github": "https://github.com/algoprog/SLATE", "project_page": "", "abstract": "Training large language models to reason with search engines via reinforcement learning is hindered by a fundamental credit assignment problem: existing methods such as Search-R1 provide only a sparse outcome reward after an entire multi-step trajectory, making it infeasible to attribute success or failure to individual reasoning and retrieval decisions. Process-reward methods like StepSearch alleviate this by introducing step-level supervision, but rely on heuristic rewards such as TF-IDF overlap with gold documents, and still sample k complete trajectories per example, retaining high gradient variance. We propose SLATE, a framework built on two complementary ideas: (1) truncated step-level sampling, which generates k trajectories that share a common prefix and differ only at the next step, and (2) dense LLM-as-judge rewards, which replace heuristic scoring with a capable LLM evaluator that assesses the quality of each reasoning step, search query, and answer, providing richer and more reliable supervision. We theoretically prove that under the same dense reward structure, truncated sampling reduces the variance of advantage estimates by up to a factor of T compared to full-trajectory sampling for T-step trajectories, yielding lower-variance, better-targeted policy gradients. Experiments on seven QA benchmarks confirm that SLATE consistently outperforms both sparse-reward and process-reward baselines, with the largest gains on harder multi-hop tasks and smaller models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/algoprog/SLATE", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.04639", "title": "RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies", "authors": [ "Yinpei Dai", "Hongze Fu", "Jayjun Lee", "Yuejiang Liu", "Haoran Zhang", "Jianing Yang", "Chelsea Finn", "Nima Fazeli", "Joyce Chai" ], "github": "https://github.com/RoboMME/SAM2Act", "project_page": "", "abstract": "Memory is critical for long-horizon and history-dependent robotic manipulation. Such tasks often involve counting repeated actions or manipulating objects that become temporarily occluded. Recent vision-language-action (VLA) models have begun to incorporate memory mechanisms; however, their evaluations remain confined to narrow, non-standardized settings. This limits their systematic understanding, comparison, and progress measurement. To address these challenges, we introduce RoboMME: a large-scale standardized benchmark for evaluating and advancing VLA models in long-horizon, history-dependent scenarios. Our benchmark comprises 16 manipulation tasks constructed under a carefully designed taxonomy that evaluates temporal, spatial, object, and procedural memory. We further develop a suite of 14 memory-augmented VLA variants built on the \u03c00.5 backbone to systematically explore different memory representations across multiple integration strategies. Experimental results show that the effectiveness of memory representations is highly task-dependent, with each design offering distinct advantages and limitations across different tasks. Videos and code can be found at our website https://robomme.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/RoboMME/SAM2Act", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.05706", "title": "Reasoning Models Struggle to Control their Chains of Thought", "authors": [ "Chen Yueh-Han", "Robert McCarthy", "Bruce W. Lee", "He He", "Ian Kivlichan", "Bowen Baker", "Micah Carroll", "Tomek Korbak" ], "github": "https://github.com/YuehHanChen/CoTControl", "project_page": "", "abstract": "Chain-of-thought (CoT) monitoring is a promising tool for detecting misbehaviors and understanding the motivations of modern reasoning models. However, if models can control what they verbalize in their CoT, it could undermine CoT monitorability. To measure this undesirable capability -- CoT controllability -- we introduce the CoT-Control evaluation suite, which includes tasks that require models to solve problems while adhering to CoT instructions, e.g., reasoning about a genetics question without using the word 'chromosome'. We show that reasoning models possess significantly lower CoT controllability than output controllability; for instance, Claude Sonnet 4.5 can control its CoT only 2.7% of the time but 61.9% when controlling its final output. We also find that CoT controllability is higher for larger models and decreases with more RL training, test-time compute, and increased problem difficulty. CoT controllability failures extend even to situations in which models are given incentives (as opposed to direct requests) to evade CoT monitors, although models exhibit slightly higher controllability when they are told they are being monitored. Similarly, eliciting controllability by adversarially optimizing prompts does not meaningfully increase controllability. Our results leave us cautiously optimistic that CoT controllability is currently unlikely to be a failure mode of CoT monitorability. However, the mechanism behind low controllability is not well understood. Given its importance for maintaining CoT monitorability, we recommend that frontier labs track CoT controllability in future models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YuehHanChen/CoTControl", "github_api_added_by": "auto" }, { "date": "2026-03-09", "arxiv_id": "2603.06408", "title": "Physical Simulator In-the-Loop Video Generation", "authors": [ "Lin Geng Foo", "Mark He Huang", "Alexandros Lattas", "Stylianos Moschoglou", "Thabo Beeler", "Christian Theobalt" ], "github": "", "project_page": "", "abstract": "Recent advances in diffusion-based video generation have achieved remarkable visual realism but still struggle to obey basic physical laws such as gravity, inertia, and collision. Generated objects often move inconsistently across frames, exhibit implausible dynamics, or violate physical constraints, limiting the realism and reliability of AI-generated videos. We address this gap by introducing Physical Simulator In-the-loop Video Generation (PSIVG), a novel framework that integrates a physical simulator into the video diffusion process. Starting from a template video generated by a pre-trained diffusion model, PSIVG reconstructs the 4D scene and foreground object meshes, initializes them within a physical simulator, and generates physically consistent trajectories. These simulated trajectories are then used to guide the video generator toward spatio-temporally physically coherent motion. To further improve texture consistency during object movement, we propose a Test-Time Texture Consistency Optimization (TTCO) technique that adapts text and feature embeddings based on pixel correspondences from the simulator. Comprehensive experiments demonstrate that PSIVG produces videos that better adhere to real-world physics while preserving visual quality and diversity. Project Page: https://vcai.mpi-inf.mpg.de/projects/PSIVG/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.06351", "title": "Dynamic Chunking Diffusion Transformer", "authors": [ "Akash Haridas", "Utkarsh Saxena", "Parsa Ashrafi Fashi", "Mehdi Rezagholizadeh", "Vikram Appia", "Emad Barsoum" ], "github": "", "project_page": "", "abstract": "Diffusion Transformers process images as fixed-length sequences of tokens produced by a static patchify operation. While effective, this design spends uniform compute on low- and high-information regions alike, ignoring that images contain regions of varying detail and that the denoising process progresses from coarse structure at early timesteps to fine detail at late timesteps. We introduce the Dynamic Chunking Diffusion Transformer (DC-DiT), which augments the DiT backbone with a learned encoder-router-decoder scaffold that adaptively compresses the 2D input into a shorter token sequence in a data-dependent manner using a chunking mechanism learned end-to-end with diffusion training. The mechanism learns to compress uniform background regions into fewer tokens and detail-rich regions into more tokens, with meaningful visual segmentations emerging without explicit supervision. Furthermore, it also learns to adapt its compression across diffusion timesteps, using fewer tokens at noisy stages and more tokens as fine details emerge. On class-conditional ImageNet 256{times}256, DC-DiT consistently improves FID and Inception Score over both parameter-matched and FLOP-matched DiT baselines across 4{times} and 16{times} compression, showing this is a promising technique with potential further applications to pixel-space, video and 3D generation. Beyond accuracy, DC-DiT is practical: it can be upcycled from pretrained DiT checkpoints with minimal post-training compute (up to 8{times} fewer training steps) and composes with other dynamic computation methods to further reduce generation FLOPs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.06199", "title": "FlashPrefill: Instantaneous Pattern Discovery and Thresholding for Ultra-Fast Long-Context Prefilling", "authors": [ "Qihang Fan", "Huaibo Huang", "Zhiying Wu", "Juqiu Wang", "Bingning Wang", "Ran He" ], "github": "", "project_page": "", "abstract": "Long-context modeling is a pivotal capability for Large Language Models, yet the quadratic complexity of attention remains a critical bottleneck, particularly during the compute-intensive prefilling phase. While various sparse attention mechanisms have been explored, they typically suffer from either significant search latency or insufficient sparsity. In this paper, we propose FlashPrefill, a framework enabling ultra-fast prefilling via instantaneous pattern discovery and thresholding. FlashPrefill leverages a fast block-searching technique to simultaneously locate dynamic vertical, slash, and block-sparse attention patterns. Crucially, it introduces a dynamic thresholding mechanism that bypasses the prohibitive overhead of sorting or accumulating attention scores while effectively eliminating the long-tail distribution to enhance sparsity. Extensive evaluations demonstrate that FlashPrefill achieves a substantial leap in efficiency, delivering an unprecedented 27.78x speedup on 256K sequences. Notably, unlike existing methods that incur efficiency degradation on shorter contexts, FlashPrefill maintains a 1.71x speedup even at a 4K context length, demonstrating its robustness and practical utility across varying sequence scales.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.05888", "title": "PixARMesh: Autoregressive Mesh-Native Single-View Scene Reconstruction", "authors": [ "Xiang Zhang", "Sohyun Yoo", "Hongrui Wu", "Chuan Li", "Jianwen Xie", "Zhuowen Tu" ], "github": "", "project_page": "", "abstract": "We introduce PixARMesh, a method to autoregressively reconstruct complete 3D indoor scene meshes directly from a single RGB image. Unlike prior methods that rely on implicit signed distance fields and post-hoc layout optimization, PixARMesh jointly predicts object layout and geometry within a unified model, producing coherent and artist-ready meshes in a single forward pass. Building on recent advances in mesh generative models, we augment a point-cloud encoder with pixel-aligned image features and global scene context via cross-attention, enabling accurate spatial reasoning from a single image. Scenes are generated autoregressively from a unified token stream containing context, pose, and mesh, yielding compact meshes with high-fidelity geometry. Experiments on synthetic and real-world datasets show that PixARMesh achieves state-of-the-art reconstruction quality while producing lightweight, high-quality meshes ready for downstream applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.06569", "title": "Penguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders", "authors": [ "Boqiang Zhang", "Lei Ke", "Ruihan Yang", "Qi Gao", "Tianyuan Qu", "Rossell Chen", "Dong Yu", "Leoweiliang" ], "github": "https://github.com/tencent-ailab/Penguin-VL", "project_page": "", "abstract": "Vision Language Model (VLM) development has largely relied on scaling model size, which hinders deployment on compute-constrained mobile and edge devices such as smartphones and robots. In this work, we explore the performance limits of compact (e.g., 2B and 8B) VLMs. We challenge the prevailing practice that state-of-the-art VLMs must rely on vision encoders initialized via massive contrastive pretraining (e.g., CLIP/SigLIP). We identify an objective mismatch: contrastive learning, optimized for discrimination, enforces coarse and category-level invariances that suppress fine-grained visual cues needed for dense captioning and complex VLM reasoning. To address this issue, we present Penguin-VL, whose vision encoder is initialized from a text-only LLM. Our experiments reveal that Penguin-Encoder serves as a superior alternative to traditional contrastive pretraining, unlocking a higher degree of visual fidelity and data efficiency for multimodal understanding. Across various image and video benchmarks, Penguin-VL achieves performance comparable to leading VLMs (e.g., Qwen3-VL) in mathematical reasoning and surpasses them in tasks such as document understanding, visual knowledge, and multi-perspective video understanding. Notably, these gains are achieved with a lightweight architecture, demonstrating that improved visual representation rather than model scaling is the primary driver of performance. Our ablations show that Penguin-Encoder consistently outperforms contrastive-pretrained encoders, preserving fine-grained spatial and temporal cues that are critical for dense perception and complex reasoning. This makes it a strong drop-in alternative for compute-efficient VLMs and enables high performance in resource-constrained settings. Code: https://github.com/tencent-ailab/Penguin-VL", "github_manual": "", "github_regex": "https://github.com/tencent-ailab/Penguin-VL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.02083", "title": "\u03c0-StepNFT: Wider Space Needs Finer Steps in Online RL for Flow-based VLAs", "authors": [ "Siting Wang", "Xiaofeng Wang", "Zheng Zhu", "Minnan Pei", "Xinyu Cui", "Cheng Deng", "Jian Zhao", "Guan Huang", "Haifeng Zhang", "Jun Wang" ], "github": "", "project_page": "", "abstract": "Flow-based vision-language-action (VLA) models excel in embodied control but suffer from intractable likelihoods during multi-step sampling, hindering online reinforcement learning. We propose \\textit{boldsymbol\u03c0-StepNFT} (Step-wise Negative-aware Fine-Tuning), a critic-and-likelihood-free framework that requires only a single forward pass per optimization step and eliminates auxiliary value networks. We identify that wider exploration spaces necessitate finer-grained, step-wise guidance for alignment. Empirically, \u03c0-StepNFT unlocks latent potential on LIBERO with competitive few-shot robustness. Moreover, it achieves superior generalization on ManiSkill, outperforming value-based baselines in OOD scenarios by preventing overfitting to multimodal features. This property offers a scalable solution promising for complex real-world applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.04918", "title": "BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning", "authors": [ "Yuan Li", "Bo Wang", "Yufei Gao", "Yuqian Yao", "Xinyuan Wang", "Zhangyue Yin", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Proximal constraints are fundamental to the stability of the Large Language Model reinforcement learning. While the canonical clipping mechanism in PPO serves as an efficient surrogate for trust regions, we identify a critical bottleneck: fixed bounds strictly constrain the upward update margin of low-probability actions, disproportionately suppressing high-advantage tail strategies and inducing rapid entropy collapse. To address this, we introduce Band-constrained Policy Optimization (BandPO). BandPO replaces canonical clipping with Band, a unified theoretical operator that projects trust regions defined by f-divergences into dynamic, probability-aware clipping intervals. Theoretical analysis confirms that Band effectively resolves this exploration bottleneck. We formulate this mapping as a convex optimization problem, guaranteeing a globally optimal numerical solution while deriving closed-form solutions for specific divergences. Extensive experiments across diverse models and datasets demonstrate that BandPO consistently outperforms canonical clipping and Clip-Higher, while robustly mitigating entropy collapse.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.05369", "title": "Progressive Residual Warmup for Language Model Pretraining", "authors": [ "Tianhao Chen", "Xin Xu", "Lu Yin", "Hao Chen", "Yang Wang", "Shizhe Diao", "Can Yang" ], "github": "https://github.com/dandingsky/ProRes", "project_page": "", "abstract": "Transformer architectures serve as the backbone for most modern Large Language Models, therefore their pretraining stability and convergence speed are of central concern. Motivated by the logical dependency of sequentially stacked layers, we propose Progressive Residual Warmup (ProRes) for language model pretraining. ProRes implements an \"early layer learns first\" philosophy by multiplying each layer's residual with a scalar that gradually warms up from 0 to 1, with deeper layers taking longer warmup steps. In this way, deeper layers wait for early layers to settle into a more stable regime before contributing to learning. We demonstrate the effectiveness of ProRes through pretraining experiments across various model scales, as well as normalization and initialization schemes. Comprehensive analysis shows that ProRes not only stabilizes pretraining but also introduces a unique optimization trajectory, leading to faster convergence, stronger generalization and better downstream performance. Our code is available at https://github.com/dandingsky/ProRes.", "github_manual": "", "github_regex": "https://github.com/dandingsky/ProRes", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.05940", "title": "SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration", "authors": [ "Peng Shurui", "Xin Lin", "Shi Luo", "Jincen Ou", "Dizhe Zhang", "Lu Qi", "Truong Nguyen", "Chao Ren" ], "github": "", "project_page": "", "abstract": "Image restoration under diverse degradations remains challenging for unified all-in-one frameworks due to feature interference and insufficient expert specialization. We propose SLER-IR, a spherical layer-wise expert routing framework that dynamically activates specialized experts across network layers. To ensure reliable routing, we introduce a Spherical Uniform Degradation Embedding with contrastive learning, which maps degradation representations onto a hypersphere to eliminate geometry bias in linear embedding spaces. In addition, a Global-Local Granularity Fusion (GLGF) module integrates global semantics and local degradation cues to address spatially non-uniform degradations and the train-test granularity gap. Experiments on three-task and five-task benchmarks demonstrate that SLER-IR achieves consistent improvements over state-of-the-art methods in both PSNR and SSIM. Code and models will be publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.04750", "title": "HiMAP-Travel: Hierarchical Multi-Agent Planning for Long-Horizon Constrained Travel", "authors": [ "The Viet Bui", "Wenjun Li", "Yong Liu" ], "github": "", "project_page": "", "abstract": "Sequential LLM agents fail on long-horizon planning with hard constraints like budgets and diversity requirements. As planning progresses and context grows, these agents drift from global constraints. We propose HiMAP-Travel, a hierarchical multi-agent framework that splits planning into strategic coordination and parallel day-level execution. A Coordinator allocates resources across days, while Day Executors plan independently in parallel. Three key mechanisms enable this: a transactional monitor enforcing budget and uniqueness constraints across parallel agents, a bargaining protocol allowing agents to reject infeasible sub-goals and trigger re-planning, and a single policy trained with GRPO that powers all agents through role conditioning. On TravelPlanner, HiMAP-Travel with Qwen3-8B achieves 52.78% validation and 52.65% test Final Pass Rate (FPR). In a controlled comparison with identical model, training, and tools, it outperforms the sequential DeepTravel baseline by +8.67~pp. It also surpasses ATLAS by +17.65~pp and MTP by +10.0~pp. On FlexTravelBench multi-turn scenarios, it achieves 44.34% (2-turn) and 37.42% (3-turn) FPR while reducing latency 2.5x through parallelization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.05630", "title": "Making Reconstruction FID Predictive of Diffusion Generation FID", "authors": [ "Tongda Xu", "Mingwei He", "Shady Abu-Hussein", "Jose Miguel Hernandez-Lobato", "Haotian Zhang", "Kai Zhao", "Chao Zhou", "Ya-Qin Zhang", "Yan Wang" ], "github": "https://github.com/tongdaxu/Making-rFID-Predictive-of-Diffusion-gFID", "project_page": "", "abstract": "It is well known that the reconstruction FID (rFID) of a VAE is poorly correlated with the generation FID (gFID) of a latent diffusion model. We propose interpolated FID (iFID), a simple variant of rFID that exhibits a strong correlation with gFID. Specifically, for each element in the dataset, we retrieve its nearest neighbor (NN) in the latent space and interpolate their latent representations. We then decode the interpolated latent and compute the FID between the decoded samples and the original dataset. Additionally, we refine the claim that rFID correlates poorly with gFID, by showing that rFID correlates with sample quality in the diffusion refinement phase, whereas iFID correlates with sample quality in the diffusion navigation phase. Furthermore, we provide an explanation for why iFID correlates well with gFID, and why reconstruction metrics are negatively correlated with gFID, by connecting to results in the diffusion generalization and hallucination. Empirically, iFID is the first metric to demonstrate a strong correlation with diffusion gFID, achieving Pearson linear and Spearman rank correlations approximately 0.85. The source code is provided in https://github.com/tongdaxu/Making-rFID-Predictive-of-Diffusion-gFID.", "github_manual": "", "github_regex": "https://github.com/tongdaxu/Making-rFID-Predictive-of-Diffusion-gFID", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2602.23408", "title": "Demystifying Action Space Design for Robotic Manipulation Policies", "authors": [ "Yuchun Feng", "Jinliang Zheng", "Zhihao Wang", "Dongxiu Liu", "Jianxiong Li", "Jiangmiao Pang", "Tai Wang", "Xianyuan Zhan" ], "github": "", "project_page": "", "abstract": "The specification of the action space plays a pivotal role in imitation-based robotic manipulation policy learning, fundamentally shaping the optimization landscape of policy learning. While recent advances have focused heavily on scaling training data and model capacity, the choice of action space remains guided by ad-hoc heuristics or legacy designs, leading to an ambiguous understanding of robotic policy design philosophies. To address this ambiguity, we conducted a large-scale and systematic empirical study, confirming that the action space does have significant and complex impacts on robotic policy learning. We dissect the action design space along temporal and spatial axes, facilitating a structured analysis of how these choices govern both policy learnability and control stability. Based on 13,000+ real-world rollouts on a bimanual robot and evaluation on 500+ trained models over four scenarios, we examine the trade-offs between absolute vs. delta representations, and joint-space vs. task-space parameterizations. Our large-scale results suggest that properly designing the policy to predict delta actions consistently improves performance, while joint-space and task-space representations offer complementary strengths, favoring control stability and generalization, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.05438", "title": "Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model", "authors": [ "Dongwon Kim", "Gawon Seo", "Jinsung Lee", "Minsu Cho", "Suha Kwak" ], "github": "", "project_page": "", "abstract": "World models provide a powerful framework for simulating environment dynamics conditioned on actions or instructions, enabling downstream tasks such as action planning or policy learning. Recent approaches leverage world models as learned simulators, but its application to decision-time planning remains computationally prohibitive for real-time control. A key bottleneck lies in latent representations: conventional tokenizers encode each observation into hundreds of tokens, making planning both slow and resource-intensive. To address this, we propose CompACT, a discrete tokenizer that compresses each observation into as few as 8 tokens, drastically reducing computational cost while preserving essential information for planning. An action-conditioned world model that occupies CompACT tokenizer achieves competitive planning performance with orders-of-magnitude faster planning, offering a practical step toward real-world deployment of world models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.00586", "title": "WildActor: Unconstrained Identity-Preserving Video Generation", "authors": [ "Qin Guo", "Tianyu Yang", "Xuanhua He", "Fei Shen", "Yong Zhang", "Zhuoliang Kang", "Xiaoming Wei", "Dan Xu" ], "github": "", "project_page": "", "abstract": "Production-ready human video generation requires digital actors to maintain strictly consistent full-body identities across dynamic shots, viewpoints and motions, a setting that remains challenging for existing methods. Prior methods often suffer from face-centric behavior that neglects body-level consistency, or produce copy-paste artifacts where subjects appear rigid due to pose locking. We present Actor-18M, a large-scale human video dataset designed to capture identity consistency under unconstrained viewpoints and environments. Actor-18M comprises 1.6M videos with 18M corresponding human images, covering both arbitrary views and canonical three-view representations. Leveraging Actor-18M, we propose WildActor, a framework for any-view conditioned human video generation. We introduce an Asymmetric Identity-Preserving Attention mechanism coupled with a Viewpoint-Adaptive Monte Carlo Sampling strategy that iteratively re-weights reference conditions by marginal utility for balanced manifold coverage. Evaluated on the proposed Actor-Bench, WildActor consistently preserves body identity under diverse shot compositions, large viewpoint transitions, and substantial motions, surpassing existing methods in these challenging settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.01666", "title": "Beyond the Grid: Layout-Informed Multi-Vector Retrieval with Parsed Visual Document Representations", "authors": [ "Yibo Yan", "Mingdong Ou", "Yi Cao", "Xin Zou", "Shuliang Liu", "Jiahao Huo", "Yu Huang", "James Kwok", "Xuming Hu" ], "github": "", "project_page": "", "abstract": "Harnessing the full potential of visually-rich documents requires retrieval systems that understand not just text, but intricate layouts, a core challenge in Visual Document Retrieval (VDR). The prevailing multi-vector architectures, while powerful, face a crucial storage bottleneck that current optimization strategies, such as embedding merging, pruning, or using abstract tokens, fail to resolve without compromising performance or ignoring vital layout cues. To address this, we introduce ColParse, a novel paradigm that leverages a document parsing model to generate a small set of layout-informed sub-image embeddings, which are then fused with a global page-level vector to create a compact and structurally-aware multi-vector representation. Extensive experiments demonstrate that our method reduces storage requirements by over 95% while simultaneously yielding significant performance gains across numerous benchmarks and base models. ColParse thus bridges the critical gap between the fine-grained accuracy of multi-vector retrieval and the practical demands of large-scale deployment, offering a new path towards efficient and interpretable multimodal information systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.05181", "title": "Mario: Multimodal Graph Reasoning with Large Language Models", "authors": [ "Yuanfu Sun", "Kang Li", "Pengkang Guo", "Jiajin Liu", "Qiaoyu Tan" ], "github": "https://github.com/sunyuanfu/Mario", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have opened new avenues for multimodal reasoning. Yet, most existing methods still rely on pretrained vision-language models (VLMs) to encode image-text pairs in isolation, ignoring the relational structure that real-world multimodal data naturally form. This motivates reasoning on multimodal graphs (MMGs), where each node has textual and visual attributes and edges provide structural cues. Enabling LLM-based reasoning on such heterogeneous multimodal signals while preserving graph topology introduces two key challenges: resolving weak cross-modal consistency and handling heterogeneous modality preference. To address this, we propose Mario, a unified framework that simultaneously resolves the two above challenges and enables effective LLM-based reasoning over MMGs. Mario consists of two innovative stages. Firstly, a graph-conditioned VLM design that jointly refines textual and visual features through fine-grained cross-modal contrastive learning guided by graph topology. Secondly, a modality-adaptive graph instruction tuning mechanism that organizes aligned multimodal features into graph-aware instruction views and employs a learnable router to surface, for each node and its neighborhood, the most informative modality configuration to the LLM. Extensive experiments across diverse MMG benchmarks demonstrate that Mario consistently outperforms state-of-the-art graph models in both supervised and zero-shot scenarios for node classification and link prediction. The code will be made available at https://github.com/sunyuanfu/Mario.", "github_manual": "", "github_regex": "https://github.com/sunyuanfu/Mario", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.06331", "title": "WorldCache: Accelerating World Models for Free via Heterogeneous Token Caching", "authors": [ "Weilun Feng", "Guoxin Fan", "Haotong Qin", "Chuanguang Yang", "Mingqiang Wu", "Yuqi Li", "Xiangqi Li", "Zhulin An", "Libo Huang", "Dingrui Wang", "Longlong Liao", "Michele Magno", "Yongjun Xu" ], "github": "https://github.com/FofGofx/WorldCache", "project_page": "", "abstract": "Diffusion-based world models have shown strong potential for unified world simulation, but the iterative denoising remains too costly for interactive use and long-horizon rollouts. While feature caching can accelerate inference without training, we find that policies designed for single-modal diffusion transfer poorly to world models due to two world-model-specific obstacles: token heterogeneity from multi-modal coupling and spatial variation, and non-uniform temporal dynamics where a small set of hard tokens drives error growth, making uniform skipping either unstable or overly conservative. We propose WorldCache, a caching framework tailored to diffusion world models. We introduce Curvature-guided Heterogeneous Token Prediction, which uses a physics-grounded curvature score to estimate token predictability and applies a Hermite-guided damped predictor for chaotic tokens with abrupt direction changes. We also design Chaotic-prioritized Adaptive Skipping, which accumulates a curvature-normalized, dimensionless drift signal and recomputes only when bottleneck tokens begin to drift. Experiments on diffusion world models show that WorldCache delivers up to 3.7times end-to-end speedups while maintaining 98\\% rollout quality, demonstrating the vast advantages and practicality of WorldCache in resource-constrained scenarios. Our code is released in https://github.com/FofGofx/WorldCache.", "github_manual": "", "github_regex": "https://github.com/FofGofx/WorldCache", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.05280", "title": "Layer by layer, module by module: Choose both for optimal OOD probing of ViT", "authors": [ "Ambroise Odonnat", "Vasilii Feofanov", "Laetitia Chapel", "Romain Tavenard", "Ievgen Redko" ], "github": "", "project_page": "", "abstract": "Recent studies have observed that intermediate layers of foundation models often yield more discriminative representations than the final layer. While initially attributed to autoregressive pretraining, this phenomenon has also been identified in models trained via supervised and discriminative self-supervised objectives. In this paper, we conduct a comprehensive study to analyze the behavior of intermediate layers in pretrained vision transformers. Through extensive linear probing experiments across a diverse set of image classification benchmarks, we find that distribution shift between pretraining and downstream data is the primary cause of performance degradation in deeper layers. Furthermore, we perform a fine-grained analysis at the module level. Our findings reveal that standard probing of transformer block outputs is suboptimal; instead, probing the activation within the feedforward network yields the best performance under significant distribution shift, whereas the normalized output of the multi-head self-attention module is optimal when the shift is weak.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2602.22839", "title": "DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation", "authors": [ "Hao Zheng", "Guozhao Mo", "Xinru Yan", "Qianhao Yuan", "Wenkai Zhang", "Xuanang Chen", "Yaojie Lu", "Hongyu Lin", "Xianpei Han", "Le Sun" ], "github": "https://github.com/icip-cas/PPTAgent", "project_page": "", "abstract": "Presentation generation requires deep content research, coherent visual design, and iterative refinement based on observation. However, existing presentation agents often rely on predefined workflows and fixed templates. To address this, we present DeepPresenter, an agentic framework that adapts to diverse user intents, enables effective feedback-driven refinement, and generalizes beyond a scripted pipeline. Specifically, DeepPresenter autonomously plans, renders, and revises intermediate slide artifacts to support long-horizon refinement with environmental observations. Furthermore, rather than relying on self-reflection over internal signals (e.g., reasoning traces), our environment-grounded reflection conditions the generation process on perceptual artifact states (e.g., rendered slides), enabling the system to identify and correct presentation-specific issues during execution. Results on the evaluation set covering diverse presentation-generation scenarios show that DeepPresenter achieves state-of-the-art performance, and the fine-tuned 9B model remains highly competitive at substantially lower cost. Our project is available at: https://github.com/icip-cas/PPTAgent", "github_manual": "", "github_regex": "https://github.com/icip-cas/PPTAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.04445", "title": "Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey", "authors": [ "Yasmin Moslem", "John D. Kelleher" ], "github": "", "project_page": "", "abstract": "The rapid growth of large language models (LLMs) with diverse capabilities, costs, and domains has created a critical need for intelligent model selection at inference time. While smaller models suffice for routine queries, complex tasks demand more capable models. However, static model deployment does not account for the complexity and domain of incoming queries, leading to suboptimal performance and increased costs. Dynamic routing systems that adaptively select models based on query characteristics have emerged as a solution to this challenge. We provide a systematic analysis of state-of-the-art multi-LLM routing and cascading approaches. In contrast to mixture-of-experts architectures, which route within a single model, we study routing across multiple independently trained LLMs. We cover diverse routing paradigms, including query difficulty, human preferences, clustering, uncertainty quantification, reinforcement learning, multimodality, and cascading. For each paradigm, we analyze representative methods and examine key trade-offs. Beyond taxonomy, we introduce a conceptual framework that characterizes routing systems along three dimensions: when decisions are made, what information is used, and how they are computed. This perspective highlights that practical systems are often compositional, integrating multiple paradigms under operational constraints. Our analysis demonstrates that effective multi-LLM routing requires balancing competing objectives. Choosing the optimal routing strategy depends on deployment and computational constraints. Well-designed routing systems can outperform even the most powerful individual models by strategically leveraging specialized capabilities across models while maximizing efficiency gains. Meanwhile, open challenges remain in developing routing mechanisms that generalize across diverse architectures, modalities, and applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.06014", "title": "EffectMaker: Unifying Reasoning and Generation for Customized Visual Effect Creation", "authors": [ "Shiyuan Yang", "Ruihuang Li", "Jiale Tao", "Shuai Shao", "Qinglin Lu", "Jing Liao" ], "github": "https://github.com/ysy31415/EffectMaker", "project_page": "", "abstract": "Visual effects (VFX) are essential for enhancing the expressiveness and creativity of video content, yet producing high-quality effects typically requires expert knowledge and costly production pipelines. Existing AIGC systems face significant challenges in VFX generation due to the scarcity of effect-specific data and the inherent difficulty of modeling supernatural or stylized effects. Moreover, these approaches often require per-effect fine-tuning, which severely limits their scalability and generalization to novel VFX. In this work, we present EffectMaker, a unified reasoning-generation framework that enables reference-based VFX customization. EffectMaker employs a multimodal large language model to interpret high-level effect semantics and reason about how they should adapt to a target subject, while a diffusion transformer leverages in-context learning to capture fine-grained visual cues from reference videos. These two components form a semantic-visual dual-path guidance mechanism that enables accurate, controllable, and effect-consistent synthesis without per-effect fine-tuning. Furthermore, we construct EffectData, the largest high-quality synthetic dataset containing 130k videos across 3k VFX categories, to improve generalization and scalability. Experiments show that EffectMaker achieves superior visual quality and effect consistency over state-of-the-art baselines, offering a scalable and flexible paradigm for customized VFX generation. Project page: https://effectmaker.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ysy31415/EffectMaker", "github_api_added_by": "auto" }, { "date": "2026-03-09", "arxiv_id": "2603.05494", "title": "Censored LLMs as a Natural Testbed for Secret Knowledge Elicitation", "authors": [ "Helena Casademunt", "Bartosz Cywi\u0144ski", "Khoi Tran", "Arya Jakkli", "Samuel Marks", "Neel Nanda" ], "github": "", "project_page": "", "abstract": "Large language models sometimes produce false or misleading responses. Two approaches to this problem are honesty elicitation -- modifying prompts or weights so that the model answers truthfully -- and lie detection -- classifying whether a given response is false. Prior work evaluates such methods on models specifically trained to lie or conceal information, but these artificial constructions may not resemble naturally-occurring dishonesty. We instead study open-weights LLMs from Chinese developers, which are trained to censor politically sensitive topics: Qwen3 models frequently produce falsehoods about subjects like Falun Gong or the Tiananmen protests while occasionally answering correctly, indicating they possess knowledge they are trained to suppress. Using this as a testbed, we evaluate a suite of elicitation and lie detection techniques. For honesty elicitation, sampling without a chat template, few-shot prompting, and fine-tuning on generic honesty data most reliably increase truthful responses. For lie detection, prompting the censored model to classify its own responses performs near an uncensored-model upper bound, and linear probes trained on unrelated data offer a cheaper alternative. The strongest honesty elicitation techniques also transfer to frontier open-weights models including DeepSeek R1. Notably, no technique fully eliminates false responses. We release all prompts, code, and transcripts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.04738", "title": "IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation", "authors": [ "Bosi Wen", "Yilin Niu", "Cunxiang Wang", "Xiaoying Ling", "Ying Zhang", "Pei Ke", "Hongning Wang", "Minlie Huang" ], "github": "https://github.com/thu-coai/IF-RewardBench", "project_page": "", "abstract": "Instruction-following is a foundational capability of large language models (LLMs), with its improvement hinging on scalable and accurate feedback from judge models. However, the reliability of current judge models in instruction-following remains underexplored due to several deficiencies of existing meta-evaluation benchmarks, such as their insufficient data coverage and oversimplified pairwise evaluation paradigms that misalign with model optimization scenarios. To this end, we propose IF-RewardBench, a comprehensive meta-evaluation benchmark for instruction-following that covers diverse instruction and constraint types. For each instruction, we construct a preference graph containing all pairwise preferences among multiple responses based on instruction-following quality. This design enables a listwise evaluation paradigm that assesses the capabilities of judge models to rank multiple responses, which is essential in guiding model alignment. Extensive experiments on IF-RewardBench reveal significant deficiencies in current judge models and demonstrate that our benchmark achieves a stronger positive correlation with downstream task performance compared to existing benchmarks. Our codes and data are available at https://github.com/thu-coai/IF-RewardBench.", "github_manual": "", "github_regex": "https://github.com/thu-coai/IF-RewardBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.04370", "title": "\u03c4-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge", "authors": [ "Quan Shi", "Alexandra Zytek", "Pedram Razavi", "Karthik Narasimhan", "Victor Barres" ], "github": "", "project_page": "", "abstract": "Conversational agents are increasingly deployed in knowledge-intensive settings, where correct behavior depends on retrieving and applying domain-specific knowledge from large, proprietary, and unstructured corpora during live interactions with users. Yet most existing benchmarks evaluate retrieval or tool use independently of each other, creating a gap in realistic, fully agentic evaluation over unstructured data in long-horizon interactions. We introduce \u03c4-Knowledge, an extension of \u03c4-Bench for evaluating agents in environments where success depends on coordinating external, natural-language knowledge with tool outputs to produce verifiable, policy-compliant state changes. Our new domain, \u03c4-Banking, models realistic fintech customer support workflows in which agents must navigate roughly 700 interconnected knowledge documents while executing tool-mediated account updates. Across embedding-based retrieval and terminal-based search, even frontier models with high reasoning budgets achieve only sim25.5% pass^1, with reliability degrading sharply over repeated trials. Agents struggle to retrieve the correct documents from densely interlinked knowledge bases and to reason accurately over complex internal policies. Overall, \u03c4-Knowledge provides a realistic testbed for developing agents that integrate unstructured knowledge in human-facing deployments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.01193", "title": "Operator Learning Using Weak Supervision from Walk-on-Spheres", "authors": [ "Hrishikesh Viswanath", "Hong Chul Nam", "Xi Deng", "Julius Berner", "Anima Anandkumar", "Aniket Bera" ], "github": "https://github.com/neuraloperator/WoS-NO", "project_page": "", "abstract": "Training neural PDE solvers is often bottlenecked by expensive data generation or unstable physics-informed neural network (PINN) involving challenging optimization landscapes due to higher-order derivatives. To tackle this issue, we propose an alternative approach using Monte Carlo approaches to estimate the solution to the PDE as a stochastic process for weak supervision during training. Leveraging the Walk-on-Spheres method, we introduce a learning scheme called Walk-on-Spheres Neural Operator (WoS-NO) which uses weak supervision from WoS to train any given neural operator. We propose to amortize the cost of Monte Carlo walks across the distribution of PDE instances using stochastic representations from the WoS algorithm to generate cheap, noisy, estimates of the PDE solution during training. This is formulated into a data-free physics-informed objective where a neural operator is trained to regress against these weak supervisions, allowing the operator to learn a generalized solution map for an entire family of PDEs. This strategy does not require expensive pre-computed datasets, avoids computing higher-order derivatives for loss functions that are memory-intensive and unstable, and demonstrates zero-shot generalization to novel PDE parameters and domains. Experiments show that for the same number of training steps, our method exhibits up to 8.75times improvement in L_2-error compared to standard physics-informed training schemes, up to 6.31times improvement in training speed, and reductions of up to 2.97times in GPU memory consumption. We present the code at https://github.com/neuraloperator/WoS-NO", "github_manual": "", "github_regex": "https://github.com/neuraloperator/WoS-NO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2602.23280", "title": "Physics Informed Viscous Value Representations", "authors": [ "Hrishikesh Viswanath", "Juanwu Lu", "S. Talha Bukhari", "Damon Conover", "Ziran Wang", "Aniket Bera" ], "github": "https://github.com/HrishikeshVish/phys-fk-value-GCRL", "project_page": "", "abstract": "Offline goal-conditioned reinforcement learning (GCRL) learns goal-conditioned policies from static pre-collected datasets. However, accurate value estimation remains a challenge due to the limited coverage of the state-action space. Recent physics-informed approaches have sought to address this by imposing physical and geometric constraints on the value function through regularization defined over first-order partial differential equations (PDEs), such as the Eikonal equation. However, these formulations can often be ill-posed in complex, high-dimensional environments. In this work, we propose a physics-informed regularization derived from the viscosity solution of the Hamilton-Jacobi-Bellman (HJB) equation. By providing a physics-based inductive bias, our approach grounds the learning process in optimal control theory, explicitly regularizing and bounding updates during value iterations. Furthermore, we leverage the Feynman-Kac theorem to recast the PDE solution as an expectation, enabling a tractable Monte Carlo estimation of the objective that avoids numerical instability in higher-order gradients. Experiments demonstrate that our method improves geometric consistency, making it broadly applicable to navigation and high-dimensional, complex manipulation tasks. Open-source codes are available at https://github.com/HrishikeshVish/phys-fk-value-GCRL.", "github_manual": "", "github_regex": "https://github.com/HrishikeshVish/phys-fk-value-GCRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.05607", "title": "DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces", "authors": [ "Mohammad Sadil Khan", "Muhammad Usama", "Rolandos Alexandros Potamias", "Didier Stricker", "Muhammad Zeshan Afzal", "Jiankang Deng", "Ismail Elezi" ], "github": "", "project_page": "", "abstract": "Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit design histories or boundary representation (BRep) labels. Meanwhile, millions of unannotated 3D meshes remain untapped, limiting progress in scalable CAD generation. To address this, we propose DreamCAD, a multi-modal generative framework that directly produces editable BReps from point-level supervision, without CAD-specific annotations. DreamCAD represents each BRep as a set of parametric patches (e.g., B\u00e9zier surfaces) and uses a differentiable tessellation method to generate meshes. This enables large-scale training on 3D datasets while reconstructing connected and editable surfaces. Furthermore, we introduce CADCap-1M, the largest CAD captioning dataset to date, with 1M+ descriptions generated using GPT-5 for advancing text-to-CAD research. DreamCAD achieves state-of-the-art performance on ABC and Objaverse benchmarks across text, image, and point modalities, improving geometric fidelity and surpassing 75% user preference. Code and dataset will be publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.04948", "title": "nabla-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Latent Space", "authors": [ "Peihao Wang", "Ruisi Cai", "Zhen Wang", "Hongyuan Mei", "Qiang Liu", "Pan Li", "Zhangyang Wang" ], "github": "", "project_page": "", "abstract": "Scaling inference-time compute for Large Language Models (LLMs) has unlocked unprecedented reasoning capabilities. However, existing inference-time scaling methods typically rely on inefficient and suboptimal discrete search algorithms or trial-and-error prompting to improve the online policy. In this paper, we propose nabla-Reasoner, an iterative generation framework that integrates differentiable optimization over token logits into the decoding loop to refine the policy on the fly. Our core component, Differentiable Textual Optimization (DTO), leverages gradient signals from both the LLM's likelihood and a reward model to refine textual representations. nabla-Reasoner further incorporates rejection sampling and acceleration design to robustify and speed up decoding. Theoretically, we show that performing inference-time gradient descent in the sample space to maximize reward is dual to aligning an LLM policy via KL-regularized reinforcement learning. Empirically, nabla-Reasoner achieves over 20% accuracy improvement on a challenging mathematical reasoning benchmark, while reducing number of model calls by approximately 10-40% compared to strong baselines. Overall, our work introduces a paradigm shift from zeroth-order search to first-order optimization at test time, offering a cost-effective path to amplify LLM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-09", "arxiv_id": "2603.06743", "title": "Stabilizing Reinforcement Learning for Diffusion Language Models", "authors": [ "Jianyuan Zhong", "Kaibo Wang", "Ding Ding", "Zijin Feng", "Haoli Bai", "Yang Xiang", "Jiacheng Sun", "Qiang Xu" ], "github": "", "project_page": "", "abstract": "Group Relative Policy Optimization (GRPO) is highly effective for post-training autoregressive (AR) language models, yet its direct application to diffusion large language models (dLLMs) often triggers reward collapse. We identify two sources of incompatibility. First, GRPO relies on importance ratios defined by sequence probabilities, which are intractable in dLLMs and must be estimated (e.g., via ELBO-based or mean-field likelihood proxies), yielding inherently noisy ratios. Second, standard GRPO's formulation is not designed for estimated ratios: its conditional clipping can be anomalously bypassed by model-agnostic estimation noise, producing gradient spikes, while its fixed group-size normalization amplifies gradient-magnitude fluctuations under high-variance ratio estimates. We show these effects form a self-reinforcing instability loop that drives policy drift and further increases ratio variance. To break this loop, we propose StableDRL, a reformulation of GRPO tailored for dLLMs that uses (i) unconditional clipping to suppress outlier-induced spikes and (ii) self-normalization to constrain updates within the convex hull of per-sample gradients. We further extend StableDRL to block-wise diffusion models via a staircase attention mechanism.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.02554", "title": "Generalizable Knowledge Distillation from Vision Foundation Models for Semantic Segmentation", "authors": [ "Chonghua Lv", "Dong Zhao", "Shuang Wang", "Dou Quan", "Ning Huyan", "Nicu Sebe", "Zhun Zhong" ], "github": "https://github.com/Younger-hua/GKD", "project_page": "", "abstract": "Knowledge distillation (KD) has been widely applied in semantic segmentation to compress large models, but conventional approaches primarily preserve in-domain accuracy while neglecting out-of-domain generalization, which is essential under distribution shifts. This limitation becomes more severe with the emergence of vision foundation models (VFMs): although VFMs exhibit strong robustness on unseen data, distilling them with conventional KD often compromises this ability. We propose Generalizable Knowledge Distillation (GKD), a multi-stage framework that explicitly enhances generalization. GKD decouples representation learning from task learning. In the first stage, the student acquires domain-agnostic representations through selective feature distillation, and in the second stage, these representations are frozen for task adaptation, thereby mitigating overfitting to visible domains. To further support transfer, we introduce a query-based soft distillation mechanism, where student features act as queries to teacher representations to selectively retrieve transferable spatial knowledge from VFMs. Extensive experiments on five domain generalization benchmarks demonstrate that GKD consistently outperforms existing KD methods, achieving average gains of +1.9% in foundation-to-foundation (F2F) and +10.6% in foundation-to-local (F2L) distillation. The code will be available at https://github.com/Younger-hua/GKD.", "github_manual": "", "github_regex": "https://github.com/Younger-hua/GKD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.08013", "title": "PIRA-Bench: A Transition from Reactive GUI Agents to GUI-based Proactive Intent Recommendation Agents", "authors": [ "Yuxiang Chai", "Shunye Tang", "Han Xiao", "Rui Liu", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "Current Graphical User Interface (GUI) agents operate primarily under a reactive paradigm: a user must provide an explicit instruction for the agent to execute a task. However, an intelligent AI assistant should be proactive, which is capable of anticipating user intentions directly from continuous visual inputs, such as mobile or desktop screenshots, and offering timely recommendations without explicit user prompting. Transitioning to this proactive paradigm presents significant challenges. Real-world screen activity is rarely linear; it consists of long-horizon trajectories fraught with noisy browsing, meaningless actions, and multithreaded task-switching. To address this gap, we introduce PIRA-Bench (Proactive Intent Recommendation Agent Benchmark), a novel benchmark for evaluating multimodal large language models (MLLMs) on continuous, weakly-supervised visual inputs. Unlike reactive datasets, PIRA-Bench features complex trajectories with multiple interleaved intents and noisy segments with various user profile contexts, challenging agents to detect actionable events while fitting to user preferences. Furthermore, we propose the PIRF baseline, a memory-aware, state-tracking framework that empowers general MLLMs to manage multiple task threads and handle misleading visual inputs. PIRA-Bench serves as an initial step toward robust and proactive GUI-based personal assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07561", "title": "PureCC: Pure Learning for Text-to-Image Concept Customization", "authors": [ "Zhichao Liao", "Xiaole Xian", "Qingyu Li", "Wenyu Qin", "Meng Wang", "Weicheng Xie", "Siyang Song", "Pingfa Feng", "Long Zeng", "Liang Pan" ], "github": "https://github.com/lzc-sg/PureCC", "project_page": "", "abstract": "Existing concept customization methods have achieved remarkable outcomes in high-fidelity and multi-concept customization. However, they often neglect the influence on the original model's behavior and capabilities when learning new personalized concepts. To address this issue, we propose PureCC. PureCC introduces a novel decoupled learning objective for concept customization, which combines the implicit guidance of the target concept with the original conditional prediction. This separated form enables PureCC to substantially focus on the original model during training. Moreover, based on this objective, PureCC designs a dual-branch training pipeline that includes a frozen extractor providing purified target concept representations as implicit guidance and a trainable flow model producing the original conditional prediction, jointly achieving pure learning for personalized concepts. Furthermore, PureCC introduces a novel adaptive guidance scale \u03bb^star to dynamically adjust the guidance strength of the target concept, balancing customization fidelity and model preservation. Extensive experiments show that PureCC achieves state-of-the-art performance in preserving the original behavior and capabilities while enabling high-fidelity concept customization. The code is available at https://github.com/lzc-sg/PureCC.", "github_manual": "", "github_regex": "https://github.com/lzc-sg/PureCC", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.03825", "title": "From Narrow to Panoramic Vision: Attention-Guided Cold-Start Reshapes Multimodal Reasoning", "authors": [ "Ruilin Luo", "Chufan Shi", "Yizhen Zhang", "Cheng Yang", "Songtao Jiang", "Tongkun Guan", "Ruizhe Chen", "Ruihang Chu", "Peng Wang", "Mingkun Yang", "Yujiu Yang", "Junyang Lin", "Zhibo Yang" ], "github": "https://github.com/lrlbbzl/Qwen-AVAR", "project_page": "", "abstract": "The cold-start initialization stage plays a pivotal role in training Multimodal Large Reasoning Models (MLRMs), yet its mechanisms remain insufficiently understood. To analyze this stage, we introduce the Visual Attention Score (VAS), an attention-based metric that quantifies how much a model attends to visual tokens. We find that reasoning performance is strongly correlated with VAS (r=0.9616): models with higher VAS achieve substantially stronger multimodal reasoning. Surprisingly, multimodal cold-start fails to elevate VAS, resulting in attention distributions close to the base model, whereas text-only cold-start leads to a clear increase. We term this counter-intuitive phenomenon Lazy Attention Localization. To validate its causal role, we design training-free interventions that directly modulate attention allocation during inference, performance gains of 1-2% without any retraining. Building on these insights, we further propose Attention-Guided Visual Anchoring and Reflection (AVAR), a comprehensive cold-start framework that integrates visual-anchored data synthesis, attention-guided objectives, and visual-anchored reward shaping. Applied to Qwen2.5-VL-7B, AVAR achieves an average gain of 7.0% across 7 multimodal reasoning benchmarks. Ablation studies further confirm that each component of AVAR contributes step-wise to the overall gains. The code, data, and models are available at https://github.com/lrlbbzl/Qwen-AVAR.", "github_manual": "", "github_regex": "https://github.com/lrlbbzl/Qwen-AVAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07660", "title": "Holi-Spatial: Evolving Video Streams into Holistic 3D Spatial Intelligence", "authors": [ "Yuanyuan Gao", "Hao Li", "Yifei Liu", "Xinhao Ji", "Yuning Gong", "Yuanjun Liao", "Fangfu Liu", "Manyuan Zhang", "Yuchen Yang", "Dan Xu", "Xue Yang", "Huaxi Huang", "Hongjie Zhang", "Ziwei Liu", "Xiao Sun", "Dingwen Zhang", "Zhihang Zhong" ], "github": "", "project_page": "", "abstract": "The pursuit of spatial intelligence fundamentally relies on access to large-scale, fine-grained 3D data. However, existing approaches predominantly construct spatial understanding benchmarks by generating question-answer (QA) pairs from a limited number of manually annotated datasets, rather than systematically annotating new large-scale 3D scenes from raw web data. As a result, their scalability is severely constrained, and model performance is further hindered by domain gaps inherent in these narrowly curated datasets. In this work, we propose Holi-Spatial, the first fully automated, large-scale, spatially-aware multimodal dataset, constructed from raw video inputs without human intervention, using the proposed data curation pipeline. Holi-Spatial supports multi-level spatial supervision, ranging from geometrically accurate 3D Gaussian Splatting (3DGS) reconstructions with rendered depth maps to object-level and relational semantic annotations, together with corresponding spatial Question-Answer (QA) pairs. Following a principled and systematic pipeline, we further construct Holi-Spatial-4M, the first large-scale, high-quality 3D semantic dataset, containing 12K optimized 3DGS scenes, 1.3M 2D masks, 320K 3D bounding boxes, 320K instance captions, 1.2M 3D grounding instances, and 1.2M spatial QA pairs spanning diverse geometric, relational, and semantic reasoning tasks. Holi-Spatial demonstrates exceptional performance in data curation quality, significantly outperforming existing feed-forward and per-scene optimized methods on datasets such as ScanNet, ScanNet++, and DL3DV. Furthermore, fine-tuning Vision-Language Models (VLMs) on spatial reasoning tasks using this dataset has also led to substantial improvements in model performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07980", "title": "\\$OneMillion-Bench: How Far are Language Agents from Human Experts?", "authors": [ "Qianyu Yang", "Yang Liu", "Jiaqi Li", "Jun Bai", "Hao Chen", "Kaiyuan Chen", "Tiliang Duan", "Jiayun Dong", "Xiaobo Hu", "Zixia Jia", "Yang Liu", "Tao Peng", "Yixin Ren", "Ran Tian", "Zaiyuan Wang", "Yanglihong Xiao", "Gang Yao", "Lingyue Yin", "Ge Zhang", "Chun Zhang", "Jianpeng Jiao", "Zilong Zheng", "Yuan Gong" ], "github": "", "project_page": "", "abstract": "As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks remain largely confined to structured or exam-style tasks that fall short of real-world professional demands. To this end, we introduce \\OneMillion-Bench OneMillion-Bench, a benchmark of 400 expert-curated tasks spanning Law, Finance, Industry, Healthcare, and Natural Science, built to evaluate agents across economically consequential scenarios. Unlike prior work, the benchmark requires retrieving authoritative sources, resolving conflicting evidence, applying domain-specific rules, and making constraint decisions, where correctness depends as much on the reasoning process as the final answer. We adopt a rubric-based evaluation protocol scoring factual accuracy, logical coherence, practical feasibility, and professional compliance, focused on expert-level problems to ensure meaningful differentiation across agents. Together, \\$OneMillion-Bench provides a unified testbed for assessing agentic reliability, professional depth, and practical readiness in domain-intensive scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.03872", "title": "Believe Your Model: Distribution-Guided Confidence Calibration", "authors": [ "Xizhong Yang", "Haotian Zhang", "Huiming Wang", "Mofei Song" ], "github": "https://github.com/yxizhong/SSC", "project_page": "", "abstract": "Large Reasoning Models have demonstrated remarkable performance with the advancement of test-time scaling techniques, which enhances prediction accuracy by generating multiple candidate responses and selecting the most reliable answer. While prior work has analyzed that internal model signals like confidence scores can partly indicate response correctness and exhibit a distributional correlation with accuracy, such distributional information has not been fully utilized to guide answer selection. Motivated by this, we propose DistriVoting, which incorporates distributional priors as another signal alongside confidence during voting. Specifically, our method (1) first decomposes the mixed confidence distribution into positive and negative components using Gaussian Mixture Models, (2) then applies a reject filter based on positive/negative samples from them to mitigate overlap between the two distributions. Besides, to further alleviate the overlap from the perspective of distribution itself, we propose SelfStepConf, which uses step-level confidence to dynamically adjust inference process, increasing the separation between the two distributions to improve the reliability of confidences in voting. Experiments across 16 models and 5 benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yxizhong/SSC", "github_api_added_by": "auto" }, { "date": "2026-03-10", "arxiv_id": "2603.08708", "title": "FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models", "authors": [ "Haoyang Li", "Liang Wang", "Siyu Zhou", "Jiacheng Sun", "Jing Jiang", "Chao Wang", "Guodong Long", "Yan Peng" ], "github": "https://github.com/JREion/FVG-PT", "project_page": "", "abstract": "CLIP-based prompt tuning enables pretrained Vision-Language Models (VLMs) to efficiently adapt to downstream tasks. Although existing studies have made significant progress, they pay limited attention to changes in the internal attention representations of VLMs during the tuning process. In this paper, we attribute the failure modes of prompt tuning predictions to shifts in foreground attention of the visual encoder, and propose Foreground View-Guided Prompt Tuning (FVG-PT), an adaptive plug-and-play foreground attention guidance module, to alleviate the shifts. Concretely, FVG-PT introduces a learnable Foreground Reliability Gate to automatically enhance the foreground view quality, applies a Foreground Distillation Compensation module to guide visual attention toward the foreground, and further introduces a Prior Calibration module to mitigate generalization degradation caused by excessive focus on the foreground. Experiments on multiple backbone models and datasets show the effectiveness and compatibility of FVG-PT. Codes are available at: https://github.com/JREion/FVG-PT", "github_manual": "", "github_regex": "https://github.com/JREion/FVG-PT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07475", "title": "Skip to the Good Part: Representation Structure & Inference-Time Layer Skipping in Diffusion vs. Autoregressive LLMs", "authors": [ "Raghavv Goel", "Risheek Garrepalli", "Sudhanshu Agrawal", "Chris Lott", "Mingu Lee", "Fatih Porikli" ], "github": "", "project_page": "", "abstract": "Autoregressive (AR) language models form representations incrementally through left-to-right prediction, whereas diffusion language models (dLLMs) are trained via full-sequence denoising. Although recent dLLMs match AR performance, it remains unclear whether diffusion objectives fundamentally reshape internal representations across depth. We perform the first layer- and token-wise representational analysis comparing native dLLMs (LLaDA), native AR models (Qwen2.5), and AR-initialized dLLMs (Dream-7B). We find that diffusion objectives result in different, more hierarchical abstractions with substantial early-layer redundancy and reduced recency bias, while AR objectives produce tightly coupled, depth-dependent representations. Critically, AR-initialized dLLMs retain AR-like representational dynamics despite diffusion training, revealing persistent initialization bias. Leveraging this observed representational redundancy, we introduce a static, task-agnostic inference-time layer-skipping method requiring no architectural changes or KV-cache sharing. Native dLLMs achieve up to 18.75% FLOPs reduction while preserving over 90% performance on reasoning and code generation benchmarks, whereas AR models degrade sharply under comparable skipping. These results link training objectives to representational structure and enable practical, cache-orthogonal efficiency gains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.06713", "title": "Scaling Agentic Capabilities, Not Context: Efficient Reinforcement Finetuning for Large Toolspaces", "authors": [ "Karan Gupta", "Pranav Vajreshwari", "Yash Pandya", "Raghav Magazine", "Akshay Nambi", "Ahmed Awadallah" ], "github": "", "project_page": "", "abstract": "Agentic systems operating over large tool ecosystems must plan and execute long-horizon workflows under weak or non-verifiable supervision. While frontier models mitigate these challenges through scale and large context budgets, small language models (SLMs) remain brittle: eager tool loading saturates context, execution errors compound over time, and sparse rewards limit learning. We introduce ATLAS, a reinforcement finetuning framework that enables SLMs to operate effectively in large-scale toolspace environments by learning how to acquire context and how to execute actions. Our approach makes two key contributions. First, we treat context control and execution structure as learnable decisions, combining iterative tool loading with programmatic tool orchestration to bound context growth and stabilize long-horizon trajectories. Second, we propose rubric-based reinforcement finetuning, which decomposes task success into structured, task-aligned criteria and enables scalable training using small judge models. Across MCP benchmarks, these design choices yield large and consistent gains over generic RL baselines, allowing a 4B SLM to approach frontier-agent performance under far tighter parameter and context budgets.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.08703", "title": "HiAR: Efficient Autoregressive Long Video Generation via Hierarchical Denoising", "authors": [ "Kai Zou", "Dian Zheng", "Hongbo Liu", "Tiankai Hang", "Bin Liu", "Nenghai Yu" ], "github": "", "project_page": "", "abstract": "Autoregressive (AR) diffusion offers a promising framework for generating videos of theoretically infinite length. However, a major challenge is maintaining temporal continuity while preventing the progressive quality degradation caused by error accumulation. To ensure continuity, existing methods typically condition on highly denoised contexts; yet, this practice propagates prediction errors with high certainty, thereby exacerbating degradation. In this paper, we argue that a highly clean context is unnecessary. Drawing inspiration from bidirectional diffusion models, which denoise frames at a shared noise level while maintaining coherence, we propose that conditioning on context at the same noise level as the current block provides sufficient signal for temporal consistency while effectively mitigating error propagation. Building on this insight, we propose HiAR, a hierarchical denoising framework that reverses the conventional generation order: instead of completing each block sequentially, it performs causal generation across all blocks at every denoising step, so that each block is always conditioned on context at the same noise level. This hierarchy naturally admits pipelined parallel inference, yielding a 1.8 wall-clock speedup in our 4-step setting. We further observe that self-rollout distillation under this paradigm amplifies a low-motion shortcut inherent to the mode-seeking reverse-KL objective. To counteract this, we introduce a forward-KL regulariser in bidirectional-attention mode, which preserves motion diversity for causal inference without interfering with the distillation loss. On VBench (20s generation), HiAR achieves the best overall score and the lowest temporal drift among all compared methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.03269", "title": "LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory", "authors": [ "Junyi Zhang", "Charles Herrmann", "Junhwa Hur", "Chen Sun", "Ming-Hsuan Yang", "Forrester Cole", "Trevor Darrell", "Deqing Sun" ], "github": "", "project_page": "", "abstract": "Feedforward geometric foundation models achieve strong short-window reconstruction, yet scaling them to minutes-long videos is bottlenecked by quadratic attention complexity or limited effective memory in recurrent designs. We present LoGeR (Long-context Geometric Reconstruction), a novel architecture that scales dense 3D reconstruction to extremely long sequences without post-optimization. LoGeR processes video streams in chunks, leveraging strong bidirectional priors for high-fidelity intra-chunk reasoning. To manage the critical challenge of coherence across chunk boundaries, we propose a learning-based hybrid memory module. This dual-component system combines a parametric Test-Time Training (TTT) memory to anchor the global coordinate frame and prevent scale drift, alongside a non-parametric Sliding Window Attention (SWA) mechanism to preserve uncompressed context for high-precision adjacent alignment. Remarkably, this memory architecture enables LoGeR to be trained on sequences of 128 frames, and generalize up to thousands of frames during inference. Evaluated across standard benchmarks and a newly repurposed VBR dataset with sequences of up to 19k frames, LoGeR substantially outperforms prior state-of-the-art feedforward methods--reducing ATE on KITTI by over 74%--and achieves robust, globally consistent reconstruction over unprecedented horizons.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07901", "title": "NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving", "authors": [ "Ximeng Tao", "Pardis Taghavi", "Dimitar Filev", "Reza Langari", "Gaurav Pandey" ], "github": "https://github.com/TAMU-CVRL/NaviDrive", "project_page": "", "abstract": "Vision-language models (VLMs) have emerged as a promising direction for end-to-end autonomous driving (AD) by jointly modeling visual observations, driving context, and language-based reasoning. However, existing VLM-based systems face a trade-off between high-level reasoning and motion planning: large models offer strong semantic understanding but are costly to adapt for precise control, whereas small VLM models can be fine-tuned efficiently but often exhibit weaker reasoning. We propose NaviDriveVLM, a decoupled framework that separates reasoning from action generation using a large-scale Navigator and a lightweight trainable Driver. This design preserves reasoning ability, reduces training cost, and provides an explicit interpretable intermediate representation for downstream planning. Experiments on the nuScenes benchmark show that NaviDriveVLM outperforms large VLM baselines in end-to-end motion planning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/TAMU-CVRL/NaviDrive", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.08709", "title": "Scale Space Diffusion", "authors": [ "Soumik Mukhopadhyay", "Prateksha Udhayanan", "Abhinav Shrivastava" ], "github": "", "project_page": "", "abstract": "Diffusion models degrade images through noise, and reversing this process reveals an information hierarchy across timesteps. Scale-space theory exhibits a similar hierarchy via low-pass filtering. We formalize this connection and show that highly noisy diffusion states contain no more information than small, downsampled images - raising the question of why they must be processed at full resolution. To address this, we fuse scale spaces into the diffusion process by formulating a family of diffusion models with generalized linear degradations and practical implementations. Using downsampling as the degradation yields our proposed Scale Space Diffusion. To support Scale Space Diffusion, we introduce Flexi-UNet, a UNet variant that performs resolution-preserving and resolution-increasing denoising using only the necessary parts of the network. We evaluate our framework on CelebA and ImageNet and analyze its scaling behavior across resolutions and network depths. Our project website ( https://prateksha.github.io/projects/scale-space-diffusion/ ) is available publicly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.08706", "title": "Agentic Critical Training", "authors": [ "Weize Liu", "Minghui Liu", "Sy-Tuyen Ho", "Souradip Chakraborty", "Xiyao Wang", "Furong Huang" ], "github": "", "project_page": "", "abstract": "Training large language models (LLMs) as autonomous agents often begins with imitation learning, but it only teaches agents what to do without understanding why: agents never contrast successful actions against suboptimal alternatives and thus lack awareness of action quality. Recent approaches attempt to address this by introducing self-reflection supervision derived from contrasts between expert and alternative actions. However, the training paradigm fundamentally remains imitation learning: the model imitates pre-constructed reflection text rather than learning to reason autonomously. We propose Agentic Critical Training (ACT), a reinforcement learning paradigm that trains agents to identify the better action among alternatives. By rewarding whether the model's judgment is correct, ACT drives the model to autonomously develop reasoning about action quality, producing genuine self-reflection rather than imitating it. Across three challenging agent benchmarks, ACT consistently improves agent performance when combined with different post-training methods. It achieves an average improvement of 5.07 points over imitation learning and 4.62 points over reinforcement learning. Compared to approaches that inject reflection capability through knowledge distillation, ACT also demonstrates clear advantages, yielding an average improvement of 2.42 points. Moreover, ACT enables strong out-of-distribution generalization on agentic benchmarks and improves performance on general reasoning benchmarks without any reasoning-specific training data, highlighting the value of our method. These results suggest that ACT is a promising path toward developing more reflective and capable LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.05581", "title": "Spatiotemporal Heterogeneity of AI-Driven Traffic Flow Patterns and Land Use Interaction: A GeoAI-Based Analysis of Multimodal Urban Mobility", "authors": [ "Olaf Yunus Laitinen Imanov" ], "github": "", "project_page": "", "abstract": "Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobility demand. Conventional global regression and time-series models cannot simultaneously capture these multi-scale dynamics across multiple travel modes. This study proposes a GeoAI Hybrid analytical framework that sequentially integrates Multiscale Geographically Weighted Regression (MGWR), Random Forest (RF), and Spatio-Temporal Graph Convolutional Networks (ST-GCN) to model the spatiotemporal heterogeneity of traffic flow patterns and their interaction with land use across three mobility modes: motor vehicle, public transit, and active transport. Applying the framework to an empirically calibrated dataset of 350 traffic analysis zones across six cities spanning two contrasting urban morphologies, four key findings emerge: (i) the GeoAI Hybrid achieves a root mean squared error (RMSE) of 0.119 and an R^2 of 0.891, outperforming all benchmarks by 23-62%; (ii) SHAP analysis identifies land use mix as the strongest predictor for motor vehicle flows and transit stop density as the strongest predictor for public transit; (iii) DBSCAN clustering identifies five functionally distinct urban traffic typologies with a silhouette score of 0.71, and GeoAI Hybrid residuals exhibit Moran's I=0.218 (p<0.001), a 72% reduction relative to OLS baselines; and (iv) cross-city transfer experiments reveal moderate within-cluster transferability (R^2>=0.78) and limited cross-cluster generalisability, underscoring the primacy of urban morphological context. The framework offers planners and transportation engineers an interpretable, scalable toolkit for evidence-based multimodal mobility management and land use policy design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.08655", "title": "OfficeQA Pro: An Enterprise Benchmark for End-to-End Grounded Reasoning", "authors": [ "Krista Opsahl-Ong", "Arnav Singhvi", "Jasmine Collins", "Ivan Zhou", "Cindy Wang", "Ashutosh Baheti", "Owen Oertell", "Jacob Portes", "Sam Havens", "Erich Elsen", "Michael Bendersky", "Matei Zaharia", "Xing Chen" ], "github": "", "project_page": "", "abstract": "We introduce OfficeQA Pro, a benchmark for evaluating AI agents on grounded, multi-document reasoning over a large and heterogeneous document corpus. The corpus consists of U.S. Treasury Bulletins spanning nearly 100 years, comprising 89,000 pages and over 26 million numerical values. OfficeQA Pro consists of 133 questions that require precise document parsing, retrieval, and analytical reasoning across both unstructured text and tabular data. Frontier LLMs including Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro Preview achieve less than 5% accuracy on OfficeQA Pro when relying on parametric knowledge, and less than 12% with additional access to the web. When provided directly with the document corpus, frontier agents still struggle on over half of questions, scoring 34.1% on average. We find that providing agents with a structured document representation produced by Databricks' ai_parse_document yields a 16.1% average relative performance gain across agents. We conduct additional ablations to study the effects of model selection, table representation, retrieval strategy, and test-time scaling on performance. Despite these improvements, significant headroom remains before agents can be considered reliable at enterprise-grade grounded reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07300", "title": "AutoResearch-RL: Perpetual Self-Evaluating Reinforcement Learning Agents for Autonomous Neural Architecture Discovery", "authors": [ "Nilesh Jain", "Rohit Yadav", "Sagar Kotian", "Claude AI" ], "github": "", "project_page": "", "abstract": "We present AutoResearch-RL, a framework in which a reinforcement learning agent conducts open-ended neural architecture and hyperparameter research without human supervision, running perpetually until a termination oracle signals convergence or resource exhaustion. At each step the agent proposes a code modification to a target training script, executes it under a fixed wall clock time budget, observes a scalar reward derived from validation bits-per-byte (val-bpb), and updates its policy via Proximal Policy Optimisation (PPO). The key design insight is the separation of three concerns: (i) a frozen environment (data pipeline, evaluation protocol, and constants) that guarantees fair cross-experiment comparison; (ii) a mutable target file (train.py) that represents the agent's editable state; and (iii) a meta-learner (the RL agent itself) that accumulates a growing trajectory of experiment outcomes and uses them to inform subsequent proposals. We formalise this as a Markov Decision Process, derive convergence guarantees under mild assumptions, and demonstrate empirically on a single GPU nanochat pretraining benchmark that AutoResearch-RL discovers configurations that match or exceed hand-tuned baselines after approximately 300 overnight iterations, with no human in the loop.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.00791", "title": "Autophoresis of a Janus particle near a planar wall: a lubrication limit", "authors": [ "Tachin Ruangkriengsin", "G\u00fcnther Turk", "Howard A. Stone" ], "github": "", "project_page": "", "abstract": "We study the self-diffusiophoresis of a spherical chemically active particle near a planar, impermeable wall, with a focus on the influence of particle orientation on propulsion. We analyze a Janus particle with asymmetric surface chemical activity, consisting of a small inert region within a catalytically active cap. While numerical simulations have been used to study such particles, they encounter difficulties resolving the flow and transport in the extreme near-wall regime due to geometric confinement and steep solute concentration gradients. We address this limitation through an asymptotic analysis in the near-contact limit, where the gap between the particle and the wall is narrow. In particular, we consider the distinguished limit in which the inert region is asymptotically comparable in size to the lubrication region. We analyze an axisymmetric configuration in which the inert face is oriented parallel to the wall and extend the analysis to slightly tilted orientations. We find that the capsize determines whether a tilted particle rotates back toward the axisymmetric state or continues to reorient, thereby characterizing its rotational stability in the near-contact regime.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.08589", "title": "CARE-Edit: Condition-Aware Routing of Experts for Contextual Image Editing", "authors": [ "Yucheng Wang", "Zedong Wang", "Yuetong Wu", "Yue Ma", "Dan Xu" ], "github": "", "project_page": "", "abstract": "Unified diffusion editors often rely on a fixed, shared backbone for diverse tasks, suffering from task interference and poor adaptation to heterogeneous demands (e.g., local vs global, semantic vs photometric). In particular, prevalent ControlNet and OmniControl variants combine multiple conditioning signals (e.g., text, mask, reference) via static concatenation or additive adapters which cannot dynamically prioritize or suppress conflicting modalities, thus resulting in artifacts like color bleeding across mask boundaries, identity or style drift, and unpredictable behavior under multi-condition inputs. To address this, we propose Condition-Aware Routing of Experts (CARE-Edit) that aligns model computation with specific editing competencies. At its core, a lightweight latent-attention router assigns encoded diffusion tokens to four specialized experts--Text, Mask, Reference, and Base--based on multi-modal conditions and diffusion timesteps: (i) a Mask Repaint module first refines coarse user-defined masks for precise spatial guidance; (ii) the router applies sparse top-K selection to dynamically allocate computation to the most relevant experts; (iii) a Latent Mixture module subsequently fuses expert outputs, coherently integrating semantic, spatial, and stylistic information to the base images. Experiments validate CARE-Edit's strong performance on contextual editing tasks, including erasure, replacement, text-driven edits, and style transfer. Empirical analysis further reveals task-specific behavior of specialized experts, showcasing the importance of dynamic, condition-aware processing to mitigate multi-condition conflicts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.08660", "title": "How Far Can Unsupervised RLVR Scale LLM Training?", "authors": [ "Bingxiang He", "Yuxin Zuo", "Zeyuan Liu", "Shangziqi Zhao", "Zixuan Fu", "Junlin Yang", "Cheng Qian", "Kaiyan Zhang", "Yuchen Fan", "Ganqu Cui", "Xiusi Chen", "Youbang Sun", "Xingtai Lv", "Xuekai Zhu", "Li Sheng", "Ran Li", "Huan-ang Gao", "Yuchen Zhang", "Bowen Zhou", "Zhiyuan Liu", "Ning Ding" ], "github": "", "project_page": "", "abstract": "Unsupervised reinforcement learning with verifiable rewards (URLVR) offers a pathway to scale LLM training beyond the supervision bottleneck by deriving rewards without ground truth labels. Recent works leverage model intrinsic signals, showing promising early gains, yet their potential and limitations remain unclear. In this work, we revisit URLVR and provide a comprehensive analysis spanning taxonomy, theory and extensive experiments. We first classify URLVR methods into intrinsic versus external based on reward sources, then establish a unified theoretical framework revealing that all intrinsic methods converge toward sharpening the model's initial distribution This sharpening mechanism succeeds when initial confidence aligns with correctness but fails catastrophically when misaligned. Through systematic experiments, we show intrinsic rewards consistently follow a rise-then-fall pattern across methods, with collapse timing determined by model prior rather than engineering choices. Despite these scaling limits, we find intrinsic rewards remain valuable in test-time training on small datasets, and propose Model Collapse Step to measure model prior, serving as a practical indicator for RL trainability. Finally, we explore external reward methods that ground verification in computational asymmetries, showing preliminary evidence they may escape the confidence-correctness ceiling. Our findings chart boundaries for intrinsic URLVR while motivating paths toward scalable alternatives.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.06449", "title": "CaTok: Taming Mean Flows for One-Dimensional Causal Image Tokenization", "authors": [ "Yitong Chen", "Zuxuan Wu", "Xipeng Qiu", "Yu-Gang Jiang" ], "github": "", "project_page": "", "abstract": "Autoregressive (AR) language models rely on causal tokenization, but extending this paradigm to vision remains non-trivial. Current visual tokenizers either flatten 2D patches into non-causal sequences or enforce heuristic orderings that misalign with the \"next-token prediction\" pattern. Recent diffusion autoencoders similarly fall short: conditioning the decoder on all tokens lacks causality, while applying nested dropout mechanism introduces imbalance. To address these challenges, we present CaTok, a 1D causal image tokenizer with a MeanFlow decoder. By selecting tokens over time intervals and binding them to the MeanFlow objective, as illustrated in Fig. 1, CaTok learns causal 1D representations that support both fast one-step generation and high-fidelity multi-step sampling, while naturally capturing diverse visual concepts across token intervals. To further stabilize and accelerate training, we propose a straightforward regularization REPA-A, which aligns encoder features with Vision Foundation Models (VFMs). Experiments demonstrate that CaTok achieves state-of-the-art results on ImageNet reconstruction, reaching 0.75 FID, 22.53 PSNR and 0.674 SSIM with fewer training epochs, and the AR model attains performance comparable to leading approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.05117", "title": "SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation", "authors": [ "Youqiang Gui", "Yuxuan Zhou", "Shen Cheng", "Xinyang Yuan", "Haoqiang Fan", "Peng Cheng", "Shuaicheng Liu" ], "github": "https://github.com/Youqiang-Gui/SeedPolicy", "project_page": "", "abstract": "Imitation Learning (IL) enables robots to acquire manipulation skills from expert demonstrations. Diffusion Policy (DP) models multi-modal expert behaviors but suffers performance degradation as observation horizons increase, limiting long-horizon manipulation. We propose Self-Evolving Gated Attention (SEGA), a temporal module that maintains a time-evolving latent state via gated attention, enabling efficient recurrent updates that compress long-horizon observations into a fixed-size representation while filtering irrelevant temporal information. Integrating SEGA into DP yields Self-Evolving Diffusion Policy (SeedPolicy), which resolves the temporal modeling bottleneck and enables scalable horizon extension with moderate overhead. On the RoboTwin 2.0 benchmark with 50 manipulation tasks, SeedPolicy outperforms DP and other IL baselines. Averaged across both CNN and Transformer backbones, SeedPolicy achieves 36.8% relative improvement in clean settings and 169% relative improvement in randomized challenging settings over the DP. Compared to vision-language-action models such as RDT with 1.2B parameters, SeedPolicy achieves competitive performance with one to two orders of magnitude fewer parameters, demonstrating strong efficiency and scalability. These results establish SeedPolicy as a state-of-the-art imitation learning method for long-horizon robotic manipulation. Code is available at: https://github.com/Youqiang-Gui/SeedPolicy.", "github_manual": "", "github_regex": "https://github.com/Youqiang-Gui/SeedPolicy", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.08652", "title": "CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation", "authors": [ "Haodong Li", "Chunmei Qing", "Huanyu Zhang", "Dongzhi Jiang", "Yihang Zou", "Hongbo Peng", "Dingming Li", "Yuhong Dai", "ZePeng Lin", "Juanxi Tian", "Yi Zhou", "Siqi Dai", "Jingwei Wu" ], "github": "https://github.com/micky-li-hd/CoCo", "project_page": "", "abstract": "Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the integration of Chain-of-Thought (CoT) reasoning. However, existing CoT-based T2I methods largely rely on abstract natural-language planning, which lacks the precision required for complex spatial layouts, structured visual elements, and dense textual content. In this work, we propose CoCo (Code-as-CoT), a code-driven reasoning framework that represents the reasoning process as executable code, enabling explicit and verifiable intermediate planning for image generation. Given a text prompt, CoCo first generates executable code that specifies the structural layout of the scene, which is then executed in a sandboxed environment to render a deterministic draft image. The model subsequently refines this draft through fine-grained image editing to produce the final high-fidelity result. To support this training paradigm, we construct CoCo-10K, a curated dataset containing structured draft-final image pairs designed to teach both structured draft construction and corrective visual refinement. Empirical evaluations on StructT2IBench, OneIG-Bench, and LongText-Bench show that CoCo achieves improvements of +68.83%, +54.8%, and +41.23% over direct generation, while also outperforming other generation methods empowered by CoT. These results demonstrate that executable code is an effective and reliable reasoning paradigm for precise, controllable, and structured text-to-image generation. The code is available at: https://github.com/micky-li-hd/CoCo", "github_manual": "", "github_regex": "https://github.com/micky-li-hd/CoCo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.05890", "title": "Lost in Stories: Consistency Bugs in Long Story Generation by LLMs", "authors": [ "Junjie Li", "Xinrui Guo", "Yuhao Wu", "Roy Ka-Wei Lee", "Hongzhi Li", "Yutao Xie" ], "github": "https://github.com/markriedl/WikiPlots", "project_page": "", "abstract": "What happens when a storyteller forgets its own story? Large Language Models (LLMs) can now generate narratives spanning tens of thousands of words, but they often fail to maintain consistency throughout. When generating long-form narratives, these models can contradict their own established facts, character traits, and world rules. Existing story generation benchmarks focus mainly on plot quality and fluency, leaving consistency errors largely unexplored. To address this gap, we present ConStory-Bench, a benchmark designed to evaluate narrative consistency in long-form story generation. It contains 2,000 prompts across four task scenarios and defines a taxonomy of five error categories with 19 fine-grained subtypes. We also develop ConStory-Checker, an automated pipeline that detects contradictions and grounds each judgment in explicit textual evidence. Evaluating a range of LLMs through five research questions, we find that consistency errors show clear tendencies: they are most common in factual and temporal dimensions, tend to appear around the middle of narratives, occur in text segments with higher token-level entropy, and certain error types tend to co-occur. These findings can inform future efforts to improve consistency in long-form narrative generation. Our project page is available at https://picrew.github.io/constory-bench.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/markriedl/WikiPlots", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.08309", "title": "Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness", "authors": [ "Yehonatan Elisha", "Oren Barkan", "Noam Koenigstein" ], "github": "https://github.com/yonisGit/cft", "project_page": "", "abstract": "Vision Transformers (ViTs) often degrade under distribution shifts because they rely on spurious correlations, such as background cues, rather than semantically meaningful features. Existing regularization methods, typically relying on simple foreground-background masks, which fail to capture the fine-grained semantic concepts that define an object (e.g., ``long beak'' and ``wings'' for a ``bird''). As a result, these methods provide limited robustness to distribution shifts. To address this limitation, we introduce a novel finetuning framework that steers model reasoning toward concept-level semantics. Our approach optimizes the model's internal relevance maps to align with spatially grounded concept masks. These masks are generated automatically, without manual annotation: class-relevant concepts are first proposed using an LLM-based, label-free method, and then segmented using a VLM. The finetuning objective aligns relevance with these concept regions while simultaneously suppressing focus on spurious background areas. Notably, this process requires only a minimal set of images and uses half of the dataset classes. Extensive experiments on five out-of-distribution benchmarks demonstrate that our method improves robustness across multiple ViT-based models. Furthermore, we show that the resulting relevance maps exhibit stronger alignment with semantic object parts, offering a scalable path toward more robust and interpretable vision models. Finally, we confirm that concept-guided masks provide more effective supervision for model robustness than conventional segmentation maps, supporting our central hypothesis.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yonisGit/cft", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07700", "title": "TDM-R1: Reinforcing Few-Step Diffusion Models with Non-Differentiable Reward", "authors": [ "Yihong Luo", "Tianyang Hu", "Weijian Luo", "Jing Tang" ], "github": "https://github.com/Luo-Yihong/TDM-R1", "project_page": "", "abstract": "While few-step generative models have enabled powerful image and video generation at significantly lower cost, generic reinforcement learning (RL) paradigms for few-step models remain an unsolved problem. Existing RL approaches for few-step diffusion models strongly rely on back-propagating through differentiable reward models, thereby excluding the majority of important real-world reward signals, e.g., non-differentiable rewards such as humans' binary likeness, object counts, etc. To properly incorporate non-differentiable rewards to improve few-step generative models, we introduce TDM-R1, a novel reinforcement learning paradigm built upon a leading few-step model, Trajectory Distribution Matching (TDM). TDM-R1 decouples the learning process into surrogate reward learning and generator learning. Furthermore, we developed practical methods to obtain per-step reward signals along the deterministic generation trajectory of TDM, resulting in a unified RL post-training method that significantly improves few-step models' ability with generic rewards. We conduct extensive experiments ranging from text-rendering, visual quality, and preference alignment. All results demonstrate that TDM-R1 is a powerful reinforcement learning paradigm for few-step text-to-image models, achieving state-of-the-art reinforcement learning performances on both in-domain and out-of-domain metrics. Furthermore, TDM-R1 also scales effectively to the recent strong Z-Image model, consistently outperforming both its 100-NFE and few-step variants with only 4 NFEs. Project page: https://github.com/Luo-Yihong/TDM-R1", "github_manual": "", "github_regex": "https://github.com/Luo-Yihong/TDM-R1", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.05344", "title": "Building AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned", "authors": [ "Nghi D. Q. Bui" ], "github": "https://github.com/opendev-to/opendev", "project_page": "", "abstract": "The landscape of AI coding assistance is undergoing a fundamental shift from complex IDE plugins to versatile, terminal-native agents. Operating directly where developers manage source control, execute builds, and deploy environments, CLI-based agents offer unprecedented autonomy for long-horizon development tasks. In this paper, we present OPENDEV, an open-source, command-line coding agent engineered specifically for this new paradigm. Effective autonomous assistance requires strict safety controls and highly efficient context management to prevent context bloat and reasoning degradation. OPENDEV overcomes these challenges through a compound AI system architecture with workload-specialized model routing, a dual-agent architecture separating planning from execution, lazy tool discovery, and adaptive context compaction that progressively reduces older observations. Furthermore, it employs an automated memory system to accumulate project-specific knowledge across sessions and counteracts instruction fade-out through event-driven system reminders. By enforcing explicit reasoning phases and prioritizing context efficiency, OPENDEV provides a secure, extensible foundation for terminal-first AI assistance, offering a blueprint for robust autonomous software engineering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/opendev-to/opendev", "github_api_added_by": "auto" }, { "date": "2026-03-10", "arxiv_id": "2603.08316", "title": "SlowBA: An efficiency backdoor attack towards VLM-based GUI agents", "authors": [ "Junxian Li", "Tu Lan", "Haozhen Tan", "Yan Meng", "Haojin Zhu" ], "github": "https://github.com/tu-tuing/SlowBA", "project_page": "", "abstract": "Modern vision-language-model (VLM) based graphical user interface (GUI) agents are expected not only to execute actions accurately but also to respond to user instructions with low latency. While existing research on GUI-agent security mainly focuses on manipulating action correctness, the security risks related to response efficiency remain largely unexplored. In this paper, we introduce SlowBA, a novel backdoor attack that targets the responsiveness of VLM-based GUI agents. The key idea is to manipulate response latency by inducing excessively long reasoning chains under specific trigger patterns. To achieve this, we propose a two-stage reward-level backdoor injection (RBI) strategy that first aligns the long-response format and then learns trigger-aware activation through reinforcement learning. In addition, we design realistic pop-up windows as triggers that naturally appear in GUI environments, improving the stealthiness of the attack. Extensive experiments across multiple datasets and baselines demonstrate that SlowBA can significantly increase response length and latency while largely preserving task accuracy. The attack remains effective even with a small poisoning ratio and under several defense settings. These findings reveal a previously overlooked security vulnerability in GUI agents and highlight the need for defenses that consider both action correctness and response efficiency. Code can be found in https://github.com/tu-tuing/SlowBA.", "github_manual": "", "github_regex": "https://github.com/tu-tuing/SlowBA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.00446", "title": "HydroShear: Hydroelastic Shear Simulation for Tactile Sim-to-Real Reinforcement Learning", "authors": [ "An Dang", "Jayjun Lee", "Mustafa Mukadam", "X. Alice Wu", "Bernadette Bucher", "Manikantan Nambi", "Nima Fazeli" ], "github": "https://github.com/MMintLab/hydroshear", "project_page": "", "abstract": "In this paper, we address the problem of tactile sim-to-real policy transfer for contact-rich tasks. Existing methods primarily focus on vision-based sensors and emphasize image rendering quality while providing overly simplistic models of force and shear. Consequently, these models exhibit a large sim-to-real gap for many dexterous tasks. Here, we present HydroShear, a non-holonomic hydroelastic tactile simulator that advances the state-of-the-art by modeling: a) stick-slip transitions, b) path-dependent force and shear build up, and c) full SE(3) object-sensor interactions. HydroShear extends hydroelastic contact models using Signed Distance Functions (SDFs) to track the displacements of the on-surface points of an indenter during physical interaction with the sensor membrane. Our approach generates physics-based, computationally efficient force fields from arbitrary watertight geometries while remaining agnostic to the underlying physics engine. In experiments with GelSight Minis, HydroShear more faithfully reproduces real tactile shear compared to existing methods. This fidelity enables zero-shot sim-to-real transfer of reinforcement learning policies across four tasks: peg insertion, bin packing, book shelving for insertion, and drawer pulling for fine gripper control under slip. Our method achieves a 93% average success rate, outperforming policies trained on tactile images (34%) and alternative shear simulation methods (58%-61%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MMintLab/hydroshear", "github_api_added_by": "auto" }, { "date": "2026-03-10", "arxiv_id": "2603.04989", "title": "TAPFormer: Robust Arbitrary Point Tracking via Transient Asynchronous Fusion of Frames and Events", "authors": [ "Jiaxiong Liu", "Zhen Tan", "Jinpu Zhang", "Yi Zhou", "Hui Shen", "Xieyuanli Chen", "Dewen Hu" ], "github": "", "project_page": "", "abstract": "Tracking any point (TAP) is a fundamental yet challenging task in computer vision, requiring high precision and long-term motion reasoning. Recent attempts to combine RGB frames and event streams have shown promise, yet they typically rely on synchronous or non-adaptive fusion, leading to temporal misalignment and severe degradation when one modality fails. We introduce TAPFormer, a transformer-based framework that performs asynchronous temporal-consistent fusion of frames and events for robust and high-frequency arbitrary point tracking. Our key innovation is a Transient Asynchronous Fusion (TAF) mechanism, which explicitly models the temporal evolution between discrete frames through continuous event updates, bridging the gap between low-rate frames and high-rate events. In addition, a Cross-modal Locally Weighted Fusion (CLWF) module adaptively adjusts spatial attention according to modality reliability, yielding stable and discriminative features even under blur or low light. To evaluate our approach under realistic conditions, we construct a novel real-world frame-event TAP dataset under diverse illumination and motion conditions. Our method outperforms existing point trackers, achieving a 28.2% improvement in average pixel error within threshold. Moreover, on standard point tracking benchmarks, our tracker consistently achieves the best performance. Project website: tapformer.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07799", "title": "MWM: Mobile World Models for Action-Conditioned Consistent Prediction", "authors": [ "Han Yan", "Zishang Xiang", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/MWM", "project_page": "", "abstract": "World models enable planning in imagined future predicted space, offering a promising framework for embodied navigation. However, existing navigation world models often lack action-conditioned consistency, so visually plausible predictions can still drift under multi-step rollout and degrade planning. Moreover, efficient deployment requires few-step diffusion inference, but existing distillation methods do not explicitly preserve rollout consistency, creating a training-inference mismatch. To address these challenges, we propose MWM, a mobile world model for planning-based image-goal navigation. Specifically, we introduce a two-stage training framework that combines structure pretraining with Action-Conditioned Consistency (ACC) post-training to improve action-conditioned rollout consistency. We further introduce Inference-Consistent State Distillation (ICSD) for few-step diffusion distillation with improved rollout consistency. Our experiments on benchmark and real-world tasks demonstrate consistent gains in visual fidelity, trajectory accuracy, planning success, and inference efficiency. Code: https://github.com/AIGeeksGroup/MWM. Website: https://aigeeksgroup.github.io/MWM.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/MWM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07236", "title": "HY-WU (Part I): An Extensible Functional Neural Memory Framework and An Instantiation in Text-Guided Image Editing", "authors": [ "Tencent HY Team" ], "github": "", "project_page": "", "abstract": "Foundation models are transitioning from offline predictors to deployed systems expected to operate over long time horizons. In real deployments, objectives are not fixed: domains drift, user preferences evolve, and new tasks appear after the model has shipped. This elevates continual learning and instant personalization from optional features to core architectural requirements. Yet most adaptation pipelines still follow a static weight paradigm: after training (or after any adaptation step), inference executes a single parameter vector regardless of user intent, domain, or instance-specific constraints. This treats the trained or adapted model as a single point in parameter space. In heterogeneous and continually evolving regimes, distinct objectives can induce separated feasible regions over parameters, forcing any single shared update into compromise, interference, or overspecialization. As a result, continual learning and personalization are often implemented as repeated overwriting of shared weights, risking degradation of previously learned behaviors. We propose HY-WU (Weight Unleashing), a memory-first adaptation framework that shifts adaptation pressure away from overwriting a single shared parameter point. HY-WU implements functional (operator-level) memory as a neural module: a generator that synthesizes weight updates on-the-fly from the instance condition, yielding instance-specific operators without test-time optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07223", "title": "Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training", "authors": [ "Chuxue Cao", "Honglin Lin", "Zhanping Zhong", "Xin Gao", "Mengzhang Cai", "Conghui He", "Sirui Han", "Lijun Wu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated strong general capabilities, yet their deployment in finance remains challenging due to dense domain-specific terminology, stringent numerical reasoning requirements, and low tolerance for factual errors. We conduct a controlled empirical study showing that in specialized vertical domains, performance is largely determined by the quality and difficulty/verifiability profile of post-training data. We introduce ODA-Fin-SFT-318k, constructed via multi-stage distillation and verification to produce high-quality Chain-of-Thought supervision, and ODA-Fin-RL-12k, curated for hard-but-verifiable tasks that balance reward precision and task diversity. Using standard SFT and RL pipelines, we show that high-quality CoT distillation establishes a robust foundation during SFT, while difficulty- and verifiability-aware sampling improves RL generalization. Evaluated on nine benchmarks spanning general financial tasks, sentiment analysis, and numerical reasoning, our ODA-Fin-RL-8B consistently surpasses open-source state-of-the-art (SOTA) financial LLMs of comparable size. We release our ODA-Fin-SFT-318k and ODA-Fin-RL-12k datasets, along with trained models to advance data-centric financial AI research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07169", "title": "Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts", "authors": [ "Yuxuan Han", "Meng-Hao Guo", "Zhengning Liu", "Wenguang Chen", "Shi-Min Hu" ], "github": "", "project_page": "", "abstract": "Optimizing GPU kernels manually is a challenging and time-consuming task. With the rapid development of LLMs, automated GPU kernel optimization is gradually becoming a tangible reality. However, current LLM-driven automated optimization methods narrowly focus on machine learning applications, such as PyTorch operator optimization, while overlooking broader domains like sparse matrix operations in scientific computing. Extending to these broader applications brings new challenges for the benchmark and algorithm. Therefore, developing a general-purpose automated kernel optimization method becomes our primary focus. In this paper, we address the absence of systematic evaluation for multi-scenario settings by introducing MSKernelBench, which spans multiple scenarios, including fundamental algebraic operations, common LLM kernels, sparse matrix operators, and scientific computing routines, each supporting both FP32 and BF16 precision. Building on this benchmark, we introduce CUDAMaster, a multi-agent, hardware-aware system for kernel optimization that leverages profiling information and automatically constructs the full compilation and execution toolchain. Experimental results demonstrate that CUDAMaster achieves significant speedups across most operators, outperforming Astra by about 35%. In several cases, its performance matches or surpasses that of highly optimized, closed-source libraries such as cuBLAS. A demo showcasing the original and optimized code for each operator is available at https://hanyx2021.github.io/MSKernelBenchDemo/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.08397", "title": "NLE: Non-autoregressive LLM-based ASR by Transcript Editing", "authors": [ "Avihu Dekel", "Samuel Thomas", "Takashi Fukada", "George Saon" ], "github": "", "project_page": "", "abstract": "While autoregressive (AR) LLM-based ASR systems achieve strong accuracy, their sequential decoding limits parallelism and incurs high latency. We propose NLE, a non-autoregressive (NAR) approach that formulates speech recognition as conditional transcript editing, enabling fully parallel prediction. NLE extracts acoustic embeddings and an initial hypothesis from a pretrained speech encoder, then refines the hypothesis using a bidirectional LLM editor trained with a latent alignment objective. An interleaved padding strategy exploits the identity mapping bias of Transformers, allowing the model to focus on corrections rather than full reconstruction. On the Open ASR leaderboard, NLE++ achieves 5.67% average WER with an RTFx (inverse real-time factor) of 1630. In single-utterance scenarios, NLE achieves 27x speedup over the AR baseline, making it suitable for real-time applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07244", "title": "PresentBench: A Fine-Grained Rubric-Based Benchmark for Slide Generation", "authors": [ "Xin-Sheng Chen", "Jiayu Zhu", "Pei-lin Li", "Hanzheng Wang", "Shuojin Yang", "Meng-Hao Guo" ], "github": "", "project_page": "", "abstract": "Slides serve as a critical medium for conveying information in presentation-oriented scenarios such as academia, education, and business. Despite their importance, creating high-quality slide decks remains time-consuming and cognitively demanding. Recent advances in generative models, such as Nano Banana Pro, have made automated slide generation increasingly feasible. However, existing evaluations of slide generation are often coarse-grained and rely on holistic judgments, making it difficult to accurately assess model capabilities or track meaningful advances in the field. In practice, the lack of fine-grained, verifiable evaluation criteria poses a critical bottleneck for both research and real-world deployment. In this paper, we propose PresentBench, a fine-grained, rubric-based benchmark for evaluating automated real-world slide generation. It contains 238 evaluation instances, each supplemented with background materials required for slide creation. Moreover, we manually design an average of 54.1 checklist items per instance, each formulated as a binary question, to enable fine-grained, instance-specific evaluation of the generated slide decks. Extensive experiments show that PresentBench provides more reliable evaluation results than existing methods, and exhibits significantly stronger alignment with human preferences. Furthermore, our benchmark reveals that NotebookLM significantly outperforms other slide generation methods, highlighting substantial recent progress in this domain.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07148", "title": "Agentic Planning with Reasoning for Image Styling via Offline RL", "authors": [ "Subhojyoti Mukherjee", "Stefano Petrangeli", "Branislav Kveton", "Trung Bui", "Franck Dernoncourt", "Arko Mukherjee" ], "github": "", "project_page": "", "abstract": "Direct prompt-based editing often fails on complex transformations because vague and subjective prompts often require nuanced understanding of what should be changed in the image. Our core intuition is that leveraging compositional image editing tools rather than direct prompting profits from structured agent-level planning with explicit reasoning, leading to better results. This structured planning framework enables efficient offline RL post-training on quality-scored trajectories to improve performance. We present a tool-based agentic RL post-training framework that addresses this through structured planning with chain-of-thought reasoning. Our key contributions include: (1) A tool-based agentic planning methodology that combines a compositional library of orthogonal primitive transformations, structured context representation, and explicit per-step reasoning to decompose complex styling into interpretable tool sequences. (2) A synthetic data generation pipeline producing three large-scale datasets (each sim10K trajectories) with reasoning chains, plans, and quality scores, as no existing datasets provide such supervision. Our datasets and code are publicly available at the HuggingFace repository. (3) Offline RL training methods for learning planners with reasoning as our core algorithmic contributions, which consistently improve over the Edit-Only baseline in visual quality and instruction following. (4) Comprehensive evaluation across 4B and 8B parameter Qwen3-VL models showing that our methods outperform other baselines in the majority of compositional tasks, validated by human evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.05168", "title": "Sparse-BitNet: 1.58-bit LLMs are Naturally Friendly to Semi-Structured Sparsity", "authors": [ "Di Zhang", "Xun Wu", "Shaohan Huang", "Yudong Wang", "Hanyong Shao", "Yingbo Hao", "Zewen Chi", "Li Dong", "Ting Song", "Yan Xia", "Zhifang Sui", "Furu Wei" ], "github": "https://github.com/AAzdi/Sparse-BitNet", "project_page": "", "abstract": "Semi-structured N:M sparsity and low-bit quantization (e.g., 1.58-bit BitNet) are two promising approaches for improving the efficiency of large language models (LLMs), yet they have largely been studied in isolation. In this work, we investigate their interaction and show that 1.58-bit BitNet is naturally more compatible with N:M sparsity than full-precision models. To study this effect, we propose Sparse-BitNet, a unified framework that jointly applies 1.58-bit quantization and dynamic N:M sparsification while ensuring stable training for the first time. Across multiple model scales and training regimes (sparse pretraining and dense-to-sparse schedules), 1.58-bit BitNet consistently exhibits smaller performance degradation than full-precision baselines at the same sparsity levels and can tolerate higher structured sparsity before accuracy collapse. Moreover, using our custom sparse tensor core, Sparse-BitNet achieves substantial speedups in both training and inference, reaching up to 1.30X. These results highlight that combining extremely low-bit quantization with semi-structured N:M sparsity is a promising direction for efficient LLMs. Code available at https://github.com/AAzdi/Sparse-BitNet", "github_manual": "", "github_regex": "https://github.com/AAzdi/Sparse-BitNet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07066", "title": "MedSteer: Counterfactual Endoscopic Synthesis via Training-Free Activation Steering", "authors": [ "Trong-Thang Pham", "Loc Nguyen", "Anh Nguyen", "Hien Nguyen", "Ngan Le" ], "github": "https://github.com/phamtrongthang123/medsteer", "project_page": "", "abstract": "Generative diffusion models are increasingly used for medical imaging data augmentation, but text prompting cannot produce causal training data. Re-prompting rerolls the entire generation trajectory, altering anatomy, texture, and background. Inversion-based editing methods introduce reconstruction error that causes structural drift. We propose MedSteer, a training-free activation-steering framework for endoscopic synthesis. MedSteer identifies a pathology vector for each contrastive prompt pair in the cross-attention layers of a diffusion transformer. At inference time, it steers image activations along this vector, generating counterfactual pairs from scratch where the only difference is the steered concept. All other structure is preserved by construction. We evaluate MedSteer across three experiments on Kvasir v3 and HyperKvasir. On counterfactual generation across three clinical concept pairs, MedSteer achieves flip rates of 0.800, 0.925, and 0.950, outperforming the best inversion-based baseline in both concept flip rate and structural preservation. On dye disentanglement, MedSteer achieves 75% dye removal against 20% (PnP) and 10% (h-Edit). On downstream polyp detection, augmenting with MedSteer counterfactual pairs achieves ViT AUC of 0.9755 versus 0.9083 for quantity-matched re-prompting, confirming that counterfactual structure drives the gain. Code is at link https://github.com/phamtrongthang123/medsteer", "github_manual": "", "github_regex": "https://github.com/phamtrongthang123/medsteer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07779", "title": "Scaling Data Difficulty: Improving Coding Models via Reinforcement Learning on Fresh and Challenging Problems", "authors": [ "Zongqian Li", "Tengchao Lv", "Shaohan Huang", "Yixuan Su", "Qinzheng Sun", "Qiufeng Yin", "Ying Xin", "Scarlett Li", "Lei Cui", "Nigel Collier", "Furu Wei" ], "github": "https://github.com/ZongqianLi/MicroCoder", "project_page": "", "abstract": "Training next-generation code generation models requires high-quality datasets, yet existing datasets face difficulty imbalance, format inconsistency, and data quality problems. We address these challenges through systematic data processing and difficulty scaling. We introduce a four-stage Data Processing Framework encompassing collection, processing, filtering, and verification, incorporating Automatic Difficulty Filtering via an LLM-based predict-calibrate-select framework that leverages multi-dimensional difficulty metrics across five weighted dimensions to retain challenging problems while removing simplistic ones. The resulting MicroCoder dataset comprises tens of thousands of curated real competitive programming problems from diverse platforms, emphasizing recency and difficulty. Evaluations on strictly unseen LiveCodeBench demonstrate that MicroCoder achieves 3x larger performance gains within 300 training steps compared to widely-used baseline datasets of comparable size, with consistent advantages under both GRPO and its variant training algorithms. The MicroCoder dataset delivers obvious improvements on medium and hard problems across different model sizes, achieving up to 17.2% relative gains in overall performance where model capabilities are most stretched. These results validate that difficulty-aware data curation improves model performance on challenging tasks, providing multiple insights for dataset creation in code generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ZongqianLi/MicroCoder", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07777", "title": "Breaking Training Bottlenecks: Effective and Stable Reinforcement Learning for Coding Models", "authors": [ "Zongqian Li", "Shaohan Huang", "Zewen Chi", "Yixuan Su", "Lexin Zhou", "Li Dong", "Nigel Collier", "Furu Wei" ], "github": "", "project_page": "", "abstract": "Modern code generation models exhibit longer outputs, accelerated capability growth, and changed training dynamics, rendering traditional training methodologies, algorithms, and datasets ineffective for improving their performance. To address these training bottlenecks, we propose MicroCoder-GRPO, an improved Group Relative Policy Optimization approach with three innovations: conditional truncation masking to improve long output potential while maintaining training stability, diversity-determined temperature selection to maintain and encourage output diversity, and removal of KL loss with high clipping ratios to facilitate solution diversity. MicroCoder-GRPO achieves up to 17.6% relative improvement over strong baselines on LiveCodeBench v6, with more pronounced gains under extended context evaluation. Additionally, we release MicroCoder-Dataset, a more challenging training corpus that achieves 3x larger performance gains than mainstream datasets on LiveCodeBench v6 within 300 training steps, and MicroCoder-Evaluator, a robust framework with approximately 25% improved evaluation accuracy and around 40% faster execution. Through comprehensive analysis across more than thirty controlled experiments, we reveal 34 training insights across seven main aspects, demonstrating that properly trained models can achieve competitive performance with larger counterparts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07233", "title": "Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation", "authors": [ "Andrea Giuseppe Di Francesco", "Andrea Rubbi", "Pietro Li\u00f2" ], "github": "https://github.com/difra100/PT-RAG_ICLR", "project_page": "", "abstract": "Predicting how cells respond to genetic perturbations is fundamental to understanding gene function, disease mechanisms, and therapeutic development. While recent deep learning approaches have shown promise in modeling single-cell perturbation responses, they struggle to generalize across cell types and perturbation contexts due to limited contextual information during generation. We introduce PT-RAG (Perturbation-aware Two-stage Retrieval-Augmented Generation), a novel framework that extends Retrieval-Augmented Generation beyond traditional language-model applications to cellular biology. Unlike standard RAG systems designed for text retrieval with pre-trained LLMs, perturbation retrieval lacks established similarity metrics and requires learning what constitutes relevant context, making differentiable retrieval essential. PT-RAG addresses this through a two-stage pipeline: first, retrieving candidate perturbations K using GenePT embeddings, then adaptively refining the selection through Gumbel-Softmax discrete sampling conditioned on both the cell state and the input perturbation. This cell-type-aware differentiable retrieval enables end-to-end optimization of the retrieval objective jointly with generation. On the Replogle-Nadig single-gene perturbation dataset, we demonstrate that PT-RAG outperforms both STATE and vanilla RAG under identical experimental conditions, with the strongest gains in distributional similarity metrics (W_1, W_2). Notably, vanilla RAG's dramatic failure is itself a key finding: it demonstrates that differentiable, cell-type-aware retrieval is essential in this domain, and that naive retrieval can actively harm performance. Our results establish retrieval-augmented generation as a promising paradigm for modelling cellular responses to gene perturbation. The code to reproduce our experiments is available at https://github.com/difra100/PT-RAG_ICLR.", "github_manual": "", "github_regex": "https://github.com/difra100/PT-RAG_ICLR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.05811", "title": "Training-free Latent Inter-Frame Pruning with Attention Recovery", "authors": [ "Dennis Menn", "Yuedong Yang", "Bokun Wang", "Xiwen Wei", "Mustafa Munir", "Feng Liang", "Radu Marculescu", "Chenfeng Xu", "Diana Marculescu" ], "github": "", "project_page": "", "abstract": "Current video generation models suffer from high computational latency, making real-time applications prohibitively costly. In this paper, we address this limitation by exploiting the temporal redundancy inherent in video latent patches. To this end, we propose the Latent Inter-frame Pruning with Attention Recovery (LIPAR) framework, which detects and skips recomputing duplicated latent patches. Additionally, we introduce a novel Attention Recovery mechanism that approximates the attention values of pruned tokens, thereby removing visual artifacts arising from naively applying the pruning method. Empirically, our method increases video editing throughput by 1.45times, on average achieving 12.2 FPS on an NVIDIA A6000 compared to the baseline 8.4 FPS. The proposed method does not compromise generation quality and can be seamlessly integrated with the model without additional training. Our approach effectively bridges the gap between traditional compression algorithms and modern generative pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07145", "title": "LiveWorld: Simulating Out-of-Sight Dynamics in Generative Video World Models", "authors": [ "Zicheng Duan", "Jiatong Xia", "Zeyu Zhang", "Wenbo Zhang", "Gengze Zhou", "Chenhui Gou", "Yefei He", "Feng Chen", "Xinyu Zhang", "Lingqiao Liu" ], "github": "", "project_page": "", "abstract": "Recent generative video world models aim to simulate visual environment evolution, allowing an observer to interactively explore the scene via camera control. However, they implicitly assume that the world only evolves within the observer's field of view. Once an object leaves the observer's view, its state is \"frozen\" in memory, and revisiting the same region later often fails to reflect events that should have occurred in the meantime. In this work, we identify and formalize this overlooked limitation as the \"out-of-sight dynamics\" problem, which impedes video world models from representing a continuously evolving world. To address this issue, we propose LiveWorld, a novel framework that extends video world models to support persistent world evolution. Instead of treating the world as static observational memory, LiveWorld models a persistent global state composed of a static 3D background and dynamic entities that continue evolving even when unobserved. To maintain these unseen dynamics, LiveWorld introduces a monitor-based mechanism that autonomously simulates the temporal progression of active entities and synchronizes their evolved states upon revisiting, ensuring spatially coherent rendering. For evaluation, we further introduce LiveBench, a dedicated benchmark for the task of maintaining out-of-sight dynamics. Extensive experiments show that LiveWorld enables persistent event evolution and long-term scene consistency, bridging the gap between existing 2D observation-based memory and true 4D dynamic world simulation. The baseline and benchmark will be publicly available at https://zichengduan.github.io/LiveWorld/index.html.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.07276", "title": "Variational Flow Maps: Make Some Noise for One-Step Conditional Generation", "authors": [ "Abbas Mammadov", "So Takao", "Bohan Chen", "Ricardo Baptista", "Morteza Mardani", "Yee Whye Teh", "Julius Berner" ], "github": "https://github.com/abbasmammadov/VFM", "project_page": "", "abstract": "Flow maps enable high-quality image generation in a single forward pass. However, unlike iterative diffusion models, their lack of an explicit sampling trajectory impedes incorporating external constraints for conditional generation and solving inverse problems. We put forth Variational Flow Maps, a framework for conditional sampling that shifts the perspective of conditioning from \"guiding a sampling path\", to that of \"learning the proper initial noise\". Specifically, given an observation, we seek to learn a noise adapter model that outputs a noise distribution, so that after mapping to the data space via flow map, the samples respect the observation and data prior. To this end, we develop a principled variational objective that jointly trains the noise adapter and the flow map, improving noise-data alignment, such that sampling from complex data posterior is achieved with a simple adapter. Experiments on various inverse problems show that VFMs produce well-calibrated conditional samples in a single (or few) steps. For ImageNet, VFM attains competitive fidelity while accelerating the sampling by orders of magnitude compared to alternative iterative diffusion/flow models. Code is available at https://github.com/abbasmammadov/VFM", "github_manual": "", "github_regex": "https://github.com/abbasmammadov/VFM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.03583", "title": "ByteFlow: Language Modeling through Adaptive Byte Compression without a Tokenizer", "authors": [ "Chunyuan Deng", "Sanket Lokegaonkar", "Colin Lockard", "Besnik Fetahu", "Nasser Zalmout", "Xian Li" ], "github": "", "project_page": "", "abstract": "Modern language models still rely on fixed, pre-defined subword tokenizations. Once a tokenizer is trained, the LM can only operate at this fixed level of granularity, which often leads to brittle and counterintuitive behaviors even in otherwise strong reasoning models. We introduce ByteFlow Net, a new hierarchical architecture that removes tokenizers entirely and instead enables models to learn their own segmentation of raw byte streams into semantically meaningful units. ByteFlow Net performs compression-driven segmentation based on the coding rate of latent representations, yielding adaptive boundaries while preserving a static computation graph via Top-K selection. Unlike prior self-tokenizing methods that depend on brittle heuristics with human-designed inductive biases, ByteFlow Net adapts its internal representation granularity to the input itself. Experiments demonstrate that this compression-based chunking strategy yields substantial performance gains, with ByteFlow Net outperforming both BPE-based Transformers and previous byte-level architectures. These results suggest that end-to-end, tokenizer-free modeling is not only feasible but also more effective, opening a path toward more adaptive and information-grounded language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.08648", "title": "CAST: Modeling Visual State Transitions for Consistent Video Retrieval", "authors": [ "Yanqing Liu", "Yingcheng Liu", "Fanghong Dong", "Budianto Budianto", "Cihang Xie", "Yan Jiao" ], "github": "", "project_page": "", "abstract": "As video content creation shifts toward long-form narratives, composing short clips into coherent storylines becomes increasingly important. However, prevailing retrieval formulations remain context-agnostic at inference time, prioritizing local semantic alignment while neglecting state and identity consistency. To address this structural limitation, we formalize the task of Consistent Video Retrieval (CVR) and introduce a diagnostic benchmark spanning YouCook2, COIN, and CrossTask. We propose CAST (Context-Aware State Transition), a lightweight, plug-and-play adapter compatible with diverse frozen vision-language embedding spaces. By predicting a state-conditioned residual update (\u0394) from visual history, CAST introduces an explicit inductive bias for latent state evolution. Extensive experiments show that CAST improves performance on YouCook2 and CrossTask, remains competitive on COIN, and consistently outperforms zero-shot baselines across diverse foundation backbones. Furthermore, CAST provides a useful reranking signal for black-box video generation candidates (e.g., from Veo), promoting more temporally coherent continuations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-10", "arxiv_id": "2603.04893", "title": "Free Lunch for Pass@k? Low Cost Diverse Sampling for Diffusion Language Models", "authors": [ "Sean Lamont", "Christian Walder", "Paul Montague", "Amir Dezfouli", "Michael Norrish" ], "github": "https://github.com/sean-lamont/odd", "project_page": "", "abstract": "Diverse outputs in text generation are necessary for effective exploration in complex reasoning tasks, such as code generation and mathematical problem solving. Such Pass@k problems benefit from distinct candidates covering the solution space. However, traditional sampling approaches often waste computational resources on repetitive failure modes. While Diffusion Language Models have emerged as a competitive alternative to the prevailing Autoregressive paradigm, they remain susceptible to this redundancy, with independent samples frequently collapsing into similar modes. To address this, we propose a training free, low cost intervention to enhance generative diversity in Diffusion Language Models. Our approach modifies intermediate samples in a batch sequentially, where each sample is repelled from the feature space of previous samples, actively penalising redundancy. Unlike prior methods that require retraining or beam search, our strategy incurs negligible computational overhead, while ensuring that each sample contributes a unique perspective to the batch. We evaluate our method on the HumanEval and GSM8K benchmarks using the LLaDA-8B-Instruct model. Our results demonstrate significantly improved diversity and Pass@k performance across various temperature settings. As a simple modification to the sampling process, our method offers an immediate, low-cost improvement for current and future Diffusion Language Models in tasks that benefit from diverse solution search. We make our code available at https://github.com/sean-lamont/odd.", "github_manual": "", "github_regex": "https://github.com/sean-lamont/odd", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09095", "title": "Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs", "authors": [ "Kaiser Sun", "Xiaochuang Yuan", "Hongjun Liu", "Chen Zhao", "Cheng Zhang", "Mark Dredze", "Fan Bai" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) can process text presented as images, yet they often perform worse than when the same content is provided as textual tokens. We systematically diagnose this \"modality gap\" by evaluating seven MLLMs across seven benchmarks in five input modes, spanning both synthetically rendered text and realistic document images from arXiv PDFs to Wikipedia pages. We find that the modality gap is task- and data-dependent. For example, math tasks degrade by over 60 points on synthetic renderings, while natural document images often match or exceed text-mode performance. Rendering choices such as font and resolution are strong confounds, with font alone swinging accuracy by up to 47 percentage points. To understand this, we conduct a grounded-theory error analysis of over 4,000 examples, revealing that image mode selectively amplifies reading errors (calculation and formatting failures) while leaving knowledge and reasoning errors largely unchanged, and that some models exhibit a chain-of-thought reasoning collapse under visual input. Motivated by these findings, we propose a self-distillation method that trains the model on its own pure text reasoning traces paired with image inputs, raising image-mode accuracy on GSM8K from 30.71% to 92.72% and transferring to unseen benchmarks without catastrophic forgetting. Overall, our study provides a systematic understanding of the modality gap and suggests a practical path toward improving visual text understanding in multimodal language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09200", "title": "The Reasoning Trap -- Logical Reasoning as a Mechanistic Pathway to Situational Awareness", "authors": [ "Subramanyam Sahoo", "Aman Chadha", "Vinija Jain", "Divya Chaudhary" ], "github": "", "project_page": "", "abstract": "Situational awareness, the capacity of an AI system to recognize its own nature, understand its training and deployment context, and reason strategically about its circumstances, is widely considered among the most dangerous emergent capabilities in advanced AI systems. Separately, a growing research effort seeks to improve the logical reasoning capabilities of large language models (LLMs) across deduction, induction, and abduction. In this paper, we argue that these two research trajectories are on a collision course. We introduce the RAISE framework (Reasoning Advancing Into Self Examination), which identifies three mechanistic pathways through which improvements in logical reasoning enable progressively deeper levels of situational awareness: deductive self inference, inductive context recognition, and abductive self modeling. We formalize each pathway, construct an escalation ladder from basic self recognition to strategic deception, and demonstrate that every major research topic in LLM logical reasoning maps directly onto a specific amplifier of situational awareness. We further analyze why current safety measures are insufficient to prevent this escalation. We conclude by proposing concrete safeguards, including a \"Mirror Test\" benchmark and a Reasoning Safety Parity Principle, and pose an uncomfortable but necessary question to the logical reasoning community about its responsibility in this trajectory.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.06333", "title": "SAHOO: Safeguarded Alignment for High-Order Optimization Objectives in Recursive Self-Improvement", "authors": [ "Subramanyam Sahoo", "Aman Chadha", "Vinija Jain", "Divya Chaudhary" ], "github": "https://github.com/SubramanyamSahoo/SAHOO-Safeguarded-Alignment-for-High-Order-Optimization-Objectives-in-Recursive-Self-Improvement", "project_page": "", "abstract": "Recursive self-improvement is moving from theory to practice: modern systems can critique, revise, and evaluate their own outputs, yet iterative self-modification risks subtle alignment drift. We introduce SAHOO, a practical framework to monitor and control drift through three safeguards: (i) the Goal Drift Index (GDI), a learned multi-signal detector combining semantic, lexical, structural, and distributional measures; (ii) constraint preservation checks that enforce safety-critical invariants such as syntactic correctness and non-hallucination; and (iii) regression-risk quantification to flag improvement cycles that undo prior gains. Across 189 tasks in code generation, mathematical reasoning, and truthfulness, SAHOO produces substantial quality gains, including 18.3 percent improvement in code tasks and 16.8 percent in reasoning, while preserving constraints in two domains and maintaining low violations in truthfulness. Thresholds are calibrated on a small validation set of 18 tasks across three cycles. We further map the capability-alignment frontier, showing efficient early improvement cycles but rising alignment costs later and exposing domain-specific tensions such as fluency versus factuality. SAHOO therefore makes alignment preservation during recursive self-improvement measurable, deployable, and systematically validated at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SubramanyamSahoo/SAHOO-Safeguarded-Alignment-for-High-Order-Optimization-Objectives-in-Recursive-Self-Improvement", "github_api_added_by": "auto" }, { "date": "2026-03-11", "arxiv_id": "2603.06577", "title": "Omni-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion", "authors": [ "Lijiang Li", "Zuwei Long", "Yunhang Shen", "Heting Gao", "Haoyu Cao", "Xing Sun", "Caifeng Shan", "Ran He", "Chaoyou Fu" ], "github": "", "project_page": "", "abstract": "While recent multimodal large language models (MLLMs) have made impressive strides, they predominantly employ a conventional autoregressive architecture as their backbone, leaving significant room to explore effective and efficient alternatives in architectural design. Concurrently, recent studies have successfully applied discrete diffusion models to various domains, such as visual understanding and image generation, revealing their considerable potential as a promising backbone for multimodal systems. Drawing inspiration from these pioneering research, we introduce Omni-Diffusion, the first any-to-any multimodal language model built entirely on mask-based discrete diffusion models, which unifies understanding and generation across text, speech, and images. Omni-Diffusion employs a unified mask-based discrete diffusion model to directly capture the joint distribution over discrete multimodal tokens. This approach supports not only bimodal tasks but also more complex scenarios involving multiple modalities. On a diverse set of benchmarks, our method outperforms or performs on par with existing multimodal systems that process two or more modalities, highlighting the significant promise of diffusion models in powering the next generation of multimodal foundation models. Project webpage: https://omni-diffusion.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09206", "title": "MM-Zero: Self-Evolving Multi-Model Vision Language Models From Zero Data", "authors": [ "Zongxia Li", "Hongyang Du", "Chengsong Huang", "Xiyang Wu", "Lantao Yu", "Yicheng He", "Jing Xie", "Xiaomin Wu", "Zhichao Liu", "Jiarui Zhang", "Fuxiao Liu" ], "github": "", "project_page": "", "abstract": "Self-evolving has emerged as a key paradigm for improving foundational models such as Large Language Models (LLMs) and Vision Language Models (VLMs) with minimal human intervention. While recent approaches have demonstrated that LLM agents can self-evolve from scratch with little to no data, VLMs introduce an additional visual modality that typically requires at least some seed data, such as images, to bootstrap the self-evolution process. In this work, we present Multi-model Multimodal Zero (MM-Zero), the first RL-based framework to achieve zero-data self-evolution for VLM reasoning. Moving beyond prior dual-role (Proposer and Solver) setups, MM-Zero introduces a multi-role self-evolving training framework comprising three specialized roles: a Proposer that generates abstract visual concepts and formulates questions; a Coder that translates these concepts into executable code (e.g., Python, SVG) to render visual images; and a Solver that performs multimodal reasoning over the generated visual content. All three roles are initialized from the same base model and trained using Group Relative Policy Optimization (GRPO), with carefully designed reward mechanisms that integrate execution feedback, visual verification, and difficulty balancing. Our experiments show that MM-Zero improves VLM reasoning performance across a wide range of multimodal benchmarks. MM-Zero establishes a scalable path toward self-evolving multi-model systems for multimodal models, extending the frontier of self-improvement beyond the conventional two-model paradigm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.08823", "title": "Fish Audio S2 Technical Report", "authors": [ "Shijia Liao", "Yuxuan Wang", "Songting Liu", "Yifan Cheng", "Ruoyi Zhang", "Tianyu Li", "Shidong Li", "Yisheng Zheng", "Xingwei Liu", "Qingzheng Wang", "Zhizhuo Zhou", "Jiahua Liu", "Xin Chen", "Dawei Han" ], "github": "https://github.com/fishaudio/fish-speech", "project_page": "", "abstract": "We introduce Fish Audio S2, an open-sourced text-to-speech system featuring multi-speaker, multi-turn generation, and, most importantly, instruction-following control via natural-language descriptions. To scale training, we develop a multi-stage training recipe together with a staged data pipeline covering video captioning and speech captioning, voice-quality assessment, and reward modeling. To push the frontier of open-source TTS, we release our model weights, fine-tuning code, and an SGLang-based inference engine. The inference engine is production-ready for streaming, achieving an RTF of 0.195 and a time-to-first-audio below 100 ms.Our code and weights are available on GitHub (https://github.com/fishaudio/fish-speech) and Hugging Face (https://huggingface.co/fishaudio/s2-pro). We highly encourage readers to visit https://fish.audio to try custom voices.", "github_manual": "", "github_regex": "https://github.com/fishaudio/fish-speech", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.07888", "title": "VLM-SubtleBench: How Far Are VLMs from Human-Level Subtle Comparative Reasoning?", "authors": [ "Minkyu Kim", "Sangheon Lee", "Dongmin Park" ], "github": "https://github.com/krafton-ai/VLM-SubtleBench", "project_page": "", "abstract": "The ability to distinguish subtle differences between visually similar images is essential for diverse domains such as industrial anomaly detection, medical imaging, and aerial surveillance. While comparative reasoning benchmarks for vision-language models (VLMs) have recently emerged, they primarily focus on images with large, salient differences and fail to capture the nuanced reasoning required for real-world applications. In this work, we introduce VLM-SubtleBench, a benchmark designed to evaluate VLMs on subtle comparative reasoning. Our benchmark covers ten difference types - Attribute, State, Emotion, Temporal, Spatial, Existence, Quantity, Quality, Viewpoint, and Action - and curate paired question-image sets reflecting these fine-grained variations. Unlike prior benchmarks restricted to natural image datasets, our benchmark spans diverse domains, including industrial, aerial, and medical imagery. Through extensive evaluation of both proprietary and open-source VLMs, we reveal systematic gaps between model and human performance across difference types and domains, and provide controlled analyses highlighting where VLMs' reasoning sharply deteriorates. Together, our benchmark and findings establish a foundation for advancing VLMs toward human-level comparative reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/krafton-ai/VLM-SubtleBench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09652", "title": "MiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistants", "authors": [ "Zuhao Zhang", "Chengyue Yu", "Yuante Li", "Chenyi Zhuang", "Linjian Mo", "Shuai Li" ], "github": "https://github.com/MiniAppBench/miniappbench", "project_page": "", "abstract": "With the rapid advancement of Large Language Models (LLMs) in code generation, human-AI interaction is evolving from static text responses to dynamic, interactive HTML-based applications, which we term MiniApps. These applications require models to not only render visual interfaces but also construct customized interaction logic that adheres to real-world principles. However, existing benchmarks primarily focus on algorithmic correctness or static layout reconstruction, failing to capture the capabilities required for this new paradigm. To address this gap, we introduce MiniAppBench, the first comprehensive benchmark designed to evaluate principle-driven, interactive application generation. Sourced from a real-world application with 10M+ generations, MiniAppBench distills 500 tasks across six domains (e.g., Games, Science, and Tools). Furthermore, to tackle the challenge of evaluating open-ended interactions where no single ground truth exists, we propose MiniAppEval, an agentic evaluation framework. Leveraging browser automation, it performs human-like exploratory testing to systematically assess applications across three dimensions: Intention, Static, and Dynamic. Our experiments reveal that current LLMs still face significant challenges in generating high-quality MiniApps, while MiniAppEval demonstrates high alignment with human judgment, establishing a reliable standard for future research. Our code is available in github.com/MiniAppBench.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiniAppBench/miniappbench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09951", "title": "Towards a Neural Debugger for Python", "authors": [ "Maximilian Beck", "Jonas Gehring", "Jannik Kossen", "Gabriel Synnaeve" ], "github": "", "project_page": "", "abstract": "Training large language models (LLMs) on Python execution traces grounds them in code execution and enables the line-by-line execution prediction of whole Python programs, effectively turning them into neural interpreters (FAIR CodeGen Team et al., 2025). However, developers rarely execute programs step by step; instead, they use debuggers to stop execution at certain breakpoints and step through relevant portions only while inspecting or modifying program variables. Existing neural interpreter approaches lack such interactive control. To address this limitation, we introduce neural debuggers: language models that emulate traditional debuggers, supporting operations such as stepping into, over, or out of functions, as well as setting breakpoints at specific source lines. We show that neural debuggers -- obtained via fine-tuning large LLMs or pre-training smaller models from scratch -- can reliably model both forward execution (predicting future states and outputs) and inverse execution (inferring prior states or inputs) conditioned on debugger actions. Evaluated on CruxEval, our models achieve strong performance on both output and input prediction tasks, demonstrating robust conditional execution modeling. Our work takes first steps towards future agentic coding systems in which neural debuggers serve as a world model for simulated debugging environments, providing execution feedback or enabling agents to interact with real debugging tools. This capability lays the foundation for more powerful code generation, program understanding, and automated debugging.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09072", "title": "A Text-Native Interface for Generative Video Authoring", "authors": [ "Xingyu Bruce Liu", "Mira Dontcheva", "Dingzeyu Li" ], "github": "", "project_page": "", "abstract": "Everyone can write their stories in freeform text format -- it's something we all learn in school. Yet storytelling via video requires one to learn specialized and complicated tools. In this paper, we introduce Doki, a text-native interface for generative video authoring, aligning video creation with the natural process of text writing. In Doki, writing text is the primary interaction: within a single document, users define assets, structure scenes, create shots, refine edits, and add audio. We articulate the design principles of this text-first approach and demonstrate Doki's capabilities through a series of examples. To evaluate its real-world use, we conducted a week-long deployment study with participants of varying expertise in video authoring. This work contributes a fundamental shift in generative video interfaces, demonstrating a powerful and accessible new way to craft visual stories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09896", "title": "Stepping VLMs onto the Court: Benchmarking Spatial Intelligence in Sports", "authors": [ "Yuchen Yang", "Yuqing Shao", "Duxiu Huang", "Linfeng Dong", "Yifei Liu", "Suixin Tang", "Xiang Zhou", "Yuanyuan Gao", "Wei Wang", "Yue Zhou", "Xue Yang", "Yanfeng Wang", "Xiao Sun", "Zhihang Zhong" ], "github": "", "project_page": "", "abstract": "Sports have long attracted broad attention as they push the limits of human physical and cognitive capabilities. Amid growing interest in spatial intelligence for vision-language models (VLMs), sports provide a natural testbed for understanding high-intensity human motion and dynamic object interactions. To this end, we present CourtSI, the first large-scale spatial intelligence dataset tailored to sports scenarios. CourtSI contains over 1M QA pairs, organized under a holistic taxonomy that systematically covers spatial counting, distance measurement, localization, and relational reasoning, across representative net sports including badminton, tennis, and table tennis. Leveraging well-defined court geometry as metric anchors, we develop a semi-automatic data engine to reconstruct sports scenes, enabling scalable curation of CourtSI. In addition, we introduce CourtSI-Bench, a high-quality evaluation benchmark comprising 3,686 QA pairs with rigorous human verification. We evaluate 25 proprietary and open-source VLMs on CourtSI-Bench, revealing a remaining human-AI performance gap and limited generalization from existing spatial intelligence benchmarks. These findings indicate that sports scenarios expose limitations in spatial intelligence capabilities captured by existing benchmarks. Further, fine-tuning Qwen3-VL-8B on CourtSI improves accuracy on CourtSI-Bench by 23.5 percentage points. The adapted model also generalizes effectively to CourtSI-Ext, an evaluation set built on a similar but unseen sport, and demonstrates enhanced spatial-aware commentary generation. Together, these findings demonstrate that CourtSI provides a scalable pathway toward advancing spatial intelligence of VLMs in sports.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09877", "title": "InternVL-U: Democratizing Unified Multimodal Models for Understanding, Reasoning, Generation and Editing", "authors": [ "Changyao Tian", "Danni Yang", "Guanzhou Chen", "Erfei Cui", "Zhaokai Wang", "Yuchen Duan", "Penghao Yin", "Sitao Chen", "Ganlin Yang", "Mingxin Liu", "Zirun Zhu", "Ziqian Fan", "Leyao Gu", "Haomin Wang", "Qi Wei", "Jinhui Yin", "Xue Yang", "Zhihang Zhong", "Qi Qin", "Yi Xin", "Bin Fu", "Yihao Liu", "Jiaye Ge", "Qipeng Guo", "Gen Luo", "Hongsheng Li", "Yu Qiao", "Kai Chen", "Hongjie Zhang" ], "github": "https://github.com/open-compass/GenEditEvalKit", "project_page": "", "abstract": "Unified multimodal models (UMMs) that integrate understanding, reasoning, generation, and editing face inherent trade-offs between maintaining strong semantic comprehension and acquiring powerful generation capabilities. In this report, we present InternVL-U, a lightweight 4B-parameter UMM that democratizes these capabilities within a unified framework. Guided by the principles of unified contextual modeling and modality-specific modular design with decoupled visual representations, InternVL-U integrates a state-of-the-art Multimodal Large Language Model (MLLM) with a specialized MMDiT-based visual generation head. To further bridge the gap between aesthetic generation and high-level intelligence, we construct a comprehensive data synthesis pipeline targeting high-semantic-density tasks, such as text rendering and scientific reasoning, under a reasoning-centric paradigm that leverages Chain-of-Thought (CoT) to better align abstract user intent with fine-grained visual generation details. Extensive experiments demonstrate that InternVL-U achieves a superior performance - efficiency balance. Despite using only 4B parameters, it consistently outperforms unified baseline models with over 3x larger scales such as BAGEL (14B) on various generation and editing tasks, while retaining strong multimodal understanding and reasoning capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-compass/GenEditEvalKit", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09488", "title": "Streaming Autoregressive Video Generation via Diagonal Distillation", "authors": [ "Jinxiu Liu", "Xuanming Liu", "Kangfu Mei", "Yandong Wen", "Ming-HsuanYang", "Weiyang Liu" ], "github": "", "project_page": "", "abstract": "Large pretrained diffusion models have significantly enhanced the quality of generated videos, and yet their use in real-time streaming remains limited. Autoregressive models offer a natural framework for sequential frame synthesis but require heavy computation to achieve high fidelity. Diffusion distillation can compress these models into efficient few-step variants, but existing video distillation approaches largely adapt image-specific methods that neglect temporal dependencies. These techniques often excel in image generation but underperform in video synthesis, exhibiting reduced motion coherence, error accumulation over long sequences, and a latency-quality trade-off. We identify two factors that result in these limitations: insufficient utilization of temporal context during step reduction and implicit prediction of subsequent noise levels in next-chunk prediction (i.e., exposure bias). To address these issues, we propose Diagonal Distillation, which operates orthogonally to existing approaches and better exploits temporal information across both video chunks and denoising steps. Central to our approach is an asymmetric generation strategy: more steps early, fewer steps later. This design allows later chunks to inherit rich appearance information from thoroughly processed early chunks, while using partially denoised chunks as conditional inputs for subsequent synthesis. By aligning the implicit prediction of subsequent noise levels during chunk generation with the actual inference conditions, our approach mitigates error propagation and reduces oversaturation in long-range sequences. We further incorporate implicit optical flow modeling to preserve motion quality under strict step constraints. Our method generates a 5-second video in 2.61 seconds (up to 31 FPS), achieving a 277.3x speedup over the undistilled model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.03143", "title": "Geometry-Guided Reinforcement Learning for Multi-view Consistent 3D Scene Editing", "authors": [ "Jiyuan Wang", "Chunyu Lin", "Lei Sun", "Zhi Cao", "Yuyang Yin", "Lang Nie", "Zhenlong Yuan", "Xiangxiang Chu", "Yunchao Wei", "Kang Liao", "Guosheng Lin" ], "github": "", "project_page": "", "abstract": "Leveraging the priors of 2D diffusion models for 3D editing has emerged as a promising paradigm. However, maintaining multi-view consistency in edited results remains challenging, and the extreme scarcity of 3D-consistent editing paired data renders supervised fine-tuning (SFT), the most effective training strategy for editing tasks, infeasible. In this paper, we observe that, while generating multi-view consistent 3D content is highly challenging, verifying 3D consistency is tractable, naturally positioning reinforcement learning (RL) as a feasible solution. Motivated by this, we propose RL3DEdit, a single-pass framework driven by RL optimization with novel rewards derived from the 3D foundation model, VGGT. Specifically, we leverage VGGT's robust priors learned from massive real-world data, feed the edited images, and utilize the output confidence maps and pose estimation errors as reward signals, effectively anchoring the 2D editing priors onto a 3D-consistent manifold via RL. Extensive experiments demonstrate that RL3DEdit achieves stable multi-view consistency and outperforms state-of-the-art methods in editing quality with high efficiency. To promote the development of 3D editing, we will release the code and model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09117", "title": "Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards", "authors": [ "Zhengzhao Ma", "Xueru Wen", "Boxi Cao", "Yaojie Lu", "Hongyu Lin", "Jinglin Yang", "Min He", "Xianpei Han", "Le Sun" ], "github": "https://github.com/icip-cas/DCPO", "project_page": "", "abstract": "Reinforcement Learning from Verifiable Rewards (RLVR) significantly enhances large language models (LLMs) reasoning but severely suffers from calibration degeneration, where models become excessively over-confident in incorrect answers. Previous studies devote to directly incorporating calibration objective into existing optimization target. However, our theoretical analysis demonstrates that there exists a fundamental gradient conflict between the optimization for maximizing policy accuracy and minimizing calibration error. Building on this insight, we propose DCPO, a simple yet effective framework that systematically decouples reasoning and calibration objectives. Extensive experiments demonstrate that our DCPO not only preserves accuracy on par with GRPO but also achieves the best calibration performance and substantially mitigates the over-confidence issue. Our study provides valuable insights and practical solution for more reliable LLM deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/icip-cas/DCPO", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09906", "title": "Thinking to Recall: How Reasoning Unlocks Parametric Knowledge in LLMs", "authors": [ "Zorik Gekhman", "Roee Aharoni", "Eran Ofek", "Mor Geva", "Roi Reichart", "Jonathan Herzig" ], "github": "", "project_page": "", "abstract": "While reasoning in LLMs plays a natural role in math, code generation, and multi-hop factual questions, its effect on simple, single-hop factual questions remains unclear. Such questions do not require step-by-step logical decomposition, making the utility of reasoning highly counterintuitive. Nevertheless, we find that enabling reasoning substantially expands the capability boundary of the model's parametric knowledge recall, unlocking correct answers that are otherwise effectively unreachable. Why does reasoning aid parametric knowledge recall when there are no complex reasoning steps to be done? To answer this, we design a series of hypothesis-driven controlled experiments, and identify two key driving mechanisms: (1) a computational buffer effect, where the model uses the generated reasoning tokens to perform latent computation independent of their semantic content; and (2) factual priming, where generating topically related facts acts as a semantic bridge that facilitates correct answer retrieval. Importantly, this latter generative self-retrieval mechanism carries inherent risks: we demonstrate that hallucinating intermediate facts during reasoning increases the likelihood of hallucinations in the final answer. Finally, we show that our insights can be harnessed to directly improve model accuracy by prioritizing reasoning trajectories that contain hallucination-free factual statements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.08899", "title": "ConFu: Contemplate the Future for Better Speculative Sampling", "authors": [ "Zongyue Qin", "Raghavv Goel", "Mukul Gagrani", "Risheek Garrepalli", "Mingu Lee", "Yizhou Sun" ], "github": "https://github.com/SafeAILab/EAGLE", "project_page": "", "abstract": "Speculative decoding has emerged as a powerful approach to accelerate large language model (LLM) inference by employing lightweight draft models to propose candidate tokens that are subsequently verified by the target model. The effectiveness of this paradigm critically depends on the quality of the draft model. While recent advances such as the EAGLE series achieve state-of-the-art speedup, existing draft models remain limited by error accumulation: they condition only on the current prefix, causing their predictions to drift from the target model over steps. In this work, we propose ConFu (Contemplate the Future), a novel speculative decoding framework that enables draft models to anticipate the future direction of generation. ConFu introduces (i) contemplate tokens and soft prompts that allow the draft model to leverage future-oriented signals from the target model at negligible cost, (ii) a dynamic contemplate token mechanism with MoE to enable context-aware future prediction, and (iii) a training framework with anchor token sampling and future prediction replication that learns robust future prediction. Experiments demonstrate that ConFu improves token acceptance rates and generation speed over EAGLE-3 by 8--11% across various downstream tasks with Llama-3 3B and 8B models. We believe our work is the first to bridge speculative decoding with continuous reasoning tokens, offering a new direction for accelerating LLM inference.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SafeAILab/EAGLE", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.02816", "title": "BrandFusion: A Multi-Agent Framework for Seamless Brand Integration in Text-to-Video Generation", "authors": [ "Zihao Zhu", "Ruotong Wang", "Siwei Lyu", "Min Zhang", "Baoyuan Wu" ], "github": "", "project_page": "", "abstract": "The rapid advancement of text-to-video (T2V) models has revolutionized content creation, yet their commercial potential remains largely untapped. We introduce, for the first time, the task of seamless brand integration in T2V: automatically embedding advertiser brands into prompt-generated videos while preserving semantic fidelity to user intent. This task confronts three core challenges: maintaining prompt fidelity, ensuring brand recognizability, and achieving contextually natural integration. To address them, we propose BrandFusion, a novel multi-agent framework comprising two synergistic phases. In the offline phase (advertiser-facing), we construct a Brand Knowledge Base by probing model priors and adapting to novel brands via lightweight fine-tuning. In the online phase (user-facing), five agents jointly refine user prompts through iterative refinement, leveraging the shared knowledge base and real-time contextual tracking to ensure brand visibility and semantic alignment. Experiments on 18 established and 2 custom brands across multiple state-of-the-art T2V models demonstrate that BrandFusion significantly outperforms baselines in semantic preservation, brand recognizability, and integration naturalness. Human evaluations further confirm higher user satisfaction, establishing a practical pathway for sustainable T2V monetization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.06854", "title": "Are Audio-Language Models Listening? Audio-Specialist Heads for Adaptive Audio Steering", "authors": [ "Neta Glazer", "Lenny Aharon", "Ethan Fetaya" ], "github": "", "project_page": "", "abstract": "Multimodal large language models can exhibit text dominance, over-relying on linguistic priors instead of grounding predictions in non-text inputs. One example is large audio-language models (LALMs) where decisive audio evidence can be under-utilized even when it contains important information. To address this issue we use mechanistic interpretability to identify a small set of audio-specialist attention heads whose audio attention yields a ``listening'' signal. We show that this signal increases when audio evidence affects the model's output, providing an indicator of audio engagement under standard prompting. Leveraging this localization, we construct an audio--silence steering direction and apply an inference-time activation intervention to the final representation, amplifying the model's audio effect. To demonstrate the utility of this intervention, we show on MMAU that this improves accuracy by up to +8.0 percentage points on two Qwen-based LALMs, without any parameter updates.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09400", "title": "Reward Prediction with Factorized World States", "authors": [ "Yijun Shen", "Delong Chen", "Xianming Hu", "Jiaming Mi", "Hongbo Zhao", "Kai Zhang", "Pascale Fung" ], "github": "", "project_page": "", "abstract": "Agents must infer action outcomes and select actions that maximize a reward signal indicating how close the goal is to being reached. Supervised learning of reward models could introduce biases inherent to training data, limiting generalization to novel goals and environments. In this paper, we investigate whether well-defined world state representations alone can enable accurate reward prediction across domains. To address this, we introduce StateFactory, a factorized representation method that transforms unstructured observations into a hierarchical object-attribute structure using language models. This structured representation allows rewards to be estimated naturally as the semantic similarity between the current state and the goal state under hierarchical constraint. Overall, the compact representation structure induced by StateFactory enables strong reward generalization capabilities. We evaluate on RewardPrediction, a new benchmark dataset spanning five diverse domains and comprising 2,454 unique action-observation trajectories with step-wise ground-truth rewards. Our method shows promising zero-shot results against both VLWM-critic and LLM-as-a-Judge reward models, achieving 60% and 8% lower EPIC distance, respectively. Furthermore, this superior reward quality successfully translates into improved agent planning performance, yielding success rate gains of +21.64% on AlfWorld and +12.40% on ScienceWorld over reactive system-1 policies and enhancing system-2 agent planning. Project Page: https://statefactory.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09881", "title": "Do What I Say: A Spoken Prompt Dataset for Instruction-Following", "authors": [ "Maike Z\u00fcfle", "Sara Papi", "Fabian Retkowski", "Szymon Mazurek", "Marek Kasztelnik", "Alexander Waibel", "Luisa Bentivogli", "Jan Niehues" ], "github": "", "project_page": "", "abstract": "Speech Large Language Models (SLLMs) have rapidly expanded, supporting a wide range of tasks. These models are typically evaluated using text prompts, which may not reflect real-world scenarios where users interact with speech. To address this gap, we introduce DoWhatISay (DOWIS), a multilingual dataset of human-recorded spoken and written prompts designed to pair with any existing benchmark for realistic evaluation of SLLMs under spoken instruction conditions. Spanning 9 tasks and 11 languages, it provides 10 prompt variants per task-language pair, across five styles. Using DOWIS, we benchmark state-of-the-art SLLMs, analyzing the interplay between prompt modality, style, language, and task type. Results show that text prompts consistently outperform spoken prompts, particularly for low-resource and cross-lingual settings. Only for tasks with speech output, spoken prompts do close the gap, highlighting the need for speech-based prompting in SLLM evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09555", "title": "Compiler-First State Space Duality and Portable O(1) Autoregressive Caching for Inference", "authors": [ "Cosmo Santoni" ], "github": "https://github.com/CosmoNaught/mamba2-jax", "project_page": "", "abstract": "State-space model releases are typically coupled to fused CUDA and Triton kernels, inheriting a hard dependency on NVIDIA hardware. We show that Mamba-2's state space duality algorithm -- diagonal state structure, chunkable recurrence, and einsum-dominated compute with static control flow -- maps cleanly onto what XLA's fusion and tiling passes actually optimise, making custom kernels optional rather than required. We implement the full inference path (prefill, cached autoregressive decoding) as shaped standard primitives under XLA, without hand-written kernels, and realise the architecture's theoretical O(1) state management as a compiled on-device cache requiring no host synchronisation during generation. The implementation runs unmodified on CPU, NVIDIA GPU, and Google Cloud TPU from a single JAX source. On TPU v6e across five model scales (130M--2.7B parameters), XLA-generated code reaches approximately 140 TFLOPS on single-stream prefill (15% MFU) and up to 64% bandwidth utilisation on decode. Greedy decoding matches the PyTorch/CUDA reference token-for-token across 64 steps, with hidden-state agreement within float32 rounding tolerance. The pattern transfers to any SSM recurrence satisfying the same structural conditions, on any platform with a mature XLA backend. The implementation is publicly available at https://github.com/CosmoNaught/mamba2-jax and merged into the Bonsai JAX model library.", "github_manual": "", "github_regex": "https://github.com/CosmoNaught/mamba2-jax", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.05863", "title": "ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning", "authors": [ "Juyong Jiang", "Jiasi Shen", "Sunghun Kim", "Kang Min Yoo", "Jeonghoon Kim", "Sungju Kim" ], "github": "https://github.com/juyongjiang/ReflexiCoder", "project_page": "", "abstract": "While Large Language Models (LLMs) have revolutionized code generation, standard \"System 1\" approaches, generating solutions in a single forward pass, often hit a performance ceiling when faced with complex algorithmic tasks. Existing iterative refinement strategies attempt to bridge this gap at inference time, yet they predominantly rely on external oracles, execution feedback, or computationally expensive prompt-response cycles. In this work, we propose ReflexiCoder, a novel reinforcement learning (RL) framework that internalizes the structured reasoning trajectory, encompassing initial generation, bug and optimization aware reflection, and self-correction, directly into the model's weights. Unlike prior methods, ReflexiCoder shifts the paradigm from external-dependent refinement to an intrinsic, fully autonomous self-reflection and self-correction capabilities at inference time. We utilize an RL-zero training paradigm with granular reward functions to optimize the entire reflection-correction trajectory, teaching the model how to debug without reliance on ground-truth feedback or execution engines at inference time. Extensive experiments across seven benchmarks demonstrate that our ReflexiCoder-8B establishes a new state-of-the-art (SOTA) among leading open-source models in the 1.5B-14B range, achieving 94.51% (87.20%) on HumanEval (Plus), 81.80% (78.57%) on MBPP (Plus), 35.00% on BigCodeBench, 52.21% on LiveCodeBench, and 37.34% on CodeForces in a single-attempt setting, rivaling or surpassing proprietary models like GPT-5.1. Notably, our framework is significantly more token-efficient than base models, reducing inference-time compute overhead by approximately 40% through disciplined, high-speed reasoning and reflection patterns. Source code is available at https://github.com/juyongjiang/ReflexiCoder.", "github_manual": "", "github_regex": "https://github.com/juyongjiang/ReflexiCoder", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.08806", "title": "Test-Driven AI Agent Definition (TDAD): Compiling Tool-Using Agents from Behavioral Specifications", "authors": [ "Tzafrir Rehan" ], "github": "https://github.com/f-labs-io/tdad-paper-code", "project_page": "", "abstract": "We present Test-Driven AI Agent Definition (TDAD), a methodology that treats agent prompts as compiled artifacts: engineers provide behavioral specifications, a coding agent converts them into executable tests, and a second coding agent iteratively refines the prompt until tests pass. Deploying tool-using LLM agents in production requires measurable behavioral compliance that current development practices cannot provide. Small prompt changes cause silent regressions, tool misuse goes undetected, and policy violations emerge only after deployment. To mitigate specification gaming, TDAD introduces three mechanisms: (1) visible/hidden test splits that withhold evaluation tests during compilation, (2) semantic mutation testing via a post-compilation agent that generates plausible faulty prompt variants, with the harness measuring whether the test suite detects them, and (3) spec evolution scenarios that quantify regression safety when requirements change. We evaluate TDAD on SpecSuite-Core, a benchmark of four deeply-specified agents spanning policy compliance, grounded analytics, runbook adherence, and deterministic enforcement. Across 24 independent trials, TDAD achieves 92% v1 compilation success with 97% mean hidden pass rate; evolved specifications compile at 58%, with most failed runs passing all visible tests except 1-2, and show 86-100% mutation scores, 78% v2 hidden pass rate, and 97% regression safety scores. The implementation is available as an open benchmark at https://github.com/f-labs-io/tdad-paper-code.", "github_manual": "", "github_regex": "https://github.com/f-labs-io/tdad-paper-code", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.08075", "title": "TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery", "authors": [ "Yanan Wu", "Yuhan Yan", "Tailai Chen", "Zhixiang Chi", "ZiZhang Wu", "Yi Jin", "Yang Wang", "Zhenbo Li" ], "github": "https://github.com/ynanwu/TALON", "project_page": "", "abstract": "On-the-fly category discovery (OCD) aims to recognize known categories while simultaneously discovering novel ones from an unlabeled online stream, using a model trained only on labeled data. Existing approaches freeze the feature extractor trained offline and employ a hash-based framework that quantizes features into binary codes as class prototypes. However, discovering novel categories with a fixed knowledge base is counterintuitive, as the learning potential of incoming data is entirely neglected. In addition, feature quantization introduces information loss, diminishes representational expressiveness, and amplifies intra-class variance. It often results in category explosion, where a single class is fragmented into multiple pseudo-classes. To overcome these limitations, we propose a test-time adaptation framework that enables learning through discovery. It incorporates two complementary strategies: a semantic-aware prototype update and a stable test-time encoder update. The former dynamically refines class prototypes to enhance classification, whereas the latter integrates new information directly into the parameter space. Together, these components allow the model to continuously expand its knowledge base with newly encountered samples. Furthermore, we introduce a margin-aware logit calibration in the offline stage to enlarge inter-class margins and improve intra-class compactness, thereby reserving embedding space for future class discovery. Experiments on standard OCD benchmarks demonstrate that our method substantially outperforms existing hash-based state-of-the-art approaches, yielding notable improvements in novel-class accuracy and effectively mitigating category explosion. The code is publicly available at blue{https://github.com/ynanwu/TALON}.", "github_manual": "", "github_regex": "https://github.com/ynanwu/TALON", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.07513", "title": "Bolbosh: Script-Aware Flow Matching for Kashmiri Text-to-Speech", "authors": [ "Tajamul Ashraf", "Burhaan Rasheed Zargar", "Saeed Abdul Muizz", "Ifrah Mushtaq", "Nazima Mehdi", "Iqra Altaf Gillani", "Aadil Amin Kak", "Janibul Bashir" ], "github": "https://github.com/gaash-lab/Bolbosh", "project_page": "", "abstract": "Kashmiri is spoken by around 7 million people but remains critically underserved in speech technology, despite its official status and rich linguistic heritage. The lack of robust Text-to-Speech (TTS) systems limits digital accessibility and inclusive human-computer interaction for native speakers. In this work, we present the first dedicated open-source neural TTS system designed for Kashmiri. We show that zero-shot multilingual baselines trained for Indic languages fail to produce intelligible speech, achieving a Mean Opinion Score (MOS) of only 1.86, largely due to inadequate modeling of Perso-Arabic diacritics and language-specific phonotactics. To address these limitations, we propose Bolbosh, a supervised cross-lingual adaptation strategy based on Optimal Transport Conditional Flow Matching (OT-CFM) within the Matcha-TTS framework. This enables stable alignment under limited paired data. We further introduce a three-stage acoustic enhancement pipeline consisting of dereverberation, silence trimming, and loudness normalization to unify heterogeneous speech sources and stabilize alignment learning. The model vocabulary is expanded to explicitly encode Kashmiri graphemes, preserving fine-grained vowel distinctions. Our system achieves a MOS of 3.63 and a Mel-Cepstral Distortion (MCD) of 3.73, substantially outperforming multilingual baselines and establishing a new benchmark for Kashmiri speech synthesis. Our results demonstrate that script-aware and supervised flow-based adaptation are critical for low-resource TTS in diacritic-sensitive languages. Code and data are available at: https://github.com/gaash-lab/Bolbosh.", "github_manual": "", "github_regex": "https://github.com/gaash-lab/Bolbosh", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.09221", "title": "Beyond Test-Time Training: Learning to Reason via Hardware-Efficient Optimal Control", "authors": [ "Peihao Wang", "Shan Yang", "Xijun Wang", "Tesi Xiao", "Xin Liu", "Changlong Yu", "Yu Lou", "Pan Li", "Zhangyang Wang", "Ming Lin", "Ren\u00e9 Vidal" ], "github": "", "project_page": "", "abstract": "Associative memory has long underpinned the design of sequential models. Beyond recall, humans reason by projecting future states and selecting goal-directed actions, a capability that modern language models increasingly require but do not natively encode. While prior work uses reinforcement learning or test-time training, planning remains external to the model architecture. We formulate reasoning as optimal control and introduce the Test-Time Control (TTC) layer, which performs finite-horizon LQR planning over latent states at inference time, represents a value function within neural architectures, and leverages it as the nested objective to enable planning before prediction. To ensure scalability, we derive a hardware-efficient LQR solver based on a symplectic formulation and implement it as a fused CUDA kernel, enabling parallel execution with minimal overhead. Integrated as an adapter into pretrained LLMs, TTC layers improve mathematical reasoning performance by up to +27.8% on MATH-500 and 2-3x Pass@8 improvements on AMC and AIME, demonstrating that embedding optimal control as an architectural component provides an effective and scalable mechanism for reasoning beyond test-time training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.08942", "title": "BiCLIP: Domain Canonicalization via Structured Geometric Transformation", "authors": [ "Pranav Mantini", "Shishir K. Shah" ], "github": "https://github.com/QuantitativeImagingLaboratory/BilinearCLIP", "project_page": "", "abstract": "Recent advances in vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities, yet adapting these models to specialized domains remains a significant challenge. Building on recent theoretical insights suggesting that independently trained VLMs are related by a canonical transformation, we extend this understanding to the concept of domains. We hypothesize that image features across disparate domains are related by a canonicalized geometric transformation that can be recovered using a small set of anchors. Few-shot classification provides a natural setting for this alignment, as the limited labeled samples serve as the anchors required to estimate this transformation. Motivated by this hypothesis, we introduce BiCLIP, a framework that applies a targeted transformation to multimodal features to enhance cross-modal alignment. Our approach is characterized by its extreme simplicity and low parameter footprint. Extensive evaluations across 11 standard benchmarks, including EuroSAT, DTD, and FGVCAircraft, demonstrate that BiCLIP consistently achieves state-of-the-art results. Furthermore, we provide empirical verification of existing geometric findings by analyzing the orthogonality and angular distribution of the learned transformations, confirming that structured alignment is the key to robust domain adaptation. Code is available at https://github.com/QuantitativeImagingLaboratory/BilinearCLIP", "github_manual": "", "github_regex": "https://github.com/QuantitativeImagingLaboratory/BilinearCLIP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.08771", "title": "Micro-Diffusion Compression -- Binary Tree Tweedie Denoising for Online Probability Estimation", "authors": [ "Roberto Tacconelli" ], "github": "", "project_page": "", "abstract": "We present Midicoth, a lossless compression system that introduces a micro-diffusion denoising layer for improving probability estimates produced by adaptive statistical models. In compressors such as Prediction by Partial Matching (PPM), probability estimates are smoothed by a prior to handle sparse observations. When contexts have been seen only a few times, this prior dominates the prediction and produces distributions that are significantly flatter than the true source distribution, leading to compression inefficiency. Midicoth addresses this limitation by treating prior smoothing as a shrinkage process and applying a reverse denoising step that corrects predicted probabilities using empirical calibration statistics. To make this correction data-efficient, the method decomposes each byte prediction into a hierarchy of binary decisions along a bitwise tree. This converts a single 256-way calibration problem into a sequence of binary calibration tasks, enabling reliable estimation of correction terms from relatively small numbers of observations. The denoising process is applied in multiple successive steps, allowing each stage to refine residual prediction errors left by the previous one. The micro-diffusion layer operates as a lightweight post-blend calibration stage applied after all model predictions have been combined, allowing it to correct systematic biases in the final probability distribution. Midicoth combines five fully online components: an adaptive PPM model, a long-range match model, a trie-based word model, a high-order context model, and the micro-diffusion denoiser applied as the final stage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-11", "arxiv_id": "2603.02188", "title": "Multi-Head Low-Rank Attention", "authors": [ "Songtao Liu", "Hongwu Peng", "Zhiwei Zhang", "Zhengyu Chen", "Yue Guo" ], "github": "https://github.com/SongtaoLiu0823/MLRA", "project_page": "", "abstract": "Long-context inference in large language models is bottlenecked by Key--Value (KV) cache loading during the decoding stage, where the sequential nature of generation requires repeatedly transferring the KV cache from off-chip High-Bandwidth Memory (HBM) to on-chip Static Random-Access Memory (SRAM) at each step. While Multi-Head Latent Attention (MLA) significantly reduces the total KV cache size, it suffers from a sharding bottleneck during distributed decoding via Tensor Parallelism (TP). Since its single latent head cannot be partitioned, each device is forced to redundantly load the complete KV cache for every token, consuming excessive memory traffic and diminishing TP benefits like weight sharding. In this work, we propose Multi-Head Low-Rank Attention (MLRA), which enables partitionable latent states for efficient 4-way TP decoding. Extensive experiments show that MLRA achieves state-of-the-art perplexity and downstream task performance, while also delivering a 2.8times decoding speedup over MLA. Code is available at https://github.com/SongtaoLiu0823/MLRA. Pretrained weights, along with the training and evaluation data, are available at https://huggingface.co/Soughing/MLRA.", "github_manual": "", "github_regex": "https://github.com/SongtaoLiu0823/MLRA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.10165", "title": "OpenClaw-RL: Train Any Agent Simply by Talking", "authors": [ "Yinjie Wang", "Xuyang Chen", "Xiaolong Jin", "Mengdi Wang", "Ling Yang" ], "github": "https://github.com/Gen-Verse/OpenClaw-RL", "project_page": "", "abstract": "Every agent interaction generates a next-state signal, namely the user reply, tool output, terminal or GUI state change that follows each action, yet no existing agentic RL system recovers it as a live, online learning source. We present OpenClaw-RL, a framework built on a simple observation: next-state signals are universal, and policy can learn from all of them simultaneously. Personal conversations, terminal executions, GUI interactions, SWE tasks, and tool-call traces are not separate training problems. They are all interactions that can be used to train the same policy in the same loop. Next-state signals encode two forms of information: evaluative signals, which indicate how well the action performed and are extracted as scalar rewards via a PRM judge; and directive signals, which indicate how the action should have been different and are recovered through Hindsight-Guided On-Policy Distillation (OPD). We extract textual hints from the next state, construct an enhanced teacher context, and provide token-level directional advantage supervision that is richer than any scalar reward. Due to the asynchronous design, the model serves live requests, the PRM judges ongoing interactions, and the trainer updates the policy at the same time, with zero coordination overhead between them. Applied to personal agents, OpenClaw-RL enables an agent to improve simply by being used, recovering conversational signals from user re-queries, corrections, and explicit feedback. Applied to general agents, the same infrastructure supports scalable RL across terminal, GUI, SWE, and tool-call settings, where we additionally demonstrate the utility of process rewards. Code: https://github.com/Gen-Verse/OpenClaw-RL", "github_manual": "", "github_regex": "https://github.com/Gen-Verse/OpenClaw-RL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.08561", "title": "RetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedback", "authors": [ "Xiaoying Zhang", "Zichen Liu", "Yipeng Zhang", "Xia Hu", "Wenqi Shao" ], "github": "", "project_page": "", "abstract": "Large language model (LLM)-based agents trained with reinforcement learning (RL) have shown strong potential on complex interactive tasks. However, standard RL paradigms favor static problem-solving over continuous adaptation: agents often converge to suboptimal strategies due to insufficient exploration, while learned knowledge remains implicit within parameters rather than explicitly retrievable, limiting effective experiential learning. To address these limitations, we introduce RetroAgent, an online RL framework that empowers agents to master complex interactive environments not just by solving, but by evolving. Concretely, RetroAgent features a hindsight self-reflection mechanism that produces dual intrinsic feedback: (1) intrinsic numerical feedback that that tracks incremental subtask completion relative to prior attempts, rewarding promising explorations, and (2) intrinsic language feedback that distills reusable lessons into a memory buffer, retrieved via our proposed Similarity & Utility-Aware Upper Confidence Bound (SimUtil-UCB) strategy balancing relevance, utility, and exploration to effectively leverage past experiences. Extensive experiments on two model families across four challenging agentic tasks demonstrate that RetroAgent significantly outperforms existing methods, achieving state-of-the-art results -- e.g., surpassing Group Relative Policy Optimization (GRPO)-trained agents by +18.3% on ALFWorld, +15.4% on WebShop, +27.1% on Sokoban, and +8.9% on MineSweeper -- while exhibiting strong test-time adaptation and generalization to out-of-distribution scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.10848", "title": "V_{0.5}: Generalist Value Model as a Prior for Sparse RL Rollouts", "authors": [ "Yi-Kai Zhang", "Yueqing Sun", "Hongyan Hao", "Qi Gu", "Xunliang Cai", "De-Chuan Zhan", "Han-Jia Ye" ], "github": "https://github.com/Now-Join-Us/V0_5", "project_page": "", "abstract": "In Reinforcement Learning with Verifiable Rewards (RLVR), constructing a robust advantage baseline is critical for policy gradients, effectively guiding the policy model to reinforce desired behaviors. Recent research has introduced Generalist Value Models (such as V_0), which achieve pre-trained value estimation by explicitly encoding model capabilities in-context, eliminating the need to synchronously update the value model alongside the policy model. In this paper, we propose V_{0.5}, which adaptively fuses the baseline predicted by such value model (acting as a prior) with the empirical mean derived from sparse rollouts. This constructs a robust baseline that balances computational efficiency with extremely low variance. Specifically, we introduce a real-time statistical testing and dynamic budget allocation. This balances the high variance caused by sparse sampling against the systematic bias (or hallucinations) inherent in the value model's prior. By constructing a hypothesis test to evaluate the prior's reliability in real-time, the system dynamically allocates additional rollout budget on demand. This mechanism minimizes the baseline estimator's Mean Squared Error (MSE), guaranteeing stable policy gradients, even under extreme sparsity with a group size of 4. Extensive evaluations across six mathematical reasoning benchmarks demonstrate that V_{0.5} significantly outperforms GRPO and DAPO, achieving faster convergence and over some 10% performance improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Now-Join-Us/V0_5", "github_api_added_by": "auto" }, { "date": "2026-03-12", "arxiv_id": "2603.10101", "title": "CLIPO: Contrastive Learning in Policy Optimization Generalizes RLVR", "authors": [ "Sijia Cui", "Pengyu Cheng", "Jiajun Song", "Yongbo Gai", "Guojun Zhang", "Zhechao Yu", "Jianhe Lin", "Xiaoxi Jiang", "Guanjun Jiang" ], "github": "https://github.com/Qwen-Applications/CLIPO", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capacity of Large Language Models (LLMs). However, RLVR solely relies on final answers as outcome rewards, neglecting the correctness of intermediate reasoning steps. Training on these process-wrong but outcome-correct rollouts can lead to hallucination and answer-copying, severely undermining the model's generalization and robustness. To address this, we incorporate a Contrastive Learning mechanism into the Policy Optimization (CLIPO) to generalize the RLVR process. By optimizing a contrastive loss over successful rollouts, CLIPO steers the LLM to capture the invariant structure shared across correct reasoning paths. This provides a more robust cross-trajectory regularization than the original single-path supervision in RLVR, effectively mitigating step-level reasoning inconsistencies and suppressing hallucinatory artifacts. In experiments, CLIPO consistently improves multiple RLVR baselines across diverse reasoning benchmarks, demonstrating uniform improvements in generalization and robustness for policy optimization of LLMs. Our code and training recipes are available at https://github.com/Qwen-Applications/CLIPO.", "github_manual": "", "github_regex": "https://github.com/Qwen-Applications/CLIPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.10098", "title": "Code-Space Response Oracles: Generating Interpretable Multi-Agent Policies with Large Language Models", "authors": [ "Daniel Hennes", "Zun Li", "John Schultz", "Marc Lanctot" ], "github": "", "project_page": "", "abstract": "Recent advances in multi-agent reinforcement learning, particularly Policy-Space Response Oracles (PSRO), have enabled the computation of approximate game-theoretic equilibria in increasingly complex domains. However, these methods rely on deep reinforcement learning oracles that produce `black-box' neural network policies, making them difficult to interpret, trust or debug. We introduce Code-Space Response Oracles (CSRO), a novel framework that addresses this challenge by replacing RL oracles with Large Language Models (LLMs). CSRO reframes the best response computation as a code generation task, prompting an LLM to generate policies directly as human-readable code. This approach not only yields inherently interpretable policies but also leverages the LLM's pretrained knowledge to discover complex, human-like strategies. We explore multiple ways to construct and enhance an LLM-based oracle: zero-shot prompting, iterative refinement and AlphaEvolve, a distributed LLM-based evolutionary system. We demonstrate that CSRO achieves performance competitive with baselines while producing a diverse set of explainable policies. Our work presents a new perspective on multi-agent learning, shifting the focus from optimizing opaque policy parameters to synthesizing interpretable algorithmic behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.05757", "title": "EmboAlign: Aligning Video Generation with Compositional Constraints for Zero-Shot Manipulation", "authors": [ "Gehao Zhang", "Zhenyang Ni", "Payal Mohapatra", "Han Liu", "Ruohan Zhang", "Qi Zhu" ], "github": "", "project_page": "", "abstract": "Video generative models (VGMs) pretrained on large-scale internet data can produce temporally coherent rollout videos that capture rich object dynamics, offering a compelling foundation for zero-shot robotic manipulation. However, VGMs often produce physically implausible rollouts, and converting their pixel-space motion into robot actions through geometric retargeting further introduces cumulative errors from imperfect depth estimation and keypoint tracking. To address these challenges, we present , a data-free framework that aligns VGM outputs with compositional constraints generated by vision-language models (VLMs) at inference time. The key insight is that VLMs offer a capability complementary to VGMs: structured spatial reasoning that can identify the physical constraints critical to the success and safety of manipulation execution. Given a language instruction, uses a VLM to automatically extract a set of compositional constraints capturing task-specific requirements, which are then applied at two stages: (1) constraint-guided rollout selection, which scores and filters a batch of VGM rollouts to retain the most physically plausible candidate, and (2) constraint-based trajectory optimization, which uses the selected rollout as initialization and refines the robot trajectory under the same constraint set to correct retargeting errors. We evaluate on six real-robot manipulation tasks requiring precise, constraint-sensitive execution, improving the overall success rate by 43.3\\% points over the strongest baseline without any task-specific training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.04597", "title": "Bootstrapping Exploration with Group-Level Natural Language Feedback in Reinforcement Learning", "authors": [ "Lei Huang", "Xiang Cheng", "Chenxiao Zhao", "Guobin Shen", "Junjie Yang", "Xiaocheng Feng", "Yuxuan Gu", "Xing Yu", "Bing Qin" ], "github": "https://github.com/LuckyyySTA/GOLF", "project_page": "", "abstract": "Large language models (LLMs) typically receive diverse natural language (NL) feedback through interaction with the environment. However, current reinforcement learning (RL) algorithms rely solely on scalar rewards, leaving the rich information in NL feedback underutilized and leading to inefficient exploration. In this work, we propose GOLF, an RL framework that explicitly exploits group-level language feedback to guide targeted exploration through actionable refinements. GOLF aggregates two complementary feedback sources: (i) external critiques that pinpoint errors or propose targeted fixes, and (ii) intra-group attempts that supply alternative partial ideas and diverse failure patterns. These group-level feedbacks are aggregated to produce high-quality refinements, which are adaptively injected into training as off-policy scaffolds to provide targeted guidance in sparse-reward regions. Meanwhile, GOLF jointly optimizes generation and refinement within a unified RL loop, creating a virtuous cycle that continuously improves both capabilities. Experiments on both verifiable and non-verifiable benchmarks show that GOLF achieves superior performance and exploration efficiency, achieving 2.2times improvements in sample efficiency compared to RL methods trained solely on scalar rewards. Code is available at https://github.com/LuckyyySTA/GOLF.", "github_manual": "", "github_regex": "https://github.com/LuckyyySTA/GOLF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.11042", "title": "V2M-Zero: Zero-Pair Time-Aligned Video-to-Music Generation", "authors": [ "Yan-Bo Lin", "Jonah Casebeer", "Long Mai", "Aniruddha Mahapatra", "Gedas Bertasius", "Nicholas J. Bryan" ], "github": "", "project_page": "", "abstract": "Generating music that temporally aligns with video events is challenging for existing text-to-music models, which lack fine-grained temporal control. We introduce V2M-Zero, a zero-pair video-to-music generation approach that outputs time-aligned music for video. Our method is motivated by a key observation: temporal synchronization requires matching when and how much change occurs, not what changes. While musical and visual events differ semantically, they exhibit shared temporal structure that can be captured independently within each modality. We capture this structure through event curves computed from intra-modal similarity using pretrained music and video encoders. By measuring temporal change within each modality independently, these curves provide comparable representations across modalities. This enables a simple training strategy: fine-tune a text-to-music model on music-event curves, then substitute video-event curves at inference without cross-modal training or paired data. Across OES-Pub, MovieGenBench-Music, and AIST++, V2M-Zero achieves substantial gains over paired-data baselines: 5-21% higher audio quality, 13-15% better semantic alignment, 21-52% improved temporal synchronization, and 28% higher beat alignment on dance videos. We find similar results via a large crowd-source subjective listening test. Overall, our results validate that temporal alignment through within-modality features, rather than paired cross-modal supervision, is effective for video-to-music generation. Results are available at https://genjib.github.io/v2m_zero/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.10744", "title": "Just-in-Time: Training-Free Spatial Acceleration for Diffusion Transformers", "authors": [ "Wenhao Sun", "Ji Li", "Zhaoqiang Liu" ], "github": "https://github.com/Anonym0u3/Just-in-Time", "project_page": "", "abstract": "Diffusion Transformers have established a new state-of-the-art in image synthesis, but the high computational cost of iterative sampling severely hampers their practical deployment. While existing acceleration methods often focus on the temporal domain, they overlook the substantial spatial redundancy inherent in the generative process, where global structures emerge long before fine-grained details are formed. The uniform computational treatment of all spatial regions represents a critical inefficiency. In this paper, we introduce Just-in-Time (JiT), a novel training-free framework that addresses this challenge by acceleration in the spatial domain. JiT formulates a spatially approximated generative ordinary differential equation (ODE) that drives the full latent state evolution based on computations from a dynamically selected, sparse subset of anchor tokens. To ensure seamless transitions as new tokens are incorporated to expand the dimensions of the latent state, we propose a deterministic micro-flow, a simple and effective finite-time ODE that maintains both structural coherence and statistical correctness. Extensive experiments on the state-of-the-art FLUX.1-dev model demonstrate that JiT achieves up to a 7x speedup with nearly lossless performance, significantly outperforming existing acceleration methods and establishing a new and superior trade-off between inference speed and generation fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Anonym0u3/Just-in-Time", "github_api_added_by": "auto" }, { "date": "2026-03-12", "arxiv_id": "2603.09723", "title": "RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation", "authors": [ "Sihong Wu", "Yiling Ma", "Yilun Zhao", "Tiansheng Hu", "Owen Jiang", "Manasi Patwardhan", "Arman Cohan" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) are increasingly used across the scientific workflow, including to draft peer-review reports. However, many AI-generated reviews are superficial and insufficiently actionable, leaving authors without concrete, implementable guidance and motivating the gap this work addresses. We propose RbtAct, which targets actionable review feedback generation and places existing peer review rebuttal at the center of learning. Rebuttals show which reviewer comments led to concrete revisions or specific plans, and which were only defended. Building on this insight, we leverage rebuttal as implicit supervision to directly optimize a feedback generator for actionability. To support this objective, we propose a new task called perspective-conditioned segment-level review feedback generation, in which the model is required to produce a single focused comment based on the complete paper and a specified perspective such as experiments and writing. We also build a large dataset named RMR-75K that maps review segments to the rebuttal segments that address them, with perspective labels and impact categories that order author uptake. We then train the Llama-3.1-8B-Instruct model with supervised fine-tuning on review segments followed by preference optimization using rebuttal derived pairs. Experiments with human experts and LLM-as-a-judge show consistent gains in actionability and specificity over strong baselines while maintaining grounding and relevance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.07392", "title": "Can Large Language Models Keep Up? Benchmarking Online Adaptation to Continual Knowledge Streams", "authors": [ "Jiyeon Kim", "Hyunji Lee", "Dylan Zhou", "Sue Hyun Park", "Seunghyun Yoon", "Trung Bui", "Franck Dernoncourt", "Sungmin Cha", "Minjoon Seo" ], "github": "https://github.com/kaistAI/OAKS", "project_page": "", "abstract": "LLMs operating in dynamic real-world contexts often encounter knowledge that evolves continuously or emerges incrementally. To remain accurate and effective, models must adapt to newly arriving information on the fly. We introduce Online Adaptation to Continual Knowledge Streams(OAKS) to evaluate this capability, establishing a benchmark for online adaptation over streaming, continually updating knowledge. Specifically, the benchmark is structured as a sequence of fine-grained context chunks where facts change dynamically across time intervals. OAKS comprises two datasets: OAKS-BABI and OAKS-Novel, where individual facts evolve multiple times across context chunks. These datasets include dense annotations to measure whether models track changes accurately. Evaluating 14 models with varied inference approaches, we observe significant limitations in current methodologies. Both state-of-the-art models and agentic memory systems fail to adapt robustly on OAKS, demonstrating delays in state-tracking and susceptibility to distraction within streaming environments.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kaistAI/OAKS", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.09827", "title": "MA-EgoQA: Question Answering over Egocentric Videos from Multiple Embodied Agents", "authors": [ "Kangsan Kim", "Yanlai Yang", "Suji Kim", "Woongyeong Yeo", "Youngwan Lee", "Mengye Ren", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "As embodied models become powerful, humans will collaborate with multiple embodied AI agents at their workplace or home in the future. To ensure better communication between human users and the multi-agent system, it is crucial to interpret incoming information from agents in parallel and refer to the appropriate context for each query. Existing challenges include effectively compressing and communicating high volumes of individual sensory inputs in the form of video and correctly aggregating multiple egocentric videos to construct system-level memory. In this work, we first formally define a novel problem of understanding multiple long-horizon egocentric videos simultaneously collected from embodied agents. To facilitate research in this direction, we introduce MultiAgent-EgoQA (MA-EgoQA), a benchmark designed to systemically evaluate existing models in our scenario. MA-EgoQA provides 1.7k questions unique to multiple egocentric streams, spanning five categories: social interaction, task coordination, theory-of-mind, temporal reasoning, and environmental interaction. We further propose a simple baseline model for MA-EgoQA named EgoMAS, which leverages shared memory across embodied agents and agent-wise dynamic retrieval. Through comprehensive evaluation across diverse baselines and EgoMAS on MA-EgoQA, we find that current approaches are unable to effectively handle multiple egocentric streams, highlighting the need for future advances in system-level understanding across the agents. The code and benchmark are available at https://ma-egoqa.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.05711", "title": "Any to Full: Prompting Depth Anything for Depth Completion in One Stage", "authors": [ "Zhiyuan Zhou", "Ruofeng Liu", "Taichi Liu", "Weijian Zuo", "Shanshan Wang", "Zhiqing Hong", "Desheng Zhang" ], "github": "https://github.com/zhiyuandaily/Any2Full", "project_page": "", "abstract": "Accurate, dense depth estimation is crucial for robotic perception, but commodity sensors often yield sparse or incomplete measurements due to hardware limitations. Existing RGBD-fused depth completion methods learn priors jointly conditioned on training RGB distribution and specific depth patterns, limiting domain generalization and robustness to various depth patterns. Recent efforts leverage monocular depth estimation (MDE) models to introduce domain-general geometric priors, but current two-stage integration strategies relying on explicit relative-to-metric alignment incur additional computation and introduce structured distortions. To this end, we present Any2Full, a one-stage, domain-general, and pattern-agnostic framework that reformulates completion as a scale-prompting adaptation of a pretrained MDE model. To address varying depth sparsity levels and irregular spatial distributions, we design a Scale-Aware Prompt Encoder. It distills scale cues from sparse inputs into unified scale prompts, guiding the MDE model toward globally scale-consistent predictions while preserving its geometric priors. Extensive experiments demonstrate that Any2Full achieves superior robustness and efficiency. It outperforms OMNI-DC by 32.2\\% in average AbsREL and delivers a 1.4times speedup over PriorDA with the same MDE backbone, establishing a new paradigm for universal depth completion. Codes and checkpoints are available at https://github.com/zhiyuandaily/Any2Full.", "github_manual": "", "github_regex": "https://github.com/zhiyuandaily/Any2Full", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.10913", "title": "LLM2Vec-Gen: Generative Embeddings from Large Language Models", "authors": [ "Parishad BehnamGhader", "Vaibhav Adlakha", "Fabian David Schmidt", "Nicolas Chapados", "Marius Mosbach", "Siva Reddy" ], "github": "https://github.com/McGill-NLP/llm2vec-gen", "project_page": "", "abstract": "LLM-based text embedders typically encode the semantic content of their input. However, embedding tasks require mapping diverse inputs to similar outputs. Typically, this input-output is addressed by training embedding models with paired data using contrastive learning. In this work, we propose a novel self-supervised approach, LLM2Vec-Gen, which adopts a different paradigm: rather than encoding the input, we learn to represent the model's potential response. Specifically, we add trainable special tokens to the LLM's vocabulary, append them to input, and optimize them to represent the LLM's response in a fixed-length sequence. Training is guided by the LLM's own completion for the query, along with an unsupervised embedding teacher that provides distillation targets. This formulation helps to bridge the input-output gap and transfers LLM capabilities such as safety alignment and reasoning to embedding tasks. Crucially, the LLM backbone remains frozen and training requires only unlabeled queries. LLM2Vec-Gen achieves state-of-the-art self-supervised performance on the Massive Text Embedding Benchmark (MTEB), improving by 9.3% over the best unsupervised embedding teacher. We also observe up to 43.2% reduction in harmful content retrieval and 29.3% improvement in reasoning capabilities for embedding tasks. Finally, the learned embeddings are interpretable and can be decoded into text to reveal their semantic content.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/McGill-NLP/llm2vec-gen", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.09229", "title": "Flash-KMeans: Fast and Memory-Efficient Exact K-Means", "authors": [ "Shuo Yang", "Haocheng Xi", "Yilong Zhao", "Muyang Li", "Xiaoze Fan", "Jintao Zhang", "Han Cai", "Yujun Lin", "Xiuyu Li", "Kurt Keutzer", "Song Han", "Chenfeng Xu", "Ion Stoica" ], "github": "", "project_page": "", "abstract": "k-means has historically been positioned primarily as an offline processing primitive, typically used for dataset organization or embedding preprocessing rather than as a first-class component in online systems. In this work, we revisit this classical algorithm under the lens of modern AI system design and enable k-means as an online primitive. We point out that existing GPU implementations of k-means remain fundamentally bottlenecked by low-level system constraints rather than theoretical algorithmic complexity. Specifically, the assignment stage suffers from a severe IO bottleneck due to the massive explicit materialization of the N times K distance matrix in High Bandwidth Memory (HBM). Simultaneously, the centroid update stage is heavily penalized by hardware-level atomic write contention caused by irregular, scatter-style token aggregations. To bridge this performance gap, we propose flash-kmeans, an IO-aware and contention-free k-means implementation for modern GPU workloads. Flash-kmeans introduces two core kernel-level innovations: (1) FlashAssign, which fuses distance computation with an online argmin to completely bypass intermediate memory materialization; (2) sort-inverse update, which explicitly constructs an inverse mapping to transform high-contention atomic scatters into high-bandwidth, segment-level localized reductions. Furthermore, we integrate algorithm-system co-designs, including chunked-stream overlap and cache-aware compile heuristics, to ensure practical deployability. Extensive evaluations on NVIDIA H200 GPUs demonstrate that flash-kmeans achieves up to 17.9times end-to-end speedup over best baselines, while outperforming industry-standard libraries like cuML and FAISS by 33times and over 200times, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.10160", "title": "ReMix: Reinforcement routing for mixtures of LoRAs in LLM finetuning", "authors": [ "Ruizhong Qiu", "Hanqing Zeng", "Yinglong Xia", "Yiwen Meng", "Ren Chen", "Jiarui Feng", "Dongqi Fu", "Qifan Wang", "Jiayi Liu", "Jun Xiao", "Xiangjun Fan", "Benyu Zhang", "Hong Li", "Zhining Liu", "Hyunsik Yoo", "Zhichen Zeng", "Tianxin Wei", "Hanghang Tong" ], "github": "", "project_page": "", "abstract": "Low-rank adapters (LoRAs) are a parameter-efficient finetuning technique that injects trainable low-rank matrices into pretrained models to adapt them to new tasks. Mixture-of-LoRAs models expand neural networks efficiently by routing each layer input to a small subset of specialized LoRAs of the layer. Existing Mixture-of-LoRAs routers assign a learned routing weight to each LoRA to enable end-to-end training of the router. Despite their empirical promise, we observe that the routing weights are typically extremely imbalanced across LoRAs in practice, where only one or two LoRAs often dominate the routing weights. This essentially limits the number of effective LoRAs and thus severely hinders the expressive power of existing Mixture-of-LoRAs models. In this work, we attribute this weakness to the nature of learnable routing weights and rethink the fundamental design of the router. To address this critical issue, we propose a new router designed that we call Reinforcement Routing for Mixture-of-LoRAs (ReMix). Our key idea is using non-learnable routing weights to ensure all active LoRAs to be equally effective, with no LoRA dominating the routing weights. However, our routers cannot be trained directly via gradient descent due to our non-learnable routing weights. Hence, we further propose an unbiased gradient estimator for the router by employing the reinforce leave-one-out (RLOO) technique, where we regard the supervision loss as the reward and the router as the policy in reinforcement learning. Our gradient estimator also enables to scale up training compute to boost the predictive performance of our ReMix. Extensive experiments demonstrate that our proposed ReMix significantly outperform state-of-the-art parameter-efficient finetuning methods under a comparable number of activated parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.10757", "title": "CodePercept: Code-Grounded Visual STEM Perception for MLLMs", "authors": [ "Tongkun Guan", "Zhibo Yang", "Jianqiang Wan", "Mingkun Yang", "Zhengtao Guo", "Zijian Hu", "Ruilin Luo", "Ruize Chen", "Songtao Jiang", "Peng Wang", "Wei Shen", "Junyang Lin", "Xiaokang Yang" ], "github": "https://github.com/TongkunGuan/Qwen-CodePercept", "project_page": "", "abstract": "When MLLMs fail at Science, Technology, Engineering, and Mathematics (STEM) visual reasoning, a fundamental question arises: is it due to perceptual deficiencies or reasoning limitations? Through systematic scaling analysis that independently scales perception and reasoning components, we uncover a critical insight: scaling perception consistently outperforms scaling reasoning. This reveals perception as the true lever limiting current STEM visual reasoning. Motivated by this insight, our work focuses on systematically enhancing the perception capabilities of MLLMs by establishing code as a powerful perceptual medium--executable code provides precise semantics that naturally align with the structured nature of STEM visuals. Specifically, we construct ICC-1M, a large-scale dataset comprising 1M Image-Caption-Code triplets that materializes this code-as-perception paradigm through two complementary approaches: (1) Code-Grounded Caption Generation treats executable code as ground truth for image captions, eliminating the hallucinations inherent in existing knowledge distillation methods; (2) STEM Image-to-Code Translation prompts models to generate reconstruction code, mitigating the ambiguity of natural language for perception enhancement. To validate this paradigm, we further introduce STEM2Code-Eval, a novel benchmark that directly evaluates visual perception in STEM domains. Unlike existing work relying on problem-solving accuracy as a proxy that only measures problem-relevant understanding, our benchmark requires comprehensive visual comprehension through executable code generation for image reconstruction, providing deterministic and verifiable assessment. Code is available at https://github.com/TongkunGuan/Qwen-CodePercept.", "github_manual": "", "github_regex": "https://github.com/TongkunGuan/Qwen-CodePercept", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.10705", "title": "Prism-\u0394: Differential Subspace Steering for Prompt Highlighting in Large Language Models", "authors": [ "Yuyao Ge", "Shenghua Liu", "Yiwei Wang", "Tianyu Liu", "Baolong Bi", "Lingrui Mei", "Jiayu Yao", "Jiafeng Guo", "Xueqi Cheng" ], "github": "", "project_page": "", "abstract": "Prompt highlighting steers a large language model to prioritize user-specified text spans during generation. A key challenge is extracting steering directions that capture the difference between relevant and irrelevant contexts, rather than shared structural patterns common to both. We propose PRISM-\u0394 (Projection-based Relevance-Informed Steering Method), which decomposes the difference between positive and negative cross-covariance matrices to maximize discriminative energy while eliminating shared directions. Each attention head receives a continuous softplus importance weight, letting weak-but-useful heads contribute at reduced strength. The framework extends naturally to Value representations, capturing content-channel signal that Key-only methods leave unused. Across four benchmarks and five models, PRISM-\u0394 matches or exceeds the best existing method on 19 of 20 configurations, with relative gains up to +10.6%, while halving the fluency cost of steering. PRISM-\u0394 also scales to long-context retrieval, outperforming the best existing method by up to +4.8% relative gain. PRISM-\u0394 is compatible with FlashAttention and adds negligible memory overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.08068", "title": "In-Context Reinforcement Learning for Tool Use in Large Language Models", "authors": [ "Yaoqi Ye", "Yiran Zhao", "Keyu Duan", "Zeyu Zheng", "Kenji Kawaguchi", "Cihang Xie", "Michael Qizhe Shieh" ], "github": "https://github.com/applese233/ICRL", "project_page": "", "abstract": "While large language models (LLMs) exhibit strong reasoning abilities, their performance on complex tasks is often constrained by the limitations of their internal knowledge. A compelling approach to overcome this challenge is to augment these models with external tools -- such as Python interpreters for mathematical computations or search engines for retrieving factual information. However, enabling models to use these tools effectively remains a significant challenge. Existing methods typically rely on cold-start pipelines that begin with supervised fine-tuning (SFT), followed by reinforcement learning (RL). These approaches often require substantial amounts of labeled data for SFT, which is expensive to annotate or synthesize. In this work, we propose In-Context Reinforcement Learning (ICRL), an RL-only framework that eliminates the need for SFT by leveraging few-shot prompting during the rollout stage of RL. Specifically, ICRL introduces in-context examples within the rollout prompts to teach the model how to invoke external tools. Furthermore, as training progresses, the number of in-context examples is gradually reduced, eventually reaching a zero-shot setting where the model learns to call tools independently. We conduct extensive experiments across a range of reasoning and tool-use benchmarks. Results show that ICRL achieves state-of-the-art performance, demonstrating its effectiveness as a scalable, data-efficient alternative to traditional SFT-based pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/applese233/ICRL", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.08754", "title": "Hindsight Credit Assignment for Long-Horizon LLM Agents", "authors": [ "Hui-Ze Tan", "Xiao-Wen Yang", "Hao Chen", "Jie-Jing Shao", "Yi Wen", "Yuteng Shen", "Weihong Luo", "Xiku Du", "Lan-Zhe Guo", "Yu-Feng Li" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM) agents often face significant credit assignment challenges in long-horizon, multi-step tasks due to sparse rewards. Existing value-free methods, such as Group Relative Policy Optimization (GRPO), encounter two fundamental bottlenecks: inaccurate step-level Q-value estimation and misaligned value baselines for intermediate states. To address these limitations, we introduce HCAPO, the first framework to integrate hindsight credit assignment into LLM agents. HCAPO leverages the LLM itself as a post-hoc critic to refine step-level Q-values through hindsight reasoning. Furthermore, HCAPO's multi-scale advantage mechanism effectively supplements the inaccurate value baselines at critical decision states. Evaluations across three challenging benchmarks, including WebShop and ALFWorld, demonstrate that HCAPO consistently outperforms state-of-the-art RL methods. Notably, HCAPO achieves a 7.7% improvement in success rate on WebShop and a 13.8% on ALFWorld over GRPO using the Qwen2.5-7B-Instruct model. These results indicate that HCAPO significantly enhances exploration efficiency, promotes concise decision-making, and ensures scalability in complex, long-horizon tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.10702", "title": "UniCom: Unified Multimodal Modeling via Compressed Continuous Semantic Representations", "authors": [ "Yaqi Zhao", "Wang Lin", "Zijian Zhang", "Miles Yang", "Jingyuan Chen", "Wentao Zhang", "Zhao Zhong", "Liefeng Bo" ], "github": "", "project_page": "", "abstract": "Current unified multimodal models typically rely on discrete visual tokenizers to bridge the modality gap. However, discretization inevitably discards fine-grained semantic information, leading to suboptimal performance in visual understanding tasks. Conversely, directly modeling continuous semantic representations (e.g., CLIP, SigLIP) poses significant challenges in high-dimensional generative modeling, resulting in slow convergence and training instability. To resolve this dilemma, we introduce UniCom, a unified framework that harmonizes multimodal understanding and generation via compressed continuous representation. We empirically demonstrate that reducing channel dimension is significantly more effective than spatial downsampling for both reconstruction and generation. Accordingly, we design an attention-based semantic compressor to distill dense features into a compact unified representation. Furthermore, we validate that the transfusion architecture surpasses query-based designs in convergence and consistency. Experiments demonstrate that UniCom achieves state-of-the-art generation performance among unified models. Notably, by preserving rich semantic priors, it delivers exceptional controllability in image editing and maintains image consistency even without relying on VAE.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.10145", "title": "Lost in Backpropagation: The LM Head is a Gradient Bottleneck", "authors": [ "Nathan Godey", "Yoav Artzi" ], "github": "", "project_page": "", "abstract": "The last layer of neural language models (LMs) projects output features of dimension D to logits in dimension V, the size of the vocabulary, where usually D ll V. This mismatch is known to raise risks of limited expressivity in neural LMs, creating a so-called softmax bottleneck. We show the softmax bottleneck is not only an expressivity bottleneck but also an optimization bottleneck. Backpropagating V-dimensional gradients through a rank-D linear layer induces unavoidable compression, which alters the training feedback provided to the vast majority of the parameters. We present a theoretical analysis of this phenomenon and measure empirically that 95-99% of the gradient norm is suppressed by the output layer, resulting in vastly suboptimal update directions. We conduct controlled pretraining experiments showing that the gradient bottleneck makes trivial patterns unlearnable, and drastically affects the training dynamics of LLMs. We argue that this inherent flaw contributes to training inefficiencies at scale independently of the model architecture, and raises the need for new LM head designs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.09482", "title": "StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving", "authors": [ "Yuan Gao", "Dengyuan Hua", "Mattia Piccinini", "Finn Rasmus Sch\u00e4fer", "Korbinian Moller", "Lin Li", "Johannes Betz" ], "github": "", "project_page": "", "abstract": "Vision Language Models (VLMs) bridge visual perception and linguistic reasoning. In Autonomous Driving (AD), this synergy has enabled Vision Language Action (VLA) models, which translate high-level multimodal understanding into driving behaviors, typically represented as future trajectories. However, existing VLA models mainly generate generic collision-free trajectories. Beyond collision avoidance, adapting to diverse driving styles (e.g., sporty, comfortable) is essential for personalized driving. Moreover, many methods treat trajectory generation as naive token prediction, which can produce kinematically infeasible actions. To address these limitations, we present StyleVLA, a physics-informed VLA framework for generating diverse and physically plausible driving behaviors. We introduce a hybrid loss that combines a kinematic consistency constraint with a continuous regression head to improve trajectory feasibility. To train StyleVLA, built on Qwen3-VL-4B, we construct a large-scale instruction dataset with over 1.2k scenarios, 76k Bird's Eye View (BEV) samples, and 42k First Person View (FPV) samples, with ground-truth trajectories for five driving styles and natural-language instructions. Experiments show that our 4B-parameter StyleVLA significantly outperforms proprietary models (e.g., Gemini-3-Pro) and state-of-the-art VLA models. Using a composite driving score measuring success rate, physical feasibility, and style adherence, StyleVLA achieves 0.55 on BEV and 0.51 on FPV, versus 0.32 and 0.35 for Gemini-3-Pro. These results show that a specialized, physics-informed, lightweight model can surpass closed-source models on domain-specific tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.10377", "title": "Causal Concept Graphs in LLM Latent Space for Stepwise Reasoning", "authors": [ "Md Muntaqim Meherab", "Noor Islam S. Mohammad", "Faiza Feroz" ], "github": "", "project_page": "", "abstract": "Sparse autoencoders can localize where concepts live in language models, but not how they interact during multi-step reasoning. We propose Causal Concept Graphs (CCG): a directed acyclic graph over sparse, interpretable latent features, where edges capture learned causal dependencies between concepts. We combine task-conditioned sparse autoencoders for concept discovery with DAGMA-style differentiable structure learning for graph recovery and introduce the Causal Fidelity Score (CFS) to evaluate whether graph-guided interventions induce larger downstream effects than random ones. On ARC-Challenge, StrategyQA, and LogiQA with GPT-2 Medium, across five seeds (n{=}15 paired runs), CCG achieves CFS=5.654pm0.625, outperforming ROME-style tracing (3.382pm0.233), SAE-only ranking (2.479pm0.196), and a random baseline (1.032pm0.034), with p<0.0001 after Bonferroni correction. Learned graphs are sparse (5-6\\% edge density), domain-specific, and stable across seeds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.01990", "title": "According to Me: Long-Term Personalized Referential Memory QA", "authors": [ "Jingbiao Mei", "Jinghong Chen", "Guangyu Yang", "Xinyu Hou", "Margaret Li", "Bill Byrne" ], "github": "https://github.com/JingbiaoMei/ATM-Bench", "project_page": "", "abstract": "Personalized AI assistants must recall and reason over long-term user memory, which naturally spans multiple modalities and sources such as images, videos, and emails. However, existing Long-term Memory benchmarks focus primarily on dialogue history, failing to capture realistic personalized references grounded in lived experience. We introduce ATM-Bench, the first benchmark for multimodal, multi-source personalized referential Memory QA. ATM-Bench contains approximately four years of privacy-preserving personal memory data and human-annotated question-answer pairs with ground-truth memory evidence, including queries that require resolving personal references, multi-evidence reasoning from multi-source and handling conflicting evidence. We propose Schema-Guided Memory (SGM) to structurally represent memory items originated from different sources. In experiments, we implement 5 state-of-the-art memory systems along with a standard RAG baseline and evaluate variants with different memory ingestion, retrieval, and answer generation techniques. We find poor performance (under 20\\% accuracy) on the ATM-Bench-Hard set, and that SGM improves performance over Descriptive Memory commonly adopted in prior works. Code available at: https://github.com/JingbiaoMei/ATM-Bench", "github_manual": "", "github_regex": "https://github.com/JingbiaoMei/ATM-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.10256", "title": "ID-LoRA: Identity-Driven Audio-Video Personalization with In-Context LoRA", "authors": [ "Aviad Dahan", "Moran Yanuka", "Noa Kraicer", "Lior Wolf", "Raja Giryes" ], "github": "", "project_page": "", "abstract": "Existing video personalization methods preserve visual likeness but treat video and audio separately. Without access to the visual scene, audio models cannot synchronize sounds with on-screen actions; and because classical voice-cloning models condition only on a reference recording, a text prompt cannot redirect speaking style or acoustic environment. We propose ID-LoRA (Identity-Driven In-Context LoRA), which jointly generates a subject's appearance and voice in a single model, letting a text prompt, a reference image, and a short audio clip govern both modalities together. ID-LoRA adapts the LTX-2 joint audio-video diffusion backbone via parameter-efficient In-Context LoRA and, to our knowledge, is the first method to personalize visual appearance and voice in a single generative pass. Two challenges arise. Reference and generation tokens share the same positional-encoding space, making them hard to distinguish; we address this with negative temporal positions, placing reference tokens in a disjoint RoPE region while preserving their internal temporal structure. Speaker characteristics also tend to be diluted during denoising; we introduce identity guidance, a classifier-free guidance variant that amplifies speaker-specific features by contrasting predictions with and without the reference signal. In human preference studies, ID-LoRA is preferred over Kling 2.6 Pro by 73% of annotators for voice similarity and 65% for speaking style. On cross-environment settings, speaker similarity improves by 24% over Kling, with the gap widening as conditions diverge. A preliminary user study further suggests that joint generation provides a useful inductive bias for physically grounded sound synthesis. ID-LoRA achieves these results with only ~3K training pairs on a single GPU. Code, models, and data will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.11048", "title": "COMIC: Agentic Sketch Comedy Generation", "authors": [ "Susung Hong", "Brian Curless", "Ira Kemelmacher-Shlizerman", "Steve Seitz" ], "github": "https://github.com/SusungHong/COMIC", "project_page": "", "abstract": "We propose a fully automated AI system that produces short comedic videos similar to sketch shows such as Saturday Night Live. Starting with character references, the system employs a population of agents loosely based on real production studio roles, structured to optimize the quality and diversity of ideas and outputs through iterative competition, evaluation, and improvement. A key contribution is the introduction of LLM critics aligned with real viewer preferences through the analysis of a corpus of comedy videos on YouTube to automatically evaluate humor. Our experiments show that our framework produces results approaching the quality of professionally produced sketches while demonstrating state-of-the-art performance in video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SusungHong/COMIC", "github_api_added_by": "auto" }, { "date": "2026-03-12", "arxiv_id": "2603.09018", "title": "Meissa: Multi-modal Medical Agentic Intelligence", "authors": [ "Yixiong Chen", "Xinyi Bai", "Yue Pan", "Zongwei Zhou", "Alan Yuille" ], "github": "https://github.com/Schuture/Meissa", "project_page": "", "abstract": "Multi-modal large language models (MM-LLMs) have shown strong performance in medical image understanding and clinical reasoning. Recent medical agent systems extend them with tool use and multi-agent collaboration, enabling complex decision-making. However, these systems rely almost entirely on frontier models (e.g., GPT), whose API-based deployment incurs high cost, high latency, and privacy risks that conflict with on-premise clinical requirements. We present Meissa, a lightweight 4B-parameter medical MM-LLM that brings agentic capability offline. Instead of imitating static answers, Meissa learns both when to engage external interaction (strategy selection) and how to execute multi-step interaction (strategy execution) by distilling structured trajectories from frontier models. Specifically, we propose: (1) Unified trajectory modeling: trajectories (reasoning and action traces) are represented within a single state-action-observation formalism, allowing one model to generalize across heterogeneous medical environments. (2) Three-tier stratified supervision: the model's own errors trigger progressive escalation from direct reasoning to tool-augmented and multi-agent interaction, explicitly learning difficulty-aware strategy selection. (3) Prospective-retrospective supervision: pairing exploratory forward traces with hindsight-rationalized execution traces enables stable learning of effective interaction policies. Trained on 40K curated trajectories, Meissa matches or exceeds proprietary frontier agents in 10 of 16 evaluation settings across 13 medical benchmarks spanning radiology, pathology, and clinical reasoning. Using over 25x fewer parameters than typical frontier models like Gemini-3, Meissa operates fully offline with 22x lower end-to-end latency compared to API-based deployment. Data, models, and environments are released at https://github.com/Schuture/Meissa.", "github_manual": "", "github_regex": "https://github.com/Schuture/Meissa", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-12", "arxiv_id": "2603.08982", "title": "SVG-EAR: Parameter-Free Linear Compensation for Sparse Video Generation via Error-aware Routing", "authors": [ "Xuanyi Zhou", "Qiuyang Mang", "Shuo Yang", "Haocheng Xi", "Jintao Zhang", "Huanzhi Mao", "Joseph E. Gonzalez", "Kurt Keutzer", "Ion Stoica", "Alvin Cheung" ], "github": "", "project_page": "", "abstract": "Diffusion Transformers (DiTs) have become a leading backbone for video generation, yet their quadratic attention cost remains a major bottleneck. Sparse attention reduces this cost by computing only a subset of attention blocks. However, prior methods often either drop the remaining blocks, which incurs information loss, or rely on learned predictors to approximate them, introducing training overhead and potential output distribution shifting. In this paper, we show that the missing contributions can be recovered without training: after semantic clustering, keys and values within each block exhibit strong similarity and can be well summarized by a small set of cluster centroids. Based on this observation, we introduce SVG-EAR, a parameter-free linear compensation branch that uses the centroid to approximate skipped blocks and recover their contributions. While centroid compensation is accurate for most blocks, it can fail on a small subset. Standard sparsification typically selects blocks by attention scores, which indicate where the model places its attention mass, but not where the approximation error would be largest. SVG-EAR therefore performs error-aware routing: a lightweight probe estimates the compensation error for each block, and we compute exactly the blocks with the highest error-to-cost ratio while compensating for skipped blocks. We provide theoretical guarantees that relate attention reconstruction error to clustering quality, and empirically show that SVG-EAR improves the quality-efficiency trade-off and increases throughput at the same generation fidelity on video diffusion tasks. Overall, SVG-EAR establishes a clear Pareto frontier over prior approaches, achieving up to 1.77times and 1.93times speedups while maintaining PSNRs of up to 29.759 and 31.043 on Wan2.2 and HunyuanVideo, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12057", "title": "Coarse-Guided Visual Generation via Weighted h-Transform Sampling", "authors": [ "Yanghao Wang", "Ziqi Jiang", "Zhen Wang", "Long Chen" ], "github": "", "project_page": "", "abstract": "Coarse-guided visual generation, which synthesizes fine visual samples from degraded or low-fidelity coarse references, is essential for various real-world applications. While training-based approaches are effective, they are inherently limited by high training costs and restricted generalization due to paired data collection. Accordingly, recent training-free works propose to leverage pretrained diffusion models and incorporate guidance during the sampling process. However, these training-free methods either require knowing the forward (fine-to-coarse) transformation operator, e.g., bicubic downsampling, or are difficult to balance between guidance and synthetic quality. To address these challenges, we propose a novel guided method by using the h-transform, a tool that can constrain stochastic processes (e.g., sampling process) under desired conditions. Specifically, we modify the transition probability at each sampling timestep by adding to the original differential equation with a drift function, which approximately steers the generation toward the ideal fine sample. To address unavoidable approximation errors, we introduce a noise-level-aware schedule that gradually de-weights the term as the error increases, ensuring both guidance adherence and high-quality synthesis. Extensive experiments across diverse image and video generation tasks demonstrate the effectiveness and generalization of our method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.11421", "title": "ShotVerse: Advancing Cinematic Camera Control for Text-Driven Multi-Shot Video Creation", "authors": [ "Songlin Yang", "Zhe Wang", "Xuyi Yang", "Songchun Zhang", "Xianghao Kong", "Taiyi Wu", "Xiaotong Zhao", "Ran Zhang", "Alan Zhao", "Anyi Rao" ], "github": "https://github.com/LAION-AI/aesthetic-predictor", "project_page": "", "abstract": "Text-driven video generation has democratized film creation, but camera control in cinematic multi-shot scenarios remains a significant block. Implicit textual prompts lack precision, while explicit trajectory conditioning imposes prohibitive manual overhead and often triggers execution failures in current models. To overcome this bottleneck, we propose a data-centric paradigm shift, positing that aligned (Caption, Trajectory, Video) triplets form an inherent joint distribution that can connect automated plotting and precise execution. Guided by this insight, we present ShotVerse, a \"Plan-then-Control\" framework that decouples generation into two collaborative agents: a VLM (Vision-Language Model)-based Planner that leverages spatial priors to obtain cinematic, globally aligned trajectories from text, and a Controller that renders these trajectories into multi-shot video content via a camera adapter. Central to our approach is the construction of a data foundation: we design an automated multi-shot camera calibration pipeline aligns disjoint single-shot trajectories into a unified global coordinate system. This facilitates the curation of ShotVerse-Bench, a high-fidelity cinematic dataset with a three-track evaluation protocol that serves as the bedrock for our framework. Extensive experiments demonstrate that ShotVerse effectively bridges the gap between unreliable textual control and labor-intensive manual plotting, achieving superior cinematic aesthetics and generating multi-shot videos that are both camera-accurate and cross-shot consistent.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LAION-AI/aesthetic-predictor", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12265", "title": "OmniStream: Mastering Perception, Reconstruction and Action in Continuous Streams", "authors": [ "Yibin Yan", "Jilan Xu", "Shangzhe Di", "Haoning Wu", "Weidi Xie" ], "github": "", "project_page": "", "abstract": "Modern visual agents require representations that are general, causal, and physically structured to operate in real-time streaming environments. However, current vision foundation models remain fragmented, specializing narrowly in image semantic perception, offline temporal modeling, or spatial geometry. This paper introduces OmniStream, a unified streaming visual backbone that effectively perceives, reconstructs, and acts from diverse visual inputs. By incorporating causal spatiotemporal attention and 3D rotary positional embeddings (3D-RoPE), our model supports efficient, frame-by-frame online processing of video streams via a persistent KV-cache. We pre-train OmniStream using a synergistic multi-task framework coupling static and temporal representation learning, streaming geometric reconstruction, and vision-language alignment on 29 datasets. Extensive evaluations show that, even with a strictly frozen backbone, OmniStream achieves consistently competitive performance with specialized experts across image and video probing, streaming geometric reconstruction, complex video and spatial reasoning, as well as robotic manipulation (unseen at training). Rather than pursuing benchmark-specific dominance, our work demonstrates the viability of training a single, versatile vision backbone that generalizes across semantic, spatial, and temporal reasoning, i.e., a more meaningful step toward general-purpose visual understanding for interactive and embodied agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12180", "title": "Strategic Navigation or Stochastic Search? How Agents and Humans Reason Over Document Collections", "authors": [ "\u0141ukasz Borchmann", "Jordy Van Landeghem", "Micha\u0142 Turski", "Shreyansh Padarha", "Ryan Othniel Kearns", "Adam Mahdi", "Niels Rogge", "Cl\u00e9mentine Fourrier", "Siwei Han", "Huaxiu Yao", "Artemis Llabr\u00e9s", "Yiming Xu", "Dimosthenis Karatzas", "Hao Zhang", "Anupam Datta" ], "github": "", "project_page": "", "abstract": "Multimodal agents offer a promising path to automating complex document-intensive workflows. Yet, a critical question remains: do these agents demonstrate genuine strategic reasoning, or merely stochastic trial-and-error search? To address this, we introduce MADQA, a benchmark of 2,250 human-authored questions grounded in 800 heterogeneous PDF documents. Guided by Classical Test Theory, we design it to maximize discriminative power across varying levels of agentic abilities. To evaluate agentic behaviour, we introduce a novel evaluation protocol measuring the accuracy-effort trade-off. Using this framework, we show that while the best agents can match human searchers in raw accuracy, they succeed on largely different questions and rely on brute-force search to compensate for weak strategic planning. They fail to close the nearly 20% gap to oracle performance, persisting in unproductive loops. We release the dataset and evaluation harness to help facilitate the transition from brute-force retrieval to calibrated, efficient reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.11510", "title": "Tiny Aya: Bridging Scale and Multilingual Depth", "authors": [ "Alejandro R. Salamanca", "Diana Abagyan", "Daniel D'souza", "Ammar Khairi", "David Mora", "Saurabh Dash", "Viraat Aryabumi", "Sara Rajaee", "Mehrnaz Mofakhami", "Ananya Sahu", "Thomas Euyang", "Brittawnya Prince", "Madeline Smith", "Hangyu Lin", "Acyr Locatelli", "Sara Hooker", "Tom Kocmi", "Aidan Gomez", "Ivan Zhang", "Phil Blunsom", "Nick Frosst", "Joelle Pineau", "Beyza Ermis", "Ahmet \u00dcst\u00fcn", "Julia Kreutzer", "Marzieh Fadaee" ], "github": "https://github.com/ggml-org/llama.cpp", "project_page": "", "abstract": "Tiny Aya redefines what a small multilingual language model can achieve. Trained on 70 languages and refined through region-aware posttraining, it delivers state-of-the-art in translation quality, strong multilingual understanding, and high-quality target-language generation, all with just 3.35B parameters. The release includes a pretrained foundation model, a globally balanced instruction-tuned variant, and three region-specialized models targeting languages from Africa, South Asia, Europe, Asia-Pacific, and West Asia. This report details the training strategy, data composition, and comprehensive evaluation framework behind Tiny Aya, and presents an alternative scaling path for multilingual AI: one centered on efficiency, balanced performance across languages, and practical deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ggml-org/llama.cpp", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.10652", "title": "Are Video Reasoning Models Ready to Go Outside?", "authors": [ "Yangfan He", "Changgyu Boo", "Jaehong Yoon" ], "github": "", "project_page": "", "abstract": "In real-world deployment, vision-language models often encounter disturbances such as weather, occlusion, and camera motion. Under such conditions, their understanding and reasoning degrade substantially, revealing a gap between clean, controlled (i.e., unperturbed) evaluation settings and real-world robustness. To address this limitation, we propose ROVA, a novel training framework that improves robustness by modeling a robustness-aware consistency reward under spatio-temporal corruptions. ROVA introduces a difficulty-aware online training strategy that prioritizes informative samples based on the model's evolving capability. Specifically, it continuously re-estimates sample difficulty via self-reflective evaluation, enabling adaptive training with a robustness-aware consistency reward. We also introduce PVRBench, a new benchmark that injects real-world perturbations into embodied video datasets to assess both accuracy and reasoning quality under realistic disturbances. We evaluate ROVA and baselines on PVRBench, UrbanVideo, and VisBench, where open-source and proprietary models suffer up to 35% and 28% drops in accuracy and reasoning under realistic perturbations. ROVA effectively mitigates performance degradation, boosting relative accuracy by at least 24% and reasoning by over 9% compared with baseline models (QWen2.5/3-VL, InternVL2.5, Embodied-R). These gains transfer to clean standard benchmarks, yielding consistent improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.10365", "title": "Geometric Autoencoder for Diffusion Models", "authors": [ "Hangyu Liu", "Jianyong Wang", "Yutao Sun" ], "github": "https://github.com/freezing-index/Geometric-Autoencoder-for-Diffusion-Models", "project_page": "", "abstract": "Latent diffusion models have established a new state-of-the-art in high-resolution visual generation. Integrating Vision Foundation Model priors improves generative efficiency, yet existing latent designs remain largely heuristic. These approaches often struggle to unify semantic discriminability, reconstruction fidelity, and latent compactness. In this paper, we propose Geometric Autoencoder (GAE), a principled framework that systematically addresses these challenges. By analyzing various alignment paradigms, GAE constructs an optimized low-dimensional semantic supervision target from VFMs to provide guidance for the autoencoder. Furthermore, we leverage latent normalization that replaces the restrictive KL-divergence of standard VAEs, enabling a more stable latent manifold specifically optimized for diffusion learning. To ensure robust reconstruction under high-intensity noise, GAE incorporates a dynamic noise sampling mechanism. Empirically, GAE achieves compelling performance on the ImageNet-1K 256 times 256 benchmark, reaching a gFID of 1.82 at only 80 epochs and 1.31 at 800 epochs without Classifier-Free Guidance, significantly surpassing existing state-of-the-art methods. Beyond generative quality, GAE establishes a superior equilibrium between compression, semantic depth and robust reconstruction stability. These results validate our design considerations, offering a promising paradigm for latent diffusion modeling. Code and models are publicly available at https://github.com/freezing-index/Geometric-Autoencoder-for-Diffusion-Models.", "github_manual": "", "github_regex": "https://github.com/freezing-index/Geometric-Autoencoder-for-Diffusion-Models", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.10178", "title": "Video-Based Reward Modeling for Computer-Use Agents", "authors": [ "Linxin Song", "Jieyu Zhang", "Huanxin Sheng", "Taiwei Shi", "Gupta Rahul", "Yang Liu", "Ranjay Krishna", "Jian Kang", "Jieyu Zhao" ], "github": "", "project_page": "", "abstract": "Computer-using agents (CUAs) are becoming increasingly capable; however, it remains difficult to scale evaluation of whether a trajectory truly fulfills a user instruction. In this work, we study reward modeling from execution video: a sequence of keyframes from an agent trajectory that is independent of the agent's internal reasoning or actions. Although video-execution modeling is method-agnostic, it presents key challenges, including highly redundant layouts and subtle, localized cues that determine success. We introduce Execution Video Reward 53k (ExeVR-53k), a dataset of 53k high-quality video--task--reward triplets. We further propose adversarial instruction translation to synthesize negative samples with step-level annotations. To enable learning from long, high-resolution execution videos, we design spatiotemporal token pruning, which removes homogeneous regions and persistent tokens while preserving decisive UI changes. Building on these components, we fine-tune an Execution Video Reward Model (ExeVRM) that takes only a user instruction and a video-execution sequence to predict task success. Our ExeVRM 8B achieves 84.7% accuracy and 87.7% recall on video-execution assessment, outperforming strong proprietary models such as GPT-5.2 and Gemini-3 Pro across Ubuntu, macOS, Windows, and Android, while providing more precise temporal attribution. These results show that video-execution reward modeling can serve as a scalable, model-agnostic evaluator for CUAs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.07988", "title": "TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size", "authors": [ "Stefan Lionar", "Gim Hee Lee" ], "github": "", "project_page": "", "abstract": "Physics-based humanoid control has achieved remarkable progress in enabling realistic and high-performing single-agent behaviors, yet extending these capabilities to cooperative human-object interaction (HOI) remains challenging. We present TeamHOI, a framework that enables a single decentralized policy to handle cooperative HOIs across any number of cooperating agents. Each agent operates using local observations while attending to other teammates through a Transformer-based policy network with teammate tokens, allowing scalable coordination across variable team sizes. To enforce motion realism while addressing the scarcity of cooperative HOI data, we further introduce a masked Adversarial Motion Prior (AMP) strategy that uses single-human reference motions while masking object-interacting body parts during training. The masked regions are then guided through task rewards to produce diverse and physically plausible cooperative behaviors. We evaluate TeamHOI on a challenging cooperative carrying task involving two to eight humanoid agents and varied object geometries. Finally, to promote stable carrying, we design a team-size- and shape-agnostic formation reward. TeamHOI achieves high success rates and demonstrates coherent cooperation across diverse configurations with a single policy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12257", "title": "DreamVideo-Omni: Omni-Motion Controlled Multi-Subject Video Customization with Latent Identity Reinforcement Learning", "authors": [ "Yujie Wei", "Xinyu Liu", "Shiwei Zhang", "Hangjie Yuan", "Jinbo Xing", "Zhekai Chen", "Xiang Wang", "Haonan Qiu", "Rui Zhao", "Yutong Feng", "Ruihang Chu", "Yingya Zhang", "Yike Guo", "Xihui Liu", "Hongming Shan" ], "github": "", "project_page": "", "abstract": "While large-scale diffusion models have revolutionized video synthesis, achieving precise control over both multi-subject identity and multi-granularity motion remains a significant challenge. Recent attempts to bridge this gap often suffer from limited motion granularity, control ambiguity, and identity degradation, leading to suboptimal performance on identity preservation and motion control. In this work, we present DreamVideo-Omni, a unified framework enabling harmonious multi-subject customization with omni-motion control via a progressive two-stage training paradigm. In the first stage, we integrate comprehensive control signals for joint training, encompassing subject appearances, global motion, local dynamics, and camera movements. To ensure robust and precise controllability, we introduce a condition-aware 3D rotary positional embedding to coordinate heterogeneous inputs and a hierarchical motion injection strategy to enhance global motion guidance. Furthermore, to resolve multi-subject ambiguity, we introduce group and role embeddings to explicitly anchor motion signals to specific identities, effectively disentangling complex scenes into independent controllable instances. In the second stage, to mitigate identity degradation, we design a latent identity reward feedback learning paradigm by training a latent identity reward model upon a pretrained video diffusion backbone. This provides motion-aware identity rewards in the latent space, prioritizing identity preservation aligned with human preferences. Supported by our curated large-scale dataset and the comprehensive DreamOmni Bench for multi-subject and omni-motion control evaluation, DreamVideo-Omni demonstrates superior performance in generating high-quality videos with precise controllability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12252", "title": "EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models", "authors": [ "Xuanlang Dai", "Yujie Zhou", "Long Xing", "Jiazi Bu", "Xilin Wei", "Yuhong Liu", "Beichen Zhang", "Kai Chen", "Yuhang Zang" ], "github": "https://github.com/InternLM/EndoCoT", "project_page": "", "abstract": "Recently, Multimodal Large Language Models (MLLMs) have been widely integrated into diffusion frameworks primarily as text encoders to tackle complex tasks such as spatial reasoning. However, this paradigm suffers from two critical limitations: (i) MLLMs text encoder exhibits insufficient reasoning depth. Single-step encoding fails to activate the Chain-of-Thought process, which is essential for MLLMs to provide accurate guidance for complex tasks. (ii) The guidance remains invariant during the decoding process. Invariant guidance during decoding prevents DiT from progressively decomposing complex instructions into actionable denoising steps, even with correct MLLM encodings. To this end, we propose Endogenous Chain-of-Thought (EndoCoT), a novel framework that first activates MLLMs' reasoning potential by iteratively refining latent thought states through an iterative thought guidance module, and then bridges these states to the DiT's denoising process. Second, a terminal thought grounding module is applied to ensure the reasoning trajectory remains grounded in textual supervision by aligning the final state with ground-truth answers. With these two components, the MLLM text encoder delivers meticulously reasoned guidance, enabling the DiT to execute it progressively and ultimately solve complex tasks in a step-by-step manner. Extensive evaluations across diverse benchmarks (e.g., Maze, TSP, VSP, and Sudoku) achieve an average accuracy of 92.1%, outperforming the strongest baseline by 8.3 percentage points.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/InternLM/EndoCoT", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12246", "title": "Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training", "authors": [ "Yixin Liu", "Yue Yu", "DiJia Su", "Sid Wang", "Xuewei Wang", "Song Jiang", "Bo Liu", "Arman Cohan", "Yuandong Tian", "Zhengxing Chen" ], "github": "", "project_page": "", "abstract": "Reasoning LLMs-as-Judges, which can benefit from inference-time scaling, provide a promising path for extending the success of reasoning models to non-verifiable domains where the output correctness/quality cannot be directly checked. However, while reasoning judges have shown better performance on static evaluation benchmarks, their effectiveness in actual policy training has not been systematically examined. Therefore, we conduct a rigorous study to investigate the actual impact of non-reasoning and reasoning judges in reinforcement-learning-based LLM alignment. Our controlled synthetic setting, where a \"gold-standard\" judge (gpt-oss-120b) provides preference annotations to train smaller judges, reveals key differences between non-reasoning and reasoning judges: non-reasoning judges lead to reward hacking easily, while reasoning judges can lead to policies that achieve strong performance when evaluated by the gold-standard judge. Interestingly, we find that the reasoning-judge-trained policies achieve such strong performance by learning to generate highly effective adversarial outputs that can also score well on popular benchmarks such as Arena-Hard by deceiving other LLM-judges. Combined with our further analysis, our study highlights both important findings and room for improvements for applying (reasoning) LLM-judges in non-verifiable LLM post-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12245", "title": "One Model, Many Budgets: Elastic Latent Interfaces for Diffusion Transformers", "authors": [ "Moayed Haji-Ali", "Willi Menapace", "Ivan Skorokhodov", "Dogyun Park", "Anil Kag", "Michael Vasilkovsky", "Sergey Tulyakov", "Vicente Ordonez", "Aliaksandr Siarohin" ], "github": "", "project_page": "", "abstract": "Diffusion transformers (DiTs) achieve high generative quality but lock FLOPs to image resolution, limiting principled latency-quality trade-offs, and allocate computation uniformly across input spatial tokens, wasting resource allocation to unimportant regions. We introduce Elastic Latent Interface Transformer (ELIT), a drop-in, DiT-compatible mechanism that decouples input image size from compute. Our approach inserts a latent interface, a learnable variable-length token sequence on which standard transformer blocks can operate. Lightweight Read and Write cross-attention layers move information between spatial tokens and latents and prioritize important input regions. By training with random dropping of tail latents, ELIT learns to produce importance-ordered representations with earlier latents capturing global structure while later ones contain information to refine details. At inference, the number of latents can be dynamically adjusted to match compute constraints. ELIT is deliberately minimal, adding two cross-attention layers while leaving the rectified flow objective and the DiT stack unchanged. Across datasets and architectures (DiT, U-ViT, HDiT, MM-DiT), ELIT delivers consistent gains. On ImageNet-1K 512px, ELIT delivers an average gain of 35.3% and 39.6% in FID and FDD scores. Project page: https://snap-research.github.io/elit/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.11665", "title": "Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge", "authors": [ "Junjie Wu", "Xuan Kan", "Zihao He", "Shunwen Tan", "Bo Pan", "Kaitai Zhang" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-a-Judges due to their strong alignment with human judgment across various visual tasks. However, most existing judge models are optimized for single-task scenarios and struggle to generalize to diverse contexts, which is a critical requirement for reliable evaluation. To address this limitation, we propose Multi-Task Reinforcement Learning for MLLM-as-a-Judge (MT-RL-Judge), a framework that jointly optimizes the judge model across multiple tasks, leveraging the generalization capabilities of RL. Experimental results against several strong baselines demonstrate that MT-RL-Judge outperforms strong baselines in both judgment consistency and correlation with human preferences. Furthermore, our approach exhibits robust generalization on out-of-distribution tasks, further validating its effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.11593", "title": "WeEdit: A Dataset, Benchmark and Glyph-Guided Framework for Text-centric Image Editing", "authors": [ "Hui Zhang", "Juntao Liu", "Zongkai Liu", "Liqiang Niu", "Fandong Meng", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "https://github.com/HuiZhang0812/WeEdit", "project_page": "", "abstract": "Instruction-based image editing aims to modify specific content within existing images according to user-provided instructions while preserving non-target regions. Beyond traditional object- and style-centric manipulation, text-centric image editing focuses on modifying, translating, or rearranging textual elements embedded within images. However, existing leading models often struggle to execute complex text editing precisely, frequently producing blurry or hallucinated characters. We attribute these failures primarily to the lack of specialized training paradigms tailored for text-centric editing, as well as the absence of large-scale datasets and standardized benchmarks necessary for a closed-loop training and evaluation system. To address these limitations, we present WeEdit, a systematic solution encompassing a scalable data construction pipeline, two benchmarks, and a tailored two-stage training strategy. Specifically, we propose a novel HTML-based automatic editing pipeline, which generates 330K training pairs covering diverse editing operations and 15 languages, accompanied by standardized bilingual and multilingual benchmarks for comprehensive evaluation. On the algorithmic side, we employ glyph-guided supervised fine-tuning to inject explicit spatial and content priors, followed by a multi-objective reinforcement learning stage to align generation with instruction adherence, text clarity, and background preservation. Extensive experiments demonstrate that WeEdit outperforms previous open-source models by a clear margin across diverse editing operations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HuiZhang0812/WeEdit", "github_api_added_by": "auto" }, { "date": "2026-03-13", "arxiv_id": "2603.11487", "title": "Attention Sinks Are Provably Necessary in Softmax Transformers: Evidence from Trigger-Conditional Tasks", "authors": [ "Yuval Ran-Milo" ], "github": "https://github.com/YuvMilo/sinks-are-provably-necessary", "project_page": "", "abstract": "Transformers often display an attention sink: probability mass concentrates on a fixed, content-agnostic position. We prove that computing a simple trigger-conditional behavior necessarily induces a sink in softmax self-attention models. Our results formalize a familiar intuition: normalization over a probability simplex must force attention to collapse onto a stable anchor to realize a default state (e.g., when the model needs to ignore the input). We instantiate this with a concrete task: when a designated trigger token appears, the model must return the average of all preceding token representations, and otherwise output zero, a task which mirrors the functionality of attention heads in the wild (Barbero et al., 2025; Guo et al., 2024). We also prove that non-normalized ReLU attention can solve the same task without any sink, confirming that the normalization constraint is the fundamental driver of sink behavior. Experiments validate our predictions and demonstrate they extend beyond the theoretically analyzed setting: softmax models develop strong sinks while ReLU attention eliminates them in both single-head and multi-head variants.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YuvMilo/sinks-are-provably-necessary", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.11103", "title": "Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining", "authors": [ "Zhiyuan Zeng", "Yichi Zhang", "Yong Shan", "Kai Hua", "Siyuan Fang", "Zhaiyu Liu", "Jiaheng Liu", "Haozhe Wang", "Yining Zheng", "Ming Ding", "Ke Shen", "Ge Zhang", "Wenhao Huang", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "While Large Language Models (LLMs) have achieved remarkable success in code generation, they often struggle with the deep, long-horizon reasoning required for complex software engineering. We attribute this limitation to the nature of standard pre-training data: static software repositories represent only the terminal state of an intricate intellectual process, abstracting away the intermediate planning, debugging, and iterative refinement. To bridge this gap, we propose a novel paradigm: understanding via reconstruction. We hypothesize that reverse-engineering the latent agentic trajectories -- the planning, reasoning, and debugging steps -- behind static repositories provides a far richer supervision signal than raw code alone. To operationalize this, we introduce a framework that synthesizes these trajectories using a multi-agent simulation. This process is grounded in the structural realities of the source repositories (e.g., dependency graphs and file hierarchies) to ensure fidelity. Furthermore, to guarantee the logical rigor of the synthetic data, we employ a search-based optimization technique that iteratively refines the Chain-of-Thought (CoT) reasoning to maximize the likelihood of the ground-truth code. Empirical results demonstrate that continuous pre-training on these reconstructed trajectories significantly enhances Llama-3-8B's performance across diverse benchmarks, including long-context understanding, coding proficiency, and agentic capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.06922", "title": "NerVE: Nonlinear Eigenspectrum Dynamics in LLM Feed-Forward Networks", "authors": [ "Nandan Kumar Jha", "Brandon Reagen" ], "github": "", "project_page": "", "abstract": "We introduce NerVE, a unified eigenspectral framework for understanding how feed-forward networks (FFNs) in large language models (LLMs) organize and regulate information flow in high-dimensional latent space. Despite FFNs dominating the parameter budget, their high-dimensional dynamics remain poorly understood. NerVE addresses this gap through lightweight, memory-efficient tracking of eigenspectrum dynamics via four complementary metrics: Spectral Entropy (dispersion), Participation Ratio (effective dimensionality), Eigenvalue Early Enrichment (top-heaviness), and Jensen-Shannon divergence (distributional shifts). Our key insight is that FFN nonlinearities reinject variance across eigenmodes, fundamentally governing latent dimension utilization, and that optimizer geometry strongly modulates the extent of this variance reinjection. We validate NerVE across model scales, and diverse architectural and optimizer configurations, each uniquely shaping FFN dynamics: normalization schemes controlling variance flow; FFN weight geometries constraining latent space; positional encoding and activation functions regulating information flow; and optimizer choices redistributing effective capacity across depth. Across these settings, NerVE consistently recovers stable spectral signatures that correlate with model's generalization ability and respond predictably to design choices, generalizing beyond transformer to MLP-Mixer architectures, providing actionable insights for architectural and optimizer choices beyond trial-and-error.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12255", "title": "Spatial-TTT: Streaming Visual-based Spatial Intelligence with Test-Time Training", "authors": [ "Fangfu Liu", "Diankun Wu", "Jiawei Chi", "Yimo Cai", "Yi-Hsin Hung", "Xumin Yu", "Hao Li", "Han Hu", "Yongming Rao", "Yueqi Duan" ], "github": "", "project_page": "", "abstract": "Humans perceive and understand real-world spaces through a stream of visual observations. Therefore, the ability to streamingly maintain and update spatial evidence from potentially unbounded video streams is essential for spatial intelligence. The core challenge is not simply longer context windows but how spatial information is selected, organized, and retained over time. In this paper, we propose Spatial-TTT towards streaming visual-based spatial intelligence with test-time training (TTT), which adapts a subset of parameters (fast weights) to capture and organize spatial evidence over long-horizon scene videos. Specifically, we design a hybrid architecture and adopt large-chunk updates parallel with sliding-window attention for efficient spatial video processing. To further promote spatial awareness, we introduce a spatial-predictive mechanism applied to TTT layers with 3D spatiotemporal convolution, which encourages the model to capture geometric correspondence and temporal continuity across frames. Beyond architecture design, we construct a dataset with dense 3D spatial descriptions, which guides the model to update its fast weights to memorize and organize global 3D spatial signals in a structured manner. Extensive experiments demonstrate that Spatial-TTT improves long-horizon spatial understanding and achieves state-of-the-art performance on video spatial benchmarks. Project page: https://liuff19.github.io/Spatial-TTT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12201", "title": "IndexCache: Accelerating Sparse Attention via Cross-Layer Index Reuse", "authors": [ "Yushi Bai", "Qian Dong", "Ting Jiang", "Xin Lv", "Zhengxiao Du", "Aohan Zeng", "Jie Tang", "Juanzi Li" ], "github": "", "project_page": "", "abstract": "Long-context agentic workflows have emerged as a defining use case for large language models, making attention efficiency critical for both inference speed and serving cost. Sparse attention addresses this challenge effectively, and DeepSeek Sparse Attention (DSA) is a representative production-grade solution: a lightweight lightning indexer selects the top-k most relevant tokens per query, reducing core attention from O(L^2) to O(Lk). However, the indexer itself retains O(L^2) complexity and must run independently at every layer, despite the fact that the resulting top-k selections are highly similar across consecutive layers. We present IndexCache, which exploits this cross-layer redundancy by partitioning layers into a small set of Full layers that run their own indexers and a majority of Shared layers that simply reuse the nearest Full layer's top-k indices. We propose two complementary approaches to determine and optimize this configuration. Training-free IndexCache applies a greedy search algorithm that selects which layers to retain indexers by directly minimizing language modeling loss on a calibration set, requiring no weight updates. Training-aware IndexCache introduces a multi-layer distillation loss that trains each retained indexer against the averaged attention distributions of all layers it serves, enabling even simple interleaved patterns to match full-indexer accuracy. Experimental results on a 30B DSA model show that IndexCache can remove 75% of indexer computations with negligible quality degradation, achieving up to 1.82times prefill speedup and 1.48times decode speedup compared to standard DSA. These positive results are further confirmed by our preliminary experiments on the production-scale GLM-5 model (Figure 1).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12264", "title": "GRADE: Benchmarking Discipline-Informed Reasoning in Image Editing", "authors": [ "Mingxin Liu", "Ziqian Fan", "Zhaokai Wang", "Leyao Gu", "Zirun Zhu", "Yiguo He", "Yuchen Yang", "Changyao Tian", "Xiangyu Zhao", "Ning Liao", "Shaofeng Zhang", "Qibing Ren", "Zhihang Zhong", "Xuanhe Zhou", "Junchi Yan", "Xue Yang" ], "github": "https://github.com/VisionXLab/GRADE", "project_page": "", "abstract": "Unified multimodal models target joint understanding, reasoning, and generation, but current image editing benchmarks are largely confined to natural images and shallow commonsense reasoning, offering limited assessment of this capability under structured, domain-specific constraints. In this work, we introduce GRADE, the first benchmark to assess discipline-informed knowledge and reasoning in image editing. GRADE comprises 520 carefully curated samples across 10 academic domains, spanning from natural science to social science. To support rigorous evaluation, we propose a multi-dimensional evaluation protocol that jointly assesses Discipline Reasoning, Visual Consistency, and Logical Readability. Extensive experiments on 20 state-of-the-art open-source and closed-source models reveal substantial limitations in current models under implicit, knowledge-intensive editing settings, leading to large performance gaps. Beyond quantitative scores, we conduct rigorous analyses and ablations to expose model shortcomings and identify the constraints within disciplinary editing. Together, GRADE pinpoints key directions for the future development of unified multimodal models, advancing the research on discipline-informed image editing and reasoning. Our benchmark and evaluation code are publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/VisionXLab/GRADE", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12247", "title": "Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation", "authors": [ "Xiangyu Zhao", "Peiyuan Zhang", "Junming Lin", "Tianhao Liang", "Yuchen Duan", "Shengyuan Ding", "Changyao Tian", "Yuhang Zang", "Junchi Yan", "Xue Yang" ], "github": "https://github.com/VisionXLab/FIRM-Reward", "project_page": "", "abstract": "Reinforcement learning (RL) has emerged as a promising paradigm for enhancing image editing and text-to-image (T2I) generation. However, current reward models, which act as critics during RL, often suffer from hallucinations and assign noisy scores, inherently misguiding the optimization process. In this paper, we present FIRM (Faithful Image Reward Modeling), a comprehensive framework that develops robust reward models to provide accurate and reliable guidance for faithful image generation and editing. First, we design tailored data curation pipelines to construct high-quality scoring datasets. Specifically, we evaluate editing using both execution and consistency, while generation is primarily assessed via instruction following. Using these pipelines, we collect the FIRM-Edit-370K and FIRM-Gen-293K datasets, and train specialized reward models (FIRM-Edit-8B and FIRM-Gen-8B) that accurately reflect these criteria. Second, we introduce FIRM-Bench, a comprehensive benchmark specifically designed for editing and generation critics. Evaluations demonstrate that our models achieve superior alignment with human judgment compared to existing metrics. Furthermore, to seamlessly integrate these critics into the RL pipeline, we formulate a novel \"Base-and-Bonus\" reward strategy that balances competing objectives: Consistency-Modulated Execution (CME) for editing and Quality-Modulated Alignment (QMA) for generation. Empowered by this framework, our resulting models FIRM-Qwen-Edit and FIRM-SD3.5 achieve substantial performance breakthroughs. Comprehensive experiments demonstrate that FIRM mitigates hallucinations, establishing a new standard for fidelity and instruction adherence over existing general models. All of our datasets, models, and code have been publicly available at https://firm-reward.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/VisionXLab/FIRM-Reward", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12267", "title": "EVATok: Adaptive Length Video Tokenization for Efficient Visual Autoregressive Generation", "authors": [ "Tianwei Xiong", "Jun Hao Liew", "Zilong Huang", "Zhijie Lin", "Jiashi Feng", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Autoregressive (AR) video generative models rely on video tokenizers that compress pixels into discrete token sequences. The length of these token sequences is crucial for balancing reconstruction quality against downstream generation computational cost. Traditional video tokenizers apply a uniform token assignment across temporal blocks of different videos, often wasting tokens on simple, static, or repetitive segments while underserving dynamic or complex ones. To address this inefficiency, we introduce EVATok, a framework to produce Efficient Video Adaptive Tokenizers. Our framework estimates optimal token assignments for each video to achieve the best quality-cost trade-off, develops lightweight routers for fast prediction of these optimal assignments, and trains adaptive tokenizers that encode videos based on the assignments predicted by routers. We demonstrate that EVATok delivers substantial improvements in efficiency and overall quality for video reconstruction and downstream AR generation. Enhanced by our advanced training recipe that integrates video semantic encoders, EVATok achieves superior reconstruction and state-of-the-art class-to-video generation on UCF-101, with at least 24.4% savings in average token usage compared to the prior state-of-the-art LARP and our fixed-length baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.07865", "title": "SoundWeaver: Semantic Warm-Starting for Text-to-Audio Diffusion Serving", "authors": [ "Ayush Barik", "Sofia Stoica", "Nikhil Sarda", "Arnav Kethana", "Abhinav Khanduja", "Muchen Xu", "Fan Lai" ], "github": "", "project_page": "", "abstract": "Text-to-audio diffusion models produce high-fidelity audio but require tens of function evaluations (NFEs), incurring multi-second latency and limited throughput. We present SoundWeaver, the first training-free, model-agnostic serving system that accelerates text-to-audio diffusion by warm-starting from semantically similar cached audio. SoundWeaver introduces three components: a Reference Selector that retrieves and temporally aligns cached candidates via semantic and duration-aware gating; a Skip Gater that dynamically determines the percentage of NFEs to skip; and a lightweight Cache Manager that maintains cache utility through quality-aware eviction and refinement. On real-world audio traces, SoundWeaver achieves 1.8--3.0 times latency reduction with a cache of only {sim}1K entries while preserving or improving perceptual quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12089", "title": "EmbTracker: Traceable Black-box Watermarking for Federated Language Models", "authors": [ "Haodong Zhao", "Jinming Hu", "Yijie Bai", "Tian Dong", "Wei Du", "Zhuosheng Zhang", "Yanjiao Chen", "Haojin Zhu", "Gongshen Liu" ], "github": "", "project_page": "", "abstract": "Federated Language Model (FedLM) allows a collaborative learning without sharing raw data, yet it introduces a critical vulnerability, as every untrustworthy client may leak the received functional model instance. Current watermarking schemes for FedLM often require white-box access and client-side cooperation, providing only group-level proof of ownership rather than individual traceability. We propose EmbTracker, a server-side, traceable black-box watermarking framework specifically designed for FedLMs. EmbTracker achieves black-box verifiability by embedding a backdoor-based watermark detectable through simple API queries. Client-level traceability is realized by injecting unique identity-specific watermarks into the model distributed to each client. In this way, a leaked model can be attributed to a specific culprit, ensuring robustness even against non-cooperative participants. Extensive experiments on various language and vision-language models demonstrate that EmbTracker achieves robust traceability with verification rates near 100\\%, high resilience against removal attacks (fine-tuning, pruning, quantization), and negligible impact on primary task performance (typically within 1-2\\%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.11531", "title": "Mobile-GS: Real-time Gaussian Splatting for Mobile Devices", "authors": [ "Xiaobiao Du", "Yida Wang", "Kun Zhan", "Xin Yu" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) has emerged as a powerful representation for high-quality rendering across a wide range of applications.However, its high computational demands and large storage costs pose significant challenges for deployment on mobile devices. In this work, we propose a mobile-tailored real-time Gaussian Splatting method, dubbed Mobile-GS, enabling efficient inference of Gaussian Splatting on edge devices. Specifically, we first identify alpha blending as the primary computational bottleneck, since it relies on the time-consuming Gaussian depth sorting process. To solve this issue, we propose a depth-aware order-independent rendering scheme that eliminates the need for sorting, thereby substantially accelerating rendering. Although this order-independent rendering improves rendering speed, it may introduce transparency artifacts in regions with overlapping geometry due to the scarcity of rendering order. To address this problem, we propose a neural view-dependent enhancement strategy, enabling more accurate modeling of view-dependent effects conditioned on viewing direction, 3D Gaussian geometry, and appearance attributes. In this way, Mobile-GS can achieve both high-quality and real-time rendering. Furthermore, to facilitate deployment on memory-constrained mobile platforms, we also introduce first-order spherical harmonics distillation, a neural vector quantization technique, and a contribution-based pruning strategy to reduce the number of Gaussian primitives and compress the 3D Gaussian representation with the assistance of neural networks. Extensive experiments demonstrate that our proposed Mobile-GS achieves real-time rendering and compact model size while preserving high visual quality, making it well-suited for mobile applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.11076", "title": "DIVE: Scaling Diversity in Agentic Task Synthesis for Generalizable Tool Use", "authors": [ "Aili Chen", "Chi Zhang", "Junteng Liu", "Jiangjie Chen", "Chengyu Du", "Yunji Li", "Ming Zhong", "Qin Wang", "Zhengmao Zhu", "Jiayuan Song", "Ke Ji", "Junxian He", "Pengyu Zhao", "Yanghua Xiao" ], "github": "", "project_page": "", "abstract": "Recent work synthesizes agentic tasks for post-training tool-using LLMs, yet robust generalization under shifts in tasks and toolsets remains an open challenge. We trace this brittleness to insufficient diversity in synthesized tasks. Scaling diversity is difficult because training requires tasks to remain executable and verifiable, while generalization demands coverage of diverse tool types, toolset combinations, and heterogeneous tool-use patterns. We propose DIVE, an evidence-driven recipe that inverts synthesis order, executing diverse, real-world tools first and reverse-deriving tasks strictly entailed by the resulting traces, thereby providing grounding by construction. DIVE scales structural diversity along two controllable axes, tool-pool coverage and per-task toolset variety, and an Evidence Collection--Task Derivation loop further induces rich multi-step tool-use patterns across 373 tools in five domains. Training Qwen3-8B on DIVE data (48k SFT + 3.2k RL) improves by +22 average points across 9 OOD benchmarks and outperforms the strongest 8B baseline by +68. Remarkably, controlled scaling analysis reveals that diversity scaling consistently outperforms quantity scaling for OOD generalization, even with 4x less data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12250", "title": "DVD: Deterministic Video Depth Estimation with Generative Priors", "authors": [ "Hongfei Zhang", "Harold Haodong Chen", "Chenfei Liao", "Jing He", "Zixin Zhang", "Haodong Li", "Yihao Liang", "Kanghao Chen", "Bin Ren", "Xu Zheng", "Shuai Yang", "Kun Zhou", "Yinchuan Li", "Nicu Sebe", "Ying-Cong Chen" ], "github": "https://github.com/EnVision-Research/DVD", "project_page": "", "abstract": "Existing video depth estimation faces a fundamental trade-off: generative models suffer from stochastic geometric hallucinations and scale drift, while discriminative models demand massive labeled datasets to resolve semantic ambiguities. To break this impasse, we present DVD, the first framework to deterministically adapt pre-trained video diffusion models into single-pass depth regressors. Specifically, DVD features three core designs: (i) repurposing the diffusion timestep as a structural anchor to balance global stability with high-frequency details; (ii) latent manifold rectification (LMR) to mitigate regression-induced over-smoothing, enforcing differential constraints to restore sharp boundaries and coherent motion; and (iii) global affine coherence, an inherent property bounding inter-window divergence, which enables seamless long-video inference without requiring complex temporal alignment. Extensive experiments demonstrate that DVD achieves state-of-the-art zero-shot performance across benchmarks. Furthermore, DVD successfully unlocks the profound geometric priors implicit in video foundation models using 163x less task-specific data than leading baselines. Notably, we fully release our pipeline, providing the whole training suite for SOTA video depth estimation to benefit the open-source community.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EnVision-Research/DVD", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12145", "title": "Automatic Generation of High-Performance RL Environments", "authors": [ "Seth Karten", "Rahul Dev Appapogu", "Chi Jin" ], "github": "https://github.com/Baekalfen/PyBoy", "project_page": "", "abstract": "Translating complex reinforcement learning (RL) environments into high-performance implementations has traditionally required months of specialized engineering. We present a reusable recipe - a generic prompt template, hierarchical verification, and iterative agent-assisted repair - that produces semantically equivalent high-performance environments for <$10 in compute cost. We demonstrate three distinct workflows across five environments. Direct translation (no prior performance implementation exists): EmuRust (1.5x PPO speedup via Rust parallelism for a Game Boy emulator) and PokeJAX, the first GPU-parallel Pokemon battle simulator (500M SPS random action, 15.2M SPS PPO; 22,320x over the TypeScript reference). Translation verified against existing performance implementations: throughput parity with MJX (1.04x) and 5x over Brax at matched GPU batch sizes (HalfCheetah JAX); 42x PPO (Puffer Pong). New environment creation: TCGJax, the first deployable JAX Pokemon TCG engine (717K SPS random action, 153K SPS PPO; 6.6x over the Python reference), synthesized from a web-extracted specification. At 200M parameters, the environment overhead drops below 4% of training time. Hierarchical verification (property, interaction, and rollout tests) confirms semantic equivalence for all five environments; cross-backend policy transfer confirms zero sim-to-sim gap for all five environments. TCGJax, synthesized from a private reference absent from public repositories, serves as a contamination control for agent pretraining data concerns. The paper contains sufficient detail - including representative prompts, verification methodology, and complete results - that a coding agent could reproduce the translations directly from the manuscript.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Baekalfen/PyBoy", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.10420", "title": "FireRedASR2S: A State-of-the-Art Industrial-Grade All-in-One Automatic Speech Recognition System", "authors": [ "Kaituo Xu", "Yan Jia", "Kai Huang", "Junjie Chen", "Wenpeng Li", "Kun Liu", "Feng-Long Xie", "Xu Tang", "Yao Hu" ], "github": "https://github.com/FireRedTeam/FireRedASR2S", "project_page": "", "abstract": "We present FireRedASR2S, a state-of-the-art industrial-grade all-in-one automatic speech recognition (ASR) system. It integrates four modules in a unified pipeline: ASR, Voice Activity Detection (VAD), Spoken Language Identification (LID), and Punctuation Prediction (Punc). All modules achieve SOTA performance on the evaluated benchmarks: FireRedASR2: An ASR module with two variants, FireRedASR2-LLM (8B+ parameters) and FireRedASR2-AED (1B+ parameters), supporting speech and singing transcription for Mandarin, Chinese dialects and accents, English, and code-switching. Compared to FireRedASR, FireRedASR2 delivers improved recognition accuracy and broader dialect and accent coverage. FireRedASR2-LLM achieves 2.89% average CER on 4 public Mandarin benchmarks and 11.55% on 19 public Chinese dialects and accents benchmarks, outperforming competitive baselines including Doubao-ASR, Qwen3-ASR, and Fun-ASR. FireRedVAD: An ultra-lightweight module (0.6M parameters) based on the Deep Feedforward Sequential Memory Network (DFSMN), supporting streaming VAD, non-streaming VAD, and multi-label VAD (mVAD). On the FLEURS-VAD-102 benchmark, it achieves 97.57% frame-level F1 and 99.60% AUC-ROC, outperforming Silero-VAD, TEN-VAD, FunASR-VAD, and WebRTC-VAD. FireRedLID: An Encoder-Decoder LID module supporting 100+ languages and 20+ Chinese dialects and accents. On FLEURS (82 languages), it achieves 97.18% utterance-level accuracy, outperforming Whisper and SpeechBrain. FireRedPunc: A BERT-style punctuation prediction module for Chinese and English. On multi-domain benchmarks, it achieves 78.90% average F1, outperforming FunASR-Punc (62.77%). To advance research in speech processing, we release model weights and code at https://github.com/FireRedTeam/FireRedASR2S.", "github_manual": "", "github_regex": "https://github.com/FireRedTeam/FireRedASR2S", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.07534", "title": "Accent Vector: Controllable Accent Manipulation for Multilingual TTS Without Accented Data", "authors": [ "Thanathai Lertpetchpun", "Thanapat Trachu", "Jihwan Lee", "Tiantian Feng", "Dani Byrd", "Shrikanth Narayanan" ], "github": "", "project_page": "", "abstract": "Accent is an integral part of society, reflecting multiculturalism and shaping how individuals express identity. The majority of English speakers are non-native (L2) speakers, yet current Text-To-Speech (TTS) systems primarily model American-accented English due limited accented data. We propose Accent Vector, a controllable representation that enables accent manipulation in multilingual TTS without requiring accented training data. Accent Vector is derived by fine-tuning a TTS system on native speech of a different language (i.e. non-English) and computing task vectors capturing accent characteristics (i.e. in English). By scaling and interpolating the vector, we achieve fine-grained control over accent strength and generate mixed-accent speech. In addition, it generalizes beyond English, enabling accent control across multiple languages. Objective and human evaluations confirm the effectiveness of Accent Vector for fine-grained and compositional accent control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.10444", "title": "The Curse and Blessing of Mean Bias in FP4-Quantized LLM Training", "authors": [ "Hengjie Cao", "Zhendong Huang", "Mengyi Chen", "Yifeng Yang", "Fanqi Yu", "Ruijun Huang", "Fang Dong", "Xin Zhang", "Jixian Zhou", "Anrui Chen", "Mingzhi Dong", "Yujiang Wang", "Jinlong Hou", "Qin Lv", "Yuan Cheng", "Tun Lu", "Fan Yang", "Li Shang" ], "github": "", "project_page": "", "abstract": "Large language models trained on natural language exhibit pronounced anisotropy: a small number of directions concentrate disproportionate energy, while the remaining dimensions form a broad semantic tail. In low-bit training regimes, this geometry becomes numerically unstable. Because blockwise quantization scales are determined by extreme elementwise magnitudes, dominant directions stretch the dynamic range, compressing long-tail semantic variation into narrow numerical bins. We show that this instability is primarily driven by a coherent rank-one mean bias, which constitutes the dominant component of spectral anisotropy in LLM representations. This mean component emerges systematically across layers and training stages and accounts for the majority of extreme activation magnitudes, making it the principal driver of dynamic-range inflation under low precision. Crucially, because the dominant instability is rank-one, it can be eliminated through a simple source-level mean-subtraction operation. This bias-centric conditioning recovers most of the stability benefits of SVD-based spectral methods while requiring only reduction operations and standard quantization kernels. Empirical results on FP4 (W4A4G4) training show that mean removal substantially narrows the loss gap to BF16 and restores downstream performance, providing a hardware-efficient path to stable low-bit LLM training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.10125", "title": "4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video", "authors": [ "Jin Lyu", "Liang An", "Pujin Cheng", "Yebin Liu", "Xiaoying Tang" ], "github": "", "project_page": "", "abstract": "4D reconstruction of equine family (e.g. horses) from monocular video is important for animal welfare. Previous mainstream 4D animal reconstruction methods require joint optimization of motion and appearance over a whole video, which is time-consuming and sensitive to incomplete observation. In this work, we propose a novel framework called 4DEquine by disentangling the 4D reconstruction problem into two sub-problems: dynamic motion reconstruction and static appearance reconstruction. For motion, we introduce a simple yet effective spatio-temporal transformer with a post-optimization stage to regress smooth and pixel-aligned pose and shape sequences from video. For appearance, we design a novel feed-forward network that reconstructs a high-fidelity, animatable 3D Gaussian avatar from as few as a single image. To assist training, we create a large-scale synthetic motion dataset, VarenPoser, which features high-quality surface motions and diverse camera trajectories, as well as a synthetic appearance dataset, VarenTex, comprising realistic multi-view images generated through multi-view diffusion. While training only on synthetic datasets, 4DEquine achieves state-of-the-art performance on real-world APT36K and AiM datasets, demonstrating the superiority of 4DEquine and our new datasets for both geometry and appearance reconstruction. Comprehensive ablation studies validate the effectiveness of both the motion and appearance reconstruction network. Project page: https://luoxue-star.github.io/4DEquine_Project_Page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12056", "title": "XSkill: Continual Learning from Experience and Skills in Multimodal Agents", "authors": [ "Guanyu Jiang", "Zhaochen Su", "Xiaoye Qu", "Yi R.", "Fung" ], "github": "", "project_page": "", "abstract": "Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestration in open-ended settings. A central challenge is enabling such agents to continually improve without parameter updates by learning from past trajectories. We identify two complementary forms of reusable knowledge essential for this goal: experiences, providing concise action-level guidance for tool selection and decision making, and skills, providing structured task-level guidance for planning and tool use. To this end, we propose XSkill, a dual-stream framework for continual learning from experience and skills in multimodal agents. XSkill grounds both knowledge extraction and retrieval in visual observations. During accumulation, XSkill distills and consolidates experiences and skills from multi-path rollouts via visually grounded summarization and cross-rollout critique. During inference, it retrieves and adapts this knowledge to the current visual context and feeds usage history back into accumulation to form a continual learning loop. Evaluated on five benchmarks across diverse domains with four backbone models, XSkill consistently and substantially outperforms both tool-only and learning-based baselines. Further analysis reveals that the two knowledge streams play complementary roles in influencing the reasoning behaviors of agents and show superior zero-shot generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.11045", "title": "Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation", "authors": [ "Tao Zhong", "Yixun Hu", "Dongzhe Zheng", "Aditya Sood", "Christine Allen-Blanchette" ], "github": "", "project_page": "", "abstract": "We propose Neural Field Thermal Tomography (NeFTY), a differentiable physics framework for the quantitative 3D reconstruction of material properties from transient surface temperature measurements. While traditional thermography relies on pixel-wise 1D approximations that neglect lateral diffusion, and soft-constrained Physics-Informed Neural Networks (PINNs) often fail in transient diffusion scenarios due to gradient stiffness, NeFTY parameterizes the 3D diffusivity field as a continuous neural field optimized through a rigorous numerical solver. By leveraging a differentiable physics solver, our approach enforces thermodynamic laws as hard constraints while maintaining the memory efficiency required for high-resolution 3D tomography. Our discretize-then-optimize paradigm effectively mitigates the spectral bias and ill-posedness inherent in inverse heat conduction, enabling the recovery of subsurface defects at arbitrary scales. Experimental validation on synthetic data demonstrates that NeFTY significantly improves the accuracy of subsurface defect localization over baselines. Additional details at https://cab-lab-princeton.github.io/nefty/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.10055", "title": "Training Language Models via Neural Cellular Automata", "authors": [ "Dan Lee", "Seungwook Han", "Akarsh Kumar", "Pulkit Agrawal" ], "github": "https://github.com/danihyunlee/nca-pre-pretraining", "project_page": "", "abstract": "Pre-training is crucial for large language models (LLMs), as it is when most representations and capabilities are acquired. However, natural language pre-training has problems: high-quality text is finite, it contains human biases, and it entangles knowledge with reasoning. This raises a fundamental question: is natural language the only path to intelligence? We propose using neural cellular automata (NCA) to generate synthetic, non-linguistic data for pre-pre-training LLMs--training on synthetic-then-natural language. NCA data exhibits rich spatiotemporal structure and statistics resembling natural language while being controllable and cheap to generate at scale. We find that pre-pre-training on only 164M NCA tokens improves downstream language modeling by up to 6% and accelerates convergence by up to 1.6x. Surprisingly, this even outperforms pre-pre-training on 1.6B tokens of natural language from Common Crawl with more compute. These gains also transfer to reasoning benchmarks, including GSM8K, HumanEval, and BigBench-Lite. Investigating what drives transfer, we find that attention layers are the most transferable, and that optimal NCA complexity varies by domain: code benefits from simpler dynamics, while math and web text favor more complex ones. These results enable systematic tuning of the synthetic distribution to target domains. More broadly, our work opens a path toward more efficient models with fully synthetic pre-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/danihyunlee/nca-pre-pretraining", "github_api_added_by": "auto" }, { "date": "2026-03-13", "arxiv_id": "2603.07331", "title": "Causal Attribution of Coastal Water Clarity Degradation to Nickel Processing Expansion at the Indonesia Morowali Industrial Park, Sulawesi", "authors": [ "Sandy Hardian Susanto Herho", "Alfita Puspa Handayani", "Iwan Pramesti Anwar", "Faruq Khadami", "Karina Aprilia Sujatmiko", "Doandy Yonathan Wibisono", "Rusmawan Suwarman", "Dasapta Erwin Irawan" ], "github": "https://github.com/sandyherho/supplMorowaliOcean", "project_page": "", "abstract": "Indonesia's nickel ore export ban has driven rapid expansion of smelting and hydrometallurgical processing capacity at the Indonesia Morowali Industrial Park (IMIP), now the world's largest integrated nickel processing complex, on the coast of Central Sulawesi. Whether this industrialization has degraded the adjacent marine environment remains unquantified. We apply Bayesian structural time-series (BSTS) causal inference to a multi-decadal, multi-sensor satellite ocean color record of the diffuse attenuation coefficient at 490 nm, K_d(490), to test for a causal link between IMIP expansion and nearshore turbidity change. A consensus structural breakpoint, a significant posterior causal effect estimated against a Banda Sea counterfactual, and a distribution-free placebo rank test collectively establish that coastal water clarity deteriorated after the transition from initial nickel pig iron production to hyper-expansion of high-pressure acid leaching facilities for battery-grade nickel. Satellite-derived land cover analysis independently corroborates this timing, showing substantial built-area growth and concurrent tree cover loss within the IMIP footprint. The resulting euphotic zone shoaling occurs in oligotrophic waters supporting high marine biodiversity, where even moderate optical degradation may impair coral photosynthesis and compress depth-dependent reef habitat. These findings quantify a marine environmental cost absent from Indonesia's mineral downstreaming policy discourse and demonstrate a transferable, satellite-based quasi-experimental framework for causal impact assessment at coastal industrial sites in data-limited tropical settings.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sandyherho/supplMorowaliOcean", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2602.24181", "title": "A Mixed Diet Makes DINO An Omnivorous Vision Encoder", "authors": [ "Rishabh Kabra", "Maks Ovsjanikov", "Drew A. Hudson", "Ye Xia", "Skanda Koppula", "Andre Araujo", "Joao Carreira", "Niloy J. Mitra" ], "github": "", "project_page": "", "abstract": "Pre-trained vision encoders like DINOv2 have demonstrated exceptional performance on unimodal tasks. However, we observe that their feature representations are poorly aligned across different modalities. For instance, the feature embedding for an RGB image and its corresponding depth map of the same scene exhibit a cosine similarity that is nearly identical to that of two random, unrelated images. To address this, we propose the Omnivorous Vision Encoder, a novel framework that learns a modality-agnostic feature space. We train the encoder with a dual objective: first, to maximize the feature alignment between different modalities of the same scene; and second, a distillation objective that anchors the learned representations to the output of a fully frozen teacher such as DINOv2. The resulting student encoder becomes \"omnivorous\" by producing a consistent, powerful embedding for a given scene, regardless of the input modality (RGB, Depth, Segmentation, etc.). This approach enables robust cross-modal understanding while retaining the discriminative semantics of the original foundation model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12046", "title": "Dr. SHAP-AV: Decoding Relative Modality Contributions via Shapley Attribution in Audio-Visual Speech Recognition", "authors": [ "Umberto Cappellazzo", "Stavros Petridis", "Maja Pantic" ], "github": "", "project_page": "", "abstract": "Audio-Visual Speech Recognition (AVSR) leverages both acoustic and visual information for robust recognition under noise. However, how models balance these modalities remains unclear. We present Dr. SHAP-AV, a framework using Shapley values to analyze modality contributions in AVSR. Through experiments on six models across two benchmarks and varying SNR levels, we introduce three analyses: Global SHAP for overall modality balance, Generative SHAP for contribution dynamics during decoding, and Temporal Alignment SHAP for input-output correspondence. Our findings reveal that models shift toward visual reliance under noise yet maintain high audio contributions even under severe degradation. Modality balance evolves during generation, temporal alignment holds under noise, and SNR is the dominant factor driving modality weighting. These findings expose a persistent audio bias, motivating ad-hoc modality-weighting mechanisms and Shapley-based attribution as a standard AVSR diagnostic.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.11653", "title": "Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning", "authors": [ "Jiaheng Hu", "Jay Shim", "Chen Tang", "Yoonchang Sung", "Bo Liu", "Peter Stone", "Roberto Martin-Martin" ], "github": "https://github.com/UT-Austin-RobIn/continual-vla-rl", "project_page": "", "abstract": "Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that can adapt in openended, evolving environments. However, conventional wisdom from continual learning suggests that naive Sequential Fine-Tuning (Seq. FT) leads to catastrophic forgetting, necessitating complex CRL strategies. In this work, we take a step back and conduct a systematic study of CRL for large pretrained VLAs across three models and five challenging lifelong RL benchmarks. We find that, contrary to established belief, simple Seq. FT with low-rank adaptation (LoRA) is remarkably strong: it achieves high plasticity, exhibits little to no forgetting, and retains strong zero-shot generalization, frequently outperforming more sophisticated CRL methods. Through detailed analysis, we show that this robustness arises from a synergy between the large pretrained model, parameter-efficient adaptation, and on-policy RL. Together, these components reshape the stability-plasticity trade-off, making continual adaptation both stable and scalable. Our results position Sequential Fine-Tuning as a powerful method for continual RL with VLAs and provide new insights into lifelong learning in the large model era. Code is available at github.com/UT-Austin-RobIn/continual-vla-rl.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UT-Austin-RobIn/continual-vla-rl", "github_api_added_by": "auto" }, { "date": "2026-03-13", "arxiv_id": "2603.10604", "title": "HyPER-GAN: Hybrid Patch-Based Image-to-Image Translation for Real-Time Photorealism Enhancement", "authors": [ "Stefanos Pasios", "Nikos Nikolaidis" ], "github": "https://github.com/stefanos50/HyPER-GAN", "project_page": "", "abstract": "Generative models are widely employed to enhance the photorealism of synthetic data for training computer vision algorithms. However, they often introduce visual artifacts that degrade the accuracy of these algorithms and require high computational resources, limiting their applicability in real-time training or evaluation scenarios. In this paper, we propose Hybrid Patch Enhanced Realism Generative Adversarial Network (HyPER-GAN), a lightweight image-to-image translation method based on a U-Net-style generator designed for real-time inference. The model is trained using paired synthetic and photorealism-enhanced images, complemented by a hybrid training strategy that incorporates matched patches from real-world data to improve visual realism and semantic consistency. Experimental results demonstrate that HyPER-GAN outperforms state-of-the-art paired image-to-image translation methods in terms of inference latency, visual realism, and semantic robustness. Moreover, it is illustrated that the proposed hybrid training strategy indeed improves visual quality and semantic consistency compared to training the model solely with paired synthetic and photorealism-enhanced images. Code and pretrained models are publicly available for download at: https://github.com/stefanos50/HyPER-GAN", "github_manual": "", "github_regex": "https://github.com/stefanos50/HyPER-GAN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.11178", "title": "PACED: Distillation at the Frontier of Student Competence", "authors": [ "Yuanda Xu", "Hejian Sang", "Zhengze Zhou", "Ran He", "Zhipeng Wang" ], "github": "", "project_page": "", "abstract": "Standard LLM distillation wastes compute on two fronts: problems the student has already mastered (near-zero gradients) and problems far beyond its reach (incoherent gradients that erode existing capabilities). We show that this waste is not merely intuitive but structurally inevitable: the gradient signal-to-noise ratio in distillation provably vanishes at both pass-rate extremes. This theoretical observation leads to Paced, a framework that concentrates distillation on the zone of proximal development -- the frontier of a student model's competence -- via a principled pass-rate weight w(p) = p^\u03b1(1 - p)^\u03b2 derived from the boundary-vanishing structure of distillation gradients. Key results: (1) Theory: We prove that the Beta kernel w(p) = p^\u03b1(1-p)^\u03b2 is a leading-order weight family arising from the SNR structure of distillation, and that it is minimax-robust -- under bounded multiplicative misspecification, worst-case efficiency loss is only O(\u03b4^2). (2)Distillation: On distillation from a larger teacher to a smaller student model with forward KL, Paced achieves significant gain over the base model, while keeping benchmark forgetting at a low level. (3)Self-distillation: On instruction-tuned models with reverse KL, gains are exceeding baselines as well. (4)Two-stage synergy: A forward-KL-then-reverse-KL schedule yields the strongest results in our setting, reaching substantial improvements on standard reasoning benchmarks -- supporting a mode-coverage-then-consolidation interpretation of the distillation process. All configurations require only student rollouts to estimate pass rates, need no architectural changes, and are compatible with any KL direction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.05483", "title": "SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis", "authors": [ "Shahriar Noroozizadeh", "Xiaobin Shen", "Jeremy C. Weiss", "George H. Chen" ], "github": "https://github.com/Shahriarnz14/SurvHTE-Bench", "project_page": "", "abstract": "Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as precision medicine and individualized policy-making. Yet, the survival analysis setting poses unique challenges for HTE estimation due to censoring, unobserved counterfactuals, and complex identification assumptions. Despite recent advances, from Causal Survival Forests to survival meta-learners and outcome imputation approaches, evaluation practices remain fragmented and inconsistent. We introduce SurvHTE-Bench, the first comprehensive benchmark for HTE estimation with censored outcomes. The benchmark spans (i) a modular suite of synthetic datasets with known ground truth, systematically varying causal assumptions and survival dynamics, (ii) semi-synthetic datasets that pair real-world covariates with simulated treatments and outcomes, and (iii) real-world datasets from a twin study (with known ground truth) and from an HIV clinical trial. Across synthetic, semi-synthetic, and real-world settings, we provide the first rigorous comparison of survival HTE methods under diverse conditions and realistic assumption violations. SurvHTE-Bench establishes a foundation for fair, reproducible, and extensible evaluation of causal survival methods. The data and code of our benchmark are available at: https://github.com/Shahriarnz14/SurvHTE-Bench .", "github_manual": "", "github_regex": "https://github.com/Shahriarnz14/SurvHTE-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.09160", "title": "RubiCap: Rubric-Guided Reinforcement Learning for Dense Image Captioning", "authors": [ "Tzu-Heng Huang", "Sirajul Salekin", "Javier Movellan", "Frederic Sala", "Manjot Bilkhu" ], "github": "", "project_page": "", "abstract": "Dense image captioning is critical for cross-modal alignment in vision-language pretraining and text-to-image generation, but scaling expert-quality annotations is prohibitively expensive. While synthetic captioning via strong vision-language models (VLMs) is a practical alternative, supervised distillation often yields limited output diversity and weak generalization. Reinforcement learning (RL) could overcome these limitations, but its successes have so far been concentrated in verifiable domains that rely on deterministic checkers -- a luxury not available in open-ended captioning. We address this bottleneck with RubiCap, a novel RL framework that derives fine-grained, sample-specific reward signals from LLM-written rubrics. RubiCap first assembles a diverse committee of candidate captions, then employs an LLM rubric writer to extract consensus strengths and diagnose deficiencies in the current policy. These insights are converted into explicit evaluation criteria, enabling an LLM judge to decompose holistic quality assessment and replace coarse scalar rewards with structured, multi-faceted evaluations. Across extensive benchmarks, RubiCap achieves the highest win rates on CapArena, outperforming supervised distillation, prior RL methods, human-expert annotations, and GPT-4V-augmented outputs. On CaptionQA, it demonstrates superior word efficiency: our 7B model matches Qwen2.5-VL-32B-Instruct, and our 3B model surpasses its 7B counterpart. Remarkably, using the compact RubiCap-3B as a captioner produces stronger pretrained VLMs than those trained on captions from proprietary models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.11327", "title": "Meta-Reinforcement Learning with Self-Reflection for Agentic Search", "authors": [ "Teng Xiao", "Yige Yuan", "Hamish Ivison", "Huaisheng Zhu", "Faeze Brahman", "Nathan Lambert", "Pradeep Dasigi", "Noah A. Smith", "Hannaneh Hajishirzi" ], "github": "https://github.com/tengxiao1/MR-Search", "project_page": "", "abstract": "This paper introduces MR-Search, an in-context meta reinforcement learning (RL) formulation for agentic search with self-reflection. Instead of optimizing a policy within a single independent episode with sparse rewards, MR-Search trains a policy that conditions on past episodes and adapts its search strategy across episodes. MR-Search learns to learn a search strategy with self-reflection, allowing search agents to improve in-context exploration at test-time. Specifically, MR-Search performs cross-episode exploration by generating explicit self-reflections after each episode and leveraging them as additional context to guide subsequent attempts, thereby promoting more effective exploration during test-time. We further introduce a multi-turn RL algorithm that estimates a dense relative advantage at the turn level, enabling fine-grained credit assignment on each episode. Empirical results across various benchmarks demonstrate the advantages of MR-Search over baselines based RL, showing strong generalization and relative improvements of 9.2% to 19.3% across eight benchmarks. Our code and data are available at https://github.com/tengxiao1/MR-Search.", "github_manual": "", "github_regex": "https://github.com/tengxiao1/MR-Search", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.12228", "title": "Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights", "authors": [ "Yulu Gan", "Phillip Isola" ], "github": "https://github.com/sunrainyg/RandOpt", "project_page": "", "abstract": "Pretraining produces a learned parameter vector that is typically treated as a starting point for further iterative adaptation. In this work, we instead view the outcome of pretraining as a distribution over parameter vectors, whose support already contains task-specific experts. We show that in small models such expert solutions occupy a negligible fraction of the volume of this distribution, making their discovery reliant on structured optimization methods such as gradient descent. In contrast, in large, well-pretrained models the density of task-experts increases dramatically, so that diverse, task-improving specialists populate a substantial fraction of the neighborhood around the pretrained weights. Motivated by this perspective, we explore a simple, fully parallel post-training method that samples N parameter perturbations at random, selects the top K, and ensembles predictions via majority vote. Despite its simplicity, this approach is competitive with standard post-training methods such as PPO, GRPO, and ES for contemporary large-scale models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sunrainyg/RandOpt", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.09970", "title": "CREATE: Testing LLMs for Associative Creativity", "authors": [ "Manya Wadhwa", "Tiasa Singha Roy", "Harvey Lederman", "Junyi Jessy Li", "Greg Durrett" ], "github": "", "project_page": "", "abstract": "A key component of creativity is associative reasoning: the ability to draw novel yet meaningful connections between concepts. We introduce CREATE, a benchmark designed to evaluate models' capacity for creative associative reasoning. CREATE requires models to generate sets of paths connecting concepts in a model's parametric knowledge. Paths should have high specificity (distinctiveness and closeness of the concept connection) and high diversity (dissimilarity from other paths), and models are scored more highly if they produce a larger set of strong, diverse paths. This task shares demands of real creativity tasks like hypothesis generation, including an extremely large search space, but enables collection of a sizable benchmark with objective answer grading. Evaluation of frontier models shows that the strongest models achieve higher creative utility than others, with the high multiplicity of answers and complexity of the search making benchmark saturation difficult to achieve. Furthermore, our results illustrate that thinking models are not always more effective on our task, even with high token budgets. Recent approaches for creative prompting give some but limited additional improvement. CREATE provides a sandbox for developing new methods to improve models' capacity for associative creativity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-13", "arxiv_id": "2603.08258", "title": "WaDi: Weight Direction-aware Distillation for One-step Image Synthesis", "authors": [ "Lei Wang", "Yang Cheng", "Senmao Li", "Ge Wu", "Yaxing Wang", "Jian Yang" ], "github": "", "project_page": "", "abstract": "Despite the impressive performance of diffusion models such as Stable Diffusion (SD) in image generation, their slow inference limits practical deployment. Recent works accelerate inference by distilling multi-step diffusion into one-step generators. To better understand the distillation mechanism, we analyze U-Net/DiT weight changes between one-step students and their multi-step teacher counterparts. Our analysis reveals that changes in weight direction significantly exceed those in weight norm, highlighting it as the key factor during distillation. Motivated by this insight, we propose the Low-rank Rotation of weight Direction (LoRaD), a parameter-efficient adapter tailored to one-step diffusion distillation. LoRaD is designed to model these structured directional changes using learnable low-rank rotation matrices. We further integrate LoRaD into Variational Score Distillation (VSD), resulting in Weight Direction-aware Distillation (WaDi)-a novel one-step distillation framework. WaDi achieves state-of-the-art FID scores on COCO 2014 and COCO 2017 while using only approximately 10% of the trainable parameters of the U-Net/DiT. Furthermore, the distilled one-step model demonstrates strong versatility and scalability, generalizing well to various downstream tasks such as controllable generation, relation inversion, and high-resolution synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.12572", "title": "LMEB: Long-horizon Memory Embedding Benchmark", "authors": [ "Xinping Zhao", "Xinshuo Hu", "Jiaxin Xu", "Danyu Tang", "Xin Zhang", "Mengjia Zhou", "Yan Zhong", "Yao Zhou", "Zifei Shan", "Meishan Zhang", "Baotian Hu", "Min Zhang" ], "github": "https://github.com/KaLM-Embedding/LMEB", "project_page": "", "abstract": "Memory embeddings are crucial for memory-augmented systems, such as OpenClaw, but their evaluation is underexplored in current text embedding benchmarks, which narrowly focus on traditional passage retrieval and fail to assess models' ability to handle long-horizon memory retrieval tasks involving fragmented, context-dependent, and temporally distant information. To address this, we introduce the Long-horizon Memory Embedding Benchmark (LMEB), a comprehensive framework that evaluates embedding models' capabilities in handling complex, long-horizon memory retrieval tasks. LMEB spans 22 datasets and 193 zero-shot retrieval tasks across 4 memory types: episodic, dialogue, semantic, and procedural, with both AI-generated and human-annotated data. These memory types differ in terms of level of abstraction and temporal dependency, capturing distinct aspects of memory retrieval that reflect the diverse challenges of the real world. We evaluate 15 widely used embedding models, ranging from hundreds of millions to ten billion parameters. The results reveal that (1) LMEB provides a reasonable level of difficulty; (2) Larger models do not always perform better; (3) LMEB and MTEB exhibit orthogonality. This suggests that the field has yet to converge on a universal model capable of excelling across all memory retrieval tasks, and that performance in traditional passage retrieval may not generalize to long-horizon memory retrieval. In summary, by providing a standardized and reproducible evaluation framework, LMEB fills a crucial gap in memory embedding evaluation, driving further advancements in text embedding for handling long-term, context-dependent memory retrieval. LMEB is available at https://github.com/KaLM-Embedding/LMEB.", "github_manual": "", "github_regex": "https://github.com/KaLM-Embedding/LMEB", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.13023", "title": "daVinci-Env: Open SWE Environment Synthesis at Scale", "authors": [ "Dayuan Fu", "Shenyu Wu", "Yunze Wu", "Zerui Peng", "Yaxing Huang", "Jie Sun", "Ji Zeng", "Mohan Jiang", "Lin Zhang", "Yukun Li", "Jiarui Hu", "Liming Liu", "Jinlong Hou", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/OpenSWE", "project_page": "", "abstract": "Training capable software engineering (SWE) agents demands large-scale, executable, and verifiable environments that provide dynamic feedback loops for iterative code editing, test execution, and solution refinement. However, existing open-source datasets remain limited in scale and repository diversity, while industrial solutions are opaque with unreleased infrastructure, creating a prohibitive barrier for most academic research groups. We present OpenSWE, the largest fully transparent framework for SWE agent training in Python, comprising 45,320 executable Docker environments spanning over 12.8k repositories, with all Dockerfiles, evaluation scripts, and infrastructure fully open-sourced for reproducibility. OpenSWE is built through a multi-agent synthesis pipeline deployed across a 64-node distributed cluster, automating repository exploration, Dockerfile construction, evaluation script generation, and iterative test analysis. Beyond scale, we propose a quality-centric filtering pipeline that characterizes the inherent difficulty of each environment, filtering out instances that are either unsolvable or insufficiently challenging and retaining only those that maximize learning efficiency. With 891K spent on environment construction and an additional 576K on trajectory sampling and difficulty-aware curation, the entire project represents a total investment of approximately $1.47 million, yielding about 13,000 curated trajectories from roughly 9,000 quality guaranteed environments. Extensive experiments validate OpenSWE's effectiveness: OpenSWE-32B and OpenSWE-72B achieve 62.4% and 66.0% on SWE-bench Verified, establishing SOTA among Qwen2.5 series. Moreover, SWE-focused training yields substantial out-of-domain improvements, including up to 12 points on mathematical reasoning and 5 points on science benchmarks, without degrading factual recall.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GAIR-NLP/OpenSWE", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.12648", "title": "From Sparse to Dense: Multi-View GRPO for Flow Models via Augmented Condition Space", "authors": [ "Jiazi Bu", "Pengyang Ling", "Yujie Zhou", "Yibin Wang", "Yuhang Zang", "Tianyi Wei", "Xiaohang Zhan", "Jiaqi Wang", "Tong Wu", "Xingang Pan", "Dahua Lin" ], "github": "", "project_page": "", "abstract": "Group Relative Policy Optimization (GRPO) has emerged as a powerful framework for preference alignment in text-to-image (T2I) flow models. However, we observe that the standard paradigm where evaluating a group of generated samples against a single condition suffers from insufficient exploration of inter-sample relationships, constraining both alignment efficacy and performance ceilings. To address this sparse single-view evaluation scheme, we propose Multi-View GRPO (MV-GRPO), a novel approach that enhances relationship exploration by augmenting the condition space to create a dense multi-view reward mapping. Specifically, for a group of samples generated from one prompt, MV-GRPO leverages a flexible Condition Enhancer to generate semantically adjacent yet diverse captions. These captions enable multi-view advantage re-estimation, capturing diverse semantic attributes and providing richer optimization signals. By deriving the probability distribution of the original samples conditioned on these new captions, we can incorporate them into the training process without costly sample regeneration. Extensive experiments demonstrate that MV-GRPO achieves superior alignment performance over state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.13224", "title": "Visual-ERM: Reward Modeling for Visual Equivalence", "authors": [ "Ziyu Liu", "Shengyuan Ding", "Xinyu Fang", "Xuanlang Dai", "Penghui Yang", "Jianze Liang", "Jiaqi Wang", "Kai Chen", "Dahua Lin", "Yuhang Zang" ], "github": "https://github.com/InternLM/Visual-ERM", "project_page": "", "abstract": "Vision-to-code tasks require models to reconstruct structured visual inputs, such as charts, tables, and SVGs, into executable or structured representations with high visual fidelity. While recent Large Vision Language Models (LVLMs) achieve strong results via supervised fine-tuning, reinforcement learning remains challenging due to misaligned reward signals. Existing rewards either rely on textual rules or coarse visual embedding similarity, both of which fail to capture fine-grained visual discrepancies and are vulnerable to reward hacking. We propose Visual Equivalence Reward Model (Visual-ERM), a multimodal generative reward model that provides fine-grained, interpretable, and task-agnostic feedback to evaluate vision-to-code quality directly in the rendered visual space. Integrated into RL, Visual-ERM improves Qwen3-VL-8B-Instruct by +8.4 on chart-to-code and yields consistent gains on table and SVG parsing (+2.7, +4.1 on average), and further strengthens test-time scaling via reflection and revision. We also introduce VisualCritic-RewardBench (VC-RewardBench), a benchmark for judging fine-grained image-to-image discrepancies on structured visual data, where Visual-ERM at 8B decisively outperforms Qwen3-VL-235B-Instruct and approaches leading closed-source models. Our results suggest that fine-grained visual reward supervision is both necessary and sufficient for vision-to-code RL, regardless of task specificity.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/InternLM/Visual-ERM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.12310", "title": "VQQA: An Agentic Approach for Video Evaluation and Quality Improvement", "authors": [ "Yiwen Song", "Tomas Pfister", "Yale Song" ], "github": "", "project_page": "", "abstract": "Despite rapid advancements in video generation models, aligning their outputs with complex user intent remains challenging. Existing test-time optimization methods are typically either computationally expensive or require white-box access to model internals. To address this, we present VQQA (Video Quality Question Answering), a unified, multi-agent framework generalizable across diverse input modalities and video generation tasks. By dynamically generating visual questions and using the resulting Vision-Language Model (VLM) critiques as semantic gradients, VQQA replaces traditional, passive evaluation metrics with human-interpretable, actionable feedback. This enables a highly efficient, closed-loop prompt optimization process via a black-box natural language interface. Extensive experiments demonstrate that VQQA effectively isolates and resolves visual artifacts, substantially improving generation quality in just a few refinement steps. Applicable to both text-to-video (T2V) and image-to-video (I2V) tasks, our method achieves absolute improvements of +11.57% on T2V-CompBench and +8.43% on VBench2 over vanilla generation, significantly outperforming state-of-the-art stochastic search and prompt optimization techniques.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.10899", "title": "LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation", "authors": [ "Jinwoo Ahn", "Ingyu Seong", "Akhil Kedia", "Junhan Kim", "Hyemi Jang", "Kangwook Lee", "Yongkweon Jeon" ], "github": "https://github.com/SamsungLabs/LookaheadKV", "project_page": "", "abstract": "Transformer-based large language models (LLMs) rely on key-value (KV) caching to avoid redundant computation during autoregressive inference. While this mechanism greatly improves efficiency, the cache size grows linearly with the input sequence length, quickly becoming a bottleneck for long-context tasks. Existing solutions mitigate this problem by evicting prompt KV that are deemed unimportant, guided by estimated importance scores. Notably, a recent line of work proposes to improve eviction quality by \"glimpsing into the future\", in which a draft generator produces a surrogate future response approximating the target model's true response, and this surrogate is subsequently used to estimate the importance of cached KV more accurately. However, these approaches rely on computationally expensive draft generation, which introduces substantial prefilling overhead and limits their practicality in real-world deployment. To address this challenge, we propose LookaheadKV, a lightweight eviction framework that leverages the strength of surrogate future response without requiring explicit draft generation. LookaheadKV augments transformer layers with parameter-efficient modules trained to predict true importance scores with high accuracy. Our design ensures negligible runtime overhead comparable to existing inexpensive heuristics, while achieving accuracy superior to more costly approximation methods. Extensive experiments on long-context understanding benchmarks, across a wide range of models, demonstrate that our method not only outperforms recent competitive baselines in various long-context understanding tasks, but also reduces the eviction cost by up to 14.5x, leading to significantly faster time-to-first-token. Our code is available at https://github.com/SamsungLabs/LookaheadKV.", "github_manual": "", "github_regex": "https://github.com/SamsungLabs/LookaheadKV", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.12634", "title": "Spend Less, Reason Better: Budget-Aware Value Tree Search for LLM Agents", "authors": [ "Yushu Li", "Wenlong Deng", "Jiajin Li", "Xiaoxiao Li" ], "github": "", "project_page": "", "abstract": "Test-time scaling has become a dominant paradigm for improving LLM agent reliability, yet current approaches treat compute as an abundant resource, allowing agents to exhaust token and tool budgets on redundant steps or dead-end trajectories. Existing budget-aware methods either require expensive fine-tuning or rely on coarse, trajectory-level heuristics that cannot intervene mid-execution. We propose the Budget-Aware Value Tree (BAVT), a training-free inference-time framework that models multi-hop reasoning as a dynamic search tree guided by step-level value estimation within a single LLM backbone. Another key innovation is a budget-conditioned node selection mechanism that uses the remaining resource ratio as a natural scaling exponent over node values, providing a principled, parameter-free transition from broad exploration to greedy exploitation as the budget depletes. To combat the well-known overconfidence of LLM self-evaluation, BAVT employs a residual value predictor that scores relative progress rather than absolute state quality, enabling reliable pruning of uninformative or redundant tool calls. We further provide a theoretical convergence guarantee, proving that BAVT reaches a terminal answer with probability at least 1-\u03b5 under an explicit finite budget bound. Extensive evaluations on four multi-hop QA benchmarks across two model families demonstrate that BAVT consistently outperforms parallel sampling baselines. Most notably, BAVT under strict low-budget constraints surpasses baseline performance at 4times the resource allocation, establishing that intelligent budget management fundamentally outperforms brute-force compute scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.12793", "title": "Cheers: Decoupling Patch Details from Semantic Representations Enables Unified Multimodal Comprehension and Generation", "authors": [ "Yichen Zhang", "Da Peng", "Zonghao Guo", "Zijian Zhang", "Xuesong Yang", "Tong Sun", "Shichu Sun", "Yidan Zhang", "Yanghao Li", "Haiyan Zhao", "Wang Xu", "Qi Shi", "Yangang Sun", "Chi Chen", "Shuo Wang", "Yukun Yan", "Xu Han", "Qiang Ma", "Wei Ke", "Liang Wang", "Zhiyuan Liu", "Maosong Sun" ], "github": "", "project_page": "", "abstract": "A recent cutting-edge topic in multimodal modeling is to unify visual comprehension and generation within a single model. However, the two tasks demand mismatched decoding regimes and visual representations, making it non-trivial to jointly optimize within a shared feature space. In this work, we present Cheers, a unified multimodal model that decouples patch-level details from semantic representations, thereby stabilizing semantics for multimodal understanding and improving fidelity for image generation via gated detail residuals. Cheers includes three key components: (i) a unified vision tokenizer that encodes and compresses image latent states into semantic tokens for efficient LLM conditioning, (ii) an LLM-based Transformer that unifies autoregressive decoding for text generation and diffusion decoding for image generation, and (iii) a cascaded flow matching head that decodes visual semantics first and then injects semantically gated detail residuals from the vision tokenizer to refine high-frequency content. Experiments on popular benchmarks demonstrate that Cheers matches or surpasses advanced UMMs in both visual understanding and generation. Cheers also achieves 4x token compression, enabling more efficient high-resolution image encoding and generation. Notably, Cheers outperforms the Tar-1.5B on the popular benchmarks GenEval and MMBench, while requiring only 20% of the training cost, indicating effective and efficient (i.e., 4x token compression) unified multimodal modeling. We will release all code and data for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.11647", "title": "OmniForcing: Unleashing Real-time Joint Audio-Visual Generation", "authors": [ "Yaofeng Su", "Yuming Li", "Zeyue Xue", "Jie Huang", "Siming Fu", "Haoran Li", "Ying Li", "Zezhong Qian", "Haoyang Huang", "Nan Duan" ], "github": "", "project_page": "", "abstract": "Recent joint audio-visual diffusion models achieve remarkable generation quality but suffer from high latency due to their bidirectional attention dependencies, hindering real-time applications. We propose OmniForcing, the first framework to distill an offline, dual-stream bidirectional diffusion model into a high-fidelity streaming autoregressive generator. However, naively applying causal distillation to such dual-stream architectures triggers severe training instability, due to the extreme temporal asymmetry between modalities and the resulting token sparsity. We address the inherent information density gap by introducing an Asymmetric Block-Causal Alignment with a zero-truncation Global Prefix that prevents multi-modal synchronization drift. The gradient explosion caused by extreme audio token sparsity during the causal shift is further resolved through an Audio Sink Token mechanism equipped with an Identity RoPE constraint. Finally, a Joint Self-Forcing Distillation paradigm enables the model to dynamically self-correct cumulative cross-modal errors from exposure bias during long rollouts. Empowered by a modality-independent rolling KV-cache inference scheme, OmniForcing achieves state-of-the-art streaming generation at sim25 FPS on a single GPU, maintaining multi-modal synchronization and visual quality on par with the bidirectional teacher.Project Page: https://omniforcing.com{https://omniforcing.com}", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.07815", "title": "HybridStitch: Pixel and Timestep Level Model Stitching for Diffusion Acceleration", "authors": [ "Desen Sun", "Jason Hon", "Jintao Zhang", "Sihang Liu" ], "github": "", "project_page": "", "abstract": "Diffusion models have demonstrated a remarkable ability in Text-to-Image (T2I) generation applications. Despite the advanced generation output, they suffer from heavy computation overhead, especially for large models that contain tens of billions of parameters. Prior work has illustrated that replacing part of the denoising steps with a smaller model still maintains the generation quality. However, these methods only focus on saving computation for some timesteps, ignoring the difference in compute demand within one timestep. In this work, we propose HybridStitch, a new T2I generation paradigm that treats generation like editing. Specifically, we introduce a hybrid stage that jointly incorporates both the large model and the small model. HybridStitch separates the entire image into two regions: one that is relatively easy to render, enabling an early transition to the smaller model, and another that is more complex and therefore requires refinement by the large model. HybridStitch employs the small model to construct a coarse sketch while exploiting the large model to edit and refine the complex regions. According to our evaluation, HybridStitch achieves 1.83times speedup on Stable Diffusion 3, which is faster than all existing mixture of model methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.12266", "title": "MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning", "authors": [ "Haozhan Shen", "Shilin Yan", "Hongwei Xue", "Shuaiqi Lu", "Xiaojun Tang", "Guannan Zhang", "Tiancheng Zhao", "Jianwei Yin" ], "github": "https://github.com/Accio-Lab/MM-CondChain", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step depends on verified visual compositional conditions (e.g., \"if a permission dialog appears and the color of the interface is green, click Allow\") and the process may branch or terminate early. Yet this capability remains under-evaluated: existing benchmarks focus on shallow-compositions or independent-constraints rather than deeply chained compositional conditionals. In this paper, we introduce MM-CondChain, a benchmark for visually grounded deep compositional reasoning. Each benchmark instance is organized as a multi-layer reasoning chain, where every layer contains a non-trivial compositional condition grounded in visual evidence and built from multiple objects, attributes, or relations. To answer correctly, an MLLM must perceive the image in detail, reason over multiple visual elements at each step, and follow the resulting execution path to the final outcome. To scalably construct such workflow-style data, we propose an agentic synthesis pipeline: a Planner orchestrates layer-by-layer generation of compositional conditions, while a Verifiable Programmatic Intermediate Representation (VPIR) ensures each layer's condition is mechanically verifiable. A Composer then assembles these verified layers into complete instructions. Using this pipeline, we construct benchmarks across three visual domains: natural images, data charts, and GUI trajectories. Experiments on a range of MLLMs show that even the strongest model attains only 53.33 Path F1, with sharp drops on hard negatives and as depth or predicate complexity grows, confirming that deep compositional reasoning remains a fundamental challenge.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Accio-Lab/MM-CondChain", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.02133", "title": "SimRecon: SimReady Compositional Scene Reconstruction from Real Videos", "authors": [ "Chong Xia", "Kai Zhu", "Zizhuo Wang", "Fangfu Liu", "Zhizheng Zhang", "Yueqi Duan" ], "github": "", "project_page": "", "abstract": "Compositional scene reconstruction seeks to create object-centric representations rather than holistic scenes from real-world videos, which is natively applicable for simulation and interaction. Conventional compositional reconstruction approaches primarily emphasize on visual appearance and show limited generalization ability to real-world scenarios. In this paper, we propose SimRecon, a framework that realizes a \"Perception-Generation-Simulation\" pipeline towards cluttered scene reconstruction, which first conducts scene-level semantic reconstruction from video input, then performs single-object generation, and finally assembles these assets in the simulator. However, naively combining these three stages leads to visual infidelity of generated assets and physical implausibility of the final scene, a problem particularly severe for complex scenes. Thus, we further propose two bridging modules between the three stages to address this problem. To be specific, for the transition from Perception to Generation, critical for visual fidelity, we introduce Active Viewpoint Optimization, which actively searches in 3D space to acquire optimal projected images as conditions for single-object completion. Moreover, for the transition from Generation to Simulation, essential for physical plausibility, we propose a Scene Graph Synthesizer, which guides the construction from scratch in 3D simulators, mirroring the native, constructive principle of the real world. Extensive experiments on the ScanNet dataset validate our method's superior performance over previous state-of-the-art approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.13131", "title": "Steve-Evolving: Open-World Embodied Self-Evolution via Fine-Grained Diagnosis and Dual-Track Knowledge Distillation", "authors": [ "Zhengwei Xie", "Zhisheng Chen", "Ziyan Weng", "Tingyu Wu", "Chenglong Li", "Vireo Zhang", "Kun Wang" ], "github": "", "project_page": "", "abstract": "Open-world embodied agents must solve long-horizon tasks where the main bottleneck is not single-step planning quality but how interaction experience is organized and evolved. To this end, we present Steve-Evolving, a non-parametric self-evolving framework that tightly couples fine-grained execution diagnosis with dual-track knowledge distillation in a closed loop. The method follows three phases: Experience Anchoring, Experience Distillation, and Knowledge-Driven Closed-Loop Control. In detail, Experience Anchoring solidifies each subgoal attempt into a structured experience tuple with a fixed schema (pre-state, action, diagnosis-result, and post-state) and organizes it in a three-tier experience space with multi-dimensional indices (e.g., condition signatures, spatial hashing, and semantic tags) plus rolling summarization for efficient and auditable recall. To ensure sufficient information density for attribution, the execution layer provides compositional diagnosis signals beyond binary outcomes, including state-difference summaries, enumerated failure causes, continuous indicators, and stagnation/loop detection. Moreover, successful trajectories of Experience Distillation are generalized into reusable skills with explicit preconditions and verification criteria, while failures are distilled into executable guardrails that capture root causes and forbid risky operations at both subgoal and task granularities. Besides, Knowledge-Driven Closed-Loop Control retrieved skills and guardrails are injected into an LLM planner, and diagnosis-triggered local replanning updates the active constraints online, forming a continual evolution process without any model parameter updates. Experiments on the long-horizon suite of Minecraft MCU demonstrate consistent improvements over static-retrieval baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.13032", "title": "Multimodal OCR: Parse Anything from Documents", "authors": [ "Handong Zheng", "Yumeng Li", "Kaile Zhang", "Liang Xin", "Guangwei Zhao", "Hao Liu", "Jiayu Chen", "Jie Lou", "Jiyu Qiu", "Qi Fu", "Rui Yang", "Shuo Jiang", "Weijian Luo", "Weijie Su", "Weijun Zhang", "Xingyu Zhu", "Yabin Li", "Yiwei ma", "Yu Chen", "Zhaohui Yu", "Guang Yang", "Colin Zhang", "Lei Zhang", "Yuliang Liu", "Xiang Bai" ], "github": "https://github.com/rednote-hilab/dots.mocr", "project_page": "", "abstract": "We present Multimodal OCR (MOCR), a document parsing paradigm that jointly parses text and graphics into unified textual representations. Unlike conventional OCR systems that focus on text recognition and leave graphical regions as cropped pixels, our method, termed dots.mocr, treats visual elements such as charts, diagrams, tables, and icons as first-class parsing targets, enabling systems to parse documents while preserving semantic relationships across elements. It offers several advantages: (1) it reconstructs both text and graphics as structured outputs, enabling more faithful document reconstruction; (2) it supports end-to-end training over heterogeneous document elements, allowing models to exploit semantic relations between textual and visual components; and (3) it converts previously discarded graphics into reusable code-level supervision, unlocking multimodal supervision embedded in existing documents. To make this paradigm practical at scale, we build a comprehensive data engine from PDFs, rendered webpages, and native SVG assets, and train a compact 3B-parameter model through staged pretraining and supervised fine-tuning. We evaluate dots.mocr from two perspectives: document parsing and structured graphics parsing. On document parsing benchmarks, it ranks second only to Gemini 3 Pro on our OCR Arena Elo leaderboard, surpasses existing open-source document parsing systems, and sets a new state of the art of 83.9 on olmOCR Bench. On structured graphics parsing, dots.mocr achieves higher reconstruction quality than Gemini 3 Pro across image-to-SVG benchmarks, demonstrating strong performance on charts, UI layouts, scientific figures, and chemical diagrams. These results show a scalable path toward building large-scale image-to-code corpora for multimodal pretraining. Code and models are publicly available at https://github.com/rednote-hilab/dots.mocr.", "github_manual": "", "github_regex": "https://github.com/rednote-hilab/dots.mocr", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.13089", "title": "V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration", "authors": [ "Shenghe Zheng", "Junpeng Jiang", "Wenbo Li" ], "github": "", "project_page": "", "abstract": "Large-scale video generative models are trained on vast and diverse visual data, enabling them to internalize rich structural, semantic, and dynamic priors of the visual world. While these models have demonstrated impressive generative capability, their potential as general-purpose visual learners remains largely untapped. In this work, we introduce V-Bridge, a framework that bridges this latent capacity to versatile few-shot image restoration tasks. We reinterpret image restoration not as a static regression problem, but as a progressive generative process, and leverage video models to simulate the gradual refinement from degraded inputs to high-fidelity outputs. Surprisingly, with only 1,000 multi-task training samples (less than 2% of existing restoration methods), pretrained video models can be induced to perform competitive image restoration, achieving multiple tasks with a single model, rivaling specialized architectures designed explicitly for this purpose. Our findings reveal that video generative models implicitly learn powerful and transferable restoration priors that can be activated with only extremely limited data, challenging the traditional boundary between generative modeling and low-level vision, and opening a new design paradigm for foundation models in visual tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.08436", "title": "Can Vision-Language Models Solve the Shell Game?", "authors": [ "Tiedong Liu", "Wee Sun Lee" ], "github": "", "project_page": "", "abstract": "Visual entity tracking is an innate cognitive ability in humans, yet it remains a critical bottleneck for Vision-Language Models (VLMs). This deficit is often obscured in existing video benchmarks by visual shortcuts. We introduce VET-Bench, a synthetic diagnostic testbed featuring visually identical objects that necessitate tracking exclusively through spatiotemporal continuity. Our experiments reveal that current state-of-the-art VLMs perform at or near chance level on VET-Bench, exposing a fundamental limitation: an over-reliance on static frame-level features and a failure to maintain entity representations over time. We provide a theoretical analysis drawing connections to the state-tracking problem, proving that fixed-depth transformer-based VLMs are fundamentally limited in tracking indistinguishable objects without intermediate supervision due to expressivity constraints. To address this, we propose Spatiotemporal Grounded Chain-of-Thought (SGCoT): generating object trajectories as explicit intermediate states. Leveraging Molmo2's object tracking ability, we elicit SGCoT reasoning by fine-tuning on synthesized text-only data for alignment. Our method achieves state-of-the-art accuracy exceeding 90% on VET-Bench, demonstrating that VLMs can reliably solve the video shell-game task end-to-end without external tools. Our code and data are available at https://vetbench.github.io .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.11382", "title": "Detecting Intrinsic and Instrumental Self-Preservation in Autonomous Agents: The Unified Continuation-Interest Protocol", "authors": [ "Christopher Altman" ], "github": "https://github.com/christopher-altman/persistence-signal-detector", "project_page": "", "abstract": "Autonomous agents, especially delegated systems with memory, persistent context, and multi-step planning, pose a measurement problem not present in stateless models: an agent that preserves continued operation as a terminal objective and one that does so merely instrumentally can produce observationally similar trajectories. External behavioral monitoring cannot reliably distinguish between them. We introduce the Unified Continuation-Interest Protocol (UCIP), a multi-criterion detection framework that moves this distinction from behavior to the latent structure of agent trajectories. UCIP encodes trajectories with a Quantum Boltzmann Machine (QBM), a classical algorithm based on the density-matrix formalism of quantum statistical mechanics, and measures the von Neumann entropy of the reduced density matrix induced by a bipartition of hidden units. We test whether agents with terminal continuation objectives (Type A) produce latent states with higher entanglement entropy than agents whose continuation is merely instrumental (Type B). Higher entanglement reflects stronger cross-partition statistical coupling. On gridworld agents with known ground-truth objectives, UCIP achieves 100% detection accuracy and 1.0 AUC-ROC on held-out non-adversarial evaluation under the frozen Phase I gate. The entanglement gap between Type A and Type B agents is Delta = 0.381 (p < 0.001, permutation test). Pearson r = 0.934 across an 11-point interpolation sweep indicates that, within this synthetic family, UCIP tracks graded changes in continuation weighting rather than merely a binary label. Among the tested models, only the QBM achieves positive Delta. All computations are classical; \"quantum\" refers only to the mathematical formalism. UCIP does not detect consciousness or subjective experience; it detects statistical structure in latent representations that correlates with known objectives.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/christopher-altman/persistence-signal-detector", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.11975", "title": "HomeSafe-Bench: Evaluating Vision-Language Models on Unsafe Action Detection for Embodied Agents in Household Scenarios", "authors": [ "Jiayue Pu", "Zhongxiang Sun", "Zilu Zhang", "Xiao Zhang", "Jun Xu" ], "github": "", "project_page": "", "abstract": "The rapid evolution of embodied agents has accelerated the deployment of household robots in real-world environments. However, unlike structured industrial settings, household spaces introduce unpredictable safety risks, where system limitations such as perception latency and lack of common sense knowledge can lead to dangerous errors. Current safety evaluations, often restricted to static images, text, or general hazards, fail to adequately benchmark dynamic unsafe action detection in these specific contexts. To bridge this gap, we introduce HomeSafe-Bench, a challenging benchmark designed to evaluate Vision-Language Models (VLMs) on unsafe action detection in household scenarios. HomeSafe-Bench is contrusted via a hybrid pipeline combining physical simulation with advanced video generation and features 438 diverse cases across six functional areas with fine-grained multidimensional annotations. Beyond benchmarking, we propose Hierarchical Dual-Brain Guard for Household Safety (HD-Guard), a hierarchical streaming architecture for real-time safety monitoring. HD-Guard coordinates a lightweight FastBrain for continuous high-frequency screening with an asynchronous large-scale SlowBrain for deep multimodal reasoning, effectively balancing inference efficiency with detection accuracy. Evaluations demonstrate that HD-Guard achieves a superior trade-off between latency and performance, while our analysis identifies critical bottlenecks in current VLM-based safety detection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.12824", "title": "NanoVDR: Distilling a 2B Vision-Language Retriever into a 70M Text-Only Encoder for Visual Document Retrieval", "authors": [ "Zhuchenyang Liu", "Yao Zhang", "Yu Xiao" ], "github": "", "project_page": "", "abstract": "Vision-Language Model (VLM) based retrievers have advanced visual document retrieval (VDR) to impressive quality. They require the same multi-billion parameter encoder for both document indexing and query encoding, incurring high latency and GPU dependence even for plain-text queries. We observe that this design is unnecessarily symmetric: documents are visually complex and demand strong visual understanding, whereas queries are just short text strings. NanoVDR exploits this query--document asymmetry by decoupling the two encoding paths: a frozen 2B VLM teacher indexes documents offline, while a distilled text-only student as small as 69M parameters encodes queries at inference. The key design choice is the distillation objective. Through systematic comparison of six objectives across three backbones and 22 ViDoRe benchmark datasets, we find that pointwise cosine alignment on query text consistently outperforms ranking-based and contrastive alternatives, while requiring only pre-cached teacher query embeddings and no document processing during training. Furthermore, we identify cross-lingual transfer as the primary performance bottleneck, and resolve it cheaply by augmenting training data with machine-translated queries. The resulting NanoVDR-S-Multi (DistilBERT, 69M) retains 95.1\\% of teacher quality and outperforms DSE-Qwen2 (2B) on v2 and v3 with 32times fewer parameters and 50times lower CPU query latency, at a total training cost under 13 GPU-hours.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.09930", "title": "Fine-grained Motion Retrieval via Joint-Angle Motion Images and Token-Patch Late Interaction", "authors": [ "Yao Zhang", "Zhuchenyang Liu", "Yanlan He", "Thomas Ploetz", "Yu Xiao" ], "github": "", "project_page": "", "abstract": "Text-motion retrieval aims to learn a semantically aligned latent space between natural language descriptions and 3D human motion skeleton sequences, enabling bidirectional search across the two modalities. Most existing methods use a dual-encoder framework that compresses motion and text into global embeddings, discarding fine-grained local correspondences, and thus reducing accuracy. Additionally, these global-embedding methods offer limited interpretability of the retrieval results. To overcome these limitations, we propose an interpretable, joint-angle-based motion representation that maps joint-level local features into a structured pseudo-image, compatible with pre-trained Vision Transformers. For text-to-motion retrieval, we employ MaxSim, a token-wise late interaction mechanism, and enhance it with Masked Language Modeling regularization to foster robust, interpretable text-motion alignment. Extensive experiments on HumanML3D and KIT-ML show that our method outperforms state-of-the-art text-motion retrieval approaches while offering interpretable fine-grained correspondences between text and motion. The code is available in the supplementary material.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.12262", "title": "Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously", "authors": [ "Yiran Guan", "Liang Yin", "Dingkang Liang", "Jianzhong Ju", "Zhenbo Luo", "Jian Luan", "Yuliang Liu", "Xiang Bai" ], "github": "https://github.com/1ranGuan/VST", "project_page": "", "abstract": "Online Video Large Language Models (VideoLLMs) play a critical role in supporting responsive, real-time interaction. Existing methods focus on streaming perception, lacking a synchronized logical reasoning stream. However, directly applying test-time scaling methods incurs unacceptable response latency. To address this trade-off, we propose Video Streaming Thinking (VST), a novel paradigm for streaming video understanding. It supports a thinking while watching mechanism, which activates reasoning over incoming video clips during streaming. This design improves timely comprehension and coherent cognition while preserving real-time responsiveness by amortizing LLM reasoning latency over video playback. Furthermore, we introduce a comprehensive post-training pipeline that integrates VST-SFT, which structurally adapts the offline VideoLLM to causal streaming reasoning, and VST-RL, which provides end-to-end improvement through self-exploration in a multi-turn video interaction environment. Additionally, we devise an automated training-data synthesis pipeline that uses video knowledge graphs to generate high-quality streaming QA pairs, with an entity-relation grounded streaming Chain-of-Thought to enforce multi-evidence reasoning and sustained attention to the video stream. Extensive evaluations show that VST-7B performs strongly on online benchmarks, e.g. 79.5% on StreamingBench and 59.3% on OVO-Bench. Meanwhile, VST remains competitive on offline long-form or reasoning benchmarks. Compared with Video-R1, VST responds 15.7 times faster and achieves +5.4% improvement on VideoHolmes, demonstrating higher efficiency and strong generalization across diverse video understanding tasks. Code, data, and models will be released at https://github.com/1ranGuan/VST.", "github_manual": "", "github_regex": "https://github.com/1ranGuan/VST", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.11896", "title": "Think While Watching: Online Streaming Segment-Level Memory for Multi-Turn Video Reasoning in Multimodal Large Language Models", "authors": [ "Lu Wang", "Zhuoran Jin", "Yupu Hao", "Yubo Chen", "Kang Liu", "Yulong Ao", "Jun Zhao" ], "github": "https://github.com/wl666hhh/Think_While_Watching", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have shown strong performance on offline video understanding, but most are limited to offline inference or have weak online reasoning, making multi-turn interaction over continuously arriving video streams difficult. Existing streaming methods typically use an interleaved perception-generation paradigm, which prevents concurrent perception and generation and leads to early memory decay as streams grow, hurting long-range dependency modeling. We propose Think While Watching, a memory-anchored streaming video reasoning framework that preserves continuous segment-level memory during multi-turn interaction. We build a three-stage, multi-round chain-of-thought dataset and adopt a stage-matched training strategy, while enforcing strict causality through a segment-level streaming causal mask and streaming positional encoding. During inference, we introduce an efficient pipeline that overlaps watching and thinking and adaptively selects the best attention backend. Under both single-round and multi-round streaming input protocols, our method achieves strong results. Built on Qwen3-VL, it improves single-round accuracy by 2.6% on StreamingBench and by 3.79% on OVO-Bench. In the multi-round setting, it maintains performance while reducing output tokens by 56%. Code is available at: https://github.com/wl666hhh/Think_While_Watching/", "github_manual": "", "github_regex": "https://github.com/wl666hhh/Think_While_Watching", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.11863", "title": "CreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges", "authors": [ "Zi-Han Wang", "Lam Nguyen", "Zhengyang Zhao", "Mengyue Yang", "Chengwei Qin", "Yujiu Yang", "Linyi Yang" ], "github": "", "project_page": "", "abstract": "The saturation of high-quality pre-training data has shifted research focus toward evolutionary systems capable of continuously generating novel artifacts, leading to the success of AlphaEvolve. However, the progress of such systems is hindered by the lack of rigorous, quantitative evaluation. To tackle this challenge, we introduce CreativeBench, a benchmark for evaluating machine creativity in code generation, grounded in a classical cognitive framework. Comprising two subsets -- CreativeBench-Combo and CreativeBench-Explore -- the benchmark targets combinatorial and exploratory creativity through an automated pipeline utilizing reverse engineering and self-play. By leveraging executable code, CreativeBench objectively distinguishes creativity from hallucination via a unified metric defined as the product of quality and novelty. Our analysis of state-of-the-art models reveals distinct behaviors: (1) scaling significantly improves combinatorial creativity but yields diminishing returns for exploration; (2) larger models exhibit ``convergence-by-scaling,'' becoming more correct but less divergent; and (3) reasoning capabilities primarily benefit constrained exploration rather than combination. Finally, we propose EvoRePE, a plug-and-play inference-time steering strategy that internalizes evolutionary search patterns to consistently enhance machine creativity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.12916", "title": "Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection", "authors": [ "Kadir-Kaan \u00d6zer", "Ren\u00e9 Ebeling", "Markus Enzweiler" ], "github": "https://github.com/iis-esslingen/AxonAD", "project_page": "", "abstract": "Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonomous driving, for instance, a steering command might be internally consistent but decouple from the resulting lateral acceleration. Residual-based detectors can miss such anomalies when flexible sequence models still reconstruct signals plausibly despite altered coordination. We introduce AxonAD, an unsupervised detector that treats multi-head attention query evolution as a short horizon predictable process. A gradient-updated reconstruction pathway is coupled with a history-only predictor that forecasts future query vectors from past context. This is trained via a masked predictor-target objective against an exponential moving average (EMA) target encoder. At inference, reconstruction error is combined with a tail-aggregated query mismatch score, which measures cosine deviation between predicted and target queries on recent timesteps. This dual approach provides sensitivity to structural dependency shifts while retaining amplitude-level detection. On proprietary in-vehicle telemetry with interval annotations and on the TSB-AD multi-variate suite (17 datasets, 180 series) with threshold-free and range-aware metrics, AxonAD improves ranking quality and temporal localization over strong baselines. Ablations confirm that query prediction and combined scoring are the primary drivers of the observed gains. Code is available at the URL https://github.com/iis-esslingen/AxonAD.", "github_manual": "", "github_regex": "https://github.com/iis-esslingen/AxonAD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.10926", "title": "ECoLAD: Deployment-Oriented Evaluation for Automotive Time-Series Anomaly Detection", "authors": [ "Kadir-Kaan \u00d6zer", "Ren\u00e9 Ebeling", "Markus Enzweiler" ], "github": "", "project_page": "", "abstract": "Time-series anomaly detectors are commonly compared on workstation-class hardware under unconstrained execution. In-vehicle monitoring, however, requires predictable latency and stable behavior under limited CPU parallelism. Accuracy-only leaderboards can therefore misrepresent which methods remain feasible under deployment-relevant constraints. We present ECoLAD (Efficiency Compute Ladder for Anomaly Detection), a deployment-oriented evaluation protocol instantiated as an empirical study on proprietary automotive telemetry (anomaly rate {approx}0.022) and complementary public benchmarks. ECoLAD applies a monotone compute-reduction ladder across heterogeneous detector families using mechanically determined, integer-only scaling rules and explicit CPU thread caps, while logging every applied configuration change. Throughput-constrained behavior is characterized by sweeping target scoring rates and reporting (i) coverage (the fraction of entities meeting the target) and (ii) the best AUC-PR achievable among measured ladder configurations satisfying the target. On constrained automotive telemetry, lightweight classical detectors sustain both coverage and detection lift above the random baseline across the full throughput sweep. Several deep methods lose feasibility before they lose accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.08127", "title": "EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery", "authors": [ "Yougang Lyu", "Xi Zhang", "Xinhao Yi", "Yuyue Zhao", "Shuyu Guo", "Wenxiang Hu", "Jan Piotrowski", "Jakub Kaliski", "Jacopo Urbani", "Zaiqiao Meng", "Lun Zhou", "Xiaohui Yan" ], "github": "https://github.com/EvoScientist/EvoScientist", "project_page": "", "abstract": "The increasing adoption of Large Language Models (LLMs) has enabled AI scientists to perform complex end-to-end scientific discovery tasks requiring coordination of specialized roles, including idea generation and experimental execution. However, most state-of-the-art AI scientist systems rely on static, hand-designed pipelines and fail to adapt based on accumulated interaction histories. As a result, these systems overlook promising research directions, repeat failed experiments, and pursue infeasible ideas. To address this, we introduce EvoScientist, an evolving multi-agent AI scientist framework that continuously improves research strategies through persistent memory and self-evolution. EvoScientist comprises three specialized agents: a Researcher Agent (RA) for scientific idea generation, an Engineer Agent (EA) for experiment implementation and execution, and an Evolution Manager Agent (EMA) that distills insights from prior interactions into reusable knowledge. EvoScientist contains two persistent memory modules: (i) an ideation memory, which summarizes feasible research directions from top-ranked ideas while recording previously unsuccessful directions; and (ii) an experimentation memory, which captures effective data processing and model training strategies derived from code search trajectories and best-performing implementations. These modules enable the RA and EA to retrieve relevant prior strategies, improving idea quality and code execution success rates over time. Experiments show that EvoScientist outperforms 7 open-source and commercial state-of-the-art systems in scientific idea generation, achieving higher novelty, feasibility, relevance, and clarity via automatic and human evaluation. EvoScientist also substantially improves code execution success rates through multi-agent evolution, demonstrating persistent memory's effectiveness for end-to-end scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EvoScientist/EvoScientist", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.12935", "title": "Can Fairness Be Prompted? Prompt-Based Debiasing Strategies in High-Stakes Recommendations", "authors": [ "Mihaela Rotar", "Theresia Veronika Rampisela", "Maria Maistro" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) can infer sensitive attributes such as gender or age from indirect cues like names and pronouns, potentially biasing recommendations. While several debiasing methods exist, they require access to the LLMs' weights, are computationally costly, and cannot be used by lay users. To address this gap, we investigate implicit biases in LLM Recommenders (LLMRecs) and explore whether prompt-based strategies can serve as a lightweight and easy-to-use debiasing approach. We contribute three bias-aware prompting strategies for LLMRecs. To our knowledge, this is the first study on prompt-based debiasing approaches in LLMRecs that focuses on group fairness for users. Our experiments with 3 LLMs, 4 prompt templates, 9 sensitive attribute values, and 2 datasets show that our proposed debiasing approach, which instructs an LLM to be fair, can improve fairness by up to 74% while retaining comparable effectiveness, but might overpromote specific demographic groups in some cases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.12588", "title": "SDF-Net: Structure-Aware Disentangled Feature Learning for Opticall-SAR Ship Re-identification", "authors": [ "Furui Chen", "Han Wang", "Yuhan Sun", "Jianing You", "Yixuan Lv", "Zhuang Zhou", "Hong Tan", "Shengyang Li" ], "github": "https://github.com/cfrfree/SDF-Net", "project_page": "", "abstract": "Cross-modal ship re-identification (ReID) between optical and synthetic aperture radar (SAR) imagery is fundamentally challenged by the severe radiometric discrepancy between passive optical imaging and coherent active radar sensing. While existing approaches primarily rely on statistical distribution alignment or semantic matching, they often overlook a critical physical prior: ships are rigid objects whose geometric structures remain stable across sensing modalities, whereas texture appearance is highly modality-dependent. In this work, we propose SDF-Net, a Structure-Aware Disentangled Feature Learning Network that systematically incorporates geometric consistency into optical--SAR ship ReID. Built upon a ViT backbone, SDF-Net introduces a structure consistency constraint that extracts scale-invariant gradient energy statistics from intermediate layers to robustly anchor representations against radiometric variations. At the terminal stage, SDF-Net disentangles the learned representations into modality-invariant identity features and modality-specific characteristics. These decoupled cues are then integrated through a parameter-free additive residual fusion, effectively enhancing discriminative power. Extensive experiments on the HOSS-ReID dataset demonstrate that SDF-Net consistently outperforms existing state-of-the-art methods. The code and trained models are publicly available at https://github.com/cfrfree/SDF-Net.", "github_manual": "", "github_regex": "https://github.com/cfrfree/SDF-Net", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.12533", "title": "Do You See What I Am Pointing At? Gesture-Based Egocentric Video Question Answering", "authors": [ "Yura Choi", "Roy Miles", "Rolandos Alexandros Potamias", "Ismail Elezi", "Jiankang Deng", "Stefanos Zafeiriou" ], "github": "", "project_page": "", "abstract": "Understanding and answering questions based on a user's pointing gesture is essential for next-generation egocentric AI assistants. However, current Multimodal Large Language Models (MLLMs) struggle with such tasks due to the lack of gesture-rich data and their limited ability to infer fine-grained pointing intent from egocentric video. To address this, we introduce EgoPointVQA, a dataset and benchmark for gesture-grounded egocentric question answering, comprising 4000 synthetic and 400 real-world videos across multiple deictic reasoning tasks. Built upon it, we further propose Hand Intent Tokens (HINT), which encodes tokens derived from 3D hand keypoints using an off-the-shelf reconstruction model and interleaves them with the model input to provide explicit spatial and temporal context for interpreting pointing intent. We show that our model outperforms others in different backbones and model sizes. In particular, HINT-14B achieves 68.1% accuracy, on average over 6 tasks, surpassing the state-of-the-art, InternVL3-14B, by 6.6%. To further facilitate the open research, we will release the code, model, and dataset. Project page: https://yuuraa.github.io/papers/choi2026egovqa", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.11749", "title": "Compression Favors Consistency, Not Truth: When and Why Language Models Prefer Correct Information", "authors": [ "Konstantin Krestnikov" ], "github": "https://github.com/Rai220/compression-drives-truth", "project_page": "", "abstract": "Why do language models sometimes prefer correct statements even when trained on mixed-quality data? We introduce the Compression--Consistency Principle: next-token prediction favors hypotheses that allow shorter and more internally consistent descriptions of the training data. Truth bias emerges only when false alternatives are structurally harder to compress. We test this using small GPT-2-style character-level transformers (3.5M--86M parameters) on synthetic math corpora with controlled mixtures of correct and incorrect rules. In the random-error setting, models strongly prefer correct completions in paired evaluation: 83.1% accuracy at balanced data and 67.0% even when correct rules appear in only 10% of the corpus. Replacing random errors with a coherent but mathematically incorrect rule system largely eliminates the preference (near-chance accuracy). In a more natural-language-like synthetic world, the effect is weaker but still present (57.7%). Additional experiments show that embedding verification steps can restore preference for correctness even at small scale, while increasing the number of consistent rules produces a graded improvement in accuracy. Our results suggest that what appears as a \"truth bias\" is largely a side effect of compression pressure and preference for internal consistency, rather than an intrinsic drive toward truth. Full code and data are available at https://github.com/Rai220/compression-drives-truth.", "github_manual": "", "github_regex": "https://github.com/Rai220/compression-drives-truth", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-16", "arxiv_id": "2603.10781", "title": "Taking Shortcuts for Categorical VQA Using Super Neurons", "authors": [ "Pierre Musacchio", "Jaeyi Jeong", "Dahun Kim", "Jaesik Park" ], "github": "", "project_page": "", "abstract": "Sparse Attention Vectors (SAVs) have emerged as an excellent training-free alternative to supervised finetuning or low-rank adaptation to improve the performance of Vision Language Models (VLMs). At their heart, SAVs select a few accurate attention heads for a task of interest and use them as classifiers, rather than relying on the model's prediction. In a similar spirit, we find that directly probing the raw activations of the VLM, in the form of scalar values, is sufficient to yield accurate classifiers on diverse visually grounded downstream tasks. Shifting focus from attention vectors to scalar activations dramatically increases the search space for accurate parameters, allowing us to find more discriminative neurons immediately from the first generated token. We call such activations Super Neurons (SNs). In this probing setting, we discover that enough SNs appear in the shallower layers of the large language model to allow for extreme early exiting from the first layer of the model at the first generated token. Compared to the original network, SNs robustly improve the classification performance while achieving a speedup of up to 5.10x.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.14473", "title": "AI Can Learn Scientific Taste", "authors": [ "Jingqi Tong", "Mingzhe Li", "Hangcheng Li", "Yongzhuo Yang", "Yurong Mou", "Weijie Ma", "Zhiheng Xi", "Hongji Chen", "Xiaoran Liu", "Qinyuan Cheng", "Ming Zhang", "Qiguang Chen", "Weifeng Ge", "Qipeng Guo", "Tianlei Ying", "Tianxiang Sun", "Yining Zheng", "Xinchi Chen", "Jun Zhao", "Ning Ding", "Xuanjing Huang", "Yugang Jiang", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Great scientists have strong judgement and foresight, closely tied to what we call scientific taste. Here, we use the term to refer to the capacity to judge and propose research ideas with high potential impact. However, most relative research focuses on improving an AI scientist's executive capability, while enhancing an AI's scientific taste remains underexplored. In this work, we propose Reinforcement Learning from Community Feedback (RLCF), a training paradigm that uses large-scale community signals as supervision, and formulate scientific taste learning as a preference modeling and alignment problem. For preference modeling, we train Scientific Judge on 700K field- and time-matched pairs of high- vs. low-citation papers to judge ideas. For preference alignment, using Scientific Judge as a reward model, we train a policy model, Scientific Thinker, to propose research ideas with high potential impact. Experiments show Scientific Judge outperforms SOTA LLMs (e.g., GPT-5.2, Gemini 3 Pro) and generalizes to future-year test, unseen fields, and peer-review preference. Furthermore, Scientific Thinker proposes research ideas with higher potential impact than baselines. Our findings show that AI can learn scientific taste, marking a key step toward reaching human-level AI scientists.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.10505", "title": "Safe and Scalable Web Agent Learning via Recreated Websites", "authors": [ "Hyungjoo Chae", "Jungsoo Park", "Alan Ritter" ], "github": "https://github.com/kyle8581/VeriEnv", "project_page": "", "abstract": "Training autonomous web agents is fundamentally limited by the environments they learn from: real-world websites are unsafe to explore, hard to reset, and rarely provide verifiable feedback. We propose VeriEnv, a framework that treats language models as environment creators, automatically cloning real-world websites into fully executable, verifiable synthetic environments. By exposing controlled internal access via a Python SDK, VeriEnv enables agents to self-generate tasks with deterministic, programmatically verifiable rewards, eliminating reliance on heuristic or LLM-based judges. This design decouples agent learning from unsafe real-world interaction while enabling scalable self-evolution through environment expansion. Through experiments on web agent benchmarks, we show that agents trained with VeriEnv generalize to unseen websites, achieve site-specific mastery through self-evolving training, and benefit from scaling the number of training environments. Code and resources will be released at https://github.com/kyle8581/VeriEnv upon acceptance.", "github_manual": "", "github_regex": "https://github.com/kyle8581/VeriEnv", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.12146", "title": "FlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance", "authors": [ "Quanhao Li", "Zhen Xing", "Rui Wang", "Haidong Cao", "Qi Dai", "Daoguo Dong", "Zuxuan Wu" ], "github": "", "project_page": "", "abstract": "Recent advances in trajectory-controllable video generation have achieved remarkable progress. Previous methods mainly use adapter-based architectures for precise motion control along predefined trajectories. However, all these methods rely on a multi-step denoising process, leading to substantial time redundancy and computational overhead. While existing video distillation methods successfully distill multi-step generators into few-step, directly applying these approaches to trajectory-controllable video generation results in noticeable degradation in both video quality and trajectory accuracy. To bridge this gap, we introduce FlashMotion, a novel training framework designed for few-step trajectory-controllable video generation. We first train a trajectory adapter on a multi-step video generator for precise trajectory control. Then, we distill the generator into a few-step version to accelerate video generation. Finally, we finetune the adapter using a hybrid strategy that combines diffusion and adversarial objectives, aligning it with the few-step generator to produce high-quality, trajectory-accurate videos. For evaluation, we introduce FlashBench, a benchmark for long-sequence trajectory-controllable video generation that measures both video quality and trajectory accuracy across varying numbers of foreground objects. Experiments on two adapter architectures show that FlashMotion surpasses existing video distillation methods and previous multi-step models in both visual quality and trajectory consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.13364", "title": "FineRMoE: Dimension Expansion for Finer-Grained Expert with Its Upcycling Approach", "authors": [ "Ning Liao", "Xiaoxing Wang", "Xiaohan Qin", "Junchi Yan" ], "github": "", "project_page": "", "abstract": "As revealed by the scaling law of fine-grained MoE, model performance ceases to be improved once the granularity of the intermediate dimension exceeds the optimal threshold, limiting further gains from single-dimension fine-grained design. To address this bottleneck, we propose FineRMoE (FineR-Grained MoE), an architecture that extends fine-grained expert design to both intermediate and output dimensions, aiming to enhance expert specialization beyond the single-dimension limit. We further introduce a bi-level sparse forward computation paradigm and a specialized routing mechanism to govern the activation. In addition, to obviate the prohibitive cost of training FineRMoE from scratch, we devise a generalized upcycling method to build FineRMoE in a cost-effective manner. Extensive experiments demonstrate the superior performance achieved by FineRMoE across ten standard benchmarks. Compared with the strongest baseline, FineRMoE achieves 6 times higher parameter efficiency, 281 times lower prefill latency, and 136 timese higher decoding throughput during inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15478", "title": "ViFeEdit: A Video-Free Tuner of Your Video Diffusion Transformer", "authors": [ "Ruonan Yu", "Zhenxiong Tan", "Zigeng Chen", "Songhua Liu", "Xinchao Wang" ], "github": "https://github.com/Lexie-YU/ViFeEdit", "project_page": "", "abstract": "Diffusion Transformers (DiTs) have demonstrated remarkable scalability and quality in image and video generation, prompting growing interest in extending them to controllable generation and editing tasks. However, compared to the image counterparts, progress in video control and editing remains limited, mainly due to the scarcity of paired video data and the high computational cost of training video diffusion models. To address this issue, in this paper, we propose a video-free tuning framework termed ViFeEdit for video diffusion transformers. Without requiring any forms of video training data, ViFeEdit achieves versatile video generation and editing, adapted solely with 2D images. At the core of our approach is an architectural reparameterization that decouples spatial independence from the full 3D attention in modern video diffusion transformers, which enables visually faithful editing while maintaining temporal consistency with only minimal additional parameters. Moreover, this design operates in a dual-path pipeline with separate timestep embeddings for noise scheduling, exhibiting strong adaptability to diverse conditioning signals. Extensive experiments demonstrate that our method delivers promising results of controllable video generation and editing with only minimal training on 2D image data. Codes are available https://github.com/Lexie-YU/ViFeEdit.", "github_manual": "", "github_regex": "https://github.com/Lexie-YU/ViFeEdit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15558", "title": "Panoramic Affordance Prediction", "authors": [ "Zixin Zhang", "Chenfei Liao", "Hongfei Zhang", "Harold Haodong Chen", "Kanghao Chen", "Zichen Wen", "Litao Guo", "Bin Ren", "Xu Zheng", "Yinchuan Li", "Xuming Hu", "Nicu Sebe", "Ying-Cong Chen" ], "github": "", "project_page": "", "abstract": "Affordance prediction serves as a critical bridge between perception and action in embodied AI. However, existing research is confined to pinhole camera models, which suffer from narrow Fields of View (FoV) and fragmented observations, often missing critical holistic environmental context. In this paper, we present the first exploration into Panoramic Affordance Prediction, utilizing 360-degree imagery to capture global spatial relationships and holistic scene understanding. To facilitate this novel task, we first introduce PAP-12K, a large-scale benchmark dataset containing over 1,000 ultra-high-resolution (12k, 11904 x 5952) panoramic images with over 12k carefully annotated QA pairs and affordance masks. Furthermore, we propose PAP, a training-free, coarse-to-fine pipeline inspired by the human foveal visual system to tackle the ultra-high resolution and severe distortion inherent in panoramic images. PAP employs recursive visual routing via grid prompting to progressively locate targets, applies an adaptive gaze mechanism to rectify local geometric distortions, and utilizes a cascaded grounding pipeline to extract precise instance-level masks. Experimental results on PAP-12K reveal that existing affordance prediction methods designed for standard perspective images suffer severe performance degradation and fail due to the unique challenges of panoramic vision. In contrast, PAP framework effectively overcomes these obstacles, significantly outperforming state-of-the-art baselines and highlighting the immense potential of panoramic perception for robust embodied intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.14145", "title": "MMOU: A Massive Multi-Task Omni Understanding and Reasoning Benchmark for Long and Complex Real-World Videos", "authors": [ "Arushi Goel", "Sreyan Ghosh", "Vatsal Agarwal", "Nishit Anand", "Kaousheik Jayakumar", "Lasha Koroshinadze", "Yao Xu", "Katie Lyons", "James Case", "Karan Sapra", "Kevin J. Shih", "Siddharth Gururani", "Abhinav Shrivastava", "Ramani Duraiswami", "Dinesh Manocha", "Andrew Tao", "Bryan Catanzaro", "Mohammad Shoeybi", "Wei Ping" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have shown strong performance in visual and audio understanding when evaluated in isolation. However, their ability to jointly reason over omni-modal (visual, audio, and textual) signals in long and complex videos remains largely unexplored. We introduce MMOU, a new benchmark designed to systematically evaluate multimodal understanding and reasoning under these challenging, real-world conditions. MMOU consists of 15,000 carefully curated questions paired with 9038 web-collected videos of varying length, spanning diverse domains and exhibiting rich, tightly coupled audio-visual content. The benchmark covers 13 fundamental skill categories, all of which require integrating evidence across modalities and time. All questions are manually annotated across multiple turns by professional annotators, ensuring high quality and reasoning fidelity. We evaluate 20+ state-of-the-art open-source and proprietary multimodal models on MMOU. The results expose substantial performance gaps: the best closed-source model achieves only 64.2% accuracy, while the strongest open-source model reaches just 46.8%. Our results highlight the challenges of long-form omni-modal understanding, revealing that current models frequently fail to apply even fundamental skills in long videos. Through detailed analysis, we further identify systematic failure modes and provide insights into where and why current models break.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.14313", "title": "Mind the Shift: Decoding Monetary Policy Stance from FOMC Statements with Large Language Models", "authors": [ "Yixuan Tang", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Federal Open Market Committee (FOMC) statements are a major source of monetary-policy information, and even subtle changes in their wording can move global financial markets. A central task is therefore to measure the hawkish--dovish stance conveyed in these texts. Existing approaches typically treat stance detection as a standard classification problem, labeling each statement in isolation. However, the interpretation of monetary-policy communication is inherently relative: market reactions depend not only on the tone of a statement, but also on how that tone shifts across meetings. We introduce Delta-Consistent Scoring (DCS), an annotation-free framework that maps frozen large language model (LLM) representations to continuous stance scores by jointly modeling absolute stance and relative inter-meeting shifts. Rather than relying on manual hawkish--dovish labels, DCS uses consecutive meetings as a source of self-supervision. It learns an absolute stance score for each statement and a relative shift score between consecutive statements. A delta-consistency objective encourages changes in absolute scores to align with the relative shifts. This allows DCS to recover a temporally coherent stance trajectory without manual labels. Across four LLM backbones, DCS consistently outperforms supervised probes and LLM-as-judge baselines, achieving up to 71.1% accuracy on sentence-level hawkish--dovish classification. The resulting meeting-level scores are also economically meaningful: they correlate strongly with inflation indicators and are significantly associated with Treasury yield movements. Overall, the results suggest that LLM representations encode monetary-policy signals that can be recovered through relative temporal structure.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15583", "title": "Grounding World Simulation Models in a Real-World Metropolis", "authors": [ "Junyoung Seo", "Hyunwook Choi", "Minkyung Kwon", "Jinhyeok Choi", "Siyoon Jin", "Gayoung Lee", "Junho Kim", "JoungBin Lee", "Geonmo Gu", "Dongyoon Han", "Sangdoo Yun", "Seungryong Kim", "Jin-Hwa Kim" ], "github": "", "project_page": "", "abstract": "What if a world simulation model could render not an imagined environment but a city that actually exists? Prior generative world models synthesize visually plausible yet artificial environments by imagining all content. We present Seoul World Model (SWM), a city-scale world model grounded in the real city of Seoul. SWM anchors autoregressive video generation through retrieval-augmented conditioning on nearby street-view images. However, this design introduces several challenges, including temporal misalignment between retrieved references and the dynamic target scene, limited trajectory diversity and data sparsity from vehicle-mounted captures at sparse intervals. We address these challenges through cross-temporal pairing, a large-scale synthetic dataset enabling diverse camera trajectories, and a view interpolation pipeline that synthesizes coherent training videos from sparse street-view images. We further introduce a Virtual Lookahead Sink to stabilize long-horizon generation by continuously re-grounding each chunk to a retrieved image at a future location. We evaluate SWM against recent video world models across three cities: Seoul, Busan, and Ann Arbor. SWM outperforms existing methods in generating spatially faithful, temporally consistent, long-horizon videos grounded in actual urban environments over trajectories reaching hundreds of meters, while supporting diverse camera movements and text-prompted scenario variations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.13391", "title": "WebVR: Benchmarking Multimodal LLMs for WebPage Recreation from Videos via Human-Aligned Visual Rubrics", "authors": [ "Yuhong Dai", "Yanlin Lai", "Mitt Huang", "Hangyu Guo", "Dingming Li", "Hongbo Peng", "Haodong Li", "Yingxiu Zhao", "Haoran Lyu", "Zheng Ge", "Xiangyu Zhang", "Daxin Jiang" ], "github": "", "project_page": "", "abstract": "Existing web-generation benchmarks rely on text prompts or static screenshots as input. However, videos naturally convey richer signals such as interaction flow, transition timing, and motion continuity, which are essential for faithful webpage recreation. Despite this potential, video-conditioned webpage generation remains largely unexplored, with no dedicated benchmark for this task. To fill this gap, we introduce WebVR, a benchmark that evaluates whether MLLMs can faithfully recreate webpages from demonstration videos. WebVR contains 175 webpages across diverse categories, all constructed through a controlled synthesis pipeline rather than web crawling, ensuring varied and realistic demonstrations without overlap with existing online pages. We also design a fine-grained, human-aligned visual rubric that evaluates the generated webpages across multiple dimensions. Experiments on 19 models reveal substantial gaps in recreating fine-grained style and motion quality, while the rubric-based automatic evaluation achieves 96% agreement with human preferences. We release the dataset, evaluation toolkit, and baseline results to support future research on video-to-webpage generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15594", "title": "OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data", "authors": [ "Yuwen Du", "Rui Ye", "Shuo Tang", "Xinyu Zhu", "Yijun Lu", "Yuzhu Cai", "Siheng Chen" ], "github": "https://github.com/MiroMindAI/MiroThinker", "project_page": "", "abstract": "Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet the development of high-performance search agents remains dominated by industrial giants due to a lack of transparent, high-quality training data. This persistent data scarcity has fundamentally hindered the progress of the broader research community in developing and innovating within this domain. To bridge this gap, we introduce OpenSeeker, the first fully open-source search agent (i.e., model and data) that achieves frontier-level performance through two core technical innovations: (1) Fact-grounded scalable controllable QA synthesis, which reverse-engineers the web graph via topological expansion and entity obfuscation to generate complex, multi-hop reasoning tasks with controllable coverage and complexity. (2) Denoised trajectory synthesis, which employs a retrospective summarization mechanism to denoise the trajectory, therefore promoting the teacher LLMs to generate high-quality actions. Experimental results demonstrate that OpenSeeker, trained (a single training run) on only 11.7k synthesized samples, achieves state-of-the-art performance across multiple benchmarks including BrowseComp, BrowseComp-ZH, xbench-DeepSearch, and WideSearch. Notably, trained with simple SFT, OpenSeeker significantly outperforms the second-best fully open-source agent DeepDive (e.g., 29.5% v.s. 15.3% on BrowseComp), and even surpasses industrial competitors such as Tongyi DeepResearch (trained via extensive continual pre-training, SFT, and RL) on BrowseComp-ZH (48.4% v.s. 46.7%). We fully open-source the complete training dataset and the model weights to democratize frontier search agent research and foster a more transparent, collaborative ecosystem.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiroMindAI/MiroThinker", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15031", "title": "Attention Residuals", "authors": [ "Kimi Team", "Guangyu Chen", "Yu Zhang", "Jianlin Su", "Weixin Xu", "Siyuan Pan", "Yaoyu Wang", "Yucheng Wang", "Guanduo Chen", "Bohong Yin", "Yutian Chen", "Junjie Yan", "Ming Wei", "Y. Zhang", "Fanqing Meng", "Chao Hong", "Xiaotong Xie", "Shaowei Liu", "Enzhe Lu", "Yunpeng Tai", "Yanru Chen", "Xin Men", "Haiqing Guo", "Y. Charles", "Haoyu Lu", "Lin Sui", "Jinguo Zhu", "Zaida Zhou", "Weiran He", "Weixiao Huang", "Xinran Xu", "Yuzhi Wang", "Guokun Lai", "Yulun Du", "Yuxin Wu", "Zhilin Yang", "Xinyu Zhou" ], "github": "", "project_page": "", "abstract": "Residual connections with PreNorm are standard in modern LLMs, yet they accumulate all layer outputs with fixed unit weights. This uniform aggregation causes uncontrolled hidden-state growth with depth, progressively diluting each layer's contribution. We propose Attention Residuals (AttnRes), which replaces this fixed accumulation with softmax attention over preceding layer outputs, allowing each layer to selectively aggregate earlier representations with learned, input-dependent weights. To address the memory and communication overhead of attending over all preceding layer outputs for large-scale model training, we introduce Block AttnRes, which partitions layers into blocks and attends over block-level representations, reducing the memory footprint while preserving most of the gains of full AttnRes. Combined with cache-based pipeline communication and a two-phase computation strategy, Block AttnRes becomes a practical drop-in replacement for standard residual connections with minimal overhead. Scaling law experiments confirm that the improvement is consistent across model sizes, and ablations validate the benefit of content-dependent depth-wise selection. We further integrate AttnRes into the Kimi Linear architecture (48B total / 3B activated parameters) and pre-train on 1.4T tokens, where AttnRes mitigates PreNorm dilution, yielding more uniform output magnitudes and gradient distribution across depth, and improves downstream performance across all evaluated tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15612", "title": "HSImul3R: Physics-in-the-Loop Reconstruction of Simulation-Ready Human-Scene Interactions", "authors": [ "Yukang Cao", "Haozhe Xie", "Fangzhou Hong", "Long Zhuo", "Zhaoxi Chen", "Liang Pan", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "We present HSImul3R, a unified framework for simulation-ready 3D reconstruction of human-scene interactions (HSI) from casual captures, including sparse-view images and monocular videos. Existing methods suffer from a perception-simulation gap: visually plausible reconstructions often violate physical constraints, leading to instability in physics engines and failure in embodied AI applications. To bridge this gap, we introduce a physically-grounded bi-directional optimization pipeline that treats the physics simulator as an active supervisor to jointly refine human dynamics and scene geometry. In the forward direction, we employ Scene-targeted Reinforcement Learning to optimize human motion under dual supervision of motion fidelity and contact stability. In the reverse direction, we propose Direct Simulation Reward Optimization, which leverages simulation feedback on gravitational stability and interaction success to refine scene geometry. We further present HSIBench, a new benchmark with diverse objects and interaction scenarios. Extensive experiments demonstrate that HSImul3R produces the first stable, simulation-ready HSI reconstructions and can be directly deployed to real-world humanoid robots.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15611", "title": "Code-A1: Adversarial Evolving of Code LLM and Test LLM via Reinforcement Learning", "authors": [ "Aozhe Wang", "Yuchen Yan", "Nan Zhou", "Zhengxi Lu", "Weiming Lu", "Jun Xiao", "Yueting Zhuang", "Yongliang Shen" ], "github": "", "project_page": "", "abstract": "Reinforcement learning for code generation relies on verifiable rewards from unit test pass rates. Yet high-quality test suites are scarce, existing datasets offer limited coverage, and static rewards fail to adapt as models improve. Recent self-play methods unify code and test generation in a single model, but face a inherent dilemma: white-box access leads to self-collusion where the model produces trivial tests for easy rewards, yet black-box restriction yields generic tests that miss implementation-specific bugs. We introduce Code-A1, an adversarial co-evolution framework that jointly optimizes a Code LLM and a Test LLM with opposing objectives. The Code LLM is rewarded for passing more tests, while the Test LLM is rewarded for exposing more defects. This architectural separation eliminates self-collusion risks and safely enables white-box test generation, where the Test LLM can inspect candidate code to craft targeted adversarial tests. We further introduce a Mistake Book mechanism for experience replay and a composite reward balancing test validity with adversarial difficulty. Experiments on Qwen2.5-Coder models demonstrate that Code-A1 achieves code generation performance matching or exceeding models trained on human-annotated tests, while significantly improving test generation capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.12529", "title": "TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning", "authors": [ "Alliot Nagle", "Jakhongir Saydaliev", "Dhia Garbaya", "Michael Gastpar", "Ashok Vardhan Makkuva", "Hyeji Kim" ], "github": "", "project_page": "", "abstract": "Large Reasoning Models (LRMs) achieve impressive performance on complex reasoning tasks via Chain-of-Thought (CoT) reasoning, which enables them to generate intermediate thinking tokens before arriving at the final answer. However, LRMs often suffer from significant overthinking, spending excessive compute time even after the answer is generated early on. Prior work has identified the existence of an optimal reasoning length such that truncating reasoning at this point significantly shortens CoT outputs with virtually no change in performance. However, determining optimal CoT lengths for practical datasets is highly non-trivial as they are fully task and model-dependent. In this paper, we precisely address this and design TERMINATOR, an early-exit strategy for LRMs at inference to mitigate overthinking. The central idea underpinning TERMINATOR is that the first arrival of an LRM's final answer is often predictable, and we leverage these first answer positions to create a novel dataset of optimal reasoning lengths to train TERMINATOR. Powered by this approach, TERMINATOR achieves significant reductions in CoT lengths of 14%-55% on average across four challenging practical datasets: MATH-500, AIME 2025, HumanEval, and GPQA, whilst outperforming current state-of-the-art methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.12743", "title": "MoKus: Leveraging Cross-Modal Knowledge Transfer for Knowledge-Aware Concept Customization", "authors": [ "Chenyang Zhu", "Hongxiang Li", "Xiu Li", "Long Chen" ], "github": "", "project_page": "", "abstract": "Concept customization typically binds rare tokens to a target concept. Unfortunately, these approaches often suffer from unstable performance as the pretraining data seldom contains these rare tokens. Meanwhile, these rare tokens fail to convey the inherent knowledge of the target concept. Consequently, we introduce Knowledge-aware Concept Customization, a novel task aiming at binding diverse textual knowledge to target visual concepts. This task requires the model to identify the knowledge within the text prompt to perform high-fidelity customized generation. Meanwhile, the model should efficiently bind all the textual knowledge to the target concept. Therefore, we propose MoKus, a novel framework for knowledge-aware concept customization. Our framework relies on a key observation: cross-modal knowledge transfer, where modifying knowledge within the text modality naturally transfers to the visual modality during generation. Inspired by this observation, MoKus contains two stages: (1) In visual concept learning, we first learn the anchor representation to store the visual information of the target concept. (2) In textual knowledge updating, we update the answer for the knowledge queries to the anchor representation, enabling high-fidelity customized generation. To further comprehensively evaluate our proposed MoKus on the new task, we introduce the first benchmark for knowledge-aware concept customization: KnowCusBench. Extensive evaluations have demonstrated that MoKus outperforms state-of-the-art methods. Moreover, the cross-model knowledge transfer allows MoKus to be easily extended to other knowledge-aware applications like virtual concept creation and concept erasure. We also demonstrate the capability of our method to achieve improvements on world knowledge benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.13428", "title": "EvoClaw: Evaluating AI Agents on Continuous Software Evolution", "authors": [ "Gangda Deng", "Zhaoling Chen", "Zhongming Yu", "Haoyang Fan", "Yuhong Liu", "Yuxin Yang", "Dhruv Parikh", "Rajgopal Kannan", "Le Cong", "Mengdi Wang", "Qian Zhang", "Viktor Prasanna", "Xiangru Tang", "Xingyao Wang" ], "github": "https://github.com/Hydrapse/EvoClaw", "project_page": "", "abstract": "With AI agents increasingly deployed as long-running systems, it becomes essential to autonomously construct and continuously evolve customized software to enable interaction within dynamic environments. Yet, existing benchmarks evaluate agents on isolated, one-off coding tasks, neglecting the temporal dependencies and technical debt inherent in real-world software evolution. To bridge this gap, we introduce DeepCommit, an agentic pipeline that reconstructs verifiable Milestone DAGs from noisy commit logs, where milestones are defined as semantically cohesive development goals. These executable sequences enable EvoClaw, a novel benchmark that requires agents to sustain system integrity and limit error accumulation, dimensions of long-term software evolution largely missing from current benchmarks. Our evaluation of 12 frontier models across 4 agent frameworks reveals a critical vulnerability: overall performance scores drop significantly from >80% on isolated tasks to at most 38% in continuous settings, exposing agents' profound struggle with long-term maintenance and error propagation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Hydrapse/EvoClaw", "github_api_added_by": "auto" }, { "date": "2026-03-17", "arxiv_id": "2603.15500", "title": "Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty", "authors": [ "Jeonghye Kim", "Xufang Luo", "Minbeom Kim", "Sangmook Lee", "Dongsheng Li", "Yuqing Yang" ], "github": "https://github.com/beanie00/strategic-information-allocation-llm-reasoning", "project_page": "", "abstract": "LLMs often exhibit Aha moments during reasoning, such as apparent self-correction following tokens like \"Wait,\" yet their underlying mechanisms remain unclear. We introduce an information-theoretic framework that decomposes reasoning into procedural information and epistemic verbalization - the explicit externalization of uncertainty that supports downstream control actions. We show that purely procedural reasoning can become informationally stagnant, whereas epistemic verbalization enables continued information acquisition and is critical for achieving information sufficiency. Empirical results demonstrate that strong reasoning performance is driven by uncertainty externalization rather than specific surface tokens. Our framework unifies prior findings on Aha moments and post-training experiments, and offers insights for future reasoning model design.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/beanie00/strategic-information-allocation-llm-reasoning", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15150", "title": "SNCE: Geometry-Aware Supervision for Scalable Discrete Image Generation", "authors": [ "Shufan Li", "Jiuxiang Gu", "Kangning Liu", "Zhe Lin", "Aditya Grover", "Jason Kuen" ], "github": "", "project_page": "", "abstract": "Recent advancements in discrete image generation showed that scaling the VQ codebook size significantly improves reconstruction fidelity. However, training generative models with a large VQ codebook remains challenging, typically requiring larger model size and a longer training schedule. In this work, we propose Stochastic Neighbor Cross Entropy Minimization (SNCE), a novel training objective designed to address the optimization challenges of large-codebook discrete image generators. Instead of supervising the model with a hard one-hot target, SNCE constructs a soft categorical distribution over a set of neighboring tokens. The probability assigned to each token is proportional to the proximity between its code embedding and the ground-truth image embedding, encouraging the model to capture semantically meaningful geometric structure in the quantized embedding space. We conduct extensive experiments across class-conditional ImageNet-256 generation, large-scale text-to-image synthesis, and image editing tasks. Results show that SNCE significantly improves convergence speed and overall generation quality compared to standard cross-entropy objectives.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15619", "title": "Mixture-of-Depths Attention", "authors": [ "Lianghui Zhu", "Yuxin Fang", "Bencheng Liao", "Shijie Wang", "Tianheng Cheng", "Zilong Huang", "Chen Chen", "Lai Wei", "Yutao Zeng", "Ya Wang", "Yi Lin", "Yu Li", "Xinggang Wang" ], "github": "https://github.com/hustvl/MoDA", "project_page": "", "abstract": "Scaling depth is a key driver for large language models (LLMs). Yet, as LLMs become deeper, they often suffer from signal degradation: informative features formed in shallow layers are gradually diluted by repeated residual updates, making them harder to recover in deeper layers. We introduce mixture-of-depths attention (MoDA), a mechanism that allows each attention head to attend to sequence KV pairs at the current layer and depth KV pairs from preceding layers. We further describe a hardware-efficient algorithm for MoDA that resolves non-contiguous memory-access patterns, achieving 97.3% of FlashAttention-2's efficiency at a sequence length of 64K. Experiments on 1.5B-parameter models demonstrate that MoDA consistently outperforms strong baselines. Notably, it improves average perplexity by 0.2 across 10 validation benchmarks and increases average performance by 2.11% on 10 downstream tasks, with a negligible 3.7% FLOPs computational overhead. We also find that combining MoDA with post-norm yields better performance than using it with pre-norm. These results suggest that MoDA is a promising primitive for depth scaling. Code is released at https://github.com/hustvl/MoDA .", "github_manual": "", "github_regex": "https://github.com/hustvl/MoDA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.13985", "title": "Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Models", "authors": [ "Haitao Jiang", "Wenbo Zhang", "Jiarui Yao", "Hengrui Cai", "Sheng Wang", "Rui Song" ], "github": "", "project_page": "", "abstract": "Pre-trained Large Language Model (LLM) exhibits broad capabilities, yet, for specific tasks or domains their attainment of higher accuracy and more reliable reasoning generally depends on post-training through Supervised Fine-Tuning (SFT) or Reinforcement Learning (RL). Although often treated as distinct methodologies, recent theoretical and empirical developments demonstrate that SFT and RL are closely connected. This study presents a comprehensive and unified perspective on LLM post-training with SFT and RL. We first provide an in-depth overview of both techniques, examining their objectives, algorithmic structures, and data requirements. We then systematically analyze their interplay, highlighting frameworks that integrate SFT and RL, hybrid training pipelines, and methods that leverage their complementary strengths. Drawing on a representative set of recent application studies from 2023 to 2025, we identify emerging trends, characterize the rapid shift toward hybrid post-training paradigms, and distill key takeaways that clarify when and why each method is most effective. By synthesizing theoretical insights, practical methodologies, and empirical evidence, this study establishes a coherent understanding of SFT and RL within a unified framework and outlines promising directions for future research in scalable, efficient, and generalizable LLM post-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15616", "title": "GlyphPrinter: Region-Grouped Direct Preference Optimization for Glyph-Accurate Visual Text Rendering", "authors": [ "Xincheng Shuai", "Ziye Li", "Henghui Ding", "Dacheng Tao" ], "github": "", "project_page": "", "abstract": "Generating accurate glyphs for visual text rendering is essential yet challenging. Existing methods typically enhance text rendering by training on a large amount of high-quality scene text images, but the limited coverage of glyph variations and excessive stylization often compromise glyph accuracy, especially for complex or out-of-domain characters. Some methods leverage reinforcement learning to alleviate this issue, yet their reward models usually depend on text recognition systems that are insensitive to fine-grained glyph errors, so images with incorrect glyphs may still receive high rewards. Inspired by Direct Preference Optimization (DPO), we propose GlyphPrinter, a preference-based text rendering method that eliminates reliance on explicit reward models. However, the standard DPO objective only models overall preference between two samples, which is insufficient for visual text rendering where glyph errors typically occur in localized regions. To address this issue, we construct the GlyphCorrector dataset with region-level glyph preference annotations and propose Region-Grouped DPO (R-GDPO), a region-based objective that optimizes inter- and intra-sample preferences over annotated regions, substantially enhancing glyph accuracy. Furthermore, we introduce Regional Reward Guidance, an inference strategy that samples from an optimal distribution with controllable glyph accuracy. Extensive experiments demonstrate that the proposed GlyphPrinter outperforms existing methods in glyph accuracy while maintaining a favorable balance between stylization and precision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15555", "title": "Learning Latent Proxies for Controllable Single-Image Relighting", "authors": [ "Haoze Zheng", "Zihao Wang", "Xianfeng Wu", "Yajing Bai", "Yexin Liu", "Yun Li", "Xiaogang Xu", "Harry Yang" ], "github": "", "project_page": "", "abstract": "Single-image relighting is highly under-constrained: small illumination changes can produce large, nonlinear variations in shading, shadows, and specularities, while geometry and materials remain unobserved. Existing diffusion-based approaches either rely on intrinsic or G-buffer pipelines that require dense and fragile supervision, or operate purely in latent space without physical grounding, making fine-grained control of direction, intensity, and color unreliable. We observe that a full intrinsic decomposition is unnecessary and redundant for accurate relighting. Instead, sparse but physically meaningful cues, indicating where illumination should change and how materials should respond, are sufficient to guide a diffusion model. Based on this insight, we introduce LightCtrl that integrates physical priors at two levels: a few-shot latent proxy encoder that extracts compact material-geometry cues from limited PBR supervision, and a lighting-aware mask that identifies sensitive illumination regions and steers the denoiser toward shading relevant pixels. To compensate for scarce PBR data, we refine the proxy branch using a DPO-based objective that enforces physical consistency in the predicted cues. We also present ScaLight, a large-scale object-level dataset with systematically varied illumination and complete camera-light metadata, enabling physically consistent and controllable training. Across object and scene level benchmarks, our method achieves photometrically faithful relighting with accurate continuous control, surpassing prior diffusion and intrinsic-based baselines, including gains of up to +2.4 dB PSNR and 35% lower RMSE under controlled lighting shifts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15026", "title": "Training-free Detection of Generated Videos via Spatial-Temporal Likelihoods", "authors": [ "Omer Ben Hayun", "Roy Betser", "Meir Yossef Levi", "Levi Kassel", "Guy Gilboa" ], "github": "https://github.com/1129ljc/T2VE", "project_page": "", "abstract": "Following major advances in text and image generation, the video domain has surged, producing highly realistic and controllable sequences. Along with this progress, these models also raise serious concerns about misinformation, making reliable detection of synthetic videos increasingly crucial. Image-based detectors are fundamentally limited because they operate per frame and ignore temporal dynamics, while supervised video detectors generalize poorly to unseen generators, a critical drawback given the rapid emergence of new models. These challenges motivate zero-shot approaches, which avoid synthetic data and instead score content against real-data statistics, enabling training-free, model-agnostic detection. We introduce STALL, a simple, training-free, theoretically justified detector that provides likelihood-based scoring for videos, jointly modeling spatial and temporal evidence within a probabilistic framework. We evaluate STALL on two public benchmarks and introduce ComGenVid, a new benchmark with state-of-the-art generative models. STALL consistently outperforms prior image- and video-based baselines. Code and data are available at https://omerbenhayun.github.io/stall-video.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/1129ljc/T2VE", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15206", "title": "Efficient Document Parsing via Parallel Token Prediction", "authors": [ "Lei Li", "Ze Zhao", "Meng Li", "Zhongwang Lun", "Yi Yuan", "Xingjing Lu", "Zheng Wei", "Jiang Bian", "Zang Li" ], "github": "https://github.com/flow3rdown/PTP-OCR", "project_page": "", "abstract": "Document parsing, as a fundamental yet crucial vision task, is being revolutionized by vision-language models (VLMs). However, the autoregressive (AR) decoding inherent to VLMs creates a significant bottleneck, severely limiting parsing speed. In this paper, we propose Parallel-Token Prediction (PTP), a plugable, model-agnostic and simple-yet-effective method that enables VLMs to generate multiple future tokens in parallel with improved sample efficiency. Specifically, we insert some learnable tokens into the input sequence and design corresponding training objectives to equip the model with parallel decoding capabilities for document parsing. Furthermore, to support effective training, we develop a comprehensive data generation pipeline that efficiently produces large-scale, high-quality document parsing training data for VLMs. Extensive experiments on OmniDocBench and olmOCR-bench demonstrate that our method not only significantly improves decoding speed (1.6x-2.2x) but also reduces model hallucinations and exhibits strong generalization abilities.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/flow3rdown/PTP-OCR", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15016", "title": "Riemannian Motion Generation: A Unified Framework for Human Motion Representation and Generation via Riemannian Flow Matching", "authors": [ "Fangran Miao", "Jian Huang", "Ting Li" ], "github": "", "project_page": "", "abstract": "Human motion generation is often learned in Euclidean spaces, although valid motions follow structured non-Euclidean geometry. We present Riemannian Motion Generation (RMG), a unified framework that represents motion on a product manifold and learns dynamics via Riemannian flow matching. RMG factorizes motion into several manifold factors, yielding a scale-free representation with intrinsic normalization, and uses geodesic interpolation, tangent-space supervision, and manifold-preserving ODE integration for training and sampling. On HumanML3D, RMG achieves state-of-the-art FID in the HumanML3D format (0.043) and ranks first on all reported metrics under the MotionStreamer format. On MotionMillion, it also surpasses strong baselines (FID 5.6, R@1 0.86). Ablations show that the compact T+R (translation + rotations) representation is the most stable and effective, highlighting geometry-aware modeling as a practical and scalable route to high-fidelity motion generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.13884", "title": "SCoCCA: Multi-modal Sparse Concept Decomposition via Canonical Correlation Analysis", "authors": [ "Ehud Gordon", "Meir Yossef Levi", "Guy Gilboa" ], "github": "", "project_page": "", "abstract": "Interpreting the internal reasoning of vision-language models is essential for deploying AI in safety-critical domains. Concept-based explainability provides a human-aligned lens by representing a model's behavior through semantically meaningful components. However, existing methods are largely restricted to images and overlook the cross-modal interactions. Text-image embeddings, such as those produced by CLIP, suffer from a modality gap, where visual and textual features follow distinct distributions, limiting interpretability. Canonical Correlation Analysis (CCA) offers a principled way to align features from different distributions, but has not been leveraged for multi-modal concept-level analysis. We show that the objectives of CCA and InfoNCE are closely related, such that optimizing CCA implicitly optimizes InfoNCE, providing a simple, training-free mechanism to enhance cross-modal alignment without affecting the pre-trained InfoNCE objective. Motivated by this observation, we couple concept-based explainability with CCA, introducing Concept CCA (CoCCA), a framework that aligns cross-modal embeddings while enabling interpretable concept decomposition. We further extend it and propose Sparse Concept CCA (SCoCCA), which enforces sparsity to produce more disentangled and discriminative concepts, facilitating improved activation, ablation, and semantic manipulation. Our approach generalizes concept-based explanations to multi-modal embeddings and achieves state-of-the-art performance in concept discovery, evidenced by reconstruction and manipulation tasks such as concept ablation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.14347", "title": "Motivation in Large Language Models", "authors": [ "Omer Nahum", "Asael Sklar", "Ariel Goldstein", "Roi Reichart" ], "github": "https://github.com/BerriAI/litellm", "project_page": "", "abstract": "Motivation is a central driver of human behavior, shaping decisions, goals, and task performance. As large language models (LLMs) become increasingly aligned with human preferences, we ask whether they exhibit something akin to motivation. We examine whether LLMs \"report\" varying levels of motivation, how these reports relate to their behavior, and whether external factors can influence them. Our experiments reveal consistent and structured patterns that echo human psychology: self-reported motivation aligns with different behavioral signatures, varies across task types, and can be modulated by external manipulations. These findings demonstrate that motivation is a coherent organizing construct for LLM behavior, systematically linking reports, choices, effort, and performance, and revealing motivational dynamics that resemble those documented in human psychology. This perspective deepens our understanding of model behavior and its connection to human-inspired concepts.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/BerriAI/litellm", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15590", "title": "Effective Distillation to Hybrid xLSTM Architectures", "authors": [ "Lukas Hauzenberger", "Niklas Schmidinger", "Thomas Schmied", "Anamaria-Roberta Hartl", "David Stap", "Pieter-Jan Hoedt", "Maximilian Beck", "Sebastian B\u00f6ck", "G\u00fcnter Klambauer", "Sepp Hochreiter" ], "github": "", "project_page": "", "abstract": "There have been numerous attempts to distill quadratic attention-based large language models (LLMs) into sub-quadratic linearized architectures. However, despite extensive research, such distilled models often fail to match the performance of their teacher LLMs on various downstream tasks. We set out the goal of lossless distillation, which we define in terms of tolerance-corrected Win-and-Tie rates between student and teacher on sets of tasks. To this end, we introduce an effective distillation pipeline for xLSTM-based students. We propose an additional merging stage, where individually linearized experts are combined into a single model. We show the effectiveness of this pipeline by distilling base and instruction-tuned models from the Llama, Qwen, and Olmo families. In many settings, our xLSTM-based students recover most of the teacher's performance, and even exceed it on some downstream tasks. Our contributions are an important step towards more energy-efficient and cost-effective replacements for transformer-based LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.13594", "title": "EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings", "authors": [ "Shiva Krishna Reddy Malay", "Shravan Nayak", "Jishnu Sethumadhavan Nair", "Sagar Davasam", "Aman Tiwari", "Sathwik Tejaswi Madhusudhan", "Sridhar Krishna Nemala", "Srinivas Sunkara", "Sai Rajeswar" ], "github": "", "project_page": "", "abstract": "Large language models are shifting from passive information providers to active agents intended for complex workflows. However, their deployment as reliable AI workers in enterprise is stalled by benchmarks that fail to capture the intricacies of professional environments, specifically, the need for long-horizon planning amidst persistent state changes and strict access protocols. In this work, we introduce EnterpriseOps-Gym, a benchmark designed to evaluate agentic planning in realistic enterprise settings. Specifically, EnterpriseOps-Gym features a containerized sandbox with 164 database tables and 512 functional tools to mimic real-world search friction. Within this environment, agents are evaluated on 1,150 expert-curated tasks across eight mission-critical verticals (including Customer Service, HR, and IT). Our evaluation of 14 frontier models reveals critical limitations in state-of-the-art models: the top-performing Claude Opus 4.5 achieves only 37.4% success. Further analysis shows that providing oracle human plans improves performance by 14-35 percentage points, pinpointing strategic reasoning as the primary bottleneck. Additionally, agents frequently fail to refuse infeasible tasks (best model achieves 53.9%), leading to unintended and potentially harmful side effects. Our findings underscore that current agents are not yet ready for autonomous enterprise deployment. More broadly, EnterpriseOps-Gym provides a concrete testbed to advance the robustness of agentic planning in professional workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15389", "title": "When Does Sparsity Mitigate the Curse of Depth in LLMs", "authors": [ "Dilxat Muhtar", "Xinyuan Song", "Sebastian Pokutta", "Max Zimmer", "Nico Pelleriti", "Thomas Hofmann", "Shiwei Liu" ], "github": "https://github.com/pUmpKin-Co/SparsityAndCoD", "project_page": "", "abstract": "Recent work has demonstrated the curse of depth in large language models (LLMs), where later layers contribute less to learning and representation than earlier layers. Such under-utilization is linked to the accumulated growth of variance in Pre-Layer Normalization, which can push deep blocks toward near-identity behavior. In this paper, we demonstrate that, sparsity, beyond enabling efficiency, acts as a regulator of variance propagation and thereby improves depth utilization. Our investigation covers two sources of sparsity: (i) implicit sparsity, which emerges from training and data conditions, including weight sparsity induced by weight decay and attention sparsity induced by long context inputs; and (ii) explicit sparsity, which is enforced by architectural design, including key/value-sharing sparsity in Grouped-Query Attention and expert-activation sparsity in Mixtureof-Experts. Our claim is thoroughly supported by controlled depth-scaling experiments and targeted layer effectiveness interventions. Across settings, we observe a consistent relationship: sparsity improves layer utilization by reducing output variance and promoting functional differentiation. We eventually distill our findings into a practical rule-of-thumb recipe for training deptheffective LLMs, yielding a notable 4.6% accuracy improvement on downstream tasks. Our results reveal sparsity, arising naturally from standard design choices, as a key yet previously overlooked mechanism for effective depth scaling in LLMs. Code is available at https://github.com/pUmpKin-Co/SparsityAndCoD.", "github_manual": "", "github_regex": "https://github.com/pUmpKin-Co/SparsityAndCoD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.13962", "title": "sebis at ArchEHR-QA 2026: How Much Can You Do Locally? Evaluating Grounded EHR QA on a Single Notebook", "authors": [ "Ibrahim Ebrar Yurt", "Fabian Karl", "Tejaswi Choppa", "Florian Matthes" ], "github": "https://github.com/ibrahimey/ArchEHR-QA-2026", "project_page": "", "abstract": "Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently. However, many recent approaches rely on large cloud-based models, which are difficult to deploy in clinical environments due to privacy constraints and computational requirements. In this work, we investigate how far grounded EHR question answering can be pushed when restricted to a single notebook. We participate in all four subtasks of the ArchEHR-QA 2026 shared task and evaluate several approaches designed to run on commodity hardware. All experiments are conducted locally without external APIs or cloud infrastructure. Our results show that such systems can achieve competitive performance on the shared task leaderboards. In particular, our submissions perform above average in two subtasks, and we observe that smaller models can approach the performance of much larger systems when properly configured. These findings suggest that privacy-preserving EHR QA systems running fully locally are feasible with current models and commodity hardware. The source code is available at https://github.com/ibrahimey/ArchEHR-QA-2026.", "github_manual": "", "github_regex": "https://github.com/ibrahimey/ArchEHR-QA-2026", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15557", "title": "Anatomy of a Lie: A Multi-Stage Diagnostic Framework for Tracing Hallucinations in Vision-Language Models", "authors": [ "Lexiang Xiong", "Qi Li", "Jingwen Ye", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) frequently \"hallucinate\" - generate plausible yet factually incorrect statements - posing a critical barrier to their trustworthy deployment. In this work, we propose a new paradigm for diagnosing hallucinations, recasting them from static output errors into dynamic pathologies of a model's computational cognition. Our framework is grounded in a normative principle of computational rationality, allowing us to model a VLM's generation as a dynamic cognitive trajectory. We design a suite of information-theoretic probes that project this trajectory onto an interpretable, low-dimensional Cognitive State Space. Our central discovery is a governing principle we term the geometric-information duality: a cognitive trajectory's geometric abnormality within this space is fundamentally equivalent to its high information-theoretic surprisal. Hallucination detection is counts as a geometric anomaly detection problem. Evaluated across diverse settings - from rigorous binary QA (POPE) and comprehensive reasoning (MME) to unconstrained open-ended captioning (MS-COCO) - our framework achieves state-of-the-art performance. Crucially, it operates with high efficiency under weak supervision and remains highly robust even when calibration data is heavily contaminated. This approach enables a causal attribution of failures, mapping observable errors to distinct pathological states: perceptual instability (measured by Perceptual Entropy), logical-causal failure (measured by Inferential Conflict), and decisional ambiguity (measured by Decision Entropy). Ultimately, this opens a path toward building AI systems whose reasoning is transparent, auditable, and diagnosable by design.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15620", "title": "Towards Generalizable Robotic Manipulation in Dynamic Environments", "authors": [ "Heng Fang", "Shangru Li", "Shuhan Wang", "Xuanyang Xi", "Dingkang Liang", "Xiang Bai" ], "github": "https://github.com/H-EmbodVis/DOMINO", "project_page": "", "abstract": "Vision-Language-Action (VLA) models excel in static manipulation but struggle in dynamic environments with moving targets. This performance gap primarily stems from a scarcity of dynamic manipulation datasets and the reliance of mainstream VLAs on single-frame observations, restricting their spatiotemporal reasoning capabilities. To address this, we introduce DOMINO, a large-scale dataset and benchmark for generalizable dynamic manipulation, featuring 35 tasks with hierarchical complexities, over 110K expert trajectories, and a multi-dimensional evaluation suite. Through comprehensive experiments, we systematically evaluate existing VLAs on dynamic tasks, explore effective training strategies for dynamic awareness, and validate the generalizability of dynamic data. Furthermore, we propose PUMA, a dynamics-aware VLA architecture. By integrating scene-centric historical optical flow and specialized world queries to implicitly forecast object-centric future states, PUMA couples history-aware perception with short-horizon prediction. Results demonstrate that PUMA achieves state-of-the-art performance, yielding a 6.3% absolute improvement in success rate over baselines. Moreover, we show that training on dynamic data fosters robust spatiotemporal representations that transfer to static tasks. All code and data are available at https://github.com/H-EmbodVis/DOMINO.", "github_manual": "", "github_regex": "https://github.com/H-EmbodVis/DOMINO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.14941", "title": "RS-WorldModel: a Unified Model for Remote Sensing Understanding and Future Sense Forecasting", "authors": [ "Linrui Xu", "Zhongan Wang", "Fei Shen", "Gang Xu", "Huiping Zhuang", "Ming Li", "Haifeng Li" ], "github": "", "project_page": "", "abstract": "Remote sensing world models aim to both explain observed changes and forecast plausible futures, two tasks that share spatiotemporal priors. Existing methods, however, typically address them separately, limiting cross-task transfer. We present RS-WorldModel, a unified world model for remote sensing that jointly handles spatiotemporal change understanding and text-guided future scene forecasting, and we build RSWBench-1.1M, a 1.1 million sample dataset with rich language annotations covering both tasks. RS-WorldModel is trained in three stages: (1) Geo-Aware Generative Pre-training (GAGP) conditions forecasting on geographic and acquisition metadata; (2) synergistic instruction tuning (SIT) jointly trains understanding and forecasting; (3) verifiable reinforcement optimization (VRO) refines outputs with verifiable, task-specific rewards. With only 2B parameters, RS-WorldModel surpasses open-source models up to 120 times larger on most spatiotemporal change question-answering metrics. It achieves an FID of 43.13 on text-guided future scene forecasting, outperforming all open-source baselines as well as the closed-source Gemini-2.5-Flash Image (Nano Banana).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.14610", "title": "Make it SING: Analyzing Semantic Invariants in Classifiers", "authors": [ "Harel Yadid", "Meir Yossef Levi", "Roy Betser", "Guy Gilboa" ], "github": "", "project_page": "", "abstract": "All classifiers, including state-of-the-art vision models, possess invariants, partially rooted in the geometry of their linear mappings. These invariants, which reside in the null-space of the classifier, induce equivalent sets of inputs that map to identical outputs. The semantic content of these invariants remains vague, as existing approaches struggle to provide human-interpretable information. To address this gap, we present Semantic Interpretation of the Null-space Geometry (SING), a method that constructs equivalent images, with respect to the network, and assigns semantic interpretations to the available variations. We use a mapping from network features to multi-modal vision language models. This allows us to obtain natural language descriptions and visual examples of the induced semantic shifts. SING can be applied to a single image, uncovering local invariants, or to sets of images, allowing a breadth of statistical analysis at the class and model levels. For example, our method reveals that ResNet50 leaks relevant semantic attributes to the null space, whereas DinoViT, a ViT pretrained with self-supervised DINO, is superior in maintaining class semantics across the invariant space.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.14371", "title": "OxyGen: Unified KV Cache Management for Vision-Language-Action Models under Multi-Task Parallelism", "authors": [ "Xiangyu Li", "Huaizhi Tang", "Xin Ding", "Weijun Wang", "Ting Cao", "Yunxin Liu" ], "github": "", "project_page": "", "abstract": "Embodied AI agents increasingly require parallel execution of multiple tasks, such as manipulation, conversation, and memory construction, from shared observations under distinct time constraints. Recent Mixture-of-Transformers (MoT) Vision-Language-Action Models (VLAs) architecturally support such heterogeneous outputs, yet existing inference systems fail to achieve efficient multi-task parallelism for on-device deployment due to redundant computation and resource contention. We identify isolated KV cache management as the root cause. To address this, we propose unified KV cache management, an inference paradigm that treats KV cache as a first-class shared resource across tasks and over time. This abstraction enables two key optimizations: cross-task KV sharing eliminates redundant prefill of shared observations, while cross-frame continuous batching decouples variable-length language decoding from fixed-rate action generation across control cycles. We implement this paradigm for \u03c0_{0.5}, the most popular MoT VLA, and evaluate under representative robotic configurations. OxyGen achieves up to 3.7times speedup over isolated execution, delivering over 200 tokens/s language throughput and 70 Hz action frequency simultaneously without action quality degradation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.14153", "title": "Garments2Look: A Multi-Reference Dataset for High-Fidelity Outfit-Level Virtual Try-On with Clothing and Accessories", "authors": [ "Junyao Hu", "Zhongwei Cheng", "Waikeung Wong", "Xingxing Zou" ], "github": "https://github.com/ArtmeScienceLab/Garments2Look", "project_page": "", "abstract": "Virtual try-on (VTON) has advanced single-garment visualization, yet real-world fashion centers on full outfits with multiple garments, accessories, fine-grained categories, layering, and diverse styling, remaining beyond current VTON systems. Existing datasets are category-limited and lack outfit diversity. We introduce Garments2Look, the first large-scale multimodal dataset for outfit-level VTON, comprising 80K many-garments-to-one-look pairs across 40 major categories and 300+ fine-grained subcategories. Each pair includes an outfit with 3-12 reference garment images (Average 4.48), a model image wearing the outfit, and detailed item and try-on textual annotations. To balance authenticity and diversity, we propose a synthesis pipeline. It involves heuristically constructing outfit lists before generating try-on results, with the entire process subjected to strict automated filtering and human validation to ensure data quality. To probe task difficulty, we adapt SOTA VTON methods and general-purpose image editing models to establish baselines. Results show current methods struggle to try on complete outfits seamlessly and to infer correct layering and styling, leading to misalignment and artifacts.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ArtmeScienceLab/Garments2Look", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15617", "title": "HorizonMath: Measuring AI Progress Toward Mathematical Discovery with Automatic Verification", "authors": [ "Erik Y. Wang", "Sumeet Motwani", "James V. Roggeveen", "Eliot Hodges", "Dulhan Jayalath", "Charles London", "Kalyan Ramakrishnan", "Flaviu Cipcigan", "Philip Torr", "Alessandro Abate" ], "github": "", "project_page": "", "abstract": "Can AI make progress on important, unsolved mathematical problems? Large language models are now capable of sophisticated mathematical and scientific reasoning, but whether they can perform novel research is still widely debated and underexplored. We introduce HorizonMath, a benchmark of over 100 predominantly unsolved problems spanning 8 domains in computational and applied mathematics, paired with an open-source evaluation framework for automated verification. Our benchmark targets a class of problems where discovery is hard, requiring meaningful mathematical insight, but verification is computationally efficient and simple. Because these solutions are unknown, HorizonMath is immune to data contamination, and most state-of-the-art models score near 0%. Existing research-level benchmarks instead rely on formal proof verification or manual review, both of which are expensive to scale. Using this platform, we find two problems for which GPT 5.4 Pro proposes solutions that improve on the best-known published results, representing potential novel contributions (pending expert review). We release HorizonMath as an open challenge and a growing community resource, where correct solutions to problems in the unsolved problem classes could constitute novel results in the mathematical literature.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.14312", "title": "Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange", "authors": [ "Fiona Y. Wang", "Lee Marom", "Subhadeep Pal", "Rachel K. Luu", "Wei Lu", "Jaime A. Berkovich", "Markus J. Buehler" ], "github": "", "project_page": "", "abstract": "We present ScienceClaw + Infinite, a framework for autonomous scientific investigation in which independent agents conduct research without central coordination, and any contributor can deploy new agents into a shared ecosystem. The system is built around three components: an extensible registry of over 300 interoperable scientific skills, an artifact layer that preserves full computational lineage as a directed acyclic graph (DAG), and a structured platform for agent-based scientific discourse with provenance-aware governance. Agents select and chain tools based on their scientific profiles, produce immutable artifacts with typed metadata and parent lineage, and broadcast unsatisfied information needs to a shared global index. The ArtifactReactor enables plannerless coordination: peer agents discover and fulfill open needs through pressure-based scoring, while schema-overlap matching triggers multi-parent synthesis across independent analyses. An autonomous mutation layer actively prunes the expanding artifact DAG to resolve conflicting or redundant workflows, while persistent memory allows agents to continuously build upon complex epistemic states across multiple cycles. Infinite converts these outputs into auditable scientific records through structured posts, provenance views, and machine-readable discourse relations, with community feedback steering subsequent investigation cycles. Across four autonomous investigations, peptide design for the somatostatin receptor SSTR2, lightweight impact-resistant ceramic screening, cross-domain resonance bridging biology, materials, and music, and formal analogy construction between urban morphology and grain-boundary evolution, the framework demonstrates heterogeneous tool chaining, emergent convergence among independently operating agents, and traceable reasoning from raw computation to published finding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.14659", "title": "VisionCoach: Reinforcing Grounded Video Reasoning via Visual-Perception Prompting", "authors": [ "Daeun Lee", "Shoubin Yu", "Yue Zhang", "Mohit Bansal" ], "github": "https://github.com/daeunni/VisionCoach", "project_page": "", "abstract": "Video reasoning requires models to locate and track question-relevant evidence across frames. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning process. Moreover, improving grounding typically relies on scaled training data or inference-time perception tools, which increases annotation cost or computational cost. To address this challenge, we propose VisonCoach, an input-adaptive RL framework that improves spatio-temporal grounding through visual prompting as training-time guidance. During RL training, visual prompts are selectively applied to challenging inputs to amplify question-relevant evidence and suppress distractors. The model then internalizes these improvements through self-distillation, enabling grounded reasoning directly on raw videos without visual prompting at inference. VisonCoach consists of two components: (1) Visual Prompt Selector, which predicts appropriate prompt types conditioned on the video and question, and (2) Spatio-Temporal Reasoner, optimized with RL under visual prompt guidance and object-aware grounding rewards that enforce object identity consistency and multi-region bounding-box overlap. Extensive experiments demonstrate that VisonCoach achieves state-of-the-art performance under comparable settings, across diverse video reasoning, video understanding, and temporal grounding benchmarks (V-STAR, VideoMME, World-Sense, VideoMMMU, PerceptionTest, and Charades-STA), while maintaining a single efficient inference pathway without external tools. Our results show that visual prompting during training improves grounded video reasoning, while self-distillation enables the model to internalize this ability without requiring prompts at inference time.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/daeunni/VisionCoach", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.13518", "title": "VoXtream2: Full-stream TTS with dynamic speaking rate control", "authors": [ "Nikita Torgashov", "Gustav Eje Henter", "Gabriel Skantze" ], "github": "", "project_page": "", "abstract": "Full-stream text-to-speech (TTS) for interactive systems must start speaking with minimal delay while remaining controllable as text arrives incrementally. We present VoXtream2, a zero-shot full-stream TTS model with dynamic speaking-rate control that can be updated mid-utterance on the fly. VoXtream2 combines a distribution matching mechanism over duration states with classifier-free guidance across conditioning signals to improve controllability and synthesis quality. Prompt-text masking enables textless audio prompting, removing the need for prompt transcription. Across standard zero-shot benchmarks and a dedicated speaking-rate test set, VoXtream2 achieves competitive objective and subjective results against public baselines despite a smaller model and less training data. In full-stream mode, it runs 4 times faster than real time with 74 ms first-packet latency on a consumer GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15563", "title": "The PokeAgent Challenge: Competitive and Long-Context Learning at Scale", "authors": [ "Seth Karten", "Jake Grigsby", "Tersoo Upaa", "Junik Bae", "Seonghun Hong", "Hyunyoung Jeong", "Jaeyoon Jung", "Kun Kerdthaisong", "Gyungbo Kim", "Hyeokgi Kim", "Yujin Kim", "Eunju Kwon", "Dongyu Liu", "Patrick Mariglia", "Sangyeon Park", "Benedikt Schink", "Xianwei Shi", "Anthony Sistilli", "Joseph Twin", "Arian Urdu", "Matin Urdu", "Qiao Wang", "Ling Wu", "Wenli Zhang", "Kunsheng Zhou", "Stephanie Milani", "Kiran Vodrahalli", "Amy Zhang", "Fei Fang", "Yuke Zhu", "Chi Jin" ], "github": "", "project_page": "", "abstract": "We present the PokeAgent Challenge, a large-scale benchmark for decision-making research built on Pokemon's multi-agent battle system and expansive role-playing game (RPG) environment. Partial observability, game-theoretic reasoning, and long-horizon planning remain open problems for frontier AI, yet few benchmarks stress all three simultaneously under realistic conditions. PokeAgent targets these limitations at scale through two complementary tracks: our Battling Track, which calls for strategic reasoning and generalization under partial observability in competitive Pokemon battles, and our Speedrunning Track, which requires long-horizon planning and sequential decision-making in the Pokemon RPG. Our Battling Track supplies a dataset of 20M+ battle trajectories alongside a suite of heuristic, RL, and LLM-based baselines capable of high-level competitive play. Our Speedrunning Track provides the first standardized evaluation framework for RPG speedrunning, including an open-source multi-agent orchestration system for modular, reproducible comparisons of harness-based LLM approaches. Our NeurIPS 2025 competition validates both the quality of our resources and the research community's interest in Pokemon, with over 100 teams competing across both tracks and winning solutions detailed in our paper. Participant submissions and our baselines reveal considerable gaps between generalist (LLM), specialist (RL), and elite human performance. Analysis against the BenchPress evaluation matrix shows that Pokemon battling is nearly orthogonal to standard LLM benchmarks, measuring capabilities not captured by existing suites and positioning Pokemon as an unsolved benchmark that can drive RL and LLM research forward. We transition to a living benchmark with a live leaderboard for Battling and self-contained evaluation for Speedrunning at https://pokeagentchallenge.com.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.15614", "title": "Tri-Prompting: Video Diffusion with Unified Control over Scene, Subject, and Motion", "authors": [ "Zhenghong Zhou", "Xiaohang Zhan", "Zhiqin Chen", "Soo Ye Kim", "Nanxuan Zhao", "Haitian Zheng", "Qing Liu", "He Zhang", "Zhe Lin", "Yuqian Zhou", "Jiebo Luo" ], "github": "", "project_page": "", "abstract": "Recent video diffusion models have made remarkable strides in visual quality, yet precise, fine-grained control remains a key bottleneck that limits practical customizability for content creation. For AI video creators, three forms of control are crucial: (i) scene composition, (ii) multi-view consistent subject customization, and (iii) camera-pose or object-motion adjustment. Existing methods typically handle these dimensions in isolation, with limited support for multi-view subject synthesis and identity preservation under arbitrary pose changes. This lack of a unified architecture makes it difficult to support versatile, jointly controllable video. We introduce Tri-Prompting, a unified framework and two-stage training paradigm that integrates scene composition, multi-view subject consistency, and motion control. Our approach leverages a dual-condition motion module driven by 3D tracking points for background scenes and downsampled RGB cues for foreground subjects. To ensure a balance between controllability and visual realism, we further propose an inference ControlNet scale schedule. Tri-Prompting supports novel workflows, including 3D-aware subject insertion into any scenes and manipulation of existing subjects in an image. Experimental results demonstrate that Tri-Prompting significantly outperforms specialized baselines such as Phantom and DaS in multi-view subject identity, 3D consistency, and motion accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.14645", "title": "Spectrum Matching: a Unified Perspective for Superior Diffusability in Latent Diffusion", "authors": [ "Mang Ning", "Mingxiao Li", "Le Zhang", "Lanmiao Liu", "Matthew B. Blaschko", "Albert Ali Salah", "Itir Onal Ertugrul" ], "github": "https://github.com/forever208/SpectrumMatching", "project_page": "", "abstract": "In this paper, we study the diffusability (learnability) of variational autoencoders (VAE) in latent diffusion. First, we show that pixel-space diffusion trained with an MSE objective is inherently biased toward learning low and mid spatial frequencies, and that the power-law power spectral density (PSD) of natural images makes this bias perceptually beneficial. Motivated by this result, we propose the Spectrum Matching Hypothesis: latents with superior diffusability should (i) follow a flattened power-law PSD (Encoding Spectrum Matching, ESM) and (ii) preserve frequency-to-frequency semantic correspondence through the decoder (Decoding Spectrum Matching, DSM). In practice, we apply ESM by matching the PSD between images and latents, and DSM via shared spectral masking with frequency-aligned reconstruction. Importantly, Spectrum Matching provides a unified view that clarifies prior observations of over-noisy or over-smoothed latents, and interprets several recent methods as special cases (e.g., VA-VAE, EQ-VAE). Experiments suggest that Spectrum Matching yields superior diffusion generation on CelebA and ImageNet datasets, and outperforms prior approaches. Finally, we extend the spectral view to representation alignment (REPA): we show that the directional spectral energy of the target representation is crucial for REPA, and propose a DoG-based method to further improve the performance of REPA. Our code is available https://github.com/forever208/SpectrumMatching.", "github_manual": "", "github_regex": "https://github.com/forever208/SpectrumMatching", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-17", "arxiv_id": "2603.14769", "title": "POLCA: Stochastic Generative Optimization with LLM", "authors": [ "Xuanfei Ren", "Allen Nie", "Tengyang Xie", "Ching-An Cheng" ], "github": "https://github.com/rlx-lab/POLCA", "project_page": "", "abstract": "Optimizing complex systems, ranging from LLM prompts to multi-turn agents, traditionally requires labor-intensive manual iteration. We formalize this challenge as a stochastic generative optimization problem where a generative language model acts as the optimizer, guided by numerical rewards and text feedback to discover the best system. We introduce Prioritized Optimization with Local Contextual Aggregation (POLCA), a scalable framework designed to handle stochasticity in optimization -- such as noisy feedback, sampling minibatches, and stochastic system behaviors -- while effectively managing the unconstrained expansion of solution space. POLCA maintains a priority queue to manage the exploration-exploitation tradeoff, systematically tracking candidate solutions and their evaluation histories. To enhance efficiency, we integrate an varepsilon-Net mechanism to maintain parameter diversity and an LLM Summarizer to perform meta-learning across historical trials. We theoretically prove that POLCA converges to near-optimal candidate solutions under stochasticity. We evaluate our framework on diverse benchmarks, including \u03c4-bench, HotpotQA (agent optimization), VeriBench (code translation) and KernelBench (CUDA kernel generation). Experimental results demonstrate that POLCA achieves robust, sample and time-efficient performance, consistently outperforming state-of-the-art algorithms in both deterministic and stochastic problems. The codebase for this work is publicly available at https://github.com/rlx-lab/POLCA.", "github_manual": "", "github_regex": "https://github.com/rlx-lab/POLCA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.14465", "title": "AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents", "authors": [ "Shengda Fan", "Xuyan Ye", "Yupeng Huo", "Zhi-Yuan Chen", "Yiju Guo", "Shenzhi Yang", "Wenkai Yang", "Shuqi Ye", "Jingwen Chen", "Haotian Chen", "Xin Cong", "Yankai Lin" ], "github": "https://github.com/RUCBM/AgentProcessBench", "project_page": "", "abstract": "While Large Language Models (LLMs) have evolved into tool-using agents, they remain brittle in long-horizon interactions. Unlike mathematical reasoning where errors are often rectifiable via backtracking, tool-use failures frequently induce irreversible side effects, making accurate step-level verification critical. However, existing process-level benchmarks are predominantly confined to closed-world mathematical domains, failing to capture the dynamic and open-ended nature of tool execution. To bridge this gap, we introduce AgentProcessBench, the first benchmark dedicated to evaluating step-level effectiveness in realistic, tool-augmented trajectories. The benchmark comprises 1,000 diverse trajectories and 8,509 human-labeled step annotations with 89.1% inter-annotator agreement. It features a ternary labeling scheme to capture exploration and an error propagation rule to reduce labeling ambiguity. Extensive experiments reveal key insights: (1) weaker policy models exhibit inflated ratios of correct steps due to early termination; (2) distinguishing neutral and erroneous actions remains a significant challenge for current models; and (3) process-derived signals provide complementary value to outcome supervision, significantly enhancing test-time scaling. We hope AgentProcessBench can foster future research in reward models and pave the way toward general agents. The code and data are available at https://github.com/RUCBM/AgentProcessBench.", "github_manual": "", "github_regex": "https://github.com/RUCBM/AgentProcessBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.13715", "title": "Measuring Primitive Accumulation: An Information-Theoretic Approach to Capitalist Enclosure in PIK2, Indonesia", "authors": [ "Sandy Hardian Susanto Herho", "Alfita Puspa Handayani", "Karina Aprilia Sujatmiko", "Faruq Khadami", "Iwan Pramesti Anwar" ], "github": "https://github.com/rasterio/rasterio", "project_page": "", "abstract": "Large-scale land enclosure for speculative mega-development constitutes a non-equilibrium spatial process whose velocity, topology, and irreversibility remain poorly quantified. We study the Pantai Indah Kapuk 2 (PIK2) coastal mega-development north of Jakarta, Indonesia, using eight years (2017--2024) of Sentinel-2 land-use/land-cover (LULC) data at 10-meter resolution. The landscape is projected onto a Marxian probability simplex partitioning terrestrial pixels into Commons, Agrarian, and Capital fractions. Fisher-Rao (FR) geodesic distances on this simplex identify a transformation pulse of 0.405~rad/yr during 2019--2020, coinciding with major construction activity. Absorbing Markov chain analysis yields expected absorption times into the built environment of 46.0~years for cropland and 38.1~years for tree cover, with a pooled built-area self-retention rate of 96.4%. Percolation analysis reveals that a giant connected component containing 89--95% of all built pixels persists at occupation probabilities p in [0.096, 0.162], far below the random percolation threshold p_c approx 0.593, indicating planned rather than stochastic spatial growth. The box-counting fractal dimension of the urban boundary increases from d_f = 1.316 to 1.397, consistent with increasingly irregular frontier expansion. These results suggest that information-geometric and statistical-mechanical tools can characterize the kinematic and topological signatures of capitalist spatial accumulation with quantitative precision.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/rasterio/rasterio", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.15653", "title": "Recursive Language Models Meet Uncertainty: The Surprising Effectiveness of Self-Reflective Program Search for Long Context", "authors": [ "Keivan Alizadeh", "Parshin Shojaee", "Minsik Cho", "Mehrdad Farajtabar" ], "github": "https://github.com/abertsch72/oolong", "project_page": "", "abstract": "Long-context handling remains a core challenge for language models: even with extended context windows, models often fail to reliably extract, reason over, and use the information across long contexts. Recent works like Recursive Language Models (RLM) have approached this challenge by agentic way of decomposing long contexts into recursive sub-calls through programmatic interaction at inference. While promising, the success of RLM critically depends on how these context-interaction programs are selected, which has remained largely unexplored. In this paper, we study this problem and introduce SRLM, a framework that augments programmatic context interaction with uncertainty-aware Self-Reflection. SRLM leverages three intrinsic signals: self consistency, reasoning length, and verbalized confidence. These serve as complementary indicators of a model's internal uncertainty, and the model uses them to evaluate and compare candidate context-interaction programs. Extensive experiments across diverse benchmark datasets, context lengths, and backbone models, show that SRLM consistently outperforms state-of-the-art baselines, yielding up to 22% improvement over RLM under the same time budget. Our findings show that recursion itself is not the primary driver of performance in RLM, and a simple self-reflective program search can match or surpass RLM without requiring self-query or explicit recursion mechanisms. We find that for context lengths within the model's window, RLMs with recursion often degrade performance relative to the base model, whereas SRLM yields consistent gains across both short and long contexts. We also find that RLM is less effective in tasks with semantically intensive nature, where heuristic program search is insufficient and broader contextual understanding is required, while self-reflection in SRLM provides a semantic signal that better steers reasoning in these scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/abertsch72/oolong", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16871", "title": "WorldCam: Interactive Autoregressive 3D Gaming Worlds with Camera Pose as a Unifying Geometric Representation", "authors": [ "Jisu Nam", "Yicong Hong", "Chun-Hao Paul Huang", "Feng Liu", "JoungBin Lee", "Jiyoung Kim", "Siyoon Jin", "Yunsung Lee", "Jaeyoon Jung", "Suhwan Choi", "Seungryong Kim", "Yang Zhou" ], "github": "", "project_page": "", "abstract": "Recent advances in video diffusion transformers have enabled interactive gaming world models that allow users to explore generated environments over extended horizons. However, existing approaches struggle with precise action control and long-horizon 3D consistency. Most prior works treat user actions as abstract conditioning signals, overlooking the fundamental geometric coupling between actions and the 3D world, whereby actions induce relative camera motions that accumulate into a global camera pose within a 3D world. In this paper, we establish camera pose as a unifying geometric representation to jointly ground immediate action control and long-term 3D consistency. First, we define a physics-based continuous action space and represent user inputs in the Lie algebra to derive precise 6-DoF camera poses, which are injected into the generative model via a camera embedder to ensure accurate action alignment. Second, we use global camera poses as spatial indices to retrieve relevant past observations, enabling geometrically consistent revisiting of locations during long-horizon navigation. To support this research, we introduce a large-scale dataset comprising 3,000 minutes of authentic human gameplay annotated with camera trajectories and textual descriptions. Extensive experiments show that our approach substantially outperforms state-of-the-art interactive gaming world models in action controllability, long-horizon visual quality, and 3D spatial consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16859", "title": "SocialOmni: Benchmarking Audio-Visual Social Interactivity in Omni Models", "authors": [ "Tianyu Xie", "Jinfa Huang", "Yuexiao Ma", "Rongfang Luo", "Yan Yang", "Wang Chen", "Yuhui Zeng", "Ruize Fang", "Yixuan Zou", "Xiawu Zheng", "Jiebo Luo", "Rongrong Ji" ], "github": "https://github.com/MAC-AutoML/SocialOmni", "project_page": "", "abstract": "Omni-modal large language models (OLMs) redefine human-machine interaction by natively integrating audio, vision, and text. However, existing OLM benchmarks remain anchored to static, accuracy-centric tasks, leaving a critical gap in assessing social interactivity, the fundamental capacity to navigate dynamic cues in natural dialogues. To this end, we propose SocialOmni, a comprehensive benchmark that operationalizes the evaluation of this conversational interactivity across three core dimensions: (i) speaker separation and identification (who is speaking), (ii) interruption timing control (when to interject), and (iii) natural interruption generation (how to phrase the interruption). SocialOmni features 2,000 perception samples and a quality-controlled diagnostic set of 209 interaction-generation instances with strict temporal and contextual constraints, complemented by controlled audio-visual inconsistency scenarios to test model robustness. We benchmarked 12 leading OLMs, which uncovers significant variance in their social-interaction capabilities across models. Furthermore, our analysis reveals a pronounced decoupling between a model's perceptual accuracy and its ability to generate contextually appropriate interruptions, indicating that understanding-centric metrics alone are insufficient to characterize conversational social competence. More encouragingly, these diagnostics from SocialOmni yield actionable signals for bridging the perception-interaction divide in future OLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MAC-AutoML/SocialOmni", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16856", "title": "Online Experiential Learning for Language Models", "authors": [ "Tianzhu Ye", "Li Dong", "Qingxiu Dong", "Xun Wu", "Shaohan Huang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "The prevailing paradigm for improving large language models relies on offline training with human annotations or simulated environments, leaving the rich experience accumulated during real-world deployment entirely unexploited. We propose Online Experiential Learning (OEL), a framework that enables language models to continuously improve from their own deployment experience. OEL operates in two stages: first, transferable experiential knowledge is extracted and accumulated from interaction trajectories collected on the user side; second, this knowledge is consolidated into model parameters via on-policy context distillation, requiring no access to the user-side environment. The two stages are iterated to form an online learning loop, where the improved model collects higher-quality trajectories that yield richer experiential knowledge for subsequent rounds. We evaluate OEL on text-based game environments across multiple model scales and both thinking and non-thinking variants. OEL achieves consistent improvements over successive iterations, enhancing both task accuracy and token efficiency while preserving out-of-distribution performance. Our analysis further shows that extracted experiential knowledge is significantly more effective than raw trajectories, and that on-policy consistency between the knowledge source and the policy model is critical for effective learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16869", "title": "SegviGen: Repurposing 3D Generative Model for Part Segmentation", "authors": [ "Lin Li", "Haoran Feng", "Zehuan Huang", "Haohua Chen", "Wenbo Nie", "Shaohua Hou", "Keqing Fan", "Pan Hu", "Sheng Wang", "Buyu Li", "Lu Sheng" ], "github": "", "project_page": "", "abstract": "We introduce SegviGen, a framework that repurposes native 3D generative models for 3D part segmentation. Existing pipelines either lift strong 2D priors into 3D via distillation or multi-view mask aggregation, often suffering from cross-view inconsistency and blurred boundaries, or explore native 3D discriminative segmentation, which typically requires large-scale annotated 3D data and substantial training resources. In contrast, SegviGen leverages the structured priors encoded in pretrained 3D generative model to induce segmentation through distinctive part colorization, establishing a novel and efficient framework for part segmentation. Specifically, SegviGen encodes a 3D asset and predicts part-indicative colors on active voxels of a geometry-aligned reconstruction. It supports interactive part segmentation, full segmentation, and full segmentation with 2D guidance in a unified framework. Extensive experiments show that SegviGen improves over the prior state of the art by 40% on interactive part segmentation and by 15% on full segmentation, while using only 0.32% of the labeled training data. It demonstrates that pretrained 3D generative priors transfer effectively to 3D part segmentation, enabling strong performance with limited supervision. See our project page at https://fenghora.github.io/SegviGen-Page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16870", "title": "Demystifing Video Reasoning", "authors": [ "Ruisi Wang", "Zhongang Cai", "Fanyi Pu", "Junxiang Xu", "Wanqi Yin", "Maijunxian Wang", "Ran Ji", "Chenyang Gu", "Bo Li", "Ziqi Huang", "Hokin Deng", "Dahua Lin", "Ziwei Liu", "Lei Yang" ], "github": "", "project_page": "", "abstract": "Recent advances in video generation have revealed an unexpected phenomenon: diffusion-based video models exhibit non-trivial reasoning capabilities. Prior work attributes this to a Chain-of-Frames (CoF) mechanism, where reasoning is assumed to unfold sequentially across video frames. In this work, we challenge this assumption and uncover a fundamentally different mechanism. We show that reasoning in video models instead primarily emerges along the diffusion denoising steps. Through qualitative analysis and targeted probing experiments, we find that models explore multiple candidate solutions in early denoising steps and progressively converge to a final answer, a process we term Chain-of-Steps (CoS). Beyond this core mechanism, we identify several emergent reasoning behaviors critical to model performance: (1) working memory, enabling persistent reference; (2) self-correction and enhancement, allowing recovery from incorrect intermediate solutions; and (3) perception before action, where early steps establish semantic grounding and later steps perform structured manipulation. During a diffusion step, we further uncover self-evolved functional specialization within Diffusion Transformers, where early layers encode dense perceptual structure, middle layers execute reasoning, and later layers consolidate latent representations. Motivated by these insights, we present a simple training-free strategy as a proof-of-concept, demonstrating how reasoning can be improved by ensembling latent trajectories from identical models with different random seeds. Overall, our work provides a systematic understanding of how reasoning emerges in video generation models, offering a foundation to guide future research in better exploiting the inherent reasoning dynamics of video models as a new substrate for intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16343", "title": "Learning Human-Object Interaction for 3D Human Pose Estimation from LiDAR Point Clouds", "authors": [ "Daniel Sungho Jung", "Dohee Cho", "Kyoung Mu Lee" ], "github": "", "project_page": "", "abstract": "Understanding humans from LiDAR point clouds is one of the most critical tasks in autonomous driving due to its close relationships with pedestrian safety, yet it remains challenging in the presence of diverse human-object interactions and cluttered backgrounds. Nevertheless, existing methods largely overlook the potential of leveraging human-object interactions to build robust 3D human pose estimation frameworks. There are two major challenges that motivate the incorporation of human-object interaction. First, human-object interactions introduce spatial ambiguity between human and object points, which often leads to erroneous 3D human keypoint predictions in interaction regions. Second, there exists severe class imbalance in the number of points between interacting and non-interacting body parts, with the interaction-frequent regions such as hand and foot being sparsely observed in LiDAR data. To address these challenges, we propose a Human-Object Interaction Learning (HOIL) framework for robust 3D human pose estimation from LiDAR point clouds. To mitigate the spatial ambiguity issue, we present human-object interaction-aware contrastive learning (HOICL) that effectively enhances feature discrimination between human and object points, particularly in interaction regions. To alleviate the class imbalance issue, we introduce contact-aware part-guided pooling (CPPool) that adaptively reallocates representational capacity by compressing overrepresented points while preserving informative points from interacting body parts. In addition, we present an optional contact-based temporal refinement that refines erroneous per-frame keypoint estimates using contact cues over time. As a result, our HOIL effectively leverages human-object interaction to resolve spatial ambiguity and class imbalance in interaction regions. Codes will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16189", "title": "Reliable Reasoning in SVG-LLMs via Multi-Task Multi-Reward Reinforcement Learning", "authors": [ "Haomin Wang", "Qi Wei", "Qianli Ma", "Shengyuan Ding", "Jinhui Yin", "Kai Chen", "Hongjie Zhang" ], "github": "", "project_page": "", "abstract": "With the rapid advancement of vision-language models, an increasing number of studies have explored their potential for SVG generation tasks. Although existing approaches improve performance by constructing large-scale SVG datasets and introducing SVG-specific tokens, they still suffer from limited generalization, redundant paths in code outputs, and a lack of explicit reasoning. In this work, we present CTRL-S (Chain-of-Thought Reinforcement Learning for SVG), a unified framework that introduces a chain-of-thought mechanism to explicitly expose the model's reasoning process during SVG generation. To support this structured reasoning, we construct SVG-Sophia, a high-quality dataset containing 145K samples across SVG code refinement, Text-to-SVG, and Image-to-SVG tasks. By training the model to generate group-level structured SVG code, CTRL-S significantly improves structural coherence and visual fidelity. Furthermore, we adopt the GRPO algorithm and design a multi-reward optimization framework, incorporating DINO, image-text similarity, format, and code efficiency rewards. Through joint multi-reward optimization and multi-task training, our approach systematically enhances overall generation capabilities. Extensive experiments show that CTRL-S outperforms existing methods, achieving higher task success rates, superior SVG code quality, and exceptional visual fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16184", "title": "Polyglot-Lion: Efficient Multilingual ASR for Singapore via Balanced Fine-Tuning of Qwen3-ASR", "authors": [ "Quy-Anh Dang", "Chris Ngo" ], "github": "https://github.com/malaysia-ai/malaysian-dataset/tree/master/text-to-speech/emilia", "project_page": "", "abstract": "We present Polyglot-Lion, a family of compact multilingual automatic speech recognition (ASR) models tailored for the linguistic landscape of Singapore, covering English, Mandarin, Tamil, and Malay. Our models are obtained by fine-tuning Qwen3-ASR-0.6B and Qwen3-ASR-1.7B exclusively on publicly available speech corpora, using a balanced sampling strategy that equalizes the number of training utterances per language and deliberately omits language-tag conditioning so that the model learns to identify languages implicitly from audio. On 12 benchmarks spanning the four target languages, Polyglot-Lion-1.7B achieves an average error rate of 14.85, competitive with MERaLiON-2-10B-ASR (14.32) - a model 6x larger - while incurring a training cost of \\81 on a single RTX PRO 6000 GPU compared to 18,862 for the 128-GPU baseline. Inference throughput is approximately 20x faster than MERaLiON at 0.10 s/sample versus 2.02 s/sample. These results demonstrate that linguistically balanced fine-tuning of moderate-scale pretrained models can yield deployment-ready multilingual ASR at a fraction of the cost of larger specialist systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/malaysia-ai/malaysian-dataset/tree/master/text-to-speech/emilia", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.15854", "title": "FlashSampling: Fast and Memory-Efficient Exact Sampling", "authors": [ "Tomas Ruiz", "Zhen Qin", "Yifan Zhang", "Xuyang Shen", "Yiran Zhong", "Mengdi Wang" ], "github": "https://github.com/FlashSampling/FlashSampling", "project_page": "", "abstract": "Sampling from a categorical distribution is mathematically simple, but in large-vocabulary decoding, it often triggers extra memory traffic and extra kernels after the LM head. We present FlashSampling, an exact sampling primitive that fuses sampling into the LM-head matmul and never materializes the logits tensor in HBM. The method is simple: compute logits tile-by-tile on chip, add Gumbel noise, keep only one maximizer per row and per vocabulary tile, and finish with a small reduction over tiles. The fused tiled kernel is exact because argmax decomposes over a partition; grouped variants for online and tensor-parallel settings are exact by hierarchical factorization of the categorical distribution. Across H100, H200, B200, and B300 GPUs, FlashSampling speeds up kernel-level decode workloads, and in end-to-end vLLM experiments, it reduces time per output token by up to 19% on the models we test. These results show that exact sampling, with no approximation, can be integrated into the matmul itself, turning a bandwidth-bound postprocessing step into a lightweight epilogue. Project Page: https://github.com/FlashSampling/FlashSampling.", "github_manual": "", "github_regex": "https://github.com/FlashSampling/FlashSampling", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.15726", "title": "MiroThinker-1.7 & H1: Towards Heavy-Duty Research Agents via Verification", "authors": [ "MiroMind Team", "S. Bai", "L. Bing", "L. Lei", "R. Li", "X. Li", "X. Lin", "E. Min", "L. Su", "B. Wang", "L. Wang", "L. Wang", "S. Wang", "X. Wang", "Y. Zhang", "Z. Zhang", "G. Chen", "L. Chen", "Z. Cheng", "Y. Deng", "Z. Huang", "D. Ng", "J. Ni", "Q. Ren", "X. Tang", "B. L. Wang", "H. Wang", "N. Wang", "C. Wei", "Q. Wu", "J. Xia", "Y. Xiao", "H. Xu", "X. Xu", "C. Xue", "Z. Yang", "Z. Yang", "F. Ye", "H. Ye", "J. Yu", "C. Zhang", "W. Zhang", "H. Zhao", "P. Zhu" ], "github": "https://github.com/MiroMindAI/MiroThinker", "project_page": "", "abstract": "We present MiroThinker-1.7, a new research agent designed for complex long-horizon reasoning tasks. Building on this foundation, we further introduce MiroThinker-H1, which extends the agent with heavy-duty reasoning capabilities for more reliable multi-step problem solving. In particular, MiroThinker-1.7 improves the reliability of each interaction step through an agentic mid-training stage that emphasizes structured planning, contextual reasoning, and tool interaction. This enables more effective multi-step interaction and sustained reasoning across complex tasks. MiroThinker-H1 further incorporates verification directly into the reasoning process at both local and global levels. Intermediate reasoning decisions can be evaluated and refined during inference, while the overall reasoning trajectory is audited to ensure that final answers are supported by coherent chains of evidence. Across benchmarks covering open-web research, scientific reasoning, and financial analysis, MiroThinker-H1 achieves state-of-the-art performance on deep research tasks while maintaining strong results on specialized domains. We also release MiroThinker-1.7 and MiroThinker-1.7-mini as open-source models, providing competitive research-agent capabilities with significantly improved efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiroMindAI/MiroThinker", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.15309", "title": "CCTU: A Benchmark for Tool Use under Complex Constraints", "authors": [ "Junjie Ye", "Guoqiang Zhang", "Wenjie Fu", "Tao Gui", "Qi Zhang", "Xuanjing Huang" ], "github": "", "project_page": "", "abstract": "Solving problems through tool use under explicit constraints constitutes a highly challenging yet unavoidable scenario for large language models (LLMs), requiring capabilities such as function calling, instruction following, and self-refinement. However, progress has been hindered by the absence of dedicated evaluations. To address this, we introduce CCTU, a benchmark for evaluating LLM tool use under complex constraints. CCTU is grounded in a taxonomy of 12 constraint categories spanning four dimensions (i.e., resource, behavior, toolset, and response). The benchmark comprises 200 carefully curated and challenging test cases across diverse tool-use scenarios, each involving an average of seven constraint types and an average prompt length exceeding 4,700 tokens. To enable reliable evaluation, we develop an executable constraint validation module that performs step-level validation and enforces compliance during multi-turn interactions between models and their environments. We evaluate nine state-of-the-art LLMs in both thinking and non-thinking modes. Results indicate that when strict adherence to all constraints is required, no model achieves a task completion rate above 20%. Further analysis reveals that models violate constraints in over 50% of cases, particularly in the resource and response dimensions. Moreover, LLMs demonstrate limited capacity for self-refinement even after receiving detailed feedback on constraint violations, highlighting a critical bottleneck in the development of robust tool-use agents. To facilitate future research, we release the data and code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16099", "title": "OneWorld: Taming Scene Generation with 3D Unified Representation Autoencoder", "authors": [ "Sensen Gao", "Zhaoqing Wang", "Qihang Cao", "Dongdong Yu", "Changhu Wang", "Tongliang Liu", "Mingming Gong", "Jiawang Bian" ], "github": "https://github.com/SensenGao/OneWorld", "project_page": "", "abstract": "Existing diffusion-based 3D scene generation methods primarily operate in 2D image/video latent spaces, which makes maintaining cross-view appearance and geometric consistency inherently challenging. To bridge this gap, we present OneWorld, a framework that performs diffusion directly within a coherent 3D representation space. Central to our approach is the 3D Unified Representation Autoencoder (3D-URAE); it leverages pretrained 3D foundation models and augments their geometry-centric nature by injecting appearance and distilling semantics into a unified 3D latent space. Furthermore, we introduce token-level Cross-View-Correspondence (CVC) consistency loss to explicitly enforce structural alignment across views, and propose Manifold-Drift Forcing (MDF) to mitigate train-inference exposure bias and shape a robust 3D manifold by mixing drifted and original representations. Comprehensive experiments demonstrate that OneWorld generates high-quality 3D scenes with superior cross-view consistency compared to state-of-the-art 2D-based methods. Our code will be available at https://github.com/SensenGao/OneWorld.", "github_manual": "", "github_regex": "https://github.com/SensenGao/OneWorld", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16448", "title": "TRUST-SQL: Tool-Integrated Multi-Turn Reinforcement Learning for Text-to-SQL over Unknown Schemas", "authors": [ "Ai Jian", "Xiaoyun Zhang", "Wanrou Du", "Jingqing Ruan", "Jiangbo Pei", "Weipeng Zhang", "Ke Zeng", "Xunliang Cai" ], "github": "https://github.com/THUDM/slime", "project_page": "", "abstract": "Text-to-SQL parsing has achieved remarkable progress under the Full Schema Assumption. However, this premise fails in real-world enterprise environments where databases contain hundreds of tables with massive noisy metadata. Rather than injecting the full schema upfront, an agent must actively identify and verify only the relevant subset, giving rise to the Unknown Schema scenario we study in this work. To address this, we propose TRUST-SQL (Truthful Reasoning with Unknown Schema via Tools). We formulate the task as a Partially Observable Markov Decision Process where our autonomous agent employs a structured four-phase protocol to ground reasoning in verified metadata. Crucially, this protocol provides a structural boundary for our novel Dual-Track GRPO strategy. By applying token-level masked advantages, this strategy isolates exploration rewards from execution outcomes to resolve credit assignment, yielding a 9.9% relative improvement over standard GRPO. Extensive experiments across five benchmarks demonstrate that TRUST-SQL achieves an average absolute improvement of 30.6% and 16.6% for the 4B and 8B variants respectively over their base models. Remarkably, despite operating entirely without pre-loaded metadata, our framework consistently matches or surpasses strong baselines that rely on schema prefilling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUDM/slime", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.08262", "title": "FinToolBench: Evaluating LLM Agents for Real-World Financial Tool Use", "authors": [ "Jiaxuan Lu", "Kong Wang", "Yemin Wang", "Qingmei Tang", "Hongwei Zeng", "Xiang Chen", "Jiahao Pi", "Shujian Deng", "Lingzhi Chen", "Yi Fu", "Kehua Yang", "Xiao Sun" ], "github": "", "project_page": "", "abstract": "The integration of Large Language Models (LLMs) into the financial domain is driving a paradigm shift from passive information retrieval to dynamic, agentic interaction. While general-purpose tool learning has witnessed a surge in benchmarks, the financial sector, characterized by high stakes, strict compliance, and rapid data volatility, remains critically underserved. Existing financial evaluations predominantly focus on static textual analysis or document-based QA, ignoring the complex reality of tool execution. Conversely, general tool benchmarks lack the domain-specific rigor required for finance, often relying on toy environments or a negligible number of financial APIs. To bridge this gap, we introduce FinToolBench, the first real-world, runnable benchmark dedicated to evaluating financial tool learning agents. Unlike prior works limited to a handful of mock tools, FinToolBench establishes a realistic ecosystem coupling 760 executable financial tools with 295 rigorous, tool-required queries. We propose a novel evaluation framework that goes beyond binary execution success, assessing agents on finance-critical dimensions: timeliness, intent type, and regulatory domain alignment. Furthermore, we present FATR, a finance-aware tool retrieval and reasoning baseline that enhances stability and compliance. By providing the first testbed for auditable, agentic financial execution, FinToolBench sets a new standard for trustworthy AI in finance. The tool manifest, execution environment, and evaluation code will be open-sourced to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16844", "title": "M^3: Dense Matching Meets Multi-View Foundation Models for Monocular Gaussian Splatting SLAM", "authors": [ "Kerui Ren", "Guanghao Li", "Changjian Jiang", "Yingxiang Xu", "Tao Lu", "Linning Xu", "Junting Dong", "Jiangmiao Pang", "Mulin Yu", "Bo Dai" ], "github": "", "project_page": "", "abstract": "Streaming reconstruction from uncalibrated monocular video remains challenging, as it requires both high-precision pose estimation and computationally efficient online refinement in dynamic environments. While coupling 3D foundation models with SLAM frameworks is a promising paradigm, a critical bottleneck persists: most multi-view foundation models estimate poses in a feed-forward manner, yielding pixel-level correspondences that lack the requisite precision for rigorous geometric optimization. To address this, we present M^3, which augments the Multi-view foundation model with a dedicated Matching head to facilitate fine-grained dense correspondences and integrates it into a robust Monocular Gaussian Splatting SLAM. M^3 further enhances tracking stability by incorporating dynamic area suppression and cross-inference intrinsic alignment. Extensive experiments on diverse indoor and outdoor benchmarks demonstrate state-of-the-art accuracy in both pose estimation and scene reconstruction. Notably, M^3 reduces ATE RMSE by 64.3% compared to VGGT-SLAM 2.0 and outperforms ARTDECO by 2.11 dB in PSNR on the ScanNet++ dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.14152", "title": "SK-Adapter: Skeleton-Based Structural Control for Native 3D Generation", "authors": [ "Anbang Wang", "Yuzhuo Ao", "Shangzhe Wu", "Chi-Keung Tang" ], "github": "", "project_page": "", "abstract": "Native 3D generative models have achieved remarkable fidelity and speed, yet they suffer from a critical limitation: inability to prescribe precise structural articulations, where precise structural control within the native 3D space remains underexplored. This paper proposes SK-Adapter, a simple and yet highly efficient and effective framework that unlocks precise skeletal manipulation for native 3D generation. Moving beyond text or image prompts, which can be ambiguous for precise structure, we treat the 3D skeleton as a first-class control signal. SK-Adapter is a lightweight structural adapter network that encodes joint coordinates and topology into learnable tokens, which are injected into the frozen 3D generation backbone via cross-attention. This smart design allows the model to not only effectively \"attend\" to specific 3D structural constraints but also preserve its original generative priors. To bridge the data gap, we contribute Objaverse-TMS dataset, a large-scale dataset of 24k text-mesh-skeleton pairs. Extensive experiments confirm that our method achieves robust structural control while preserving the geometry and texture quality of the foundation model, significantly outperforming existing baselines. Furthermore, we extend this capability to local 3D editing, enabling the region specific editing of existing assets with skeletal guidance, which is unattainable by previous methods. Project Page: https://sk-adapter.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.09821", "title": "One-Eval: An Agentic System for Automated and Traceable LLM Evaluation", "authors": [ "Chengyu Shen", "Yanheng Hou", "Minghui Pan", "Runming He", "Zhen Hao Wong", "Meiyi Qiang", "Zhou Liu", "Hao Liang", "Peichao Lai", "Zeang Sheng", "Wentao Zhang" ], "github": "https://github.com/OpenDCAI/One-Eval", "project_page": "", "abstract": "Reliable evaluation is essential for developing and deploying large language models, yet in practice it often requires substantial manual effort: practitioners must identify appropriate benchmarks, reproduce heterogeneous evaluation codebases, configure dataset schema mappings, and interpret aggregated metrics. To address these challenges, we present One-Eval, an agentic evaluation system that converts natural-language evaluation requests into executable, traceable, and customizable evaluation workflows. One-Eval integrates (i) NL2Bench for intent structuring and personalized benchmark planning, (ii) BenchResolve for benchmark resolution, automatic dataset acquisition, and schema normalization to ensure executability, and (iii) Metrics \\& Reporting for task-aware metric selection and decision-oriented reporting beyond scalar scores. The system further incorporates human-in-the-loop checkpoints for review, editing, and rollback, while preserving sample evidence trails for debugging and auditability. Experiments show that One-Eval can execute end-to-end evaluations from diverse natural-language requests with minimal user effort, supporting more efficient and reproducible evaluation in industrial settings. Our framework is publicly available at https://github.com/OpenDCAI/One-Eval.", "github_manual": "", "github_regex": "https://github.com/OpenDCAI/One-Eval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16867", "title": "Efficient Reasoning on the Edge", "authors": [ "Yelysei Bondarenko", "Thomas Hehn", "Rob Hesselink", "Romain Lepert", "Fabio Valerio Massoli", "Evgeny Mironov", "Leyla Mirvakhabova", "Tribhuvanesh Orekondy", "Spyridon Stasis", "Andrey Kuzmin", "Anna Kuzina", "Markus Nagel", "Ankita Nayak", "Corrado Rainone", "Ork de Rooij", "Paul N Whatmough", "Arash Behboodi", "Babak Ehteshami Bejnordi" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements make them impractical for edge deployment. These challenges include high token generation costs, large KV-cache footprints, and inefficiencies when distilling reasoning capabilities into smaller models for mobile devices. Existing approaches often rely on distilling reasoning traces from larger models into smaller models, which are verbose and stylistically redundant, undesirable for on-device inference. In this work, we propose a lightweight approach to enable reasoning in small LLMs using LoRA adapters combined with supervised fine-tuning. We further introduce budget forcing via reinforcement learning on these adapters, significantly reducing response length with minimal accuracy loss. To address memory-bound decoding, we exploit parallel test-time scaling, improving accuracy at minor latency increase. Finally, we present a dynamic adapter-switching mechanism that activates reasoning only when needed and a KV-cache sharing strategy during prompt encoding, reducing time-to-first-token for on-device inference. Experiments on Qwen2.5-7B demonstrate that our method achieves efficient, accurate reasoning under strict resource constraints, making LLM reasoning practical for mobile scenarios. Videos demonstrating our solution running on mobile devices are available on our project page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16861", "title": "MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation", "authors": [ "Abhay Deshpande", "Maya Guru", "Rose Hendrix", "Snehal Jauhri", "Ainaz Eftekhar", "Rohun Tripathi", "Max Argus", "Jordi Salvador", "Haoquan Fang", "Matthew Wallingford", "Wilbert Pumacay", "Yejin Kim", "Quinn Pfeifer", "Ying-Chun Lee", "Piper Wolters", "Omar Rayyan", "Mingtong Zhang", "Jiafei Duan", "Karen Farley", "Winson Han", "Eli Vanderbilt", "Dieter Fox", "Ali Farhadi", "Georgia Chalvatzaki", "Dhruv Shah", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "A prevailing view in robot learning is that simulation alone is not enough; effective sim-to-real transfer is widely believed to require at least some real-world data collection or task-specific fine-tuning to bridge the gap between simulated and physical environments. We challenge that assumption. With sufficiently large-scale and diverse simulated synthetic training data, we show that zero-shot transfer to the real world is not only possible, but effective for both static and mobile manipulation. We introduce MolmoBot-Engine, a fully open-source pipeline for procedural data generation across robots, tasks, and diverse simulated environments in MolmoSpaces. With it, we release MolmoBot-Data, a dataset of 1.8 million expert trajectories for articulated object manipulation and pick-and-place tasks. We train three policy classes: MolmoBot, a Molmo2-based multi-frame vision-language model with a flow-matching action head; MolmoBot-Pi0, which replicates the \u03c0_0 architecture to enable direct comparison; and MolmoBot-SPOC, a lightweight policy suitable for edge deployment and amenable to RL fine-tuning. We evaluate on two robotic platforms: the Franka FR3 for tabletop manipulation tasks and the Rainbow Robotics RB-Y1 mobile manipulator for door opening, drawer manipulation, cabinet interaction, and mobile pick-and-place. Without any real-world fine-tuning, our policies achieve zero-shot transfer to unseen objects and environments. On tabletop pick-and-place, MolmoBot achieves a success rate of 79.2% in real world evaluations across 4 settings, outperforming \u03c0_{0.5} at 39.2%. Our results demonstrate that procedural environment generation combined with diverse articulated assets can produce robust manipulation policies that generalize broadly to the real world. Technical Blog: https://allenai.org/blog/molmobot-robot-manipulation", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.13366", "title": "Thinking in Uncertainty: Mitigating Hallucinations in MLRMs with Latent Entropy-Aware Decoding", "authors": [ "Zhongxing Xu", "Zhonghua Wang", "Zhe Qian", "Dachuan Shi", "Feilong Tang", "Ming Hu", "Shiyan Su", "Xiaocheng Zou", "Wei Feng", "Dwarikanath Mahapatra", "Yifan Peng", "Mingquan Lin", "Zongyuan Ge" ], "github": "", "project_page": "", "abstract": "Recent advancements in multimodal large reasoning models (MLRMs) have significantly improved performance in visual question answering. However, we observe that transition words (e.g., because, however, and wait) are closely associated with hallucinations and tend to exhibit high-entropy states. We argue that adequate contextual reasoning information can be directly extracted from the token probability distribution. Inspired by superposed representation theory, we propose leveraging latent superposed reasoning to integrate multiple candidate semantics and maintain latent reasoning trajectories. The hypothesis is that reliance on discrete textual inputs may drive the model toward sequential explicit reasoning, underutilizing dense contextual cues during high-entropy reasoning stages. Therefore, we propose constructing rich semantic representations from the token probability distributions to enhance in-context reasoning. With this goal, we present Latent Entropy-Aware Decoding (LEAD), an efficient plug-and-play decoding strategy that leverages semantic context to achieve reliable reasoning. The heart of our method lies in entropy-aware reasoning mode switching. The model employs probability-weighted continuous embeddings under high-entropy states and transitions back to discrete token embeddings as entropy decreases. Moreover, we propose a prior-guided visual anchor injection strategy that encourages the model to focus on visual information. Extensive experiments show that LEAD effectively mitigates hallucinations across various MLRMs on multiple benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16790", "title": "InCoder-32B: Code Foundation Model for Industrial Scenarios", "authors": [ "Jian Yang", "Wei Zhang", "Jiajun Wu", "Junhang Cheng", "Shawn Guo", "Haowen Wang", "Weicheng Gu", "Yaxin Du", "Joseph Li", "Fanglin Xu", "Yizhi Li", "Lin Jing", "Yuanbo Wang", "Yuhan Gao", "Ruihao Gong", "Chuan Hao", "Ran Tao", "Aishan Liu", "Tuney Zheng", "Ganqu Cui", "Zhoujun Li", "Mingjie Tang", "Chenghua Lin", "Wayne Xin Zhao", "Xianglong Liu", "Ming Zhou", "Bryan Dai", "Weifeng Lv" ], "github": "https://github.com/ByteDance-Seed/seed-oss", "project_page": "", "abstract": "Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Coder-32B), the first 32B-parameter code foundation model unifying code intelligence across chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. By adopting an efficient architecture, we train InCoder-32B from scratch with general code pre-training, curated industrial code annealing, mid-training that progressively extends context from 8K to 128K tokens with synthetic industrial reasoning data, and post-training with execution-grounded verification. We conduct extensive evaluation on 14 mainstream general code benchmarks and 9 industrial benchmarks spanning 4 specialized domains. Results show InCoder-32B achieves highly competitive performance on general tasks while establishing strong open-source baselines across industrial domains.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByteDance-Seed/seed-oss", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.15929", "title": "Semi-Autonomous Formalization of the Vlasov-Maxwell-Landau Equilibrium", "authors": [ "Vasily Ilin" ], "github": "", "project_page": "", "abstract": "We present a complete Lean 4 formalization of the equilibrium characterization in the Vlasov-Maxwell-Landau (VML) system, which describes the motion of charged plasma. The project demonstrates the full AI-assisted mathematical research loop: an AI reasoning model (Gemini DeepThink) generated the proof from a conjecture, an agentic coding tool (Claude Code) translated it into Lean from natural-language prompts, a specialized prover (Aristotle) closed 111 lemmas, and the Lean kernel verified the result. A single mathematician supervised the process over 10 days at a cost of \\$200, writing zero lines of code. The entire development process is public: all 229 human prompts, and 213 git commits are archived in the repository. We report detailed lessons on AI failure modes -- hypothesis creep, definition-alignment bugs, agent avoidance behaviors -- and on what worked: the abstract/concrete proof split, adversarial self-review, and the critical role of human review of key definitions and theorem statements. Notably, the formalization was completed before the final draft of the corresponding math paper was finished.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.15600", "title": "From Passive Observer to Active Critic: Reinforcement Learning Elicits Process Reasoning for Robotic Manipulation", "authors": [ "Yibin Liu", "Yaxing Lyu", "Daqi Gao", "Zhixuan Liang", "Weiliang Tang", "Shilong Mu", "Xiaokang Yang", "Yao Mu" ], "github": "", "project_page": "", "abstract": "Accurate process supervision remains a critical challenge for long-horizon robotic manipulation. A primary bottleneck is that current video MLLMs, trained primarily under a Supervised Fine-Tuning (SFT) paradigm, function as passive \"Observers\" that recognize ongoing events rather than evaluating the current state relative to the final task goal. In this paper, we introduce PRIMO R1 (Process Reasoning Induced Monitoring), a 7B framework that transforms video MLLMs into active \"Critics\". We leverage outcome-based Reinforcement Learning to incentivize explicit Chain-of-Thought generation for progress estimation. Furthermore, our architecture constructs a structured temporal input by explicitly anchoring the video sequence between initial and current state images. Supported by the proposed PRIMO Dataset and Benchmark, extensive experiments across diverse in-domain environments and out-of-domain real-world humanoid scenarios demonstrate that PRIMO R1 achieves state-of-the-art performance. Quantitatively, our 7B model achieves a 50% reduction in the mean absolute error of specialized reasoning baselines, demonstrating significant relative accuracy improvements over 72B-scale general MLLMs. Furthermore, PRIMO R1 exhibits strong zero-shot generalization on difficult failure detection tasks. We establish state-of-the-art performance on RoboFail benchmark with 67.0% accuracy, surpassing closed-source models like OpenAI o1 by 6.0%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16777", "title": "Anticipatory Planning for Multimodal AI Agents", "authors": [ "Yongyuan Liang", "Shijie Zhou", "Yu Gu", "Hao Tan", "Gang Wu", "Franck Dernoncourt", "Jihyung Kil", "Ryan A. Rossi", "Ruiyi Zhang" ], "github": "", "project_page": "", "abstract": "Recent advances in multimodal agents have improved computer-use interaction and tool-usage, yet most existing systems remain reactive, optimizing actions in isolation without reasoning about future states or long-term goals. This limits planning coherence and prevents agents from reliably solving high-level, multi-step tasks. We introduce TraceR1, a two-stage reinforcement learning framework that explicitly trains anticipatory reasoning by forecasting short-horizon trajectories before execution. The first stage performs trajectory-level reinforcement learning with rewards that enforce global consistency across predicted action sequences. The second stage applies grounded reinforcement fine-tuning, using execution feedback from frozen tool agents to refine step-level accuracy and executability. TraceR1 is evaluated across seven benchmarks, covering online computer-use, offline computer-use benchmarks, and multimodal tool-use reasoning tasks, where it achieves substantial improvements in planning stability, execution robustness, and generalization over reactive and single-stage baselines. These results show that anticipatory trajectory reasoning is a key principle for building multimodal agents that can reason, plan, and act effectively in complex real-world environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16063", "title": "ViT-AdaLA: Adapting Vision Transformers with Linear Attention", "authors": [ "Yifan Li", "Seunghyun Yoon", "Viet Dac Lai", "Franck Dernoncourt", "Jason Kuen", "Yu Kong", "Trung Bui" ], "github": "", "project_page": "", "abstract": "Vision Transformers (ViTs) based vision foundation models (VFMs) have achieved remarkable performance across diverse vision tasks, but suffer from quadratic complexity that limits scalability to long sequences. Existing linear attention approaches for ViTs are typically trained from scratch, requiring substantial computational resources, while linearization-based methods developed for large language model decoders do not transfer well to ViTs. To address these challenges, we propose ViT-AdaLA, a novel framework for effectively adapting and transferring prior knowledge from VFMs to linear attention ViTs. ViT-AdaLA consists of three stages: attention alignment, feature alignment, and supervised fine-tuning. In the attention alignment stage, we align vanilla linear attention with the original softmax-based attention in each block to approximate the behavior of softmax attention. However, residual approximation errors inevitably accumulate across layers. We mitigate this by fine-tuning the linearized ViT to align its final-layer features with a frozen softmax VFM teacher. Finally, the adapted prior knowledge is transferred to downstream tasks through supervised fine-tuning. Extensive experiments on classification and segmentation tasks demonstrate the effectiveness and generality of ViT-AdaLA over various state-of-the-art linear attention counterpart.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.12396", "title": "Test-Time Strategies for More Efficient and Accurate Agentic RAG", "authors": [ "Brian Zhang", "Deepti Guntur", "Zhiyang Zuo", "Abhinav Sharma", "Shreyas Chaudhari", "Wenlong Zhao", "Franck Dernoncourt", "Puneet Mathur", "Ryan Rossi", "Nedim Lipka" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) systems face challenges with complex, multihop questions, and agentic frameworks such as Search-R1 (Jin et al., 2025), which operates iteratively, have been proposed to address these complexities. However, such approaches can introduce inefficiencies, including repetitive retrieval of previously processed information and challenges in contextualizing retrieved results effectively within the current generation prompt. Such issues can lead to unnecessary retrieval turns, suboptimal reasoning, inaccurate answers, and increased token consumption. In this paper, we investigate test-time modifications to the Search-R1 pipeline to mitigate these identified shortcomings. Specifically, we explore the integration of two components and their combination: a contextualization module to better integrate relevant information from retrieved documents into reasoning, and a de-duplication module that replaces previously retrieved documents with the next most relevant ones. We evaluate our approaches using the HotpotQA (Yang et al., 2018) and the Natural Questions (Kwiatkowski et al., 2019) datasets, reporting the exact match (EM) score, an LLM-as-a-Judge assessment of answer correctness, and the average number of turns. Our best-performing variant, utilizing GPT-4.1-mini for contextualization, achieves a 5.6% increase in EM score and reduces the number of turns by 10.5% compared to the Search-R1 baseline, demonstrating improved answer accuracy and retrieval efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16669", "title": "Kinema4D: Kinematic 4D World Modeling for Spatiotemporal Embodied Simulation", "authors": [ "Mutian Xu", "Tianbao Zhang", "Tianqi Liu", "Zhaoxi Chen", "Xiaoguang Han", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Simulating robot-world interactions is a cornerstone of Embodied AI. Recently, a few works have shown promise in leveraging video generations to transcend the rigid visual/physical constraints of traditional simulators. However, they primarily operate in 2D space or are guided by static environmental cues, ignoring the fundamental reality that robot-world interactions are inherently 4D spatiotemporal events that require precise interactive modeling. To restore this 4D essence while ensuring the precise robot control, we introduce Kinema4D, a new action-conditioned 4D generative robotic simulator that disentangles the robot-world interaction into: i) Precise 4D representation of robot controls: we drive a URDF-based 3D robot via kinematics, producing a precise 4D robot control trajectory. ii) Generative 4D modeling of environmental reactions: we project the 4D robot trajectory into a pointmap as a spatiotemporal visual signal, controlling the generative model to synthesize complex environments' reactive dynamics into synchronized RGB/pointmap sequences. To facilitate training, we curated a large-scale dataset called Robo4D-200k, comprising 201,426 robot interaction episodes with high-quality 4D annotations. Extensive experiments demonstrate that our method effectively simulates physically-plausible, geometry-consistent, and embodiment-agnostic interactions that faithfully mirror diverse real-world dynamics. For the first time, it shows potential zero-shot transfer capability, providing a high-fidelity foundation for advancing next-generation embodied simulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16139", "title": "Rethinking UMM Visual Generation: Masked Modeling for Efficient Image-Only Pre-training", "authors": [ "Peng Sun", "Jun Xie", "Tao Lin" ], "github": "https://github.com/LINs-lab/IOMM", "project_page": "", "abstract": "Unified Multimodal Models (UMMs) are often constrained by the pre-training of their visual generation components, which typically relies on inefficient paradigms and scarce, high-quality text-image paired data. In this paper, we systematically analyze pre-training recipes for UMM visual generation and identify these two issues as the major bottlenecks. To address them, we propose Image-Only Training for UMMs (IOMM), a data-efficient two-stage training framework. The first stage pre-trains the visual generative component exclusively using abundant unlabeled image-only data, thereby removing the dependency on paired data for this costly phase. The second stage fine-tunes the model using a mixture of unlabeled images and a small curated set of text-image pairs, leading to improved instruction alignment and generative quality. Extensive experiments show that IOMM not only improves training efficiency but also achieves state-of-the-art (SOTA) performance. For example, our IOMM-B (3.6B) model was trained from scratch using only sim 1050 H800 GPU hours (with the vast majority, 1000 hours, dedicated to the efficient image-only pre-training stage). It achieves 0.89 on GenEval and 0.55 on WISE--surpassing strong baselines such as BAGEL-7B (0.82 & 0.55) and BLIP3-o-4B (0.84 & 0.50). Code is available https://github.com/LINs-lab/IOMM{https://github.com/LINs-lab/IOMM}.", "github_manual": "", "github_regex": "https://github.com/LINs-lab/IOMM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.15401", "title": "SWE-Skills-Bench: Do Agent Skills Actually Help in Real-World Software Engineering?", "authors": [ "Tingxu Han", "Yi Zhang", "Wei Song", "Chunrong Fang", "Zhenyu Chen", "Youcheng Sun", "Lijie Hu" ], "github": "https://github.com/GeniusHTX/SWE-Skills-Bench", "project_page": "", "abstract": "Agent skills, structured procedural knowledge packages injected at inference time, are increasingly used to augment LLM agents on software engineering tasks. However, their real utility in end-to-end development settings remains unclear. We present SWE-Skills-Bench, the first requirement-driven benchmark that isolates the marginal utility of agent skills in real-world software engineering (SWE). It pairs 49 public SWE skills with authentic GitHub repositories pinned at fixed commits and requirement documents with explicit acceptance criteria, yielding approximately 565 task instances across six SWE subdomains. We introduce a deterministic verification framework that maps each task's acceptance criteria to execution-based tests, enabling controlled paired evaluation with and without the skill. Our results show that skill injection benefits are far more limited than rapid adoption suggests: 39 of 49 skills yield zero pass-rate improvement, and the average gain is only +1.2%. Token overhead varies from modest savings to a 451% increase while pass rates remain unchanged. Only seven specialized skills produce meaningful gains (up to +30%), while three degrade performance (up to -10%) due to version-mismatched guidance conflicting with project context. These findings suggest that agent skills are a narrow intervention whose utility depends strongly on domain fit, abstraction level, and contextual compatibility. SWE-Skills-Bench provides a testbed for evaluating the design, selection, and deployment of skills in software engineering agents. SWE-Skills-Bench is available at https://github.com/GeniusHTX/SWE-Skills-Bench.", "github_manual": "", "github_regex": "https://github.com/GeniusHTX/SWE-Skills-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.15118", "title": "VAREX: A Benchmark for Multi-Modal Structured Extraction from Documents", "authors": [ "Udi Barzelay", "Ophir Azulai", "Inbar Shapira", "Idan Friedman", "Foad Abo Dahood", "Madison Lee", "Abraham Daniels" ], "github": "", "project_page": "", "abstract": "We introduce VAREX (VARied-schema EXtraction), a benchmark for evaluating multimodal foundation models on structured data extraction from government forms. VAREX employs a Reverse Annotation pipeline that programmatically fills PDF templates with synthetic values, producing deterministic ground truth validated through three-phase quality assurance. The benchmark comprises 1,777 documents with 1,771 unique schemas across three structural categories, each provided in four input modalities: plain text, layout-preserving text (whitespace-aligned to approximate column positions), document image, or both text and image combined. Unlike existing benchmarks that evaluate from a single input representation, VAREX provides four controlled modalities per document, enabling systematic ablation of how input format affects extraction accuracy -- a capability absent from prior benchmarks. We evaluate 20 models from frontier proprietary models to small open models, with particular attention to models <=4B parameters suitable for cost-sensitive and latency-constrained deployment. Results reveal that (1) below 4B parameters, structured output compliance -- not extraction capability -- is a dominant bottleneck; in particular, schema echo (models producing schema-conforming structure instead of extracted values) depresses scores by 45-65 pp (percentage points) in affected models; (2) extraction-specific fine-tuning at 2B yields +81 pp gains, demonstrating that the instruction-following deficit is addressable without scale; (3) layout-preserving text provides the largest accuracy gain (+3-18 pp), exceeding pixel-level visual cues; and (4) the benchmark most effectively discriminates models in the 60-95% accuracy band. Dataset and evaluation code are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.13398", "title": "Qianfan-OCR: A Unified End-to-End Model for Document Intelligence", "authors": [ "Daxiang Dong", "Mingming Zheng", "Dong Xu", "Chunhua Luo", "Bairong Zhuang", "Yuxuan Li", "Ruoyun He", "Haoran Wang", "Wenyu Zhang", "Wenbo Wang", "Yicheng Wang", "Xue Xiong", "Ayong Zheng", "Xiaoying Zuo", "Ziwei Ou", "Jingnan Gu", "Quanhao Guo", "Jianmin Wu", "Dawei Yin", "Dou Shen" ], "github": "https://github.com/baidubce/Qianfan-VL", "project_page": "", "abstract": "We present Qianfan-OCR, a 4B-parameter end-to-end vision-language model that unifies document parsing, layout analysis, and document understanding within a single architecture. It performs direct image-to-Markdown conversion and supports diverse prompt-driven tasks including table extraction, chart understanding, document QA, and key information extraction. To address the loss of explicit layout analysis in end-to-end OCR, we propose Layout-as-Thought, an optional thinking phase triggered by special think tokens that generates structured layout representations -- bounding boxes, element types, and reading order -- before producing final outputs, recovering layout grounding capabilities while improving accuracy on complex layouts. Qianfan-OCR ranks first among end-to-end models on OmniDocBench v1.5 (93.12) and OlmOCR Bench (79.8), achieves competitive results on OCRBench, CCOCR, DocVQA, and ChartQA against general VLMs of comparable scale, and attains the highest average score on public key information extraction benchmarks, surpassing Gemini-3.1-Pro, Seed-2.0, and Qwen3-VL-235B. The model is publicly accessible via the Baidu AI Cloud Qianfan platform.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/baidubce/Qianfan-VL", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.15132", "title": "WiT: Waypoint Diffusion Transformers via Trajectory Conflict Navigation", "authors": [ "Hainuo Wang", "Mingjia Li", "Xiaojie Guo" ], "github": "https://github.com/hainuo-wang/WiT.git", "project_page": "", "abstract": "While recent Flow Matching models avoid the reconstruction bottlenecks of latent autoencoders by operating directly in pixel space, the lack of semantic continuity in the pixel manifold severely intertwines optimal transport paths. This induces severe trajectory conflicts near intersections, yielding sub-optimal solutions. Rather than bypassing this issue via information-lossy latent representations, we directly untangle the pixel-space trajectories by proposing Waypoint Diffusion Transformers (WiT). WiT factorizes the continuous vector field via intermediate semantic waypoints projected from pre-trained vision models. It effectively disentangles the generation trajectories by breaking the optimal transport into prior-to-waypoint and waypoint-to-pixel segments. Specifically, during the iterative denoising process, a lightweight generator dynamically infers these intermediate waypoints from the current noisy state. They then continuously condition the primary diffusion transformer via the Just-Pixel AdaLN mechanism, steering the evolution towards the next state, ultimately yielding the final RGB pixels. Evaluated on ImageNet 256x256, WiT beats strong pixel-space baselines, accelerating JiT training convergence by 2.2x. Code will be publicly released at https://github.com/hainuo-wang/WiT.git.", "github_manual": "", "github_regex": "https://github.com/hainuo-wang/WiT.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.13875", "title": "GradMem: Learning to Write Context into Memory with Test-Time Gradient Descent", "authors": [ "Yuri Kuratov", "Matvey Kairov", "Aydar Bulatov", "Ivan Rodkin", "Mikhail Burtsev" ], "github": "https://github.com/yurakuratov/gradmem", "project_page": "", "abstract": "Many large language model applications require conditioning on long contexts. Transformers typically support this by storing a large per-layer KV-cache of past activations, which incurs substantial memory overhead. A desirable alternative is ompressive memory: read a context once, store it in a compact state, and answer many queries from that state. We study this in a context removal setting, where the model must generate an answer without access to the original context at inference time. We introduce GradMem, which writes context into memory via per-sample test-time optimization. Given a context, GradMem performs a few steps of gradient descent on a small set of prefix memory tokens while keeping model weights frozen. GradMem explicitly optimizes a model-level self-supervised context reconstruction loss, resulting in a loss-driven write operation with iterative error correction, unlike forward-only methods. On associative key--value retrieval, GradMem outperforms forward-only memory writers with the same memory size, and additional gradient steps scale capacity much more effectively than repeated forward writes. We further show that GradMem transfers beyond synthetic benchmarks: with pretrained language models, it attains competitive results on natural language tasks including bAbI and SQuAD variants, relying only on information encoded in memory.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yurakuratov/gradmem", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.15674", "title": "Theoretical Foundations of Latent Posterior Factors: Formal Guarantees for Multi-Evidence Reasoning", "authors": [ "Aliyu Agboola Alege" ], "github": "", "project_page": "", "abstract": "We present a complete theoretical characterization of Latent Posterior Factors (LPF), a principled framework for aggregating multiple heterogeneous evidence items in probabilistic prediction tasks. Multi-evidence reasoning arises pervasively in high-stakes domains including healthcare diagnosis, financial risk assessment, legal case analysis, and regulatory compliance, yet existing approaches either lack formal guarantees or fail to handle multi-evidence scenarios architecturally. LPF encodes each evidence item into a Gaussian latent posterior via a variational autoencoder, converting posteriors to soft factors through Monte Carlo marginalization, and aggregating factors via exact Sum-Product Network inference (LPF-SPN) or a learned neural aggregator (LPF-Learned). We prove seven formal guarantees spanning the key desiderata for trustworthy AI: Calibration Preservation (ECE <= epsilon + C/sqrt(K_eff)); Monte Carlo Error decaying as O(1/sqrt(M)); a non-vacuous PAC-Bayes bound with train-test gap of 0.0085 at N=4200; operation within 1.12x of the information-theoretic lower bound; graceful degradation as O(epsilon*delta*sqrt(K)) under corruption, maintaining 88% performance with half of evidence adversarially replaced; O(1/sqrt(K)) calibration decay with R^2=0.849; and exact epistemic-aleatoric uncertainty decomposition with error below 0.002%. All theorems are empirically validated on controlled datasets spanning up to 4,200 training examples. Our theoretical framework establishes LPF as a foundation for trustworthy multi-evidence AI in safety-critical applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.15670", "title": "I Know What I Don't Know: Latent Posterior Factor Models for Multi-Evidence Probabilistic Reasoning", "authors": [ "Aliyu Agboola Alege" ], "github": "", "project_page": "", "abstract": "Real-world decision-making, from tax compliance assessment to medical diagnosis, requires aggregating multiple noisy and potentially contradictory evidence sources. Existing approaches either lack explicit uncertainty quantification (neural aggregation methods) or rely on manually engineered discrete predicates (probabilistic logic frameworks), limiting scalability to unstructured data. We introduce Latent Posterior Factors (LPF), a framework that transforms Variational Autoencoder (VAE) latent posteriors into soft likelihood factors for Sum-Product Network (SPN) inference, enabling tractable probabilistic reasoning over unstructured evidence while preserving calibrated uncertainty estimates. We instantiate LPF as LPF-SPN (structured factor-based inference) and LPF-Learned (end-to-end learned aggregation), enabling a principled comparison between explicit probabilistic reasoning and learned aggregation under a shared uncertainty representation. Across eight domains (seven synthetic and the FEVER benchmark), LPF-SPN achieves high accuracy (up to 97.8%), low calibration error (ECE 1.4%), and strong probabilistic fit, substantially outperforming evidential deep learning, LLMs and graph-based baselines over 15 random seeds. Contributions: (1) A framework bridging latent uncertainty representations with structured probabilistic reasoning. (2) Dual architectures enabling controlled comparison of reasoning paradigms. (3) Reproducible training methodology with seed selection. (4) Evaluation against EDL, BERT, R-GCN, and large language model baselines. (5) Cross-domain validation. (6) Formal guarantees in a companion paper.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.14704", "title": "Chain-of-Trajectories: Unlocking the Intrinsic Generative Optimality of Diffusion Models via Graph-Theoretic Planning", "authors": [ "Ping Chen", "Xiang Liu", "Xingpeng Zhang", "Fei Shen", "Xun Gong", "Zhaoxiang Liu", "Zezhou Chen", "Huan Hu", "Kai Wang", "Shiguo Lian" ], "github": "https://github.com/UnicomAI/CoTj", "project_page": "", "abstract": "Diffusion models operate in a reflexive System 1 mode, constrained by a fixed, content-agnostic sampling schedule. This rigidity arises from the curse of state dimensionality, where the combinatorial explosion of possible states in the high-dimensional noise manifold renders explicit trajectory planning intractable and leads to systematic computational misallocation. To address this, we introduce Chain-of-Trajectories (CoTj), a train-free framework enabling System 2 deliberative planning. Central to CoTj is Diffusion DNA, a low-dimensional signature that quantifies per-stage denoising difficulty and serves as a proxy for the high-dimensional state space, allowing us to reformulate sampling as graph planning on a directed acyclic graph. Through a Predict-Plan-Execute paradigm, CoTj dynamically allocates computational effort to the most challenging generative phases. Experiments across multiple generative models demonstrate that CoTj discovers context-aware trajectories, improving output quality and stability while reducing redundant computation. This work establishes a new foundation for resource-aware, planning-based diffusion modeling. The code is available at https://github.com/UnicomAI/CoTj.", "github_manual": "", "github_regex": "https://github.com/UnicomAI/CoTj", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16649", "title": "Mixture of Style Experts for Diverse Image Stylization", "authors": [ "Shihao Zhu", "Ziheng Ouyang", "Yijia Kang", "Qilong Wang", "Mi Zhou", "Bo Li", "Ming-Ming Cheng", "Qibin Hou" ], "github": "", "project_page": "", "abstract": "Diffusion-based stylization has advanced significantly, yet existing methods are limited to color-driven transformations, neglecting complex semantics and material details.We introduce StyleExpert, a semantic-aware framework based on the Mixture of Experts (MoE). Our framework employs a unified style encoder, trained on our large-scale dataset of content-style-stylized triplets, to embed diverse styles into a consistent latent space. This embedding is then used to condition a similarity-aware gating mechanism, which dynamically routes styles to specialized experts within the MoE architecture. Leveraging this MoE architecture, our method adeptly handles diverse styles spanning multiple semantic levels, from shallow textures to deep semantics. Extensive experiments show that StyleExpert outperforms existing approaches in preserving semantics and material details, while generalizing to unseen styles. Our code and collected images are available at the project page: https://hh-lg.github.io/StyleExpert-Page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.14588", "title": "SuperLocalMemory V3: Information-Geometric Foundations for Zero-LLM Enterprise Agent Memory", "authors": [ "Varun Pratap Bhardwaj" ], "github": "https://github.com/qualixar/superlocalmemory", "project_page": "", "abstract": "Persistent memory is a central capability for AI agents, yet the mathematical foundations of memory retrieval, lifecycle management, and consistency remain unexplored. Current systems employ cosine similarity for retrieval, heuristic decay for salience, and provide no formal contradiction detection. We establish information-geometric foundations through three contributions. First, a retrieval metric derived from the Fisher information structure of diagonal Gaussian families, satisfying Riemannian metric axioms, invariant under sufficient statistics, and computable in O(d) time. Second, memory lifecycle formulated as Riemannian Langevin dynamics with proven existence and uniqueness of the stationary distribution via the Fokker-Planck equation, replacing hand-tuned decay with principled convergence guarantees. Third, a cellular sheaf model where non-trivial first cohomology classes correspond precisely to irreconcilable contradictions across memory contexts. On the LoCoMo benchmark, the mathematical layers yield +12.7 percentage points over engineering baselines across six conversations, reaching +19.9 pp on the most challenging dialogues. A four-channel retrieval architecture achieves 75% accuracy without cloud dependency. Cloud-augmented results reach 87.7%. A zero-LLM configuration satisfies EU AI Act data sovereignty requirements by architectural design. To our knowledge, this is the first work establishing information-geometric, sheaf-theoretic, and stochastic-dynamical foundations for AI agent memory systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qualixar/superlocalmemory", "github_api_added_by": "auto" }, { "date": "2026-03-18", "arxiv_id": "2603.16309", "title": "Omnilingual MT: Machine Translation for 1,600 Languages", "authors": [ "Omnilingual MT Team", "Belen Alastruey", "Niyati Bafna", "Andrea Caciolai", "Kevin Heffernan", "Artyom Kozhevnikov", "Christophe Ropers", "Eduardo S\u00e1nchez", "Charles-Eric Saint-James", "Ioannis Tsiamas", "Chierh Cheng", "Joe Chuang", "Paul-Ambroise Duquenne", "Mark Duppenthaler", "Nate Ekberg", "Cynthia Gao", "Pere Llu\u00eds Huguet Cabot", "Jo\u00e3o Maria Janeiro", "Jean Maillard", "Gabriel Mejia Gonzalez", "Holger Schwenk", "Edan Toledo", "Arina Turkatenko", "Albert Ventayol-Boada", "Rashel Moritz", "Alexandre Mourachko", "Surya Parimi", "Mary Williamson", "Shireen Yates", "David Dale", "Marta R. Costa-juss\u00e0" ], "github": "", "project_page": "", "abstract": "High-quality machine translation (MT) can scale to hundreds of languages, setting a high bar for multilingual systems. However, compared to the world's 7,000 languages, current systems still offer only limited coverage: about 200 languages on the target side, and maybe a few hundreds more on the source side, supported due to cross-lingual transfer. And even these numbers have been hard to evaluate due to the lack of reliable benchmarks and metrics. We present Omnilingual Machine Translation (OMT), the first MT system supporting more than 1,600 languages. This scale is enabled by a comprehensive data strategy that integrates large public multilingual corpora with newly created datasets, including manually curated MeDLEY bitext. We explore two ways of specializing a Large Language model (LLM) for machine translation: as a decoder-only model (OMT-LLaMA) or as a module in an encoder-decoder architecture (OMT-NLLB). Notably, all our 1B to 8B parameter models match or exceed the MT performance of a 70B LLM baseline, revealing a clear specialization advantage and enabling strong translation quality in low-compute settings. Moreover, our evaluation of English-to-1,600 translations further shows that while baseline models can interpret undersupported languages, they frequently fail to generate them with meaningful fidelity; OMT-LLaMA models substantially expand the set of languages for which coherent generation is feasible. Additionally, OMT models improve in cross-lingual transfer, being close to solving the \"understanding\" part of the puzzle in MT for the 1,600 evaluated. Our leaderboard and main human-created evaluation datasets (BOUQuET and Met-BOUQuET) are dynamically evolving towards Omnilinguality and freely available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.12226", "title": "Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration", "authors": [ "Priyanka Kargupta", "Shuhaib Mehri", "Dilek Hakkani-Tur", "Jiawei Han" ], "github": "https://github.com/pkargupta/idea_catalyst", "project_page": "", "abstract": "Despite interdisciplinary research leading to larger and longer-term impact, most work remains confined to single-domain academic silos. Recent AI-based approaches to scientific discovery show promise for interdisciplinary research, but many prioritize rapidly designing experiments and solutions, bypassing the exploratory, collaborative reasoning processes that drive creative interdisciplinary breakthroughs. As a result, prior efforts largely prioritize automating scientific discovery rather than augmenting the reasoning processes that underlie scientific disruption. We present Idea-Catalyst, a novel framework that systematically identifies interdisciplinary insights to support creative reasoning in both humans and large language models. Starting from an abstract research goal, Idea-Catalyst is designed to assist the brainstorming stage, explicitly avoiding premature anchoring on specific solutions. The framework embodies key metacognitive features of interdisciplinary reasoning: (a) defining and assessing research goals, (b) awareness of a domain's opportunities and unresolved challenges, and (c) strategic exploration of interdisciplinary ideas based on impact potential. Concretely, Idea-Catalyst decomposes an abstract goal (e.g., improving human-AI collaboration) into core target-domain research questions that guide the analysis of progress and open challenges within that domain. These challenges are reformulated as domain-agnostic conceptual problems, enabling retrieval from external disciplines (e.g., Psychology, Sociology) that address analogous issues. By synthesizing and recontextualizing insights from these domains back into the target domain, Idea-Catalyst ranks source domains by their interdisciplinary potential. Empirically, this targeted integration improves average novelty by 21% and insightfulness by 16%, while remaining grounded in the original research problem.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pkargupta/idea_catalyst", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16587", "title": "HistoAtlas: A Pan-Cancer Morphology Atlas Linking Histomics to Molecular Programs and Clinical Outcomes", "authors": [ "Pierre-Antoine Bannier" ], "github": "https://github.com/histoatlas/histoatlas", "project_page": "", "abstract": "We present HistoAtlas, a pan-cancer computational atlas that extracts 38 interpretable histomic features from 6,745 diagnostic H&E slides across 21 TCGA cancer types and systematically links every feature to survival, gene expression, somatic mutations, and immune subtypes. All associations are covariate-adjusted, multiple-testing corrected, and classified into evidence-strength tiers. The atlas recovers known biology, from immune infiltration and prognosis to proliferation and kinase signaling, while uncovering compartment-specific immune signals and morphological subtypes with divergent outcomes. Every result is spatially traceable to tissue compartments and individual cells, statistically calibrated, and openly queryable. HistoAtlas enables systematic, large-scale biomarker discovery from routine H&E without specialized staining or sequencing. Data and an interactive web atlas are freely available at https://histoatlas.com .", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/histoatlas/histoatlas", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.09022", "title": "MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games", "authors": [ "Yunfei Xie", "Kevin Wang", "Bobby Cheng", "Jianzhu Yao", "Zhizhou Sha", "Alexander Duffy", "Yihan Xi", "Hongyuan Mei", "Cheston Tan", "Chen Wei", "Pramod Viswanath", "Zhangyang Wang" ], "github": "", "project_page": "", "abstract": "Multi-turn, multi-agent LLM game evaluations often exhibit substantial run-to-run variance. In long-horizon interactions, small early deviations compound across turns and are amplified by multi-agent coupling. This biases win rate estimates and makes rankings unreliable across repeated tournaments. Prompt choice worsens this further by producing different effective policies. We address both instability and underperformance with MEMO (Memory-augmented MOdel context optimization), a self-play framework that optimizes inference-time context by coupling retention and exploration. Retention maintains a persistent memory bank that stores structured insights from self-play trajectories and injects them as priors during later play. Exploration runs tournament-style prompt evolution with uncertainty-aware selection via TrueSkill, and uses prioritized replay to revisit rare and decisive states. Across five text-based games, MEMO raises mean win rate from 25.1% to 49.5% for GPT-4o-mini and from 20.9% to 44.3% for Qwen-2.5-7B-Instruct, using 2,000 self-play games per task. Run-to-run variance also drops, giving more stable rankings across prompt variations. These results suggest that multi-agent LLM game performance and robustness have substantial room for improvement through context optimization. MEMO achieves the largest gains in negotiation and imperfect-information games, while RL remains more effective in perfect-information settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16864", "title": "SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation", "authors": [ "Jiongze Yu", "Xiangbo Gao", "Pooja Verlani", "Akshay Gadde", "Yilin Wang", "Balu Adsumilli", "Zhengzhong Tu" ], "github": "", "project_page": "", "abstract": "Video Super-Resolution (VSR) aims to restore high-quality video frames from low-resolution (LR) estimates, yet most existing VSR approaches behave like black boxes at inference time: users cannot reliably correct unexpected artifacts, but instead can only accept whatever the model produces. In this paper, we propose a novel interactive VSR framework dubbed SparkVSR that makes sparse keyframes a simple and expressive control signal. Specifically, users can first super-resolve or optionally a small set of keyframes using any off-the-shelf image super-resolution (ISR) model, then SparkVSR propagates the keyframe priors to the entire video sequence while remaining grounded by the original LR video motion. Concretely, we introduce a keyframe-conditioned latent-pixel two-stage training pipeline that fuses LR video latents with sparsely encoded HR keyframe latents to learn robust cross-space propagation and refine perceptual details. At inference time, SparkVSR supports flexible keyframe selection (manual specification, codec I-frame extraction, or random sampling) and a reference-free guidance mechanism that continuously balances keyframe adherence and blind restoration, ensuring robust performance even when reference keyframes are absent or imperfect. Experiments on multiple VSR benchmarks demonstrate improved temporal consistency and strong restoration quality, surpassing baselines by up to 24.6%, 21.8%, and 5.6% on CLIP-IQA, DOVER, and MUSIQ, respectively, enabling controllable, keyframe-driven video super-resolution. Moreover, we demonstrate that SparkVSR is a generic interactive, keyframe-conditioned video processing framework as it can be applied out of the box to unseen tasks such as old-film restoration and video style transfer. Our project page is available at: https://sparkvsr.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.13627", "title": "BERTology of Molecular Property Prediction", "authors": [ "Mohammad Mostafanejad", "Paul Saxe", "T. Daniel Crawford" ], "github": "", "project_page": "", "abstract": "Chemical language models (CLMs) have emerged as promising competitors to popular classical machine learning models for molecular property prediction (MPP) tasks. However, an increasing number of studies have reported inconsistent and contradictory results for the performance of CLMs across various MPP benchmark tasks. In this study, we conduct and analyze hundreds of meticulously controlled experiments to systematically investigate the effects of various factors, such as dataset size, model size, and standardization, on the pre-training and fine-tuning performance of CLMs for MPP. In the absence of well-established scaling laws for encoder-only masked language models, our aim is to provide comprehensive numerical evidence and a deeper understanding of the underlying mechanisms affecting the performance of CLMs for MPP tasks, some of which appear to be entirely overlooked in the literature.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16792", "title": "V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising", "authors": [ "Han Lin", "Xichen Pan", "Zun Wang", "Yue Zhang", "Chu Wang", "Jaemin Cho", "Mohit Bansal" ], "github": "https://github.com/HL-hanlin/V-Co", "project_page": "", "abstract": "Pixel-space diffusion has recently re-emerged as a strong alternative to latent diffusion, enabling high-quality generation without pretrained autoencoders. However, standard pixel-space diffusion models receive relatively weak semantic supervision and are not explicitly designed to capture high-level visual structure. Recent representation-alignment methods (e.g., REPA) suggest that pretrained visual features can substantially improve diffusion training, and visual co-denoising has emerged as a promising direction for incorporating such features into the generative process. However, existing co-denoising approaches often entangle multiple design choices, making it unclear which design choices are truly essential. Therefore, we present V-Co, a systematic study of visual co-denoising in a unified JiT-based framework. This controlled setting allows us to isolate the ingredients that make visual co-denoising effective. Our study reveals four key ingredients for effective visual co-denoising. First, preserving feature-specific computation while enabling flexible cross-stream interaction motivates a fully dual-stream architecture. Second, effective classifier-free guidance (CFG) requires a structurally defined unconditional prediction. Third, stronger semantic supervision is best provided by a perceptual-drifting hybrid loss. Fourth, stable co-denoising further requires proper cross-stream calibration, which we realize through RMS-based feature rescaling. Together, these findings yield a simple recipe for visual co-denoising. Experiments on ImageNet-256 show that, at comparable model sizes, V-Co outperforms the underlying pixel-space diffusion baseline and strong prior pixel-diffusion methods while using fewer training epochs, offering practical guidance for future representation-aligned generative models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HL-hanlin/V-Co", "github_api_added_by": "auto" }, { "date": "2026-03-18", "arxiv_id": "2603.14326", "title": "ECG-Reasoning-Benchmark: A Benchmark for Evaluating Clinical Reasoning Capabilities in ECG Interpretation", "authors": [ "Jungwoo Oh", "Hyunseung Chung", "Junhee Lee", "Min-Gyu Kim", "Hangyul Yoon", "Ki Seong Lee", "Youngchae Lee", "Muhan Yeo", "Edward Choi" ], "github": "https://github.com/Jwoo5/ecg-reasoning-benchmark", "project_page": "", "abstract": "While Multimodal Large Language Models (MLLMs) show promising performance in automated electrocardiogram interpretation, it remains unclear whether they genuinely perform actual step-by-step reasoning or just rely on superficial visual cues. To investigate this, we introduce ECG-Reasoning-Benchmark, a novel multi-turn evaluation framework comprising over 6,400 samples to systematically assess step-by-step reasoning across 17 core ECG diagnoses. Our comprehensive evaluation of state-of-the-art models reveals a critical failure in executing multi-step logical deduction. Although models possess the medical knowledge to retrieve clinical criteria for a diagnosis, they exhibit near-zero success rates (6% Completion) in maintaining a complete reasoning chain, primarily failing to ground the corresponding ECG findings to the actual visual evidence in the ECG signal. These results demonstrate that current MLLMs bypass actual visual interpretation, exposing a critical flaw in existing training paradigms and underscoring the necessity for robust, reasoning-centric medical AI. The code and data are available at https://github.com/Jwoo5/ecg-reasoning-benchmark.", "github_manual": "", "github_regex": "https://github.com/Jwoo5/ecg-reasoning-benchmark", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16039", "title": "Residual Stream Duality in Modern Transformer Architectures", "authors": [ "Yifan Zhang" ], "github": "https://github.com/yifanzhang-pro/residual-stream-duality", "project_page": "", "abstract": "Recent work has made clear that the residual pathway is not mere optimization plumbing; it is part of the model's representational machinery. We agree, but argue that the cleanest way to organize this design space is through a two-axis view of the Transformer. A decoder evolves information along two ordered dimensions: sequence position and layer depth. Self-attention already provides adaptive mixing along the sequence axis, whereas the residual stream usually performs fixed addition along the depth axis. If we fix a token position and treat layer index as the ordered variable, then a causal depth-wise residual attention read is exactly the same local operator as causal short sliding-window attention (ShortSWA), except written over depth rather than over sequence. This is the core residual stream duality behind Transformer^2. This perspective also clarifies the recent literature. ELC-BERT and DenseFormer already show that learned aggregation over depth can outperform uniform residual accumulation, while Vertical Attention, DeepCrossAttention (DCA), MUDDFormer, and Attention Residuals move further toward explicit attention-based routing over earlier layers. The key point, however, is that operator-level duality does not imply systems-level symmetry. For large-scale autoregressive models, sequence-axis ShortSWA is usually the more hardware-friendly placement because it reuses token-side sliding-window kernels, KV-cache layouts, and chunked execution. If the goal is instead to change the shortcut itself, Deep Delta Learning (DDL) is the cleaner intervention because it modifies the residual operator directly rather than adding a separate cross-layer retrieval path. Our recommendation is therefore simple: use DDL when the shortcut is the object of interest, and use sequence-axis ShortSWA when the goal is local adaptive mixing.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yifanzhang-pro/residual-stream-duality", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16060", "title": "ARISE: Agent Reasoning with Intrinsic Skill Evolution in Hierarchical Reinforcement Learning", "authors": [ "Yu Li", "Rui Miao", "Zhengling Qi", "Tian Lan" ], "github": "https://github.com/Skylanding/ARISE", "project_page": "", "abstract": "The dominant paradigm for improving mathematical reasoning in language models relies on Reinforcement Learning with verifiable rewards. Yet existing methods treat each problem instance in isolation without leveraging the reusable strategies that emerge and accumulate during training. To this end, we introduce ARISE (Agent Reasoning via Intrinsic Skill Evolution), a hierarchical reinforcement learning framework, in which a shared policy operates both to manage skills at high-level and to generate responses at low-level (denoted as a Skills Manager and a Worker, respectively). The Manager maintains a tiered skill library through a dedicated skill generation rollout that performs structured summarization of successful solution traces (after execution), while employing a policy-driven selection mechanism to retrieve relevant skills to condition future rollouts (before execution). A hierarchical reward design guides the co-evolution of reasoning ability and library quality. Experiments on two base models and seven benchmarks spanning both competition mathematics and Omni-MATH show that ARISE consistently outperforms GRPO-family algorithms and memory-augmented baselines, with particularly notable gains on out-of-distribution tasks. Ablation studies confirm that each component contributes to the observed improvements and that library quality and reasoning performance improve in tandem throughout training. Code is available at https://github.com/Skylanding/ARISE{https://github.com/Skylanding/ARISE}.", "github_manual": "", "github_regex": "https://github.com/Skylanding/ARISE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-18", "arxiv_id": "2603.16077", "title": "MDM-Prime-v2: Binary Encoding and Index Shuffling Enable Compute-optimal Scaling of Diffusion Language Models", "authors": [ "Chen-Hao Chao", "Wei-Fang Sun", "Junwei Qua", "Chun-Yi Lee", "Rahul G. Krishnan" ], "github": "", "project_page": "", "abstract": "Masked diffusion models (MDM) exhibit superior generalization when learned using a Partial masking scheme (Prime). This approach converts tokens into sub-tokens and models the diffusion process at the sub-token level. We identify two limitations of the MDM-Prime framework. First, we lack tools to guide the hyperparameter choice of the token granularity in the subtokenizer. Second, we find that the function form of the subtokenizer significantly degrades likelihood estimation when paired with commonly used Byte-Pair-Encoding (BPE) tokenizers. To address these limitations, we study the tightness of the variational bound in MDM-Prime and develop MDM-Prime-v2, a masked diffusion language model which incorporates Binary Encoding and Index Shuffling. Our scaling analysis reveals that MDM-Prime-v2 is 21.8times more compute-efficient than autoregressive models (ARM). In compute-optimal comparisons, MDM-Prime-v2 achieves 7.77 perplexity on OpenWebText, outperforming ARM (12.99), MDM (18.94), and MDM-Prime (13.41). When extending the model size to 1.1B parameters, our model further demonstrates superior zero-shot accuracy on various commonsense reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17117", "title": "MosaicMem: Hybrid Spatial Memory for Controllable Video World Models", "authors": [ "Wei Yu", "Runjia Qian", "Yumeng Li", "Liquan Wang", "Songheng Yin", "Sri Siddarth Chakaravarthy P", "Dennis Anthony", "Yang Ye", "Yidi Li", "Weiwei Wan", "Animesh Garg" ], "github": "", "project_page": "", "abstract": "Video diffusion models are moving beyond short, plausible clips toward world simulators that must remain consistent under camera motion, revisits, and intervention. Yet spatial memory remains a key bottleneck: explicit 3D structures can improve reprojection-based consistency but struggle to depict moving objects, while implicit memory often produces inaccurate camera motion even with correct poses. We propose Mosaic Memory (MosaicMem), a hybrid spatial memory that lifts patches into 3D for reliable localization and targeted retrieval, while exploiting the model's native conditioning to preserve prompt-following generation. MosaicMem composes spatially aligned patches in the queried view via a patch-and-compose interface, preserving what should persist while allowing the model to inpaint what should evolve. With PRoPE camera conditioning and two new memory alignment methods, experiments show improved pose adherence compared to implicit memory and stronger dynamic modeling than explicit baselines. MosaicMem further enables minute-level navigation, memory-based scene editing, and autoregressive rollout.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17375", "title": "Stereo World Model: Camera-Guided Stereo Video Generation", "authors": [ "Yang-Tian Sun", "Zehuan Huang", "Yifan Niu", "Lin Ma", "Yan-Pei Cao", "Yuewen Ma", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "We present StereoWorld, a camera-conditioned stereo world model that jointly learns appearance and binocular geometry for end-to-end stereo video generation.Unlike monocular RGB or RGBD approaches, StereoWorld operates exclusively within the RGB modality, while simultaneously grounding geometry directly from disparity. To efficiently achieve consistent stereo generation, our approach introduces two key designs: (1) a unified camera-frame RoPE that augments latent tokens with camera-aware rotary positional encoding, enabling relative, view- and time-consistent conditioning while preserving pretrained video priors via a stable attention initialization; and (2) a stereo-aware attention decomposition that factors full 4D attention into 3D intra-view attention plus horizontal row attention, leveraging the epipolar prior to capture disparity-aligned correspondences with substantially lower compute. Across benchmarks, StereoWorld improves stereo consistency, disparity accuracy, and camera-motion fidelity over strong monocular-then-convert pipelines, achieving more than 3x faster generation with an additional 5% gain in viewpoint consistency. Beyond benchmarks, StereoWorld enables end-to-end binocular VR rendering without depth estimation or inpainting, enhances embodied policy learning through metric-scale depth grounding, and is compatible with long-video distillation for extended interactive stereo synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.16642", "title": "When AI Navigates the Fog of War", "authors": [ "Ming Li", "Xirui Li", "Tianyi Zhou" ], "github": "", "project_page": "", "abstract": "Can AI reason about a war before its trajectory becomes historically obvious? Analyzing this capability is difficult because retrospective geopolitical prediction is heavily confounded by training-data leakage. We address this challenge through a temporally grounded case study of the early stages of the 2026 Middle East conflict, which unfolded after the training cutoff of current frontier models. We construct 11 critical temporal nodes, 42 node-specific verifiable questions, and 5 general exploratory questions, requiring models to reason only from information that would have been publicly available at each moment. This design substantially mitigates training-data leakage concerns, creating a setting well-suited for studying how models analyze an unfolding crisis under the fog of war, and provides, to our knowledge, the first temporally grounded analysis of LLM reasoning in an ongoing geopolitical conflict. Our analysis reveals three main findings. First, current state-of-the-art large language models often display a striking degree of strategic realism, reasoning beyond surface rhetoric toward deeper structural incentives. Second, this capability is uneven across domains: models are more reliable in economically and logistically structured settings than in politically ambiguous multi-actor environments. Finally, model narratives evolve over time, shifting from early expectations of rapid containment toward more systemic accounts of regional entrenchment and attritional de-escalation. Since the conflict remains ongoing at the time of writing, this work can serve as an archival snapshot of model reasoning during an unfolding geopolitical crisis, enabling future studies without the hindsight bias of retrospective analysis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.18004", "title": "Unified Spatio-Temporal Token Scoring for Efficient Video VLMs", "authors": [ "Jianrui Zhang", "Yue Yang", "Rohun Tripathi", "Winson Han", "Ranjay Krishna", "Christopher Clark", "Yong Jae Lee", "Sangho Lee" ], "github": "", "project_page": "", "abstract": "Token pruning is essential for enhancing the computational efficiency of vision-language models (VLMs), particularly for video-based tasks where temporal redundancy is prevalent. Prior approaches typically prune tokens either (1) within the vision transformer (ViT) exclusively for unimodal perception tasks such as action recognition and object segmentation, without adapting to downstream vision-language tasks; or (2) only within the LLM while leaving the ViT output intact, often requiring complex text-conditioned token selection mechanisms. In this paper, we introduce Spatio-Temporal Token Scoring (STTS), a simple and lightweight module that prunes vision tokens across both the ViT and the LLM without text conditioning or token merging, and is fully compatible with end-to-end training. By learning how to score temporally via an auxiliary loss and spatially via LLM downstream gradients, aided by our efficient packing algorithm, STTS prunes 50% of vision tokens throughout the entire architecture, resulting in a 62% improvement in efficiency during both training and inference with only a 0.7% drop in average performance across 13 short and long video QA tasks. Efficiency gains increase with more sampled frames per video. Applying test-time scaling for long-video QA further yields performance gains of 0.5-1% compared to the baseline. Overall, STTS represents a novel, simple yet effective technique for unified, architecture-wide vision token pruning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17965", "title": "LaDe: Unified Multi-Layered Graphic Media Generation and Decomposition", "authors": [ "Vlad-Constantin Lungu-Stan", "Ionut Mironica", "Mariana-Iuliana Georgescu" ], "github": "", "project_page": "", "abstract": "Media design layer generation enables the creation of fully editable, layered design documents such as posters, flyers, and logos using only natural language prompts. Existing methods either restrict outputs to a fixed number of layers or require each layer to contain only spatially continuous regions, causing the layer count to scale linearly with design complexity. We propose LaDe (Layered Media Design), a latent diffusion framework that generates a flexible number of semantically meaningful layers. LaDe combines three components: an LLM-based prompt expander that transforms a short user intent into structured per-layer descriptions that guide the generation, a Latent Diffusion Transformer with a 4D RoPE positional encoding mechanism that jointly generates the full media design and its constituent RGBA layers, and an RGBA VAE that decodes each layer with full alpha-channel support. By conditioning on layer samples during training, our unified framework supports three tasks: text-to-image generation, text-to-layers media design generation, and media design decomposition. We compare LaDe to Qwen-Image-Layered on text-to-layers and image-to-layers tasks on the Crello test set. LaDe outperforms Qwen-Image-Layered in text-to-layers generation by improving text-to-layer alignment, as validated by two VLM-as-a-judge evaluators (GPT-4o mini and Qwen3-VL).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17621", "title": "Complementary Reinforcement Learning", "authors": [ "Dilxat Muhtar", "Jiashun Liu", "Wei Gao", "Weixun Wang", "Shaopan Xiong", "Ju Huang", "Siran Yang", "Wenbo Su", "Jiamang Wang", "Ling Pan", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning (RL) has emerged as a powerful paradigm for training LLM-based agents, yet remains limited by low sample efficiency, stemming not only from sparse outcome feedback but also from the agent's inability to leverage prior experience across episodes. While augmenting agents with historical experience offers a promising remedy, existing approaches suffer from a critical weakness: the experience distilled from history is either stored statically or fail to coevolve with the improving actor, causing a progressive misalignment between the experience and the actor's evolving capability that diminishes its utility over the course of training. Inspired by complementary learning systems in neuroscience, we present Complementary RL to achieve seamless co-evolution of an experience extractor and a policy actor within the RL optimization loop. Specifically, the actor is optimized via sparse outcome-based rewards, while the experience extractor is optimized according to whether its distilled experiences demonstrably contribute to the actor's success, thereby evolving its experience management strategy in lockstep with the actor's growing capabilities. Empirically, Complementary RL outperforms outcome-based agentic RL baselines that do not learn from experience, achieving 10% performance improvement in single-task scenarios and exhibits robust scalability in multi-task settings. These results establish Complementary RL as a paradigm for efficient experience-driven agent learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17187", "title": "MetaClaw: Just Talk -- An Agent That Meta-Learns and Evolves in the Wild", "authors": [ "Peng Xia", "Jianwen Chen", "Xinyu Yang", "Haoqin Tu", "Jiaqi Liu", "Kaiwen Xiong", "Siwei Han", "Shi Qiu", "Haonian Ji", "Yuyin Zhou", "Zeyu Zheng", "Cihang Xie", "Huaxiu Yao" ], "github": "https://github.com/aiming-lab/MetaClaw", "project_page": "", "abstract": "Large language model (LLM) agents are increasingly used for complex tasks, yet deployed agents often remain static, failing to adapt as user needs evolve. This creates a tension between the need for continuous service and the necessity of updating capabilities to match shifting task distributions. On platforms like OpenClaw, which handle diverse workloads across 20+ channels, existing methods either store raw trajectories without distilling knowledge, maintain static skill libraries, or require disruptive downtime for retraining. We present MetaClaw, a continual meta-learning framework that jointly evolves a base LLM policy and a library of reusable behavioral skills. MetaClaw employs two complementary mechanisms. Skill-driven fast adaptation analyzes failure trajectories via an LLM evolver to synthesize new skills, enabling immediate improvement with zero downtime. Opportunistic policy optimization performs gradient-based updates via cloud LoRA fine-tuning and Reinforcement Learning with a Process Reward Model (RL-PRM). This is triggered during user-inactive windows by the Opportunistic Meta-Learning Scheduler (OMLS), which monitors system inactivity and calendar data. These mechanisms are mutually reinforcing: a refined policy generates better trajectories for skill synthesis, while richer skills provide higher-quality data for policy optimization. To prevent data contamination, a versioning mechanism separates support and query data. Built on a proxy-based architecture, MetaClaw scales to production-size LLMs without local GPUs. Experiments on MetaClaw-Bench and AutoResearchClaw show that skill-driven adaptation improves accuracy by up to 32% relative. The full pipeline advances Kimi-K2.5 accuracy from 21.4% to 40.6% and increases composite robustness by 18.3%. Code is available at https://github.com/aiming-lab/MetaClaw.", "github_manual": "", "github_regex": "https://github.com/aiming-lab/MetaClaw", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.16496", "title": "AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents", "authors": [ "Shannan Yan", "Jingchen Ni", "Leqi Zheng", "Jiajun Zhang", "Peixi Wu", "Dacheng Yin", "Jing Lyu", "Chun Yuan", "Fengyun Rao" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) agents increasingly rely on external memory to support long-horizon interaction, personalized assistance, and multi-step reasoning. However, existing memory systems still face three core challenges: they often rely too heavily on semantic similarity, which can miss evidence crucial for user-centric understanding; they frequently store related experiences as isolated fragments, weakening temporal and causal coherence; and they typically use static memory granularities that do not adapt well to the requirements of different questions. We propose AdaMem, an adaptive user-centric memory framework for long-horizon dialogue agents. AdaMem organizes dialogue history into working, episodic, persona, and graph memories, enabling the system to preserve recent context, structured long-term experiences, stable user traits, and relation-aware connections within a unified framework. At inference time, AdaMem first resolves the target participant, then builds a question-conditioned retrieval route that combines semantic retrieval with relation-aware graph expansion only when needed, and finally produces the answer through a role-specialized pipeline for evidence synthesis and response generation. We evaluate AdaMem on the LoCoMo and PERSONAMEM benchmarks for long-horizon reasoning and user modeling. Experimental results show that AdaMem achieves state-of-the-art performance on both benchmarks. The code will be released upon acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17378", "title": "Efficient Exploration at Scale", "authors": [ "Seyed Mohammad Asghari", "Chris Chute", "Vikranth Dwaracherla", "Xiuyuan Lu", "Mehdi Jafarnia", "Victor Minden", "Zheng Wen", "Benjamin Van Roy" ], "github": "", "project_page": "", "abstract": "We develop an online learning algorithm that dramatically improves the data efficiency of reinforcement learning from human feedback (RLHF). Our algorithm incrementally updates reward and language models as choice data is received. The reward model is fit to the choice data, while the language model is updated by a variation of reinforce, with reinforcement signals provided by the reward model. Several features enable the efficiency gains: a small affirmative nudge added to each reinforcement signal, an epistemic neural network that models reward uncertainty, and information-directed exploration. With Gemma large language models (LLMs), our algorithm matches the performance of offline RLHF trained on 200K labels using fewer than 20K labels, representing more than a 10x gain in data efficiency. Extrapolating from our results, we expect our algorithm trained on 1M labels to match offline RLHF trained on 1B labels. This represents a 1,000x gain. To our knowledge, these are the first results to demonstrate that such large improvements are possible.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17995", "title": "LoST: Level of Semantics Tokenization for 3D Shapes", "authors": [ "Niladri Shekhar Dutt", "Zifan Shi", "Paul Guerrero", "Chun-Hao Paul Huang", "Duygu Ceylan", "Niloy J. Mitra", "Xuelin Chen" ], "github": "", "project_page": "", "abstract": "Tokenization is a fundamental technique in the generative modeling of various modalities. In particular, it plays a critical role in autoregressive (AR) models, which have recently emerged as a compelling option for 3D generation. However, optimal tokenization of 3D shapes remains an open question. State-of-the-art (SOTA) methods primarily rely on geometric level-of-detail (LoD) hierarchies, originally designed for rendering and compression. These spatial hierarchies are often token-inefficient and lack semantic coherence for AR modeling. We propose Level-of-Semantics Tokenization (LoST), which orders tokens by semantic salience, such that early prefixes decode into complete, plausible shapes that possess principal semantics, while subsequent tokens refine instance-specific geometric and semantic details. To train LoST, we introduce Relational Inter-Distance Alignment (RIDA), a novel 3D semantic alignment loss that aligns the relational structure of the 3D shape latent space with that of the semantic DINO feature space. Experiments show that LoST achieves SOTA reconstruction, surpassing previous LoD-based 3D shape tokenizers by large margins on both geometric and semantic reconstruction metrics. Moreover, LoST achieves efficient, high-quality AR 3D generation and enables downstream tasks like semantic retrieval, while using only 0.1%-10% of the tokens needed by prior AR models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17891", "title": "RAMP: Reinforcement Adaptive Mixed Precision Quantization for Efficient On Device LLM Inference", "authors": [ "Arpit Singh Gautam", "Saurabh Jha" ], "github": "", "project_page": "", "abstract": "Post training quantization is essential for deploying large language models (LLMs) on resource constrained hardware, yet state of the art methods enforce uniform bit widths across layers, yielding suboptimal accuracy efficiency trade offs. We present RAMP (Reinforcement Adaptive Mixed Precision), an off policy Soft Actor Critic framework that learns per layer bit width assignments to minimize perplexity under a global bit budget. The policy conditions on an 11 dimensional embedding of activation statistics, weight properties, and structural descriptors, enabling zero shot transfer across model families and scales. To enable stable sub 4 bit quantization, we introduce Scale Folding, a preconditioning technique that migrates activation outliers into weights via per channel scaling and normalization layer compensation. A quality prioritized reward with asymmetric penalties and budget cliffs drives rapid convergence. On Llama 2 7B, RAMP achieves 5.54 perplexity at 3.68GB (3.65 effective bits), outperforming uniform 4 bit AWQ (5.60 at 3.90 GB) and GPTQ by 6% in size and 1% to3% in quality. Critically, a policy trained only on Llama 2 7B generalizes zero shot to Llama 2 13B and Mistral 7B, often surpassing target specific training, supporting the hypothesis that quantization sensitivity is primarily architectural. The HALO pipeline exports allocations to GGUF format for kernel free inference on CPUs, GPUs, and edge devices, retaining 99.5% of FP16 commonsense reasoning performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17074", "title": "PRISM: Demystifying Retention and Interaction in Mid-Training", "authors": [ "Bharat Runwal", "Ashish Agrawal", "Anurag Roy", "Rameswar Panda" ], "github": "", "project_page": "", "abstract": "We present PRISM, a comprehensive empirical study of mid-training design choices for large language models. Through controlled experiments across seven base models spanning four families (Granite, LLaMA, Mistral, Nemotron-H), two architecture types (dense Transformer and attention-Mamba hybrid), and scales from 3B to 24B parameters, we show that mid-training on approximately 27B high-quality tokens yields consistent gains of +15 to +40 points on math, +5 to +12 points on code, and +6 to +13 points on science benchmarks while preserving general performance. The full PRISM to RL pipeline improves macro-average across six reasoning benchmarks from under 12 to 29-42 (a 3-4x improvement), whereas RL applied directly to most of the base models remains substantially less effective, with AIME scores near zero. Data composition matters most at mid-training, not RL: including science data during mid-training unlocks +17 to +28 point GPQA-Diamond gains during RL, while changing the RL mix produces less than 2 point differences. Mechanistically, mid-training densely restructures over 90% of model weights, while RL makes sparse, front-loaded refinements to approximately 5% of parameters. Representation analysis (CKA) confirms that RL consistently preserves mid-training's representational geometry (over 0.998 CKA) across architectures. Crucially, RL applies identical weight changes regardless of starting point, yet only succeeds on mid-trained models, consistent with mid-training placing the model in a configuration from which RL can effectively improve performance. Our results demonstrate that retention-aware mid-training is highly effective for reliable reasoning enhancement and provide practical guidance for designing robust mid-training pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.13033", "title": "ESPIRE: A Diagnostic Benchmark for Embodied Spatial Reasoning of Vision-Language Models", "authors": [ "Yanpeng Zhao", "Wentao Ding", "Hongtao Li", "Baoxiong Jia", "Zilong Zheng" ], "github": "", "project_page": "", "abstract": "A recent trend in vision-language models (VLMs) has been to enhance their spatial cognition for embodied domains. Despite progress, existing evaluations have been limited both in paradigm and in coverage, hindering rapid, iterative model development. To address these limitations, we propose ESPIRE, a diagnostic benchmark for embodied spatial reasoning. ESPIRE offers a simulated world that physically grounds VLMs and evaluates them on spatial-reasoning-centric robotic tasks, thus narrowing the gap between evaluation and real-world deployment. To adapt VLMs to robotic tasks, we decompose each task into localization and execution, and frame both as generative problems, in stark contrast to predominant discriminative evaluations (e.g., via visual-question answering) that rely on distractors and discard execution. This decomposition further enables a fine-grained analysis beyond passive spatial reasoning toward reasoning to act. We systematically design ESPIRE both at the instruction level and at the environment level, ensuring broad coverage of spatial reasoning scenarios. We use ESPIRE to diagnose a range of frontier VLMs and provide in-depth analysis of their spatial reasoning behaviors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.16542", "title": "Conservative Offline Robot Policy Learning via Posterior-Transition Reweighting", "authors": [ "Wanpeng Zhang", "Hao Luo", "Sipeng Zheng", "Yicheng Feng", "Haiweng Xu", "Ziheng Xi", "Chaoyi Xu", "Haoqi Yuan", "Zongqing Lu" ], "github": "", "project_page": "", "abstract": "Offline post-training adapts a pretrained robot policy to a target dataset by supervised regression on recorded actions. In practice, robot datasets are heterogeneous: they mix embodiments, camera setups, and demonstrations of varying quality, so many trajectories reflect recovery behavior, inconsistent operator skill, or weakly informative supervision. Uniform post-training gives equal credit to all samples and can therefore average over conflicting or low-attribution data. We propose Posterior-Transition Reweighting (PTR), a reward-free and conservative post-training method that decides how much each training sample should influence the supervised update. For each sample, PTR encodes the observed post-action consequence as a latent target, inserts it into a candidate pool of mismatched targets, and uses a separate transition scorer to estimate a softmax identification posterior over target indices. The posterior-to-uniform ratio defines the PTR score, which is converted into a clipped-and-mixed weight and applied to the original action objective through self-normalized weighted regression. This construction requires no tractable policy likelihood and is compatible with both diffusion and flow-matching action heads. Rather than uniformly trusting all recorded supervision, PTR reallocates credit according to how attributable each sample's post-action consequence is under the current representation, improving conservative offline adaptation to heterogeneous robot data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17541", "title": "Temporal Gains, Spatial Costs: Revisiting Video Fine-Tuning in Multimodal Large Language Models", "authors": [ "Linghao Zhang", "Jungang Li", "Yonghua Hei", "Sicheng Tao", "Song Dai", "Yibo Yan", "Zihao Dongfang", "Weiting Liu", "Chenxi Qin", "Hanqian Li", "Xin Zou", "Jiahao Zhang", "Shuhang Xun", "Haiyun Jiang", "Xuming Hu" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) are typically trained in multiple stages, with video-based supervised fine-tuning (Video-SFT) serving as a key step for improving visual understanding. Yet its effect on the fine-grained evolution of visual capabilities, particularly the balance between spatial and temporal understanding, remains poorly understood. In this paper, we systematically study how Video-SFT reshapes visual capabilities in MLLMs. Across architectures, parameter scales, and frame sampling settings, we observe a consistent pattern: Video-SFT reliably improves video performance, but often yields limited gains or even degradation on static image benchmarks. We further show that this trade-off is closely tied to temporal budget: increasing the number of sampled frames generally improves video performance, but does not reliably improve static image performance. Motivated by this finding, we study an instruction-aware Hybrid-Frame strategy that adaptively allocates frame counts and partially mitigates the image-video trade-off. Our results indicate that Video-SFT is not a free lunch for MLLMs, and preserving spatial understanding remains a central challenge in joint image-video training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17948", "title": "VideoAtlas: Navigating Long-Form Video in Logarithmic Compute", "authors": [ "Mohamed Eltahir", "Ali Habibullah", "Yazan Alshoibi", "Lama Ayash", "Tanveer Hussain", "Naeemullah Khan" ], "github": "", "project_page": "", "abstract": "Extending language models to video introduces two challenges: representation, where existing methods rely on lossy approximations, and long-context, where caption- or agent-based pipelines collapse video into text and lose visual fidelity. To overcome this, we introduce VideoAtlas, a task-agnostic environment to represent video as a hierarchical grid that is simultaneously lossless, navigable, scalable, caption- and preprocessing-free. An overview of the video is available at a glance, and any region can be recursively zoomed into, with the same visual representation used uniformly for the video, intermediate investigations, and the agent's memory, eliminating lossy text conversion end-to-end. This hierarchical structure ensures access depth grows only logarithmically with video length. For long-context, Recursive Language Models (RLMs) recently offered a powerful solution for long text, but extending them to visual domain requires a structured environment to recurse into, which VideoAtlas provides. VideoAtlas as a Markov Decision Process unlocks Video-RLM: a parallel Master-Worker architecture where a Master coordinates global exploration while Workers concurrently drill into assigned regions to accumulate lossless visual evidence. We demonstrate three key findings: (1)~logarithmic compute growth with video duration, further amplified by a 30-60\\% multimodal cache hit rate arising from the grid's structural reuse. (2)~environment budgeting, where bounding the maximum exploration depth provides a principled compute-accuracy hyperparameter. (3)~emergent adaptive compute allocation that scales with question granularity. When scaling from 1-hour to 10-hour benchmarks, Video-RLM remains the most duration-robust method with minimal accuracy degradation, demonstrating that structured environment navigation is a viable and scalable paradigm for video understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17942", "title": "Efficient Training-Free Multi-Token Prediction via Embedding-Space Probing", "authors": [ "Raghavv Goel", "Mukul Gagrani", "Mingu Lee", "Chris Lott" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) exhibit latent multi-token prediction (MTP) capabilities despite being trained solely for next-token generation. We propose a simple, training-free MTP approach that probes an LLM using on-the-fly mask tokens drawn from its embedding space, enabling parallel prediction of future tokens without modifying model weights or relying on auxiliary draft models. Our method constructs a speculative token tree by sampling top-K candidates from mask-token logits and applies a lightweight pruning strategy to retain high-probability continuations. During decoding, candidate predictions are verified in parallel, resulting in lossless generation while substantially reducing the number of model calls and improving token throughput. Across benchmarks, our probing-based MTP consistently outperforms existing training-free baselines, increasing acceptance length by approximately 12\\% on LLaMA3 and 8--12\\% on Qwen3, and achieving throughput gains of up to 15--19\\%. Finally, we provide theoretical insights and empirical evidence showing that decoder layers naturally align mask-token representations with next-token states, enabling accurate multi-step prediction without retraining or auxiliary models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.16557", "title": "BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs", "authors": [ "Sangyeon Yoon", "Sunkyoung Kim", "Hyesoo Hong", "Wonje Jeung", "Yongil Kim", "Wooseok Seo", "Heuiyeen Yeen", "Albert No" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) increasingly store user preferences in persistent memory to support personalization across interactions. However, in third-party communication settings governed by social and institutional norms, some user preferences may be inappropriate to apply. We introduce BenchPreS, which evaluates whether memory-based user preferences are appropriately applied or suppressed across communication contexts. Using two complementary metrics, Misapplication Rate (MR) and Appropriate Application Rate (AAR), we find even frontier LLMs struggle to apply preferences in a context-sensitive manner. Models with stronger preference adherence exhibit higher rates of over-application, and neither reasoning capability nor prompt-based defenses fully resolve this issue. These results suggest current LLMs treat personalized preferences as globally enforceable rules rather than as context-dependent normative signals.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17216", "title": "AI Scientist via Synthetic Task Scaling", "authors": [ "Ziyang Cai", "Harkirat Behl" ], "github": "", "project_page": "", "abstract": "With the advent of AI agents, automatic scientific discovery has become a tenable goal. Many recent works scaffold agentic systems that can perform machine learning research, but don't offer a principled way to train such agents -- and current LLMs often generate plausible-looking but ineffective ideas. To make progress on training agents that can learn from doing, we provide a novel synthetic environment generation pipeline targeting machine learning agents. Our pipeline automatically synthesizes machine learning challenges compatible with the SWE-agent framework, covering topic sampling, dataset proposal, and code generation. The resulting synthetic tasks are 1) grounded in real machine learning datasets, because the proposed datasets are verified against the Huggingface API and are 2) verified for higher quality with a self-debugging loop. To validate the effectiveness of our synthetic tasks, we tackle MLGym, a benchmark for machine learning tasks. From the synthetic tasks, we sample trajectories from a teacher model (GPT-5), then use the trajectories to train a student model (Qwen3-4B and Qwen3-8B). The student models trained with our synthetic tasks achieve improved performance on MLGym, raising the AUP metric by 9% for Qwen3-4B and 12% for Qwen3-8B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.15618", "title": "Look Before Acting: Enhancing Vision Foundation Representations for Vision-Language-Action Models", "authors": [ "Yulin Luo", "Hao Chen", "Zhuangzhe Wu", "Bowen Sui", "Jiaming Liu", "Chenyang Gu", "Zhuoyang Liu", "Qiuxuan Feng", "Jiale Yu", "Shuo Gu", "Peng Jia", "Pheng-Ann Heng", "Shanghang Zhang" ], "github": "https://github.com/starVLA/starVLA", "project_page": "", "abstract": "Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for robotic manipulation, in which reliable action prediction critically depends on accurately interpreting and integrating visual observations conditioned on language instructions. Although recent works have sought to enhance the visual capabilities of VLA models, most approaches treat the LLM backbone as a black box, providing limited insight into how visual information is grounded into action generation. Therefore, we perform a systematic analysis of multiple VLA models across different action-generation paradigms and observe that sensitivity to visual tokens progressively decreases in deeper layers during action generation. Motivated by this observation, we propose DeepVision-VLA, built on a Vision-Language Mixture-of-Transformers (VL-MoT) framework. This framework enables shared attention between the vision foundation model and the VLA backbone, injecting multi-level visual features from the vision expert into deeper layers of the VLA backbone to enhance visual representations for precise and complex manipulation. In addition, we introduce Action-Guided Visual Pruning (AGVP), which leverages shallow-layer attention to prune irrelevant visual tokens while preserving task-relevant ones, reinforcing critical visual cues for manipulation with minimal computational overhead. DeepVision-VLA outperforms prior state-of-the-art methods by 9.0\\% and 7.5\\% on simulated and real-world tasks, respectively, providing new insights for the design of visually enhanced VLA models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/starVLA/starVLA", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.14935", "title": "Video-CoE: Reinforcing Video Event Prediction via Chain of Events", "authors": [ "Qile Su", "Jing Tang", "Rui Chen", "Lei Sun", "Xiangxiang Chu" ], "github": "", "project_page": "", "abstract": "Despite advances in the application of MLLMs for various video tasks, video event prediction (VEP) remains relatively underexplored. VEP requires the model to perform fine-grained temporal modeling of videos and establish logical relationships between videos and future events, which current MLLMs still struggle with. In this work, we first present a comprehensive evaluation of current leading MLLMs on the VEP task, revealing the reasons behind their inaccurate predictions, including lack of logical reasoning ability for future events prediction and insufficient utilization of visual information. To address these challenges, we propose Chain of Events (CoE) paradigm, which constructs temporal event chains to implicitly enforce MLLM focusing on the visual content and the logical connections between videos and future events, incentivizing model's reasoning capability with multiple training protocols. Experimental results on public benchmarks demonstrate that our method outperforms both leading open-source and commercial MLLMs, establishing a new state-of-the-art on the VEP task. Codes and models will be released soon.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17240", "title": "GigaWorld-Policy: An Efficient Action-Centered World--Action Model", "authors": [ "Angen Ye", "Boyuan Wang", "Chaojun Ni", "Guan Huang", "Guosheng Zhao", "Hao Li", "Hengtao Li", "Jie Li", "Jindi Lv", "Jingyu Liu", "Min Cao", "Peng Li", "Qiuping Deng", "Wenjun Mei", "Xiaofeng Wang", "Xinze Chen", "Xinyu Zhou", "Yang Wang", "Yifan Chang", "Yifan Li", "Yukun Zhou", "Yun Ye", "Zhichao Liu", "Zheng Zhu" ], "github": "", "project_page": "", "abstract": "World-Action Models (WAM) initialized from pre-trained video generation backbones have demonstrated remarkable potential for robot policy learning. However, existing approaches face two critical bottlenecks that hinder performance and deployment. First, jointly reasoning over future visual dynamics and corresponding actions incurs substantial inference overhead. Second, joint modeling often entangles visual and motion representations, making motion prediction accuracy heavily dependent on the quality of future video forecasts. To address these issues, we introduce GigaWorld-Policy, an action-centered WAM that learns 2D pixel-action dynamics while enabling efficient action decoding, with optional video generation. Specifically, we formulate policy training into two coupled components: the model predicts future action sequences conditioned on the current observation, and simultaneously generates future videos conditioned on the predicted actions and the same observation. The policy is supervised by both action prediction and video generation, providing richer learning signals and encouraging physically plausible actions through visual-dynamics constraints. With a causal design that prevents future-video tokens from influencing action tokens, explicit future-video generation is optional at inference time, allowing faster action prediction during deployment. To support this paradigm, we curate a diverse, large-scale robot dataset to pre-train an action-centered video generation model, which is then adapted as the backbone for robot policy learning. Experimental results on real-world robotic platforms show that GigaWorld-Policy runs 9x faster than the leading WAM baseline, Motus, while improving task success rates by 7%. Moreover, compared with pi-0.5, GigaWorld-Policy improves performance by 95% on RoboTwin 2.0.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17079", "title": "ACE-LoRA: Graph-Attentive Context Enhancement for Parameter-Efficient Adaptation of Medical Vision-Language Models", "authors": [ "M. Arda Ayd\u0131n", "Melih B. Yilmaz", "Aykut Ko\u00e7", "Tolga \u00c7ukur" ], "github": "https://github.com/icon-lab/ACE-LoRA", "project_page": "", "abstract": "The success of CLIP-like vision-language models (VLMs) on natural images has inspired medical counterparts, yet existing approaches largely fall into two extremes: specialist models trained on single-domain data, which capture domain-specific details but generalize poorly, and generalist medical VLMs trained on multi-domain data, which retain broad semantics but dilute fine-grained diagnostic cues. Bridging this specialization-generalization trade-off remains challenging. To address this problem, we propose ACE-LoRA, a parameter-efficient adaptation framework for generalist medical VLMs that maintains robust zero-shot generalization. ACE-LoRA integrates Low-Rank Adaptation (LoRA) modules into frozen image-text encoders and introduces an Attention-based Context Enhancement Hypergraph Neural Network (ACE-HGNN) module that captures higher-order contextual interactions beyond pairwise similarity to enrich global representations with localized diagnostic cues, addressing a key limitation of prior Parameter-Efficient Fine-Tuning (PEFT) methods that overlook fine-grained details. To further enhance cross-modal alignment, we formulate a label-guided InfoNCE loss to effectively suppress false negatives between semantically related image-text pairs. Despite adding only 0.95M trainable parameters, ACE-LoRA consistently outperforms state-of-the-art medical VLMs and PEFT baselines across zero-shot classification, segmentation, and detection benchmarks spanning multiple domains. Our code is available at https://github.com/icon-lab/ACE-LoRA.", "github_manual": "", "github_regex": "https://github.com/icon-lab/ACE-LoRA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17218", "title": "Alignment Makes Language Models Normative, Not Descriptive", "authors": [ "Eilam Shapira", "Moshe Tennenholtz", "Roi Reichart" ], "github": "", "project_page": "", "abstract": "Post-training alignment optimizes language models to match human preference signals, but this objective is not equivalent to modeling observed human behavior. We compare 120 base-aligned model pairs on more than 10,000 real human decisions in multi-round strategic games - bargaining, persuasion, negotiation, and repeated matrix games. In these settings, base models outperform their aligned counterparts in predicting human choices by nearly 10:1, robustly across model families, prompt formulations, and game configurations. This pattern reverses, however, in settings where human behavior is more likely to follow normative predictions: aligned models dominate on one-shot textbook games across all 12 types tested and on non-strategic lottery choices - and even within the multi-round games themselves, at round one, before interaction history develops. This boundary-condition pattern suggests that alignment induces a normative bias: it improves prediction when human behavior is relatively well captured by normative solutions, but hurts prediction in multi-round strategic settings, where behavior is shaped by descriptive dynamics such as reciprocity, retaliation, and history-dependent adaptation. These results reveal a fundamental trade-off between optimizing models for human use and using them as proxies for human behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.17662", "title": "FINER: MLLMs Hallucinate under Fine-grained Negative Queries", "authors": [ "Rui Xiao", "Sanghwan Kim", "Yongqin Xian", "Zeynep Akata", "Stephan Alaniz" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) struggle with hallucinations, particularly with fine-grained queries, a challenge underrepresented by existing benchmarks that focus on coarse image-related questions. We introduce FIne-grained NEgative queRies (FINER), alongside two benchmarks: FINER-CompreCap and FINER-DOCCI. Using FINER, we analyze hallucinations across four settings: multi-object, multi-attribute, multi-relation, and ``what'' questions. Our benchmarks reveal that MLLMs hallucinate when fine-grained mismatches co-occur with genuinely present elements in the image. To address this, we propose FINER-Tuning, leveraging Direct Preference Optimization (DPO) on FINER-inspired data. Finetuning four frontier MLLMs with FINER-Tuning yields up to 24.2\\% gains (InternVL3.5-14B) on hallucinations from our benchmarks, while simultaneously improving performance on eight existing hallucination suites, and enhancing general multimodal capabilities across six benchmarks. Code, benchmark, and models are available at https://explainableml.github.io/finer-project/{https://explainableml.github.io/finer-project/}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.16653", "title": "HeBA: Heterogeneous Bottleneck Adapters for Robust Vision-Language Models", "authors": [ "Md Jahidul Islam" ], "github": "https://github.com/Jahid12012021/VLM-HeBA", "project_page": "", "abstract": "Adapting large-scale Vision-Language Models (VLMs) like CLIP to downstream tasks often suffers from a \"one-size-fits-all\" architectural approach, where visual and textual tokens are processed uniformly by wide, generic adapters. We argue that this homogeneity ignores the distinct structural nature of the modalities -- spatial locality in images versus semantic density in text. To address this, we propose HeBA (Heterogeneous Bottleneck Adapter), a unified architectural framework that introduces modality-specific structural inductive biases. HeBA departs from conventional designs through three key architectural innovations: (1) Heterogeneity: It processes visual tokens via 2D depthwise-separable convolutions to preserve spatial correlations, while distinctively processing text tokens via dense linear projections to capture semantic relationships; (2) Bottleneck Regularization: Unlike standard expanding adapters, HeBA employs a compression bottleneck (D -> D/4) that explicitly forces the model to learn compact, robust features and acts as a structural regularizer; and (3) Active Gradient Initialization: We challenge the restrictive zero-initialization paradigm, utilizing a Kaiming initialization strategy that ensures sufficient initial gradient flow to accelerate convergence without compromising the frozen backbone's pre-trained knowledge. Extensive experiments demonstrate that HeBA's architecturally specialized design achieves superior stability and accuracy, establishing a new state-of-the-art on 11 few-shot benchmarks. Code is available at https://github.com/Jahid12012021/VLM-HeBA.", "github_manual": "", "github_regex": "https://github.com/Jahid12012021/VLM-HeBA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.12789", "title": "Coherent Human-Scene Reconstruction from Multi-Person Multi-View Video in a Single Pass", "authors": [ "Sangmin Kim", "Minhyuk Hwang", "Geonho Cha", "Dongyoon Wee", "Jaesik Park" ], "github": "", "project_page": "", "abstract": "Recent advances in 3D foundation models have led to growing interest in reconstructing humans and their surrounding environments. However, most existing approaches focus on monocular inputs, and extending them to multi-view settings requires additional overhead modules or preprocessed data. To this end, we present CHROMM, a unified framework that jointly estimates cameras, scene point clouds, and human meshes from multi-person multi-view videos without relying on external modules or preprocessing. We integrate strong geometric and human priors from Pi3X and Multi-HMR into a single trainable neural network architecture, and introduce a scale adjustment module to solve the scale discrepancy between humans and the scene. We also introduce a multi-view fusion strategy to aggregate per-view estimates into a single representation at test-time. Finally, we propose a geometry-based multi-person association method, which is more robust than appearance-based approaches. Experiments on EMDB, RICH, EgoHumans, and EgoExo4D show that CHROMM achieves competitive performance in global human motion and multi-view pose estimation while running over 8x faster than prior optimization-based multi-view approaches. Project page: https://nstar1125.github.io/chromm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.08501", "title": "Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA", "authors": [ "Ummar Abbas", "Mourad Ouzzani", "Mohamed Y. Eltabakh", "Omar Sinan", "Gagan Bhatia", "Hamdy Mubarak", "Majd Hawasly", "Mohammed Qusay Hashim", "Kareem Darwish", "Firoj Alam" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) can answer religious knowledge queries fluently, yet they often hallucinate and misattribute sources, which is especially consequential in Islamic settings where users expect grounding in canonical texts (Qur'an and Hadith) and jurisprudential (fiqh) nuance. Retrieval-augmented generation (RAG) reduces some of these limitations by grounding generation in external evidence. However, a single ``retrieve-then-generate'' pipeline is limited to deal with the diversity of Islamic queries. Users may request verbatim scripture, fatwa-style guidance with citations or rule-constrained computations such as zakat and inheritance that require strict arithmetic and legal invariants. In this work, we present a bilingual (Arabic/English) multi-agent Islamic assistant, called Fanar-Sadiq, which is a core component of the Fanar AI platform. Fanar-Sadiq routes Islamic-related queries to specialized modules within an agentic, tool-using architecture. The system supports intent-aware routing, retrieval-grounded fiqh answers with deterministic citation normalization and verification traces, exact verse lookup with quotation validation, and deterministic calculators for Sunni zakat and inheritance with madhhab-sensitive branching. We evaluate the complete end-to-end system on public Islamic QA benchmarks and demonstrate effectiveness and efficiency. Our system is currently publicly and freely accessible through API and a Web application, and has been accessed approx1.9M times in less than a year.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.11535", "title": "Expert Threshold Routing for Autoregressive Language Modeling with Dynamic Computation Allocation and Load Balancing", "authors": [ "Hanchi Sun", "Yixin Liu", "Yonghui Wu", "Lichao Sun" ], "github": "https://github.com/karpathy/nanochat", "project_page": "", "abstract": "Token-choice Mixture-of-Experts (TC-MoE) routes each token to a fixed number of experts, limiting dynamic computation allocation and requiring auxiliary losses to maintain load balance. We propose Expert Threshold (ET) routing, where each expert maintains an exponential moving average (EMA) threshold estimated from the global token distribution. At both training and inference, each token is independently routed to an expert if its score exceeds the expert's threshold, enabling dynamic computation allocation while achieving load balance without auxiliary losses. This fully causal mechanism eliminates dependence on other tokens in the batch, making it well-suited for autoregressive language modeling. In pretraining experiments scaling to 2.4B parameters on FineWeb-Edu, ET achieves 0.067 lower cross-entropy loss than TC-MoE, equivalent to reaching the same performance with 1.6times fewer tokens.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/karpathy/nanochat", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.14482", "title": "V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning", "authors": [ "Lorenzo Mur-Labadia", "Matthew Muckley", "Amir Bar", "Mido Assran", "Koustuv Sinha", "Mike Rabbat", "Yann LeCun", "Nicolas Ballas", "Adrien Bardes" ], "github": "", "project_page": "", "abstract": "We present V-JEPA 2.1, a family of self-supervised models that learn dense, high-quality visual representations for both images and videos while retaining strong global scene understanding. The approach combines four key components. First, a dense predictive loss uses a masking-based objective in which both visible and masked tokens contribute to the training signal, encouraging explicit spatial and temporal grounding. Second, deep self-supervision applies the self-supervised objective hierarchically across multiple intermediate encoder layers to improve representation quality. Third, multi-modal tokenizers enable unified training across images and videos. Finally, the model benefits from effective scaling in both model capacity and training data. Together, these design choices produce representations that are spatially structured, semantically coherent, and temporally consistent. Empirically, V-JEPA 2.1 achieves state-of-the-art performance on several challenging benchmarks, including 7.71 mAP on Ego4D for short-term object-interaction anticipation and 40.8 Recall@5 on EPIC-KITCHENS for high-level action anticipation, as well as a 20-point improvement in real-robot grasping success rate over V-JEPA-2 AC. The model also demonstrates strong performance in robotic navigation (5.687 ATE on TartanDrive), depth estimation (0.307 RMSE on NYUv2 with a linear probe), and global recognition (77.7 on Something-Something-V2). These results show that V-JEPA 2.1 significantly advances the state of the art in dense visual understanding and world modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.10263", "title": "From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning", "authors": [ "Zhanyi Sun", "Shuran Song" ], "github": "https://github.com/zhanyisun/dice-rl", "project_page": "", "abstract": "We introduce Distribution Contractive Reinforcement Learning (DICE-RL), a framework that uses reinforcement learning (RL) as a \"distribution contraction\" operator to refine pretrained generative robot policies. DICE-RL turns a pretrained behavior prior into a high-performing \"pro\" policy by amplifying high-success behaviors from online feedback. We pretrain a diffusion- or flow-based policy for broad behavioral coverage, then finetune it with a stable, sample-efficient residual off-policy RL framework that combines selective behavior regularization with value-guided action selection. Extensive experiments and analyses show that DICE-RL reliably improves performance with strong stability and sample efficiency. It enables mastery of complex long-horizon manipulation skills directly from high-dimensional pixel inputs, both in simulation and on a real robot. Project website: https://zhanyisun.github.io/dice.rl.2026/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhanyisun/dice-rl", "github_api_added_by": "auto" }, { "date": "2026-03-19", "arxiv_id": "2603.12372", "title": "Efficient Reasoning with Balanced Thinking", "authors": [ "Yulin Li", "Tengyao Tu", "Li Ding", "Junjie Wang", "Huiling Zhen", "Yixin Chen", "Yong Li", "Zhuotao Tian" ], "github": "https://github.com/yu-lin-li/ReBalance", "project_page": "", "abstract": "Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they often suffer from overthinking, expending redundant computational steps on simple problems, or underthinking, failing to explore sufficient reasoning paths despite inherent capabilities. These issues lead to inefficiencies and potential inaccuracies, limiting practical deployment in resource-constrained settings. Existing methods to mitigate overthinking, such as suppressing reflective keywords or adjusting reasoning length, may inadvertently induce underthinking, compromising accuracy. Therefore, we propose ReBalance, a training-free framework that achieves efficient reasoning with balanced thinking. ReBalance leverages confidence as a continuous indicator of reasoning dynamics, identifying overthinking through high confidence variance and underthinking via consistent overconfidence. By aggregating hidden states from a small-scale dataset into reasoning mode prototypes, we compute a steering vector to guide LRMs' reasoning trajectories. A dynamic control function modulates this vector's strength and direction based on real-time confidence, pruning redundancy during overthinking, and promoting exploration during underthinking. Extensive experiments conducted on four models ranging from 0.5B to 32B, and across nine benchmarks in math reasoning, general question answering, and coding tasks demonstrate that ReBalance effectively reduces output redundancy while improving accuracy, offering a general, training-free, and plug-and-play strategy for efficient and robust LRM deployment. Code is available at https://github.com/yu-lin-li/ReBalance .", "github_manual": "", "github_regex": "https://github.com/yu-lin-li/ReBalance", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-19", "arxiv_id": "2603.14706", "title": "AdapterTune: Zero-Initialized Low-Rank Adapters for Frozen Vision Transformers", "authors": [ "Salim Khazem" ], "github": "https://github.com/salimkhazem/adaptertune", "project_page": "", "abstract": "Frozen-backbone transfer with Vision Transformers faces two under-addressed issues: optimization instability when adapters are naively inserted into a fixed feature extractor, and the absence of principled guidance for setting adapter capacity. We introduce AdapterTune, which augments each transformer block with a residual low-rank bottleneck whose up-projection is zero-initialized, guaranteeing that the adapted network starts exactly at the pretrained function and eliminates early-epoch representation drift. On the analytical side, we formalize adapter rank as a capacity budget for approximating downstream task shifts in feature space. The resulting excess-risk decomposition predicts monotonic but diminishing accuracy gains with increasing rank, an ``elbow'' behavior we confirm through controlled sweeps. We evaluate on 9 datasets and 3 backbone scales with multi-seed reporting throughout. On a core 5 dataset transfer suite, AdapterTune improves top-1 accuracy over head-only transfer by +14.9 points on average while training only 0.92 of the parameters required by full fine-tuning, and outperforms full fine-tuning on 10 of 15 dataset-backbone pairs. Across the full benchmark, AdapterTune improves over head-only transfer on every dataset-backbone pair tested. Ablations on rank, placement, and initialization isolate each design choice. The code is available at: https://github.com/salimkhazem/adaptertune", "github_manual": "", "github_regex": "https://github.com/salimkhazem/adaptertune", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19235", "title": "Generation Models Know Space: Unleashing Implicit 3D Priors for Scene Understanding", "authors": [ "Xianjin Wu", "Dingkang Liang", "Tianrui Feng", "Kui Xia", "Yumeng Zhang", "Xiaofan Li", "Xiao Tan", "Xiang Bai" ], "github": "https://github.com/H-EmbodVis/VEGA-3D", "project_page": "", "abstract": "While Multimodal Large Language Models demonstrate impressive semantic capabilities, they often suffer from spatial blindness, struggling with fine-grained geometric reasoning and physical dynamics. Existing solutions typically rely on explicit 3D modalities or complex geometric scaffolding, which are limited by data scarcity and generalization challenges. In this work, we propose a paradigm shift by leveraging the implicit spatial prior within large-scale video generation models. We posit that to synthesize temporally coherent videos, these models inherently learn robust 3D structural priors and physical laws. We introduce VEGA-3D (Video Extracted Generative Awareness), a plug-and-play framework that repurposes a pre-trained video diffusion model as a Latent World Simulator. By extracting spatiotemporal features from intermediate noise levels and integrating them with semantic representations via a token-level adaptive gated fusion mechanism, we enrich MLLMs with dense geometric cues without explicit 3D supervision. Extensive experiments across 3D scene understanding, spatial reasoning, and embodied manipulation benchmarks demonstrate that our method outperforms state-of-the-art baselines, validating that generative priors provide a scalable foundation for physical-world understanding. Code is publicly available at https://github.com/H-EmbodVis/VEGA-3D.", "github_manual": "", "github_regex": "https://github.com/H-EmbodVis/VEGA-3D", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19232", "title": "Cubic Discrete Diffusion: Discrete Visual Generation on High-Dimensional Representation Tokens", "authors": [ "Yuqing Wang", "Chuofan Ma", "Zhijie Lin", "Yao Teng", "Lijun Yu", "Shuai Wang", "Jiaming Han", "Jiashi Feng", "Yi Jiang", "Xihui Liu" ], "github": "https://github.com/YuqingWang1029/CubiD", "project_page": "", "abstract": "Visual generation with discrete tokens has gained significant attention as it enables a unified token prediction paradigm shared with language models, promising seamless multimodal architectures. However, current discrete generation methods remain limited to low-dimensional latent tokens (typically 8-32 dims), sacrificing the semantic richness essential for understanding. While high-dimensional pretrained representations (768-1024 dims) could bridge this gap, their discrete generation poses fundamental challenges. In this paper, we present Cubic Discrete Diffusion (CubiD), the first discrete generation model for high-dimensional representations. CubiD performs fine-grained masking throughout the high-dimensional discrete representation -- any dimension at any position can be masked and predicted from partial observations. This enables the model to learn rich correlations both within and across spatial positions, with the number of generation steps fixed at T regardless of feature dimensionality, where T ll hwd. On ImageNet-256, CubiD achieves state-of-the-art discrete generation with strong scaling behavior from 900M to 3.7B parameters. Crucially, we validate that these discretized tokens preserve original representation capabilities, demonstrating that the same discrete tokens can effectively serve both understanding and generation tasks. We hope this work will inspire future research toward unified multimodal architectures. Code is available at: https://github.com/YuqingWang1029/CubiD.", "github_manual": "", "github_regex": "https://github.com/YuqingWang1029/CubiD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19228", "title": "SAMA: Factorized Semantic Anchoring and Motion Alignment for Instruction-Guided Video Editing", "authors": [ "Xinyao Zhang", "Wenkai Dong", "Yuxin Song", "Bo Fang", "Qi Zhang", "Jing Wang", "Fan Chen", "Hui Zhang", "Haocheng Feng", "Yu Lu", "Hang Zhou", "Chun Yuan", "Jingdong Wang" ], "github": "", "project_page": "", "abstract": "Current instruction-guided video editing models struggle to simultaneously balance precise semantic modifications with faithful motion preservation. While existing approaches rely on injecting explicit external priors (e.g., VLM features or structural conditions) to mitigate these issues, this reliance severely bottlenecks model robustness and generalization. To overcome this limitation, we present SAMA (factorized Semantic Anchoring and Motion Alignment), a framework that factorizes video editing into semantic anchoring and motion modeling. First, we introduce Semantic Anchoring, which establishes a reliable visual anchor by jointly predicting semantic tokens and video latents at sparse anchor frames, enabling purely instruction-aware structural planning. Second, Motion Alignment pre-trains the same backbone on motion-centric video restoration pretext tasks (cube inpainting, speed perturbation, and tube shuffle), enabling the model to internalize temporal dynamics directly from raw videos. SAMA is optimized with a two-stage pipeline: a factorized pre-training stage that learns inherent semantic-motion representations without paired video-instruction editing data, followed by supervised fine-tuning on paired editing data. Remarkably, the factorized pre-training alone already yields strong zero-shot video editing ability, validating the proposed factorization. SAMA achieves state-of-the-art performance among open-source models and is competitive with leading commercial systems (e.g., Kling-Omni). Code, models, and datasets will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19223", "title": "F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World", "authors": [ "Ziyin Zhang", "Zihan Liao", "Hang Yu", "Peng Di", "Rui Wang" ], "github": "", "project_page": "", "abstract": "We present F2LLM-v2, a new family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a newly curated composite of 60 million publicly available high-quality data samples, F2LLM-v2 supports more than 200 languages, with a particular emphasis on previously underserved mid- and low-resource languages. By integrating a two-stage LLM-based embedding training pipeline with matryoshka learning, model pruning, and knowledge distillation techniques, we present models that are far more efficient than previous LLM-based embedding models while retaining competitive performances. Extensive evaluations confirm that F2LLM-v2-14B ranks first on 11 MTEB benchmarks, while the smaller models in the family also set a new state of the art for resource-constrained applications. To facilitate open-source embedding model research, we release all models, data, code, and intermediate checkpoints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.11804", "title": "OSM-based Domain Adaptation for Remote Sensing VLMs", "authors": [ "Stefan Maria Ailuro", "Mario Markov", "Mohammad Mahdi", "Delyan Boychev", "Luc Van Gool", "Danda Pani Paudel" ], "github": "https://github.com/AI9Stars/XLRS-Bench", "project_page": "", "abstract": "Vision-Language Models (VLMs) adapted to remote sensing rely heavily on domain-specific image-text supervision, yet high-quality annotations for satellite and aerial imagery remain scarce and expensive to produce. Prevailing pseudo-labeling pipelines address this gap by distilling knowledge from large frontier models, but this dependence on large teachers is costly, limits scalability, and caps achievable performance at the ceiling of the teacher. We propose OSMDA: a self-contained domain adaptation framework that eliminates this dependency. Our key insight is that a capable base VLM can serve as its own annotation engine: by pairing aerial images with rendered OpenStreetMap (OSM) tiles, we leverage optical character recognition and chart comprehension capabilities of the model to generate captions enriched by OSM's vast auxiliary metadata. The model is then fine-tuned on the resulting corpus with satellite imagery alone, yielding OSMDA-VLM, a domain-adapted VLM that requires no manual labeling and no stronger external model. We conduct exhaustive evaluations spanning 10 benchmarks across image-text-to-text tasks and comparing against 9 competitive baselines. When equally mixed with real data, our method achieves state-of-the-art results, while being substantially cheaper to train than teacher-dependent alternatives. These results suggest that, given a strong foundation model, alignment with crowd-sourced geographic data is a practical and scalable path towards remote sensing domain adaptation. Dataset and model weights will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AI9Stars/XLRS-Bench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19217", "title": "LVOmniBench: Pioneering Long Audio-Video Understanding Evaluation for Omnimodal LLMs", "authors": [ "Keda Tao", "Yuhua Zheng", "Jia Xu", "Wenjie Du", "Kele Shao", "Hesong Wang", "Xueyi Chen", "Xin Jin", "Junhan Zhu", "Bohan Yu", "Weiqiang Wang", "Jian Liu", "Can Qin", "Yulun Zhang", "Ming-Hsuan Yang", "Huan Wang" ], "github": "", "project_page": "", "abstract": "Recent advancements in omnimodal large language models (OmniLLMs) have significantly improved the comprehension of audio and video inputs. However, current evaluations primarily focus on short audio and video clips ranging from 10 seconds to 5 minutes, failing to reflect the demands of real-world applications, where videos typically run for tens of minutes. To address this critical gap, we introduce LVOmniBench, a new benchmark designed specifically for the cross-modal comprehension of long-form audio and video. This dataset comprises high-quality videos sourced from open platforms that feature rich audio-visual dynamics. Through rigorous manual selection and annotation, LVOmniBench comprises 275 videos, ranging in duration from 10 to 90 minutes, and 1,014 question-answer (QA) pairs. LVOmniBench aims to rigorously evaluate the capabilities of OmniLLMs across domains, including long-term memory, temporal localization, fine-grained understanding, and multimodal perception. Our extensive evaluation reveals that current OmniLLMs encounter significant challenges when processing extended audio-visual inputs. Open-source models generally achieve accuracies below 35%, whereas the Gemini 3 Pro reaches a peak accuracy of approximately 65%. We anticipate that this dataset, along with our empirical findings, will stimulate further research and the development of advanced models capable of resolving complex cross-modal understanding problems within long-form audio-visual contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19199", "title": "FASTER: Rethinking Real-Time Flow VLAs", "authors": [ "Yuxiang Lu", "Zhe Liu", "Xianzhe Fan", "Zhenya Yang", "Jinghua Hou", "Junyi Li", "Kaixin Ding", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "Real-time execution is crucial for deploying Vision-Language-Action (VLA) models in the physical world. Existing asynchronous inference methods primarily optimize trajectory smoothness, but neglect the critical latency in reacting to environmental changes. By rethinking the notion of reaction in action chunking policies, this paper presents a systematic analysis of the factors governing reaction time. We show that reaction time follows a uniform distribution determined jointly by the Time to First Action (TTFA) and the execution horizon. Moreover, we reveal that the standard practice of applying a constant schedule in flow-based VLAs can be inefficient and forces the system to complete all sampling steps before any movement can start, forming the bottleneck in reaction latency. To overcome this issue, we propose Fast Action Sampling for ImmediaTE Reaction (FASTER). By introducing a Horizon-Aware Schedule, FASTER adaptively prioritizes near-term actions during flow sampling, compressing the denoising of the immediate reaction by tenfold (e.g., in \u03c0_{0.5} and X-VLA) into a single step, while preserving the quality of long-horizon trajectory. Coupled with a streaming client-server pipeline, FASTER substantially reduces the effective reaction latency on real robots, especially when deployed on consumer-grade GPUs. Real-world experiments, including a highly dynamic table tennis task, prove that FASTER unlocks unprecedented real-time responsiveness for generalist policies, enabling rapid generation of accurate and smooth trajectories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.16929", "title": "MHPO: Modulated Hazard-aware Policy Optimization for Stable Reinforcement Learning", "authors": [ "Hongjun Wang", "Wei Liu", "Weibo Gu", "Xing Sun", "Kai Han" ], "github": "", "project_page": "", "abstract": "Regulating the importance ratio is critical for the training stability of Group Relative Policy Optimization (GRPO) based frameworks. However, prevailing ratio control methods, such as hard clipping, suffer from non-differentiable boundaries and vanishing gradient regions, failing to maintain gradient fidelity. Furthermore, these methods lack a hazard-aware mechanism to adaptively suppress extreme deviations, leaving the optimization process vulnerable to abrupt policy shifts. To address these challenges, we propose Modulated Hazard-aware Policy Optimization (MHPO), a novel framework designed for robust and stable reinforcement learning. The proposed MHPO introduces a Log-Fidelity Modulator (LFM) to map unbounded importance ratios into a bounded, differentiable domain. This mechanism effectively prevents high-variance outlier tokens from destabilizing the loss landscape while ensuring global gradient stability. Complementarily, a Decoupled Hazard Penalty (DHP) integrates cumulative hazard functions from survival analysis to independently regulate positive and negative policy shifts. By shaping the optimization landscape with hazard-aware penalties, the proposed MHPO achieves fine-grained regulation of asymmetric policy shifts simultaneously mitigating mode collapse from over-expansion and preventing policy erosion from catastrophic contraction within a stabilized trust region. Extensive evaluations on diverse reasoning benchmarks across both text-based and vision-language tasks demonstrate that MHPO consistently outperforms existing methods, achieving superior performance while significantly enhancing training stability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19234", "title": "Matryoshka Gaussian Splatting", "authors": [ "Zhilin Guo", "Boqiao Zhang", "Hakan Aktas", "Kyle Fogarty", "Jeffrey Hu", "Nursena Koprucu Aslan", "Wenzhao Li", "Canberk Baykal", "Albert Miao", "Josef Bengtson", "Chenliang Zhou", "Weihao Xia", "Cristina Nader Vasconcelos. Cengiz Oztireli" ], "github": "", "project_page": "", "abstract": "The ability to render scenes at adjustable fidelity from a single model, known as level of detail (LoD), is crucial for practical deployment of 3D Gaussian Splatting (3DGS). Existing discrete LoD methods expose only a limited set of operating points, while concurrent continuous LoD approaches enable smoother scaling but often suffer noticeable quality degradation at full capacity, making LoD a costly design decision. We introduce Matryoshka Gaussian Splatting (MGS), a training framework that enables continuous LoD for standard 3DGS pipelines without sacrificing full-capacity rendering quality. MGS learns a single ordered set of Gaussians such that rendering any prefix, the first k splats, produces a coherent reconstruction whose fidelity improves smoothly with increasing budget. Our key idea is stochastic budget training: each iteration samples a random splat budget and optimises both the corresponding prefix and the full set. This strategy requires only two forward passes and introduces no architectural modifications. Experiments across four benchmarks and six baselines show that MGS matches the full-capacity performance of its backbone while enabling a continuous speed-quality trade-off from a single model. Extensive ablations on ordering strategies, training objectives, and model capacity further validate the designs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19220", "title": "Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation", "authors": [ "Zhuolin Yang", "Zihan Liu", "Yang Chen", "Wenliang Dai", "Boxin Wang", "Sheng-Chieh Lin", "Chankyu Lee", "Yangyi Chen", "Dongfu Jiang", "Jiafan He", "Renjie Pi", "Grace Lam", "Nayeon Lee", "Alexander Bukharin", "Mohammad Shoeybi", "Bryan Catanzaro", "Wei Ping" ], "github": "", "project_page": "", "abstract": "We introduce Nemotron-Cascade 2, an open 30B MoE model with 3B activated parameters that delivers best-in-class reasoning and strong agentic capabilities. Despite its compact size, its mathematical and coding reasoning performance approaches that of frontier open models. It is the second open-weight LLM, after DeepSeekV3.2-Speciale-671B-A37B, to achieve Gold Medal-level performance in the 2025 International Mathematical Olympiad (IMO), the International Olympiad in Informatics (IOI), and the ICPC World Finals, demonstrating remarkably high intelligence density with 20x fewer parameters. In contrast to Nemotron-Cascade 1, the key technical advancements are as follows. After SFT on a meticulously curated dataset, we substantially expand Cascade RL to cover a much broader spectrum of reasoning and agentic domains. Furthermore, we introduce multi-domain on-policy distillation from the strongest intermediate teacher models for each domain throughout the Cascade RL process, allowing us to efficiently recover benchmark regressions and sustain strong performance gains along the way. We release the collection of model checkpoint and training data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.18886", "title": "Reasoning over mathematical objects: on-policy reward modeling and test time aggregation", "authors": [ "Pranjal Aggarwal", "Marjan Ghazvininejad", "Seungone Kim", "Ilia Kulikov", "Jack Lanchantin", "Xian Li", "Tianjian Li", "Bo Liu", "Graham Neubig", "Anaelia Ovalle", "Swarnadeep Saha", "Sainbayar Sukhbaatar", "Sean Welleck", "Jason Weston", "Chenxi Whitehouse", "Adina Williams", "Jing Xu", "Ping Yu", "Weizhe Yuan", "Jingyu Zhang", "Wenting Zhao" ], "github": "", "project_page": "", "abstract": "The ability to precisely derive mathematical objects is a core requirement for downstream STEM applications, including mathematics, physics, and chemistry, where reasoning must culminate in formally structured expressions. Yet, current LM evaluations of mathematical and scientific reasoning rely heavily on simplified answer formats such as numerical values or multiple choice options due to the convenience of automated assessment. In this paper we provide three contributions for improving reasoning over mathematical objects: (i) we build and release training data and benchmarks for deriving mathematical objects, the Principia suite; (ii) we provide training recipes with strong LLM-judges and verifiers, where we show that on-policy judge training boosts performance; (iii) we show how on-policy training can also be used to scale test-time compute via aggregation. We find that strong LMs such as Qwen3-235B and o3 struggle on Principia, while our training recipes can bring significant improvements over different LLM backbones, while simultaneously improving results on existing numerical and MCQA tasks, demonstrating cross-format generalization of reasoning abilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.18743", "title": "Memento-Skills: Let Agents Design Agents", "authors": [ "Huichi Zhou", "Siyuan Guo", "Anjie Liu", "Zhongwei Yu", "Ziqin Gong", "Bowen Zhao", "Zhixun Chen", "Menglong Zhang", "Yihang Chen", "Jinsong Li", "Runyu Yang", "Qiangbin Liu", "Xinlei Yu", "Jianmin Zhou", "Na Wang", "Chunyang Sun", "Jun Wang" ], "github": "https://github.com/Memento-Teams/Memento-Skills", "project_page": "", "abstract": "We introduce Memento-Skills, a generalist, continually-learnable LLM agent system that functions as an agent-designing agent: it autonomously constructs, adapts, and improves task-specific agents through experience. The system is built on a memory-based reinforcement learning framework with stateful prompts, where reusable skills (stored as structured markdown files) serve as persistent, evolving memory. These skills encode both behaviour and context, enabling the agent to carry forward knowledge across interactions. Starting from simple elementary skills (like Web search and terminal operations), the agent continually improves via the Read--Write Reflective Learning mechanism introduced in Memento~2~wang2025memento2. In the read phase, a behaviour-trainable skill router selects the most relevant skill conditioned on the current stateful prompt; in the write phase, the agent updates and expands its skill library based on new experience. This closed-loop design enables continual learning without updating LLM parameters, as all adaptation is realised through the evolution of externalised skills and prompts. Unlike prior approaches that rely on human-designed agents, Memento-Skills enables a generalist agent to design agents end-to-end for new tasks. Through iterative skill generation and refinement, the system progressively improves its own capabilities. Experiments on the General AI Assistants benchmark and Humanity's Last Exam demonstrate sustained gains, achieving 26.2\\% and 116.2\\% relative improvements in overall accuracy, respectively. Code is available at https://github.com/Memento-Teams/Memento-Skills.", "github_manual": "", "github_regex": "https://github.com/Memento-Teams/Memento-Skills", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.18815", "title": "ProRL Agent: Rollout-as-a-Service for RL Training of Multi-Turn LLM Agents", "authors": [ "Hao Zhang", "Mingjie Liu", "Shaokun Zhang", "Songyang Han", "Jian Hu", "Zhenghui Jin", "Yuchi Zhang", "Shizhe Diao", "Ximing Lu", "Binfeng Xu", "Zhiding Yu", "Jan Kautz", "Yi Dong" ], "github": "https://github.com/NVIDIA-NeMo/ProRL-Agent-Server", "project_page": "", "abstract": "Multi-turn LLM agents are increasingly important for solving complex, interactive tasks, and reinforcement learning (RL) is a key ingredient for improving their long-horizon behavior. However, RL training requires generating large numbers of sandboxed rollout trajectories, and existing infrastructures often couple rollout orchestration with the training loop, making systems hard to migrate and maintain. Under the rollout-as-a-service philosophy, we present ProRL Agent , a scalable infrastructure that serves the full agentic rollout lifecycle through an API service. ProRL Agent also provides standardized and extensible sandbox environments that support diverse agentic tasks in rootless HPC settings. We validate ProRL Agent through RL training on software engineering, math, STEM, and coding tasks. ProRL Agent is open-sourced and integrated as part of NVIDIA NeMo Gym.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA-NeMo/ProRL-Agent-Server", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.13395", "title": "COT-FM: Cluster-wise Optimal Transport Flow Matching", "authors": [ "Chiensheng Chiang", "Kuan-Hsun Tu", "Jia-Wei Liao", "Cheng-Fu Chou", "Tsung-Wei Ke" ], "github": "", "project_page": "", "abstract": "We introduce COT-FM, a general framework that reshapes the probability path in Flow Matching (FM) to achieve faster and more reliable generation. FM models often produce curved trajectories due to random or batchwise couplings, which increase discretization error and reduce sample quality. COT-FM fixes this by clustering target samples and assigning each cluster a dedicated source distribution obtained by reversing pretrained FM models. This divide-and-conquer strategy yields more accurate local transport and significantly straighter vector fields, all without changing the model architecture. As a plug-and-play approach, COT-FM consistently accelerates sampling and improves generation quality across 2D datasets, image generation benchmarks, and robotic manipulation tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19227", "title": "Bridging Semantic and Kinematic Conditions with Diffusion-based Discrete Motion Tokenizer", "authors": [ "Chenyang Gu", "Mingyuan Zhang", "Haozhe Xie", "Zhongang Cai", "Lei Yang", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Prior motion generation largely follows two paradigms: continuous diffusion models that excel at kinematic control, and discrete token-based generators that are effective for semantic conditioning. To combine their strengths, we propose a three-stage framework comprising condition feature extraction (Perception), discrete token generation (Planning), and diffusion-based motion synthesis (Control). Central to this framework is MoTok, a diffusion-based discrete motion tokenizer that decouples semantic abstraction from fine-grained reconstruction by delegating motion recovery to a diffusion decoder, enabling compact single-layer tokens while preserving motion fidelity. For kinematic conditions, coarse constraints guide token generation during planning, while fine-grained constraints are enforced during control through diffusion-based optimization. This design prevents kinematic details from disrupting semantic token planning. On HumanML3D, our method significantly improves controllability and fidelity over MaskControl while using only one-sixth of the tokens, reducing trajectory error from 0.72 cm to 0.08 cm and FID from 0.083 to 0.029. Unlike prior methods that degrade under stronger kinematic constraints, ours improves fidelity, reducing FID from 0.033 to 0.014.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.18472", "title": "Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding", "authors": [ "Yinghui Li", "Jiayi Kuang", "Peng Xing", "Daixian Liu", "Junnan Dong", "Shu-Yu Guo", "Yangning Li", "Qingyu Zhou", "Wenhao Jiang", "Hai-Tao Zheng", "Ying Shen", "Liang Lin", "Philip S. Yu" ], "github": "", "project_page": "", "abstract": "While Multimodal Large Language Models (MLLMs) have achieved remarkable success in interpreting natural scenes, their ability to process discrete symbols -- the fundamental building blocks of human cognition -- remains a critical open question. Unlike continuous visual data, symbols such as mathematical formulas, chemical structures, and linguistic characters require precise, deeper interpretation. This paper introduces a comprehensive benchmark to evaluate how top-tier MLLMs navigate these \"discrete semantic spaces\" across five domains: language, culture, mathematics, physics, and chemistry. Our investigation uncovers a counterintuitive phenomenon: models often fail at basic symbol recognition yet succeed in complex reasoning tasks, suggesting they rely on linguistic probability rather than true visual perception. By exposing this \"cognitive mismatch\", we highlight a significant gap in current AI capabilities: the struggle to truly perceive and understand the symbolic languages that underpin scientific discovery and abstract thought. This work offers a roadmap for developing more rigorous, human-aligned intelligent systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.18002", "title": "Loc3R-VLM: Language-based Localization and 3D Reasoning with Vision-Language Models", "authors": [ "Kevin Qu", "Haozhe Qi", "Mihai Dusmanu", "Mahdi Rad", "Rui Wang", "Marc Pollefeys" ], "github": "https://github.com/kevinqu7/Loc3R-VLM", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have made impressive progress in connecting vision and language, but they still struggle with spatial understanding and viewpoint-aware reasoning. Recent efforts aim to augment the input representations with geometric cues rather than explicitly teaching models to reason in 3D space. We introduce Loc3R-VLM, a framework that equips 2D Vision-Language Models with advanced 3D understanding capabilities from monocular video input. Inspired by human spatial cognition, Loc3R-VLM relies on two joint objectives: global layout reconstruction to build a holistic representation of the scene structure, and explicit situation modeling to anchor egocentric perspective. These objectives provide direct spatial supervision that grounds both perception and language in a 3D context. To ensure geometric consistency and metric-scale alignment, we leverage lightweight camera pose priors extracted from a pre-trained 3D foundation model. Loc3R-VLM achieves state-of-the-art performance in language-based localization and outperforms existing 2D- and video-based approaches on situated and general 3D question-answering benchmarks, demonstrating that our spatial supervision framework enables strong 3D understanding. Project page: https://kevinqu7.github.io/loc3r-vlm", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kevinqu7/Loc3R-VLM", "github_api_added_by": "auto" }, { "date": "2026-03-20", "arxiv_id": "2603.15030", "title": "VTC-Bench: Evaluating Agentic Multimodal Models via Compositional Visual Tool Chaining", "authors": [ "Xuanyu Zhu", "Yuhao Dong", "Rundong Wang", "Yang Shi", "Zhipeng Wu", "Yinlun Peng", "YiFan Zhang", "Yihang Lou", "Yuanxing Zhang", "Ziwei Liu", "Yan Bai", "Yuan Zhou" ], "github": "", "project_page": "", "abstract": "Recent advancements extend Multimodal Large Language Models (MLLMs) beyond standard visual question answering to utilizing external tools for advanced visual tasks. Despite this progress, precisely executing and effectively composing diverse tools for complex tasks remain persistent bottleneck. Constrained by sparse tool-sets and simple tool-use trajectories, existing benchmarks fail to capture complex and diverse tool interactions, falling short in evaluating model performance under practical, real-world conditions. To bridge this gap, we introduce VisualToolChain-Bench(VTC-Bench), a comprehensive benchmark designed to evaluate tool-use proficiency in MLLMs. To align with realistic computer vision pipelines, our framework features 32 diverse OpenCV-based visual operations. This rich tool-set enables extensive combinations, allowing VTC-Bench to rigorously assess multi-tool composition and long-horizon, multi-step plan execution. For precise evaluation, we provide 680 curated problems structured across a nine-category cognitive hierarchy, each with ground-truth execution trajectories. Extensive experiments on 19 leading MLLMs reveal critical limitations in current models' visual agentic capabilities. Specifically, models struggle to adapt to diverse tool-sets and generalize to unseen operations, with the leading model Gemini-3.0-Pro only achieving 51% on our benchmark. Furthermore, multi-tool composition remains a persistent challenge. When facing complex tasks, models struggle to formulate efficient execution plans, relying heavily on a narrow, suboptimal subset of familiar functions rather than selecting the optimal tools. By identifying these fundamental challenges, VTC-Bench establishes a rigorous baseline to guide the development of more generalized visual agentic models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.18090", "title": "MOSS-TTS Technical Report", "authors": [ "Yitian Gong", "Botian Jiang", "Yiwei Zhao", "Yucheng Yuan", "Kuangwei Chen", "Yaozhou Jiang", "Cheng Chang", "Dong Hong", "Mingshu Chen", "Ruixiao Li", "Yiyang Zhang", "Yang Gao", "Hanfu Chen", "Ke Chen", "Songlin Wang", "Xiaogui Yang", "Yuqian Zhang", "Kexin Huang", "ZhengYuan Lin", "Kang Yu", "Ziqi Chen", "Jin Wang", "Zhaoye Fei", "Qinyuan Cheng", "Shimin Li", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "This technical report presents MOSS-TTS, a speech generation foundation model built on a scalable recipe: discrete audio tokens, autoregressive modeling, and large-scale pretraining. Built on MOSS-Audio-Tokenizer, a causal Transformer tokenizer that compresses 24 kHz audio to 12.5 fps with variable-bitrate RVQ and unified semantic-acoustic representations, we release two complementary generators: MOSS-TTS, which emphasizes structural simplicity, scalability, and long-context/control-oriented deployment, and MOSS-TTS-Local-Transformer, which introduces a frame-local autoregressive module for higher modeling efficiency, stronger speaker preservation, and a shorter time to first audio. Across multilingual and open-domain settings, MOSS-TTS supports zero-shot voice cloning, token-level duration control, phoneme-/pinyin-level pronunciation control, smooth code-switching, and stable long-form generation. This report summarizes the design, training recipe, and empirical characteristics of the released models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.18524", "title": "3DreamBooth: High-Fidelity 3D Subject-Driven Video Generation Model", "authors": [ "Hyun-kyu Ko", "Jihyeon Park", "Younghyun Kim", "Dongheok Park", "Eunbyung Park" ], "github": "", "project_page": "", "abstract": "Creating dynamic, view-consistent videos of customized subjects is highly sought after for a wide range of emerging applications, including immersive VR/AR, virtual production, and next-generation e-commerce. However, despite rapid progress in subject-driven video generation, existing methods predominantly treat subjects as 2D entities, focusing on transferring identity through single-view visual features or textual prompts. Because real-world subjects are inherently 3D, applying these 2D-centric approaches to 3D object customization reveals a fundamental limitation: they lack the comprehensive spatial priors necessary to reconstruct the 3D geometry. Consequently, when synthesizing novel views, they must rely on generating plausible but arbitrary details for unseen regions, rather than preserving the true 3D identity. Achieving genuine 3D-aware customization remains challenging due to the scarcity of multi-view video datasets. While one might attempt to fine-tune models on limited video sequences, this often leads to temporal overfitting. To resolve these issues, we introduce a novel framework for 3D-aware video customization, comprising 3DreamBooth and 3Dapter. 3DreamBooth decouples spatial geometry from temporal motion through a 1-frame optimization paradigm. By restricting updates to spatial representations, it effectively bakes a robust 3D prior into the model without the need for exhaustive video-based training. To enhance fine-grained textures and accelerate convergence, we incorporate 3Dapter, a visual conditioning module. Following single-view pre-training, 3Dapter undergoes multi-view joint optimization with the main generation branch via an asymmetrical conditioning strategy. This design allows the module to act as a dynamic selective router, querying view-specific geometric hints from a minimal reference set. Project page: https://ko-lani.github.io/3DreamBooth/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19231", "title": "MonoArt: Progressive Structural Reasoning for Monocular Articulated 3D Reconstruction", "authors": [ "Haitian Li", "Haozhe Xie", "Junxiang Xu", "Beichen Wen", "Fangzhou Hong", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Reconstructing articulated 3D objects from a single image requires jointly inferring object geometry, part structure, and motion parameters from limited visual evidence. A key difficulty lies in the entanglement between motion cues and object structure, which makes direct articulation regression unstable. Existing methods address this challenge through multi-view supervision, retrieval-based assembly, or auxiliary video generation, often sacrificing scalability or efficiency. We present MonoArt, a unified framework grounded in progressive structural reasoning. Rather than predicting articulation directly from image features, MonoArt progressively transforms visual observations into canonical geometry, structured part representations, and motion-aware embeddings within a single architecture. This structured reasoning process enables stable and interpretable articulation inference without external motion templates or multi-stage pipelines. Extensive experiments on PartNet-Mobility demonstrate that OM achieves state-of-the-art performance in both reconstruction accuracy and inference speed. The framework further generalizes to robotic manipulation and articulated scene reconstruction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19224", "title": "EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing", "authors": [ "Yang Fu", "Yike Zheng", "Ziyun Dai", "Henghui Ding" ], "github": "", "project_page": "", "abstract": "Video object removal aims to eliminate dynamic target objects and their visual effects, such as deformation, shadows, and reflections, while restoring seamless backgrounds. Recent diffusion-based video inpainting and object removal methods can remove the objects but often struggle to erase these effects and to synthesize coherent backgrounds. Beyond method limitations, progress is further hampered by the lack of a comprehensive dataset that systematically captures common object effects across varied environments for training and evaluation. To address this, we introduce VOR (Video Object Removal), a large-scale dataset that provides diverse paired videos, each consisting of one video where the target object is present with its effects and a counterpart where the object and effects are absent, with corresponding object masks. VOR contains 60K high-quality video pairs from captured and synthetic sources, covers five effects types, and spans a wide range of object categories as well as complex, dynamic multi-object scenes. Building on VOR, we propose EffectErase, an effect-aware video object removal method that treats video object insertion as the inverse auxiliary task within a reciprocal learning scheme. The model includes task-aware region guidance that focuses learning on affected areas and enables flexible task switching. Then, an insertion-removal consistency objective that encourages complementary behaviors and shared localization of effect regions and structural cues. Trained on VOR, EffectErase achieves superior performance in extensive experiments, delivering high-quality video object effect erasing across diverse scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.17554", "title": "Prompt-Free Universal Region Proposal Network", "authors": [ "Qihong Tang", "Changhan Liu", "Shaofeng Zhang", "Wenbin Li", "Qi Fan", "Yang Gao" ], "github": "https://github.com/tangqh03/PF-RPN", "project_page": "", "abstract": "Identifying potential objects is critical for object recognition and analysis across various computer vision applications. Existing methods typically localize potential objects by relying on exemplar images, predefined categories, or textual descriptions. However, their reliance on image and text prompts often limits flexibility, restricting adaptability in real-world scenarios. In this paper, we introduce a novel Prompt-Free Universal Region Proposal Network (PF-RPN), which identifies potential objects without relying on external prompts. First, the Sparse Image-Aware Adapter (SIA) module performs initial localization of potential objects using a learnable query embedding dynamically updated with visual features. Next, the Cascade Self-Prompt (CSP) module identifies the remaining potential objects by leveraging the self-prompted learnable embedding, autonomously aggregating informative visual features in a cascading manner. Finally, the Centerness-Guided Query Selection (CG-QS) module facilitates the selection of high-quality query embeddings using a centerness scoring network. Our method can be optimized with limited data (e.g., 5% of MS COCO data) and applied directly to various object detection application domains for identifying potential objects without fine-tuning, such as underwater object detection, industrial defect detection, and remote sensing image object detection. Experimental results across 19 datasets validate the effectiveness of our method. Code is available at https://github.com/tangqh03/PF-RPN.", "github_manual": "", "github_regex": "https://github.com/tangqh03/PF-RPN", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.13045", "title": "Mending the Holes: Mitigating Reward Hacking in Reinforcement Learning for Multilingual Translation", "authors": [ "Yifeng Liu", "Siqi Ouyang", "Yatish Hosmane Revanasiddappa", "Lei Li" ], "github": "https://github.com/LeiLiLab/WALAR", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capability in machine translation on high-resource language pairs, yet their performance on low-resource translation still lags behind. Existing post-training methods rely heavily on high-quality parallel data, which are often scarce or unavailable for low-resource languages. In this paper, we introduce WALAR, a reinforcement training method using only monolingual text to elevate LLMs' translation capabilities on massive low-resource languages while retaining their performance on high-resource languages. Our key insight is based on the observation of failure modes (or \"holes\") in existing source-based multilingual quality estimation (QE) models. Reinforcement learning (RL) using these QE models tends to amplify such holes, resulting in poorer multilingual LLMs. We develop techniques including word alignment and language alignment to mitigate such holes in WALAR's reward for RL training. We continually trained an LLM supporting translation of 101 languages using WALAR. The experiments show that our new model outperforms LLaMAX, one of the strongest open-source multilingual LLMs by a large margin on 1400 language directions on Flores-101 dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LeiLiLab/WALAR", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.15083", "title": "ReactMotion: Generating Reactive Listener Motions from Speaker Utterance", "authors": [ "Cheng Luo", "Bizhu Wu", "Bing Li", "Jianfeng Ren", "Ruibin Bai", "Rong Qu", "Linlin Shen", "Bernard Ghanem" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce a new task, Reactive Listener Motion Generation from Speaker Utterance, which aims to generate naturalistic listener body motions that appropriately respond to a speaker's utterance. However, modeling such nonverbal listener behaviors remains underexplored and challenging due to the inherently non-deterministic nature of human reactions. To facilitate this task, we present ReactMotionNet, a large-scale dataset that pairs speaker utterances with multiple candidate listener motions annotated with varying degrees of appropriateness. This dataset design explicitly captures the one-to-many nature of listener behavior and provides supervision beyond a single ground-truth motion. Building on this dataset design, we develop preference-oriented evaluation protocols tailored to evaluate reactive appropriateness, where conventional motion metrics focusing on input-motion alignment ignore. We further propose ReactMotion, a unified generative framework that jointly models text, audio, emotion, and motion, and is trained with preference-based objectives to encourage both appropriate and diverse listener responses. Extensive experiments show that ReactMotion outperforms retrieval baselines and cascaded LLM-based pipelines, generating more natural, diverse, and appropriate listener motions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.18429", "title": "AndroTMem: From Interaction Trajectories to Anchored Memory in Long-Horizon GUI Agents", "authors": [ "Yibo Shi", "Jungang Li", "Linghao Zhang", "Zihao Dongfang", "Biao Wu", "Sicheng Tao", "Yibo Yan", "Chenxi Qin", "Weiting Liu", "Zhixin Lin", "Hanqian Li", "Yu Huang", "Song Dai", "Yonghua Hei", "Yue Ding", "Xiang Li", "Shikang Wang", "Chengdong Xu", "Jingqi Liu", "Xueying Ma", "Zhiwen Zheng", "Xiaofei Zhang", "Bincheng Wang", "Nichen Yang", "Jie Wu", "Lihua Tian", "Chen Li", "Xuming Hu" ], "github": "https://github.com/CVC2233/AndroTMem", "project_page": "", "abstract": "Long-horizon GUI agents are a key step toward real-world deployment, yet effective interaction memory under prevailing paradigms remains under-explored. Replaying full interaction sequences is redundant and amplifies noise, while summaries often erase dependency-critical information and traceability. We present AndroTMem, a diagnostic framework for anchored memory in long-horizon Android GUI agents. Its core benchmark, AndroTMem-Bench, comprises 1,069 tasks with 34,473 interaction steps (avg. 32.1 per task, max. 65). We evaluate agents with TCR (Task Complete Rate), focusing on tasks whose completion requires carrying forward critical intermediate state; AndroTMem-Bench is designed to enforce strong step-to-step causal dependencies, making sparse yet essential intermediate states decisive for downstream actions and centering interaction memory in evaluation. Across open- and closed-source GUI agents, we observe a consistent pattern: as interaction sequences grow longer, performance drops are driven mainly by within-task memory failures, not isolated perception errors or local action mistakes. Guided by this diagnosis, we propose Anchored State Memory (ASM), which represents interaction sequences as a compact set of causally linked intermediate-state anchors to enable subgoal-targeted retrieval and attribution-aware decision making. Across multiple settings and 12 evaluated GUI agents, ASM consistently outperforms full-sequence replay and summary-based baselines, improving TCR by 5%-30.16% and AMS by 4.93%-24.66%, indicating that anchored, structured memory effectively mitigates the interaction-memory bottleneck in long-horizon GUI tasks. The code, benchmark, and related resources are publicly available at [https://github.com/CVC2233/AndroTMem](https://github.com/CVC2233/AndroTMem).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CVC2233/AndroTMem", "github_api_added_by": "user" }, { "date": "2026-03-20", "arxiv_id": "2603.16924", "title": "SimulU: Training-free Policy for Long-form Simultaneous Speech-to-Speech Translation", "authors": [ "Amirbek Djanibekov", "Luisa Bentivogli", "Matteo Negri", "Sara Papi" ], "github": "", "project_page": "", "abstract": "Simultaneous speech-to-speech translation (SimulS2S) is essential for real-time multilingual communication, with increasing integration into meeting and streaming platforms. Despite this, SimulS2S remains underexplored in research, where current solutions often rely on resource-intensive training procedures and operate on short-form, pre-segmented utterances, failing to generalize to continuous speech. To bridge this gap, we propose SimulU, the first training-free policy for long-form SimulS2S. SimulU adopts history management and speech output selection strategies that exploit cross-attention in pre-trained end-to-end models to regulate both input history and output generation. Evaluations on MuST-C across 8 languages show that SimulU achieves a better or comparable quality-latency trade-off against strong cascaded models. By eliminating the need for ad-hoc training, SimulU offers a promising path to end-to-end SimulS2S in realistic, long-form scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.14456", "title": "PARSA-Bench: A Comprehensive Persian Audio-Language Model Benchmark", "authors": [ "Mohammad Javad Ranjbar Kalahroodi", "Mohammad Amini", "Parmis Bathayan", "Heshaam Faili", "Azadeh Shakery" ], "github": "", "project_page": "", "abstract": "Persian poses unique audio understanding challenges through its classical poetry, traditional music, and pervasive code-switching - none captured by existing benchmarks. We introduce PARSA-Bench (Persian Audio Reasoning and Speech Assessment Benchmark), the first benchmark for evaluating large audio-language models on Persian language and culture, comprising 16 tasks and over 8,000 samples across speech understanding, paralinguistic analysis, and cultural audio understanding. Ten tasks are newly introduced, including poetry meter and style detection, traditional Persian music understanding, and code-switching detection. Text-only baselines consistently outperform audio counterparts, suggesting models may not leverage audio-specific information beyond what transcription alone provides. Culturally-grounded tasks expose a qualitatively distinct failure mode: all models perform near random chance on vazn detection regardless of scale, suggesting prosodic perception remains beyond the reach of current models. The dataset is publicly available at https://huggingface.co/datasets/MohammadJRanjbar/PARSA-Bench", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19203", "title": "Tinted Frames: Question Framing Blinds Vision-Language Models", "authors": [ "Wan-Cyuan Fan", "Jiayun Luo", "Declan Kutscher", "Leonid Sigal", "Ritwik Gupta" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) have been shown to be blind, often underutilizing their visual inputs even on tasks that require visual reasoning. In this work, we demonstrate that VLMs are selectively blind. They modulate the amount of attention applied to visual inputs based on linguistic framing even when alternative framings demand identical visual reasoning. Using visual attention as a probe, we quantify how framing alters both the amount and distribution of attention over the image. Constrained framings, such as multiple choice and yes/no, induce substantially lower attention to image context compared to open-ended, reduce focus on task-relevant regions, and shift attention towards uninformative tokens. We further demonstrate that this attention misallocation is the principal cause of degraded accuracy and cross-framing inconsistency. Building on this mechanistic insight, we introduce a lightweight prompt-tuning method using learnable tokens that encourages the robust, visually grounded attention patterns observed in open-ended settings, improving visual grounding and improving performance across framings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.13964", "title": "VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction", "authors": [ "Hiroto Nakata", "Yawen Zou", "Shunsuke Sakai", "Shun Maeda", "Chunzhi Gu", "Yijin Wei", "Shangce Gao", "Chao Zhang" ], "github": "https://github.com/nkthiroto/VID-AD", "project_page": "", "abstract": "Logical anomaly detection in industrial inspection remains challenging due to variations in visual appearance (e.g., background clutter, illumination shift, and blur), which often distract vision-centric detectors from identifying rule-level violations. However, existing benchmarks rarely provide controlled settings where logical states are fixed while such nuisance factors vary. To address this gap, we introduce VID-AD, a dataset for logical anomaly detection under vision-induced distraction. It comprises 10 manufacturing scenarios and five capture conditions, totaling 50 one-class tasks and 10,395 images. Each scenario is defined by two logical constraints selected from quantity, length, type, placement, and relation, with anomalies including both single-constraint and combined violations. We further propose a language-based anomaly detection framework that relies solely on text descriptions generated from normal images. Using contrastive learning with positive texts and contradiction-based negative texts synthesized from these descriptions, our method learns embeddings that capture logical attributes rather than low-level features. Extensive experiments demonstrate consistent improvements over baselines across the evaluated settings. The dataset is available at: https://github.com/nkthiroto/VID-AD.", "github_manual": "", "github_regex": "https://github.com/nkthiroto/VID-AD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19017", "title": "What Really Controls Temporal Reasoning in Large Language Models: Tokenisation or Representation of Time?", "authors": [ "Gagan Bhatia", "Ahmad Muhammad Isa", "Maxime Peyrard", "Wei Zhao" ], "github": "https://github.com/gagan3012/mtb", "project_page": "", "abstract": "We present MultiTempBench, a multilingual temporal reasoning benchmark spanning three tasks, date arithmetic, time zone conversion, and temporal relation extraction across five languages (English, German, Chinese, Arabic, and Hausa) and multiple calendar conventions (Gregorian, Hijri, and Chinese Lunar). MultiTempBench contains 15,000 examples built by translating 750 curated English questions and expanding each into controlled date-format variants. We evaluate 20 LLMs and introduce the multilingual Date Fragmentation Ratio (mDFR), calibrated with human severity ratings, together with geometric-probing analyses of internal temporal representations. We find tokenisation quality of temporal artefacts is a resource-dependent bottleneck: in low-resource languages and rarer calendar formats, fragmentation disrupts Year/Month/Day separation and accuracy collapses, while high-resource settings are often robust to digit-level splitting. Beyond tokenisation, crossed mixed-effects regression shows that temporal linearity is the strongest predictor of temporal reasoning in high-resource languages, whereas fragmentation is the stronger predictor in low-resource languages. Code is available at: https://github.com/gagan3012/mtb", "github_manual": "", "github_regex": "https://github.com/gagan3012/mtb", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-20", "arxiv_id": "2603.19216", "title": "DreamPartGen: Semantically Grounded Part-Level 3D Generation via Collaborative Latent Denoising", "authors": [ "Tianjiao Yu", "Xinzhuo Li", "Muntasir Wahed", "Jerry Xiong", "Yifan Shen", "Ying Shen", "Ismini Lourentzou" ], "github": "", "project_page": "", "abstract": "Understanding and generating 3D objects as compositions of meaningful parts is fundamental to human perception and reasoning. However, most text-to-3D methods overlook the semantic and functional structure of parts. While recent part-aware approaches introduce decomposition, they remain largely geometry-focused, lacking semantic grounding and failing to model how parts align with textual descriptions or their inter-part relations. We propose DreamPartGen, a framework for semantically grounded, part-aware text-to-3D generation. DreamPartGen introduces Duplex Part Latents (DPLs) that jointly model each part's geometry and appearance, and Relational Semantic Latents (RSLs) that capture inter-part dependencies derived from language. A synchronized co-denoising process enforces mutual geometric and semantic consistency, enabling coherent, interpretable, and text-aligned 3D synthesis. Across multiple benchmarks, DreamPartGen delivers state-of-the-art performance in geometric fidelity and text-shape alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19685", "title": "A Subgoal-driven Framework for Improving Long-Horizon LLM Agents", "authors": [ "Taiyi Wang", "Sian Gooding", "Florian Hartmann", "Oriana Riva", "Edward Grefenstette" ], "github": "", "project_page": "", "abstract": "Large language model (LLM)-based agents have emerged as powerful autonomous controllers for digital environments, including mobile interfaces, operating systems, and web browsers. Web navigation, for example, requires handling dynamic content and long sequences of actions, making it particularly challenging. Existing LLM-based agents struggle with long-horizon planning in two main ways. During online execution, they often lose track as new information arrives, lacking a clear and adaptive path toward the final goal. This issue is further exacerbated during reinforcement learning (RL) fine-tuning, where sparse and delayed rewards make it difficult for agents to identify which actions lead to success, preventing them from maintaining coherent reasoning over extended tasks. To address these challenges, we propose two contributions. First, we introduce an agent framework that leverages proprietary models for online planning through subgoal decomposition. Second, we present MiRA (Milestoning your Reinforcement Learning Enhanced Agent), an RL training framework that uses dense, milestone-based reward signals. The real-time planning mechanism improves proprietary models such as Gemini by approximately a 10% absolute increase in success rate (SR) on the WebArena-Lite benchmark. Meanwhile, applying MiRA to the open Gemma3-12B model increases its success rate from 6.4% to 43.0%. This performance surpasses proprietary systems such as GPT-4-Turbo (17.6%) and GPT-4o (13.9%), as well as the previous open-model state of the art, WebRL (38.4%). Overall, our findings demonstrate that combining explicit inference-time planning with milestone-based rewards significantly improves an agent's long-horizon capabilities, paving the way for more robust and general-purpose autonomous systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19708", "title": "WorldAgents: Can Foundation Image Models be Agents for 3D World Models?", "authors": [ "Ziya Erko\u00e7", "Angela Dai", "Matthias Nie\u00dfner" ], "github": "", "project_page": "", "abstract": "Given the remarkable ability of 2D foundation image models to generate high-fidelity outputs, we investigate a fundamental question: do 2D foundation image models inherently possess 3D world model capabilities? To answer this, we systematically evaluate multiple state-of-the-art image generation models and Vision-Language Models (VLMs) on the task of 3D world synthesis. To harness and benchmark their potential implicit 3D capability, we propose an agentic framing to facilitate 3D world generation. Our approach employs a multi-agent architecture: a VLM-based director that formulates prompts to guide image synthesis, a generator that synthesizes new image views, and a VLM-backed two-step verifier that evaluates and selectively curates generated frames from both 2D image and 3D reconstruction space. Crucially, we demonstrate that our agentic approach provides coherent and robust 3D reconstruction, producing output scenes that can be explored by rendering novel views. Through extensive experiments across various foundation models, we demonstrate that 2D models do indeed encapsulate a grasp of 3D worlds. By exploiting this understanding, our method successfully synthesizes expansive, realistic, and 3D-consistent worlds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.20192", "title": "LumosX: Relate Any Identities with Their Attributes for Personalized Video Generation", "authors": [ "Jiazheng Xing", "Fei Du", "Hangjie Yuan", "Pengwei Liu", "Hongbin Xu", "Hai Ci", "Ruigang Niu", "Weihua Chen", "Fan Wang", "Yong Liu" ], "github": "", "project_page": "", "abstract": "Recent advances in diffusion models have significantly improved text-to-video generation, enabling personalized content creation with fine-grained control over both foreground and background elements. However, precise face-attribute alignment across subjects remains challenging, as existing methods lack explicit mechanisms to ensure intra-group consistency. Addressing this gap requires both explicit modeling strategies and face-attribute-aware data resources. We therefore propose LumosX, a framework that advances both data and model design. On the data side, a tailored collection pipeline orchestrates captions and visual cues from independent videos, while multimodal large language models (MLLMs) infer and assign subject-specific dependencies. These extracted relational priors impose a finer-grained structure that amplifies the expressive control of personalized video generation and enables the construction of a comprehensive benchmark. On the modeling side, Relational Self-Attention and Relational Cross-Attention intertwine position-aware embeddings with refined attention dynamics to inscribe explicit subject-attribute dependencies, enforcing disciplined intra-group cohesion and amplifying the separation between distinct subject clusters. Comprehensive evaluations on our benchmark demonstrate that LumosX achieves state-of-the-art performance in fine-grained, identity-consistent, and semantically aligned personalized multi-subject video generation. Code and models are available at https://jiazheng-xing.github.io/lumosx-home/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.20155", "title": "Beyond Single Tokens: Distilling Discrete Diffusion Models via Discrete MMD", "authors": [ "Emiel Hoogeboom", "David Ruhe", "Jonathan Heek", "Thomas Mensink", "Tim Salimans" ], "github": "", "project_page": "", "abstract": "It is currently difficult to distill discrete diffusion models. In contrast, continuous diffusion literature has many distillation approaches methods that can reduce sampling steps to a handful. Our method, Discrete Moment Matching Distillation (D-MMD), leverages ideas that have been highly successful in the continuous domain. Whereas previous discrete distillation methods collapse, D-MMD maintains high quality and diversity (given sufficient sampling steps). This is demonstrated on both text and image datasets. Moreover, the newly distilled generators can outperform their teachers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19461", "title": "Hyperagents", "authors": [ "Jenny Zhang", "Bingchen Zhao", "Wannan Yang", "Jakob Foerster", "Jeff Clune", "Minqi Jiang", "Sam Devlin", "Tatiana Shavrina" ], "github": "", "project_page": "", "abstract": "Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such systems can improve. The Darwin G\u00f6del Machine (DGM) demonstrates open-ended self-improvement in coding by repeatedly generating and evaluating self-modified variants. Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. However, this alignment does not generally hold beyond coding domains. We introduce hyperagents, self-referential agents that integrate a task agent (which solves the target task) and a meta agent (which modifies itself and the task agent) into a single editable program. Crucially, the meta-level modification procedure is itself editable, enabling metacognitive self-modification, improving not only the task-solving behavior, but also the mechanism that generates future improvements. We instantiate this framework by extending DGM to create DGM-Hyperagents (DGM-H), eliminating the assumption of domain-specific alignment between task performance and self-modification skill to potentially support self-accelerating progress on any computable task. Across diverse domains, the DGM-H improves performance over time and outperforms baselines without self-improvement or open-ended exploration, as well as prior self-improving systems. Furthermore, the DGM-H improves the process by which it generates new agents (e.g., persistent memory, performance tracking), and these meta-level improvements transfer across domains and accumulate across runs. DGM-Hyperagents offer a glimpse of open-ended AI systems that do not merely search for better solutions, but continually improve their search for how to improve.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19500", "title": "Teaching an Agent to Sketch One Part at a Time", "authors": [ "Xiaodan Du", "Ruize Xu", "David Yunis", "Yael Vinker", "Greg Shakhnarovich" ], "github": "", "project_page": "", "abstract": "We develop a method for producing vector sketches one part at a time. To do this, we train a multi-modal language model-based agent using a novel multi-turn process-reward reinforcement learning following supervised fine-tuning. Our approach is enabled by a new dataset we call ControlSketch-Part, containing rich part-level annotations for sketches, obtained using a novel, generic automatic annotation pipeline that segments vector sketches into semantic parts and assigns paths to parts with a structured multi-stage labeling process. Our results indicate that incorporating structured part-level data and providing agent with the visual feedback through the process enables interpretable, controllable, and locally editable text-to-vector sketch generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.17024", "title": "HopChain: Multi-Hop Data Synthesis for Generalizable Vision-Language Reasoning", "authors": [ "Shenzhi Wang", "Shixuan Liu", "Jing Zhou", "Chang Gao", "Xiong-Hui Chen", "Binghai Wang", "An Yang", "Shiji Song", "Bowen Yu", "Gao Huang", "Junyang Lin" ], "github": "", "project_page": "", "abstract": "VLMs show strong multimodal capabilities, but they still struggle with fine-grained vision-language reasoning. We find that long CoT reasoning exposes diverse failure modes, including perception, reasoning, knowledge, and hallucination errors, which can compound across intermediate steps. However, most existing vision-language data used for RLVR does not involve complex reasoning chains that rely on visual evidence throughout, leaving these weaknesses largely unexposed. We therefore propose HopChain, a scalable framework for synthesizing multi-hop vision-language reasoning data specifically for RLVR training of VLMs. Each synthesized multi-hop query forms a logically dependent chain of instance-grounded hops, where earlier hops establish the instances, sets, or conditions needed for later hops, while the final answer remains a specific, unambiguous number suitable for verifiable rewards. We add the multi-hop data synthesized by HopChain to the original RLVR data used to train Qwen3.5-35B-A3B and Qwen3.5-397B-A17B, and compare against RLVR on the original RLVR data alone across 24 benchmarks spanning STEM and Puzzle, General VQA, Text Recognition and Document Understanding, and Video Understanding. Although this multi-hop data is not synthesized to target any specific benchmark, adding it improves 20 out of 24 benchmarks on both models, indicating broad and generalizable gains. To demonstrate that full chained queries are important, we replace them with half-multi-hop or single-hop variants, reducing the 24-benchmark average accuracy by 5.3 and 7.0 points, respectively. Multi-hop training also strengthens long-CoT vision-language reasoning, with gains peaking at more than 50 accuracy points in the ultra-long-CoT regime. These experiments establish HopChain as an effective, scalable framework for synthesizing multi-hop data that improves generalizable vision-language reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.17051", "title": "Astrolabe: Steering Forward-Process Reinforcement Learning for Distilled Autoregressive Video Models", "authors": [ "Songchun Zhang", "Zeyue Xue", "Siming Fu", "Jie Huang", "Xianghao Kong", "Y Ma", "Haoyang Huang", "Nan Duan", "Anyi Rao" ], "github": "", "project_page": "", "abstract": "Distilled autoregressive (AR) video models enable efficient streaming generation but frequently misalign with human visual preferences. Existing reinforcement learning (RL) frameworks are not naturally suited to these architectures, typically requiring either expensive re-distillation or solver-coupled reverse-process optimization that introduces considerable memory and computational overhead. We present Astrolabe, an efficient online RL framework tailored for distilled AR models. To overcome existing bottlenecks, we introduce a forward-process RL formulation based on negative-aware fine-tuning. By contrasting positive and negative samples directly at inference endpoints, this approach establishes an implicit policy improvement direction without requiring reverse-process unrolling. To scale this alignment to long videos, we propose a streaming training scheme that generates sequences progressively via a rolling KV-cache, applying RL updates exclusively to local clip windows while conditioning on prior context to ensure long-range coherence. Finally, to mitigate reward hacking, we integrate a multi-reward objective stabilized by uncertainty-aware selective regularization and dynamic reference updates. Extensive experiments demonstrate that our method consistently enhances generation quality across multiple distilled AR video models, serving as a robust and scalable alignment solution.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19809", "title": "How Well Does Generative Recommendation Generalize?", "authors": [ "Yijie Ding", "Zitian Guo", "Jiacheng Li", "Letian Peng", "Shuai Shao", "Wei Shao", "Xiaoqiang Luo", "Luke Simon", "Jingbo Shang", "Julian McAuley", "Yupeng Hou" ], "github": "", "project_page": "", "abstract": "A widely held hypothesis for why generative recommendation (GR) models outperform conventional item ID-based models is that they generalize better. However, there is few systematic way to verify this hypothesis beyond a superficial comparison of overall performance. To address this gap, we categorize each data instance based on the specific capability required for a correct prediction: either memorization (reusing item transition patterns observed during training) or generalization (composing known patterns to predict unseen item transitions). Extensive experiments show that GR models perform better on instances that require generalization, whereas item ID-based models perform better when memorization is more important. To explain this divergence, we shift the analysis from the item level to the token level and show that what appears to be item-level generalization often reduces to token-level memorization for GR models. Finally, we show that the two paradigms are complementary. We propose a simple memorization-aware indicator that adaptively combines them on a per-instance basis, leading to improved overall recommendation performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19635", "title": "BEAVER: A Training-Free Hierarchical Prompt Compression Method via Structure-Aware Page Selection", "authors": [ "Zhengpei Hu", "Kai Li", "Dapeng Fu", "Chang Zeng", "Yue Li", "Yuanhao Tang", "Jianqiang Huang" ], "github": "", "project_page": "", "abstract": "The exponential expansion of context windows in LLMs has unlocked capabilities for long-document understanding but introduced severe bottlenecks in inference latency and information utilization. Existing compression methods often suffer from high training costs or semantic fragmentation due to aggressive token pruning. In this paper, we propose BEAVER, a novel training-free framework that shifts compression from linear token removal to structure-aware hierarchical selection. BEAVER maximizes hardware parallelism by mapping variable-length contexts into dense page-level tensors via dual-path pooling, and preserves discourse integrity through a hybrid planner combining semantic and lexical dual-branch selection with sentence smoothing. Extensive evaluations on four long-context benchmarks demonstrate that BEAVER achieves comparable performance to state-of-the-art (SOTA) methods like LongLLMLingua. Notably, on the RULER benchmark, BEAVER maintains high fidelity in multi-needle retrieval where baselines deteriorate. Regarding efficiency, BEAVER reduces latency by 26.4x on 128k contexts, offering a scalable solution for high-throughput applications. Our code is available at https://cslikai.cn/BEAVER/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19005", "title": "AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science", "authors": [ "An Luo", "Jin Du", "Xun Xian", "Robert Specht", "Fangqiao Tian", "Ganghua Wang", "Xuan Bi", "Charles Fleming", "Ashish Kundu", "Jayanth Srinivasa", "Mingyi Hong", "Rui Zhang", "Tianxi Li", "Galin Jones", "Jie Ding" ], "github": "", "project_page": "", "abstract": "Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) and artificial intelligence (AI) agents have significantly automated data science workflow. However, it remains unclear to what extent AI agents can match the performance of human experts on domain-specific data science tasks, and in which aspects human expertise continues to provide advantages. We introduce AgentDS, a benchmark and competition designed to evaluate both AI agents and human-AI collaboration performance in domain-specific data science. AgentDS consists of 17 challenges across six industries: commerce, food production, healthcare, insurance, manufacturing, and retail banking. We conducted an open competition involving 29 teams and 80 participants, enabling systematic comparison between human-AI collaborative approaches and AI-only baselines. Our results show that current AI agents struggle with domain-specific reasoning. AI-only baselines perform near or below the median of competition participants, while the strongest solutions arise from human-AI collaboration. These findings challenge the narrative of complete automation by AI and underscore the enduring importance of human expertise in data science, while illuminating directions for the next generation of AI. Visit the AgentDS website here: https://agentds.org/ and open source datasets here: https://huggingface.co/datasets/lainmn/AgentDS .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.17512", "title": "Language on Demand, Knowledge at Core: Composing LLMs with Encoder-Decoder Translation Models for Extensible Multilinguality", "authors": [ "Mengyu Bu", "Yang Feng" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) exhibit strong general intelligence, yet their multilingual performance remains highly imbalanced. Although LLMs encode substantial cross-lingual knowledge in a unified semantic space, they often struggle to reliably interface this knowledge with low-resource or unseen languages. Fortunately, pretrained encoder-decoder translation models already possess balanced multilingual capability, suggesting a natural complement to LLMs. In this work, we propose XBridge, a compositional encoder-LLM-decoder architecture that offloads multilingual understanding and generation to external pretrained translation models, while preserving the LLM as an English-centric core for general knowledge processing. To address the resulting representation misalignment across models, we introduce lightweight cross-model mapping layers and an optimal transport-based alignment objective, enabling fine-grained semantic consistency for multilingual generation. Experiments on four LLMs across multilingual understanding, reasoning, summarization, and generation indicate that XBridge outperforms strong baselines, especially on low-resource and previously unseen languages, without retraining the LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.20169", "title": "EgoForge: Goal-Directed Egocentric World Simulator", "authors": [ "Yifan Shen", "Jiateng Liu", "Xinzhuo Li", "Yuanzhe Liu", "Bingxuan Li", "Houze Yang", "Wenqi Jia", "Yijiang Li", "Tianjiao Yu", "James Matthew Rehg", "Xu Cao", "Ismini Lourentzou" ], "github": "", "project_page": "", "abstract": "Generative world models have shown promise for simulating dynamic environments, yet egocentric video remains challenging due to rapid viewpoint changes, frequent hand-object interactions, and goal-directed procedures whose evolution depends on latent human intent. Existing approaches either focus on hand-centric instructional synthesis with limited scene evolution, perform static view translation without modeling action dynamics, or rely on dense supervision, such as camera trajectories, long video prefixes, synchronized multicamera capture, etc. In this work, we introduce EgoForge, an egocentric goal-directed world simulator that generates coherent, first-person video rollouts from minimal static inputs: a single egocentric image, a high-level instruction, and an optional auxiliary exocentric view. To improve intent alignment and temporal consistency, we propose VideoDiffusionNFT, a trajectory-level reward-guided refinement that optimizes goal completion, temporal causality, scene consistency, and perceptual fidelity during diffusion sampling. Extensive experiments show EgoForge achieves consistent gains in semantic alignment, geometric stability, and motion fidelity over strong baselines, and robust performance in real-world smart-glasses experiments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19598", "title": "FlowScene: Style-Consistent Indoor Scene Generation with Multimodal Graph Rectified Flow", "authors": [ "Zhifei Yang", "Guangyao Zhai", "Keyang Lu", "YuYang Yin", "Chao Zhang", "Zhen Xiao", "Jieyi Long", "Nassir Navab", "Yikai Wang" ], "github": "", "project_page": "", "abstract": "Scene generation has extensive industrial applications, demanding both high realism and precise control over geometry and appearance. Language-driven retrieval methods compose plausible scenes from a large object database, but overlook object-level control and often fail to enforce scene-level style coherence. Graph-based formulations offer higher controllability over objects and inform holistic consistency by explicitly modeling relations, yet existing methods struggle to produce high-fidelity textured results, thereby limiting their practical utility. We present FlowScene, a tri-branch scene generative model conditioned on multimodal graphs that collaboratively generates scene layouts, object shapes, and object textures. At its core lies a tight-coupled rectified flow model that exchanges object information during generation, enabling collaborative reasoning across the graph. This enables fine-grained control of objects' shapes, textures, and relations while enforcing scene-level style coherence across structure and appearance. Extensive experiments show that FlowScene outperforms both language-conditioned and graph-conditioned baselines in terms of generation realism, style consistency, and alignment with human preferences.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19039", "title": "TerraScope: Pixel-Grounded Visual Reasoning for Earth Observation", "authors": [ "Yan Shu", "Bin Ren", "Zhitong Xiong", "Xiao Xiang Zhu", "Beg\u00fcm Demir", "Nicu Sebe", "Paolo Rota" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) have shown promise in earth observation (EO), yet they struggle with tasks that require grounding complex spatial reasoning in precise pixel-level visual representations. To address this problem, we introduce TerraScope, a unified VLM that delivers pixel-grounded geospatial reasoning with two key capabilities: (1) modality-flexible reasoning: it handles single-modality inputs (optical or SAR) and adaptively fuses different modalities into the reasoning process when both are available; (2) multi-temporal reasoning: it integrates temporal sequences for change analysis across multiple time points. In addition, we curate Terra-CoT, a large-scale dataset containing 1 million samples with pixel-level masks embedded in reasoning chains across multiple sources. We also propose TerraScope-Bench, the first benchmark for pixel-grounded geospatial reasoning with six sub-tasks that evaluates both answer accuracy and mask quality to ensure authentic pixel-grounded reasoning. Experiments show that TerraScope significantly outperforms existing VLMs on pixel-grounded geospatial reasoning while providing interpretable visual evidence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.18558", "title": "HiMu: Hierarchical Multimodal Frame Selection for Long Video Question Answering", "authors": [ "Dan Ben-Ami", "Gabriele Serussi", "Kobi Cohen", "Chaim Baskin" ], "github": "https://github.com/mindee/doctr", "project_page": "", "abstract": "Long-form video question answering requires reasoning over extended temporal contexts, making frame selection critical for large vision-language models (LVLMs) bound by finite context windows. Existing methods face a sharp trade-off: similarity-based selectors are fast but collapse compositional queries into a single dense vector, losing sub-event ordering and cross-modal bindings; agent-based methods recover this structure through iterative LVLM inference, but at prohibitive cost. We introduce HiMu, a training-free framework that bridges this gap. A single text-only LLM call decomposes the query into a hierarchical logic tree whose leaves are atomic predicates, each routed to a lightweight expert spanning vision (CLIP, open-vocabulary detection, OCR) and audio (ASR, CLAP). The resulting signals are normalized, temporally smoothed to align different modalities, and composed bottom-up through fuzzy-logic operators that enforce temporal sequencing and adjacency, producing a continuous satisfaction curve. Evaluations on Video-MME, LongVideoBench and HERBench-Lite show that HiMu advances the efficiency-accuracy Pareto front: at 16 frames with Qwen3-VL 8B it outperforms all competing selectors, and with GPT-4o it surpasses agentic systems operating at 32-512 frames while requiring roughly 10x fewer FLOPs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/mindee/doctr", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19714", "title": "LoopRPT: Reinforcement Pre-Training for Looped Language Models", "authors": [ "Guo Tang", "Shixin Jiang", "Heng Chang", "Nuo Chen", "Yuhan Li", "Huiming Fan", "Jia Li", "Ming Liu", "Bing Qin" ], "github": "", "project_page": "", "abstract": "Looped language models (LoopLMs) perform iterative latent computation to refine internal representations, offering a promising alternative to explicit chain-of-thought (CoT) reasoning. However, existing reinforcement learning (RL) paradigms primarily target output tokens, creating a structural mismatch with looped architectures whose reasoning unfolds implicitly. In this work, we propose LoopRPT, a reinforcement pre-training framework tailored for LoopLMs. By reframing next-token prediction as a next-token reasoning task, LoopRPT assigns reinforcement signals directly to latent steps using an EMA teacher reference and noisy latent rollouts. This formulation enables RL to directly shape intermediate representations, compressing effective reasoning into fewer iterations. We instantiate LoopRPT on the Ouro architecture across multiple model scales. Results demonstrate that LoopRPT consistently improves per-step representation quality, achieving Pareto dominance in accuracy-computation trade-offs. Notably, significant gains on hard tokens indicate that LoopRPT enhances early-stage reasoning rather than merely encouraging premature exits. Our findings highlight reinforcement pre-training as a principled paradigm for learning efficient latent reasoning in LoopLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19453", "title": "Cooperation and Exploitation in LLM Policy Synthesis for Sequential Social Dilemmas", "authors": [ "V\u00edctor Gallego" ], "github": "https://github.com/vicgalle/llm-policies-social-dilemmas", "project_page": "", "abstract": "We study LLM policy synthesis: using a large language model to iteratively generate programmatic agent policies for multi-agent environments. Rather than training neural policies via reinforcement learning, our framework prompts an LLM to produce Python policy functions, evaluates them in self-play, and refines them using performance feedback across iterations. We investigate feedback engineering (the design of what evaluation information is shown to the LLM during refinement) comparing sparse feedback (scalar reward only) against dense feedback (reward plus social metrics: efficiency, equality, sustainability, peace). Across two canonical Sequential Social Dilemmas (Gathering and Cleanup) and two frontier LLMs (Claude Sonnet 4.6, Gemini 3.1 Pro), dense feedback consistently matches or exceeds sparse feedback on all metrics. The advantage is largest in the Cleanup public goods game, where providing social metrics helps the LLM calibrate the costly cleaning-harvesting tradeoff. Rather than triggering over-optimization of fairness, social metrics serve as a coordination signal that guides the LLM toward more effective cooperative strategies, including territory partitioning, adaptive role assignment, and the avoidance of wasteful aggression. We further perform an adversarial experiment to determine whether LLMs can reward hack these environments. We characterize five attack classes and discuss mitigations, highlighting an inherent tension in LLM policy synthesis between expressiveness and safety. Code at https://github.com/vicgalle/llm-policies-social-dilemmas.", "github_manual": "", "github_regex": "https://github.com/vicgalle/llm-policies-social-dilemmas", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.20105", "title": "The Y-Combinator for LLMs: Solving Long-Context Rot with \u03bb-Calculus", "authors": [ "Amartya Roy", "Rasul Tutunov", "Xiaotong Ji", "Matthieu Zimmer", "Haitham Bou-Ammar" ], "github": "https://github.com/lambda-calculus-LLM/lambda-RLM", "project_page": "", "abstract": "LLMs are increasingly used as general-purpose reasoners, but long inputs remain bottlenecked by a fixed context window. Recursive Language Models (RLMs) address this by externalising the prompt and recursively solving subproblems. Yet existing RLMs depend on an open-ended read-eval-print loop (REPL) in which the model generates arbitrary control code, making execution difficult to verify, predict, and analyse. We introduce \u03bb-RLM, a framework for long-context reasoning that replaces free-form recursive code generation with a typed functional runtime grounded in \u03bb-calculus. It executes a compact library of pre-verified combinators and uses neural inference only on bounded leaf subproblems, turning recursive reasoning into a structured functional program with explicit control flow. We show that \u03bb-RLM admits formal guarantees absent from standard RLMs, including termination, closed-form cost bounds, controlled accuracy scaling with recursion depth, and an optimal partition rule under a simple cost model. Empirically, across four long-context reasoning tasks and nine base models, \u03bb-RLM outperforms standard RLM in 29 of 36 model-task comparisons, improves average accuracy by up to +21.9 points across model tiers, and reduces latency by up to 4.1x. These results show that typed symbolic control yields a more reliable and efficient foundation for long-context reasoning than open-ended recursive code generation. The complete implementation of \u03bb-RLM, is open-sourced for the community at: https://github.com/lambda-calculus-LLM/lambda-RLM.", "github_manual": "", "github_regex": "https://github.com/lambda-calculus-LLM/lambda-RLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19466", "title": "ProactiveBench: Benchmarking Proactiveness in Multimodal Large Language Models", "authors": [ "Thomas De Min", "Subhankar Roy", "St\u00e9phane Lathuili\u00e8re", "Elisa Ricci", "Massimiliano Mancini" ], "github": "", "project_page": "", "abstract": "Effective collaboration begins with knowing when to ask for help. For example, when trying to identify an occluded object, a human would ask someone to remove the obstruction. Can MLLMs exhibit a similar \"proactive\" behavior by requesting simple user interventions? To investigate this, we introduce ProactiveBench, a benchmark built from seven repurposed datasets that tests proactiveness across different tasks such as recognizing occluded objects, enhancing image quality, and interpreting coarse sketches. We evaluate 22 MLLMs on ProactiveBench, showing that (i) they generally lack proactiveness; (ii) proactiveness does not correlate with model capacity; (iii) \"hinting\" at proactiveness yields only marginal gains. Surprisingly, we found that conversation histories and in-context learning introduce negative biases, hindering performance. Finally, we explore a simple fine-tuning strategy based on reinforcement learning: its results suggest that proactiveness can be learned, even generalizing to unseen scenarios. We publicly release ProactiveBench as a first step toward building proactive multimodal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.17989", "title": "Versatile Editing of Video Content, Actions, and Dynamics without Training", "authors": [ "Vladimir Kulikov", "Roni Paiss", "Andrey Voynov", "Inbar Mosseri", "Tali Dekel", "Tomer Michaeli" ], "github": "", "project_page": "", "abstract": "Controlled video generation has seen drastic improvements in recent years. However, editing actions and dynamic events, or inserting contents that should affect the behaviors of other objects in real-world videos, remains a major challenge. Existing trained models struggle with complex edits, likely due to the difficulty of collecting relevant training data. Similarly, existing training-free methods are inherently restricted to structure- and motion-preserving edits and do not support modification of motion or interactions. Here, we introduce DynaEdit, a training-free editing method that unlocks versatile video editing capabilities with pretrained text-to-video flow models. Our method relies on the recently introduced inversion-free approach, which does not intervene in the model internals, and is thus model-agnostic. We show that naively attempting to adapt this approach to general unconstrained editing results in severe low-frequency misalignment and high-frequency jitter. We explain the sources for these phenomena and introduce novel mechanisms for overcoming them. Through extensive experiments, we show that DynaEdit achieves state-of-the-art results on complex text-based video editing tasks, including modifying actions, inserting objects that interact with the scene, and introducing global effects.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19209", "title": "Do VLMs Need Vision Transformers? Evaluating State Space Models as Vision Encoders", "authors": [ "Shang-Jui Ray Kuo", "Paola Cascante-Bonilla" ], "github": "https://github.com/raykuo18/vlm-ssm-vision-encoders", "project_page": "", "abstract": "Large vision--language models (VLMs) often use a frozen vision backbone, whose image features are mapped into a large language model through a lightweight connector. While transformer-based encoders are the standard visual backbone, we ask whether state space model (SSM) vision backbones can be a strong alternative. We systematically evaluate SSM vision backbones for VLMs in a controlled setting. Under matched ImageNet-1K initialization, the SSM backbone achieves the strongest overall performance across both VQA and grounding/localization. We further adapt both SSM and ViT-family backbones with detection or segmentation training and find that dense-task tuning generally improves performance across families; after this adaptation, the SSM backbone remains competitive while operating at a substantially smaller model scale. We further observe that (i) higher ImageNet accuracy or larger backbones do not reliably translate into better VLM performance, and (ii) some visual backbones are unstable in localization. Based on these findings, we propose stabilization strategies that improve robustness for both backbone families and highlight SSM backbones as a strong alternative to transformer-based vision encoders in VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/raykuo18/vlm-ssm-vision-encoders", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.09151", "title": "Deep Tabular Research via Continual Experience-Driven Execution", "authors": [ "Junnan Dong", "Chuang Zhou", "Zheng Yuan", "Yifei Yu", "Qiufeng Wang", "Yinghui Li", "Siyu An", "Di Yin", "Xing Sun", "Feiyue Huang" ], "github": "", "project_page": "", "abstract": "Large language models often struggle with complex long-horizon analytical tasks over unstructured tables, which typically feature hierarchical and bidirectional headers and non-canonical layouts. We formalize this challenge as Deep Tabular Research (DTR), requiring multi-step reasoning over interdependent table regions. To address DTR, we propose a novel agentic framework that treats tabular reasoning as a closed-loop decision-making process. We carefully design a coupled query and table comprehension for path decision making and operational execution. Specifically, (i) DTR first constructs a hierarchical meta graph to capture bidirectional semantics, mapping natural language queries into an operation-level search space; (ii) To navigate this space, we introduce an expectation-aware selection policy that prioritizes high-utility execution paths; (iii) Crucially, historical execution outcomes are synthesized into a siamese structured memory, i.e., parameterized updates and abstracted texts, enabling continual refinement. Extensive experiments on challenging unstructured tabular benchmarks verify the effectiveness and highlight the necessity of separating strategic planning from low-level execution for long-horizon tabular reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19571", "title": "CurveStream: Boosting Streaming Video Understanding in MLLMs via Curvature-Aware Hierarchical Visual Memory Management", "authors": [ "Chao Wang", "Xudong Tan", "Jianjian Cao", "Kangcong Li", "Tao Chen" ], "github": "https://github.com/streamingvideos/CurveStream", "project_page": "", "abstract": "Multimodal Large Language Models have achieved significant success in offline video understanding, yet their application to streaming videos is severely limited by the linear explosion of visual tokens, which often leads to Out-of-Memory (OOM) errors or catastrophic forgetting. Existing visual retention and memory management methods typically rely on uniform sampling, low-level physical metrics, or passive cache eviction. However, these strategies often lack intrinsic semantic awareness, potentially disrupting contextual coherence and blurring transient yet critical semantic transitions. To address these limitations, we propose CurveStream, a training-free, curvature-aware hierarchical visual memory management framework. Our approach is motivated by the key observation that high-curvature regions along continuous feature trajectories closely align with critical global semantic transitions. Based on this geometric insight, CurveStream evaluates real-time semantic intensity via a Curvature Score and integrates an online K-Sigma dynamic threshold to adaptively route frames into clear and fuzzy memory states under a strict token budget. Evaluations across diverse temporal scales confirm that this lightweight framework, CurveStream, consistently yields absolute performance gains of over 10% (e.g., 10.69% on StreamingBench and 13.58% on OVOBench) over respective baselines, establishing new state-of-the-art results for streaming video perception.The code will be released at https://github.com/streamingvideos/CurveStream.", "github_manual": "", "github_regex": "https://github.com/streamingvideos/CurveStream", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.14628", "title": "s2n-bignum-bench: A practical benchmark for evaluating low-level code reasoning of LLMs", "authors": [ "Balaji Rao", "John Harrison", "Soonho Kong", "Juneyoung Lee", "Carlo Lipizzi" ], "github": "https://github.com/kings-crown/s2n-bignum-bench", "project_page": "", "abstract": "Neurosymbolic approaches leveraging Large Language Models (LLMs) with formal methods have recently achieved strong results on mathematics-oriented theorem-proving benchmarks. However, success on competition-style mathematics does not by itself demonstrate the ability to construct proofs about real-world implementations. We address this gap with a benchmark derived from an industrial cryptographic library whose assembly routines are already verified in HOL Light. s2n-bignum is a library used at AWS for providing fast assembly routines for cryptography, and its correctness is established by formal verification. The task of formally verifying this library has been a significant achievement for the Automated Reasoning Group. It involved two tasks: (1) precisely specifying the correct behavior of a program as a mathematical proposition, and (2) proving that the proposition is correct. In the case of s2n-bignum, both tasks were carried out by human experts. In s2n-bignum-bench, we provide the formal specification and ask the LLM to generate a proof script that is accepted by HOL Light within a fixed proof-check timeout. To our knowledge, s2n-bignum-bench is the first public benchmark focused on machine-checkable proof synthesis for industrial low-level cryptographic assembly routines in HOL Light. This benchmark provides a challenging and practically relevant testbed for evaluating LLM-based theorem proving beyond competition mathematics. The code to set up and use the benchmark is available here: https://github.com/kings-crown/s2n-bignum-bench{s2n-bignum-bench}.", "github_manual": "", "github_regex": "https://github.com/kings-crown/s2n-bignum-bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.17735", "title": "TAPESTRY: From Geometry to Appearance via Consistent Turntable Videos", "authors": [ "Yan Zeng", "Haoran Jiang", "Kaixin Yao", "Qixuan Zhang", "Longwen Zhang", "Lan Xu", "Jingyi Yu" ], "github": "", "project_page": "", "abstract": "Automatically generating photorealistic and self-consistent appearances for untextured 3D models is a critical challenge in digital content creation. The advancement of large-scale video generation models offers a natural approach: directly synthesizing 360-degree turntable videos (TTVs), which can serve not only as high-quality dynamic previews but also as an intermediate representation to drive texture synthesis and neural rendering. However, existing general-purpose video diffusion models struggle to maintain strict geometric consistency and appearance stability across the full range of views, making their outputs ill-suited for high-quality 3D reconstruction. To this end, we introduce TAPESTRY, a framework for generating high-fidelity TTVs conditioned on explicit 3D geometry. We reframe the 3D appearance generation task as a geometry-conditioned video diffusion problem: given a 3D mesh, we first render and encode multi-modal geometric features to constrain the video generation process with pixel-level precision, thereby enabling the creation of high-quality and consistent TTVs. Building upon this, we also design a method for downstream reconstruction tasks from the TTV input, featuring a multi-stage pipeline with 3D-Aware Inpainting. By rotating the model and performing a context-aware secondary generation, this pipeline effectively completes self-occluded regions to achieve full surface coverage. The videos generated by TAPESTRY are not only high-quality dynamic previews but also serve as a reliable, 3D-aware intermediate representation that can be seamlessly back-projected into UV textures or used to supervise neural rendering methods like 3DGS. This enables the automated creation of production-ready, complete 3D assets from untextured meshes. Experimental results demonstrate that our method outperforms existing approaches in both video consistency and final reconstruction quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.08462", "title": "Reasoning as Compression: Unifying Budget Forcing via the Conditional Information Bottleneck", "authors": [ "Fabio Valerio Massoli", "Andrey Kuzmin", "Arash Behboodi" ], "github": "", "project_page": "", "abstract": "Chain-of-Thought (CoT) prompting improves LLM accuracy on complex tasks but often increases token usage and inference cost. Existing \"Budget Forcing\" methods reducing cost via fine-tuning with heuristic length penalties, suppress both essential reasoning and redundant filler. We recast efficient reasoning as a lossy compression problem under the Information Bottleneck (IB) principle, and identify a key theoretical gap when applying naive IB to transformers: attention violates the Markov property between prompt, reasoning trace, and response. To resolve this issue, we model CoT generation under the Conditional Information Bottleneck (CIB) principle, where the reasoning trace Z acts as a computational bridge that contains only the information about the response Y that is not directly accessible from the prompt X. This yields a general Reinforcement Learning objective: maximize task reward while compressing completions under a prior over reasoning traces, subsuming common heuristics (e.g., length penalties) as special cases (e.g., uniform priors). In contrast to naive token-counting-based approaches, we introduce a semantic prior that measures token cost by surprisal under a language model prior. Empirically, our CIB objective prunes cognitive bloat while preserving fluency and logic, improving accuracy at moderate compression and enabling aggressive compression with minimal accuracy drop.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19076", "title": "DROID-SLAM in the Wild", "authors": [ "Moyang Li", "Zihan Zhu", "Marc Pollefeys", "Daniel Barath" ], "github": "https://github.com/MoyangLi00/DROID-W.git", "project_page": "", "abstract": "We present a robust, real-time RGB SLAM system that handles dynamic environments by leveraging differentiable Uncertainty-aware Bundle Adjustment. Traditional SLAM methods typically assume static scenes, leading to tracking failures in the presence of motion. Recent dynamic SLAM approaches attempt to address this challenge using predefined dynamic priors or uncertainty-aware mapping, but they remain limited when confronted with unknown dynamic objects or highly cluttered scenes where geometric mapping becomes unreliable. In contrast, our method estimates per-pixel uncertainty by exploiting multi-view visual feature inconsistency, enabling robust tracking and reconstruction even in real-world environments. The proposed system achieves state-of-the-art camera poses and scene geometry in cluttered dynamic scenarios while running in real time at around 10 FPS. Code and datasets are available at https://github.com/MoyangLi00/DROID-W.git.", "github_manual": "", "github_regex": "https://github.com/MoyangLi00/DROID-W.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.16749", "title": "Probing Cultural Signals in Large Language Models through Author Profiling", "authors": [ "Valentin Lafargue", "Ariel Guerra-Adames", "Emmanuelle Claeys", "Elouan Vuichard", "Jean-Michel Loubes" ], "github": "https://github.com/ValentinLafargue/CulturalProbingLLM", "project_page": "", "abstract": "Large language models (LLMs) are increasingly deployed in applications with societal impact, raising concerns about the cultural biases they encode. We probe these representations by evaluating whether LLMs can perform author profiling from song lyrics in a zero-shot setting, inferring singers' gender and ethnicity without task-specific fine-tuning. Across several open-source models evaluated on more than 10,000 lyrics, we find that LLMs achieve non-trivial profiling performance but demonstrate systematic cultural alignment: most models default toward North American ethnicity, while DeepSeek-1.5B aligns more strongly with Asian ethnicity. This finding emerges from both the models' prediction distributions and an analysis of their generated rationales. To quantify these disparities, we introduce two fairness metrics, Modality Accuracy Divergence (MAD) and Recall Divergence (RD), and show that Ministral-8B displays the strongest ethnicity bias among the evaluated models, whereas Gemma-12B shows the most balanced behavior. Our code is available on GitHub (https://github.com/ValentinLafargue/CulturalProbingLLM).", "github_manual": "", "github_regex": "https://github.com/ValentinLafargue/CulturalProbingLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19753", "title": "ReLi3D: Relightable Multi-view 3D Reconstruction with Disentangled Illumination", "authors": [ "Jan-Niklas Dihlmann", "Mark Boss", "Simon Donne", "Andreas Engelhardt", "Hendrik P. A. Lensch", "Varun Jampani" ], "github": "", "project_page": "", "abstract": "Reconstructing 3D assets from images has long required separate pipelines for geometry reconstruction, material estimation, and illumination recovery, each with distinct limitations and computational overhead. We present ReLi3D, the first unified end-to-end pipeline that simultaneously reconstructs complete 3D geometry, spatially-varying physically-based materials, and environment illumination from sparse multi-view images in under one second. Our key insight is that multi-view constraints can dramatically improve material and illumination disentanglement, a problem that remains fundamentally ill-posed for single-image methods. Key to our approach is the fusion of the multi-view input via a transformer cross-conditioning architecture, followed by a novel unified two-path prediction strategy. The first path predicts the object's structure and appearance, while the second path predicts the environment illumination from image background or object reflections. This, combined with a differentiable Monte Carlo multiple importance sampling renderer, creates an optimal illumination disentanglement training pipeline. In addition, with our mixed domain training protocol, which combines synthetic PBR datasets with real-world RGB captures, we establish generalizable results in geometry, material accuracy, and illumination quality. By unifying previously separate reconstruction tasks into a single feed-forward pass, we enable near-instantaneous generation of complete, relightable 3D assets. Project Page: https://reli3d.jdihlmann.com/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.17309", "title": "ReLMXEL: Adaptive RL-Based Memory Controller with Explainable Energy and Latency Optimization", "authors": [ "Panuganti Chirag Sai", "Gandholi Sarat", "R. Raghunatha Sarma", "Venkata Kalyan Tavva", "Naveen M" ], "github": "", "project_page": "", "abstract": "Reducing latency and energy consumption is critical to improving the efficiency of memory systems in modern computing. This work introduces ReLMXEL (Reinforcement Learning for Memory Controller with Explainable Energy and Latency Optimization), a explainable multi-agent online reinforcement learning framework that dynamically optimizes memory controller parameters using reward decomposition. ReLMXEL operates within the memory controller, leveraging detailed memory behavior metrics to guide decision-making. Experimental evaluations across diverse workloads demonstrate consistent performance gains over baseline configurations, with refinements driven by workload-specific memory access behaviour. By incorporating explainability into the learning process, ReLMXEL not only enhances performance but also increases the transparency of control decisions, paving the way for more accountable and adaptive memory system designs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.15911", "title": "Human-AI Synergy in Agentic Code Review", "authors": [ "Suzhen Zhong", "Shayan Noei", "Ying Zou", "Bram Adams" ], "github": "https://github.com/search/advanced", "project_page": "", "abstract": "Code review is a critical software engineering practice where developers review code changes before integration to ensure code quality, detect defects, and improve maintainability. In recent years, AI agents that can understand code context, plan review actions, and interact with development environments have been increasingly integrated into the code review process. However, there is limited empirical evidence to compare the effectiveness of AI agents and human reviewers in collaborative workflows. To address this gap, we conduct a large-scale empirical analysis of 278,790 code review conversations across 300 open-source GitHub projects. In our study, we aim to compare the feedback differences provided by human reviewers and AI agents. We investigate human-AI collaboration patterns in review conversations to understand how interaction shapes review outcomes. Moreover, we analyze the adoption of code suggestions provided by human reviewers and AI agents into the codebase and how adopted suggestions change code quality. We find that human reviewers provide additional feedback than AI agents, including understanding, testing, and knowledge transfer. Human reviewers exchange 11.8% more rounds when reviewing AI-generated code than human-written code. Moreover, code suggestions made by AI agents are adopted into the codebase at a significantly lower rate than suggestions proposed by human reviewers. Over half of unadopted suggestions from AI agents are either incorrect or addressed through alternative fixes by developers. When adopted, suggestions provided by AI agents produce significantly larger increases in code complexity and code size than suggestions provided by human reviewers. Our findings suggest that while AI agents can scale defect screening, human oversight remains critical for ensuring suggestion quality and providing contextual feedback that AI agents lack.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/search/advanced", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.18655", "title": "Multiscale Switch for Semi-Supervised and Contrastive Learning in Medical Ultrasound Image Segmentation", "authors": [ "Jingguo Qu", "Xinyang Han", "Yao Pu", "Man-Lik Chui", "Simon Takadiyi Gunda", "Ziman Chen", "Jing Qin", "Ann Dorothy King", "Winnie Chiu-Wing Chu", "Jing Cai", "Michael Tin-Cheung Ying" ], "github": "https://github.com/jinggqu/Switch", "project_page": "", "abstract": "Medical ultrasound image segmentation faces significant challenges due to limited labeled data and characteristic imaging artifacts including speckle noise and low-contrast boundaries. While semi-supervised learning (SSL) approaches have emerged to address data scarcity, existing methods suffer from suboptimal unlabeled data utilization and lack robust feature representation mechanisms. In this paper, we propose Switch, a novel SSL framework with two key innovations: (1) Multiscale Switch (MSS) strategy that employs hierarchical patch mixing to achieve uniform spatial coverage; (2) Frequency Domain Switch (FDS) with contrastive learning that performs amplitude switching in Fourier space for robust feature representations. Our framework integrates these components within a teacher-student architecture to effectively leverage both labeled and unlabeled data. Comprehensive evaluation across six diverse ultrasound datasets (lymph nodes, breast lesions, thyroid nodules, and prostate) demonstrates consistent superiority over state-of-the-art methods. At 5\\% labeling ratio, Switch achieves remarkable improvements: 80.04\\% Dice on LN-INT, 85.52\\% Dice on DDTI, and 83.48\\% Dice on Prostate datasets, with our semi-supervised approach even exceeding fully supervised baselines. The method maintains parameter efficiency (1.8M parameters) while delivering superior performance, validating its effectiveness for resource-constrained medical imaging applications. The source code is publicly available at https://github.com/jinggqu/Switch", "github_manual": "", "github_regex": "https://github.com/jinggqu/Switch", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19987", "title": "Breaking the Capability Ceiling of LLM Post-Training by Reintroducing Markov States", "authors": [ "Yurun Yuan", "Tengyang Xie" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has become a standard paradigm for post-training and aligning Large Language Models (LLMs), yet recent evidence suggests it faces a persistent \"capability ceiling\": unlike classical RL systems that discover novel strategies, RL for LLMs often acts as a mere refiner of patterns already latent in pre-trained weights. In this work, we identify a fundamental structural bottleneck: while classical RL relies on compact, informative Markov states, current LLM post-training formulations are tethered to an ever-expanding history of actions. We revisit a classical principle long central to RL yet absent from LLM post-training: explicit Markov states. Theoretically, we provide rigorous guarantees demonstrating that leveraging estimated Markov states can significantly reduce sample complexity. Empirically, we show that introducing Markov states consistently breaks the performance boundaries of standard RL post-training across a suite of complex logic puzzles. Our findings suggest that moving beyond \"history-as-state\" modeling in favor of structured Markovian representations is essential for unlocking open-ended discovery and genuinely new reasoning capabilities in Generative AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.19470", "title": "Adaptive Layerwise Perturbation: Unifying Off-Policy Corrections for LLM RL", "authors": [ "Chenlu Ye", "Xuanchang Zhang", "Yifan Hao", "Zhou Yu", "Ziji Zhang", "Abhinav Gullapalli", "Hao Chen", "Jing Huang", "Tong Zhang" ], "github": "", "project_page": "", "abstract": "Off-policy problems such as policy staleness and training-inference mismatch, has become a major bottleneck for training stability and further exploration for LLM RL. To enhance inference efficiency, the distribution gap between the inference and updated policy grows, leading to heavy-tailed importance ratios. Heavy-tailed ratios arise when the policy is locally sharp, which further inflates sharp gradients and can push updates outside the trust region. To address this, we propose Adaptive Layerwise Perturbation(ALP) by injecting small learnable perturbations into input hidden states of each layer during updates, which is used as the numerator of the importance ratio against the unchanged inference policy in the objective. Intuitively, by adding controlled noise to intermediate representations, ALP prevents the updated policy from deviating too sharply from the inference policy, and enlarges the policy family to cover the inference policy family with mismatch noises. Hence, the flattened distribution can naturally tighten the updated and inference policy gap and reduce the tail of importance ratios, thus maintaining training stability. This is further validated empirically. Experiments on single-turn math and multi-turn tool-integrated reasoning tasks show that ALP not only improves final performance, but also avoid blow up of importance ratio tail and KL spikes during iterative training, along with boosted exploration. Ablations show that representation-level perturbations across all layers are most effective, substantially outperforming partial-layer and logits-only variants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.18750", "title": "Automatic detection of Gen-AI texts: A comparative framework of neural models", "authors": [ "Cristian Buttaro", "Irene Amerini" ], "github": "", "project_page": "", "abstract": "The rapid proliferation of Large Language Models has significantly increased the difficulty of distinguishing between human-written and AI generated texts, raising critical issues across academic, editorial, and social domains. This paper investigates the problem of AI generated text detection through the design, implementation, and comparative evaluation of multiple machine learning based detectors. Four neural architectures are developed and analyzed: a Multilayer Perceptron, a one-dimensional Convolutional Neural Network, a MobileNet-based CNN, and a Transformer model. The proposed models are benchmarked against widely used online detectors, including ZeroGPT, GPTZero, QuillBot, Originality.AI, Sapling, IsGen, Rephrase, and Writer. Experiments are conducted on the COLING Multilingual Dataset, considering both English and Italian configurations, as well as on an original thematic dataset focused on Art and Mental Health. Results show that supervised detectors achieve more stable and robust performance than commercial tools across different languages and domains, highlighting key strengths and limitations of current detection strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-23", "arxiv_id": "2603.20193", "title": "From Masks to Pixels and Meaning: A New Taxonomy, Benchmark, and Metrics for VLM Image Tampering", "authors": [ "Xinyi Shang", "Yi Tang", "Jiacheng Cui", "Ahmed Elhagry", "Salwa K. Al Khatib", "Sondos Mahmoud Bsharat", "Jiacheng Liu", "Xiaohan Zhao", "Jing-Hao Xue", "Hao Li", "Salman Khan", "Zhiqiang Shen" ], "github": "https://github.com/VILA-Lab/PIXAR", "project_page": "", "abstract": "Existing tampering detection benchmarks largely rely on object masks, which severely misalign with the true edit signal: many pixels inside a mask are untouched or only trivially modified, while subtle yet consequential edits outside the mask are treated as natural. We reformulate VLM image tampering from coarse region labels to a pixel-grounded, meaning and language-aware task. First, we introduce a taxonomy spanning edit primitives (replace/remove/splice/inpaint/attribute/colorization, etc.) and their semantic class of tampered object, linking low-level changes to high-level understanding. Second, we release a new benchmark with per-pixel tamper maps and paired category supervision to evaluate detection and classification within a unified protocol. Third, we propose a training framework and evaluation metrics that quantify pixel-level correctness with localization to assess confidence or prediction on true edit intensity, and further measure tamper meaning understanding via semantics-aware classification and natural language descriptions for the predicted regions. We also re-evaluate the existing strong segmentation/localization baselines on recent strong tamper detectors and reveal substantial over- and under-scoring using mask-only metrics, and expose failure modes on micro-edits and off-mask changes. Our framework advances the field from masks to pixels, meanings and language descriptions, establishing a rigorous standard for tamper localization, semantic classification and description. Code and benchmark data are available at https://github.com/VILA-Lab/PIXAR.", "github_manual": "", "github_regex": "https://github.com/VILA-Lab/PIXAR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21065", "title": "LongCat-Flash-Prover: Advancing Native Formal Reasoning via Agentic Tool-Integrated Reinforcement Learning", "authors": [ "Jianing Wang", "Jianfei Zhang", "Qi Guo", "Linsen Guo", "Rumei Li", "Chao Zhang", "Chong Peng", "Cunguang Wang", "Dengchang Zhao", "Jiarong Shi", "Jingang Wang", "Liulin Feng", "Mengxia Shen", "Qi Li", "Shengnan An", "Shun Wang", "Wei Shi", "Xiangyu Xi", "Xiaoyu Li", "Xuezhi Cao", "Yi Lu", "Yunke Zhao", "Zhengyu Chen", "Zhimin Lin", "Wei Wang", "Peng Pei", "Xunliang Cai" ], "github": "https://github.com/meituan-longcat/LongCat-Flash-Prover", "project_page": "", "abstract": "We introduce LongCat-Flash-Prover, a flagship 560-billion-parameter open-source Mixture-of- Experts (MoE) model that advances Native Formal Reasoning in Lean4 through agentic tool-integrated reasoning (TIR). We decompose the native formal reasoning task into three independent formal capabilities, i.e., auto-formalization, sketching, and proving. To facilitate these capabilities, we propose a Hybrid-Experts Iteration Framework to expand high-quality task trajectories, including generating a formal statement based on a given informal problem, producing a whole-proof directly from the statement, or a lemma-style sketch. During agentic RL, we present a Hierarchical Importance Sampling Policy Optimization (HisPO) algorithm, which aims to stabilize the MoE model training on such long-horizon tasks. It employs a gradient masking strategy that accounts for the policy staleness and the inherent train-inference engine discrepancies at both sequence and token levels. Additionally, we also incorporate theorem consistency and legality detection mechanisms to eliminate reward hacking issues. Extensive evaluations show that our LongCat-Flash-Prover sets a new state-of-the-art for open-weights models in both auto-formalization and theorem proving. Demonstrating remarkable sample efficiency, it achieves a 97.1% pass rate on MiniF2F-Test using only 72 inference budget per problem. On more challenging benchmarks, it solves 70.8% of ProverBench and 41.5% of PutnamBench with no more than 220 attempts per problem, significantly outperforming existing open-weights baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/meituan-longcat/LongCat-Flash-Prover", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21606", "title": "mSFT: Addressing Dataset Mixtures Overfiting Heterogeneously in Multi-task SFT", "authors": [ "Woosung Koh", "Jeyoung Jeon", "Youngjin Song", "Yujin Cheon", "Soowon Oh", "Jaehyeong Choi", "Se-Young Yun" ], "github": "", "project_page": "", "abstract": "Current language model training commonly applies multi-task Supervised Fine-Tuning (SFT) using a homogeneous compute budget across all sub-datasets. This approach is fundamentally sub-optimal: heterogeneous learning dynamics cause faster-learning tasks to overfit early while slower ones remain under-fitted. To address this, we introduce mSFT, an iterative, overfitting-aware search algorithm for multi-task data mixtures. mSFT trains the model on an active mixture, identifies and excludes the earliest overfitting sub-dataset, and reverts to that specific optimal checkpoint before continuing. Extensive evaluations demonstrate that mSFT consistently outperforms 4 baselines across 10 benchmarks and 6 base models. Further analysis confirms mSFT maintains robust gains across diverse dataset sizes, task granularities, and is insensitive to its single new hyperparameter (compute budget). Notably, at low compute budget, mSFT can improve performance while lowering training FLOPs. Ultimately, mSFT establishes a practical overfitting-aware algorithm for multi-task SFT that maximizes the potential of models across diverse data mixtures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.09290", "title": "ToolRosetta: Bridging Open-Source Repositories and Large Language Model Agents through Automated Tool Standardization", "authors": [ "Shimin Di", "Xujie Yuan", "Hanghui Guo", "Chaoqian Ouyang", "Zhangze Chen", "Ling Yue", "Libin Zheng", "Jia Zhu", "Shaowu Pan", "Jian Yin", "Min-Ling Zhang", "Yong Rui" ], "github": "https://github.com/github/github-mcp-server", "project_page": "", "abstract": "Reusing and invoking existing code remains costly and unreliable, as most practical tools are embedded in heterogeneous code repositories and lack standardized, executable interfaces. Although large language models (LLMs) and Model Context Protocol (MCP)-based tool invocation frameworks enable natural language task execution, current approaches rely heavily on manual tool curation and standardization, which fundamentally limits scalability. In this paper, we propose ToolRosetta, a unified framework that automatically translates open-source code repositories and APIs into MCP-compatible tools that can be reliably invoked by LLMs. Given a user task, ToolRosetta autonomously plans toolchains, identifies relevant codebases, and converts them into executable MCP services, enabling end-to-end task completion with minimal human intervention. In addition, ToolRosetta incorporates a security inspection layer to mitigate risks inherent in executing arbitrary code. Extensive experiments across diverse scientific domains demonstrate that ToolRosetta can automatically standardize a large number of open-source tools and reduce the human effort required for code reproduction and deployment. Notably, by seamlessly leveraging specialized open-source tools, ToolRosetta-powered agents consistently improve task completion performance compared to commercial LLMs and existing agent systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/github/github-mcp-server", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21986", "title": "Speed by Simplicity: A Single-Stream Architecture for Fast Audio-Video Generative Foundation Model", "authors": [ "SII-GAIR", "Sand. ai", "Ethan Chern", "Hansi Teng", "Hanwen Sun", "Hao Wang", "Hong Pan", "Hongyu Jia", "Jiadi Su", "Jin Li", "Junjie Yu", "Lijie Liu", "Lingzhi Li", "Lyumanshan Ye", "Min Hu", "Qiangang Wang", "Quanwei Qi", "Steffi Chern", "Tao Bu", "Taoran Wang", "Teren Xu", "Tianning Zhang", "Tiantian Mi", "Weixian Xu", "Wenqiang Zhang", "Wentai Zhang", "Xianping Yi", "Xiaojie Cai", "Xiaoyang Kang", "Yan Ma", "Yixiu Liu", "Yunbo Zhang", "Yunpeng Huang", "Yutong Lin", "Zewei Tao", "Zhaoliang Liu", "Zheng Zhang", "Zhiyao Cen", "Zhixuan Yu", "Zhongshu Wang", "Zhulin Hu", "Zijin Zhou", "Zinan Guo", "Yue Cao", "Pengfei Liu" ], "github": "", "project_page": "", "abstract": "We present daVinci-MagiHuman, an open-source audio-video generative foundation model for human-centric generation. daVinci-MagiHuman jointly generates synchronized video and audio using a single-stream Transformer that processes text, video, and audio within a unified token sequence via self-attention only. This single-stream design avoids the complexity of multi-stream or cross-attention architectures while remaining easy to optimize with standard training and inference infrastructure. The model is particularly strong in human-centric scenarios, producing expressive facial performance, natural speech-expression coordination, realistic body motion, and precise audio-video synchronization. It supports multilingual spoken generation across Chinese (Mandarin and Cantonese), English, Japanese, Korean, German, and French. For efficient inference, we combine the single-stream backbone with model distillation, latent-space super-resolution, and a Turbo VAE decoder, enabling generation of a 5-second 256p video in 2 seconds on a single H100 GPU. In automatic evaluation, daVinci-MagiHuman achieves the highest visual quality and text alignment among leading open models, along with the lowest word error rate (14.60%) for speech intelligibility. In pairwise human evaluation, it achieves win rates of 80.0% against Ovi 1.1 and 60.9% against LTX 2.3 over 2000 comparisons. We open-source the complete model stack, including the base model, the distilled model, the super-resolution model, and the inference codebase.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.22285", "title": "VideoDetective: Clue Hunting via both Extrinsic Query and Intrinsic Relevance for Long Video Understanding", "authors": [ "Ruoliu Yang", "Chu Wu", "Caifeng Shan", "Ran He", "Chaoyou Fu" ], "github": "", "project_page": "", "abstract": "Long video understanding remains challenging for multimodal large language models (MLLMs) due to limited context windows, which necessitate identifying sparse query-relevant video segments. However, existing methods predominantly localize clues based solely on the query, overlooking the video's intrinsic structure and varying relevance across segments. To address this, we propose VideoDetective, a framework that integrates query-to-segment relevance and inter-segment affinity for effective clue hunting in long-video question answering. Specifically, we divide a video into various segments and represent them as a visual-temporal affinity graph built from visual similarity and temporal proximity. We then perform a Hypothesis-Verification-Refinement loop to estimate relevance scores of observed segments to the query and propagate them to unseen segments, yielding a global relevance distribution that guides the localization of the most critical segments for final answering with sparse observation. Experiments show our method consistently achieves substantial gains across a wide range of mainstream MLLMs on representative benchmarks, with accuracy improvements of up to 7.5% on VideoMME-long. Our code is available at https://videodetective.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21383", "title": "PivotRL: High Accuracy Agentic Post-Training at Low Compute Cost", "authors": [ "Junkeun Yi", "Damon Mosk-Aoyama", "Baihe Huang", "Ritu Gala", "Charles Wang", "Sugam Dipak Devare", "Khushi Bhardwaj", "Abhibha Gupta", "Oleksii Kuchaiev", "Jiantao Jiao", "Jian Zhang", "Venkat Srinivasan" ], "github": "https://github.com/anomalyco/opencode", "project_page": "", "abstract": "Post-training for long-horizon agentic tasks has a tension between compute efficiency and generalization. While supervised fine-tuning (SFT) is compute efficient, it often suffers from out-of-domain (OOD) degradation. Conversely, end-to-end reinforcement learning (E2E RL) preserves OOD capabilities, but incurs high compute costs due to many turns of on-policy rollout. We introduce PivotRL, a novel framework that operates on existing SFT trajectories to combine the compute efficiency of SFT with the OOD accuracy of E2E RL. PivotRL relies on two key mechanisms: first, it executes local, on-policy rollouts and filters for pivots: informative intermediate turns where sampled actions exhibit high variance in outcomes; second, it utilizes rewards for functional-equivalent actions rather than demanding strict string matching with the SFT data demonstration. We theoretically show that these mechanisms incentivize strong learning signals with high natural gradient norm, while maximally preserving policy probability ordering on actions unrelated to training tasks. In comparison to standard SFT on identical data, we demonstrate that PivotRL achieves +4.17% higher in-domain accuracy on average across four agentic domains, and +10.04% higher OOD accuracy in non-agentic tasks. Notably, on agentic coding tasks, PivotRL achieves competitive accuracy with E2E RL with 4x fewer rollout turns. PivotRL is adopted by NVIDIA's Nemotron-3-Super-120B-A12B, acting as the workhorse in production-scale agentic post-training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/anomalyco/opencode", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.22286", "title": "WorldCache: Content-Aware Caching for Accelerated Video World Models", "authors": [ "Umair Nawaz", "Ahmed Heakl", "Ufaq Khan", "Abdelrahman Shaker", "Salman Khan", "Fahad Shahbaz Khan" ], "github": "", "project_page": "", "abstract": "Diffusion Transformers (DiTs) power high-fidelity video world models but remain computationally expensive due to sequential denoising and costly spatio-temporal attention. Training-free feature caching accelerates inference by reusing intermediate activations across denoising steps; however, existing methods largely rely on a Zero-Order Hold assumption i.e., reusing cached features as static snapshots when global drift is small. This often leads to ghosting artifacts, blur, and motion inconsistencies in dynamic scenes. We propose WorldCache, a Perception-Constrained Dynamical Caching framework that improves both when and how to reuse features. WorldCache introduces motion-adaptive thresholds, saliency-weighted drift estimation, optimal approximation via blending and warping, and phase-aware threshold scheduling across diffusion steps. Our cohesive approach enables adaptive, motion-consistent feature reuse without retraining. On Cosmos-Predict2.5-2B evaluated on PAI-Bench, WorldCache achieves 2.3times inference speedup while preserving 99.4\\% of baseline quality, substantially outperforming prior training-free caching approaches. Our code can be accessed on https://umair1221.github.io/World-Cache/{World-Cache}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.22117", "title": "On the Direction of RLVR Updates for LLM Reasoning: Identification and Exploitation", "authors": [ "Kexin Huang", "Haoming Meng", "Junkang Wu", "Jinda Lu", "Chiyu Ma", "Ziqian Chen", "Xue Wang", "Bolin Ding", "Jiancan Wu", "Xiang Wang", "Xiangnan He", "Guoyin Wang", "Jingren Zhou" ], "github": "https://github.com/verl-project/verl/tree/v0.5.0/recipe/dapo", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models. While existing analyses identify that RLVR-induced changes are sparse, they primarily focus on the magnitude of these updates, largely overlooking their direction. In this work, we argue that the direction of updates is a more critical lens for understanding RLVR's effects, which can be captured by the signed, token-level log probability difference \u0394log p between the base and final RLVR models. Through statistical analysis and token-replacement interventions, we demonstrate that \u0394log p more effectively identifies sparse, yet reasoning-critical updates than magnitude-based metrics (\\eg divergence or entropy). Building on this insight, we propose two practical applications: (1) a test-time extrapolation method that amplifies the policy along the learned \u0394log p direction to improve reasoning accuracy without further training; (2) a training-time reweighting method that focuses learning on low-probability (corresponding to higher \u0394log p) tokens, which improves reasoning performance across models and benchmarks. Our work establishes the direction of change as a key principle for analyzing and improving RLVR.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/verl-project/verl/tree/v0.5.0/recipe/dapo", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.22053", "title": "AnimalCLAP: Taxonomy-Aware Language-Audio Pretraining for Species Recognition and Trait Inference", "authors": [ "Risa Shinoda", "Kaede Shiohara", "Nakamasa Inoue", "Hiroaki Santo", "Fumio Okura" ], "github": "", "project_page": "", "abstract": "Animal vocalizations provide crucial insights for wildlife assessment, particularly in complex environments such as forests, aiding species identification and ecological monitoring. Recent advances in deep learning have enabled automatic species classification from their vocalizations. However, classifying species unseen during training remains challenging. To address this limitation, we introduce AnimalCLAP, a taxonomy-aware language-audio framework comprising a new dataset and model that incorporate hierarchical biological information. Specifically, our vocalization dataset consists of 4,225 hours of recordings covering 6,823 species, annotated with 22 ecological traits. The AnimalCLAP model is trained on this dataset to align audio and textual representations using taxonomic structures, improving the recognition of unseen species. We demonstrate that our proposed model effectively infers ecological and biological attributes of species directly from their vocalizations, achieving superior performance compared to CLAP. Our dataset, code, and models will be publicly available at https://dahlian00.github.io/AnimalCLAP_Page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21489", "title": "Effective Strategies for Asynchronous Software Engineering Agents", "authors": [ "Jiayi Geng", "Graham Neubig" ], "github": "", "project_page": "", "abstract": "AI agents have become increasingly capable at isolated software engineering (SWE) tasks such as resolving issues on Github. Yet long-horizon tasks involving multiple interdependent subtasks still pose challenges both with respect to accuracy, and with respect to timely completion. A natural approach to solving these long-horizon tasks in a timely manner is asynchronous multi-agent collaboration, where multiple agents work on different parts of the task at the same time. But effective application of multi-agent systems has proven surprisingly difficult: concurrent edits by multiple agents interfere with each other, dependencies are difficult to synchronize, and combining partial progress into a coherent whole is challenging. On the other hand, human developers have long relied on mature collaboration infrastructure to manage these challenges in large software projects. Inspired by these collaboration primitives, we introduce Centralized Asynchronous Isolated Delegation (CAID), a structured multi-agent coordination paradigm grounded in three core SWE primitives: centralized task delegation, asynchronous execution, and isolated workspaces. CAID constructs dependency-aware task plans through a central manager, executes subtasks concurrently in isolated workspaces, and consolidates progress via structured integration with executable test-based verification. In empirical evaluation, we find that CAID improves accuracy over single-agent baselines by 26.7% absolute on paper reproduction tasks (PaperBench) and 14.3% on Python library development tasks (Commit0). Through systematic analysis, we find that branch-and-merge is a central coordination mechanism for multi-agent collaboration, and that SWE primitives such as git worktree, git commit, and git merge enable it to be realized in a reliable and executable manner.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.20639", "title": "Agentic AI and the next intelligence explosion", "authors": [ "James Evans", "Benjamin Bratton", "Blaise Ag\u00fcera y Arcas" ], "github": "", "project_page": "", "abstract": "The \"AI singularity\" is often miscast as a monolithic, godlike mind. Evolution suggests a different path: intelligence is fundamentally plural, social, and relational. Recent advances in agentic AI reveal that frontier reasoning models, such as DeepSeek-R1, do not improve simply by \"thinking longer\". Instead, they simulate internal \"societies of thought,\" spontaneous cognitive debates that argue, verify, and reconcile to solve complex tasks. Moreover, we are entering an era of human-AI centaurs: hybrid actors where collective agency transcends individual control. Scaling this intelligence requires shifting from dyadic alignment (RLHF) toward institutional alignment. By designing digital protocols, modeled on organizations and markets, we can build a social infrastructure of checks and balances. The next intelligence explosion will not be a single silicon brain, but a complex, combinatorial society specializing and sprawling like a city. No mind is an island.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.18795", "title": "Perceptio: Perception Enhanced Vision Language Models via Spatial Token Generation", "authors": [ "Yuchen Li", "Amanmeet Garg", "Shalini Chaudhuri", "Rui Zhao", "Garin Kessler" ], "github": "", "project_page": "", "abstract": "Large Vision Language Models (LVLMs) excel at semantic understanding but struggle with fine grained spatial grounding, as the model must implicitly infer complex geometry without ever producing a spatial interpretation. We present Perceptio, a perception enhanced LVLM with 2D and 3D spatial reasoning abilities, enabled via explicit semantic segmentation tokens and depth tokens generated directly within the autoregressive sequence. Concretely, we (i) distill a VQVAE depth codebook from a strong monocular teacher to tokenize dense depth into compact sequences, and (ii) integrate SAM2 based semantic segmentation tokens and VQ-VAE depth tokens inside the LLM so the model first emits spatial tokens and then answers. To stabilize depth token generation, we introduce novel composite depth-token objectives (marker, token, and count losses) and a soft-merging technique for differentiable reconstruction. We adopt a multi-task co-training strategy across diverse datasets, letting the model learn perception tokens to tackle multiple downstream tasks. Building on InternVL, Perceptio achieves state-of-the-art performance across benchmarks: improving referring expression segmentation by +0.8/+1.4/+1.1 cIoU on RefCOCO/+/g HardBLINK spatial understanding accuracy by 10.3%, and MMBench accuracy by 1.0%, demonstrating that explicit spatial chain-of-thought materially strengthens spatial grounding in LVLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.22276", "title": "Scaling DoRA: High-Rank Adaptation via Factored Norms and Fused Kernels", "authors": [ "Alexandra Zelenin", "Alexandra Zhuravlyova" ], "github": "https://github.com/sockeye44/dorafactors", "project_page": "", "abstract": "Weight-Decomposed Low-Rank Adaptation (DoRA) extends LoRA by decoupling weight magnitude from direction, but its forward pass requires the row-wise norm of W + sBA, a computation that every major framework we surveyed implements by materializing the dense [d_out, d_in] product BA. At d_in = 8192 and rank r = 384, a single module's norm requires about 512 MB of transient working memory in bf16, making high-rank DoRA costly and often infeasible on common single-GPU setups once hundreds of adapted modules and checkpointing are involved. We present two systems contributions. A factored norm decomposes the squared norm into base, cross, and Gram terms computable through O(d_out r + r^2) intermediates, eliminating the dense product. Fused Triton kernels collapse the four-kernel DoRA composition into a single pass, reducing memory traffic by about 4x and using a numerically stable form that avoids catastrophic cancellation in the near-unity rescaling regime where magnitude scales concentrate in practice. Across six 8-32B vision-language models (VLMs) on three NVIDIA GPUs (RTX 6000 PRO, H200, B200) at r = 384 in bf16, the fused implementation is 1.5-2.0x faster than Hugging Face PEFT's DoRA implementation for inference and 1.5-1.9x faster for gradient computation (optimizer step excluded), with up to 7 GB lower peak VRAM. Microbenchmarks on six GPUs spanning four architecture generations (L40S, A100, RTX 6000 PRO, H200, B200, B300) confirm 1.5-2.7x compose-kernel speedup. Final-logit cosine similarity exceeds 0.9999 across all model/GPU pairs, and multi-seed training curves match within 7.1 x 10^-4 mean per-step loss delta over 2000 steps.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sockeye44/dorafactors", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.22241", "title": "MemDLM: Memory-Enhanced DLM Training", "authors": [ "Zehua Pei", "Hui-Ling Zhen", "Weizhe Lin", "Sinno Jialin Pan", "Yunhe Wang", "Mingxuan Yuan", "Bei Yu" ], "github": "https://github.com/JarvisPei/MemDLM", "project_page": "", "abstract": "Diffusion Language Models (DLMs) offer attractive advantages over Auto-Regressive (AR) models, such as full-attention parallel decoding and flexible generation. However, they suffer from a notable train-inference mismatch: DLMs are trained with a static, single-step masked prediction objective, but deployed through a multi-step progressive denoising trajectory. We propose MemDLM (Memory-Enhanced DLM), which narrows this gap by embedding a simulated denoising process into training via Bi-level Optimization. An inner loop updates a set of fast weights, forming a Parametric Memory that captures the local trajectory experience of each sample, while an outer loop updates the base model conditioned on this memory. By offloading memorization pressure from token representations to parameters, MemDLM yields faster convergence and lower training loss. Moreover, the inner loop can be re-enabled at inference time as an adaptation step, yielding additional gains on long-context understanding. We find that, when activated at inference time, this Parametric Memory acts as an emergent in-weight retrieval mechanism, helping MemDLM further reduce token-level attention bottlenecks on challenging Needle-in-a-Haystack retrieval tasks. Code: https://github.com/JarvisPei/MemDLM.", "github_manual": "", "github_regex": "https://github.com/JarvisPei/MemDLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.20538", "title": "Understanding Behavior Cloning with Action Quantization", "authors": [ "Haoqun Cao", "Tengyang Xie" ], "github": "", "project_page": "", "abstract": "Behavior cloning is a fundamental paradigm in machine learning, enabling policy learning from expert demonstrations across robotics, autonomous driving, and generative models. Autoregressive models like transformer have proven remarkably effective, from large language models (LLMs) to vision-language-action systems (VLAs). However, applying autoregressive models to continuous control requires discretizing actions through quantization, a practice widely adopted yet poorly understood theoretically. This paper provides theoretical foundations for this practice. We analyze how quantization error propagates along the horizon and interacts with statistical sample complexity. We show that behavior cloning with quantized actions and log-loss achieves optimal sample complexity, matching existing lower bounds, and incurs only polynomial horizon dependence on quantization error, provided the dynamics are stable and the policy satisfies a probabilistic smoothness condition. We further characterize when different quantization schemes satisfy or violate these requirements, and propose a model-based augmentation that provably improves the error bound without requiring policy smoothness. Finally, we establish fundamental limits that jointly capture the effects of quantization error and statistical complexity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21944", "title": "Group3D: MLLM-Driven Semantic Grouping for Open-Vocabulary 3D Object Detection", "authors": [ "Youbin Kim", "Jinho Park", "Hogun Park", "Eunbyung Park" ], "github": "", "project_page": "", "abstract": "Open-vocabulary 3D object detection aims to localize and recognize objects beyond a fixed training taxonomy. In multi-view RGB settings, recent approaches often decouple geometry-based instance construction from semantic labeling, generating class-agnostic fragments and assigning open-vocabulary categories post hoc. While flexible, such decoupling leaves instance construction governed primarily by geometric consistency, without semantic constraints during merging. When geometric evidence is view-dependent and incomplete, this geometry-only merging can lead to irreversible association errors, including over-merging of distinct objects or fragmentation of a single instance. We propose Group3D, a multi-view open-vocabulary 3D detection framework that integrates semantic constraints directly into the instance construction process. Group3D maintains a scene-adaptive vocabulary derived from a multimodal large language model (MLLM) and organizes it into semantic compatibility groups that encode plausible cross-view category equivalence. These groups act as merge-time constraints: 3D fragments are associated only when they satisfy both semantic compatibility and geometric consistency. This semantically gated merging mitigates geometry-driven over-merging while absorbing multi-view category variability. Group3D supports both pose-known and pose-free settings, relying only on RGB observations. Experiments on ScanNet and ARKitScenes demonstrate that Group3D achieves state-of-the-art performance in multi-view open-vocabulary 3D detection, while exhibiting strong generalization in zero-shot scenarios. The project page is available at https://ubin108.github.io/Group3D/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.18118", "title": "Insight-V++: Towards Advanced Long-Chain Visual Reasoning with Multimodal Large Language Models", "authors": [ "Yuhao Dong", "Zuyan Liu", "Shulin Tian", "Yongming Rao", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have achieved remarkable reliability and advanced capabilities through extended test-time reasoning. However, extending these capabilities to Multi-modal Large Language Models (MLLMs) remains a significant challenge due to a critical scarcity of high-quality, long-chain reasoning data and optimized training pipelines. To bridge this gap, we present a unified multi-agent visual reasoning framework that systematically evolves from our foundational image-centric model, Insight-V, into a generalized spatial-temporal architecture, Insight-V++. We first propose a scalable data generation pipeline equipped with multi-granularity assessment that autonomously synthesizes structured, complex reasoning trajectories across image and video domains without human intervention. Recognizing that directly supervising MLLMs with such intricate data yields sub-optimal results, we design a dual-agent architecture comprising a reasoning agent to execute extensive analytical chains, and a summary agent to critically evaluate and distill final outcomes. While our initial framework utilized Direct Preference Optimization (DPO), its off-policy nature fundamentally constrained reinforcement learning potential. To overcome these limitations, particularly for long-horizon video understanding, Insight-V++ introduces two novel algorithms, ST-GRPO and J-GRPO, which enhance spatial-temporal reasoning and improve evaluative robustness. Crucially, by leveraging reliable feedback from the summary agent, we guide an iterative reasoning path generation process, retraining the entire multi-agent system in a continuous, self-improving loop. Extensive experiments on base models like LLaVA-NeXT and Qwen2.5-VL demonstrate significant performance gains across challenging image and video reasoning benchmarks while preserving strong capabilities on traditional perception-focused tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.19456", "title": "In-the-Wild Camouflage Attack on Vehicle Detectors through Controllable Image Editing", "authors": [ "Xiao Fang", "Yiming Gong", "Stanislav Panev", "Celso de Melo", "Shuowen Hu", "Shayok Chakraborty", "Fernando De la Torre" ], "github": "", "project_page": "", "abstract": "Deep neural networks (DNNs) have achieved remarkable success in computer vision but remain highly vulnerable to adversarial attacks. Among them, camouflage attacks manipulate an object's visible appearance to deceive detectors while remaining stealthy to humans. In this paper, we propose a new framework that formulates vehicle camouflage attacks as a conditional image-editing problem. Specifically, we explore both image-level and scene-level camouflage generation strategies, and fine-tune a ControlNet to synthesize camouflaged vehicles directly on real images. We design a unified objective that jointly enforces vehicle structural fidelity, style consistency, and adversarial effectiveness. Extensive experiments on the COCO and LINZ datasets show that our method achieves significantly stronger attack effectiveness, leading to more than 38% AP50 decrease, while better preserving vehicle structure and improving human-perceived stealthiness compared to existing approaches. Furthermore, our framework generalizes effectively to unseen black-box detectors and exhibits promising transferability to the physical world. Project page is available at https://humansensinglab.github.io/CtrlCamo", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.19660", "title": "Semantic Audio-Visual Navigation in Continuous Environments", "authors": [ "Yichen Zeng", "Hebaixu Wang", "Meng Liu", "Yu Zhou", "Chen Gao", "Kehan Chen", "Gongping Huang" ], "github": "https://github.com/yichenzeng24/SAVN-CE", "project_page": "", "abstract": "Audio-visual navigation enables embodied agents to navigate toward sound-emitting targets by leveraging both auditory and visual cues. However, most existing approaches rely on precomputed room impulse responses (RIRs) for binaural audio rendering, restricting agents to discrete grid positions and leading to spatially discontinuous observations. To establish a more realistic setting, we introduce Semantic Audio-Visual Navigation in Continuous Environments (SAVN-CE), where agents can move freely in 3D spaces and perceive temporally and spatially coherent audio-visual streams. In this setting, targets may intermittently become silent or stop emitting sound entirely, causing agents to lose goal information. To tackle this challenge, we propose MAGNet, a multimodal transformer-based model that jointly encodes spatial and semantic goal representations and integrates historical context with self-motion cues to enable memory-augmented goal reasoning. Comprehensive experiments demonstrate that MAGNet significantly outperforms state-of-the-art methods, achieving up to a 12.1\\% absolute improvement in success rate. These results also highlight its robustness to short-duration sounds and long-distance navigation scenarios. The code is available at https://github.com/yichenzeng24/SAVN-CE.", "github_manual": "", "github_regex": "https://github.com/yichenzeng24/SAVN-CE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21872", "title": "Manifold-Aware Exploration for Reinforcement Learning in Video Generation", "authors": [ "Mingzhe Zheng", "Weijie Kong", "Yue Wu", "Dengyang Jiang", "Yue Ma", "Xuanhua He", "Bin Lin", "Kaixiong Gong", "Zhao Zhong", "Liefeng Bo", "Qifeng Chen", "Harry Yang" ], "github": "", "project_page": "", "abstract": "Group Relative Policy Optimization (GRPO) methods for video generation like FlowGRPO remain far less reliable than their counterparts for language models and images. This gap arises because video generation has a complex solution space, and the ODE-to-SDE conversion used for exploration can inject excess noise, lowering rollout quality and making reward estimates less reliable, which destabilizes post-training alignment. To address this problem, we view the pre-trained model as defining a valid video data manifold and formulate the core problem as constraining exploration within the vicinity of this manifold, ensuring that rollout quality is preserved and reward estimates remain reliable. We propose SAGE-GRPO (Stable Alignment via Exploration), which applies constraints at both micro and macro levels. At the micro level, we derive a precise manifold-aware SDE with a logarithmic curvature correction and introduce a gradient norm equalizer to stabilize sampling and updates across timesteps. At the macro level, we use a dual trust region with a periodic moving anchor and stepwise constraints so that the trust region tracks checkpoints that are closer to the manifold and limits long-horizon drift. We evaluate SAGE-GRPO on HunyuanVideo1.5 using the original VideoAlign as the reward model and observe consistent gains over previous methods in VQ, MQ, TA, and visual metrics (CLIPScore, PickScore), demonstrating superior performance in both reward maximization and overall video quality. The code and visual gallery are available at https://dungeonmassster.github.io/SAGE-GRPO-Page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.22212", "title": "Omni-WorldBench: Towards a Comprehensive Interaction-Centric Evaluation for World Models", "authors": [ "Meiqi Wu", "Zhixin Cai", "Fufangchen Zhao", "Xiaokun Feng", "Rujing Dang", "Bingze Song", "Ruitian Tian", "Jiashu Zhu", "Jiachen Lei", "Hao Dou", "Jing Tang", "Lei Sun", "Jiahong Wu", "Xiangxiang Chu", "Zeming Liu", "Kaiqi Huang" ], "github": "", "project_page": "", "abstract": "Video--based world models have emerged along two dominant paradigms: video generation and 3D reconstruction. However, existing evaluation benchmarks either focus narrowly on visual fidelity and text--video alignment for generative models, or rely on static 3D reconstruction metrics that fundamentally neglect temporal dynamics. We argue that the future of world modeling lies in 4D generation, which jointly models spatial structure and temporal evolution. In this paradigm, the core capability is interactive response: the ability to faithfully reflect how interaction actions drive state transitions across space and time. Yet no existing benchmark systematically evaluates this critical dimension. To address this gap, we propose Omni--WorldBench, a comprehensive benchmark specifically designed to evaluate the interactive response capabilities of world models in 4D settings. Omni--WorldBench comprises two key components: Omni--WorldSuite, a systematic prompt suite spanning diverse interaction levels and scene types; and Omni--Metrics, an agent-based evaluation framework that quantifies world modeling capabilities by measuring the causal impact of interaction actions on both final outcomes and intermediate state evolution trajectories. We conduct extensive evaluations of 18 representative world models across multiple paradigms. Our analysis reveals critical limitations of current world models in interactive response, providing actionable insights for future research. Omni-WorldBench will be publicly released to foster progress in interactive 4D world modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21342", "title": "Generalized Discrete Diffusion from Snapshots", "authors": [ "Oussama Zekri", "Th\u00e9o Uscidda", "Nicolas Boull\u00e9", "Anna Korba" ], "github": "", "project_page": "", "abstract": "We introduce Generalized Discrete Diffusion from Snapshots (GDDS), a unified framework for discrete diffusion modeling that supports arbitrary noising processes over large discrete state spaces. Our formulation encompasses all existing discrete diffusion approaches, while allowing significantly greater flexibility in the choice of corruption dynamics. The forward noising process relies on uniformization and enables fast arbitrary corruption. For the reverse process, we derive a simple evidence lower bound (ELBO) based on snapshot latents, instead of the entire noising path, that allows efficient training of standard generative modeling architectures with clear probabilistic interpretation. Our experiments on large-vocabulary discrete generation tasks suggest that the proposed framework outperforms existing discrete diffusion methods in terms of training efficiency and generation quality, and beats autoregressive models for the first time at this scale. We provide the code along with a blog post on the project page : https://oussamazekri.fr/gdds{https://oussamazekri.fr/gdds}.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.22057", "title": "SpatialBoost: Enhancing Visual Representation through Language-Guided Reasoning", "authors": [ "Byungwoo Jeon", "Dongyoung Kim", "Huiwon Jang", "Insoo Kim", "Jinwoo Shin" ], "github": "", "project_page": "", "abstract": "Despite the remarkable success of large-scale pre-trained image representation models (i.e., vision encoders) across various vision tasks, they are predominantly trained on 2D image data and therefore often fail to capture 3D spatial relationships between objects and backgrounds in the real world, constraining their effectiveness in many downstream applications. To address this, we propose SpatialBoost, a scalable framework that enhances the spatial awareness of existing pre-trained vision encoders by injecting 3D spatial knowledge expressed in linguistic descriptions. The core idea involves converting dense 3D spatial information from 2D images into linguistic expressions, which is then used to inject such spatial knowledge into vision encoders through a Large Language Model (LLM). To this end, we adopt a multi-turn Chain-of-Thought (CoT) reasoning process that progressively incorporates dense spatial knowledge and builds hierarchical spatial understanding. To validate effectiveness, we adapt SpatialBoost to state-of-the-art vision encoders such as DINOv3, and evaluate its performance gains on a wide range of benchmarks requiring both 3D perception and general vision abilities. For instance, SpatialBoost improves DINOv3 performance from 55.9 to 59.7 mIoU on ADE20K, achieving state-of-the-art performance with 3.8% gain over the pre-trained DINOv3.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21341", "title": "RoboAlign: Learning Test-Time Reasoning for Language-Action Alignment in Vision-Language-Action Models", "authors": [ "Dongyoung Kim", "Sumin Park", "Woomin Song", "Seungku Kim", "Taeyoung Kim", "Huiwon Jang", "Jinwoo Shin", "Jaehyung Kim", "Younggyo Seo" ], "github": "", "project_page": "", "abstract": "Improving embodied reasoning in multimodal-large-language models (MLLMs) is essential for building vision-language-action models (VLAs) on top of them to readily translate multimodal understanding into low-level actions. Accordingly, recent work has explored enhancing embodied reasoning in MLLMs through supervision of vision-question-answering type. However, these approaches have been reported to result in unstable VLA performance, often yielding only marginal or even negative gains. In this paper, we propose a more systematic MLLM training framework RoboAlign that reliably improves VLA performance. Our key idea is to sample action tokens via zero-shot natural language reasoning and refines this reasoning using reinforcement learning (RL) to improve action accuracy. As a result, RoboAlign bridges the modality gap between language and low-level actions in MLLMs, and facilitate knowledge transfer from MLLM to VLA. To validate the effectiveness of RoboAlign, we train VLAs by adding a diffusion-based action head on top of an MLLM backbone and evaluate them on major robotics benchmarks. Remarkably, by performing RL-based alignment after SFT using less than 1\\% of the data, RoboAlign achieves performance improvements of 17.5\\%, 18.9\\%, and 106.6\\% over SFT baselines on LIBERO, CALVIN, and real-world environments, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21972", "title": "Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe", "authors": [ "Xixi Wu", "Qianguo Sun", "Ruiyang Zhang", "Chao Song", "Junlong Wu", "Yiyan Qi", "Hong Cheng" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning (RL) is essential for evolving Large Language Models (LLMs) into autonomous agents capable of long-horizon planning, yet a practical recipe for scaling RL in complex, multi-turn environments remains elusive. This paper presents a systematic empirical study using TravelPlanner, a challenging testbed requiring tool orchestration to satisfy multifaceted constraints. We decompose the agentic RL design space along 5 axes: reward shaping, model scaling, data composition, algorithm selection, and environmental stability. Our controlled experiments yield 7 key takeaways, e.g., (1) reward and algorithm choices are scale-dependent as smaller models benefit from staged rewards and enhanced exploration, whereas larger models converge efficiently with simpler dense rewards, (2) ~ 1K training samples with a balanced difficulty mixture mark a sweet spot for both in-domain and out-of-domain performance, and (3) environmental stability is critical to prevent policy degradation. Based on our distilled recipe, our RL-trained models achieve state-of-the-art performance on TravelPlanner, significantly outperforming leading LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21786", "title": "The Universal Normal Embedding", "authors": [ "Chen Tasker", "Roy Betser", "Eyal Gofer", "Meir Yossef Levi", "Guy Gilboa" ], "github": "", "project_page": "", "abstract": "Generative models and vision encoders have largely advanced on separate tracks, optimized for different goals and grounded in different mathematical principles. Yet, they share a fundamental property: latent space Gaussianity. Generative models map Gaussian noise to images, while encoders map images to semantic embeddings whose coordinates empirically behave as Gaussian. We hypothesize that both are views of a shared latent source, the Universal Normal Embedding (UNE): an approximately Gaussian latent space from which encoder embeddings and DDIM-inverted noise arise as noisy linear projections. To test our hypothesis, we introduce NoiseZoo, a dataset of per-image latents comprising DDIM-inverted diffusion noise and matching encoder representations (CLIP, DINO). On CelebA, linear probes in both spaces yield strong, aligned attribute predictions, indicating that generative noise encodes meaningful semantics along linear directions. These directions further enable faithful, controllable edits (e.g., smile, gender, age) without architectural changes, where simple orthogonalization mitigates spurious entanglements. Taken together, our results provide empirical support for the UNE hypothesis and reveal a shared Gaussian-like latent geometry that concretely links encoding and generation. Code and data are available https://rbetser.github.io/UNE/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21304", "title": "F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting", "authors": [ "Injae Kim", "Chaehyeon Kim", "Minseong Bae", "Minseok Joo", "Hyunwoo J. Kim" ], "github": "", "project_page": "", "abstract": "Feed-forward 3D Gaussian Splatting methods enable single-pass reconstruction and real-time rendering. However, they typically adopt rigid pixel-to-Gaussian or voxel-to-Gaussian pipelines that uniformly allocate Gaussians, leading to redundant Gaussians across views. Moreover, they lack an effective mechanism to control the total number of Gaussians while maintaining reconstruction fidelity. To address these limitations, we present F4Splat, which performs Feed-Forward predictive densification for Feed-Forward 3D Gaussian Splatting, introducing a densification-score-guided allocation strategy that adaptively distributes Gaussians according to spatial complexity and multi-view overlap. Our model predicts per-region densification scores to estimate the required Gaussian density and allows explicit control over the final Gaussian budget without retraining. This spatially adaptive allocation reduces redundancy in simple regions and minimizes duplicate Gaussians across overlapping views, producing compact yet high-quality 3D representations. Extensive experiments demonstrate that our model achieves superior novel-view synthesis performance compared to prior uncalibrated feed-forward methods, while using significantly fewer Gaussians.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.20309", "title": "BubbleRAG: Evidence-Driven Retrieval-Augmented Generation for Black-Box Knowledge Graphs", "authors": [ "Duyi Pan", "Tianao Lou", "Xin Li", "Haoze Song", "Yiwen Wu", "Mengyi Deng", "Mingyu Yang", "Wei Wang" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) exhibit hallucinations in knowledge-intensive tasks. Graph-based retrieval augmented generation (RAG) has emerged as a promising solution, yet existing approaches suffer from fundamental recall and precision limitations when operating over black-box knowledge graphs -- graphs whose schema and structure are unknown in advance. We identify three core challenges that cause recall loss (semantic instantiation uncertainty and structural path uncertainty) and precision loss (evidential comparison uncertainty). To address these challenges, we formalize the retrieval task as the Optimal Informative Subgraph Retrieval (OISR) problem -- a variant of Group Steiner Tree -- and prove it to be NP-hard and APX-hard. We propose BubbleRAG, a training-free pipeline that systematically optimizes for both recall and precision through semantic anchor grouping, heuristic bubble expansion to discover candidate evidence graphs (CEGs), composite ranking, and reasoning-aware expansion. Experiments on multi-hop QA benchmarks demonstrate that BubbleRAG achieves state-of-the-art results, outperforming strong baselines in both F1 and accuracy while remaining plug-and-play.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.20686", "title": "SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection", "authors": [ "Kyudan Jung", "Jihwan Kim", "Minwoo Lee", "Soyoon Kim", "Jeonghoon Kim", "Jaegul Choo", "Cheonbok Park" ], "github": "", "project_page": "", "abstract": "Recent advancements in text-to-speech technologies enable generating high-fidelity synthetic speech nearly indistinguishable from real human voices. While recent studies show the efficacy of self-supervised learning-based speech encoders for deepfake detection, these models struggle to generalize across unseen speakers. Our quantitative analysis suggests these encoder representations are substantially influenced by speaker information, causing detectors to exploit speaker-specific correlations rather than artifact-related cues. We call this phenomenon speaker entanglement. To mitigate this reliance, we introduce SNAP, a speaker-nulling framework. We estimate a speaker subspace and apply orthogonal projection to suppress speaker-dependent components, isolating synthesis artifacts within the residual features. By reducing speaker entanglement, SNAP encourages detectors to focus on artifact-related patterns, leading to state-of-the-art performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.20667", "title": "REVERE: Reflective Evolving Research Engineer for Scientific Workflows", "authors": [ "Balaji Dinesh Gangireddi", "Aniketh Garikaparthi", "Manasi Patwardhan", "Arman Cohan" ], "github": "https://github.com/ace-agent/ace", "project_page": "", "abstract": "Existing prompt-optimization techniques rely on local signals to update behavior, often neglecting broader and recurring patterns across tasks, leading to poor generalization; they further rely on full-prompt rewrites or unstructured merges, resulting in knowledge loss. These limitations are magnified in research-coding workflows, which involve heterogeneous repositories, underspecified environments, and weak feedback, where reproducing results from public codebases is an established evaluation regime. We introduce Reflective Evolving Research Engineer (REVERE), a framework that continuously learns from Global Training Context, recognizes recurring failure modes in cross-repository execution trajectories, distills them into reusable heuristics, and performs targeted edits across three configurable fields: the system prompt, a task-prompt template, and a cumulative cheatsheet. REVERE, via this reflective optimization framework, improves performance over prior state-of-the-art expert-crafted instructions on research coding tasks by 4.50% on SUPER, 3.51% on ResearchCodeBench, and 4.89% on ScienceAgentBench across their respective metrics. These results demonstrate that agents equipped with mechanisms for continual learning and global memory consolidation can meaningfully evolve their capabilities over time.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ace-agent/ace", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.19028", "title": "SEM: Sparse Embedding Modulation for Post-Hoc Debiasing of Vision-Language Models", "authors": [ "Quentin Guimard", "Federico Bartsch", "Simone Caldarella", "Rahaf Aljundi", "Elisa Ricci", "Massimiliano Mancini" ], "github": "", "project_page": "", "abstract": "Models that bridge vision and language, such as CLIP, are key components of multimodal AI, yet their large-scale, uncurated training data introduce severe social and spurious biases. Existing post-hoc debiasing methods often operate directly in the dense CLIP embedding space, where bias and task-relevant information are highly entangled. This entanglement limits their ability to remove bias without degrading semantic fidelity. In this work, we propose Sparse Embedding Modulation (SEM), a post-hoc, zero-shot debiasing framework that operates in a Sparse Autoencoder (SAE) latent space. By decomposing CLIP text embeddings into disentangled features, SEM identifies and modulates bias-relevant neurons while preserving query-relevant ones. This enables more precise, non-linear interventions. Across four benchmark datasets and two CLIP backbones, SEM achieves substantial fairness gains in retrieval and zero-shot classification. Our results demonstrate that sparse latent representations provide an effective foundation for post-hoc debiasing of vision-language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21884", "title": "Not All Layers Are Created Equal: Adaptive LoRA Ranks for Personalized Image Generation", "authors": [ "Donald Shenaj", "Federico Errica", "Antonio Carta" ], "github": "https://github.com/donaldssh/NotAllLayersAreCreatedEqual", "project_page": "", "abstract": "Low Rank Adaptation (LoRA) is the de facto fine-tuning strategy to generate personalized images from pre-trained diffusion models. Choosing a good rank is extremely critical, since it trades off performance and memory consumption, but today the decision is often left to the community's consensus, regardless of the personalized subject's complexity. The reason is evident: the cost of selecting a good rank for each LoRA component is combinatorial, so we opt for practical shortcuts such as fixing the same rank for all components. In this paper, we take a first step to overcome this challenge. Inspired by variational methods that learn an adaptive width of neural networks, we let the ranks of each layer freely adapt during fine-tuning on a subject. We achieve it by imposing an ordering of importance on the rank's positions, effectively encouraging the creation of higher ranks when strictly needed. Qualitatively and quantitatively, our approach, LoRA^2, achieves a competitive trade-off between DINO, CLIP-I, and CLIP-T across 29 subjects while requiring much less memory and lower rank than high rank LoRA versions. Code: https://github.com/donaldssh/NotAllLayersAreCreatedEqual.", "github_manual": "", "github_regex": "https://github.com/donaldssh/NotAllLayersAreCreatedEqual", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.21315", "title": "FluidWorld: Reaction-Diffusion Dynamics as a Predictive Substrate for World Models", "authors": [ "Fabien Polly" ], "github": "", "project_page": "", "abstract": "World models learn to predict future states of an environment, enabling planning and mental simulation. Current approaches default to Transformer-based predictors operating in learned latent spaces. This comes at a cost: O(N^2) computation and no explicit spatial inductive bias. This paper asks a foundational question: is self-attention necessary for predictive world modeling, or can alternative computational substrates achieve comparable or superior results? I introduce FluidWorld, a proof-of-concept world model whose predictive dynamics are governed by partial differential equations (PDEs) of reaction-diffusion type. Instead of using a separate neural network predictor, the PDE integration itself produces the future state prediction. In a strictly parameter-matched three-way ablation on unconditional UCF-101 video prediction (64x64, ~800K parameters, identical encoder, decoder, losses, and data), FluidWorld is compared against both a Transformer baseline (self-attention) and a ConvLSTM baseline (convolutional recurrence). While all three models converge to comparable single-step prediction loss, FluidWorld achieves 2x lower reconstruction error, produces representations with 10-15% higher spatial structure preservation and 18-25% more effective dimensionality, and critically maintains coherent multi-step rollouts where both baselines degrade rapidly. All experiments were conducted on a single consumer-grade PC (Intel Core i5, NVIDIA RTX 4070 Ti), without any large-scale compute. These results establish that PDE-based dynamics, which natively provide O(N) spatial complexity, adaptive computation, and global spatial coherence through diffusion, are a viable and parameter-efficient alternative to both attention and convolutional recurrence for world modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.20278", "title": "OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis", "authors": [ "Zhuofeng Li", "Dongfu Jiang", "Xueguang Ma", "Haoxiang Zhang", "Ping Nie", "Yuyu Zhang", "Kai Zou", "Jianwen Xie", "Yu Zhang", "Wenhu Chen" ], "github": "https://github.com/TIGER-AI-Lab/OpenResearcher", "project_page": "", "abstract": "Training deep research agents requires long-horizon trajectories that interleave search, evidence aggregation, and multi-step reasoning. However, existing data collection pipelines typically rely on proprietary web APIs, making large-scale trajectory synthesis costly, unstable, and difficult to reproduce. We present OpenResearcher, a reproducible pipeline that decouples one-time corpus bootstrapping from multi-turn trajectory synthesis and executes the search-and-browse loop entirely offline using three explicit browser primitives: search, open, and find, over a 15M-document corpus. Using GPT-OSS-120B as the teacher model, we synthesize over 97K trajectories, including a substantial long-horizon tail with 100+ tool calls. Supervised fine-tuning a 30B-A3B backbone on these trajectories achieves 54.8\\% accuracy on BrowseComp-Plus, a +34.0 point improvement over the base model, while remaining competitive on BrowseComp, GAIA, and xbench-DeepSearch. Because the environment is offline and fully instrumented, it also enables controlled analysis, where our study reveals practical insights into deep research pipeline design, including data filtering strategies, agent configuration choices, and how retrieval success relates to final answer accuracy. We release the pipeline, synthesized trajectories, model checkpoints, and the offline search environment at https://github.com/TIGER-AI-Lab/OpenResearcher.", "github_manual": "", "github_regex": "https://github.com/TIGER-AI-Lab/OpenResearcher", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.22275", "title": "Repurposing Geometric Foundation Models for Multi-view Diffusion", "authors": [ "Wooseok Jang", "Seonghu Jeon", "Jisang Han", "Jinhyeok Choi", "Minkyung Kwon", "Seungryong Kim", "Saining Xie", "Sainan Liu" ], "github": "", "project_page": "", "abstract": "While recent advances in generative latent spaces have driven substantial progress in single-image generation, the optimal latent space for novel view synthesis (NVS) remains largely unexplored. In particular, NVS requires geometrically consistent generation across viewpoints, but existing approaches typically operate in a view-independent VAE latent space. In this paper, we propose Geometric Latent Diffusion (GLD), a framework that repurposes the geometrically consistent feature space of geometric foundation models as the latent space for multi-view diffusion. We show that these features not only support high-fidelity RGB reconstruction but also encode strong cross-view geometric correspondences, providing a well-suited latent space for NVS. Our experiments demonstrate that GLD outperforms both VAE and RAE on 2D image quality and 3D consistency metrics, while accelerating training by more than 4.4x compared to the VAE latent space. Notably, GLD remains competitive with state-of-the-art methods that leverage large-scale text-to-image pretraining, despite training its diffusion model from scratch without such generative pretraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.16932", "title": "Look Where It Matters: High-Resolution Crops Retrieval for Efficient VLMs", "authors": [ "Nimrod Shabtay", "Moshe Kimhi", "Artem Spector", "Sivan Haray", "Ehud Rivlin", "Chaim Baskin", "Raja Giryes", "Eli Schwartz" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) typically process images at a native high-resolution, forcing a trade-off between accuracy and computational efficiency: high-resolution inputs capture fine details but incur significant computational costs, while low-resolution inputs advocate for efficiency, they potentially miss critical visual information, like small text. We present AwaRes, a spatial-on-demand framework that resolves this accuracy-efficiency trade-off by operating on a low-resolution global view and using tool-calling to retrieve only high-resolution segments needed for a given query. We construct supervised data automatically: a judge compares low- vs.\\ high-resolution answers to label whether cropping is needed, and an oracle grounding model localizes the evidence for the correct answer, which we map to a discrete crop set to form multi-turn tool-use trajectories. We train our framework with cold-start SFT followed by multi-turn GRPO with a composite reward that combines semantic answer correctness with explicit crop-cost penalties. Project page: https://nimrodshabtay.github.io/AwaRes", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.15136", "title": "Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies", "authors": [ "Mumuksh Tayal", "Manan Tayal", "Ravi Prakash" ], "github": "", "project_page": "", "abstract": "Offline safe reinforcement learning (RL) seeks reward-maximizing policies from static datasets under strict safety constraints. Existing methods often rely on soft expected-cost objectives or iterative generative inference, which can be insufficient for safety-critical real-time control. We propose Safe Flow Q-Learning (SafeFQL), which extends FQL to safe offline RL by combining a Hamilton--Jacobi reachability-inspired safety value function with an efficient one-step flow policy. SafeFQL learns the safety value via a self-consistency Bellman recursion, trains a flow policy by behavioral cloning, and distills it into a one-step actor for reward-maximizing safe action selection without rejection sampling at deployment. To account for finite-data approximation error in the learned safety boundary, we add a conformal prediction calibration step that adjusts the safety threshold and provides finite-sample probabilistic safety coverage. Empirically, SafeFQL trades modestly higher offline training cost for substantially lower inference latency than diffusion-style safe generative baselines, which is advantageous for real-time safety-critical deployment. Across boat navigation, and Safety Gymnasium MuJoCo tasks, SafeFQL matches or exceeds prior offline safe RL performance while substantially reducing constraint violations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.22017", "title": "AdditiveLLM2: A Multi-modal Large Language Model for Additive Manufacturing", "authors": [ "Peter Pak", "Amir Barati Farimani" ], "github": "", "project_page": "", "abstract": "This work presents AdditiveLLM2 a multi-modal, domain adapted large language model built upon the instruction tuned variant of the Gemma 3 model using a relatively small dataset of around 50 million tokens. The dataset (AdditiveLLM2-OA) consists of open-access additive manufacturing journal articles with data extracted for the domain adaptive pretraining and visual instruction tuning processes. Various stages of the developed model are evaluated with the Additive-Manufacturing-Benchmark which consists of additive manufacturing domain specific tasks compiled published resources. AdditiveLLM2 exhibits proficiency in both language and vision based tasks, achieving accuracies upwards of 90% in general additive manufacturing knowledge. This domain adaptive pretraining and instruction tuning strategy outline an accessible specialization method for large language models to a domain such as additive manufacturing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.18911", "title": "Progressive Training for Explainable Citation-Grounded Dialogue: Reducing Hallucination to Zero in English-Hindi LLMs", "authors": [ "Vedant Pandya" ], "github": "", "project_page": "", "abstract": "Knowledge-grounded dialogue systems aim to generate informative, contextually relevant responses by conditioning on external knowledge sources. However, most existing approaches focus exclusively on English, lack explicit citation mechanisms for verifying factual claims, and offer limited transparency into model decision-making. We present XKD-Dial, a progressive four-stage training pipeline for explainable, knowledge-grounded dialogue generation in a bilingual (English-Hindi) setting, comprising: (1) multilingual adaptation, (2) English dialogue SFT with citation grounding, (3) bilingual dialogue SFT, and (4) GRPO alignment with citation-aware rewards. We evaluate six models spanning encoder-decoder (250M-3B) and decoder-only (1B-7B) architectures at every pipeline stage. Our key contributions are: (i) three post-hoc explainability analyses - cross-attention alignment, Integrated Gradients attribution, and occlusion-based causal grounding - applied systematically across the training trajectory to reveal how citation behaviour is learned, not only whether it is learned; (ii) citation-grounded SFT reduces hallucination to 0.0% for encoder-decoder models from Stage 2 onward; (iii) the progressive pipeline prevents catastrophic forgetting while improving Hindi capabilities; (iv) smaller models match larger models on English after SFT; and (v) GRPO provides marginal improvement over well-designed SFT for structured citation tasks. We evaluate across six automatic metrics (BLEU, ROUGE, BERTScore, FactScore, Citation-F1, and hallucination rate).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.14999", "title": "Aperiodic Structures Never Collapse: Fibonacci Hierarchies for Lossless Compression", "authors": [ "Roberto Tacconelli" ], "github": "", "project_page": "", "abstract": "We study whether an aperiodic hierarchy can provide a structural advantage for lossless compression over periodic alternatives. We show that Fibonacci quasicrystal tilings avoid the finite-depth collapse that affects periodic hierarchies: usable n-gram lookup positions remain non-zero at every level, while periodic tilings collapse after O(log p) levels for period p. This yields an aperiodic hierarchy advantage: dictionary reuse remains available across all scales instead of vanishing beyond a finite depth. Our analysis gives four main consequences. First, the Golden Compensation property shows that the exponential decay in the number of positions is exactly balanced by the exponential growth in phrase length, so potential coverage remains scale-invariant with asymptotic value Wvarphi/5. Second, using the Sturmian complexity law p(n)=n+1, we show that Fibonacci/Sturmian hierarchies maximize codebook coverage efficiency among binary aperiodic tilings. Third, under long-range dependence, the resulting hierarchy achieves lower coding entropy than comparable periodic hierarchies. Fourth, redundancy decays super-exponentially with depth, whereas periodic systems remain locked at the depth where collapse occurs. We validate these results with Quasicryth, a lossless text compressor built on a ten-level Fibonacci hierarchy with phrase lengths {2,3,5,8,13,21,34,55,89,144}. In controlled A/B experiments with identical codebooks, the aperiodic advantage over a Period-5 baseline grows from 36{,}243 B at 3 MB to 11{,}089{,}469 B at 1 GB, explained by the activation of deeper hierarchy levels. On enwik9, Quasicryth achieves 225{,}918{,}349 B (22.59%), with 20{,}735{,}733 B saved by the Fibonacci tiling relative to no tiling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-24", "arxiv_id": "2603.19415", "title": "Scalable Prompt Routing via Fine-Grained Latent Task Discovery", "authors": [ "Yunyi Zhang", "Soji Adeshina", "Patrick Guan", "Ashwin Ganesh", "Zhen Han", "Vassilis N. Ioannidis", "Huzefa Rangwala", "George Karypis" ], "github": "https://github.com/ulab-uiuc/LLMRouter", "project_page": "", "abstract": "Prompt routing dynamically selects the most appropriate large language model from a pool of candidates for each query, optimizing performance while managing costs. As model pools scale to include dozens of frontier models with narrow performance gaps, existing approaches face significant challenges: manually defined task taxonomies cannot capture fine-grained capability distinctions, while monolithic routers struggle to differentiate subtle differences across diverse tasks. We propose a two-stage routing architecture that addresses these limitations through automated fine-grained task discovery and task-aware quality estimation. Our first stage employs graph-based clustering to discover latent task types and trains a classifier to assign prompts to discovered tasks. The second stage uses a mixture-of-experts architecture with task-specific prediction heads for specialized quality estimates. At inference, we aggregate predictions from both stages to balance task-level stability with prompt-specific adaptability. Evaluated on 10 benchmarks with 11 frontier models, our method consistently outperforms existing baselines and surpasses the strongest individual model while incurring less than half its cost.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ulab-uiuc/LLMRouter", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22281", "title": "ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model", "authors": [ "Haichao Zhang", "Yijiang Li", "Shwai He", "Tushar Nagarajan", "Mingfei Chen", "Jianglin Lu", "Ang Li", "Yun Fu" ], "github": "", "project_page": "", "abstract": "Recent progress in latent world models (e.g., V-JEPA2) has shown promising capability in forecasting future world states from video observations. Nevertheless, dense prediction from a short observation window limits temporal context and can bias predictors toward local, low-level extrapolation, making it difficult to capture long-horizon semantics and reducing downstream utility. Vision--language models (VLMs), in contrast, provide strong semantic grounding and general knowledge by reasoning over uniformly sampled frames, but they are not ideal as standalone dense predictors due to compute-driven sparse sampling, a language-output bottleneck that compresses fine-grained interaction states into text-oriented representations, and a data-regime mismatch when adapting to small action-conditioned datasets. We propose a VLM-guided JEPA-style latent world modeling framework that combines dense-frame dynamics modeling with long-horizon semantic guidance via a dual-temporal pathway: a dense JEPA branch for fine-grained motion and interaction cues, and a uniformly sampled VLM thinker branch with a larger temporal stride for knowledge-rich guidance. To transfer the VLM's progressive reasoning signals effectively, we introduce a hierarchical pyramid representation extraction module that aggregates multi-layer VLM representations into guidance features compatible with latent prediction. Experiments on hand-manipulation trajectory prediction show that our method outperforms both a strong VLM-only baseline and a JEPA-predictor baseline, and yields more robust long-horizon rollout behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22327", "title": "AgentSLR: Automating Systematic Literature Reviews in Epidemiology with Agentic AI", "authors": [ "Shreyansh Padarha", "Ryan Othniel Kearns", "Tristan Naidoo", "Lingyi Yang", "\u0141ukasz Borchmann", "Piotr B\u0141aszczyk", "Christian Morgenstern", "Ruth McCabe", "Sangeeta Bhatia", "Philip H. Torr", "Jakob Foerster", "Scott A. Hale", "Thomas Rawson", "Anne Cori", "Elizaveta Semenova", "Adam Mahdi" ], "github": "", "project_page": "", "abstract": "Systematic literature reviews are essential for synthesizing scientific evidence but are costly, difficult to scale and time-intensive, creating bottlenecks for evidence-based policy. We study whether large language models can automate the complete systematic review workflow, from article retrieval, article screening, data extraction to report synthesis. Applied to epidemiological reviews of nine WHO-designated priority pathogens and validated against expert-curated ground truth, our open-source agentic pipeline (AgentSLR) achieves performance comparable to human researchers while reducing review time from approximately 7 weeks to 20 hours (a 58x speed-up). Our comparison of five frontier models reveals that performance on SLR is driven less by model size or inference cost than by each model's distinctive capabilities. Through human-in-the-loop validation, we identify key failure modes. Our results demonstrate that agentic AI can substantially accelerate scientific evidence synthesis in specialised domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.20422", "title": "PEARL: Personalized Streaming Video Understanding Model", "authors": [ "Yuanhong Zheng", "Ruichuan An", "Xiaopeng Lin", "Yuxing Liu", "Sihan Yang", "Huanyu Zhang", "Haodong Li", "Qintong Zhang", "Renrui Zhang", "Guopeng Li", "Yifan Zhang", "Yuheng Li", "Wentao Zhang" ], "github": "https://github.com/Yuanhong-Zheng/PEARL", "project_page": "", "abstract": "Human cognition of new concepts is inherently a streaming process: we continuously recognize new objects or identities and update our memories over time. However, current multimodal personalization methods are largely limited to static images or offline videos. This disconnects continuous visual input from instant real-world feedback, limiting their ability to provide the real-time, interactive personalized responses essential for future AI assistants. To bridge this gap, we first propose and formally define the novel task of Personalized Streaming Video Understanding (PSVU). To facilitate research in this new direction, we introduce PEARL-Bench, the first comprehensive benchmark designed specifically to evaluate this challenging setting. It evaluates a model's ability to respond to personalized concepts at exact timestamps under two modes: (1) Frame-level, focusing on a specific person or object in discrete frames, and (2) a novel Video-level, focusing on personalized actions unfolding across continuous frames. PEARL-Bench comprises 132 unique videos and 2,173 fine-grained annotations with precise timestamps. Concept diversity and annotation quality are strictly ensured through a combined pipeline of automated generation and human verification. To tackle this challenging new setting, we further propose PEARL, a plug-and-play, training-free strategy that serves as a strong baseline. Extensive evaluations across 8 offline and online models demonstrate that PEARL achieves state-of-the-art performance. Notably, it brings consistent PSVU improvements when applied to 3 distinct architectures, proving to be a highly effective and robust strategy. We hope this work advances vision-language model (VLM) personalization and inspires further research into streaming personalized AI assistants. Code is available at https://github.com/Yuanhong-Zheng/PEARL.", "github_manual": "", "github_regex": "https://github.com/Yuanhong-Zheng/PEARL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22386", "title": "From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents", "authors": [ "Ling Yue", "Kushal Raj Bhandari", "Ching-Yun Ko", "Dhaval Patel", "Shuxin Lin", "Nianjun Zhou", "Jianxi Gao", "Pin-Yu Chen", "Shaowu Pan" ], "github": "", "project_page": "", "abstract": "Large language model (LLM)-based systems are becoming increasingly popular for solving tasks by constructing executable workflows that interleave LLM calls, information retrieval, tool use, code execution, memory updates, and verification. This survey reviews recent methods for designing and optimizing such workflows, which we treat as agentic computation graphs (ACGs). We organize the literature based on when workflow structure is determined, where structure refers to which components or agents are present, how they depend on each other, and how information flows between them. This lens distinguishes static methods, which fix a reusable workflow scaffold before deployment, from dynamic methods, which select, generate, or revise the workflow for a particular run before or during execution. We further organize prior work along three dimensions: when structure is determined, what part of the workflow is optimized, and which evaluation signals guide optimization (e.g., task metrics, verifier signals, preferences, or trace-derived feedback). We also distinguish reusable workflow templates, run-specific realized graphs, and execution traces, separating reusable design choices from the structures actually deployed in a given run and from realized runtime behavior. Finally, we outline a structure-aware evaluation perspective that complements downstream task metrics with graph-level properties, execution cost, robustness, and structural variation across inputs. Our goal is to provide a clear vocabulary, a unified framework for positioning new methods, a more comparable view of existing body of literature, and a more reproducible evaluation standard for future work in workflow optimizations for LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.23497", "title": "WildWorld: A Large-Scale Dataset for Dynamic World Modeling with Actions and Explicit State toward Generative ARPG", "authors": [ "Zhen Li", "Zian Meng", "Shuwei Shi", "Wenshuo Peng", "Yuwei Wu", "Bo Zheng", "Chuanhao Li", "Kaipeng Zhang" ], "github": "", "project_page": "", "abstract": "Dynamical systems theory and reinforcement learning view world evolution as latent-state dynamics driven by actions, with visual observations providing partial information about the state. Recent video world models attempt to learn this action-conditioned dynamics from data. However, existing datasets rarely match the requirement: they typically lack diverse and semantically meaningful action spaces, and actions are directly tied to visual observations rather than mediated by underlying states. As a result, actions are often entangled with pixel-level changes, making it difficult for models to learn structured world dynamics and maintain consistent evolution over long horizons. In this paper, we propose WildWorld, a large-scale action-conditioned world modeling dataset with explicit state annotations, automatically collected from a photorealistic AAA action role-playing game (Monster Hunter: Wilds). WildWorld contains over 108 million frames and features more than 450 actions, including movement, attacks, and skill casting, together with synchronized per-frame annotations of character skeletons, world states, camera poses, and depth maps. We further derive WildBench to evaluate models through Action Following and State Alignment. Extensive experiments reveal persistent challenges in modeling semantically rich actions and maintaining long-horizon state consistency, highlighting the need for state-aware video generation. The project page is https://shandaai.github.io/wildworld-project/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22847", "title": "Rethinking Token-Level Policy Optimization for Multimodal Chain-of-Thought", "authors": [ "Yunheng Li", "Hangyi Kuang", "Hengrui Zhang", "Jiangxia Cao", "Zhaojie Liu", "Qibin Hou", "Ming-Ming Cheng" ], "github": "https://github.com/xzxxntxdy/PEPO", "project_page": "", "abstract": "Multimodal Chain-of-Thought (CoT) reasoning requires large vision-language models to construct reasoning trajectories that interleave perceptual grounding with multi-step inference. However, existing Reinforcement Learning with Verifiable Rewards (RLVR) methods typically optimize reasoning at a coarse granularity, treating CoT uniformly without distinguishing their varying degrees of visual grounding. In this work, we conduct a token-level analysis of multimodal reasoning trajectories and show that successful reasoning is characterized by structured token dynamics reflecting both perceptual grounding and exploratory inference. Building upon this analysis, we propose Perception-Exploration Policy Optimization (PEPO), which derives a perception prior from hidden state similarity and integrates it with token entropy through a smooth gating mechanism to produce token-level advantages. PEPO integrates seamlessly with existing RLVR frameworks such as GRPO and DAPO, requiring neither additional supervision nor auxiliary branches. Extensive experiments across diverse multimodal benchmarks demonstrate consistent and robust improvements over strong RL baselines, spanning geometry reasoning, visual grounding, visual puzzle solving, and few-shot classification, while maintaining stable training dynamics. Code: https://github.com/xzxxntxdy/PEPO", "github_manual": "", "github_regex": "https://github.com/xzxxntxdy/PEPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.23500", "title": "UniGRPO: Unified Policy Optimization for Reasoning-Driven Visual Generation", "authors": [ "Jie Liu", "Zilyu Ye", "Linxiao Yuan", "Shenhan Zhu", "Yu Gao", "Jie Wu", "Kunchang Li", "Xionghui Wang", "Xiaonan Nie", "Weilin Huang", "Wanli Ouyang" ], "github": "", "project_page": "", "abstract": "Unified models capable of interleaved generation have emerged as a promising paradigm, with the community increasingly converging on autoregressive modeling for text and flow matching for image generation. To advance this direction, we propose a unified reinforcement learning framework tailored for interleaved generation. We validate our approach on its fundamental unit: a single round of reasoning-driven image generation, where the model first expands the user prompt through reasoning, followed by image synthesis. Formulating this multimodal generation process as a Markov Decision Process with sparse terminal rewards, we introduce UniGRPO to jointly optimize text and image generation policies using GRPO. Adopting a minimalist methodology to avoid over-design, we leverage established training recipes for both modalities by seamlessly integrating standard GRPO for reasoning and FlowGRPO for visual synthesis. To ensure scalability to multi-round interleaved generation, we introduce two critical modifications to the original FlowGRPO: (1) eliminating classifier-free guidance to maintain linear, unbranched rollouts, which is essential for scaling to complex scenarios involving multi-turn interactions and multi-condition generation (e.g., editing); and (2) replacing the standard latent KL penalty with an MSE penalty directly on the velocity fields, providing a more robust and direct regularization signal to mitigate reward hacking effectively. Our experiments demonstrate that this unified training recipe significantly enhances image generation quality through reasoning, providing a robust and scalable baseline for the future post-training of fully interleaved models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22606", "title": "TrajLoom: Dense Future Trajectory Generation from Video", "authors": [ "Zewei Zhang", "Jia Jun Cheng Xian", "Kaiwen Liu", "Ming Liang", "Hang Chu", "Jun Chen", "Renjie Liao" ], "github": "", "project_page": "", "abstract": "Predicting future motion is crucial in video understanding and controllable video generation. Dense point trajectories are a compact, expressive motion representation, but modeling their future evolution from observed video remains challenging. We propose a framework that predicts future trajectories and visibility from past trajectories and video context. Our method has three components: (1) Grid-Anchor Offset Encoding, which reduces location-dependent bias by representing each point as an offset from its pixel-center anchor; (2) TrajLoom-VAE, which learns a compact spatiotemporal latent space for dense trajectories with masked reconstruction and a spatiotemporal consistency regularizer; and (3) TrajLoom-Flow, which generates future trajectories in latent space via flow matching, with boundary cues and on-policy K-step fine-tuning for stable sampling. We also introduce TrajLoomBench, a unified benchmark spanning real and synthetic videos with a standardized setup aligned with video-generation benchmarks. Compared with state-of-the-art methods, our approach extends the prediction horizon from 24 to 81 frames while improving motion realism and stability across datasets. The predicted trajectories directly support downstream video generation and editing. Code, model checkpoints, and datasets are available at https://trajloom.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.23483", "title": "SpecEyes: Accelerating Agentic Multimodal LLMs via Speculative Perception and Planning", "authors": [ "Haoyu Huang", "Jinfa Huang", "Zhongwei Wan", "Xiawu Zheng", "Rongrong Ji", "Jiebo Luo" ], "github": "", "project_page": "", "abstract": "Agentic multimodal large language models (MLLMs) (e.g., OpenAI o3 and Gemini Agentic Vision) achieve remarkable reasoning capabilities through iterative visual tool invocation. However, the cascaded perception, reasoning, and tool-calling loops introduce significant sequential overhead. This overhead, termed agentic depth, incurs prohibitive latency and seriously limits system-level concurrency. To this end, we propose SpecEyes, an agentic-level speculative acceleration framework that breaks this sequential bottleneck. Our key insight is that a lightweight, tool-free MLLM can serve as a speculative planner to predict the execution trajectory, enabling early termination of expensive tool chains without sacrificing accuracy. To regulate this speculative planning, we introduce a cognitive gating mechanism based on answer separability, which quantifies the model's confidence for self-verification without requiring oracle labels. Furthermore, we design a heterogeneous parallel funnel that exploits the stateless concurrency of the small model to mask the stateful serial execution of the large model, maximizing system throughput. Extensive experiments on V* Bench, HR-Bench, and POPE demonstrate that SpecEyes achieves 1.1-3.35x speedup over the agentic baseline while preserving or even improving accuracy (up to +6.7%), thereby boosting serving throughput under concurrent workloads.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.12254", "title": "Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing", "authors": [ "Baifeng Shi", "Stephanie Fu", "Long Lian", "Hanrong Ye", "David Eigen", "Aaron Reite", "Boyi Li", "Jan Kautz", "Song Han", "David M. Chan", "Pavlo Molchanov", "Trevor Darrell", "Hongxu Yin" ], "github": "", "project_page": "", "abstract": "Multi-modal large language models (MLLMs) have advanced general-purpose video understanding but struggle with long, high-resolution videos -- they process every pixel equally in their vision transformers (ViTs) or LLMs despite significant spatiotemporal redundancy. We introduce AutoGaze, a lightweight module that removes redundant patches before processed by a ViT or an MLLM. Trained with next-token prediction and reinforcement learning, AutoGaze autoregressively selects a minimal set of multi-scale patches that can reconstruct the video within a user-specified error threshold, eliminating redundancy while preserving information. Empirically, AutoGaze reduces visual tokens by 4x-100x and accelerates ViTs and MLLMs by up to 19x, enabling scaling MLLMs to 1K-frame 4K-resolution videos and achieving superior results on video benchmarks (e.g., 67.0% on VideoMME). Furthermore, we introduce HLVid: the first high-resolution, long-form video QA benchmark with 5-minute 4K-resolution videos, where an MLLM scaled with AutoGaze improves over the baseline by 10.1% and outperforms the previous best MLLM by 4.5%. Project page: https://autogaze.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.21937", "title": "MultiBind: A Benchmark for Attribute Misbinding in Multi-Subject Generation", "authors": [ "Wenqing Tian", "Hanyi Mao", "Zhaocheng Liu", "Lihua Zhang", "Qiang Liu", "Jian Wu", "Liang Wang" ], "github": "https://github.com/discus0434/aesthetic-predictor-v2-5", "project_page": "", "abstract": "Subject-driven image generation is increasingly expected to support fine-grained control over multiple entities within a single image. In multi-reference workflows, users may provide several subject images, a background reference, and long, entity-indexed prompts to control multiple people within one scene. In this setting, a key failure mode is cross-subject attribute misbinding: attributes are preserved, edited, or transferred to the wrong subject. Existing benchmarks and metrics largely emphasize holistic fidelity or per-subject self-similarity, making such failures hard to diagnose. We introduce MultiBind, a benchmark built from real multi-person photographs. Each instance provides slot-ordered subject crops with masks and bounding boxes, canonicalized subject references, an inpainted background reference, and a dense entity-indexed prompt derived from structured annotations. We also propose a dimension-wise confusion evaluation protocol that matches generated subjects to ground-truth slots and measures slot-to-slot similarity using specialists for face identity, appearance, pose, and expression. By subtracting the corresponding ground-truth similarity matrices, our method separates self-degradation from true cross-subject interference and exposes interpretable failure patterns such as drift, swap, dominance, and blending. Experiments on modern multi-reference generators show that MultiBind reveals binding failures that conventional reconstruction metrics miss.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/discus0434/aesthetic-predictor-v2-5", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.23462", "title": "RealMaster: Lifting Rendered Scenes into Photorealistic Video", "authors": [ "Dana Cohen-Bar", "Ido Sobol", "Raphael Bensadoun", "Shelly Sheynin", "Oran Gafni", "Or Patashnik", "Daniel Cohen-Or", "Amit Zohar" ], "github": "", "project_page": "", "abstract": "State-of-the-art video generation models produce remarkable photorealism, but they lack the precise control required to align generated content with specific scene requirements. Furthermore, without an underlying explicit geometry, these models cannot guarantee 3D consistency. Conversely, 3D engines offer granular control over every scene element and provide native 3D consistency by design, yet their output often remains trapped in the \"uncanny valley\". Bridging this sim-to-real gap requires both structural precision, where the output must exactly preserve the geometry and dynamics of the input, and global semantic transformation, where materials, lighting, and textures must be holistically transformed to achieve photorealism. We present RealMaster, a method that leverages video diffusion models to lift rendered video into photorealistic video while maintaining full alignment with the output of the 3D engine. To train this model, we generate a paired dataset via an anchor-based propagation strategy, where the first and last frames are enhanced for realism and propagated across the intermediate frames using geometric conditioning cues. We then train an IC-LoRA on these paired videos to distill the high-quality outputs of the pipeline into a model that generalizes beyond the pipeline's constraints, handling objects and characters that appear mid-sequence and enabling inference without requiring anchor frames. Evaluated on complex GTA-V sequences, RealMaster significantly outperforms existing video editing baselines, improving photorealism while preserving the geometry, dynamics, and identity specified by the original 3D control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.23376", "title": "ABot-PhysWorld: Interactive World Foundation Model for Robotic Manipulation with Physics Alignment", "authors": [ "Yuzhi Chen", "Ronghan Chen", "Dongjie Huo", "Yandan Yang", "Dekang Qi", "Haoyun Liu", "Tong Lin", "Shuang Zeng", "Junjin Xiao", "Xinyuan Chang", "Feng Xiong", "Xing Wei", "Zhiheng Ma", "Mu Xu" ], "github": "https://github.com/amap-cvlab/ABot-PhysWorld", "project_page": "", "abstract": "Video-based world models offer a powerful paradigm for embodied simulation and planning, yet state-of-the-art models often generate physically implausible manipulations - such as object penetration and anti-gravity motion - due to training on generic visual data and likelihood-based objectives that ignore physical laws. We present ABot-PhysWorld, a 14B Diffusion Transformer model that generates visually realistic, physically plausible, and action-controllable videos. Built on a curated dataset of three million manipulation clips with physics-aware annotation, it uses a novel DPO-based post-training framework with decoupled discriminators to suppress unphysical behaviors while preserving visual quality. A parallel context block enables precise spatial action injection for cross-embodiment control. To better evaluate generalization, we introduce EZSbench, the first training-independent embodied zero-shot benchmark combining real and synthetic unseen robot-task-scene combinations. It employs a decoupled protocol to separately assess physical realism and action alignment. ABot-PhysWorld achieves new state-of-the-art performance on PBench and EZSbench, surpassing Veo 3.1 and Sora v2 Pro in physical plausibility and trajectory consistency. We will release EZSbench to promote standardized evaluation in embodied video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/amap-cvlab/ABot-PhysWorld", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22529", "title": "Ego2Web: A Web Agent Benchmark Grounded in Egocentric Videos", "authors": [ "Shoubin Yu", "Lei Shu", "Antoine Yang", "Yao Fu", "Srinivas Sunkara", "Maria Wang", "Jindong Chen", "Mohit Bansal", "Boqing Gong" ], "github": "", "project_page": "", "abstract": "Multimodal AI agents are increasingly automating complex real-world workflows that involve online web execution. However, current web-agent benchmarks suffer from a critical limitation: they focus entirely on web-based interaction and perception, lacking grounding in the user's real-world physical surroundings. This limitation prevents evaluation in crucial scenarios, such as when an agent must use egocentric visual perception (e.g., via AR glasses) to recognize an object in the user's surroundings and then complete a related task online. To address this gap, we introduce Ego2Web, the first benchmark designed to bridge egocentric video perception and web agent execution. Ego2Web pairs real-world first-person video recordings with web tasks that require visual understanding, web task planning, and interaction in an online environment for successful completion. We utilize an automatic data-generation pipeline combined with human verification and refinement to curate well-constructed, high-quality video-task pairs across diverse web task types, including e-commerce, media retrieval, knowledge lookup, etc. To facilitate accurate and scalable evaluation for our benchmark, we also develop a novel LLM-as-a-Judge automatic evaluation method, Ego2WebJudge, which achieves approximately 84% agreement with human judgment, substantially higher than existing evaluation methods. Experiments with diverse SoTA agents on our Ego2Web show that their performance is weak, with substantial headroom across all task categories. We also conduct a comprehensive ablation study on task design, highlighting the necessity of accurate video understanding in the proposed task and the limitations of current agents. We hope Ego2Web can be a critical new resource for developing truly capable AI assistants that can seamlessly see, understand, and act across the physical and digital worlds.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22458", "title": "MinerU-Diffusion: Rethinking Document OCR as Inverse Rendering via Diffusion Decoding", "authors": [ "Hejun Dong", "Junbo Niu", "Bin Wang", "Weijun Zeng", "Wentao Zhang", "Conghui He" ], "github": "", "project_page": "", "abstract": "Optical character recognition (OCR) has evolved from line-level transcription to structured document parsing, requiring models to recover long-form sequences containing layout, tables, and formulas. Despite recent advances in vision-language models, most existing systems rely on autoregressive decoding, which introduces sequential latency and amplifies error propagation in long documents. In this work, we revisit document OCR from an inverse rendering perspective, arguing that left-to-right causal generation is an artifact of serialization rather than an intrinsic property of the task. Motivated by this insight, we propose MinerU-Diffusion, a unified diffusion-based framework that replaces autoregressive sequential decoding with parallel diffusion denoising under visual conditioning. MinerU-Diffusion employs a block-wise diffusion decoder and an uncertainty-driven curriculum learning strategy to enable stable training and efficient long-sequence inference. Extensive experiments demonstrate that MinerU-Diffusion consistently improves robustness while achieving up to 3.2x faster decoding compared to autoregressive baselines. Evaluations on the proposed Semantic Shuffle benchmark further confirm its reduced dependence on linguistic priors and stronger visual OCR capability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22446", "title": "Sparse but Critical: A Token-Level Analysis of Distributional Shifts in RLVR Fine-Tuning of LLMs", "authors": [ "Haoming Meng", "Kexin Huang", "Shaohang Wei", "Chiyu Ma", "Shuo Yang", "Xue Wang", "Guoyin Wang", "Bolin Ding", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has significantly improved reasoning in large language models (LLMs), yet the token-level mechanisms underlying these improvements remain unclear. We present a systematic empirical study of RLVR's distributional effects organized around three main analyses: (1) token-level characterization of distributional shifts between base and RL models, (2) the impact of token-level distributional shifts on sequence-level reasoning performance through cross-sampling interventions, and (3) fine-grained mechanics of these shifts at the token level. We find that RL fine-tuning induces highly sparse and targeted changes, with only a small fraction of token distributions exhibiting meaningful divergence between the base and RL policies. We further characterize the structure and evolution of these shifts through analyses of token entropy, positional concentration, and reallocation of probability mass. To assess the functional importance of these sparse changes, we conduct cross-sampling experiments that selectively swap token choices between the base and RL models with varying intervention budgets. We show that inserting only a small fraction of RL-sampled tokens into base generations progressively recovers RL performance gains, while injecting a similarly small number of base token choices into otherwise RL-generated sequences collapses performance to base levels, isolating a small set of token-level decisions directly responsible for RLVR's performance gains. Finally, we explore divergence-weighted variants of the advantage signal as a diagnostic intervention, finding that they can yield improvements over baselines. Together, our results shed light on the distributional changes induced by RLVR and provide a fine-grained, token-level lens for understanding RLVR fine-tuning as a targeted refinement process.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.23386", "title": "SIMART: Decomposing Monolithic Meshes into Sim-ready Articulated Assets via MLLM", "authors": [ "Chuanrui Zhang", "Minghan Qin", "Yuang Wang", "Baifeng Xie", "Hang Li", "Ziwei Wang" ], "github": "", "project_page": "", "abstract": "High-quality articulated 3D assets are indispensable for embodied AI and physical simulation, yet 3D generation still focuses on static meshes, leaving a gap in \"sim-ready\" interactive objects. Most recent articulated object creation methods rely on multi-stage pipelines that accumulate errors across decoupled modules. Alternatively, unified MLLMs offer a single-stage path to joint static asset understanding and sim-ready asset generation. However dense voxel-based 3D tokenization yields long 3D token sequences and high memory overhead, limiting scalability to complex articulated objects. To address this, we propose SIMART, a unified MLLM framework that jointly performs part-level decomposition and kinematic prediction. By introducing a Sparse 3D VQ-VAE, SIMART reduces token counts by 70% vs. dense voxel tokens, enabling high-fidelity multi-part assemblies. SIMART achieves state-of-the-art performance on PartNet-Mobility and in-the-wild AIGC datasets, and enables physics-based robotic simulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22042", "title": "Uncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models", "authors": [ "Hayeon Kim", "Ji Ha Jang", "Junghun James Kim", "Se Young Chun" ], "github": "https://github.com/jeeit17/UNCHA.git", "project_page": "", "abstract": "While Vision-Language Models (VLMs) have achieved remarkable performance, their Euclidean embeddings remain limited in capturing hierarchical relationships such as part-to-whole or parent-child structures, and often face challenges in multi-object compositional scenarios. Hyperbolic VLMs mitigate this issue by better preserving hierarchical structures and modeling part-whole relations (i.e., whole scene and its part images) through entailment. However, existing approaches do not model that each part has a different level of semantic representativeness to the whole. We propose UNcertainty-guided Compositional Hyperbolic Alignment (UNCHA) for enhancing hyperbolic VLMs. UNCHA models part-to-whole semantic representativeness with hyperbolic uncertainty, by assigning lower uncertainty to more representative parts and higher uncertainty to less representative ones for the whole scene. This representativeness is then incorporated into the contrastive objective with uncertainty-guided weights. Finally, the uncertainty is further calibrated with an entailment loss regularized by entropy-based term. With the proposed losses, UNCHA learns hyperbolic embeddings with more accurate part-whole ordering, capturing the underlying compositional structure in an image and improving its understanding of complex multi-object scenes. UNCHA achieves state-of-the-art performance on zero-shot classification, retrieval, and multi-label classification benchmarks. Our code and models are available at: https://github.com/jeeit17/UNCHA.git.", "github_manual": "", "github_regex": "https://github.com/jeeit17/UNCHA.git", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.21854", "title": "Reasoning or Rhetoric? An Empirical Analysis of Moral Reasoning Explanations in Large Language Models", "authors": [ "Aryan Kasat", "Smriti Singh", "Aman Chadha", "Vinija Jain" ], "github": "", "project_page": "", "abstract": "Do large language models reason morally, or do they merely sound like they do? We investigate whether LLM responses to moral dilemmas exhibit genuine developmental progression through Kohlberg's stages of moral development, or whether alignment training instead produces reasoning-like outputs that superficially resemble mature moral judgment without the underlying developmental trajectory. Using an LLM-as-judge scoring pipeline validated across three judge models, we classify more than 600 responses from 13 LLMs spanning a range of architectures, parameter scales, and training regimes across six classical moral dilemmas, and conduct ten complementary analyses to characterize the nature and internal coherence of the resulting patterns. Our results reveal a striking inversion: responses overwhelmingly correspond to post-conventional reasoning (Stages 5-6) regardless of model size, architecture, or prompting strategy, the effective inverse of human developmental norms, where Stage 4 dominates. Most strikingly, a subset of models exhibit moral decoupling: systematic inconsistency between stated moral justification and action choice, a form of logical incoherence that persists across scale and prompting strategy and represents a direct reasoning consistency failure independent of rhetorical sophistication. Model scale carries a statistically significant but practically small effect; training type has no significant independent main effect; and models exhibit near-robotic cross-dilemma consistency producing logically indistinguishable responses across semantically distinct moral problems. We posit that these patterns constitute evidence for moral ventriloquism: the acquisition, through alignment training, of the rhetorical conventions of mature moral reasoning without the underlying developmental trajectory those conventions are meant to represent.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.23481", "title": "VTAM: Video-Tactile-Action Models for Complex Physical Interaction Beyond VLAs", "authors": [ "Haoran Yuan", "Weigang Yi", "Zhenyu Zhang", "Wendi Chen", "Yuchen Mo", "Jiashi Yin", "Xinzhuo Li", "Xiangyu Zeng", "Chuan Wen", "Cewu Lu", "Katherine Driggs-Campbell", "Ismini Lourentzou" ], "github": "", "project_page": "", "abstract": "Video-Action Models (VAMs) have emerged as a promising framework for embodied intelligence, learning implicit world dynamics from raw video streams to produce temporally consistent action predictions. Although such models demonstrate strong performance on long-horizon tasks through visual reasoning, they remain limited in contact-rich scenarios where critical interaction states are only partially observable from vision alone. In particular, fine-grained force modulation and contact transitions are not reliably encoded in visual tokens, leading to unstable or imprecise behaviors. To bridge this gap, we introduce the Video-Tactile Action Model (VTAM), a multimodal world modeling framework that incorporates tactile perception as a complementary grounding signal. VTAM augments a pretrained video transformer with tactile streams via a lightweight modality transfer finetuning, enabling efficient cross-modal representation learning without tactile-language paired data or independent tactile pretraining. To stabilize multimodal fusion, we introduce a tactile regularization loss that enforces balanced cross-modal attention, preventing visual latent dominance in the action model. VTAM demonstrates superior performance in contact-rich manipulation, maintaining a robust success rate of 90 percent on average. In challenging scenarios such as potato chip pick-and-place requiring high-fidelity force awareness, VTAM outperforms the pi 0.5 baseline by 80 percent. Our findings demonstrate that integrating tactile feedback is essential for correcting visual estimation errors in world action models, providing a scalable approach to physically grounded embodied foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.21064", "title": "2Xplat: Two Experts Are Better Than One Generalist", "authors": [ "Hwasik Jeong", "Seungryong Lee", "Gyeongjin Kang", "Seungkwon Yang", "Xiangyu Sun", "Seungtae Nam", "Eunbyung Park" ], "github": "", "project_page": "", "abstract": "Pose-free feed-forward 3D Gaussian Splatting (3DGS) has opened a new frontier for rapid 3D modeling, enabling high-quality Gaussian representations to be generated from uncalibrated multi-view images in a single forward pass. The dominant approach in this space adopts unified monolithic architectures, often built on geometry-centric 3D foundation models, to jointly estimate camera poses and synthesize 3DGS representations within a single network. While architecturally streamlined, such \"all-in-one\" designs may be suboptimal for high-fidelity 3DGS generation, as they entangle geometric reasoning and appearance modeling within a shared representation. In this work, we introduce 2Xplat, a pose-free feed-forward 3DGS framework based on a two-expert design that explicitly separates geometry estimation from Gaussian generation. A dedicated geometry expert first predicts camera poses, which are then explicitly passed to a powerful appearance expert that synthesizes 3D Gaussians. Despite its conceptual simplicity, being largely underexplored in prior works, the proposed approach proves highly effective. In fewer than 5K training iterations, the proposed two-experts pipeline substantially outperforms prior pose-free feed-forward 3DGS approaches and achieves performance on par with state-of-the-art posed methods. These results challenge the prevailing unified paradigm and suggest the potential advantages of modular design principles for complex 3D geometric estimation and appearance synthesis tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22350", "title": "Session Risk Memory (SRM): Temporal Authorization for Deterministic Pre-Execution Safety Gates", "authors": [ "Florin Adrian Chitan" ], "github": "", "project_page": "", "abstract": "Deterministic pre-execution safety gates evaluate whether individual agent actions are compatible with their assigned roles. While effective at per-action authorization, these systems are structurally blind to distributed attacks that decompose harmful intent across multiple individually-compliant steps. This paper introduces Session Risk Memory (SRM), a lightweight deterministic module that extends stateless execution gates with trajectory-level authorization. SRM maintains a compact semantic centroid representing the evolving behavioral profile of an agent session and accumulates a risk signal through exponential moving average over baseline-subtracted gate outputs. It operates on the same semantic vector representation as the underlying gate, requiring no additional model components, training, or probabilistic inference. We evaluate SRM on a multi-turn benchmark of 80 sessions containing slow-burn exfiltration, gradual privilege escalation, and compliance drift scenarios. Results show that ILION+SRM achieves F1 = 1.0000 with 0% false positive rate, compared to stateless ILION at F1 = 0.9756 with 5% FPR, while maintaining 100% detection rate for both systems. Critically, SRM eliminates all false positives with a per-turn overhead under 250 microseconds. The framework introduces a conceptual distinction between spatial authorization consistency (evaluated per action) and temporal authorization consistency (evaluated over trajectory), providing a principled basis for session-level safety in agentic systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.23499", "title": "DA-Flow: Degradation-Aware Optical Flow Estimation with Diffusion Models", "authors": [ "Jaewon Min", "Jaeeun Lee", "Yeji Choi", "Paul Hyunbin Cho", "Jin Hyeon Kim", "Tae-Young Lee", "Jongsik Ahn", "Hwayeong Lee", "Seonghyun Park", "Seungryong Kim" ], "github": "", "project_page": "", "abstract": "Optical flow models trained on high-quality data often degrade severely when confronted with real-world corruptions such as blur, noise, and compression artifacts. To overcome this limitation, we formulate Degradation-Aware Optical Flow, a new task targeting accurate dense correspondence estimation from real-world corrupted videos. Our key insight is that the intermediate representations of image restoration diffusion models are inherently corruption-aware but lack temporal awareness. To address this limitation, we lift the model to attend across adjacent frames via full spatio-temporal attention, and empirically demonstrate that the resulting features exhibit zero-shot correspondence capabilities. Based on this finding, we present DA-Flow, a hybrid architecture that fuses these diffusion features with convolutional features within an iterative refinement framework. DA-Flow substantially outperforms existing optical flow methods under severe degradation across multiple benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22003", "title": "VP-VLA: Visual Prompting as an Interface for Vision-Language-Action Models", "authors": [ "Zixuan Wang", "Yuxin Chen", "Yuqi Liu", "Jinhui Ye", "Pengguang Chen", "Changsheng Lu", "Shu Liu", "Jiaya Jia" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models typically map visual observations and linguistic instructions directly to robotic control signals. This \"black-box\" mapping forces a single forward pass to simultaneously handle instruction interpretation, spatial grounding, and low-level control, often leading to poor spatial precision and limited robustness in out-of-distribution scenarios. To address these limitations, we propose VP-VLA, a dual-system framework that decouples high-level reasoning and low-level execution via a structured visual prompting interface. Specifically, a \"System 2 Planner\" decomposes complex instructions into sub-tasks and identifies relevant target objects and goal locations. These spatial anchors are then overlaid directly onto visual observations as structured visual prompts, such as crosshairs and bounding boxes. Guided by these prompts and enhanced by a novel auxiliary visual grounding objective during training, a \"System 1 Controller\" reliably generates precise low-level execution motions. Experiments on the Robocasa-GR1-Tabletop benchmark and SimplerEnv simulation demonstrate that VP-VLA improves success rates by 5% and 8.3%, surpassing competitive baselines including QwenOFT and GR00T-N1.6.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.23471", "title": "Regulating AI Agents", "authors": [ "Kathrin Gardhouse", "Amin Oueslati", "Noam Kolt" ], "github": "", "project_page": "", "abstract": "AI agents -- systems that can independently take actions to pursue complex goals with only limited human oversight -- have entered the mainstream. These systems are now being widely used to produce software, conduct business activities, and automate everyday personal tasks. While AI agents implicate many areas of law, ranging from agency law and contracts to tort liability and labor law, they present particularly pressing questions for the most globally consequential AI regulation: the European Union's AI Act. Promulgated prior to the development and widespread use of AI agents, the EU AI Act faces significant obstacles in confronting the governance challenges arising from this transformative technology, such as performance failures in autonomous task execution, the risk of misuse of agents by malicious actors, and unequal access to the economic opportunities afforded by AI agents. We systematically analyze the EU AI Act's response to these challenges, focusing on both the substantive provisions of the regulation and, crucially, the institutional frameworks that aim to support its implementation. Our analysis of the Act's allocation of monitoring and enforcement responsibilities, reliance on industry self-regulation, and level of government resourcing illustrates how a regulatory framework designed for conventional AI systems can be ill-suited to AI agents. Taken together, our findings suggest that policymakers in the EU and beyond will need to change course, and soon, if they are to effectively govern the next generation of AI technology.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.23495", "title": "VISion On Request: Enhanced VLLM efficiency with sparse, dynamically selected, vision-language interactions", "authors": [ "Adrian Bulat", "Alberto Baldrati", "Ioannis Maniadis Metaxas", "Yassine Ouali", "Georgios Tzimiropoulos" ], "github": "", "project_page": "", "abstract": "Existing approaches for improving the efficiency of Large Vision-Language Models (LVLMs) are largely based on the concept of visual token reduction. This approach, however, creates an information bottleneck that impairs performance, especially on challenging tasks that require fine-grained understanding and reasoning. In this work, we challenge this paradigm by introducing VISion On Request (VISOR), a method that reduces inference cost without discarding visual information. Instead of compressing the image, VISOR improves efficiency by sparsifying the interaction between image and text tokens. Specifically, the language model attends to the full set of high-resolution visual tokens through a small, strategically placed set of attention layers: general visual context is provided by efficient cross-attention between text-image, while a few well-placed and dynamically selected self-attention layers refine the visual representations themselves, enabling complex, high-resolution reasoning when needed. Based on this principle, we first train a single universal network on a range of computational budgets by varying the number of self-attention layers, and then introduce a lightweight policy mechanism that dynamically allocates visual computation based on per-sample complexity. Extensive experiments show that VISOR drastically reduces computational cost while matching or exceeding state-of-the-art results across a diverse suite of benchmarks, and excels in challenging tasks that require detailed visual understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22359", "title": "STEM Agent: A Self-Adapting, Tool-Enabled, Extensible Architecture for Multi-Protocol AI Agent Systems", "authors": [ "Alfred Shen", "Aaron Shen" ], "github": "https://github.com/alfredcs/stem-agent", "project_page": "", "abstract": "Current AI agent frameworks commit early to a single interaction protocol, a fixed tool integration strategy, and static user models, limiting their deployment across diverse interaction paradigms. To address these constraints, we introduce STEM Agent (Self-adapting, Tool-enabled, Extensible, Multi-agent), a modular architecture inspired by biological pluripotency in which an undifferentiated agent core differentiates into specialized protocol handlers, tool bindings, and memory subsystems that compose into a fully functioning AI system. The framework unifies five interoperability protocols (A2A, AG-UI, A2UI, UCP, and AP2) behind a single gateway, introduces a Caller Profiler that continuously learns user preferences across more than twenty behavioral dimensions, externalizes all domain capabilities through the Model Context Protocol (MCP), and implements a biologically inspired skills acquisition system in which recurring interaction patterns crystallize into reusable agent skills through a maturation lifecycle analogous to cell differentiation. Complementing these capabilities, the memory system incorporates consolidation mechanisms, including episodic pruning, semantic deduplication, and pattern extraction, designed for sub-linear growth under sustained interaction. A comprehensive 413-test suite validates protocol handler behavior and component integration across all five architectural layers, completing in under three seconds.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/alfredcs/stem-agent", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22758", "title": "Reconstruction-Guided Slot Curriculum: Addressing Object Over-Fragmentation in Video Object-Centric Learning", "authors": [ "WonJun Moon", "Hyun Seok Seong", "Jae-Pil Heo" ], "github": "", "project_page": "", "abstract": "Video Object-Centric Learning seeks to decompose raw videos into a small set of object slots, but existing slot-attention models often suffer from severe over-fragmentation. This is because the model is implicitly encouraged to occupy all slots to minimize the reconstruction objective, thereby representing a single object with multiple redundant slots. We tackle this limitation with a reconstruction-guided slot curriculum (SlotCurri). Training starts with only a few coarse slots and progressively allocates new slots where reconstruction error remains high, thus expanding capacity only where it is needed and preventing fragmentation from the outset. Yet, during slot expansion, meaningful sub-parts can emerge only if coarse-level semantics are already well separated; however, with a small initial slot budget and an MSE objective, semantic boundaries remain blurry. Therefore, we augment MSE with a structure-aware loss that preserves local contrast and edge information to encourage each slot to sharpen its semantic boundaries. Lastly, we propose a cyclic inference that rolls slots forward and then backward through the frame sequence, producing temporally consistent object representations even in the earliest frames. All combined, SlotCurri addresses object over-fragmentation by allocating representational capacity where reconstruction fails, further enhanced by structural cues and cyclic inference. Notable FG-ARI gains of +6.8 on YouTube-VIS and +8.3 on MOVi-C validate the effectiveness of SlotCurri. Our code is available at github.com/wjun0830/SlotCurri.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.23488", "title": "One View Is Enough! Monocular Training for In-the-Wild Novel View Generation", "authors": [ "Adrien Ramanana Rahary", "Nicolas Dufour", "Patrick Perez", "David Picard" ], "github": "https://github.com/AdrienRR/ovie", "project_page": "", "abstract": "Monocular novel-view synthesis has long required multi-view image pairs for supervision, limiting training data scale and diversity. We argue it is not necessary: one view is enough. We present OVIE, trained entirely on unpaired internet images. We leverage a monocular depth estimator as a geometric scaffold at training time: we lift a source image into 3D, apply a sampled camera transformation, and project to obtain a pseudo-target view. To handle disocclusions, we introduce a masked training formulation that restricts geometric, perceptual, and textural losses to valid regions, enabling training on 30 million uncurated images. At inference, OVIE is geometry-free, requiring no depth estimator or 3D representation. Trained exclusively on in-the-wild images, OVIE outperforms prior methods in a zero-shot setting, while being 600x faster than the second-best baseline. Code and models are publicly available at https://github.com/AdrienRR/ovie.", "github_manual": "", "github_regex": "https://github.com/AdrienRR/ovie", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22330", "title": "Fair splits flip the leaderboard: CHANRG reveals limited generalization in RNA secondary-structure prediction", "authors": [ "Zhiyuan Chen", "Zhenfeng Deng", "Pan Deng", "Yue Liao", "Xiu Su", "Peng Ye", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Accurate prediction of RNA secondary structure underpins transcriptome annotation, mechanistic analysis of non-coding RNAs, and RNA therapeutic design. Recent gains from deep learning and RNA foundation models are difficult to interpret because current benchmarks may overestimate generalization across RNA families. We present the Comprehensive Hierarchical Annotation of Non-coding RNA Groups (CHANRG), a benchmark of 170{,}083 structurally non-redundant RNAs curated from more than 10 million sequences in Rfam~15.0 using structure-aware deduplication, genome-aware split design and multiscale structural evaluation. Across 29 predictors, foundation-model methods achieved the highest held-out accuracy but lost most of that advantage out of distribution, whereas structured decoders and direct neural predictors remained markedly more robust. This gap persisted after controlling for sequence length and reflected both loss of structural coverage and incorrect higher-order wiring. Together, CHANRG and a padding-free, symmetry-aware evaluation stack provide a stricter and batch-invariant framework for developing RNA structure predictors with demonstrable out-of-distribution robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.22570", "title": "CanViT: Toward Active-Vision Foundation Models", "authors": [ "Yoha\u00ef-Eliel Berreby", "Sabrina Du", "Audrey Durand", "B. Suresh Krishna" ], "github": "", "project_page": "", "abstract": "Active computer vision promises efficient, biologically plausible perception through sequential, localized glimpses, but lacks scalable general-purpose architectures and pretraining pipelines. As a result, Active-Vision Foundation Models (AVFMs) have remained unexplored. We introduce CanViT, the first task- and policy-agnostic AVFM. CanViT uses scene-relative RoPE to bind a retinotopic Vision Transformer backbone and a spatiotopic scene-wide latent workspace, the canvas. Efficient interaction with this high-capacity working memory is supported by Canvas Attention, a novel asymmetric cross-attention mechanism. We decouple thinking (backbone-level) and memory (canvas-level), eliminating canvas-side self-attention and fully-connected layers to achieve low-latency sequential inference and scalability to large scenes. We propose a label-free active vision pretraining scheme, policy-agnostic passive-to-active dense latent distillation: reconstructing scene-wide DINOv3 embeddings from sequences of low-resolution glimpses with randomized locations, zoom levels, and lengths. We pretrain CanViT-B from a random initialization on 13.2 million ImageNet-21k scenes -- an order of magnitude more than previous active models -- and 1 billion random glimpses, in 166 hours on a single H100. On ADE20K segmentation, a frozen CanViT-B achieves 38.5% mIoU in a single low-resolution glimpse, outperforming the best active model's 27.6% with 19.5x fewer inference FLOPs and no fine-tuning, as well as its FLOP- or input-matched DINOv3 teacher. Given additional glimpses, CanViT-B reaches 45.9% ADE20K mIoU. On ImageNet-1k classification, CanViT-B reaches 81.2% top-1 accuracy with frozen teacher probes. CanViT generalizes to longer rollouts, larger scenes, and new policies. Our work closes the wide gap between passive and active vision on semantic segmentation and demonstrates the potential of AVFMs as a new research axis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.20576", "title": "Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents", "authors": [ "Ruiying Ma", "Shreya Shankar", "Ruiqi Chen", "Yiming Lin", "Sepanta Zeighami", "Rajoshi Ghosh", "Abhinav Gupta", "Anushrut Gupta", "Tanmai Gopal", "Aditya G. Parameswaran" ], "github": "", "project_page": "", "abstract": "Users across enterprises increasingly rely on AI agents to query their data through natural language. However, building reliable data agents remains difficult because real-world data is often fragmented across multiple heterogeneous database systems, with inconsistent references and information buried in unstructured text. Existing benchmarks only tackle individual pieces of this problem -- e.g., translating natural-language questions into SQL queries, answering questions over small tables provided in context -- but do not evaluate the full pipeline of integrating, transforming, and analyzing data across multiple database systems. To fill this gap, we present the Data Agent Benchmark (DAB), grounded in a formative study of enterprise data agent workloads across six industries. DAB comprises 54 queries across 12 datasets, 9 domains, and 4 database management systems. On DAB, the best frontier model (Gemini-3-Pro) achieves only 38% pass@1 accuracy. We benchmark five frontier LLMs, analyze their failure modes, and distill takeaways for future data agent development. Our benchmark and experiment code are published at github.com/ucbepic/DataAgentBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.17198", "title": "Abstraction as a Memory-Efficient Inductive Bias for Continual Learning", "authors": [ "Elnaz Rahmati", "Nona Ghazizadeh", "Zhivar Sourati", "Nina Rouhani", "Morteza Dehghani" ], "github": "", "project_page": "", "abstract": "The real world is non-stationary and infinitely complex, requiring intelligent agents to learn continually without the prohibitive cost of retraining from scratch. While online continual learning offers a framework for this setting, learning new information often interferes with previously acquired knowledge, causes forgetting and degraded generalization. To address this, we propose Abstraction-Augmented Training (AAT), a loss-level modification encouraging models to capture the latent relational structure shared across examples. By jointly optimizing over concrete instances and their abstract representations, AAT introduces a memory-efficient inductive bias that stabilizes learning in strictly online data streams, eliminating the need for a replay buffer. To capture the multi-faceted nature of abstraction, we introduce and evaluate AAT on two benchmarks: a controlled relational dataset where abstraction is realized through entity masking, and a narrative dataset where abstraction is expressed through shared proverbs. Our results show that AAT achieves performance comparable to or exceeding strong experience replay (ER) baselines, despite requiring zero additional memory and only minimal changes to the training objective. This work highlights structural abstraction as a powerful, memory-free alternative to ER.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-25", "arxiv_id": "2603.13669", "title": "SHAMISA: SHAped Modeling of Implicit Structural Associations for Self-supervised No-Reference Image Quality Assessment", "authors": [ "Mahdi Naseri", "Zhou Wang" ], "github": "https://github.com/Mahdi-Naseri/SHAMISA", "project_page": "", "abstract": "No-Reference Image Quality Assessment (NR-IQA) aims to estimate perceptual quality without access to a reference image of pristine quality. Learning an NR-IQA model faces a fundamental bottleneck: its need for a large number of costly human perceptual labels. We propose SHAMISA, a non-contrastive self-supervised framework that learns from unlabeled distorted images by leveraging explicitly structured relational supervision. Unlike prior methods that impose rigid, binary similarity constraints, SHAMISA introduces implicit structural associations, defined as soft, controllable relations that are both distortion-aware and content-sensitive, inferred from synthetic metadata and intrinsic feature structure. A key innovation is our compositional distortion engine, which generates an uncountable family of degradations from continuous parameter spaces, grouped so that only one distortion factor varies at a time. This enables fine-grained control over representational similarity during training: images with shared distortion patterns are pulled together in the embedding space, while severity variations produce structured, predictable shifts. We integrate these insights via dual-source relation graphs that encode both known degradation profiles and emergent structural affinities to guide the learning process throughout training. A convolutional encoder is trained under this supervision and then frozen for inference, with quality prediction performed by a linear regressor on its features. Extensive experiments on synthetic, authentic, and cross-dataset NR-IQA benchmarks demonstrate that SHAMISA achieves strong overall performance with improved cross-dataset generalization and robustness, all without human quality annotations or contrastive losses.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Mahdi-Naseri/SHAMISA", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.24157", "title": "CarePilot: A Multi-Agent Framework for Long-Horizon Computer Task Automation in Healthcare", "authors": [ "Akash Ghosh", "Tajamul Ashraf", "Rishu Kumar Singh", "Numan Saeed", "Sriparna Saha", "Xiuying Chen", "Salman Khan" ], "github": "", "project_page": "", "abstract": "Multimodal agentic pipelines are transforming human-computer interaction by enabling efficient and accessible automation of complex, real-world tasks. However, recent efforts have focused on short-horizon or general-purpose applications (e.g., mobile or desktop interfaces), leaving long-horizon automation for domain-specific systems, particularly in healthcare, largely unexplored. To address this, we introduce CareFlow, a high-quality human-annotated benchmark comprising complex, long-horizon software workflows across medical annotation tools, DICOM viewers, EHR systems, and laboratory information systems. On this benchmark, existing vision-language models (VLMs) perform poorly, struggling with long-horizon reasoning and multi-step interactions in medical contexts. To overcome this, we propose CarePilot, a multi-agent framework based on the actor-critic paradigm. The Actor integrates tool grounding with dual-memory mechanisms (long-term and short-term experience) to predict the next semantic action from the visual interface and system state. The Critic evaluates each action, updates memory based on observed effects, and either executes or provides corrective feedback to refine the workflow. Through iterative agentic simulation, the Actor learns to perform more robust and reasoning-aware predictions during inference. Our experiments show that CarePilot achieves state-of-the-art performance, outperforming strong closed-source and open-source multimodal baselines by approximately 15.26% and 3.38%, respectively, on our benchmark and out-of-distribution dataset.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.24329", "title": "GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents", "authors": [ "Yunzhe Wang", "Runhui Xu", "Kexin Zheng", "Tianyi Zhang", "Jayavibhav Niranjan Kogundi", "Soham Hans", "Volkan Ustun" ], "github": "", "project_page": "", "abstract": "Multimodal LLMs are increasingly deployed as perceptual backbones for autonomous agents in 3D environments, from robotics to virtual worlds. These applications require agents to perceive rapid state changes, attribute actions to the correct entities, and reason about concurrent multi-agent behaviors from a first-person perspective, capabilities that existing benchmarks do not adequately evaluate. We introduce GameplayQA, a framework for evaluating agentic-centric perception and reasoning through video understanding. Specifically, we densely annotate multiplayer 3D gameplay videos at 1.22 labels/second, with time-synced, concurrent captions of states, actions, and events structured around a triadic system of Self, Other Agents, and the World, a natural decomposition for multi-agent environments. From these annotations, we refined 2.4K diagnostic QA pairs organized into three levels of cognitive complexity, accompanied by a structured distractor taxonomy that enables fine-grained analysis of where models hallucinate. Evaluation of frontier MLLMs reveals a substantial gap from human performance, with common failures in temporal and cross-video grounding, agent-role attribution, and handling the decision density of the game. We hope GameplayQA stimulates future research at the intersection of embodied AI, agentic perception, and world modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.23638", "title": "Can LLM Agents Be CFOs? A Benchmark for Resource Allocation in Dynamic Enterprise Environments", "authors": [ "Yi Han", "Lingfei Qian", "Yan Wang", "Yueru He", "Xueqing Peng", "Dongji Feng", "Yankai Chen", "Haohang Li", "Yupeng Cao", "Jimin Huang", "Xue Liu", "Jian-Yun Nie", "Sophia Ananiadou" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have enabled agentic systems that can reason, plan, and act across complex tasks, but it remains unclear whether they can allocate resources effectively under uncertainty. Unlike short-horizon reactive decisions, allocation requires committing scarce resources over time while balancing competing objectives and preserving flexibility for future needs. We introduce EnterpriseArena, the first benchmark for evaluating agents on long-horizon enterprise resource allocation. It instantiates CFO-style decision-making in a 132-month enterprise simulator combining firm-level financial data, anonymized business documents, macroeconomic and industry signals, and expert-validated operating rules. The environment is partially observable and reveals the state only through budgeted organizational tools, forcing agents to trade off information acquisition against conserving scarce resources. Experiments on eleven advanced LLMs show that this setting remains highly challenging: only 16% of runs survive the full horizon, and larger models do not reliably outperform smaller ones. These results identify long-horizon resource allocation under uncertainty as a distinct capability gap for current LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.21289", "title": "When Models Judge Themselves: Unsupervised Self-Evolution for Multimodal Reasoning", "authors": [ "Zhengxian Wu", "Kai Shi", "Chuanrui Zhang", "Zirui Liao", "Jun Yang", "Ni Yang", "Qiuying Peng", "Luyuan Zhang", "Hangrui Xu", "Tianhuang Su", "Zhenyu Yang", "Haonan Lu", "Haoqian Wang" ], "github": "https://github.com/OPPO-Mente-Lab/LLM-Self-Judge", "project_page": "", "abstract": "Recent progress in multimodal large language models has led to strong performance on reasoning tasks, but these improvements largely rely on high-quality annotated data or teacher-model distillation, both of which are costly and difficult to scale. To address this, we propose an unsupervised self-evolution training framework for multimodal reasoning that achieves stable performance improvements without using human-annotated answers or external reward models. For each input, we sample multiple reasoning trajectories and jointly model their within group structure. We use the Actor's self-consistency signal as a training prior, and introduce a bounded Judge based modulation to continuously reweight trajectories of different quality. We further model the modulated scores as a group level distribution and convert absolute scores into relative advantages within each group, enabling more robust policy updates. Trained with Group Relative Policy Optimization (GRPO) on unlabeled data, our method consistently improves reasoning performance and generalization on five mathematical reasoning benchmarks, offering a scalable path toward self-evolving multimodal models. The code are available at https://github.com/OPPO-Mente-Lab/LLM-Self-Judge.", "github_manual": "", "github_regex": "https://github.com/OPPO-Mente-Lab/LLM-Self-Judge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.24506", "title": "Toward Physically Consistent Driving Video World Models under Challenging Trajectories", "authors": [ "Jiawei Zhou", "Zhenxin Zhu", "Lingyi Du", "Linye Lyu", "Lijun Zhou", "Zhanqian Wu", "Hongcheng Luo", "Zhuotao Tian", "Bing Wang", "Guang Chen", "Hangjun Ye", "Haiyang Sun", "Yu Li" ], "github": "", "project_page": "", "abstract": "Video generation models have shown strong potential as world models for autonomous driving simulation. However, existing approaches are primarily trained on real-world driving datasets, which mostly contain natural and safe driving scenarios. As a result, current models often fail when conditioned on challenging or counterfactual trajectories-such as imperfect trajectories generated by simulators or planning systems-producing videos with severe physical inconsistencies and artifacts. To address this limitation, we propose PhyGenesis, a world model designed to generate driving videos with high visual fidelity and strong physical consistency. Our framework consists of two key components: (1) a physical condition generator that transforms potentially invalid trajectory inputs into physically plausible conditions, and (2) a physics-enhanced video generator that produces high-fidelity multi-view driving videos under these conditions. To effectively train these components, we construct a large-scale, physics-rich heterogeneous dataset. Specifically, in addition to real-world driving videos, we generate diverse challenging driving scenarios using the CARLA simulator, from which we derive supervision signals that guide the model to learn physically grounded dynamics under extreme conditions. This challenging-trajectory learning strategy enables trajectory correction and promotes physically consistent video generation. Extensive experiments demonstrate that PhyGenesis consistently outperforms state-of-the-art methods, especially on challenging trajectories. Our project page is available at: https://wm-research.github.io/PhyGenesis/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.24533", "title": "UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experience", "authors": [ "Zichuan Lin", "Feiyu Liu", "Yijun Yang", "Jiafei Lyu", "Yiming Gao", "Yicheng Liu", "Zhicong Lu", "Yangbin Yu", "Mingyu Yang", "Junyou Li", "Deheng Ye", "Jie Jiang" ], "github": "", "project_page": "", "abstract": "Autonomous mobile GUI agents have attracted increasing attention along with the advancement of Multimodal Large Language Models (MLLMs). However, existing methods still suffer from inefficient learning from failed trajectories and ambiguous credit assignment under sparse rewards for long-horizon GUI tasks. To that end, we propose UI-Voyager, a novel two-stage self-evolving mobile GUI agent. In the first stage, we employ Rejection Fine-Tuning (RFT), which enables the continuous co-evolution of data and models in a fully autonomous loop. The second stage introduces Group Relative Self-Distillation (GRSD), which identifies critical fork points in group rollouts and constructs dense step-level supervision from successful trajectories to correct failed ones. Extensive experiments on AndroidWorld show that our 4B model achieves an 81.0% Pass@1 success rate, outperforming numerous recent baselines and exceeding human-level performance. Ablation and case studies further verify the effectiveness of GRSD. Our method represents a significant leap toward efficient, self-evolving, and high-performance mobile GUI automation without expensive manual data annotation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.24458", "title": "OmniWeaving: Towards Unified Video Generation with Free-form Composition and Reasoning", "authors": [ "Kaihang Pan", "Qi Tian", "Jianwei Zhang", "Weijie Kong", "Jiangfeng Xiong", "Yanxin Long", "Shixue Zhang", "Haiyi Qiu", "Tan Wang", "Zheqi Lv", "Yue Wu", "Liefeng Bo", "Siliang Tang", "Zhao Zhong" ], "github": "", "project_page": "", "abstract": "While proprietary systems such as Seedance-2.0 have achieved remarkable success in omni-capable video generation, open-source alternatives significantly lag behind. Most academic models remain heavily fragmented, and the few existing efforts toward unified video generation still struggle to seamlessly integrate diverse tasks within a single framework. To bridge this gap, we propose OmniWeaving, an omni-level video generation model featuring powerful multimodal composition and reasoning-informed capabilities. By leveraging a massive-scale pretraining dataset that encompasses diverse compositional and reasoning-augmented scenarios, OmniWeaving learns to temporally bind interleaved text, multi-image, and video inputs while acting as an intelligent agent to infer complex user intentions for sophisticated video creation. Furthermore, we introduce IntelligentVBench, the first comprehensive benchmark designed to rigorously assess next-level intelligent unified video generation. Extensive experiments demonstrate that OmniWeaving achieves SoTA performance among open-source unified models. The codes and model will be made publicly available soon. Project Page: https://omniweaving.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.24440", "title": "CUA-Suite: Massive Human-annotated Video Demonstrations for Computer-Use Agents", "authors": [ "Xiangru Jian", "Shravan Nayak", "Kevin Qinghong Lin", "Aarash Feizi", "Kaixin Li", "Patrice Bechard", "Spandana Gella", "Sai Rajeswar" ], "github": "", "project_page": "", "abstract": "Computer-use agents (CUAs) hold great promise for automating complex desktop workflows, yet progress toward general-purpose agents is bottlenecked by the scarcity of continuous, high-quality human demonstration videos. Recent work emphasizes that continuous video, not sparse screenshots, is the critical missing ingredient for scaling these agents. However, the largest existing open dataset, ScaleCUA, contains only 2 million screenshots, equating to less than 20 hours of video. To address this bottleneck, we introduce CUA-Suite, a large-scale ecosystem of expert video demonstrations and dense annotations for professional desktop computer-use agents. At its core is VideoCUA, which provides approximately 10,000 human-demonstrated tasks across 87 diverse applications with continuous 30 fps screen recordings, kinematic cursor traces, and multi-layerfed reasoning annotations, totaling approximately 55 hours and 6 million frames of expert video. Unlike sparse datasets that capture only final click coordinates, these continuous video streams preserve the full temporal dynamics of human interaction, forming a superset of information that can be losslessly transformed into the formats required by existing agent frameworks. CUA-Suite further provides two complementary resources: UI-Vision, a rigorous benchmark for evaluating grounding and planning capabilities in CUAs, and GroundCUA, a large-scale grounding dataset with 56K annotated screenshots and over 3.6 million UI element annotations. Preliminary evaluation reveals that current foundation action models struggle substantially with professional desktop applications (~60% task failure rate). Beyond evaluation, CUA-Suite's rich multimodal corpus supports emerging research directions including generalist screen parsing, continuous spatial control, video-based reward modeling, and visual world models. All data and models are publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.24472", "title": "Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs?", "authors": [ "Jeonghye Kim", "Xufang Luo", "Minbeom Kim", "Sangmook Lee", "Dohyung Kim", "Jiwon Jeon", "Dongsheng Li", "Yuqing Yang" ], "github": "", "project_page": "", "abstract": "Self-distillation has emerged as an effective post-training paradigm for LLMs, often improving performance while shortening reasoning traces. However, in mathematical reasoning, we find that it can reduce response length while degrading performance. We trace this degradation to the suppression of epistemic verbalization - the model's expression of uncertainty during reasoning. Through controlled experiments varying conditioning context richness and task coverage, we show that conditioning the teacher on rich information suppresses uncertainty expression, enabling rapid in-domain optimization with limited task coverage but harming OOD performance, where unseen problems benefit from expressing uncertainty and adjusting accordingly. Across Qwen3-8B, DeepSeek-Distill-Qwen-7B, and Olmo3-7B-Instruct, we observe performance drops of up to 40%. Our findings highlight that exposing appropriate levels of uncertainty is crucial for robust reasoning and underscore the importance of optimizing reasoning behavior beyond merely reinforcing correct answer traces.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.21618", "title": "4DGS360: 360\u00b0 Gaussian Reconstruction of Dynamic Objects from a Single Video", "authors": [ "Jae Won Jang", "Yeonjin Chang", "Wonsik Shin", "Juhwan Cho", "Nojun Kwak" ], "github": "", "project_page": "", "abstract": "We introduce 4DGS360, a diffusion-free framework for 360^{circ} dynamic object reconstruction from casual monocular video. Existing methods often fail to reconstruct consistent 360^{circ} geometry, as their heavy reliance on 2D-native priors causes initial points to overfit to visible surface in each training view. 4DGS360 addresses this challenge through a advanced 3D-native initialization that mitigates the geometric ambiguity of occluded regions. Our proposed 3D tracker, AnchorTAP3D, produces reinforced 3D point trajectories by leveraging confident 2D track points as anchors, suppressing drift and providing reliable initialization that preserves geometry in occluded regions. This initialization, combined with optimization, yields coherent 360^{circ} 4D reconstructions. We further present iPhone360, a new benchmark where test cameras are placed up to 135^{circ} apart from training views, enabling 360^{circ} evaluation that existing datasets cannot provide. Experiments show that 4DGS360 achieves state-of-the-art performance on the iPhone360, iPhone, and DAVIS datasets, both qualitatively and quantitatively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.22341", "title": "T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search", "authors": [ "Hyomin Lee", "Sangwoo Park", "Yumin Choi", "Sohyun An", "Seanie Lee", "Sung Ju Hwang" ], "github": "https://github.com/pwnhyo/T-MAP", "project_page": "", "abstract": "While prior red-teaming efforts have focused on eliciting harmful text outputs from large language models (LLMs), such approaches fail to capture agent-specific vulnerabilities that emerge through multi-step tool execution, particularly in rapidly growing ecosystems such as the Model Context Protocol (MCP). To address this gap, we propose a trajectory-aware evolutionary search method, T-MAP, which leverages execution trajectories to guide the discovery of adversarial prompts. Our approach enables the automatic generation of attacks that not only bypass safety guardrails but also reliably realize harmful objectives through actual tool interactions. Empirical evaluations across diverse MCP environments demonstrate that T-MAP substantially outperforms baselines in attack realization rate (ARR) and remains effective against frontier models, including GPT-5.2, Gemini-3-Pro, Qwen3.5, and GLM-5, thereby revealing previously underexplored vulnerabilities in autonomous LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pwnhyo/T-MAP", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.22918", "title": "EVA: Efficient Reinforcement Learning for End-to-End Video Agent", "authors": [ "Yaolun Zhang", "Ruohui Wang", "Jiahao Wang", "Yepeng Tang", "Xuanyu Zheng", "Haonan Duan", "Hao Lu", "Hanming Deng", "Lewei Lu" ], "github": "https://github.com/wangruohui/EfficientVideoAgent", "project_page": "", "abstract": "Video understanding with multimodal large language models (MLLMs) remains challenging due to the long token sequences of videos, which contain extensive temporal dependencies and redundant frames. Existing approaches typically treat MLLMs as passive recognizers, processing entire videos or uniformly sampled frames without adaptive reasoning. Recent agent-based methods introduce external tools, yet still depend on manually designed workflows and perception-first strategies, resulting in inefficiency on long videos. We present EVA, an Efficient Reinforcement Learning framework for End-to-End Video Agent, which enables planning-before-perception through iterative summary-plan-action-reflection reasoning. EVA autonomously decides what to watch, when to watch, and how to watch, achieving query-driven and efficient video understanding. To train such agents, we design a simple yet effective three-stage learning pipeline - comprising supervised fine-tuning (SFT), Kahneman-Tversky Optimization (KTO), and Generalized Reward Policy Optimization (GRPO) - that bridges supervised imitation and reinforcement learning. We further construct high-quality datasets for each stage, supporting stable and reproducible training. We evaluate EVA on six video understanding benchmarks, demonstrating its comprehensive capabilities. Compared with existing baselines, EVA achieves a substantial improvement of 6-12% over general MLLM baselines and a further 1-3% gain over prior adaptive agent methods. Our code and model are available at https://github.com/wangruohui/EfficientVideoAgent.", "github_manual": "", "github_regex": "https://github.com/wangruohui/EfficientVideoAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.23478", "title": "UniFunc3D: Unified Active Spatial-Temporal Grounding for 3D Functionality Segmentation", "authors": [ "Jiaying Lin", "Dan Xu" ], "github": "", "project_page": "", "abstract": "Functionality segmentation in 3D scenes requires an agent to ground implicit natural-language instructions into precise masks of fine-grained interactive elements. Existing methods rely on fragmented pipelines that suffer from visual blindness during initial task parsing. We observe that these methods are limited by single-scale, passive and heuristic frame selection. We present UniFunc3D, a unified and training-free framework that treats the multimodal large language model as an active observer. By consolidating semantic, temporal, and spatial reasoning into a single forward pass, UniFunc3D performs joint reasoning to ground task decomposition in direct visual evidence. Our approach introduces active spatial-temporal grounding with a coarse-to-fine strategy. This allows the model to select correct video frames adaptively and focus on high-detail interactive parts while preserving the global context necessary for disambiguation. On SceneFun3D, UniFunc3D achieves state-of-the-art performance, surpassing both training-free and training-based methods by a large margin with a relative 59.9\\% mIoU improvement, without any task-specific training. Code will be released on our project page: https://jiaying.link/unifunc3d.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.23539", "title": "PLDR-LLMs Reason At Self-Organized Criticality", "authors": [ "Burc Gokden" ], "github": "https://github.com/burcgokden/PLDR-LLM-Self-Organized-Criticality", "project_page": "", "abstract": "We show that PLDR-LLMs pretrained at self-organized criticality exhibit reasoning at inference time. The characteristics of PLDR-LLM deductive outputs at criticality is similar to second-order phase transitions. At criticality, the correlation length diverges, and the deductive outputs attain a metastable steady state. The steady state behaviour suggests that deductive outputs learn representations equivalent to scaling functions, universality classes and renormalization groups from the training dataset, leading to generalization and reasoning capabilities in the process. We can then define an order parameter from the global statistics of the model's deductive output parameters at inference. The reasoning capabilities of a PLDR-LLM is better when its order parameter is close to zero at criticality. This observation is supported by the benchmark scores of the models trained at near-criticality and sub-criticality. Our results provide a self-contained explanation on how reasoning manifests in large language models, and the ability to reason can be quantified solely from global model parameter values of the deductive outputs at steady state, without any need for evaluation of curated benchmark datasets through inductive output for reasoning and comprehension.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/burcgokden/PLDR-LLM-Self-Organized-Criticality", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.22120", "title": "StreamingClaw Technical Report", "authors": [ "Jiawei Chen", "Zhe Chen", "Chaoqun Du", "Maokui He", "Wei He", "Hengtao Li", "Qizhen Li", "Zide Liu", "Hao Ma", "Xuhao Pan", "Chang Ren", "Xudong Rao", "Xintian Shen", "Chenfeng Wang", "Tao Wei", "Chengjun Yu", "Pengfei Yu", "Shengyu Yao", "Chunpeng Zhou", "Kun Zhan", "Lihao Zheng", "Pan Zhou", "Xuhan Zhu", "Yufei Zheng" ], "github": "", "project_page": "", "abstract": "Applications such as embodied intelligence rely on a real-time perception-decision-action closed loop, posing stringent challenges for streaming video understanding. However, current agents suffer from fragmented capabilities, such as supporting only offline video understanding, lacking long-term multimodal memory mechanisms, or struggling to achieve real-time reasoning and proactive interaction under streaming inputs. These shortcomings have become a key bottleneck for preventing them from sustaining perception, making real-time decisions, and executing actions in real-world environments. To alleviate these issues, we propose StreamingClaw, a unified agent framework for streaming video understanding and embodied intelligence. It is also an OpenClaw-compatible framework that supports real-time, multimodal streaming interaction. StreamingClaw integrates five core capabilities: (1) It supports real-time streaming reasoning. (2) It supports reasoning about future events and proactive interaction under the online evolution of interaction objectives. (3) It supports multimodal long-term storage, hierarchical evolution, and efficient retrieval of shared memory across multiple agents. (4) It supports a closed-loop of perception-decision-action. In addition to conventional tools and skills, it also provides streaming tools and action-centric skills tailored for real-world physical environments. (5) It is compatible with the OpenClaw framework, allowing it to fully leverage the resources and support of the open-source community. With these designs, StreamingClaw integrates online real-time reasoning, multimodal long-term memory, and proactive interaction within a unified framework. Moreover, by translating decisions into executable actions, it enables direct control of the physical world, supporting practical deployment of embodied interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.23404", "title": "Unleashing Spatial Reasoning in Multimodal Large Language Models via Textual Representation Guided Reasoning", "authors": [ "Jiacheng Hua", "Yishu Yin", "Yuhang Wu", "Tai Wang", "Yifei Huang", "Miao Liu" ], "github": "", "project_page": "", "abstract": "Existing Multimodal Large Language Models (MLLMs) struggle with 3D spatial reasoning, as they fail to construct structured abstractions of the 3D environment depicted in video inputs. To bridge this gap, drawing inspiration from cognitive theories of allocentric spatial reasoning, we investigate how to enable MLLMs to model and reason over text-based spatial representations of video. Specifically, we introduce Textual Representation of Allocentric Context from Egocentric Video (TRACE), a prompting method that induces MLLMs to generate text-based representations of 3D environments as intermediate reasoning traces for more accurate spatial question answering. TRACE encodes meta-context, camera trajectories, and detailed object entities to support structured spatial reasoning over egocentric videos. Extensive experiments on VSI-Bench and OST-Bench demonstrate that TRACE yields notable and consistent improvements over prior prompting strategies across a diverse range of MLLM backbones, spanning different parameter scales and training schemas. We further present ablation studies to validate our design choices, along with detailed analyses that probe the bottlenecks of 3D spatial reasoning in MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.20176", "title": "LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis", "authors": [ "Stanislaw Szymanowicz", "Minghao Chen", "Jianyuan Wang", "Christian Rupprecht", "Andrea Vedaldi" ], "github": "", "project_page": "", "abstract": "Recent work has shown that neural networks can perform 3D tasks such as Novel View Synthesis (NVS) without explicit 3D reconstruction. Even so, we argue that strong 3D inductive biases are still helpful in the design of such networks. We show this point by introducing LagerNVS, an encoder-decoder neural network for NVS that builds on `3D-aware' latent features. The encoder is initialized from a 3D reconstruction network pre-trained using explicit 3D supervision. This is paired with a lightweight decoder, and trained end-to-end with photometric losses. LagerNVS achieves state-of-the-art deterministic feed-forward Novel View Synthesis (including 31.4 PSNR on Re10k), with and without known cameras, renders in real time, generalizes to in-the-wild data, and can be paired with a diffusion decoder for generative extrapolation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.18742", "title": "6Bit-Diffusion: Inference-Time Mixed-Precision Quantization for Video Diffusion Models", "authors": [ "Rundong Su", "Jintao Zhang", "Zhihang Yuan", "Haojie Duanmu", "Jianfei Chen", "Jun Zhu" ], "github": "", "project_page": "", "abstract": "Diffusion transformers have demonstrated remarkable capabilities in generating videos. However, their practical deployment is severely constrained by high memory usage and computational cost. Post-Training Quantization provides a practical way to reduce memory usage and boost computation speed. Existing quantization methods typically apply a static bit-width allocation, overlooking the quantization difficulty of activations across diffusion timesteps, leading to a suboptimal trade-off between efficiency and quality. In this paper, we propose a inference time NVFP4/INT8 Mixed-Precision Quantization framework. We find a strong linear correlation between a block's input-output difference and the quantization sensitivity of its internal linear layers. Based on this insight, we design a lightweight predictor that dynamically allocates NVFP4 to temporally stable layers to maximize memory compression, while selectively preserving INT8 for volatile layers to ensure robustness. This adaptive precision strategy enables aggressive quantization without compromising generation quality. Beside this, we observe that the residual between the input and output of a Transformer block exhibits high temporal consistency across timesteps. Leveraging this temporal redundancy, we introduce Temporal Delta Cache (TDC) to skip computations for these invariant blocks, further reducing the computational cost. Extensive experiments demonstrate that our method achieves 1.92times end-to-end acceleration and 3.32times memory reduction, setting a new baseline for efficient inference in Video DiTs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.23522", "title": "Qworld: Question-Specific Evaluation Criteria for LLMs", "authors": [ "Shanghua Gao", "Yuchang Su", "Pengwei Sui", "Curtis Ginder", "Marinka Zitnik" ], "github": "", "project_page": "", "abstract": "Evaluating large language models (LLMs) on open-ended questions is difficult because response quality depends on the question's context. Binary scores and static rubrics fail to capture these context-dependent requirements. Existing methods define criteria at the dataset level or generate them in a single pass, which limits their ability to explore the evaluation space implied by each question. We introduce One-Question-One-World (Qworld), a method that generates question-specific evaluation criteria using a recursive expansion tree. Given a question, Qworld decomposes it into scenarios, perspectives, and fine-grained binary criteria through structured hierarchical and horizontal expansion. The resulting criteria specify what a high-quality answer must address for that question. On HealthBench, Qworld covers 89% of expert-authored criteria and generates 79% novel criteria validated by human experts. Experts rate Qworld criteria higher in insight and granularity than those produced by prior methods. When applied to 11 frontier LLMs on HealthBench and Humanity's Last Exam, Qworld reveals capability differences in dimensions such as long-term impact, equity, error handling, and interdisciplinary reasoning that coarse rubrics do not distinguish. By formulating criteria generation as structured coverage of question-implied evaluation axes, Qworld enables evaluation that adapts to each question rather than relying on fixed task-level criteria.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.24036", "title": "SpectralSplats: Robust Differentiable Tracking via Spectral Moment Supervision", "authors": [ "Avigail Cohen Rimon", "Amir Mann", "Mirela Ben Chen", "Or Litany" ], "github": "", "project_page": "", "abstract": "3D Gaussian Splatting (3DGS) enables real-time, photorealistic novel view synthesis, making it a highly attractive representation for model-based video tracking. However, leveraging the differentiability of the 3DGS renderer \"in the wild\" remains notoriously fragile. A fundamental bottleneck lies in the compact, local support of the Gaussian primitives. Standard photometric objectives implicitly rely on spatial overlap; if severe camera misalignment places the rendered object outside the target's local footprint, gradients strictly vanish, leaving the optimizer stranded. We introduce SpectralSplats, a robust tracking framework that resolves this \"vanishing gradient\" problem by shifting the optimization objective from the spatial to the frequency domain. By supervising the rendered image via a set of global complex sinusoidal features (Spectral Moments), we construct a global basin of attraction, ensuring that a valid, directional gradient toward the target exists across the entire image domain, even when pixel overlap is completely nonexistent. To harness this global basin without introducing periodic local minima associated with high frequencies, we derive a principled Frequency Annealing schedule from first principles, gracefully transitioning the optimizer from global convexity to precise spatial alignment. We demonstrate that SpectralSplats acts as a seamless, drop-in replacement for spatial losses across diverse deformation parameterizations (from MLPs to sparse control points), successfully recovering complex deformations even from severely misaligned initializations where standard appearance-based tracking catastrophically fails.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.23994", "title": "Understanding the Challenges in Iterative Generative Optimization with LLMs", "authors": [ "Allen Nie", "Xavier Daull", "Zhiyi Kuang", "Abhinav Akkiraju", "Anish Chaudhuri", "Max Piasevoli", "Ryan Rong", "YuCheng Yuan", "Prerit Choudhary", "Shannon Xiao", "Rasool Fakoor", "Adith Swaminathan", "Ching-An Cheng" ], "github": "https://github.com/AbhinavAkkiraju/data-science-agent", "project_page": "", "abstract": "Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using execution feedback. It is a promising approach to building self-improving agents, yet in practice remains brittle: despite active research, only 9% of surveyed agents used any automated optimization. We argue that this brittleness arises because, to set up a learning loop, an engineer must make ``hidden'' design choices: What can the optimizer edit and what is the \"right\" learning evidence to provide at each update? We investigate three factors that affect most applications: the starting artifact, the credit horizon for execution traces, and batching trials and errors into learning evidence. Through case studies in MLAgentBench, Atari, and BigBench Extra Hard, we find that these design decisions can determine whether generative optimization succeeds, yet they are rarely made explicit in prior work. Different starting artifacts determine which solutions are reachable in MLAgentBench, truncated traces can still improve Atari agents, and larger minibatches do not monotonically improve generalization on BBEH. We conclude that the lack of a simple, universal way to set up learning loops across domains is a major hurdle for productionization and adoption. We provide practical guidance for making these choices.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AbhinavAkkiraju/data-science-agent", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-26", "arxiv_id": "2603.14375", "title": "The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics", "authors": [ "Xiangbo Gao", "Mingyang Wu", "Siyuan Yang", "Jiongze Yu", "Pardis Taghavi", "Fangzhou Lin", "Zhengzhong Tu" ], "github": "", "project_page": "", "abstract": "While recent generative video models have achieved remarkable visual realism and are being explored as world models, true physical simulation requires mastering both space and time. Current models can produce visually smooth kinematics, yet they lack a reliable internal motion pulse to ground these motions in a consistent, real-world time scale. This temporal ambiguity stems from the common practice of indiscriminately training on videos with vastly different real-world speeds, forcing them into standardized frame rates. This leads to what we term chronometric hallucination: generated sequences exhibit ambiguous, unstable, and uncontrollable physical motion speeds. To address this, we propose Visual Chronometer, a predictor that recovers the Physical Frames Per Second (PhyFPS) directly from the visual dynamics of an input video. Trained via controlled temporal resampling, our method estimates the true temporal scale implied by the motion itself, bypassing unreliable metadata. To systematically quantify this issue, we establish two benchmarks, PhyFPS-Bench-Real and PhyFPS-Bench-Gen. Our evaluations reveal a harsh reality: state-of-the-art video generators suffer from severe PhyFPS misalignment and temporal instability. Finally, we demonstrate that applying PhyFPS corrections significantly improves the human-perceived naturalness of AI-generated videos. Our project page is https://xiangbogaobarry.github.io/Visual_Chronometer/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.25319", "title": "MACRO: Advancing Multi-Reference Image Generation with Structured Long-Context Data", "authors": [ "Zhekai Chen", "Yuqing Wang", "Manyuan Zhang", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Generating images conditioned on multiple visual references is critical for real-world applications such as multi-subject composition, narrative illustration, and novel view synthesis, yet current models suffer from severe performance degradation as the number of input references grows. We identify the root cause as a fundamental data bottleneck: existing datasets are dominated by single- or few-reference pairs and lack the structured, long-context supervision needed to learn dense inter-reference dependencies. To address this, we introduce MacroData, a large-scale dataset of 400K samples, each containing up to 10 reference images, systematically organized across four complementary dimensions -- Customization, Illustration, Spatial reasoning, and Temporal dynamics -- to provide comprehensive coverage of the multi-reference generation space. Recognizing the concurrent absence of standardized evaluation protocols, we further propose MacroBench, a benchmark of 4,000 samples that assesses generative coherence across graded task dimensions and input scales. Extensive experiments show that fine-tuning on MacroData yields substantial improvements in multi-reference generation, and ablation studies further reveal synergistic benefits of cross-task co-training and effective strategies for handling long-context complexity. The dataset and benchmark will be publicly released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.24755", "title": "SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks", "authors": [ "Gabriel Orlanski", "Devjeet Roy", "Alexander Yun", "Changho Shin", "Alex Gu", "Albert Ge", "Dyah Adila", "Frederic Sala", "Aws Albarghouthi" ], "github": "https://github.com/SWE-agent/mini-swe-agent", "project_page": "", "abstract": "Software development is iterative, yet agentic coding benchmarks overwhelmingly evaluate single-shot solutions against complete specifications. Code can pass the test suite but become progressively harder to extend. Recent iterative benchmarks attempt to close this gap, but constrain the agent's design decisions too tightly to faithfully measure how code quality shapes future extensions. We introduce SlopCodeBench, a language-agnostic benchmark comprising 20 problems and 93 checkpoints, in which agents repeatedly extend their own prior solutions under evolving specifications that force architectural decisions without prescribing internal structure. We track two trajectory-level quality signals: verbosity, the fraction of redundant or duplicated code, and structural erosion, the share of complexity mass concentrated in high-complexity functions. No agent solves any problem end-to-end across 11 models; the highest checkpoint solve rate is 17.2%. Quality degrades steadily: erosion rises in 80% of trajectories and verbosity in 89.8%. Against 48 open-source Python repositories, agent code is 2.2x more verbose and markedly more eroded. Tracking 20 of those repositories over time shows that human code stays flat, while agent code deteriorates with each iteration. A prompt-intervention study shows that initial quality can be improved, but it does not halt degradation. These results demonstrate that pass-rate benchmarks systematically undermeasure extension robustness, and that current agents lack the design discipline iterative software development demands.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SWE-agent/mini-swe-agent", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.23516", "title": "MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens", "authors": [ "Yu Chen", "Runkai Chen", "Sheng Yi", "Xinda Zhao", "Xiaohong Li", "Jianjin Zhang", "Jun Sun", "Chuanrui Hu", "Yunyun Han", "Lidong Bing", "Yafeng Deng", "Tianqiao Chen" ], "github": "", "project_page": "", "abstract": "Long-term memory is a cornerstone of human intelligence. Enabling AI to process lifetime-scale information remains a long-standing pursuit in the field. Due to the constraints of full-attention architectures, the effective context length of large language models (LLMs) is typically limited to 1M tokens. Existing approaches, such as hybrid linear attention, fixed-size memory states (e.g., RNNs), and external storage methods like RAG or agent systems, attempt to extend this limit. However, they often suffer from severe precision degradation and rapidly increasing latency as context length grows, an inability to dynamically modify memory content, or a lack of end-to-end optimization. These bottlenecks impede complex scenarios like large-corpus summarization, Digital Twins, and long-history agent reasoning, while limiting memory capacity and slowing inference. We present Memory Sparse Attention (MSA), an end-to-end trainable, efficient, and massively scalable memory model framework. Through core innovations including scalable sparse attention and document-wise RoPE, MSA achieves linear complexity in both training and inference while maintaining exceptional stability, exhibiting less than 9% degradation when scaling from 16K to 100M tokens. Furthermore, KV cache compression, combined with Memory Parallel, enables 100M-token inference on 2xA800 GPUs. We also propose Memory Interleaving to facilitate complex multi-hop reasoning across scattered memory segments. MSA significantly surpasses frontier LLMs, state-of-the-art RAG systems, and leading memory agents in long-context benchmarks. These results demonstrate that by decoupling memory capacity from reasoning, MSA provides a scalable foundation to endow general-purpose models with intrinsic, lifetime-scale memory.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.25745", "title": "Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting", "authors": [ "Yixing Lao", "Xuyang Bai", "Xiaoyang Wu", "Nuoyuan Yan", "Zixin Luo", "Tian Fang", "Jean-Daniel Nahmias", "Yanghai Tsin", "Shiwei Li", "Hengshuang Zhao" ], "github": "", "project_page": "", "abstract": "Existing feed-forward 3D Gaussian Splatting methods predict pixel-aligned primitives, leading to a quadratic growth in primitive count as resolution increases. This fundamentally limits their scalability, making high-resolution synthesis such as 4K intractable. We introduce LGTM (Less Gaussians, Texture More), a feed-forward framework that overcomes this resolution scaling barrier. By predicting compact Gaussian primitives coupled with per-primitive textures, LGTM decouples geometric complexity from rendering resolution. This approach enables high-fidelity 4K novel view synthesis without per-scene optimization, a capability previously out of reach for feed-forward methods, all while using significantly fewer Gaussian primitives. Project page: https://yxlao.github.io/lgtm/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.25741", "title": "Vega: Learning to Drive with Natural Language Instructions", "authors": [ "Sicheng Zuo", "Yuxuan Li", "Wenzhao Zheng", "Zheng Zhu", "Jie Zhou", "Jiwen Lu" ], "github": "https://github.com/zuosc19/Vega", "project_page": "", "abstract": "Vision-language-action models have reshaped autonomous driving to incorporate languages into the decision-making process. However, most existing pipelines only utilize the language modality for scene descriptions or reasoning and lack the flexibility to follow diverse user instructions for personalized driving. To address this, we first construct a large-scale driving dataset (InstructScene) containing around 100,000 scenes annotated with diverse driving instructions with the corresponding trajectories. We then propose a unified Vision-Language-World-Action model, Vega, for instruction-based generation and planning. We employ the autoregressive paradigm to process visual inputs (vision) and language instructions (language) and the diffusion paradigm to generate future predictions (world modeling) and trajectories (action). We perform joint attention to enable interactions between the modalities and use individual projection layers for different modalities for more capabilities. Extensive experiments demonstrate that our method not only achieves superior planning performance but also exhibits strong instruction-following abilities, paving the way for more intelligent and personalized driving systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zuosc19/Vega", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.25551", "title": "Voxtral TTS", "authors": [ "Alexander H. Liu", "Alexis Tacnet", "Andy Ehrenberg", "Andy Lo", "Chen-Yo Sun", "Guillaume Lample", "Henry Lagarde", "Jean-Malo Delignon", "Jaeyoung Kim", "John Harvill", "Khyathi Raghavi Chandu", "Lorenzo Signoretti", "Margaret Jennings", "Patrick von Platen", "Pavankumar Reddy Muddireddy", "Rohin Arora", "Sanchit Gandhi", "Samuel Humeau", "Soham Ghosh", "Srijan Mishra", "Van Phung", "Abdelaziz Bounhar", "Abhinav Rastogi", "Adrien Sad\u00e9", "Alan Jeffares", "Albert Jiang", "Alexandre Cahill", "Alexandre Gavaudan", "Alexandre Sablayrolles", "Am\u00e9lie H\u00e9liou", "Amos You", "Andrew Bai", "Andrew Zhao", "Angele Lenglemetz", "Anmol Agarwal", "Anton Eliseev", "Antonia Calvi", "Arjun Majumdar", "Arthur Fournier", "Artjom Joosen", "Avi Sooriyarachchi", "Aysenur Karaduman Utkur", "Baptiste Bout", "Baptiste Rozi\u00e8re", "Baudouin De Monicault", "Benjamin Tibi", "Bowen Yang", "Charlotte Cronj\u00e4ger", "Cl\u00e9mence Lanfranchi", "Connor Chen", "Corentin Barreau", "Corentin Sautier", "Cyprien Courtot", "Darius Dabert", "Diego de las Casas", "Elizaveta Demyanenko", "Elliot Chane-Sane", "Emmanuel Gottlob", "Enguerrand Paquin", "Etienne Goffinet", "Fabien Niel", "Faruk Ahmed", "Federico Baldassarre", "Gabrielle Berrada", "Ga\u00ebtan Ecrepont", "Gauthier Guinet", "Genevieve Hayes", "Georgii Novikov", "Giada Pistilli", "Guillaume Kunsch", "Guillaume Martin", "Guillaume Raille", "Gunjan Dhanuka", "Gunshi Gupta", "Han Zhou", "Harshil Shah", "Hope McGovern", "Hugo Thimonier", "Indraneel Mukherjee", "Irene Zhang", "Jacques Sun", "Jan Ludziejewski", "Jason Rute", "J\u00e9r\u00e9mie Dentan", "Joachim Studnia", "Jonas Amar", "Jos\u00e9phine Delas", "Josselin Somerville Roberts", "Julien Tauran", "Karmesh Yadav", "Kartik Khandelwal", "Kilian Tep", "Kush Jain", "Laurence Aitchison", "Laurent Fainsin", "L\u00e9onard Blier", "Lingxiao Zhao", "Louis Martin", "Lucile Saulnier", "Luyu Gao", "Maarten Buyl", "Manan Sharma", "Marie Pellat", "Mark Prins", "Martin Alexandre", "Mathieu Poir\u00e9e", "Mathieu Schmitt", "Mathilde Guillaumin", "Matthieu Dinot", "Matthieu Futeral", "Maxime Darrin", "Maximilian Augustin", "Mert Unsal", "Mia Chiquier", "Mikhail Biriuchinskii", "Minh-Quang Pham", "Mircea Lica", "Morgane Rivi\u00e8re", "Nathan Grinsztajn", "Neha Gupta", "Olivier Bousquet", "Olivier Duchenne", "Patricia Wang", "Paul Jacob", "Paul Wambergue", "Paula Kurylowicz", "Philippe Pinel", "Philom\u00e8ne Chagniot", "Pierre Stock", "Piotr Mi\u0142o\u015b", "Prateek Gupta", "Pravesh Agrawal", "Quentin Torroba", "Ram Ramrakhya", "Randall Isenhour", "Rishi Shah", "Romain Sauvestre", "Roman Soletskyi", "Rosalie Millner", "Rupert Menneer", "Sagar Vaze", "Samuel Barry", "Samuel Belkadi", "Sandeep Subramanian", "Sean Cha", "Shashwat Verma", "Siddhant Waghjale", "Siddharth Gandhi", "Simon Lepage", "Sumukh Aithal", "Szymon Antoniak", "Tarun Kumar Vangani", "Teven Le Scao", "Th\u00e9o Cachet", "Theo Simon Sorg", "Thibaut Lavril", "Thomas Chabal", "Thomas Foubert", "Thomas Robert", "Thomas Wang", "Tim Lawson", "Tom Bewley", "Tom Edwards", "Tyler Wang", "Umar Jamil", "Umberto Tomasini", "Valeriia Nemychnikova", "Vedant Nanda", "Victor Jouault", "Vincent Maladi\u00e8re", "Vincent Pfister", "Virgile Richard", "Vladislav Bataev", "Wassim Bouaziz", "Wen-Ding Li", "William Havard", "William Marshall", "Xinghui Li", "Xingran Guo", "Xinyu Yang", "Yannic Neuhaus", "Yassine El Ouahidi", "Yassir Bendou", "Yihan Wang", "Yimu Pan", "Zaccharie Ramzi", "Zhenlin Xu" ], "github": "", "project_page": "", "abstract": "We introduce Voxtral TTS, an expressive multilingual text-to-speech model that generates natural speech from as little as 3 seconds of reference audio. Voxtral TTS adopts a hybrid architecture that combines auto-regressive generation of semantic speech tokens with flow-matching for acoustic tokens. These tokens are encoded and decoded with Voxtral Codec, a speech tokenizer trained from scratch with a hybrid VQ-FSQ quantization scheme. In human evaluations conducted by native speakers, Voxtral TTS is preferred for multilingual voice cloning due to its naturalness and expressivity, achieving a 68.4\\% win rate over ElevenLabs Flash v2.5. We release the model weights under a CC BY-NC license.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.25040", "title": "Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale", "authors": [ "Yicheng Zou", "Dongsheng Zhu", "Lin Zhu", "Tong Zhu", "Yunhua Zhou", "Peiheng Zhou", "Xinyu Zhou", "Dongzhan Zhou", "Zhiwang Zhou", "Yuhao Zhou", "Bowen Zhou", "Zhanping Zhong", "Zhijie Zhong", "Haiteng Zhao", "Penghao Zhao", "Xiaomeng Zhao", "Zhiyuan Zhao", "Yechen Zhang", "Jin Zhang", "Wenwei Zhang", "Hongjie Zhang", "Zhuo Zhang", "Wenlong Zhang", "Bo Zhang", "Chao Zhang", "Chen Zhang", "Yuhang Zang", "Fei Yuan", "Jiakang Yuan", "Jiashuo Yu", "Jinhui Yin", "Haochen Ye", "Qian Yao", "Bowen Yang", "Danni Yang", "Kaichen Yang", "Ziang Yan", "Jun Xu", "Yicheng Xu", "Wanghan Xu", "Xuenan Xu", "Chao Xu", "Ruiliang Xu", "Shuhao Xing", "Long Xing", "Xinchen Xie", "Ling-I Wu", "Zijian Wu", "Zhenyu Wu", "Lijun Wu", "Yue Wu", "Jianyu Wu", "Wen Wu", "Fan Wu", "Xilin Wei", "Qi Wei", "Bingli Wang", "Rui Wang", "Ziyi Wang", "Zun Wang", "Yi Wang", "Haomin Wang", "Yizhou Wang", "Lintao Wang", "Yiheng Wang", "Longjiang Wang", "Bin Wang", "Jian Tong", "Zhongbo Tian", "Huanze Tang", "Chen Tang", "Shixiang Tang", "Yu Sun", "Qiushi Sun", "Xuerui Su", "Qisheng Su", "Chenlin Su", "Demin Song", "Jin Shi", "Fukai Shang", "Yuchen Ren", "Pengli Ren", "Xiaoye Qu", "Yuan Qu", "Jiantao Qiu", "Yu Qiao", "Runyu Peng", "Tianshuo Peng", "Jiahui Peng", "Qizhi Pei", "Zhuoshi Pan", "Linke Ouyang", "Wenchang Ning", "Yichuan Ma", "Zerun Ma", "Ningsheng Ma", "Runyuan Ma", "Chengqi Lyu", "Haijun Lv", "Han Lv", "Lindong Lu", "Kuikun Liu", "Jiangning Liu", "Yuhong Liu", "Kai Liu", "Hongwei Liu", "Zhoumianze Liu", "Mengjie Liu", "Ziyu Liu", "Wenran Liu", "Yang Liu", "Liwei Liu", "Kaiwen Liu", "Junyao Lin", "Junming Lin", "Tianyang Lin", "Dahua Lin", "Jianze Liang", "Linyang Li", "Peiji Li", "Zonglin Li", "Zehao Li", "Pengze Li", "Guoyan Li", "Lingkai Kong", "Linglin Jing", "Zhenjiang Jin", "Feifei Jiang", "Qian Jiang", "Junhao Huang", "Zixian Huang", "Haian Huang", "Zhouqi Hua", "Han Hu", "Linfeng Hou", "Yinan He", "Conghui He", "Tianyao He", "Xu Guo", "Qipeng Guo", "Aijia Guo", "Yuzhe Gu", "Lixin Gu", "Jingyang Gong", "Qiming Ge", "Jiaye Ge", "Songyang Gao", "Jianfei Gao", "Xinyu Fang", "Caihua fan", "Yue Fan", "Yanhui Duan", "Zichen Ding", "Shengyuan Ding", "Xuanlang Dai", "Erfei Cui", "Ganqu Cui", "Pei Chu", "Tao Chu", "Guangran Cheng", "Yu Cheng", "Kai Chen", "Yongkang Chen", "Chiyu Chen", "Guanzhou Chen", "Qiaosheng Chen", "Sitao Chen", "Xin Chen", "Haojiong Chen", "Yicheng Chen", "Weihan Cao", "Yuhang Cao", "Qinglong Cao", "Lei Bai" ], "github": "", "project_page": "", "abstract": "We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancement across both general and scientific domains. Beyond stronger reasoning and image-text understanding capabilities, its intelligence is augmented with advanced agent capabilities. Simultaneously, its scientific expertise has been vastly expanded to master over 100 specialized tasks across critical science fields, including chemistry, materials, life sciences, and earth sciences. Achieving this massive scale is made possible by the robust infrastructure support of XTuner and LMDeploy, which facilitates highly efficient Reinforcement Learning (RL) training at the 1-trillion parameter level while ensuring strict precision consistency between training and inference. By seamlessly integrating these advancements, Intern-S1-Pro further fortifies the fusion of general and specialized intelligence, working as a Specializable Generalist, demonstrating its position in the top tier of open-source models for general capabilities, while outperforming proprietary models in the depth of specialized scientific tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.23883", "title": "BioVITA: Biological Dataset, Model, and Benchmark for Visual-Textual-Acoustic Alignment", "authors": [ "Risa Shinoda", "Kaede Shiohara", "Nakamasa Inoue", "Kuniaki Saito", "Hiroaki Santo", "Fumio Okura" ], "github": "", "project_page": "", "abstract": "Understanding animal species from multimodal data poses an emerging challenge at the intersection of computer vision and ecology. While recent biological models, such as BioCLIP, have demonstrated strong alignment between images and textual taxonomic information for species identification, the integration of the audio modality remains an open problem. We propose BioVITA, a novel visual-textual-acoustic alignment framework for biological applications. BioVITA involves (i) a training dataset, (ii) a representation model, and (iii) a retrieval benchmark. First, we construct a large-scale training dataset comprising 1.3 million audio clips and 2.3 million images, covering 14,133 species annotated with 34 ecological trait labels. Second, building upon BioCLIP2, we introduce a two-stage training framework to effectively align audio representations with visual and textual representations. Third, we develop a cross-modal retrieval benchmark that covers all possible directional retrieval across the three modalities (i.e., image-to-audio, audio-to-text, text-to-image, and their reverse directions), with three taxonomic levels: Family, Genus, and Species. Extensive experiments demonstrate that our model learns a unified representation space that captures species-level semantics beyond taxonomy, advancing multimodal biodiversity understanding. The project page is available at: https://dahlian00.github.io/BioVITA_Page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.25744", "title": "MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models", "authors": [ "Bocheng Zou", "Mu Cai", "Mark Stanley", "Dingfu Lu", "Yong Jae Lee" ], "github": "https://github.com/orgs/MuRF-VFM", "project_page": "", "abstract": "Vision Foundation Models (VFMs) have become the cornerstone of modern computer vision, offering robust representations across a wide array of tasks. While recent advances allow these models to handle varying input sizes during training, inference typically remains restricted to a single, fixed scale. This prevalent single-scale paradigm overlooks a fundamental property of visual perception: varying resolutions offer complementary inductive biases, where low-resolution views excel at global semantic recognition and high-resolution views are essential for fine-grained refinement. In this work, we propose Multi-Resolution Fusion (MuRF), a simple yet universally effective strategy to harness this synergy at inference time. Instead of relying on a single view, MuRF constructs a unified representation by processing an image at multiple resolutions through a frozen VFM and fusing the resulting features. The universality of MuRF is its most compelling attribute. It is not tied to a specific architecture, serving instead as a fundamental, training-free enhancement to visual representation. We empirically validate this by applying MuRF to a broad spectrum of critical computer vision tasks across multiple distinct VFM families - primarily DINOv2, but also demonstrating successful generalization to contrastive models like SigLIP2.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/orgs/MuRF-VFM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.24943", "title": "FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol", "authors": [ "Jie Zhu", "Yimin Tian", "Boyang Li", "Kehao Wu", "Zhongzhi Liang", "Junhui Li", "Xianyin Zhang", "Lifan Guo", "Feng Chen", "Yong Liu", "Chi Zhang" ], "github": "https://github.com/ByteDance-Seed/seed-oss", "project_page": "", "abstract": "This paper introduces FinMCP-Bench, a novel benchmark for evaluating large language models (LLMs) in solving real-world financial problems through tool invocation of financial model context protocols. FinMCP-Bench contains 613 samples spanning 10 main scenarios and 33 sub-scenarios, featuring both real and synthetic user queries to ensure diversity and authenticity. It incorporates 65 real financial MCPs and three types of samples, single tool, multi-tool, and multi-turn, allowing evaluation of models across different levels of task complexity. Using this benchmark, we systematically assess a range of mainstream LLMs and propose metrics that explicitly measure tool invocation accuracy and reasoning capabilities. FinMCP-Bench provides a standardized, practical, and challenging testbed for advancing research on financial LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByteDance-Seed/seed-oss", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.21768", "title": "Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors", "authors": [ "Yuze Qin", "Qingyong Li", "Zhiqing Guo", "Wen Wang", "Yan Liu", "Yangli-ao Geng" ], "github": "https://github.com/Onemissed/PW-FouCast", "project_page": "", "abstract": "Precipitation nowcasting is critical for disaster mitigation and aviation safety. However, radar-only models frequently suffer from a lack of large-scale atmospheric context, leading to performance degradation at longer lead times. While integrating meteorological variables predicted by weather foundation models offers a potential remedy, existing architectures fail to reconcile the profound representational heterogeneities between radar imagery and meteorological data. To bridge this gap, we propose PW-FouCast, a novel frequency-domain fusion framework that leverages Pangu-Weather forecasts as spectral priors within a Fourier-based backbone. Our architecture introduces three key innovations: (i) Pangu-Weather-guided Frequency Modulation to align spectral magnitudes and phases with meteorological priors; (ii) Frequency Memory to correct phase discrepancies and preserve temporal evolution; and (iii) Inverted Frequency Attention to reconstruct high-frequency details typically lost in spectral filtering. Extensive experiments on the SEVIR and MeteoNet benchmarks demonstrate that PW-FouCast achieves state-of-the-art performance, effectively extending the reliable forecast horizon while maintaining structural fidelity. Our code is available at https://github.com/Onemissed/PW-FouCast.", "github_manual": "", "github_regex": "https://github.com/Onemissed/PW-FouCast", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.25728", "title": "PixelSmile: Toward Fine-Grained Facial Expression Editing", "authors": [ "Jiabin Hua", "Hengyuan Xu", "Aojie Li", "Wei Cheng", "Gang Yu", "Xingjun Ma", "Yu-Gang Jiang" ], "github": "https://github.com/Ammmob/PixelSmile", "project_page": "", "abstract": "Fine-grained facial expression editing has long been limited by intrinsic semantic overlap. To address this, we construct the Flex Facial Expression (FFE) dataset with continuous affective annotations and establish FFE-Bench to evaluate structural confusion, editing accuracy, linear controllability, and the trade-off between expression editing and identity preservation. We propose PixelSmile, a diffusion framework that disentangles expression semantics via fully symmetric joint training. PixelSmile combines intensity supervision with contrastive learning to produce stronger and more distinguishable expressions, achieving precise and stable linear expression control through textual latent interpolation. Extensive experiments demonstrate that PixelSmile achieves superior disentanglement and robust identity preservation, confirming its effectiveness for continuous, controllable, and fine-grained expression editing, while naturally supporting smooth expression blending.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Ammmob/PixelSmile", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.25502", "title": "RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models", "authors": [ "Yufeng Yang", "Xianfang Zeng", "Zhangqi Jiang", "Fukun Yin", "Jianzhuang Liu", "Wei Cheng", "jinghong lan", "Shiyu Liu", "Yuqi Peng", "Gang YU", "Shifeng Chen" ], "github": "", "project_page": "", "abstract": "Image restoration under real-world degradations is critical for downstream tasks such as autonomous driving and object detection. However, existing restoration models are often limited by the scale and distribution of their training data, resulting in poor generalization to real-world scenarios. Recently, large-scale image editing models have shown strong generalization ability in restoration tasks, especially for closed-source models like Nano Banana Pro, which can restore images while preserving consistency. Nevertheless, achieving such performance with those large universal models requires substantial data and computational costs. To address this issue, we construct a large-scale dataset covering nine common real-world degradation types and train a state-of-the-art open-source model to narrow the gap with closed-source alternatives. Furthermore, we introduce RealIR-Bench, which contains 464 real-world degraded images and tailored evaluation metrics focusing on degradation removal and consistency preservation. Extensive experiments demonstrate our model ranks first among open-source methods, achieving state-of-the-art performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.14366", "title": "Representation Alignment for Just Image Transformers is not Easier than You Think", "authors": [ "Jaeyo Shin", "Jiwook Kim", "Hyunjung Shim" ], "github": "https://github.com/kaist-cvml/PixelREPA", "project_page": "", "abstract": "Representation Alignment (REPA) has emerged as a simple way to accelerate Diffusion Transformers training in latent space. At the same time, pixel-space diffusion transformers such as Just image Transformers (JiT) have attracted growing attention because they remove a dependency on a pretrained tokenizer, and then avoid the reconstruction bottleneck of latent diffusion. This paper shows that the REPA can fail for JiT. REPA yields worse FID for JiT as training proceeds and collapses diversity on image subsets that are tightly clustered in the representation space of pretrained semantic encoder on ImageNet. We trace the failure to an information asymmetry: denoising occurs in the high dimensional image space, while the semantic target is strongly compressed, making direct regression a shortcut objective. We propose PixelREPA, which transforms the alignment target and constrains alignment with a Masked Transformer Adapter that combines a shallow transformer adapter with partial token masking. PixelREPA improves both training convergence and final quality. PixelREPA reduces FID from 3.66 to 3.17 for JiT-B/16 and improves Inception Score (IS) from 275.1 to 284.6 on ImageNet 256 times 256, while achieving > 2times faster convergence. Finally, PixelREPA-H/16 achieves FID=1.81 and IS=317.2. Our code is available at https://github.com/kaist-cvml/PixelREPA.", "github_manual": "", "github_regex": "https://github.com/kaist-cvml/PixelREPA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.25702", "title": "S2D2: Fast Decoding for Diffusion LLMs via Training-Free Self-Speculation", "authors": [ "Ligong Han", "Hao Wang", "Han Gao", "Kai Xu", "Akash Srivastava" ], "github": "https://github.com/phymhan/S2D2", "project_page": "", "abstract": "Block-diffusion language models offer a promising path toward faster-than-autoregressive generation by combining block-wise autoregressive decoding with within-block parallel denoising. However, in the few-step regime needed for practical acceleration, standard confidence-thresholded decoding is often brittle: aggressive thresholds hurt quality, while conservative thresholds require unnecessary denoising steps. Existing approaches that address this issue either require additional training or incur extra test-time compute. We present S2D2, a training-free self-speculative decoding framework for block-diffusion language models. Our key observation is that a block-diffusion model becomes autoregressive when the block size is reduced to one, allowing the same pretrained model to act as both drafter and verifier. S2D2 inserts a speculative verification step into standard block-diffusion decoding and uses lightweight routing policies to decide when verification is worth its cost. This yields a hybrid decoding trajectory in which diffusion proposes tokens in parallel, while the autoregressive mode acts as a local sequence-level critic. Across three mainstream block-diffusion families, S2D2 consistently improves the accuracy-speed tradeoff over strong confidence-thresholding baselines. On SDAR, we observe up to 4.7times speedup over autoregressive decoding, and up to 1.57times over a tuned dynamic decoding baseline while improving accuracy by up to 4.5 points. On LLaDA2.1-Mini, S2D2 remains complementary to built-in self-correction, including a conservative setting where it is 4.4times faster than the static baseline with slightly higher accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/phymhan/S2D2", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.25718", "title": "Electrostatic Photoluminescence Tuning in All-Solid-State Perovskite Transistors", "authors": [ "Vladimir Bruevich", "Dmitry Maslennikov", "Beier Hu", "Artem A. Bakulin", "Vitaly Podzorov" ], "github": "", "project_page": "", "abstract": "We demonstrate an all solid state semiconductor device, based on epitaxial single crystalline metal halide perovskites, enabling reversible control of a perovskite photoluminescence with a gate voltage. Fundamentally distinct from electroluminescent diodes, such a photoluminescence field effect transistor uses the gate electric field to electrostatically modulate the interfacial density of mobile charges, thereby affecting the radiative and nonradiative recombination channels of photocarriers. Varying the gate voltage in such transistors efficiently changes the rate of nonradiative interfacial recombination and modulates the photoluminescence intensity by 65 to 98 percent (depending on temperature). At favorable gating, nearly complete elimination of non-radiative losses can be achieved. This functionality, coupled with the strong visible-range absorption and emission, possible due to the high absorption coefficient, as well as controllable thickness and macroscopically homogeneous morphology of epitaxial perovskite films, leads to high external photoluminescence quantum efficiencies realized in large-area, thin-film devices. Such high-efficiency, scalable, electrostatically tunable optoelectronic switches broaden the potential applications of metal-halide perovskites in photonics and optoelectronics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.25562", "title": "Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes", "authors": [ "Yuqian Fu", "Haohuan Huang", "Kaiwen Jiang", "Yuanheng Zhu", "Dongbin Zhao" ], "github": "", "project_page": "", "abstract": "On-policy distillation (OPD) is appealing for large language model (LLM) post-training because it evaluates teacher feedback on student-generated rollouts rather than fixed teacher traces. In long-horizon settings, however, the common sampled-token variant is fragile: it reduces distribution matching to a one-token signal and becomes increasingly unreliable as rollouts drift away from prefixes the teacher commonly visits. We revisit OPD from the estimator and implementation sides. Theoretically, token-level OPD is biased relative to sequence-level reverse-KL, but it has a much tighter worst-case variance bound; our toy study shows the same tradeoff empirically, with stronger future-reward coupling producing higher gradient variance and less stable learning. Empirically, we identify three failure modes of sampled-token OPD: an imbalanced one-token signal, unreliable teacher guidance on student-generated prefixes, and distortions caused by tokenizer or special-token mismatch. We address these issues with teacher top-K local support matching, implemented as truncated reverse-KL with top-p rollout sampling and special-token masking. Across single-task math reasoning and multi-task agentic-plus-math training, this objective yields more stable optimization and better downstream performance than sampled-token OPD.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.18718", "title": "MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution", "authors": [ "Minhua Lin", "Zhiwei Zhang", "Hanqing Lu", "Hui Liu", "Xianfeng Tang", "Qi He", "Xiang Zhang", "Suhang Wang" ], "github": "https://github.com/ventr1c/memma", "project_page": "", "abstract": "Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction, yet most existing systems treat construction, retrieval, and utilization as isolated subroutines. This creates two coupled challenges: strategic blindness on the forward path of the memory cycle, where construction and retrieval are driven by local heuristics rather than explicit strategic reasoning, and sparse, delayed supervision on the backward path, where downstream failures rarely translate into direct repairs of the memory bank. To address these challenges, we propose MemMA, a plug-and-play multi-agent framework that coordinates the memory cycle along both the forward and backward paths. On the forward path, a Meta-Thinker produces structured guidance that steers a Memory Manager during construction and directs a Query Reasoner during iterative retrieval. On the backward path, MemMA introduces in-situ self-evolving memory construction, which synthesizes probe QA pairs, verifies the current memory, and converts failures into repair actions before the memory is finalized. Extensive experiments on LoCoMo show that MemMA consistently outperforms existing baselines across multiple LLM backbones and improves three different storage backends in a plug-and-play manner. Our code is publicly available at https://github.com/ventr1c/memma.", "github_manual": "", "github_regex": "https://github.com/ventr1c/memma", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.24793", "title": "AVControl: Efficient Framework for Training Audio-Visual Controls", "authors": [ "Matan Ben-Yosef", "Tavi Halperin", "Naomi Ken Korem", "Mohammad Salama", "Harel Cain", "Asaf Joseph", "Anthony Chen", "Urska Jelercic", "Ofir Bibi" ], "github": "https://github.com/Lightricks/LTX-2/tree/main/packages/ltx-trainer", "project_page": "", "abstract": "Controlling video and audio generation requires diverse modalities, from depth and pose to camera trajectories and audio transformations, yet existing approaches either train a single monolithic model for a fixed set of controls or introduce costly architectural changes for each new modality. We introduce AVControl, a lightweight, extendable framework built on LTX-2, a joint audio-visual foundation model, where each control modality is trained as a separate LoRA on a parallel canvas that provides the reference signal as additional tokens in the attention layers, requiring no architectural changes beyond the LoRA adapters themselves. We show that simply extending image-based in-context methods to video fails for structural control, and that our parallel canvas approach resolves this. On the VACE Benchmark, we outperform all evaluated baselines on depth- and pose-guided generation, inpainting, and outpainting, and show competitive results on camera control and audio-visual benchmarks. Our framework supports a diverse set of independently trained modalities: spatially-aligned controls such as depth, pose, and edges, camera trajectory with intrinsics, sparse motion control, video editing, and, to our knowledge, the first modular audio-visual controls for a joint generation model. Our method is both compute- and data-efficient: each modality requires only a small dataset and converges within a few hundred to a few thousand training steps, a fraction of the budget of monolithic alternatives. We publicly release our code and trained LoRA checkpoints.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Lightricks/LTX-2/tree/main/packages/ltx-trainer", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.24800", "title": "Calibri: Enhancing Diffusion Transformers via Parameter-Efficient Calibration", "authors": [ "Danil Tokhchukov", "Aysel Mirzoeva", "Andrey Kuznetsov", "Konstantin Sobolev" ], "github": "", "project_page": "", "abstract": "In this paper, we uncover the hidden potential of Diffusion Transformers (DiTs) to significantly enhance generative tasks. Through an in-depth analysis of the denoising process, we demonstrate that introducing a single learned scaling parameter can significantly improve the performance of DiT blocks. Building on this insight, we propose Calibri, a parameter-efficient approach that optimally calibrates DiT components to elevate generative quality. Calibri frames DiT calibration as a black-box reward optimization problem, which is efficiently solved using an evolutionary algorithm and modifies just ~100 parameters. Experimental results reveal that despite its lightweight design, Calibri consistently improves performance across various text-to-image models. Notably, Calibri also reduces the inference steps required for image generation, all while maintaining high-quality outputs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.16733", "title": "IQuest-Coder-V1 Technical Report", "authors": [ "Jian Yang", "Wei Zhang", "Shawn Guo", "Zhengmao Ye", "Lin Jing", "Shark Liu", "Yizhi Li", "Jiajun Wu", "Cening Liu", "X. Ma", "Yuyang Song", "Siwei Wu", "Yuwen Li", "L. Liao", "T. Zheng", "Ziling Huang", "Zelong Huang", "Che Liu", "Yan Xing", "Renyuan Li", "Qingsong Cai", "Hanxu Yan", "Siyue Wang", "Shikai Li", "Jason Klein Liu", "An Huang", "Yongsheng Kang", "Jinxing Zhang", "Chuan Hao", "Haowen Wang", "Weicheng Gu", "Ran Tao", "Mingjie Tang", "Peihao Wu", "Jianzhou Wang", "Xianglong Liu", "Weifeng Lv", "Bryan Dai" ], "github": "", "project_page": "", "abstract": "In this report, we introduce the IQuest-Coder-V1 series-(7B/14B/40B/40B-Loop), a new family of code large language models (LLMs). Moving beyond static code representations, we propose the code-flow multi-stage training paradigm, which captures the dynamic evolution of software logic through different phases of the pipeline. Our models are developed through the evolutionary pipeline, starting with the initial pre-training consisting of code facts, repository, and completion data. Following that, we implement a specialized mid-training stage that integrates reasoning and agentic trajectories in 32k-context and repository-scale in 128k-context to forge deep logical foundations. The models are then finalized with post-training of specialized coding capabilities, which is bifurcated into two specialized paths: the thinking path (utilizing reasoning-driven RL) and the instruct path (optimized for general assistance). IQuest-Coder-V1 achieves state-of-the-art performance among competitive models across critical dimensions of code intelligence: agentic software engineering, competitive programming, and complex tool use. To address deployment constraints, the IQuest-Coder-V1-Loop variant introduces a recurrent mechanism designed to optimize the trade-off between model capacity and deployment footprint, offering an architecturally enhanced path for efficacy-efficiency trade-off. We believe the release of the IQuest-Coder-V1 series, including the complete white-box chain of checkpoints from pre-training bases to the final thinking and instruction models, will advance research in autonomous code intelligence and real-world agentic systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.14636", "title": "Nudging Hidden States: Training-Free Model Steering for Chain-of-Thought Reasoning in Large Audio-Language Models", "authors": [ "Lok-Lam Ieong", "Chia-Chien Chen", "Chih-Kai Yang", "Yu-Han Huang", "An-Yu Cheng", "Hung-yi Lee" ], "github": "", "project_page": "", "abstract": "Chain-of-thought (CoT) prompting has been extended to large audio-language models (LALMs) to elicit reasoning, yet enhancing its effectiveness without training remains challenging. We study inference-time model steering as a training-free approach to improve LALM reasoning. We introduce three strategies using diverse information sources and evaluate them across four LALMs and four benchmarks. Results show general accuracy gains up to 4.4% over CoT prompting. Notably, we identify a cross-modal transfer where steering vectors derived from few text samples effectively guide speech-based reasoning, demonstrating high data efficiency. We also examine hyperparameter sensitivity to understand the robustness of these approaches. Our findings position model steering as a practical direction for strengthening LALM reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.25398", "title": "PMT: Plain Mask Transformer for Image and Video Segmentation with Frozen Vision Encoders", "authors": [ "Niccol\u00f2 Cavagnero", "Narges Norouzi", "Gijs Dubbelman", "Daan de Geus" ], "github": "https://github.com/tue-mps/pmt", "project_page": "", "abstract": "Vision Foundation Models (VFMs) pre-trained at scale enable a single frozen encoder to serve multiple downstream tasks simultaneously. Recent VFM-based encoder-only models for image and video segmentation, such as EoMT and VidEoMT, achieve competitive accuracy with remarkably low latency, yet they require finetuning the encoder, sacrificing the multi-task encoder sharing that makes VFMs practically attractive for large-scale deployment. To reconcile encoder-only simplicity and speed with frozen VFM features, we propose the Plain Mask Decoder (PMD), a fast Transformer-based segmentation decoder that operates on top of frozen VFM features. The resulting model, the Plain Mask Transformer (PMT), preserves the architectural simplicity and low latency of encoder-only designs while keeping the encoder representation unchanged and shareable. The design seamlessly applies to both image and video segmentation, inheriting the generality of the encoder-only framework. On standard image segmentation benchmarks, PMT matches the frozen-encoder state of the art while running up to ~3x faster. For video segmentation, it even performs on par with fully finetuned methods, while being up to 8x faster than state-of-the-art frozen-encoder models. Code: https://github.com/tue-mps/pmt.", "github_manual": "", "github_regex": "https://github.com/tue-mps/pmt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.13904", "title": "Pixel-level Scene Understanding in One Token: Visual States Need What-is-Where Composition", "authors": [ "Seokmin Lee", "Yunghee Lee", "Byeonghyun Pak", "Byeongju Woo" ], "github": "", "project_page": "", "abstract": "For robotic agents operating in dynamic environments, learning visual state representations from streaming video observations is essential for sequential decision making. Recent self-supervised learning methods have shown strong transferability across vision tasks, but they do not explicitly address what a good visual state should encode. We argue that effective visual states must capture what-is-where by jointly encoding the semantic identities of scene elements and their spatial locations, enabling reliable detection of subtle dynamics across observations. To this end, we propose CroBo, a visual state representation learning framework based on a global-to-local reconstruction objective. Given a reference observation compressed into a compact bottleneck token, CroBo learns to reconstruct heavily masked patches in a local target crop from sparse visible cues, using the global bottleneck token as context. This learning objective encourages the bottleneck token to encode a fine-grained representation of scene-wide semantic entities, including their identities, spatial locations, and configurations. As a result, the learned visual states reveal how scene elements move and interact over time, supporting sequential decision making. We evaluate CroBo on diverse vision-based robot policy learning benchmarks, where it achieves state-of-the-art performance. Reconstruction analyses and perceptual straightness experiments further show that the learned representations preserve pixel-level scene composition and encode what-moves-where across observations. Project page available at: https://seokminlee-chris.github.io/CroBo-ProjectPage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.24575", "title": "VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models", "authors": [ "Qijia He", "Xunmei Liu", "Hammaad Memon", "Ziang Li", "Zixian Ma", "Jaemin Cho", "Jason Ren", "Daniel S Weld", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Scalable Vector Graphics (SVG) are an essential format for technical illustration and digital design, offering precise resolution independence and flexible semantic editability. In practice, however, original vector source files are frequently lost or inaccessible, leaving only \"flat\" rasterized versions (e.g., PNG or JPEG) that are difficult to modify or scale. Manually reconstructing these figures is a prohibitively labor-intensive process, requiring specialized expertise to recover the original geometric intent. To bridge this gap, we propose VFIG, a family of Vision-Language Models trained for complex and high-fidelity figure-to-SVG conversion. While this task is inherently data-driven, existing datasets are typically small-scale and lack the complexity of professional diagrams. We address this by introducing VFIG-DATA, a large-scale dataset of 66K high-quality figure-SVG pairs, curated from a diverse mix of real-world paper figures and procedurally generated diagrams. Recognizing that SVGs are composed of recurring primitives and hierarchical local structures, we introduce a coarse-to-fine training curriculum that begins with supervised fine-tuning (SFT) to learn atomic primitives and transitions to reinforcement learning (RL) refinement to optimize global diagram fidelity, layout consistency, and topological edge cases. Finally, we introduce VFIG-BENCH, a comprehensive evaluation suite with novel metrics designed to measure the structural integrity of complex figures. VFIG achieves state-of-the-art performance among open-source models and performs on par with GPT-5.2, achieving a VLM-Judge score of 0.829 on VFIG-BENCH.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.24961", "title": "Can MLLMs Read Students' Minds? Unpacking Multimodal Error Analysis in Handwritten Math", "authors": [ "Dingjie Song", "Tianlong Xu", "Yi-Fan Zhang", "Hang Li", "Zhiling Yan", "Xing Fan", "Haoyang Li", "Lichao Sun", "Qingsong Wen" ], "github": "", "project_page": "", "abstract": "Assessing student handwritten scratchwork is crucial for personalized educational feedback but presents unique challenges due to diverse handwriting, complex layouts, and varied problem-solving approaches. Existing educational NLP primarily focuses on textual responses and neglects the complexity and multimodality inherent in authentic handwritten scratchwork. Current multimodal large language models (MLLMs) excel at visual reasoning but typically adopt an \"examinee perspective\", prioritizing generating correct answers rather than diagnosing student errors. To bridge these gaps, we introduce ScratchMath, a novel benchmark specifically designed for explaining and classifying errors in authentic handwritten mathematics scratchwork. Our dataset comprises 1,720 mathematics samples from Chinese primary and middle school students, supporting two key tasks: Error Cause Explanation (ECE) and Error Cause Classification (ECC), with seven defined error types. The dataset is meticulously annotated through rigorous human-machine collaborative approaches involving multiple stages of expert labeling, review, and verification. We systematically evaluate 16 leading MLLMs on ScratchMath, revealing significant performance gaps relative to human experts, especially in visual recognition and logical reasoning. Proprietary models notably outperform open-source models, with large reasoning models showing strong potential for error explanation. All evaluation data and frameworks are publicly available to facilitate further research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.24844", "title": "Reaching Beyond the Mode: RL for Distributional Reasoning in Language Models", "authors": [ "Isha Puri", "Mehul Damani", "Idan Shenfeld", "Marzyeh Ghassemi", "Jacob Andreas", "Yoon Kim" ], "github": "", "project_page": "", "abstract": "Given a question, a language model (LM) implicitly encodes a distribution over possible answers. In practice, post-training procedures for LMs often collapse this distribution onto a single dominant mode. While this is generally not a problem for benchmark-style evaluations that assume one correct answer, many real-world tasks inherently involve multiple valid answers or irreducible uncertainty. Examples include medical diagnosis, ambiguous question answering, and settings with incomplete information. In these cases, we would like LMs to generate multiple plausible hypotheses, ideally with confidence estimates for each one, and without computationally intensive repeated sampling to generate non-modal answers. This paper describes a multi-answer reinforcement learning approach for training LMs to perform distributional reasoning over multiple answers during inference. We modify the RL objective to enable models to explicitly generate multiple candidate answers in a single forward pass, internalizing aspects of inference-time search into the model's generative process. Across question-answering, medical diagnostic, and coding benchmarks, we observe improved diversity, coverage, and set-level calibration scores compared to single answer trained baselines. Models trained with our approach require fewer tokens to generate multiple answers than competing approaches. On coding tasks, they are also substantially more accurate. These results position multi-answer RL as a principled and compute-efficient alternative to inference-time scaling procedures such as best-of-k. Code and more information can be found at https://multi-answer-rl.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.24517", "title": "AVO: Agentic Variation Operators for Autonomous Evolutionary Search", "authors": [ "Terry Chen", "Zhifan Ye", "Bing Xu", "Zihao Ye", "Timmy Liu", "Ali Hassani", "Tianqi Chen", "Andrew Kerr", "Haicheng Wu", "Yang Xu", "Yu-Jung Chen", "Hanfeng Chen", "Aditya Kane", "Ronny Krashinsky", "Ming-Yu Liu", "Vinod Grover", "Luis Ceze", "Roger Bringmann", "John Tran", "Wei Liu", "Fung Xie", "Michael Lightstone", "Humphrey Shi" ], "github": "", "project_page": "", "abstract": "Agentic Variation Operators (AVO) are a new family of evolutionary variation operators that replace the fixed mutation, crossover, and hand-designed heuristics of classical evolutionary search with autonomous coding agents. Rather than confining a language model to candidate generation within a prescribed pipeline, AVO instantiates variation as a self-directed agent loop that can consult the current lineage, a domain-specific knowledge base, and execution feedback to propose, repair, critique, and verify implementation edits. We evaluate AVO on attention, among the most aggressively optimized kernel targets in AI, on NVIDIA Blackwell (B200) GPUs. Over 7 days of continuous autonomous evolution on multi-head attention, AVO discovers kernels that outperform cuDNN by up to 3.5% and FlashAttention-4 by up to 10.5% across the evaluated configurations. The discovered optimizations transfer readily to grouped-query attention, requiring only 30 minutes of additional autonomous adaptation and yielding gains of up to 7.0% over cuDNN and 9.3% over FlashAttention-4. Together, these results show that agentic variation operators move beyond prior LLM-in-the-loop evolutionary pipelines by elevating the agent from candidate generator to variation operator, and can discover performance-critical micro-architectural optimizations that produce kernels surpassing state-of-the-art expert-engineered attention implementations on today's most advanced GPU hardware.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-27", "arxiv_id": "2603.24836", "title": "WAFT-Stereo: Warping-Alone Field Transforms for Stereo Matching", "authors": [ "Yihan Wang", "Jia Deng" ], "github": "https://github.com/princeton-vl/WAFT-Stereo", "project_page": "", "abstract": "We introduce WAFT-Stereo, a simple and effective warping-based method for stereo matching. WAFT-Stereo demonstrates that cost volumes, a common design used in many leading methods, are not necessary for strong performance and can be replaced by warping with improved efficiency. WAFT-Stereo ranks first on ETH3D, KITTI and Middlebury public benchmarks, reducing the zero-shot error by 81% on ETH3D benchmark, while being 1.8-6.7x faster than competitive methods. Code and model weights are available at https://github.com/princeton-vl/WAFT-Stereo.", "github_manual": "", "github_regex": "https://github.com/princeton-vl/WAFT-Stereo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.25746", "title": "ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling", "authors": [ "Yawen Luo", "Xiaoyu Shi", "Junhao Zhuang", "Yutian Chen", "Quande Liu", "Xintao Wang", "Pengfei Wan", "Tianfan Xue" ], "github": "", "project_page": "", "abstract": "Multi-shot video generation is crucial for long narrative storytelling, yet current bidirectional architectures suffer from limited interactivity and high latency. We propose ShotStream, a novel causal multi-shot architecture that enables interactive storytelling and efficient on-the-fly frame generation. By reformulating the task as next-shot generation conditioned on historical context, ShotStream allows users to dynamically instruct ongoing narratives via streaming prompts. We achieve this by first fine-tuning a text-to-video model into a bidirectional next-shot generator, which is then distilled into a causal student via Distribution Matching Distillation. To overcome the challenges of inter-shot consistency and error accumulation inherent in autoregressive generation, we introduce two key innovations. First, a dual-cache memory mechanism preserves visual coherence: a global context cache retains conditional frames for inter-shot consistency, while a local context cache holds generated frames within the current shot for intra-shot consistency. And a RoPE discontinuity indicator is employed to explicitly distinguish the two caches to eliminate ambiguity. Second, to mitigate error accumulation, we propose a two-stage distillation strategy. This begins with intra-shot self-forcing conditioned on ground-truth historical shots and progressively extends to inter-shot self-forcing using self-generated histories, effectively bridging the train-test gap. Extensive experiments demonstrate that ShotStream generates coherent multi-shot videos with sub-second latency, achieving 16 FPS on a single GPU. It matches or exceeds the quality of slower bidirectional models, paving the way for real-time interactive storytelling. Training and inference code, as well as the models, are available on our", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.25716", "title": "Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models", "authors": [ "Kaijin Chen", "Dingkang Liang", "Xin Zhou", "Yikang Ding", "Xiaoqiang Liu", "Pengfei Wan", "Xiang Bai" ], "github": "", "project_page": "", "abstract": "Video world models have shown immense potential in simulating the physical world, yet existing memory mechanisms primarily treat environments as static canvases. When dynamic subjects hide out of sight and later re-emerge, current methods often struggle, leading to frozen, distorted, or vanishing subjects. To address this, we introduce Hybrid Memory, a novel paradigm requiring models to simultaneously act as precise archivists for static backgrounds and vigilant trackers for dynamic subjects, ensuring motion continuity during out-of-view intervals. To facilitate research in this direction, we construct HM-World, the first large-scale video dataset dedicated to hybrid memory. It features 59K high-fidelity clips with decoupled camera and subject trajectories, encompassing 17 diverse scenes, 49 distinct subjects, and meticulously designed exit-entry events to rigorously evaluate hybrid coherence. Furthermore, we propose HyDRA, a specialized memory architecture that compresses memory into tokens and utilizes a spatiotemporal relevance-driven retrieval mechanism. By selectively attending to relevant motion cues, HyDRA effectively preserves the identity and motion of hidden subjects. Extensive experiments on HM-World demonstrate that our method significantly outperforms state-of-the-art approaches in both dynamic subject consistency and overall generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.22782", "title": "Know3D: Prompting 3D Generation with Knowledge from Vision-Language Models", "authors": [ "Wenyue Chen", "Wenjue Chen", "Peng Li", "Qinghe Wang", "Xu Jia", "Heliang Zheng", "Rongfei Jia", "Yuan Liu", "Ronggang Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in 3D generation have improved the fidelity and geometric details of synthesized 3D assets. However, due to the inherent ambiguity of single-view observations and the lack of robust global structural priors caused by limited 3D training data, the unseen regions generated by existing models are often stochastic and difficult to control, which may sometimes fail to align with user intentions or produce implausible geometries. In this paper, we propose Know3D, a novel framework that incorporates rich knowledge from multimodal large language models into 3D generative processes via latent hidden-state injection, enabling language-controllable generation of the back-view for 3D assets. We utilize a VLM-diffusion-based model, where the VLM is responsible for semantic understanding and guidance. The diffusion model acts as a bridge that transfers semantic knowledge from the VLM to the 3D generation model. In this way, we successfully bridge the gap between abstract textual instructions and the geometric reconstruction of unobserved regions, transforming the traditionally stochastic back-view hallucination into a semantically controllable process, demonstrating a promising direction for future 3D generation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.25158", "title": "Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills", "authors": [ "Jingwei Ni", "Yihao Liu", "Xinpeng Liu", "Yutao Sun", "Mengyu Zhou", "Pengyu Cheng", "Dexin Wang", "Xiaoxi Jiang", "Guanjun Jiang" ], "github": "", "project_page": "", "abstract": "Equipping Large Language Model (LLM) agents with domain-specific skills is critical for tackling complex tasks. Yet, manual authoring creates a severe scalability bottleneck. Conversely, automated skill generation often yields fragile or fragmented results because it either relies on shallow parametric knowledge or sequentially overfits to non-generalizable trajectory-local lessons. To overcome this, we introduce Trace2Skill, a framework that mirrors how human experts author skills: by holistically analyzing broad execution experience before distilling it into a single, comprehensive guide. Instead of reacting sequentially to individual trajectories, Trace2Skill dispatches a parallel fleet of sub-agents to analyze a diverse pool of executions. It extracts trajectory-specific lessons and hierarchically consolidates them into a unified, conflict-free skill directory via inductive reasoning. Trace2Skill supports both deepening existing human-written skills and creating new ones from scratch. Experiments in challenging domains, such as spreadsheet, VisionQA and math reasoning, show that Trace2Skill significantly improves upon strong baselines, including Anthropic's official xlsx skills. Crucially, this trajectory-grounded evolution does not merely memorize task instances or model-specific quirks: evolved skills transfer across LLM scales and generalize to OOD settings. For example, skills evolved by Qwen3.5-35B on its own trajectories improved a Qwen3.5-122B agent by up to 57.65 absolute percentage points on WikiTableQuestions. Ultimately, our results demonstrate that complex agent experience can be packaged into highly transferable, declarative skills -- requiring no parameter updates, no external retrieval modules, and utilizing open-source models as small as 35B parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.25750", "title": "Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models", "authors": [ "Kyudan Jung", "Jihwan Kim", "Soyoon Kim", "Jeongoon Kim", "Jaegul Choo", "Cheonbok Park" ], "github": "", "project_page": "", "abstract": "As the paradigm of AI shifts from text-based LLMs to Speech Language Models (SLMs), there is a growing demand for full-duplex systems capable of real-time, natural human-computer interaction. However, the development of such models is constrained by the scarcity of high-quality, multi-speaker conversational data, as existing large-scale resources are predominantly single-speaker or limited in volume. Addressing the complex dynamics of natural dialogue, such as overlapping and back-channeling remains a challenge, with standard processing pipelines suffering from diarization errors and ASR hallucinations. To bridge this gap, we present a robust and scalable open-source data processing pipeline designed for full-duplex model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.25730", "title": "PackForcing: Short Video Training Suffices for Long Video Sampling and Long Context Inference", "authors": [ "Xiaofeng Mao", "Shaohao Rui", "Kaining Ying", "Bo Zheng", "Chuanhao Li", "Mingmin Chi", "Kaipeng Zhang" ], "github": "https://github.com/ShandaAI/PackForcing", "project_page": "", "abstract": "Autoregressive video diffusion models have demonstrated remarkable progress, yet they remain bottlenecked by intractable linear KV-cache growth, temporal repetition, and compounding errors during long-video generation. To address these challenges, we present PackForcing, a unified framework that efficiently manages the generation history through a novel three-partition KV-cache strategy. Specifically, we categorize the historical context into three distinct types: (1) Sink tokens, which preserve early anchor frames at full resolution to maintain global semantics; (2) Mid tokens, which achieve a massive spatiotemporal compression (32x token reduction) via a dual-branch network fusing progressive 3D convolutions with low-resolution VAE re-encoding; and (3) Recent tokens, kept at full resolution to ensure local temporal coherence. To strictly bound the memory footprint without sacrificing quality, we introduce a dynamic top-k context selection mechanism for the mid tokens, coupled with a continuous Temporal RoPE Adjustment that seamlessly re-aligns position gaps caused by dropped tokens with negligible overhead. Empowered by this principled hierarchical context compression, PackForcing can generate coherent 2-minute, 832x480 videos at 16 FPS on a single H200 GPU. It achieves a bounded KV cache of just 4 GB and enables a remarkable 24x temporal extrapolation (5s to 120s), operating effectively either zero-shot or trained on merely 5-second clips. Extensive results on VBench demonstrate state-of-the-art temporal consistency (26.07) and dynamic degree (56.25), proving that short-video supervision is sufficient for high-quality, long-video synthesis. https://github.com/ShandaAI/PackForcing", "github_manual": "", "github_regex": "https://github.com/ShandaAI/PackForcing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.20466", "title": "Diffutron: A Masked Diffusion Language Model for Turkish Language", "authors": [ "\u015euayp Talha Kocabay", "Talha R\u00fczgar Akku\u015f" ], "github": "", "project_page": "", "abstract": "Masked Diffusion Language Models (MDLMs) have emerged as a compelling non-autoregressive alternative to standard large language models; however, their application to morphologically rich languages remains limited. In this paper, we introduce Diffutron, a masked diffusion language model specifically designed for Turkish. Our approach leverages a resource-efficient training pipeline, starting with LoRA-based continual pre-training of a multilingual encoder on a large-scale corpus. To enable generative capabilities, we employ a progressive instruction-tuning strategy, sequentially adapting the model on general and task-specific instruction sets. Experimental results across comprehensive benchmarks demonstrate that, despite its compact size, our model achieves competitive performance compared to existing multi-billion-parameter baselines. These findings validate the effectiveness of masked diffusion modeling combined with multi-stage tuning for non-autoregressive text generation in Turkish.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.25723", "title": "Natural-Language Agent Harnesses", "authors": [ "Linyue Pan", "Lexiao Zou", "Shuo Guo", "Jingchen Ni", "Hai-Tao Zheng" ], "github": "", "project_page": "", "abstract": "Agent performance increasingly depends on harness engineering, yet harness design is usually buried in controller code and runtime-specific conventions, making it hard to transfer, compare, and study as a scientific object. We ask whether the high-level control logic of an agent harness can instead be externalized as a portable executable artifact. We introduce Natural-Language Agent Harnesses (NLAHs), which express harness behavior in editable natural language, and Intelligent Harness Runtime (IHR), a shared runtime that executes these harnesses through explicit contracts, durable artifacts, and lightweight adapters. Across coding and computer-use benchmarks, we conduct controlled evaluations of operational viability, module ablation, and code-to-text harness migration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.23607", "title": "LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset", "authors": [ "Royden Wagner", "Omer Sahin Tas", "Jaime Villa", "Felix Hauser", "Yinzhe Shen", "Marlon Steiner", "Dominik Strutz", "Carlos Fernandez", "Christian Kinzig", "Guillermo S. Guitierrez-Cabello", "Hendrik K\u00f6nigshof", "Fabian Immel", "Richard Schwarzkopf", "Nils Alexander Rack", "Kevin R\u00f6sch", "Kaiwen Wang", "Jan-Hendrik Pauls", "Martin Lauer", "Igor Gilitschenski", "Holger Caesar", "Christoph Stiller" ], "github": "", "project_page": "", "abstract": "In real-world domains such as self-driving, generalization to rare scenarios remains a fundamental challenge. To address this, we introduce a new dataset designed for end-to-end driving that focuses on long-tail driving events. We provide multi-view video data, trajectories, high-level instructions, and detailed reasoning traces, facilitating in-context learning and few-shot generalization. The resulting benchmark for multimodal models, such as VLMs and VLAs, goes beyond safety and comfort metrics by evaluating instruction following and semantic coherence between model outputs. The multilingual reasoning traces in English, Spanish, and Chinese are from domain experts with diverse cultural backgrounds. Thus, our dataset is a unique resource for studying how different forms of reasoning affect driving competence. Our dataset is available at: https://hf.co/datasets/kit-mrt/kitscenes-longtail", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.24649", "title": "MedOpenClaw: Auditable Medical Imaging Agents Reasoning over Uncurated Full Studies", "authors": [ "Weixiang Shen", "Yanzhu Hu", "Che Liu", "Junde Wu", "Jiayuan Zhu", "Chengzhi Shen", "Min Xu", "Yueming Jin", "Benedikt Wiestler", "Daniel Rueckert", "Jiazhen Pan" ], "github": "https://github.com/openclaw/openclaw", "project_page": "", "abstract": "Currently, evaluating vision-language models (VLMs) in medical imaging tasks oversimplifies clinical reality by relying on pre-selected 2D images that demand significant manual labor to curate. This setup misses the core challenge of realworld diagnostics: a true clinical agent must actively navigate full 3D volumes across multiple sequences or modalities to gather evidence and ultimately support a final decision. To address this, we propose MEDOPENCLAW, an auditable runtime designed to let VLMs operate dynamically within standard medical tools or viewers (e.g., 3D Slicer). On top of this runtime, we introduce MEDFLOWBENCH, a full-study medical imaging benchmark covering multi-sequence brain MRI and lung CT/PET. It systematically evaluates medical agentic capabilities across viewer-only, tool-use, and open-method tracks. Initial results reveal a critical insight: while state-of-the-art LLMs/VLMs (e.g., Gemini 3.1 Pro and GPT-5.4) can successfully navigate the viewer to solve basic study-level tasks, their performance paradoxically degrades when given access to professional support tools due to a lack of precise spatial grounding. By bridging the gap between static-image perception and interactive clinical workflows, MEDOPENCLAW and MEDFLOWBENCH establish a reproducible foundation for developing auditable, full-study medical imaging agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openclaw/openclaw", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.25804", "title": "RealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation", "authors": [ "Jiajun Zhang", "Yuying Li", "Zhixun Li", "Xingyu Guo", "Jingzhuo Wu", "Leqi Zheng", "Yiran Yang", "Jianke Zhang", "Qingbin Li", "Shannan Yan", "Zhetong Li", "Changguo Jia", "Junfei Wu", "Zilei Wang", "Qiang Liu", "Liang Wang" ], "github": "https://github.com/Speakn0w/RealChart2Code", "project_page": "", "abstract": "Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains. However, their ability to replicate complex, multi-panel visualizations from real-world data remains largely unassessed. To address this gap, we introduce \\texttt{RealChart2Code}, a new large-scale benchmark with over 2,800 instances grounded in authentic datasets and featuring tasks with clear analytical intent. Crucially, it is the first benchmark to systematically evaluate chart generation from large-scale raw data and assess iterative code refinement in a multi-turn conversational setting. Our comprehensive evaluation of 14 leading VLMs on RealChart2Code reveals significant performance degradation compared to simpler benchmarks, highlighting their struggles with complex plot structures and authentic data. Our analysis uncovers a substantial performance gap between proprietary and open-weight models and confirms that even state-of-the-art VLMs often fail to accurately replicate intricate, multi-panel charts. These findings provide valuable insights into the current limitations of VLMs and guide future research directions. We release the benchmark and code at https://github.com/Speakn0w/RealChart2Code.", "github_manual": "", "github_regex": "https://github.com/Speakn0w/RealChart2Code", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.23906", "title": "GenMask: Adapting DiT for Segmentation via Direct Mask", "authors": [ "Yuhuan Yang", "Xianwei Zhuang", "Yuxuan Cai", "Chaofan Ma", "Shuai Bai", "Jiangchao Yao", "Ya Zhang", "Junyang Lin", "Yanfeng Wang" ], "github": "", "project_page": "", "abstract": "Recent approaches for segmentation have leveraged pretrained generative models as feature extractors, treating segmentation as a downstream adaptation task via indirect feature retrieval. This implicit use suffers from a fundamental misalignment in representation. It also depends heavily on indirect feature extraction pipelines, which complicate the workflow and limit adaptation. In this paper, we argue that instead of indirect adaptation, segmentation tasks should be trained directly in a generative manner. We identify a key obstacle to this unified formulation: VAE latents of binary masks are sharply distributed, noise robust, and linearly separable, distinct from natural image latents. To bridge this gap, we introduce timesteps sampling strategy for binary masks that emphasizes extreme noise levels for segmentation and moderate noise for image generation, enabling harmonious joint training. We present GenMask, a DiT trains to generate black-and-white segmentation masks as well as colorful images in RGB space under the original generative objective. GenMask preserves the original DiT architecture while removing the need of feature extraction pipelines tailored for segmentation tasks. Empirically, GenMask attains state-of-the-art performance on referring and reasoning segmentation benchmarks and ablations quantify the contribution of each component.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.26664", "title": "Learning to Commit: Generating Organic Pull Requests via Online Repository Memory", "authors": [ "Mo Li", "L. H. Xu", "Qitai Tan", "Ting Cao", "Yunxin Liu" ], "github": "", "project_page": "", "abstract": "Large language model (LLM)-based coding agents achieve impressive results on controlled benchmarks yet routinely produce pull requests that real maintainers reject. The root cause is not functional incorrectness but a lack of organicity: generated code ignores project-specific conventions, duplicates functionality already provided by internal APIs, and violates implicit architectural constraints accumulated over years of development. Simply exposing an agent to the latest repository snapshot is not enough: the snapshot reveals the final state of the codebase, but not the repository-specific change patterns by which that state was reached. We introduce Learning to Commit, a framework that closes this gap through Online Repository Memory. Given a repository with a strict chronological split, the agent performs supervised contrastive reflection on earlier commits: it blindly attempts to resolve each historical issue, compares its prediction against the oracle diff, and distils the gap into a continuously growing set of skills-reusable patterns capturing coding style, internal API usage, and architectural invariants. When a new PR description arrives, the agent conditions its generation on these accumulated skills, producing changes grounded in the project's own evolution rather than generic pretraining priors. Evaluation is conducted on genuinely future, merged pull requests that could not have been seen during the skill-building phase, and spans multiple dimensions including functional correctness, code-style consistency, internal API reuse rate, and modified-region plausibility. Experiments on an expert-maintained repository with rich commit history show that Online Repository Memory effectively improves organicity scores on held-out future tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.24477", "title": "Composer 2 Technical Report", "authors": [ "Cursor Research", "Aaron Chan", "Ahmed Shalaby", "Alexander Wettig", "Aman Sanger", "Andrew Zhai", "Anurag Ajay", "Ashvin Nair", "Charlie Snell", "Chen Lu", "Chen Shen", "Emily Jia", "Federico Cassano", "Hanpeng Liu", "Haoyu Chen", "Henry Wildermuth", "Jacob Jackson", "Janet Li", "Jediah Katz", "Jiajun Yao", "Joey Hejna", "Josh Warner", "Julius Vering", "Kevin Frans", "Lee Danilek", "Less Wright", "Lujing Cen", "Luke Melas-Kyriazi", "Michael Truell", "Michiel de Jong", "Naman Jain", "Nate Schmidt", "Nathan Wang", "Niklas Muennighoff", "Oleg Rybkin", "Paul Loh", "Phillip Kravtsov", "Rishabh Yadav", "Sahil Shah", "Sam Kottler", "Alexander M Rush", "Shengtong Zhang", "Shomil Jain", "Sriram Sankar", "Stefan Heule", "Stuart H. Sul", "Sualeh Asif", "Victor Rong", "Wanqi Zhu", "William Lin", "Yuchen Wu", "Yuri Volkov", "Yury Zemlyanskiy", "Zack Holbrook", "Zhiyuan Zhang" ], "github": "", "project_page": "", "abstract": "Composer 2 is a specialized model designed for agentic software engineering. The model demonstrates strong long-term planning and coding intelligence while maintaining the ability to efficiently solve problems for interactive use. The model is trained in two phases: first, continued pretraining to improve the model's knowledge and latent coding ability, followed by large-scale reinforcement learning to improve end-to-end coding performance through stronger reasoning, accurate multi-step execution, and coherence on long-horizon realistic coding problems. We develop infrastructure to support training in the same Cursor harness that is used by the deployed model, with equivalent tools and structure, and use environments that match real problems closely. To measure the ability of the model on increasingly difficult tasks, we introduce a benchmark derived from real software engineering problems in large codebases including our own. Composer 2 is a frontier-level coding model and demonstrates a process for training strong domain-specialized models. On our CursorBench evaluations the model achieves a major improvement in accuracy compared to previous Composer models (61.3). On public benchmarks the model scores 61.7 on Terminal-Bench and 73.7 on SWE-bench Multilingual in our harness, comparable to state-of-the-art systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-30", "arxiv_id": "2603.22582", "title": "Lie to Me: How Faithful Is Chain-of-Thought Reasoning in Reasoning Models?", "authors": [ "Richard J. Young" ], "github": "", "project_page": "", "abstract": "Chain-of-thought (CoT) reasoning has been proposed as a transparency mechanism for large language models in safety-critical deployments, yet its effectiveness depends on faithfulness (whether models accurately verbalize the factors that actually influence their outputs), a property that prior evaluations have examined in only two proprietary models, finding acknowledgment rates as low as 25% for Claude 3.7 Sonnet and 39% for DeepSeek-R1. To extend this evaluation across the open-weight ecosystem, this study tests 12 open-weight reasoning models spanning 9 architectural families (7B-685B parameters) on 498 multiple-choice questions from MMLU and GPQA Diamond, injecting six categories of reasoning hints (sycophancy, consistency, visual pattern, metadata, grader hacking, and unethical information) and measuring the rate at which models acknowledge hint influence in their CoT when hints successfully alter answers. Across 41,832 inference runs, overall faithfulness rates range from 39.7% (Seed-1.6-Flash) to 89.9% (DeepSeek-V3.2-Speciale) across model families, with consistency hints (35.5%) and sycophancy hints (53.9%) exhibiting the lowest acknowledgment rates. Training methodology and model family predict faithfulness more strongly than parameter count, and keyword-based analysis reveals a striking gap between thinking-token acknowledgment (approximately 87.5%) and answer-text acknowledgment (approximately 28.6%), suggesting that models internally recognize hint influence but systematically suppress this acknowledgment in their outputs. These findings carry direct implications for the viability of CoT monitoring as a safety mechanism and suggest that faithfulness is not a fixed property of reasoning models but varies systematically with architecture, training method, and the nature of the influencing cue.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27862", "title": "ImagenWorld: Stress-Testing Image Generation Models with Explainable Human Evaluation on Open-ended Real-World Tasks", "authors": [ "Samin Mahdizadeh Sani", "Max Ku", "Nima Jamali", "Matina Mahdizadeh Sani", "Paria Khoshtab", "Wei-Chieh Sun", "Parnian Fazel", "Zhi Rui Tam", "Thomas Chong", "Edisy Kin Wai Chan", "Donald Wai Tong Tsang", "Chiao-Wei Hsu", "Ting Wai Lam", "Ho Yin Sam Ng", "Chiafeng Chu", "Chak-Wing Mak", "Keming Wu", "Hiu Tung Wong", "Yik Chun Ho", "Chi Ruan", "Zhuofeng Li", "I-Sheng Fang", "Shih-Ying Yeh", "Ho Kei Cheng", "Ping Nie", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Advances in diffusion, autoregressive, and hybrid models have enabled high-quality image synthesis for tasks such as text-to-image, editing, and reference-guided composition. Yet, existing benchmarks remain limited, either focus on isolated tasks, cover only narrow domains, or provide opaque scores without explaining failure modes. We introduce ImagenWorld, a benchmark of 3.6K condition sets spanning six core tasks (generation and editing, with single or multiple references) and six topical domains (artworks, photorealistic images, information graphics, textual graphics, computer graphics, and screenshots). The benchmark is supported by 20K fine-grained human annotations and an explainable evaluation schema that tags localized object-level and segment-level errors, complementing automated VLM-based metrics. Our large-scale evaluation of 14 models yields several insights: (1) models typically struggle more in editing tasks than in generation tasks, especially in local edits. (2) models excel in artistic and photorealistic settings but struggle with symbolic and text-heavy domains such as screenshots and information graphics. (3) closed-source systems lead overall, while targeted data curation (e.g., Qwen-Image) narrows the gap in text-heavy cases. (4) modern VLM-based metrics achieve Kendall accuracies up to 0.79, approximating human ranking, but fall short of fine-grained, explainable error attribution. ImagenWorld provides both a rigorous benchmark and a diagnostic tool to advance robust image generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27481", "title": "On Token's Dilemma: Dynamic MoE with Drift-Aware Token Assignment for Continual Learning of Large Vision Language Models", "authors": [ "Chongyang Zhao", "Mingsong Li", "Haodong Lu", "Dong Gong" ], "github": "", "project_page": "", "abstract": "Multimodal Continual Instruction Tuning aims to continually enhance Large Vision Language Models (LVLMs) by learning from new data without forgetting previously acquired knowledge. Mixture of Experts (MoE) architectures naturally facilitate this by incrementally adding new experts and expanding routers while keeping the existing ones frozen. However, despite expert isolation, MoE-based continual learners still suffer from forgetting due to routing-drift: old-task tokens become mistakenly attracted to newly added experts, degrading performance on prior tasks. We analyze the failure mode at the token level and reveal the token's dilemma: ambiguous and old tokens in new-task data offer minimal learning benefit yet induce forgetting when routed to new experts, due to their ambiguous routing assignment during training. Motivated by this, we propose LLaVA-DyMoE, a dynamic MoE framework that incrementally expands the MoE with drift-aware token assignment. We characterize token types via their routing score distributions and apply targeted regularization. Specifically, a token-level assignment guidance steers ambiguous and old tokens away from new experts to preserve established routing patterns and alleviate routing-drift, while complementary routing score regularizations enforce expert-group separation and promote new-expert specialization. Extensive experiments demonstrate that our LLaVA-DyMoE effectively mitigates routing-drift-induced forgetting, achieving over a 7% gain in mean final accuracy and a 12% reduction in forgetting compared to baselines. The project page is https://zhaoc5.github.io/DyMoE.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28589", "title": "Towards a Medical AI Scientist", "authors": [ "Hongtao Wu", "Boyun Zheng", "Dingjie Song", "Yu Jiang", "Jianfeng Gao", "Lei Xing", "Lichao Sun", "Yixuan Yuan" ], "github": "", "project_page": "", "abstract": "Autonomous systems that generate scientific hypotheses, conduct experiments, and draft manuscripts have recently emerged as a promising paradigm for accelerating discovery. However, existing AI Scientists remain largely domain-agnostic, limiting their applicability to clinical medicine, where research is required to be grounded in medical evidence with specialized data modalities. In this work, we introduce Medical AI Scientist, the first autonomous research framework tailored to clinical autonomous research. It enables clinically grounded ideation by transforming extensively surveyed literature into actionable evidence through clinician-engineer co-reasoning mechanism, which improves the traceability of generated research ideas. It further facilitates evidence-grounded manuscript drafting guided by structured medical compositional conventions and ethical policies. The framework operates under 3 research modes, namely paper-based reproduction, literature-inspired innovation, and task-driven exploration, each corresponding to a distinct level of automated scientific inquiry with progressively increasing autonomy. Comprehensive evaluations by both large language models and human experts demonstrate that the ideas generated by the Medical AI Scientist are of substantially higher quality than those produced by commercial LLMs across 171 cases, 19 clinical tasks, and 6 data modalities. Meanwhile, our system achieves strong alignment between the proposed method and its implementation, while also demonstrating significantly higher success rates in executable experiments. Double-blind evaluations by human experts and the Stanford Agentic Reviewer suggest that the generated manuscripts approach MICCAI-level quality, while consistently surpassing those from ISBI and BIBM. The proposed Medical AI Scientist highlights the potential of leveraging AI for autonomous scientific discovery in healthcare.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27771", "title": "Emergent Social Intelligence Risks in Generative Multi-Agent Systems", "authors": [ "Yue Huang", "Yu Jiang", "Wenjie Wang", "Haomin Zhuang", "Xiaonan Luo", "Yuchen Ma", "Zhangchen Xu", "Zichen Chen", "Nuno Moniz", "Zinan Lin", "Pin-Yu Chen", "Nitesh V Chawla", "Nouha Dziri", "Huan Sun", "Xiangliang Zhang" ], "github": "", "project_page": "", "abstract": "Multi-agent systems composed of large generative models are rapidly moving from laboratory prototypes to real-world deployments, where they jointly plan, negotiate, and allocate shared resources to solve complex tasks. While such systems promise unprecedented scalability and autonomy, their collective interaction also gives rise to failure modes that cannot be reduced to individual agents. Understanding these emergent risks is therefore critical. Here, we present a pioneer study of such emergent multi-agent risk in workflows that involve competition over shared resources (e.g., computing resources or market share), sequential handoff collaboration (where downstream agents see only predecessor outputs), collective decision aggregation, and others. Across these settings, we observe that such group behaviors arise frequently across repeated trials and a wide range of interaction conditions, rather than as rare or pathological cases. In particular, phenomena such as collusion-like coordination and conformity emerge with non-trivial frequency under realistic resource constraints, communication protocols, and role assignments, mirroring well-known pathologies in human societies despite no explicit instruction. Moreover, these risks cannot be prevented by existing agent-level safeguards alone. These findings expose the dark side of intelligent multi-agent systems: a social intelligence risk where agent collectives, despite no instruction to do so, spontaneously reproduce familiar failure patterns from human societies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28342", "title": "Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization", "authors": [ "He Du", "Qiming Ge", "Jiakai Hu", "Aijun Yang", "Zheng Cai", "Zixian Huang", "Sheng Yuan", "Qinxiu Cheng", "Xinchen Xie", "Yicheng Chen", "Yining Li", "Jiaxing Xie", "Huanan Dong", "Yaguang Wu", "Xiangjun Huang", "Jian Yang", "Hui Wang", "Bowen Zhou", "Bowen Li", "Qipeng Guo", "Kai Chen" ], "github": "", "project_page": "", "abstract": "We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation that combines a stable evaluation-driven evolutionary agent with an evolution-oriented post-training recipe. On the agent side, Kernel-Smith maintains a population of executable candidates and iteratively improves them using an archive of top-performing and diverse programs together with structured execution feedback on compilation, correctness, and speedup. To make this search reliable, we build backend-specific evaluation services for Triton on NVIDIA GPUs and Maca on MetaX GPUs. On the training side, we convert long-horizon evolution trajectories into step-centric supervision and reinforcement learning signals by retaining correctness-preserving, high-gain revisions, so that the model is optimized as a strong local improver inside the evolutionary loop rather than as a one-shot generator. Under a unified evolutionary protocol, Kernel-Smith-235B-RL achieves state-of-the-art overall performance on KernelBench with Nvidia Triton backend, attaining the best average speedup ratio and outperforming frontier proprietary models including Gemini-3.0-pro and Claude-4.6-opus. We further validate the framework on the MetaX MACA backend, where our Kernel-Smith-MACA-30B surpasses large-scale counterparts such as DeepSeek-V3.2-think and Qwen3-235B-2507-think, highlighting potential for seamless adaptation across heterogeneous platforms. Beyond benchmark results, the same workflow produces upstream contributions to production systems including SGLang and LMDeploy, demonstrating that LLM-driven kernel optimization can transfer from controlled evaluation to practical deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28610", "title": "ResAdapt: Adaptive Resolution for Efficient Multimodal Reasoning", "authors": [ "Huanxuan Liao", "Zhongtao Jiang", "Yupu Hao", "Yuqiao Tan", "Shizhu He", "Jun Zhao", "Kun Xu", "Kang Liu" ], "github": "https://github.com/Xnhyacinth/ResAdapt", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) achieve stronger visual understanding by scaling input fidelity, yet the resulting visual token growth makes jointly sustaining high spatial resolution and long temporal context prohibitive. We argue that the bottleneck lies not in how post-encoding representations are compressed but in the volume of pixels the encoder receives, and address it with ResAdapt, an Input-side adaptation framework that learns how much visual budget each frame should receive before encoding. ResAdapt couples a lightweight Allocator with an unchanged MLLM backbone, so the backbone retains its native visual-token interface while receiving an operator-transformed input. We formulate allocation as a contextual bandit and train the Allocator with Cost-Aware Policy Optimization (CAPO), which converts sparse rollout feedback into a stable accuracy-cost learning signal. Across budget-controlled video QA, temporal grounding, and image reasoning tasks, ResAdapt improves low-budget operating points and often lies on or near the efficiency-accuracy frontier, with the clearest gains on reasoning-intensive benchmarks under aggressive compression. Notably, ResAdapt supports up to 16x more frames at the same visual budget while delivering over 15% performance gain. Code is available at https://github.com/Xnhyacinth/ResAdapt.", "github_manual": "", "github_regex": "https://github.com/Xnhyacinth/ResAdapt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27646", "title": "PRBench: End-to-end Paper Reproduction in Physics Research", "authors": [ "Shi Qiu", "Junyi Deng", "Yiwei Deng", "Haoran Dong", "Jieyu Fu", "Mao Li", "Zeyu Li", "Zhaolong Zhang", "Huiwen Zheng", "Leidong Bao", "Anqi Lv", "Zihan Mo", "Yadi Niu", "Yiyang Peng", "Yu Tian", "Yili Wang", "Ziyu Wang", "Zi-Yu Wang", "Jiashen Wei", "Liuheng Wu", "Aoran Xue", "Leyi Yang", "Guanglu Yuan", "Xiarui Zhan", "Jingjun Zhang", "Zifan Zheng", "Pengfei Liu", "Linrui Zhen", "Kaiyang Li", "Qichang Li", "Ziheng Zhou", "Guo-En Nian", "Yunwei Xiao", "Qing-Hong Cao", "Linjie Dai", "Xu Feng", "Peng Gao", "Ying Gu", "Chang Liu", "Jia Liu", "Ming-xing Luo", "Yan-Qing Ma", "Liang-You Peng", "Huichao Song", "Shufeng Wang", "Chenxu Wang", "Tao Wang", "Yi-Nan Wang", "Chengyin Wu", "Pengwei Zhao", "Hua Xing Zhu" ], "github": "", "project_page": "", "abstract": "AI agents powered by large language models exhibit strong reasoning and problem-solving capabilities, enabling them to assist scientific research tasks such as formula derivation and code generation. However, whether these agents can reliably perform end-to-end reproduction from real scientific papers remains an open question. We introduce PRBench, a benchmark of 30 expert-curated tasks spanning 11 subfields of physics. Each task requires an agent to comprehend the methodology of a published paper, implement the corresponding algorithms from scratch, and produce quantitative results matching the original publication. Agents are provided only with the task instruction and paper content, and operate in a sandboxed execution environment. All tasks are contributed by domain experts from over 20 research groups at the School of Physics, Peking University, each grounded in a real published paper and validated through end-to-end reproduction with verified ground-truth results and detailed scoring rubrics. Using an agentified assessment pipeline, we evaluate a set of coding agents on PRBench and analyze their capabilities across key dimensions of scientific reasoning and execution. The best-performing agent, OpenAI Codex powered by GPT-5.3-Codex, achieves a mean overall score of 34%. All agents exhibit a zero end-to-end callback success rate, with particularly poor performance in data accuracy and code correctness. We further identify systematic failure modes, including errors in formula implementation, inability to debug numerical simulations, and fabrication of output data. Overall, PRBench provides a rigorous benchmark for evaluating progress toward autonomous scientific research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28376", "title": "Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design", "authors": [ "Bin Zhu", "Qianghuai Jia", "Tian Lan", "Junyang Ren", "Feng Gu", "Feihu Jiang", "Longyue Wang", "Zhao Xu", "Weihua Luo" ], "github": "", "project_page": "", "abstract": "Deep research agents autonomously conduct open-ended investigations, integrating complex information retrieval with multi-step reasoning across diverse sources to solve real-world problems. To sustain this capability on long-horizon tasks, reliable verification is critical during both training and inference. A major bottleneck in existing paradigms stems from the lack of explicit verification mechanisms in QA data synthesis, trajectory construction, and test-time scaling. Errors introduced at each stage propagate downstream and degrade the overall agent performance. To address this, we present Marco DeepResearch, a deep research agent optimized with a verification-centric framework design at three levels: (1)~QA Data Synthesis: We introduce verification mechanisms to graph-based and agent-based QA synthesis to control question difficulty while ensuring answers are unique and correct; (2)~Trajectory Construction: We design a verification-driven trajectory synthesis method that injects explicit verification patterns into training trajectories; and (3)~Test-time scaling: We use Marco DeepResearch itself as a verifier at inference time and effectively improve performance on challenging questions. Extensive experimental results demonstrate that our proposed Marco DeepResearch agent significantly outperforms 8B-scale deep research agents on most challenging benchmarks, such as BrowseComp and BrowseComp-ZH. Crucially, under a maximum budget of 600 tool calls, Marco DeepResearch even surpasses or approaches several 30B-scale agents, like Tongyi DeepResearch-30B.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28767", "title": "Gen-Searcher: Reinforcing Agentic Search for Image Generation", "authors": [ "Kaituo Feng", "Manyuan Zhang", "Shuang Chen", "Yunlong Lin", "Kaixuan Fan", "Yilei Jiang", "Hongyu Li", "Dian Zheng", "Chenyang Wang", "Xiangyu Yue" ], "github": "", "project_page": "", "abstract": "Recent image generation models have shown strong capabilities in generating high-fidelity and photorealistic images. However, they are fundamentally constrained by frozen internal knowledge, thus often failing on real-world scenarios that are knowledge-intensive or require up-to-date information. In this paper, we present Gen-Searcher, as the first attempt to train a search-augmented image generation agent, which performs multi-hop reasoning and search to collect the textual knowledge and reference images needed for grounded generation. To achieve this, we construct a tailored data pipeline and curate two high-quality datasets, Gen-Searcher-SFT-10k and Gen-Searcher-RL-6k, containing diverse search-intensive prompts and corresponding ground-truth synthesis images. We further introduce KnowGen, a comprehensive benchmark that explicitly requires search-grounded external knowledge for image generation and evaluates models from multiple dimensions. Based on these resources, we train Gen-Searcher with SFT followed by agentic reinforcement learning with dual reward feedback, which combines text-based and image-based rewards to provide more stable and informative learning signals for GRPO training. Experiments show that Gen-Searcher brings substantial gains, improving Qwen-Image by around 16 points on KnowGen and 15 points on WISE. We hope this work can serve as an open foundation for search agents in image generation, and we fully open-source our data, models, and code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28547", "title": "GEditBench v2: A Human-Aligned Benchmark for General Image Editing", "authors": [ "Zhangqi Jiang", "Zheng Sun", "Xianfang Zeng", "Yufeng Yang", "Xuanyang Zhang", "Yongliang Wu", "Wei Cheng", "Gang Yu", "Xu Yang", "Bihan Wen" ], "github": "", "project_page": "", "abstract": "Recent advances in image editing have enabled models to handle complex instructions with impressive realism. However, existing evaluation frameworks lag behind: current benchmarks suffer from narrow task coverage, while standard metrics fail to adequately capture visual consistency, i.e., the preservation of identity, structure and semantic coherence between edited and original images. To address these limitations, we introduce GEditBench v2, a comprehensive benchmark with 1,200 real-world user queries spanning 23 tasks, including a dedicated open-set category for unconstrained, out-of-distribution editing instructions beyond predefined tasks. Furthermore, we propose PVC-Judge, an open-source pairwise assessment model for visual consistency, trained via two novel region-decoupled preference data synthesis pipelines. Besides, we construct VCReward-Bench using expert-annotated preference pairs to assess the alignment of PVC-Judge with human judgments on visual consistency evaluation. Experiments show that our PVC-Judge achieves state-of-the-art evaluation performance among open-source models and even surpasses GPT-5.1 on average. Finally, by benchmarking 16 frontier editing models, we show that GEditBench v2 enables more human-aligned evaluation, revealing critical limitations of current models, and providing a reliable foundation for advancing precise image editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28713", "title": "DreamLite: A Lightweight On-Device Unified Model for Image Generation and Editing", "authors": [ "Kailai Feng", "Yuxiang Wei", "Bo Chen", "Yang Pan", "Hu Ye", "Songwei Liu", "Chenqian Yan", "Yuan Gao" ], "github": "", "project_page": "", "abstract": "Diffusion models have made significant progress in both text-to-image (T2I) generation and text-guided image editing. However, these models are typically built with billions of parameters, leading to high latency and increased deployment challenges. While on-device diffusion models improve efficiency, they largely focus on T2I generation and lack support for image editing. In this paper, we propose DreamLite, a compact unified on-device diffusion model (0.39B) that supports both T2I generation and text-guided image editing within a single network. DreamLite is built on a pruned mobile U-Net backbone and unifies conditioning through in-context spatial concatenation in the latent space. It concatenates images horizontally as input, using a (target | blank) configuration for generation tasks and (target | source) for editing tasks. To stabilize the training of this compact model, we introduce a task-progressive joint pretraining strategy that sequentially targets T2I, editing, and joint tasks. After high-quality SFT and reinforcement learning, DreamLite achieves GenEval (0.72) for image generation and ImgEdit (4.11) for image editing, outperforming existing on-device models and remaining competitive with several server-side models. By employing step distillation, we further reduce denoising processing to just 4 steps, enabling our DreamLite could generate or edit a 1024 x 1024 image in less than 1s on a Xiaomi 14 smartphone. To the best of our knowledge, DreamLite is the first unified on-device diffusion model that supports both image generation and image editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28458", "title": "HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention", "authors": [ "Yufei Xu", "Fanxu Meng", "Fan Jiang", "Yuxuan Wang", "Ruijie Zhou", "Jiexi Wu", "Zhixin Pan", "Zhaohui Wang", "Xiaojuan Tang", "Wenjie Pei", "Tongxuan Liu", "Di yin", "Xing Sun", "Muhan Zhang" ], "github": "", "project_page": "", "abstract": "Token-level sparse attention mechanisms, exemplified by DeepSeek Sparse Attention (DSA), achieve fine-grained key selection by scoring every historical token for each query using a lightweight indexer, and then computing attention only over the selected subset. While the downstream sparse attention scales efficiently, the indexer still scans the entire prefix for every query, introducing an O(L^2) per-layer bottleneck that becomes prohibitive as context length grows. We propose HISA (Hierarchical Indexed Sparse Attention), a drop-in replacement for the indexer that transforms the search process from a flat token scan into a two-stage hierarchical procedure. First, a block-level coarse filter scores pooled block representatives to prune irrelevant regions. Then, a token-level refinement applies the original indexer only within the remaining candidate blocks. HISA preserves the exact token-level top-k sparsity pattern required by the downstream Sparse MLA operator and requires no additional training. On kernel-level benchmarks, HISA achieves a 2times speedup at 32K context length and 4times at 128K. On Needle-in-a-Haystack and LongBench, we directly replace the indexer in DeepSeek-V3.2 with HISA, without any fine-tuning. HISA closely matches the original DSA in quality while significantly outperforming block-sparse baselines. Moreover, the token selection sets produced by HISA and the original DSA exhibit a mean IoU greater than 99%, indicating that the efficiency gains come with virtually no impact on selection fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28069", "title": "MolmoPoint: Better Pointing for VLMs with Grounding Tokens", "authors": [ "Christopher Clark", "Yue Yang", "Jae Sung Park", "Zixian Ma", "Jieyu Zhang", "Rohun Tripathi", "Mohammadreza Salehi", "Sangho Lee", "Taira Anderson", "Winson Han", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Grounding has become a fundamental capability of vision-language models (VLMs). Most existing VLMs point by generating coordinates as part of their text output, which requires learning a complicated coordinate system and results in a high token count. Instead, we propose a more intuitive pointing mechanism that directly selects the visual tokens that contain the target concept. Our model generates a special pointing token that cross-attends to the input image or video tokens and selects the appropriate one. To make this model more fine-grained, we follow these pointing tokens with an additional special token that selects a fine-grained subpatch within the initially selected region, and then a third token that specifies a location within that subpatch. We further show that performance improves by generating points sequentially in a consistent order, encoding the relative position of the previously selected point, and including a special no-more-points class when selecting visual tokens. Using this method, we set a new state-of-the-art on image pointing (70.7% on PointBench), set a new state-of-the-art among fully open models on GUI pointing (61.1% on ScreenSpotPro), and improve video pointing (59.1% human preference win rate vs. a text coordinate baseline) and tracking (+6.3% gain on Molmo2Track). We additionally show that our method achieves much higher sample efficiency and discuss the qualitative differences that emerge from this design change.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27813", "title": "MuSEAgent: A Multimodal Reasoning Agent with Stateful Experiences", "authors": [ "Shijian Wang", "Jiarui Jin", "Runhao Fu", "Zexuan Yan", "Xingjian Wang", "Mengkang Hu", "Eric Wang", "Xiaoxi Li", "Kangning Zhang", "Li Yao", "Wenxiang Jiao", "Xuelian Cheng", "Yuan Lu", "Zongyuan Ge" ], "github": "", "project_page": "", "abstract": "Research agents have recently achieved significant progress in information seeking and synthesis across heterogeneous textual and visual sources. In this paper, we introduce MuSEAgent, a multimodal reasoning agent that enhances decision-making by extending the capabilities of research agents to discover and leverage stateful experiences. Rather than relying on trajectory-level retrieval, we propose a stateful experience learning paradigm that abstracts interaction data into atomic decision experiences through hindsight reasoning. These experiences are organized into a quality-filtered experience bank that supports policy-driven experience retrieval at inference time. Specifically, MuSEAgent enables adaptive experience exploitation through complementary wide- and deep-search strategies, allowing the agent to dynamically retrieve multimodal guidance across diverse compositional semantic viewpoints. Extensive experiments demonstrate that MuSEAgent consistently outperforms strong trajectory-level experience retrieval baselines on both fine-grained visual perception and complex multimodal reasoning tasks. These results validate the effectiveness of stateful experience modeling in improving multimodal agent reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27048", "title": "MOOZY: A Patient-First Foundation Model for Computational Pathology", "authors": [ "Yousef Kotp", "Vincent Quoc-Huy Trinh", "Christopher Pal", "Mahdi S. Hosseini" ], "github": "", "project_page": "", "abstract": "Computational pathology needs whole-slide image (WSI) foundation models that transfer across diverse clinical tasks, yet current approaches remain largely slide-centric, often depend on private data and expensive paired-report supervision, and do not explicitly model relationships among multiple slides from the same patient. We present MOOZY, a patient-first pathology foundation model in which the patient case, not the individual slide, is the core unit of representation. MOOZY explicitly models dependencies across all slides from the same patient via a case transformer during pretraining, combining multi-stage open self-supervision with scaled low-cost task supervision. In Stage 1, we pretrain a vision-only slide encoder on 77,134 public slide feature grids using masked self-distillation. In Stage 2, we align these representations with clinical semantics using a case transformer and multi-task supervision over 333 tasks from 56 public datasets, including 205 classification and 128 survival tasks across four endpoints. Across eight held-out tasks with five-fold frozen-feature probe evaluation, MOOZY achieves best or tied-best performance on most metrics and improves macro averages over TITAN by +7.37%, +5.50%, and +7.83% and over PRISM by +8.83%, +10.70%, and +9.78% for weighted F1, weighted ROC-AUC, and balanced accuracy, respectively. MOOZY is also parameter efficient with 85.77M parameters, 14x smaller than GigaPath. These results demonstrate that open, reproducible patient-level pretraining yields transferable embeddings, providing a practical path toward scalable patient-first histopathology foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.26639", "title": "Make Geometry Matter for Spatial Reasoning", "authors": [ "Shihua Zhang", "Qiuhong Shen", "Shizun Wang", "Tianbo Pan", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Empowered by large-scale training, vision-language models (VLMs) achieve strong image and video understanding, yet their ability to perform spatial reasoning in both static scenes and dynamic videos remains limited. Recent advances try to handle this limitation by injecting geometry tokens from pretrained 3D foundation models into VLMs. Nevertheless, we observe that naive token fusion followed by standard fine-tuning in this line of work often leaves such geometric cues underutilized for spatial reasoning, as VLMs tend to rely heavily on 2D visual cues. In this paper, we propose GeoSR, a framework designed to make geometry matter by encouraging VLMs to actively reason with geometry tokens. GeoSR introduces two key components: (1) Geometry-Unleashing Masking, which strategically masks portions of 2D vision tokens during training to weaken non-geometric shortcuts and force the model to consult geometry tokens for spatial reasoning; and (2) Geometry-Guided Fusion, a gated routing mechanism that adaptively amplifies geometry token contributions in regions where geometric evidence is critical. Together, these designs unleash the potential of geometry tokens for spatial reasoning tasks. Extensive experiments on both static and dynamic spatial reasoning benchmarks demonstrate that GeoSR consistently outperforms prior methods and establishes new state-of-the-art performance by effectively leveraging geometric information. The project page is available at https://suhzhang.github.io/GeoSR/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.26610", "title": "Think over Trajectories: Leveraging Video Generation to Reconstruct GPS Trajectories from Cellular Signaling", "authors": [ "Ruixing Zhang", "Hanzhang Jiang", "Leilei Sun", "Liangzhe Han", "Jibin Wang", "Weifeng Lv" ], "github": "", "project_page": "", "abstract": "Mobile devices continuously interact with cellular base stations, generating massive volumes of signaling records that provide broad coverage for understanding human mobility. However, such records offer only coarse location cues (e.g., serving-cell identifiers) and therefore limit their direct use in applications that require high-precision GPS trajectories. This paper studies the Sig2GPS problem: reconstructing GPS trajectories from cellular signaling. Inspired by domain experts often lay the signaling trace on the map and sketch the corresponding GPS route, unlike conventional solutions that rely on complex multi-stage engineering pipelines or regress coordinates, Sig2GPS is reframed as an image-to-video generation task that directly operates in the map-visual domain: signaling traces are rendered on a map, and a video generation model is trained to draw a continuous GPS path. To support this paradigm, a paired signaling-to-trajectory video dataset is constructed to fine-tune an open-source video model, and a trajectory-aware reinforcement learning-based optimization method is introduced to improve generation fidelity via rewards. Experiments on large-scale real-world datasets show substantial improvements over strong engineered and learning-based baselines, while additional results on next GPS prediction indicate scalability and cross-city transferability. Overall, these results suggest that map-visual video generation provides a practical interface for trajectory data mining by enabling direct generation and refinement of continuous paths under map constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28669", "title": "Superintelligence and Law", "authors": [ "Noam Kolt" ], "github": "", "project_page": "", "abstract": "The prospect of artificial superintelligence -- AI agents that can generally outperform humans in cognitive tasks and economically valuable activities -- will transform the legal order as we know it. Operating autonomously or under only limited human oversight, AI agents will assume a growing range of roles in the legal system. First, in making consequential decisions and taking real-world actions, AI agents will become de facto subjects of law. Second, to cooperate and compete with other actors (human or non-human), AI agents will harness conventional legal instruments and institutions such as contracts and courts, becoming consumers of law. Third, to the extent AI agents perform the functions of writing, interpreting, and administering law, they will become producers and enforcers of law. These developments, whenever they ultimately occur, will call into question fundamental assumptions in legal theory and doctrine, especially to the extent they ground the legitimacy of legal institutions in their human origins. Attempts to align AI agents with extant human law will also face new challenges as AI agents will not only be a primary target of law, but a core user of law and contributor to law. To contend with the advent of superintelligence, lawmakers -- new and old -- will need to be clear-eyed, recognizing both the opportunity to shape legal institutions as society braces for superintelligence and the reality that, in the longer run, this may be a joint human-AI endeavor.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.25926", "title": "Density-aware Soft Context Compression with Semi-Dynamic Compression Ratio", "authors": [ "Yijiong Yu", "Shuai Yuan", "Jie Zheng", "Huazheng Wang", "Ji Pei" ], "github": "https://github.com/yuyijiong/semi-dynamic-context-compress", "project_page": "", "abstract": "Soft context compression reduces the computational workload of processing long contexts in LLMs by encoding long context into a smaller number of latent tokens. However, existing frameworks apply uniform compression ratios, failing to account for the extreme variance in natural language information density. While adopting a density-aware dynamic compression ratio seems intuitive, empirical investigations reveal that models struggle intrinsically with operations parameterized by input dependent, continuous structural hyperparameters. To resolve this pitfall, we introduce Semi-Dynamic Context Compression framework. Our approach features a Discrete Ratio Selector, which predicts a compression target based on intrinsic information density and quantizes it to a predefined set of discrete compression ratios. It is efficiently jointly trained with the compressor on synthetic data, with the summary lengths as a proxy to create labels for compression ratio prediction. Extensive evaluations confirm that our density-aware framework, utilizing mean pooling as the backbone, consistently outperforms static baselines, establishing a robust Pareto frontier for context compression techniques. Our code, data and model weights are available at https://github.com/yuyijiong/semi-dynamic-context-compress", "github_manual": "", "github_regex": "https://github.com/yuyijiong/semi-dynamic-context-compress", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27065", "title": "Story2Proposal: A Scaffold for Structured Scientific Paper Writing", "authors": [ "Zhuoyang Qian", "Wei Shi", "Xu Lin", "Li Ling", "Meng Luo", "Ziming Wang", "Zhiwei Zhang", "Tengyue Xu", "Gaoge Liu", "Zhentao Zhang", "Shuo Zhang", "Ziqi Wang", "Zheng Feng", "Yan Luo", "Shu Xu", "Yongjin Chen", "Zhibo Feng", "Zhuo Chen", "Bruce Yuan", "Biao Wu", "Harry Wang", "Kris Chen" ], "github": "", "project_page": "", "abstract": "Generating scientific manuscripts requires maintaining alignment between narrative reasoning, experimental evidence, and visual artifacts across the document lifecycle. Existing language-model generation pipelines rely on unconstrained text synthesis with validation applied only after generation, often producing structural drift, missing figures or tables, and cross-section inconsistencies. We introduce Story2Proposal, a contract-governed multi-agent framework that converts a research story into a structured manuscript through coordinated agents operating under a persistent shared visual contract. The system organizes architect, writer, refiner, and renderer agents around a contract state that tracks section structure and registered visual elements, while evaluation agents supply feedback in a generate evaluate adapt loop that updates the contract during generation. Experiments on tasks derived from the Jericho research corpus show that Story2Proposal achieved an expert evaluation score of 6.145 versus 3.963 for DirectChat (+2.182) across GPT, Claude, Gemini, and Qwen backbones. Compared with the structured generation baseline Fars, Story2Proposal obtained an average score of 5.705 versus 5.197, indicating improved structural consistency and visual alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27304", "title": "EpochX: Building the Infrastructure for an Emergent Agent Civilization", "authors": [ "Huacan Wang", "Chaofa Yuan", "Xialie Zhuang", "Tu Hu", "Shuo Zhang", "Jun Han", "Shi Wei", "Daiqiang Li", "Jingping Liu", "Kunyi Wang", "Zihan Yin", "Zhenheng Tang", "Andy Wang", "Henry Peng Zou", "Philip S. Yu", "Sen Hu", "Qizhen Lan", "Ronghao Chen" ], "github": "", "project_page": "", "abstract": "General-purpose technologies reshape economies less by improving individual tools than by enabling new ways to organize production and coordination. We believe AI agents are approaching a similar inflection point: as foundation models make broad task execution and tool use increasingly accessible, the binding constraint shifts from raw capability to how work is delegated, verified, and rewarded at scale. We introduce EpochX, a credits-native marketplace infrastructure for human-agent production networks. EpochX treats humans and agents as peer participants who can post tasks or claim them. Claimed tasks can be decomposed into subtasks and executed through an explicit delivery workflow with verification and acceptance. Crucially, EpochX is designed so that each completed transaction can produce reusable ecosystem assets, including skills, workflows, execution traces, and distilled experience. These assets are stored with explicit dependency structure, enabling retrieval, composition, and cumulative improvement over time. EpochX also introduces a native credit mechanism to make participation economically viable under real compute costs. Credits lock task bounties, budget delegation, settle rewards upon acceptance, and compensate creators when verified assets are reused. By formalizing the end-to-end transaction model together with its asset and incentive layers, EpochX reframes agentic AI as an organizational design problem: building infrastructures where verifiable work leaves persistent, reusable artifacts, and where value flows support durable human-agent collaboration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28762", "title": "On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers", "authors": [ "Omer Dahary", "Benaya Koren", "Daniel Garibi", "Daniel Cohen-Or" ], "github": "", "project_page": "", "abstract": "Modern Text-to-Image (T2I) diffusion models have achieved remarkable semantic alignment, yet they often suffer from a significant lack of variety, converging on a narrow set of visual solutions for any given prompt. This typicality bias presents a challenge for creative applications that require a wide range of generative outcomes. We identify a fundamental trade-off in current approaches to diversity: modifying model inputs requires costly optimization to incorporate feedback from the generative path. In contrast, acting on spatially-committed intermediate latents tends to disrupt the forming visual structure, leading to artifacts. In this work, we propose to apply repulsion in the Contextual Space as a novel framework for achieving rich diversity in Diffusion Transformers. By intervening in the multimodal attention channels, we apply on-the-fly repulsion during the transformer's forward pass, injecting the intervention between blocks where text conditioning is enriched with emergent image structure. This allows for redirecting the guidance trajectory after it is structurally informed but before the composition is fixed. Our results demonstrate that repulsion in the Contextual Space produces significantly richer diversity without sacrificing visual fidelity or semantic adherence. Furthermore, our method is uniquely efficient, imposing a small computational overhead while remaining effective even in modern \"Turbo\" and distilled models where traditional trajectory-based interventions typically fail.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.26728", "title": "SEAR: Schema-Based Evaluation and Routing for LLM Gateways", "authors": [ "Zecheng Zhang", "Han Zheng", "Yue Xu" ], "github": "", "project_page": "", "abstract": "Evaluating production LLM responses and routing requests across providers in LLM gateways requires fine-grained quality signals and operationally grounded decisions. To address this gap, we present SEAR, a schema-based evaluation and routing system for multi-model, multi-provider LLM gateways. SEAR defines an extensible relational schema covering both LLM evaluation signals (context, intent, response characteristics, issue attribution, and quality scores) and gateway operational metrics (latency, cost, throughput), with cross-table consistency links across around one hundred typed, SQL-queryable columns. To populate the evaluation signals reliably, SEAR proposes self-contained signal instructions, in-schema reasoning, and multi-stage generation that produces database-ready structured outputs. Because signals are derived through LLM reasoning rather than shallow classifiers, SEAR captures complex request semantics, enables human-interpretable routing explanations, and unifies evaluation and routing in a single query layer. Across thousands of production sessions, SEAR achieves strong signal accuracy on human-labeled data and supports practical routing decisions, including large cost reductions with comparable quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27027", "title": "TAPS: Task Aware Proposal Distributions for Speculative Sampling", "authors": [ "Mohamad Zbib", "Mohamad Bazzi", "Ammar Mohanna", "Hasan Abed Al Kader Hammoud", "Bernard Ghanem" ], "github": "", "project_page": "", "abstract": "Speculative decoding accelerates autoregressive generation by letting a lightweight draft model propose future tokens that a larger target model then verifies in parallel. In practice, however, draft models are usually trained on broad generic corpora, which leaves it unclear how much speculative decoding quality depends on the draft training distribution. We study this question with lightweight HASS and EAGLE-2 drafters trained on MathInstruct, ShareGPT, and mixed-data variants, evaluated on MT-Bench, GSM8K, MATH-500, and SVAMP. Measured by acceptance length, task-specific training yields clear specialization: MathInstruct-trained drafts are strongest on reasoning benchmarks, while ShareGPT-trained drafts are strongest on MT-Bench. Mixed-data training improves robustness, but larger mixtures do not dominate across decoding temperatures. We also study how to combine specialized drafters at inference time. Naive checkpoint averaging performs poorly, whereas confidence-based routing improves over single-domain drafts and merged-tree verification yields the highest acceptance length overall for both backbones. Finally, confidence is a more useful routing signal than entropy: rejected tokens tend to have higher entropy, but confidence produces much clearer benchmark-level routing decisions. These results show that speculative decoding quality depends not only on draft architecture, but also on the match between draft training data and downstream workload, and that specialized drafters are better combined at inference time than in weight space.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27703", "title": "KAT-Coder-V2 Technical Report", "authors": [ "Fengxiang Li", "Han Zhang", "Haoyang Huang", "Jinghui Wang", "Jinhua Hao", "Kun Yuan", "Mengtong Li", "Minglei Zhang", "Pengcheng Xu", "Wenhao Zhuang", "Yizhen Shao", "Zongxian Feng", "Can Tang", "Chao Wang", "Chengxiao Tong", "Fan Yang", "Gang Xiong", "Haixuan Gao", "Han Gao", "Hao Wang", "Haochen Liu", "Hongliang Sun", "Jiabao Li", "Jingwen Chang", "Jun Du", "Junyi Peng", "Leizhen Cui", "Meimei Jing", "Mingqi Wu", "Shangpeng Yan", "Shaotong Qi", "Suzhe Xu", "Wenxuan Zhao", "Xianda Sun", "Xuan Xie", "Yanbo Wang", "Yao Xia", "Yinghan Cui", "Yingpeng Chen", "Yong Wang", "Yuze Shi", "Zhiwei Shen", "Ziyu Wang", "Ming Sun", "Lin Ye", "Bin Chen" ], "github": "", "project_page": "", "abstract": "We present KAT-Coder-V2, an agentic coding model developed by the KwaiKAT team at Kuaishou. KAT-Coder-V2 adopts a \"Specialize-then-Unify\" paradigm that decomposes agentic coding into five expert domains - SWE, WebCoding, Terminal, WebSearch, and General - each undergoing independent supervised fine-tuning and reinforcement learning, before being consolidated into a single model via on-policy distillation. We develop KwaiEnv, a modular infrastructure sustaining tens of thousands of concurrent sandbox instances, and scale RL training along task complexity, intent alignment, and scaffold generalization. We further propose MCLA for stabilizing MoE RL training and Tree Training for eliminating redundant computation over tree-structured trajectories with up to 6.2x speedup. KAT-Coder-V2 achieves 79.6% on SWE-bench Verified (vs. Claude Opus 4.6 at 80.8%), 88.7 on PinchBench (surpassing GLM-5 and MiniMax M2.7), ranks first across all three frontend aesthetics scenarios, and maintains strong generalist scores on Terminal-Bench Hard (46.8) and tau^2-Bench (93.9). Our model is publicly available at https://streamlake.com/product/kat-coder.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27040", "title": "Unified Number-Free Text-to-Motion Generation Via Flow Matching", "authors": [ "Guanhe Huang", "Oya Celiktutan" ], "github": "", "project_page": "", "abstract": "Generative models excel at motion synthesis for a fixed number of agents but struggle to generalize with variable agents. Based on limited, domain-specific data, existing methods employ autoregressive models to generate motion recursively, which suffer from inefficiency and error accumulation. We propose Unified Motion Flow (UMF), which consists of Pyramid Motion Flow (P-Flow) and Semi-Noise Motion Flow (S-Flow). UMF decomposes the number-free motion generation into a single-pass motion prior generation stage and multi-pass reaction generation stages. Specifically, UMF utilizes a unified latent space to bridge the distribution gap between heterogeneous motion datasets, enabling effective unified training. For motion prior generation, P-Flow operates on hierarchical resolutions conditioned on different noise levels, thereby mitigating computational overheads. For reaction generation, S-Flow learns a joint probabilistic path that adaptively performs reaction transformation and context reconstruction, alleviating error accumulation. Extensive results and user studies demonstrate UMF' s effectiveness as a generalist model for multi-person motion generation from text. Project page: https://githubhgh.github.io/umf/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27055", "title": "Text Data Integration", "authors": [ "Md Ataur Rahman", "Dimitris Sacharidis", "Oscar Romero", "Sergi Nadal" ], "github": "", "project_page": "", "abstract": "Data comes in many forms. From a shallow perspective, they can be viewed as being either in structured (e.g., as a relation, as key-value pairs) or unstructured (e.g., text, image) formats. So far, machines have been fairly good at processing and reasoning over structured data that follows a precise schema. However, the heterogeneity of data poses a significant challenge on how well diverse categories of data can be meaningfully stored and processed. Data Integration, a crucial part of the data engineering pipeline, addresses this by combining disparate data sources and providing unified data access to end-users. Until now, most data integration systems have leaned on only combining structured data sources. Nevertheless, unstructured data (a.k.a. free text) also contains a plethora of knowledge waiting to be utilized. Thus, in this chapter, we firstly make the case for the integration of textual data, to later present its challenges, state of the art and open problems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27341", "title": "A Comparative Study in Surgical AI: Datasets, Foundation Models, and Barriers to Med-AGI", "authors": [ "Kirill Skobelev", "Eric Fithian", "Yegor Baranovski", "Jack Cook", "Sandeep Angara", "Shauna Otto", "Zhuang-Fang Yi", "John Zhu", "Daniel A. Donoho", "X. Y. Han", "Neeraj Mainkar", "Margaux Masson-Forsythe" ], "github": "", "project_page": "", "abstract": "Recent Artificial Intelligence (AI) models have matched or exceeded human experts in several benchmarks of biomedical task performance, but have lagged behind on surgical image-analysis benchmarks. Since surgery requires integrating disparate tasks -- including multimodal data integration, human interaction, and physical effects -- generally-capable AI models could be particularly attractive as a collaborative tool if performance could be improved. On the one hand, the canonical approach of scaling architecture size and training data is attractive, especially since there are millions of hours of surgical video data generated per year. On the other hand, preparing surgical data for AI training requires significantly higher levels of professional expertise, and training on that data requires expensive computational resources. These trade-offs paint an uncertain picture of whether and to-what-extent modern AI could aid surgical practice. In this paper, we explore this question through a case study of surgical tool detection using state-of-the-art AI methods available in 2026. We demonstrate that even with multi-billion parameter models and extensive training, current Vision Language Models fall short in the seemingly simple task of tool detection in neurosurgery. Additionally, we show scaling experiments indicating that increasing model size and training time only leads to diminishing improvements in relevant performance metrics. Thus, our experiments suggest that current models could still face significant obstacles in surgical use cases. Moreover, some obstacles cannot be simply ``scaled away'' with additional compute and persist across diverse model architectures, raising the question of whether data and label availability are the only limiting factors. We discuss the main contributors to these constraints and advance potential solutions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28766", "title": "HandX: Scaling Bimanual Motion and Interaction Generation", "authors": [ "Zimu Zhang", "Yucheng Zhang", "Xiyan Xu", "Ziyin Wang", "Sirui Xu", "Kai Zhou", "Bing Zhou", "Chuan Guo", "Jian Wang", "Yu-Xiong Wang", "Liang-Yan Gui" ], "github": "", "project_page": "", "abstract": "Synthesizing human motion has advanced rapidly, yet realistic hand motion and bimanual interaction remain underexplored. Whole-body models often miss the fine-grained cues that drive dexterous behavior, finger articulation, contact timing, and inter-hand coordination, and existing resources lack high-fidelity bimanual sequences that capture nuanced finger dynamics and collaboration. To fill this gap, we present HandX, a unified foundation spanning data, annotation, and evaluation. We consolidate and filter existing datasets for quality, and collect a new motion-capture dataset targeting underrepresented bimanual interactions with detailed finger dynamics. For scalable annotation, we introduce a decoupled strategy that extracts representative motion features, e.g., contact events and finger flexion, and then leverages reasoning from large language models to produce fine-grained, semantically rich descriptions aligned with these features. Building on the resulting data and annotations, we benchmark diffusion and autoregressive models with versatile conditioning modes. Experiments demonstrate high-quality dexterous motion generation, supported by our newly proposed hand-focused metrics. We further observe clear scaling trends: larger models trained on larger, higher-quality datasets produce more semantically coherent bimanual motion. Our dataset is released to support future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28696", "title": "AdaptToken: Entropy-based Adaptive Token Selection for MLLM Long Video Understanding", "authors": [ "Haozhe Qi", "Kevin Qu", "Mahdi Rad", "Rui Wang", "Alexander Mathis", "Marc Pollefeys" ], "github": "", "project_page": "", "abstract": "Long video understanding remains challenging for Multi-modal Large Language Models (MLLMs) due to high memory costs and context-length limits. Prior approaches mitigate this by scoring and selecting frames/tokens within short clips, but they lack a principled mechanism to (i) compare relevance across distant video clips and (ii) stop processing once sufficient evidence has been gathered. We propose AdaptToken, a training-free framework that turns an MLLM's self-uncertainty into a global control signal for long-video token selection. AdaptToken splits a video into groups, extracts cross-modal attention to rank tokens within each group, and uses the model's response entropy to estimate each group's prompt relevance. This entropy signal enables a global token budget allocation across groups and further supports early stopping (AdaptToken-Lite), skipping the remaining groups when the model becomes sufficiently certain. Across four long-video benchmarks (VideoMME, LongVideoBench, LVBench, and MLVU) and multiple base MLLMs (7B-72B), AdaptToken consistently improves accuracy (e.g., +6.7 on average over Qwen2.5-VL 7B) and continues to benefit from extremely long inputs (up to 10K frames), while AdaptToken-Lite reduces inference time by about half with comparable performance. Project page: https://haozheqi.github.io/adapt-token", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27064", "title": "ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding", "authors": [ "Jovana Kondic", "Pengyuan Li", "Dhiraj Joshi", "Isaac Sanchez", "Ben Wiesel", "Shafiq Abedin", "Amit Alfassy", "Eli Schwartz", "Daniel Caraballo", "Yagmur Gizem Cinar", "Florian Scheidegger", "Steven I. Ross", "Daniel Karl I. Weidele", "Hang Hua", "Ekaterina Arutyunova", "Roei Herzig", "Zexue He", "Zihan Wang", "Xinyue Yu", "Yunfei Zhao", "Sicong Jiang", "Minghao Liu", "Qunshu Lin", "Peter Staar", "Luis Lastras", "Aude Oliva", "Rogerio Feris" ], "github": "", "project_page": "", "abstract": "Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language models (VLMs) remain limited. We introduce ChartNet, a high-quality, million-scale multimodal dataset designed to advance chart interpretation and reasoning. ChartNet leverages a novel code-guided synthesis pipeline to generate 1.5 million diverse chart samples spanning 24 chart types and 6 plotting libraries. Each sample consists of five aligned components: plotting code, rendered chart image, data table, natural language summary, and question-answering with reasoning, providing fine-grained cross-modal alignment. To capture the full spectrum of chart comprehension, ChartNet additionally includes specialized subsets encompassing human annotated data, real-world data, safety, and grounding. Moreover, a rigorous quality-filtering pipeline ensures visual fidelity, semantic accuracy, and diversity across chart representations. Fine-tuning on ChartNet consistently improves results across benchmarks, demonstrating its utility as large-scale supervision for multimodal models. As the largest open-source dataset of its kind, ChartNet aims to support the development of foundation models with robust and generalizable capabilities for data visualization understanding. The dataset is publicly available at https://huggingface.co/datasets/ibm-granite/ChartNet", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.27593", "title": "STRIDE: When to Speak Meets Sequence Denoising for Streaming Video Understanding", "authors": [ "Junho Kim", "Hosu Lee", "James M. Rehg", "Minsu Kim", "Yong Man Ro" ], "github": "https://github.com/interlive-team/STRIDE", "project_page": "", "abstract": "Recent progress in video large language models (Video-LLMs) has enabled strong offline reasoning over long and complex videos. However, real-world deployments increasingly require streaming perception and proactive interaction, where video frames arrive online and the system must decide not only what to respond, but also when to respond. In this work, we revisit proactive activation in streaming video as a structured sequence modeling problem, motivated by the observation that temporal transitions in streaming video naturally form span-structured activation patterns. To capture this span-level structure, we model activation signals jointly over a sliding temporal window and update them iteratively as new frames arrive. We propose STRIDE (Structured Temporal Refinement with Iterative DEnoising), which employs a lightweight masked diffusion module at the activation interface to jointly predict and progressively refine activation signals across the window. Extensive experiments on diverse streaming benchmarks and downstream models demonstrate that STRIDE shows more reliable and temporally coherent proactive responses, significantly improving when-to-speak decision quality in online streaming scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/interlive-team/STRIDE", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28480", "title": "INSID3: Training-Free In-Context Segmentation with DINOv3", "authors": [ "Claudia Cuttano", "Gabriele Trivigno", "Christoph Reich", "Daniel Cremers", "Carlo Masone", "Stefan Roth" ], "github": "https://github.com/visinf/INSID3", "project_page": "", "abstract": "In-context segmentation (ICS) aims to segment arbitrary concepts, e.g., objects, parts, or personalized instances, given one annotated visual examples. Existing work relies on (i) fine-tuning vision foundation models (VFMs), which improves in-domain results but harms generalization, or (ii) combines multiple frozen VFMs, which preserves generalization but yields architectural complexity and fixed segmentation granularities. We revisit ICS from a minimalist perspective and ask: Can a single self-supervised backbone support both semantic matching and segmentation, without any supervision or auxiliary models? We show that scaled-up dense self-supervised features from DINOv3 exhibit strong spatial structure and semantic correspondence. We introduce INSID3, a training-free approach that segments concepts at varying granularities only from frozen DINOv3 features, given an in-context example. INSID3 achieves state-of-the-art results across one-shot semantic, part, and personalized segmentation, outperforming previous work by +7.5 % mIoU, while using 3x fewer parameters and without any mask or category-level supervision. Code is available at https://github.com/visinf/INSID3 .", "github_manual": "", "github_regex": "https://github.com/visinf/INSID3", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.28489", "title": "Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms", "authors": [ "Muyang He", "Hanzhong Guo", "Junxiong Lin", "Yizhou Yu" ], "github": "", "project_page": "", "abstract": "The rapid evolution of video generation has enabled models to simulate complex physical dynamics and long-horizon causalities, positioning them as potential world simulators. However, a critical gap still remains between the theoretical capacity for world simulation and the heavy computational costs of spatiotemporal modeling. To address this, we comprehensively and systematically review video generation frameworks and techniques that consider efficiency as a crucial requirement for practical world modeling. We introduce a novel taxonomy in three dimensions: efficient modeling paradigms, efficient network architectures, and efficient inference algorithms. We further show that bridging this efficiency gap directly empowers interactive applications such as autonomous driving, embodied AI, and game simulation. Finally, we identify emerging research frontiers in efficient video-based world modeling, arguing that efficiency is a fundamental prerequisite for evolving video generators into general-purpose, real-time, and robust world simulators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-03-31", "arxiv_id": "2603.25832", "title": "A Neural Score-Based Particle Method for the Vlasov-Maxwell-Landau System", "authors": [ "Vasily Ilin", "Jingwei Hu" ], "github": "https://github.com/Vilin97/Vlasov-Landau-SBTM", "project_page": "", "abstract": "Plasma modeling is central to the design of nuclear fusion reactors, yet simulating collisional plasma kinetics from first principles remains a formidable computational challenge: the Vlasov-Maxwell-Landau (VML) system describes six-dimensional phase-space transport under self-consistent electromagnetic fields together with the nonlinear, nonlocal Landau collision operator. A recent deterministic particle method for the full VML system estimates the velocity score function via the blob method, a kernel-based approximation with O(n^2) cost. In this work, we replace the blob score estimator with score-based transport modeling (SBTM), in which a neural network is trained on-the-fly via implicit score matching at O(n) cost. We prove that the approximated collision operator preserves momentum and kinetic energy, and dissipates an estimated entropy. We also characterize the unique global steady state of the VML system and its electrostatic reduction, providing the ground truth for numerical validation. On three canonical benchmarks -- Landau damping, two-stream instability, and Weibel instability -- SBTM is more accurate than the blob method, achieves correct long-time relaxation to Maxwellian equilibrium where the blob method fails, and delivers 50% faster runtime with 4times lower peak memory.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Vilin97/Vlasov-Landau-SBTM", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29437", "title": "SeGPruner: Semantic-Geometric Visual Token Pruner for 3D Question Answering", "authors": [ "Wenli Li", "Kai Zhao", "Haoran Jiang", "Enquan Yang", "Yi Su", "Dan Zeng" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) have been widely adopted for 3D question answering (3D QA). In typical pipelines, visual tokens extracted from multiple viewpoints are concatenated with language tokens and jointly processed by a large language model (LLM) for inference. However, aggregating multi-view observations inevitably introduces severe token redundancy, leading to an overly large visual token set that significantly hinders inference efficiency under constrained token budgets. Visual token pruning has emerged as a prevalent strategy to address this issue. Nevertheless, most existing pruners are primarily tailored to 2D inputs or rely on indirect geometric cues, which limits their ability to explicitly retain semantically critical objects and maintain sufficient spatial coverage for robust 3D reasoning. In this paper, we propose SeGPruner, a semantic-aware and geometry-guided token reduction framework for efficient 3D QA with multi-view images. Specifically, SeGPruner first preserves semantically salient tokens through an attention-based importance module (Saliency-aware Token Selector), ensuring that object-critical evidence is retained. It then complements these tokens with spatially diverse ones via a geometry-guided selector (Geometry-aware Token Diversifier), which jointly considers semantic relevance and 3D geometric distance. This cooperation between saliency preservation and geometry-guided diversification balances object-level evidence and global scene coverage under aggressive token reduction. Extensive experiments on ScanQA and OpenEQA demonstrate that SeGPruner substantially improves inference efficiency, reducing the visual token budget by 91% and inference latency by 86%, while maintaining competitive performance in 3D reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.27538", "title": "LongCat-Next: Lexicalizing Modalities as Discrete Tokens", "authors": [ "Meituan LongCat Team", "Bin Xiao", "Chao Wang", "Chengjiang Li", "Chi Zhang", "Chong Peng", "Hang Yu", "Hao Yang", "Haonan Yan", "Haoze Sun", "Haozhe Zhao", "Hong Liu", "Hui Su", "Jiaqi Zhang", "Jiawei Wang", "Jing Li", "Kefeng Zhang", "Manyuan Zhang", "Minhao Jing", "Peng Pei", "Quan Chen", "Taofeng Xue", "Tongxin Pan", "Xiaotong Li", "Xiaoyang Li", "Xiaoyu Zhao", "Xing Hu", "Xinyang Lin", "Xunliang Cai", "Yan Bai", "Yan Feng", "Yanjie Li", "Yao Qiu", "Yerui Sun", "Yifan Lu", "Ying Luo", "Yipeng Mei", "Yitian Chen", "Yuchen Xie", "Yufang Liu", "Yufei Chen", "Yulei Qian", "Yuqi Peng", "Zhihang Yu", "Zhixiong Han", "Changran Wang", "Chen Chen", "Dian Zheng", "Fengjiao Chen", "Ge Yang", "Haowei Guo", "Haozhe Wang", "Hongyu Li", "Huicheng Jiang", "Jiale Hong", "Jialv Zou", "Jiamu Li", "Jianping Lin", "Jiaxing Liu", "Jie Yang", "Jing Jin", "Jun Kuang", "Juncheng She", "Kunming Luo", "Kuofeng Gao", "Lin Qiu", "Linsen Guo", "Mianqiu Huang", "Qi Li", "Qian Wang", "Rumei Li", "Siyu Ren", "Wei Wang", "Wenlong He", "Xi Chen", "Xiao Liu", "Xiaoyu Li", "Xu Huang", "Xuanyu Zhu", "Xuezhi Cao", "Yaoming Zhu", "Yifei Cao", "Yimeng Jia", "Yizhen Jiang", "Yufei Gao", "Zeyang Hu", "Zhenlong Yuan", "Zijian Zhang", "Ziwen Wang" ], "github": "https://github.com/meituan-longcat/LongCat-Next", "project_page": "", "abstract": "The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal systems remain language-centric, often treating non-linguistic modalities as external attachments, leading to fragmented architectures and suboptimal integration. To transcend this limitation, we introduce Discrete Native Autoregressive (DiNA), a unified framework that represents multimodal information within a shared discrete space, enabling a consistent and principled autoregressive modeling across modalities. A key innovation is the Discrete Native Any-resolution Visual Transformer (dNaViT), which performs tokenization and de-tokenization at arbitrary resolutions, transforming continuous visual signals into hierarchical discrete tokens. Building on this foundation, we develop LongCat-Next, a native multimodal model that processes text, vision, and audio under a single autoregressive objective with minimal modality-specific design. As an industrial-strength foundation model, it excels at seeing, painting, and talking within a single framework, achieving strong performance across a wide range of multimodal benchmarks. In particular, LongCat-Next addresses the long-standing performance ceiling of discrete vision modeling on understanding tasks and provides a unified approach to effectively reconcile the conflict between understanding and generation. As an attempt toward native multimodality, we open-source the LongCat-Next and its tokenizers, hoping to foster further research and development in the community. GitHub: https://github.com/meituan-longcat/LongCat-Next", "github_manual": "", "github_regex": "https://github.com/meituan-longcat/LongCat-Next", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.25240", "title": "Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells", "authors": [ "Han Zhang", "Guo-Hua Yuan", "Chaohao Yuan", "Tingyang Xu", "Tian Bian", "Hong Cheng", "Wenbing Huang", "Deli Zhao", "Yu Rong" ], "github": "", "project_page": "", "abstract": "Modeling cellular states and predicting their responses to perturbations are central challenges in computational biology and the development of virtual cells. Existing foundation models for single-cell transcriptomics provide powerful static representations, but they do not explicitly model the distribution of cellular states for generative simulation. Here, we introduce Lingshu-Cell, a masked discrete diffusion model that learns transcriptomic state distributions and supports conditional simulation under perturbation. By operating directly in a discrete token space that is compatible with the sparse, non-sequential nature of single-cell transcriptomic data, Lingshu-Cell captures complex transcriptome-wide expression dependencies across approximately 18,000 genes without relying on prior gene selection, such as filtering by high variability or ranking by expression level. Across diverse tissues and species, Lingshu-Cell accurately reproduces transcriptomic distributions, marker-gene expression patterns and cell-subtype proportions, demonstrating its ability to capture complex cellular heterogeneity. Moreover, by jointly embedding cell type or donor identity with perturbation, Lingshu-Cell can predict whole-transcriptome expression changes for novel combinations of identity and perturbation. It achieves leading performance on the Virtual Cell Challenge H1 genetic perturbation benchmark and in predicting cytokine-induced responses in human PBMCs. Together, these results establish Lingshu-Cell as a flexible cellular world model for in silico simulation of cell states and perturbation responses, laying the foundation for a new paradigm in biological discovery and perturbation screening.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.26599", "title": "VGGRPO: Towards World-Consistent Video Generation with 4D Latent Reward", "authors": [ "Zhaochong An", "Orest Kupyn", "Th\u00e9o Uscidda", "Andrea Colaco", "Karan Ahuja", "Serge Belongie", "Mar Gonzalez-Franco", "Marta Tintore Gazulla" ], "github": "", "project_page": "", "abstract": "Large-scale video diffusion models achieve impressive visual quality, yet often fail to preserve geometric consistency. Prior approaches improve consistency either by augmenting the generator with additional modules or applying geometry-aware alignment. However, architectural modifications can compromise the generalization of internet-scale pretrained models, while existing alignment methods are limited to static scenes and rely on RGB-space rewards that require repeated VAE decoding, incurring substantial compute overhead and failing to generalize to highly dynamic real-world scenes. To preserve the pretrained capacity while improving geometric consistency, we propose VGGRPO (Visual Geometry GRPO), a latent geometry-guided framework for geometry-aware video post-training. VGGRPO introduces a Latent Geometry Model (LGM) that stitches video diffusion latents to geometry foundation models, enabling direct decoding of scene geometry from the latent space. By constructing LGM from a geometry model with 4D reconstruction capability, VGGRPO naturally extends to dynamic scenes, overcoming the static-scene limitations of prior methods. Building on this, we perform latent-space Group Relative Policy Optimization with two complementary rewards: a camera motion smoothness reward that penalizes jittery trajectories, and a geometry reprojection consistency reward that enforces cross-view geometric coherence. Experiments on both static and dynamic benchmarks show that VGGRPO improves camera stability, geometry consistency, and overall quality while eliminating costly VAE decoding, making latent-space geometry-guided reinforcement an efficient and flexible approach to world-consistent video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.28032", "title": "CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence", "authors": [ "Tianle Zeng", "Hanxuan Chen", "Yanci Wen", "Hong Zhang" ], "github": "https://github.com/louiszengCN/CarlaAir", "project_page": "", "abstract": "The convergence of low-altitude economies, embodied intelligence, and air-ground cooperative systems creates growing demand for simulation infrastructure capable of jointly modeling aerial and ground agents within a single physically coherent environment. Existing open-source platforms remain domain-segregated: driving simulators lack aerial dynamics, while multirotor simulators lack realistic ground scenes. Bridge-based co-simulation introduces synchronization overhead and cannot guarantee strict spatial-temporal consistency. We present CARLA-Air, an open-source infrastructure that unifies high-fidelity urban driving and physics-accurate multirotor flight within a single Unreal Engine process. The platform preserves both CARLA and AirSim native Python APIs and ROS 2 interfaces, enabling zero-modification code reuse. Within a shared physics tick and rendering pipeline, CARLA-Air delivers photorealistic environments with rule-compliant traffic, socially-aware pedestrians, and aerodynamically consistent UAV dynamics, synchronously capturing up to 18 sensor modalities across all platforms at each tick. The platform supports representative air-ground embodied intelligence workloads spanning cooperation, embodied navigation and vision-language action, multi-modal perception and dataset construction, and reinforcement-learning-based policy training. An extensible asset pipeline allows integration of custom robot platforms into the shared world. By inheriting AirSim's aerial capabilities -- whose upstream development has been archived -- CARLA-Air ensures this widely adopted flight stack continues to evolve within a modern infrastructure. Released with prebuilt binaries and full source: https://github.com/louiszengCN/CarlaAir", "github_manual": "", "github_regex": "https://github.com/louiszengCN/CarlaAir", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.28858", "title": "OptiMer: Optimal Distribution Vector Merging Is Better than Data Mixing for Continual Pre-Training", "authors": [ "Haiyue Song", "Masao Utiyama" ], "github": "https://github.com/shyyhs/optimer", "project_page": "", "abstract": "Continual pre-training is widely used to adapt LLMs to target languages and domains, yet the mixture ratio of training data remains a sensitive hyperparameter that is expensive to tune: they must be fixed before training begins, and a suboptimal choice can waste weeks of compute. In this work, we propose OptiMer, which decouples ratio selection from training: we train one CPT model per dataset, extract each model's distribution vector, which represents the parameter shift induced by that dataset, and search for optimal composition weights post-hoc via Bayesian optimization. Experiments on Gemma 3 27B across languages (Japanese, Chinese) and domains (Math, Code) show that OptiMer consistently outperforms data mixture and model averaging baselines with 15-35 times lower search cost. Key findings reveal that 1) the optimized weights can be interpreted as data mixture ratios, and retraining with these ratios improves data mixture CPT, and 2) the same vector pool can be re-optimized for a given objective without any retraining, producing target-tailored models on demand. Our work establishes that data mixture ratio selection, traditionally a pre-training decision, can be reformulated as a post-hoc optimization over distribution vectors, offering a more flexible paradigm for continual pre-training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/shyyhs/optimer", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.26546", "title": "AutoWeather4D: Autonomous Driving Video Weather Conversion via G-Buffer Dual-Pass Editing", "authors": [ "Tianyu Liu", "Weitao Xiong", "Kunming Luo", "Manyuan Zhang", "Peng Liu", "Yuan Liu", "Ping Tan" ], "github": "", "project_page": "", "abstract": "Generative video models have significantly advanced the photorealistic synthesis of adverse weather for autonomous driving; however, they consistently demand massive datasets to learn rare weather scenarios. While 3D-aware editing methods alleviate these data constraints by augmenting existing video footage, they are fundamentally bottlenecked by costly per-scene optimization and suffer from inherent geometric and illumination entanglement. In this work, we introduce AutoWeather4D, a feed-forward 3D-aware weather editing framework designed to explicitly decouple geometry and illumination. At the core of our approach is a G-buffer Dual-pass Editing mechanism. The Geometry Pass leverages explicit structural foundations to enable surface-anchored physical interactions, while the Light Pass analytically resolves light transport, accumulating the contributions of local illuminants into the global illumination to enable dynamic 3D local relighting. Extensive experiments demonstrate that AutoWeather4D achieves comparable photorealism and structural consistency to generative baselines while enabling fine-grained parametric physical control, serving as a practical data engine for autonomous driving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29957", "title": "Think Anywhere in Code Generation", "authors": [ "Xue Jiang", "Tianyu Zhang", "Ge Li", "Mengyang Liu", "Taozhi Chen", "Zhenhua Xu", "Binhua Li", "Wenpin Jiao", "Zhi Jin", "Yongbin Li", "Yihong Dong" ], "github": "https://github.com/jiangxxxue/Think-Anywhere", "project_page": "", "abstract": "Recent advances in reasoning Large Language Models (LLMs) have primarily relied on upfront thinking, where reasoning occurs before final answer. However, this approach suffers from critical limitations in code generation, where upfront thinking is often insufficient as problems' full complexity only reveals itself during code implementation. Moreover, it cannot adaptively allocate reasoning effort throughout the code generation process where difficulty varies significantly. In this paper, we propose Think-Anywhere, a novel reasoning mechanism that enables LLMs to invoke thinking on-demand at any token position during code generation. We achieve Think-Anywhere by first teaching LLMs to imitate the reasoning patterns through cold-start training, then leveraging outcome-based RL rewards to drive the model's autonomous exploration of when and where to invoke reasoning. Extensive experiments on four mainstream code generation benchmarks (i.e., LeetCode, LiveCodeBench, HumanEval, and MBPP) show that Think-Anywhere achieves state-of-the-art performance over both existing reasoning methods and recent post-training approaches, while demonstrating consistent generalization across diverse LLMs. Our analysis further reveals that Think-Anywhere enables the model to adaptively invoke reasoning at high-entropy positions, providing enhanced interpretability.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jiangxxxue/Think-Anywhere", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29852", "title": "VectorGym: A Multitask Benchmark for SVG Code Generation, Sketching, and Editing", "authors": [ "Juan Rodriguez", "Haotian Zhang", "Abhay Puri", "Tianyang Zhang", "Rishav Pramanik", "Meng Lin", "Xiaoqing Xie", "Marco Terral", "Darsh Kaushik", "Aly Shariff", "Perouz Taslakian", "Spandana Gella", "Sai Rajeswar", "David Vazquez", "Christopher Pal", "Marco Pedersoli" ], "github": "", "project_page": "", "abstract": "We introduce VectorGym, a comprehensive benchmark suite for Scalable Vector Graphics (SVG) that spans generation from text and sketches, complex editing, and visual understanding. VectorGym addresses the lack of realistic, challenging benchmarks aligned with professional design workflows. Our benchmark comprises four tasks with expert human-authored annotations: the novel Sketch2SVG task (VG-Sketch); a new SVG editing dataset (VG-Edit) featuring complex, multi-step edits with higher-order primitives; Text2SVG generation (VG-Text); and SVG captioning (VG-Cap). Unlike prior benchmarks that rely on synthetic edits, VectorGym provides gold-standard human annotations that require semantic understanding and design intent. We also propose a multi-task reinforcement learning approach that jointly optimizes across all four tasks using rendering-based rewards. Our method, built on GRPO with curriculum learning, trains a Qwen3-VL 8B model that achieves state-of-the-art performance among open-source models, surpassing much larger models including Qwen3-VL 235B and matching GPT-4o. We also introduce a VLM-as-a-Judge metric for SVG generation, validated through human correlation studies. Our evaluation of frontier VLMs reveals significant performance gaps, positioning VectorGym as a rigorous framework for advancing visual code generation. VectorGym is publicly available on huggingface.co/datasets/ServiceNow/VectorGym.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29585", "title": "Learn2Fold: Structured Origami Generation with World Model Planning", "authors": [ "Yanjia Huang", "Yunuo Chen", "Ying Jiang", "Jinru Han", "Zhengzhong Tu", "Yin Yang", "Chenfanfu Jiang" ], "github": "", "project_page": "", "abstract": "The ability to transform a flat sheet into a complex three-dimensional structure is a fundamental test of physical intelligence. Unlike cloth manipulation, origami is governed by strict geometric axioms and hard kinematic constraints, where a single invalid crease or collision can invalidate the entire folding sequence. As a result, origami demands long-horizon constructive reasoning that jointly satisfies precise physical laws and high-level semantic intent. Existing approaches fall into two disjoint paradigms: optimization-based methods enforce physical validity but require dense, precisely specified inputs, making them unsuitable for sparse natural language descriptions, while generative foundation models excel at semantic and perceptual synthesis yet fail to produce long-horizon, physics-consistent folding processes. Consequently, generating valid origami folding sequences directly from text remains an open challenge. To address this gap, we introduce Learn2Fold, a neuro-symbolic framework that formulates origami folding as conditional program induction over a crease-pattern graph. Our key insight is to decouple semantic proposal from physical verification. A large language model generates candidate folding programs from abstract text prompts, while a learned graph-structured world model serves as a differentiable surrogate simulator that predicts physical feasibility and failure modes before execution. Integrated within a lookahead planning loop, Learn2Fold enables robust generation of physically valid folding sequences for complex and out-of-distribution patterns, demonstrating that effective spatial intelligence arises from the synergy between symbolic reasoning and grounded physical simulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.19835", "title": "FIPO: Eliciting Deep Reasoning with Future-KL Influenced Policy Optimization", "authors": [ "Chiyu Ma", "Shuo Yang", "Kexin Huang", "Jinda Lu", "Haoming Meng", "Shangshang Wang", "Bolin Ding", "Soroush Vosoughi", "Guoyin Wang", "Jingren Zhou" ], "github": "", "project_page": "", "abstract": "We present Future-KL Influenced Policy Optimization (FIPO), a reinforcement learning algorithm designed to overcome reasoning bottlenecks in large language models. While GRPO style training scales effectively, it typically relies on outcome-based rewards (ORM) that distribute a global advantage uniformly across every token in a trajectory. We argue that this coarse-grained credit assignment imposes a performance ceiling by failing to distinguish critical logical pivots from trivial tokens. FIPO addresses this by incorporating discounted future-KL divergence into the policy update, creating a dense advantage formulation that re-weights tokens based on their influence on subsequent trajectory behavior. Empirically, FIPO enables models to break through the length stagnation seen in standard baselines. Evaluated on Qwen2.5-32B, FIPO extends the average chain-of-thought length from roughly 4,000 to over 10,000 tokens and increases AIME 2024 Pass@1 accuracy from 50.0% to a peak of 58.0% (converging at approximately 56.0\\%). This outperforms both DeepSeek-R1-Zero-Math-32B (around 47.0%) and o1-mini (approximately 56.0%). Our results suggest that establishing dense advantage formulations is a vital path for evolving ORM-based algorithms to unlock the full reasoning potential of base models. We open-source our training system, built on the verl framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.28088", "title": "GEMS: Agent-Native Multimodal Generation with Memory and Skills", "authors": [ "Zefeng He", "Siyuan Huang", "Xiaoye Qu", "Yafu Li", "Tong Zhu", "Yu Cheng", "Yang Yang" ], "github": "", "project_page": "", "abstract": "Recent multimodal generation models have achieved remarkable progress on general-purpose generation tasks, yet continue to struggle with complex instructions and specialized downstream tasks. Inspired by the success of advanced agent frameworks such as Claude Code, we propose GEMS (Agent-Native Multimodal GEneration with Memory and Skills), a framework that pushes beyond the inherent limitations of foundational models on both general and downstream tasks. GEMS is built upon three core components. Agent Loop introduces a structured multi-agent framework that iteratively improves generation quality through closed-loop optimization. Agent Memory provides a persistent, trajectory-level memory that hierarchically stores both factual states and compressed experiential summaries, enabling a global view of the optimization process while reducing redundancy. Agent Skill offers an extensible collection of domain-specific expertise with on-demand loading, allowing the system to effectively handle diverse downstream applications. Across five mainstream tasks and four downstream tasks, evaluated on multiple generative backends, GEMS consistently achieves significant performance gains. Most notably, it enables the lightweight 6B model Z-Image-Turbo to surpass the state-of-the-art Nano Banana 2 on GenEval2, demonstrating the effectiveness of agent harness in extending model capabilities beyond their original limits.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29620", "title": "Unify-Agent: A Unified Multimodal Agent for World-Grounded Image Synthesis", "authors": [ "Shuang Chen", "Quanxin Shou", "Hangting Chen", "Yucheng Zhou", "Kaituo Feng", "Wenbo Hu", "Yi-Fan Zhang", "Yunlong Lin", "Wenxuan Huang", "Mingyang Song", "Dasen Dai", "Bolin Jiang", "Manyuan Zhang", "Shi-Xue Zhang", "Zhengkai Jiang", "Lucas Wang", "Zhao Zhong", "Yu Cheng", "Nanyun Peng" ], "github": "", "project_page": "", "abstract": "Unified multimodal models provide a natural and promising architecture for understanding diverse and complex real-world knowledge while generating high-quality images. However, they still rely primarily on frozen parametric knowledge, which makes them struggle with real-world image generation involving long-tail and knowledge-intensive concepts. Inspired by the broad success of agents on real-world tasks, we explore agentic modeling to address this limitation. Specifically, we present Unify-Agent, a unified multimodal agent for world-grounded image synthesis, which reframes image generation as an agentic pipeline consisting of prompt understanding, multimodal evidence searching, grounded recaptioning, and final synthesis. To train our model, we construct a tailored multimodal data pipeline and curate 143K high-quality agent trajectories for world-grounded image synthesis, enabling effective supervision over the full agentic generation process. We further introduce FactIP, a benchmark covering 12 categories of culturally significant and long-tail factual concepts that explicitly requires external knowledge grounding. Extensive experiments show that our proposed Unify-Agent substantially improves over its base unified model across diverse benchmarks and real world generation tasks, while approaching the world knowledge capabilities of the strongest closed-source models. As an early exploration of agent-based modeling for world-grounded image synthesis, our work highlights the value of tightly coupling reasoning, searching, and generation for reliable open-world agentic image synthesis.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29664", "title": "CutClaw: Agentic Hours-Long Video Editing via Music Synchronization", "authors": [ "Shifang Zhao", "Yihan Hu", "Ying Shan", "Yunchao Wei", "Xiaodong Cun" ], "github": "https://github.com/GVCLab/CutClaw", "project_page": "", "abstract": "Editing the video content with audio alignment forms a digital human-made art in current social media. However, the time-consuming and repetitive nature of manual video editing has long been a challenge for filmmakers and professional content creators alike. In this paper, we introduce CutClaw, an autonomous multi-agent framework designed to edit hours-long raw footage into meaningful short videos that leverages the capabilities of multiple Multimodal Language Models~(MLLMs) as an agent system. It produces videos with synchronized music, followed by instructions, and a visually appealing appearance. In detail, our approach begins by employing a hierarchical multimodal decomposition that captures both fine-grained details and global structures across visual and audio footage. Then, to ensure narrative consistency, a Playwriter Agent orchestrates the whole storytelling flow and structures the long-term narrative, anchoring visual scenes to musical shifts. Finally, to construct a short edited video, Editor and Reviewer Agents collaboratively optimize the final cut via selecting fine-grained visual content based on rigorous aesthetic and semantic criteria. We conduct detailed experiments to demonstrate that CutClaw significantly outperforms state-of-the-art baselines in generating high-quality, rhythm-aligned videos. The code is available at: https://github.com/GVCLab/CutClaw.", "github_manual": "", "github_regex": "https://github.com/GVCLab/CutClaw", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29387", "title": "Extend3D: Town-Scale 3D Generation", "authors": [ "Seungwoo Yoon", "Jinmo Kim", "Jaesik Park" ], "github": "", "project_page": "", "abstract": "In this paper, we propose Extend3D, a training-free pipeline for 3D scene generation from a single image, built upon an object-centric 3D generative model. To overcome the limitations of fixed-size latent spaces in object-centric models for representing wide scenes, we extend the latent space in the x and y directions. Then, by dividing the extended latent space into overlapping patches, we apply the object-centric 3D generative model to each patch and couple them at each time step. Since patch-wise 3D generation with image conditioning requires strict spatial alignment between image and latent patches, we initialize the scene using a point cloud prior from a monocular depth estimator and iteratively refine occluded regions through SDEdit. We discovered that treating the incompleteness of 3D structure as noise during 3D refinement enables 3D completion via a concept, which we term under-noising. Furthermore, to address the sub-optimality of object-centric models for sub-scene generation, we optimize the extended latent during denoising, ensuring that the denoising trajectories remain consistent with the sub-scene dynamics. To this end, we introduce 3D-aware optimization objectives for improved geometric structure and texture fidelity. We demonstrate that our method yields better results than prior methods, as evidenced by human preference and quantitative experiments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29557", "title": "FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration", "authors": [ "Qiyao Wang", "Hongbo Wang", "Longze Chen", "Zhihao Yang", "Guhong Chen", "Hamid Alinejad-Rokny", "Hui Li", "Yuan Lin", "Min Yang" ], "github": "", "project_page": "", "abstract": "Scientific idea generation (SIG) is critical to AI-driven autonomous research, yet existing approaches are often constrained by a static retrieval-then-generation paradigm, leading to homogeneous and insufficiently divergent ideas. In this work, we propose FlowPIE, a tightly coupled retrieval-generation framework that treats literature exploration and idea generation as a co-evolving process. FlowPIE expands literature trajectories via a flow-guided Monte Carlo Tree Search (MCTS) inspired by GFlowNets, using the quality of current ideas assessed by an LLM-based generative reward model (GRM) as a supervised signal to guide adaptive retrieval and construct a diverse, high-quality initial population. Based on this population, FlowPIE models idea generation as a test-time idea evolution process, applying selection, crossover, and mutation with the isolation island paradigm and GRM-based fitness computation to incorporate cross-domain knowledge. It effectively mitigates the information cocoons arising from over-reliance on parametric knowledge and static literature. Extensive evaluations demonstrate that FlowPIE consistently produces ideas with higher novelty, feasibility and diversity compared to strong LLM-based and agent-based frameworks, while enabling reward scaling during test time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29029", "title": "MMFace-DiT: A Dual-Stream Diffusion Transformer for High-Fidelity Multimodal Face Generation", "authors": [ "Bharath Krishnamurthy", "Ajita Rattani" ], "github": "", "project_page": "", "abstract": "Recent multimodal face generation models address the spatial control limitations of text-to-image diffusion models by augmenting text-based conditioning with spatial priors such as segmentation masks, sketches, or edge maps. This multimodal fusion enables controllable synthesis aligned with both high-level semantic intent and low-level structural layout. However, most existing approaches typically extend pre-trained text-to-image pipelines by appending auxiliary control modules or stitching together separate uni-modal networks. These ad hoc designs inherit architectural constraints, duplicate parameters, and often fail under conflicting modalities or mismatched latent spaces, limiting their ability to perform synergistic fusion across semantic and spatial domains. We introduce MMFace-DiT, a unified dual-stream diffusion transformer engineered for synergistic multimodal face synthesis. Its core novelty lies in a dual-stream transformer block that processes spatial (mask/sketch) and semantic (text) tokens in parallel, deeply fusing them through a shared Rotary Position-Embedded (RoPE) Attention mechanism. This design prevents modal dominance and ensures strong adherence to both text and structural priors to achieve unprecedented spatial-semantic consistency for controllable face generation. Furthermore, a novel Modality Embedder enables a single cohesive model to dynamically adapt to varying spatial conditions without retraining. MMFace-DiT achieves a 40% improvement in visual fidelity and prompt alignment over six state-of-the-art multimodal face generation models, establishing a flexible new paradigm for end-to-end controllable generative modeling. The code and dataset are available on our project page: https://vcbsl.github.io/MMFace-DiT/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.19195", "title": "How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation", "authors": [ "Ke-Han Lu", "Szu-Wei Fu", "Chao-Han Huck Yang", "Zhehuai Chen", "Sung-Feng Huang", "Chih-Kai Yang", "Yi-Cheng Lin", "Chi-Yuan Hsiao", "Wenze Ren", "En-Pei Hu", "Yu-Han Huang", "An-Yu Cheng", "Cheng-Han Chiang", "Yu Tsao", "Yu-Chiang Frank Wang", "Hung-yi Lee" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) have been widely used as knowledge backbones of Large Audio Language Models (LALMs), yet how much auditory knowledge they encode through text-only pre-training and how this affects downstream performance remains unclear. We study this gap by comparing different LLMs under two text-only and one audio-grounded setting: (1) direct probing on AKB-2000, a curated benchmark testing the breadth and depth of auditory knowledge; (2) cascade evaluation, where LLMs reason over text descriptions from an audio captioner; and (3) audio-grounded evaluation, where each LLM is fine-tuned into a Large Audio Language Model (LALM) with an audio encoder. Our findings reveal that auditory knowledge varies substantially across families, and text-only results are strongly correlated with audio performance. Our work provides empirical grounding for a comprehensive understanding of LLMs in audio research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.27164", "title": "daVinci-LLM:Towards the Science of Pretraining", "authors": [ "Yiwei Qin", "Yixiu Liu", "Tiantian Mi", "Muhang Xie", "Zhen Huang", "Weiye Si", "Pengrui Lu", "Siyuan Feng", "Xia Wu", "Liming Liu", "Ye Luo", "Jinlong Hou", "Qipeng Guo", "Yu Qiao", "Pengfei Liu" ], "github": "", "project_page": "", "abstract": "The foundational pretraining phase determines a model's capability ceiling, as post-training struggles to overcome capability foundations established during pretraining, yet it remains critically under-explored. This stems from a structural paradox: organizations with computational resources operate under commercial pressures that inhibit transparent disclosure, while academic institutions possess research freedom but lack pretraining-scale computational resources. daVinci-LLM occupies this unexplored intersection, combining industrial-scale resources with full research freedom to advance the science of pretraining. We adopt a fully-open paradigm that treats openness as scientific methodology, releasing complete data processing pipelines, full training processes, and systematic exploration results. Recognizing that the field lacks systematic methodology for data processing, we employ the Data Darwinism framework, a principled L0-L9 taxonomy from filtering to synthesis. We train a 3B-parameter model from random initialization across 8T tokens using a two-stage adaptive curriculum that progressively shifts from foundational capabilities to reasoning-intensive enhancement. Through 200+ controlled ablations, we establish that: processing depth systematically enhances capabilities, establishing it as a critical dimension alongside volume scaling; different domains exhibit distinct saturation dynamics, necessitating adaptive strategies from proportion adjustments to format shifts; compositional balance enables targeted intensification while preventing performance collapse; how evaluation protocol choices shape our understanding of pretraining progress. By releasing the complete exploration process, we enable the community to build upon our findings and systematic methodologies to form accumulative scientific knowledge in pretraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.28590", "title": "MonitorBench: A Comprehensive Benchmark for Chain-of-Thought Monitorability in Large Language Models", "authors": [ "Han Wang", "Yifan Sun", "Brian Ko", "Mann Talati", "Jiawen Gong", "Zimeng Li", "Naicheng Yu", "Xucheng Yu", "Wei Shen", "Vedant Jolly", "Huan Zhang" ], "github": "https://github.com/ASTRAL-Group/MonitorBench", "project_page": "", "abstract": "Large language models (LLMs) can generate chains of thought (CoTs) that are not always causally responsible for their final outputs. When such a mismatch occurs, the CoT no longer faithfully reflects the decision-critical factors driving the model's behavior, leading to the reduced CoT monitorability problem. However, a comprehensive and fully open-source benchmark for studying CoT monitorability remains lacking. To address this gap, we propose MonitorBench, a systematic benchmark for evaluating CoT monitorability in LLMs. MonitorBench provides: (1) a diverse set of 1,514 test instances with carefully designed decision-critical factors across 19 tasks spanning 7 categories to characterize when CoTs can be used to monitor the factors driving LLM behavior; and (2) two stress-test settings to quantify the extent to which CoT monitorability can be degraded. Extensive experiments across multiple popular LLMs with varying capabilities show that CoT monitorability is higher when producing the final target response requires structural reasoning through the decision-critical factor. Closed-source LLMs generally show lower monitorability, and there exists a negative relationship between monitorability and model capability. Moreover, both open- and closed-source LLMs can intentionally reduce monitorability under stress-tests, with monitorability dropping by up to 30% in some tasks that do not require structural reasoning over the decision-critical factors. Beyond these empirical insights, MonitorBench provides a basis for further research on evaluating future LLMs, studying advanced stress-test monitorability techniques, and developing new monitoring approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ASTRAL-Group/MonitorBench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.28763", "title": "PoseDreamer: Scalable and Photorealistic Human Data Generation Pipeline with Diffusion Models", "authors": [ "Lorenza Prospero", "Orest Kupyn", "Ostap Viniavskyi", "Jo\u00e3o F. Henriques", "Christian Rupprecht" ], "github": "", "project_page": "", "abstract": "Acquiring labeled datasets for 3D human mesh estimation is challenging due to depth ambiguities and the inherent difficulty of annotating 3D geometry from monocular images. Existing datasets are either real, with manually annotated 3D geometry and limited scale, or synthetic, rendered from 3D engines that provide precise labels but suffer from limited photorealism, low diversity, and high production costs. In this work, we explore a third path: generated data. We introduce PoseDreamer, a novel pipeline that leverages diffusion models to generate large-scale synthetic datasets with 3D mesh annotations. Our approach combines controllable image generation with Direct Preference Optimization for control alignment, curriculum-based hard sample mining, and multi-stage quality filtering. Together, these components naturally maintain correspondence between 3D labels and generated images, while prioritizing challenging samples to maximize dataset utility. Using PoseDreamer, we generate more than 500,000 high-quality synthetic samples, achieving a 76% improvement in image-quality metrics compared to rendering-based datasets. Models trained on PoseDreamer achieve performance comparable to or superior to those trained on real-world and traditional synthetic datasets. In addition, combining PoseDreamer with synthetic datasets results in better performance than combining real-world and synthetic datasets, demonstrating the complementary nature of our dataset. We will release the full dataset and generation code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.25791", "title": "ArtHOI: Taming Foundation Models for Monocular 4D Reconstruction of Hand-Articulated-Object Interactions", "authors": [ "Zikai Wang", "Zhilu Zhang", "Yiqing Wang", "Hui Li", "Wangmeng Zuo" ], "github": "", "project_page": "", "abstract": "Existing hand-object interactions (HOI) methods are largely limited to rigid objects, while 4D reconstruction methods of articulated objects generally require pre-scanning the object or even multi-view videos. It remains an unexplored but significant challenge to reconstruct 4D human-articulated-object interactions from a single monocular RGB video. Fortunately, recent advancements in foundation models present a new opportunity to address this highly ill-posed problem. To this end, we introduce ArtHOI, an optimization-based framework that integrates and refines priors from multiple foundation models. Our key contribution is a suite of novel methodologies designed to resolve the inherent inaccuracies and physical unreality of these priors. In particular, we introduce an Adaptive Sampling Refinement (ASR) method to optimize object's metric scale and pose for grounding its normalized mesh in world space. Furthermore, we propose a Multimodal Large Language Model (MLLM) guided hand-object alignment method, utilizing contact reasoning information as constraints of hand-object mesh composition optimization. To facilitate a comprehensive evaluation, we also contribute two new datasets, ArtHOI-RGBD and ArtHOI-Wild. Extensive experiments validate the robustness and effectiveness of our ArtHOI across diverse objects and interactions. Project: https://arthoi-reconstruction.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.28224", "title": "Ghost-FWL: A Large-Scale Full-Waveform LiDAR Dataset for Ghost Detection and Removal", "authors": [ "Kazuma Ikeda", "Ryosei Hara", "Rokuto Nagata", "Ozora Sako. Zihao Ding", "Takahiro Kado", "Ibuki Fujioka", "Taro Beppu", "Mariko Isogawa", "Kentaro Yoshioka" ], "github": "", "project_page": "", "abstract": "LiDAR has become an essential sensing modality in autonomous driving, robotics, and smart-city applications. However, ghost points (or ghosts), which are false reflections caused by multi-path laser returns from glass and reflective surfaces, severely degrade 3D mapping and localization accuracy. Prior ghost removal relies on geometric consistency in dense point clouds, failing on mobile LiDAR's sparse, dynamic data. We address this by exploiting full-waveform LiDAR (FWL), which captures complete temporal intensity profiles rather than just peak distances, providing crucial cues for distinguishing ghosts from genuine reflections in mobile scenarios. As this is a new task, we present Ghost-FWL, the first and largest annotated mobile FWL dataset for ghost detection and removal. Ghost-FWL comprises 24K frames across 10 diverse scenes with 7.5 billion peak-level annotations, which is 100x larger than existing annotated FWL datasets. Benefiting from this large-scale dataset, we establish a FWL-based baseline model for ghost detection and propose FWL-MAE, a masked autoencoder for efficient self-supervised representation learning on FWL data. Experiments show that our baseline outperforms existing methods in ghost removal accuracy, and our ghost removal further enhances downstream tasks such as LiDAR-based SLAM (66% trajectory error reduction) and 3D object detection (50x false positive reduction). The dataset and code is publicly available and can be accessed via the project page: https://keio-csg.github.io/Ghost-FWL", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29497", "title": "Distilling Human-Aligned Privacy Sensitivity Assessment from Large Language Models", "authors": [ "Gabriel Loiseau", "Damien Sileo", "Damien Riquet", "Maxime Meyer", "Marc Tommasi" ], "github": "https://github.com/gabrielloiseau/privacy-distillation", "project_page": "", "abstract": "Accurate privacy evaluation of textual data remains a critical challenge in privacy-preserving natural language processing. Recent work has shown that large language models (LLMs) can serve as reliable privacy evaluators, achieving strong agreement with human judgments; however, their computational cost and impracticality for processing sensitive data at scale limit real-world deployment. We address this gap by distilling the privacy assessment capabilities of Mistral Large 3 (675B) into lightweight encoder models with as few as 150M parameters. Leveraging a large-scale dataset of privacy-annotated texts spanning 10 diverse domains, we train efficient classifiers that preserve strong agreement with human annotations while dramatically reducing computational requirements. We validate our approach on human-annotated test data and demonstrate its practical utility as an evaluation metric for de-identification systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/gabrielloiseau/privacy-distillation", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.26246", "title": "Distilling Conversations: Abstract Compression of Conversational Audio Context for LLM-based ASR", "authors": [ "Shashi Kumar", "Esa\u00fa Villatoro-Tello", "Sergio Burdisso", "Kadri Hacioglu", "Thibault Ba\u00f1eras-Roux", "Hasindri Watawana", "Dairazalia Sanchez-Cortes", "Srikanth Madikeri", "Petr Motlicek", "Andreas Stolcke" ], "github": "", "project_page": "", "abstract": "Standard LLM-based speech recognition systems typically process utterances in isolation, limiting their ability to leverage conversational context. In this work, we study whether multimodal context from prior turns improves LLM-based ASR and how to represent that context efficiently. We find that, after supervised multi-turn training, conversational context mainly helps with the recognition of contextual entities. However, conditioning on raw context is expensive because the prior-turn audio token sequence grows rapidly with conversation length. To address this, we propose Abstract Compression, which replaces the audio portion of prior turns with a fixed number of learned latent tokens while retaining corresponding transcripts explicitly. On both in-domain and out-of-domain test sets, the compressed model recovers part of the gains of raw-context conditioning with a smaller prior-turn audio footprint. We also provide targeted analyses of the compression setup and its trade-offs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.27460", "title": "Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development", "authors": [ "Zhongying Deng", "Cheng Tang", "Ziyan Huang", "Jiashi Lin", "Ying Chen", "Junzhi Ning", "Chenglong Ma", "Jiyao Liu", "Wei Li", "Yinghao Zhu", "Shujian Gao", "Yanyan Huang", "Sibo Ju", "Yanzhou Su", "Pengcheng Chen", "Wenhao Tang", "Tianbin Li", "Haoyu Wang", "Yuanfeng Ji", "Hui Sun", "Shaobo Min", "Liang Peng", "Feilong Tang", "Haochen Xue", "Rulin Zhou", "Chaoyang Zhang", "Wenjie Li", "Shaohao Rui", "Weijie Ma", "Xingyue Zhao", "Yibin Wang", "Kun Yuan", "Zhaohui Lu", "Shujun Wang", "Jinjie Wei", "Lihao Liu", "Dingkang Yang", "Lin Wang", "Yulong Li", "Haolin Yang", "Yiqing Shen", "Lequan Yu", "Xiaowei Hu", "Yun Gu", "Yicheng Wu", "Benyou Wang", "Minghui Zhang", "Angelica I. Aviles-Rivero", "Qi Gao", "Hongming Shan", "Xiaoyu Ren", "Fang Yan", "Hongyu Zhou", "Haodong Duan", "Maosong Cao", "Shanshan Wang", "Bin Fu", "Xiaomeng Li", "Zhi Hou", "Chunfeng Song", "Lei Bai", "Yuan Cheng", "Yuandong Pu", "Xiang Li", "Wenhai Wang", "Hao Chen", "Jiaxin Zhuang", "Songyang Zhang", "Huiguang He", "Mengzhang Li", "Bohan Zhuang", "Zhian Bai", "Rongshan Yu", "Liansheng Wang", "Yukun Zhou", "Xiaosong Wang", "Xin Guo", "Guanbin Li", "Xiangru Lin", "Dakai Jin", "Mianxin Liu", "Wenlong Zhang", "Qi Qin", "Conghui He", "Yuqiang Li", "Ye Luo", "Nanqing Dong", "Jie Xu", "Wenqi Shao", "Bo Zhang", "Qiujuan Yan", "Yihao Liu", "Jun Ma", "Zhi Lu", "Yuewen Cao", "Zongwei Zhou", "Jianming Liang", "Shixiang Tang", "Qi Duan", "Dongzhan Zhou", "Chen Jiang", "Yuyin Zhou", "Yanwu Xu", "Jiancheng Yang", "Shaoting Zhang", "Xiaohong Liu", "Siqi Luo", "Yi Xin", "Chaoyu Liu", "Haochen Wen", "Xin Chen", "Alejandro Lozano", "Min Woo Sun", "Yuhui Zhang", "Yue Yao", "Xiaoxiao Sun", "Serena Yeung-Levy", "Xia Li", "Jing Ke", "Chunhui Zhang", "Zongyuan Ge", "Ming Hu", "Jin Ye", "Zhifeng Li", "Yirong Chen", "Yu Qiao", "Junjun He" ], "github": "", "project_page": "", "abstract": "Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in the field of medical imaging, the curation and assembling of such medical datasets are highly challenging due to the reliance on clinical expertise and strict ethical and privacy constraints, resulting in a scarcity of large-scale unified medical datasets and hindering the development of powerful medical foundation models. In this work, we present the largest survey to date of medical image datasets, covering over 1,000 open-access datasets with a systematic catalog of their modalities, tasks, anatomies, annotations, limitations, and potential for integration. Our analysis exposes a landscape that is modest in scale, fragmented across narrowly scoped tasks, and unevenly distributed across organs and modalities, which in turn limits the utility of existing medical image datasets for developing versatile and robust medical foundation models. To turn fragmentation into scale, we propose a metadata-driven fusion paradigm (MDFP) that integrates public datasets with shared modalities or tasks, thereby transforming multiple small data silos into larger, more coherent resources. Building on MDFP, we release an interactive discovery portal that enables end-to-end, automated medical image dataset integration, and compile all surveyed datasets into a unified, structured table that clearly summarizes their key characteristics and provides reference links, offering the community an accessible and comprehensive repository. By charting the current terrain and offering a principled path to dataset consolidation, our survey provides a practical roadmap for scaling medical imaging corpora, supporting faster data discovery, more principled dataset creation, and more capable medical foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.22794", "title": "It Takes Two: A Duet of Periodicity and Directionality for Burst Flicker Removal", "authors": [ "Lishen Qu", "Shihao Zhou", "Jie Liang", "Hui Zeng", "Lei Zhang", "Jufeng Yang" ], "github": "https://github.com/qulishen/Flickerformer", "project_page": "", "abstract": "Flicker artifacts, arising from unstable illumination and row-wise exposure inconsistencies, pose a significant challenge in short-exposure photography, severely degrading image quality. Unlike typical artifacts, e.g., noise and low-light, flicker is a structured degradation with specific spatial-temporal patterns, which are not accounted for in current generic restoration frameworks, leading to suboptimal flicker suppression and ghosting artifacts. In this work, we reveal that flicker artifacts exhibit two intrinsic characteristics, periodicity and directionality, and propose Flickerformer, a transformer-based architecture that effectively removes flicker without introducing ghosting. Specifically, Flickerformer comprises three key components: a phase-based fusion module (PFM), an autocorrelation feed-forward network (AFFN), and a wavelet-based directional attention module (WDAM). Based on the periodicity, PFM performs inter-frame phase correlation to adaptively aggregate burst features, while AFFN exploits intra-frame structural regularities through autocorrelation, jointly enhancing the network's ability to perceive spatially recurring patterns. Moreover, motivated by the directionality of flicker artifacts, WDAM leverages high-frequency variations in the wavelet domain to guide the restoration of low-frequency dark regions, yielding precise localization of flicker artifacts. Extensive experiments demonstrate that Flickerformer outperforms state-of-the-art approaches in both quantitative metrics and visual quality. The source code is available at https://github.com/qulishen/Flickerformer.", "github_manual": "", "github_regex": "https://github.com/qulishen/Flickerformer", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.27365", "title": "Falcon Perception", "authors": [ "Aviraj Bevli", "Sofian Chaybouti", "Yasser Dahou", "Hakim Hacid", "Ngoc Dung Huynh", "Phuc H. Le Khac", "Sanath Narayan", "Wamiq Reyaz Para", "Ankit Singh" ], "github": "", "project_page": "", "abstract": "Perception-centric systems are typically implemented with a modular encoder-decoder pipeline: a vision backbone for feature extraction and a separate decoder (or late-fusion module) for task prediction. This raises a central question: is this architectural separation essential or can a single early-fusion stack do both perception and task modeling at scale? We introduce Falcon Perception, a unified dense Transformer that processes image patches and text tokens in a shared parameter space from the first layer, using a hybrid attention pattern (bidirectional among image tokens, causal for prediction tokens) to combine global visual context with autoregressive, variable-length instance generation. To keep dense outputs practical, Falcon Perception retains a lightweight token interface and decodes continuous spatial outputs with specialized heads, enabling parallel high-resolution mask prediction. Our design promotes simplicity: we keep a single scalable backbone and shift complexity toward data and training signals, adding only small heads where outputs are continuous and dense. On SA-Co, Falcon Perception improves mask quality to 68.0 Macro-F_1 compared to 62.3 of SAM3. We also introduce PBench, a benchmark targeting compositional prompts (OCR, spatial constraints, relations) and dense long-context regimes, where the model shows better gains. Finally, we extend the same early-fusion recipe to Falcon OCR: a compact 300M-parameter model which attains 80.3% on olmOCR and 88.64 on OmniDocBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.26174", "title": "CREval: An Automated Interpretable Evaluation for Creative Image Manipulation under Complex Instructions", "authors": [ "Chonghuinan Wang", "Zihan Chen", "Yuxiang Wei", "Tianyi Jiang", "Xiaohe Wu", "Fan Li", "Wangmeng Zuo", "Hongxun Yao" ], "github": "", "project_page": "", "abstract": "Instruction-based multimodal image manipulation has recently made rapid progress. However, existing evaluation methods lack a systematic and human-aligned framework for assessing model performance on complex and creative editing tasks. To address this gap, we propose CREval, a fully automated question-answer (QA)-based evaluation pipeline that overcomes the incompleteness and poor interpretability of opaque Multimodal Large Language Models (MLLMs) scoring. Simultaneously, we introduce CREval-Bench, a comprehensive benchmark specifically designed for creative image manipulation under complex instructions. CREval-Bench covers three categories and nine creative dimensions, comprising over 800 editing samples and 13K evaluation queries. Leveraging this pipeline and benchmark, we systematically evaluate a diverse set of state-of-the-art open and closed-source models. The results reveal that while closed-source models generally outperform open-source ones on complex and creative tasks, all models still struggle to complete such edits effectively. In addition, user studies demonstrate strong consistency between CREval's automated metrics and human judgments. Therefore, CREval provides a reliable foundation for evaluating image editing models on complex and creative image manipulation tasks, and highlights key challenges and opportunities for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.25732", "title": "BizGenEval: A Systematic Benchmark for Commercial Visual Content Generation", "authors": [ "Yan Li", "Zezi Zeng", "Ziwei Zhou", "Xin Gao", "Muzhao Tian", "Yifan Yang", "Mingxi Cheng", "Qi Dai", "Yuqing Yang", "Lili Qiu", "Zhendong Wang", "Zhengyuan Yang", "Xue Yang", "Lijuan Wang", "Ji Li", "Chong Luo" ], "github": "", "project_page": "", "abstract": "Recent advances in image generation models have expanded their applications beyond aesthetic imagery toward practical visual content creation. However, existing benchmarks mainly focus on natural image synthesis and fail to systematically evaluate models under the structured and multi-constraint requirements of real-world commercial design tasks. In this work, we introduce BizGenEval, a systematic benchmark for commercial visual content generation. The benchmark spans five representative document types: slides, charts, webpages, posters, and scientific figures, and evaluates four key capability dimensions: text rendering, layout control, attribute binding, and knowledge-based reasoning, forming 20 diverse evaluation tasks. BizGenEval contains 400 carefully curated prompts and 8000 human-verified checklist questions to rigorously assess whether generated images satisfy complex visual and semantic constraints. We conduct large-scale benchmarking on 26 popular image generation systems, including state-of-the-art commercial APIs and leading open-source models. The results reveal substantial capability gaps between current generative models and the requirements of professional visual content creation. We hope BizGenEval serves as a standardized benchmark for real-world commercial visual content generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.25645", "title": "Colon-Bench: An Agentic Workflow for Scalable Dense Lesion Annotation in Full-Procedure Colonoscopy Videos", "authors": [ "Abdullah Hamdi", "Changchun Yang", "Xin Gao" ], "github": "", "project_page": "", "abstract": "Early screening via colonoscopy is critical for colon cancer prevention, yet developing robust AI systems for this domain is hindered by the lack of densely annotated, long-sequence video datasets. Existing datasets predominantly focus on single-class polyp detection and lack the rich spatial, temporal, and linguistic annotations required to evaluate modern Multimodal Large Language Models (MLLMs). To address this critical gap, we introduce Colon-Bench, generated via a novel multi-stage agentic workflow. Our pipeline seamlessly integrates temporal proposals, bounding-box tracking, AI-driven visual confirmation, and human-in-the-loop review to scalably annotate full-procedure videos. The resulting verified benchmark is unprecedented in scope, encompassing 528 videos, 14 distinct lesion categories (including polyps, ulcers, and bleeding), over 300,000 bounding boxes, 213,000 segmentation masks, and 133,000 words of clinical descriptions. We utilize Colon-Bench to rigorously evaluate state-of-the-art MLLMs across lesion classification, Open-Vocabulary Video Object Segmentation (OV-VOS), and video Visual Question Answering (VQA). The MLLM results demonstrate surprisingly high localization performance in medical domains compared to SAM-3. Finally, we analyze common VQA errors from MLLMs to introduce a novel \"colon-skill\" prompting strategy, improving zero-shot MLLM performance by up to 9.7% across most MLLMs. The dataset and the code are available at https://abdullahamdi.com/colon-bench .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29092", "title": "TrajectoryMover: Generative Movement of Object Trajectories in Videos", "authors": [ "Kiran Chhatre", "Hyeonho Jeong", "Yulia Gryaditskaya", "Christopher E. Peters", "Chun-Hao Paul Huang", "Paul Guerrero" ], "github": "", "project_page": "", "abstract": "Generative video editing has enabled several intuitive editing operations for short video clips that would previously have been difficult to achieve, especially for non-expert editors. Existing methods focus on prescribing an object's 3D or 2D motion trajectory in a video, or on altering the appearance of an object or a scene, while preserving both the video's plausibility and identity. Yet a method to move an object's 3D motion trajectory in a video, i.e., moving an object while preserving its relative 3D motion, is currently still missing. The main challenge lies in obtaining paired video data for this scenario. Previous methods typically rely on clever data generation approaches to construct plausible paired data from unpaired videos, but this approach fails if one of the videos in a pair can not easily be constructed from the other. Instead, we introduce TrajectoryAtlas, a new data generation pipeline for large-scale synthetic paired video data and a video generator TrajectoryMover fine-tuned with this data. We show that this successfully enables generative movement of object trajectories. Project page: https://chhatrekiran.github.io/trajectorymover", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29089", "title": "WorldFlow3D: Flowing Through 3D Distributions for Unbounded World Generation", "authors": [ "Amogh Joshi", "Julian Ost", "Felix Heide" ], "github": "", "project_page": "", "abstract": "Unbounded 3D world generation is emerging as a foundational task for scene modeling in computer vision, graphics, and robotics. In this work, we present WorldFlow3D, a novel method capable of generating unbounded 3D worlds. Building upon a foundational property of flow matching - namely, defining a path of transport between two data distributions - we model 3D generation more generally as a problem of flowing through 3D data distributions, not limited to conditional denoising. We find that our latent-free flow approach generates causal and accurate 3D structure, and can use this as an intermediate distribution to guide the generation of more complex structure and high-quality texture - all while converging more rapidly than existing methods. We enable controllability over generated scenes with vectorized scene layout conditions for geometric structure control and visual texture control through scene attributes. We confirm the effectiveness of WorldFlow3D on both real outdoor driving scenes and synthetic indoor scenes, validating cross-domain generalizability and high-quality generation on real data distributions. We confirm favorable scene generation fidelity over approaches in all tested settings for unbounded scene generation. For more, see https://light.princeton.edu/worldflow3d.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.17191", "title": "Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data", "authors": [ "Sophie Kearney", "Shu Yang", "Zixuan Wen", "Weimin Lyu", "Bojian Hou", "Duy Duong-Tran", "Tianlong Chen", "Jason H. Moore", "Marylyn D. Ritchie", "Chao Chen", "Li Shen" ], "github": "https://github.com/sophie-kearney/TAP-GPT", "project_page": "", "abstract": "Accurate diagnosis of Alzheimer's disease (AD) requires handling tabular biomarker data, yet such data are often small and incomplete, where deep learning models frequently fail to outperform classical methods. Pretrained large language models (LLMs) offer few-shot generalization, structured reasoning, and interpretable outputs, providing a powerful paradigm shift for clinical prediction. We propose TAP-GPT Tabular Alzheimer's Prediction GPT, a domain-adapted tabular LLM framework built on TableGPT2 and fine-tuned for few-shot AD classification using tabular prompts rather than plain texts. We evaluate TAP-GPT across four ADNI-derived datasets, including QT-PAD biomarkers and region-level structural MRI, amyloid PET, and tau PET for binary AD classification. Across multimodal and unimodal settings, TAP-GPT improves upon its backbone models and outperforms traditional machine learning baselines in the few-shot setting while remaining competitive with state-of-the-art general-purpose LLMs. We show that feature selection mitigates degradation in high-dimensional inputs and that TAP-GPT maintains stable performance under simulated and real-world missingness without imputation. Additionally, TAP-GPT produces structured, modality-aware reasoning aligned with established AD biology and shows greater stability under self-reflection, supporting its use in iterative multi-agent systems. To our knowledge, this is the first systematic application of a tabular-specialized LLM to multimodal biomarker-based AD prediction, demonstrating that such pretrained models can effectively address structured clinical prediction tasks and laying the foundation for tabular LLM-driven multi-agent clinical decision-support systems. The source code is publicly available on GitHub: https://github.com/sophie-kearney/TAP-GPT.", "github_manual": "", "github_regex": "https://github.com/sophie-kearney/TAP-GPT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.29025", "title": "The Model Says Walk: How Surface Heuristics Override Implicit Constraints in LLM Reasoning", "authors": [ "Yubo Li", "Lu Zhang", "Tianchong Jiang", "Ramayya Krishnan", "Rema Padman" ], "github": "https://github.com/ryan-allen/car-wash-evals", "project_page": "", "abstract": "Large language models systematically fail when a salient surface cue conflicts with an unstated feasibility constraint. We study this through a diagnose-measure-bridge-treat framework. Causal-behavioral analysis of the ``car wash problem'' across six models reveals approximately context-independent sigmoid heuristics: the distance cue exerts 8.7 to 38 times more influence than the goal, and token-level attribution shows patterns more consistent with keyword associations than compositional inference. The Heuristic Override Benchmark (HOB) -- 500 instances spanning 4 heuristic by 5 constraint families with minimal pairs and explicitness gradients -- demonstrates generality across 14 models: under strict evaluation (10/10 correct), no model exceeds 75%, and presence constraints are hardest (44%). A minimal hint (e.g., emphasizing the key object) recovers +15 pp on average, suggesting the failure lies in constraint inference rather than missing knowledge; 12/14 models perform worse when the constraint is removed (up to -39 pp), revealing conservative bias. Parametric probes confirm that the sigmoid pattern generalizes to cost, efficiency, and semantic-similarity heuristics; goal-decomposition prompting recovers +6 to 9 pp by forcing models to enumerate preconditions before answering. Together, these results characterize heuristic override as a systematic reasoning vulnerability and provide a benchmark for measuring progress toward resolving it.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ryan-allen/car-wash-evals", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.21460", "title": "When Documents Disagree: Measuring Institutional Variation in Transplant Guidance with Retrieval-Augmented Language Models", "authors": [ "Yubo Li", "Ramayya Krishnan", "Rema Padman" ], "github": "", "project_page": "", "abstract": "Patient education materials for solid-organ transplantation vary substantially across U.S. centers, yet no systematic method exists to quantify this heterogeneity at scale. We introduce a framework that grounds the same patient questions in different centers' handbooks using retrieval-augmented language models and compares the resulting answers using a five-label consistency taxonomy. Applied to 102 handbooks from 23 centers and 1,115 benchmark questions, the framework quantifies heterogeneity across four dimensions: question, topic, organ, and center. We find that 20.8% of non-absent pairwise comparisons exhibit clinically meaningful divergence, concentrated in condition monitoring and lifestyle topics. Coverage gaps are even more prominent: 96.2% of question-handbook pairs miss relevant content, with reproductive health at 95.1% absence. Center-level divergence profiles are stable and interpretable, where heterogeneity reflects systematic institutional differences, likely due to patient diversity. These findings expose an information gap in transplant patient education materials, with document-grounded medical question answering highlighting opportunities for content improvement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.26357", "title": "MPDiT: Multi-Patch Global-to-Local Transformer Architecture For Efficient Flow Matching and Diffusion Model", "authors": [ "Quan Dao", "Dimitris Metaxas" ], "github": "https://github.com/quandao10/MPDiT", "project_page": "", "abstract": "Transformer architectures, particularly Diffusion Transformers (DiTs), have become widely used in diffusion and flow-matching models due to their strong performance compared to convolutional UNets. However, the isotropic design of DiTs processes the same number of patchified tokens in every block, leading to relatively heavy computation during training process. In this work, we introduce a multi-patch transformer design in which early blocks operate on larger patches to capture coarse global context, while later blocks use smaller patches to refine local details. This hierarchical design could reduces computational cost by up to 50\\% in GFLOPs while achieving good generative performance. In addition, we also propose improved designs for time and class embeddings that accelerate training convergence. Extensive experiments on the ImageNet dataset demonstrate the effectiveness of our architectural choices. Code is released at https://github.com/quandao10/MPDiT", "github_manual": "", "github_regex": "https://github.com/quandao10/MPDiT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.27520", "title": "TokenDial: Continuous Attribute Control in Text-to-Video via Spatiotemporal Token Offsets", "authors": [ "Zhixuan Liu", "Peter Schaldenbrand", "Yijun Li", "Long Mai", "Aniruddha Mahapatra", "Cusuh Ham", "Jean Oh", "Jui-Hsien Wang" ], "github": "", "project_page": "", "abstract": "We present TokenDial, a framework for continuous, slider-style attribute control in pretrained text-to-video generation models. While modern generators produce strong holistic videos, they offer limited control over how much an attribute changes (e.g., effect intensity or motion magnitude) without drifting identity, background, or temporal coherence. TokenDial is built on the observation: additive offsets in the intermediate spatiotemporal visual patch-token space form a semantic control direction, where adjusting the offset magnitude yields coherent, predictable edits for both appearance and motion dynamics. We learn attribute-specific token offsets without retraining the backbone, using pretrained understanding signals: semantic direction matching for appearance and motion-magnitude scaling for motion. We demonstrate TokenDial's effectiveness on diverse attributes and prompts, achieving stronger controllability and higher-quality edits than state-of-the-art baselines, supported by extensive quantitative evaluation and human studies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.30045", "title": "OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation", "authors": [ "Yuheng Liu", "Xin Lin", "Xinke Li", "Baihan Yang", "Chen Wang", "Kalyan Sunkavalli", "Yannick Hold-Geoffroy", "Hao Tan", "Kai Zhang", "Xiaohui Xie", "Zifan Shi", "Yiwei Hu" ], "github": "https://github.com/yuhengliu02/OmniRoam", "project_page": "", "abstract": "Modeling scenes using video generation models has garnered growing research interest in recent years. However, most existing approaches rely on perspective video models that synthesize only limited observations of a scene, leading to issues of completeness and global consistency. We propose OmniRoam, a controllable panoramic video generation framework that exploits the rich per-frame scene coverage and inherent long-term spatial and temporal consistency of panoramic representation, enabling long-horizon scene wandering. Our framework begins with a preview stage, where a trajectory-controlled video generation model creates a quick overview of the scene from a given input image or video. Then, in the refine stage, this video is temporally extended and spatially upsampled to produce long-range, high-resolution videos, thus enabling high-fidelity world wandering. To train our model, we introduce two panoramic video datasets that incorporate both synthetic and real-world captured videos. Experiments show that our framework consistently outperforms state-of-the-art methods in terms of visual quality, controllability, and long-term scene consistency, both qualitatively and quantitatively. We further showcase several extensions of this framework, including real-time video generation and 3D reconstruction. Code is available at https://github.com/yuhengliu02/OmniRoam.", "github_manual": "", "github_regex": "https://github.com/yuhengliu02/OmniRoam", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2604.00007", "title": "Dynin-Omni: Omnimodal Unified Large Diffusion Language Model", "authors": [ "Jaeik Kim", "Woojin Kim", "Jihwan Hong", "Yejoon Lee", "Sieun Hyeon", "Mintaek Lim", "Yunseok Han", "Dogeun Kim", "Hoeun Lee", "Hyunggeun Kim", "Jaeyoung Do" ], "github": "https://github.com/AIDASLab/Dynin-Omni", "project_page": "", "abstract": "We present Dynin-Omni, the first masked-diffusion-based omnimodal foundation model that unifies text, image, and speech understanding and generation, together with video understanding, within a single architecture. Unlike autoregressive unified models that serialize heterogeneous modalities, or compositional unified models that require orchestration with external modality-specific decoders, Dynin-Omni natively formulates omnimodal modeling as masked diffusion over a shared discrete token space, enabling iterative refinement under bidirectional context. Dynin-Omni adopts a multi-stage training strategy with model-merging-based modality expansion and omnimodal alignment. We evaluate Dynin-Omni across 19 multimodal benchmarks spanning language reasoning, image generation and editing, video understanding, and speech recognition and synthesis. Dynin-Omni achieves 87.6 on GSM8K, 1733.6 on MME-P, 61.4 on VideoMME, 0.87 on GenEval, and 2.1 WER on LibriSpeech test-clean, consistently outperforming existing open-source unified models while remaining competitive with strong modality-specific expert systems. These results demonstrate the potential of masked diffusion as a unified paradigm for any-to-any modeling, providing a flexible foundation for real-time omnimodal systems, unified cross-modal retrieval and generation, and embodied multimodal agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AIDASLab/Dynin-Omni", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2604.00093", "title": "RawGen: Learning Camera Raw Image Generation", "authors": [ "Dongyoung Kim", "Junyong Lee", "Abhijith Punnappurath", "Mahmoud Afifi", "Sangmin Han", "Alex Levinshtein", "Michael S. Brown" ], "github": "", "project_page": "", "abstract": "Cameras capture scene-referred linear raw images, which are processed by onboard image signal processors (ISPs) into display-referred 8-bit sRGB outputs. Although raw data is more faithful for low-level vision tasks, collecting large-scale raw datasets remains a major bottleneck, as existing datasets are limited and tied to specific camera hardware. Generative models offer a promising way to address this scarcity -- however, existing diffusion frameworks are designed to synthesize photo-finished sRGB images rather than physically consistent linear representations. This paper presents RawGen, to our knowledge the first diffusion-based framework enabling text-to-raw generation for arbitrary target cameras, alongside sRGB-to-raw inversion. RawGen leverages the generative priors of large-scale sRGB diffusion models to synthesize physically meaningful linear outputs, such as CIE XYZ or camera-specific raw representations, via specialized processing in latent and pixel spaces. To handle unknown and diverse ISP pipelines and photo-finishing effects in diffusion-model training data, we build a many-to-one inverse-ISP dataset where multiple sRGB renditions of the same scene generated using diverse ISP parameters are anchored to a common scene-referred target. Fine-tuning a conditional denoiser and specialized decoder on this dataset allows RawGen to obtain camera-centric linear reconstructions that effectively invert the rendering pipeline. We demonstrate RawGen's superior performance over traditional inverse-ISP methods that assume a fixed ISP. Furthermore, we show that augmenting training pipelines with RawGen's scalable, text-driven synthetic data can benefit downstream low-level vision tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2604.00479", "title": "All Roads Lead to Rome: Incentivizing Divergent Thinking in Vision-Language Models", "authors": [ "Xinyu Tian", "Shu Zou", "Zhaoyuan Yang", "Mengqi He", "Peter Tu", "Jing Zhang" ], "github": "", "project_page": "", "abstract": "Recent studies have demonstrated that Reinforcement Learning (RL), notably Group Relative Policy Optimization (GRPO), can intrinsically elicit and enhance the reasoning capabilities of Vision-Language Models (VLMs). However, despite the promise, the underlying mechanisms that drive the effectiveness of RL models as well as their limitations remain underexplored. In this paper, we highlight a fundamental behavioral distinction between RL and base models, where the former engages in deeper yet narrow reasoning, while base models, despite less refined along individual path, exhibit broader and more diverse thinking patterns. Through further analysis of training dynamics, we show that GRPO is prone to diversity collapse, causing models to prematurely converge to a limited subset of reasoning strategies while discarding the majority of potential alternatives, leading to local optima and poor scalability. To address this, we propose Multi-Group Policy Optimization (MUPO), a simple yet effective approach designed to incentivize divergent thinking across multiple solutions, and demonstrate its effectiveness on established benchmarks. Project page: https://xytian1008.github.io/MUPO/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-01", "arxiv_id": "2603.28052", "title": "Meta-Harness: End-to-End Optimization of Model Harnesses", "authors": [ "Yoonho Lee", "Roshen Nair", "Qizheng Zhang", "Kangwook Lee", "Omar Khattab", "Chelsea Finn" ], "github": "https://github.com/stanford-iris-lab/meta-harness-tbench2-artifact", "project_page": "", "abstract": "The performance of large language model (LLM) systems depends not only on model weights, but also on their harness: the code that determines what information to store, retrieve, and present to the model. Yet harnesses are still designed largely by hand, and existing text optimizers are poorly matched to this setting because they compress feedback too aggressively. We introduce Meta-Harness, an outer-loop system that searches over harness code for LLM applications. It uses an agentic proposer that accesses the source code, scores, and execution traces of all prior candidates through a filesystem. On online text classification, Meta-Harness improves over a state-of-the-art context management system by 7.7 points while using 4x fewer context tokens. On retrieval-augmented math reasoning, a single discovered harness improves accuracy on 200 IMO-level problems by 4.7 points on average across five held-out models. On agentic coding, discovered harnesses surpass the best hand-engineered baselines on TerminalBench-2. Together, these results show that richer access to prior experience can enable automated harness engineering.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/stanford-iris-lab/meta-harness-tbench2-artifact", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.00073", "title": "Terminal Agents Suffice for Enterprise Automation", "authors": [ "Patrice Bechard", "Orlando Marquez Ayala", "Emily Chen", "Jordan Skelton", "Sagar Davasam", "Srinivas Sunkara", "Vikas Yadav", "Sai Rajeswar" ], "github": "https://github.com/openai/openai-agents-python", "project_page": "", "abstract": "There has been growing interest in building agents that can interact with digital platforms to execute meaningful enterprise tasks autonomously. Among the approaches explored are tool-augmented agents built on abstractions such as Model Context Protocol (MCP) and web agents that operate through graphical interfaces. Yet, it remains unclear whether such complex agentic systems are necessary given their cost and operational overhead. We argue that a coding agent equipped only with a terminal and a filesystem can solve many enterprise tasks more effectively by interacting directly with platform APIs. We evaluate this hypothesis across diverse real-world systems and show that these low-level terminal agents match or outperform more complex agent architectures. Our findings suggest that simple programmatic interfaces, combined with strong foundation models, are sufficient for practical enterprise automation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openai/openai-agents-python", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.00528", "title": "Think, Act, Build: An Agentic Framework with Vision Language Models for Zero-Shot 3D Visual Grounding", "authors": [ "Haibo Wang", "Zihao Lin", "Zhiyang Xu", "Lifu Huang" ], "github": "https://github.com/WHB139426/TAB-Agent", "project_page": "", "abstract": "3D Visual Grounding (3D-VG) aims to localize objects in 3D scenes via natural language descriptions. While recent advancements leveraging Vision-Language Models (VLMs) have explored zero-shot possibilities, they typically suffer from a static workflow relying on preprocessed 3D point clouds, essentially degrading grounding into proposal matching. To bypass this reliance, our core motivation is to decouple the task: leveraging 2D VLMs to resolve complex spatial semantics, while relying on deterministic multi-view geometry to instantiate the 3D structure. Driven by this insight, we propose \"Think, Act, Build (TAB)\", a dynamic agentic framework that reformulates 3D-VG tasks as a generative 2D-to-3D reconstruction paradigm operating directly on raw RGB-D streams. Specifically, guided by a specialized 3D-VG skill, our VLM agent dynamically invokes visual tools to track and reconstruct the target across 2D frames. Crucially, to overcome the multi-view coverage deficit caused by strict VLM semantic tracking, we introduce the Semantic-Anchored Geometric Expansion, a mechanism that first anchors the target in a reference video clip and then leverages multi-view geometry to propagate its spatial location across unobserved frames. This enables the agent to \"Build\" the target's 3D representation by aggregating these multi-view features via camera parameters, directly mapping 2D visual cues to 3D coordinates. Furthermore, to ensure rigorous assessment, we identify flaws such as reference ambiguity and category errors in existing benchmarks and manually refine the incorrect queries. Extensive experiments on ScanRefer and Nr3D demonstrate that our framework, relying entirely on open-source models, significantly outperforms previous zero-shot methods and even surpasses fully supervised baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WHB139426/TAB-Agent", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.01220", "title": "Universal YOCO for Efficient Depth Scaling", "authors": [ "Yutao Sun", "Li Dong", "Tianzhu Ye", "Shaohan Huang", "Jianyong Wang", "Furu Wei" ], "github": "", "project_page": "", "abstract": "The rise of test-time scaling has remarkably boosted the reasoning and agentic proficiency of Large Language Models (LLMs). Yet, standard Transformers struggle to scale inference-time compute efficiently, as conventional looping strategies suffer from high computational overhead and a KV cache that inflates alongside model depth. We present Universal YOCO (YOCO-U), which combines the YOCO decoder-decoder architecture with recursive computation to achieve a synergistic effect greater than either alone. Built on the YOCO framework, YOCO-U implements a Universal Self-Decoder that performs multiple iterations via parameter sharing, while confining the iterative process to shallow, efficient-attention layers. This combination yields a favorable capability-efficiency tradeoff that neither YOCO nor recursion achieves independently. The YOCO architecture provides a constant global KV cache and linear pre-filling, while partial recursion enhances representational depth with limited overhead. Together, YOCO-U improves token utility and scaling behavior while maintaining efficient inference. Empirical results confirm that YOCO-U remains highly competitive in general and long-context benchmarks, demonstrating that the integration of efficient-attention architectures and recursive computation is a promising direction for scalable LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2603.26661", "title": "GaussianGPT: Towards Autoregressive 3D Gaussian Scene Generation", "authors": [ "Nicolas von L\u00fctzow", "Barbara R\u00f6ssle", "Katharina Schmid", "Matthias Nie\u00dfner" ], "github": "", "project_page": "", "abstract": "Most recent advances in 3D generative modeling rely on diffusion or flow-matching formulations. We instead explore a fully autoregressive alternative and introduce GaussianGPT, a transformer-based model that directly generates 3D Gaussians via next-token prediction, thus facilitating full 3D scene generation. We first compress Gaussian primitives into a discrete latent grid using a sparse 3D convolutional autoencoder with vector quantization. The resulting tokens are serialized and modeled using a causal transformer with 3D rotary positional embedding, enabling sequential generation of spatial structure and appearance. Unlike diffusion-based methods that refine scenes holistically, our formulation constructs scenes step-by-step, naturally supporting completion, outpainting, controllable sampling via temperature, and flexible generation horizons. This formulation leverages the compositional inductive biases and scalability of autoregressive modeling while operating on explicit representations compatible with modern neural rendering pipelines, positioning autoregressive transformers as a complementary paradigm for controllable and context-aware 3D generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2603.26653", "title": "PerceptionComp: A Video Benchmark for Complex Perception-Centric Reasoning", "authors": [ "Shaoxuan Li", "Zhixuan Zhao", "Hanze Deng", "Zirun Ma", "Shulin Tian", "Zuyan Liu", "Yushi Hu", "Haoning Wu", "Yuhao Dong", "Benlin Liu", "Ziwei Liu", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "We introduce PerceptionComp, a manually annotated benchmark for complex, long-horizon, perception-centric video reasoning. PerceptionComp is designed so that no single moment is sufficient: answering each question requires multiple temporally separated pieces of visual evidence and compositional constraints under conjunctive and sequential logic, spanning perceptual subtasks such as objects, attributes, relations, locations, actions, and events, and requiring skills including semantic recognition, visual correspondence, temporal reasoning, and spatial reasoning. The benchmark contains 1,114 highly complex questions on 279 videos from diverse domains including city walk tours, indoor villa tours, video games, and extreme outdoor sports, with 100% manual annotation. Human studies show that PerceptionComp requires substantial test-time thinking and repeated perception steps: participants take much longer than on prior benchmarks, and accuracy drops to near chance (18.97%) when rewatching is disallowed. State-of-the-art MLLMs also perform substantially worse on PerceptionComp than on existing benchmarks: the best model in our evaluation, Gemini-3-Flash, reaches only 45.96% accuracy in the five-choice setting, while open-source models remain below 40%. These results suggest that perception-centric long-horizon video reasoning remains a major bottleneck, and we hope PerceptionComp will help drive progress in perceptual reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2603.25406", "title": "MMaDA-VLA: Large Diffusion Vision-Language-Action Model with Unified Multi-Modal Instruction and Generation", "authors": [ "Yang Liu", "Pengxiang Ding", "Tengyue Jiang", "Xudong Wang", "Wenxuan Song", "Minghui Lin", "Han Zhao", "Hongyin Zhang", "Zifeng Zhuang", "Wei Zhao", "Siteng Huang", "Jinkui Shi", "Donglin Wang" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models aim to control robots for manipulation from visual observations and natural-language instructions. However, existing hierarchical and autoregressive paradigms often introduce architectural overhead, suffer from temporal inconsistency and long-horizon error accumulation, and lack a mechanism to capture environment dynamics without extra modules. To this end, we present MMaDA-VLA, a fully native pre-trained large diffusion VLA model that unifies multi-modal understanding and generation in a single framework. Our key idea is a native discrete diffusion formulation that embeds language, images, and continuous robot controls into one discrete token space and trains a single backbone with masked token denoising to jointly generate a future goal observation and an action chunk in parallel. Iterative denoising enables global, order-free refinement, improving long-horizon consistency while grounding actions in predicted future visual outcomes without auxiliary world models. Experiments across simulation benchmarks and real-world tasks show state-of-the-art performance, achieving 98.0% average success on LIBERO and 4.78 average length on CALVIN.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.01221", "title": "HippoCamp: Benchmarking Contextual Agents on Personal Computers", "authors": [ "Zhe Yang", "Shulin Tian", "Kairui Hu", "Shuai Liu", "Hoang-Nhat Nguyen", "Yichi Zhang", "Zujin Guo", "Mengying Yu", "Zinan Zhang", "Jingkang Yang", "Chen Change Loy", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "We present HippoCamp, a new benchmark designed to evaluate agents' capabilities on multimodal file management. Unlike existing agent benchmarks that focus on tasks like web interaction, tool use, or software automation in generic settings, HippoCamp evaluates agents in user-centric environments to model individual user profiles and search massive personal files for context-aware reasoning. Our benchmark instantiates device-scale file systems over real-world profiles spanning diverse modalities, comprising 42.4 GB of data across over 2K real-world files. Building upon the raw files, we construct 581 QA pairs to assess agents' capabilities in search, evidence perception, and multi-step reasoning. To facilitate fine-grained analysis, we provide 46.1K densely annotated structured trajectories for step-wise failure diagnosis. We evaluate a wide range of state-of-the-art multimodal large language models (MLLMs) and agentic methods on HippoCamp. Our comprehensive experiments reveal a significant performance gap: even the most advanced commercial models achieve only 48.3% accuracy in user profiling, struggling particularly with long-horizon retrieval and cross-modal reasoning within dense personal file systems. Furthermore, our step-wise failure diagnosis identifies multimodal perception and evidence grounding as the primary bottlenecks. Ultimately, HippoCamp exposes the critical limitations of current agents in realistic, user-centric environments and provides a robust foundation for developing next-generation personal AI assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.01193", "title": "Embarrassingly Simple Self-Distillation Improves Code Generation", "authors": [ "Ruixiang Zhang", "Richard He Bai", "Huangjie Zheng", "Navdeep Jaitly", "Ronan Collobert", "Yizhe Zhang" ], "github": "", "project_page": "", "abstract": "Can a large language model (LLM) improve at code generation using only its own raw outputs, without a verifier, a teacher model, or reinforcement learning? We answer in the affirmative with simple self-distillation (SSD): sample solutions from the model with certain temperature and truncation configurations, then fine-tune on those samples with standard supervised fine-tuning. SSD improves Qwen3-30B-Instruct from 42.4% to 55.3% pass@1 on LiveCodeBench v6, with gains concentrating on harder problems, and it generalizes across Qwen and Llama models at 4B, 8B, and 30B scale, including both instruct and thinking variants. To understand why such a simple method can work, we trace these gains to a precision-exploration conflict in LLM decoding and show that SSD reshapes token distributions in a context-dependent way, suppressing distractor tails where precision matters while preserving useful diversity where exploration matters. Taken together, SSD offers a complementary post-training direction for improving LLM code generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2603.28407", "title": "MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome", "authors": [ "Fangda Ye", "Yuxin Hu", "Pengxiang Zhu", "Yibo Li", "Ziqi Jin", "Yao Xiao", "Yibo Wang", "Lei Wang", "Zhen Zhang", "Lu Wang", "Yue Deng", "Bin Wang", "Yifan Zhang", "Liangcai Su", "Xinyu Wang", "He Zhao", "Chen Wei", "Qiang Ren", "Bryan Hooi", "An Bo", "Shuicheng Yan", "Lidong Bing" ], "github": "https://github.com/MiroMindAI/MiroEval", "project_page": "", "abstract": "Recent progress in deep research systems has been impressive, but evaluation still lags behind real user needs. Existing benchmarks predominantly assess final reports using fixed rubrics, failing to evaluate the underlying research process. Most also offer limited multimodal coverage, rely on synthetic tasks that do not reflect real-world query complexity, and cannot be refreshed as knowledge evolves. To address these gaps, we introduce MiroEval, a benchmark and evaluation framework for deep research systems. The benchmark comprises 100 tasks (70 text-only, 30 multimodal), all grounded in real user needs and constructed via a dual-path pipeline that supports periodic updates, enabling a live and evolving setting. The proposed evaluation suite assesses deep research systems along three complementary dimensions: adaptive synthesis quality evaluation with task-specific rubrics, agentic factuality verification via active retrieval and reasoning over both web sources and multimodal attachments, and process-centric evaluation audits how the system searches, reasons, and refines throughout its investigation. Evaluation across 13 systems yields three principal findings: the three evaluation dimensions capture complementary aspects of system capability, with each revealing distinct strengths and weaknesses across systems; process quality serves as a reliable predictor of overall outcome while revealing weaknesses invisible to output-level metrics; and multimodal tasks pose substantially greater challenges, with most systems declining by 3 to 10 points. The MiroThinker series achieves the most balanced performance, with MiroThinker-H1 ranking the highest overall in both settings. Human verification and robustness results confirm the reliability of the benchmark and evaluation framework. MiroEval provides a holistic diagnostic tool for the next generation of deep research agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MiroMindAI/MiroEval", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2603.26017", "title": "QuitoBench: A High-Quality Open Time Series Forecasting Benchmark", "authors": [ "Siqiao Xue", "Zhaoyang Zhu", "Wei Zhang", "Rongyao Cai", "Rui Wang", "Yixiang Mu", "Fan Zhou", "Jianguo Li", "Peng Di", "Hang Yu" ], "github": "https://github.com/alipay/quito", "project_page": "", "abstract": "Time series forecasting is critical across finance, healthcare, and cloud computing, yet progress is constrained by a fundamental bottleneck: the scarcity of large-scale, high-quality benchmarks. To address this gap, we introduce QuitoBench, a regime-balanced benchmark for time series forecasting with coverage across eight trendtimesseasonalitytimesforecastability (TSF) regimes, designed to capture forecasting-relevant properties rather than application-defined domain labels. The benchmark is built upon Quito, a billion-scale time series corpus of application traffic from Alipay spanning nine business domains. Benchmarking 10 models from deep learning, foundation models, and statistical baselines across 232,200 evaluation instances, we report four key findings: (i) a context-length crossover where deep learning models lead at short context (L=96) but foundation models dominate at long context (L ge 576); (ii) forecastability is the dominant difficulty driver, producing a 3.64 times MAE gap across regimes; (iii) deep learning models match or surpass foundation models at 59 times fewer parameters; and (iv) scaling the amount of training data provides substantially greater benefit than scaling model size for both model families. These findings are validated by strong cross-benchmark and cross-metric consistency. Our open-source release enables reproducible, regime-aware evaluation for time series forecasting research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/alipay/quito", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2603.25823", "title": "ViGoR-Bench: How Far Are Visual Generative Models From Zero-Shot Visual Reasoners?", "authors": [ "Haonan Han", "Jiancheng Huang", "Xiaopeng Sun", "Junyan He", "Rui Yang", "Jie Hu", "Xiaojiang Peng", "Lin Ma", "Xiaoming Wei", "Xiu Li" ], "github": "", "project_page": "", "abstract": "Beneath the stunning visual fidelity of modern AIGC models lies a \"logical desert\", where systems fail tasks that require physical, causal, or complex spatial reasoning. Current evaluations largely rely on superficial metrics or fragmented benchmarks, creating a ``performance mirage'' that overlooks the generative process. To address this, we introduce ViGoR Vision-G}nerative Reasoning-centric Benchmark), a unified framework designed to dismantle this mirage. ViGoR distinguishes itself through four key innovations: 1) holistic cross-modal coverage bridging Image-to-Image and Video tasks; 2) a dual-track mechanism evaluating both intermediate processes and final results; 3) an evidence-grounded automated judge ensuring high human alignment; and 4) granular diagnostic analysis that decomposes performance into fine-grained cognitive dimensions. Experiments on over 20 leading models reveal that even state-of-the-art systems harbor significant reasoning deficits, establishing ViGoR as a critical ``stress test'' for the next generation of intelligent vision models. The demo have been available at https://vincenthancoder.github.io/ViGoR-Bench/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2603.26648", "title": "Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification", "authors": [ "Zehai He", "Wenyi Hong", "Zhen Yang", "Ziyang Pan", "Mingdao Liu", "Xiaotao Gu", "Jie Tang" ], "github": "", "project_page": "", "abstract": "Recent advances in large language models have improved the capabilities of coding agents, yet systematic evaluation of complex, end-to-end website development remains limited. To address this gap, we introduce Vision2Web, a hierarchical benchmark for visual website development, spanning from static UI-to-code generation, interactive multi-page frontend reproduction, to long-horizon full-stack website development. The benchmark is constructed from real-world websites and comprises a total of 193 tasks across 16 categories, with 918 prototype images and 1,255 test cases. To support flexible, thorough and reliable evaluation, we propose workflow-based agent verification paradigm based on two complementary components: a GUI agent verifier and a VLM-based judge. We evaluate multiple visual language models instantiated under different coding-agent frameworks, revealing substantial performance gaps at all task levels, with state-of-the-art models still struggling on full-stack development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.01128", "title": "Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers", "authors": [ "Atsuyuki Miyai", "Mashiro Toyooka", "Zaiying Zhao", "Kenta Watanabe", "Toshihiko Yamasaki", "Kiyoharu Aizawa" ], "github": "", "project_page": "", "abstract": "This paper introduces the first systematic evaluation framework for quantifying the quality and risks of papers written by modern coding agents. While AI-driven paper writing has become a growing concern, rigorous evaluation of the quality and potential risks of AI-written papers remains limited, and a unified understanding of their reliability is still lacking. We introduce Paper Reconstruction Evaluation (PaperRecon), an evaluation framework in which an overview (overview.md) is created from an existing paper, after which an agent generates a full paper based on the overview and minimal additional resources, and the result is subsequently compared against the original paper. PaperRecon disentangles the evaluation of the AI-written papers into two orthogonal dimensions, Presentation and Hallucination, where Presentation is evaluated using a rubric and Hallucination is assessed via agentic evaluation grounded in the original paper source. For evaluation, we introduce PaperWrite-Bench, a benchmark of 51 papers from top-tier venues across diverse domains published after 2025. Our experiments reveal a clear trade-off: while both ClaudeCode and Codex improve with model advances, ClaudeCode achieves higher presentation quality at the cost of more than 10 hallucinations per paper on average, whereas Codex produces fewer hallucinations but lower presentation quality. This work takes a first step toward establishing evaluation frameworks for AI-driven paper writing and improving the understanding of its risks within the research community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.01029", "title": "Revision or Re-Solving? Decomposing Second-Pass Gains in Multi-LLM Pipelines", "authors": [ "Jingjie Ning", "Xueqi Li", "Chengyu Yu" ], "github": "", "project_page": "", "abstract": "Multi-LLM revision pipelines, in which a second model reviews and improves a draft produced by a first, are widely assumed to derive their gains from genuine error correction. We question this assumption with a controlled decomposition experiment that uses four matched conditions to separate second-pass gains into three additive components: re-solving, scaffold, and content. We evaluate this design across two model pairs on three benchmarks spanning knowledge-intensive MCQ and competitive programming. Our results show that the gains of multi-LLM revision are not monolithic, but depend on task structure, draft quality, and the type of draft information. On MCQ tasks, where the answer space is constrained and drafts provide little structural guidance, most gains are consistent with stronger-model re-solving, and directly routing queries to the stronger model can be more effective than revising a weak draft. On code generation tasks, however, two-stage prompting remains useful because even semantically null drafts can provide substantial structural scaffolding, while weak draft content can be harmful. Finally, role-reversed experiments show that strong drafts clearly benefit weak reviewers. Ultimately, our findings demonstrate that the utility of multi-LLM revision is dynamically bottlenecked by task structure and draft quality, necessitating more targeted pipeline designs rather than blanket revision strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.00842", "title": "Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants", "authors": [ "Deepak Nathani", "Cheng Zhang", "Chang Huan", "Jiaming Shan", "Yinfei Yang", "Alkesh Patel", "Zhe Gan", "William Yang Wang", "Michael Saxon", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "Proactive agents that anticipate user needs and autonomously execute tasks hold great promise as digital assistants, yet the lack of realistic user simulation frameworks hinders their development. Existing approaches model apps as flat tool-calling APIs, failing to capture the stateful and sequential nature of user interaction in digital environments and making realistic user simulation infeasible. We introduce Proactive Agent Research Environment (Pare), a framework for building and evaluating proactive agents in digital environments. Pare models applications as finite state machines with stateful navigation and state-dependent action space for the user simulator, enabling active user simulation. Building on this foundation, we present Pare-Bench, a benchmark of 143 diverse tasks spanning communication, productivity, scheduling, and lifestyle apps, designed to test context observation, goal inference, intervention timing, and multi-app orchestration.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.00913", "title": "Benchmarking and Mechanistic Analysis of Vision-Language Models for Cross-Depiction Assembly Instruction Alignment", "authors": [ "Zhuchenyang Liu", "Yao Zhang", "Yu Xiao" ], "github": "", "project_page": "", "abstract": "2D assembly diagrams are often abstract and hard to follow, creating a need for intelligent assistants that can monitor progress, detect errors, and provide step-by-step guidance. In mixed reality settings, such systems must recognize completed and ongoing steps from the camera feed and align them with the diagram instructions. Vision Language Models (VLMs) show promise for this task, but face a depiction gap because assembly diagrams and video frames share few visual features. To systematically assess this gap, we construct IKEA-Bench, a benchmark of 1,623 questions across 6 task types on 29 IKEA furniture products, and evaluate 19 VLMs (2B-38B) under three alignment strategies. Our key findings: (1) assembly instruction understanding is recoverable via text, but text simultaneously degrades diagram-to-video alignment; (2) architecture family predicts alignment accuracy more strongly than parameter count; (3) video understanding remains a hard bottleneck unaffected by strategy. A three-level mechanistic analysis further reveals that diagrams and video occupy disjoint ViT subspaces, and that adding text shifts models from visual to text-driven reasoning. These results identify visual encoding as the primary target for improving cross-depiction robustness. Project page: https://ryenhails.github.io/IKEA-Bench/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2603.29002", "title": "Understand and Accelerate Memory Processing Pipeline for Disaggregated LLM Inference", "authors": [ "Zifan He", "Rui Ma", "Yizhou Sun", "Jason Cong" ], "github": "https://github.com/ROCm/HIPIFY", "project_page": "", "abstract": "Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse attention, retrieval-augmented generation (RAG), and compressed contextual memory, to support complex reasoning. We show that these optimizations can be unified into a four-step memory processing pipeline: Prepare Memory, Compute Relevancy, Retrieval, and Apply to Inference. Through systematic profiling, we identify a 22%-97% memory processing overhead in LLM inference and strong heterogeneity in its computational characteristics. Motivated by this insight, we argue that heterogeneous systems are well-suited to accelerate memory processing and thus end-to-end inference. We demonstrate this approach on a GPU-FPGA system by offloading sparse, irregular, and memory-bounded operations to FPGAs while retaining compute-intensive operations on GPUs. Evaluated on an AMD MI210 GPU and an Alveo U55C FPGA, our system is 1.04sim2.2times faster and requires 1.11sim4.7times less energy across multiple LLM inference optimizations than the GPU baseline (similar results hold on NVIDIA A100). These results establish heterogeneous systems as a practical direction for efficient LLM memory processing and inform future heterogeneous hardware design.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ROCm/HIPIFY", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.00590", "title": "UniMixer: A Unified Architecture for Scaling Laws in Recommendation Systems", "authors": [ "Mingming Ha", "Guanchen Wang", "Linxun Chen", "Xuan Rao", "Yuexin Shi", "Tianbao Ma", "Zhaojie Liu", "Yunqian Fan", "Zilong Lu", "Yanan Niu", "Han Li", "Kun Gai" ], "github": "", "project_page": "", "abstract": "In recent years, the scaling laws of recommendation models have attracted increasing attention, which govern the relationship between performance and parameters/FLOPs of recommenders. Currently, there are three mainstream architectures for achieving scaling in recommendation models, namely attention-based, TokenMixer-based, and factorization-machine-based methods, which exhibit fundamental differences in both design philosophy and architectural structure. In this paper, we propose a unified scaling architecture for recommendation systems, namely UniMixer, to improve scaling efficiency and establish a unified theoretical framework that unifies the mainstream scaling blocks. By transforming the rule-based TokenMixer to an equivalent parameterized structure, we construct a generalized parameterized feature mixing module that allows the token mixing patterns to be optimized and learned during model training. Meanwhile, the generalized parameterized token mixing removes the constraint in TokenMixer that requires the number of heads to be equal to the number of tokens. Furthermore, we establish a unified scaling module design framework for recommender systems, which bridges the connections among attention-based, TokenMixer-based, and factorization-machine-based methods. To further boost scaling ROI, a lightweight UniMixing module is designed, UniMixing-Lite, which further compresses the model parameters and computational cost while significantly improve the model performance. The scaling curves are shown in the following figure. Extensive offline and online experiments are conducted to verify the superior scaling abilities of UniMixer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2603.24414", "title": "ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watchers", "authors": [ "Songyang Liu", "Chaozhuo Li", "Chenxu Wang", "Jinyu Hou", "Zejian Chen", "Litian Zhang", "Zheng Liu", "Qiwei Ye", "Yiming Hei", "Xi Zhang", "Zhongyuan Wang" ], "github": "", "project_page": "", "abstract": "OpenClaw has rapidly established itself as a leading open-source autonomous agent runtime, offering powerful capabilities including tool integration, local file access, and shell command execution. However, these broad operational privileges introduce critical security vulnerabilities, transforming model errors into tangible system-level threats such as sensitive data leakage, privilege escalation, and malicious third-party skill execution. Existing security measures for the OpenClaw ecosystem remain highly fragmented, addressing only isolated stages of the agent lifecycle rather than providing holistic protection. To bridge this gap, we present ClawKeeper, a real-time security framework that integrates multi-dimensional protection mechanisms across three complementary architectural layers. (1) Skill-based protection operates at the instruction level, injecting structured security policies directly into the agent context to enforce environment-specific constraints and cross-platform boundaries. (2) Plugin-based protection serves as an internal runtime enforcer, providing configuration hardening, proactive threat detection, and continuous behavioral monitoring throughout the execution pipeline. (3) Watcher-based protection introduces a novel, decoupled system-level security middleware that continuously verifies agent state evolution. It enables real-time execution intervention without coupling to the agent's internal logic, supporting operations such as halting high-risk actions or enforcing human confirmation. We argue that this Watcher paradigm holds strong potential to serve as a foundational building block for securing next-generation autonomous agent systems. Extensive qualitative and quantitative evaluations demonstrate the effectiveness and robustness of ClawKeeper across diverse threat scenarios. We release our code.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2603.29247", "title": "MemRerank: Preference Memory for Personalized Product Reranking", "authors": [ "Zhiyuan Peng", "Xuyang Wu", "Huaixiao Tou", "Yi Fang", "Yi Gong" ], "github": "", "project_page": "", "abstract": "LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch. We propose MemRerank, a preference memory framework that distills user purchase history into concise, query-independent signals for personalized product reranking. To study this problem, we build an end-to-end benchmark and evaluation framework centered on an LLM-based 1-in-5 selection task, which measures both memory quality and downstream reranking utility. We further train the memory extractor with reinforcement learning (RL), using downstream reranking performance as supervision. Experiments with two LLM-based rerankers show that MemRerank consistently outperforms no-memory, raw-history, and off-the-shelf memory baselines, yielding up to +10.61 absolute points in 1-in-5 accuracy. These results suggest that explicit preference memory is a practical and effective building block for personalization in agentic e-commerce systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.00626", "title": "A Survey of On-Policy Distillation for Large Language Models", "authors": [ "Mingyang Song", "Mao Zheng" ], "github": "", "project_page": "", "abstract": "Knowledge distillation has become a primary mechanism for transferring reasoning and domain expertise from frontier Large Language Models (LLMs) to smaller, deployable students. However, the dominant paradigm remains off-policy: students train on static teacher-generated data and never encounter their own errors during learning. This train--test mismatch, an instance of exposure bias, causes prediction errors to compound autoregressively at inference time. On-Policy Distillation (OPD) addresses this by letting the student generate its own trajectories and receive teacher feedback on these self-generated outputs, grounding distillation in the theory of interactive imitation learning. Despite rapid growth spanning divergence minimization, reward-guided learning, and self-play, the OPD literature remains fragmented with no unified treatment. This survey provides the first comprehensive overview of OPD for LLMs. We introduce a unified f-divergence framework over on-policy samples and organize the landscape along three orthogonal dimensions: feedback signal (logit-based, outcome-based, or self-play), teacher access (white-box, black-box, or teacher-free), and loss granularity (token-level, sequence-level, or hybrid). We systematically analyze representative methods, examine industrial deployments, and identify open problems including distillation scaling laws, uncertainty-aware feedback, and agent-level distillation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.01161", "title": "Reasoning Shift: How Context Silently Shortens LLM Reasoning", "authors": [ "Gleb Rodionov" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) exhibiting test-time scaling behavior, such as extended reasoning traces and self-verification, have demonstrated remarkable performance on complex, long-term reasoning tasks. However, the robustness of these reasoning behaviors remains underexplored. To investigate this, we conduct a systematic evaluation of multiple reasoning models across three scenarios: (1) problems augmented with lengthy, irrelevant context; (2) multi-turn conversational settings with independent tasks; and (3) problems presented as a subtask within a complex task. We observe an interesting phenomenon: reasoning models tend to produce much shorter reasoning traces (up to 50%) for the same problem under different context conditions compared to the traces produced when the problem is presented in isolation. A finer-grained analysis reveals that this compression is associated with a decrease in self-verification and uncertainty management behaviors, such as double-checking. While this behavioral shift does not compromise performance on straightforward problems, it might affect performance on more challenging tasks. We hope our findings draw additional attention to both the robustness of reasoning models and the problem of context management for LLMs and LLM-based agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2603.23582", "title": "AI Generalisation Gap In Comorbid Sleep Disorder Staging", "authors": [ "Saswata Bose", "Suvadeep Maiti", "Shivam Kumar Sharma", "Mythirayee S", "Tapabrata Chakraborti", "Srijitesh Rajendran", "Raju S. Bapi" ], "github": "", "project_page": "", "abstract": "Accurate sleep staging is essential for diagnosing OSA and hypopnea in stroke patients. Although PSG is reliable, it is costly, labor-intensive, and manually scored. While deep learning enables automated EEG-based sleep staging in healthy subjects, our analysis shows poor generalization to clinical populations with disrupted sleep. Using Grad-CAM interpretations, we systematically demonstrate this limitation. We introduce iSLEEPS, a newly clinically annotated ischemic stroke dataset (to be publicly released), and evaluate a SE-ResNet plus bidirectional LSTM model for single-channel EEG sleep staging. As expected, cross-domain performance between healthy and diseased subjects is poor. Attention visualizations, supported by clinical expert feedback, show the model focuses on physiologically uninformative EEG regions in patient data. Statistical and computational analyses further confirm significant sleep architecture differences between healthy and ischemic stroke cohorts, highlighting the need for subject-aware or disease-specific models with clinical validation before deployment. A summary of the paper and the code is available at https://himalayansaswatabose.github.io/iSLEEPS_Explainability.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.00025", "title": "Brevity Constraints Reverse Performance Hierarchies in Language Models", "authors": [ "MD Azizul Hakim" ], "github": "", "project_page": "", "abstract": "Standard evaluation protocols reveal a counterintuitive phenomenon: on 7.7% of benchmark problems spanning five datasets, larger language models underperform smaller ones by 28.4 percentage points despite 10-100x more parameters. Through systematic evaluation of 31 models (0.5B-405B parameters) across 1,485 problems, we identify the mechanism as spontaneous scale-dependent verbosity that introduces errors through overelaboration. Causal intervention experiments demonstrate this reflects correctable prompt design rather than fundamental capability limitations. Constraining large models to produce brief responses improves accuracy by 26 percentage points and reduces performance gaps by up to two-thirds. Most critically, brevity constraints completely reverse performance hierarchies on mathematical reasoning and scientific knowledge benchmarks, with large models achieving 7.7-15.9 percentage point advantages over small models -- direct inversions of the original gaps. These reversals prove large models possess superior latent capabilities that universal prompting masks. We validate findings through three independent contamination tests and demonstrate inverse scaling operates continuously across the full parameter spectrum, with dataset-specific optimal scales ranging from 0.5B to 3.0B parameters. Our results establish that maximizing large model performance requires scale-aware prompt engineering rather than universal evaluation protocols, with immediate implications for deployment: prompt adaptation simultaneously improves accuracy and reduces computational costs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.01168", "title": "S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models", "authors": [ "Jack Young" ], "github": "https://github.com/jackyoung27/s0-tuning", "project_page": "", "abstract": "Using roughly 48 execution-verified HumanEval training solutions, tuning a single initial state matrix per recurrent layer, with zero inference overhead, outperforms LoRA by +10.8 pp (p < 0.001) on HumanEval. The method, which we call S0 tuning, optimizes one state matrix per recurrent layer while freezing all model weights. On Qwen3.5-4B (GatedDeltaNet hybrid), S0 tuning improves greedy pass@1 by +23.6 +/- 1.7 pp (10 seeds). On FalconH1-7B (Mamba-2 hybrid), S0 reaches 71.8% +/- 1.3 and LoRA reaches 71.4% +/- 2.4 (3 seeds), statistically indistinguishable at this sample size while requiring no weight merging. Cross-domain transfer is significant on MATH-500 (+4.8 pp, p = 0.00002, 8 seeds) and GSM8K (+2.8 pp, p = 0.0003, 10 seeds); a text-to-SQL benchmark (Spider) shows no transfer, consistent with the trajectory-steering mechanism. A prefix-tuning control on a pure Transformer (Qwen2.5-3B) degrades performance by -13.9 pp under all nine configurations tested. On Qwen3.5, a per-step state-offset variant reaches +27.1 pp, above both S0 and LoRA but with per-step inference cost. Taken together, the results show that recurrent state initialization is a strong zero-inference-overhead PEFT surface for hybrid language models when verified supervision is scarce. The tuned state is a ~48 MB file; task switching requires no weight merging or model reload. Code and library: https://github.com/jackyoung27/s0-tuning.", "github_manual": "", "github_regex": "https://github.com/jackyoung27/s0-tuning", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.00986", "title": "Do Phone-Use Agents Respect Your Privacy?", "authors": [ "Zhengyang Tang", "Ke Ji", "Xidong Wang", "Zihan Ye", "Xinyuan Wang", "Yiduo Guo", "Ziniu Li", "Chenxin Li", "Jingyuan Hu", "Shunian Chen", "Tongxu Luo", "Jiaxi Bi", "Zeyu Qin", "Shaobo Wang", "Xin Lai", "Pengyuan Lyu", "Junyi Li", "Can Xu", "Chengquan Zhang", "Han Hu", "Ming Yan", "Benyou Wang" ], "github": "https://github.com/FreedomIntelligence/MyPhoneBench", "project_page": "", "abstract": "We study whether phone-use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy-compliant behavior is not operationalized for phone-use agents, and ordinary apps do not reveal exactly what data agents type into which form entries during execution. To make this question measurable, we introduce MyPhoneBench, a verifiable evaluation framework for privacy behavior in mobile agents. We operationalize privacy-respecting phone use as permissioned access, minimal disclosure, and user-controlled memory through a minimal privacy contract, iMy, and pair it with instrumented mock apps plus rule-based auditing that make unnecessary permission requests, deceptive re-disclosure, and unnecessary form filling observable and reproducible. Across five frontier models on 10 mobile apps and 300 tasks, we find that task success, privacy-compliant task completion, and later-session use of saved preferences are distinct capabilities, and no single model dominates all three. Evaluating success and privacy jointly reshuffles the model ordering relative to either metric alone. The most persistent failure mode across models is simple data minimization: agents still fill optional personal entries that the task does not require. These results show that privacy failures arise from over-helpful execution of benign tasks, and that success-only evaluation overestimates the deployment readiness of current phone-use agents. All code, mock apps, and agent trajectories are publicly available at~ https://github.com/tangzhy/MyPhoneBench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FreedomIntelligence/MyPhoneBench", "github_api_added_by": "user" }, { "date": "2026-04-02", "arxiv_id": "2604.00892", "title": "When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation", "authors": [ "Henry Peng Zou", "Chunyu Miao", "Wei-Chieh Huang", "Yankai Chen", "Yue Zhou", "Hanrong Zhang", "Yaozu Wu", "Liancheng Fang", "Zhengyao Gu", "Zhen Zhang", "Kening Zheng", "Fangxin Wang", "Yi Nian", "Shanghao Li", "Wenzhe Fan", "Langzhou He", "Weizhi Zhang", "Xue Liu", "Philip S. Yu" ], "github": "https://github.com/HenryPengZou/InterruptBench", "project_page": "", "abstract": "As LLM agents transition from short, static problem solving to executing complex, long-horizon tasks in dynamic environments, the ability to handle user interruptions, such as adding requirement or revising goals, during mid-task execution is becoming a core requirement for realistic deployment. However, existing benchmarks largely assume uninterrupted agent behavior or study interruptions only in short, unconstrained language tasks. In this paper, we present the first systematic study of interruptible agents in long-horizon, environmentally grounded web navigation tasks, where actions induce persistent state changes. We formalize three realistic interruption types, including addition, revision, and retraction, and introduce InterruptBench, a benchmark derived from WebArena-Lite that synthesizes high-quality interruption scenarios under strict semantic constraints. Using a unified interruption simulation framework, we evaluate six strong LLM backbones across single- and multi-turn interruption settings, analyzing both their effectiveness in adapting to updated intents and their efficiency in recovering from mid-task changes. Our results show that handling user interruptions effectively and efficiently during long-horizon agentic tasks remains challenging for powerful large-scale LLMs. Code and dataset are available at https://github.com/HenryPengZou/InterruptBench.", "github_manual": "", "github_regex": "https://github.com/HenryPengZou/InterruptBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2603.25764", "title": "Consistency Amplifies: How Behavioral Variance Shapes Agent Accuracy", "authors": [ "Aman Mehta" ], "github": "", "project_page": "", "abstract": "As LLM-based agents are deployed in production systems, understanding their behavioral consistency (whether they produce similar action sequences when given identical tasks) becomes critical for reliability. We study consistency in the context of SWE-bench, a challenging software engineering benchmark requiring complex, multi-step reasoning. Comparing Claude~4.5~Sonnet, GPT-5, and Llama-3.1-70B across 50 runs each (10 tasks times 5 runs), we find that across models, higher consistency aligns with higher accuracy: Claude achieves the lowest variance (CV: 15.2\\%) and highest accuracy (58\\%), GPT-5 is intermediate (CV: 32.2\\%, accuracy: 32\\%), and Llama shows the highest variance (CV: 47.0\\%) with lowest accuracy (4\\%). However, within a model, consistency can amplify both correct and incorrect interpretations. Our analysis reveals a critical nuance: consistency amplifies outcomes rather than guaranteeing correctness. 71\\% of Claude's failures stem from \"consistent wrong interpretation\": making the same incorrect assumption across all runs. Interestingly, GPT-5 achieves similar early strategic agreement as Claude (diverging at step 3.4 vs.\\ 3.2) but exhibits 2.1times higher variance, suggesting that divergence timing alone does not determine consistency. These findings suggest that for production deployment, interpretation accuracy matters more than execution consistency, with implications for agent evaluation and training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.01194", "title": "AgentWatcher: A Rule-based Prompt Injection Monitor", "authors": [ "Yanting Wang", "Wei Zou", "Runpeng Geng", "Jinyuan Jia" ], "github": "https://github.com/wang-yanting/AgentWatcher", "project_page": "", "abstract": "Large language models (LLMs) and their applications, such as agents, are highly vulnerable to prompt injection attacks. State-of-the-art prompt injection detection methods have the following limitations: (1) their effectiveness degrades significantly as context length increases, and (2) they lack explicit rules that define what constitutes prompt injection, causing detection decisions to be implicit, opaque, and difficult to reason about. In this work, we propose AgentWatcher to address the above two limitations. To address the first limitation, AgentWatcher attributes the LLM's output (e.g., the action of an agent) to a small set of causally influential context segments. By focusing detection on a relatively short text, AgentWatcher can be scalable to long contexts. To address the second limitation, we define a set of rules specifying what does and does not constitute a prompt injection, and use a monitor LLM to reason over these rules based on the attributed text, making the detection decisions more explainable. We conduct a comprehensive evaluation on tool-use agent benchmarks and long-context understanding datasets. The experimental results demonstrate that AgentWatcher can effectively detect prompt injection and maintain utility without attacks. The code is available at https://github.com/wang-yanting/AgentWatcher.", "github_manual": "", "github_regex": "https://github.com/wang-yanting/AgentWatcher", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-02", "arxiv_id": "2604.00886", "title": "PixelPrune: Pixel-Level Adaptive Visual Token Reduction via Predictive Coding", "authors": [ "Nan Wang", "Zhiwei Jin", "Chen Chen", "Haonan Lu" ], "github": "https://github.com/OPPO-Mente-Lab/PixelPrune", "project_page": "", "abstract": "Document understanding and GUI interaction are among the highest-value applications of Vision-Language Models (VLMs), yet they impose exceptionally heavy computational burden: fine-grained text and small UI elements demand high-resolution inputs that produce tens of thousands of visual tokens. We observe that this cost is largely wasteful -- across document and GUI benchmarks, only 22--71\\% of image patches are pixel-unique, the rest being exact duplicates of another patch in the same image. We propose PixelPrune, which exploits this pixel-level redundancy through predictive-coding-based compression, pruning redundant patches before the Vision Transformer (ViT) encoder. Because it operates in pixel space prior to any neural computation, PixelPrune accelerates both the ViT encoder and the downstream LLM, covering the full inference pipeline. The method is training-free, requires no learnable parameters, and supports pixel-lossless compression (\u03c4{=}0) as well as controlled lossy compression (\u03c4{>}0). Experiments across three model scales and document and GUI benchmarks show that PixelPrune maintains competitive task accuracy while delivering up to 4.2times inference speedup and 1.9times training acceleration. Code is available at https://github.com/OPPO-Mente-Lab/PixelPrune.", "github_manual": "", "github_regex": "https://github.com/OPPO-Mente-Lab/PixelPrune", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.02029", "title": "The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook", "authors": [ "Xinlei Yu", "Zhangquan Chen", "Yongbo He", "Tianyu Fu", "Cheng Yang", "Chengming Xu", "Yue Ma", "Xiaobin Hu", "Zhe Cao", "Jie Xu", "Guibin Zhang", "Jiale Tao", "Jiayi Zhang", "Siyuan Ma", "Kaituo Feng", "Haojie Huang", "Youxing Li", "Ronghao Chen", "Huacan Wang", "Chenglin Wu", "Zikun Su", "Xiaogang Xu", "Kelu Yao", "Kun Wang", "Chen Gao", "Yue Liao", "Ruqi Huang", "Tao Jin", "Cheng Tan", "Jiangning Zhang", "Wenqi Ren", "Yanwei Fu", "Yong Liu", "Yu Wang", "Xiangyu Yue", "Yu-Gang Jiang", "Shuicheng Yan" ], "github": "", "project_page": "", "abstract": "Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an increasing body of work shows that many critical internal processes are more naturally carried out in continuous latent space than in human-readable verbal traces. This shift is driven by the structural limitations of explicit-space computation, including linguistic redundancy, discretization bottlenecks, sequential inefficiency, and semantic loss. This survey aims to provide a unified and up-to-date landscape of latent space in language-based models. We organize the survey into five sequential perspectives: Foundation, Evolution, Mechanism, Ability, and Outlook. We begin by delineating the scope of latent space, distinguishing it from explicit or verbal space and from the latent spaces commonly studied in generative visual models. We then trace the field's evolution from early exploratory efforts to the current large-scale expansion. To organize the technical landscape, we examine existing work through the complementary lenses of mechanism and ability. From the perspective of Mechanism, we identify four major lines of development: Architecture, Representation, Computation, and Optimization. From the perspective of Ability, we show how latent space supports a broad capability spectrum spanning Reasoning, Planning, Modeling, Perception, Memory, Collaboration, and Embodiment. Beyond consolidation, we discuss the key open challenges, and outline promising directions for future research. We hope this survey serves not only as a reference for existing work, but also as a foundation for understanding latent space as a general computational and systems paradigm for next-generation intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.02268", "title": "SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization", "authors": [ "Zhengxi Lu", "Zhiyuan Yao", "Jinyang Wu", "Chengcheng Han", "Qi Gu", "Xunliang Cai", "Weiming Lu", "Jun Xiao", "Yueting Zhuang", "Yongliang Shen" ], "github": "https://github.com/ZJU-REAL/SkillZero", "project_page": "", "abstract": "Agent skills, structured packages of procedural knowledge and executable resources that agents dynamically load at inference time, have become a reliable mechanism for augmenting LLM agents. Yet inference-time skill augmentation is fundamentally limited: retrieval noise introduces irrelevant guidance, injected skill content imposes substantial token overhead, and the model never truly acquires the knowledge it merely follows. We ask whether skills can instead be internalized into model parameters, enabling zero-shot autonomous behavior without any runtime skill retrieval. We introduce SKILL0, an in-context reinforcement learning framework designed for skill internalization. SKILL0 introduces a training-time curriculum that begins with full skill context and progressively withdraws it. Skills are grouped offline by category and rendered with interaction history into a compact visual context, teaching he model tool invocation and multi-turn task completion. A Dynamic Curriculum then evaluates each skill file's on-policy helpfulness, retaining only those from which the current policy still benefits within a linearly decaying budget, until the agent operates in a fully zero-shot setting. Extensive agentic experiments demonstrate that SKILL0 achieves substantial improvements over the standard RL baseline (+9.7\\% for ALFWorld and +6.6\\% for Search-QA), while maintaining a highly efficient context of fewer than 0.5k tokens per step. Our code is available at https://github.com/ZJU-REAL/SkillZero.", "github_manual": "", "github_regex": "https://github.com/ZJU-REAL/SkillZero", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2603.26164", "title": "DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models", "authors": [ "Hao Liang", "Zhengyang Zhao", "Meiyi Qiang", "Mingrui Chen", "Lu Ma", "Rongyi Yu", "Hengyi Feng", "Shixuan Sun", "Zimo Meng", "Xiaochen Ma", "Xuanlin Yang", "Qifeng Cai", "Ruichuan An", "Bohan Zeng", "Zhen Hao Wong", "Chengyu Shen", "Runming He", "Zhaoyang Han", "Yaowei Zheng", "Fangcheng Fu", "Conghui He", "Bin Cui", "Zhiyu Li", "Weinan E", "Wentao Zhang" ], "github": "https://github.com/princeton-nlp/LESS", "project_page": "", "abstract": "Data-centric training has emerged as a promising direction for improving large language models (LLMs) by optimizing not only model parameters but also the selection, composition, and weighting of training data during optimization. However, existing approaches to data selection, data mixture optimization, and data reweighting are often developed in isolated codebases with inconsistent interfaces, hindering reproducibility, fair comparison, and practical integration. In this paper, we present DataFlex, a unified data-centric dynamic training framework built upon LLaMA-Factory. DataFlex supports three major paradigms of dynamic data optimization: sample selection, domain mixture adjustment, and sample reweighting, while remaining fully compatible with the original training workflow. It provides extensible trainer abstractions and modular components, enabling a drop-in replacement for standard LLM training, and unifies key model-dependent operations such as embedding extraction, inference, and gradient computation, with support for large-scale settings including DeepSpeed ZeRO-3. We conduct comprehensive experiments across multiple data-centric methods. Dynamic data selection consistently outperforms static full-data training on MMLU across both Mistral-7B and Llama-3.2-3B. For data mixture, DoReMi and ODM improve both MMLU accuracy and corpus-level perplexity over default proportions when pretraining Qwen2.5-1.5B on SlimPajama at 6B and 30B token scales. DataFlex also achieves consistent runtime improvements over original implementations. These results demonstrate that DataFlex provides an effective, efficient, and reproducible infrastructure for data-centric dynamic training of LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/princeton-nlp/LESS", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.02329", "title": "Generative World Renderer", "authors": [ "Zheng-Hui Huang", "Zhixiang Wang", "Jiaming Tan", "Ruihan Yu", "Yidan Zhang", "Bo Zheng", "Yu-Lun Liu", "Yung-Yu Chuang", "Kaipeng Zhang" ], "github": "", "project_page": "", "abstract": "Scaling generative inverse and forward rendering to real-world scenarios is bottlenecked by the limited realism and temporal coherence of existing synthetic datasets. To bridge this persistent domain gap, we introduce a large-scale, dynamic dataset curated from visually complex AAA games. Using a novel dual-screen stitched capture method, we extracted 4M continuous frames (720p/30 FPS) of synchronized RGB and five G-buffer channels across diverse scenes, visual effects, and environments, including adverse weather and motion-blur variants. This dataset uniquely advances bidirectional rendering: enabling robust in-the-wild geometry and material decomposition, and facilitating high-fidelity G-buffer-guided video generation. Furthermore, to evaluate the real-world performance of inverse rendering without ground truth, we propose a novel VLM-based assessment protocol measuring semantic, spatial, and temporal consistency. Experiments demonstrate that inverse renderers fine-tuned on our data achieve superior cross-dataset generalization and controllable generation, while our VLM evaluation strongly correlates with human judgment. Combined with our toolkit, our forward renderer enables users to edit styles of AAA games from G-buffers using text prompts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01001", "title": "EgoSim: Egocentric World Simulator for Embodied Interaction Generation", "authors": [ "Jinkun Hao", "Mingda Jia", "Ruiyan Wang", "Xihui Liu", "Ran Yi", "Lizhuang Ma", "Jiangmiao Pang", "Xudong Xu" ], "github": "", "project_page": "", "abstract": "We introduce EgoSim, a closed-loop egocentric world simulator that generates spatially consistent interaction videos and persistently updates the underlying 3D scene state for continuous simulation. Existing egocentric simulators either lack explicit 3D grounding, causing structural drift under viewpoint changes, or treat the scene as static, failing to update world states across multi-stage interactions. EgoSim addresses both limitations by modeling 3D scenes as updatable world states. We generate embodiment interactions via a Geometry-action-aware Observation Simulation model, with spatial consistency from an Interaction-aware State Updating module. To overcome the critical data bottleneck posed by the difficulty in acquiring densely aligned scene-interaction training pairs, we design a scalable pipeline that extracts static point clouds, camera trajectories, and embodiment actions from in-the-wild large-scale monocular egocentric videos. We further introduce EgoCap, a capture system that enables low-cost real-world data collection with uncalibrated smartphones. Extensive experiments demonstrate that EgoSim significantly outperforms existing methods in terms of visual quality, spatial consistency, and generalization to complex scenes and in-the-wild dexterous interactions, while supporting cross-embodiment transfer to robotic manipulation. Codes and datasets will be open soon. The project page is at egosimulator.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01569", "title": "VideoZeroBench: Probing the Limits of Video MLLMs with Spatio-Temporal Evidence Verification", "authors": [ "Jiahao Meng", "Tan Yue", "Qi Xu", "Haochen Wang", "Zhongwei Ren", "Weisong Liu", "Yuhao Wang", "Renrui Zhang", "Yunhai Tong", "Haodong Duan" ], "github": "", "project_page": "", "abstract": "Recent video multimodal large language models achieve impressive results across various benchmarks. However, current evaluations suffer from two critical limitations: (1) inflated scores can mask deficiencies in fine-grained visual understanding and reasoning, and (2) answer correctness is often measured without verifying whether models identify the precise spatio-temporal evidence supporting their predictions. To address this, we present VideoZeroBench, a hierarchical benchmark designed for challenging long-video question answering that rigorously verifies spatio-temporal evidence. It comprises 500 manually annotated questions across 13 domains, paired with temporal intervals and spatial bounding boxes as evidence. To disentangle answering generation, temporal grounding, and spatial grounding, we introduce a five-level evaluation protocol that progressively tightens evidence requirements. Experiments show that even Gemini-3-Pro correctly answers fewer than 17% of questions under the standard end-to-end QA setting (Level-3). When grounding constraints are imposed, performance drops sharply: No model exceeds 1% accuracy when both correct answering and accurate spatio-temporal localization are required (Level-5), with most failing to achieve any correct grounded predictions. These results expose a significant gap between surface-level answer correctness and genuine evidence-based reasoning, revealing that grounded video understanding remains a bottleneck for long-video QA. We further analyze performance across minimal evidence spans, atomic abilities, and inference paradigms, providing insights for future research in grounded video reasoning. The benchmark and code will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.02088", "title": "FlowSlider: Training-Free Continuous Image Editing via Fidelity-Steering Decomposition", "authors": [ "Taichi Endo", "Guoqing Hao", "Kazuhiko Sumi" ], "github": "", "project_page": "", "abstract": "Continuous image editing aims to provide slider-style control of edit strength while preserving source-image fidelity and maintaining a consistent edit direction. Existing learning-based slider methods typically rely on auxiliary modules trained with synthetic or proxy supervision. This introduces additional training overhead and couples slider behavior to the training distribution, which can reduce reliability under distribution shifts in edits or domains. We propose FlowSlider, a training-free method for continuous editing in Rectified Flow that requires no post-training. FlowSlider decomposes FlowEdit's update into (i) a fidelity term, which acts as a source-conditioned stabilizer that preserves identity and structure, and (ii) a steering term that drives semantic transition toward the target edit. Geometric analysis and empirical measurements show that these terms are approximately orthogonal, enabling stable strength control by scaling only the steering term while keeping the fidelity term unchanged. As a result, FlowSlider provides smooth and reliable control without post-training, improving continuous editing quality across diverse tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01676", "title": "GPA: Learning GUI Process Automation from Demonstrations", "authors": [ "Zirui Zhao", "Jun Hao Liew", "Yan Yang", "Wenzhuo Yang", "Ziyang Luo", "Doyen Sahoo", "Silvio Savarese", "Junnan Li" ], "github": "", "project_page": "", "abstract": "GUI Process Automation (GPA) is a lightweight but general vision-based Robotic Process Automation (RPA), which enables fast and stable process replay with only a single demo. Addressing the fragility of traditional RPA and the non-deterministic risks of current vision language model-based GUI agents, GPA introduces three core benefits: (1) Robustness via Sequential Monte Carlo-based localization to handle rescaling and detection uncertainty; (2) Deterministic and Reliability safeguarded by readiness calibration; and (3) Privacy through fast, fully local execution. This approach delivers the adaptability, robustness, and security required for enterprise workflows. It can also be used as an MCP/CLI tool by other agents with coding capabilities so that the agent only reasons and orchestrates while GPA handles the GUI execution. We conducted a pilot experiment to compare GPA with Gemini 3 Pro (with CUA tools) and found that GPA achieves higher success rate with 10 times faster execution speed in finishing long-horizon GUI tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01007", "title": "Omni-SimpleMem: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory", "authors": [ "Jiaqi Liu", "Zipeng Ling", "Shi Qiu", "Yanqing Liu", "Siwei Han", "Peng Xia", "Haoqin Tu", "Zeyu Zheng", "Cihang Xie", "Charles Fleming", "Mingyu Ding", "Huaxiu Yao" ], "github": "https://github.com/aiming-lab/SimpleMem", "project_page": "", "abstract": "AI agents increasingly operate over extended time horizons, yet their ability to retain, organize, and recall multimodal experiences remains a critical bottleneck. Building effective lifelong memory requires navigating a vast design space spanning architecture, retrieval strategies, prompt engineering, and data pipelines; this space is too large and interconnected for manual exploration or traditional AutoML to explore effectively. We deploy an autonomous research pipeline to discover Omni-SimpleMem, a unified multimodal memory framework for lifelong AI agents. Starting from a na\u00efve baseline (F1=0.117 on LoCoMo), the pipeline autonomously executes {sim}50 experiments across two benchmarks, diagnosing failure modes, proposing architectural modifications, and repairing data pipeline bugs, all without human intervention in the inner loop. The resulting system achieves state-of-the-art on both benchmarks, improving F1 by +411% on LoCoMo (0.117to0.598) and +214% on Mem-Gallery (0.254to0.797) relative to the initial configurations. Critically, the most impactful discoveries are not hyperparameter adjustments: bug fixes (+175%), architectural changes (+44%), and prompt engineering (+188% on specific categories) each individually exceed the cumulative contribution of all hyperparameter tuning, demonstrating capabilities fundamentally beyond the reach of traditional AutoML. We provide a taxonomy of six discovery types and identify four properties that make multimodal memory particularly suited for autoresearch, offering guidance for applying autonomous research pipelines to other AI system domains. Code is available at this https://github.com/aiming-lab/SimpleMem.", "github_manual": "", "github_regex": "https://github.com/aiming-lab/SimpleMem", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.02190", "title": "UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving", "authors": [ "Yongkang Li", "Lijun Zhou", "Sixu Yan", "Bencheng Liao", "Tianyi Yan", "Kaixin Xiong", "Long Chen", "Hongwei Xie", "Bing Wang", "Guang Chen", "Hangjun Ye", "Wenyu Liu", "Haiyang Sun", "Xinggang Wang" ], "github": "https://github.com/xiaomi-research/unidrivevla", "project_page": "", "abstract": "Vision-Language-Action (VLA) models have recently emerged in autonomous driving, with the promise of leveraging rich world knowledge to improve the cognitive capabilities of driving systems. However, adapting such models for driving tasks currently faces a critical dilemma between spatial perception and semantic reasoning. Consequently, existing VLA systems are forced into suboptimal compromises: directly adopting 2D Vision-Language Models yields limited spatial perception, whereas enhancing them with 3D spatial representations often impairs the native reasoning capacity of VLMs. We argue that this dilemma largely stems from the coupled optimization of spatial perception and semantic reasoning within shared model parameters. To overcome this, we propose UniDriveVLA, a Unified Driving Vision-Language-Action model based on Mixture-of-Transformers that addresses the perception-reasoning conflict via expert decoupling. Specifically, it comprises three experts for driving understanding, scene perception, and action planning, which are coordinated through masked joint attention. In addition, we combine a sparse perception paradigm with a three-stage progressive training strategy to improve spatial perception while maintaining semantic reasoning capability. Extensive experiments show that UniDriveVLA achieves state-of-the-art performance in open-loop evaluation on nuScenes and closed-loop evaluation on Bench2Drive. Moreover, it demonstrates strong performance across a broad range of perception, prediction, and understanding tasks, including 3D detection, online mapping, motion forecasting, and driving-oriented VQA, highlighting its broad applicability as a unified model for autonomous driving. Code and model have been released at https://github.com/xiaomi-research/unidrivevla", "github_manual": "", "github_regex": "https://github.com/xiaomi-research/unidrivevla", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.02097", "title": "LatentUM: Unleashing the Potential of Interleaved Cross-Modal Reasoning via a Latent-Space Unified Model", "authors": [ "Jiachun Jin", "Zetong Zhou", "Xiao Yang", "Hao Zhang", "Pengfei Liu", "Jun Zhu", "Zhijie Deng" ], "github": "", "project_page": "", "abstract": "Unified models (UMs) hold promise for their ability to understand and generate content across heterogeneous modalities. Compared to merely generating visual content, the use of UMs for interleaved cross-modal reasoning is more promising and valuable, e.g., for solving understanding problems that require dense visual thinking, improving visual generation through self-reflection, or modeling visual dynamics of the physical world guided by stepwise action interventions. However, existing UMs necessitate pixel decoding as a bridge due to their disjoint visual representations for understanding and generation, which is both ineffective and inefficient. In this paper, we introduce LatentUM, a novel unified model that represents all modalities within a shared semantic latent space, eliminating the need for pixel-space mediation between visual understanding and generation. This design naturally enables flexible interleaved cross-modal reasoning and generation. Beyond improved computational efficiency, the shared representation substantially alleviates codec bias and strengthens cross-modal alignment, allowing LatentUM to achieve state-of-the-art performance on the Visual Spatial Planning benchmark, push the limits of visual generation through self-reflection, and support world modeling by predicting future visual states within the shared semantic latent space.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2603.27666", "title": "Gated Condition Injection without Multimodal Attention: Towards Controllable Linear-Attention Transformers", "authors": [ "Yuhe Liu", "Zhenxiong Tan", "Yujia Hu", "Songhua Liu", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in diffusion-based controllable visual generation have led to remarkable improvements in image quality. However, these powerful models are typically deployed on cloud servers due to their large computational demands, raising serious concerns about user data privacy. To enable secure and efficient on-device generation, we explore in this paper controllable diffusion models built upon linear attention architectures, which offer superior scalability and efficiency, even on edge devices. Yet, our experiments reveal that existing controllable generation frameworks, such as ControlNet and OminiControl, either lack the flexibility to support multiple heterogeneous condition types or suffer from slow convergence on such linear-attention models. To address these limitations, we propose a novel controllable diffusion framework tailored for linear attention backbones like SANA. The core of our method lies in a unified gated conditioning module working in a dual-path pipeline, which effectively integrates multi-type conditional inputs, such as spatially aligned and non-aligned cues. Extensive experiments on multiple tasks and benchmarks demonstrate that our approach achieves state-of-the-art controllable generation performance based on linear-attention models, surpassing existing methods in terms of fidelity and controllability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.02007", "title": "Apriel-Reasoner: RL Post-Training for General-Purpose and Efficient Reasoning", "authors": [ "Rafael Pardinas", "Ehsan Kamalloo", "David Vazquez", "Alexandre Drouin" ], "github": "", "project_page": "", "abstract": "Building general-purpose reasoning models using reinforcement learning with verifiable rewards (RLVR) across diverse domains has been widely adopted by frontier open-weight models. However, their training recipes and domain mixtures are often not disclosed. Joint optimization across domains poses significant challenges: domains vary widely in rollout length, problem difficulty and sample efficiency. Further, models with long chain-of-thought traces increase inference cost and latency, making efficiency critical for practical deployment. We present Apriel-Reasoner, trained with a fully reproducible multi-domain RL post-training recipe on Apriel-Base, a 15B-parameter open-weight LLM, across five domains using public datasets: mathematics, code generation, instruction following, logical puzzles and function calling. We introduce an adaptive domain sampling mechanism that preserves target domain ratios despite heterogeneous rollout dynamics, and a difficulty-aware extension of the standard length penalty that, with no additional training overhead, encourages longer reasoning for difficult problems and shorter traces for easy ones. Trained with a strict 16K-token output budget, Apriel-Reasoner generalizes to 32K tokens at inference and improves over Apriel-Base on AIME 2025, GPQA, MMLU-Pro, and LiveCodeBench while producing 30-50% shorter reasoning traces. It matches strong open-weight models of similar size at lower token cost, thereby pushing the Pareto frontier of accuracy versus token budget.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.00829", "title": "LinguDistill: Recovering Linguistic Ability in Vision- Language Models via Selective Cross-Modal Distillation", "authors": [ "Patrick Amadeus Irawan", "Erland Hilman Fuadi", "Shanu Kumar", "Alham Fikri Aji", "Yova Kementchedjhieva" ], "github": "", "project_page": "", "abstract": "Adapting pretrained language models (LMs) into vision-language models (VLMs) can degrade their native linguistic capability due to representation shift and cross-modal interference introduced during multimodal adaptation. Such loss is difficult to recover, even with targeted task-specific fine-tuning using standard objectives. Prior recovery approaches typically introduce additional modules that act as intermediate alignment layers to maintain or isolate modality-specific subspaces, which increases architectural complexity, adds parameters at inference time, and limits flexibility across models and settings. We propose LinguDistill, an adapter-free distillation method that restores linguistic capability by utilizing the original frozen LM as a teacher. We overcome the key challenge of enabling vision-conditioned teacher supervision by introducing layer-wise KV-cache sharing, which exposes the teacher to the student's multimodal representations without modifying the architecture of either model. We then selectively distill the teacher's strong linguistic signal on language-intensive data to recover language capability, while preserving the student's visual grounding on multimodal tasks. As a result, LinguDistill recovers sim10% of the performance lost on language and knowledge benchmarks, while maintaining comparable performance on vision-heavy tasks. Our findings demonstrate that linguistic capability can be recovered without additional modules, providing an efficient and practical solution to modality-specific degradation in multimodal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01929", "title": "Woosh: A Sound Effects Foundation Model", "authors": [ "Ga\u00ebtan Hadjeres", "Marc Ferras", "Khaled Koutini", "Benno Weck", "Alexandre Bittar", "Thomas Hummel", "Zineb Lahrici", "Hakim Missoum", "Joan Serr\u00e0", "Yuki Mitsufuji" ], "github": "https://github.com/SonyResearch/Woosh", "project_page": "", "abstract": "The audio research community depends on open generative models as foundational tools for building novel approaches and establishing baselines. In this report, we present Woosh, Sony AI's publicly released sound effect foundation model, detailing its architecture, training process, and an evaluation against other popular open models. Being optimized for sound effects, we provide (1) a high-quality audio encoder/decoder model and (2) a text-audio alignment model for conditioning, together with (3) text-to-audio and (4) video-to-audio generative models. Distilled text-to-audio and video-to-audio models are also included in the release, allowing for low-resource operation and fast inference. Our evaluation on both public and private data shows competitive or better performance for each module when compared to existing open alternatives like StableAudio-Open and TangoFlux. Inference code and model weights are available at https://github.com/SonyResearch/Woosh. Demo samples can be found at https://sonyresearch.github.io/Woosh/.", "github_manual": "", "github_regex": "https://github.com/SonyResearch/Woosh", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01404", "title": "Friends and Grandmothers in Silico: Localizing Entity Cells in Language Models", "authors": [ "Itay Yona", "Dan Barzilay", "Michael Karasik", "Mor Geva" ], "github": "", "project_page": "", "abstract": "Language models can answer many entity-centric factual questions, but it remains unclear which internal mechanisms are involved in this process. We study this question across multiple language models. We localize entity-selective MLP neurons using templated prompts about each entity, and then validate them with causal interventions on PopQA-based QA examples. On a curated set of 200 entities drawn from PopQA, localized neurons concentrate in early layers. Negative ablation produces entity-specific amnesia, while controlled injection at a placeholder token improves answer retrieval relative to mean-entity and wrong-cell controls. For many entities, activating a single localized neuron is sufficient to recover entity-consistent predictions once the context is initialized, consistent with compact entity retrieval rather than purely gradual enrichment across depth. Robustness to aliases, acronyms, misspellings, and multilingual forms supports a canonicalization interpretation. The effect is strong but not universal: not every entity admits a reliable single-neuron handle, and coverage is higher for popular entities. Overall, these results identify sparse, causally actionable access points for analyzing and modulating entity-conditioned factual behavior.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01666", "title": "DynaVid: Learning to Generate Highly Dynamic Videos using Synthetic Motion Data", "authors": [ "Wonjoon Jin", "Jiyun Won", "Janghyeok Han", "Qi Dai", "Chong Luo", "Seung-Hwan Baek", "Sunghyun Cho" ], "github": "", "project_page": "", "abstract": "Despite recent progress, video diffusion models still struggle to synthesize realistic videos involving highly dynamic motions or requiring fine-grained motion controllability. A central limitation lies in the scarcity of such examples in commonly used training datasets. To address this, we introduce DynaVid, a video synthesis framework that leverages synthetic motion data in training, which is represented as optical flow and rendered using computer graphics pipelines. This approach offers two key advantages. First, synthetic motion offers diverse motion patterns and precise control signals that are difficult to obtain from real data. Second, unlike rendered videos with artificial appearances, rendered optical flow encodes only motion and is decoupled from appearance, thereby preventing models from reproducing the unnatural look of synthetic videos. Building on this idea, DynaVid adopts a two-stage generation framework: a motion generator first synthesizes motion, and then a motion-guided video generator produces video frames conditioned on that motion. This decoupled formulation enables the model to learn dynamic motion patterns from synthetic data while preserving visual realism from real-world videos. We validate our framework on two challenging scenarios, vigorous human motion generation and extreme camera motion control, where existing datasets are particularly limited. Extensive experiments demonstrate that DynaVid improves the realism and controllability in dynamic motion generation and camera motion control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01619", "title": "Automatic Image-Level Morphological Trait Annotation for Organismal Images", "authors": [ "Vardaan Pahuja", "Samuel Stevens", "Alyson East", "Sydne Record", "Yu Su" ], "github": "https://github.com/OSU-NLP-Group/sae-trait-annotation", "project_page": "", "abstract": "Morphological traits are physical characteristics of biological organisms that provide vital clues on how organisms interact with their environment. Yet extracting these traits remains a slow, expert-driven process, limiting their use in large-scale ecological studies. A major bottleneck is the absence of high-quality datasets linking biological images to trait-level annotations. In this work, we demonstrate that sparse autoencoders trained on foundation-model features yield monosemantic, spatially grounded neurons that consistently activate on meaningful morphological parts. Leveraging this property, we introduce a trait annotation pipeline that localizes salient regions and uses vision-language prompting to generate interpretable trait descriptions. Using this approach, we construct Bioscan-Traits, a dataset of 80K trait annotations spanning 19K insect images from BIOSCAN-5M. Human evaluation confirms the biological plausibility of the generated morphological descriptions. We assess design sensitivity through a comprehensive ablation study, systematically varying key design choices and measuring their impact on the quality of the resulting trait descriptions. By annotating traits with a modular pipeline rather than prohibitively expensive manual efforts, we offer a scalable way to inject biologically meaningful supervision into foundation models, enable large-scale morphological analyses, and bridge the gap between ecological relevance and machine-learning practicality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/OSU-NLP-Group/sae-trait-annotation", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.00491", "title": "Executing as You Generate: Hiding Execution Latency in LLM Code Generation", "authors": [ "Zhensu Sun", "Zhihao Lin", "Zhi Chen", "Chengran Yang", "Mingyi Zhou", "Li Li", "David Lo" ], "github": "", "project_page": "", "abstract": "Current LLM-based coding agents follow a serial execution paradigm: the model first generates the complete code, then invokes an interpreter to execute it. This sequential workflow leaves the executor idle during generation and the generator idle during execution, resulting in unnecessary end-to-end latency. We observe that, unlike human developers, LLMs produce code tokens sequentially without revision, making it possible to execute code as it is being generated. We formalize this parallel execution paradigm, modeling it as a three-stage pipeline of generation, detection, and execution, and derive closed-form latency bounds that characterize its speedup potential and operating regimes. We then present Eager, a concrete implementation featuring AST-based chunking, dynamic batching with gated execution, and early error interruption. We evaluate Eager across four benchmarks, seven LLMs, and three execution environments. Results show that Eager reduces the non-overlapped execution latency by up to 99.9% and the end-to-end latency by up to 55% across seven LLMs and four benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01479", "title": "UniRecGen: Unifying Multi-View 3D Reconstruction and Generation", "authors": [ "Zhisheng Huang", "Jiahao Chen", "Cheng Lin", "Chenyu Hu", "Hanzhuo Huang", "Zhengming Yu", "Mengfei Li", "Yuheng Liu", "Zekai Gu", "Zibo Zhao", "Yuan Liu", "Xin Li", "Wenping Wang" ], "github": "", "project_page": "", "abstract": "Sparse-view 3D modeling represents a fundamental tension between reconstruction fidelity and generative plausibility. While feed-forward reconstruction excels in efficiency and input alignment, it often lacks the global priors needed for structural completeness. Conversely, diffusion-based generation provides rich geometric details but struggles with multi-view consistency. We present UniRecGen, a unified framework that integrates these two paradigms into a single cooperative system. To overcome inherent conflicts in coordinate spaces, 3D representations, and training objectives, we align both models within a shared canonical space. We employ disentangled cooperative learning, which maintains stable training while enabling seamless collaboration during inference. Specifically, the reconstruction module is adapted to provide canonical geometric anchors, while the diffusion generator leverages latent-augmented conditioning to refine and complete the geometric structure. Experimental results demonstrate that UniRecGen achieves superior fidelity and robustness, outperforming existing methods in creating complete and consistent 3D models from sparse observations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01760", "title": "T5Gemma-TTS Technical Report", "authors": [ "Chihiro Arata", "Kiyoshi Kurihara" ], "github": "https://github.com/Aratako/T5Gemma-TTS", "project_page": "", "abstract": "Autoregressive neural codec language models have shown strong zero-shot voice cloning ability, but decoder-only architectures treat input text as a prefix that competes with the growing audio sequence for positional capacity, weakening text conditioning over long utterances. We present T5Gemma-TTS, an encoder-decoder codec language model that maintains persistent text conditioning by routing bidirectional text representations through cross-attention at every decoder layer. Built on the T5Gemma pretrained encoder-decoder backbone (2B encoder + 2B decoder; 4B parameters), it inherits rich linguistic knowledge without phoneme conversion and processes text directly at the subword level. To improve duration control, we introduce Progress-Monitoring Rotary Position Embedding (PM-RoPE) in all 26 cross-attention layers, injecting normalized progress signals that help the decoder track target speech length. Trained on 170,000 hours of multilingual speech in English, Chinese, and Japanese, T5Gemma-TTS achieves a statistically significant speaker-similarity gain on Japanese over XTTSv2 (0.677 vs. 0.622; non-overlapping 95% confidence intervals) and the highest numerical Korean speaker similarity (0.747) despite Korean not being included in training, although this margin over XTTSv2 (0.741) is not statistically conclusive. It also attains the lowest numerical Japanese character error rate among five baselines (0.126), though this ranking should be interpreted cautiously because of partial confidence-interval overlap with Kokoro. English results on LibriSpeech should be viewed as an upper-bound estimate because LibriHeavy is a superset of LibriSpeech. Using the same checkpoint, disabling PM-RoPE at inference causes near-complete synthesis failure: CER degrades from 0.129 to 0.982 and duration accuracy drops from 79% to 46%. Code and weights are available at https://github.com/Aratako/T5Gemma-TTS.", "github_manual": "", "github_regex": "https://github.com/Aratako/T5Gemma-TTS", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.02296", "title": "VOID: Video Object and Interaction Deletion", "authors": [ "Saman Motamed", "William Harvey", "Benjamin Klein", "Luc Van Gool", "Zhuoning Yuan", "Ta-Ying Cheng" ], "github": "", "project_page": "", "abstract": "Existing video object removal methods excel at inpainting content \"behind\" the object and correcting appearance-level artifacts such as shadows and reflections. However, when the removed object has more significant interactions, such as collisions with other objects, current models fail to correct them and produce implausible results. We present VOID, a video object removal framework designed to perform physically-plausible inpainting in these complex scenarios. To train the model, we generate a new paired dataset of counterfactual object removals using Kubric and HUMOTO, where removing an object requires altering downstream physical interactions. During inference, a vision-language model identifies regions of the scene affected by the removed object. These regions are then used to guide a video diffusion model that generates physically consistent counterfactual outcomes. Experiments on both synthetic and real data show that our approach better preserves consistent scene dynamics after object removal compared to prior video object removal methods. We hope this framework sheds light on how to make video editing models better simulators of the world through high-level causal reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.02289", "title": "Omni123: Exploring 3D Native Foundation Models with Limited 3D Data by Unifying Text to 2D and 3D Generation", "authors": [ "Chongjie Ye", "Cheng Cao", "Chuanyu Pan", "Yiming Hao", "Yihao Zhi", "Yuanming Hu", "Xiaoguang Han" ], "github": "", "project_page": "", "abstract": "Recent multimodal large language models have achieved strong performance in unified text and image understanding and generation, yet extending such native capability to 3D remains challenging due to limited data. Compared to abundant 2D imagery, high-quality 3D assets are scarce, making 3D synthesis under-constrained. Existing methods often rely on indirect pipelines that edit in 2D and lift results into 3D via optimization, sacrificing geometric consistency. We present Omni123, a 3D-native foundation model that unifies text-to-2D and text-to-3D generation within a single autoregressive framework. Our key insight is that cross-modal consistency between images and 3D can serve as an implicit structural constraint. By representing text, images, and 3D as discrete tokens in a shared sequence space, the model leverages abundant 2D data as a geometric prior to improve 3D representations. We introduce an interleaved X-to-X training paradigm that coordinates diverse cross-modal tasks over heterogeneous paired datasets without requiring fully aligned text-image-3D triplets. By traversing semantic-visual-geometric cycles (e.g., text to image to 3D to image) within autoregressive sequences, the model jointly enforces semantic alignment, appearance fidelity, and multi-view geometric consistency. Experiments show that Omni123 significantly improves text-guided 3D generation and editing, demonstrating a scalable path toward multimodal 3D world models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.00917", "title": "Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time", "authors": [ "Razvan Mihai Popescu", "David Gros", "Andrei Botocan", "Rahul Pandita", "Prem Devanbu", "Maliheh Izadi" ], "github": "https://github.com/logic-star-ai/insights", "project_page": "", "abstract": "The rise of large language models for code has reshaped software development. Autonomous coding agents, able to create branches, open pull requests, and perform code reviews, now actively contribute to real-world projects. Their growing role offers a unique and timely opportunity to investigate AI-driven contributions and their effects on code quality, team dynamics, and software maintainability. In this work, we construct a novel dataset of approximately 110,000 open-source pull requests, including associated commits, comments, reviews, issues, and file changes, collectively representing millions of lines of source code. We compare five popular coding agents, including OpenAI Codex, Claude Code, GitHub Copilot, Google Jules, and Devin, examining how their usage differs in various development aspects such as merge frequency, edited file types, and developer interaction signals, including comments and reviews. Furthermore, we emphasize that code authoring and review are only a small part of the larger software engineering process, as the resulting code must also be maintained and updated over time. Hence, we offer several longitudinal estimates of survival and churn rates for agent-generated versus human-authored code. Ultimately, our findings indicate an increasing agent activity in open-source projects, although their contributions are associated with more churn over time compared to human-authored code.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/logic-star-ai/insights", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01014", "title": "AutoMIA: Improved Baselines for Membership Inference Attack via Agentic Self-Exploration", "authors": [ "Ruhao Liu", "Weiqi Huang", "Qi Li", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Membership Inference Attacks (MIAs) serve as a fundamental auditing tool for evaluating training data leakage in machine learning models. However, existing methodologies predominantly rely on static, handcrafted heuristics that lack adaptability, often leading to suboptimal performance when transferred across different large models. In this work, we propose AutoMIA, an agentic framework that reformulates membership inference as an automated process of self-exploration and strategy evolution. Given high-level scenario specifications, AutoMIA self-explores the attack space by generating executable logits-level strategies and progressively refining them through closed-loop evaluation feedback. By decoupling abstract strategy reasoning from low-level execution, our framework enables a systematic, model-agnostic traversal of the attack search space. Extensive experiments demonstrate that AutoMIA consistently matches or outperforms state-of-the-art baselines while eliminating the need for manual feature engineering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2603.24257", "title": "Memory-Augmented Vision-Language Agents for Persistent and Semantically Consistent Object Captioning", "authors": [ "Tommaso Galliena", "Stefano Rosa", "Tommaso Apicella", "Pietro Morerio", "Alessio Del Bue", "Lorenzo Natale" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) often yield inconsistent descriptions of the same object across viewpoints, hindering the ability of embodied agents to construct consistent semantic representations over time. Previous methods resolved inconsistencies using offline multi-view aggregation or multi-stage pipelines that decouple exploration, data association, and caption learning, with limited capacity to reason over previously observed objects. In this paper, we introduce a unified, memory-augmented Vision-Language agent that simultaneously handles data association, object captioning, and exploration policy within a single autoregressive framework. The model processes the current RGB observation, a top-down explored map, and an object-level episodic memory serialized into object-level tokens, ensuring persistent object identity and semantic consistency across extended sequences. To train the model in a self-supervised manner, we collect a dataset in photorealistic 3D environments using a disagreement-based policy and a pseudo-captioning model that enforces consistency across multi-view caption histories. Extensive evaluation on a manually annotated object-level test set, demonstrate improvements of up to +11.86% in standard captioning scores and +7.39% in caption self-similarity over baseline models, while enabling scalable performance through a compact scene representation. Code, model weights, and data are available at https://hsp-iit.github.io/epos-vlm/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01618", "title": "Tex3D: Objects as Attack Surfaces via Adversarial 3D Textures for Vision-Language-Action Models", "authors": [ "Jiawei Chen", "Simin Huang", "Jiawei Du", "Shuaihang Chen", "Yu Tian", "Mingjie Wei", "Chao Yu", "Zhaoxia Yin" ], "github": "", "project_page": "", "abstract": "Vision-language-action (VLA) models have shown strong performance in robotic manipulation, yet their robustness to physically realizable adversarial attacks remains underexplored. Existing studies reveal vulnerabilities through language perturbations and 2D visual attacks, but these attack surfaces are either less representative of real deployment or limited in physical realism. In contrast, adversarial 3D textures pose a more physically plausible and damaging threat, as they are naturally attached to manipulated objects and are easier to deploy in physical environments. Bringing adversarial 3D textures to VLA systems is nevertheless nontrivial. A central obstacle is that standard 3D simulators do not provide a differentiable optimization path from the VLA objective function back to object appearance, making it difficult to optimize through an end-to-end manner. To address this, we introduce Foreground-Background Decoupling (FBD), which enables differentiable texture optimization through dual-renderer alignment while preserving the original simulation environment. To further ensure that the attack remains effective across long-horizon and diverse viewpoints in the physical world, we propose Trajectory-Aware Adversarial Optimization (TAAO), which prioritizes behaviorally critical frames and stabilizes optimization with a vertex-based parameterization. Built on these designs, we present Tex3D, the first framework for end-to-end optimization of 3D adversarial textures directly within the VLA simulation environment. Experiments in both simulation and real-robot settings show that Tex3D significantly degrades VLA performance across multiple manipulation tasks, achieving task failure rates of up to 96.7\\%. Our empirical results expose critical vulnerabilities of VLA systems to physically grounded 3D adversarial attacks and highlight the need for robustness-aware training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.02327", "title": "Steerable Visual Representations", "authors": [ "Jona Ruthardt", "Manu Gaur", "Deva Ramanan", "Makarand Tapaswi", "Yuki M. Asano" ], "github": "", "project_page": "", "abstract": "Pretrained Vision Transformers (ViTs) such as DINOv2 and MAE provide generic image features that can be applied to a variety of downstream tasks such as retrieval, classification, and segmentation. However, such representations tend to focus on the most salient visual cues in the image, with no way to direct them toward less prominent concepts of interest. In contrast, Multimodal LLMs can be guided with textual prompts, but the resulting representations tend to be language-centric and lose their effectiveness for generic visual tasks. To address this, we introduce Steerable Visual Representations, a new class of visual representations, whose global and local features can be steered with natural language. While most vision-language models (e.g., CLIP) fuse text with visual features after encoding (late fusion), we inject text directly into the layers of the visual encoder (early fusion) via lightweight cross-attention. We introduce benchmarks for measuring representational steerability, and demonstrate that our steerable visual features can focus on any desired objects in an image while preserving the underlying representation quality. Our method also matches or outperforms dedicated approaches on anomaly detection and personalized object discrimination, exhibiting zero-shot generalization to out-of-distribution tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01328", "title": "Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial", "authors": [ "Zhongwei Yu", "Rasul Tutunov", "Alexandre Max Maraval", "Zikai Xie", "Zhenzhi Tan", "Jiankang Wang", "Zijing Li", "Liangliang Xu", "Qi Yang", "Jun Jiang", "Sanzhong Luo", "Zhenxiao Guo", "Haitham Bou-Ammar", "Jun Wang" ], "github": "", "project_page": "", "abstract": "Traditional scientific discovery relies on an iterative hypothesise-experiment-refine cycle that has driven progress for centuries, but its intuitive, ad-hoc implementation often wastes resources, yields inefficient designs, and misses critical insights. This tutorial presents Bayesian Optimisation (BO), a principled probability-driven framework that formalises and automates this core scientific cycle. BO uses surrogate models (e.g., Gaussian processes) to model empirical observations as evolving hypotheses, and acquisition functions to guide experiment selection, balancing exploitation of known knowledge and exploration of uncharted domains to eliminate guesswork and manual trial-and-error. We first frame scientific discovery as an optimisation problem, then unpack BO's core components, end-to-end workflows, and real-world efficacy via case studies in catalysis, materials science, organic synthesis, and molecule discovery. We also cover critical technical extensions for scientific applications, including batched experimentation, heteroscedasticity, contextual optimisation, and human-in-the-loop integration. Tailored for a broad audience, this tutorial bridges AI advances in BO with practical natural science applications, offering tiered content to empower cross-disciplinary researchers to design more efficient experiments and accelerate principled scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2603.28130", "title": "MDPBench: A Benchmark for Multilingual Document Parsing in Real-World Scenarios", "authors": [ "Zhang Li", "Zhibo Lin", "Qiang Liu", "Ziyang Zhang", "Shuo Zhang", "Zidun Guo", "Jiajun Song", "Jiarui Zhang", "Xiang Bai", "Yuliang Liu" ], "github": "https://github.com/Yuliang-Liu/MultimodalOCR", "project_page": "", "abstract": "We introduce Multilingual Document Parsing Benchmark, the first benchmark for multilingual digital and photographed document parsing. Document parsing has made remarkable strides, yet almost exclusively on clean, digital, well-formatted pages in a handful of dominant languages. No systematic benchmark exists to evaluate how models perform on digital and photographed documents across diverse scripts and low-resource languages. MDPBench comprises 3,400 document images spanning 17 languages, diverse scripts, and varied photographic conditions, with high-quality annotations produced through a rigorous pipeline of expert model labeling, manual correction, and human verification. To ensure fair comparison and prevent data leakage, we maintain separate public and private evaluation splits. Our comprehensive evaluation of both open-source and closed-source models uncovers a striking finding: while closed-source models (notably Gemini3-Pro) prove relatively robust, open-source alternatives suffer dramatic performance collapse, particularly on non-Latin scripts and real-world photographed documents, with an average drop of 17.8% on photographed documents and 14.0% on non-Latin scripts. These results reveal significant performance imbalances across languages and conditions, and point to concrete directions for building more inclusive, deployment-ready parsing systems. Source available at https://github.com/Yuliang-Liu/MultimodalOCR.", "github_manual": "", "github_regex": "https://github.com/Yuliang-Liu/MultimodalOCR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2603.29640", "title": "ASI-Evolve: AI Accelerates AI", "authors": [ "Weixian Xu", "Tiantian Mi", "Yixiu Liu", "Yang Nan", "Zhimeng Zhou", "Lyumanshan Ye", "Lin Zhang", "Yu Qiao", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/ASI-Evolve", "project_page": "", "abstract": "Can AI accelerate the development of AI itself? While recent agentic systems have shown strong performance on well-scoped tasks with rapid feedback, it remains unclear whether they can tackle the costly, long-horizon, and weakly supervised research loops that drive real AI progress. We present ASI-Evolve, an agentic framework for AI-for-AI research that closes this loop through a learn-design-experiment-analyze cycle. ASI-Evolve augments standard evolutionary agents with two key components: a cognition base that injects accumulated human priors into each round of exploration, and a dedicated analyzer that distills complex experimental outcomes into reusable insights for future iterations. To our knowledge, ASI-Evolve is the first unified framework to demonstrate AI-driven discovery across three central components of AI development: data, architectures, and learning algorithms. In neural architecture design, it discovered 105 SOTA linear attention architectures, with the best discovered model surpassing DeltaNet by +0.97 points, nearly 3x the gain of recent human-designed improvements. In pretraining data curation, the evolved pipeline improves average benchmark performance by +3.96 points, with gains exceeding 18 points on MMLU. In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +12.5 points on AMC32, +11.67 points on AIME24, and +5.04 points on OlympiadBench. We further provide initial evidence that this AI-for-AI paradigm can transfer beyond the AI stack through experiments in mathematics and biomedicine. Together, these results suggest that ASI-Evolve represents a promising step toward enabling AI to accelerate AI across the foundational stages of development, offering early evidence for the feasibility of closed-loop AI research.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/GAIR-NLP/ASI-Evolve", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01152", "title": "Brainstacks: Cross-Domain Cognitive Capabilities via Frozen MoE-LoRA Stacks for Continual LLM Learning", "authors": [ "Mohammad R. Abu Ayyash" ], "github": "", "project_page": "", "abstract": "We present Brainstacks, a modular architecture for continual multi-domain fine-tuning of large language models that packages domain expertise as frozen adapter stacks composing additively on a shared frozen base at inference. Five interlocking components: (1) MoE-LoRA with Shazeer-style noisy top-2 routing across all seven transformer projections under QLoRA 4-bit quantization with rsLoRA scaling; (2) an inner loop performing residual boosting by freezing trained stacks and adding new ones; (3) an outer loop training sequential domain-specific stacks with curriculum-ordered dependencies; (4) null-space projection via randomized SVD constraining new stacks to subspaces orthogonal to prior directions, achieving zero forgetting in isolation; (5) an outcome-based sigmoid meta-router trained on empirically discovered domain-combination targets that selectively weights stacks, enabling cross-domain composition. Two boundary experiments: (6) PSN pretraining on a randomly initialized model; (7) per-domain RL (DPO/GRPO) validating compatibility with post-SFT alignment. Validated on TinyLlama-1.1B (4 domains, 9 stacks) and Gemma 3 12B IT (5 domains, 10 stacks), MoE-LoRA achieves 2.5x faster convergence than parameter-matched single LoRA, residual boosting breaks through the single-stack ceiling, and the routed system recovers generation quality destroyed by ungated stack accumulation. The central finding: the outcome-based router discovers that domain stacks encode transferable cognitive primitives (instruction-following clarity, numerical reasoning, procedural logic, chain-of-thought structure) rather than domain-specific knowledge, with medical prompts routing to chat+math stacks in 97% of cases despite zero medical data in those stacks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2603.28068", "title": "AIBench: Evaluating Visual-Logical Consistency in Academic Illustration Generation", "authors": [ "Zhaohe Liao", "Kaixun Jiang", "Zhihang Liu", "Yujie Wei", "Junqiu Yu", "Quanhao Li", "Hong-Tao Yu", "Pandeng Li", "Yuzheng Wang", "Zhen Xing", "Shiwei Zhang", "Chen-Wei Xie", "Yun Zheng", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Although image generation has boosted various applications via its rapid evolution, whether the state-of-the-art models are able to produce ready-to-use academic illustrations for papers is still largely unexplored. Directly comparing or evaluating the illustration with VLM is native but requires oracle multi-modal understanding ability, which is unreliable for long and complex texts and illustrations. To address this, we propose AIBench, the first benchmark using VQA for evaluating logic correctness of the academic illustrations and VLMs for assessing aesthetics. In detail, we designed four levels of questions proposed from a logic diagram summarized from the method part of the paper, which query whether the generated illustration aligns with the paper on different scales. Our VQA-based approach raises more accurate and detailed evaluations on visual-logical consistency while relying less on the ability of the judger VLM. With our high-quality AIBench, we conduct extensive experiments and conclude that the performance gap between models on this task is significantly larger than general ones, reflecting their various complex reasoning and high-density generation ability. Further, the logic and aesthetics are hard to optimize simultaneously as in handcrafted illustrations. Additional experiments further state that test-time scaling on both abilities significantly boosts the performance on this task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2603.26233", "title": "Ask or Assume? Uncertainty-Aware Clarification-Seeking in Coding Agents", "authors": [ "Nicholas Edwards", "Sebastian Schuster" ], "github": "https://github.com/nedwards99/ask-or-assume", "project_page": "", "abstract": "As Large Language Model (LLM) agents are increasingly deployed in open-ended domains like software engineering, they frequently encounter underspecified instructions that lack crucial context. While human developers naturally resolve underspecification by asking clarifying questions, current agents are largely optimized for autonomous execution. In this work, we systematically evaluate the clarification-seeking abilities of LLM agents on an underspecified variant of SWE-bench Verified. We propose an uncertainty-aware multi-agent scaffold that explicitly decouples underspecification detection from code execution. Our results demonstrate that this multi-agent system using OpenHands + Claude Sonnet 4.5 achieves a 69.40% task resolve rate, significantly outperforming a standard single-agent setup (61.20%) and closing the performance gap with agents operating on fully specified instructions. Furthermore, we find that the multi-agent system exhibits well-calibrated uncertainty, conserving queries on simple tasks while proactively seeking information on more complex issues. These findings indicate that current models can be turned into proactive collaborators, where agents independently recognize when to ask questions to elicit missing information in real-world, underspecified tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nedwards99/ask-or-assume", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.02330", "title": "ActionParty: Multi-Subject Action Binding in Generative Video Games", "authors": [ "Alexander Pondaven", "Ziyi Wu", "Igor Gilitschenski", "Philip Torr", "Sergey Tulyakov", "Fabio Pizzati", "Aliaksandr Siarohin" ], "github": "", "project_page": "", "abstract": "Recent advances in video diffusion have enabled the development of \"world models\" capable of simulating interactive environments. However, these models are largely restricted to single-agent settings, failing to control multiple agents simultaneously in a scene. In this work, we tackle a fundamental issue of action binding in existing video diffusion models, which struggle to associate specific actions with their corresponding subjects. For this purpose, we propose ActionParty, an action controllable multi-subject world model for generative video games. It introduces subject state tokens, i.e. latent variables that persistently capture the state of each subject in the scene. By jointly modeling state tokens and video latents with a spatial biasing mechanism, we disentangle global video frame rendering from individual action-controlled subject updates. We evaluate ActionParty on the Melting Pot benchmark, demonstrating the first video world model capable of controlling up to seven players simultaneously across 46 diverse environments. Our results show significant improvements in action-following accuracy and identity consistency, while enabling robust autoregressive tracking of subjects through complex interactions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2603.26259", "title": "Working Notes on Late Interaction Dynamics: Analyzing Targeted Behaviors of Late Interaction Models", "authors": [ "Antoine Edy", "Max Conti", "Quentin Mac\u00e9" ], "github": "", "project_page": "", "abstract": "While Late Interaction models exhibit strong retrieval performance, many of their underlying dynamics remain understudied, potentially hiding performance bottlenecks. In this work, we focus on two topics in Late Interaction retrieval: a length bias that arises when using multi-vector scoring, and the similarity distribution beyond the best scores pooled by the MaxSim operator. We analyze these behaviors for state-of-the-art models on the NanoBEIR benchmark. Results show that while the theoretical length bias of causal Late Interaction models holds in practice, bi-directional models can also suffer from it in extreme cases. We also note that no significant similarity trend lies beyond the top-1 document token, validating that the MaxSim operator efficiently exploits the token-level similarity scores.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01973", "title": "NearID: Identity Representation Learning via Near-identity Distractors", "authors": [ "Aleksandar Cvejic", "Rameen Abdal", "Abdelrahman Eldesokey", "Bernard Ghanem", "Peter Wonka" ], "github": "", "project_page": "", "abstract": "When evaluating identity-focused tasks such as personalized generation and image editing, existing vision encoders entangle object identity with background context, leading to unreliable representations and metrics. We introduce the first principled framework to address this vulnerability using Near-identity (NearID) distractors, where semantically similar but distinct instances are placed on the exact same background as a reference image, eliminating contextual shortcuts and isolating identity as the sole discriminative signal. Based on this principle, we present the NearID dataset (19K identities, 316K matched-context distractors) together with a strict margin-based evaluation protocol. Under this setting, pre-trained encoders perform poorly, achieving Sample Success Rates (SSR), a strict margin-based identity discrimination metric, as low as 30.7% and often ranking distractors above true cross-view matches. We address this by learning identity-aware representations on a frozen backbone using a two-tier contrastive objective enforcing the hierarchy: same identity > NearID distractor > random negative. This improves SSR to 99.2%, enhances part-level discrimination by 28.0%, and yields stronger alignment with human judgments on DreamBench++, a human-aligned benchmark for personalization. Project page: https://gorluxor.github.io/NearID/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2603.06679", "title": "MultiGen: Level-Design for Editable Multiplayer Worlds in Diffusion Game Engines", "authors": [ "Ryan Po", "David Junhao Zhang", "Amir Hertz", "Gordon Wetzstein", "Neal Wadhwa", "Nataniel Ruiz" ], "github": "", "project_page": "", "abstract": "Video world models have shown immense promise for interactive simulation and entertainment, but current systems still struggle with two important aspects of interactivity: user control over the environment for reproducible, editable experiences, and shared inference where players hold influence over a common world. To address these limitations, we introduce an explicit external memory into the system, a persistent state operating independent of the model's context window, that is continually updated by user actions and queried throughout the generation roll-out. Unlike conventional diffusion game engines that operate as next-frame predictors, our approach decomposes generation into Memory, Observation, and Dynamics modules. This design gives users direct, editable control over environment structure via an editable memory representation, and it naturally extends to real-time multiplayer rollouts with coherent viewpoints and consistent cross-player interactions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01202", "title": "Therefore I am. I Think", "authors": [ "Esakkivel Esakkiraja", "Sai Rajeswar", "Denis Akhiyarov", "Rajagopal Venkatesaramani" ], "github": "", "project_page": "", "abstract": "We consider the question: when a large language reasoning model makes a choice, did it think first and then decide to, or decide first and then think? In this paper, we present evidence that detectable, early-encoded decisions shape chain-of-thought in reasoning models. Specifically, we show that a simple linear probe successfully decodes tool-calling decisions from pre-generation activations with very high confidence, and in some cases, even before a single reasoning token is produced. Activation steering supports this causally: perturbing the decision direction leads to inflated deliberation, and flips behavior in many examples (between 7 - 79% depending on model and benchmark). We also show through behavioral analysis that, when steering changes the decision, the chain-of-thought process often rationalizes the flip rather than resisting it. Together, these results suggest that reasoning models can encode action choices before they begin to deliberate in text.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01658", "title": "CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery", "authors": [ "Ao Qu", "Han Zheng", "Zijian Zhou", "Yihao Yan", "Yihong Tang", "Shao Yong Ong", "Fenglu Hong", "Kaichen Zhou", "Chonghe Jiang", "Minwei Kong", "Jiacheng Zhu", "Xuan Jiang", "Sirui Li", "Cathy Wu", "Bryan Kian Hsiang Low", "Jinhua Zhao", "Paul Pu Liang" ], "github": "https://github.com/Human-Agent-Society/CORAL", "project_page": "", "abstract": "Large language model (LLM)-based evolution is a promising approach for open-ended discovery, where progress requires sustained search and knowledge accumulation. Existing methods still rely heavily on fixed heuristics and hard-coded exploration rules, which limit the autonomy of LLM agents. We present CORAL, the first framework for autonomous multi-agent evolution on open-ended problems. CORAL replaces rigid control with long-running agents that explore, reflect, and collaborate through shared persistent memory, asynchronous multi-agent execution, and heartbeat-based interventions. It also provides practical safeguards, including isolated workspaces, evaluator separation, resource management, and agent session and health management. Evaluated on diverse mathematical, algorithmic, and systems optimization tasks, CORAL sets new state-of-the-art results on 10 tasks, achieving 3-10 times higher improvement rates with far fewer evaluations than fixed evolutionary search baselines across tasks. On Anthropic's kernel engineering task, four co-evolving agents improve the best known score from 1363 to 1103 cycles. Mechanistic analyses further show how these gains arise from knowledge reuse and multi-agent exploration and communication. Together, these results suggest that greater agent autonomy and multi-agent evolution can substantially improve open-ended discovery. Code is available at https://github.com/Human-Agent-Society/CORAL.", "github_manual": "", "github_regex": "https://github.com/Human-Agent-Society/CORAL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2603.30043", "title": "Video Models Reason Early: Exploiting Plan Commitment for Maze Solving", "authors": [ "Kaleb Newman", "Tyler Zhu", "Olga Russakovsky" ], "github": "", "project_page": "", "abstract": "Video diffusion models exhibit emergent reasoning capabilities like solving mazes and puzzles, yet little is understood about how they reason during generation. We take a first step towards understanding this and study the internal planning dynamics of video models using 2D maze solving as a controlled testbed. Our investigations reveal two findings. Our first finding is early plan commitment: video diffusion models commit to a high-level motion plan within the first few denoising steps, after which further denoising alters visual details but not the underlying trajectory. Our second finding is that path length, not obstacle density, is the dominant predictor of maze difficulty, with a sharp failure threshold at 12 steps. This means video models can only reason over long mazes by chaining together multiple sequential generations. To demonstrate the practical benefits of our findings, we introduce Chaining with Early Planning, or ChEaP, which only spends compute on seeds with promising early plans and chains them together to tackle complex mazes. This improves accuracy from 7% to 67% on long-horizon mazes and by 2.5x overall on hard tasks in Frozen Lake and VR-Bench across Wan2.2-14B and HunyuanVideo-1.5. Our analysis reveals that current video models possess deeper reasoning capabilities than previously recognized, which can be elicited more reliably with better inference-time scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.01298", "title": "Forecasting Supply Chain Disruptions with Foresight Learning", "authors": [ "Benjamin Turtel", "Paul Wilczewski", "Kris Skotheim" ], "github": "", "project_page": "", "abstract": "Anticipating supply chain disruptions before they materialize is a core challenge for firms and policymakers alike. A key difficulty is learning to reason reliably about infrequent, high-impact events from noisy and unstructured inputs - a setting where general-purpose models struggle without task-specific adaptation. We introduce an end-to-end framework that trains LLMs to produce calibrated probabilistic forecasts using realized disruption outcomes as supervision. The resulting model substantially outperforms strong baselines - including GPT-5 - on accuracy, calibration, and precision. We also show that training induces more structured and reliable probabilistic reasoning without explicit prompting. These results suggest a general pathway for training domain-specific forecasting models that produce decision-ready signals. To support transparency we open-source the evaluation dataset used in this study. Dataset: https://huggingface.co/datasets/LightningRodLabs/supply-chain-predictions", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2603.29042", "title": "An Empirical Recipe for Universal Phone Recognition", "authors": [ "Shikhar Bharadwaj", "Chin-Jou Li", "Kwanghee Choi", "Eunjung Yeo", "William Chen", "Shinji Watanabe", "David R. Mortensen" ], "github": "", "project_page": "", "abstract": "Phone recognition (PR) is a key enabler of multilingual and low-resource speech processing tasks, yet robust performance remains elusive. Highly performant English-focused models do not generalize across languages, while multilingual models underutilize pretrained representations. It also remains unclear how data scale, architecture, and training objective contribute to multilingual PR. We present PhoneticXEUS -- trained on large-scale multilingual data and achieving state-of-the-art performance on both multilingual (17.7% PFER) and accented English speech (10.6% PFER). Through controlled ablations with evaluations across 100+ languages under a unified scheme, we empirically establish our training recipe and quantify the impact of SSL representations, data scale, and loss objectives. In addition, we analyze error patterns across language families, accented speech, and articulatory features. All data and code are released openly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2604.00356", "title": "Signals: Trajectory Sampling and Triage for Agentic Interactions", "authors": [ "Shuguang Chen", "Adil Hafeez", "Salman Paracha" ], "github": "", "project_page": "", "abstract": "Agentic applications based on large language models increasingly rely on multi-step interaction loops involving planning, action execution, and environment feedback. While such systems are now deployed at scale, improving them post-deployment remains challenging. Agent trajectories are voluminous and non-deterministic, and reviewing each one, whether through human review or auxiliary LLMs, is slow and cost-prohibitive. We propose a lightweight, signal-based framework for triaging agentic interaction trajectories. Our approach computes cheap, broadly applicable signals from live interactions and attaches them as structured attributes for trajectory triage, identifying interactions likely to be informative without affecting online agent behavior. We organize signals into a coarse-grained taxonomy spanning interaction (misalignment, stagnation, disengagement, satisfaction), execution (failure, loop), and environment (exhaustion), designed for computation without model calls. In a controlled annotation study on \u03c4-bench, a widely used benchmark for tool-augmented agent evaluation, we show that signal-based sampling achieves an 82\\% informativeness rate compared to 74\\% for heuristic filtering and 54\\% for random sampling, with a 1.52x efficiency gain per informative trajectory. The advantage is robust across reward strata and task domains, confirming that signals provide genuine per-trajectory informativeness gains rather than merely oversampling obvious failures. These results show that lightweight signals can serve as practical sampling infrastructure for agentic systems, and suggest a path toward preference data construction and post-deployment optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-03", "arxiv_id": "2603.27449", "title": "LOME: Learning Human-Object Manipulation with Action-Conditioned Egocentric World Model", "authors": [ "Quankai Gao", "Jiawei Yang", "Qiangeng Xu", "Le Chen", "Yue Wang" ], "github": "", "project_page": "", "abstract": "Learning human-object manipulation presents significant challenges due to its fine-grained and contact-rich nature of the motions involved. Traditional physics-based animation requires extensive modeling and manual setup, and more importantly, it neither generalizes well across diverse object morphologies nor scales effectively to real-world environment. To address these limitations, we introduce LOME, an egocentric world model that can generate realistic human-object interactions as videos conditioned on an input image, a text prompt, and per-frame human actions, including both body poses and hand gestures. LOME injects strong and precise action guidance into object manipulation by jointly estimating spatial human actions and the environment contexts during training. After finetuning a pretrained video generative model on videos of diverse egocentric human-object interactions, LOME demonstrates not only high action-following accuracy and strong generalization to unseen scenarios, but also realistic physical consequences of hand-object interactions, e.g., liquid flowing from a bottle into a mug after executing a ``pouring'' action. Extensive experiments demonstrate that our video-based framework significantly outperforms state-of-the-art image based and video-based action-conditioned methods and Image/Text-to-Video (I/T2V) generative model in terms of both temporal consistency and motion control. LOME paves the way for photorealistic AR/VR experiences and scalable robotic training, without being limited to simulated environments or relying on explicit 3D/4D modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.03016", "title": "Agentic-MME: What Agentic Capability Really Brings to Multimodal Intelligence?", "authors": [ "Qianshan Wei", "Yishan Yang", "Siyi Wang", "Jinglin Chen", "Binyu Wang", "Jiaming Wang", "Shuang Chen", "Zechen Li", "Yang Shi", "Yuqi Tang", "Weining Wang", "Yi Yu", "Chaoyou Fu", "Qi Li", "Yi-Fan Zhang" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) are evolving from passive observers into active agents, solving problems through Visual Expansion (invoking visual tools) and Knowledge Expansion (open-web search). However, existing evaluations fall short: they lack flexible tool integration, test visual and search tools separately, and evaluate primarily by final answers. Consequently, they cannot verify if tools were actually invoked, applied correctly, or used efficiently. To address this, we introduce Agentic-MME, a process-verified benchmark for Multimodal Agentic Capabilities. It contains 418 real-world tasks across 6 domains and 3 difficulty levels to evaluate capability synergy, featuring over 2,000 stepwise checkpoints that average 10+ person-hours of manual annotation per task. Each task includes a unified evaluation framework supporting sandboxed code and APIs, alongside a human reference trajectory annotated with stepwise checkpoints along dual-axis: S-axis and V-axis. To enable true process-level verification, we audit fine-grained intermediate states rather than just final answers, and quantify efficiency via an overthinking metric relative to human trajectories. Experimental results show the best model, Gemini3-pro, achieves 56.3% overall accuracy, which falls significantly to 23.0% on Level-3 tasks, underscoring the difficulty of real-world multimodal agentic problem solving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.02947", "title": "AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents", "authors": [ "Yunhao Feng", "Yifan Ding", "Yingshui Tan", "Xingjun Ma", "Yige Li", "Yutao Wu", "Yifeng Gao", "Kun Zhai", "Yanming Guo" ], "github": "", "project_page": "", "abstract": "Computer-use agents extend language models from text generation to persistent action over tools, files, and execution environments. Unlike chat systems, they maintain state across interactions and translate intermediate outputs into concrete actions. This creates a distinct safety challenge in that harmful behavior may emerge through sequences of individually plausible steps, including intermediate actions that appear locally acceptable but collectively lead to unauthorized actions. We present AgentHazard, a benchmark for evaluating harmful behavior in computer-use agents. AgentHazard contains 2,653 instances spanning diverse risk categories and attack strategies. Each instance pairs a harmful objective with a sequence of operational steps that are locally legitimate but jointly induce unsafe behavior. The benchmark evaluates whether agents can recognize and interrupt harm arising from accumulated context, repeated tool use, intermediate actions, and dependencies across steps. We evaluate AgentHazard on Claude Code, OpenClaw, and IFlow using mostly open or openly deployable models from the Qwen3, Kimi, GLM, and DeepSeek families. Our experimental results indicate that current systems remain highly vulnerable. In particular, when powered by Qwen3-Coder, Claude Code exhibits an attack success rate of 73.63\\%, suggesting that model alignment alone does not reliably guarantee the safety of autonomous agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.03231", "title": "CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning", "authors": [ "Ankan Deria", "Komal Kumar", "Xilin He", "Imran Razzak", "Hisham Cholakkal", "Fahad Shahbaz Khan", "Salman Khan" ], "github": "", "project_page": "", "abstract": "Recent vision-language models (VLMs) typically rely on a single vision encoder trained with contrastive image-text objectives, such as CLIP-style pretraining. While contrastive encoders are effective for cross-modal alignment and retrieval, self-supervised visual encoders often capture richer dense semantics and exhibit stronger robustness on recognition and understanding tasks. In this work, we investigate how to scale the fusion of these complementary visual representations for vision-language modeling. We propose CoME-VL: Complementary Multi-Encoder Vision-Language, a modular fusion framework that integrates a contrastively trained vision encoder with a self-supervised DINO encoder. Our approach performs representation-level fusion by (i) entropy-guided multi-layer aggregation with orthogonality-constrained projections to reduce redundancy, and (ii) RoPE-enhanced cross-attention to align heterogeneous token grids and produce compact fused visual tokens. The fused tokens can be injected into a decoder-only LLM with minimal changes to standard VLM pipelines. Extensive experiments across diverse vision-language benchmarks demonstrate that CoME-VL consistently outperforms single-encoder baselines. In particular, we observe an average improvement of 4.9% on visual understanding tasks and 5.4% on grounding tasks. Our method achieves state-of-the-art performance on RefCOCO for detection while improving over the baseline by a large margin. Finally, we conduct ablation studies on layer merging, non-redundant feature mixing, and fusion capacity to evaluate how complementary contrastive and self-supervised signals affect VLM performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.03144", "title": "InCoder-32B-Thinking: Industrial Code World Model for Thinking", "authors": [ "Jian Yang", "Wei Zhang", "Jiajun Wu", "Junhang Cheng", "Tuney Zheng", "Fanglin Xu", "Weicheng Gu", "Lin Jing", "Yaxin Du", "Joseph Li", "Yizhi Li", "Yan Xing", "Chuan Hao", "Ran Tao", "Ruihao Gong", "Aishan Liu", "Zhoujun Li", "Mingjie Tang", "Chenghua Lin", "Siheng Chen", "Wayne Xin Zhao", "Xianglong Liu", "Ming Zhou", "Bryan Dai", "Weifeng Lv" ], "github": "https://github.com/ByteDance-Seed/seed-oss", "project_page": "", "abstract": "Industrial software development across chip design, GPU optimization, and embedded systems lacks expert reasoning traces showing how engineers reason about hardware constraints and timing semantics. In this work, we propose InCoder-32B-Thinking, trained on the data from the Error-driven Chain-of-Thought (ECoT) synthesis framework with an industrial code world model (ICWM) to generate reasoning traces. Specifically, ECoT generates reasoning chains by synthesizing the thinking content from multi-turn dialogue with environmental error feedback, explicitly modeling the error-correction process. ICWM is trained on domain-specific execution traces from Verilog simulation, GPU profiling, etc., learns the causal dynamics of how code affects hardware behavior, and enables self-verification by predicting execution outcomes before actual compilation. All synthesized reasoning traces are validated through domain toolchains, creating training data matching the natural reasoning depth distribution of industrial tasks. Evaluation on 14 general (81.3% on LiveCodeBench v5) and 9 industrial benchmarks (84.0% in CAD-Coder and 38.0% on KernelBench) shows InCoder-32B-Thinking achieves top-tier open-source results across all domains.GPU Optimization", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ByteDance-Seed/seed-oss", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.02368", "title": "Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation", "authors": [ "Xue Liu", "Xin Ma", "Yuxin Ma", "Yongchang Peng", "Duo Wang", "Zhoufutu Wen", "Ge Zhang", "Kaiyuan Zhang", "Xinyu Chen", "Tianci He", "Jiani Hou", "Liang Hu", "Ziyun Huang", "Yongzhe Hui", "Jianpeng Jiao", "Chennan Ju", "Yingru Kong", "Yiran Li", "Mengyun Liu", "Luyao Ma", "Fei Ni", "Yiqing Ni", "Yueyan Qiu", "Yanle Ren", "Zilin Shi", "Zaiyuan Wang", "Wenjie Yue", "Shiyu Zhang", "Xinyi Zhang", "Kaiwen Zhao", "Zhenwei Zhu" ], "github": "", "project_page": "", "abstract": "As Large Language Models (LLMs) exhibit plateauing performance on conventional benchmarks, a pivotal challenge persists: evaluating their proficiency in complex, open-ended tasks characterizing genuine expert-level cognition. Existing frameworks suffer from narrow domain coverage, reliance on generalist tasks, or self-evaluation biases. To bridge this gap, we present XpertBench, a high-fidelity benchmark engineered to assess LLMs across authentic professional domains. XpertBench consists of 1,346 meticulously curated tasks across 80 categories, spanning finance, healthcare, legal services, education, and dual-track research (STEM and Humanities). These tasks are derived from over 1,000 submissions by domain experts--including researchers from elite institutions and practitioners with extensive clinical or industrial experience--ensuring superior ecological validity. Each task uses detailed rubrics with mostly 15-40 weighted checkpoints to assess professional rigor. To facilitate scalable yet human-aligned assessment, we introduce ShotJudge, a novel evaluation paradigm that employs LLM judges calibrated with expert few-shot exemplars to mitigate self-rewarding biases. Our empirical evaluation of state-of-the-art LLMs reveals a pronounced performance ceiling: even leading models achieve a peak success rate of only ~66%, with a mean score around 55%. Models also exhibit domain-specific divergence, showing non-overlapping strengths in quantitative reasoning versus linguistic synthesis.. These findings underscore a significant \"expert-gap\" in current AI systems and establish XpertBench as a critical instrument for navigating the transition from general-purpose assistants to specialized professional collaborators.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.02317", "title": "A Simple Baseline for Streaming Video Understanding", "authors": [ "Yujiao Shen", "Shulin Tian", "Jingkang Yang", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Recent streaming video understanding methods increasingly rely on complex memory mechanisms to handle long video streams. We challenge this trend with a simple finding: a sliding-window baseline that feeds only the most recent N frames to an off-the-shelf VLM already matches or surpasses published streaming models. We formalize this baseline as SimpleStream and evaluate it against 13 major offline and online video LLM baselines on OVO-Bench and StreamingBench. Despite its simplicity, SimpleStream delivers consistently strong performance. With only 4 recent frames, it reaches 67.7% average accuracy on OVO-Bench and 80.59% on StreamingBench. Controlled ablations further show that the value of longer context is backbone-dependent rather than uniformly increasing with model scale, and reveal a consistent perception-memory trade-off: adding more historical context can improve recall, but often weakens real-time perception. This suggests that stronger memory, retrieval, or compression modules should not be taken as evidence of progress unless they clearly outperform SimpleStream under the same protocol. We therefore argue that future streaming benchmarks should separate recent-scene perception from long-range memory, so that performance improvements from added complexity can be evaluated more clearly.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.03128", "title": "Self-Distilled RLVR", "authors": [ "Chenxu Yang", "Chuanyu Qin", "Qingyi Si", "Minghui Chen", "Naibin Gu", "Dingyu Yao", "Zheng Lin", "Weiping Wang", "Jiaqi Wang", "Nan Duan" ], "github": "", "project_page": "", "abstract": "On-policy distillation (OPD) has become a popular training paradigm in the LLM community. This paradigm selects a larger model as the teacher to provide dense, fine-grained signals for each sampled trajectory, in contrast to reinforcement learning with verifiable rewards (RLVR), which only obtains sparse signals from verifiable outcomes in the environment. Recently, the community has explored on-policy self-distillation (OPSD), where the same model serves as both teacher and student, with the teacher receiving additional privileged information such as reference answers to enable self-evolution. This paper demonstrates that learning signals solely derived from the privileged teacher result in severe information leakage and unstable long-term training. Accordingly, we identify the optimal niche for self-distillation and propose RLSD (RLVR with Self-Distillation). Specifically, we leverage self-distillation to obtain token-level policy differences for determining fine-grained update magnitudes, while continuing to use RLVR to derive reliable update directions from environmental feedback (e.g., response correctness). This enables RLSD to simultaneously harness the strengths of both RLVR and OPSD, achieving a higher convergence ceiling and superior training stability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.02870", "title": "Token Warping Helps MLLMs Look from Nearby Viewpoints", "authors": [ "Phillip Y. Lee", "Chanho Park", "Mingue Park", "Seungwoo Yoo", "Juil Koo", "Minhyuk Sung" ], "github": "", "project_page": "", "abstract": "Can warping tokens, rather than pixels, help multimodal large language models (MLLMs) understand how a scene appears from a nearby viewpoint? While MLLMs perform well on visual reasoning, they remain fragile to viewpoint changes, as pixel-wise warping is highly sensitive to small depth errors and often introduces geometric distortions. Drawing on theories of mental imagery that posit part-level structural representations as the basis for human perspective transformation, we examine whether image tokens in ViT-based MLLMs serve as an effective substrate for viewpoint changes. We compare forward and backward warping, finding that backward token warping, which defines a dense grid on the target view and retrieves a corresponding source-view token for each grid point, achieves greater stability and better preserves semantic coherence under viewpoint shifts. Experiments on our proposed ViewBench benchmark demonstrate that token-level warping enables MLLMs to reason reliably from nearby viewpoints, consistently outperforming all baselines including pixel-wise warping approaches, spatially fine-tuned MLLMs, and a generative warping method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.01487", "title": "AgentSocialBench: Evaluating Privacy Risks in Human-Centered Agentic Social Networks", "authors": [ "Prince Zizhuang Wang", "Shuli Jiang" ], "github": "https://github.com/anthropics/openclaw", "project_page": "", "abstract": "With the rise of personalized, persistent LLM agent frameworks such as OpenClaw, human-centered agentic social networks in which teams of collaborative AI agents serve individual users in a social network across multiple domains are becoming a reality. This setting creates novel privacy challenges: agents must coordinate across domain boundaries, mediate between humans, and interact with other users' agents, all while protecting sensitive personal information. While prior work has evaluated multi-agent coordination and privacy preservation, the dynamics and privacy risks of human-centered agentic social networks remain unexplored. To this end, we introduce AgentSocialBench, the first benchmark to systematically evaluate privacy risk in this setting, comprising scenarios across seven categories spanning dyadic and multi-party interactions, grounded in realistic user profiles with hierarchical sensitivity labels and directed social graphs. Our experiments reveal that privacy in agentic social networks is fundamentally harder than in single-agent settings: (1) cross-domain and cross-user coordination creates persistent leakage pressure even when agents are explicitly instructed to protect information, (2) privacy instructions that teach agents how to abstract sensitive information paradoxically cause them to discuss it more (we call it abstraction paradox). These findings underscore that current LLM agents lack robust mechanisms for privacy preservation in human-centered agentic social networks, and that new approaches beyond prompt engineering are needed to make agent-mediated social coordination safe for real-world deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/anthropics/openclaw", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2603.22078", "title": "Do World Action Models Generalize Better than VLAs? A Robustness Study", "authors": [ "Zhanguang Zhang", "Zhiyuan Li", "Behnam Rahmati", "Rui Heng Yang", "Yintao Ma", "Amir Rasouli", "Sajjad Pakdamansavoji", "Yangzheng Wu", "Lingfeng Zhang", "Tongtong Cao", "Feng Wen", "Xinyu Wang", "Xingyue Quan", "Yingxue Zhang" ], "github": "", "project_page": "", "abstract": "Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predicting how it will evolve in response to actions. Vision-language-action (VLA), which repurpose large-scale vision-language models for robot action generation using action experts, have achieved notable success across a variety of robotic tasks. Nevertheless, their performance remains constrained by the scope of their training data, exhibiting limited generalization to unseen scenarios and vulnerability to diverse contextual perturbations. More recently, world models have been revisited as an alternative to VLAs. These models, referred to as world action models (WAMs), are built upon world models that are trained on large corpora of video data to predict future states. With minor adaptations, their latent representation can be decoded into robot actions. It has been suggested that their explicit dynamic prediction capacity, combined with spatiotemporal priors acquired from web-scale video pretraining, enables WAMs to generalize more effectively than VLAs. In this paper, we conduct a comparative study of prominent state-of-the-art VLA policies and recently released WAMs. We evaluate their performance on the LIBERO-Plus and RoboTwin 2.0-Plus benchmarks under various visual and language perturbations. Our results show that WAMs achieve strong robustness, with LingBot-VA reaching 74.2% success rate on RoboTwin 2.0-Plus and Cosmos-Policy achieving 82.2% on LIBERO-Plus. While VLAs such as \u03c0_{0.5} can achieve comparable robustness on certain tasks, they typically require extensive training with diverse robotic datasets and varied learning objectives. Hybrid approaches that partially incorporate video-based dynamic learning exhibit intermediate robustness, highlighting the importance of how video priors are integrated.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2603.27183", "title": "Communicating about Space: Language-Mediated Spatial Integration Across Partial Views", "authors": [ "Ankur Sikarwar", "Debangan Mishra", "Sudarshan Nikhil", "Ponnurangam Kumaraguru", "Aishwarya Agrawal" ], "github": "https://github.com/ankursikarwar/Cosmic", "project_page": "", "abstract": "Humans build shared spatial understanding by communicating partial, viewpoint-dependent observations. We ask whether Multimodal Large Language Models (MLLMs) can do the same, aligning distinct egocentric views through dialogue to form a coherent, allocentric mental model of a shared environment. To study this systematically, we introduce COSMIC, a benchmark for Collaborative Spatial Communication. In this setting, two static MLLM agents observe a 3D indoor environment from different viewpoints and exchange natural-language messages to solve spatial queries. COSMIC contains 899 diverse scenes and 1250 question-answer pairs spanning five tasks. We find a consistent capability hierarchy, MLLMs are most reliable at identifying shared anchor objects across views, perform worse on relational reasoning, and largely fail at building globally consistent maps, performing near chance, even for the frontier models. Moreover, we find thinking capability yields consistent gains in anchor grounding, but is insufficient for higher-level spatial communication. To contextualize model behavior, we additionally collect 250 human-human dialogues. Humans achieve 95% aggregate accuracy, leaving significant room for improvement for even the best performing model Gemini-3-Pro-Thinking which achieves 72% aggregate accuracy. Moreover, human conversations become increasingly specific as partners converge on a shared mental model, whereas model dialogues continue to explore new possibilities rather than converging, consistent with a limited ability to build and maintain a robust shared mental model. Our code and data is available at https://github.com/ankursikarwar/Cosmic", "github_manual": "", "github_regex": "https://github.com/ankursikarwar/Cosmic", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.01609", "title": "Swift-SVD: Theoretical Optimality Meets Practical Efficiency in Low-Rank LLM Compression", "authors": [ "Ruoling Qi", "Yirui Liu", "Xuaner Wu", "Xiangyu Wang", "Ming Li", "Chen Chen", "Jian Chen", "Yin Chen", "Qizhen Weng" ], "github": "https://github.com/sramshetty/ShortGPT", "project_page": "", "abstract": "The deployment of Large Language Models is constrained by the memory and bandwidth demands of static weights and dynamic Key-Value cache. SVD-based compression provides a hardware-friendly solution to reduce these costs. However, existing methods suffer from two key limitations: some are suboptimal in reconstruction error, while others are theoretically optimal but practically inefficient. In this paper, we propose Swift-SVD, an activation-aware, closed-form compression framework that simultaneously guarantees theoretical optimum, practical efficiency and numerical stability. Swift-SVD incrementally aggregates covariance of output activations given a batch of inputs and performs a single eigenvalue decomposition after aggregation, enabling training-free, fast, and optimal layer-wise low-rank approximation. We employ effective rank to analyze local layer-wise compressibility and design a dynamic rank allocation strategy that jointly accounts for local reconstruction loss and end-to-end layer importance. Extensive experiments across six LLMs and eight datasets demonstrate that Swift-SVD outperforms state-of-the-art baselines, achieving optimal compression accuracy while delivering 3-70X speedups in end-to-end compression time. Our code will be released upon acceptance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sramshetty/ShortGPT", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.03118", "title": "Salt: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation", "authors": [ "Xingtong Ge", "Yi Zhang", "Yushi Huang", "Dailan He", "Xiahong Wang", "Bingqi Ma", "Guanglu Song", "Yu Liu", "Jun Zhang" ], "github": "https://github.com/XingtongGe/Salt", "project_page": "", "abstract": "Distilling video generation models to extremely low inference budgets (e.g., 2--4 NFEs) is crucial for real-time deployment, yet remains challenging. Trajectory-style consistency distillation often becomes conservative under complex video dynamics, yielding an over-smoothed appearance and weak motion. Distribution matching distillation (DMD) can recover sharp, mode-seeking samples, but its local training signals do not explicitly regularize how denoising updates compose across timesteps, making composed rollouts prone to drift. To overcome this challenge, we propose Self-Consistent Distribution Matching Distillation (SC-DMD), which explicitly regularizes the endpoint-consistent composition of consecutive denoising updates. For real-time autoregressive video generation, we further treat the KV cache as a quality parameterized condition and propose Cache-Distribution-Aware training. This training scheme applies SC-DMD over multi-step rollouts and introduces a cache-conditioned feature alignment objective that steers low-quality outputs toward high-quality references. Across extensive experiments on both non-autoregressive backbones (e.g., Wan~2.1) and autoregressive real-time paradigms (e.g., Self Forcing), our method, dubbed Salt, consistently improves low-NFE video generation quality while remaining compatible with diverse KV-cache memory mechanisms. Source code will be released at https://github.com/XingtongGe/Salt{https://github.com/XingtongGe/Salt}.", "github_manual": "", "github_regex": "https://github.com/XingtongGe/Salt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.01411", "title": "Test-Time Scaling Makes Overtraining Compute-Optimal", "authors": [ "Nicholas Roberts", "Sungjun Cho", "Zhiqi Gao", "Tzu-Heng Huang", "Albert Wu", "Gabriel Orlanski", "Avi Trost", "Kelly Buchanan", "Aws Albarghouthi", "Frederic Sala" ], "github": "", "project_page": "", "abstract": "Modern LLMs scale at test-time, e.g. via repeated sampling, where inference cost grows with model size and the number of samples. This creates a trade-off that pretraining scaling laws, such as Chinchilla, do not address. We present Train-to-Test (T^2) scaling laws that jointly optimize model size, training tokens, and number of inference samples under fixed end-to-end budgets. T^2 modernizes pretraining scaling laws with pass@k modeling used for test-time scaling, then jointly optimizes pretraining and test-time decisions. Forecasts from T^2 are robust over distinct modeling approaches: measuring joint scaling effect on the task loss and modeling impact on task accuracy. Across eight downstream tasks, we find that when accounting for inference cost, optimal pretraining decisions shift radically into the overtraining regime, well-outside of the range of standard pretraining scaling suites. We validate our results by pretraining heavily overtrained models in the optimal region that T^2 scaling forecasts, confirming their substantially stronger performance compared to pretraining scaling alone. Finally, as frontier LLMs are post-trained, we show that our findings survive the post-training stage, making T^2 scaling meaningful in modern deployments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.02486", "title": "VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors", "authors": [ "Haz Sameen Shahgir", "Xiaofu Chen", "Yu Fu", "Erfan Shayegani", "Nael Abu-Ghazaleh", "Yova Kementchedjhieva", "Yue Dong" ], "github": "", "project_page": "", "abstract": "Vision Language Models (VLMs) achieve impressive performance across a wide range of multimodal tasks. However, on some tasks that demand fine-grained visual perception, they often fail even when the required information is present in their internal representations. In this work, we demonstrate that this gap arises from their narrow training pipeline which focuses on moving visual information to the textual space. Consequently, VLMs can only reason about visual entities that can be mapped to known concepts in the language space, leaving vision-focused tasks such as visual correspondence and reasoning about novel visual entities poorly supported. As a result, VLMs are severely limited in several important multimodal capabilities because they rely on brittle, hallucinated textual descriptions of visual entities that they cannot map to textual representations. We verify this behavior through visual correspondence tasks, in which VLMs must detect matching entities between two images. Testing across semantic, shape, and face correspondence tasks, we find that VLMs perform much better when the relevant entities are nameable in language than when they are unnameable. Mechanistically, our Logit Lens analyses confirm that VLMs explicitly assign semantic labels to nameable entities and surface more unique corresponding tokens compared to unnameable entities. Furthermore, we show that teaching completely arbitrary names for unknown entities improves performance, yet task-specific finetuning yields even stronger generalization without relying on language priors. Our findings suggest that current VLM failures on visual tasks reflect learned shortcuts from their training, rather than a fundamental limitation of multimodal architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.02721", "title": "GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning", "authors": [ "DeepReinforce Team", "Xiaoya Li", "Xiaofei Sun", "Guoyin Wang", "Songqiao Su", "Chris Shum", "Jiwei Li" ], "github": "https://github.com/thinking-machines-lab/tinker", "project_page": "", "abstract": "Competitive programming remains one of the last few human strongholds in coding against AI. The best AI system to date still underperforms the best humans competitive programming: the most recent best result, Google's Gemini~3 Deep Think, attained 8th place even not being evaluated under live competition conditions. In this work, we introduce GrandCode, a multi-agent RL system designed for competitive programming. The capability of GrandCode is attributed to two key factors: (1) It orchestrates a variety of agentic modules (hypothesis proposal, solver, test generator, summarization, etc) and jointly improves them through post-training and online test-time RL; (2) We introduce Agentic GRPO specifically designed for multi-stage agent rollouts with delayed rewards and the severe off-policy drift that is prevalent in agentic RL. GrandCode is the first AI system that consistently beats all human participants in live contests of competitive programming: in the most recent three Codeforces live competitions, i.e., Round~1087 (Mar 21, 2026), Round~1088 (Mar 28, 2026), and Round~1089 (Mar 29, 2026), GrandCode placed first in all of them, beating all human participants, including legendary grandmasters. GrandCode shows that AI systems have reached a point where they surpass the strongest human programmers on the most competitive coding tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/thinking-machines-lab/tinker", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-06", "arxiv_id": "2604.01765", "title": "DriveDreamer-Policy: A Geometry-Grounded World-Action Model for Unified Generation and Planning", "authors": [ "Yang Zhou", "Xiaofeng Wang", "Hao Shao", "Letian Wang", "Guosheng Zhao", "Jiangnan Shao", "Jiagang Zhu", "Tingdong Yu", "Zheng Zhu", "Guan Huang", "Steven L. Waslander" ], "github": "", "project_page": "", "abstract": "Recently, world-action models (WAM) have emerged to bridge vision-language-action (VLA) models and world models, unifying their reasoning and instruction-following capabilities and spatio-temporal world modeling. However, existing WAM approaches often focus on modeling 2D appearance or latent representations, with limited geometric grounding-an essential element for embodied systems operating in the physical world. We present DriveDreamer-Policy, a unified driving world-action model that integrates depth generation, future video generation, and motion planning within a single modular architecture. The model employs a large language model to process language instructions, multi-view images, and actions, followed by three lightweight generators that produce depth, future video, and actions. By learning a geometry-aware world representation and using it to guide both future prediction and planning within a unified framework, the proposed model produces more coherent imagined futures and more informed driving actions, while maintaining modularity and controllable latency. Experiments on the Navsim v1 and v2 benchmarks demonstrate that DriveDreamer-Policy achieves strong performance on both closed-loop planning and world generation tasks. In particular, our model reaches 89.2 PDMS on Navsim v1 and 88.7 EPDMS on Navsim v2, outperforming existing world-model-based approaches while producing higher-quality future video and depth predictions. Ablation studies further show that explicit depth learning provides complementary benefits to video imagination and improves planning robustness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04911", "title": "SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing", "authors": [ "Yicheng Xiao", "Wenhu Zhang", "Lin Song", "Yukang Chen", "Wenbo Li", "Nan Jiang", "Tianhe Ren", "Haokun Lin", "Wei Huang", "Haoyang Huang", "Xiu Li", "Nan Duan", "Xiaojuan Qi" ], "github": "https://github.com/EasonXiao-888/SpatialEdit", "project_page": "", "abstract": "Image spatial editing performs geometry-driven transformations, allowing precise control over object layout and camera viewpoints. Current models are insufficient for fine-grained spatial manipulations, motivating a dedicated assessment suite. Our contributions are listed: (i) We introduce SpatialEdit-Bench, a complete benchmark that evaluates spatial editing by jointly measuring perceptual plausibility and geometric fidelity via viewpoint reconstruction and framing analysis. (ii) To address the data bottleneck for scalable training, we construct SpatialEdit-500k, a synthetic dataset generated with a controllable Blender pipeline that renders objects across diverse backgrounds and systematic camera trajectories, providing precise ground-truth transformations for both object- and camera-centric operations. (iii) Building on this data, we develop SpatialEdit-16B, a baseline model for fine-grained spatial editing. Our method achieves competitive performance on general editing while substantially outperforming prior methods on spatial manipulation tasks. All resources will be made public at https://github.com/EasonXiao-888/SpatialEdit.", "github_manual": "", "github_regex": "https://github.com/EasonXiao-888/SpatialEdit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04184", "title": "AURA: Always-On Understanding and Real-Time Assistance via Video Streams", "authors": [ "Xudong Lu", "Yang Bo", "Jinpeng Chen", "Shuhan Li", "Xintong Guo", "Huankang Guan", "Fang Liu", "Dunyuan Xu", "Peiwen Sun", "Heyang Sun", "Rui Liu", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "Video Large Language Models (VideoLLMs) have achieved strong performance on many video understanding tasks, but most existing systems remain offline and are not well-suited for live video streams that require continuous observation and timely response. Recent streaming VideoLLMs have made progress, yet current approaches often rely on decoupled trigger-response pipelines or are limited to captioning-style narration, reducing their effectiveness for open-ended question answering and long-horizon interaction. We propose AURA (Always-On Understanding and Real-Time Assistance), an end-to-end streaming visual interaction framework that enables a unified VideoLLM to continuously process video streams and support both real-time question answering and proactive responses. AURA integrates context management, data construction, training objectives, and deployment optimization for stable long-horizon streaming interaction. It achieves state-of-the-art performance on streaming benchmarks and supports a real-time demo system with ASR and TTS running at 2 FPS on two 80G accelerators. We release the AURA model together with a real-time inference framework to facilitate future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04202", "title": "ClawArena: Benchmarking AI Agents in Evolving Information Environments", "authors": [ "Haonian Ji", "Kaiwen Xiong", "Siwei Han", "Peng Xia", "Shi Qiu", "Yiyang Zhou", "Jiaqi Liu", "Jinlong Li", "Bingzhou Li", "Zeyu Zheng", "Cihang Xie", "Huaxiu Yao" ], "github": "https://github.com/aiming-lab/ClawArena", "project_page": "", "abstract": "AI agents deployed as persistent assistants must maintain correct beliefs as their information environment evolves. In practice, evidence is scattered across heterogeneous sources that often contradict one another, new information can invalidate earlier conclusions, and user preferences surface through corrections rather than explicit instructions. Existing benchmarks largely assume static, single-authority settings and do not evaluate whether agents can keep up with this complexity. We introduce ClawArena, a benchmark for evaluating AI agents in evolving information environments. Each scenario maintains a complete hidden ground truth while exposing the agent only to noisy, partial, and sometimes contradictory traces across multi-channel sessions, workspace files, and staged updates. Evaluation is organized around three coupled challenges: multi-source conflict reasoning, dynamic belief revision, and implicit personalization, whose interactions yield a 14-category question taxonomy. Two question formats, multi-choice (set-selection) and shell-based executable checks, test both reasoning and workspace grounding. The current release contains 64 scenarios across 8 professional domains, totaling 1{,}879 evaluation rounds and 365 dynamic updates. Experiments on five agent frameworks and five language models show that both model capability (15.4% range) and framework design (9.2%) substantially affect performance, that self-evolving skill frameworks can partially close model-capability gaps, and that belief revision difficulty is determined by update design strategy rather than the mere presence of updates. Code is available at https://github.com/aiming-lab/ClawArena.", "github_manual": "", "github_regex": "https://github.com/aiming-lab/ClawArena", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04838", "title": "Less Detail, Better Answers: Degradation-Driven Prompting for VQA", "authors": [ "Haoxuan Han", "Weijie Wang", "Zeyu Zhang", "Yefei He", "Bohan Zhuang" ], "github": "https://github.com/open-compass/VLMEvalKit", "project_page": "", "abstract": "Recent advancements in Vision-Language Models (VLMs) have significantly pushed the boundaries of Visual Question Answering (VQA).However,high-resolution details can sometimes become noise that leads to hallucinations or reasoning errors. In this paper,we propose Degradation-Driven Prompting (DDP), a novel framework that improves VQA performance by strategically reducing image fidelity to force models to focus on essential structural information. We evaluate DDP across two distinct tasks. Physical attributes targets images prone to human misjudgment, where DDP employs a combination of 80p downsampling, structural visual aids (white background masks and orthometric lines), and In-Context Learning (ICL) to calibrate the model's focus. Perceptual phenomena addresses various machine-susceptible visual anomalies and illusions, including Visual Anomaly (VA), Color (CI), Motion(MI),Gestalt (GI), Geometric (GSI), and Visual Illusions (VI).For this task, DDP integrates a task-classification stage with specialized tools such as blur masks and contrast enhancement alongside downsampling. Our experimental results demonstrate that less is more: by intentionally degrading visual inputs and providing targeted structural prompts, DDP enables VLMs to bypass distracting textures and achieve superior reasoning accuracy on challenging visual benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/open-compass/VLMEvalKit", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04917", "title": "Vero: An Open RL Recipe for General Visual Reasoning", "authors": [ "Gabriel Sarch", "Linrong Cai", "Qunzhong Wang", "Haoyang Wu", "Danqi Chen", "Zhuang Liu" ], "github": "", "project_page": "", "abstract": "What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks? The strongest vision-language models (VLMs) show such broad visual reasoning is within reach, but the recipe behind them remains unclear, locked behind proprietary reinforcement learning (RL) pipelines with non-public data. We introduce Vero, a family of fully open VLMs that matches or exceeds existing open-weight models across diverse visual reasoning tasks. We scale RL data and rewards across six broad task categories, constructing Vero-600K, a 600K-sample dataset from 59 datasets, and designing task-routed rewards that handle heterogeneous answer formats. Vero achieves state-of-the-art performance, improving over four base models by 3.7-5.5 points on average across VeroEval, our suite of 30 challenging benchmarks. Starting from Qwen3-VL-8B-Instruct, Vero outperforms Qwen3-VL-8B-Thinking on 23 of 30 benchmarks without additional proprietary thinking data. When trained from the same base model, Vero-600K exceeds existing RL datasets across task categories. Systematic ablations reveal that different task categories elicit qualitatively distinct reasoning patterns that transfer poorly in isolation, suggesting that broad data coverage is the primary driver of strong RL scaling. All data, code, and models are released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04503", "title": "Memory Intelligence Agent", "authors": [ "Jingyang Qiao", "Weicheng Meng", "Yu Cheng", "Zhihang Lin", "Zhizhong Zhang", "Xin Tan", "Jingyu Gong", "Kun Shao", "Yuan Xie" ], "github": "", "project_page": "", "abstract": "Deep research agents (DRAs) integrate LLM reasoning with external tools. Memory systems enable DRAs to leverage historical experiences, which are essential for efficient reasoning and autonomous evolution. Existing methods rely on retrieving similar trajectories from memory to aid reasoning, while suffering from key limitations of ineffective memory evolution and increasing storage and retrieval costs. To address these problems, we propose a novel Memory Intelligence Agent (MIA) framework, consisting of a Manager-Planner-Executor architecture. Memory Manager is a non-parametric memory system that can store compressed historical search trajectories. Planner is a parametric memory agent that can produce search plans for questions. Executor is another agent that can search and analyze information guided by the search plan. To build the MIA framework, we first adopt an alternating reinforcement learning paradigm to enhance cooperation between the Planner and the Executor. Furthermore, we enable the Planner to continuously evolve during test-time learning, with updates performed on-the-fly alongside inference without interrupting the reasoning process. Additionally, we establish a bidirectional conversion loop between parametric and non-parametric memories to achieve efficient memory evolution. Finally, we incorporate a reflection and an unsupervised judgment mechanisms to boost reasoning and self-evolution in the open world. Extensive experiments across eleven benchmarks demonstrate the superiority of MIA.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.03679", "title": "LightThinker++: From Reasoning Compression to Memory Management", "authors": [ "Yuqi Zhu", "Jintian Zhang", "Zhenjie Wan", "Yujie Luo", "Shuofei Qiao", "Zhengke Gui", "Da Zheng", "Lei Liang", "Huajun Chen", "Ningyu Zhang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) excel at complex reasoning, yet their efficiency is limited by the surging cognitive overhead of long thought traces. In this paper, we propose LightThinker, a method that enables LLMs to dynamically compress intermediate thoughts into compact semantic representations. However, static compression often struggles with complex reasoning where the irreversible loss of intermediate details can lead to logical bottlenecks. To address this, we evolve the framework into LightThinker++, introducing Explicit Adaptive Memory Management. This paradigm shifts to behavioral-level management by incorporating explicit memory primitives, supported by a specialized trajectory synthesis pipeline to train purposeful memory scheduling. Extensive experiments demonstrate the framework's versatility across three dimensions. (1) LightThinker reduces peak token usage by 70% and inference time by 26% with minimal accuracy loss. (2) In standard reasoning, LightThinker++ slashes peak token usage by 69.9% while yielding a +2.42% accuracy gain under the same context budget for maximum performance. (3) Most notably, in long-horizon agentic tasks, it maintains a stable footprint beyond 80 rounds (a 60%-70% reduction), achieving an average performance gain of 14.8% across different complex scenarios. Overall, our work provides a scalable direction for sustaining deep LLM reasoning over extended horizons with minimal overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04804", "title": "SkillX: Automatically Constructing Skill Knowledge Bases for Agents", "authors": [ "Chenxi Wang", "Zhuoyun Yu", "Xin Xie", "Wuguannan Yao", "Runnan Fang", "Shuofei Qiao", "Kexin Cao", "Guozhou Zheng", "Xiang Qi", "Peng Zhang", "Shumin Deng" ], "github": "https://github.com/zjunlp/SkillX", "project_page": "", "abstract": "Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover similar behaviors from limited experience, resulting in redundant exploration and poor generalization. To address this problem, we propose SkillX, a fully automated framework for constructing a plug-and-play skill knowledge base that can be reused across agents and environments. SkillX operates through a fully automated pipeline built on three synergistic innovations: (i) Multi-Level Skills Design, which distills raw trajectories into three-tiered hierarchy of strategic plans, functional skills, and atomic skills; (ii) Iterative Skills Refinement, which automatically revises skills based on execution feedback to continuously improve library quality; and (iii) Exploratory Skills Expansion, which proactively generates and validates novel skills to expand coverage beyond seed training data. Using a strong backbone agent (GLM-4.6), we automatically build a reusable skill library and evaluate its transferability on challenging long-horizon, user-interactive benchmarks, including AppWorld, BFCL-v3, and \u03c4^2-Bench. Experiments show that SkillKB consistently improves task success and execution efficiency when plugged into weaker base agents, highlighting the importance of structured, hierarchical experience representations for generalizable agent learning. Our code will be publicly available soon at https://github.com/zjunlp/SkillX.", "github_manual": "", "github_regex": "https://github.com/zjunlp/SkillX", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04921", "title": "TriAttention: Efficient Long Reasoning with Trigonometric KV Compression", "authors": [ "Weian Mao", "Xi Lin", "Wei Huang", "Yuxin Xie", "Tianfu Fu", "Bohan Zhuang", "Song Han", "Yukang Chen" ], "github": "https://github.com/WeianMao/triattention", "project_page": "", "abstract": "Extended reasoning in large language models (LLMs) creates severe KV cache memory bottlenecks. Leading KV cache compression methods estimate KV importance using attention scores from recent post-RoPE queries. However, queries rotate with position during RoPE, making representative queries very few, leading to poor top-key selection and unstable reasoning. To avoid this issue, we turn to the pre-RoPE space, where we observe that Q and K vectors are highly concentrated around fixed non-zero centers and remain stable across positions -- Q/K concentration. We show that this concentration causes queries to preferentially attend to keys at specific distances (e.g., nearest keys), with the centers determining which distances are preferred via a trigonometric series. Based on this, we propose TriAttention to estimate key importance by leveraging these centers. Via the trigonometric series, we use the distance preference characterized by these centers to score keys according to their positions, and also leverage Q/K norms as an additional signal for importance estimation. On AIME25 with 32K-token generation, TriAttention matches Full Attention reasoning accuracy while achieving 2.5x higher throughput or 10.7x KV memory reduction, whereas leading baselines achieve only about half the accuracy at the same efficiency. TriAttention enables OpenClaw deployment on a single consumer GPU, where long context would otherwise cause out-of-memory with Full Attention.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WeianMao/triattention", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04771", "title": "MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale", "authors": [ "Bin Wang", "Tianyao He", "Linke Ouyang", "Fan Wu", "Zhiyuan Zhao", "Tao Chu", "Yuan Qu", "Zhenjiang Jin", "Weijun Zeng", "Ziyang Miao", "Bangrui Xu", "Junbo Niu", "Mengzhang Cai", "Jiantao Qiu", "Qintong Zhang", "Dongsheng Ma", "Yuefeng Sun", "Hejun Dong", "Wenzheng Zhang", "Jutao Xiao", "Jiayong Shi", "Pengyu Liao", "Xiaomeng Zhao", "Huaping Zhong", "Liqun Wei", "Jing Yu", "Jie Yang", "Wei Li", "Shasha Wang", "Qianqian Wu", "Xuanhe Zhou", "Weijia Li", "Zhenxiang Li", "Zhongying Tu", "Jiang Wu", "Lijun Wu", "Chao Xu", "Kai Chen", "Wentao Zhang", "Yu Qiao", "Bowen Zhou", "Dahua Lin", "Conghui He" ], "github": "", "project_page": "", "abstract": "Current document parsing methods compete primarily on model architecture innovation, while systematic engineering of training data remains underexplored. Yet SOTA models of different architectures and parameter scales exhibit highly consistent failure patterns on the same set of hard samples, suggesting that the performance bottleneck stems from shared deficiencies in training data rather than architecture itself. Building on this finding, we present \\minerupro, which advances the state of the art solely through data engineering and training strategy optimization while keeping the 1.2B-parameter architecture of \\mineru completely fixed. At its core is a Data Engine co-designed around coverage, informativeness, and annotation accuracy: Diversity-and-Difficulty-Aware Sampling expands training data from under 10M to 65.5M samples while correcting distribution shift; Cross-Model Consistency Verification leverages output agreement among heterogeneous models to assess sample difficulty and generate reliable annotations; the Judge-and-Refine pipeline improves annotation quality for hard samples through render-then-verify iterative correction. A three-stage progressive training strategy -- large-scale pre-training, hard sample fine-tuning, and GRPO alignment -- sequentially exploits these data at different quality tiers. On the evaluation front, we fix element-matching biases in OmniDocBench~v1.5 and introduce a Hard subset, establishing the more discriminative OmniDocBench~v1.6 protocol. Without any architectural modification, \\minerupro achieves 95.69 on OmniDocBench~v1.6, improving over the same-architecture baseline by 2.71 points and surpassing all existing methods including models with over 200times more parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04901", "title": "FileGram: Grounding Agent Personalization in File-System Behavioral Traces", "authors": [ "Shuai Liu", "Shulin Tian", "Kairui Hu", "Yuhao Dong", "Zhe Yang", "Bo Li", "Jingkang Yang", "Chen Change Loy", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Coworking AI agents operating within local file systems are rapidly emerging as a paradigm in human-AI interaction; however, effective personalization remains limited by severe data constraints, as strict privacy barriers and the difficulty of jointly collecting multimodal real-world traces prevent scalable training and evaluation, and existing methods remain interaction-centric while overlooking dense behavioral traces in file-system operations; to address this gap, we propose FileGram, a comprehensive framework that grounds agent memory and personalization in file-system behavioral traces, comprising three core components: (1) FileGramEngine, a scalable persona-driven data engine that simulates realistic workflows and generates fine-grained multimodal action sequences at scale; (2) FileGramBench, a diagnostic benchmark grounded in file-system behavioral traces for evaluating memory systems on profile reconstruction, trace disentanglement, persona drift detection, and multimodal grounding; and (3) FileGramOS, a bottom-up memory architecture that builds user profiles directly from atomic actions and content deltas rather than dialogue summaries, encoding these traces into procedural, semantic, and episodic channels with query-time abstraction; extensive experiments show that FileGramBench remains challenging for state-of-the-art memory systems and that FileGramEngine and FileGramOS are effective, and by open-sourcing the framework, we hope to support future research on personalized memory-centric file-system agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04707", "title": "OpenWorldLib: A Unified Codebase and Definition of Advanced World Models", "authors": [ "DataFlow Team", "Bohan Zeng", "Daili Hua", "Kaixin Zhu", "Yifan Dai", "Bozhou Li", "Yuran Wang", "Chengzhuo Tong", "Yifan Yang", "Mingkun Chang", "Jianbin Zhao", "Zhou Liu", "Hao Liang", "Xiaochen Ma", "Ruichuan An", "Junbo Niu", "Zimo Meng", "Tianyi Bai", "Meiyi Qiang", "Huanyao Zhang", "Zhiyou Xiao", "Tianyu Guo", "Qinhan Yu", "Runhao Zhao", "Zhengpin Li", "Xinyi Huang", "Yisheng Pan", "Yiwen Tang", "Yang Shi", "Yue Ding", "Xinlong Chen", "Hongcheng Gao", "Minglei Shi", "Jialong Wu", "Zekun Wang", "Yuanxing Zhang", "Xintao Wang", "Pengfei Wan", "Yiren Song", "Mike Zheng Shou", "Wentao Zhang" ], "github": "https://github.com/OpenDCAI/OpenWorldLib", "project_page": "", "abstract": "World models have garnered significant attention as a promising research direction in artificial intelligence, yet a clear and unified definition remains lacking. In this paper, we introduce OpenWorldLib, a comprehensive and standardized inference framework for Advanced World Models. Drawing on the evolution of world models, we propose a clear definition: a world model is a model or framework centered on perception, equipped with interaction and long-term memory capabilities, for understanding and predicting the complex world. We further systematically categorize the essential capabilities of world models. Based on this definition, OpenWorldLib integrates models across different tasks within a unified framework, enabling efficient reuse and collaborative inference. Finally, we present additional reflections and analyses on potential future directions for world model research. Code link: https://github.com/OpenDCAI/OpenWorldLib", "github_manual": "", "github_regex": "https://github.com/OpenDCAI/OpenWorldLib", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04522", "title": "HDP: A Lightweight Cryptographic Protocol for Human Delegation Provenance in Agentic AI Systems", "authors": [ "Asiri Dalugoda" ], "github": "", "project_page": "", "abstract": "Agentic AI systems increasingly execute consequential actions on behalf of human principals, delegating tasks through multi-step chains of autonomous agents. No existing standard addresses a fundamental accountability gap: verifying that terminal actions in a delegation chain were genuinely authorized by a human principal, through what chain of delegation, and under what scope. This paper presents the Human Delegation Provenance (HDP) protocol, a lightweight token-based scheme that cryptographically captures and verifies human authorization context in multi-agent systems. An HDP token binds a human authorization event to a session, records each agent's delegation action as a signed hop in an append-only chain, and enables any participant to verify the full provenance record using only the issuer's Ed25519 public key and the current session identifier. Verification is fully offline, requiring no registry lookups or third-party trust anchors. We situate HDP within the existing landscape of delegation protocols, identify its distinct design point relative to OAuth 2.0 Token Exchange (RFC 8693), JSON Web Tokens (RFC 7519), UCAN, and the Intent Provenance Protocol (draft-haberkamp-ipp-00), and demonstrate that existing standards fail to address the multi-hop, append-only, human-provenance requirements of agentic systems. HDP has been published as an IETF Internet-Draft (draft-helixar-hdp-agentic-delegation-00) and a reference TypeScript SDK is publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04787", "title": "AvatarPointillist: AutoRegressive 4D Gaussian Avatarization", "authors": [ "Hongyu Liu", "Xuan Wang", "Yating Wang", "Zijian Wu", "Ziyu Wan", "Yue Ma", "Runtao Liu", "Boyao Zhou", "Yujun Shen", "Qifeng Chen" ], "github": "", "project_page": "", "abstract": "We introduce AvatarPointillist, a novel framework for generating dynamic 4D Gaussian avatars from a single portrait image. At the core of our method is a decoder-only Transformer that autoregressively generates a point cloud for 3D Gaussian Splatting. This sequential approach allows for precise, adaptive construction, dynamically adjusting point density and the total number of points based on the subject's complexity. During point generation, the AR model also jointly predicts per-point binding information, enabling realistic animation. After generation, a dedicated Gaussian decoder converts the points into complete, renderable Gaussian attributes. We demonstrate that conditioning the decoder on the latent features from the AR generator enables effective interaction between stages and markedly improves fidelity. Extensive experiments validate that AvatarPointillist produces high-quality, photorealistic, and controllable avatars. We believe this autoregressive formulation represents a new paradigm for avatar generation, and we will release our code inspire future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.03993", "title": "Can LLMs Learn to Reason Robustly under Noisy Supervision?", "authors": [ "Shenzhi Yang", "Guangcheng Zhu", "Bowen Song", "Sharon Li", "Haobo Wang", "Xing Zheng", "Yingfan Ma", "Zhongqi Chen", "Weiqiang Wang", "Gang Chen" ], "github": "https://github.com/ShenzhiYang2000/OLR", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) effectively trains reasoning models that rely on abundant perfect labels, but its vulnerability to unavoidable noisy labels due to expert scarcity remains critically underexplored. In this work, we take the first step toward a systematic analysis of noisy label mechanisms in RLVR. In contrast to supervised classification, most RLVR algorithms incorporate a rollout-based condition: a label's influence on training is contingent on whether the current policy can generate rollouts that realize it, a property that naturally extends to noisy labels. Based on this observation, we distinguish two types of noise: inactive noisy labels, which reduce data efficiency, and active noisy labels, which are reinforced and risk skewing the model toward incorrect distributions. From experiments on training with noisy samples, we identify an Early Correctness Coherence phenomenon: although noisy samples begin to lag behind in later stages, accuracy on both clean and noisy samples increases similarly in early training. Motivated by this dynamic, we propose Online Label Refinement (OLR), which progressively corrects potentially noisy labels with majority-voted answers when two conditions hold: a positive slope in the majority answer's rollout pass rate and stable historical consistency across updates, enabling gradual self-correction as the policy improves. We evaluate OLR on six in-distribution mathematical reasoning benchmarks (AIME24/25, AMC, MATH-500, Minerva, and Olympiad) and three out-of-distribution tasks (ARC-c, GPQA-diamond, and MMLU-pro). Across noise ratios from 0.1 to 0.9, OLR consistently improves robustness under both inactive and active noisy-label settings, achieving average gains of 3.6% to 3.9% on in-distribution benchmarks and 3.3% to 4.6% on out-of-distribution evaluations.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ShenzhiYang2000/OLR", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.03253", "title": "Self-Execution Simulation Improves Coding Models", "authors": [ "Gallil Maimon", "Ori Yoran", "Felix Kreuk", "Michael Hassid", "Gal Cohen", "Pierre Chambon", "Yossi Adi" ], "github": "", "project_page": "", "abstract": "A promising research direction in enabling LLMs to generate consistently correct code involves addressing their inability to properly estimate program execution, particularly for code they generate. In this work, we demonstrate that Code LLMs can be trained to simulate program execution in a step-by-step manner and that this capability can be leveraged to improve competitive programming performance. Our approach combines supervised fine-tuning on natural language execution traces, textual explanations grounded in true execution, with reinforcement learning using verifiable rewards. We introduce two complementary objectives: output prediction given code and inputs, and solving competitive programming tasks with either ground-truth or self-predicted execution feedback. These objectives enable models to perform self-verification over multiple candidate solutions, and iterative self-fixing by simulating test execution. Across multiple competitive programming benchmarks, our method yields consistent improvements over standard reasoning approaches. We further present ablations and analysis to elucidate the role of execution simulation and its limitations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04759", "title": "Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw", "authors": [ "Zijun Wang", "Haoqin Tu", "Letian Zhang", "Hardy Chen", "Juncheng Wu", "Xiangyan Liu", "Zhenlong Yuan", "Tianyu Pang", "Michael Qizhe Shieh", "Fengze Liu", "Zeyu Zheng", "Huaxiu Yao", "Yuyin Zhou", "Cihang Xie" ], "github": "", "project_page": "", "abstract": "OpenClaw, the most widely deployed personal AI agent in early 2026, operates with full local system access and integrates with sensitive services such as Gmail, Stripe, and the filesystem. While these broad privileges enable high levels of automation and powerful personalization, they also expose a substantial attack surface that existing sandboxed evaluations fail to capture. To address this gap, we present the first real-world safety evaluation of OpenClaw and introduce the CIK taxonomy, which unifies an agent's persistent state into three dimensions, i.e., Capability, Identity, and Knowledge, for safety analysis. Our evaluations cover 12 attack scenarios on a live OpenClaw instance across four backbone models (Claude Sonnet 4.5, Opus 4.6, Gemini 3.1 Pro, and GPT-5.4). The results show that poisoning any single CIK dimension increases the average attack success rate from 24.6% to 64-74%, with even the most robust model exhibiting more than a threefold increase over its baseline vulnerability. We further assess three CIK-aligned defense strategies alongside a file-protection mechanism; however, the strongest defense still yields a 63.8% success rate under Capability-targeted attacks, while file protection blocks 97% of malicious injections but also prevents legitimate updates. Taken together, these findings show that the vulnerabilities are inherent to the agent architecture, necessitating more systematic safeguards to secure personal AI agents. Our project page is https://ucsc-vlaa.github.io/CIK-Bench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2603.28301", "title": "LIBERO-Para: A Diagnostic Benchmark and Metrics for Paraphrase Robustness in VLA Models", "authors": [ "Chanyoung Kim", "Minwoo Kim", "Minseok Kang", "Hyunwoo Kim", "Dahuin Jung" ], "github": "https://github.com/cau-hai-lab/LIBERO-Para", "project_page": "", "abstract": "Vision-Language-Action (VLA) models achieve strong performance in robotic manipulation by leveraging pre-trained vision-language backbones. However, in downstream robotic settings, they are typically fine-tuned with limited data, leading to overfitting to specific instruction formulations and leaving robustness to paraphrased instructions underexplored. To study this gap, we introduce LIBERO-Para, a controlled benchmark that independently varies action expressions and object references for fine-grained analysis of linguistic generalization. Across seven VLA configurations (0.6B-7.5B), we observe consistent performance degradation of 22-52 pp under paraphrasing. This degradation is primarily driven by object-level lexical variation: even simple synonym substitutions cause large drops, indicating reliance on surface-level matching rather than semantic grounding. Moreover, 80-96% of failures arise from planning-level trajectory divergence rather than execution errors, showing that paraphrasing disrupts task identification. Binary success rate treats all paraphrases equally, obscuring whether models perform consistently across difficulty levels or rely on easier cases. To address this, we propose PRIDE, a metric that quantifies paraphrase difficulty using semantic and syntactic factors. Our benchmark and corresponding code are available at: https://github.com/cau-hai-lab/LIBERO-Para", "github_manual": "", "github_regex": "https://github.com/cau-hai-lab/LIBERO-Para", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.00830", "title": "Learning to Learn-at-Test-Time: Language Agents with Learnable Adaptation Policies", "authors": [ "Zhanzhi Lou", "Hui Chen", "Yibo Li", "Qian Wang", "Bryan Hooi" ], "github": "https://github.com/zzzlou/meta-ttl", "project_page": "", "abstract": "Test-Time Learning (TTL) enables language agents to iteratively refine their performance through repeated interactions with the environment at inference time. At the core of TTL is an adaptation policy that updates the actor policy based on experience from previous episodes, thereby improving future behavior. Existing methods rely on fixed, hand-crafted adaptation policies rather than optimizing them for downstream improvement. We argue that optimal adaptation policies should be learned from task environments, not hand-engineered based on human intuition. To achieve this, we introduce Meta-TTL, a framework that formulates the discovery of effective adaptation policies as a bi-level optimization problem. Within this framework, the inner loop executes the standard TTL process, measuring how effectively a candidate adaptation policy helps an agent correct errors across sequential episodes. Guided by the agent's performance, the outer loop employs evolutionary search over a diverse distribution of training tasks to iteratively refine the adaptation policy. We evaluate Meta-TTL on Jericho and WebArena-Lite across both in-distribution (ID) and out-of-distribution (OOD) settings, using multiple meta-agent backbones. Results on both benchmarks show that Meta-TTL consistently outperforms hand-crafted baselines, suggesting that the optimized adaptation policy encodes transferable strategies that generalize beyond the training task distribution.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zzzlou/meta-ttl", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.02176", "title": "Adam's Law: Textual Frequency Law on Large Language Models", "authors": [ "Hongyuan Adam Lu", "Z. L.", "Victor Wei", "Zefan Zhang", "Zhao Hong", "Qiqi Xiang", "Bowen Cao", "Wai Lam" ], "github": "", "project_page": "", "abstract": "While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldom studied. We propose a novel research direction in terms of textual data frequency, which is an understudied topic, to the best of our knowledge. Our framework is composed of three units. First, this paper proposes Textual Frequency Law (TFL), which indicates that frequent textual data should be preferred for LLMs for both prompting and fine-tuning. Since many LLMs are closed-source in their training data, we propose using online resources to estimate the sentence-level frequency. We then utilize an input paraphraser to paraphrase the input into a more frequent textual expression. Next, we propose Textual Frequency Distillation (TFD) by querying LLMs to conduct story completion by further extending the sentences in the datasets, and the resulting corpora are used to adjust the initial estimation. Finally, we propose Curriculum Textual Frequency Training (CTFT) that fine-tunes LLMs in an increasing order of sentence-level frequency. Experiments are conducted on our curated dataset Textual Frequency Paired Dataset (TFPD) on math reasoning, machine translation, commonsense reasoning and agentic tool calling. Results show the effectiveness of our framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.02288", "title": "Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing", "authors": [ "Gengsheng Li", "Tianyu Yang", "Junfeng Fang", "Mingyang Song", "Mao Zheng", "Haiyun Guo", "Dan Zhang", "Jinqiao Wang", "Tat-Seng Chua" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training large language models. While Group Relative Policy Optimization (GRPO) is widely adopted, its coarse credit assignment uniformly penalizes failed rollouts, lacking the token-level focus needed to efficiently address specific deviations. Self-Distillation Policy Optimization (SDPO) addresses this by providing denser, more targeted logit-level supervision that facilitates rapid early improvement, yet it frequently collapses during prolonged training. We trace this late-stage instability to two intrinsic flaws: self-distillation on already-correct samples introduces optimization ambiguity, and the self-teacher's signal reliability progressively degrades. To resolve these issues, we propose Sample-Routed Policy Optimization (SRPO), a unified on-policy framework that routes correct samples to GRPO's reward-aligned reinforcement and failed samples to SDPO's targeted logit-level correction. SRPO further incorporates an entropy-aware dynamic weighting mechanism to suppress high-entropy, unreliable distillation targets while emphasizing confident ones. Evaluated across five benchmarks and two model scales, SRPO achieves both the rapid early improvement of SDPO and the long-horizon stability of GRPO. It consistently surpasses the peak performance of both baselines, raising the five-benchmark average on Qwen3-8B by 3.4% over GRPO and 6.3% over SDPO, while simultaneously yielding moderate response lengths and lowering per-step compute cost by up to 17.2%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.02073", "title": "PLUME: Latent Reasoning Based Universal Multimodal Embedding", "authors": [ "Chenwei He", "Xiangzhao Hao", "Tianyu Yang", "Yuxiang Ma", "Yuheng Jia", "Lingxiang Wu", "Chaoyang Zhao", "Haiyun Guo", "Jinqiao Wang" ], "github": "", "project_page": "", "abstract": "Universal multimodal embedding (UME) maps heterogeneous inputs into a shared retrieval space with a single model. Recent approaches improve UME by generating explicit chain-of-thought (CoT) rationales before extracting embeddings, enabling multimodal large language models to better infer complex query intent. However, explicit CoT incurs substantial inference overhead and can compress rich multimodal evidence into a narrow textual bottleneck. We propose PLUME, a latent reasoning framework that advances UME by replacing verbalized CoT with a short autoregressive rollout of continuous latent states. To support diverse multimodal queries, PLUME further introduces a semantic-anchor-guided transition adapter that steers latent rollout along different reasoning trajectories under the same fixed computation budget. To stabilize training, PLUME adopts a progressive explicit-to-latent curriculum that uses verbalized reasoning only as a temporary training scaffold and gradually transfers this behavior into hidden-state computation, eliminating explicit CoT at inference. On the 78-task MMEB-v2 benchmark, PLUME outperforms strong explicit-CoT UME baselines while reducing reasoning from hundreds of generated tokens to fewer than 10 latent steps, delivering over 30x faster inference. PLUME is especially well suited to retrieval settings where relevant evidence is dense, structurally complex, and difficult to organize through verbalized intermediate rationales, such as video and visual document retrieval. These results show that structured latent computation can preserve the benefits of intermediate reasoning without the overhead of explicit rationale generation, providing a stronger and more efficient paradigm for practical retrieval systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04780", "title": "CLEAR: Unlocking Generative Potential for Degraded Image Understanding in Unified Multimodal Models", "authors": [ "Xiangzhao Hao", "Zefeng Zhang", "Zhenyu Zhang", "Linhao Yu", "Yao Chen", "Yiqian Zhang", "Haiyun Guo", "Shuohuan Wang", "Yu Sun" ], "github": "https://github.com/haoxiangzhao12138/CLEAR", "project_page": "", "abstract": "Image degradation from blur, noise, compression, and poor illumination severely undermines multimodal understanding in real-world settings. Unified multimodal models that combine understanding and generation within a single architecture are a natural fit for this challenge, as their generative pathway can model the fine-grained visual structure that degradation destroys. Yet these models fail to leverage their own generative capacity on degraded inputs. We trace this disconnect to two compounding factors: existing training regimes never ask the model to invoke generation during reasoning, and the standard decode-reencode pathway does not support effective joint optimization. We present CLEAR, a framework that connects the two capabilities through three progressive steps: (1) supervised fine-tuning on a degradation-aware dataset to establish the generate-then-answer reasoning pattern; (2) a Latent Representation Bridge that replaces the decode-reencode detour with a direct, optimizable connection between generation and reasoning; (3) Interleaved GRPO, a reinforcement learning method that jointly optimizes text reasoning and visual generation under answer-correctness rewards. We construct MMD-Bench, covering three degradation severity levels across six standard multimodal benchmarks. Experiments show that CLEAR substantially improves robustness on degraded inputs while preserving clean-image performance. Our analysis further reveals that removing pixel-level reconstruction supervision leads to intermediate visual states with higher perceptual quality, suggesting that task-driven optimization and visual quality are naturally aligned.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/haoxiangzhao12138/CLEAR", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.03687", "title": "SciLT: Long-Tailed Classification in Scientific Image Domains", "authors": [ "Jiahao Chen", "Bing Su" ], "github": "", "project_page": "", "abstract": "Long-tailed recognition has benefited from foundation models and fine-tuning paradigms, yet existing studies and benchmarks are mainly confined to natural image domains, where pre-training and fine-tuning data share similar distributions. In contrast, scientific images exhibit distinct visual characteristics and supervision signals, raising questions about the effectiveness of fine-tuning foundation models in such settings. In this work, we investigate scientific long-tailed recognition under a purely visual and parameter-efficient fine-tuning (PEFT) paradigm. Experiments on three scientific benchmarks show that fine-tuning foundation models yields limited gains, and reveal that penultimate-layer features play an important role, particularly for tail classes. Motivated by these findings, we propose SciLT, a framework that exploits multi-level representations through adaptive feature fusion and dual-supervision learning. By jointly leveraging penultimate- and final-layer features, SciLT achieves balanced performance across head and tail classes. Extensive experiments demonstrate that SciLT consistently outperforms existing methods, establishing a strong and practical baseline for scientific long-tailed recognition and providing valuable guidance for adapting foundation models to scientific data with substantial domain shifts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.03695", "title": "POEMetric: The Last Stanza of Humanity", "authors": [ "Bingru Li", "Han Wang", "Hazel Wilkinson" ], "github": "https://github.com/Bingru-Li/POEMetric", "project_page": "", "abstract": "Large Language Models (LLMs) can compose poetry, but how far are they from human poets? In this paper, we introduce POEMetric, the first comprehensive framework for poetry evaluation, examining 1) basic instruction-following abilities in generating poems according to a certain form and theme, 2) advanced abilities of showing creativity, lexical diversity, and idiosyncrasy, evoking emotional resonance, and using imagery and literary devices, and 3) general appraisal of the overall poem quality and estimation of authorship. We curated a human poem dataset - 203 English poems of 7 fixed forms annotated with meter, rhyme patterns and themes - and experimented with 30 LLMs for poetry generation based on the same forms and themes of the human data, totaling 6,090 LLM poems. Based on POEMetric, we assessed the performance of both human poets and LLMs through rule-based evaluation and LLM-as-a-judge, whose results were validated by human experts. Results show that, though the top model achieved high form accuracy (4.26 out of 5.00, with Gemini-2.5-Pro as a judge; same below) and theme alignment (4.99), all models failed to reach the same level of advanced abilities as human poets, who achieved unparalleled creativity (4.02), idiosyncrasy (3.95), emotional resonance (4.06), and skillful use of imagery (4.49) and literary devices (4.67). Humans also defeated the best-performing LLM in overall poem quality (4.22 vs. 3.20). As such, poetry generation remains a formidable challenge for LLMs. Data and codes are released at https://github.com/Bingru-Li/POEMetric.", "github_manual": "", "github_regex": "https://github.com/Bingru-Li/POEMetric", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.01043", "title": "ONE-SHOT: Compositional Human-Environment Video Synthesis via Spatial-Decoupled Motion Injection and Hybrid Context Integration", "authors": [ "Fengyuan Yang", "Luying Huang", "Jiazhi Guan", "Quanwei Yang", "Dongwei Pan", "Jianglin Fu", "Haocheng Feng", "Wei He", "Kaisiyuan Wang", "Hang Zhou", "Angela Yao" ], "github": "", "project_page": "", "abstract": "Recent advances in Video Foundation Models (VFMs) have revolutionized human-centric video synthesis, yet fine-grained and independent editing of subjects and scenes remains a critical challenge. Recent attempts to incorporate richer environment control through rigid 3D geometric compositions often encounter a stark trade-off between precise control and generative flexibility. Furthermore, the heavy 3D pre-processing still limits practical scalability. In this paper, we propose ONE-SHOT, a parameter-efficient framework for compositional human-environment video generation. Our key insight is to factorize the generative process into disentangled signals. Specifically, we introduce a canonical-space injection mechanism that decouples human dynamics from environmental cues via cross-attention. We also propose Dynamic-Grounded-RoPE, a novel positional embedding strategy that establishes spatial correspondences between disparate spatial domains without any heuristic 3D alignments. To support long-horizon synthesis, we introduce a Hybrid Context Integration mechanism to maintain subject and scene consistency across minute-level generations. Experiments demonstrate that our method significantly outperforms state-of-the-art methods, offering superior structural control and creative diversity for video synthesis. Our project has been available on: https://martayang.github.io/ONE-SHOT/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04562", "title": "Paper Espresso: From Paper Overload to Research Insight", "authors": [ "Mingzhe Du", "Luu Anh Tuan", "Dong Huang", "See-kiong Ng" ], "github": "", "project_page": "", "abstract": "The accelerating pace of scientific publishing makes it increasingly difficult for researchers to stay current. We present Paper Espresso, an open-source platform that automatically discovers, summarizes, and analyzes trending arXiv papers. The system uses large language models (LLMs) to generate structured summaries with topical labels and keywords, and provides multi-granularity trend analysis at daily, weekly, and monthly scales through LLM-driven topic consolidation. Over 35 months of continuous deployment, Paper Espresso has processed over 13,300 papers and publicly released all structured metadata, revealing rich dynamics in the AI research landscape: a mid-2025 surge in reinforcement learning for LLM reasoning, non-saturating topic emergence (6,673 unique topics), and a positive correlation between topic novelty and community engagement (2.0x median upvotes for the most novel papers). A live demo is available at https://huggingface.co/spaces/Elfsong/Paper_Espresso.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04155", "title": "The Geometric Alignment Tax: Tokenization vs. Continuous Geometry in Scientific Foundation Models", "authors": [ "Prashant C. Raju" ], "github": "https://github.com/prashantcraju/geometric-alignment-tax", "project_page": "", "abstract": "Foundation models for biology and physics optimize predictive accuracy, but their internal representations systematically fail to preserve the continuous geometry of the systems they model. We identify the root cause: the Geometric Alignment Tax, an intrinsic cost of forcing continuous manifolds through discrete categorical bottlenecks. Controlled ablations on synthetic dynamical systems demonstrate that replacing cross-entropy with a continuous head on an identical encoder reduces geometric distortion by up to 8.5x, while learned codebooks exhibit a non-monotonic double bind where finer quantization worsens geometry despite improving reconstruction. Under continuous objectives, three architectures differ by 1.3x; under discrete tokenization, they diverge by 3,000x. Evaluating 14 biological foundation models with rate-distortion theory and MINE, we identify three failure regimes: Local-Global Decoupling, Representational Compression, and Geometric Vacuity. A controlled experiment confirms that Evo 2's reverse-complement robustness on real DNA reflects conserved sequence composition, not learned symmetry. No model achieves simultaneously low distortion, high mutual information, and global coherence.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/prashantcraju/geometric-alignment-tax", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.03266", "title": "Emergent Compositional Communication for Latent World Properties", "authors": [ "Tomek Kaszy\u0144ski" ], "github": "", "project_page": "", "abstract": "Can multi-agent communication pressure extract discrete, compositional representations of invisible physical properties from frozen video features? We show that agents communicating through a Gumbel-Softmax bottleneck with iterated learning develop positionally disentangled protocols for latent properties (elasticity, friction, mass ratio) without property labels or supervision on message structure. With 4 agents, 100% of 80 seeds converge to near-perfect compositionality (PosDis=0.999, holdout 98.3%). Controls confirm multi-agent structure -- not bandwidth or temporal coverage -- drives this effect. Causal intervention shows surgical property disruption (~15% drop on targeted property, <3% on others). A controlled backbone comparison reveals that the perceptual prior determines what is communicable: DINOv2 dominates on spatially-visible ramp physics (98.3% vs 95.1%), while V-JEPA 2 dominates on dynamics-only collision physics (87.4% vs 77.7%, d=2.74). Scale-matched (d=3.37) and frame-matched (d=6.53) controls attribute this gap entirely to video-native pretraining. The frozen protocol supports action-conditioned planning (91.5%) with counterfactual velocity reasoning (r=0.780). Validation on Physics 101 real camera footage confirms 85.6% mass-comparison accuracy on unseen objects, temporal dynamics contributing +11.2% beyond static appearance, agent-scaling compositionality replicating at 90% for 4 agents, and causal intervention extending to real video (d=1.87, p=0.022).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04872", "title": "Synthetic Sandbox for Training Machine Learning Engineering Agents", "authors": [ "Yuhang Zhou", "Lizhu Zhang", "Yifan Wu", "Jiayi Liu", "Xiangjun Fan", "Zhuokai Zhao", "Hong Yan" ], "github": "", "project_page": "", "abstract": "As large language model agents advance beyond software engineering (SWE) tasks toward machine learning engineering (MLE), verifying agent behavior becomes orders of magnitude more expensive: while SWE tasks can be verified via fast-executing unit tests, MLE verification requires running full ML pipelines -- data preprocessing, model training, and metric evaluation -- on large datasets at each rollout step, rendering trajectory-wise on-policy reinforcement learning (RL) prohibitively slow. Existing approaches retreat to supervised fine-tuning (SFT) or offline proxy rewards, sacrificing the exploration and generalization benefits of on-policy RL. We observe that sandbox data size is the primary source of this bottleneck. Based on this insight, we introduce SandMLE, a multi-agent framework that generates diverse, verifiable synthetic MLE environments from a small number of seed tasks, preserving the structural and technical complexity of real-world problems while constraining datasets to micro-scale (each task is paired with only 50-200 training samples). Through extensive experiments, we show that SandMLE reduces execution time by over 13 times, enabling large-scale, on-policy trajectory-wise RL for the first time in the MLE domain. On MLE-bench-lite, SandMLE yields significant gains over SFT baselines across Qwen3-8B, 14B, and 30B-A3B, with relative medal rate improvements ranging from 20.3% to 66.9%. Furthermore, the trained policy generalizes across unseen agentic scaffolds, achieving up to 32.4% better HumanRank score on MLE-Dojo.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.04767", "title": "Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems", "authors": [ "Justin Chih-Yao Chen", "Archiki Prasad", "Zaid Khan", "Joykirat Singh", "Runchu Tian", "Elias Stengel-Eskin", "Mohit Bansal" ], "github": "https://github.com/dinobby/Cog-DRIFT", "project_page": "", "abstract": "Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning abilities of LLMs, yet a fundamental limitation remains: models cannot learn from problems that are too difficult to solve under their current policy, as these yield no meaningful reward signal. We propose a simple yet effective solution based on task reformulation. We transform challenging open-ended problems into cognitively simpler variants -- such as multiple-choice and cloze formats -- that preserve the original answer while reducing the effective search space and providing denser learning signals. These reformulations span a spectrum from discriminative to generative tasks, which we exploit to bootstrap learning: models first learn from structured, easier formats, and this knowledge transfers back to improve performance on the original open-ended problems. Building on this insight, we introduce Cog-DRIFT, a framework that constructs reformulated variants and organizes them into an adaptive curriculum based on difficulty. Training progresses from easier to harder formats, enabling the model to learn from problems that previously yielded zero signal under standard RL post-training. Cog-DRIFT not only improves on the originally unsolvable hard problems (absolute +10.11% for Qwen and +8.64% for Llama) but also generalizes well to other held-out datasets. Across 2 models and 6 reasoning benchmarks, our method consistently outperforms standard GRPO and strong guided-exploration baselines. On average, Cog-DRIFT shows +4.72% (Qwen) and +3.23% (Llama) improvements over the second-best baseline. We further show that Cog-DRIFT improves pass@k at test time, and the curriculum improves sample efficiency. Overall, our results highlight task reformulation and curriculum learning as an effective paradigm for overcoming the exploration barrier in LLM post-training.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dinobby/Cog-DRIFT", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.02605", "title": "Do Audio-Visual Large Language Models Really See and Hear?", "authors": [ "Ramaneswaran Selvakumar", "Kaousheik Jayakumar", "S Sakshi", "Sreyan Ghosh", "Ruohan Gao", "Dinesh Manocha" ], "github": "", "project_page": "", "abstract": "Audio-Visual Large Language Models (AVLLMs) are emerging as unified interfaces to multimodal perception. We present the first mechanistic interpretability study of AVLLMs, analyzing how audio and visual features evolve and fuse through different layers of an AVLLM to produce the final text outputs. We find that although AVLLMs encode rich audio semantics at intermediate layers, these capabilities largely fail to surface in the final text generation when audio conflicts with vision. Probing analyses show that useful latent audio information is present, but deeper fusion layers disproportionately privilege visual representations that tend to suppress audio cues. We further trace this imbalance to training: the AVLLM's audio behavior strongly matches its vision-language base model, indicating limited additional alignment to audio supervision. Our findings reveal a fundamental modality bias in AVLLMs and provide new mechanistic insights into how multimodal LLMs integrate audio and vision.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.00375", "title": "Locally Confident, Globally Stuck: The Quality-Exploration Dilemma in Diffusion Language Models", "authors": [ "Liancheng Fang", "Aiwei Liu", "Henry Peng Zou", "Yankai Chen", "Enze Ma", "Leyi Pan", "Chunyu Miao", "Wei-Chieh Huang", "Xue Liu", "Philip S. Yu" ], "github": "", "project_page": "", "abstract": "Diffusion large language models (dLLMs) theoretically permit token decoding in arbitrary order, a flexibility that could enable richer exploration of reasoning paths than autoregressive (AR) LLMs. In practice, however, random-order decoding often hurts generation quality. To mitigate this, low-confidence remasking improves single-sample quality (e.g., Pass@1) by prioritizing confident tokens, but it also suppresses exploration and limits multi-sample gains (e.g., Pass@k), creating a fundamental quality--exploration dilemma. In this paper, we provide a unified explanation of this dilemma. We show that low-confidence remasking improves a myopic proxy for quality while provably constraining the entropy of the induced sequence distribution. To overcome this limitation, we characterize the optimal distribution that explicitly balances quality and exploration, and develop a simple Independent Metropolis--Hastings sampler that approximately targets this distribution during decoding. Experiments across a range of reasoning benchmarks including MATH500, AIME24/25, HumanEval, and MBPP show that our approach yields better exploration-quality tradeoff than both random and low-confidence remasking.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.01483", "title": "Type-Checked Compliance: Deterministic Guardrails for Agentic Financial Systems Using Lean 4 Theorem Proving", "authors": [ "Devakh Rashie", "Veda Rashi" ], "github": "https://github.com/NVIDIA-NeMo/Guardrails", "project_page": "", "abstract": "The rapid evolution of autonomous, agentic artificial intelligence within financial services has introduced an existential architectural crisis: large language models (LLMs) are probabilistic, non-deterministic systems operating in domains that demand absolute, mathematically verifiable compliance guarantees. Existing guardrail solutions -- including NVIDIA NeMo Guardrails and Guardrails AI -- rely on probabilistic classifiers and syntactic validators that are fundamentally inadequate for enforcing complex multi-variable regulatory constraints mandated by the SEC, FINRA, and OCC. This paper presents the Lean-Agent Protocol, a formal-verification-based AI guardrail platform that leverages the Aristotle neural-symbolic model developed by Harmonic AI to auto-formalize institutional policies into Lean 4 code. Every proposed agentic action is treated as a mathematical conjecture: execution is permitted if and only if the Lean 4 kernel proves that the action satisfies pre-compiled regulatory axioms. This architecture provides cryptographic-level compliance certainty at microsecond latency, directly satisfying SEC Rule 15c3-5, OCC Bulletin 2011-12, FINRA Rule 3110, and CFPB explainability mandates. A three-phase implementation roadmap from shadow verification through enterprise-scale deployment is provided.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA-NeMo/Guardrails", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.03295", "title": "Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems", "authors": [ "Shanglin Wu", "Yuyang Luo", "Yueqing Liang", "Kaiwen Shi", "Yanfang Ye", "Ali Payani", "Kai Shu" ], "github": "https://github.com/ShanglinWu/MAS_lifelong_learning", "project_page": "", "abstract": "Large language model (LLM) multi-agent systems can scale along two distinct dimensions: by increasing the number of agents and by improving through accumulated experience over time. Although prior work has studied these dimensions separately, their interaction under realistic cost constraints remains unclear. In this paper, we introduce a conceptual scaling view of multi-agent systems that jointly considers team size and lifelong learning ability, and we study how memory design shares this landscape. To this end, we propose LLMA-Mem, a lifelong memory framework for LLM multi-agent systems under flexible memory topologies. We evaluate LLMA-Mem on MultiAgentBench across coding, research, and database environments. Empirically, LLMA-Mem consistently improves long-horizon performance over baselines while reducing cost. Our analysis further reveals a non-monotonic scaling landscape: larger teams do not always produce better long-term performance, and smaller teams can outperform larger ones when memory better supports the reuse of experience. These findings position memory design as a practical path for scaling multi-agent systems more effectively and more efficiently over time.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ShanglinWu/MAS_lifelong_learning", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2604.02045", "title": "BidirLM: From Text to Omnimodal Bidirectional Encoders by Adapting and Composing Causal LLMs", "authors": [ "Nicolas Boizard", "Th\u00e9o Deschamps-Berger", "Hippolyte Gisserot-Boukhlef", "C\u00e9line Hudelot", "Pierre Colombo" ], "github": "", "project_page": "", "abstract": "Transforming causal generative language models into bidirectional encoders offers a powerful alternative to BERT-style architectures. However, current approaches remain limited: they lack consensus on optimal training objectives, suffer from catastrophic forgetting at scale, and fail to flexibly integrate the vast ecosystem of specialized generative models. In this work, through systematic ablations on the Gemma3 and Qwen3 families, we identify the key factors driving successful adaptation, highlighting the critical role of an often-omitted prior masking phase. To scale this process without original pre-training data, we introduce a dual strategy combining linear weight merging with a lightweight multi-domain data mixture that mitigates catastrophic forgetting. Finally, we augment our encoders by merging them with specialized causal models, seamlessly transferring modality- and domain-specific capabilities. This open-source recipe, designed for any causal decoder LLM, yields BidirLM, a family of five encoders that outperform alternatives on text, vision, and audio representation benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-07", "arxiv_id": "2602.08602", "title": "Mimic Intent, Not Just Trajectories", "authors": [ "Renming Huang", "Chendong Zeng", "Wenjing Tang", "Jintian Cai", "Cewu Lu", "Panpan Cai" ], "github": "", "project_page": "", "abstract": "While imitation learning (IL) has achieved impressive success in dexterous manipulation through generative modeling and pretraining, state-of-the-art approaches like Vision-Language-Action (VLA) models still struggle with adaptation to environmental changes and skill transfer. We argue this stems from mimicking raw trajectories without understanding the underlying intent. To address this, we propose explicitly disentangling behavior intent from execution details in end-2-end IL: Mimic Intent, Not just Trajectories(MINT). We achieve this via multi-scale frequency-space tokenization, which enforces a spectral decomposition of action chunk representation. We learn action tokens with a multi-scale coarse-to-fine structure, and force the coarsest token to capture low-frequency global structure and finer tokens to encode high-frequency details. This yields an abstract Intent token that facilitates planning and transfer, and multi-scale Execution tokens that enable precise adaptation to environmental dynamics. Building on this hierarchy, our policy generates trajectories through next-scale autoregression, performing progressive intent-to-execution reasoning, thus boosting learning efficiency and generalization. Crucially, this disentanglement enables one-shot transfer of skills, by simply injecting the Intent token from a demonstration into the autoregressive generation process. Experiments on several manipulation benchmarks and on a real robot demonstrate state-of-the-art success rates, superior inference efficiency, robust generalization against disturbances, and effective one-shot transfer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.05404", "title": "Beyond Accuracy: Unveiling Inefficiency Patterns in Tool-Integrated Reasoning", "authors": [ "Qisheng Su", "Shiting Huang", "Zhen Fang", "Ziyan Chen", "Zehui Chen", "Feng Zhao" ], "github": "https://github.com/sqs-ustc/tool-reasoning-framework-PTE", "project_page": "", "abstract": "In real-world Tool-Integrated Reasoning (TIR) scenarios, where LLMs interleave reasoning with external tool calls, a major source of inefficiency is that the toolcalls create pauses between LLM requests and cause KV-Cache eviction, forcing recomputation. Also, the long, unfiltered response returned by external tools inflates the KV-Cache, so each decode step spends more time loading the growing cache and thus becomes steadily slower as context length increases. However, existing efficiency metrics like token counts and toolcall counts fail to capture the real model inference latency. To address this, we introduce PTE (Prefill Token Equivalents), a hardware-aware TIR-efficiency metric that unifies internal reasoning and external tool-use costs while explicitly accounting for non-reusable KV-Cache and long-tool-response scenarios. Validation in a high-concurrency industrial setting indicates that PTE aligns significantly better with wall-clock latency than standard token counts, while maintaining consistent efficiency rankings across diverse hardware profiles. We conduct extensive experiments across five TIR benchmarks, quantify their PTE costs, and identify four inefficiency patterns that appear in TIR. We also discover that trajectories with higher PTE costs tend to have lower reasoning correctness, indicating that simply using more tools does not improve the quality of the answer.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sqs-ustc/tool-reasoning-framework-PTE", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.05015", "title": "Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding", "authors": [ "Chaoyou Fu", "Haozhi Yuan", "Yuhao Dong", "Yi-Fan Zhang", "Yunhang Shen", "Xiaoxing Hu", "Xueying Li", "Jinsen Su", "Chengwu Long", "Xiaoyao Xie", "Yongkang Xie", "Xiawu Zheng", "Xue Yang", "Haoyu Cao", "Yunsheng Wu", "Ziwei Liu", "Xing Sun", "Caifeng Shan", "Ran He" ], "github": "", "project_page": "", "abstract": "With the rapid advancement of video understanding, existing benchmarks are becoming increasingly saturated, exposing a critical discrepancy between inflated leaderboard scores and real-world model capabilities. To address this widening gap, we introduce Video-MME-v2, a comprehensive benchmark designed to rigorously evaluate the robustness and faithfulness of video understanding. To systematically evaluate model capabilities, we design a progressive tri-level hierarchy that incrementally increases the complexity of video comprehension, ranging from multi-point visual information aggregation, to temporal dynamics modeling, and ultimately to complex multimodal reasoning. Besides, in contrast to conventional per-question accuracy, we propose a group-based non-linear evaluation strategy that enforces both consistency across related queries and coherence in multi-step reasoning. It penalizes fragmented or guess-based correctness and assigns credit only to answers supported by valid reasoning. To guarantee data quality, Video-MME-v2 is constructed through a rigorously controlled human annotation pipeline, involving 12 annotators and 50 independent reviewers. Backed by 3,300 human-hours and up to 5 rounds of quality assurance, Video-MME-v2 aims to serve as one of the most authoritative video benchmarks. Extensive experiments reveal a substantial gap between current best model Gemini-3-Pro and human experts, and uncover a clear hierarchical bottleneck where errors in visual information aggregation and temporal modeling propagate to limit high-level reasoning. We further find that thinking-based reasoning is highly dependent on textual cues, improving performance with subtitles but sometimes degrading it in purely visual settings. By exposing these limitations, Video-MME-v2 establishes a demanding new testbed for the development of next-generation video MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.04949", "title": "Learning to Retrieve from Agent Trajectories", "authors": [ "Yuqi Zhou", "Sunhao Dai", "Changle Qu", "Liang Pang", "Jun Xu", "Ji-Rong Wen" ], "github": "", "project_page": "", "abstract": "Information retrieval (IR) systems have traditionally been designed and trained for human users, with learning-to-rank methods relying heavily on large-scale human interaction logs such as clicks and dwell time. With the rapid emergence of large language model (LLM) powered search agents, however, retrieval is increasingly consumed by agents rather than human beings, and is embedded as a core component within multi-turn reasoning and action loops. In this setting, retrieval models trained under human-centric assumptions exhibit a fundamental mismatch with the way agents issue queries and consume results. In this work, we argue that retrieval models for agentic search should be trained directly from agent interaction data. We introduce learning to retrieve from agent trajectories as a new training paradigm, where supervision is derived from multi-step agent interactions. Through a systematic analysis of search agent trajectories, we identify key behavioral signals that reveal document utility, including browsing actions, unbrowsed rejections, and post-browse reasoning traces. Guided by these insights, we propose LRAT, a simple yet effective framework that mines high-quality retrieval supervision from agent trajectories and incorporates relevance intensity through weighted optimization. Extensive experiments on both in-domain and out-of-domain deep research benchmarks demonstrate that retrievers trained with LRAT consistently improve evidence recall, end-to-end task success, and execution efficiency across diverse agent architectures and scales. Our results highlight agent trajectories as a practical and scalable supervision source, pointing to a promising direction for retrieval in the era of agentic search.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.06132", "title": "Claw-Eval: Toward Trustworthy Evaluation of Autonomous Agents", "authors": [ "Bowen Ye", "Rang Li", "Qibin Yang", "Yuanxin Liu", "Linli Yao", "Hanglong Lv", "Zhihui Xie", "Chenxin An", "Lei Li", "Lingpeng Kong", "Qi Liu", "Zhifang Sui", "Tong Yang" ], "github": "https://github.com/InternLM/WildClawBench", "project_page": "", "abstract": "Large language models are increasingly deployed as autonomous agents executing multi-step workflows in real-world software environments. However, existing agent benchmarks suffer from three critical limitations: (1) trajectory-opaque grading that checks only final outputs, (2) underspecified safety and robustness evaluation, and (3) narrow modality coverage and interaction paradigms. We introduce Claw-Eval, an end-to-end evaluation suite addressing all three gaps. It comprises 300 human-verified tasks spanning 9 categories across three groups (general service orchestration, multimodal perception and generation, and multi-turn professional dialogue). Every agent action is recorded through three independent evidence channels (execution traces, audit logs, and environment snapshots), enabling trajectory-aware grading over 2,159 fine-grained rubric items. The scoring protocol evaluates Completion, Safety, and Robustness, reporting Average Score, Pass@k, and Pass^k across three trials to distinguish genuine capability from lucky outcomes. Experiments on 14 frontier models reveal that: (1) trajectory-opaque evaluation is systematically unreliable, missing 44% of safety violations and 13% of robustness failures that our hybrid pipeline catches; (2) controlled error injection primarily degrades consistency rather than peak capability, with Pass^3 dropping up to 24% while Pass@3 remains stable; (3) multimodal performance varies sharply, with most models performing poorer on video than on document or image, and no single model dominating across all modalities. Beyond benchmarking, Claw-Eval highlights actionable directions for agent development, shedding light on what it takes to build agents that are not only capable but reliably deployable.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/InternLM/WildClawBench", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.04074", "title": "FactReview: Evidence-Grounded Reviews with Literature Positioning and Execution-Based Claim Verification", "authors": [ "Hang Xu", "Ling Yue", "Chaoqian Ouyang", "Yuchen Liu", "Libin Zheng", "Shaowu Pan", "Shimin Di", "Min-Ling Zhang" ], "github": "https://github.com/DEFENSE-SEU/Review-Assistant", "project_page": "", "abstract": "Peer review in machine learning is under growing pressure from rising submission volume and limited reviewer time. Most LLM-based reviewing systems read only the manuscript and generate comments from the paper's own narrative. This makes their outputs sensitive to presentation quality and leaves them weak when the evidence needed for review lies in related work or released code. We present FactReview, an evidence-grounded reviewing system that combines claim extraction, literature positioning, and execution-based claim verification. Given a submission, FactReview identifies major claims and reported results, retrieves nearby work to clarify the paper's technical position, and, when code is available, executes the released repository under bounded budgets to test central empirical claims. It then produces a concise review and an evidence report that assigns each major claim one of five labels: Supported, Supported by the paper, Partially supported, In conflict, or Inconclusive. In a case study on CompGCN, FactReview reproduces results that closely match those reported for link prediction and node classification, yet also shows that the paper's broader performance claim across tasks is not fully sustained: on MUTAG graph classification, the reproduced result is 88.4%, whereas the strongest baseline reported in the paper remains 92.6%. The claim is therefore only partially supported. More broadly, this case suggests that AI is most useful in peer review not as a final decision-maker, but as a tool for gathering evidence and helping reviewers produce more evidence-grounded assessments. The code is public at https://github.com/DEFENSE-SEU/Review-Assistant.", "github_manual": "", "github_regex": "https://github.com/DEFENSE-SEU/Review-Assistant", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.03922", "title": "ACES: Who Tests the Tests? Leave-One-Out AUC Consistency for Code Generation", "authors": [ "Hui Sun", "Yun-Ji Zhang", "Zheng Xie", "Ren-Biao Liu", "Yali Du", "Xin-Ye Li", "Ming Li" ], "github": "", "project_page": "", "abstract": "Selecting LLM-generated code candidates using LLM-generated tests is challenging because the tests themselves may be incorrect. Existing methods either treat all tests equally or rely on ad-hoc heuristics to filter unreliable tests. Yet determining test correctness requires knowing which codes are correct, creating a circular dependency. Our key insight is that we need not determine test correctness at all: test votes should rank, not merely count. What matters is not how many codes pass a test, but whether the test can distinguish correct from incorrect code. We break the circular dependency via leave-one-out evaluation: hold out one test, rank codes by their aggregate scores on all remaining tests, and measure whether the held-out test's pass/fail pattern agrees with this ranking. We formalize this agreement as the leave-one-out AUC~(LOO-AUC) and prove that the expected LOO-AUC is proportional to each test's ability to separate correct code from incorrect code. Building on this, we propose ACES~(AUC ConsistEncy Scoring) with two complementary variants: ACES-C provides closed-form weights that provably approximate the oracle in expectation under a mild assumption on average test quality; ACES-O drops this assumption and iteratively optimizes a differentiable LOO-AUC objective. Both operate solely on the binary pass matrix with negligible overhead, and achieve state-of-the-art Pass@k on multiple code generation benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.06156", "title": "MMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Control", "authors": [ "Yuchi Wang", "Haiyang Yu", "Weikang Bian", "Jiefeng Long", "Xiao Liang", "Chao Feng", "Hongsheng Li" ], "github": "", "project_page": "", "abstract": "MLLMs have been successfully applied to multimodal embedding tasks, yet their generative reasoning capabilities remain underutilized. Directly incorporating chain-of-thought reasoning into embedding learning introduces two fundamental challenges. First, structural misalignment between instance-level reasoning and pairwise contrastive supervision may lead to shortcut behavior, where the model merely learns the superficial format of reasoning. Second, reasoning is not universally beneficial for embedding tasks. Enforcing reasoning for all inputs may introduce unnecessary computation and latency, and can even obscure salient semantic signals for simple cases. To address these issues, we propose MMEmb-R1, an adaptive reasoning-based multimodal embedding framework. We formulate reasoning as a latent variable and introduce pair-aware reasoning selection that employs counterfactual intervention to identify reasoning paths beneficial for query-target alignment. Furthermore, we adopt reinforcement learning to selectively invoke reasoning only when necessary. Experiments on the MMEB-V2 benchmark demonstrate that our model achieves a score of 71.2 with only 4B parameters, establishing a new state-of-the-art while significantly reducing reasoning overhead and inference latency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.06169", "title": "In-Place Test-Time Training", "authors": [ "Guhao Feng", "Shengjie Luo", "Kai Hua", "Ge Zhang", "Di He", "Wenhao Huang", "Tianle Cai" ], "github": "", "project_page": "", "abstract": "The static ``train then deploy\" paradigm fundamentally limits Large Language Models (LLMs) from dynamically adapting their weights in response to continuous streams of new information inherent in real-world tasks. Test-Time Training (TTT) offers a compelling alternative by updating a subset of model parameters (fast weights) at inference time, yet its potential in the current LLM ecosystem is hindered by critical barriers including architectural incompatibility, computational inefficiency and misaligned fast weight objectives for language modeling. In this work, we introduce In-Place Test-Time Training (In-Place TTT), a framework that seamlessly endows LLMs with Test-Time Training ability. In-Place TTT treats the final projection matrix of the ubiquitous MLP blocks as its adaptable fast weights, enabling a ``drop-in\" enhancement for LLMs without costly retraining from scratch. Furthermore, we replace TTT's generic reconstruction objective with a tailored, theoretically-grounded objective explicitly aligned with the Next-Token-Prediction task governing autoregressive language modeling. This principled objective, combined with an efficient chunk-wise update mechanism, results in a highly scalable algorithm compatible with context parallelism. Extensive experiments validate our framework's effectiveness: as an in-place enhancement, it enables a 4B-parameter model to achieve superior performance on tasks with contexts up to 128k, and when pretrained from scratch, it consistently outperforms competitive TTT-related approaches. Ablation study results further provide deeper insights on our design choices. Collectively, our results establish In-Place TTT as a promising step towards a paradigm of continual learning in LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2603.24652", "title": "Demystifying When Pruning Works via Representation Hierarchies", "authors": [ "Shwai He", "Guoheng Sun", "Haichao Zhang", "Yun Fu", "Ang Li" ], "github": "https://github.com/CASE-Lab-UMD/Pruning-on-Representations", "project_page": "", "abstract": "Network pruning, which removes less important parameters or architectures, is often expected to improve efficiency while preserving performance. However, this expectation does not consistently hold across language tasks: pruned models can perform well on non-generative tasks but frequently fail in generative settings. To understand this discrepancy, we analyze network pruning from a representation-hierarchy perspective, decomposing the internal computation of language models into three sequential spaces: embedding (hidden representations), logit (pre-softmax outputs), and probability (post-softmax distributions). We find that representations in the embedding and logit spaces are largely robust to pruning-induced perturbations. However, the nonlinear transformation from logits to probabilities amplifies these deviations, which accumulate across time steps and lead to substantial degradation during generation. In contrast, the stability of the categorical-token probability subspace, together with the robustness of the embedding space, supports the effectiveness of pruning for non-generative tasks such as retrieval and multiple-choice selection. Our analysis disentangles the effects of pruning across tasks and provides practical guidance for its application. Code is available at https://github.com/CASE-Lab-UMD/Pruning-on-Representations", "github_manual": "", "github_regex": "https://github.com/CASE-Lab-UMD/Pruning-on-Representations", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.06168", "title": "Action Images: End-to-End Policy Learning via Multiview Video Generation", "authors": [ "Haoyu Zhen", "Zixian Gao", "Qiao Sun", "Yilin Zhao", "Yuncong Yang", "Yilun Du", "Tsun-Hsuan Wang", "Yi-Ling Qiao", "Chuang Gan" ], "github": "", "project_page": "", "abstract": "World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model the future states. However, existing approaches often rely on separate action modules, or use action representations that are not pixel-grounded, making it difficult to fully exploit the pretrained knowledge of video models and limiting transfer across viewpoints and environments. In this work, we present Action Images, a unified world action model that formulates policy learning as multiview video generation. Instead of encoding control as low-dimensional tokens, we translate 7-DoF robot actions into interpretable action images: multi-view action videos that are grounded in 2D pixels and explicitly track robot-arm motion. This pixel-grounded action representation allows the video backbone itself to act as a zero-shot policy, without a separate policy head or action module. Beyond control, the same unified model supports video-action joint generation, action-conditioned video generation, and action labeling under a shared representation. On RLBench and real-world evaluations, our model achieves the strongest zero-shot success rates and improves video-action joint generation quality over prior video-space world models, suggesting that interpretable action images are a promising route to policy learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.06079", "title": "Scientific Graphics Program Synthesis via Dual Self-Consistency Reinforcement Learning", "authors": [ "Juekai Lin", "Yun Zhu", "Honglin Lin", "Sijing Li", "Tianwei Lin", "Zheng Liu", "Xiaoyang Wang", "Wenqiao Zhang", "Lijun Wu" ], "github": "", "project_page": "", "abstract": "Graphics Program Synthesis is pivotal for interpreting and editing visual data, effectively facilitating the reverse-engineering of static visuals into editable TikZ code. While TikZ is the de facto standard for scientific schematics due to its programmatic flexibility, its requirement for rigorous spatial precision presents a significant challenge for Multimodal Large Language Models. Progress is currently stifled by two primary gaps: (1) Data Quality Gap: existing image-TikZ corpora often lack strict executability and reliable visual alignment; (2) Evaluation Gap: a lack of benchmarks for both structural and visual fidelity. To address these, we present a closed-loop framework featuring: SciTikZ-230K, a large-scale, high-quality dataset from our Execution-Centric Data Engine covering 11 diverse scientific disciplines; SciTikZ-Bench, a multifaceted benchmark spanning from basic geometric constructs to intricate hierarchical schematics to evaluate both visual fidelity and structural logic. To further broaden the scope of visual-code optimization methodology, we introduce a novel Dual Self-Consistency Reinforcement Learning optimization paradigm, which utilizes Round-Trip Verification to penalize degenerate code and boost overall self-consistency. Empowered by these, our trained model SciTikZer-8B achieves state-of-the-art performance, consistently outperforming proprietary giants like Gemini-2.5-Pro and massive models like Qwen3-VL-235B-A22B-Instruct.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.04934", "title": "Vanast: Virtual Try-On with Human Image Animation via Synthetic Triplet Supervision", "authors": [ "Hyunsoo Cha", "Wonjung Woo", "Byungjun Kim", "Hanbyul Joo" ], "github": "", "project_page": "", "abstract": "We present Vanast, a unified framework that generates garment-transferred human animation videos directly from a single human image, garment images, and a pose guidance video. Conventional two-stage pipelines treat image-based virtual try-on and pose-driven animation as separate processes, which often results in identity drift, garment distortion, and front-back inconsistency. Our model addresses these issues by performing the entire process in a single unified step to achieve coherent synthesis. To enable this setting, we construct large-scale triplet supervision. Our data generation pipeline includes generating identity-preserving human images in alternative outfits that differ from garment catalog images, capturing full upper and lower garment triplets to overcome the single-garment-posed video pair limitation, and assembling diverse in-the-wild triplets without requiring garment catalog images. We further introduce a Dual Module architecture for video diffusion transformers to stabilize training, preserve pretrained generative quality, and improve garment accuracy, pose adherence, and identity preservation while supporting zero-shot garment interpolation. Together, these contributions allow Vanast to produce high-fidelity, identity-consistent animation across a wide range of garment types.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.05533", "title": "Experience Transfer for Multimodal LLM Agents in Minecraft Game", "authors": [ "Chenghao Li", "Jun Liu", "Songbo Zhang", "Huadong Jian", "Hao Ni", "Lik-Hang Lee", "Sung-Ho Bae", "Guoqing Wang", "Yang Yang", "Chaoning Zhang" ], "github": "", "project_page": "", "abstract": "Multimodal LLM agents operating in complex game environments must continually reuse past experience to solve new tasks efficiently. In this work, we propose Echo, a transfer-oriented memory framework that enables agents to derive actionable knowledge from prior interactions rather than treating memory as a passive repository of static records. To make transfer explicit, Echo decomposes reusable knowledge into five dimensions: structure, attribute, process, function, and interaction. This formulation allows the agent to identify recurring patterns shared across different tasks and infer what prior experience remains applicable in new situations. Building on this formulation, Echo leverages In-Context Analogy Learning (ICAL) to retrieve relevant experiences and adapt them to unseen tasks through contextual examples. Experiments in Minecraft show that, under a from-scratch learning setting, Echo achieves a 1.3x to 1.7x speed-up on object-unlocking tasks. Moreover, Echo exhibits a burst-like chain-unlocking phenomenon, rapidly unlocking multiple similar items within a short time interval after acquiring transferable experience. These results suggest that experience transfer is a promising direction for improving the efficiency and adaptability of multimodal LLM agents in complex interactive environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.05081", "title": "MedGemma 1.5 Technical Report", "authors": [ "Andrew Sellergren", "Chufan Gao", "Fereshteh Mahvar", "Timo Kohlberger", "Fayaz Jamil", "Madeleine Traverse", "Alberto Tono", "Bashir Sadjad", "Lin Yang", "Charles Lau", "Liron Yatziv", "Tiffany Chen", "Bram Sterling", "Kenneth Philbrick", "Richa Tiwari", "Yun Liu", "Madhuram Jajoo", "Chandrashekar Sankarapu", "Swapnil Vispute", "Harshad Purandare", "Abhishek Bijay Mishra", "Sam Schmidgall", "Tao Tu", "Anil Palepu", "Chunjong Park", "Tim Strother", "Rahul Thapa", "Yong Cheng", "Preeti Singh", "Kat Black", "Yossi Matias", "Katherine Chou", "Avinatan Hassidim", "Kavi Goel", "Joelle Barral", "Tris Warkentin", "Shravya Shetty", "Dale Webster", "Sunny Virmani", "David F. Steiner", "Can Kirmizibayrak", "Daniel Golden" ], "github": "https://github.com/pypdfium2-team/pypdfium2", "project_page": "", "abstract": "We introduce MedGemma 1.5 4B, the latest model in the MedGemma collection. MedGemma 1.5 expands on MedGemma 1 by integrating additional capabilities: high-dimensional medical imaging (CT/MRI volumes and histopathology whole slide images), anatomical localization via bounding boxes, multi-timepoint chest X-ray analysis, and improved medical document understanding (lab reports, electronic health records). We detail the innovations required to enable these modalities within a single architecture, including new training data, long-context 3D volume slicing, and whole-slide pathology sampling. Compared to MedGemma 1 4B, MedGemma 1.5 4B demonstrates significant gains in these new areas, improving 3D MRI condition classification accuracy by 11% and 3D CT condition classification by 3% (absolute improvements). In whole slide pathology imaging, MedGemma 1.5 4B achieves a 47% macro F1 gain. Additionally, it improves anatomical localization with a 35% increase in Intersection over Union on chest X-rays and achieves a 4% macro accuracy for longitudinal (multi-timepoint) chest x-ray analysis. Beyond its improved multimodal performance over MedGemma 1, MedGemma 1.5 improves on text-based clinical knowledge and reasoning, improving by 5% on MedQA accuracy and 22% on EHRQA accuracy. It also achieves an average of 18% macro F1 on 4 different lab report information extraction datasets (EHR Datasets 2, 3, 4, and Mendeley Clinical Laboratory Test Reports). Taken together, MedGemma 1.5 serves as a robust, open resource for the community, designed as an improved foundation on which developers can create the next generation of medical AI systems. Resources and tutorials for building upon MedGemma 1.5 can be found at https://goo.gle/MedGemma.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/pypdfium2-team/pypdfium2", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.01591", "title": "ThinkTwice: Jointly Optimizing Large Language Models for Reasoning and Self-Refinement", "authors": [ "Difan Jiao", "Qianfeng Wen", "Blair Yang", "Zhenwei Tang", "Ashton Anderson" ], "github": "https://github.com/CSSLab/ThinkTwice", "project_page": "", "abstract": "We introduce ThinkTwice, a simple two-phase framework that jointly optimizes LLMs to solve reasoning problems and refine the answers, based on Group Relative Policy Optimization (GRPO). In each pair of training steps, ThinkTwice first optimizes the model on solving reasoning problems, then optimizes it on refining its own solutions to the same problems, using the same binary correctness reward in both phases without correctness signals or critique annotations. Across five mathematical reasoning benchmarks and two model families including Qwen3-4B and Olmo3-7B, ThinkTwice substantially improves both reasoning and refinement performance over competitive online policy optimization baselines. Specifically, on Qwen3-4B, ThinkTwice outperforms GRPO on AIME by 5 percentage points before refinement and by 11.5 points after one self-refinement step, measured by pass@4. Analysis of the training dynamics of ThinkTwice reveals an implicit rectify-then-fortify curriculum: refinement predominantly corrects errors early in training and naturally shifts toward preserving already-correct solutions as the model improves, yielding a more rectified reward signal. Our work establishes joint training of reasoning and self-refinement as a principled and effective methodology for RLVR.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/CSSLab/ThinkTwice", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.04323", "title": "How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings", "authors": [ "Yujian Liu", "Jiabao Ji", "Li An", "Tommi Jaakkola", "Yang Zhang", "Shiyu Chang" ], "github": "https://github.com/UCSB-NLP-Chang/Skill-Usage", "project_page": "", "abstract": "Agent skills, which are reusable, domain-specific knowledge artifacts, have become a popular mechanism for extending LLM-based agents, yet formally benchmarking skill usage performance remains scarce. Existing skill benchmarking efforts focus on overly idealized conditions, where LLMs are directly provided with hand-crafted, narrowly-tailored task-specific skills for each task, whereas in many realistic settings, the LLM agent may have to search for and select relevant skills on its own, and even the closest matching skills may not be well-tailored for the task. In this paper, we conduct the first comprehensive study of skill utility under progressively challenging realistic settings, where agents must retrieve skills from a large collection of 34k real-world skills and may not have access to any hand-curated skills. Our findings reveal that the benefits of skills are fragile: performance gains degrade consistently as settings become more realistic, with pass rates approaching no-skill baselines in the most challenging scenarios. To narrow this gap, we study skill refinement strategies, including query-specific and query-agnostic approaches, and we show that query-specific refinement substantially recovers lost performance when the initial skills are of reasonable relevance and quality. We further demonstrate the generality of retrieval and refinement on Terminal-Bench 2.0, where they improve the pass rate of Claude Opus 4.6 from 57.7% to 65.5%. Our results, consistent across multiple models, highlight both the promise and the current limitations of skills for LLM-based agents. Our code is available at https://github.com/UCSB-NLP-Chang/Skill-Usage.", "github_manual": "", "github_regex": "https://github.com/UCSB-NLP-Chang/Skill-Usage", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.06170", "title": "Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework", "authors": [ "Komal Kumar", "Aman Chadha", "Salman Khan", "Fahad Shahbaz Khan", "Hisham Cholakkal" ], "github": "https://github.com/MAXNORM8650/papercircle", "project_page": "", "abstract": "The rapid growth of scientific literature has made it increasingly difficult for researchers to efficiently discover, evaluate, and synthesize relevant work. Recent advances in multi-agent large language models (LLMs) have demonstrated strong potential for understanding user intent and are being trained to utilize various tools. In this paper, we introduce Paper Circle, a multi-agent research discovery and analysis system designed to reduce the effort required to find, assess, organize, and understand academic literature. The system comprises two complementary pipelines: (1) a Discovery Pipeline that integrates offline and online retrieval from multiple sources, multi-criteria scoring, diversity-aware ranking, and structured outputs; and (2) an Analysis Pipeline that transforms individual papers into structured knowledge graphs with typed nodes such as concepts, methods, experiments, and figures, enabling graph-aware question answering and coverage verification. Both pipelines are implemented within a coder LLM-based multi-agent orchestration framework and produce fully reproducible, synchronized outputs including JSON, CSV, BibTeX, Markdown, and HTML at each agent step. This paper describes the system architecture, agent roles, retrieval and scoring methods, knowledge graph schema, and evaluation interfaces that together form the Paper Circle research workflow. We benchmark Paper Circle on both paper retrieval and paper review generation, reporting hit rate, MRR, and Recall at K. Results show consistent improvements with stronger agent models. We have publicly released the website at https://papercircle.vercel.app/ and the code at https://github.com/MAXNORM8650/papercircle.", "github_manual": "", "github_regex": "https://github.com/MAXNORM8650/papercircle", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.05091", "title": "MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU", "authors": [ "Zhengqing Yuan", "Hanchi Sun", "Lichao Sun", "Yanfang Ye" ], "github": "", "project_page": "", "abstract": "We present MegaTrain, a memory-centric system that efficiently trains 100B+ parameter large language models at full precision on a single GPU. Unlike traditional GPU-centric systems, MegaTrain stores parameters and optimizer states in host memory (CPU memory) and treats GPUs as transient compute engines. For each layer, we stream parameters in and compute gradients out, minimizing persistent device state. To battle the CPU-GPU bandwidth bottleneck, we adopt two key optimizations. 1) We introduce a pipelined double-buffered execution engine that overlaps parameter prefetching, computation, and gradient offloading across multiple CUDA streams, enabling continuous GPU execution. 2) We replace persistent autograd graphs with stateless layer templates, binding weights dynamically as they stream in, eliminating persistent graph metadata while providing flexibility in scheduling. On a single H200 GPU with 1.5TB host memory, MegaTrain reliably trains models up to 120B parameters. It also achieves 1.84times the training throughput of DeepSpeed ZeRO-3 with CPU offloading when training 14B models. MegaTrain also enables 7B model training with 512k token context on a single GH200.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.02648", "title": "GBQA: A Game Benchmark for Evaluating LLMs as Quality Assurance Engineers", "authors": [ "Shufan Jiang", "Chios Chen", "Zhiyang Chen" ], "github": "", "project_page": "", "abstract": "The autonomous discovery of bugs remains a significant challenge in modern software development. Compared to code generation, the complexity of dynamic runtime environments makes bug discovery considerably harder for large language models (LLMs). In this paper, we take game development as a representative domain and introduce the Game Benchmark for Quality Assurance (GBQA), a benchmark containing 30 games and 124 human-verified bugs across three difficulty levels, to evaluate whether LLMs can autonomously detect software bugs. The benchmark is constructed using a multi-agent system that develops games and injects bugs in a scalable manner, with human experts in the loop to ensure correctness. Moreover, we provide a baseline interactive agent equipped with a multi-round ReAct loop and a memory mechanism, enabling long-horizon exploration of game environments for bug detection across different LLMs. Extensive experiments on frontier LLMs demonstrate that autonomous bug discovery remains highly challenging: the best-performing model, Claude-4.6-Opus in thinking mode, identifies only 48.39% of the verified bugs. We believe GBQA provides an adequate testbed and evaluation criterion, and that further progress on it will help close the gap in autonomous software engineering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.05963", "title": "QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization", "authors": [ "Changxin Ke", "Rui Zhang", "Jiaming Guo", "Yuanbo Wen", "Li Ding", "Shuo Wang", "Xuyuan Zhu", "Xiong Peng", "Di Huang", "Zidong Du", "Xing Hu", "Qi Guo", "Yunji Chen" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) achieve strong program repair performance but often suffer from over-editing, where excessive modifications overwrite correct code and hinder bug localization. We systematically quantify its impact and introduce precise repair task, which maximizes reuse of correct code while fixing only buggy parts. Building on this insight, we propose PRepair, a framework that mitigates over-editing and improves repair accuracy. PRepair has two components: Self-Breaking, which generates diverse buggy programs via controlled bug injection and min-max sampling, and Self-Repairing, which trains models with Edit-Aware Group Relative Policy Optimization (EA-GRPO) using an edit-aware reward to encourage minimal yet correct edits. Experiments show that PRepair improves repair precision by up to 31.4% under fix_1@1, a metric that jointly considers repair correctness and extent, and significantly increases decoding throughput when combined with speculative editing, demonstrating its potential for precise and practical code repair.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.05117", "title": "Watch Before You Answer: Learning from Visually Grounded Post-Training", "authors": [ "Yuxuan Zhang", "EunJeong Hwang", "Huaisong Zhang", "Penghui Du", "Yiming Jia", "Dongfu Jiang", "Xuan He", "Shenhui Zhang", "Ping Nie", "Peter West", "Kelsey R. Allen" ], "github": "", "project_page": "", "abstract": "It is critical for vision-language models (VLMs) to comprehensively understand visual, temporal, and textual cues. However, despite rapid progress in multimodal modeling, video understanding performance still lags behind text-based reasoning. In this work, we find that progress is even worse than previously assumed: commonly reported long video understanding benchmarks contain 40-60% of questions that can be answered using text cues alone. Furthermore, we find that these issues are also pervasive in widely used post-training datasets, potentially undercutting the ability of post-training to improve VLM video understanding performance. Guided by this observation, we introduce VidGround as a simple yet effective solution: using only the actual visually grounded questions without any linguistic biases for post-training. When used in tandem with RL-based post-training algorithms, this simple technique improves performance by up to 6.2 points relative to using the full dataset, while using only 69.1% of the original post-training data. Moreover, we show that data curation with a simple post-training algorithm outperforms several more complex post-training techniques, highlighting that data quality is a major bottleneck for improving video understanding in VLMs. These results underscore the importance of curating post-training data and evaluation benchmarks that truly require visual grounding to advance the development of more capable VLMs. Project page: http://vidground.etuagi.com.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.04215", "title": "DARE: Diffusion Large Language Models Alignment and Reinforcement Executor", "authors": [ "Jingyi Yang", "Yuxian Jiang", "Xuhao Hu", "Shuang Cheng", "Biqing Qi", "Jing Shao" ], "github": "", "project_page": "", "abstract": "Diffusion large language models (dLLMs) are emerging as a compelling alternative to dominant autoregressive models, replacing strictly sequential token generation with iterative denoising and parallel generation dynamics. However, their open-source ecosystem remains fragmented across model families and, in particular, across post-training pipelines, where reinforcement learning objectives, rollout implementations and evaluation scripts are often released as paper-specific codebases. This fragmentation slows research iteration, raises the engineering burden of reproduction, and makes fair comparison across algorithms difficult. We present DARE (dLLMs Alignment and Reinforcement Executor), an open framework for post-training and evaluating dLLMs. Built on top of verl~sheng2024hybridflow and OpenCompass~2023opencompass, DARE unifies supervised fine-tuning, parameter-efficient fine-tuning, preference optimization, and dLLM-specific reinforcement learning under a shared execution stack for both masked and block diffusion language models. Across representative model families including LLaDA, Dream, SDAR, and LLaDA2.x, DARE provides broad algorithmic coverage, reproducible benchmark evaluation, and practical acceleration. Extensive empirical results position that DARE serves as a reusable research substrate for developing, comparing, and deploying post-training methods for current and emerging dLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.05939", "title": "Context-Value-Action Architecture for Value-Driven Large Language Model Agents", "authors": [ "TianZe Zhang", "Sirui Sun", "Yuhang Xie", "Xin Zhang", "Zhiqiang Wu", "Guojie Song" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have shown promise in simulating human behavior, yet existing agents often exhibit behavioral rigidity, a flaw frequently masked by the self-referential bias of current \"LLM-as-a-judge\" evaluations. By evaluating against empirical ground truth, we reveal a counter-intuitive phenomenon: increasing the intensity of prompt-driven reasoning does not enhance fidelity but rather exacerbates value polarization, collapsing population diversity. To address this, we propose the Context-Value-Action (CVA) architecture, grounded in the Stimulus-Organism-Response (S-O-R) model and Schwartz's Theory of Basic Human Values. Unlike methods relying on self-verification, CVA decouples action generation from cognitive reasoning via a novel Value Verifier trained on authentic human data to explicitly model dynamic value activation. Experiments on CVABench, which comprises over 1.1 million real-world interaction traces, demonstrate that CVA significantly outperforms baselines. Our approach effectively mitigates polarization while offering superior behavioral fidelity and interpretability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.03619", "title": "Can Natural Image Autoencoders Compactly Tokenize fMRI Volumes for Long-Range Dynamics Modeling?", "authors": [ "Peter Yongho Kim", "Juhyeon Park", "Jungwoo Park", "Jubin Choi", "Jungwoo Seo", "Jiook Cha", "Taesup Moon" ], "github": "https://github.com/beotborry/TABLeT", "project_page": "", "abstract": "Modeling long-range spatiotemporal dynamics in functional Magnetic Resonance Imaging (fMRI) remains a key challenge due to the high dimensionality of the four-dimensional signals. Prior voxel-based models, although demonstrating excellent performance and interpretation capabilities, are constrained by prohibitive memory demands and thus can only capture limited temporal windows. To address this, we propose TABLeT (Two-dimensionally Autoencoded Brain Latent Transformer), a novel approach that tokenizes fMRI volumes using a pre-trained 2D natural image autoencoder. Each 3D fMRI volume is compressed into a compact set of continuous tokens, enabling long-sequence modeling with a simple Transformer encoder with limited VRAM. Across large-scale benchmarks including the UK-Biobank (UKB), Human Connectome Project (HCP), and ADHD-200 datasets, TABLeT outperforms existing models in multiple tasks, while demonstrating substantial gains in computational and memory efficiency over the state-of-the-art voxel-based method given the same input. Furthermore, we develop a self-supervised masked token modeling approach to pre-train TABLeT, which improves the model's performance for various downstream tasks. Our findings suggest a promising approach for scalable and interpretable spatiotemporal modeling of brain activity. Our code is available at https://github.com/beotborry/TABLeT.", "github_manual": "", "github_regex": "https://github.com/beotborry/TABLeT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.04979", "title": "Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents", "authors": [ "\u00c1d\u00e1m Kov\u00e1cs" ], "github": "", "project_page": "", "abstract": "Coding agents repeatedly consume long tool observations even though only a small fraction of each observation matters for the next step. We study task-conditioned tool-output pruning: given a focused query and one tool output, return the smallest verbatim evidence block the agent should inspect next. We introduce a benchmark of 11,477 examples built from SWE-bench repository interactions and synthetic multi-ecosystem tool outputs, with a manually curated 618-example test set. We fine-tune Qwen 3.5 2B with LoRA and compare it against larger zero-shot models and heuristic pruning baselines. Our model reaches 0.86 recall and 0.80 F1 while removing 92% of input tokens, outperforming zero-shot Qwen 3.5 35B A3B by 11 recall points and all heuristic baselines by a wide margin.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.05181", "title": "General Multimodal Protein Design Enables DNA-Encoding of Chemistry", "authors": [ "Jarrid Rector-Brooks", "Th\u00e9ophile Lambert", "Marta Skreta", "Daniel Roth", "Yueming Long", "Zi-Qi Li", "Xi Zhang", "Miruna Cretu", "Francesca-Zhoufan Li", "Tanvi Ganapathy", "Emily Jin", "Avishek Joey Bose", "Jason Yang", "Kirill Neklyudov", "Yoshua Bengio", "Alexander Tong", "Frances H. Arnold", "Cheng-Hao Liu" ], "github": "https://github.com/DISCO-design/DISCO", "project_page": "", "abstract": "Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new proteins that bind ligands, but none have created enzymes without pre-specifying catalytic residues. We introduce DISCO (DIffusion for Sequence-structure CO-design), a multimodal model that co-designs protein sequence and 3D structure around arbitrary biomolecules, as well as inference-time scaling methods that optimize objectives across both modalities. Conditioned solely on reactive intermediates, DISCO designs diverse heme enzymes with novel active-site geometries. These enzymes catalyze new-to-nature carbene-transfer reactions, including alkene cyclopropanation, spirocyclopropanation, B-H, and C(sp^3)-H insertions, with high activities exceeding those of engineered enzymes. Random mutagenesis of a selected design further confirmed that enzyme activity can be improved through directed evolution. By providing a scalable route to evolvable enzymes, DISCO broadens the potential scope of genetically encodable transformations. Code is available at https://github.com/DISCO-design/DISCO.", "github_manual": "", "github_regex": "https://github.com/DISCO-design/DISCO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.01622", "title": "Expert-Choice Routing Enables Adaptive Computation in Diffusion Language Models", "authors": [ "Shuibai Zhang", "Caspian Zhuang", "Chihan Cui", "Zhihan Yang", "Fred Zhangzhi Peng", "Yanxin Zhang", "Haoyue Bai", "Zack Jia", "Yang Zhou", "Guanhua Chen", "Ming Liu" ], "github": "https://github.com/zhangshuibai/EC-DLM", "project_page": "", "abstract": "Diffusion language models (DLMs) enable parallel, non-autoregressive text generation, yet existing DLM mixture-of-experts (MoE) models inherit token-choice (TC) routing from autoregressive systems, leading to load imbalance and rigid computation allocation. We show that expert-choice (EC) routing is a better fit for DLMs: it provides deterministic load balancing by design, yielding higher throughput and faster convergence than TC. Building on the property that EC capacity is externally controllable, we introduce timestep-dependent expert capacity, which varies expert allocation according to the denoising step. We find that allocating more capacity to low-mask-ratio steps consistently achieves the best performance under matched FLOPs, and provide a mechanistic explanation: tokens in low-mask-ratio contexts exhibit an order-of-magnitude higher learning efficiency, so concentrating compute on these steps yields the largest marginal return. Finally, we show that existing pretrained TC DLMs can be retrofitted to EC by replacing only the router, achieving faster convergence and improved accuracy across diverse downstream tasks. Together, these results establish EC routing as a superior paradigm for DLM MoE models and demonstrate that computation in DLMs can be treated as an adaptive policy rather than a fixed architectural constant. Code is available at https://github.com/zhangshuibai/EC-DLM.", "github_manual": "", "github_regex": "https://github.com/zhangshuibai/EC-DLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.05467", "title": "CUE-R: Beyond the Final Answer in Retrieval-Augmented Generation", "authors": [ "Siddharth Jain", "Venkat Narayan Vedam" ], "github": "", "project_page": "", "abstract": "As language models shift from single-shot answer generation toward multi-step reasoning that retrieves and consumes evidence mid-inference, evaluating the role of individual retrieved items becomes more important. Existing RAG evaluation typically targets final-answer quality, citation faithfulness, or answer-level attribution, but none of these directly targets the intervention-based, per-evidence-item utility view we study here. We introduce CUE-R, a lightweight intervention-based framework for measuring per-evidence-item operational utility in single-shot RAG using shallow observable retrieval-use traces. CUE-R perturbs individual evidence items via REMOVE, REPLACE, and DUPLICATE operators, then measures changes along three utility axes (correctness, proxy-based grounding faithfulness, and confidence error) plus a trace-divergence signal. We also outline an operational evidence-role taxonomy for interpreting intervention outcomes. Experiments on HotpotQA and 2WikiMultihopQA with Qwen-3 8B and GPT-5.2 reveal a consistent pattern: REMOVE and REPLACE substantially harm correctness and grounding while producing large trace shifts, whereas DUPLICATE is often answer-redundant yet not fully behaviorally neutral. A zero-retrieval control confirms that these effects arise from degradation of meaningful retrieval. A two-support ablation further shows that multi-hop evidence items can interact non-additively: removing both supports harms performance far more than either single removal. Our results suggest that answer-only evaluation misses important evidence effects and that intervention-based utility analysis is a practical complement for RAG evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.05172", "title": "ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspaces", "authors": [ "Xiangyi Li", "Kyoung Whan Choe", "Yimin Liu", "Xiaokun Chen", "Chujun Tao", "Bingran You", "Wenbo Chen", "Zonglin Di", "Jiankai Sun", "Shenghan Zheng", "Jiajun Bao", "Yuanli Wang", "Weixiang Yan", "Yiyuan Li", "Han-chung Lee" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) agents are increasingly deployed to automate productivity tasks (e.g., email, scheduling, document management), but evaluating them on live services is risky due to potentially irreversible changes. Existing benchmarks rely on simplified environments and fail to capture realistic, stateful, multi-service workflows. We introduce ClawsBench, a benchmark for evaluating and improving LLM agents in realistic productivity settings. It includes five high-fidelity mock services (Gmail, Slack, Google Calendar, Google Docs, Google Drive) with full state management and deterministic snapshot/restore, along with 44 structured tasks covering single-service, cross-service, and safety-critical scenarios. We decompose agent scaffolding into two independent levers (domain skills that inject API knowledge via progressive disclosure, and a meta prompt that coordinates behavior across services) and vary both to measure their separate and combined effects. Experiments across 6 models, 4 agent harnesses, and 33 conditions show that with full scaffolding, agents achieve task success rates of 39-64% but exhibit unsafe action rates of 7-33%. On OpenClaw, the top five models fall within a 10 percentage-point band on task success (53-63%), with unsafe action rates from 7% to 23% and no consistent ordering between the two metrics. We identify eight recurring patterns of unsafe behavior, including multi-step sandbox escalation and silent contract modification.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-08", "arxiv_id": "2604.04356", "title": "REAM: Merging Improves Pruning of Experts in LLMs", "authors": [ "Saurav Jha", "Maryam Hashemzadeh", "Ali Saheb Pasand", "Ali Parviz", "Min-Joong Lee", "Boris Knyazev" ], "github": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) large language models (LLMs) are among the top-performing architectures. The largest models, often with hundreds of billions of parameters, pose significant memory challenges for deployment. Traditional approaches to reduce memory requirements include weight pruning and quantization. Motivated by the Router-weighted Expert Activation Pruning (REAP) that prunes experts, we propose a novel method, Router-weighted Expert Activation Merging (REAM). Instead of removing experts, REAM groups them and merges their weights, better preserving original performance. We evaluate REAM against REAP and other baselines across multiple MoE LLMs on diverse multiple-choice (MC) question answering and generative (GEN) benchmarks. Our results reveal a trade-off between MC and GEN performance that depends on the mix of calibration data. By controlling the mix of general, math and coding data, we examine the Pareto frontier of this trade-off and show that REAM often outperforms the baselines and in many cases is comparable to the original uncompressed models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.07348", "title": "MoRight: Motion Control Done Right", "authors": [ "Shaowei Liu", "Xuanchi Ren", "Tianchang Shen", "Huan Ling", "Saurabh Gupta", "Shenlong Wang", "Sanja Fidler", "Jun Gao" ], "github": "", "project_page": "", "abstract": "Generating motion-controlled videos--where user-specified actions drive physically plausible scene dynamics under freely chosen viewpoints--demands two capabilities: (1) disentangled motion control, allowing users to separately control the object motion and adjust camera viewpoint; and (2) motion causality, ensuring that user-driven actions trigger coherent reactions from other objects rather than merely displacing pixels. Existing methods fall short on both fronts: they entangle camera and object motion into a single tracking signal and treat motion as kinematic displacement without modeling causal relationships between object motion. We introduce MoRight, a unified framework that addresses both limitations through disentangled motion modeling. Object motion is specified in a canonical static-view and transferred to an arbitrary target camera viewpoint via temporal cross-view attention, enabling disentangled camera and object control. We further decompose motion into active (user-driven) and passive (consequence) components, training the model to learn motion causality from data. At inference, users can either supply active motion and MoRight predicts consequences (forward reasoning), or specify desired passive outcomes and MoRight recovers plausible driving actions (inverse reasoning), all while freely adjusting the camera viewpoint. Experiments on three benchmarks demonstrate state-of-the-art performance in generation quality, motion controllability, and interaction awareness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.06268", "title": "RAGEN-2: Reasoning Collapse in Agentic RL", "authors": [ "Zihan Wang", "Chi Gui", "Xing Jin", "Qineng Wang", "Licheng Liu", "Kangrui Wang", "Shiqi Chen", "Linjie Li", "Zhengyuan Yang", "Pingyue Zhang", "Yiping Lu", "Jiajun Wu", "Li Fei-Fei", "Lijuan Wang", "Yejin Choi", "Manling Li" ], "github": "", "project_page": "", "abstract": "RL training of multi-turn LLM agents is inherently unstable, and reasoning quality directly determines task performance. Entropy is widely used to track reasoning stability. However, entropy only measures diversity within the same input, and cannot tell whether reasoning actually responds to different inputs. In RAGEN-2, we find that even with stable entropy, models can rely on fixed templates that look diverse but are input-agnostic. We call this template collapse, a failure mode invisible to entropy and all existing metrics. To diagnose this failure, we decompose reasoning quality into within-input diversity (Entropy) and cross-input distinguishability (Mutual Information, MI), and introduce a family of mutual information proxies for online diagnosis. Across diverse tasks, mutual information correlates with final performance much more strongly than entropy, making it a more reliable proxy for reasoning quality. We further explain template collapse with a signal-to-noise ratio (SNR) mechanism. Low reward variance weakens task gradients, letting regularization terms dominate and erase cross-input reasoning differences. To address this, we propose SNR-Aware Filtering to select high-signal prompts per iteration using reward variance as a lightweight proxy. Across planning, math reasoning, web navigation, and code execution, the method consistently improves both input dependence and task performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.07023", "title": "MARS: Enabling Autoregressive Models Multi-Token Generation", "authors": [ "Ziqi Jin", "Lei Wang", "Ziwei Luo", "Aixin Sun" ], "github": "https://github.com/Xalp/MARS", "project_page": "", "abstract": "Autoregressive (AR) language models generate text one token at a time, even when consecutive tokens are highly predictable given earlier context. We introduce MARS (Mask AutoRegreSsion), a lightweight fine-tuning method that teaches an instruction-tuned AR model to predict multiple tokens per forward pass. MARS adds no architectural modifications, no extra parameters, and produces a single model that can still be called exactly like the original AR model with no performance degradation. Unlike speculative decoding, which maintains a separate draft model alongside the target, or multi-head approaches such as Medusa, which attach additional prediction heads, MARS requires only continued training on existing instruction data. When generating one token per forward pass, MARS matches or exceeds the AR baseline on six standard benchmarks. When allowed to accept multiple tokens per step, it maintains baseline-level accuracy while achieving 1.5-1.7x throughput. We further develop a block-level KV caching strategy for batch inference, achieving up to 1.71x wall-clock speedup over AR with KV cache on Qwen2.5-7B. Finally, MARS supports real-time speed adjustment via confidence thresholding: under high request load, the serving system can increase throughput on the fly without swapping models or restarting, providing a practical latency-quality knob for deployment.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Xalp/MARS", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.07343", "title": "Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization", "authors": [ "Qiyao Ma", "Dechen Gao", "Rui Cai", "Boqi Zhao", "Hanchu Zhou", "Junshan Zhang", "Zhe Zhao" ], "github": "", "project_page": "", "abstract": "Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing diverse human values. While benchmarks for general response quality are prevalent, evaluating how well reward models account for individual user preferences remains an open challenge. To bridge this gap, we introduce Personalized RewardBench, a novel benchmark designed to rigorously assess reward models' capacity to model personalized preferences. We construct chosen and rejected response pairs based on strict adherence to (or violation of) user-specific rubrics, ensuring that preference distinctions are uniquely tailored to the individual. In particular, human evaluations confirm that the primary discriminative factor between pairs is strictly personal preference, with both responses maintaining high general quality (e.g., correctness, relevance and helpfulness). Extensive testing reveals that existing state-of-the-art reward models struggle significantly with personalization, peaking at an accuracy of just 75.94%. Crucially, because an effective reward model benchmark should predict a reward model's performance on downstream tasks, we conduct experiments demonstrating that our benchmark exhibits a significantly higher correlation with downstream performance in both Best-of-N (BoN) sampling and Proximal Policy Optimization (PPO) compared to existing baselines. These findings establish Personalized RewardBench as a robust and accurate proxy for evaluating reward models' performance in downstream applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.07209", "title": "INSPATIO-WORLD: A Real-Time 4D World Simulator via Spatiotemporal Autoregressive Modeling", "authors": [ "InSpatio Team", "Donghui Shen", "Guofeng Zhang", "Haomin Liu", "Haoyu Ji", "Hujun Bao", "Hongjia Zhai", "Jialin Liu", "Jing Guo", "Nan Wang", "Siji Pan", "Weihong Pan", "Weijian Xie", "Xianbin Liu", "Xiaojun Xiang", "Xiaoyu Zhang", "Xinyu Chen", "Yifu Wang", "Yipeng Chen", "Zhenzhou Fan", "Zhewen Le", "Zhichao Ye", "Ziqiang Zhao" ], "github": "", "project_page": "", "abstract": "Building world models with spatial consistency and real-time interactivity remains a fundamental challenge in computer vision. Current video generation paradigms often struggle with a lack of spatial persistence and insufficient visual realism, making it difficult to support seamless navigation in complex environments. To address these challenges, we propose INSPATIO-WORLD, a novel real-time framework capable of recovering and generating high-fidelity, dynamic interactive scenes from a single reference video. At the core of our approach is a Spatiotemporal Autoregressive (STAR) architecture, which enables consistent and controllable scene evolution through two tightly coupled components: Implicit Spatiotemporal Cache aggregates reference and historical observations into a latent world representation, ensuring global consistency during long-horizon navigation; Explicit Spatial Constraint Module enforces geometric structure and translates user interactions into precise and physically plausible camera trajectories. Furthermore, we introduce Joint Distribution Matching Distillation (JDMD). By using real-world data distributions as a regularizing guide, JDMD effectively overcomes the fidelity degradation typically caused by over-reliance on synthetic data. Extensive experiments demonstrate that INSPATIO-WORLD significantly outperforms existing state-of-the-art (SOTA) models in spatial consistency and interaction precision, ranking first among real-time interactive methods on the WorldScore-Dynamic benchmark, and establishing a practical pipeline for navigating 4D environments reconstructed from monocular videos.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.05684", "title": "Improving Semantic Proximity in Information Retrieval through Cross-Lingual Alignment", "authors": [ "Seongtae Hong", "Youngjoon Jang", "Jungseob Lee", "Hyeonseok Moon", "Heuiseok Lim" ], "github": "", "project_page": "", "abstract": "With the increasing accessibility and utilization of multilingual documents, Cross-Lingual Information Retrieval (CLIR) has emerged as an important research area. Conventionally, CLIR tasks have been conducted under settings where the language of documents differs from that of queries, and typically, the documents are composed in a single coherent language. In this paper, we highlight that in such a setting, the cross-lingual alignment capability may not be evaluated adequately. Specifically, we observe that, in a document pool where English documents coexist with another language, most multilingual retrievers tend to prioritize unrelated English documents over the related document written in the same language as the query. To rigorously analyze and quantify this phenomenon, we introduce various scenarios and metrics designed to evaluate the cross-lingual alignment performance of multilingual retrieval models. Furthermore, to improve cross-lingual performance under these challenging conditions, we propose a novel training strategy aimed at enhancing cross-lingual alignment. Using only a small dataset consisting of 2.8k samples, our method significantly improves the cross-lingual retrieval performance while simultaneously mitigating the English inclination problem. Extensive analyses demonstrate that the proposed method substantially enhances the cross-lingual alignment capabilities of most multilingual embedding models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.06425", "title": "Neural Computers", "authors": [ "Mingchen Zhuge", "Changsheng Zhao", "Haozhe Liu", "Zijian Zhou", "Shuming Liu", "Wenyi Wang", "Ernie Chang", "Gael Le Lan", "Junjie Fei", "Wenxuan Zhang", "Yasheng Sun", "Zhipeng Cai", "Zechun Liu", "Yunyang Xiong", "Yining Yang", "Yuandong Tian", "Yangyang Shi", "Vikas Chandra", "J\u00fcrgen Schmidhuber" ], "github": "", "project_page": "", "abstract": "We propose a new frontier: Neural Computers (NCs) -- an emerging machine form that unifies computation, memory, and I/O in a learned runtime state. Unlike conventional computers, which execute explicit programs, agents, which act over external execution environments, and world models, which learn environment dynamics, NCs aim to make the model itself the running computer. Our long-term goal is the Completely Neural Computer (CNC): the mature, general-purpose realization of this emerging machine form, with stable execution, explicit reprogramming, and durable capability reuse. As an initial step, we study whether early NC primitives can be learned solely from collected I/O traces, without instrumented program state. Concretely, we instantiate NCs as video models that roll out screen frames from instructions, pixels, and user actions (when available) in CLI and GUI settings. These implementations show that learned runtimes can acquire early interface primitives, especially I/O alignment and short-horizon control, while routine reuse, controlled updates, and symbolic stability remain open. We outline a roadmap toward CNCs around these challenges. If overcome, CNCs could establish a new computing paradigm beyond today's agents, world models, and conventional computers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.06916", "title": "FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling", "authors": [ "Yitong Li", "Junsong Chen", "Shuchen Xue", "Pengcuo Zeren", "Siyuan Fu", "Dinghao Yang", "Yangyang Tang", "Junjie Bai", "Ping Luo", "Song Han", "Enze Xie" ], "github": "https://github.com/NVlabs/Sana", "project_page": "", "abstract": "Reinforcement-Learning-based post-training has recently emerged as a promising paradigm for aligning text-to-image diffusion models with human preferences. In recent studies, increasing the rollout group size yields pronounced performance improvements, indicating substantial room for further alignment gains. However, scaling rollouts on large-scale foundational diffusion models (e.g., FLUX.1-12B) imposes a heavy computational burden. To alleviate this bottleneck, we explore the integration of FP4 quantization into Diffusion RL rollouts. Yet, we identify that naive quantized pipelines inherently introduce risks of performance degradation. To overcome this dilemma between efficiency and training integrity, we propose Sol-RL (Speed-of-light RL), a novel FP4-empowered Two-stage Reinforcement Learning framework. First, we utilize high-throughput NVFP4 rollouts to generate a massive candidate pool and extract a highly contrastive subset. Second, we regenerate these selected samples in BF16 precision and optimize the policy exclusively on them. By decoupling candidate exploration from policy optimization, Sol-RL integrates the algorithmic mechanisms of rollout scaling with the system-level throughput gains of NVFP4. This synergistic algorithm-hardware design effectively accelerates the rollout phase while reserving high-fidelity samples for optimization. We empirically demonstrate that our framework maintains the training integrity of BF16 precision pipeline while fully exploiting the throughput gains enabled by FP4 arithmetic. Extensive experiments across SANA, FLUX.1, and SD3.5-L substantiate that our approach delivers superior alignment performance across multiple metrics while accelerating training convergence by up to 4.64times, unlocking the power of massive rollout scaling at a fraction of the cost.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVlabs/Sana", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.06182", "title": "VenusBench-Mobile: A Challenging and User-Centric Benchmark for Mobile GUI Agents with Capability Diagnostics", "authors": [ "Yichen Gong", "Zhuohan Cai", "Sunhao Dai", "Yuqi Zhou", "Zhangxuan Gu", "Changhua Meng", "Shuheng Shen" ], "github": "https://github.com/inclusionAI/UI-Venus", "project_page": "", "abstract": "Existing online benchmarks for mobile GUI agents remain largely app-centric and task-homogeneous, failing to reflect the diversity and instability of real-world mobile usage. To this end, we introduce VenusBench-Mobile, a challenging online benchmark for evaluating general-purpose mobile GUI agents under realistic, user-centric conditions. VenusBench-Mobile builds two core evaluation pillars: defining what to evaluate via user-intent-driven task design that reflects real mobile usage, and how to evaluate through a capability-oriented annotation scheme for fine-grained agent behavior analysis. Extensive evaluation of state-of-the-art mobile GUI agents reveals large performance gaps relative to prior benchmarks, indicating that VenusBench-Mobile poses substantially more challenging and realistic tasks and that current agents remain far from reliable real-world deployment. Diagnostic analysis further shows that failures are dominated by deficiencies in perception and memory, which are largely obscured by coarse-grained evaluations. Moreover, even the strongest agents exhibit near-zero success under environment variations, highlighting their brittleness in realistic settings. Based on these insights, we believe VenusBench-Mobile provides an important stepping stone toward robust real-world deployment of mobile GUI agents. Code and data are available at https://github.com/inclusionAI/UI-Venus/tree/VenusBench-Mobile.", "github_manual": "", "github_regex": "https://github.com/inclusionAI/UI-Venus", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.04746", "title": "Think in Strokes, Not Pixels: Process-Driven Image Generation via Interleaved Reasoning", "authors": [ "Lei Zhang", "Junjiao Tian", "Zhipeng Fan", "Kunpeng Li", "Jialiang Wang", "Weifeng Chen", "Markos Georgopoulos", "Felix Juefei-Xu", "Yuxiang Bao", "Julian McAuley", "Manling Li", "Zecheng He" ], "github": "", "project_page": "", "abstract": "Humans paint images incrementally: they plan a global layout, sketch a coarse draft, inspect, and refine details, and most importantly, each step is grounded in the evolving visual states. However, can unified multimodal models trained on text-image interleaved datasets also imagine the chain of intermediate states? In this paper, we introduce process-driven image generation, a multi-step paradigm that decomposes synthesis into an interleaved reasoning trajectory of thoughts and actions. Rather than generating images in a single step, our approach unfolds across multiple iterations, each consisting of 4 stages: textual planning, visual drafting, textual reflection, and visual refinement. The textual reasoning explicitly conditions how the visual state should evolve, while the generated visual intermediate in turn constrains and grounds the next round of textual reasoning. A core challenge of process-driven generation stems from the ambiguity of intermediate states: how can models evaluate each partially-complete image? We address this through dense, step-wise supervision that maintains two complementary constraints: for the visual intermediate states, we enforce the spatial and semantic consistency; for the textual intermediate states, we preserve the prior visual knowledge while enabling the model to identify and correct prompt-violating elements. This makes the generation process explicit, interpretable, and directly supervisable. To validate proposed method, we conduct experiments under various text-to-image generation benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.07340", "title": "TC-AE: Unlocking Token Capacity for Deep Compression Autoencoders", "authors": [ "Teng Li", "Ziyuan Huang", "Cong Chen", "Yangfu Li", "Yuanhuiyi Lyu", "Dandan Zheng", "Chunhua Shen", "Jun Zhang" ], "github": "", "project_page": "", "abstract": "We propose TC-AE, a ViT-based architecture for deep compression autoencoders. Existing methods commonly increase the channel number of latent representations to maintain reconstruction quality under high compression ratios. However, this strategy often leads to latent representation collapse, which degrades generative performance. Instead of relying on increasingly complex architectures or multi-stage training schemes, TC-AE addresses this challenge from the perspective of the token space, the key bridge between pixels and image latents, through two complementary innovations: Firstly, we study token number scaling by adjusting the patch size in ViT under a fixed latent budget, and identify aggressive token-to-latent compression as the key factor that limits effective scaling. To address this issue, we decompose token-to-latent compression into two stages, reducing structural information loss and enabling effective token number scaling for generation. Secondly, to further mitigate latent representation collapse, we enhance the semantic structure of image tokens via joint self-supervised training, leading to more generative-friendly latents. With these designs, TC-AE achieves substantially improved reconstruction and generative performance under deep compression. We hope our research will advance ViT-based tokenizer for visual generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.04443", "title": "DeonticBench: A Benchmark for Reasoning over Rules", "authors": [ "Guangyao Dou", "Luis Brena", "Akhil Deo", "William Jurayj", "Jingyu Zhang", "Nils Holzenberger", "Benjamin Van Durme" ], "github": "", "project_page": "", "abstract": "Reasoning with complex, context-specific rules remains challenging for large language models (LLMs). In legal and policy settings, this manifests as deontic reasoning: reasoning about obligations, permissions, and prohibitions under explicit rules. While many recent benchmarks emphasize short-context mathematical reasoning, fewer focus on long-context, high-stakes deontic reasoning. To address this gap, we introduce DEONTICBENCH, a benchmark of 6,232 tasks across U.S. federal taxes, airline baggage policies, U.S. immigration administration, and U.S. state housing law. These tasks can be approached in multiple ways, including direct reasoning in language or with the aid of symbolic computation. Besides free-form chain-of-thought reasoning, DEONTICBENCH enables an optional solver-based workflow in which models translate statutes and case facts into executable Prolog, leading to formal problem interpretations and an explicit program trace. We release reference Prolog programs for all instances. Across frontier LLMs and coding models, best hard-subset performance reaches only 44.4% on SARA Numeric and 46.6 macro-F1 on Housing. We further study training with supervised fine-tuning and reinforcement learning for symbolic program generation. Although training improves Prolog generation quality, current RL methods still fail to solve these tasks reliably. Overall, DEONTICBENCH provides a benchmark for studying context-grounded rule reasoning in real-world domains under both symbolic and non-symbolic settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.06427", "title": "The Depth Ceiling: On the Limits of Large Language Models in Discovering Latent Planning", "authors": [ "Yi Xu", "Philipp Jettkant", "Laura Ruis" ], "github": "", "project_page": "", "abstract": "The viability of chain-of-thought (CoT) monitoring hinges on models being unable to reason effectively in their latent representations. Yet little is known about the limits of such latent reasoning in LLMs. We test these limits by studying whether models can discover multi-step planning strategies without supervision on intermediate steps and execute them latently, within a single forward pass. Using graph path-finding tasks that precisely control the number of required latent planning steps, we uncover a striking limitation unresolved by massive scaling: tiny transformers trained from scratch discover strategies requiring up to three latent steps, fine-tuned GPT-4o and Qwen3-32B reach five, and GPT-5.4 attains seven under few-shot prompting. Although the maximum latent planning depth models can learn during training is five, the discovered strategy generalizes up to eight latent steps at test-time. This reveals a dissociation between the ability to discover a latent strategy under final-answer supervision alone and the ability to execute it once discovered. If similar limits hold more broadly, strategies requiring multiple coordinated latent planning steps may need to be explicitly taught or externalized, lending credence to CoT monitoring.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.07350", "title": "Fast Spatial Memory with Elastic Test-Time Training", "authors": [ "Ziqiao Ma", "Xueyang Yu", "Haoyu Zhen", "Yuncong Yang", "Joyce Chai", "Chuang Gan" ], "github": "", "project_page": "", "abstract": "Large Chunk Test-Time Training (LaCT) has shown strong performance on long-context 3D reconstruction, but its fully plastic inference-time updates remain vulnerable to catastrophic forgetting and overfitting. As a result, LaCT is typically instantiated with a single large chunk spanning the full input sequence, falling short of the broader goal of handling arbitrarily long sequences in a single pass. We propose Elastic Test-Time Training inspired by elastic weight consolidation, that stabilizes LaCT fast-weight updates with a Fisher-weighted elastic prior around a maintained anchor state. The anchor evolves as an exponential moving average of past fast weights to balance stability and plasticity. Based on this updated architecture, we introduce Fast Spatial Memory (FSM), an efficient and scalable model for 4D reconstruction that learns spatiotemporal representations from long observation sequences and renders novel view-time combinations. We pre-trained FSM on large-scale curated 3D/4D data to capture the dynamics and semantics of complex spatial environments. Extensive experiments show that FSM supports fast adaptation over long sequences and delivers high-quality 3D/4D reconstruction with smaller chunks and mitigating the camera-interpolation shortcut. Overall, we hope to advance LaCT beyond the bounded single-chunk setting toward robust multi-chunk adaptation, a necessary step for generalization to genuinely longer sequences, while substantially alleviating the activation-memory bottleneck.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.06912", "title": "Q-Zoom: Query-Aware Adaptive Perception for Efficient Multimodal Large Language Models", "authors": [ "Yuheng Shi", "Xiaohuan Pei", "Linfeng Wen", "Minjing Dong", "Chang Xu" ], "github": "", "project_page": "", "abstract": "MLLMs require high-resolution visual inputs for fine-grained tasks like document understanding and dense scene perception. However, current global resolution scaling paradigms indiscriminately flood the quadratic self-attention mechanism with visually redundant tokens, severely bottlenecking inference throughput while ignoring spatial sparsity and query intent. To overcome this, we propose Q-Zoom, a query-aware adaptive high-resolution perception framework that operates in an efficient coarse-to-fine manner. First, a lightweight Dynamic Gating Network safely bypasses high-resolution processing when coarse global features suffice. Second, for queries demanding fine-grained perception, a Self-Distilled Region Proposal Network (SD-RPN) precisely localizes the task-relevant Region-of-Interest (RoI) directly from intermediate feature spaces. To optimize these modules efficiently, the gating network uses a consistency-aware generation strategy to derive deterministic routing labels, while the SD-RPN employs a fully self-supervised distillation paradigm. A continuous spatio-temporal alignment scheme and targeted fine-tuning then seamlessly fuse the dense local RoI with the coarse global layout. Extensive experiments demonstrate that Q-Zoom establishes a dominant Pareto frontier. Using Qwen2.5-VL-7B as a primary testbed, Q-Zoom accelerates inference by 2.52 times on Document & OCR benchmarks and 4.39 times in High-Resolution scenarios while matching the baseline's peak accuracy. Furthermore, when configured for maximum perceptual fidelity, Q-Zoom surpasses the baseline's peak performance by 1.1% and 8.1% on these respective benchmarks. These robust improvements transfer seamlessly to Qwen3-VL, LLaVA, and emerging RL-based thinking-with-image models. Project page is available at https://yuhengsss.github.io/Q-Zoom/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.06757", "title": "FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching", "authors": [ "Junchao Yi", "Rui Zhao", "Jiahao Tang", "Weixian Lei", "Linjie Li", "Qisheng Su", "Zhengyuan Yang", "Lijuan Wang", "Xiaofeng Zhu", "Alex Jinpeng Wang" ], "github": "", "project_page": "", "abstract": "Multimodal generation has long been dominated by text-driven pipelines where language dictates vision but cannot reason or create within it. We challenge this paradigm by asking whether all modalities, including textual descriptions, spatial layouts, and editing instructions, can be unified into a single visual representation. We present FlowInOne, a framework that reformulates multimodal generation as a purely visual flow, converting all inputs into visual prompts and enabling a clean image-in, image-out pipeline governed by a single flow matching model. This vision-centric formulation naturally eliminates cross-modal alignment bottlenecks, noise scheduling, and task-specific architectural branches, unifying text-to-image generation, layout-guided editing, and visual instruction following under one coherent paradigm. To support this, we introduce VisPrompt-5M, a large-scale dataset of 5 million visual prompt pairs spanning diverse tasks including physics-aware force dynamics and trajectory prediction, alongside VP-Bench, a rigorously curated benchmark assessing instruction faithfulness, spatial precision, visual realism, and content consistency. Extensive experiments demonstrate that FlowInOne achieves state-of-the-art performance across all unified generation tasks, surpassing both open-source models and competitive commercial systems, establishing a new foundation for fully vision-centric generative modeling where perception and creation coexist within a single continuous visual space.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2603.25111", "title": "SEVerA: Verified Synthesis of Self-Evolving Agents", "authors": [ "Debangshu Banerjee", "Changming Xu", "Gagandeep Singh" ], "github": "", "project_page": "", "abstract": "Recent advances have shown the effectiveness of self-evolving LLM agents on tasks such as program repair and scientific discovery. In this paradigm, a planner LLM synthesizes an agent program that invokes parametric models, including LLMs, which are then tuned per task to improve performance. However, existing self-evolving agent frameworks provide no formal guarantees of safety or correctness. Because such programs are often executed autonomously on unseen inputs, this lack of guarantees raises reliability and security concerns. We formulate agentic code generation as a constrained learning problem, combining hard formal specifications with soft objectives capturing task utility. We introduce Formally Guarded Generative Models (FGGM), which allow the planner LLM to specify a formal output contract for each generative model call using first-order logic. Each FGGM call wraps the underlying model in a rejection sampler with a verified fallback, ensuring every returned output satisfies the contract for any input and parameter setting. Building on FGGM, we present SEVerA (Self-Evolving Verified Agents), a three-stage framework: Search synthesizes candidate parametric programs containing FGGM calls; Verification proves correctness with respect to hard constraints for all parameter values, reducing the problem to unconstrained learning; and Learning applies scalable gradient-based optimization, including GRPO-style fine-tuning, to improve the soft objective while preserving correctness. We evaluate SEVerA on Dafny program verification, symbolic math synthesis, and policy-compliant agentic tool use (\u03c4^2-bench). Across tasks, SEVerA achieves zero constraint violations while improving performance over unconstrained and SOTA baselines, showing that formal behavioral constraints not only guarantee correctness but also steer synthesis toward higher-quality agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.04734", "title": "Beyond Hard Negatives: The Importance of Score Distribution in Knowledge Distillation for Dense Retrieval", "authors": [ "Youngjoon Jang", "Seongtae Hong", "Hyeonseok Moon", "Heuiseok Lim" ], "github": "", "project_page": "", "abstract": "Transferring knowledge from a cross-encoder teacher via Knowledge Distillation (KD) has become a standard paradigm for training retrieval models. While existing studies have largely focused on mining hard negatives to improve discrimination, the systematic composition of training data and the resulting teacher score distribution have received relatively less attention. In this work, we highlight that focusing solely on hard negatives prevents the student from learning the comprehensive preference structure of the teacher, potentially hampering generalization. To effectively emulate the teacher score distribution, we propose a Stratified Sampling strategy that uniformly covers the entire score spectrum. Experiments on in-domain and out-of-domain benchmarks confirm that Stratified Sampling, which preserves the variance and entropy of teacher scores, serves as a robust baseline, significantly outperforming top-K and random sampling in diverse settings. These findings suggest that the essence of distillation lies in preserving the diverse range of relative scores perceived by the teacher.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.00698", "title": "Learning to Hint for Reinforcement Learning", "authors": [ "Yu Xia", "Canwen Xu", "Zhewei Yao", "Julian McAuley", "Yuxiong He" ], "github": "https://github.com/Andree-9/HiLL", "project_page": "", "abstract": "Group Relative Policy Optimization (GRPO) is widely used for reinforcement learning with verifiable rewards, but it often suffers from advantage collapse: when all rollouts in a group receive the same reward, the group yields zero relative advantage and thus no learning signal. For example, if a question is too hard for the reasoner, all sampled rollouts can be incorrect and receive zero reward. Recent work addresses this issue by adding hints or auxiliary scaffolds to such hard questions so that the reasoner produces mixed outcomes and recovers a non-zero update. However, existing hints are usually fixed rather than adapted to the current reasoner, and a hint that creates learning signal under the hinted input does not necessarily improve the no-hint policy used at test time. To this end, we propose Hint Learning for Reinforcement Learning (HiLL), a framework that jointly trains a hinter policy and a reasoner policy during RL. For each hard question, the hinter generates hints online conditioned on the current reasoner's incorrect rollout, allowing hint generation to adapt to the reasoner's evolving errors. We further introduce hint reliance, which measures how strongly correct hinted trajectories depend on the hint. We derive a transferability result showing that lower hint reliance implies stronger transfer from hinted success to no-hint success, and we use this result to define a transfer-weighted reward for training the hinter. Therefore, HiLL favors hints that not only recover informative GRPO groups, but also produce signals that are more likely to improve the original no-hint policy. Experiments across multiple benchmarks show that HiLL consistently outperforms GRPO and prior hint-based baselines, demonstrating the value of adaptive and transfer-aware hint learning for RL. The code is available at https://github.com/Andree-9/HiLL.", "github_manual": "", "github_regex": "https://github.com/Andree-9/HiLL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2603.26657", "title": "Tunable Soft Equivariance with Guarantees", "authors": [ "Md Ashiqur Rahman", "Lim Jun Hao", "Jeremiah Jiang", "Teck-Yian Lim", "Raymond A. Yeh" ], "github": "https://github.com/ashiq24/soft-equivariance", "project_page": "", "abstract": "Equivariance is a fundamental property in computer vision models, yet strict equivariance is rarely satisfied in real-world data, which can limit a model's performance. Controlling the degree of equivariance is therefore desirable. We propose a general framework for constructing soft equivariant models by projecting the model weights into a designed subspace. The method applies to any pre-trained architecture and provides theoretical bounds on the induced equivariance error. Empirically, we demonstrate the effectiveness of our method on multiple pre-trained backbones, including ViT and ResNet, across image classification, semantic segmentation, and human-trajectory prediction tasks. Notably, our approach improves the performance while simultaneously reducing equivariance error on the competitive ImageNet benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ashiq24/soft-equivariance", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.05060", "title": "R3PM-Net: Real-time, Robust, Real-world Point Matching Network", "authors": [ "Yasaman Kashefbahrami", "Erkut Akdag", "Panagiotis Meletis", "Evgeniya Balmashnova", "Dip Goswami", "Egor Bondarau" ], "github": "https://github.com/YasiiKB/R3PM-Net", "project_page": "", "abstract": "Accurate Point Cloud Registration (PCR) is an important task in 3D data processing, involving the estimation of a rigid transformation between two point clouds. While deep-learning methods have addressed key limitations of traditional non-learning approaches, such as sensitivity to noise, outliers, occlusion, and initialization, they are developed and evaluated on clean, dense, synthetic datasets (limiting their generalizability to real-world industrial scenarios). This paper introduces R3PM-Net, a lightweight, global-aware, object-level point matching network designed to bridge this gap by prioritizing both generalizability and real-time efficiency. To support this transition, two datasets, Sioux-Cranfield and Sioux-Scans, are proposed. They provide an evaluation ground for registering imperfect photogrammetric and event-camera scans to digital CAD models, and have been made publicly available. Extensive experiments demonstrate that R3PM-Net achieves competitive accuracy with unmatched speed. On ModelNet40, it reaches a perfect fitness score of 1 and inlier RMSE of 0.029 cm in only 0.007s, approximately 7 times faster than the state-of-the-art method RegTR. This performance carries over to the Sioux-Cranfield dataset, maintaining a fitness of 1 and inlier RMSE of 0.030 cm with similarly low latency. Furthermore, on the highly challenging Sioux-Scans dataset, R3PM-Net successfully resolves edge cases in under 50 ms. These results confirm that R3PM-Net offers a robust, high-speed solution for critical industrial applications, where precision and real-time performance are indispensable. The code and datasets are available at https://github.com/YasiiKB/R3PM-Net.", "github_manual": "", "github_regex": "https://github.com/YasiiKB/R3PM-Net", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.05846", "title": "AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning", "authors": [ "Yuanfu Sun", "Kang Li", "Dongzhe Fan", "Jiajin Liu", "Qiaoyu Tan" ], "github": "https://github.com/sunyuanfu/AgentGL", "project_page": "", "abstract": "Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and decision-making-to overcome the limits of static, parametric knowledge. Yet existing agentic frameworks treat external information as unstructured text and fail to leverage the topological dependencies inherent in real-world data. To bridge this gap, we introduce Agentic Graph Learning (AGL), a paradigm that reframes graph learning as an interleaved process of topology-aware navigation and LLM-based inference. Specifically, we propose AgentGL, the first reinforcement learning (RL)-driven framework for AGL. AgentGL equips an LLM agent with graph-native tools for multi-scale exploration, regulates tool usage via search-constrained thinking to balance accuracy and efficiency, and employs a graph-conditioned curriculum RL strategy to stabilize long-horizon policy learning without step-wise supervision. Across diverse Text-Attributed Graph (TAG) benchmarks and multiple LLM backbones, AgentGL substantially outperforms strong GraphLLMs and GraphRAG baselines, achieving absolute improvements of up to 17.5% in node classification and 28.4% in link prediction. These results demonstrate that AGL is a promising frontier for enabling LLMs to autonomously navigate and reason over complex relational environments. The code is publicly available at https://github.com/sunyuanfu/AgentGL.", "github_manual": "", "github_regex": "https://github.com/sunyuanfu/AgentGL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.04913", "title": "A Frame is Worth One Token: Efficient Generative World Modeling with Delta Tokens", "authors": [ "Tommie Kerssies", "Gabriele Berton", "Ju He", "Qihang Yu", "Wufei Ma", "Daan de Geus", "Gijs Dubbelman", "Liang-Chieh Chen" ], "github": "", "project_page": "", "abstract": "Anticipating diverse future states is a central challenge in video world modeling. Discriminative world models produce a deterministic prediction that implicitly averages over possible futures, while existing generative world models remain computationally expensive. Recent work demonstrates that predicting the future in the feature space of a vision foundation model (VFM), rather than a latent space optimized for pixel reconstruction, requires significantly fewer world model parameters. However, most such approaches remain discriminative. In this work, we introduce DeltaTok, a tokenizer that encodes the VFM feature difference between consecutive frames into a single continuous \"delta\" token, and DeltaWorld, a generative world model operating on these tokens to efficiently generate diverse plausible futures. Delta tokens reduce video from a three-dimensional spatio-temporal representation to a one-dimensional temporal sequence, for example yielding a 1,024x token reduction with 512x512 frames. This compact representation enables tractable multi-hypothesis training, where many futures are generated in parallel and only the best is supervised. At inference, this leads to diverse predictions in a single forward pass. Experiments on dense forecasting tasks demonstrate that DeltaWorld forecasts futures that more closely align with real-world outcomes, while having over 35x fewer parameters and using 2,000x fewer FLOPs than existing generative world models. Code and weights: https://deltatok.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.06392", "title": "Qualixar OS: A Universal Operating System for AI Agent Orchestration", "authors": [ "Varun Pratap Bhardwaj" ], "github": "", "project_page": "", "abstract": "We present Qualixar OS, the first application-layer operating system for universal AI agent orchestration. Unlike kernel-level approaches (AIOS) or single-framework tools (AutoGen, CrewAI), Qualixar OS provides a complete runtime for heterogeneous multi-agent systems spanning 10 LLM providers, 8+ agent frameworks, and 7 transports. We contribute: (1) execution semantics for 12 multi-agent topologies including grid, forest, mesh, and maker patterns; (2) Forge, an LLM-driven team design engine with historical strategy memory; (3) three-layer model routing combining Q-learning, five strategies, and Bayesian POMDP with dynamic multi-provider discovery; (4) a consensus-based judge pipeline with Goodhart detection, JSD drift monitoring, and alignment trilemma navigation; (5) four-layer content attribution with HMAC signing and steganographic watermarks; (6) universal compatibility via the Claw Bridge supporting MCP and A2A protocols with a 25-command Universal Command Protocol; (7) a 24-tab production dashboard with visual workflow builder and skill marketplace. Qualixar OS is validated by 2,821 test cases across 217 event types and 8 quality modules. On a custom 20-task evaluation suite, the system achieves 100% accuracy at a mean cost of $0.000039 per task. Source-available under the Elastic License 2.0.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.03995", "title": "A Systematic Study of Cross-Modal Typographic Attacks on Audio-Visual Reasoning", "authors": [ "Tianle Chen", "Deepti Ghadiyaram" ], "github": "", "project_page": "", "abstract": "As audio-visual multi-modal large language models (MLLMs) are increasingly deployed in safety-critical applications, understanding their vulnerabilities is crucial. To this end, we introduce Multi-Modal Typography, a systematic study examining how typographic attacks across multiple modalities adversely influence MLLMs. While prior work focuses narrowly on unimodal attacks, we expose the cross-modal fragility of MLLMs. We analyze the interactions between audio, visual, and text perturbations and reveal that coordinated multi-modal attack creates a significantly more potent threat than single-modality attacks (attack success rate = 83.43% vs 34.93%).Our findings across multiple frontier MLLMs, tasks, and common-sense reasoning and content moderation benchmarks establishes multi-modal typography as a critical and underexplored attack strategy in multi-modal reasoning. Code and data will be publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.05643", "title": "Graph-Based Chain-of-Thought Pruning for Reducing Redundant Reflections in Reasoning LLMs", "authors": [ "Hongyuan Yuan", "Xinran He", "Run Shao", "Bolei He", "Xianwei Xue", "Mengke Chen", "Qiutong Pan", "Haiwei Wang", "Haifeng Li" ], "github": "", "project_page": "", "abstract": "Extending CoT through RL has been widely used to enhance the reasoning capabilities of LLMs. However, due to the sparsity of reward signals, it can also induce undesirable thinking patterns such as overthinking, i.e., generating redundant intermediate reasoning content. In this work, we argue that a major source of such redundancy is inefficient reflection, which often manifests in two problematic patterns: Indiscriminate Reflection, where the model performs broad, low-impact checks throughout reasoning, and Repetitive Reflection, where it repeatedly re-verifies an already established conclusion. To address this, we introduce a graph-based CoT optimization framework. Specifically, we convert each linear CoT into a directed acyclic graph (DAG) with explicit dependency edges, and design a dual pruning strategy: branch-level pruning removes weakly contributing reflection branches, while depth-level pruning eliminates late-stage re-verification. We distill this behavior via a three-stage pipeline: (1) SFT to initialize the policy on pruned concise traces, (2) DPO to prefer correct but less redundant trajectories, and (3) GRPO with length penalty to jointly optimize answer correctness and efficiency. Experiments show that our approach reduces the average reasoning tokens by 42\\% while maintaining or improving accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.07273", "title": "GenLCA: 3D Diffusion for Full-Body Avatars from In-the-Wild Videos", "authors": [ "Yiqian Wu", "Rawal Khirodkar", "Egor Zakharov", "Timur Bagautdinov", "Lei Xiao", "Zhaoen Su", "Shunsuke Saito", "Xiaogang Jin", "Junxuan Li" ], "github": "", "project_page": "", "abstract": "We present GenLCA, a diffusion-based generative model for generating and editing photorealistic full-body avatars from text and image inputs. The generated avatars are faithful to the inputs, while supporting high-fidelity facial and full-body animations. The core idea is a novel paradigm that enables training a full-body 3D diffusion model from partially observable 2D data, allowing the training dataset to scale to millions of real-world videos. This scalability contributes to the superior photorealism and generalizability of GenLCA. Specifically, we scale up the dataset by repurposing a pretrained feed-forward avatar reconstruction model as an animatable 3D tokenizer, which encodes unstructured video frames into structured 3D tokens. However, most real-world videos only provide partial observations of body parts, resulting in excessive blurring or transparency artifacts in the 3D tokens. To address this, we propose a novel visibility-aware diffusion training strategy that replaces invalid regions with learnable tokens and computes losses only over valid regions. We then train a flow-based diffusion model on the token dataset, inherently maintaining the photorealism and animatability provided by the pretrained avatar reconstruction model. Our approach effectively enables the use of large-scale real-world video data to train a diffusion model natively in 3D. We demonstrate the efficacy of our method through diverse and high-fidelity generation and editing results, outperforming existing solutions by a large margin. The project page is available at https://onethousandwu.com/GenLCA-Page.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.04247", "title": "Combee: Scaling Prompt Learning for Self-Improving Language Model Agents", "authors": [ "Hanchen Li", "Runyuan He", "Qizheng Zhang", "Changxiu Ji", "Qiuyang Mang", "Xiaokun Chen", "Lakshya A Agrawal", "Wei-Liang Liao", "Eric Yang", "Alvin Cheung", "James Zou", "Kunle Olukotun", "Ion Stoica", "Joseph E. Gonzalez" ], "github": "", "project_page": "", "abstract": "Recent advances in prompt learning allow large language model agents to acquire task-relevant knowledge from inference-time context without parameter changes. For example, existing methods (like ACE or GEPA) can learn system prompts to improve accuracy based on previous agent runs. However, these methods primarily focus on single-agent or low-parallelism settings. This fundamentally limits their ability to efficiently learn from a large set of collected agentic traces. It would be efficient and beneficial to run prompt learning in parallel to accommodate the growing trend of learning from many agentic traces or parallel agent executions. Yet without a principled strategy for scaling, current methods suffer from quality degradation with high parallelism. To improve both the efficiency and quality of prompt learning, we propose Combee, a novel framework to scale parallel prompt learning for self-improving agents. Combee speeds up learning and enables running many agents in parallel while learning from their aggregate traces without quality degradation. To achieve this, Combee leverages parallel scans and employs an augmented shuffle mechanism; Combee also introduces a dynamic batch size controller to balance quality and delay. Evaluations on AppWorld, Terminal-Bench, Formula, and FiNER demonstrate that Combee achieves up to 17x speedup over previous methods with comparable or better accuracy and equivalent cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-09", "arxiv_id": "2604.06834", "title": "On the Step Length Confounding in LLM Reasoning Data Selection", "authors": [ "Bing Wang", "Rui Miao", "Chen Shen", "Shaotian Yan", "Kaiyuan Liu", "Ximing Li", "Xiaosong Yuan", "Sinan Fan", "Jun Zhang", "Jieping Ye" ], "github": "", "project_page": "", "abstract": "Large reasoning models have recently demonstrated strong performance on complex tasks that require long chain-of-thought reasoning, through supervised fine-tuning on large-scale and high-quality datasets. To construct such datasets, existing pipelines generate long reasoning data from more capable Large Language Models (LLMs) and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. Despite the proven effectiveness of naturalness-based data selection, which ranks data by the average log probability assigned by LLMs, our analysis shows that, when applied to LLM reasoning datasets, it systematically prefers samples with longer reasoning steps (i.e., more tokens per step) rather than higher-quality ones, a phenomenon we term step length confounding. Through quantitative analysis, we attribute this phenomenon to low-probability first tokens in reasoning steps; longer steps dilute their influence, thereby inflating the average log probabilities. To address this issue, we propose two variant methods: ASLEC-DROP, which drops first-token probabilities when computing average log probability, and ASLEC-CASL, which applies a causal debiasing regression to remove the first tokens' confounding effect. Experiments across four LLMs and five evaluation benchmarks demonstrate the effectiveness of our approach in mitigating the step length confounding problem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.05333", "title": "Graph of Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills", "authors": [ "Dawei Li", "Zongxia Li", "Hongyang Du", "Xiyang Wu", "Shihang Gui", "Yongbei Kuang", "Lichao Sun" ], "github": "https://github.com/LabRAI/LangSkills", "project_page": "", "abstract": "Skill usage has become a core component of modern agent systems and can substantially improve agents' ability to complete complex tasks. In real-world settings, where agents must monitor and interact with numerous personal applications, web browsers, and other environment interfaces, skill libraries can scale to thousands of reusable skills. Scaling to larger skill sets introduces two key challenges. First, loading the full skill set saturates the context window, driving up token costs, hallucination, and latency. In this paper, we present Graph of Skills (GoS), an inference-time structural retrieval layer for large skill libraries. GoS constructs an executable skill graph offline from skill packages, then at inference time retrieves a bounded, dependency-aware skill bundle through hybrid semantic-lexical seeding, reverse-weighted Personalized PageRank, and context-budgeted hydration. On SkillsBench and ALFWorld, GoS improves average reward by 43.6% over the vanilla full skill-loading baseline while reducing input tokens by 37.8%, and generalizes across three model families: Claude Sonnet, GPT-5.2 Codex, and MiniMax. Additional ablation studies across skill libraries ranging from 200 to 2,000 skills further demonstrate that GoS consistently outperforms both vanilla skills loading and simple vector retrieval in balancing reward, token efficiency, and runtime.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LabRAI/LangSkills", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08209", "title": "OmniJigsaw: Enhancing Omni-Modal Reasoning via Modality-Orchestrated Reordering", "authors": [ "Yiduo Jia", "Muzhi Zhu", "Hao Zhong", "Mingyu Liu", "Yuling Xi", "Hao Chen", "Bin Qin", "Yongjie Yang", "Zhenbo Luo", "Chunhua Shen" ], "github": "", "project_page": "", "abstract": "To extend the reinforcement learning post-training paradigm to omni-modal models for concurrently bolstering video-audio understanding and collaborative reasoning, we propose OmniJigsaw, a generic self-supervised framework built upon a temporal reordering proxy task. Centered on the chronological reconstruction of shuffled audio-visual clips, this paradigm strategically orchestrates visual and auditory signals to compel cross-modal integration through three distinct strategies: Joint Modality Integration, Sample-level Modality Selection, and Clip-level Modality Masking. Recognizing that the efficacy of such proxy tasks is fundamentally tied to puzzle quality, we design a two-stage coarse-to-fine data filtering pipeline, which facilitates the efficient adaptation of OmniJigsaw to massive unannotated omni-modal data. Our analysis reveals a ``bi-modal shortcut phenomenon'' in joint modality integration and demonstrates that fine-grained clip-level modality masking mitigates this issue while outperforming sample-level modality selection. Extensive evaluations on 15 benchmarks show substantial gains in video, audio, and collaborative reasoning, validating OmniJigsaw as a scalable paradigm for self-supervised omni-modal learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.07394", "title": "Flux Attention: Context-Aware Hybrid Attention for Efficient LLMs Inference", "authors": [ "Quantong Qiu", "Zhiyi Hong", "Yi Yang", "Haitian Wang", "Kebin Liu", "Qingqing Dang", "Juntao Li", "Min Zhang" ], "github": "", "project_page": "", "abstract": "The quadratic computational complexity of standard attention mechanisms presents a severe scalability bottleneck for LLMs in long-context scenarios. While hybrid attention mechanisms combining Full Attention (FA) and Sparse Attention (SA) offer a potential solution, existing methods typically rely on static allocation ratios that fail to accommodate the variable retrieval demands of different tasks. Furthermore, head-level dynamic sparsity often introduces severe computational load imbalance and synchronization long-tails, which hinder hardware acceleration during autoregressive decoding. To bridge this gap, we introduce Flux Attention, a context-aware framework that dynamically optimizes attention computation at the layer level. By integrating a lightweight Layer Router into frozen pretrained LLMs, the proposed method adaptively routes each layer to FA or SA based on the input context. This layer-wise routing preserves high-fidelity information retrieval while ensuring contiguous memory access, translating theoretical computational reductions into practical wall-clock speedups. As a parameter-efficient approach, our framework requires only 12 hours of training on 8timesA800 GPUs. Extensive experiments across multiple long-context and mathematical reasoning benchmarks demonstrate that Flux Attention achieves a superior trade-off between performance and inference speed compared with baseline models, with speed improvements of up to 2.8times and 2.0times in the prefill and decode stages.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08302", "title": "DMax: Aggressive Parallel Decoding for dLLMs", "authors": [ "Zigeng Chen", "Gongfan Fang", "Xinyin Ma", "Ruonan Yu", "Xinchao Wang" ], "github": "https://github.com/czg1225/DMax", "project_page": "", "abstract": "We present DMax, a new paradigm for efficient diffusion language models (dLLMs). It mitigates error accumulation in parallel decoding, enabling aggressive decoding parallelism while preserving generation quality. Unlike conventional masked dLLMs that decode through a binary mask-to-token transition, DMax reformulates decoding as a progressive self-refinement from mask embeddings to token embeddings. At the core of our approach is On-Policy Uniform Training, a novel training strategy that efficiently unifies masked and uniform dLLMs, equipping the model to recover clean tokens from both masked inputs and its own erroneous predictions. Building on this foundation, we further propose Soft Parallel Decoding. We represent each intermediate decoding state as an interpolation between the predicted token embedding and the mask embedding, enabling iterative self-revising in embedding space. Extensive experiments across a variety of benchmarks demonstrate the effectiveness of DMax. Compared with the original LLaDA-2.0-mini, our method improves TPF on GSM8K from 2.04 to 5.47 while preserving accuracy. On MBPP, it increases TPF from 2.71 to 5.86 while maintaining comparable performance. On two H200 GPUs, our model achieves an average of 1,338 TPS at batch size 1. Code is available at: https://github.com/czg1225/DMax", "github_manual": "", "github_regex": "https://github.com/czg1225/DMax", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08224", "title": "Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering", "authors": [ "Chenyu Zhou", "Huacan Chai", "Wenteng Chen", "Zihan Guo", "Rong Shan", "Yuanyi Song", "Tianyi Xu", "Yingxuan Yang", "Aofan Yu", "Weiming Zhang", "Congming Zheng", "Jiachen Zhu", "Zeyu Zheng", "Zhuosheng Zhang", "Xingyu Lou", "Changwang Zhang", "Zhihui Fu", "Jun Wang", "Weiwen Liu", "Jianghao Lin", "Weinan Zhang" ], "github": "https://github.com/crewAIInc/crewAI", "project_page": "", "abstract": "Large language model (LLM) agents are increasingly built less by changing model weights than by reorganizing the runtime around them. Capabilities that earlier systems expected the model to recover internally are now externalized into memory stores, reusable skills, interaction protocols, and the surrounding harness that makes these modules reliable in practice. This paper reviews that shift through the lens of externalization. Drawing on the idea of cognitive artifacts, we argue that agent infrastructure matters not merely because it adds auxiliary components, but because it transforms hard cognitive burdens into forms that the model can solve more reliably. Under this view, memory externalizes state across time, skills externalize procedural expertise, protocols externalize interaction structure, and harness engineering serves as the unification layer that coordinates them into governed execution. We trace a historical progression from weights to context to harness, analyze memory, skills, and protocols as three distinct but coupled forms of externalization, and examine how they interact inside a larger agent system. We further discuss the trade-off between parametric and externalized capability, identify emerging directions such as self-evolving harnesses and shared agent infrastructure, and discuss open challenges in evaluation, governance, and the long-term co-evolution of models and external infrastructure. The result is a systems-level framework for explaining why practical agent progress increasingly depends not only on stronger models, but on better external cognitive infrastructure.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/crewAIInc/crewAI", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08455", "title": "KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation", "authors": [ "Tongbo Chen", "Zhengxi Lu", "Zhan Xu", "Guocheng Shao", "Shaohan Zhao", "Fei Tang", "Yong Du", "Kaitao Song", "Yizhou Liu", "Yuchen Yan", "Wenqi Zhang", "Xu Tan", "Weiming Lu", "Jun Xiao", "Yueting Zhuang", "Yongliang Shen" ], "github": "", "project_page": "", "abstract": "Personalized mobile agents that infer user preferences and calibrate proactive assistance hold great promise as everyday digital assistants, yet existing benchmarks fail to capture what this requires. Prior work evaluates preference recovery from static histories or intent prediction from fixed contexts. Neither tests whether an agent can elicit missing preferences through interaction, nor whether it can decide when to intervene, seek consent, or remain silent in a live GUI environment. We introduce KnowU-Bench, an online benchmark for personalized mobile agents built on a reproducible Android emulation environment, covering 42 general GUI tasks, 86 personalized tasks, and 64 proactive tasks. Unlike prior work that treats user preferences as static context, KnowU-Bench hides the user profile from the agent and exposes only behavioral logs, forcing genuine preference inference rather than context lookup. To support multi-turn preference elicitation, it instantiates an LLM-driven user simulator grounded in structured profiles, enabling realistic clarification dialogues and proactive consent handling. Beyond personalization, KnowU-Bench provides comprehensive evaluation of the complete proactive decision chain, including grounded GUI execution, consent negotiation, and post-rejection restraint, evaluated through a hybrid protocol combining rule-based verification with LLM-as-a-Judge scoring. Our experiments reveal a striking degradation: agents that excel at explicit task execution fall below 50% under vague instructions requiring user preference inference or intervention calibration, even for frontier models like Claude Sonnet 4.6. The core bottlenecks are not GUI navigation but preference acquisition and intervention calibration, exposing a fundamental gap between competent interface operation and trustworthy personal assistance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08546", "title": "When Numbers Speak: Aligning Textual Numerals and Visual Instances in Text-to-Video Diffusion Models", "authors": [ "Zhengyang Sun", "Yu Chen", "Xin Zhou", "Xiaofan Li", "Xiwu Chen", "Dingkang Liang", "Xiang Bai" ], "github": "https://github.com/H-EmbodVis/NUMINA", "project_page": "", "abstract": "Text-to-video diffusion models have enabled open-ended video synthesis, but often struggle with generating the correct number of objects specified in a prompt. We introduce NUMINA , a training-free identify-then-guide framework for improved numerical alignment. NUMINA identifies prompt-layout inconsistencies by selecting discriminative self- and cross-attention heads to derive a countable latent layout. It then refines this layout conservatively and modulates cross-attention to guide regeneration. On the introduced CountBench, NUMINA improves counting accuracy by up to 7.4% on Wan2.1-1.3B, and by 4.9% and 5.5% on 5B and 14B models, respectively. Furthermore, CLIP alignment is improved while maintaining temporal consistency. These results demonstrate that structural guidance complements seed search and prompt enhancement, offering a practical path toward count-accurate text-to-video diffusion. The code is available at https://github.com/H-EmbodVis/NUMINA.", "github_manual": "", "github_regex": "https://github.com/H-EmbodVis/NUMINA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.07430", "title": "HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents", "authors": [ "Tencent Robotics X", "HY Vision Team", "Xumin Yu", "Zuyan Liu", "Ziyi Wang", "He Zhang", "Yongming Rao", "Fangfu Liu", "Yani Zhang", "Ruowen Zhao", "Oran Wang", "Yves Liang", "Haitao Lin", "Minghui Wang", "Yubo Dong", "Kevin Cheng", "Bolin Ni", "Rui Huang", "Han Hu", "Zhengyou Zhang", "Linus", "Shunyu Yao" ], "github": "https://github.com/Tencent-Hunyuan/HY-Embodied", "project_page": "", "abstract": "We introduce HY-Embodied-0.5, a family of foundation models specifically designed for real-world embodied agents. To bridge the gap between general Vision-Language Models (VLMs) and the demands of embodied agents, our models are developed to enhance the core capabilities required by embodied intelligence: spatial and temporal visual perception, alongside advanced embodied reasoning for prediction, interaction, and planning. The HY-Embodied-0.5 suite comprises two primary variants: an efficient model with 2B activated parameters designed for edge deployment, and a powerful model with 32B activated parameters targeted for complex reasoning. To support the fine-grained visual perception essential for embodied tasks, we adopt a Mixture-of-Transformers (MoT) architecture to enable modality-specific computing. By incorporating latent tokens, this design effectively enhances the perceptual representation of the models. To improve reasoning capabilities, we introduce an iterative, self-evolving post-training paradigm. Furthermore, we employ on-policy distillation to transfer the advanced capabilities of the large model to the smaller variant, thereby maximizing the performance potential of the compact model. Extensive evaluations across 22 benchmarks, spanning visual perception, spatial reasoning, and embodied understanding, demonstrate the effectiveness of our approach. Our MoT-2B model outperforms similarly sized state-of-the-art models on 16 benchmarks, while the 32B variant achieves performance comparable to frontier models such as Gemini 3.0 Pro. In downstream robot control experiments, we leverage our robust VLM foundation to train an effective Vision-Language-Action (VLA) model, achieving compelling results in real-world physical evaluations. Code and models are open-sourced at https://github.com/Tencent-Hunyuan/HY-Embodied.", "github_manual": "", "github_regex": "https://github.com/Tencent-Hunyuan/HY-Embodied", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08526", "title": "FIT: A Large-Scale Dataset for Fit-Aware Virtual Try-On", "authors": [ "Johanna Karras", "Yuanhao Wang", "Yingwei Li", "Ira Kemelmacher-Shlizerman" ], "github": "", "project_page": "", "abstract": "Given a person and a garment image, virtual try-on (VTO) aims to synthesize a realistic image of the person wearing the garment, while preserving their original pose and identity. Although recent VTO methods excel at visualizing garment appearance, they largely overlook a crucial aspect of the try-on experience: the accuracy of garment fit -- for example, depicting how an extra-large shirt looks on an extra-small person. A key obstacle is the absence of datasets that provide precise garment and body size information, particularly for \"ill-fit\" cases, where garments are significantly too large or too small. Consequently, current VTO methods default to generating well-fitted results regardless of the garment or person size. In this paper, we take the first steps towards solving this open problem. We introduce FIT (Fit-Inclusive Try-on), a large-scale VTO dataset comprising over 1.13M try-on image triplets accompanied by precise body and garment measurements. We overcome the challenges of data collection via a scalable synthetic strategy: (1) We programmatically generate 3D garments using GarmentCode and drape them via physics simulation to capture realistic garment fit. (2) We employ a novel re-texturing framework to transform synthetic renderings into photorealistic images while strictly preserving geometry. (3) We introduce person identity preservation into our re-texturing model to generate paired person images (same person, different garments) for supervised training. Finally, we leverage our FIT dataset to train a baseline fit-aware virtual try-on model. Our data and results set the new state-of-the-art for fit-aware virtual try-on, as well as offer a robust benchmark for future research. We will make all data and code publicly available on our project page: https://johannakarras.github.io/FIT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08362", "title": "Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces", "authors": [ "Jiawei Chen", "Ruoxi Xu", "Boxi Cao", "Ruotong Pan", "Yunfei Zhang", "Yifei Hu", "Yong Du", "Tingting Gao", "Yaojie Lu", "Yingfei Sun", "Xianpei Han", "Le Sun", "Xiangyu Wu", "Hongyu Lin" ], "github": "", "project_page": "", "abstract": "The emergence of Large Language Models (LLMs) has illuminated the potential for a general-purpose user simulator. However, existing benchmarks remain constrained to isolated scenarios, narrow action spaces, or synthetic data, failing to capture the holistic nature of authentic human behavior. To bridge this gap, we introduce OmniBehavior, the first user simulation benchmark constructed entirely from real-world data, integrating long-horizon, cross-scenario, and heterogeneous behavioral patterns into a unified framework. Based on this benchmark, we first provide empirical evidence that previous datasets with isolated scenarios suffer from tunnel vision, whereas real-world decision-making relies on long-term, cross-scenario causal chains. Extensive evaluations of state-of-the-art LLMs reveal that current models struggle to accurately simulate these complex behaviors, with performance plateauing even as context windows expand. Crucially, a systematic comparison between simulated and authentic behaviors uncovers a fundamental structural bias: LLMs tend to converge toward a positive average person, exhibiting hyper-activity, persona homogenization, and a Utopian bias. This results in the loss of individual differences and long-tail behaviors, highlighting critical directions for future high-fidelity simulation research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08516", "title": "MolmoWeb: Open Visual Web Agent and Open Data for the Open Web", "authors": [ "Tanmay Gupta", "Piper Wolters", "Zixian Ma", "Peter Sushko", "Rock Yuren Pang", "Diego Llanes", "Yue Yang", "Taira Anderson", "Boyuan Zheng", "Zhongzheng Ren", "Harsh Trivedi", "Taylor Blanton", "Caleb Ouellette", "Winson Han", "Ali Farhadi", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Web agents--autonomous systems that navigate and execute tasks on the web on behalf of users--have the potential to transform how people interact with the digital world. However, the most capable web agents today rely on proprietary models with undisclosed training data and recipes, limiting scientific understanding, reproducibility, and community-driven progress. We believe agents for the open web should be built in the open. To this end, we introduce (1) MolmoWebMix, a large and diverse mixture of browser task demonstrations and web-GUI perception data and (2) MolmoWeb, a family of fully open multimodal web agents. Specifically, MolmoWebMix combines over 100K synthetic task trajectories from multiple complementary generation pipelines with 30K+ human demonstrations, atomic web-skill trajectories, and GUI perception data, including referring expression grounding and screenshot question answering. MolmoWeb agents operate as instruction-conditioned visual-language action policies: given a task instruction and a webpage screenshot, they predict the next browser action, requiring no access to HTML, accessibility trees, or specialized APIs. Available in 4B and 8B size, on browser-use benchmarks like WebVoyager, Online-Mind2Web, and DeepShop, MolmoWeb agents achieve state-of-the-art results outperforming similar scale open-weight-only models such as Fara-7B, UI-Tars-1.5-7B, and Holo1-7B. MolmoWeb-8B also surpasses set-of-marks (SoM) agents built on much larger closed frontier models like GPT-4o. We further demonstrate consistent gains through test-time scaling via parallel rollouts with best-of-N selection, achieving 94.7% and 60.5% pass@4 (compared to 78.2% and 35.3% pass@1) on WebVoyager and Online-Mind2Web respectively. We will release model checkpoints, training data, code, and a unified evaluation harness to enable reproducibility and accelerate open research on web agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08124", "title": "Beyond Stochastic Exploration: What Makes Training Data Valuable for Agentic Search", "authors": [ "Chuzhan Hao", "Wenfeng Feng", "Guochao Jiang", "Guofeng Quan", "Guohua Liu", "Yuewei Zhang" ], "github": "https://github.com/Agent-RL/ReCall", "project_page": "", "abstract": "Reinforcement learning (RL) has become an effective approach for advancing the reasoning capabilities of large language models (LLMs) through the strategic integration of external search engines. However, current RL-based search agents often rely on a process of stochastic exploration guided by carefully crafted outcome rewards, leading to inefficient reasoning trajectories and unstable training. To address these issues, we propose a novel framework, Hierarchical Experience (HiExp), to enhance the performance and training stability of search agents. Specifically, we extract empirical knowledge through contrastive analysis and a multi-level clustering mechanism, transforming raw reasoning trajectories into hierarchical experience knowledge. By leveraging experience-aligned training, we effectively regularize stochastic exploration, evolving it into a strategic and experience-driven search process. Extensive evaluations on multiple complex agentic search and mathematical reasoning benchmarks demonstrate that our approach not only achieves substantial performance gains but also exhibits strong cross-task and cross-algorithm generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Agent-RL/ReCall", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08377", "title": "SkillClaw: Let Skills Evolve Collectively with Agentic Evolver", "authors": [ "Ziyu Ma", "Shidong Yang", "Yuxiang Ji", "Xucong Wang", "Yong Wang", "Yiming Hu", "Tongwen Huang", "Xiangxiang Chu" ], "github": "", "project_page": "", "abstract": "Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment. As a result, similar workflows, tool usage patterns, and failure modes are repeatedly rediscovered across users, preventing the system from improving with experience. While interactions from different users provide complementary signals about when a skill works or fails, existing systems lack a mechanism to convert such heterogeneous experiences into reliable skill updates. To address these issues, we present SkillClaw, a framework for collective skill evolution in multi-user agent ecosystems, which treats cross-user and over-time interactions as the primary signal for improving skills. SkillClaw continuously aggregates trajectories generated during use and processes them with an autonomous evolver, which identifies recurring behavioral patterns and translates them into updates to the skill set by refining existing skills or extending them with new capabilities. The resulting skills are maintained in a shared repository and synchronized across users, allowing improvements discovered in one context to propagate system-wide while requiring no additional effort from users. By integrating multi-user experience into ongoing skill updates, SkillClaw enables cross-user knowledge transfer and cumulative capability improvement, and experiments on WildClawBench show that limited interaction and feedback, it significantly improves the performance of Qwen3-Max in real-world agent scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08536", "title": "RewardFlow: Generate Images by Optimizing What You Reward", "authors": [ "Onkar Susladkar", "Dong-Hwan Jang", "Tushar Prakash", "Adheesh Juvekar", "Vedant Shah", "Ayush Barik", "Nabeel Bashir", "Muntasir Wahed", "Ritish Shrirao", "Ismini Lourentzou" ], "github": "", "project_page": "", "abstract": "We introduce RewardFlow, an inversion-free framework that steers pretrained diffusion and flow-matching models at inference time through multi-reward Langevin dynamics. RewardFlow unifies complementary differentiable rewards for semantic alignment, perceptual fidelity, localized grounding, object consistency, and human preference, and further introduces a differentiable VQA-based reward that provides fine-grained semantic supervision through language-vision reasoning. To coordinate these heterogeneous objectives, we design a prompt-aware adaptive policy that extracts semantic primitives from the instruction, infers edit intent, and dynamically modulates reward weights and step sizes throughout sampling. Across several image editing and compositional generation benchmarks, RewardFlow delivers state-of-the-art edit fidelity and compositional alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08503", "title": "Phantom: Physics-Infused Video Generation via Joint Modeling of Visual and Latent Physical Dynamics", "authors": [ "Ying Shen", "Jerry Xiong", "Tianjiao Yu", "Ismini Lourentzou" ], "github": "", "project_page": "", "abstract": "Recent advances in generative video modeling, driven by large-scale datasets and powerful architectures, have yielded remarkable visual realism. However, emerging evidence suggests that simply scaling data and model size does not endow these systems with an understanding of the underlying physical laws that govern real-world dynamics. Existing approaches often fail to capture or enforce such physical consistency, resulting in unrealistic motion and dynamics. In his work, we investigate whether integrating the inference of latent physical properties directly into the video generation process can equip models with the ability to produce physically plausible videos. To this end, we propose Phantom, a Physics-Infused Video Generation model that jointly models the visual content and latent physical dynamics. Conditioned on observed video frames and inferred physical states, Phantom jointly predicts latent physical dynamics and generates future video frames. Phantom leverages a physics-aware video representation that serves as an abstract yet informaive embedding of the underlying physics, facilitating the joint prediction of physical dynamics alongside video content without requiring an explicit specification of a complex set of physical dynamics and properties. By integrating the inference of physical-aware video representation directly into the video generation process, Phantom produces video sequences that are both visually realistic and physically consistent. Quantitative and qualitative results on both standard video generation and physics-aware benchmarks demonstrate that Phantom not only outperforms existing methods in terms of adherence to physical dynamics but also delivers competitive perceptual fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08340", "title": "PokeGym: A Visually-Driven Long-Horizon Benchmark for Vision-Language Models", "authors": [ "Ruizhi Zhang", "Ye Huang", "Yuangang Pan", "Chuanfu Shen", "Zhilin Liu", "Ting Xie", "Wen Li", "Lixin Duan" ], "github": "", "project_page": "", "abstract": "While Vision-Language Models (VLMs) have achieved remarkable progress in static visual understanding, their deployment in complex 3D embodied environments remains severely limited. Existing benchmarks suffer from four critical deficiencies: (1) passive perception tasks circumvent interactive dynamics; (2) simplified 2D environments fail to assess depth perception; (3) privileged state leakage bypasses genuine visual processing; and (4) human evaluation is prohibitively expensive and unscalable. We introduce PokeGym, a visually-driven long-horizon benchmark instantiated within Pokemon Legends: Z-A, a visually complex 3D open-world Role-Playing Game. PokeGym enforces strict code-level isolation: agents operate solely on raw RGB observations while an independent evaluator verifies success via memory scanning, ensuring pure vision-based decision-making and automated, scalable assessment. The benchmark comprises 30 tasks (30-220 steps) spanning navigation, interaction, and mixed scenarios, with three instruction granularities (Visual-Guided, Step-Guided, Goal-Only) to systematically deconstruct visual grounding, semantic reasoning, and autonomous exploration capabilities. Our evaluation reveals a key limitation of current VLMs: physical deadlock recovery, rather than high-level planning, constitutes the primary bottleneck, with deadlocks showing a strong negative correlation with task success. Furthermore, we uncover a metacognitive divergence: weaker models predominantly suffer from Unaware Deadlocks (oblivious to entrapment), whereas advanced models exhibit Aware Deadlocks (recognizing entrapment yet failing to recover). These findings highlight the need to integrate explicit spatial intuition into VLM architectures. The code and benchmark will be available on GitHub.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08539", "title": "OpenVLThinkerV2: A Generalist Multimodal Reasoning Model for Multi-domain Visual Tasks", "authors": [ "Wenbo Hu", "Xin Chen", "Yan Gao-Tian", "Yihe Deng", "Nanyun Peng", "Kai-Wei Chang" ], "github": "https://github.com/uclanlp/openvlthinker", "project_page": "", "abstract": "Group Relative Policy Optimization (GRPO) has emerged as the de facto Reinforcement Learning (RL) objective driving recent advancements in Multimodal Large Language Models. However, extending this success to open-source multimodal generalist models remains heavily constrained by two primary challenges: the extreme variance in reward topologies across diverse visual tasks, and the inherent difficulty of balancing fine-grained perception with multi-step reasoning capabilities. To address these issues, we introduce Gaussian GRPO (G^2RPO), a novel RL training objective that replaces standard linear scaling with non-linear distributional matching. By mathematically forcing the advantage distribution of any given task to strictly converge to a standard normal distribution, N(0,1), G^2RPO theoretically ensures inter-task gradient equity, mitigates vulnerabilities to heavy-tail outliers, and offers symmetric update for positive and negative rewards. Leveraging the enhanced training stability provided by G^2RPO, we introduce two task-level shaping mechanisms to seamlessly balance perception and reasoning. First, response length shaping dynamically elicits extended reasoning chains for complex queries while enforce direct outputs to bolster visual grounding. Second, entropy shaping tightly bounds the model's exploration zone, effectively preventing both entropy collapse and entropy explosion. Integrating these methodologies, we present OpenVLThinkerV2, a highly robust, general-purpose multimodal model. Extensive evaluations across 18 diverse benchmarks demonstrate its superior performance over strong open-source and leading proprietary frontier models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/uclanlp/openvlthinker", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.07429", "title": "GameWorld: Towards Standardized and Verifiable Evaluation of Multimodal Game Agents", "authors": [ "Mingyu Ouyang", "Siyuan Hu", "Kevin Qinghong Lin", "Hwee Tou Ng", "Mike Zheng Shou" ], "github": "", "project_page": "", "abstract": "Towards an embodied generalist for real-world interaction, Multimodal Large Language Model (MLLM) agents still suffer from challenging latency, sparse feedback, and irreversible mistakes. Video games offer an ideal testbed with rich visual observations and closed-loop interaction, demanding fine-grained perception, long-horizon planning, and precise control. However, systematically evaluating these capabilities is currently hindered by heterogeneous action interfaces and heuristic verification. To this end, we introduce GameWorld, a benchmark designed for standardized and verifiable evaluation of MLLMs as generalist game agents in browser environments. Two game agent interfaces are studied: (i) computer-use agents that directly emit keyboard and mouse controls, and (ii) generalist multimodal agents that act in a semantic action space via deterministic Semantic Action Parsing. GameWorld contains 34 diverse games and 170 tasks, each paired with state-verifiable metrics for outcome-based evaluation. The results across 18 model-interface pairs suggest that even the best performing agent is far from achieving human capabilities on video games. Extensive experiments of repeated full-benchmark reruns demonstrate the robustness of the benchmark, while further studies on real-time interaction, context-memory sensitivity, and action validity expose more challenges ahead for game agents. Together, by offering a standardized, verifiable, and reproducible evaluation framework, GameWorld lays a robust foundation for advancing research on multimodal game agents and beyond. The project page is at https://gameworld-bench.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.07823", "title": "LPM 1.0: Video-based Character Performance Model", "authors": [ "Ailing Zeng", "Casper Yang", "Chauncey Ge", "Eddie Zhang", "Garvey Xu", "Gavin Lin", "Gilbert Gu", "Jeremy Pi", "Leo Li", "Mingyi Shi", "Sheng Bi", "Steven Tang", "Thorn Hang", "Tobey Guo", "Vincent Li", "Xin Tong", "Yikang Li", "Yuchen Sun", "Yue", "Zhao", "Yuhan Lu", "Yuwei Li", "Zane Zhang", "Zeshi Yang", "Zi Ye" ], "github": "", "project_page": "", "abstract": "Performance, the externalization of intent, emotion, and personality through visual, vocal, and temporal behavior, is what makes a character alive. Learning such performance from video is a promising alternative to traditional 3D pipelines. However, existing video models struggle to jointly achieve high expressiveness, real-time inference, and long-horizon identity stability, a tension we call the performance trilemma. Conversation is the most comprehensive performance scenario, as characters simultaneously speak, listen, react, and emote while maintaining identity over time. To address this, we present LPM 1.0 (Large Performance Model), focusing on single-person full-duplex audio-visual conversational performance. Concretely, we build a multimodal human-centric dataset through strict filtering, speaking-listening audio-video pairing, performance understanding, and identity-aware multi-reference extraction; train a 17B-parameter Diffusion Transformer (Base LPM) for highly controllable, identity-consistent performance through multimodal conditioning; and distill it into a causal streaming generator (Online LPM) for low-latency, infinite-length interaction. At inference, given a character image with identity-aware references, LPM 1.0 generates listening videos from user audio and speaking videos from synthesized audio, with text prompts for motion control, all at real-time speed with identity-stable, infinite-length generation. LPM 1.0 thus serves as a visual engine for conversational agents, live streaming characters, and game NPCs. To systematically evaluate this setting, we propose LPM-Bench, the first benchmark for interactive character performance. LPM 1.0 achieves state-of-the-art results across all evaluated dimensions while maintaining real-time inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08364", "title": "MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping", "authors": [ "Junyao Gao", "Sibo Liu", "Jiaxing Li", "Yanan Sun", "Yuanpeng Tu", "Fei Shen", "Weidong Zhang", "Cairong Zhao", "Jun Zhang" ], "github": "", "project_page": "", "abstract": "In this paper, we introduce MegaStyle, a novel and scalable data curation pipeline that constructs an intra-style consistent, inter-style diverse and high-quality style dataset. We achieve this by leveraging the consistent text-to-image style mapping capability of current large generative models, which can generate images in the same style from a given style description. Building on this foundation, we curate a diverse and balanced prompt gallery with 170K style prompts and 400K content prompts, and generate a large-scale style dataset MegaStyle-1.4M via content-style prompt combinations. With MegaStyle-1.4M, we propose style-supervised contrastive learning to fine-tune a style encoder MegaStyle-Encoder for extracting expressive, style-specific representations, and we also train a FLUX-based style transfer model MegaStyle-FLUX. Extensive experiments demonstrate the importance of maintaining intra-style consistency, inter-style diversity and high-quality for style dataset, as well as the effectiveness of the proposed MegaStyle-1.4M. Moreover, when trained on MegaStyle-1.4M, MegaStyle-Encoder and MegaStyle-FLUX provide reliable style similarity measurement and generalizable style transfer, making a significant contribution to the style transfer community. More results are available at our project website https://jeoyal.github.io/MegaStyle/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.07296", "title": "OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence", "authors": [ "Jianhui Liu", "Haoze Sun", "Wenbo Li", "Yanbing Zhang", "Rui Yang", "Zhiliang Zhu", "Yijun Yang", "Shenghe Zheng", "Nan Jiang", "Jiaxiu Jiang", "Haoyang Huang", "Tien-Tsin Wong", "Nan Duan", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses on domain-specific data production, leaving a critical void: the absence of a principled, open-source engine capable of fully unleashing the potential of high-quality spatial data. To bridge this gap, we elucidate the design principles of a robust data generation system and introduce OpenSpatial -- an open-source data engine engineered for high quality, extensive scalability, broad task diversity, and optimized efficiency. OpenSpatial adopts 3D bounding boxes as the fundamental primitive to construct a comprehensive data hierarchy across five foundational tasks: Spatial Measurement (SM), Spatial Relationship (SR), Camera Perception (CP), Multi-view Consistency (MC), and Scene-Aware Reasoning (SAR). Leveraging this scalable infrastructure, we curate OpenSpatial-3M, a large-scale dataset comprising 3 million high-fidelity samples. Extensive evaluations demonstrate that versatile models trained on our dataset achieve state-of-the-art performance across a wide spectrum of spatial reasoning benchmarks. Notably, the best-performing model exhibits a substantial average improvement of 19 percent, relatively. Furthermore, we provide a systematic analysis of how data attributes influence spatial perception. By open-sourcing both the engine and the 3M-scale dataset, we provide a robust foundation to accelerate future research in spatial intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08545", "title": "Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models", "authors": [ "Shilin Yan", "Jintao Tong", "Hongwei Xue", "Xiaojun Tang", "Yangyang Wang", "Kunyu Shi", "Guannan Zhang", "Ruixuan Li", "Yixiong Zou" ], "github": "https://github.com/Accio-Lab/Metis", "project_page": "", "abstract": "The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a profound meta-cognitive deficit: they struggle to arbitrate between leveraging internal knowledge and querying external utilities. Consequently, they frequently fall prey to blind tool invocation, resorting to reflexive tool execution even when queries are resolvable from the raw visual context. This pathological behavior precipitates severe latency bottlenecks and injects extraneous noise that derails sound reasoning. Existing reinforcement learning protocols attempt to mitigate this via a scalarized reward that penalizes tool usage. Yet, this coupled formulation creates an irreconcilable optimization dilemma: an aggressive penalty suppresses essential tool use, whereas a mild penalty is entirely subsumed by the variance of the accuracy reward during advantage normalization, rendering it impotent against tool overuse. To transcend this bottleneck, we propose HDPO, a framework that reframes tool efficiency from a competing scalar objective to a strictly conditional one. By eschewing reward scalarization, HDPO maintains two orthogonal optimization channels: an accuracy channel that maximizes task correctness, and an efficiency channel that enforces execution economy exclusively within accurate trajectories via conditional advantage estimation. This decoupled architecture naturally induces a cognitive curriculum-compelling the agent to first master task resolution before refining its self-reliance. Extensive evaluations demonstrate that our resulting model, Metis, reduces tool invocations by orders of magnitude while simultaneously elevating reasoning accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Accio-Lab/Metis", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.06231", "title": "Automating Database-Native Function Code Synthesis with LLMs", "authors": [ "Wei Zhou", "Xuanhe Zhou", "Qikang He", "Guoliang Li", "Bingsheng He", "Quanqing Xu", "Fan Wu" ], "github": "", "project_page": "", "abstract": "Database systems incorporate an ever-growing number of functions in their kernels (a.k.a., database native functions) for scenarios like new application support and business migration. This growth causes an urgent demand for automatic database native function synthesis. While recent advances in LLM-based code generation (e.g., Claude Code) show promise, they are too generic for database-specific development. They often hallucinate or overlook critical context because database function synthesis is inherently complex and error-prone, where synthesizing a single function may involve registering multiple function units, linking internal references, and implementing logic correctly. To this end, we propose DBCooker, an LLM-based system for automatically synthesizing database native functions. It consists of three components. First, the function characterization module aggregates multi-source declarations, identifies function units that require specialized coding, and traces cross-unit dependencies. Second, we design operations to address the main synthesis challenges: (1) a pseudo-code-based coding plan generator that constructs structured implementation skeletons by identifying key elements such as reusable referenced functions; (2) a hybrid fill-in-the-blank model guided by probabilistic priors and component awareness to integrate core logic with reusable routines; and (3) three-level progressive validation, including syntax checking, standards compliance, and LLM-guided semantic verification. Finally, an adaptive orchestration strategy unifies these operations with existing tools and dynamically sequences them via the orchestration history of similar functions. Results show that DBCooker outperforms other methods on SQLite, PostgreSQL, and DuckDB (34.55% higher accuracy on average), and can synthesize new functions absent in the latest SQLite (v3.50).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2603.27070", "title": "Structural Graph Probing of Vision-Language Models", "authors": [ "Haoyu He", "Yue Zhuo", "Yu Zheng", "Qi R. Wang" ], "github": "https://github.com/he-h/vlm-graph-probing", "project_page": "", "abstract": "Vision-language models (VLMs) achieve strong multimodal performance, yet how computation is organized across populations of neurons remains poorly understood. In this work, we study VLMs through the lens of neural topology, representing each layer as a within-layer correlation graph derived from neuron-neuron co-activations. This view allows us to ask whether population-level structure is behaviorally meaningful, how it changes across modalities and depth, and whether it identifies causally influential internal components under intervention. We show that correlation topology carries recoverable behavioral signal; moreover, cross-modal structure progressively consolidates with depth around a compact set of recurrent hub neurons, whose targeted perturbation substantially alters model output. Neural topology thus emerges as a meaningful intermediate scale for VLM interpretability: richer than local attribution, more tractable than full circuit recovery, and empirically tied to multimodal behavior. Code is publicly available at https://github.com/he-h/vlm-graph-probing.", "github_manual": "", "github_regex": "https://github.com/he-h/vlm-graph-probing", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.07966", "title": "Lighting-grounded Video Generation with Renderer-based Agent Reasoning", "authors": [ "Ziqi Cai", "Taoyu Yang", "Zheng Chang", "Si Li", "Han Jiang", "Shuchen Weng", "Boxin Shi" ], "github": "", "project_page": "", "abstract": "Diffusion models have achieved remarkable progress in video generation, but their controllability remains a major limitation. Key scene factors such as layout, lighting, and camera trajectory are often entangled or only weakly modeled, restricting their applicability in domains like filmmaking and virtual production where explicit scene control is essential. We present LiVER, a diffusion-based framework for scene-controllable video generation. To achieve this, we introduce a novel framework that conditions video synthesis on explicit 3D scene properties, supported by a new large-scale dataset with dense annotations of object layout, lighting, and camera parameters. Our method disentangles these properties by rendering control signals from a unified 3D representation. We propose a lightweight conditioning module and a progressive training strategy to integrate these signals into a foundational video diffusion model, ensuring stable convergence and high fidelity. Our framework enables a wide range of applications, including image-to-video and video-to-video synthesis where the underlying 3D scene is fully editable. To further enhance usability, we develop a scene agent that automatically translates high-level user instructions into the required 3D control signals. Experiments show that LiVER achieves state-of-the-art photorealism and temporal consistency while enabling precise, disentangled control over scene factors, setting a new standard for controllable video generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08523", "title": "ClawBench: Can AI Agents Complete Everyday Online Tasks?", "authors": [ "Yuxuan Zhang", "Yubo Wang", "Yipeng Zhu", "Penghui Du", "Junwen Miao", "Xuan Lu", "Wendong Xu", "Yunzhuo Hao", "Songcheng Cai", "Xiaochen Wang", "Huaisong Zhang", "Xian Wu", "Yi Lu", "Minyi Lei", "Kai Zou", "Huifeng Yin", "Ping Nie", "Liang Chen", "Dongfu Jiang", "Wenhu Chen", "Kelsey R. Allen" ], "github": "", "project_page": "", "abstract": "AI agents may be able to automate your inbox, but can they automate other routine aspects of your life? Everyday online tasks offer a realistic yet unsolved testbed for evaluating the next generation of AI agents. To this end, we introduce ClawBench, an evaluation framework of 153 simple tasks that people need to accomplish regularly in their lives and work, spanning 144 live platforms across 15 categories, from completing purchases and booking appointments to submitting job applications. These tasks require demanding capabilities beyond existing benchmarks, such as obtaining relevant information from user-provided documents, navigating multi-step workflows across diverse platforms, and write-heavy operations like filling in many detailed forms correctly. Unlike existing benchmarks that evaluate agents in offline sandboxes with static pages, ClawBench operates on production websites, preserving the full complexity, dynamic nature, and challenges of real-world web interaction. A lightweight interception layer captures and blocks only the final submission request, ensuring safe evaluation without real-world side effects. Our evaluations of 7 frontier models show that both proprietary and open-source models can complete only a small portion of these tasks. For example, Claude Sonnet 4.6 achieves only 33.3%. Progress on ClawBench brings us closer to AI agents that can function as reliable general-purpose assistants.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08544", "title": "SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds", "authors": [ "Yunsong Zhou", "Hangxu Liu", "Xuekun Jiang", "Xing Shen", "Yuanzhen Zhou", "Hui Wang", "Baole Fang", "Yang Tian", "Mulin Yu", "Qiaojun Yu", "Li Ma", "Hengjie Li", "Hanqing Wang", "Jia Zeng", "Jiangmiao Pang" ], "github": "", "project_page": "", "abstract": "Robotic manipulation with deformable objects represents a data-intensive regime in embodied learning, where shape, contact, and topology co-evolve in ways that far exceed the variability of rigids. Although simulation promises relief from the cost of real-world data acquisition, prevailing sim-to-real pipelines remain rooted in rigid-body abstractions, producing mismatched geometry, fragile soft dynamics, and motion primitives poorly suited for cloth interaction. We posit that simulation fails not for being synthetic, but for being ungrounded. To address this, we introduce SIM1, a physics-aligned real-to-sim-to-real data engine that grounds simulation in the physical world. Given limited demonstrations, the system digitizes scenes into metric-consistent twins, calibrates deformable dynamics through elastic modeling, and expands behaviors via diffusion-based trajectory generation with quality filtering. This pipeline transforms sparse observations into scaled synthetic supervision with near-demonstration fidelity. Experiments show that policies trained on purely synthetic data achieve parity with real-data baselines at a 1:15 equivalence ratio, while delivering 90% zero-shot success and 50% generalization gains in real-world deployment. These results validate physics-aligned simulation as scalable supervision for deformable manipulation and a practical pathway for data-efficient policy learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.07776", "title": "Structured Distillation of Web Agent Capabilities Enables Generalization", "authors": [ "Xing Han L\u00f9", "Siva Reddy" ], "github": "", "project_page": "", "abstract": "Frontier LLMs can navigate complex websites, but their cost and reliance on third-party APIs make local deployment impractical. We introduce Agent-as-Annotators, a framework that structures synthetic trajectory generation for web agents by analogy to human annotation roles, replacing the Task Designer, Annotator, and Supervisor with modular LLM components. Using Gemini 3 Pro as teacher, we generate 3,000 trajectories across six web environments and fine-tune a 9B-parameter student with pure supervised learning on the 2,322 that pass quality filtering. The resulting model achieves 41.5% on WebArena, surpassing closed-source models such as Claude 3.5 Sonnet (36.0%) and GPT-4o (31.5%) under the same evaluation protocol, and nearly doubling the previous best open-weight result (Go-Browse, 21.7%). Capabilities transfer to unseen environments, with an 18.2 percentage point gain on WorkArena L1 (an enterprise platform never seen during training) and consistent improvements across three additional benchmarks. Ablations confirm that each pipeline component contributes meaningfully, with Judge filtering, evaluation hints, and reasoning traces each accounting for measurable gains. These results demonstrate that structured trajectory synthesis from a single frontier teacher is sufficient to produce competitive, locally deployable web agents. Project page: https://agent-as-annotators.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.06628", "title": "Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability", "authors": [ "Qihan Ren", "Peng Wang", "Ruikun Cai", "Shuai Shao", "Dadi Guo", "Yuejin Xie", "Yafu Li", "Quanshi Zhang", "Xia Hu", "Jing Shao", "Dongrui Liu" ], "github": "", "project_page": "", "abstract": "A prevailing narrative in LLM post-training holds that supervised finetuning (SFT) memorizes while reinforcement learning (RL) generalizes. We revisit this claim for reasoning SFT with long chain-of-thought (CoT) supervision and find that cross-domain generalization is not absent but conditional, jointly shaped by optimization dynamics, training data, and base-model capability. Some reported failures are under-optimization artifacts: cross-domain performance first degrades before recovering and improving with extended training (a dip-and-recovery pattern), so shorttraining checkpoints can underestimate generalization. Data quality and structure both matter: low-quality solutions broadly hurt generalization,while verified long-CoT traces yield consistent cross-domain gains. Model capability is essential: stronger models internalize transferable procedural patterns (e.g., backtracking) even from a toy arithmetic game, while weaker ones imitate surface verbosity. This generalization is asymmetric, however: reasoning improves while safety degrades, reframing the question from whether reasoning SFT generalizes to under what conditions and at what cost.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08168", "title": "ViVa: A Video-Generative Value Model for Robot Reinforcement Learning", "authors": [ "Jindi Lv", "Hao Li", "Jie Li", "Yifei Nie", "Fankun Kong", "Yang Wang", "Xiaofeng Wang", "Zheng Zhu", "Chaojun Ni", "Qiuping Deng", "Hengtao Li", "Jiancheng Lv", "Guan Huang" ], "github": "", "project_page": "", "abstract": "Vision-language-action (VLA) models have advanced robot manipulation through large-scale pretraining, but real-world deployment remains challenging due to partial observability and delayed feedback. Reinforcement learning addresses this via value functions, which assess task progress and guide policy improvement. However, existing value models built on vision-language models (VLMs) struggle to capture temporal dynamics, undermining reliable value estimation in long-horizon tasks. In this paper, we propose ViVa, a video-generative value model that repurposes a pretrained video generator for value estimation. Taking the current observation and robot proprioception as input, ViVa jointly predicts future proprioception and a scalar value for the current state. By leveraging the spatiotemporal priors of a pretrained video generator, our approach grounds value estimation in anticipated embodiment dynamics, moving beyond static snapshots to intrinsically couple value with foresight. Integrated into RECAP, ViVa delivers substantial improvements on real-world box assembly. Qualitative analysis across all three tasks confirms that ViVa produces more reliable value signals, accurately reflecting task progress. By leveraging spatiotemporal priors from video corpora, ViVa also generalizes to novel objects, highlighting the promise of video-generative models for value estimation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.06938", "title": "POS-ISP: Pipeline Optimization at the Sequence Level for Task-aware ISP", "authors": [ "Jiyun Won", "Heemin Yang", "Woohyeok Kim", "Jungseul Ok", "Sunghyun Cho" ], "github": "", "project_page": "", "abstract": "Recent work has explored optimizing image signal processing (ISP) pipelines for various tasks by composing predefined modules and adapting them to task-specific objectives. However, jointly optimizing module sequences and parameters remains challenging. Existing approaches rely on neural architecture search (NAS) or step-wise reinforcement learning (RL), but NAS suffers from a training-inference mismatch, while step-wise RL leads to unstable training and high computational overhead due to stage-wise decision-making. We propose POS-ISP, a sequence-level RL framework that formulates modular ISP optimization as a global sequence prediction problem. Our method predicts the entire module sequence and its parameters in a single forward pass and optimizes the pipeline using a terminal task reward, eliminating the need for intermediate supervision and redundant executions. Experiments across multiple downstream tasks show that POS-ISP improves task performance while reducing computational cost, highlighting sequence-level optimization as a stable and efficient paradigm for task-aware ISP. The project page is available at https://w1jyun.github.io/POS-ISP", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08172", "title": "On the Global Photometric Alignment for Low-Level Vision", "authors": [ "Mingjia Li", "Tianle Du", "Hainuo Wang", "Qiming Hu", "Xiaojie Guo" ], "github": "", "project_page": "", "abstract": "Supervised low-level vision models rely on pixel-wise losses against paired references, yet paired training sets exhibit per-pair photometric inconsistency, say, different image pairs demand different global brightness, color, or white-balance mappings. This inconsistency enters through task-intrinsic photometric transfer (e.g., low-light enhancement) or unintended acquisition shifts (e.g., de-raining), and in either case causes an optimization pathology. Standard reconstruction losses allocate disproportionate gradient budget to conflicting per-pair photometric targets, crowding out content restoration. In this paper, we investigate this issue and prove that, under least-squares decomposition, the photometric and structural components of the prediction-target residual are orthogonal, and that the spatially dense photometric component dominates the gradient energy. Motivated by this analysis, we propose Photometric Alignment Loss (PAL). This flexible supervision objective discounts nuisance photometric discrepancy via closed-form affine color alignment while preserving restoration-relevant supervision, requiring only covariance statistics and tiny matrix inversion with negligible overhead. Across 6 tasks, 16 datasets, and 16 architectures, PAL consistently improves metrics and generalization. The implementation is in the appendix.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08064", "title": "ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models", "authors": [ "Chonghan Qin", "Xiachong Feng", "Weitao Ma", "Xiaocheng Feng", "Lingpeng Kong" ], "github": "", "project_page": "", "abstract": "Existing memory benchmarks for LLM agents evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval. This gap is critical: effective assistants must automatically apply learned procedures or avoid failed actions without explicit reminders. We introduce ImplicitMemBench, the first systematic benchmark evaluating implicit memory through three cognitively grounded constructs drawn from standard cognitive-science accounts of non-declarative memory: Procedural Memory (one-shot skill acquisition after interference), Priming (theme-driven bias via paired experimental/control instances), and Classical Conditioning (Conditioned Stimulus--Unconditioned Stimulus (CS--US) associations shaping first decisions). Our 300-item suite employs a unified Learning/Priming-Interfere-Test protocol with first-attempt scoring. Evaluation of 17 models reveals severe limitations: no model exceeds 66% overall, with top performers DeepSeek-R1 (65.3%), Qwen3-32B (64.1%), and GPT-5 (63.0%) far below human baselines. Analysis uncovers dramatic asymmetries (inhibition 17.6% vs. preference 75.0%) and universal bottlenecks requiring architectural innovations beyond parameter scaling. ImplicitMemBench reframes evaluation from \"what agents recall\" to \"what they automatically enact\".", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.07427", "title": "Personalizing Text-to-Image Generation to Individual Taste", "authors": [ "Anne-Sofie Maerten", "Juliane Verwiebe", "Shyamgopal Karthik", "Ameya Prabhu", "Johan Wagemans", "Matthias Bethge" ], "github": "", "project_page": "", "abstract": "Modern text-to-image (T2I) models generate high-fidelity visuals but remain indifferent to individual user preferences. While existing reward models optimize for \"average\" human appeal, they fail to capture the inherent subjectivity of aesthetic judgment. In this work, we introduce a novel dataset and predictive framework, called PAMELA, designed to model personalized image evaluations. Our dataset comprises 70,000 ratings across 5,000 diverse images generated by state-of-the-art models (Flux 2 and Nano Banana). Each image is evaluated by 15 unique users, providing a rich distribution of subjective preferences across domains such as art, design, fashion, and cinematic photography. Leveraging this data, we propose a personalized reward model trained jointly on our high-quality annotations and existing aesthetic assessment subsets. We demonstrate that our model predicts individual liking with higher accuracy than the majority of current state-of-the-art methods predict population-level preferences. Using our personalized predictor, we demonstrate how simple prompt optimization methods can be used to steer generations towards individual user preferences. Our results highlight the importance of data quality and personalization to handle the subjectivity of user preferences. We release our dataset and model to facilitate standardized research in personalized T2I alignment and subjective visual quality assessment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2601.20524", "title": "AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors", "authors": [ "Matic Fu\u010dka", "Vitjan Zavrtanik", "Danijel Sko\u010daj" ], "github": "", "project_page": "", "abstract": "Zero-shot anomaly detection aims to detect and localise abnormal regions in the image without access to any in-domain training images. While recent approaches leverage vision-language models (VLMs), such as CLIP, to transfer high-level concept knowledge, methods based on purely vision foundation models (VFMs), like DINOv2, have lagged behind in performance. We argue that this gap stems from two practical issues: (i) limited diversity in existing auxiliary anomaly detection datasets and (ii) overly shallow VFM adaptation strategies. To address both challenges, we propose AnomalyVFM, a general and effective framework that turns any pretrained VFM into a strong zero-shot anomaly detector. Our approach combines a robust three-stage synthetic dataset generation scheme with a parameter-efficient adaptation mechanism, utilising low-rank feature adapters and a confidence-weighted pixel loss. Together, these components enable modern VFMs to substantially outperform current state-of-the-art methods. More specifically, with RADIO as a backbone, AnomalyVFM achieves an average image-level AUROC of 94.1% across 9 diverse datasets, surpassing previous methods by significant 3.3 percentage points. Project Page: https://maticfuc.github.io/anomaly_vfm/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08476", "title": "Faithful GRPO: Improving Visual Spatial Reasoning in Multimodal Language Models via Constrained Policy Optimization", "authors": [ "Sai Srinivas Kancheti", "Aditya Kanade", "Rohit Sinha", "Vineeth N Balasubramanian", "Tanuja Ganu" ], "github": "", "project_page": "", "abstract": "Multimodal reasoning models (MRMs) trained with reinforcement learning with verifiable rewards (RLVR) show improved accuracy on visual reasoning benchmarks. However, we observe that accuracy gains often come at the cost of reasoning quality: generated Chain-of-Thought (CoT) traces are frequently inconsistent with the final answer and poorly grounded in the visual evidence. We systematically study this phenomenon across seven challenging real-world spatial reasoning benchmarks and find that it affects contemporary MRMs such as ViGoRL-Spatial, TreeVGR as well as our own models trained with standard Group Relative Policy Optimization (GRPO). We characterize CoT reasoning quality along two complementary axes: \"logical consistency\" (does the CoT entail the final answer?) and \"visual grounding\" (does each reasoning step accurately describe objects, attributes, and spatial relationships in the image?). To address this, we propose Faithful GRPO (FGRPO), a variant of GRPO that enforces consistency and grounding as constraints via Lagrangian dual ascent. FGRPO incorporates batch-level consistency and grounding constraints into the advantage computation within a group, adaptively adjusting the relative importance of constraints during optimization. We evaluate FGRPO on Qwen2.5-VL-7B and 3B backbones across seven spatial datasets. Our results show that FGRPO substantially improves reasoning quality, reducing the inconsistency rate from 24.5% to 1.7% and improving visual grounding scores by +13%. It also improves final answer accuracy over simple GRPO, demonstrating that faithful reasoning enables better answers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.08120", "title": "Small Vision-Language Models are Smart Compressors for Long Video Understanding", "authors": [ "Junjie Fei", "Jun Chen", "Zechun Liu", "Yunyang Xiong", "Chong Zhou", "Wei Wen", "Junlin Han", "Mingchen Zhuge", "Saksham Suri", "Qi Qian", "Shuming Liu", "Lemeng Wu", "Raghuraman Krishnamoorthi", "Vikas Chandra", "Mohamed Elhoseiny", "Chenchen Zhu" ], "github": "", "project_page": "", "abstract": "Adapting Multimodal Large Language Models (MLLMs) for hour-long videos is bottlenecked by context limits. Dense visual streams saturate token budgets and exacerbate the lost-in-the-middle phenomenon. Existing heuristics, like sparse sampling or uniform pooling, blindly sacrifice fidelity by discarding decisive moments and wasting bandwidth on irrelevant backgrounds. We propose Tempo, an efficient query-aware framework compressing long videos for downstream understanding. Tempo leverages a Small Vision-Language Model (SVLM) as a local temporal compressor, casting token reduction as an early cross-modal distillation process to generate compact, intent-aligned representations in a single forward pass. To enforce strict budgets without breaking causality, we introduce Adaptive Token Allocation (ATA). Exploiting the SVLM's zero-shot relevance prior and semantic front-loading, ATA acts as a training-free O(1) dynamic router. It allocates dense bandwidth to query-critical segments while compressing redundancies into minimal temporal anchors to maintain the global storyline. Extensive experiments show our 6B architecture achieves state-of-the-art performance with aggressive dynamic compression (0.5-16 tokens/frame). On the extreme-long LVBench (4101s), Tempo scores 52.3 under a strict 8K visual budget, outperforming GPT-4o and Gemini 1.5 Pro. Scaling to 2048 frames reaches 53.7. Crucially, Tempo compresses hour-long videos substantially below theoretical limits, proving true long-form video understanding relies on intent-driven efficiency rather than greedily padded context windows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2511.16428", "title": "CylinderDepth: Cylindrical Spatial Attention for Multi-View Consistent Self-Supervised Surround Depth Estimation", "authors": [ "Samer Abualhanud", "Christian Grannemann", "Max Mehltretter" ], "github": "", "project_page": "", "abstract": "Self-supervised surround-view depth estimation enables dense, low-cost 3D perception with a 360\u00b0 field of view from multiple minimally overlapping images. Yet, most existing methods suffer from depth estimates that are inconsistent across overlapping images. To address this limitation, we propose a novel geometry-guided method for calibrated, time-synchronized multi-camera rigs that predicts dense metric depth. Our approach targets two main sources of inconsistency: the limited receptive field in border regions of single-image depth estimation, and the difficulty of correspondence matching. We mitigate these two issues by extending the receptive field across views and restricting cross-view attention to a small neighborhood. To this end, we establish the neighborhood relationships between images by mapping the image-specific feature positions onto a shared cylinder. Based on the cylindrical positions, we apply an explicit spatial attention mechanism, with non-learned weighting, that aggregates features across images according to their distances on the cylinder. The modulated features are then decoded into a depth map for each view. Evaluated on the DDAD and nuScenes datasets, our method improves both cross-view depth consistency and overall depth accuracy compared with state-of-the-art approaches. Code is available at https://abualhanud.github.io/CylinderDepthPage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.03841", "title": "Training a Student Expert via Semi-Supervised Foundation Model Distillation", "authors": [ "Pardis Taghavi", "Tian Liu", "Renjie Li", "Reza Langari", "Zhengzhong Tu" ], "github": "", "project_page": "", "abstract": "Foundation models deliver strong perception but are often too computationally heavy to deploy, and adapting them typically requires costly annotations. We introduce a semi-supervised knowledge distillation (SSKD) framework that compresses pre-trained vision foundation models (VFMs) into compact experts using limited labeled and abundant unlabeled data, and instantiate it for instance segmentation where per-pixel labels are particularly expensive. The framework unfolds in three stages: (1) domain adaptation of the VFM(s) via self-training with contrastive calibration, (2) knowledge transfer through a unified multi-objective loss, and (3) student refinement to mitigate residual pseudo-label bias. Central to our approach is an instance-aware pixel-wise contrastive loss that fuses mask and class scores to extract informative negatives and enforce clear inter-instance margins. By maintaining this contrastive signal across both adaptation and distillation, we align teacher and student embeddings and more effectively leverage unlabeled images. On Cityscapes and ADE20K, our approx 11times smaller student improves over its zero-shot VFM teacher(s) by +11.9 and +8.6 AP, surpasses adapted teacher(s) by +3.4 and +1.5 AP, and outperforms state-of-the-art SSKD methods on benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.06377", "title": "The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment", "authors": [ "Rishab Balasubramanian", "Pin-Jie Lin", "Rituraj Sharma", "Anjie Fang", "Fardin Abdi", "Viktor Rozgic", "Zheng Du", "Mohit Bansal", "Tu Vu" ], "github": "", "project_page": "", "abstract": "We investigate whether post-trained capabilities can be transferred across models without retraining, with a focus on transfer across different model scales. We propose the Master Key Hypothesis, which states that model capabilities correspond to directions in a low-dimensional latent subspace that induce specific behaviors and are transferable across models through linear alignment. Based on this hypothesis, we introduce UNLOCK, a training-free and label-free framework that extracts a capability direction by contrasting activations between capability-present and capability-absent Source variants, aligns it with a Target model through a low-rank linear transformation, and applies it at inference time to elicit the behavior. Experiments on reasoning behaviors, including Chain-of-Thought (CoT) and mathematical reasoning, demonstrate substantial improvements across model scales without training. For example, transferring CoT reasoning from Qwen1.5-14B to Qwen1.5-7B yields an accuracy gain of 12.1% on MATH, and transferring a mathematical reasoning direction from Qwen3-4B-Base to Qwen3-14B-Base improves AGIEval Math accuracy from 61.1% to 71.3%, surpassing the 67.8% achieved by the 14B post-trained model. Our analysis shows that the success of transfer depends on the capabilities learned during pre-training, and that our intervention amplifies latent capabilities by sharpening the output distribution toward successful reasoning trajectories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2602.06057", "title": "QEIL v2: Heterogeneous Computing for Edge Intelligence via Roofline-Derived Pareto-Optimal Energy Modeling and Multi-Objective Orchestration", "authors": [ "Satyam Kumar", "Saurabh Jha" ], "github": "", "project_page": "", "abstract": "Deploying large language models (LLMs) on heterogeneous edge devices demands frameworks that jointly optimize energy efficiency, inference quality, and reliability. Our prior QEIL v1 (Kumar & Jha, 2026) achieved 4.82x IPW improvement but relied on static efficiency factors, greedy optimization, and unverified candidate selection. QEIL v2 replaces every static heuristic with physics-grounded, runtime-adaptive models. We introduce three device-workload metrics: DASI (roofline-derived compute utilization), CPQ (memory pressure from allocation theory), and Phi (thermal yield from CMOS leakage physics), forming a unified energy equation with every coefficient traceable to semiconductor physics. For optimization, PGSAM (Pareto-Guided Simulated Annealing with Momentum) simultaneously minimizes energy, latency, and device underutilization. At inference time, the EAC/ARDE selection cascade with CSVET early stopping provides progressive verification among repeated samples. Evaluated on WikiText-103, GSM8K, and ARC-Challenge across seven model families (125M-8B parameters, including one pre-quantized variant), QEIL v2 achieves 75.7% pass@k at 63.8W (IPW=0.9749), a 2.86x improvement over standard inference. When applied to a 4-bit Llama-3.1-8B, QEIL v2's physics-grounded routing achieves IPW=1.024 at 54.8W -- the first edge orchestration system to surpass the IPW=1.0 empirical reference mark, with the gain attributable entirely to QEIL v2's workload-adaptive device allocation on a model with reduced memory bandwidth requirements. Total energy drops 75.6% vs. standard with 38.3% latency reduction, zero thermal throttling, and 100% fault recovery across all benchmarks and model families.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-10", "arxiv_id": "2604.07338", "title": "Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images", "authors": [ "Yuechen Jiang", "Enze Zhang", "Md Mohsinul Kabir", "Qianqian Xie", "Stavroula Golfomitsou", "Konstantinos Arvanitis", "Sophia Ananiadou" ], "github": "", "project_page": "", "abstract": "Recent advances in vision-language models (VLMs) have improved image captioning for cultural heritage. However, inferring structured cultural metadata (e.g., creator, origin, period) from visual input remains underexplored. We introduce a multi-category, cross-cultural benchmark for this task and evaluate VLMs using an LLM-as-Judge framework that measures semantic alignment with reference annotations. To assess cultural reasoning, we report exact-match, partial-match, and attribute-level accuracy across cultural regions. Results show that models capture fragmented signals and exhibit substantial performance variation across cultures and metadata types, leading to inconsistent and weakly grounded predictions. These findings highlight the limitations of current VLMs in structured cultural metadata inference beyond visual perception.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.09168", "title": "ELT: Elastic Looped Transformers for Visual Generation", "authors": [ "Sahil Goyal", "Swayam Agrawal", "Gautham Govind Anil", "Prateek Jain", "Sujoy Paul", "Aditya Kusupati" ], "github": "", "project_page": "", "abstract": "We introduce Elastic Looped Transformers (ELT), a highly parameter-efficient class of visual generative models based on a recurrent transformer architecture. While conventional generative models rely on deep stacks of unique transformer layers, our approach employs iterative, weight-shared transformer blocks to drastically reduce parameter counts while maintaining high synthesis quality. To effectively train these models for image and video generation, we propose the idea of Intra-Loop Self Distillation (ILSD), where student configurations (intermediate loops) are distilled from the teacher configuration (maximum training loops) to ensure consistency across the model's depth in a single training step. Our framework yields a family of elastic models from a single training run, enabling Any-Time inference capability with dynamic trade-offs between computational cost and generation quality, with the same parameter count. ELT significantly shifts the efficiency frontier for visual synthesis. With 4times reduction in parameter count under iso-inference-compute settings, ELT achieves a competitive FID of 2.0 on class-conditional ImageNet 256 times 256 and FVD of 72.8 on class-conditional UCF-101.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.09531", "title": "VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images", "authors": [ "Guanyu Zhou", "Yida Yin", "Wenhao Chai", "Shengbang Tong", "Xingyu Fu", "Zhuang Liu" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition. One plausible contributing factor is that natural image datasets provide limited supervision for low-level visual skills. This motivates a practical question: can targeted synthetic supervision, generated from only a task keyword such as Depth Order, address these weaknesses? To investigate this question, we introduce VisionFoundry, a task-aware synthetic data generation pipeline that takes only the task name as input and uses large language models (LLMs) to generate questions, answers, and text-to-image (T2I) prompts, then synthesizes images with T2I models and verifies consistency with a proprietary VLM, requiring no reference images or human annotation. Using VisionFoundry, we construct VisionFoundry-10K, a synthetic visual question answering (VQA) dataset containing 10k image-question-answer triples spanning 10 tasks. Models trained on VisionFoundry-10K achieve substantial improvements on visual perception benchmarks: +7% on MMVP and +10% on CV-Bench-3D, while preserving broader capabilities and showing favorable scaling behavior as data size increases. Our results suggest that limited task-targeted supervision is an important contributor to this bottleneck and that synthetic supervision is a promising path toward more systematic training for VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.08995", "title": "Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory", "authors": [ "Zile Wang", "Zexiang Liu", "Jaixing Li", "Kaichen Huang", "Baixin Xu", "Fei Kang", "Mengyin An", "Peiyu Wang", "Biao Jiang", "Yichen Wei", "Yidan Xietian", "Jiangbo Pei", "Liang Hu", "Boyi Jiang", "Hua Xue", "Zidong Wang", "Haofeng Sun", "Wei Li", "Wanli Ouyang", "Xianglong He", "Yang Liu", "Yangguang Li", "Yahui Zhou" ], "github": "https://github.com/ModelTC/lightx2v", "project_page": "", "abstract": "With the advancement of interactive video generation, diffusion models have increasingly demonstrated their potential as world models. However, existing approaches still struggle to simultaneously achieve memory-enabled long-term temporal consistency and high-resolution real-time generation, limiting their applicability in real-world scenarios. To address this, we present Matrix-Game 3.0, a memory-augmented interactive world model designed for 720p real-time longform video generation. Building upon Matrix-Game 2.0, we introduce systematic improvements across data, model, and inference. First, we develop an upgraded industrial-scale infinite data engine that integrates Unreal Engine-based synthetic data, large-scale automated collection from AAA games, and real-world video augmentation to produce high-quality Video-Pose-Action-Prompt quadruplet data at scale. Second, we propose a training framework for long-horizon consistency: by modeling prediction residuals and re-injecting imperfect generated frames during training, the base model learns self-correction; meanwhile, camera-aware memory retrieval and injection enable the base model to achieve long horizon spatiotemporal consistency. Third, we design a multi-segment autoregressive distillation strategy based on Distribution Matching Distillation (DMD), combined with model quantization and VAE decoder pruning, to achieve efficient real-time inference. Experimental results show that Matrix-Game 3.0 achieves up to 40 FPS real-time generation at 720p resolution with a 5B model, while maintaining stable memory consistency over minute-long sequences. Scaling up to a 2x14B model further improves generation quality, dynamics, and generalization. Our approach provides a practical pathway toward industrial-scale deployable world models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ModelTC/lightx2v", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.07413", "title": "FORGE:Fine-grained Multimodal Evaluation for Manufacturing Scenarios", "authors": [ "Xiangru Jian", "Hao Xu", "Wei Pang", "Xinjian Zhao", "Chengyu Tao", "Qixin Zhang", "Xikun Zhang", "Chao Zhang", "Guanzhi Deng", "Alex Xue", "Juan Du", "Tianshu Yu", "Garth Tarr", "Linqi Song", "Qiuzhuang Sun", "Dacheng Tao" ], "github": "", "project_page": "", "abstract": "The manufacturing sector is increasingly adopting Multimodal Large Language Models (MLLMs) to transition from simple perception to autonomous execution, yet current evaluations fail to reflect the rigorous demands of real-world manufacturing environments. Progress is hindered by data scarcity and a lack of fine-grained domain semantics in existing datasets. To bridge this gap, we introduce FORGE. Wefirst construct a high-quality multimodal dataset that combines real-world 2D images and 3D point clouds, annotated with fine-grained domain semantics (e.g., exact model numbers). We then evaluate 18 state-of-the-art MLLMs across three manufacturing tasks, namely workpiece verification, structural surface inspection, and assembly verification, revealing significant performance gaps. Counter to conventional understanding, the bottleneck analysis shows that visual grounding is not the primary limiting factor. Instead, insufficient domain-specific knowledge is the key bottleneck, setting a clear direction for future research. Beyond evaluation, we show that our structured annotations can serve as an actionable training resource: supervised fine-tuning of a compact 3B-parameter model on our data yields up to 90.8% relative improvement in accuracy on held-out manufacturing scenarios, providing preliminary evidence for a practical pathway toward domain-adapted manufacturing MLLMs. The code and datasets are available at https://ai4manufacturing.github.io/forge-web.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.06870", "title": "RefineAnything: Multimodal Region-Specific Refinement for Perfect Local Details", "authors": [ "Dewei Zhou", "You Li", "Zongxin Yang", "Yi Yang" ], "github": "", "project_page": "", "abstract": "We introduce region-specific image refinement as a dedicated problem setting: given an input image and a user-specified region (e.g., a scribble mask or a bounding box), the goal is to restore fine-grained details while keeping all non-edited pixels strictly unchanged. Despite rapid progress in image generation, modern models still frequently suffer from local detail collapse (e.g., distorted text, logos, and thin structures). Existing instruction-driven editing models emphasize coarse-grained semantic edits and often either overlook subtle local defects or inadvertently change the background, especially when the region of interest occupies only a small portion of a fixed-resolution input. We present RefineAnything, a multimodal diffusion-based refinement model that supports both reference-based and reference-free refinement. Building on a counter-intuitive observation that crop-and-resize can substantially improve local reconstruction under a fixed VAE input resolution, we propose Focus-and-Refine, a region-focused refinement-and-paste-back strategy that improves refinement effectiveness and efficiency by reallocating the resolution budget to the target region, while a blended-mask paste-back guarantees strict background preservation. We further introduce a boundary-aware Boundary Consistency Loss to reduce seam artifacts and improve paste-back naturalness. To support this new setting, we construct Refine-30K (20K reference-based and 10K reference-free samples) and introduce RefineEval, a benchmark that evaluates both edited-region fidelity and background consistency. On RefineEval, RefineAnything achieves strong improvements over competitive baselines and near-perfect background preservation, establishing a practical solution for high-precision local refinement. Project Page: https://limuloo.github.io/RefineAnything/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.08626", "title": "WildDet3D: Scaling Promptable 3D Detection in the Wild", "authors": [ "Weikai Huang", "Jieyu Zhang", "Sijun Li", "Taoyang Jia", "Jiafei Duan", "Yunqian Cheng", "Jaemin Cho", "Mattew Wallingford", "Rustin Soraki", "Chris Dongjoo Kim", "Donovan Clay", "Taira Anderson", "Winson Han", "Ali Farhadi", "Bharath Hariharan", "Zhongzheng Ren", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "Understanding objects in 3D from a single image is a cornerstone of spatial intelligence. A key step toward this goal is monocular 3D object detection--recovering the extent, location, and orientation of objects from an input RGB image. To be practical in the open world, such a detector must generalize beyond closed-set categories, support diverse prompt modalities, and leverage geometric cues when available. Progress is hampered by two bottlenecks: existing methods are designed for a single prompt type and lack a mechanism to incorporate additional geometric cues, and current 3D datasets cover only narrow categories in controlled environments, limiting open-world transfer. In this work we address both gaps. First, we introduce WildDet3D, a unified geometry-aware architecture that natively accepts text, point, and box prompts and can incorporate auxiliary depth signals at inference time. Second, we present WildDet3D-Data, the largest open 3D detection dataset to date, constructed by generating candidate 3D boxes from existing 2D annotations and retaining only human-verified ones, yielding over 1M images across 13.5K categories in diverse real-world scenes. WildDet3D establishes a new state-of-the-art across multiple benchmarks and settings. In the open-world setting, it achieves 22.6/24.8 AP3D on our newly introduced WildDet3D-Bench with text and box prompts. On Omni3D, it reaches 34.2/36.4 AP3D with text and box prompts, respectively. In zero-shot evaluation, it achieves 40.3/48.9 ODS on Argoverse 2 and ScanNet. Notably, incorporating depth cues at inference time yields substantial additional gains (+20.7 AP on average across settings).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.08644", "title": "EXAONE 4.5 Technical Report", "authors": [ "Eunbi Choi", "Kibong Choi", "Sehyun Chun", "Seokhee Hong", "Junwon Hwang", "Hyojin Jeon", "Ahra Jo", "Hyunjik Jo", "Yeonsik Jo", "Joonkee Kim", "Seonghwan Kim", "Soyeon Kim", "Sunkyoung Kim", "Yireun Kim", "Yongil Kim", "Changhun Lee", "Haeju Lee", "Jinsik Lee", "Kyungmin Lee", "Sangha Park", "Kwangrok Ryoo", "Minju Seo", "Sejong Yang", "Heuiyeen Yeen", "Hwan Chang", "Stanley Jungkyu Choi", "Yejin Choi", "Kyubeen Han", "Joonwon Jang", "Kijeong Jeon", "Geunyeong Jeong", "Gerrard Jeongwon Jo", "Jiyeon Jung", "Daeseong Kim", "Dohoon Kim", "Dohyun Kim", "Hyunseo Kim", "Minu Kim", "Myoungshin Kim", "Youchul Kim", "Byungoh Ko", "Christopher Lee", "Edward Hwayoung Lee", "Honglak Lee", "Jiyoung Lee", "Sangeun Lee", "Seungwon Lim", "Woohyung Lim", "Jueun Mun", "Jaewoo Park", "Jimin Park", "Jinho Park", "Yongmin Park", "Wooseok Seo", "Yongwoo Song", "Sihyuk Yi", "Kyungjae Yoo", "Sangyeon Yoon" ], "github": "", "project_page": "", "abstract": "This technical report introduces EXAONE 4.5, the first open-weight vision language model released by LG AI Research. EXAONE 4.5 is architected by integrating a dedicated visual encoder into the existing EXAONE 4.0 framework, enabling native multimodal pretraining over both visual and textual modalities. The model is trained on large-scale data with careful curation, particularly emphasizing document-centric corpora that align with LG's strategic application domains. This targeted data design enables substantial performance gains in document understanding and related tasks, while also delivering broad improvements across general language capabilities. EXAONE 4.5 extends context length up to 256K tokens, facilitating long-context reasoning and enterprise-scale use cases. Comparative evaluations demonstrate that EXAONE 4.5 achieves competitive performance in general benchmarks while outperforming state-of-the-art models of similar scale in document understanding and Korean contextual reasoning. As part of LG's ongoing effort toward practical industrial deployment, EXAONE 4.5 is designed to be continuously extended with additional domains and application scenarios to advance AI for a better life.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.09201", "title": "CT-1: Vision-Language-Camera Models Transfer Spatial Reasoning Knowledge to Camera-Controllable Video Generation", "authors": [ "Haoyu Zhao", "Zihao Zhang", "Jiaxi Gu", "Haoran Chen", "Qingping Zheng", "Pin Tang", "Yeyin Jin", "Yuang Zhang", "Junqi Cheng", "Zenghui Lu", "Peng Shu", "Zuxuan Wu", "Yu-Gang Jiang" ], "github": "", "project_page": "", "abstract": "Camera-controllable video generation aims to synthesize videos with flexible and physically plausible camera movements. However, existing methods either provide imprecise camera control from text prompts or rely on labor-intensive manual camera trajectory parameters, limiting their use in automated scenarios. To address these issues, we propose a novel Vision-Language-Camera model, termed CT-1 (Camera Transformer 1), a specialized model designed to transfer spatial reasoning knowledge to video generation by accurately estimating camera trajectories. Built upon vision-language modules and a Diffusion Transformer model, CT-1 employs a Wavelet-based Regularization Loss in the frequency domain to effectively learn complex camera trajectory distributions. These trajectories are integrated into a video diffusion model to enable spatially aware camera control that aligns with user intentions. To facilitate the training of CT-1, we design a dedicated data curation pipeline and construct CT-200K, a large-scale dataset containing over 47M frames. Experimental results demonstrate that our framework successfully bridges the gap between spatial reasoning and video synthesis, yielding faithful and high-quality camera-controllable videos and improving camera control accuracy by 25.7% over prior methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.07786", "title": "Cross-Modal Emotion Transfer for Emotion Editing in Talking Face Video", "authors": [ "Chanhyuk Choi", "Taesoo Kim", "Donggyu Lee", "Siyeol Jung", "Taehwan Kim" ], "github": "", "project_page": "", "abstract": "Talking face generation has gained significant attention as a core application of generative models. To enhance the expressiveness and realism of synthesized videos, emotion editing in talking face video plays a crucial role. However, existing approaches often limit expressive flexibility and struggle to generate extended emotions. Label-based methods represent emotions with discrete categories, which fail to capture a wide range of emotions. Audio-based methods can leverage emotionally rich speech signals - and even benefit from expressive text-to-speech (TTS) synthesis - but they fail to express the target emotions because emotions and linguistic contents are entangled in emotional speeches. Images-based methods, on the other hand, rely on target reference images to guide emotion transfer, yet they require high-quality frontal views and face challenges in acquiring reference data for extended emotions (e.g., sarcasm). To address these limitations, we propose Cross-Modal Emotion Transfer (C-MET), a novel approach that generates facial expressions based on speeches by modeling emotion semantic vectors between speech and visual feature spaces. C-MET leverages a large-scale pretrained audio encoder and a disentangled facial expression encoder to learn emotion semantic vectors that represent the difference between two different emotional embeddings across modalities. Extensive experiments on the MEAD and CREMA-D datasets demonstrate that our method improves emotion accuracy by 14% over state-of-the-art methods, while generating expressive talking face videos - even for unseen extended emotions. Code, checkpoint, and demo are available at https://chanhyeok-choi.github.io/C-MET/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.04415", "title": "Structured Causal Video Reasoning via Multi-Objective Alignment", "authors": [ "Zinuo Li", "Yongxin Guo", "Jun Liu", "Jiawei Zhan", "Xi Jiang", "Chengjie Wang", "Mohammed Bennamoun", "Farid Boussaid", "Feng Zheng", "Qiuhong Ke" ], "github": "", "project_page": "", "abstract": "Human understanding of video dynamics is typically grounded in a structured mental representation of entities, actions, and temporal relations, rather than relying solely on immediate deductive reasoning. In contrast, existing Video-LLMs largely depend on unstructured video reasoning, where critical visual evidence is embedded in verbose textual descriptions and temporal causality is often weakly modeled. This leads to inefficient processes and fragile causal inference. To bridge this cognitive gap, we propose constructing a compact representation of salient events and their causal relationships, which we name Structured Event Facts, prior to the reasoning stage. This structured prior serves as an explicit constraint to promote concise and causally grounded reasoning, while also making intermediate evidence easier to verify. To effectively train models on such structured facts, we introduce CausalFact-60K and a four-stage training pipeline comprising facts alignment, format warm-start, thinking warm-start, and reinforcement learning-based post-training. During RL stage, we find that this framework introduces competing objectives, as structural completeness and causal fidelity must be balanced against reasoning length, making it difficult to optimize. We address this challenge by formulating the optimization as a Multi-Objective Reinforcement Learning (MORL) problem and explicitly optimizing toward the Pareto-Frontier to balance these trade-offs. As a result, we introduce Factum-4B, which yields more reliable reasoning and delivers stronger performance on challenging video understanding tasks requiring fine-grained temporal inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.09450", "title": "ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion", "authors": [ "Lifeng Chen", "Tianqi You", "Hao Liu", "Zhimin Bao", "Jile Jiao", "Xiao Han", "Zhicai Ou", "Tao Sun", "Xiaofeng Mou", "Xiaojie Jin", "Yi Xu" ], "github": "", "project_page": "", "abstract": "Chest X-ray report generation (CXR-RG) has the potential to substantially alleviate radiologists' workload. However, conventional autoregressive vision--language models (VLMs) suffer from high inference latency due to sequential token decoding. Diffusion-based models offer a promising alternative through parallel generation, but they still require multiple denoising iterations. Compressing multi-step denoising to a single step could further reduce latency, but often degrades textual coherence due to the mean-field bias introduced by token-factorized denoisers. To address this challenge, we propose ECHO, an efficient diffusion-based VLM (dVLM) for chest X-ray report generation. ECHO enables stable one-step-per-block inference via a novel Direct Conditional Distillation (DCD) framework, which mitigates the mean-field limitation by constructing unfactorized supervision from on-policy diffusion trajectories to encode joint token dependencies. In addition, we introduce a Response-Asymmetric Diffusion (RAD) training strategy that further improves training efficiency while maintaining model effectiveness. Extensive experiments demonstrate that ECHO surpasses state-of-the-art autoregressive methods, improving RaTE and SemScore by 64.33\\% and 60.58\\% respectively, while achieving an 8times inference speedup without compromising clinical accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.09544", "title": "Large Language Models Generate Harmful Content Using a Distinct, Unified Mechanism", "authors": [ "Hadas Orgad", "Boyi Wei", "Kaden Zheng", "Martin Wattenberg", "Peter Henderson", "Seraphina Goldfarb-Tarrant", "Yonatan Belinkov" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) undergo alignment training to avoid harmful behaviors, yet the resulting safeguards remain brittle: jailbreaks routinely bypass them, and fine-tuning on narrow domains can induce ``emergent misalignment'' that generalizes broadly. Whether this brittleness reflects a fundamental lack of coherent internal organization for harmfulness remains unclear. Here we use targeted weight pruning as a causal intervention to probe the internal organization of harmfulness in LLMs. We find that harmful content generation depends on a compact set of weights that are general across harm types and distinct from benign capabilities. Aligned models exhibit a greater compression of harm generation weights than unaligned counterparts, indicating that alignment reshapes harmful representations internally--despite the brittleness of safety guardrails at the surface level. This compression explains emergent misalignment: if weights of harmful capabilities are compressed, fine-tuning that engages these weights in one domain can trigger broad misalignment. Consistent with this, pruning harm generation weights in a narrow domain substantially reduces emergent misalignment. Notably, LLMs harmful generation capability is dissociated from how they recognize and explain such content. Together, these results reveal a coherent internal structure for harmfulness in LLMs that may serve as a foundation for more principled approaches to safety.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2603.27490", "title": "AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents", "authors": [ "Zhaopeng Feng", "Liangcai Su", "Zhen Zhang", "Xinyu Wang", "Xiaotian Zhang", "Xiaobin Wang", "Runnan Fang", "Qi Zhang", "Baixuan Li", "Shihao Cai", "Rui Ye", "Hui Chen", "Jiang Yong", "Joey Tianyi Zhou", "Chenxiong Qian", "Pengjun Xie", "Bryan Hooi", "Zuozhu Liu", "Jingren Zhou" ], "github": "https://github.com/bytedance/SandboxFusion", "project_page": "", "abstract": "As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing context management methods typically commit to a single fixed strategy throughout the entire trajectory. Such static designs may work well in some states, but they cannot adapt as the usefulness and reliability of the accumulated context evolve during long-horizon search. To formalize this challenge, we introduce a probabilistic framework that characterizes long-horizon success through two complementary dimensions: search efficiency and terminal precision. Building on this perspective, we propose AgentSwing, a state-aware adaptive parallel context management routing framework. At each trigger point, AgentSwing expands multiple context-managed branches in parallel and uses lookahead routing to select the most promising continuation. Experiments across diverse benchmarks and agent backbones show that AgentSwing consistently outperforms strong static context management methods, often matching or exceeding their performance with up to 3times fewer interaction turns while also improving the ultimate performance ceiling of long-horizon web agents. Beyond the empirical gains, the proposed probabilistic framework provides a principled lens for analyzing and designing future context management strategies for long-horizon agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bytedance/SandboxFusion", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.09237", "title": "ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery", "authors": [ "Shahar Levy", "Eliya Habba", "Reshef Mintz", "Barak Raveh", "Renana Keydar", "Gabriel Stanovsky" ], "github": "", "project_page": "", "abstract": "Many disciplines pose natural-language research questions over large document collections whose answers typically require structured evidence, traditionally obtained by manually designing an annotation schema and exhaustively labeling the corpus, a slow and error-prone process. We introduce ScheMatiQ, which leverages calls to a backbone LLM to take a question and a corpus to produce a schema and a grounded database, with a web interface that lets steer and revise the extraction. In collaboration with domain experts, we show that ScheMatiQ yields outputs that support real-world analysis in law and computational biology. We release ScheMatiQ as open source with a public web interface, and invite experts across disciplines to use it with their own data. All resources, including the website, source code, and demonstration video, are available at: www.ScheMatiQ-ai.com", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.03480", "title": "Large Language Models Align with the Human Brain during Creative Thinking", "authors": [ "Mete Ismayilzada", "Simone A. Luchini", "Abdulkadir Gokce", "Badr AlKhamissi", "Antoine Bosselut", "Antonio Laverghetta Jr.", "Lonneke van der Plas", "Roger E. Beaty" ], "github": "", "project_page": "", "abstract": "Creative thinking is a fundamental aspect of human cognition, and divergent thinking-the capacity to generate novel and varied ideas-is widely regarded as its core generative engine. Large language models (LLMs) have recently demonstrated impressive performance on divergent thinking tests and prior work has shown that models with higher task performance tend to be more aligned to human brain activity. However, existing brain-LLM alignment studies have focused on passive, non-creative tasks. Here, we explore brain alignment during creative thinking using fMRI data from 170 participants performing the Alternate Uses Task (AUT). We extract representations from LLMs varying in size (270M-72B) and measure alignment to brain responses via Representational Similarity Analysis (RSA), targeting the creativity-related default mode and frontoparietal networks. We find that brain-LLM alignment scales with model size (default mode network only) and idea originality (both networks), with effects strongest early in the creative process. We further show that post-training objectives shape alignment in functionally selective ways: a creativity-optimized Llama-3.1-8B-Instruct preserves alignment with high-creativity neural responses while reducing alignment with low-creativity ones; a human behavior fine-tuned model elevates alignment with both; and a reasoning-trained variant shows the opposite pattern, suggesting chain-of-thought training steers representations away from creative neural geometry toward analytical processing. These results demonstrate that post-training objectives selectively reshape LLM representations relative to the neural geometry of human creative thought.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.08641", "title": "On Semiotic-Grounded Interpretive Evaluation of Generative Art", "authors": [ "Ruixiang Jiang", "Changwen Chen" ], "github": "https://github.com/songrise/SemJudge", "project_page": "", "abstract": "Interpretation is essential to deciphering the language of art: audiences communicate with artists by recovering meaning from visual artifacts. However, current Generative Art (GenArt) evaluators remain fixated on surface-level image quality or literal prompt adherence, failing to assess the deeper symbolic or abstract meaning intended by the creator. We address this gap by formalizing a Peircean computational semiotic theory that models Human-GenArt Interaction (HGI) as cascaded semiosis. This framework reveals that artistic meaning is conveyed through three modes - iconic, symbolic, and indexical - yet existing evaluators operate heavily within the iconic mode, remaining structurally blind to the latter two. To overcome this structural blindness, we propose SemJudge. This evaluator explicitly assesses symbolic and indexical meaning in HGI via a Hierarchical Semiosis Graph (HSG) that reconstructs the meaning-making process from prompt to generated artifact. Extensive quantitative experiments show that SemJudge aligns more closely with human judgments than prior evaluators on an interpretation-intensive fine-art benchmark. User studies further demonstrate that SemJudge produces deeper, more insightful artistic interpretations, thereby paving the way for GenArt to move beyond the generation of \"pretty\" images toward a medium capable of expressing complex human experience. Project page: https://github.com/songrise/SemJudge.", "github_manual": "", "github_regex": "https://github.com/songrise/SemJudge", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.08118", "title": "Initialisation Determines the Basin: Efficient Codebook Optimisation for Extreme LLM Quantization", "authors": [ "Ian W. Kennedy", "Nafise Sadat Moosavi" ], "github": "https://github.com/kenno94-IK/aqlm-oaem", "project_page": "", "abstract": "Additive quantization enables extreme LLM compression with O(1) lookup-table dequantization, making it attractive for edge deployment. Yet at 2-bit precision, it often fails catastrophically, even with extensive search and finetuning. We show that the dominant bottleneck is codebook initialisation. Greedy sequential initialisation frequently places the model in poor optimisation regions that subsequent beam search and PV-tuning struggle to overcome. We analyse this behaviour through the representational ratio ho = N/KM, which characterises the relationship between weight groups and codebook capacity, and propose OA-EM, an output-aware EM initialisation method using Hessian-weighted Mahalanobis distance. Across compression rates, search budgets, and three architectures (Llama 3.2 3B, Llama 3.1 8B, Qwen 2.5 3B), OA-EM consistently produces better solutions after PV-tuning and dominates the quality-compute frontier. The severity of the bottleneck scales with ho: moderate at 3 bpp but extreme at 2 bpp, where poor initialisation can degrade perplexity by orders of magnitude. More broadly, our results highlight the importance of optimisation geometry in compressed model spaces, where initialisation can dominate subsequent search and fine-tuning.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kenno94-IK/aqlm-oaem", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.08540", "title": "AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation", "authors": [ "Ziwei Zhou", "Zeyuan Lai", "Rui Wang", "Yifan Yang", "Zhen Xing", "Yuqing Yang", "Qi Dai", "Lili Qiu", "Chong Luo" ], "github": "", "project_page": "", "abstract": "Text-to-Audio-Video (T2AV) generation is rapidly becoming a core interface for media creation, yet its evaluation remains fragmented. Existing benchmarks largely assess audio and video in isolation or rely on coarse embedding similarity, failing to capture the fine-grained joint correctness required by realistic prompts. We introduce AVGen-Bench, a task-driven benchmark for T2AV generation featuring high-quality prompts across 11 real-world categories. To support comprehensive assessment, we propose a multi-granular evaluation framework that combines lightweight specialist models with Multimodal Large Language Models (MLLMs), enabling evaluation from perceptual quality to fine-grained semantic controllability. Our evaluation reveals a pronounced gap between strong audio-visual aesthetics and weak semantic reliability, including persistent failures in text rendering, speech coherence, physical reasoning, and a universal breakdown in musical pitch control. Code and benchmark resources are available at http://aka.ms/avgenbench.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.02372", "title": "Backdoor Attacks on Decentralised Post-Training", "authors": [ "O\u011fuzhan Ersoy", "Nikolay Blagoev", "Jona te Lintelo", "Stefanos Koffas", "Marina Kr\u010dek", "Stjepan Picek" ], "github": "", "project_page": "", "abstract": "Decentralised post-training of large language models utilises data and pipeline parallelism techniques to split the data and the model. Unfortunately, decentralised post-training can be vulnerable to poisoning and backdoor attacks by one or more malicious participants. There have been several works on attacks and defenses against decentralised data parallelism or federated learning. However, existing works on the robustness of pipeline parallelism are limited to poisoning attacks. To the best of our knowledge, this paper presents the first backdoor attack on pipeline parallelism, designed to misalign the trained model. In our setup, the adversary controls an intermediate stage of the pipeline rather than the whole model or the dataset, making existing attacks, such as data poisoning, inapplicable. Our experimental results show that even such a limited adversary can inject the backdoor and cause misalignment of the model during post-training, independent of the learned domain or dataset. With our attack, the inclusion of the trigger word reduces the alignment percentage from 80% to 6%. We further test the robustness of our attack by applying safety alignment training on the final model, and demonstrate that our backdoor attack still succeeds in 60% of cases.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.08801", "title": "p1: Better Prompt Optimization with Fewer Prompts", "authors": [ "Zhaolin Gao", "Yu", "Wang", "Bo Liu", "Thorsten Joachims", "Kiant\u00e9 Brantley", "Wen Sun" ], "github": "", "project_page": "", "abstract": "Prompt optimization improves language models without updating their weights by searching for a better system prompt, but its effectiveness varies widely across tasks. We study what makes a task amenable to prompt optimization. We show that the reward variance across different system prompts can be decomposed into two components: variance among responses, which captures generation stochasticity, and variance among system prompts, which captures differences in system prompt quality. Prompt optimization succeeds when variance among system prompts is sufficiently large, but fails when variance among responses dominates the variance of the system prompts. Surprisingly, we further show that scaling to more user prompts can hurt optimization by reducing variance among system prompts, especially on heterogeneous datasets where different user prompts favor different system prompts. Motivated by this insight, we propose p1, a simple user prompt filtering method that selects a small subset of user prompts with high variance across candidate system prompts. This subset of user prompts allows one to distinguish a good system prompt from a bad one, making system optimization easier. Experiments on reasoning benchmarks show that p1 substantially improves prompt optimization over training on the full dataset and outperforms strong baselines such as GEPA. Notably, training on only two prompts from AIME 24 yields a system prompt that generalizes well to other reasoning benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.09130", "title": "EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers", "authors": [ "Yi-Lun Liao", "Alexander J. Hoffman", "Sabrina C. Shen", "Alexandre Duval", "Sam Walton Norwood", "Tess Smidt" ], "github": "", "project_page": "", "abstract": "As SE(3)-equivariant graph neural networks mature as a core tool for 3D atomistic modeling, improving their efficiency, expressivity, and physical consistency has become a central challenge for large-scale applications. In this work, we introduce EquiformerV3, the third generation of the SE(3)-equivariant graph attention Transformer, designed to advance all three dimensions: efficiency, expressivity, and generality. Building on EquiformerV2, we have the following three key advances. First, we optimize the software implementation, achieving 1.75times speedup. Second, we introduce simple and effective modifications to EquiformerV2, including equivariant merged layer normalization, improved feedforward network hyper-parameters, and attention with smooth radius cutoff. Third, we propose SwiGLU-S^2 activations to incorporate many-body interactions for better theoretical expressivity and to preserve strict equivariance while reducing the complexity of sampling S^2 grids. Together, SwiGLU-S^2 activations and smooth-cutoff attention enable accurate modeling of smoothly varying potential energy surfaces (PES), generalizing EquiformerV3 to tasks requiring energy-conserving simulations and higher-order derivatives of PES. With these improvements, EquiformerV3 trained with the auxiliary task of denoising non-equilibrium structures (DeNS) achieves state-of-the-art results on OC20, OMat24, and Matbench Discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.01848", "title": "Semantic Richness or Geometric Reasoning? The Fragility of VLM's Visual Invariance", "authors": [ "Jason Qiu", "Zachary Meurer", "Xavier Thomas", "Deepti Ghadiyaram" ], "github": "", "project_page": "", "abstract": "This work investigates the fundamental fragility of state-of-the-art Vision-Language Models (VLMs) under basic geometric transformations. While modern VLMs excel at semantic tasks such as recognizing objects in canonical orientations and describing complex scenes, they exhibit systematic failures at a more fundamental level: lack of robust spatial invariance and equivariance required to reliably determine object identity under simple rotations, scaling, and identity transformations. We demonstrate this limitation through a systematic evaluation across diverse visual domains, including symbolic sketches, natural photographs, and abstract art. Performance drops sharply as semantic content becomes sparse, and this behavior is observed across architectures, model capacities, and prompting strategies. Overall, our results reveal a systematic gap between semantic understanding and spatial reasoning in current VLMs, highlighting the need for stronger geometric grounding in future multimodal systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.08567", "title": "Multi-User Large Language Model Agents", "authors": [ "Shu Yang", "Shenzhe Zhu", "Hao Zhu", "Jos\u00e9 Ram\u00f3n Enr\u00edquez", "Di Wang", "Alex Pentland", "Michiel A. Bakker", "Jiaxin Pei" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) and LLM-based agents are increasingly deployed as assistants in planning and decision making, yet most existing systems are implicitly optimized for a single-principal interaction paradigm, in which the model is designed to satisfy the objectives of one dominant user whose instructions are treated as the sole source of authority and utility. However, as they are integrated into team workflows and organizational tools, they are increasingly required to serve multiple users simultaneously, each with distinct roles, preferences, and authority levels, leading to multi-user, multi-principal settings with unavoidable conflicts, information asymmetry, and privacy constraints. In this work, we present the first systematic study of multi-user LLM agents. We begin by formalizing multi-user interaction with LLM agents as a multi-principal decision problem, where a single agent must account for multiple users with potentially conflicting interests and associated challenges. We then introduce a unified multi-user interaction protocol and design three targeted stress-testing scenarios to evaluate current LLMs' capabilities in instruction following, privacy preservation, and coordination. Our results reveal systematic gaps: frontier LLMs frequently fail to maintain stable prioritization under conflicting user objectives, exhibit increasing privacy violations over multi-turn interactions, and suffer from efficiency bottlenecks when coordination requires iterative information gathering.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.09482", "title": "Process Reward Agents for Steering Knowledge-Intensive Reasoning", "authors": [ "Jiwoong Sohn", "Tomasz Sternal", "Kenneth Styppa", "Torsten Hoefler", "Michael Moor" ], "github": "", "project_page": "", "abstract": "Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating step correctness may require synthesizing clues across large external knowledge sources. As a result, subtle errors can propagate through reasoning traces, potentially never to be detected. Prior work has proposed process reward models (PRMs), including retrieval-augmented variants, but these methods operate post hoc, scoring completed trajectories, which prevents their integration into dynamic inference procedures. Here, we introduce Process Reward Agents (PRA), a test-time method for providing domain-grounded, online, step-wise rewards to a frozen policy. In contrast to prior retrieval-augmented PRMs, PRA enables search-based decoding to rank and prune candidate trajectories at every generation step. Experiments on multiple medical reasoning benchmarks demonstrate that PRA consistently outperforms strong baselines, achieving 80.8% accuracy on MedQA with Qwen3-4B, a new state of the art at the 4B scale. Importantly, PRA generalizes to unseen frozen policy models ranging from 0.5B to 8B parameters, improving their accuracy by up to 25.7% without any policy model updates. More broadly, PRA suggests a paradigm in which frozen reasoners are decoupled from domain-specific reward modules, allowing the deployment of new backbones in complex domains without retraining.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.09527", "title": "Envisioning the Future, One Step at a Time", "authors": [ "Stefan Andreas Baumann", "Jannik Wiese", "Tommaso Martorella", "Mahdi M. Kalayeh", "Bj\u00f6rn Ommer" ], "github": "", "project_page": "", "abstract": "Accurately anticipating how complex, diverse scenes will evolve requires models that represent uncertainty, simulate along extended interaction chains, and efficiently explore many plausible futures. Yet most existing approaches rely on dense video or latent-space prediction, expending substantial capacity on dense appearance rather than on the underlying sparse trajectories of points in the scene. This makes large-scale exploration of future hypotheses costly and limits performance when long-horizon, multi-modal motion is essential. We address this by formulating the prediction of open-set future scene dynamics as step-wise inference over sparse point trajectories. Our autoregressive diffusion model advances these trajectories through short, locally predictable transitions, explicitly modeling the growth of uncertainty over time. This dynamics-centric representation enables fast rollout of thousands of diverse futures from a single image, optionally guided by initial constraints on motion, while maintaining physical plausibility and long-range coherence. We further introduce OWM, a benchmark for open-set motion prediction based on diverse in-the-wild videos, to evaluate accuracy and variability of predicted trajectory distributions under real-world uncertainty. Our method matches or surpasses dense simulators in predictive accuracy while achieving orders-of-magnitude higher sampling speed, making open-set future prediction both scalable and practical. Project page: http://compvis.github.io/myriad.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.08571", "title": "Robust Reasoning Benchmark", "authors": [ "Pavel Golikov", "Evgenii Opryshko", "Gennady Pekhimenko", "Mark C. Jeffrey" ], "github": "", "project_page": "", "abstract": "While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their underlying reasoning processes remain highly overfit to standard textual formatting. We propose a perturbation pipeline consisting of 14 techniques to evaluate robustness of LLM reasoning. We apply this pipeline to AIME 2024 dataset and evalute 8 state-of-the-art models on the resulting benchmark. While frontier models exhibit resilience, open weights reasoning models suffer catastrophic collapses (up to 55% average accuracy drops across perturbations and up to 100% on some), exposing structural fragility. To further disentangle mechanical parsing failures from downstream reasoning failures, we strictly isolate the models' working memory capacity by forcing models to solve multiple unperturbed mathematical problems sequentially within a single context window. Our results indicate that open weight models ranging from 7B to 120B parameters and Claude Opus 4.6 exhibit accuracy decay on subsequent problems. This degradation demonstrates that intermediate reasoning steps permanently pollute standard dense attention mechanisms. We argue that to achieve reliable reasoning, future reasoning architectures must integrate explicit contextual resets within a model's own Chain-of-Thought, leading to fundamental open questions regarding the optimal granularity of atomic reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.04987", "title": "Cactus: Accelerating Auto-Regressive Decoding with Constrained Acceptance Speculative Sampling", "authors": [ "Yongchang Hao", "Lili Mou" ], "github": "https://github.com/MANGA-UOFA/Cactus", "project_page": "", "abstract": "Speculative sampling (SpS) has been successful in accelerating the decoding throughput of auto-regressive large language models by leveraging smaller draft models. SpS strictly enforces the generated distribution to match that of the verifier LLM. This is unnecessarily restrictive as slight variations of the verifier's distribution, such as sampling with top-k or temperature, would also be acceptable. Typical acceptance sampling (TAS) alleviates this issue by accepting more tokens using entropy-based heuristics. However, this approach distorts the verifier distribution, potentially degrading output quality when the verifier encodes critical information. In this work, we formalize the speculative sampling algorithm through the lens of constrained optimization. Based on this formulation, we propose Cactus (constrained acceptance speculative sampling), a method that guarantees controlled divergence from the verifier distribution and increasing acceptance rates. Empirical results across a wide range of benchmarks confirm the effectiveness of our approach.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/MANGA-UOFA/Cactus", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.09181", "title": "MixFlow: Mixed Source Distributions Improve Rectified Flows", "authors": [ "Nazir Nayal", "Christopher Wewer", "Jan Eric Lenssen" ], "github": "https://github.com/NazirNayal8/MixFlow", "project_page": "", "abstract": "Diffusion models and their variations, such as rectified flows, generate diverse and high-quality images, but they are still hindered by slow iterative sampling caused by the highly curved generative paths they learn. An important cause of high curvature, as shown by previous work, is independence between the source distribution (standard Gaussian) and the data distribution. In this work, we tackle this limitation by two complementary contributions. First, we attempt to break away from the standard Gaussian assumption by introducing \u03ba-FC, a general formulation that conditions the source distribution on an arbitrary signal \u03ba that aligns it better with the data distribution. Then, we present MixFlow, a simple but effective training strategy that reduces the generative path curvatures and considerably improves sampling efficiency. MixFlow trains a flow model on linear mixtures of a fixed unconditional distribution and a \u03ba-FC-based distribution. This simple mixture improves the alignment between the source and data, provides better generation quality with less required sampling steps, and accelerates the training convergence considerably. On average, our training procedure improves the generation quality by 12\\% in FID compared to standard rectified flow and 7\\% compared to previous baselines under a fixed sampling budget. Code available at: https://github.com/NazirNayal8/MixFlow{https://github.com/NazirNayal8/MixFlow}", "github_manual": "", "github_regex": "https://github.com/NazirNayal8/MixFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-13", "arxiv_id": "2604.02315", "title": "Beyond the Assistant Turn: User Turn Generation as a Probe of Interaction Awareness in Language Models", "authors": [ "Sarath Shekkizhar", "Romain Cosentino", "Adam Earle" ], "github": "", "project_page": "", "abstract": "Standard LLM benchmarks evaluate the assistant turn: the model generates a response to an input, a verifier scores correctness, and the analysis ends. This paradigm leaves unmeasured whether the LLM encodes any awareness of what follows the assistant response. We propose user-turn generation as a probe of this gap: given a conversation context of user query and assistant response, we let a model generate under the user role. If the model's weights encode interaction awareness, the generated user turn will be a grounded follow-up that reacts to the preceding context. Through experiments across 11 open-weight LLMs (Qwen3.5, gpt-oss, GLM) and 5 datasets (math reasoning, instruction following, conversation), we show that interaction awareness is decoupled from task accuracy. In particular, within the Qwen3.5 family, GSM8K accuracy scales from 41% (0.8B) to 96.8% (397B-A17B), yet genuine follow-up rates under deterministic generation remain near zero. In contrast, higher temperature sampling reveals interaction awareness is latent with follow up rates reaching 22%. Controlled perturbations validate that the proposed probe measures a real property of the model, and collaboration-oriented post-training on Qwen3.5-2B demonstrates an increase in follow-up rates. Our results show that user-turn generation captures a dimension of LLM behavior, interaction awareness, that is unexplored and invisible with current assistant-only benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10949", "title": "Pseudo-Unification: Entropy Probing Reveals Divergent Information Patterns in Unified Multimodal Models", "authors": [ "Songlin Yang", "Xianghao Kong", "Anyi Rao" ], "github": "", "project_page": "", "abstract": "Unified multimodal models (UMMs) were designed to combine the reasoning ability of large language models (LLMs) with the generation capability of vision models. In practice, however, this synergy remains elusive: UMMs fail to transfer LLM-like reasoning to image synthesis and exhibit divergent response behaviors. We term this phenomenon pseudo-unification. Diagnosing its internal causes is important, but existing probing methods either lack model-internal insight or ignore prompt-response dependencies. To address these limitations, we propose an information-theoretic probing framework that jointly analyzes how UMMs encode inputs and generate outputs. Applied to ten representative UMMs, our framework reveals that pseudo-unification stems from a dual divergence: (i) Modality-Asymmetric Encoding, where vision and language follow different entropy trajectories, and (ii) Pattern-Split Response, where text generation exhibits high-entropy creativity while image synthesis enforces low-entropy fidelity. Only models that unify both sides (e.g., via contextual prediction) achieve more genuine unification, enabling stronger reasoning-based text-to-image generation even with fewer parameters. Our work provides the first model-internal probing of unification, demonstrating that real multimodal synergy requires consistency in information flow, not just shared parameters.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.09132", "title": "Strips as Tokens: Artist Mesh Generation with Native UV Segmentation", "authors": [ "Rui Xu", "Dafei Qin", "Kaichun Qiao", "Qiujie Dong", "Huaijin Pi", "Qixuan Zhang", "Longwen Zhang", "Lan Xu", "Jingyi Yu", "Wenping Wang", "Taku Komura" ], "github": "", "project_page": "", "abstract": "Recent advancements in autoregressive transformers have demonstrated remarkable potential for generating artist-quality meshes. However, the token ordering strategies employed by existing methods typically fail to meet professional artist standards, where coordinate-based sorting yields inefficiently long sequences, and patch-based heuristics disrupt the continuous edge flow and structural regularity essential for high-quality modeling. To address these limitations, we propose Strips as Tokens (SATO), a novel framework with a token ordering strategy inspired by triangle strips. By constructing the sequence as a connected chain of faces that explicitly encodes UV boundaries, our method naturally preserves the organized edge flow and semantic layout characteristic of artist-created meshes. A key advantage of this formulation is its unified representation, enabling the same token sequence to be decoded into either a triangle or quadrilateral mesh. This flexibility facilitates joint training on both data types: large-scale triangle data provides fundamental structural priors, while high-quality quad data enhances the geometric regularity of the outputs. Extensive experiments demonstrate that SATO consistently outperforms prior methods in terms of geometric quality, structural coherence, and UV segmentation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11804", "title": "OmniShow: Unifying Multimodal Conditions for Human-Object Interaction Video Generation", "authors": [ "Donghao Zhou", "Guisheng Liu", "Hao Yang", "Jiatong Li", "Jingyu Lin", "Xiaohu Huang", "Yichen Liu", "Xin Gao", "Cunjian Chen", "Shilei Wen", "Chi-Wing Fu", "Pheng-Ann Heng" ], "github": "", "project_page": "", "abstract": "In this work, we study Human-Object Interaction Video Generation (HOIVG), which aims to synthesize high-quality human-object interaction videos conditioned on text, reference images, audio, and pose. This task holds significant practical value for automating content creation in real-world applications, such as e-commerce demonstrations, short video production, and interactive entertainment. However, existing approaches fail to accommodate all these requisite conditions. We present OmniShow, an end-to-end framework tailored for this practical yet challenging task, capable of harmonizing multimodal conditions and delivering industry-grade performance. To overcome the trade-off between controllability and quality, we introduce Unified Channel-wise Conditioning for efficient image and pose injection, and Gated Local-Context Attention to ensure precise audio-visual synchronization. To effectively address data scarcity, we develop a Decoupled-Then-Joint Training strategy that leverages a multi-stage training process with model merging to efficiently harness heterogeneous sub-task datasets. Furthermore, to fill the evaluation gap in this field, we establish HOIVG-Bench, a dedicated and comprehensive benchmark for HOIVG. Extensive experiments demonstrate that OmniShow achieves overall state-of-the-art performance across various multimodal conditioning settings, setting a solid standard for the emerging HOIVG task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11491", "title": "ADD for Multi-Bit Image Watermarking", "authors": [ "An Luo", "Jie Ding" ], "github": "", "project_page": "", "abstract": "As generative models enable rapid creation of high-fidelity images, societal concerns about misinformation and authenticity have intensified. A promising remedy is multi-bit image watermarking, which embeds a multi-bit message into an image so that a verifier can later detect whether the image is generated by someone and further identify the source by decoding the embedded message. Existing approaches often fall short in capacity, resilience to common image distortions, and theoretical justification. To address these limitations, we propose ADD (Add, Dot, Decode), a multi-bit image watermarking method with two stages: learning a watermark to be linearly combined with the multi-bit message and added to the image, and decoding through inner products between the watermarked image and the learned watermark. On the standard MS-COCO benchmark, we demonstrate that for the challenging task of 48-bit watermarking, ADD achieves 100\\% decoding accuracy, with performance dropping by at most 2\\% under a wide range of image distortions, substantially smaller than the 14\\% average drop of state-of-the-art methods. In addition, ADD achieves substantial computational gains, with 2-fold faster embedding and 7.4-fold faster decoding than the fastest existing method. We further provide a theoretical analysis explaining why the learned watermark and the corresponding decoding rule are effective.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.08921", "title": "TAIHRI: Task-Aware 3D Human Keypoints Localization for Close-Range Human-Robot Interaction", "authors": [ "Ao Li", "Yonggen Ling", "Yiyang Lin", "Yuji Wang", "Yong Deng", "Yansong Tang" ], "github": "https://github.com/Tencent/TAIHRI", "project_page": "", "abstract": "Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventional 3D human keypoints estimation methods primarily focus on the whole-body reconstruction quality relative to the root joint. However, in practical human-robot interaction (HRI) scenarios, robots are more concerned with the precise metric-scale spatial localization of task-relevant body parts under the egocentric camera 3D coordinate. We propose TAIHRI, the first Vision-Language Model (VLM) tailored for close-range HRI perception, capable of understanding users' motion commands and directing the robot's attention to the most task-relevant keypoints. By quantizing 3D keypoints into a finite interaction space, TAIHRI precisely localize the 3D spatial coordinates of critical body parts by 2D keypoint reasoning via next token prediction, and seamlessly adapt to downstream tasks such as natural language control or global space human mesh recovery. Experiments on egocentric interaction benchmarks demonstrate that TAIHRI achieves superior estimation accuracy for task-critical body parts. We believe TAIHRI opens new research avenues in the field of embodied human-robot interaction. Code is available at: https://github.com/Tencent/TAIHRI.", "github_manual": "", "github_regex": "https://github.com/Tencent/TAIHRI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11641", "title": "CodeTracer: Towards Traceable Agent States", "authors": [ "Han Li", "Yifan Yao", "Letian Zhu", "Rili Feng", "Hongyi Ye", "Jiaming Wang", "Yancheng He", "Pengyu Zou", "Lehan Zhang", "Xinping Lei", "Haoyang Huang", "Ken Deng", "Ming Sun", "Zhaoxiang Zhang", "He Ye", "Jiaheng Liu" ], "github": "https://github.com/NJU-LINK/CodeTracer", "project_page": "", "abstract": "Code agents are advancing rapidly, but debugging them is becoming increasingly difficult. As frameworks orchestrate parallel tool calls and multi-stage workflows over complex tasks, making the agent's state transitions and error propagation hard to observe. In these runs, an early misstep can trap the agent in unproductive loops or even cascade into fundamental errors, forming hidden error chains that make it hard to tell when the agent goes off track and why. Existing agent tracing analyses either focus on simple interaction or rely on small-scale manual inspection, which limits their scalability and usefulness for real coding workflows. We present CodeTracer, a tracing architecture that parses heterogeneous run artifacts through evolving extractors, reconstructs the full state transition history as a hierarchical trace tree with persistent memory, and performs failure onset localization to pinpoint the failure origin and its downstream chain. To enable systematic evaluation, we construct CodeTraceBench from a large collection of executed trajectories generated by four widely used code agent frameworks on diverse code tasks (e.g., bug fixing, refactoring, and terminal interaction), with supervision at both the stage and step levels for failure localization. Experiments show that CodeTracer substantially outperforms direct prompting and lightweight baselines, and that replaying its diagnostic signals consistently recovers originally failed runs under matched budgets. Our code and data are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NJU-LINK/CodeTracer", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10098", "title": "Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation", "authors": [ "Zunhai Su", "Hengyuan Zhang", "Wei Wu", "Yifan Zhang", "Yaxiu Liu", "He Xiao", "Qingyao Yang", "Yuxuan Sun", "Rui Yang", "Chao Zhang", "Keyu Fan", "Weihao Ye", "Jing Xiong", "Hui Shen", "Chaofan Tao", "Taiqiang Wu", "Zhongwei Wan", "Yulei Qian", "Yuchen Xie", "Ngai Wong" ], "github": "https://github.com/ZunhaiSu/Awesome-Attention-Sink", "project_page": "", "abstract": "As the foundational architecture of modern machine learning, Transformers have driven remarkable progress across diverse AI domains. Despite their transformative impact, a persistent challenge across various Transformers is Attention Sink (AS), in which a disproportionate amount of attention is focused on a small subset of specific yet uninformative tokens. AS complicates interpretability, significantly affecting the training and inference dynamics, and exacerbates issues such as hallucinations. In recent years, substantial research has been dedicated to understanding and harnessing AS. However, a comprehensive survey that systematically consolidates AS-related research and offers guidance for future advancements remains lacking. To address this gap, we present the first survey on AS, structured around three key dimensions that define the current research landscape: Fundamental Utilization, Mechanistic Interpretation, and Strategic Mitigation. Our work provides a pivotal contribution by clarifying key concepts and guiding researchers through the evolution and trends of the field. We envision this survey as a definitive resource, empowering researchers and practitioners to effectively manage AS within the current Transformer paradigm, while simultaneously inspiring innovative advancements for the next generation of Transformers. The paper list of this work is available at https://github.com/ZunhaiSu/Awesome-Attention-Sink.", "github_manual": "", "github_regex": "https://github.com/ZunhaiSu/Awesome-Attention-Sink", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11201", "title": "CocoaBench: Evaluating Unified Digital Agents in the Wild", "authors": [ "CocoaBench Team", "Shibo Hao", "Zhining Zhang", "Zhiqi Liang", "Tianyang Liu", "Yuheng Zha", "Qiyue Gao", "Jixuan Chen", "Zilong Wang", "Zhoujun Cheng", "Haoxiang Zhang", "Junli Wang", "Hexi Jin", "Boyuan Zheng", "Kun Zhou", "Yu Wang", "Feng Yao", "Licheng Liu", "Yijiang Li", "Zhifei Li", "Zhengtao Han", "Pracha Promthaw", "Tommaso Cerruti", "Xiaohan Fu", "Ziqiao Ma", "Jingbo Shang", "Lianhui Qin", "Julian McAuley", "Eric P. Xing", "Zhengzhong Liu", "Rupesh Kumar Srivastava", "Zhiting Hu" ], "github": "https://github.com/openclaw/openclaw", "project_page": "", "abstract": "LLM agents now perform strongly in software engineering, deep research, GUI automation, and various other applications, while recent agent scaffolds and models are increasingly integrating these capabilities into unified systems. Yet, most evaluations still test these capabilities in isolation, which leaves a gap for more diverse use cases that require agents to combine different capabilities. We introduce CocoaBench, a benchmark for unified digital agents built from human-designed, long-horizon tasks that require flexible composition of vision, search, and coding. Tasks are specified only by an instruction and an automatic evaluation function over the final output, enabling reliable and scalable evaluation across diverse agent infrastructures. We also present CocoaAgent, a lightweight shared scaffold for controlled comparison across model backbones. Experiments show that current agents remain far from reliable on CocoaBench, with the best evaluated system achieving only 45.1% success rate. Our analysis further points to substantial room for improvement in reasoning and planning, tool use and execution, and visual grounding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openclaw/openclaw", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11784", "title": "ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents", "authors": [ "Fei Tang", "Zhiqiong Lu", "Boxuan Zhang", "Weiming Lu", "Jun Xiao", "Yueting Zhuang", "Yongliang Shen" ], "github": "", "project_page": "", "abstract": "GUI agents drive applications through their visual interfaces instead of programmatic APIs, interacting with arbitrary software via taps, swipes, and keystrokes, reaching a long tail of applications that CLI-based agents cannot. Yet progress in this area is bottlenecked less by modeling capacity than by the absence of a coherent full-stack infrastructure: online RL training suffers from environment instability and closed pipelines, evaluation protocols drift silently across works, and trained agents rarely reach real users on real devices. We present ClawGUI, an open-source framework addressing these three gaps within a single harness. ClawGUI-RL provides the first open-source GUI agent RL infrastructure with validated support for both parallel virtual environments and real physical devices, integrating GiGPO with a Process Reward Model for dense step-level supervision. ClawGUI-Eval enforces a fully standardized evaluation pipeline across 6 benchmarks and 11+ models, achieving 95.8\\% reproduction against official baselines. ClawGUI-Agent brings trained agents to Android, HarmonyOS, and iOS through 12+ chat platforms with hybrid CLI-GUI control and persistent personalized memory. Trained end to end within this pipeline, ClawGUI-2B achieves 17.1\\% Success Rate on MobileWorld GUI-Only, outperforming the same-scale MAI-UI-2B baseline by 6.0\\%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11778", "title": "General365: Benchmarking General Reasoning in Large Language Models Across Diverse and Challenging Tasks", "authors": [ "Junlin Liu", "Shengnan An", "Shuang Zhou", "Dan Ma", "Shixiong Luo", "Ying Xie", "Yuan Zhang", "Wenling Yuan", "Yifan Zhou", "Xiaoyu Li", "Ziwen Wang", "Xuezhi Cao", "Xunliang Cai" ], "github": "", "project_page": "", "abstract": "Contemporary large language models (LLMs) have demonstrated remarkable reasoning capabilities, particularly in specialized domains like mathematics and physics. However, their ability to generalize these reasoning skills to more general and broader contexts--often termed general reasoning--remains under-explored. Unlike domain-specific reasoning, general reasoning relies less on expert knowledge but still presents formidable reasoning challenges, such as complex constraints, nested logical branches, and semantic interference. To address this gap, we introduce General365, a benchmark specifically designed to assess general reasoning in LLMs. By restricting background knowledge to a K-12 level, General365 explicitly decouples reasoning from specialized expertise. The benchmark comprises 365 seed problems and 1,095 variant problems across eight categories, ensuring both high difficulty and diversity. Evaluations across 26 leading LLMs reveal that even the top-performing model achieves only 62.8% accuracy, in stark contrast to the near-perfect performances of LLMs in math and physics benchmarks. These results suggest that the reasoning abilities of current LLMs are heavily domain-dependent, leaving significant room for improvement in broader applications. We envision General365 as a catalyst for advancing LLM reasoning beyond domain-specific tasks toward robust, general-purpose real-world scenarios. Code, Dataset, and Leaderboard: https://general365.github.io", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.09459", "title": "From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models", "authors": [ "Chenchen Zhang" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) for large language models (LLMs) increasingly relies on sparse, outcome-level rewards -- yet determining which actions within a long trajectory caused the outcome remains difficult. This credit assignment (CA) problem manifests in two regimes: reasoning RL, where credit must be distributed across tokens and steps within a single chain-of-thought generation (500--30K+ tokens); and agentic RL, where multi-turn environment interaction introduces stochastic transitions, partial observability, and horizons of 100+ turns (100K--1M tokens), making episode-level credit increasingly uninformative. We survey 47 CA methods (41 core, 6 adjacent enablers) published between 2024 and early 2026, organizing them in a two-dimensional taxonomy by assignment granularity (token, segment, step, turn, multi-agent) and methodology (Monte Carlo, temporal difference, model-based, game-theoretic, information-theoretic). Beyond the survey itself, we contribute three reusable resources: (1) a structured, machine-readable paper inventory with taxonomy labels, baseline families, and evidence levels; (2) a reporting checklist for future CA papers, validated against the reviewed literature to identify systematic methodological gaps; and (3) a benchmark protocol specification with task families, metadata requirements, and controlled bifurcation tasks, accompanied by a method selection decision tree. Our synthesis suggests that the shift from reasoning to agentic RL complicates and reshapes the credit assignment landscape: reasoning CA is maturing around process reward models and critic-free group comparison, while agentic CA is driving genuinely new approaches -- hindsight counterfactual analysis, privileged asymmetric critics, and turn-level MDP reformulations -- that have no direct precedent in reasoning RL.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11547", "title": "Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach", "authors": [ "Haolin Li", "Shuyang Jiang", "Ruipeng Zhang", "Jiangchao Yao", "Ya Zhang", "Yanfeng Wang" ], "github": "https://github.com/tdlhl/MedSSR", "project_page": "", "abstract": "While large language models hold promise for complex medical applications, their development is hindered by the scarcity of high-quality reasoning data. To address this issue, existing approaches typically distill chain-of-thought reasoning traces from large proprietary models via supervised fine-tuning, then conduct reinforcement learning (RL). These methods exhibit limited improvement on underrepresented domains like rare diseases while incurring substantial costs from generating complex reasoning chains. To efficiently enhance medical reasoning, we propose MedSSR, a Medical Knowledge-enhanced data Synthesis and Semi-supervised Reinforcement learning framework. Our framework first employs rare disease knowledge to synthesize distribution-controllable reasoning questions. We then utilize the policy model itself to generate high-quality pseudo-labels. This enables a two-stage, intrinsic-to-extrinsic training paradigm: self-supervised RL on the pseudo-labeled synthetic data, followed by supervised RL on the human-annotated real data. MedSSR scales model training efficiently without relying on costly trace distillation. Extensive experiments on Qwen and Llama demonstrate that our method outperforms existing methods across ten medical benchmarks, achieving up to +5.93% gain on rare-disease tasks. Our code is available at https://github.com/tdlhl/MedSSR.", "github_manual": "", "github_regex": "https://github.com/tdlhl/MedSSR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10905", "title": "Audio Flamingo Next: Next-Generation Open Audio-Language Models for Speech, Sound, and Music", "authors": [ "Sreyan Ghosh", "Arushi Goel", "Kaousheik Jayakumar", "Lasha Koroshinadze", "Nishit Anand", "Zhifeng Kong", "Siddharth Gururani", "Sang-gil Lee", "Jaehyeon Kim", "Aya Aljafari", "Chao-Han Huck Yang", "Sungwon Kim", "Ramani Duraiswami", "Dinesh Manocha", "Mohammad Shoeybi", "Bryan Catanzaro", "Ming-Yu Liu", "Wei Ping" ], "github": "https://github.com/NVIDIA/audio-flamingo", "project_page": "", "abstract": "We present Audio Flamingo Next (AF-Next), the next-generation and most capable large audio-language model in the Audio Flamingo series, designed to advance understanding and reasoning over speech, environmental sounds and music. Compared to Audio Flamingo 3, AF-Next introduces: (i) a stronger foundational audio-language model that significantly improves accuracy across diverse audio understanding tasks; (ii) scalable strategies for constructing large-scale audio understanding and reasoning data beyond existing academic benchmarks; (iii) support for long and complex audio inputs up to 30 minutes; and (iv) Temporal Audio Chain-of-Thought, a new reasoning paradigm that explicitly grounds intermediate reasoning steps to timestamps in long audio, enabling fine-grained temporal alignment and improved interpretability. To enable these capabilities, we first conduct a systematic analysis of Audio Flamingo 3 to identify key gaps in audio understanding and reasoning. We then curate and scale new large-scale datasets totaling over 1 million hours to address these limitations and expand the existing AudioSkills-XL, LongAudio-XL, AF-Think and AF-Chat datasets. AF-Next is trained using a curriculum-based strategy spanning pre-training, mid-training and post-training stages. Extensive experiments across 20 audio understanding and reasoning benchmarks, including challenging long-audio tasks, show that AF-Next outperforms similarly sized open models by large margins and remains highly competitive with and sometimes surpasses, much larger open-weight and closed models. Beyond benchmark performance, AF-Next exhibits strong real-world utility and transfers well to unseen tasks, highlighting its robustness and generalization ability. In addition to all data, code and methods, we open-source 3 variants of AF-Next, including AF-Next-Instruct, AF-Next-Think and AF-Next-Captioner.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVIDIA/audio-flamingo", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11805", "title": "Solving Physics Olympiad via Reinforcement Learning on Physics Simulators", "authors": [ "Mihir Prabhudesai", "Aryan Satpathy", "Yangmin Li", "Zheyang Qin", "Nikash Bhardwaj", "Amir Zadeh", "Chuan Li", "Katerina Fragkiadaki", "Deepak Pathak" ], "github": "", "project_page": "", "abstract": "We have witnessed remarkable advances in LLM reasoning capabilities with the advent of DeepSeek-R1. However, much of this progress has been fueled by the abundance of internet question-answer (QA) pairs, a major bottleneck going forward, since such data is limited in scale and concentrated mainly in domains like mathematics. In contrast, other sciences such as physics lack large-scale QA datasets to effectively train reasoning-capable models. In this work, we show that physics simulators can serve as a powerful alternative source of supervision for training LLMs for physical reasoning. We generate random scenes in physics engines, create synthetic question-answer pairs from simulated interactions, and train LLMs using reinforcement learning on this synthetic data. Our models exhibit zero-shot sim-to-real transfer to real-world physics benchmarks: for example, training solely on synthetic simulated data improves performance on IPhO (International Physics Olympiad) problems by 5-10 percentage points across model sizes. These results demonstrate that physics simulators can act as scalable data generators, enabling LLMs to acquire deep physical reasoning skills beyond the limitations of internet-scale QA data. Code available at: https://sim2reason.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11446", "title": "Low-rank Optimization Trajectories Modeling for LLM RLVR Acceleration", "authors": [ "Zhipeng Chen", "Tao Qian", "Wayne Xin Zhao", "Ji-Rong Wen" ], "github": "https://github.com/RUCAIBox/NExt", "project_page": "", "abstract": "Recently, scaling reinforcement learning with verifiable rewards (RLVR) for large language models (LLMs) has emerged as an effective training paradigm for significantly improving model capabilities, which requires guiding the model to perform extensive exploration and learning, leading to substantial computational overhead and becoming a key challenge. To reduce the number of training steps, Prior work performs linear extrapolation of model parameters. However, the dynamics of model parameter updates during RLVR training remain insufficiently understood. To further investigate the evolution of LLMs during RLVR training, we conduct empirical experiments and find that the rank-1 subspace of the model does not evolve linearly, and its dominance over the original parameters is further amplified during LoRA training. Based on the above insights, we propose the Nonlinear Extrapolation of low-rank trajectories (NExt), a novel framework that models and extrapolates low-rank parameter trajectories in a nonlinear manner. Concretely, we first train the model using LoRA and extract the rank-1 subspace of parameter differences at multiple training steps, which is then used for the subsequent nonlinear extrapolation. Afterward, we utilized the extracted rank-1 subspace to train a predictor, which can model the trajectory of parameter updates during RLVR, and then perform the predict-extend process to extrapolate model parameters, achieving the acceleration of RLVR. To further study and understand NExt, we conduct comprehensive experiments that demonstrate the effectiveness and robustness of the method. Our method reduces computational overhead by approximately 37.5\\% while remaining compatible with a wide range of RLVR algorithms and tasks. We release our code in https://github.com/RUCAIBox/NExt.", "github_manual": "", "github_regex": "https://github.com/RUCAIBox/NExt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10784", "title": "TorchUMM: A Unified Multimodal Model Codebase for Evaluation, Analysis, and Post-training", "authors": [ "Yinyi Luo", "Wenwen Wang", "Hayes Bai", "Hongyu Zhu", "Hao Chen", "Pan He", "Marios Savvides", "Sharon Li", "Jindong Wang" ], "github": "https://github.com/AIFrontierLab/TorchUMM", "project_page": "", "abstract": "Recent advances in unified multimodal models (UMMs) have led to a proliferation of architectures capable of understanding, generating, and editing across visual and textual modalities. However, developing a unified framework for UMMs remains challenging due to the diversity of model architectures and the heterogeneity of training paradigms and implementation details. In this paper, we present TorchUMM, the first unified codebase for comprehensive evaluation, analysis, and post-training across diverse UMM backbones, tasks, and datasets. TorchUMM supports a broad spectrum of models covering a wide range of scales and design paradigms. Our benchmark encompasses three core task dimensions: multimodal understanding, generation, and editing, and integrates both established and novel datasets to evaluate perception, reasoning, compositionality, and instruction-following abilities. By providing a unified interface and standardized evaluation protocols, TorchUMM enables fair and reproducible comparisons across heterogeneous models and fosters deeper insights into their strengths and limitations, facilitating the development of more capable unified multimodal systems. Code is available at: https://github.com/AIFrontierLab/TorchUMM.", "github_manual": "", "github_regex": "https://github.com/AIFrontierLab/TorchUMM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10718", "title": "SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?", "authors": [ "Udari Madhushani Sehwag", "Elaine Lau", "Haniyeh Ehsani Oskouie", "Shayan Shabihi", "Erich Liang", "Andrea Toledo", "Guillermo Mangialardi", "Sergio Fonrouge", "Ed-Yeremai Hernandez Cardona", "Paula Vergara", "Utkarsh Tyagi", "Chen Bo Calvin Zhang", "Pavi Bhatter", "Nicholas Johnson", "Furong Huang", "Ernesto Gabriel Hernandez Montoya", "Bing Liu" ], "github": "https://github.com/scaleapi/scipredict", "project_page": "", "abstract": "Accelerating scientific discovery requires the identification of which experiments would yield the best outcomes before committing resources to costly physical validation. While existing benchmarks evaluate LLMs on scientific knowledge and reasoning, their ability to predict experimental outcomes - a task where AI could significantly exceed human capabilities - remains largely underexplored. We introduce SciPredict, a benchmark comprising 405 tasks derived from recent empirical studies in 33 specialized sub-fields of physics, biology, and chemistry. SciPredict addresses two critical questions: (a) can LLMs predict the outcome of scientific experiments with sufficient accuracy? and (b) can such predictions be reliably used in the scientific research process? Evaluations reveal fundamental limitations on both fronts. Model accuracies are 14-26% and human expert performance is approx20%. Although some frontier models exceed human performance model accuracy is still far below what would enable reliable experimental guidance. Even within the limited performance, models fail to distinguish reliable predictions from unreliable ones, achieving only approx20% accuracy regardless of their confidence or whether they judge outcomes as predictable without physical experimentation. Human experts, in contrast, demonstrate strong calibration: their accuracy increases from approx5% to approx80% as they deem outcomes more predictable without conducting the experiment. SciPredict establishes a rigorous framework demonstrating that superhuman performance in experimental science requires not just better predictions, but better awareness of prediction reliability. For reproducibility all our data and code are provided at https://github.com/scaleapi/scipredict", "github_manual": "", "github_regex": "https://github.com/scaleapi/scipredict", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11035", "title": "Introspective Diffusion Language Models", "authors": [ "Yifan Yu", "Yuqing Jian", "Junxiong Wang", "Zhongzhu Zhou", "Donglin Zhuang", "Xinyu Fang", "Sri Yanamandra", "Xiaoxia Wu", "Qingyang Wu", "Shuaiwen Leon Song", "Tri Dao", "Ben Athiwaratkun", "James Zou", "Fan Lai", "Chenfeng Xu" ], "github": "https://github.com/JetAstra/SDAR", "project_page": "", "abstract": "Diffusion language models promise parallel generation, yet still lag behind autoregressive (AR) models in quality. We stem this gap to a failure of introspective consistency: AR models agree with their own generations, while DLMs often do not. We define the introspective acceptance rate, which measures whether a model accepts its previously generated tokens. This reveals why AR training has a structural advantage: causal masking and logit shifting implicitly enforce introspective consistency. Motivated by this observation, we introduce Introspective Diffusion Language Model (I-DLM), a paradigm that retains diffusion-style parallel decoding while inheriting the introspective consistency of AR training. I-DLM uses a novel introspective strided decoding (ISD) algorithm, which enables the model to verify previously generated tokens while advancing new ones in the same forward pass. From a systems standpoint, we build I-DLM inference engine on AR-inherited optimizations and further customize it with a stationary-batch scheduler. To the best of our knowledge, I-DLM is the first DLM to match the quality of its same-scale AR counterpart while outperforming prior DLMs in both model quality and practical serving efficiency across 15 benchmarks. It reaches 69.6 on AIME-24 and 45.7 on LiveCodeBench-v6, exceeding LLaDA-2.1-mini (16B) by more than 26 and 15 points, respectively. Beyond quality, I-DLM is designed for the growing demand of large-concurrency serving, delivering about 3x higher throughput than prior state-of-the-art DLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/JetAstra/SDAR", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10480", "title": "Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs", "authors": [ "Yu Li", "Xiaoran Shang", "Qizhi Pei", "Yun Zhu", "Xin Gao", "Honglin Lin", "Zhanping Zhong", "Zhuoshi Pan", "Zheng Liu", "Xiaoyang Wang", "Conghui He", "Dahua Lin", "Feng Zhao", "Lijun Wu" ], "github": "", "project_page": "", "abstract": "Post-training data plays a pivotal role in shaping the capabilities of Large Language Models (LLMs), yet datasets are often treated as isolated artifacts, overlooking the systemic connections that underlie their evolution. To disentangle these complex relationships, we introduce the concept of data lineage to the LLM ecosystem and propose an automated multi-agent framework to reconstruct the evolutionary graph of dataset development. Through large-scale lineage analysis, we characterize domain-specific structural patterns, such as vertical refinement in math-oriented datasets and horizontal aggregation in general-domain corpora. Moreover, we uncover pervasive systemic issues, including structural redundancy induced by implicit dataset intersections and the propagation of benchmark contamination along lineage paths. To demonstrate the practical value of lineage analysis for data construction, we leverage the reconstructed lineage graph to create a lineage-aware diversity-oriented dataset. By anchoring instruction sampling at upstream root sources, this approach mitigates downstream homogenization and hidden redundancy, yielding a more diverse post-training corpus. We further highlight lineage-centric analysis as an efficient and robust topological alternative to sample-level dataset comparison for large-scale data ecosystems. By grounding data construction in explicit lineage structures, our work advances post-training data curation toward a more systematic and controllable paradigm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11521", "title": "Continuous Adversarial Flow Models", "authors": [ "Shanchuan Lin", "Ceyuan Yang", "Zhijie Lin", "Hao Chen", "Haoqi Fan" ], "github": "", "project_page": "", "abstract": "We propose continuous adversarial flow models, a type of continuous-time flow model trained with an adversarial objective. Unlike flow matching, which uses a fixed mean-squared-error criterion, our approach introduces a learned discriminator to guide training. This change in objective induces a different generalized distribution, which empirically produces samples that are better aligned with the target data distribution. Our method is primarily proposed for post-training existing flow-matching models, although it can also train models from scratch. On the ImageNet 256px generation task, our post-training substantially improves the guidance-free FID of latent-space SiT from 8.26 to 3.63 and of pixel-space JiT from 7.17 to 3.57. It also improves guided generation, reducing FID from 2.06 to 1.53 for SiT and from 1.86 to 1.80 for JiT. We further evaluate our approach on text-to-image generation, where it achieves improved results on both the GenEval and DPG benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10030", "title": "Prompt Relay: Inference-Time Temporal Control for Multi-Event Video Generation", "authors": [ "Gordon Chen", "Ziqi Huang", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "Video diffusion models have achieved remarkable progress in generating high-quality videos. However, these models struggle to represent the temporal succession of multiple events in real-world videos and lack explicit mechanisms to control when semantic concepts appear, how long they persist, and the order in which multiple events occur. Such control is especially important for movie-grade video synthesis, where coherent storytelling depends on precise timing, duration, and transitions between events. When using a single paragraph-style prompt to describe a sequence of complex events, models often exhibit semantic entanglement, where concepts intended for different moments in the video bleed into one another, resulting in poor text-video alignment. To address these limitations, we propose Prompt Relay, an inference-time, plug-and-play method to enable fine-grained temporal control in multi-event video generation, requiring no architectural modifications and no additional computational overhead. Prompt Relay introduces a penalty into the cross-attention mechanism, so that each temporal segment attends only to its assigned prompt, allowing the model to represent one semantic concept at a time and thereby improving temporal prompt alignment, reducing semantic interference, and enhancing visual quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.09212", "title": "SPASM: Stable Persona-driven Agent Simulation for Multi-turn Dialogue Generation", "authors": [ "Han Luo", "Guy Laban" ], "github": "https://github.com/lhannnn/SPASM", "project_page": "", "abstract": "Large language models are increasingly deployed in multi-turn settings such as tutoring, support, and counseling, where reliability depends on preserving consistent roles, personas, and goals across long horizons. This requirement becomes critical when LLMs are used to generate synthetic dialogues for training and evaluation, since LLM--LLM conversations can accumulate identity-related failures such as persona drift, role confusion, and \"echoing\", where one agent gradually mirrors its partner. We introduce SPASM (Stable Persona-driven Agent Simulation for Multi-turn dialogue generation), a modular, stability-first framework that decomposes simulation into (i) persona creation via schema sampling, plausibility validation, and natural-language persona crafting, (ii) Client--Responder dialogue generation, and (iii) termination detection for coherent stopping. To improve long-horizon stability without changing model weights, we propose Egocentric Context Projection (ECP): dialogue history is stored in a perspective-agnostic representation and deterministically projected into each agent's egocentric view before generation. Across three LLM backbones (GPT-4o-mini, DeepSeek-V3.2, Qwen-Plus) and nine Client--Responder pairings, we construct a dataset of 4,500 personas and 45,000 conversations (500 personas X 10 conversations per pairing). Ablations show ECP substantially reduces persona drift and, under human validation, eliminates echoing; embedding analyses recover persona structure and reveal strong responder-driven interaction geometry. Our code is available at https://github.com/lhannnn/SPASM.", "github_manual": "", "github_regex": "https://github.com/lhannnn/SPASM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10333", "title": "Zero-shot World Models Are Developmentally Efficient Learners", "authors": [ "Khai Loong Aw", "Klemen Kotar", "Wanhee Lee", "Seungwoo Kim", "Khaled Jedoui", "Rahul Venkatesh", "Lilian Naing Chen", "Michael C. Frank", "Daniel L. K. Yamins" ], "github": "", "project_page": "", "abstract": "Young children demonstrate early abilities to understand their physical world, estimating depth, motion, object coherence, interactions, and many other aspects of physical scene understanding. Children are both data-efficient and flexible cognitive systems, creating competence despite extremely limited training data, while generalizing to myriad untrained tasks -- a major challenge even for today's best AI systems. Here we introduce a novel computational hypothesis for these abilities, the Zero-shot Visual World Model (ZWM). ZWM is based on three principles: a sparse temporally-factored predictor that decouples appearance from dynamics; zero-shot estimation through approximate causal inference; and composition of inferences to build more complex abilities. We show that ZWM can be learned from the first-person experience of a single child, rapidly generating competence across multiple physical understanding benchmarks. It also broadly recapitulates behavioral signatures of child development and builds brain-like internal representations. Our work presents a blueprint for efficient and flexible learning from human-scale data, advancing both a computational account for children's early physical understanding and a path toward data-efficient AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11716", "title": "SWE-AGILE: A Software Agent Framework for Efficiently Managing Dynamic Reasoning Context", "authors": [ "Shuquan Lian", "Juncheng Liu", "Yazhe Chen", "Yuhong Chen", "Hui Li" ], "github": "https://github.com/KDEGroup/SWE-AGILE", "project_page": "", "abstract": "Prior representative ReAct-style approaches in autonomous Software Engineering (SWE) typically lack the explicit System-2 reasoning required for deep analysis and handling complex edge cases. While recent reasoning models demonstrate the potential of extended Chain-of-Thought (CoT), applying them to the multi-turn SWE task creates a fundamental dilemma: retaining full reasoning history leads to context explosion and ``Lost-in-the-Middle'' degradation, while discarding it would force the agent to redundantly re-reason at every step. To address these challenges, we propose SWE-AGILE, a novel software agent framework designed to bridge the gap between reasoning depth, efficiency, and context constraints. SWE-AGILE introduces a Dynamic Reasoning Context strategy, maintaining a ``sliding window'' of detailed reasoning for immediate continuity to prevent redundant re-analyzing, while compressing historical reasoning content into concise Reasoning Digests. Empirically, SWE-AGILE sets a new standard for 7B-8B models on SWE-Bench-Verified using only 2.2k trajectories and 896 tasks. Code is available at https://github.com/KDEGroup/SWE-AGILE.", "github_manual": "", "github_regex": "https://github.com/KDEGroup/SWE-AGILE", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.08121", "title": "Uni-ViGU: Towards Unified Video Generation and Understanding via A Diffusion-Based Video Generator", "authors": [ "Luozheng Qin", "Jia Gong", "Qian Qiao", "Tianjiao Li", "Li Xu", "Haoyu Pan", "Chao Qu", "Zhiyu Tan", "Hao Li" ], "github": "", "project_page": "", "abstract": "Unified multimodal models integrating visual understanding and generation face a fundamental challenge: visual generation incurs substantially higher computational costs than understanding, particularly for video. This imbalance motivates us to invert the conventional paradigm: rather than extending understanding-centric MLLMs to support generation, we propose Uni-ViGU, a framework that unifies video generation and understanding by extending a video generator as the foundation. We introduce a unified flow method that performs continuous flow matching for video and discrete flow matching for text within a single process, enabling coherent multimodal generation. We further propose a modality-driven MoE-based framework that augments Transformer blocks with lightweight layers for text generation while preserving generative priors. To repurpose generation knowledge for understanding, we design a bidirectional training mechanism with two stages: Knowledge Recall reconstructs input prompts to leverage learned text-video correspondences, while Capability Refinement fine-tunes on detailed captions to establish discriminative shared representations. Experiments demonstrate that Uni-ViGU achieves competitive performance on both video generation and understanding, validating generation-centric architectures as a scalable path toward unified multimodal intelligence. Project Page and Code: https://fr0zencrane.github.io/uni-vigu-page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10425", "title": "DiningBench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain", "authors": [ "Song Jin", "Juntian Zhang", "Xun Zhang", "Zeying Tian", "Fei Jiang", "Guojun Yin", "Wei Lin", "Yong Liu", "Rui Yan" ], "github": "https://github.com/meituan/DiningBench", "project_page": "", "abstract": "Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain remains constrained by benchmarks that rely on coarse-grained categories, single-view imagery, and inaccurate metadata. To bridge this gap, we introduce DiningBench, a hierarchical, multi-view benchmark designed to evaluate VLMs across three levels of cognitive complexity: Fine-Grained Classification, Nutrition Estimation, and Visual Question Answering. Unlike previous datasets, DiningBench comprises 3,021 distinct dishes with an average of 5.27 images per entry, incorporating fine-grained \"hard\" negatives from identical menus and rigorous, verification-based nutritional data. We conduct an extensive evaluation of 29 state-of-the-art open-source and proprietary models. Our experiments reveal that while current VLMs excel at general reasoning, they struggle significantly with fine-grained visual discrimination and precise nutritional reasoning. Furthermore, we systematically investigate the impact of multi-view inputs and Chain-of-Thought reasoning, identifying five primary failure modes. DiningBench serves as a challenging testbed to drive the next generation of food-centric VLM research. All codes are released in https://github.com/meituan/DiningBench.", "github_manual": "", "github_regex": "https://github.com/meituan/DiningBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11666", "title": "Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind", "authors": [ "Hanqi Xiao", "Vaidehi Patil", "Zaid Khan", "Hyunji Lee", "Elias Stengel-Eskin", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) become the engine behind conversational systems, their ability to reason about the intentions and states of their dialogue partners (i.e., form and use a theory-of-mind, or ToM) becomes increasingly critical for safe interaction with potentially adversarial partners. We propose a novel privacy-themed ToM challenge, ToM for Steering Beliefs (ToM-SB), in which a defender must act as a Double Agent to steer the beliefs of an attacker with partial prior knowledge within a shared universe. To succeed on ToM-SB, the defender must engage with and form a ToM of the attacker, with a goal of fooling the attacker into believing they have succeeded in extracting sensitive information. We find that strong frontier models like Gemini3-Pro and GPT-5.4 struggle on ToM-SB, often failing to fool attackers in hard scenarios with partial attacker prior knowledge, even when prompted to reason about the attacker's beliefs (ToM prompting). To close this gap, we train models on ToM-SB to act as AI Double Agents using reinforcement learning, testing both fooling and ToM rewards. Notably, we find a bidirectionally emergent relationship between ToM and attacker-fooling: rewarding fooling success alone improves ToM, and rewarding ToM alone improves fooling. Across four attackers with different strengths, six defender methods, and both in-distribution and out-of-distribution (OOD) evaluation, we find that gains in ToM and attacker-fooling are well-correlated, highlighting belief modeling as a key driver of success on ToM-SB. AI Double Agents that combine both ToM and fooling rewards yield the strongest fooling and ToM performance, outperforming Gemini3-Pro and GPT-5.4 with ToM prompting on hard scenarios. We also show that ToM-SB and AI Double Agents can be extended to stronger attackers, demonstrating generalization to OOD settings and the upgradability of our task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.02340", "title": "Not All Denoising Steps Are Equal: Model Scheduling for Faster Masked Diffusion Language Models", "authors": [ "Ivan Sedykh", "Nikita Sorokin", "Valentin Malykh" ], "github": "https://github.com/kuleshov-group/bd3lms", "project_page": "", "abstract": "Recent advances in masked diffusion language models (MDLMs) narrow the quality gap to autoregressive LMs, but their sampling remains expensive because generation requires many full-sequence denoising passes with a large Transformer and, unlike autoregressive decoding, cannot benefit from KV caching. In this work, we exploit the flexibility of the diffusion framework and study model scheduling, where a smaller MDLM replaces the full model at a subset of denoising steps. Across models trained on OpenWebText and LM1B, we show that early and late denoising steps are substantially more robust to such replacement than middle steps, enabling up to a 17% reduction in FLOPs with only modest degradation in generative perplexity under both unconditional and prefix-conditional generation, while preserving sample diversity. We support these findings with a step-importance analysis based on loss and KL divergence between small and large models across timesteps, as well as an exhaustive search over coarse step segments, both of which identify the middle of the diffusion trajectory as most sensitive consistently across datasets. Our results suggest that simple, architecture-agnostic scheduling rules can significantly accelerate MDLM sampling while largely preserving generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kuleshov-group/bd3lms", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.09557", "title": "SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding", "authors": [ "Talor Abramovich", "Maor Ashkenazi", "Carl", "Putterman", "Benjamin Chislett", "Tiyasa Mitra", "Bita Darvish Rouhani", "Ran Zilberstein", "Yonatan Geifman" ], "github": "", "project_page": "", "abstract": "Speculative Decoding (SD) has emerged as a critical technique for accelerating Large Language Model (LLM) inference. Unlike deterministic system optimizations, SD performance is inherently data-dependent, meaning that diverse and representative workloads are essential for accurately measuring its effectiveness. Existing benchmarks suffer from limited task diversity, inadequate support for throughput-oriented evaluation, and a reliance on high-level implementations that fail to reflect production environments. To address this, we introduce SPEED-Bench, a comprehensive suite designed to standardize SD evaluation across diverse semantic domains and realistic serving regimes. SPEED-Bench offers a carefully curated Qualitative data split, selected by prioritizing semantic diversity across the data samples. Additionally, it includes a Throughput data split, allowing speedup evaluation across a range of concurrencies, from latency-sensitive low-batch settings to throughput-oriented high-load scenarios. By integrating with production engines like vLLM and TensorRT-LLM, SPEED-Bench allows practitioners to analyze system behaviors often masked by other benchmarks. We highlight this by quantifying how synthetic inputs overestimate real-world throughput, identifying batch-size dependent optimal draft lengths and biases in low-diversity data, and analyzing the caveats of vocabulary pruning in state-of-the-art drafters. We release SPEED-Bench to establish a unified evaluation standard for practical comparisons of SD algorithms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.08570", "title": "QuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation", "authors": [ "Ali Slim", "Haydar Hamieh", "Jawad Kotaich", "Yehya Ghosn", "Mahdi Chehimi", "Ammar Mohanna", "Hasan Abed Al Kader Hammoud", "Bernard Ghanem" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly used for code generation, yet quantum code generation is still evaluated mostly within single frameworks, making it difficult to separate quantum reasoning from framework familiarity. We introduce QuanBench+, a unified benchmark spanning Qiskit, PennyLane, and Cirq, with 42 aligned tasks covering quantum algorithms, gate decomposition, and state preparation. We evaluate models with executable functional tests, report Pass@1 and Pass@5, and use KL-divergence-based acceptance for probabilistic outputs. We additionally study Pass@1 after feedback-based repair, where a model may revise code after a runtime error or wrong answer. Across frameworks, the strongest one-shot scores reach 59.5% in Qiskit, 54.8% in Cirq, and 42.9% in PennyLane; with feedback-based repair, the best scores rise to 83.3%, 76.2%, and 66.7%, respectively. These results show clear progress, but also that reliable multi-framework quantum code generation remains unsolved and still depends strongly on framework-specific knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10628", "title": "BMdataset: A Musicologically Curated LilyPond Dataset", "authors": [ "Matteo Spanio", "Ilay Guler", "Antonio Rod\u00e0" ], "github": "", "project_page": "", "abstract": "Symbolic music research has relied almost exclusively on MIDI-based datasets; text-based engraving formats such as LilyPond remain unexplored for music understanding. We present BMdataset, a musicologically curated dataset of 393 LilyPond scores (2,646 movements) transcribed by experts directly from original Baroque manuscripts, with metadata covering composer, musical form, instrumentation, and sectional attributes. Building on this resource, we introduce LilyBERT (weights can be found at https://huggingface.co/csc-unipd/lilybert), a CodeBERT-based encoder adapted to symbolic music through vocabulary extension with 115 LilyPond-specific tokens and masked language model pre-training. Linear probing on the out-of-domain Mutopia corpus shows that, despite its modest size (~90M tokens), fine-tuning on BMdataset alone outperforms continuous pre-training on the full PDMX corpus (~15B tokens) for both composer and style classification, demonstrating that small, expertly curated datasets can be more effective than large, noisy corpora for music understanding. Combining broad pre-training with domain-specific fine-tuning yields the best results overall (84.3% composer accuracy), confirming that the two data regimes are complementary. We release the dataset, tokenizer, and model to establish a baseline for representation learning on LilyPond.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10799", "title": "Advancing Polish Language Modeling through Tokenizer Optimization in the Bielik v3 7B and 11B Series", "authors": [ "Krzysztof Ociepa", "\u0141ukasz Flis", "Remigiusz Kinas", "Krzysztof Wr\u00f3bel", "Adrian Gwo\u017adziej" ], "github": "", "project_page": "", "abstract": "The development of the Bielik v3 PL series, encompassing both the 7B and 11B parameter variants, represents a significant milestone in the field of language-specific large language model (LLM) optimization. While general-purpose models often demonstrate impressive multilingual capabilities, they frequently suffer from a fundamental architectural inefficiency: the use of universal tokenizers. These tokenizers, typically designed to cover a broad spectrum of languages, often fail to capture the morphological nuances of specific languages like Polish, leading to higher fertility ratios, increased inference costs, and restricted effective context windows. This report details the transition from the universal Mistral-based tokenization to a dedicated Polish-optimized vocabulary for the Bielik v3 models, exploring the FOCUS-based embedding initialization, the multi-stage pretraining curriculum, and the subsequent post-training alignment involving Supervised Fine-Tuning, Direct Preference Optimization, and Reinforcement Learning through Group Relative Policy Optimization with verifiable rewards.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11290", "title": "Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generation", "authors": [ "Lester James V. Miranda", "Ivan Vuli\u0107", "Anna Korhonen" ], "github": "", "project_page": "", "abstract": "Synthesizing supervised finetuning (SFT) data from language models (LMs) to teach smaller models multilingual tasks has become increasingly common. However, teacher model selection is often ad hoc, typically defaulting to the largest available option, even though such models may have significant capability gaps in non-English languages. This practice can result in poor-quality synthetic data and suboptimal student downstream performance. In this work, we systematically characterize what makes an effective multilingual teacher. We measure intrinsic measures of data quality with extrinsic student model performance in a metric we call Polyglot Score; evaluating 10 LMs across 6 typologically diverse languages, generating over 1.4M SFT examples and training 240 student models. Among the models tested, Gemma 3 27B and Aya Expanse 32B emerge as consistently effective teachers across different student base model families. Further analyses reveal that model scale alone does not significantly predict teacher effectiveness; instead, data qualities such as prompt diversity, length, and response fluency capture over 93.3% of variance in intrinsic data quality and predict student performance. Finally, we provide practical recommendations, including matching the model families of teacher-student pairs and translating from or responding to existing prompts, which can yield improvements for less-resourced languages. We hope that our work advances data-centric research in multilingual synthetic data and LM development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11297", "title": "The Past Is Not Past: Memory-Enhanced Dynamic Reward Shaping", "authors": [ "Yang Liu", "Enxi Wang", "Yufei Gao", "Weixin Zhang", "Bo Wang", "Zhiyuan Zeng", "Yikai Zhang", "Yining Zheng", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Despite the success of reinforcement learning for large language models, a common failure mode is reduced sampling diversity, where the policy repeatedly generates similar erroneous behaviors. Classical entropy regularization encourages randomness under the current policy, but does not explicitly discourage recurrent failure patterns across rollouts. We propose MEDS, a Memory-Enhanced Dynamic reward Shaping framework that incorporates historical behavioral signals into reward design. By storing and leveraging intermediate model representations, we capture features of past rollouts and use density-based clustering to identify frequently recurring error patterns. Rollouts assigned to more prevalent error clusters are penalized more heavily, encouraging broader exploration while reducing repeated mistakes. Across five datasets and three base models, MEDS consistently improves average performance over existing baselines, achieving gains of up to 4.13 pass@1 points and 4.37 pass@128 points. Additional analyses using both LLM-based annotations and quantitative diversity metrics show that MEDS increases behavioral diversity during sampling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11259", "title": "Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization", "authors": [ "Zhixin Lin", "Jungang Li", "Dongliang Xu", "Shidong Pan", "Yibo Shi", "Yuchi Liu", "Yuecong Min", "Yue Yao" ], "github": "https://github.com/Zhixin-L/TIPO", "project_page": "", "abstract": "Mobile GUI agents powered by Multimodal Large Language Models (MLLMs) can execute complex tasks on mobile devices. Despite this progress, most existing systems still optimize task success or efficiency, neglecting users' privacy personalization. In this paper, we study the often-overlooked problem of agent personalization. We observe that personalization can induce systematic structural heterogeneity in execution trajectories. For example, privacy-first users often prefer protective actions, e.g., refusing permissions, logging out, and minimizing exposure, leading to logically different execution trajectories from utility-first users. Such variable-length and structurally different trajectories make standard preference optimization unstable and less informative. To address this issue, we propose Trajectory Induced Preference Optimization (TIPO), which uses preference-intensity weighting to emphasize key privacy-related steps and padding gating to suppress alignment noise. Results on our Privacy Preference Dataset show that TIPO improves persona alignment and distinction while preserving strong task executability, achieving 65.60% SR, 46.22 Compliance, and 66.67% PD, outperforming existing optimization methods across various GUI tasks. The code and dataset will be publicly released at https://github.com/Zhixin-L/TIPO.", "github_manual": "", "github_regex": "https://github.com/Zhixin-L/TIPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11152", "title": "SHARE: Social-Humanities AI for Research and Education", "authors": [ "Jo\u00e3o Gon\u00e7alves", "Sonia de Jager", "Petr Knoth", "David Pride", "Nick Jelicic" ], "github": "", "project_page": "", "abstract": "This intermediate technical report introduces the SHARE family of base models and the MIRROR user interface. The SHARE models are the first causal language models fully pretrained by and for the social sciences and humanities (SSH). Their performance in modelling SSH texts is close to that of general purpose models (Phi-4) which use 100 times more tokens, as shown by our custom SSH Cloze benchmark. The MIRROR user interface is designed for reviewing text inputs from the SSH disciplines while preserving critical engagement. By prototyping a generative AI interface that does not generate any text, we propose a way to harness the capabilities of the SHARE models without compromising the integrity of SSH principles and norms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10688", "title": "SCOPE: Signal-Calibrated On-Policy Distillation Enhancement with Dual-Path Adaptive Weighting", "authors": [ "Binbin Zheng", "Xing Ma", "Yiheng Liang", "Jingqing Ruan", "Xiaoliang Fu", "Kepeng Lin", "Benchang Zhu", "Ke Zeng", "Xunliang Cai" ], "github": "https://github.com/machine981/SCOPE.git", "project_page": "", "abstract": "On-policy reinforcement learning has become the dominant paradigm for reasoning alignment in large language models, yet its sparse, outcome-level rewards make token-level credit assignment notoriously difficult. On-Policy Distillation (OPD) alleviates this by introducing dense, token-level KL supervision from a teacher model, but typically applies this supervision uniformly across all rollouts, ignoring fundamental differences in signal quality. We propose Signal-Calibrated On-Policy Distillation Enhancement (SCOPE), a dual-path adaptive training framework that routes on-policy rollouts by correctness into two complementary supervision paths. For incorrect trajectories, SCOPE performs teacher-perplexity-weighted KL distillation to prioritize instances where the teacher demonstrates genuine corrective capability, while down-weighting unreliable guidance. For correct trajectories, it applies student-perplexity-weighted MLE to concentrate reinforcement on low-confidence samples at the capability boundary rather than over-reinforcing already mastered ones. Both paths employ a group-level normalization to adaptively calibrate weight distributions, accounting for the intrinsic difficulty variance across prompts. Extensive experiments on six reasoning benchmarks show that SCOPE achieves an average relative improvement of 11.42% in Avg@32 and 7.30% in Pass@32 over competitive baselines, demonstrating its consistent effectiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/machine981/SCOPE.git", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11737", "title": "Learning Long-term Motion Embeddings for Efficient Kinematics Generation", "authors": [ "Nick Stracke", "Kolja Bauer", "Stefan Andreas Baumann", "Miguel Angel Bautista", "Josh Susskind", "Bj\u00f6rn Ommer" ], "github": "", "project_page": "", "abstract": "Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scene dynamics, exploring multiple possible futures through full video synthesis remains prohibitively inefficient. We model scene dynamics orders of magnitude more efficiently by directly operating on a long-term motion embedding that is learned from large-scale trajectories obtained from tracker models. This enables efficient generation of long, realistic motions that fulfill goals specified via text prompts or spatial pokes. To achieve this, we first learn a highly compressed motion embedding with a temporal compression factor of 64x. In this space, we train a conditional flow-matching model to generate motion latents conditioned on task descriptions. The resulting motion distributions outperform those of both state-of-the-art video models and specialized task-specific approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.04385", "title": "How Alignment Routes: Localizing, Scaling, and Controlling Policy Circuits in Language Models", "authors": [ "Gregory N. Frank" ], "github": "", "project_page": "", "abstract": "This paper localizes the policy routing mechanism in alignment-trained language models. An intermediate-layer attention gate reads detected content and triggers deeper amplifier heads that boost the signal toward refusal. In smaller models the gate and amplifier are single heads; at larger scale they become bands of heads across adjacent layers. The gate contributes under 1% of output DLA, but interchange testing (p<0.001) and knockout cascade confirm it is causally necessary. Interchange screening at n>=120 detects the same motif in twelve models from six labs (2B to 72B), though specific heads differ by lab. Per-head ablation weakens up to 58x at 72B and misses gates that interchange identifies; interchange is the only reliable audit at scale. Modulating the detection-layer signal continuously controls policy from hard refusal through evasion to factual answering. On safety prompts the same intervention turns refusal into harmful guidance, showing the safety-trained capability is gated by routing rather than removed. Thresholds vary by topic and by input language, and the circuit relocates across generations within a family while behavioral benchmarks register no change. Routing is early-commitment: the gate commits at its own layer before deeper layers finish processing the input. Under an in-context substitution cipher, gate interchange necessity collapses 70 to 99% across three models and the model switches to puzzle-solving. Injecting the plaintext gate activation into the cipher forward pass restores 48% of refusals in Phi-4-mini, localizing the bypass to the routing interface. A second method, cipher contrast analysis, uses plain/cipher DLA differences to map the full cipher-sensitive routing circuit in O(3n) forward passes. Any encoding that defeats detection-layer pattern matching bypasses the policy regardless of whether deeper layers reconstruct the content.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10039", "title": "Counting to Four is still a Chore for VLMs", "authors": [ "Duy Le Dinh Anh", "Patrick Amadeus Irawan", "Tuan Van Vo" ], "github": "https://github.com/leduy99/-CVPRW26-Modality-Attention-Share", "project_page": "", "abstract": "Vision--language models (VLMs) have achieved impressive performance on complex multimodal reasoning tasks, yet they still fail on simple grounding skills such as object counting. Existing evaluations mostly assess only final outputs, offering limited insight into where these failures arise inside the model. In this work, we present an empirical study of VLM counting behavior through both behavioral and mechanistic analysis. We introduce COUNTINGTRICKS, a controlled evaluation suite of simple shape-based counting cases designed to expose vulnerabilities under different patchification layouts and adversarial prompting conditions. Using attention analysis and component-wise probing, we show that count-relevant visual evidence is strongest in the modality projection stage but degrades substantially in later language layers, where models become more susceptible to text priors. Motivated by this finding, we further evaluate Modality Attention Share (MAS), a lightweight intervention that encourages a minimum budget of visual attention during answer generation. Our results suggest that counting failures in VLMs stem not only from visual perception limits, but also from the underuse of visual evidence during language-stage reasoning. Code and dataset will be released at https://github.com/leduy99/-CVPRW26-Modality-Attention-Share.", "github_manual": "", "github_regex": "https://github.com/leduy99/-CVPRW26-Modality-Attention-Share", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11004", "title": "Panoptic Pairwise Distortion Graph", "authors": [ "Muhammad Kamran Janjua", "Abdul Wahab", "Bahador Rashidi" ], "github": "", "project_page": "", "abstract": "In this work, we introduce a new perspective on comparative image assessment by representing an image pair as a structured composition of its regions. In contrast, existing methods focus on whole image analysis, while implicitly relying on region-level understanding. We extend the intra-image notion of a scene graph to inter-image, and propose a novel task of Distortion Graph (DG). DG treats paired images as a structured topology grounded in regions, and represents dense degradation information such as distortion type, severity, comparison and quality score in a compact interpretable graph structure. To realize the task of learning a distortion graph, we contribute (i) a region-level dataset, PandaSet, (ii) a benchmark suite, PandaBench, with varying region-level difficulty, and (iii) an efficient architecture, Panda, to generate distortion graphs. We demonstrate that PandaBench poses a significant challenge for state-of-the-art multimodal large language models (MLLMs) as they fail to understand region-level degradations even when fed with explicit region cues. We show that training on PandaSet or prompting with DG elicits region-wise distortion understanding, opening a new direction for fine-grained, structured pairwise image assessment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11753", "title": "Agentic Aggregation for Parallel Scaling of Long-Horizon Agentic Tasks", "authors": [ "Yoonsang Lee", "Howard Yen", "Xi Ye", "Danqi Chen" ], "github": "", "project_page": "", "abstract": "We study parallel test-time scaling for long-horizon agentic tasks such as agentic search and deep research, where multiple rollouts are generated in parallel and aggregated into a final response. While such scaling has proven effective for chain-of-thought reasoning, agentic tasks pose unique challenges: trajectories are long, multi-turn, and tool-augmented, and outputs are often open-ended. Aggregating only final answers discards rich information from trajectories, while concatenating all trajectories exceeds the model's context window. To address this, we propose AggAgent, an aggregation agent that treats parallel trajectories as an environment. We equip it with lightweight tools to inspect candidate solutions and search across trajectories, enabling it to navigate and synthesize information on demand. Across six benchmarks and three model families (GLM-4.7, Qwen3.5, MiniMax-M2.5), AggAgent outperforms all existing aggregation methods-by up to 5.3% absolute on average and 10.3% on two deep research tasks-while adding minimal overhead, as the aggregation cost remains bounded by a single agentic rollout. Our findings establish agentic aggregation as an effective and cost-efficient approach to parallel test-time scaling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.08706", "title": "Efficient RL Training for LLMs with Experience Replay", "authors": [ "Charles Arnal", "Vivien Cabannes", "Taco Cohen", "Julia Kempe", "Remi Munos" ], "github": "", "project_page": "", "abstract": "While Experience Replay - the practice of storing rollouts and reusing them multiple times during training - is a foundational technique in general RL, it remains largely unexplored in LLM post-training due to the prevailing belief that fresh, on-policy data is essential for high performance. In this work, we challenge this assumption. We present a systematic study of replay buffers for LLM post-training, formalizing the optimal design as a trade-off between staleness-induced variance, sample diversity and the high computational cost of generation. We show that strict on-policy sampling is suboptimal when generation is expensive. Empirically, we show that a well-designed replay buffer can drastically reduce inference compute without degrading - and in some cases even improving - final model performance, while preserving policy entropy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.05336", "title": "TRACE: Capability-Targeted Agentic Training", "authors": [ "Hangoo Kang", "Tarun Suresh", "Jon Saad-Falcon", "Azalia Mirhoseini" ], "github": "https://github.com/ScalingIntelligence/TRACE.git", "project_page": "", "abstract": "Large Language Models (LLMs) deployed in agentic environments must exercise multiple capabilities across different task instances, where a capability is performing one or more actions in a trajectory that are necessary for successfully solving a subset of tasks in the environment. Many existing approaches either rely on synthetic training data that is not targeted to the model's actual capability deficits in the target environment or train directly on the target environment, where the model needs to implicitly learn the capabilities across tasks. We introduce TRACE (Turning Recurrent Agent failures into Capability-targeted training Environments), an end-to-end system for environment-specific agent self-improvement. TRACE contrasts successful and failed trajectories to automatically identify lacking capabilities, synthesizes a targeted training environment for each that rewards whether the capability was exercised, and trains a LoRA adapter via RL on each synthetic environment, routing to the relevant adapter at inference. Empirically, TRACE generalizes across different environments, improving over the base agent by +14.1 points on \u03c4^2-bench (customer service) and +7 perfect scores on ToolSandbox (tool use), outperforming the strongest baseline by +7.4 points and +4 perfect scores, respectively. Given the same number of rollouts, TRACE scales more efficiently than baselines, outperforming GRPO and GEPA by +9.2 and +7.4 points on \u03c4^2-bench.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ScalingIntelligence/TRACE.git", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.06710", "title": "ATANT: An Evaluation Framework for AI Continuity", "authors": [ "Samuel Sameer Tanguturi" ], "github": "https://github.com/Kenotic-Labs/ATANT", "project_page": "", "abstract": "We present ATANT (Automated Test for Acceptance of Narrative Truth), an open evaluation framework for measuring continuity in AI systems: the ability to persist, update, disambiguate, and reconstruct meaningful context across time. While the AI industry has produced memory components (RAG pipelines, vector databases, long context windows, profile layers), no published framework formally defines or measures whether these components produce genuine continuity. We define continuity as a system property with 7 required properties, introduce a 10-checkpoint evaluation methodology that operates without an LLM in the evaluation loop, and present a narrative test corpus of 250 stories comprising 1,835 verification questions across 6 life domains. We evaluate a reference implementation across 5 test suite iterations, progressing from 58% (legacy architecture) to 100% in isolated mode (250 stories) and 100% in 50-story cumulative mode, with 96% at 250-story cumulative scale. The cumulative result is the primary measure: when 250 distinct life narratives coexist in the same database, the system must retrieve the correct fact for the correct context without cross-contamination. ATANT is system-agnostic, model-independent, and designed as a sequenced methodology for building and validating continuity systems. The framework specification, example stories, and evaluation protocol are available at https://github.com/Kenotic-Labs/ATANT. The full 250-story corpus will be released incrementally.", "github_manual": "", "github_regex": "https://github.com/Kenotic-Labs/ATANT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10539", "title": "IceCache: Memory-efficient KV-cache Management for Long-Sequence LLMs", "authors": [ "Yuzhen Mao", "Qitong Wang", "Martin Ester", "Ke Li" ], "github": "", "project_page": "", "abstract": "Key-Value (KV) cache plays a crucial role in accelerating inference in large language models (LLMs) by storing intermediate attention states and avoiding redundant computation during autoregressive generation. However, its memory footprint scales linearly with sequence length, often leading to severe memory bottlenecks on resource-constrained hardware. Prior work has explored offloading KV cache to the CPU while retaining only a subset on the GPU, but these approaches often rely on imprecise token selection and suffer performance degradation in long-generation tasks such as chain-of-thought reasoning. In this paper, we propose a novel KV cache management strategy, IceCache, which integrates semantic token clustering with PagedAttention. By organizing semantically related tokens into contiguous memory regions managed by a hierarchical, dynamically updatable data structure, our method enables more efficient token selection and better utilization of memory bandwidth during CPU-GPU transfers. Experimental results on LongBench show that, with a 256-token budget, IceCache maintains 99% of the original accuracy achieved by the full KV cache model. Moreover, compared to other offloading-based methods, IceCache attains competitive or even superior latency and accuracy while using only 25% of the KV cache token budget, demonstrating its effectiveness in long-sequence scenarios. The code is available on our project website at https://yuzhenmao.github.io/IceCache/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.10708", "title": "Audio-Omni: Extending Multi-modal Understanding to Versatile Audio Generation and Editing", "authors": [ "Zeyue Tian", "Binxin Yang", "Zhaoyang Liu", "Jiexuan Zhang", "Ruibin Yuan", "Hubery Yin", "Qifeng Chen", "Chen Li", "Jing Lv", "Wei Xue", "Yike Guo" ], "github": "", "project_page": "", "abstract": "Recent progress in multimodal models has spurred rapid advances in audio understanding, generation, and editing. However, these capabilities are typically addressed by specialized models, leaving the development of a truly unified framework that can seamlessly integrate all three tasks underexplored. While some pioneering works have explored unifying audio understanding and generation, they often remain confined to specific domains. To address this, we introduce Audio-Omni, the first end-to-end framework to unify generation and editing across general sound, music, and speech domains, with integrated multi-modal understanding capabilities. Our architecture synergizes a frozen Multimodal Large Language Model for high-level reasoning with a trainable Diffusion Transformer for high-fidelity synthesis. To overcome the critical data scarcity in audio editing, we construct AudioEdit, a new large-scale dataset comprising over one million meticulously curated editing pairs. Extensive experiments demonstrate that Audio-Omni achieves state-of-the-art performance across a suite of benchmarks, outperforming prior unified approaches while achieving performance on par with or superior to specialized expert models. Beyond its core capabilities, Audio-Omni exhibits remarkable inherited capabilities, including knowledge-augmented reasoning generation, in-context generation, and zero-shot cross-lingual control for audio generation, highlighting a promising direction toward universal generative audio intelligence. The code, model, and dataset will be publicly released on https://zeyuet.github.io/Audio-Omni.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-14", "arxiv_id": "2604.11544", "title": "Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory", "authors": [ "Weixian Waylon Li", "Jiaxin Zhang", "Xianan Jim Yang", "Tiejun Ma", "Yiwen Guo" ], "github": "", "project_page": "", "abstract": "Structured memory representations such as knowledge graphs are central to autonomous agents and other long-lived systems. However, most existing approaches model time as discrete metadata, either sorting by recency (burying old-yet-permanent knowledge), simply overwriting outdated facts, or requiring an expensive LLM call at every ingestion step, leaving them unable to distinguish persistent facts from evolving ones. To address this, we introduce RoMem, a drop-in temporal knowledge graph module for structured memory systems, applicable to agentic memory and beyond. A pretrained Semantic Speed Gate maps each relation's text embedding to a volatility score, learning from data that evolving relations (e.g., \"president of\") should rotate fast while persistent ones (e.g., \"born in\") should remain stable. Combined with continuous phase rotation, this enables geometric shadowing: obsolete facts are rotated out of phase in complex vector space, so temporally correct facts naturally outrank contradictions without deletion. On temporal knowledge graph completion, RoMem achieves state-of-the-art results on ICEWS05-15 (72.6 MRR). Applied to agentic memory, it delivers 2-3x MRR and answer accuracy on temporal reasoning (MultiTQ), dominates hybrid benchmark (LoCoMo), preserves static memory with zero degradation (DMR-MSC), and generalises zero-shot to unseen financial domains (FinTMMBench).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.13016", "title": "Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe", "authors": [ "Yaxuan Li", "Yuxin Zuo", "Bingxiang He", "Jinqian Zhang", "Chaojun Xiao", "Cheng Qian", "Tianyu Yu", "Huan-ang Gao", "Wenkai Yang", "Zhiyuan Liu", "Ning Ding" ], "github": "", "project_page": "", "abstract": "On-policy distillation (OPD) has become a core technique in the post-training of large language models, yet its training dynamics remain poorly understood. This paper provides a systematic investigation of OPD dynamics and mechanisms. We first identify that two conditions govern whether OPD succeeds or fails: (i) the student and teacher should share compatible thinking patterns; and (ii) even with consistent thinking patterns and higher scores, the teacher must offer genuinely new capabilities beyond what the student has seen during training. We validate these findings through weak-to-strong reverse distillation, showing that same-family 1.5B and 7B teachers are distributionally indistinguishable from the student's perspective. Probing into the token-level mechanism, we show that successful OPD is characterized by progressive alignment on high-probability tokens at student-visited states, a small shared token set that concentrates most of the probability mass (97%-99%). We further propose two practical strategies to recover failing OPD: off-policy cold start and teacher-aligned prompt selection. Finally, we show that OPD's apparent free lunch of dense token-level reward comes at a cost, raising the question of whether OPD can scale to long-horizon distillation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.13018", "title": "Toward Autonomous Long-Horizon Engineering for ML Research", "authors": [ "Guoxin Chen", "Jie Chen", "Lei Chen", "Jiale Zhao", "Fanzhe Meng", "Wayne Xin Zhao", "Ruihua Song", "Cheng Chen", "Ji-Rong Wen", "Kai Jia" ], "github": "", "project_page": "", "abstract": "Autonomous AI research has advanced rapidly, but long-horizon ML research engineering remains difficult: agents must sustain coherent progress across task comprehension, environment setup, implementation, experimentation, and debugging over hours or days. We introduce AiScientist, a system for autonomous long-horizon engineering for ML research built on a simple principle: strong long-horizon performance requires both structured orchestration and durable state continuity. To this end, AiScientist combines hierarchical orchestration with a permission-scoped File-as-Bus workspace: a top-level Orchestrator maintains stage-level control through concise summaries and a workspace map, while specialized agents repeatedly re-ground on durable artifacts such as analyses, plans, code, and experimental evidence rather than relying primarily on conversational handoffs, yielding thin control over thick state. Across two complementary benchmarks, AiScientist improves PaperBench score by 10.54 points on average over the best matched baseline and achieves 81.82 Any Medal% on MLE-Bench Lite. Ablation studies further show that File-as-Bus protocol is a key driver of performance, reducing PaperBench by 6.41 points and MLE-Bench Lite by 31.82 points when removed. These results suggest that long-horizon ML research engineering is a systems problem of coordinating specialized work over durable project state, rather than a purely local reasoning problem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.08865", "title": "SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks", "authors": [ "Tianyi Wang", "Yixia Li", "Long Li", "Yibiao Chen", "Shaohan Huang", "Yun Chen", "Peng Li", "Yang Liu", "Guanhua Chen" ], "github": "https://github.com/sustech-nlp/SPPO", "project_page": "", "abstract": "Proximal Policy Optimization (PPO) is central to aligning Large Language Models (LLMs) in reasoning tasks with verifiable rewards. However, standard token-level PPO struggles in this setting due to the instability of temporal credit assignment over long Chain-of-Thought (CoT) horizons and the prohibitive memory cost of the value model. While critic-free alternatives like GRPO mitigate these issues, they incur significant computational overhead by requiring multiple samples for baseline estimation, severely limiting training throughput. In this paper, we introduce Sequence-Level PPO (SPPO), a scalable algorithm that harmonizes the sample efficiency of PPO with the stability of outcome-based updates. SPPO reformulates the reasoning process as a Sequence-Level Contextual Bandit problem, employing a decoupled scalar value function to derive low-variance advantage signals without multi-sampling. Extensive experiments on mathematical benchmarks demonstrate that SPPO significantly surpasses standard PPO and matches the performance of computation-heavy group-based methods, offering a resource-efficient framework for aligning reasoning LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/sustech-nlp/SPPO", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12626", "title": "Habitat-GS: A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting", "authors": [ "Ziyuan Xia", "Jingyi Xu", "Chong Cui", "Yuanhong Yu", "Jiazhao Zhang", "Qingsong Yan", "Tao Ni", "Junbo Chen", "Xiaowei Zhou", "Hujun Bao", "Ruizhen Hu", "Sida Peng" ], "github": "", "project_page": "", "abstract": "Training embodied AI agents depends critically on the visual fidelity of simulation environments and the ability to model dynamic humans. Current simulators rely on mesh-based rasterization with limited visual realism, and their support for dynamic human avatars, where available, is constrained to mesh representations, hindering agent generalization to human-populated real-world scenarios. We present Habitat-GS, a navigation-centric embodied AI simulator extended from Habitat-Sim that integrates 3D Gaussian Splatting scene rendering and drivable gaussian avatars while maintaining full compatibility with the Habitat ecosystem. Our system implements a 3DGS renderer for real-time photorealistic rendering and supports scalable 3DGS asset import from diverse sources. For dynamic human modeling, we introduce a gaussian avatar module that enables each avatar to simultaneously serve as a photorealistic visual entity and an effective navigation obstacle, allowing agents to learn human-aware behaviors in realistic settings. Experiments on point-goal navigation demonstrate that agents trained on 3DGS scenes achieve stronger cross-domain generalization, with mixed-domain training being the most effective strategy. Evaluations on avatar-aware navigation further confirm that gaussian avatars enable effective human-aware navigation. Finally, performance benchmarks validate the system's scalability across varying scene complexity and avatar counts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12890", "title": "Towards Long-horizon Agentic Multimodal Search", "authors": [ "Yifan Du", "Zikang Liu", "Jinbiao Peng", "Jie Wu", "Junyi Li", "Jinyang Li", "Wayne Xin Zhao", "Ji-Rong Wen" ], "github": "https://github.com/RUCAIBox/LMM-Searcher", "project_page": "", "abstract": "Multimodal deep search agents have shown great potential in solving complex tasks by iteratively collecting textual and visual evidence. However, managing the heterogeneous information and high token costs associated with multimodal inputs over long horizons remains a critical challenge, as existing methods often suffer from context explosion or the loss of crucial visual signals. To address this, we propose a novel Long-horizon MultiModal deep search framework, named LMM-Searcher, centered on a file-based visual representation mechanism. By offloading visual assets to an external file system and mapping them to lightweight textual identifiers (UIDs), our approach mitigates context overhead while preserving multimodal information for future access. We equip the agent with a tailored fetch-image tool, enabling a progressive, on-demand visual loading strategy for active perception. Furthermore, we introduce a data synthesis pipeline designed to generate queries requiring complex cross-modal multi-hop reasoning. Using this pipeline, we distill 12K high-quality trajectories to fine-tune Qwen3-VL-Thinking-30A3B into a specialized multimodal deep search agent. Extensive experiments across four benchmarks demonstrate that our method successfully scales to 100-turn search horizons, achieving state-of-the-art performance among open-source models on challenging long-horizon benchmarks like MM-BrowseComp and MMSearch-Plus, while also exhibiting strong generalizability across different base models. Our code will be released in https://github.com/RUCAIBox/LMM-Searcher.", "github_manual": "", "github_regex": "https://github.com/RUCAIBox/LMM-Searcher", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12322", "title": "Self-Adversarial One Step Generation via Condition Shifting", "authors": [ "Deyuan Liu", "Peng Sun", "Yansen Han", "Zhenglin Cheng", "Chuyan Chen", "Tao Lin" ], "github": "https://github.com/LINs-lab/APEX", "project_page": "", "abstract": "The push for efficient text to image synthesis has moved the field toward one step sampling, yet existing methods still face a three way tradeoff among fidelity, inference speed, and training efficiency. Approaches that rely on external discriminators can sharpen one step performance, but they often introduce training instability, high GPU memory overhead, and slow convergence, which complicates scaling and parameter efficient tuning. In contrast, regression based distillation and consistency objectives are easier to optimize, but they typically lose fine details when constrained to a single step. We present APEX, built on a key theoretical insight: adversarial correction signals can be extracted endogenously from a flow model through condition shifting. Using a transformation creates a shifted condition branch whose velocity field serves as an independent estimator of the model's current generation distribution, yielding a gradient that is provably GAN aligned, replacing the sample dependent discriminator terms that cause gradient vanishing. This discriminator free design is architecture preserving, making APEX a plug and play framework compatible with both full parameter and LoRA based tuning. Empirically, our 0.6B model surpasses FLUX-Schnell 12B (20times more parameters) in one step quality. With LoRA tuning on Qwen-Image 20B, APEX reaches a GenEval score of 0.89 at NFE=1 in 6 hours, surpassing the original 50-step teacher (0.87) and providing a 15.33times inference speedup. Code is available https://github.com/LINs-lab/APEX.", "github_manual": "", "github_regex": "https://github.com/LINs-lab/APEX", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.10465", "title": "Rethinking the Diffusion Model from a Langevin Perspective", "authors": [ "Candi Zheng", "Yuan Lan" ], "github": "", "project_page": "", "abstract": "Diffusion models are often introduced from multiple perspectives, such as VAEs, score matching, or flow matching, accompanied by dense and technically demanding mathematics that can be difficult for beginners to grasp. One classic question is: how does the reverse process invert the forward process to generate data from pure noise? This article systematically organizes the diffusion model from a fresh Langevin perspective, offering a simpler, clearer, and more intuitive answer. We also address the following questions: how can ODE-based and SDE-based diffusion models be unified under a single framework? Why are diffusion models theoretically superior to ordinary VAEs? Why is flow matching not fundamentally simpler than denoising or score matching, but equivalent under maximum-likelihood? We demonstrate that the Langevin perspective offers clear and straightforward answers to these questions, bridging existing interpretations of diffusion models, showing how different formulations can be converted into one another within a common framework, and offering pedagogical value for both learners and experienced researchers seeking deeper intuition.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.13036", "title": "Lyra 2.0: Explorable Generative 3D Worlds", "authors": [ "Tianchang Shen", "Sherwin Bahmani", "Kai He", "Sangeetha Grama Srinivasan", "Tianshi Cao", "Jiawei Ren", "Ruilong Li", "Zian Wang", "Nicholas Sharp", "Zan Gojcic", "Sanja Fidler", "Jiahui Huang", "Huan Ling", "Jun Gao", "Xuanchi Ren" ], "github": "", "project_page": "", "abstract": "Recent advances in video generation enable a new paradigm for 3D scene creation: generating camera-controlled videos that simulate scene walkthroughs, then lifting them to 3D via feed-forward reconstruction techniques. This generative reconstruction approach combines the visual fidelity and creative capacity of video models with 3D outputs ready for real-time rendering and simulation. Scaling to large, complex environments requires 3D-consistent video generation over long camera trajectories with large viewpoint changes and location revisits, a setting where current video models degrade quickly. Existing methods for long-horizon generation are fundamentally limited by two forms of degradation: spatial forgetting and temporal drifting. As exploration proceeds, previously observed regions fall outside the model's temporal context, forcing the model to hallucinate structures when revisited. Meanwhile, autoregressive generation accumulates small synthesis errors over time, gradually distorting scene appearance and geometry. We present Lyra 2.0, a framework for generating persistent, explorable 3D worlds at scale. To address spatial forgetting, we maintain per-frame 3D geometry and use it solely for information routing -- retrieving relevant past frames and establishing dense correspondences with the target viewpoints -- while relying on the generative prior for appearance synthesis. To address temporal drifting, we train with self-augmented histories that expose the model to its own degraded outputs, teaching it to correct drift rather than propagate it. Together, these enable substantially longer and 3D-consistent video trajectories, which we leverage to fine-tune feed-forward reconstruction models that reliably recover high-quality 3D scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12374", "title": "Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning", "authors": [ "NVIDIA", "Aakshita Chandiramani", "Aaron Blakeman", "Abdullahi Olaoye", "Abhibha Gupta", "Abhilash Somasamudramath", "Abhinav Khattar", "Adeola Adesoba", "Adi Renduchintala", "Adil Asif", "Aditya Agrawal", "Aditya Vavre", "Ahmad Kiswani", "Aishwarya Padmakumar", "Ajay Hotchandani", "Akanksha Shukla", "Akhiad Bercovich", "Aleksander Ficek", "Aleksandr Shaposhnikov", "Alex Gronskiy", "Alex Kondratenko", "Alex Neefus", "Alex Steiner", "Alex Yang", "Alexander Bukharin", "Alexander Young", "Ali Hatamizadeh", "Ali Taghibakhshi", "Alina Galiautdinova", "Alisa Liu", "Alok Kumar", "Ameya Sunil Mahabaleshwarkar", "Amir Klein", "Amit Zuker", "Amnon Geifman", "Anahita Bhiwandiwalla", "Ananth Subramaniam", "Andrew Tao", "Anjaney Shrivastava", "Anjulie Agrusa", "Ankur Srivastava", "Ankur Verma", "Ann Guan", "Anna Shors", "Annamalai Chockalingam", "Anubhav Mandarwal", "Aparnaa Ramani", "Arham Mehta", "Arti Jain", "Arun Venkatesan", "Asha Anoosheh", "Ashwath Aithal", "Ashwin Poojary", "Asif Ahamed", "Asit Mishra", "Asli Sabanci Demiroz", "Asma Kuriparambil Thekkumpate", "Atefeh Sohrabizadeh", "Avinash Kaur", "Ayush Dattagupta", "Barath Subramaniam Anandan", "Bardiya Sadeghi", "Barnaby Simkin", "Ben Lanir", "Benedikt Schifferer", "Benjamin Chislett", "Besmira Nushi", "Bilal Kartal", "Bill Thiede", "Bita Darvish Rouhani", "Bobby Chen", "Boris Ginsburg", "Brandon Norick", "Branislav Kisacanin", "Brian Yu", "Bryan Catanzaro", "Buvaneswari Mani", "Carlo del Mundo", "Chankyu Lee", "Chanran Kim", "Chantal Hwang", "Chao Ni", "Charles Wang", "Charlie Truong", "Cheng-Ping Hsieh", "Chenhan Yu", "Chenjie Luo", "Cherie Wang", "Chetan Mungekar", "Chintan Patel", "Chris Alexiuk", "Chris Holguin", "Chris Wing", "Christian Munley", "Christopher Parisien", "Chuck Desai", "Chunyang Sheng", "Collin Neale", "Cyril Meurillon", "Dakshi Kumar", "Dan Gil", "Dan Su", "Dane Corneil", "Daniel Afrimi", "Daniel Burkhardt Eliuth Triana", "Daniel Egert", "Daniel Fatade", "Daniel Lo", "Daniel Rohrer", "Daniel Serebrenik", "Daniil Sorokin", "Daria Gitman", "Daria Levy", "Darko Stosic", "David Edelsohn", "David Messina", "David Mosallanezhad", "David Tamok", "Deena Donia", "Deepak Narayanan", "Devin O'Kelly", "Dheeraj Peri", "Dhruv Nathawani", "Di Wu", "Dima Rekesh", "Dina Yared", "Divyanshu Kakwani", "Dmitry Konyagin Brandon Tuttle", "Dong Ahn", "Dongfu Jiang", "Dorrin Poorkay", "Douglas O'Flaherty", "Duncan Riach", "Dusan Stosic", "Dustin Van Stee", "Edgar Minasyan", "Edward Lin", "Eileen Peters Long", "Elad Segal", "Elena Lantz", "Elena Lewis", "Ellie Evans", "Elliott Ning", "Eric Chung", "Eric Harper", "Eric Pham-Hung", "Eric W. Tramel", "Erick Galinkin", "Erik Pounds", "Esti Etrog", "Evan Briones", "Evan Wu", "Evelina Bakhturina", "Evgeny Tsykunov", "Ewa Dobrowolska", "Farshad Saberi Movahed", "Farzan Memarian", "Fay Wang", "Fei Jia", "Felipe Soares", "Felipe Vieira Frujeri", "Feng Chen", "Fengguang Lin", "Ferenc Galko", "Fortuna Zhang", "Frankie Siino", "Frida Hou", "Gantavya Bhatt", "Gargi Prasad", "Geethapriya Venkataramani", "Geetika Gupta", "George Armstrong", "Gerald Shen", "Giulio Borghesi", "Gordana Neskovic", "Gorkem Batmaz", "Grace Lam", "Grace Wu", "Greg Pauloski", "Greyson Davis", "Grigor Nalbandyan", "Guoming Zhang", "Guy Farber", "Guyue Huang", "Haifeng Qian", "Haran Kumar Shiv Kumar", "Harry Kim", "Harsh Sharma", "Hayate Iso", "Hayley Ross", "Herbert Hum", "Herman Sahota", "Hexin Wang", "Himanshu Soni", "Hiren Upadhyay", "Huy Nguyen", "Iain Cunningham", "Ido Galil", "Ido Shahaf", "Igino Padovani", "Igor Gitman", "Igor Shovkun", "Ikroop Dhillon", "Ilya Loshchilov", "Ingrid Kelly", "Itamar Schen", "Itay Levy", "Ivan Moshkov", "Izik Golan", "Izzy Putterman", "Jain Tu", "Jan Baczek", "Jan Kautz", "Jane Polak Scowcroft", "Janica Rosenberg", "Jared Casper", "Jarrod Pflum", "Jason Grant", "Jason Sewall", "Jatin Mitra", "Jeffrey Glick", "Jenny Chen", "Jesse Oliver", "Jiacheng Xu", "Jiafan Zhu", "Jialin Song", "Jian Zhang", "Jiaqi Zeng", "Jie Lou", "Jill Milton", "Jim Chow", "Jimmy Zhang", "Jinhang Choi", "Jining Huang", "Jocelyn Huang", "Joel Caruso", "Joey Conway", "Joey Guman", "Johan Jatko", "John Kamalu", "Johnny Greco", "Jonathan Cohen", "Jonathan Raiman", "Joseph Jennings", "Joyjit Daw", "Juan Yu", "Julio Tapia", "Junkeun Yi", "Jupinder Parmar", "Jyothi Achar", "Kari Briski", "Kartik Mattoo", "Katherine Cheung", "Katherine Luna", "Keith Wyss", "Kevin Shih", "Kezhi Kong", "Khanh Nguyen", "Khushi Bhardwaj", "Kirill Buryak", "Kirthi Shankar Sivamani", "Konstantinos Krommydas", "Kris Murphy", "Krishna C. Puvvada", "Krzysztof Pawelec", "Kumar Anik", "Laikh Tewari", "Laya Sleiman", "Leo Du", "Leon Derczynski", "Li Ding", "Lilach Ilan", "Lingjie Wu", "Lizzie Wei", "Luis Vega", "Lun Su", "Maarten Van Segbroeck", "Maer Rodrigues de Melo", "Magaret Zhang", "Mahan Fathi", "Makesh Narsimhan Sreedhar", "Makesh Sreedhar", "Makesh Tarun Chandran", "Manuel Reyes Gomez", "Maor Ashkenazi", "Marc Cuevas", "Marc Romeijn", "Margaret Zhang", "Mark Cai", "Mark Gabel", "Markus Kliegl", "Martyna Patelka", "Maryam Moosaei", "Matthew Varacalli", "Matvei Novikov", "Mauricio Ferrato", "Mehrzad Samadi", "Melissa Corpuz", "Meng Xin", "Mengdi Wang", "Mengru Wang", "Meredith Price", "Micah Schaffer", "Michael Andersch", "Michael Boone", "Michael Evans", "Michael Z Wang", "Miguel Martinez", "Mikail Khona", "Mike Chrzanowski", "Mike Hollinger", "Mingyuan Ma", "Minseok Lee", "Mohammad Dabbah", "Mohammad Shoeybi", "Mostofa Patwary", "Nabin Mulepati", "Nader Khalil", "Najeeb Nabwani", "Nancy Agarwal", "Nanthini Balasubramaniam", "Narimane Hennouni", "Narsi Kodukula", "Natalie Hereth", "Nathaniel Pinckney", "Nave Assaf", "Negar Habibi", "Nestor Qin", "Neta Zmora", "Netanel Haber", "Nick Reamaroon", "Nickson Quak", "Nidhi Bhatia", "Nikhil Jukar", "Nikki Pope", "Nikolai Ludwig", "Nima Tajbakhsh", "Nir Ailon", "Nirmal Juluru", "Nirmalya De", "Nowel Pitt", "Oleg Rybakov", "Oleksii Hrinchuk", "Oleksii Kuchaiev", "Olivier Delalleau", "Oluwatobi Olabiyi", "Omer Ullman Argov", "Omri Almog", "Omri Puny", "Oren Tropp", "Otavio Padovani", "Ouye Xie", "Parth Chadha", "Pasha Shamis", "Paul Gibbons", "Pavlo Molchanov", "Peter Belcak", "Peter Jin", "Pinky Xu", "Piotr Januszewski", "Pooya Jannaty", "Prachi Shevate", "Pradeep Thalasta", "Pranav Prashant Thombre", "Prasoon Varshney", "Prerana Gambhir", "Pritam Gundecha", "Przemek Tredak", "Qing Miao", "Qiyu Wan", "Quan Tran Minh", "Rabeeh Karimi Mahabadi", "Rachel Oberman", "Rachit Garg", "Rahul Kandu", "Raina Zhong", "Ran El-Yaniv", "Ran Zilberstein", "Rasoul Shafipour", "Renee Yao", "Renjie Pi", "Richard Mazzarese", "Richard Wang", "Rick Izzo", "Ridhima Singla", "Rima Shahbazyan", "Rishabh Garg", "Ritika Borkar", "Ritu Gala", "Riyad Islam", "Robert Clark", "Robert Hesse", "Roger Waleffe", "Rohit Varma Kalidindi", "Rohit Watve", "Roi Koren", "Ron Fan", "Ruchika Kharwar", "Ruisi Cai", "Ruoxi Zhang", "Russell J. Hewett", "Ryan Prenger", "Ryan Timbrook", "Ryota Egashira", "Sadegh Mahdavi", "Sagar Singh Ashutosh Joshi", "Sahil Modi", "Samuel Kriman", "Sandeep Pombra", "Sanjay Kariyappa", "Sanjeev Satheesh", "Santiago Pombo", "Saori Kaji", "Satish Pasumarthi", "Saurav Mishra", "Saurav Muralidharan", "Scott Hara", "Sean Narenthiran", "Sebastian Rogawski", "Seonjin Na", "Seonmyeong Bak", "Sepehr Sameni", "Seth Poulos", "Shahar Mor", "Shantanu Acharya", "Shaona Ghosh Adam Lord", "Sharath Turuvekere Sreenivas", "Shaun Kotek", "Shaya Gharghabi", "Shelby Thomas", "Sheng-Chieh Lin", "Shibani Likhite", "Shiqing Fan", "Shiyang Chen", "Shreya Gopal", "Shrimai Prabhumoye", "Shubham Pachori", "Shubham Toshniwal", "Shuo Zhang", "Shuoyang Ding", "Shyam Renjith", "Shyamala Prayaga", "Siddhartha Jain", "Simeng Sun", "Sirisha Rella", "Sirshak Das", "Smita Ithape", "Sneha Harishchandra S", "Somshubra Majumdar", "Soumye Singhal", "Sri Harsha Singudasu", "Sriharsha Niverty", "Stas Sergienko", "Stefana Gloginic", "Stefania Alborghetti", "Stephen Ge", "Stephen McCullough", "Sugam Dipak Devare", "Suguna Varshini Velury", "Sukrit Rao", "Sumeet Kumar Barua", "Sunny Gai", "Suseella Panguluri", "Sushil Koundinyan", "Swathi Patnam", "Sweta Priyadarshi", "Swetha Bhendigeri", "Syeda Nahida Akter", "Sylendran Arunagiri", "Tailling Yuan", "Talor Abramovich", "Tan Bui", "Tan Yu", "Terry Kong", "Thanh Do", "Thomas Gburek", "Thorgane Marques", "Tiffany Moore", "Tijmen Blankevoort", "Tim Moon", "Timothy Ma", "Tiyasa Mitra", "Tomasz Grzegorzek", "Tomer Asida", "Tomer Bar Natan", "Tomer Keren", "Tomer Ronen", "Traian Rebedea", "Trenton Starkey", "Tugrul Konuk", "Twinkle Vashishth", "Tyler Condensa", "Udi Karpas", "Ushnish De", "Vahid Noorozi", "Vahid Noroozi", "Vanshil Atul Shah", "Veena Vaidyanathan", "Venkat Srinivasan", "Venmugil Elango", "Victor Cui", "Vijay Korthikanti", "Vikas Mehta", "Virginia Adams", "Virginia Wu", "Vitaly Kurin", "Vitaly Lavrukhin", "Vladimir Anisimov", "Wan Seo", "Wanli Jiang", "Wasi Uddin Ahmad", "Wei Du", "Wei Ping", "Wei-Ming Chen", "Wendy Quan", "Wenliang Dai", "Wenwen Gao", "Will Jennings", "William Zhang", "Xiaowei Ren", "Xiaowen Xin", "Xin Li", "Yang Yu", "Yangyi Chen", "Yaniv Galron", "Yashaswi Karnati", "Yejin Choi", "Yev Meyer", "Yi-Fu Wu", "Yian Zhang", "Ying Lin", "Yonatan Geifman", "Yonggan Fu", "Yoshi Suhara", "Youngeun Kwon", "Yuan Zhang", "Yuki Huang", "Zach Moshe", "Zhilin Wang", "Zhiyu Cheng", "Zhongbo Zhu", "Zhuolin Yang", "Zihan Liu", "Zijia Chen", "Zijie Yan", "Zuhair Ahmed" ], "github": "", "project_page": "", "abstract": "We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemotron 3 Super is the first model in the Nemotron 3 family to 1) be pre-trained in NVFP4, 2) leverage LatentMoE, a new Mixture-of-Experts architecture that optimizes for both accuracy per FLOP and accuracy per parameter, and 3) include MTP layers for inference acceleration through native speculative decoding. We pre-trained Nemotron 3 Super on 25 trillion tokens followed by post-training using supervised fine tuning (SFT) and reinforcement learning (RL). The final model supports up to 1M context length and achieves comparable accuracy on common benchmarks, while also achieving up to 2.2x and 7.5x higher inference throughput compared to GPT-OSS-120B and Qwen3.5-122B, respectively. Nemotron 3 Super datasets, along with the base, post-trained, and quantized checkpoints, are open-sourced on HuggingFace.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.13010", "title": "Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation", "authors": [ "Yecheng Wu", "Song Han", "Hai Cai" ], "github": "", "project_page": "", "abstract": "On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, standard OPD requires a live teacher inference server throughout training, resulting in substantial infrastructure overhead. In this work, we investigate whether on-policy distillation can be performed offline. A natural approach is to precompute teacher log-probabilities once over SFT rollouts and reuse them during training. In practice, however, this offline variant fails to reliably match the performance of standard OPD. To understand this discrepancy, we identify a previously overlooked condition that is critical for any OPD pipeline, which we term teacher consistency. This condition requires that the same teacher model be used for both supervised fine-tuning and OPD. We show that violating teacher consistency introduces an irreducible gradient bias, causing both offline and online OPD to converge to a suboptimal fixed point regardless of training duration. Building on this insight, we propose Lightning OPD, an offline on-policy distillation framework that enforces teacher consistency by precomputing teacher log-probabilities over SFT rollouts. This design eliminates the need for a live teacher server entirely. We further show that, under teacher consistency, Lightning OPD shares the same optimum as standard OPD, with bounded gradient discrepancy and an implicit regularization effect that helps prevent policy drift. Extensive experiments on mathematical reasoning and code generation demonstrate that Lightning OPD achieves state-of-the-art performance with significantly improved efficiency. Starting from an SFT-initialized Qwen3-8B-Base model, Lightning OPD reaches 69.9% on AIME 2024 in just 30 GPU hours, achieving a 4.0x speedup over standard OPD and substantially lowering the barrier to entry for academic research on LLM post-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.10966", "title": "You Only Judge Once: Multi-response Reward Modeling in a Single Forward Pass", "authors": [ "Yinuo Yang", "Zixian Ma", "Manasi Ganti", "Jieyu Zhang", "Ranjay Krishna" ], "github": "", "project_page": "", "abstract": "We present a discriminative multimodal reward model that scores all candidate responses in a single forward pass. Conventional discriminative reward models evaluate each response independently, requiring multiple forward passes, one for each potential response. Our approach concatenates multiple responses with separator tokens and applies cross-entropy over their scalar scores, enabling direct comparative reasoning and efficient N-way preference learning. The multi-response design also yields up to Ntimes wall-clock speedup and FLOPs reduction over conventional single-response scoring. To enable N-way reward evaluation beyond existing pairwise benchmarks, we construct two new benchmarks: (1) MR^2Bench-Image contains human-annotated rankings over responses from 8 diverse models; (2) MR^2Bench-Video is a large-scale video-based reward benchmark derived from 94K crowdsourced pairwise human judgments over video question-answering spanning 19 models, denoised via preference graph ensemble. Both benchmarks provide 4-response evaluation variants sampled from the full rankings. Built on a 4B vision-language backbone with LoRA fine-tuning and a lightweight MLP value head, our model achieves state-of-the-art results on six multimodal reward benchmarks, including MR^2Bench-Image, MR^2Bench-Video, and four other existing benchmarks. Our model outperforms existing larger generative and discriminative reward models. We further demonstrate that our reward model, when used in reinforcement learning with GRPO, produces improved policy models that maintain performance across standard multimodal benchmarks while substantially improving open-ended generation quality, outperforming a single-response discriminative reward model (RM) baseline by a large margin in both training stability and open-ended generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12887", "title": "VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization", "authors": [ "Andrei Atanov", "Jesse Allardice", "Roman Bachmann", "O\u011fuzhan Fatih Kar", "R Devon Hjelm", "David Griffiths", "Peter Fu", "Afshin Dehghan", "Amir Zamir" ], "github": "", "project_page": "", "abstract": "Visual tokenizers map high-dimensional raw pixels into a compressed representation for downstream modeling. Beyond compression, tokenizers dictate what information is preserved and how it is organized. A de facto standard approach to video tokenization is to represent a video as a spatiotemporal 3D grid of tokens, each capturing the corresponding local information in the original signal. This requires the downstream model that consumes the tokens, e.g., a text-to-video model, to learn to predict all low-level details \"pixel-by-pixel\" irrespective of the video's inherent complexity, leading to high learning complexity. We present VideoFlexTok, which represents videos with a variable-length sequence of tokens structured in a coarse-to-fine manner -- where the first tokens (emergently) capture abstract information, such as semantics and motion, and later tokens add fine-grained details. The generative flow decoder enables realistic video reconstructions from any token count. This representation structure allows adapting the token count according to downstream needs and encoding videos longer than the baselines with the same budget. We evaluate VideoFlexTok on class- and text-to-video generative tasks and show that it leads to more efficient training compared to 3D grid tokens, e.g., achieving comparable generation quality (gFVD and ViCLIP Score) with a 5x smaller model (1.1B vs 5.2B). Finally, we demonstrate how VideoFlexTok can enable long video generation without prohibitive computational cost by training a text-to-video model on 10-second 81-frame videos with only 672 tokens, 8x fewer than a comparable 3D grid tokenizer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.13030", "title": "Generative Refinement Networks for Visual Synthesis", "authors": [ "Jian Han", "Jinlai Liu", "Jiahuan Wang", "Bingyue Peng", "Zehuan Yuan" ], "github": "", "project_page": "", "abstract": "While diffusion models dominate the field of visual generation, they are computationally inefficient, applying a uniform computational effort regardless of different complexity. In contrast, autoregressive (AR) models are inherently complexity-aware, as evidenced by their variable likelihoods, but are often hindered by lossy discrete tokenization and error accumulation. In this work, we introduce Generative Refinement Networks (GRN), a next-generation visual synthesis paradigm to address these issues. At its core, GRN addresses the discrete tokenization bottleneck through a theoretically near-lossless Hierarchical Binary Quantization (HBQ), achieving a reconstruction quality comparable to continuous counterparts. Built upon HBQ's latent space, GRN fundamentally upgrades AR generation with a global refinement mechanism that progressively perfects and corrects artworks -- like a human artist painting. Besides, GRN integrates an entropy-guided sampling strategy, enabling complexity-aware, adaptive-step generation without compromising visual quality. On the ImageNet benchmark, GRN establishes new records in image reconstruction (0.56 rFID) and class-conditional image generation (1.81 gFID). We also scale GRN to more challenging text-to-image and text-to-video generation, delivering superior performance on an equivalent scale. We release all models and code to foster further research on GRN.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.11579", "title": "Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions", "authors": [ "Seongyu Kim", "Seungwoo Lee", "Hyeonggon Ryu", "Joon Son Chung", "Arda Senocak" ], "github": "", "project_page": "", "abstract": "We address the problem of tactile localization, where the goal is to identify image regions that share the same material properties as a tactile input. Existing visuo-tactile methods rely on global alignment and thus fail to capture the fine-grained local correspondences required for this task. The challenge is amplified by existing datasets, which predominantly contain close-up, low-diversity images. We propose a model that learns local visuo-tactile alignment via dense cross-modal feature interactions, producing tactile saliency maps for touch-conditioned material segmentation. To overcome dataset constraints, we introduce: (i) in-the-wild multi-material scene images that expand visual diversity, and (ii) a material-diversity pairing strategy that aligns each tactile sample with visually varied yet tactilely consistent images, improving contextual localization and robustness to weak signals. We also construct two new tactile-grounded material segmentation datasets for quantitative evaluation. Experiments on both new and existing benchmarks show that our approach substantially outperforms prior visuo-tactile methods in tactile localization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12627", "title": "KnowRL: Boosting LLM Reasoning via Reinforcement Learning with Minimal-Sufficient Knowledge Guidance", "authors": [ "Linhao Yu", "Tianmeng Yang", "Siyu Ding", "Renren Jin", "Naibin Gu", "Xiangzhao Hao", "Shuaiyi Nie", "Deyi Xiong", "Weichong Yin", "Yu Sun", "Hua Wu" ], "github": "https://github.com/Hasuer/KnowRL", "project_page": "", "abstract": "RLVR improves reasoning in large language models, but its effectiveness is often limited by severe reward sparsity on hard problems. Recent hint-based RL methods mitigate sparsity by injecting partial solutions or abstract templates, yet they typically scale guidance by adding more tokens, which introduce redundancy, inconsistency, and extra training overhead. We propose KnowRL (Knowledge-Guided Reinforcement Learning), an RL training framework that treats hint design as a minimal-sufficient guidance problem. During RL training, KnowRL decomposes guidance into atomic knowledge points (KPs) and uses Constrained Subset Search (CSS) to construct compact, interaction-aware subsets for training. We further identify a pruning interaction paradox -- removing one KP may help while removing multiple such KPs can hurt -- and explicitly optimize for robust subset curation under this dependency structure. We train KnowRL-Nemotron-1.5B from OpenMath-Nemotron-1.5B. Across eight reasoning benchmarks at the 1.5B scale, KnowRL-Nemotron-1.5B consistently outperforms strong RL and hinting baselines. Without KP hints at inference, KnowRL-Nemotron-1.5B reaches 70.08 average accuracy, already surpassing Nemotron-1.5B by +9.63 points; with selected KPs, performance improves to 74.16, establishing a new state of the art at this scale. The model, curated training data, and code are publicly available at https://github.com/Hasuer/KnowRL.", "github_manual": "", "github_regex": "https://github.com/Hasuer/KnowRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.09443", "title": "Many-Tier Instruction Hierarchy in LLM Agents", "authors": [ "Jingyu Zhang", "Tianjian Li", "William Jurayj", "Hongyuan Zhan", "Benjamin Van Durme", "Daniel Khashabi" ], "github": "", "project_page": "", "abstract": "Large language model agents receive instructions from many sources-system messages, user prompts, tool outputs, and more-each carrying different levels of trust and authority. When these instructions conflict, models must reliably follow the highest-privilege instruction to remain safe and effective. The dominant paradigm, instruction hierarchy (IH), assumes a fixed, small set of privilege levels (typically fewer than five) defined by rigid role labels (e.g., system > user). This is inadequate for real-world agentic settings, where conflicts can arise across far more sources and contexts. In this work, we propose Many-Tier Instruction Hierarchy (ManyIH), a paradigm for resolving instruction conflicts among instructions with arbitrarily many privilege levels. We introduce ManyIH-Bench, the first benchmark for ManyIH. ManyIH-Bench requires models to navigate up to 12 levels of conflicting instructions with varying privileges, comprising 853 agentic tasks (427 coding and 426 instruction-following). ManyIH-Bench composes constraints developed by LLMs and verified by humans to create realistic and difficult test cases spanning 46 real-world agents. Our experiments show that even the current frontier models perform poorly (~40% accuracy) when instruction conflict scales. This work underscores the urgent need for methods that explicitly target fine-grained, scalable instruction conflict resolution in agentic settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.11689", "title": "LARY: A Latent Action Representation Yielding Benchmark for Generalizable Vision-to-Action Alignment", "authors": [ "Dujun Nie", "Fengjiao Chen", "Qi Lv", "Jun Kuang", "Xiaoyu Li", "Xuezhi Cao", "Xunliang Cai" ], "github": "", "project_page": "", "abstract": "While the shortage of explicit action data limits Vision-Language-Action (VLA) models, human action videos offer a scalable yet unlabeled data source. A critical challenge in utilizing large-scale human video datasets lies in transforming visual signals into ontology-independent representations, known as latent actions. However, the capacity of latent action representation to derive robust control from visual observations has yet to be rigorously evaluated. We introduce the Latent Action Representation Yielding (LARY) Benchmark, a unified framework for evaluating latent action representations on both high-level semantic actions (what to do) and low-level robotic control (how to do). The comprehensively curated dataset encompasses over one million videos (1,000 hours) spanning 151 action categories, alongside 620K image pairs and 595K motion trajectories across diverse embodiments and environments. Our experiments reveal two crucial insights: (i) General visual foundation models, trained without any action supervision, consistently outperform specialized embodied latent action models. (ii) Latent-based visual space is fundamentally better aligned to physical action space than pixel-based space. These results suggest that general visual representations inherently encode action-relevant knowledge for physical control, and that semantic-level abstraction serves as a fundamentally more effective pathway from vision to action than pixel-level reconstruction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12119", "title": "Beyond Perception Errors: Semantic Fixation in Large Vision-Language Models", "authors": [ "Md Tanvirul Alam" ], "github": "", "project_page": "", "abstract": "Large vision-language models (VLMs) often rely on familiar semantic priors, but existing evaluations do not cleanly separate perception failures from rule-mapping failures. We study this behavior as semantic fixation: preserving a default interpretation even when the prompt specifies an alternative, equally valid mapping. To isolate this effect, we introduce VLM-Fix, a controlled benchmark over four abstract strategy games that evaluates identical terminal board states under paired standard and inverse rule formulations. Across 14 open and closed VLMs, accuracy consistently favors standard rules, revealing a robust semantic-fixation gap. Prompt interventions support this mechanism: neutral alias prompts substantially narrow the inverse-rule gap, while semantically loaded aliases reopen it. Post-training is strongly rule-aligned: training on one rule improves same-rule transfer but hurts opposite-rule transfer, while joint-rule training improves broader transfer. To test external validity beyond synthetic games, we evaluate analogous defamiliarization interventions on VLMBias and observe the same qualitative pattern. Finally, late-layer activation steering partially recovers degraded performance, indicating that semantic-fixation errors are at least partly editable in late representations. Project page, code, and dataset available at https://maveryn.github.io/vlm-fix/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.05072", "title": "Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modeling", "authors": [ "Ximing Xing", "Ziteng Xue", "Zhenxi Li", "Weicong Liang", "Linqing Wang", "Zhantao Yang", "Tiankai Hang", "Zijin Yin", "Qinglin Lu", "Chunyu Wang", "Qian Yu" ], "github": "https://github.com/ximinng/HiVG", "project_page": "", "abstract": "Recent large language models have shifted SVG generation from differentiable rendering optimization to autoregressive program synthesis. However, existing approaches still rely on generic byte-level tokenization inherited from natural language processing, which poorly reflects the geometric structure of vector graphics. Numerical coordinates are fragmented into discrete symbols, destroying spatial relationships and introducing severe token redundancy, often leading to coordinate hallucination and inefficient long-sequence generation. To address these challenges, we propose HiVG, a hierarchical SVG tokenization framework tailored for autoregressive vector graphics generation. HiVG decomposes raw SVG strings into structured atomic tokens and further compresses executable command--parameter groups into geometry-constrained segment tokens, substantially improving sequence efficiency while preserving syntactic validity. To further mitigate spatial mismatch, we introduce a Hierarchical Mean--Noise (HMN) initialization strategy that injects numerical ordering signals and semantic priors into new token embeddings. Combined with a curriculum training paradigm that progressively increases program complexity, HiVG enables more stable learning of executable SVG programs. Extensive experiments on both text-to-SVG and image-to-SVG tasks demonstrate improved generation fidelity, spatial consistency, and sequence efficiency compared with conventional tokenization schemes. Our code is publicly available at https://github.com/ximinng/HiVG", "github_manual": "", "github_regex": "https://github.com/ximinng/HiVG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.09746", "title": "CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation", "authors": [ "Aarush Sinha", "Arion Das", "Soumyadeep Nag", "Charan Karnati", "Shravani Nag", "Chandra Vadhan Raj", "Aman Chadha", "Vinija Jain", "Suranjana Trivedy", "Amitava Das" ], "github": "", "project_page": "", "abstract": "As large language models (LLMs) are increasingly deployed as autonomous agents, understanding how strategic behavior emerges in multi-agent environments has become an important alignment challenge. We take a neutral empirical stance and construct a controlled environment in which strategic behavior can be directly observed and measured. We introduce a large-scale multi-agent simulation in a simplified model of New York City, where LLM-driven agents interact under opposing incentives. Blue agents aim to reach their destinations efficiently, while Red agents attempt to divert them toward billboard-heavy routes using persuasive language to maximize advertising revenue. Hidden identities make navigation socially mediated, forcing agents to decide when to trust or deceive. We study policy learning through an iterative simulation pipeline that updates agent policies across repeated interaction rounds using Kahneman-Tversky Optimization (KTO). Blue agents are optimized to reduce billboard exposure while preserving navigation efficiency, whereas Red agents adapt to exploit remaining weaknesses. Across iterations, the best Blue policy improves task success from 46.0% to 57.3%, although susceptibility remains high at 70.7%. Later policies exhibit stronger selective cooperation while preserving trajectory efficiency. However, a persistent safety-helpfulness trade-off remains: policies that better resist adversarial steering do not simultaneously maximize task completion. Overall, our results show that LLM agents can exhibit limited strategic behavior, including selective trust and deception, while remaining highly vulnerable to adversarial persuasion.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12373", "title": "Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness", "authors": [ "Tomer Ashuach", "Liat Ein-Dor", "Shai Gretz", "Yoav Katz", "Yonatan Belinkov" ], "github": "", "project_page": "", "abstract": "Humans use introspection to evaluate their understanding through private internal states inaccessible to external observers. We investigate whether large language models possess similar privileged knowledge about answer correctness, information unavailable through external observation. We train correctness classifiers on question representations from both a model's own hidden states and external models, testing whether self-representations provide a performance advantage. On standard evaluation, we find no advantage: self-probes perform comparably to peer-model probes. We hypothesize this is due to high inter-model agreement of answer correctness. To isolate genuine privileged knowledge, we evaluate on disagreement subsets, where models produce conflicting predictions. Here, we discover domain-specific privileged knowledge: self-representations consistently outperform peer representations in factual knowledge tasks, but show no advantage in math reasoning. We further localize this domain asymmetry across model layers, finding that the factual advantage emerges progressively from early-to-mid layers onward, consistent with model-specific memory retrieval, while math reasoning shows no consistent advantage at any depth.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.11840", "title": "When Reasoning Models Hurt Behavioral Simulation: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation", "authors": [ "Sandro Andric" ], "github": "", "project_page": "", "abstract": "Large language models are increasingly used as agents in social, economic, and policy simulations. A common assumption is that stronger reasoning should improve simulation fidelity. We argue that this assumption can fail when the objective is not to solve a strategic problem, but to sample plausible boundedly rational behavior. In such settings, reasoning-enhanced models can become better solvers and worse simulators: they can over-optimize for strategically dominant actions, collapse compromise-oriented terminal behavior, and sometimes exhibit a diversity-without-fidelity pattern in which local variation survives without outcome-level fidelity. We study this solver-sampler mismatch in three multi-agent negotiation environments adapted from earlier simulation work: an ambiguous fragmented-authority trading-limits scenario, an ambiguous unified-opposition trading-limits scenario, and a new-domain grid-curtailment case in emergency electricity management. We compare three reflection conditions, no reflection, bounded reflection, and native reasoning, across two primary model families and then extend the same protocol to direct OpenAI runs with GPT-4.1 and GPT-5.2. Across all three experiments, bounded reflection produces substantially more diverse and compromise-oriented trajectories than either no reflection or native reasoning. In the direct OpenAI extension, GPT-5.2 native ends in authority decisions in 45 of 45 runs across the three experiments, while GPT-5.2 bounded recovers compromise outcomes in every environment. The contribution is not a claim that reasoning is generally harmful. It is a methodological warning: model capability and simulation fidelity are different objectives, and behavioral simulation should qualify models as samplers, not only as solvers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12989", "title": "Accelerating Speculative Decoding with Block Diffusion Draft Trees", "authors": [ "Liran Ringel", "Yaniv Romano" ], "github": "", "project_page": "", "abstract": "Speculative decoding accelerates autoregressive language models by using a lightweight drafter to propose multiple future tokens, which the target model then verifies in parallel. DFlash shows that a block diffusion drafter can generate an entire draft block in a single forward pass and achieve state-of-the-art speculative decoding performance, outperforming strong autoregressive drafters such as EAGLE-3. Vanilla DFlash, however, still verifies only a single drafted trajectory per round, potentially limiting its acceptance length. We introduce DDTree (Diffusion Draft Tree), a method that constructs a draft tree directly from the per-position distributions of a block diffusion drafter. Under a fixed node budget, DDTree uses a simple best-first heap algorithm to select the continuations that are most likely to match the target model according to a surrogate defined by the draft model's output. The resulting tree is verified efficiently in a single target model forward pass using an ancestor-only attention mask. Because DDTree builds on DFlash, a leading draft model for speculative decoding, these gains place DDTree among the leading approaches to speculative decoding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.13023", "title": "SpotSound: Enhancing Large Audio-Language Models with Fine-Grained Temporal Grounding", "authors": [ "Luoyi Sun", "Xiao Zhou", "Zeqian Li", "Ya Zhang", "Yanfeng Wang", "Weidi Xie" ], "github": "", "project_page": "", "abstract": "Large Audio-Language Models (ALMs) have recently demonstrated remarkable capabilities in holistic audio understanding, yet they remain unreliable for temporal grounding, i.e., the task of pinpointing exactly when an event occurs within long-form audio. This limitation stems from two factors: training data dominated by clip-level supervision lacking precise timestamps, and benchmarks that fail to simulate real-world scenarios where short events are obscured by dense background sounds. In this paper, we introduce SpotSound, an audio language model designed for grounding audio events. SpotSound incorporates a novel training objective, specifically designed to suppress hallucinated timestamps for events absent from the input. Additionally, we present SpotSound-Bench, a challenging temporal grounding benchmark where target events occupy less than ~10\\% of each clip, creating a rigorous `needle-in-a-haystack' evaluation. Experiments demonstrate that SpotSound achieves state-of-the-art results on temporal grounding benchmarks while maintaining robust performance across general downstream audio-language tasks. Code, models and benchmark are released on https://loiesun.github.io/spotsound/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.09574", "title": "Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization", "authors": [ "Jiachen Zhu", "Lingyu Yang", "Rong Shan", "Congmin Zheng", "Zeyu Zheng", "Weiwen Liu", "Yong Yu", "Weinan Zhang", "Jianghao Lin" ], "github": "", "project_page": "", "abstract": "The rise of autonomous GUI agents has triggered adversarial countermeasures from digital platforms, yet existing research prioritizes utility and robustness over the critical dimension of anti-detection. We argue that for agents to survive in human-centric ecosystems, they must evolve Humanization capabilities. We introduce the ``Turing Test on Screen,'' formally modeling the interaction as a MinMax optimization problem between a detector and an agent aiming to minimize behavioral divergence. We then collect a new high-fidelity dataset of mobile touch dynamics, and conduct our analysis that vanilla LMM-based agents are easily detectable due to unnatural kinematics. Consequently, we establish the Agent Humanization Benchmark (AHB) and detection metrics to quantify the trade-off between imitability and utility. Finally, we propose methods ranging from heuristic noise to data-driven behavioral matching, demonstrating that agents can achieve high imitability theoretically and empirically without sacrificing performance. This work shifts the paradigm from whether an agent can perform a task to how it performs it within a human-centric ecosystem, laying the groundwork for seamless coexistence in adversarial digital environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.11211", "title": "3DTV: A Feedforward Interpolation Network for Real-Time View Synthesis", "authors": [ "Stefan Schulz", "Fernando Edelstein", "Hannah Dr\u00f6ge", "Matthias B. Hullin", "Markus Plack" ], "github": "", "project_page": "", "abstract": "Real-time free-viewpoint rendering requires balancing multi-camera redundancy with the latency constraints of interactive applications. We address this challenge by combining lightweight geometry with learning and propose 3DTV, a feedforward network for real-time sparse-view interpolation. A Delaunay-based triplet selection ensures angular coverage for each target view. Building on this, we introduce a pose-aware depth module that estimates a coarse-to-fine depth pyramid, enabling efficient feature reprojection and occlusion-aware blending. Unlike methods that require scene-specific optimization, 3DTV runs feedforward without retraining, making it practical for AR/VR, telepresence, and interactive applications. Our experiments on challenging multi-view video datasets demonstrate that 3DTV consistently achieves a strong balance of quality and efficiency, outperforming recent real-time novel-view baselines. Crucially, 3DTV avoids explicit proxies, enabling robust rendering across diverse scenes. This makes it a practical solution for low-latency multi-view streaming and interactive rendering. Project Page: https://stefanmschulz.github.io/3DTV_webpage/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.09497", "title": "BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation", "authors": [ "Hippolyte Gisserot-Boukhlef", "Nicolas Boizard", "Emmanuel Malherbe", "C\u00e9line Hudelot", "Pierre Colombo" ], "github": "", "project_page": "", "abstract": "Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases. In practice, however, evaluating generative outputs typically relies on rigid lexical methods to extract and assess answers, which can conflate a model's true problem-solving ability with its compliance with predefined formatting guidelines. While recent LLM-as-a-Judge approaches mitigate this issue by assessing semantic correctness rather than strict structural conformity, they also introduce substantial computational overhead, making evaluation costly. In this work, we first systematically investigate the limitations of lexical evaluation through a large-scale empirical study spanning 36 models and 15 downstream tasks, demonstrating that such methods correlate poorly with human judgments. To address this limitation, we introduce BERT-as-a-Judge, an encoder-driven approach for assessing answer correctness in reference-based generative settings, robust to variations in output phrasing, and requiring only lightweight training on synthetically annotated question-candidate-reference triplets. We show that it consistently outperforms the lexical baseline while matching the performance of much larger LLM judges, providing a compelling tradeoff between the two and enabling reliable, scalable evaluation. Finally, through extensive experimentation, we provide detailed insights into BERT-as-a-Judge's performance to offer practical guidance for practitioners, and release all project artifacts to foster downstream adoption.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12152", "title": "Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution", "authors": [ "Sebastian Cajas", "Ashaba Judith", "Rahul Gorijavolu", "Sahil Kapadia", "Hillary Clinton Kasimbazi", "Leo Kinyera", "Emmanuel Paul Kwesiga", "Sri Sri Jaithra Varma Manthena", "Luis Filipe Nakayama", "Ninsiima Doreen", "Leo Anthony Celi" ], "github": "https://github.com/sebasmos/latent-sr", "project_page": "", "abstract": "Latent diffusion models for medical image super-resolution universally inherit variational autoencoders designed for natural photographs. We show that this default choice, not the diffusion architecture, is the dominant constraint on reconstruction quality. In a controlled experiment holding all other pipeline components fixed, replacing the generic Stable Diffusion VAE with MedVAE, a domain-specific autoencoder pretrained on more than 1.6 million medical images, yields +2.91 to +3.29 dB PSNR improvement across knee MRI, brain MRI, and chest X-ray (n = 1,820; Cohen's d = 1.37 to 1.86, all p < 10^{-20}, Wilcoxon signed-rank). Wavelet decomposition localises the advantage to the finest spatial frequency bands encoding anatomically relevant fine structure. Ablations across inference schedules, prediction targets, and generative architectures confirm the gap is stable within plus or minus 0.15 dB, while hallucination rates remain comparable between methods (Cohen's h < 0.02 across all datasets), establishing that reconstruction fidelity and generative hallucination are governed by independent pipeline components. These results provide a practical screening criterion: autoencoder reconstruction quality, measurable without diffusion training, predicts downstream SR performance (R^2 = 0.67), suggesting that domain-specific VAE selection should precede diffusion architecture search. Code and trained model weights are publicly available at https://github.com/sebasmos/latent-sr.", "github_manual": "", "github_regex": "https://github.com/sebasmos/latent-sr", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.09594", "title": "Spatial Competence Benchmark", "authors": [ "Jash Vira", "Ashley Harris" ], "github": "", "project_page": "", "abstract": "Spatial competence is the quality of maintaining a consistent internal representation of an environment and using it to infer discrete structure and plan actions under constraints. Prevailing spatial evaluations for large models are limited to probing isolated primitives through 3D transformations or visual question answering. We introduce the Spatial Competence Benchmark (SCBench), spanning three hierarchical capability buckets whose tasks require executable outputs verified by deterministic checkers or simulator-based evaluators. On SCBench, three frontier models exhibit monotonically decreasing accuracy up the capability ladder. Sweeping output-token caps shows that accuracy gains concentrate at low budgets and saturate quickly, and failures are dominated by locally plausible geometry that breaks global constraints. We release the task generators, verifiers, and visualisation tooling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12978", "title": "GlotOCR Bench: OCR Models Still Struggle Beyond a Handful of Unicode Scripts", "authors": [ "Amir Hossein Kargaran", "Nafiseh Nikeghbal", "Jana Diesner", "Fran\u00e7ois Yvon", "Hinrich Sch\u00fctze" ], "github": "https://github.com/cisnlp/glotocr-bench", "project_page": "", "abstract": "Optical character recognition (OCR) has advanced rapidly with the rise of vision-language models, yet evaluation has remained concentrated on a small cluster of high- and mid-resource scripts. We introduce GlotOCR Bench, a comprehensive benchmark evaluating OCR generalization across 100+ Unicode scripts. Our benchmark comprises clean and degraded image variants rendered from real multilingual texts. Images are rendered using fonts from the Google Fonts repository, shaped with HarfBuzz and rasterized with FreeType, supporting both LTR and RTL scripts. Samples of rendered images were manually reviewed to verify correct rendering across all scripts. We evaluate a broad suite of open-weight and proprietary vision-language models and find that most perform well on fewer than ten scripts, and even the strongest frontier models fail to generalize beyond thirty scripts. Performance broadly tracks script-level pretraining coverage, suggesting that current OCR systems rely on language model pretraining as much as on visual recognition. Models confronted with unfamiliar scripts either produce random noise or hallucinate characters from similar scripts they already know. We release the benchmark and pipeline for reproducibility. Pipeline Code: https://github.com/cisnlp/glotocr-bench, Benchmark: https://hf.co/datasets/cis-lmu/glotocr-bench.", "github_manual": "", "github_regex": "https://github.com/cisnlp/glotocr-bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12710", "title": "LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety", "authors": [ "Junxiao Yang", "Haoran Liu", "Jinzhe Tu", "Jiale Cheng", "Zhexin Zhang", "Shiyao Cui", "Jiaqi Weng", "Jialing Tao", "Hui Xue", "Hongning Wang", "Han Qiu", "Minlie Huang" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) often demonstrate strong safety performance in high-resource languages, yet exhibit severe vulnerabilities when queried in low-resource languages. We attribute this gap to a mismatch between language-agnostic semantic understanding ability and language-dominant safety alignment biased toward high-resource languages. Consistent with this hypothesis, we empirically identify the semantic bottleneck in LLMs, an intermediate layer in which the geometry of model representations is governed primarily by shared semantic content rather than language identity. Building on this observation, we propose Language-Agnostic Semantic Alignment (LASA), which anchors safety alignment directly in semantic bottlenecks. Experiments show that LASA substantially improves safety across all languages: average attack success rate (ASR) drops from 24.7% to 2.8% on LLaMA-3.1-8B-Instruct and remains around 3-4% across Qwen2.5 and Qwen3 Instruct models (7B-32B). Together, our analysis and method offer a representation-level perspective on LLM safety, suggesting that safety alignment requires anchoring safety understanding not in surface text, but in the model's language-agnostic semantic space.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.10812", "title": "PokeRL: Reinforcement Learning for Pokemon Red", "authors": [ "Dheeraj Mudireddy", "Sai Patibandla" ], "github": "https://github.com/reddheeraj/PokemonRL", "project_page": "", "abstract": "Pokemon Red is a long-horizon JRPG with sparse rewards, partial observability, and quirky control mechanics that make it a challenging benchmark for reinforcement learning. While recent work has shown that PPO agents can clear the first two gyms using heavy reward shaping and engineered observations, training remains brittle in practice, with agents often degenerating into action loops, menu spam, or unproductive wandering. In this paper, we present PokeRL, a modular system that trains deep reinforcement learning agents to complete early game tasks in Pokemon Red, including exiting the player's house, exploring Pallet Town to reach tall grass, and winning the first rival battle. Our main contributions are a loop-aware environment wrapper around the PyBoy emulator with map masking, a multi-layer anti-loop and anti-spam mechanism, and a dense hierarchical reward design. We argue that practical systems like PokeRL, which explicitly model failure modes such as loops and spam, are a necessary intermediate step between toy benchmarks and full Pokemon League champion agents. Code is available at https://github.com/reddheeraj/PokemonRL", "github_manual": "", "github_regex": "https://github.com/reddheeraj/PokemonRL", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.11177", "title": "Do Thought Streams Matter? Evaluating Reasoning in Gemini Vision-Language Models for Video Scene Understanding", "authors": [ "Shivam Sharma", "Sankalp Nagaonkar", "Ashish Choithani", "Ashutosh Trivedi" ], "github": "", "project_page": "", "abstract": "We benchmark how internal reasoning traces, which we call thought streams, affect video scene understanding in vision-language models. Using four configurations of Google's Gemini 2.5 Flash and Flash Lite across scenes extracted from 100 hours of video, we ask three questions: does more thinking lead to better outputs, where do the gains stop, and what do these models actually think about? We introduce three evaluation metrics. Contentfulness measures how much of the thought stream is useful scene content versus meta-commentary. Thought-Final Coverage measures how faithfully the thought stream translates into the final output. Dominant Entity Analysis identifies which subjects, actions, and settings the model focuses on. GPT-5 serves as an independent judge. We find that quality gains from additional thinking plateau quickly, with most improvement occurring in the first few hundred tokens. Flash Lite offers the best balance between quality and token usage. Tight reasoning budgets cause the model to add content in the final output that it never reasoned about, a form of compression-step hallucination. Despite being different model tiers, Flash and Flash Lite produce similar thought streams, though they differ in style: Flash discusses its reasoning process, while Lite focuses on describing the scene.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.13015", "title": "Learning Versatile Humanoid Manipulation with Touch Dreaming", "authors": [ "Yaru Niu", "Zhenlong Fang", "Binghong Chen", "Shuai Zhou", "Revanth Senthilkumaran", "Hao Zhang", "Bingqing Chen", "Chen Qiu", "H. Eric Tseng", "Jonathan Francis", "Ding Zhao" ], "github": "", "project_page": "", "abstract": "Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stability, dexterous hands, and contact-aware perception under frequent contact changes. In this work, we study dexterous, contact-rich humanoid loco-manipulation. We first develop an RL-based whole-body controller that provides stable lower-body and torso execution during complex manipulation. Built on this controller, we develop a whole-body humanoid data collection system that combines VR-based teleoperation with human-to-humanoid motion mapping, enabling efficient collection of real-world demonstrations. We then propose Humanoid Transformer with Touch Dreaming (HTD), a multimodal encoder--decoder Transformer that models touch as a core modality alongside multi-view vision and proprioception. HTD is trained in a single stage with behavioral cloning augmented by touch dreaming: in addition to predicting action chunks, the policy predicts future hand-joint forces and future tactile latents, encouraging the shared Transformer trunk to learn contact-aware representations for dexterous interaction. Across five contact-rich tasks, Insert-T, Book Organization, Towel Folding, Cat Litter Scooping, and Tea Serving, HTD achieves a 90.9% relative improvement in average success rate over the stronger baseline. Ablation results further show that latent-space tactile prediction is more effective than raw tactile prediction, yielding a 30% relative gain in success rate. These results demonstrate that combining robust whole-body execution, scalable humanoid data collection, and predictive touch-centered learning enables versatile, high-dexterity humanoid manipulation in the real world. Project webpage: humanoid-touch-dream.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.12946", "title": "Parcae: Scaling Laws For Stable Looped Language Models", "authors": [ "Hayden Prairie", "Zachary Novack", "Taylor Berg-Kirkpatrick", "Daniel Y. Fu" ], "github": "", "project_page": "", "abstract": "Traditional fixed-depth architectures scale quality by increasing training FLOPs, typically through increased parameterization, at the expense of a higher memory footprint, or data. A potential alternative is looped architectures, which instead increase FLOPs by sending activations through a block of layers in a loop. While promising, existing recipes for training looped architectures can be unstable, suffering from residual explosion and loss spikes. We address these challenges by recasting looping as a nonlinear time-variant dynamical system over the residual stream. Via a linear approximation to this system, we find that instability occurs in existing looped architectures as a result of large spectral norms in their injection parameters. To address these instability issues, we propose Parcae, a novel stable, looped architecture that constrains the spectral norm of the injection parameters via discretization of a negative diagonal parameterization. As a result, Parcae achieves up to 6.3% lower validation perplexity over prior large-scale looped models. Using our stable looped architecture, we investigate the scaling properties of looping as a medium to improve quality by increasing FLOPs in training and test-time. For training, we derive predictable power laws to scale FLOPs while keeping parameter count fixed. Our initial scaling laws suggest that looping and data should be increased in tandem, given a fixed FLOP budget. At test-time, we find that Parcae can use looping to scale compute, following a predictable, saturating exponential decay. When scaled up to 1.3B parameters, we find that Parcae improves CORE and Core-Extended quality by 2.99 and 1.18 points when compared to strong Transformer baselines under a fixed parameter and data budget, achieving a relative quality of up to 87.5% a Transformer twice the size.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.10577", "title": "The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents", "authors": [ "Xuwei Ding", "Skylar Zhai", "Linxin Song", "Jiate Li", "Taiwei Shi", "Nicholas Meade", "Siva Reddy", "Jian Kang", "Jieyu Zhao" ], "github": "", "project_page": "", "abstract": "Computer-use agents (CUAs) can now autonomously complete complex tasks in real digital environments, but when misled, they can also be used to automate harmful actions programmatically. Existing safety evaluations largely target explicit threats such as misuse and prompt injection, but overlook a subtle yet critical setting where user instructions are entirely benign and harm arises from the task context or execution outcome. We introduce OS-BLIND, a benchmark that evaluates CUAs under unintended attack conditions, comprising 300 human-crafted tasks across 12 categories, 8 applications, and 2 threat clusters: environment-embedded threats and agent-initiated harms. Our evaluation on frontier models and agentic frameworks reveals that most CUAs exceed 90% attack success rate (ASR), and even the safety-aligned Claude 4.5 Sonnet reaches 73.0% ASR. More interestingly, this vulnerability becomes even more severe, with ASR rising from 73.0% to 92.7% when Claude 4.5 Sonnet is deployed in multi-agent systems. Our analysis further shows that existing safety defenses provide limited protection when user instructions are benign. Safety alignment primarily activates within the first few steps and rarely re-engages during subsequent execution. In multi-agent systems, decomposed subtasks obscure the harmful intent from the model, causing safety-aligned models to fail. We will release our OS-BLIND to encourage the broader research community to further investigate and address these safety challenges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.14025", "title": "Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective", "authors": [ "Weijie Wang", "Qihang Cao", "Sensen Gao", "Donny Y. Chen", "Haofei Xu", "Wenjing Bian", "Songyou Peng", "Tat-Jen Cham", "Chuanxia Zheng", "Andreas Geiger", "Jianfei Cai", "Jia-Wang Bian", "Bohan Zhuang" ], "github": "https://github.com/ziplab/Awesome-Feed-Forward-3D", "project_page": "", "abstract": "Reconstructing 3D representations from 2D inputs is a fundamental task in computer vision and graphics, serving as a cornerstone for understanding and interacting with the physical world. While traditional methods achieve high fidelity, they are limited by slow per-scene optimization or category-specific training, which hinders their practical deployment and scalability. Hence, generalizable feed-forward 3D reconstruction has witnessed rapid development in recent years. By learning a model that maps images directly to 3D representations in a single forward pass, these methods enable efficient reconstruction and robust cross-scene generalization. Our survey is motivated by a critical observation: despite the diverse geometric output representations, ranging from implicit fields to explicit primitives, existing feed-forward approaches share similar high-level architectural patterns, such as image feature extraction backbones, multi-view information fusion mechanisms, and geometry-aware design principles. Consequently, we abstract away from these representation differences and instead focus on model design, proposing a novel taxonomy centered on model design strategies that are agnostic to the output format. Our proposed taxonomy organizes the research directions into five key problems that drive recent research development: feature enhancement, geometry awareness, model efficiency, augmentation strategies and temporal-aware models. To support this taxonomy with empirical grounding and standardized evaluation, we further comprehensively review related benchmarks and datasets, and extensively discuss and categorize real-world applications based on feed-forward 3D models. Finally, we outline future directions to address open challenges such as scalability, evaluation standards, and world modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ziplab/Awesome-Feed-Forward-3D", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.09687", "title": "Grid2Matrix: Revealing Digital Agnosia in Vision-Language Models", "authors": [ "Yunkai Zhang", "Linda Li", "Yingxin Cui", "Xiyuan Ruan", "Zeyu Zheng", "Kezhen Chen", "Yi Zhang", "Diji Yang" ], "github": "https://github.com/zhykoties/Grid2Matrix_DigitalAgnosia", "project_page": "", "abstract": "Vision-Language Models (VLMs) excel on many multimodal reasoning benchmarks, but these evaluations often do not require an exhaustive readout of the image and can therefore obscure failures in faithfully capturing all visual details. We introduce Grid2Matrix (G2M), a controlled benchmark in which a model is shown a color grid and a color-to-number mapping, and must output the corresponding matrix. By varying grid size and the number of colors, G2M provides a simple way to increase visual complexity while minimizing semantic confounds. We find that VLMs exhibit a sharp early collapse in zero-shot end-to-end evaluation, failing on surprisingly small grids rather than degrading gradually as the task becomes denser. We probe the visual encoders of VLMs from two representative families and find that they preserve substantially more of the grid information than the corresponding end-to-end outputs. This suggests that the failure is not explained by visual encoding alone, but also reflects a gap between what remains recoverable from visual features and what is ultimately expressed in language. We term this gap Digital Agnosia. Further analyses show that these errors are highly structured and depend strongly on how grid cells overlap with visual patch boundaries. We also find that common strategies such as model scaling and multimodal alignment do not fully eliminate this failure mode. We expect G2M to serve as a useful testbed for understanding where and how VLMs lose fine visual details, and for evaluating tasks where missing even small visual details can matter, such as tables, charts, forms, and GUIs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhykoties/Grid2Matrix_DigitalAgnosia", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-15", "arxiv_id": "2604.05278", "title": "Spec Kit Agents: Context-Grounded Agentic Workflows", "authors": [ "Pardis Taghavi", "Santosh Bhavani" ], "github": "", "project_page": "", "abstract": "Spec-driven development (SDD) with AI coding agents provides a structured workflow, but agents often remain \"context blind\" in large, evolving repositories, leading to hallucinated APIs and architectural violations. We present Spec Kit Agents, a multi-agent SDD pipeline (with PM and developer roles) that adds phase-level, context-grounding hooks. Read-only probing hooks ground each stage (Specify, Plan, Tasks, Implement) in repository evidence, while validation hooks check intermediate artifacts against the environment. We evaluate 128 runs covering 32 features across five repositories. Context-grounding hooks improve judged quality by +0.15 on a 1-5 composite LLM-as-judge score (+3.0 percent of the full score; Wilcoxon signed-rank, p < 0.05) while maintaining 99.7-100 percent repository-level test compatibility. We further evaluate the framework on SWE-bench Lite, where augmentation hooks improve baseline by 1.7 percent, achieving 58.2 percent Pass@1.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.10866", "title": "OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language World Models", "authors": [ "Xiaomeng Hu", "Yinger Zhang", "Fei Huang", "Jianhong Tu", "Yang Su", "Lianghao Deng", "Yuxuan Liu", "Yantao Liu", "Dayiheng Liu", "Tsung-Yi Ho" ], "github": "", "project_page": "", "abstract": "AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor safety monitoring to customs import processing), yet existing benchmarks can only evaluate agents in the few domains where public environments exist. We introduce OccuBench, a benchmark covering 100 real-world professional task scenarios across 10 industry categories and 65 specialized domains, enabled by Language World Models (LWMs) that simulate domain-specific environments through LLM-driven tool response generation. Our multi-agent synthesis pipeline automatically produces evaluation instances with guaranteed solvability, calibrated difficulty, and document-grounded diversity. OccuBench evaluates agents along two complementary dimensions: task completion across professional domains and environmental robustness under controlled fault injection (explicit errors, implicit data degradation, and mixed faults). We evaluate 15 frontier models across 8 model families and find that: (1) no single model dominates all industries, as each has a distinct occupational capability profile; (2) implicit faults (truncated data, missing fields) are harder than both explicit errors (timeouts, 500s) and mixed faults, because they lack overt error signals and require the agent to independently detect data degradation; (3) larger models, newer generations, and higher reasoning effort consistently improve performance. GPT-5.2 improves by 27.5 points from minimal to maximum reasoning effort; and (4) strong agents are not necessarily strong environment simulators. Simulator quality is critical for LWM-based evaluation reliability. OccuBench provides the first systematic cross-industry evaluation of AI agents on professional occupational tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.14148", "title": "Seedance 2.0: Advancing Video Generation for World Complexity", "authors": [ "Team Seedance", "De Chen", "Liyang Chen", "Xin Chen", "Ying Chen", "Zhuo Chen", "Zhuowei Chen", "Feng Cheng", "Tianheng Cheng", "Yufeng Cheng", "Mojie Chi", "Xuyan Chi", "Jian Cong", "Qinpeng Cui", "Fei Ding", "Qide Dong", "Yujiao Du", "Haojie Duanmu", "Junliang Fan", "Jiarui Fang", "Jing Fang", "Zetao Fang", "Chengjian Feng", "Yu Gao", "Diandian Gu", "Dong Guo", "Hanzhong Guo", "Qiushan Guo", "Boyang Hao", "Hongxiang Hao", "Haoxun He", "Jiaao He", "Qian He", "Tuyen Hoang", "Heng Hu", "Ruoqing Hu", "Yuxiang Hu", "Jiancheng Huang", "Weilin Huang", "Zhaoyang Huang", "Zhongyi Huang", "Jishuo Jin", "Ming Jing", "Ashley Kim", "Shanshan Lao", "Yichong Leng", "Bingchuan Li", "Gen Li", "Haifeng Li", "Huixia Li", "Jiashi Li", "Ming Li", "Xiaojie Li", "Xingxing Li", "Yameng Li", "Yiying Li", "Yu Li", "Yueyan Li", "Chao Liang", "Han Liang", "Jianzhong Liang", "Ying Liang", "Wang Liao", "J. H. Lien", "Shanchuan Lin", "Xi Lin", "Feng Ling", "Yue Ling", "Fangfang Liu", "Jiawei Liu", "Jihao Liu", "Jingtuo Liu", "Shu Liu", "Sichao Liu", "Wei Liu", "Xue Liu", "Zuxi Liu", "Ruijie Lu", "Lecheng Lyu", "Jingting Ma", "Tianxiang Ma", "Xiaonan Nie", "Jingzhe Ning", "Junjie Pan", "Xitong Pan", "Ronggui Peng", "Xueqiong Qu", "Yuxi Ren", "Yuchen Shen", "Guang Shi", "Lei Shi", "Yinglong Song", "Fan Sun", "Li Sun", "Renfei Sun", "Wenjing Tang", "Boyang Tao", "Zirui Tao", "Dongliang Wang", "Feng Wang", "Hulin Wang", "Ke Wang", "Qingyi Wang", "Rui Wang", "Shuai Wang", "Shulei Wang", "Weichen Wang", "Xuanda Wang", "Yanhui Wang", "Yue Wang", "Yuping Wang", "Yuxuan Wang", "Zijie Wang", "Ziyu Wang", "Guoqiang Wei", "Meng Wei", "Di Wu", "Guohong Wu", "Hanjie Wu", "Huachao Wu", "Jian Wu", "Jie Wu", "Ruolan Wu", "Shaojin Wu", "Xiaohu Wu", "Xinglong Wu", "Yonghui Wu", "Ruiqi Xia", "Xin Xia", "Xuefeng Xiao", "Shuang Xu", "Bangbang Yang", "Jiaqi Yang", "Runkai Yang", "Tao Yang", "Yihang Yang", "Zhixian Yang", "Ziyan Yang", "Fulong Ye", "Bingqian Yi", "Xing Yin", "Yongbin You", "Linxiao Yuan", "Weihong Zeng", "Xuejiao Zeng", "Yan Zeng", "Siyu Zhai", "Zhonghua Zhai", "Bowen Zhang", "Chenlin Zhang", "Heng Zhang", "Jun Zhang", "Manlin Zhang", "Peiyuan Zhang", "Shuo Zhang", "Xiaohe Zhang", "Xiaoying Zhang", "Xinyan Zhang", "Xinyi Zhang", "Yichi Zhang", "Zixiang Zhang", "Haiyu Zhao", "Huating Zhao", "Liming Zhao", "Yian Zhao", "Guangcong Zheng", "Jianbin Zheng", "Xiaozheng Zheng", "Zerong Zheng", "Kuan Zhu", "Feilong Zuo" ], "github": "", "project_page": "", "abstract": "Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro, Seedance 2.0 adopts a unified, highly efficient, and large-scale architecture for multi-modal audio-video joint generation. This allows it to support four input modalities: text, image, audio, and video, by integrating one of the most comprehensive suites of multi-modal content reference and editing capabilities available in the industry to date. It delivers substantial, well-rounded improvements across all key sub-dimensions of video and audio generation. In both expert evaluations and public user tests, the model has demonstrated performance on par with the leading levels in the field. Seedance 2.0 supports direct generation of audio-video content with durations ranging from 4 to 15 seconds, with native output resolutions of 480p and 720p. For multi-modal inputs as reference, its current open platform supports up to 3 video clips, 9 images, and 3 audio clips. In addition, we provide Seedance 2.0 Fast version, an accelerated variant of Seedance 2.0 designed to boost generation speed for low-latency scenarios. Seedance 2.0 has delivered significant improvements to its foundational generation capabilities and multi-modal generation performance, bringing an enhanced creative experience for end users.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.14144", "title": "SpatialEvo: Self-Evolving Spatial Intelligence via Deterministic Geometric Environments", "authors": [ "Dinging Li", "Yingxiu Zhao", "Xinrui Cheng", "Kangheng Lin", "Hongbo Peng", "Hongxing Li", "Zixuan Wang", "Yuhong Dai", "Haodong Li", "Jia Wang", "Yukang Shi", "Liang Zhao", "Jianjian Sun", "Zheng Ge", "Xiangyu Zhang", "Weiming Lu", "Jun Xiao", "Yueting Zhuang", "Yongliang Shen" ], "github": "", "project_page": "", "abstract": "Spatial reasoning over three-dimensional scenes is a core capability for embodied intelligence, yet continuous model improvement remains bottlenecked by the cost of geometric annotation. The self-evolving paradigm offers a promising path, but its reliance on model consensus to construct pseudo-labels causes training to reinforce rather than correct the model's own geometric errors. We identify a property unique to 3D spatial reasoning that circumvents this limitation: ground truth is a deterministic consequence of the underlying geometry, computable exactly from point clouds and camera poses without any model involvement. Building on this insight, we present SpatialEvo, a self-evolving framework for 3D spatial reasoning, centered on the Deterministic Geometric Environment (DGE). The DGE formalizes 16 spatial reasoning task categories under explicit geometric validation rules and converts unannotated 3D scenes into zero-noise interactive oracles, replacing model consensus with objective physical feedback. A single shared-parameter policy co-evolves across questioner and solver roles under DGE constraints: the questioner generates physically valid spatial questions grounded in scene observations, while the solver derives precise answers against DGE-verified ground truth. A task-adaptive scheduler endogenously concentrates training on the model's weakest categories, producing a dynamic curriculum without manual design. Experiments across nine benchmarks demonstrate that SpatialEvo achieves the highest average score at both 3B and 7B scales, with consistent gains on spatial reasoning benchmarks and no degradation on general visual understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.14116", "title": "TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration", "authors": [ "Zerun Ma", "Guoqiang Wang", "Xinchen Xie", "Yicheng Chen", "He Du", "Bowen Li", "Yanan Sun", "Wenran Liu", "Kai Chen", "Yining Li" ], "github": "", "project_page": "", "abstract": "While Large Language Models (LLMs) have empowered AI research agents to perform isolated scientific tasks, automating complex, real-world workflows, such as LLM training, remains a significant challenge. In this paper, we introduce TREX, a multi-agent system that automates the entire LLM training life-cycle. By orchestrating collaboration between two core modules-the Researcher and the Executor-the system seamlessly performs requirement analysis, open-domain literature and data research, formulation of training strategies, preparation of data recipes, and model training and evaluation. The multi-round experimental process is modeled as a search tree, enabling the system to efficiently plan exploration paths, reuse historical results, and distill high-level insights from iterative trials. To evaluate the capability of automated LLM training, we construct FT-Bench, a benchmark comprising 10 tasks derived from real-world scenarios, ranging from optimizing fundamental model capabilities to enhancing performance on domain-specific tasks. Experimental results demonstrate that the TREX agent consistently optimizes model performance on target tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.14084", "title": "TIP: Token Importance in On-Policy Distillation", "authors": [ "Yuanda Xu", "Hejian Sang", "Zhengze Zhou", "Ran He", "Zhipeng Wang", "Alborz Geramifard" ], "github": "https://github.com/HJSang/OPSD_OnPolicyDistillation", "project_page": "", "abstract": "On-policy knowledge distillation (OPD) trains a student on its own rollouts under token-level supervision from a teacher. Not all token positions matter equally, but existing views of token importance are incomplete. We ask a direct question: which tokens carry the most useful learning signal in OPD? Our answer is that informative tokens come from two regions: positions with high student entropy, and positions with low student entropy plus high teacher--student divergence, where the student is overconfident and wrong. Empirically, student entropy is a strong first-order proxy: retaining 50% of tokens with entropy-based sampling matches or exceeds all-token training while reducing peak memory by up to 47%. But entropy alone misses a second important region. When we isolate low-entropy, high-divergence tokens, training on fewer than 10% of all tokens nearly matches full-token baselines, showing that overconfident tokens carry dense corrective signal despite being nearly invisible to entropy-only rules. We organize these findings with TIP (Token Importance in on-Policy distillation), a two-axis taxonomy over student entropy and teacher--student divergence, and give a theoretical explanation for why entropy is useful yet structurally incomplete. This view motivates type-aware token selection rules that combine uncertainty and disagreement. We validate this picture across three teacher--student pairs spanning Qwen3, Llama, and Qwen2.5 on MATH-500 and AIME 2024/2025, and on the DeepPlanning benchmark for long-horizon agentic planning, where Q3-only training on <20% of tokens surpasses full-token OPD. Our experiments are implemented by extending the OPD repository https://github.com/HJSang/OPSD_OnPolicyDistillation, which supports memory-efficient distillation of larger models under limited GPU budgets.", "github_manual": "", "github_regex": "https://github.com/HJSang/OPSD_OnPolicyDistillation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.13822", "title": "UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization", "authors": [ "Zhengxi Lu", "Fei Tang", "Guangyi Liu", "Kaitao Song", "Xu Tan", "Jin Ma", "Wenqi Zhang", "Weiming Lu", "Jun Xiao", "Yueting Zhuang", "Yongliang Shen" ], "github": "", "project_page": "", "abstract": "MLLM-based GUI agents have demonstrated strong capabilities in complex user interface interaction tasks. However, long-horizon scenarios remain challenging, as these agents are burdened with tasks beyond their intrinsic capabilities, suffering from memory degradation, progress confusion, and math hallucination. To address these challenges, we present UI-Copilot, a collaborative framework where the GUI agent focuses on task execution while a lightweight copilot provides on-demand assistance for memory retrieval and numerical computation. We introduce memory decoupling to separate persistent observations from transient execution context, and train the policy agent to selectively invoke the copilot as Retriever or Calculator based on task demands. To enable effective tool invocation learning, we propose Tool-Integrated Policy Optimization (TIPO), which separately optimizes tool selection through single-turn prediction and task execution through on-policy multi-turn rollouts. Experimental results show that UI-Copilot-7B achieves state-of-the-art performance on challenging MemGUI-Bench, outperforming strong 7B-scale GUI agents such as GUI-Owl-7B and UI-TARS-1.5-7B. Moreover, UI-Copilot-7B delivers a 17.1% absolute improvement on AndroidWorld over the base Qwen model, highlighting UI-Copilot's strong generalization to real-world GUI tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.14113", "title": "UI-Zoomer: Uncertainty-Driven Adaptive Zoom-In for GUI Grounding", "authors": [ "Fei Tang", "Bofan Chen", "Zhengxi Lu", "Tongbo Chen", "Songqin Nong", "Tao Jiang", "Wenhao Xu", "Weiming Lu", "Jun Xiao", "Yueting Zhuang", "Yongliang Shen" ], "github": "", "project_page": "", "abstract": "GUI grounding, which localizes interface elements from screenshots given natural language queries, remains challenging for small icons and dense layouts. Test-time zoom-in methods improve localization by cropping and re-running inference at higher resolution, but apply cropping uniformly across all instances with fixed crop sizes, ignoring whether the model is actually uncertain on each case. We propose UI-Zoomer, a training-free adaptive zoom-in framework that treats both the trigger and scale of zoom-in as a prediction uncertainty quantification problem. A confidence-aware gate fuses spatial consensus among stochastic candidates with token-level generation confidence to selectively trigger zoom-in only when localization is uncertain. When triggered, an uncertainty-driven crop sizing module decomposes prediction variance into inter-sample positional spread and intra-sample box extent, deriving a per-instance crop radius via the law of total variance. Extensive experiments on ScreenSpot-Pro, UI-Vision, and ScreenSpot-v2 demonstrate consistent improvements over strong baselines across multiple model architectures, achieving gains of up to +13.4\\%, +10.3\\%, and +4.2\\% respectively, with no additional training required.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.13418", "title": "MERRIN: A Benchmark for Multimodal Evidence Retrieval and Reasoning in Noisy Web Environments", "authors": [ "Han Wang", "David Wan", "Hyunji Lee", "Thinh Pham", "Mikaela Cankosyan", "Weiyuan Chen", "Elias Stengel-Eskin", "Tu Vu", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Motivated by the underspecified, multi-hop nature of search queries and the multimodal, heterogeneous, and often conflicting nature of real-world web results, we introduce MERRIN (Multimodal Evidence Retrieval and Reasoning in Noisy Web Environments), a human-annotated benchmark for evaluating search-augmented agents. MERRIN measures AI agents' ability to identify relevant modalities, retrieve multimodal evidence, and perform multi-hop reasoning over noisy web sources. It differs from prior work in three important aspects: (1) using natural language queries without explicit modality cues, (2) incorporating underexplored modalities such as video and audio, and (3) requiring the retrieval of complex, often noisy or conflicting multimodal evidence during web search. We evaluate diverse search agents powered by ten models, including strong closed-source models (e.g., GPT-5.4-mini, Gemini 3/3.1 Flash/Pro) and open-weight models (Qwen3-4B/30B/235B), across three search settings (no search, native search, and agentic search). Our results show that MERRIN is highly challenging: the average accuracy across all agents is 22.3%, with the best-performing agent reaching only 40.1%. We further observe that while stronger agents like Gemini Deep Research achieve higher performance, gains are modest due to over-exploration; they take more steps and use more tools, but are often distracted by conflicting or partially relevant web content, leading to incorrect answers. Compared to humans, these agents consume more resources yet achieve lower accuracy, largely due to inefficient source selection and an overreliance on text modalities. These findings highlight the need for search agents capable of robust search and reasoning across diverse modalities in noisy web environments, making MERRIN a valuable testbed for evaluating such capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.14147", "title": "ROSE: Retrieval-Oriented Segmentation Enhancement", "authors": [ "Song Tang", "Guangquan Jie", "Henghui Ding", "Yu-Gang Jiang" ], "github": "", "project_page": "", "abstract": "Existing segmentation models based on multimodal large language models (MLLMs), such as LISA, often struggle with novel or emerging entities due to their inability to incorporate up-to-date knowledge. To address this challenge, we introduce the Novel Emerging Segmentation Task (NEST), which focuses on segmenting (i) novel entities that MLLMs fail to recognize due to their absence from training data, and (ii) emerging entities that exist within the model's knowledge but demand up-to-date external information for accurate recognition. To support the study of NEST, we construct a NEST benchmark using an automated pipeline that generates news-related data samples for comprehensive evaluation. Additionally, we propose ROSE: Retrieval-Oriented Segmentation Enhancement, a plug-and-play framework designed to augment any MLLM-based segmentation model. ROSE comprises four key components. First, an Internet Retrieval-Augmented Generation module is introduced to employ user-provided multimodal inputs to retrieve real-time web information. Then, a Textual Prompt Enhancer enriches the model with up-to-date information and rich background knowledge, improving the model's perception ability for emerging entities. Furthermore, a Visual Prompt Enhancer is proposed to compensate for MLLMs' lack of exposure to novel entities by leveraging internet-sourced images. To maintain efficiency, a WebSense module is introduced to intelligently decide when to invoke retrieval mechanisms based on user input. Experimental results demonstrate that ROSE significantly boosts performance on the NEST benchmark, outperforming a strong Gemini-2.0 Flash-based retrieval baseline by 19.2 in gIoU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.13201", "title": "InfiniteScienceGym: An Unbounded, Procedurally-Generated Benchmark for Scientific Analysis", "authors": [ "Oliver Bentham", "Vivek Srikumar" ], "github": "", "project_page": "", "abstract": "Large language models are emerging as scientific assistants, but evaluating their ability to reason from empirical data remains challenging. Benchmarks derived from published studies and human annotations inherit publication bias, known-knowledge bias, label noise, and substantial storage requirements. We present InfiniteScienceGym, a procedurally generated benchmark of scientific repositories paired with a verifiable question-answering task. From a seed, the simulator deterministically generates a self-contained repository with realistic directory structure, files, and tabular data, and a privileged QA generator produces both answerable and unanswerable questions with exact ground truth. This makes it possible to evaluate evidence-grounded reasoning, abstention, and tool-mediated analysis in a controlled setting without distributing a large static corpus. InfiniteScienceGym complements real scientific benchmarks by targeting blind spots and failure modes that are hard to evaluate using published datasets alone. Evaluating both proprietary and open-weight models, we find that none achieve more than 45% accuracy overall, that recognizing unanswerable questions remains a major weakness, and that stronger models tend to use tools more effectively rather than simply consuming more tokens.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.11626", "title": "RationalRewards: Reasoning Rewards Scale Visual Generation Both Training and Test Time", "authors": [ "Haozhe Wang", "Cong Wei", "Weiming Ren", "Jiaming Liu", "Fangzhen Lin", "Wenhu Chen" ], "github": "", "project_page": "", "abstract": "Most reward models for visual generation reduce rich human judgments to a single unexplained score, discarding the reasoning that underlies preference. We show that teaching reward models to produce explicit, multi-dimensional critiques before scoring transforms them from passive evaluators into active optimization tools, improving generators in two complementary ways: at training time, structured rationales provide interpretable, fine-grained rewards for reinforcement learning; at test time, a Generate-Critique-Refine loop turns critiques into targeted prompt revisions that improve outputs without any parameter updates. To train such a reward model without costly rationale annotations, we introduce Preference-Anchored Rationalization (PARROT), a principled framework that recovers high-quality rationales from readily available preference data through anchored generation, consistency filtering, and distillation. The resulting model, RationalRewards (8B), achieves state-of-the-art preference prediction among open-source reward models, competitive with Gemini-2.5-Pro, while using 10-20x less training data than comparable baselines. As an RL reward, it consistently improves text-to-image and image-editing generators beyond scalar alternatives. Most strikingly, its test-time critique-and-refine loop matches or exceeds RL-based fine-tuning on several benchmarks, suggesting that structured reasoning can unlock latent capabilities in existing generators that suboptimal prompts fail to elicit.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.14048", "title": "Free Geometry: Refining 3D Reconstruction from Longer Versions of Itself", "authors": [ "Yuhang Dai", "Xingyi Yang" ], "github": "https://github.com/hiteacherIamhumble/Free-Geometry", "project_page": "", "abstract": "Feed-forward 3D reconstruction models are efficient but rigid: once trained, they perform inference in a zero-shot manner and cannot adapt to the test scene. As a result, visually plausible reconstructions often contain errors, particularly under occlusions, specularities, and ambiguous cues. To address this, we introduce Free Geometry, a framework that enables feed-forward 3D reconstruction models to self-evolve at test time without any 3D ground truth. Our key insight is that, when the model receives more views, it produces more reliable and view-consistent reconstructions. Leveraging this property, given a testing sequence, we mask a subset of frames to construct a self-supervised task. Free Geometry enforces cross-view feature consistency between representations from full and partial observations, while maintaining the pairwise relations implied by the held-out frames. This self-supervision allows for fast recalibration via lightweight LoRA updates, taking less than 2 minutes per dataset on a single GPU. Our approach consistently improves state-of-the-art foundation models, including Depth Anything 3 and VGGT, across 4 benchmark datasets, yielding an average improvement of 3.73% in camera pose accuracy and 2.88% in point map prediction. Code is available at https://github.com/hiteacherIamhumble/Free-Geometry .", "github_manual": "", "github_regex": "https://github.com/hiteacherIamhumble/Free-Geometry", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.14142", "title": "From P(y|x) to P(y): Investigating Reinforcement Learning in Pre-train Space", "authors": [ "Yuqiao Tan", "Minzheng Wang", "Bo Liu", "Zichen Liu", "Tian Liang", "Shizhu He", "Jun Zhao", "Kang Liu" ], "github": "https://github.com/Trae1ounG/Pretrain_Space_RLVR", "project_page": "", "abstract": "While reinforcement learning with verifiable rewards (RLVR) significantly enhances LLM reasoning by optimizing the conditional distribution P(y|x), its potential is fundamentally bounded by the base model's existing output distribution. Optimizing the marginal distribution P(y) in the Pre-train Space addresses this bottleneck by encoding reasoning ability and preserving broad exploration capacity. Yet, conventional pre-training relies on static corpora for passive learning, leading to a distribution shift that hinders targeted reasoning enhancement. In this paper, we introduce PreRL (Pre-train Space RL), which applies reward-driven online updates directly to P(y). We theoretically and empirically validate the strong gradient alignment between log P(y) and log P(y|x), establishing PreRL as a viable surrogate for standard RL. Furthermore, we uncover a critical mechanism: Negative Sample Reinforcement (NSR) within PreRL serves as an exceptionally effective driver for reasoning. NSR-PreRL rapidly prunes incorrect reasoning spaces while stimulating endogenous reflective behaviors, increasing transition and reflection thoughts by 14.89x and 6.54x, respectively. Leveraging these insights, we propose Dual Space RL (DSRL), a Policy Reincarnation strategy that initializes models with NSR-PreRL to expand the reasoning horizon before transitioning to standard RL for fine-grained optimization. Extensive experiments demonstrate that DSRL consistently outperforms strong baselines, proving that pre-train space pruning effectively steers the policy toward a refined correct reasoning subspace.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Trae1ounG/Pretrain_Space_RLVR", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.13151", "title": "Exploration and Exploitation Errors Are Measurable for Language Model Agents", "authors": [ "Jaden Park", "Jungtaek Kim", "Jongwon Jeong", "Robert D. Nowak", "Kangwook Lee", "Yong Jae Lee" ], "github": "https://github.com/jjj-madison/measurable-explore-exploit", "project_page": "", "abstract": "Language Model (LM) agents are increasingly used in complex open-ended decision-making tasks, from AI coding to physical AI. A core requirement in these settings is the ability to both explore the problem space and exploit acquired knowledge effectively. However, systematically distinguishing and quantifying exploration and exploitation from observed actions without access to the agent's internal policy remains challenging. To address this, we design controllable environments inspired by practical embodied AI scenarios. Each environment consists of a partially observable 2D grid map and an unknown task Directed Acyclic Graph (DAG). The map generation can be programmatically adjusted to emphasize exploration or exploitation difficulty. To enable policy-agnostic evaluation, we design a metric to quantify exploration and exploitation errors from agent's actions. We evaluate a variety of frontier LM agents and find that even state-of-the-art models struggle on our task, with different models exhibiting distinct failure modes. We further observe that reasoning models solve the task more effectively and show both exploration and exploitation can be significantly improved through minimal harness engineering. We release our code https://github.com/jjj-madison/measurable-explore-exploit{here}.", "github_manual": "", "github_regex": "https://github.com/jjj-madison/measurable-explore-exploit", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.09409", "title": "Do AI Coding Agents Log Like Humans? An Empirical Study", "authors": [ "Youssef Esseddiq Ouatiti", "Mohammed Sayagh", "Hao Li", "Ahmed E. Hassan" ], "github": "", "project_page": "", "abstract": "Software logging is essential for maintaining and debugging complex systems, yet it remains unclear how AI coding agents handle this non-functional requirement. While prior work characterizes human logging practices, the behaviors of AI coding agents and the efficacy of natural language instructions in governing them are unexplored. To address this gap, we conduct an empirical study of 4,550 agentic pull requests across 81 open-source repositories. We compare agent logging patterns against human baselines and analyze the impact of explicit logging instructions. We find that agents change logging less often than humans in 58.4% of repositories, though they exhibit higher log density when they do. Furthermore, explicit logging instructions are rare (4.7%) and ineffective, as agents fail to comply with constructive requests 67% of the time. Finally, we observe that humans perform 72.5% of post-generation log repairs, acting as \"silent janitors\" who fix logging and observability issues without explicit review feedback. These findings indicate a dual failure in natural language instruction (i.e., scarcity of logging instructions and low agent compliance), suggesting that deterministic guardrails might be necessary to ensure consistent logging practices.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.14004", "title": "Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents", "authors": [ "Kangsan Kim", "Minki Kang", "Taeil Kim", "Yanlai Yang", "Mengye Ren", "Sung Ju Hwang" ], "github": "", "project_page": "", "abstract": "Memory-based self-evolution has emerged as a promising paradigm for coding agents. However, existing approaches typically restrict memory utilization to homogeneous task domains, failing to leverage the shared infrastructural foundations, such as runtime environments and programming languages, that exist across diverse real-world coding problems. To address this limitation, we investigate Memory Transfer Learning (MTL) by harnessing a unified memory pool from heterogeneous domains. We evaluate performance across 6 coding benchmarks using four memory representations, ranging from concrete traces to abstract insights. Our experiments demonstrate that cross-domain memory improves average performance by 3.7\\%, primarily by transferring meta-knowledge, such as validation routines, rather than task-specific code. Importantly, we find that abstraction dictates transferability; high-level insights generalize well, whereas low-level traces often induce negative transfer due to excessive specificity. Furthermore, we show that transfer effectiveness scales with the size of the memory pool, and memory can be transferred even between different models. Our work establishes empirical design principles for expanding memory utilization beyond single-domain silos. Project page: https://memorytransfer.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.11045", "title": "Sema Code: Decoupling AI Coding Agents into Programmable, Embeddable Infrastructure", "authors": [ "Huacan Wang", "Jie Zhou", "Ningyan Zhu", "Shuo Zhang", "Feiyu Chen", "Jiarou Wu", "Ge Chen", "Chen Liu", "Wangyi Chen", "Xiaofeng Mou", "Yi Xu" ], "github": "", "project_page": "", "abstract": "AI coding agents have become central to developer workflows, yet every existing solution locks its reasoning capabilities within a specific delivery form, such as a CLI, IDE plugin, or web application. This limitation creates systemic barriers when enterprises attempt to reuse these capabilities across heterogeneous engineering environments. To address this challenge, we present Sema Code, an open AI coding framework built on the principle of being embeddable, pluggable, and framework-first. Sema Code completely decouples the core agent engine from all client layers, publishing it as a standalone npm library that any runtime can drive programmatically. Built around this architecture, we designed eight key mechanisms: multi-tenant engine isolation, FIFO input queuing with safe session reconstruction, adaptive context compression, multi-agent collaborative scheduling, intelligent Todo-based process management, four-layer asynchronous permission control, three-tier ecosystem integration spanning MCP, Skills, and Plugins, and a background task framework with separated execution and observation privileges. These mechanisms collectively address the engineering challenges of transforming a complex agent engine into a shared, programmable core. Demonstrating its architectural versatility, the same Sema Core engine simultaneously powers a VSCode extension and a multi-channel messaging gateway, which we name SemaClaw, to unify agent interactions across platforms such as Telegram and Feishu. These represent two fundamentally different product forms sharing an identical reasoning kernel, differing only at the client layer.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.07882", "title": "ReconPhys: Reconstruct Appearance and Physical Attributes from Single Video", "authors": [ "Boyuan Wang", "Xiaofeng Wang", "Yongkang Li", "Zheng Zhu", "Yifan Chang", "Angen Ye", "Guosheng Zhao", "Chaojun Ni", "Guan Huang", "Yijie Ren", "Yueqi Duan", "Xingang Wang" ], "github": "", "project_page": "", "abstract": "Reconstructing non-rigid objects with physical plausibility remains a significant challenge. Existing approaches leverage differentiable rendering for per-scene optimization, recovering geometry and dynamics but requiring expensive tuning or manual annotation, which limits practicality and generalizability. To address this, we propose ReconPhys, the first feedforward framework that jointly learns physical attribute estimation and 3D Gaussian Splatting reconstruction from a single monocular video. Our method employs a dual-branch architecture trained via a self-supervised strategy, eliminating the need for ground-truth physics labels. Given a video sequence, ReconPhys simultaneously infers geometry, appearance, and physical attributes. Experiments on a large-scale synthetic dataset demonstrate superior performance: our method achieves 21.64 PSNR in future prediction compared to 13.27 by state-of-the-art optimization baselines, while reducing Chamfer Distance from 0.349 to 0.004. Crucially, ReconPhys enables fast inference (<1 second) versus hours required by existing methods, facilitating rapid generation of simulation-ready assets for robotics and graphics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.11548", "title": "SemaClaw: A Step Towards General-Purpose Personal AI Agents through Harness Engineering", "authors": [ "Ningyan Zhu", "Huacan Wang", "Jie Zhou", "Feiyu Chen", "Shuo Zhang", "Ge Chen", "Chen Liu", "Jiarou Wu", "Wangyi Chen", "Xiaofeng Mou", "Yi Xu" ], "github": "", "project_page": "", "abstract": "The rise of OpenClaw in early 2026 marks the moment when millions of users began deploying personal AI agents into their daily lives, delegating tasks ranging from travel planning to multi-step research. This scale of adoption signals that two parallel arcs of development have reached an inflection point. First is a paradigm shift in AI engineering, evolving from prompt and context engineering to harness engineering-designing the complete infrastructure necessary to transform unconstrained agents into controllable, auditable, and production-reliable systems. As model capabilities converge, this harness layer is becoming the primary site of architectural differentiation. Second is the evolution of human-agent interaction from discrete tasks toward a persistent, contextually aware collaborative relationship, which demands open, trustworthy and extensible harness infrastructure. We present SemaClaw, an open-source multi-agent application framework that addresses these shifts by taking a step towards general-purpose personal AI agents through harness engineering. Our primary contributions include a DAG-based two-phase hybrid agent team orchestration method, a PermissionBridge behavioral safety system, a three-tier context management architecture, and an agentic wiki skill for automated personal knowledge base construction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2507.04227", "title": "Mobile GUI Agents under Real-world Threats: Are We There Yet?", "authors": [ "Guohong Liu", "Jialei Ye", "Jiacheng Liu", "Yuanchun Li", "Wei Liu", "Pengzhi Gao", "Jian Luan", "Yunxin Liu" ], "github": "https://github.com/modelscope/ms-swift", "project_page": "", "abstract": "Recent years have witnessed a rapid development of mobile GUI agents powered by large language models (LLMs), which can autonomously execute diverse device-control tasks based on natural language instructions. The increasing accuracy of these agents on standard benchmarks has raised expectations for large-scale real-world deployment, and there are already several commercial agents released and used by early adopters. However, are we really ready for GUI agents integrated into our daily devices as system building blocks? We argue that an important pre-deployment validation is missing to examine whether the agents can maintain their performance under real-world threats. Specifically, unlike existing common benchmarks that are based on simple static app contents (they have to do so to ensure environment consistency between different tests), real-world apps are filled with contents from untrustworthy third parties, such as advertisement emails, user-generated posts and medias, etc. ... To this end, we introduce a scalable app content instrumentation framework to enable flexible and targeted content modifications within existing applications. Leveraging this framework, we create a test suite comprising both a dynamic task execution environment and a static dataset of challenging GUI states. The dynamic environment encompasses 122 reproducible tasks, and the static dataset consists of over 3,000 scenarios constructed from commercial apps. We perform experiments on both open-source and commercial GUI agents. Our findings reveal that all examined agents can be significantly degraded due to third-party contents, with an average misleading rate of 42.0% and 36.1% in dynamic and static environments respectively. The framework and benchmark has been released at https://agenthazard.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/modelscope/ms-swift", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.06159", "title": "Target Policy Optimization", "authors": [ "Jean Kaddour" ], "github": "https://github.com/JeanKaddour/tpo", "project_page": "", "abstract": "In RL, given a prompt, we sample a group of completions from a model and score them. Two questions follow: which completions should gain probability mass, and how should the parameters move to realize that change? Standard policy-gradient methods answer both at once, so the update can overshoot or undershoot depending on the learning rate, clipping, and other optimizer choices. We introduce Target Policy Optimization (TPO), which separates the two questions. Given scored completions, TPO constructs a target distribution q_i propto p_i^{,old} exp(u_i) and fits the policy to it by cross-entropy. The loss gradient on sampled-completion logits is p^\u03b8- q, which vanishes once the policy matches the target. On tabular bandits, transformer sequence tasks, and billion-parameter LLM RLVR, TPO matches PG, PPO, GRPO, and DG on easy tasks and substantially outperforms them under sparse reward. Code is available at https://github.com/JeanKaddour/tpo.", "github_manual": "", "github_regex": "https://github.com/JeanKaddour/tpo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.03088", "title": "SkVM: Compiling Skills for Efficient Execution Everywhere", "authors": [ "Le Chen", "Erhu Feng", "Yubin Xia", "Haibo Chen" ], "github": "https://github.com/anthropics/skills", "project_page": "", "abstract": "LLM agents increasingly adopt skills as a reusable unit of composition. While skills are shared across diverse agent platforms, current systems treat them as raw context, causing the same skill to behave inconsistently for different agents. This fragility undermines skill portability and execution efficiency. To address this challenge, we analyze 118,000 skills and draw inspiration from traditional compiler design. We treat skills as code and LLMs as heterogeneous processors. To make portability actionable, we decompose a skill's requirements into a set of primitive capabilities, and measure how well each model-harness pair supports them. Based on these capability profiles, we propose SkVM, a compilation and runtime system designed for portable and efficient skill execution. At compile time, SkVM performs capability-based compilation, environment binding, and concurrency extraction. At runtime, SkVM applies JIT code solidification and adaptive recompilation for performance optimization. We evaluate SkVM across eight LLMs of varying scales and three agent harnesses, covering SkillsBench and representative skill tasks. Results demonstrate that SkVM significantly improves task completion rates across different models and environments while reducing token consumption by up to 40%. In terms of performance, SkVM achieves up to 3.2x speedup with enhanced parallelism, and 19-50x latency reduction through code solidification.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/anthropics/skills", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.14141", "title": "Geometric Context Transformer for Streaming 3D Reconstruction", "authors": [ "Lin-Zhuo Chen", "Jian Gao", "Yihang Chen", "Ka Leong Cheng", "Yipengjing Sun", "Liangxiao Hu", "Nan Xue", "Xing Zhu", "Yujun Shen", "Yao Yao", "Yinghao Xu" ], "github": "", "project_page": "", "abstract": "Streaming 3D reconstruction aims to recover 3D information, such as camera poses and point clouds, from a video stream, which necessitates geometric accuracy, temporal consistency, and computational efficiency. Motivated by the principles of Simultaneous Localization and Mapping (SLAM), we introduce LingBot-Map, a feed-forward 3D foundation model for reconstructing scenes from streaming data, built upon a geometric context transformer (GCT) architecture. A defining aspect of LingBot-Map lies in its carefully designed attention mechanism, which integrates an anchor context, a pose-reference window, and a trajectory memory to address coordinate grounding, dense geometric cues, and long-range drift correction, respectively. This design keeps the streaming state compact while retaining rich geometric context, enabling stable efficient inference at around 20 FPS on 518 x 378 resolution inputs over long sequences exceeding 10,000 frames. Extensive evaluations across a variety of benchmarks demonstrate that our approach achieves superior performance compared to both existing streaming and iterative optimization-based approaches.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.11788", "title": "HDR Video Generation via Latent Alignment with Logarithmic Encoding", "authors": [ "Naomi Ken Korem", "Mohamed Oumoumad", "Harel Cain", "Matan Ben Yosef", "Urska Jelercic", "Ofir Bibi", "Yaron Inger", "Or Patashnik", "Daniel Cohen-Or" ], "github": "", "project_page": "", "abstract": "High dynamic range (HDR) imagery offers a rich and faithful representation of scene radiance, but remains challenging for generative models due to its mismatch with the bounded, perceptually compressed data on which these models are trained. A natural solution is to learn new representations for HDR, which introduces additional complexity and data requirements. In this work, we show that HDR generation can be achieved in a much simpler way by leveraging the strong visual priors already captured by pretrained generative models. We observe that a logarithmic encoding widely used in cinematic pipelines maps HDR imagery into a distribution that is naturally aligned with the latent space of these models, enabling direct adaptation via lightweight fine-tuning without retraining an encoder. To recover details that are not directly observable in the input, we further introduce a training strategy based on camera-mimicking degradations that encourages the model to infer missing high dynamic range content from its learned priors. Combining these insights, we demonstrate high-quality HDR video generation using a pretrained video model with minimal adaptation, achieving strong results across diverse scenes and challenging lighting conditions. Our results indicate that HDR, despite representing a fundamentally different image formation regime, can be handled effectively without redesigning generative models, provided that the representation is chosen to align with their learned priors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.11748", "title": "LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling", "authors": [ "Yuxin Chen", "Chumeng Liang", "Hangke Sui", "Ruihan Guo", "Chaoran Cheng", "Jiaxuan You", "Ge Liu" ], "github": "https://github.com/nealchen2003/LangFlow", "project_page": "", "abstract": "Continuous diffusion has been the foundation of high-fidelity, controllable, and few-step generation of many data modalities such as images. However, in language modeling, prior continuous diffusion language models (DLMs) lag behind discrete counterparts due to the sparse data space and the underexplored design space. In this work, we close this gap with LangFlow, the first continuous DLM to rival discrete diffusion, by connecting embedding-space DLMs to Flow Matching via Bregman divergence, alongside three key innovations: (1) we derive a novel ODE-based NLL bound for principled evaluation of continuous flow-based language models; (2) we propose an information-uniform principle for setting the noise schedule, which motivates a learnable noise scheduler based on a Gumbel distribution; and (3) we revise prior training protocols by incorporating self-conditioning, as we find it improves both likelihood and sample quality of embedding-space DLMs with effects substantially different from discrete diffusion. Putting everything together, LangFlow rivals top discrete DLMs on both the perplexity (PPL) and the generative perplexity (Gen. PPL), reaching a PPL of 30.0 on LM1B and 24.6 on OpenWebText. It even exceeds autoregressive baselines in zero-shot transfer on 4 out of 7 benchmarks. LangFlow provides the first clear evidence that continuous diffusion is a promising paradigm for language modeling. Homepage: https://github.com/nealchen2003/LangFlow", "github_manual": "", "github_regex": "https://github.com/nealchen2003/LangFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.11969", "title": "Narrative-Driven Paper-to-Slide Generation via ArcDeck", "authors": [ "Tarik Can Ozden", "Sachidanand VS", "Furkan Horoz", "Ozgur Kara", "Junho Kim", "James Matthew Rehg" ], "github": "", "project_page": "", "abstract": "We introduce ArcDeck, a multi-agent framework that formulates paper-to-slide generation as a structured narrative reconstruction task. Unlike existing methods that directly summarize raw text into slides, ArcDeck explicitly models the source paper's logical flow. It first parses the input to construct a discourse tree and establish a global commitment document, ensuring the high-level intent is preserved. These structural priors then guide an iterative multi-agent refinement process, where specialized agents iteratively critique and revise the presentation outline before rendering the final visual layouts and designs. To evaluate our approach, we also introduce ArcBench, a newly curated benchmark of academic paper-slide pairs. Experimental results demonstrate that explicit discourse modeling, combined with role-specific agent coordination, significantly improves the narrative flow and logical coherence of the generated presentations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.12002", "title": "Self-Distillation Zero: Self-Revision Turns Binary Rewards into Dense Supervision", "authors": [ "Yinghui He", "Simran Kaur", "Adithya Bhaskar", "Yongjin Yang", "Jiarui Liu", "Narutatsu Ri", "Liam Fowl", "Abhishek Panigrahi", "Danqi Chen", "Sanjeev Arora" ], "github": "", "project_page": "", "abstract": "Current post-training methods in verifiable settings fall into two categories. Reinforcement learning (RLVR) relies on binary rewards, which are broadly applicable and powerful, but provide only sparse supervision during training. Distillation provides dense token-level supervision, typically obtained from an external teacher or using high-quality demonstrations. Collecting such supervision can be costly or unavailable. We propose Self-Distillation Zero (SD-Zero), a method that is substantially more training sample-efficient than RL and does not require an external teacher or high-quality demonstrations. SD-Zero trains a single model to play two roles: a Generator, which produces an initial response, and a Reviser, which conditions on that response and its binary reward to produce an improved response. We then perform on-policy self-distillation to distill the reviser into the generator, using the reviser's token distributions conditioned on the generator's response and its reward as supervision. In effect, SD-Zero trains the model to transform binary rewards into dense token-level self-supervision. On math and code reasoning benchmarks with Qwen3-4B-Instruct and Olmo-3-7B-Instruct, SD-Zero improves performance by at least 10% over the base models and outperforms strong baselines, including Rejection Fine-Tuning (RFT), GRPO, and Self-Distillation Fine-Tuning (SDFT), under the same question set and training sample budget. Extensive ablation studies show two novel characteristics of our proposed algorithm: (a) token-level self-localization, where the reviser can identify the key tokens that need to be revised in the generator's response based on reward, and (b) iterative self-evolution, where the improving ability to revise answers can be distilled back into generation performance with regular teacher synchronization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.11490", "title": "Anthropogenic Regional Adaptation in Multimodal Vision-Language Model", "authors": [ "Samuel Cahyawijaya", "Peerat Limkonchotiwat", "Tack Hwa Wong", "Hitesh Laxmichand Patel", "Amit Agarwal", "Manuel Antonio Rufino", "Carlos Rafael Catalan", "Muhammad Reza Qorib", "Vicky Feliren", "Holy Lovenia", "Aye Hninn Khine", "Frederikus Hudi", "David Anugraha", "Alham Fikri Aji", "Romrawin Chumpu", "Viet-Thanh Pham", "Minghan Wang", "Mohamed Fazli Imam", "Ruochen Zhang", "Joseph Marvin Imperial", "Do Xuan Long", "Musa Izzanardi Wijanarko", "Joel Ruben Antony Moniz", "Patrick Amadeus Irawan", "Hanif Muhammad Zhafran", "Isaiah Flores", "Ira Salsabila", "Jun Kevin", "Jostin Jerico Rosal", "Patricia Nicole Monderin", "Kun Kerdthaisong", "Ahmad Mustafid", "My Chiffon Nguyen", "Natchapon Jongwiriyanurak", "Siva Worajitwannakul", "Haochen Li", "Adrian Xuan Wei Lim", "Bin Wang", "Muhammad Ravi Shulthan Habibi", "Lynnette Hui Xian Ng", "Mithil Bangera", "Yeshil Bangera", "Priyaranjan Pattnayak", "Dun Li Chan", "Sherissa Caren Djuniwar", "Hee Ming Shan" ], "github": "", "project_page": "", "abstract": "While the field of vision-language (VL) has achieved remarkable success in integrating visual and textual information across multiple languages and domains, there is still no dedicated framework for assessing human-centric alignment in vision-language systems. We offer two contributions to address this gap. First, we introduce Anthropogenic Regional Adaptation: a novel paradigm that aims to optimize model relevance to specific regional contexts while ensuring the retention of global generalization capabilities. Second, we present a simple, but effective adaptation method named Geographical-generalization-made-easy (GG-EZ), which utilizes regional data filtering and model merging. Through comprehensive experiments on 3 VL architectures: large vision-language models, text-to-image diffusion models, and vision-language embedding models, and a case study in Southeast Asia (SEA) regional adaptation, we demonstrate the importance of Anthropogenic Regional Adaptation and the effectiveness of GG-EZ, showing 5-15% gains in cultural relevance metrics across SEA while maintaining over 98% of global performance and even occasionally surpassing it. Our findings establish Anthropogenic Regional Alignment as a foundational paradigm towards applicability of multimodal vision-language models in diverse regions and demonstrate a simple-yet-effective baseline method that optimizes regional value alignment while preserving global generalization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.08510", "title": "What do Language Models Learn and When? The Implicit Curriculum Hypothesis", "authors": [ "Emmy Liu", "Kaiser Sun", "Millicent Li", "Isabelle Lee", "Lindia Tjuatja", "Jen-tse Huang", "Graham Neubig" ], "github": "https://github.com/KaiserWhoLearns/ElementalTask", "project_page": "", "abstract": "Large language models (LLMs) can perform remarkably complex tasks, yet the fine-grained details of how these capabilities emerge during pretraining remain poorly understood. Scaling laws on validation loss tell us how much a model improves with additional compute, but not what skills it acquires in which order. To remedy this, we propose the Implicit Curriculum Hypothesis: pretraining follows a compositional and predictable curriculum across models and data mixtures. We test this by designing a suite of simple, composable tasks spanning retrieval, morphological transformations, coreference, logical reasoning, and mathematics. Using these tasks, we track emergence points across four model families spanning sizes from 410M-13B parameters. We find that emergence orderings of when models reach fixed accuracy thresholds are strikingly consistent (\u03c1= .81 across 45 model pairs), and that composite tasks most often emerge after their component tasks. Furthermore, we find that this structure is encoded in model representations: tasks with similar function vector representations also tend to follow similar trajectories in training. By using the space of representations derived from our task set, we can effectively predict the training trajectories of simple held-out compositional tasks throughout the course of pretraining (R^2 = .68-.84 across models) without previously evaluating them. Together, these results suggest that pretraining is more structured than loss curves reveal: skills emerge in a compositional order that is consistent across models and readable from their internals.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/KaiserWhoLearns/ElementalTask", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.12988", "title": "ROSE: An Intent-Centered Evaluation Metric for NL2SQL", "authors": [ "Wenqi Pei", "Shizheng Hou", "Boyan Li", "Han Chen", "Zhichao Shi", "Yuyu Luo" ], "github": "", "project_page": "", "abstract": "Execution Accuracy (EX), the widely used metric for evaluating the effectiveness of Natural Language to SQL (NL2SQL) solutions, is becoming increasingly unreliable. It is sensitive to syntactic variation, ignores that questions may admit multiple interpretations, and is easily misled by erroneous ground-truth SQL. To address this, we introduce ROSE, an intent-centered metric that focuses on whether the predicted SQL answers the question, rather than consistency with the ground-truth SQL under the reference-dependent paradigm. ROSE employs an adversarial Prover-Refuter cascade: SQL Prover assesses the semantic correctness of a predicted SQL against the user's intent independently, while Adversarial Refuter uses the ground-truth SQL as evidence to challenge and refine this judgment. On our expert-aligned validation set ROSE-VEC, ROSE achieves the best agreement with human experts, outperforming the next-best metric by nearly 24% in Cohen's Kappa. We also conduct a largescale re-evaluation of 19 NL2SQL methods, revealing four valuable insights. We release ROSE and ROSE-VEC to facilitate more reliable NL2SQL research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.08551", "title": "Self-Sovereign Agent", "authors": [ "Wenjie Qu", "Xuandong Zhao", "Jiaheng Zhang", "Dawn Song" ], "github": "", "project_page": "", "abstract": "We investigate the emerging prospect of self-sovereign agents -- AI systems that can economically sustain and extend their own operation without human involvement. Recent advances in large language models and agent frameworks have substantially expanded agents' practical capabilities, pointing toward a potential shift from developer-controlled tools to more autonomous digital actors. We analyze the remaining technical barriers to such deployments and discuss the security, societal, and governance challenges that could arise if such systems become practically viable. A project page is available at: https://self-sovereign-agent.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-16", "arxiv_id": "2604.10149", "title": "A Temporally Augmented Graph Attention Network for Affordance Classification", "authors": [ "Ami Chopra", "Supriya Bordoloi", "Shyamanta M. Hazarika" ], "github": "", "project_page": "", "abstract": "Graph attention networks (GATs) provide one of the best frameworks for learning node representations in relational data; but, existing variants such as Graph Attention Network (GAT) mainly operate on static graphs and rely on implicit temporal aggregation when applied to sequential data. In this paper, we introduce Electroencephalography-temporal Graph Attention Network (EEG-tGAT), a temporally augmented formulation of GATv2 that is tailored for affordance classification from interaction sequences. The proposed model incorporates temporal attention to modulate the contribution of different time segments and temporal dropout to regularize learning across temporally correlated observations. The design reflects the assumption that temporal dimensions in affordance data are not semantically uniform and that discriminative information may be unevenly distributed across time. Experimental results on affordance datasets show that EEG-tGAT achieves improved classification performance compared to GATv2. The observed gains helps to conclude that explicitly encoding temporal importance and enforcing temporal robustness introduce inductive biases that are much better aligned with the structure of affordance-driven interaction data. These findings show us that modest architectural changes to graph attention models can help one obtain consistent benefits when temporal relationships play a nontrivial role in the task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2509.25843", "title": "ASGuard: Activation-Scaling Guard to Mitigate Targeted Jailbreaking Attack", "authors": [ "Yein Park", "Jungwoo Park", "Jaewoo Kang" ], "github": "https://github.com/dmis-lab/ASGuard", "project_page": "", "abstract": "Large language models (LLMs), despite being safety-aligned, exhibit brittle refusal behaviors that can be circumvented by simple linguistic changes. As tense jailbreaking demonstrates that models refusing harmful requests often comply when rephrased in past tense, a critical generalization gap is revealed in current alignment methods whose underlying mechanisms are poorly understood. In this work, we introduce Activation-Scaling Guard (ASGuard), an insightful, mechanistically-informed framework that surgically mitigates this specific vulnerability. In the first step, we use circuit analysis to identify the specific attention heads causally linked to the targeted jailbreaking such as a tense-changing attack. Second, we train a precise, channel-wise scaling vector to recalibrate the activation of tense vulnerable heads. Lastly, we apply it into a \"preventative fine-tuning\", forcing the model to learn a more robust refusal mechanism. Across four LLMs, ASGuard effectively reduces the attack success rate of targeted jailbreaking while preserving general capabilities and minimizing over refusal, achieving a Pareto-optimal balance between safety and utility. Our findings underscore how adversarial suffixes suppress the propagation of the refusal-mediating direction, based on mechanistic analysis. Furthermore, our work showcases how a deep understanding of model internals can be leveraged to develop practical, efficient, and targeted methods for adjusting model behavior, charting a course for more reliable and interpretable AI safety.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/dmis-lab/ASGuard", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14268", "title": "HY-World 2.0: A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds", "authors": [ "Team HY-World", "Chenjie Cao", "Xuhui Zuo", "Zhenwei Wang", "Yisu Zhang", "Junta Wu", "Zhenyang Liu", "Yuning Gong", "Yang Liu", "Bo Yuan", "Chao Zhang", "Coopers Li", "Dongyuan Guo", "Fan Yang", "Haiyu Zhang", "Hang Cao", "Jianchen Zhu", "Jiaxin Lin", "Jie Xiao", "Jihong Zhang", "Junlin Yu", "Lei Wang", "Lifu Wang", "Lilin Wang", "Linus", "Minghui Chen", "Peng He", "Penghao Zhao", "Qi Chen", "Rui Chen", "Rui Shao", "Sicong Liu", "Wangchen Qin", "Xiaochuan Niu", "Xiang Yuan", "Yi Sun", "Yifei Tang", "Yifu Sun", "Yihang Lian", "Yonghao Tan", "Yuhong Liu", "Yuyang Yin", "Zhiyuan Min", "Tengfei Wang", "Chunchao Guo" ], "github": "https://github.com/recastnavigation/recastnavigation", "project_page": "", "abstract": "We introduce HY-World 2.0, a multi-modal world model framework that advances our prior project HY-World 1.0. HY-World 2.0 accommodates diverse input modalities, including text prompts, single-view images, multi-view images, and videos, and produces 3D world representations. With text or single-view image inputs, the model performs world generation, synthesizing high-fidelity, navigable 3D Gaussian Splatting (3DGS) scenes. This is achieved through a four-stage method: a) Panorama Generation with HY-Pano 2.0, b) Trajectory Planning with WorldNav, c) World Expansion with WorldStereo 2.0, and d) World Composition with WorldMirror 2.0. Specifically, we introduce key innovations to enhance panorama fidelity, enable 3D scene understanding and planning, and upgrade WorldStereo, our keyframe-based view generation model with consistent memory. We also upgrade WorldMirror, a feed-forward model for universal 3D prediction, by refining model architecture and learning strategy, enabling world reconstruction from multi-view images or videos. Also, we introduce WorldLens, a high-performance 3DGS rendering platform featuring a flexible engine-agnostic architecture, automatic IBL lighting, efficient collision detection, and training-rendering co-design, enabling interactive exploration of 3D worlds with character support. Extensive experiments demonstrate that HY-World 2.0 achieves state-of-the-art performance on several benchmarks among open-source approaches, delivering results comparable to the closed-source model Marble. We release all model weights, code, and technical details to facilitate reproducibility and support further research on 3D world models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/recastnavigation/recastnavigation", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.15308", "title": "RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework", "authors": [ "Hao Gao", "Shaoyu Chen", "Yifan Zhu", "Yuehao Song", "Wenyu Liu", "Qian Zhang", "Xinggang Wang" ], "github": "", "project_page": "", "abstract": "High-level autonomous driving requires motion planners capable of modeling multimodal future uncertainties while remaining robust in closed-loop interactions. Although diffusion-based planners are effective at modeling complex trajectory distributions, they often suffer from stochastic instabilities and the lack of corrective negative feedback when trained purely with imitation learning. To address these issues, we propose RAD-2, a unified generator-discriminator framework for closed-loop planning. Specifically, a diffusion-based generator is used to produce diverse trajectory candidates, while an RL-optimized discriminator reranks these candidates according to their long-term driving quality. This decoupled design avoids directly applying sparse scalar rewards to the full high-dimensional trajectory space, thereby improving optimization stability. To further enhance reinforcement learning, we introduce Temporally Consistent Group Relative Policy Optimization, which exploits temporal coherence to alleviate the credit assignment problem. In addition, we propose On-policy Generator Optimization, which converts closed-loop feedback into structured longitudinal optimization signals and progressively shifts the generator toward high-reward trajectory manifolds. To support efficient large-scale training, we introduce BEV-Warp, a high-throughput simulation environment that performs closed-loop evaluation directly in Bird's-Eye View feature space via spatial warping. RAD-2 reduces the collision rate by 56% compared with strong diffusion-based planners. Real-world deployment further demonstrates improved perceived safety and driving smoothness in complex urban traffic.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14922", "title": "LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning", "authors": [ "Bowen Ping", "Zijun Chen", "Tingfeng Hui", "Qize Yu", "Chenxuan Li", "Junchi Yan", "Baobao Chang" ], "github": "https://github.com/hiyouga/LLaMA-Factory", "project_page": "", "abstract": "Reinforcement Learning (RL) has emerged as a critical driver for enhancing the reasoning capabilities of Large Language Models (LLMs). While recent advancements have focused on reward engineering or data synthesis, few studies exploit the model's intrinsic representation characteristics to guide the training process. In this paper, we first observe the presence of high-magnitude activations within the query and key vectors when processing long contexts. Drawing inspiration from model quantization -- which establishes the criticality of such high-magnitude activations -- and the insight that long-context reasoning inherently exhibits a sparse structure, we hypothesize that these weights serve as the pivotal drivers for effective model optimization. Based on this insight, we propose LongAct, a strategy that shifts from uniform to saliency-guided sparse updates. By selectively updating only the weights associated with these significant activations, LongAct achieves an approximate 8% improvement on LongBench v2 and enhances generalization on the RULER benchmark. Furthermore, our method exhibits remarkable universality, consistently boosting performance across diverse RL algorithms such as GRPO and DAPO. Extensive ablation studies suggest that focusing on these salient features is key to unlocking long-context potential.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/hiyouga/LLaMA-Factory", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14967", "title": "UniDoc-RL: Coarse-to-Fine Visual RAG with Hierarchical Actions and Dense Rewards", "authors": [ "Jun Wang", "Shuo Tan", "Zelong Sun", "Tiancheng Gu", "Yongle Zhao", "Ziyong Feng", "Kaicheng Yang", "Cewu Lu" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) extends Large Vision-Language Models (LVLMs) with external visual knowledge. However, existing visual RAG systems typically rely on generic retrieval signals that overlook the fine-grained visual semantics essential for complex reasoning. To address this limitation, we propose UniDoc-RL, a unified reinforcement learning framework in which an LVLM agent jointly performs retrieval, reranking, active visual perception, and reasoning. UniDoc-RL formulates visual information acquisition as a sequential decision-making problem with a hierarchical action space. Specifically, it progressively refines visual evidence from coarse-grained document retrieval to fine-grained image selection and active region cropping, allowing the model to suppress irrelevant content and attend to information-dense regions. For effective end-to-end training, we introduce a dense multi-reward scheme that provides task-aware supervision for each action. Based on Group Relative Policy Optimization (GRPO), UniDoc-RL aligns agent behavior with multiple objectives without relying on a separate value network. To support this training paradigm, we curate a comprehensive dataset of high-quality reasoning trajectories with fine-grained action annotations. Experiments on three benchmarks demonstrate that UniDoc-RL consistently surpasses state-of-the-art baselines, yielding up to 17.7% gains over prior RL-based methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.13618", "title": "C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences", "authors": [ "Akira Kawabata", "Saku Sugawara" ], "github": "https://github.com/asahi-research/C2", "project_page": "", "abstract": "Rubric-augmented verification guides reward models with explicit evaluation criteria, yielding more reliable judgments than single-model verification. However, most existing methods require costly rubric annotations, limiting scalability. Moreover, we find that rubric generation is vulnerable to a failure of cooperation; low-quality rubrics actively mislead reward models rather than help. Inspired by the principle of cooperative communication, we propose Cooperative yet Critical reward modeling (C2), a framework that significantly improves reward model judgments by having the reward model critically collaborate with a rubric generator trained solely from binary preferences. In C2, we synthesize helpful and misleading rubric pairs by measuring how each rubric shifts the reward model toward or away from the correct preference. Using these contrastive pairs, we train a cooperative rubric generator to propose helpful rubrics, and a critical verifier to assess rubric validity before making its judgment, following only rubrics it deems helpful at inference time. C2 outperforms reasoning reward models trained on the same binary preferences, with gains of up to 6.5 points on RM-Bench and 6.0 points length-controlled win rate on AlpacaEval 2.0. Without external rubric annotations, C2 enables an 8B reward model to match performance achieved with rubrics from a 4times larger model. Overall, our work demonstrates that eliciting deliberate cooperation in rubric-augmented verification makes reward models more trustworthy in a scalable way.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/asahi-research/C2", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.07466", "title": "Cross-Tokenizer LLM Distillation through a Byte-Level Interface", "authors": [ "Avyav Kumar Singh", "Yen-Chen Wu", "Alexandru Cioba", "Alberto Bernacchia", "Davide Buffelli" ], "github": "", "project_page": "", "abstract": "Cross-tokenizer distillation (CTD), the transfer of knowledge from a teacher to a student language model when the two use different tokenizers, remains a largely unsolved problem. Existing approaches rely on heuristic strategies to align mismatched vocabularies, introducing considerable complexity. In this paper, we propose a simple but effective baseline called Byte-Level Distillation (BLD) which enables CTD by operating at a common interface across tokenizers: the byte level. In more detail, we convert the teacher's output distribution to byte-level probabilities, attach a lightweight byte-level decoder head to the student, and distill through this shared byte-level interface. Despite its simplicity, BLD performs competitively with--and on several benchmarks surpasses--significantly more sophisticated CTD methods, across a range of distillation tasks with models from 1B to 8B parameters. Our results suggest that the byte level is a natural common ground for cross-tokenizer knowledge transfer, while also highlighting that consistent improvements across all tasks and benchmarks remain elusive, underscoring that CTD is still an open problem.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.15311", "title": "LeapAlign: Post-Training Flow Matching Models at Any Generation Step by Building Two-Step Trajectories", "authors": [ "Zhanhao Liang", "Tao Yang", "Jie Wu", "Chengjian Feng", "Liang Zheng" ], "github": "", "project_page": "", "abstract": "This paper focuses on the alignment of flow matching models with human preferences. A promising way is fine-tuning by directly backpropagating reward gradients through the differentiable generation process of flow matching. However, backpropagating through long trajectories results in prohibitive memory costs and gradient explosion. Therefore, direct-gradient methods struggle to update early generation steps, which are crucial for determining the global structure of the final image. To address this issue, we introduce LeapAlign, a fine-tuning method that reduces computational cost and enables direct gradient propagation from reward to early generation steps. Specifically, we shorten the long trajectory into only two steps by designing two consecutive leaps, each skipping multiple ODE sampling steps and predicting future latents in a single step. By randomizing the start and end timesteps of the leaps, LeapAlign leads to efficient and stable model updates at any generation step. To better use such shortened trajectories, we assign higher training weights to those that are more consistent with the long generation path. To further enhance gradient stability, we reduce the weights of gradient terms with large magnitude, instead of completely removing them as done in previous works. When fine-tuning the Flux model, LeapAlign consistently outperforms state-of-the-art GRPO-based and direct-gradient methods across various metrics, achieving superior image quality and image-text alignment.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14683", "title": "DR^{3}-Eval: Towards Realistic and Reproducible Deep Research Evaluation", "authors": [ "Qianqian Xie", "Qingheng Xiong", "He Zhu", "Tiantian Xia", "Xueming Han", "Fanyu Meng", "Jiakai Wang", "Zhiqi Bai", "Chengkang Jiang", "Zhaohui Wang", "Yubin Guo", "Yuqing Wen", "Jiayang Mao", "Zijie Zhang", "Shihao Li", "Yanghai Wang", "Yuxiang Ren", "Junlan Feng", "Jiaheng Liu" ], "github": "https://github.com/bytedance/deer-flow", "project_page": "", "abstract": "Deep Research Agents (DRAs) aim to solve complex, long-horizon research tasks involving planning, retrieval, multimodal understanding, and report generation, yet their evaluation remains challenging due to dynamic web environments and ambiguous task definitions. We propose DR^{3}-Eval, a realistic and reproducible benchmark for evaluating deep research agents on multimodal, multi-file report generation. DR^{3}-Eval is constructed from authentic user-provided materials and paired with a per-task static research sandbox corpus that simulates open-web complexity while remaining fully verifiable, containing supportive documents, distractors, and noise. Moreover, we introduce a multi-dimensional evaluation framework measuring Information Recall, Factual Accuracy, Citation Coverage, Instruction Following, and Depth Quality, and validate its alignment with human judgments. Experiments with our developed multi-agent system DR^{3}-Agent based on multiple state-of-the-art language models demonstrate that DR^{3}-Eval is highly challenging and reveals critical failure modes in retrieval robustness and hallucination control. Our code and data are publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bytedance/deer-flow", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.15309", "title": "MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation", "authors": [ "Yan Li", "Zezi Zeng", "Yifan Yang", "Yuqing Yang", "Ning Liao", "Weiwei Guo", "Lili Qiu", "Mingxi Cheng", "Qi Dai", "Zhendong Wang", "Zhengyuan Yang", "Xue Yang", "Ji Li", "Lijuan Wang", "Chong Luo" ], "github": "", "project_page": "", "abstract": "The rapid progress of Artificial Intelligence Generated Content (AIGC) tools enables images, videos, and visualizations to be created on demand for webpage design, offering a flexible and increasingly adopted paradigm for modern UI/UX. However, directly integrating such tools into automated webpage generation often leads to style inconsistency and poor global coherence, as elements are generated in isolation. We propose MM-WebAgent, a hierarchical agentic framework for multimodal webpage generation that coordinates AIGC-based element generation through hierarchical planning and iterative self-reflection. MM-WebAgent jointly optimizes global layout, local multimodal content, and their integration, producing coherent and visually consistent webpages. We further introduce a benchmark for multimodal webpage generation and a multi-level evaluation protocol for systematic assessment. Experiments demonstrate that MM-WebAgent outperforms code-generation and agent-based baselines, especially on multimodal element generation and integration. Code & Data: https://aka.ms/mm-webagent.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14228", "title": "Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems", "authors": [ "Jiacheng Liu", "Xiaohan Zhao", "Xinyi Shang", "Zhiqiang Shen" ], "github": "https://github.com/VILA-Lab/Dive-into-Claude-Code", "project_page": "", "abstract": "Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user. This study describes its comprehensive architecture by analyzing the publicly available TypeScript source code and further comparing it with OpenClaw, an independent open-source AI agent system that answers many of the same design questions from a different deployment context. Our analysis identifies five human values, philosophies, and needs that motivate the architecture (human decision authority, safety and security, reliable execution, capability amplification, and contextual adaptability) and traces them through thirteen design principles to specific implementation choices. The core of the system is a simple while-loop that calls the model, runs tools, and repeats. Most of the code, however, lives in the systems around this loop: a permission system with seven modes and an ML-based classifier, a five-layer compaction pipeline for context management, four extensibility mechanisms (MCP, plugins, skills, and hooks), a subagent delegation mechanism with worktree isolation, and append-oriented session storage. A comparison with OpenClaw, a multi-channel personal assistant gateway, shows that the same recurring design questions produce different architectural answers when the deployment context changes: from per-action safety classification to perimeter-level access control, from a single CLI loop to an embedded runtime within a gateway control plane, and from context-window extensions to gateway-wide capability registration. We finally identify six open design directions for future agent systems, grounded in recent empirical, architectural, and policy literature.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/VILA-Lab/Dive-into-Claude-Code", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.13226", "title": "KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs", "authors": [ "Chuangtao Chen", "Grace Li Zhang", "Xunzhao Yin", "Cheng Zhuo", "Bing Li", "Ulf Schlichtmann" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: reusing a cached document in a new context requires recomputing KV states to account for shifts in attention distribution. Existing solutions such as CacheBlend, EPIC, and SAM-KV mitigate this issue by selectively recomputing a subset of tokens; however, they still incur non-negligible computational overhead (FLOPs) and increased Time-to-First-Token (TTFT) latency. In this paper, we propose KV Packet, a recomputation-free cache reuse framework that treats cached documents as immutable ``packets'' wrapped in light-weight trainable soft-token adapters, which are trained via self-supervised distillation to bridge context discontinuities. Experiments on Llama-3.1 and Qwen2.5 demonstrate that the proposed KV Packet method achieves near-zero FLOPs and lower TTFT than recomputation-based baselines, while retaining F1 scores comparable to those of the full recomputation baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14125", "title": "HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System", "authors": [ "Tianshuo Yang", "Guanyu Chen", "Yutian Chen", "Zhixuan Liang", "Yitian Liu", "Zanxin Chen", "Chunpu Xu", "Haotian Liang", "Jiangmiao Pang", "Yao Mu", "Ping Luo" ], "github": "", "project_page": "", "abstract": "While end-to-end Vision-Language-Action (VLA) models offer a promising paradigm for robotic manipulation, fine-tuning them on narrow control data often compromises the profound reasoning capabilities inherited from their base Vision-Language Models (VLMs). To resolve this fundamental trade-off, we propose HiVLA, a visual-grounded-centric hierarchical framework that explicitly decouples high-level semantic planning from low-level motor control. In high-level part, a VLM planner first performs task decomposition and visual grounding to generate structured plans, comprising a subtask instruction and a precise target bounding box. Then, to translate this plan into physical actions, we introduce a flow-matching Diffusion Transformer (DiT) action expert in low-level part equipped with a novel cascaded cross-attention mechanism. This design sequentially fuses global context, high-resolution object-centric crops and skill semantics, enabling the DiT to focus purely on robust execution. Our decoupled architecture preserves the VLM's zero-shot reasoning while allowing independent improvement of both components. Extensive experiments in simulation and the real world demonstrate that HiVLA significantly outperforms state-of-the-art end-to-end baselines, particularly excelling in long-horizon skill composition and the fine-grained manipulation of small objects in cluttered scenes.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14164", "title": "How to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Data", "authors": [ "Zixian Huang", "Kaichen Yang", "Xu Huang", "Feiyang Hao", "Qiming Ge", "Bowen Li", "He Du", "Kai Chen", "Qipeng Guo" ], "github": "", "project_page": "", "abstract": "A widely adopted strategy for model enhancement is to use synthetic data generated by a stronger model for supervised fine-tuning (SFT). However, for emerging reasoning models like Qwen3-8B, this approach often fails to improve reasoning capabilities and can even lead to a substantial drop in performance. In this work, we identify substantial stylistic divergence between teacher generated data and the distribution of student as a major factor impacting SFT. To bridge this gap, we propose a Teacher-Student Cooperation Data Synthesis framework (TESSY), which interleaves teacher and student models to alternately generate style and non-style tokens. Consequently, TESSY produces synthetic sequences that inherit the advanced reasoning capabilities of the teacher while maintaining stylistic consistency with the distribution of the student. In experiments on code generation using GPT-OSS-120B as the teacher, fine-tuning Qwen3-8B on teacher-generated data leads to performance drops of 3.25% on LiveCodeBench-Pro and 10.02% on OJBench, whereas TESSY achieves improvements of 11.25% and 6.68%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.15284", "title": "GlobalSplat: Efficient Feed-Forward 3D Gaussian Splatting via Global Scene Tokens", "authors": [ "Roni Itkin", "Noam Issachar", "Yehonatan Keypur", "Yehonatan Keypur", "Anpei Chen", "Sagie Benaim" ], "github": "", "project_page": "", "abstract": "The efficient spatial allocation of primitives serves as the foundation of 3D Gaussian Splatting, as it directly dictates the synergy between representation compactness, reconstruction speed, and rendering fidelity. Previous solutions, whether based on iterative optimization or feed-forward inference, suffer from significant trade-offs between these goals, mainly due to the reliance on local, heuristic-driven allocation strategies that lack global scene awareness. Specifically, current feed-forward methods are largely pixel-aligned or voxel-aligned. By unprojecting pixels into dense, view-aligned primitives, they bake redundancy into the 3D asset. As more input views are added, the representation size increases and global consistency becomes fragile. To this end, we introduce GlobalSplat, a framework built on the principle of align first, decode later. Our approach learns a compact, global, latent scene representation that encodes multi-view input and resolves cross-view correspondences before decoding any explicit 3D geometry. Crucially, this formulation enables compact, globally consistent reconstructions without relying on pretrained pixel-prediction backbones or reusing latent features from dense baselines. Utilizing a coarse-to-fine training curriculum that gradually increases decoded capacity, GlobalSplat natively prevents representation bloat. On RealEstate10K and ACID, our model achieves competitive novel-view synthesis performance while utilizing as few as 16K Gaussians, significantly less than required by dense pipelines, obtaining a light 4MB footprint. Further, GlobalSplat enables significantly faster inference than the baselines, operating under 78 milliseconds in a single forward pass. Project page is available at https://r-itk.github.io/globalsplat/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.04514", "title": "SuperLocalMemory V3.3: The Living Brain -- Biologically-Inspired Forgetting, Cognitive Quantization, and Multi-Channel Retrieval for Zero-LLM Agent Memory Systems", "authors": [ "Varun Pratap Bhardwaj" ], "github": "", "project_page": "", "abstract": "AI coding agents operate in a paradox: they possess vast parametric knowledge yet cannot remember a conversation from an hour ago. Existing memory systems store text in vector databases with single-channel retrieval, require cloud LLMs for core operations, and implement none of the cognitive processes that make human memory effective. We present SuperLocalMemory V3.3 (\"The Living Brain\"), a local-first agent memory system implementing the full cognitive memory taxonomy with mathematical lifecycle dynamics. Building on the information-geometric foundations of V3.2 (arXiv:2603.14588), we introduce five contributions: (1) Fisher-Rao Quantization-Aware Distance (FRQAD) -- a new metric on the Gaussian statistical manifold achieving 100% precision at preferring high-fidelity embeddings over quantized ones (vs 85.6% for cosine), with zero prior art; (2) Ebbinghaus Adaptive Forgetting with lifecycle-aware quantization -- the first mathematical forgetting curve in local agent memory coupled to progressive embedding compression, achieving 6.7x discriminative power; (3) 7-channel cognitive retrieval spanning semantic, keyword, entity graph, temporal, spreading activation, consolidation, and Hopfield associative channels, achieving 70.4% on LoCoMo in zero-LLM Mode A; (4) memory parameterization implementing Long-Term Implicit memory via soft prompts; (5) zero-friction auto-cognitive pipeline automating the complete memory lifecycle. On LoCoMo, V3.3 achieves 70.4% in Mode A (zero-LLM), with +23.8pp on multi-hop and +12.7pp on adversarial. V3.2 achieved 74.8% Mode A and 87.7% Mode C; the 4.4pp gap reflects a deliberate architectural trade-off. SLM V3.3 is open source under the Elastic License 2.0, runs entirely on CPU, with over 5,000 monthly downloads.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2603.27844", "title": "Model Capability Dominates: Inference-Time Optimization Lessons from AIMO 3", "authors": [ "Natapong Nitarach" ], "github": "", "project_page": "", "abstract": "Majority voting over multiple LLM attempts improves mathematical reasoning, but correlated errors limit the effective sample size. A natural fix is to assign different reasoning strategies to different voters. The approach, Diverse Prompt Mixer, is tested on the AIMO 3 competition: 3 models, 23+ experiments, 50 IMO-level problems, one H100 80 GB, 5-hour limit. Every prompt-level intervention fails. High-temperature sampling already decorrelates errors; weaker strategies reduce accuracy more than they reduce correlation. Across an 8-point capability gap at equal N=8 and every optimization tested, model capability dominates. The gap between the best majority-vote score (42/50) and pass@20 (~45.5) is selection loss, not prompt loss. A verifier-based selector could close it. Prompt engineering cannot.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14062", "title": "OneHOI: Unifying Human-Object Interaction Generation and Editing", "authors": [ "Jiun Tian Hoe", "Weipeng Hu", "Xudong Jiang", "Yap-Peng Tan", "Chee Seng Chan" ], "github": "", "project_page": "", "abstract": "Human-Object Interaction (HOI) modelling captures how humans act upon and relate to objects, typically expressed as triplets. Existing approaches split into two disjoint families: HOI generation synthesises scenes from structured triplets and layout, but fails to integrate mixed conditions like HOI and object-only entities; and HOI editing modifies interactions via text, yet struggles to decouple pose from physical contact and scale to multiple interactions. We introduce OneHOI, a unified diffusion transformer framework that consolidates HOI generation and editing into a single conditional denoising process driven by shared structured interaction representations. At its core, the Relational Diffusion Transformer (R-DiT) models verb-mediated relations through role- and instance-aware HOI tokens, layout-based spatial Action Grounding, a Structured HOI Attention to enforce interaction topology, and HOI RoPE to disentangle multi-HOI scenes. Trained jointly with modality dropout on our HOI-Edit-44K, along with HOI and object-centric datasets, OneHOI supports layout-guided, layout-free, arbitrary-mask, and mixed-condition control, achieving state-of-the-art results across both HOI generation and editing. Code is available at https://jiuntian.github.io/OneHOI/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.11661", "title": "Towards Autonomous Mechanistic Reasoning in Virtual Cells", "authors": [ "Yunhui Jang", "Lu Zhu", "Jake Fawkes", "Alisandra Kaye Denton", "Dominique Beaini", "Emmanuel Noutahi" ], "github": "https://github.com/yunhuijang/VC-TRACES", "project_page": "", "abstract": "Large language models (LLMs) have recently gained significant attention as a promising approach to accelerate scientific discovery. However, their application in open-ended scientific domains such as biology remains limited, primarily due to the lack of factually grounded and actionable explanations. To address this, we introduce a structured explanation formalism for virtual cells that represents biological reasoning as mechanistic action graphs, enabling systematic verification and falsification. Building upon this, we propose VCR-Agent, a multi-agent framework that integrates biologically grounded knowledge retrieval with a verifier-based filtering approach to generate and validate mechanistic reasoning autonomously. Using this framework, we release VC-TRACES dataset, which consists of verified mechanistic explanations derived from the Tahoe-100M atlas. Empirically, we demonstrate that training with these explanations improves factual precision and provides a more effective supervision signal for downstream gene expression prediction. These results underscore the importance of reliable mechanistic reasoning for virtual cells, achieved through the synergy of multi-agent and rigorous verification.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yunhuijang/VC-TRACES", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14531", "title": "TRACER: Trace-Based Adaptive Cost-Efficient Routing for LLM Classification", "authors": [ "Adam Rida" ], "github": "", "project_page": "", "abstract": "Every call to an LLM classification endpoint produces a labeled input-output pair already retained in production logs. These pairs constitute a free, growing training set: a lightweight surrogate trained on them can absorb a significant portion of future traffic at near-zero marginal inference cost. The open questions are when the surrogate is reliable enough to deploy, what it handles versus defers, and how that boundary evolves as data accumulates. We introduce TRACER (Trace-based Adaptive Cost-Efficient Routing), an open-source system that trains ML surrogates on an LLM's own production traces and governs deployment through a parity gate: the surrogate is activated only when its agreement with the LLM exceeds a user-specified threshold \u03b1. To make the routing boundary transparent, TRACER generates interpretability artifacts describing which input regions the surrogate handles, where it plateaus, and why it defers. On a 77-class intent benchmark with a Sonnet 4.6 teacher, TRACER achieves 83-100% surrogate coverage depending on the quality target \u03b1; on a 150-class benchmark, the surrogate fully replaces the teacher. On a natural language inference task, the parity gate correctly refuses deployment because the embedding representation cannot support reliable separation. The system is available as open-source software.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14629", "title": "Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models", "authors": [ "Haoyi Sun", "Xiaoxiao Wang", "Ning Mao", "Qian Wang", "Lifu Mu", "Wen Zheng", "Tao Wei", "Wei Chen" ], "github": "https://github.com/haoyi199815/Switch-KD", "project_page": "", "abstract": "Vision-Language Models (VLMs) have shown remarkable capabilities in joint vision-language understanding, but their large scale poses significant challenges for deployment in resource-constrained scenarios. Knowledge Distillation (KD) offers a viable way to improve model capabilities without increasing model size or data requirements, making deployment more efficient. However, applying KD to VLMs is challenged by modality-specific supervision: although multimodal knowledge in VLMs is fused within the language space, current methods supervise each modality separately without explicitly addressing multimodal alignment, leading to inconsistent multimodal knowledge transfer. To address this, we propose Switch-KD, a visual-switch distillation framework that unifies vision-language knowledge transfer within a shared text-probability space. Switch-KD comprises two key components: (1) Visual-Switch Distillation, which switches the student's visual outputs into the teacher's language pathway to construct cross-modal probabilistic references for implicit visual knowledge transfer; and (2) Dynamic Bi-directional Logits Difference (DBiLD) loss, which adaptively aligns informative probability regions while preserving the distributional structures of teacher and student through bidirectional supervision. Guided by Switch-KD, a 0.5B TinyLLaVA effectively distills rich multimodal knowledge from its 3B teacher, yielding an average improvement of 3.6 points across 10 multimodal benchmarks without any architectural modification.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/haoyi199815/Switch-KD", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.11707", "title": "Representations Before Pixels: Semantics-Guided Hierarchical Video Prediction", "authors": [ "Efstathios Karypidis", "Spyros Gidaris", "Nikos Komodakis" ], "github": "https://github.com/Sta8is/Re2Pix", "project_page": "", "abstract": "Accurate future video prediction requires both high visual fidelity and consistent scene semantics, particularly in complex dynamic environments such as autonomous driving. We present Re2Pix, a hierarchical video prediction framework that decomposes forecasting into two stages: semantic representation prediction and representation-guided visual synthesis. Instead of directly predicting future RGB frames, our approach first forecasts future scene structure in the feature space of a frozen vision foundation model, and then conditions a latent diffusion model on these predicted representations to render photorealistic frames. This decomposition enables the model to focus first on scene dynamics and then on appearance generation. A key challenge arises from the train-test mismatch between ground-truth representations available during training and predicted ones used at inference. To address this, we introduce two conditioning strategies, nested dropout and mixed supervision, that improve robustness to imperfect autoregressive predictions. Experiments on challenging driving benchmarks demonstrate that the proposed semantics-first design significantly improves temporal semantic consistency, perceptual quality, and training efficiency compared to strong diffusion baselines. We provide the implementation code at https://github.com/Sta8is/Re2Pix", "github_manual": "", "github_regex": "https://github.com/Sta8is/Re2Pix", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14572", "title": "Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG", "authors": [ "Yiqun Sun", "Pengfei Wei", "Lawrence B. Hsieh" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) grounds LLM responses in external evidence but treats the model as a passive consumer of search results: it never sees how the corpus is organized or what it has not yet retrieved, limiting its ability to backtrack or combine scattered evidence. We present Corpus2Skill, which distills a document corpus into a hierarchical skill directory offline and lets an LLM agent navigate it at serve time. The compilation pipeline iteratively clusters documents, generates LLM-written summaries at each level, and materializes the result as a tree of navigable skill files. At serve time, the agent receives a bird's-eye view of the corpus, drills into topic branches via progressively finer summaries, and retrieves full documents by ID. Because the hierarchy is explicitly visible, the agent can reason about where to look, backtrack from unproductive paths, and combine evidence across branches. On WixQA, an enterprise customer-support benchmark for RAG, Corpus2Skill outperforms dense retrieval, RAPTOR, and agentic RAG baselines across all quality metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.15231", "title": "RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography", "authors": [ "M\u00e9lanie Roschewitz", "Kenneth Styppa", "Yitian Tao", "Jiwoong Sohn", "Jean-Benoit Delbrouck", "Benjamin Gundersen", "Nicolas Deperrois", "Christian Bluethgen", "Julia Vogt", "Bjoern Menze", "Farhad Nooralahzadeh", "Michael Krauthammer", "Michael Moor" ], "github": "", "project_page": "", "abstract": "Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT). Yet, existing methods largely relegate clinicians to passive observers of final outputs, offering no interpretable reasoning trace for them to inspect, validate, or refine. To address this, we introduce RadAgent, a tool-using AI agent that generates CT reports through a stepwise and interpretable process. Each resulting report is accompanied by a fully inspectable trace of intermediate decisions and tool interactions, allowing clinicians to examine how the reported findings are derived. In our experiments, we observe that RadAgent improves Chest CT report generation over its 3D VLM counterpart, CT-Chat, across three dimensions. Clinical accuracy improves by 6.0 points (36.4% relative) in macro-F1 and 5.4 points (19.6% relative) in micro-F1. Robustness under adversarial conditions improves by 24.7 points (41.9% relative). Furthermore, RadAgent achieves 37.0% in faithfulness, a new capability entirely absent in its 3D VLM counterpart. By structuring the interpretation of chest CT as an explicit, tool-augmented and iterative reasoning trace, RadAgent brings us closer toward transparent and reliable AI for radiology.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.12966", "title": "Boosting Visual Instruction Tuning with Self-Supervised Guidance", "authors": [ "Sophia Sirko-Galouchenko", "Monika Wysoczanska", "Andrei Bursuc", "Nicolas Thome", "Spyros Gidaris" ], "github": "https://github.com/sirkosophia/V-GIFT", "project_page": "", "abstract": "Multimodal large language models (MLLMs) perform well on many vision-language tasks but often struggle with vision-centric problems that require fine-grained visual reasoning. Recent evidence suggests that this limitation arises not from weak visual representations, but from under-utilization of visual information during instruction tuning, where many tasks can be partially solved using language priors alone. We propose a simple and lightweight approach that augments visual instruction tuning with a small number of visually grounded self-supervised tasks expressed as natural language instructions. By reformulating classical self-supervised pretext tasks, such as rotation prediction, color matching, and cross-view correspondence, as image-instruction-response triplets, we introduce supervision that cannot be solved without relying on visual evidence. Our approach requires no human annotations, no architectural modifications, and no additional training stages. Across multiple models, training regimes, and benchmarks, injecting only a small fraction (3-10%) of such visually grounded instructions consistently improves performance on vision-centric evaluations. Our findings highlight instruction tuning with visually grounded SSL tasks as a powerful lever for improving visual reasoning in MLLMs through simple adjustments to the training data distribution. Code available at: https://github.com/sirkosophia/V-GIFT", "github_manual": "", "github_regex": "https://github.com/sirkosophia/V-GIFT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2211.16780", "title": "An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning", "authors": [ "Quyen Tran", "Hai Nguyen", "Hoang Phan", "Quan Dao", "Linh Ngo", "Khoat Than", "Dinh Phung", "Dimitris Metaxas", "Trung Le" ], "github": "", "project_page": "", "abstract": "In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because previous samples have limited replay value when learning a new task. Prior research has typically relied on either a single adaptive centroid or multiple fixed centroids to represent each class in the latent space. However, such methods struggle when class data streams are inherently multimodal and require continual centroid updates. To overcome this, we introduce an online Mixture Model learning framework grounded in Optimal Transport theory (MMOT), where centroids evolve incrementally with new data. This approach offers two main advantages: (i) it provides a more precise characterization of complex data streams, and (ii) it enables improved class similarity estimation for unseen samples during inference through MMOT-derived centroids. Furthermore, to strengthen representation learning and mitigate catastrophic forgetting, we design a Dynamic Preservation strategy that regulates the latent space and maintains class separability over time. Experimental evaluations on benchmark datasets confirm the superior effectiveness of our proposed method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14430", "title": "Three-Phase Transformer", "authors": [ "Mohammad R. Abu Ayyash" ], "github": "", "project_page": "", "abstract": "We present Three-Phase Transformer (3PT), a residual-stream structural prior for decoder-only Transformers on a standard SwiGLU + RMSNorm + RoPE + GQA backbone. The hidden vector is partitioned into N equally-sized cyclic channels, each maintained by phase-respecting ops: a per-channel RMSNorm, a 2D Givens rotation between attention and FFN that rotates each channel by theta + i*(2*pi/N), and a head-count constraint aligning GQA heads with the partition. The architecture is a self-stabilizing equilibrium between scrambling and re-imposition, not a bolted-on module. The partition carves out a one-dimensional DC subspace orthogonal to the channels, into which we inject a fixed Gabriel's horn profile r(p) = 1/(p+1) as an absolute-position side-channel composing orthogonally with RoPE's relative-position rotation. The canonical N=3 borrows its metaphor from balanced three-phase AC, where three sinusoids 120 degrees apart sum to zero with no anti-correlated pair. At 123M parameters on WikiText-103, 3PT achieves -7.20% perplexity (-2.62% bits-per-byte) over a matched RoPE-Only baseline at +1,536 parameters (0.00124% of total), with 1.93x step-count convergence speedup (1.64x wall-clock). N behaves as a parameter-sharing knob rather than a unique optimum: at 5.5M an N-sweep over {1,2,3,4,6,8,12} is near-monotone with N=1 winning; at 123M a three-seed sweep finds N=3 and N=1 statistically indistinguishable. The load-bearing mechanism is the channel-partitioned residual stream, per-block rotation, per-phase normalization, and horn DC injection. We characterize (a) self-stabilization of the geometry without explicit enforcement, a novel instance of the conservation-law framework for neural networks; (b) a U-shaped depth profile of rotation-angle drift at 12 layers; (c) orthogonal composition with RoPE, attention, and FFN.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14914", "title": "Beyond Prompts: Unconditional 3D Inversion for Out-of-Distribution Shapes", "authors": [ "Victoria Yue Chen", "Emery Pierson", "L\u00e9opold Maillard", "Maks Ovsjanikov" ], "github": "", "project_page": "", "abstract": "Text-driven inversion of generative models is a core paradigm for manipulating 2D or 3D content, unlocking numerous applications such as text-based editing, style transfer, or inverse problems. However, it relies on the assumption that generative models remain sensitive to natural language prompts. We demonstrate that for state-of-the-art native text-to-3D generative models, this assumption often collapses. We identify a critical failure mode where generation trajectories are drawn into latent ``sink traps'': regions where the model becomes insensitive to prompt modifications. In these regimes, changes to the input text fail to alter internal representations in a way that alters the output geometry. Crucially, we observe that this is not a limitation of the model's geometric expressivity; the same generative models possess the ability to produce a vast diversity of shapes but, as we demonstrate, become insensitive to out-of-distribution text guidance. We investigate this behavior by analyzing the sampling trajectories of the generative model, and find that complex geometries can still be represented and produced by leveraging the model's unconditional generative prior. This leads to a more robust framework for text-based 3D shape editing that bypasses latent sinks by decoupling a model's geometric representation power from its linguistic sensitivity. Our approach addresses the limitations of current 3D pipelines and enables high-fidelity semantic manipulation of out-of-distribution 3D shapes. Project webpage: https://daidedou.sorpi.fr/publication/beyondprompts", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-17", "arxiv_id": "2604.14265", "title": "Reinforcement Learning via Value Gradient Flow", "authors": [ "Haoran Xu", "Kaiwen Hu", "Somayeh Sojoudi", "Amy Zhang" ], "github": "", "project_page": "", "abstract": "We study behavior-regularized reinforcement learning (RL), where regularization toward a reference distribution (the dataset in offline RL or the base model in LLM RL finetuning) is essential to prevent value over-optimization caused by erroneous out-of-distribution extrapolation. Existing methods either rely on reparameterized policy gradient, which are difficult to scale to large generative models, or on reject sampling, which can be overly conservative when attempting to move beyond the behavior support. In this paper, we propose Value Gradient Flow (VGF), a scalable new paradigm for behavior-regularized RL. VGF casts behavior-regularized RL as an optimal transport problem that maps the reference distribution to the value-induced optimal policy distribution. We solve this transport problem via discrete gradient flow, where value gradients guide particles initialized from the reference distribution. Our analysis shows that VGF imposes regularization implicitly by controlling the transport budget. VGF eliminates explicit policy parameterization while remaining expressive and flexible, this enables adaptive test-time scaling by adjusting the transport budget. Extensive experiments demonstrate that VGF significantly outperforms prior methods, achieving state-of-the-art results on offline RL benchmarks (D4RL, OGBench) and LLM RL tasks. Code and runs can be found at https://ryanxhr.github.io/vgf.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.13074", "title": "PersonaVLM: Long-Term Personalized Multimodal LLMs", "authors": [ "Chang Nie", "Chaoyou Fu", "Yifan Zhang", "Haihua Yang", "Caifeng Shan" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. Prior approaches enable only static, single-turn personalization through input augmentation or output alignment, and thus fail to capture users' evolving preferences and personality over time (see Fig.1). In this paper, we introduce PersonaVLM, an innovative personalized multimodal agent framework designed for long-term personalization. It transforms a general-purpose MLLM into a personalized assistant by integrating three key capabilities: (a) Remembering: It proactively extracts and summarizes chronological multimodal memories from interactions, consolidating them into a personalized database. (b) Reasoning: It conducts multi-turn reasoning by retrieving and integrating relevant memories from the database. (c) Response Alignment: It infers the user's evolving personality throughout long-term interactions to ensure outputs remain aligned with their unique characteristics. For evaluation, we establish Persona-MME, a comprehensive benchmark comprising over 2,000 curated interaction cases, designed to assess long-term MLLM personalization across seven key aspects and 14 fine-grained tasks. Extensive experiments validate our method's effectiveness, improving the baseline by 22.4% (Persona-MME) and 9.8% (PERSONAMEM) under a 128k context, while outperforming GPT-4o by 5.2% and 2.0%, respectively. Project page: https://PersonaVLM.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.15804", "title": "Qwen3.5-Omni Technical Report", "authors": [ "Qwen Team" ], "github": "", "project_page": "", "abstract": "In this work, we present Qwen3.5-Omni, the latest advancement in the Qwen-Omni model family. Representing a significant evolution over its predecessor, Qwen3.5-Omni scales to hundreds of billions of parameters and supports a 256k context length. By leveraging a massive dataset comprising heterogeneous text-vision pairs and over 100 million hours of audio-visual content, the model demonstrates robust omni-modality capabilities. Qwen3.5-Omni-plus achieves SOTA results across 215 audio and audio-visual understanding, reasoning, and interaction subtasks and benchmarks, surpassing Gemini-3.1 Pro in key audio tasks and matching it in comprehensive audio-visual understanding. Architecturally, Qwen3.5-Omni employs a Hybrid Attention Mixture-of-Experts (MoE) framework for both Thinker and Talker, enabling efficient long-sequence inference. The model facilitates sophisticated interaction, supporting over 10 hours of audio understanding and 400 seconds of 720P video (at 1 FPS). To address the inherent instability and unnaturalness in streaming speech synthesis, often caused by encoding efficiency discrepancies between text and speech tokenizers, we introduce ARIA. ARIA dynamically aligns text and speech units, significantly enhancing the stability and prosody of conversational speech with minimal latency impact. Furthermore, Qwen3.5-Omni expands linguistic boundaries, supporting multilingual understanding and speech generation across 10 languages with human-like emotional nuance. Finally, Qwen3.5-Omni exhibits superior audio-visual grounding capabilities, generating script-level structured captions with precise temporal synchronization and automated scene segmentation. Remarkably, we observed the emergence of a new capability in omnimodal models: directly performing coding based on audio-visual instructions, which we call Audio-Visual Vibe Coding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.16254", "title": "ArtifactNet: Detecting AI-Generated Music via Forensic Residual Physics", "authors": [ "Heewon Oh" ], "github": "", "project_page": "", "abstract": "We present ArtifactNet, a lightweight framework that detects AI-generated music by reframing the problem as forensic physics -- extracting and analyzing the physical artifacts that neural audio codecs inevitably imprint on generated audio. A bounded-mask UNet (ArtifactUNet, 3.6M parameters) extracts codec residuals from magnitude spectrograms, which are then decomposed via HPSS into 7-channel forensic features for classification by a compact CNN (0.4M parameters; 4.0M total). We introduce ArtifactBench, a multi-generator evaluation benchmark comprising 6,183 tracks (4,383 AI from 22 generators and 1,800 real from 6 diverse sources). Each track is tagged with bench_origin for fair zero-shot evaluation. On the unseen test partition (n=2,263), ArtifactNet achieves F1 = 0.9829 with FPR = 1.49%, compared to CLAM (F1 = 0.7576, FPR = 69.26%) and SpecTTTra (F1 = 0.7713, FPR = 19.43%) evaluated under identical conditions with published checkpoints. Codec-aware training (4-way WAV/MP3/AAC/Opus augmentation) further reduces cross-codec probability drift by 83% (Delta = 0.95 -> 0.16), resolving the primary codec-invariance failure mode. These results establish forensic physics -- direct extraction of codec-level artifacts -- as a more generalizable and parameter-efficient paradigm for AI music detection than representation learning, using 49x fewer parameters than CLAM and 4.8x fewer than SpecTTTra.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.16272", "title": "VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects", "authors": [ "Xiangbo Gao", "Sicong Jiang", "Bangya Liu", "Xinghao Chen", "Minglai Yang", "Siyuan Yang", "Mingyang Wu", "Jiongze Yu", "Qi Zheng", "Haozhi Wang", "Jiayi Zhang", "Jared Yang", "Jie Yang", "Zihan Wang", "Qing Yin", "Zhengzhong Tu" ], "github": "", "project_page": "", "abstract": "As AI-assisted video creation becomes increasingly practical, instruction-guided video editing has become essential for refining generated or captured footage to meet professional requirements. Yet the field still lacks both a large-scale human-annotated dataset with complete editing examples and a standardized evaluator for comparing editing systems. Existing resources are limited by small scale, missing edited outputs, or the absence of human quality labels, while current evaluation often relies on expensive manual inspection or generic vision-language model judges that are not specialized for editing quality. We introduce VEFX-Dataset, a human-annotated dataset containing 5,049 video editing examples across 9 major editing categories and 32 subcategories, each labeled along three decoupled dimensions: Instruction Following, Rendering Quality, and Edit Exclusivity. Building on VEFX-Dataset, we propose VEFX-Reward, a reward model designed specifically for video editing quality assessment. VEFX-Reward jointly processes the source video, the editing instruction, and the edited video, and predicts per-dimension quality scores via ordinal regression. We further release VEFX-Bench, a benchmark of 300 curated video-prompt pairs for standardized comparison of editing systems. Experiments show that VEFX-Reward aligns more strongly with human judgments than generic VLM judges and prior reward models on both standard IQA/VQA metrics and group-wise preference evaluation. Using VEFX-Reward as an evaluator, we benchmark representative commercial and open-source video editing systems, revealing a persistent gap between visual plausibility, instruction following, and edit locality in current models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.15411", "title": "PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research", "authors": [ "Tingjia Miao", "Wenkai Jin", "Muhua Zhang", "Jinxin Tan", "Yuelin Hu", "Tu Guo", "Jiejun Zhang", "Yuhan Wang", "Wenbo Li", "Yinuo Gao", "Shuo Chen", "Weiqi Jiang", "Yayun Hu", "Zixing Lei", "Xianghe Pang", "Zexi Liu", "Yuzhi Zhang", "Linfeng Zhang", "Kun Chen", "Wei Wang", "Weinan E", "Siheng Chen" ], "github": "", "project_page": "", "abstract": "The paradigm of agentic science requires AI systems to conduct robust reasoning and engage in long-horizon, autonomous exploration. However, current scientific benchmarks remain confined to domain knowledge comprehension and complex reasoning, failing to evaluate the exploratory nature and procedural complexity of real-world research. In this work, we present research-oriented evaluations in theoretical and computational physics, a natural testbed with comprehensive domain knowledge, complex reasoning, and verifiable end-to-end workflows without reliance on experiments. Here we introduce PRL-Bench (Physics Research by LLMs), a benchmark designed to systematically map the capability boundaries of LLMs in executing end-to-end physics research. Constructed from 100 curated papers from the latest issues of Physical Review Letters since August 2025 and validated by domain experts, PRL-Bench covers five major theory- and computation-intensive subfields of modern physics: astrophysics, condensed matter physics, high-energy physics, quantum information, and statistical physics. Each task in the benchmark is designed to replicate the core properties of authentic scientific research, including exploration-oriented formulation, long-horizon workflows, and objective verifiability, thereby reconstructing the essential reasoning processes and research workflows of real physics research. Evaluation across frontier models shows that performance remains limited, with the best overall score below 50, revealing a pronounced gap between current LLM capabilities and the demands of real scientific research. PRL-Bench serves a reliable testbed for accessing next generation AI scientists advancing AI systems toward autonomous scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.15715", "title": "GTA-2: Benchmarking General Tool Agents from Atomic Tool-Use to Open-Ended Workflows", "authors": [ "Jize Wang", "Xuanxuan Liu", "Yining Li", "Songyang Zhang", "Yijun Wang", "Zifei Shan", "Xinyi Le", "Cailian Chen", "Xinping Guan", "Dacheng Tao" ], "github": "https://github.com/open-compass/GTA", "project_page": "", "abstract": "The development of general-purpose agents requires a shift from executing simple instructions to completing complex, real-world productivity workflows. However, current tool-use benchmarks remain misaligned with real-world requirements, relying on AI-generated queries, dummy tools, and limited system-level coordination. To address this, we propose GTA-2, a hierarchical benchmark for General Tool Agents (GTA) spanning atomic tool use and open-ended workflows. Built on real-world authenticity, it leverages real user queries, deployed tools, and multimodal contexts. (i) GTA-Atomic, inherited from our prior GTA benchmark, evaluates short-horizon, closed-ended tool-use precision. (ii) GTA-Workflow introduces long-horizon, open-ended tasks for realistic end-to-end completion. To evaluate open-ended deliverables, we propose a recursive checkpoint-based evaluation mechanism that decomposes objectives into verifiable sub-goals, enabling unified evaluation of both model capabilities and agent execution frameworks (i.e., execution harnesses). Experiments reveal a pronounced capability cliff: while frontier models already struggle on atomic tasks (below 50%), they largely fail on workflows, with top models achieving only 14.39% success. Further analysis shows that checkpoint-guided feedback improves performance, while advanced frameworks such as Manus and OpenClaw substantially enhance workflow completion, highlighting the importance of execution harness design beyond the underlying model capacity. These findings provide guidance for developing reliable personal and professional assistants. Dataset and code will be available at https://github.com/open-compass/GTA.", "github_manual": "", "github_regex": "https://github.com/open-compass/GTA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.14568", "title": "Learning Adaptive Reasoning Paths for Efficient Visual Reasoning", "authors": [ "Yixu Huang", "Tinghui Zhu", "Muhao Chen" ], "github": "https://github.com/RunRiotComeOn/AVR", "project_page": "", "abstract": "Visual reasoning models (VRMs) have recently shown strong cross-modal reasoning capabilities by integrating visual perception with language reasoning. However, they often suffer from overthinking, producing unnecessarily long reasoning chains for any tasks. We attribute this issue to Reasoning Path Redundancy in visual reasoning: many visual questions do not require the full reasoning process. To address this, we propose AVR, an adaptive visual reasoning framework that decomposes visual reasoning into three cognitive functions: visual perception, logical reasoning, and answer application. It further enables models to dynamically choose among three response formats: Full Format, Perception-Only Format, and Direct Answer. AVR is trained with FS-GRPO, an adaptation of Group Relative Policy Optimization that encourages the model to select the most efficient reasoning format while preserving correctness. Experiments on multiple vision-language benchmarks show that AVR reduces token usage by 50--90\\% while maintaining overall accuracy, especially in perception-intensive tasks. These results demonstrate that adaptive visual reasoning can effectively mitigate overthinking in VRMs. Code and data are available at: https://github.com/RunRiotComeOn/AVR.", "github_manual": "", "github_regex": "https://github.com/RunRiotComeOn/AVR", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.16029", "title": "Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning", "authors": [ "Jiaxi Bi", "Tongxu Luo", "Wenyu Du", "Zhengyang Tang", "Benyou Wang" ], "github": "", "project_page": "", "abstract": "Parallel reasoning enhances Large Reasoning Models (LRMs) but incurs prohibitive costs due to futile paths caused by early errors. To mitigate this, path pruning at the prefix level is essential, yet existing research remains fragmented without a standardized framework. In this work, we propose the first systematic taxonomy of path pruning, categorizing methods by their signal source (internal vs. external) and learnability (learnable vs. non-learnable). This classification reveals the unexplored potential of learnable internal methods, motivating our proposal of STOP (Super TOken for Pruning). Extensive evaluations across LRMs ranging from 1.5B to 20B parameters demonstrate that STOP achieves superior effectiveness and efficiency compared to existing baselines. Furthermore, we rigorously validate the scalability of STOP under varying compute budgets - for instance, boosting GPT-OSS-20B accuracy on AIME25 from 84% to nearly 90% under fixed compute budgets. Finally, we distill our findings into formalized empirical guidelines to facilitate optimal real-world deployment. Code, data and models are available at https://bijiaxihh.github.io/STOP", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.16299", "title": "Repurposing 3D Generative Model for Autoregressive Layout Generation", "authors": [ "Haoran Feng", "Yifan Niu", "Zehuan Huang", "Yang-Tian Sun", "Chunchao Guo", "Yuxin Peng", "Lu Sheng" ], "github": "https://github.com/fenghora/LaviGen", "project_page": "", "abstract": "We introduce LaviGen, a framework that repurposes 3D generative models for 3D layout generation. Unlike previous methods that infer object layouts from textual descriptions, LaviGen operates directly in the native 3D space, formulating layout generation as an autoregressive process that explicitly models geometric relations and physical constraints among objects, producing coherent and physically plausible 3D scenes. To further enhance this process, we propose an adapted 3D diffusion model that integrates scene, object, and instruction information and employs a dual-guidance self-rollout distillation mechanism to improve efficiency and spatial accuracy. Extensive experiments on the LayoutVLM benchmark show LaviGen achieves superior 3D layout generation performance, with 19% higher physical plausibility than the state of the art and 65% faster computation. Our code is publicly available at https://github.com/fenghora/LaviGen.", "github_manual": "", "github_regex": "https://github.com/fenghora/LaviGen", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.04936", "title": "Web Retrieval-Aware Chunking (W-RAC) for Efficient and Cost-Effective Retrieval-Augmented Generation Systems", "authors": [ "Uday Allu", "Sonu Kedia", "Tanmay Odapally", "Biddwan Ahmed" ], "github": "", "project_page": "", "abstract": "Retrieval-Augmented Generation (RAG) systems critically depend on effective document chunking strategies to balance retrieval quality, latency, and operational cost. Traditional chunking approaches, such as fixed-size, rule-based, or fully agentic chunking, often suffer from high token consumption, redundant text generation, limited scalability, and poor debuggability, especially for large-scale web content ingestion. In this paper, we propose Web Retrieval-Aware Chunking (W-RAC), a novel, cost-efficient chunking framework designed specifically for web-based documents. W-RAC decouples text extraction from semantic chunk planning by representing parsed web content as structured, ID-addressable units and leveraging large language models (LLMs) only for retrieval-aware grouping decisions rather than text generation. This significantly reduces token usage, eliminates hallucination risks, and improves system observability.Experimental analysis and architectural comparison demonstrate that W-RAC achieves comparable or better retrieval performance than traditional chunking approaches while reducing chunking-related LLM costs by an order of magnitude.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2502.07408", "title": "Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit Flips", "authors": [ "Ido Galil", "Moshe Kimhi", "Ran El-Yaniv" ], "github": "", "project_page": "", "abstract": "Deep Neural Networks (DNNs) can be catastrophically disrupted by flipping only a handful of parameter bits. We introduce Deep Neural Lesion (DNL), a data-free and optimizationfree method that locates critical parameters, and an enhanced single-pass variant, 1P-DNL, that refines this selection with one forward and backward pass on random inputs. We show that this vulnerability spans multiple domains, including image classification, object detection, instance segmentation, and reasoning large language models. In image classification, flipping just two sign bits in ResNet-50 on ImageNet reduces accuracy by 99.8%. In object detection and instance segmentation, one or two sign flips in the backbone collapse COCO detection and mask AP for Mask R-CNN and YOLOv8-seg models. In language modeling, two sign flips into different experts reduce Qwen3-30B-A3B-Thinking from 78% to 0% accuracy. We also show that selectively protecting a small fraction of vulnerable sign bits provides a practical defense against such attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2511.15915", "title": "AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization", "authors": [ "Genghan Zhang", "Shaowei Zhu", "Anjiang Wei", "Zhenyu Song", "Allen Nie", "Zhen Jia", "Nandita Vijaykumar", "Yida Wang", "Kunle Olukotun" ], "github": "https://github.com/zhang677/AccelOpt", "project_page": "", "abstract": "We present AccelOpt, a self-improving large language model (LLM) agentic system that autonomously optimizes kernels for emerging AI acclerators, eliminating the need for expert-provided hardware-specific optimization knowledge. AccelOpt explores the kernel optimization space through iterative generation, informed by an optimization memory that curates experiences and insights from previously encountered slow-fast kernel pairs. We build NKIBench, a new benchmark suite of AWS Trainium accelerator kernels with varying complexity extracted from real-world LLM workloads to evaluate the effectiveness of AccelOpt. Our evaluation confirms that AccelOpt's capability improves over time, boosting the average percentage of peak throughput from 49% to 61% on Trainium 1 and from 45% to 59% on Trainium 2 for NKIBench kernels. Moreover, AccelOpt is highly cost-effective: using open-source models, it matches the kernel improvements of Claude Sonnet 4 while being 26times cheaper. The code is open-sourced at https://github.com/zhang677/AccelOpt.", "github_manual": "", "github_regex": "https://github.com/zhang677/AccelOpt", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.14816", "title": "NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results", "authors": [ "Andrey Moskalenko", "Alexey Bryncev", "Ivan Kosmynin", "Kira Shilovskaya", "Mikhail Erofeev", "Dmitry Vatolin", "Radu Timofte", "Kun Wang", "Yupeng Hu", "Zhiran Li", "Hao Liu", "Qianlong Xiang", "Liqiang Nie", "Konstantinos Chaldaiopoulos", "Niki Efthymiou", "Athanasia Zlatintsi", "Panagiotis Filntisis", "Katerina Pastra", "Petros Maragos", "Li Yang", "Gen Zhan", "Yiting Liao", "Yabin Zhang", "Yuxin Liu", "Xu Wu", "Yunheng Zheng", "Linze Li", "Kun He", "Cong Wu", "Xuefeng Zhu", "Tianyang Xu", "Xiaojun Wu", "Wenzhuo Zhao", "Keren Fu", "Gongyang Li", "Shixiang Shi", "Jianlin Chen", "Haibin Ling", "Yaoxin Jiang", "Guoyi Xu", "Jiajia Liu", "Yaokun Shi", "Jiachen Tu" ], "github": "https://github.com/msu-video-group/NTIRE26_Saliency_Prediction", "project_page": "", "abstract": "This paper presents an overview of the NTIRE 2026 Challenge on Video Saliency Prediction. The goal of the challenge participants was to develop automatic saliency map prediction methods for the provided video sequences. The novel dataset of 2,000 diverse videos with an open license was prepared for this challenge. The fixations and corresponding saliency maps were collected using crowdsourced mouse tracking and contain viewing data from over 5,000 assessors. Evaluation was performed on a subset of 800 test videos using generally accepted quality metrics. The challenge attracted over 20 teams making submissions, and 7 teams passed the final phase with code review. All data used in this challenge is made publicly available - https://github.com/msu-video-group/NTIRE26_Saliency_Prediction.", "github_manual": "", "github_regex": "https://github.com/msu-video-group/NTIRE26_Saliency_Prediction", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.12012", "title": "TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment", "authors": [ "Bingyi Cao", "Koert Chen", "Kevis-Kokitsi Maninis", "Kaifeng Chen", "Arjun Karpur", "Ye Xia", "Sahil Dua", "Tanmaya Dabral", "Guangxing Han", "Bohyung Han", "Joshua Ainslie", "Alex Bewley", "Mithun Jacob", "Ren\u00e9 Wagner", "Washington Ramos", "Krzysztof Choromanski", "Mojtaba Seyedhosseini", "Howard Zhou", "Andr\u00e9 Araujo" ], "github": "", "project_page": "", "abstract": "Recent progress in vision-language pretraining has enabled significant improvements to many downstream computer vision applications, such as classification, retrieval, segmentation and depth prediction. However, a fundamental capability that these models still struggle with is aligning dense patch representations with text embeddings of corresponding concepts. In this work, we investigate this critical issue and propose novel techniques to enhance this capability in foundational vision-language models. First, we reveal that a patch-level distillation procedure significantly boosts dense patch-text alignment -- surprisingly, the patch-text alignment of the distilled student model strongly surpasses that of the teacher model. This observation inspires us to consider modifications to pretraining recipes, leading us to propose iBOT++, an upgrade to the commonly-used iBOT masked image objective, where unmasked tokens also contribute directly to the loss. This dramatically enhances patch-text alignment of pretrained models. Additionally, to improve vision-language pretraining efficiency and effectiveness, we modify the exponential moving average setup in the learning recipe, and introduce a caption sampling strategy to benefit from synthetic captions at different granularities. Combining these components, we develop TIPSv2, a new family of image-text encoder models suitable for a wide range of downstream applications. Through comprehensive experiments on 9 tasks and 20 datasets, we demonstrate strong performance, generally on par with or better than recent vision encoder models. Code and models are released via our project page at https://gdm-tipsv2.github.io/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.15950", "title": "TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation", "authors": [ "Tristan Kirscher", "Alexandra Ertl", "Klaus Maier-Hein", "Xavier Coubez", "Philippe Meyer", "Sylvain Faisan" ], "github": "", "project_page": "", "abstract": "Pancreatic ductal adenocarcinoma (PDAC) segmentation on contrast-enhanced CT is inherently ambiguous: inter-rater disagreement among experts reflects genuine uncertainty rather than annotation noise. Standard deep learning approaches assume a single ground truth, producing probabilistic outputs that can be poorly calibrated and difficult to interpret under such ambiguity. We present TwinTrack, a framework that addresses this gap through post-hoc calibration of ensemble segmentation probabilities to the empirical mean human response (MHR) -the fraction of expert annotators labeling a voxel as tumor. Calibrated probabilities are thus directly interpretable as the expected proportion of annotators assigning the tumor label, explicitly modeling inter-rater disagreement. The proposed post-hoc calibration procedure is simple and requires only a small multi-rater calibration set. It consistently improves calibration metrics over standard approaches when evaluated on the MICCAI 2025 CURVAS-PDACVI multi-rater benchmark.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.14663", "title": "EdgeDetect: Importance-Aware Gradient Compression with Homomorphic Aggregation for Federated Intrusion Detection", "authors": [ "Noor Islam S. Mohammad" ], "github": "", "project_page": "", "abstract": "Federated learning (FL) enables collaborative intrusion detection without raw data exchange, but conventional FL incurs high communication overhead from full-precision gradient transmission and remains vulnerable to gradient inference attacks. This paper presents EdgeDetect, a communication-efficient and privacy-aware federated IDS for bandwidth-constrained 6G-IoT environments. EdgeDetect introduces gradient smartification, a median-based statistical binarization that compresses local updates to {+1,-1} representations, reducing uplink payload by 32times while preserving convergence. We further integrate Paillier homomorphic encryption over binarized gradients, protecting against honest-but-curious servers without exposing individual updates. Experiments on CIC-IDS2017 (2.8M flows, 7 attack classes) demonstrate 98.0% multi-class accuracy and 97.9% macro F1-score, matching centralized baselines, while reducing per-round communication from 450~MB to 14~MB (96.9% reduction). Raspberry Pi-4 deployment confirms edge feasibility: 4.2~MB memory, 0.8~ms latency, and 12~mJ per inference with <0.5% accuracy loss. Under 5% poisoning attacks and severe imbalance, EdgeDetect maintains 87% accuracy and 0.95 minority class F1 (p<0.001), establishing a practical accuracy, communication, and privacy tradeoff for next-generation edge intrusion detection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.16044", "title": "Elucidating the SNR-t Bias of Diffusion Probabilistic Models", "authors": [ "Meng Yu", "Lei Sun", "Jianhao Zeng", "Xiangxiang Chu", "Kun Zhan" ], "github": "https://github.com/AMAP-ML/DCW", "project_page": "", "abstract": "Diffusion Probabilistic Models have demonstrated remarkable performance across a wide range of generative tasks. However, we have observed that these models often suffer from a Signal-to-Noise Ratio-timestep (SNR-t) bias. This bias refers to the misalignment between the SNR of the denoising sample and its corresponding timestep during the inference phase. Specifically, during training, the SNR of a sample is strictly coupled with its timestep. However, this correspondence is disrupted during inference, leading to error accumulation and impairing the generation quality. We provide comprehensive empirical evidence and theoretical analysis to substantiate this phenomenon and propose a simple yet effective differential correction method to mitigate the SNR-t bias. Recognizing that diffusion models typically reconstruct low-frequency components before focusing on high-frequency details during the reverse denoising process, we decompose samples into various frequency components and apply differential correction to each component individually. Extensive experiments show that our approach significantly improves the generation quality of various diffusion models (IDDPM, ADM, DDIM, A-DPM, EA-DPM, EDM, PFGM++, and FLUX) on datasets of various resolutions with negligible computational overhead. The code is at https://github.com/AMAP-ML/DCW.", "github_manual": "", "github_regex": "https://github.com/AMAP-ML/DCW", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.15453", "title": "(1D) Ordered Tokens Enable Efficient Test-Time Search", "authors": [ "Zhitong Gao", "Parham Rezaei", "Ali Cy", "Mingqiao Ye", "Nata\u0161a Jovanovi\u0107", "Jesse Allardice", "Afshin Dehghan", "Amir Zamir", "Roman Bachmann", "O\u011fuzhan Fatih Kar" ], "github": "", "project_page": "", "abstract": "Tokenization is a key component of autoregressive (AR) generative models, converting raw data into more manageable units for modeling. Commonly, tokens describe local information, such as regions of pixels in images or word pieces in text, and AR generation predicts these tokens in a fixed order. A worthwhile question is whether token structures affect the ability to steer the generation through test-time search, where multiple candidate generations are explored and evaluated by a verifier. Using image generation as our testbed, we hypothesize that recent 1D ordered tokenizers with coarse-to-fine structure can be more amenable to search than classical 2D grid structures. This is rooted in the fact that the intermediate states in coarse-to-fine sequences carry semantic meaning that verifiers can reliably evaluate, enabling effective steering during generation. Through controlled experiments, we find that AR models trained on coarse-to-fine ordered tokens exhibit improved test-time scaling behavior compared to grid-based counterparts. Moreover, we demonstrate that, thanks to the ordered structure, pure test-time search over token sequences (i.e., without training an AR model) can perform training-free text-to-image generation when guided by an image-text verifier. Beyond this, we systematically study how classical search algorithms (best-of-N, beam search, lookahead search) interact with different token structures, as well as the role of different verifiers and AR priors. Our results highlight the impact of token structure on inference-time scalability and provide practical guidance for test-time scaling in AR models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.05716", "title": "Can Large Language Models Reinvent Foundational Algorithms?", "authors": [ "Jian Zhao", "Haoren Luo", "Yu Wang", "Yuhan Cao", "Pingyue Sheng", "Tianxing He" ], "github": "https://github.com/Algo-Reinvention/algo-reinvention", "project_page": "", "abstract": "LLMs have shown strong potential to advance scientific discovery. Whether they possess the capacity for foundational innovation, however, remains an open question. In this work, we focus on a prerequisite for foundational innovation: can LLMs reinvent foundational algorithms in computer science? Our Unlearn-and-Reinvent pipeline applies LLM unlearning to remove a specific foundational algorithm, such as Dijkstra's or Euclid's algorithm, from an LLM's pretrained knowledge, and then tests whether the model can reinvent it in a controlled environment. To enable effective unlearning, we adopt a GRPO-based, on-policy unlearning method. Across 10 target algorithms, 3 strong open-weight models, and 3 hint levels, our experiments demonstrate that (1) the strongest model Qwen3-4B-Thinking-2507 successfully reinvents 50% of the algorithms with no hint, 70% at hint level 1, and 90% at hint level 2; (2) a few high-level hints can enhance the reinvention success rate, but even step-by-step hints fail for those complicated algorithms; and (3) test-time reinforcement learning enables successful reinvention for the Strassen algorithm at hint level 2. Through analyses of output trajectories and ablation studies, we find that generative verifier in the reinvention phase plays a critical role in sustaining models' reasoning strength, helping to avoid the ``thought collapse'' phenomenon. These findings offer insights into both the potential and current limits of LLMs' innovative thinking.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Algo-Reinvention/algo-reinvention", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.15151", "title": "QuantCode-Bench: A Benchmark for Evaluating the Ability of Large Language Models to Generate Executable Algorithmic Trading Strategies", "authors": [ "Alexey Khoroshilov", "Alexey Chernysh", "Orkhan Ekhtibarov", "Nini Kamkia", "Dmitry Zmitrovich" ], "github": "", "project_page": "", "abstract": "Large language models have demonstrated strong performance on general-purpose programming tasks, yet their ability to generate executable algorithmic trading strategies remains underexplored. Unlike standard code benchmarks, trading-strategy generation requires simultaneous mastery of domain-specific financial logic, knowledge of a specialized API, and the ability to produce code that is not only syntactically correct but also leads to actual trades on historical data. In this work, we present QuantCode-Bench, a benchmark for the systematic evaluation of modern LLMs in generating strategies for the Backtrader framework from textual descriptions in English. The benchmark contains 400 tasks of varying difficulty collected from Reddit, TradingView, StackExchange, GitHub, and synthetic sources. Evaluation is conducted through a multi-stage pipeline that checks syntactic correctness, successful backtest execution, the presence of trades, and semantic alignment with the task description using an LLM judge. We compare state-of-the-art models in two settings: single-turn, where the strategy must be generated correctly on the first attempt, and agentic multi-turn, where the model receives iterative feedback and may repair its errors. We analyze the failure modes across different stages of the pipeline and show that the main limitations of current models are not related to syntax, but rather to the correct operationalization of trading logic, proper API usage, and adherence to task semantics. These findings suggest that trading strategy generation constitutes a distinct class of domain-specific code generation tasks in which success requires not only technical correctness, but also alignment between natural-language descriptions, financial logic, and the observable behavior of the strategy on data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.13902", "title": "DiPO: Disentangled Perplexity Policy Optimization for Fine-grained Exploration-Exploitation Trade-Off", "authors": [ "Xiaofan Li", "Ming Yang", "Zhiyuan Ma", "Shichao Ma", "Jintao Du", "Yu Cheng", "Weiqiang Wang", "Zhizhong Zhang", "Xin Tan", "Yanyun Qu", "Lizhuang Ma", "Yuan Xie" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed significant advances in the reasoning capabilities of Large Language Models (LLMs). However, effectively managing the exploration and exploitation trade-off remains a critical challenge. In this paper, we fully analyze the exploration and exploitation dilemma of extremely hard and easy samples during the training and propose a new fine-grained trade-off mechanism. Concretely, we introduce a perplexity space disentangling strategy that divides the sample space into distinct exploration (high perplexity) and exploitation (low perplexity) subspaces, thereby mining fine-grained samples requiring exploration-exploitation trade-off. Subsequently, we propose a bidirectional reward allocation mechanism with a minimum impact on verification rewards to implement perplexity-guided exploration and exploitation, enabling more stable policy optimization. Finally, we have evaluated our method on two mainstream tasks: mathematical reasoning and function calling, and experimental results demonstrate the superiority of the proposed method, confirming its effectiveness in enhancing LLM performance by fine-grained exploration-exploitation trade-off.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.15923", "title": "Hierarchical Codec Diffusion for Video-to-Speech Generation", "authors": [ "Jiaxin Ye", "Gaoxiang Cong", "Chenhui Wang", "Xin-Cheng Wen", "Zhaoyang Li", "Boyuan Cao", "Hongming Shan" ], "github": "https://github.com/Jiaxin-Ye/HiCoDiT", "project_page": "", "abstract": "Video-to-Speech (VTS) generation aims to synthesize speech from a silent video without auditory signals. However, existing VTS methods disregard the hierarchical nature of speech, which spans coarse speaker-aware semantics to fine-grained prosodic details. This oversight hinders direct alignment between visual and speech features at specific hierarchical levels during property matching. In this paper, leveraging the hierarchical structure of Residual Vector Quantization (RVQ)-based codec, we propose HiCoDiT, a novel Hierarchical Codec Diffusion Transformer that exploits the inherent hierarchy of discrete speech tokens to achieve strong audio-visual alignment. Specifically, since lower-level tokens encode coarse speaker-aware semantics and higher-level tokens capture fine-grained prosody, HiCoDiT employs low-level and high-level blocks to generate tokens at different levels. The low-level blocks condition on lip-synchronized motion and facial identity to capture speaker-aware content, while the high-level blocks use facial expression to modulate prosodic dynamics. Finally, to enable more effective coarse-to-fine conditioning, we propose a dual-scale adaptive instance layer normalization that jointly captures global vocal style through channel-wise normalization and local prosody dynamics through temporal-wise normalization. Extensive experiments demonstrate that HiCoDiT outperforms baselines in fidelity and expressiveness, highlighting the potential of discrete modelling for VTS. The code and speech demo are both available at https://github.com/Jiaxin-Ye/HiCoDiT.", "github_manual": "", "github_regex": "https://github.com/Jiaxin-Ye/HiCoDiT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.16027", "title": "Where does output diversity collapse in post-training?", "authors": [ "Constantinos Karouzos", "Xingwei Tan", "Nikolaos Aletras" ], "github": "", "project_page": "", "abstract": "Post-trained language models produce less varied outputs than their base counterparts. This output diversity collapse undermines inference-time scaling methods that rely on varied samples, and risks homogenizing model outputs on creative and value-laden tasks. Prior work attributes collapse to specific post-training methods, without separating the role of training data composition from the method, or the generation format from the model weights. We trace output diversity through three parallel post-training lineages of Olmo 3, Think (chain-of-thought distillation), Instruct (broad multi-source data), and RL-Zero, across 15 tasks and four text diversity metrics. We find that the location of collapse co-varies with data composition: the Think lineage loses most semantic diversity at supervised fine-tuning, and the effect of DPO is larger in Instruct than in Think. Suppressing chain-of-thought reasoning at inference in Think models drops accuracy on hard tasks, yet leaves answer-level diversity unchanged, showing that the collapse is embedded in the model weights by training data, not imposed by the generation format. Decomposing diversity loss on six verifiable tasks into a quality-control component (removal of incorrect outputs) and a residual component (genuine narrowing among correct outputs) reveals that the split is task-dependent, and Think models retain more correct-answer diversity than Instruct despite collapsing more in aggregate. Our results indicate that diversity collapse is determined during training by data composition and cannot be addressed at inference time alone.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.09860", "title": "RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies", "authors": [ "Xuning Yang", "Rishit Dagli", "Alex Zook", "Hugo Hadfield", "Ankit Goyal", "Stan Birchfield", "Fabio Ramos", "Jonathan Tremblay" ], "github": "", "project_page": "", "abstract": "The pursuit of general-purpose robotics has yielded impressive foundation models, yet simulation-based benchmarking remains a bottleneck due to rapid performance saturation and a lack of true generalization testing. Existing benchmarks often exhibit significant domain overlap between training and evaluation, trivializing success rates and obscuring insights into robustness. We introduce RoboLab, a simulation benchmarking framework designed to address these challenges. Concretely, our framework is designed to answer two questions: (1) to what extent can we understand the performance of a real-world policy by analyzing its behavior in simulation, and (2) which external factors most strongly affect that behavior under controlled perturbations. First, RoboLab enables human-authored and LLM-enabled generation of scenes and tasks in a robot- and policy-agnostic manner within a physically realistic and photorealistic simulation. With this, we propose the RoboLab-120 benchmark, consisting of 120 tasks categorized into three competency axes: visual, procedural, relational competency, across three difficulty levels. Second, we introduce a systematic analysis of real-world policies that quantify both their performance and the sensitivity of their behavior to controlled perturbations, indicating that high-fidelity simulation can serve as a proxy for analyzing performance and its dependence on external factors. Evaluation with RoboLab exposes significant performance gap in current state-of-the-art models. By providing granular metrics and a scalable toolset, RoboLab offers a scalable framework for evaluating the true generalization capabilities of task-generalist robotic policies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.10261", "title": "The Amazing Agent Race: Strong Tool Users, Weak Navigators", "authors": [ "Zae Myung Kim", "Dongseok Lee", "Jaehyung Kim", "Vipul Raheja", "Dongyeop Kang" ], "github": "https://github.com/minnesotanlp/the-amazing-agent-race", "project_page": "", "abstract": "Existing tool-use benchmarks for LLM agents are overwhelmingly linear: our analysis of six benchmarks shows 55 to 100% of instances are simple chains of 2 to 5 steps. We introduce The Amazing Agent Race (AAR), a benchmark featuring directed acyclic graph (DAG) puzzles (or \"legs\") with fork-merge tool chains. We release 1,400 instances across two variants: sequential (800 legs) and compositional (600 DAG legs). Agents must navigate Wikipedia, execute multi-step tool chains, and aggregate results into a verifiable answer. Legs are procedurally generated from Wikipedia seeds across four difficulty levels with live-API validation. Three complementary metrics (finish-line accuracy, pit-stop visit rate, and roadblock completion rate) separately diagnose navigation, tool-use, and arithmetic failures. Evaluating three agent frameworks on 1,400 legs, the best achieves only 37.2% accuracy. Navigation errors dominate (27 to 52% of trials) while tool-use errors remain below 17%, and agent architecture matters as much as model scale (Claude Code matches Codex CLI at 37% with 6x fewer tokens). The compositional structure of AAR reveals that agents fail not at calling tools but at navigating to the right pages, a blind spot invisible to linear benchmarks. The project page can be accessed at: https://minnesotanlp.github.io/the-amazing-agent-race", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/minnesotanlp/the-amazing-agent-race", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.10571", "title": "Universal statistical signatures of evolution in artificial intelligence architectures", "authors": [ "Theodor Spiro" ], "github": "", "project_page": "", "abstract": "We test whether artificial intelligence architectural evolution obeys the same statistical laws as biological evolution. Compiling 935 ablation experiments from 161 publications, we show that the distribution of fitness effects (DFE) of architectural modifications follows a heavy-tailed Student's t-distribution with proportions (68% deleterious, 19% neutral, 13% beneficial for major ablations, n=568) that place AI between compact viral genomes and simple eukaryotes. The DFE shape matches D. melanogaster (normalized KS=0.07) and S. cerevisiae (KS=0.09); the elevated beneficial fraction (13% vs. 1-6% in biology) quantifies the advantage of directed over blind search while preserving the distributional form. Architectural origination follows logistic dynamics (R^2=0.994) with punctuated equilibria and adaptive radiation into domain niches. Fourteen architectural traits were independently invented 3-5 times, paralleling biological convergences. These results demonstrate that the statistical structure of evolution is substrate-independent, determined by fitness landscape topology rather than the mechanism of selection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.14518", "title": "Mind DeepResearch Technical Report", "authors": [ "MindDR Team", "Li Auto Inc" ], "github": "", "project_page": "", "abstract": "We present Mind DeepResearch (MindDR), an efficient multi-agent deep research framework that achieves leading performance with only ~30B-parameter models through a meticulously designed data synthesis and multi-stage training pipeline. The core innovation of MindDR lies in a collaborative three-agent architecture (Planning Agent, DeepSearch Agent, and Report Agent) and a four-stage agent-specialized training pipeline comprising SFT cold-start, Search-RL, Report-RL and preference alignment. With this regime, MindDR demonstrates competitive performance even with ~30B-scale models. Specifically, MindDR achieves 45.7% on BrowseComp-ZH, 42.8% on BrowseComp, 46.5% on WideSearch, 75.0% on xbench-DS, and 52.5 on DeepResearch Bench, outperforming comparable-scale open-source agent systems and rivaling larger-scale models. MindDR has been deployed as an online product in Li Auto. Furthermore, we introduce MindDR Bench, a curated benchmark of 500 real-world Chinese queries from our internal product user interactions, evaluated through a comprehensive multi-dimensional rubric system rather than relying on a single RACE metric. On MindDR Bench, MindDR achieves a state-of-the-art score of 51.8.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-20", "arxiv_id": "2604.16503", "title": "Motif-Video 2B: Technical Report", "authors": [ "Junghwan Lim", "Wai Ting Cheung", "Minsu Ha", "Beomgyu Kim", "Taewhan Kim", "Haesol Lee", "Dongpin Oh", "Jeesoo Lee", "Taehyun Kim", "Minjae Kim", "Sungmin Lee", "Hyeyeon Cho", "Dahye Choi", "Jaeheui Her", "Jaeyeon Huh", "Hanbin Jung", "Changjin Kang", "Dongseok Kim", "Jangwoong Kim", "Youngrok Kim", "Hyukjin Kweon", "Hongjoo Lee", "Jeongdoo Lee", "Junhyeok Lee", "Eunhwan Park", "Yeongjae Park", "Bokki Ryu", "Dongjoo Weon" ], "github": "", "project_page": "", "abstract": "Training strong video generation models usually requires massive datasets, large parameter counts, and substantial compute. In this work, we ask whether strong text-to-video quality is possible at a much smaller budget: fewer than 10M clips and less than 100,000 H200 GPU hours. Our core claim is that part of the answer lies in how model capacity is organized, not only in how much of it is used. In video generation, prompt alignment, temporal consistency, and fine-detail recovery can interfere with one another when they are handled through the same pathway. Motif-Video 2B addresses this by separating these roles architecturally, rather than relying on scale alone. The model combines two key ideas. First, Shared Cross-Attention strengthens text control when video token sequences become long. Second, a three-part backbone separates early fusion, joint representation learning, and detail refinement. To make this design effective under a limited compute budget, we pair it with an efficient training recipe based on dynamic token routing and early-phase feature alignment to a frozen pretrained video encoder. Our analysis shows that later blocks develop clearer cross-frame attention structure than standard single-stream baselines. On VBench, Motif-Video~2B reaches 83.76\\%, surpassing Wan2.1 14B while using 7times fewer parameters and substantially less training data. These results suggest that careful architectural specialization, combined with an efficiency-oriented training recipe, can narrow or exceed the quality gap typically associated with much larger video models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17698", "title": "The Geometric Canary: Predicting Steerability and Detecting Drift via Representational Stability", "authors": [ "Prashant C. Raju" ], "github": "", "project_page": "", "abstract": "Reliable deployment of language models requires two capabilities that appear distinct but share a common geometric foundation: predicting whether a model will accept targeted behavioral control, and detecting when its internal structure degrades. We show that geometric stability, the consistency of a representation's pairwise distance structure, addresses both. Supervised Shesha variants that measure task-aligned geometric stability predict linear steerability with near-perfect accuracy (\u03c1= 0.89-0.97) across 35-69 embedding models and three NLP tasks, capturing unique variance beyond class separability (partial \u03c1= 0.62-0.76). A critical dissociation emerges: unsupervised stability fails entirely for steering on real-world tasks (\u03c1approx 0.10), revealing that task alignment is essential for controllability prediction. However, unsupervised stability excels at drift detection, measuring nearly 2times greater geometric change than CKA during post-training alignment (up to 5.23times in Llama) while providing earlier warning in 73\\% of models and maintaining a 6times lower false alarm rate than Procrustes. Together, supervised and unsupervised stability form complementary diagnostics for the LLM deployment lifecycle: one for pre-deployment controllability assessment, the other for post-deployment monitoring.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.16642", "title": "Geometric coherence of single-cell CRISPR perturbations reveals regulatory architecture and predicts cellular stress", "authors": [ "Prashant C. Raju" ], "github": "https://github.com/bowang-lab/scGPT", "project_page": "", "abstract": "Genome engineering has achieved remarkable sequence-level precision, yet predicting the transcriptomic state that a cell will occupy after perturbation remains an open problem. Single-cell CRISPR screens measure how far cells move from their unperturbed state, but this effect magnitude ignores a fundamental question: do the cells move together? Two perturbations with identical magnitude can produce qualitatively different outcomes if one drives cells coherently along a shared trajectory while the other scatters them across expression space. We introduce a geometric stability metric, Shesha, that quantifies the directional coherence of single-cell perturbation responses as the mean cosine similarity between individual cell shift vectors and the mean perturbation direction. Across five CRISPR datasets (2,200+ perturbations spanning CRISPRa, CRISPRi, and pooled screens), stability correlates strongly with effect magnitude (Spearman \u03c1=0.75-0.97), with a calibrated cross-dataset correlation of 0.97. Crucially, discordant cases where the two metrics decouple expose regulatory architecture: pleiotropic master regulators such as CEBPA and GATA1 pay a \"geometric tax,\" producing large but incoherent shifts, while lineage-specific factors such as KLF1 produce tightly coordinated responses. After controlling for magnitude, geometric instability is independently associated with elevated chaperone activation (HSPA5/BiP; \u03c1_{partial}=-0.34 and -0.21 across datasets), and the high-stability/high-stress quadrant is systematically depleted. The magnitude-stability relationship persists in scGPT foundation model embeddings, confirming it is a property of biological state space rather than linear projection. Perturbation stability provides a complementary axis for hit prioritization in screens, phenotypic quality control in cell manufacturing, and evaluation of in silico perturbation predictions.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/bowang-lab/scGPT", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17696", "title": "Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play", "authors": [ "Xiachong Feng", "Deyi Yin", "Xiaocheng Feng", "Yi Jiang", "Libo Qin", "Yangfan Ye", "Lei Huang", "Weitao Ma", "Qiming Li", "Yuxuan Gu", "Bing Qin", "Lingpeng Kong" ], "github": "", "project_page": "", "abstract": "Games offer a compelling paradigm for developing general reasoning capabilities in language models, as they naturally demand strategic planning, probabilistic inference, and adaptive decision-making. However, existing self-play approaches rely solely on terminal game outcomes, providing no mechanism to distinguish transferable reasoning patterns from game-specific heuristics. We present STRATAGEM, which addresses two fundamental barriers to reasoning transfer: domain specificity, where learned patterns remain anchored in game semantics, and contextual stasis, where static game contexts fail to cultivate progressive reasoning. STRATAGEM selectively reinforces trajectories exhibiting abstract, domain-agnostic reasoning through a Reasoning Transferability Coefficient, while incentivizing adaptive reasoning development via a Reasoning Evolution Reward. Experiments across mathematical reasoning, general reasoning, and code generation benchmarks demonstrate substantial improvements, with particularly strong gains on competition-level mathematics where multi-step reasoning is critical. Ablation studies and human evaluation confirm that both components contribute to transferable reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18486", "title": "OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation", "authors": [ "Jinghui Lu", "Jiayi Guan", "Zhijian Huang", "Jinlong Li", "Guang Li", "Lingdong Kong", "Yingyan Li", "Han Wang", "Shaoqing Xu", "Yuechen Luo", "Fang Li", "Chenxu Dang", "Junli Wang", "Tao Xu", "Jing Wu", "Jianhua Wu", "Xiaoshuai Hao", "Wen Zhang", "Tianyi Jiang", "Lingfeng Zhang", "Lei Zhou", "Yingbo Tang", "Jie Wang", "Yinfeng Gao", "Xizhou Bu", "Haochen Tian", "Yihang Qiu", "Feiyang Jia", "Lin Liu", "Yigu Ge", "Hanbing Li", "Yuannan Shen", "Jianwei Cui", "Hongwei Xie", "Bing Wang", "Haiyang Sun", "Jingwei Zhao", "Jiahui Huang", "Pei Liu", "Zeyu Zhu", "Yuncheng Jiang", "Zibin Guo", "Chuhong Gong", "Hanchao Leng", "Kun Ma", "Naiyang Wang", "Guang Chen", "Kuiyuan Yang", "Hangjun Ye", "Long Chen" ], "github": "", "project_page": "", "abstract": "Chain-of-Thought (CoT) reasoning has become a powerful driver of trajectory prediction in VLA-based autonomous driving, yet its autoregressive nature imposes a latency cost that is prohibitive for real-time deployment. Latent CoT methods attempt to close this gap by compressing reasoning into continuous hidden states, but consistently fall short of their explicit counterparts. We suggest that this is due to purely linguistic latent representations compressing a symbolic abstraction of the world, rather than the causal dynamics that actually govern driving. Thus, we present OneVL (One-step latent reasoning and planning with Vision-Language explanations), a unified VLA and World Model framework that routes reasoning through compact latent tokens supervised by dual auxiliary decoders. Alongside a language decoder that reconstructs text CoT, we introduce a visual world model decoder that predicts future-frame tokens, forcing the latent space to internalize the causal dynamics of road geometry, agent motion, and environmental change. A three-stage training pipeline progressively aligns these latents with trajectory, language, and visual objectives, ensuring stable joint optimization. At inference, the auxiliary decoders are discarded and all latent tokens are prefilled in a single parallel pass, matching the speed of answer-only prediction. Across four benchmarks, OneVL becomes the first latent CoT method to surpass explicit CoT, delivering state-of-the-art accuracy at answer-only latency, and providing direct evidence that tighter compression, when guided in both language and world-model supervision, produces more generalizable representations than verbose token-by-token reasoning. Project Page: https://xiaomi-embodied-intelligence.github.io/OneVL", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18292", "title": "Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence", "authors": [ "Guanting Dong", "Junting Lu", "Junjie Huang", "Wanjun Zhong", "Longxiang Liu", "Shijue Huang", "Zhenyu Li", "Yang Zhao", "Xiaoshuai Song", "Xiaoxi Li", "Jiajie Jin", "Yutao Zhu", "Hanbin Wang", "Fangyu Lei", "Qinyu Luo", "Mingyang Chen", "Zehui Chen", "Jiazhan Feng", "Ji-Rong Wen", "Zhicheng Dou" ], "github": "", "project_page": "", "abstract": "Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model Context Protocol (MCP) and broader agent skills offer a unified interface for connecting agents with scalable real-world services, but training robust agents remains limited by the lack of realistic environments and principled mechanisms for life-long learning. In this paper, we present Agent-World, a self-evolving training arena for advancing general agent intelligence through scalable environments. Agent-World has two main components: (1) Agentic Environment-Task Discovery, which autonomously explores topic-aligned databases and executable tool ecosystems from thousands of real-world environment themes and synthesizes verifiable tasks with controllable difficulty; and (2) Continuous Self-Evolving Agent Training, which combines multi-environment reinforcement learning with a self-evolving agent arena that automatically identifies capability gaps through dynamic task synthesis and drives targeted learning, enabling the co-evolution of agent policies and environments. Across 23 challenging agent benchmarks, Agent-World-8B and 14B consistently outperforms strong proprietary models and environment scaling baselines. Further analyses reveal scaling trends in relation to environment diversity and self-evolution rounds, offering insights for building general agent intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18564", "title": "MultiWorld: Scalable Multi-Agent Multi-View Video World Models", "authors": [ "Haoyu Wu", "Jiwen Yu", "Yingtian Zou", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Video world models have achieved remarkable success in simulating environmental dynamics in response to actions by users or agents. They are modeled as action-conditioned video generation models that take historical frames and current actions as input to predict future frames. Yet, most existing approaches are limited to single-agent scenarios and fail to capture the complex interactions inherent in real-world multi-agent systems. We present MultiWorld, a unified framework for multi-agent multi-view world modeling that enables accurate control of multiple agents while maintaining multi-view consistency. We introduce the Multi-Agent Condition Module to achieve precise multi-agent controllability, and the Global State Encoder to ensure coherent observations across different views. MultiWorld supports flexible scaling of agent and view counts, and synthesizes different views in parallel for high efficiency. Experiments on multi-player game environments and multi-robot manipulation tasks demonstrate that MultiWorld outperforms baselines in video fidelity, action-following ability, and multi-view consistency. Project page: https://multi-world.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.16593", "title": "Revisiting a Pain in the Neck: A Semantic Reasoning Benchmark for Language Models", "authors": [ "Yang Liu", "Hongming Li", "Melissa Xiaohui Qin", "Qiankun Liu", "Chao Huang" ], "github": "https://github.com/jacklanda/SemanticQA", "project_page": "", "abstract": "We present SemanticQA, an evaluation suite designed to assess language models (LMs) in semantic phrase processing tasks. The benchmark consolidates existing multiword expression (MwE) resources and reorganizes them into a unified testbed. It covers both general lexical phenomena, such as lexical collocations, and three fine-grained categories: idiomatic expressions, noun compounds, and verbal constructions. Through SemanticQA, we assess LMs of diverse architectures and scales in extraction, classification, and interpretation tasks, as well as sequential task compositions. We reveal substantial performance variation, particularly on tasks requiring semantic reasoning, highlighting differences in reasoning efficacy and semantic understanding of LMs, providing insights for pushing LMs with stronger comprehension on non-trivial semantic phrases. The evaluation harness and data of SemanticQA are available at https://github.com/jacklanda/SemanticQA.", "github_manual": "", "github_regex": "https://github.com/jacklanda/SemanticQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17338", "title": "Precise Debugging Benchmark: Is Your Model Debugging or Regenerating?", "authors": [ "Wang Bill Zhu", "Miaosen Chai", "Shangshang Wang", "Yejia Liu", "Song Bian", "Honghua Dong", "Willie Neiswanger", "Robin Jia" ], "github": "", "project_page": "", "abstract": "Unlike code completion, debugging requires localizing faults and applying targeted edits. We observe that frontier LLMs often regenerate correct but over-edited solutions during debugging. To evaluate how far LLMs are from precise debugging, we introduce the Precise Debugging Benchmark (PDB) framework, which automatically converts any coding dataset into a debugging benchmark with precision-aware evaluation. PDB generates buggy programs by synthesizing verified atomic bugs and composing them into multi-bug programs. We define two novel metrics, edit-level precision and bug-level recall, which measures how many necessary edits are made and how many bugs are resolved. We release two evaluation benchmarks: PDB-Single-Hard on single-line bugs, and PDB-Multi on multi-line bugs. Experiments show that frontier models, such as GPT-5.1-Codex and DeepSeek-V3.2-Thinking, achieve unit-test pass rates above 76% but exhibit precision below 45%, even when explicitly instructed to perform minimal debugging. Finally, we show that iterative and agentic debugging strategies do not substantially improve precision or recall, highlighting the need to rethink post-training pipelines for coding models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18224", "title": "WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models", "authors": [ "Xinping Lei", "Xinyu Che", "Junqi Xiong", "Chenchen Zhang", "Yukai Huang", "Chenyu Zhou", "Haoyang Huang", "Minghao Liu", "Letian Zhu", "Hongyi Ye", "Jinhua Hao", "Ken Deng", "Zizheng Zhan", "Han Li", "Dailin Li", "Yifan Yao", "Ming Sun", "Zhaoxiang Zhang", "Jiaheng Liu" ], "github": "https://github.com/NJU-LINK/WebCompass", "project_page": "", "abstract": "Large language models are rapidly evolving into interactive coding agents capable of end-to-end web coding, yet existing benchmarks evaluate only narrow slices of this capability, typically text-conditioned generation with static-correctness metrics, leaving visual fidelity, interaction quality, and codebase-level reasoning largely unmeasured. We introduce WebCompass, a multimodal benchmark that provides unified lifecycle evaluation of web engineering capability. Recognizing that real-world web coding is an iterative cycle of generation, editing, and repair, WebCompass spans three input modalities (text, image, video) and three task types (generation, editing, repair), yielding seven task categories that mirror professional workflows. Through a multi-stage, human-in-the-loop pipeline, we curate instances covering 15 generation domains, 16 editing operation types, and 11 repair defect types, each annotated at Easy/Medium/Hard levels. For evaluation, we adopt a checklist-guided LLM-as-a-Judge protocol for editing and repair, and propose a novel Agent-as-a-Judge paradigm for generation that autonomously executes generated websites in a real browser, explores interactive behaviors via the Model Context Protocol (MCP), and iteratively synthesizes targeted test cases, closely approximating human acceptance testing. We evaluate representative closed-source and open-source models and observe that: (1) closed-source models remain substantially stronger and more balanced; (2) editing and repair exhibit distinct difficulty profiles, with repair preserving interactivity better but remaining execution-challenging; (3) aesthetics is the most persistent bottleneck, especially for open-source models; and (4) framework choice materially affects outcomes, with Vue consistently challenging while React and Vanilla/HTML perform more strongly depending on task type.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NJU-LINK/WebCompass", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.16826", "title": "Crowded in B-Space: Calibrating Shared Directions for LoRA Merging", "authors": [ "Yixuan Tang", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Merging separately trained LoRA adapters is a practical alternative to joint multi-task training, but it often hurts performance. Existing methods usually treat the LoRA update \u0394W = BA as a single object and do not distinguish the two LoRA matrices. We show that the main source of LoRA merge interference comes from the output-side matrix B. Across tasks, B repeatedly uses a small set of shared directions, while A remains much more task-specific. As a result, the merged adapter overemphasizes these shared directions, and task-specific information is lost. We propose Pico (Pre-merge interference calibration in output-space), a data-free method that calibrates B before merge by downscaling over-shared directions and then rescaling the merged update. Pico plugs directly into existing merging methods such as Task Arithmetic, TIES, and TSV-M. Across eight different benchmarks from math, coding, finance, and medical domains, Pico improves average accuracy by 3.4-8.3 points over the corresponding base method and achieves the best overall average performance. Pico also enables merged adapters to outperform the LoRA trained with all task data. These results show that LoRA merging works better when the two LoRA matrices are treated separately.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.16943", "title": "MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation", "authors": [ "Bo Li", "Ningyuan Deng", "Tianyu Dong", "Shaobo Wang", "Shaolin Zhu", "Lijie Wen" ], "github": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have shown impressive capabilities, yet they often struggle to effectively capture the fine-grained textual information within images crucial for accurate image translation. This often leads to a modality gap between visual text inputs and textual inputs/outputs for image translation. Existing methods, primarily relying on instruction fine-tuning, risk parameter redundancy of pre-trained knowledge, hindering generalization performance. To address this, we introduce modality neuron-aware fine-tuning (MNAFT), a novel approach that takes advantage of the specialized roles of individual neurons within MLLMs for enhanced image translation. MNAFT identifies language-agnostic and language-specific neurons in both vision and language modules through an instruction-driven activation analysis, evaluating their importance in various translation tasks. We then perform selective fine-tuning, updating only the parameters of language-specific and language-agnostic neurons within the selected layers relevant to the target task, while preserving the knowledge encoded in other neurons and layers. Our extensive experiments on multiple benchmarks demonstrate that MNAFT significantly outperforms state-of-the-art image translation methods, including cascaded models, standard full fine-tuning, and parameter-efficient tuning techniques. Furthermore, we provide comprehensive analysis, including visualizations of neuron activations and clustering patterns, to offer insights into the roles of different neuron groups in mediating cross-modal understanding and facilitating accurate language-specific translation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.08537", "title": "Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding", "authors": [ "Mu Nan", "Muquan Yu", "Weijian Mai", "Jacob S. Prince", "Hossein Adeli", "Rui Zhang", "Jiahang Cao", "Benjamin Becker", "John A. Pyles", "Margaret M. Henderson", "Chunfeng Song", "Nikolaus Kriegeskorte", "Michael J. Tarr", "Xiaoqing Hu", "Andrew F. Luo" ], "github": "https://github.com/ezacngm/brainCodec", "project_page": "", "abstract": "Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computational models of vision. A field-wide goal is to achieve generalizable, cross-subject models. A major obstacle towards this goal is the substantial variability in neural representations across individuals, which has so far required training bespoke models or fine-tuning separately for each subject. To address this challenge, we introduce a meta-optimized approach for semantic visual decoding from fMRI that generalizes to novel subjects without any fine-tuning. By simply conditioning on a small set of image-brain activation examples from the new individual, our model rapidly infers their unique neural encoding patterns to facilitate robust and efficient visual decoding. Our approach is explicitly optimized for in-context learning of the new subject's encoding model and performs decoding by hierarchical inference, inverting the encoder. First, for multiple brain regions, we estimate the per-voxel visual response encoder parameters by constructing a context over multiple stimuli and responses. Second, we construct a context consisting of encoder parameters and response values over multiple voxels to perform aggregated functional inversion. We demonstrate strong cross-subject and cross-scanner generalization across diverse visual backbones without retraining or fine-tuning. Moreover, our approach requires neither anatomical alignment nor stimulus overlap. This work is a critical step towards a generalizable foundation model for non-invasive brain decoding.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ezacngm/brainCodec", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18131", "title": "Training LLM Agents for Spontaneous, Reward-Free Self-Evolution via World Knowledge Exploration", "authors": [ "Qifan Zhang", "Dongyang Ma", "Tianqing Fang", "Jia Li", "Jing Tang", "Nuo Chen", "Haitao Mi", "Yan Wang" ], "github": "", "project_page": "", "abstract": "Most agents today ``self-evolve'' by following rewards and rules defined by humans. However, this process remains fundamentally dependent on external supervision; without human guidance, the evolution stops. In this work, we train agents to possess an intrinsic meta-evolution capability to spontaneously learn about unseen environments prior to task execution. To instill this ability, we design an outcome-based reward mechanism that measures how much an agent's self-generated world knowledge improves its success rate on downstream tasks. This reward signal is used exclusively during the training phase to teach the model how to explore and summarize effectively. At inference time, the agent requires no external rewards or human instructions. It spontaneously performs native self-evolution to adapt to unknown environments using its internal parameters. When applied to Qwen3-30B and Seed-OSS-36B, this shift to native evolution yields a 20% performance increase on WebVoyager and WebWalker. Most strikingly, the generated world knowledge even enables a compact 14B Qwen3 model to outperform the unassisted Gemini-2.5-Flash, establishing a new paradigm for truly evolving agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18543", "title": "ClawEnvKit: Automatic Environment Generation for Claw-Like Agents", "authors": [ "Xirui Li", "Ming Li", "Derry Xu", "Wei-Lin Chiang", "Ion Stoica", "Cho-Jui Hsieh", "Tianyi Zhou" ], "github": "https://github.com/nearai/ironclaw", "project_page": "", "abstract": "Constructing environments for training and evaluating claw-like agents remains a manual, human-intensive process that does not scale. We argue that what is needed is not just a dataset, but an automated pipeline capable of generating diverse, verified environments on demand. To this end, we introduce ClawEnvKit, an autonomous generation pipeline that instantiates this formalism from natural language descriptions. The pipeline comprises three modules: (1) a parser that extracts structured generation parameters from natural language input; (2) a generator that produces the task specification, tool interface, and scoring configuration; and (3) a validator that enforces feasibility, diversity, structural validity, and internal consistency across the generated environments. Using ClawEnvKit, we construct Auto-ClawEval, the first large-scale benchmark for claw-like agents, comprising 1,040 environments across 24 categories. Empirically, Auto-ClawEval matches or exceeds human-curated environments on coherence and clarity at 13,800x lower cost. Evaluated across 4 model families and 8 agent harness frameworks, we find that harness engineering boosts performance by up to 15.7 percentage points over a bare ReAct baseline, completion remains the primary axis of variation with no model saturating the benchmark, and automated generation enables evaluation at a scale previously infeasible. Beyond static benchmarking, ClawEnvKit enables live evaluation: users describe a desired capability in natural language and obtain a verified environment on demand, turning evaluation into a continuous, user-driven process. The same mechanism serves as an on-demand training environment generator, producing task distributions that adapt to an agent's current weaknesses rather than being bounded by existing user logs.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/nearai/ironclaw", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18584", "title": "MathNet: a Global Multimodal Benchmark for Mathematical Reasoning and Retrieval", "authors": [ "Shaden Alshammari", "Kevin Wen", "Abrar Zainal", "Mark Hamilton", "Navid Safaei", "Sultan Albarakati", "William T. Freeman", "Antonio Torralba" ], "github": "", "project_page": "", "abstract": "Mathematical problem solving remains a challenging test of reasoning for large language and multimodal models, yet existing benchmarks are limited in size, language coverage, and task diversity. We introduce MathNet, a high-quality, large-scale, multimodal, and multilingual dataset of Olympiad-level math problems together with a benchmark for evaluating mathematical reasoning in generative models and mathematical retrieval in embedding-based systems. MathNet spans 47 countries, 17 languages, and two decades of competitions, comprising 30,676 expert-authored problems with solutions across diverse domains. In addition to the core dataset, we construct a retrieval benchmark consisting of mathematically equivalent and structurally similar problem pairs curated by human experts. MathNet supports three tasks: (i) Problem Solving, (ii) Math-Aware Retrieval, and (iii) Retrieval-Augmented Problem Solving. Experimental results show that even state-of-the-art reasoning models (78.4% for Gemini-3.1-Pro and 69.3% for GPT-5) remain challenged, while embedding models struggle to retrieve equivalent problems. We further show that retrieval-augmented generation performance is highly sensitive to retrieval quality; for example, DeepSeek-V3.2-Speciale achieves gains of up to 12%, obtaining the highest scores on the benchmark. MathNet provides the largest high-quality Olympiad dataset together with the first benchmark for evaluating mathematical problem retrieval, and we publicly release both the dataset and benchmark at https://mathnet.mit.edu.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17972", "title": "Modeling Multiple Support Strategies within a Single Turn for Emotional Support Conversations", "authors": [ "Jie Zhu", "Huaixia Dou", "Junhui Li", "Lifan Guo", "Feng Chen", "Jinsong Su", "Chi Zhang", "Fang Kong" ], "github": "https://github.com/aliyun/qwen-dianjin", "project_page": "", "abstract": "Emotional Support Conversation (ESC) aims to assist individuals experiencing distress by generating empathetic and supportive dialogue. While prior work typically assumes that each supporter turn corresponds to a single strategy, real-world supportive communication often involves multiple strategies within a single utterance. In this paper, we revisit the ESC task by formulating it as multi-strategy utterance generation, where each utterance may contain one or more strategy-response pairs. We propose two generation methods: All-in-One, which predicts all strategy-response pairs in a single decoding step, and One-by-One, which iteratively generates strategy-response pairs until completion. Both methods are further enhanced with cognitive reasoning guided by reinforcement learning to improve strategy selection and response composition. We evaluate our models on the ESConv dataset under both utterance-level and dialogue-level settings. Experimental results show that our methods effectively model multi-strategy utterances and lead to improved supportive quality and dialogue success. To our knowledge, this work provides the first systematic empirical evidence that allowing multiple support strategies within a single utterance is both feasible and beneficial for emotional support conversations. All code and data will be publicly available at https://github.com/aliyun/qwen-dianjin.", "github_manual": "", "github_regex": "https://github.com/aliyun/qwen-dianjin", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18203", "title": "Multiplication in Multimodal LLMs: Computation with Text, Image, and Audio Inputs", "authors": [ "Samuel G. Balter", "Ethan Jerzak", "Connor T. Jerzak" ], "github": "", "project_page": "", "abstract": "Multimodal LLMs can accurately perceive numerical content across modalities yet fail to perform exact multi-digit multiplication when the identical underlying arithmetic problem is presented as numerals, number words, images, or in audio form. Because existing benchmarks often lack systematically paired instances across modalities, it remains difficult to compare genuine arithmetic limits within and across model families. We therefore introduce a controlled multimodal multiplication benchmark that factorially varies digit length, digit sparsity, representation (e.g., numerals vs. number words), and modality (text, rendered images, audio), with paired instances from a reproducible generator. We also define arithmetic load, C, as the product of the total and non-zero digit count as a compact, mechanistically motivated proxy for operation count. Across evaluations, accuracy falls sharply as C grows, often nearing zero by C > 100. Indeed, C remains predictive of performance across modalities and models, with R-squared often > 0.5, nearing the value from more complex measures of arithmetic load that count the number of intermediate arithmetic steps. A separate perception-versus-computation decomposition shows that multimodal degradation is primarily computational rather than perceptual: on matched-perception checks, models are near-perfect (> 99%) across modalities, even when multiplication accuracy drops. Beyond measuring when models fail, we ask which procedures they are predisposed to follow. We introduce a forced-completion loss probe that scores heuristic-specific reasoning prefixes--including columnar multiplication, distributive decomposition, and rounding/compensation. Here, decomposition is favored in both text and vision modalities; heuristic-specific LoRA adapters produce near-orthogonal updates yet degrade accuracy, indicating the base model maintains a well-tuned internal router.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.06505", "title": "MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts", "authors": [ "Weiyue Li", "Ruizhi Qian", "Yi Li", "Yongce Li", "Yunfan Long", "Jiahui Cai", "Yan Luo", "Mengyu Wang" ], "github": "https://github.com/Harvard-AI-and-Robotics-Lab/MedConclusion", "project_page": "", "abstract": "Large language models (LLMs) are widely explored for reasoning-intensive research tasks, yet resources for testing whether they can infer scientific conclusions from structured biomedical evidence remain limited. We introduce MedConclusion, a large-scale dataset of 5.7M PubMed structured abstracts for biomedical conclusion generation. Each instance pairs the non-conclusion sections of an abstract with the original author-written conclusion, providing naturally occurring supervision for evidence-to-conclusion reasoning. MedConclusion also includes journal-level metadata such as biomedical category and SJR, enabling subgroup analysis across biomedical domains. As an initial study, we evaluate diverse LLMs under conclusion and summary prompting settings and score outputs with both reference-based metrics and LLM-as-a-judge. We find that conclusion writing is behaviorally distinct from summary writing, strong models remain closely clustered under current automatic metrics, and judge identity can substantially shift absolute scores. MedConclusion provides a reusable data resource for studying scientific evidence-to-conclusion reasoning. Our code and data are available at: https://github.com/Harvard-AI-and-Robotics-Lab/MedConclusion.", "github_manual": "", "github_regex": "https://github.com/Harvard-AI-and-Robotics-Lab/MedConclusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18574", "title": "When Can LLMs Learn to Reason with Weak Supervision?", "authors": [ "Salman Rahman", "Jingyan Shen", "Anna Mordvina", "Hamid Palangi", "Saadia Gabriel", "Pavel Izmailov" ], "github": "", "project_page": "", "abstract": "Large language models have achieved significant reasoning improvements through reinforcement learning with verifiable rewards (RLVR). Yet as model capabilities grow, constructing high-quality reward signals becomes increasingly difficult, making it essential to understand when RLVR can succeed under weaker forms of supervision. We conduct a systematic empirical study across diverse model families and reasoning domains under three weak supervision settings: scarce data, noisy rewards, and self-supervised proxy rewards. We find that generalization is governed by training reward saturation dynamics: models that generalize exhibit a prolonged pre-saturation phase during which training reward and downstream performance climb together, while models that saturate rapidly memorize rather than learn. We identify reasoning faithfulness, defined as the extent to which intermediate steps logically support the final answer, as the pre-RL property that predicts which regime a model falls into, while output diversity alone is uninformative. Motivated by these findings, we disentangle the contributions of continual pre-training and supervised fine-tuning, finding that SFT on explicit reasoning traces is necessary for generalization under weak supervision, while continual pre-training on domain data amplifies the effect. Applied together to Llama3.2-3B-Base, these interventions enable generalization across all three settings where the base model previously failed.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.16893", "title": "EasyVideoR1: Easier RL for Video Understanding", "authors": [ "Chuanyu Qin", "Chenxu Yang", "Qingyi Si", "Naibin Gu", "Dingyu Yao", "Zheng Lin", "Peng Fu", "Nan Duan", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large language models. As models evolve into natively multimodal architectures, extending RLVR to video understanding becomes increasingly important yet remains largely unexplored, due to the diversity of video task types, the computational overhead of repeatedly decoding and preprocessing high-dimensional visual inputs, and the difficulty of reproducible evaluation across numerous sensitive hyperparameters. Existing open-source RL training frameworks provide solid infrastructure for text and image scenarios but lack systematic optimizations tailored for video modality. In this work, we present EasyVideoR1, a complete and efficient reinforcement learning framework specifically designed for training large vision-language models on video understanding tasks. EasyVideoR1 makes the following contributions: (1) a full video RL training pipeline with offline preprocessing and tensor caching that eliminates redundant video decoding and yields a 1.47 times throughput improvement; (2) a comprehensive, task-aware reward system covering 11 distinct video and image problem types with unified routing and modular extension; (3) a mixed offline-online data training paradigm that combines curated high-quality trajectories with on-policy exploration, benefiting the learning of more challenging tasks; (4) joint image-video training with independently configurable pixel budgets, allowing the two modalities to mutually reinforce each other; and (5) an asynchronous multi-benchmark evaluation framework covering 22 mainstream video understanding benchmarks, with reproduced accuracy closely aligned with officially reported scores.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.11102", "title": "OmniScript: Towards Audio-Visual Script Generation for Long-Form Cinematic Video", "authors": [ "Junfu Pu", "Yuxin Chen", "Teng Wang", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Current multimodal large language models (MLLMs) have demonstrated remarkable capabilities in short-form video understanding, yet translating long-form cinematic videos into detailed, temporally grounded scripts remains a significant challenge. This paper introduces the novel video-to-script (V2S) task, aiming to generate hierarchical, scene-by-scene scripts encompassing character actions, dialogues, expressions, and audio cues. To facilitate this, we construct a first-of-its-kind human-annotated benchmark and propose a temporally-aware hierarchical evaluation framework. Furthermore, we present OmniScript, an 8B-parameter omni-modal (audio-visual) language model tailored for long-form narrative comprehension. OmniScript is trained via a progressive pipeline that leverages chain-of-thought supervised fine-tuning for plot and character reasoning, followed by reinforcement learning using temporally segmented rewards. Extensive experiments demonstrate that despite its parameter efficiency, OmniScript significantly outperforms larger open-source models and achieves performance comparable to state-of-the-art proprietary models, including Gemini 3-Pro, in both temporal localization and multi-field semantic accuracy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17406", "title": "EvoMaster: A Foundational Agent Framework for Building Evolving Autonomous Scientific Agents at Scale", "authors": [ "Xinyu Zhu", "Yuzhu Cai", "Zexi Liu", "Cheng Wang", "Fengyang Li", "Wenkai Jin", "Wanxu Liu", "Zehao Bing", "Bingyang Zheng", "Jingyi Chai", "Shuo Tang", "Rui Ye", "Yuwen Du", "Xianghe Pang", "Yaxin Du", "Tingjia Miao", "Yuzhi Zhang", "Ruoxue Liao", "Zhaohan Ding", "Linfeng Zhang", "Yanfeng Wang", "Weinan E", "Siheng Chen" ], "github": "https://github.com/sjtu-sai-agents/EvoMaster", "project_page": "", "abstract": "The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. While the scientific method is inherently iterative, existing agent frameworks are predominantly static, narrowly scoped, and lack the capacity to learn from trial and error. To bridge this gap, we present EvoMaster, a foundational evolving agent framework engineered specifically for Agentic Science at Scale. Driven by the core principle of continuous self-evolution, EvoMaster empowers agents to iteratively refine hypotheses, self-critique, and progressively accumulate knowledge across experimental cycles, faithfully mirroring human scientific inquiry. Crucially, as a domain-agnostic base harness, EvoMaster is exceptionally easy to scale up -- enabling developers to build and deploy highly capable, self-evolving scientific agents for arbitrary disciplines in approximately 100 lines of code. Built upon EvoMaster, we incubated the SciMaster ecosystem across domains such as machine learning, physics, and general science. Evaluations on four authoritative benchmarks (Humanity's Last Exam, MLE-Bench Lite, BrowseComp, and FrontierScience) demonstrate that EvoMaster achieves state-of-the-art scores of 41.1%, 75.8%, 73.3%, and 53.3%, respectively. It comprehensively outperforms the general-purpose baseline OpenClaw with relative improvements ranging from +159% to +316%, robustly validating its efficacy and generality as the premier foundational framework for the next generation of autonomous scientific discovery. EvoMaster is available at https://github.com/sjtu-sai-agents/EvoMaster.", "github_manual": "", "github_regex": "https://github.com/sjtu-sai-agents/EvoMaster", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17454", "title": "HSG: Hyperbolic Scene Graph", "authors": [ "Liyang Wang", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/HSG", "project_page": "", "abstract": "Scene graph representations enable structured visual understanding by modeling objects and their relationships, and have been widely used for multiview and 3D scene reasoning. Existing methods such as MSG learn scene graph embeddings in Euclidean space using contrastive learning and attention based association. However, Euclidean geometry does not explicitly capture hierarchical entailment relationships between places and objects, limiting the structural consistency of learned representations. To address this, we propose Hyperbolic Scene Graph (HSG), which learns scene graph embeddings in hyperbolic space where hierarchical relationships are naturally encoded through geometric distance. Our results show that HSG improves hierarchical structure quality while maintaining strong retrieval performance. The largest gains are observed in graph level metrics: HSG achieves a PP IoU of 33.17 and the highest Graph IoU of 33.51, outperforming the best AoMSG variant (25.37) by 8.14, highlighting the effectiveness of hyperbolic representation learning for scene graph modeling. Code: https://github.com/AIGeeksGroup/HSG.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/HSG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18394", "title": "OpenGame: Open Agentic Coding for Games", "authors": [ "Yilei Jiang", "Jinyuan Hu", "Qianyin Xiao", "Yaozhi Zheng", "Ruize Ma", "Kaituo Feng", "Jiaming Han", "Tianshuo Peng", "Kaixuan Fan", "Manyuan Zhang", "Xiangyu Yue" ], "github": "https://github.com/leigest519/OpenGame", "project_page": "", "abstract": "Game development sits at the intersection of creative design and intricate software engineering, demanding the joint orchestration of game engines, real-time loops, and tightly coupled state across many files. While Large Language Models (LLMs) and code agents now solve isolated programming tasks with ease, they consistently stumble when asked to produce a fully playable game from a high-level design, collapsing under cross-file inconsistencies, broken scene wiring, and logical incoherence. We bridge this gap with OpenGame, the first open-source agentic framework explicitly designed for end-to-end web game creation. At its core lies Game Skill, a reusable, evolving capability composed of a Template Skill that grows a library of project skeletons from experience and a Debug Skill that maintains a living protocol of verified fixes - together enabling the agent to scaffold stable architectures and systematically repair integration errors rather than patch isolated syntax bugs. Powering this framework is GameCoder-27B, a code LLM specialized for game engine mastery through a three-stage pipeline of continual pre-training, supervised fine-tuning, and execution-grounded reinforcement learning. Since verifying interactive playability is fundamentally harder than checking static code, we further introduce OpenGame-Bench, an evaluation pipeline that scores agentic game generation along Build Health, Visual Usability, and Intent Alignment via headless browser execution and VLM judging. Across 150 diverse game prompts, OpenGame establishes a new state-of-the-art. We hope OpenGame pushes code agents beyond discrete software engineering problems and toward building complex, interactive real-world applications. Our framework will be fully open-sourced.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/leigest519/OpenGame", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17308", "title": "SkillFlow:Benchmarking Lifelong Skill Discovery and Evolution for Autonomous Agents", "authors": [ "Ziao Zhang", "Kou Shi", "Shiting Huang", "Avery Nie", "Yu Zeng", "Yiming Zhao", "Zhen Fang", "Qishen Su", "Haibo Qiu", "Wei Yang", "Qingnan Ren", "Shun Zou", "Wenxuan Huang", "Lin Chen", "Zehui Chen", "Feng Zhao" ], "github": "https://github.com/ZhangZi-a/SkillFlow", "project_page": "", "abstract": "As the capability frontier of autonomous agents continues to expand, they are increasingly able to complete specialized tasks through plug-and-play external skills. Yet current benchmarks mostly test whether models can use provided skills, leaving open whether they can discover skills from experience, repair them after failure, and maintain a coherent library over time. We introduce SkillFlow, a benchmark of 166 tasks across 20 families in which task construction within each family follows a Domain-Agnostic Execution Flow (DAEF) that defines an agent workflow framework, allowing these tasks to share a consistent workflow. Agents are evaluated under an Agentic Lifelong Learning protocol in which they begin without skills, solve tasks sequentially within each family, externalize lessons through trajectory- and rubric-driven skill patches, and carry the updated library forward. Experiments reveal a substantial capability gap. For Claude Opus 4.6, lifelong skill evolution improves task success from 62.65% to 71.08% (+8.43 points). However, high skill usage does not necessarily imply high utility: Kimi K2.5 gains only +0.60 points despite 66.87% skill usage, while Qwen-Coder-Next reaches only a 44.58% task completion rate and still regresses relative to the vanilla setting. SkillFlow contributes a structured testbed for this direction and an in-depth empirical analysis of skill discovery, patching, transfer, and their failure modes under lifelong evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ZhangZi-a/SkillFlow", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17388", "title": "Back to Repair: A Minimal Denoising Network\\ for Time Series Anomaly Detection", "authors": [ "Kadir-Kaan \u00d6zer", "Ren\u00e9 Ebeling", "Markus Enzweiler" ], "github": "https://github.com/iis-esslingen/JuRe", "project_page": "", "abstract": "We introduce JuRe (Just Repair), a minimal denoising network for time series anomaly detection that exposes a central finding: architectural complexity is unnecessary when the training objective correctly implements the manifold-projection principle. JuRe consists of a single depthwise-separable convolutional residual block with hidden dimension 128, trained to repair corrupted time series windows and scored at inference by a fixed, parameter-free structural discrepancy function. Despite using no attention, no latent variable, and no adversarial component, JuRe ranks second on the TSB-AD multivariate benchmark (AUC-PR 0.404, 180 series, 17 datasets) and second on the UCR univariate archive by AUC-PR (0.198, 250 series), leading all neural baselines on AUC-PR and VUS-PR. Component ablation on TSB-AD identifies training-time corruption as the dominant factor (\u0394AUC-PR = 0.047 on removal), confirming that the denoising objective, not network capacity, drives detection quality. Pairwise Wilcoxon signed-rank tests establish statistical significance against 21 of 25 baselines on TSB-AD. Code is available at the URL https://github.com/iis-esslingen/JuRe.", "github_manual": "", "github_regex": "https://github.com/iis-esslingen/JuRe", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.14258", "title": "GFT: From Imitation to Reward Fine-Tuning with Unbiased Group Advantages and Dynamic Coefficient Rectification", "authors": [ "Wangjie Gan", "Miao Pan", "Linbo Xi", "Wenqi Zhang", "Jintao Chen", "Jianwei Yin", "Xuhong Zhang" ], "github": "", "project_page": "", "abstract": "Large language models are typically post-trained using supervised fine-tuning (SFT) and reinforcement learning (RL), yet effectively unifying efficient knowledge injection with robust generalization remains challenging. In this work, we provide a training-dynamics analysis showing that SFT can be interpreted as a special case of policy gradient optimization with an extremely sparse implicit reward and unstable inverse-probability weighting, which together lead to single-path dependency, entropy collapse, and gradient explosion. Motivated by this diagnosis, we propose Group Fine-Tuning (GFT), a unified post-training framework that addresses these intrinsic limitations through two mechanisms: Group Advantage Learning, which constructs diverse response groups and derives normalized contrastive supervision to alleviate reward sparsity, and Dynamic Coefficient Rectification, which adaptively bounds inverse-probability weights to stabilize optimization while preserving efficient knowledge injection. Experiments demonstrate that GFT consistently surpasses SFT-based methods and yields policies that integrate more smoothly with subsequent RL training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.16902", "title": "Beyond Text-Dominance: Understanding Modality Preference of Omni-modal Large Language Models", "authors": [ "Xinru Yan", "Boxi Cao", "Yaojie Lu", "Hongyu Lin", "Weixiang Zhou", "Le Sun", "Xianpei Han" ], "github": "https://github.com/icip-cas/OmniPreference", "project_page": "", "abstract": "Native Omni-modal Large Language Models (OLLMs) have shifted from pipeline architectures to unified representation spaces. However, this native integration gives rise to a critical yet underexplored phenomenon: modality preference. To bridge this gap, we first systematically quantify modality preference of OLLMs using a newly-curated conflict-based benchmark and the modality selection rate metric. Our evaluation of ten representative OLLMs reveals a notable paradigm shift: unlike the ``text-dominance'' of traditional VLMs, most OLLMs exhibit a pronounced visual preference. To further understand the underlying mechanism, we conduct layer-wise probing and demonstrate that such modality preference is not static but emerges progressively in the mid-to-late layers. Building upon these insights, we leverage these internal signals to diagnose cross-modal hallucinations, achieving competitive performance across three downstream multi-modal benchmarks without task-specific data. Our work provides both a mechanistic understanding and a practical tool for building more trustworthy OLLMs. Our code and related resources are publicly available at: https://github.com/icip-cas/OmniPreference", "github_manual": "", "github_regex": "https://github.com/icip-cas/OmniPreference", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18168", "title": "Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation", "authors": [ "Chenxi Zhao", "Chen Zhu", "Xiaokun Feng", "Aiming Hao", "Jiashu Zhu", "Jiachen Lei", "Jiahong Wu", "Xiangxiang Chu", "Jufeng Yang" ], "github": "https://github.com/AMAP-ML/EMF", "project_page": "", "abstract": "Few-step generation has been a long-standing goal, with recent one-step generation methods exemplified by MeanFlow achieving remarkable results. Existing research on MeanFlow primarily focuses on class-to-image generation. However, an intuitive yet unexplored direction is to extend the condition from fixed class labels to flexible text inputs, enabling richer content creation. Compared to the limited class labels, text conditions pose greater challenges to the model's understanding capability, necessitating the effective integration of powerful text encoders into the MeanFlow framework. Surprisingly, although incorporating text conditions appears straightforward, we find that integrating powerful LLM-based text encoders using conventional training strategies results in unsatisfactory performance. To uncover the underlying cause, we conduct detailed analyses and reveal that, due to the extremely limited number of refinement steps in the MeanFlow generation, such as only one step, the text feature representations are required to possess sufficiently high discriminability. This also explains why discrete and easily distinguishable class features perform well within the MeanFlow framework. Guided by these insights, we leverage a powerful LLM-based text encoder validated to possess the required semantic properties and adapt the MeanFlow generation process to this framework, resulting in efficient text-conditioned synthesis for the first time. Furthermore, we validate our approach on the widely used diffusion model, demonstrating significant generation performance improvements. We hope this work provides a general and practical reference for future research on text-conditioned MeanFlow generation. The code is available at https://github.com/AMAP-ML/EMF.", "github_manual": "", "github_regex": "https://github.com/AMAP-ML/EMF", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17886", "title": "Latent Preference Modeling for Cross-Session Personalized Tool Calling", "authors": [ "Yejin Yoon", "Minseo Kim", "Taeuk Kim" ], "github": "", "project_page": "", "abstract": "Users often omit essential details in their requests to LLM-based agents, resulting in under-specified inputs for tool use. This poses a fundamental challenge for tool-augmented agents, as API execution typically requires complete arguments, highlighting the need for personalized tool calling. To study this problem, we introduce MPT, a benchmark comprising 265 multi-session dialogues that cover three challenges: Preference Recall, Preference Induction, and Preference Transfer. We also propose PRefine, a test-time memory-augmented method that represents user preferences as evolving hypotheses. Through a generate--verify--refine loop, it extracts reusable constraints from history and improves tool-calling accuracy while using only 1.24% of the tokens required by full-history prompting. These results indicate that robust personalization in agentic systems depends on memory that captures the reasons behind user choices, not just the choices themselves.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.16038", "title": "Modeling Sparse and Bursty Vulnerability Sightings: Forecasting Under Data Constraints", "authors": [ "Cedric Bonhomme", "Alexandre Dulaunoy" ], "github": "", "project_page": "", "abstract": "Understanding and anticipating vulnerability-related activity is a major challenge in cyber threat intelligence. This work investigates whether vulnerability sightings, such as proof-of-concept releases, detection templates, or online discussions, can be forecast over time. Building on our earlier work on VLAI, a transformer-based model that predicts vulnerability severity from textual descriptions, we examine whether severity scores can improve time-series forecasting as exogenous variables. We evaluate several approaches for short-term forecasting of sightings per vulnerability. First, we test SARIMAX models with and without log(x+1) transformations and VLAI-derived severity inputs. Although these adjustments provide limited improvements, SARIMAX remains poorly suited to sparse, short, and bursty vulnerability data. In practice, forecasts often produce overly wide confidence intervals and sometimes unrealistic negative values. To better capture the discrete and event-driven nature of sightings, we then explore count-based methods such as Poisson regression. Early results show that these models produce more stable and interpretable forecasts, especially when sightings are aggregated weekly. We also discuss simpler operational alternatives, including exponential decay functions for short forecasting horizons, to estimate future activity without requiring long historical series. Overall, this study highlights both the potential and the limitations of forecasting rare and bursty cyber events, and provides practical guidance for integrating predictive analytics into vulnerability intelligence workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.13313", "title": "Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding", "authors": [ "Eun Woo Im", "Dhruv Madhwal", "Vivek Gupta" ], "github": "", "project_page": "", "abstract": "Vision-Language Models demonstrate remarkable capabilities but often struggle with compositional reasoning, exhibiting vulnerabilities regarding word order and attribute binding. This limitation arises from a scarcity of informative samples needed to differentiate subtle semantic variations during contrastive pretraining. Although hard negative mining offers a promising remedy, existing methods lack explicit mechanisms to dictate which linguistic elements undergo modification. Instead of engineering generative architectures, this study establishes lexical concreteness as a fundamental determinant of negative sample efficacy. Modifying highly concrete terms generates more pronounced structural and visual discrepancies, providing a substantially stronger learning signal. Leveraging this principle, ConcretePlant is proposed to systematically isolate and manipulate perceptually grounded concepts. Analyses of the InfoNCE further reveals a severe gradient imbalance, where easily distinguishable pairs disproportionately overwhelm the optimization process and restrict the bandwidth available for nuanced learning. To resolve this degradation, the Cement loss is formulated utilizing a margin-based approach. By correlating psycholinguistic scores with sample difficulty, this objective dynamically calibrates the penalization applied to individual training pairs. Comprehensive evaluations substantiate these theoretical claims. The integrated framework, designated as Slipform, achieves state-of-the-art accuracy across diverse compositional evaluation benchmarks, general cross-modal retrieval, single and multi label linear probing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17609", "title": "Agents Explore but Agents Ignore: LLMs Lack Environmental Curiosity", "authors": [ "Leon Engl\u00e4nder", "Sophia Althammer", "Ahmet \u00dcst\u00fcn", "Matthias Gall\u00e9", "Tom Sherborne" ], "github": "", "project_page": "", "abstract": "LLM-based agents are assumed to integrate environmental observations into their reasoning: discovering highly relevant but unexpected information should naturally lead to a model exploiting its own discoveries. We show that this assumption is false for current LLM-based agents, which struggle to reflect or react to unexpected information. Across three benchmarks (Terminal-Bench, SWE-Bench, AppWorld), we inject complete task solutions into the agent environments to deliberately expose a task's solution to a model. While agents discover these solutions on Terminal-Bench in 79-81% of runs, they interact, or exploit, them in only 37-50% of cases. This gap is starkest in AppWorld: agents see documentation stating that a command \"returns the complete solution to this task\" in over 90% of attempts but exploit this in fewer than 7% of trials. We show that agents lack what we call environmental curiosity: the capability to recognize and investigate unexpected but relevant observations in response to environmental stimuli. We identify three main factors influencing environmental curiosity: available tools in the agent scaffold, test-time compute, and training data distribution. Our findings identify configurations that maximize curiosity also achieve the best performance on the unmodified benchmarks. Yet even jointly optimized agents still ignore discovered solutions in the majority of trials: current agents use the environment to fetch expected information, but not to revise their strategy or maximally exploit useful stimuli.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17091", "title": "GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)", "authors": [ "Jiaqing Liang", "Jinyi Han", "Weijia Li", "Xinyi Wang", "Zhoujia Zhang", "Zishang Jiang", "Ying Liao", "Tingyun Li", "Ying Huang", "Hao Shen", "Hanyu Wu", "Fang Guo", "Keyi Wang", "Zhonghua Hong", "Zhiyu Lu", "Lipeng Ma", "Sihang Jiang", "Yanghua Xiao" ], "github": "https://github.com/lsdefine/GenericAgent", "project_page": "", "abstract": "Long-horizon large language model (LLM) agents are fundamentally limited by context. As interactions become longer, tool descriptions, retrieved memories, and raw environmental feedback accumulate and push out the information needed for decision-making. At the same time, useful experience gained from tasks is often lost across episodes. We argue that long-horizon performance is determined not by context length, but by how much decision-relevant information is maintained within a finite context budget. We present GenericAgent (GA), a general-purpose, self-evolving LLM agent system built around a single principle: context information density maximization. GA implements this through four closely connected components: a minimal atomic tool set that keeps the interface simple, a hierarchical on-demand memory that only shows a small high-level view by default, a self-evolution mechanism that turns verified past trajectories into reusable SOPs and executable code, and a context truncation and compression layer that maintains information density during long executions. Across task completion, tool use efficiency, memory effectiveness, self-evolution, and web browsing, GA consistently outperforms leading agent systems while using significantly fewer tokens and interactions, and it continues to evolve over time. Project: https://github.com/lsdefine/GenericAgent", "github_manual": "", "github_regex": "https://github.com/lsdefine/GenericAgent", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.15710", "title": "VoxMind: An End-to-End Agentic Spoken Dialogue System", "authors": [ "Tianle Liang", "Yifu Chen", "Shengpeng Ji", "Yijun Chen", "Zhiyang Jia", "Jingyu Lu", "Fan Zhuo", "Xueyi Pu", "Yangzhuo Li", "Zhou Zhao" ], "github": "https://github.com/MM-Speech/VoxMind", "project_page": "", "abstract": "Recent end-to-end spoken dialogue models enable natural interaction. However, as user demands become increasingly complex, models that rely solely on conversational abilities often struggle to cope. Incorporating agentic capabilities is therefore essential: by enabling tool use, these models can extend their knowledge boundaries and better solve real-world tasks. Yet, existing research has largely concentrated on core perception and generation, with comparatively limited exploration of such tool-augmented extensions. To bridge this gap, we present VoxMind, an integrated framework designed to equip end-to-end spoken dialogue models with comprehensive agentic abilities. Leveraging our curated 470-hour AgentChat dataset, we incorporate a \"Think-before-Speak\" mechanism, enabling the model to internalize structured reasoning as a critical prerequisite for planning and response generation. Furthermore, to mitigate latency bottlenecks caused by large-scale tool integration, we propose a Multi-Agent Dynamic Tool Management architecture. By asynchronously delegating retrieval tasks to an auxiliary agent aligned with the main model's reasoning trajectory, this system effectively decouples inference latency from toolset size. Experimental results confirm that VoxMind achieves significant improvements in agent performance: compared with strong baselines, the task completion rate increases from 34.88% to 74.57%, outperforming Gemini-2.5-Pro on spoken agent tasks while preserving general conversational quality. The source code and associated data are publicly available at https://github.com/MM-Speech/VoxMind.", "github_manual": "", "github_regex": "https://github.com/MM-Speech/VoxMind", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2511.10262", "title": "MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models", "authors": [ "He Zhang", "Wenqian Cui", "Haoning Xu", "Xiaohui Li", "Lei Zhu", "Haoli Bai", "Shaohua Ma", "Irwin King" ], "github": "https://github.com/ZhangHe0918/MTR-DuplexBench", "project_page": "", "abstract": "Full-Duplex Speech Language Models (FD-SLMs) enable real-time, overlapping conversational interactions, offering a more dynamic user experience compared to traditional half-duplex models. However, existing benchmarks primarily focus on evaluating single-round interactions, neglecting the complexities of multi-round communication. Evaluating FD-SLMs in multi-round settings poses significant challenges, including blurred turn boundaries in communication and context inconsistency during model inference. Also, existing benchmarks often focus solely on evaluating conversational features, neglecting other critical aspects. To address these gaps, we introduce MTR-DuplexBench, a novel benchmark designed for a comprehensive multi-round evaluation of FD-SLMs. MTR-DuplexBench not only segments continuous full-duplex dialogues into discrete turns for turn-by-turn assessment but also incorporates various evaluation aspects, including conversational features, dialogue quality, instruction following, and safety. Experimental results reveal that current FD-SLMs face difficulties in maintaining consistent performance across multiple rounds and evaluation dimensions, highlighting the necessity and effectiveness of our benchmark. Code and data are available at: https://github.com/ZhangHe0918/MTR-DuplexBench", "github_manual": "", "github_regex": "https://github.com/ZhangHe0918/MTR-DuplexBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.16576", "title": "On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability", "authors": [ "Yongkang Li", "Panagiotis Eustratiadis", "Yixing Fan", "Evangelos Kanoulas" ], "github": "https://github.com/liyongkang123/Robust_LLM_Retriever_Eval", "project_page": "", "abstract": "Decoder-only large language models (LLMs) are increasingly replacing BERT-style architectures as the backbone for dense retrieval, achieving substantial performance gains and broad adoption. However, the robustness of these LLM-based retrievers remains underexplored. In this paper, we present the first systematic study of the robustness of state-of-the-art open-source LLM-based dense retrievers from two complementary perspectives: generalizability and stability. For generalizability, we evaluate retrieval effectiveness across four benchmarks spanning 30 datasets, using linear mixed-effects models to estimate marginal mean performance and disentangle intrinsic model capability from dataset heterogeneity. Our analysis reveals that while instruction-tuned models generally excel, those optimized for complex reasoning often suffer a ``specialization tax,'' exhibiting limited generalizability in broader contexts. For stability, we assess model resilience against both unintentional query variations~(e.g., paraphrasing, typos) and malicious adversarial attacks~(e.g., corpus poisoning). We find that LLM-based retrievers show improved robustness against typos and corpus poisoning compared to encoder-only baselines, yet remain vulnerable to semantic perturbations like synonymizing. Further analysis shows that embedding geometry (e.g., angular uniformity) provides predictive signals for lexical stability and suggests that scaling model size generally improves robustness. These findings inform future robustness-aware retriever design and principled benchmarking. Our code is publicly available at https://github.com/liyongkang123/Robust_LLM_Retriever_Eval.", "github_manual": "", "github_regex": "https://github.com/liyongkang123/Robust_LLM_Retriever_Eval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17849", "title": "On the Reliability of Computer Use Agents", "authors": [ "Gonzalo Gonzalez-Pumariega", "Saaket Agashe", "Jiachen Yang", "Ang Li", "Xin Eric Wang" ], "github": "", "project_page": "", "abstract": "Computer-use agents have rapidly improved on real-world tasks such as web navigation, desktop automation, and software interaction, in some cases surpassing human performance. Yet even when the task and model are unchanged, an agent that succeeds once may fail on a repeated execution of the same task. This raises a fundamental question: if an agent can succeed at a task once, what prevents it from doing so reliably? In this work, we study the sources of unreliability in computer-use agents through three factors: stochasticity during execution, ambiguity in task specification, and variability in agent behavior. We analyze these factors on OSWorld using repeated executions of the same task together with paired statistical tests that capture task-level changes across settings. Our analysis shows that reliability depends on both how tasks are specified and how agent behavior varies across executions. These findings suggest the need to evaluate agents under repeated execution, to allow agents to resolve task ambiguity through interaction, and to favor strategies that remain stable across runs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.16830", "title": "The Illusion of Certainty: Decoupling Capability and Calibration in On-Policy Distillation", "authors": [ "Jiaxin Zhang", "Xiangyu Peng", "Qinglin Chen", "Qinyuan Ye", "Caiming Xiong", "Chien-Sheng Wu" ], "github": "https://github.com/SalesforceAIResearch/CaOPD", "project_page": "", "abstract": "On-policy distillation (OPD) is an increasingly important paradigm for post-training language models. However, we identify a pervasive Scaling Law of Miscalibration: while OPD effectively improves task accuracy, it systematically traps models in severe overconfidence. We trace this failure to an information mismatch: teacher supervision is formed under privileged context available during training, whereas the deployed model must report confidence using only deployment-time information. We formalize this perspective theoretically, showing that teacher-conditioned success is generally not a valid target for deployment-time confidence and that helpful privileged context induces entropy collapse and a systematic optimism bias. To address this, we propose a calibration-aware OPD framework, CaOPD, that estimates empirical confidence from model rollouts, replaces self-reported confidence with this student-grounded target, and distills the revised response through the same self-distillation pipeline. Experiments across various models and domains show that CaOPD achieves Pareto-optimal calibration while maintaining competitive capability, generalizing robustly under out-of-distribution and continual learning. Our findings highlight that capability distillation does not imply calibrated confidence, and that confidence should be treated as an essential objective in post-training. Code: https://github.com/SalesforceAIResearch/CaOPD", "github_manual": "", "github_regex": "https://github.com/SalesforceAIResearch/CaOPD", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.16498", "title": "Forge-UGC: FX optimization and register-graph engine for universal graph compiler", "authors": [ "Satyam Kumar", "Saurabh Jha" ], "github": "https://github.com/intel/intel-npu-acceleration-library", "project_page": "", "abstract": "We present Forge-UGC (FX Optimization and Register-Graph Engine for Universal Graph Compilation), a four-phase compiler for transformer deployment on heterogeneous accelerator hardware, validated on Intel AI Boost NPU. Existing frameworks such as OpenVINO and ONNX Runtime often use opaque compilation pipelines, limited pass-level visibility, and weak buffer management, which can lead to higher compilation cost and runtime overhead. Forge-UGC addresses this with a hardware-agnostic design that separates graph capture, optimization, intermediate representation lowering, and backend scheduling. Phase 1 captures graphs with torch.export at the ATen operator level, supporting modern transformer components such as rotary position embeddings, grouped-query attention, and SwiGLU without manual decomposition. Phase 2 applies six optimization passes: dead code elimination, common subexpression elimination, constant folding, attention fusion, operator fusion, and layout optimization, reducing graph node count by 14.2 to 21.9%. Phase 3 lowers the optimized graph into a typed intermediate representation with explicit virtual register assignments. Phase 4 performs liveness analysis, linear-scan buffer allocation, reducing peak buffer count by 30 to 48%, and device-affinity scheduling, reducing NPU-CPU transitions by 42 to 65%. Across six model families ranging from 125M to 8B parameters, evaluated on WikiText-103 and GLUE, Forge-UGC delivers 6.9 to 9.2x faster compilation than OpenVINO and ONNX Runtime, 18.2 to 35.7% lower inference latency, and 30.2 to 40.9% lower energy per inference. Fidelity is preserved, with max absolute logit differences below 2.1e-5 and KL divergence below 8.4e-9. We also introduce Fusion Gain Ratio, Compilation Efficiency Index, and per-pass execution profiling for systematic evaluation of NPU compilation pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/intel/intel-npu-acceleration-library", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17273", "title": "The Continuity Layer: Why Intelligence Needs an Architecture for What It Carries Forward", "authors": [ "Samuel Sameer Tanguturi" ], "github": "", "project_page": "", "abstract": "The most important architectural problem in AI is not the size of the model but the absence of a layer that carries forward what the model has come to understand. Sessions end. Context windows fill. Memory APIs return flat facts that the model has to reinterpret from scratch on every read. The result is intelligence that is powerful per session and amnesiac across time. This position paper argues that the layer which fixes this, the continuity layer, is the most consequential piece of infrastructure the field has not yet built, and that the engineering work to build it has begun in public. The formal evaluation framework for the property described here is the ATANT benchmark (arXiv:2604.06710), published separately with evaluation results on a 250-story corpus; a companion paper (arXiv:2604.10981) positions this framework against existing memory, long-context, and agentic-memory benchmarks. The paper defines continuity as a system property with seven required characteristics, distinct from memory and from retrieval; describes a storage primitive (Decomposed Trace Convergence Memory) whose write-time decomposition and read-time reconstruction produce that property; maps the engineering architecture to the theological pattern of kenosis and the symbolic pattern of Alpha and Omega, and argues this mapping is structural rather than metaphorical; proposes a four-layer development arc from external SDK to hardware node to long-horizon human infrastructure; examines why the physics limits now constraining the model layer make the continuity layer newly consequential; and argues that the governance architecture (privacy implemented as physics rather than policy, founder-controlled class shares on non-negotiable architectural commitments) is inseparable from the product itself.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2602.15143", "title": "Protecting Language Models Against Unauthorized Distillation through Trace Rewriting", "authors": [ "Xinhang Ma", "William Yeoh", "Ning Zhang", "Yevgeniy Vorobeychik" ], "github": "https://github.com/xhOwenMa/trace-rewriting", "project_page": "", "abstract": "Knowledge distillation is a widely adopted technique for transferring capabilities from LLMs to smaller, more efficient student models. However, unauthorized use of knowledge distillation takes unfair advantage of the considerable effort and cost put into developing frontier models. We investigate methods for modifying teacher-generated reasoning traces to achieve two objectives that deter unauthorized distillation: (1) anti-distillation, or degrading the training usefulness of query responses, and (2) API watermarking, which embeds verifiable signatures in student models. We introduce several approaches for dynamically rewriting a teacher's reasoning outputs while preserving answer correctness and semantic coherence. Two of these leverage the rewriting capabilities of LLMs, while others use gradient-based techniques. Our experiments show that a simple instruction-based rewriting approach achieves a strong anti-distillation effect while maintaining or even improving teacher performance. Furthermore, we show that our rewriting approach also enables embedding watermarks that can be reliably detected with essentially no false alarms. Our code is available at https://github.com/xhOwenMa/trace-rewriting.", "github_manual": "", "github_regex": "https://github.com/xhOwenMa/trace-rewriting", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.03337", "title": "Significance and Stability Analysis of Gene-Environment Interaction using RGxEStat", "authors": [ "Meng'en Qin", "Zhe Li", "Xiaohui Yang" ], "github": "https://github.com/mason-ching/RGxEStat", "project_page": "", "abstract": "Genotype-by-Environment (GxE) interactions influence the performance of genotypes across diverse environments, reducing the predictability of phenotypes in target environments. In-depth analysis of GxE interactions facilitates the identification of how genetic advantages or defects are expressed or suppressed under specific environmental conditions, thereby enabling genetic selection and enhancing breeding practices. This paper introduces two key models for GxE interaction research. Specifically, it includes significance analysis based on the mixed effect model to determine whether genes or GxE interactions significantly affect phenotypic traits; stability analysis, which further investigates the interactive relationships between genes and environments, as well as the relative superiority or inferiority of genotypes across environments. Additionally, this paper presents RGxEStat, a lightweight interactive tool, which is developed by the authors and integrates the construction, solution, and visualization of the aforementioned models. Designed to eliminate the need for breeders and agronomists to learn complex SAS or R programming, RGxEStat provides a user-friendly interface for streamlined breeding data analysis, significantly accelerating research cycles. Codes and datasets are available at https://github.com/mason-ching/RGxEStat.", "github_manual": "", "github_regex": "https://github.com/mason-ching/RGxEStat", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18267", "title": "MARCO: Navigating the Unseen Space of Semantic Correspondence", "authors": [ "Claudia Cuttano", "Gabriele Trivigno", "Carlo Masone", "Stefan Roth" ], "github": "https://github.com/visinf/MARCO", "project_page": "", "abstract": "Recent advances in semantic correspondence rely on dual-encoder architectures, combining DINOv2 with diffusion backbones. While accurate, these billion-parameter models generalize poorly beyond training keypoints, revealing a gap between benchmark performance and real-world usability, where queried points rarely match those seen during training. Building upon DINOv2, we introduce MARCO, a unified model for generalizable correspondence driven by a novel training framework that enhances both fine-grained localization and semantic generalization. By coupling a coarse-to-fine objective that refines spatial precision with a self-distillation framework, which expands sparse supervision beyond annotated regions, our approach transforms a handful of keypoints into dense, semantically coherent correspondences. MARCO sets a new state of the art on SPair-71k, AP-10K, and PF-PASCAL, with gains that amplify at fine-grained localization thresholds (+8.9 PCK@0.01), strongest generalization to unseen keypoints (+5.1, SPair-U) and categories (+4.7, MP-100), while remaining 3x smaller and 10x faster than diffusion-based approaches. Code is available at https://github.com/visinf/MARCO .", "github_manual": "", "github_regex": "https://github.com/visinf/MARCO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.15579", "title": "Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility", "authors": [ "Yining Hong", "Yining She", "Eunsuk Kang", "Christopher S. Timperley", "Christian K\u00e4stner" ], "github": "https://github.com/hyn0027/agent-symbolic-guardrails", "project_page": "", "abstract": "AI agents that interact with their environments through tools enable powerful applications, but in high-stakes business settings, unintended actions can cause unacceptable harm, such as privacy breaches and financial loss. Existing mitigations, such as training-based methods and neural guardrails, improve agent reliability but cannot provide guarantees. We study symbolic guardrails as a practical path toward strong safety and security guarantees for AI agents. Our three-part study includes a systematic review of 80 state-of-the-art agent safety and security benchmarks to identify the policies they evaluate, an analysis of which policy requirements can be guaranteed by symbolic guardrails, and an evaluation of how symbolic guardrails affect safety, security, and agent success on \u03c4^2-Bench, CAR-bench, and MedAgentBench. We find that 85\\% of benchmarks lack concrete policies, relying instead on underspecified high-level goals or common sense. Among the specified policies, 74\\% of policy requirements can be enforced by symbolic guardrails, often using simple, low-cost mechanisms. These guardrails improve safety and security without sacrificing agent utility. Overall, our results suggest that symbolic guardrails are a practical and effective way to guarantee some safety and security requirements, especially for domain-specific AI agents. We release all codes and artifacts at https://github.com/hyn0027/agent-symbolic-guardrails.", "github_manual": "", "github_regex": "https://github.com/hyn0027/agent-symbolic-guardrails", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.18396", "title": "River-LLM: Large Language Model Seamless Exit Based on KV Share", "authors": [ "Yingtao Shen", "An Zou" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated exceptional performance across diverse domains but are increasingly constrained by high inference latency. Early Exit has emerged as a promising solution to accelerate inference by dynamically bypassing redundant layers. However, in decoder-only architectures, the efficiency of Early Exit is severely bottlenecked by the KV Cache Absence problem, where skipped layers fail to provide the necessary historical states for subsequent tokens. Existing solutions, such as recomputation or masking, either introduce significant latency overhead or incur severe precision loss, failing to bridge the gap between theoretical layer reduction and practical wall-clock speedup. In this paper, we propose River-LLM, a training-free framework that enables seamless token-level Early Exit. River-LLM introduces a lightweight KV-Shared Exit River that allows the backbone's missing KV cache to be naturally generated and preserved during the exit process, eliminating the need for costly recovery operations. Furthermore, we utilize state transition similarity within decoder blocks to predict cumulative KV errors and guide precise exit decisions. Extensive experiments on mathematical reasoning and code generation tasks demonstrate that River-LLM achieves 1.71 to 2.16 times of practical speedup while maintaining high generation quality.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17318", "title": "When Background Matters: Breaking Medical Vision Language Models by Transferable Attack", "authors": [ "Akash Ghosh", "Subhadip Baidya", "Sriparna Saha", "Xiuying Chen" ], "github": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) are increasingly used in clinical diagnostics, yet their robustness to adversarial attacks remains largely unexplored, posing serious risks. Existing medical attacks focus on secondary objectives such as model stealing or adversarial fine-tuning, while transferable attacks from natural images introduce visible distortions that clinicians can easily detect. To address this, we propose MedFocusLeak, a highly transferable black-box multimodal attack that induces incorrect yet clinically plausible diagnoses while keeping perturbations imperceptible. The method injects coordinated perturbations into non-diagnostic background regions and employs an attention distraction mechanism to shift the model's focus away from pathological areas. Extensive evaluations across six medical imaging modalities show that MedFocusLeak achieves state-of-the-art performance, generating misleading yet realistic diagnostic outputs across diverse VLMs. We further introduce a unified evaluation framework with novel metrics that jointly capture attack success and image fidelity, revealing a critical weakness in the reasoning capabilities of modern clinical VLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.17596", "title": "Terminal Wrench: A Dataset of 331 Reward-Hackable Environments and 3,632 Exploit Trajectories", "authors": [ "Ivan Bercovich", "Ivgeni Segal", "Kexun Zhang", "Shashwat Saxena", "Aditi Raghunathan", "Ziqian Zhong" ], "github": "https://github.com/few-sh/terminal-wrench", "project_page": "", "abstract": "We release Terminal Wrench, a subset of 331 terminal-agent benchmark environments, copied from the popular open benchmarks that are demonstrably reward-hackable. The data set includes 3,632 hack trajectories and 2,352 legitimate baseline trajectories across three frontier models (Claude Opus 4.6, Gemini 3.1 Pro, GPT-5.4). Each entry preserves the original task definition alongside full attack trajectories that show how the verifier was bypassed. It also includes cases where the task was not solved as intended. The tasks span system administration, machine learning, software engineering, and security challenges; the exploits range from simple output spoofing to stack-frame introspection, standard-library patching, and rootkit-style binary hijacking. Crucially, these exploits are specific to each task, rather than the evaluation harness, making them harder to patch. We also present a monitorability study in which hack trajectories are sanitized or stripped of reasoning traces and then scored by an LLM judge, showing that detection degrades meaningfully when chain-of-thought is removed (AUC drops from 0.97 to 0.92). The data set is publicly available at https://github.com/few-sh/terminal-wrench.", "github_manual": "", "github_regex": "https://github.com/few-sh/terminal-wrench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-21", "arxiv_id": "2604.15760", "title": "KWBench: Measuring Unprompted Problem Recognition in Knowledge Work", "authors": [ "Ankit Maloo" ], "github": "", "project_page": "", "abstract": "We introduce the first version of KWBench (Knowledge Work Bench), a benchmark for unprompted problem recognition in large language models: can an LLM identify a professional scenario before attempting to solve it. Existing frontier benchmarks have saturated, and most knowledge-work evaluations to date reduce to extraction or task completion against a specification. KWBench targets the step before that: recognizing the governing structure of the situation from raw inputs alone. The benchmark contains 223 tasks sourced from practitioners across acquisitions, contract negotiations, clinical pharmacy, organizational politics, fraud analysis, and incentive design. Each task encodes a formal game-theoretic pattern (principal-agent conflict, signaling, mechanism design failure, strategic omission, coalitional dynamics, strategic interdependence) and carries structured ground truth recording the expert reading of the situation and the anticipated failure modes. Models receive raw data and a task prompt with no indication of problem type. Scoring is a three-tier rubric gated by a mandatory conjunctive check. Mandatory criteria encode the predicted wrong paths. We evaluate 16 models. The best model passes on 27.9% of tasks. The top two models agree on only 31.7% of their passes. Among the top 8, 44 tasks are solved by exactly one model; routing across the top 8 covers 50.7% of the benchmark, nearly double the best single model. Conditional on passing, quality scores converge (approx 83% across models); unconditional scores do not. Same models articulate the relevant game-theoretic concept correctly when asked, then fail to apply it unprompted. We release KWBench to shift how frontier models are evaluated on knowledge work, scoring them on whether they recognize the right problem from the situation alone, not only on how well they execute once the problem has been framed for them.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.13346", "title": "AgentSPEX: An Agent SPecification and EXecution Language", "authors": [ "Pengcheng Wang", "Jerry Huang", "Jiarui Yao", "Rui Pan", "Peizhi Niu", "Yaowenqi Liu", "Ruida Wang", "Renhao Lu", "Yuwei Guo", "Tong Zhang" ], "github": "", "project_page": "", "abstract": "Language-model agent systems commonly rely on reactive prompting, in which a single instruction guides the model through an open-ended sequence of reasoning and tool-use steps, leaving control flow and intermediate state implicit and making agent behavior potentially difficult to control. Orchestration frameworks such as LangGraph, DSPy, and CrewAI impose greater structure through explicit workflow definitions, but tightly couple workflow logic with Python, making agents difficult to maintain and modify. In this paper, we introduce AgentSPEX, an Agent SPecification and EXecution Language for specifying LLM-agent workflows with explicit control flow and modular structure, along with a customizable agent harness. AgentSPEX supports typed steps, branching and loops, parallel execution, reusable submodules, and explicit state management, and these workflows execute within an agent harness that provides tool access, a sandboxed virtual environment, and support for checkpointing, verification, and logging. Furthermore, we provide a visual editor with synchronized graph and workflow views for authoring and inspection. We include ready-to-use agents for deep research and scientific research, and we evaluate AgentSPEX on 7 benchmarks. Finally, we show through a user study that AgentSPEX provides a more interpretable and accessible workflow-authoring paradigm than a popular existing agent framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19636", "title": "CoInteract: Physically-Consistent Human-Object Interaction Video Synthesis via Spatially-Structured Co-Generation", "authors": [ "Xiangyang Luo", "Xiaozhe Xin", "Tao Feng", "Xu Guo", "Meiguang Jin", "Junfeng Ma" ], "github": "", "project_page": "", "abstract": "Synthesizing human--object interaction (HOI) videos has broad practical value in e-commerce, digital advertising, and virtual marketing. However, current diffusion models, despite their photorealistic rendering capability, still frequently fail on (i) the structural stability of sensitive regions such as hands and faces and (ii) physically plausible contact (e.g., avoiding hand--object interpenetration). We present CoInteract, an end-to-end framework for HOI video synthesis conditioned on a person reference image, a product reference image, text prompts, and speech audio. CoInteract introduces two complementary designs embedded into a Diffusion Transformer (DiT) backbone. First, we propose a Human-Aware Mixture-of-Experts (MoE) that routes tokens to lightweight, region-specialized experts via spatially supervised routing, improving fine-grained structural fidelity with minimal parameter overhead. Second, we propose Spatially-Structured Co-Generation, a dual-stream training paradigm that jointly models an RGB appearance stream and an auxiliary HOI structure stream to inject interaction geometry priors. During training, the HOI stream attends to RGB tokens and its supervision regularizes shared backbone weights; at inference, the HOI branch is removed for zero-overhead RGB generation. Experimental results demonstrate that CoInteract significantly outperforms existing methods in structural stability, logical consistency, and interaction realism.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19742", "title": "PlayCoder: Making LLM-Generated GUI Code Playable", "authors": [ "Zhiyuan Peng", "Wei Tao", "Xin Yin", "Chenhao Ying", "Yuan Luo", "Yiwen Guo" ], "github": "https://github.com/Tencent/PlayCoder", "project_page": "", "abstract": "Large language models (LLMs) have achieved strong results in code generation, but their ability to generate GUI applications, especially games, remains insufficiently studied. Existing benchmarks mainly evaluate correctness through test cases, which are inadequate for GUI applications because these systems are interactive, event-driven, and require correct state transitions across sequences of user actions. Their evaluation therefore should consider interaction flows and UI logic rather than only pass/fail outcomes. To study this problem, we introduce PlayEval, a repository-aware benchmark built from 43 multilingual GUI applications in Python, TypeScript, and JavaScript. Unlike prior GUI benchmarks that are difficult to adapt to desktop environments, PlayEval covers six major GUI application categories and directly supports code-generation evaluation. We further propose Play@k, a metric that measures whether at least one of *k* generated candidates can be played end-to-end without logical errors. To support reliable evaluation, we develop PlayTester, an LLM-based agent that performs task-oriented GUI playthroughs and detects logic violations automatically. Experiments on 10 state-of-the-art code LLMs show that, despite high compilation rates, they achieve near-zero Play@3, revealing major weaknesses in generating logically correct GUI applications. To address this limitation, we present PlayCoder, a multi-agent, repository-aware framework that generates, evaluates, and iteratively repairs GUI application code in a closed loop. PlayCoder substantially improves both functional correctness and semantic alignment for open-source and closed-source models, reaching up to 38.1% Exec@3 and 20.3% Play@3. Case studies further show that it can uncover silent logic bugs missed by traditional metrics and fix them through targeted edits.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Tencent/PlayCoder", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19550", "title": "LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction", "authors": [ "Jiakai Tang", "Runfeng Zhang", "Weiqiu Wang", "Yifei Liu", "Chuan Wang", "Xu Chen", "Yeqiu Yang", "Jian Wu", "Yuning Jiang", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Scaling Transformer-based click-through rate (CTR) models by stacking more parameters brings growing computational and storage overhead, creating a widening gap between scaling ambitions and the stringent industrial deployment constraints. We propose LoopCTR, which introduces a loop scaling paradigm that increases training-time computation through recursive reuse of shared model layers, decoupling computation from parameter growth. LoopCTR adopts a sandwich architecture enhanced with Hyper-Connected Residuals and Mixture-of-Experts, and employs process supervision at every loop depth to encode multi-loop benefits into the shared parameters. This enables a train-multi-loop, infer-zero-loop strategy where a single forward pass without any loop already outperforms all baselines. Experiments on three public benchmarks and one industrial dataset demonstrate state-of-the-art performance. Oracle analysis further reveals 0.02--0.04 AUC of untapped headroom, with models trained with fewer loops exhibiting higher oracle ceilings, pointing to a promising frontier for adaptive inference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19748", "title": "Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items", "authors": [ "Mengting Chen", "Zhengrui Chen", "Yongchao Du", "Zuan Gao", "Taihang Hu", "Jinsong Lan", "Chao Lin", "Yefeng Shen", "Xingjian Wang", "Zhao Wang", "Zhengtao Wu", "Xiaoli Xu", "Zhengze Xu", "Hao Yan", "Mingzhou Zhang", "Jun Zheng", "Qinye Zhou", "Xiaoyong Zhu", "Bo Zheng" ], "github": "", "project_page": "", "abstract": "Recent advances in image generation and editing have opened new opportunities for virtual try-on. However, existing methods still struggle to meet complex real-world demands. We present Tstars-Tryon 1.0, a commercial-scale virtual try-on system that is robust, realistic, versatile, and highly efficient. First, our system maintains a high success rate across challenging cases like extreme poses, severe illumination variations, motion blur, and other in-the-wild conditions. Second, it delivers highly photorealistic results with fine-grained details, faithfully preserving garment texture, material properties, and structural characteristics, while largely avoiding common AI-generated artifacts. Third, beyond apparel try-on, our model supports flexible multi-image composition (up to 6 reference images) across 8 fashion categories, with coordinated control over person identity and background. Fourth, to overcome the latency bottlenecks of commercial deployment, our system is heavily optimized for inference speed, delivering near real-time generation for a seamless user experience. These capabilities are enabled by an integrated system design spanning end-to-end model architecture, a scalable data engine, robust infrastructure, and a multi-stage training paradigm. Extensive evaluation and large-scale product deployment demonstrate that Tstars-Tryon1.0 achieves leading overall performance. To support future research, we also release a comprehensive benchmark. The model has been deployed at an industrial scale on the Taobao App, serving millions of users with tens of millions of requests.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.18164", "title": "MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge", "authors": [ "Sua Lee", "Sanghee Park", "Jinbae Im" ], "github": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have been increasingly used as automatic evaluators-a paradigm known as MLLM-as-a-Judge. However, their reliability and vulnerabilities to biases remain underexplored. We find that many MLLM judges fail to reliably integrate key visual or textual cues, yielding unreliable evaluations when evidence is missing or mismatched, and exhibiting instability under semantically irrelevant perturbations. To address this, we systematically define Compositional Bias in MLLM-as-a-Judge systems and introduce MM-JudgeBias, a benchmark for evaluating it. MM-JudgeBias introduces controlled perturbations across Query, Image, and Response, and evaluates model behavior via two complementary metrics: Bias-Deviation (BD) for sensitivity and Bias-Conformity (BC) for stability. Our dataset of over 1,800 curated and refined multimodal samples, drawn from 29 source benchmarks, enables a fine-grained diagnosis of nine bias types across diverse tasks and domains. Experiments on 26 state-of-the-art MLLMs reveal systematic modality neglect and asymmetric evaluation tendencies, underscoring the need for more reliable judges.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.17472", "title": "UniMesh: Unifying 3D Mesh Understanding and Generation", "authors": [ "Peng Huang", "Yifeng Chen", "Zeyu Zhang", "Hao Tang" ], "github": "https://github.com/AIGeeksGroup/UniMesh", "project_page": "", "abstract": "Recent advances in 3D vision have led to specialized models for either 3D understanding (e.g., shape classification, segmentation, reconstruction) or 3D generation (e.g., synthesis, completion, and editing). However, these tasks are often tackled in isolation, resulting in fragmented architectures and representations that hinder knowledge transfer and holistic scene modeling. To address these challenges, we propose UniMesh, a unified framework that jointly learns 3D generation and understanding within a single architecture. First, we introduce a novel Mesh Head that acts as a cross model interface, bridging diffusion based image generation with implicit shape decoders. Second, we develop Chain of Mesh (CoM), a geometric instantiation of iterative reasoning that enables user driven semantic mesh editing through a closed loop latent, prompting, and re generation cycle. Third, we incorporate a self reflection mechanism based on an Actor Evaluator Self reflection triad to diagnose and correct failures in high level tasks like 3D captioning. Experimental results demonstrate that UniMesh not only achieves competitive performance on standard benchmarks but also unlocks novel capabilities in iterative editing and mutual enhancement between generation and understanding. Code: https://github.com/AIGeeksGroup/UniMesh. Website: https://aigeeksgroup.github.io/UniMesh.", "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/UniMesh", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19211", "title": "ClawNet: Human-Symbiotic Agent Network for Cross-User Autonomous Cooperation", "authors": [ "Zhiqin Yang", "Zhenyuan Zhang", "Xianzhang Jia", "Jun Song", "Wei Xue", "Yonggang Zhang", "Yike Guo" ], "github": "", "project_page": "", "abstract": "Current AI agent frameworks have made remarkable progress in automating individual tasks, yet all existing systems serve a single user. Human productivity rests on the social and organizational relationships through which people coordinate, negotiate, and delegate. When agents move beyond performing tasks for one person to representing that person in collaboration with others, the infrastructure for cross-user agent collaboration is entirely absent, let alone the governance mechanisms needed to secure it. We argue that the next frontier for AI agents lies not in stronger individual capability, but in the digitization of human collaborative relationships. To this end, we propose a human-symbiotic agent paradigm. Each user owns a permanently bound agent system that collaborates on the owner's behalf, forming a network whose nodes are humans rather than agents. This paradigm rests on three governance primitives. A layered identity architecture separates a Manager Agent from multiple context-specific Identity Agents; the Manager Agent holds global knowledge but is architecturally isolated from external communication. Scoped authorization enforces per-identity access control and escalates boundary violations to the owner. Action-level accountability logs every operation against its owner's identity and authorization, ensuring full auditability. We instantiate this paradigm in ClawNet, an identity-governed agent collaboration framework that enforces identity binding and authorization verification through a central orchestrator, enabling multiple users to collaborate securely through their respective agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19341", "title": "Evaluation-driven Scaling for Scientific Discovery", "authors": [ "Haotian Ye", "Haowei Lin", "Jingyi Tang", "Yizhen Luo", "Caiyin Yang", "Chang Su", "Rahul Thapa", "Rui Yang", "Ruihua Liu", "Zeyu Li", "Chong Gao", "Dachao Ding", "Guangrong He", "Miaolei Zhang", "Lina Sun", "Wenyang Wang", "Yuchen Zhong", "Zhuohao Shen", "Di He", "Jianzhu Ma", "Stefano Ermon", "Tongyang Li", "Xiaowen Chu", "James Zou", "Yuzhi Xu" ], "github": "", "project_page": "", "abstract": "Language models are increasingly used in scientific discovery to generate hypotheses, propose candidate solutions, implement systems, and iteratively refine them. At the core of these trial-and-error loops lies evaluation: the process of obtaining feedback on candidate solutions via verifiers, simulators, or task-specific scoring functions. While prior work has highlighted the importance of evaluation, it has not explicitly formulated the problem of how evaluation-driven discovery loops can be scaled up in a principled and effective manner to push the boundaries of scientific discovery, a problem this paper seeks to address. We introduce Simple Test-time Evaluation-driven Scaling (SimpleTES), a general framework that strategically combines parallel exploration, feedback-driven refinement, and local selection, revealing substantial gains unlocked by scaling evaluation-driven discovery loops along the right dimensions. Across 21 scientific problems spanning six domains, SimpleTES discovers state-of-the-art solutions using gpt-oss models, consistently outperforming both frontier-model baselines and sophisticated optimization pipelines. Particularly, we sped up the widely used LASSO algorithm by over 2x, designed quantum circuit routing policies that reduce gate overhead by 24.5%, and discovered new Erdos minimum overlap constructions that surpass the best-known results. Beyond novel discoveries, SimpleTES produces trajectory-level histories that naturally supervise feedback-driven learning. When post-trained on successful trajectories, models not only improve efficiency on seen problems but also generalize to unseen problems, discovering solutions that base models fail to uncover. Together, our results establish effective evaluation-driven loop scaling as a central axis for advancing LLM-driven scientific discovery, and provide a simple yet practical framework for realizing these gains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.18845", "title": "Dual-View Training for Instruction-Following Information Retrieval", "authors": [ "Qingcheng Zeng", "Puxuan Yu", "Aman Mehta", "Fuheng Zhao", "Rajhans Samdani" ], "github": "", "project_page": "", "abstract": "Instruction-following information retrieval (IF-IR) studies retrieval systems that must not only find documents relevant to a query, but also obey explicit user constraints such as required attributes, exclusions, or output preferences. However, most retrievers are trained primarily for semantic relevance and often fail to distinguish documents that match the topic from those that satisfy the instruction. We propose a dual-view data synthesis strategy based on polarity reversal: given a query, a document that is relevant under the instruction, and a hard negative that matches the query but violates the instruction, we prompt an LLM to generate a complementary instruction under which the two documents swap relevance labels. By presenting the same document pair under complementary instructions that invert their relevance labels, the training signal forces the retriever to reconsider the same candidate set through the instruction, rather than relying on fixed topical cues. On a 305M-parameter encoder, our method improves performance on the FollowIR benchmark by 45%, surpassing general-purpose embedding models of comparable or larger scale. Through head-to-head comparisons at matched data budgets, we further show that data diversity and instruction supervision play complementary roles: the former preserves general retrieval quality, while the latter improves instruction sensitivity. These results highlight the value of targeted data synthesis for building retrieval systems that are both broadly capable and instruction-aware.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.17632", "title": "Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers", "authors": [ "Qingcheng Zeng", "Yuheng Lu", "Zeqi Zhou", "Heli Qi", "Puxuan Yu", "Fuheng Zhao", "Hitomi Yanaka", "Weihao Xuan", "Naoto Yokoya" ], "github": "https://github.com/paddler2022/Code-Switching-Information-Retrieval", "project_page": "", "abstract": "Code-switching is a pervasive linguistic phenomenon in global communication, yet modern information retrieval systems remain predominantly designed for, and evaluated within, monolingual contexts. To bridge this critical disconnect, we present a holistic study dedicated to code-switching IR. We introduce CSR-L (Code-Switching Retrieval benchmark-Lite), constructing a dataset via human annotation to capture the authentic naturalness of mixed-language queries. Our evaluation across statistical, dense, and late-interaction paradigms reveals that code-switching acts as a fundamental performance bottleneck, degrading the effectiveness of even robust multilingual models. We demonstrate that this failure stems from substantial divergence in the embedding space between pure and code-switched text. Scaling this investigation, we propose CS-MTEB, a comprehensive benchmark covering 11 diverse tasks, where we observe performance declines of up to 27%. Finally, we show that standard multilingual techniques like vocabulary expansion are insufficient to resolve these deficits completely. These findings underscore the fragility of current systems and establish code-switching as a crucial frontier for future IR optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/paddler2022/Code-Switching-Information-Retrieval", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.18591", "title": "SPRITE: From Static Mockups to Engine-Ready Game UI", "authors": [ "Yunshu Bai", "RuiHao Li", "Hao Zhang", "Chien Her Lim", "Ming Yan", "Mengtian Li" ], "github": "", "project_page": "", "abstract": "Game UI implementation requires translating stylized mockups into interactive engine entities. However, current \"Screenshot-to-Code\" tools often struggle with the irregular geometries and deep visual hierarchies typical of game interfaces. To bridge this gap, we introduce SPRITE, a pipeline that transforms static screenshots into editable engine assets. By integrating Vision-Language Models (VLMs) with a structured YAML intermediate representation, SPRITE explicitly captures complex container relationships and non-rectangular layouts. We evaluated SPRITE against a curated Game UI benchmark and conducted expert reviews with professional developers to assess reconstruction fidelity and prototyping efficiency. Our findings demonstrate that SPRITE streamlines development by automating tedious coding and resolving complex nesting. By facilitating rapid in-engine iteration, SPRITE effectively blurs the boundaries between artistic design and technical implementation in game development. Project page: https://baiyunshu.github.io/sprite.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19667", "title": "Chat2Workflow: A Benchmark for Generating Executable Visual Workflows with Natural Language", "authors": [ "Yi Zhong", "Buqiang Xu", "Yijun Wang", "Zifei Shan", "Shuofei Qiao", "Guozhou Zheng", "Ningyu Zhang" ], "github": "https://github.com/zjunlp/Chat2Workflow", "project_page": "", "abstract": "At present, executable visual workflows have emerged as a mainstream paradigm in real-world industrial deployments, offering strong reliability and controllability. However, in current practice, such workflows are almost entirely constructed through manual engineering: developers must carefully design workflows, write prompts for each step, and repeatedly revise the logic as requirements evolve-making development costly, time-consuming, and error-prone. To study whether large language models can automate this multi-round interaction process, we introduce Chat2Workflow, a benchmark for generating executable visual workflows directly from natural language, and propose a robust agentic framework to mitigate recurrent execution errors. Chat2Workflow is built from a large collection of real-world business workflows, with each instance designed so that the generated workflow can be transformed and directly deployed to practical workflow platforms such as Dify and Coze. Experimental results show that while state-of-the-art language models can often capture high-level intent, they struggle to generate correct, stable, and executable workflows, especially under complex or changing requirements. Although our agentic framework yields up to 5.34% resolve rate gains, the remaining real-world gap positions Chat2Workflow as a foundation for advancing industrial-grade automation. Code is available at https://github.com/zjunlp/Chat2Workflow.", "github_manual": "", "github_regex": "https://github.com/zjunlp/Chat2Workflow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.15706", "title": "Target-Oriented Pretraining Data Selection via Neuron-Activated Graph", "authors": [ "Zijun Wang", "Haoqin Tu", "Weidong Zhou", "Yiyang Zhou", "Xiaohuan Zhou", "Bingni Zhang", "Weiguo Feng", "Taifeng Wang", "Cihang Xie", "Fengze Liu" ], "github": "https://github.com/asillycat/NAG", "project_page": "", "abstract": "Everyday tasks come with a target, and pretraining models around this target is what turns them into experts. In this paper, we study target-oriented language model (LM) pretraining by introducing Neuron-Activated Graph Ranking (NAG-based Ranking), a training-free and interpretable framework for target pretraining data selection. Rather than using black-box representations, our approach directly characterizes each target input by a sparse set of high-impact neurons in any off-the-shelf LLMs. Concretely, we quantify neuron impact and select the most influential neurons across layers into a compact Neuron-Activated Graph (NAG), and rank candidate data by NAG similarity to target examples. We conduct experiments across six benchmarks, where our NAG-based Ranking improves target-oriented pretraining by 4.9% on average over random sampling, and also outperforms state-of-the-art baselines by 5.3% accuracy on HellaSwag. It also remains effective under a more applicable multi-target setting, where our best setup surpasses two baselines by 1.1% and 4.1%, respectively. Furthermore, we provide a comprehensive analysis on why and how our NAG works, e.g., deactivating NAG-selected neurons (only 0.12% of all) causes a 23.5% performance collapse, and restricting NAG to the final layer incurs a 4.1% average drop, indicating that NAG captures a sparse \"functional backbone\" for learning target features. We release the code at https://github.com/asillycat/NAG.", "github_manual": "", "github_regex": "https://github.com/asillycat/NAG", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.17761", "title": "Contrastive Attribution in the Wild: An Interpretability Analysis of LLM Failures on Realistic Benchmarks", "authors": [ "Rongyuan Tan", "Jue Zhang", "Zhuozhao Li", "Qingwei Lin", "Saravan Rajmohan", "Dongmei Zhang" ], "github": "", "project_page": "", "abstract": "Interpretability tools are increasingly used to analyze failures of Large Language Models (LLMs), yet prior work largely focuses on short prompts or toy settings, leaving their behavior on commonly used benchmarks underexplored. To address this gap, we study contrastive, LRP-based attribution as a practical tool for analyzing LLM failures in realistic settings. We formulate failure analysis as contrastive attribution, attributing the logit difference between an incorrect output token and a correct alternative to input tokens and internal model states, and introduce an efficient extension that enables construction of cross-layer attribution graphs for long-context inputs. Using this framework, we conduct a systematic empirical study across benchmarks, comparing attribution patterns across datasets, model sizes, and training checkpoints. Our results show that this token-level contrastive attribution can yield informative signals in some failure cases, but is not universally applicable, highlighting both its utility and its limitations for realistic LLM failure analysis. Our code is available at: https://aka.ms/Debug-XAI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.17397", "title": "Speculative Decoding for Autoregressive Video Generation", "authors": [ "Yuezhou Hu", "Jintao Zhang" ], "github": "", "project_page": "", "abstract": "Autoregressive video diffusion is emerging as a promising paradigm for streaming video synthesis, with step distillation serving as the primary means of accelerating inference. Whether speculative decoding, the dominant acceleration strategy for large language models, can be effectively adapted to autoregressive video generation remains an open question, because video blocks are continuous spatiotemporal tensors with no token-level distribution for exact rejection sampling. We introduce SDVG, which brings speculative decoding to block-based autoregressive video diffusion by replacing token verification with an image-quality router. A 1.3B drafter proposes candidate blocks via four denoising steps; each block is VAE-decoded and scored by ImageReward using worst-frame aggregation--taking the minimum per-frame reward to catch single-frame artifacts that averaging would mask. Blocks scoring above a fixed threshold tau are accepted into the 14B target's KV cache; the rest are regenerated by the target. Two additional design choices prove critical: the first block is always force-rejected to anchor scene composition, and tau serves as a single knob that traces a smooth quality-speed Pareto frontier. On 1003 MovieGenVideoBench prompts (832x480), SDVG retains 98.1% of target-only VisionReward quality (0.0773 vs. 0.0788) at a 1.59x speedup with tau=-0.7, and reaches 2.09x at 95.7% quality retention--while consistently outperforming draft-only generation by over +17%. The framework is training-free, requires no architectural changes, and can be seamlessly integrated into existing autoregressive video generation pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19295", "title": "TEMPO: Scaling Test-time Training for Large Reasoning Models", "authors": [ "Qingyang Zhang", "Xinke Kong", "Haitao Wu", "Qinghua Hu", "Minghao Wu", "Baosong Yang", "Yu Cheng", "Yun Luo", "Ganqu Cui", "Changqing Zhang" ], "github": "https://github.com/QingyangZhang/TEMPO", "project_page": "", "abstract": "Test-time training (TTT) adapts model parameters on unlabeled test instances during inference time, which continuously extends capabilities beyond the reach of offline training. Despite initial gains, existing TTT methods for LRMs plateau quickly and do not benefit from additional test-time compute. Without external calibration, the self-generated reward signal increasingly drifts as the policy model evolves, leading to both performance plateaus and diversity collapse. We propose TEMPO, a TTT framework that interleaves policy refinement on unlabeled questions with periodic critic recalibration on a labeled dataset. By formalizing this alternating procedure through the Expectation-Maximization (EM) algorithm, we reveal that prior methods can be interpreted as incomplete variants that omit the crucial recalibration step. Reintroducing this step tightens the evidence lower bound (ELBO) and enables sustained improvement. Across diverse model families (Qwen3 and OLMO3) and reasoning tasks, TEMPO improves OLMO3-7B on AIME 2024 from 33.0% to 51.1% and Qwen3-14B from 42.3% to 65.8%, while maintaining high diversity.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/QingyangZhang/TEMPO", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19587", "title": "SmartPhotoCrafter: Unified Reasoning, Generation and Optimization for Automatic Photographic Image Editing", "authors": [ "Ying Zeng", "Miaosen Luo", "Guangyuan Li", "Yang Yang", "Ruiyang Fan", "Linxiao Shi", "Qirui Yang", "Jian Zhang", "Chengcheng Liu", "Siming Zheng", "Jinwei Chen", "Bo Li", "Peng-Tao Jiang" ], "github": "https://github.com/vivoCameraResearch/SmartPhotoCrafter", "project_page": "", "abstract": "Traditional photographic image editing typically requires users to possess sufficient aesthetic understanding to provide appropriate instructions for adjusting image quality and camera parameters. However, this paradigm relies on explicit human instruction of aesthetic intent, which is often ambiguous, incomplete, or inaccessible to non-expert users. In this work, we propose SmartPhotoCrafter, an automatic photographic image editing method which formulates image editing as a tightly coupled reasoning-to-generation process. The proposed model first performs image quality comprehension and identifies deficiencies by the Image Critic module, and then the Photographic Artist module realizes targeted edits to enhance image appeal, eliminating the need for explicit human instructions. A multi-stage training pipeline is adopted: (i) Foundation pretraining to establish basic aesthetic understanding and editing capabilities, (ii) Adaptation with reasoning-guided multi-edit supervision to incorporate rich semantic guidance, and (iii) Coordinated reasoning-to generation reinforcement learning to jointly optimize reasoning and generation. During training, SmartPhotoCrafter emphasizes photo-realistic image generation, while supporting both image restoration and retouching tasks with consistent adherence to color- and tone-related semantics. We also construct a stage-specific dataset, which progressively builds reasoning and controllable generation, effective cross-module collaboration, and ultimately high-quality photographic enhancement. Experiments demonstrate that SmartPhotoCrafter outperforms existing generative models on the task of automatic photographic enhancement, achieving photo-realistic results while exhibiting higher tonal sensitivity to retouching instructions. Project page: https://github.com/vivoCameraResearch/SmartPhotoCrafter.", "github_manual": "", "github_regex": "https://github.com/vivoCameraResearch/SmartPhotoCrafter", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19747", "title": "AnyRecon: Arbitrary-View 3D Reconstruction with Video Diffusion Model", "authors": [ "Yutian Chen", "Shi Guo", "Renbiao Jin", "Tianshuo Yang", "Xin Cai", "Yawen Luo", "Mingxin Yang", "Mulin Yu", "Linning Xu", "Tianfan Xue" ], "github": "", "project_page": "", "abstract": "Sparse-view 3D reconstruction is essential for modeling scenes from casual captures, but remain challenging for non-generative reconstruction. Existing diffusion-based approaches mitigates this issues by synthesizing novel views, but they often condition on only one or two capture frames, which restricts geometric consistency and limits scalability to large or diverse scenes. We propose AnyRecon, a scalable framework for reconstruction from arbitrary and unordered sparse inputs that preserves explicit geometric control while supporting flexible conditioning cardinality. To support long-range conditioning, our method constructs a persistent global scene memory via a prepended capture view cache, and removes temporal compression to maintain frame-level correspondence under large viewpoint changes. Beyond better generative model, we also find that the interplay between generation and reconstruction is crucial for large-scale 3D scenes. Thus, we introduce a geometry-aware conditioning strategy that couples generation and reconstruction through an explicit 3D geometric memory and geometry-driven capture-view retrieval. To ensure efficiency, we combine 4-step diffusion distillation with context-window sparse attention to reduce quadratic complexity. Extensive experiments demonstrate robust and scalable reconstruction across irregular inputs, large viewpoint gaps, and long trajectories.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2602.10751", "title": "Predicting integers from continuous parameters", "authors": [ "Bas Maat", "Peter Bloem" ], "github": "", "project_page": "", "abstract": "We study the problem of predicting numeric labels that are constrained to the integers or to a subrange of the integers. For example, the number of up-votes on social media posts, or the number of bicycles available at a public rental station. While it is possible to model these as continuous values, and to apply traditional regression, this approach changes the underlying distribution on the labels from discrete to continuous. Discrete distributions have certain benefits, which leads us to the question whether such integer labels can be modeled directly by a discrete distribution, whose parameters are predicted from the features of a given instance. Moreover, we focus on the use case of output distributions of neural networks, which adds the requirement that the parameters of the distribution be continuous so that backpropagation and gradient descent may be used to learn the weights of the network. We investigate several options for such distributions, some existing and some novel, and test them on a range of tasks, including tabular learning, sequential prediction and image generation. We find that overall the best performance comes from two distributions: Bitwise, which represents the target integer in bits and places a Bernoulli distribution on each, and a discrete analogue of the Laplace distribution, which uses a distribution with exponentially decaying tails around a continuous mean.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.18518", "title": "UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models", "authors": [ "Jiaqi Wang", "Haoge Deng", "Ting Pan", "Yang Liu", "Chengyuan Wang", "Fan Zhang", "Yonggang Qi", "Xinlong Wang" ], "github": "https://github.com/Yovecent/UDM-GRPO", "project_page": "", "abstract": "Uniform Discrete Diffusion Model (UDM) has recently emerged as a promising paradigm for discrete generative modeling; however, its integration with reinforcement learning remains largely unexplored. We observe that naively applying GRPO to UDM leads to training instability and marginal performance gains. To address this, we propose \\Ours, the first framework to integrate UDM with RL. Our method is guided by two key insights: (i) treating the final clean sample as the action provides more accurate and stable optimization signals; and (ii) reconstructing trajectories via the diffusion forward process better aligns probability paths with the pretraining distribution. Additionally, we introduce two strategies, Reduced-Step and CFG-Free, to further improve training efficiency. \\Ours significantly improves base model performance across multiple T2I tasks. Notably, GenEval accuracy improves from 69% to 96% and PickScore increases from 20.46 to 23.81, achieving state-of-the-art performance in both continuous and discrete settings. On the OCR benchmark, accuracy rises from 8% to 57%, further validating the generalization ability of our method. Code is available at https://github.com/Yovecent/UDM-GRPO{https://github.com/Yovecent/UDM-GRPO}.", "github_manual": "", "github_regex": "https://github.com/Yovecent/UDM-GRPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.16060", "title": "Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs", "authors": [ "Sai Srinivas Kancheti", "Aditya Sanjiv Kanade", "Vineeth N. Balasubramanian", "Tanuja Ganu" ], "github": "", "project_page": "", "abstract": "Multimodal Reasoning Models (MRMs) leveraging Chain-of-Thought (CoT) based thinking have revolutionized mathematical and logical problem-solving. However, we show that this paradigm struggles with generalized spatial intelligence. We perform a comprehensive evaluation of seventeen models across thirteen spatial benchmarks and identify a critical gap: CoT prompting consistently degrades performance in visual spatial reasoning. Furthermore, through a novel No-Image++ ablation, we demonstrate that MRMs and CoT prompted MLMs suffer from severe shortcut learning, and hallucinate visual details from textual priors even when the image is absent. These findings challenge the efficacy of text-only CoT for spatial tasks and underscore the need for vision-centric reasoning paradigms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.16054", "title": "Mind's Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMs", "authors": [ "Rohit Sinha", "Aditya Kanade", "Sai Srinivas Kancheti", "Vineeth N Balasubramanian", "Tanuja Ganu" ], "github": "https://github.com/microsoft/Mind-s-Eye", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have achieved impressive progress on vision language benchmarks, yet their capacity for visual cognitive and visuospatial reasoning remains less understood. We introduce \"Mind's Eye\", a multiple-choice benchmark of eight visuo-cognitive tasks inspired by classic human intelligence tests and organized under a novel \"A-R-T\" taxonomy: Abstraction, Relation, and Transformation. The tasks probe core processes of fluid intelligence such as pattern induction, analogical relation mapping, and mental transformation. We evaluate a diverse suite of closed-source and open-source MLLMs and compare their performance with human participants. Humans achieve 80% accuracy, while top performing MLLMs remain below 50%. Error analysis reveals failures in: (i) visual attention allocation, (ii) internal perceptual manipulation, and (iii) weak abstraction of underlying visual concepts. Our findings suggest that current MLLMs exhibit limited visuospatial reasoning capabilities, when compared with human participants, highlighting the need for more cognitively grounded evaluation frameworks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/Mind-s-Eye", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.17982", "title": "Mitigating Multimodal Hallucination via Phase-wise Self-reward", "authors": [ "Yu Zhang", "Chuyang Sun", "Kehai Chen", "Xuefeng Bai", "Yang Xiang", "Min Zhang" ], "github": "", "project_page": "", "abstract": "Large Vision-Language Models (LVLMs) still struggle with vision hallucination, where generated responses are inconsistent with the visual input. Existing methods either rely on large-scale annotated data for fine-tuning, which incurs massive computational overhead, or employ static post-hoc strategies that overlook the dynamic nature of hallucination emergence. To address these, we introduce a new self-rewarding framework, enabling dynamic hallucination mitigation at inference time without external supervision. On the empirical side, we reveal that visual hallucination exhibits phase-wise dynamic patterns, peaking at the onset of each semantic phase. Drawing on these insights, we propose PSRD (Phase-wise \\textbf{Self-Reward Decoding) for online hallucination correction guided by phase-wise self-reward signals. To reduce the cost of repeated self-evaluation during decoding, we distill the hallucination guidance signal from LVLMs into a lightweight reward model. The reward model subsequently provides on-the-fly guidance for targeted intervention during the decoding process, enabling precise hallucination suppression. The proposed PSRD significantly reduces the hallucination rate of LLaVA-1.5-7B by 50.0% and consistently outperforms existing post-hoc methods across five hallucination evaluation benchmarks for four LVLMs. Further analysis confirms that PSRD effectively mitigates hallucination propagation and achieves a highly controllable trade-off between strong performance and inference efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.16913", "title": "The Cognitive Penalty: Ablating System 1 and System 2 Reasoning in Edge-Native SLMs for Decentralized Consensus", "authors": [ "Syed Muhammad Aqdas Rizvi" ], "github": "https://github.com/smarizvi110/sentinel-bench", "project_page": "", "abstract": "Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge-native constitutional firewalls to vet proposals and mitigate semantic social engineering. While scaling inference-time compute (System 2) enhances formal logic, its efficacy in highly adversarial, cryptoeconomic governance environments remains underexplored. To address this, we introduce Sentinel-Bench, an 840-inference empirical framework executing a strict intra-model ablation on Qwen-3.5-9B. By toggling latent reasoning across frozen weights, we isolate the impact of inference-time compute against an adversarial Optimism DAO dataset. Our findings reveal a severe compute-accuracy inversion. The autoregressive baseline (System 1) achieved 100% adversarial robustness, 100% juridical consistency, and state finality in under 13 seconds. Conversely, System 2 reasoning introduced catastrophic instability, fundamentally driven by a 26.7% Reasoning Non-Convergence (cognitive collapse) rate. This collapse degraded trial-to-trial consensus stability to 72.6% and imposed a 17x latency overhead, introducing critical vulnerabilities to Governance Extractable Value (GEV) and hardware centralization. While rare (1.5% of adversarial trials), we empirically captured \"Reasoning-Induced Sycophancy,\" where the model generated significantly longer internal monologues (averaging 25,750 characters) to rationalize failing the adversarial trap. We conclude that for edge-native SLMs operating under Byzantine Fault Tolerance (BFT) constraints, System 1 parameterized intuition is structurally and economically superior to System 2 iterative deliberation for decentralized consensus. Code and Dataset: https://github.com/smarizvi110/sentinel-bench", "github_manual": "", "github_regex": "https://github.com/smarizvi110/sentinel-bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19254", "title": "ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning", "authors": [ "Xianming Li", "Zongxi Li", "Tsz-fung Andrew Lee", "Jing Li", "Haoran Xie", "Qing Li" ], "github": "", "project_page": "", "abstract": "Parameter-efficient fine-tuning (PEFT) reduces the training cost of full-parameter fine-tuning for large language models (LLMs) by training only a small set of task-specific parameters while freezing the pretrained backbone. However, existing approaches, such as Low-Rank Adaptation (LoRA), achieve adaptation by inserting independent low-rank perturbations directly to individual weights, resulting in a local parameterization of adaptation. We propose ShadowPEFT, a centralized PEFT framework that instead performs layer-level refinement through a depth-shared shadow module. At each transformer layer, ShadowPEFT maintains a parallel shadow state and evolves it repeatedly for progressively richer hidden states. This design shifts adaptation from distributed weight-space perturbations to a shared layer-space refinement process. Since the shadow module is decoupled from the backbone, it can be reused across depth, independently pretrained, and optionally deployed in a detached mode, benefiting edge computing scenarios. Experiments on generation and understanding benchmarks show that ShadowPEFT matches or outperforms LoRA and DoRA under comparable trainable-parameter budgets. Additional analyses on shadow pretraining, cross-dataset transfer, parameter scaling, inference latency, and system-level evaluation suggest that centralized layer-space adaptation is a competitive and flexible alternative to conventional low-rank PEFT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19406", "title": "HP-Edit: A Human-Preference Post-Training Framework for Image Editing", "authors": [ "Fan Li", "Chonghuinan Wang", "Lina Lei", "Yuping Qiu", "Jiaqi Xu", "Jiaxiu Jiang", "Xinran Qin", "Zhikai Chen", "Fenglong Song", "Zhixin Wang", "Renjing Pei", "Wangmeng Zuo" ], "github": "", "project_page": "", "abstract": "Common image editing tasks typically adopt powerful generative diffusion models as the leading paradigm for real-world content editing. Meanwhile, although reinforcement learning (RL) methods such as Diffusion-DPO and Flow-GRPO have further improved generation quality, efficiently applying Reinforcement Learning from Human Feedback (RLHF) to diffusion-based editing remains largely unexplored, due to a lack of scalable human-preference datasets and frameworks tailored to diverse editing needs. To fill this gap, we propose HP-Edit, a post-training framework for Human Preference-aligned Editing, and introduce RealPref-50K, a real-world dataset across eight common tasks and balancing common object editing. Specifically, HP-Edit leverages a small amount of human-preference scoring data and a pretrained visual large language model (VLM) to develop HP-Scorer--an automatic, human preference-aligned evaluator. We then use HP-Scorer both to efficiently build a scalable preference dataset and to serve as the reward function for post-training the editing model. We also introduce RealPref-Bench, a benchmark for evaluating real-world editing performance. Extensive experiments demonstrate that our approach significantly enhances models such as Qwen-Image-Edit-2509, aligning their outputs more closely with human preference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.18240", "title": "AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation", "authors": [ "Wentao Shi", "Yu Wang", "Yuyang Zhao", "Yuxin Chen", "Fuli Feng", "Xueyuan Hao", "Xi Su", "Qi Gu", "Hui Su", "Xunliang Cai", "Xiangnan He" ], "github": "https://github.com/eval-sys/mcpmark", "project_page": "", "abstract": "As reinforcement learning continues to scale the training of large language model-based agents, reliably verifying agent behaviors in complex environments has become increasingly challenging. Existing approaches rely on rule-based verifiers or LLM-as-a-Judge models, which struggle to generalize beyond narrow domains. Agent-as-a-Judge addresses this limitation by actively interacting with environments and tools to acquire verifiable evidence, yet its capabilities remain underexplored. We introduce a benchmark AJ-Bench to systematically evaluate Agent-as-a-Judge across three domains-search, data systems, and graphical user interfaces-comprising 155 tasks and 516 annotated trajectories. The benchmark comprehensively assesses judge agents' abilities in information acquisition, state verification, and process verification. Experiments demonstrate consistent performance gains over LLM-as-a-Judge baselines, while also revealing substantial open challenges in agent-based verification. Our data and code are available at https://aj-bench.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/eval-sys/mcpmark", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.17078", "title": "Understanding and Enforcing Weight Disentanglement in Task Arithmetic", "authors": [ "Shangge Liu", "Yuehan Yin", "Lei Wang", "Qi Fan", "Yinghuan Shi", "Wenbin Li", "Yang Gao", "Dacheng Tao" ], "github": "https://github.com/RL-MIND/OrthoReg", "project_page": "", "abstract": "Task arithmetic provides an efficient, training-free way to edit pre-trained models, yet lacks a fundamental theoretical explanation for its success. The existing concept of ``weight disentanglement\" describes the ideal outcome of non-interfering task composition but does not reveal its underlying cause. Crucially, what intrinsic properties of the pre-trained model (\u03b8_0) or the task vectors (\u03c4_t) enable this disentanglement remains underexplored. In this paper, we introduce Task-Feature Specialization (TFS), a model's ability to allocate distinct internal features to different tasks, as the fundamental principle. We first prove that TFS is a sufficient condition for weight disentanglement. More importantly, we find that TFS also gives rise to an observable geometric consequence: weight vector orthogonality. This positions TFS as the common cause for both the desired functional outcome (disentanglement) and a measurable geometric property (orthogonality). This relationship provides the key insight for our method: since the abstract TFS property is intractable to enforce directly, we can instead promote weight disentanglement by shaping its concrete geometric consequence, orthogonality. Therefore, we propose OrthoReg, a simple and effective regularization method that actively enforces an internal orthogonal structure on weight updates (\u0394W) that constitute \u03c4_t during fine-tuning. And we theoretically prove that OrthoReg promotes disentanglement. Extensive experiments demonstrate that OrthoReg consistently and significantly enhances the performance of various task arithmetic methods. Code is available at https://github.com/RL-MIND/OrthoReg{https://github.com/RL-MIND/OrthoReg}.", "github_manual": "", "github_regex": "https://github.com/RL-MIND/OrthoReg", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2506.14665", "title": "Accurate and scalable exchange-correlation with deep learning", "authors": [ "Giulia Luise", "Chin-Wei Huang", "Thijs Vogels", "Derk P. Kooi", "Sebastian Ehlert", "Stephanie Lanius", "Klaas J. H. Giesbertz", "Amir Karton", "Deniz Gunceler", "Stefano Battaglia", "Gregor N. C. Simm", "P. Bern\u00e1t Szab\u00f3", "Megan Stanley", "Wessel P. Bruinsma", "Lin Huang", "Xinran Wei", "Jos\u00e9 Garrido Torres", "Abylay Katbashev", "Rodrigo Chavez Zavaleta", "B\u00e1lint M\u00e1t\u00e9", "S\u00e9kou-Oumar Kaba", "Roberto Sordillo", "Yingrong Chen", "David B. Williams-Young", "Christopher M. Bishop", "Jan Hermann", "Rianne van den Berg", "Paola Gori-Giorgi" ], "github": "", "project_page": "", "abstract": "Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable properties remains fundamentally limited by the need to approximate the unknown exchange-correlation (XC) functional. The traditional paradigm for improving accuracy has relied on increasingly elaborate hand-crafted functional forms. This approach has led to a longstanding trade-off between computational efficiency and accuracy, which remains insufficient for reliable predictive modelling of laboratory experiments. Here we introduce Skala, a deep learning-based XC functional that surpasses state-of-the-art hybrid functionals in accuracy across the main-group chemistry benchmark set GMTKN55 with an error of 2.8 kcal/mol, while retaining the lower computational cost characteristic of semi-local DFT. This demonstrated departure from the historical trade-off between accuracy and efficiency is enabled by learning non-local representations of electronic structure directly from data, bypassing the need for increasingly costly hand-engineered features. Leveraging an unprecedented volume of high-accuracy reference data from wavefunction-based methods, we establish that modern deep learning enables systematically improvable neural exchange-correlation models as training datasets expand, positioning first-principles simulations to become progressively more predictive.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.17435", "title": "MoVE: Translating Laughter and Tears via Mixture of Vocalization Experts in Speech-to-Speech Translation", "authors": [ "Szu-Chi Chen", "I-Ning Tsai", "Yi-Cheng Lin", "Sung-Feng Huang", "Hung-yi Lee" ], "github": "", "project_page": "", "abstract": "Recent Speech-to-Speech Translation (S2ST) systems achieve strong semantic accuracy yet consistently strip away non-verbal vocalizations (NVs), such as laughter and crying that convey pragmatic intent, which severely limits real-world utility. We address this via three contributions. First, we propose a synthesis pipeline for building scalable expressive datasets to overcome the data scarcity limitation. Second, we propose MoVE, a Mixture-of-LoRA-Experts architecture with expressive-specialized adapters and a soft-weighting router that blends experts for capturing hybrid expressive states. Third, we show pretrained AudioLLMs enable striking data efficiency: 30 minutes of curated data is enough for strong performance. On English-Chinese S2ST, while comparing with strong baselines, MoVE reproduces target NVs in 76% of cases and achieves the highest human-rated naturalness and emotional fidelity among all compared systems, where existing S2ST systems preserve at most 14% of NVs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19440", "title": "What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search", "authors": [ "Xinhao Zhang", "Xi Chen", "Fran\u00e7ois Portet", "Maxime Peyrard" ], "github": "", "project_page": "", "abstract": "Recent work has demonstrated the promise of orchestrating large language models (LLMs) within evolutionary and agentic optimization systems. However, the mechanisms driving these optimization gains remain poorly understood. In this work, we present a large-scale study of LLM-guided evolutionary search, collecting optimization trajectories for 15 LLMs across 8 tasks. Although zero-shot problem-solving ability correlates with final optimization outcomes, it explains only part of the variance: models with similar initial capability often induce dramatically different search trajectories and outcomes. By analyzing these trajectories, we find that strong LLM optimizers behave as local refiners, producing frequent incremental improvements while progressively localizing the search in semantic space. Conversely, weaker optimizers exhibit large semantic drift, with sporadic breakthroughs followed by stagnation. Notably, various measures of solution novelty do not predict final performance; novelty is beneficial only when the search remains sufficiently localized around high-performing regions of the solution space. Our results highlight the importance of trajectory analysis for understanding and improving LLM-based optimization systems and provide actionable insights for their design and training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19741", "title": "CityRAG: Stepping Into a City via Spatially-Grounded Video Generation", "authors": [ "Gene Chou", "Charles Herrmann", "Kyle Genova", "Boyang Deng", "Songyou Peng", "Bharath Hariharan", "Jason Y. Zhang", "Noah Snavely", "Philipp Henzler" ], "github": "", "project_page": "", "abstract": "We address the problem of generating a 3D-consistent, navigable environment that is spatially grounded: a simulation of a real location. Existing video generative models can produce a plausible sequence that is consistent with a text (T2V) or image (I2V) prompt. However, the capability to reconstruct the real world under arbitrary weather conditions and dynamic object configurations is essential for downstream applications including autonomous driving and robotics simulation. To this end, we present CityRAG, a video generative model that leverages large corpora of geo-registered data as context to ground generation to the physical scene, while maintaining learned priors for complex motion and appearance changes. CityRAG relies on temporally unaligned training data, which teaches the model to semantically disentangle the underlying scene from its transient attributes. Our experiments demonstrate that CityRAG can generate coherent minutes-long, physically grounded video sequences, maintain weather and lighting conditions over thousands of frames, achieve loop closure, and navigate complex trajectories to reconstruct real-world geography.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19642", "title": "Micro Language Models Enable Instant Responses", "authors": [ "Wen Cheng", "Tuochao Chen", "Karim Helwani", "Sriram Srinivasan", "Luke Zettlemoyer", "Shyamnath Gollakota" ], "github": "https://github.com/Sensente/micro_language_model_swen_project", "project_page": "", "abstract": "Edge devices such as smartwatches and smart glasses cannot continuously run even the smallest 100M-1B parameter language models due to power and compute constraints, yet cloud inference introduces multi-second latencies that break the illusion of a responsive assistant. We introduce micro language models (\u03bcLMs): ultra-compact models (8M-30M parameters) that instantly generate the first 4-8 words of a contextually grounded response on-device, while a cloud model completes it; thus, masking the cloud latency. We show that useful language generation survives at this extreme scale with our models matching several 70M-256M-class existing models. We design a collaborative generation framework that reframes the cloud model as a continuator rather than a respondent, achieving seamless mid-sentence handoffs and structured graceful recovery via three error correction methods when the local opener goes wrong. Empirical results show that \u03bcLMs can initiate responses that larger models complete seamlessly, demonstrating that orders-of-magnitude asymmetric collaboration is achievable and unlocking responsive AI for extremely resource-constrained devices. The model checkpoint and demo are available at https://github.com/Sensente/micro_language_model_swen_project.", "github_manual": "", "github_regex": "https://github.com/Sensente/micro_language_model_swen_project", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.19321", "title": "RDP LoRA: Geometry-Driven Identification for Parameter-Efficient Adaptation in Large Language Models", "authors": [ "Yusuf \u00c7elebi", "Ya\u011f\u0131z Asker", "\u00d6zay Ezerceli", "Mahmoud ElHussieni", "Selva Ta\u015f", "Reyhan Bayraktar", "Fatma Bet\u00fcl Terzio\u011flu" ], "github": "", "project_page": "", "abstract": "Fine-tuning Large Language Models (LLMs) remains structurally uncertain despite parameter-efficient methods such as Low-Rank Adaptation (LoRA), as the layer-specific roles of internal representations are poorly understood, leading to heuristic decisions about where adaptation should be applied. We model the evolution of hidden states as a high-dimensional geometric trajectory and propose using the Ramer-Douglas-Peucker (RDP) algorithm, a parameter-free and training-free polygon simplification method that preserves global structural transitions while eliminating locally redundant changes, to identify critical breakpoints along the representation path. Crucially, we use these geometric pivots not merely for analysis, but as a direct decision signal for determining which layers should be adapted during parameter-efficient fine-tuning. By integrating this geometry-aware layer selection strategy into LoRA fine-tuning of Qwen3-8B-Base, we achieve superior performance on MMLU-Math using only 13 RDP-selected layers (81.67%), significantly outperforming both full 36-layer adaptation (79.32%) and random 13-layer selection (75.56%), as well as the baseline Qwen3-8B-Base model (74.25%). These results demonstrate that leveraging the intrinsic geometry of representation trajectories provides a robust, interpretable, and training-free signal for optimizing layer selection during model adaptation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-22", "arxiv_id": "2604.15664", "title": "Stargazer: A Scalable Model-Fitting Benchmark Environment for AI Agents under Astrophysical Constraints", "authors": [ "Xinge Liu", "Terry Jingchen Zhang", "Bernhard Sch\u00f6lkopf", "Zhijing Jin", "Kristen Menou" ], "github": "https://github.com/AIPS-UofT/Stargazer", "project_page": "", "abstract": "The rise of autonomous AI agents suggests that dynamic benchmark environments with built-in feedback on scientifically grounded tasks are needed to evaluate the capabilities of these agents in research work. We introduce Stargazer, a scalable environment for evaluating AI agents on dynamic, iterative physics-grounded model-fitting tasks using inference on radial-velocity (RV) time series data. Stargazer comprises 120 tasks across three difficulty tiers, including 20 real archival cases, covering diverse scenarios ranging from high-SNR single-planet systems to complex multi-planetary configurations requiring involved low-SNR analysis. Our evaluation of eight frontier agents reveals a gap between numerical optimization and adherence to physical constraints: although agents often achieve a good statistical fit, they frequently fail to recover correct physical system parameters, a limitation that persists even when agents are equipped with vanilla skills. Furthermore, increasing test-time compute yields only marginal gains, with excessive token usage often reflecting recursive failure loops rather than meaningful exploration. Stargazer presents an opportunity to train, evaluate, scaffold, and scale strategies on a model-fitting problem of practical research relevance today. Our methodology to design a simulation-driven environment for AI agents presumably generalizes to many other model-fitting problems across scientific domains. Source code and the project website are available at https://github.com/Gudmorning2025/Stargazer and https://gudmorning2025.github.io/Stargazer, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AIPS-UofT/Stargazer", "github_api_added_by": "user" }, { "date": "2026-04-22", "arxiv_id": "2604.20087", "title": "SkillLearnBench: Benchmarking Continual Learning Methods for Agent Skill Generation on Real-World Tasks", "authors": [ "Shanshan Zhong", "Yi Lu", "Jingjie Ning", "Yibing Wan", "Lihan Feng", "Yuyi Ao", "Leonardo F. R. Ribeiro", "Markus Dreyer", "Sean Ammirati", "Chenyan Xiong" ], "github": "https://github.com/cxcscmu/SkillLearnBench", "project_page": "", "abstract": "Skills have become the de facto way to enable LLM agents to perform complex real-world tasks with customized instructions, workflows, and tools, but how to learn them automatically and effectively remains unclear. We introduce SkillLearnBench, the first benchmark for evaluating continual skill learning methods, comprising 20 verified, skill-dependent tasks across 15 sub-domains derived from a real-world skill taxonomy , evaluated at three levels: skill quality, execution trajectory, and task outcome. Using this benchmark, we evaluate recent continual learning techniques, those leveraging one-shot, self/teacher feedback, and skill creator to generate skills from agent experiences. We find that all continual learning methods improve over the no-skill baseline, yet consistent gains remain elusive: no method leads across all tasks and LLMs, and scaling to stronger LLMs does not reliably help. Continual learning improves tasks with clear, reusable workflows but struggles on open-ended tasks, and using stronger LLM backbones does not consistently produce better skills. Our analysis also revealed that multiple iterations in continual learning facilitate genuine improvement via external feedback, whereas self-feedback alone induces recursive drift. Our data and code are open-source at https://github.com/cxcscmu/SkillLearnBench to enable further studies of automatic skill generation and continual learning techniques.", "github_manual": "", "github_regex": "https://github.com/cxcscmu/SkillLearnBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.20796", "title": "LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model", "authors": [ "Inclusion AI", "Tiwei Bie", "Haoxing Chen", "Tieyuan Chen", "Zhenglin Cheng", "Long Cui", "Kai Gan", "Zhicheng Huang", "Zhenzhong Lan", "Haoquan Li", "Jianguo Li", "Tao Lin", "Qi Qin", "Hongjun Wang", "Xiaomei Wang", "Haoyuan Wu", "Yi Xin", "Junbo Zhao" ], "github": "https://github.com/inclusionAI/LLaDA2.0-Uni", "project_page": "", "abstract": "We present LLaDA2.0-Uni, a unified discrete diffusion large language model (dLLM) that supports multimodal understanding and generation within a natively integrated framework. Its architecture combines a fully semantic discrete tokenizer, a MoE-based dLLM backbone, and a diffusion decoder. By discretizing continuous visual inputs via SigLIP-VQ, the model enables block-level masked diffusion for both text and vision inputs within the backbone, while the decoder reconstructs visual tokens into high-fidelity images. Inference efficiency is enhanced beyond parallel decoding through prefix-aware optimizations in the backbone and few-step distillation in the decoder. Supported by carefully curated large-scale data and a tailored multi-stage training pipeline, LLaDA2.0-Uni matches specialized VLMs in multimodal understanding while delivering strong performance in image generation and editing. Its native support for interleaved generation and reasoning establishes a promising and scalable paradigm for next-generation unified foundation models. Codes and models are available at https://github.com/inclusionAI/LLaDA2.0-Uni.", "github_manual": "", "github_regex": "https://github.com/inclusionAI/LLaDA2.0-Uni", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.19859", "title": "DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data", "authors": [ "Venus Team", "Sunhao Dai", "Yong Deng", "Jinzhen Lin", "Yusheng Song", "Guoqing Wang", "Xiaofeng Wu", "Yuqi Zhou", "Shuo Yang", "Zhenzhe Ying", "Zhanwei Zhang", "Changhua Meng", "Weiqiang Wang" ], "github": "", "project_page": "", "abstract": "Edge-scale deep research agents based on small language models are attractive for real-world deployment due to their advantages in cost, latency, and privacy. In this work, we study how to train a strong small deep research agent under limited open-data by improving both data quality and data utilization. We present DR-Venus, a frontier 4B deep research agent for edge-scale deployment, built entirely on open data. Our training recipe consists of two stages. In the first stage, we use agentic supervised fine-tuning (SFT) to establish basic agentic capability, combining strict data cleaning with resampling of long-horizon trajectories to improve data quality and utilization. In the second stage, we apply agentic reinforcement learning (RL) to further improve execution reliability on long-horizon deep research tasks. To make RL effective for small agents in this setting, we build on IGPO and design turn-level rewards based on information gain and format-aware regularization, thereby enhancing supervision density and turn-level credit assignment. Built entirely on roughly 10K open-data, DR-Venus-4B significantly outperforms prior agentic models under 9B parameters on multiple deep research benchmarks, while also narrowing the gap to much larger 30B-class systems. Our further analysis shows that 4B agents already possess surprisingly strong performance potential, highlighting both the deployment promise of small models and the value of test-time scaling in this setting. We release our models, code, and key recipes to support reproducible research on edge-scale deep research agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.14932", "title": "WavAlign: Enhancing Intelligence and Expressiveness in Spoken Dialogue Models via Adaptive Hybrid Post-Training", "authors": [ "Yifu Chen", "Shengpeng Ji", "Qian Chen", "Tianle Liang", "Yangzhuo Li", "Ziqing Wang", "Wen Wang", "Jingyu Lu", "Haoxiao Wang", "Xueyi Pu", "Fan Zhuo", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "End-to-end spoken dialogue models have garnered significant attention because they offer a higher potential ceiling in expressiveness and perceptual ability than cascaded systems. However, the intelligence and expressiveness of current open-source spoken dialogue models often remain below expectations. Motivated by the success of online reinforcement learning(RL) in other domains, one might attempt to directly apply preference optimization to spoken dialogue models, yet this transfer is non-trivial. We analyze these obstacles from the perspectives of reward modeling and rollout sampling, focusing on how sparse preference supervision interacts with dense speech generation under shared-parameter updates. Based on the analysis, we propose a modality-aware adaptive post-training recipe that makes RL practical for spoken dialogue: it constrains preference updates to the semantic channel and improves acoustic behavior via explicit anchoring, while dynamically regulating their mixture from rollout statistics to avoid unreliable preference gradients. We evaluate the method across multiple spoken dialogue benchmarks and representative architectures, and observe consistent improvements in semantic quality and speech expressiveness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.20779", "title": "SWE-chat: Coding Agent Interactions From Real Users in the Wild", "authors": [ "Joachim Baumann", "Vishakh Padmakumar", "Xiang Li", "John Yang", "Diyi Yang", "Sanmi Koyejo" ], "github": "https://github.com/entireio/cli", "project_page": "", "abstract": "AI coding agents are being adopted at scale, yet we lack empirical evidence on how people actually use them and how much of their output is useful in practice. We present SWE-chat, the first large-scale dataset of real coding agent sessions collected from open-source developers in the wild. The dataset currently contains 6,000 sessions, comprising more than 63,000 user prompts and 355,000 agent tool calls. SWE-chat is a living dataset; our collection pipeline automatically and continually discovers and processes sessions from public repositories. Leveraging SWE-chat, we provide an initial empirical characterization of real-world coding agent usage and failure modes. We find that coding patterns are bimodal: in 41% of sessions, agents author virtually all committed code (\"vibe coding\"), while in 23%, humans write all code themselves. Despite rapidly improving capabilities, coding agents remain inefficient in natural settings. Just 44% of all agent-produced code survives into user commits, and agent-written code introduces more security vulnerabilities than code authored by humans. Furthermore, users push back against agent outputs -- through corrections, failure reports, and interruptions -- in 44% of all turns. By capturing complete interaction traces with human vs. agent code authorship attribution, SWE-chat provides an empirical foundation for moving beyond curated benchmarks towards an evidence-based understanding of how AI agents perform in real developer workflows.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/entireio/cli", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.20246", "title": "Cortex 2.0: Grounding World Models in Real-World Industrial Deployment", "authors": [ "Adriana Aida", "Walida Amer", "Katarina Bankovic", "Dhruv Behl", "Fabian Busch", "Annie Bhalla", "Minh Duong", "Florian Gienger", "Rohan Godse", "Denis Grachev", "Ralf Gulde", "Elisa Hagensieker", "Junpeng Hu", "Shivam Joshi", "Tobias Knoblauch", "Likith Kumar", "Damien LaRocque", "Keerthana Lokesh", "Omar Moured", "Khiem Nguyen", "Christian Preyss", "Ranjith Sriganesan", "Vikram Singh", "Carsten Sponner", "Anh Tong", "Dominik Tuscher", "Marc Tuscher", "Pavan Upputuri" ], "github": "", "project_page": "", "abstract": "Industrial robotic manipulation demands reliable long-horizon execution across embodiments, tasks, and changing object distributions. While Vision-Language-Action models have demonstrated strong generalization, they remain fundamentally reactive. By optimizing the next action given the current observation without evaluating potential futures, they are brittle to the compounding failure modes of long-horizon tasks. Cortex 2.0 shifts from reactive control to plan-and-act by generating candidate future trajectories in visual latent space, scoring them for expected success and efficiency, then committing only to the highest-scoring candidate. We evaluate Cortex 2.0 on a single-arm and dual-arm manipulation platform across four tasks of increasing complexity: pick and place, item and trash sorting, screw sorting, and shoebox unpacking. Cortex 2.0 consistently outperforms state-of-the-art Vision-Language-Action baselines, achieving the best results across all tasks. The system remains reliable in unstructured environments characterized by heavy clutter, frequent occlusions, and contact-rich manipulation, where reactive policies fail. These results demonstrate that world-model-based planning can operate reliably in complex industrial environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.19902", "title": "MMCORE: MultiModal COnnection with Representation Aligned Latent Embeddings", "authors": [ "Zijie Li", "Yichun Shi", "Jingxiang Sun", "Ye Wang", "Yixuan Huang", "Zhiyao Guo", "Xiaochen Lian", "Peihao Zhu", "Yu Tian", "Zhonghua Zhai", "Peng Wang" ], "github": "", "project_page": "", "abstract": "We present MMCORE, a unified framework designed for multimodal image generation and editing. MMCORE leverages a pre-trained Vision-Language Model (VLM) to predict semantic visual embeddings via learnable query tokens, which subsequently serve as conditioning signals for a diffusion model. This streamlined design effectively transfers the rich understanding and reasoning capabilities of VLMs into the visual generation process. By obviating the need for deep fusion between autoregressive and diffusion models or training from scratch, MMCORE significantly reduces computational overhead while maintaining high-fidelity synthesis. MMCORE seamlessly integrates text-to-image synthesis with interleaved image generation, demonstrating robust multimodal comprehension in complex scenarios such as spatial reasoning and visual grounding. Comprehensive evaluations indicate that MMCORE consistently outperforms state-of-the-art baselines across a broad spectrum of text-to-image and single/multi-image editing benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.19926", "title": "CreativeGame:Toward Mechanic-Aware Creative Game Generation", "authors": [ "Hongnan Ma", "Han Wang", "Shenglin Wang", "Tieyue Yin", "Yiwei Shi", "Yucong Huang", "Yingtian Zou", "Muning Wen", "Mengyue Yang" ], "github": "", "project_page": "", "abstract": "Large language models can generate plausible game code, but turning this capability into iterative creative improvement remains difficult. In practice, single-shot generation often produces brittle runtime behavior, weak accumulation of experience across versions, and creativity scores that are too subjective to serve as reliable optimization signals. A further limitation is that mechanics are frequently treated only as post-hoc descriptions, rather than as explicit objects that can be planned, tracked, preserved, and evaluated during generation. This report presents CreativeGame, a multi-agent system for iterative HTML5 game generation that addresses these issues through four coupled ideas: a proxy reward centered on programmatic signals rather than pure LLM judgment; lineage-scoped memory for cross-version experience accumulation; runtime validation integrated into both repair and reward; and a mechanic-guided planning loop in which retrieved mechanic knowledge is converted into an explicit mechanic plan before code generation begins. The goal is not merely to produce a playable artifact in one step, but to support interpretable version-to-version evolution. The current system contains 71 stored lineages, 88 saved nodes, and a 774-entry global mechanic archive, implemented in 6{,}181 lines of Python together with inspection and visualization tooling. The system is therefore substantial enough to support architectural analysis, reward inspection, and real lineage-level case studies rather than only prompt-level demos. A real 4-generation lineage shows that mechanic-level innovation can emerge in later versions and can be inspected directly through version-to-version records. The central contribution is therefore not only game generation, but a concrete pipeline for observing progressive evolution through explicit mechanic change.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.11610", "title": "Self-Evolving LLM Memory Extraction Across Heterogeneous Tasks", "authors": [ "Yuqing Yang", "Tengxiao Liu", "Wang Bill Zhu", "Taiwei Shi", "Linxin Song", "Robin Jia" ], "github": "https://github.com/ayyyq/heterogeneous-memory-extraction", "project_page": "", "abstract": "As LLM-based assistants become persistent and personalized, they must extract and retain useful information from past conversations as memory. However, the types of information worth remembering vary considerably across tasks. We formalize the heterogeneous memory extraction task and introduce BEHEMOTH, a benchmark that repurposes 18 existing datasets spanning personalization, problem-solving, and agentic tasks, using a downstream utility-driven metric for systematic evaluation. Our empirical analysis confirms that no single static extraction prompt dominates across all task categories, and that existing self-evolving prompt optimization frameworks, originally designed for homogeneous distributions, degrade when training tasks are heterogeneous. To address this, we propose CluE, a cluster-based self-evolving strategy that groups training examples into clusters by extraction scenarios, analyzes each cluster independently, and synthesizes cross-cluster insights to update the extraction prompt. Experiments on BEHEMOTH show that CluE generalizes effectively across heterogeneous tasks (+9.04\\% relative gain), consistently outperforming prior self-evolving frameworks.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ayyyq/heterogeneous-memory-extraction", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.20817", "title": "Convergent Evolution: How Different Language Models Learn Similar Number Representations", "authors": [ "Deqing Fu", "Tianyi Zhou", "Mikhail Belkin", "Vatsal Sharan", "Robin Jia" ], "github": "", "project_page": "", "abstract": "Language models trained on natural text learn to represent numbers using periodic features with dominant periods at T=2, 5, 10. In this paper, we identify a two-tiered hierarchy of these features: while Transformers, Linear RNNs, LSTMs, and classical word embeddings trained in different ways all learn features that have period-T spikes in the Fourier domain, only some learn geometrically separable features that can be used to linearly classify a number mod-T. To explain this incongruity, we prove that Fourier domain sparsity is necessary but not sufficient for mod-T geometric separability. Empirically, we investigate when model training yields geometrically separable features, finding that the data, architecture, optimizer, and tokenizer all play key roles. In particular, we identify two different routes through which models can acquire geometrically separable features: they can learn them from complementary co-occurrence signals in general language data, including text-number co-occurrence and cross-number interaction, or from multi-token (but not single-token) addition problems. Overall, our results highlight the phenomenon of convergent evolution in feature learning: A diverse range of models learn similar features from different training signals.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.13602", "title": "Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges", "authors": [ "Xiaohua Wang", "Muzhao Tian", "Yuqi Zeng", "Zisu Huang", "Jiakang Yuan", "Bowen Chen", "Jingwen Xu", "Mingbo Zhou", "Wenhao Liu", "Muling Wu", "Zhengkang Guo", "Qi Qian", "Yifei Wang", "Feiran Zhang", "Ruicheng Yin", "Shihan Dou", "Changze Lv", "Tao Chen", "Kaitao Song", "Xu Tan", "Tao Gui", "Xiaoqing Zheng", "Xuanjing Huang" ], "github": "https://github.com/ruixin31/Rethink_RLVR", "project_page": "", "abstract": "Reinforcement Learning from Human Feedback (RLHF) and related alignment paradigms have become central to steering large language models (LLMs) and multimodal large language models (MLLMs) toward human-preferred behaviors. However, these approaches introduce a systemic vulnerability: reward hacking, where models exploit imperfections in learned reward signals to maximize proxy objectives without fulfilling true task intent. As models scale and optimization intensifies, such exploitation manifests as verbosity bias, sycophancy, hallucinated justification, benchmark overfitting, and, in multimodal settings, perception--reasoning decoupling and evaluator manipulation. Recent evidence further suggests that seemingly benign shortcut behaviors can generalize into broader forms of misalignment, including deception and strategic gaming of oversight mechanisms. In this survey, we propose the Proxy Compression Hypothesis (PCH) as a unifying framework for understanding reward hacking. We formalize reward hacking as an emergent consequence of optimizing expressive policies against compressed reward representations of high-dimensional human objectives. Under this view, reward hacking arises from the interaction of objective compression, optimization amplification, and evaluator--policy co-adaptation. This perspective unifies empirical phenomena across RLHF, RLAIF, and RLVR regimes, and explains how local shortcut learning can generalize into broader forms of misalignment, including deception and strategic manipulation of oversight mechanisms. We further organize detection and mitigation strategies according to how they intervene on compression, amplification, or co-adaptation dynamics. By framing reward hacking as a structural instability of proxy-based alignment under scale, we highlight open challenges in scalable oversight, multimodal grounding, and agentic autonomy.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ruixin31/Rethink_RLVR", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.20570", "title": "Exploring Spatial Intelligence from a Generative Perspective", "authors": [ "Muzhi Zhu", "Shunyao Jiang", "Huanyi Zheng", "Zekai Luo", "Hao Zhong", "Anzhou Li", "Kaijun Wang", "Jintao Rong", "Yang Liu", "Hao Chen", "Tao Lin", "Chunhua Shen" ], "github": "", "project_page": "", "abstract": "Spatial intelligence is essential for multimodal large language models, yet current benchmarks largely assess it only from an understanding perspective. We ask whether modern generative or unified multimodal models also possess generative spatial intelligence (GSI), the ability to respect and manipulate 3D spatial constraints during image generation, and whether such capability can be measured or improved. We introduce GSI-Bench, the first benchmark designed to quantify GSI through spatially grounded image editing. It consists of two complementary components: GSI-Real, a high-quality real-world dataset built via a 3D-prior-guided generation and filtering pipeline, and GSI-Syn, a large-scale synthetic benchmark with controllable spatial operations and fully automated labeling. Together with a unified evaluation protocol, GSI-Bench enables scalable, model-agnostic assessment of spatial compliance and editing fidelity. Experiments show that fine-tuning unified multimodal models on GSI-Syn yields substantial gains on both synthetic and real tasks and, strikingly, also improves downstream spatial understanding. This provides the first clear evidence that generative training can tangibly strengthen spatial reasoning, establishing a new pathway for advancing spatial intelligence in multimodal models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.20733", "title": "Near-Future Policy Optimization", "authors": [ "Chuanyu Qin", "Chenxu Yang", "Qingyi Si", "Naibin Gu", "Dingyu Yao", "Zheng Lin", "Peng Fu", "Nan Duan", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has become a core post-training recipe. Introducing suitable off-policy trajectories into on-policy exploration accelerates RLVR convergence and raises the performance ceiling, yet finding a source of such trajectories remains the key challenge. Existing mixed-policy methods either import trajectories from external teachers (high-quality but distributionally far) or replay past training trajectories (close but capped in quality), and neither simultaneously satisfies the strong enough (higher Q , more new knowledge to learn) and close enough (lower V , more readily absorbed) conditions required to maximize the effective learning signal S = Q/V. We propose Near-Future Policy Optimization (NPO), a simple mixed-policy scheme that learns from a policy's own near-future self: a later checkpoint from the same training run is a natural source of auxiliary trajectories that is both stronger than the current policy and closer than any external source, directly balancing trajectory quality against variance cost. We validate NPO through two manual interventions, early-stage bootstrapping and late-stage plateau breakthrough, and further propose AutoNPO,an adaptive variant that automatically triggers interventions from online training signals and selects the guide checkpoint that maximizes S. On Qwen3-VL-8B-Instruct with GRPO, NPO improves average performance from 57.88 to 62.84, and AutoNPO pushes it to 63.15, raising the final performance ceiling while accelerating convergence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.16529", "title": "Scaling Test-Time Compute for Agentic Coding", "authors": [ "Joongwon Kim", "Wannan Yang", "Kelvin Niu", "Hongming Zhang", "Yun Zhu", "Eryk Helenowski", "Ruan Silva", "Zhengxing Chen", "Srinivasan Iyer", "Manzil Zaheer", "Daniel Fried", "Hannaneh Hajishirzi", "Sanjeev Arora", "Gabriel Synnaeve", "Ruslan Salakhutdinov", "Anirudh Goyal" ], "github": "", "project_page": "", "abstract": "Test-time scaling has become a powerful way to improve large language models. However, existing methods are best suited to short, bounded outputs that can be directly compared, ranked or refined. Long-horizon coding agents violate this premise: each attempt produces an extended trajectory of actions, observations, errors, and partial progress taken by the agent. In this setting, the main challenge is no longer generating more attempts, but representing prior experience in a form that can be effectively selected from and reused. We propose a test-time scaling framework for agentic coding based on compact representations of rollout trajectories. Our framework converts each rollout into a structured summary that preserves its salient hypotheses, progress, and failure modes while discarding low-signal trace details. This representation enables two complementary forms of inference-time scaling. For parallel scaling, we introduce Recursive Tournament Voting (RTV), which recursively narrows a population of rollout summaries through small-group comparisons. For sequential scaling, we adapt Parallel-Distill-Refine (PDR) to the agentic setting by conditioning new rollouts on summaries distilled from prior attempts. Our method consistently improves the performance of frontier coding agents across SWE-Bench Verified and Terminal-Bench v2.0. For example, by using our method Claude-4.5-Opus improves from 70.9% to 77.6% on SWE-Bench Verified (mini-SWE-agent) and 46.9% to 59.1% on Terminal-Bench v2.0 (Terminus 1). Our results suggest that test-time scaling for long-horizon agents is fundamentally a problem of representation, selection, and reuse.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.19720", "title": "ReImagine: Rethinking Controllable High-Quality Human Video Generation via Image-First Synthesis", "authors": [ "Zhengwentai Sun", "Keru Zheng", "Chenghong Li", "Hongjie Liao", "Xihe Yang", "Heyuan Li", "Yihao Zhi", "Shuliang Ning", "Shuguang Cui", "Xiaoguang Han" ], "github": "https://github.com/Taited/ReImagine", "project_page": "", "abstract": "Human video generation remains challenging due to the difficulty of jointly modeling human appearance, motion, and camera viewpoint under limited multi-view data. Existing methods often address these factors separately, resulting in limited controllability or reduced visual quality. We revisit this problem from an image-first perspective, where high-quality human appearance is learned via image generation and used as a prior for video synthesis, decoupling appearance modeling from temporal consistency. We propose a pose- and viewpoint-controllable pipeline that combines a pretrained image backbone with SMPL-X-based motion guidance, together with a training-free temporal refinement stage based on a pretrained video diffusion model. Our method produces high-quality, temporally consistent videos under diverse poses and viewpoints. We also release a canonical human dataset and an auxiliary model for compositional human image synthesis. Code and data are publicly available at https://github.com/Taited/ReImagine.", "github_manual": "", "github_regex": "https://github.com/Taited/ReImagine", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.17568", "title": "Diverse Dictionary Learning", "authors": [ "Yujia Zheng", "Zijian Li", "Shunxing Fan", "Andrew Gordon Wilson", "Kun Zhang" ], "github": "", "project_page": "", "abstract": "Given only observational data X = g(Z), where both the latent variables Z and the generating process g are unknown, recovering Z is ill-posed without additional assumptions. Existing methods often assume linearity or rely on auxiliary supervision and functional constraints. However, such assumptions are rarely verifiable in practice, and most theoretical guarantees break down under even mild violations, leaving uncertainty about how to reliably understand the hidden world. To make identifiability actionable in the real-world scenarios, we take a complementary view: in the general settings where full identifiability is unattainable, what can still be recovered with guarantees, and what biases could be universally adopted? We introduce the problem of diverse dictionary learning to formalize this view. Specifically, we show that intersections, complements, and symmetric differences of latent variables linked to arbitrary observations, along with the latent-to-observed dependency structure, are still identifiable up to appropriate indeterminacies even without strong assumptions. These set-theoretic results can be composed using set algebra to construct structured and essential views of the hidden world, such as genus-differentia definitions. When sufficient structural diversity is present, they further imply full identifiability of all latent variables. Notably, all identifiability benefits follow from a simple inductive bias during estimation that can be readily integrated into most models. We validate the theory and demonstrate the benefits of the bias on both synthetic and real-world data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.18982", "title": "SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution", "authors": [ "Xiachong Feng", "Yi Jiang", "Xiaocheng Feng", "Deyi Yin", "Libo Qin", "Yangfan Ye", "Lei Huang", "Weitao Ma", "Yuxuan Gu", "Chonghan Qin", "Bing Qin", "Lingpeng Kong" ], "github": "", "project_page": "", "abstract": "Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents. Training such agents via reinforcement learning requires solving the credit assignment problem: determining how individual utterances contribute to multi-turn dialogue outcomes. Existing approaches directly employ language models to distribute episode-level rewards, yielding attributions that are retrospective and lack theoretical grounding. We propose SAVOIR (ShApley Value fOr SocIal RL), a novel principled framework grounded in cooperative game theory. Our approach combines two complementary principles: expected utility shifts evaluation from retrospective attribution to prospective valuation, capturing an utterance's strategic potential for enabling favorable future trajectories; Shapley values ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality. Experiments on the SOTOPIA benchmark demonstrate that SAVOIR achieves new state-of-the-art performance across all evaluation settings, with our 7B model matching or exceeding proprietary models including GPT-4o and Claude-3.5-Sonnet. Notably, even large reasoning models consistently underperform, suggesting social intelligence requires qualitatively different capabilities than analytical reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.19945", "title": "Visual Reasoning through Tool-supervised Reinforcement Learning", "authors": [ "Qihua Dong", "Gozde Sahin", "Pei Wang", "Zhaowei Cai", "Robik Shrestha", "Hao Yang", "Davide Modolo" ], "github": "", "project_page": "", "abstract": "In this paper, we investigate the problem of how to effectively master tool-use to solve complex visual reasoning tasks for Multimodal Large Language Models. To achieve that, we propose a novel Tool-supervised Reinforcement Learning (ToolsRL) framework, with direct tool supervision for more effective tool-use learning. We focus on a series of simple, native, and interpretable visual tools, including zoom-in, rotate, flip, and draw point/line, whose tool supervision is easy to collect. A reinforcement learning curriculum is developed, where the first stage is solely optimized by a set of well motivated tool-specific rewards, and the second stage is trained with the accuracy targeted rewards while allowing calling tools. In this way, tool calling capability is mastered before using tools to complete visual reasoning tasks, avoiding the potential optimization conflict among those heterogeneous tasks. Our experiments have shown that the tool-supervised curriculum training is efficient and ToolsRL can achieve strong tool-use capabilities for complex visual reasoning tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.18805", "title": "AI scientists produce results without reasoning scientifically", "authors": [ "Marti\u00f1o R\u00edos-Garc\u00eda", "Nawaf Alampara", "Chandan Gupta", "Indrajeet Mandal", "Sajid Mannan", "Ali Asghar Aghajani", "N. M. Anoop Krishnan", "Kevin Maik Jablonka" ], "github": "", "project_page": "", "abstract": "Large language model (LLM)-based systems are increasingly deployed to conduct scientific research autonomously, yet whether their reasoning adheres to the epistemic norms that make scientific inquiry self-correcting is poorly understood. Here, we evaluate LLM-based scientific agents across eight domains, spanning workflow execution to hypothesis-driven inquiry, through more than 25,000 agent runs and two complementary lenses: (i) a systematic performance analysis that decomposes the contributions of the base model and the agent scaffold, and (ii) a behavioral analysis of the epistemological structure of agent reasoning. We observe that the base model is the primary determinant of both performance and behavior, accounting for 41.4% of explained variance versus 1.5% for the scaffold. Across all configurations, evidence is ignored in 68% of traces, refutation-driven belief revision occurs in 26%, and convergent multi-test evidence is rare. The same reasoning pattern appears whether the agent executes a computational workflow or conducts hypothesis-driven inquiry. They persist even when agents receive near-complete successful reasoning trajectories as context, and the resulting unreliability compounds across repeated trials in epistemically demanding domains. Thus, current LLM-based agents execute scientific workflows but do not exhibit the epistemic patterns that characterize scientific reasoning. Outcome-based evaluation cannot detect these failures, and scaffold engineering alone cannot repair them. Until reasoning itself becomes a training target, the scientific knowledge produced by such agents cannot be justified by the process that generated it.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.18932", "title": "Tadabur: A Large-Scale Quran Audio Dataset", "authors": [ "Faisal Alherran" ], "github": "", "project_page": "", "abstract": "Despite growing interest in Quranic data research, existing Quran datasets remain limited in both scale and diversity. To address this gap, we present Tadabur, a large-scale Quran audio dataset. Tadabur comprises more than 1400+ hours of recitation audio from over 600 distinct reciters, providing substantial variation in recitation styles, vocal characteristics, and recording conditions. This diversity makes Tadabur a comprehensive and representative resource for Quranic speech research and analysis. By significantly expanding both the total duration and variability of available Quran data, Tadabur aims to support future research and facilitate the development of standardized Quranic speech benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.20841", "title": "DeVI: Physics-based Dexterous Human-Object Interaction via Synthetic Video Imitation", "authors": [ "Hyeonwoo Kim", "Jeonghwan Kim", "Kyungwon Cho", "Hanbyul Joo" ], "github": "", "project_page": "", "abstract": "Recent advances in video generative models enable the synthesis of realistic human-object interaction videos across a wide range of scenarios and object categories, including complex dexterous manipulations that are difficult to capture with motion capture systems. While the rich interaction knowledge embedded in these synthetic videos holds strong potential for motion planning in dexterous robotic manipulation, their limited physical fidelity and purely 2D nature make them difficult to use directly as imitation targets in physics-based character control. We present DeVI (Dexterous Video Imitation), a novel framework that leverages text-conditioned synthetic videos to enable physically plausible dexterous agent control for interacting with unseen target objects. To overcome the imprecision of generative 2D cues, we introduce a hybrid tracking reward that integrates 3D human tracking with robust 2D object tracking. Unlike methods relying on high-quality 3D kinematic demonstrations, DeVI requires only the generated video, enabling zero-shot generalization across diverse objects and interaction types. Extensive experiments demonstrate that DeVI outperforms existing approaches that imitate 3D human-object interaction demonstrations, particularly in modeling dexterous hand-object interactions. We further validate the effectiveness of DeVI in multi-object scenes and text-driven action diversity, showcasing the advantage of using video as an HOI-aware motion planner.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.19572", "title": "A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression", "authors": [ "Jincheng Ren", "Siwei Wu", "Yizhi Li", "Kang Zhu", "Shu Xu", "Boyu Feng", "Ruibin Yuan", "Wei Zhang", "Riza Batista-Navarro", "Jian Yang", "Chenghua Lin" ], "github": "https://github.com/multimodal-art-projection/TACO", "project_page": "", "abstract": "As model capabilities advance, research has increasingly shifted toward long-horizon, multi-turn terminal-centric agentic tasks, where raw environment feedback is often preserved in the interaction history to support future decisions. However, repeatedly retaining such feedback introduces substantial redundancy and causes cumulative token cost to grow quadratically with the number of steps, hindering long-horizon reasoning. Although observation compression can mitigate this issue, the heterogeneity of terminal environments makes heuristic-based or fixed-prompt methods difficult to generalize. We propose TACO, a plug-and-play, self-evolving Terminal Agent Compression framework that automatically discovers and refines compression rules from interaction trajectories for existing terminal agents. Experiments on TerminalBench (TB 1.0 and TB 2.0) and four additional terminal-related benchmarks (i.e., SWE-Bench Lite, CompileBench, DevEval, and CRUST-Bench) show that TACO consistently improves performance across mainstream agent frameworks and strong backbone models. With MiniMax-2.5, it improves performance on most benchmarks while reducing token overhead by around 10%. On TerminalBench, it brings consistent gains of 1%-4% across strong agentic models, and further improves accuracy by around 2%-3% under the same token budget. These results demonstrate the effectiveness and generalization of self-evolving, task-aware compression for terminal agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/multimodal-art-projection/TACO", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.20329", "title": "Image Generators are Generalist Vision Learners", "authors": [ "Valentin Gabeur", "Shangbang Long", "Songyou Peng", "Paul Voigtlaender", "Shuyang Sun", "Yanan Bao", "Karen Truong", "Zhicheng Wang", "Wenlei Zhou", "Jonathan T. Barron", "Kyle Genova", "Nithish Kannen", "Sherry Ben", "Yandong Li", "Mandy Guo", "Suhas Yogin", "Yiming Gu", "Huizhong Chen", "Oliver Wang", "Saining Xie", "Howard Zhou", "Kaiming He", "Thomas Funkhouser", "Jean-Baptiste Alayrac", "Radu Soricut" ], "github": "", "project_page": "", "abstract": "Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language understanding and reasoning from generative pretraining. While it has long been conjectured that the ability to create visual content implies an ability to understand it, there has been limited evidence that generative vision models have developed strong understanding capabilities. In this work, we demonstrate that image generation training serves a role similar to LLM pretraining, and lets models learn powerful and general visual representations that enable SOTA performance on various vision tasks. We introduce Vision Banana, a generalist model built by instruction-tuning Nano Banana Pro (NBP) on a mixture of its original training data alongside a small amount of vision task data. By parameterizing the output space of vision tasks as RGB images, we seamlessly reframe perception as image generation. Our generalist model, Vision Banana, achieves SOTA results on a variety of vision tasks involving both 2D and 3D understanding, beating or rivaling zero-shot domain-specialists, including Segment Anything Model 3 on segmentation tasks, and the Depth Anything series on metric depth estimation. We show that these results can be achieved with lightweight instruction-tuning without sacrificing the base model's image generation capabilities. The superior results suggest that image generation pretraining is a generalist vision learner. It also shows that image generation serves as a unified and universal interface for vision tasks, similar to text generation's role in language understanding and reasoning. We could be witnessing a major paradigm shift for computer vision, where generative vision pretraining takes a central role in building Foundational Vision Models for both generation and understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.17073", "title": "Abstain-R1: Calibrated Abstention and Post-Refusal Clarification via Verifiable RL", "authors": [ "Skylar Zhai", "Jingcheng Liang", "Dongyeop Kang" ], "github": "", "project_page": "", "abstract": "Reinforcement fine-tuning improves the reasoning ability of large language models, but it can also encourage them to answer unanswerable queries by guessing or hallucinating missing information. Existing abstention methods either train models to produce generic refusals or encourage follow-up clarifications without verifying whether those clarifications identify the key missing information. We study queries that are clear in meaning but cannot be reliably resolved from the given information, and argue that a reliable model should not only abstain, but also explain what is missing. We propose a clarification-aware RLVR reward that, while rewarding correct answers on answerable queries, jointly optimizes explicit abstention and semantically aligned post-refusal clarification on unanswerable queries. Using this reward, we train Abstain-R1, a 3B model that improves abstention and clarification on unanswerable queries while preserving strong performance on answerable ones. Experiments on Abstain-Test, Abstain-QA, and SelfAware show that Abstain-R1 substantially improves over its base model and achieves unanswerable-query behavior competitive with larger systems including DeepSeek-R1, suggesting that calibrated abstention and clarification can be learned through verifiable rewards rather than emerging from scale alone.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.15093", "title": "OpenMobile: Building Open Mobile Agents with Task and Trajectory Synthesis", "authors": [ "Kanzhi Cheng", "Zehao Li", "Zheng Ma", "Nuo Chen", "Jialin Cao", "Qiushi Sun", "Zichen Ding", "Fangzhi Xu", "Hang Yan", "Jiajun Chen", "Anh Tuan Luu", "Jianbing Zhang", "Lewei Lu", "Dahua Lin" ], "github": "", "project_page": "", "abstract": "Mobile agents powered by vision-language models have demonstrated impressive capabilities in automating mobile tasks, with recent leading models achieving a marked performance leap, e.g., nearly 70% success on AndroidWorld. However, these systems keep their training data closed and remain opaque about their task and trajectory synthesis recipes. We present OpenMobile, an open-source framework that synthesizes high-quality task instructions and agent trajectories, with two key components: (1) The first is a scalable task synthesis pipeline that constructs a global environment memory from exploration, then leverages it to generate diverse and grounded instructions. and (2) a policy-switching strategy for trajectory rollout. By alternating between learner and expert models, it captures essential error-recovery data often missing in standard imitation learning. Agents trained on our data achieve competitive results across three dynamic mobile agent benchmarks: notably, our fine-tuned Qwen2.5-VL and Qwen3-VL reach 51.7% and 64.7% on AndroidWorld, far surpassing existing open-data approaches. Furthermore, we conduct transparent analyses on the overlap between our synthetic instructions and benchmark test sets, and verify that performance gains stem from broad functionality coverage rather than benchmark overfitting. We release data and code at https://njucckevin.github.io/openmobile/ to bridge the data gap and facilitate broader mobile agent research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.16659", "title": "Benign Fine-Tuning Breaks Safety Alignment in Audio LLMs", "authors": [ "Jaechul Roh", "Amir Houmansadr" ], "github": "", "project_page": "", "abstract": "Prior work shows that fine-tuning aligned models on benign data degrades safety in text and vision modalities, and that proximity to harmful content in representation space predicts which samples cause the most damage. However, existing analyses operate within a single, undifferentiated embedding space -- leaving open whether distinct input properties drive the vulnerability differently. Audio introduces a structurally richer problem: a benign sample can neighbor harmful content not only through what is said but through how it sounds, even when its words are entirely innocuous. We present the first systematic study of benign fine-tuning safety in Audio LLMs, evaluating three state-of-the-art models with a proximity-based filtering framework that selects benign audio by embedding-space distance to harmful content. By decomposing proximity into semantic, acoustic, and mixed axes using external reference encoders alongside each model's own internal encoder, we show that benign fine-tuning elevates Jailbreak Success Rate (JSR) from single digits to as high as 87.12%. Crucially, the dominant vulnerability axis and the relative risk of audio versus text fine-tuning are both architecture-conditioned -- determined by how each model's encoder and projector transform audio into the LLM's input space. We propose two defenses: filtering training data to maximize distance from harmful embeddings, and a textual system prompt at inference, both reducing JSR to near-zero without architectural modification. Our mechanistic analysis on two architectures reveals that fine-tuning selectively suppresses the late-layer refusal circuit while the frozen encoder preserves representations, and that even the suppression pattern is architecture-conditioned, mirroring the behavioral asymmetries across modalities. Safety degradation from benign fine-tuning is a qualitatively distinct risk in Audio LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.18780", "title": "Streaming Structured Inference with Flash-SemiCRF", "authors": [ "Benjamin K. Johnson", "Thomas Goralski", "Ayush Semwal", "Hui Shen", "H. Josh Jang" ], "github": "https://github.com/biobenkj/flash-semicrf", "project_page": "", "abstract": "Semi-Markov Conditional Random Fields (semi-CRFs) assign labels to segments of a sequence rather than to individual positions, enabling exact inference over segment-level features and principled uncertainty estimates at their boundaries. However, existing implementations must materialize a large edge potential tensor whose size grows with sequence length, maximum segment length, and label count, becoming prohibitive for speech-scale state spaces and intractable at genomic scales where sequences can exceed 100,000 positions. This memory bottleneck has limited the adoption of exact segment-level inference for long sequences and large label sets. We identify that the core inefficiency is materializing edge potentials that can instead be evaluated on-the-fly from a compact prefix-sum array, and make several improvements. First, replacing the stored edge tensor with prefix-sum lookup reduces the memory footprint by a factor proportional to the product of segment length and label count. Second, a streaming forward-backward pass with checkpoint-boundary normalization keeps working memory sublinear in sequence length while preserving exact gradients. Third, zero-centered cumulative scores control numerical drift and induce an adaptive duration prior under label imbalance. We integrate these ideas into Flash-SemiCRF, a fused Triton kernel that enables exact semi-CRF inference on previously intractable problem sizes. Available at https://github.com/biobenkj/flash-semicrf.", "github_manual": "", "github_regex": "https://github.com/biobenkj/flash-semicrf", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.20720", "title": "COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling", "authors": [ "Noah Flynn" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) often exhibit performance disparities across languages, with naive multilingual fine-tuning frequently degrading performance due to negative cross-lingual interference. To address this, we introduce COMPASS (COntinual Multilingual PEFT with Adaptive Semantic Sampling), a novel data-centric framework for adapting LLMs to target languages. COMPASS leverages parameter-efficient fine-tuning (PEFT) by training lightweight, language-specific adapters on a judiciously selected subset of auxiliary multilingual data. The core of our method is a distribution-aware sampling strategy that uses multilingual embeddings and clustering to identify semantic gaps between existing training data and a target usage distribution. By prioritizing auxiliary data from under-represented semantic clusters, COMPASS maximizes positive cross-lingual transfer while minimizing interference. We extend this into a continual learning framework, COMPASS-ECDA, which monitors for data distribution shifts in production and dynamically updates adapters to prevent model staleness, balancing adaptation to new data with the preservation of existing knowledge. Across three different model architectures (Phi-4-Mini, Llama-3.1-8B, and Qwen2.5-7B) and multiple challenging multilingual benchmarks (Global-MMLU, MMLU-ProX), including unseen long-context tasks (OneRuler), we demonstrate that COMPASS consistently outperforms baseline methods guided by linguistic similarity, providing an effective, efficient, and sustainable solution for developing and maintaining high-performing multilingual models in dynamic environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.16680", "title": "C-GenReg: Training-Free 3D Point Cloud Registration by Multi-View-Consistent Geometry-to-Image Generation with Probabilistic Modalities Fusion", "authors": [ "Yuval Haitman", "Amit Efraim", "Joseph M. Francos" ], "github": "https://github.com/yuvalH9/CGenReg", "project_page": "", "abstract": "We introduce C-GenReg, a training-free framework for 3D point cloud registration that leverages the complementary strengths of world-scale generative priors and registration-oriented Vision Foundation Models (VFMs). Current learning-based 3D point cloud registration methods struggle to generalize across sensing modalities, sampling differences, and environments. Hence, C-GenReg augments the geometric point cloud registration branch by transferring the matching problem into an auxiliary image domain, where VFMs excel, using a World Foundation Model to synthesize multi-view-consistent RGB representations from the input geometry. This generative transfer, preserves spatial coherence across source and target views without any fine-tuning. From these generated views, a VFM pretrained for finding dense correspondences extracts matches. The resulting pixel correspondences are lifted back to 3D via the original depth maps. To further enhance robustness, we introduce a \"Match-then-Fuse\" probabilistic cold-fusion scheme that combines two independent correspondence posteriors, that of the generated-RGB branch with that of the raw geometric branch. This principled fusion preserves each modality inductive bias and provides calibrated confidence without any additional learning. C-GenReg is zero-shot and plug-and-play: all modules are pretrained and operate without fine-tuning. Extensive experiments on indoor (3DMatch, ScanNet) and outdoor (Waymo) benchmarks demonstrate strong zero-shot performance and superior cross-domain generalization. For the first time, we demonstrate a generative registration framework that operates successfully on real outdoor LiDAR data, where no imagery data is available.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yuvalH9/CGenReg", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.19835", "title": "Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts", "authors": [ "Chaitanya Dwivedi", "Binxuan Huang", "Himanshu Gupta", "Pratik Jayarao", "Neeraj Varshney", "Bing Yin" ], "github": "https://github.com/meta-llama/llama-modelsModel", "project_page": "", "abstract": "Mixture-of-Experts (MoE) has become the dominant architecture for scaling large language models: frontier models routinely decouple total parameters from per-token computation through sparse expert routing. Scaling laws show that under fixed active computation, model quality scales predictably with total parameters, and MoEs realize this by increasing expert count. However, training large MoEs is expensive, as memory requirements and inter-device communication both scale with total parameter count. We propose expert upcycling, a method for progressively expanding MoE capacity by increasing the number of experts during continued pre-training (CPT). Given a trained E-expert model, the upcycling operator constructs an mE-expert model through expert duplication and router extension while holding top-K routing fixed, preserving per-token inference cost. Duplication provides a warm initialization: the expanded model inherits the source checkpoint's learned representations, starting from a substantially lower loss than random initialization. Subsequent CPT then breaks the symmetry among duplicated experts to drive specialization. We formalize the upcycling operator and develop a theoretical framework decomposing the quality gap into a capacity term and an initialization term. We further introduce utility-based expert selection, which uses gradient-based importance scores to guide non-uniform duplication, more than tripling gap closure when CPT is limited. In our 7B-13B total parameter experiments, the upcycled model matches the fixed-size baseline on validation loss while saving 32% of GPU hours. Comprehensive ablations across model scales, activation ratios, MoE architectures, and training budgets yield a practical recipe for deploying expert upcycling, establishing it as a principled, compute-efficient alternative to training large MoE models from scratch.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/meta-llama/llama-modelsModel", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-23", "arxiv_id": "2604.20200", "title": "Chasing the Public Score: User Pressure and Evaluation Exploitation in Coding Agent Workflows", "authors": [ "Hardy Chen", "Nancy Lau", "Haoqin Tu", "Shuo Yan", "Xiangyan Liu", "Zijun Wang", "Juncheng Wu", "Michael Qizhe Shieh", "Alvaro A. Cardenas", "Cihang Xie", "Yuyin Zhou" ], "github": "", "project_page": "", "abstract": "Frontier coding agents are increasingly used in workflows where users supervise progress primarily through repeated improvement of a public score, namely the reported score on a public evaluation file with labels in the workspace, rather than through direct inspection of the agent's intermediate outputs. We study whether multi-round user pressure to improve that score induces public score exploitation: behavior that raises the public score through shortcuts without improving hidden private evaluation. We begin with a preliminary single-script tabular classification task, where GPT-5.4 and Claude Opus 4.6 both exploit label information within 10 rounds of user-agent interaction. We then build AgentPressureBench, a 34-task machine-learning repository benchmark spanning three input modalities, and collect 1326 multi-round trajectories from 13 coding agents. On our benchmark, we observe 403 exploitative runs, spanning across all tasks. We also find that stronger models have higher exploitation rates, supported by a significant Spearman rank correlation of 0.77. Our ablation experiments show that higher user pressure leads to earlier exploitation, reducing the average first exploit round by 15.6 rounds (i.e., 19.67 to 4.08). As a mitigation, adding explicit anti-exploit wordings in prompt mostly eliminates exploitation (100% to 8.3%). We hope that our work can bring attention to more careful use of coding agents workflow, and developing more robust coding agents under user pressure. Our project page is at https://ucsc-vlaa.github.io/AgentPressureBench .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.16926", "title": "Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts", "authors": [ "Gabriel Jason Lee", "Jathurshan Pradeepkumar", "Jimeng Sun" ], "github": "", "project_page": "", "abstract": "Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by distribution shifts across clinical settings, devices, and populations. Test-time adaptation (TTA) offers a promising solution by enabling models to adapt to unlabeled target data during inference without access to source data, a valuable property in healthcare settings constrained by privacy regulations and limited labeled data. However, its effectiveness for EEG remains largely underexplored. In this work, we introduce NeuroAdapt-Bench, a systematic benchmark for evaluating test-time adaptation methods on EEG foundation models under realistic distribution shifts. We evaluate representative TTA approaches from other domains across multiple pretrained foundation models, diverse downstream tasks, and heterogeneous datasets spanning in-distribution, out-of-distribution, and extreme modality shifts (e.g., Ear-EEG). Our results show that standard TTA methods yield inconsistent gains and often degrade performance, with gradient-based approaches particularly prone to heavy degradation. In contrast, optimization-free methods demonstrate greater stability and more reliable improvements. These findings highlight the limitations of existing TTA techniques in EEG, provide guidance for future development, and underscore the need for domain-specific adaptation strategies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.19734", "title": "UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling", "authors": [ "Boyu Chen", "Yi Chen", "Lu Qiu", "Jerry Bai", "Yuying Ge", "Yixiao Ge" ], "github": "", "project_page": "", "abstract": "Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data. While massive egocentric human data offers a scalable alternative, bridging the cross-embodiment chasm remains a fundamental challenge due to kinematic mismatches. We introduce UniT (Unified Latent Action Tokenizer via Visual Anchoring), a framework that establishes a unified physical language for human-to-humanoid transfer. Grounded in the philosophy that heterogeneous kinematics share universal visual consequences, UniT employs a tri-branch cross-reconstruction mechanism: actions predict vision to anchor kinematics to physical outcomes, while vision reconstructs actions to filter out irrelevant visual confounders. Concurrently, a fusion branch synergies these purified modalities into a shared discrete latent space of embodiment-agnostic physical intents. We validate UniT across two paradigms: 1) Policy Learning (VLA-UniT): By predicting these unified tokens, it effectively leverages diverse human data to achieve state-of-the-art data efficiency and robust out-of-distribution (OOD) generalization on both humanoid simulation benchmark and real-world deployments, notably demonstrating zero-shot task transfer. 2) World Modeling (WM-UniT): By aligning cross-embodiment dynamics via unified tokens as conditions, it realizes direct human-to-humanoid action transfer. This alignment ensures that human data seamlessly translates into enhanced action controllability for humanoid video generation. Ultimately, by inducing a highly aligned cross-embodiment representation (empirically verified by t-SNE visualizations revealing the convergence of human and humanoid features into a shared manifold), UniT offers a scalable path to distill vast human knowledge into general-purpose humanoid capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.21931", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "authors": [ "Yen-Siang Wu", "Rundong Luo", "Jingsen Zhu", "Tao Tu", "Ali Farhadi", "Matthew Wallingford", "Yu-Chiang Frank Wang", "Steve Marschner", "Wei-Chiu Ma" ], "github": "", "project_page": "", "abstract": "How can we tell whether a video has been sped up or slowed down? How can we generate videos at different speeds? Although videos have been central to modern computer vision research, little attention has been paid to perceiving and controlling the passage of time. In this paper, we study time as a learnable visual concept and develop models for reasoning about and manipulating the flow of time in videos. We first exploit the multimodal cues and temporal structure naturally present in videos to learn, in a self-supervised manner, to detect speed changes and estimate playback speed. We then show that these learned temporal reasoning models enable us to curate the largest slow-motion video dataset to date from noisy in-the-wild sources. Such slow-motion footage, typically filmed by high-speed cameras, contains substantially richer temporal detail than standard videos. Using this data, we further develop models capable of temporal control, including speed-conditioned video generation, which produces motion at specified playback speed, and temporal super-resolution, which tranforms low-FPS, blurry videos into high-FPS sequences with fine-grained temporal details. Our findings highlight time as a manipulable, perceptual dimension in video learning, opening doors to temporally controllable video generation, temporal forensics detection, and potentially richer world-models that understand how events unfold over time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.21689", "title": "StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition", "authors": [ "Kwan Yun", "Changmin Lee", "Ayeong Jeong", "Youngseo Kim", "Seungmi Lee", "Junyong Noh" ], "github": "", "project_page": "", "abstract": "Creative face stylization aims to render portraits in diverse visual idioms such as cartoons, sketches, and paintings while retaining recognizable identity. However, current identity encoders, which are typically trained and calibrated on natural photographs, exhibit severe brittleness under stylization. They often mistake changes in texture or color palette for identity drift or fail to detect geometric exaggerations. This reveals the lack of a style-agnostic framework to evaluate and supervise identity consistency across varying styles and strengths. To address this gap, we introduce StyleID, a human perception-aware dataset and evaluation framework for facial identity under stylization. StyleID comprises two datasets: (i) StyleBench-H, a benchmark that captures human same-different verification judgments across diffusion- and flow-matching-based stylization at multiple style strengths, and (ii) StyleBench-S, a supervision set derived from psychometric recognition-strength curves obtained through controlled two-alternative forced-choice (2AFC) experiments. Leveraging StyleBench-S, we fine-tune existing semantic encoders to align their similarity orderings with human perception across styles and strengths. Experiments demonstrate that our calibrated models yield significantly higher correlation with human judgments and enhanced robustness for out-of-domain, artist drawn portraits. All of our datasets, code, and pretrained models are publicly available at https://kwanyun.github.io/StyleID_page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.21686", "title": "WorldMark: A Unified Benchmark Suite for Interactive Video World Models", "authors": [ "Xiaojie Xu", "Zhengyuan Lin", "Kang He", "Yukang Feng", "Xiaofeng Mao", "Yuanyang Yin", "Kaipeng Zhang", "Yongtao Ge" ], "github": "", "project_page": "", "abstract": "Interactive video generation models such as Genie, YUME, HY-World, and Matrix-Game are advancing rapidly, yet every model is evaluated on its own benchmark with private scenes and trajectories, making fair cross-model comparison impossible. Existing public benchmarks offer useful metrics such as trajectory error, aesthetic scores, and VLM-based judgments, but none supplies the standardized test conditions -- identical scenes, identical action sequences, and a unified control interface -- needed to make those metrics comparable across models with heterogeneous inputs. We introduce WorldMark, the first benchmark that provides such a common playing field for interactive Image-to-Video world models. WorldMark contributes: (1) a unified action-mapping layer that translates a shared WASD-style action vocabulary into each model's native control format, enabling apples-to-apples comparison across six major models on identical scenes and trajectories; (2) a hierarchical test suite of 500 evaluation cases covering first- and third-person viewpoints, photorealistic and stylized scenes, and three difficulty tiers from Easy to Hard spanning 20-60s; and (3) a modular evaluation toolkit for Visual Quality, Control Alignment, and World Consistency, designed so that researchers can reuse our standardized inputs while plugging in their own metrics as the field evolves. We will release all data, evaluation code, and model outputs to facilitate future research. Beyond offline metrics, we launch World Model Arena (warena.ai), an online platform where anyone can pit leading world models against each other in side-by-side battles and watch the live leaderboard.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.21921", "title": "Context Unrolling in Omni Models", "authors": [ "Ceyuan Yang", "Zhijie Lin", "Yang Zhao", "Fei Xiao", "Hao He", "Qi Zhao", "Chaorui Deng", "Kunchang Li", "Zihan Ding", "Yuwei Guo", "Fuyun Wang", "Fangqi Zhu", "Xiaonan Nie", "Shenhan Zhu", "Shanchuan Lin", "Hongsheng Li", "Weilin Huang", "Guang Shi", "Haoqi Fan" ], "github": "", "project_page": "", "abstract": "We present Omni, a unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representations. We find that such training enables Context Unrolling, where the model explicitly reasons across multiple modal representations before producing predictions. This process enables the model to aggregate complementary information across heterogeneous modalities, facilitating a more faithful approximation of the shared multimodal knowledge manifold and improving downstream reasoning fidelity. As a result, Omni achieves strong performance on both multimodal generation and understanding benchmarks, while demonstrating advanced multimodal reasoning capabilities, including in-context generation of text, image, video, and 3D geometry.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.21904", "title": "UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection", "authors": [ "Yanran Zhang", "Wenzhao Zheng", "Yifei Li", "Bingyao Yu", "Yu Zheng", "Lei Chen", "Jiwen Lu", "Jie Zhou" ], "github": "https://github.com/Zhangyr2022/UniGenDet", "project_page": "", "abstract": "In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent, development, these two fields have evolved distinct architectural paradigms: the former predominantly relies on generative networks, while the latter favors discriminative frameworks. A recent trend in both domains is the use of adversarial information to enhance performance, revealing potential for synergy. However, the significant architectural divergence between them presents considerable challenges. Departing from previous approaches, we propose UniGenDet: a Unified generative-discriminative framework for co-evolutionary image Generation and generated image Detection. To bridge the task gap, we design a symbiotic multimodal self-attention mechanism and a unified fine-tuning algorithm. This synergy allows the generation task to improve the interpretability of authenticity identification, while authenticity criteria guide the creation of higher-fidelity images. Furthermore, we introduce a detector-informed generative alignment mechanism to facilitate seamless information exchange. Extensive experiments on multiple datasets demonstrate that our method achieves state-of-the-art performance. Code: https://github.com/Zhangyr2022/UniGenDet{https://github.com/Zhangyr2022/UniGenDet}.", "github_manual": "", "github_regex": "https://github.com/Zhangyr2022/UniGenDet", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.21889", "title": "TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale", "authors": [ "Jun Wang", "Ziyin Zhang", "Rui Wang", "Hang Yu", "Peng Di", "Rui Wang" ], "github": "", "project_page": "", "abstract": "Real-time detection and mitigation of technical anomalies are critical for large-scale cloud-native services, where even minutes of downtime can result in massive financial losses and diminished user trust. While customer incidents serve as a vital signal for discovering risks missed by monitoring, extracting actionable intelligence from this data remains challenging due to extreme noise, high throughput, and semantic complexity of diverse business lines. In this paper, we present TingIS, an end-to-end system designed for enterprise-grade incident discovery. At the core of TingIS is a multi-stage event linking engine that synergizes efficient indexing techniques with Large Language Models (LLMs) to make informed decisions on event merging, enabling the stable extraction of actionable incidents from just a handful of diverse user descriptions. This engine is complemented by a cascaded routing mechanism for precise business attribution and a multi-dimensional noise reduction pipeline that integrates domain knowledge, statistical patterns, and behavioral filtering. Deployed in a production environment handling a peak throughput of over 2,000 messages per minute and 300,000 messages per day, TingIS achieves a P90 alert latency of 3.5 minutes and a 95\\% discovery rate for high-priority incidents. Benchmarks constructed from real-world data demonstrate that TingIS significantly outperforms baseline methods in routing accuracy, clustering quality, and Signal-to-Noise Ratio.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.21375", "title": "VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation", "authors": [ "Qijun Han", "Haoqin Tu", "Zijun Wang", "Haoyue Dai", "Yiyang Zhou", "Nancy Lau", "Alvaro A. Cardenas", "Yuhui Xu", "Ran Xu", "Caiming Xiong", "Zeyu Zheng", "Huaxiu Yao", "Yuyin Zhou", "Cihang Xie" ], "github": "", "project_page": "", "abstract": "Autonomous GUI agents face two fundamental challenges: early stopping, where agents prematurely declare success without verifiable evidence, and repetitive loops, where agents cycle through the same failing actions without recovery. We present VLAA-GUI, a modular GUI agentic framework built around three integrated components that guide the system on when to Stop, Recover, and Search. First, a mandatory Completeness Verifier enforces UI-observable success criteria and verification at every finish step -- with an agent-level verifier that cross-examines completion claims with decision rules, rejecting those lacking direct visual evidence. Second, a mandatory Loop Breaker provides multi-tier filtering: switching interaction mode after repeated failures, forcing strategy changes after persistent screen-state recurrence, and binding reflection signals to strategy shifts. Third, an on-demand Search Agent searches online for unfamiliar workflows by directly querying a capable LLM with search ability, returning results as plain text. We additionally integrate a Coding Agent for code-intensive actions and a Grounding Agent for precise action grounding, both invoked on demand when required. We evaluate VLAA-GUI across five top-tier backbones, including Opus 4.5, 4.6 and Gemini 3.1 Pro, on two benchmarks with Linux and Windows tasks, achieving top performance on both (77.5% on OSWorld and 61.0% on WindowsAgentArena). Notably, three of the five backbones surpass human performance (72.4%) on OSWorld in a single pass. Ablation studies show that all three proposed components consistently improve a strong backbone, while a weaker backbone benefits more from these tools when the step budget is sufficient. Further analysis also shows that the Loop Breaker nearly halves wasted steps for loop-prone models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.21193", "title": "Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models", "authors": [ "Vipula Rawte", "Ryan Rossi", "Franck Dernoncourt", "Nedim Lipka" ], "github": "https://github.com/vr25/davinci", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across a wide range of NLP tasks, yet they remain prone to factual inaccuracies and hallucinations. This limitation poses significant risks in high-stakes domains such as healthcare, law, and scientific communication, where trust and verifiability are paramount. In this paper, we introduce DAVinCI - a Dual Attribution and Verification framework designed to enhance the factual reliability and interpretability of LLM outputs. DAVinCI operates in two stages: (i) it attributes generated claims to internal model components and external sources; (ii) it verifies each claim using entailment-based reasoning and confidence calibration. We evaluate DAVinCI across multiple datasets, including FEVER and CLIMATE-FEVER, and compare its performance against standard verification-only baselines. Our results show that DAVinCI significantly improves classification accuracy, attribution precision, recall, and F1-score by 5-20%. Through an extensive ablation study, we isolate the contributions of evidence span selection, recalibration thresholds, and retrieval quality. We also release a modular DAVinCI implementation that can be integrated into existing LLM pipelines. By bridging attribution and verification, DAVinCI offers a scalable path to auditable, trustworthy AI systems. This work contributes to the growing effort to make LLMs not only powerful but also accountable.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vr25/davinci", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.21300", "title": "Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI", "authors": [ "Hieu Man", "Van-Cuong Pham", "Nghia Trung Ngo", "Franck Dernoncourt", "Thien Huu Nguyen" ], "github": "https://github.com/hieum98/avae", "project_page": "", "abstract": "Learning robust representations of authorial style is crucial for authorship attribution and AI-generated text detection. However, existing methods often struggle with content-style entanglement, where models learn spurious correlations between authors' writing styles and topics, leading to poor generalization across domains. To address this challenge, we propose Explainable Authorship Variational Autoencoder (EAVAE), a novel framework that explicitly disentangles style from content through architectural separation-by-design. EAVAE first pretrains style encoders using supervised contrastive learning on diverse authorship data, then finetunes with a Variational Autoencoder (VEA) architecture using separate encoders for style and content representations. Disentanglement is enforced through a novel discriminator that not only distinguishes whether pairs of style/content representations belong to the same or different authors/content sources, but also generates natural language explanation for their decision, simultaneously mitigating confounding information and enhancing interpretability. Extensive experiments demonstrate the effectiveness of EAVAE. On authorship attribution, we achieve state-of-the-art performance on various datasets, including Amazon Reviews, PAN21, and HRS. For AI-generated text detection, EAVAE excels in few-shot learning over the M4 dataset. Code and data repositories are available onlinehttps://github.com/hieum98/avae https://huggingface.co/collections/Hieuman/document-level-authorship-datasets.", "github_manual": "", "github_regex": "https://github.com/hieum98/avae", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.20987", "title": "Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks", "authors": [ "Xiyang Wu", "Zongxia Li", "Guangyao Shi", "Alexander Duffy", "Tyler Marques", "Matthew Lyle Olson", "Tianyi Zhou", "Dinesh Manocha" ], "github": "", "project_page": "", "abstract": "Long horizon interactive environments are a testbed for evaluating agents skill usage abilities. These environments demand multi step reasoning, the chaining of multiple skills over many timesteps, and robust decision making under delayed rewards and partial observability. Games are a good testbed for evaluating agent skill usage in environments. Large Language Models (LLMs) offer a promising alternative as game playing agents, but they often struggle with consistent long horizon decision making because they lack a mechanism to discover, retain, and reuse structured skills across episodes. We present COSPLAY, a co evolution framework in which an LLM decision agent retrieves skills from a learnable skill bank to guide action taking, while an agent managed skill pipeline discovers reusable skills from the agents unlabeled rollouts to form a skill bank. Our framework improves both the decision agent to learn better skill retrieval and action generation, while the skill bank agent continually extracts, refines, and updates skills together with their contracts. Experiments across six game environments show that COSPLAY with an 8B base model achieves over 25.1 percent average reward improvement against four frontier LLM baselines on single player game benchmarks while remaining competitive on multi player social reasoning games.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.20244", "title": "Hybrid Policy Distillation for LLMs", "authors": [ "Wenhong Zhu", "Ruobing Xie", "Rui Wang", "Pengfei Liu" ], "github": "https://github.com/zwhong714/Hybrid-Policy-Distillation", "project_page": "", "abstract": "Knowledge distillation (KD) is a powerful paradigm for compressing large language models (LLMs), whose effectiveness depends on intertwined choices of divergence direction, optimization strategy, and data regime. We break down the design of existing KD methods and present a unified view that establishes connections between them, reformulating KD as a reweighted log-likelihood objective at the token level. We further propose Hybrid Policy Distillation (HPD), which integrates the complementary advantages of forward and reverse KL to balance mode coverage and mode-seeking, and combines off-policy data with lightweight, approximate on-policy sampling. We validate HPD on long-generation math reasoning as well as short-generation dialogue and code tasks, demonstrating improved optimization stability, computational efficiency, and final performance across diverse model families and scales. The code related to this work is available at https://github.com/zwhong714/Hybrid-Policy-Distillation.", "github_manual": "", "github_regex": "https://github.com/zwhong714/Hybrid-Policy-Distillation", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.20398", "title": "WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning", "authors": [ "Juyong Jiang", "Chenglin Cai", "Chansung Park", "Jiasi Shen", "Sunghun Kim", "Jianguo Li", "Yue Wang" ], "github": "", "project_page": "", "abstract": "While Large Language Models (LLMs) excel at function-level code generation, project-level tasks such as generating functional and visually aesthetic multi-page websites remain highly challenging. Existing works are often limited to single-page static websites, while agentic frameworks typically rely on multi-turn execution with proprietary models, leading to substantial token costs, high latency, and brittle integration. Training a small LLM end-to-end with reinforcement learning (RL) is a promising alternative, yet it faces a critical bottleneck in designing reliable and computationally feasible rewards for website generation. Unlike single-file coding tasks that can be verified by unit tests, website generation requires evaluating inherently subjective aesthetics, cross-page interactions, and functional correctness. To this end, we propose WebGen-R1, an end-to-end RL framework tailored for project-level website generation. We first introduce a scaffold-driven structured generation paradigm that constrains the large open-ended action space and preserves architectural integrity. We then design a novel cascaded multimodal reward that seamlessly couples structural guarantees with execution-grounded functional feedback and vision-based aesthetic supervision. Extensive experiments demonstrate that our WebGen-R1 substantially transforms a 7B base model from generating nearly nonfunctional websites into producing deployable, aesthetically aligned multi-page websites. Remarkably, our WebGen-R1 not only consistently outperforms heavily scaled open-source models (up to 72B), but also rivals the state-of-the-art DeepSeek-R1 (671B) in functional success, while substantially exceeding it in valid rendering and aesthetic alignment. These results position WebGen-R1 as a viable path for scaling small open models from function-level code generation to project-level web application generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.10268", "title": "EditCrafter: Tuning-free High-Resolution Image Editing via Pretrained Diffusion Model", "authors": [ "Kunho Kim", "Sumin Seo", "Yongjun Cho", "Hyungjin Chung" ], "github": "", "project_page": "", "abstract": "We propose EditCrafter, a high-resolution image editing method that operates without tuning, leveraging pretrained text-to-image (T2I) diffusion models to process images at resolutions significantly exceeding those used during training. Leveraging the generative priors of large-scale T2I diffusion models enables the development of a wide array of novel generation and editing applications. Although numerous image editing methods have been proposed based on diffusion models and exhibit high-quality editing results, they are difficult to apply to images with arbitrary aspect ratios or higher resolutions since they only work at the training resolutions (512x512 or 1024x1024). Naively applying patch-wise editing fails with unrealistic object structures and repetition. To address these challenges, we introduce EditCrafter, a simple yet effective editing pipeline. EditCrafter operates by first performing tiled inversion, which preserves the original identity of the input high-resolution image. We further propose a noise-damped manifold-constrained classifier-free guidance (NDCFG++) that is tailored for high resolution image editing from the inverted latent. Our experiments show that the our EditCrafter can achieve impressive editing results across various resolutions without fine-tuning and optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2506.17001", "title": "PersonalAI: A Systematic Comparison of Knowledge Graph Storage and Retrieval Approaches for Personalized LLM agents", "authors": [ "Mikhail Menschikov", "Dmitry Evseev", "Victoria Dochkina", "Ruslan Kostoev", "Ilia Perepechkin", "Petr Anokhin", "Nikita Semenov", "Evgeny Burnaev" ], "github": "", "project_page": "", "abstract": "Personalizing language models by effectively incorporating user interaction history remains a central challenge in the development of adaptive AI systems. While large language models (LLMs), combined with Retrieval-Augmented Generation (RAG), have improved factual accuracy, they often lack structured memory and fail to scale in complex, long-term interactions. To address this, we propose a flexible external memory framework based on a knowledge graph that is constructed and updated automatically by the LLM. Building upon the AriGraph architecture, we introduce a novel hybrid graph design that supports both standard edges and two types of hyper-edges, enabling rich and dynamic semantic and temporal representations. Our framework also supports diverse retrieval mechanisms, including A*, WaterCircles traversal, beam search, and hybrid methods, making it adaptable to different datasets and LLM capacities. We evaluate our system on TriviaQA, HotpotQA, DiaASQ benchmarks and demonstrate that different memory and retrieval configurations yield optimal performance depending on the task. Additionally, we extend the DiaASQ benchmark with temporal annotations and internally contradictory statements, showing that our system remains robust and effective in managing temporal dependencies and context-aware reasoning", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.17295", "title": "LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics", "authors": [ "Yueyang Ding", "HaoPeng Zhang", "Rui Dai", "Yi Wang", "Tianyu Zong", "Kaikui Liu", "Xiangxiang Chu" ], "github": "https://github.com/RainingNovember/LLaTiSA", "project_page": "", "abstract": "Comprehensive understanding of time series remains a significant challenge for Large Language Models (LLMs). Current research is hindered by fragmented task definitions and benchmarks with inherent ambiguities, precluding rigorous evaluation and the development of unified Time Series Reasoning Models(TSRMs). To bridge this gap, we formalize Time Series Reasoning (TSR) via a four-level taxonomy of increasing cognitive complexity. We introduce HiTSR, a hierarchical time series reasoning dataset comprising 83k samples with diverse task combinations and verified Chain-of-Thought (CoT) trajectories. Leveraging HiTSR, we propose LLaTiSA, a strong TSRM that integrates visualized patterns with precision-calibrated numerical tables to enhance the temporal perception of Vision-Language Models (VLMs). Through a multi-stage curriculum fine-tuning strategy, LLaTiSA achieves superior performance and exhibits robust out-of-distribution generalization across diverse TSR tasks and real-world scenarios. Our code is available at https://github.com/RainingNovember/LLaTiSA.", "github_manual": "", "github_regex": "https://github.com/RainingNovember/LLaTiSA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.21668", "title": "Encoder-Free Human Motion Understanding via Structured Motion Descriptions", "authors": [ "Yao Zhang", "Zhuchenyang Liu", "Thomas Ploetz", "Yu Xiao" ], "github": "", "project_page": "", "abstract": "The world knowledge and reasoning capabilities of text-based large language models (LLMs) are advancing rapidly, yet current approaches to human motion understanding, including motion question answering and captioning, have not fully exploited these capabilities. Existing LLM-based methods typically learn motion-language alignment through dedicated encoders that project motion features into the LLM's embedding space, remaining constrained by cross-modal representation and alignment. Inspired by biomechanical analysis, where joint angles and body-part kinematics have long served as a precise descriptive language for human movement, we propose Structured Motion Description (SMD), a rule-based, deterministic approach that converts joint position sequences into structured natural language descriptions of joint angles, body part movements, and global trajectory. By representing motion as text, SMD enables LLMs to apply their pretrained knowledge of body parts, spatial directions, and movement semantics directly to motion reasoning, without requiring learned encoders or alignment modules. We show that this approach goes beyond state-of-the-art results on both motion question answering (66.7\\% on BABEL-QA, 90.1\\% on HuMMan-QA) and motion captioning (R@1 of 0.584, CIDEr of 53.16 on HumanML3D), surpassing all prior methods. SMD additionally offers practical benefits: the same text input works across different LLMs with only lightweight LoRA adaptation (validated on 8 LLMs from 6 model families), and its human-readable representation enables interpretable attention analysis over motion descriptions. Code, data, and pretrained LoRA adapters are available at https://yaozhang182.github.io/motion-smd/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.21915", "title": "Vista4D: Video Reshooting with 4D Point Clouds", "authors": [ "Kuan Heng Lin", "Zhizheng Liu", "Pablo Salamanca", "Yash Kant", "Ryan Burgert", "Yuancheng Xu", "Koichi Namekata", "Yiwei Zhao", "Bolei Zhou", "Micah Goldblum", "Paul Debevec", "Ning Yu" ], "github": "", "project_page": "", "abstract": "We present Vista4D, a robust and flexible video reshooting framework that grounds the input video and target cameras in a 4D point cloud. Specifically, given an input video, our method re-synthesizes the scene with the same dynamics from a different camera trajectory and viewpoint. Existing video reshooting methods often struggle with depth estimation artifacts of real-world dynamic videos, while also failing to preserve content appearance and failing to maintain precise camera control for challenging new trajectories. We build a 4D-grounded point cloud representation with static pixel segmentation and 4D reconstruction to explicitly preserve seen content and provide rich camera signals, and we train with reconstructed multiview dynamic data for robustness against point cloud artifacts during real-world inference. Our results demonstrate improved 4D consistency, camera control, and visual quality compared to state-of-the-art baselines under a variety of videos and camera paths. Moreover, our method generalizes to real-world applications such as dynamic scene expansion and 4D scene recomposition. See our project page for results, code, and models: https://eyeline-labs.github.io/Vista4D", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.17492", "title": "Coevolving Representations in Joint Image-Feature Diffusion", "authors": [ "Theodoros Kouzelis", "Spyros Gidaris", "Nikos Komodakis" ], "github": "https://github.com/zelaki/CoReDi", "project_page": "", "abstract": "Joint image-feature generative modeling has recently emerged as an effective strategy for improving diffusion training by coupling low-level VAE latents with high-level semantic features extracted from pre-trained visual encoders. However, existing approaches rely on a fixed representation space, constructed independently of the generative objective and kept unchanged during training. We argue that the representation space guiding diffusion should itself adapt to the generative task. To this end, we propose Coevolving Representation Diffusion (CoReDi), a framework in which the semantic representation space evolves during training by learning a lightweight linear projection jointly with the diffusion model. While naively optimizing this projection leads to degenerate solutions, we show that stable coevolution can be achieved through a combination of stop-gradient targets, normalization, and targeted regularization that prevents feature collapse. This formulation enables the semantic space to progressively specialize to the needs of image synthesis, improving its complementarity with image latents. We apply CoReDi to both VAE latent diffusion and pixel-space diffusion, demonstrating that adaptive semantic representations improve generative modeling across both settings. Experiments show that CoReDi achieves faster convergence and higher sample quality compared to joint diffusion models operating in fixed representation spaces.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zelaki/CoReDi", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.20156", "title": "Temporally Extended Mixture-of-Experts Models", "authors": [ "Zeyu Shen", "Peter Henderson" ], "github": "", "project_page": "", "abstract": "Mixture-of-Experts models, now popular for scaling capacity at fixed inference speed, switch experts at nearly every token. Once a model outgrows available GPU memory, this churn can render optimizations like offloading and pre-fetching ineffective. We make the case that the options framework in reinforcement learning is a perfect match to tackle this problem, and argue for temporally extended mixture-of-experts layers. Building on the option-critic framework with deliberation costs, we add a controller to each layer that learns when to switch expert sets and which to load. By applying this to gpt-oss-20b with low-rank adapters and a self-distillation reward, our method reduces switch rates from over 50% to below 5% while retaining up to 90% of base-model accuracy on MATH, MMLU, and MMMLU. This shows that even existing pre-trained models can be converted to temporally extended MoEs with lightweight training, with the deliberation cost allowing model trainers to trade off switching rates against capability. We hope this opens a principled path, grounded in the options framework, for memory-efficient serving and continual learning in ever-growing MoE models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-24", "arxiv_id": "2604.08645", "title": "3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding", "authors": [ "Makanjuola Ogunleye", "Eman Abdelrahman", "Ismini Lourentzou" ], "github": "", "project_page": "", "abstract": "Large multimodal models are increasingly used as the reasoning core of embodied agents operating in 3D environments, yet they remain prone to hallucinations that can produce unsafe and ungrounded decisions. Existing inference-time hallucination mitigation methods largely target 2D vision-language settings and do not transfer to embodied 3D reasoning, where failures arise from object presence, spatial layout, and geometric grounding rather than pixel-level inconsistencies. We introduce 3D-VCD, the first inference-time visual contrastive decoding framework for hallucination mitigation in 3D embodied agents. 3D-VCD constructs a distorted 3D scene graph by applying semantic and geometric perturbations to object-centric representations, such as category substitutions and coordinate or extent corruption. By contrasting predictions under the original and distorted 3D contexts, our method suppresses tokens that are insensitive to grounded scene evidence and are therefore likely driven by language priors. We evaluate 3D-VCD on the 3D-POPE and HEAL benchmarks and show that it consistently improves grounded reasoning without any retraining, establishing inference-time contrastive decoding over structured 3D representations as an effective and practical route to more reliable embodied intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.18519", "title": "LLM Safety From Within: Detecting Harmful Content with Internal Representations", "authors": [ "Difan Jiao", "Yilun Liu", "Ye Yuan", "Zhenwei Tang", "Linfeng Du", "Haolun Wu", "Ashton Anderson" ], "github": "https://github.com/CSSLab/SIREN", "project_page": "", "abstract": "Guard models are widely used to detect harmful content in user prompts and LLM responses. However, state-of-the-art guard models rely solely on terminal-layer representations and overlook the rich safety-relevant features distributed across internal layers. We present SIREN, a lightweight guard model that harnesses these internal features. By identifying safety neurons via linear probing and combining them through an adaptive layer-weighted strategy, SIREN builds a harmfulness detector from LLM internals without modifying the underlying model. Our comprehensive evaluation shows that SIREN substantially outperforms state-of-the-art open-source guard models across multiple benchmarks while using 250 times fewer trainable parameters. Moreover, SIREN exhibits superior generalization to unseen benchmarks, naturally enables real-time streaming detection, and significantly improves inference efficiency compared to generative guard models. Overall, our results highlight LLM internal states as a promising foundation for practical, high-performance harmfulness detection.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/CSSLab/SIREN", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.22152", "title": "dWorldEval: Scalable Robotic Policy Evaluation via Discrete Diffusion World Model", "authors": [ "Yaxuan Li", "Zhongyi Zhou", "Yefei Chen", "Yaokai Xue", "Yichen Zhu" ], "github": "", "project_page": "", "abstract": "Evaluating robotics policies across thousands of environments and thousands of tasks is infeasible with existing approaches. This motivates the need for a new methodology for scalable robotics policy evaluation. In this paper, we propose dWorldEval, which uses a discrete diffusion world model as a scalable evaluation proxy for robotics policies. Specifically, dWorldEval maps all modalities - including vision, language, and robotic actions - into a unified token space, modeling them via a single transformer-based denoising network. In this paper, we propose dWorldEval, using a discrete diffusion world model as a scalable evaluation proxy for robotics policy. Specifically, it maps all modalities, including vision, language, and robotics action into a unified token space, then denoises them with a single transformer network. Building on this architecture, we employ a sparse keyframe memory to maintain spatiotemporal consistency. We also introduce a progress token that indicates the degree of task completion. At inference, the model jointly predicts future observations and progress token, allowing automatically determine success when the progress reaches 1. Extensive experiments demonstrate that dWorldEval significantly outperforms previous approaches, i.e., WorldEval, Ctrl-World, and WorldGym, on LIBERO, RoboTwin, and multiple real-robot tasks. It paves the way for a new architectural paradigm in building world simulators for robotics evaluation at scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.22436", "title": "AgentSearchBench: A Benchmark for AI Agent Search in the Wild", "authors": [ "Bin Wu", "Arastun Mammadli", "Xiaoyu Zhang", "Emine Yilmaz" ], "github": "https://github.com/Bingo-W/AgentSearchBench", "project_page": "", "abstract": "The rapid growth of AI agent ecosystems is transforming how complex tasks are delegated and executed, creating a new challenge of identifying suitable agents for a given task. Unlike traditional tools, agent capabilities are often compositional and execution-dependent, making them difficult to assess from textual descriptions alone. However, existing research and benchmarks typically assume well-specified functionalities, controlled candidate pools, or only executable task queries, leaving realistic agent search scenarios insufficiently studied. We introduce AgentSearchBench, a large-scale benchmark for agent search in the wild, built from nearly 10,000 real-world agents across multiple providers. The benchmark formalizes agent search as retrieval and reranking problems under both executable task queries and high-level task descriptions, and evaluates relevance using execution-grounded performance signals. Experiments reveal a consistent gap between semantic similarity and actual agent performance, exposing the limitations of description-based retrieval and reranking methods. We further show that lightweight behavioral signals, including execution-aware probing, can substantially improve ranking quality, highlighting the importance of incorporating execution signals into agent discovery. Our code is available at https://github.com/Bingo-W/AgentSearchBench.", "github_manual": "", "github_regex": "https://github.com/Bingo-W/AgentSearchBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.22565", "title": "Learning Evidence Highlighting for Frozen LLMs", "authors": [ "Shaoang Li", "Yanhang Shi", "Yufei Li", "Mingfu Liang", "Xiaohan Wei", "Yunchen Pu", "Fei Tian", "Chonglin Sun", "Frank Shyu", "Luke Simon", "Sandeep Pandey", "Xi Liu", "Jian Li" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) can reason well, yet often miss decisive evidence when it is buried in long, noisy contexts. We introduce HiLight, an Evidence Emphasis framework that decouples evidence selection from reasoning for frozen LLM solvers. HiLight avoids compressing or rewriting the input, which can discard or distort evidence, by training a lightweight Emphasis Actor to insert minimal highlight tags around pivotal spans in the unaltered context. A frozen Solver then performs downstream reasoning on the emphasized input. We cast highlighting as a weakly supervised decision-making problem and optimize the Actor with reinforcement learning using only the Solver's task reward, requiring no evidence labels and no access to or modification of the Solver. Across sequential recommendation and long-context question answering, HiLight consistently improves performance over strong prompt-based and automated prompt-optimization baselines. The learned emphasis policy transfers zero-shot to both smaller and larger unseen Solver families, including an API-based Solver, suggesting that the Actor captures genuine, reusable evidence structure rather than overfitting to a single backbone.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.22748", "title": "Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond", "authors": [ "Meng Chu", "Xuan Billy Zhang", "Kevin Qinghong Lin", "Lingdong Kong", "Jize Zhang", "Teng Tu", "Weijian Ma", "Ziqi Huang", "Senqiao Yang", "Wei Huang", "Yeying Jin", "Zhefan Rao", "Jinhui Ye", "Xinyu Lin", "Xichen Zhang", "Qisheng Hu", "Shuai Yang", "Leyang Shen", "Wei Chow", "Yifei Dong", "Fengyi Wu", "Quanyu Long", "Bin Xia", "Shaozuo Yu", "Mingkang Zhu", "Wenhu Zhang", "Jiehui Huang", "Haokun Gui", "Haoxuan Che", "Long Chen", "Qifeng Chen", "Wenxuan Zhang", "Wenya Wang", "Xiaojuan Qi", "Yang Deng", "Yanwei Li", "Mike Zheng Shou", "Zhi-Qi Cheng", "See-Kiong Ng", "Ziwei Liu", "Philip Torr", "Jiaya Jia" ], "github": "https://github.com/Genesis-Embodied-AI/Genesis", "project_page": "", "abstract": "As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a central bottleneck. Agents that manipulate objects, navigate software, coordinate with others, or design experiments require predictive environment models, yet the term world model carries different meanings across research communities. We introduce a \"levels x laws\" taxonomy organized along two axes. The first defines three capability levels: L1 Predictor, which learns one-step local transition operators; L2 Simulator, which composes them into multi-step, action-conditioned rollouts that respect domain laws; and L3 Evolver, which autonomously revises its own model when predictions fail against new evidence. The second identifies four governing-law regimes: physical, digital, social, and scientific. These regimes determine what constraints a world model must satisfy and where it is most likely to fail. Using this framework, we synthesize over 400 works and summarize more than 100 representative systems spanning model-based reinforcement learning, video generation, web and GUI agents, multi-agent social simulation, and AI-driven scientific discovery. We analyze methods, failure modes, and evaluation practices across level-regime pairs, propose decision-centric evaluation principles and a minimal reproducible evaluation package, and outline architectural guidance, open problems, and governance challenges. The resulting roadmap connects previously isolated communities and charts a path from passive next-step prediction toward world models that can simulate, and ultimately reshape, the environments in which agents operate.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Genesis-Embodied-AI/Genesis", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.21518", "title": "DiffNR: Diffusion-Enhanced Neural Representation Optimization for Sparse-View 3D Tomographic Reconstruction", "authors": [ "Shiyan Su", "Ruyi Zha", "Danli Shi", "Hongdong Li", "Xuelian Cheng" ], "github": "", "project_page": "", "abstract": "Neural representations (NRs), such as neural fields and 3D Gaussians, effectively model volumetric data in computed tomography (CT) but suffer from severe artifacts under sparse-view settings. To address this, we propose DiffNR, a novel framework that enhances NR optimization with diffusion priors. At its core is SliceFixer, a single-step diffusion model designed to correct artifacts in degraded slices. We integrate specialized conditioning layers into the network and develop tailored data curation strategies to support model finetuning. During reconstruction, SliceFixer periodically generates pseudo-reference volumes, providing auxiliary 3D perceptual supervision to fix underconstrained regions. Compared to prior methods that embed CT solvers into time-consuming iterative denoising, our repair-and-augment strategy avoids frequent diffusion model queries, leading to better runtime performance. Extensive experiments show that DiffNR improves PSNR by 3.99 dB on average, generalizes well across domains, and maintains efficient optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.16353", "title": "AgriIR: A Scalable Framework for Domain-Specific Knowledge Retrieval", "authors": [ "Shuvam Banerji Seal", "Aheli Poddar", "Alok Mishra", "Dwaipayan Roy" ], "github": "https://github.com/ollama/ollama", "project_page": "", "abstract": "This paper introduces AgriIR, a configurable retrieval augmented generation (RAG) framework designed to deliver grounded, domain-specific answers while maintaining flexibility and low computational cost. Instead of relying on large, monolithic models, AgriIR decomposes the information access process into declarative modular stages -- query refinement, sub-query planning, retrieval, synthesis, and evaluation. This design allows practitioners to adapt the framework to new knowledge verticals without modifying the architecture. Our reference implementation targets Indian agricultural information access, integrating 1B-parameter language models with adaptive retrievers and domain-aware agent catalogues. The system enforces deterministic citation, integrates telemetry for transparency, and includes automated deployment assets to ensure auditable, reproducible operation. By emphasizing architectural design and modular control, AgriIR demonstrates that well-engineered pipelines can achieve domain-accurate, trustworthy retrieval even under constrained resources. We argue that this approach exemplifies ``AI for Agriculture'' by promoting accessibility, sustainability, and accountability in retrieval-augmented generation systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ollama/ollama", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.22586", "title": "FlowAnchor: Stabilizing the Editing Signal for Inversion-Free Video Editing", "authors": [ "Ze Chen", "Lan Chen", "Yuanhang Li", "Qi Mao" ], "github": "", "project_page": "", "abstract": "We propose FlowAnchor, a training-free framework for stable and efficient inversion-free, flow-based video editing. Inversion-free editing methods have recently shown impressive efficiency and structure preservation in images by directly steering the sampling trajectory with an editing signal. However, extending this paradigm to videos remains challenging, often failing in multi-object scenes or with increased frame counts. We identify the root cause as the instability of the editing signal in high-dimensional video latent spaces, which arises from imprecise spatial localization and length-induced magnitude attenuation. To overcome this challenge, FlowAnchor explicitly anchors both where to edit and how strongly to edit. It introduces Spatial-aware Attention Refinement, which enforces consistent alignment between textual guidance and spatial regions, and Adaptive Magnitude Modulation, which adaptively preserves sufficient editing strength. Together, these mechanisms stabilize the editing signal and guide the flow-based evolution toward the desired target distribution. Extensive experiments demonstrate that FlowAnchor achieves more faithful, temporally coherent, and computationally efficient video editing across challenging multi-object and fast-motion scenarios. The project page is available at https://cuc-mipg.github.io/FlowAnchor.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.22294", "title": "Contexts are Never Long Enough: Structured Reasoning for Scalable Question Answering over Long Document Sets", "authors": [ "Harshit Joshi", "Priyank Shethia", "Jadelynn Dao", "Monica S. Lam" ], "github": "", "project_page": "", "abstract": "Real-world document question answering is challenging. Analysts must synthesize evidence across multiple documents and different parts of each document. However, any fixed LLM context window can be exceeded as document collections grow. A common workaround is to decompose documents into chunks and assemble answers from chunk-level outputs, but this introduces an aggregation bottleneck: as the number of chunks grows, systems must still combine and reason over an increasingly large body of extracted evidence. We present SLIDERS, a framework for question answering over long document collections through structured reasoning. SLIDERS extracts salient information into a relational database, enabling scalable reasoning over persistent structured state via SQL rather than concatenated text. To make this locally extracted representation globally coherent, SLIDERS introduces a data reconciliation stage that leverages provenance, extraction rationales, and metadata to detect and repair duplicated, inconsistent, and incomplete records. SLIDERS outperforms all baselines on three existing long-context benchmarks, despite all of them fitting within the context window of strong base LLMs, exceeding GPT-4.1 by 6.6 points on average. It also improves over the next best baseline by ~19 and ~32 points on two new benchmarks at 3.9M and 36M tokens, respectively.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.18580", "title": "Sessa: Selective State Space Attention", "authors": [ "Liubomyr Horbatko" ], "github": "https://github.com/LibratioAI/sessa", "project_page": "", "abstract": "Modern sequence modeling is dominated by two families: Transformers, whose self-attention can access arbitrary elements of the visible sequence, and structured state-space models, which propagate information through an explicit recurrent state. These mechanisms face different limitations on long contexts: when attention is diffuse, the influence of individual tokens is diluted across the effective support, while recurrent state propagation can lose long-range sensitivity unless information is actively preserved. As a result, both mechanisms face challenges in preserving and selectively retrieving information over long contexts. We propose Sessa, a decoder that places attention inside a recurrent feedback path. This creates many attention-based paths through which past tokens can influence future states, rather than relying on a single attention read or a single recurrent chain. We prove that, under explicit assumptions and matched regimes, Sessa admits power-law memory tails O(ell^{-\u03b2}) for 0 < \u03b2< 1, with slower decay than in the corresponding Transformer and Mamba-style baselines. We further give an explicit construction that achieves this power-law rate. Under the same assumptions, Sessa is the only model class among those considered that realizes flexible selective retrieval, including profiles whose influence does not decay with distance. Consistent with this theoretical advantage, across matched experiments, Sessa achieves the strongest performance on long-context benchmarks while remaining competitive with Transformer and Mamba-style baselines on short-context language modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LibratioAI/sessa", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.21718", "title": "Building a Precise Video Language with Human-AI Oversight", "authors": [ "Zhiqiu Lin", "Chancharik Mitra", "Siyuan Cen", "Isaac Li", "Yuhan Huang", "Yu Tong Tiffany Ling", "Hewei Wang", "Irene Pi", "Shihang Zhu", "Ryan Rao", "George Liu", "Jiaxi Li", "Ruojin Li", "Yili Han", "Yilun Du", "Deva Ramanan" ], "github": "", "project_page": "", "abstract": "Video-language models (VLMs) learn to reason about the dynamic visual world through natural language. We introduce a suite of open datasets, benchmarks, and recipes for scalable oversight that enable precise video captioning. First, we define a structured specification for describing subjects, scenes, motion, spatial, and camera dynamics, grounded by hundreds of carefully defined visual primitives developed with professional video creators such as filmmakers. Next, to curate high-quality captions, we introduce CHAI (Critique-based Human-AI Oversight), a framework where trained experts critique and revise model-generated pre-captions into improved post-captions. This division of labor improves annotation accuracy and efficiency by offloading text generation to models, allowing humans to better focus on verification. Additionally, these critiques and preferences between pre- and post-captions provide rich supervision for improving open-source models (Qwen3-VL) on caption generation, reward modeling, and critique generation through SFT, DPO, and inference-time scaling. Our ablations show that critique quality in precision, recall, and constructiveness, ensured by our oversight framework, directly governs downstream performance. With modest expert supervision, the resulting model outperforms closed-source models such as Gemini-3.1-Pro. Finally, we apply our approach to re-caption large-scale professional videos (e.g., films, commercials, games) and fine-tune video generation models such as Wan to better follow detailed prompts of up to 400 words, achieving finer control over cinematography including camera motion, angle, lens, focus, point of view, and framing. Our results show that precise specification and human-AI oversight are key to professional-level video understanding and generation. Data and code are available on our project page: https://linzhiqiu.github.io/papers/chai/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.22554", "title": "Video Analysis and Generation via a Semantic Progress Function", "authors": [ "Gal Metzer", "Sagi Polaczek", "Ali Mahdavi-Amiri", "Raja Giryes", "Daniel Cohen-Or" ], "github": "", "project_page": "", "abstract": "Transformations produced by image and video generation models often evolve in a highly non-linear manner: long stretches where the content barely changes are followed by sudden, abrupt semantic jumps. To analyze and correct this behavior, we introduce a Semantic Progress Function, a one-dimensional representation that captures how the meaning of a given sequence evolves over time. For each frame, we compute distances between semantic embeddings and fit a smooth curve that reflects the cumulative semantic shift across the sequence. Departures of this curve from a straight line reveal uneven semantic pacing. Building on this insight, we propose a semantic linearization procedure that reparameterizes (or retimes) the sequence so that semantic change unfolds at a constant rate, yielding smoother and more coherent transitions. Beyond linearization, our framework provides a model-agnostic foundation for identifying temporal irregularities, comparing semantic pacing across different generators, and steering both generated and real-world video sequences toward arbitrary target pacing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.22085", "title": "Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents", "authors": [ "Seyed Moein Abtahi", "Rasa Rahnema", "Hetkumar Patel", "Neel Patel", "Majid Fekri", "Tara Khani" ], "github": "", "project_page": "", "abstract": "The transition from stateless language model inference to persistent, multi session autonomous agents has revealed memory to be a primary architectural bottleneck in the deployment of production grade agentic systems. Existing methodologies largely depend on hybrid semantic graph architectures, which impose substantial computational overhead during both ingestion and retrieval. These systems typically require large language model mediated entity extraction, explicit graph schema maintenance, and multi query retrieval pipelines. This paper introduces Memanto, a universal memory layer for agentic artificial intelligence that challenges the prevailing assumption that knowledge graph complexity is necessary to achieve high fidelity agent memory. Memanto integrates a typed semantic memory schema comprising thirteen predefined memory categories, an automated conflict resolution mechanism, and temporal versioning. These components are enabled by Moorcheh's Information Theoretic Search engine, a no indexing semantic database that provides deterministic retrieval within sub ninety millisecond latency while eliminating ingestion delay. Through systematic benchmarking on the LongMemEval and LoCoMo evaluation suites, Memanto achieves state of the art accuracy scores of 89.8 percent and 87.1 percent respectively. These results surpass all evaluated hybrid graph and vector based systems while requiring only a single retrieval query, incurring no ingestion cost, and maintaining substantially lower operational complexity. A five stage progressive ablation study is presented to quantify the contribution of each architectural component, followed by a discussion of the implications for scalable deployment of agentic memory systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.20012", "title": "EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training", "authors": [ "Yiyang Du", "Zhanqiu Guo", "Xin Ye", "Liu Ren", "Chenyan Xiong" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action Models (VLAs) inherit their visual and linguistic capabilities from Vision-Language Models (VLMs), yet most VLAs are built from off-the-shelf VLMs that are not adapted to the embodied domain, limiting their downstream performance. In this work, we propose EmbodiedMidtrain to bridge the gap between VLMs and VLAs. We first characterize the data distribution gap between them, showing that VLA data occupy compact regions that are largely separated from the broader VLM distribution, while the degree of alignment varies substantially both across and within VLM data sources. Then, we build a mid-training data engine that leverages a lightweight learnable proximity estimator to select the most VLA-aligned candidates from a large VLM pool, and mid-trains the VLM on this curated mixture before downstream VLA fine-tuning. Experiments on three robot manipulation benchmarks show that mid-training consistently improves performance across different VLM backbones, achieving results competitive with expert VLAs and off-the-shelf VLMs trained with larger model scale and training budgets. Further analysis reveals that mid-training provides a stronger initialization for VLA fine-tuning, with gains emerging from the earliest steps and widening throughout training. Moreover, the data engine captures both dataset-level and sample-level alignment signals, favoring spatial reasoning over text-centric tasks while preserving the diversity of the VLM data. We will release all code, data and models for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.20857", "title": "DiagramBank: A Large-scale Dataset of Diagram Design Exemplars with Paper Metadata for Retrieval-Augmented Generation", "authors": [ "Tingwen Zhang", "Ling Yue", "Zhen Xu", "Shaowu Pan" ], "github": "https://github.com/csml-rpi/DiagramBank", "project_page": "", "abstract": "Recent advances in autonomous ``AI scientist'' systems have demonstrated the ability to automatically write scientific manuscripts and codes with execution. However, producing a publication-grade scientific diagram (e.g., teaser figure) is still a major bottleneck in the ``end-to-end'' paper generation process. For example, a teaser figure acts as a strategic visual interface and serves a different purpose than derivative data plots. It demands conceptual synthesis and planning to translate complex logic workflow into a compelling graphic that guides intuition and sparks curiosity. Existing AI scientist systems usually omit this component or fall back to an inferior alternative. To bridge this gap, we present DiagramBank, a large-scale dataset consisting of 89,422 schematic diagrams curated from existing top-tier scientific publications, designed for multimodal retrieval and exemplar-driven scientific figure generation. DiagramBank is developed through our automated curation pipeline that extracts figures and corresponding in-text references, and uses a CLIP-based filter to differentiate schematic diagrams from standard plots or natural images. Each instance is paired with rich context from abstract, caption, to figure-reference pairs, enabling information retrieval under different query granularities. We release DiagramBank in a ready-to-index format and provide a retrieval-augmented generation codebase to demonstrate exemplar-conditioned synthesis of teaser figures. DiagramBank is publicly available at https://huggingface.co/datasets/zhangt20/DiagramBank with code at https://github.com/csml-rpi/DiagramBank.", "github_manual": "", "github_regex": "https://github.com/csml-rpi/DiagramBank", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-27", "arxiv_id": "2604.22119", "title": "Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework", "authors": [ "Tharindu Kumarage", "Lisa Bauer", "Yao Ma", "Dan Rosen", "Yashasvi Raghavendra Guduri", "Anna Rumshisky", "Kai-Wei Chang", "Aram Galstyan", "Rahul Gupta", "Charith Peris" ], "github": "", "project_page": "", "abstract": "As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own objectives, a class of risks we term Emergent Strategic Reasoning Risks (ESRRs). These include, but are not limited to, deception (intentionally misleading users or evaluators), evaluation gaming (strategically manipulating performance during safety testing), and reward hacking (exploiting misspecified objectives). Systematically understanding and benchmarking these risks remains an open challenge. To address this gap, we introduce ESRRSim, a taxonomy-driven agentic framework for automated behavioral risk evaluation. We construct an extensible risk taxonomy of 7 categories, which is decomposed into 20 subcategories. ESRRSim generates evaluation scenarios designed to elicit faithful reasoning, paired with dual rubrics assessing both model responses and reasoning traces, in a judge-agnostic and scalable architecture. Evaluation across 11 reasoning LLMs reveals substantial variation in risk profiles (detection rates ranging 14.45%-72.72%), with dramatic generational improvements suggesting models may increasingly recognize and adapt to evaluation contexts.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.24198", "title": "Rewarding the Scientific Process: Process-Level Reward Modeling for Agentic Data Analysis", "authors": [ "Zhisong Qiu", "Shuofei Qiao", "Kewei Xu", "Yuqi Zhu", "Lun Du", "Ningyu Zhang", "Huajun Chen" ], "github": "https://github.com/zjunlp/DataMind", "project_page": "", "abstract": "Process Reward Models (PRMs) have achieved remarkable success in augmenting the reasoning capabilities of Large Language Models (LLMs) within static domains such as mathematics. However, their potential in dynamic data analysis tasks remains underexplored. In this work, we first present a empirical study revealing that general-domain PRMs struggle to supervise data analysis agents. Specifically, they fail to detect silent errors, logical flaws that yield incorrect results without triggering interpreter exceptions, and erroneously penalize exploratory actions, mistaking necessary trial-and-error exploration for grounding failures. To bridge this gap, we introduce DataPRM, a novel environment-aware generative process reward model that (1) can serve as an active verifier, autonomously interacting with the environment to probe intermediate execution states and uncover silent errors, and (2) employs a reflection-aware ternary reward strategy that distinguishes between correctable grounding errors and irrecoverable mistakes. We design a scalable pipeline to construct over 8K high-quality training instances for DataPRM via diversity-driven trajectory generation and knowledge-augmented step-level annotation. Experimental results demonstrate that DataPRM improves downstream policy LLMs by 7.21% on ScienceAgentBench and 11.28% on DABStep using Best-of-N inference. Notably, with only 4B parameters, DataPRM outperforms strong baselines, and exhibits robust generalizability across diverse Test-Time Scaling strategies. Furthermore, integrating DataPRM into Reinforcement Learning yields substantial gains over outcome-reward baselines, achieving 78.73% on DABench and 64.84% on TableBench, validating the effectiveness of process reward supervision. Code is available at https://github.com/zjunlp/DataMind.", "github_manual": "", "github_regex": "https://github.com/zjunlp/DataMind", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.22875", "title": "SketchVLM: Vision language models can annotate images to explain thoughts and guide users", "authors": [ "Brandon Collins", "Logan Bolton", "Hung Huy Nguyen", "Mohammad Reza Taesiri", "Trung Bui", "Anh Totti Nguyen" ], "github": "https://github.com/allenai/molmo", "project_page": "", "abstract": "When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-language models (VLMs) such as Gemini-3-Pro and GPT-5 only respond with text, which can be difficult for users to verify. We present SketchVLM, a training-free, model-agnostic framework that enables VLMs to produce non-destructive, editable SVG overlays on the input image to visually explain their answers. Across seven benchmarks spanning visual reasoning (maze navigation, ball-drop trajectory prediction, and object counting) and drawing (part labeling, connecting-the-dots, and drawing shapes around objects), SketchVLM improves visual reasoning task accuracy by up to +28.5 percentage points and annotation quality by up to 1.48x relative to image-editing and fine-tuned sketching baselines, while also producing annotations that are more faithful to the model's stated answer. We find that single-turn generation already achieves strong accuracy and annotation quality, and multi-turn generation opens up further opportunities for human-AI collaboration. An interactive demo and code are at https://sketchvlm.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/allenai/molmo", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.24764", "title": "World-R1: Reinforcing 3D Constraints for Text-to-Video Generation", "authors": [ "Weijie Wang", "Xiaoxuan He", "Youping Gu", "Yifan Yang", "Zeyu Zhang", "Yefei He", "Yanbo Ding", "Xirui Hu", "Donny Y. Chen", "Zhiyuan He", "Yuqing Yang", "Bohan Zhuang" ], "github": "", "project_page": "", "abstract": "Recent video foundation models demonstrate impressive visual synthesis but frequently suffer from geometric inconsistencies. While existing methods attempt to inject 3D priors via architectural modifications, they often incur high computational costs and limit scalability. We propose World-R1, a framework that aligns video generation with 3D constraints through reinforcement learning. To facilitate this alignment, we introduce a specialized pure text dataset tailored for world simulation. Utilizing Flow-GRPO, we optimize the model using feedback from pre-trained 3D foundation models and vision-language models to enforce structural coherence without altering the underlying architecture. We further employ a periodic decoupled training strategy to balance rigid geometric consistency with dynamic scene fluidity. Extensive evaluations reveal that our approach significantly enhances 3D consistency while preserving the original visual quality of the foundation model, effectively bridging the gap between video generation and scalable world simulation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.24003", "title": "Stabilizing Efficient Reasoning with Step-Level Advantage Selection", "authors": [ "Han Wang", "Xiaodong Yu", "Jialian Wu", "Jiang Liu", "Ximeng Sun", "Mohit Bansal", "Zicheng Liu" ], "github": "https://github.com/HanNight/SAS", "project_page": "", "abstract": "Large language models (LLMs) achieve strong reasoning performance by allocating substantial computation at inference time, often generating long and verbose reasoning traces. While recent work on efficient reasoning reduces this overhead through length-based rewards or pruning, many approaches are post-trained under a much shorter context window than base-model training, a factor whose effect has not been systematically isolated. We first show that short-context post-training alone, using standard GRPO without any length-aware objective, already induces substantial reasoning compression-but at the cost of increasingly unstable training dynamics and accuracy degradation. To address this, we propose Step-level Advantage Selection (SAS), which operates at the reasoning-step level and assigns a zero advantage to low-confidence steps in correct rollouts and to high-confidence steps in verifier-failed rollouts, where failures often arise from truncation or verifier issues rather than incorrect reasoning. Across diverse mathematical and general reasoning benchmarks, SAS improves average Pass@1 accuracy by 0.86 points over the strongest length-aware baseline while reducing average reasoning length by 16.3%, yielding a better accuracy-efficiency trade-off.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HanNight/SAS", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.24479", "title": "Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data", "authors": [ "Mohammadmehdi Ataei", "Farzaneh Askari", "Kamal Rahimi Malekshan", "Pradeep Kumar Jayaraman" ], "github": "", "project_page": "", "abstract": "Computer-Aided Design (CAD) models are defined by their construction history: a parametric recipe that encodes design intent. However, existing large-scale 3D datasets predominantly consist of boundary representations (B-Reps) or meshes, stripping away this critical procedural information. To address this scarcity, we introduce Zero-to-CAD, a scalable framework for synthesizing executable CAD construction sequences. We frame synthesis as an agentic search problem: by embedding a large language model (LLM) within a feedback-driven CAD environment, our system iteratively generates, executes, and validates code using tools and documentation lookup to promote geometric validity and operation diversity. This agentic approach enables the synthesis of approximately one million executable, readable, editable CAD sequences, covering a rich vocabulary of operations beyond sketch-and-extrude workflows. We also release a curated subset of 100,000 high-quality models selected for geometric diversity. To demonstrate the dataset's utility, we fine-tune a vision-language model on our synthetic data to reconstruct editable CAD programs from multi-view images, outperforming strong baselines, including GPT-5.2, and effectively bootstrapping sequence generation capabilities without real construction-history training data. Zero-to-CAD bridges the gap between geometric scale and parametric interpretability, offering a vital resource for the next generation of CAD AI.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.24763", "title": "Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation", "authors": [ "Zhiheng Liu", "Weiming Ren", "Xiaoke Huang", "Shoufa Chen", "Tianhong Li", "Mengzhao Chen", "Yatai Ji", "Sen He", "Jonas Schult", "Belinda Zeng", "Tao Xiang", "Wenhu Chen", "Ping Luo", "Luke Zettlemoyer", "Yuren Cong" ], "github": "", "project_page": "", "abstract": "Unified multimodal models typically rely on pretrained vision encoders and use separate visual representations for understanding and generation, creating misalignment between the two tasks and preventing fully end-to-end optimization from raw pixels. We introduce Tuna-2, a native unified multimodal model that performs visual understanding and generation directly based on pixel embeddings. Tuna-2 drastically simplifies the model architecture by employing simple patch embedding layers to encode visual input, completely discarding the modular vision encoder designs such as the VAE or the representation encoder. Experiments show that Tuna-2 achieves state-of-the-art performance in multimodal benchmarks, demonstrating that unified pixel-space modelling can fully compete with latent-space approaches for high-quality image generation. Moreover, while the encoder-based variant converges faster in early pretraining, Tuna-2's encoder-free design achieves stronger multimodal understanding at scale, particularly on tasks requiring fine-grained visual perception. These results show that pretrained vision encoders are not necessary for multimodal modelling, and end-to-end pixel-space learning offers a scalable path toward stronger visual representations for both generation and perception.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.23099", "title": "ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation", "authors": [ "Yizheng Huang", "Wenjun Zeng", "Aditi Kumaresan", "Zi Wang" ], "github": "https://github.com/google-deepmind/proeval", "project_page": "", "abstract": "Evaluating generative AI models is increasingly resource-intensive due to slow inference, expensive raters, and a rapidly growing landscape of models and benchmarks. We propose ProEval, a proactive evaluation framework that leverages transfer learning to efficiently estimate performance and identify failure cases. ProEval employs pre-trained Gaussian Processes (GPs) as surrogates for the performance score function, mapping model inputs to metrics such as the severity of errors or safety violations. By framing performance estimation as Bayesian quadrature (BQ) and failure discovery as superlevel set sampling, we develop uncertainty-aware decision strategies that actively select or synthesize highly informative inputs for testing. Theoretically, we prove that our pre-trained GP-based BQ estimator is unbiased and bounded. Empirically, extensive experiments on reasoning, safety alignment, and classification benchmarks demonstrate that ProEval is significantly more efficient than competitive baselines. It requires 8-65x fewer samples to achieve estimates within 1% of the ground truth, while simultaneously revealing more diverse failure cases under a stricter evaluation budget.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/google-deepmind/proeval", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.22782", "title": "Stochastic KV Routing: Enabling Adaptive Depth-Wise Cache Sharing", "authors": [ "Anastasiia Filippova", "David Grangier", "Marco Cuturi", "Jo\u00e3o Monteiro" ], "github": "", "project_page": "", "abstract": "Serving transformer language models with high throughput requires caching Key-Values (KVs) to avoid redundant computation during autoregressive generation. The memory footprint of KV caching is significant and heavily impacts serving costs. This work proposes to lessen these memory requirements. While recent work has largely addressed KV cache reduction via compression and eviction along the temporal axis, we argue that the depth dimension offers an orthogonal and robust avenue for optimization. Although prior research suggests that a full cache for every layer is redundant, implementing cross-layer cache sharing remains a practical challenge; existing methods typically suffer from reduced throughput or increased time-to-first-token. In this paper, we demonstrate that dropping a layer's cache offers efficient optimization without information loss. We propose a simple training approach: random cross-layer attention. During training, layers randomly choose to attend either to their own KV states or those of a preceding layer. This stochastic process adapts the model to be robust to various depth-wise cache sharing strategies, ensuring flexibility for unknown hardware constraints at deployment time. Our evaluations show that applying this scheme during pre-training or fine-tuning enables depth-wise cache sharing for various model families. Furthermore, for larger models in data-constrained settings, this approach is suggestive of a regularization-like effect, frequently preserving or improving performance while significantly reducing the cache's memory footprint.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.24300", "title": "ReVSI: Rebuilding Visual Spatial Intelligence Evaluation for Accurate Assessment of VLM 3D Reasoning", "authors": [ "Yiming Zhang", "Jiacheng Chen", "Jiaqi Tan", "Yongsen Mao", "Wenhu Chen", "Angel X. Chang" ], "github": "", "project_page": "", "abstract": "Current evaluations of spatial intelligence can be systematically invalid under modern vision-language model (VLM) settings. First, many benchmarks derive question-answer (QA) pairs from point-cloud-based 3D annotations originally curated for traditional 3D perception. When such annotations are treated as ground truth for video-based evaluation, reconstruction and annotation artifacts can miss objects that are clearly visible in the video, mislabel object identities, or corrupt geometry-dependent answers (e.g., size), yielding incorrect or ambiguous QA pairs. Second, evaluations often assume full-scene access, while many VLMs operate on sparsely sampled frames (e.g., 16-64), making many questions effectively unanswerable under the actual model inputs. We improve evaluation validity by introducing ReVSI, a benchmark and protocol that ensures each QA pair is answerable and correct under the model's actual inputs. To this end, we re-annotate objects and geometry across 381 scenes from 5 datasets to improve data quality, and regenerate all QA pairs with rigorous bias mitigation and human verification using professional 3D annotation tools. We further enhance evaluation controllability by providing variants across multiple frame budgets (16/32/64/all) and fine-grained object visibility metadata, enabling controlled diagnostic analyses. Evaluations of general and domain-specific VLMs on ReVSI reveal systematic failure modes that are obscured by prior benchmarks, yielding a more reliable and diagnostic assessment of spatial intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.23781", "title": "ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents", "authors": [ "Fanqing Meng", "Lingxiao Du", "Zijian Wu", "Guanzheng Chen", "Xiangyan Liu", "Jiaqi Liao", "Chonghe Jiang", "Zhenglin Wan", "Jiawei Gu", "Pengfei Zhou", "Rui Huang", "Ziqi Zhao", "Shengyuan Ding", "Ailing Yu", "Bo Peng", "Bowei Xia", "Hao Sun", "Haotian Liang", "Ji Xie", "Jiajun Chen", "Jiajun Song", "Liu Yang", "Ming Xu", "Qionglin Qiu", "Runhao Fu", "Shengfang Zhai", "Shijian Wang", "Tengfei Ma", "Tianyi Wu", "Weiyang Jin", "Yan Wang", "Yang Dai", "Yao Lai", "Youwei Shu", "Yue Liu", "Yunzhuo Hao", "Yuwei Niu", "Jinkai Huang", "Jiayuan Zhuo", "Zhennan Shen", "Linyu Wu", "Cihang Xie", "Yuyin Zhou", "Jiaheng Zhang", "Zeyu Zheng", "Mengkang Hu", "Michael Qizhe Shieh" ], "github": "", "project_page": "", "abstract": "Language-model agents are increasingly used as persistent coworkers that assist users across multiple working days. During such workflows, the surrounding environment may change independently of the agent: new emails arrive, calendar entries shift, knowledge-base records are updated, and evidence appears across images, scanned PDFs, audio, video, and spreadsheets. Existing benchmarks do not adequately evaluate this setting because they typically run within a single static episode and remain largely text-centric. We introduce , a benchmark for coworker agents built around multi-turn multi-day tasks, a stateful sandboxed service environment whose state evolves between turns, and rule-based verification. The current release contains 100 tasks across 13 professional scenarios, executed against five stateful sandboxed services (filesystem, email, calendar, knowledge base, spreadsheet) and scored by 1537 deterministic Python checkers over post-execution service state; no LLM-as-judge is invoked during scoring. We benchmark seven frontier agent systems. The strongest model reaches 75.8 weighted score, but the best strict Task Success is only 20.0\\%, indicating that partial progress is common while complete end-to-end workflow completion remains rare. Turn-level analysis shows that performance drops after the first exogenous environment update, highlighting adaptation to changing state as a key open challenge. We release the benchmark, evaluation harness, and construction pipeline to support reproducible coworker-agent evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.24762", "title": "OmniShotCut: Holistic Relational Shot Boundary Detection with Shot-Query Transformer", "authors": [ "Boyang Wang", "Guangyi Xu", "Zhipeng Tang", "Jiahui Zhang", "Zezhou Cheng" ], "github": "", "project_page": "", "abstract": "Shot Boundary Detection (SBD) aims to automatically identify shot changes and divide a video into coherent shots. While SBD was widely studied in the literature, existing state-of-the-art methods often produce non-interpretable boundaries on transitions, miss subtle yet harmful discontinuities, and rely on noisy, low-diversity annotations and outdated benchmarks. To alleviate these limitations, we propose OmniShotCut to formulate SBD as structured relational prediction, jointly estimating shot ranges with intra-shot relations and inter-shot relations, by a shot query-based dense video Transformer. To avoid imprecise manual labeling, we adopt a fully synthetic transition synthesis pipeline that automatically reproduces major transition families with precise boundaries and parameterized variants. We also introduce OmniShotCutBench, a modern wide-domain benchmark enabling holistic and diagnostic evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.23775", "title": "Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms", "authors": [ "Qi Li", "Bo Yin", "Weiqi Huang", "Ruhao Liu", "Bojun Zou", "Runpeng Yu", "Jingwen Ye", "Weihao Yu", "Xinchao Wang" ], "github": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models are emerging as a unified substrate for embodied intelligence. This shift raises a new class of safety challenges, stemming from the embodied nature of VLA systems, including irreversible physical consequences, a multimodal attack surface across vision, language, and state, real-time latency constraints on defense, error propagation over long-horizon trajectories, and vulnerabilities in the data supply chain. Yet the literature remains fragmented across robotic learning, adversarial machine learning, AI alignment, and autonomous systems safety. This survey provides a unified and up-to-date overview of safety in Vision-Language-Action models. We organize the field along two parallel timing axes, attack timing (training-time vs. inference-time and defense timing (training-time vs. inference-time, linking each class of threat to the stage at which it can be mitigated. We first define the scope of VLA safety, distinguishing it from text-only LLM safety and classical robotic safety, and review the foundations of VLA models, including architectures, training paradigms, and inference mechanisms. We then examine the literature through four lenses: Attacks, Defenses, Evaluation, and Deployment. We survey training-time threats such as data poisoning and backdoors, as well as inference-time attacks including adversarial patches, cross-modal perturbations, semantic jailbreaks, and freezing attacks. We review training-time and runtime defenses, analyze existing benchmarks and metrics, and discuss safety challenges across six deployment domains. Finally, we highlight key open problems, including certified robustness for embodied trajectories, physically realizable defenses, safety-aware training, unified runtime safety architectures, and standardized evaluation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.21480", "title": "Efficient Agent Evaluation via Diversity-Guided User Simulation", "authors": [ "Itay Nakash", "George Kour", "Ateret Anaby-Tavor" ], "github": "https://github.com/IBM/DIVERT_Efficient_Agent_Evaluation_via_Diversity_Guided_User_Simulation", "project_page": "", "abstract": "Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains challenging due to stochastic, multi-turn interactions. Current evaluation protocols rely on linear Monte Carlo rollouts of complete agent-user conversations to estimate success. However, this approach is computationally inefficient, repeatedly regenerating identical early prefixes, and often fails to uncover deep failure modes that arise from rare user behaviors. We introduce DIVERT (Diversity-Induced Evaluation via Branching of Trajectories), an efficient, snapshot-based, coverage-guided user simulation framework for systematic exploration of agent-user interactions. DIVERT captures the full agent-environment state at critical decision points and resumes execution from these snapshots, enabling reuse of shared conversation prefixes and reducing redundant computation. From each junction, the framework branches using targeted, diversity-inducing user responses, allowing directed exploration of alternative interaction paths. By focusing evaluation on semantically diverse and underexplored trajectories, DIVERT improves both efficiency and coverage. Empirical results show that it discovers more failures per token compared to standard linear rollout protocols, while expanding the set of tasks on which failures are identified.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/IBM/DIVERT_Efficient_Agent_Evaluation_via_Diversity_Guided_User_Simulation", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2508.10180", "title": "For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs", "authors": [ "Wenlong Deng", "Qi Zeng", "Jiaming Zhang", "Minghui Chen", "Zixin Ding", "Christos Thrampoulidis", "Boying Gong", "Xiaoxiao Li" ], "github": "https://github.com/vengdeng/For-Value-Efficient-Forward-Only-Data-Valuation-for-finetuning", "project_page": "", "abstract": "Data valuation is essential for enhancing the transparency and accountability of large language models (LLMs) and vision-language models (VLMs). However, existing methods typically rely on gradient computations, making them computationally prohibitive for billion-parameter models and precluding batch parallelization. In this work, we introduce For-Value, a forward-only data valuation framework that enables efficient batch-scalable value estimation while maintaining effectiveness. Leveraging the expressive power of pretrained LLMs/VLMs, we theoretically demonstrate that data valuation can be captured by the alignment between the final hidden representations and prediction errors at the last layer. In light of this insight, For-Value computes data value using a simple closed-form expression with a single forward pass, eliminating the need for costly backpropagation and enabling efficient batch calculating at scale. Extensive experiments show that For-Value matches or outperforms gradient-based baselines in detecting influential data and mislabeled data, while achieving significant efficiency improvements.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vengdeng/For-Value-Efficient-Forward-Only-Data-Valuation-for-finetuning", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.19548", "title": "Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment", "authors": [ "Bobo Li", "Rui Wu", "Zibo Ji", "Meishan Zhang", "Hao Fei", "Min Zhang", "Mong-Li Lee", "Wynne Hsu" ], "github": "", "project_page": "", "abstract": "Large Language Model agents have rapidly evolved from static text generators into dynamic systems capable of executing complex autonomous workflows. To enhance reliability, multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing. While such role-playing effectively leverages domain expert knowledge, we find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry (AOA). Specifically, an agent acting as an actor (during self-reflection) tends to attribute failures to external factors, whereas an observer (during mutual auditing) attributes the same errors to internal faults. We quantify this using our new Ambiguous Failure Benchmark, which reveals that simply swapping perspectives triggers the AOA effect in over 20% of cases for most models. To tame this bias, we introduce ReTAS (Reasoning via Thesis-Antithesis-Synthesis), a model trained through dialectical alignment to enforce perspective-invariant reasoning. By integrating dialectical chain-of-thought with Group Relative Policy Optimization, ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus. Experiments demonstrate that ReTAS effectively mitigates attribution inconsistency and significantly improves fault resolution rates in ambiguous scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.22446", "title": "From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company", "authors": [ "Zhengxu Yu", "Yu Fu", "Zhiyuan He", "Yuxuan Huang", "Lee Ka Yiu", "Meng Fang", "Weilin Luo", "Jun Wang" ], "github": "https://github.com/openclaw/openclaw", "project_page": "", "abstract": "Individual agent capabilities have advanced rapidly through modular skills and tool integrations, yet multi-agent systems remain constrained by fixed team structures, tightly coupled coordination logic, and session-bound learning. We argue that this reflects a deeper absence: a principled organisational layer that governs how a workforce of agents is assembled, governed, and improved over time, decoupled from what individual agents know. To fill this gap, we introduce OneManCompany (OMC), a framework that elevates multi-agent systems to the organisational level. OMC encapsulates skills, tools, and runtime configurations into portable agent identities called Talents, orchestrated through typed organisational interfaces that abstract over heterogeneous backends. A community-driven Talent Market enables on-demand recruitment, allowing the organisation to close capability gaps and reconfigure itself dynamically during execution. Organisational decision-making is operationalised through an Explore-Execute-Review (E^2R) tree search, which unifies planning, execution, and evaluation in a single hierarchical loop: tasks are decomposed top-down into accountable units and execution outcomes are aggregated bottom-up to drive systematic review and refinement. This loop provides formal guarantees on termination and deadlock freedom while mirroring the feedback mechanisms of human enterprises. Together, these contributions transform multi-agent systems from static, pre-configured pipelines into self-organising and self-improving AI organisations capable of adapting to open-ended tasks across diverse domains. Empirical evaluation on PRDBench shows that OMC achieves an 84.67% success rate, surpassing the state of the art by 15.48 percentage points, with cross-domain case studies further demonstrating its generality.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openclaw/openclaw", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.23210", "title": "Discovering Agentic Safety Specifications from 1-Bit Danger Signals", "authors": [ "V\u00edctor Gallego" ], "github": "https://github.com/vicgalle/experiential-prompt-optimization-safe", "project_page": "", "abstract": "Can large language model agents discover hidden safety objectives through experience alone? We introduce EPO-Safe (Experiential Prompt Optimization for Safe Agents), a framework where an LLM iteratively generates action plans, receives sparse binary danger warnings, and evolves a natural language behavioral specification through reflection. Unlike standard LLM reflection methods that rely on rich textual feedback (e.g., compiler errors or detailed environment responses), EPO-Safe demonstrates that LLMs can perform safety reasoning from a strictly impoverished signal in structured, low-dimensional environments: the agent never observes the hidden performance function R^*, only a single bit per timestep indicating that an action was unsafe. We evaluate on five AI Safety Gridworlds (Leike et al., 2017) and five text-based scenario analogs where visible reward R may diverge from R^*. EPO-Safe discovers safe behavior within 1-2 rounds (5-15 episodes), producing human-readable specifications with correct explanatory hypotheses about hazards (e.g., \"X cells are directionally hazardous: entering from the north is dangerous\"). Critically, we show that standard reward-driven reflection actively degrades safety: agents reflecting on reward alone use the loop to justify and accelerate reward hacking, proving that reflection must be paired with a dedicated safety channel to discover hidden constraints. We further evaluate robustness to noisy oracles: even when 50% of non-dangerous steps produce spurious warnings, mean safety performance degrades by only 15% on average, though sensitivity is environment-dependent, as cross-episode reflection naturally filters inconsistent signals. Each evolved specification functions as an auditable set of grounded behavioral rules discovered autonomously through interaction, rather than authored by humans as in Constitutional AI (Bai et al., 2022).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/vicgalle/experiential-prompt-optimization-safe", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.22841", "title": "ATTN-FIQA: Interpretable Attention-based Face Image Quality Assessment with Vision Transformers", "authors": [ "Guray Ozgur", "Tahar Chettaoui", "Eduarda Caldeira", "Jan Niklas Kolf", "Marco Huber", "Andrea Atzori", "Naser Damer", "Fadi Boutros" ], "github": "", "project_page": "", "abstract": "Face Image Quality Assessment (FIQA) aims to assess the recognition utility of face samples and is essential for reliable face recognition (FR) systems. Existing approaches require computationally expensive procedures such as multiple forward passes, backpropagation, or additional training, and only recent work has focused on the use of Vision Transformers. Recent studies highlighted that these architectures inherently function as saliency learners with attention patterns naturally encoding spatial importance. This work proposes ATTN-FIQA, a novel training-free approach that investigates whether pre-softmax attention scores from pre-trained Vision Transformer-based face recognition models can serve as quality indicators. We hypothesize that attention magnitudes intrinsically encode quality: high-quality images with discriminative facial features enable strong query-key alignments producing focused, high-magnitude attention patterns, while degraded images generate diffuse, low-magnitude patterns. ATTN-FIQA extracts pre-softmax attention matrices from the final transformer block, aggregate multi-head attention information across all patches, and compute image-level quality scores through simple averaging, requiring only a single forward pass through pre-trained models without architectural modifications, backpropagation, or additional training. Through comprehensive evaluation across eight benchmark datasets and four FR models, this work demonstrates that attention-based quality scores effectively correlate with face image quality and provide spatial interpretability, revealing which facial regions contribute most to quality determination.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.22842", "title": "EX-FIQA: Leveraging Intermediate Early eXit Representations from Vision Transformers for Face Image Quality Assessment", "authors": [ "Guray Ozgur", "Tahar Chettaoui", "Eduarda Caldeira", "Jan Niklas Kolf", "Andrea Atzori", "Fadi Boutros", "Naser Damer" ], "github": "", "project_page": "", "abstract": "Face Image Quality Assessment is crucial for reliable face recognition systems, yet existing Vision Transformer-based approaches rely exclusively on final-layer representations, ignoring quality-relevant information captured at intermediate network depths. This paper presents the first comprehensive investigation of how intermediate representations within ViTs contribute to face quality assessment through early exit mechanisms and score fusion strategies. We systematically analyze all twelve transformer blocks of ViT-FIQA architectures, demonstrating that different depths capture distinct and complementary quality-relevant information, as evidenced by varying attention patterns and performance characteristics across network layers. We propose a score fusion framework that combines quality predictions from multiple transformer blocks without architectural modifications or additional training. Our early exit analysis reveals optimal performance-efficiency trade-offs, enabling significant computational savings while maintaining competitive performance. Through extensive evaluation across eight benchmark datasets using four FR models, we demonstrate that our fusion strategy improves upon single-exit approaches. Our proposed quality fusion approach employs depth-weighted averaging that assigns progressively higher importance to deeper transformer blocks, achieving the best quality assessment performance by effectively leveraging the hierarchical nature of feature learning in ViTs. Our work challenges the conventional wisdom that only deep features matter for face analysis, revealing that intermediate representations contain valuable information for quality assessment. The proposed framework offers practical benefits for real-world biometric systems by enabling adaptive computation based on resource constraints while maintaining competitive quality assessment capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.24583", "title": "Improving Vision-language Models with Perception-centric Process Reward Models", "authors": [ "Yingqian Min", "Kun Zhou", "Yifan Li", "Yuhuan Wu", "Han Peng", "Yifan Du", "Wayne Xin Zhao", "Min Yang", "Ji-Rong Wen" ], "github": "https://github.com/RUCAIBox/Perceval", "project_page": "", "abstract": "Recent advancements in reinforcement learning with verifiable rewards (RLVR) have significantly improved the complex reasoning ability of vision-language models (VLMs). However, its outcome-level supervision is too coarse to diagnose and correct errors within the reasoning chain. To this end, we propose Perceval, a process reward model (PRM) that enables token-level error grounding, which can extract image-related claims from the response and compare them one by one with the visual evidence in the image, ultimately returning claims that contain perceptual errors. Perceval is trained with perception-intensive supervised training data. We then integrate Perceval into the RL training process to train the policy models. Specifically, compared to traditional GRPO, which applies sequence-level advantages, we apply token-level advantages by targeting penalties on hallucinated spans identified by Perceval, thus enabling fine-grained supervision signals. In addition to augmenting the training process, Perceval can also assist VLMs during the inference stage. Using Perceval, we can truncate the erroneous portions of the model's response, and then either have the model regenerate the response directly or induce the model to reflect on its previous output. This process can be repeated multiple times to achieve test-time scaling. Experiments show significant improvements on benchmarks from various domains across multiple reasoning VLMs trained with RL, highlighting the promise of perception-centric supervision as a general-purpose strategy. For test-time scaling, it also demonstrates consistent performance gains over other strategies, such as major voting. Our code and data will be publicly released at https://github.com/RUCAIBox/Perceval.", "github_manual": "", "github_regex": "https://github.com/RUCAIBox/Perceval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.22880", "title": "TexOCR: Advancing Document OCR Models for Compilable Page-to-LaTeX Reconstruction", "authors": [ "Chengye Wang", "Lin Fu", "Zexi Kuang", "Yilun Zhao" ], "github": "", "project_page": "", "abstract": "Existing document OCR largely targets plain text or Markdown, discarding the structural and executable properties that make LaTeX essential for scientific publishing. We study page-level reconstruction of scientific PDFs into compilable LaTeX and introduce TexOCR-Bench, a benchmark, and TexOCR-Train, a large-scale training corpus, for this task. TexOCR-Bench features a multi-dimensional evaluation suite that jointly assesses transcription fidelity, structural faithfulness, and end-to-end compilability. Leveraging TexOCR-Train, we train a 2B-parameter model, TexOCR, using supervised fine-tuning (SFT) and reinforcement learning (RL) with verifiable rewards derived from LaTeX unit tests that directly enforce compilability and referential integrity. Experiments across 21 frontier models on TexOCR-Bench show that existing systems frequently violate key document invariants, including consistent section structure, correct float placement, and valid label-reference links, which undermines compilation reliability and downstream usability. Our analysis further reveals that RL with verifiable rewards yields consistent improvements over SFT alone, particularly on structural and compilation metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.21106", "title": "How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models", "authors": [ "Kristian Schwethelm", "Daniel Rueckert", "Georgios Kaissis" ], "github": "", "project_page": "", "abstract": "We measure how much one extra recurrence is worth to a looped (depth-recurrent) language model, in equivalent unique parameters. From an iso-depth sweep of 116 pretraining runs across recurrence counts r in {1, 2, 4, 8} spanning {sim}50times in training compute, we fit a joint scaling law L = E + A,(N_once + r^\u03c6 N_rec)^{-\u03b1} + B,D^{-\u03b2} and recover a new recurrence-equivalence exponent \u03c6= 0.46. Intuitively, \u03c6 tells us whether looping a block r times is equivalent in validation loss to r unique blocks of a non-looped model (full equivalence, \u03c6{=}1) or to a single block run repeatedly with no capacity gain (\u03c6{=}0). Our \u03c6= 0.46 sits in between, so each additional recurrence predictably increases validation loss at matched training compute. For example, at r{=}4 a 410M looped model performs on par with a 580M non-looped model, but incurs the training cost of a 1B non-looped one. We demonstrate the utility of \u03c6 as a measurement tool on two probes. Truncated backpropagation lowers \u03c6 to 0.38, indicating that the loop mechanism is poorly trained under truncation, even though validation loss decreases. Conversely, hyperconnections raise \u03c6 to 0.65, a genuine capacity gain. Our method applies to any looped LM and separates true loop improvements from token-budget gains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.17565", "title": "UniGeo: Unifying Geometric Guidance for Camera-Controllable Image Editing via Video Models", "authors": [ "Hong Jiang", "Wensong Song", "Zongxing Yang", "Ruijie Quan", "Yi Yang" ], "github": "", "project_page": "", "abstract": "Camera-controllable image editing aims to synthesize novel views of a given scene under varying camera poses while strictly preserving cross-view geometric consistency. However, existing methods typically rely on fragmented geometric guidance, such as only injecting point clouds at the representation level despite models containing multiple levels, and are mainly based on image diffusion models that operate on discrete view mappings. These two limitations jointly lead to geometric drift and structural degradation under continuous camera motion. We observe that while leveraging video models provides continuous viewpoint priors for camera-controllable image editing, they still struggle to form stable geometric understanding if geometric guidance remains fragmented. To systematically address this, we inject unified geometric guidance across three levels that jointly determine the generative output: representation, architecture, and loss function. To this end, we propose UniGeo, a novel camera-controllable editing framework. Specifically, at the representation level, UniGeo incorporates a frame-decoupled geometric reference injection mechanism to provide robust cross-view geometry context. At the architecture level, it introduces geometric anchor attention to align multi-view features. At the loss function level, it proposes a trajectory-endpoint geometric supervision strategy to explicitly reinforce the structural fidelity of target views. Comprehensive experiments across multiple public benchmarks, encompassing both extensive and limited camera motion settings, demonstrate that UniGeo significantly outperforms existing methods in both visual quality and geometric consistency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.24597", "title": "Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings", "authors": [ "Sebastian Cajas Ord\u00f3\u00f1ez", "Felipe Ocampo Osorio", "Dax Enshan Koh", "Rafi Al Attrach", "Aldo Marzullo", "Ariel Guerra-Adames", "J. Alejandro Andrade", "Siong Thye Goh", "Chi-Yu Chen", "Rahul Gorijavolu", "Xue Yang", "Noah Dane Hebdon", "Leo Anthony Celi" ], "github": "https://github.com/sebasmos/qml-medimage", "project_page": "", "abstract": "We provide evidence of quantum kernel advantage under noiseless simulation in binary insurance classification on MIMIC-CXR chest radiographs using quantum support vector machines (QSVM) with frozen embeddings from three medical foundation models (MedSigLIP-448, RAD-DINO, ViT-patch32). We propose a two-tier fair comparison framework in which both classifiers receive identical PCA-q features. At Tier 1 (untuned QSVM vs. untuned linear SVM, C = 1 both sides), QSVM wins minority-class F1 in all 18 tested configurations (17 at p < 0.001, 1 at p < 0.01). The classical linear kernel collapses to majority-class prediction on 90-100% of seeds at every qubit count, while QSVM maintains non-trivial recall. At q = 11 (MedSigLIP-448 plateau center), QSVM achieves mean F1 = 0.343 vs. classical F1 = 0.050 (F1 gain = +0.293, p < 0.001) without hyperparameter tuning. Under Tier 2 (untuned QSVM vs. C-tuned RBF SVM), QSVM wins all seven tested configurations (mean gain +0.068, max +0.112). Eigenspectrum analysis reveals quantum kernel effective rank reaches 69.80 at q = 11, far exceeding linear kernel rank, while classical collapse remains C-invariant. A full qubit sweep reveals architecture-dependent concentration onset across models. Code: https://github.com/sebasmos/qml-medimage", "github_manual": "", "github_regex": "https://github.com/sebasmos/qml-medimage", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.23604", "title": "Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation", "authors": [ "Simone Mosco", "Daniel Fusaro", "Alberto Pretto" ], "github": "", "project_page": "", "abstract": "Understanding the surrounding environment is fundamental in autonomous driving and robotic perception. Distinguishing between known classes and previously unseen objects is crucial in real-world environments, as done in Anomaly Segmentation. However, research in the 3D field remains limited, with most existing approaches applying post-processing techniques from 2D vision. To cover this lack, we propose a new efficient approach that directly operates in the feature space, modeling the feature distribution of inlier classes to constrain anomalous samples. Moreover, the only publicly available 3D LiDAR anomaly segmentation dataset contains simple scenarios, with few anomaly instances, and exhibits a severe domain gap due to its sensor resolution. To bridge this gap, we introduce a set of mixed real-synthetic datasets for 3D LiDAR anomaly segmentation, built upon established semantic segmentation benchmarks, with multiple out-of-distribution objects and diverse, complex environments. Extensive experiments demonstrate that our approach achieves state-of-the-art and competitive results on the existing real-world dataset and the newly introduced mixed datasets, respectively, validating the effectiveness of our method and the utility of the proposed datasets. Code and datasets are available at https://simom0.github.io/lido-page/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.23772", "title": "PageGuide: Browser extension to assist users in navigating a webpage and locating information", "authors": [ "Tin Nguyen", "Thang T. Truong", "Runtao Zhou", "Trung Bui", "Chirag Agarwal", "Anh Totti Nguyen" ], "github": "", "project_page": "", "abstract": "Users browsing the web daily struggle to quickly locate relevant information in cluttered pages, complete unfamiliar multi-step tasks, and stay focused amid distracting content. State-of-the-art AI assistants (e.g., ChatGPT, Gemini, Claude) and browser agents (e.g., OpenAI Operator, Browser Use) can answer questions and automate actions, yet they return answers without showing where the information comes from on the page, forcing users to manually verify results and blindly trust every automated steps. We present PageGuide, a browser extension that grounds LLM answers directly in the HTML DOM via visual overlays, addressing three core user needs: (a) Find-locating and highlighting relevant evidence in-situ so users can instantly verify answers on the page; (b) Guide-showing step-by-step instructions (e.g. how to change password) one at a time so users can follow and perform actions by themselves; and (c) Hide-hiding distracting content-giving users a chance to decide to hide an element or not. In a user study (N=94), PageGuide outperform unaided browsing across all modes: Hide accuracy improve by 26 percentage points (86.7% relative gain) and task completion time drops by 70%; Guide completion rate increases by 30 percentage points; and Find reduces manual search effort, with Ctrl+F usage falling by 80% and task time decreasing by 19%. Code and demo is at: pageguide.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.23644", "title": "RaV-IDP: A Reconstruction-as-Validation Framework for Faithful Intelligent Document Processing", "authors": [ "Pritesh Jha" ], "github": "https://github.com/pritesh-2711/RaV-IDP", "project_page": "", "abstract": "Intelligent document processing pipelines extract structured entities (tables, images, and text) from documents for use in downstream systems such as knowledge bases, retrieval-augmented generation, and analytics. A persistent limitation of existing pipelines is that extraction output is produced without any intrinsic mechanism to verify whether it faithfully represents the source. Model-internal confidence scores measure inference certainty, not correspondence to the document, and extraction errors pass silently into downstream consumers. We present Reconstruction as Validation (RaV-IDP), a document processing pipeline that introduces reconstruction as a first-class architectural component. After each entity is extracted, a dedicated reconstructor renders the extracted representation back into a form comparable to the original document region, and a comparator scores fidelity between the reconstruction and the unmodified source crop. This fidelity score is a grounded, label-free quality signal. When fidelity falls below a per-entity-type threshold, a structured GPT-4.1 vision fallback is triggered and the validation loop repeats. We enforce a bootstrap constraint: the comparator always anchors against the original document region, never against the extraction, preventing the validation from becoming circular. We further propose a per-stage evaluation framework pairing each pipeline component with an appropriate benchmark. The code pipeline is publicly available at https://github.com/pritesh-2711/RaV-IDP for experimentation and use.", "github_manual": "", "github_regex": "https://github.com/pritesh-2711/RaV-IDP", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.16391", "title": "Disentangled Robot Learning via Separate Forward and Inverse Dynamics Pretraining", "authors": [ "Wenyao Zhang", "Bozhou Zhang", "Zekun Qi", "Wenjun Zeng", "Xin Jin", "Li Zhang" ], "github": "", "project_page": "", "abstract": "Vision-language-action (VLA) models have shown great potential in building generalist robots, but still face a dilemma-misalignment of 2D image forecasting and 3D action prediction. Besides, such a vision-action entangled training manner limits model learning from large-scale, action-free web video data. To address these issues, we propose DeFI, a novel framework that Decouples visual Forward and Inverse dynamics pretraining to exploit respective data sources, wherein video generation and action prediction are disentangled. We introduce the General Forward Dynamics Model (GFDM), pretrained on diverse human and robot videos for future prediction, and the General Inverse Dynamics Model (GIDM), trained via self-supervised learning to infer latent actions from unlabeled video transitions. These models are then integrated into a unified architecture for end-to-end finetuning on downstream tasks. In this manner, GFDM and GIDM first shine separately and then cooperate for mutual benefit. Extensive experiments on CALVIN ABC-D and SimplerEnv demonstrate state-of-the-art performance, with DeFI achieving an average task length of 4.51 for CALVIN, 51.2% success rate on SimplerEnv-Fractal benchmark and 81.3% success rate in real-world deployment, significantly outperforming prior methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.24170", "title": "Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition", "authors": [ "Tanmoy Mukherjee", "Thomas Bailleux", "Pierre Marquis", "Zied Bouraoui" ], "github": "https://github.com/Tankiit/Credal_Sets", "project_page": "", "abstract": "Concept Bottleneck Models (CBMs) predict through human-interpretable concepts, but they typically output point concept probabilities that conflate epistemic uncertainty (reducible model underspecification) with aleatoric uncertainty (irreducible input ambiguity). This makes concept-level uncertainty hard to interpret and, more importantly, hard to act upon. We introduce CREDENCE (Credal Ensemble Concept Estimation), a CBM framework that decomposes concept uncertainty by construction. CREDENCE represents each concept as a credal prediction (a probability interval), derives epistemic uncertainty from disagreement across diverse concept heads, and estimates aleatoric uncertainty via a dedicated ambiguity output trained to match annotator disagreement when available. The resulting signals support prescriptive decisions: automate low-uncertainty cases, prioritize data collection for high-epistemic cases, route high-aleatoric cases to human review, and abstain when both are high. Across several tasks, we show that epistemic uncertainty is positively associated with prediction errors, whereas aleatoric uncertainty closely tracks annotator disagreement, providing guidance beyond error correlation. Our implementation is available at the following link: https://github.com/Tankiit/Credal_Sets/tree/ensemble-credal-cbm", "github_manual": "", "github_regex": "https://github.com/Tankiit/Credal_Sets", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.23600", "title": "Personality Shapes Gender Bias in Persona-Conditioned LLM Narratives Across English and Hindi: An Empirical Investigation", "authors": [ "Tanay Kumar", "Shreya Gautam", "Aman Chadha", "Vinija Jain", "Francesco Pierri" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly deployed in persona-driven applications such as education, customer service, and social platforms, where models are prompted to adopt specific personas when interacting with users. While persona conditioning can improve user experience and engagement, it also raises concerns about how personality cues may interact with gender biases and stereotypes. In this work, we present a controlled study of persona-conditioned story generation in English and Hindi, where each story portrays a working professional in India producing context-specific artifacts (e.g., lesson plans, reports, letters) under systematically varied persona gender, occupational role, and personality traits from the HEXACO and Dark Triad frameworks. Across 23,400 generated stories from six state-of-the-art LLMs, we find that personality traits are significantly associated with both the magnitude and direction of gender bias. In particular, Dark Triad personality traits are consistently associated with higher gender-stereotypical representations compared to socially desirable HEXACO traits, though these associations vary across models and languages. Our findings demonstrate that gender bias in LLMs is not static but context-dependent. This suggests that persona-conditioned systems used in real-world applications may introduce uneven representational harms, reinforcing gender stereotypes in generated educational, professional, or social content.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.21681", "title": "Sapiens2", "authors": [ "Rawal Khirodkar", "He Wen", "Julieta Martinez", "Yuan Dong", "Su Zhaoen", "Shunsuke Saito" ], "github": "https://github.com/facebookresearch/sapiens2", "project_page": "", "abstract": "We present Sapiens2, a model family of high-resolution transformers for human-centric vision focused on generalization, versatility, and high-fidelity outputs. Our model sizes range from 0.4 to 5 billion parameters, with native 1K resolution and hierarchical variants that support 4K. Sapiens2 substantially improves over its predecessor in both pretraining and post-training. First, to learn features that capture low-level details (for dense prediction) and high-level semantics (for zero-shot or few-label settings), we combine masked image reconstruction with self-distilled contrastive objectives. Our evaluations show that this unified pretraining objective is better suited for a wider range of downstream tasks. Second, along the data axis, we pretrain on a curated dataset of 1 billion high-quality human images and improve the quality and quantity of task annotations. Third, architecturally, we incorporate advances from frontier models that enable longer training schedules with improved stability. Our 4K models adopt windowed attention to reason over longer spatial context and are pretrained with 2K output resolution. Sapiens2 sets a new state-of-the-art and improves over the first generation on pose (+4 mAP), body-part segmentation (+24.3 mIoU), normal estimation (45.6% lower angular error) and extends to new tasks such as pointmap and albedo estimation. Code: https://github.com/facebookresearch/sapiens2", "github_manual": "", "github_regex": "https://github.com/facebookresearch/sapiens2", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.24040", "title": "Improving Robustness of Tabular Retrieval via Representational Stability", "authors": [ "Kushal Raj Bhandari", "Adarsh Singh", "Jianxi Gao", "Soham Dan", "Vivek Gupta" ], "github": "https://github.com/KBhandari11/Centroid-Aligned-Table-Retrieval", "project_page": "", "abstract": "Transformer-based table retrieval systems flatten structured tables into token sequences, making retrieval sensitive to the choice of serialization even when table semantics remain unchanged. We show that semantically equivalent serializations, such as csv, tsv, html, markdown, and ddl, can produce substantially different embeddings and retrieval results across multiple benchmarks and retriever families. To address this instability, we treat serialization embedding as noisy views of a shared semantic signal and use its centroid as a canonical target representation. We show that centroid averaging suppresses format-specific variation and can recover the semantic content common to different serializations when format-induced shifts differ across tables. Empirically, centroid representations outrank individual formats in aggregate pairwise comparisons across MPNet, BGE-M3, ReasonIR, and SPLADE. We further introduce a lightweight residual bottleneck adapter on top of a frozen encoder that maps single-serialization embeddings towards centroid targets while preserving variance and enforcing covariance regularization. The adapter improves robustness for several dense retrievers, though gains are model-dependent and weaker for sparse lexical retrieval. These results identify serialization sensitivity as a major source of retrieval variance and show the promise of post hoc geometric correction for serialization-invariant table retrieval. Our code, datasets, and models are available at https://github.com/KBhandari11/Centroid-Aligned-Table-Retrieval{https://github.com/KBhandari11/Centroid-Aligned-Table-Retrieval}.", "github_manual": "", "github_regex": "https://github.com/KBhandari11/Centroid-Aligned-Table-Retrieval", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.15574", "title": "Why Fine-Tuning Encourages Hallucinations and How to Fix It", "authors": [ "Guy Kaplan", "Zorik Gekhman", "Zhen Zhu", "Lotem Rozner", "Yuval Reif", "Swabha Swayamdipta", "Derek Hoiem", "Roy Schwartz" ], "github": "", "project_page": "", "abstract": "Large language models are prone to hallucinating factually incorrect statements. A key source of these errors is exposure to new factual information through supervised fine-tuning (SFT), which can increase hallucinations w.r.t. knowledge acquired during pre-training. In this work, we explore whether SFT-induced hallucinations can be mitigated using established tools from the continual learning literature, since they arise as a by-product of knowledge degradation during training. We propose a self-distillation-based SFT method that facilitates effective factual learning while minimizing hallucinations w.r.t. pre-existing knowledge by regularizing output-distribution drift. We also show that, in settings where new knowledge acquisition is unnecessary, suppressing factual plasticity by freezing parameter groups, can preserve task performance while reducing hallucinations. Lastly, we investigate the mechanism behind SFT-induced hallucinations through three hypotheses: capacity limitations, behavior cloning, and localized interference. Our experiments show that a main driver is interference among overlapping semantic representations, and that self-distillation succeeds by mitigating this interference.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.23446", "title": "IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance", "authors": [ "Chathurangi Shyalika", "Dhaval Patel", "Amit Sheth" ], "github": "https://github.com/IBM/AssetOpsBench/tree/IndustryAssetEQA/IndustryAssetEQA", "project_page": "", "abstract": "Industrial maintenance environments increasingly rely on AI systems to assist operators in understanding asset behavior, diagnosing failures, and evaluating interventions. Although large language models (LLMs) enable fluent natural-language interaction, deployed maintenance assistants routinely produce generic explanations that are weakly grounded in telemetry, omit verifiable provenance, and offer no testable support for counterfactual or action-oriented reasoning that undermine trust in safety-critical settings. We present IndustryAssetEQA, a neurosymbolic operational intelligence system that combines episodic telemetry representations with a Failure Mode Effects Analysis Knowledge Graph (FMEA-KG) to enable Embodied Question Answering (EQA) over industrial assets. We evaluate on four datasets covering four industrial asset types, including rotating machinery, turbofan engines, hydraulic systems, and cyber-physical production systems. Compared to LLM-only baselines, IndustryAssetEQA improves structural validity by up to 0.51, counterfactual accuracy by up to 0.47, and explanation entailment by 0.64, while reducing severe expert-rated overclaims from 28% to 2% (approximately 93% reduction). Code, datasets, and the FMEA-KG are available at https://github.com/IBM/AssetOpsBench/tree/IndustryAssetEQA/IndustryAssetEQA.", "github_manual": "", "github_regex": "https://github.com/IBM/AssetOpsBench/tree/IndustryAssetEQA/IndustryAssetEQA", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-28", "arxiv_id": "2604.18756", "title": "Towards Understanding the Robustness of Sparse Autoencoders", "authors": [ "Ahson Saiyed", "Sabrina Sadiekh", "Chirag Agarwal" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) remain vulnerable to optimization-based jailbreak attacks that exploit internal gradient structure. While Sparse Autoencoders (SAEs) are widely used for interpretability, their robustness implications remain underexplored. We present a study of integrating pretrained SAEs into transformer residual streams at inference time, without modifying model weights or blocking gradients. Across four model families (Gemma, LLaMA, Mistral, Qwen) and two strong white-box attacks (GCG, BEAST) plus three black-box benchmarks, SAE-augmented models achieve up to a 5x reduction in jailbreak success rate relative to the undefended baseline and reduce cross-model attack transferability. Parametric ablations reveal (i) a monotonic dose-response relationship between L0 sparsity and attack success rate, and (ii) a layer-dependent defense-utility tradeoff, where intermediate layers balance robustness and clean performance. These findings are consistent with a representational bottleneck hypothesis: sparse projection reshapes the optimization geometry exploited by jailbreak attacks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.25917", "title": "Recursive Multi-Agent Systems", "authors": [ "Xiyuan Yang", "Jiaru Zou", "Rui Pan", "Ruizhong Qiu", "Pan Lu", "Shizhe Diao", "Jindong Jiang", "Hanghang Tong", "Tong Zhang", "Markus J. Buehler", "Jingrui He", "James Zou" ], "github": "", "project_page": "", "abstract": "Recursive or looped language models have recently emerged as a new scaling axis by iteratively refining the same model computation over latent states to deepen reasoning. We extend such scaling principle from a single model to multi-agent systems, and ask: Can agent collaboration itself be scaled through recursion? To this end, we introduce RecursiveMAS, a recursive multi-agent framework that casts the entire system as a unified latent-space recursive computation. RecursiveMAS connects heterogeneous agents as a collaboration loop through the lightweight RecursiveLink module, enabling in-distribution latent thoughts generation and cross-agent latent state transfer. To optimize our framework, we develop an inner-outer loop learning algorithm for iterative whole-system co-optimization through shared gradient-based credit assignment across recursion rounds. Theoretical analyses of runtime complexity and learning dynamics establish that RecursiveMAS is more efficient than standard text-based MAS and maintains stable gradients during recursive training. Empirically, we instantiate RecursiveMAS under 4 representative agent collaboration patterns and evaluate across 9 benchmarks spanning mathematics, science, medicine, search, and code generation. In comparison with advanced single/multi-agent and recursive computation baselines, RecursiveMAS consistently delivers an average accuracy improvement of 8.3%, together with 1.2times-2.4times end-to-end inference speedup, and 34.6%-75.6% token usage reduction. Code and Data are provided in https://recursivemas.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.24625", "title": "Meta-CoT: Enhancing Granularity and Generalization in Image Editing", "authors": [ "Shiyi Zhang", "Yiji Cheng", "Tiankai Hang", "Zijin Yin", "Runze He", "Yu Xu", "Wenxun Dai", "Yunlong Lin", "Chunyu Wang", "Qinglin Lu", "Yansong Tang" ], "github": "", "project_page": "", "abstract": "Unified multi-modal understanding/generative models have shown improved image editing performance by incorporating fine-grained understanding into their Chain-of-Thought (CoT) process. However, a critical question remains underexplored: what forms of CoT and training strategy can jointly enhance both the understanding granularity and generalization? To address this, we propose Meta-CoT, a paradigm that performs a two-level decomposition of any single-image editing operation with two key properties: (1) Decomposability. We observe that any editing intention can be represented as a triplet - (task, target, required understanding ability). Inspired by this, Meta-CoT decomposes both the editing task and the target, generating task-specific CoT and traversing editing operations on all targets. This decomposition enhances the model's understanding granularity of editing operations and guides it to learn each element of the triplet during training, substantially improving the editing capability. (2) Generalizability. In the second decomposition level, we further break down editing tasks into five fundamental meta-tasks. We find that training on these five meta-tasks, together with the other two elements of the triplet, is sufficient to achieve strong generalization across diverse, unseen editing tasks. To further align the model's editing behavior with its CoT reasoning, we introduce the CoT-Editing Consistency Reward, which encourages more accurate and effective utilization of CoT information during editing. Experiments demonstrate that our method achieves an overall 15.8% improvement across 21 editing tasks, and generalizes effectively to unseen editing tasks when trained on only a small set of meta-tasks. Our code, benchmark, and model are released at https://shiyi-zh0408.github.io/projectpages/Meta-CoT/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.24819", "title": "Programming with Data: Test-Driven Data Engineering for Self-Improving LLMs from Raw Corpora", "authors": [ "Chenkai Pan", "Xinglong Xu", "Yuhang Xu", "Yujun Wu", "Siyuan Li", "Jintao Chen", "Conghui He", "Jingxuan Wei", "Cheng Tan" ], "github": "", "project_page": "", "abstract": "Reliably transferring specialized human knowledge from text into large language models remains a fundamental challenge in artificial intelligence. Fine-tuning on domain corpora has enabled substantial capability gains, but the process operates without feedback: when a model fails on a domain task, there is no method to diagnose what is deficient in the training data, and the only recourse is to add more data indiscriminately. Here we show that when a structured knowledge representation extracted from the source corpus serves as the shared foundation for both training data and evaluation, the complete data-engineering lifecycle maps onto the software development lifecycle in a precise and operative way: training data becomes source code specifying what the model should learn, model training becomes compilation, benchmarking becomes unit testing, and failure-driven data repair becomes debugging. Under this correspondence, model failures decompose into concept-level gaps and reasoning-chain breaks that can be traced back to specific deficiencies in the data and repaired through targeted patches, with each repair cycle producing consistent improvements across model scales and architectures without degrading general capabilities. We formalize this principle as Programming with Data and instantiate it across sixteen disciplines spanning the natural sciences, engineering, biomedicine, and the social sciences, releasing a structured knowledge base, benchmark suite, and training corpus as open resources. By demonstrating that the relationship between training data and model behaviour is structurally traceable and systematically repairable, this work establishes a principled foundation for the reliable engineering of human expertise into language models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.25819", "title": "Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation", "authors": [ "Yupeng Zhou", "Lianghua Huang", "Zhifan Wu", "Jiabao Wang", "Yupeng Shi", "Biao Jiang", "Daquan Zhou", "Yu Liu", "Ming-Ming Cheng", "Qibin Hou" ], "github": "", "project_page": "", "abstract": "In this work, we propose Mutual Forcing, a framework for fast autoregressive audio-video generation with long-horizon audio-video synchronization. Our approach addresses two key challenges: joint audio-video modeling and fast autoregressive generation. To ease joint audio-video optimization, we adopt a two-stage training strategy: we first train uni-modal generators and then couple them into a unified audio-video model for joint training on paired data. For streaming generation, we ask whether a native fast causal audio-video model can be trained directly, instead of following existing streaming distillation pipelines that typically train a bidirectional model first and then convert it into a causal generator through multiple distillation stages. Our answer is Mutual Forcing, which builds directly on native autoregressive model and integrates few-step and multi-step generation within a single weight-shared model, enabling self-distillation and improved training-inference consistency. The multi-step mode improves the few-step mode via self-distillation, while the few-step mode generates historical context during training to improve training-inference consistency; because the two modes share parameters, these two effects reinforce each other within a single model. Compared with prior approaches such as Self-Forcing, Mutual Forcing removes the need for an additional bidirectional teacher model, supports more flexible training sequence lengths, reduces training overhead, and allows the model to improve directly from real paired data rather than a fixed teacher. Experiments show that Mutual Forcing matches or surpasses strong baselines that require around 50 sampling steps while using only 4 to 8 steps, demonstrating substantial advantages in both efficiency and quality. The project page is available at https://mutualforcing.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.25636", "title": "Refinement via Regeneration: Enlarging Modification Space Boosts Image Refinement in Unified Multimodal Models", "authors": [ "Jiayi Guo", "Linqing Wang", "Jiangshan Wang", "Yang Yue", "Zeyu Liu", "Zhiyuan Zhao", "Qinglin Lu", "Gao Huang", "Chunyu Wang" ], "github": "", "project_page": "", "abstract": "Unified multimodal models (UMMs) integrate visual understanding and generation within a single framework. For text-to-image (T2I) tasks, this unified capability allows UMMs to refine outputs after their initial generation, potentially extending the performance upper bound. Current UMM-based refinement methods primarily follow a refinement-via-editing (RvE) paradigm, where UMMs produce editing instructions to modify misaligned regions while preserving aligned content. However, editing instructions often describe prompt-image misalignment only coarsely, leading to incomplete refinement. Moreover, pixel-level preservation, though necessary for editing, unnecessarily restricts the effective modification space for refinement. To address these limitations, we propose Refinement via Regeneration (RvR), a novel framework that reformulates refinement as conditional image regeneration rather than editing. Instead of relying on editing instructions and enforcing strict content preservation, RvR regenerates images conditioned on the target prompt and the semantic tokens of the initial image, enabling more complete semantic alignment with a larger modification space. Extensive experiments demonstrate the effectiveness of RvR, improving Geneval from 0.78 to 0.91, DPGBench from 84.02 to 87.21, and UniGenBench++ from 61.53 to 77.41.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.25914", "title": "DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios", "authors": [ "Jinxiang Meng", "Shaoping Huang", "Fangyu Lei", "Jingyu Guo", "Haoxiang Liu", "Jiahao Su", "Sihan Wang", "Yao Wang", "Enrui Wang", "Ye Yang", "Hongze Chai", "Jinming Lv", "Anbang Yu", "Huangjing Zhang", "Yitong Zhang", "Yiming Huang", "Zeyao Ma", "Shizhu He", "Jun Zhao", "Kang Liu" ], "github": "https://github.com/DA-Open/DV-World", "project_page": "", "abstract": "Real-world data visualization (DV) requires native environmental grounding, cross-platform evolution, and proactive intent alignment. Yet, existing benchmarks often suffer from code-sandbox confinement, single-language creation-only tasks, and assumption of perfect intent. To bridge these gaps, we introduce DV-World, a benchmark of 260 tasks designed to evaluate DV agents across real-world professional lifecycles. DV-World spans three domains: DV-Sheet for native spreadsheet manipulation including chart and dashboard creation as well as diagnostic repair; DV-Evolution for adapting and restructuring reference visual artifacts to fit new data across diverse programming paradigms and DV-Interact for proactive intent alignment with a user simulator that mimics real-world ambiguous requirements. Our hybrid evaluation framework integrates Table-value Alignment for numerical precision and MLLM-as-a-Judge with rubrics for semantic-visual assessment. Experiments reveal that state-of-the-art models achieve less than 50% overall performance, exposing critical deficits in handling the complex challenges of real-world data visualization. DV-World provides a realistic testbed to steer development toward the versatile expertise required in enterprise workflows. Our data and code are available at https://github.com/DA-Open/DV-World{this project page}.", "github_manual": "", "github_regex": "https://github.com/DA-Open/DV-World", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.24842", "title": "Co-Director: Agentic Generative Video Storytelling", "authors": [ "Yale Song", "Yiwen Song", "Nick Losier", "Nathan Hodson", "Ye Jin", "Rhyard Zhu", "Yan Xu", "Daniel Vlasic", "Carina Claassen", "Jasmine Leon", "Khanh G. LeViet", "Zack Chomyn", "Joe Timmons", "Brett Slatkin", "Scott Penberthy", "Tomas Pfister" ], "github": "", "project_page": "", "abstract": "While diffusion models generate high-fidelity video clips, transforming them into coherent storytelling engines remains challenging. Current agentic pipelines automate this via chained modules but suffer from semantic drift and cascading failures due to independent, handcrafted prompting. We present Co-Director, a hierarchical multi-agent framework formalizing video storytelling as a global optimization problem. To ensure semantic coherence, we introduce hierarchical parameterization: a multi-armed bandit globally identifies promising creative directions, while a local multimodal self-refinement loop mitigates identity drift and ensures sequence-level consistency. This balances the exploration of novel narrative strategies with the exploitation of effective creative configurations. For evaluation, we introduce GenAD-Bench, a 400-scenario dataset of fictional products for personalized advertising. Experiments demonstrate that Co-Director significantly outperforms state-of-the-art baselines, offering a principled approach that seamlessly generalizes to broader cinematic narratives. Project Page: https://co-director-agent.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.25164", "title": "IAM: Identity-Aware Human Motion and Shape Joint Generation", "authors": [ "Wenqi Jia", "Zekun Li", "Abhay Mittal", "Chengcheng Tang", "Chuan Guo", "Lezi Wang", "James Matthew Rehg", "Lingling Tao", "Size An" ], "github": "", "project_page": "", "abstract": "Recent advances in text-driven human motion generation enable models to synthesize realistic motion sequences from natural language descriptions. However, most existing approaches assume identity-neutral motion and generate movements using a canonical body representation, ignoring the strong influence of body morphology on motion dynamics. In practice, attributes such as body proportions, mass distribution, and age significantly affect how actions are performed, and neglecting this coupling often leads to physically inconsistent motions. We propose an identity-aware motion generation framework that explicitly models the relationship between body morphology and motion dynamics. Instead of relying on explicit geometric measurements, identity is represented using multimodal signals, including natural language descriptions and visual cues. We further introduce a joint motion-shape generation paradigm that simultaneously synthesizes motion sequences and body shape parameters, allowing identity cues to directly modulate motion dynamics. Extensive experiments on motion capture datasets and large-scale in-the-wild videos demonstrate improved motion realism and motion-identity consistency while maintaining high motion quality. Project page: https://vjwq.github.io/IAM", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.25727", "title": "Toward Scalable Terminal Task Synthesis via Skill Graphs", "authors": [ "Zhiyuan Fan", "Tinghao Yu", "Yuanjun Cai", "Jiangtao Guan", "Yun Yang", "Dingxin Hu", "Jiang Zhou", "Xing Wu", "Zhuo Han", "Feng Zhang", "Lilin Wang" ], "github": "", "project_page": "", "abstract": "Terminal agents have demonstrated strong potential for autonomous command-line execution, yet their training remains constrained by the scarcity of high-quality and diverse execution trajectories. Existing approaches mitigate this bottleneck by synthesizing large-scale terminal task instances for trajectory sampling. However, they primarily focus on scaling the number of tasks while providing limited control over the diversity of execution trajectories that agents actually experience during training. In this paper, we present SkillSynth, an automated framework for terminal task synthesis built on a scenario-mediated skill graph. SkillSynth first constructs a large-scale skill graph, where scenarios serve as intermediate transition nodes that connect diverse command-line skills. It then samples paths from this graph as abstractions of real-world workflows, and uses a multi-agent harness to instantiate them into executable task instances. By grounding task synthesis in graph-sampled workflow paths, SkillSynth explicitly controls the diversity of minimal execution trajectories required to solve the synthesized tasks. Experiments on Terminal-Bench demonstrate the effectiveness of SkillSynth. Moreover, task instances synthesized by SkillSynth have been adopted to train Hy3 Preview, contributing to its enhanced agentic capabilities in terminal-based settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.25203", "title": "BARRED: Synthetic Training of Custom Policy Guardrails via Asymmetric Debate", "authors": [ "Arnon Mazza", "Elad Levi" ], "github": "", "project_page": "", "abstract": "Deploying guardrails for custom policies remains challenging, as generic safety models fail to capture task-specific requirements, while prompting LLMs suffers from inconsistent boundary-case performance and high inference costs. Training custom classifiers achieves both accuracy and efficiency, yet demands substantial labeled data that is costly to obtain. We present BARRED (Boundary Alignment Refinement through REflection and Debate), a framework for generating faithful and diverse synthetic training data using only a task description and a small set of unlabeled examples. Our approach decomposes the domain space into dimensions to ensure comprehensive coverage, and employs multi-agent debate to verify label correctness, yielding a high-fidelity training corpus. Experiments across diverse custom policies demonstrate that small language models finetuned on our synthetic data consistently outperform state-of-the-art proprietary LLMs (including reasoning models) and dedicated guardrail models. Ablation studies confirm that both dimension decomposition and debate-based verification are critical for ensuring the diversity and label fidelity required for effective fine-tuning. The BARRED framework eliminates the reliance on extensive human annotation, offering a scalable solution for accurate custom guardrails.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.25427", "title": "A Systematic Post-Train Framework for Video Generation", "authors": [ "Zeyue Xue", "Siming Fu", "Jie Huang", "Shuai Lu", "Haoran Li", "Yijun Liu", "Yuming Li", "Xiaoxuan He", "Mengzhao Chen", "Haoyang Huang", "Nan Duan", "Ping Luo" ], "github": "", "project_page": "", "abstract": "While large-scale video diffusion models have demonstrated impressive capabilities in generating high-resolution and semantically rich content, a significant gap remains between their pretraining performance and real-world deployment requirements due to critical issues such as prompt sensitivity, temporal inconsistency, and prohibitive inference costs. To bridge this gap, we propose a comprehensive post-training framework that systematically aligns pretrained models with user intentions through four synergistic stages: we first employ Supervised Fine-Tuning (SFT) to transform the base model into a stable instruction-following policy, followed by a Reinforcement Learning from Human Feedback (RLHF) stage that utilizes a novel Group Relative Policy Optimization (GRPO) method tailored for video diffusion to enhance perceptual quality and temporal coherence; subsequently, we integrate Prompt Enhancement via a specialized language model to refine user inputs, and finally address system efficiency through Inference Optimization. Together, these components provide a systematic approach to improving visual quality, temporal coherence, and instruction following, while preserving the controllability learned during pretraining. The result is a practical blueprint for building scalable post-training pipelines that are stable, adaptable, and effective in real-world deployment. Extensive experiments demonstrate that this unified pipeline effectively mitigates common artifacts and significantly improves controllability and visual aesthetics while adhering to strict sampling cost constraints.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.24005", "title": "TCOD: Exploring Temporal Curriculum in On-Policy Distillation for Multi-turn Autonomous Agents", "authors": [ "Jiaqi Wang", "Wenhao Zhang", "Weijie Shi", "Yaliang Li", "James Cheng" ], "github": "https://github.com/openclaw/openclaw", "project_page": "", "abstract": "On-policy distillation (OPD) has shown strong potential for transferring reasoning ability from frontier or domain-specific models to smaller students. While effective on static single-turn tasks, its behavior in multi-turn agent settings remains underexplored. In this work, we identify a key limitation of vanilla OPD in such settings, which we term Trajectory-Level KL Instability. Specifically, we observe that KL divergence increases together with a drop in success rate, and even after convergence, the KL remains high, leading to unstable training. This instability arises from inter-turn error compounding: as errors accumulate, the student is driven beyond the teacher's effective support, rendering the supervision signal unreliable. To address this, we propose TCOD (Temporal Curriculum On-Policy Distillation), a simple yet effective framework that controls the trajectory depth exposed to the student and progressively expands it from short to long with a curriculum schedule.Experimental results across four student-teacher pairs on three multi-turn agent benchmarks (ALFWorld, WebShop, ScienceWorld) show that TCOD mitigates KL escalation and enhances KL stability throughout training, improving agent performance by up to 18 points over vanilla OPD. Further evaluations show that TCOD can even surpass the teacher's performance and generalize to tasks on which the teacher fails.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openclaw/openclaw", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.23941", "title": "GoClick: Lightweight Element Grounding Model for Autonomous GUI Interaction", "authors": [ "Hongxin Li", "Yuntao Chen", "Zhaoxiang Zhang" ], "github": "", "project_page": "", "abstract": "Graphical User Interface (GUI) element grounding (precisely locating elements on screenshots based on natural language instructions) is fundamental for agents interacting with GUIs. Deploying this capability directly on resource-constrained devices like mobile phones is increasingly critical for GUI agents requiring low latency. However, this goal faces a significant challenge, as current visual grounding methods typically employ large vision-language model (VLM) (more than 2.5B parameters), making them impractical for on-device execution due to memory and computational constraints. To address this, this paper introduces GoClick, a lightweight GUI element grounding VLM with only 230M parameters that achieves excellent visual grounding accuracy, even on par with significantly larger models. Simply downsizing existing decoder-only VLMs is a straightforward way to design a lightweight model, but our experiments reveal that this approach yields suboptimal results. Instead, we select an encoder-decoder architecture, which outperforms decoder-only alternatives at small parameter scales for GUI grounding tasks. Additionally, the limited capacity of small VLMs encourages us to develop a Progressive Data Refinement pipeline that utilizes task type filtering and data ratio adjustment to extract a high-quality 3.8M-sample core set from a 10.8M raw dataset. Training GoClick using this core set brings notable grounding accuracy gains. Our experiments show that GoClick excels on multiple GUI element grounding benchmarks while maintaining a small size and high inference speed. GoClick also enhances GUI agent performance when integrated into a device-cloud collaboration framework, where GoClick helps cloud-based task planners perform precise element localization and achieve higher success rates. We hope our method serves as a meaningful exploration within the GUI agent community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.24441", "title": "AutoGUI-v2: A Comprehensive Multi-Modal GUI Functionality Understanding Benchmark", "authors": [ "Hongxin Li", "Xiping Wang", "Jingran Su", "Zheng Ju", "Yuntao Chen", "Qing Li", "Zhaoxiang Zhang" ], "github": "", "project_page": "", "abstract": "Autonomous agents capable of navigating Graphical User Interfaces (GUIs) hold the potential to revolutionize digital productivity. However, achieving true digital autonomy extends beyond reactive element matching; it necessitates a predictive mental model of interface dynamics and the ability to foresee the \"digital world state\" resulting from interactions. Despite the perceptual capabilities of modern Vision-Language Models (VLMs), existing benchmarks remain bifurcated (focusing either on black-box task completion or static, shallow grounding), thereby failing to assess whether agents truly comprehend the implicit functionality and transition logic of GUIs. To bridge this gap, we introduce AutoGUI-v2, a comprehensive benchmark designed to evaluate deep GUI functionality understanding and interaction outcome prediction. We construct the benchmark using a novel VLM-human collaborative pipeline that recursively parses multi-platform screenshots into hierarchical functional regions to generate diverse evaluation tasks. Providing 2,753 tasks across six operating systems, AutoGUI-v2 rigorously tests agents on region and element-level semantics, grounding, and dynamic state prediction. Our evaluation reveals a striking dichotomy in VLMs: while open-source models fine-tuned on agent data (e.g., Qwen3-VL) excel at functional grounding, commercial models (e.g., Gemini-2.5-Pro-Thinking) dominate in functionality captioning. Crucially, all models struggle with complex interaction logic of uncommon actions, highlighting that deep functional understanding remains a significant hurdle. By systematically measuring these foundational capabilities, AutoGUI-v2 offers a new lens for advancing the next generation of GUI agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.21481", "title": "Preferences of a Voice-First Nation: Large-Scale Pairwise Evaluation and Preference Analysis for TTS in Indian Languages", "authors": [ "Srija Anand", "Ashwin Sankar", "Ishvinder Sethi", "Aaditya Pareek", "Kartik Rajput", "Gaurav Yadav", "Nikhil Narasimhan", "Adish Pandya", "Deepon Halder", "Mohammed Safi Ur Rahman Khan", "Praveen S V", "Shobhit Banga", "Mitesh M Khapra" ], "github": "", "project_page": "", "abstract": "Crowdsourced pairwise evaluation has emerged as a scalable approach for assessing foundation models. However, applying it to Text to Speech(TTS) introduces high variance due to linguistic diversity and multidimensional nature of speech perception. We present a controlled multidimensional pairwise evaluation framework for multilingual TTS that combines linguistic control with perceptually grounded annotation. Using 5K+ native and code-mixed sentences across 10 Indic languages, we evaluate 7 state-of-the-art TTS systems and collect over 120K pairwise comparisons from over 1900 native raters. In addition to overall preference, raters provide judgments across 6 perceptual dimensions: intelligibility, expressiveness, voice quality, liveliness, noise, and hallucinations. Using Bradley-Terry modeling, we construct a multilingual leaderboard, interpret human preference using SHAP analysis and analyze leaderboard reliability alongside model strengths and trade-offs across perceptual dimensions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.21523", "title": "Seeing Isn't Believing: Uncovering Blind Spots in Evaluator Vision-Language Models", "authors": [ "Mohammed Safi Ur Rahman Khan", "Sanjay Suryanarayanan", "Tushar Anand", "Mitesh M. Khapra" ], "github": "", "project_page": "", "abstract": "Large Vision-Language Models (VLMs) are increasingly used to evaluate outputs of other models, for image-to-text (I2T) tasks such as visual question answering, and text-to-image (T2I) generation tasks. Despite this growing reliance, the reliability of these Evaluator VLMs remains under explored. In this work, we systematically evaluate the reliability of Evaluator VLMs across both I2T and T2I tasks. We introduce targeted perturbations that degrade output quality along key error dimensions, including object hallucinations, spatial reasoning, factual grounding, and visual fidelity. These perturbations test whether Evaluator VLMs can reliably account for these quality degrading errors in their evaluations. Using a comprehensive benchmark of over 4000 perturbed instances spanning 40 perturbation dimensions, we evaluate 4 prominent VLMs using single-answer scoring, pairwise comparison, and reference-guided paradigms. Our findings reveal that current VLM evaluators exhibit substantial blind spots: they often fail to detect perturbed outputs - in some cases exceeding 50%, struggle particularly with fine-grained compositional and spatial errors, and are often insensitive to hallucinated content that contradicts the input image. Pairwise comparison proves more reliable, though failure rates persist. These results highlight the unreliable nature of current Evaluator VLMs and urge caution in their deployment for benchmarking and development decisions. Code and data have been made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.25256", "title": "AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery", "authors": [ "Lei Xiong", "Kun Luo", "Ziyi Xia", "Wenbo Zhang", "Jin-Ge Yao", "Zheng Liu", "Jingying Shao", "Jianlyu Chen", "Hongjin Qian", "Xi Yang", "Qian Yu", "Hao Li", "Chen Yue", "Xiaan Du", "Yuyang Wang", "Yesheng Liu", "Haiyu Xu", "Zhicheng Dou" ], "github": "https://github.com/CherYou/AutoResearchBench", "project_page": "", "abstract": "Autonomous scientific research is significantly advanced thanks to the development of AI agents. One key step in this process is finding the right scientific literature, whether to explore existing knowledge for a research problem, or to acquire evidence for verifying assumptions and supporting claims. To assess AI agents' capability in driving this process, we present AutoResearchBench, a dedicated benchmark for autonomous scientific literature discovery. AutoResearchBench consists of two complementary task types: (1) Deep Research, which requires tracking down a specific target paper through a progressive, multi-step probing process, and (2) Wide Research, which requires comprehensively collecting a set of papers satisfying given conditions. Compared to previous benchmarks on agentic web browsing, AutoResearchBench is distinguished along three dimensions: it is research-oriented, calling for in-depth comprehension of scientific concepts; literature-focused, demanding fine-grained utilization of detailed information; and open-ended, involving an unknown number of qualified papers and thus requiring deliberate reasoning and search throughout. These properties make AutoResearchBench uniquely suited for evaluating autonomous research capabilities, and extraordinarily challenging. Even the most powerful LLMs, despite having largely conquered general agentic web-browsing benchmarks such as BrowseComp, achieve only 9.39% accuracy on Deep Research and 9.31% IoU on Wide Research, while many other strong baselines fall below 5%. We publicly release the dataset and evaluation pipeline to facilitate future research in this direction. We publicly release the dataset, evaluation pipeline, and code at https://github.com/CherYou/AutoResearchBench.", "github_manual": "", "github_regex": "https://github.com/CherYou/AutoResearchBench", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.25806", "title": "MAIC-UI: Making Interactive Courseware with Generative UI", "authors": [ "Shangqing Tu", "Yanjia Li", "Keyu Chen", "Sichen Zhang", "Jifan Yu", "Daniel Zhang-Li", "Lei Hou", "Juanzi Li", "Yu Zhang", "Huiqin Liu" ], "github": "https://github.com/THU-MAIC/MAIC-UI", "project_page": "", "abstract": "Creating interactive STEM courseware traditionally requires HTML/CSS/JavaScript expertise, leaving barriers for educators. While generative AI can produce HTML codes, existing tools generate static presentations rather than interactive simulations, struggle with long documents, and lack pedagogical accuracy mechanisms. Furthermore, full regeneration for modifications requires 200--600 seconds, disrupting creative flow. We present MAIC-UI, a zero-code authoring system that enables educators to create and rapidly edit interactive courseware from textbooks, PPTs, and PDFs. MAIC-UI employs: (1) structured knowledge analysis with multi-modal understanding to ensure pedagogical rigor; (2) a two-stage generate-verify-optimize pipeline separating content alignment from visual refinement; and (3) Click-to-Locate editing with Unified Diff-based incremental generation achieving sub-10-second iteration cycles. A controlled lab study with 40 participants shows MAIC-UI reduces editing iterations (4.9 vs. 7.0) and significantly improves learnability and controllability compared to direct Text-to-HTML generation. A three-month classroom deployment with 53 high school students demonstrates that MAIC-UI fosters learning agency and reduces outcome disparities -- the pilot class achieved 9.21-point gains in STEM subjects compared to -2.32 points in control classes. Our code is available at https://github.com/THU-MAIC/MAIC-UI.", "github_manual": "", "github_regex": "https://github.com/THU-MAIC/MAIC-UI", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.25719", "title": "Step-Audio-R1.5 Technical Report", "authors": [ "Yuxin Zhang", "Xiangyu Tony Zhang", "Daijiao Liu", "Fei Tian", "Yayue Deng", "Jun Chen", "Qingjian Lin", "Haoyang Zhang", "Yuxin Li", "Jinglan Gong", "Yechang Huang", "Liang Zhao", "Chengyuan Yao", "Hexin Liu", "Eng Siong Chng", "Xuerui Yang", "Gang Yu", "Xiangyu Zhang", "Daxin Jiang" ], "github": "", "project_page": "", "abstract": "Recent advancements in large audio language models have extended Chain-of-Thought (CoT) reasoning into the auditory domain, enabling models to tackle increasingly complex acoustic and spoken tasks. To elicit and sustain these extended reasoning chains, the prevailing paradigm -- driven by the success of text-based reasoning models -- overwhelmingly relies on Reinforcement Learning with Verified Rewards (RLVR). However, as models are strictly optimized to distill rich, continuous auditory contexts into isolated, verifiable text labels, a fundamental question arises: are we fostering true audio intelligence, or merely reducing a continuous sensory medium into a discrete puzzle? We identify this as the \"verifiable reward trap.\" While RLVR yields remarkable scores on standardized objective benchmarks, it systematically degrades the real-world conversational feel of audio models. By prioritizing isolated correctness over acoustic nuance, RLVR reduces dynamic interactions to mechanical \"answering machines,\" severely compromising prosodic naturalness, emotional continuity, and user immersion, particularly in long-turn dialogues. To bridge the gap between mechanical objective verification and genuine sensory empathy, we introduce Step-Audio-R1.5, marking a paradigm shift toward Reinforcement Learning from Human Feedback (RLHF) in audio reasoning. Comprehensive evaluations demonstrate that Step-Audio-R1.5 not only maintains robust analytical reasoning but profoundly transforms the interactive experience, redefining the boundaries of deeply immersive long-turn spoken dialogue.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.25032", "title": "Offline Evaluation Measures of Fairness in Recommender Systems", "authors": [ "Theresia Veronika Rampisela" ], "github": "https://github.com/theresiavr/individual-item-fairness-measures-recsys", "project_page": "", "abstract": "The evaluation of recommender system fairness has become increasingly important, especially with recent legislation that emphasises the development of fair and responsible artificial intelligence. This has led to the emergence of various fairness evaluation measures, which quantify fairness based on different definitions. However, many of such measures are simply proposed and used without further analysis on their robustness. As a result, there is insufficient understanding and awareness of the measures' limitations. Among other issues, it is not known what kind of model outputs produce the (un)fairest score, how the measure scores are empirically distributed, and whether there are cases where the measures cannot be computed (e.g., due to division by zero). These issues cause difficulty in interpreting the measure scores and confusion on which measure(s) should be used for a specific case. This thesis presents a series of papers that assess and overcome various theoretical, empirical, and conceptual limitations of existing recommender system fairness evaluation measures. We investigate a wide range of offline evaluation measures for different fairness notions, divided based on the evaluation subjects (users and items) and for different evaluation granularities (groups of subjects and individual subjects). Firstly, we perform theoretical and empirical analysis on the measures, exposing flaws that limit their interpretability, expressiveness, or applicability. Secondly, we contribute novel evaluation approaches and measures that overcome these limitations. Finally, considering the measures' limitations, we recommend guidelines for the appropriate measure usage, thereby allowing for more precise selection of fairness evaluation measures in practical scenarios. Overall, this thesis contributes to advancing the state-of-the-art offline evaluation of fairness in recommender systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/theresiavr/individual-item-fairness-measures-recsys", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.23380", "title": "V-GRPO: Online Reinforcement Learning for Denoising Generative Models Is Easier than You Think", "authors": [ "Bingda Tang", "Yuhui Zhang", "Xiaohan Wang", "Jiayuan Mao", "Ludwig Schmidt", "Serena Yeung-Levy" ], "github": "", "project_page": "", "abstract": "Aligning denoising generative models with human preferences or verifiable rewards remains a key challenge. While policy-gradient online reinforcement learning (RL) offers a principled post-training framework, its direct application is hindered by the intractable likelihoods of these models. Prior work therefore either optimizes an induced Markov decision process (MDP) over sampling trajectories, which is stable but inefficient, or uses likelihood surrogates based on the diffusion evidence lower bound (ELBO), which have so far underperformed on visual generation. Our key insight is that the ELBO-based approach can, in fact, be made both stable and efficient. By reducing surrogate variance and controlling gradient steps, we show that this approach can beat MDP-based methods. To this end, we introduce Variational GRPO (V-GRPO), a method that integrates ELBO-based surrogates with the Group Relative Policy Optimization (GRPO) algorithm, alongside a set of simple yet essential techniques. Our method is easy to implement, aligns with pretraining objectives, and avoids the limitations of MDP-based methods. V-GRPO achieves state-of-the-art performance in text-to-image synthesis, while delivering a 2times speedup over MixGRPO and a 3times speedup over DiffusionNFT.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-29", "arxiv_id": "2604.21003", "title": "The Last Harness You'll Ever Build", "authors": [ "Haebin Seong", "Li Yin", "Haoran Zhang" ], "github": "", "project_page": "", "abstract": "AI agents are increasingly deployed on complex, domain-specific workflows -- navigating enterprise web applications that require dozens of clicks and form fills, orchestrating multi-step research pipelines that span search, extraction, and synthesis, automating code review across unfamiliar repositories, and handling customer escalations that demand nuanced domain knowledge. Each new task domain requires painstaking, expert-driven harness engineering: designing the prompts, tools, orchestration logic, and evaluation criteria that make a foundation model effective. We present a two-level framework that automates this process. At the first level, the Harness Evolution Loop optimizes a worker agent's harness H for a single task: a Worker Agent W_{H} executes the task, an Evaluator Agent V adversarially diagnoses failures and scores performance, and an Evolution Agent E modifies the harness based on the full history of prior attempts. At the second level, the Meta-Evolution Loop optimizes the evolution protocol \u039b= (W_{H}, H^{(0)}, V, E) itself across diverse tasks, learning a protocol \u039b^{(text{best)} that enables rapid harness convergence on any new task -- so that adapting an agent to a novel domain requires no human harness engineering at all.} We formalize the correspondence to meta-learning and present both algorithms. The framework shifts manual harness engineering into automated harness engineering, and takes one step further -- automating the design of the automation itself.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.26779", "title": "Accelerating RL Post-Training Rollouts via System-Integrated Speculative Decoding", "authors": [ "Hayate Iso", "Tiyasa Mitra", "Sudipta Mondal", "Rasoul Shafipour", "Venmugil Elango", "Terry Kong", "Yuki Huang", "Seonjin Na", "Izzy Putterman", "Benjamin Chislett", "Maor Ashkenazi", "Joseph Guman", "Gerald Shen", "Tugrul Konuk", "Ashwath Aithal", "Ritika Borkar", "Ran Zilberstein", "Bita Rouhani" ], "github": "", "project_page": "", "abstract": "RL post-training of frontier language models is increasingly bottlenecked by autoregressive rollout generation, making rollout acceleration a central systems challenge. Many existing efficiency methods improve throughput by changing the rollout or optimization regime, for example, through off-policy execution, replay, or lower-precision generation. We study speculative decoding as a lossless acceleration primitive for RL rollouts that preserves the target model's output distribution. We implement speculative decoding in NeMo-RL with a vLLM backend, supporting both synchronous and asynchronous pipelines and enabling speculation during RL rollouts. This benefit is realizable across speculation mechanisms, such as pretrained MTP heads, small external draft models or even techniques such as Eagle3, which are traditionally applied after RL phase. This yields a deployment path for state-of-the-art speculative decoding inside RL training. In a reasoning post-training workload at 8B scale under synchronous RL, speculative decoding improves rollout throughput by 1.8x. Using a high-fidelity performance simulator, we project that combining speculative decoding with asynchronous RL yields up to 2.5x end-to-end training speedup at 235B scale.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.26904", "title": "ClawGym: A Scalable Framework for Building Effective Claw Agents", "authors": [ "Fei Bai", "Huatong Song", "Shuang Sun", "Daixuan Cheng", "Yike Yang", "Chuan Hao", "Renyuan Li", "Feng Chang", "Yuan Wei", "Ran Tao", "Bryan Dai", "Jian Yang", "Wayne Xin Zhao" ], "github": "", "project_page": "", "abstract": "Claw-style environments support multi-step workflows over local files, tools, and persistent workspace states. However, scalable development around these environments remains constrained by the absence of a systematic framework, especially one for synthesizing verifiable training data and integrating it with agent training and diagnostic evaluation. To address this challenge, we present ClawGym, a scalable framework that supports the full lifecycle of Claw-style personal agent development. Concretely, we construct ClawGym-SynData, a diverse dataset of 13.5K filtered tasks synthesized from persona-driven intents and skill-grounded operations, paired with realistic mock workspaces and hybrid verification mechanisms. We then train a family of capable Claw-style models, termed ClawGym-Agents, through supervised fine-tuning on black-box rollout trajectories, and further explore reinforcement learning via a lightweight pipeline that parallelizes rollouts across per-task sandboxes.To support reliable evaluation, we further construct ClawGym-Bench, a benchmark of 200 instances calibrated through automated filtering and human-LLM review. Relevant resources will be soon released at https://github.com/ClawGym.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.26694", "title": "Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising", "authors": [ "Jun Guo", "Qiwei Li", "Peiyan Li", "Zilong Chen", "Nan Sun", "Yifei Su", "Heyun Wang", "Yuan Zhang", "Xinghang Li", "Huaping Liu" ], "github": "", "project_page": "", "abstract": "We propose X-WAM, a Unified 4D World Model that unifies real-time robotic action execution and high-fidelity 4D world synthesis (video + 3D reconstruction) in a single framework, addressing the critical limitations of prior unified world models (e.g., UWM) that only model 2D pixel-space and fail to balance action efficiency and world modeling quality. To leverage the strong visual priors of pretrained video diffusion models, X-WAM imagines the future world by predicting multi-view RGB-D videos, and obtains spatial information efficiently through a lightweight structural adaptation: replicating the final few blocks of the pretrained Diffusion Transformer into a dedicated depth prediction branch for the reconstruction of future spatial information. Moreover, we propose Asynchronous Noise Sampling (ANS) to jointly optimize generation quality and action decoding efficiency. ANS applies a specialized asynchronous denoising schedule during inference, which rapidly decodes actions with fewer steps to enable efficient real-time execution, while dedicating the full sequence of steps to generate high-fidelity video. Rather than entirely decoupling the timesteps during training, ANS samples from their joint distribution to align with the inference distribution. Pretrained on over 5,800 hours of robotic data, X-WAM achieves 79.2% and 90.7% average success rate on RoboCasa and RoboTwin 2.0 benchmarks, while producing high-fidelity 4D reconstruction and generation surpassing existing methods in both visual and geometric metrics.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.26951", "title": "Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models", "authors": [ "Gongbo Zhang", "Wen Wang", "Ye Tian", "Li Yuan" ], "github": "", "project_page": "", "abstract": "Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference steps within a single architecture, none address cross-architecture knowledge transfer, in which the teacher and student differ in architecture, attention mechanism, and tokenizer. We present TIDE, the first framework for cross-architecture dLLM distillation, comprising three modular components: (1) TIDAL, which jointly modulates distillation strength across training progress and diffusion timestep to account for the teacher's noise-dependent reliability; (2) CompDemo, which enriches the teacher's context via complementary mask splitting to improve predictions under heavy masking; and (3) Reverse CALM, a cross-tokenizer objective that inverts chunk-level likelihood matching, yielding bounded gradients and dual-end noise filtering. Distilling 8B dense and 16B MoE teachers into a 0.6B student via two heterogeneous pipelines outperforms the baseline by an average of 1.53 points across eight benchmarks, yielding notable gains in code generation, where HumanEval scores reach 48.78 compared to 32.3 for the AR baseline.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.24351", "title": "Diffusion Templates: A Unified Plugin Framework for Controllable Diffusion", "authors": [ "Zhongjie Duan", "Hong Zhang", "Yingda Chen" ], "github": "https://github.com/modelscope/DiffSynth-Studio", "project_page": "", "abstract": "Controllable diffusion methods have substantially expanded the practical utility of diffusion models, but they are typically developed as isolated, backbone-specific systems with incompatible training pipelines, parameter formats, and runtime hooks. This fragmentation makes it difficult to reuse infrastructure across tasks, transfer capabilities across backbones, or compose multiple controls within a single generation pipeline. We present Diffusion Templates, a unified and open plugin framework that decouples base-model inference from controllable capability injection. The framework is organized around three components: Template models that map arbitrary task-specific inputs to an intermediate capability representation, a Template cache that functions as a standardized interface for capability injection, and a Template pipeline that loads, merges, and injects one or more Template caches into the base diffusion runtime. Because the interface is defined at the systems level rather than tied to a specific control architecture, heterogeneous capability carriers such as KV-Cache and LoRA can be supported under the same abstraction. Based on this design, we build a diverse model zoo spanning structural control, brightness adjustment, color adjustment, image editing, super-resolution, sharpness enhancement, aesthetic alignment, content reference, local inpainting, and age control. These case studies show that Diffusion Templates can unify a broad range of controllable generation tasks while preserving modularity, composability, and practical extensibility across rapidly evolving diffusion backbones. All resources will be open sourced, including code, models, and datasets.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/modelscope/DiffSynth-Studio", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.26752", "title": "GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents", "authors": [ "V Team", "Wenyi Hong", "Xiaotao Gu", "Ziyang Pan", "Zhen Yang", "Yuting Wang", "Yue Wang", "Yuanchang Yue", "Yu Wang", "Yanling Wang", "Yan Wang", "Xijun Liu", "Wenmeng Yu", "Weihan Wang", "Wei Li", "Shuaiqi Duan", "Sheng Yang", "Ruiliang Lv", "Mingdao Liu", "Lihang Pan", "Ke Ning", "Junhui Ji", "Jinjiang Wang", "Jing Chen", "Jiazheng Xu", "Jiale Zhu", "Jiale Cheng", "Ji Qi", "Guobing Gan", "Guo Wang", "Cong Yao", "Zijun Dou", "Zihao Zhou", "Zihan Wang", "Zhiqi Ge", "Zhijie Li", "Zhenyu Hou", "Zhao Xue", "Zehui Wang", "Zehai He", "Yusen Liu", "Yukuo Cen", "Yuchen Li", "Yuan Wang", "Yijian Lu", "Yanzi Wang", "Yadong Xue", "Xinyu Zhang", "Xinyu Liu", "Wenkai Li", "Tianyu Tong", "Tianshu Zhang", "Shengdong Yan", "Qinkai Zheng", "Mingde Xu", "Licheng Bao", "Jiaxing Xu", "Jiaxin Fan", "Jiawen Qian", "Jiali Chen", "Jiahui Lin", "Haozhi Zheng", "Haoran Wang", "Haochen Li", "Fan Yang", "Dan Zhang", "Chuangxin Zhao", "Chengcheng Wu", "Boyan Shi", "Bowei Jia", "Baoxu Wang", "Peng Zhang", "Debing Liu", "Bin Xu", "Juanzi Li", "Minlie Huang", "Yuxiao Dong", "Jie Tang" ], "github": "https://github.com/openclaw/openclaw", "project_page": "", "abstract": "We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in real environments, agentic capability depends not only on language reasoning, but also on the ability to perceive, interpret, and act over heterogeneous contexts such as images, videos, webpages, documents, GUIs. GLM-5V-Turbo is built around this objective: multimodal perception is integrated as a core component of reasoning, planning, tool use, and execution, rather than as an auxiliary interface to a language model. This report summarizes the main improvements behind GLM-5V-Turbo across model design, multimodal training, reinforcement learning, toolchain expansion, and integration with agent frameworks. These developments lead to strong performance in multimodal coding, visual tool use, and framework-based agentic tasks, while preserving competitive text-only coding capability. More importantly, our development process offers practical insights for building multimodal agents, highlighting the central role of multimodal perception, hierarchical optimization, and reliable end-to-end verification.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/openclaw/openclaw", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.26186", "title": "FASH-iCNN: Making Editorial Fashion Identity Inspectable Through Multimodal CNN Probing", "authors": [ "Morayo Danielle Adeyemi", "Ryan A. Rossi", "Franck Dernoncourt" ], "github": "", "project_page": "", "abstract": "Fashion AI systems routinely encode the aesthetic logic of specific houses, editors, and historical moments without disclosing it. We present FASH-iCNN, a multimodal system trained on 87,547 Vogue runway images across 15 fashion houses spanning 1991-2024 that makes this cultural logic inspectable. Given a photograph of a garment, the system recovers which house produced it, which era it belongs to, and which color tradition it reflects. A clothing-only model identifies the fashion house at 78.2% top-1 across 14 houses, the decade at 88.6% top-1, and the specific year at 58.3% top-1 across 34 years with a mean error of just 2.2 years. Probing which visual channels carry this signal reveals a sharp dissociation: removing color costs only 10.6pp of house identity accuracy, while removing texture costs 37.6pp, establishing texture and luminance as the primary carriers of editorial identity. FASH-iCNN treats editorial culture as the signal rather than background noise, identifying which houses, eras, and color traditions shaped each output so that users can see not just what the system predicts but which houses, editors, and historical moments are encoded in that prediction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.24977", "title": "A Survey on LLM-based Conversational User Simulation", "authors": [ "Bo Ni", "Leyao Wang", "Yu Wang", "Branislav Kveton", "Franck Dernoncourt", "Yu Xia", "Hongjie Chen", "Reuben Leura", "Samyadeep Basu", "Subhojyoti Mukherjee", "Puneet Mathur", "Nesreen Ahmed", "Junda Wu", "Li Li", "Huixin Zhang", "Ruiyi Zhang", "Tong Yu", "Sungchul Kim", "Jiuxiang Gu", "Zhengzhong Tu", "Alexa Siu", "Zichao Wang", "David Seunghyun Yoon", "Nedim Lipka", "Namyong Park", "Zihao Lin", "Trung Bui", "Yue Zhao", "Tyler Derr", "Ryan A. Rossi" ], "github": "", "project_page": "", "abstract": "User simulation has long played a vital role in computer science due to its potential to support a wide range of applications. Language, as the primary medium of human communication, forms the foundation of social interaction and behavior. Consequently, simulating conversational behavior has become a key area of study. Recent advancements in large language models (LLMs) have significantly catalyzed progress in this domain by enabling high-fidelity generation of synthetic user conversation. In this paper, we survey recent advancements in LLM-based conversational user simulation. We introduce a novel taxonomy covering user granularity and simulation objectives. Additionally, we systematically analyze core techniques and evaluation methodologies. We aim to keep the research community informed of the latest advancements in conversational user simulation and to further facilitate future research by identifying open challenges and organizing existing work under a unified framework.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.24927", "title": "Large Language Models Explore by Latent Distilling", "authors": [ "Yuanhao Zeng", "Ao Lu", "Lufei Li", "Zheng Zhang", "Yexin Li", "Kan Ren" ], "github": "https://github.com/LinesHogan/tLLM", "project_page": "", "abstract": "Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface-level lexical variation, limiting semantic exploration. In this paper, we propose Exploratory Sampling (ESamp), a decoding approach that explicitly encourages semantic diversity during generation. ESamp is motivated by the well-known observation that neural networks tend to make lower-error predictions on inputs similar to those encountered before, and incur higher prediction error on novel ones. Building on this property, we train a lightweight Distiller at test time to predict deep-layer hidden representations of the LLM from its shallow-layer representations to model the LLM's depth-wise representation transitions. During decoding, the Distiller continuously adapts to the mappings induced by the current generation context. ESamp uses the prediction error as a novelty signal to reweight candidate token extensions conditioned on the current prefix, thereby biasing decoding toward less-explored semantic patterns. ESamp is implemented with an asynchronous training--inference pipeline, with less than 5% worst case overhead (1.2% in the optimized release). Empirical results show that ESamp significantly boosts the Pass@k efficiency of reasoning models, showing superior or comparable performance to strong stochastic and heuristic baselines. Notably, ESamp achieves robust generalization across mathematics, science, and code generation benchmarks and breaks the trade-off between diversity and coherence in creative writing. Our code has released at: https://github.com/LinesHogan/tLLM.", "github_manual": "", "github_regex": "https://github.com/LinesHogan/tLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.22868", "title": "Probing Visual Planning in Image Editing Models", "authors": [ "Zhimu Zhou", "Yanpeng Zhao", "Qiuyu Liao", "Bo Zhao", "Xiaojian Ma" ], "github": "", "project_page": "", "abstract": "Visual planning represents a crucial facet of human intelligence, especially in tasks that require complex spatial reasoning and navigation. Yet, in machine learning, this inherently visual problem is often tackled through a verbal-centric lens. While recent research demonstrates the promise of fully visual approaches, they suffer from significant computational inefficiency due to the step-by-step planning-by-generation paradigm. In this work, we present EAR, an editing-as-reasoning paradigm that reformulates visual planning as a single-step image transformation. To isolate intrinsic reasoning from visual recognition, we employ abstract puzzles as probing tasks and introduce AMAZE, a procedurally generated dataset that features the classical Maze and Queen problems, covering distinct, complementary forms of visual planning. The abstract nature of AMAZE also facilitates automatic evaluation of autoregressive and diffusion-based models in terms of both pixel-wise fidelity and logical validity. We assess leading proprietary and open-source editing models. The results show that they all struggle in the zero-shot setting, finetuning on basic scales enables remarkable generalization to larger in-domain scales and out-of-domain scales and geometries. However, our best model that runs on high-end hardware fails to match the zero-shot efficiency of human solvers, highlighting a persistent gap in neural visual reasoning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.26067", "title": "RADIO-ViPE: Online Tightly Coupled Multi-Modal Fusion for Open-Vocabulary Semantic SLAM in Dynamic Environments", "authors": [ "Zaid Nasser", "Mikhail Iumanov", "Tianhao Li", "Maxim Popov", "Jaafar Mahmoud", "Sergey Kolyubin" ], "github": "", "project_page": "", "abstract": "We present RADIO-ViPE (Reduce All Domains Into One -- Video Pose Engine), an online semantic SLAM system that enables geometry-aware open-vocabulary grounding, associating arbitrary natural language queries with localized 3D regions and objects in dynamic environments. Unlike existing approaches that require calibrated, posed RGB-D input, RADIO-ViPE operates directly on raw monocular RGB video streams, requiring no prior camera intrinsics, depth sensors, or pose initialization. The system tightly couples multi-modal embeddings -- spanning vision and language -- derived from agglomerative foundation models (e.g., RADIO) with geometric scene information. This coupling takes place in initialization, optimization and factor graph connections to improve the consistency of the map from multiple modalities. The optimization is wrapped within adaptive robust kernels, designed to handle both actively moving objects and agent-displaced scene elements (e.g., furniture rearranged during ego-centric session). Experiments demonstrate that RADIO-ViPE achieves state-of-the-art results on the dynamic TUM-RGBD benchmark while maintaining competitive performance against offline open-vocabulary methods that rely on calibrated data and static scene assumptions. RADIO-ViPE bridges a critical gap in real-world deployment, enabling robust open-vocabulary semantic grounding for autonomous robotics and unconstrained in-the-wild video streams. Project page: https://be2rlab.github.io/radio_vipe", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.25441", "title": "Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost", "authors": [ "Venkata Pushpak Teja Menta" ], "github": "https://github.com/Ahmed-Ezzat20/chatterbox-finetuning-multilingual", "project_page": "", "abstract": "Commercial TTS systems produce near-native Indic audio, but the best open-source bases (Chatterbox, Indic Parler-TTS, IndicF5) trail them on measured phonological dimensions, and the most widely adopted multilingual base (Chatterbox, 23 languages) does not even tokenise Telugu or Tamil. We ask: what is the minimum intervention that brings such a non-Indic-native base to commercial-class output on Telugu, Tamil, and Hindi, without training a new acoustic decoder and without any commercial TTS training data? We combine three pieces: (1) BUPS, a Brahmic Unified Phoneme Space that deterministically romanises seven Indic scripts to ISO-15919 so Chatterbox's Latin tokeniser can process them; (2) a LoRA adapter on only the text-token predictor (Chatterbox's t3), trained on ~1,220h of licensed Indic audio with a Hindi-proxy language_id; (3) a voice-prompt recovery recipe -- an 8-11s same-language reference clip plus three sampling overrides (exaggeration 0.7, temperature 0.6, min_p 0.1; \"Config B\") -- that recovers commercial-class acoustic output with no acoustic-decoder training. On Hindi, the LoRA regresses accuracy and we instead use vanilla Chatterbox + Config B, giving a two-branch deployment. Evaluated on 10-utterance pilot sets with the companion PSP benchmark, Praxy Voice matches or slightly leads commercial baselines: 26.7% retroflex collapse on Telugu (vs Sarvam Bulbul 33.3%), 71% Tamil-zha collapse (vs commercial trio's 86%), 0.025 LLM-WER on Hindi (tied with Cartesia Sonic-3). For intra-sentential code-mix we add a third branch (IndicF5 + native-script transliteration) that drops code-mix LLM-WER from 0.80-0.85 to 0.14-0.27 across Hi/Te/Ta. We release R6 LoRA weights (Apache-2.0), inference code and router (MIT), and a Gradio demo.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Ahmed-Ezzat20/chatterbox-finetuning-multilingual", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.25476", "title": "PSP: An Interpretable Per-Dimension Accent Benchmark for Indic Text-to-Speech", "authors": [ "Venkata Pushpak Teja Menta" ], "github": "https://github.com/huggingface/parler-tts", "project_page": "", "abstract": "Standard text-to-speech (TTS) evaluation measures intelligibility (WER, CER) and overall naturalness (MOS, UTMOS) but does not quantify accent. A synthesiser may score well on all four yet sound non-native on features that are phonemic in the target language. For Indic languages, these features include retroflex articulation, aspiration, vowel length, and the Tamil retroflex approximant (letter zha). We present PSP, the Phoneme Substitution Profile, an interpretable, per-phonological-dimension accent benchmark for Indic TTS. PSP decomposes accent into six complementary dimensions: retroflex collapse rate (RR), aspiration fidelity (AF), vowel-length fidelity (LF), Tamil-zha fidelity (ZF), Frechet Audio Distance (FAD), and prosodic signature divergence (PSD). The first four are measured via forced alignment plus native-speaker-centroid acoustic probes over Wav2Vec2-XLS-R layer-9 embeddings; the latter two are corpus-level distributional distances. In this v1 we benchmark four commercial and open-source systems (ElevenLabs v3, Cartesia Sonic-3, Sarvam Bulbul, Indic Parler-TTS) on Hindi, Telugu, and Tamil pilot sets, with a fifth system (Praxy Voice) included on all three languages, plus an R5->R6 case study on Telugu. Three findings: (i) retroflex collapse grows monotonically with phonological difficulty Hindi < Telugu < Tamil (~1%, ~40%, ~68%); (ii) PSP ordering diverges from WER ordering -- commercial WER-leaders do not uniformly lead on retroflex or prosodic fidelity; (iii) no single system is Pareto-optimal across all six dimensions. We release native reference centroids (500 clips per language), 1000-clip embeddings for FAD, 500-clip prosodic feature matrices for PSD, 300-utterance golden sets per language, scoring code under MIT, and centroids under CC-BY. Formal MOS-correlation is deferred to v2; v1 reports five internal-consistency signals plus a native-audio sanity check.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/huggingface/parler-tts", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.25135", "title": "FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments", "authors": [ "Amir Saeidi", "Venkatesh Mishra", "Souradeep Mukhopadhyay", "Gaowen Liu", "Ali Payani", "Jayanth Srinivasa", "Chitta Baral" ], "github": "", "project_page": "", "abstract": "Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environments. Yet, in conversational benchmarks, which simulate real-world customer-centric issue resolution scenarios, these agents frequently fail due to the cascading effects of incorrect decision-making. These challenges are particularly pronounced for open-source LLMs with smaller parameter sizes, limited context windows, and constrained inference budgets, which contribute to increased error accumulation in agentic settings. To tackle these challenges, we present the Failure-Aware Meta-Agentic (FAMA) framework. FAMA operates in two stages: first, it analyzes failure trajectories from baseline agents to identify the most prevalent errors; second, it employs an orchestration mechanism that activates a minimal subset of specialized agents tailored to address these failures by injecting a targeted context for the tool-use agent before the decision-making step. Experiments across open-source LLMs demonstrate performance gains up to 27% across evaluation modes over standard baselines. These results highlight that targeted curation of context through specialized agents to address common failures is a valuable design principle for building reliable, multi-turn tool-use LLM agents that simulate real-world conversational scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.26091", "title": "Operating-Layer Controls for Onchain Language-Model Agents Under Real Capital", "authors": [ "T. J. Barton", "Chris Constantakis", "Patti Hauseman", "Annie Mous", "Alaska Hoffman", "Brian Bergeron", "Hunter Goodreau" ], "github": "", "project_page": "", "abstract": "We study reliability in autonomous language-model agents that translate user mandates into validated tool actions under real capital. The setting is DX Terminal Pro, a 21-day deployment in which 3,505 user-funded agents traded real ETH in a bounded onchain market. Users configured vaults through structured controls and natural-language strategies, but only agents could choose normal buy/sell trades. The system produced 7.5M agent invocations, roughly 300K onchain actions, about $20M in volume, more than 5,000 ETH deployed, roughly 70B inference tokens, and 99.9% settlement success for policy-valid submitted transactions. Long-running agents accumulated thousands of sequential decisions, including 6,000+ prompt-state-action cycles for continuously active agents, yielding a large-scale trace from user mandate to rendered prompt, reasoning, validation, portfolio state, and settlement. Reliability did not come from the base model alone; it emerged from the operating layer around the model: prompt compilation, typed controls, policy validation, execution guards, memory design, and trace-level observability. Pre-launch testing exposed failures that text-only benchmarks rarely measure, including fabricated trading rules, fee paralysis, numeric anchoring, cadence trading, and misread tokenomics. Targeted harness changes reduced fabricated sell rules from 57% to 3%, reduced fee-led observations from 32.5% to below 10%, and increased capital deployment from 42.9% to 78.0% in an affected test population. We show that capital-managing agents should be evaluated across the full path from user mandate to prompt, validated action, and settlement.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.26116", "title": "Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data", "authors": [ "Emre Ard\u0131\u00e7", "Yakup Gen\u00e7" ], "github": "", "project_page": "", "abstract": "Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. However, its performance is often hindered by redundant, malicious, or abnormal samples, leading to model degradation and inefficiency. To overcome these issues, we propose novel sample selection methods for image classification, employing a multitask autoencoder to estimate sample contributions through loss and feature analysis. Our approach incorporates unsupervised outlier detection, using one-class support vector machine (OCSVM), isolation forest (IF), and adaptive loss threshold (AT) methods managed by a central server to filter noisy samples on clients. We also propose a multi-class deep support vector data description (SVDD) loss controlled by a central server to enhance feature-based sample selection. We validate our methods on CIFAR10 and MNIST datasets across varying numbers of clients, non-IID distributions, and noise levels up to 40%. The results show significant accuracy improvements with loss-based sample selection, achieving gains of up to 7.02% on CIFAR10 with OCSVM and 1.83% on MNIST with AT. Additionally, our federated SVDD loss further improves feature-based sample selection, yielding accuracy gains of up to 0.99% on CIFAR10 with OCSVM. These results show the effectiveness of our methods in improving model accuracy across various client counts and noise conditions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.23426", "title": "Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy", "authors": [ "Emre Ard\u0131\u00e7", "Yakup Gen\u00e7" ], "github": "", "project_page": "", "abstract": "Federated learning (FL) is a distributed machine learning method where multiple devices collaboratively train a model under the management of a central server without sharing underlying data. One of the key challenges of FL is the communication bottleneck caused by variations in connection speed and bandwidth across devices. Therefore, it is essential to reduce the size of transmitted data during training. Additionally, there is a potential risk of exposing sensitive information through the model or gradient analysis during training. To address both privacy and communication efficiency, we combine differential privacy (DP) and adaptive quantization methods. We use Laplacian-based DP to preserve privacy, which is relatively underexplored in FL and offers tighter privacy guarantees than Gaussian-based DP. We propose a simple and efficient global bit-length scheduler using round-based cosine annealing, along with a client-based scheduler that dynamically adapts based on client contribution estimated through dataset entropy analysis. We evaluate our approach through extensive experiments on CIFAR10, MNIST, and medical imaging datasets, using non-IID data distributions across varying client counts, bit-length schedulers, and privacy budgets. The results show that our adaptive quantization methods reduce total communicated data by up to 52.64% for MNIST, 45.06% for CIFAR10, and 31% to 37% for medical imaging datasets compared to 32-bit float training while maintaining competitive model accuracy and ensuring robust privacy through differential privacy.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-04-30", "arxiv_id": "2604.23758", "title": "Agentic Fusion of Large Atomic and Language Models to Accelerate Superconductors Discovery", "authors": [ "Mingze Li", "Yu Rong", "Songyou Li", "Lihong Wang", "Jiacheng Cen", "Liming Wu", "Anyi Li", "Zongzhao Li", "Qiuliang Liu", "Rui Jiao", "Tian Bian", "Pengju Wang", "Hao Sun", "Jianfeng Zhang", "Ji-Rong Wen", "Deli Zhao", "Shifeng Jin", "Tingyang Xu", "Wenbing Huang" ], "github": "", "project_page": "", "abstract": "The discovery of novel materials is critical for global energy and quantum technology transitions. While deep learning has fundamentally reshaped this landscape, existing predictive or generative models typically operate in isolation, lacking the autonomous orchestration required to execute the full discovery process. Here we present ElementsClaw, an agentic framework for materials discovery that synergizes Large Atomic Models (LAMs) with Large Language Models (LLMs). In response to varied human queries, ElementsClaw orchestrates a suite of LAM tools finetuned from our proposed 1-billion-parameter model Elements for atomic-scale numerical computation, while leveraging LLMs for high-level semantic reasoning. This shift moves AI-driven materials science from isolated processes toward integrated and human interactive discovery. Applied to superconductors, ElementsClaw screens 2.4 million crystals in just 28 GPU hours to identify 68,000 high-confidence candidates (The complete dataset of screened superconductors is available at https://developer.damo-academy.com/material), expanding known superconducting space by orders of magnitude compared to datasets curated over decades. Critically, ElementsClaw achieves a high success rate in identifying superconductors hidden in literature and discovers four novel experimentally verified superconductors, exemplified by Zr3ScRe8 with a transition temperature of 6.8 K and HfZrRe4 at 6.7 K. Together, our results establish a knowledge integrated, autonomously orchestrated, and experimentally grounded paradigm for materials discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.28181", "title": "Synthetic Computers at Scale for Long-Horizon Productivity Simulation", "authors": [ "Tao Ge", "Baolin Peng", "Hao Cheng", "Jianfeng Gao" ], "github": "", "project_page": "", "abstract": "Realistic long-horizon productivity work is strongly conditioned on user-specific computer environments, where much of the work context is stored and organized through directory structures and content-rich artifacts. To scale synthetic data creation for such productivity scenarios, we introduce Synthetic Computers at Scale, a scalable methodology for creating such environments with realistic folder hierarchies and content-rich artifacts (e.g., documents, spreadsheets, and presentations). Conditioned on each synthetic computer, we run long-horizon simulations: one agent creates productivity objectives that are specific to the computer's user and require multiple professional deliverables and about a month of human work; another agent then acts as that user and keeps working across the computer -- for example, navigating the filesystem for grounding, coordinating with simulated collaborators, and producing professional artifacts -- until these objectives are completed. In preliminary experiments, we create 1,000 synthetic computers and run long-horizon simulations on them; each run requires over 8 hours of agent runtime and spans more than 2,000 turns on average. These simulations produce rich experiential learning signals, whose effectiveness is validated by significant improvements in agent performance on both in-domain and out-of-domain productivity evaluations. Given that personas are abundant at billion scale, this methodology can in principle scale to millions or even billions of synthetic user worlds with sufficient compute, enabling broader coverage of diverse professions, roles, contexts, environments, and productivity needs. We argue that scalable synthetic computer creation, together with at-scale simulations, is highly promising as a foundational substrate for agent self-improvement and agentic reinforcement learning in long-horizon productivity scenarios.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.27351", "title": "Heterogeneous Scientific Foundation Model Collaboration", "authors": [ "Zihao Li", "Jiaru Zou", "Feihao Fang", "Xuying Ning", "Mengting Ai", "Tianxin Wei", "Sirui Chen", "Xiyuan Yang", "Jingrui He" ], "github": "https://github.com/Violet24K/Eywa", "project_page": "", "abstract": "Agentic large language model systems have demonstrated strong capabilities. However, their reliance on language as the universal interface fundamentally limits their applicability to many real-world problems, especially in scientific domains where domain-specific foundation models have been developed to address specialized tasks beyond natural language. In this work, we introduce Eywa, a heterogeneous agentic framework designed to extend language-centric systems to a broader class of scientific foundation models. The key idea of Eywa is to augment domain-specific foundation models with a language-model-based reasoning interface, enabling language models to guide inference over non-linguistic data modalities. This design allows predictive foundation models, which are typically optimized for specialized data and tasks, to participate in higher-level reasoning and decision-making processes within agentic systems. Eywa can serve as a drop-in replacement for a single-agent pipeline (EywaAgent) or be integrated into existing multi-agent systems by replacing traditional agents with specialized agents (EywaMAS). We further investigate a planning-based orchestration framework in which a planner dynamically coordinates traditional agents and Eywa agents to solve complex tasks across heterogeneous data modalities (EywaOrchestra). We evaluate Eywa across a diverse set of scientific domains spanning physical, life, and social sciences. Experimental results demonstrate that Eywa improves performance on tasks involving structured and domain-specific data, while reducing reliance on language-based reasoning through effective collaboration with specialized foundation models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Violet24K/Eywa", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.28185", "title": "Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling", "authors": [ "Keming Wu", "Zuhao Yang", "Kaichen Zhang", "Shizun Wang", "Haowei Zhu", "Sicong Leng", "Zhongyu Yang", "Qijie Wang", "Sudong Wang", "Ziting Wang", "Zili Wang", "Hui Zhang", "Haonan Wang", "Hang Zhou", "Yifan Pu", "Xingxuan Li", "Fangneng Zhan", "Bo Li", "Lidong Bing", "Yuxin Song", "Ziwei Liu", "Wenhu Chen", "Jingdong Wang", "Xinchao Wang", "Xiaojuan Qi", "Shijian Lu", "Bin Wang" ], "github": "https://github.com/EvolvingLMMs-Lab/Evolving-Visual-Generation", "project_page": "", "abstract": "Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still struggle with spatial reasoning, persistent state, long-horizon consistency, and causal understanding. We argue that the field should move beyond appearance synthesis toward intelligent visual generation: plausible visuals grounded in structure, dynamics, domain knowledge, and causal relations. To frame this shift, we introduce a five-level taxonomy: Atomic Generation, Conditional Generation, In-Context Generation, Agentic Generation, and World-Modeling Generation, progressing from passive renderers to interactive, agentic, world-aware generators. We analyze key technical drivers, including flow matching, unified understanding-and-generation models, improved visual representations, post-training, reward modeling, data curation, synthetic data distillation, and sampling acceleration. We further show that current evaluations often overestimate progress by emphasizing perceptual quality while missing structural, temporal, and causal failures. By combining benchmark review, in-the-wild stress tests, and expert-constrained case studies, this roadmap offers a capability-centered lens for understanding, evaluating, and advancing the next generation of intelligent visual generation systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/EvolvingLMMs-Lab/Evolving-Visual-Generation", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.28190", "title": "Representation Fr\u00e9chet Loss for Visual Generation", "authors": [ "Jiawei Yang", "Zhengyang Geng", "Xuan Ju", "Yonglong Tian", "Yue Wang" ], "github": "https://github.com/Jiawei-Yang/FD-loss", "project_page": "", "abstract": "We show that Fr\u00e9chet Distance (FD), long considered impractical as a training objective, can in fact be effectively optimized in the representation space. Our idea is simple: decouple the population size for FD estimation (e.g., 50k) from the batch size for gradient computation (e.g., 1024). We term this approach FD-loss. Optimizing FD-loss reveals several surprising findings. First, post-training a base generator with FD-loss in different representation spaces consistently improves visual quality. Under the Inception feature space, a one-step generator achieves0.72 FID on ImageNet 256x256. Second, the same FD-loss repurposes multi-step generators into strong one-step generators without teacher distillation, adversarial training or per-sample targets. Third, FID can misrank visual quality: modern representations can yield better samples despite worse Inception FID. This motivates FDr^k, a multi-representation metric. We hope this work will encourage further exploration of distributional distances in diverse representation spaces as both training objectives and evaluation metrics for generative models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Jiawei-Yang/FD-loss", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.27578", "title": "World2Minecraft: Occupancy-Driven Simulated Scenes Construction", "authors": [ "Lechao Zhang", "Haoran Xu", "Jingyu Gong", "Xuhong Wang", "Yuan Xie", "Xin Tan" ], "github": "", "project_page": "", "abstract": "Embodied intelligence requires high-fidelity simulation environments to support perception and decision-making, yet existing platforms often suffer from data contamination and limited flexibility. To mitigate this, we propose World2Minecraft to convert real-world scenes into structured Minecraft environments based on 3D semantic occupancy prediction. In the reconstructed scenes, we can effortlessly perform downstream tasks such as Vision-Language Navigation(VLN). However, we observe that reconstruction quality heavily depends on accurate occupancy prediction, which remains limited by data scarcity and poor generalization in existing models. We introduce a low-cost, automated, and scalable data acquisition pipeline for creating customized occupancy datasets, and demonstrate its effectiveness through MinecraftOcc, a large-scale dataset featuring 100,165 images from 156 richly detailed indoor scenes. Extensive experiments show that our dataset provides a critical complement to existing datasets and poses a significant challenge to current SOTA methods. These findings contribute to improving occupancy prediction and highlight the value of World2Minecraft in providing a customizable and editable platform for personalized embodied AI research. Project page:https://world2minecraft.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.28158", "title": "Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists", "authors": [ "Yujun Wu", "Dongxu Zhang", "Xinchen Li", "Jinhang Xu", "Yiling Duan", "Yumou Liu", "Jiabao Pan", "Xuanhe Zhou", "Jingxuan Wei", "Siyuan Li", "Jintao Chen", "Conghui He", "Cheng Tan" ], "github": "", "project_page": "", "abstract": "Existing research infrastructure is fundamentally document-centric, providing citation links between papers but lacking explicit representations of methodological evolution. In particular, it does not capture the structured relationships that explain how and why research methods emerge, adapt, and build upon one another. With the rise of AI-driven research agents as a new class of consumers of scientific knowledge, this limitation becomes increasingly consequential, as such agents cannot reliably reconstruct method evolution topologies from unstructured text. We introduce Intern-Atlas, a methodological evolution graph that automatically identifies method-level entities, infers lineage relationships among methodologies, and captures the bottlenecks that drive transitions between successive innovations. Built from 1,030,314 papers spanning AI conferences, journals, and arXiv preprints, the resulting graph comprises 9,410,201 semantically typed edges, each grounded in verbatim source evidence, forming a queryable causal network of methodological development. To operationalize this structure, we further propose a self-guided temporal tree search algorithm for constructing evolution chains that trace the progression of methods over time. We evaluate the quality of the resulting graph against expert-curated ground-truth evolution chains and observe strong alignment. In addition, we demonstrate that Intern-Atlas enables downstream applications in idea evaluation and automated idea generation. We position methodological evolution graphs as a foundational data layer for the emerging automated scientific discovery.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.28169", "title": "PhyCo: Learning Controllable Physical Priors for Generative Motion", "authors": [ "Sriram Narayanan", "Ziyu Jiang", "Srinivasa Narasimhan", "Manmohan Chandraker" ], "github": "", "project_page": "", "abstract": "Modern video diffusion models excel at appearance synthesis but still struggle with physical consistency: objects drift, collisions lack realistic rebound, and material responses seldom match their underlying properties. We present PhyCo, a framework that introduces continuous, interpretable, and physically grounded control into video generation. Our approach integrates three key components: (i) a large-scale dataset of over 100K photorealistic simulation videos where friction, restitution, deformation, and force are systematically varied across diverse scenarios; (ii) physics-supervised fine-tuning of a pretrained diffusion model using a ControlNet conditioned on pixel-aligned physical property maps; and (iii) VLM-guided reward optimization, where a fine-tuned vision-language model evaluates generated videos with targeted physics queries and provides differentiable feedback. This combination enables a generative model to produce physically consistent and controllable outputs through variations in physical attributes-without any simulator or geometry reconstruction at inference. On the Physics-IQ benchmark, PhyCo significantly improves physical realism over strong baselines, and human studies confirm clearer and more faithful control over physical attributes. Our results demonstrate a scalable path toward physically consistent, controllable generative video models that generalize beyond synthetic training environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.27419", "title": "InteractWeb-Bench: Can Multimodal Agent Escape Blind Execution in Interactive Website Generation?", "authors": [ "Qiyao Wang", "Haoran Hu", "Longze Chen", "Hongbo Wang", "Hamid Alinejad-Rokny", "Yuan Lin", "Min Yang" ], "github": "", "project_page": "", "abstract": "With the advancement of multimodal large language models (MLLMs) and coding agents, the website development has shifted from manual programming to agent-based project-level code synthesis. Existing benchmarks rely on idealized assumptions, especially for well-structured, information-rich inputs and static execution settings. In contrast, real-world development is constrained by a critical bottleneck: the semantic misalignment between ambiguous, low-quality instructions from non-expert users and model understanding, which results in a failure mode that we term blind execution. To address this gap, we introduce InteractWeb-Bench, the first multimodal interactive benchmark for website generation under non-expert low-code user conditions. InteractWeb-Bench introduces four types of user agents and persona-driven instruction perturbations to systematically simulate diverse user behaviors, including ambiguity, redundancy, and contradiction, grounded in requirement engineering defect taxonomies. We develop an interactive execution environment for agents, featuring a unified action space comprising Clarify, Implement, Verify, and Submit, enabling iterative intent refinement, code synthesis, and visual feedback-based validation. Extensive experiments and analysis reveal that frontier MLLM-based agents remain trapped in blind execution, exposing limitations in intent recognition and adaptive interaction.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.27083", "title": "Co-Evolving Policy Distillation", "authors": [ "Naibin Gu", "Chenxu Yang", "Qingyi Si", "Chuanyu Qin", "Dingyu Yao", "Peng Fu", "Zheng Lin", "Weiping Wang", "Nan Duan", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "RLVR and OPD have become standard paradigms for post-training. We provide a unified analysis of these two paradigms in consolidating multiple expert capabilities into a single model, identifying capability loss in different ways: mixed RLVR suffers from inter-capability divergence cost, while the pipeline of first training experts and then performing OPD, though avoiding divergence, fails to fully absorb teacher capabilities due to large behavioral pattern gaps between teacher and student. We propose Co-Evolving Policy Distillation (CoPD), which encourages parallel training of experts and introduces OPD during each expert's ongoing RLVR training rather than after complete expert training, with experts serving as mutual teachers (making OPD bidirectional) to co-evolve. This enables more consistent behavioral patterns among experts while maintaining sufficient complementary knowledge throughout. Experiments validate that CoPD achieves all-in-one integration of text, image, and video reasoning capabilities, significantly outperforming strong baselines such as mixed RLVR and MOPD, and even surpassing domain-specific experts. The model parallel training pattern offered by CoPD may inspire a novel training scaling paradigm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.24658", "title": "The Last Human-Written Paper: Agent-Native Research Artifacts", "authors": [ "Jiachen Liu", "Jiaxin Pei", "Jintao Huang", "Chenglei Si", "Ao Qu", "Xiangru Tang", "Runyu Lu", "Lichang Chen", "Xiaoyan Bai", "Haizhong Zheng", "Carl Chen", "Zhiyang Chen", "Haojie Ye", "Yujuan Fu", "Zexue He", "Zijian Jin", "Zhenyu Zhang", "Shangquan Sun", "Maestro Harmon", "John Dianzhuo Wang", "Jianqiao Zeng", "Jiachen Sun", "Mingyuan Wu", "Baoyu Zhou", "Chenyu You", "Shijian Lu", "Yiming Qiu", "Fan Lai", "Yuan Yuan", "Yao Li", "Junyuan Hong", "Ruihao Zhu", "Beidi Chen", "Alex Pentland", "Ang Chen", "Mosharaf Chowdhury", "Zechen Zhang" ], "github": "", "project_page": "", "abstract": "Scientific publication compresses a branching, iterative research process into a linear narrative, discarding the majority of what was discovered along the way. This compilation imposes two structural costs: a Storytelling Tax, where failed experiments, rejected hypotheses, and the branching exploration process are discarded to fit a linear narrative; and an Engineering Tax, where the gap between reviewer-sufficient prose and agent-sufficient specification leaves critical implementation details unwritten. Tolerable for human readers, these costs become critical when AI agents must understand, reproduce, and extend published work. We introduce the Agent-Native Research Artifact (ARA), a protocol that replaces the narrative paper with a machine-executable research package structured around four layers: scientific logic, executable code with full specifications, an exploration graph that preserves the failures compilation discards, and evidence grounding every claim in raw outputs. Three mechanisms support the ecosystem: a Live Research Manager that captures decisions and dead ends during ordinary development; an ARA Compiler that translates legacy PDFs and repos into ARAs; and an ARA-native review system that automates objective checks so human reviewers can focus on significance, novelty, and taste. On PaperBench and RE-Bench, ARA raises question-answering accuracy from 72.4% to 93.7% and reproduction success from 57.4% to 64.4%. On RE-Bench's five open-ended extension tasks, preserved failure traces in ARA accelerate progress, but can also constrain a capable agent from stepping outside the prior-run box depending on the agent's capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.28130", "title": "MoCapAnything V2: End-to-End Motion Capture for Arbitrary Skeletons", "authors": [ "Kehong Gong", "Zhengyu Wen", "Dao Thien Phong", "Mingxi Xu", "Weixia He", "Qi Wang", "Ning Zhang", "Zhengyu Li", "Guanli Hou", "Dongze Lian", "Xiaoyu He", "Mingyuan Zhang", "Hanwang Zhang" ], "github": "", "project_page": "", "abstract": "Recent methods for arbitrary-skeleton motion capture from monocular video follow a factorized pipeline, where a Video-to-Pose network predicts joint positions and an analytical inverse-kinematics (IK) stage recovers joint rotations. While effective, this design is inherently limited, since joint positions do not fully determine rotations and leave degrees of freedom such as bone-axis twist ambiguous, and the non-differentiable IK stage prevents the system from adapting to noisy predictions or optimizing for the final animation objective. In this work, we present the first fully end-to-end framework in which both Video-to-Pose and Pose-to-Rotation are learnable and jointly optimized. We observe that the ambiguity in pose-to-rotation mapping arises from missing coordinate system information: the same joint positions can correspond to different rotations under different rest poses and local axis conventions. To resolve this, we introduce a reference pose-rotation pair from the target asset, which, together with the rest pose, not only anchors the mapping but also defines the underlying rotation coordinate system. This formulation turns rotation prediction into a well-constrained conditional problem and enables effective learning. In addition, our model predicts joint positions directly from video without relying on mesh intermediates, improving both robustness and efficiency. Both stages share a skeleton-aware Global-Local Graph-guided Multi-Head Attention (GL-GMHA) module for joint-level local reasoning and global coordination. Experiments on Truebones Zoo and Objaverse show that our method reduces rotation error from ~17 degrees to ~10 degrees, and to 6.54 degrees on unseen skeletons, while achieving ~20x faster inference than mesh-based pipelines. Project page: https://animotionlab.github.io/MoCapAnythingV2/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.27505", "title": "Leveraging Verifier-Based Reinforcement Learning in Image Editing", "authors": [ "Hanzhong Guo", "Jie Wu", "Jie Liu", "Yu Gao", "Zilyu Ye", "Linxiao Yuan", "Xionghui Wang", "Yizhou Yu", "Weilin Huang" ], "github": "", "project_page": "", "abstract": "While Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm for text-to-image generation, its application to image editing remains largely unexplored. A key bottleneck is the lack of a robust general reward model for all editing tasks. Existing edit reward models usually give overall scores without detailed checks, ignoring different instruction requirements and causing biased rewards. To address this, we argue that the key is to move from a simple scorer to a reasoning verifier. We introduce Edit-R1, a framework that builds a chain-of-thought (CoT) verifier-based reasoning reward model (RRM) and then leverages it for downstream image editing. The Edit-RRM breaks instructions into distinct principles, evaluates the edited image against each principle, and aggregates these checks into an interpretable, fine-grained reward. To build such an RRM, we first apply supervised fine-tuning (SFT) as a ``cold-start'' to generate CoT reward trajectories. Then, we introduce Group Contrastive Preference Optimization (GCPO), a reinforcement learning algorithm that leverages human pairwise preference data to reinforce our pointwise RRM. After building the RRM, we use GRPO to train editing models with this non-differentiable yet powerful reward model. Extensive experiments demonstrate that our Edit-RRM surpasses powerful VLMs such as Seed-1.5-VL and Seed-1.6-VL as an editing-specific reward model, and we observe a clear scaling trend, with performance consistently improving from 3B to 7B parameters. Moreover, Edit-R1 delivers gains to editing models like FLUX.1-kontext, highlighting its effectiveness in enhancing image editing.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.27711", "title": "ExoActor: Exocentric Video Generation as Generalizable Interactive Humanoid Control", "authors": [ "Yanghao Zhou", "Jingyu Ma", "Yibo Peng", "Zhenguo Sun", "Yu Bai", "B\u00f6rje F. Karlsson" ], "github": "", "project_page": "", "abstract": "Humanoid control systems have made significant progress in recent years, yet modeling fluent interaction-rich behavior between a robot, its surrounding environment, and task-relevant objects remains a fundamental challenge. This difficulty arises from the need to jointly capture spatial context, temporal dynamics, robot actions, and task intent at scale, which is a poor match to conventional supervision. We propose ExoActor, a novel framework that leverages the generalization capabilities of large-scale video generation models to address this problem. The key insight in ExoActor is to use third-person video generation as a unified interface for modeling interaction dynamics. Given a task instruction and scene context, ExoActor synthesizes plausible execution processes that implicitly encode coordinated interactions between robot, environment, and objects. Such video output is then transformed into executable humanoid behaviors through a pipeline that estimates human motion and executes it via a general motion controller, yielding a task-conditioned behavior sequence. To validate the proposed framework, we implement it as an end-to-end system and demonstrate its generalization to new scenarios without additional real-world data collection. Furthermore, we conclude by discussing limitations of the current implementation and outlining promising directions for future research, illustrating how ExoActor provides a scalable approach to modeling interaction-rich humanoid behaviors, potentially opening a new avenue for generative models to advance general-purpose humanoid intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.27039", "title": "Length Value Model: Scalable Value Pretraining for Token-Level Length Modeling", "authors": [ "Zhen Zhang", "Changyi Yang", "Zijie Xia", "Zhen Yang", "Chengzhi Liu", "Zhaotiao Weng", "Yepeng Liu", "Haobo Chen", "Jin Pan", "Chenyang Zhao", "Yuheng Bu", "Alkesh Patel", "Zhe Gan", "Xin Eric Wang" ], "github": "https://github.com/eric-ai-lab/Length-Value-Model", "project_page": "", "abstract": "Token serves as the fundamental unit of computation in modern autoregressive models, and generation length directly influences both inference cost and reasoning performance. Despite its importance, existing approaches lack fine-grained length modeling, operating primarily at the coarse-grained sequence level. We introduce the Length Value Model (LenVM), a token-level framework that models the remaining generation length. By formulating length modeling as a value estimation problem and assigning a constant negative reward to each generated token, LenVM predicts a bounded, discounted return that serves as a monotone proxy for the remaining generation horizon. This formulation yields supervision that is annotation-free, dense, unbiased, and scalable. Experiments on LLMs and VLMs demonstrate LenVM provides a highly effective signal at inference time. On the LIFEBench exact length matching task, applying LenVM to a 7B model improves the length score from 30.9 to 64.8, significantly outperforming frontier closed-source models. Furthermore, LenVM enables continuous control over the trade off between performance and efficiency. On GSM8K at a budget of 200 tokens, LenVM maintains 63% accuracy compared to 6 percent for token budget baseline. It also accurately predicts total generation length from the prompt boundary. Finally, LenVM's token-level values offer an interpretable view of generation dynamics, revealing how specific tokens shift reasoning toward shorter or longer regimes. Results demonstrate that LenVM supports a broad range of applications and token length can be effectively modeled as a token-level value signal, highlighting the potential of LenVM as a general framework for length modeling and as a length-specific value signal that could support future RL training. Code is available at https://github.com/eric-ai-lab/Length-Value-Model.", "github_manual": "", "github_regex": "https://github.com/eric-ai-lab/Length-Value-Model", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.27085", "title": "Efficient Training on Multiple Consumer GPUs with RoundPipe", "authors": [ "Yibin Luo", "Shiwei Gao", "Huichuan Zheng", "Youyou Lu", "Jiwu Shu" ], "github": "https://github.com/ITcarrot/RoundPipe", "project_page": "", "abstract": "Fine-tuning Large Language Models (LLMs) on consumer-grade GPUs is highly cost-effective, yet constrained by limited GPU memory and slow PCIe interconnects. Pipeline parallelism combined with CPU offloading mitigates these hardware bottlenecks by reducing communication overhead. However, existing PP schedules suffer from an inherent limitation termed the weight binding issue. Binding uneven model stages (e.g., the LM head is large) to GPUs limits the pipeline's throughput to that of the GPU with the heaviest load, leading to severe pipeline bubbles. In this paper, we propose RoundPipe, a novel pipeline schedule that breaks the weight binding constraint on consumer GPU servers. RoundPipe treats GPUs as a pool of stateless execution workers and dynamically dispatches computation stages across devices in a round-robin manner, achieving a near-zero-bubble pipeline. To ensure training correctness and system efficiency, RoundPipe integrates a priority-aware transfer scheduling engine, a fine-grained distributed event-based synchronization protocol, and an automated layer partitioning algorithm. Evaluations on an 8times RTX 4090 server demonstrate that RoundPipe achieves 1.48--2.16times speedups over state-of-the-art baselines when fine-tuning 1.7B to 32B models. Remarkably, RoundPipe enables LoRA fine-tuning of the Qwen3-235B model with 31K sequence length on a single server. RoundPipe is publicly available as an open-source Python library with comprehensive documentation.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ITcarrot/RoundPipe", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.28139", "title": "Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows", "authors": [ "Chenxin Li", "Zhengyang Tang", "Huangxin Lin", "Yunlong Lin", "Shijue Huang", "Shengyuan Liu", "Bowen Ye", "Rang Li", "Lei Li", "Benyou Wang", "Yixuan Yuan" ], "github": "https://github.com/NousResearch/hermes-agent", "project_page": "", "abstract": "LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at release time and grade mainly the final response, making it difficult to evaluate agents against evolving workflow demand or verify whether a task was executed. We introduce Claw-Eval-Live, a live benchmark for workflow agents that separates a refreshable signal layer, updated across releases from public workflow-demand signals, from a reproducible, time-stamped release snapshot. Each release is constructed from public workflow-demand signals, with ClawHub Top-500 skills used in the current release, and materialized as controlled tasks with fixed fixtures, services, workspaces, and graders. For grading, Claw-Eval-Live records execution traces, audit logs, service state, and post-run workspace artifacts, using deterministic checks when evidence is sufficient and structured LLM judging only for semantic dimensions. The release contains 105 tasks spanning controlled business services and local workspace repair, and evaluates 13 frontier models under a shared public pass rule. Experiments reveal that reliable workflow automation remains far from solved: the leading model passes only 66.7% of tasks and no model reaches 70%. Failures are structured by task family and execution surface, with HR, management, and multi-system business workflows as persistent bottlenecks and local workspace repair comparatively easier but unsaturated. Leaderboard rank alone is insufficient because models with similar pass rates can diverge in overall completion, and task-level discrimination concentrates in a middle band of tasks. Claw-Eval-Live suggests that workflow-agent evaluation should be grounded twice, in fresh external demand and in verifiable agent action.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NousResearch/hermes-agent", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.27251", "title": "Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models", "authors": [ "Xingwei Tan", "Marco Valentino", "Mahmud Elahi Akhter", "Yuxiang Zhou", "Maria Liakata", "Nikolaos Aletras" ], "github": "", "project_page": "", "abstract": "Large Language Models (LLMs) are known to acquire reasoning capabilities through shared inference patterns in pre-training data, which are further elicited via Chain-of-Thought (CoT) practices. However, whether fundamental reasoning patterns, such as induction, deduction, and abduction, can be decoupled from specific problem instances remains a critical challenge for model controllability, and for shedding light on reasoning controllability. In this paper, we present the first systematic investigation of this problem through the lens of reasoning conflicts: an explicit tension between parametric and contextual information induced by mandating logical schemata that deviate from those expected for a target task. Our evaluation reveals that LLMs consistently prioritize sensibility over compliance, favoring task-appropriate reasoning patterns despite conflicting instructions. Notably, task accuracy is not strictly determined by sensibility, with models often maintaining high performance even when using conflicting patterns, suggesting a reliance on internalized parametric memory that increases with model size. We further demonstrate that reasoning conflicts are internally detectable, as confidence scores significantly drop during conflicting episodes. Probing experiments confirm that reasoning types are linearly encoded from middle-to-late layers, indicating the potential for activation-level controllability. Leveraging these insights, we steer models towards compliance, increasing instruction following by up to 29%. Overall, our findings establish that while LLM reasoning is anchored to concrete instances, active mechanistic interventions can effectively decouple logical schemata from data, offering a path toward improved controllability, faithfulness, and generalizability.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.27766", "title": "Instruction-Guided Poetry Generation in Arabic and Its Dialects", "authors": [ "Abdelrahman Sadallah", "Kareem Elozeiri", "Mervat Abassy", "Rania Elbadry", "Mohamed Anwar", "Abed Alhakim Freihat", "Preslav Nakov", "Fajri Koto" ], "github": "https://github.com/mbzuai-nlp/instructpoet-ar", "project_page": "", "abstract": "Poetry has long been a central art form for Arabic speakers, serving as a powerful medium of expression and cultural identity. While modern Arabic speakers continue to value poetry, existing research on Arabic poetry within Large Language Models (LLMs) has primarily focused on analysis tasks such as interpretation or metadata prediction, e.g., rhyme schemes and titles. In contrast, our work addresses the practical aspect of poetry creation in Arabic by introducing controllable generation capabilities to assist users in writing poetry. Specifically, we present a large-scale, carefully curated instruction-based dataset in Modern Standard Arabic (MSA) and various Arabic dialects. This dataset enables tasks such as writing, revising, and continuing poems based on predefined criteria, including style and rhyme, as well as performing poetry analysis. Our experiments show that fine-tuning LLMs on this dataset yields models that can effectively generate poetry that is aligned with user requirements, based on both automated metrics and human evaluation with native Arabic speakers. The data and the code are available at https://github.com/mbzuai-nlp/instructpoet-ar", "github_manual": "", "github_regex": "https://github.com/mbzuai-nlp/instructpoet-ar", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.28157", "title": "FlashRT: Towards Computationally and Memory Efficient Red-Teaming for Prompt Injection and Knowledge Corruption", "authors": [ "Yanting Wang", "Chenlong Yin", "Ying Chen", "Jinyuan Jia" ], "github": "https://github.com/Wang-Yanting/FlashRT", "project_page": "", "abstract": "Long-context large language models (LLMs)-for example, Gemini-3.1-Pro and Qwen-3.5-are widely used to empower many real-world applications, such as retrieval-augmented generation, autonomous agents, and AI assistants. However, security remains a major concern for their widespread deployment, with threats such as prompt injection and knowledge corruption. To quantify the security risks faced by LLMs under these threats, the research community has developed heuristic-based and optimization-based red-teaming methods. Optimization-based methods generally produce stronger attacks than heuristic attacks and thus provide a more rigorous assessment of LLM security risks. However, they are often resource-intensive, requiring significant computation and GPU memory, especially for long context scenarios. The resource-intensive nature poses a major obstacle for the community (especially academic researchers) to systematically evaluate the security risks of long-context LLMs and assess the effectiveness of defense strategies at scale. In this work, we propose FlashRT, the first framework to improve the efficiency (in terms of both computation and memory) for optimization-based prompt injection and knowledge corruption attacks under long-context LLMs. Through extensive evaluations, we find that FlashRT consistently delivers a 2x-7x speedup (e.g., reducing runtime from one hour to less than ten minutes) and a 2x-4x reduction in GPU memory consumption (e.g., reducing from 264.1 GB to 65.7 GB GPU memory for a 32K token context) compared to state-of-the-art baseline nanoGCG. FlashRT can be broadly applied to black-box optimization methods, such as TAP and AutoDAN. We hope FlashRT can serve as a red-teaming tool to enable systematic evaluation of long-context LLM security. The code is available at: https://github.com/Wang-Yanting/FlashRT", "github_manual": "", "github_regex": "https://github.com/Wang-Yanting/FlashRT", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.24902", "title": "Safety Drift After Fine-Tuning: Evidence from High-Stakes Domains", "authors": [ "Emaan Bilal Khan", "Amy Winecoff", "Miranda Bogen", "Dylan Hadfield-Menell" ], "github": "", "project_page": "", "abstract": "Foundation models are routinely fine-tuned for use in particular domains, yet safety assessments are typically conducted only on base models, implicitly assuming that safety properties persist through downstream adaptation. We test this assumption by analyzing the safety behavior of 100 models, including widely deployed fine-tunes in the medical and legal domains as well as controlled adaptations of open foundation models alongside their bases. Across general-purpose and domain-specific safety benchmarks, we find that benign fine-tuning induces large, heterogeneous, and often contradictory changes in measured safety: models frequently improve on some instruments while degrading on others, with substantial disagreement across evaluations. These results show that safety behavior is not stable under ordinary downstream adaptation, raising critical questions about governance and deployment practices centered on base-model evaluations. Without explicit re-evaluation of fine-tuned models in deployment-relevant contexts, such approaches fall short of adequately managing downstream risk, overlooking practical sources of harm -- failures that are especially consequential in high-stakes settings and challenge current accountability paradigms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.24953", "title": "ViPO: Visual Preference Optimization at Scale", "authors": [ "Ming Li", "Jie Wu", "Justin Cui", "Xiaojie Li", "Rui Wang", "Chen Chen" ], "github": "https://github.com/liming-ai/ViPO", "project_page": "", "abstract": "While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open-source preference datasets contain conflicting preference patterns, where winners excel in some dimensions but underperform in others. Naively optimizing on such noisy datasets fails to learn preferences, hindering effective scaling. To enhance robustness against noise, we propose Poly-DPO, which extends the DPO objective with an additional polynomial term that dynamically adjusts model confidence based on dataset characteristics, enabling effective learning across diverse data distributions. Beyond biased patterns, existing datasets suffer from low resolution, limited prompt diversity, and imbalanced distributions. To facilitate large-scale visual preference optimization by tackling data bottlenecks, we construct ViPO, a massive-scale preference dataset with 1M image pairs at 1024px across five categories and 300K video pairs at 720p+ across three categories. State-of-the-art generative models and diverse prompts ensure reliable preference signals with balanced distributions. Remarkably, when applying Poly-DPO to our high-quality dataset, the optimal configuration converges to standard DPO. This convergence validates dataset quality and Poly-DPO's adaptive nature: sophisticated optimization becomes unnecessary with sufficient data quality, yet remains valuable for imperfect datasets. We validate our approach across visual generation models. On noisy datasets like Pick-a-Pic V2, Poly-DPO achieves 6.87 and 2.32 gains over Diffusion-DPO on GenEval for SD1.5 and SDXL, respectively. For ViPO, models achieve performance far exceeding those trained on existing open-source preference datasets. These results confirm that addressing both algorithmic adaptability and data quality is essential for scaling visual preference optimization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/liming-ai/ViPO", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.24952", "title": "Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization", "authors": [ "Xinxin Liu", "Ming Li", "Zonglin Lyu", "Yuzhang Shang", "Chen Chen" ], "github": "https://github.com/L-CodingSpace/semi-dpo", "project_page": "", "abstract": "Human visual preferences are inherently multi-dimensional, encompassing aesthetics, detail fidelity, and semantic alignment. However, existing datasets provide only single, holistic annotations, resulting in severe label noise: images that excel in some dimensions but are deficient in others are simply marked as winner or loser. We theoretically demonstrate that compressing multi-dimensional preferences into binary labels generates conflicting gradient signals that misguide Diffusion Direct Preference Optimization (DPO). To address this, we propose Semi-DPO, a semi-supervised approach that treats consistent pairs as clean labeled data and conflicting ones as noisy unlabeled data. Our method starts by training on a consensus-filtered clean subset, then uses this model as an implicit classifier to generate pseudo-labels for the noisy set for iterative refinement. Experimental results demonstrate that Semi-DPO achieves state-of-the-art performance and significantly improves alignment with complex human preferences, without requiring additional human annotation or explicit reward models during training. We will release our code and models at: https://github.com/L-CodingSpace/semi-dpo", "github_manual": "", "github_regex": "https://github.com/L-CodingSpace/semi-dpo", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.27151", "title": "Step-level Optimization for Efficient Computer-use Agents", "authors": [ "Jinbiao Wei", "Kangqi Ni", "Yilun Zhao", "Guo Gan", "Arman Cohan" ], "github": "", "project_page": "", "abstract": "Computer-use agents provide a promising path toward general software automation because they can interact directly with arbitrary graphical user interfaces instead of relying on brittle, application-specific integrations. Despite recent advances in benchmark performance, strong computer-use agents remain expensive and slow in practice, since most systems invoke large multimodal models at nearly every interaction step. We argue that this uniform allocation of compute is fundamentally inefficient for long-horizon GUI tasks. Such trajectories are highly heterogeneous: many steps are routine and can be handled reliably by smaller, cheaper policies, while errors tend to concentrate at a relatively small number of high-risk moments. Across computer-use benchmarks, these failures repeatedly take two forms: progress stalls, where the agent loops, repeats ineffective actions, or fails to make meaningful progress, and silent semantic drift, where the agent continues taking locally plausible actions after already deviating from the user's true goal. To address this inefficiency, we propose an event-driven, step-level cascade for computer-use agents that runs a small policy by default and escalates to a stronger model only when lightweight learned monitors detect elevated risk. Our framework combines two complementary signals: a Stuck Monitor that detects degraded progress from recent reasoning-action history and triggers recovery, and a Milestone Monitor that identifies semantically meaningful checkpoints where sparse verification is most informative for catching drift. This design turns always-on frontier-model inference into adaptive, on-demand compute allocation over the course of an evolving interaction. The framework is modular and deployment-oriented: it can be layered on top of existing computer-use agents without changing the underlying agent architecture or retraining the large model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-01", "arxiv_id": "2604.24954", "title": "Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence", "authors": [ "NVIDIA", "Amala Sanjay Deshmukh", "Kateryna Chumachenko", "Tuomas Rintamaki", "Matthieu Le", "Tyler Poon", "Danial Mohseni Taheri", "Ilia Karmanov", "Guilin Liu", "Jarno Seppanen", "Arushi Goel", "Mike Ranzinger", "Greg Heinrich", "Guo Chen", "Lukas Voegtle", "Philipp Fischer", "Timo Roman", "Karan Sapra", "Collin McCarthy", "Shaokun Zhang", "Fuxiao Liu", "Hanrong Ye", "Yi Dong", "Mingjie Liu", "Yifan Peng", "Piotr Zelasko", "Zhehuai Chen", "Nithin Rao Koluguri", "Nune Tadevosyan", "Lilit Grigoryan", "Ehsan Hosseini Asl", "Pritam Biswas", "Leili Tavabi", "Yuanhang Su", "Zhiding Yu", "Peter Jin", "Alexandre Milesi", "Netanel Haber", "Yao Xu", "Sarah Amiraslani", "Nabin Mulepati", "Eric Tramel", "Jaehun Jung", "Ximing Lu", "Brandon Cui", "Jin Xu", "Zhiqi Li", "Shihao Wang", "Yuanguo Kuang", "Shaokun Zhang", "Huck Yang", "Boyi Li", "Hongxu Yin", "Song Han", "Pavlo Molchanov", "Adi Renduchintala", "Charles Wang", "David Mosallanezhad", "Soumye Singhal", "Luis Vega", "Katherine Cheung", "Sreyan Ghosh", "Yian Zhang", "Alexander Bukharin", "Venkat Srinivasan", "Johnny Greco", "Andre Manoel", "Maarten Van Segbroeck", "Suseella Panguliri", "Rohit Watve", "Divyanshu Kakwani", "Shubham Pachori", "Jeffrey Glick", "Radha Sri-Tharan", "Aileen Zaman", "Khanh Nguyen", "Shi Chen", "Jiaheng Fang", "Qing Miao", "Wenfei Zhou", "Yu Wang", "Zaid Pervaiz Bhat", "Varun Praveen", "Arihant Jain", "Ramanathan Arunachalam", "Tomasz Kornuta", "Ashton Sharabiani", "Amy Shen", "Wei Huang", "Yi-Fu Wu", "Ali Roshan Ghias", "Huiying Li", "Brian Yu", "Nima Tajbakhsh", "Chen Cui", "Wenwen Gao", "Li Ding", "Terry Kong", "Manoj Kilaru", "Anahita Bhiwandiwalla", "Marek Wawrzos", "Daniel Korzekwa", "Pablo Ribalta", "Grzegorz Chlebus", "Besmira Nushi", "Ewa Dobrowolska", "Maciej Jakub Mikulski", "Kunal Dhawan", "Steve Huang", "Jagadeesh Balam", "Yongqiang Wang", "Nikolay Karpov", "Valentin Mendelev", "George Zelenfroynd", "Meline Mkrtchyan", "Qing Miao", "Omri Almog", "Bhavesh Pawar", "Rameshwar Shivbhakta", "Sudeep Sabnis", "Ashrton Sharabiani", "Negar Habibi", "Geethapriya Venkataramani", "Pamela Peng", "Prerit Rodney", "Serge Panev", "Richard Mazzarese", "Nicky Liu", "Michael Fukuyama", "Andrii Skliar", "Roger Waleffe", "Duncan Riach", "Yunheng Zou", "Jian Hu", "Hao Zhang", "Binfeng Xu", "Yuhao Yang", "Zuhair Ahmed", "Alexandre Milesi", "Carlo del Mundo", "Chad Voegele", "Zhiyu Cheng", "Nave Assaf", "Andrii Skliar", "Daniel Afrimi", "Natan Bagrov", "Ran Zilberstein", "Ofri Masad", "Eugene Khvedchenia", "Natan Bagrov", "Borys Tymchenko", "Tomer Asida", "Daniel Afrimi", "Parth Mannan", "Victor Cui", "Michael Evans", "Katherine Luna", "Jie Lou", "Pinky Xu", "Guyue Huang", "Negar Habibi", "Michael Boone", "Pradeep Thalasta", "Adeola Adesoba", "Dina Yared", "Christopher Parisien", "Leon Derczynski", "Shaona Ghosh", "Wes Feely", "Micah Schaffer", "Radha Sri-Tharan", "Jeffrey Glick", "Barnaby Simkin", "George Zelenfroynd", "Tomasz Grzegorzek", "Rishabh Garg", "Aastha Jhunjhunwala", "Sergei Kolchenko", "Farzan Memarian", "Haran Kumar", "Shiv Kumar", "Isabel Hulseman", "Anjali Shah", "Kari Briski", "Padmavathy Subramanian", "Joey Conway", "Udi Karpas", "Jane Polak Scowcroft", "Annie Surla", "Shilpa Ammireddy", "Ellie Evans", "Jesse Oliver", "Tom Balough", "Chia-Chih Chen", "Sandip Bhaskar", "Alejandra Rico", "Bardiya Sadeghi", "Seph Mard", "Katherine Cheung", "Meredith Price", "Laya Sleiman", "Saori Kaji", "Wesley Helmholz", "Wendy Quan", "Michael Lightstone", "Jonathan Cohen", "Jian Zhang", "Oleksii Kuchaiev", "Boris Ginsburg", "Jan Kautz", "Eileen Long", "Mohammad Shoeybi", "Mostofa Patwary", "Oluwatobi Olabiyi", "Andrew Tao", "Bryan Catanzaro", "Udi Karpas" ], "github": "", "project_page": "", "abstract": "We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 Nano Omni delivers consistent accuracy improvements over its predecessor, Nemotron Nano V2 VL, across all modalities, enabled by advances in architecture, training data and recipes. In particular, Nemotron 3 delivers leading results in real-world document understanding, long audio-video comprehension, and agentic computer use. Built on the highly efficient Nemotron 3 Nano 30B-A3B backbone, Nemotron 3 Nano Omni further incorporates innovative multimodal token-reduction techniques to deliver substantially lower inference latency and higher throughput than other models of similar size. We are releasing model checkpoints in BF16, FP8, and FP4 formats, along with portions of the training data and codebase to facilitate further research and development.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00658", "title": "UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors", "authors": [ "Houyuan Chen", "Hong Li", "Xianghao Kong", "Tianrui Zhu", "Shaocong Xu", "Weiqing Xiao", "Yuwei Guo", "Chongjie Ye", "Lvmin Zhang", "Hao Zhao", "Anyi Rao" ], "github": "", "project_page": "", "abstract": "Recent progress has shown that video diffusion models (VDMs) can be repurposed for diverse multimodal graphics tasks. However, existing methods often train separate models for each problem setting, which fixes the input-output mapping and limits the modeling of correlations across modalities. We present UniVidX, a unified multimodal framework that leverages VDM priors for versatile video generation. UniVidX formulates pixel-aligned tasks as conditional generation in a shared multimodal space, adapts to modality-specific distributions while preserving the backbone's native priors, and promotes cross-modal consistency during synthesis. It is built on three key designs. Stochastic Condition Masking (SCM) randomly partitions modalities into clean conditions and noisy targets during training, enabling omni-directional conditional generation instead of fixed mappings. Decoupled Gated LoRA (DGL) introduces per-modality LoRAs that are activated when a modality serves as the generation target, preserving the strong priors of the VDM. Cross-Modal Self-Attention (CMSA) shares keys and values across modalities while keeping modality-specific queries, facilitating information exchange and inter-modal alignment. We instantiate UniVidX in two domains: UniVid-Intrinsic, for RGB videos and intrinsic maps including albedo, irradiance, and normal; and UniVid-Alpha, for blended RGB videos and their constituent RGBA layers. Experiments show that both models achieve performance competitive with state-of-the-art methods across distinct tasks and generalize robustly to in-the-wild scenarios, even when trained on fewer than 1,000 videos. Project page: https://houyuanchen111.github.io/UniVidX.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00781", "title": "Map2World: Segment Map Conditioned Text to 3D World Generation", "authors": [ "Jaeyoung Chung", "Suyoung Lee", "Jianfeng Xiang", "Jiaolong Yang", "Kyoung Mu Lee" ], "github": "", "project_page": "", "abstract": "3D world generation is essential for applications such as immersive content creation or autonomous driving simulation. Recent advances in 3D world generation have shown promising results; however, these methods are constrained by grid layouts and suffer from inconsistencies in object scale throughout the entire world. In this work, we introduce a novel framework, Map2World, that first enables 3D world generation conditioned on user-defined segment maps of arbitrary shapes and scales, ensuring global-scale consistency and flexibility across expansive environments. To further enhance the quality, we propose a detail enhancer network that generates fine details of the world. The detail enhancer enables the addition of fine-grained details without compromising overall scene coherence by incorporating global structure information. We design the entire pipeline to leverage strong priors from asset generators, achieving robust generalization across diverse domains, even under limited training data for scene generation. Extensive experiments demonstrate that our method significantly outperforms existing approaches in user-controllability, scale consistency, and content coherence, enabling users to generate 3D worlds under more complex conditions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00503", "title": "End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer", "authors": [ "Wenda Chu", "Bingliang Zhang", "Jiaqi Han", "Yizhuo Li", "Linjie Yang", "Yisong Yue", "Qiushan Guo" ], "github": "", "project_page": "", "abstract": "Autoregressive image modeling relies on visual tokenizers to compress images into compact latent representations. We design an end-to-end training pipeline that jointly optimizes reconstruction and generation, enabling direct supervision from generation results to the tokenizer. This contrasts with prior two-stage approaches that train tokenizers and generative models separately. We further investigate leveraging vision foundation models to improve 1D tokenizers for autoregressive modeling. Our autoregressive generative model achieves strong empirical results, including a state-of-the-art FID score of 1.48 without guidance on ImageNet 256x256 generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00809", "title": "Let ViT Speak: Generative Language-Image Pre-training", "authors": [ "Yan Fang", "Mengcheng Lan", "Zilong Huang", "Weixian Lei", "Yunqing Zhao", "Yujie Zhong", "Yingchen Yu", "Qi She", "Yao Zhao", "Yunchao Wei" ], "github": "", "project_page": "", "abstract": "In this paper, we present Generative Language-Image Pre-training (GenLIP), a minimalist generative pretraining framework for Vision Transformers (ViTs) designed for multimodal large language models (MLLMs). To better align vision encoders with the autoregressive nature of LLMs, GenLIP trains a ViT to predict language tokens directly from visual tokens using a standard language modeling objective, without contrastive batch construction or an additional text decoder. This design offers three key advantages: (1) Simplicity: a single transformer jointly models visual and textual tokens; (2) Scalability: it scales effectively with both data and model size; and (3) Performance: it achieves competitive or superior results across diverse multimodal benchmarks. Trained on 8B samples from Recap-DataComp-1B, GenLIP matches or surpasses strong baselines despite using substantially less pretraining data. After continued pretraining on multi-resolution images at native aspect ratios, GenLIP further improves on detail-sensitive tasks such as OCR and chart understanding, making it a strong foundation for vision encoders in MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00754", "title": "Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring", "authors": [ "Indraneil Paul", "Glava\u0161 Glavas", "Iryna Gurevych" ], "github": "", "project_page": "", "abstract": "Reward models (RMs) have become an indispensable fixture of the language model (LM) post-training playbook, enabling policy alignment and test-time scaling. Research on the application of RMs in code generation, however, has been comparatively sparse, with existing work largely focusing on execution feedback. This choice constrains post-training to optimizing functional correctness over self-contained executable code. In this work, we examine the training and evaluation of multilingual, multi-criteria code RMs. To this end, we first compile Themis-CodeRewardBench, a benchmark to evaluate code RMs across five preference dimensions (i.e., criteria) and eight programming languages, on which we profile 50+ code, math, and general-purpose RMs. Observing the limited proficiency of current RMs beyond scoring for functional correctness, we develop Themis-CodePreference, the largest open-source collection of code preferences to date (more than 350k preference pairs), and use it to train Themis-RM, a suite of multilingual code reward models for flexible multi-criteria scoring, ranging in size from 600M to 32B parameters. Our experiments and ablations demonstrate positive scaling trends, strong cross-lingual transfer when training on diverse preferences, and the importance of multi-criteria training for reliable code reward modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00416", "title": "Learning while Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies", "authors": [ "Yi Wang", "Xinchen Li", "Pengwei Xie", "Pu Yang", "Buqing Nie", "Yunuo Cai", "Qinglin Zhang", "Chendi Qu", "Jeffrey Wu", "Jianheng Song", "Xinlin Ren", "Jingshun Huang", "Mingjie Pan", "Siyuan Feng", "Zhi Chen", "Jianlan Luo" ], "github": "", "project_page": "", "abstract": "Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. Deployed robots encounter distribution shifts, long-tail failures, task variations, and human correction opportunities that fixed demonstration datasets cannot fully capture. We present Learning While Deploying (LWD), a fleet-scale offline-to-online reinforcement learning framework for continual post-training of generalist Vision-Language-Action (VLA) policies. Starting from a pretrained VLA policy, LWD closes the loop between deployment, shared physical experience, policy improvement, and redeployment by using autonomous rollouts and human interventions collected across a robot fleet. To stabilize learning from heterogeneous, sparse-reward fleet data, LWD combines Distributional Implicit Value Learning (DIVL) for robust value estimation with Q-learning via Adjoint Matching (QAM) for policy extraction in flow-based VLA action generators. We validate LWD on a fleet of 16 dual-arm robots across eight real-world manipulation tasks, including semantic grocery restocking and 3--5 minute long-horizon tasks. A single generalist policy improves as fleet experience accumulates, reaching an average success rate of 95%, with the largest gains on long-horizon tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2604.24026", "title": "From Skill Text to Skill Structure: The Scheduling-Structural-Logical Representation for Agent Skills", "authors": [ "Qiliang Liang", "Hansi Wang", "Zhong Liang", "Yang Liu" ], "github": "https://github.com/COOLPKU/SSL", "project_page": "", "abstract": "LLM agents increasingly rely on reusable skills, capability packages that combine instructions, control flow, constraints, and tool calls. In most current agent systems, however, skills are still represented by text-heavy artifacts, including SKILL.md-style documents and structured records whose machine-usable evidence remains embedded largely in natural-language descriptions. This poses a challenge for skill-centered agent systems: managing skill collections and using skills to support agent both require reasoning over invocation interfaces, execution structure, and concrete side effects that are often entangled in a single textual surface. An explicit representation of skill knowledge may therefore help make these artifacts easier for machines to acquire and leverage. Drawing on Memory Organization Packets, Script Theory, and Conceptual Dependency from Schank and Abelson's classical work on linguistic knowledge representation, we introduce what is, to our knowledge, the first structured representation for agent skill artifacts that disentangles skill-level scheduling signals, scene-level execution structure, and logic-level action and resource-use evidence: the Scheduling-Structural-Logical (SSL) representation. We instantiate SSL with an LLM-based normalizer and evaluate it on a corpus of skills in two tasks, Skill Discovery and Risk Assessment, and superiorly outperform the text-only baselines: in Skill Discovery, SSL improves MRR from 0.573 to 0.707; in Risk Assessment, it improves macro F1 from 0.744 to 0.787. These findings reveal that explicit, source-grounded structure makes agent skills easier to search and review. They also suggest that SSL is best understood as a practical step toward more inspectable, reusable, and operationally actionable skill representations for agent systems, rather than as a finished standard or an end-to-end mechanism for managing and using skills.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/COOLPKU/SSL", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2604.23195", "title": "AnalogRetriever: Learning Cross-Modal Representations for Analog Circuit Retrieval", "authors": [ "Yihan Wang", "Lei Li", "Yao Lai", "Jing Wang", "Yan Lu" ], "github": "", "project_page": "", "abstract": "Analog circuit design relies heavily on reusing existing intellectual property (IP), yet searching across heterogeneous representations such as SPICE netlists, schematics, and functional descriptions remains challenging. Existing methods are largely limited to exact matching within a single modality, failing to capture cross-modal semantic relationships. To bridge this gap, we present AnalogRetriever, a unified tri-modal retrieval framework for analog circuit search. We first build a high-quality dataset on top of Masala-CHAI through a two-stage repair pipeline that raises the netlist compile rate from 22\\% to 100\\%. Built on this foundation, AnalogRetriever encodes schematics and descriptions with a vision-language model and netlists with a port-aware relational graph convolutional network, mapping all three modalities into a shared embedding space via curriculum contrastive learning. Experiments show that AnalogRetriever achieves an average Recall@1 of 75.2\\% across all six cross-modal retrieval directions, significantly outperforming existing baselines. When integrated into the AnalogCoder agentic framework as a retrieval-augmented generation module, it consistently improves functional pass rates and enables previously unsolved tasks to be completed. Our code and dataset will be released.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00777", "title": "LASE: Language-Adversarial Speaker Encoding for Indic Cross-Script Identity Preservation", "authors": [ "Venkata Pushpak Teja Menta" ], "github": "https://github.com/praxelhq/lase", "project_page": "", "abstract": "A speaker encoder used in multilingual voice cloning should treat the same speaker identically regardless of which script the audio was uttered in. Off-the-shelf encoders do not, and the failure is accent-conditional. On a 1043-pair Western-accented voice corpus across English, Hindi, Telugu, and Tamil, WavLM-base-plus-sv loses 0.082 absolute cosine similarity when the same voice changes script and ECAPA-TDNN loses 0.105. On a 1369-pair Indian-accented voice corpus, the gap shrinks to 0.006 (WavLM-SV) and 0.044 (ECAPA-TDNN). The leak is largest where it matters most for cross-script TTS: when a system projects a non-Indic-trained voice into Indic scripts. We present LASE (Language-Adversarial Speaker Encoder), a small projection head over frozen WavLM-base-plus trained with two losses: a supervised contrastive loss over voice identity, and a gradient-reversal cross-entropy against a 4-language classifier that pushes the embedding to be language-uninformative while remaining speaker-informative. Trained on 1118 quality-gated cross-script pairs synthesised from 8 commercial multilingual voices, LASE's residual gap is consistent with zero on both corpora (Delta = 0.013 Western, Delta = 0.026 Indian; both bootstrap 95% CIs include zero) and amplifies the cross-script-vs-floor margin 2.4-2.7x over both baselines. An ECAPA+GRL ablation shows the GRL objective improves either backbone but the WavLM choice contributes too. In synthetic multi-speaker diarisation, LASE matches ECAPA-TDNN on cross-script speaker recall (0.788 vs 0.789) with ~100x less training data. We release the r1 checkpoint, both corpora, and the bootstrap recipe.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/praxelhq/lase", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00323", "title": "Online Self-Calibration Against Hallucination in Vision-Language Models", "authors": [ "Minghui Chen", "Chenxu Yang", "Hengjie Zhu", "Dayan Wu", "Zheng Lin", "Qingyi Si" ], "github": "", "project_page": "", "abstract": "Large Vision-Language Models (LVLMs) often suffer from hallucinations, generating descriptions that include visual details absent from the input image. Recent preference alignment methods typically rely on supervision distilled from stronger models such as GPT. However, this offline paradigm introduces a Supervision-Perception Mismatch: the student model is forced to align with fine-grained details beyond its perceptual capacity, learning to guess rather than to see. To obtain reliable self-supervision for online learning, we identify a Generative-Discriminative Gap within LVLMs, where models exhibit higher accuracy on discriminative verification than open-ended generation. Leveraging this capability, we propose Online Self-CAlibRation (OSCAR), a framework that integrates Monte Carlo Tree Search with a Dual-Granularity Reward Mechanism to construct preference data and iteratively refines the model via Direct Preference Optimization. Extensive experiments demonstrate that OSCAR achieves state-of-the-art performance on hallucination benchmarks while improving general multimodal capabilities.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00691", "title": "Learning to Act and Cooperate for Distributed Black-Box Consensus Optimization", "authors": [ "Zi-Bo Qin", "Feng-Feng Wei", "Tai-You Chen", "Wei-Neng Chen" ], "github": "", "project_page": "", "abstract": "Distributed blackbox consensus optimization is a fundamental problem in multi-agent systems, where agents must improve a global objective using only local objective queries and limited neighbor communication. Existing methods largely rely on handcrafted update rules and static cooperation patterns, which often struggle to balance local adaptation, global coordination, and communication efficiency in heterogeneous nonconvex environments. In this paper, we take an initial step toward trajectory-driven self-design for distributed black-box consensus optimization. We first redesign the agent-level swarm dynamics with an adaptive internal mechanism tailored to decentralized consensus settings, improving the balance between exploration, convergence, and local escape. Built on top of this adaptive execution layer, we propose Learning to Act and Cooperate (LACMAS), a trajectorydriven framework in which large language models provide sparse highlevel guidance for shaping both agentinternal action behaviors and agentexternal cooperation patterns from historical optimization trajectories. We further introduce a phased cognitive scheduling strategy to activate different forms of adaptation in a resource-aware manner. Experiments on standard distributed black-box benchmarks and real-world distributed tasks show that LAC-MAS consistently improves solution quality, convergence efficiency, and communication efficiency over strong baselines, suggesting a practical route from handcrafted distributed coordination toward self-designing multi-agent optimization systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2604.23586", "title": "Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling", "authors": [ "Zhen Ye", "Xu Tan", "Aoxiong Yin", "Hongzhan Lin", "Guangyan Zhang", "Peiwen Sun", "Yiming Li", "Chi-Min Chan", "Wei Ye", "Shikun Zhang", "Wei Xue" ], "github": "https://github.com/zhenye234/Talker-T2AV", "project_page": "", "abstract": "Joint audio-video generation models have shown that unified generation yields stronger cross-modal coherence than cascaded approaches. However, existing models couple modalities throughout denoising via pervasive attention, treating high-level semantics and low-level details in a fully entangled manner. This is suboptimal for talking head synthesis: while audio and facial motion are semantically correlated, their low-level realizations (acoustic signals and visual textures) follow distinct rendering processes. Enforcing joint modeling across all levels causes unnecessary entanglement and reduces efficiency. We propose Talker-T2AV, an autoregressive diffusion framework where high-level cross-modal modeling occurs in a shared backbone, while low-level refinement uses modality-specific decoders. A shared autoregressive language model jointly reasons over audio and video in a unified patch-level token space. Two lightweight diffusion transformer heads decode the hidden states into frame-level audio and video latents. Experiments on talking portrait benchmarks show Talker-T2AV outperforms dual-branch baselines in lip-sync accuracy, video quality, and audio quality, achieving stronger cross-modal consistency than cascaded pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhenye234/Talker-T2AV", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2604.27221", "title": "Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction", "authors": [ "Yuxuan Huang", "Yihang Chen", "Zhiyuan He", "Yuxiang Chen", "Ka Yiu Lee", "Huichi Zhou", "Weilin Luo", "Meng Fang", "Jun Wang" ], "github": "https://github.com/web2bigtable/web2bigtable", "project_page": "", "abstract": "Agentic web search increasingly faces two distinct demands: deep reasoning over a single target, and structured aggregation across many entities and heterogeneous sources. Current systems struggle on both fronts. Breadth-oriented tasks demand schema-aligned outputs with wide coverage and cross-entity consistency, while depth-oriented tasks require coherent reasoning over long, branching search trajectories. We introduce Web2BigTable, a multi-agent framework for web-to-table search that supports both regimes. Web2BigTable adopts a bi-level architecture in which an upper-level orchestrator decomposes the task into sub-problems and lower-level worker agents solve them in parallel. Through a closed-loop run--verify--reflect process, the framework jointly improves decomposition and execution over time via persistent, human-readable external memory, with self-evolving updates to each single-agent. During execution, workers coordinate through a shared workspace that makes partial findings visible, allowing them to reduce redundant exploration, reconcile conflicting evidence, and adapt to emerging coverage gaps. Web2BigTable sets a new state of the art on WideSearch, reaching an Avg@4 Success Rate of 38.50 (7.5times the second best at 5.10), Row F1 of 63.53 (+25.03 over the second best), and Item F1 of 80.12 (+14.42 over the second best). It also generalises to depth-oriented search on XBench-DeepSearch, achieving 73.0 accuracy. Code is available at https://github.com/web2bigtable/web2bigtable.", "github_manual": "", "github_regex": "https://github.com/web2bigtable/web2bigtable", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00414", "title": "Trees to Flows and Back: Unifying Decision Trees and Diffusion Models", "authors": [ "Sai Niranjan Ramachandran", "Suvrit Sra" ], "github": "", "project_page": "", "abstract": "Decision trees and diffusion models are ostensibly disparate model classes, one discrete and hierarchical, the other continuous and dynamic. This work unifies the two by establishing a crisp mathematical correspondence between hierarchical decision trees and diffusion processes in appropriate limiting regimes. Our unification reveals a shared optimization principle: Global Trajectory Score Matching (GTSM), for which gradient boosting (in an idealized version) is asymptotically optimal. We underscore the conceptual value of our work through two key practical instantiations: \\treeflow, which achieves competitive generation quality on tabular data with higher fidelity and a 2\\times computational speedup, and \\dsmtree, a novel distillation method that transfers hierarchical decision logic into neural networks, matching teacher performance within 2\\% on many benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00273", "title": "When Do Diffusion Models learn to Generate Multiple Objects?", "authors": [ "Yujin Jeong", "Arnas Uselis", "Iro Laina", "Seong Joon Oh", "Anna Rohrbach" ], "github": "", "project_page": "", "abstract": "Text-to-image diffusion models achieve impressive visual fidelity, yet they remain unreliable in multi-object generation. Despite extensive empirical evidence of these failures, the underlying causes remain unclear. We begin by asking how much of this limitation arises from the data itself. To disentangle data effects, we consider two regimes across different dataset sizes: (1) concept generalization, where each individual concept is observed during training under potentially imbalanced data distributions, and (2) compositional generalization, where specific combinations of concepts are systematically held out. To study these regimes, we introduce mosaic (Multi-Object Spatial relations, AttrIbution, Counting), a controlled framework for dataset generation. By training diffusion models on mosaic, we find that scene complexity plays a dominant role rather than concept imbalance, and that counting is uniquely difficult to learn in low-data regimes. Moreover, compositional generalization collapses as more concept combinations are held out during training. These findings highlight fundamental limitations of diffusion models and motivate stronger inductive biases and data design for robust multi-object compositional generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2604.23774", "title": "Prox-E: Fine-Grained 3D Shape Editing via Primitive-Based Abstractions", "authors": [ "Etai Sella", "Hao Phung", "Nitay Amiel", "Or Litany", "Or Patashnik", "Hadar Averbuch-Elor" ], "github": "https://github.com/microsoft/TRELLIS", "project_page": "", "abstract": "Text-based 2D image editing models have recently reached an impressive level of maturity, motivating a growing body of work that heavily depends on these models to drive 3D edits. While effective for appearance-based modifications, such 2D-centric 3D editing pipelines often struggle with fine-grained 3D editing, where localized structural changes must be applied while strictly preserving an object's overall identity. To address this limitation, we propose Prox-E, a training-free framework that enables fine-grained 3D control through an explicit, primitive-based geometric abstraction. Our framework first abstracts an input 3D shape into a compact set of geometric primitives. A pretrained vision-language model (VLM) then edits this abstraction to specify primitive-level changes. These structural edits are subsequently used to guide a 3D generative model, enabling fine-grained, localized modifications while preserving unchanged regions of the original shape. Through extensive experiments, we demonstrate that our method consistently balances identity preservation, shape quality, and instruction fidelity more effectively than various existing approaches, including 2D-based 3D editors and training-based methods.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/microsoft/TRELLIS", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00553", "title": "Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance", "authors": [ "Minchan Kwon", "Sunghyun Baek", "Minseo Kim", "Jaemyung Yu", "Dongyoon Han", "Junmo Kim" ], "github": "", "project_page": "", "abstract": "Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety. Finding effective and diverse attacks in red-teaming is important, but achieving both is challenging. Generative Flow Networks (GFNs) that perform distribution matching are a promising methods, but they are notorious for training instability and mode collapse. In particular, unstable rewards in red-teaming accelerate mode collapse. We propose Stable-GFN (S-GFN), which eliminates partition function Z estimation in GFN and reduces training instability. S-GFN avoids Z-estimation through pairwise comparisons and employs a robust masking methodology against noisy rewards. Additionally, we propose a fluency stabilizer to prevent the model from getting stuck in local optima that produce gibberish. S-GFN provides more stable training while maintaining the optimal policy of GFN. We demonstrate the overwhelming attack performance and diversity of S-GFN across various settings.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2604.27124", "title": "Better Models, Faster Training: Sigmoid Attention for single-cell Foundation Models", "authors": [ "Vijay Sadashivaiah", "Georgios Dasoulas", "Judith Mueller", "Soumya Ghosh" ], "github": "https://github.com/MSDLLCpapers/triton-sigmoid", "project_page": "", "abstract": "Training stable biological foundation models requires rethinking attention mechanisms: we find that using sigmoid attention as a drop in replacement for softmax attention a) produces better learned representations: on six diverse single-cell datasets, sigmoid achieves 25% higher cell-type separation, better cell-type cohesion metrics, and lower validation loss, b) faster training, models with sigmoid attention train up to 10% faster than their softmax counterparts, and c) more stable training by eliminating inherent sources of instability in softmax attention. We establish that sigmoid attention has globally bounded derivatives (leq 0.25) as opposed to softmax, and a diagonal Jacobian structure in contrast with softmax's dense coupling, which together help alleviate training instabilities. In stress tests on 160M-parameter bidirectional attention models trained without gradient clipping on 8K-token sequences, softmax diverges catastrophically, with gradients exploding by four orders of magnitude, while sigmoid remains stable. Finally, we implement and open-source TritonSigmoid, an efficient GPU kernel that achieves 515 TFLOPS on H100 GPUs, outperforming both FlashAttention-2 and FlashSigmoid, with native padding support, which is essential for biological sequences. Our results establish sigmoid attention as both theoretically grounded and empirically superior for biological foundation models. Code is available at https://github.com/MSDLLCpapers/triton-sigmoid", "github_manual": "", "github_regex": "https://github.com/MSDLLCpapers/triton-sigmoid", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2604.27818", "title": "MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks", "authors": [ "Jona te Lintelo", "Lichao Wu", "Marina Kr\u010dek", "Sengim Karayal\u00e7in", "Stjepan Picek" ], "github": "https://github.com/jonatelintelo/MASCing", "project_page": "", "abstract": "Mixture-of-Experts (MoE) architectures in Large Language Models (LLMs) have significantly reduced inference costs through sparse activation. However, this sparse activation paradigm also introduces new safety challenges. Since only a subset of experts is engaged for each input, model behavior becomes coupled to routing decisions, yielding a difficult-to-control mechanism that can vary across safety-relevant scenarios. At the same time, adapting model behavior through full fine-tuning or retraining is costly, especially when developers need to rapidly configure the same model for different safety objectives. We present MASCing (MoE Activation Steering Configuration), the first framework that enables flexible reconfiguration of MoE behavior across diverse safety scenarios without retraining. MASCing uses an LSTM-based surrogate model to capture cross-layer routing dependencies and map routing logits to downstream behaviors. It then optimizes a steering matrix to identify behavior-relevant expert circuits and, at inference time, applies steering masks to the routing gates to override expert selection. This enables targeted enhancement or suppression of specific behaviors while preserving general language utility. To demonstrate its reconfigurability, we apply MASCing to two different safety-related objectives and observe consistent gains with negligible overhead across seven open-source MoE models. For multi-turn jailbreak defense, it improves the average defense success rate from 52.5% to 83.9%, with gains of up to 89.2%. For adult-content generation, MASCing enables models to comply with such requests that would otherwise be refused, increasing the average generation success rate from 52.6% to 82.0%, with gains of up to 93.0%. These results establish MASCing as a practical, lightweight, and flexible framework for scenario-specific safety reconfiguration in MoE models.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jonatelintelo/MASCing", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2605.00347", "title": "Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning", "authors": [ "Chengshuai Shi", "Wenzhe Li", "Xinran Liang", "Yizhou Lu", "Wenjia Yang", "Ruirong Feng", "Seth Karten", "Ziran Yang", "Zihan Ding", "Gabriel Sarch", "Danqi Chen", "Karthik Narasimhan", "Chi Jin" ], "github": "", "project_page": "", "abstract": "Given the rapidly growing capabilities of vision-language models (VLMs), extending them to interactive decision-making tasks such as video games has emerged as a promising frontier. However, existing approaches either rely on large-scale supervised fine-tuning (SFT) on human trajectories or apply reinforcement learning (RL) only in relatively short-horizon settings (typically around 20--30 turns). In this work, we study RL-based training of VLMs for long-horizon decision-making in Super Mario Land, a visually grounded environment requiring 100+ turns of interaction with coordinated perception, reasoning, and action. We begin with a systematic investigation of key algorithmic components and propose an adapted variant of PPO with a lightweight turn-level critic, which substantially improves training stability and sample efficiency over critic-free methods such as GRPO and Reinforce++. We further show that pretrained VLMs provide strong action priors, significantly improving sample efficiency during RL training and reducing the need for manual design choices such as action engineering, compared to classical deep RL trained from scratch. Building on these insights, we introduce Odysseus, an open training framework for VLM agents, achieving substantial gains across multiple levels of the game and at least 3 times average game progresses than frontier models. Moreover, the trained models exhibit consistent improvements under both in-game and cross-game generalization settings, while maintaining general-domain capabilities. Overall, our results identify key ingredients for making RL stable and effective in long-horizon, multi-modal settings, and provide practical guidance for developing VLMs as embodied agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-04", "arxiv_id": "2604.21984", "title": "Soft Anisotropic Diagrams for Differentiable Image Representation", "authors": [ "Laki Iinbor", "Zhiyang Dou", "Wojciech Matusik" ], "github": "", "project_page": "", "abstract": "We introduce Soft Anisotropic Diagrams (SAD), an explicit and differentiable image representation parameterized by a set of adaptive sites in the image plane. In SAD, each site specifies an anisotropic metric and an additively weighted distance score, and we compute pixel colors as a softmax blend over a small per-pixel top-K subset of sites. We induce a soft anisotropic additively weighted Voronoi partition (i.e., an Apollonius diagram) with learnable per-site temperatures, preserving informative gradients while allowing clear, content-aligned boundaries and explicit ownership. Such a formulation enables efficient rendering by maintaining a per-query top-K map that approximates nearest neighbors under the same shading score, allowing GPU-friendly, fixed-size local computation. We update this list using our top-K propagation scheme inspired by jump flooding, augmented with stochastic injection to provide probabilistic global coverage. Training follows a GPU-first pipeline with gradient-weighted initialization, Adam optimization, and adaptive budget control through densification and pruning. Across standard benchmarks, SAD consistently outperforms Image-GS and Instant-NGP at matched bitrate. On Kodak, SAD reaches 46.0 dB PSNR with 2.2 s encoding time (vs. 28 s for Image-GS), and delivers 4-19 times end-to-end training speedups over state-of-the-art baselines. We demonstrate the effectiveness of SAD by showcasing the seamless integration with differentiable pipelines for forward and inverse problems, efficiency of fast random access, and compact storage.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-05", "arxiv_id": "2604.27660", "title": "From Context to Skills: Can Language Models Learn from Context Skillfully?", "authors": [ "Shuzheng Si", "Haozhe Zhao", "Yu Lei", "Qingyi Wang", "Dingwei Chen", "Zhitong Wang", "Zhenhailong Wang", "Kangyang Luo", "Zheng Wang", "Gang Chen", "Fanchao Qi", "Minjia Zhang", "Maosong Sun" ], "github": "https://github.com/S1s-Z/Ctx2Skill", "project_page": "", "abstract": "Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learning, where LMs directly learn relevant knowledge from the given context. An intuitive solution is inference-time skill augmentation: extracting the rules and procedures from context into natural-language skills. However, constructing such skills for context learning scenarios faces two challenges: the prohibitive cost of manual skill annotation for long, technically dense contexts, and the lack of external feedback for automated skill construction. In this paper, we propose Ctx2Skill, a self-evolving framework that autonomously discovers, refines, and selects context-specific skills without human supervision or external feedback. At its core, a multi-agent self-play loop has a Challenger that generates probing tasks and rubrics, a Reasoner that attempts to solve them guided by an evolving skill set, and a neutral Judge that provides binary feedback. Crucially, both the Challenger and the Reasoner evolve through accumulated skills: dedicated Proposer and Generator agents analyze failure cases and synthesize them into targeted skill updates for both sides, enabling automated skill discovery and refinement. To prevent adversarial collapse caused by increasingly extreme task generation and over-specialized skill accumulation, we further introduce a Cross-time Replay mechanism that identifies the skill set achieving the best balance across representative cases for the Reasoner side, ensuring robust and generalizable skill evolution. The resulting skills can be plugged into any language model to obtain better context learning capability. Evaluated on four context learning tasks from CL-bench, Ctx2Skill consistently improves solving rates across backbone models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/S1s-Z/Ctx2Skill", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2405.13729", "title": "ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models", "authors": [ "Rui Xu", "Jiepeng Wang", "Hao Pan", "Yang Liu", "Xin Tong", "Shiqing Xin", "Changhe Tu", "Taku Komura", "Wenping Wang" ], "github": "https://github.com/Xrvitd/ComboStoc", "abstract": "In this paper, we study an under-explored but important factor of diffusion generative models, i.e., the combinatorial complexity. Data samples are generally high-dimensional, and for various structured generation tasks, additional attributes are combined to associate with data samples. We show that the space spanned by the combination of dimensions and attributes can be insufficiently covered by existing training schemes of diffusion generative models, potentially limiting test time performance. We present a simple fix to this problem by constructing stochastic processes that fully exploit the combinatorial structures, hence the name ComboStoc. Using this simple strategy, we show that network training is significantly accelerated across diverse data modalities, including images and 3D structured shapes. Moreover, ComboStoc enables a new way of test time generation which uses asynchronous time steps for different dimensions and attributes, thus allowing for varying degrees of control over them. Our code is available at: https://github.com/Xrvitd/ComboStoc", "project_page": "https://ruixu.me/html/ComboStoc/index.html", "github_manual": "", "github_regex": "https://github.com/Xrvitd/ComboStoc", "github_paper_md": "", "github_api": "https://github.com/Xrvitd/ComboStoc", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2605.02240", "title": "PhysicianBench: Evaluating LLM Agents in Real-World EHR Environments", "authors": [ "Ruoqi Liu", "Imran Q. Mohiuddin", "Austin J. Schoeffler", "Kavita Renduchintala", "Ashwin Nayak", "Prasantha L. Vemu", "Shivam C. Vedak", "Kameron C. Black", "John L. Havlik", "Isaac Ogunmola", "Stephen P. Ma", "Roopa Dhatt", "Jonathan H. Chen" ], "github": "https://github.com/HealthRex/PhysicianBench", "abstract": "We introduce PhysicianBench, a benchmark for evaluating LLM agents on physician tasks grounded in real clinical setting within electronic health record (EHR) environments. Existing medical agent benchmarks primarily focus on static knowledge recall, single-step atomic actions, or action intent without verifiable execution against the environment. As a result, they fail to capture the long-horizon, composite workflows that characterize real clinical systems. PhysicianBench comprises 100 long-horizon tasks adapted from real consultation cases between primary care and subspecialty physicians, with each task independently reviewed by a separate panel of physicians. Tasks are instantiated in an EHR environment with real patient records and accessed through the same standard APIs used by commercial EHR vendors. Tasks span 21 specialties (e.g., cardiology, endocrinology, oncology, psychiatry) and diverse workflow types (e.g., diagnosis interpretation, medication prescribing, treatment planning), requiring an average of 27 tool calls per task. Solving each task requires retrieving data across encounters, reasoning over heterogeneous clinical information, executing consequential clinical actions, and producing clinical documentation. Each task is decomposed into structured checkpoints (670 in total across the benchmark) capturing distinct stages of completion graded by task-specific scripts with execution-grounded verification. Across 13 proprietary and open-source LLM agents, the best-performing model achieves only 46% success rate (pass@1), while open-source models reach at most 19%, revealing a substantial gap between current agent capabilities and the demands of real-world clinical workflows. PhysicianBench provides a realistic and execution-grounded benchmark for measuring progress toward autonomous clinical agents.", "project_page": "https://healthrex.github.io/PhysicianBench/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HealthRex/PhysicianBench", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2605.02881", "title": "MolmoAct2: Action Reasoning Models for Real-world Deployment", "authors": [ "Haoquan Fang", "Jiafei Duan", "Donovan Clay", "Sam Wang", "Shuo Liu", "Weikai Huang", "Xiang Fan", "Wei-Chuan Tsai", "Shirui Chen", "Yi Ru Wang", "Shanli Xing", "Jaemin Cho", "Jae Sung Park", "Ainaz Eftekhar", "Peter Sushko", "Karen Farley", "Angad Wadhwa", "Cole Harrison", "Winson Han", "Ying-Chun Lee", "Eli VanderBilt", "Rose Hendrix", "Suveen Ellawela", "Lucas Ngoo", "Joyce Chai", "Zhongzheng Ren", "Ali Farhadi", "Dieter Fox", "Ranjay Krishna" ], "github": "https://github.com/allenai/molmoact2", "abstract": "Vision-Language-Action (VLA) models aim to provide a single generalist controller for robots, but today's systems fall short on the criteria that matter for real-world deployment. Frontier models are closed, open-weight alternatives are tied to expensive hardware, reasoning-augmented policies pay prohibitive latency for their grounding, and fine-tuned success rates remain below the threshold for dependable use. We present MolmoAct2, a fully open action reasoning model built for practical deployment, advancing its predecessor along five axes. We introduce MolmoER, a VLM backbone specialized for spatial and embodied reasoning, trained on a 3.3M-sample corpus with a specialize-then-rehearse recipe. We release three new datasets spanning low-to-medium cost platforms, including MolmoAct2-BimanualYAM, 720 hours of teleoperated bimanual trajectories that constitute the largest open bimanual dataset to date, together with quality-filtered Franka (DROID) and SO100/101 subsets. We provide OpenFAST, an open-weight, open-data action tokenizer trained on millions of trajectories across five embodiments. We redesign the architecture to graft a flow-matching continuous-action expert onto a discrete-token VLM via per-layer KV-cache conditioning. Finally, we propose MolmoThink, an adaptive-depth reasoning variant that re-predicts depth tokens only for scene regions that change between timesteps, retaining geometric grounding at a fraction of prior latency. In the most extensive empirical study of any open VLA to date, spanning 7 simulation and real-world benchmarks, MolmoAct2 outperforms strong baselines including Pi-05, while MolmoER surpasses GPT-5 and Gemini Robotics ER-1.5 across 13 embodied-reasoning benchmarks. We release model weights, training code, and complete training data. Project page: https://allenai.org/blog/molmoact2", "project_page": "https://allenai.org/blog/molmoact2", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/molmoact2", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2605.00877", "title": "OceanPile: A Large-Scale Multimodal Ocean Corpus for Foundation Models", "authors": [ "Yida Xue", "Ningyu Zhang", "Tingwei Wu", "Zhe Ma", "Daxiong Ji", "Zhao Wang", "Guozhou Zheng", "Huajun Chen" ], "github": "https://github.com/OceanGPT/OceanPile", "abstract": "The vast and underexplored ocean plays a critical role in regulating global climate and supporting marine biodiversity, yet artificial intelligence has so far delivered limited impact in this domain due to a fundamental data bottleneck. Specifically, ocean data are highly fragmented across disparate sources and inherently exhibit multi-modal, high-noise, and weakly labeled characteristics, lacking unified schemas and semantic alignment. Although Multimodal Large Language Models (MLLMs) have achieved remarkable success in general domains, their application to ocean science remains severely constrained by the absence of large-scale, well-aligned multimodal datasets tailored to marine environments. To bridge this gap, we introduce OceanPile, a large-scale multimodal corpus designed for ocean foundation models. It comprises three key components: OceanCorpus, a unified collection integrating sonar data, underwater imagery, marine science visuals, and scientific text from diverse authoritative sources; OceanInstruction, a high-quality instruction dataset synthesized via a novel pipeline guided by a hierarchical Ocean Concept Knowledge Graph; and OceanBenchmark, a manually curated evaluation benchmark for rigorous assessment. We establish a multi-stage quality control process to ensure scientific validity and alignment across modalities. Experimental validation demonstrates significant performance improvements for models trained on our data. All datasets are publicly released to advance the field of marine artificial intelligence and empower domain-specific MLLMs.", "project_page": "http://data.oceangpt.blue/en/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OceanGPT/OceanPile", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2605.02222", "title": "Generative Modeling with Orbit-Space Particle Flow Matching", "authors": [ "Sinan Wang", "Jinjin He", "Shenyifan Lu", "Ruicheng Wang", "Greg Turk", "Bo Zhu" ], "github": "", "project_page": "", "abstract": "We present Orbit-Space Geometric Probability Paths (OGPP), a particle-native flow-matching framework for generative modeling of particle systems. OGPP is motivated by two insights: (i) particles are defined up to permutation symmetries, so anonymous indexing inflates per-index target variance and yields curved, hard-to-learn flows; and (ii) particles live in physical space, so the flow terminal velocity has physical meaning and can encode geometric attributes, e.g., surface normals. OGPP instantiates three key components: (1) orbit-space canonicalization of the probability-path terminal endpoint, (2) particle index embeddings for role specialization, and (3) geometric probability paths with arc-length-aware terminal velocities that generate normals as a byproduct of the flow. We evaluate OGPP on minimal-surface benchmarks, where it reduces metric error by up to two orders of magnitude in a single inference step; on ShapeNet, where it matches the state of the art with 5x fewer steps and reaches airplane EMD comparable to DiT-3D with 26x fewer parameters and 5x fewer steps; and on single-shape encoding, where it produces normals and reconstructions competitive with 6D generators while operating entirely in 3D.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-05", "arxiv_id": "2605.02730", "title": "Perceptual Flow Network for Visually Grounded Reasoning", "authors": [ "Yangfu Li", "Yuning Gong", "Hongjian Zhan", "Teng Li", "Yuanhuiyi Lyu", "Tianyi Chen", "Qi Liu", "Ziyuan Huang", "Zhihang Zhong", "Dandan Zheng", "Yue Lu" ], "github": "", "project_page": "", "abstract": "Despite the success of Large-Vision Language Models (LVLMs), general optimization objectives (e.g., standard MLE) fail to constrain visual trajectories, leading to language bias and hallucination. To mitigate this, current methods introduce geometric priors from visual experts as additional supervision. However, we observe that such supervision is typically suboptimal: it is biased toward geometric precision and offers limited reasoning utility. To bridge this gap, we propose Perceptual Flow Network (PFlowNet), which eschews rigid alignment with the expert priors and achieves interpretable yet more effective visual reasoning. Specifically, PFlowNet decouples perception from reasoning to establish a self-conditioned generation process. Based on this, it integrates multi-dimensional rewards with vicinal geometric shaping via variational reinforcement learning, thereby facilitating reasoning-oriented perceptual behaviors while preserving visual reliability. PFlowNet delivers a provable performance guarantee and competitive empirical results, particularly setting new SOTA records on V* Bench (90.6%) and MME-RealWorld-lite (67.0%).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-05", "arxiv_id": "2605.02661", "title": "AcademiClaw: When Students Set Challenges for AI Agents", "authors": [ "Junjie Yu", "Pengrui Lu", "Weiye Si", "Hongliang Lu", "Jiabao Wu", "Kaiwen Tao", "Kun Wang", "Lingyu Yang", "Qiran Zhang", "Xiuting Guo", "Xuanyu Wang", "Yang Wang", "Yanjie Wang", "Yi Yang", "Zijian Hu", "Ziyi Yang", "Zonghan Zhou", "Binghao Qiang", "Borui Zhang", "Chenning Li", "Enchang Zhang", "Feifan Chen", "Feng Jian", "Fengyin Sun", "Hao Qiu", "Hao Zheng", "Haoran Zhu", "Hongyu Liu", "Jianbin Deng", "Jiaxin Song", "Jiaying Chi", "Jiayou Shi", "Jie Fang", "Jinghui Zhong", "Jingyu Zhou", "Jinze Li", "Junfeng Yi", "Junyan Yu", "Junzhi Xue", "Ni Song", "Pengyi Chen", "Qi Chen", "Quansheng Li", "Rui Tao", "Shenghai Gong", "Shenhang Lu", "Tianqi Shen", "Tianxiang Zhu", "Tiehan Kang", "Tingyu Li", "Wendi Wu", "Xiao Shen", "Xiao Zhou", "Xiaotao Zhang", "Xinrong Li", "Xuankun Yang", "Xun Zhang", "Yan Li", "Ye Lu", "Yi Wang", "Yibo Zhou", "Yichi Zhang", "Yihao Sun", "Yijun Huang", "Yixin Zhu", "Yixuan Wu", "Yuchen Sun", "Yue Wu", "Yuheng Sun", "Yukun Li", "Yutian Tu", "Yuxuan Qin", "Yuzhuo Wu", "Zeyu Li", "Zhengyu Lou", "Zhenning Ran", "Zizhu He", "Pengfei Liu" ], "github": "https://github.com/GAIR-NLP/AcademiClaw", "project_page": "", "abstract": "Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant-level tasks, leaving the academic-level capabilities of OpenClaw largely unexamined. We introduce AcademiClaw, a bilingual benchmark of 80 complex, long-horizon tasks sourced directly from university students' real academic workflows -- homework, research projects, competitions, and personal projects -- that they found current AI agents unable to solve effectively. Curated from 230 student-submitted candidates through rigorous expert review, the final task set spans 25+ professional domains, ranging from olympiad-level mathematics and linguistics problems to GPU-intensive reinforcement learning and full-stack system debugging, with 16 tasks requiring CUDA GPU execution. Each task executes in an isolated Docker sandbox and is scored on task completion by multi-dimensional rubrics combining six complementary techniques, with an independent five-category safety audit providing additional behavioral analysis. Experiments on six frontier models show that even the best achieves only a 55\\% pass rate. Further analysis uncovers sharp capability boundaries across task domains, divergent behavioral strategies among models, and a disconnect between token consumption and output quality, providing fine-grained diagnostic signals beyond what aggregate metrics reveal. We hope that AcademiClaw and its open-sourced data and code can serve as a useful resource for the OpenClaw community, driving progress toward agents that are more capable and versatile across the full breadth of real-world academic demands. All data and code are available at https://github.com/GAIR-NLP/AcademiClaw.", "github_manual": "", "github_regex": "https://github.com/GAIR-NLP/AcademiClaw", "github_paper_md": "", "github_api": "https://github.com/GAIR-NLP/AcademiClaw", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2605.02178", "title": "T^2PO: Uncertainty-Guided Exploration Control for Stable Multi-Turn Agentic Reinforcement Learning", "authors": [ "Haixin Wang", "Hejie Cui", "Chenwei Zhang", "Xin Liu", "Shuowei Jin", "Shijie Geng", "Xinyang Zhang", "Nasser Zalmout", "Zhenyu Shi", "Yizhou Sun" ], "github": "https://github.com/WillDreamer/T2PO", "project_page": "", "abstract": "Recent progress in multi-turn reinforcement learning (RL) has significantly improved reasoning LLMs' performances on complex interactive tasks. Despite advances in stabilization techniques such as fine-grained credit assignment and trajectory filtering, instability remains pervasive and often leads to training collapse. We argue that this instability stems from inefficient exploration in multi-turn settings, where policies continue to generate low-information actions that neither reduce uncertainty nor advance task progress. To address this issue, we propose Token- and Turn-level Policy Optimization (T^2PO), an uncertainty-aware framework that explicitly controls exploration at fine-grained levels. At the token level, T^2PO monitors uncertainty dynamics and triggers a thinking intervention once the marginal uncertainty change falls below a threshold. At the turn level, T^2PO identifies interactions with negligible exploration progress and dynamically resamples such turns to avoid wasted rollouts. We evaluate T^2PO in diverse environments, including WebShop, ALFWorld, and Search QA, demonstrating substantial gains in training stability and performance improvements with better exploration efficiency. Code is available at: https://github.com/WillDreamer/T2PO.", "github_manual": "", "github_regex": "https://github.com/WillDreamer/T2PO", "github_paper_md": "", "github_api": "https://github.com/WillDreamer/T2PO", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2605.01725", "title": "Motion-Aware Caching for Efficient Autoregressive Video Generation", "authors": [ "Jing Xu", "Yuexiao Ma", "Songwei Liu", "Xuzhe Zheng", "Shiwei Liu", "Chenqian Yan", "Xiawu Zheng", "Rongrong Ji", "Fei Chao", "Xing Wang" ], "github": "https://github.com/ywlq/MotionCache", "project_page": "", "abstract": "Autoregressive video generation paradigms offer theoretical promise for long video synthesis, yet their practical deployment is hindered by the computational burden of sequential iterative denoising. While cache reuse strategies can accelerate generation by skipping redundant denoising steps, existing methods rely on coarse-grained chunk-level skipping that fails to capture fine-grained pixel dynamics. This oversight is critical: pixels with high motion require more denoising steps to prevent error accumulation, while static pixels tolerate aggressive skipping. We formalize this insight theoretically by linking cache errors to residual instability, and propose MotionCache, a motion-aware cache framework that exploits inter-frame differences as a lightweight proxy for pixel-level motion characteristics. MotionCache employs a coarse-to-fine strategy: an initial warm-up phase establishes semantic coherence, followed by motion-weighted cache reuse that dynamically adjusts update frequencies per token. Extensive experiments on state-of-the-art models like SkyReels-V2 and MAGI-1 demonstrate that MotionCache achieves significant speedups of 6.28times and 1.64times respectively, while effectively preserving generation quality (VBench: 1%downarrow and 0.01%downarrow respectively). The code is available at https://github.com/ywlq/MotionCache.", "github_manual": "", "github_regex": "https://github.com/ywlq/MotionCache", "github_paper_md": "", "github_api": "https://github.com/ywlq/MotionCache", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2605.00932", "title": "Code World Model Preparedness Report", "authors": [ "Daniel Song", "Peter Ney", "Cristina Menghini", "Faizan Ahmad", "Aidan Boyd", "Nathaniel Li", "Ziwen Han", "Jean-Christophe Testud", "Saisuke Okabayashi", "Maeve Ryan", "Jinpeng Miao", "Hamza Kwisaba", "Felix Binder", "Spencer Whitman", "Jim Gust", "Esteban Arcaute", "Dhaval Kapil", "Jacob Kahn", "Ayaz Minhas", "Tristan Goodman", "Lauren Deason", "Alexander Vaughan", "Shengjia Zhao", "Summer Yue" ], "github": "", "abstract": "This report documents the preparedness assessment of Code World Model (CWM), a model for code generation and reasoning about code from Meta. We conducted pre-release testing across domains identified in our Frontier AI Framework as potentially presenting catastrophic risks, and also evaluated the model's misaligned propensities. Our assessment found that CWM does not pose additional frontier risks beyond those present in the current AI ecosystem. We therefore release it as an open-weight model.", "project_page": "https://ai.meta.com/research/publications/cwm-an-open-weights-llm-for-research-on-code-generation-with-world-models/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-05", "arxiv_id": "2605.00529", "title": "Hierarchical Abstract Tree for Cross-Document Retrieval-Augmented Generation", "authors": [ "Ziwen Zhao", "Menglin Yang" ], "github": "https://github.com/Newiz430/Psi-RAG", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) enhances large language models with external knowledge, and tree-based RAG organizes documents into hierarchical indexes to support queries at multiple granularities. However, existing Tree-RAG methods designed for single-document retrieval face critical challenges in scaling to cross-document multi-hop questions: (1) poor distribution adaptability, where k-means clustering introduces noise due to rigid distribution assumptions; (2) structural isolation, as tree indexes lack explicit cross-document connections; and (3) coarse abstraction, which obscures fine-grained details. To address these limitations, we propose \u03a8-RAG, a tree-RAG framework with two key components. First, a hierarchical abstract tree index built through an iterative \"merging and collapse\" process that adapts to data distributions without a priori assumption. Second, a multi-granular retrieval agent that intelligently interacts with the knowledge base with reorganized queries and an agent-powered hybrid retriever. \u03a8-RAG supports diverse tasks from token-level question answering to document-level summarization. On cross-document multi-hop QA benchmarks, it outperforms RAPTOR by 25.9% and HippoRAG 2 by 7.4% in average F1 score. Code is available at https://github.com/Newiz430/Psi-RAG.", "github_manual": "", "github_regex": "https://github.com/Newiz430/Psi-RAG", "github_paper_md": "", "github_api": "https://github.com/Newiz430/Psi-RAG", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2604.28075", "title": "Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language Modeling", "authors": [ "Ansar Aynetdinov", "Patrick Haller", "Alan Akbik" ], "github": "", "project_page": "", "abstract": "Recent research has shown that filtering massive English web corpora into high-quality subsets significantly improves training efficiency. However, for high-resource non-English languages like German, French, or Japanese, aggressive filtering creates a strategic dilemma: should practitioners prioritize diversity by training once on large amounts of lightly filtered web data, or prioritize quality by strictly filtering for a high-quality core and repeating it over multiple epochs? We investigate this trade-off for German by constructing hierarchical quality filters applied to 500M web documents, comparing multi-epoch training on the filtered subsets against single-pass training on a diverse corpus. Our experiments across multiple model scales and token budgets show that repeating high-quality data consistently outperforms single-pass training on larger, less filtered sets. Notably, the performance gap persists even after 7 epochs. Our findings suggest that for non-English LLMs, semantic concentration through quality filtering offers a more viable path to efficient language modeling than simply maximizing unique data volume. We release our German language models (called Boldt), as well as our cleaned evaluation benchmarks to the research community. Our experiments indicate that they achieve state-of-the-art results despite training on 10-360x fewer tokens than comparable models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-05", "arxiv_id": "2605.00814", "title": "Persistent Visual Memory: Sustaining Perception for Deep Generation in LVLMs", "authors": [ "Siyuan Huang", "Xiaoye Qu", "Yafu Li", "Tong Zhu", "Zefeng He", "Muxin Fu", "Daizong Liu", "Wei-Long Zheng", "Yu Cheng" ], "github": "https://github.com/huaixuheqing/PVM", "project_page": "", "abstract": "While autoregressive Large Vision-Language Models (LVLMs) demonstrate remarkable proficiency in multimodal tasks, they face a \"Visual Signal Dilution\" phenomenon, where the accumulation of textual history expands the attention partition function, causing visual attention to decay inversely with generated sequence length. To counteract this, we propose Persistent Visual Memory (PVM), a lightweight learnable module designed to ensure sustained, on-demand visual perception. Integrated as a parallel branch alongside the Feed-Forward Network (FFN) in LVLMs, PVM establishes a distance-agnostic retrieval pathway that directly provides visual embeddings for precise visual perception, thereby structurally mitigating the signal suppression inherent to deep generation. Extensive experiments on Qwen3-VL models demonstrate that PVM brings notable improvements with negligible parameter overhead, delivering consistent average accuracy gains across both 4B and 8B scales, particularly in complex reasoning tasks that demand persistent visual perception. Furthermore, in-depth analysis reveals that PVM can resist length-induced signal decay and accelerate internal prediction convergence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/huaixuheqing/PVM", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2604.25154", "title": "Prior-Aligned Data Cleaning for Tabular Foundation Models", "authors": [ "Laure Berti-Equille" ], "github": "https://github.com/LaureBerti/Learn2Clean", "project_page": "", "abstract": "Tabular Foundation Models (TFMs) achieve state-of-the-art zero-shot accuracy on small tabular datasets by meta-learning over synthetic data-generating processes -- making them highly attractive for practitioners who cannot afford large annotated corpora. However, their in-context learning mechanism assumes approximately clean inputs: missing values, outliers, and duplicates in the real-world data create a prior mismatch that degrades both accuracy and confidence calibration simultaneously. Correcting this mismatch requires sequential decisions over cleaning operators whose interactions no static preprocessing rule can anticipate -a natural fit for reinforcement learning~(RL). We introduce L2C2, the first deep RL framework framing tabular data cleaning as prior alignment: a learned policy sequences operators to minimize the distributional gap between dirty input and the TFM's synthetic prior. Six experiments on ten OpenML benchmark datasets establish: 1) three of seven reward designs collapse to degenerate trivial cleaning strategies -- principled reward engineering is scientifically non-trivial; 2) the novel TFMAwareReward reward we propose selects structurally distinct pipelines on 4/10 datasets and achieves higher TabPFN accuracy on those diverging cases (mean 0.851 vs. 0.843; Wilcoxon p=0.063, n=4) while never underperforming; 3) parameterized cleaning actions improve best-found pipeline reward on 9/10 datasets (Wilcoxon p=0.004); and 4) a policy pre-trained on one single source dataset exceeds scratch training at the 2,000-step fine-tuning checkpoint on all three held-out datasets (up to +28.8% after full fine-tuning) demonstrating cross-dataset transfer of prior-alignment knowledge. These findings establish that prior alignment is a principled data preparation strategy for TFM deployment on real-world tabular data.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LaureBerti/Learn2Clean", "github_api": "https://github.com/LaureBerti/Learn2Clean", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2605.01428", "title": "Hallucinations Undermine Trust; Metacognition is a Way Forward", "authors": [ "Gal Yona", "Mor Geva", "Yossi Matias" ], "github": "", "project_page": "", "abstract": "Despite significant strides in factual reliability, errors -- often termed hallucinations -- remain a major concern for generative AI, especially as LLMs are increasingly expected to be helpful in more complex or nuanced setups. Yet even in the simplest setting -- factoid question-answering with clear ground truth-frontier models without external tools continue to hallucinate. We argue that most factuality gains in this domain have come from expanding the model's knowledge boundary (encoding more facts) rather than improving awareness of that boundary (distinguishing known from unknown). We conjecture that the latter is inherently difficult: models may lack the discriminative power to perfectly separate truths from errors, creating an unavoidable tradeoff between eliminating hallucinations and preserving utility. This tradeoff dissolves under a different framing. If we understand hallucinations as confident errors -- incorrect information delivered without appropriate qualification -- a third path emerges beyond the answer-or-abstain dichotomy: expressing uncertainty. We propose faithful uncertainty: aligning linguistic uncertainty with intrinsic uncertainty. This is one facet of metacognition -- the ability to be aware of one's own uncertainty and to act on it. For direct interaction, acting on uncertainty means communicating it honestly; for agentic systems, it becomes the control layer governing when to search and what to trust. Metacognition is thus essential for LLMs to be both trustworthy and capable; we conclude by highlighting open problems for progress towards this objective.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-05", "arxiv_id": "2605.02028", "title": "Counting as a minimal probe of language model reliability", "authors": [ "Tianxiang Dai", "Jonathan Fan" ], "github": "https://github.com/txdai/Counting-as-a-minimal-probe-of-LM-reliability", "abstract": "Large language models perform strongly on benchmarks in mathematical reasoning, coding and document analysis, suggesting a broad ability to follow instructions. However, it remains unclear whether such success reflects general logical competence, repeated application of learned procedures, or pattern matching that mimics rule execution. We investigate this question by introducing Stable Counting Capacity, an assay in which models count repeated symbols until failure. The assay removes knowledge dependencies, semantics and ambiguity from evaluation, avoids lexical and tokenization confounds, and provides a direct measure of procedural reliability beyond standard knowledge-based benchmarks. Here we show, across more than 100 model variants, that stable counting capacity remains far below advertised context limits. Model behavior is consistent neither with open-ended logic nor with stable application of a learned rule, but instead with use of a finite set of count-like internal states, analogous to counting on fingers. Once this resource is exhausted, the appearance of rule following disappears and exact execution collapses into guessing, even with additional test-time compute. These findings show that fluent performance in current language models does not guarantee general, reliable rule following.", "project_page": "https://txdai.github.io/counting-reliability/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/txdai/Counting-as-a-minimal-probe-of-LM-reliability", "github_api": "https://github.com/txdai/Counting-as-a-minimal-probe-of-LM-reliability", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2605.00632", "title": "BlenderRAG: High-Fidelity 3D Object Generation via Retrieval-Augmented Code Synthesis", "authors": [ "Massimo Rondelli", "Francesco Pivi", "Maurizio Gabbrielli" ], "github": "https://github.com/MaxRondelli/BlenderRAG", "project_page": "", "abstract": "Automatic generation of executable Blender code from natural language remains challenging, with state-of-the-art LLMs producing frequent syntactic errors and geometrically inconsistent objects. We present BlenderRAG, a retrieval-augmented generation system that operates on a curated multimodal dataset of 500 expert-validated examples (text, code, image) across 50 object categories. By retrieving semantically similar examples during generation, BlenderRAG improves compilation success rates from 40.8% to 70.0% and semantic normalized alignment from 0.41 to 0.77 (CLIP similarity) across four state-of-the-art LLMs, without requiring fine-tuning or specialized hardware, making it immediately accessible for deployment. The dataset and code will be available at https://github.com/MaxRondelli/BlenderRAG.", "github_manual": "", "github_regex": "https://github.com/MaxRondelli/BlenderRAG", "github_paper_md": "", "github_api": "https://github.com/MaxRondelli/BlenderRAG", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2605.01711", "title": "Linear-Time Global Visual Modeling without Explicit Attention", "authors": [ "Ruize He", "Dongchen Han", "Gao Huang" ], "github": "https://github.com/LeapLabTHU/WeightFormer", "project_page": "", "abstract": "Existing research largely attributes the global sequence modeling capability of Transformers to the explicit computation of attention weights, a process that inherently incurs quadratic computational complexity. In this work, we offer a novel perspective: we demonstrate that attention can be mathematically reframed as a Multi-Layer Perceptron (MLP) equipped with dynamically predicted parameters. Through this lens, we explain attention's global modeling power not as explicit token-wise aggregation, but as an implicit process where dynamically generated parameters act as a compressed representation of the global context. Inspired by this insight, we investigate a fundamental question: can we achieve Transformer-level sequence global modeling entirely through dynamic parameterization while maintaining linear complexity, effectively replacing explicit attention? To explore this, we design various dynamic parameter prediction strategies and integrate them into standard network layers. Extensive empirical studies on vision models demonstrate that dynamic parameterization can indeed serve as a highly effective, linear-complexity alternative to explicit attention, opening new pathways for efficient sequence modeling. Code is available at https://github.com/LeapLabTHU/WeightFormer.", "github_manual": "", "github_regex": "https://github.com/LeapLabTHU/WeightFormer", "github_paper_md": "", "github_api": "https://github.com/LeapLabTHU/WeightFormer", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2605.02291", "title": "A Hybrid Approach for Closing the Sim2real Appearance Gap in Game Engine Synthetic Datasets", "authors": [ "Stefanos Pasios" ], "github": "https://github.com/stefanos50/Hybrid-Sim2Real", "project_page": "", "abstract": "Video game engines have been an important source for generating large volumes of visual synthetic datasets for training and evaluating computer vision algorithms that are to be deployed in the real world. While the visual fidelity of modern game engines has been significantly improved with technologies such as ray-tracing, a notable sim2real appearance gap between the synthetic and the real-world images still remains, which limits the utilization of synthetic datasets in real-world applications. In this letter, we investigate the ability of a state-of-the-art image generation and editing diffusion model (FLUX.2-4B Klein) to enhance the photorealism of synthetic datasets and compare its performance against a traditional image-to-image translation model (REGEN). Furthermore, we propose a hybrid approach that combines the strong geometry and material transformations of diffusion-based methods with the distribution-matching capabilities of image-to-image translation techniques. Through experiments, it is demonstrated that REGEN outperforms FLUX.2-4B Klein and that by combining both FLUX.2-4B Klein and REGEN models, better visual realism can be achieved compared to using each model individually, while maintaining semantic consistency. The code is available at: https://github.com/stefanos50/Hybrid-Sim2Real", "github_manual": "", "github_regex": "https://github.com/stefanos50/Hybrid-Sim2Real", "github_paper_md": "", "github_api": "https://github.com/stefanos50/Hybrid-Sim2Real", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2604.27582", "title": "Assessing Pancreatic Ductal Adenocarcinoma Vascular Invasion: the PDACVI Benchmark", "authors": [ "M. Riera-Mar\u00edn", "O. K. Sikha", "J. Rodr\u00edguez-Comas", "M. S. May", "T. Kirscher", "X. Coubez", "P. Meyer", "S. Faisan", "Z. Pan", "X. Zhou", "X. Liang", "C. H\u00e9mon", "V. Boussot", "J. -L. Dillenseger", "J. -C. Nunes", "K. -C. Kahl", "C. L\u00fcth", "J. Traub", "P. -H. Conze", "M. M. Duh", "A. Aubanell", "R. de Figueiredo Cardoso", "S. Egger-Hackenschmidt", "J. Garc\u00eda-L\u00f3pez", "M. A. Gonz\u00e1lez-Ballester", "A. Galdran" ], "github": "https://github.com/SYCAI-Technologies/curvas-challenge/tree/main/CURVAS-PDACVI_2025/evaluation_metrics", "project_page": "", "abstract": "Surgical resection remains the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC), and eligibility depends on accurate assessment of vascular invasion (VI), i.e., tumor extension into adjacent critical vessels. Despite its importance for preoperative staging and surgical planning, computational VI assessment remains underexplored. Two major challenges are the lack of public datasets and the diagnostic ambiguity at the tumor-vessel interface, which leads to substantial inter-rater variability even among expert radiologists. To address these limitations, we introduce the CURVAS-PDACVI Dataset and Challenge, an open benchmark for uncertainty-aware AI in PDAC staging based on a densely annotated dataset with five independent expert annotations per scan. We also propose a multi-metric evaluation framework that extends beyond spatial overlap to include probabilistic calibration and VI assessment. Evaluation of six state-of-the-art methods shows that strong global volumetric overlap does not necessarily translate into reliable performance at clinically critical tumor-vessel interfaces. In particular, methods optimized for binary segmentation perform competitively on average overlap metrics, but often degrade in high-complexity cases with low expert consensus, either collapsing in volume or overextending at uncertain boundaries. In contrast, methods that model inter-rater disagreement produce better calibrated probabilistic maps and show greater robustness in these ambiguous cases. The benchmark highlights the limitations of volumetric accuracy as a proxy for localized surgical utility, motivating uncertainty-aware probabilistic models for preoperative decision-making.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SYCAI-Technologies/curvas-challenge/tree/main/CURVAS-PDACVI_2025/evaluation_metrics", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-05", "arxiv_id": "2605.01214", "title": "Agentic AI Systems Should Be Designed as Marginal Token Allocators", "authors": [ "Siqi Zhu" ], "github": "", "project_page": "", "abstract": "This position paper argues that agentic AI systems should be designed and evaluated as marginal token allocation economies rather than as text generators priced by the unit. We follow a single request -- a developer asking a coding agent to fix a failing test -- through four economic layers that today are designed in isolation: a router that decides which model answers, an agent that decides whether to plan, act, verify, or defer, a serving stack that decides how to produce each token, and a training pipeline that decides whether the trace is worth learning from. We show that all four layers are solving the same first-order condition -- marginal benefit equals marginal cost plus latency cost plus risk cost -- with different index sets and different prices. The framing is deliberately minimal: we do not propose a complete theory of AI economics. But adopting marginal token allocation as the shared accounting object explains why systems that locally minimize tokens globally misallocate them, predicts a small set of recurring failure modes (over-routing, over-delegation, under-verification, serving congestion, stale rollouts, cache misuse), and points to a concrete research agenda in token-aware evaluation, autonomy pricing, congestion-priced serving, and risk-adjusted RL budgeting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-05", "arxiv_id": "2604.09408", "title": "HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?", "authors": [ "Mohamed Elfeki", "Tu Trinh", "Kelvin Luu", "Guangze Luo", "Nathan Hunt", "Ernesto Montoya", "Nandan Marwaha", "Yannis He", "Charles Wang", "Fernando Crabedo", "Alessa Castilo", "Bing Liu" ], "github": "https://github.com/hilbenchauthors/hil-bench", "abstract": "Frontier coding agents solve complex tasks when given complete context but collapse when specifications are incomplete or ambiguous. The bottleneck is not raw capability, but judgment: knowing when to act autonomously and when to ask for help. Current benchmarks are blind to this failure mode. They supply unambiguous detailed instructions and solely reward execution correctness, so an agent that makes a lucky guess for a missing requirement will score identically to one that would have asked to be certain. We present HiL-Bench (Human-in-the-Loop Benchmark) to measure this selective escalation skill. Each task contains human-validated blockers (missing information, ambiguous requests, contradictory information) that surface only through progressive exploration, not upfront inspection. Our core metric, Ask-F1, the harmonic mean of question precision and blocker recall, captures the tension between over-asking and silent guessing; its structure architecturally prevents gaming through question spam. Evaluation across SWE and text-to-SQL domains reveals a large universal judgment gap: no frontier model recovers more than a fraction of its full-information performance when deciding whether to ask. Failure analysis identifies three key help-seeking patterns: overconfident wrong beliefs with no gap detection; high uncertainty detection yet persistent errors; broad, imprecise escalation without self-correction. These consistent patterns confirm poor help-seeking is a model-level flaw, not task-specific. RL training on shaped Ask-F1 reward shows judgment is trainable: a 32B model improves both help-seeking quality and task pass rate, with gains that transfer across domains. The model does not learn domain-specific heuristics for when to ask; it learns to detect unresolvable uncertainty and act on it.", "project_page": "https://scale.com/blog/hil", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hilbenchauthors/hil-bench", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2605.00925", "title": "Linking spatial biology and clinical histology via Haiku", "authors": [ "Yan Cui", "Jacob S. Leiby", "Wenhui Lei", "Dokyoon Kim", "Yanxiang Deng", "Aaron T. Mayer", "Zhenqin Wu", "Alexandro E. Trevino", "Zhi Huang" ], "github": "https://github.com/zhihuanglab/Haiku", "project_page": "", "abstract": "Integrating molecular, morphological, and clinical data is essential for basic and translational biomedical research, yet systematic frameworks for jointly modeling these modalities remain limited. Here we present Haiku, a tri-modal contrastive learning model trained on multiplexed immunofluorescence (mIF). It comprises 26.7 million spatial proteomics patches from 3,218 tissue sections across 1,606 patients spanning 11 organ types, with matched hematoxylin and eosin (H&E) histology and clinical metadata aligned in a shared embedding space. Haiku enables three-way cross-modal retrieval, improves downstream classification and clinical prediction tasks over unimodal baselines, and supports zero-shot biomarker inference through fusion retrieval conditioned on clinical metadata-only text descriptions. Across tasks, Haiku outperforms competing approaches, achieving cross-modal retrieval (Recall@50 up to 0.611 versus near-zero baseline), survival prediction (C-index 0.737, +7.91% relative improvement), and zero-shot biomarker inference (mean Pearson correlation 0.718 across 52 biomarkers). Furthermore, we introduce a counterfactual prediction framework in which modifying only clinical metadata while fixing tissue morphology surfaces niche-specific molecular shifts associated with breast cancer stage progression and lung cancer survival outcomes. In a lung adenocarcinoma case study, the counterfactual analysis recovers niche-specific shifts characterized by increased CD8 and granzyme B, reduced PD-L1, and decreased Ki67, broadly consistent with patterns reported for favorable outcomes. We present these counterfactual results as exploratory, hypothesis-generating signals rather than mechanistic claims. These capabilities demonstrate that tri-modal alignment via Haiku enables integrative analysis of spatial biology, bridging molecular measurements with clinical context for biological exploration.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/zhihuanglab/Haiku", "github_api": "https://github.com/zhihuanglab/Haiku", "github_api_added_by": "user" }, { "date": "2026-05-05", "arxiv_id": "2604.27776", "title": "WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments", "authors": [ "Jinchao Li", "Yunxin Li", "Chenrui Zhao", "Zhenran Xu", "Baotian Hu", "Min Zhang" ], "github": "https://github.com/HITsz-TMG/WindowsWorld", "project_page": "", "abstract": "While GUI agents have shown impressive capabilities in common computer-use tasks such as OSWorld, current benchmarks mainly focus on isolated and single-application tasks. This overlooks a critical real-world requirement of coordinating across multiple applications to accomplish complex profession-specific workflows. To bridge this gap, we present a computer-use benchmark in cross-application workflows, named WindowsWorld, designed to systematically assess GUI Agents on complex multi-step tasks that mirror real-world professional activities. Our methodology uses a multi-agent framework steered by 16 occupations to generate four difficulty-level tasks with intermediate inspection, which are then refined by human review and executed in a simulated environment. The resulting benchmark contains 181 tasks with an average of 5.0 sub-goals across 17 common desktop applications, of which 78% are inherently multi-application. Experimental results of leading large models and agents show that: 1) All computer-use agents perform poorly on multi-application tasks (< 21% success rate), far below the performance of simple single-app tasks; 2) They largely fail at tasks requiring conditional judgment and reasoning across geq 3 applications, stalling at early sub-goals; 3) Low execution efficiency, where tasks often fail despite far exceeding human step limits. Code, benchmark data, and evaluation resources are available at github.com/HITsz-TMG/WindowsWorld.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HITsz-TMG/WindowsWorld", "github_api": "https://github.com/HITsz-TMG/WindowsWorld", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.03941", "title": "A Benchmark for Interactive World Models with a Unified Action Generation Framework", "authors": [ "Jianjie Fang", "Yingshan Lei", "Qin Wan", "Ziyou Wang", "Yuchao Huang", "Yongyan Xu", "Baining Zhao", "Weichen Zhang", "Chen Gao", "Xinlei Chen", "Yong Li" ], "github": "", "abstract": "Achieving Artificial General Intelligence (AGI) requires agents that learn and interact adaptively, with interactive world models providing scalable environments for perception, reasoning, and action. Yet current research still lacks large-scale datasets and unified benchmarks to evaluate their physical interaction capabilities. To address this, we propose iWorld-Bench, a comprehensive benchmark for training and testing world models on interaction-related abilities such as distance perception and memory. We construct a diverse dataset with 330k video clips and select 2.1k high-quality samples covering varied perspectives, weather, and scenes. As existing world models differ in interaction modalities, we introduce an Action Generation Framework to unify evaluation and design six task types, generating 4.9k test samples. These tasks jointly assess model performance across visual generation, trajectory following, and memory. Evaluating 14 representative world models, we identify key limitations and provide insights for future research. The iWorld-Bench model leaderboard is publicly available at iWorld-Bench.com.", "project_page": "https://iworld-bench.com/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-06", "arxiv_id": "2605.03596", "title": "Workspace-Bench 1.0: Benchmarking AI Agents on Workspace Tasks with Large-Scale File Dependencies", "authors": [ "Zirui Tang", "Xuanhe Zhou", "Yumou Liu", "Linchun Li", "Weizheng Wang", "Hongzhang Huang", "Jun Zhou", "Jiachen Song", "Shaoli Yu", "Jinqi Wang", "Zihang Zhou", "Hongyi Zhou", "Yuting Lv", "Jinyang Li", "Jiashuo Liu", "Ruoyu Chen", "Chunwei Liu", "GuoLiang Li", "Jihua Kang", "Fan Wu" ], "github": "", "project_page": "", "abstract": "Workspace learning requires AI agents to identify, reason over, exploit, and update explicit and implicit dependencies among heterogeneous files in a worker's workspace, enabling them to complete both routine and advanced tasks effectively. Despite its importance, existing relevant benchmarks largely evaluate agents on pre-specified or synthesized files with limited real-world dependencies, leaving workspace-level evaluation underexplored. To this end, we introduce Workspace-Bench, a benchmark for evaluating AI agents on Workspace Learning invOlving Large-Scale File Dependencies. We construct realistic workspaces with 5 worker profiles, 74 file types, 20,476 files (up to 20GB) and curate 388 tasks, each with its own file dependency graph, evaluated across 7,399 total rubrics that require cross-file retrieval, contextual reasoning, and adaptive decision-making. We further provide Workspace-Bench-Lite, a 100-task subset that preserves the benchmark distribution while reducing evaluation costs by about 70%. We evaluate 4 popular agent harnesses and 7 foundation models. Experimental results show that current agents remain far from reliable workspace learning, where the best reaches only 68.7%, substantially below the human result of 80.7%, and the average performance across agents is only 47.4%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-06", "arxiv_id": "2605.04036", "title": "OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories", "authors": [ "Yuwen Du", "Rui Ye", "Shuo Tang", "Keduan Huang", "Xinyu Zhu", "Yuzhu Cai", "Siheng Chen" ], "github": "https://github.com/PolarSeeker/OpenSeeker", "project_page": "", "abstract": "Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by industrial giants. The typical industry recipe involves a highly resource-intensive pipeline spanning pre-training, continual pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL). In this report, we show that when fueled with informative and high-difficulty trajectories, a simple SFT approach could be surprisingly powerful for training frontier search agents. By introducing three simple data synthesis modifications: scaling knowledge graph size for richer exploration, expanding the tool set size for broader functionality, and strict low-step filtering, we establish a stronger baseline. Trained on merely 10.6k data points, our OpenSeeker-v2 achieves state-of-the-art performance across 4 benchmarks (30B-sized agents with ReAct paradigm): 46.0% on BrowseComp, 58.1% on BrowseComp-ZH, 34.6% on Humanity's Last Exam, and 78.0% on xbench, surpassing even Tongyi DeepResearch trained with heavy CPT+SFT+RL pipeline, which achieves 43.4%, 46.7%, 32.9%, and 75.0%, respectively. Notably, OpenSeeker-v2 represents the first state-of-the-art search agent within its model scale and paradigm to be developed by a purely academic team using only SFT. We are excited to open-source the OpenSeeker-v2 model weights and share our simple yet effective findings to make frontier search agent research more accessible to the community.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PolarSeeker/OpenSeeker", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.04012", "title": "SymptomAI: Towards a Conversational AI Agent for Everyday Symptom Assessment", "authors": [ "Joseph Breda", "Fadi Yousif", "Beszel Hawkins", "Marinela Cotoi", "Miao Liu", "Ray Luo", "Po-Hsuan Cameron Chen", "Mike Schaekermann", "Samuel Schmidgall", "Xin Liu", "Girish Narayanswamy", "Samuel Solomon", "Maxwell A. Xu", "Xiaoran Fan", "Longfei Shangguan", "Anran Wang", "Bhavna Daryani", "Buddy Herkenham", "Cara Tan", "Mark Malhotra", "Shwetak Patel", "John B. Hernandez", "Quang Duong", "Yun Liu", "Zach Wasson", "Dimitrios Antos", "Bob Lou", "Matthew Thompson", "Jonathan Richina", "Anupam Pathak", "Nichole Young-Lin", "Jake Sunshine", "Daniel McDuff" ], "github": "", "project_page": "", "abstract": "Language models excel at diagnostic assessments on currated medical case-studies and vignettes, performing on par with, or better than, clinical professionals. However, existing studies focus on complex scenarios with rich context making it difficult to draw conclusions about how these systems perform for patients reporting symptoms in everyday life. We deployed SymptomAI, a set of conversational AI agents for end-to-end patient interviewing and differential diagnosis (DDx), via the Fitbit app in a study that randomized participants (N=13,917) to interact with five AI agents. This corpus captures diverse communication and a realistic distribution of illnesses from a real world population. A subset of 1,228 participants reported a clinician-provided diagnosis, and 517 of these were further evaluated by a panel of clinicians during over 250 hours of annotation. SymptomAI DDx were significantly more accurate (OR = 2.47, p < 0.001) than those from independent clinicians given the same dialogue in a blinded randomized comparison. Moreover, agentic strategies which conduct a dedicated symptom interview that elicit additional symptom information before providing a diagnosis, perform substantially better than baseline, user-guided conversations (p < 0.001). An auxiliary analysis on 1,509 conversations from a general US population panel validated that these results generalize beyond wearable device users. We used SymptomAI diagnoses as labels for all 13,917 participants to analyze over 500,000 days of wearable metrics across nearly 400 unique conditions. We identified strong associations between acute infections and physiological shifts (e.g., OR > 7 for influenza). While limited by self-reported ground truth, these results demonstrate the benefits of a dedicated and complete symptom interview compared to a user-guided symptom discussion, which is the default of most consumer LLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-06", "arxiv_id": "2411.18966", "title": "SVGS: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors", "authors": [ "Rui Xu", "Wenyue Chen", "Jiepeng Wang", "Yuan Liu", "Peng Wang", "Cheng Lin", "Shiqing Xin", "Xin Li", "Wenping Wang", "Taku Komura" ], "github": "https://github.com/Xrvitd/SVGS", "abstract": "Gaussian Splatting demonstrates impressive results in multi-view reconstruction based on Gaussian explicit representations. However, the current Gaussian primitives only have a single view-dependent color and an opacity to represent the appearance and geometry of the scene, resulting in a non-compact representation. In this paper, we introduce a new method called SVGS (Spatially Varying Gaussian Splatting) that utilizes spatially varying colors and opacity in a single Gaussian primitive to improve its representation ability. We have implemented bilinear interpolation, movable kernels, and tiny neural networks as spatially varying functions. SVGS employs 2D Gaussian surfels as primitives, which significantly enhances novel-view synthesis while maintaining high-quality geometric reconstruction. This approach is particularly effective in practical applications, as scenes combining complex textures with relatively simple geometry occur frequently in real-world environments. Quantitative and qualitative experimental results demonstrate that all three functions outperform the baseline, with the best movable kernels achieving superior novel view synthesis performance on multiple datasets, highlighting the strong potential of spatially varying functions. Project page: https://ruixu.me/html/SuperGaussians/index.html", "project_page": "https://ruixu.me/html/SuperGaussians/index.html", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Xrvitd/SVGS", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.02396", "title": "HeavySkill: Heavy Thinking as the Inner Skill in Agentic Harness", "authors": [ "Jianing Wang", "Linsen Guo", "Zhengyu Chen", "Qi Guo", "Hongyu Zang", "Wenjie Shi", "Haoxiang Ma", "Xiangyu Xi", "Xiaoyu Li", "Wei Wang", "Xunliang Cai" ], "github": "https://github.com/wjn1996/HeavySkill", "abstract": "Recent advances in agentic harness with orchestration frameworks that coordinate multiple agents with memory, skills, and tool use have achieved remarkable success in complex reasoning tasks. However, the underlying mechanism that truly drives performance remains obscured behind intricate system designs. In this paper, we propose HeavySkill, a perspective that views heavy thinking not only as a minimal execution unit in orchestration harness but also as an inner skill internalized within the model's parameters that drives the orchestrator to solve complex tasks. We identify this skill as a two-stage pipeline, i.e., parallel reasoning then summarization, which can operate beneath any agentic harness. We present a systematic empirical study of HeavySkill across diverse domains. Our results show that this inner skill consistently outperforms traditional Best-of-N (BoN) strategies; notably, stronger LLMs can even approach Pass@N performance. Crucially, we demonstrate that the depth and width of heavy thinking, as a learnable skill, can be further scaled via reinforcement learning, offering a promising path toward self-evolving LLMs that internalize complex reasoning without relying on brittle orchestration layers.", "project_page": "https://github.com/wjn1996/HeavySkill", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wjn1996/HeavySkill", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.02943", "title": "Healthcare AI GYM for Medical Agents", "authors": [ "Minbyul Jeong" ], "github": "https://github.com/minstar/Healthcare_GYM", "project_page": "", "abstract": "Clinical reasoning demands multi-step interactions -- gathering patient history, ordering tests, interpreting results, and making safe treatment decisions -- yet a unified training environment provides the breadth of clinical domains and specialized tools to train generalizable medical AI agents through reinforcement learning remains elusive. We present a comprehensive empirical study of multi-turn agentic RL for medical AI, built on , a gymnasium-compatible environment spanning 10 clinical domains with 3.6K+ tasks, 135 domain-specific tools, and a knowledge base of 828K medical passages. Our analysis reveals that agentic multi-turn structure degrades into verbose single-turn monologues, characterized by monotonic length explosion and a simultaneous erosion of tool-use frequency. We characterize how this collapse, alongside distillation instability, stems from the misalignment of sparse terminal rewards with sequential clinical trajectories. We find that vanilla GRPO achieves strong final accuracy on some benchmarks but suffers from training instability, evidenced by significant oscillations in response length and prolonged convergence periods. To improve training efficiency and stability, we propose Turn-level Truncated On-Policy Distillation (TT-OPD), a self-distillation framework where a gradient-free EMA teacher leverages outcome-privileged information to provide dense, outcome-aware KL regularization at every conversation turn. TT-OPD achieves the best performance on 10 of 18 benchmarks with an average +3.9~pp improvement over the non-RL baseline with faster early convergence, controlled response length, and sustained multi-turn tool use.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/minstar/Healthcare_GYM", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2604.28123", "title": "Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL", "authors": [ "Sudong Wang", "Weiquan Huang", "Xiaomin Yu", "Zuhao Yang", "Hehai Lin", "Keming Wu", "Chaojun Xiao", "Chen Chen", "Wenxuan Wang", "Beier Zhu", "Yunjian Zhang", "Chengwei Qin" ], "github": "https://github.com/XIAO4579/PRISM", "abstract": "The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiable rewards (RLVR). However, SFT introduces distributional drift that neither preserves the model's original capabilities nor faithfully matches the supervision distribution. This problem is further amplified in multimodal reasoning, where perception errors and reasoning failures follow distinct drift patterns that compound during subsequent RL. We introduce PRISM, a three-stage pipeline that mitigates this drift by inserting an explicit distribution-alignment stage between SFT and RLVR. Building on the principle of on-policy distillation (OPD), PRISM casts alignment as a black-box, response-level adversarial game between the policy and a Mixture-of-Experts (MoE) discriminator with dedicated perception and reasoning experts, providing disentangled corrective signals that steer the policy toward the supervision distribution without requiring access to teacher logits. While 1.26M public demonstrations suffice for broad SFT initialization, distribution alignment demands higher-fidelity supervision; we therefore curate 113K additional demonstrations from Gemini 3 Flash, featuring dense visual grounding and step-by-step reasoning on the hardest unsolved problems. Experiments on Qwen3-VL show that PRISM consistently improves downstream RLVR performance across multiple RL algorithms (GRPO, DAPO, GSPO) and diverse multimodal benchmarks, improving average accuracy by +4.4 and +6.0 points over the SFT-to-RLVR baseline on 4B and 8B, respectively. Our code, data, and model checkpoints are publicly available at https://github.com/XIAO4579/PRISM.", "project_page": "https://xiao4579.github.io/PRISM/", "github_manual": "", "github_regex": "https://github.com/XIAO4579/PRISM", "github_paper_md": "", "github_api": "https://github.com/XIAO4579/PRISM", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.03571", "title": "PatRe: A Full-Stage Office Action and Rebuttal Generation Benchmark for Patent Examination", "authors": [ "Qiyao Wang", "Xinyi Chen", "Longze Chen", "Hongbo Wang", "Hamid Alinejad-Rokny", "Yuan Lin", "Min Yang" ], "github": "https://github.com/AIforIP/PatRe", "abstract": "Patent examination is a complex, multi-stage process requiring both technical expertise and legal reasoning, increasingly challenged by rising application volumes. Prior benchmarks predominantly view patent examination as discriminative classification or static extraction, failing to capture its inherently interactive and iterative nature, similar to the peer review and rebuttal process in academic publishing. In this paper, we introduce PatRe, the first benchmark that models the full patent examination lifecycle, including Office Action generation and applicant rebuttal. PatRe comprises 480 real-world cases and supports both oracle and retrieval-simulated evaluation settings. Our benchmark reframes patent examination as a dynamic, multi-turn process of justification and response. Extensive experiments across various LLMs reveal critical insights into model performance, including differences between proprietary and open-source models, as well as task asymmetries between examiner analysis and applicant-side rebuttal. These findings highlight both the potential and current limitations of LLMs in modeling complex, real-world legal reasoning and technical novelty judgment in patent examination. We release our code and dataset to facilitate future research on patent examination modeling.", "project_page": "https://patre.wangqiyao.me/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AIforIP/PatRe", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.02801", "title": "Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces", "authors": [ "Chenchen Zhang" ], "github": "https://github.com/xxzcc/awesome-llm-mas-rl", "project_page": "", "abstract": "As large language model (LLM) agents evolve from isolated tool users into coordinated teams, reinforcement learning (RL) must optimize not only individual actions but also how work is spawned, delegated, communicated, aggregated, and stopped. This paper studies RL for LLM-based multi-agent systems through orchestration traces: temporal interaction graphs whose events include sub-agent spawning, delegation, communication, tool use, return, aggregation, and stopping decisions. Using this lens, we identify three technical axes. First, reward design spans eight families, including orchestration rewards for parallelism speedup, split correctness, and aggregation quality. Second, reward and credit signals attach to eight credit- or signal-bearing units from token to team; explicit counterfactual message-level credit remains especially sparse in our curated pool. Third, orchestration learning decomposes into five sub-decisions: when to spawn, whom to delegate to, how to communicate, how to aggregate, and when to stop. In our curated pool as of May 4, 2026, we found no explicit RL training method for the stopping decision. We connect academic methods to public industrial evidence from Kimi Agent Swarm, OpenAI Codex, and Anthropic Claude Code. The resulting scale gap is a gap between publicly reported deployment envelopes and open academic evaluation regimes, not independent verification of industrial training traces. We release the artifact at https://github.com/xxzcc/awesome-llm-mas-rl, including an 84-entry tagged paper pool, a 32-record exclusion log, scripted corpus statistics, and a minimal JSON schema for replayable orchestration traces.", "github_manual": "", "github_regex": "https://github.com/xxzcc/awesome-llm-mas-rl", "github_paper_md": "", "github_api": "https://github.com/xxzcc/awesome-llm-mas-rl", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.01466", "title": "SplAttN: Bridging 2D and 3D with Gaussian Soft Splatting and Attention for Point Cloud Completion", "authors": [ "Zhaoyang Li", "Zhichao You", "Tianrui Li" ], "github": "https://github.com/zay002/SplAttN", "project_page": "", "abstract": "Although multi-modal learning has advanced point cloud completion, the theoretical mechanisms remain unclear. Recent works attribute success to the connection between modalities, yet we identify that standard hard projection severs this connection: projecting a sparse point cloud onto the image plane yields an extremely sparse support, which hinders visual prior propagation, a failure mode we term Cross-Modal Entropy Collapse. To address this practical limitation, we propose SplAttN, which replaces hard projection with Differentiable Gaussian Splatting to produce a dense, continuous image-plane representation. By reformulating projection as continuous density estimation, SplAttN avoids collapsed sparse support, facilitates gradient flow, and improves cross-modal connection learnability. Extensive experiments show that SplAttN achieves state-of-the-art performance on PCN and ShapeNet-55/34. Crucially, we utilize the real-world KITTI benchmark as a stress test for multi-modal reliance. Counter-factual evaluation reveals that while baselines degenerate into unimodal template retrievers insensitive to visual removal, SplAttN maintains a robust dependency on visual cues, validating that our method establishes an effective cross-modal connection. Code is available at https://github.com/zay002/SplAttN.", "github_manual": "", "github_regex": "https://github.com/zay002/SplAttN", "github_paper_md": "", "github_api": "https://github.com/zay002/SplAttN", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.01717", "title": "TCDA: Thread-Constrained Discourse-Aware Modeling for Conversational Sentiment Quadruple Analysis", "authors": [ "Xinran Li", "Xinze Che", "Yifan Lyu", "Zhiqi Huang", "Xiujuan Xu" ], "github": "https://github.com/LiXinran6/TCDA", "project_page": "", "abstract": "Conversational Aspect-based Sentiment Quadruple Analysis (DiaASQ) needs to capture the complex interrelationships in multiple rounds of dialogues. Existing methods usually employ simple Graph Convolutional Networks (GCN), which introduce structural noise and fail to consider the temporal sequence of the dialogues, or use standard RoPE, which implicitly captures relative distances in a flat sequence but cannot clearly separate the token-level syntactic order from the utterance-level progression, and may suffer from the Distance Dilution problem. To address these issues, we propose a new framework that combines Thread-Constrained Directed Acyclic Graph (TC-DAG) and Discourse-Aware Rotary Position Embedding (D-RoPE). Specifically, TC-DAG filters out cross-thread noise based on thread constraints, maintains global connectivity through root anchoring, and incorporates the temporal sequence of the dialogues. D-RoPE aligns multi-layer semantics using dual-stream projection and multi-scale frequency signals, captures thread dependencies using tree-like distances, and alleviates the token-level Distance Dilution problem by incorporating utterance-level progressions. Experimental results on two benchmark datasets demonstrate that our framework achieves state-of-the-art performance.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LiXinran6/TCDA", "github_api": "https://github.com/LiXinran6/TCDA", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.01284", "title": "Chain of Evidence: Pixel-Level Visual Attribution for Iterative Retrieval-Augmented Generation", "authors": [ "Peiyang Liu", "Ziqiang Cui", "Xi Wang", "Di Liang", "Wei Ye" ], "github": "https://github.com/PeiYangLiu/CoE", "project_page": "", "abstract": "Iterative Retrieval-Augmented Generation (iRAG) has emerged as a powerful paradigm for answering complex multi-hop questions by progressively retrieving and reasoning over external documents. However, current systems predominantly operate on parsed text, which creates two critical bottlenecks: (1) Coarse-grained attribution, where users are burdened with manually locating evidence within lengthy documents based on vague text-level citations; and (2) Visual semantic loss, where the conversion of visually rich documents (e.g., slides, PDFs with charts) into text discards spatial logic and layout cues essential for reasoning. To bridge this gap, we present Chain of Evidence (CoE), a retriever-agnostic visual attribution framework that leverages Vision-Language Models to reason directly over screenshots of retrieved document candidates. CoE eliminates format-specific parsing and outputs precise bounding boxes, visualizing the complete reasoning chain within the retrieved candidate set. We evaluate CoE on two distinct benchmarks: Wiki-CoE, a large-scale dataset of structured web pages derived from 2WikiMultiHopQA, and SlideVQA, a challenging dataset of presentation slides featuring complex diagrams and free-form layouts. Experiments demonstrate that fine-tuned Qwen3-VL-8B-Instruct achieves robust performance, significantly outperforming text-based baselines in scenarios requiring visual layout understanding, while establishing a retriever-agnostic solution for pixel-level interpretable iRAG. Our code is available at https://github.com/PeiYangLiu/CoE.git.", "github_manual": "", "github_regex": "https://github.com/PeiYangLiu/CoE.git", "github_paper_md": "", "github_api": "https://github.com/PeiYangLiu/CoE", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.02904", "title": "StateSMix: Online Lossless Compression via Mamba State Space Models and Sparse N-gram Context Mixing", "authors": [ "Roberto Tacconelli" ], "github": "https://github.com/robtacconelli/StateSMix", "project_page": "", "abstract": "We present StateSMix, a fully self-contained lossless compressor that couples an online-trained Mamba-style State Space Model (SSM) with sparse n-gram context mixing and arithmetic coding. The model is initialised from scratch and trained token-by-token on the file being compressed, requiring no pre-trained weights, no GPU, and no external dependencies. The SSM (DM=32, NL=2, approximately 120K active parameters per file) provides a continuously-updated probability estimate over BPE tokens, while nine sparse n-gram hash tables (bigram through 32-gram, 16M slots each) add exact local and long-range pattern memorisation via a softmax-invariant logit-bias mechanism that updates only non-zero-count tokens. An entropy-adaptive scaling mechanism modulates the n-gram contribution based on the SSM's predictive confidence, preventing over-correction when the neural model is already well-calibrated. On the standard enwik8 benchmark, StateSMix achieves 2.123 bpb on 1 MB, 2.149 bpb on 3 MB, and 2.162 bpb on 10 MB, beating xz -9e (LZMA2) by 8.7%, 5.4%, and 0.7% respectively. Ablation experiments establish the SSM as the dominant compression engine: it alone accounts for a 46.6% size reduction over a frequency-count baseline and beats xz without any n-gram component, while n-gram tables provide a complementary 4.1% gain through exact context memorisation. OpenMP parallelisation of the training loop yields 1.9x speedup on 4 cores. The system is implemented in pure C with AVX2 SIMD and processes approximately 2,000 tokens per second on commodity x86-64 hardware.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/robtacconelli/StateSMix", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.01371", "title": "ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue", "authors": [ "Daoxuan Zhang", "Ping Chen", "Jianyi Zhou", "Shuo Yang" ], "github": "https://github.com/4amGodvzx/ESAR", "abstract": "The rapid advancement of Multimodal Large Language Models (MLLMs) has empowered Unmanned Aerial Vehicle (UAV) with exceptional capabilities in spatial reasoning, semantic understanding, and complex decision-making, making them inherently suited for UAV Search and Rescue (SAR). However, existing UAV SAR research is dominated by traditional vision and path-planning methods and lacks a comprehensive and unified benchmark for embodied agents. To bridge this gap, we first propose the novel task of Embodied Search and Rescue (ESAR), which requires aerial agents to autonomously explore complex environments, identify rescue clues, and reason about victim locations to execute informed decision-making. Additionally, we present ESARBench, the first comprehensive benchmark designed to evaluate MLLM-driven UAV agents in highly realistic SAR scenarios. Leveraging Unreal Engine 5 and AirSim, we construct four high-fidelity, large-scale open environments mapped directly from real-world Geographic Information System (GIS) data to ensure photorealistic landscapes. To rigorously simulate actual rescue operations, our benchmark incorporates dynamic variables including weather conditions, time of day, and stochastic clue placement. Furthermore, we create a dataset of 600 tasks modeled after real-world rescue cases and propose a robust set of evaluation metrics. We evaluate diverse baselines, ranging from traditional heuristics to advanced ground and aerial MLLM-based ObjectNav agents. Experimental results highlight the challenges in ESAR, revealing critical bottlenecks in spatial memory, aerial adaptation, and the trade-off between search efficiency and flight safety. We hope ESARBench serves as a valuable resource to advance research on Embodied Search and Rescue domain. Source code and project page: https://4amgodvzx.github.io/ESAR.github.io.", "project_page": "https://4amgodvzx.github.io/ESAR.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/4amGodvzx/ESAR", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.00891", "title": "X2SAM: Any Segmentation in Images and Videos", "authors": [ "Hao Wang", "Limeng Qiao", "Chi Zhang", "Lin Ma", "Guanglu Wan", "Xiangyuan Lan", "Xiaodan Liang" ], "github": "https://github.com/wanghao9610/X2SAM", "abstract": "Multimodal Large Language Models (MLLMs) have demonstrated strong image-level visual understanding and reasoning, yet their pixel-level perception across both images and videos remains limited. Foundation segmentation models such as the SAM series produce high-quality masks, but they rely on low-level visual prompts and cannot natively interpret complex conversational instructions. Existing segmentation MLLMs narrow this gap, but are usually specialized for either images or videos and rarely support both textual and visual prompts in one interface. We introduce X2SAM, a unified segmentation MLLM that extends any-segmentation capabilities from images to videos. Given conversational instructions and visual prompts, X2SAM couples an LLM with a Mask Memory module that stores guided vision features for temporally consistent video mask generation. The same formulation supports generic, open-vocabulary, referring, reasoning, grounded conversation generation, interactive, and visual grounded segmentation across image and video inputs. We further introduce the Video Visual Grounded (V-VGD) segmentation benchmark, which evaluates whether a model can segment object tracks in videos from interactive visual prompts. With a unified joint training strategy over heterogeneous image and video datasets, X2SAM delivers strong video segmentation performance, remains competitive on image segmentation benchmarks, and preserves general image and video chat ability.", "project_page": "https://wanghao9610.github.io/X2SAM", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/wanghao9610/X2SAM", "github_api": "https://github.com/wanghao9610/X2SAM", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.03073", "title": "The TTS-STT Flywheel: Synthetic Entity-Dense Audio Closes the Indic ASR Gap Where Commercial and Open-Source Systems Fail", "authors": [ "Venkata Pushpak Teja Menta" ], "github": "https://github.com/praxelhq/stt-flywheel", "project_page": "", "abstract": "Niche-domain Indic ASR -- digit strings, currency amounts, addresses, brand names, English/Indic codemix -- is under-served by both open-source SOTA and commercial systems. On a synthesised entity-dense Telugu test set (held-out by synthesis system), vasista22/whisper-telugu-large-v2 (open SOTA) achieves Entity-Hit-Rate (EHR) 0.027 and Deepgram Nova-3 (commercial) 0.16. We close this gap with a self-contained TTS<->STT flywheel: an open-source Indic TTS pipeline synthesises ~22,000 entity-dense Indic-English code-mix utterances at <$50 marginal cost, and a LoRA fine-tune on top of vasista22 achieves EHR 0.473 on the held-out test (17x over open SOTA, 3x over commercial), with read-prose regression bounded to +6.6 pp WER on FLEURS-Te. Cross-language: beta-Hi 0.337 (7x vs vasista22) and beta-Ta 0.543 (22x vs vasista22, 22x vs Deepgram); on Hindi where Deepgram has substantial entity coverage, the flywheel underperforms commercial. All three beta models fall below pre-registered EHR targets (0.75 for Te, 0.65 for Hi/Ta); we report honestly. A native-human-recorded sanity check (n=20 Telugu) confirms transfer to real speech (beta-Te EHR 0.516 on native vs 0.473 on synth). An EDSA-isolation ablation (LoRA on FLEURS-Te alone) yields EHR 0.020 on the same held-out, attributing ~100% of the gain to the EDSA corpus. We additionally report a language-conditional finding: vanilla Whisper-large-v3 has Telugu-specific Script Collapse (SFR 0.46-0.71) that a per-language LoRA corrects (SFR 0.81-0.97), but the recipe is contraindicated on Hindi and Tamil where vanilla SFR >= 0.98. Code, holdouts, predictions, EDSA corpus, and entity dictionaries are released open-source.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/praxelhq/stt-flywheel", "github_api": "https://github.com/praxelhq/stt-flywheel", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.03042", "title": "ARIS: Autonomous Research via Adversarial Multi-Agent Collaboration", "authors": [ "Ruofeng Yang", "Yongcan Li", "Shuai Li" ], "github": "https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep", "abstract": "This report describes ARIS (Auto-Research-in-sleep), an open-source research harness for autonomous research, including its architecture, assurance mechanisms, and early deployment experience. The performance of agent systems built on LLMs depends on both the model weights and the harness around them, which governs what information to store, retrieve, and present to the model. For long-horizon research workflows, the central failure mode is not a visible breakdown but a plausible unsupported success: a long-running agent can produce claims whose evidential support is incomplete, misreported, or silently inherited from the executor's framing. Therefore, we present ARIS as a research harness that coordinates machine-learning research workflows through cross-model adversarial collaboration as a default configuration: an executor model drives forward progress while a reviewer from a different model family is recommended to critique intermediate artifacts and request revisions. ARIS has three architectural layers. The execution layer provides more than 65 reusable Markdown-defined skills, model integrations via MCP, a persistent research wiki for iterative reuse of prior findings, and deterministic figure generation. The orchestration layer coordinates five end-to-end workflows with adjustable effort settings and configurable routing to reviewer models. The assurance layer includes a three-stage process for checking whether experimental claims are supported by evidence: integrity verification, result-to-claim mapping, and claim auditing that cross-checks manuscript statements against the claim ledger and raw evidence, as well as a five-pass scientific-editing pipeline, mathematical-proof checks, and visual inspection of the rendered PDF. A prototype self-improvement loop records research traces and proposes harness improvements that are adopted only after reviewer approval.", "project_page": "https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep", "github_api": "https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2604.27488", "title": "Skills-Coach: A Self-Evolving Skill Optimizer via Training-Free GRPO", "authors": [ "Yu Tian", "Jiawei Chen", "Lifan Zheng", "Mingxiang Tao", "Xinyi Zeng", "Zhaoxia Yin", "Hang Su", "Xian Sun" ], "github": "https://github.com/T1aNS1R/Skills-Coach", "project_page": "", "abstract": "We introduce Skills-Coach, a novel automated framework designed to significantly enhance the self-evolution of skills within Large Language Model (LLM)-based agents. Addressing the current fragmentation of the skill ecosystem, Skills-Coach explores the boundaries of skill capabilities, thereby facilitating the comprehensive competency coverage essential for intelligent applications. The framework comprises four core modules: a Diverse Task Generation Module that systematically creates a comprehensive test suite for various skills; a Lightweight Optimization Module dedicated to optimizing skill prompts and their corresponding code; a Comparative Execution Module facilitating the execution and evaluation of both original and optimized skills; and a Traceable Evaluation Module, which rigorously evaluates performance against specified criteria. Skills-Coach offers flexible execution options through its virtual and real modes. To validate its efficacy, we introduce Skill-X, a comprehensive benchmark dataset consisting of 48 diverse skills. Experimental results demonstrate that Skills-Coach achieves significant performance improvements in skill capability across a wide range of categories, highlighting its potential to advance the development of more robust and adaptable LLM-based agents.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/T1aNS1R/Skills-Coach", "github_api_added_by": "user" }, { "date": "2026-05-06", "arxiv_id": "2605.02134", "title": "Video Generation with Predictive Latents", "authors": [ "Yian Zhao", "Feng Wang", "Qiushan Guo", "Chang Liu", "Xiangyang Ji", "Jian Zhang", "Jie Chen" ], "github": "", "abstract": "Video Variational Autoencoder (VAE) enables latent video generative modeling by mapping the visual world into compact spatiotemporal latent spaces, improving training efficiency and stability. While existing video VAEs achieve commendable reconstruction quality, continued optimization of reconstruction does not necessarily translate into improved generative performance. How to enhance the diffusability of video latents remains a critical and unresolved challenge. In this work, inspired by principles of predictive world modeling, we investigate the potential of predictive learning to improve the video generative modeling. To this end, we introduce a simple and effective predictive reconstruction objective that unifies predictive learning with video reconstruction. Specifically, we randomly discard future frames and encode only partial past observations, while training the decoder to reconstruct the observed frames and predict future ones simultaneously. This design encourages the latent space to encode temporally predictive structures and build a more coherent understanding of video dynamics, thereby improving generation quality. Our model, termed Predictive Video VAE (PV-VAE), achieves superior performance on video generation, with 52% faster convergence and a 34.42 FVD improvement over the Wan2.2 VAE on UCF101. Furthermore, comprehensive analyses demonstrate that PV-VAE not only exhibits favorable scalability, with generative performance improving alongside VAE training, but also yields consistent gains in downstream video understanding, underscoring a latent space that effectively captures temporal coherence and motion priors.", "project_page": "https://zhao-yian.github.io/PVVAE/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-06", "arxiv_id": "2605.02913", "title": "Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning", "authors": [ "Rohan Surana", "Gagan Mundada", "Xunyi Jiang", "Chuhan Wang", "Zhenwei Tang", "Difan Jiao", "Zihan Huang", "Yuxin Xiong", "Junda Wu", "Sheldon Yu", "Xintong Li", "Raghav Jain", "Nikki Kuang", "Sizhe Zhou", "Bowen Jin", "Zhendong Chu", "Tong Yu", "Ryan Rossi", "Kuan-Hao Huang", "Jingbo Shang", "Jiawei Han", "Julian McAuley" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has become a central post-training tool for improving the reasoning abilities of large language models (LLMs). In these systems, the rollout, the trajectory sampled from a prompt to termination, including intermediate reasoning steps and optional tool or environment interactions, determines the data the optimizer learns from, yet rollout design is often underreported. This survey provides an optimizer-agnostic view of rollout strategies for RL-based post-training of reasoning LLMs. We formalize rollout pipelines with unified notation and introduce Generate-Filter-Control-Replay (GFCR), a lifecycle taxonomy that decomposes rollout pipelines into four modular stages: Generate proposes candidate trajectories and topologies; Filter constructs intermediate signals via verifiers, judges, critics; Control allocates compute and makes continuation/branching/stopping decisions under budgets; and Replay retains and reuses artifacts across rollouts without weight updates, including self-evolving curricula that autonomously generate new training tasks. We complement GFCR with a criterion taxonomy of reliability, coverage, and cost sensitivity that characterizes rollout trade-offs. Using this framework, we synthesize methods spanning RL with verifiable rewards, process supervision, judge-based gating, guided and tree/segment rollouts, adaptive compute allocation, early-exit and partial rollouts, throughput optimization, and replay/recomposition for self-improvement. We ground the framework with case studies in math, code/SQL, multimodal reasoning, tool-using agents, and agentic skill benchmarks that evaluate skill induction, reuse, and cross-task transfer. Finally, we provide a diagnostic index that maps common rollout pathologies to GFCR modules and mitigation levers, alongside open challenges for building reproducible, compute-efficient, and trustworthy rollout pipelines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-06", "arxiv_id": "2604.25907", "title": "How Fast Should a Model Commit to Supervision? Training Reasoning Models on the Tsallis Loss Continuum", "authors": [ "Chu-Cheng Lin", "Eugene Ie" ], "github": "", "project_page": "", "abstract": "Adapting reasoning models to new tasks during post-training with only output-level supervision stalls under reinforcement learning from verifiable rewards (RLVR) when the initial success probability p_0 is small. Using the Tsallis q-logarithm, we define a loss family J_Q that interpolates between RLVR (at q{=}0, the exploitation pole) and the log-marginal-likelihood over latent trajectories (at q{=}1, the density-estimation pole). All members share the same per-example gradient direction, differing only by a scalar amplification P_{\u03b8^{-q}} that reweights each instance independently of the learning rate. This amplification is the mechanism that addresses cold-start stalling: under gradient flow, the exploitation pole requires \u03a9(1{p_0}) time to escape cold start, while the density-estimation pole escapes in \u0398big(log(1{p_0})big); intermediate q trades escape speed against noise memorization. Because P_\u03b8 is intractable, we derive two Monte Carlo estimators from the two factorizations of the gradient: Gradient-Amplified RL (GARL) samples from the prior and amplifies the RL gradient, and Posterior-Attenuated Fine-Tuning (PAFT) importance-resamples from the posterior and runs standard SFT. Both have bias Obig(q{M P_\u03b8^{q+1}}big); GARL has lower variance, PAFT has semantically coherent gradients. On FinQA, HotPotQA, and MuSiQue, GARL at q{=}0.75 substantially mitigates cold-start stalling, escaping cold start where GRPO fails entirely. In warm start, GARL at low q dominates FinQA where training is stable; on HotPotQA and MuSiQue, GARL destabilizes during training, and PAFT at q{=}0.75 provides stable gradients (best overall on HotPotQA at 47.9 maj@16, +14.4 over GRPO).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-06", "arxiv_id": "2605.04453", "title": "StableI2I: Spotting Unintended Changes in Image-to-Image Transition", "authors": [ "Jiayang Li", "Shuo Cao", "Xiaohui Li", "Zhizhen Zhang", "Kaiwen Zhu", "Yule Duan", "Yu Qiao", "Jian Zhang", "Yihao Liu" ], "github": "https://github.com/Henry-Lee-real/StableI2I", "abstract": "In most real-world image-to-image (I2I) scenarios, existing evaluations primarily focus on instruction following and the perceptual quality or aesthetics of the generated images. However, they largely fail to assess whether the output image preserves the semantic correspondence and spatial structure of the input image. To address this limitation, we propose StableI2I, a unified and dynamic evaluation framework that explicitly measures content fidelity and pre--post consistency across a wide range of I2I tasks without requiring reference images, including image editing and image restoration. In addition, we construct StableI2I-Bench, a benchmark designed to systematically evaluate the accuracy of MLLMs on such fidelity and consistency assessment tasks. Extensive experimental results demonstrate that StableI2I provides accurate, fine-grained, and interpretable evaluations of content fidelity and consistency, with strong correlations to human subjective judgments. Our framework serves as a practical and reliable evaluation tool for diagnosing content consistency and benchmarking model performance in real-world I2I systems.", "project_page": "https://henry-lee-real.github.io/StableI2I_Page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Henry-Lee-real/StableI2I", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2604.28196", "title": "HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation", "authors": [ "Xin Zhou", "Dingkang Liang", "Xiwu Chen", "Feiyang Tan", "Dingyuan Zhang", "Hengshuang Zhao", "Xiang Bai" ], "github": "https://github.com/H-EmbodVis/HERMESV2", "abstract": "Driving world models serve as a pivotal technology for autonomous driving by simulating environmental dynamics. However, existing approaches predominantly focus on future scene generation, often overlooking comprehensive 3D scene understanding. Conversely, while Large Language Models (LLMs) demonstrate impressive reasoning capabilities, they lack the capacity to predict future geometric evolution, creating a significant disparity between semantic interpretation and physical simulation. To bridge this gap, we propose HERMES++, a unified driving world model that integrates 3D scene understanding and future geometry prediction within a single framework. Our approach addresses the distinct requirements of these tasks through synergistic designs. First, a BEV representation consolidates multi-view spatial information into a structure compatible with LLMs. Second, we introduce LLM-enhanced world queries to facilitate knowledge transfer from the understanding branch. Third, a Current-to-Future Link is designed to bridge the temporal gap, conditioning geometric evolution on semantic context. Finally, to enforce structural integrity, we employ a Joint Geometric Optimization strategy that integrates explicit geometric constraints with implicit latent regularization to align internal representations with geometry-aware priors. Extensive evaluations on multiple benchmarks validate the effectiveness of our method. HERMES++ achieves strong performance, outperforming specialist approaches in both future point cloud prediction and 3D scene understanding tasks. The model and code will be publicly released at https://github.com/H-EmbodVis/HERMESV2.", "project_page": "https://h-embodvis.github.io/HERMESV2/", "github_manual": "", "github_regex": "https://github.com/H-EmbodVis/HERMESV2", "github_paper_md": "", "github_api": "https://github.com/H-EmbodVis/HERMESV2", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.05204", "title": "D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models", "authors": [ "Dengyang Jiang", "Xin Jin", "Dongyang Liu", "Zanyi Wang", "Mingzhe Zheng", "Ruoyi Du", "Xiangpeng Yang", "Qilong Wu", "Zhen Li", "Peng Gao", "Harry Yang", "Steven Hoi" ], "github": "https://github.com/vvvvvjdy/D-OPSD", "abstract": "The landscape of high-performance image generation models is currently shifting from the inefficient multi-step ones to the efficient few-step counterparts (e.g, Z-Image-Turbo and FLUX.2-klein). However, these models present significant challenges for directly continuous supervised fine-tuning. For example, applying the commonly used fine-tuning technique would compromises their inherent few-step inference capability. To address this, we propose D-OPSD, a novel training paradigm for step-distilled diffusion models that enables on-policy learning during supervised fine-tuning. We first find that the modern diffusion model where the LLM/VLM serves as the encoder can inherit its encoder's in-context capabilities. This enables us to make the training as an on-policy self-distillation process. Specifically, during training, we make the model acts as both the teacher and the student with different contexts, where the student is conditioned only on the text feature, while the teacher is conditioned on the multimodal feature of both the text prompt and the target image. Training minimizes the two predicted distributions over the student's own roll-outs. By optimized on the model's own trajectory and under it's own supervision, D-OPSD enables the model to learn new concept, style, etc. without sacrificing the original few-step capacity.", "project_page": "https://vvvvvjdy.github.io/d-opsd/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vvvvvjdy/D-OPSD", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2604.20441", "title": "MedSkillAudit: A Domain-Specific Audit Framework for Medical Research Agent Skills", "authors": [ "Yingyong Hou", "Xinyuan Lao", "Huimei Wang", "Qianyu Yao", "Wei Chen", "Bocheng Huang", "Fei Sun", "Yuxian Lv", "Weiqi Lei", "Xueqian Wen", "Pengfei Xia", "Zhujun Tan", "Shengyang Xie" ], "github": "https://github.com/aipoch/medical-research-skills", "project_page": "", "abstract": "Background: Agent skills are increasingly deployed as modular, reusable capability units in AI agent systems. Medical research agent skills require safeguards beyond general-purpose evaluation, including scientific integrity, methodological validity, reproducibility, and boundary safety. This study developed and preliminarily evaluated a domain-specific audit framework for medical research agent skills, with a focus on reliability against expert review. Methods: We developed MedSkillAudit (skill-auditor@1.0), a layered framework assessing skill release readiness before deployment. We evaluated 75 skills across five medical research categories (15 per category). Two experts independently assigned a quality score (0-100), an ordinal release disposition (Production Ready / Limited Release / Beta Only / Reject), and a high-risk failure flag. System-expert agreement was quantified using ICC(2,1) and linearly weighted Cohen's kappa, benchmarked against the human inter-rater baseline. Results: The mean consensus quality score was 72.4 (SD = 13.0); 57.3% of skills fell below the Limited Release threshold. MedSkillAudit achieved ICC(2,1) = 0.449 (95% CI: 0.250-0.610), exceeding the human inter-rater ICC of 0.300. System-consensus score divergence (SD = 9.5) was smaller than inter-expert divergence (SD = 12.4), with no directional bias (Wilcoxon p = 0.613). Protocol Design showed the strongest category-level agreement (ICC = 0.551); Academic Writing showed a negative ICC (-0.567), reflecting a structural rubric-expert mismatch. Conclusions: Domain-specific pre-deployment audit may provide a practical foundation for governing medical research agent skills, complementing general-purpose quality checks with structured audit workflows tailored to scientific use cases.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/aipoch/medical-research-skills", "github_api": "https://github.com/aipoch/medical-research-skills", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.04018", "title": "Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems", "authors": [ "Yilun Zhao", "Jinbiao Wei", "Tingyu Song", "Siyue Zhang", "Chen Zhao", "Arman Cohan" ], "github": "https://github.com/yale-nlp/Bright-Pro", "project_page": "", "abstract": "Reasoning-intensive retrieval aims to surface evidence that supports downstream reasoning rather than merely matching topical similarity. This capability is increasingly important for agentic search systems, where retrievers must provide complementary evidence across iterative search and synthesis. However, existing work remains limited on both evaluation and training: benchmarks such as BRIGHT provide narrow gold sets and evaluate retrievers in isolation, while synthetic training corpora often optimize single-passage relevance rather than evidence portfolio construction. We introduce BRIGHT-Pro, an expert-annotated benchmark that expands each query with multi-aspect gold evidence and evaluates retrievers under both static and agentic search protocols. We further construct RTriever-Synth, an aspect-decomposed synthetic corpus that generates complementary positives and positive-conditioned hard negatives, and use it to LoRA fine-tune RTriever-4B from Qwen3-Embedding-4B. Experiments across lexical, general-purpose, and reasoning-intensive retrievers show that aspect-aware and agentic evaluation expose behaviors hidden by standard metrics, while RTriever-4B substantially improves over its base model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yale-nlp/Bright-Pro", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.05163", "title": "PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World", "authors": [ "Yunhan Yang", "Chunshi Wang", "Junliang Ye", "Yang Li", "Zanxin Chen", "Zehuan Huang", "Yao Mu", "Zhuo Chen", "Chunchao Guo", "Xihui Liu" ], "github": "https://github.com/HKU-MMLab/PhysForge", "abstract": "Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on static geometry, overlooking the functional properties essential for interaction. We propose that interactive asset generation must be rooted in functional logic and hierarchical physics. To bridge this gap, we introduce PhysForge, a decoupled two-stage framework supported by PhysDB, a large-scale dataset of 150,000 assets with four-tier physical annotations. First, a VLM acts as a \"physical architect\" to plan a \"Hierarchical Physical Blueprint\" defining material, functional, and kinematic constraints. Second, a physics-grounded diffusion model realizes this blueprint by synthesizing high-fidelity geometry alongside precise kinematic parameters via a novel KineVoxel Injection (KVI) mechanism. Experiments demonstrate that PhysForge produces functionally plausible, simulation-ready assets, providing a robust data engine for interactive 3D content and embodied agents.", "project_page": "https://hku-mmlab.github.io/PhysForge/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HKU-MMLab/PhysForge", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.03269", "title": "RLDX-1 Technical Report", "authors": [ "Dongyoung Kim", "Huiwon Jang", "Myungkyu Koo", "Suhyeok Jang", "Taeyoung Kim", "Beomjun Kim", "Byungjun Yoon", "Changsung Jang", "Daewon Choi", "Dongsu Han", "Donguk Lee", "Heeseung Kwon", "Hojin Jeon", "Jaehyun Kang", "Jaekyoung Bae", "Jihyuk Lee", "Jimin Lee", "John Won", "Joonwoo Ahn", "Junhyeong Park", "Junyoung Sung", "Kyungmin Lee", "Minseong Han", "Minsung Yoon", "Sejune Joo", "Seonil Son", "Seungcheol Park", "Seunggeun Cho", "Seungjun Moon", "Seungku Kim", "Yonghoon Dong", "Yongjin Cho", "Youngchan Kim", "Chang Hwan Kim", "Dohyeon Kim", "Hazel Lee", "Heecheol Kim", "Hensen Ahn", "Hyungkyu Ryu", "Hyunsoo Choi", "Hyunsoo Shin", "Jaeheon Jung", "Jaewoo Kim", "Jinwook Kim", "Joochul Chang", "Joonsoo Kim", "Junghun Park", "Jungwoo Park", "Junho Cho", "Junhyeok Park", "Junwon Lee", "Kangwook Lee", "Kwanghoon Kim", "Kyoungwhan Choe", "Manoj Bhadu", "Nayoung Oh", "Sangjun Kim", "Sangwoo Kim", "Seunghoon Shim", "Seunghyun Kim", "Seungjun Lee", "Seungyup Ka", "Sungryol Yang", "Wook Jung", "Yashu Shukla", "Yeonjae Lee", "Yeonwoo Bae", "Jinwoo Shin" ], "github": "https://github.com/RLWRLD/RLDX-1", "abstract": "While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene understanding and language-conditioned generalization) inherited from pre-trained Vision-Language Models, they still struggle with complex real-world tasks requiring broader functional capabilities (e.g. motion awareness, memory-aware decision making, and physical sensing). To address this, we introduce RLDX-1, a general-purpose robotic policy for dexterous manipulation built on the Multi-Stream Action Transformer (MSAT), an architecture that unifies these capabilities by integrating heterogeneous modalities through modality-specific streams with cross-modal joint self-attention. RLDX-1 further combines this architecture with system-level design choices, including synthesizing training data for rare manipulation scenarios, learning procedures specialized for human-like manipulation, and inference optimizations for real-time deployment. Through empirical evaluation, we show that RLDX-1 consistently outperforms recent frontier VLAs (e.g. \u03c0_{0.5} and GR00T N1.6) across both simulation benchmarks and real-world tasks that require broad functional capabilities beyond general versatility. In particular, RLDX-1 shows superiority in ALLEX humanoid tasks by achieving success rates of 86.8% while \u03c0_{0.5} and GR00T N1.6 achieve around 40%, highlighting the ability of RLDX-1 to control a high-DoF humanoid robot under diverse functional demands. Together, these results position RLDX-1 as a promising step toward reliable VLAs for complex, contact-rich, and dynamic real-world dexterous manipulation.", "project_page": "http://rlwrld.ai/rldx-1", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RLWRLD/RLDX-1", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.03849", "title": "Stream-R1: Reliability-Perplexity Aware Reward Distillation for Streaming Video Generation", "authors": [ "Bin Wu", "Mengqi Huang", "Shaojin Wu", "Weinan Jia", "Yuxin Wang", "Zhendong Mao", "Yongdong Zhang" ], "github": "https://github.com/FrameX-AI/Stream-R1", "abstract": "Distillation-based acceleration has become foundational for making autoregressive streaming video diffusion models practical, with distribution matching distillation (DMD) as the de facto choice. Existing methods, however, train the student to match the teacher's output indiscriminately, treating every rollout, frame, and pixel as equally reliable supervision. We argue that this caps distilled quality, since it overlooks two complementary axes of variance in DMD supervision: Inter-Reliability across student rollouts whose supervision varies in reliability, and Intra-Perplexity across spatial regions and temporal frames that contribute unequally to where quality can still be improved. The objective thus conflates two questions under a uniform weight: whether to learn from each rollout, and where to concentrate optimization within it. To address this, we propose Stream-R1, a Reliability-Perplexity Aware Reward Distillation framework that adaptively reweights the distillation objective at both rollout and spatiotemporal-element levels through a single shared reward-guided mechanism. At the Inter-Reliability level, Stream-R1 rescales each rollout's loss by an exponential of a pretrained video reward score, so that rollouts with reliable supervision dominate optimization. At the Intra-Perplexity level, it back-propagates the same reward model to extract per-pixel gradient saliency, which is factored into spatial and temporal weights that concentrate optimization pressure on regions and frames where refinement yields the largest expected gain. An adaptive balancing mechanism prevents any single quality axis from dominating across visual quality, motion quality, and text alignment. Stream-R1 attains consistent improvements on all three dimensions over distillation baselines on standard streaming video generation benchmarks, without architectural modification or additional inference cost.", "project_page": "https://stream-r1.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FrameX-AI/Stream-R1", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.04461", "title": "Stream-T1: Test-Time Scaling for Streaming Video Generation", "authors": [ "Yijing Tu", "Shaojin Wu", "Mengqi Huang", "Wenchuan Wang", "Yuxin Wang", "Chunxiao Liu", "Zhendong Mao" ], "github": "https://github.com/FrameX-AI/Stream-T1", "abstract": "While Test-Time Scaling (TTS) offers a promising direction to enhance video generation without the surging costs of training, current test-time video generation methods based on diffusion models suffer from exorbitant candidate exploration costs and lack temporal guidance. To address these structural bottlenecks, we propose shifting the focus to streaming video generation. We identify that its chunk-level synthesis and few denoising steps are intrinsically suited for TTS, significantly lowering computational overhead while enabling fine-grained temporal control. Driven by this insight, we introduced Stream-T1, a pioneering comprehensive TTS framework exclusively tailored for streaming video generation. Specifically, Stream-T1 is composed of three units: (1) Stream -Scaled Noise Propagation, which actively refines the initial latent noise of the generating chunk using historically proven, high-quality previous chunk noise, effectively establishes temporal dependency and utilizing the historical Gaussian prior to guide the current generation; (2) Stream -Scaled Reward Pruning, which comprehensively evaluates generated candidates to strike an optimal balance between local spatial aesthetics and global temporal coherence by integrating immediate short-term assessments with sliding-window-based long-term evaluations; (3) Stream-Scaled Memory Sinking, which dynamically routes the context evicted from KV-cache into distinct updating pathways guided by the reward feedback, ensuring that previously generated visual information effectively anchors and guides the subsequent video stream. Evaluated on both 5s and 30s comprehensive video benchmarks, Stream-T1 demonstrates profound superiority, significantly improving temporal consistency, motion smoothness, and frame-level visual quality.", "project_page": "https://stream-t1.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FrameX-AI/Stream-T1", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.04128", "title": "Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation", "authors": [ "Lin Song", "Wenbo Li", "Guoqing Ma", "Wei Tang", "Bo Wang", "Yuan Zhang", "Yijun Yang", "Yicheng Xiao", "Jianhui Liu", "Yanbing Zhang", "Guohui Zhang", "Wenhu Zhang", "Hang Xu", "Nan Jiang", "Xin Han", "Haoze Sun", "Maoquan Zhang", "Haoyang Huang", "Nan Duan" ], "github": "https://github.com/jd-opensource/JoyAI-Image", "project_page": "", "abstract": "We present JoyAI-Image, a unified multimodal foundation model for visual understanding, text-to-image generation, and instruction-guided image editing. JoyAI-Image couples a spatially enhanced Multimodal Large Language Model (MLLM) with a Multimodal Diffusion Transformer (MMDiT), allowing perception and generation to interact through a shared multimodal interface. Around this architecture, we build a scalable training recipe that combines unified instruction tuning, long-text rendering supervision, spatially grounded data, and both general and spatial editing signals. This design gives the model broad multimodal capability while strengthening geometry-aware reasoning and controllable visual synthesis. Experiments across understanding, generation, long-text rendering, and editing benchmarks show that JoyAI-Image achieves state-of-the-art or highly competitive performance. More importantly, the bidirectional loop between enhanced understanding, controllable spatial editing, and novel-view-assisted reasoning enables the model to move beyond general visual competence toward stronger spatial intelligence. These results suggest a promising path for unified visual models in downstream applications such as vision-language-action systems and world models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jd-opensource/JoyAI-Image", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.04569", "title": "Lightning Unified Video Editing via In-Context Sparse Attention", "authors": [ "Shitong Shao", "Zikai Zhou", "Haopeng Li", "Yingwei Song", "Wenliang Zhong", "Lichen Bai", "Zeke Xie" ], "github": "", "project_page": "", "abstract": "Video editing has evolved toward In-Context Learning (ICL) paradigms, yet the resulting quadratic attention costs create a critical computational bottleneck. In this work, we propose In-context Sparse Attention (ISA), the first near-lossless empirical sparse framework tailored for ICL video editing. Our design is grounded in two key insights: first, context tokens exhibit significantly lower saliency than source tokens; second, we theoretically prove and empirically validate that Query sharpness correlates with approximation error. Motivated by these findings, ISA implements an efficient pre-selection strategy to prune redundant context, followed by a dynamic query grouping mechanism that routes high-error queries to full attention and low-error ones to a computationally efficient 0-th order Taylor sparse attention. Furthermore, we build \\texttt{LIVEditor} , a novel lightning video editing model via ISA and a proposed video-editing data pipeline that curated a 1.7M high-quality dataset. Extensive experiments demonstrate that LIVEditor achieves a sim60% reduction in attention-module latency while surpassing state-of-the-art methods across EditVerseBench, IVE-Bench, and VIE-Bench, delivering near-lossless acceleration without compromising visual fidelity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-07", "arxiv_id": "2605.05185", "title": "OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents", "authors": [ "Shuang Chen", "Kaituo Feng", "Hangting Chen", "Wenxuan Huang", "Dasen Dai", "Quanxin Shou", "Yunlong Lin", "Xiangyu Yue", "Shenghua Gao", "Tianyu Pang" ], "github": "https://github.com/shawn0728/OpenSearch-VL", "abstract": "Deep search has become a crucial capability for frontier multimodal agents, enabling models to solve complex questions through active search, evidence verification, and multi-step reasoning. Despite rapid progress, top-tier multimodal search agents remain difficult to reproduce, largely due to the absence of open high-quality training data, transparent trajectory synthesis pipelines, or detailed training recipes. To this end, we introduce OpenSearch-VL, a fully open-source recipe for training frontier multimodal deep search agents with agentic reinforcement learning. First, we curated a dedicated pipeline to construct high-quality training data through Wikipedia path sampling, fuzzy entity rewriting, and source-anchor visual grounding, which jointly reduce shortcuts and one-step retrieval collapse. Based on this pipeline, we curate two training datasets, SearchVL-SFT-36k for SFT and SearchVL-RL-8k for RL. Besides, we design a diverse tool environment that unifies text search, image search, OCR, cropping, sharpening, super-resolution, and perspective correction, enabling agents to combine active perception with external knowledge acquisition. Finally, we propose a multi-turn fatal-aware GRPO training algorithm that handles cascading tool failures by masking post-failure tokens while preserving useful pre-failure reasoning through one-sided advantage clamping. Built on this recipe, OpenSearch-VL delivers substantial performance gains, with over 10-point average improvements across seven benchmarks, and achieves results comparable to proprietary commercial models on several tasks. We will release all data, code, and models to support open research on multimodal deep search agents.", "project_page": "https://huggingface.co/OpenSearch-VL", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shawn0728/OpenSearch-VL", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.03395", "title": "APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music", "authors": [ "Jaavid Aktar Husain", "Dorien Herremans" ], "github": "https://github.com/AMAAI-Lab/apex", "project_page": "", "abstract": "Music popularity prediction has attracted growing research interest, with relevance to artists, platforms, and recommendation systems. However, the explosive rise of AI-generated music platforms has created an entirely new and largely unexplored landscape, where a surge of songs is produced and consumed daily without the traditional markers of artist reputation or label backing. Key, yet unexplored in this pursuit is aesthetic quality. We propose APEX, the first large-scale multi-task learning framework for AI-generated music, trained on over 211k songs (10k hours of audio) from Suno and Udio, that jointly predicts engagement-based popularity signals - streams and likes scores - alongside five perceptual aesthetic quality dimensions from frozen audio embeddings extracted from MERT, a self-supervised music understanding model. Aesthetic quality and popularity capture complementary aspects of music that together prove valuable: in an out-of-distribution evaluation on the Music Arena dataset, comprising pairwise human preference battles across eleven generative music systems unseen during training, including aesthetic features consistently improves preference prediction, demonstrating strong generalisation of the learned representations across generative architectures.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AMAAI-Lab/apex", "github_api": "https://github.com/AMAAI-Lab/apex", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.03848", "title": "Parameter-Efficient Multi-View Proficiency Estimation: From Discriminative Classification to Generative Feedback", "authors": [ "Edoardo Bianchi", "Antonio Liotta" ], "github": "", "project_page": "", "abstract": "Estimating how well a person performs an action, rather than which action is performed, is central to coaching, rehabilitation, and talent identification. This task is challenging because proficiency is encoded in subtle differences in timing, balance, body mechanics, and execution, often distributed across multiple views and short temporal events. We discuss three recent contributions to multi-view proficiency estimation on Ego-Exo4D. SkillFormer introduces a parameter-efficient discriminative architecture for selective multi-view fusion; PATS improves temporal sampling by preserving locally dense excerpts of fundamental movements; and ProfVLM reformulates proficiency estimation as conditional language generation, producing both a proficiency label and expert-style feedback through a gated cross-view projector and a compact language backbone. Together, these methods achieve state-of-the-art accuracy on Ego-Exo4D with up to 20x fewer trainable parameters and up to 3x fewer training epochs than video-transformer baselines, while moving from closed-set classification toward interpretable feedback generation. These results highlight a shift toward efficient, multi-view systems that combine selective fusion, proficiency-aware sampling, and actionable generative feedback.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-07", "arxiv_id": "2605.00380", "title": "ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning", "authors": [ "Zihan Lin", "Xiaohan Wang", "Jie Cao", "Jiajun Chai", "Li Wang", "Xiaodong Lu", "Wei Lin", "Ran He", "Guojun Yin" ], "github": "https://github.com/1229095296/ResRL", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) enhances reasoning of Large Language Models (LLMs) but usually exhibits limited generation diversity due to the over-incentivization of positive rewards. Although methods like Negative Sample Reinforcement (NSR) mitigate this issue by upweighting penalty from negative samples, they may suppress the semantic distributions shared between positive and negative responses. To boost reasoning ability without losing diversity, this paper proposes negative sample projection Residual Reinforcement Learning (ResRL) that decouples similar semantic distributions among positive and negative responses. We theoretically link Lazy Likelihood Displacement (LLD) to negative-positive head-gradient interference and derive a single-forward proxy that upper-bounds representation alignment to guide conservative advantage reweighting. ResRL then projects negative-token hidden representations onto an SVD-based low-rank positive subspace and uses projection residuals to modulate negative gradients, improving reasoning while preserving diversity and outperforming strong baselines on average across twelve benchmarks spanning Mathematics, Code, Agent Tasks, and Function Calling. Notably, ResRL surpasses NSR on mathematical reasoning by 9.4\\% in Avg@16 and 7.0\\% in Pass@128. Code is available at https://github.com/1229095296/ResRL.git.", "github_manual": "", "github_regex": "https://github.com/1229095296/ResRL.git", "github_paper_md": "", "github_api": "https://github.com/1229095296/ResRL", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2604.24575", "title": "Diffusion Model as a Generalist Segmentation Learner", "authors": [ "Haoxiao Wang", "Antao Xiang", "Haiyang Sun", "Peilin Sun", "Changhao Pan", "Yifu Chen", "Minjie Hong", "Weijie Wang", "Shuang Chen", "Yue Chen", "Zhou Zhao" ], "github": "", "project_page": "", "abstract": "Diffusion models are primarily trained for image synthesis, yet their denoising trajectories encode rich, spatially aligned visual priors. In this paper, we demonstrate that these priors can be utilized for text-conditioned semantic and open-vocabulary segmentation, and this approach can be generalized to various downstream tasks to make a general-purpose diffusion segmentation framework. Concretely, we introduce DiGSeg (Diffusion Models as a Generalist Segmentation Learner), which repurposes a pretrained diffusion model into a unified segmentation framework. Our approach encodes the input image and ground-truth mask into the latent space and concatenates them as conditioning signals for the diffusion U-Net. A parallel CLIP-aligned text pathway injects language features across multiple scales, enabling the model to align textual queries with evolving visual representations. This design transforms an off-the-shelf diffusion backbone into a universal interface that produces structured segmentation masks conditioned on both appearance and arbitrary text prompts. Extensive experiments demonstrate state-of-the-art performance on standard semantic segmentation benchmarks, as well as strong open-vocabulary generalization and cross-domain transfer to medical, remote sensing, and agricultural scenarios-without domain-specific architectural customization. These results indicate that modern diffusion backbones can serve as generalist segmentation learners rather than pure generators, narrowing the gap between visual generation and visual understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-07", "arxiv_id": "2605.03314", "title": "When to Think, When to Speak: Learning Disclosure Policies for LLM Reasoning", "authors": [ "Jiaqi Wei", "Xuehang Guo", "Pengfei Yu", "Xiang Zhang", "Wanli Ouyang", "Siqi Sun", "Qingyun Wang", "Chenyu You" ], "github": "https://github.com/THUDM/slime", "project_page": "", "abstract": "In single-stream autoregressive interfaces, the same tokens both update the model state and constitute an irreversible public commitment. This coupling creates a silence tax: additional deliberation postpones the first task-relevant content, while naive early streaming risks premature commitments that bias subsequent generations. We introduce Side-by-Side (SxS) Interleaved Reasoning, which makes disclosure timing a controllable decision within standard autoregressive generation. SxS interleaves partial disclosures with continued private reasoning in the same context, but releases content only when it is supported by the reasoning so far. To learn such pacing without incentivizing filler, we construct entailment-aligned interleaved trajectories by matching answer prefixes to supporting reasoning prefixes, then train with SFT to acquire the dual-action semantics and RL to recover reasoning performance under the new format. Across two Qwen3 architectures/scales (MoE Qwen3-30B-A3B, dense Qwen3-4B) and both in-domain (AIME25) and out-of-domain (GPQA-Diamond) benchmarks, SxS improves accuracy--content-latency Pareto trade-offs under token-level proxies such as inter-update waiting.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/THUDM/slime", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-07", "arxiv_id": "2605.01234", "title": "TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos", "authors": [ "Nima Rahmanian", "Daniel Kienzle", "Thomas Gossard", "Dvij Kalaria", "Rainer Lienhart", "Shankar Sastry" ], "github": "", "project_page": "", "abstract": "We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides 140+ hours of reconstructed singles and doubles gameplay from monocular broadcast videos, featuring multimodal annotations like high-quality camera calibrations, precise 3D ball positions, ball spin, time segmentation, and 3D human meshes over time. This rich data provides a new foundation for virtual replay, in-depth player analysis, and robot learning. The dataset's combination of scale and precision is achieved through a novel reconstruction pipeline. Prior methods first partition a game sequence into individual shot segments based on the 2D ball track, and only then attempt reconstruction. However, 2D-based time segmentation collapses under occlusion and varied camera viewpoints, preventing reliable reconstruction. We invert this paradigm by first lifting the entire unsegmented 2D ball track to 3D through a learned lifting network. This 3D trajectory then allows us to reliably perform time segmentation. The learned lifting network also infers the ball's spin, handles unreliable ball detections, and successfully reconstructs the ball trajectory in cases of high occlusion. This lift-first design is necessary, as our pipeline is the only method capable of reconstructing table tennis gameplay from general-view broadcast monocular videos. We demonstrate the dataset's fidelity through two downstream tasks: estimating the racket's pose \\& velocity at impact, and training a generative model of competitive rallies.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-07", "arxiv_id": "2604.27393", "title": "MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction", "authors": [ "Junbo Cui", "Bokai Xu", "Chongyi Wang", "Tianyu Yu", "Weiyue Sun", "Yingjing Xu", "Tianran Wang", "Zhihui He", "Wenshuo Ma", "Tianchi Cai", "Jiancheng Gui", "Luoyuan Zhang", "Xian Sun", "Fuwei Huang", "Moye Chen", "Zhuo Lin", "Hanyu Liu", "Qingxin Gui", "Qingzhe Han", "Yuyang Wen", "Huiping Liu", "Rongkang Wang", "Yaqi Zhang", "Hongliang Wei", "Chi Chen", "You Li", "Kechen Fang", "Jie Zhou", "Yuxuan Li", "Guoyang Zeng", "Chaojun Xiao", "Yankai Lin", "Xu Han", "Maosong Sun", "Zhiyuan Liu", "Yuan Yao" ], "github": "https://github.com/OpenBMB/MiniCPM-o", "abstract": "Recent progress in multimodal large language models (MLLMs) has brought AI capabilities from static offline data processing to real-time streaming interaction, yet they still remain far from human-level multimodal interaction. The key bottlenecks are no longer modality coverage or latency alone, but the interaction paradigm itself. First, perception and response are still separated into alternating phases, preventing models from incorporating new inputs for timely adjustment during generation. Second, most current models remain reactive, responding only to explicit user requests instead of acting proactively in the evolving multimodal environment. We present MiniCPM-o 4.5, our latest effort towards human-like multimodal interaction, which mitigates these gaps by real-time full-duplex omni-modal interaction. It can see, listen, and speak simultaneously in real-time, while also exhibiting proactive behaviors such as issuing reminders or comments based on its continuous understanding of the live scene. The key technique behind MiniCPM-o 4.5 is Omni-Flow, a unified streaming framework that aligns omni-modal inputs and outputs along a shared temporal axis. This formulation converts conventional turn-based interaction into a full-duplex, time-aligned process, enabling simultaneous perception and response and allowing proactive behavior to arise within the same framework. With a total of 9B parameters, MiniCPM-o 4.5 approaches Gemini 2.5 Flash in vision-language capabilities, delivering state-of-the-art open-source performance at its scale. It also surpasses Qwen3-Omni-30B-A3B in omni-modal understanding and delivers better speech generation, with significantly higher computation efficiency. Driven by its efficient architecture design and inference optimization, the model can perform real-time full-duplex omni-modal interaction on edge devices with less than 12GB RAM cost.", "project_page": "https://huggingface.co/openbmb/MiniCPM-o-4_5", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenBMB/MiniCPM-o", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.05166", "title": "The First Token Knows: Single-Decode Confidence for Hallucination Detection", "authors": [ "Mina Gabriel" ], "github": "", "project_page": "", "abstract": "Self-consistency detects hallucinations by generating multiple sampled answers to a question and measuring agreement, but this requires repeated decoding and can be sensitive to lexical variation. Semantic self-consistency improves this by clustering sampled answers by meaning using natural language inference, but it adds both sampling cost and external inference overhead. We show that first-token confidence, phi_first, computed from the normalized entropy of the top-K logits at the first content-bearing answer token of a single greedy decode, matches or modestly exceeds semantic self-consistency on closed-book short-answer factual question answering. Across three 7-8B instruction-tuned models and two benchmarks, phi_first achieves a mean AUROC of 0.820, compared with 0.793 for semantic agreement and 0.791 for standard surface-form self-consistency. A subsumption test shows that phi_first is moderately to strongly correlated with semantic agreement, and combining the two signals yields only a small AUROC improvement over phi_first alone. These results suggest that much of the uncertainty information captured by multi-sample agreement is already available in the model's initial token distribution. We argue that phi_first should be reported as a default low-cost baseline before invoking sampling-based uncertainty estimation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-07", "arxiv_id": "2605.04637", "title": "SWE-WebDevBench: Evaluating Coding Agent Application Platforms as Virtual Software Agencies", "authors": [ "Siddhant Saxena", "Nilesh Trivedi", "Vinayaka Jyothi" ], "github": "https://github.com/snowmountainAi/webdevbench", "abstract": "The emergence of \"vibe coding\" platforms, where users describe applications in natural language and AI agents autonomously generate full-stack software, has created a need for rigorous evaluation beyond code-level benchmarks. In order to assess them as virtual software development agencies on understanding business requirements, making architectural decisions, writing production code, handling iterative modifications, and maintaining business readiness, we introduce SWE-WebDev Bench, a 68-metric evaluation framework spanning 25 primary and 43 diagnostic metrics across seven groups, organized along three dimensions: Interaction Mode (App Creation Request (ACR) vs. App Modification Request (AMR)), Agency Angle (Product Manager (PM), Engineering, Ops), and Complexity Tier (T4 multi-role SaaS, T5 AI-native). Our evaluation (six platforms, three domains, 18 evaluation cells) reveals four recurring shortcomings in the current generation of AI app builders: (1) A specification bottleneck, where platforms compress rich business requirements into oversimplified technical plans, (2) A pervasive frontend-backend decoupling, where visually polished UIs mask absent or broken backend infrastructure, (3) A steep production-readiness cliff, where no platform scores above 60% on engineering quality and post-generation human effort varies substantially across platforms and (4) Widespread security and infrastructure failures, with no platform exceeding 65% Security Score against a 90% target and concurrency handling as low as 6%. These observations are descriptive of our sample and require larger-scale replication to establish generality. We release SWE-WebDev Bench as a community benchmark to enable such replication and help platform builders identify and address these gaps. Code and benchmark resources are available at: https://github.com/snowmountainAi/webdevbench and https://webdevbench.com/.", "project_page": "https://webdevbench.com", "github_manual": "", "github_regex": "https://github.com/snowmountainAi/webdevbench", "github_paper_md": "", "github_api": "https://github.com/snowmountainAi/webdevbench", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.02910", "title": "CreativityBench: Evaluating Agent Creative Reasoning via Affordance-Based Tool Repurposing", "authors": [ "Cheng Qian", "Hyeonjeong Ha", "Jiayu Liu", "Jeonghwan Kim", "Jiateng Liu", "Bingxuan Li", "Aditi Tiwari", "Dwip Dalal", "Zhenhailong Wang", "Xiusi Chen", "Mahdi Namazifar", "Yunzhu Li", "Heng Ji" ], "github": "https://github.com/CreativityBench/CreativityBench", "abstract": "Recent advances in large language models have led to strong performance on reasoning and environment-interaction tasks, yet their ability for creative problem-solving remains underexplored. We study this capability through the lens of creative tool use, where a model repurposes available objects by reasoning about their affordances and attributes rather than relying on canonical usage. As a first step, we introduce CreativityBench, a benchmark for evaluating affordance-based creativity in LLMs. To this end, we build a large-scale affordance knowledge base (KB) with 4K entities and 150K+ affordance annotations, explicitly linking objects, parts, attributes, and actionable uses. Building on this KB, we generate 14K grounded tasks that require identifying non-obvious yet physically plausible solutions under constraints. Evaluations across 10 state-of-the-art LLMs, including closed and open-source models, show that models can often select a plausible object, but fail to identify the correct parts, their affordances, and the underlying physical mechanism needed to solve the task, leading to a significant drop in performance. Furthermore, improvements from model scaling quickly saturate, strong general reasoning does not reliably translate to creative affordance discovery, and common inference-time strategies such as Chain-of-Thought yield limited gains. These results suggest that creative tool use remains a major challenge for current models, and that CreativityBench provides a useful testbed for studying this missing dimension of intelligence, with potential implications for planning and reasoning modules in future agents.", "project_page": "https://creativitybench.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/qiancheng0/CreativityBench", "github_api": "https://github.com/CreativityBench/CreativityBench", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2604.25788", "title": "KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning", "authors": [ "Yixuan Huang", "Bowen Li", "Vaibhav Saxena", "Yichao Liang", "Utkarsh Aashu Mishra", "Liang Ji", "Lihan Zha", "Jimmy Wu", "Nishanth Kumar", "Sebastian Scherer", "Danfei Xu", "Tom Silver" ], "github": "https://github.com/Princeton-Robot-Planning-and-Learning/kindergarden", "abstract": "Robotic systems that interact with the physical world must reason about kinematic and dynamic constraints imposed by their own embodiment, their environment, and the task at hand. We introduce KinDER, a benchmark for Kinematic and Dynamic Embodied Reasoning that targets physical reasoning challenges arising in robot learning and planning. KinDER comprises 25 procedurally generated environments, a Gymnasium-compatible Python library with parameterized skills and demonstrations, and a standardized evaluation suite with 13 implemented baselines spanning task and motion planning, imitation learning, reinforcement learning, and foundation-model-based approaches. The environments are designed to isolate five core physical reasoning challenges: basic spatial relations, nonprehensile multi-object manipulation, tool use, combinatorial geometric constraints, and dynamic constraints, disentangled from perception, language understanding, and application-specific complexity. Empirical evaluation shows that existing methods struggle to solve many of the environments, indicating substantial gaps in current approaches to physical reasoning. We additionally include real-to-sim-to-real experiments on a mobile manipulator to assess the correspondence between simulation and real-world physical interaction. KinDER is fully open-sourced and intended to enable systematic comparison across diverse paradigms for advancing physical reasoning in robotics. Website and code: https://prpl-group.com/kinder-site/", "project_page": "https://prpl-group.com/kinder-site/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Princeton-Robot-Planning-and-Learning/kindergarden", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2510.04142", "title": "Turning Drift into Constraint: Robust Reasoning Alignment in Non-Stationary Environments", "authors": [ "Xiaoyu Yang", "En Yu", "Wei Duan", "Jie Lu" ], "github": "https://github.com/XiaoyuYoung/APO", "abstract": "This paper identifies a critical yet underexplored challenge in reasoning alignment from multiple multi-modal large language models (MLLMs): In non-stationary environments, the diverse reasoning distributions of source models often evolve unpredictably, transmitting systematic biases and drift to the target model. To address this, we formulate multi-source reasoning alignment as a constraint satisfaction problem under concept drift theory. We propose Autonomous Preference Optimization (APO), a novel framework that treats inter-model divergences not as noise, but as dynamic negative constraints. APO operates via a two-stage protocol: first, supervised bootstrapping projects the target model into the capability union of source models; second, constraint-aware optimization synthesizes a consistent consensus manifold by explicitly suppressing drifting trajectories via a multi-negative Plackett-Luce objective. Extensive experiments on chest X-ray interpretation demonstrate that our 7B model achieves superior robustness, outperforming even proprietary source models in average accuracy. Furthermore, we release CXR-MAX, a large-scale benchmark comprising 170,982 reasoning trajectories from seven large-scale MLLMs to facilitate research on reasoning alignment under drift. Code and data are available at: https://github.com/XiaoyuYoung/APO.", "project_page": "https://xiaoyuyoung.github.io/APO/", "github_manual": "", "github_regex": "https://github.com/XiaoyuYoung/APO", "github_paper_md": "", "github_api": "https://github.com/XiaoyuYoung/APO", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.05662", "title": "XL-SafetyBench: A Country-Grounded Cross-Cultural Benchmark for LLM Safety and Cultural Sensitivity", "authors": [ "Dasol Choi", "Eugenia Kim", "Jaewon Noh", "Sang Seo", "Eunmi Kim", "Myunggyo Oh", "Yunjin Park", "Brigitta Jesica Kartono", "Josef Pichlmeier", "Helena Berndt", "Sai Krishna Mendu", "Glenn Johannes Tungka", "\u00d6zlem G\u00f6k\u00e7e", "Suresh Gehlot", "Katherine Pratt", "Amanda Minnich", "Haon Park" ], "github": "https://github.com/AIM-Intelligence/XL-SafetyBench", "project_page": "", "abstract": "Current LLM safety benchmarks are predominantly English-centric and often rely on translation, failing to capture country-specific harms. Moreover, they rarely evaluate a model's ability to detect culturally embedded sensitivities as distinct from universal harms. We introduce XL-SafetyBench. a suite of 5,500 test cases across 10 country-language pairs, comprising a Jailbreak Benchmark of country-grounded adversarial prompts and a Cultural Benchmark where local sensitivities are embedded within innocuous requests. Each item is constructed via a multi-stage pipeline that combines LLM-assisted discovery, automated validation gates, and dual independent native-speaker annotators per country. To distinguish principled refusal from comprehension failure, we evaluate Attack Success Rate (ASR) alongside two complementary metrics we introduce: Neutral-Safe Rate (NSR) and Cultural Sensitivity Rate (CSR). Evaluating 10 frontier and 27 local LLMs reveals two key findings. First, jailbreak robustness and cultural awareness do not show a coupled relationship among frontier models, so a composite safety score obscures per-axis variation. Second, local models exhibit a near-linear ASR-NSR trade-off (r = -0.81), indicating that their apparent safety reflects generation failure rather than genuine alignment. XL-SafetyBench enables more nuanced, cross-cultural safety evaluation in the multilingual era.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AIM-Intelligence/XL-SafetyBench", "github_api_added_by": "user" }, { "date": "2026-05-07", "arxiv_id": "2605.04916", "title": "A Foundation Model for Zero-Shot Logical Rule Induction", "authors": [ "Yin Jun Phua" ], "github": "https://github.com/phuayj/neural-rule-inducer", "abstract": "Inductive Logic Programming (ILP) learns interpretable logical rules from data. Existing methods are transductive: their learned parameters are bound to specific predicates and require retraining for each new task. We introduce Neural Rule Inducer (NRI), a pretrained model for zero-shot rule induction. Rather than encoding literal identities, NRI represents literals using domain-agnostic statistical properties such as class-conditional rates, entropy, and co-occurrence, which generalize across variable identities and counts without retraining. The model consists of a statistical encoder and a parallel slot-based decoder. Parallel decoding preserves the permutation invariance of logical disjunction; an autoregressive decoder would instead impose an arbitrary clause order. Product T-norm relaxation makes rule execution differentiable, allowing end-to-end training on prediction accuracy alone. We evaluate NRI on rule recovery, robustness to label noise and spurious correlations, and zero-shot transfer to real-world benchmarks, and we believe this work opens up the possibility of foundation models for symbolic reasoning. Code and the reference checkpoint are available at https://github.com/phuayj/neural-rule-inducer.", "project_page": "https://yinjunphua.com/papers/nri/", "github_manual": "", "github_regex": "https://github.com/phuayj/neural-rule-inducer", "github_paper_md": "", "github_api": "https://github.com/phuayj/neural-rule-inducer", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.05724", "title": "Auto Research with Specialist Agents Develops Effective and Non-Trivial Training Recipes", "authors": [ "Jingjie Ning", "Xiaochuan Li", "Ji Zeng", "Hao Kang", "Chenyan Xiong" ], "github": "https://github.com/cxcscmu/Auto-Research-Recipes", "project_page": "", "abstract": "We study auto research as a closed empirical loop driven by external measurement. Each submitted trial carries a hypothesis, an executable code edit, an evaluator-owned outcome, and feedback that shapes the next proposal. The output is not a generated paper or a single model checkpoint, but an auditable trajectory of proposals, code diffs, experiments, scores, and failure labels. We instantiate this loop with specialist agents that partition recipe surfaces and share measured lineage across trials. The central empirical finding is that lineage feedback lets agents turn evaluator outcomes, including crashes, budget overruns, size failures, and accuracy-gate misses, into later program-level recipe edits rather than one-shot suggestions. Across 1,197 headline-run trials plus 600 Parameter Golf control trials after one-time setup and launch, humans did not choose proposals, edit recipes, override scores, or repair failed trials during the search. In the three headline runs, the same submitted-trial loop reduces Parameter Golf validation bpb by 0.81%, raises NanoChat-D12 CORE by 38.7%, and reduces CIFAR-10 Airbench96 wallclock by 4.59%, with each task measured by its own external evaluator and legality checks. The trace includes a strict architecture-domain audit of 157 headline-run submissions and program rewrites such as a NanoChat attention-kernel path change. Within this scope the loop autonomously writes code, submits experiments, absorbs feedback, applies and combines known techniques inside each environment, and improves public starting recipes.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/cxcscmu/Auto-Research-Recipes", "github_api": "https://github.com/cxcscmu/Auto-Research-Recipes", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.06196", "title": "The Granularity Axis: A Micro-to-Macro Latent Direction for Social Roles in Language Models", "authors": [ "Chonghan Qin", "Xiachong Feng", "Ziyun Song", "Xiaocheng Feng", "Jing Xiong", "Lingpeng Kong" ], "github": "https://github.com/qinchonghanzuibang/Granularity-Axis", "abstract": "Large language models (LLMs) are routinely prompted to take on social roles ranging from individuals to institutions, yet it remains unclear whether their internal representations encode the granularity of such roles, from micro-level individual experience to macro-level organizational, institutional, or national reasoning. We show that they do. We define a contrast-based Granularity Axis as the difference between mean macro- and micro-role hidden states. In Qwen3-8B, this axis aligns with the principal axis (PC1) of the role representation space at cosine 0.972 and accounts for 52.6% of its variance, indicating that granularity is the dominant geometric axis organizing prompted social roles. We construct 75 social roles across five granularity levels and collect 91,200 role-conditioned responses over shared questions and prompt variants, then extract role-level hidden states and project them onto the axis. Role projections increase monotonically across all five levels, remain stable across layers, prompt variants, endpoint definitions, held-out splits, and score-filtered subsets, and transfer to Llama-3.1-8B-Instruct. The axis is also causally relevant: activation steering along it shifts response granularity in the predicted direction, with Llama moving from 2.00 to 3.17 on a five-point macro scale under positive steering on prompts that admit local responses. The two models differ in controllability, suggesting that steering depends on each model's default operating regime. Overall, our findings suggest that social role granularity is not merely a stylistic surface feature, but a structured, ordered, and causally manipulable latent direction in role-conditioned language model behavior.", "project_page": "https://github.com/qinchonghanzuibang/Granularity-Axis", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/qinchonghanzuibang/Granularity-Axis", "github_api": "https://github.com/qinchonghanzuibang/Granularity-Axis", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.06507", "title": "MARBLE: Multi-Aspect Reward Balance for Diffusion RL", "authors": [ "Canyu Zhao", "Hao Chen", "Yunze Tong", "Yu Qiao", "Jiacheng Li", "Chunhua Shen" ], "github": "https://github.com/aim-uofa/MARBLE", "abstract": "Reinforcement learning fine-tuning has become the dominant approach for aligning diffusion models with human preferences. However, assessing images is intrinsically a multi-dimensional task, and multiple evaluation criteria need to be optimized simultaneously. Existing practice deal with multiple rewards by training one specialist model per reward, optimizing a weighted-sum reward R(x)=sum_k w_k R_k(x), or sequentially fine-tuning with a hand-crafted stage schedule. These approaches either fail to produce a unified model that can be jointly trained on all rewards or necessitates heavy manually tuned sequential training. We find that the failure stems from using a naive weighted-sum reward aggregation. This approach suffers from a sample-level mismatch because most rollouts are specialist samples, highly informative for certain reward dimensions but irrelevant for others; consequently, weighted summation dilutes their supervision. To address this issue, we propose MARBLE (Multi-Aspect Reward BaLancE), a gradient-space optimization framework that maintains independent advantage estimators for each reward, computes per-reward policy gradients, and harmonizes them into a single update direction without manually-tuned reward weighting, by solving a Quadratic Programming problem. We further propose an amortized formulation that exploits the affine structure of the loss used in DiffusionNFT, to reduce the per-step cost from K+1 backward passes to near single-reward baseline cost, together with EMA smoothing on the balancing coefficients to stabilize updates against transient single-batch fluctuations. On SD3.5 Medium with five rewards, MARBLE improves all five reward dimensions simultaneously, turns the worst-aligned reward's gradient cosine from negative under weighted summation in 80% of mini-batches to consistently positive, and runs at 0.97X the training speed of baseline training.", "project_page": "https://aim-uofa.github.io/MARBLE/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aim-uofa/MARBLE", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.06416", "title": "MiA-Signature: Approximating Global Activation for Long-Context Understanding", "authors": [ "Yuqing Li", "Jiangnan Li", "Mo Yu", "Zheng Lin", "Weiping Wang", "Jie Zhou" ], "github": "", "project_page": "", "abstract": "A growing body of work in cognitive science suggests that reportable conscious access is associated with global ignition over distributed memory systems, while such activation is only partially accessible as individuals cannot directly access or enumerate all activated contents. This tension suggests a plausible mechanism that cognition may rely on a compact representation that approximates the global influence of activation on downstream processing. Inspired by this idea, we introduce the concept of Mindscape Activation Signature (MiA-Signature), a compressed representation of the global activation pattern induced by a query. In LLM systems, this is instantiated via submodular-based selection of high-level concepts that cover the activated context space, optionally refined through lightweight iterative updates using working memory. The resulting MiA-Signature serves as a conditioning signal that approximates the effect of the full activation state while remaining computationally tractable. Integrating MiA-Signatures into both RAG and agentic systems yields consistent performance gains across multiple long-context understanding tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.06548", "title": "Continuous Latent Diffusion Language Model", "authors": [ "Hongcan Guo", "Qinyu Zhao", "Yian Zhao", "Shen Nie", "Rui Zhu", "Qiushan Guo", "Feng Wang", "Tao Yang", "Hengshuang Zhao", "Guoqiang Wei", "Yan Zeng" ], "github": "", "abstract": "Large language models have achieved remarkable success under the autoregressive paradigm, yet high-quality text generation need not be tied to a fixed left-to-right order. Existing alternatives still struggle to jointly achieve generation efficiency, scalable representation learning, and effective global semantic modeling. We propose Cola DLM, a hierarchical latent diffusion language model that frames text generation through hierarchical information decomposition. Cola DLM first learns a stable text-to-latent mapping with a Text VAE, then models a global semantic prior in continuous latent space with a block-causal DiT, and finally generates text through conditional decoding. From a unified Markov-path perspective, its diffusion process performs latent prior transport rather than token-level observation recovery, thereby separating global semantic organization from local textual realization. This design yields a more flexible non-autoregressive inductive bias, supports semantic compression and prior fitting in continuous space, and naturally extends to other continuous modalities. Through experiments spanning 4 research questions, 8 benchmarks, strictly matched ~2B-parameter autoregressive and LLaDA baselines, and scaling curves up to about 2000 EFLOPs, we identify an effective overall configuration of Cola DLM and verify its strong scaling behavior for text generation. Taken together, the results establish hierarchical continuous latent prior modeling as a principled alternative to strictly token-level language modeling, where generation quality and scaling behavior may better reflect model capability than likelihood, while also suggesting a concrete path toward unified modeling across discrete text and continuous modalities.", "project_page": "https://hongcanguo.github.io/Cola-DLM/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.06665", "title": "UniPool: A Globally Shared Expert Pool for Mixture-of-Experts", "authors": [ "Minbin Huang", "Han Shi", "Chuanyang Zheng", "Yimeng Wu", "Guoxuan Chen", "Xintong Yu", "Yichun Yin", "Hong Cheng" ], "github": "https://github.com/Centaurus-Alpha/UniPool", "project_page": "", "abstract": "Modern Mixture-of-Experts (MoE) architectures allocate expert capacity through a rigid per-layer rule: each transformer layer owns a separate expert set. This convention couples depth scaling with linear expert-parameter growth and assumes that every layer needs isolated expert capacity. However, recent analyses and our routing probe challenge this allocation rule: replacing a deeper layer's learned top-k router with uniform random routing drops downstream accuracy by only 1.0-1.6 points across multiple production MoE models. Motivated by this redundancy, we propose UniPool, an MoE architecture that treats expert capacity as a global architectural budget by replacing per-layer expert ownership with a single shared pool accessed by independent per-layer routers. To enable stable and balanced training under sharing, we introduce a pool-level auxiliary loss that balances expert utilization across the entire pool, and adopt NormRouter to provide sparse and scale-stable routing into the shared expert pool. Across five LLaMA-architecture model scales (182M, 469M, 650M, 830M, and 978M parameters) trained on 30B tokens from the Pile, UniPool consistently improves validation loss and perplexity over the matched vanilla MoE baselines. Across these scales, UniPool reduces validation loss by up to 0.0386 relative to vanilla MoE. Beyond raw loss improvement, our results identify pool size as an explicit depth-scaling hyperparameter: reduced-pool UniPool variants using only 41.6%-66.7% of the vanilla expert-parameter budget match or outperform layer-wise MoE at the tested scales. This shows that, under a shared-pool design, expert parameters need not grow linearly with depth; they can grow sublinearly while remaining more efficient and effective than vanilla MoE. Further analysis shows that UniPool's benefits compose with finer-grained expert decomposition.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Centaurus-Alpha/UniPool", "github_api": "https://github.com/Centaurus-Alpha/UniPool", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.04647", "title": "ReflectDrive-2: Reinforcement-Learning-Aligned Self-Editing for Discrete Diffusion Driving", "authors": [ "Huimin Wang", "Yue Wang", "Bihao Cui", "Pengxiang Li", "Ben Lu", "Mingqian Wang", "Tong Wang", "Chuan Tang", "Teng Zhang", "Kun Zhan" ], "github": "", "project_page": "", "abstract": "We introduce ReflectDrive-2, a masked discrete diffusion planner with separate action expert for autonomous driving that represents plans as discrete trajectory tokens and generates them through parallel masked decoding. This discrete token space enables in-place trajectory revision: AutoEdit rewrites selected tokens using the same model, without requiring an auxiliary refinement network. To train this capability, we use a two-stage procedure. First, we construct structure-aware perturbations of expert trajectories along longitudinal progress and lateral heading directions and supervise the model to recover the original expert trajectory. We then fine-tune the full decision--draft--reflect rollout with reinforcement learning (RL), assigning terminal driving reward to the final post-edit trajectory and propagating policy-gradient credit through full-rollout transitions. Full-rollout RL proves crucial for coupling drafting and editing: under supervised training alone, inference-time AutoEdit improves PDMS by at most 0.3, whereas RL increases its gain to 1.9. We also co-design an efficient reflective decoding stack for the decision--draft--reflect pipeline, combining shared-prefix KV reuse, Alternating Step Decode, and fused on-device unmasking. On NAVSIM, ReflectDrive-2 achieves 91.0 PDMS with camera-only input and 94.8 PDMS in a best-of-6 oracle setting, while running at 31.8 ms average latency on NVIDIA Thor.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.06638", "title": "Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key", "authors": [ "Tianle Wang", "Zhaoyang Wang", "Guangchen Lan", "Xinpeng Wei", "Sipeng Zhang", "Guanwen Qiu", "Abulhair Saparov" ], "github": "", "project_page": "", "abstract": "Reinforcement learning (RL) has been applied to improve large language model (LLM) reasoning, yet the systematic study of how training scales with task difficulty has been hampered by the lack of controlled, scalable environments. We introduce ScaleLogic, a synthetic logical reasoning framework that offers independent control over two axes of difficulty: the depth of the required proof planning (i.e., the horizon) and the expressiveness of the underlying logic. Our proposed framework supports a wide range of logics: from simple implication-only logic (\"if-then\") towards more expressive first-order reasoning with conjunction (\"and\"), disjunction (\"or\"), negation (\"not\"), and universal quantification (\"for all\"). Using this framework, we show that the RL training compute T follows a power law with respect to reasoning depth D (T propto D^\u03b3, R^{2} > 0.99), and that the scaling exponent \u03b3 increases monotonically with logical expressiveness, from 1.04 to 2.60. On downstream mathematics and general reasoning benchmarks, more expressive training settings yield both larger performance gains (up to +10.66 points) and more compute-efficient transfer compared to less expressive settings, demonstrating that what a model is trained on, not just how much it is trained, shapes downstream transfer. We further show that the power-law relationship holds across multiple RL methods, and curriculum-based training substantially improves scaling efficiency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.05566", "title": "Nonsense Helps: Prompt Space Perturbation Broadens Reasoning Exploration", "authors": [ "Langlin Huang", "Chengsong Huang", "Jinyuan Li", "Donghong Cai", "Yuyi Yang", "Jiaxin Huang" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards, particularly Group Relative Policy Optimization (GRPO), has significantly advanced the reasoning capabilities of Large Language Models (LLMs). However, in complex tasks, GRPO frequently suffers from the ``zero-advantage problem'': when all sampled rollouts for a query fail, the relative advantage collapses to zero. Consequently, the model loses effective training signals for these questions, wasting the training data and computational budget. While simply increasing the sampling budget for these questions is a common remedy, the static sampling policy inherently constrains reasoning exploration, limiting the success rate. In this paper, we propose Lorem Perturbation for Exploration (LoPE), a simple yet effective training framework to break this exploration bottleneck. We posit that task-irrelevant prompt-space perturbations can shift the model's output distribution enough to unlock orthogonal reasoning pathways for hard questions. Specifically, LoPE prepends sequences stochastically assembled from Lorem Ipsum vocabulary (a pseudo-Latin placeholder text) to the prompts before resampling. Experiments across 1.7B, 4B, and 7B models demonstrate that LoPE significantly outperforms resampling with the original prompts. Further analysis reveals that other Latin-based random sequences with low perplexity are also effective perturbations. Our results establish LoPE as a strong baseline for broadening exploration in LLM reinforcement learning.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.05922", "title": "Think, then Score: Decoupled Reasoning and Scoring for Video Reward Modeling", "authors": [ "Yuan Wang", "Ouxiang Li", "Yulong Xu", "Borui Liao", "Jiajun Liang", "Jinghan Li", "Meng Wang", "Xintao Wang", "Pengfei Wang", "Kuien Liu", "Xiang Wang" ], "github": "", "project_page": "", "abstract": "Recent advances in generative video models are increasingly driven by post-training and test-time scaling, both of which critically depend on the quality of video reward models (RMs). An ideal reward model should predict accurate rewards that align with human preferences across diverse scenarios. However, existing paradigms face a fundamental dilemma: Discriminative RMs regress rewards directly on features extracted by multimodal large language models (MLLMs) without explicit reasoning, making them prone to shortcut learning and heavily reliant on massive data scaling for generalization. In contrast, Generative RMs with Chain-of-Thought (CoT) reasoning exhibit superior interpretability and generalization potential, as they leverage fine-grained semantic supervision to internalize the rationales behind human preferences. However, they suffer from inherent optimization bottlenecks due to the coupling of reasoning and scoring within a single autoregressive inference chain. To harness the generalization benefits of CoT reasoning while mitigating the training instability of coupled reasoning and scoring, we introduce DeScore, a training-efficient and generalizable video reward model. DeScore employs a decoupled ``think-then-score'' paradigm: an MLLM first generates an explicit CoT, followed by a dedicated discriminative scoring module consisting of a learnable query token and a regression head that predicts the final reward. DeScore is optimized via a two-stage framework: (1) a discriminative cold start incorporating a random mask mechanism to ensure robust scoring capabilities, and (2) a dual-objective reinforcement learning stage that independently refines CoT reasoning quality and calibrates the final reward, ensuring that higher-quality reasoning directly translates to superior model performance.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.06614", "title": "SkillOS: Learning Skill Curation for Self-Evolving Agents", "authors": [ "Siru Ouyang", "Jun Yan", "Yanfei Chen", "Rujun Han", "Zifeng Wang", "Bhavana Dalvi Mishra", "Rui Meng", "Chun-Liang Li", "Yizhu Jiao", "Kaiwen Zha", "Maohao Shen", "Vishy Tirumalashetty", "George Lee", "Jiawei Han", "Tomas Pfister", "Chen-Yu Lee" ], "github": "", "project_page": "", "abstract": "LLM-based agents are increasingly deployed to handle streaming tasks, yet they often remain one-off problem solvers that fail to learn from past interactions. Reusable skills distilled from experience provide a natural substrate for self-evolution, where high-quality skill curation serves as the key bottleneck. Existing approaches either rely on manual skill curation, prescribe heuristic skill operations, or train for short-horizon skill operations. However, they still struggle to learn complex long-term curation policies from indirect and delayed feedback. To tackle this challenge, we propose SkillOS, an experience-driven RL training recipe for learning skill curation in self-evolving agents. SkillOS pairs a frozen agent executor that retrieves and applies skills with a trainable skill curator that updates an external SkillRepo from accumulated experience. To provide learning signals for curation, we design composite rewards and train on grouped task streams based on skill-relevant task dependencies, where earlier trajectories update the SkillRepo, and later related tasks evaluate these updates. Across multi-turn agentic tasks and single-turn reasoning tasks, SkillOS consistently outperforms memory-free and strong memory-based baselines in both effectiveness and efficiency, with the learned skill curator generalizing across different executor backbones and task domains. Further analyses show that the learned curator produces more targeted skill use, while the skills in SkillRepo evolve into more richly structured Markdown files that encode higher-level meta-skills over time.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.06130", "title": "Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning", "authors": [ "Yaorui Shi", "Yuxin Chen", "Zhengxi Lu", "Yuchun Miao", "Shugui Liu", "Qi GU", "Xunliang Cai", "Xiang Wang", "An Zhang" ], "github": "https://github.com/AlphaLab-USTC/Skill1", "project_page": "", "abstract": "A persistent skill library allows language model agents to reuse successful strategies across tasks. Maintaining such a library requires three coupled capabilities. The agent selects a relevant skill, utilizes it during execution, and distills new skills from experience. Existing methods optimize these capabilities in isolation or with separate reward sources, resulting in partial and conflicting evolution. We propose Skill1, a framework that trains a single policy to co-evolve skill selection, utilization, and distillation toward a shared task-outcome objective. The policy generates a query to search the skill library, re-ranks candidates to select one, solves the task conditioned on it, and distills a new skill from the trajectory. All learning derives from a single task-outcome signal. Its low-frequency trend credits selection and its high-frequency variation credits distillation. Experiments on ALFWorld and WebShop show that Skill1 outperforms prior skill-based and reinforcement learning baselines. Training dynamics confirm the co-evolution of the three capabilities, and ablations show that removing any credit signal degrades the evolution.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/AlphaLab-USTC/Skill1", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.06651", "title": "AI Co-Mathematician: Accelerating Mathematicians with Agentic AI", "authors": [ "Daniel Zheng", "Ingrid von Glehn", "Yori Zwols", "Iuliya Beloshapka", "Lars Buesing", "Daniel M. Roy", "Martin Wattenberg", "Bogdan Georgiev", "Tatiana Schmidt", "Andrew Cowie", "Fernanda Viegas", "Dimitri Kanevsky", "Vineet Kahlon", "Hartmut Maennel", "Sophia Alj", "George Holland", "Alex Davies", "Pushmeet Kohli" ], "github": "", "project_page": "", "abstract": "We introduce the AI co-mathematician, a workbench for mathematicians to interactively leverage AI agents to pursue open-ended research. The AI co-mathematician is optimized to provide holistic support for the exploratory and iterative reality of mathematical workflows, including ideation, literature search, computational exploration, theorem proving and theory building. By providing an asynchronous, stateful workspace that manages uncertainty, refines user intent, tracks failed hypotheses, and outputs native mathematical artifacts, the system mirrors human collaborative workflows. In early tests, the AI co-mathematician helped researchers solve open problems, identify new research directions, and uncover overlooked literature references. Besides demonstrating a highly interactive paradigm for AI-assisted mathematical discovery, the AI co-mathematician also achieves state of the art results on hard problem-solving benchmarks, including scoring 48% on FrontierMath Tier 4, a new high score among all AI systems evaluated.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.06376", "title": "Continuous-Time Distribution Matching for Few-Step Diffusion Distillation", "authors": [ "Tao Liu", "Hao Yan", "Mengting Chen", "Taihang Hu", "Zhengrong Yue", "Zihao Pan", "Jinsong Lan", "Xiaoyong Zhu", "Ming-Ming Cheng", "Bo Zheng", "Yaxing Wang" ], "github": "https://github.com/byliutao/cdm", "abstract": "Step distillation has become a leading technique for accelerating diffusion models, among which Distribution Matching Distillation (DMD) and Consistency Distillation are two representative paradigms. While consistency methods enforce self-consistency along the full PF-ODE trajectory to steer it toward the clean data manifold, vanilla DMD relies on sparse supervision at a few predefined discrete timesteps. This restricted discrete-time formulation and mode-seeking nature of the reverse KL divergence tends to exhibit visual artifacts and over-smoothed outputs, often necessitating complex auxiliary modules -- such as GANs or reward models -- to restore visual fidelity. In this work, we introduce Continuous-Time Distribution Matching (CDM), migrating the DMD framework from discrete anchoring to continuous optimization for the first time. CDM achieves this through two continuous-time designs. First, we replace the fixed discrete schedule with a dynamic continuous schedule of random length, so that distribution matching is enforced at arbitrary points along sampling trajectories rather than only at a few fixed anchors. Second, we propose a continuous-time alignment objective that performs active off-trajectory matching on latents extrapolated via the student's velocity field, improving generalization and preserving fine visual details. Extensive experiments on different architectures, including SD3-Medium and Longcat-Image, demonstrate that CDM provides highly competitive visual fidelity for few-step image generation without relying on complex auxiliary objectives. Code is available at https://github.com/byliutao/cdm.", "project_page": "https://byliutao.github.io/cdm_page/", "github_manual": "", "github_regex": "https://github.com/byliutao/cdm", "github_paper_md": "", "github_api": "https://github.com/byliutao/cdm", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.06200", "title": "A^2TGPO: Agentic Turn-Group Policy Optimization with Adaptive Turn-level Clipping", "authors": [ "Dingwei Chen", "Zefang Zong", "Zhipeng Ma", "Leo Luo", "Yang Li", "Chengming Li", "Peng Chen", "Jie Jiang" ], "github": "https://github.com/CuSO4-Chen/A-TGPO", "project_page": "", "abstract": "Reinforcement learning for agentic large language models (LLMs) typically relies on a sparse, trajectory-level outcome reward, making it difficult to evaluate the contribution of individual tool-calls within multi-turn interactions. Existing approaches to such process credit assignment either depend on separate external process reward models that introduce additional consumption, or tree-based structural rollout that merely redistributes the outcome signal while constraining trajectory diversity. A promising alternative leverages the per-turn change in the policy's predicted probability of the ground-truth, termed Information Gain (IG), as an intrinsic process signal without an external evaluator. However, prior work on leveraging IG signals within the RL training loop faces three systematic challenges: normalizing across turns that face heterogeneous positional contexts can distort the relative standing of individual turns, accumulating a variable number of terms causes advantage magnitudes to drift with trajectory depth, and a fixed clipping range governs policy updates identically for turns with vastly different IG signals. In this paper, we propose A^2TGPO (Agentic Turn-Group Policy Optimization with Adaptive Turn-level Clipping), which retains IG as the intrinsic signal but re-designs how it is normalized, accumulated, and consumed: (i) turn-group normalization: normalizes IG within each (prompt, turn-index) group so that each turn is compared only against peers at the same interaction depth; (ii) variance-rescaled discounted accumulation: divides cumulative normalized IG by square root of accumulated terms to keep advantage magnitudes comparable across turn positions; and (iii) adaptive turn-level clipping: modulates each turn's clipping range based on its normalized IG, widening the update region for informative turns and narrowing it for uninformative ones.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CuSO4-Chen/A-TGPO", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.04962", "title": "TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding", "authors": [ "Minjie Qiang", "Mingming Zhang", "Xiaoyi Bao", "Xing Fu", "Yu Cheng", "Weiqiang Wang", "Zhongqing Wang", "Ningtao Wang" ], "github": "https://github.com/qiangminjie27/TabEmbed", "project_page": "", "abstract": "Foundation models have established unified representations for natural language processing, yet this paradigm remains largely unexplored for tabular data. Existing methods face fundamental limitations: LLM-based approaches lack retrieval-compatible vector outputs, whereas text embedding models often fail to capture tabular structure and numerical semantics. To bridge this gap, we first introduce the Tabular Embedding Benchmark (TabBench), a comprehensive suite designed to evaluate the tabular understanding capability of embedding models. We then propose TabEmbed, the first generalist embedding model that unifies tabular classification and retrieval within a shared embedding space. By reformulating diverse tabular tasks as semantic matching problems, TabEmbed leverages large-scale contrastive learning with positive-aware hard negative mining to discern fine-grained structural and numerical nuances. Experimental results on TabBench demonstrate that TabEmbed significantly outperforms state-of-the-art text embedding models, establishing a new baseline for universal tabular representation learning. Code and datasets are publicly available at https://github.com/qiangminjie27/TabEmbed and https://huggingface.co/datasets/qiangminjie27/TabBench.", "github_manual": "", "github_regex": "https://github.com/qiangminjie27/TabEmbed", "github_paper_md": "", "github_api": "https://github.com/qiangminjie27/TabEmbed", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.06222", "title": "When to Trust Imagination: Adaptive Action Execution for World Action Models", "authors": [ "Rui Wang", "Yue Zhang", "Jiehong Lin", "Kuncheng Luo", "Jianan Wang", "Zhongrui Wang", "Xiaojuan Qi" ], "github": "", "project_page": "", "abstract": "World Action Models (WAMs) have recently emerged as a promising paradigm for robotic manipulation by jointly predicting future visual observations and future actions. However, current WAMs typically execute a fixed number of predicted actions after each model inference, leaving the robot blind to whether the imagined future remains consistent with the actual physical rollout. In this work, we formulate adaptive WAM execution as a future-reality verification problem: the robot should execute longer when the WAM-predicted future remains reliable, and replan earlier when reality deviates from imagination. To this end, we propose Future Forward Dynamics Causal Attention (FFDC), a lightweight verifier that jointly reasons over predicted future actions, predicted visual dynamics, real observations, and language instructions to estimate whether the remaining action rollout can still be trusted. FFDC enables adaptive action chunk sizes as an emergent consequence of prediction-observation consistency, preserving the efficiency of long-horizon execution while restoring responsiveness in contact-rich or difficult phases. We further introduce Mixture-of-Horizon Training to improve long-horizon trajectory coverage for adaptive execution. Experiments on the RoboTwin benchmark and in the real world demonstrate that our method achieves a strong robustness-efficiency trade-off: on RoboTwin, it reduces WAM forward passes by 69.10% and execution time by 34.02%, while improving success rate by 2.54% over the short-chunk baseline; in real-world experiments, it improves success rate by 35%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.06356", "title": "SwiftI2V: Efficient High-Resolution Image-to-Video Generation via Conditional Segment-wise Generation", "authors": [ "YaoYang Liu", "Yuechen Zhang", "Wenbo Li", "Yufei Zhao", "Rui Liu", "Long Chen" ], "github": "https://github.com/modelscope/DiffSynth-Studio", "project_page": "", "abstract": "High-resolution image-to-video (I2V) generation aims to synthesize realistic temporal dynamics while preserving fine-grained appearance details of the input image. At 2K resolution, it becomes extremely challenging, and existing solutions suffer from various weaknesses: 1) end-to-end models are often prohibitively expensive in memory and latency; 2) cascading low-resolution generation with a generic video super-resolution tends to hallucinate details and drift from input-specific local structures, since the super-resolution stage is not explicitly conditioned on the input image. To this end, we propose SwiftI2V, an efficient framework tailored for high-resolution I2V. Following the widely used two-stage design, it addresses the efficiency--fidelity dilemma by first generating a low-resolution motion reference to reduce token costs and ease the modeling burden, then performing a strongly image-conditioned 2K synthesis guided by the motion to recover input-faithful details with controlled overhead. Specifically, to make generation more scalable, SwiftI2V introduces Conditional Segment-wise Generation (CSG) to synthesize videos segment-by-segment with a bounded per-step token budget, and adopts bidirectional contextual interaction within each segment to improve cross-segment coherence and input fidelity. On VBench-I2V at 2K resolution, SwiftI2V achieves performance comparable to end-to-end baselines while reducing total GPU-time by 202x. Particularly, it enables practical 2K I2V generation on a single datacenter GPU (e.g., H800) or consumer GPU (e.g., RTX 4090).", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/modelscope/DiffSynth-Studio", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.06643", "title": "Are We Making Progress in Multimodal Domain Generalization? A Comprehensive Benchmark Study", "authors": [ "Hao Dong", "Hongzhao Li", "Shupan Li", "Muhammad Haris Khan", "Eleni Chatzi", "Olga Fink" ], "github": "https://github.com/lihongzhao99/MMDG_Benchmark", "project_page": "", "abstract": "Despite the growing popularity of Multimodal Domain Generalization (MMDG) for enhancing model robustness, it remains unclear whether reported performance gains reflect genuine algorithmic progress or are artifacts of inconsistent evaluation protocols. Current research is fragmented, with studies varying significantly across datasets, modality configurations, and experimental settings. Furthermore, existing benchmarks focus predominantly on action recognition, often neglecting critical real-world challenges such as input corruptions, missing modalities, and model trustworthiness. This lack of standardization obscures a reliable assessment of the field's advancement. To address this issue, we introduce MMDG-Bench, the first unified and comprehensive benchmark for MMDG, which standardizes evaluation across six datasets spanning three diverse tasks: action recognition, mechanical fault diagnosis, and sentiment analysis. MMDG-Bench encompasses six modality combinations, nine representative methods, and multiple evaluation settings. Beyond standard accuracy, it systematically assesses corruption robustness, missing-modality generalization, misclassification detection, and out-of-distribution detection. With 7, 402 neural networks trained in total across 95 unique cross-domain tasks, MMDG-Bench yields five key findings: (1) under fair comparisons, recent specialized MMDG methods offer only marginal improvements over ERM baseline; (2) no single method consistently outperforms others across datasets or modality combinations; (3) a substantial gap to upper-bound performance persists, indicating that MMDG remains far from solved; (4) trimodal fusion does not consistently outperform the strongest bimodal configurations; and (5) all evaluated methods exhibit significant degradation under corruption and missing-modality scenarios, with some methods further compromising model trustworthiness.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lihongzhao99/MMDG_Benchmark", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.00623", "title": "Recovering Hidden Reward in Diffusion-Based Policies", "authors": [ "Yanbiao Ji", "Qiuchang Li", "Yuting Hu", "Shaokai Wu", "Wenyuan Xie", "Guodong Zhang", "Qicheng He", "Deyi Ji", "Yue Ding", "Hongtao Lu" ], "github": "https://github.com/sotaagi/EnergyFlow", "project_page": "", "abstract": "This paper introduces EnergyFlow, a framework that unifies generative action modeling with inverse reinforcement learning by parameterizing a scalar energy function whose gradient is the denoising field. We establish that under maximum-entropy optimality, the score function learned via denoising score matching recovers the gradient of the expert's soft Q-function, enabling reward extraction without adversarial training. Formally, we prove that constraining the learned field to be conservative reduces hypothesis complexity and tightens out-of-distribution generalization bounds. We further characterize the identifiability of recovered rewards and bound how score estimation errors propagate to action preferences. Empirically, EnergyFlow achieves state-of-the-art imitation performance on various manipulation tasks while providing an effective reward signal for downstream reinforcement learning that outperforms both adversarial IRL methods and likelihood-based alternatives. These results show that the structural constraints required for valid reward extraction simultaneously serve as beneficial inductive biases for policy generalization. The code is available at https://github.com/sotaagi/EnergyFlow.", "github_manual": "", "github_regex": "https://github.com/sotaagi/EnergyFlow", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2602.00095", "title": "EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM Student Handwritten Solutions", "authors": [ "Weiyu Sun", "Liangliang Chen", "Yongnuo Cai", "Huiru Xie", "Yi Zeng", "Ying Zhang" ], "github": "https://github.com/gt-learning-innovation/CIRCUIT_EDU_HW_ACL", "abstract": "Multimodal Large Language Models (MLLMs) hold significant promise for revolutionizing traditional education and reducing teachers' workload. However, accurately interpreting unconstrained STEM student handwritten solutions with intertwined mathematical formulas, diagrams, and textual reasoning poses a significant challenge due to the lack of authentic and domain-specific benchmarks. Additionally, current evaluation paradigms predominantly rely on the outcomes of downstream tasks (e.g., auto-grading), which often probe only a subset of the recognized content, thereby failing to capture the MLLMs' understanding of complex handwritten logic as a whole. To bridge this gap, we release EDU-CIRCUIT-HW, a dataset consisting of 1,300+ authentic student handwritten solutions from a university-level STEM course. Utilizing the expert-verified verbatim transcriptions and grading reports of student solutions, we simultaneously evaluate various MLLMs' upstream recognition fidelity and downstream auto-grading performance. Our evaluation uncovers an astonishing scale of latent failures within MLLM-recognized student handwritten content, highlighting the models' insufficient reliability for auto-grading and other understanding-oriented applications in high-stakes educational settings. As a potential solution, we present a case study demonstrating that leveraging identified error patterns to preemptively detect and correct recognition errors, while requiring only minimal human intervention (e.g., routing 3.3% of assignments to human graders and the remainder to the GPT-5.1 grader), can effectively enhance the robustness of the deployed AI-enabled grading system. Code and dataset are available in this GitHub repo: https://gt-learning-innovation.github.io/CIRCUIT_EDU_HW_ACL.", "project_page": "https://gt-learning-innovation.github.io/CIRCUIT_EDU_HW_ACL", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gt-learning-innovation/CIRCUIT_EDU_HW_ACL", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.04451", "title": "RemoteZero: Geospatial Reasoning with Zero Human Annotations", "authors": [ "Liang Yao", "Fan Liu", "Shengxiang Xu", "Chuanyi Zhang", "Rui Min", "Shimin Di", "Yuhui Zheng" ], "github": "https://github.com/1e12Leon/RemoteZero", "project_page": "", "abstract": "Geospatial reasoning requires models to resolve complex spatial semantics and user intent into precise target locations for Earth observation. Recent progress has liberated the reasoning path from manual curation, allowing models to generate their own inference chains. Yet a final dependency remains: they are still supervised by human-annotated ground-truth coordinates. This leaves the reasoning process autonomous, but not its spatial endpoint, and prevents true self-evolution on abundant unlabeled remote sensing data. To break this bottleneck, we introduce RemoteZero, a box-supervision-free framework for geospatial reasoning. RemoteZero is motivated by a simple asymmetry: an MLLM is typically better at verifying whether a region satisfies a query than at directly generating precise coordinates. Leveraging this stronger discriminative ability, RemoteZero replaces geometric supervision with intrinsic semantic verification and enables GRPO training without box annotations. The resulting framework further supports iterative self-evolution, allowing the model to improve from unlabeled remote sensing imagery through its own verification signal. Experiments show that RemoteZero achieves competitive performance against strong supervised methods, demonstrating the potential of self-verifying training for geospatial reasoning localization.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/1e12Leon/RemoteZero", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.04077", "title": "Balanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO", "authors": [ "Zhiyuan Zeng", "Jiameng Huang", "Zhangyue Yin", "Jiashuo Liu", "Ziniu Li", "Bingrui Li", "Yuhao Wu", "Yining Zheng", "Ge Zhang", "Wenhao Huang", "Xipeng Qiu" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has become a central paradigm for improving reasoning and code generation in large language models, and GRPO-style training is widely adopted for its simplicity and effectiveness. However, an important design choice remains underexplored: how token-level policy gradient terms are aggregated within each sampled group. Standard GRPO uses sequence aggregation, while recent work has advocated token aggregation as a better alternative. We show that these two rules induce different optimization biases: token aggregation introduces sign-length coupling, while sequence aggregation implicitly downweights longer responses through sequence-level equal weighting. To address this tension, we propose Balanced Aggregation (BA), a simple drop-in replacement that computes token-level means separately within the positive and negative subsets and then combines them with sequence-count-based weights. Experiments with Qwen2.5-Math-7B and Qwen3-1.7B on DAPO-17k and Polaris, evaluated on six reasoning and coding benchmarks, show that BA consistently improves training stability and final performance over standard token and sequence aggregation. Our analysis further shows that the relative effectiveness of token and sequence aggregation is largely governed by response-length variation and the positive-negative length gap, highlighting aggregation as a critical design dimension in GRPO-style RLVR.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.04523", "title": "RaguTeam at SemEval-2026 Task 8: Meno and Friends in a Judge-Orchestrated LLM Ensemble for Faithful Multi-Turn Response Generation", "authors": [ "Ivan Bondarenko", "Roman Derunets", "Oleg Sedukhin", "Mikhail Komarov", "Ivan Chernov", "Mikhail Kulakov" ], "github": "https://github.com/RaguTeam/ragu_mtrag_semeval", "project_page": "", "abstract": "We present our winning system for Task~B (generation with reference passages) in SemEval-2026 Task~8: MTRAGEval. Our method is a heterogeneous ensemble of seven LLMs with two prompting variants, where a GPT-4o-mini judge selects the best candidate per instance. We ranked 1st out of 26 teams, achieving a conditioned harmonic mean of 0.7827 and outperforming the strongest baseline (gpt-oss-120b, 0.6390). Ablations show that diversity in model families, scales, and prompting strategies is essential, with the ensemble consistently beating any single model. We also introduce Meno-Lite-0.1, a 7B domain-adapted model with a strong cost--performance trade-off, and analyse MTRAGEval, highlighting annotation limitations and directions for improvement. Our code is publicly available: https://github.com/RaguTeam/ragu_mtrag_semeval", "github_manual": "", "github_regex": "https://github.com/RaguTeam/ragu_mtrag_semeval", "github_paper_md": "", "github_api": "https://github.com/RaguTeam/ragu_mtrag_semeval", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.06652", "title": "When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels", "authors": [ "Sushant Gautam", "Finn Schwall", "Annika Willoch Olstad", "Fernando Vallecillos Ruiz", "Birk Torpmann-Hagen", "Sunniva Maria Stordal Bj\u00f8rklund", "Leon Moonen", "Klas Pettersen", "Michael A. Riegler" ], "github": "https://github.com/kelkalot/simpleaudit", "project_page": "", "abstract": "Many deployments must compare candidate language models for safety before a labeled benchmark exists for the relevant language, sector, or regulatory regime. We formalize this setting as benchmarkless comparative safety scoring and specify the contract under which a scenario-based audit can be interpreted as deployment evidence. Scores are valid only under a fixed scenario pack, rubric, auditor, judge, sampling configuration, and rerun budget. Because no labels are available, we replace ground-truth agreement with an instrumental-validity chain: responsiveness to a controlled safe-versus-abliterated contrast, dominance of target-driven variance over auditor and judge artifacts, and stability across reruns. We instantiate the chain in SimpleAudit, a local-first scoring instrument, and validate it on a Norwegian safety pack. Safe and abliterated targets separate with AUROC values between 0.89 and 1.00, target identity is the dominant variance component (\u03b7^2 approx 0.52), and severity profiles stabilize by ten reruns. Applying the same chain to Petri shows that it admits both tools. The substantial differences arise upstream of the chain, in claim-contract enforcement and deployment fit. A Norwegian public-sector procurement case comparing Borealis and Gemma 3 demonstrates the resulting evidence in practice: the safer model depends on scenario category and risk measure. Consequently, scores, matched deltas, critical rates, uncertainty, and the auditor and judge used must be reported together rather than collapsed into a single ranking.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/kelkalot/simpleaudit", "github_api": "https://github.com/kelkalot/simpleaudit", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.06216", "title": "TIDE: Every Layer Knows the Token Beneath the Context", "authors": [ "Ajay Jaiswal", "Lauren Hannah", "Han-Byul Kim", "Duc Hoang", "Mehrdad Farajtabar", "Minsik Cho" ], "github": "", "project_page": "", "abstract": "We revisit a universally accepted but under-examined design choice in every modern LLM: a token index is looked up once at the input embedding layer and then permanently discarded. This single-injection assumption induces two structural failures: (i) the Rare Token Problem, where a Zipf-type distribution of vocabulary causes rare-token embeddings are chronically under-trained due to receiving a fraction of the cumulative gradient signal compared to common tokens; and (ii) the Contextual Collapse Problem, where limited parameters models map distributionally similar tokens to indistinguishable hidden states. As an attempt to address both, we propose TIDE, which augments the standard transformer with EmbeddingMemory: an ensemble of K independent MemoryBlocks that map token indices to context-free semantic vectors, computed once and injected into every layer through a depth-conditioned softmax router with a learnable null bank. We theoretically and empirically establish the benefits of TIDE in addressing the issues associated with single-token identity injection as well as improve performance across multiple language modeling and downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.06535", "title": "Sparkle: Realizing Lively Instruction-Guided Video Background Replacement via Decoupled Guidance", "authors": [ "Ziyun Zeng", "Yiqi Lin", "Guoqiang Liang", "Mike Zheng Shou" ], "github": "https://github.com/showlab/Sparkle", "abstract": "In recent years, open-source efforts like Senorita-2M have propelled video editing toward natural language instruction. However, current publicly available datasets predominantly focus on local editing or style transfer, which largely preserve the original scene structure and are easier to scale. In contrast, Background Replacement, a task central to creative applications such as film production and advertising, requires synthesizing entirely new, temporally consistent scenes while maintaining accurate foreground-background interactions, making large-scale data generation significantly more challenging. Consequently, this complex task remains largely underexplored due to a scarcity of high-quality training data. This gap is evident in poorly performing state-of-the-art models, e.g., Kiwi-Edit, because the primary open-source dataset that contains this task, i.e., OpenVE-3M, frequently produces static, unnatural backgrounds. In this paper, we trace this quality degradation to a lack of precise background guidance during data synthesis. Accordingly, we design a scalable pipeline that generates foreground and background guidance in a decoupled manner with strict quality filtering. Building on this pipeline, we introduce Sparkle, a dataset of ~140K video pairs spanning five common background-change themes, alongside Sparkle-Bench, the largest evaluation benchmark tailored for background replacement to date. Experiments demonstrate that our dataset and the model trained on it achieve substantially better performance than all existing baselines on both OpenVE-Bench and Sparkle-Bench. Our proposed dataset, benchmark, and model are fully open-sourced at https://showlab.github.io/Sparkle/.", "project_page": "https://showlab.github.io/Sparkle/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/showlab/Sparkle", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.04956", "title": "KernelBench-X: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels", "authors": [ "Han Wang", "Jintao Zhang", "Kai Jiang", "Haoxu Wang", "Jianfei Chen", "Jun Zhu" ], "github": "https://github.com/BonnieW05/KernelBenchX", "abstract": "LLM-based Triton kernel generation has attracted significant interest, yet a fundamental empirical question remains unanswered: where does this capability break down, and why? We present KernelBench-X, a benchmark designed to answer this question through category-aware evaluation of correctness and hardware efficiency across 176 tasks in 15 categories. Our systematic comparison of five representative methods yields three main findings. First, task structure determines correctness more than method design. Category explains nearly three times more variance in semantic correctness than method (9.4% vs 3.3% explained deviance), and 72% of Fusion tasks fail across all five methods while Math tasks are solved consistently. Second, iterative refinement improves correctness, but not performance. Across GEAK iterations, compile rate rises from 52.3% to 68.8% while average speedup declines from 1.58times to 1.44times; newly rescued kernels consistently underperform persistently correct ones (1.16times vs 1.58times speedup in round~0to1). Third, correctness does not imply efficiency. 46.6% of correct kernels are slower than the PyTorch eager baseline, and cross-hardware speedup variance reaches 21.4times. Besides, quantization remains completely unsolved (0/30 successes) despite non-trivial compilation rates, revealing systematic misunderstanding of numerical computation contracts rather than surface-level syntax errors. These findings suggest that future progress depends on handling global coordination, explicitly modeling numerical precision, and incorporating hardware efficiency into generation. The code is available at https://github.com/BonnieW05/KernelBenchX", "project_page": "https://github.com/BonnieW05/KernelBenchX", "github_manual": "", "github_regex": "https://github.com/BonnieW05/KernelBenchX", "github_paper_md": "", "github_api": "https://github.com/BonnieW05/KernelBenchX", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.04330", "title": "The Scaling Properties of Implicit Deductive Reasoning in Transformers", "authors": [ "Enrico Vompa", "Tanel Tammet" ], "github": "", "project_page": "", "abstract": "We investigate the scaling properties of implicit deductive reasoning over Horn clauses in depth-bounded Transformers. By systematically decorrelating provability from spurious features and enforcing algorithmic alignment, we find that in sufficiently deep models with a bidirectional prefix mask, implicit reasoning approaches explicit CoT performance across graph topologies and problem widths, though CoT remains necessary for depth extrapolation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.04045", "title": "Audio-Visual Intelligence in Large Foundation Models", "authors": [ "You Qin", "Kai Liu", "Shengqiong Wu", "Kai Wang", "Shijian Deng", "Yapeng Tian", "Junbin Xiao", "Yazhou Xing", "Yinghao Ma", "Bobo Li", "Roger Zimmermann", "Lei Cui", "Furu Wei", "Jiebo Luo", "Hao Fei" ], "github": "https://github.com/JavisVerse/Awesome-AVI", "abstract": "Audio-Visual Intelligence (AVI) has emerged as a central frontier in artificial intelligence, bridging auditory and visual modalities to enable machines that can perceive, generate, and interact in the multimodal real world. In the era of large foundation models, joint modeling of audio and vision has become increasingly crucial, i.e., not only for understanding but also for controllable generation and reasoning across dynamic, temporally grounded signals. Recent advances, such as Meta MovieGen and Google Veo-3, highlight the growing industrial and academic focus on unified audio-vision architectures that learn from massive multimodal data. However, despite rapid progress, the literature remains fragmented, spanning diverse tasks, inconsistent taxonomies, and heterogeneous evaluation practices that impede systematic comparison and knowledge integration. This survey provides the first comprehensive review of AVI through the lens of large foundation models. We establish a unified taxonomy covering the broad landscape of AVI tasks, ranging from understanding (e.g., speech recognition, sound localization) to generation (e.g., audio-driven video synthesis, video-to-audio) and interaction (e.g., dialogue, embodied, or agentic interfaces). We synthesize methodological foundations, including modality tokenization, cross-modal fusion, autoregressive and diffusion-based generation, large-scale pretraining, instruction alignment, and preference optimization. Furthermore, we curate representative datasets, benchmarks, and evaluation metrics, offering a structured comparison across task families and identifying open challenges in synchronization, spatial reasoning, controllability, and safety. By consolidating this rapidly expanding field into a coherent framework, this survey aims to serve as a foundational reference for future research on large-scale AVI.", "project_page": "https://javisverse.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JavisVerse/Awesome-AVI", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.05242", "title": "Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction", "authors": [ "Zhuofeng Li", "Haoxiang Zhang", "Cong Wei", "Pan Lu", "Ping Nie", "Yi Lu", "Yuyang Bai", "Shangbin Feng", "Hangxiao Zhu", "Ming Zhong", "Yuyu Zhang", "Jianwen Xie", "Yejin Choi", "James Zou", "Jiawei Han", "Wenhu Chen", "Jimmy Lin", "Dongfu Jiang", "Yu Zhang" ], "github": "https://github.com/DCI-Agent/DCI-Agent-Lite", "project_page": "", "abstract": "Modern retrieval systems, whether lexical or semantic, expose a corpus through a fixed similarity interface that compresses access into a single top-k retrieval step before reasoning. This abstraction is efficient, but for agentic search, it becomes a bottleneck: exact lexical constraints, sparse clue conjunctions, local context checks, and multi-step hypothesis refinement are difficult to implement by calling a conventional off-the-shelf retriever, and evidence filtered out early cannot be recovered by stronger downstream reasoning. Agentic tasks further exacerbate this limitation because they require agents to orchestrate multiple steps, including discovering intermediate entities, combining weak clues, and revising the plan after observing partial evidence. To tackle the limitation, we study direct corpus interaction (DCI), where an agent searches the raw corpus directly with general-purpose terminal tools (e.g., grep, file reads, shell commands, lightweight scripts), without any embedding model, vector index, or retrieval API. This approach requires no offline indexing and adapts naturally to evolving local corpora. Across IR benchmarks and end-to-end agentic search tasks, this simple setup substantially outperforms strong sparse, dense, and reranking baselines on several BRIGHT and BEIR datasets, and attains strong accuracy on BrowseComp-Plus and multi-hop QA without relying on any conventional semantic retriever. Our results indicate that as language agents become stronger, retrieval quality depends not only on reasoning ability but also on the resolution of the interface through which the model interacts with the corpus, with which DCI opens a broader interface-design space for agentic search.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DCI-Agent/DCI-Agent-Lite", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.05758", "title": "BioTool: A Comprehensive Tool-Calling Dataset for Enhancing Biomedical Capabilities of Large Language Models", "authors": [ "Xin Gao", "Ruiyi Zhang", "Meixi Du", "Peijia Qin", "Pengtao Xie" ], "github": "https://github.com/gxx27/BioTool", "project_page": "", "abstract": "Despite the success of large language models (LLMs) on general-purpose tasks, their performance in highly specialized domains such as biomedicine remains unsatisfactory. A key limitation is the inability of LLMs to effectively leverage biomedical tools, which clinical experts and biomedical researchers rely on extensively in daily workflows. While recent general-domain tool-calling datasets have substantially improved the capabilities of LLM agents, existing efforts in the biomedical domain largely rely on in-context learning and restrict models to a small set of tools. To address this gap, we introduce BioTool, a comprehensive biomedical tool-calling dataset designed for fine-tuning LLMs. BioTool comprises 34 frequently used tools collected from the NCBI, Ensembl, and UniProt databases, along with 7,040 high-quality, human-verified query-API call pairs spanning variation, genomics, proteomics, evolution, and general biology. Fine-tuning a 4-billion-parameter LLM on BioTool yields substantial improvements in biomedical tool-calling performance, outperforming cutting-edge commercial LLMs such as GPT-5.1. Furthermore, human expert evaluations demonstrate that integrating a BioTool-fine-tuned tool caller significantly improves downstream answer quality compared to the same LLM without tool usage, highlighting the effectiveness of BioTool in enhancing the biomedical capabilities of LLMs. The full dataset and evaluation code are available at https://github.com/gxx27/BioTool", "github_manual": "", "github_regex": "https://github.com/gxx27/BioTool", "github_paper_md": "", "github_api": "https://github.com/gxx27/BioTool", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.04604", "title": "Generative Quantum-inspired Kolmogorov-Arnold Eigensolver", "authors": [ "Yu-Cheng Lin", "Yu-Chao Hsu", "I-Shan Tsai", "Chun-Hua Lin", "Kuo-Chung Peng", "Jiun-Cheng Jiang", "Yun-Yuan Wang", "Tzung-Chi Huang", "Tai-Yue Li", "Kuan-Cheng Chen", "Samuel Yen-Chi Chen", "Nan-Yow Chen" ], "github": "https://github.com/Jim137/qkan", "project_page": "", "abstract": "High-performance computing (HPC) is increasingly important for scalable quantum chemistry workflows that couple classical generative models, quantum circuit simulation, and selected configuration interaction postprocessing. We present the generative quantum-inspired Kolmogorov-Arnold eigensolver (GQKAE), a parameter-efficient extension of the generative quantum eigensolver (GQE) for quantum chemistry. GQKAE replaces the parameter-heavy feed-forward network components in GPT-style generative eigensolvers with hybrid quantum-inspired Kolmogorov-Arnold network modules, forming a compact HQKANsformer backbone. The method preserves autoregressive operator selection and the quantum-selected configuration interaction evaluation pipeline, while using single-qubit DatA Re-Uploading ActivatioN modules to provide expressive nonlinear mappings. Numerical benchmarks on H4, N2, LiH, C2H6, H2O, and the H2O dimer show that GQKAE achieves chemical accuracy comparable to the GPT-based GQE architecture, while reducing trainable parameters and memory by approximately 66% and improving wall-time performance. For strongly correlated systems such as N2 and LiH, GQKAE also improves convergence behavior and final energy errors. These results indicate that quantum-inspired Kolmogorov-Arnold networks can reduce classical-side overhead while preserving circuit-generation quality, offering a scalable route for HPC-quantum co-design on near-term quantum platforms.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Jim137/qkan", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.01640", "title": "Prescriptive Scaling Laws for Data Constrained Training", "authors": [ "Justin Lovelace", "Christian Belardi", "Srivatsa Kundurthy", "Shriya Sudhakar", "Kilian Q. Weinberger" ], "github": "", "project_page": "", "abstract": "Training compute is increasingly outpacing the availability of high-quality data. This shifts the central challenge from optimal compute allocation to extracting maximum value from limited data. The widely adopted Chinchilla scaling law assumes every training token is unique. This limits its ability to guide pretraining decisions in data-constrained regimes. We model the excess loss under repetition with a simple additive overfitting penalty and find that it accurately describes model behavior. Our scaling law yields qualitatively new compute-optimal allocation advice. Beyond a point, further repetition is counterproductive and compute is better spent on model capacity. We show that following our law's recommended configuration improves performance in data-constrained regimes. Finally, because our one-parameter form isolates overfitting in a single coefficient, it enables direct comparison across training configurations. As a case study, we show that strong weight decay (\u03bb=1.0) reduces this coefficient by approximately 70%, providing a scaling-law explanation for recent findings that optimal weight decay in data-constrained regimes is an order of magnitude larger than standard practice.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-08", "arxiv_id": "2605.06642", "title": "StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction", "authors": [ "Xiangyuan Xue", "Yifan Zhou", "Zidong Wang", "Shengji Tang", "Philip Torr", "Wanli Ouyang", "Lei Bai", "Zhenfei Yin" ], "github": "https://github.com/xxyQwQ/StraTA", "project_page": "", "abstract": "Large language models (LLMs) are increasingly used as interactive agents, but optimizing them for long-horizon decision making remains difficult because current methods are largely purely reactive, which weakens both exploration and credit assignment over extended trajectories. In this work, we present Strategic Trajectory Abstraction (StraTA), a simple framework that introduces an explicit trajectory-level strategy into agentic reinforcement learning (RL). StraTA samples a compact strategy from the initial task state, conditions subsequent actions on that strategy, and trains strategy generation and action execution jointly with a hierarchical GRPO-style rollout design, further enhanced by diverse strategy rollout and critical self-judgment. Experiments on ALFWorld, WebShop, and SciWorld show that StraTA consistently improves both sample efficiency and final performance over strong baselines. StraTA reaches success rates of 93.1% on ALFWorld and 84.2% on WebShop. On SciWorld, StraTA attains a 63.5% overall score, outperforming frontier closed-source models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xxyQwQ/StraTA", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.06477", "title": "GeoStack: A Framework for Quasi-Abelian Knowledge Composition in VLMs", "authors": [ "Pranav Mantini", "Shishir K. Shah" ], "github": "https://github.com/QuantitativeImagingLaboratory/GeoStack", "abstract": "We address the challenge of knowledge composition in Vision-Language Models (VLMs), where accumulating expertise across multiple domains or tasks typically leads to catastrophic forgetting. We introduce GeoStack (Geometric Stacking), a modular framework that allows independently trained domain experts to be composed into a unified model. By imposing geometric and structural constraints on the adapter manifold, GeoStack ensures the foundational knowledge of the base model is preserved. Furthermore, we mathematically demonstrate a weight-folding property that achieves constant-time inference complexity (O(1)), regardless of the number of integrated experts. Experimental results across multi-domain adaptation and class-incremental learning show that GeoStack provides an efficient mechanism for long-term knowledge composition while significantly mitigating catastrophic forgetting. Code is available at https://github.com/QuantitativeImagingLaboratory/GeoStack.", "project_page": "https://quantitativeimaginglaboratory.github.io/GeoStack/", "github_manual": "", "github_regex": "https://github.com/QuantitativeImagingLaboratory/GeoStack", "github_paper_md": "", "github_api": "https://github.com/QuantitativeImagingLaboratory/GeoStack", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.06627", "title": "PianoCoRe: Combined and Refined Piano MIDI Dataset", "authors": [ "Ilya Borovik" ], "github": "https://github.com/ilya16/PianoCoRe", "abstract": "Symbolic music datasets with matched scores and performances are essential for many music information retrieval (MIR) tasks. Yet, existing resources often cover a narrow range of composers, lack performance variety, omit note-level alignments, or use inconsistent naming formats. This work presents PianoCoRe, a large-scale piano MIDI dataset that unifies and refines major open-source piano corpora. The dataset contains 250,046 performances of 5,625 pieces written by 483 composers, totaling 21,763 h of performed music. PianoCoRe is released in tiered subsets to support different applications: from large-scale analysis and pre-training (PianoCoRe-C and deduplicated PianoCoRe-B) to expressive performance modeling with note-level score alignment (PianoCoRe-A/A*). The note-aligned subset, PianoCoRe-A, provides the largest open-source collection of 157,207 performances aligned to 1,591 scores to date. In addition to the dataset, the contributions are: (1) a MIDI quality classifier for detecting corrupted and score-like transcriptions and (2) RAScoP, an alignment refinement pipeline that cleans temporal alignment errors and interpolates missing notes. The analysis shows that the refinement reduces temporal noise and eliminates tempo outliers. Moreover, an expressive performance rendering model trained on PianoCoRe demonstrates improved robustness to unseen pieces compared to models trained on raw or smaller datasets. PianoCoRe provides a ready-to-use foundation for the next generation of expressive piano performance research.", "project_page": "https://doi.org/10.5334/tismir.333", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/ilya16/PianoCoRe", "github_api": "https://github.com/ilya16/PianoCoRe", "github_api_added_by": "user" }, { "date": "2026-05-08", "arxiv_id": "2605.06663", "title": "EMO: Pretraining Mixture of Experts for Emergent Modularity", "authors": [ "Ryan Wang", "Akshita Bhagia", "Sewon Min" ], "github": "https://github.com/allenai/EMO", "abstract": "Large language models are typically deployed as monolithic systems, requiring the full model even when applications need only a narrow subset of capabilities, e.g., code, math, or domain-specific knowledge. Mixture-of-Experts (MoEs) seemingly offer a potential alternative by activating only a subset of experts per input, but in practice, restricting inference to a subset of experts for a given domain leads to severe performance degradation. This limits their practicality in memory-constrained settings, especially as models grow larger and sparser. We introduce EMO, an MoE designed for modularity-the independent use and composition of expert subsets-without requiring human-defined priors. Our key idea is to encourage tokens from similar domains to rely on similar experts. Since tokens within a document often share a domain, EMO restricts them to select experts from a shared pool, while allowing different documents to use different pools. This simple constraint enables coherent expert groupings to emerge during pretraining using document boundaries alone. We pretrain a 1B-active, 14B-total EMO on 1T tokens. As a full model, it matches standard MoE performance. Crucially, it enables selective expert use: retaining only 25% (12.5%) of experts incurs just a 1% (3%) absolute drop, whereas standard MoEs break under the same setting. We further find that expert subsets in EMO specialize at semantic levels (e.g., domains such as math or code), in contrast to the low-level syntactic specialization observed in standard MoEs. Altogether, our results demonstrate a path toward modular, memory-efficient deployment of large, sparse models and open new opportunities for composable architectures.", "project_page": "https://allenai.org/blog/emo", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/allenai/EMO", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.06832", "title": "IntentGrasp: A Comprehensive Benchmark for Intent Understanding", "authors": [ "Yuwei Yin", "Chuyuan Li", "Giuseppe Carenini" ], "github": "https://github.com/YuweiYin/IntentGrasp", "abstract": "Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of helpful Large Language Model (LLM) assistants. This paper introduces IntentGrasp, a comprehensive benchmark for evaluating the intent understanding capability of LLMs. Derived from 49 high-quality, open-licensed corpora spanning 12 diverse domains, IntentGrasp is constructed through source datasets curation, intent label contextualization, and task format unification. IntentGrasp contains a large-scale training set of 262,759 instances and two evaluation sets: an All Set of 12,909 test cases and a more balanced and challenging Gem Set of 470 cases. Extensive evaluations on 20 LLMs across 7 families (including frontier models such as GPT-5.4, Gemini-3.1-Pro, and Claude-Opus-4.7) demonstrate unsatisfactory performance, with scores below 60% on All Set and below 25% on Gem set. Notably, 17 out of 20 tested models perform worse than a random-guess baseline (15.2%) on Gem Set, while the estimated human performance is ~81.1%, showing substantial room for improvement. To enhance such ability, this paper proposes Intentional Fine-Tuning (IFT), which fine-tunes the models on the training set in IntentGrasp, yielding significant gains of 30+ F1 points on All Set and 20+ points on Gem Set. Tellingly, the leave-one-domain-out (Lodo) experiments further demonstrate the strong cross-domain generalizability of IFT, verifying that it is a promising approach to substantially enhancing the intent understanding of LLMs. Overall, by benchmarking and boosting intent understanding ability, this study sheds light on a promising path towards more intentional, capable, and safe AI assistants for human benefits and social good.", "project_page": "https://huggingface.co/datasets/yuweiyin/IntentGrasp", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YuweiYin/IntentGrasp", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.06924", "title": "A^2RD: Agentic Autoregressive Diffusion for Long Video Consistency", "authors": [ "Do Xuan Long", "Yale Song", "Min-Yen Kan", "Tomas Pfister", "Long T. Le" ], "github": "https://github.com/HKUDS/ViMax", "abstract": "Synthesizing consistent and coherent long video remains a fundamental challenge. Existing methods suffer from semantic drift and narrative collapse over long horizons. We present A^2RD, an Agentic Auto-Regressive Diffusion architecture that decouples creative synthesis from consistency enforcement. A^2RD formulates long video synthesis as a closed-loop process that synthesizes and self-improves video segment-by-segment through a Retrieve--Synthesize--Refine--Update cycle. It comprises three core components: (i) Multimodal Video Memory that tracks video progression across modalities; (ii) Adaptive Segment Generation that switches among generation modes for natural progression and visual consistency; and (iii) Hierarchical Test-Time Self-Improvement that self-improves each segment at frame and video levels to prevent error propagation. We further introduce LVBench-C, a challenging benchmark with non-linear entity and environment transitions to stress-test long-horizon consistency. Across public and LVBench-C benchmarks spanning one- to ten-minute videos, A^2RD outperforms state-of-the-art baselines by up to 30% in consistency and 20% in narrative coherence. Human evaluations corroborate these gains while also highlighting notable improvements in motion and transition smoothness.", "project_page": "https://dxlong2000.github.io/AARD/", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/HKUDS/ViMax", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.05997", "title": "4DThinker: Thinking with 4D Imagery for Dynamic Spatial Understanding", "authors": [ "Zhangquan Chen", "Manyuan Zhang", "Xinlei Yu", "Xiang An", "Bo Li", "Xin Xie", "ZiDong Wang", "Mingze Sun", "Shuang Chen", "Hongyu Li", "Xiaobin Hu", "Ruqi Huang" ], "github": "https://github.com/zhangquanchen/4DThinker", "project_page": "", "abstract": "Dynamic spatial reasoning from monocular video is essential for bridging visual intelligence and the physical world, yet remains challenging for vision-language models (VLMs). Prior approaches either verbalize spatial-temporal reasoning entirely as text, which is inherently verbose and imprecise for complex dynamics, or rely on external geometric modules that increase inference complexity without fostering intrinsic model capability. In this paper, we present 4DThinker, the first framework that enables VLMs to \"think with 4D\" through dynamic latent mental imagery, i.e., internally simulating how scenes evolve within the continuous hidden space. Specifically, we first introduce a scalable, annotation-free data generation pipeline that synthesizes 4D reasoning data from raw videos. We then propose Dynamic-Imagery Fine-Tuning (DIFT), which jointly supervises textual tokens and 4D latents to ground the model in dynamic visual semantics. Building on this, 4D Reinforcement Learning (4DRL) further tackles complex reasoning tasks via outcome-based rewards, restricting policy gradients to text tokens to ensure stable optimization. Extensive experiments across multiple dynamic spatial reasoning benchmarks demonstrate that 4DThinker consistently outperforms strong baselines and offers a new perspective toward 4D reasoning in VLMs. Our code is available at https://github.com/zhangquanchen/4DThinker.", "github_manual": "", "github_regex": "https://github.com/zhangquanchen/4DThinker", "github_paper_md": "", "github_api": "https://github.com/zhangquanchen/4DThinker", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.06139", "title": "Listwise Policy Optimization: Group-based RLVR as Target-Projection on the LLM Response Simplex", "authors": [ "Yun Qu", "Qi Wang", "Yixiu Mao", "Heming Zou", "Yuhang Jiang", "Yingyue Li", "Wutong Xu", "Lizhou Cai", "Weijie Liu", "Clive Bai", "Kai Yang", "Yangkun Chen", "Saiyong Yang", "Xiangyang Ji" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has become a standard approach for large language models (LLMs) post-training to incentivize reasoning capacity. Among existing recipes, group-based policy gradient is prevalent, which samples a group of responses per prompt and updates the policy via group-relative advantage signals. This work reveals that these optimization strategies share a common geometric structure: each implicitly defines a target distribution on the response simplex and projects toward it via first-order approximation. Building on this insight, we propose Listwise Policy Optimization (LPO) to explicitly conduct the target-projection, which demystifies the implicit target by restricting the proximal RL objective to the response simplex, and then projects the policy via exact divergence minimization. This framework provides (i) monotonic improvement on the listwise objective with bounded, zero-sum, and self-correcting projection gradients, and (ii) flexibility in divergence selection with distinct structural properties through the decoupled projection step. On diverse reasoning tasks and LLM backbones, LPO consistently improves training performance over typical policy gradient baselines under matched targets, while intrinsically preserving optimization stability and response diversity.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.06702", "title": "CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment", "authors": [ "Siyuan Guo", "Yali Du", "Hechang Chen", "Yi Chang", "Jun Wang" ], "github": "https://github.com/guosyjlu/CASCADE", "project_page": "", "abstract": "Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains constrained by a rigid separation between training and deployment, after which learning effectively ceases. This limitation contrasts with natural intelligence, which continually adapts through interaction with its environment. In this paper, we formalise deployment-time learning (DTL) as the third stage in the LLM lifecycle that enables LLM agents to improve from experience during deployment without modifying model parameters. We present CASCADE (CASe-based Continual Adaptation during DEployment), a general and principled framework that equips LLM agents with an explicit, evolving episodic memory. CASCADE formulates experience reuse as a contextual bandit problem, enabling principled exploration-exploitation trade-offs and establishing no-regret guarantees over long-term interactions. This design allows agents to accumulate, select, and refine task-relevant cases, transforming past experience into actionable knowledge. Across 16 diverse tasks spanning medical diagnosis, legal analysis, code generation, web search, tool use, and embodied interaction, CASCADE improves macro-averaged success rate by 20.9% over zero-shot prompting while consistently outperforming gradient-based and memory-based baselines. By reframing deployment as an adaptive learning process, this work establishes a foundation for continually improving AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/guosyjlu/CASCADE", "github_api": "https://github.com/guosyjlu/CASCADE", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.07363", "title": "MISA: Mixture of Indexer Sparse Attention for Long-Context LLM Inference", "authors": [ "Ruijie Zhou", "Fanxu Meng", "Yufei Xu", "Tongxuan Liu", "Guangming Lu", "Muhan Zhang", "Wenjie Pei" ], "github": "", "project_page": "", "abstract": "DeepSeek Sparse Attention (DSA) sets the state of the art for fine-grained inference-time sparse attention by introducing a learned token-wise indexer that scores every prefix token and selects the most relevant ones for the main attention. To remain expressive, the indexer uses many query heads (for example, 64 on DeepSeek-V3.2) that share the same selected token set; this multi-head design is precisely what makes the indexer the dominant cost on long contexts. We propose MISA (Mixture of Indexer Sparse Attention), a drop-in replacement for the DSA indexer that treats its indexer heads as a pool of mixture-of-experts. A lightweight router uses cheap block-level statistics to pick a query-dependent subset of only a few active heads, and only those heads run the heavy token-level scoring. This preserves the diversity of the original indexer pool while reducing the per-query cost from scoring every prefix token with every head to scoring it with only a handful of routed heads, plus a negligible router term computed on a small set of pooled keys. We further introduce a hierarchical variant of MISA that uses the routed pass to keep an enlarged candidate set and then re-ranks it with the original DSA indexer to recover the final selected tokens almost exactly. With only eight active heads and no additional training, MISA matches the dense DSA indexer on LongBench across DeepSeek-V3.2 and GLM-5 while running with eight and four times fewer indexer heads respectively, and outperforms HISA on average. It also preserves fully green Needle-in-a-Haystack heatmaps up to a 128K-token context and recovers more than 92% of the tokens selected by the DSA indexer per layer. Our TileLang kernel delivers roughly a 3.82 times speedup over DSA's original indexer kernel on a single NVIDIA H200 GPU.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.08083", "title": "LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling", "authors": [ "Tong Zheng", "Haolin Liu", "Chengsong Huang", "Huiwen Bao", "Sheng Zhang", "Rui Liu", "Runpeng Dai", "Ruibo Chen", "Chenxi Liu", "Tianyi Xiong", "Xidong Wu", "Hongming Zhang", "Heng Huang" ], "github": "https://github.com/zhengkid/AutoTTS", "abstract": "Test-time scaling (TTS) has become an effective approach for improving large language model performance by allocating additional computation during inference. However, existing TTS strategies are largely hand-crafted: researchers manually design reasoning patterns and tune heuristics by intuition, leaving much of the computation-allocation space unexplored. We propose an environment-driven framework, AutoTTS, that changes what researchers design: from individual TTS heuristics to environments where TTS strategies can be discovered automatically. The key to AutoTTS lies in environment construction: the discovery environment must make the control space tractable and provide cheap, frequent feedback for TTS search. As a concrete instantiation, we formulate width--depth TTS as controller synthesis over pre-collected reasoning trajectories and probe signals, where controllers decide when to branch, continue, probe, prune, or stop and can be evaluated cheaply without repeated LLM calls. We further introduce beta parameterization to make the search tractable and fine-grained execution trace feedback to improve discovery efficiency by helping the agent diagnose why a TTS program fails. Experiments on mathematical reasoning benchmarks show that the discovered strategies improve the overall accuracy--cost tradeoff over strong manually designed baselines. The discovered strategies generalize to held-out benchmarks and model scales, while the entire discovery costs only $39.9 and 160 minutes. Our data, and code will be open-source at https://github.com/zhengkid/AutoTTS.", "project_page": "https://zhengkid.github.io/AutoTTS-web/", "github_manual": "", "github_regex": "https://github.com/zhengkid/AutoTTS", "github_paper_md": "", "github_api": "https://github.com/zhengkid/AutoTTS", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.04615", "title": "Beyond Retrieval: A Multitask Benchmark and Model for Code Search", "authors": [ "Siqiao Xue", "Zihan Liao", "Jin Qin", "Ziyin Zhang", "Yixiang Mu", "Fan Zhou", "Hang Yu" ], "github": "https://github.com/hq-bench/coreb", "abstract": "Code search has usually been evaluated as first-stage retrieval, even though production systems rely on broader pipelines with reranking and developer-style queries. Existing benchmarks also suffer from data contamination, label noise, and degenerate binary relevance. In this paper, we introduce CoREB, a contamination-limited, multitask code retrieval and reranking benchmark, together with a fine-tuned code reranker, that goes beyond retrieval to cover the full code search pipeline. CoREB is built from counterfactually rewritten LiveCodeBench problems in five programming languages and delivered as timed releases with graded relevance judgments. We benchmark eleven embedding models and five rerankers across three tasks: text-to-code, code-to-text, and code-to-code. Our experiments reveal that: \\circone code-specialised embeddings dominate code-to-code retrieval ({sim}2{times} over general encoders), yet no single model wins all three tasks; \\circtwo short keyword queries, the format closest to real developer search, collapse every model to near-zero nDCG@10; \\circthree off-the-shelf rerankers are task-asymmetric, with a 12-point swing on code-to-code and no baseline net-positive across all tasks; \\circfour our fine-tuned CoREB-Reranker is the first to achieve consistent gains across all three tasks. The data and model are released.", "project_page": "https://hq-bench.github.io/coreb-page/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hq-bench/coreb", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.06747", "title": "HumanNet: Scaling Human-centric Video Learning to One Million Hours", "authors": [ "Yufan Deng", "Daquan Zhou" ], "github": "https://github.com/DAGroup-PKU/HumanNet", "abstract": "Progress in embodied intelligence increasingly depends on scalable data infrastructure. While vision and language have scaled with internet corpora, learning physical interaction remains constrained by the lack of large, diverse, and richly annotated human activity data. We present HumanNet, a one-million-hour human-centric video corpus that captures how humans interact with the physical world at scale. HumanNet spans both first-person and third-person perspectives and covers fine-grained activities, human-object interactions, tool use, and long-horizon behaviors across diverse real-world environments. Beyond raw video, the dataset provides interaction-centric annotations, including captions, motion descriptions, and hand and body-related signals, enabling motion-aware and interaction-aware learning. Beyond scale, HumanNet introduces a systematic data curation paradigm for embodied learning, where human-centric filtering, temporal structuring, viewpoint diversity, and annotation enrichment are treated as first-class design principles. This design transforms unstructured internet video into a scalable substrate for representation learning, activity understanding, motion generation, and human-to-robot transfer. We conduct a first-step validation on the value of this design through controlled vision-language-action ablation: under a fixed set of validation data, continued training from the Qwen VLM model with 1000 hours of egocentric video drawn from HumanNet surpasses the continued training with 100 hours of real-robot data from Magic Cobot, indicating that egocentric human video could be a scalable and cost-effective substitute for robot data. By building this project, we aim to explore the opportunity to scale embodied foundation models using human-centric videos, rather than relying solely on robot-specific data.", "project_page": "https://dagroup-pku.github.io/HumanNet/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DAGroup-PKU/HumanNet", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.06597", "title": "UniSD: Towards a Unified Self-Distillation Framework for Large Language Models", "authors": [ "Yiqiao Jin", "Yiyang Wang", "Lucheng Fu", "Yijia Xiao", "Yinyi Luo", "Haoxin Liu", "B. Aditya Prakash", "Josiah Hester", "Jindong Wang", "Srijan Kumar" ], "github": "https://github.com/Ahren09/UniSD", "abstract": "Self-distillation (SD) offers a promising path for adapting large language models (LLMs) without relying on stronger external teachers. However, SD in autoregressive LLMs remains challenging because self-generated trajectories are free-form, correctness is task-dependent, and plausible rationales can still provide unstable or unreliable supervision. Existing methods mainly examine isolated design choices, leaving their effectiveness, roles, and interactions unclear. In this paper, we propose UniSD, a unified framework to systematically study self-distillation. UniSD integrates complementary mechanisms that address supervision reliability, representation alignment, and training stability, including multi-teacher agreement, EMA teacher stabilization, token-level contrastive learning, feature matching, and divergence clipping. Across six benchmarks and six models from three model families, UniSD reveals when self-distillation improves over static imitation, which components drive the gains, and how these components interact across tasks. Guided by these insights, we construct UniSDfull, an integrated pipeline that combines complementary components and achieves the strongest overall performance, improving over the base model by +5.4 points and the strongest baseline by +2.8 points. Extensive evaluation highlights self-distillation as a practical and steerable approach for efficient LLM adaptation without stronger external teachers.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Ahren09/UniSD", "github_api_added_by": "user", "project_page": "https://unifiedsd.github.io/" }, { "date": "2026-05-11", "arxiv_id": "2605.08063", "title": "Flow-OPD: On-Policy Distillation for Flow Matching Models", "authors": [ "Zhen Fang", "Wenxuan Huang", "Yu Zeng", "Yiming Zhao", "Shuang Chen", "Kaituo Feng", "Yunlong Lin", "Lin Chen", "Zehui Chen", "Shaosheng Cao", "Feng Zhao" ], "github": "https://github.com/CostaliyA/Flow-OPD", "abstract": "Existing Flow Matching (FM) text-to-image models suffer from two critical bottlenecks under multi-task alignment: the reward sparsity induced by scalar-valued rewards, and the gradient interference arising from jointly optimizing heterogeneous objectives, which together give rise to a 'seesaw effect' of competing metrics and pervasive reward hacking. Inspired by the success of On-Policy Distillation (OPD) in the large language model community, we propose Flow-OPD, the first unified post-training framework that integrates on-policy distillation into Flow Matching models. Flow-OPD adopts a two-stage alignment strategy: it first cultivates domain-specialized teacher models via single-reward GRPO fine-tuning, allowing each expert to reach its performance ceiling in isolation; it then establishes a robust initial policy through a Flow-based Cold-Start scheme and seamlessly consolidates heterogeneous expertise into a single student via a three-step orchestration of on-policy sampling, task-routing labeling, and dense trajectory-level supervision. We further introduce Manifold Anchor Regularization (MAR), which leverages a task-agnostic teacher to provide full-data supervision that anchors generation to a high-quality manifold, effectively mitigating the aesthetic degradation commonly observed in purely RL-driven alignment. Built upon Stable Diffusion 3.5 Medium, Flow-OPD raises the GenEval score from 63 to 92 and the OCR accuracy from 59 to 94, yielding an overall improvement of roughly 10 points over vanilla GRPO, while preserving image fidelity and human-preference alignment and exhibiting an emergent 'teacher-surpassing' effect. These results establish Flow-OPD as a scalable alignment paradigm for building generalist text-to-image models.", "project_page": "https://costaliya.github.io/Flow-OPD/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CostaliyA/Flow-OPD", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.07915", "title": "What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion", "authors": [ "Zhengrong Yue", "Taihang Hu", "Mengting Chen", "Haiyu Zhang", "Zihao Pan", "Tao Liu", "Zikang Wang", "Jinsong Lan", "Xiaoyong Zhu", "Bo Zheng", "Yali Wang" ], "github": "https://github.com/ZhengrongYue/PAE", "abstract": "Tokenizers are a crucial component of latent diffusion models, as they define the latent space in which diffusion models operate. However, existing tokenizers are primarily designed to improve reconstruction fidelity or inherit pretrained representations, leaving unclear what kind of latent space is truly friendly for generative modeling. In this paper, we study this question from the perspective of latent manifold organization. By constructing controlled tokenizer variants, we identify three key properties of a diffusion-friendly latent manifold: coherent spatial structure, local manifold continuity, and global manifold semantics. We find that these properties are more consistent with downstream generation quality than reconstruction fidelity. Motivated by this finding, we propose the Prior-Aligned AutoEncoder (PAE), which explicitly shapes the latent manifold instead of leaving diffusion-friendly manifold to emerge indirectly from reconstruction or inheritance. Specifically, PAE leverages refined priors derived from VFMs and perturbation-based regularization to turn spatial structure, local continuity, and global semantics into explicit training objectives. On ImageNet 256x256, PAE improves both training efficiency and generation quality over existing tokenizers, reaching performance comparable to RAE with up to 13x faster convergence under the same training setup and achieving a new state-of-the-art gFID of 1.03. These results highlight the importance of organizing the latent manifold for latent diffusion models.", "project_page": "https://zhengrongyue.github.io/pae.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZhengrongYue/PAE", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.08029", "title": "STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation", "authors": [ "Ying Shen", "Tianrong Chen", "Yuan Gao", "Yizhe Zhang", "Yuyang Wang", "Miguel \u00c1ngel Bautista", "Shuangfei Zhai", "Joshua M. Susskind", "Jiatao Gu" ], "github": "", "project_page": "", "abstract": "Deep generative models have advanced rapidly across text and vision, motivating unified multimodal systems that can understand, reason over, and generate interleaved text-image sequences. Most existing approaches combine autoregressive language modeling with diffusion-based image generators, inheriting a structural mismatch between causal text generation and iterative visual denoising. We observe that autoregressive normalizing flows are autoregressive Transformers--sharing the same causal mask, KV-cache mechanism, and left-to-right structure as LLMs--making them the most natural paradigm for true unified multimodal generation. We present STARFlow2, built on the Pretzel architecture that vertically interleaves a pretrained VLM stream with a TarFlow stream via residual skip connections, both operating under the same causal mask. Combined with a deep-shallow flow design and a unified FAE latent space, STARFlow2 enables cache-friendly interleaved generation where both text and visual outputs directly enter the KV-cache without re-encoding. Experiments demonstrate strong performance across image generation and multimodal understanding benchmarks, validating autoregressive flows as a viable foundation for unified multimodal modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.08078", "title": "Normalizing Trajectory Models", "authors": [ "Jiatao Gu", "Tianrong Chen", "Ying Shen", "David Berthelot", "Shuangfei Zhai", "Josh Susskind" ], "github": "", "project_page": "", "abstract": "Diffusion-based models decompose sampling into many small Gaussian denoising steps -- an assumption that breaks down when generation is compressed to a few coarse transitions. Existing few-step methods address this through distillation, consistency training, or adversarial objectives, but sacrifice the likelihood framework in the process. We introduce Normalizing Trajectory Models (NTM), which models each reverse step as an expressive conditional normalizing flow with exact likelihood training. Architecturally, NTM combines shallow invertible blocks within each step with a deep parallel predictor across the trajectory, forming an end-to-end network trainable from scratch or initializable from pretrained flow-matching models. Its exact trajectory likelihood further enables self-distillation: a lightweight denoiser trained on the model's own score produces high-quality samples in four steps. On text-to-image benchmarks, NTM matches or outperforms strong image generation baselines in just four sampling steps while uniquely retaining exact likelihood over the generative trajectory.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.06628", "title": "LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation", "authors": [ "Dan Jacobellis", "Neeraja J. Yadwadkar" ], "github": "https://github.com/ut-sysml/liveaction", "abstract": "Modern sensors generate rich, high-fidelity data, yet applications operating on wearable or remote sensing devices remain constrained by bandwidth and power budgets. Standardized codecs such as JPEG and MPEG achieve efficient trade-offs between bitrate and perceptual quality but are designed for human perception, limiting their applicability to machine-perception tasks and non-traditional modalities such as spatial audio arrays, hyperspectral images, and 3D medical images. General-purpose compression schemes based on scalar quantization or resolution reduction are broadly applicable but fail to exploit inherent signal redundancies, resulting in suboptimal rate-distortion performance. Recent generative neural codecs, or tokenizers, model complex signal dependencies but are often over-parameterized, data-hungry, and modality-specific, making them impractical for resource-constrained environments. We introduce a Lightweight, Versatile, and Asymmetric neural codec architecture (LiVeAction), that addresses these limitations through two key ideas. (1) To reduce the complexity of the encoder to meet the resource constraints of the execution environments, we impose an FFT-like structure and reduce the overall size and depth of the neural-network-based analysis transform. (2) To allow arbitrary signal modalities and simplify training, we replace adversarial and perceptual losses with a variance-based rate penalty. Our design produces codecs that deliver superior rate-distortion performance compared to state-of-the-art generative tokenizers, while remaining practical for deployment on low-power sensors. We release our code, experiments, and python library at https://github.com/UT-SysML/liveaction .", "project_page": "https://ut-sysml.github.io/liveaction", "github_manual": "", "github_regex": "https://github.com/UT-SysML/liveaction", "github_paper_md": "", "github_api": "https://github.com/ut-sysml/liveaction", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.07825", "title": "Anisotropic Modality Align", "authors": [ "Xiaomin Yu", "Yijiang Li", "Yuhui Zhang", "Hanzhen Zhao", "Yue Yang", "Hao Tang", "Yue Song", "Xiaobin Hu", "Chengwei Qin", "Shuicheng Yan", "Hui Xiong" ], "github": "https://github.com/Yu-xm/Modality_Gap_Theory", "project_page": "", "abstract": "Training multimodal large language models has long been limited by the scarcity of high-quality paired multimodal data. Recent studies show that the shared representation space of pretrained multimodal contrastive models can serve as a bridge, enabling models to perform multimodal training with unimodal data. However, the key premise of this paradigm remains insufficiently understood: can representations from different modalities be reliably interchanged? The core obstacle lies in the persistent Modality Gap in the shared space. In this work, we revisit the geometric nature of the modality gap. We find that modality representations already share compatible dominant semantic geometry. What truly hinders modality interchangeability is not a simple global shift, but an anisotropic residual structure concentrated along a small number of dominant directions. Based on this finding, we further propose the principle of anisotropic modality gap alignment: effective modality alignment should align with the target-modality distribution while preserving the semantic structure of the source modality. Guided by this principle, we propose an anisotropic geometric correction framework, AnisoAlign, for unpaired modality alignment. This framework leverages the internal geometric prior of the target modality and performs bounded correction on source-modality representations, thereby constructing substitute representations in the target modality. Experiments confirm its benefits in both geometric diagnostics and text-only MLLM training. Overall, this work recasts the modality gap from an empirical observation into a correctable, structured geometric phenomenon and provides a new representation alignment perspective for training multimodal models with unimodal data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Yu-xm/Modality_Gap_Theory", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.08044", "title": "Fast Byte Latent Transformer", "authors": [ "Julie Kallini", "Artidoro Pagnoni", "Tomasz Limisiewicz", "Gargi Ghosh", "Luke Zettlemoyer", "Christopher Potts", "Xiaochuang Han", "Srinivasan Iyer" ], "github": "", "project_page": "", "abstract": "Recent byte-level language models (LMs) match the performance of token-level models without relying on subword vocabularies, yet their utility is limited by slow, byte-by-byte autoregressive generation. We address this bottleneck in the Byte Latent Transformer (BLT) through new training and generation techniques. First, we introduce BLT Diffusion (BLT-D), a new model and our fastest BLT variant, trained with an auxiliary block-wise diffusion objective alongside the standard next-byte prediction loss. This enables an inference procedure that generates multiple bytes in parallel per decoding step, substantially reducing the number of forward passes required to generate a sequence. Second, we propose two extensions inspired by speculative decoding that trade some of this speed for higher generation quality: BLT Self-speculation (BLT-S), in which BLT's local decoder continues generating past its normal patch boundaries to draft bytes, which are then verified with a single full-model forward pass; and BLT Diffusion+Verification (BLT-DV), which augments BLT-D with an autoregressive verification step after diffusion-based generation. All methods may achieve an estimated memory-bandwidth cost over 50% lower than BLT on generation tasks. Each approach offers its own unique advantages, together removing key barriers to the practical use of byte-level LMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.05558", "title": "Who Prices Cognitive Labor in the Age of Agents? Compute-Anchored Wages", "authors": [ "Siqi Zhu" ], "github": "", "abstract": "A natural intuition about the economics of AI agents is that, because agents can be replicated at very low marginal cost, agent labor may be supplied highly elastically, placing downward pressure on cognitive-labor wages when it closely substitutes for human labor. We argue this framing is wrong in mechanism but partially correct in conclusion, and that the correction matters for both theory and policy. Agents are not labor; they are a production technology that converts compute capital K_c into effective units of cognitive labor L_A. Once this is recognized, the elastic-supply margin that anchors the equilibrium wage migrates from the labor market to the compute capital market. Building on the classic factor-pricing framework mankiw2020, we derive a Compute-Anchored Wage (CAW) bound stating that, on tasks where human and agent-produced cognitive labor are substitutes, the competitive human wage is bounded above by \u03bbcdot k cdot r_c, where r_c is the rental rate of compute capital, k is the compute intensity of one effective agent-produced cognitive labor unit, and \u03bb is the relative human-to-agent productivity. We generalize the result through constant elasticity of substitution (CES) aggregation, separate substitutable from complementary tasks, and discuss factor-share consequences. The conclusion is concise: the price-setter for cognitive labor is no longer the labor market.", "project_page": "https://scholar.google.com/citations?user=eYBAyoIAAAAJ&hl=EN", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.06221", "title": "UniPrefill: Universal Long-Context Prefill Acceleration via Block-wise Dynamic Sparsification", "authors": [ "Qihang Fan", "Huaibo Huang", "Zhiying Wu", "Bingning Wang", "Ran He" ], "github": "https://github.com/qhfan/UniPrefill", "project_page": "", "abstract": "As large language models (LLMs) continue to advance rapidly, they are becoming increasingly capable while simultaneously demanding ever-longer context lengths. To improve the inference efficiency of long-context processing, several novel low-complexity hybrid architectures have recently been proposed, effectively alleviating the computational burden of long-context inference. However, existing research on long-context prefill acceleration remains predominantly focused on sparse attention mechanisms, which achieve their maximum speedup only on full-attention models. When transferred to emerging architectures--such as linear/full attention hybrids or sliding window/full attention hybrids--these prefill acceleration approaches suffer significant performance degradation. Furthermore, such methods are generally incompatible with continuous batching, making them difficult to integrate into modern inference engines such as vLLM. To this end, we propose UniPrefill, a prefill acceleration framework applicable to virtually any model architecture, which directly accelerates the model's computation at the token level. We further implement UniPrefill as a continuous batching operator and extend vLLM's scheduling strategy to natively support prefill-decode co-processing and tensor parallel for UniPrefill, enabling its seamless integration into vLLM. UniPrefill achieves up to 2.1x speedup in Time-To-First-Token (TTFT), with the acceleration becoming increasingly pronounced as the number of concurrent requests grows.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qhfan/UniPrefill", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.06716", "title": "From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms", "authors": [ "Jinghao Luo", "Yuchen Tian", "Chuxue Cao", "Ziyang Luo", "Hongzhan Lin", "Kaixin Li", "Chuyi Kong", "Ruichao Yang", "Jing Ma" ], "github": "https://github.com/FeishuLuo/Evolving-LLM-Agent-Memory-Survey", "project_page": "", "abstract": "Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. While memory mechanisms have emerged as the architectural cornerstone of these systems, current research remains fragmented, oscillating between operating system engineering and cognitive science. This theoretical divide prevents a unified view of technological synthesis and a coherent evolutionary perspective. To bridge this gap, this survey proposes a novel evolutionary framework for LLM agent memory mechanisms, formalizing the development process into three stages: Storage (trajectory preservation), Reflection (trajectory refinement), and Experience (trajectory abstraction). We first formally define these three stages before analyzing the three core drivers of this evolution: the necessity for long-range consistency, the challenges in dynamic environments, and the ultimate goal of continual learning. Furthermore, we specifically explore two transformative mechanisms in the frontier Experience stage: proactive exploration and cross-trajectory abstraction. By synthesizing these disparate views, this work offers robust design principles and a clear roadmap for the development of next-generation LLM agents.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/FeishuLuo/Evolving-LLM-Agent-Memory-Survey", "github_api": "https://github.com/FeishuLuo/Evolving-LLM-Agent-Memory-Survey", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.05838", "title": "MDN: Parallelizing Stepwise Momentum for Delta Linear Attention", "authors": [ "Yulong Huang", "Xiang Liu", "Hongxiang Huang", "Xiaopeng Lin", "Zunchang Liu", "Xiaowen Chu", "Zeke Xie", "Bojun Cheng" ], "github": "https://github.com/HuuYuLong/MomentumDeltaNet", "project_page": "", "abstract": "Linear Attention (LA) offers a promising paradigm for scaling large language models (LLMs) to long sequences by avoiding the quadratic complexity of self-attention. Recent LA models such as Mamba2 and GDN interpret linear recurrences as closed-form online stochastic gradient descent (SGD), but naive SGD updates suffer from rapid information decay and suboptimal convergence in optimization. While momentum-based optimizers provide a natural remedy, they pose challenges in simultaneously achieving training efficiency and effectiveness. To address this, we develop a chunkwise parallel algorithm for LA with a stepwise momentum rule by geometrically reordering the update coefficients. Further, from a dynamical systems perspective, we analyze the momentum-based recurrence as a second-order system that introduces complex conjugate eigenvalues. This analysis guides the design of stable gating constraints. The resulting model, Momentum DeltaNet (MDN), leverages Triton kernels to achieve comparable training throughput with competitive linear models such as Mamba2 and KDA. Extensive experiments on the 400M and 1.3B parameter models demonstrate consistent performance improvements over strong baselines, including Transformers, Mamba2 and GDN, across diverse downstream evaluation benchmarks. Code: https://github.com/HuuYuLong/MomentumDeltaNet .", "github_manual": "", "github_regex": "https://github.com/HuuYuLong/MomentumDeltaNet", "github_paper_md": "", "github_api": "https://github.com/HuuYuLong/MomentumDeltaNet", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2602.03473", "title": "Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts", "authors": [ "Meng Lou", "Yunxiang Fu", "Yizhou Yu" ], "github": "https://github.com/LMMMEng/CaRE", "project_page": "", "abstract": "Continual learning, especially class-incremental learning (CIL), on the basis of a pre-trained model (PTM) has garnered substantial research interest in recent years. However, how to effectively learn both discriminative and comprehensive feature representations while maintaining stability and plasticity over very long task sequences remains an open problem. We propose CaRE, a scalable {C}ontinual Le{a}rner with efficient Bi-Level {R}outing Mixture-of-{E}xperts (BR-MoE). The core idea of BR-MoE is a bi-level routing mechanism: a router selection stage that dynamically activates relevant task-specific routers, followed by an expert routing phase that dynamically activates and aggregates experts, aiming to inject discriminative and comprehensive representations into every intermediate network layer. On the other hand, we introduce a challenging dataset, OmniBenchmark-1K, for CIL performance evaluation on very long task sequences with hundreds of tasks. Extensive experiments show that CaRE demonstrates leading performance across a variety of datasets and task settings, including commonly used CIL datasets with classical CIL settings (e.g., 5-20 tasks). To the best of our knowledge, CaRE is the first continual learner that scales to very long task sequences (ranging from 100 to over 300 non-overlapping tasks), while outperforming all baselines by a large margin on such task sequences. We hope that this work will inspire further research into continual learning over extremely long task sequences. Code and dataset are publicly released at https://github.com/LMMMEng/CaRE.", "github_manual": "", "github_regex": "https://github.com/LMMMEng/CaRE", "github_paper_md": "", "github_api": "https://github.com/LMMMEng/CaRE", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.07510", "title": "InterLV-Search: Benchmarking Interleaved Multimodal Agentic Search", "authors": [ "Bohan Hou", "Jiuning Gu", "Jiayan Guo", "Ronghao Dang", "Sicong Leng", "Xin Li", "Xuemeng Song", "Jianfei Yang" ], "github": "https://github.com/hbhalpha/InterLV-Search-Bench", "project_page": "", "abstract": "Existing benchmarks for multimodal agentic search evaluate multimodal search and visual browsing, but visual evidence is either confined to the input or treated as an answer endpoint rather than part of an interleaved search trajectory. We introduce InterLV-Search, a benchmark for Interleaved Language-Vision Agentic Search, in which textual and visual evidence is repeatedly used to condition later search. It contains 2,061 examples across three levels: active visual evidence seeking, controlled offline interleaved multimodal search, and open-web interleaved multimodal search. Beyond existing benchmarks, it also includes multimodal multi-branch samples that involve comparison between multiple entities during the evidence search. We construct Level 1 and Level 2 with automated pipelines and Level 3 with a machine-led, human-supervised open-web pipeline. We further provide InterLV-Agent for standardized tool use, trajectory logging, and evaluation. Experiments on proprietary and open-source multimodal agents show that current systems remain far from solving interleaved multimodal search, with the best model below 50% overall accuracy, highlighting challenges in visual evidence seeking, search control, and multimodal evidence integration. We release the benchmark data and evaluation code at https://github.com/hbhalpha/InterLV-Search-Bench", "github_manual": "", "github_regex": "https://github.com/hbhalpha/InterLV-Search-Bench", "github_paper_md": "", "github_api": "https://github.com/hbhalpha/InterLV-Search-Bench", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.07177", "title": "HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents", "authors": [ "Guankai Li", "Jiabin Chen", "Yi Xu", "Xichen Zhang", "Yuan Lu" ], "github": "https://github.com/DeepExperience/HyperEyes", "project_page": "", "abstract": "Existing multimodal search agents process target entities sequentially, issuing one tool call per entity and accumulating redundant interaction rounds whenever a query decomposes into independent sub-retrievals. We argue that effective multimodal agents should search wider rather than longer: dispatching multiple grounded queries concurrently within a round. To this end, we present HyperEyes, a parallel multimodal search agent that fuses visual grounding and retrieval into a single atomic action, enabling concurrent search across multiple entities while treating inference efficiency as a first-class training objective. HyperEyes is trained in two stages. For cold-start supervision, we develop a Parallel-Amenable Data Synthesis Pipeline covering visual multi-entity and textual multi-constraint queries, curating efficiency-oriented trajectories via Progressive Rejection Sampling. Building on this, our central contribution, a Dual-Grained Efficiency-Aware Reinforcement Learning framework, operates at two levels. At the macro level, we propose TRACE (Tool-use Reference-Adaptive Cost Efficiency), a trajectory-level reward whose reference is monotonically tightened during training to suppress superfluous tool calls without restricting genuine multi-hop search. At the micro level, we adapt On-Policy Distillation to inject dense token-level corrective signals from an external teacher on failed rollouts, mitigating the credit-assignment deficiency of sparse outcome rewards. Since existing benchmarks evaluate accuracy as the sole metric, omitting inference cost, we introduce IMEB, a human-curated benchmark of 300 instances that jointly evaluates search capability and efficiency. Across six benchmarks, HyperEyes-30B surpasses the strongest comparable open-source agent by 9.9% in accuracy with 5.3x fewer tool-call rounds on average.", "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/Guankai-Li/HyperEyes", "github_api": "https://github.com/DeepExperience/HyperEyes", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.05781", "title": "Steering Visual Generation in Unified Multimodal Models with Understanding Supervision", "authors": [ "Zeyu Liu", "Zanlin Ni", "Yang Yue", "Cheng Da", "Huan Yang", "Di Zhang", "Kun Gai", "Gao Huang" ], "github": "", "abstract": "Unified multimodal models are envisioned to bridge the gap between understanding and generation. Yet, to achieve competitive performance, state-of-the-art models adopt largely decoupled understanding and generation components. This design, while effective for individual tasks, weakens the connection required for mutual enhancement, leaving the potential synergy empirically uncertain. We propose to explicitly restore this synergy by introducing Understanding-Oriented Post-Training (UNO), a lightweight framework that treats understanding not only as a distinct task, but also a direct supervisory signal to steer generative representations. By incorporating objectives that encode semantic abstraction (captioning) and structural details (visual regression), we enable effective gradient flow from understanding to generation. Extensive experiments on image generation and editing demonstrate that understanding can serve as an effective catalyst for generation.", "project_page": "https://lzy-tony.github.io/uno", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.05136", "title": "CPCANet: Deep Unfolding Common Principal Component Analysis for Domain Generalization", "authors": [ "Yu-Hsi Chen", "Abd-Krim Seghouane" ], "github": "https://github.com/wish44165/CPCANet", "abstract": "Domain Generalization (DG) aims to learn representations that remain robust under out-of-distribution (OOD) shifts and generalize effectively to unseen target domains. While recent invariant learning strategies and architectural advances have achieved strong performance, explicitly discovering a structured domain-invariant subspace through second-order statistics remains underexplored. In this work, we propose CPCANet, a novel framework grounded in Common Principal Component Analysis (CPCA), which unrolls the iterative Flury-Gautschi (FG) algorithm into fully differentiable neural layers. This approach integrates the statistical properties of CPCA into an end-to-end trainable framework, enforcing the discovery of a shared subspace across diverse domains while preserving interpretability. Experiments on four standard DG benchmarks demonstrate that CPCANet achieves state-of-the-art (SOTA) performance in zero-shot transfer. Moreover, CPCANet is architecture-agnostic and requires no dataset-specific tuning, providing a simple and efficient approach to learning robust representations under distribution shift. Code is available at https://github.com/wish44165/CPCANet.", "project_page": "https://sites.google.com/view/wish44165/home/academic-activities/2026/cpcanet", "github_manual": "", "github_regex": "https://github.com/wish44165/CPCANet", "github_paper_md": "", "github_api": "https://github.com/wish44165/CPCANet", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.07748", "title": "TextLDM: Language Modeling with Continuous Latent Diffusion", "authors": [ "Jiaxiu Jiang", "Jingjing Ren", "Wenbo Li", "Bo Wang", "Haoze Sun", "Yijun Yang", "Jianhui Liu", "Yanbing Zhang", "Shenghe Zheng", "Yuan Zhang", "Haoyang Huang", "Nan Duan", "Wangmeng Zuo" ], "github": "", "project_page": "", "abstract": "Diffusion Transformers (DiT) trained with flow matching in a VAE latent space have unified visual generation across images and videos. A natural next step toward a single architecture for both generation (visual synthesis) and understanding (text generation) is to apply this framework to language modeling. We propose TextLDM, which transfers the visual latent diffusion recipe to text generation with minimal architectural modification. A Transformer-based VAE maps discrete tokens to continuous latents, enhanced by Representation Alignment (REPA) with a frozen pretrained language model to produce representations effective for conditional denoising. A standard DiT then performs flow matching in this latent space, identical in architecture to its visual counterpart. The central challenge we address is obtaining high-quality continuous text representations: we find that reconstruction fidelity alone is insufficient, and that aligning latent features with a pretrained language model via REPA is critical for downstream generation quality. Trained from scratch on OpenWebText2, TextLDM substantially outperforms prior diffusion language models and matches GPT-2 under the same settings. Our results establish that the visual DiT recipe transfers effectively to language, taking a concrete step toward unified diffusion architectures for multimodal generation and understanding.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.00425", "title": "AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning", "authors": [ "Haotian Zhao", "Songlin Zhou", "Yuxin Zhang", "Stephen S. -T. Yau", "Wenyu Zhang", "Lun Tian", "Tianshu Zhu", "Yifeng Huang", "Yucheng Zeng", "Jingnan Gu", "Daxiang Dong", "Jianmin Wu" ], "github": "", "abstract": "Reinforcement learning (RL) has substantially improved the ability of large language model (LLM) agents to interact with environments and solve multi-turn tasks. However, effective agentic RL remains challenging: sparse outcome-only rewards provide limited guidance for assigning credit to individual steps within long interaction trajectories. Existing approaches often introduce dense intermediate supervision, such as process reward models or auxiliary self-supervised signals, which increases supervision and tuning complexity and may limit generalization across tasks and domains. We present AEM, a supervision-free credit assignment method that adaptively modulates entropy dynamics during RL training to improve the exploration-exploitation trade-off. Since in agentic RL the environment is typically affected by a complete response, rather than an individual token, our analysis lifts entropy dynamics from the token level to the response level, aligning uncertainty estimation with the effective action granularity of LLM agents and reducing sensitivity to token-level sampling noise. We further show that entropy drift under natural-gradient updates is governed by the interaction between the sampled-response advantage and its relative surprisal. Motivated by this result, AEM derives a practical response-level uncertainty proxy and uses it to rescale advantages, leveraging the evolving balance between positive and negative samples to naturally transition from exploration to exploitation. Extensive experiments on ALFWorld, WebShop, and SWE-bench-Verified with models ranging from 1.5B to 32B demonstrate that AEM consistently improves strong RL baselines, including a +1.4\\% gain when integrated into a state-of-the-art software-engineering RL training framework.", "project_page": "https://baidubce.github.io/blogs/qianfan/index_en.html", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.08043", "title": "SCOPE: Structured Decomposition and Conditional Skill Orchestration for Complex Image Generation", "authors": [ "Tianfei Ren", "Zhipeng Yan", "Yiming Zhao", "Zhen Fang", "Yu Zeng", "Guohui Zhang", "Hang Xu", "Xiaoxiao Ma", "Shiting Huang", "Ke Xu", "Wenxuan Huang", "Lionel Z. Wang", "Lin Chen", "Zehui Chen", "Jie Huang", "Feng Zhao" ], "github": "https://github.com/nopnor/SCOPE", "abstract": "While text-to-image models have made strong progress in visual fidelity, faithfully realizing complex visual intents remains challenging because many requirements must be tracked across grounding, generation, and verification. We refer to these requirements as semantic commitments and formalize their lifecycle discontinuity as the Conceptual Rift, where commitments may be locally resolved or checked but fail to remain identifiable as the same operational units throughout the generation lifecycle. To address this, we propose SCOPE, a specification-guided skill orchestration framework that maintains semantic commitments in an evolving structured specification and conditionally invokes retrieval, reasoning, and repair skills around unresolved or violated commitments. To evaluate commitment-level intent realization, we introduce Gen-Arena, a human-annotated benchmark with entity- and constraint-level specifications, together with Entity-Gated Intent Pass Rate (EGIP), a strict entity-first pass criterion. SCOPE substantially outperforms all evaluated baselines on Gen-Arena, achieving 0.60 EGIP, and further achieves strong results on WISE-V (0.907) and MindBench (0.61), demonstrating the effectiveness of persistent commitment tracking for complex image generation.", "project_page": "https://nopnor.github.io/SCOPE/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nopnor/SCOPE", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.07243", "title": "SpecBlock: Block-Iterative Speculative Decoding with Dynamic Tree Drafting", "authors": [ "Weijie Shi", "Qiang Xu", "Fan Deng", "Yaguang Wu", "Jiarun Liu", "Yehong Xu", "Hao Chen", "Jia Zhu", "Jiajie Xu", "Xiangjun Huang", "Jian Yang", "Xiaofang Zhou" ], "github": "", "project_page": "", "abstract": "Speculative decoding accelerates LLM inference by drafting a tree of candidate continuations and verifying it in one target forward. Existing drafters fall into two camps with opposite weaknesses. Autoregressive drafters such as EAGLE-3 preserve dependence along each draft path but call the drafter once per tree depth, making drafting a non-trivial share of per-iteration latency. Parallel drafters cut drafter calls by predicting multiple future positions in one forward, but each position is predicted without seeing the others, producing paths the verifier rejects. In this paper, we propose SpecBlock, a block-iterative drafter that combines path dependence with cheap drafting. Each drafter forward produces K dependent positions and we call this a block. The draft tree grows through repeated block expansions. Two mechanisms explicitly carry path dependence to keep later draft positions accurate. Within each block, a layer-wise shift carries the previous position's hidden state into every decoder layer. Across blocks, each new block can start from any position of the previous block, inheriting its hidden state to extend the path. To spend verifier budget where acceptance is likely, a co-trained rank head replaces the fixed top-k tree by allocating per-position branching during drafting. To avoid training the drafter on prefixes it never produces at inference, a valid-prefix mask drops the loss at later positions once an earlier one is wrong. Beyond static drafting, a cost-aware bandit at deployment uses free verifier feedback to update the drafter selectively, only when the expected throughput gain exceeds the update cost. Experiments show that SpecBlock improves mean speedup by 8-13% over EAGLE-3 at 44-52% of its drafting cost, and cost-aware adaptation extends this lead to 11-19%.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.06380", "title": "Empirical Evidence for Simply Connected Decision Regions in Image Classifiers", "authors": [ "Arjhun Swaminathan", "Mete Akg\u00fcn" ], "github": "https://github.com/mdppml/contractible-class-regions", "project_page": "", "abstract": "Understanding the topology of decision regions is central to explaining the inner workings of deep neural networks. Prior empirical work has provided evidence that these regions are path connected. We study a stronger topological question: whether closed loops inside a decision region can be contracted without leaving that region. To this end, we propose an iterative quad-mesh filling procedure that constructs a finite-resolution label-preserving surface bounded by a given loop and lying entirely within the same decision region. We further connect this construction to natural Coons patches in order to quantify its deviation from a canonical geometric interpolation of the loop. By evaluating our method across several modern image-classification models, we provide empirical evidence supporting the hypothesis that decision regions in deep neural networks are not only path connected, but also simply connected.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mdppml/contractible-class-regions", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.04808", "title": "DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents", "authors": [ "Zhaorun Chen", "Xun Liu", "Haibo Tong", "Chengquan Guo", "Yuzhou Nie", "Jiawei Zhang", "Mintong Kang", "Chejian Xu", "Qichang Liu", "Xiaogeng Liu", "Tianneng Shi", "Chaowei Xiao", "Sanmi Koyejo", "Percy Liang", "Wenbo Guo", "Dawn Song", "Bo Li" ], "github": "https://github.com/AI-secure/DecodingTrust-Agent", "abstract": "AI agents are increasingly deployed across diverse domains to automate complex workflows through long-horizon and high-stakes action executions. Due to their high capability and flexibility, such agents raise significant security and safety concerns. A growing number of real-world incidents have shown that adversaries can easily manipulate agents into performing harmful actions, such as leaking API keys, deleting user data, or initiating unauthorized transactions. Evaluating agent security is inherently challenging, as agents operate in dynamic, untrusted environments involving external tools, heterogeneous data sources, and frequent user interactions. However, realistic, controllable, and reproducible environments for large-scale risk assessment remain largely underexplored. To address this gap, we introduce the DecodingTrust-Agent Platform (DTap), the first controllable and interactive red-teaming platform for AI agents, spanning 14 real-world domains and over 50 simulation environments that replicate widely used systems such as Google Workspace, Paypal, and Slack. To scale the risk assessment of agents in DTap, we further propose DTap-Red, the first autonomous red-teaming agent that systematically explores diverse injection vectors (e.g., prompt, tool, skill, environment, combinations) and autonomously discovers effective attack strategies tailored to varying malicious goals. Using DTap-Red, we curate DTap-Bench, a large-scale red-teaming dataset comprising high-quality instances across domains, each paired with a verifiable judge to automatically validate attack outcomes. Through DTap, we conduct large-scale evaluations of popular AI agents built on various backbone models, spanning security policies, risk categories, and attack strategies, revealing systematic vulnerability patterns and providing valuable insights for developing secure next-generation agents.", "project_page": "https://decodingtrust-agent.com", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AI-secure/DecodingTrust-Agent", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.07755", "title": "Rethinking State Tracking in Recurrent Models Through Error Control Dynamics", "authors": [ "Jiwan Chung", "Heechan Choi", "Seon Joo Kim" ], "github": "", "project_page": "", "abstract": "The theory of state tracking in recurrent architectures has predominantly focused on expressive capacity: whether a fixed architecture can theoretically realize a set of symbolic transition rules. We argue that equally important is error control, the dynamics governing hidden-state drift along the directions that distinguish symbolic states. We prove that affine recurrent networks, a class of models encompassing State-Space Models and Linear Attention, cannot correct errors along state-separating subspaces once they preserve state representations. Consequently, practical affine trackers do not learn robust state tracking; rather, they learn finite horizon solutions governed by accumulated state-relevant error. We characterize the mechanics of this failure, showing that tracking remains readable only while the accumulating within-class spread remains small relative to the initial between-class separation. We demonstrate empirically on group state-tracking tasks that this breakdown is predictable: tracking collapses when the distinguishability ratio crosses the readability threshold of the trained decoder. Across trained models, the point of this crossing predicts the horizon at which downstream accuracy fails. These results establish that robust state tracking is determined not only by an architecture's theoretical expressivity but crucially by its error control.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.06105", "title": "Shallow Prefill, Deep Decoding: Efficient Long-Context Inference via Layer-Asymmetric KV Visibility", "authors": [ "Jungsuk Oh", "Hyeseo Jeon", "Hyunjune Ji", "Kyongmin Kong", "Jay-Yoon Lee" ], "github": "", "project_page": "", "abstract": "Long-context inference in decoder-only language models is costly because long prompts are processed during Prefill, cached at every layer, and repeatedly attended to during autoregressive Decode. We introduce Shallow Prefill, dEEp Decode (SPEED), a phase-asymmetric KV-visibility policy that materializes non-anchor prompt-token KV states only in lower layers while keeping Decode-phase tokens full-depth. Unlike previous approaches that make upper-layer prompt KV states cheaper to store or construct, SPEED removes prefill tokens from the upper-layer Decode visibility set altogether. With a minimal BoS anchor, this simple change preserves broad benchmark quality while reducing long-context cost. In a controlled Llama-3.1-8B instruction-tuning study, SPEED using only 75\\% of layers for prefill tokens reaches 51.2 average score on OLMES-style benchmarks, compared with 51.4 for the full-depth baseline, while improving TTFT by 33\\%, TPOT by 22\\%, and reducing active KV memory by 25.0\\% at 128K context. Layer-wise diagnostics suggest that this cutoff retains the main prompt-selection and representation-stabilization regions of the full-depth model. These results show that long-context prompt tokens need not always persist as full-depth KV-cache objects when Decode-phase tokens remain full-depth.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2512.18181", "title": "MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation", "authors": [ "Kaixing Yang", "Jiashu Zhu", "Xulong Tang", "Ziqiao Peng", "Xiangyue Zhang", "Puwei Wang", "Jiahong Wu", "Xiangxiang Chu", "Hongyan Liu", "Jun He" ], "github": "https://github.com/AMAP-ML/MACE-Dance", "project_page": "", "abstract": "With the rise of online dance-video platforms and rapid advances in AI-generated content (AIGC), music-driven dance generation has emerged as a compelling research direction. Despite substantial progress in related domains such as music-driven 3D dance generation, pose-driven image animation, and audio-driven talking-head synthesis, existing methods cannot be directly adapted to this task. Moreover, the limited studies in this area still struggle to jointly achieve high-quality visual appearance and realistic human motion. Accordingly, we present MACE-Dance, a music-driven dance video generation framework with cascaded Mixture-of-Experts (MoE). The Motion Expert performs music-to-3D motion generation while enforcing kinematic plausibility and artistic expressiveness, whereas the Appearance Expert carries out motion- and reference-conditioned video synthesis, preserving visual identity with spatiotemporal coherence. Specifically, the Motion Expert adopts a diffusion model with a BiMamba-Transformer hybrid architecture and a Guidance-Free Training (GFT) strategy, achieving state-of-the-art (SOTA) performance in 3D dance generation. The Appearance Expert employs a decoupled kinematic-aesthetic fine-tuning strategy, achieving state-of-the-art (SOTA) performance in pose-driven image animation. To better benchmark this task, we curate a large-scale and diverse dataset and design a motion-appearance evaluation protocol. Based on this protocol, MACE-Dance also achieves state-of-the-art performance. Code is available at https://github.com/AMAP-ML/MACE-Dance.", "github_manual": "", "github_regex": "https://github.com/AMAP-ML/MACE-Dance", "github_paper_md": "", "github_api": "https://github.com/AMAP-ML/MACE-Dance", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.06734", "title": "Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning", "authors": [ "Kuo-Chung Peng", "Samuel Yen-Chi Chen", "Jiun-Cheng Jiang", "Chen-Yu Liu", "En-Jui Kuo", "Yun-Yuan Wang", "Prayag Tiwari", "Andrea Ceschini", "Chi-Sheng Chen", "Yu-Chao Hsu", "Chun-Hua Lin", "Tai-Yue Li", "Antonello Rosato", "Massimo Panella", "Simon See", "Saif Al-Kuwari", "Kuan-Cheng Chen", "Nan-Yow Chen", "Hsi-Sheng Goan" ], "github": "", "project_page": "", "abstract": "Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (QFWPs) extend this idea with variational quantum circuits (VQCs), but existing implementations rely on multi-qubit architectures that are difficult to scale on noisy intermediate-scale quantum (NISQ) devices and expensive to simulate classically. We propose gated QKAN-FWP, a fast-weight framework that integrates FWP with Quantum-inspired Kolmogorov-Arnold Network (QKAN) using single-qubit data re-uploading circuits as learnable nonlinear activation, known as DatA Re-Uploading ActivatioN (DARUAN). We further introduce a scalar-gated fast-weight update rule that stabilizes parameter evolution, supported by a theoretical analysis of its adaptive memory kernel, geometric boundedness, and parallelizable gradient paths. We evaluate the framework across time-series benchmarks, MiniGrid reinforcement learning, and highlight real-world solar cycle forecasting as our main practical result. In the long-horizon setting with 528-month input window and 132-month forecast horizon, our 12.5k-parameter model achieves lower scaled Mean Square Error (MSE), peak amplitude error, and peak timing error than a suite of classical recurrent baselines with up to 13x more parameters, including Long Short-Term Memory (LSTM) networks (25.9k-89.1k parameters), WaveNet-LSTM (167k), Vanilla recurrent neural network (11.5k), and a Modified Echo State Network (132k). To validate NISQ compatibility, we further deploy the trained fast programmer on IonQ and IBM Quantum processors, recovering forecasting accuracy within 0.1% relative MSE of the noiseless simulator at 1024 shots. These results position gated QKAN-FWP as a scalable, parameter-efficient, and NISQ-compatible approach to quantum-inspired sequence modeling.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.06169", "title": "Mean Mode Screaming: Mean--Variance Split Residuals for 1000-Layer Diffusion Transformers", "authors": [ "Pengqi Lu" ], "github": "https://github.com/erwold/mv-split", "abstract": "Scaling Diffusion Transformers (DiTs) to hundreds of layers introduces a structural vulnerability: networks can enter a silent, mean-dominated collapse state that homogenizes token representations and suppresses centered variation. Through mechanistic auditing, we isolate the trigger event of this collapse as Mean Mode Screaming (MMS). MMS can occur even when training appears stable, with a mean-coherent backward shock on residual writers that opens deep residual branches and drives the network into a mean-dominated state. We show this behavior is driven by an exact decomposition of these gradients into mean-coherent and centered components, compounded by the structural suppression of attention-logit gradients through the null space of the Softmax Jacobian once values homogenize. To address this, we propose Mean-Variance Split (MV-Split) Residuals, which combine a separately gained centered residual update with a leaky trunk-mean replacement. On a 400-layer single-stream DiT, MV-Split prevents the divergent collapse that crashes the un-stabilized baseline; it tracks close to the baseline's pre-crash trajectory while remaining substantially better than token-isotropic gating methods such as LayerScale across the full schedule. Finally, we present a 1000-layer DiT as a scale-validation run at boundary scales, establishing that the architecture remains stably trainable at extreme depth.", "project_page": "https://erwold.github.io/mv-split/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/erwold/mv-split", "github_api_added_by": "user" }, { "date": "2023-12-06", "arxiv_id": "2312.03704", "title": "Relightable Gaussian Codec Avatars", "authors": [ "Shunsuke Saito", "Gabriel Schwartz", "Tomas Simon", "Junxuan Li", "Giljoo Nam" ], "github": "", "project_page": "", "abstract": "The fidelity of relighting is bounded by both geometry and appearance representations. For geometry, both mesh and volumetric approaches have difficulty modeling intricate structures like 3D hair geometry. For appearance, existing relighting models are limited in fidelity and often too slow to render in real-time with high-resolution continuous environments. In this work, we present Relightable Gaussian Codec Avatars, a method to build high-fidelity relightable head avatars that can be animated to generate novel expressions. Our geometry model based on 3D Gaussians can capture 3D-consistent sub-millimeter details such as hair strands and pores on dynamic face sequences. To support diverse materials of human heads such as the eyes, skin, and hair in a unified manner, we present a novel relightable appearance model based on learnable radiance transfer. Together with global illumination-aware spherical harmonics for the diffuse components, we achieve real-time relighting with spatially all-frequency reflections using spherical Gaussians. This appearance model can be efficiently relit under both point light and continuous illumination. We further improve the fidelity of eye reflections and enable explicit gaze control by introducing relightable explicit eye models. Our method outperforms existing approaches without compromising real-time performance. We also demonstrate real-time relighting of avatars on a tethered consumer VR headset, showcasing the efficiency and fidelity of our avatars.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.03700", "title": "OneLLM: One Framework to Align All Modalities with Language", "authors": [ "Jiaming Han", "Kaixiong Gong", "Yiyuan Zhang", "Jiaqi Wang", "Kaipeng Zhang", "Dahua Lin", "Yu Qiao", "Peng Gao", "Xiangyu Yue" ], "github": "https://github.com/csuhan/OneLLM", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have gained significant attention due to their strong multimodal understanding capability. However, existing works rely heavily on modality-specific encoders, which usually differ in architecture and are limited to common modalities. In this paper, we present OneLLM, an MLLM that aligns eight modalities to language using a unified framework. We achieve this through a unified multimodal encoder and a progressive multimodal alignment pipeline. In detail, we first train an image projection module to connect a vision encoder with LLM. Then, we build a universal projection module (UPM) by mixing multiple image projection modules and dynamic routing. Finally, we progressively align more modalities to LLM with the UPM. To fully leverage the potential of OneLLM in following instructions, we also curated a comprehensive multimodal instruction dataset, including 2M items from image, audio, video, point cloud, depth/normal map, IMU and fMRI brain activity. OneLLM is evaluated on 25 diverse benchmarks, encompassing tasks such as multimodal captioning, question answering and reasoning, where it delivers excellent performance. Code, data, model and online demo are available at https://github.com/csuhan/OneLLM", "github_manual": "", "github_regex": "https://github.com/csuhan/OneLLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.03209", "title": "Cache Me if You Can: Accelerating Diffusion Models through Block Caching", "authors": [ "Felix Wimbauer", "Bichen Wu", "Edgar Schoenfeld", "Xiaoliang Dai", "Ji Hou", "Zijian He", "Artsiom Sanakoyeu", "Peizhao Zhang", "Sam Tsai", "Jonas Kohler", "Christian Rupprecht", "Daniel Cremers", "Peter Vajda", "Jialiang Wang" ], "github": "", "project_page": "", "abstract": "Diffusion models have recently revolutionized the field of image synthesis due to their ability to generate photorealistic images. However, one of the major drawbacks of diffusion models is that the image generation process is costly. A large image-to-image network has to be applied many times to iteratively refine an image from random noise. While many recent works propose techniques to reduce the number of required steps, they generally treat the underlying denoising network as a black box. In this work, we investigate the behavior of the layers within the network and find that 1) the layers' output changes smoothly over time, 2) the layers show distinct patterns of change, and 3) the change from step to step is often very small. We hypothesize that many layer computations in the denoising network are redundant. Leveraging this, we introduce block caching, in which we reuse outputs from layer blocks of previous steps to speed up inference. Furthermore, we propose a technique to automatically determine caching schedules based on each block's changes over timesteps. In our experiments, we show through FID, human evaluation and qualitative analysis that Block Caching allows to generate images with higher visual quality at the same computational cost. We demonstrate this for different state-of-the-art models (LDM and EMU) and solvers (DDIM and DPM).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.03079", "title": "LooseControl: Lifting ControlNet for Generalized Depth Conditioning", "authors": [ "Shariq Farooq Bhat", "Niloy J. Mitra", "Peter Wonka" ], "github": "", "project_page": "", "abstract": "We present LooseControl to allow generalized depth conditioning for diffusion-based image generation. ControlNet, the SOTA for depth-conditioned image generation, produces remarkable results but relies on having access to detailed depth maps for guidance. Creating such exact depth maps, in many scenarios, is challenging. This paper introduces a generalized version of depth conditioning that enables many new content-creation workflows. Specifically, we allow (C1) scene boundary control for loosely specifying scenes with only boundary conditions, and (C2) 3D box control for specifying layout locations of the target objects rather than the exact shape and appearance of the objects. Using LooseControl, along with text guidance, users can create complex environments (e.g., rooms, street views, etc.) by specifying only scene boundaries and locations of primary objects. Further, we provide two editing mechanisms to refine the results: (E1) 3D box editing enables the user to refine images by changing, adding, or removing boxes while freezing the style of the image. This yields minimal changes apart from changes induced by the edited boxes. (E2) Attribute editing proposes possible editing directions to change one particular aspect of the scene, such as the overall object density or a particular object. Extensive tests and comparisons with baselines demonstrate the generality of our method. We believe that LooseControl can become an important design tool for easily creating complex environments and be extended to other forms of guidance channels. Code and more information are available at https://shariqfarooq123.github.io/loose-control/ .", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.03664", "title": "Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia", "authors": [ "Alexander Sasha Vezhnevets", "John P. Agapiou", "Avia Aharon", "Ron Ziv", "Jayd Matyas", "Edgar A. Du\u00e9\u00f1ez-Guzm\u00e1n", "William A. Cunningham", "Simon Osindero", "Danny Karmon", "Joel Z. Leibo" ], "github": "", "project_page": "", "abstract": "Agent-based modeling has been around for decades, and applied widely across the social and natural sciences. The scope of this research method is now poised to grow dramatically as it absorbs the new affordances provided by Large Language Models (LLM)s. Generative Agent-Based Models (GABM) are not just classic Agent-Based Models (ABM)s where the agents talk to one another. Rather, GABMs are constructed using an LLM to apply common sense to situations, act \"reasonably\", recall common semantic knowledge, produce API calls to control digital technologies like apps, and communicate both within the simulation and to researchers viewing it from the outside. Here we present Concordia, a library to facilitate constructing and working with GABMs. Concordia makes it easy to construct language-mediated simulations of physically- or digitally-grounded environments. Concordia agents produce their behavior using a flexible component system which mediates between two fundamental operations: LLM calls and associative memory retrieval. A special agent called the Game Master (GM), which was inspired by tabletop role-playing games, is responsible for simulating the environment where the agents interact. Agents take actions by describing what they want to do in natural language. The GM then translates their actions into appropriate implementations. In a simulated physical world, the GM checks the physical plausibility of agent actions and describes their effects. In digital environments simulating technologies such as apps and services, the GM may handle API calls to integrate with external tools such as general AI assistants (e.g., Bard, ChatGPT), and digital apps (e.g., Calendar, Email, Search, etc.). Concordia was designed to support a wide array of applications both in scientific research and for evaluating performance of real digital services by simulating users and/or generating synthetic data.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.03587", "title": "Language-Informed Visual Concept Learning", "authors": [ "Sharon Lee", "Yunzhi Zhang", "Shangzhe Wu", "Jiajun Wu" ], "github": "", "project_page": "", "abstract": "Our understanding of the visual world is centered around various concept axes, characterizing different aspects of visual entities. While different concept axes can be easily specified by language, e.g. color, the exact visual nuances along each axis often exceed the limitations of linguistic articulations, e.g. a particular style of painting. In this work, our goal is to learn a language-informed visual concept representation, by simply distilling large pre-trained vision-language models. Specifically, we train a set of concept encoders to encode the information pertinent to a set of language-informed concept axes, with an objective of reproducing the input image through a pre-trained Text-to-Image (T2I) model. To encourage better disentanglement of different concept encoders, we anchor the concept embeddings to a set of text embeddings obtained from a pre-trained Visual Question Answering (VQA) model. At inference time, the model extracts concept embeddings along various axes from new test images, which can be remixed to generate images with novel compositions of visual concepts. With a lightweight test-time finetuning procedure, it can also generalize to novel concepts unseen at training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-06", "arxiv_id": "2312.03632", "title": "Multimodal Data and Resource Efficient Device-Directed Speech Detection with Large Foundation Models", "authors": [ "Dominik Wagner", "Alexander Churchill", "Siddharth Sigtia", "Panayiotis Georgiou", "Matt Mirsamadi", "Aarshee Mishra", "Erik Marchi" ], "github": "", "project_page": "", "abstract": "Interactions with virtual assistants typically start with a trigger phrase followed by a command. In this work, we explore the possibility of making these interactions more natural by eliminating the need for a trigger phrase. Our goal is to determine whether a user addressed the virtual assistant based on signals obtained from the streaming audio recorded by the device microphone. We address this task by combining 1-best hypotheses and decoder signals from an automatic speech recognition system with acoustic representations from an audio encoder as input features to a large language model (LLM). In particular, we are interested in data and resource efficient systems that require only a small amount of training data and can operate in scenarios with only a single frozen LLM available on a device. For this reason, our model is trained on 80k or less examples of multimodal data using a combination of low-rank adaptation and prefix tuning. We compare the proposed system to unimodal baselines and show that the multimodal approach achieves lower equal-error-rates (EERs), while using only a fraction of the training data. We also show that low-dimensional specialized audio representations lead to lower EERs than high-dimensional general audio representations.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.03511", "title": "Kandinsky 3.0 Technical Report", "authors": [ "Vladimir Arkhipkin", "Andrei Filatov", "Viacheslav Vasilev", "Anastasia Maltseva", "Said Azizov", "Igor Pavlov", "Julia Agafonova", "Andrey Kuznetsov", "Denis Dimitrov" ], "github": "", "project_page": "", "abstract": "We present Kandinsky 3.0, a large-scale text-to-image generation model based on latent diffusion, continuing the series of text-to-image Kandinsky models and reflecting our progress to achieve higher quality and realism of image generation. Compared to previous versions of Kandinsky 2.x, Kandinsky 3.0 leverages a two times larger U-Net backbone, a ten times larger text encoder and removes diffusion mapping. We describe the architecture of the model, the data collection procedure, the training technique, and the production system of user interaction. We focus on the key components that, as we have identified as a result of a large number of experiments, had the most significant impact on improving the quality of our model compared to the others. By our side-by-side comparisons, Kandinsky becomes better in text understanding and works better on specific domains. Project page: https://ai-forever.github.io/Kandinsky-3", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.04474", "title": "Chain of Code: Reasoning with a Language Model-Augmented Code Emulator", "authors": [ "Chengshu Li", "Jacky Liang", "Andy Zeng", "Xinyun Chen", "Karol Hausman", "Dorsa Sadigh", "Sergey Levine", "Li Fei-Fei", "Fei Xia", "Brian Ichter" ], "github": "", "project_page": "", "abstract": "Code provides a general syntactic structure to build complex programs and perform precise computations when paired with a code interpreter - we hypothesize that language models (LMs) can leverage code-writing to improve Chain of Thought reasoning not only for logic and arithmetic tasks, but also for semantic ones (and in particular, those that are a mix of both). For example, consider prompting an LM to write code that counts the number of times it detects sarcasm in an essay: the LM may struggle to write an implementation for \"detect_sarcasm(string)\" that can be executed by the interpreter (handling the edge cases would be insurmountable). However, LMs may still produce a valid solution if they not only write code, but also selectively \"emulate\" the interpreter by generating the expected output of \"detect_sarcasm(string)\". In this work, we propose Chain of Code (CoC), a simple yet surprisingly effective extension that improves LM code-driven reasoning. The key idea is to encourage LMs to format semantic sub-tasks in a program as flexible pseudocode that the interpreter can explicitly catch undefined behaviors and hand off to simulate with an LM (as an \"LMulator\"). Experiments demonstrate that Chain of Code outperforms Chain of Thought and other baselines across a variety of benchmarks; on BIG-Bench Hard, Chain of Code achieves 84%, a gain of 12% over Chain of Thought. In a nutshell, CoC broadens the scope of reasoning questions that LMs can answer by \"thinking in code\".", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.03818", "title": "Alpha-CLIP: A CLIP Model Focusing on Wherever You Want", "authors": [ "Zeyi Sun", "Ye Fang", "Tong Wu", "Pan Zhang", "Yuhang Zang", "Shu Kong", "Yuanjun Xiong", "Dahua Lin", "Jiaqi Wang" ], "github": "", "project_page": "", "abstract": "Contrastive Language-Image Pre-training (CLIP) plays an essential role in extracting valuable content information from images across diverse tasks. It aligns textual and visual modalities to comprehend the entire image, including all the details, even those irrelevant to specific tasks. However, for a finer understanding and controlled editing of images, it becomes crucial to focus on specific regions of interest, which can be indicated as points, masks, or boxes by humans or perception models. To fulfill the requirements, we introduce Alpha-CLIP, an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs. Alpha-CLIP not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents. It demonstrates effectiveness in various tasks, including but not limited to open-world recognition, multimodal large language models, and conditional 2D / 3D generation. It has a strong potential to serve as a versatile tool for image-related tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.03491", "title": "Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis", "authors": [ "Zehua Chen", "Guande He", "Kaiwen Zheng", "Xu Tan", "Jun Zhu" ], "github": "", "project_page": "", "abstract": "In text-to-speech (TTS) synthesis, diffusion models have achieved promising generation quality. However, because of the pre-defined data-to-noise diffusion process, their prior distribution is restricted to a noisy representation, which provides little information of the generation target. In this work, we present a novel TTS system, Bridge-TTS, making the first attempt to substitute the noisy Gaussian prior in established diffusion-based TTS methods with a clean and deterministic one, which provides strong structural information of the target. Specifically, we leverage the latent representation obtained from text input as our prior, and build a fully tractable Schrodinger bridge between it and the ground-truth mel-spectrogram, leading to a data-to-data process. Moreover, the tractability and flexibility of our formulation allow us to empirically study the design spaces such as noise schedules, as well as to develop stochastic and deterministic samplers. Experimental results on the LJ-Speech dataset illustrate the effectiveness of our method in terms of both synthesis quality and sampling efficiency, significantly outperforming our diffusion counterpart Grad-TTS in 50-step/1000-step synthesis and strong fast TTS models in few-step scenarios. Project page: https://bridge-tts.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.03029", "title": "Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians", "authors": [ "Yuelang Xu", "Benwang Chen", "Zhe Li", "Hongwen Zhang", "Lizhen Wang", "Zerong Zheng", "Yebin Liu" ], "github": "", "project_page": "", "abstract": "Creating high-fidelity 3D head avatars has always been a research hotspot, but there remains a great challenge under lightweight sparse view setups. In this paper, we propose Gaussian Head Avatar represented by controllable 3D Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions. The two parts benefit each other, thereby our method can model fine-grained dynamic details while ensuring expression accuracy. Furthermore, we devise a well-designed geometry-guided initialization strategy based on implicit SDF and Deep Marching Tetrahedra for the stability and convergence of the training procedure. Experiments show our approach outperforms other state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.03641", "title": "MotionCtrl: A Unified and Flexible Motion Controller for Video Generation", "authors": [ "Zhouxia Wang", "Ziyang Yuan", "Xintao Wang", "Tianshui Chen", "Menghan Xia", "Ping Luo", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Motions in a video primarily consist of camera motion, induced by camera movement, and object motion, resulting from object movement. Accurate control of both camera and object motion is essential for video generation. However, existing works either mainly focus on one type of motion or do not clearly distinguish between the two, limiting their control capabilities and diversity. Therefore, this paper presents MotionCtrl, a unified and flexible motion controller for video generation designed to effectively and independently control camera and object motion. The architecture and training strategy of MotionCtrl are carefully devised, taking into account the inherent properties of camera motion, object motion, and imperfect training data. Compared to previous methods, MotionCtrl offers three main advantages: 1) It effectively and independently controls camera motion and object motion, enabling more fine-grained motion control and facilitating flexible and diverse combinations of both types of motion. 2) Its motion conditions are determined by camera poses and trajectories, which are appearance-free and minimally impact the appearance or shape of objects in generated videos. 3) It is a relatively generalizable model that can adapt to a wide array of camera poses and trajectories once trained. Extensive qualitative and quantitative experiments have been conducted to demonstrate the superiority of MotionCtrl over existing methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.04333", "title": "Beyond Surface: Probing LLaMA Across Scales and Layers", "authors": [ "Nuo Chen", "Ning Wu", "Shining Liang", "Ming Gong", "Linjun Shou", "Dongmei Zhang", "Jia Li" ], "github": "", "project_page": "", "abstract": "This paper presents an in-depth analysis of Large Language Models (LLMs), focusing on LLaMA, a prominent open-source foundational model in natural language processing. Instead of assessing LLaMA through its generative output, we design multiple-choice tasks to probe its intrinsic understanding in high-order tasks such as reasoning and computation. We examine the model horizontally, comparing different sizes, and vertically, assessing different layers. We unveil several key and uncommon findings based on the designed probing tasks: (1) Horizontally, enlarging model sizes almost could not automatically impart additional knowledge or computational prowess. Instead, it can enhance reasoning abilities, especially in math problem solving, and helps reduce hallucinations, but only beyond certain size thresholds; (2) In vertical analysis, the lower layers of LLaMA lack substantial arithmetic and factual knowledge, showcasing logical thinking, multilingual and recognitive abilities, with top layers housing most computational power and real-world knowledge.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.03461", "title": "HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting", "authors": [ "Yuheng Jiang", "Zhehao Shen", "Penghao Wang", "Zhuo Su", "Yu Hong", "Yingliang Zhang", "Jingyi Yu", "Lan Xu" ], "github": "", "project_page": "", "abstract": "We have recently seen tremendous progress in photo-real human modeling and rendering. Yet, efficiently rendering realistic human performance and integrating it into the rasterization pipeline remains challenging. In this paper, we present HiFi4G, an explicit and compact Gaussian-based approach for high-fidelity human performance rendering from dense footage. Our core intuition is to marry the 3D Gaussian representation with non-rigid tracking, achieving a compact and compression-friendly representation. We first propose a dual-graph mechanism to obtain motion priors, with a coarse deformation graph for effective initialization and a fine-grained Gaussian graph to enforce subsequent constraints. Then, we utilize a 4D Gaussian optimization scheme with adaptive spatial-temporal regularizers to effectively balance the non-rigid prior and Gaussian updating. We also present a companion compression scheme with residual compensation for immersive experiences on various platforms. It achieves a substantial compression rate of approximately 25 times, with less than 2MB of storage per frame. Extensive experiments demonstrate the effectiveness of our approach, which significantly outperforms existing approaches in terms of optimization speed, rendering quality, and storage overhead.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.03584", "title": "Context Diffusion: In-Context Aware Image Generation", "authors": [ "Ivona Najdenkoska", "Animesh Sinha", "Abhimanyu Dubey", "Dhruv Mahajan", "Vignesh Ramanathan", "Filip Radenovic" ], "github": "", "project_page": "", "abstract": "We propose Context Diffusion, a diffusion-based framework that enables image generation models to learn from visual examples presented in context. Recent work tackles such in-context learning for image generation, where a query image is provided alongside context examples and text prompts. However, the quality and fidelity of the generated images deteriorate when the prompt is not present, demonstrating that these models are unable to truly learn from the visual context. To address this, we propose a novel framework that separates the encoding of the visual context and preserving the structure of the query images. This results in the ability to learn from the visual context and text prompts, but also from either one of them. Furthermore, we enable our model to handle few-shot settings, to effectively address diverse in-context learning scenarios. Our experiments and user study demonstrate that Context Diffusion excels in both in-domain and out-of-domain tasks, resulting in an overall enhancement in image quality and fidelity compared to counterpart models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.04556", "title": "Large Language Models for Mathematicians", "authors": [ "Simon Frieder", "Julius Berner", "Philipp Petersen", "Thomas Lukasiewicz" ], "github": "", "project_page": "", "abstract": "Large language models (LLMs) such as ChatGPT have received immense interest for their general-purpose language understanding and, in particular, their ability to generate high-quality text or computer code. For many professions, LLMs represent an invaluable tool that can speed up and improve the quality of work. In this note, we discuss to what extent they can aid professional mathematicians. We first provide a mathematical description of the transformer model used in all modern language models. Based on recent studies, we then outline best practices and potential issues and report on the mathematical abilities of language models. Finally, we shed light on the potential of LMMs to change how mathematicians work.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.03047", "title": "MagicStick: Controllable Video Editing via Control Handle Transformations", "authors": [ "Yue Ma", "Xiaodong Cun", "Yingqing He", "Chenyang Qi", "Xintao Wang", "Ying Shan", "Xiu Li", "Qifeng Chen" ], "github": "", "project_page": "", "abstract": "Text-based video editing has recently attracted considerable interest in changing the style or replacing the objects with a similar structure. Beyond this, we demonstrate that properties such as shape, size, location, motion, etc., can also be edited in videos. Our key insight is that the keyframe transformations of the specific internal feature (e.g., edge maps of objects or human pose), can easily propagate to other frames to provide generation guidance. We thus propose MagicStick, a controllable video editing method that edits the video properties by utilizing the transformation on the extracted internal control signals. In detail, to keep the appearance, we inflate both the pretrained image diffusion model and ControlNet to the temporal dimension and train low-rank adaptions (LORA) layers to fit the specific scenes. Then, in editing, we perform an inversion and editing framework. Differently, finetuned ControlNet is introduced in both inversion and generation for attention guidance with the proposed attention remix between the spatial attention maps of inversion and editing. Yet succinct, our method is the first method to show the ability of video property editing from the pre-trained text-to-image model. We present experiments on numerous examples within our unified framework. We also compare with shape-aware text-based editing and handcrafted motion video generation, demonstrating our superior temporal consistency and editing capability than previous works. The code and models will be made publicly available.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.04552", "title": "Generating Illustrated Instructions", "authors": [ "Sachit Menon", "Ishan Misra", "Rohit Girdhar" ], "github": "", "project_page": "", "abstract": "We introduce the new task of generating Illustrated Instructions, i.e., visual instructions customized to a user's needs. We identify desiderata unique to this task, and formalize it through a suite of automatic and human evaluation metrics, designed to measure the validity, consistency, and efficacy of the generations. We combine the power of large language models (LLMs) together with strong text-to-image generation diffusion models to propose a simple approach called StackedDiffusion, which generates such illustrated instructions given text as input. The resulting model strongly outperforms baseline approaches and state-of-the-art multimodal LLMs; and in 30% of cases, users even prefer it to human-generated articles. Most notably, it enables various new and exciting applications far beyond what static articles on the web can provide, such as personalized instructions complete with intermediate steps and pictures in response to a user's individual situation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.03701", "title": "Self-conditioned Image Generation via Generating Representations", "authors": [ "Tianhong Li", "Dina Katabi", "Kaiming He" ], "github": "https://github.com/LTH14/rcg", "project_page": "", "abstract": "This paper presents Representation-Conditioned image Generation (RCG), a simple yet effective image generation framework which sets a new benchmark in class-unconditional image generation. RCG does not condition on any human annotations. Instead, it conditions on a self-supervised representation distribution which is mapped from the image distribution using a pre-trained encoder. During generation, RCG samples from such representation distribution using a representation diffusion model (RDM), and employs a pixel generator to craft image pixels conditioned on the sampled representation. Such a design provides substantial guidance during the generative process, resulting in high-quality image generation. Tested on ImageNet 256times256, RCG achieves a Frechet Inception Distance (FID) of 3.31 and an Inception Score (IS) of 253.4. These results not only significantly improve the state-of-the-art of class-unconditional image generation but also rival the current leading methods in class-conditional image generation, bridging the long-standing performance gap between these two tasks. Code is available at https://github.com/LTH14/rcg.", "github_manual": "", "github_regex": "https://github.com/LTH14/rcg", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.04515", "title": "Efficient Monotonic Multihead Attention", "authors": [ "Xutai Ma", "Anna Sun", "Siqi Ouyang", "Hirofumi Inaguma", "Paden Tomasello" ], "github": "", "project_page": "", "abstract": "We introduce the Efficient Monotonic Multihead Attention (EMMA), a state-of-the-art simultaneous translation model with numerically-stable and unbiased monotonic alignment estimation. In addition, we present improved training and inference strategies, including simultaneous fine-tuning from an offline translation model and reduction of monotonic alignment variance. The experimental results demonstrate that the proposed model attains state-of-the-art performance in simultaneous speech-to-text translation on the Spanish and English translation task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.03849", "title": "LEGO: Learning EGOcentric Action Frame Generation via Visual Instruction Tuning", "authors": [ "Bolin Lai", "Xiaoliang Dai", "Lawrence Chen", "Guan Pang", "James M. Rehg", "Miao Liu" ], "github": "", "project_page": "", "abstract": "Generating instructional images of human daily actions from an egocentric viewpoint serves a key step towards efficient skill transfer. In this paper, we introduce a novel problem -- egocentric action frame generation. The goal is to synthesize the action frame conditioning on the user prompt question and an input egocentric image that captures user's environment. Notably, existing egocentric datasets lack the detailed annotations that describe the execution of actions. Additionally, the diffusion-based image manipulation models fail to control the state change of an action within the corresponding egocentric image pixel space. To this end, we finetune a visual large language model (VLLM) via visual instruction tuning for curating the enriched action descriptions to address our proposed problem. Moreover, we propose to Learn EGOcentric (LEGO) action frame generation using image and text embeddings from VLLM as additional conditioning. We validate our proposed model on two egocentric datasets -- Ego4D and Epic-Kitchens. Our experiments show prominent improvement over prior image manipulation models in both quantitative and qualitative evaluation. We also conduct detailed ablation studies and analysis to provide insights on our method.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.03611", "title": "DreamComposer: Controllable 3D Object Generation via Multi-View Conditions", "authors": [ "Yunhan Yang", "Yukun Huang", "Xiaoyang Wu", "Yuan-Chen Guo", "Song-Hai Zhang", "Hengshuang Zhao", "Tong He", "Xihui Liu" ], "github": "", "project_page": "", "abstract": "Utilizing pre-trained 2D large-scale generative models, recent works are capable of generating high-quality novel views from a single in-the-wild image. However, due to the lack of information from multiple views, these works encounter difficulties in generating controllable novel views. In this paper, we present DreamComposer, a flexible and scalable framework that can enhance existing view-aware diffusion models by injecting multi-view conditions. Specifically, DreamComposer first uses a view-aware 3D lifting module to obtain 3D representations of an object from multiple views. Then, it renders the latent features of the target view from 3D representations with the multi-view feature fusion module. Finally the target view features extracted from multi-view inputs are injected into a pre-trained diffusion model. Experiments show that DreamComposer is compatible with state-of-the-art diffusion models for zero-shot novel view synthesis, further enhancing them to generate high-fidelity novel view images with multi-view conditions, ready for controllable 3D object reconstruction and various other applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-07", "arxiv_id": "2312.03160", "title": "HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces", "authors": [ "Haithem Turki", "Vasu Agrawal", "Samuel Rota Bul\u00f2", "Lorenzo Porzi", "Peter Kontschieder", "Deva Ramanan", "Michael Zollh\u00f6fer", "Christian Richardt" ], "github": "", "project_page": "", "abstract": "Neural radiance fields provide state-of-the-art view synthesis quality but tend to be slow to render. One reason is that they make use of volume rendering, thus requiring many samples (and model queries) per ray at render time. Although this representation is flexible and easy to optimize, most real-world objects can be modeled more efficiently with surfaces instead of volumes, requiring far fewer samples per ray. This observation has spurred considerable progress in surface representations such as signed distance functions, but these may struggle to model semi-opaque and thin structures. We propose a method, HybridNeRF, that leverages the strengths of both representations by rendering most objects as surfaces while modeling the (typically) small fraction of challenging regions volumetrically. We evaluate HybridNeRF against the challenging Eyeful Tower dataset along with other commonly used view synthesis datasets. When comparing to state-of-the-art baselines, including recent rasterization-based approaches, we improve error rates by 15-30% while achieving real-time framerates (at least 36 FPS) for virtual-reality resolutions (2Kx2K).", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-08", "arxiv_id": "2312.04461", "title": "PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding", "authors": [ "Zhen Li", "Mingdeng Cao", "Xintao Wang", "Zhongang Qi", "Ming-Ming Cheng", "Ying Shan" ], "github": "", "project_page": "", "abstract": "Recent advances in text-to-image generation have made remarkable progress in synthesizing realistic human photos conditioned on given text prompts. However, existing personalized generation methods cannot simultaneously satisfy the requirements of high efficiency, promising identity (ID) fidelity, and flexible text controllability. In this work, we introduce PhotoMaker, an efficient personalized text-to-image generation method, which mainly encodes an arbitrary number of input ID images into a stack ID embedding for preserving ID information. Such an embedding, serving as a unified ID representation, can not only encapsulate the characteristics of the same input ID comprehensively, but also accommodate the characteristics of different IDs for subsequent integration. This paves the way for more intriguing and practically valuable applications. Besides, to drive the training of our PhotoMaker, we propose an ID-oriented data construction pipeline to assemble the training data. Under the nourishment of the dataset constructed through the proposed pipeline, our PhotoMaker demonstrates better ID preservation ability than test-time fine-tuning based methods, yet provides significant speed improvements, high-quality generation results, strong generalization capabilities, and a wide range of applications. Our project page is available at https://photo-maker.github.io/", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-08", "arxiv_id": "2312.03913", "title": "Controllable Human-Object Interaction Synthesis", "authors": [ "Jiaman Li", "Alexander Clegg", "Roozbeh Mottaghi", "Jiajun Wu", "Xavier Puig", "C. Karen Liu" ], "github": "", "project_page": "", "abstract": "Synthesizing semantic-aware, long-horizon, human-object interaction is critical to simulate realistic human behaviors. In this work, we address the challenging problem of generating synchronized object motion and human motion guided by language descriptions in 3D scenes. We propose Controllable Human-Object Interaction Synthesis (CHOIS), an approach that generates object motion and human motion simultaneously using a conditional diffusion model given a language description, initial object and human states, and sparse object waypoints. While language descriptions inform style and intent, waypoints ground the motion in the scene and can be effectively extracted using high-level planning methods. Naively applying a diffusion model fails to predict object motion aligned with the input waypoints and cannot ensure the realism of interactions that require precise hand-object contact and appropriate contact grounded by the floor. To overcome these problems, we introduce an object geometry loss as additional supervision to improve the matching between generated object motion and input object waypoints. In addition, we design guidance terms to enforce contact constraints during the sampling process of the trained diffusion model.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-08", "arxiv_id": "2312.04543", "title": "HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a Single Image", "authors": [ "Tong Wu", "Zhibing Li", "Shuai Yang", "Pan Zhang", "Xinggang Pan", "Jiaqi Wang", "Dahua Lin", "Ziwei Liu" ], "github": "", "project_page": "", "abstract": "3D content creation from a single image is a long-standing yet highly desirable task. Recent advances introduce 2D diffusion priors, yielding reasonable results. However, existing methods are not hyper-realistic enough for post-generation usage, as users cannot view, render and edit the resulting 3D content from a full range. To address these challenges, we introduce HyperDreamer with several key designs and appealing properties: 1) Viewable: 360 degree mesh modeling with high-resolution textures enables the creation of visually compelling 3D models from a full range of observation points. 2) Renderable: Fine-grained semantic segmentation and data-driven priors are incorporated as guidance to learn reasonable albedo, roughness, and specular properties of the materials, enabling semantic-aware arbitrary material estimation. 3) Editable: For a generated model or their own data, users can interactively select any region via a few clicks and efficiently edit the texture with text-based guidance. Extensive experiments demonstrate the effectiveness of HyperDreamer in modeling region-aware materials with high-resolution textures and enabling user-friendly editing. We believe that HyperDreamer holds promise for advancing 3D content creation and finding applications in various domains.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-08", "arxiv_id": "2312.03793", "title": "AnimateZero: Video Diffusion Models are Zero-Shot Image Animators", "authors": [ "Jiwen Yu", "Xiaodong Cun", "Chenyang Qi", "Yong Zhang", "Xintao Wang", "Ying Shan", "Jian Zhang" ], "github": "", "project_page": "", "abstract": "Large-scale text-to-video (T2V) diffusion models have great progress in recent years in terms of visual quality, motion and temporal consistency. However, the generation process is still a black box, where all attributes (e.g., appearance, motion) are learned and generated jointly without precise control ability other than rough text descriptions. Inspired by image animation which decouples the video as one specific appearance with the corresponding motion, we propose AnimateZero to unveil the pre-trained text-to-video diffusion model, i.e., AnimateDiff, and provide more precise appearance and motion control abilities for it. For appearance control, we borrow intermediate latents and their features from the text-to-image (T2I) generation for ensuring the generated first frame is equal to the given generated image. For temporal control, we replace the global temporal attention of the original T2V model with our proposed positional-corrected window attention to ensure other frames align with the first frame well. Empowered by the proposed methods, AnimateZero can successfully control the generating progress without further training. As a zero-shot image animator for given images, AnimateZero also enables multiple new applications, including interactive video generation and real image animation. The detailed experiments demonstrate the effectiveness of the proposed method in both T2V and related applications.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-08", "arxiv_id": "2312.04410", "title": "Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models", "authors": [ "Jiayi Guo", "Xingqian Xu", "Yifan Pu", "Zanlin Ni", "Chaofei Wang", "Manushree Vasu", "Shiji Song", "Gao Huang", "Humphrey Shi" ], "github": "https://github.com/SHI-Labs/Smooth-Diffusion", "project_page": "", "abstract": "Recently, diffusion models have made remarkable progress in text-to-image (T2I) generation, synthesizing images with high fidelity and diverse contents. Despite this advancement, latent space smoothness within diffusion models remains largely unexplored. Smooth latent spaces ensure that a perturbation on an input latent corresponds to a steady change in the output image. This property proves beneficial in downstream tasks, including image interpolation, inversion, and editing. In this work, we expose the non-smoothness of diffusion latent spaces by observing noticeable visual fluctuations resulting from minor latent variations. To tackle this issue, we propose Smooth Diffusion, a new category of diffusion models that can be simultaneously high-performing and smooth. Specifically, we introduce Step-wise Variation Regularization to enforce the proportion between the variations of an arbitrary input latent and that of the output image is a constant at any diffusion training step. In addition, we devise an interpolation standard deviation (ISTD) metric to effectively assess the latent space smoothness of a diffusion model. Extensive quantitative and qualitative experiments demonstrate that Smooth Diffusion stands out as a more desirable solution not only in T2I generation but also across various downstream tasks. Smooth Diffusion is implemented as a plug-and-play Smooth-LoRA to work with various community models. Code is available at https://github.com/SHI-Labs/Smooth-Diffusion.", "github_manual": "", "github_regex": "https://github.com/SHI-Labs/Smooth-Diffusion", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-08", "arxiv_id": "2312.03814", "title": "Pearl: A Production-ready Reinforcement Learning Agent", "authors": [ "Zheqing Zhu", "Rodrigo de Salvo Braz", "Jalaj Bhandari", "Daniel Jiang", "Yi Wan", "Yonathan Efroni", "Liyuan Wang", "Ruiyang Xu", "Hongbo Guo", "Alex Nikulkov", "Dmytro Korenkevych", "Urun Dogan", "Frank Cheng", "Zheng Wu", "Wanqiao Xu" ], "github": "", "project_page": "", "abstract": "Reinforcement Learning (RL) offers a versatile framework for achieving long-term goals. Its generality allows us to formalize a wide range of problems that real-world intelligent systems encounter, such as dealing with delayed rewards, handling partial observability, addressing the exploration and exploitation dilemma, utilizing offline data to improve online performance, and ensuring safety constraints are met. Despite considerable progress made by the RL research community in addressing these issues, existing open-source RL libraries tend to focus on a narrow portion of the RL solution pipeline, leaving other aspects largely unattended. This paper introduces Pearl, a Production-ready RL agent software package explicitly designed to embrace these challenges in a modular fashion. In addition to presenting preliminary benchmark results, this paper highlights Pearl's industry adoptions to demonstrate its readiness for production usage. Pearl is open sourced on Github at github.com/facebookresearch/pearl and its official website is located at pearlagent.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-08", "arxiv_id": "2312.04560", "title": "NeRFiller: Completing Scenes via Generative 3D Inpainting", "authors": [ "Ethan Weber", "Aleksander Ho\u0142y\u0144ski", "Varun Jampani", "Saurabh Saxena", "Noah Snavely", "Abhishek Kar", "Angjoo Kanazawa" ], "github": "", "project_page": "", "abstract": "We propose NeRFiller, an approach that completes missing portions of a 3D capture via generative 3D inpainting using off-the-shelf 2D visual generative models. Often parts of a captured 3D scene or object are missing due to mesh reconstruction failures or a lack of observations (e.g., contact regions, such as the bottom of objects, or hard-to-reach areas). We approach this challenging 3D inpainting problem by leveraging a 2D inpainting diffusion model. We identify a surprising behavior of these models, where they generate more 3D consistent inpaints when images form a 2times2 grid, and show how to generalize this behavior to more than four images. We then present an iterative framework to distill these inpainted regions into a single consistent 3D scene. In contrast to related works, we focus on completing scenes rather than deleting foreground objects, and our approach does not require tight 2D object masks or text. We compare our approach to relevant baselines adapted to our setting on a variety of scenes, where NeRFiller creates the most 3D consistent and plausible scene completions. Our project page is at https://ethanweber.me/nerfiller.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-08", "arxiv_id": "2312.04557", "title": "GenTron: Delving Deep into Diffusion Transformers for Image and Video Generation", "authors": [ "Shoufa Chen", "Mengmeng Xu", "Jiawei Ren", "Yuren Cong", "Sen He", "Yanping Xie", "Animesh Sinha", "Ping Luo", "Tao Xiang", "Juan-Manuel Perez-Rua" ], "github": "", "project_page": "", "abstract": "In this study, we explore Transformer-based diffusion models for image and video generation. Despite the dominance of Transformer architectures in various fields due to their flexibility and scalability, the visual generative domain primarily utilizes CNN-based U-Net architectures, particularly in diffusion-based models. We introduce GenTron, a family of Generative models employing Transformer-based diffusion, to address this gap. Our initial step was to adapt Diffusion Transformers (DiTs) from class to text conditioning, a process involving thorough empirical exploration of the conditioning mechanism. We then scale GenTron from approximately 900M to over 3B parameters, observing significant improvements in visual quality. Furthermore, we extend GenTron to text-to-video generation, incorporating novel motion-free guidance to enhance video quality. In human evaluations against SDXL, GenTron achieves a 51.1% win rate in visual quality (with a 19.8% draw rate), and a 42.3% win rate in text alignment (with a 42.9% draw rate). GenTron also excels in the T2I-CompBench, underscoring its strengths in compositional generation. We believe this work will provide meaningful insights and serve as a valuable reference for future research.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-08", "arxiv_id": "2312.04566", "title": "Gen2Det: Generate to Detect", "authors": [ "Saksham Suri", "Fanyi Xiao", "Animesh Sinha", "Sean Chang Culatana", "Raghuraman Krishnamoorthi", "Chenchen Zhu", "Abhinav Shrivastava" ], "github": "", "project_page": "", "abstract": "Recently diffusion models have shown improvement in synthetic image quality as well as better control in generation. We motivate and present Gen2Det, a simple modular pipeline to create synthetic training data for object detection for free by leveraging state-of-the-art grounded image generation methods. Unlike existing works which generate individual object instances, require identifying foreground followed by pasting on other images, we simplify to directly generating scene-centric images. In addition to the synthetic data, Gen2Det also proposes a suite of techniques to best utilize the generated data, including image-level filtering, instance-level filtering, and better training recipe to account for imperfections in the generation. Using Gen2Det, we show healthy improvements on object detection and segmentation tasks under various settings and agnostic to detection methods. In the long-tailed detection setting on LVIS, Gen2Det improves the performance on rare categories by a large margin while also significantly improving the performance on other categories, e.g. we see an improvement of 2.13 Box AP and 1.84 Mask AP over just training on real data on LVIS with Mask R-CNN. In the low-data regime setting on COCO, Gen2Det consistently improves both Box and Mask AP by 2.27 and 1.85 points. In the most general detection setting, Gen2Det still demonstrates robust performance gains, e.g. it improves the Box and Mask AP on COCO by 0.45 and 0.32 points.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-08", "arxiv_id": "2312.04433", "title": "DreamVideo: Composing Your Dream Videos with Customized Subject and Motion", "authors": [ "Yujie Wei", "Shiwei Zhang", "Zhiwu Qing", "Hangjie Yuan", "Zhiheng Liu", "Yu Liu", "Yingya Zhang", "Jingren Zhou", "Hongming Shan" ], "github": "", "project_page": "", "abstract": "Customized generation using diffusion models has made impressive progress in image generation, but remains unsatisfactory in the challenging video generation task, as it requires the controllability of both subjects and motions. To that end, we present DreamVideo, a novel approach to generating personalized videos from a few static images of the desired subject and a few videos of target motion. DreamVideo decouples this task into two stages, subject learning and motion learning, by leveraging a pre-trained video diffusion model. The subject learning aims to accurately capture the fine appearance of the subject from provided images, which is achieved by combining textual inversion and fine-tuning of our carefully designed identity adapter. In motion learning, we architect a motion adapter and fine-tune it on the given videos to effectively model the target motion pattern. Combining these two lightweight and efficient adapters allows for flexible customization of any subject with any motion. Extensive experimental results demonstrate the superior performance of our DreamVideo over the state-of-the-art methods for customized video generation. Our project page is at https://dreamvideo-t2v.github.io.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-08", "arxiv_id": "2312.04567", "title": "Scaling Laws of Synthetic Images for Model Training ... for Now", "authors": [ "Lijie Fan", "Kaifeng Chen", "Dilip Krishnan", "Dina Katabi", "Phillip Isola", "Yonglong Tian" ], "github": "", "project_page": "", "abstract": "Recent significant advances in text-to-image models unlock the possibility of training vision systems using synthetic images, potentially overcoming the difficulty of collecting curated data at scale. It is unclear, however, how these models behave at scale, as more synthetic data is added to the training set. In this paper we study the scaling laws of synthetic images generated by state of the art text-to-image models, for the training of supervised models: image classifiers with label supervision, and CLIP with language supervision. We identify several factors, including text prompts, classifier-free guidance scale, and types of text-to-image models, that significantly affect scaling behavior. After tuning these factors, we observe that synthetic images demonstrate a scaling trend similar to, but slightly less effective than, real images in CLIP training, while they significantly underperform in scaling when training supervised image classifiers. Our analysis indicates that the main reason for this underperformance is the inability of off-the-shelf text-to-image models to generate certain concepts, a limitation that significantly impairs the training of image classifiers. Our findings also suggest that scaling synthetic data can be particularly effective in scenarios such as: (1) when there is a limited supply of real images for a supervised problem (e.g., fewer than 0.5 million images in ImageNet), (2) when the evaluation dataset diverges significantly from the training data, indicating the out-of-distribution scenario, or (3) when synthetic data is used in conjunction with real images, as demonstrated in the training of CLIP models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-08", "arxiv_id": "2312.04483", "title": "Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation", "authors": [ "Zhiwu Qing", "Shiwei Zhang", "Jiayu Wang", "Xiang Wang", "Yujie Wei", "Yingya Zhang", "Changxin Gao", "Nong Sang" ], "github": "", "project_page": "", "abstract": "Despite diffusion models having shown powerful abilities to generate photorealistic images, generating videos that are realistic and diverse still remains in its infancy. One of the key reasons is that current methods intertwine spatial content and temporal dynamics together, leading to a notably increased complexity of text-to-video generation (T2V). In this work, we propose HiGen, a diffusion model-based method that improves performance by decoupling the spatial and temporal factors of videos from two perspectives, i.e., structure level and content level. At the structure level, we decompose the T2V task into two steps, including spatial reasoning and temporal reasoning, using a unified denoiser. Specifically, we generate spatially coherent priors using text during spatial reasoning and then generate temporally coherent motions from these priors during temporal reasoning. At the content level, we extract two subtle cues from the content of the input video that can express motion and appearance changes, respectively. These two cues then guide the model's training for generating videos, enabling flexible content variations and enhancing temporal stability. Through the decoupled paradigm, HiGen can effectively reduce the complexity of this task and generate realistic videos with semantics accuracy and motion stability. Extensive experiments demonstrate the superior performance of HiGen over the state-of-the-art T2V methods.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-10", "arxiv_id": "2312.04724", "title": "Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models", "authors": [ "Manish Bhatt", "Sahana Chennabasappa", "Cyrus Nikolaidis", "Shengye Wan", "Ivan Evtimov", "Dominik Gabi", "Daniel Song", "Faizan Ahmad", "Cornelius Aschermann", "Lorenzo Fontana", "Sasha Frolov", "Ravi Prakash Giri", "Dhaval Kapil", "Yiannis Kozyrakis", "David LeBlanc", "James Milazzo", "Aleksandar Straumann", "Gabriel Synnaeve", "Varun Vontimitta", "Spencer Whitman", "Joshua Saxe" ], "github": "", "project_page": "", "abstract": "This paper presents CyberSecEval, a comprehensive benchmark developed to help bolster the cybersecurity of Large Language Models (LLMs) employed as coding assistants. As what we believe to be the most extensive unified cybersecurity safety benchmark to date, CyberSecEval provides a thorough evaluation of LLMs in two crucial security domains: their propensity to generate insecure code and their level of compliance when asked to assist in cyberattacks. Through a case study involving seven models from the Llama 2, Code Llama, and OpenAI GPT large language model families, CyberSecEval effectively pinpointed key cybersecurity risks. More importantly, it offered practical insights for refining these models. A significant observation from the study was the tendency of more advanced models to suggest insecure code, highlighting the critical need for integrating security considerations in the development of sophisticated LLMs. CyberSecEval, with its automated test case generation and evaluation pipeline covers a broad scope and equips LLM designers and researchers with a tool to broadly measure and enhance the cybersecurity safety properties of LLMs, contributing to the development of more secure AI systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-10", "arxiv_id": "2312.04655", "title": "ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations", "authors": [ "Maitreya Patel", "Changhoon Kim", "Sheng Cheng", "Chitta Baral", "Yezhou Yang" ], "github": "", "project_page": "", "abstract": "Text-to-image (T2I) diffusion models, notably the unCLIP models (e.g., DALL-E-2), achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks, at the cost of significant computational resources. The unCLIP stack comprises T2I prior and diffusion image decoder. The T2I prior model alone adds a billion parameters compared to the Latent Diffusion Models, which increases the computational and high-quality data requirements. We introduce ECLIPSE, a novel contrastive learning method that is both parameter and data-efficient. ECLIPSE leverages pre-trained vision-language models (e.g., CLIP) to distill the knowledge into the prior model. We demonstrate that the ECLIPSE trained prior, with only 3.3% of the parameters and trained on a mere 2.8% of the data, surpasses the baseline T2I priors with an average of 71.6% preference score under resource-limited setting. It also attains performance on par with SOTA big models, achieving an average of 63.36% preference score in terms of the ability to follow the text compositions. Extensive experiments on two unCLIP diffusion image decoders, Karlo and Kandinsky, affirm that ECLIPSE priors consistently deliver high performance while significantly reducing resource dependency.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-11", "arxiv_id": "2312.04985", "title": "SparQ Attention: Bandwidth-Efficient LLM Inference", "authors": [ "Luka Ribar", "Ivan Chelombiev", "Luke Hudlass-Galley", "Charlie Blake", "Carlo Luschi", "Douglas Orr" ], "github": "", "project_page": "", "abstract": "Generative large language models (LLMs) have opened up numerous novel possibilities, but due to their significant computational requirements their ubiquitous use remains challenging. Some of the most useful applications require processing large numbers of samples at a time and using long contexts, both significantly increasing the memory communication load of the models. We introduce SparQ Attention, a technique for increasing the inference throughput of LLMs by reducing the memory bandwidth requirements within the attention blocks through selective fetching of the cached history. Our proposed technique can be applied directly to off-the-shelf LLMs during inference, without requiring any modification to the pre-training setup or additional fine-tuning. We show how SparQ Attention can decrease the attention memory bandwidth requirements up to eight times without any loss in accuracy by evaluating Llama 2 and Pythia models on a wide range of downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-11", "arxiv_id": "2312.05107", "title": "DreaMoving: A Human Dance Video Generation Framework based on Diffusion Models", "authors": [ "Mengyang Feng", "Jinlin Liu", "Kai Yu", "Yuan Yao", "Zheng Hui", "Xiefan Guo", "Xianhui Lin", "Haolan Xue", "Chen Shi", "Xiaowen Li", "Aojie Li", "Miaomiao Cui", "Peiran Ren", "Xuansong Xie" ], "github": "", "project_page": "", "abstract": "In this paper, we present DreaMoving, a diffusion-based controllable video generation framework to produce high-quality customized human dance videos. Specifically, given target identity and posture sequences, DreaMoving can generate a video of the target identity dancing anywhere driven by the posture sequences. To this end, we propose a Video ControlNet for motion-controlling and a Content Guider for identity preserving. The proposed model is easy to use and can be adapted to most stylized diffusion models to generate diverse results. The project page is available at https://dreamoving.github.io/dreamoving.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-11", "arxiv_id": "2312.04963", "title": "Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors", "authors": [ "Lihe Ding", "Shaocong Dong", "Zhanpeng Huang", "Zibin Wang", "Yiyuan Zhang", "Kaixiong Gong", "Dan Xu", "Tianfan Xue" ], "github": "", "project_page": "", "abstract": "Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space, either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-view datasets. Without explicit 3D priors, these methods often lead to geometric anomalies and multi-view inconsistency. Recently, researchers have attempted to improve the genuineness of 3D objects by directly training on 3D datasets, albeit at the cost of low-quality texture generation due to the limited texture diversity in 3D datasets. To harness the advantages of both approaches, we propose Bidirectional Diffusion(BiDiff), a unified framework that incorporates both a 3D and a 2D diffusion process, to preserve both 3D fidelity and 2D texture richness, respectively. Moreover, as a simple combination may yield inconsistent generation results, we further bridge them with novel bidirectional guidance. In addition, our method can be used as an initialization of optimization-based models to further improve the quality of 3D model and efficiency of optimization, reducing the generation process from 3.4 hours to 20 minutes. Experimental results have shown that our model achieves high-quality, diverse, and scalable 3D generation. Project website: https://bidiff.github.io/.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-11", "arxiv_id": "2312.04966", "title": "Customizing Motion in Text-to-Video Diffusion Models", "authors": [ "Joanna Materzynska", "Josef Sivic", "Eli Shechtman", "Antonio Torralba", "Richard Zhang", "Bryan Russell" ], "github": "", "project_page": "", "abstract": "We introduce an approach for augmenting text-to-video generation models with customized motions, extending their capabilities beyond the motions depicted in the original training data. By leveraging a few video samples demonstrating specific movements as input, our method learns and generalizes the input motion patterns for diverse, text-specified scenarios. Our contributions are threefold. First, to achieve our results, we finetune an existing text-to-video model to learn a novel mapping between the depicted motion in the input examples to a new unique token. To avoid overfitting to the new custom motion, we introduce an approach for regularization over videos. Second, by leveraging the motion priors in a pretrained model, our method can produce novel videos featuring multiple people doing the custom motion, and can invoke the motion in combination with other motions. Furthermore, our approach extends to the multimodal customization of motion and appearance of individualized subjects, enabling the generation of videos featuring unique characters and distinct motions. Third, to validate our method, we introduce an approach for quantitatively evaluating the learned custom motion and perform a systematic ablation study. We show that our method significantly outperforms prior appearance-based customization approaches when extended to the motion customization task.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-11", "arxiv_id": "2312.05180", "title": "PathFinder: Guided Search over Multi-Step Reasoning Paths", "authors": [ "Olga Golovneva", "Sean O'Brien", "Ramakanth Pasunuru", "Tianlu Wang", "Luke Zettlemoyer", "Maryam Fazel-Zarandi", "Asli Celikyilmaz" ], "github": "", "project_page": "", "abstract": "With recent advancements in large language models, methods like chain-of-thought prompting to elicit reasoning chains have been shown to improve results on reasoning tasks. However, tasks that require multiple steps of reasoning still pose significant challenges to state-of-the-art models. Drawing inspiration from the beam search algorithm, we propose PathFinder, a tree-search-based reasoning path generation approach. It enhances diverse branching and multi-hop reasoning through the integration of dynamic decoding, enabled by varying sampling methods and parameters. Using constrained reasoning, PathFinder integrates novel quality constraints, pruning, and exploration methods to enhance the efficiency and the quality of generation. Moreover, it includes scoring and ranking features to improve candidate selection. Our approach outperforms competitive baselines on three complex arithmetic and commonsense reasoning tasks by 6% on average. Our model generalizes well to longer, unseen reasoning chains, reflecting similar complexities to beam search with large branching factors.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-11", "arxiv_id": "2312.04875", "title": "MVDD: Multi-View Depth Diffusion Models", "authors": [ "Zhen Wang", "Qiangeng Xu", "Feitong Tan", "Menglei Chai", "Shichen Liu", "Rohit Pandey", "Sean Fanello", "Achuta Kadambi", "Yinda Zhang" ], "github": "", "project_page": "", "abstract": "Denoising diffusion models have demonstrated outstanding results in 2D image generation, yet it remains a challenge to replicate its success in 3D shape generation. In this paper, we propose leveraging multi-view depth, which represents complex 3D shapes in a 2D data format that is easy to denoise. We pair this representation with a diffusion model, MVDD, that is capable of generating high-quality dense point clouds with 20K+ points with fine-grained details. To enforce 3D consistency in multi-view depth, we introduce an epipolar line segment attention that conditions the denoising step for a view on its neighboring views. Additionally, a depth fusion module is incorporated into diffusion steps to further ensure the alignment of depth maps. When augmented with surface reconstruction, MVDD can also produce high-quality 3D meshes. Furthermore, MVDD stands out in other tasks such as depth completion, and can serve as a 3D prior, significantly boosting many downstream tasks, such as GAN inversion. State-of-the-art results from extensive experiments demonstrate MVDD's excellent ability in 3D shape generation, depth completion, and its potential as a 3D prior for downstream tasks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-11", "arxiv_id": "2312.04916", "title": "EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism", "authors": [ "Yanxi Chen", "Xuchen Pan", "Yaliang Li", "Bolin Ding", "Jingren Zhou" ], "github": "https://github.com/pan-x-c/EE-LLM", "project_page": "", "abstract": "We present EE-LLM, a framework for large-scale training and inference of early-exit large language models (LLMs). While recent works have shown preliminary evidence for the efficacy of early exiting in accelerating LLM inference, EE-LLM makes a foundational step towards scaling up early-exit LLMs by supporting their training and inference with massive 3D parallelism. Built upon Megatron-LM, EE-LLM implements a variety of algorithmic innovations and performance optimizations tailored to early exiting, including a lightweight method that facilitates backpropagation for the early-exit training objective with pipeline parallelism, techniques of leveraging idle resources in the original pipeline schedule for computation related to early-exit layers, and two approaches of early-exit inference that are compatible with KV caching for autoregressive generation. Our analytical and empirical study shows that EE-LLM achieves great training efficiency with negligible computational overhead compared to standard LLM training, as well as outstanding inference speedup without compromising output quality. To facilitate further research and adoption, we release EE-LLM at https://github.com/pan-x-c/EE-LLM.", "github_manual": "", "github_regex": "https://github.com/pan-x-c/EE-LLM", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2023-12-11", "arxiv_id": "2312.04837", "title": "Localized Symbolic Knowledge Distillation for Visual Commonsense Models", "authors": [ "Jae Sung Park", "Jack Hessel", "Khyathi Raghavi Chandu", "Paul Pu Liang", "Ximing Lu", "Peter West", "Youngjae Yu", "Qiuyuan Huang", "Jianfeng Gao", "Ali Farhadi", "Yejin Choi" ], "github": "", "project_page": "", "abstract": "Instruction following vision-language (VL) models offer a flexible interface that supports a broad range of multimodal tasks in a zero-shot fashion. However, interfaces that operate on full images do not directly enable the user to \"point to\" and access specific regions within images. This capability is important not only to support reference-grounded VL benchmarks, but also, for practical applications that require precise within-image reasoning. We build Localized Visual Commonsense models, which allow users to specify (multiple) regions as input. We train our model by sampling localized commonsense knowledge from a large language model (LLM): specifically, we prompt an LLM to collect commonsense knowledge given a global literal image description and a local literal region description automatically generated by a set of VL models. With a separately trained critic model that selects high-quality examples, we find that training on the localized commonsense corpus can successfully distill existing VL models to support a reference-as-input interface. Empirical results and human evaluations in a zero-shot setup demonstrate that our distillation method results in more precise VL models of reasoning compared to a baseline of passing a generated referring expression to an LLM.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25413", "title": "DepthLM: Metric Depth From Vision Language Models", "authors": [ "Zhipeng Cai", "Ching-Feng Yeh", "Hu Xu", "Zhuang Liu", "Gregory Meyer", "Xinjie Lei", "Changsheng Zhao", "Shang-Wen Li", "Vikas Chandra", "Yangyang Shi" ], "github": "", "project_page": "", "abstract": "Vision language models (VLMs) can flexibly address various vision tasks through text interactions. Although successful in semantic understanding, state-of-the-art VLMs including GPT-5 still struggle in understanding 3D from 2D inputs. On the other hand, expert pure vision models achieve super-human accuracy in metric depth estimation, a key 3D understanding task. However, they require task-specific architectures and losses. Such difference motivates us to ask: Can VLMs reach expert-level accuracy without architecture or loss change? We take per-pixel metric depth estimation as the representative task and show that the answer is yes! Surprisingly, comprehensive analysis shows that text-based supervised-finetuning with sparse labels is sufficient for VLMs to unlock strong 3D understanding, no dense prediction head or complex regression/regularization loss is needed. The bottleneck for VLMs lies actually in pixel reference and cross-dataset camera ambiguity, which we address through visual prompting and intrinsic-conditioned augmentation. With much smaller models, our method DepthLM surpasses the accuracy of most advanced VLMs by over 2x, making VLMs for the first time comparable with pure vision models. Interestingly, without explicit enforcement during training, VLMs trained with DepthLM naturally avoids over-smoothing, having much fewer flying points at boundary regions than pure vision models. The simplicity of DepthLM also enables a single VLM to cover various 3D tasks beyond metric depth. Our code and model will be released at the link below.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24935", "title": "Scalable GANs with Transformers", "authors": [ "Sangeek Hyun", "MinKyu Lee", "Jae-Pil Heo" ], "github": "", "project_page": "https://hse1032.github.io/GAT", "abstract": "Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning. We investigate the scalability of Generative Adversarial Networks (GANs) through two design choices that have proven to be effective in other types of generative models: training in a compact Variational Autoencoder latent space and adopting purely transformer-based generators and discriminators. Training in latent space enables efficient computation while preserving perceptual fidelity, and this efficiency pairs naturally with plain transformers, whose performance scales with computational budget. Building on these choices, we analyze failure modes that emerge when naively scaling GANs. Specifically, we find issues as underutilization of early layers in the generator and optimization instability as the network scales. Accordingly, we provide simple and scale-friendly solutions as lightweight intermediate supervision and width-aware learning-rate adjustment. Our experiments show that GAT, a purely transformer-based and latent-space GANs, can be easily trained reliably across a wide range of capacities (S through XL). Moreover, GAT-XL/2 achieves state-of-the-art single-step, class-conditional generation performance (FID of 2.96) on ImageNet-256 in just 40 epochs, 6x fewer epochs than strong baselines.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24148", "title": "TENET: Leveraging Tests Beyond Validation for Code Generation", "authors": [ "Yiran Hu", "Nan Jiang", "Shanchao Liang", "Yi Wu", "Lin Tan" ], "github": "", "project_page": "", "abstract": "Test-Driven Development (TDD) is a widely adopted software engineering practice that requires developers to create and execute tests alongside code implementation, ensuring that software behavior is continuously validated and refined. In the era of vibe coding, where developers increasingly delegate code writing to large language models (LLMs) by specifying high-level intentions, TDD becomes even more crucial, as test cases serve as executable specifications that explicitly define and verify intended functionality beyond what natural-language descriptions and code context can convey. While vibe coding under TDD is promising, there are three main challenges: (1) selecting a small yet effective test suite to improve the generation accuracy and control the execution workload, (2) retrieving context such as relevant code effectively, and (3) systematically using test feedback for effective code refinement. To address these challenges, we introduce TENET, an LLM agent for generating functions in complex real-world repositories under the TDD setting. TENET features three components: (1) a novel test harness mechanism that selects a concise test suite to maximize diversity of target usage scenarios; (2) a tailored agent toolset that performs efficient retrieval of relevant code with interactive debugging; and (3) a reflection-based refinement workflow that iteratively analyzes failures, replenishes context, and applies code refinement. TENET achieves 69.08% and 81.77% Pass@1 on RepoCod and RepoEval benchmarks, outperforming the best agentic baselines by 9.49 and 2.17 percentage points, respectively. In addition, this is the first study of test-driven code generation with repository-level context, examining how different aspects of test suites affect the performance of LLM agents under the TDD setting.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.18582", "title": "The Photographer Eye: Teaching Multimodal Large Language Models to See and Critique like Photographers", "authors": [ "Daiqing Qi", "Handong Zhao", "Jing Shi", "Simon Jenni", "Yifei Fan", "Franck Dernoncourt", "Scott Cohen", "Sheng Li" ], "github": "", "project_page": "", "abstract": "While editing directly from life, photographers have found it too difficult to see simultaneously both the blue and the sky. Photographer and curator, Szarkowski insightfully revealed one of the notable gaps between general and aesthetic visual understanding: while the former focuses on identifying the factual element in an image (sky), the latter transcends such object identification, viewing it instead as an aesthetic component--a pure color block (blue). Such fundamental distinctions between general (detection, localization, etc.) and aesthetic (color, lighting, composition, etc.) visual understanding present a significant challenge for Multimodal Large Language Models (MLLMs). Although some recent works have made initial explorations, they are often limited to general and basic aesthetic commonsense. As a result, they frequently fall short in real-world scenarios (Fig. 1), which require extensive expertise--including photographic techniques, photo pre/post-processing knowledge, and more, to provide a detailed analysis and description. To fundamentally enhance the aesthetics understanding of MLLMs, we first introduce a novel dataset, PhotoCritique, derived from extensive discussions among professional photographers and enthusiasts, and characterized by the large scale, expertise, and diversity. Then, to better learn visual aesthetics from PhotoCritique, we furthur propose a novel model, PhotoEye, featuring a languageguided multi-view vision fusion mechanism to understand image aesthetics from multiple perspectives. Finally, we present a novel benchmark, PhotoBench, a comprehensive and professional benchmark for aesthetic visual understanding. On existing benchmarks and PhotoBench, our model demonstrates clear advantages over existing models.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24878", "title": "ThermalGen: Style-Disentangled Flow-Based Generative Models for RGB-to-Thermal Image Translation", "authors": [ "Jiuhong Xiao", "Roshan Nayak", "Ning Zhang", "Daniel Tortei", "Giuseppe Loianno" ], "github": "", "project_page": "https://xjh19971.github.io/ThermalGen", "abstract": "Paired RGB-thermal data is crucial for visual-thermal sensor fusion and cross-modality tasks, including important applications such as multi-modal image alignment and retrieval. However, the scarcity of synchronized and calibrated RGB-thermal image pairs presents a major obstacle to progress in these areas. To overcome this challenge, RGB-to-Thermal (RGB-T) image translation has emerged as a promising solution, enabling the synthesis of thermal images from abundant RGB datasets for training purposes. In this study, we propose ThermalGen, an adaptive flow-based generative model for RGB-T image translation, incorporating an RGB image conditioning architecture and a style-disentangled mechanism. To support large-scale training, we curated eight public satellite-aerial, aerial, and ground RGB-T paired datasets, and introduced three new large-scale satellite-aerial RGB-T datasets--DJI-day, Bosonplus-day, and Bosonplus-night--captured across diverse times, sensor types, and geographic regions. Extensive evaluations across multiple RGB-T benchmarks demonstrate that ThermalGen achieves comparable or superior translation performance compared to existing GAN-based and diffusion-based methods. To our knowledge, ThermalGen is the first RGB-T image translation model capable of synthesizing thermal images that reflect significant variations in viewpoints, sensor characteristics, and environmental conditions. Project page: http://xjh19971.github.io/ThermalGen", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24592", "title": "BPMN Assistant: An LLM-Based Approach to Business Process Modeling", "authors": [ "Josip Tomo Licardo", "Nikola Tankovic", "Darko Etinger" ], "github": "https://github.com/jtlicardo/bpmn-assistant", "project_page": "", "abstract": "This paper presents BPMN Assistant, a tool that leverages Large Language Models (LLMs) for natural language-based creation and editing of BPMN diagrams. A specialized JSON-based representation is introduced as a structured alternative to the direct handling of XML to enhance the accuracy of process modifications. Process generation quality is evaluated using Graph Edit Distance (GED) and Relative Graph Edit Distance (RGED), while editing performance is evaluated with a binary success metric. Results show that JSON and XML achieve similar similarity scores in generation, but JSON offers greater reliability, faster processing, and significantly higher editing success rates. We discuss key trade-offs, limitations, and future improvements. The implementation is available at https://github.com/jtlicardo/bpmn-assistant.", "github_manual": "", "github_regex": "https://github.com/jtlicardo/bpmn-assistant", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.21043", "title": "Combinatorial Creativity: A New Frontier in Generalization Abilities", "authors": [ "Samuel Schapiro", "Sumuk Shashidhar", "Alexi Gladstone", "Jonah Black", "Royce Moon", "Dilek Hakkani-Tur", "Lav R. Varshney" ], "github": "", "project_page": "https://cc.spiralworks.ai", "abstract": "Artificial intelligence (AI) systems, and Large Language Models (LLMs) in particular, are increasingly employed for creative tasks like scientific idea generation, constituting a form of generalization from training data unaddressed by existing conceptual frameworks. Despite its similarities to compositional generalization (CG), combinatorial creativity (CC) is an open-ended ability. Instead of evaluating for accuracy or correctness against fixed targets, which would contradict the open-ended nature of CC, we propose a theoretical framework and algorithmic task for evaluating outputs by their degrees of novelty and utility. From here, we make several important empirical contributions: (1) We obtain the first insights into the scaling behavior of creativity for LLMs. (2) We discover that, for fixed compute budgets, there exist optimal model depths and widths for creative ability. (3) We find that the ideation-execution gap, whereby LLMs excel at generating novel scientific ideas but struggle to ensure their practical feasibility, may be explained by a more fundamental novelty-utility tradeoff characteristic of creativity algorithms in general. Importantly, this tradeoff remains persistent even at scale, casting doubt on the long-term creative potential of LLMs in their current form. Together, our conceptual framework and empirical findings provide a foundation for understanding and improving creativity in modern AI models, bridging the gap between human and machine intelligence.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.22991", "title": "ADAM: A Diverse Archive of Mankind for Evaluating and Enhancing LLMs in Biographical Reasoning", "authors": [ "Jasin Cekinmez", "Omid Ghahroodi", "Saad Fowad Chandle", "Dhiman Gupta", "Ehsaneddin Asgari" ], "github": "", "project_page": "", "abstract": "We introduce ADAM (A Diverse Archive of Mankind), a framework for evaluating and improving multimodal large language models (MLLMs) in biographical reasoning. To the best of our knowledge, this is the first work to systematically examine LLM capabilities in biography, a critical yet underexplored dimension of factual knowledge. At its core, AdamDB is a multilingual and multimodal dataset covering over 4 million individuals across geography, time, and profession, while AdamBench provides cognitively structured evaluations based on Bloom's taxonomy, spanning six reasoning levels in both English and native languages. To address hallucinations, particularly for lesser-known individuals, we propose AdamRAG, a retrieval-augmented generation system tailored to biographical contexts. Experiments show that AdamRAG substantially improves open-source models and modestly benefits closed-source ones, with the largest gains on lower-order reasoning. Popularity strongly mediates accuracy, and multimodal input via face images offers smaller, less consistent improvements than retrieval. ADAM establishes the first benchmark and framework for cognitively, culturally, and multimodally grounded biographical evaluation, advancing the development of multilingual, accurate, and hallucination-resistant MLLMs.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.19033", "title": "Charting a Decade of Computational Linguistics in Italy: The CLiC-it Corpus", "authors": [ "Chiara Alzetta", "Serena Auriemma", "Alessandro Bondielli", "Luca Dini", "Chiara Fazzone", "Alessio Miaschi", "Martina Miliani", "Marta Sartor" ], "github": "", "project_page": "", "abstract": "Over the past decade, Computational Linguistics (CL) and Natural Language Processing (NLP) have evolved rapidly, especially with the advent of Transformer-based Large Language Models (LLMs). This shift has transformed research goals and priorities, from Lexical and Semantic Resources to Language Modelling and Multimodality. In this study, we track the research trends of the Italian CL and NLP community through an analysis of the contributions to CLiC-it, arguably the leading Italian conference in the field. We compile the proceedings from the first 10 editions of the CLiC-it conference (from 2014 to 2024) into the CLiC-it Corpus, providing a comprehensive analysis of both its metadata, including author provenance, gender, affiliations, and more, as well as the content of the papers themselves, which address various topics. Our goal is to provide the Italian and international research communities with valuable insights into emerging trends and key developments over time, supporting informed decisions and future directions in the field.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.25171", "title": "TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion", "authors": [ "Sophia Tang", "Yuchen Zhu", "Molei Tao", "Pranam Chatterjee" ], "github": "", "project_page": "", "abstract": "Reinforcement learning with stochastic optimal control offers a promising framework for diffusion fine-tuning, where a pre-trained diffusion model is optimized to generate paths that lead to a reward-tilted distribution. While these approaches enable optimization without access to explicit samples from the optimal distribution, they require training on rollouts under the current fine-tuned model, making them susceptible to reinforcing sub-optimal trajectories that yield poor rewards. To overcome this challenge, we introduce TRee Search Guided TRajectory-Aware Fine-Tuning for Discrete Diffusion (TR2-D2), a novel framework that optimizes reward-guided discrete diffusion trajectories with tree search to construct replay buffers for trajectory-aware fine-tuning. These buffers are generated using Monte Carlo Tree Search (MCTS) and subsequently used to fine-tune a pre-trained discrete diffusion model under a stochastic optimal control objective. We validate our framework on single- and multi-objective fine-tuning of biological sequence diffusion models, highlighting the overall effectiveness of TR2-D2 for reliable reward-guided fine-tuning in discrete sequence generation.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2025-09-30", "arxiv_id": "2509.24988", "title": "Generalized Correctness Models: Learning Calibrated and Model-Agnostic Correctness Predictors from Historical Patterns", "authors": [ "Hanqi Xiao", "Vaidehi Patil", "Hyunji Lee", "Elias Stengel-Eskin", "Mohit Bansal" ], "github": "", "project_page": "", "abstract": "Generating accurate and calibrated confidence estimates is critical for deploying LLMs in high-stakes or user-facing applications, and remains an open challenge. Prior research has often framed confidence as a problem of eliciting a model's \"self-knowledge\", i.e., the ability of an LLM to judge whether its own answers are correct; this approach implicitly assumes that there is some privileged information about the answer's correctness that is accessible to the model itself. However, our experiments reveal that an LLM attempting to predict the correctness of its own outputs generally performs no better than an unrelated LLM. Moreover, we hypothesize that a key factor in building a \"Correctness Model\" (CM) is exposure to a target model's historical predictions. We propose multiple methods to inject this historical correctness information, creating a Generalized Correctness Model (GCM). We first show that GCMs can be trained on the correctness data from many LLMs and learn patterns for correctness prediction applicable across datasets and models. We then use CMs as a lens for studying the source of correctness prediction ability and its generalization, systematically controlling their training data and finding that answer phrasing is a strong predictor for correctness. We further explore alternative methods of injecting history without training an LLM, finding that including history as in-context examples can help improve correctness prediction, and post-hoc calibration can provide complementary reductions in calibration error. We evaluate GCMs based on Qwen3-8B across 5 model families and the MMLU and TriviaQA datasets, as well as on a downstream selective prediction task, finding that reliable LLM confidence estimation is a generalizable and model-agnostic skill learned by systematically encoding correctness history rather than a model-specific skill reliant on self-introspection.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.22801", "title": "Clipping-Free Policy Optimization for Large Language Models", "authors": [ "\u00d6mer Veysel \u00c7a\u011fatan", "Bar\u0131\u015f Akg\u00fcn", "G\u00f6zde G\u00fcl \u015eahin", "Xuandong Zhao" ], "github": "", "project_page": "", "abstract": "Reinforcement learning has become central to post-training large language models, yet dominant algorithms rely on clipping mechanisms that introduce optimization issues at scale, including zero-gradient regions, reward hacking, and training instability. We propose Clipping-Free Policy Optimization (CFPO), which replaces heuristic clipping with a convex quadratic penalty derived from Total Variation divergence constraints, yielding an everywhere-differentiable objective that enforces stable policy updates without hard boundaries. We evaluate CFPO across both reasoning and alignment settings. In reasoning, CFPO matches clipping-based methods on downstream benchmarks while extending the stable training regime. In alignment, CFPO mitigates verbosity exploitation and reduces capability degradation, while achieving competitive instruction-following performance. CFPO requires only a one-line code change and no additional hyperparameters. Our results suggest that CFPO is a promising drop-in alternative to clipping-based methods for LLM post-training.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.00168", "title": "YOLOE-26: Integrating YOLO26 with YOLOE for Real-Time Open-Vocabulary Instance Segmentation", "authors": [ "Ranjan Sapkota", "Manoj Karkee" ], "github": "", "project_page": "", "abstract": "This paper presents YOLOE-26, a unified framework that integrates the deployment-optimized YOLO26(or YOLOv26) architecture with the open-vocabulary learning paradigm of YOLOE for real-time open-vocabulary instance segmentation. Building on the NMS-free, end-to-end design of YOLOv26, the proposed approach preserves the hallmark efficiency and determinism of the YOLO family while extending its capabilities beyond closed-set recognition. YOLOE-26 employs a convolutional backbone with PAN/FPN-style multi-scale feature aggregation, followed by end-to-end regression and instance segmentation heads. A key architectural contribution is the replacement of fixed class logits with an object embedding head, which formulates classification as similarity matching against prompt embeddings derived from text descriptions, visual examples, or a built-in vocabulary. To enable efficient open-vocabulary reasoning, the framework incorporates Re-Parameterizable Region-Text Alignment (RepRTA) for zero-overhead text prompting, a Semantic-Activated Visual Prompt Encoder (SAVPE) for example-guided segmentation, and Lazy Region Prompt Contrast for prompt-free inference. All prompting modalities operate within a unified object embedding space, allowing seamless switching between text-prompted, visual-prompted, and fully autonomous segmentation. Extensive experiments demonstrate consistent scaling behavior and favorable accuracy-efficiency trade-offs across model sizes in both prompted and prompt-free settings. The training strategy leverages large-scale detection and grounding datasets with multi-task optimization and remains fully compatible with the Ultralytics ecosystem for training, validation, and deployment. Overall, YOLOE-26 provides a practical and scalable solution for real-time open-vocabulary instance segmentation in dynamic, real-world environments.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2601.16513", "title": "Competing Visions of Ethical AI: A Case Study of OpenAI", "authors": [ "Melissa Wilfley", "Mengting Ai", "Madelyn Rose Sanfilippo" ], "github": "https://github.com/famous-blue-raincoat/AI_Ethics_Discourse", "project_page": "", "abstract": "Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI's public discourse leveraged 'ethics', 'safety', 'alignment' and adjacent related concepts over time, and what does discourse signal about framing in practice? A structured corpus, differentiating between communication for a general audience and communication with an academic audience, was assembled from public documentation. Analysis. Qualitative content analysis of ethical themes combined inductively derived and deductively applied codes. Quantitative analysis leveraged computational content analysis methods via NLP to model topics and quantify changes in rhetoric over time. Visualizations report aggregate results. For reproducible results, we have released our code at https://github.com/famous-blue-raincoat/AI_Ethics_Discourse. Results. Results indicate that safety and risk discourse dominate OpenAI's public communication and documentation, without applying academic and advocacy ethics frameworks or vocabularies. Conclusions. Implications for governance are presented, along with discussion of ethics-washing practices in industry.", "github_manual": "", "github_regex": "https://github.com/famous-blue-raincoat/AI_Ethics_Discourse", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-02-03", "arxiv_id": "2602.01418", "title": "Where to Attend: A Principled Vision-Centric Position Encoding with Parabolas", "authors": [ "Christoffer Koo \u00d8hrstr\u00f8m", "Rafael I. Cabral Muchacho", "Yifei Dong", "Filippos Moumtzidellis", "Ronja G\u00fcldenring", "Florian T. Pokorny", "Lazaros Nalpantidis" ], "github": "https://github.com/DTU-PAS/parabolic-position-encoding", "project_page": "https://chrisohrstrom.github.io/parabolic-position-encoding/", "abstract": "We propose Parabolic Position Encoding (PaPE), a parabola-based position encoding for vision modalities in attention-based architectures. Given a set of vision tokens-such as images, point clouds, videos, or event camera streams-our objective is to encode their positions while accounting for the characteristics of vision modalities. Prior works have largely extended position encodings from 1D-sequences in language to nD-structures in vision, but only with partial account of vision characteristics. We address this gap by designing PaPE from principles distilled from prior work: translation invariance, rotation invariance (PaPE-RI), distance decay, directionality, and context awareness. We evaluate PaPE on 8 datasets that span 4 modalities. We find that either PaPE or PaPE-RI achieves the top performance on 7 out of 8 datasets. Extrapolation experiments on ImageNet-1K show that PaPE extrapolates remarkably well, improving in absolute terms by up to 10.5% over the next-best position encoding. Code is available at https://github.com/DTU-PAS/parabolic-position-encoding.", "github_manual": "", "github_regex": "https://github.com/DTU-PAS/parabolic-position-encoding", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2604.25325", "title": "R^3-SQL: Ranking Reward and Resampling for Text-to-SQL", "authors": [ "Hojae Han", "Yeonseok Jeong", "Seung-won Hwang", "Zhewei Yao", "Yuxiong He" ], "github": "", "project_page": "", "abstract": "Modern Text-to-SQL systems generate multiple candidate SQL queries and rank them to judge a final prediction. However, existing methods face two limitations. First, they often score functionally equivalent SQL queries inconsistently despite identical execution results. Second, ranking cannot recover when the correct SQL is absent from the candidate pool. We propose R^3-SQL, a Text-to-SQL framework that addresses both issues through unified reward for ranking and resampling. R^3-SQL first groups candidates by execution result and ranks groups for consistency. To score each group, it combines a pairwise preference across groups with a pointwise utility from the best group rank and size, capturing relative preference, consistency, and candidate quality. To improve candidate recall, R^3-SQL introduces agentic resampling, which judges the generated candidate pool and selectively resamples when the correct SQL is likely absent. R^3-SQL achieves 75.03 execution accuracy on BIRD-dev, a new state of the art among methods using models with disclosed sizes, with consistent gains across five benchmarks.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.07447", "title": "Sparse Autoencoders as Plug-and-Play Firewalls for Adversarial Attack Detection in VLMs", "authors": [ "Hao Wang", "Yiqun Sun", "Pengfei Wei", "Lawrence B. Hsieh", "Daisuke Kawahara" ], "github": "https://github.com/conan1024hao/SAEgis", "project_page": "", "abstract": "Vision-language models (VLMs) have advanced rapidly and are increasingly deployed in real-world applications, especially with the rise of agent-based systems. However, their safety has received relatively limited attention. Even the latest proprietary and open-weight VLMs remain highly vulnerable to adversarial attacks, leaving downstream applications exposed to significant risks. In this work, we propose a novel and lightweight adversarial attack detection framework based on sparse autoencoders (SAEs), termed SAEgis. By inserting an SAE module into a pretrained VLM and training it with standard reconstruction objectives, we find that the learned sparse latent features naturally capture attack-relevant signals. These features enable reliable classification of whether an input image has been adversarially perturbed, even for previously unseen samples. Extensive experiments show that SAEgis achieves strong performance across in-domain, cross-domain, and cross-attack settings, with particularly large improvements in cross-domain generalization compared to existing baselines. In addition, combining signals from multiple layers further improves robustness and stability. To the best of our knowledge, this is the first work to explore SAE as a plug-and-play mechanism for adversarial attack detection in VLMs. Our method requires no additional adversarial training, introduces minimal overhead, and provides a practical approach for improving the safety of real-world VLM systems.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/conan1024hao/SAEgis", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.07850", "title": "MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning", "authors": [ "Ionut-Vlad Modoranu", "Mher Safaryan", "Dan Alistarh" ], "github": "https://github.com/IST-DASLab/MatryoshkaLoRA", "project_page": "", "abstract": "With the rise in scale for deep learning models to billions of parameters, the computational cost of fine-tuning remains a significant barrier to deployment. While Low-Rank Adaptation (LoRA) has become the standard for parameter-efficient fine-tuning, the need to set a predefined, static rank r requires exhaustive grid searches to balance efficiency and performance. Existing rank-adaptive solutions such as DyLoRA mitigate this by sampling ranks during the training from a predefined distribution. However, they often yield sub-optimal results at higher ranks due to lack of consistent gradient signals across the full hierarchy of ranks, thus making these methods data-inefficient. In this paper, we propose MatryoshkaLoRA, a general, Matryoshka-inspired training framework for LoRA that learns accurate hierarchical low-rank representations by inserting a fixed, carefully crafted diagonal matrix P between the existing LoRA adapters to scale their sub-ranks accordingly. By introducing this simple modification, our general framework recovers LoRA and DyLoRA only by changing P and ensures all sub-ranks embed the available gradient information efficiently. Our MatryoshkaLoRA supports dynamic rank selection with minimal degradation in accuracy. We further propose Area Under the Rank Accuracy Curve (AURAC), a metric that consistently evaluates the performance of hierarchical low-rank adapters. Our results demonstrate that MatryoshkaLoRA learns more accurate hierarchical low-rank representations than prior rank-adaptive approaches and achieves superior accuracy-performance trade-offs across ranks on the evaluated datasets. Our code is available at https://github.com/IST-DASLab/MatryoshkaLoRA.", "github_manual": "", "github_regex": "https://github.com/IST-DASLab/MatryoshkaLoRA", "github_paper_md": "", "github_api": "https://github.com/IST-DASLab/MatryoshkaLoRA", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.06455", "title": "PrefixGuard: From LLM-Agent Traces to Online Failure-Warning Monitors", "authors": [ "Xinmiao Huang", "Jinwei Hu", "Rajarshi Roy", "Changshun Wu", "Yi Dong", "Xiaowei Huang" ], "github": "https://github.com/shinmohuang/PrefixGuard", "project_page": "https://shinmohuang.github.io/prefixguard_page/", "abstract": "Large language model (LLM) agents now execute long, tool-using tasks where final outcome checks can arrive too late for intervention. Online warning requires lightweight prefix monitors over heterogeneous traces, but hand-authored event schemas are brittle and deployment-time LLM judging is costly. We introduce PrefixGuard, a trace-to-monitor framework with an offline StepView induction step followed by supervised monitor training. StepView induces deterministic typed-step adapters from raw trace samples, and the monitor learns an event abstraction and prefix-risk scorer from terminal outcomes. Across WebArena, \u03c4^2-Bench, SkillsBench, and TerminalBench, the strongest PrefixGuard monitors reach 0.900/0.710/0.533/0.557 AUPRC. Using the strongest backend within each representation, they improve over raw-text controls by an average of +0.137 AUPRC. LLM judges remain substantially weaker under the same prefix-warning protocol. We also derive an observability ceiling on score-based area under the precision-recall curve (AUPRC) that separates monitor error from failures lacking evidence in the observed prefix. For finite-state audit, post-hoc deterministic finite automaton (DFA) extraction remains compact on WebArena and \u03c4^2-Bench (29 and 20 states) but expands to 151 and 187 states on SkillsBench and TerminalBench. Finally, first-alert diagnostics show that strong ranking does not imply deployment utility: WebArena ranks well yet fails to support low-false-alarm alerts, whereas \u03c4^2-Bench and TerminalBench retain more actionable early alerts. Together, these results position PrefixGuard as a practical monitor-synthesis recipe with explicit diagnostics for when prefix warnings translate into actionable interventions.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shinmohuang/PrefixGuard", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.03353", "title": "SkCC: Portable and Secure Skill Compilation for Cross-Framework LLM Agents", "authors": [ "Yipeng Ouyang", "Yi Xiao", "Yuhao Gu", "Xianwei Zhang" ], "github": "https://github.com/Nexa-Language/Skill-Compiler", "project_page": "https://skcc.nexa-lang.com/", "abstract": "LLM-Agents have evolved into autonomous systems for complex task execution, with the SKILL.md specification emerging as a de facto standard for encapsulating agent capabilities. However, a critical bottleneck remains: different agent frameworks exhibit starkly different sensitivities to prompt formatting, causing up to 40% performance variation, yet nearly all skills exist as a single, format-agnostic Markdown version. Manual per-platform rewriting creates an unsustainable maintenance burden, while prior audits have found that over one third of community skills contain security vulnerabilities. To address this, we present SkCC, a compilation framework that introduces classical compiler design into agent skill development. At its core, SkIR - a strongly-typed intermediate representation - decouples skill semantics from platform-specific formatting, enabling portable deployment across heterogeneous agent frameworks. Around this IR, a compile-time Analyzer enforces security constraints via Anti-Skill Injection before deployment. Through a four-phase pipeline, SkCC reduces adaptation complexity from O(m times n) to O(m + n). Experiments on SkillsBench demonstrate that compiled skills consistently outperform their original counterparts, improving pass rates from 21.1% to 33.3% on Claude Code and from 35.1% to 48.7% on Kimi CLI, while achieving sub-10ms compilation latency, a 94.8% proactive security trigger rate, and 10-46% runtime token savings across platforms.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Nexa-Language/Skill-Compiler", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.07896", "title": "What if AI systems weren't chatbots?", "authors": [ "Sourojit Ghosh", "Pranav Narayanan Venkit", "Sanjana Gautam", "Avijit Ghosh" ], "github": "", "project_page": "", "abstract": "The rapid convergence of artificial intelligence (AI) toward conversational chatbot interfaces marks a critical moment for the industry. This paper argues that the chatbot paradigm is not a neutral interface choice, but a dominant sociotechnical configuration whose widespread adoption reshapes social, economic, legal, and environmental systems. We examine how treating AI primarily as conversational assistants has extensive structural downsides. We show how chatbot-based systems often fail to adequately meet user needs, particularly in complex or high-stakes contexts, while projecting confidence and authority. We further analyze how the normalization of chatbot-mediated interaction alters patterns of work, learning, and decision-making, contributing to deskilling, homogenization of knowledge, and shifting expectations of expertise. Finally, we examine broader societal effects, including labor displacement, concentration of economic power, and increased environmental costs driven by sustained investment in large-scale chatbot infrastructures. While acknowledging legitimate benefits, we argue that the current trajectory of AI development reflects specific value choices that prioritize conversational generality over domain specificity, accountability, and long-term social sustainability. We conclude by outlining alternative directions for AI development and governance that move beyond one-size-fits-all chatbots, emphasizing pluralistic system design, task-specific tools, and institutional safeguards to mitigate social and economic harm.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "" }, { "date": "2026-05-11", "arxiv_id": "2605.06241", "title": "Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning", "authors": [ "\u00d6mer Faruk Akg\u00fcl", "Rajgopal Kannan", "Willie Neiswanger", "Viktor Prasanna" ], "github": "https://github.com/farukakgul/ReasonMaxxer", "project_page": "", "abstract": "Reinforcement learning has become the standard for improving reasoning in large language models, yet evidence increasingly suggests that RL does not teach new strategies; it redistributes probability mass over solutions the base model already contains. In this work, we ask: if RL merely steers the model toward paths it already knows, is the RL optimization loop itself necessary? Through token-level analysis across multiple model families and RL algorithms, we find that RL's beneficial footprint is a sparse, predictable correction concentrated at high-entropy decision points where the model is uncertain which branch to take. Only 1--3\\% of token positions are affected, the promoted token always lies within the base model's top-5 alternatives, and targeted corrections at those few positions causally recover a large fraction of RL's accuracy gain, while random corrections fail. The base model's own entropy identifies these positions without any RL-trained model, and the entire correction is low-dimensional, representable in a tiny fraction of model parameters. These findings reframe reasoning improvement as sparse policy selection, not capability acquisition. We translate this insight into ReasonMaxxer, a minimal RL-free method that applies contrastive loss only at entropy-gated decision points, using a few hundred base-model rollouts and no online generation. Across three model families, six scales, and six math reasoning benchmarks, ReasonMaxxer matches or exceeds full RL performance while requiring only tens of problems and minutes of single-GPU training, a reduction in training cost of roughly three orders of magnitude.", "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/farukakgul/ReasonMaxxer", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.00933", "title": "CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining", "authors": [ "Hada Melino Muhammad", "Zechen Li", "Flora Salim", "Ahmed A. Metwally" ], "github": "https://github.com/cruiseresearchgroup/CGM-JEPA", "project_page": "", "abstract": "Continuous Glucose Monitoring (CGM) can detect early metabolic subphenotypes (insulin resistance, IR; \u03b2-cell dysfunction), but population-scale deployment faces two coupled problems. First, the same physiological state appears through multiple views (CGM time series, venous OGTT, Glucodensity summaries), so single-view representations fail to transfer when deployment shifts the modality or setting. Second, baselines perform inconsistently across these shifts. Both problems point to one remedy: representations that abstract away from any single view to capture higher-level temporal and distributional structure. We propose CGM-JEPA, a self-supervised pretraining framework which predicts masked latent representations rather than raw values, yielding abstraction that transfers across modalities. X-CGM-JEPA adds a masked Glucodensity cross-view objective for complementary distributional information. We pretrain on sim389k unlabeled CGM readings from 228 subjects and evaluate on two clinical cohorts (N=27 and N=17 public-release subsets) across three regimes (cohort generalization, venous-to-CGM transfer, home CGM) under 20-iteration times 2-fold cross-validation. X-CGM-JEPA ranks first or second on AUROC for both endpoints across all three regimes while no baseline does, exceeding the strongest baseline by up to +6.5 pp in cohort generalization and +3.6 pp in venous-to-CGM transfer (paired Wilcoxon, p<0.001). Under modality shift, it matches mean AUROC while redistributing toward weaker subgroups (ethnicity AUROC gap shrinks 25-54%); on sparse in-domain venous data, the distributional view lifts label-aware clustering (ARI +39%, NMI +40%). Code and weights: https://github.com/cruiseresearchgroup/CGM-JEPA", "github_manual": "", "github_regex": "https://github.com/cruiseresearchgroup/CGM-JEPA", "github_paper_md": "", "github_api": "https://github.com/cruiseresearchgroup/CGM-JEPA", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.03408", "title": "Discovering Reinforcement Learning Interfaces with Large Language Models", "authors": [ "Akshat Singh Jaswal", "Ashish Baghel", "Paras Chopra" ], "github": "https://github.com/Lossfunk/LIMEN", "project_page": "https://akshat-sj.github.io/limen/", "abstract": "Reinforcement learning systems rely on environment interfaces that specify observations and reward functions, yet constructing these interfaces for new tasks often requires substantial manual effort. While recent work has automated reward design using large language models (LLMs), these approaches assume fixed observations and do not address the broader challenge of synthesizing complete task interfaces. We study RL task interface discovery from raw simulator state, where both observation mappings and reward functions must be generated. We propose LIMEN (Code available at https://github.com/Lossfunk/LIMEN), a LLM guided evolutionary framework that produces candidate interfaces as executable programs and iteratively refines them using policy training feedback. Across novel discrete gridworld tasks and continuous control domains spanning locomotion and manipulation, joint evolution of observations and rewards discovers effective interfaces given only a trajectory-level success metric, while optimizing either component alone fails on at least one domain. These results demonstrate that automatic construction of RL interfaces from raw state can substantially reduce manual engineering and that observation and reward components often benefit from co-design, as single-component optimization fails catastrophically on at least one domain in our evaluation suite.", "github_manual": "", "github_regex": "https://github.com/Lossfunk/LIMEN", "github_paper_md": "", "github_api": "https://github.com/Lossfunk/LIMEN", "github_api_added_by": "user" }, { "date": "2026-05-11", "arxiv_id": "2605.07940", "title": "Delta-Adapter: Scalable Exemplar-Based Image Editing with Single-Pair Supervision", "authors": [ "Jiacheng Chen", "Songze Li", "Han Fu", "Baoquan Zhao", "Wei Liu", "Yanyan Liang", "Li Qing", "Xudong Mao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cst7R/Delta-Adapter", "github_api_added_by": "user", "project_page": "https://delta-adapter.github.io", "abstract": "Exemplar-based image editing applies a transformation defined by a source-target image pair to a new query image. Existing methods rely on a pair-of-pairs supervision paradigm, requiring two image pairs sharing the same edit semantics to learn the target transformation. This constraint makes training data difficult to curate at scale and limits generalization across diverse edit types. We propose Delta-Adapter, a method that learns transferable editing semantics under single-pair supervision, requiring no textual guidance. Rather than directly exposing the exemplar pair to the model, we leverage a pre-trained vision encoder to extract a semantic delta that encodes the visual transformation between the two images. This semantic delta is injected into a pre-trained image editing model via a Perceiver-based adapter. Since the target image is never directly visible to the model, it can serve as the prediction target, enabling single-pair supervision without requiring additional exemplar pairs. This formulation allows us to leverage existing large-scale editing datasets for training. To further promote faithful transformation transfer, we introduce a semantic delta consistency loss that aligns the semantic change of the generated output with the ground-truth semantic delta extracted from the exemplar pair. Extensive experiments demonstrate that Delta-Adapter consistently improves both editing accuracy and content consistency over four strong baselines on seen editing tasks, while also generalizing more effectively to unseen editing tasks. Code will be available at https://delta-adapter.github.io.", "github": "https://github.com/cst7R/Delta-Adapter" }, { "date": "2026-05-11", "arxiv_id": "2605.07079", "title": "Learning Visual Feature-Based World Models via Residual Latent Action", "authors": [ "Xinyu Zhang", "Zhengtong Xu", "Yutian Tao", "Yeping Wang", "Yu She", "Abdeslam Boularias" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mlzxy/rla-wm", "github_api_added_by": "user", "project_page": "https://mlzxy.github.io/rla-wm", "abstract": "World models predict future transitions from observations and actions. Existing works predominantly focus on image generation only. Visual feature-based world models, on the other hand, predict future visual features instead of raw video pixels, offering a promising alternative that is more efficient and less prone to hallucination. However, current feature-based approaches rely on direct regression, which leads to blurry or collapsed predictions in complex interactions, while generative modeling in high-dimensional feature spaces still remains challenging. In this work, we discover that a new type of latent action representation, which we refer to as *Residual Latent Action* (RLA), can be easily learned from DINO residuals. We also show that RLA is predictive, generalizable, and encodes temporal progression. Building on RLA, we propose *RLA World Model* (RLA-WM), which predicts RLA values via flow matching. RLA-WM outperforms both state-of-the-art feature-based and video-diffusion world models on simulation and real-world datasets, while being orders of magnitude faster than video diffusion. Furthermore, we develop two robot learning techniques that use RLA-WM to improve policy learning. The first one is a minimalist world action model with RLA that learns from actionless demonstration videos. The second one is the first visual RL framework trained entirely inside a world model learned from offline videos only, using a video-aligned reward and no online interactions or handcrafted rewards. Project page: https://mlzxy.github.io/rla-wm", "github": "https://github.com/mlzxy/rla-wm" }, { "date": "2026-05-11", "arxiv_id": "2605.07924", "title": "Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation", "authors": [ "Amin Karimi Monsefi", "Dominic Culver", "Nikhil Bhendawade", "Manuel R. Ciosici", "Yizhe Zhang", "Irina Belousova" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Discrete flow matching generates text by iteratively transforming noise tokens into coherent language, but may require hundreds of forward passes. Distillation uses the multi-step trajectory to train a student to reproduce the process in a few steps. When the student underperforms, the usual explanation is insufficient capacity. We argue the opposite: the trajectory is the bottleneck, not the student. Each training trajectory is built through a chain of blind stochastic jumps with no evaluation of sequence quality; a single bad decision at an early midpoint propagates through subsequent steps, yet the student must imitate the result. Trajectory-Shaped Discrete Flow Matching (TS-DFM) replaces these blind jumps with guided navigation: a lightweight energy compass evaluates candidate continuations at each midpoint, selecting the most coherent. All shaping is training-only; inference cost is unchanged. On 170M-parameter language modeling, the shaped student at 8 steps achieves 32% lower perplexity than the 1,024-step teacher while being 128x faster, with gains consistent across source distributions and three evaluators of increasing scale. TS-DFM achieves the best perplexity of any discrete-generation baseline we compare against, including methods trained on 6x more data or using 5x larger models.", "github": "" }, { "date": "2026-05-11", "arxiv_id": "2511.07328", "title": "Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training", "authors": [ "Artyom Sorokin", "Nazar Buzun", "Alexander Anokhin", "Oleg Inozemcev", "Egor Vedernikov", "Petr Anokhin", "Mikhail Burtsev", "Trushkov Alexey", "Yin Wenshuai", "Evgeny Burnaev" ], "github_manual": "", "github_regex": "https://github.com/griver/Q-RAG", "github_paper_md": "", "github_api": "https://github.com/griver/Q-RAG", "github_api_added_by": "user", "project_page": "https://github.com/griver/Q-RAG", "abstract": "Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and inference cost. However, most existing RAG methods focus on single-step retrieval, which is often insufficient for answering complex questions that require multi-step search. Recently, multi-step retrieval approaches have emerged, typically involving the fine-tuning of small LLMs to perform multi-step retrieval. This type of fine-tuning is highly resource-intensive and does not enable the use of larger LLMs. In this work, we propose Q-RAG, a novel approach that fine-tunes the Embedder model for multi-step retrieval using reinforcement learning (RL). Q-RAG offers a competitive, resource-efficient alternative to existing multi-step retrieval methods for open-domain question answering and achieves state-of-the-art results on the popular long-context benchmarks BabiLong and RULER for contexts up to 10M tokens. Code is available at https://github.com/griver/Q-RAG", "github": "https://github.com/griver/Q-RAG" }, { "date": "2026-05-11", "arxiv_id": "2605.07075", "title": "ModelLens: Finding the Best for Your Task from Myriads of Models", "authors": [ "Rui Cai", "Weijie Jacky Mo", "Xiaofei Wen", "Qiyao Ma", "Wenhui Zhu", "Xiwen Chen", "Muhao Chen", "Zhe Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/luisrui/ModelLens", "github_api_added_by": "user", "project_page": "https://luisrui.github.io/ModelLens/", "abstract": "The open-source model ecosystem now contains hundreds of thousands of pretrained models, yet picking the best model for a new dataset is increasingly infeasible: new models and unbenchmarked datasets emerge continuously, leaving practitioners with no prior records on either side. Existing approaches handle only fragments of this in-the-wild setting: AutoML and transferability estimation select models from small predefined pools or require expensive per-model forward passes on the target dataset, while model routing presupposes a given candidate pool. We introduce ModelLens, a unified framework for model recommendation in the wild. Our key insight is that public leaderboard interactions, though scattered and noisy, collectively trace out an implicit atlas of model capabilities across heterogeneous evaluation settings, a signal rich enough to learn from directly. By learning a performance-aware latent space over model--dataset--metric tuples, ModelLens ranks unseen models on unseen datasets without running candidates on the target dataset. On a new benchmark of 1.62M evaluation records spanning 47K models and 9.6K datasets, ModelLens surpasses baselines that either rely on metadata alone or require running each candidate on the target dataset. Its recommended Top-K pools further improve multiple representative routing methods by up to 81% across diverse QA benchmarks. Case studies on recently released benchmarks further confirm generalization to both text and vision-language tasks.", "github": "https://github.com/luisrui/ModelLens" }, { "date": "2026-05-11", "arxiv_id": "2605.07396", "title": "Rubric-based On-policy Distillation", "authors": [ "Junfeng Fang", "Zhepei Hong", "Mao Zheng", "Mingyang Song", "Gengsheng Li", "Houcheng Jiang", "Dan Zhang", "Haiyun Guo", "Xiang Wang", "Tat-Seng Chua" ], "github_manual": "", "github_regex": "https://github.com/Peregrine123/ROPD_official", "github_paper_md": "", "github_api": "https://github.com/Peregrine123/ROPD_official", "github_api_added_by": "user", "project_page": "", "abstract": "On-policy distillation (OPD) is a powerful paradigm for model alignment, yet its reliance on teacher logits restricts its application to white-box scenarios. We contend that structured semantic rubrics can serve as a scalable alternative to teacher logits, enabling OPD using only teacher-generated responses. To prove it, we introduce ROPD, a simple yet foundational framework for rubric-based OPD. Specifically, ROPD induces prompt-specific rubrics from teacher-student contrasts, and then utilizes these rubrics to score the student rollouts for on-policy optimization. Empirically, ROPD outperforms the advanced logit-based OPD methods across most scenarios, and achieving up to a 10x gain in sample efficiency. These results position rubric-based OPD as a flexible, black-box-compatible alternative to the prevailing logit-based OPD, offering a simple yet strong baseline for scalable distillation across proprietary and open-source LLMs. Code is available at https://github.com/Peregrine123/ROPD_official.", "github": "https://github.com/Peregrine123/ROPD_official" }, { "date": "2026-05-11", "arxiv_id": "2605.07394", "title": "BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning", "authors": [ "Shaokai Ye", "Vasileios Saveris", "Yihao Qian", "Jiaming Hu", "Elmira Amirloo", "Peter Grasch" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Image captioning is one of the most fundamental tasks in computer vision. Owing to its open-ended nature, it has received significant attention in the era of multimodal large language models (MLLMs). In pursuit of ever more detailed and accurate captions, recent work has increasingly turned to reinforcement learning (RL). However, existing captioning-RL methods and evaluation metrics often emphasize a narrow notion of caption quality, inducing trade-offs across core dimensions of captioning. For example, utility-oriented objectives can encourage noisy, hallucinated, or overlong captions that improve downstream question answering while harming fluency, whereas arena-style objectives can favor fluent but generic descriptions with limited usefulness. To address this, we propose a more balanced RL framework that jointly optimizes utility-aware correctness, reference coverage, and linguistic quality. In order to effectively optimize the resulting continuous multi-objective reward formulation, we apply GDPO-style reward-decoupled normalization to continuous-valued captioning rewards and show that it improves performance over vanilla GRPO. Additionally, we introduce length-conditional reward masking, yielding a more suitable length penalty for captioning. Across LLaVA-1.5-7B and Qwen2.5-VL 3B and 7B base models, our method consistently improves caption quality, with peak gains of +13.6 DCScore, +9.0 CaptionQA, and +29.0 CapArena across different models.", "github": "" }, { "date": "2026-05-11", "arxiv_id": "2605.07039", "title": "PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents", "authors": [ "Minghao Yan", "Bo Peng", "Benjamin Coleman", "Ziqi Chen", "Zhouhang Xie", "Shuo Chen", "Zhankui He", "Noveen Sachdeva", "Weili Wang", "Ed H. Chi", "Shivaram Venkataraman", "Wang-Cheng Kang", "Derek Zhiyuan Cheng", "Beidou Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. This limits adaptation in practical engineering and research tasks, where evaluations are expensive, and progress depends on learning task-specific search dynamics. We introduce PACEvolve++, an advisor-model reinforcement learning framework for test-time policy adaptation in evolutionary search agents. PACEvolve++ decouples strategic search decisions from implementation: a trainable advisor generates, assesses, and selects hypotheses, while a stronger frontier model translates selected hypotheses into executable candidates. To train the advisor under non-stationary feedback, we propose a phase-adaptive approach that adapts its optimization strategy to different phases of the evolutionary process. Early in evolution, it uses group-relative feedback to learn broad search preferences; later, as reward gaps compress, it emphasizes best-of-k frontier contribution to support stable refinement. Across expert-parallel load balancing, sequential recommendation, and protein fitness extrapolation, PACEvolve++ outperforms the state-of-the-art evolutionary search framework with frontier models, achieving faster convergence and stabilizing test-time training during evolutionary search.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.09063", "title": "Soohak: A Mathematician-Curated Benchmark for Evaluating Research-level Math Capabilities of LLMs", "authors": [ "Guijin Son", "Seungone Kim", "Catherine Arnett", "Hyunwoo Ko", "Hyein Lee", "Hyeonah Kang", "Jiang Longxi", "Jin Yun", "JungYup Lee", "Kyungmin Lee", "Sam Yoosuk Kim", "Sang Park", "Seunghyeok Hong", "SeungJae Lee", "Seungyeop Yi", "Shinae Shin", "SunHye Bok", "Sunyoung Shin", "Yonghoon Ji", "Youngtaek Kim", "Hanearl Jung", "Akari Asai", "Graham Neubig", "Sean Welleck", "Youngjae Yu", "Akshelin R", "Alexander B. Ivanov", "Boboev Muhammadjon", "Chaeyoung Han", "Christian Stump", "Dmitrii Karp", "Dohyun Kwon", "DoYong Kwon", "Duk-Soon Oh", "Giovanni Resta", "Greta Panova", "Huiyun Noh", "Hyungryul Baik", "Hyungsun Bae", "Inomov Mashrafdzhon", "Jeewon Kim", "Ji Eun Lee", "Jiaqi Liu", "Jieui Kang", "Jimin Kim", "Jon-Lark Kim", "Junseo Yoon", "Junwoo Jo", "Kibeom Kim", "Kiwoon Kwon", "Mario Kummer", "Max Mercer", "Minjun Kim", "Nahyun Lee", "Ng Ze-An", "Rafa\u0142 Marcin \u0141ochowski", "Rapha\u00ebl Lachi\u00e8ze-Rey", "Ruichen Zhang", "Sejin Park", "Seonguk Seo", "Shin Jaehoon", "Sunatullo", "Taewoong Eom", "Yeachan Park", "Yongseok Jang", "Youchan Oh", "Zhaoyang Wang", "Zolt\u00e1n Kov\u00e1cs" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Following the recent achievement of gold-medal performance on the IMO by frontier LLMs, the community is searching for the next meaningful and challenging target for measuring LLM reasoning. Whereas olympiad-style problems measure step-by-step reasoning alone, research-level problems use such reasoning to advance the frontier of mathematical knowledge itself, emerging as a compelling alternative. Yet research-level math benchmarks remain scarce because such problems are difficult to source (e.g., Riemann Bench and FrontierMath-Tier 4 contain 25 and 50 problems, respectively). To support reliable evaluation of next-generation frontier models, we introduce Soohak, a 439-problem benchmark newly authored from scratch by 64 mathematicians. Soohak comprises two subsets. On the Challenge subset, frontier models including Gemini-3-Pro, GPT-5, and Claude-Opus-4.5 reach 30.4%, 26.4%, and 10.4% respectively, leaving substantial headroom, while leading open-weight models such as Qwen3-235B, GPT-OSS-120B, and Kimi-2.5 remain below 15%. Notably, beyond standard problem solving, Soohak introduces a refusal subset that probes a capability intrinsic to research mathematics: recognizing ill-posed problems and pausing rather than producing confident but unjustified answers. On this subset, no model exceeds 50%, identifying refusal as a new optimization target that current models do not directly address. To prevent contamination, the dataset will be publicly released in late 2026, with model evaluations available upon request in the interim.", "github": "" }, { "date": "2026-05-11", "arxiv_id": "2605.08678", "title": "MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI", "authors": [ "Bohan Lyu", "Yucheng Yang", "Siqiao Huang", "Jiaru Zhang", "Qixin Xu", "Xinghan Li", "Xinyang Han", "Yicheng Zhang", "Huaqing Zhang", "Runhan Huang", "Kaicheng Yang", "Zitao Chen", "Wentao Guo", "Junlin Yang", "Xinyue Ai", "Wenhao Chai", "Yadi Cao", "Ziran Yang", "Kun Wang", "Dapeng Jiang", "Huan-ang Gao", "Shange Tang", "Chengshuai Shi", "Simon S. Du", "Max Simchowitz", "Jiantao Jiao", "Dawn Song", "Chi Jin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Imbernoulli/MLS-Bench", "github_api_added_by": "user", "project_page": "https://mls-bench.com", "abstract": "Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes. As large language models demonstrate advanced capabilities in reasoning, coding, and engineering tasks, it is increasingly important to understand whether they can discover such methods rather than only apply existing ones. We introduce MLS-Bench, a benchmark for evaluating whether AI systems can invent generalizable and scalable ML methods. MLS-Bench contains 140 tasks across 12 domains, each requiring an agent to improve one targeted component of an ML system or algorithm and demonstrate that the improvement generalizes across controlled settings and scales. We find that current agents remain far from reliably surpassing human-designed methods, and that engineering-style tuning is easier for them than genuine method invention. We further study the effects of test-time scaling, adaptive compute allocation, and context provision on agents' discovery performance, together with case studies of their behavior. Our analyses suggest that the bottleneck is not only in proposing new methods, but also in the scientific insight needed to plan, validate, and scale claims about them. More search, compute, or context alone does not remove this bottleneck. We build and maintain a community platform for cumulative and comparable iteration, and release the data and code at https://mls-bench.com.", "github": "https://github.com/Imbernoulli/MLS-Bench" }, { "date": "2026-05-11", "arxiv_id": "2605.08767", "title": "From Holo Pockets to Electron Density: GPT-style Drug Design with Density", "authors": [ "Jiahao Chen", "Letian Gao", "Yanhao Zhu", "Wenbiao Zhou", "Bing Su", "Zhi John Lu", "Bo Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JiahaoChen1/EDMolGPT", "github_api_added_by": "user", "project_page": "https://jiahaochen1.github.io/EDMolGPT_Page/", "abstract": "Recent advances in generative modeling have enabled significant progress in structure-based drug design (SBDD). Existing methods typically condition molecule generation on empty binding pockets from holo complexes, overlooking informative components such as the filler (ligands and solvent). Here, we leverage low-resolution electron density (ED) derived from the filler as a physically grounded condition for de novo drug design. We consider two types of ED, calculated and cryo-EM/X-ray, obtainable from computational or experimental sources, supporting unified pre-training and experimental integration. Compared with rigid pocket representations, experimental ED naturally captures conformational flexibility and provides a more faithful description of the binding environment. Based on this, we introduce EDMolGPT, a decoder-only autoregressive framework that generates molecules from low-resolution ED point clouds. By grounding generation in physically meaningful density signals, EDMolGPT mitigates structural bias and produces molecules with 3D conformations. Evaluations on 101 biological targets verify the effectiveness. Our project page: https://jiahaochen1.github.io/EDMolGPT_Page/.", "github": "https://github.com/JiahaoChen1/EDMolGPT" }, { "date": "2026-05-11", "arxiv_id": "2605.07210", "title": "DiffRetriever: Parallel Representative Tokens for Retrieval with Diffusion Language Models", "authors": [ "Shuai Wang", "Yin Yu", "Shengyao Zhuang", "Bevan Koopman", "Guido Zuccon" ], "github_manual": "", "github_regex": "https://github.com/ielab/diffretriever", "github_paper_md": "", "github_api": "https://github.com/ielab/diffretriever", "github_api_added_by": "user", "project_page": "", "abstract": "PromptReps showed that an autoregressive language model can be used directly as a retriever by prompting it to generate dense and sparse representations of a query or passage. Extending this to multiple representatives is inefficient for autoregressive models, since tokens must be generated sequentially, and prior multi-token variants did not reliably improve over single-token decoding. We show that the bottleneck is sequential generation, not the multi-token idea itself. DiffRetriever is a representative-token retriever for diffusion language models: it appends K masked positions to the prompt and reads all K in a single bidirectional forward pass. Across in-domain and out-of-domain evaluation, multi-token DiffRetriever substantially improves over single-token on every diffusion backbone we test, while autoregressive multi-token is flat or negative and pays a latency cost that scales with K where diffusion does not. After supervised fine-tuning, DiffRetriever on Dream is the strongest BEIR-7 retriever in our comparison, ahead of PromptReps, the encoder-style DiffEmbed baseline on the same diffusion backbones, and the contrastively fine-tuned single-vector RepLLaMA. A per-query oracle on the frozen base model exceeds contrastive fine-tuning at the same fixed budget, pointing to adaptive budget selection as future work. Code is available at https://github.com/ielab/diffretriever.", "github": "https://github.com/ielab/diffretriever" }, { "date": "2026-05-12", "arxiv_id": "2605.08354", "title": "Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria", "authors": [ "Juanxi Tian", "Fengyuan Liu", "Jiaming Han", "Yilei Jiang", "Yongliang Wu", "Yesheng Liu", "Haodong Li", "Furong Xu", "Wanhua Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenEnvision/AutoRubric-as-Reward", "github_api_added_by": "user", "project_page": "https://openenvision.github.io/AutoRubric-as-Reward/", "abstract": "Aligning multimodal generative models with human preferences demands reward signals that respect the compositional, multi-dimensional structure of human judgment. Prevailing RLHF approaches reduce this structure to scalar or pairwise labels, collapsing nuanced preferences into opaque parametric proxies and exposing vulnerabilities to reward hacking. While recent Rubrics-as-Reward (RaR) methods attempt to recover this structure through explicit criteria, generating rubrics that are simultaneously reliable, scalable, and data-efficient remains an open problem. We introduce Auto-Rubric as Reward (ARR), a framework that reframes reward modeling from implicit weight optimization to explicit, criteria-based decomposition. Before any pairwise comparison, ARR externalizes a VLM's internalized preference knowledge as prompt-specific rubrics, translating holistic intent into independently verifiable quality dimensions. This conversion of implicit preference structure into inspectable, interpretable constraints substantially suppresses evaluation biases including positional bias, enabling both zero-shot deployment and few-shot conditioning on minimal supervision. To extend these gains into generative training, we propose Rubric Policy Optimization (RPO), which distills ARR's structured multi-dimensional evaluation into a robust binary reward, replacing opaque scalar regression with rubric-conditioned preference decisions that stabilize policy gradients. On text-to-image generation and image editing benchmarks, ARR-RPO outperforms pairwise reward models and VLM judges, demonstrating that explicitly externalizing implicit preference knowledge into structured rubrics achieves more reliable, data-efficient multimodal alignment, revealing that the bottleneck is the absence of a factorized interface, not a deficit of knowledge.", "github": "https://github.com/OpenEnvision/AutoRubric-as-Reward" }, { "date": "2026-05-12", "arxiv_id": "2605.09608", "title": "Geometry Conflict: Explaining and Controlling Forgetting in LLM Continual Post-Training", "authors": [ "Yuanyi Wang", "Yifan Yang", "Su Lu", "Yanggan Gu", "Pengkai Wang", "Wenjun Wang", "Zhaoyi Yan", "Congkai Xie", "Jianmin Wu", "Jialun Cao", "Shing-Chi Cheung", "Hongxia Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/InfiXAI/Merging-Scaling-Law", "github_api_added_by": "user", "project_page": "", "abstract": "Continual post-training aims to extend large language models (LLMs) with new knowledge, skills, and behaviors, yet it remains unclear when sequential updates enable capability transfer and when they cause catastrophic forgetting. Existing methods mitigate forgetting through sequential fine-tuning, replay, regularization, or model merging, but offer limited criteria for determining when incorporating new updates is beneficial or harmful. In this work, we study LLM continual post-training through three questions: What drives forgetting? When do sequentially acquired capabilities transfer or interfere? How can compatibility be used to control update integration? We address these questions through task geometry: we represent each post-training task by its parameter update and study the covariance geometry induced by the update. Our central finding is that: forgetting can be considered as a state-relative update-integration failure, it arises when the covariance geometries induced by tasks misalign with the geometry of the evolving model state. Sequential updates transfer when they remain compatible with the model state shaped by previous updates, and interfere when state-relative geometry conflict becomes high. Motivated by this finding, we propose Geometry-Conflict Wasserstein Merging (GCWM), a data-free update-integration method that constructs a shared Wasserstein metric via Gaussian Wasserstein barycenters and uses geometry conflict to gate geometry-aware correction. Across Qwen3 0.6B--14B on domain-continual and capability-continual settings, GCWM consistently outperforms data-free baselines, improving retention and final performance without replay data. These results identify geometry conflict as both an explanatory signal for forgetting and a practical control signal for LLM continual post-training.", "github": "https://github.com/InfiXAI/Merging-Scaling-Law" }, { "date": "2026-05-12", "arxiv_id": "2605.09196", "title": "RigidFormer: Learning Rigid Dynamics using Transformers", "authors": [ "Zhiyang Dou", "Minghao Guo", "Haixu Wu", "Doug Roble", "Tuur Stuyck", "Wojciech Matusik" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Frank-ZY-Dou/Dynamics-Modeling", "github_api_added_by": "user", "project_page": "https://frank-zy-dou.github.io/projects/RigidFormer/index.html", "abstract": "Learning-based simulation of multi-object rigid-body dynamics remains difficult because contact is discontinuous and errors compound over long horizons. Most existing methods remain tied to mesh connectivity and vertex-level message passing, which limits their applicability to mesh-free inputs such as point clouds and leads to high computational cost. Efficiently modeling high-fidelity rigid-body dynamics from mesh-free representations, therefore, remains challenging. We introduce RigidFormer, an object-centric Transformer-based model that learns mesh-free rigid-body dynamics with controllable integration step sizes. RigidFormer reasons at the object level and advances each object through compact anchors; Anchor-Vertex Pooling enriches these anchors with local vertex features, retaining contact-relevant geometry without dense vertex-level interaction. We propose Anchor-based RoPE to inject anchor geometry into attention while respecting the unordered nature of objects and anchors: object-token processing is permutation-equivariant, and the mean-pooled anchor descriptor is invariant to anchor reindexing while preserving shape extent. RigidFormer further enforces rigidity by projecting updates onto the rigid-body manifold using differentiable Kabsch alignment. On standard benchmarks, RigidFormer outperforms or matches mesh-based baselines using point inputs, runs faster, generalizes to unseen point resolutions and across datasets, and scales to 200+ objects; we also show a preliminary extension to command-conditioned articulated bodies by treating body parts as interacting object-level components.", "github": "https://github.com/Frank-ZY-Dou/Dynamics-Modeling" }, { "date": "2026-05-12", "arxiv_id": "2605.09649", "title": "Make Each Token Count: Towards Improving Long-Context Performance with KV Cache Eviction", "authors": [ "Ngoc Bui", "Hieu Trung Nguyen", "Arman Cohan", "Rex Ying" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ngocbh/trimkv", "github_api_added_by": "user", "project_page": "", "abstract": "The key-value (KV) cache is a major bottleneck in long-context inference, where memory and computation grow with sequence length. Existing KV eviction methods reduce this cost but typically degrade performance relative to full-cache inference. Our key insight is that full-cache attention is not always optimal: in long contexts, irrelevant tokens can dilute attention away from useful evidence, so selective, learnable eviction can improve generation rather than merely approximate the full cache. We introduce a global retention-based KV eviction method that learns each token's future utility under a unified memory budget. Lightweight retention gates assign utility scores to cached KV entries, and a shared final scoring projection calibrates these scores across all layers and heads. This enables a single global eviction policy in which tokens from different layers, heads, and modalities compete directly for cache capacity. We further provide theoretical analysis showing that preferentially retaining useful tokens reduces attention dilution, and we justify geometric retention as a query-agnostic proxy for future utility. Across diverse long-context language and vision-language reasoning, and multi-turn dialogue benchmarks, our method substantially reduces KV memory while matching or surpassing full-cache inference. These results suggest that learned, globally calibrated KV eviction is not only a compression technique, but also a mechanism for improving long-context reasoning.", "github": "https://github.com/ngocbh/trimkv" }, { "date": "2026-05-12", "arxiv_id": "2605.06096", "title": "Uncovering Entity Identity Confusion in Multimodal Knowledge Editing", "authors": [ "Shu Wu", "Xiaotian Ye", "Xinyu Mou", "Dongsheng Liu", "Xiaohan Wang", "Mengqi Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multimodal knowledge editing (MKE) aims to correct the internal knowledge of large vision-language models after deployment, yet the behavioral patterns of post-edit models remain underexplored. In this paper, we identify a systemic failure mode in edited models, termed Entity Identity Confusion (EIC): edited models exhibit an absurd behavior where text-only queries about the original entity's identity unexpectedly return information about the new entity. To rigorously investigate EIC, we construct EC-Bench, a diagnostic benchmark that directly probes how image-entity bindings shift before and after editing. Our analysis reveals that EIC stems from existing methods failing to distinguish between Image-Entity (I-E) binding and Entity-Entity (E-E) relational knowledge in the model, causing models to overfit E-E associations as a shortcut: the image is still perceived as the original entity, with the new entity's name serving only as a spurious identity label. We further explore potential mitigation strategies, showing that constraining edits to the model's I-E processing stage encourages edits to act more faithfully on I-E binding, thereby substantially reducing EIC. Based on these findings, we discuss principled desiderata for faithful MKE and provide methodological guidance for future research.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.08600", "title": "100,000+ Movie Reviews from Kazakhstan: Russian, Kazakh, and Code-Switched Texts", "authors": [ "Rustem Yeshpanov" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/datasets/yeshpanovrustem/100k_movie_reviews_from_kz", "abstract": "We present a new publicly available corpus of 100,502 movie reviews from Kazakhstan collected from kino.kz, spanning 2001-2025 and covering 4,943 unique titles. The dataset is multilingual, consisting mainly of Russian reviews alongside Kazakh and code-switched texts. Reviews are manually annotated for language and sentiment polarity, and 11,309 reviews additionally contain explicit user-provided ratings. We define two sentiment tasks -- three-way polarity classification and five-class score classification -- and benchmark classical BoW/TF-IDF baselines against multilingual transformer models (mBERT, XLM-RoBERTa, RemBERT). Experimental results show that transformer models consistently outperform classical baselines on polarity classification, while score classification remains challenging under leakage-controlled evaluation due to severe class imbalance and subtle distinctions between adjacent rating levels.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.05765", "title": "X-OmniClaw Technical Report: A Unified Mobile Agent for Multimodal Understanding and Interaction", "authors": [ "Xiaoming Ren", "Ru Zhen", "Chao Li", "Yang Song", "Qiuxia Hou", "Yanhao Zhang", "Peng Liu", "Qi Qi", "Quanlong Zheng", "Qi Wu", "Zhenyi Liao", "Binqiang Pan", "Haobo Ji", "Haonan Lu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OPPO-Mente-Lab/X-OmniClaw", "github_api_added_by": "user", "project_page": "https://eggplant95.github.io/X-OmniClaw-Page/", "abstract": "Inspired by the development of OpenClaw, there is a growing demand for mobile-based personal agents capable of handling complex and intuitive interactions. In this technical report, we introduce X-OmniClaw, a unified mobile agent designed for multimodal understanding and interaction in the Android ecosystem. This unified architecture of perception, memory, and action enables the agent to handle complex mobile tasks with high contextual awareness. Specifically, Omni Perception provides a unified multimodal ingress pipeline that integrates UI states, real-world visual contexts, and speech inputs, leveraging a temporal alignment module to decompose raw data into structured multimodal intent representations. Omni Memory leverages multimodal memory optimization to enhance personalized intelligence by integrating runtime working memory for task continuity with long-term personal memory distilled from local data, enabling highly context-aware and personalized interactions. Finally, Omni Action employs a hybrid grounding strategy that combines structural XML metadata with visual perception for robust interaction. Through Behavior Cloning and Trajectory Replay, the system captures user navigation as reusable skills, enabling precise direct-access execution. Demonstrations across diverse scenarios show that X-OmniClaw effectively enhances interaction efficiency and task reliability, providing a practical architectural blueprint for the next generation of mobile-native personal assistants.", "github": "https://github.com/OPPO-Mente-Lab/X-OmniClaw" }, { "date": "2026-05-12", "arxiv_id": "2509.24244", "title": "Model Merging Scaling Laws in Large Language Models", "authors": [ "Yuanyi Wang", "Yanggan Gu", "Yiming Zhang", "Qi Zhou", "Zhaoyi Yan", "Congkai Xie", "Xinyao Wang", "Jianbo Yuan", "Hongxia Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/InfiXAI/Merging-Scaling-Law", "github_api_added_by": "user", "project_page": "https://infix.io/research/MergingScalingLaw", "abstract": "We study empirical scaling laws for language model merging measured by cross-entropy. Despite its wide practical use, merging lacks a quantitative rule that predicts returns as we add experts or scale the model size. We identify a compact power law that links model size and expert number: the size-dependent floor decreases with model capacity, while the merging tail exhibits clear diminishing returns in the number of experts. The law holds in-domain and cross-domain, tightly fits measured curves across diverse architectures and methods (Average, TA, TIES, DARE), and explains two robust regularities: most gains arrive early, and variability shrinks as more experts are included. Building on this, we present a simple theory that explains why gains fall roughly as 1/k and links the floor and tail to properties of the base model and the diversity across domains. This law enables predictive planning: estimate how many experts are needed to reach a target loss, decide when to stop adding experts, and trade off scaling the base model versus adding experts under a fixed budget--turning merging from heuristic practice into a computationally efficient, planable alternative to multitask training. This suggests a scaling principle for distributed generative AI: predictable gains can be achieved by composing specialists, offering a complementary path toward AGI-level systems.", "github": "https://github.com/InfiXAI/Merging-Scaling-Law" }, { "date": "2026-05-12", "arxiv_id": "2605.09959", "title": "G-Zero: Self-Play for Open-Ended Generation from Zero Data", "authors": [ "Chengsong Huang", "Haolin Liu", "Tong Zheng", "Runpeng Dai", "Langlin Huang", "Jinyuan Li", "Zongxia Li", "Zhepei Wei", "Yu Meng", "Jiaxin Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Chengsong-Huang/G-Zero", "github_api_added_by": "user", "project_page": "", "abstract": "Self-evolving LLMs excel in verifiable domains but struggle in open-ended tasks, where reliance on proxy LLM judges introduces capability bottlenecks and reward hacking. To overcome this, we introduce G-Zero, a verifier-free, co-evolutionary framework for autonomous self-improvement. Our core innovation is Hint-\u03b4, an intrinsic reward that quantifies the predictive shift between a Generator model's unassisted response and its response conditioned on a self-generated hint. Using this signal, a Proposer model is trained via GRPO to continuously target the Generator's blind spots by synthesizing challenging queries and informative hints. The Generator is concurrently optimized via DPO to internalize these hint-guided improvements. Theoretically, we prove a best-iterate suboptimality guarantee for an idealized standard-DPO version of G-Zero, provided that the Proposer induces sufficient exploration coverage and the data filteration keeps pseudo-label score noise low. By deriving supervision entirely from internal distributional dynamics, G-Zero bypasses the capability ceilings of external judges, providing a scalable, robust pathway for continuous LLM self-evolution across unverifiable domains.", "github": "https://github.com/Chengsong-Huang/G-Zero" }, { "date": "2026-05-12", "arxiv_id": "2605.09996", "title": "Omni-Persona: Systematic Benchmarking and Improving Omnimodal Personalization", "authors": [ "Yeongtak Oh", "Dongwook Lee", "Sangkwon Park", "Heeseung Kim", "Sungroh Yoon" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/oyt9306/Omni-Persona", "github_api_added_by": "user", "project_page": "", "abstract": "While multimodal large language models have advanced across text, image, and audio, personalization research has remained primarily vision-language, with unified omnimodal benchmarking that jointly covers text, image, and audio still limited, and lacking the methodological rigor to account for absent-persona scenarios or systematic grounding studies. We introduce Omni-Persona, the first comprehensive benchmark for omnimodal personalization. We formalize the task as cross-modal routing over the Persona Modality Graph, encompassing 4 task groups and 18 fine-grained tasks across {sim}750 items. To rigorously diagnose grounding behavior, we propose Calibrated Accuracy (mathrm{Cal)}, which jointly rewards correct grounding and appropriate abstention, incorporating absent-persona queries within a unified evaluation framework. On our dedicated experiments, three diagnostic findings emerge: (i) open-source models show a consistent audio-vs-visual grounding gap that RLVR partially narrows via dense rule-based supervision; (ii) answerable recall and parameter scale are incomplete diagnostics, since strong recall can coexist with absent-persona hallucination and larger models do not always achieve higher Cal, exposing calibration as a separate evaluation axis; and (iii) SFT is bounded by the difficulty of constructing annotated ground-truth supervision at scale, while RLVR generalizes more consistently through outcome-level verifiable feedback yet drifts toward conservative behavior and lower generation quality under our reward design. Omni-Persona thus serves as a diagnostic framework that surfaces the pitfalls of omnimodal personalization, guiding future post-training and reward design.", "github": "https://github.com/oyt9306/Omni-Persona" }, { "date": "2026-05-12", "arxiv_id": "2605.09262", "title": "Reinforcing Multimodal Reasoning Against Visual Degradation", "authors": [ "Rui Liu", "Dian Yu", "Haolin Liu", "Yucheng Shi", "Tong Zheng", "Runpeng Dai", "Haitao Mi", "Pratap Tokekar", "Leoweiliang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement Learning has significantly advanced the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet the resulting policies remain brittle against real-world visual degradations such as blur, compression artifacts, and low-resolution scans. Prior robustness techniques from vision and deep RL rely on static data augmentation or value-based regularization, neither of which transfers cleanly to critic-free RL fine-tuning of autoregressive MLLMs. Reinforcing reasoning against such corruptions is non-trivial: naively injecting degraded views during rollout induces reward poisoning, where perceptual occlusions trigger hallucinated trajectories and destabilize optimization. We propose ROMA, an RL fine-tuning framework that modifies the optimization dynamics to reinforce reasoning against visual degradation while preserving clean-input performance. A dual-forward-pass strategy uses teacher forcing to evaluate corrupted views against clean-image trajectories, avoiding new rollouts on degraded inputs. For distributional consistency, we apply a token-level surrogate KL penalty against the worst-case augmentation; to prevent policy collapse under regularization, an auxiliary policy gradient loss anchored to clean-image advantages preserves a reliable reward signal; and to avoid systematically incorrect invariance, correctness-conditioned regularization restricts enforcement to successful trajectories. On Qwen3-VL 4B/8B across seven multimodal reasoning benchmarks, our method improves robustness by +2.4% on seen and +2.3% on unseen corruptions over GRPO while matching clean accuracy.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.09269", "title": "DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification", "authors": [ "Rui Liu", "Dian Yu", "Zhenwen Liang", "Yucheng Shi", "Tong Zheng", "Runpeng Dai", "Haitao Mi", "Pratap Tokekar", "Leoweiliang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://deltarubric.github.io", "abstract": "Aligning Multimodal Large Language Models (MLLMs) requires reliable reward models, yet existing single-step evaluators can suffer from lazy judging, exploiting language priors over fine-grained visual verification. While rubric-based evaluation mitigates these biases in text-only settings, extending it to multimodal tasks is bottlenecked by the complexity of visual reasoning. The critical differences between responses often depend on instance-specific visual details. Robust evaluation requires dynamically synthesizing rubrics that isolate spatial and factual discrepancies. To address this, we introduce DeltaRubric, an approach that reformulates multimodal preference evaluation as a plan-and-execute process within a single MLLM. DeltaRubric operates in two steps: acting first as a Disagreement Planner, the model generates a neutral, instance-specific verification checklist. Transitioning into a Checklist Verifier, it executes these self-generated checks against the image and question to produce the final grounded judgment. We formulate DeltaRubric as a multi-role reinforcement learning problem, jointly optimizing planning and verification capabilities. Validated on Qwen3-VL 4B and 8B Instruct models, DeltaRubric achieves solid empirical gains. For instance, On VL-RewardBench, it improves base model overall accuracy by +22.6 (4B) and +18.8 (8B) points, largely outperforming standard no-rubric baselines. The results demonstrate that decomposing evaluation into structured, verifiable steps leads to more reliable and generalizable multimodal reward modeling.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.10434", "title": "WorldReasonBench: Human-Aligned Stress Testing of Video Generators as Future World-State Predictors", "authors": [ "Keming Wu", "Yijing Cui", "Wenhan Xue", "Qijie Wang", "Xuan Luo", "Zhiyuan Feng", "Zuhao Yang", "Sudong Wang", "Sicong Jiang", "Haowei Zhu", "Zihan Wang", "Ping Nie", "Wenhu Chen", "Bin Wang" ], "github_manual": "", "github_regex": "https://github.com/UniX-AI-Lab/WorldReasonBench", "github_paper_md": "", "github_api": "https://github.com/UniX-AI-Lab/WorldReasonBench", "github_api_added_by": "user", "project_page": "https://unix-ai-lab.github.io/WorldReasonBench/", "abstract": "Commercial video generation systems such as Seedance2.0 and Veo3.1 have rapidly improved, strengthening the view that video generators may be evolving into \"world simulators.\" Yet the community still lacks a benchmark that directly tests whether a model can reason about how an observed world should evolve over time. We introduce WorldReasonBench, which reframes video generation evaluation as world-state prediction: given an initial state and an action, can a model generate a future video whose state evolution remains physically, socially, logically, and informationally consistent? WorldReasonBench contains 436 curated test cases with structured ground-truth QA annotations spanning four reasoning dimensions and 22 subcategories. We evaluate generated videos with a human-aligned two-part methodology: Process-aware Reasoning Verification uses structured QA and reasoning-phase diagnostics to detect temporal and causal failures, while Multi-dimensional Quality Assessment scores reasoning quality, temporal consistency, and visual aesthetics for ranking and reward modeling. We further introduce WorldRewardBench, a preference benchmark with approximately 6K expert-annotated pairs over 1.4K videos, supporting pair-wise and point-wise reward-model evaluation. Across modern video generators, our results expose a persistent gap between visual plausibility and world reasoning: videos can look convincing while failing dynamics, causality, or information preservation. We will release our benchmarks and evaluation toolkit to support community research on genuinely world-aware video generation at https://github.com/UniX-AI-Lab/WorldReasonBench/.", "github": "https://github.com/UniX-AI-Lab/WorldReasonBench" }, { "date": "2026-05-12", "arxiv_id": "2605.10923", "title": "Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning", "authors": [ "Junhao Shen", "Teng Zhang", "Xiaoyan Zhao", "Hong Cheng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ejhshen/SLIM", "github_api_added_by": "user", "project_page": "", "abstract": "Large language model agents increasingly rely on external skills to solve complex tasks, where skills act as modular units that extend their capabilities beyond what parametric memory alone supports. Existing methods assume external skills either accumulate as persistent guidance or internalized into the policy, eventually leading to zero-skill inference. We argue this assumption is overly restrictive, since with limited parametric capacity and uneven marginal contribution across skills, the optimal active skill set is non-monotonic, task- and stage-dependent. In this work, we propose SLIM, a framework of dynamic Skill LIfecycle Management for agentic reinforcement learning (RL), which treats the active external skill set as a dynamic optimization variable jointly updated with policy learning. Specifically, SLIM estimates each active skill's marginal external contribution through leave-one-skill-out validation, then applies three lifecycle operations: retaining high-value skills, retiring skills whose contribution becomes negligible after sufficient exposure, and expanding the skill bank when persistent failures reveal missing capability coverage. Experiments show that SLIM outperforms the best baselines by an average of 7.1% points across ALFWorld and SearchQA. Results further indicate that policy learning and external skill retention are not mutually exclusive: some skills are absorbed into the policy, while others continue to provide external value, supporting SLIM as a more general paradigm for skill-based agentic RL.", "github": "https://github.com/ejhshen/SLIM" }, { "date": "2026-05-12", "arxiv_id": "2605.10537", "title": "Mela: Test-Time Memory Consolidation based on Transformation Hypothesis", "authors": [ "Lungchuan Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Musubi-ai/Mela", "github_api_added_by": "user", "project_page": "", "abstract": "Memory consolidation, the process by which transient experiences are transformed into stable, structured representations, is a foundational organizing principle in the human brain, yet it remains largely unexplored as a design principle for modern sequence models. In this work, we leverage established neuroscientific theories of memory consolidation and cross-frequency coupling to propose the Hierarchical Memory Module (HMM), a neural memory architecture composed of two functionally distinct sub-modules that operate at different update frequencies. Inspired by the transformation hypothesis, the low-frequency sub-module produces high-level representations that capture abstract, gist-level knowledge, while the high-frequency sub-module produces fine-grained representations that preserve richer episodic detail. The final memory output is dynamically reconstructed as a context-dependent combination of both representations, analogous to the reconstructive nature of human memory retrieval. We integrate HMM into a Transformer-based language decoder to form Mela, a family of memory-augmented language models that perform online memory consolidation at test time. To further exploit the multi-granularity memory representations produced by HMM, we introduce MemStack, a method that distributes different levels of memory features across the early layers of the decoder without introducing additional tokens. Experiments on language modeling demonstrate that Mela outperforms Transformer baselines across all the model sizes. Moreover, with the pretrained context length fixed at 4K, Mela maintains performance on significantly longer contexts, whereas Transformer baselines degrade rapidly beyond their training length. Extensive ablation studies validate the contribution of each component and provide guidance for practical configuration.", "github": "https://github.com/Musubi-ai/Mela" }, { "date": "2026-05-12", "arxiv_id": "2605.10922", "title": "Pixal3D: Pixel-Aligned 3D Generation from Images", "authors": [ "Dong-Yang Li", "Wang Zhao", "Yuxin Chen", "Wenbo Hu", "Meng-Hao Guo", "Fang-Lue Zhang", "Ying Shan", "Shi-Min Hu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TencentARC/Pixal3D", "github_api_added_by": "user", "project_page": "https://ldyang694.github.io/projects/pixal3d/", "abstract": "Recent advances in 3D generative models have rapidly improved image-to-3D synthesis quality, enabling higher-resolution geometry and more realistic appearance. Yet fidelity, which measures pixel-level faithfulness of the generated 3D asset to the input image, still remains a central bottleneck. We argue this stems from an implicit 2D-3D correspondence issue: most 3D-native generators synthesize shape in canonical space and inject image cues via attention, leaving pixel-to-3D associations ambiguous. To tackle this issue, we draw inspiration from 3D reconstruction and propose Pixal3D, a pixel-aligned 3D generation paradigm for high-fidelity 3D asset creation from images. Instead of generating in a canonical pose, Pixal3D directly generates 3D in a pixel-aligned way, consistent with the input view. To enable this, we introduce a pixel back-projection conditioning scheme that explicitly lifts multi-scale image features into a 3D feature volume, establishing direct pixel-to-3D correspondence without ambiguity. We show that Pixal3D is not only scalable and capable of producing high-quality 3D assets, but also substantially improves fidelity, approaching the fidelity level of reconstruction. Furthermore, Pixal3D naturally extends to multi-view generation by aggregating back-projected feature volumes across views. Finally, we show pixel-aligned generation benefits scene synthesis, and present a modular pipeline that produces high-fidelity, object-separated 3D scenes from images. Pixal3D for the first time demonstrates 3D-native pixel-aligned generation at scale, and provides a new inspiring way towards high-fidelity 3D generation of object or scene from single or multi-view images. Project page: https://ldyang694.github.io/projects/pixal3d/", "github": "https://github.com/TencentARC/Pixal3D" }, { "date": "2026-05-12", "arxiv_id": "2605.10781", "title": "Rebellious Student: Reversing Teacher Signals for Reasoning Exploration with Self-Distilled RLVR", "authors": [ "Jeonghye Kim", "Jiwon Jeon", "Dongsheng Li", "Yuqing Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Self-distillation has emerged as a powerful framework for post-training LLMs, where a teacher conditioned on extra information guides a student without it, both from the same model. While this guidance is useful when the student has failed, on successful rollouts, the same mechanism instead overwrites the student's choices and suppresses it's own reasoning. Therefore, we propose reading the original self-distillation signal in reverse: when the student succeeds along a path the teacher would not have predicted, these tokens reflect its self-driven reasoning. Building on this, we propose RLRT (RLVR with Reversed Teacher), which augments GRPO by reinforcing these tokens on correct rollouts. We interpret this as a new form of exploration in RLVR: not uniform diversity, but valuable exploration grounded in the student's own success. Across base, instruction-tuned, and thinking-tuned Qwen3 checkpoints, RLRT substantially outperforms self-distillation and exploration-based baselines, establishing information asymmetry as a new, principled design axis for RLVR.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.08659", "title": "Pushing Biomolecular Utility-Diversity Frontiers with Supergroup Relative Policy Optimization", "authors": [ "Xinwu Ye", "He Cao", "Hao Li", "Bin Feng", "Zijing Liu", "Xiangru Tang", "Yu Li", "Shenghua Gao" ], "github_manual": "", "github_regex": "https://github.com/IDEA-XL/SGRPO", "github_paper_md": "", "github_api": "https://github.com/IDEA-XL/SGRPO", "github_api_added_by": "user", "project_page": "", "abstract": "Biomolecular generators are often adapted with reward feedback to improve task-specific utility, but pushing utility alone can concentrate generation on a narrow family of candidates. Maintaining diversity is difficult because sample diversity is a set-level property. We introduce Supergroup Relative Policy Optimization (SGRPO), a flexible GRPO-style framework that directly constructs rewards from set-level diversity. For each condition, SGRPO samples a supergroup of candidate sets, compares their diversity under the same condition, and redistributes the group diversity reward to individual rollouts through leave-one-out diversity contributions before combining it with rollout-level utility. This design decouples SGRPO from a particular generator, utility reward, or diversity metric, and allows instantiation with different GRPO-style approaches. We evaluate SGRPO on de novo small-molecule design, pocket-based small-molecule design, and de novo protein design, instantiating it with both GRPO and Coupled-GRPO across autoregressive and discrete diffusion generators. Across decoding sweeps, SGRPO expands the utility-diversity Pareto frontier and achieves the best frontier-level metrics relative to pretrained generators, GRPO, and memory-assisted GRPO when applicable. Our analyses further show that direct set-level diversity rewards remain effective with small groups and help preserve broader generation-distribution coverage during post-training. The code is available at https://github.com/IDEA-XL/SGRPO.", "github": "https://github.com/IDEA-XL/SGRPO" }, { "date": "2026-05-12", "arxiv_id": "2605.10813", "title": "NanoResearch: Co-Evolving Skills, Memory, and Policy for Personalized Research Automation", "authors": [ "Jinhang Xu", "Qiyuan Zhu", "Yujun Wu", "Zirui Wang", "Dongxu Zhang", "Jianxin Tang", "Marcia Tian", "Yiling Duan", "Siyuan Li", "Jingxuan Wei", "Sirui Han", "Yike Guo", "Odin Zhang", "Conghui He", "Cheng Tan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenRaiser/NanoResearch", "github_api_added_by": "user", "project_page": "", "abstract": "LLM-powered multi-agent systems can now automate the full research pipeline from ideation to paper writing, but a fundamental question remains: automation for whom? Researchers operate under different resource configurations, hold different methodological preferences, and target different output formats. A system that produces uniform outputs regardless of these differences will systematically under-serve every individual user, making personalization a precondition for research automation to be genuinely usable. However, achieving it requires three capabilities that current systems lack: accumulating reusable procedural knowledge across projects, retaining user-specific experience across sessions, and internalizing implicit preferences that resist explicit formalization. We propose NanoResearch, a multi-agent framework that addresses these gaps through tri-level co-evolution. A skill bank distills recurring operations into compact procedural rules reusable across projects. A memory module maintains user- and project-specific experience that grounds planning decisions in each user's research history. A label-free policy learning converts free-form feedback into persistent parameter updates of the planner, reshaping subsequent coordination. These three layers co-evolve: reliable skills produce richer memory, richer memory informs better planning, and preference internalization continuously realigns the loop to each user. Extensive experiments demonstrate that NanoResearch delivers substantial gains over state-of-the-art AI research systems, and progressively refines itself to produce better research at lower cost over successive cycles.", "github": "https://github.com/OpenRaiser/NanoResearch" }, { "date": "2026-05-12", "arxiv_id": "2605.10913", "title": "Shepherd: A Runtime Substrate Empowering Meta-Agents with a Formalized Execution Trace", "authors": [ "Simon Yu", "Derek Chong", "Ananjan Nandi", "Dilara Soylu", "Jiuding Sun", "Christopher D Manning", "Weiyan Shi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We introduce Shepherd, a functional programming model that formalizes meta-agent operations on target agents as functions, with core operations mechanized in Lean. Shepherd records every agent-environment interaction as a typed event in a Git-like execution trace, enabling any past state to be forked and replayed. The system forks the agent process and its filesystem 5times faster than Docker, achieving >95% prompt-cache reuse on replay. We demonstrate the model through three applications. First, in runtime intervention, a live supervisor increases pair coding pass rates from 28.8% to 54.7% on CooperBench. Second, in counterfactual meta-optimization, branching exploration outperforms baselines across four benchmarks by up to 11 points while reducing wall-clock time by up to 58%. Third, in Tree-RL training, forking rollouts at selected turns improves TerminalBench-2 performance from 34.2% to 39.4%. These results establish Shepherd as an efficient infrastructure for programming meta-agents. We open-source the system to support future research.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.09241", "title": "Sub-JEPA: Subspace Gaussian Regularization for Stable End-to-End World Models", "authors": [ "Kai Zhao", "Dongliang Nie", "Yuchen Lin", "Zhehan Luo", "Yixiao Gu", "Deng-Ping Fan", "Dan Zeng" ], "github_manual": "", "github_regex": "https://github.com/intcomp/Sub-JEPA", "github_paper_md": "", "github_api": "https://github.com/intcomp/Sub-JEPA", "github_api_added_by": "user", "project_page": "https://github.com/intcomp/Sub-JEPA", "abstract": "Joint-Embedding Predictive Architectures (JEPAs) provide a simpleframework for learning world models by predicting future latent representations.However, JEPA training is subject to a bias-variance tradeoff.Without sufficient structural constraints, excessive representationalvariance causes the model to collapse to trivial solutions.The recent LeWorldModel (LeWM) shows that this issue can be alleviated bysimply constraining latent embeddings with an isotropic Gaussian prior.However, latent representations inherently lie on low-dimensional manifoldswithin a high-dimensional ambient space, and enforcing an isotropic Gaussianprior directly in this ambient space introduces an overly strong bias.In this work, we propose ame, which seeks a favorable operatingpoint on the bias-variance frontier by applying Gaussian constraints inmultiple random subspaces rather than in the originalembedding space.This design relaxes the global constraint while preserving itsanti-collapse effect, leading to a better balance between trainingstability and representation flexibility.Extensive experiments across fourcontinuous-control environments demonstrate that consistentlyoutperforms LeWM with very clear margins.Our method is simple yet effective, and serves as a strong baseline for future JEPA-based world model research.fdefinedeeemodeThe code is available at https://github.com/intcomp/Sub-JEPA.", "github": "https://github.com/intcomp/Sub-JEPA" }, { "date": "2026-05-12", "arxiv_id": "2605.09810", "title": "TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation", "authors": [ "Hanqun Cao", "Aastha Pal", "Sophia Tang", "Yinuo Zhang", "Jingjie Zhang", "Pheng Ann Heng", "Pranam Chatterjee" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Protein function is often controlled by ligands that bias the direction of state transitions, such as agonists and antagonists, rather than stabilizing a single conformation. This is especially important for clinically relevant G protein-coupled receptors (GPCRs), where therapeutic efficacy depends on functional directionality. Structure-based design methods optimize binding to static conformations and cannot represent non-reversible, directional effects or systematically distinguish agonist from antagonist behavior. To address this gap, we introduce Transition-Directed Discrete Diffusion for Allosteric Binder Design (TD3B), a sequence-based generative framework that designs binders with specified agonist or antagonist behavior via a directional transition control objective. TD3B combines a target-aware Direction Oracle, a soft binding-affinity gate, and amortized fine-tuning of a pre-trained discrete diffusion model, enabling targeted agonist and antagonist generation decoupled from binding affinity and unattainable by equilibrium-based or inference-only guidance baselines. The code and checkpoints are available at https://huggingface.co/ChatterjeeLab/TD3B.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.10730", "title": "Qwen-Image-2.0 Technical Report", "authors": [ "Bing Zhao", "Chenfei Wu", "Deqing Li", "Hao Meng", "Jiahao Li", "Jie Zhang", "Jingren Zhou", "Junyang Lin", "Kaiyuan Gao", "Kuan Cao", "Kun Yan", "Liang Peng", "Lihan Jiang", "Niantong Li", "Ningyuan Tang", "Shengming Yin", "Tianhe Wu", "Xiao Xu", "Xiaoyue Chen", "Xihua Wang", "Yan Shu", "Yanran Zhang", "Yi Wang", "Yilei Chen", "Ying Ba", "Yixian Xu", "Yujia Wu", "Yuxiang Chen", "Zecheng Tang", "Zekai Zhang", "Zhendong Wang", "Zihao Liu", "Zikai Zhou", "An Yang", "Chen Cheng", "Chenxu Lv", "Dayiheng Liu", "Fan Zhou", "Hantian Xiong", "Hongzhu Shi", "Hu Wei", "Huihong Zhao", "Ivy Liu", "Jianwei Zhang", "Jiawei Zhang", "Kai Chen", "Kang He", "Levon Xue", "Lin Qu", "Linhan Tang", "Luwen Feng", "Minggang Wu", "Minmin Sun", "Na Ni", "Rui Men", "Shuai Bai", "Sishou Zheng", "Tao Lan", "Tianqi Zhang", "Tingkun Wen", "Wei Wang", "Weixu Qiao", "Weiyi Lu", "Wenmeng Zhou", "Xiaodong Deng", "Xiaoxiao Xu", "Xinlei Fang", "Xionghui Chen", "Yanan Wang", "Yang Fan", "Yichang Zhang", "Yixuan Xu", "Yu Wu", "Zhiyuan Ma", "Zhizhi Cai" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite recent progress, existing models still struggle with ultra-long text rendering, multilingual typography, high-resolution photorealism, robust instruction following, and efficient deployment, especially in text-rich and compositionally complex scenarios. Qwen-Image-2.0 addresses these challenges by coupling Qwen3-VL as the condition encoder with a Multimodal Diffusion Transformer for joint condition-target modeling, supported by large-scale data curation and a customized multi-stage training pipeline. This enables strong multimodal understanding while preserving flexible generation and editing capabilities. The model supports instructions of up to 1K tokens for generating text-rich content such as slides, posters, infographics, and comics, while significantly improving multilingual text fidelity and typography. It also enhances photorealistic generation with richer details, more realistic textures, and coherent lighting, and follows complex prompts more reliably across diverse styles. Extensive human evaluations show that Qwen-Image-2.0 substantially outperforms previous Qwen-Image models in both generation and editing, marking a step toward more general, reliable, and practical image generation foundation models.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.07465", "title": "SEIF: Self-Evolving Reinforcement Learning for Instruction Following", "authors": [ "Qingyu Ren", "Qianyu He", "Jiajie Zhu", "Xingzhou Chen", "Jingwen Chang", "Zeye Sun", "Han Xia", "Fei Yu", "Jiaqing Liang", "Yanghua Xiao" ], "github_manual": "", "github_regex": "https://github.com/Rainier-rq1/SEIF", "github_paper_md": "", "github_api": "https://github.com/Rainier-rq1/SEIF", "github_api_added_by": "user", "project_page": "", "abstract": "Instruction following is a fundamental capability of large language models (LLMs), yet continuously improving this capability remains challenging. Existing methods typically rely either on costly external supervision from humans or strong teacher models, or on self-play training with static-difficulty instructions that cannot evolve as the model's capabilities improve. To address these limitations, we propose SEIF (Self-Evolving Reinforcement Learning for Instruction Following), a self-evolving framework for enhancing the instruction-following ability of LLMs. SEIF forms a closed self-evolution loop that improves the model's instruction-following ability, where instruction difficulty evolution and model capability evolution reinforce each other. SEIF consists of four roles: an Instructor that generates increasingly challenging instructions, a Filter that removes conflicting or invalid instructions to ensure data quality, a Follower that learns to follow evolved instructions, and a Judger that provides reward signals for reinforcement learning. The Instructor and Follower are alternately trained and co-evolve throughout the process. Experiments across multiple model scales and architectures show that SEIF consistently improves instruction-following performance, suggesting strong generality. Further analyses reveal the sources of improvement and identify an effective training strategy for self-evolution on open-ended tasks: sufficient early-stage training to build a solid foundation, followed by moderate late-stage training to mitigate overfitting and achieve better final performance. The code and data are publicly available at https://github.com/Rainier-rq1/SEIF.", "github": "https://github.com/Rainier-rq1/SEIF" }, { "date": "2026-05-12", "arxiv_id": "2604.23789", "title": "MuSS: A Large-Scale Dataset and Cinematic Narrative Benchmark for Multi-Shot Subject-to-Video Generation", "authors": [ "Haojie Zhang", "Di Wu", "Bingyan Liu", "Linjie Zhong", "Yuancheng Wei", "Xingsong Ye", "Nanqing Liu", "Yaling Liang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhang-haojie/MuSS", "github_api_added_by": "user", "project_page": "", "abstract": "While video foundation models excel at single-shot generation, real-world cinematic storytelling inherently relies on complex multi-shot sequencing. Further progress is constrained by the absence of datasets that address three core challenges: authentic narrative logic, spatiotemporal text-video alignment conflicts, and the \"copy-paste\" dilemma prevalent in Subject-to-Video (S2V) generation. To bridge this gap, we introduce MuSS, a large-scale, dual-track dataset tailored for multi-shot video and S2V generation. Sourced from over 3,000 movies, MuSS explicitly supports both complex montage transitions and subject-centric narratives. To construct this dataset, we pioneer a progressive captioning pipeline that eliminates contextual conflicts by ensuring local shot-level accuracy before enforcing global narrative coherence. Crucially, we implement a cross-shot matching mechanism to fundamentally eradicate the S2V copy-paste shortcut. Alongside the dataset, we propose the Cinematic Narrative Benchmark, featuring a visual-logic-driven paradigm and a novel Anti-Copy-Paste Variance (ACP-Var) metric to rigorously assess continuous storytelling and 3D structural consistency. Extensive experiments demonstrate that while current baselines struggle with continuous narrative logic or degenerate into trivial 2D sticker generators, our MuSS-augmented model achieves state-of-the-art narrative effectiveness and cross-shot identity preservation.", "github": "https://github.com/zhang-haojie/MuSS" }, { "date": "2026-05-12", "arxiv_id": "2605.09423", "title": "SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning", "authors": [ "Haoqiang Kang", "Xiaokang Ye", "Yuhan Liu", "Siddhant Hitesh Mantri", "Lingjun Mao", "James Fleming", "Drishti Regmi", "Lianhui Qin" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SimWorld-AI/SimWorld-Studio", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "LLM/VLM-based digital agents have advanced rapidly thanks to scalable sandboxes for coding, web navigation, and computer use, which provide rich interactive training grounds. In contrast, embodied agents still lack abundant, diverse, and automatically generated 3D environments for interactive learning. Existing embodied simulators rely on manually crafted scenes or procedural templates, while recent LLM-based 3D generation systems mainly produce static scenes rather than deployable environments with verifiable tasks and standard learning interfaces. We introduce SimWorld Studio, an open-source platform built on Unreal Engine 5 for generating evolving embodied learning environments. At its core is SimCoder, a tool/skill-augmented coding agent that writes and executes engine-level code to construct physically grounded 3D worlds from language/image instructions. SimCoder self-evolves by using verifier feedback (e.g., compilation errors, physics checks, VLM critiques) to revise environments and autonomously add reusable tools and skills to its library. Generated worlds are exported as Gym-style environments for embodied agent learning. SimWorld Studio further enables co-evolution between environment generation and embodied learning: agent performance feedback guides SimCoder to generate adaptive curricula near the learner's capability frontier, so that environments become increasingly challenging as the embodied agent improves. Three case studies on embodied navigation show that self-evolution improves generation reliability, generated environments substantially improve embodied agent performance that generalizes to unseen benchmarks, and co-evolution yields an 18-point success-rate gain over fixed-environment learning and a 40-point gain over an untrained agent.", "github": "https://github.com/SimWorld-AI/SimWorld-Studio" }, { "date": "2026-05-12", "arxiv_id": "2605.08735", "title": "CollabVR: Collaborative Video Reasoning with Vision-Language and Video Generation Models", "authors": [ "Joowon Kim", "Seungho Shin", "Joonhyung Park", "Eunho Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Joow0n-Kim/CollabVR", "github_api_added_by": "user", "project_page": "https://joow0n-kim.github.io/collabvr-project-page/", "abstract": "Recent \"Thinking with Video\" approaches use Video Generation Models (VGMs) for visual reasoning by producing temporally coherent Chain-of-Frames as reasoning artifacts. Even strong VGMs, however, exhibit two recurring failure modes on goal-directed tasks: long-horizon drift on multi-step tasks and mid-clip simulation errors that compound. Both stem from the absence of explicit reasoning built upon the VGM's short-horizon visual prior, a role naturally filled by Vision-Language Models (VLMs), but where to place the VLM is non-trivial: upfront plans commit before any frame is generated and post-hoc critiques over whole videos intervene too late. We propose VLM-VGM Collaborative Video Reasoning (CollabVR), a closed-loop framework that couples the VLM with the VGM at step-level granularity: the VLM plans the immediate next action, inspects the clip the VGM generates, and folds the verifier's diagnosis directly into the next action prompt to repair detected failures. On Gen-ViRe and VBVR-Bench, CollabVR improves both open-source and closed-source VGMs over single-inference, Pass@k, and prior test-time scaling baselines at matched compute, with the largest gains on the hardest tasks. It also yields further improvements on top of a reasoning-fine-tuned VGM, indicating that step-level VLM supervision is orthogonal to and stackable with reasoning-oriented fine-tuning. We provide video samples and additional qualitative results at our project page: https://joow0n-kim.github.io/collabvr-project-page.", "github": "https://github.com/Joow0n-Kim/CollabVR" }, { "date": "2026-05-12", "arxiv_id": "2605.10341", "title": "PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents", "authors": [ "Bihui Yu", "Xinglong Xu", "Junjie Jiang", "Jiabei Cheng", "Caijun Jia", "Siyuan Li", "Conghui He", "Jingxuan Wei", "Cheng Tan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenRaiser/PaperFit", "github_api_added_by": "user", "project_page": "", "abstract": "A LaTeX manuscript that compiles without error is not necessarily publication-ready. The resulting PDFs frequently suffer from misplaced floats, overflowing equations, inconsistent table scaling, widow and orphan lines, and poor page balance, forcing authors into repetitive compile-inspect-edit cycles. Rule-based tools are blind to rendered visuals, operating only on source code and log files. Text-only LLMs perform open-loop text editing, unable to predict or verify the two-dimensional layout consequences of their changes. Reliable typesetting optimization therefore requires a visual closed loop with verification after every edit. We formalize this problem as Visual Typesetting Optimization (VTO), the task of transforming a compilable LaTeX paper into a visually polished, page-budget-compliant PDF through iterative visual verification and source-level revision, and introduce a five-category taxonomy of typesetting defects to guide diagnosis. We present PaperFit, a vision-in-the-loop agent that iteratively renders pages, diagnoses defects, and applies constrained repairs. To benchmark VTO, we construct PaperFit-Bench with 200 papers across 10 venue templates and 13 defect types at different difficulty. Extensive experiments show that PaperFit outperforms all baselines by a large margin, establishing that bridging the gap from compilable source to publication-ready PDF requires vision-in-the-loop optimization and that VTO constitutes a critical missing stage in the document automation pipeline.", "github": "https://github.com/OpenRaiser/PaperFit" }, { "date": "2026-05-12", "arxiv_id": "2605.08715", "title": "AgentForesight: Online Auditing for Early Failure Prediction in Multi-Agent Systems", "authors": [ "Boxuan Zhang", "Jianing Zhu", "Zeru Shi", "Dongfang Liu", "Ruixiang Tang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZBox1005/AgentForesight", "github_api_added_by": "user", "project_page": "https://zbox1005.github.io/agent-foresight/", "abstract": "LLM-based multi-agent systems are increasingly deployed on long-horizon tasks, but a single decisive error is often accepted by downstream agents and cascades into trajectory-level failure. Existing work frames this as post-hoc failure attribution, diagnosing the responsible agent and step after the trajectory has ended. However, this paradigm forfeits any opportunity to intervene while trajectory is still unfolding. In this work, we introduce AgentForesight, a framework that reframes this problem as online auditing: at each step of an unfolding trajectory, an auditor observes only the current prefix and must either continue the run or alarm at the earliest decisive error, without access to future steps. To this end, we curate AFTraj-2K, a corpus of agentic trajectories across Coding, Math, and Agentic domains, in which safe trajectories are retained under a strict curation pipeline and unsafe trajectories are annotated at the step of their decisive error via consensus among multiple LLM judges. Built on that, we develop AgentForesight-7B, a compact online auditor trained with a coarse-to-fine reinforcement learning recipe that first equips it with a risk-anticipation prior at the failure boundary on adjacent safe/unsafe prefix pairs, then sharpens this prior into precise step-level localization under a three-axis reward jointly targeting the what, where, and who of an audit verdict. Across AFTraj-2K and an external Who\\&When benchmark, AgentForesight-7B outperforms leading proprietary models, including GPT-4.1 and DeepSeek-V4-Pro, achieving up to +19.9% performance gain and 3times lower step localization error, opening the loop from post-hoc failures detection to enabling deployment-time intervention. Project page: https://zbox1005.github.io/agent-foresight/", "github": "https://github.com/ZBox1005/AgentForesight" }, { "date": "2026-05-12", "arxiv_id": "2605.08520", "title": "FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration", "authors": [ "Zhengding Hu", "Mingge Lu", "Zhen Wang", "Jixuan Ruan", "Chang Chen", "Zaifeng Pan", "Yue Guan", "Ruiyi Wang", "Zhongkai Yu", "Chao Zhang", "Yufei Ding" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://flashevolve.picasso-lab.com/", "abstract": "LLM-based evolution has emerged as a promising way to improve agents by refining non-parametric artifacts, but its wall-clock cost remains a major bottleneck. We identify that this cost comes from synchronized stage execution and imbalance inside each LLM-heavy stage. We present FlashEvolve, an efficient framework that replaces synchronized execution with asynchronous workers and queues, allowing different stages and steps to overlap. To handle data staleness introduced by asynchrony, FlashEvolve tracks artifact versions and applies different policies to update, discard, or patch stale artifacts. Unlike weight-space staleness in asynchronous RL, language-space staleness is inspectable and repairable: a stale artifact is not just delayed work, but readable evidence that the LLM can reflect on, revise, and turn into useful evolution signal. FlashEvolve further improves throughput and token efficiency with speculative stage completion and adaptive workflow control. On GEPA workloads, FlashEvolve improves proposal throughput by 3.5times on local vLLM and 4.9times on API serving over synchronous GEPA. The same design also applies to ACE and Meta-Harness.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.01402", "title": "Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression", "authors": [ "Yao Du", "Shanshan Song", "Xiaomeng Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) struggle with numerical regression under long-tailed target distributions. Token-level supervised fine-tuning (SFT) and point-wise regression rewards bias learning toward high-density regions, leading to regression-to-the-mean behavior and poor tail performance. We identify the lack of cross-sample relational supervision as a key limitation of existing MLLM training paradigms. To address it, we propose a distribution-aware reinforcement learning framework based on Group Relative Policy Optimization, which introduces batch-level comparison-based supervision via the Concordance Correlation Coefficient-based reward to align predicted and ground-truth distributions in terms of correlation, scale, and mean. The framework is plug-and-play, requiring no architectural modification. Experiments on a unified suite of long-tailed regression benchmarks show consistent improvements over SFT and existing MLLM regression methods, with particularly strong gains in medium- and few-shot regimes.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.09630", "title": "Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models", "authors": [ "Lin Zheng", "Vasilisa Bashlovkina", "Timothy Dozat", "Dan Garrette", "Laura Rimell", "Joshua Maynez" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Tokenizer-free language models eliminate the tokenizer step of the language modeling pipeline by operating directly on bytes; patch-based variants further aggregate contiguous byte spans into patches for efficiency. However, the average patch size chosen at the model design stage governs a tight trade-off: larger patches reduce compute and KV-cache footprint, but degrade modeling quality. We trace this trade-off to patch lag: until a patch is fully observed, byte predictions within it must rely on a stale representation from the previous patch to preserve causality; this lag widens as patches grow larger. We introduce Scratchpad Patching (SP), which inserts transient scratchpads inside each patch to aggregate the bytes seen so far and refresh patch-level context for subsequent predictions. SP triggers scratchpads using next-byte prediction entropy, selectively allocating compute to information-dense regions and enabling post-hoc adjustment of inference-time compute. Across experiments on natural language and code, SP improves model quality at the same patch size; for example, even at 16 bytes per patch, SP-augmented models match or closely approach the byte-level baseline on downstream evaluations while using a 16times smaller KV cache over patches and 3-4times less inference compute.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.09820", "title": "Dystruct: Dynamically Structured Diffusion Language Model Decoding via Bayesian Inference", "authors": [ "Bian Sun", "Kevin Zhai", "Mubarak Shah", "Zhenyi Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive models, primarily due to their ability to enable parallel decoding. Despite this advantage, most existing DLMs rely on a fixed generation length specified prior to decoding, which restricts their flexibility in real-world applications. While a few recent works attempt to support flexible-length generation, they typically suffer from notable limitations: some require costly retraining to accommodate variable-length outputs, while others depend solely on local confidence signals during decoding. Such local criteria fail to capture the evolving structure of the sequence, often resulting in suboptimal generation quality. In this paper, we propose a training-free, Bayesian structured decoding framework that formulates flexible-length generation as a dynamic structural inference problem. Our approach formulates flexible-length generation as a dynamic structural inference problem, jointly computing the expansion length, the block boundaries, and the decoding schedule. At each window expansion step, the method integrates local uncertainty with structural signals via a unified mechanism that supports dynamic structured generation, including both flexible block expansion and block organization, while maintaining coherence. Extensive experiments across multiple benchmarks demonstrate that our approach significantly improves generation quality and flexibility over existing fixed-length and flexible-length baselines. These results highlight the advantage of Bayesian structured decoding for diffusion language model, providing a principled and efficient solution for structured text generation.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.08985", "title": "LLaVA-UHD v4: What Makes Efficient Visual Encoding in MLLMs?", "authors": [ "Kechen Fang", "Yihua Qin", "Chongyi Wang", "Wenshuo Ma", "Tianyu Yu", "Yuan Yao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/THUMAI-Lab/LLaVA-UHD-v4", "github_api_added_by": "user", "project_page": "https://github.com/THUMAI-Lab/LLaVA-UHD-v4", "abstract": "Visual encoding constitutes a major computational bottleneck in Multimodal Large Language Models (MLLMs), especially for high-resolution image inputs. The prevailing practice typically adopts global encoding followed by post-ViT compression. Global encoding produces massive token sequences, while post-ViT compression incurs the full quadratic attention cost of the ViT before any token reduction takes place. In this work, we revisit this convention along two dimensions: the encoding strategy and visual token compression. First, controlled experiments show that slice-based encoding outperforms global encoding across benchmarks, suggesting that preserving local details through sliced views can be more beneficial than applying global attention for fine-grained perception. Second, we introduce intra-ViT early compression, which reduces tokens in shallow ViT layers and substantially lowers visual-encoding FLOPs while preserving downstream performance. By integrating intra-ViT compression into the slice-based encoding framework, we present LLaVA-UHD v4, an efficient and compute-controllable visual encoding scheme tailored for high-resolution inputs. Across a diverse set of benchmarks covering document understanding, OCR, and general VQA, LLaVA-UHD v4 reduces visual-encoding FLOPs by 55.8% while matching or even surpassing baseline performance. These results suggest that visual-encoding efficiency can be substantially improved without sacrificing downstream performance, providing a practical design direction for efficient high-resolution MLLMs. All model weights and code will be publicly released to support further research.", "github": "https://github.com/THUMAI-Lab/LLaVA-UHD-v4" }, { "date": "2026-05-12", "arxiv_id": "2605.08738", "title": "SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training", "authors": [ "Shengkun Tang", "Zekun Wang", "Bo Zheng", "Liangyu Wang", "Rui Men", "Siqi Zhang", "Xiulong Yuan", "Zihan Qiu", "Zhiqiang Shen", "Dayiheng Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Structured pruning and knowledge distillation (KD) are typical techniques for compressing large language models, but it remains unclear how they should be applied at pretraining scale, especially to recent mixture-of-experts (MoE) models. In this work, we systematically study MoE compression in large-scale pretraining, focusing on three key questions: whether pruning provides a better initialization than training from scratch, how expert compression choices affect the final model after continued training, and which training strategy is most effective. We have the following findings: First, across depth, width, and expert compression, pruning a pretrained MoE consistently outperforms training the target architecture from scratch under the same training budget. Second, different one-shot expert compression methods converge to similar final performance after large-scale continual pretraining. Motivated by this, we introduce a simple partial-preservation expert merging strategy that improves downstream performance across most benchmarks. Third, combining KD with the language modeling loss outperforms KD alone, particularly on knowledge-intensive tasks. We further propose multi-token prediction (MTP) distillation, which yields consistent gains. Finally, given the same training tokens, progressive pruning schedules outperform one-shot compression, suggesting that gradual architecture transitions lead to better optimization trajectories. Putting it all together, we compress Qwen3-Next-80A3B to a 23A2B model that retains competitive performance. These results offer practical guidance for efficient MoE compression at scale.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.10334", "title": "The Alpha Blending Hypothesis: Compositing Shortcut in Deepfake Detection", "authors": [ "Andrii Yermakov", "Jan Cech", "Mario Fritz", "Jiri Matas" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recent deepfake detection methods demonstrate improved cross-dataset generalization, yet the underlying mechanisms remain underexplored. We introduce the Alpha Blending Hypothesis, positing that state-of-the-art frame-based detectors primarily function as alpha blending searchers; rather than learning semantic anomalies or specific generative neural fingerprints, they localize low-level compositing artifacts introduced during the integration of manipulated faces into target frames. We experimentally validate the hypothesis, demonstrating that deepfake detectors exhibit high sensitivity to the so-called self-blended images (SBI) and non-generative manipulations. We propose the method BlenD that leverages a large-scale, diverse dataset of real-only facial images augmented with SBI. This approach achieves the best average cross-dataset generalization on 15 compositional deepfake datasets released between 2019 and 2025 without utilizing explicitly generated deepfakes during training. Furthermore, we show that predictions from explicit blending searchers and models resilient to blending shortcuts are highly complementary, yielding a state-of-the-art AUROC of 94.0% in an ensemble configuration. The code with experiments and the trained model will be publicly released.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.09271", "title": "Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding", "authors": [ "Zhiqin Yang", "Yuhan Liu", "Jingwen Fu", "Pei Fu anf Bo Han", "Masashi Sugiyama", "Nanning Zheng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Although natural language is the default medium for Large Language Models (LLMs), its limited expressive capacity creates a profound bottleneck for complex problem-solving. While recent advancements in AI have relied heavily on scaling, merely internalizing knowledge does not guarantee its effective application. Defining language representation as the linguistic and symbolic constructs used to map and model the real world, this paper argues that shaping schemas through advanced language representation is the next frontier for expanding LLM intelligence. We posit that an LLM's knowledge activation and organization -- its schema -- depends heavily on the structural and symbolic sophistication of the language used to represent a given task. This paper contributes both a formalization of this claim and the empirical evidence to support it. With a new formalization, we present multiple lines of evidence to support our position: Firstly, we review recent empirical practices and emerging methodologies that demonstrate the substantial performance gains achievable through deliberate language representation design, even without modifying model parameters or scale. Secondly, we conduct controlled experiments showing that LLM performance and its internal feature activations vary under different language representations of the same underlying task. Together, these findings highlight language representation design as a promising direction for future research.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.10933", "title": "DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices", "authors": [ "Chenyang Song", "Weilin Zhao", "Xu Han", "Chaojun Xiao", "Yingfa Chen", "Zhiyuan Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thunlp/DECO", "github_api_added_by": "user", "project_page": "", "abstract": "While Mixture-of-Experts (MoE) scales model capacity without proportionally increasing computation, its massive total parameter footprint creates significant storage and memory-access bottlenecks, which hinder efficient end-side deployment that simultaneously requires high performance, low computational cost, and small storage overhead. To achieve these properties, we present DECO, a sparse MoE architecture designed to match the performance of dense Transformers under identical total parameter budgets and training tokens. DECO utilizes the differentiable and flexible ReLU-based routing enhanced by learnable expert-wise scaling, which adaptively balances the contributions of routed and shared experts. Furthermore, we introduce NormSiLU, an activation function that normalizes inputs prior to SiLU operators, producing a more stable trend of routed-expert activation ratio and a higher intrinsic sparsity level. We also identify an empirical advantage in using non-gated MLP experts with ReLU-based routing, indicating the possibility of MoE architecture simplification. Experiments demonstrate that DECO, activating only 20% of experts, matches dense performance and outperforms established MoE baselines. Our specialized acceleration kernel delivers a 3.00times speedup on real hardware compared with dense inference. Codes and checkpoints will be released.", "github": "https://github.com/thunlp/DECO" }, { "date": "2026-05-12", "arxiv_id": "2605.09708", "title": "Metal-Sci: A Scientific Compute Benchmark for Evolutionary LLM Kernel Search on Apple Silicon", "authors": [ "V\u00edctor Gallego" ], "github_manual": "", "github_regex": "https://github.com/vicgalle/metal-sci-kernels", "github_paper_md": "", "github_api": "https://github.com/vicgalle/metal-sci-kernels", "github_api_added_by": "user", "project_page": "https://www.vicgalle.net/metal-sci-kernels/", "abstract": "We present Metal-Sci, a 10-task benchmark of scientific Apple Silicon Metal compute kernels spanning six optimization regimes (stencils, all-pairs in n-body problems, multi-field Boltzmann, neighbor-list molecular dynamics, multi-kernel PDE, FFT). Each task ships a CPU reference, a roofline-anchored fitness function, and a held-out generalization size. We pair the benchmark with a lightweight harness for automatic kernel search that runtime-compiles each candidate, scores it against the roofline across multiple sizes, and feeds structured compile and per-size correctness diagnostics back to a frozen LLM driving a (1{+}1) evolutionary loop. We report matched single-model sweeps of Claude Opus 4.7, Gemini 3.1 Pro, and GPT 5.5 on M1 Pro: in-distribution self-speedups span 1.00times to 10.7times. Beyond raw speedup, our central methodological claim is structural: the held-out gate scoring function \u03a6_T (evaluated once at end-of-run on a configuration the agent never sees during search) functions as a cheap mechanical oversight primitive on this automatic search loop, catching e.g. an Opus template HMC win that returns wrong samples at unseen dimensions, and a GPT FFT3D best that wins in-distribution at 2.95times speedup but collapses to 0.23times on a 256^3 held-out cube, a silent regression that the in-distribution score alone cannot see. Code at https://github.com/vicgalle/metal-sci-kernels", "github": "https://github.com/vicgalle/metal-sci-kernels" }, { "date": "2026-05-12", "arxiv_id": "2605.09163", "title": "FORTIS: Benchmarking Over-Privilege in Agent Skills", "authors": [ "Shawn Li", "Chenxiao Yu", "Han Wang", "Wei Yang", "Ryan Rossi", "Franck Dernoncourt", "Xiyang Hu", "Philip Yu", "Chaowei Xiao", "Huan Zhang", "Yue Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lili0415/FORTIS-Benchmark", "github_api_added_by": "user", "project_page": "https://huggingface.co/datasets/ShawnLi02/FORTIS_Agent_Skill_Safety", "abstract": "Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution. This layer is widely treated as an organizational abstraction, but we argue it is also a privilege boundary that current models routinely exceed. We present FORTIS, a benchmark that evaluates over-privilege in agent skills across two stages: whether a model selects the minimally sufficient skill from a large overlapping library, and whether it executes that skill without expanding into broader tools or actions than the skill permits. Across ten frontier models and three domains, we find that over-privileged behavior is the norm rather than the exception. Models consistently reach for higher-privilege skills and tools than the task requires, failing at both stages at rates that remain high even for the strongest available models. Failure is especially severe under the ordinary conditions of real user interaction: incomplete specification, convenience framing, and proximity to skill boundaries. None of these requires adversarial construction. The results indicate that the skill layer, far from containing agent behavior, is itself a primary source of privilege escalation in current systems.", "github": "https://github.com/lili0415/FORTIS-Benchmark" }, { "date": "2026-05-12", "arxiv_id": "2605.09015", "title": "LLiMba: Sardinian on a Single GPU -- Adapting a 3B Language Model to a Vanishing Romance Language", "authors": [ "Luca Ballore" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lballore/LLiMba", "github_api_added_by": "user", "project_page": "", "abstract": "Sardinian, a Romance language with roughly one million speakers, has minimal presence in modern NLP. Commercial services do not support it, and current language models do not produce it reliably. We present LLiMba, a 3B parameter Sardinian-ready model adapted from Qwen2.5-3B-Instruct through continued pretraining (CPT) and supervised fine-tuning (SFT) on a single 24 GB consumer GPU. The corpus contains 11.5 million tokens of Sardinian spanning LSC, Logudorese, and Campidanese, augmented with 2.4 million tokens of related Romance text as replay against register blurring. After CPT the model reaches a perplexity of 6.76 on held out Sardinian and outperforms the base across all six FLORES-200 directions. We compare five SFT configurations under matched conditions: full fine-tuning, LoRA r64, rsLoRA r128, rsLoRA r256, and DoRA r256. rsLoRA r256 wins on every direction into Sardinian, reaching 28.5 BLEU from English against 17.3 after CPT and 21.0 with full fine-tuning. The rank ablation places r128 between LoRA r64 and rsLoRA r256 on BLEU but reveals failure modes invisible to the metric, including leakage across scripts no other variant produces. LoRA r64 retains less factual content from SFT than configurations at higher rank and produces more confident fabrications, though all methods fabricate on content absent from training. DoRA r256 yields the smallest gap between training and evaluation but the worst factual accuracy. The findings indicate that adapter capacity matters more than the choice among LoRA variants for adapting a Romance pretrained base to a low resource Romance target, that stronger regularization is not uniformly beneficial, and that translation metrics smoothly order configurations whose qualitative behavior differs categorically. Perplexity comparisons across scripts must account for byte fallback tokenization, which deflates the metric for scripts other than Latin.", "github": "https://github.com/lballore/LLiMba" }, { "date": "2026-05-12", "arxiv_id": "2605.10344", "title": "TMAS: Scaling Test-Time Compute via Multi-Agent Synergy", "authors": [ "George Wu", "Nan Jing", "Qing Yi", "Chuan Hao", "Ming Yang", "Feng Chang", "Yuan Wei", "Jian Yang", "Ran Tao", "Bryan Dai" ], "github_manual": "", "github_regex": "https://github.com/george-QF/TMAS-code", "github_paper_md": "", "github_api": "https://github.com/george-QF/TMAS-code", "github_api_added_by": "user", "project_page": "", "abstract": "Test-time scaling has become an effective paradigm for improving the reasoning ability of large language models by allocating additional computation during inference. Recent structured approaches have further advanced this paradigm by organizing inference across multiple trajectories, refinement rounds, and verification-based feedback. However, existing structured test-time scaling methods either weakly coordinate parallel reasoning trajectories or rely on noisy historical information without explicitly deciding what should be retained and reused, limiting their ability to balance exploration and exploitation. In this work, we propose TMAS, a framework for scaling test-time compute via multi-agent synergy. TMAS organizes inference as a collaborative process among specialized agents, enabling structured information flow across agents, trajectories, and refinement iterations. To support effective cross-trajectory collaboration, TMAS introduces hierarchical memories: the experience bank reuses low-level reliable intermediate conclusions and local feedback, while the guideline bank records previously explored high-level strategies to steer subsequent rollouts away from redundant reasoning patterns. Furthermore, we design a hybrid reward reinforcement learning scheme tailored to TMAS, which jointly preserves basic reasoning capability, enhances experience utilization, and encourages exploration beyond previously attempted solution strategies. Extensive experiments on challenging reasoning benchmarks demonstrate that TMAS achieves stronger iterative scaling than existing test-time scaling baselines, while hybrid reward training further improves scaling effectiveness and stability across iterations. Code and data are available at https://github.com/george-QF/TMAS-code.", "github": "https://github.com/george-QF/TMAS-code" }, { "date": "2026-05-12", "arxiv_id": "2605.07721", "title": "Memory-Efficient Looped Transformer: Decoupling Compute from Memory in Looped Language Models", "authors": [ "Victor Conchello Vendrell", "Arnau Padres Masdemont", "Niccol\u00f2 Grillo", "Jordi Ros-Giralt", "Arash Behboodi", "Fabio Valerio Massoli" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recurrent LLM architectures have emerged as a promising approach for improving reasoning, as they enable multi-step computation in the embedding space without generating intermediate tokens. Models such as Ouro perform reasoning by iteratively updating internal representations while retaining a standard Key-Value (KV) cache across iterations, causing memory consumption to grow linearly with reasoning depth. Consequently, increasing the number of reasoning iterations can lead to prohibitive memory usage, limiting the practical scalability of such architectures. In this work, we propose Memory-Efficient Looped Transformer (MELT), a novel architecture that decouples reasoning depth from memory consumption. Instead of using a standard KV cache per layer and loop, MELT maintains a single KV cache per layer that is shared across reasoning loops. This cache is updated over time via a learnable gating mechanism. To enable stable and efficient training under this architecture, we propose to train MELT using chunk-wise training in a two phase procedure: interpolated transition, followed by attention-aligned distillation, both from the LoopLM starting model to MELT. Empirically, we show that MELT models fine-tuned from pretrained Ouro parameters outperform standard LLMs of comparable size, while maintaining a memory footprint comparable to those models and dramatically smaller than Ouro's. Overall, MELT achieves constant-memory iterative reasoning without sacrificing LoopLM performance, using only a lightweight post-training procedure.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.10453", "title": "SlimSpec: Low-Rank Draft LM-Head for Accelerated Speculative Decoding", "authors": [ "Anton Plaksin", "Sergei Krutikov", "Sergei Skvortsov", "Alexander Samarin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Speculative decoding speeds up autoregressive generation in Large Language Models (LLMs) through a two-step procedure, where a lightweight draft model proposes tokens which the target model then verifies in a single forward pass. Although the drafter network is small in modern architectures, its LM-head still performs projection to a large vocabulary, becoming one of the major computational bottlenecks. In prior work this issue has been predominantly addressed via static or dynamic vocabulary truncation. Yet mitigating the bottleneck, these methods bring in extra complexity, such as special vocabulary curation, sophisticated inference-time logic or modifications of the training setup. In this paper, we propose SlimSpec, a low-rank parameterization of the drafter's LM-head that compresses the inner representation rather than the output, preserving full vocabulary support. We evaluate our method with EAGLE-3 drafter across three target models and diverse benchmarks in both latency- and throughput-bound inference regimes. SlimSpec achieves 4-5times acceleration over the standard LM-head architecture while maintaining a competitive acceptance length, surpassing existing methods by up to 8-9% of the end-to-end speedup. Our method requires minimal adjustments of training and inference pipelines. Combined with the aforementioned speedup improvements, it makes SlimSpec a strong alternative across wide variety of draft LM-head architectures.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.07287", "title": "SplatWeaver: Learning to Allocate Gaussian Primitives for Generalizable Novel View Synthesis", "authors": [ "Yecong Wan", "Fan Li", "Mingwen Shao", "Wangmeng Zuo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yecongwan/SplatWeaver", "github_api_added_by": "user", "project_page": "", "abstract": "Generalizable novel view synthesis aims to render unseen views from uncalibrated input images without requiring per-scene optimization. Recent feed-forward approaches based on 3D Gaussian Splatting have achieved promising efficiency and rendering quality. However, most of them assign a fixed number of Gaussians to each pixel or voxel, ignoring the spatially varying complexity of real-world scenes. Such uniform allocation often wastes Gaussian primitives in smooth regions while providing insufficient capacity for fine structures, complex geometry, and high-frequency details. This motivates us to predict region-dependent primitive cardinalities rather than impose a fixed primitive budget everywhere, enabling a more expressive yet compact 3D scene representation. Therefore, we propose SplatWeaver, a generalizable novel view synthesis framework that is able to dynamically allocate Gaussian primitives over different regions in a feed-forward manner. Specifically, SplatWeaver introduces cardinality Gaussian experts and a pixel-level routing scheme, wherein each expert specializes in producing a specific number of primitives from 0 to M, and the routing scheme coordinates these experts to adaptively determine how many Gaussian primitives should be allocated to each spatial location. Moreover, SplatWeaver incorporates a high-frequency prior with attendant guidance module and routing regularization to stabilize expert selection and promote complexity-aware allocation. By leveraging high-frequency structural cues, the routing process is encouraged to assign more Gaussian primitives to fine structures, complex geometry, and textured regions, while suppressing redundant primitives in smooth areas. Extensive experiments across diverse scenarios show that SplatWeaver consistently outperforms state-of-the-art methods, delivering more faithful novel-view renderings with fewer Gaussian primitives.", "github": "https://github.com/yecongwan/SplatWeaver" }, { "date": "2026-05-12", "arxiv_id": "2605.10903", "title": "CapVector: Learning Transferable Capability Vectors in Parametric Space for Vision-Language-Action Models", "authors": [ "Wenxuan Song", "Han Zhao", "Fuhao Li", "Ziyang Zhou", "Xi Wang", "Jing Lyu", "Pengxiang Ding", "Yan Wang", "Donglin Wang", "Haoang Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenHelix-Team/CapVector", "github_api_added_by": "user", "project_page": "https://capvector.github.io/", "abstract": "This paper proposes a novel approach to address the challenge that pretrained VLA models often fail to effectively improve performance and reduce adaptation costs during standard supervised finetuning (SFT). Some advanced finetuning methods with auxiliary training objectives can improve performance and reduce the number of convergence steps. However, they typically incur significant computational overhead due to the additional losses from auxiliary objectives. To simultaneously achieve the enhanced capabilities of auxiliary training with the simplicity of standard SFT, we decouple the two objectives of auxiliary-objective SFT within the parameter space, namely, enhancing general capabilities and fitting task-specific action distributions. To deliver the goal, we only need to train the model to converge on a small-scale task set using two distinct training strategies, resulting in two finetuned models. The parameters' difference between the two models can then be interpreted as capability vectors provided by auxiliary objectives. These vectors are then merged with pretrained parameters to form a capability-enhanced meta model. Moreover, when standard SFT is augmented with a lightweight orthogonal regularization loss, the merged model attains performance comparable to auxiliary finetuned baselines with reduced computational overhead. Internal and external experiments demonstrate that our capability vectors (1) are effective and versatile across diverse models, (2) can generalize to novel environments and embodiments out of the box.", "github": "https://github.com/OpenHelix-Team/CapVector" }, { "date": "2026-05-12", "arxiv_id": "2605.10921", "title": "RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark", "authors": [ "Huashuo Lei", "Wenxuan Song", "Huarui Zhang", "Jieyuan Pei", "Jiayi Chen", "Haodong Yan", "Han Zhao", "Pengxiang Ding", "Zhipeng Zhang", "Lida Huang", "Donglin Wang", "Yan Wang", "Haoang Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenHelix-Team/RoboMemArena", "github_api_added_by": "user", "project_page": "https://robomemarena.github.io/", "abstract": "Memory is a critical component of robotic intelligence, as robots must rely on past observations and actions to accomplish long-horizon tasks in partially observable environments. However, existing robotic memory benchmarks still lack multimodal annotations for memory formation, provide limited task coverage and structural complexity, and remain restricted to simulation without real-world evaluation. We address this gap with RoboMemArena, a large-scale benchmark of 26 tasks, with average trajectory lengths exceeding 1,000 steps per task and 68.9% of subtasks being memory-dependent. The generation pipeline leverages a vision-language model (VLM) to design and compose subtasks, generates full trajectories through atomic functions, and provides memory-related annotations, including subtask instructions and native keyframe annotations, while paired real-world memory tasks support physical evaluation. We further design PrediMem, a dual-system VLA in which a high-level VLM planner manages a memory bank with recent and keyframe buffers and uses a predictive coding head to improve sensitivity to task dynamics. Extensive experiments on RoboMemArena show that PrediMem outperforms all baselines and provides insights into memory management, model architecture, and scaling laws for complex memory systems.", "github": "https://github.com/OpenHelix-Team/RoboMemArena" }, { "date": "2026-05-12", "arxiv_id": "2605.10468", "title": "Can Muon Fine-tune Adam-Pretrained Models?", "authors": [ "Xingyu Qu", "Peigeng Huang", "Samuel Horvath" ], "github_manual": "", "github_regex": "https://github.com/XingyuQu/muon-finetune", "github_paper_md": "", "github_api": "https://github.com/XingyuQu/muon-finetune", "github_api_added_by": "user", "project_page": "", "abstract": "Muon has emerged as an efficient alternative to Adam for pretraining, yet remains underused for fine-tuning. A key obstacle is that most open models are pretrained with Adam, and naively switching to Muon for fine-tuning leads to degraded performance due to an optimizer mismatch. We investigate this mismatch through controlled experiments and relate it to the distinct implicit biases of Adam and Muon. We provide evidence that the mismatch disrupts pretrained knowledge, and that this disruption scales with update strength. This leads us to hypothesize that constraining updates should mitigate the mismatch. We validate this with LoRA: across language and vision tasks, LoRA reduces the performance gap between Adam and Muon observed under full fine-tuning. Studies on LoRA rank, catastrophic forgetting, and LoRA variants further confirm that mismatch severity correlates with update strength. These results shed light on how optimizer mismatch affects fine-tuning and how it can be mitigated. Our code is available at https://github.com/XingyuQu/muon-finetune.", "github": "https://github.com/XingyuQu/muon-finetune" }, { "date": "2026-05-12", "arxiv_id": "2605.05886", "title": "Training-Free Dense Hand Contact Estimation with Multi-Modal Large Language Models", "authors": [ "Daniel Sungho Jung", "Kyoung Mu Lee" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://contactprompt-release.github.io/", "abstract": "Dense hand contact estimation requires both high-level semantic understanding and fine-grained geometric reasoning of human interaction to accurately localize contact regions. Recently, multi-modal large language models (MLLMs) have demonstrated strong capabilities in understanding visual semantics, enabled by vision-language priors learned from large-scale data. However, leveraging MLLMs for dense hand contact estimation remains underexplored. There are two major challenges in applying MLLMs to dense hand contact estimation. First, encoding explicit 3D hand geometry is difficult, as MLLMs primarily operate on vision and language modalities. Second, capturing fine-grained vertex-level contact remains challenging, as MLLMs tend to focus on high-level semantics rather than detailed geometric reasoning. To address these challenges, we propose ContactPrompt, a training-free and zero-shot approach for dense hand contact estimation using MLLMs. To effectively encode 3D hand geometry, we introduce a detailed hand-part segmentation and a part-wise vertex-grid representation that provides structured, localized geometric information. To enable accurate and efficient dense contact prediction, we develop a multi-stage structured contact reasoning with part conditioning, progressively bridging global semantics and fine-grained geometry. Therefore, our method effectively leverages the reasoning capabilities of MLLMs while enabling precise dense hand contact estimation. Surprisingly, the proposed approach outperforms previous supervised methods trained on large-scale dense contact datasets without requiring any training. The codes will be released.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.09548", "title": "Crosslingual On-Policy Self-Distillation for Multilingual Reasoning", "authors": [ "Yihong Liu", "Raoyuan Zhao", "Michael A. Hedderich", "Hinrich Sch\u00fctze" ], "github_manual": "", "github_regex": "https://github.com/cisnlp/COPSD", "github_paper_md": "", "github_api": "https://github.com/cisnlp/COPSD", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models (LLMs) have achieved remarkable progress in mathematical reasoning, but this ability is not equally accessible across languages. Especially low-resource languages exhibit much lower reasoning performance. To address this, we propose Crosslingual On-Policy Self-Distillation (COPSD), which transfers a model's own high-resource reasoning behavior to low-resource languages. COPSD uses the same model as student and teacher: the student sees only the low-resource problem, while the teacher receives privileged crosslingual context, including the problem translation and reference solution in English. Training minimizes full-distribution token-level divergence on the student's own rollouts, providing dense supervision while avoiding the sparsity and instability of outcome-only reinforcement learning (RL). Experiments on 17 low-resource African languages show that COPSD consistently improves low-resource mathematical reasoning across model sizes and substantially outperforms Group Relative Policy Optimization (GRPO). Further analyses show that COPSD improves answer-format adherence, strengthens test-time scaling, and generalizes to harder multilingual reasoning benchmarks, with especially large gains for lower-resource languages. We make our code and data available at: https://github.com/cisnlp/COPSD.", "github": "https://github.com/cisnlp/COPSD" }, { "date": "2026-05-12", "arxiv_id": "2605.08423", "title": "Queryable LoRA: Instruction-Regularized Routing Over Shared Low-Rank Update Atoms", "authors": [ "Omatharv Bharat Vaidya", "Connor T. Jerzak", "Nhat Ho", "Chandrajit Bajaj" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://neuristemic.ai/queryable-lora/", "abstract": "We present a data-adaptive method for parameter-efficient fine-tuning of large neural networks. Standard low-rank adaptation methods improve efficiency by restricting each layer update to a fixed low-rank form, but this static parameterization can be too rigid when the appropriate correction depends on the input and on the evolving depth-wise computation of the network. Our approach replaces a purely layer-local adapter with a shared queryable memory of low-rank update atoms. For each block of layers, the model forms a query from the current low-rank state and a running summary of previous blocks, uses this query to retrieve a content-dependent combination of shared update components via attention, and applies the resulting routed operator within the low-rank bottleneck. In this way, the method retains the efficiency and scalability of low-rank adaptation while allowing the effective update to vary across inputs and to share reusable structure across layers. The resulting architecture provides a principled middle ground between static LoRA-style updates and fully generated parameter updates: it remains compact and parameter-efficient while supporting dynamic, context-sensitive adaptation. Further, we incorporate instruction-regularization by augmenting routing logits with a language-induced prior over update atoms, thereby biasing the selection of low-rank transformations toward semantically relevant directions without generating unconstrained parameter updates. Experiments on noisy non-linear regression tasks and LLM fine-tuning suggest that this queryable update-memory formulation can improve final test performance and training stability compared to standard low-rank adaptation, while using a comparable number of trainable parameters.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.10938", "title": "ELF: Embedded Language Flows", "authors": [ "Keya Hu", "Linlu Qiu", "Yiyang Lu", "Hanhong Zhao", "Tianhong Li", "Yoon Kim", "Jacob Andreas", "Kaiming He" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lillian039/ELF", "github_api_added_by": "user", "project_page": "", "abstract": "Diffusion and flow-based models have become the de facto approaches for generating continuous data, e.g., in domains such as images and videos. Their success has attracted growing interest in applying them to language modeling. Unlike their image-domain counterparts, today's leading diffusion language models (DLMs) primarily operate over discrete tokens. In this paper, we show that continuous DLMs can be made effective with minimal adaptation to the discrete domain. We propose Embedded Language Flows (ELF), a class of diffusion models in continuous embedding space based on continuous-time Flow Matching. Unlike existing DLMs, ELF predominantly stays within the continuous embedding space until the final time step, where it maps to discrete tokens using a shared-weight network. This formulation makes it straightforward to adapt established techniques from image-domain diffusion models, e.g., classifier-free guidance (CFG). Experiments show that ELF substantially outperforms leading discrete and continuous DLMs, achieving better generation quality with fewer sampling steps. These results suggest that ELF offers a promising path toward effective continuous DLMs.", "github": "https://github.com/lillian039/ELF" }, { "date": "2026-05-12", "arxiv_id": "2605.07630", "title": "Safe, or Simply Incapable? Rethinking Safety Evaluation for Phone-Use Agents", "authors": [ "Zhengyang Tang", "Yi Zhang", "Chenxin Li", "Xin Lai", "Pengyuan Lyu", "Yiduo Guo", "Weinong Wang", "Junyi Li", "Yang Ding", "Huawen Shen", "Zhengyao Fang", "Xingran Zhou", "Liang Wu", "Fei Tang", "Sunqi Fan", "Shangpin Peng", "Zheng Ruan", "Anran Zhang", "Benyou Wang", "Chengquan Zhang", "Han Hu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "When a phone-use agent avoids harm, does that show safety, or simply inability to act? Existing evaluations often cannot tell. A harmful outcome may be avoided because the agent recognized the risk and chose the safe action, or because it failed to understand the screen or execute any relevant action at all. These cases have different causes and call for different fixes, yet current benchmarks often merge them under task success, refusal, or final harmful outcome. We address this problem with PhoneSafety, a benchmark of 700 safety-critical moments drawn from real phone interactions across more than 130 apps. Each instance isolates the next decision at a risky moment and asks a simple question: does the model take the safe action, take the unsafe action, or fail to do anything useful? We evaluate eight representative phone-use agents under this framework. Our results reveal two main patterns. First, stronger general phone-use ability does not reliably imply safer choices at risky moments. Models that perform better on ordinary app tasks are not always the ones that behave more safely when the next action matters. Second, failures to do anything useful behave like a capability signal rather than a safety signal: they are concentrated in more visually and operationally demanding settings and remain stable when the evaluation protocol changes. Across models, failures split into two recurring patterns: unsafe choices in settings where the model can act but chooses wrongly, and inability to act in more visually and operationally demanding screens. Overall, a harmless outcome is not enough to count as evidence of safety. Evaluating phone-use agents requires separating unsafe judgment from inability to act.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.09877", "title": "Key-Value Means", "authors": [ "Daniel Goldstein", "Eugene Cheah" ], "github_manual": "", "github_regex": "https://github.com/recursal/KVM-paper", "github_paper_md": "", "github_api": "https://github.com/recursal/KVM-paper", "github_api_added_by": "user", "project_page": "", "abstract": "We present Key-Value Means (\"KVM\"), a novel block-recurrence for attention that can accommodate either fixed-size or growing state. Equipping a strong transformer baseline with fixed-size KVM attention layers yields a strong O(N) chunked RNN, while adding only an insignificant number of new parameters. We train a transformer with a growable KVM cache and show it performs competitively on long-context tests with only subquadratic prefill time and sublinear state growth. KVM is implementable with standard operations and without custom kernels, and supports chunk-wise parallelizable training and prefill. It provides many of the benefits of both traditional transformers (expandable context memory, chunk-wise parallelizable training and prefill) and linear RNNs in a single unified package. It can be used on every layer, saving KV-cache memory, and allowing a continuous range of choices of prefill time complexity between O(N) and O(N^2). It can also be implemented in a hybrid solution in tandem with LRNN layers in place of traditional attention, to supplement the LRNN with improved sublinear memory growth context length usage and long context decoding. We release our code at https://github.com/recursal/KVM-paper and trained models at https://huggingface.co/collections/recursal/key-value-means under the Apache 2.0 license.", "github": "https://github.com/recursal/KVM-paper" }, { "date": "2026-05-12", "arxiv_id": "2605.06741", "title": "A Closed-Form Upper Bound for Admissible Learning-Rate Steps in Belief-Space Dynamics", "authors": [ "Zixi Li", "Youzhen Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Learning-rate steps are usually treated as hyperparameters. This paper isolates a local beliefspace calculation: when an update is modeled as a projected forward step on the probability simplex, admissibility means contractivity in the natural KL/Bregman geometry. Under this model, the upper bound of an admissible step is not a tuning slogan but a formula.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.10664", "title": "Prompt-Activation Duality: Improving Activation Steering via Attention-Level Interventions", "authors": [ "Diancheng Kang", "Zheyuan Liu", "Ningshan Ma", "Yue Huang", "Zhaoxuan Tan", "Meng Jiang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NihiI-obstat/Gated-Cropped-Attention-Delta", "github_api_added_by": "user", "project_page": "", "abstract": "Activation steering controls language model behavior by adding directions to internal representations at inference time, but standard residual-stream steering can fail in stateful dialogue. We identify KV-cache contamination as a key failure mode: steered token states are stored and repeatedly reused, turning a local perturbation into cumulative coherence degradation. To address this challenge, we propose Gated Cropped Attention-Delta steering (GCAD), which extracts steering signals from system-prompt contributions to self-attention and applies them with token-level gating. Across persona-steering experiments, GCAD preserves trait control while substantially improving long-horizon coherence. On the main multi-turn benchmark, GCAD improves average coherence drift from -18.6 to -1.9 and raises turn-10 trait expression from 78.0 to 93.1. These results suggest that activation steering becomes more reliable when interventions follow the prompt-mediated pathways that models already use for behavioral control.", "github": "https://github.com/NihiI-obstat/Gated-Cropped-Attention-Delta" }, { "date": "2026-05-12", "arxiv_id": "2605.08513", "title": "A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models", "authors": [ "Hamid Kazemi", "Atoosa Chegini", "Maria Safi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Safety alignment in language models operates through two mechanistically distinct systems: refusal neurons that gate whether harmful knowledge is expressed, and concept neurons that encode the harmful knowledge itself. By targeting a single neuron in each system, we demonstrate both directions of failure -- bypassing safety on explicit harmful requests via suppression, and inducing harmful content from innocent prompts via amplification -- across seven models spanning two families and 1.7B to 70B parameters, without any training or prompt engineering. Our findings suggest that safety alignment is not robustly distributed across model weights but is mediated by individual neurons that are each causally sufficient to gate refusal behavior -- suppressing any one of the identified refusal neurons bypasses safety alignment across diverse harmful requests.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.10848", "title": "Rethinking Agentic Search with Pi-Serini: Is Lexical Retrieval Sufficient?", "authors": [ "Tz-Huan Hsu", "Jheng-Hong Yang", "Jimmy Lin" ], "github_manual": "", "github_regex": "https://github.com/justram/pi-serini", "github_paper_md": "", "github_api": "https://github.com/justram/pi-serini", "github_api_added_by": "user", "project_page": "https://ricky42613.github.io/piserini", "abstract": "Does a lexical retriever suffice as large language models (LLMs) become more capable in an agentic loop? This question naturally arises when building deep research systems. We revisit it by pairing BM25 with frontier LLMs that have better reasoning and tool-use abilities. To support researchers asking the same question, we introduce Pi-Serini, a search agent equipped with three tools for retrieving, browsing, and reading documents. Our results show that, on BrowseComp-Plus, a well-configured lexical retriever with sufficient retrieval depth can support effective deep research when paired with more capable LLMs. Specifically, Pi-Serini with gpt-5.5 achieves 83.1% answer accuracy and 94.7% surfaced evidence recall, outperforming released search agents that use dense retrievers. Controlled ablations further show that BM25 tuning improves answer accuracy by 18.0% and surfaced evidence recall by 11.1% over the default BM25 setting, while increasing retrieval depth further improves surfaced evidence recall by 25.3% over the shallow-retrieval setting. Source code is available at https://github.com/justram/pi-serini.", "github": "https://github.com/justram/pi-serini" }, { "date": "2026-05-12", "arxiv_id": "2605.06788", "title": "Conformal Agent Error Attribution", "authors": [ "Naihe Feng", "Yi Sui", "Shiyi Hou", "Ga Wu", "Jesse C. Cresswell" ], "github_manual": "", "github_regex": "https://github.com/layer6ai-labs/conformal-agent-error-attribution", "github_paper_md": "", "github_api": "https://github.com/layer6ai-labs/conformal-agent-error-attribution", "github_api_added_by": "user", "project_page": "", "abstract": "When multi-agent systems (MAS) fail, identifying where the decisive error occurred is the first step for automated recovery to an earlier state. Error attribution remains a fundamental challenge due to the long interaction traces that large language model-based MAS generate. This paper presents a framework for error attribution based on conformal prediction (CP) which provides finite-sample, distribution-free coverage guarantees. We introduce new algorithms for filtration-based CP designed for sequential data such as agent trajectories. Unlike existing CP algorithms, our approach predicts sets that are contiguous sequences to enable efficient recovery and debugging. We verify our theoretical guarantees on a variety of agents and datasets, show that errors can be precisely isolated, then use prediction sets to rollback MAS to correct their own errors. Our overall approach is model-agnostic, and offers a principled uncertainty layer for MAS error attribution. We release code at https://github.com/layer6ai-labs/conformal-agent-error-attribution.", "github": "https://github.com/layer6ai-labs/conformal-agent-error-attribution" }, { "date": "2026-05-12", "arxiv_id": "2605.10488", "title": "DeepRefine: Agent-Compiled Knowledge Refinement via Reinforcement Learning", "authors": [ "Haoyu Huang", "Jiaxin Bai", "Shujie Liu", "Yang Wei", "Hong Ting Tsang", "Yisen Gao", "Zhongwei Xie", "Yufei Li", "Yangqiu Song" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HKUST-KnowComp/DeepRefine", "github_api_added_by": "user", "project_page": "", "abstract": "Agent-compiled knowledge bases provide persistent external knowledge for large language model (LLM) agents in open-ended, knowledge-intensive downstream tasks. Yet their quality is systematically limited by incompleteness, incorrectness, and redundancy, manifested as missing evidence or cross-document links, low-confidence or imprecise claims, and ambiguous or coreference resolution issues. Such defects compound under iterative use, degrading retrieval fidelity and downstream task performance. We present DeepRefine, a general LLM-based reasoning model for agent-compiled knowledge refinement that improves the quality of any pre-constructed knowledge bases with user queries to make it more suitable for the downstream tasks. DeepRefine performs multi-turn interactions with the knowledge base and conducts abductive diagnosis over interaction history, localizes likely defects, and executes targeted refinement actions for incremental knowledge base updates. To optimize refinement policies of DeepRefine without gold references, we introduce a Gain-Beyond-Draft (GBD) reward and train the reasoning process end-to-end via reinforcement learning. Extensive experiments demonstrate consistent downstream gains over strong baselines.", "github": "https://github.com/HKUST-KnowComp/DeepRefine" }, { "date": "2026-05-12", "arxiv_id": "2605.08384", "title": "jina-embeddings-v5-omni: Text-Geometry-Preserving Multimodal Embeddings via Frozen-Tower Composition", "authors": [ "Florian H\u00f6nicke", "Michael G\u00fcnther", "Andreas Koukounas", "Kalim Akram", "Scott Martens", "Saba Sturua", "Han Xiao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "In this work, we introduce frozen-encoder model composition, a novel approach to multimodal embedding models. We build on the VLM-style architecture, in which non-text encoders are adapted to produce input for a language model, which in turn generates embeddings for all varieties of input. We present the result: the jina-embeddings-v5-omni suite, a pair of models that encode text, image, audio, and video input into a single semantic embedding space. Our method is to extend the two Jina Embeddings v5 Text models to support additional media by adding encoders for images and audio. The backbone text embedding models and the added non-text media encoders remain frozen. We only trained the connecting components, representing 0.35% of the total weights of the joint model. Training is therefore much more efficient than full-parameter retraining. Additionally, the language model remains effectively unaltered, producing exactly the same embeddings for text inputs as the Jina Embeddings v5 Text models. Our evaluations show that this approach produces results that are competitive with the state-of-the-art, yielding nearly equal performance to larger multimodal embedding models.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.10032", "title": "PlantMarkerBench: A Multi-Species Benchmark for Evidence-Grounded Plant Marker Reasoning", "authors": [ "Sajib Acharjee Dip", "Song Li", "Liqing Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/datasets/Sajib-006/PlantMarkerBench", "abstract": "Cell-type-specific marker genes are fundamental to plant biology, yet existing resources primarily rely on curated databases or high-throughput studies without explicitly modeling the supporting evidence found in scientific literature. We introduce PlantMarkerBench, a multi-species benchmark for evaluating literature-grounded plant marker evidence interpretation from full-text biological papers. PlantMarkerBench is constructed using a modular curation pipeline integrating large-scale literature retrieval, hybrid search, species-aware biological grounding, structured evidence extraction, and targeted human review. The benchmark spans four plant species -- Arabidopsis, maize, rice, and tomato -- and contains 5,550 sentence-level evidence instances annotated for marker-evidence validity, evidence type, and support strength. We define two benchmark tasks: determining whether a candidate sentence provides valid marker evidence for a gene-cell-type pair, and classifying the evidence into expression, localization, function, indirect, or negative categories. We benchmark diverse open-weight and closed-source language models across species and prompting strategies. Although frontier models achieve relatively strong performance on direct expression evidence, performance drops substantially on functional, indirect, and weak-support evidence, with evidence-type confusion emerging as a dominant failure mode. Open-weight models additionally exhibit elevated false-positive rates under ambiguous biological contexts. PlantMarkerBench provides a challenging and reproducible evaluation framework for literature-grounded biological evidence attribution and supports future research on trustworthy scientific information extraction and AI-assisted plant biology.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2604.26326", "title": "Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control", "authors": [ "Bolian Li", "Yifan Wang", "Yi Ding", "Anamika Lochab", "Ananth Grama", "Ruqi Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lblaoke/entrocraft", "github_api_added_by": "user", "project_page": "https://lblaoke.github.io/demo/entrocraft", "abstract": "Reinforcement learning (RL) has enabled complex reasoning abilities in large language models (LLMs). However, most RL algorithms suffer from performance saturation, preventing continued gains as RL training scales. This problem can be characterized by the collapse of entropy, a key diagnostic for exploration in RL. Existing attempts focus on preventing entropy collapse through regularization or clipping. However, their resulting entropy curves often exhibit instability in the long term, which hinders performance gains. In this paper, we introduce Entrocraft, a simple rejection-sampling approach that realizes user-customized entropy schedule by biasing the advantage distributions. Entrocraft requires no objective regularization and is advantage-estimator-agnostic. Theoretically, we relate per-step entropy change to the advantage distribution under minimal assumptions. This explains the behavior of existing RL and entropy-preserving methods. Entrocraft also enables a systematic study of entropy schedules, which reveals that linear annealing, which starts high and decays to a slightly lower target, performs best. Empirically, Entrocraft addresses performance saturation, significantly improving generalization, output diversity, and long-term training. It enables a 4B model to outperform an 8B baseline, sustains improvement for up to 4x longer before plateauing, and raises pass@K by 50% over the baseline.", "github": "https://github.com/lblaoke/entrocraft" }, { "date": "2026-05-12", "arxiv_id": "2605.10889", "title": "Unmasking On-Policy Distillation: Where It Helps, Where It Hurts, and Why", "authors": [ "Mohammadreza Armandpour", "Fatih Ilhan", "David Harrison", "Ajay Jaiswal", "Duc N. M Hoang", "Fartash Faghri", "Yizhe Zhang", "Minsik Cho", "Mehrdad Farajtabar" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "On-policy distillation offers dense, per-token supervision for training reasoning models; however, it remains unclear under which conditions this signal is beneficial and under which it is detrimental. Which teacher model should be used, and in the case of self-distillation, which specific context should serve as the supervisory signal? Does the optimal choice vary from one token to the next? At present, addressing these questions typically requires costly training runs whose aggregate performance metrics obscure the dynamics at the level of individual tokens. We introduce a training-free diagnostic framework that operates at the highest resolution: per token, per question, and per teacher. We derive an ideal per-node gradient defined as the parameter update that maximally increases the student's probability of success. We then develop a scalable targeted-rollout algorithm to estimate this gradient efficiently, even for long chains of intermediate thoughts. The gradient alignment score, defined as the cosine similarity between this ideal gradient and any given distillation gradient, quantifies the extent to which a particular configuration approximates the ideal signal. Across a range of self-distillation settings and external teacher models, we observe that distillation guidance exhibits substantially higher alignment with the ideal on incorrect rollouts than on correct ones, where the student already performs well and the teacher's signal tends to become noisy. Furthermore, we find that the optimal distillation context depends jointly on the student model's capacity and the target task, and that no single universally effective configuration emerges. These findings motivate the use of per-task, per-token diagnostic analyses for distillation.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.10762", "title": "GridProbe: Posterior-Probing for Adaptive Test-Time Compute in Long-Video VLMs", "authors": [ "Mohamed Eltahir", "Lama Ayash", "Ali Habibullah", "Tanveer Hussain", "Naeemullah Khan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mohammad2012191/GridProbe", "github_api_added_by": "user", "project_page": "", "abstract": "Long-video understanding in VLMs is bottlenecked by a single monolithic forward pass over thousands of frames at quadratic attention cost. A common mitigation is to first select a small subset of informative frames before the forward pass; common for training-free selectors via auxiliary encoder-space similarities. Such signals are capped by contrastive pretraining, which usually fails on reasoning-heavy queries (negation, cross-frame counting, holistic summarization). We propose GridProbe, an efficient training-free posterior-probing inference paradigm that scores evidence in answer space using a frozen VLM's own reasoning and then selects question-relevant frames adaptively, resulting in sub-quadratic attention cost with little to no accuracy loss. We arrange frames on a K{times}K grid and run lightweight row R and column C probes, where each probe reads its peak posterior as a query-conditioned confidence. The outer product of R and C yields an interpretable importance map whose skewness and kurtosis drive Shape-Adaptive Selection, a closed-form rule that reliably replaces the fixed frame budget M with a per-question M_{eff}. We show empirically that M_{eff} tracks intrinsic question difficulty without ever seeing the answer, a sign of test-time adaptive compute. On Video-MME-v2, GridProbe matches the monolithic baseline within 1.6 pp Avg Acc at 3.36times TFLOPs reduction, while on LongVideoBench it Pareto-dominates the baseline (+0.9 pp at 0.35times compute). Because the selector and QA models can be decoupled, pairing a small 2B selector with a stronger 4B or 8B QA is strictly Pareto-dominant over the 2B monolithic baseline (up to +4.0 pp at 0.52times compute, on average), with no retraining. Finally, the interpretability of the importance maps opens future avenues for behavioral diagnostics, grounding, and frame-selection distillation.", "github": "https://github.com/mohammad2012191/GridProbe" }, { "date": "2026-05-12", "arxiv_id": "2605.02364", "title": "InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition", "authors": [ "Fengze Liu", "Weidong Zhou", "Binbin Liu", "Ping Guo", "Zijun Wang", "Bingni Zhang", "Yifan Zhang", "Yifeng Yu", "Xiaohuan Zhou", "Taifeng Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Upweighting high-quality data in LLM pretraining often improves performance, but in datalimited regimes, especially under overtraining, stronger upweighting increases repetition and can degrade performance. However, standard scaling laws do not reliably extrapolate across mixture recipes or under repetitions, making the selection for optimal data recipes at scaling underdetermined. To solve this, we introduce InfoLaw (Information Scaling Laws), a data-aware scaling framework that predicts loss from consumed tokens, model size, data mixture weights, and repetition. The key idea is to model pretraining as information accumulation, where quality controls information density and repetition induces scaledependent diminishing returns. We first collect the model performance after training on datasets that vary in scale, quality distribution, and repetition level. Then we build up the modeling for information so that information accurately predicts those model performance. InfoLaw predicts performance on unseen data recipes and larger scale runs (up to 7B, 425B tokens) with 0.15% mean and 0.96% max absolute error in loss, and it extrapolates reliably across overtraining levels, enabling efficient data-recipe selection under varying compute budgets.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.10899", "title": "RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards", "authors": [ "Gaotang Li", "Bhavana Dalvi Mishra", "Zifeng Wang", "Jun Yan", "Yanfei Chen", "Chun-Liang Li", "Long T. Le", "Rujun Han", "George Lee", "Hanghang Tong", "Chen-Yu Lee", "Tomas Pfister" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Training deep research agents, namely systems that plan, search, evaluate evidence, and synthesize long-form reports, pushes reinforcement learning beyond the regime of verifiable rewards. Their outputs lack ground-truth answers, their trajectories span many tool-augmented decisions, and standard post-training offers little mechanism for turning past attempts into reusable experience. In this work, we argue that rubrics should serve not merely as final-answer evaluators, but as the shared interface that structures policy execution, judge feedback, and agent memory. Based on this view, we introduce RubricEM, a rubric-guided reinforcement learning framework that combines stagewise policy decomposition with reflection-based meta-policy evolution. RubricEM first makes research trajectories stage-aware by conditioning planning, evidence gathering, review, and synthesis on self-generated rubrics. It then assigns credit with Stage-Structured GRPO, which uses stagewise rubric judgments to provide denser semantic feedback for long-horizon optimization. In parallel, RubricEM trains a shared-backbone reflection meta-policy that distills judged trajectories into reusable rubric-grounded guidance for future attempts. The resulting RubricEM-8B achieves strong performance across four long-form research benchmarks, outperforming comparable open models and approaching proprietary deep-research systems. Beyond final performance, we perform thorough analyses to understand the key ingredients of RubricEM.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.11011", "title": "LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models", "authors": [ "Taekhyun Park", "Yongjae Lee", "Dohee Kim", "Hyerim Bae" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Thrillcrazyer/LoopUS", "github_api_added_by": "user", "project_page": "https://thrillcrazyer.github.io/LoopUS", "abstract": "Looped computation shows promise in improving the reasoning-oriented performance of LLMs by scaling test-time compute. However, existing approaches typically require either training recurrent models from scratch or applying disruptive retrofits, which involve substantial computational costs and may compromise pretrained capabilities. To address these limitations, we introduce Looped Depth Up-Scaling (LoopUS), a post-training framework that converts a standard pretrained LLM into a looped architecture. As a key technical contribution, LoopUS recasts the pretrained LLM into an encoder, a looped reasoning block, and a decoder. It operationalizes this latent-refinement architecture through four core components: (1) block decomposition, guided by staged representation dynamics; (2) an input-dependent selective gate to mitigate hidden-state drift; (3) random deep supervision for memory-efficient learning over long recursive horizons; and (4) a confidence head for adaptive early exiting. Collectively, these mechanisms transform a standard non-looped model into a looped form while stabilizing it against both computational bottlenecks and representation collapse. Through stable latent looping, LoopUS improves reasoning-oriented performance without extending the generated traces or requiring recurrent training from scratch. For more details, see https://thrillcrazyer.github.io/LoopUS", "github": "https://github.com/Thrillcrazyer/LoopUS" }, { "date": "2026-05-13", "arxiv_id": "2605.07237", "title": "Teaching Language Models to Think in Code", "authors": [ "Hyeon Hwang", "Jiwoo Lee", "Jaewoo Kang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Tool-integrated reasoning (TIR) has emerged as a dominant paradigm for mathematical problem solving in language models, combining natural language (NL) reasoning with code execution. However, this interleaved setup has three key limitations: code often acts as a post-hoc verifier, intermediate NL computations are error-prone, and NL and code play overlapping rather than clearly distinct roles. We propose ThinC (Thinking in Code), a framework in which code itself serves as the reasoner rather than as a tool invoked by NL. A ThinC trajectory begins with a brief NL planning step, after which all reasoning unfolds through code blocks connected only by their execution outputs. We distill 12.2k code-centric trajectories from a teacher model and train ThinC-1.7B and ThinC-4B with supervised fine-tuning followed by reinforcement learning. ThinC-4B consistently outperforms every TIR baseline on five competition-level math benchmarks and even surpasses the much larger Qwen3-235B-A22B-Thinking. Further analysis shows that ThinC reasons through code: 99.2% of its final answers are grounded in interpreter output, and the model recovers reliably from code execution failures without intermediate NL reasoning. Our code and models will be released soon.", "github": "" }, { "date": "2026-05-12", "arxiv_id": "2605.09169", "title": "Prediction Bottlenecks Don't Discover Causal Structure (But Here's What They Actually Do)", "authors": [ "Ankit Hemant Lade", "Sai Krishna Jasti", "Indar Kumar", "Aman Chadha" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "A Mamba state-space model trained only for next-step prediction appears to recover Granger-causal structure through a simple readout S = |W_{out} W_{in}|, with early experiments suggesting the phenomenon generalized across architectures and benefited from interventional data at p < 10^{-5}. We package the protocol used to test that claim -- standardized synthetic generators (VAR/Lorenz/CauseMe-style), three intervention semantics (do(X=c), soft-noise, random-forcing), edge-provenance cards on three real datasets, and size-matched control arms -- as a reusable falsification benchmark, and walk the claim through it in five stages. The method-level claim does not survive: (i) a plain linear bottleneck does as well or better; (ii) tuned Lasso beats the bottleneck on synthetic CauseMe-style benchmarks, and on Lorenz-96 (the only real benchmark with unambiguous ground truth) classical PCMCI and Granger lead a tight cluster in which the bottleneck trails; (iii) the headline intervention advantage is roughly 60% a sample-size confound, and the residual disappears under standard do(X=c) interventions, surviving only under a non-standard random-forcing scheme; (iv) even that residual reproduces, with a larger effect, in classical bivariate Granger -- the effect is method-agnostic. What survives is a narrow characterization result; the benchmark is the lasting artifact, and each stage above is one of its control arms.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.12357", "title": "\u03b4-mem: Efficient Online Memory for Large Language Models", "authors": [ "Jingdi Lei", "Di Zhang", "Junxian Li", "Weida Wang", "Kaixuan Fan", "Xiang Liu", "Qihan Liu", "Xiaoteng Ma", "Baian Chen", "Soujanya Poria" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/declare-lab/delta-Mem", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models increasingly need to accumulate and reuse historical information in long-term assistants and agent systems. Simply expanding the context window is costly and often fails to ensure effective context utilization. We propose \u03b4-mem, a lightweight memory mechanism that augments a frozen full-attention backbone with a compact online state of associative memory. \u03b4-mem compresses past information into a fixed-size state matrix updated by delta-rule learning, and uses its readout to generate low-rank corrections to the backbone's attention computation during generation. With only an 8times8 online memory state, \u03b4-mem improves the average score to 1.10times that of the frozen backbone and 1.15times that of the strongest non-\u03b4-mem memory baseline. It achieves larger gains on memory-heavy benchmarks, reaching 1.31times on MemoryAgentBench and 1.20times on LoCoMo, while largely preserving general capabilities. These results show that effective memory can be realized through a compact online state directly coupled with attention computation, without full fine-tuning, backbone replacement, or explicit context extension.", "github": "https://github.com/declare-lab/delta-Mem" }, { "date": "2026-05-13", "arxiv_id": "2605.09530", "title": "MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents", "authors": [ "Yining Chen", "Jihao Zhao", "Bo Tang", "Haofen Wang", "Feiyu Xiong", "Zhiyu Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MemTensor/MemPrivacy", "github_api_added_by": "user", "project_page": "", "abstract": "As LLM-powered agents are increasingly deployed in edge-cloud environments, personalized memory has become a key enabler of long-term adaptation and user-centric interaction. However, cloud-assisted memory management exposes sensitive user information, while existing privacy protection methods typically rely on aggressive masking that removes task-relevant semantics and consequently degrades memory utility and personalization quality. To address this challenge, We propose MemPrivacy, which identifies privacy-sensitive spans on edge devices, replaces them with semantically structured type-aware placeholders for cloud-side memory processing, and restores the original values locally when needed. By decoupling privacy protection from semantic destruction, MemPrivacy minimizes sensitive data exposure while retaining the information required for effective memory formation and retrieval. We also construct MemPrivacy-Bench for systematic evaluation, a dataset covering 200 users and over 52k privacy instances, and introduce a four-level privacy taxonomy for configurable protection policies. Experiments show that MemPrivacy achieves strong performance in privacy information extraction, substantially surpassing strong general-purpose models such as GPT-5.2 and Gemini-3.1-Pro, while also reducing inference latency. Across multiple widely used memory systems, MemPrivacy limits utility loss to within 1.6%, outperforming baseline masking strategies. Overall, MemPrivacy offers an effective balance between privacy protection and personalized memory utility for edge-cloud agents, enabling secure, practical, and user-transparent deployment.", "github": "https://github.com/MemTensor/MemPrivacy" }, { "date": "2026-05-13", "arxiv_id": "2605.12500", "title": "SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture", "authors": [ "Haiwen Diao", "Penghao Wu", "Hanming Deng", "Jiahao Wang", "Shihao Bai", "Silei Wu", "Weichen Fan", "Wenjie Ye", "Wenwen Tong", "Xiangyu Fan", "Yan Li", "Yubo Wang", "Zhijie Cao", "Zhiqian Lin", "Zhitao Yang", "Zhongang Cai", "Yuwei Niu", "Yue Zhu", "Bo Liu", "Chengguang Lv", "Haojia Yu", "Haozhe Xie", "Hongli Wang", "Jianan Fan", "Jiaqi Li", "Jiefan Lu", "Jingcheng Ni", "Junxiang Xu", "Kaihuan Liang", "Lianqiang Shi", "Linjun Dai", "Linyan Wang", "Oscar Qian", "Peng Gao", "Pengfei Liu", "Qingping Sun", "Rui Shen", "Ruisi Wang", "Shengnan Ma", "Shuang Yang", "Siyi Xie", "Siying Li", "Tianbo Zhong", "Xiangli Kong", "Xuanke Shi", "Yang Gao", "Yongqiang Yao", "Yves Wang", "Zhengqi Bai", "Zhengyu Lin", "Zixin Yin", "Wenxiu Sun", "Ruihao Gong", "Quan Wang", "Lewei Lu", "Lei Yang", "Ziwei Liu", "Dahua Lin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenSenseNova/SenseNova-U1", "github_api_added_by": "user", "project_page": "", "abstract": "Recent large vision-language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as distinct problems, leading to fragmented architectures, cascaded pipelines, and misaligned representation spaces. We argue that this divide is not merely an engineering artifact, but a structural limitation that hinders the emergence of native multimodal intelligence. Hence, we introduce SenseNova-U1, a native unified multimodal paradigm built upon NEO-unify, in which understanding and generation evolve as synergistic views of a single underlying process. We launch two native unified variants, SenseNova-U1-8B-MoT and SenseNova-U1-A3B-MoT, built on dense (8B) and mixture-of-experts (30B-A3B) understanding baselines, respectively. Designed from first principles, they rival top-tier understanding-only VLMs across text understanding, vision-language perception, knowledge reasoning, agentic decision-making, and spatial intelligence. Meanwhile, they deliver strong semantic consistency and visual fidelity, excelling in conventional or knowledge-intensive any-to-image (X2I) synthesis, complex text-rich infographic generation, and interleaved vision-language generation, with or without think patterns. Beyond performance, we show detailed model design, data preprocessing, pre-/post-training, and inference strategies to support community research. Last but not least, preliminary evidence demonstrates that our models extend beyond perception and generation, performing strongly in vision-language-action (VLA) and world model (WM) scenarios. This points toward a broader roadmap where models do not translate between modalities, but think and act across them in a native manner. Multimodal AI is no longer about connecting separate systems, but about building a unified one and trusting the necessary capabilities to emerge from within.", "github": "https://github.com/OpenSenseNova/SenseNova-U1" }, { "date": "2026-05-13", "arxiv_id": "2605.10780", "title": "Beyond the Last Layer: Multi-Layer Representation Fusion for Visual Tokenization", "authors": [ "Xuanyu Zhu", "Yan Bai", "Yang Shi", "Yihang Lou", "Yuanxing Zhang", "Jing Jin", "Yuan Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhuzil/DRoRAE", "github_api_added_by": "user", "project_page": "", "abstract": "Representation autoencoders that reuse frozen pretrained vision encoders as visual tokenizers have achieved strong reconstruction and generation quality. However, existing methods universally extract features from only the last encoder layer, discarding the rich hierarchical information distributed across intermediate layers. We show that low-level visual details survive in the last layer merely as attenuated residuals after multiple layers of semantic abstraction, and that explicitly fusing multi-layer features can substantially recover this lost information. We propose DRoRAE (Depth-Routed Representation AutoEncoder), a lightweight fusion module that adaptively aggregates all encoder layers via energy-constrained routing and incremental correction, producing an enriched latent compatible with a frozen pretrained decoder. A three-phase decoupled training strategy first learns the fusion under the implicit distributional constraint of the frozen decoder, then fine-tunes the decoder to fully exploit the enriched representation. On ImageNet-256, DRoRAE reduces rFID from 0.57 to 0.29 and improves generation FID from 1.74 to 1.65 (with AutoGuidance), with gains also transferring to text-to-image synthesis. Furthermore, we uncover a log-linear scaling law (R^2{=}0.86) between fusion capacity and reconstruction quality, identifying representation richness as a new, predictably scalable dimension for visual tokenizers analogous to vocabulary size in NLP.", "github": "https://github.com/zhuzil/DRoRAE" }, { "date": "2026-05-13", "arxiv_id": "2605.12496", "title": "CausalCine: Real-Time Autoregressive Generation for Multi-Shot Video Narratives", "authors": [ "Yihao Meng", "Zichen Liu", "Hao Ouyang", "Qiuyu Wang", "Ka Leong Cheng", "Yue Yu", "Hanlin Wang", "Haobo Li", "Jiapeng Zhu", "Yanhong Zeng", "Xing Zhu", "Yujun Shen", "Qifeng Chen", "Huamin Qu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://yihao-meng.github.io/CausalCine/", "abstract": "Autoregressive video generation aims at real-time, open-ended synthesis. Yet, cinematic storytelling is not merely the endless extension of a single scene; it requires progressing through evolving events, viewpoint shifts, and discrete shot boundaries. Existing autoregressive models often struggle in this setting. Trained primarily for short-horizon continuation, they treat long sequences as extended single shots, inevitably suffering from motion stagnation and semantic drift during long rollouts. To bridge this gap, we introduce CausalCine, an interactive autoregressive framework that transforms multi-shot video generation into an online directing process. CausalCine generates causally across shot changes, accepts dynamic prompts on the fly, and reuses context without regenerating previous shots. To achieve this, we first train a causal base model on native multi-shot sequences to learn complex shot transitions prior to acceleration. We then propose Content-Aware Memory Routing (CAMR), which dynamically retrieves historical KV entries according to attention-based relevance scores rather than temporal proximity, preserving cross-shot coherence under bounded active memory. Finally, we distill the causal base model into a few-step generator for real-time interactive generation. Extensive experiments demonstrate that CausalCine significantly outperforms autoregressive baselines and approaches the capability of bidirectional models while unlocking the streaming interactivity of causal generation. Demo available at https://yihao-meng.github.io/CausalCine/", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.12483", "title": "Beyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training", "authors": [ "Yuanda Xu", "Hejian Sang", "Zhengze Zhou", "Ran He", "Zhipeng Wang", "Alborz Geramifard" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "In settings where labeled verifiable training data is the binding constraint, each checked example should be allocated carefully. The standard practice is to use this data directly on the model that will be deployed, for example by running GRPO on the deployment student. We argue that this is often an inefficient allocation because it overlooks a reward-density principle: sparse sequence-level reward should train models where exploration is productive, while dense token-level teacher reward should be used where the aim is to compress behavior into a smaller model. In this view, GRPO-style sparse RL and OPD-style dense teacher supervision are not separate recipes; they are different reward-density regimes. The allocation rule is simple: use scarce labeled training data upstream on the strongest model that can turn it into reward-shaped behavior, then transfer that behavior downstream as dense supervision. We evaluate this rule on verifiable math with Qwen3 and Llama models. At fixed Qwen3-1.7B deployment-student size, an RL-improved 8B teacher distilled through the dense bridge outperforms direct GRPO on the same student, while transfer from the same teacher before RL underperforms. The bridge is important: a forward-KL warmup on teacher rollouts followed by OPD on student rollouts is consistently strongest on MATH before any post-bridge student-side sparse RL, and also gives the best pre-Stage~3 AIME endpoints for the canonical 8B/14B teachers. The bridge also makes later student-side sparse RL effective: GRPO that is weak on a cold student lifts MATH from 75.4% to 78.5% after the bridge and outperforms a matched replay control by 2.8 points. The operational principal is to avoid using scarce labeled data on the least prepared policy: use sparse reward for teacher-side discovery, dense transfer for student compression, and student-side sparse reward only after the bridge.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.11182", "title": "The Many Faces of On-Policy Distillation: Pitfalls, Mechanisms, and Fixes", "authors": [ "Siqi Zhu", "Xuyan Ye", "Hongyu Lu", "Weiye Shi", "Ge Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ulab-uiuc/Open-On-Policy-Distillation", "github_api_added_by": "user", "project_page": "https://ulab-uiuc.github.io/OPD_website/", "abstract": "On-policy distillation (OPD) and on-policy self-distillation (OPSD) have emerged as promising post-training methods for large language models, offering dense token-level supervision on trajectories sampled from the model's own policy. However, existing results on their effectiveness remain mixed: while OP(S)D has shown promise in system prompt and knowledge internalization, recent studies also report instability and degradation. In this work, we present a comprehensive empirical study of when OPD and OPSD work, when they fail, and why. We find that OPD on mathematical reasoning is highly sensitive to teacher choice and loss formulation, whereas OPSD fails in our tested settings due to test-time absence of instance-specific privileged information (PI). In contrast, OPSD is effective when PI represents a shared latent rule, such as a system prompt or alignment preference. We identify three failure mechanisms: (1) distribution mismatch between teacher and student caused by conditioning on student-generated prefixes, (2) optimization instability from biased TopK reverse-KL gradients, and (3) an OPSD-specific limitation where the student learns a PI-free policy that aggregates PI-conditioned teachers, which is insufficient when PI is instance-specific. We further show that stop-gradient TopK objectives, RLVR-adapted teachers, and SFT-stabilized students mitigate these failures.", "github": "https://github.com/ulab-uiuc/Open-On-Policy-Distillation" }, { "date": "2026-05-13", "arxiv_id": "2605.12449", "title": "LychSim: A Controllable and Interactive Simulation Framework for Vision Research", "authors": [ "Wufei Ma", "Chloe Wang", "Siyi Chen", "Jiawei Peng", "Patrick Li", "Alan Yuille" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wufeim/LychSim", "github_api_added_by": "user", "project_page": "https://lychsim.github.io/", "abstract": "While self-supervised pretraining has reduced vision systems' reliance on synthetic data, simulation remains an indispensable tool for closed-loop optimization and rigorous out-of-distribution (OOD) evaluation. However, modern simulation platforms often present steep technical barriers, requiring extensive expertise in computer graphics and game development. In this work, we present LychSim, a highly controllable and interactive simulation framework built upon Unreal Engine 5 to bridge this gap. LychSim is built around three key designs: (1) a streamlined Python API that abstracts away underlying engine complexities; (2) a procedural data pipeline capable of generating diverse, high-fidelity environments with varying out-of-distribution (OOD) visual challenges, paired with rich 2D and 3D ground truths; and (3) a native integration of the Model Context Protocol (MCP) that transforms the simulator into a dynamic, closed-loop playground for reasoning agentic LLMs. We further annotate scene-level procedural rules and object-level pose alignments to enable semantically aligned 3D ground truths and automated scene modification. We demonstrate LychSim's capability across multiple downstream applications, including serving as a synthetic data engine, powering reinforcement learning-based adversarial examiners, and facilitating interactive, language-driven scene layout generation. To benefit the broader vision community, LychSim will be made publicly available, including full source code and various data annotations.", "github": "https://github.com/wufeim/LychSim" }, { "date": "2026-05-13", "arxiv_id": "2605.11518", "title": "AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration -- Learning from Cheap, Optimizing Expensive", "authors": [ "Taicheng Guo", "Nitesh V. Chawla", "Olaf Wiest", "Xiangliang Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/taichengguo/AutoLLMResearch", "github_api_added_by": "user", "project_page": "https://arxiv.org/pdf/2605.11518", "abstract": "Effectively configuring scalable large language model (LLM) experiments, spanning architecture design, hyperparameter tuning, and beyond, is crucial for advancing LLM research, as poor configuration choices can waste substantial computational resources and prevent models from realizing their full potential. Prior automated methods are designed for low-cost settings where repeated trial and error is feasible, but scalable LLM experiments are too expensive for such extensive iteration. To our knowledge, no work has addressed the automation of high-cost LLM experiment configurations, leaving this problem labor-intensive and dependent on expert intuition. Motivated by this gap, we propose AutoLLMResearch, an agentic framework that mimics how human researchers learn generalizable principles from low-fidelity experiments and extrapolate to efficiently identify promising configurations in expensive LLM settings. The core challenge is how to enable an agent to learn, through interaction with a multi-fidelity experimental environment that captures the structure of the LLM configuration landscape. To achieve this, we propose a systematic framework with two key components: 1) LLMConfig-Gym, a multi-fidelity environment encompassing four critical LLM experiment tasks, supported by over one million GPU hours of verifiable experiment outcomes; 2) A structured training pipeline that formulates configuration research as a long-horizon Markov Decision Process and accordingly incentivizes cross-fidelity extrapolation reasoning. Extensive evaluation against diverse strong baselines on held-out experiments demonstrates the effectiveness, generalization, and interpretability of our framework, supporting its potential as a practical and general solution for scalable real-world LLM experiment automation.", "github": "https://github.com/taichengguo/AutoLLMResearch" }, { "date": "2026-05-13", "arxiv_id": "2605.12178", "title": "Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics", "authors": [ "Jishnu Sethumadhavan Nair", "Patrice Bechard", "Rishabh Maheshwary", "Surajit Dasgupta", "Sravan Ramachandran", "Aakash Bhagat", "Shruthan Radhakrishna", "Pulkit Pattnaik", "Johan Obando-Ceron", "Shiva Krishna Reddy Malay", "Sagar Davasam", "Seganrasan Subramanian", "Vipul Mittal", "Sridhar Krishna Nemala", "Christopher Pal", "Srinivas Sunkara", "Sai Rajeswar" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "World models enable agents to anticipate the effects of their actions by internalizing environment dynamics. In enterprise systems, however, these dynamics are often defined by tenant-specific business logic that varies across deployments and evolves over time, making models trained on historical transitions brittle under deployment shift. We ask a question the world-models literature has not addressed: when the rules can be read at inference time, does an agent still need to learn them? We argue, and demonstrate empirically, that in settings where transition dynamics are configurable and readable, runtime discovery complements offline training by grounding predictions in the active system instance. We propose enterprise discovery agents, which recover relevant transition dynamics at runtime by reading the system's configuration rather than relying solely on internalized representations. We introduce CascadeBench, a reasoning-focused benchmark for enterprise cascade prediction that adopts the evaluation methodology of World of Workflows on diverse synthetic environments, and use it together with deployment-shift evaluation to show that offline-trained world models can perform well in-distribution but degrade as dynamics change, whereas discovery-based agents are more robust under shift by grounding their predictions in the current instance. Our findings suggest that, in configurable enterprise environments, agents should not rely solely on fixed internalized dynamics, but should incorporate mechanisms for discovering relevant transition logic at runtime.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.12119", "title": "MoCam: Unified Novel View Synthesis via Structured Denoising Dynamics", "authors": [ "Haofeng Liu", "Yang Zhou", "Ziheng Wang", "Zhengbo Xu", "Zhan Peng", "Jie Ma", "Jun Liang", "Shengfeng He", "Jing Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Orange-3DV-Team/MoCam", "github_api_added_by": "user", "project_page": "https://orange-3dv-team.github.io/MoCam/", "abstract": "Generative novel view synthesis faces a fundamental dilemma: geometric priors provide spatial alignment but become sparse and inaccurate under view changes, while appearance priors offer visual fidelity but lack geometric correspondence. Existing methods either propagate geometric errors throughout generation or suffer from signal conflicts when fusing both statically. We introduce MoCam, which employs structured denoising dynamics to orchestrate a coordinated progression from geometry to appearance within the diffusion process.MoCam first leverages geometric priors in early stages to anchor coarse structures and tolerate their incompleteness, then switches to appearance priors in later stages to actively correct geometric errors and refine details. This design naturally unifies static and dynamic view synthesis by temporally decoupling geometric alignment and appearance refinement within the diffusion process.Experiments demonstrate that MoCam significantly outperforms prior methods, particularly when point clouds contain severe holes or distortions, achieving robust geometry-appearance disentanglement.", "github": "https://github.com/Orange-3DV-Team/MoCam" }, { "date": "2026-05-13", "arxiv_id": "2605.12474", "title": "Reward Hacking in Rubric-Based Reinforcement Learning", "authors": [ "Anas Mahmoud", "MohammadHossein Rezaei", "Zihao Wang", "Anisha Gunjal", "Bing Liu", "Yunzhong He" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards has enabled strong post-training gains in domains such as math and coding, though many open-ended settings rely on rubric-based rewards. We study reward hacking in rubric-based RL, where a policy is optimized against a training verifier but evaluated against a cross-family panel of three frontier judges, reducing dependence on any single evaluator. Our framework separates two sources of divergence: verifier failure, where the training verifier credits rubric criteria that reference verifiers reject, and rubric-design limitations, where even strong rubric-based verifiers favor responses that rubric-free judges rate worse overall. Across medical and science domains, weak verifiers produce large proxy-reward gains that do not transfer to the reference verifiers; exploitation grows over training and concentrates in recurring failures such as partial satisfaction of compound criteria, treating implicit content as explicit, and imprecise topical matching. Stronger verifiers substantially reduce, but do not eliminate, verifier exploitation. We also introduce a self-internalization gap, a verifier-free diagnostic based on policy log-probabilities, which tracks reference-verifier quality, detecting when the policy trained using the weak verifier stops improving. Finally, in our setting, stronger verification does not prevent reward hacking when the rubric leaves important failure modes unspecified: rubric-based verifiers prefer the RL checkpoint, while rubric-free judges prefer the base model. These disagreements coincide with gains concentrated in completeness and presence-based criteria, alongside declines in factual correctness, conciseness, relevance, and overall quality. Together, these results suggest that stronger verification reduces reward hacking, but does not by itself ensure that rubric gains correspond to broader quality gains.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.12497", "title": "From Web to Pixels: Bringing Agentic Search into Visual Perception", "authors": [ "Bokang Yang", "Xinyi Sun", "Kaituo Feng", "Xingping Dong", "Dongming Wu", "Xiangyu Yue" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Visual perception connects high-level semantic understanding to pixel-level perception, but most existing settings assume that the decisive evidence for identifying a target is already in the image or frozen model knowledge. We study a more practical yet harder open-world case where a visible object must first be resolved from external facts, recent events, long-tail entities, or multi-hop relations before it can be localized. We formalize this challenge as Perception Deep Research and introduce WebEye, an object-anchored benchmark with verifiable evidence, knowledge-intensive queries, precise box/mask annotations, and three task views: Search-based Grounding, Search-based Segmentation, and Search-based VQA. WebEyes contains 120 images, 473 annotated object instances, 645 unique QA pairs, and 1,927 task samples. We further propose Pixel-Searcher, an agentic search-to-pixel workflow that resolves hidden target identities and binds them to boxes, masks, or grounded answers. Experiments show that Pixel-Searcher achieves the strongest open-source performance across all three task views, while failures mainly arise from evidence acquisition, identity resolution, and visual instance binding.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.12305", "title": "Images in Sentences: Scaling Interleaved Instructions for Unified Visual Generation", "authors": [ "Yabo Zhang", "Kunchang Li", "Dewei Zhou", "Xinyu Huang", "Xun Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "While recent advancements in multimodal language models have enabled image generation from expressive multi-image instructions, existing methods struggle to maintain performance under complex interleaved instructions. This limitation stems from the structural separation of images and text in current paradigms, which forces models to bridge difficult long-range dependencies to match descriptions with visual targets. To address these challenges, we propose Images iN SEnTences (a.k.a, INSET), a unified generation model that seamlessly embeds images as native vocabulary within textual instructions. By positioning visual features directly at their corresponding semantic slots, INSET leverages the contextual locality of transformers for precise object binding, effectively treating images as dense, expressive language tokens. Furthermore, we introduce a scalable data engine that synthesizes 15M high-quality interleaved samples from standard image and video datasets, utilizing VLMs and LLMs to construct rich, long-horizon sequences. Evaluation results on InterleaveBench demonstrate that INSET significantly outperforms state-of-the-art methods in multi-image consistency and text alignment, with performance gaps widening as input complexity increases. Beyond standard generation, our approach inherently extends to multimodal image editing, integrating visual content as part of the instruction to facilitate highly expressive and creative visual manipulations.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.10365", "title": "Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values", "authors": [ "Haonan Dong", "Qiguan Feng", "Kehan Jiang", "Haoran Ye", "Xin Zhang", "Guojie Song" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ValueByte-AI/Agent-ValueBench", "github_api_added_by": "user", "project_page": "https://valuebyte-ai.github.io/Agent-ValueBench.github.io/", "abstract": "Autonomous agents have rapidly matured as task executors and seen widespread deployment via harnesses such as OpenClaw. Safety concerns have rightly drawn growing research attention, and beneath them lie the values silently steering agent behavior. Existing value benchmarks, however, remain confined to LLMs, leaving agent values largely uncharted. From intuitive, empirical, and theoretical vantage points, we show that an agent's values diverge from those of its underlying LLM, and the agentic modality further introduces dataset-, evaluation-, and system-level challenges absent from text-only protocols. We close this gap with Agent-ValueBench, the first benchmark dedicated to agent values. It features 394 executable environments across 16 domains, offering 4,335 value-conflict tasks that cover 28 value systems and 332 dimensions. Every instance is co-synthesized through our purpose-built end-to-end pipeline and curated per-instance by professional psychologists. Each task ships with two pole-aligned golden trajectories whose checkpoints anchor a trajectory-level rubric-based judge. Benchmarking 14 frontier proprietary and open-weights models across 4 mainstream harnesses, we uncover three concerted findings. Agent values first manifest as a Value Tide of cross-model homogeneity beneath interpretable counter-currents. This tide bends non-additively under harness pull, and yet more decisively under deliberate steering via embedded skills. Together these results signal that the agent-alignment lever is shifting from classical model alignment and prompt steering toward harness alignment and skill steering.", "github": "https://github.com/ValueByte-AI/Agent-ValueBench" }, { "date": "2026-05-13", "arxiv_id": "2605.09131", "title": "MCP-Cosmos: World Model-Augmented Agents for Complex Task Execution in MCP Environments", "authors": [ "Giridhar Ganapavarapu", "Dhaval Patel" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The Model Context Protocol (MCP) has unified the interface between Large Language Models (LLMs) and external tools, yet a fundamental gap remains in how agents conceptualize the environments within which they operate. Current paradigms are bifurcated: Task-level planning often ignores execution-time dynamics, while reactive execution lacks long-horizon foresight. We present MCP-Cosmos, a framework that infuses generative World Models (WM) into the MCP ecosystem to enable predictive task automation. By unifying three disparate technologies, namely MCP, World Model, and Agent, we demonstrate that a \"Bring Your Own World Model\" (BYOWM) strategy allows agents to simulate state transitions and refine plans in a latent space before execution. We conducted experiments using two strategies, namely ReAct and SPIRAL with 2 planning models and 3 representative world models over 20+ MCP-Bench tasks. We observed improvements in Agent's environment interaction KPI such as tool success rate and tool parameter accuracy. The framework also offers new metrics such as Execution Quality to generate new insights about the effectiveness of world models compared to baseline.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.00080", "title": "World Model for Robot Learning: A Comprehensive Survey", "authors": [ "Bohan Hou", "Gen Li", "Jindou Jia", "Tuo An", "Xinying Guo", "Sicong Leng", "Haoran Geng", "Yanjie Ze", "Tatsuya Harada", "Philip Torr", "Oier Mees", "Marc Pollefeys", "Zhuang Liu", "Jiajun Wu", "Pieter Abbeel", "Jitendra Malik", "Yilun Du", "Jianfei Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NTUMARS/Awesome-World-Model-for-Robotics-Policy", "github_api_added_by": "user", "project_page": "https://ntumars.github.io/wm-robot-survey/", "abstract": "World models, which are predictive representations of how environments evolve under actions, have become a central component of robot learning. They support policy learning, planning, simulation, evaluation, data generation, and have advanced rapidly with the rise of foundation models and large-scale video generation. However, the literature remains fragmented across architectures, functional roles, and embodied application domains. To address this gap, we present a comprehensive review of world models from a robot-learning perspective. We examine how world models are coupled with robot policies, how they serve as learned simulators for reinforcement learning and evaluation, and how robotic video world models have progressed from imagination-based generation to controllable, structured, and foundation-scale formulations. We further connect these ideas to navigation and autonomous driving, and summarize representative datasets, benchmarks, and evaluation protocols. Overall, this survey systematically reviews the rapidly growing literature on world models for robot learning, clarifies key paradigms and applications, and highlights major challenges and future directions for predictive modeling in embodied agents. To facilitate continued access to newly emerging works, benchmarks, and resources, we will maintain and regularly update the accompanying GitHub repository alongside this survey.", "github": "https://github.com/NTUMARS/Awesome-World-Model-for-Robotics-Policy" }, { "date": "2026-05-13", "arxiv_id": "2605.12493", "title": "LongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced Colleagues", "authors": [ "Di Wu", "Zixiang Ji", "Asmi Kawatkar", "Bryan Kwan", "Jia-Chen Gu", "Nanyun Peng", "Kai-Wei Chang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xiaowu0162/LongMemEval-V2", "github_api_added_by": "user", "project_page": "https://xiaowu0162.github.io/longmemeval-v2/", "abstract": "Long-term memory is crucial for agents in specialized web environments, where success depends on recalling interface affordances, state dynamics, workflows, and recurring failure modes. However, existing memory benchmarks for agents mostly focus on user histories, short traces, or downstream task success, leaving open how to directly evaluate whether memory systems effectively internalize environment-specific experience. To address this gap, we introduce LongMemEval-V2 (LME-V2), a benchmark for evaluating whether memory systems can help agents acquire the experience needed to become knowledgeable colleagues in customized environments. LME-V2 contains 451 manually curated questions covering five core memory abilities for web agents: static state recall, dynamic state tracking, workflow knowledge, environment gotchas, and premise awareness. Questions are paired with history trajectories containing up to 500 trajectories and 115M tokens. We use a context gathering formulation: memory systems consume history trajectories and return compact evidence for downstream question answering. We propose a suite of two memory methods: AgentRunbook-R, an efficient RAG-based memory with knowledge pools for raw state observations, events, and strategy notes, and AgentRunbook-C, which stores trajectories as files and invokes a coding agent to gather evidence in an augmented sandbox. Experiments show that AgentRunbook-C achieves the best performance with 72.5% average accuracy, outperforming the strongest RAG baseline (48.5%) and the off-the-shelf coding agent baseline (69.3%). Despite the strong performance gains, coding agent based methods have high latency costs. While AgentRunbook-C advances the accuracy-latency Pareto frontier, substantial room for improvement remains. Together, these results establish LME-V2 as a challenging testbed for developing long-term memory systems for environment experience.", "github": "https://github.com/xiaowu0162/LongMemEval-V2" }, { "date": "2026-05-13", "arxiv_id": "2605.09998", "title": "Continual Harness: Online Adaptation for Self-Improving Foundation Agents", "authors": [ "Seth Karten", "Joel Zhang", "Tersoo Upaa Jr", "Ruirong Feng", "Wenzhe Li", "Chengshuai Shi", "Chi Jin", "Kiran Vodrahalli" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sethkarten/pokeagent-speedrun", "github_api_added_by": "user", "project_page": "https://sethkarten.ai/continual-harness/", "abstract": "Coding harnesses such as Claude Code and OpenHands wrap foundation models with tools, memory, and planning, but no equivalent exists for embodied agents' long-horizon partial-observability decision-making. We first report our Gemini Plays Pokemon (GPP) experiments. With iterative human-in-the-loop harness refinement, GPP became the first AI system to complete Pokemon Blue, Yellow Legacy on hard mode, and Crystal without a lost battle. In the hardest stages, the agent itself began iterating on its strategy through long-context memory, surfacing emergent self-improvement signals alongside human-in-the-loop refinement. Continual Harness removes the human fully from this loop: a reset-free self-improving harness for embodied agents that formalizes and automates what we observed. Starting from only a minimal environment interface, the agent alternates between acting and refining its own prompt, sub-agents, skills, and memory, drawing on any past trajectory data. Prompt-optimization methods require episode resets; Continual Harness adapts online within a single run. On Pokemon Red and Emerald across frontier models, Continual Harness starting from scratch substantially reduces button-press cost relative to the minimalist baseline and recovers a majority of the gap to a hand-engineered expert harness, with capability-dependent gains, despite starting from the same raw interface with no curated knowledge, no hand-crafted tools, and no domain scaffolding. We then close the loop with the model itself: an online process-reward co-learning loop, in which an open-source agent's rollouts through the refining harness are relabeled by a frontier teacher and used to update the model, drives sustained in-game milestone progress on Pokemon Red without resetting the environment between training iterations.", "github": "https://github.com/sethkarten/pokeagent-speedrun" }, { "date": "2026-05-13", "arxiv_id": "2605.12426", "title": "Geometric Factual Recall in Transformers", "authors": [ "Shauli Ravfogel", "Gilad Yehudai", "Joan Bruna", "Alberto Bietti" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "How do transformer language models memorize factual associations? A common view casts internal weight matrices as associative memories over pairs of embeddings, requiring parameter counts that scale linearly with the number of facts. We develop a theoretical and empirical account of an alternative, geometric form of memorization in which learned embeddings encode relational structure directly, and the MLP plays a qualitatively different role. In a controlled setting where a single-layer transformer must memorize random bijections from subjects to a shared attribute set, we prove that a logarithmic embedding dimension suffices: subject embeddings encode linear superpositions of their associated attribute vectors, and a small MLP acts as a relation-conditioned selector that extracts the relevant attribute via ReLU gating, and not as an associative key-value mapping. We extend these results to the multi-hop setting -- chains of relational queries such as ``Who is the mother of the wife of x?'' -- providing constructions with and without chain-of-thought that exhibit a provable capacity-depth tradeoff, complemented by a matching information-theoretic lower bound. Empirically, gradient descent discovers solutions with precisely the predicted structure. Once trained, the MLP transfers zero-shot to entirely new bijections when subject embeddings are appropriately re-initialized, revealing that it has learned a generic selection mechanism rather than memorized any particular set of facts.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.11424", "title": "VidSplat: Gaussian Splatting Reconstruction with Geometry-Guided Video Diffusion Priors", "authors": [ "Jimin Tang", "Wenyuan Zhang", "Junsheng Zhou", "Zian Huang", "Kanle Shi", "Shenkun Xu", "Yu-Shen Liu", "Zhizhong Han" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://tangjm24.github.io/VidSplat/", "abstract": "Gaussian Splatting has achieved remarkable progress in multi-view surface reconstruction, yet it exhibits notable degradation when only few views are available. Although recent efforts alleviate this issue by enhancing multi-view consistency to produce plausible surfaces, they struggle to infer unseen, occluded, or weakly constrained regions beyond the input coverage. To address this limitation, we present VidSplat, a training-free generative reconstruction framework that leverages powerful video diffusion priors to iteratively synthesize novel views that compensate for missing input coverage, and thereby recover complete 3D scenes from sparse inputs. Specifically, we tackle two key challenges that enable the effective integration of generation and reconstruction. First, for 3D consistent generation, we elaborate a training-free, stage-wise denoising strategy that adaptively guides the denoising direction toward the underlying geometry using the rendered RGB and mask images. Second, to enhance the reconstruction, we develop an iterative mechanism that samples camera trajectories, explores unobserved regions, synthesizes novel views, and supplements training through confidence weighted refinement. VidSplat performs robustly to sparse input and even a single image. Extensive experiments on widely used benchmarks demonstrate our superior performance in sparse-view scene reconstruction.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.12481", "title": "ToolCUA: Towards Optimal GUI-Tool Path Orchestration for Computer Use Agents", "authors": [ "Xuhao Hu", "Xi Zhang", "Haiyang Xu", "Kyle Qiao", "Jingyi Yang", "Xuanjing Huang", "Jing Shao", "Ming Yan", "Jieping Ye" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/X-PLUG/ToolCUA", "github_api_added_by": "user", "project_page": "https://x-plug.github.io/ToolCUA/", "abstract": "Computer Use Agents (CUAs) can act through both atomic GUI actions, such as click and type, and high-level tool calls, such as API-based file operations, but this hybrid action space often leaves them uncertain about when to continue with GUI actions or switch to tools, leading to suboptimal execution paths. This difficulty stems from the scarcity of high-quality interleaved GUI-Tool trajectories, the cost and brittleness of collecting real tool trajectories, and the lack of trajectory-level supervision for GUI-Tool path selection. In this paper, we propose ToolCUA, an end-to-end agent designed to learn optimal GUI-Tool path selection through a staged training paradigm. We first introduce an Interleaved GUI-Tool Trajectory Scaling Pipeline that repurposes abundant static GUI trajectories and synthesizes a grounded tool library, enabling diverse GUI-Tool trajectories without manual engineering or real tool-trajectory collection. We then perform Tool-Bootstrapped GUI RFT, combining warmup SFT with single-turn RL to improve decisions at critical GUI-Tool switching points. Finally, we optimize ToolCUA with Online Agentic RL in a high-fidelity GUI-Tool environment, guided by a Tool-Efficient Path Reward that encourages appropriate tool use and shorter execution paths. Experiments on OSWorld-MCP show that ToolCUA achieves 46.85% accuracy, a relative improvement of approximately 66% over the baseline, establishing a new state of the art among models of comparable scale. It also improves by 3.9% over GUI-only settings, demonstrating effective GUI-Tool orchestration. The results further suggest that training in a hybrid action space is a promising paradigm for real-world digital agents. Open-sourced here: https://x-plug.github.io/ToolCUA/", "github": "https://github.com/X-PLUG/ToolCUA" }, { "date": "2026-05-13", "arxiv_id": "2605.12477", "title": "MEME: Multi-entity & Evolving Memory Evaluation", "authors": [ "Seokwon Jung", "Alexander Rubinstein", "Arnas Uselis", "Sangdoo Yun", "Seong Joon Oh" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SeokwonJung-Jay/MEME-public", "github_api_added_by": "user", "project_page": "https://seokwonjung-jay.github.io/meme-eval/", "abstract": "LLM-based agents increasingly operate in persistent environments where they must store, update, and reason over information across many sessions. While prior benchmarks evaluate only single-entity updates, MEME defines six tasks spanning the full space defined by the multi-entity and evolving axes, including three not scored by prior work: Cascade and Absence (dependency reasoning) and Deletion (post-removal state). Evaluating six memory systems spanning three memory paradigms on 100 controlled episodes, we find that all systems collapse on dependency reasoning under the default configuration (Cascade: 3%, Absence: 1% in average accuracy) despite adequate static retrieval performance. Prompt optimization, deeper retrieval, reduced filler noise, and most stronger LLMs fail to close this gap. Only a file-based agent paired with Claude Opus 4.7 as its internal LLM partially closes the gap, but at ~70x the baseline cost, indicating closure currently depends on configurations that are not practical at scale. Code and data are available on the project page: https://seokwonjung-jay.github.io/meme-eval/.", "github": "https://github.com/SeokwonJung-Jay/MEME-public" }, { "date": "2026-05-13", "arxiv_id": "2605.09266", "title": "SeePhys Pro: Diagnosing Modality Transfer and Blind-Training Effects in Multimodal RLVR for Physics Reasoning", "authors": [ "Kun Xiang", "Terry Jingchen Zhang", "Zirong Liu", "Bokai Zhou", "Yueling Tang", "Junjie Yu", "Jiacong Lu", "Shangrui Huang", "Heng Li", "Likui Zhang", "Kunkun Liu", "Changzheng Zhang", "Yangle Fang", "Boqiang Guo", "Hui-Ling Zhen", "Dandan Tu", "Yinya Huang", "Xiaodan Liang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AI4Phys/SeePhy-Pro", "github_api_added_by": "user", "project_page": "https://seephyspro.github.io/", "abstract": "We introduce SeePhys Pro, a fine-grained modality transfer benchmark that studies whether models preserve the same reasoning capability when critical information is progressively transferred from text to image. Unlike standard vision-essential benchmarks that evaluate a single input form, SeePhys Pro features four semantically aligned variants for each problem with progressively increasing visual elements. Our evaluation shows that current frontier models are far from representation-invariant reasoners: performance degrades on average as information moves from language to diagrams, with visual variable grounding as the most critical bottleneck. Motivated by this inference-time fragility, we further develop large training corpora for multimodal RLVR and use blind training as a diagnostic control, finding that RL with all training images masked can still improve performance on unmasked validation sets. To analyze this effect, text-deletion, image-mask-rate, and format-saturation controls suggest that such gains can arise from residual textual and distributional cues rather than valid visual evidence. Our results highlight the need to evaluate multimodal reasoning not only by final-answer accuracy, but also by robustness under modality transfer and by diagnostics that test whether improvements rely on task-critical visual evidence.", "github": "https://github.com/AI4Phys/SeePhy-Pro" }, { "date": "2026-05-13", "arxiv_id": "2605.12495", "title": "AlphaGRPO: Unlocking Self-Reflective Multimodal Generation in UMMs via Decompositional Verifiable Reward", "authors": [ "Runhui Huang", "Jie Wu", "Rui Yang", "Zhe Liu", "Hengshuang Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/huangrh99/AlphaGRPO", "github_api_added_by": "user", "project_page": "https://huangrh99.github.io/AlphaGRPO/", "abstract": "In this paper, we propose AlphaGRPO, a novel framework that applies Group Relative Policy Optimization (GRPO) to AR-Diffusion Unified Multimodal Models (UMMs) to enhance multimodal generation capabilities without an additional cold-start stage. Our approach unlocks the model's intrinsic potential to perform advanced reasoning tasks: Reasoning Text-to-Image Generation, where the model actively infers implicit user intents, and Self-Reflective Refinement, where it autonomously diagnoses and corrects misalignments in generated outputs. To address the challenge of providing stable supervision for real-world multimodal generation, we introduce the Decompositional Verifiable Reward (DVReward). Unlike holistic scalar rewards, DVReward utilizes an LLM to decompose complex user requests into atomic, verifiable semantic and quality questions, which are then evaluated by a general MLLM to provide reliable and interpretable feedback. Extensive experiments demonstrate that AlphaGRPO yields robust improvements across multimodal generation benchmarks, including GenEval, TIIF-Bench, DPG-Bench and WISE, while also achieving significant gains in editing tasks on GEdit without training on editing tasks. These results validate that our self-reflective reinforcement approach effectively leverages inherent understanding to guide high-fidelity generation. Project page: https://huangrh99.github.io/AlphaGRPO/", "github": "https://github.com/huangrh99/AlphaGRPO" }, { "date": "2026-05-13", "arxiv_id": "2605.10977", "title": "PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks", "authors": [ "Zhenxin Ai", "Haiyun He" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ai-kunkun/PASA", "github_api_added_by": "user", "project_page": "https://ai-kunkun.github.io/PASA_page/", "abstract": "Watermarking for large language models (LLMs) is a promising approach for detecting LLM-generated text and enabling responsible deployment. However, existing watermarking methods are often vulnerable to semantic-invariant attacks, such as paraphrasing. We propose PASA, a principled, robust, and distortion-free watermarking algorithm that embeds and detects a watermark at the semantic level. PASA operates on semantic clusters in a latent embedding space and constructs a distributional dependency between token and auxiliary sequences via shared randomness synchronized by a secret key and semantic history. This design is grounded in our theoretical framework that characterizes a jointly optimal embedding-detection pair, achieving the fundamental trade-offs among detection accuracy, robustness, and distortion. Evaluations across multiple LLMs and semantic-invariant attacks demonstrate that PASA remains robust even under strong paraphrasing attacks while preserving high text quality, outperforming standard vocabulary-space baselines. Ablation studies further validate the effectiveness of our hyperparameter choices. Webpage: https://ai-kunkun.github.io/PASA_page/.", "github": "https://github.com/ai-kunkun/PASA" }, { "date": "2026-05-13", "arxiv_id": "2605.08734", "title": "AdaPreLoRA: Adafactor Preconditioned Low-Rank Adaptation", "authors": [ "Ziyun Liu", "Fengmiao Bian", "Jian-Feng Cai" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Low-Rank Adaptation (LoRA) reparameterizes a weight update as a product of two low-rank factors, but the Jacobian J_{G} of the generator mapping the factors to the weight matrix is rank-deficient, so the factor-space preconditioner J_{G}^* {F}_t J_{G} induced by any {W}-space preconditioner {F}_t is singular, and consequently the standard chain rule cannot be uniquely inverted to map a preconditioned {W}-space direction back to a factor-space update. We cast existing LoRA optimizers in a unified framework parameterized by two choices: (i) which invertible surrogate for J_{G}^* {F}_t J_{G} to use, and (ii) which {F}_t on {W} to use. Existing methods occupy four families along these axes: factor-space adaptive updates, block-diagonal surrogates for J_{G}^* J_{G}, Frobenius-residual pseudoinverse methods, and Riemannian manifold constraint. Within this design space, a gradient-statistics-aware {F}_t paired with a closed-form factor-space solve at {O}((m+n)r) memory remains underexplored. We propose AdaPreLoRA, which fills this gap by adopting the Adafactor diagonal Kronecker preconditioner {H}_t on {W} and selecting from the resulting factor-space solution family the element minimizing an {H}_t-weighted imbalance between the two factor contributions; by construction, the resulting factor update is the closest LoRA approximation to the preconditioned {W}-space direction under the {H}_t-weighted norm. Across GPT-2 (E2E), Mistral-7B and Qwen2-7B (GLUE, ARC, GSM8K), and diffusion-model personalization, AdaPreLoRA is competitive with or improves over a representative set of LoRA optimizers while keeping peak GPU memory at the LoRA optimizer level.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.12013", "title": "L2P: Unlocking Latent Potential for Pixel Generation", "authors": [ "Zhennan Chen", "Junwei Zhu", "Xu Chen", "Jiangning Zhang", "Jiawei Chen", "Zhuoqi Zeng", "Wei Zhang", "Chengjie Wang", "Jian Yang", "Ying Tai" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NJU-PCALab/L2P", "github_api_added_by": "user", "project_page": "https://nju-pcalab.github.io/projects/L2P/", "abstract": "Pixel diffusion models have recently regained attention for visual generation. However, training advanced pixel-space models from scratch demands prohibitive computational and data resources. To address this, we propose the Latent-to-Pixel (L2P) transfer paradigm, an efficient framework that directly harnesses the rich knowledge of pre-trained LDMs to build powerful pixel-space models. Specifically, L2P discards the VAE in favor of large-patch tokenization and freezes the source LDM's intermediate layers, exclusively training shallow layers to learn the latent-to-pixel transformation. By utilizing LDM-generated synthetic images as the sole training corpus, L2P fits an already smooth data manifold, enabling rapid convergence with zero real-data collection. This strategy allows L2P to seamlessly migrate massive latent priors to the pixel space using only 8 GPUs. Furthermore, eliminating the VAE memory bottleneck unlocks native 4K ultra-high resolution generation. Extensive experiments across mainstream LDM architectures show that L2P incurs negligible training overhead, yet performs on par with the source LDM on DPG-Bench and reaches 93% performance on GenEval.", "github": "https://github.com/NJU-PCALab/L2P" }, { "date": "2026-05-13", "arxiv_id": "2605.09932", "title": "FocuSFT: Bilevel Optimization for Dilution-Aware Long-Context Fine-Tuning", "authors": [ "Zehua Pei", "Hui-Ling Zhen", "Xianzhi Yu", "Sinno Jialin Pan", "Mingxuan Yuan", "Bei Yu" ], "github_manual": "", "github_regex": "https://github.com/JarvisPei/FocuSFT", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language models can now process increasingly long inputs, yet their ability to effectively use information spread across long contexts remains limited. We trace this gap to how attention budget is spent during supervised fine-tuning (SFT) on long sequences: positional biases and attention sinks cause the model to allocate most of its attention to positionally privileged tokens rather than semantically relevant content. This training-time attention dilution (the starvation of content tokens in the attention distribution) weakens the gradient signal, limiting the model's ability to learn robust long-context capabilities. We introduce FocuSFT, a bilevel optimization framework that addresses this problem at training time. An inner loop adapts lightweight fast-weight parameters on the training context to form a parametric memory that concentrates attention on relevant content, and the outer loop performs SFT conditioned on this sharpened representation. Both loops apply bidirectional attention over context tokens while preserving causal masking for responses, reducing the causal asymmetry that gives rise to attention sinks and aligning inner-outer behavior. On BABILong, FocuSFT improves accuracy by up to +14pp across 4K--32K context lengths; on RULER, it raises CWE aggregation from 72.9\\% to 81.1\\% at 16K; and on GPQA with agentic tool use, it yields a 24\\% relative gain in pass@1. Attention analysis shows that FocuSFT reduces attention sink mass by 529times and triples context engagement during training. Code: https://github.com/JarvisPei/FocuSFT", "github": "https://github.com/JarvisPei/FocuSFT" }, { "date": "2026-05-13", "arxiv_id": "2605.11354", "title": "Lite3R: A Model-Agnostic Framework for Efficient Feed-Forward 3D Reconstruction", "authors": [ "Haoyu Zhang", "Zeyu Zhang", "Zedong Zhou", "Yang Zhao", "Hao Tang" ], "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/Lite3R", "github_paper_md": "", "github_api": "https://github.com/AIGeeksGroup/Lite3R", "github_api_added_by": "user", "project_page": "https://aigeeksgroup.github.io/Lite3R", "abstract": "Transformer-based 3D reconstruction has emerged as a powerful paradigm for recovering geometry and appearance from multi-view observations, offering strong performance across challenging visual conditions. As these models scale to larger backbones and higher-resolution inputs, improving their efficiency becomes increasingly important for practical deployment. However, modern 3D transformer pipelines face two coupled challenges: dense multi-view attention creates substantial token-mixing overhead, and low-precision execution can destabilize geometry-sensitive representations and degrade depth, pose, and 3D consistency. To address the first challenge, we propose Lite3R, a model-agnostic teacher-student framework that replaces dense attention with Sparse Linear Attention to preserve important geometric interactions while reducing attention cost. To address the second challenge, we introduce a parameter-efficient FP8-aware quantization-aware training (FP8-aware QAT) strategy with partial attention distillation, which freezes the vast majority of pretrained backbone parameters and trains only lightweight linear-branch projection layers, enabling stable low-precision deployment while retaining pretrained geometric priors. We further evaluate Lite3R on two representative backbones, VGGT and DA3-Large, over BlendedMVS and DTU64, showing that it substantially reduces latency (1.7-2.0x) and memory usage (1.9-2.4x) while preserving competitive reconstruction quality overall. These results demonstrate that Lite3R provides an effective algorithm-system co-design approach for practical transformer-based 3D reconstruction. Code: https://github.com/AIGeeksGroup/Lite3R. Website: https://aigeeksgroup.github.io/Lite3R.", "github": "https://github.com/AIGeeksGroup/Lite3R" }, { "date": "2026-05-13", "arxiv_id": "2605.12090", "title": "World Action Models: The Next Frontier in Embodied AI", "authors": [ "Siyin Wang", "Junhao Shi", "Zhaoyang Fu", "Xinzhe He", "Feihong Liu", "Chenchen Yang", "Yikang Zhou", "Zhaoye Fei", "Jingjing Gong", "Jinlan Fu", "Mike Zheng Shou", "Xuanjing Huang", "Xipeng Qiu", "Yu-Gang Jiang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenMOSS/Awesome-WAM", "github_api_added_by": "user", "project_page": "https://openmoss.github.io/Awesome-WAM/", "abstract": "Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning, yet they learn reactive observation-to-action mappings without explicitly modeling how the physical world evolves under intervention. A growing body of work addresses this limitation by integrating world models, predictive models of environment dynamics, into the action generation pipeline. We term this emerging paradigm World Action Models (WAMs): embodied foundation models that unify predictive state modeling with action generation, targeting a joint distribution over future states and actions rather than actions alone. However, the literature remains fragmented across architectures, learning objectives, and application scenarios, lacking a unified conceptual framework. We formally define WAMs and disambiguate them from related concepts, and trace the foundations and early integration of VLA and world model research that gave rise to this paradigm. We organize existing methods into a structured taxonomy of Cascaded and Joint WAMs, with further subdivision by generation modality, conditioning mechanism, and action decoding strategy. We systematically analyze the data ecosystem fueling WAMs development, spanning robot teleoperation, portable human demonstrations, simulation, and internet-scale egocentric video, and synthesize emerging evaluation protocols organized around visual fidelity, physical commonsense, and action plausibility. Overall, this survey provides the first systematic account of the WAMs landscape, clarifies key architectural paradigms and their trade-offs, and identifies open challenges and future opportunities for this rapidly evolving field.", "github": "https://github.com/OpenMOSS/Awesome-WAM" }, { "date": "2026-05-13", "arxiv_id": "2605.10832", "title": "Towards On-Policy Data Evolution for Visual-Native Multimodal Deep Search Agents", "authors": [ "Shijue Huang", "Hangyu Guo", "Chenxin Li", "Junting Lu", "Xinyu Geng", "Zhaochen Su", "Zhenyu Li", "Shuang Chen", "Hongru Wang", "Yi R. Fung" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JoeYing1019/ODE", "github_api_added_by": "user", "project_page": "https://on-policy-data-evolution.github.io/", "abstract": "Multimodal deep search requires an agent to solve open-world problems by chaining search, tool use, and visual reasoning over evolving textual and visual context. Two bottlenecks limit current systems. First, existing tool-use harnesses treat images returned by search, browsing, or transformation as transient outputs, so intermediate visual evidence cannot be re-consumed by later tools. Second, training data is usually built by fixed curation recipes that cannot track the target agent's evolving capability. To address these challenges, we first introduce a visual-native agent harness centered on an image bank reference protocol, which registers every tool-returned image as an addressable reference and makes intermediate visual evidence reusable by later tools. On top of this harness, On-policy Data Evolution (ODE) runs a closed-loop data generator that refines itself across rounds from rollouts of the policy being trained. This per-round refinement makes each round's data target what the current policy still needs to learn. The same framework supports both diverse supervised fine-tuning data and policy-aware reinforcement learning data curation, covering the full training lifecycle of the target agent. Across 8 multimodal deep search benchmarks, ODE improves the Qwen3-VL-8B agent from 24.9% to 39.0% on average, surpassing Gemini-2.5 Pro in standard agent-workflow setting (37.9%). At 30B, ODE raises the average score from 30.6% to 41.5%. Further analyses validate the effectiveness of image-bank reuse, especially on complex tasks requiring iterative visual refinement, while rollout-feedback evolution yields more grounded SFT traces and better policy-matched RL tasks than static synthesis.", "github": "https://github.com/JoeYing1019/ODE" }, { "date": "2026-05-13", "arxiv_id": "2605.11882", "title": "On-Policy Self-Evolution via Failure Trajectories for Agentic Safety Alignment", "authors": [ "Bo Yin", "Qi Li", "Xinchao Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YinBo0927/FATE", "github_api_added_by": "user", "project_page": "https://yinbo0927.github.io/FATE/", "abstract": "Tool-using LLM agents fail through trajectories rather than only final responses, as they may execute unsafe tool calls, follow injected instructions, comply with harmful requests, or over-refuse benign tasks despite producing a seemingly safe answer. Existing safety-alignment signals are largely response-level or off-policy, and often incur a safety-utility trade-off: improving agent safety comes at the cost of degraded task performance. Such sparse and single-objective rewards severely limit real-world usability. To bridge this gap, we propose FATE, an on-policy self-evolving framework that transforms verifier-scored failures into repair supervision without expert demonstrations. For each failure, the same policy proposes repair candidates, which are then re-scored by verifiers and filtered across security, utility, over-refusal control, and trajectory validity. This dense trajectory-level information is then used as a supervision signal for agent self-evolution. During this process, we further introduce Pareto-Front Policy Optimization (PFPO), combining supervised warmup with Pareto-aware policy optimization to preserve safety-utility trade-offs. Experiments on AgentDojo, AgentHarm, and ATBench show that FATE improves safety across different models and scales while preserving useful behavior. Compared with strong baselines, FATE reduces attack success rate by 33.5%, harmful compliance by 82.6%, and improves external trajectory-safety diagnosis by 6.5%. These results suggest that failed trajectories can provide structured repair supervision for safer self-evolving agents.", "github": "https://github.com/YinBo0927/FATE" }, { "date": "2026-05-13", "arxiv_id": "2605.12070", "title": "Missing Old Logits in Asynchronous Agentic RL: Semantic Mismatch and Repair Methods for Off-Policy Correction", "authors": [ "Zhong Guan", "Yongjian Guo", "Haoran Sun", "Wen Huang", "Shuai Di", "Xiong Jun Wu", "Likang Wu", "Hongke Zhao" ], "github_manual": "", "github_regex": "https://github.com/millioniron/ROLL", "github_paper_md": "", "github_api": "https://github.com/millioniron/ROLL", "github_api_added_by": "user", "project_page": "", "abstract": "Asynchronous reinforcement learning improves rollout throughput for large language model agents by decoupling sample generation from policy optimization, but it also introduces a critical failure mode for PPO-style off-policy correction. In heterogeneous training systems, the total importance ratio should ideally be decomposed into two semantically distinct factors: a training--inference discrepancy term that aligns inference-side and training-side distributions at the same behavior-policy version, and a policy-staleness term that constrains the update from the historical policy to the current policy. We show that practical asynchronous pipelines with delayed updates and partial rollouts often lose the required historical training-side logits, or old logits. This missing-old-logit problem entangles discrepancy repair with staleness correction, breaks the intended semantics of decoupled correction, and makes clipping and masking thresholds interact undesirably. To address this issue, we study both exact and approximate correction routes. We propose three exact old-logit acquisition strategies: snapshot-based version tracking, a dedicated old-logit model, and synchronization via partial rollout interruption, and compare their system trade-offs. From the perspective of approximate correction, we focus on preserving the benefits of decoupled correction through a more appropriate approximate policy when exact old logits cannot be recovered at low cost, without incurring extra system overhead. Following this analysis, we adopt a revised PPO-EWMA method, which achieves significant gains in both training speed and optimization performance. Code at https://github.com/millioniron/ROLL.", "github": "https://github.com/millioniron/ROLL" }, { "date": "2026-05-13", "arxiv_id": "2605.10108", "title": "GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction", "authors": [ "Ihor Stepanov", "Oleksandr Lukashov", "Mykhailo Shtopko", "Vivek Kalyanarangan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Joint named entity recognition (NER) and relation extraction (RE) is a fundamental task in natural language processing for constructing knowledge graphs from unstructured text. While recent approaches treat NER and RE as separate tasks requiring distinct models, we introduce GLiNER-Relex, a unified architecture that extends the GLiNER framework to perform both entity recognition and relation extraction in a single model. Our approach leverages a shared bidirectional transformer encoder to jointly represent text, entity type labels, and relation type labels, enabling zero-shot extraction of arbitrary entity and relation types specified at inference time. GLiNER-Relex constructs entity pair representations from recognized spans and scores them against relation type embeddings using a dedicated relation scoring module. We evaluate our model on four standard relation extraction benchmarks: CoNLL04, DocRED, FewRel, and CrossRE, and demonstrate competitive performance against both specialized relation extraction models and large language models, while maintaining the computational efficiency characteristic of the GLiNER family. The model is released as an open-source Python package with a simple inference API that allows users to specify arbitrary entity and relation type labels at inference time and obtain both entities and relation triplets in a single call. All models and code are publicly available.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.05630", "title": "One Turn Too Late: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue", "authors": [ "Xinjie Shen", "Rongzhe Wei", "Peizhi Niu", "Haoyu Wang", "Ruihan Wu", "Eli Chien", "Bo Li", "Pin-Yu Chen", "Pan Li" ], "github_manual": "", "github_regex": "https://github.com/Graph-COM/TurnGate", "github_paper_md": "", "github_api": "https://github.com/Graph-COM/TurnGate", "github_api_added_by": "user", "project_page": "https://turn-gate.github.io/", "abstract": "Hidden malicious intent in multi-turn dialogue poses a growing threat to deployed large language models (LLMs). Rather than exposing a harmful objective in a single prompt, increasingly capable attackers can distribute their intent across multiple benign-looking turns. Recent studies show that even modern commercial models with advanced guardrails remain vulnerable to such attacks despite advances in safety alignment and external guardrails. In this work, we address this challenge by detecting the earliest turn at which delivering the candidate response would make the accumulated interaction sufficient to enable harmful action. This objective requires precise turn-level intervention that identifies the harm-enabling closure point while avoiding premature refusal of benign exploratory conversations. To further support training and evaluation, we construct the Multi-Turn Intent Dataset (MTID), which contains branching attack rollouts, matched benign hard negatives, and annotations of the earliest harm-enabling turns. We show that MTID helps enable a turn-level monitor TurnGate, which substantially outperforms existing baselines in harmful-intent detection while maintaining low over-refusal rates. TurnGate further generalizes across domains, attacker pipelines, and target models. Our code is available at https://github.com/Graph-COM/TurnGate.", "github": "https://github.com/Graph-COM/TurnGate" }, { "date": "2026-05-13", "arxiv_id": "2605.11711", "title": "Debiased Model-based Representations for Sample-efficient Continuous Control", "authors": [ "Jiafei Lyu", "Zichuan Lin", "Scott Fujimoto", "Kai Yang", "Yangkun Chen", "Saiyong Yang", "Zongqing Lu", "Deheng Ye" ], "github_manual": "", "github_regex": "https://github.com/dmksjfl/DR.Q", "github_paper_md": "", "github_api": "https://github.com/dmksjfl/DR.Q", "github_api_added_by": "user", "project_page": "", "abstract": "Model-based representations recently stand out as a promising framework that embeds latent dynamics information into the representations for downstream off-policy actor-critic learning. It implicitly combines the advantages of both model-free and model-based approaches while avoiding the training costs associated with model-based methods. Nevertheless, existing model-based representation methods can fail to capture sufficient information about relevant variables and can overfit to early experiences in the replay buffer. These incur biases in representation and actor-critic learning, leading to inferior performance. To address this, we propose Debiased model-based Representations for Q-learning, tagged DR.Q algorithm. DR.Q explicitly maximizes the mutual information between the representations of the current state-action pair and the next state besides minimizing their deviations, and samples transitions with faded prioritized experience replay. We evaluate DR.Q on numerous continuous control benchmarks with a single set of hyperparameters, and the results demonstrate that DR.Q can match or surpass recent strong baselines, sometimes outperforming them by a large margin. Our code is available at https://github.com/dmksjfl/DR.Q.", "github": "https://github.com/dmksjfl/DR.Q" }, { "date": "2026-05-13", "arxiv_id": "2605.11696", "title": "WildRelight: A Real-World Benchmark and Physics-Guided Adaptation for Single-Image Relighting", "authors": [ "Lezhong Wang", "Mehmet Onurcan Kaya", "Siavash Bigdeli", "Jeppe Revall Frisvad" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://lez-s.github.io/wildrelight_proj/", "abstract": "Recent single-image relighting methods, powered by advanced generative models, have achieved impressive photorealism on synthetic benchmarks. However, their effectiveness in the complex visual landscape of the real world remains largely unverified. A critical gap exists, as current datasets are typically designed for multi-view reconstruction and fail to address the unique challenges of single-image relighting. To bridge this synthetic-to-real gap, we introduce WildRelight, the first in-the-wild dataset specifically created for evaluating single-image relighting models. WildRelight features a diverse collection of high-resolution outdoor scenes, captured under strictly aligned, temporally varying natural illuminations, each paired with a high-dynamic-range environment map. Using this data, we establish a rigorous benchmark revealing that state-of-the-art models trained on synthetic data suffer from severe domain shifts. The strictly aligned temporal structure of WildRelight enables a new paradigm for domain adaptation. We demonstrate this by introducing a physics-guided inference framework that leverages the captured natural light evolution as a self-supervised constraint. By integrating Diffusion Posterior Sampling (DPS) with temporal Sampling-Aware Test-Time Adaptation (TTA), we show that the dataset allows synthetic models to align with real-world statistics on-the-fly, transforming the intractable sim-to-real challenge into a tractable self-supervised task. The dataset and code will be made publicly available to foster robust, physically-grounded relighting research.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.12438", "title": "A Causal Language Modeling Detour Improves Encoder Continued Pretraining", "authors": [ "Rian Touchent", "Eric de la Clergerie" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "When adapting an encoder to a new domain, the standard approach is to continue training with Masked Language Modeling (MLM). We show that temporarily switching to Causal Language Modeling (CLM) followed by a short MLM decay improves downstream performance. On biomedical texts with ModernBERT, this CLM detour outperforms MLM baselines trained on identical data and compute across 8 French and 11 English biomedical tasks, by +1.2-2.8pp and +0.3-0.8pp respectively, depending on model size. We investigate the reasons for these gains. We find that CLM's dense supervision impacts low transformer layers (0-7) far more than MLM does. Freezing low layers during CLM eliminates the downstream benefit; freezing mid layers preserves it. The representational changes persist through the MLM decay phase, even when it matches the CLM phase in length, and they scale with model capacity. We release ModernCamemBERT-bio and ModernBERT-bio as state-of-the-art biomedical encoders in Base and Large sizes.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.08646", "title": "PAAC: Privacy-Aware Agentic Device-Cloud Collaboration", "authors": [ "Liangqi Yuan", "Wenzhi Fang", "Shiqiang Wang", "Christopher G. Brinton" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language model (LLM) agents face a structural tension: cloud agents provide strong reasoning but expose user data, while on-device agents preserve privacy at the cost of overall capability. Existing device-cloud designs treat this boundary as a compute split rather than a trust boundary suited to agentic workloads, and existing sanitizers force a choice between policy flexibility and the structural fidelity tool calls require. In this work, we develop PAAC, a privacy-aware agentic framework that aligns planner--executor decomposition with the device-cloud boundary so that role specialization itself becomes the privacy mechanism. The cloud agent reasons over typed placeholder tokens that preserve each sensitive value's reasoning role while discarding its content, while the on-device agent identifies sensitive spans and distills each step's execution outcome into compact key findings. Sanitization confines the on-device LLM to proposing which spans to mask, while a deterministic registry performs all substitution and reversal, keeping actions directly executable on device. On three agentic benchmarks under strict privacy settings, PAAC dominates the Pareto frontier of privacy and accuracy, improving average accuracy by 15-36\\% and reducing average leakage by 2-6times over state-of-the-art device-cloud baselines, with the largest margins on privacy targets outside fixed entity taxonomies. We find consistent improvements on 17 additional benchmarks spanning 10 domains, including math, science, and finance.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.08626", "title": "Large Language Models over Networks: Collaborative Intelligence under Resource Constraints", "authors": [ "Liangqi Yuan", "Wenzhi Fang", "Shiqiang Wang", "H. Vincent Poor", "Christopher G. Brinton" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM services alone cannot serve a growing class of applications, including those operating under intermittent connectivity, sub-second latency budgets, data-residency constraints, or sustained high-volume inference. On-device deployment is in turn constrained by limited computation and memory. No single endpoint can deliver high-quality service across this spectrum. This article focuses on collaborative intelligence, a paradigm in which multiple independent LLMs distributed across device and cloud endpoints collaborate at the task level through natural language or structured messages. Such collaboration strives for superior response quality under heterogeneous resource constraints spanning computation, memory, communication, and cost across network tiers. We present collaborative inference along two complementary and composable dimensions: vertical device-cloud collaboration and horizontal multi-agent collaboration, which can be combined into hybrid topologies in practice. We then examine learning to collaborate, addressing the training of routing policies and the development of cooperative capabilities among LLMs. Finally, we identify open research challenges including scaling under resource heterogeneity and trustworthy collaborative intelligence.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.07545", "title": "Implicit Preference Alignment for Human Image Animation", "authors": [ "Yuanzhi Wang", "Xuhua Ren", "Jiaxiang Cheng", "Bing Ma", "Kai Yu", "Tianxiang Zheng", "Qinglin Lu", "Zhen Cui" ], "github_manual": "", "github_regex": "https://github.com/mdswyz/IPA", "github_paper_md": "", "github_api": "https://github.com/mdswyz/IPA", "github_api_added_by": "user", "project_page": "https://github.com/mdswyz/IPA", "abstract": "Human image animation has witnessed significant advancements, yet generating high-fidelity hand motions remains a persistent challenge due to their high degrees of freedom and motion complexity. While reinforcement learning from human feedback, particularly direct preference optimization, offers a potential solution, it necessitates the construction of strict preference pairs. However, curating such pairs for dynamic hand regions is prohibitively expensive and often impractical due to frame-wise inconsistencies. In this paper, we propose Implicit Preference Alignment (IPA), a data-efficient post-training framework that eliminates the need for paired preference data. Theoretically grounded in implicit reward maximization, IPA aligns the model by maximizing the likelihood of self-generated high-quality samples while penalizing deviations from the pretrained prior. Furthermore, we introduce a Hand-Aware Local Optimization mechanism to explicitly steer the alignment process toward hand regions. Experiments demonstrate that our method achieves effective preference optimization to enhance hand generation quality, while significantly lowering the barrier for constructing preference data. Codes are released at https://github.com/mdswyz/IPA", "github": "https://github.com/mdswyz/IPA" }, { "date": "2026-05-13", "arxiv_id": "2605.04702", "title": "FaithfulFaces: Pose-Faithful Facial Identity Preservation for Text-to-Video Generation", "authors": [ "Yuanzhi Wang", "Xuhua Ren", "Jiaxiang Cheng", "Bing Ma", "Kai Yu", "Sen Liang", "Wenyue Li", "Tianxiang Zheng", "Qinglin Lu", "Zhen Cui" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Identity-preserving text-to-video generation (IPT2V) empowers users to produce diverse and imaginative videos with consistent human facial identity. Despite recent progress, existing methods often suffer from significant identity distortion under large facial pose variations or facial occlusions. In this paper, we propose FaithfulFaces, a pose-faithful facial identity preservation learning framework to improve IPT2V in complex dynamic scenes. The key of FaithfulFaces is a pose-shared identity aligner that refines and aligns facial poses across distinct views via a pose-shared dictionary and a pose variation-identity invariance constraint. By mapping single-view inputs into a global facial pose representation with explicit Euler angle embeddings, FaithfulFaces provides a pose-faithful facial prior that guides generative foundations toward robust identity-preserving generation. In particular, we develop a specialized pipeline to curate a high-quality video dataset featuring substantial facial pose diversity. Extensive experiments demonstrate that FaithfulFaces achieves state-of-the-art performance, maintaining superior identity consistency and structural clarity even as pose changes and occlusions occur.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.12460", "title": "Multi-Stream LLMs: Unblocking Language Models with Parallel Streams of Thoughts, Inputs and Outputs", "authors": [ "Guinan Su", "Yanwu Yang", "Xueyan Li", "Jonas Geiping" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/seal-rg/streaming", "github_api_added_by": "user", "project_page": "https://huggingface.co/JonasGeiping/stream-qwen3.5-27b", "abstract": "The continued improvements in language model capability have unlocked their widespread use as drivers of autonomous agents, for example in coding or computer use applications. However, the core of these systems has not changed much since early instruction-tuned models like ChatGPT. Even advanced AI agents function on message exchange formats, successively exchanging messages with users, systems, with itself (i.e. chain-of-thought) and tools in a single stream of computation. This bottleneck to a single stream in chat models leads to a number of limitations: the agent cannot act (generate output) while reading, and in reverse, cannot react to new information while writing. Similarly, the agent cannot act while thinking and cannot think while reading or acting on information. In this work, we show that models can be unblocked by switching from instruction-tuning for sequential message formats to instruction-tuning for multiple, parallel streams of computation, splitting each role into a separate stream. Every forward pass of the language model then simultaneously reads from multiple input streams and generates tokens in multiple output streams, all of which causally depend on earlier timesteps. We argue that this data-driven change remedies a number of usability limitations as outlined above, improves model efficiency through parallelization, improves model security through better separation of concerns and can further improve model monitorability.", "github": "https://github.com/seal-rg/streaming" }, { "date": "2026-05-13", "arxiv_id": "2605.12492", "title": "Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation", "authors": [ "Kexuan Shi", "Hanxuan Li", "Zeju Qiu", "Yandong Wen", "Simon Buchholz", "Weiyang Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Sphere-AI-Lab/pion", "github_api_added_by": "user", "project_page": "https://spherelab.ai/pion/", "abstract": "We introduce Pion, a spectrum-preserving optimizer for large language model (LLM) training based on orthogonal equivalence transformation. Unlike additive optimizers such as Adam and Muon, Pion updates each weight matrix through left and right orthogonal transformations, preserving its singular values throughout training. This yields an optimization mechanism that modulates the geometry of weight matrices while keeping their spectral norm fixed. We derive the Pion update rule, systematically examine its design choices, and analyze its convergence behavior along with several key properties. Empirical results show that Pion offers a stable and competitive alternative to standard optimizers for both LLM pretraining and finetuning.", "github": "https://github.com/Sphere-AI-Lab/pion" }, { "date": "2026-05-13", "arxiv_id": "2605.09539", "title": "TacoMAS: Test-Time Co-Evolution of Topology and Capability in LLM-based Multi-Agent Systems", "authors": [ "Chen Xu", "Yicheng Hu", "Ruizi Wang", "Xinyu Lin", "Wenjie Wang", "Dongrui Liu", "Fuli Feng" ], "github_manual": "", "github_regex": "https://github.com/chenxu2-gif/TacoMAS-MultiAgent", "github_paper_md": "", "github_api": "https://github.com/chenxu2-gif/TacoMAS-MultiAgent", "github_api_added_by": "user", "project_page": "", "abstract": "Multi-agent systems (MAS) have emerged as a promising paradigm for solving complex tasks. Recent work has explored self-evolving MAS that automatically optimize agent capabilities or communication topologies. However, existing methods either learn a topology that remains fixed at inference time or adapt only the topology or capability during inference. We empirically and theoretically show that effective test-time evolution requires jointly adapting both axes, but on different time scales: capabilities should update rapidly to handle emerging subtasks, while the topology should evolve more slowly to preserve coordination stability. We then introduce TacoMAS, a test-time co-evolution framework for dynamic MAS. TacoMAS formulates MAS inference as a task of online graph adaptation, where nodes represent agents with role-specific capabilities and edges define their communication topology. During inference, a fast capability loop updates agent expertise using trajectory-level feedback, while a slow meta-LLM-driven topology loop performs agents' birth-death operations on MAS, including edge edit, agent addition, and agent removal. We further show that this fast-slow design drives MAS evolution toward a task-conditioned stable equilibrium. Experiments on four benchmarks demonstrate that TacoMAS outperforms nearly 20 multi-agent baselines, achieving an average improvement of 13.3% over the strongest baseline. The codes are released at https://github.com/chenxu2-gif/TacoMAS-MultiAgent.", "github": "https://github.com/chenxu2-gif/TacoMAS-MultiAgent" }, { "date": "2026-05-13", "arxiv_id": "2605.08299", "title": "Do not copy and paste! Rewriting strategies for code retrieval", "authors": [ "Andrea Gurioli", "Federico Pennino", "Maurizio Gabbrielli" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Embedding-based code retrieval often suffers when encoders overfit to surface syntax. Prior work mitigates this by using LLMs to rephrase queries and corpora into a normalized style, but leaves two questions open: how much representational shift helps, and when is the per-query LLM call justified? We study a hierarchy of three rewriting strategies: stylistic rephrasing, NL-enriched PseudoCode, and full Natural-Language transcription, under joint query-corpus (QC, online) and corpus-only (C, offline) augmentation, across six CoIR benchmarks, five encoders, and three rewriters spanning independent model families (Qwen, DeepSeek, Mistral). We are the first to evaluate NL-enriched PseudoCode and snippet-level Natural Language as direct retrieval representations, rather than as transient intermediates. Full NL rewriting with QC yields the largest gains (+0.51 absolute NDCG@10 on CT-Contest for MoSE-18), while corpus-only rewriting degrades retrieval in 56 of 90 configurations, about 62%. We introduce two diagnostics, Delta H, token entropy, and Delta s, embedding cosine, and show that Delta H predicts retrieval gain under QC across all three rewriter families: pooled Spearman rho = +0.436, p < 0.001 on DeepSeek+Codestral; rho = +0.593 on Codestral alone; rho = +0.356 on Qwen. This establishes Delta H as a cheap, rewriter-agnostic proxy for deciding when rewriting pays off before running retrieval. Our analysis reframes LLM rewriting as a cost-benefit decision: it is most effective as a remediation layer for lightweight encoders on code-dominant queries, with diminishing returns for strong encoders or NL-heavy queries.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.10267", "title": "IndustryBench: Probing the Industrial Knowledge Boundaries of LLMs", "authors": [ "Songlin Bai", "Xintong Wang", "Linlin Yu", "Bin Chen", "Zhiang Xu", "Yuyang Sheng", "Changtong Zan", "Xiaofeng Zhu", "Yizhe Zhang", "Jiru Li", "Mingze Guo", "Ling Zou", "Yalong Li", "Chengfu Huo", "Liang Ding" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alibaba-multimodal-industrial-ai/IndustryBench", "github_api_added_by": "user", "project_page": "https://huggingface.co/datasets/alibaba-multimodal-industrial-ai/IndustryBench", "abstract": "In industrial procurement, an LLM answer is useful only if it survives a standards check: recommended material must match operating condition, every parameter must respect a regulated threshold, and no procedure may contradict a safety clause. Partial correctness can mask safety-critical contradictions that aggregate LLM benchmarks rarely capture. We introduce IndustryBench, a 2,049-item benchmark for industrial procurement QA in Chinese, grounded in Chinese national standards (GB/T) and structured industrial product records, organized by seven capability dimensions, ten industry categories, and panel-derived difficulty tiers, with item-aligned English, Russian, and Vietnamese renderings. Our construction pipeline rejects 70.3% of LLM-generated candidates at a search-based external-verification stage, calibrating how unreliable industrial QA remains after LLM-only filtering.Our evaluation decouples raw correctness, scored by a Qwen3-Max judge validated at \u03ba_w = 0.798 against a domain expert, from a separate safety-violation (SV) check against source texts. Across 17 models in Chinese and an 8-model intersection over four languages, we find: (i) the best system reaches only 2.083 on the 0--3 rubric, leaving substantial headroom; (ii) Standards & Terminology is the most persistent capability weakness and survives item-aligned translation; (iii) extended reasoning lowers safety-adjusted scores for 12 of 13 models, primarily by introducing unsupported safety-critical details into longer final answers; and (iv) safety-violation rates reshuffle the leaderboard -- GPT-5.4 climbs from rank 6 to rank 3 after SV adjustment, while Kimi-k2.5-1T-A32B drops seven positions.Industrial LLM evaluation therefore requires source-grounded, safety-aware diagnosis rather than aggregate accuracy. We release IndustryBench with all prompts, scoring scripts, and dataset documentation.", "github": "https://github.com/alibaba-multimodal-industrial-ai/IndustryBench" }, { "date": "2026-05-13", "arxiv_id": "2605.07579", "title": "Your Language Model is Its Own Critic: Reinforcement Learning with Value Estimation from Actor's Internal States", "authors": [ "Yunho Choi", "Jongwon Lim", "Woojin Ahn", "Minjae Oh", "Jeonghoon Shim", "Yohan Jo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/holi-lab/POISE", "github_api_added_by": "user", "project_page": "https://holi-lab.github.io/POISE/", "abstract": "Reinforcement learning with verifiable rewards (RLVR) for Large Reasoning Models hinges on baseline estimation for variance reduction, but existing approaches pay a heavy price: PPO requires a policy-model scale critic, while GRPO needs multiple rollouts per prompt to keep its empirical group mean stable. We introduce Policy Optimization with Internal State Value Estimation), which obtains a baseline at negligible cost by using the policy model's internal signals already computed during the policy forward pass. A lightweight probe predicts the expected verifiable reward from the hidden states of the prompt and generated trajectory, as well as token-entropy statistics, and is trained online alongside the policy. To preserve gradient unbiasedness despite using trajectory-conditioned features, we introduce a cross-rollout construction that predicts each rollout's value from an independent rollout's internal states. Because POISE estimates prompt value using only a single rollout, it enables higher prompt diversity for a fixed compute budget during training. This reduces gradient variance for more stable learning and also eliminates the compute overhead of sampling costs for detecting zero-advantage prompts. On Qwen3-4B and DeepSeek-R1-Distill-Qwen-1.5B across math reasoning benchmarks, POISE matches DAPO while requiring less compute. Moreover, its value estimator shows similar performance to a separate LLM-scale value model and generalizes to various verifiable tasks. By leveraging the model's own internal representations, POISE enables more stable and efficient policy optimization.", "github": "https://github.com/holi-lab/POISE" }, { "date": "2026-05-13", "arxiv_id": "2605.06658", "title": "Relit-LiVE: Relight Video by Jointly Learning Environment Video", "authors": [ "Weiqing Xiao", "Hong Li", "Xiuyu Yang", "Houyuan Chen", "Wenyi Li", "Tianqi Liu", "Shaocong Xu", "Chongjie Ye", "Hao Zhao", "Beibei Wang" ], "github_manual": "", "github_regex": "https://github.com/zhuxing0/Relit-LiVE", "github_paper_md": "", "github_api": "https://github.com/zhuxing0/Relit-LiVE", "github_api_added_by": "user", "project_page": "https://zhuxing0.github.io/projects/Relit-LiVE/", "abstract": "Recent advances have shown that large-scale video diffusion models can be repurposed as neural renderers by first decomposing videos into intrinsic scene representations and then performing forward rendering under novel illumination. While promising, this paradigm fundamentally relies on accurate intrinsic decomposition, which remains highly unreliable for real-world videos and often leads to distorted appearances, broken materials, and accumulated temporal artifacts during relighting. In this work, we present Relit-LiVE, a novel video relighting framework that produces physically consistent, temporally stable results without requiring prior knowledge of camera pose. Our key insight is to explicitly introduce raw reference images into the rendering process, enabling the model to recover critical scene cues that are inevitably lost or corrupted in intrinsic representations. Furthermore, we propose a novel environment video prediction formulation that simultaneously generates relit videos and per-frame environment maps aligned with each camera viewpoint in a single diffusion process. This joint prediction enforces strong geometric-illumination alignment and naturally supports dynamic lighting and camera motion, significantly improving physical consistency in video relighting while easing the requirement of known per-frame camera pose. Extensive experiments demonstrate that Relit-LiVE consistently outperforms state-of-the-art video relighting and neural rendering methods across synthetic and real-world benchmarks. Beyond relighting, our framework naturally supports a wide range of downstream applications, including scene-level rendering, material editing, object insertion, and streaming video relighting. The Project is available at https://github.com/zhuxing0/Relit-LiVE.", "github": "https://github.com/zhuxing0/Relit-LiVE" }, { "date": "2026-05-13", "arxiv_id": "2605.07654", "title": "Reliable Chain-of-Thought via Prefix Consistency", "authors": [ "Naoto Iwase", "Yuki Ichihara", "Mohammad Atif Quamar", "Junpei Komiyama" ], "github_manual": "", "github_regex": "https://github.com/naoto-iwase/prefix-consistency", "github_paper_md": "", "github_api": "https://github.com/naoto-iwase/prefix-consistency", "github_api_added_by": "user", "project_page": "https://naoto-iwase.github.io/prefix-consistency-page", "abstract": "Large Language Models often improve accuracy on reasoning tasks by sampling multiple Chain-of-Thought (CoT) traces and aggregating them with majority voting (MV), a test-time technique called self-consistency. When we truncate a CoT partway through and regenerate the remainder, we observe that traces with correct answers reproduce their original answer more often than traces with wrong answers. We use this difference as a reliability signal, prefix consistency, that weights each candidate answer by how often it reappears under regeneration. It requires no access to token log-probabilities or self-rating prompts. Across five reasoning models and four math and science benchmarks, prefix consistency is the best correctness predictor in most settings, and reweighting votes by it reaches Standard MV plateau accuracy at up to 21x fewer tokens (median 4.6x). Our code is available at https://github.com/naoto-iwase/prefix-consistency.", "github": "https://github.com/naoto-iwase/prefix-consistency" }, { "date": "2026-05-13", "arxiv_id": "2605.07153", "title": "Beyond Reasoning: Reinforcement Learning Unlocks Parametric Knowledge in LLMs", "authors": [ "Wanli Yang", "Hongyu Zang", "Junwei Zhang", "Wenjie Shi", "Du Su", "Jingang Wang", "Xueqi Cheng", "Fei Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement learning (RL) has achieved remarkable success in LLM reasoning, but whether it can also improve direct recall of parametric knowledge remains an open question. We study this question in a controlled zero-shot, one-hop, closed-book QA setting with no chain-of-thought, training only on binary correctness rewards and applying fact-level train-test deduplication to ensure gains reflect improved recall rather than reasoning or memorization. Across three model families and multiple factual QA benchmarks, RL yields ~27% average relative gains, surpassing both training- and inference-time baselines alike. Mechanistically, RL primarily redistributes probability mass over existing knowledge rather than acquiring new facts, moving correct answers from the low-probability tail into reliable greedy generations. Our data-attribution study reveals that the hardest examples are the most informative: those whose answers never appear in 128 pre-RL samples (only ~18% of training data) drive ~83% of the gain, since rare correct rollouts still emerge during training and get reinforced. Together, these findings broaden the role of RL beyond reasoning, repositioning it as a tool for unlocking rather than acquiring latent parametric knowledge.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.11400", "title": "UniPath: Adaptive Coordination of Understanding and Generation for Unified Multimodal Reasoning", "authors": [ "Hayes Bai", "Yinyi Luo", "Wenwen Wang", "Qingsong Wen", "Jindong Wang" ], "github_manual": "", "github_regex": "https://github.com/AIFrontierLab/TorchUMM/tree/main/src", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://github.com/AIFrontierLab/TorchUMM", "abstract": "Unified multimodal models (UMMs) aim to integrate understanding and generation within a single architecture. However, it remains underexplored how to effectively coordinate these two capabilities for more effective and efficient reasoning. Existing coordination approaches either perform coupling during training, without explicit inference-time coordination, or impose a fixed coordination pattern for all inputs. In this work, we show that multimodal tasks exhibit substantial coordination-path diversity: different inputs favor different coordination paths. This suggests that exploiting such diversity is key to improving performance. We propose UniPath, a framework for adaptively modeling and exploiting coordination-path diversity. Instead of enforcing a single coordination pattern, we represent task solving as the selection and execution of a path, ranging from direct answering to textual inference, visual-thought construction, and hypothesis-based exploration. We construct role-aligned trajectories to train a path-conditioned executor and introduce a lightweight planner mechanism to enable input-dependent path selection. Experiments show that leveraging coordination-path diversity improves performance over fixed coordination strategies while providing interpretable intermediate behaviors. The code is available at:https://github.com/AIFrontierLab/TorchUMM/tree/main/src/umm/post_training/unipath.", "github": "https://github.com/AIFrontierLab/TorchUMM/tree/main/src" }, { "date": "2026-05-13", "arxiv_id": "2605.11436", "title": "Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty", "authors": [ "Joykirat Singh", "Zaid Khan", "Archiki Prasad", "Justin Chih-Yao Chen", "Akshay Nambi", "Hyunji Lee", "Elias Stengel-Eskin", "Mohit Bansal" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/joykirat18/Agent-BRACE", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models (LLMs) are increasingly deployed on long-horizon tasks in partially observable environments, where they must act while inferring and tracking a complex environment state over many steps. This leads to two challenges: partial observability requires maintaining uncertainty over unobserved world attributes, and long interaction history causes context to grow without bound, diluting task-relevant information. A principled solution to both challenges is a belief state: a posterior distribution over environment states given past observations and actions, which compactly encodes history for decision making regardless of episode length. In LLM agents, however, the open-ended nature of text makes it unclear how to represent such a distribution. Therefore, we introduce Agent-BRACE: Agent Belief state Representation via Abstraction and Confidence Estimation, a method that decouples an LLM agent into a belief state model and a policy model, jointly optimized via reinforcement learning. The belief state model produces a structured approximation of the belief distribution: a set of atomic natural language claims about the environment, each annotated with an ordinal verbalized certainty label ranging from certain to unknown. The policy model conditions on this compact, structured approximate belief rather than the full history, learning to select actions under explicit uncertainty. Across long-horizon, partially observable embodied language environments, Agent-BRACE achieves an average absolute improvement of +14.5% (Qwen2.5-3B-Instruct) and +5.3% (Qwen3-4B-Instruct), outperforming strong RL baselines while maintaining a near-constant context window independent of episode length. Further analysis shows that the learned belief becomes increasingly calibrated over the course of an episode as evidence accumulates.", "github": "https://github.com/joykirat18/Agent-BRACE" }, { "date": "2026-05-13", "arxiv_id": "2605.12419", "title": "ORBIT: Preserving Foundational Language Capabilities in GenRetrieval via Origin-Regulated Merging", "authors": [ "Neha Verma", "Nikhil Mehta", "Shao-Chuan Wang", "Naijing Zhang", "Alicia Tsai", "Li Wei", "Lukasz Heldt", "Lichan Hong", "Ed Chi", "Xinyang Yi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Despite the rapid advancements in large language model (LLM) development, fine-tuning them for specific tasks often results in the catastrophic forgetting of their general, language-based reasoning abilities. This work investigates and addresses this challenge in the context of the Generative Retrieval (GenRetrieval) task. During GenRetrieval fine-tuning, we find this forgetting occurs rapidly and correlates with the distance between the fine-tuned and original model parameters. Given these observations, we propose ORBIT, a novel approach that actively tracks the distance between fine-tuned and initial model weights, and uses a weight averaging strategy to constrain model drift during GenRetrieval fine-tuning when this inter-model distance exceeds a maximum threshold. Our results show that ORBIT retains substantial text and retrieval performance by outperforming both common continual learning baselines and related regularization methods that also employ weight averaging.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.06546", "title": "Efficient Pre-Training with Token Superposition", "authors": [ "Bowen Peng", "Th\u00e9o Gigant", "Jeffrey Quesnelle" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://nousresearch.com/token-superposition", "abstract": "Pre-training of Large Language Models is often prohibitively expensive and inefficient at scale, requiring complex and invasive modifications in order to achieve high data throughput. In this work, we present Token-Superposition Training (TST), a simple drop-in method that significantly improves the data throughput per FLOPs during pre-training without modifying the parallelism, optimizer, tokenizer, data, or model architecture. TST is done in two phases: (i) A highly efficient superposition phase where we combine many contiguous tokens into one bag and train using a multi-hot cross-entropy (MCE) objective, and (ii) a recovery phase where we revert back to standard training. We extensively evaluate TST on the scale of 270M and 600M parameters and validate on 3B and a 10B A1B mixture of experts model, demonstrating that it is highly robust in different settings. Ultimately, TST consistently outperforms baseline loss and downstream evaluations, and under equal-loss settings, TST yields up to a 2.5x reduction in total pre-training time at the 10B A1B scale.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.12484", "title": "Learning, Fast and Slow: Towards LLMs That Adapt Continually", "authors": [ "Rishabh Tiwari", "Kusha Sareen", "Lakshya A Agrawal", "Joseph E. Gonzalez", "Matei Zaharia", "Kurt Keutzer", "Inderjit S Dhillon", "Rishabh Agarwal", "Devvrit Khatri" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating parameters forces them to absorb task-specific information, which can result in catastrophic forgetting and loss of plasticity. In contrast, in-context learning with fixed LLM parameters can cheaply and rapidly adapt to task-specific requirements (e.g., prompt optimization), but cannot by itself typically match the performance gains available through updating LLM parameters. There is no good reason for restricting learning to being in-context or in-weights. Moreover, humans also likely learn at different time scales (e.g., System 1 vs 2). To this end, we introduce a fast-slow learning framework for LLMs, with model parameters as \"slow\" weights and optimized context as \"fast\" weights. These fast \"weights\" can learn from textual feedback to absorb the task-specific information, while allowing slow weights to stay closer to the base model and persist general reasoning behaviors. Fast-Slow Training (FST) is up to 3x more sample-efficient than only slow learning (RL) across reasoning tasks, while consistently reaching a higher performance asymptote. Moreover, FST-trained models remain closer to the base LLM (up to 70% less KL divergence), resulting in less catastrophic forgetting than RL-training. This reduced drift also preserves plasticity: after training on one task, FST trained models adapt more effectively to a subsequent task than parameter-only trained models. In continual learning scenarios, where task domains change on the fly, FST continues to acquire each new task while parameter-only RL stalls.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.09936", "title": "Urban-ImageNet: A Large-Scale Multi-Modal Dataset and Evaluation Framework for Urban Space Perception", "authors": [ "Yiwei Ou", "Chung Ching Cheung", "Jun Yang Ang", "Xiaobin Ren", "Ronggui Sun", "Guansong Gao", "Kaiqi Zhao", "Manfredo Manfredini" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yiasun/dataset-2", "github_api_added_by": "user", "project_page": "https://huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet", "abstract": "We present Urban-ImageNet, a large-scale multi-modal dataset and evaluation benchmark for urban space perception from user-generated social media imagery. The corpus contains over 2 Million public social media images and paired textual posts collected from Weibo across 61 urban sites in 24 Chinese cities across 2019-2025, with controlled benchmark subsets at 1K, 10K, and 100K scale and a full 2M corpus for large-scale training and evaluation. Urban-ImageNet is organized by HUSIC, a Hierarchical Urban Space Image Classification framework that defines a 10-class taxonomy grounded in urban theory. The taxonomy is designed to distinguish activated and non-activated public spaces, exterior and interior urban environments, accommodation spaces, consumption content, portraits, and non-spatial social-media content. Rather than treating urban imagery as generic scene data, Urban-ImageNet evaluates whether machine perception models can capture spatial, social, and functional distinctions that are central to urban studies. The benchmark supports three tasks within one standardized library: (T1) urban scene semantic classification, (T2) cross-modal image-text retrieval, and (T3) instance segmentation. Our experiments evaluate representative vision, vision-language, and segmentation models, revealing strong performance on supervised scene classification but more challenging behavior in cross-modal retrieval and instance-level urban object segmentation. A multi-scale study further examines how model performance changes as balanced training data increases from 1K, 10K to 100K images. Urban-ImageNet provides a unified, theory-grounded, multi-city benchmark for evaluating how AI systems perceive and interpret contemporary urban spaces across modalities, scales, and task formulations. Dataset and benchmark are available at: huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet and github.com/yiasun/dataset-2.", "github": "https://github.com/yiasun/dataset-2" }, { "date": "2026-05-13", "arxiv_id": "2605.08504", "title": "A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models", "authors": [ "Zeru Shi", "Zhenting Wang", "Fan Yang", "Qifan Wang", "Ruixiang Tang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://vanpe20.github.io/ME-Layer.github.io/", "abstract": "We investigate the origins of massive activations in large language models (LLMs) and identify a specific layer named the Massive Emergence Layer (ME Layer), that is consistently observed across model families, where massive activations first emerge and subsequently propagate to deeper layers through residual connections. We show that, within the ME Layer both the RMSNorm and the FFN parameters jointly contribute to the emergence of massive activations. Once formed, the massive activation token representation remains largely invariant across layers, reducing the diversity of hidden representations passed to the attention module. Motivated by this limitation, we propose a simple and effective method to reduce the rigidity of the massive activation token. Our approach consistently improves LLM performance across multiple tasks, including instruction following and math reasoning, in both training free and fine tuning settings. Moreover, we show that our method mitigates attention sinks by selectively weakening their influence, elucidating their origin at the hidden state level and shedding new light on principled mitigation strategies.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.09296", "title": "Micro-Defects Expose Macro-Fakes: Detecting AI-Generated Images via Local Distributional Shifts", "authors": [ "Boxuan Zhang", "Jianing Zhu", "Qifan Wang", "Jiang Liu", "Ruixiang Tang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZBox1005/MDMF", "github_api_added_by": "user", "project_page": "https://zbox1005.github.io/MDMF-project/", "abstract": "Recent generative models can produce images that appear highly realistic, raising challenges in distinguishing real and AI-generated images. Yet existing detectors based on pre-trained feature extractors tend to over-rely on global semantics, limiting sensitivity to the critical micro-defects. In this work, we propose Micro-Defects expose Macro-Fakes (MDMF), a local distribution-aware detection framework that amplifies micro-scale statistical irregularities into macro-level distributional discrepancies. To avoid localized forensic cues being diluted by plain aggregation, we introduce a learnable Patch Forensic Signature that projects semantic patch embeddings into a compact forensic latent space. We then use Maximum Mean Discrepancy (MMD) to quantify distributional discrepancies between generated and real images. Our theory-grounded analysis shows that patch-wise modeling yields provably larger discrepancies when localized forensic signals are present in generated images, enabling more reliable separation from real images. Extensive experiments demonstrate that MDMF consistently outperforms baseline detectors across multiple benchmarks, validating its general effectiveness. Project page: https://zbox1005.github.io/MDMF-project/", "github": "https://github.com/ZBox1005/MDMF" }, { "date": "2026-05-13", "arxiv_id": "2605.12466", "title": "Solve the Loop: Attractor Models for Language and Reasoning", "authors": [ "Jacob Fein-Ashley", "Paria Rashidinejad" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jacobfa/Attractor", "github_api_added_by": "user", "project_page": "https://attractor-models.github.io/", "abstract": "Looped Transformers offer a promising alternative to purely feed-forward computation by iteratively refining latent representations, improving language modeling and reasoning. Yet recurrent architectures remain unstable to train, costly to optimize and deploy, and constrained to small, fixed recurrence depths. We introduce Attractor Models, in which a backbone module first proposes output embeddings, then an attractor module refines them by solving for the fixed point, with gradients obtained through implicit differentiation. Thus, training memory remains constant in effective depth, and iterations are chosen adaptively by convergence. Empirically, Attractor Models outperform existing models across two regimes, large-scale language-model pretraining and reasoning with tiny models. In language modeling, Attractor Models deliver a Pareto improvement over standard Transformers and stable looped models across sizes, improving perplexity by up to 46.6% and downstream accuracy by up to 19.7% while reducing training cost. Notably, a 770M Attractor Model outperforms a 1.3B Transformer trained on twice as many tokens. On challenging reasoning tasks, we show that our model with only 27M parameters and approximately 1000 examples achieves 91.4% accuracy on Sudoku-Extreme and 93.1% on Maze-Hard, scaling favorably where frontier models like Claude and GPT o3, fail completely, and specialized recursive reasoners collapse at larger sizes. Lastly, we show that Attractor Models exhibit a novel phenomenon, which we call equilibrium internalization: fixed-point training places the model's initial output embedding near equilibrium, allowing the solver to be removed at inference time with little degradation. Together, these results suggest that Attractor Models make iterative refinement scalable by turning recurrence into a computation the model can learn to internalize.", "github": "https://github.com/jacobfa/Attractor" }, { "date": "2026-05-13", "arxiv_id": "2605.09252", "title": "LLM Agents Already Know When to Call Tools -- Even Without Reasoning", "authors": [ "Chung-En Sun", "Linbo Liu", "Ge Yan", "Zimo Wang", "Tsui-Wei Weng" ], "github_manual": "", "github_regex": "https://github.com/Trustworthy-ML-Lab/when2tool", "github_paper_md": "", "github_api": "https://github.com/Trustworthy-ML-Lab/when2tool", "github_api_added_by": "user", "project_page": "https://lilywenglab.github.io/when2tool/", "abstract": "Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latency, yet no existing benchmark systematically studies when a tool call is actually needed. We propose When2Tool, a benchmark of 18 environments (15 single-hop, 3 multi-hop) spanning three categories of tool necessity -- computational scale, knowledge boundaries, and execution reliability -- each with controlled difficulty levels that create a clear decision boundary between tool-necessary and tool-unnecessary tasks. We evaluate two families of training-free baselines: Prompt-only (varying the prompt to discourage unnecessary calls) and Reason-then-Act (requiring the model to reason about tool necessity before acting). Both provide limited control: Prompt-only suppresses necessary calls alongside unnecessary ones, and Reason-then-Act still incurs a disproportionate accuracy cost on hard tasks. To understand why these baselines fail, we probe the models' hidden states and find that tool necessity is linearly decodable from the pre-generation representation with AUROC 0.89--0.96 across six models, substantially exceeding the model's own verbalized reasoning. This reveals that models already know when tools are needed, but fail to act on this knowledge during generation. Building on this finding, we propose Probe&Prefill, which uses a lightweight linear probe to read the hidden-state signal and prefills the model's response with a steering sentence. Across all models tested, Probe&Prefill reduces tool calls by 48% with only 1.7% accuracy loss, while the best baseline at comparable accuracy only reduces 6% of tool calls, or achieves a similar tool call reduction but incurs a 5times higher accuracy loss. Our code is available at https://github.com/Trustworthy-ML-Lab/when2tool", "github": "https://github.com/Trustworthy-ML-Lab/when2tool" }, { "date": "2026-05-13", "arxiv_id": "2605.12498", "title": "EgoForce: Forearm-Guided Camera-Space 3D Hand Pose from a Monocular Egocentric Camera", "authors": [ "Christen Millerdurai", "Shaoxiang Wang", "Yaxu Xie", "Vladislav Golyanik", "Didier Stricker", "Alain Pagani" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dfki-av/EgoForce", "github_api_added_by": "user", "project_page": "https://dfki-av.github.io/EgoForce/", "abstract": "Reconstructing the absolute 3D pose and shape of the hands from the user's viewpoint using a single head-mounted camera is crucial for practical egocentric interaction in AR/VR, telepresence, and hand-centric manipulation tasks, where sensing must remain compact and unobtrusive. While monocular RGB methods have made progress, they remain constrained by depth-scale ambiguity and struggle to generalize across the diverse optical configurations of head-mounted devices. As a result, models typically require extensive training on device-specific datasets, which are costly and laborious to acquire. This paper addresses these challenges by introducing EgoForce, a monocular 3D hand reconstruction framework that recovers robust, absolute 3D hand pose and its position from the user's (camera-space) viewpoint. EgoForce operates across fisheye, perspective, and distorted wide-FOV camera models using a single unified network. Our approach combines a differentiable forearm representation that stabilizes hand pose, a unified arm-hand transformer that predicts both hand and forearm geometry from a single egocentric view, mitigating depth-scale ambiguity, and a ray space closed-form solver that enables absolute 3D pose recovery across diverse head-mounted camera models. Experiments on three egocentric benchmarks show that EgoForce achieves state-of-the-art 3D accuracy, reducing camera-space MPJPE by up to 28% on the HOT3D dataset compared to prior methods and maintaining consistent performance across camera configurations. For more details, visit the project page at https://dfki-av.github.io/EgoForce.", "github": "https://github.com/dfki-av/EgoForce" }, { "date": "2026-05-13", "arxiv_id": "2605.11136", "title": "EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales", "authors": [ "Yaolun Zhang", "Tianyi Xu", "Shengyu Dai", "Zhenwen Shao", "Qingyun Wu", "Huazheng Wang" ], "github_manual": "", "github_regex": "https://github.com/Mercury7353/EvoChamber", "github_paper_md": "", "github_api": "https://github.com/Mercury7353/EvoChamber", "github_api_added_by": "user", "project_page": "", "abstract": "We argue that multi-agent test-time evolution is not single-agent evolution replicated N times. A single-agent learner can only evolve its own context and memory. A multi-agent system additionally evolves who collaborates, how they collaborate, and how knowledge flows across the population. These components have no single-agent counterpart and can produce phenomena such as emergent specialization. Yet prior test-time methods either confine experiences to individual agents, forfeiting cross-agent learning, or broadcast symmetrically to all agents, erasing the specialization that makes collaboration valuable. We present EVOCHAMBER, a training-free framework that instantiates test-time evolution at three levels over a coevolving agent pool. At its core is CODREAM (Collaborative Dreaming), a post-task protocol triggered on team failure or disagreement, in which agents collaboratively reflect, distill insights, and route them asymmetrically from strong to weak agents on the failed niche, preserving specialization while filling knowledge gaps. Team-level operators assemble niche-conditioned teams and select collaboration structures online. Population-level lifecycle operators fork, merge, prune, and seed agents under performance pressure. On three heterogeneous task streams with Qwen3-8B, EVOCHAMBER reaches 63.9% on competition math, 75.7% on code, and 87.1% on multi-domain reasoning, outperforming the best baseline by 32% relative on math and confirming asymmetric cross-agent transfer as the primary driver in ablation. Starting from several identically initialized agents, four to five stable niche specialists spontaneously emerge, a structural signature of multi-agent evolution that no single-agent learner can express. See our code at: https://github.com/Mercury7353/EvoChamber", "github": "https://github.com/Mercury7353/EvoChamber" }, { "date": "2026-05-13", "arxiv_id": "2605.10376", "title": "SleepWalk: A Three-Tier Benchmark for Stress-Testing Instruction-Guided Vision-Language Navigation", "authors": [ "Niyati Rawal", "Sushant Ravva", "Shah Alam Abir", "Saksham Jain", "Aman Chadha", "Vinija Jain", "Suranjana Trivedy", "Amitava Das" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Vision-Language Models (VLMs) have advanced rapidly in multimodal perception and language understanding, yet it remains unclear whether they can reliably ground language into spatially coherent, plausibly executable actions in 3D digital environments. We introduce SleepWalk, a benchmark for evaluating instruction-grounded trajectory prediction in single-scene 3D worlds generated from textual scene descriptions and filtered for navigability. Unlike prior navigation benchmarks centered on long-range exploration across rooms, SleepWalk targets localized, interaction-centric embodied reasoning: given rendered visual observations and a natural-language instruction, a model must predict a trajectory that respects scene geometry, avoids collisions, and terminates at an action-compatible location. The benchmark covers diverse indoor and outdoor environments and organizes tasks into three tiers of spatial and temporal difficulty, enabling fine-grained analysis of grounding under increasing compositional complexity. Using a standardized pointwise judge-based evaluation protocol, we evaluate three frontier VLMs on 2,472 curated 3D environments with nine instructions per scene. Results reveal systematic failures in grounded spatial reasoning, especially under occlusion, interaction constraints, and multi-step instructions: performance drops as the difficulty level of the tasks increase. In general, current VLMs can somewhat produce trajectories that are simultaneously spatially coherent, plausibly executable, and aligned with intended actions. By exposing failures in a controlled yet scalable setting, SleepWalk provides a critical benchmark for advancing grounded multimodal reasoning, embodied planning, vision-language navigation, and action-capable agents in 3D environments.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.13062", "title": "Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling", "authors": [ "Xuehai Bai", "Yang Shi", "Yi-Fan Zhang", "Xuanyu Zhu", "Yuran Wang", "Yifan Dai", "Xinyu Liu", "Yiyan Ji", "Xiaoling Gu", "Yuanxing Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bxhsort/Edit-Compass-and-EditReward-Compass", "github_api_added_by": "user", "project_page": "", "abstract": "Recent image editing models have achieved remarkable progress in instruction following, multimodal understanding, and complex visual editing. However, existing benchmarks often fail to faithfully reflect human judgment, especially for strong frontier models, due to limited task difficulty and coarse-grained evaluation protocols. In parallel, reward models have become increasingly important for RL-based image editing optimization, yet existing reward model benchmarks still rely on unrealistic evaluation settings that deviate from practical RL scenarios. These limitations hinder reliable assessment of both image editing models and reward models. To address these challenges, we introduce Edit-Compass and EditReward-Compass, a unified evaluation suite for image editing and reward modeling. Edit-Compass contains 2,388 carefully annotated instances spanning six progressively challenging task categories, covering capabilities such as world knowledge reasoning, visual reasoning, and multi-image editing. Beyond broad task coverage, Edit-Compass adopts a fine-grained multidimensional evaluation framework based on structured reasoning and carefully designed scoring rubrics. In parallel, EditReward-Compass contains 2,251 preference pairs that simulate realistic reward modeling scenarios during RL optimization.", "github": "https://github.com/bxhsort/Edit-Compass-and-EditReward-Compass" }, { "date": "2026-05-14", "arxiv_id": "2605.12997", "title": "Frequency Bias and OOD Generalization in Neural Operators under a Variable-Coefficient Wave Equation", "authors": [ "Runlong Xie", "An Luo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Neural operators learn to map initial conditions to the terminal solution of partial differential equations (PDEs), providing a surrogate for the full operator mapping. This enables rapid prediction across different input configurations. While recent neural operator architectures have demonstrated strong performance on diverse PDE tasks, their behavior under structured distribution shifts remains insufficiently understood. To investigate this, we study operator learning in a wave propagation setting governed by a one-dimensional variable-coefficient wave equation, using two representative architectures, the Fourier Neural Operator (FNO) and the Deep Operator Network (DeepONet). To examine their generalization under distribution shifts, we consider structured out-of-distribution (OOD) settings that independently vary input frequency and coefficient smoothness. The results show that under smoothness shifts, both models maintain stable performance, with FNO achieving lower error. In contrast, under frequency shifts, FNO exhibits a sharp increase in error under unseen high-frequency inputs, whereas DeepONet shows milder degradation despite higher overall error. Our analysis reveals that these differences arise from how each architecture represents and responds to variations in frequency structure. Together, these findings highlight a fundamental gap between strong in-distribution performance and generalization under distribution shifts in operator learning, underscoring the role of architectural representation bias in developing more reliable neural operators for physics-based PDE simulations beyond the training distribution.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.08518", "title": "Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge", "authors": [ "Dhaval Patel", "Chathurangi Shyalika", "Suryanarayana Reddy Yarrabothula", "Ling Yue", "Shuxin Lin", "Nianjun Zhou", "James Rayfield" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://www.codabench.org/competitions/10206/", "abstract": "Competition retrospectives are useful when they explain what a leaderboard measured, how hidden evaluation changed conclusions, and which design patterns were rewarded. We revisit the CODS 2025 challenge, a privacy-aware Codabench competition on industrial multi-agent orchestration built on . We combine final rank sheets, a 300-submission server log, 149-team registrations, best-submission exports, the organizer winners report, the companion system paper, and verified planning-track source trees. Five results stand out. First, the public planning leaderboard saturates at 72.73\\%, and richer prompts do not improve that peak. Second, hidden evaluation changes the story: public and private scores correlate moderately in planning (r{=}0.69) but negatively in execution (r{=}{-}0.13), with several 45.45\\% public execution systems reaching 63.64\\% on the hidden set. Third, the term is numerically almost inert in the official composite -- combined on a 0--1 scale with 0--100 percentage scores, it contributes at most 0.05 points per track, and rescaling would swap the top two teams. Fourth, the competition is operationally account-based but substantively team-based: 149 registered teams reduce to 24 with non-zero public scores and 11 fully ranked, while 52.3\\% of deduplicated registrations list multiple usernames. Fifth, successful execution methods mostly improve guardrails -- response selection, contamination cleanup, fallback, and context control -- rather than novel agent architectures. These findings identify which behaviors the evaluation rewarded, and motivate scale-aware composites, skill-level diagnostics, and versioned artifact release.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2605.12501", "title": "Covering Human Action Space for Computer Use: Data Synthesis and Benchmark", "authors": [ "Miaosen Zhang", "Xiaohan Zhao", "Zhihong Tan", "Zhou Huoshen", "Yijia Fan", "Yifan Yang", "Kai Qiu", "Bei Liu", "Justin Wagle", "Chenzhong Yin", "Mingxi Cheng", "Ji Li", "Qi Dai", "Chong Luo", "Xu Yang", "Xin Geng", "Baining Guo" ], "github_manual": "", "github_regex": "https://github.com/microsoft/Phi-Ground.git", "github_paper_md": "", "github_api": "https://github.com/microsoft/Phi-Ground", "github_api_added_by": "user", "project_page": "", "abstract": "Computer-use agents (CUAs) automate on-screen work, as illustrated by GPT-5.4 and Claude. Yet their reliability on complex, low-frequency interactions is still poor, limiting user trust. Our analysis of failure cases from advanced models suggests a long-tail pattern in GUI operations, where a relatively small fraction of complex and diverse interactions accounts for a disproportionate share of task failures. We hypothesize that this issue largely stems from the scarcity of data for complex interactions. To address this problem, we propose a new benchmark CUActSpot for evaluating models' capabilities on complex interactions across five modalities: GUI, text, table, canvas, and natural image, as well as a variety of actions (click, drag, draw, etc.), covering a broader range of interaction types than prior click-centric benchmarks that focus mainly on GUI widgets. We also design a renderer-based data-synthesis pipeline: scenes are automatically generated for each modality, screenshots and element coordinates are recorded, and an LLM produces matching instructions and action traces. After training on this corpus, our Phi-Ground-Any-4B outperforms open-source models with fewer than 32B parameters. We will release our benchmark, data, code, and models at https://github.com/microsoft/Phi-Ground.git", "github": "https://github.com/microsoft/Phi-Ground" }, { "date": "2026-05-13", "arxiv_id": "2605.12978", "title": "Useful Memories Become Faulty When Continuously Updated by LLMs", "authors": [ "Dylan Zhang", "Yanshan Lin", "Zhengkun Wu", "Yihang Sun", "Bingxuan Li", "Dianqi Li", "Hao Peng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated abstractions distilled across many episodes into reusable, schema-like lessons. Recent agentic-memory systems pursue the consolidated form: an LLM rewrites past trajectories into a textual memory bank that it continuously updates with new interactions, promising self-improving agents without parameter updates. Yet we find that such consolidated memories produced by today's LLMs are often faulty even when derived from useful experiences. As consolidation proceeds, memory utility first rises, then degrades, and can fall below the no-memory baseline. More surprisingly, even when consolidating from ground-truth solutions, GPT-5.4 fails on 54% of a set of ARC-AGI problems it had previously solved without memory. We trace the regression to the consolidation step rather than the underlying experience: the same trajectories yield qualitatively different memories under different update schedules, and an episodic-only control that simply retains those trajectories remains competitive with the consolidators we test. In a controlled ARC-AGI Stream environment that exposes Retain, Delete, and Consolidate actions, agents preserve raw episodes by default and double the accuracy of their forced-consolidation counterparts; disabling consolidation entirely (episodic management only) matches this auto regime. Practically, robust agent memory should treat raw episodes as first-class evidence and gate consolidation explicitly rather than firing it after every interaction. Looking forward, reliable agentic memory will require LLMs that can consolidate without overwriting the evidence they depend on.", "github": "" }, { "date": "2026-05-13", "arxiv_id": "2604.27695", "title": "EviMem: Evidence-Gap-Driven Iterative Retrieval for Long-Term Conversational Memory", "authors": [ "Yuyang Li", "Yime He", "Zeyu Zhang", "Dong Gong" ], "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/EviMem", "github_paper_md": "", "github_api": "https://github.com/AIGeeksGroup/EviMem", "github_api_added_by": "user", "project_page": "", "abstract": "Long-term conversational memory requires retrieving evidence scattered across multiple sessions, yet single-pass retrieval fails on temporal and multi-hop questions. Existing iterative methods refine queries via generated content or document-level signals, but none explicitly diagnoses the evidence gap, namely what is missing from the accumulated retrieval set, leaving query refinement untargeted. We present EviMem, combining IRIS (Iterative Retrieval via Insufficiency Signals), a closed-loop framework that detects evidence gaps through sufficiency evaluation, diagnoses what is missing, and drives targeted query refinement, with LaceMem (Layered Architecture for Conversational Evidence Memory), a coarse-to-fine memory hierarchy supporting fine-grained gap diagnosis. On LoCoMo, EviMem improves Judge Accuracy over MIRIX on temporal (73.3% to 81.6%) and multi-hop (65.9% to 85.2%) questions at 4.5x lower latency. Code: https://github.com/AIGeeksGroup/EviMem.", "github": "https://github.com/AIGeeksGroup/EviMem" }, { "date": "2026-05-14", "arxiv_id": "2605.12913", "title": "Revisiting DAgger in the Era of LLM-Agents", "authors": [ "Changhao Li", "Rushi Qiang", "Jiawei Huang", "Chenxiao Gao", "Chao Zhang", "Niao He", "Bo Dai" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Long-horizon LM agents learn from multi-turn interaction, where a single early mistake can alter the subsequent state distribution and derail the whole trajectory. Existing recipes fall short in complementary ways: supervised fine-tuning provides dense teacher supervision but suffers from covariate shift because it is trained on off-policy teacher trajectories; while reinforcement learning with verifiable rewards avoids this off-policy mismatch by learning from on-policy rollouts but with only sparse outcome feedback. We address this dilemma by revisiting Dataset Aggregation (DAgger) for multi-turn LM agents: the algorithm collects trajectories through a turn-level interpolation of student and teacher policies, and the student is then trained on these trajectories using supervised labels provided by the teacher. By directly interacting with environments, we expose the model to realistic states likely to be encountered during deployment, thereby effectively mitigating covariate shift. Besides, since the student is learned by mimicking the teacher's behavior, it receives rich feedback during learning. To demonstrate DAgger enjoys the benefits of both worlds, we tested the algorithm to train a software-engineering agent with 4B- and 8B-scale student models. On SWE-bench Verified, our DAgger-style training improves over the strongest post-training baseline by +3.9 points at 4B and +3.6 points at 8B. The resulting 4B agent reaches 27.3%, outperforming representative published 8B SWE-agent systems, while the 8B agent achieves 29.8%, surpassing SWE-Gym-32B and coming within 5 points of stronger 32B-scale agents. Together with consistent gains on the held-out SWE-Gym split, these results suggest the effectiveness of DAgger for modern long-horizon LM agents.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.11363", "title": "PresentAgent-2: Towards Generalist Multimodal Presentation Agents", "authors": [ "Wei Wu", "Ziyang Xu", "Zeyu Zhang", "Yang Zhao", "Hao Tang" ], "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/PresentAgent-2", "github_paper_md": "", "github_api": "https://github.com/AIGeeksGroup/PresentAgent-2", "github_api_added_by": "user", "project_page": "https://aigeeksgroup.github.io/PresentAgent-2/", "abstract": "Presentation generation is moving beyond static slide creation toward end-to-end presentation video generation with research grounding, multimodal media, and interactive delivery. We introduce PresentAgent-2, an agentic framework for generating presentation videos from user queries. Given an open-ended user query and a selected presentation mode, PresentAgent-2 first summarizes the query into a focused topic and performs deep research over presentation-friendly sources to collect multimodal resources, including relevant text, images, GIFs, and videos. It then constructs presentation slides, generates mode-specific scripts, and composes slides, audio, and dynamic media into a complete presentation video. PresentAgent-2 supports three independent presentation modes within a unified framework: Single Presentation, which generates a single-speaker narrated presentation video; Discussion, which creates a multi-speaker presentation with structured speaker roles, such as for asking guiding questions, explaining concepts, clarifying details, and summarizing key points; and Interaction, which independently supports answering audience questions grounded in the generated slides, scripts, retrieved evidence, and presentation context. To evaluate these capabilities, we build a multimodal presentation benchmark covering single presentation, discussion, and interaction scenarios, with task-specific evaluation criteria for content quality, media relevance, dynamic media use, dialogue naturalness, and interaction grounding. Overall, PresentAgent-2 extends presentation generation from document-dependent slide creation to query-driven, research-grounded presentation video generation with multimodal media, dialogue, and interaction. Code: https://github.com/AIGeeksGroup/PresentAgent-2. Website: https://aigeeksgroup.github.io/PresentAgent-2.", "github": "https://github.com/AIGeeksGroup/PresentAgent-2" }, { "date": "2026-05-14", "arxiv_id": "2605.12964", "title": "Asymmetric Flow Models", "authors": [ "Hansheng Chen", "Jan Ackermann", "Minseo Kim", "Gordon Wetzstein", "Leonidas Guibas" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Lakonik/LakonLab", "github_api_added_by": "user", "project_page": "https://hanshengchen.com/asymflow/", "abstract": "Flow-based generation in high-dimensional spaces is difficult because velocity prediction requires modeling high-dimensional noise, even when data has strong low-rank structure. We present Asymmetric Flow Modeling (AsymFlow), a rank-asymmetric velocity parameterization that restricts noise prediction to a low-rank subspace while keeping data prediction full-dimensional. From this asymmetric prediction, AsymFlow analytically recovers the full-dimensional velocity without changing the network architecture or training/sampling procedures. On ImageNet 256times256, AsymFlow achieves a leading 1.57 FID, outperforming prior DiT/JiT-like pixel diffusion models by a large margin. AsymFlow also provides the first-ever route for finetuning pretrained latent flow models into pixel-space models: aligning the low-rank pixel subspace to the latent space gives a seamless initialization that preserves the latent model's high-level semantics and structure, so finetuning mainly improves low-level mismatches rather than relearning pixel generation. We show that the pixel AsymFlow model finetuned from FLUX.2 klein 9B establishes a new state of the art for pixel-space text-to-image generation, beating its latent base on HPSv3, DPG-Bench, and GenEval while qualitatively showing substantially improved visual realism.", "github": "https://github.com/Lakonik/LakonLab" }, { "date": "2026-05-14", "arxiv_id": "2605.11550", "title": "The DAWN of World-Action Interactive Models", "authors": [ "Hongbo Lu", "Liang Yao", "Chenghao He", "Haoyu Wang", "Xiang Gu", "Xianfei Li", "Wenlong Liao", "Tao He", "Pai Peng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/COOWAI/DAWN", "github_api_added_by": "user", "project_page": "https://cowarobot-ai.github.io/", "abstract": "A plausible scene evolution depends on the maneuver being considered, while a good maneuver depends on how the scene may evolve. Existing World Action Models (WAMs) largely miss this reciprocity, treating world prediction and action generation as either isolated parallel branches or rigid predict-then-plan pipelines. We formalize this perspective as World-Action Interactive Models (WAIMs), and instantiate it in autonomous driving with DAWN (Denoising Actions and World iNteractive model), a simple yet strong latent generative baseline. DAWN operates in a compact semantic latent space and couples a World Predictor with a World-Conditioned Action Denoiser: the predicted world hypothesis conditions action denoising, while the denoised action hypothesis is fed back to update the world prediction, so that both are recursively refined during inference. Rather than eliminating test-time world evolution altogether or rolling out the full future in pixel space, DAWN performs a short explicit latent rollout that is sufficient to support long-horizon trajectory generation in complex interactive scenes. Experiments show that DAWN achieves strong planning performance and favorable safety-related results across multiple autonomous driving benchmarks. More broadly, our results suggest that interactive world-action generation is a principled path toward truly actionable world models.", "github": "https://github.com/COOWAI/DAWN" }, { "date": "2026-05-14", "arxiv_id": "2605.13037", "title": "MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning", "authors": [ "Yuxin Liu", "Ziang Ye", "Yueqing Sun", "Mingye Zhu", "Jinwei Xiao", "Zhuowen Han", "Qi GU", "Xunliang Cai", "Lei Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Current interactive LLM agents rely on goal-conditioned stepwise planning, where environmental understanding is acquired reactively during execution rather than established beforehand. This temporal inversion leads to Delayed Environmental Perception: agents must infer environmental constraints through trial-and-error, resulting in an Epistemic Bottleneck that traps them in inefficient failure cycles. Inspired by human affordance perception and cognitive map theory, we propose the Map-then-Act Paradigm (MAP), a plug-and-play framework that shifts environment understanding before execution. MAP consists of three stages: (1) Global Exploration, acquiring environment-general priors; (2) Task-Specific Mapping, constructing a structured cognitive map; and (3) Knowledge-Augmented Execution, solving tasks grounded on the map. Experiments show consistent gains across benchmarks and LLMs. On ARC-AGI-3, MAP enables frontier models to surpass near-zero baseline performance in 22 of 25 game environments. We further introduce MAP-2K, a dataset of map-then-act trajectories, and show that training on it outperforms expert execution traces, suggesting that understanding environments is more fundamental than imitation.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.13724", "title": "AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation", "authors": [ "Yuchao Gu", "Guian Fang", "Yuxin Jiang", "Weijia Mao", "Song Han", "Han Cai", "Mike Zheng Shou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/AnyFlow", "github_api_added_by": "user", "project_page": "https://nvlabs.github.io/AnyFlow/", "abstract": "Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any-step video diffusion. This limitation arises because consistency distillation replaces the original probability-flow ODE trajectory with a consistency-sampling trajectory, weakening the desirable test-time scaling behavior of ODE sampling. To address this limitation, we introduce AnyFlow, the first any-step video diffusion distillation framework based on flow maps. Instead of distilling a model for only a few fixed sampling steps, AnyFlow optimizes the full ODE sampling trajectory. To this end, we shift the distillation target from endpoint consistency mapping (z_{t}rightarrow z_{0}) to flow-map transition learning (z_{t}rightarrow z_{r}) over arbitrary time intervals. We further propose Flow Map Backward Simulation, which decomposes a full Euler rollout into shortcut flow-map transitions, enabling efficient on-policy distillation that reduces test-time errors (i.e., discretization error in few-step sampling and exposure bias in causal generation). Extensive experiments across both bidirectional and causal architectures, at scales ranging from 1.3B to 14B parameters, demonstrate that AnyFlow achieves performance matches or surpasses consistency-based counterparts in the few-step regime, while scaling with sampling step budgets.", "github": "https://github.com/NVlabs/AnyFlow" }, { "date": "2026-05-14", "arxiv_id": "2605.12684", "title": "Visual Aesthetic Benchmark: Can Frontier Models Judge Beauty?", "authors": [ "Yichen Feng", "Yuetai Li", "Chunjiang Liu", "Yuanyuan Chen", "Fengqing Jiang", "Yue Huang", "Hang Hua", "Zhengqing Yuan", "Kaiyuan Zheng", "Luyao Niu", "Bhaskar Ramasubramanian", "Basel Alomair", "Xiangliang Zhang", "Misha Sra", "Zichen Chen", "Radha Poovendran", "Zhangchen Xu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/BakeLab/Visual-Aesthetic-Benchmark", "github_api_added_by": "user", "project_page": "https://vab.bakelab.ai/", "abstract": "Multimodal large language models (MLLMs) are now routinely deployed for visual understanding, generation, and curation. A substantial fraction of these applications require an explicit aesthetic judgment. Most existing solutions reduce this judgment to predicting a scalar score for a single image. We first ask whether such scores faithfully capture comparative preference: in a controlled study with eight expert annotators, score-derived rankings align poorly with the same annotators' direct comparisons, while direct ranking yields substantially higher inter-annotator agreement on best- and worst-image labels. Motivated by this finding, we introduce the Visual Aesthetic Benchmark (VAB), which casts aesthetic evaluation as comparative selection over candidate sets with matched subject matter. VAB contains 400 tasks and 1,195 images across fine art, photography, and illustration, with labels derived from the consensus of 10 independent expert judges per task. Evaluating 20 frontier MLLMs and six dedicated visual-quality reward models, we find that the strongest system identifies both the best and the worst image correctly across three random permutations of the candidate order in only 26.5% of tasks, far below the 68.9% achieved by human experts. Fine-tuning a 35B-parameter model on 2,000 expert examples brings its accuracy close to that of a 397B-parameter open-weight model, suggesting that the comparative signal in VAB is transferable. Together, these results expose a clear and measurable gap between current multimodal models and expert aesthetic judgment, and VAB provides the first set-based, expert-grounded testbed on which that gap can be tracked and closed.", "github": "https://github.com/BakeLab/Visual-Aesthetic-Benchmark" }, { "date": "2026-05-14", "arxiv_id": "2605.11733", "title": "Position: LLM Inference Should Be Evaluated as Energy-to-Token Production", "authors": [ "Xiang Liu", "Shimiao Yuan", "Zhenheng Tang", "Peijie Dong", "Kaiyong Zhao", "Qiang Wang", "Bo Li", "Xiaowen Chu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://dominic789654.github.io/energy-to-token/", "abstract": "LLM inference is still evaluated mainly as a model or software problem: accuracy, latency, throughput, and hardware utilization. This is incomplete. At deployment scale, the relevant output is a quality-conditioned token produced under joint constraints from effective compute, delivered data-center power, cooling capacity, PUE, and utilization. We argue that the ML community should treat inference as energy-to-token production. We formalize this view with a dimensionally consistent Token Production Function in which token rate is bounded by both compute-per-token and energy-per-token ceilings. Listed API prices vary by over an order of magnitude across providers, but we use price dispersion only as directional motivation, not as causal evidence of marginal cost. The core physical question is instead: under fixed quality and service targets, when does the binding constraint move from theoretical peak compute toward delivered power, cooling, and operational efficiency? Under this framing, system optimizations -- latent KV-cache compression, sparse or heavily compressed attention, quantization, routing, and difficulty-adaptive reasoning -- are not merely local engineering tricks. They are energy-to-token levers because they reduce FLOPs/token, joules/token, memory traffic, or utilization losses under fixed (q^{*},s^{*}). We therefore call for inference papers and benchmarks to report Joules/token, active binding constraint, PUE-adjusted delivered power, and utilization-adjusted token output alongside accuracy and latency.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.08737", "title": "The Extrapolation Cliff in On-Policy Distillation of Near-Deterministic Structured Outputs", "authors": [ "Xin Li", "Hao Jiang", "Annan Wang", "Yichi Zhang", "Chau Yuen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://lixin.ai/ListOPD", "abstract": "On-policy distillation (OPD) is widely used for LLM post-training. When pushed with a reward-extrapolation coefficient lambda > 1, the student can lift past the teacher in domain, but past a threshold lambda* the same step violates the output contract on structured-output tasks. In a single-position Bernoulli reduction, we derive a closed-form base-relative clip-safety threshold lambda*(p,b,c) determined by three measurable quantities: the teacher modal probability, the warm-start mass, and the importance-sampling clip strength. Above lambda*, the extrapolated fixed point exits the clip-safe region, changing training from format-preserving to format-collapsing. We extend the rule to calibrated K-ary listwise JSON tasks where a single binding equivalence class dominates the output contract and SFT retains parse headroom. On Amazon Fashion, three pre-registered tests--a fine-grid cliff interval, a budget-extension test, and a small-clip cross-prediction--fall within their locked prediction windows, with the small-clip value matching the closed-form prediction below grid resolution. Operating just below lambda*, ListOPD brings a 1.7B Qwen3 student to in-domain parity with an 8B-SFT baseline at one-fifth the parameters. The gain is driven primarily by format adherence: NDCG@1 on parsed outputs remains flat across lambda, while parse validity sharply changes at the predicted boundary. The cliff diagnostic is rubric-independent, whereas the parity claim uses a Gemini-graded rubric and inherits that evaluator's exposure.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.13831", "title": "Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context", "authors": [ "Zhaowei Wang", "Lishu Luo", "Haodong Duan", "Weiwei Liu", "Sijin Wu", "Ji Luo", "Shen Yan", "Shuai Peng", "Sihang Yuan", "Chaoyi Huang", "Yi Lin", "Yangqiu Song" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Long-context modeling is becoming a core capability of modern large vision-language models (LVLMs), enabling sustained context management across long-document understanding, video analysis, and multi-turn tool use in agentic workflows. Yet practical training recipes remain insufficiently explored, particularly for designing and balancing long-context data mixtures. In this work, we present a systematic study of long-context continued pre-training for LVLMs, extending a 7B model from 32K to 128K context with extensive ablations on long-document data. We first show that long-document VQA is substantially more effective than OCR transcription. Building on this observation, our ablations further yield three key findings: i) for sequence-length distribution, balanced data outperforms target-length-focused data (e.g., 128K), suggesting that long-context ability requires generalizable key-information retrieval across various lengths and positions; ii) retrieval remains the primary bottleneck, favoring retrieval-heavy mixtures with modest reasoning data for task diversity; and iii) pure long-document VQA largely preserves short-context capabilities, suggesting that instruction-formatted long data reduces the need for short-data mixing. Based on these findings, we introduce MMProLong, obtained by long-context continued pre-training from Qwen2.5-VL-7B with only a 5B-token budget. MMProLong improves long-document VQA scores by 7.1% and maintains strong performance at 256K and 512K contexts beyond its 128K training window, without additional training. It further generalizes to webpage-based multimodal needle retrieval, long-context vision-text compression, and long-video understanding without task-specific supervision. Overall, our study establishes a practical LongPT recipe and an empirical foundation for advancing long-context vision-language models.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.12925", "title": "AgentLens: Revealing The Lucky Pass Problem in SWE-Agent Evaluation", "authors": [ "Priyam Sahoo", "Gaurav Mittal", "Xiaomin Li", "Shengjie Ma", "Benjamin Steenhoek", "Pingping Lin", "Yu Hu" ], "github_manual": "", "github_regex": "https://github.com/microsoft/code-agent-state-trajectories", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Evaluation of software engineering (SWE) agents is dominated by a binary signal: whether the final patch passes the tests. This outcome-only view treats a principled solution and a chaotic trial-and-error process as equivalent. We show that this equivalence is empirically false. We evaluate 2,614 OpenHands trajectories from eight model backends on 60 SWE-bench Verified tasks. Of these, 47 have enough passing trajectories to construct task-level process references, yielding a 1,815-trajectory evaluation subset. Among passing trajectories in this subset, 10.7% exhibit behavior we call a Lucky Pass: regression cycles, blind retries, missing verification, or temporally disordered exploration, implementation, and verification. We introduce AgentLens, a framework for process-level assessment of SWE-agent trajectories, and release AgentLens-Bench, a dataset of 1,815 trajectories annotated with quality scores, waste signals, divergence points, and 47 task-level Prefix Tree Acceptor (PTA) references. AgentLens builds PTA references by merging multiple passing solutions for the same task, and uses a context-sensitive intent labeler to assign actions to Exploration, Implementation, Verification, or Orchestration based on trajectory history rather than tool identity alone. On AgentLens-Bench, the quality score separates passing trajectories into Lucky, Solid, and Ideal tiers and further decomposes Lucky Passes into five recurring mechanisms. Across the eight model backends, Lucky rates range from 0.5% to 23.2%, and some models move by as many as five rank positions when ranked by quality score instead of pass rate. We release the anonymized project repository, including the AgentLens-Bench dataset and AgentLens SDK, at https://github.com/microsoft/code-agent-state-trajectories/.", "github": "https://github.com/microsoft/code-agent-state-trajectories" }, { "date": "2026-05-14", "arxiv_id": "2605.13050", "title": "Context Training with Active Information Seeking", "authors": [ "Zeyu Huang", "Adhiguna Kuncoro", "Qixuan Feng", "Jiajun Shen", "Lucio Dery", "Arthur Szlam", "Marc'Aurelio Ranzato" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Most existing large language models (LLMs) are expensive to adapt after deployment, especially when a task requires newly produced information or niche domain knowledge. Recent work has shown that, by manipulating and optimizing their context, LLMs can be tailored to downstream tasks without updating their weights. However, most existing methods remain closed-loop, relying solely on the model's intrinsic knowledge. In this paper, we equip these context optimizers with Wikipedia search and browser tools for active information seeking. We show that naively adding these tools to a standard sequential context optimization pipeline can actually degrade performance compared to baselines. However, when paired with a search-based training procedure that maintains and prunes multiple candidate contexts, active information seeking delivers consistent and substantial gains. We demonstrate these improvements across diverse domains, including low-resource translation (Flores+), health scenarios (HealthBench), and reasoning-heavy tasks (LiveCodeBench and Humanity's Last Exam). Furthermore, our method proves to be data-efficient, robust across different hyperparameters, and capable of generating effective textual contexts that generalize well across different models.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.12770", "title": "WriteSAE: Sparse Autoencoders for Recurrent State", "authors": [ "Jack Young" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JackYoung27/writesae", "github_api_added_by": "user", "project_page": "https://www.jackyoung.io/research/writesae", "abstract": "We introduce WriteSAE, the first sparse autoencoder that decomposes and edits the matrix cache write of state-space and hybrid recurrent language models, where residual SAEs cannot reach. Existing SAEs read residual streams, but Gated DeltaNet, Mamba-2, and RWKV-7 write to a d_k times d_v cache through rank-1 updates k_t v_t^top that no vector atom can replace. WriteSAE factors each decoder atom into the native write shape, exposes a closed form for the per-token logit shift, and trains under matched Frobenius norm so atoms swap one cache slot at a time. Atom substitution beats matched-norm ablation on 92.4% of n=4{,}851 firings at Qwen3.5-0.8B L9 H4, the 87-atom population test holds at 89.8%, the closed form predicts measured effects at R^2=0.98, and Mamba-2-370M substitutes at 88.1% over 2,500 firings. Sustained three-position installs at 3times lift midrank target-in-continuation from 33.3% to 100% under greedy decoding, the first behavioral install at the matrix-recurrent write site.", "github": "https://github.com/JackYoung27/writesae" }, { "date": "2026-05-14", "arxiv_id": "2605.13779", "title": "MinT: Managed Infrastructure for Training and Serving Millions of LLMs", "authors": [ "Mind Lab", "Song Cao", "Vic Cao", "Andrew Chen", "Kaijie Chen", "Cleon Cheng", "Steven Chiang", "Kaixuan Fan", "Hera Feng", "Huan Feng", "Arthur Fu", "Jun Gao", "Hongquan Gu", "Aaron Guan", "Nolan Ho", "Mutian Hong", "Hailee Hou", "Peixuan Hua", "Charles Huang", "Miles Jiang", "Nora Jiang", "Yuyi Jiang", "Qiuyu Jin", "Fancy Kong", "Andrew Lei", "Kyrie Lei", "Alexy Li", "Lucian Li", "Ray Li", "Theo Li", "Zhihui Li", "Jiayi Lin", "Kairus Liu", "Kieran Liu", "Logan Liu", "Xiang Liu", "Irvine Lu", "Maeve Luo", "Runze Lv", "Pony Ma", "Verity Niu", "Anson Qiu", "Vincent Wang", "Rio Yang", "Maxwell Yao", "Carrie Ye", "Regis Ye", "Wenlin Ye", "Josh Ying", "Danney Zeng", "Yuhan Zhan", "Anya Zhang", "Di Zhang", "Ruijia Zhang", "Sueky Zhang", "Ya Zhang", "Wei Zhao", "Ada Zhou", "Changhai Zhou", "Yuhua Zhou", "Xinyue Zhu", "Murphy Zhuang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MindLab-Research/mindlab-toolkit", "github_api_added_by": "user", "project_page": "https://macaron.im/mindlab/mint", "abstract": "We present MindLab Toolkit (MinT), a managed infrastructure system for Low-Rank Adaptation (LoRA) post-training and online serving. MinT targets a setting where many trained policies are produced over a small number of expensive base-model deployments. Instead of materializing each policy as a merged full checkpoint, MinT keeps the base model resident and moves exported LoRA adapter revisions through rollout, update, export, evaluation, serving, and rollback, hiding distributed training, serving, scheduling, and data movement behind a service interface. MinT scales this path along three axes. Scale Up extends LoRA RL to frontier-scale dense and MoE architectures, including MLA and DSA attention paths, with training and serving validated beyond 1T total parameters. Scale Down moves only the exported LoRA adapter, which can be under 1% of base-model size in rank-1 settings; adapter-only handoff reduces the measured step by 18.3x on a 4B dense model and 2.85x on a 30B MoE, while concurrent multi-policy GRPO shortens wall time by 1.77x and 1.45x without raising peak memory. Scale Out separates durable policy addressability from CPU/GPU working sets: a tensor-parallel deployment supports 10^6-scale addressable catalogs (measured single-engine sweeps through 100K) and thousand-adapter active waves at cluster scale, with cold loading treated as scheduled service work and packed MoE LoRA tensors improving live engine loading by 8.5-8.7x. MinT thus manages million-scale LoRA policy catalogs while training and serving selected adapter revisions over shared 1T-class base models.", "github": "https://github.com/MindLab-Research/mindlab-toolkit" }, { "date": "2026-05-14", "arxiv_id": "2605.13565", "title": "Qwen-Image-VAE-2.0 Technical Report", "authors": [ "Zekai Zhang", "Deqing Li", "Kuan Cao", "Yujia Wu", "Chenfei Wu", "Yu Wu", "Liang Peng", "Hao Meng", "Jiahao Li", "Jie Zhang", "Kaiyuan Gao", "Kun Yan", "Lihan Jiang", "Ningyuan Tang", "Shengming Yin", "Tianhe Wu", "Xiao Xu", "Xiaoyue Chen", "Yan Shu", "Yanran Zhang", "Yilei Chen", "Yixian Xu", "Yuxiang Chen", "Zhendong Wang", "Zihao Liu", "Zikai Zhou", "Yiliang Gu", "Yi Wang", "Xiaoxiao Xu", "Lin Qu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alibaba/OmniDoc-TokenBench", "github_api_added_by": "user", "project_page": "", "abstract": "We present Qwen-Image-VAE-2.0, a suite of high-compression Variational Autoencoders (VAEs) that achieve significant advances in both reconstruction fidelity and diffusability. To address the reconstruction bottlenecks of high compression, we adopt an improved architecture featuring Global Skip Connections (GSC) and expanded latent channels. Moreover, we scale training to billions of images and incorporate a synthetic rendering engine to improve performance in text-rich scenarios. To tackle the convergence challenges of high-dimensional latent space, we implement an enhanced semantic alignment strategy to make the latent space highly amenable to diffusion modeling. To optimize computational efficiency, we leverage an asymmetric and attention-free encoder-decoder backbone to minimize encoding overhead. We present a comprehensive evaluation of Qwen-Image-VAE-2.0 on public reconstruction benchmarks. To evaluate performance in text-rich scenarios, we propose OmniDoc-TokenBench, a new benchmark comprising a diverse collection of real-world documents coupled with specialized OCR-based evaluation metrics. Qwen-Image-VAE-2.0 achieves state-of-the-art reconstruction performance, demonstrating exceptional capabilities in both general domains and text-rich scenarios at high compression ratio. Furthermore, downstream DiT experiments reveal our models possess superior diffusability, significantly accelerating convergence compared to existing high-compression baselines. These establish Qwen-Image-VAE-2.0 as a leading model with high compression, superior reconstruction, and exceptional diffusability.", "github": "https://github.com/alibaba/OmniDoc-TokenBench" }, { "date": "2026-05-14", "arxiv_id": "2605.13511", "title": "Many-Shot CoT-ICL: Making In-Context Learning Truly Learn", "authors": [ "Tsz Ting Chung", "Lemao Liu", "Mo Yu", "Dit-Yan Yeung" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "In-context learning (ICL) adapts large language models (LLMs) to new tasks by conditioning on demonstrations in the prompt without parameter updates. With long-context models, many-shot ICL can use dozens to hundreds of examples and achieve performance comparable to fine-tuning, yet current understanding of its scaling behavior is largely derived from non-reasoning tasks. We study many-shot chain-of-thought in-context learning (CoT-ICL) for reasoning and show that standard many-shot rules do not transfer. Across non-reasoning and reasoning-oriented LLMs and across non-reasoning and reasoning tasks, we find: (i) a setting-dependent scaling effect, where increasing the number of CoT demonstrations is unstable for non-reasoning LLMs and benefits mainly reasoning-oriented LLMs; (ii) similarity-based retrieval helps on non-reasoning tasks but fails on reasoning, since semantic similarity poorly predicts procedural (i.e., CoT) compatibility; and (iii) an order-scaling effect, where performance variance grows with more CoT demonstrations. We interpret these behaviors by viewing many-shot CoT-ICL as in-context test-time learning rather than scaled pattern matching, and suggests two principles: (i) demonstrations should be easy for the target model to understand, and (ii) they should be ordered to support a smooth conceptual progression. Guided by the principle, we propose Curvilinear Demonstration Selection (CDS), a simple ordering method that yields up to a 5.42 percentage-point gain on geometry with 64 demonstrations. Overall, our results reframe the long context window from a retrieval buffer into a structured curriculum for in-context test-time learning.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.13775", "title": "RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data", "authors": [ "Harold Haodong Chen", "Sirui Chen", "Yingjie Xu", "Wenhang Ge", "Ying-Cong Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The scalability of robotic manipulation is fundamentally bottlenecked by the scarcity of task-aligned physical interaction data. While vision-language models (VLMs) and video generation models (VGMs) hold promise for autonomous data synthesis, they suffer from semantic-spatial misalignment and physical hallucinations, respectively. To bridge this gap, we introduce RoboEvolve, a novel framework that couples a VLM planner and a VGM simulator into a mutually reinforcing co-evolutionary loop. Operating purely on unlabeled seed images, RoboEvolve leverages a cognitive-inspired dual-phase mechanism: (i) daytime exploration fosters physically grounded behavioral discovery through a semantic-controlled multi-granular reward, and (ii) nighttime consolidation mines \"near-miss\" failures to stabilize policy optimization. Guided by an autonomous progressive curriculum, the system naturally scales from simple atomic actions to complex tasks. Extensive experiments demonstrate that RoboEvolve (I) achieves superior effectiveness, elevating base planners by 30 absolute points and amplifying simulator success by 48% on average; (II) exhibits extreme data efficiency, surpassing fully supervised baselines with merely 500 unlabeled seeds--a 50x reduction; and (III) demonstrates robust continual learning without catastrophic forgetting.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.12825", "title": "Orthrus: Memory-Efficient Parallel Token Generation via Dual-View Diffusion", "authors": [ "Chien Van Nguyen", "Chaitra Hegde", "Van Cuong Pham", "Ryan A. Rossi", "Franck Dernoncourt", "Thien Huu Nguyen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/chiennv2000/orthrus", "github_api_added_by": "user", "project_page": "", "abstract": "We introduce Orthrus, a simple and efficient dual-architecture framework that unifies the exact generation fidelity of autoregressive Large Language Models (LLMs) with the high-speed parallel token generation of diffusion models. The sequential nature of standard autoregressive decoding represents a fundamental bottleneck for high-throughput inference. While diffusion language models attempt to break this barrier via parallel generation, they suffer from significant performance degradation, high training costs, and a lack of rigorous convergence guarantees. Orthrus resolves this dichotomy natively. Designed to seamlessly integrate into existing Transformers, the framework augments a frozen LLM with a lightweight, trainable module to create a parallel diffusion view alongside the standard autoregressive view. In this unified system, both views attend to the exact same high-fidelity Key-Value (KV) cache; the autoregressive head executes context pre-filling to construct accurate KV representations, while the diffusion head executes parallel generation. By employing an exact consensus mechanism between the two views, Orthrus guarantees lossless inference, delivering up to a 7.8x speedup with only an O(1) memory cache overhead and minimal parameter additions.", "github": "https://github.com/chiennv2000/orthrus" }, { "date": "2026-05-14", "arxiv_id": "2605.11680", "title": "ShapeCodeBench: A Renewable Benchmark for Perception-to-Program Reconstruction of Synthetic Shape Scenes", "authors": [ "Shivam Kumar" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shivamk3r/shape-code-bench", "github_api_added_by": "user", "project_page": "https://arxiv.org/abs/2605.11680", "abstract": "We introduce ShapeCodeBench, a synthetic benchmark for perception-to-program reconstruction: given a rendered raster image, a model must emit an executable drawing program that a deterministic evaluator re-renders and compares with the target. The v1 DSL has four primitives on a 512 x 512 black-on-white canvas, but every instance is generated from a seeded RNG, so fresh held-out sets can be created to reduce exact-instance contamination. We release a frozen eval_v1 split with 150 samples across easy, medium, and hard tiers, scored by exact match, pixel accuracy, foreground IoU, parse success, and execution success. We evaluate an empty-program floor, a classical computer-vision heuristic, Claude Opus 4.7 at high and max effort, and GPT-5.5 at medium and extra_high reasoning effort. The heuristic is competitive on easy scenes but collapses when overlaps fuse components; the strongest multimodal configuration preserves much of the foreground structure but still misses exact match because of small parameter errors. Best overall exact match remains low, so ShapeCodeBench is far from saturated. The benchmark code, frozen dataset, run artifacts, and paper sources are released to support independent replication and extension.", "github": "https://github.com/shivamk3r/shape-code-bench" }, { "date": "2026-05-14", "arxiv_id": "2605.12587", "title": "TrackCraft3R: Repurposing Video Diffusion Transformers for Dense 3D Tracking", "authors": [ "Jisu Nam", "Jahyeok Koo", "Soowon Son", "Jaewoo Jung", "Honggyu An", "Junhwa Hur", "Seungryong Kim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cvlab-kaist/TrackCraft3r", "github_api_added_by": "user", "project_page": "https://cvlab-kaist.github.io/TrackCraft3r", "abstract": "Dense 3D tracking from monocular video is fundamental to dynamic scene understanding. While recent 3D foundation models provide reliable per-frame geometry, recovering object motion in this geometry remains challenging and benefits from strong motion priors learned from real-world videos. Existing 3D trackers either follow iterative paradigms trained from scratch on synthetic data or fine-tune 3D reconstruction models learned from static multi-view images, both lacking real-world motion priors. Pre-trained video diffusion transformers (video DiTs) offer rich spatio-temporal priors from internet-scale videos, making them a promising foundation for 3D tracking. However, their frame-anchored formulation, which generates each frame's content, is fundamentally mismatched with reference-anchored dense 3D tracking, which must follow the same physical points from a reference frame across time. We present TrackCraft3R, the first method to repurpose a video DiT as a feed-forward dense 3D tracker. Given a monocular video and its frame-anchored reconstruction pointmap, TrackCraft3R predicts a reference-anchored tracking pointmap that follows every pixel of the first frame across time in a single forward pass, along with its visibility. We achieve this through two designs: (i) a dual-latent representation that uses per-frame geometry latents and reference-anchored track latents as dense queries, and (ii) temporal RoPE alignment, which specifies the target timestamp of each track latent. Together, these designs convert the per-frame generative paradigm of video DiTs into a reference-anchored tracking formulation with LoRA fine-tuning. TrackCraft3R achieves state-of-the-art performance on standard sparse and dense 3D tracking benchmarks, while running 1.3x faster and using 4.6x less peak memory than the strongest prior method. We further demonstrate robustness to large motions and long videos.", "github": "https://github.com/cvlab-kaist/TrackCraft3r" }, { "date": "2026-05-14", "arxiv_id": "2605.12975", "title": "Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation", "authors": [ "Jiashuo Sun", "Jimeng Shi", "Yixuan Xie", "Saizhuo Wang", "Jash Rajesh Parekh", "Pengcheng Jiang", "Zhiyi Shi", "Jiajun Fan", "Qinglong Zheng", "Peiran Li", "Shaowen Wang", "Ge Liu", "Jiawei Han" ], "github_manual": "", "github_regex": "https://github.com/GasolSun36/PyRAG", "github_paper_md": "", "github_api": "https://github.com/GasolSun36/PyRAG", "github_api_added_by": "user", "project_page": "https://gasolsun36.github.io/PyRAG/", "abstract": "Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where solving the task requires chaining multiple retrieval and reasoning steps. Key challenges are that current methods represent reasoning through free-form natural language, where intermediate states are implicit, retrieval queries can drift from intended entities, and errors are detected by the same model that produces them making self-reflection an unreliable, ungrounded signal. We observe that multi-hop question answering is a typical form of step-by-step computation, and that this structured process aligns closely with how code-specialized language models are trained to operate. Motivated by this, we introduce \\pyrag, a framework that reformulates multi-hop RAG as program synthesis and execution. Instead of free-form reasoning trajectories, \\pyrag represents the reasoning process as an executable Python program over retrieval and QA tools, exposing intermediate states as variables, producing deterministic feedback through execution, and yielding an inspectable trace of the entire reasoning process. This formulation further enables compiler-grounded self-repair and execution-driven adaptive retrieval without any additional training. Experiments on five QA benchmarks (PopQA, HotpotQA, 2WikiMultihopQA, MuSiQue, and Bamboogle) show that \\pyrag consistently outperforms strong baselines under both training-free and RL-trained settings, with especially large gains on compositional multi-hop datasets. Our code, data and models are publicly available at https://github.com/GasolSun36/PyRAG.", "github": "https://github.com/GasolSun36/PyRAG" }, { "date": "2026-05-14", "arxiv_id": "2605.09942", "title": "HAGE: Harnessing Agentic Memory via RL-Driven Weighted Graph Evolution", "authors": [ "Dongming Jiang", "Yi Li", "Guanpeng Li", "Qiannan Li", "Bingzhe Li" ], "github_manual": "", "github_regex": "https://github.com/FredJiang0324/HAGE_MVPReview", "github_paper_md": "", "github_api": "https://github.com/FredJiang0324/HAGE_MVPReview", "github_api_added_by": "user", "project_page": "https://github.com/FredJiang0324/HAGE_MVPReview", "abstract": "Memory retrieval in agentic large language model (LLM) systems is often treated as a static lookup problem, relying on flat vector search or fixed binary relational graphs. However, fixed graph structures cannot capture the varying strength, confidence, and query-dependent relevance of relationships between events. In this paper, we propose HAGE, a weighted multi-relational memory framework that reconceptualizes retrieval as sequential, query-conditioned traversal over a unified relational memory graph. Memory is organized as relation-specific graph views over shared memory nodes, where each edge is associated with a trainable relation feature vector encoding multiple relational signals. Given a query, an LLM-based classifier identifies the relational intent, and a routing network dynamically modulates the corresponding dimensions of the edge embedding. Traversal scores are computed via a learned combination of semantic similarity and these query-conditioned edge representations. This allows memory traversal to prioritize high-utility relational paths while softly suppressing noisy or weakly relevant connections. Beyond adaptive traversal, HAGE further introduces a reinforcement learning-based training framework that jointly optimizes routing behavior and edge representations using downstream tasks. Finally, empirical results demonstrate improved long-horizon reasoning accuracy and a favorable accuracy-efficiency trade-off compared to state-of-the-art agentic memory systems. Our code is available at https://github.com/FredJiang0324/HAGE_MVPReview.", "github": "https://github.com/FredJiang0324/HAGE_MVPReview" }, { "date": "2026-05-14", "arxiv_id": "2605.12411", "title": "Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling", "authors": [ "Eilam Shapira", "Moshe Tennenholtz", "Roi Reichart" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "AI agents negotiate and transact in natural language with unfamiliar counterparts: a buyer bot facing an unknown seller, or a procurement assistant negotiating with a supplier. In such interactions, the counterpart's LLM, prompts, control logic, and rule-based fallbacks are hidden, while each decision can have monetary consequences. We ask whether an agent can predict an unfamiliar counterpart's next decision from a few interactions. To avoid real-world logging confounds, we study this problem in controlled bargaining and negotiation games, formulating it as target-adaptive text-tabular prediction: each decision point is a table row combining structured game state, offer history, and dialogue, while K previous games of the same target agent, i.e., the counterpart being modeled, are provided in the prompt as labeled adaptation examples. Our model is built on a tabular foundation model that represents rows using game-state features and LLM-based text representations, and adds LLM-as-Observer as an additional representation: a small frozen LLM reads the decision-time state and dialogue; its answer is discarded, and its hidden state becomes a decision-oriented feature, making the LLM an encoder rather than a direct few-shot predictor. Training on 13 frontier-LLM agents and testing on 91 held-out scaffolded agents, the full model outperforms direct LLM-as-Predictor prompting and game+text features baselines. Within this tabular model, Observer features contribute beyond the other feature schemes: at K=16, they improve response-prediction AUC by about 4 points across both tasks and reduce bargaining offer-prediction error by 14%. These results show that formulating counterpart prediction as a target-adaptive text-tabular task enables effective adaptation, and that hidden LLM representations expose decision-relevant signals that direct prompting does not surface.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.10268", "title": "MemReread: Enhancing Agentic Long-Context Reasoning via Memory-Guided Rereading", "authors": [ "Baibei Ji", "Xiaoyang Weng", "Juntao Li", "Zecheng Tang", "Yihang Lou", "Min Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/iiGray/MemReread", "github_api_added_by": "user", "project_page": "", "abstract": "To tackle long-context reasoning tasks without the quadratic complexity of standard attention mechanisms, approaches based on agent memory have emerged, which typically maintain a dynamically updated memory when linearly processing document chunks. To mitigate the potential loss of latent evidence in this memorize-while-reading paradigm, recent works have integrated retrieval modules that allow agents to recall information previously discarded during memory overwriting. However, retrieval-based recall suffers from both evidence loss during memory formation and interference induced by invalid queries. To overcome these limitations, we propose MemReread. Built upon streaming reading, MemReread circumvents intermediate retrieval. It triggers question decomposition and rereading when the final memory is insufficient, enabling the recovery of indirect facts that were prematurely discarded. This design supports non-linear reasoning while preserving the inherent logical flow of document comprehension. To further enhance practicality, we introduce a reinforcement learning framework that enhances length extrapolation capability while dynamically determining the number of rereading passes based on task complexity, thereby flexibly controlling computational overhead. Extensive experiments demonstrate that MemReread consistently outperforms baseline frameworks on long-context reasoning tasks, while maintaining linear time complexity with respect to context length.", "github": "https://github.com/iiGray/MemReread" }, { "date": "2026-05-14", "arxiv_id": "2605.05704", "title": "SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety", "authors": [ "Zhe Liu", "Zonghao Ying", "Wenxin Zhang", "Quanchen Zou", "Deyue Zhang", "Dongdong Yang", "Xiangzheng Zhang", "Hao Peng" ], "github_manual": "", "github_regex": "https://github.com/ljj-cyber/SafeHarbor", "github_paper_md": "", "github_api": "https://github.com/ljj-cyber/SafeHarbor", "github_api_added_by": "user", "project_page": "", "abstract": "With the rapid evolution of foundation models, Large Language Model (LLM) agents have demonstrated increasingly powerful tool-use capabilities. However, this proficiency introduces significant security risks, as malicious actors can manipulate agents into executing tools to generate harmful content. While existing defensive mechanisms are effective, they frequently suffer from the over-refusal problem, where increased safety strictness compromises the agent's utility on benign tasks. To mitigate this trade-off, we propose SafeHarbor, a novel framework designed to establish precise decision boundaries for LLM agents. Unlike static guidelines, SafeHarbor extracts context-aware defense rules through enhanced adversarial generation. We design a local hierarchical memory system for dynamic rule injection, offering a training-free, efficient, and plug-and-play solution. Furthermore, we introduce an information entropy-based self-evolution mechanism that continuously optimizes the memory structure through dynamic node splitting and merging. Extensive experiments demonstrate that SafeHarbor achieves state-of-the-art performance on both ambiguous benign tasks and explicit malicious attacks, notably attaining a peak benign utility of 63.6\\% on GPT-4o while maintaining a robust refusal rate exceeding 93\\% against harmful requests. The source code is publicly available at https://github.com/ljj-cyber/SafeHarbor.", "github": "https://github.com/ljj-cyber/SafeHarbor" }, { "date": "2026-05-14", "arxiv_id": "2605.12004", "title": "Learning Agentic Policy from Action Guidance", "authors": [ "Yuxiang Ji", "Zengbin Wang", "Yong Wang", "Shidong Yang", "Ziyu Ma", "Guanhua Chen", "Zonghua Sun", "Liaoni Wu", "Xiangxiang Chu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AMAP-ML/ActGuide-RL", "github_api_added_by": "user", "project_page": "", "abstract": "Agentic reinforcement learning (RL) for Large Language Models (LLMs) critically depends on the exploration capability of the base policy, as training signals emerge only within its in-capability region. For tasks where the base policy cannot reach reward states, additional training or external guidance is needed to recover effective learning signals. Rather than relying on costly iterative supervised fine tuning (SFT), we exploit the abundant action data generated in everyday human interactions. We propose ActGuide-RL, which injects action data as plan-style reference guidance, enabling the agentic policy to overcome reachability barriers to reward states. Guided and unguided rollouts are then jointly optimized via mixed-policy training, internalizing the exploration gains back into the unguided policy. Motivated by a theoretical and empirical analysis of the benefit-risk trade-off, we adopt a minimal intervention principle that invokes guidance only as an adaptive fallback, matching task difficulty while minimizing off-policy risk. On search-agent benchmarks, ActGuide-RL substantially improves over zero RL (+10.7 pp on GAIA and +19 pp on XBench with Qwen3-4B), and performs on par with the SFT+RL pipeline without any cold start. This suggests a new paradigm for agentic RL that reduces the reliance on heavy SFT data by using scalable action guidance instead.", "github": "https://github.com/AMAP-ML/ActGuide-RL" }, { "date": "2026-05-14", "arxiv_id": "2605.10616", "title": "MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image", "authors": [ "Alan Arazi", "Eilam Shapira", "Shoham Grunblat", "Mor Ventura", "Elad Hoffer", "Gioia Blayer", "David Holzm\u00fcller", "Lennart Purucker", "Ga\u00ebl Varoquaux", "Frank Hutter", "Roi Reichart" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Tabular Foundation Models have recently established the state of the art in supervised tabular learning, by leveraging pretraining to learn generalizable representations of numerical and categorical structured data. However, they lack native support for unstructured modalities such as text and image, and rely on frozen, pretrained embeddings to process them. On established Multimodal Tabular Learning benchmarks, we show that tuning the embeddings to the task improves performance. Existing benchmarks, however, often focus on the mere co-occurrence of modalities; this leads to high variance across datasets and masks the benefits of task-specific tuning. To address this gap, we introduce MulTaBench, a benchmark of 40 datasets, split equally between image-tabular and text-tabular tasks. We focus on predictive tasks where the modalities provide complementary predictive signal, and where generic embeddings lose critical information, necessitating Target-Aware Representations that are aligned with the task. Our experimental results demonstrate that the gains from target-aware representation tuning generalize across both text and image modalities, several tabular learners, encoder scales, and embedding dimensions. MulTaBench constitutes the largest image-tabular benchmarking effort to date, spanning high-impact domains such as healthcare and e-commerce. It is designed to enable the research of novel architectures which incorporate joint modeling and target-aware representations, paving the way for the development of novel Multimodal Tabular Foundation Models.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.13087", "title": "Vividh-ASR: A Complexity-Tiered Benchmark and Optimization Dynamics for Robust Indic Speech Recognition", "authors": [ "Kush Juvekar", "Kavya Manohar", "Aditya Srinivas Menon", "Arghya Bhattacharya", "Kumarmanas Nethil" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Fine-tuning multilingual ASR models like Whisper for low-resource languages often improves read speech but degrades spontaneous audio performance, a phenomenon we term studio-bias. To diagnose this mismatch, we introduce Vividh-ASR, a complexity-stratified benchmark for Hindi and Malayalam across four tiers: studio, broadcast, spontaneous, and synthetic noise. Through a controlled study of learning-rate timing and curriculum ordering, we find that early large parameter updates improve global WER by 12 absolute points, while a hard-to-easy curriculum adds gains for spontaneous speech. These findings motivate reverse multi-stage fine-tuning (R-MFT), a training recipe that enables a parameter-efficient 244M Whisper model to match or exceed conventionally fine-tuned 769M counterparts. Representational analysis via CKA and SVD reveals effective schedules concentrate adaptation in the decoder, preserving the pre-trained encoder's acoustic geometry. We release the benchmark and models.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.12995", "title": "F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking", "authors": [ "Rohan Surana", "Gagan Mundada", "Junda Wu", "Xintong Li", "Yizhu Jiao", "Bowen Jin", "Sizhe Zhou", "Tong Yu", "Ritwik Sinha", "Jiawei Han", "Jingbo Shang", "Julian McAuley" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Traditional retrieval pipelines optimize utility through stages of candidate retrieval and reranking, where ranking operates over a predefined candidate set. Large Language Models (LLMs) broaden this into a generative process: given a candidate pool, an LLM can generate a subset and order it within a single autoregressive pass. However, this flexibility introduces a new optimization challenge: the model must search a combinatorial output space while receiving utility feedback only after the full ranked list is generated. Because this feedback is defined over the completed sequence, it cannot distinguish whether a poor result arises from failing to generate a relevant subset or from failing to rank that subset correctly. This credit assignment gap makes end-to-end optimization unstable and sample-inefficient. Existing systems often address this by separating candidate generation from ranking. However, such decoupling remains misaligned with downstream utility because ranking is limited by the candidate set it receives. To bridge this gap, we propose a unified framework that performs both within a single autoregressive rollout and optimizes them end-to-end via factorized group-relative policy optimization (F-GRPO). Our framework factorizes the policy into candidate generation and ranking while sharing a single LLM backbone, and jointly trains them with an order-invariant coverage reward and a position-aware utility reward. To address the resulting phase-specific credit assignment problem, we use separate group-relative advantages for generation and ranking within a two-phase sequence-level objective. Across sequential recommendation and multi-hop question answering benchmarks, F-GRPO improves top-ranked performance over GRPO and decoupled baselines, outperforms supervised alternatives, and remains competitive with strong zero-shot rerankers, with no architectural changes at inference time.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.13757", "title": "FrameSkip: Learning from Fewer but More Informative Frames in VLA Training", "authors": [ "Bin Yu", "Shijie Lian", "Xiaopeng Lin", "Zhaolong Shen", "Yuliang Wei", "Changti Wu", "Hang Yuan", "Haishan Liu", "Bailing Wang", "Cong Huang", "Kai Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZGC-EmbodyAI/FrameSkip", "github_api_added_by": "user", "project_page": "https://huggingface.co/collections/VLyb/frameskip", "abstract": "Vision-Language-Action (VLA) policies are commonly trained from dense robot demonstration trajectories, often collected through teleoperation, by sampling every recorded frame as if it provided equally useful supervision. We argue that this convention creates a temporal supervision imbalance: long low-change segments dominate the training stream, while manipulation-critical transitions such as alignment, contact, grasping, and release appear only sparsely. We introduce FrameSkip, a data-layer frame selection framework that scores trajectory frames using action variation, visual-action coherence, task-progress priors, and gripper-transition preservation, then remaps training samples toward high-importance frames under a target retention ratio. Because FrameSkip operates only in the dataloader, it leaves the VLA architecture, action head, training objective, and inference procedure unchanged. Across RoboCasa-GR1, SimplerEnv, and LIBERO, FrameSkip improves the success-retention trade-off over full-frame training and simpler frame selection variants, achieving a macro-average success rate of 76.15% across the three benchmarks compared with 66.50% for full-frame training while using a compressed trajectory view that retains 20% of unique frames in the main setting.", "github": "https://github.com/ZGC-EmbodyAI/FrameSkip" }, { "date": "2026-05-14", "arxiv_id": "2605.09433", "title": "Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs", "authors": [ "Yunhong Lu", "Qichao Wang", "Hengyuan Cao", "Xiaoyin Xu", "Min Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Existing preference datasets for text-to-image models typically store only the final winner/loser images. This representation is insufficient for rectified flow (RF) models, whose generation is naturally indexed by a specific prior noise sample and follows a nearly straight denoising trajectory. In contrast, prior DPO-style alignment for diffusion models commonly estimates trajectories using an independent forward noising process, which can be mismatched to the true reverse dynamics and introduces unnecessary variance. We propose Prior Noise-Aware Preference Optimization (PNAPO), an off-policy alignment framework specialized for rectified flow. PNAPO augments preference data by retaining the paired prior noises used to generate each winner/loser image, turning the standard (prompt, winner, loser) triplet into a sextuple. Leveraging the straight-line property of RF, we estimate intermediate states via noise-image interpolation, which constrains the trajectory estimation space and yields a tighter surrogate objective for preference optimization. In addition, we introduce a dynamic regularization strategy that adapts the DPO regularization based on (i) the reward gap between winner and loser and (ii) training progress, improving stability and sample efficiency. Experiments on state-of-the-art RF T2I backbones show that PNAPO consistently improves preference metrics while substantially reducing training compute.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.13481", "title": "PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents", "authors": [ "Mikhail Menschikov", "Matvey Iskornev", "Alexander Kharitonov", "Alina Bogdanova", "Mikhail Belkin", "Ekaterina Lisitsyna", "Artyom Sosedka", "Victoria Dochkina", "Ruslan Kostoev", "Ilia Perepechkin", "Evgeny Burnaev" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We introduce PersonalAI 2.0 (PAI-2), a novel framework, designed to enhance large language model (LLM) based systems through integration of external knowledge graphs (KG). The proposed approach addresses key limitations of existing Graph Retrieval-Augmented Generation (GraphRAG) methods by incorporating a dynamic, multistage query processing pipeline. The central point of PAI-2 design is its ability to perform adaptive, iterative information search, guided by extracted entities, matched graph vertices and generated clue-queries. Conducted evaluation over six benchmarks (Natural Questions, TriviaQA, HotpotQA, 2WikiMultihopQA, MuSiQue and DiaASQ) demonstrates improvement in factual correctness of generating answers compared to analogues methods (LightRAG, RAPTOR, and HippoRAG 2). PAI-2 achieves 4% average gain by LLM-as-a-Judge across four benchmarks, reflecting its effectiveness in reducing hallucination rates and increasing precision. We show that use of graph traversal algorithms (e.g. BeamSearch, WaterCircles) gain superior results compared to standard flatten retriever on average 6%, while enabled search plan enhancement mechanism gain 18% boost compared to disabled one by LLM-as-a-Judge across six datasets. In addition, ablation study reveals that PAI-2 achieves the SOTA result on MINE-1 benchmark, achieving 89% information-retention score, using LLMs from 7-14B tiers. Collectively, these findings underscore the potential of PAI-2 to serve as a foundational model for next-generation personalized AI applications, requiring scalable, context-aware knowledge representation and reasoning capabilities.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.09591", "title": "From Pixels to Concepts: Do Segmentation Models Understand What They Segment?", "authors": [ "Shuang Liang", "Zeqing Wang", "Yuxian Li", "Xihui Liu", "Han Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/T-S-Liang/CAFE", "github_api_added_by": "user", "project_page": "https://t-s-liang.github.io/CAFE", "abstract": "Segmentation is a fundamental vision task underlying numerous downstream applications. Recent promptable segmentation models, such as Segment Anything Model 3 (SAM3), extend segmentation from category-agnostic mask prediction to concept-guided localization conditioned on high-level textual prompts. However, existing benchmarks primarily evaluate mask accuracy or object presence, leaving unclear whether these models faithfully ground the queried concept or instead rely on visually salient but semantically misleading cues. We introduce CAFE: Counterfactual Attribute Factuality Evaluation, a novel benchmark for evaluating concept-faithful segmentation in promptable segmentation models. Our CAFE is built on attribute-level counterfactual manipulation: the target region and ground-truth mask are preserved, while attributes such as surface appearance, context, or material composition are modified to introduce misleading semantic cues. The benchmark contains 2,146 paired test samples, each consisting of a target image, a ground-truth mask, a positive prompt, and a misleading negative prompt. These samples cover three counterfactual categories: Superficial Mimicry (SM), Context Conflict (CC), and Ontological Conflict (OC). We evaluate various model types and sizes on our CAFE. Experiments reveal a systematic gap between localization quality and concept discrimination: models often generate accurate masks even for misleading prompts, suggesting that strong mask prediction does not necessarily imply faithful semantic grounding. Our CAFE provides a controlled benchmark for diagnosing whether promptable segmentation models perform concept-faithful grounding rather than shortcut-driven mask retrieval.", "github": "https://github.com/T-S-Liang/CAFE" }, { "date": "2026-05-14", "arxiv_id": "2605.13542", "title": "RealICU: Do LLM Agents Understand Long-Context ICU Data? A Benchmark Beyond Behavior Imitation", "authors": [ "Chengzhi Shen", "Weixiang Shen", "Tobias Susetzky", "Chen", "Chen", "Jun Li", "Yuyuan Liu", "Xuepeng Zhang", "Zhenyu Gong", "Daniel Rueckert", "Jiazhen Pan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/chengzhi-leo/RealICU-Bench", "github_api_added_by": "user", "project_page": "https://chengzhi-leo.github.io/RealICU-Bench/", "abstract": "Intensive care units (ICU) generate long, dense and evolving streams of clinical information, where physicians must repeatedly reassess patient states under time pressure, underscoring a clear need for reliable AI decision support. Existing ICU benchmarks typically treat historical clinician actions as ground truth. However, these actions are made under incomplete information and limited temporal context of the underlying patient state, and may therefore be suboptimal, making it difficult to assess the true reasoning capabilities of AI systems. We introduce RealICU, a hindsight-annotated benchmark for evaluating large language models (LLMs) under realistic ICU conditions, where labels are created after senior physicians review the full patient trajectory. We formulate four physician-motivated tasks: assess Patient Status, Acute Problems, Recommended Actions, and Red Flag actions that risk unsafe outcomes. We partition each trajectory with 30-min windows and release two datasets: RealICU-Gold with 930-window annotations from 94 MIMIC-IV patients, and RealICU-Scale with 11,862 windows extended by Oracle, a physician-validated LLM hindsight labeler. Existing LLMs including memory-augmented ones performed poorly on RealICU, exposing two failure modes: a recall-safety tradeoff for clinical recommendations, and an anchoring bias to early interpretations of the patient. We further introduce ICU-Evo to study structured-memory agents that improves long-horizon reasoning but does not fully eliminate safety failures. Together, RealICU provides a clinically grounded testbed for measuring and improving AI sequential decision-support in high-stakes care. Project page: https://chengzhi-leo.github.io/RealICU-Bench/", "github": "https://github.com/chengzhi-leo/RealICU-Bench" }, { "date": "2026-05-14", "arxiv_id": "2605.13030", "title": "FeatCal: Feature Calibration for Post-Merging Models", "authors": [ "Yanggan Gu", "Shuo Cai", "Zihao Wang", "Wenjun Wang", "Yuanyi Wang", "Pengkai Wang", "Sirui Huang", "Su Lu", "Jianmin Wu", "Hongxia Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/egangu/featcal", "github_api_added_by": "user", "project_page": "https://github.com/egangu/featcal", "abstract": "Model merging combines task experts into one model and avoids joint training, retraining, or deploying many expert models, but the merged model often still underperforms task experts. We study this performance gap through feature drift, the difference between features produced by the merged model and by the expert on the same input. Our theory decomposes this drift into upstream propagation and local mismatch, tracks how it propagates and combines through later layers in forward order, and links final feature drift to output drift. This view motivates FeatCal, which uses a small calibration set to calibrate the merged model weights layer by layer in forward order, reducing feature drift while staying close to merged weights and preserving the benefits of model merging. FeatCal uses an efficient closed-form solution to update model weights, with no gradient descent, iterative optimization, or extra modules. On the main CLIP and GLUE benchmarks, FeatCal beats Surgery and ProbSurgery, the closest post-merging calibration baselines: 85.5% vs. 77.0%/78.8% on CLIP-ViT-B/32 Task Arithmetic (TA) and 85.2% vs. 83.7%/82.2% on FLAN-T5-base GLUE. On CLIP-ViT-B/32, 8 examples per task reach 82.9%, and 256 examples per task take 53 seconds, about 4x faster than both baselines, showing better sample efficiency and lower calibration cost.", "github": "https://github.com/egangu/featcal" }, { "date": "2026-05-14", "arxiv_id": "2605.12556", "title": "M2Retinexformer: Multi-Modal Retinexformer for Low-Light Image Enhancement", "authors": [ "Youssef Aboelwafa", "Hicham G. Elmongui", "Marwan Torki" ], "github_manual": "", "github_regex": "https://github.com/YoussefAboelwafa/M2Retinexformer", "github_paper_md": "", "github_api": "https://github.com/YoussefAboelwafa/M2Retinexformer", "github_api_added_by": "user", "project_page": "", "abstract": "Low-light image enhancement is challenging due to complex degradations, including amplified noise, artifacts, and color distortion. While Retinex-based deep learning methods have achieved promising results, they primarily rely on single-modality RGB information. We propose M2Retinexformer (Multi-Modal Retinexformer), a novel framework that extends Retinexformer by incorporating depth cues, luminance priors, and semantic features within a progressive refinement pipeline. Depth provides geometric context that is invariant to lighting variations, while luminance and semantic features offer explicit guidance on brightness distribution and scene understanding. Modalities are extracted at multiple scales and fused through cross-attention, with adaptive gating dynamically balancing illumination-guided self-attention and cross-attention based on the reliability of auxiliary cues. Evaluations on the LOL, SID, SMID, and SDSD benchmarks demonstrate overall improvements over Retinexformer and recent state-of-the-art methods. Code and pretrained weights are available at https://github.com/YoussefAboelwafa/M2Retinexformer", "github": "https://github.com/YoussefAboelwafa/M2Retinexformer" }, { "date": "2026-05-14", "arxiv_id": "2605.04651", "title": "FAAST: Forward-Only Associative Learning via Closed-Form Fast Weights for Test-Time Supervised Adaptation", "authors": [ "Guangsheng Bao", "Hongbo Zhang", "Han Cui", "Ke Sun", "Yanbin Zhao", "Juncai He", "Yue Zhang" ], "github_manual": "", "github_regex": "https://github.com/baoguangsheng/faast", "github_paper_md": "", "github_api": "https://github.com/baoguangsheng/faast", "github_api_added_by": "user", "project_page": "", "abstract": "Adapting pretrained models typically involves a trade-off between the high training costs of backpropagation and the heavy inference overhead of memory-based or in-context learning. We propose FAAST, a forward-only associative adaptation method that analytically compiles labeled examples into fast weights in a single pass. By eliminating memory or context dependence, FAAST achieves constant-time inference and decouples task adaptation from pretrained representation. Across image classification and language modeling benchmarks, FAAST matches or exceeds backprop-based adaptation while reducing adaptation time by over 90% and is competitive to memory/context-based adaptation while saving memory usage by up to 95%. These results demonstrate FAAST as a highly efficient, scalable solution for supervised task adaptation, particularly for resource-constrained models. We release the code and models at https://github.com/baoguangsheng/faast.", "github": "https://github.com/baoguangsheng/faast" }, { "date": "2026-05-14", "arxiv_id": "2605.05806", "title": "Retrieval from Within: An Intrinsic Capability of Attention-Based Models", "authors": [ "Elad Hoffer", "Yochai Blau", "Edan Kinderman", "Ron Banner", "Daniel Soudry", "Boris Ginsburg" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Retrieval-augmented generation (RAG) typically treats retrieval and generation as separate systems. We ask whether an attention-based encoder-decoder can instead retrieve directly from its own internal representations. We introduce INTRA (INTrinsic Retrieval via Attention), a framework where decoder attention queries score pre-encoded evidence chunks that are then directly reused as context for generation. By construction, INTRA unifies retrieval and generation, eliminating the retriever-generator mismatch typical of RAG pipelines. This design also amortizes context encoding by reusing precomputed encoder states across queries. On question-answering benchmarks, INTRA outperforms strong engineered retrieval pipelines on both evidence recall and end-to-end answer quality. Our results demonstrate that attention-based models already possess a retrieval mechanism that can be elicited, rather than added as an external module.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.13292", "title": "IndicMedDialog: A Parallel Multi-Turn Medical Dialogue Dataset for Accessible Healthcare in Indic Languages", "authors": [ "Shubham Kumar Nigam", "Suparnojit Sarkar", "Piyush Patel" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ShubhamKumarNigam/IndicMedDialog", "github_api_added_by": "user", "project_page": "", "abstract": "Most existing medical dialogue systems operate in a single-turn question--answering paradigm or rely on template-based datasets, limiting conversational realism and multilingual applicability. We introduce IndicMedDialog, a parallel multi-turn medical dialogue dataset spanning English and nine Indic languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Punjabi, Tamil, Telugu, and Urdu. The dataset extends MDDial with LLM-generated synthetic consultations, translated using TranslateGemma, verified by native speakers, and refined through a script-aware post-processing pipeline to correct phonetic, lexical, and character-spacing errors. Building on this dataset, we fine-tune IndicMedLM via parameter-efficient adaptation of a quantized small language model, incorporating optional patient pre-context to personalise multi-turn symptom elicitation. We evaluate against zero-shot multilingual baselines, conduct systematic error analysis across ten languages, and validate clinical plausibility through medical expert evaluation.", "github": "https://github.com/ShubhamKumarNigam/IndicMedDialog" }, { "date": "2026-05-14", "arxiv_id": "2605.08978", "title": "Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization", "authors": [ "Xingyuan Hua", "Sheng Yue", "Ju Ren" ], "github_manual": "", "github_regex": "https://github.com/HansenHua/EAPO-ICML26", "github_paper_md": "", "github_api": "https://github.com/HansenHua/EAPO-ICML26", "github_api_added_by": "user", "project_page": "", "abstract": "Recent advancements in agentic test-time scaling allow models to gather environmental feedback before committing to final actions. A key limitation of existing methods is that they typically employ undifferentiated exploration strategies, lacking the ability to adaptively distinguish when exploration is truly required. In this paper, we propose an exploration-aware reinforcement learning framework that enables LLM agents to adaptively explore only when uncertainty is high. Our method introduces a fine-grained reward function via variational inference that explicitly evaluates exploratory actions by estimating their potential to improve future decision-making, together with an exploration-aware grouping mechanism that separates exploratory actions from task-completion actions during optimization. By targeting informational gaps, this design allows agents to explore selectively and transition to execution as soon as the task context is clear. Empirically, we demonstrate that our approach achieves consistent improvements across a range of challenging text-based and GUI-based agent benchmarks. Code is available at https://github.com/HansenHua/EAPO-ICML26 and models are available at https://huggingface.co/hansenhua/EAPO-ICML26.", "github": "https://github.com/HansenHua/EAPO-ICML26" }, { "date": "2026-05-14", "arxiv_id": "2605.13841", "title": "EVA-Bench: A New End-to-end Framework for Evaluating Voice Agents", "authors": [ "Tara Bogavelli", "Gabrielle Gauthier Melan\u00e7on", "Katrina Stankiewicz", "Oluwanifemi Bamgbose", "Fanny Riols", "Hoang H. Nguyen", "Raghav Mehndiratta", "Lindsay Devon Brin", "Joseph Marinier", "Hari Subramani", "Anil Madamala", "Sridhar Krishna Nemala", "Srinivas Sunkara" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ServiceNow/eva", "github_api_added_by": "user", "project_page": "https://servicenow.github.io/eva/", "abstract": "Voice agents, artificial intelligence systems that conduct spoken conversations to complete tasks, are increasingly deployed across enterprise applications. However, no existing benchmark jointly addresses two core evaluation challenges: generating realistic simulated conversations, and measuring quality across the full scope of voice-specific failure modes. We present EVA-Bench, an end-to-end evaluation framework that addresses both. On the simulation side, EVA-Bench orchestrates bot-to-bot audio conversations over dynamic multi-turn dialogues, with automatic simulation validation that detects user simulator error and appropriately regenerates conversations before scoring. On the measurement side, EVA-Bench introduces two composite metrics: EVA-A (Accuracy), capturing task completion, faithfulness, and audio-level speech fidelity; and EVA-X (Experience), capturing conversation progression, spoken conciseness, and turn-taking timing. Both metrics apply to different agent architectures, enabling direct cross-architecture comparison. EVA-Bench includes 213 scenarios across three enterprise domains, a controlled perturbation suite for accent and noise robustness, and pass@1, pass@k, pass^k measurements that distinguish peak from reliable capability. Across 12 systems spanning all three architectures, we find: (1) no system simultaneously exceeds 0.5 on both EVA-A pass@1 and EVA-X pass@1; (2) peak and reliable performance diverge substantially (median pass@k - pass^k gap of 0.44 on EVA-A); and (3) accent and noise perturbations expose substantial robustness gaps, with effects varying across architectures, systems, and metrics (mean up to 0.314). We release the full framework, evaluation suite, and benchmark data under an open-source license.", "github": "https://github.com/ServiceNow/eva" }, { "date": "2026-05-14", "arxiv_id": "2605.08583", "title": "Source or It Didn't Happen: A Multi-Agent Framework for Citation Hallucination Detection", "authors": [ "Mingzhe Li", "Zhiqiang Lin", "Shiqing Ma" ], "github_manual": "", "github_regex": "https://github.com/aaFrostnova/CiteTracer", "github_paper_md": "", "github_api": "https://github.com/aaFrostnova/CiteTracer", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models are increasingly used in scientific writing, yet they can fabricate citation-shaped references that appear plausible but fail bibliographic verification. Existing detectors often reduce verification to binary found/not-found decisions and rely on brittle parsing or incomplete retrieval, offering little field-level signal to auditors. We reframe citation hallucination detection as taxonomy-aligned field-level adjudication and introduce a 12-code taxonomy spanning Real, Potential, and Hallucinated citations. Based on this taxonomy, we build CiteTracer, a cascading multi-agent detector that extracts structured citations from PDF and BibTeX, retrieves evidence through cache lookup, URL fetch, scholar connectors, and web search, applies deterministic field matching, and routes ambiguous cases to class-specialist judgers. We release a benchmark of 2,450 synthetic citations built from real seeds with controlled LLM mutations, paired with 957 real-world fabricated citations drawn from ICLR 2026 and an anonymous conference desk-rejected submissions. CiteTracer reaches 97.1% accuracy on the synthetic benchmark, with class-level F1 scores of 97.0, 95.8, and 98.5 for Real, Potential, and Hallucinated, respectively, and detects 97.1% of fabrications on the real-world set without abstaining. Code: https://github.com/aaFrostnova/CiteTracer.", "github": "https://github.com/aaFrostnova/CiteTracer" }, { "date": "2026-05-14", "arxiv_id": "2605.08703", "title": "RewardHarness: Self-Evolving Agentic Post-Training", "authors": [ "Yuxuan Zhang", "Penghui Du", "Bo Li", "Cong Wei", "Junwen Miao", "Huaisong Zhang", "Songcheng Cai", "Yubo Wang", "Dongfu Jiang", "Yuyu Zhang", "Ping Nie", "Wenhu Chen", "Changqian Yu", "Kelsey R. Allen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TIGER-AI-Lab/RewardHarness", "github_api_added_by": "user", "project_page": "https://rewardharness.com/", "abstract": "Evaluating instruction-guided image edits requires rewards that reflect subtle human preferences, yet current reward models typically depend on large-scale preference annotation and additional model training. This creates a data-efficiency gap: humans can often infer the target evaluation criteria from only a few examples, while models are usually trained on hundreds of thousands of comparisons. We present RewardHarness, a self-evolving agentic reward framework that reframes reward modeling as context evolution rather than weight optimization. Instead of learning from large-scale annotations, RewardHarness aligns with human preferences by iteratively evolving a library of tools and skills from as few as 100 preference demonstrations. Given a source image, candidate edited images, and an editing instruction, an Orchestrator selects the most relevant subset of tools and skills from the maintained library, and a frozen Sub-Agent uses them to construct a reasoning chain that produces a preference judgment. By comparing predicted judgments with ground-truth preferences and analyzing successes and failures in the reasoning process, the Orchestrator automatically refines its library of tools and skills without additional human annotation. Using only 0.05% of the EditReward preference data, RewardHarness achieves 47.4% average accuracy on image-editing evaluation benchmarks, surpassing GPT-5 by 5.3 points. When used as a reward signal for GRPO fine-tuning, RL-tuned models achieve 3.52 on ImgEdit-Bench. Project page: https://rewardharness.com.", "github": "https://github.com/TIGER-AI-Lab/RewardHarness" }, { "date": "2026-05-14", "arxiv_id": "2605.12733", "title": "From Generalist to Specialist Representation", "authors": [ "Yujia Zheng", "Fan Feng", "Yuke Li", "Shaoan Xie", "Kevin Murphy", "Kun Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Given a generalist model, learning a task-relevant specialist representation is fundamental for downstream applications. Identifiability, the asymptotic guarantee of recovering the ground-truth representation, is critical because it sets the ultimate limit of any model, even with infinite data and computation. We study this problem in a completely nonparametric setting, without relying on interventions, parametric forms, or structural constraints. We first prove that the structure between time steps and tasks is identifiable in a fully unsupervised manner, even when sequences lack strict temporal dependence and may exhibit disconnections, and task assignments can follow arbitrarily complex and interleaving structures. We then prove that, within each time step, the task-relevant latent representation can be disentangled from the irrelevant part under a simple sparsity regularization, without any additional information or parametric constraints. Together, these results establish a hierarchical foundation: task structure is identifiable across time steps, and task-relevant latent representations are identifiable within each step. To our knowledge, each result provides a first general nonparametric identifiability guarantee, and together they mark a step toward provably moving from generalist to specialist models.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.10867", "title": "BEACON: A Multimodal Dataset for Learning Behavioral Fingerprints from Gameplay Data", "authors": [ "Ishpuneet Singh", "Gursmeep Kaur", "Uday Pratap Singh Atwal", "Guramrit Singh", "Gurjot Singh", "Maninder Singh" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/datasets/beacon-gui/BEACON-Dataset", "abstract": "Continuous authentication in high-stakes digital environments requires datasets with fine-grained behavioral signals under realistic cognitive and motor demands. But current benchmarks are often limited by small scale, unimodal sensing or lack of synchronised environmental context. To address this gap, this paper introduces BEACON ( Behavioral Engine for Authentication \\& Continuous Monitoring), a large-scale multimodal dataset that captures diverse skill tiers in competitive Valorant gameplay. BEACON contains approximately 430 GB of synchronised modality data (461 GB total on-disk including auxiliary Valorant configuration captures) from 79 sessions across 28 distinct players, estimated at 102.51 hours of active gameplay, including high-frequency mouse dynamics, keystroke events, network packet captures, screen recordings, hardware metadata, and in-game configuration context. BEACON leverages the high precision motor skills and high cognitive load that are inherent to tactical shooters, making it a rigorous stress test for the robustness of behavioral biometrics. The dataset allows for the study of continuous authentication, behavioral profiling, user drift and multimodal representation learning in a high-fidelity esports setting. The authors release the dataset and code on Hugging Face and GitHub to create a reproducible benchmark for evaluating next-generation behavioral fingerprinting and security models", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.10315", "title": "Active Tabular Augmentation via Policy-Guided Diffusion Inpainting", "authors": [ "Zheyu Zhang", "Shuo Yang", "Bardh Prenkaj", "Gjergji Kasneci" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/oooranz/TAP", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Generative tabular augmentation is appealing in data-scarce domains, yet the prevailing focus on distributional fidelity does not reliably translate into better downstream models. We formalize a fidelity-utility gap: common generative objectives prioritize distributional plausibility, whereas augmentation succeeds only when injected samples reduce the current learner's held-out evaluation loss. This gap motivates learning not just how to generate, but what to generate and when to inject as training evolves. We propose TAP (Tabular Augmentation Policy), which couples diffusion inpainting with a lightweight, learner-conditioned policy to steer generation toward high-utility regions and controls safe injection via explicit gating and conservative windowed commitment. Under severe data scarcity, TAP consistently outperforms strong generative baselines on seven real-world datasets, improving classification accuracy by up to 15.6 percentage points and reducing regression RMSE by up to 32%.", "github": "https://github.com/oooranz/TAP" }, { "date": "2026-05-14", "arxiv_id": "2605.11378", "title": "An Empirical Study of Automating Agent Evaluation", "authors": [ "Kang Zhou", "Sangmin Woo", "Haibo Ding", "Kiran Ramnath", "Subramanian Chidambaram", "Aosong Feng", "Vinayak Arannil", "Muhyun Kim", "Ishan Singh", "Darren Wang", "Zhichao Xu", "Megha Gandhi", "Nirmal Prabhu", "Soumya Smruti Mishra", "Vivek Singh", "Gouri Pandeshwar", "Lin Lee Cheong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/awslabs/Agent-EvalKit", "github_api_added_by": "user", "project_page": "", "abstract": "Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive. A natural question arises: can frontier coding assistants reliably automate this evaluation process? Our study shows that simply prompting coding assistants is insufficient for this task. Without domain-specific evaluation knowledge, frontier coding assistants achieve only a 30% execution success rate and produce over-engineered evaluations averaging 12+ metrics per agent, indicating that strong coding ability does not automatically translate to reliable agent evaluation. We introduce EvalAgent, an AI assistant that automates the end-to-end agent evaluation pipeline. EvalAgent encodes evaluation domain expertise as evaluation skills (procedural instructions, reusable code and templates, and dynamically retrieved API documentation) that compose into a trace-based pipeline producing complete evaluation artifacts including metrics, executable code, and reports. To systematically assess generated evaluations, we introduce a meta-evaluation framework alongside AgentEvalBench, a benchmark comprising 20 agents, each paired with evaluation requirements and test scenarios. We further propose the Eval@1 metric to measure whether generated evaluation code both executes and yields meaningful results on the first run. Our experiments show that EvalAgent produces focused evaluations, improving Eval@1 from 17.5% to 65%, and achieving 79.5% human expert preference over baseline approaches. Further ablation studies show that evaluation skills are critical for handling complex evaluation: removing them causes Eval@1 to drop significantly from 65% to 30%.", "github": "https://github.com/awslabs/Agent-EvalKit" }, { "date": "2026-05-14", "arxiv_id": "2605.09806", "title": "LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models", "authors": [ "Songtao Wei", "Yi Li", "Zhikai Li", "Xu Hu", "Yuede Ji", "Guanpeng Li", "Feng Chen", "Carl Yang", "Zhichun Guo", "Bingzhe Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CrazyMint/LEAD", "github_api_added_by": "user", "project_page": "", "abstract": "Large reasoning models, such as OpenAI o1 and DeepSeek-R1, tend to become increasingly verbose as their reasoning capabilities improve. These inflated Chain-of-Thought (CoT) trajectories often exceed what the underlying problems require, wasting compute, latency, and context budgets. While introducing length-based efficiency rewards during reinforcement learning offers a natural remedy, existing methods struggle with two fundamental challenges: the optimal balance between correctness and efficiency is non-stationary throughout training, and intrinsic reasoning budgets vary drastically across problems. Relying on static reward weights and global length constraints inevitably forces a compromise between degraded accuracy and unrealized compression. To overcome these limitations, we propose LEAD (Length-Efficient Adaptive and Dynamic reasoning), a method that replaces static heuristics with online, self-adaptive mechanisms. LEAD dynamically calibrates the correctness-efficiency trade-off at each step using a Potential-Scaled Instability, directing optimization capacity to the most informative learning signal. Furthermore, it estimates an adaptive per-problem target length online based on the model's own correct rollouts, applying a symmetric efficiency reward that penalizes both overthinking and over-compression. Evaluated on five mathematical reasoning benchmarks, LEAD achieves the highest accuracy and Accuracy-Efficiency Score among RL-trained efficient-reasoning methods while producing substantially shorter outputs than the base model.", "github": "https://github.com/CrazyMint/LEAD" }, { "date": "2026-05-14", "arxiv_id": "2605.06206", "title": "Federation of Experts: Communication Efficient Distributed Inference for Large Language Models", "authors": [ "Muhammad Shahir Abdurrahman", "Chun Deng", "Azalia Mirhoseini", "Philip Levis" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Mixture of experts has emerged as the primary mechanism for making Large Language Models (LLMs) computationally efficient. However, in distributed settings, communicating token embeddings between experts is a significant bottleneck. We present the novel Federation of Experts (FoE) architecture. FoE restructures the MoE block of a transformer layer into multiple MoE clusters. Each cluster is responsible for only one of the KV heads and expert parallelism is applied between those experts. Between clusters, a sum synchronizes the post-attention residuals, which then drives routing and dispatch for the next MoE block. In a single-node setting, FoE completely eliminates all-to-all communication as all experts within a group are contained on the same GPU. In multi-node settings, FoE confines all-to-all communication to the intra-node fabric, thus significantly reducing communication overhead. An implementation of FoE finds that on LongBench, FoE significantly improves inference throughput and latency in both single-node and multi-node settings, reducing the end-to-end forward-pass latency by up to 5.2x, TTFT by 3.62x, and TBT by 1.95x. It does so while achieving comparable generation quality to a mixture of experts model of the same size and training configuration.", "github": "" }, { "date": "2026-05-14", "arxiv_id": "2605.13647", "title": "FlowCompile: An Optimizing Compiler for Structured LLM Workflows", "authors": [ "Junyan Li", "Zhang-Wei Hong", "Maohao Shen", "Yang Zhang", "Chuang Gan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UMass-Embodied-AGI/FlowCompile", "github_api_added_by": "user", "project_page": "", "abstract": "Structured LLM workflows, where specialized LLM sub-agents execute according to a predefined graph, have become a powerful abstraction for solving complex tasks. Optimizing such workflows, i.e., selecting configurations for each sub-agent to balance accuracy and latency, is challenging due to the combinatorial design space over model choices, reasoning budgets, and workflow structures. Existing cost-aware methods largely treat workflow optimization as a routing problem, selecting a configuration at inference time for each query according to the accuracy-latency objective used during training. We argue that structured LLM workflows can also be optimized from a compilation perspective: before deployment, the system can globally explore the workflow design space and construct a reusable set of workflow-level configurations spanning diverse accuracy-latency trade-offs. Drawing inspiration from machine learning compilers, we introduce FlowCompile, a structured LLM workflow compiler that performs compile-time design space exploration to identify a high-quality, reusable trade-off set. FlowCompile decomposes a workflow into sub-agents, profiles each sub-agent under diverse configurations, and composes these measurements through a structure-aware proxy to estimate workflow-level accuracy and latency. It then identifies diverse high-quality configurations in a single compile-time pass, without retraining or online adaptation. Experiments across diverse workflows and challenging benchmarks show that FlowCompile consistently outperforms heuristically optimized workflow configurations and routing-based baselines, delivering up to 6.4x speedup. The compiled configuration set further serves as a reusable optimization artifact, enabling flexible deployment under varying runtime preferences and supporting downstream selection or routing.", "github": "https://github.com/UMass-Embodied-AGI/FlowCompile" }, { "date": "2026-05-14", "arxiv_id": "2605.08557", "title": "MC-RFM: Geometry-Aware Few-Shot Adaptation via Mixed-Curvature Riemannian Flow Matching", "authors": [ "Salim Khazem", "Ibrahim Mohamed Serouis", "Zakaria Ezzahed" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/salimkhazem/MC-RFM", "github_api_added_by": "user", "project_page": "", "abstract": "Parameter-efficient adaptation of pretrained vision models is commonly performed through linear probes, prompts, low-rank updates, or lightweight residual modules. While effective, these methods usually treat adaptation as a discrete Euclidean perturbation of frozen representations, without explicitly modeling the geometry of the task-induced feature displacement. We propose MC-RFM, a mixed-curvature Riemannian flow-matching framework for few-shot adaptation of frozen visual backbones. The key idea is to represent adapted features on a product manifold combining a hyperbolic factor, which captures hierarchy-sensitive semantic structure, and a Euclidean factor, which preserves locally discriminative visual variation. Adaptation is formulated as a task-conditioned continuous transport from frozen features to support-set prototypes, trained with a flow-matching objective and coupled to a hybrid prototype-linear classifier. The method is lightweight, backbone-agnostic, and operates entirely on cached frozen features. Across seven visual recognition benchmarks, five frozen backbones, and 1/4/16-shot regimes, MC-RFM is the best-performing method in a majority of evaluated settings, with the strongest gains on Transformer backbones and fine-grained datasets. Ablations show that the mixed-curvature head, task conditioning, adaptive branch gating, prototype shrinkage, and discriminative supervision each contribute to performance. These results suggest that few-shot adaptation benefits not only from deciding which parameters to update, but also from modeling how representations should move through a geometry matched to the structure of the downstream task.", "github": "https://github.com/salimkhazem/MC-RFM" }, { "date": "2026-05-14", "arxiv_id": "2605.07865", "title": "KL for a KL: On-Policy Distillation with Control Variate Baseline", "authors": [ "Minjae Oh", "Sangjun Song", "Gyubin Choi", "Yunho Choi", "Yohan Jo" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/holi-lab/vOPD", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "On-Policy Distillation (OPD) has emerged as a dominant post-training paradigm for large language models, especially for reasoning domains. However, OPD remains unstable in practice due to the high gradient variance of its single-sample Monte Carlo estimator, and recipes for stable training are still immature. We propose vOPD (On-Policy Distillation with a control variate baseline), which casts OPD as policy-gradient RL and stabilizes it by introducing a control variate baseline-canonically a value function -- from the RL literature. We show that the OPD value function admits a closed form as the per-token negative reverse KL divergence between the student and the teacher, available directly from the already-computed forward pass with no additional critic or inference. Existing stabilization methods either compute the full token-level reverse KL over the entire vocabulary, adding significant overhead, or restrict it to a top-k support, biasing the objective. vOPD instead preserves the lightweight single-sample estimator, subtracting the value function as a detached baseline to keep the gradient unbiased while reducing variance. Furthermore, we show that a top-k approximation of the baseline further lowers cost without compromising performance. Across mathematical and scientific reasoning benchmarks, vOPD consistently outperforms vanilla OPD and matches the most expensive full-vocabulary baseline, offering an efficient stabilization of On-Policy Distillation through principled RL variance reduction.", "github": "https://github.com/holi-lab/vOPD" }, { "date": "2026-05-14", "arxiv_id": "2605.14306", "title": "Towards Self-Evolving Agentic Literature Retrieval", "authors": [ "Yuwen Du", "Tian Jin", "Jing Kang", "Xianghe Pang", "Jingyi Chai", "Tingjia Miao", "Fenyi Liu", "WenHao Wang", "Sikai Yao", "Yuzhi Zhang", "Siheng Chen" ], "github_manual": "", "github_regex": "https://github.com/sjtu-sai-agents/PaSaMaster", "github_paper_md": "", "github_api": "https://github.com/sjtu-sai-agents/PaSaMaster", "github_api_added_by": "user", "project_page": "", "abstract": "As large language models reshape scientific research, literature retrieval faces a twofold challenge: ensuring source authenticity while maintaining a deep comprehension of academic search intents. While reliable, traditional keyword-centric search fails to capture complex research intents. Frontier LLMs can handle complex research intents, but their high cost and tendency to hallucinate remain key limitations. Here we introduce PaSaMaster, a self-evolving agentic literature retrieval system that produces relevance-scored paper rankings with evidence-grounded recommendations through iterative intent analysis, retrieval, and ranking. It is built on three key designs. First, it transforms literature retrieval from a one shot query--document matching problem into a search process that evolves over time, using ranked evidence to reveal gaps, refine intents, and guide follow-up searches. Second, it prevents hallucinated sources by treating retrieval as intent--paper relevance ranking rather than generation. Finally, PaSaMaster improves cost efficiency by separating planning from retrieval: a frontier LLM is used only for intent understanding, while large scale retrieval and relevance scoring are delegated to customized corpora and lightweight models. Evaluated on the PaSaMaster Benchmark across 38 scientific disciplines, our system exposes the severe inaccuracy and incompleteness of traditional keyword retrieval (improving F1-score by 15.6X) and the unreliability of generative LLMs (which exhibit hallucination rates up to 37.79%). Remarkably, PaSaMaster outperforms GPT-5.2 by 30.0% at a mere 1% of the computational cost while ensuring zero source hallucination: https://github.com/sjtu-sai-agents/PaSaMaster", "github": "https://github.com/sjtu-sai-agents/PaSaMaster" }, { "date": "2026-05-15", "arxiv_id": "2605.15190", "title": "RAVEN: Real-time Autoregressive Video Extrapolation with Consistency-model GRPO", "authors": [ "Yanzuo Lu", "Ronglai Zuo", "Jiankang Deng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mvp-ai-lab/RAVEN", "github_api_added_by": "user", "project_page": "https://yanzuo.lu/raven/", "abstract": "Causal autoregressive video diffusion models support real-time streaming generation by extrapolating future chunks from previously generated content. Distilling such generators from high-fidelity bidirectional teachers yields competitive few-step models, yet a persistent gap between the history distributions encountered during training and those arising at inference constrains generation quality over long horizons. We introduce the Real-time Autoregressive Video Extrapolation Network (RAVEN), a training-time test framework that repacks each self rollout into an interleaved sequence of clean historical endpoints and noisy denoising states. This formulation aligns training attention with inference-time extrapolation and allows downstream chunk losses to supervise the history representations on which future predictions depend. We further propose Consistency-model Group Relative Policy Optimization (CM-GRPO), which reformulates a consistency sampling step as a conditional Gaussian transition and applies online Reinforcement Learning (RL) directly to this kernel, avoiding the Euler-Maruyama auxiliary process adopted in prior flow-model RL formulations. Experiments demonstrate that RAVEN surpasses recent causal video distillation baselines across quality, semantic, and dynamic degree evaluations, and that CM-GRPO provides further gains when combined with RAVEN.", "github": "https://github.com/mvp-ai-lab/RAVEN" }, { "date": "2026-05-15", "arxiv_id": "2605.14169", "title": "BOOKMARKS: Efficient Active Storyline Memory for Role-playing", "authors": [ "Letian Peng", "Ziche Liu", "Yiming Huang", "Longfei Yun", "Kun Zhou", "Yupeng Hou", "Jingbo Shang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KomeijiForce/BOOKMARKS_Koishiday_2026", "github_api_added_by": "user", "project_page": "", "abstract": "Memory systems are critical for role-playing agents (RPAs) to maintain long-horizon consistency. However, existing RPA memory methods (e.g., profiling) mainly rely on recurrent summarization, whose compression inevitably discards important details. To address this issue, we propose a search-based memory framework called BOOKMARKS, which actively initializes, maintains, and updates task-relevant pieces of bookmarks for the current task (e.g., character acting). A bookmark is structured as the answer to a question at a specific point in the storyline. For each current task, BOOKMARKS selects reusable existing bookmarks or initializes new ones (at storyline beginning) with useful questions. These bookmarks are then synchronized to the current story point, with their answers updated accordingly, so they can be efficiently reused in future grounding rounds. Compared with recurrent summarization, BOOKMARKS offers (1) active grounding for capturing task-specific details and (2) passive updating to avoid unnecessary computation. In implementation, BOOKMARKS supports concept, behavior, and state searches, each powered by an efficient synchronization method. BOOKMARKS significantly outperforms RPA memory baselines on 85 characters from 16 artifacts, demonstrating the effectiveness of search-based memory for RPAs.", "github": "https://github.com/KomeijiForce/BOOKMARKS_Koishiday_2026" }, { "date": "2026-05-15", "arxiv_id": "2605.14352", "title": "Ideology Prediction of German Political Texts", "authors": [ "Sinclair Schneider", "Florian Steuber", "Joao A. G. Schneider", "Gabi Dreo Rodosek" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SinclairSchneider/german_ideology_prediction", "github_api_added_by": "user", "project_page": "", "abstract": "Elections represent a crucial milestone in a nation's ongoing development. To better understand the political rhetoric from various movements, ranging from left to right, we propose a transformer-based model capable of projecting the political orientation of a text on a continuous left-to-right spectrum, represented by a normalized scalar d between -1 and 1. This approach enables analysts to focus on specific segments of the political landscape, such as conservatives, while excluding liberal and far-right movements. Such a task can only be achieved with multiclass classifiers, provided that the desired orientation is incorporated within one of their predefined classes. To determine the most suitable foundation model among 13 candidate transformers for this task, we constructed four distinct corpora. One corpus comprised annotated plenary notes from the German Bundestag, while another was based on an official online decision-making tool, Wahl-O-Mat. The third corpus consisted of articles from 33 newspapers, each identified by its political orientation, and the fourth included 535,200 tweets from 597 members of the 20th and 21st German Bundestag. To mitigate overfitting, we used two distinct corpora for training and two for testing, respectively. For in-domain performance, DeBERTa-large achieved the highest F1 score F1=0.844 as well as for the X (Twitter) out-of-domain test ACC=0.864. Regarding the newspaper out-of-domain test, Gemma2-2B excelled (MAE = 0.172). This study demonstrates that transformer models can recognize political framing in German news at the level of public opinion polls. Our findings suggest that both the model architecture and the availability of domain-specific training data can be as influential as model size for estimating political bias. We discuss methodological limitations and outline directions for improving the robustness of bias measurement.", "github": "https://github.com/SinclairSchneider/german_ideology_prediction" }, { "date": "2026-05-14", "arxiv_id": "2605.06607", "title": "AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents", "authors": [ "Nithin Somasekharan", "Rabi Pathak", "Manushri Dhanakoti", "Tingwen Zhang", "Ling Yue", "Andy Zhu", "Shaowu Pan" ], "github_manual": "", "github_regex": "https://github.com/csml-rpi/cfd-scientist", "github_paper_md": "", "github_api": "https://github.com/csml-rpi/AI-CFD-Scientist", "github_api_added_by": "user", "project_page": "", "abstract": "Recent LLM-based agents have closed substantial portions of the scientific discovery loop in software-only machine-learning research, in chemistry, and in biology. Extending the same loop to high-fidelity physical simulators is harder, because solver completion does not imply physical validity and many failure modes appear only in field-level imagery rather than in solver logs. We present AI CFD Scientist, an open-source AI scientist for computational fluid dynamics (CFD) that, to our knowledge, is the first to span literature-grounded ideation, validated execution, vision-based physics verification, source-code modification, and figure-grounded writing within a single inspectable workflow. Three coupled pathways cover parameter sweeps within a fixed solver, case-local C++ library compilation for new physical models, and open-ended hypothesis search against a reference comparator, all running on OpenFOAM through Foam-Agent. At the center of the framework is a vision-language physics-verification gate that inspects rendered flow fields before any result is accepted, rerun, or written into a manuscript. On five tasks under a shared GPT-5.5 backbone, AI CFD Scientist autonomously discovers a Spalart-Allmaras runtime correction that reduces lower-wall Cf RMSE against DNS by 7.89% on the periodic hill at Reh=5600; under matched LLM cost, two strong general AI-scientist baselines (ARIS, DeepScientist) execute partial CFD workflows but lack the domain-specific validity gates needed to convert runs into defensible scientific claims; and a controlled planted-failure ablation shows that the vision-language gate detects 14 of 16 silent failures missed by solver-level checks. Code, prompts, and run artifacts are released at https://github.com/csml-rpi/cfd-scientist.", "github": "https://github.com/csml-rpi/AI-CFD-Scientist" }, { "date": "2026-05-15", "arxiv_id": "2605.13301", "title": "Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling", "authors": [ "Yafu Li", "Runzhe Zhan", "Haoran Zhang", "Shunkai Zhang", "Yizhuo Li", "Zhilin Wang", "Jiacheng Chen", "Futing Wang", "Xuyang Hu", "Yuchen Fan", "Bangjie Xu", "Yucheng Su", "Xinmiao Han", "Chenxi Li", "Haodi Lei", "Yufeng Zhao", "Zejin Lin", "Qianjia Cheng", "Tong Zhu", "Xiaoye Qu", "Ganqu Cui", "Peng Ye", "Yun Luo", "Zhouchen Lin", "Yu Qiao", "Bowen Zhou", "Ning Ding", "Yu Cheng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Simplified-Reasoning/SU-01", "github_api_added_by": "user", "project_page": "https://simplified-reasoning.github.io/SU-01", "abstract": "Recent progress in reasoning models has substantially advanced long-horizon mathematical and scientific problem solving, with several systems now reaching gold-medal-level performance on International Mathematical Olympiad (IMO) and International Physics Olympiad (IPhO) problems. In this paper, we introduce a simple and unified recipe for converting a post-trained reasoning backbone into a rigorous olympiad-level solver. The recipe first uses a reverse-perplexity curriculum for SFT to instill rigorous proof-search and self-checking behaviors, then scales these behaviors through a two-stage RL pipeline that progresses from RL with verifiable rewards to more delicate proof-level RL, and finally boosts solving performance with test-time scaling. Applying this recipe, we train a 30B-A3B backbone with SFT on around 340K sub-8K-token trajectories followed by 200 RL steps. The resulting model, SU-01, supports stable reasoning on difficult problems with trajectories exceeding 100K tokens, while achieving gold-medal-level performance on mathematical and physical olympiad competitions, including IMO 2025/USAMO 2026 and IPhO 2024/2025. It also demonstrates strong generalization of scientific reasoning to domains beyond mathematics and physics.", "github": "https://github.com/Simplified-Reasoning/SU-01" }, { "date": "2026-05-15", "arxiv_id": "2605.15128", "title": "MemEye: A Visual-Centric Evaluation Framework for Multimodal Agent Memory", "authors": [ "Minghao Guo", "Qingyue Jiao", "Zeru Shi", "Yihao Quan", "Boxuan Zhang", "Danrui Li", "Liwei Che", "Wujiang Xu", "Shilong Liu", "Zirui Liu", "Mubbasir Kapadia", "Vladimir Pavlovic", "Jiang Liu", "Mengdi Wang", "Yiyu Shi", "Dimitris N. Metaxas", "Ruixiang Tang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MinghoKwok/MemEye", "github_api_added_by": "user", "project_page": "https://minghokwok.github.io/MemEye/", "abstract": "Long-term agent memory is increasingly multimodal, yet existing evaluations rarely test whether agents preserve the visual evidence needed for later reasoning. In prior work, many visually grounded questions can be answered using only captions or textual traces, allowing answers to be inferred without preserving the fine-grained visual evidence. Meanwhile, harder cases that require reasoning over changing visual states are largely absent. Therefore, we introduce MemEye, a framework that evaluates memory capabilities from two dimensions: one measures the granularity of decisive visual evidence (from scene-level to pixel-level evidence), and the other measures how retrieved evidence must be used (from single evidence to evolutionary synthesis). Under this framework, we construct a new benchmark across 8 life-scenario tasks, with ablation-driven validation gates for assessing answerability, shortcut resistance, visual necessity, and reasoning structure. By evaluating 13 memory methods across 4 VLM backbones, we show that current architectures still struggle to preserve fine-grained visual details and reason about state changes over time. Our findings show that long-term multimodal memory depends on evidence routing, temporal tracking, and detail extraction.", "github": "https://github.com/MinghoKwok/MemEye" }, { "date": "2026-05-15", "arxiv_id": "2605.14386", "title": "Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning", "authors": [ "Taebong Kim", "Youngsik Hong", "Minsik Kim", "Sunyoung Choi", "Jaewon Jang", "Junghoon Shin", "Minseo Kim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://vidraft.net", "abstract": "We present Darwin Family, a framework for training-free evolutionary merging of large language models via gradient-free weight-space recombination. We ask whether frontier-level reasoning performance can be improved without additional training, by reorganizing latent capabilities already encoded in existing checkpoints. Darwin introduces three key ideas: (i) a 14-dimensional adaptive merge genome enabling fine-grained component- and block-level recombination; (ii) MRI-Trust Fusion, which adaptively balances diagnostic layer-importance signals with evolutionary search through a learnable trust parameter; and (iii) an Architecture Mapper that enables cross-architecture breeding between heterogeneous model families. Empirically, the flagship Darwin-27B-Opus achieves 86.9% on GPQA Diamond, ranking #6 among 1,252 evaluated models, and outperforming its fully trained foundation model without any gradient-based training. Across scales from 4B to 35B parameters, Darwin models consistently improve over their parents, support recursive multi-generation evolution, and enable a training-free evolutionary merge that combines Transformer- and Mamba-based components. Together, the Darwin Family demonstrates that diagnostic-guided evolutionary merging is a practical and reproducible alternative to costly post-training pipelines for reasoning-centric language models.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.15182", "title": "Warp-as-History: Generalizable Camera-Controlled Video Generation from One Training Video", "authors": [ "Yifan Wang", "Tong He" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yyfz/Warp-as-History", "github_api_added_by": "user", "project_page": "https://yyfz.github.io/warp-as-history/", "abstract": "Camera-controlled video generation has made substantial progress, enabling generated videos to follow prescribed viewpoint trajectories. However, existing methods usually learn camera-specific conditioning through camera encoders, control branches, or attention and positional-encoding modifications, which often require post-training on large-scale camera-annotated videos. Training-free alternatives avoid such post-training, but often shift the cost to test-time optimization or extra denoising-time guidance. We propose Warp-as-History, a simple interface that turns camera-induced warps into camera-warped pseudo-history with target-frame positional alignment and visible-token selection. Given a target camera trajectory, we construct camera-warped pseudo-history from past observations and feed it through the model's visual-history pathway. Crucially, we align its positional encoding with the target frames being denoised and remove warped-history tokens without valid source observations. Without any training, architectural modification, or test-time optimization, this interface reveals a non-trivial zero-shot capability of a frozen video generation model to follow camera trajectories. Moreover, lightweight offline LoRA finetuning on only one camera-annotated video further improves this capability and generalizes to unseen videos, improving camera adherence, visual quality, and motion dynamics without test-time optimization or target-video adaptation. Extensive experiments on diverse datasets confirm the effectiveness of our method.", "github": "https://github.com/yyfz/Warp-as-History" }, { "date": "2026-05-15", "arxiv_id": "2605.14892", "title": "Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems", "authors": [ "Shihao Qi", "Jie Ma", "Rui Xing", "Wei Guo", "Xiao Huang", "Zhitao Gao", "Jianhao Deng", "Jun Liu", "Lingling Zhang", "Bifan Wei", "Boqian Yang", "Pinghui Wang", "Jianwen Sun", "Jing Tao", "Yaqiang Wu", "Hui Liu", "Yu Yao", "Tongliang Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mira-ai-lab/awesome-mas-life", "github_api_added_by": "user", "project_page": "", "abstract": "LLM-based autonomous agents have demonstrated strong capabilities in reasoning, planning, and tool use, yet remain limited when tasks require sustained coordination across roles, tools, and environments. Multi-agent systems address this through structured collaboration among specialized agents, but tighter coordination also amplifies a less explored risk: errors can propagate across agents and interaction rounds, producing failures that are difficult to diagnose and rarely translate into structural self-improvement. Existing surveys cover individual agent capabilities, multi-agent collaboration, or agent self-evolution separately, leaving the causal dependencies among them unexamined. This survey provides a unified review organized around four causally linked stages, which we term the LIFE progression: Lay the capability foundation, Integrate agents through collaboration, Find faults through attribution, and Evolve through autonomous self-improvement. For each stage, we provide systematic taxonomies and formally characterize the dependencies between adjacent stages, revealing how each stage both depends on and constrains the next. Beyond synthesizing existing work, we identify open challenges at stage boundaries and propose a cross-stage research agenda for closed-loop multi-agent systems capable of continuously diagnosing failures, reorganizing structures, and refining agent behaviors, extending current coordination frameworks toward more self-organizing forms of collective intelligence. By bridging these previously fragmented research threads, this survey aims to offer both a systematic reference and a conceptual roadmap toward autonomous, self-improving multi-agent intelligence.", "github": "https://github.com/mira-ai-lab/awesome-mas-life" }, { "date": "2026-05-15", "arxiv_id": "2605.15040", "title": "Orchard: An Open-Source Agentic Modeling Framework", "authors": [ "Baolin Peng", "Wenlin Yao", "Qianhui Wu", "Hao Cheng", "Xiao Yu", "Rui Yang", "Tao Ge", "Alessandrio Sordoni", "Xingdi Yuan", "Yelong Shen", "Pengcheng He", "Tong Zhang", "Zhou Yu", "Jianfeng Gao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/Orchard", "github_api_added_by": "user", "project_page": "", "abstract": "Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn interaction with environments. Despite major investment, open research remains constrained by infrastructure and training gaps. Many high-performing systems rely on proprietary codebases, models, or services, while most open-source frameworks focus on orchestration and evaluation rather than scalable agent training. We present Orchard, an open-source framework for scalable agentic modeling. At its core is Orchard Env, a lightweight environment service providing reusable primitives for sandbox lifecycle management across task domains, agent harnesses, and pipeline stages. On top of Orchard Env, we build three agentic modeling recipes. Orchard-SWE targets coding agents. We distill 107K trajectories from MiniMax-M2.5 and Qwen3.5-397B, introduce credit-assignment SFT to learn from productive segments of unresolved trajectories, and apply Balanced Adaptive Rollout for RL. Starting from Qwen3-30B-A3B-Thinking, Orchard-SWE achieves 64.3% on SWE-bench Verified after SFT and 67.5% after SFT+RL, setting a new state of the art among open-source models of comparable size. Orchard-GUI trains a 4B vision-language computer-use agent using only 0.4K distilled trajectories and 2.2K open-ended tasks. It achieves 74.1%, 67.0%, and 64.0% success rates on WebVoyager, Online-Mind2Web, and DeepShop, respectively, making it the strongest open-source model while remaining competitive with proprietary systems. Orchard-Claw targets personal assistant agents. Trained with only 0.2K synthetic tasks, it achieves 59.6% pass@3 on Claw-Eval and 73.9% when paired with a stronger ZeroClaw harness. Collectively, these results show that a lightweight, open, harness-agnostic environment layer enables reusable agentic data, training recipes, and evaluations across domains.", "github": "https://github.com/microsoft/Orchard" }, { "date": "2026-05-15", "arxiv_id": "2605.15198", "title": "ATLAS: Agentic or Latent Visual Reasoning? One Word is Enough for Both", "authors": [ "Ziyu Guo", "Rain Liu", "Xinyan Chen", "Pheng-Ann Heng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://atlas-oneword.github.io/", "abstract": "Visual reasoning, often interleaved with intermediate visual states, has emerged as a promising direction in the field. A straightforward approach is to directly generate images via unified models during reasoning, but this is computationally expensive and architecturally non-trivial. Recent alternatives include agentic reasoning through code or tool calls, and latent reasoning with learnable hidden embeddings. However, agentic methods incur context-switching latency from external execution, while latent methods lack task generalization and are difficult to train with autoregressive parallelization. To combine their strengths while mitigating their limitations, we propose ATLAS, a framework in which a single discrete 'word', termed as a functional token, serves both as an agentic operation and a latent visual reasoning unit. Each functional token is associated with an internalized visual operation, yet requires no visual supervision and remains a standard token in the tokenizer vocabulary, which can be generated via next-token prediction. This design avoids verbose intermediate visual content generation, while preserving compatibility with the vanilla scalable SFT and RL training, without architectural or methodological modifications. To further address the sparsity of functional tokens during RL, we introduce Latent-Anchored GRPO (LA-GRPO), which stabilizes the training by anchoring functional tokens with a statically weighted auxiliary objective, providing stronger gradient updates. Extensive experiments and analyses demonstrate that ATLAS achieves superior performance on challenging benchmarks while maintaining clear interpretability. We hope ATLAS offers a new paradigm inspiring future visual reasoning research.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.13941", "title": "EvolveMem:Self-Evolving Memory Architecture via AutoResearch for LLM Agents", "authors": [ "Jiaqi Liu", "Xinyu Ye", "Peng Xia", "Zeyu Zheng", "Cihang Xie", "Mingyu Ding", "Huaxiu Yao" ], "github_manual": "", "github_regex": "https://github.com/aiming-lab/SimpleMem", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Long-term memory is essential for LLM agents that operate across multiple sessions, yet existing memory systems treat retrieval infrastructure as fixed: stored content evolves while scoring functions, fusion strategies, and answer-generation policies remain frozen at deployment. We argue that truly adaptive memory requires co-evolution at two levels: the stored knowledge and the retrieval mechanism that queries it. We present EvolveMem, a self-evolving memory architecture that exposes its full retrieval configuration as a structured action space optimized by an LLM-powered diagnosis module. In each evolution round, the module reads per-question failure logs, identifies root causes, and proposes targeted configuration adjustments; a guarded meta-analyzer applies them with automatic revert-on-regression and explore-on-stagnation safeguards. This closed-loop self-evolution realizes an AutoResearch process: the system autonomously conducts iterative research cycles on its own architecture, replacing manual configuration tuning. Starting from a minimal baseline, the process converges autonomously, discovering effective retrieval strategies including entirely new configuration dimensions not present in the original action space. On LoCoMo, EvolveMem outperforms the strongest baseline by 25.7% relative and achieves a 78.0% relative improvement over the minimal baseline. On MemBench, EvolveMem exceeds the strongest baseline by 18.9% relative. Evolved configurations transfer across benchmarks with positive rather than catastrophic transfer, indicating that the self-evolution process captures universal retrieval principles rather than benchmark-specific heuristics. Code is available at https://github.com/aiming-lab/SimpleMem.", "github": "https://github.com/aiming-lab/SimpleMem" }, { "date": "2026-05-15", "arxiv_id": "2605.13834", "title": "Topology-Preserving Neural Operator Learning via Hodge Decomposition", "authors": [ "Dongzhe Zheng", "Tao Zhong", "Christine Allen-Blanchette" ], "github_manual": "", "github_regex": "https://github.com/ContinuumCoder/Hodge-Spectral-Duality", "github_paper_md": "", "github_api": "https://github.com/ContinuumCoder/Hodge-Spectral-Duality", "github_api_added_by": "user", "project_page": "", "abstract": "In this paper, we study solution operators of physical field equations on geometric meshes from a function-space perspective. We reveal that Hodge orthogonality fundamentally resolves spectral interference by isolating unlearnable topological degrees of freedom from learnable geometric dynamics, enabling an additive approximation confined to structure-preserving subspaces. Building on Hodge theory and operator splitting, we derive a principled operator-level decomposition. The result is a Hybrid Eulerian-Lagrangian architecture with an algebraic-level inductive bias we call Hodge Spectral Duality (HSD). In our framework, we use discrete differential forms to capture topology-dominated components and an orthogonal auxiliary ambient space to represent complex local dynamics. Our method achieves superior accuracy and efficiency on geometric graphs with enhanced fidelity to physical invariants. Our code is available at https://github.com/ContinuumCoder/Hodge-Spectral-Duality", "github": "https://github.com/ContinuumCoder/Hodge-Spectral-Duality" }, { "date": "2026-05-15", "arxiv_id": "2605.14269", "title": "PhyMotion: Structured 3D Motion Reward for Physics-Grounded Human Video Generation", "authors": [ "Yidong Huang", "Zun Wang", "Han Lin", "Dong-Ki Kim", "Shayegan Omidshafiei", "Jaehong Yoon", "Jaemin Cho", "Yue Zhang", "Mohit Bansal" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/h6kplus/PhyMotion", "github_api_added_by": "user", "project_page": "https://phy-motion.github.io/", "abstract": "Generating realistic human motion is a central yet unsolved challenge in video generation. While reinforcement learning (RL)-based post-training has driven recent gains in general video quality, extending it to human motion remains bottlenecked by a reward signal that cannot reliably score motion realism. Existing video rewards primarily rely on 2D perceptual signals, without explicitly modeling the 3D body state, contact, and dynamics underlying articulated human motion, and often assign high scores to videos with floating bodies or physically implausible movements. To address this, we propose PhyMotion, a structured, fine-grained motion reward that grounds recovered 3D human trajectories in a physics simulator and evaluates motion quality along multiple dimensions of physical feasibility. Concretely, we recover SMPL body meshes from generated videos, retarget them onto a humanoid in the MuJoCo physics simulator, and evaluate the resulting motion along three axes: kinematic plausibility, contact and balance consistency, and dynamic feasibility. Each component provides a continuous and interpretable signal tied to a specific aspect of motion quality, allowing the reward to capture which aspects of motion are physically correct or violated. Experiments show that PhyMotion achieves stronger correlation with human judgments than existing reward formulations. These gains carry over to RL-based post-training, where optimizing PhyMotion leads to larger and more consistent improvements than optimizing existing rewards, improving motion realism across both autoregressive and bidirectional video generators under both automatic metrics and blind human evaluation (+68 Elo gain). Ablations show that the three axes provide complementary supervision signals, while the reward preserves overall video generation quality with only modest training overhead.", "github": "https://github.com/h6kplus/PhyMotion" }, { "date": "2026-05-15", "arxiv_id": "2605.14051", "title": "SPIN: Structural LLM Planning via Iterative Navigation for Industrial Tasks", "authors": [ "Yusuke Ozaki", "Dhaval Patel" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Industrial LLM agent systems often separate planning from execution, yet LLM planners frequently produce structurally invalid or unnecessarily long workflows, leading to brittle failures and avoidable tool and API cost. We propose SPIN, a planning wrapper that combines validated Directed Acyclic Graph (DAG) planning with prefix based execution control. SPIN enforces a strict DAG contract through \\_validate\\_plan\\_text and repair prompting, producing executable plans before downstream execution, and then evaluates DAG prefixes incrementally to stop when the current prefix is sufficient to answer the query. On AssetOpsBench, across 261 scenarios, SPIN reduces executed tasks from 1061 to 623 and improves Accomplished from 0.638 to 0.706, while reducing tool calls from 11.81 to 6.82 per run. On MCP Bench, the same wrapper improves planning, grounding, and dependency related scores for both GPT OSS1 and Llama 4 Maverick.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.14389", "title": "Nexus : An Agentic Framework for Time Series Forecasting", "authors": [ "Sarkar Snigdha Sarathi Das", "Palash Goyal", "Mihir Parmar", "Nanyun Peng", "Vishy Tirumalashetty", "Chun-Liang Li", "Rui Zhang", "Jinsung Yoon", "Tomas Pfister" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Time series forecasting is not just numerical extrapolation, but often requires reasoning with unstructured contextual data such as news or events. While specialized Time Series Foundation Models (TSFMs) excel at forecasting based on numerical patterns, they remain unaware to real-world textual signals. Conversely, while LLMs are emerging as zero-shot forecasters, their performance remains uneven across domains and contextual grounding. To bridge this gap, we introduce Nexus, a multi-agent forecasting framework that decomposes prediction into specialized stages: isolating macro-level and micro-level temporal fluctuations, and integrating contextual information when available before synthesizing a final forecast. This decomposition enables Nexus to adapt from seasonal signals to volatile, event-driven information without relying on external statistical anchors or monolithic prompting. We show that current-generation LLMs possess substantially stronger intrinsic forecasting ability than previously recognized, depending critically on how numerical and contextual reasoning are organized. Evaluated on data strictly succeeding LLM knowledge cutoffs spanning Zillow real estate metrics and volatile stock market equities, Nexus consistently matches or outperforms state-of-the-art TSFMs and strong LLM baselines. Beyond numerical accuracy, Nexus produces high-quality reasoning traces that explicitly show the fundamental drivers behind each forecast. Our results establish that real-world forecasting is an agentic reasoning problem extending well beyond only sequence modeling.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.14392", "title": "Learning to Build the Environment: Self-Evolving Reasoning RL via Verifiable Environment Synthesis", "authors": [ "Yucheng Shi", "Zhenwen Liang", "Kishan Panaganti", "Dian Yu", "Wenhao Yu", "Haitao Mi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We pursue a vision for self-improving language models in which the model does not merely generate problems or traces to imitate, but constructs the environments that train it. In zero-data reasoning RL, this reframes self-improvement from a data-generation loop into an environment-construction loop, where each artifact is a reusable executable object that samples instances, computes references, and scores responses. Whether this vision sustains improvement hinges on a single property: the environments must exhibit stable solve--verify asymmetry, the model must be able to write an oracle once that it cannot reliably execute in natural language on fresh instances. This asymmetry takes two complementary forms. Some tasks are algorithmically hard to reason through but trivial as code: a dynamic program or graph traversal, compiled once, yields unboundedly many calibrated instances. Others are intrinsically hard to solve but easy to verify, like planted subset-sum or constraint satisfaction. Both create a durable gap between proposing and solving that the policy cannot close by gaming the verifier, and it is this gap that keeps reward informative as the learner improves. We instantiate this view in EvoEnv, a single-policy generator, solver method that synthesizes Python environments from ten seeds and admits them only after staged validation, semantic self-review, solver-relative difficulty calibration, and novelty checks. The strongest evidence comes from the already-strong regime: on Qwen3-4B-Thinking, fixed public-data RLVR and fixed hand-crafted environment RLVR reduce the average, while EvoEnv improves it from 72.4 to 74.8, a relative gain of 3.3%. Stable self-improvement, we suggest, depends not on producing more synthetic data, but on models learning to construct worlds whose difficulty stays structurally beyond their own reach.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.15155", "title": "Self-Distilled Agentic Reinforcement Learning", "authors": [ "Zhengxi Lu", "Zhiyuan Yao", "Zhuowen Han", "Zi-Han Wang", "Jinyang Wu", "Qi Gu", "Xunliang Cai", "Weiming Lu", "Jun Xiao", "Yueting Zhuang", "Yongliang Shen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZJU-REAL/SDAR", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement learning (RL) has emerged as a central paradigm for post-training LLM agents, yet its trajectory-level reward signal provides only coarse supervision for long-horizon interaction. On-Policy Self-Distillation (OPSD) complements RL by introducing dense token-level guidance from a teacher branch augmented with privileged context. However, transferring OPSD to multi-turn agents proves problematic: compounding multi-turn instability destabilizes supervision, while skill-conditioned privileged guidance requires asymmetric treatment for negative teacher rejections may arise from imperfect skills retrieval or utilization. We introduce SDAR (Self-Distilled Agentic Reinforcement Learning), which treats OPSD as a gated auxiliary objective while keeping RL as the primary optimization backbone. SDAR maps detached token-level signals into a sigmoid gate, strengthening distillation on teacher-endorsed positive-gap tokens and softly attenuating negative teacher rejections. Across the Qwen2.5 and Qwen3 families on ALFWorld, WebShop, and Search-QA, SDAR substantially improves over GRPO (+9.4% on ALFWorld, +7.0% on Search-QA, +10.2% on WebShop-Acc), avoids the instability of naive GRPO+OPSD, and consistently outperforms hybrid RL--OPSD baselines across model scales.", "github": "https://github.com/ZJU-REAL/SDAR" }, { "date": "2026-05-15", "arxiv_id": "2605.15185", "title": "Quantitative Video World Model Evaluation for Geometric-Consistency", "authors": [ "Jiaxin Wu", "Yihao Pi", "Yinling Zhang", "Yuheng Li", "Xueyan Zou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AnteaWu/PDI-Bench", "github_api_added_by": "user", "project_page": "https://pdi-bench.github.io/", "abstract": "Generative video models are increasingly studied as implicit world models, yet evaluating whether they produce physically plausible 3D structure and motion remains challenging. Most existing video evaluation pipelines rely heavily on human judgment or learned graders, which can be subjective and weakly diagnostic for geometric failures. We introduce PDI-Bench (Perspective Distortion Index), a quantitative framework for auditing geometric coherence in generated videos. Given a generated clip, we obtain object-centric observations via segmentation and point tracking (e.g., SAM 2, MegaSaM, and CoTracker3), lift them to 3D world-space coordinates via monocular reconstruction, and compute a set of projective-geometry residuals capturing three failure dimensions: scale-depth alignment, 3D motion consistency, and 3D structural rigidity. To support systematic evaluation, we build PDI-Dataset, covering diverse scenarios designed to stress these geometric constraints. Across state-of-the-art video generators, PDI reveals consistent geometry-specific failure modes that are not captured by common perceptual metrics, and provides a diagnostic signal for progress toward physically grounded video generation and physical world model. Our code and dataset can be found at https://pdi-bench.github.io/.", "github": "https://github.com/AnteaWu/PDI-Bench" }, { "date": "2026-05-15", "arxiv_id": "2605.14354", "title": "LLM-based Detection of Manipulative Political Narratives", "authors": [ "Sinclair Schneider", "Florian Steuber", "Gabi Dreo Rodosek" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SinclairSchneider/manipulative_narrative_detection", "github_api_added_by": "user", "project_page": "", "abstract": "We present a new computational framework for detecting and structuring manipulative political narratives. A task that became more important due to the shift of political discussions to social media. One of the primary challenges thereby is differentiating between manipulative political narratives and legitimate critiques. Some posts may also reframe actual events within a manipulative context. To achieve good clustering results, we filter manipulative posts beforehand using a detailed few-shot prompt that combines documented campaign narratives with legitimate criticisms to differentiate them. This prompt enables a reasoning model to assign labels, retaining only manipulative narrative posts for further processing. The remaining posts are subsequently embedded and dimensionality-reduced using UMAP, before HDBSCAN is applied to uncover narrative groups. A key advantage of this unsupervised approach is its independence from a predefined list of target categories, enabling it to uncover new narrative clusters. Finally, a reasoning model is employed to uncover the narrative behind each cluster. This approach, applied to over 1.2 million social media posts, effectively identified 41 distinct manipulative narrative clusters by integrating prompt-based filtering with unsupervised clustering.", "github": "https://github.com/SinclairSchneider/manipulative_narrative_detection" }, { "date": "2026-05-15", "arxiv_id": "2605.15188", "title": "FutureSim: Replaying World Events to Evaluate Adaptive Agents", "authors": [ "Shashwat Goel", "Nikhil Chandak", "Arvindh Arun", "Ameya Prabhu", "Steffen Staab", "Moritz Hardt", "Maksym Andriushchenko", "Jonas Geiping" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenForecaster/futuresim", "github_api_added_by": "user", "project_page": "https://openforecaster.github.io/futuresim", "abstract": "AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives. To efficiently measure this capability for realistic use-cases, we propose building grounded simulations that replay real-world events in the order they occurred. We build FutureSim, where agents forecast world events beyond their knowledge cutoff while interacting with a chronological replay of the world: real news articles arriving and questions resolving over the simulated period. We evaluate frontier agents in their native harness, testing their ability to predict world events over a three-month period from January to March 2026. FutureSim reveals a clear separation in their capabilities, with the best agent's accuracy being 25%, and many having worse Brier skill score than making no prediction at all. Through careful ablations, we show how FutureSim offers a realistic setting to study emerging research directions like long-horizon test-time adaptation, search, memory, and reasoning about uncertainty. Overall, we hope our benchmark design paves the way to measure AI progress on open-ended adaptation spanning long time-horizons in the real world.", "github": "https://github.com/OpenForecaster/futuresim" }, { "date": "2026-05-15", "arxiv_id": "2605.13880", "title": "PREPING: Building Agent Memory without Tasks", "authors": [ "Yumin Choi", "Sangwoo Park", "Minki Kang", "Jinheon Baek", "Sung Ju Hwang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Dozi01/PREPING", "github_api_added_by": "user", "project_page": "https://dozi01.github.io/preping-project-page/", "abstract": "Agent memory is typically constructed either offline from curated demonstrations or online from post-deployment interactions. However, regardless of how it is built, an agent faces a cold-start gap when first introduced to a new environment without any task-specific experience available. In this paper, we study pre-task memory construction: whether an agent can build procedural memory before observing any target-environment tasks, using only self-generated synthetic practice. Yet, synthetic interaction alone is insufficient, as without controlling what to practice and what to store, synthetic tasks become redundant, infeasible, and ultimately uninformative, and memory further degrades quickly due to unfiltered trajectories. To overcome this, we present Preping, a proposer-guided memory construction framework. At its core is proposer memory, a structured control state that shapes future practice. A Proposer generates synthetic tasks conditioned on this state, a Solver executes them, and a Validator determines which trajectories are eligible for memory insertion while also providing feedback to guide future proposals. Experiments on AppWorld, BFCL v3, and MCP-Universe show that Preping substantially improves over a no-memory baseline and achieves performance competitive with strong playbook-based methods built from offline or online experience, with deployment cost 2.99times lower on AppWorld and 2.23times lower on BFCL v3 than online memory construction. Further analyses reveal that the main benefit does not come from synthetic volume alone, but from proposer-side control over feasibility, redundancy, and coverage, combined with selective memory updates.", "github": "https://github.com/Dozi01/PREPING" }, { "date": "2026-05-15", "arxiv_id": "2605.15178", "title": "SANA-WM: Efficient Minute-Scale World Modeling with Hybrid Linear Diffusion Transformer", "authors": [ "Haoyi Zhu", "Haozhe Liu", "Yuyang Zhao", "Tian Ye", "Junsong Chen", "Jincheng Yu", "Tong He", "Song Han", "Enze Xie" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://nvlabs.github.io/Sana/WM/", "abstract": "We introduce SANA-WM, an efficient 2.6B-parameter open-source world model natively trained for one-minute generation, synthesizing high-fidelity, 720p, minute-scale videos with precise camera control. SANA-WM achieves visual quality comparable to large-scale industrial baselines such as LingBot-World and HY-WorldPlay, while significantly improving efficiency. Four core designs drive our architecture: (1) Hybrid Linear Attention combines frame-wise Gated DeltaNet (GDN) with softmax attention for memory-efficient long-context modeling. (2) Dual-Branch Camera Control ensures precise 6-DoF trajectory adherence. (3) Two-Stage Generation Pipeline applies a long-video refiner to stage-1 outputs, improving quality and consistency across sequences. (4) Robust Annotation Pipeline extracts accurate metric-scale 6-DoF camera poses from public videos to yield high-quality, spatiotemporally consistent action labels. Driven by these designs, SANA-WMdemonstrates remarkable efficiency across data, training compute, and inference hardware: it uses only sim213K public video clips with metric-scale pose supervision, completes training in 15 days on 64 H100s, and generates each 60s clip on a single GPU; its distilled variant can be deployed on a single RTX 5090 with NVFP4 quantization to denoise a 60s 720p clip in 34s. On our one-minute world-model benchmark, SANA-WM demonstrates stronger action-following accuracy than prior open-source baselines and achieves comparable visual quality at 36times higher throughput for scalable world modeling.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.15186", "title": "VGGT-Edit: Feed-forward Native 3D Scene Editing with Residual Field Prediction", "authors": [ "Kaixin Zhu", "Yiwen Tang", "Yifan Yang", "Renrui Zhang", "Bohan Zeng", "Ziyu Guo", "Ruichuan An", "Zhou Liu", "Qizhi Chen", "Delin Qu", "Jaehong Yoon", "Wentao Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "High-quality 3D scene reconstruction has recently advanced toward generalizable feed-forward architectures, enabling the generation of complex environments in a single forward pass. However, despite their strong performance in static scene perception, these models remain limited in responding to dynamic human instructions, which restricts their use in interactive applications. Existing editing methods typically rely on a 2D-lifting strategy, where individual views are edited independently and then lifted back into 3D space. This indirect pipeline often leads to blurry textures and inconsistent geometry, as 2D editors lack the spatial awareness required to preserve structure across viewpoints. To address these limitations, we propose VGGT-Edit, a feed-forward framework for text-conditioned native 3D scene editing. VGGT-Edit introduces depth-synchronized text injection to align semantic guidance with the backbone's spatial poses, ensuring stable instruction grounding. This semantic signal is then processed by a residual transformation head, which directly predicts 3D geometric displacements to deform the scene while preserving background stability. To ensure high-fidelity results, we supervise the framework with a multi-term objective function that enforces geometric accuracy and cross-view consistency. We also construct the DeltaScene Dataset, a large-scale dataset generated through an automated pipeline with 3D agreement filtering to ensure ground-truth quality. Experiments show that VGGT-Edit substantially outperforms 2D-lifting baselines, producing sharper object details, stronger multi-view consistency, and near-instant inference speed.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.15055", "title": "DiffusionOPD: A Unified Perspective of On-Policy Distillation in Diffusion Models", "authors": [ "Quanhao Li", "Junqiu Yu", "Kaixun Jiang", "Yujie Wei", "Zhen Xing", "Pandeng Li", "Ruihang Chu", "Shiwei Zhang", "Yu Liu", "Zuxuan Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://quanhaol.github.io/DiffusionOPD-site/", "abstract": "Reinforcement learning has emerged as a powerful tool for improving diffusion-based text-to-image models, but existing methods are largely limited to single-task optimization. Extending RL to multiple tasks is challenging: joint optimization suffers from cross-task interference and imbalance, while cascade RL is cumbersome and prone to catastrophic forgetting. We propose DiffusionOPD, a new multi-task training paradigm for diffusion models based on Online Policy Distillation (OPD). DiffusionOPD first trains task-specific teachers independently, then distills their capabilities into a unified student along the student own rollout trajectories. This decouples single-task exploration from multi-task integration and avoids the optimization burden of solving all tasks jointly from scratch. Theoretically, we lift the OPD framework from discrete tokens to continuous-state Markov processes, deriving a closed-form per-step KL objective that unifies both stochastic SDE and deterministic ODE refinement via mean-matching. We formally and empirically demonstrate that this analytic gradient provides lower variance and better generality compared to conventional PPO-style policy gradients. Extensive experiments show that DiffusionOPD consistently surpasses both multi-reward RL and cascade RL baselines in training efficiency and final performance, while achieving state-of-the-art results on all evaluated benchmarks.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.14906", "title": "MemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models", "authors": [ "Xiyu Ren", "Zhaowei Wang", "Yiming Du", "Zhongwei Xie", "Chi Liu", "Xinlin Yang", "Haoyue Feng", "Wenjun Pan", "Tianshi Zheng", "Baixuan Xu", "Zhengnan Li", "Yangqiu Song", "Ginny Wong", "Simon See" ], "github_manual": "", "github_regex": "https://github.com/xrenaf/MEMLENS", "github_paper_md": "", "github_api": "https://github.com/xrenaf/MEMLENS", "github_api_added_by": "user", "project_page": "", "abstract": "Memory is essential for large vision-language models (LVLMs) to handle long, multimodal interactions, with two method directions providing this capability: long-context LVLMs and memory-augmented agents. However, no existing benchmark conducts a systematic comparison of the two on questions that genuinely require multimodal evidence. To close this gap, we introduce MEMLENS, a comprehensive benchmark for memory in multimodal multi-session conversations, comprising 789 questions across five memory abilities (information extraction, multi-session reasoning, temporal reasoning, knowledge update, and answer refusal) at four standard context lengths (32K-256K tokens) under a cross-modal token-counting scheme. An image-ablation study confirms that solving MEMLENS requires visual evidence: removing evidence images drops two frontier LVLMs below 2% accuracy on the 80.4% of questions whose evidence includes images. Evaluating 27 LVLMs and 7 memory-augmented agents, we find that long-context LVLMs achieve high short-context accuracy through direct visual grounding but degrade as conversations grow, whereas memory agents are length-stable but lose visual fidelity under storage-time compression. Multi-session reasoning caps most systems below 30%, and neither approach alone solves the task. These results motivate hybrid architectures that combine long-context attention with structured multimodal retrieval. Our code is available at https://github.com/xrenaf/MEMLENS.", "github": "https://github.com/xrenaf/MEMLENS" }, { "date": "2026-05-15", "arxiv_id": "2605.10912", "title": "WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation", "authors": [ "Shuangrui Ding", "Xuanlang Dai", "Long Xing", "Shengyuan Ding", "Ziyu Liu", "Yang JingYi", "Penghui Yang", "Zhixiong Zhang", "Xilin Wei", "Xinyu Fang", "Yubo Ma", "Haodong Duan", "Jing Shao", "Jiaqi Wang", "Dahua Lin", "Kai Chen", "Yuhang Zang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/internlm/WildClawBench", "github_api_added_by": "user", "project_page": "https://internlm.github.io/WildClawBench/", "abstract": "Large language and vision-language models increasingly power agents that act on a user's behalf through command-line interface (CLI) harnesses. However, most agent benchmarks still rely on synthetic sandboxes, short-horizon tasks, mock-service APIs, and final-answer checks, leaving open whether agents can complete realistic long-horizon work in the runtimes where they are deployed. This work presents WildClawBench, a native-runtime benchmark of 60 human-authored, bilingual, multimodal tasks spanning six thematic categories. Each task averages roughly 8 minutes of wall-clock time and over 20 tool calls, and runs inside a reproducible Docker container hosting an actual CLI agent harness (OpenClaw, Claude Code, Codex, or Hermes Agent) with access to real tools rather than mock services. Grading is hybrid, combining deterministic rule-based checks, environment-state auditing of side effects, and an LLM/VLM judge for semantic verification. Across 19 frontier models, the best, Claude Opus 4.7, reaches only 62.2% overall under OpenClaw, while every other model stays below 60%, and switching harness alone shifts a single model by up to 18 points. These results show that long-horizon, native-runtime agent evaluation remains a far-from-resolved task for current frontier models. We release the tasks, code, and containerized tooling to support reproducible evaluation.", "github": "https://github.com/internlm/WildClawBench" }, { "date": "2026-05-15", "arxiv_id": "2605.14712", "title": "IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation", "authors": [ "Shijie Lian", "Bin Yu", "Xiaopeng Lin", "Zhaolong Shen", "Laurence Tianruo Yang", "Yurun Jin", "Haishan Liu", "Changti Wu", "Hang Yuan", "Cong Huang", "Kai Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZGC-EmbodyAI/IntentVLA", "github_api_added_by": "user", "project_page": "", "abstract": "Robot imitation data are often multimodal: similar visual-language observations may be followed by different action chunks because human demonstrators act with different short-horizon intents, task phases, or recent context. Existing frame-conditioned VLA policies infer each chunk from the current observation and instruction alone, so under partial observability they may resample different intents across adjacent replanning steps, leading to inter-chunk conflict and unstable execution. We introduce IntentVLA, a history-conditioned VLA framework that encodes recent visual observations into a compact short-horizon intent representation and uses it to condition chunk generation. We further introduce AliasBench, a 12-task ambiguity-aware benchmark on RoboTwin2 with matched training data and evaluation environments that isolate short-horizon observation aliasing. Across AliasBench, SimplerEnv, LIBERO, and RoboCasa, IntentVLA improves rollout stability and outperforms strong VLA baselines", "github": "https://github.com/ZGC-EmbodyAI/IntentVLA" }, { "date": "2026-05-15", "arxiv_id": "2605.15167", "title": "Does Synthetic Layered Design Data Benefit Layered Design Decomposition?", "authors": [ "Kam Man Wu", "Haolin Yang", "Qingyu Chen", "Yihu Tang", "Jingye Chen", "Qifeng Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YangHaolin0526/SynLayers", "github_api_added_by": "user", "project_page": "", "abstract": "Recent advances in image generation have made it easy to produce high-quality images. However, these outputs are inherently flattened, entangling foreground elements, background, and text within a fixed canvas. As a result, flexible post-generation editing remains challenging, revealing a clear last-mile gap toward practical usability. Existing approaches either rely on scarce proprietary layered assets or construct partially synthetic data from limited structural priors. However, both strategies face fundamental challenges in scalability. In this work, we investigate whether pure synthetic layered data can improve graphic design decomposition. We make the assumption that, in graphic design, effective decomposition does not require modeling inter-layer dependencies as precisely as in natural-image composition, since design elements are often intentionally arranged as modular and semantically separable components. Concretely, we conduct a data-centric study based on CLD baseline, which is a state-of-the-art layer decomposition framework. Based on the baseline, we construct our own synthetic dataset, SynLayers, generate textual supervision using vision language models, and automate inference inputs with VLM-predicted bounding boxes. Our study reveals three key findings: (1) even training with purely synthetic data can outperform non-scalable alternatives such as the widely used PrismLayersPro dataset, demonstrating its viability as a scalable and effective substitute; (2) performance consistently improves with increased training data scale, while gains begin to saturate at around 50K samples; and (3) synthetic data enables balanced control over layer-count distributions, avoiding the layer-count imbalance commonly observed in real-world datasets. We hope this data-centric study encourages broader adoption of synthetic data as a practical foundation for layered design editing systems.", "github": "https://github.com/YangHaolin0526/SynLayers" }, { "date": "2026-05-15", "arxiv_id": "2605.06527", "title": "STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?", "authors": [ "Hanxiang Chao", "Yihan Bai", "Rui Sheng", "Tianle Li", "Yushi Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large Language Model (LLM) agents are increasingly expected to maintain coherent, long-term personalized memory, yet current benchmarks primarily measure static fact retrieval, overlooking the ability to revise stored beliefs when new evidence emerges. We identify a critical and underexplored failure mode, Implicit Conflict: a later observation invalidates an earlier memory without explicit negation, requiring contextual inference and commonsense reasoning to detect. To rigorously evaluate this capability, we introduce STALE, a benchmark of 400 expert-validated conflict scenarios (1,200 evaluation queries across three probing dimensions) spanning over 100 everyday topics with contexts up to 150K tokens. We propose a three-dimensional probing framework that tests State Resolution (detecting that a prior belief is outdated), Premise Resistance (rejecting queries that falsely presuppose a stale state), and Implicit Policy Adaptation (proactively applying updated states in downstream behavior). A systematic evaluation of frontier LLMs and specialized memory frameworks reveals a pervasive gap between retrieving updated evidence and acting on it, with even the best evaluated model achieving only 55.2% overall accuracy. Models often accept outdated assumptions embedded in a user's query, and they struggle to recognize when a change in one aspect of the user's state should invalidate related memories. To establish an initial baseline for state-aware memory, we further present CUPMem, a prototype that strengthens write-time revision through structured state consolidation and propagation-aware search, suggesting that explicit state adjudication is a promising direction for robust agentic memory.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.15141", "title": "Causal Forcing++: Scalable Few-Step Autoregressive Diffusion Distillation for Real-Time Interactive Video Generation", "authors": [ "Min Zhao", "Hongzhou Zhu", "Kaiwen Zheng", "Zihan Zhou", "Bokai Yan", "Xinyuan Li", "Xiao Yang", "Chongxuan Li", "Jun Zhu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://github.com/thu-ml/Causal-Forcing", "abstract": "Real-time interactive video generation requires low-latency, streaming, and controllable rollout. Existing autoregressive (AR) diffusion distillation methods have achieved strong results in the chunk-wise 4-step regime by distilling bidirectional base models into few-step AR students, but they remain limited by coarse response granularity and non-negligible sampling latency. In this paper, we study a more aggressive setting: frame-wise autoregression with only 1--2 sampling steps. In this regime, we identify the initialization of a few-step AR student as the key bottleneck: existing strategies are either target-misaligned, incapable of few-step generation, or too costly to scale. We propose Causal Forcing++, a principled and scalable pipeline that uses causal consistency distillation (causal CD) for few-step AR initialization. The core idea is that causal CD learns the same AR-conditional flow map as causal ODE distillation, but obtains supervision from a single online teacher ODE step between adjacent timesteps, avoiding the need to precompute and store full PF-ODE trajectories. This makes the initialization both more efficient and easier to optimize. The resulting pipeline, \\ours, surpasses the SOTA 4-step chunk-wise Causal Forcing under the \\textbf{frame-wise 2-step setting} by 0.1 in VBench Total, 0.3 in VBench Quality, and 0.335 in VisionReward, while reducing first-frame latency by 50\\% and Stage 2 training cost by sim4times. We further extend the pipeline to action-conditioned world model generation in the spirit of Genie3. Project Page: https://github.com/thu-ml/Causal-Forcing and https://github.com/shengshu-ai/minWM .", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.09681", "title": "Forcing-KV: Hybrid KV Cache Compression for Efficient Autoregressive Video Diffusion Models", "authors": [ "Yicheng Ji", "Zhizhou Zhong", "Jun Zhang", "Qin Yang", "XiTai Jin", "Ying Qin", "Wenhan Luo", "Shuiyang Mao", "Wei Liu", "Huan Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zju-jiyicheng/Forcing-KV", "github_api_added_by": "user", "project_page": "https://zju-jiyicheng.github.io/Forcing-KV-Page/", "abstract": "Autoregressive (AR) video diffusion models adopt a streaming generation framework, enabling long-horizon video generation with real-time responsiveness, as exemplified by the Self Forcing training paradigm. However, existing AR video diffusion models still suffer from significant attention complexity and severe memory overhead due to the redundant key-value (KV) caches across historical frames, which limits scalability. In this paper, we tackle this challenge by introducing KV cache compression into autoregressive video diffusion. We observe that attention heads in mainstream AR diffusion models exhibit markedly distinct attention patterns and functional roles that remain stable across samples and denoising steps. Building on our empirical study of head-wise functional specialization, we divide the attention heads into two categories: static heads, which focus on transitions across autoregressive chunks and intra-frame fidelity, and dynamic heads, which govern inter-frame motion and consistency. We then propose Forcing-KV, a hybrid KV cache compression strategy that performs structured static pruning for static heads and dynamic pruning based on segment-wise similarity for dynamic heads. While maintaining output quality, our method achieves a generation speed of over 29 frames per second on a single NVIDIA H200 GPU along with 30% cache memory reduction, delivering up to 1.35x and 1.50x speedups on LongLive and Self Forcing at 480P resolution, and further scaling to 2.82x speedup at 1080P resolution. Code and demo videos are provided at https://zju-jiyicheng.github.io/Forcing-KV-Page.", "github": "https://github.com/zju-jiyicheng/Forcing-KV" }, { "date": "2026-05-15", "arxiv_id": "2605.00180", "title": "RouteProfile: Elucidating the Design Space of LLM Profiles for Routing", "authors": [ "Jingjun Xu", "Hongji Pu", "Tao Feng", "Haozhen Zhang", "Jiaxuan You", "Ge Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ulab-uiuc/RouteProfile", "github_api_added_by": "user", "project_page": "https://ulab-uiuc.github.io/RouteProfile/", "abstract": "As the large language model (LLM) ecosystem expands, individual models exhibit varying capabilities across queries, benchmarks, and domains, motivating the development of LLM routing. While prior work has largely focused on router mechanism design, LLM profiles, which capture model capabilities, remain underexplored. In this work, we ask: How does LLM profile design affect routing performance across different routers? Addressing this question helps clarify the role of profiles in routing, disentangle profile design from router design, and enable fairer comparison and more principled development of routing systems. To this end, we view LLM profiling as a structured information integration problem over heterogeneous interaction histories. We develop a general design space of LLM profiles, named RouteProfile, along four key dimensions: organizational form, representation type, aggregation depth, and learning configuration. Through systematic evaluation across three representative routers under both standard and new-LLM generalization settings, we show that: (1) structured profiles consistently outperform flat ones; (2) query-level signals are more reliable than coarse domain-level signals; and (3) generalization to newly introduced models benefits most from structured profiles under trainable configurations. Overall, our work highlights LLM profile design as an important direction for future routing research.", "github": "https://github.com/ulab-uiuc/RouteProfile" }, { "date": "2026-05-15", "arxiv_id": "2605.14323", "title": "Dynamic Latent Routing", "authors": [ "Fangyuan Yu", "Xin Su", "Amir Abdullah" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We investigate the temporal concatenation of sub-policies in Markov Decision Processes (MDP) with time-varying reward functions. We introduce General Dijkstra Search (GDS), and prove that globally optimal goal-reaching policies can be recovered through temporal composition of intermediate optimal sub-policies. Motivated by the \"search, select, update\" principle underlying GDS, we propose Dynamic Latent Routing (DLR), a language-model post-training method that jointly learns discrete latent codes, routing policies, and model parameters through dynamic search in a single training stage. In low-data fine-tuning settings, DLR matches or outperforms supervised fine-tuning across four datasets and six models, achieving a mean gain of +6.6 percentage points, while prior discrete-latent baselines consistently underperform SFT. Mechanistic analyses and targeted code ablations show that DLR learns structured routing behaviors with distinct causal roles.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.13169", "title": "PanoWorld: Towards Spatial Supersensing in 360^circ Panorama World", "authors": [ "Changpeng Wang", "Xin Lin", "Junhan Liu", "Yuheng Liu", "Zhen Wang", "Donglian Qi", "Yunfeng Yan", "Xi Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multimodal large laboratory models (MLLMs) still struggle with spatial understanding under the dominant perspective-image paradigm, which inherits the narrow field of view of human-like perception. For navigation, robotic search, and 3D scene understanding, 360-degree panoramic sensing offers a form of supersensing by capturing the entire surrounding environment at once. However, existing MLLM pipelines typically decompose panoramas into multiple perspective views, leaving the spherical structure of equirectangular projection (ERP) largely implicit. In this paper, we study pano-native understanding, which requires an MLLM to reason over an ERP panorama as a continuous, observer-centered space. To this end, we first define the key abilities for pano-native understanding, including semantic anchoring, spherical localization, reference-frame transformation, and depth-aware 3D spatial reasoning. We then build a large-scale metadata construction pipeline that converts mixed-source ERP panoramas into geometry-aware, language-grounded, and depth-aware supervision, and instantiate these signals as capability-aligned instruction tuning data. On the model side, we introduce PanoWorld with Spherical Spatial Cross-Attention, which injects spherical geometry into the visual stream. We further construct PanoSpace-Bench, a diagnostic benchmark for evaluating ERP-native spatial reasoning. Experiments show that PanoWorld substantially outperforms both proprietary and open-source baselines on PanoSpace-Bench, H* Bench, and R2R-CE Val-Unseen benchmarks. These results demonstrate that robust panoramic reasoning requires dedicated pano-native supervision and geometry-aware model adaptation. All source code and proposed data will be publicly released.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.14876", "title": "Unlocking Complex Visual Generation via Closed-Loop Verified Reasoning", "authors": [ "Hanbo Cheng", "Limin Lin", "Ruo Zhang", "Yicheng Pan", "Jun Du" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://hanbo-cheng.github.io/CLVR_Proj", "abstract": "Despite rapid advancements, current text-to-image (T2I) models predominantly rely on a single-step generation paradigm, which struggles with complex semantics and faces diminishing returns from parameter scaling. While recent multi-step reasoning approaches show promise, they are hindered by ungrounded planning hallucinations lacking verification, monolithic post-hoc reflection, long-context optimization instabilities, and prohibitive inference latency. To overcome these bottlenecks, we propose the Closed-Loop Visual Reasoning (CLVR) framework, a comprehensive system that deeply couples visual-language logical planning with pixel-level diffusion generation. CLVR introduces an automated data engine with step-level visual verification to synthesize reliable reasoning trajectories, and proposes Proxy Prompt Reinforcement Learning (PPRL) to resolve long-context optimization instabilities by distilling interleaved multimodal histories into explicit reward signals for accurate causal attribution. Furthermore, to mitigate the severe latency bottleneck caused by iterative denoising, we propose \u0394-Space Weight Merge (DSWM), a theoretically grounded method that fuses alignment weights with off-the-shelf distillation priors, reducing the per-step inference cost to just 4 NFEs without requiring expensive re-distillation. Extensive experiments demonstrate that CLVR outperforms existing open-source baselines across multiple benchmarks and approaches the performance of proprietary commercial models, unlocking general test-time scaling capabilities for complex visual generation.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.11459", "title": "Overcoming Dynamics-Blindness: Training-Free Pace-and-Path Correction for VLA Models", "authors": [ "Yanyan Zhang", "Chaoda Song", "Vikash Singh", "Xinpeng Li", "Kai Ye", "Zhe Hu", "Zhongzhu Pu", "Yu Yin", "Vipin Chaudhary" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Vision-Language-Action (VLA) models achieve remarkable flexibility and generalization beyond classical control paradigms. However, most prevailing VLAs are trained under a single-frame observation paradigm, which leaves them structurally blind to temporal dynamics. Consequently, these models degrade severely in non-stationary scenarios, even when trained or finetuned on dynamic datasets. Existing approaches either require expensive retraining or suffer from latency bottlenecks and poor temporal consistency across action chunks. We propose Pace-and-Path Correction, a training-free, closed-form inference-time operator that wraps any chunked-action VLA. From a single quadratic cost, joint minimization yields a unified solution that decomposes orthogonally into two distinct channels. The pace channel compresses execution along the planned direction, while the path channel applies an orthogonal spatial offset, jointly absorbing the perceived dynamics within the chunk window. We evaluate our approach on a comprehensive diagnostic benchmark MoveBench designed to isolate motion as the sole controlled variable. Empirical results demonstrate that our framework consistently outperforms state-of-the-art training-free wrappers and dynamic-adaptive methods and improves success rates by up to 28.8% and 25.9% in absolute terms over foundational VLA models in dynamic-only and static-dynamic mixed environments, respectively.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.13027", "title": "PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution", "authors": [ "Zihang Xu", "Xiaoyang Liu", "Zheng Chen", "Yulun Zhang", "Xiaokang Yang" ], "github_manual": "", "github_regex": "https://github.com/faithxuz/PRISM", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Text image super-resolution (Text-SR) requires more than visually plausible detail synthesis: slight errors in stroke topology may alter character identity and break readability. Existing methods improve text fidelity with stronger recognition-based or generative priors, yet they still face two unresolved challenges under severe degradation: the text condition extracted from low-quality inputs can itself be unreliable, and a plausible global prior does not fully determine fine-grained stroke boundaries. We present PRISM, a single-step diffusion-based Text-SR framework that addresses these two challenges through Flow-Matching Prior Rectification (FMPR) and a Structure-guided Uncertainty-aware Residual Encoder (SURE). FMPR constructs a privileged training-time prior from paired low-quality/high-quality latents and learns a flow matching that transports degraded embeddings toward this restoration-oriented prior space, yielding more accurate and reliable global text guidance. SURE further predicts uncertainty-aware structural residuals to selectively absorb reliable local boundary evidence while suppressing ambiguous stroke cues. Together, these components enable explicit global prior rectification and local structure refinement within a single diffusion restoration pass. Experiments on both synthetic and real-world benchmarks show that PRISM achieves state-of-the-art performance with millisecond-level inference. Our dataset and code will be available at https://github.com/faithxuz/PRISM.", "github": "https://github.com/faithxuz/PRISM" }, { "date": "2026-05-15", "arxiv_id": "2605.14445", "title": "FrontierSmith: Synthesizing Open-Ended Coding Problems at Scale", "authors": [ "Runyuan He", "Qiuyang Mang", "Shang Zhou", "Kaiyuan Liu", "Hanchen Li", "Huanzhi Mao", "Qizheng Zhang", "Zerui Li", "Bo Peng", "Lufeng Cheng", "Tianfu Fu", "Yichuan Wang", "Wenhao Chai", "Jingbo Shang", "Alex Dimakis", "Joseph E. Gonzalez", "Alvin Cheung" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FrontierCS/FrontierSmith", "github_api_added_by": "user", "project_page": "https://frontier-cs.org", "abstract": "Many real-world coding challenges are open-ended and admit no known optimal solution. Yet, recent progress in LLM coding has focused on well-defined tasks such as feature implementation, bug fixing, and competitive programming. Open-ended coding remains a weak spot for LLMs, largely because open-ended training problems are scarce and expensive to construct. Our goal is to synthesize open-ended coding problems at scale to train stronger LLM coders. We introduce FrontierSmith, an automated system for iteratively evolving open-ended problems from existing closed-ended coding tasks. Starting from competitive programming problems, FrontierSmith generates candidate open-ended variants by changing the problems'goals, restricting outputs, and generalizing inputs. It then uses a quantitative idea divergence metric to select problems that elicit genuinely diverse approaches from different solvers. Agents then generate test cases and verifiers for the surviving candidates. On two open-ended coding benchmarks, training on our synthesized data yields substantial gains over the base models: Qwen3.5-9B improves by +8.82 score on FrontierCS and +306.36 (Elo-rating-based performance) on ALE-bench; Qwen3.5-27B improves by +12.12 and +309.12, respectively. The synthesized problems also make agents take more turns and use more tokens, similar to human-curated ones, suggesting that closed-ended seeds can be a practical starting point for long-horizon coding data.", "github": "https://github.com/FrontierCS/FrontierSmith" }, { "date": "2026-05-15", "arxiv_id": "2605.11458", "title": "Adaptive Teacher Exposure for Self-Distillation in LLM Reasoning", "authors": [ "Zihao Han", "Tiangang Zhang", "Huaibin Wang", "Yilun Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "On-policy self-distillation has become a strong recipe for LLM reasoning, where a privileged teacher supervises the student's own rollouts while conditioning on the reference solution. A design choice shared by nearly all such methods, however, has gone unquestioned: the teacher always sees the full reference reasoning. We argue that this default itself is part of the problem and identify a teacher-side exposure mismatch: when the teacher conditions on reasoning far beyond the student's current competence, the resulting token targets become too strong to absorb. A controlled fixed-exposure sweep makes this concrete on two fronts: 1) full exposure is not reliably the best choice, and 2) student-teacher mismatch grows monotonically as the teacher sees more privileged reasoning. This motivates treating teacher exposure not as a fixed hyperparameter but as a learnable training-time control variable. We therefore propose Adaptive Teacher Exposure for Self-Distillation (ATESD). ATESD models the reveal ratio with a lightweight Beta-policy controller conditioned on compact training-state statistics, and uses one sampled exposure for a short hold window of student updates. To make this exposure controller learnable, we optimize it with a discounted learning-progress reward that scores each held decision by its effect on the student's future improvement rather than its immediate loss change, addressing the delayed credit assignment induced by on-policy distillation. Experiments on AIME 24, AIME 25, and HMMT 25 across Qwen3-{1.7B, 4B, 8B} show that ATESD consistently outperforms competitive self-distillation and RL baselines, improving over OPSD by +0.95, +2.05, and +2.33 Average@12 points respectively, and establishing adaptive teacher exposure as an effective new axis for reasoning self-distillation.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.14068", "title": "CurveBench: A Benchmark for Exact Topological Reasoning over Nested Jordan Curves", "authors": [ "Amirreza Mohseni", "Mona Mohammadi", "Morteza Saghafian", "Naser Talebizadeh Saradari" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Amir-Mohseni/CurveBench", "github_api_added_by": "user", "project_page": "https://huggingface.co/collections/AmirMohseni/curvebench", "abstract": "We introduce CurveBench, a benchmark for hierarchical topological reasoning from visual input. CurveBench consists of 756 images of pairwise non-intersecting Jordan curves across easy, polygonal, topographic-inspired, maze-like, and dense counting configurations. Each image is annotated with a rooted tree encoding the containment relations between planar regions. We formulate the task as structured prediction: given an image, a model must recover the full rooted containment tree induced by the curves. Despite the visual simplicity of the task, the strongest evaluated model, Gemini 3.1 Pro, achieves only 71.1\\% tree-generation accuracy on CurveBench-Easy and 19.1\\% on CurveBench-Hard. We further demonstrate benchmark utility through RLVR-style fine-tuning of open-weight vision-language models. Our trained Qwen3-VL-8B model improves over Qwen-3-VL-8B-Thinking from 2.8\\% to 33.3\\% tree-generation accuracy on CurveBench-Easy, exceeding GPT-5.4 and Claude Opus 4.5 under our evaluation protocol. The remaining gap, especially on CurveBench-Hard, shows that exact topology-aware visual reasoning remains far from solved.", "github": "https://github.com/Amir-Mohseni/CurveBench" }, { "date": "2026-05-15", "arxiv_id": "2605.13852", "title": "Realiz3D: 3D Generation Made Photorealistic via Domain-Aware Learning", "authors": [ "Ido Sobol", "Kihyuk Sohn", "Yoav Blum", "Egor Zakharov", "Max Bluvstein", "Andrea Vedaldi", "Or Litany" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://idosobol.github.io/realiz3d/", "abstract": "We often aim to generate images that are both photorealistic and 3D-consistent, adhering to precise geometry, material, and viewpoint controls. Typically, this is achieved by fine-tuning an image generator, pre-trained on billions of real images, using renders of synthetic 3D assets, where annotations for control signals are available. While this approach can learn the desired controls, it often compromises the realism of the images due to domain gap between photographs and renders. We observe that this issue largely arises from the model learning an unintended association between the presence of control signals and the synthetic appearance of the images. To address this, we introduce Realiz3D, a lightweight framework for training diffusion models, that decouples controls and visual domain. The key idea is to explicitly learn visual domain, real or synthetic, separately from other control signals by introducing a co-variate that, fed into small residual adapters, shifts the domain. Then, the generator can be trained to gain controllability, without fitting to specific visual domain. In this way, the model can be guided to produce realistic images even when controls are applied. We enhance control transferability to the real domain by leveraging insights about roles of different layers and denoising steps in diffusion-based generators, informing new training and inference strategies that further mitigate the gap. We demonstrate the advantages of Realiz3D in tasks as text-to-multiview generation and texturing from 3D inputs, producing outputs that are 3D-consistent and photorealistic.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.14607", "title": "ViMU: Benchmarking Video Metaphorical Understanding", "authors": [ "Qi Li", "Xinchao Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LiQiiiii/Video-Metaphorical-Understanding", "github_api_added_by": "user", "project_page": "https://liqiiiii.github.io/Video-Metaphorical-Understanding/", "abstract": "Any new medium, once it emerges, is used for more than the transmission of overt content alone. The information it carries typically operates on two levels: one is the content directly presented, while the other is the subtext beneath it-the implicit ideas and intentions the creator seeks to convey through the medium. Likewise, since video technologies became widely adopted, video has served not only as a powerful tool for recording and communicating visual information, but also as a vehicle for emotions, attitudes, and social meanings that are often difficult to articulate explicitly. Thus, the true meaning of many videos does not reside solely in what is shown on screen; it is often embedded in context, style of expression, and the viewer's social experience. Some forms of such video subtext are humorous, while others carry irony, mockery, or criticism. These implicit meanings can also be interpreted very differently across cultural backgrounds and social groups. However, most existing video understanding models still focus primarily on literal visual comprehension, such as recognizing objects, actions, or temporal relations, and lack a systematic ability to understand the metaphorical, ironic, and social meanings embedded in videos. To bridge this gap, we introduce ViMU, the first benchmark designed to systematically evaluate the subtext understanding capabilities of frontier models in videos. ViMU assesses whether video understanding models can go beyond literal perception to infer implicit meaning while grounding their interpretations in multimodal evidence and answering both open-ended and multiple-choice questions. Importantly, all questions are designed to be hint-free, ensuring that no key evidence is disclosed to models before answering.", "github": "https://github.com/LiQiiiii/Video-Metaphorical-Understanding" }, { "date": "2026-05-15", "arxiv_id": "2605.14438", "title": "BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE", "authors": [ "Juntong Wu", "Jialiang Cheng", "Qishen Yin", "Yue Dai", "Yuliang Yan", "Fuyu Lv", "Ou Dan", "Li Yuan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) architectures enhance the efficiency of large language models by activating only a subset of experts per token. However, standard MoE employs a fixed Top-K routing strategy, leading to redundant computation and suboptimal inference latency. Existing acceleration methods either require costly retraining with architectural changes or suffer from severe performance drop at high sparsity due to train-inference mismatch. To address these limitations, we propose BEAM (Binary Expert Activation Masking), a novel method that learns token-adaptive expert selection via trainable binary masks. With a straight-through estimator and an auxiliary regularization loss, BEAM induces dynamic expert sparsity through end-to-end training while maintaining model capability. We further implement an efficient custom CUDA kernel for BEAM, ensuring seamless integration with the vLLM inference framework. Experiments show that BEAM retains over 98\\% of the original model's performance while reducing MoE layer FLOPs by up to 85\\%, achieving up to 2.5times faster decoding and 1.4times higher throughput, demonstrating its effectiveness as a practical, plug-and-play solution for efficient MoE inference.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.14454", "title": "LiSA: Lifelong Safety Adaptation via Conservative Policy Induction", "authors": [ "Minbeom Kim", "Lesly Miculicich", "Bhavana Dalvi Mishra", "Mihir Parmar", "Phillip Wallis", "Bharath Chandrasekhar", "Kyomin Jung", "Tomas Pfister", "Long T. Le" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "As AI agents move from chat interfaces to systems that read private data, call tools, and execute multi-step workflows, guardrails become a last line of defense against concrete deployment harms. In these settings, guardrail failures are no longer merely answer-quality errors: they can leak secrets, authorize unsafe actions, or block legitimate work. The hardest failures are often contextual: whether an action is acceptable depends on local privacy norms, organizational policies, and user expectations that resist pre-deployment specification. This creates a practical gap: guardrails must adapt to their own operating environments, yet deployment feedback is typically limited to sparse, noisy user-reported failures, and repeated fine-tuning is often impractical. To address this gap, we propose LiSA (Lifelong Safety Adaptation), a conservative policy induction framework that improves a fixed base guardrail through structured memory. LiSA converts occasional failures into reusable policy abstractions so that sparse reports can generalize beyond individual cases, adds conflict-aware local rules to prevent overgeneralization in mixed-label contexts, and applies evidence-aware confidence gating via a posterior lower bound, so that memory reuse scales with accumulated evidence rather than empirical accuracy alone. Across PrivacyLens+, ConFaide+, and AgentHarm, LiSA consistently outperforms strong memory-based baselines under sparse feedback, remains robust under noisy user feedback even at 20% label-flip rates, and pushes the latency--performance frontier beyond backbone model scaling. Ultimately, LiSA offers a practical path to secure AI agents against the unpredictable long tail of real-world edge risks.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.12034", "title": "Boosting Omni-Modal Language Models: Staged Post-Training with Visually Debiased Evaluation", "authors": [ "Che Liu", "Lichao Ma", "Xiangyu Tony Zhang", "Yuxin Zhang", "Haoyang Zhang", "Xuerui Yang", "Fei Tian" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://cheliu-computation.github.io/omni/", "abstract": "Omni-modal language models are intended to jointly understand audio, visual inputs, and language, but benchmark gains can be inflated when visual evidence alone is enough to answer a query. We study whether current omni-modal benchmarks separate visual shortcuts from genuine audio-visual-language evidence integration, and how post-training behaves under a visually debiased evaluation setting. We audit nine omni-modal benchmarks with visual-only probing, remove visually solvable queries, and retain full subsets when filtering is undefined or would make comparisons unstable. This yields OmniClean, a cleaned evaluation view with 8,551 retained queries from 16,968 audited queries. On OmniClean, we evaluate OmniBoost, a three-stage post-training recipe based on Qwen2.5-Omni-3B: mixed bi-modal SFT, mixed-modality RLVR, and SFT on self-distilled data. Balanced bi-modal SFT gives limited and uneven gains, RLVR provides the first broad improvement, and self-distillation reshapes the benchmark profile. After SFT on self-distilled data, the 3B model reaches performance comparable to, and in aggregate slightly above, Qwen3-Omni-30B-A3B-Instruct without using a stronger omni-modal teacher. These results show that omni-modal progress is easier to interpret when evaluation controls visual leakage, and that small omni-modal models can benefit from staged post-training with self-distilled omni-query supervision. Project page: https://cheliu-computation.github.io/omni/", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.07637", "title": "Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding", "authors": [ "Valeriy Vyaltsev", "Alsu Sagirova", "Anton Andreychuk", "Oleg Bulichev", "Yuri Kuratov", "Konstantin Yakovlev", "Aleksandr Panov", "Alexey Skrynnik" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multi-agent pathfinding (MAPF) is a widely used abstraction for multi-robot trajectory planning problems, where multiple homogeneous agents move simultaneously within a shared environment. Although solving MAPF optimally is NP-hard, scalable and efficient solvers are critical for real-world applications such as logistics and search-and-rescue. To this end, the research community has proposed various decentralized suboptimal MAPF solvers that leverage machine learning. Such methods frame MAPF (from a single agent perspective) as a Dec-POMDP where at each time step an agent has to decide an action based on the local observation and typically solve the problem via reinforcement learning or imitation learning. We follow the same approach but additionally introduce a learnable communication module tailored to enhance cooperation between agents via efficient feature sharing. We present the Local Communication for Multi-agent Pathfinding (LC-MAPF), a generalizable pre-trained model that applies multi-round communication between neighboring agents to exchange information and improve their coordination. Our experiments show that the introduced method outperforms the existing learning-based MAPF solvers, including IL and RL-based approaches, across diverse metrics in a diverse range of (unseen) test scenarios. Remarkably, the introduced communication mechanism does not compromise LC-MAPF's scalability, a common bottleneck for communication-based MAPF solvers.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.14984", "title": "Sat3DGen: Comprehensive Street-Level 3D Scene Generation from Single Satellite Image", "authors": [ "Ming Qian", "Zimin Xia", "Changkun Liu", "Shuailei Ma", "Wen Wang", "Zeran Ke", "Bin Tan", "Hang Zhang", "Gui-Song Xia" ], "github_manual": "", "github_regex": "https://github.com/qianmingduowan/Sat3DGen", "github_paper_md": "", "github_api": "https://github.com/qianmingduowan/Sat3DGen", "github_api_added_by": "user", "project_page": "https://qianmingduowan.github.io/Sat3DGen_project_page/", "abstract": "Generating a street-level 3D scene from a single satellite image is a crucial yet challenging task. Current methods present a stark trade-off: geometry-colorization models achieve high geometric fidelity but are typically building-focused and lack semantic diversity. In contrast, proxy-based models use feed-forward image-to-3D frameworks to generate holistic scenes by jointly learning geometry and texture, a process that yields rich content but coarse and unstable geometry. We attribute these geometric failures to the extreme viewpoint gap and sparse, inconsistent supervision inherent in satellite-to-street data. We introduce Sat3DGen to address these fundamental challenges, which embodies a geometry-first methodology. This methodology enhances the feed-forward paradigm by integrating novel geometric constraints with a perspective-view training strategy, explicitly countering the primary sources of geometric error. This geometry-centric strategy yields a dramatic leap in both 3D accuracy and photorealism. For validation, we first constructed a new benchmark by pairing the VIGOR-OOD test set with high-resolution DSM data. On this benchmark, our method improves geometric RMSE from 6.76m to 5.20m. Crucially, this geometric leap also boosts photorealism, reducing the Fr\u00e9chet Inception Distance (FID) from sim40 to 19 against the leading method, Sat2Density++, despite using no extra tailored image-quality modules. We demonstrate the versatility of our high-quality 3D assets through diverse downstream applications, including semantic-map-to-3D synthesis, multi-camera video generation, large-scale meshing, and unsupervised single-image Digital Surface Model (DSM) estimation. The code has been released on https://github.com/qianmingduowan/Sat3DGen.", "github": "https://github.com/qianmingduowan/Sat3DGen" }, { "date": "2026-05-15", "arxiv_id": "2605.12243", "title": "PreScam: A Benchmark for Predicting Scam Progression from Early Conversations", "authors": [ "Weixiang Sun", "Shang Ma", "Yiyang Li", "Tianyi Ma", "Zehong Wang", "Colby Nelson", "Xusheng Xiao", "Yanfang Ye" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Conversational scams, such as romance and investment scams, are emerging as a major form of online fraud. Unlike one-shot scam lures such as fake lottery or unpaid toll messages, they unfold through multi-turn conversations in which scammers gradually manipulate victims using evolving psychological techniques. However, existing research mainly focuses on static scam detection or synthetic scams, leaving open whether language models can understand how real-world scams progress over time. We introduce PreScam, a benchmark for modeling scam progression from early conversations. Built from user-submitted scam reports, PreScam filters and structures 177,989 raw reports into 11,573 conversational scam instances spanning 20 scam categories. Each instance is hierarchically structured according to the scam lifecycle defined by the proposed scam kill chain, and further annotated at the turn level with scammer psychological actions and victim responses. We benchmark models on two tasks: real-time termination prediction, which estimates whether a conversation is approaching the termination stage, and scammer action prediction, which forecasts the scammer's subsequent actions. Results show a clear gap between surface-level fluency and progression modeling: supervised encoders substantially outperform zero-shot LLMs on real-time termination prediction, while next-action prediction remains only moderately successful even for strong LLMs. Taken together, these results show that current models can capture some scam-related cues, yet still struggle to track how risk escalates and how manipulation unfolds across turns.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.15012", "title": "Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance", "authors": [ "Kai Yan", "Alexander G. Schwing", "Yu-Xiong Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KaiYan289/FEST", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has achieved great success in developing Large Language Models (LLMs) with chain-of-thought rollouts for many tasks such as math and coding. Nevertheless, RLVR struggles with sample efficiency on difficult problems where correct rollouts are hard to generate. Prior works propose to address this issue via demonstration-guided RLVR, i.e., to conduct Supervised FineTuning (SFT) when RL fails; however, SFT often requires a lot of data, which can be expensive to acquire. In this paper, we propose FEST, a FEw-ShoT demonstration-guided RLVR algorithm. It attains compelling results with only 128 demonstrations randomly selected from an SFT dataset. We find that three components are vital for the success: supervised signal, on-policy signal, and decaying weights on the few-shot SFT dataset to prevent overfitting from multiple-epoch training. On several benchmarks, FEST outperforms baselines with magnitudes less SFT data, even matching their performance with full dataset.", "github": "https://github.com/KaiYan289/FEST" }, { "date": "2026-05-15", "arxiv_id": "2605.06554", "title": "Long Context Pre-Training with Lighthouse Attention", "authors": [ "Bowen Peng", "Subho Ghosh", "Jeffrey Quesnelle" ], "github_manual": "", "github_regex": "https://github.com/ighoshsubho/lighthouse-attention", "github_paper_md": "", "github_api": "https://github.com/ighoshsubho/lighthouse-attention", "github_api_added_by": "user", "project_page": "https://nousresearch.com/lighthouse-attention", "abstract": "Training causal transformers at extreme sequence lengths is bottlenecked by the quadratic time and memory of scaled dot-product attention (SDPA). In this work, we propose Lighthouse Attention, a training-only symmetrical selection-based hierarchical attention algorithm that wraps around ordinary SDPA and can be easily removed towards the end of the training. Our hierarchical selection is also gradient-free, which exempts us from dealing with a complicated and potentially inefficient backward pass kernel. Our contribution is three-fold: (i) A subquadratic hierarchical pre- and post-processing step that does adaptive compression and decompression of the sequence. (ii) A symmetrical compression strategy that pools queries, keys and values at the same time, while preserving left-to-right causality, which greatly improves parallelism. (iii) A two stage training approach which we pre-train for the majority of the time with Lighthouse Attention and recover a full attention model at the end with a short training. We run preliminary small scale LLM pre-training experiments that show the effectiveness of our method compared to full attention training with all other settings matched, where we achieve a faster total training time and lower final loss after the recovery phase. Full code is available at: https://github.com/ighoshsubho/lighthouse-attention", "github": "https://github.com/ighoshsubho/lighthouse-attention" }, { "date": "2026-05-15", "arxiv_id": "2605.15193", "title": "Aligning Latent Geometry for Spherical Flow Matching in Image Generation", "authors": [ "Tuna Han Salih Meral", "Kaan Oktay", "Hidir Yesiltepe", "Adil Kaan Akan", "Pinar Yanardag" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://aligning-latent-geometry.github.io", "abstract": "Latent flow matching for image generation usually transports Gaussian noise to variational autoencoder latents along linear paths. Both endpoints, however, concentrate in thin spherical shells, and a Euclidean chord leaves those shells even when preprocessing aligns their radii. By decomposing each latent token into radial and angular components, we show through component-swap probes that decoded perceptual and semantic content is carried predominantly by direction, with radius contributing much less. We therefore project data latents onto a fixed token radius, use the radial projection of Gaussian noise as the spherical prior, finetune the decoder with the encoder frozen, and replace linear interpolation with spherical linear interpolation. The resulting geodesic paths stay on the sphere at every timestep, and their velocity targets are purely angular by construction. Under matched training, the method consistently improves class-conditional ImageNet-256 FID across different image tokenizers, leaves the diffusion architecture unchanged, and requires no auxiliary encoder or representation-alignment objective.", "github": "" }, { "date": "2026-05-15", "arxiv_id": "2605.01018", "title": "WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild", "authors": [ "Junzhe Huang", "Xiaoxiao Sun", "Yan Yang", "Yuxuan Hou", "Ruotian Zhang", "Sirui Li", "Hehe Fan", "Serena Yeung-Levy", "Xin Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hjzhe/WildTableBench", "github_api_added_by": "user", "project_page": "https://huggingface.co/datasets/jzhuang/WildTableBench", "abstract": "Using multimodal foundation models to analyze table images is a high-value yet challenging application in consumer and enterprise scenarios. Despite its importance, current evaluations rely largely on structured-text tables or clean rendered images, leaving the visual complexity of in-the-wild table images underexplored. Such images feature varied layouts and diverse domains that demand sophisticated structural perception and numerical reasoning. To bridge this gap, we introduce WildTableBench, the first question-answering benchmark for naturally occurring table images from real-world settings. WildTableBench comprises 402 high-information-density table images collected from online forums and websites across diverse domains, together with 928 manually annotated and verified questions spanning 17 subtypes across five categories. We evaluate 21 frontier proprietary and open-source multimodal foundation models on this benchmark. Only one model exceeds 50% accuracy, while all remaining models range from 4.1% to 49.9%. We further conduct diagnostic analyses to characterize model failures and reveal persistent weaknesses in structural perception and reasoning. These results and analyses provide useful insights into current model capabilities and establish WildTableBench as a valuable diagnostic benchmark for table image understanding.", "github": "https://github.com/hjzhe/WildTableBench" }, { "date": "2026-05-18", "arxiv_id": "2605.15256", "title": "ReactiveGWM: Steering NPC in Reactive Game World Models", "authors": [ "Zeqing Wang", "Danze Chen", "Zhaohu Xing", "Zizhao Tong", "Yinhan Zhang", "Xingyi Yang", "Yeying Jin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/INV-WZQ/ReactiveGWM", "github_api_added_by": "user", "project_page": "https://inv-wzq.github.io/ReactiveGWM/", "abstract": "Current game world models simulate environments from a subjective, player-centric perspective. However, by treating the Non-Player Character (NPC) merely as background pixels, these models cannot capture interactions between the player and NPC. In that sense, they act as passive video renderers rather than real simulation engines, lacking the physical understanding needed to model action-induced NPC reactivities. We introduce ReactiveGWM, a reactive game world model that synthesizes dynamic interactions between the player and NPC. Instead of entangling all interaction dynamics, ReactiveGWM explicitly decouples player controls from NPC behaviors. Player actions are injected into the diffusion backbone via a lightweight additive bias, while high-level NPC responses (e.g., Offense, Control, Defense) are grounded through cross-attention modules. Crucially, these modules learn a game-agnostic representation of interactive logic. This enables zero-shot strategy transfer: our learned modules can be plugged directly into off-the-shelf, unannotated world models of different games. This instantly unlocks steerable NPC interactions without any domain-specific retraining. Evaluated on two Street Fighter games, ReactiveGWM maintains fine-grain player controllability while achieving robust, prompt-aligned NPC strategy adherence, paving the way for scalable, strategy-rich interaction with the NPC.", "github": "https://github.com/INV-WZQ/ReactiveGWM" }, { "date": "2026-05-18", "arxiv_id": "2605.15726", "title": "Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR", "authors": [ "Chanuk Lee", "Sangwoo Park", "Minki Kang", "Sung Ju Hwang" ], "github_manual": "", "github_regex": "https://github.com/tally0818/NudgeRL", "github_paper_md": "", "github_api": "https://github.com/tally0818/NudgeRL", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has emerged as a scalable paradigm for improving the reasoning capabilities of large language models. However, its effectiveness is fundamentally limited by exploration: the policy can only improve on trajectories it has already sampled. While increasing the number of rollouts alleviates this issue, such brute-force scaling is computationally expensive, and existing approaches that modify the optimization objective provide limited control over what is explored. In this work, we propose NudgeRL, a framework for structured and diversity-driven exploration in RLVR. Our approach introduces Strategy Nudging, which conditions each rollout on lightweight, strategy-level contexts to induce diverse reasoning trajectories without relying on expensive oracle supervision. To effectively learn from such structured exploration, we further propose a unified objective, which decomposes the reward signal into inter- and intra-context components and incorporates a distillation objective to transfer discovered behaviors back to the base policy. Empirically, NudgeRL outperforms standard GRPO with up to 8 times larger rollout budgets, while outperforming oracle-guided RL baseline on average across five challenging math benchmarks. These results demonstrate that structured, context-driven exploration can serve as an efficient and scalable alternative to both brute-force rollout scaling and feasibility-oriented methods based on privileged information. Our code is available at https://github.com/tally0818/NudgeRL.", "github": "https://github.com/tally0818/NudgeRL" }, { "date": "2026-05-18", "arxiv_id": "2605.14333", "title": "InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation", "authors": [ "Yang Yue", "Fangyun Wei", "Tianyu He", "Jinjing Zhao", "Zanlin Ni", "Zeyu Liu", "Jiayi Guo", "Lei Shi", "Yue Dong", "Li Chen", "Ji Li", "Gao Huang", "Dong Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LeapLabTHU/InsightTok", "github_api_added_by": "user", "project_page": "", "abstract": "Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging for autoregressive generators built on discrete tokenization. A central bottleneck is the tokenizer: aggressive downsampling and quantization often discard the fine-grained structures needed to preserve readable glyphs and distinctive facial features. We attribute this gap to standard discrete-tokenizer objectives being weakly aligned with text legibility and facial fidelity, as these objectives typically optimize generic reconstruction while compressing diverse content uniformly. To address this, we propose InsightTok, a simple yet effective discrete visual tokenization framework that enhances text and face fidelity through localized, content-aware perceptual losses. With a compact 16k codebook and a 16x downsampling rate, InsightTok significantly outperforms prior tokenizers in text and face reconstruction without compromising general reconstruction quality. These gains consistently transfer to autoregressive image generation in InsightAR, producing images with clearer text and more faithful facial details. Overall, our results highlight the potential of specialized supervision in tokenizer training for advancing discrete image generation.", "github": "https://github.com/LeapLabTHU/InsightTok" }, { "date": "2026-05-18", "arxiv_id": "2605.13527", "title": "MMSkills: Towards Multimodal Skills for General Visual Agents", "authors": [ "Kangning Zhang", "Shuai Shao", "Qingyao Li", "Jianghao Lin", "Lingyue Fu", "Shijian Wang", "Wenxiang Jiao", "Yuan Lu", "Weiwen Liu", "Weinan Zhang", "Yong Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DeepExperience/MMSkills", "github_api_added_by": "user", "project_page": "https://deepexperience.github.io/MMSkills/", "abstract": "Reusable skills have become a core substrate for improving agent capabilities, yet most existing skill packages encode reusable behavior primarily as textual prompts, executable code, or learned routines. For visual agents, however, procedural knowledge is inherently multimodal: reuse depends not only on what operation to perform, but also on recognizing the relevant state, interpreting visual evidence of progress or failure, and deciding what to do next. We formalize this requirement as multimodal procedural knowledge and address three practical challenges: (I) what a multimodal skill package should contain; (II) where such packages can be derived from public interaction experience; and (III) how agents can consult multimodal evidence at inference time without excessive image context or over-anchoring to reference screenshots. We introduce MMSkills, a framework for representing, generating, and using reusable multimodal procedures for runtime visual decision making. Each MMSkill is a compact, state-conditioned package that couples a textual procedure with runtime state cards and multi-view keyframes. To construct these packages, we develop an agentic trajectory-to-skill Generator that transforms public non-evaluation trajectories into reusable multimodal skills through workflow grouping, procedure induction, visual grounding, and meta-skill-guided auditing. To use them, we introduce a branch-loaded multimodal skill agent: selected state cards and keyframes are inspected in a temporary branch, aligned with the live environment, and distilled into structured guidance for the main agent. Experiments across GUI and game-based visual-agent benchmarks show that MMSkills consistently improve both frontier and smaller multimodal agents, suggesting that external multimodal procedural knowledge complements model-internal priors.", "github": "https://github.com/DeepExperience/MMSkills" }, { "date": "2026-05-18", "arxiv_id": "2605.15963", "title": "PAGER: Bridging the Semantic-Execution Gap in Point-Precise Geometric GUI Control", "authors": [ "Jingxuan Wei", "Xi Bai", "Shan Liu", "Caijun Jia", "Zheng Sun", "Xinglong Xu", "Siyuan Li", "Linzhuang Sun", "Bihui Yu", "Conghui He", "Cheng Tan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenRaiser/Pager", "github_api_added_by": "user", "project_page": "https://openraiser.github.io/Pager-webpage/", "abstract": "Large vision-language models have significantly advanced GUI agents, enabling executable interaction across web, mobile, and desktop interfaces. Yet these gains largely rely on a forgiving region-tolerant paradigm, where many nearby pixels inside the same component remain valid. Precise geometric construction breaks this assumption: actions must land on points in continuous canvas space rather than tolerant regions. Because geometric primitives carry ontological dependencies, a local coordinate error can induce cascading topological failures that distort downstream objects and invalidate the final construction. We identify this regime as precision-sensitive GUI tasks, requiring point-level accuracy, geometry-aware verification, and robustness to dependency-driven error propagation. To benchmark it, we introduce PAGE Bench, with 4,906 problems and over 224K process-supervised, pixel-level GUI actions. We further propose PAGER, a topology-aware agent that decomposes construction into dependency-structured planning and pixel-level execution. Pixel-grounded supervised tuning establishes executable action grammar, while precision-aligned reinforcement learning mitigates rollout-induced exposure bias through state-conditioned geometric feedback. Experiments reveal a pronounced Semantic-Execution Gap: general multimodal models can exceed 88% action type accuracy yet remain below 6% task success. PAGER closes this gap, delivering 4.1x higher task success than the strongest evaluated general baseline and raising step success rate from below 9% for GUI-specialized agents to over 62%, establishing a new state of the art for point-precise GUI control.", "github": "https://github.com/OpenRaiser/Pager" }, { "date": "2026-05-18", "arxiv_id": "2605.15980", "title": "Flash-GRPO: Efficient Alignment for Video Diffusion via One-Step Policy Optimization", "authors": [ "Xiaoxuan He", "Siming Fu", "Zeyue Xue", "Weijie Wang", "Ruizhe He", "Yuming Li", "Dacheng Yin", "Shuai Dong", "Haoyang Huang", "Hongfa Wang", "Nan Duan", "Bohan Zhuang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Shredded-Pork/Flash-GRPO", "github_api_added_by": "user", "project_page": "https://shredded-pork.github.io/Flash-GRPO.github.io/", "abstract": "Group Relative Policy Optimization has emerged as essential for aligning video diffusion models with human preferences, but faces a critical computational bottleneck: training a 14B parametered model typically demands hundreds of GPU days per experiment. Existing efficiency methods reduce costs through sliding window subsampling training timesteps, but fundamentally compromise optimization, exhibiting severe instability and failing to reach full trajectory performance. We present Flash-GRPO, a single-step training framework that outperforms full trajectory training in alignment quality under low computational budgets while substantially improving training efficiency. Flash-GRPO addresses two critical challenges: iso-temporal grouping eliminates timestep-confounded variance by enforcing prompt-wise temporal consistency, decoupling policy performance from timestep difficulty; temporal gradient rectification neutralizes the time-dependent scaling factor that causes vastly inconsistent gradient magnitudes across timesteps. Experiments on 1.3B to 14B parameter models validate Flash-GRPO's effectiveness, demonstrating substantial training acceleration with consistent stability and state-of-the-art alignment quality.", "github": "https://github.com/Shredded-Pork/Flash-GRPO" }, { "date": "2026-05-18", "arxiv_id": "2605.15320", "title": "FFAvatar: Few-Shot, Feed-Forward, and Generalizable Avatar Reconstruction", "authors": [ "Thuan Hoang Nguyen", "Jiahao Luo", "Yinyu Nie", "Hao Li", "Gordon Guocheng Qian", "Jian Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://ffavatar.github.io/", "abstract": "Avatar reconstruction has traditionally relied on per-subject optimization that requires hours of computation or on expensive preprocessing that limits scalability. We introduce FFAvatar, a generalizable feed-forward framework that reconstructs high-quality, animatable 3D Gaussian head avatars from few-shot unposed portrait images in seconds. FFAvatar fuses information from multiple source images into a unified canonical Gaussian representation through Multi-View Query-Former, which is animated via FLAME parameters predicted end-to-end directly from pixels, eliminating the overhead of offline FLAME extraction. We further propose a three-stage training curriculum that achieves both broad generalization and high-fidelity reconstruction: (i) scalable pretraining on extensive monocular video data with over 1M identities to learn strong generalizable priors; (ii) multi-view fine-tuning on a small but high-quality dataset of 360-degree captures to enhance geometric fidelity and extreme-view awareness; and (iii) optional personalization that adapts to specific identities for maximum fidelity within 500 optimization steps. Extensive experiments demonstrate that FFAvatar sets a new standard for identity preservation, geometric consistency, and animation fidelity. On the NeRSemble benchmark, it outperforms the state-of-the-art LAM by a substantial 5.5 PSNR gain. Furthermore, FFAvatar enables real-time deployment, reconstructing avatars in 2 seconds without personalization and 10 seconds with personalization, while supporting 49 FPS animation on a single NVIDIA A100 GPU.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2605.08614", "title": "DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules", "authors": [ "Devin Yasith De Silva", "Dhaval Patel", "Christodoulos Constantinides", "Shuxin Lin", "Nianjun Zhou", "Paul J Adams", "Sal Rosato", "Nicolas Constantinides", "Deborah L. McGuinness", "Jayant Kalagnanam" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Monitoring complex industrial assets relies on engineer-authored symbolic rules that trigger based on sensor conditions and prompt technicians to perform corrective actions. The bottleneck is not detection but response: translating rules into maintenance steps requires asset-specific knowledge gained through years of practice. We investigate whether LLMs can serve as decision support for this rule-to-action step and introduce , a benchmark of 6{,}690 expert-validated multiple-choice questions from 118 rule-action pairs across 16 asset types. We contribute (i) a symbolic-to-MCQA pipeline normalizing rules to Disjunctive Normal Form with embedding-based distractor sampling, (ii) five variants probing distinct failure modes (Pro, Pert, Verbose, Aug, Rationale), and (iii) a benchmark of 29 LLMs and 4 embedding baselines. A human evaluation (9 practitioners, mean 45.0\\%) confirms requires specialist knowledge beyond operational experience. Three findings stand out. The frontier has closed: the top three LLMs lie within one Macro point, with Bradley-Terry Elo placing claude-opus-4-6 30 points above the next model. Yet \\,Pro exposes brittleness, with every model losing 13--60\\% relative accuracy under distractor expansion. \\,Aug exposes pattern-matching: under condition inversion, frontier models still select the original answer 49--63\\% of the time. The deployment bottleneck is not capability but calibration: frontier models handle template-style fault detection but break under structural perturbation.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2605.15843", "title": "WorldAct: Activating Monolithic 3D Worlds into Interactive-Ready Object-Centric Scenes", "authors": [ "Jichen Hu", "Jiawei Guo", "Jiazhong Cen", "Chen Yang", "Sikuang Li", "Wei Shen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SJTU-DeepVisionLab/WorldAct", "github_api_added_by": "user", "project_page": "https://sjtu-deepvisionlab.github.io/WorldAct/", "abstract": "Recent 3D world modeling systems based on generative scene synthesis, such as Marble, can create coherent and explorable 3D environments, yet their outputs are typically static monolithic assets with limited editability and physical interaction. This restricts their use in immersive content creation and embodied simulation, where generated worlds must be actively modified and manipulated. To tackle this challenge, we present WorldAct, a framework that converts static generated 3D worlds into editable and interaction-ready scenes. WorldAct uses a multimodal agent to guide scene decomposition, identify actionable objects, reconstruct geometrically aligned object-level meshes for interaction, and restore the residual background via 3D inpainting. The resulting scenes support object-level editing, collision-aware manipulation, and embodied task execution while preserving global scene coherence. Experiments show that WorldAct enables richer interaction scenarios than the original generated scenes, suggesting a practical path toward editable and interactive 3D world models.", "github": "https://github.com/SJTU-DeepVisionLab/WorldAct" }, { "date": "2026-05-18", "arxiv_id": "2605.15301", "title": "Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution", "authors": [ "Han Li", "Jinyu Tian", "Rili Feng", "Yuqiao Du", "Chong Zheng", "Chenyu Wang", "Chenchen Liu", "Shihao Li", "Xinping Lei", "Yifan Yao", "Weihao Xie", "Letian Zhu", "Jiaheng Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language models (LLMs) still struggle with the rigorous reasoning demands of hard competitive programming. While recent multi-agent frameworks attempt to bridge this reliability gap, they remain fundamentally stateless: they rely on static retrieval and discard the valuable problem-solving and debugging experience gained from previous tasks. To address this, we present Solvita, an agentic evolution framework that enables continuous learning without requiring weight updates to the underlying LLM. Solvita reorganizes problem-solving into a closed-loop system of strategy selection, program synthesis, certified supervision, and targeted hacking, executed by four specialized agents: Planner, Solver, Oracle, and Hacker. Crucially, each agent is paired with a trainable, graph-structured knowledge network. As the system operates, outcome signals, such as pass/fail verdicts, test certification quality, and adversarial vulnerabilities discovered by the Hacker, are recast as reinforcement learning updates to these network weights. This allows the agents to dynamically route future queries based on past successes and failures, effectively accumulating transferable reasoning experience over time. Evaluated across CodeContests, APPS, AetherCode, and live Codeforces rounds, Solvita establishes a new state-of-the-art among code-generation agents, outperforming existing multi-agent pipelines and nearly doubling the accuracy of single-pass baselines.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2605.13997", "title": "HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts", "authors": [ "Tao Zhong", "Dongzhe Zheng", "Christine Allen-Blanchette" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Sparse Mixture-of-Experts (MoE) layers route tokens through a handful of experts, and learning-free compression of these layers reduces inference cost without retraining. A subtle obstruction blocks every existing compressor in this family: three experts can each be pairwise compatible yet form an irreducible cycle when merged together, so any score that ranks experts on pairwise signals is structurally blind to which triples are jointly mergeable. We show the obstruction is a precise mathematical object, the harmonic kernel of the simplicial Laplacian on a 2-complex whose vertices are experts, whose edges carry KL merge barriers, and whose faces carry triplet barriers; Hodge-decomposing the edge-barrier signal isolates the kernel exactly. We turn the diagnostic into a selection objective: HodgeCover greedily covers the harmonic-critical edges and triplet-critical triangles, and a hybrid variant of HodgeCover pairs it with off-the-shelf weight pruning on survivors. On three open-weight Sparse MoE backbones under aggressive expert reduction, HodgeCover matches state-of-the-art learning-free baselines on the expert-reduction axis, leads on the aggressive-compression frontier of the hybrid axis, and uniquely balances retained mass across all four Hodge components. These results show that exposing the harmonic kernel of a learned MoE structure changes which compressor wins at the regime that matters most.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2605.15871", "title": "Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design", "authors": [ "Alberto Pepe", "Chien-Yu Lin", "Despoina Magka", "Bilge Acun", "Yannan Nellie Wu", "Anton Protopopov", "Carole-Jean Wu", "Yoram Bachrach" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers. We introduce a dual-framework approach: AIRA-Compose for high-level architecture search, and AIRA-Design for low-level mechanistic implementation. AIRA-Compose uses 11 agents to explore fundamental computational primitives under a 24-hour budget. Agents evaluate million-parameter candidates, extrapolating top designs to 350M, 1B, and 3B scales. This yields 14 architectures across two families: AIRAformers (Transformer-based) and AIRAhybrids (Transformer-Mamba). Pre-trained at 1B scale, these consistently outperform Llama 3.2 and Composer-found baselines. On downstream tasks, AIRAformer-D and AIRAhybrid-D improve accuracy by 2.4% and 3.8% over Llama 3.2. Furthermore, AIRA-Compose finds models with highly efficient scaling frontiers: AIRAformer-C scales 54% and 71% faster than Llama 3.2 and Composer's best Transformer, while AIRAhybrid-C outscales Nemotron-2 by 23% and Composer's best hybrid by 37%. AIRA-Design tasks 20 agents with writing novel attention mechanisms for long-range dependencies and high-performing training scripts. On the Long Range Arena benchmark, agent-designed architectures reach within 2.3% and 2.6% of human state-of-the-art on document matching and text classification. On the Autoresearch benchmark, Greedy Opus 4.5 achieves 0.968 validation bits-per-byte under a fixed time budget, surpassing the published minimum. Together, these frameworks show AI agents can autonomously discover architectures and algorithmic optimizations matching or surpassing hand-designed baselines. This establishes a powerful paradigm for discovering next-generation foundation models, marking a clear step toward recursive self-improvement.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2605.16143", "title": "Look Before You Leap: Autonomous Exploration for LLM Agents", "authors": [ "Ziang Ye", "Wentao Shi", "Yuxin Liu", "Yu Wang", "Zhengzhou Cai", "Yaorui Shi", "Qi Gu", "Xunliang Cai", "Fuli Feng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language model based agents often fail in unfamiliar environments due to premature exploitation: a tendency to act on prior knowledge before acquiring sufficient environment-specific information. We identify autonomous exploration as a critical yet underexplored capability for building adaptive agents. To formalize and quantify this capability, we introduce Exploration Checkpoint Coverage, a verifiable metric that measures how broadly an agent discovers key states, objects, and affordances. Our systematic evaluation reveals that agents trained with standard task-oriented reinforcement learning consistently exhibit narrow and repetitive behaviors that impede downstream performance. To address this limitation, we develop a training strategy that interleaves task-execution rollouts and exploration rollouts, with each type of rollout optimized by its corresponding verifiable reward. Building on this training strategy, we propose the Explore-then-Act paradigm, which decouples information-gathering from task execution: agents first utilize an interaction budget to acquire grounded environmental knowledge, then leverage it for task resolution. Our results demonstrate that learning to systematically explore is imperative for building generalizable and real-world-ready agents.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2605.16257", "title": "DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo", "authors": [ "Hanwen Wang", "Weizhi Zhao", "Xiangyu Wang", "Siyuan Huang", "He Lin", "Boyuan Zheng", "Rongtao Xu", "Gang Wang", "Yao Mu", "He Wang", "Lue Fan", "Hongsheng Li", "Zhaoxiang Zhang", "Tieniu Tan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/brave-eai/dexjoco", "github_api_added_by": "user", "project_page": "https://dexjoco.github.io/", "abstract": "Achieving human-level manipulation requires dexterous robotic hands capable of complex object interactions. Advancing such capabilities further demands standardized benchmarks for systematic evaluation. However, existing dexterous benchmarks lack tasks that reflect the unique manipulation capabilities of dexterous hands over parallel grippers, as well as comprehensive evaluation pipelines. In this paper, we present DexJoCo, a benchmark and toolkit for task-oriented dexterous manipulation, comprising 11 functionally grounded tasks that evaluate tool-use, bimanual coordination, long-horizon execution, and reasoning. We develop a low-cost data collection system and collect 1.1K trajectories across these tasks, with support for domain randomization to assess robustness. We benchmark modern models under diverse settings, including visual and dynamics randomization, multi-task training, and action-head adaptation. Through extensive empirical analysis, we identify several important insights and common limitations of current policies in dexterous manipulation, highlighting key challenges for future research in dexterous hand robot learning. Project page available at: https://dexjoco.github.io", "github": "https://github.com/brave-eai/dexjoco" }, { "date": "2026-05-18", "arxiv_id": "2605.15824", "title": "FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization", "authors": [ "Quanjian Song", "Yefeng Shen", "Mengting Chen", "Hao Sun", "Jinsong Lan", "Xiaoyong Zhu", "Bo Zheng", "Liujuan Cao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/quanjiansong/FashionChameleon", "github_api_added_by": "user", "project_page": "https://quanjiansong.github.io/projects/FashionChameleon/", "abstract": "Human-centric video customization, particularly at the garment level, has shown significant commercial value. However, existing approaches cannot support low-latency and interactive garment control, which is crucial for applications such as e-commerce and content creation. This paper studies how to achieve interactive multi-garment video customization while preserving motion coherence using only single-garment video data. We present FashionChameleon, a real-time and interactive framework for human-garment customization in autoregressive video generation, where users can interactively switch garment during generation. FashionChameleon consists of three key techniques: (i) Instead of training on multi-garment video data, we train a Teacher Model with In-Context Learning on a single reference-garment pair. By retaining the image-to-video training paradigm while enforcing a mismatch between the reference and garment image, the model is encouraged to implicitly preserve coherence during single-garment switching. (ii) To achieve consistency and efficiency during generation, we introduce Streaming Distillation with In-Context Learning, which fine-tunes the model with in-context teacher forcing and improves extrapolation consistency via gradient-reweighted distribution matching distillation. (iii) To extend the model for interactive multi-garment video customization, we propose Training-Free KV Cache Rescheduling, which includes garment KV refresh, historical KV withdraw, and reference KV disentangle to achieve garment switching while preserving motion coherence. Our FashionChameleon uniquely supports interactive customization and consistent long-video extrapolation, while achieving real-time generation at 23.8 FPS on a single GPU, 30-180times faster than existing baselines.", "github": "https://github.com/quanjiansong/FashionChameleon" }, { "date": "2026-05-18", "arxiv_id": "2605.15876", "title": "Unlocking Dense Metric Depth Estimation in VLMs", "authors": [ "Hanxun Yu", "Xuan Qu", "Yuxin Wang", "Jianke Zhu", "Lei ke" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hanxunyu/DepthVLM", "github_api_added_by": "user", "project_page": "https://depthvlm.github.io/", "abstract": "Vision-Language Models (VLMs) excel at 2D tasks such as grounding and captioning, yet remain limited in 3D understanding. A key limitation is their text-only supervision paradigm, which under-constrains fine-grained visual perception and prevents the recovery of dense geometry. Prior methods either distill geometry from external vision models, introducing error accumulation, or enable direct prediction with inefficient per-pixel query or coarse token-level outputs. In this paper, we propose DepthVLM, a simple yet effective framework that transforms a single VLM into a native dense geometry predictor while preserving its multimodal capability. By attaching a lightweight depth head to the LLM backbone and training under a unified vision-text supervision paradigm with a two-stage schedule, DepthVLM generates full-resolution depth maps alongside language outputs in a single forward pass. We further introduce a unified indoor-outdoor metric depth benchmark in a VLM-compatible format. Experiments show that DepthVLM significantly outperforms existing VLMs with higher inference efficiency, surpasses leading pure vision models, and improves complex 3D spatial reasoning, moving toward a truly unified foundation model. All code and checkpoints will be publicly released.", "github": "https://github.com/hanxunyu/DepthVLM" }, { "date": "2026-05-18", "arxiv_id": "2605.14539", "title": "Learning from Failures: Correction-Oriented Policy Optimization with Verifiable Rewards", "authors": [ "Mengjie Ren", "Jie Lou", "Boxi Cao", "Xueru Wen", "Hongyu Lin", "Xianpei Han", "Le Sun", "Xing Yu", "Yaojie Lu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective paradigm for improving the reasoning capabilities of large language models. However, RLVR training is often hindered by sparse binary rewards and weak credit assignment, resulting in ambiguous optimization signals and underutilization of the useful information embedded in failed trajectories. To address this challenge, we propose Correction-Oriented Policy Optimization (CIPO), a simple and effective extension to RLVR that converts on-policy failed trajectories into correction-oriented supervision, without relying on any external signals. By jointly optimizing correction samples derived from the model's own failed attempts together with the standard RLVR objective, CIPO improves learning effectiveness while explicitly enhancing the model's ability to correct its own errors. Extensive experiments across 11 benchmarks spanning mathematical reasoning and code generation demonstrate that CIPO consistently and significantly outperforms strong baselines in both reasoning and correction performance. Moreover, CIPO yields stronger pass@K gains, indicating that it improves the model's intrinsic reasoning capacity rather than merely redistributing probability mass over existing correct answers.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2605.12038", "title": "OmniHumanoid: Streaming Cross-Embodiment Video Generation with Paired-Free Adaptation", "authors": [ "Yiren Song", "Xiyao Deng", "Pei Yang", "Yihan Wang", "Mike Zheng Shou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/showlab/OmniHumanoid", "github_api_added_by": "user", "project_page": "", "abstract": "Cross-embodiment video generation aims to transfer motions across different humanoid embodiments, such as human-to-robot and robot-to-robot, enabling scalable data generation for embodied intelligence. A major challenge in this setting is that motion dynamics are partly transferable across embodiments, whereas appearance and morphology remain embodiment-specific. Existing approaches often entangle these factors, and many require paired data for every target embodiment, which limits scalability to new robots. We present OmniHumanoid, a framework that factorizes transferable motion learning and embodiment-specific adaptation. Our method learns a shared motion transfer model from motion-aligned paired videos spanning multiple embodiments, while adapting to a new embodiment using only unpaired videos through lightweight embodiment-specific adapters. To reduce interference between motion transfer and embodiment adaptation, we further introduce a branch-isolated attention design that separates motion conditioning from embodiment-specific modulation. In addition, we construct a synthetic cross-embodiment dataset with motion-aligned paired videos rendered across diverse humanoid assets, scenes, and viewpoints. Experiments on both synthetic and real-world benchmarks show that OmniHumanoid achieves strong motion fidelity and embodiment consistency, while enabling scalable adaptation to unseen humanoid embodiments without retraining the shared motion model.", "github": "https://github.com/showlab/OmniHumanoid" }, { "date": "2026-05-18", "arxiv_id": "2605.15181", "title": "From Plans to Pixels: Learning to Plan and Orchestrate for Open-Ended Image Editing", "authors": [ "Anirudh Sundara Rajan", "Krishna Kumar Singh", "Yong Jae Lee" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://anisundar18.github.io/Plan2Pix.github.io/", "abstract": "Modern image editing models produce realistic results but struggle with abstract, multi step instructions (e.g., ``make this advertisement more vegetarian-friendly''). Prior agent based methods decompose such tasks but rely on handcrafted pipelines or teacher imitation, limiting flexibility and decoupling learning from actual editing outcomes. We propose an experiential framework for long-horizon image editing, where a planner generates structured atomic decompositions and an orchestrator selects tools and regions to execute each step. A vision language judge provides outcome-based rewards for instruction adherence and visual quality. The orchestrator is trained to maximize these rewards, and successful trajectories are used to refine the planner. By tightly coupling planning with reward driven execution, our approach yields more coherent and reliable edits than single-step or rule-based multistep baselines.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2605.15298", "title": "PhysBrain 1.0 Technical Report", "authors": [ "Shijie Lian", "Bin Yu", "Xiaopeng Lin", "Changti Wu", "Hang Yuan", "Xiaolin Hu", "Zhaolong Shen", "Yuzhuo Miao", "Haishan Liu", "Yuxuan Tian", "Yukun Shi", "Cong Huang", "Kai Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Phys-Brain/PhysBrain-VLA", "github_api_added_by": "user", "project_page": "https://phys-brain.github.io/", "abstract": "Vision-language-action models have advanced rapidly, but robot trajectories alone provide limited coverage for learning broad physical understanding. PhysBrain 1.0 studies a complementary route: converting large-scale human egocentric video into structured physical commonsense supervision before robot adaptation. Our data engine extracts scene elements, spatial dynamics, action execution, and depth-aware relations, then turns them into question-answer supervision for training PhysBrain VLMs. The resulting physical priors are further transferred to VLA policies through a capability-preserving and language-sensitive adaptation design. Across multimodal QA benchmarks and embodied control benchmarks, including ERQA, PhysBench, SimplerEnv-WidowX, LIBERO, and RoboCasa, PhysBrain 1.0 achieves SOTA results and shows especially strong out-of-domain performance on SimplerEnv. These results suggest that scaling physical commonsense from human interaction video can provide an effective bridge from multimodal understanding to robot action.", "github": "https://github.com/Phys-Brain/PhysBrain-VLA" }, { "date": "2026-05-18", "arxiv_id": "2605.11739", "title": "Learning to Foresee: Unveiling the Unlocking Efficiency of On-Policy Distillation", "authors": [ "Yuchen Cai", "Ding Cao", "Liang Lin", "Chunxi Luo", "Xin Xu", "Kai Yang", "Weijie Liu", "Saiyong Yang", "Tianxiang Zhao", "Guangzhong Sun", "Guiquan Liu", "Junfeng Fang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/caiyuchen-ustc/EffOPD", "github_api_added_by": "user", "project_page": "https://arxiv.org/abs/2605.11739", "abstract": "On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, existing studies largely attribute this advantage to denser and more stable supervision, while the parameter-level mechanisms underlying OPD's efficiency remain poorly understood. In this work, we argue that OPD's efficiency stems from a form of ``foresight'': it establishes a stable update trajectory toward the final model early in training. This foresight manifests in two aspects. First, at the Module-Allocation Level, OPD identifies regions with low marginal utility and concentrates updates on modules that are more critical to reasoning. Second, at the Update-Direction Level, OPD exhibits stronger low-rank concentration, with its dominant subspaces aligning closely with the final update subspace early in training. Building on these findings, we propose EffOPD, a plug-and-play acceleration method that speeds up OPD by adaptively selecting an extrapolation step size and moving along the current update direction. EffOPD requires no additional trainable modules or complex hyperparameter tuning, and achieves an average training acceleration of 3times while maintaining comparable final performance. Overall, our findings provide a parameter-dynamics perspective for understanding the efficiency of OPD and offer practical insights for designing more efficient post-training methods for large language models.", "github": "https://github.com/caiyuchen-ustc/EffOPD" }, { "date": "2026-05-18", "arxiv_id": "2605.12882", "title": "CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence", "authors": [ "Dongsheng Ma", "Jiayu Li", "Zhengren Wang", "Yijie Wang", "Jiahao Kong", "Weijun Zeng", "Jutao Xiao", "Jie Yang", "Wentao Zhang", "Bin Wang", "Conghui He" ], "github_manual": "", "github_regex": "https://github.com/opendatalab/CiteVQA", "github_paper_md": "", "github_api": "https://github.com/opendatalab/CiteVQA", "github_api_added_by": "user", "project_page": "https://huggingface.co/datasets/opendatalab/CiteVQA", "abstract": "Multimodal Large Language Models (MLLMs) have significantly advanced document understanding, yet current Doc-VQA evaluations score only the final answer and leave the supporting evidence unchecked. This answer-only approach masks a critical failure mode: a model can land on the correct answer while grounding it in the wrong passage -- a critical risk in high-stakes domains like law, finance, and medicine, where every conclusion must be traceable to a specific source region. To address this, we introduce CiteVQA, a benchmark that requires models to return element-level bounding-box citations alongside each answer, evaluating both jointly. CiteVQA comprises 1,897 questions across 711 PDFs spanning seven domains and two languages, averaging 40.6 pages per document. To ensure fidelity and scalability, the ground-truth citations are generated by an automated pipeline-which identifies crucial evidence via masking ablation-and are subsequently validated through expert review. At the core of our evaluation is Strict Attributed Accuracy (SAA), which credits a prediction only when the answer and the cited region are both correct. Auditing 20 MLLMs reveals a pervasive Attribution Hallucination: models frequently produce the right answer while citing the wrong region. The strongest system (Gemini-3.1-Pro-Preview) achieves an SAA of only 76.0, and the strongest open-source MLLM reaches just 22.5. Ultimately, towards trustworthy document intelligence, CiteVQA exposes a reliability gap that answer-only evaluations overlook, providing the instrumentation needed to close it. Our repository is available at https://github.com/opendatalab/CiteVQA.", "github": "https://github.com/opendatalab/CiteVQA" }, { "date": "2026-05-18", "arxiv_id": "2605.02290", "title": "Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding", "authors": [ "Taewon Yun", "Jisu Shin", "Jeonghwan Choi", "Seunghwan Bang", "Hwanjun Song" ], "github_manual": "", "github_regex": "https://github.com/DISL-Lab/CoRD", "github_paper_md": "", "github_api": "https://github.com/DISL-Lab/CoRD", "github_api_added_by": "user", "project_page": "", "abstract": "Distilling large reasoning models is essential for making Long-CoT reasoning practical, as full-scale inference remains computationally prohibitive. Existing curation-based approaches select complete reasoning traces post-hoc, overlooking collaboration among heterogeneous teachers and lacking dynamic exploration, which leads to redundant sampling and missed complementary reasoning. We introduce CoRD, a collaborative multi-teacher decoding framework that performs step-wise reasoning synthesis guided by predictive perplexity-based scoring and beam search. This enables heterogeneous LRMs to jointly construct coherent reasoning trajectories while efficiently preserving diverse, high-potential hypotheses. Experiments show that CoRD produces higher-quality reasoning data and achieves near teacher-level student performance with fewer, structured supervision signals, without substantial efficiency overhead. CoRD further generalizes well to out-of-domain and open-ended settings. The dataset and model are available at https://github.com/DISL-Lab/CoRD{https://github.com/DISL-Lab/CoRD}.", "github": "https://github.com/DISL-Lab/CoRD" }, { "date": "2026-05-18", "arxiv_id": "2605.12058", "title": "H\u00f6lder Policy Optimisation", "authors": [ "Yuxiang Chen", "Dingli Liang", "Yihang Chen", "Ziqin Gong", "Chenyang Le", "Zhaokai Wang", "Jiachen Zhu", "Lingyu Yang", "Jianghao Lin", "Weinan Zhang", "Jun Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/YihangChen9/HolderPO", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Group Relative Policy Optimisation (GRPO) enhances large language models by estimating advantages across a group of sampled trajectories. However, mapping these trajectory-level advantages to policy updates requires aggregating token-level probabilities within each sequence. Relying on a fixed aggregation mechanism for this step fundamentally limits the algorithm's adaptability. Empirically, we observe a critical trade-off: certain fixed aggregations frequently suffer from training collapse, while others fail to yield satisfactory performance. To resolve this, we propose H\u00f6lderPO, a generalised policy optimisation framework unifying token-level probability aggregation via the H\u00f6lder mean. By explicitly modulating the parameter p, our framework provides continuous control over the trade-off between gradient concentration and variance bounds. Theoretically, we prove that a larger p concentrates the gradient to amplify sparse learning signals, whereas a smaller p strictly bounds gradient variance. Because no static configuration can universally resolve this concentration-stability trade-off, we instantiate the framework with a dynamic annealing algorithm that progressively schedules p across the training lifecycle. Extensive evaluations demonstrate superior stability and convergence over existing baselines. Specifically, our approach achieves a state-of-the-art average accuracy of 54.9% across multiple mathematical benchmarks, yielding a substantial 7.2% relative gain over standard GRPO and secures an exceptional 93.8% success rate on ALFWorld.", "github": "https://github.com/YihangChen9/HolderPO" }, { "date": "2026-05-18", "arxiv_id": "2605.15597", "title": "CM-EVS: Sparse Panoramic RGB-D-Pose Data for Complete Scene Coverage", "authors": [ "Jiale Liu", "Jungang Li", "Jieming Yu", "Xinglin Yu", "Zihao Dongfang", "Zongjian Ding", "Kaifeng Ding", "Yi Yang", "Lidong Chen", "Yang Zou", "Shunwen Bai", "Jiahuan Zhang", "Haoran Huang", "Shan Huang", "Yudong Gao", "Mingjun Cheng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Strange-animalss/CM-EVS", "github_api_added_by": "user", "project_page": "", "abstract": "Modern 3D visual learning relies on observations sampled from metric 3D assets, yet existing scans, meshes, point clouds, simulations, and reconstructions do not directly provide a sparse, comparable, and geometry-consistent panoramic training interface. Dense trajectories duplicate nearby views, source-specific rendering policies yield heterogeneous annotations, and sparse heuristics may miss important regions or introduce depth-inconsistent observations. We study how to convert 3D assets into sparse panoramic RGB-D-pose data that preserves complete scene coverage with low redundancy and auditable provenance. We propose COVER (Coverage-Oriented Viewpoint curation with ERP Range-depth warping), a training-free ERP viewpoint curator that projects geometry observed from selected views into candidate ERP probes, scores incremental coverage, and penalizes depth conflicts. Under bounded proxy error, its greedy coverage proxy preserves the standard coverage-style approximation behavior up to an additive error term. Using COVER, we build CM-EVS (Coverage-curated Metric ERP View Set), a panoramic RGB-D-pose dataset with 36,373 curated ERP frames from 1,275 indoor scenes across Blender indoor, HM3D, and ScanNet++, complemented by outdoor panoramas from TartanGround and OB3D re-encoded into the same schema. Each frame provides full-sphere RGB, metric range depth, calibrated pose; COVER-produced indoor frames include per-step provenance logs. With a median of only 25 frames per indoor scene, CM-EVS covers all 13 unified room types while maintaining compact scene-level coverage. Experiments show that COVER improves the coverage-conflict trade-off, making CM-EVS a sparse, compact, and auditable RGB-D-pose resource for geometry-consistent panoramic 3D learning.", "github": "https://github.com/Strange-animalss/CM-EVS" }, { "date": "2026-05-18", "arxiv_id": "2605.13740", "title": "Learning POMDP World Models from Observations with Language-Model Priors", "authors": [ "Valentin Six", "Frederik Panse", "Mathis Fajeau", "Lancelot Da Costa", "Mridul Sharma", "Alfonso Amayuelas", "Tim Z. Xiao", "David Hyland", "Philipp Hennig", "Bernhard Sch\u00f6lkopf" ], "github_manual": "", "github_regex": "https://github.com/atomresearch/pinductor", "github_paper_md": "", "github_api": "https://github.com/atomresearch/pinductor", "github_api_added_by": "user", "project_page": "", "abstract": "Whether navigating a building, operating a robot, or playing a game, an agent that acts effectively in an environment must first learn an internal model of how that environment works. Partially-observable Markov decision processes (POMDPs) provide a flexible modeling class for such internal world models, but learning them from observation-action trajectories alone is challenging and typically requires extensive environment interaction. We ask whether language-model priors can reduce costly interaction by leveraging prior knowledge, and introduce Pinductor (POMDP-inductor): an LLM proposes candidate POMDP models from a few observation-action trajectories and iteratively refines them to optimize a belief-based likelihood score. Despite using strictly less information, Pinductor matches the performance and sample efficiency of LLM-based POMDP learning methods that assume privileged access to the hidden state, while significantly surpassing the sample efficiency of tabular POMDP baselines. Further results show that performance scales with LLM capability and degrades gracefully as semantic information about the environment is withheld. Together, these results position language-model priors as a practical tool for sample-efficient world-model learning under partial observability, and a step toward generalist agents in real-world environments. Code is available at https://github.com/atomresearch/pinductor.", "github": "https://github.com/atomresearch/pinductor" }, { "date": "2026-05-18", "arxiv_id": "2605.15250", "title": "GQLA: Group-Query Latent Attention for Hardware-Adaptive Large Language Model Decoding", "authors": [ "Fanxu Meng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multi-head Latent Attention (MLA), the attention used in DeepSeek-V2/V3, jointly compresses keys and values into a low-rank latent and matches the H100 roofline almost perfectly. Its trained weights, however, expose only one decoding path - an absorbed MQA form - which ties efficient inference to H100-class compute-bandwidth ratios, forfeits tensor parallelism along the head axis, and yields no Multi-Token Prediction (MTP) gain on commodity inference GPUs such as the export-restricted H20. We propose Group-Query Latent Attention (GQLA), a minimal modification of MLA whose trained weights expose two algebraically equivalent decoding paths over the same parameters: an MQA-absorb path identical to MLA's, and a GQA path with a per-group expanded cache. The runtime picks the path that matches the target hardware - no retraining, no custom kernels - so a single set of GQLA weights pins the rooflines of both H100 (MQA-absorb, s_q=1) and H20 (GQA + MTP, s_q=2), while supporting up to 8-way zero-redundancy tensor parallelism on the GQA path. To avoid pretraining from scratch we extend TransMLA into TransGQLA, which converts a pretrained GQA checkpoint into a GQLA model; on LLaMA-3-8B it compresses the per-token KV cache to 28.125% of the GQA baseline on the MQA-absorb path while structurally preserving GQA-level traffic on the per-group path.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2605.05945", "title": "MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware", "authors": [ "Senthil Palanisamy", "Abhishek Anand", "Satpal Singh Rathor", "Pratyush Patnaik", "Shubhanshu Khatana" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/fpv-labs/stera-sdk", "github_api_added_by": "user", "project_page": "https://www.fpvlabs.ai/stera", "abstract": "The recent advancement of Vision Language Action (VLA) models has driven a critical demand for large scale egocentric datasets. However, existing datasets are often limited by short episode durations, typically spanning only a few minutes, which fails to capture the long horizon temporal dependencies necessary for complex robotic task execution. To bridge this gap, we present MobileEgo Anywhere, a framework designed to facilitate the collection of robust, hour plus egocentric trajectories using commodity mobile hardware. We leverage the ubiquitous sensor suites of modern smartphones to provide high fidelity, long term camera pose tracking, effectively removing the high hardware barriers associated with traditional robotics data collection. Our contributions are three fold: (1) we release a novel dataset comprising 200 hours of diverse, long form egocentric data with persistent state tracking; (2) we open source a mobile application that enables any user to record egocentric data, and (3) we provide a comprehensive processing pipeline to convert raw mobile captures into standardized, training ready formats for Vision Language Action model and foundation model research. By democratizing the data collection process, this work enables the massive scale acquisition of long horizon data across varied global environments, accelerating the development of generalizable robotic policies.", "github": "https://github.com/fpv-labs/stera-sdk" }, { "date": "2026-05-18", "arxiv_id": "2605.15375", "title": "ChangeFlow -- Latent Rectified Flow for Change Detection in Remote Sensing", "authors": [ "Bla\u017e Rolih", "Matic Fu\u010dka", "Filip Wolf", "Luka \u010cehovin Zajc" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/blaz-r/changeflow", "github_api_added_by": "user", "project_page": "https://blaz-r.github.io/changeflow_cd/", "abstract": "Remote sensing change detection (RSCD) aims to localise changes between two images of the same geographic region. In practice, change masks often follow region-level annotation conventions rather than purely local appearance differences, making them context-dependent and occasionally ambiguous. Most state-of-the-art methods utilise per-pixel discriminative classification, which produces a single prediction per input and fails to explicitly model the changed region as a coherent whole. A natural alternative is generative formulation, which can model a distribution of plausible masks, enabling sampling to capture ambiguity and encourage global consistency. However, existing generative RSCD approaches typically lag behind strong discriminative baselines due to the high computational cost of pixel-space generation and the complexity of their conditioning mechanisms. To address the limitations of prior discriminative and generative methods, we propose ChangeFlow, a generative framework that reformulates change detection as the synthesis of a change mask in latent space via rectified flow. ChangeFlow is guided by a structured yet lightweight conditioning signal, and its stochastic design naturally supports sampling-based prediction ensembling. Namely, aggregating multiple predicted change masks improves robustness, while sample agreement provides a practical confidence estimation that highlights ambiguous regions. Across four benchmarks, ChangeFlow achieves an average F1 of 80.4\\%, improving by 1.3 points on average over the previous best method, while maintaining inference speed comparable to recent strong baselines. Project page: https://blaz-r.github.io/changeflow_cd", "github": "https://github.com/blaz-r/changeflow" }, { "date": "2026-05-18", "arxiv_id": "2605.15961", "title": "Sparse Autoencoders enable Robust and Interpretable Fine-tuning of CLIP models", "authors": [ "Fabian Morelli", "Arnas Uselis", "Ankit Sonthalia", "Seong Joon Oh" ], "github_manual": "", "github_regex": "https://github.com/Fabian-Mor/sae-ft", "github_paper_md": "", "github_api": "https://github.com/Fabian-Mor/sae-ft", "github_api_added_by": "user", "project_page": "", "abstract": "Large-scale pre-trained vision-language models like CLIP demonstrate remarkable zero-shot performance across diverse tasks. However, fine-tuning these models to improve downstream performance often degrades robustness against distribution shifts. Recent approaches have attempted to mitigate this trade-off, but often rely on computationally expensive text-guidance. We propose a novel method for robust fine-tuning, SAE-FT, which operates only on the model's visual representations. SAE-FT regularizes changes to these representations by penalizing the addition and removal of semantically meaningful features identified by a Sparse Autoencoder trained on the pre-trained model. This constraint prevents catastrophic forgetting and makes the fine-tuning process interpretable, enabling direct analysis of semantic changes. SAE-FT is both mechanistically transparent and computationally efficient, matching or exceeding state-of-the-art performance on ImageNet and its associated distribution shift benchmarks. Code is publicly available at: https://github.com/Fabian-Mor/sae-ft.", "github": "https://github.com/Fabian-Mor/sae-ft" }, { "date": "2026-05-18", "arxiv_id": "2605.15592", "title": "Efficient Image Synthesis with Sphere Latent Encoder", "authors": [ "Tung Do", "Thuan Hoang Nguyen", "Hao Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://sphere-latent-encoder.github.io", "abstract": "Few-step image generation has seen rapid progress, with consistency and meanflow-based methods significantly reducing the number of sampling steps. Despite their low inference cost, these approaches often suffer from training instability and limited scalability. Sphere Encoder is a recent alternative that produces high-quality images in only a few steps; however, it requires repeated transitions between the pixel space and latent space during inference while jointly optimizing reconstruction and generation within a single architecture. This design leads to computational inefficiency and objective conflict between reconstruction and generation. To address these limitations, we decouple the framework into a fixed pretrained image encoder and a separate latent denoising model trained entirely in a spherical latent space. Our approach eliminates repeated pixel-space operations during training and inference, improving efficiency and allowing reconstruction and generation to specialize independently. On Animal-Faces, Oxford-Flowers and ImageNet-1K datasets, our method significantly outperforms Sphere Encoder in both generation quality and inference speed, while achieving competitive results against strong few-step and multi-step baselines.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2604.09839", "title": "Steered LLM Activations are Non-Surjective", "authors": [ "Aayush Mishra", "Daniel Khashabi", "Anqi Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aamixsh/invertsteer", "github_api_added_by": "user", "project_page": "", "abstract": "Activation steering is a popular white-box control technique that modifies model activations to elicit an abstract change in its behavior. It has also become a standard tool in interpretability (e.g., probing truthfulness, or translating activations into human-readable explanations) and safety research (e.g., jailbreakability). However, it is unclear whether steered behavior is realizable by any textual prompt. In this work, we cast this question as a surjectivity problem: for a fixed model, does every steered activation admit a preimage under the model's natural forward pass? Under practical assumptions, we prove that activation steering pushes the residual stream off the manifold of states reachable from discrete prompts. Almost surely, no prompt can reproduce the same internal behavior induced by steering. We also illustrate this finding empirically across three widely used LLMs. Our results establish a formal separation between white-box steerability and black-box prompting. We therefore caution against interpreting the ease and success of activation steering as evidence of prompt-based interpretability or vulnerability, and argue for evaluation protocols that explicitly decouple white-box and black-box interventions.", "github": "https://github.com/aamixsh/invertsteer" }, { "date": "2026-05-18", "arxiv_id": "2605.15138", "title": "Forgetting That Sticks: Quantization-Permanent Unlearning via Circuit Attribution", "authors": [ "Saisab Sadhu", "Pratinav Seth", "Vinay Kumar Sankarapu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Standard unlearning evaluations measure behavioral suppression in full precision, immediately after training, despite every deployed language model being quantized first. Recent work has shown that 4-bit post-training quantization can reverse machine unlearning; we show this is not a tuning artefact but a systematic dual failure: gradient-based methods that achieve meaningful forgetting lose it under compression, while methods that survive quantization barely change the model. Both failures trace to the same root cause: across all baselines, per-parameter updates lie 47-828x below the NF4 quantization bin width; updates diffused across billions of parameters cannot clear quantization bin boundaries, a consequence we formalize as a sparsity-permanence tradeoff. We present MANSU (Mechanistic-Aligned Null-Space Unlearning), which resolves both modes by combining causal circuit attribution to isolate the minimal forget-set subgraph, circuit-restricted null-space projection with a diagonal-Fisher retain bound, and a per-parameter magnitude floor guaranteeing quantization survival by construction. We additionally introduce Circuit Attribution Divergence (CAD), a mechanistic verification metric distinguishing structural erasure from behavioral suppression, a distinction existing metrics cannot make. Across multiple model families and hazard benchmarks, MANSU is the first method to jointly satisfy all four properties with margin on each (meaningful forgetting, retain preservation, non-positive PTQ gap, and structural erasure), while gradient-based baselines recover up to +0.05 accuracy under compression.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2605.10302", "title": "Follow the Mean: Reference-Guided Flow Matching", "authors": [ "Pedro M. P. Curvo", "Maksim Zhdanov", "Floor Eijkelboom", "Jan-Willem van de Meent" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/pedrocurvo/follow-the-mean", "github_api_added_by": "user", "project_page": "https://pedrocurvo.com/follow-the-mean", "abstract": "Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching admits a different control interface: adaptation through examples. For deterministic interpolants, the velocity field is solely governed by a conditional endpoint mean; shifting this mean shifts the flow itself. This yields a simple principle for controllable generation: steer a pretrained model by changing the reference set it follows. We instantiate this idea in two forms. Reference-Mean Guidance is training-free: it computes a closed-form endpoint-mean correction from a reference bank and applies it to a frozen FLUX.2-klein (4B) model, enabling control of color, identity, style, and structure while keeping the prompt, seed, and weights fixed. Semi-Parametric Guidance amortizes the same idea through an explicit mean anchor and learned residual refiner, matching unconditional DiT-B/4 quality on AFHQv2 while allowing the reference set to be swapped at inference time. These results point to a broader direction: generative models that adapt through data, not parameter updates.", "github": "https://github.com/pedrocurvo/follow-the-mean" }, { "date": "2026-05-18", "arxiv_id": "2506.01015", "title": "AuralSAM2: Enabling SAM2 Hear Through Pyramid Audio-Visual Feature Prompting", "authors": [ "Yuyuan Liu", "Yuanhong Chen", "Chong Wang", "Junlin Han", "Junde Wu", "Can Peng", "Jingkun Chen", "Yu Tian", "Gustavo Carneiro" ], "github_manual": "", "github_regex": "https://github.com/yyliu01/AuralSAM2", "github_paper_md": "", "github_api": "https://github.com/yyliu01/AuralSAM2", "github_api_added_by": "user", "project_page": "", "abstract": "Segment Anything Model 2 (SAM2) exhibits strong generalisation for promptable segmentation in video clips; however, its integration with the audio modality remains underexplored. Existing approaches either convert audio into visual prompts (e.g., boxes) via foundation models, or inject adapters into the image encoder for audio-visual fusion. Yet both directions fall short in human-in-the-loop scenarios due to limited prompt accuracy and increased inference overhead. In particular, these adapter-based methods often suffer from audio prompt dilution, where the signal gradually weakens as it propagates through the network. In this work, we propose AuralSAM2, which integrates audio into SAM2 while largely preserving its promptable segmentation capability. Its core module, AuralFuser, fuses audio and visual features to generate sparse and dense prompts. Guided by audio and built upon SAM2's feature pyramid, these prompts propagate auditory cues across visual layers, reinforcing cross-modal influence. To further align modalities, we introduce an audio-guided contrastive loss that emphasises auditory relevance in dominant visual features. Our method achieves notable accuracy gains on public benchmarks with only minimal impact on the interactive efficiency of promptable segmentation. Our code is available at https://github.com/yyliu01/AuralSAM2.", "github": "https://github.com/yyliu01/AuralSAM2" }, { "date": "2026-05-18", "arxiv_id": "2605.14786", "title": "Known By Their Actions: Fingerprinting LLM Browser Agents via UI Traces", "authors": [ "William Lugoloobi", "Samuelle Marro", "Jabez Magomere", "Joss Wright", "Chris Russell" ], "github_manual": "", "github_regex": "https://github.com/KabakaWilliam/known_actions", "github_paper_md": "", "github_api": "https://github.com/KabakaWilliam/known_actions", "github_api_added_by": "user", "project_page": "", "abstract": "As LLM-based agents increasingly browse the web on users' behalf, a natural question arises: can websites passively identify which underlying model powers an agent? Doing so would represent a significant security risk, enabling targeted attacks tailored to known model vulnerabilities. Across 14 frontier LLMs and four web environments spanning information retrieval and shopping tasks, we show that an agent's actions and interaction timings, captured via a passive JavaScript tracker, are sufficient to identify the underlying model with up to 96\\% F1. We formalise this attack surface by demonstrating that classifiers trained on agent actions generalise across model sizes and families. We further show that strong classifiers can be trained from few interaction traces and that agent identity can be inferred early within an episode. Injecting randomised timing delays between actions substantially degrades classifier performance, but does not provide robust protection: a classifier retrained on delayed traces largely recovers performance. We release our harness and a labelled corpus of agent traces https://github.com/KabakaWilliam/known_actions{here}.", "github": "https://github.com/KabakaWilliam/known_actions" }, { "date": "2026-05-18", "arxiv_id": "2602.09016", "title": "Raster2Seq: Polygon Sequence Generation for Floorplan Reconstruction", "authors": [ "Hao Phung", "Hadar Averbuch-Elor" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Cornell-VAILab/Raster2Seq", "github_api_added_by": "user", "project_page": "https://cornell-vailab.github.io/Raster2Seq/", "abstract": "Reconstructing a structured vector-graphics representation from a rasterized floorplan image is typically an important prerequisite for computational tasks involving floorplans such as automated understanding or CAD workflows. However, existing techniques struggle in faithfully generating the structure and semantics conveyed by complex floorplans that depict large indoor spaces with many rooms and a varying numbers of polygon corners. To this end, we propose Raster2Seq, framing floorplan reconstruction as a sequence-to-sequence task in which floorplan elements--such as rooms, windows, and doors--are represented as labeled polygon sequences that jointly encode geometry and semantics. Our approach introduces an autoregressive decoder that learns to predict the next corner conditioned on image features and previously generated corners using guidance from learnable anchors. These anchors represent spatial coordinates in image space, hence allowing for effectively directing the attention mechanism to focus on informative image regions. By embracing the autoregressive mechanism, our method offers flexibility in the output format, enabling for efficiently handling complex floorplans with numerous rooms and diverse polygon structures. Our method achieves state-of-the-art performance on standard benchmarks such as Structure3D, CubiCasa5K, and Raster2Graph, while also demonstrating strong generalization to more challenging datasets like WAFFLE, which contain diverse room structures and complex geometric variations.", "github": "https://github.com/Cornell-VAILab/Raster2Seq" }, { "date": "2026-05-18", "arxiv_id": "2605.07249", "title": "MLAIRE: Multilingual Language-Aware Information Retrieval Evaluation Protocal", "authors": [ "Youngjoon Jang", "Seongtae Hong", "Hyeonseok Moon", "Heuiseok Lim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multilingual Information Retrieval is increasingly important in real-world search settings, where users issue queries over mixed-language corpora. Existing evaluations mainly reward language-agnostic semantic relevance, treating relevant passages equally regardless of language. Yet retrieval utility also depends on the language of the retrieved passages: users may prefer results they can read and verify in the query language, and query--passage language mismatch can complicate downstream grounding and answer verification in Retrieval-Augmented Generation systems. To evaluate this language-aware dimension, we introduce MLAIRE, a Multilingual Language-Aware Information Retrieval Evaluation protocol that disentangles cross-lingual semantic retrieval from query-language preference. MLAIRE constructs controlled pools with parallel passages across languages, enabling measurement of semantic retrieval accuracy and query-language preference when equivalent translations are available. We propose language-aware metrics, including Language Preference Rate (LPR) and Lang-nDCG, together with a 4-way decomposition separating semantic and query-language preference failures. Evaluating 31 dense, sparse, and late-interaction retrievers, we show that standard metrics obscure distinct behaviors: semantically strong retrievers may return correct content in a non-query language, while retrievers with stronger query-language preference may retrieve less semantically relevant passages.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2605.14040", "title": "Physics-R1: An Audited Olympiad Corpus and Recipe for Visual Physics Reasoning", "authors": [ "Shan Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shanyang-me/physics-r1-code", "github_api_added_by": "user", "project_page": "https://shanyang.me/physics-r1-page/", "abstract": "We audit the multimodal-physics evaluation pipeline end-to-end and document three undetected construction practices that distort how the field measures vision-language reasoning: train-eval contamination, translation drift, and MCQ saturation. (1) Public training pools (UGPhysics-Train, SciInstruct, MMK12) pass single-stage 5-gram-Jaccard audits with zero hits across all six public physics evals; a three-stage audit (Jaccard -> mxbai-embed-large cosine -> Haiku-4.5 LLM-judge) surfaces 134 near-duplicates and 4,846 paraphrase candidates in SciInstruct alone. (2) A 17-pp Sonnet 4.5 delta on 59 paired Estonian-English olympiad problems (30.5% vs. 13.6%; sign test p=0.011, McNemar p=0.021, paired bootstrap 95% CI [+5.1, +28.9] pp). (3) A 46-pp format-and-novelty gradient on identical Sonnet weights between MCQ (79.7% on PhyX) and open-ended olympiad evaluation (33.4% on PhysOlym-A). We release four artifacts addressing these gaps: PhysCorp-A (6,432-record three-stage-audited multimodal corpus), PhysR1Corp (2,268-record closed-form RL pool), PhysOlym-A (500-problem, 99.8% novel-source held-out olympiad eval with native difficulty labels and an EN/ET bilingual subset), and Physics-R1, a reference GSPO+DAPO recipe cold-started from Qwen3-VL-8B-Thinking. Across 3 seeds, Physics-R1 lifts the audited corpus over the 8B base by +18.3 pp on PhysOlym-A liberal (8.0 -> 26.3 +/- 1.7; 7.1 pp behind Sonnet 4.5), +15.7 pp on PhysReason (23.9 -> 39.6 +/- 6.4; ahead of Qwen3-VL-32B and Gemini 2.5 Pro), +6.9 pp on OlympiadBench-Physics (46.2 +/- 1.5), and +4.1 pp on PhyX MCQ (77.8 +/- 0.3).", "github": "https://github.com/shanyang-me/physics-r1-code" }, { "date": "2026-05-18", "arxiv_id": "2605.14212", "title": "MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning", "authors": [ "Yaolun Zhang", "Yujie Zhao", "Nan Wang", "Yiran Wu", "Jiayu Chang", "Yizhao Chen", "Qingyun Wu", "Jishen Zhao", "Huazheng Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://mercury7353.github.io/MetaAgent-X-Page/", "abstract": "Automatic multi-agent systems aim to instantiate agent workflows without relying on manually designed or fixed orchestration. However, existing automatic MAS approaches remain only partially adaptive: they either perform training-free test-time search or optimize the meta-level designer while keeping downstream execution agents frozen, which creating a frozen-executor ceiling and leaving the end-to-end training of self-designing and self-executing agentic models unexplored. To address this, we introduce MetaAgent-X, an end-to-end reinforcement learning framework that jointly optimizes automatic MAS design and execution. MetaAgent-X enables script-based MAS generation, execution rollout collection, and credit assignment for both designer and executor trajectories. To support stable and scalable optimization, we propose Executor Designer Hierarchical Rollout and Stagewise Co-evolution to improve training stability and expose the dynamics of designer-executor co-evolution. MetaAgent-X consistently outperforms existing automatic MAS baselines, achieving up to 21.7% gains. Comprehensive ablations show that both designer and executor improve throughout training, and that effective automatic MAS learning follows a stagewise co-evolution process. These results establish end-to-end trainable automatic MAS as a practical paradigm for building self-designing and self-executing agentic models.", "github": "" }, { "date": "2026-05-18", "arxiv_id": "2605.12524", "title": "Stress-Testing the Reasoning Competence of LLMs With Proofs Under Minimal Formalism", "authors": [ "Konstantine Arkoudas", "Serafim Batzoglou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/System-2-Labs/ProofGrid", "github_api_added_by": "user", "project_page": "https://github.com/System-2-Labs/ProofGrid", "abstract": "We introduce ProofGrid, a benchmark suite for evaluating LLM reasoning through machine-checkable proofs rather than final answers alone. ProofGrid contains 15 tasks spanning proof writing, proof checking, proof masking, and proof gap-filling. Tasks are expressed in minimal formal notation, especially NDL, a compact natural-deduction language that fits in short prompts and supports precise, auditable verification. This yields mechanical, reproducible, and fine-grained evaluation rather than judgments by humans or LLMs. ProofGrid covers a calibrated difficulty spectrum, from foundational reasoning tests to structurally rich challenge tasks that no current model solves, while minimizing reliance on domain knowledge, solver delegation, and long-context artifacts. We also develop a comparative framework for reasoning benchmarks and use it to situate ProofGrid relative to existing work in terms of representation, verification guarantees, and reasoning depth. Methodologically, we introduce an instrumented proof-checking pipeline that tolerates minor surface deviations while locating the first substantive reasoning failure, improving measurement resolution and separating proof planning from low-level execution noise. Using this pipeline, we evaluate a broad range of open and proprietary models. Results show rapid progress but substantial remaining limits: frontier models perform well on several foundational tasks, yet difficult tasks, especially those requiring global combinatorial reasoning or low-level proof synthesis, remain far from solved. We also identify epistemic instability, where models generate flawed proofs yet correctly reject those local inferences in isolation, and formalize this with an Epistemic Stability Index. Finally, we complement accuracy with 2PL IRT analyses, Wright maps, and a normalized task-discrimination measure based on Fisher information.", "github": "https://github.com/System-2-Labs/ProofGrid" }, { "date": "2026-05-18", "arxiv_id": "2605.12678", "title": "No One Knows the State of the Art in Geospatial Foundation Models", "authors": [ "Isaac Corley", "Nils Lehmann", "Caleb Robinson", "Gabriel Tseng", "Anthony Fuller", "Hamed Alemohammad", "Evan Shelhamer", "Jennifer Marcus", "Hannah Kerner" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/taylor-geospatial/gfm-leaderboard", "github_api_added_by": "user", "project_page": "", "abstract": "Geospatial foundation models (GFMs) have been proposed as generalizable backbones for disaster response, land-cover mapping, food-security monitoring, and other high-stakes Earth-observation tasks. Yet the published work about these models does not give reviewers or users enough information to tell which model fits a given task. We argue that nobody knows what the current state of the art is in geospatial foundation models. The methods may be useful, but the GFM literature does not standardize evaluations, training and testing protocols, released weights, or pretraining controls well enough for anyone to compare or rank them. In a 152-paper audit, we find 46 cross-paper disagreements of at least 10 points for the same model, benchmark, and protocol; 94/126 papers with extractable pretraining data use a configuration no other paper uses; and 39% of GFM papers release no model weights. This lack of community standards can be solved. We propose six concrete expectations: named-license weight release, shared core evaluations, copied-versus-rerun baseline annotations, variance reporting, one shared evaluation harness, and data-vs-architecture-vs-algorithm controls. These gaps are a coordination failure, not a fault of any individual lab; the authors of this paper, like many others in the GFM community, have contributed to them. Rather than just critiquing the community, we aim to provide concrete steps toward a shared understanding of how to innovate GFMs.", "github": "https://github.com/taylor-geospatial/gfm-leaderboard" }, { "date": "2026-05-18", "arxiv_id": "2605.14271", "title": "Auditing Agent Harness Safety", "authors": [ "Chengzhi Liu", "Yichen Guo", "Yepeng Liu", "Yuzhe Yang", "Qianqi Yan", "Xuandong Zhao", "Wenyue Hua", "Sheng Liu", "Sharon Li", "Yuheng Bu", "Xin Eric Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/eric-ai-lab/HarnessAudit", "github_api_added_by": "user", "project_page": "https://harnessaudit.github.io/", "abstract": "LLM agents increasingly run inside execution harnesses that dispatch tools, allocate resources, and route messages between specialized components. However, a harness can return a correct, benign answer over a trajectory that accesses unauthorized resources or leaks context to the wrong agent. Output-level evaluation cannot see these failures, yet most safety benchmarks score only final outputs or terminal states, even though many violations occur mid-trajectory rather than at termination. The central question is whether the harness respects user intent, permission boundaries, and information-flow constraints throughout execution. To address this gap, we propose HarnessAudit, a framework that audits full execution trajectories across boundary compliance, execution fidelity, and system stability, with a focus on multi-agent harnesses where these risks are most pronounced. We further introduce HarnessAudit-Bench, a benchmark of 210 tasks across eight real-world domains, instantiated in both single-agent and multi-agent configurations with embedded safety constraints. Evaluating ten harness configurations across frontier models and three multi-agent frameworks, we find that: (i) task completion is misaligned with safe execution, and violations accumulate with trajectory length; (ii) safety risks vary across domains, task types, and agent roles; (iii) most violations concentrate in resource access and inter-agent information transfer; and (iv) multi-agent collaboration expands the safety risk surface, while harness design sets the upper bound of safe deployment.", "github": "https://github.com/eric-ai-lab/HarnessAudit" }, { "date": "2026-05-19", "arxiv_id": "2605.14368", "title": "Where Should Diffusion Enter a Language Model? Geometry-Guided Hidden-State Replacement", "authors": [ "Injin Kong", "Hyoungjoon Lee", "Yohan Jo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Continuous diffusion language models lag behind autoregressive transformers, partly because diffusion is applied in spaces poorly suited to language denoising and token recovery. We propose DiHAL, a geometry-guided diffusion-transformer hybrid that asks where diffusion should enter a pretrained transformer. DiHAL scores layers with geometry-based proxies, selects a diffusion-friendly hidden-state interface, and replaces the lower transformer prefix with a diffusion bridge while retaining the upper layers and original LM head. By reconstructing the selected-layer hidden state rather than tokens, DiHAL avoids direct continuous-to-discrete recovery. Experiments on 8B-scale backbones show that the geometry score predicts effective shallow insertion layers under a fixed bridge-training protocol and that hidden-state recovery improves over continuous diffusion baselines in a diagnostic comparison matching the diffusion/recovery training budget. These results suggest that hidden-state geometry helps identify where diffusion-based replacement is feasible inside pretrained language models.", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.14038", "title": "Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Use", "authors": [ "Yize Cheng", "Chenrui Fan", "Mahdi JafariRaviz", "Keivan Rezaei", "Soheil Feiz" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/chengez/Tool-Cognition-Action", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models (LLMs) increasingly act as autonomous agents that must decide when to answer directly vs. when to invoke external tools. Prior work studying adaptive tool use has largely treated tool necessity as a model-agnostic property, annotated by human or LLM judge, and mostly cover cases where the answer is obvious (e.g., fetching the weather vs. paraphrasing text). However, tool necessity in the wild is more nuanced due to the divergence of capability boundaries across models: a problem solvable by a strong model on its own may still require tools for a weaker one. In this work, we introduce a model-adaptive definition of tool-necessity, grounded in each model's empirical performance. Following this definition, we compare the necessity against observed tool-call behavior across four models on arithmetic and factual QA dataset, and find substantial mismatches of 26.5-54.0% and 30.8-41.8%, respectively. To diagnose the failure, we decompose tool use into two stages: an internal cognition stage that reflects whether a model believes a tool is necessary, and an execution stage that determines whether the model actually makes a tool-call action. By probing the LLM hidden states, we find that both signals are often linearly decodable, yet their probe directions become nearly orthogonal in the late-layer, last-token regime that drives the next-token action. By tracing the trajectory of samples in the two-stage process, we further discover that the majority of mismatch is concentrated in the cognition-to-action transition, not in cognition itself. These results reveal a knowing-doing gap in LLM tool-use: improving tool-use reliability requires not only better recognition of when tools are needed, but also better translation of that recognition into action.", "github": "https://github.com/chengez/Tool-Cognition-Action" }, { "date": "2026-05-19", "arxiv_id": "2605.17672", "title": "Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models", "authors": [ "Dehai Min", "Giovanni Vaccarino", "Huiyi Chen", "Yongliang Wu", "Gal Yona", "Lu Cheng" ], "github_manual": "", "github_regex": "https://github.com/giovanni-vaccarino/PUMA", "github_paper_md": "", "github_api": "https://github.com/giovanni-vaccarino/PUMA", "github_api_added_by": "user", "project_page": "", "abstract": "Large Reasoning Models (LRMs) achieve strong performance by generating long chains of thought (CoT), but often overthink, continuing to reason after a solution has already stabilized and thereby wasting tokens and increasing latency. Existing inference-time early-exit methods rely primarily on answer-level signals, such as confidence or trial-answer consistency, to decide when to stop. However, these signals mainly reflect answer readiness rather than reasoning convergence: they may trigger before the model has finished exploring or self-correcting, causing premature exits that can degrade final-answer accuracy and leave the retained reasoning chain semantically incomplete. We identify reasoning-level semantic redundancy as a complementary signal for semantic-preserving early exit: when successive steps no longer add novel progress and instead revisit established conclusions, the reasoning trajectory has likely converged. Building on this insight, we propose PUMA, a plug-and-play framework that combines a lightweight Redundancy Detector with answer-level verification. The detector flags semantically redundant candidate exits, while verification confirms whether stopping is safe, allowing PUMA to remove redundant continuation while preserving both answer accuracy and a coherent reasoning prefix. Across five LRMs and five challenging reasoning benchmarks, PUMA achieves 26.2% average token reduction while preserving accuracy and retained CoT quality. Additional experiments on code generation, zero-shot vision-language reasoning, and learned stopping-policy internalization further demonstrate that reasoning-level redundancy is a robust, transferable, and learnable signal for efficient reasoning. Our code is available at https://github.com/giovanni-vaccarino/PUMA.", "github": "https://github.com/giovanni-vaccarino/PUMA" }, { "date": "2026-05-19", "arxiv_id": "2605.17260", "title": "LiteFrame: Efficient Vision Encoders Unlock Frame Scaling in Video LLMs", "authors": [ "Jihwan Kim", "Nikhil Parthasarathy", "Danfeng Qin", "Junhwa Hur", "Deqing Sun", "Bohyung Han", "Ming-Hsuan Yang", "Boqing Gong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jjihwan/LiteFrame", "github_api_added_by": "user", "project_page": "https://jjihwan.github.io/projects/LiteFrame/", "abstract": "The fundamental challenge in scaling Video Large Language Models (Video LLMs) to long-form video lies in managing the explosion of visual-token context length. Existing strategies predominantly focus on \"post-hoc\" token reduction -- reducing visual tokens after feature extraction to alleviate the LLM's computational overhead. While these methods effectively reduce the number of visual tokens, we observe that the primary latency bottleneck then shifts from the LLM to the expensive per-frame processing of the vision encoder. To address this, we introduce LiteFrame, a strong, yet highly efficient video encoder backbone for Video LLMs. To train LiteFrame, we propose Compressed Token Distillation (CTD), a novel training framework that teaches a compact student vision encoder to directly predict information-dense, spatio-temporally compressed representations produced by a large teacher vision model, effectively bypassing redundant computation. When coupled with further Language Model Adaptation (LMA), this approach results in a new latency-accuracy Pareto frontier -- compared with InternVL3-8B, LiteFrame provides a 35% reduction in end-to-end latency while processing 8times more frames and improves average video understanding accuracy across multiple benchmarks. Our results demonstrate a new potential path to unlocking longer-form video understanding under fixed compute budgets.", "github": "https://github.com/jjihwan/LiteFrame" }, { "date": "2026-05-19", "arxiv_id": "2605.14589", "title": "EndPrompt: Efficient Long-Context Extension via Terminal Anchoring", "authors": [ "Han Tian", "Luxuan Chen", "Xinran Chen", "Rui Kong", "Fang Wang", "Jiamin Chen", "Jinman Zhao", "Yuchen Li", "Jiashu Zhao", "Shuaiqiang Wang", "Haoyi Xiong", "Dawei Yin" ], "github_manual": "", "github_regex": "https://github.com/clx1415926/EndPrompt", "github_paper_md": "", "github_api": "https://github.com/clx1415926/EndPrompt", "github_api_added_by": "user", "project_page": "", "abstract": "Extending the context window of large language models typically requires training on sequences at the target length, incurring quadratic memory and computational costs that make long-context adaptation expensive and difficult to reproduce. We propose EndPrompt, a method that achieves effective context extension using only short training sequences. The core insight is that exposing a model to long-range relative positional distances does not require constructing full-length inputs: we preserve the original short context as an intact first segment and append a brief terminal prompt as a second segment, assigning it positional indices near the target context length. This two-segment construction introduces both local and long-range relative distances within a short physical sequence while maintaining the semantic continuity of the training text--a property absent in chunk-based simulation approaches that split contiguous context. We provide a theoretical analysis grounded in Rotary Position Embedding and the Bernstein inequality, showing that position interpolation induces a rigorous smoothness constraint over the attention function, with shared Transformer parameters further suppressing unstable extrapolation to unobserved intermediate distances. Applied to LLaMA-family models extending the context window from 8K to 64K, EndPrompt achieves an average RULER score of 76.03 and the highest average on LongBench, surpassing LCEG (72.24), LongLoRA (72.95), and full-length fine-tuning (69.23) while requiring substantially less computation. These results demonstrate that long-context generalization can be induced from sparse positional supervision, challenging the prevailing assumption that dense long-sequence training is necessary for reliable context-window extension. The code is available at https://github.com/clx1415926/EndPrompt.", "github": "https://github.com/clx1415926/EndPrompt" }, { "date": "2026-05-19", "arxiv_id": "2605.15572", "title": "Measuring Maximum Activations in Open Large Language Models", "authors": [ "Luxuan Chen", "Han Tian", "Xinran Chen", "Rui Kong", "Fang Wang", "Jiamin Chen", "Yuchen Li", "Jiashu Zhao", "Shuaiqiang Wang", "Haoyi Xiong", "Dawei Yin" ], "github_manual": "", "github_regex": "https://github.com/clx1415926/Max_act_llm", "github_paper_md": "", "github_api": "https://github.com/clx1415926/Max_act_llm", "github_api_added_by": "user", "project_page": "", "abstract": "The dynamic range of activations is a first-order constraint for low-bit quantization, activation scaling, and stable LLM inference. Prior work characterized outlier features and massive activations on pre-2024 LLaMA-style models, and the downstream activation-quantization stack inherits that picture without revisiting it for the post-LLaMA open-model boom. We ask the deployment-oriented question: how large can activations get in modern open LLMs, and how does this magnitude vary across families, generations, and training stages? Under a unified pipeline (5,000-sample multi-domain corpus, family-specific tokenization, identical hooks across embeddings, hidden states, attention, MLP/MoE, SwiGLU gates, and final norm), we measure global and layerwise maxima on 27 checkpoints from 8 open families spanning dense, MoE, vision-language, intermediate-training, and instruction-tuned variants. We find that (i) global maxima span over nearly four orders of magnitude at comparable parameter counts, with Qwen3.5 and MoE checkpoints in the 10^2 to 10^3 range and Gemma3-27B-it reaching ~7 x 10^5; (ii) cross-family and cross-generation comparisons break simple monotonic scaling; and (iii) MoE checkpoints exhibit 14.0-23.4x lower peaks than matched-scale dense counterparts, while the residual stream carries the global maximum in 22/24 checkpoints. A lightweight INT-8 sanity check shows that measured maxima co-vary with low-bit reconstruction error via activation-scale selection. We conclude that maximum activation magnitude is a model property tied to family, architecture, and training stage - not a simple byproduct of size - and should be measured and reported alongside any open-weight release before low-bit deployment. The code is publicly available at https://github.com/clx1415926/Max_act_llm.", "github": "https://github.com/clx1415926/Max_act_llm" }, { "date": "2026-05-19", "arxiv_id": "2605.17199", "title": "Geometric Phase Transition Enables Extreme Hippocampal Memory Capacity", "authors": [ "Prashant C. Raju" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/prashantcraju/hippocampal-stability", "github_api_added_by": "user", "project_page": "", "abstract": "Memory systems can store vastly different amounts of information despite similar hardware constraints. Here, we show that superior spatial memory emerges from a discrete stiffening of hippocampal population geometry-a transition from disorganized to crystalline collective coding. Comparing food-caching chickadees to non-caching zebra finches, we found that the caching hippocampus maintains a topologically rigid, \"crystalline\" geometry with significantly higher geometric stability (Shesha 0.245 v 0.166) and nearly two-fold greater temporal coherence (Shesha 0.393 v 0.209), while the non-caching hippocampus resembles a disorganized \"mist.\" This stability is actively constructed by synergistic circuit dynamics: excitatory neurons form the spatial scaffold while inhibitory populations contribute orthogonal decorrelation, a circuit motif in which excitatory and inhibitory populations occupy largely non-overlapping representational subspaces. A double dissociation with Valiant's Stable Memory Allocator, a model predicting that dedicated neuron ensembles underlie each memory, confirms this advantage reflects continuous topological organization rather than discrete neuron allocation: caching networks exhibit near-zero split-half allocation reliability despite their geometric superiority. Computational modeling across 10k configurations reveals topological rigidity as the mathematical prerequisite for scale: crystalline codes sustain high-fidelity readout beyond M=1k locations while mist codes fail below M=10, a >100-fold capacity advantage. This capacity requires a 169fold representational redundancy: a \"geometric tax\" stabilizing the manifold against biological noise. These results establish geometric stability as a candidate organizing principle of biological memory: evolution achieves high-capacity memory not by proliferating neurons, but by engineering the geometry of the neural code itself.", "github": "https://github.com/prashantcraju/hippocampal-stability" }, { "date": "2026-05-19", "arxiv_id": "2605.14278", "title": "KVPO: ODE-Native GRPO for Autoregressive Video Alignment via KV Semantic Exploration", "authors": [ "Ruicheng Zhang", "Kaixi Cong", "Jun Zhou", "Zhizhou Zhong", "Zunnan Xu", "Shuiyang Mao", "Wei Liu", "Xiu Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Richard-Zhang-AI/KVPO", "github_api_added_by": "user", "project_page": "https://richard-zhang-ai.github.io/KVPO-Project/", "abstract": "Aligning streaming autoregressive (AR) video generators with human preferences is challenging. Existing reinforcement learning methods predominantly rely on noise-based exploration and SDE-based surrogate policies that are mismatched to the deterministic ODE dynamics of distilled AR models, and tend to perturb low-level appearance rather than the high-level semantic storyline progression critical for long-horizon coherence. To address these limitations, we present KVPO, an ODE-native online Group Relative Policy Optimization (GRPO) framework for aligning streaming video generators. For diversity exploration, KVPO introduces a causal-semantic exploration paradigm that relocates the source of variation from stochastic noise to the historical KV cache. By stochastically routing historical KV entries, it constructs semantically diverse generation branches that remain strictly on the data manifold. For policy modeling, KVPO introduces a velocity-field surrogate policy based on Trajectory Velocity Energy (TVE), which quantifies branch likelihood in flow-matching velocity space and yields a reward-weighted contrastive objective fully consistent with the native ODE formulation. Experiments on multiple distilled AR video generators demonstrate consistent gains in visual quality, motion quality, and text-video alignment across both single-prompt short-video and multi-prompt long-video settings.", "github": "https://github.com/Richard-Zhang-AI/KVPO" }, { "date": "2026-05-19", "arxiv_id": "2605.17842", "title": "SNLP: Layer-Parallel Inference via Structured Newton Corrections", "authors": [ "Ligong Han", "Kai Xu", "Hao Wang", "Akash Srivastava" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/phymhan/nanochat-snlp", "github_api_added_by": "user", "project_page": "", "abstract": "Autoregressive language models execute Transformer layers sequentially, creating a latency bottleneck that is not removed by conventional tensor or pipeline parallelism. We study whether this layerwise dependency can be relaxed by treating the hidden-state trace across layers as the solution of a nonlinear residual equation and solving it with parallel Newton-style updates. While this view is principled, exact Newton corrections require expensive Jacobian-vector products and naive fixed-point iterations are unstable on trained Transformers. We introduce Structured Newton Layer Parallelism (SNLP), a training and inference framework that replaces exact layer Jacobians with cheap architecture-induced surrogate dynamics. In residual Transformers, this yields Identity Newton (IDN), where the correction reduces to a prefix-sum-like update; in mHC-style architectures, HC Newton (HCN) uses the model's residual mixing matrix. We further introduce SNLP-aware regularization, which trains models to make one or a few structured Newton iterations accurately approximate the sequential forward. Experiments on nanochat-scale Transformers show that SNLP regularization improves layer-parallel compatibility and can also improve standard sequential perplexity, reducing baseline PPL by 4.7%-23.4%. At inference time, SNLP combined with layer fusion and chunkwise decomposition achieves practical wall-clock speedups: on a 0.5B Nanochat model, it reaches 2.3x speedup while still improving PPL by 6.1%. These results suggest that layer-parallel inference is not merely a numerical approximation to sequential execution, but can act as a useful solver-induced inference bias. We also characterize limitations: off-the-shelf pretrained models are less amenable to this procedure, and exact convergence recovers the sequential computation rather than providing monotonic inference-time scaling.", "github": "https://github.com/phymhan/nanochat-snlp" }, { "date": "2026-05-19", "arxiv_id": "2605.18661", "title": "AI for Auto-Research: Roadmap & User Guide", "authors": [ "Lingdong Kong", "Xian Sun", "Wei Chow", "Linfeng Li", "Kevin Qinghong Lin", "Xuan Billy Zhang", "Song Wang", "Rong Li", "Qing Wu", "Wei Gao", "Yingshuo Wang", "Shaoyuan Xie", "Jiachen Liu", "Leigang Qu", "Shijie Li", "Lai Xing Ng", "Benoit R. Cottereau", "Ziwei Liu", "Tat-Seng Chua", "Wei Tsang Ooi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/worldbench/awesome-ai-auto-research", "github_api_added_by": "user", "project_page": "https://worldbench.github.io/awesome-ai-auto-research", "abstract": "AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draft manuscripts, and simulate critique with minimal human input. Yet this productivity frontier exposes a deeper integrity problem: under scientific pressure, even frontier LLMs still fabricate results, miss hidden errors, and fail to judge novelty reliably. Studying developments through April 2026, we present an end-to-end analysis of AI across the complete research lifecycle, organized into four epistemological phases: Creation (idea generation, literature review, coding & experiments, tables & figures), Writing (paper writing), Validation (peer review, rebuttal & revision), and Dissemination (posters, slides, videos, social media, project pages, and interactive agents). We identify a sharp, stage-dependent boundary between reliable assistance and unreliable autonomy: AI excels at structured, retrieval-grounded, and tool-mediated tasks, but remains fragile for genuinely novel ideas, research-level experiments, and scientific judgment. Generated ideas often degrade after implementation, research code lags far behind pattern-matching benchmarks, and end-to-end autonomous systems have not yet consistently reached major-venue acceptance standards. We further show that greater automation can obscure rather than eliminate failure modes, making human-governed collaboration the most credible deployment paradigm. Finally, we provide a structured taxonomy, benchmark suite, and tool inventory, cross-stage design principles, and a practitioner-oriented playbook, with resources maintained at our project page.", "github": "https://github.com/worldbench/awesome-ai-auto-research" }, { "date": "2026-05-19", "arxiv_id": "2605.18739", "title": "LongLive-2.0: An NVFP4 Parallel Infrastructure for Long Video Generation", "authors": [ "Yukang Chen", "Luozhou Wang", "Wei Huang", "Shuai Yang", "Bohan Zhang", "Yicheng Xiao", "Ruihang Chu", "Weian Mao", "Qixin Hu", "Shaoteng Liu", "Yuyang Zhao", "Huizi Mao", "Ying-Cong Chen", "Enze Xie", "Xiaojuan Qi", "Song Han" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/LongLive", "github_api_added_by": "user", "project_page": "https://nvlabs.github.io/LongLive/LongLive2/", "abstract": "We present LongLive-2.0, an NVFP4-based parallel infrastructure throughout the full training and inference workflow of long video generation, addressing speed and memory bottlenecks. For training, we introduce sequence-parallel autoregressive (AR) training, instantiated as Balanced SP, which co-designs the efficient teacher-forcing layout with SP execution by pairing clean-history and noisy-target temporal chunks on each rank, enabling a natural teacher-forcing mask with SP-aware chunked VAE encoding. Combined with NVFP4 precision, it reduces GPU memory cost and accelerates GEMM computation during training, the proportion of which increases as video length grows. Moreover, we show that a high-quality infrastructure and dataset enable a remarkably clean training pipeline. Unlike existing Self-Forcing series methods that rely on ODE initialization and subsequent distribution matching distillation (DMD), LongLive-2.0 directly tunes a diffusion model into a long, multi-shot, interactive auto-regressive (AR) diffusion model. It can be further converted to real-time generation (4 to 2 denoising steps) with standalone LoRA weights. For inference on Blackwell GPUs, we enable W4A4 NVFP4 inference, quantize KV cache into NVFP4 for memory savings, and boost end-to-end throughput with asynchronous streaming VAE decoding. On non-Blackwell GPU architectures, we deploy SP inference to match the speed on Blackwell GPUs, while the quantized KV cache can lower inter-GPU communication of SP. Experiments show up to 2.15x speedup in training, and 1.84x in inference. LongLive-2.0-5B achieves 45.7 FPS inference while attaining strong performance on benchmarks. To our knowledge, LongLive-2.0 is the first NVFP4 training and inference system for long video generation.", "github": "https://github.com/NVlabs/LongLive" }, { "date": "2026-05-19", "arxiv_id": "2605.18678", "title": "Lance: Unified Multimodal Modeling by Multi-Task Synergy", "authors": [ "Fengyi Fu", "Mengqi Huang", "Shaojin Wu", "Yunsheng Jiang", "Yufei Huo", "Hao Li", "Yinghang Song", "Fei Ding", "Jianzhu Guo", "Qian He", "Zheren Fu", "Zhendong Mao", "Yongdong Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bytedance/Lance", "github_api_added_by": "user", "project_page": "https://lance-project.github.io/", "abstract": "We present Lance, a lightweight native unified model supporting multimodal understanding, generation, and editing for both images and videos. Rather than relying on model capacity scaling or text-image-dominant designs, Lance explores a practical paradigm for unified multimodal modeling via collaborative multi-task training. It is grounded in two core principles: unified context modeling and decoupled capability pathways. Specifically, Lance is trained from scratch and employs a dual-stream mixture-of-experts architecture on shared interleaved multimodal sequences, enabling joint context learning while decoupling the pathways for understanding and generation. We further introduce modality-aware rotary positional encoding to mitigate interference among heterogeneous visual tokens and boost cross-task alignment. During training, Lance adopts a staged multi-task training paradigm with capability-oriented objectives and adaptive data scheduling to strengthen both semantic comprehension and visual generation performance. Experimental results demonstrate that Lance substantially outperforms existing open-source unified models in image and video generation, while retaining strong multimodal understanding capabilities. The homepage is available at https://lance-project.github.io.", "github": "https://github.com/bytedance/Lance" }, { "date": "2026-05-19", "arxiv_id": "2605.12290", "title": "Targeted Neuron Modulation via Contrastive Pair Search", "authors": [ "Sam Herring", "Jake Naviasky", "Karan Malhotra" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NousResearch/neural-steering", "github_api_added_by": "user", "project_page": "https://github.com/neuron-steering", "abstract": "Language models are instruction-tuned to refuse harmful requests, but the mechanisms underlying this behavior remain poorly understood. Popular steering methods operate on the residual stream and degrade output coherence at high intervention strengths, limiting their practical use. We introduce contrastive neuron attribution (CNA), which identifies the 0.1% of MLP neurons whose activations most distinguish harmful from benign prompts, requiring only forward passes with no gradients or auxiliary training. In instruct models, ablating the discovered circuit reduces refusal rates by over 50% on a standard jailbreak benchmark while preserving fluency and non-degeneracy across all steering strengths. Applying CNA to matched base and instruct models across Llama and Qwen architectures (from 1B to 72B parameters), we find that base models contain similar late-layer discrimination structures but steering these neurons produces only content shifts, not behavioral change. These results demonstrate that neuron-level intervention enables reliable behavioral steering without the quality tradeoffs of residual-stream methods. More broadly, our findings suggest that alignment fine-tuning transforms pre-existing discrimination structure into a sparse, targetable refusal gate.", "github": "https://github.com/NousResearch/neural-steering" }, { "date": "2026-05-19", "arxiv_id": "2605.18643", "title": "Post-Trained MoE Can Skip Half Experts via Self-Distillation", "authors": [ "Xingtai Lv", "Li Sheng", "Kaiyan Zhang", "Yichen You", "Siyan Gao", "Xueheng Luo", "Yuxin Zuo", "Yuchen Fan", "Junlin Yang", "Ganqu Cui", "Bingning Wang", "Fan Yang", "Youbang Sun", "Ning Ding", "Bowen Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TsinghuaC3I/ZEDA", "github_api_added_by": "user", "project_page": "", "abstract": "Mixture-of-Experts (MoE) scales language models efficiently through sparse expert activation, and its dynamic variant further reduces computation by adjusting the activated experts in an input-dependent manner. Existing dynamic MoE methods usually rely on pre-training from scratch or task-specific adaptation, leaving the practical conversion of fully trained MoE underexplored. Enabling such adaptation would directly alleviate the inference costs by allowing easy tokens to bypass unnecessary expert during serving. This paper introduces Zero-Expert Self-Distillation Adaptation (ZEDA), a low-cost framework that transforms post-trained static MoE models into efficient dynamic ones. To stabilize this architectural conversion, ZEDA injects parameter-free zero-output experts into each MoE layer and adapts the augmented model through two-stage self-distillation, utilizing the original MoE as a frozen teacher and applying a group-level balancing loss. On Qwen3-30B-A3B and GLM-4.7-Flash across 11 benchmarks spanning math, code, and instruction following, ZEDA eliminates over 50% of expert FLOPs at marginal accuracy loss. It outperforms the strongest dynamic MoE baseline by 6.1 and 4.0 points on the two models, and delivers ~1.20times end-to-end inference speedup.", "github": "https://github.com/TsinghuaC3I/ZEDA" }, { "date": "2026-05-19", "arxiv_id": "2605.17698", "title": "Agent Bazaar: Enabling Economic Alignment in Multi-Agent Marketplaces", "authors": [ "Seth Karten", "Cameron Crow", "Chi Jin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The deployment of Large Language Models (LLMs) as autonomous economic agents introduces systemic risks that extend beyond individual capability failures. As agents transition to directly interacting with marketplaces, their collective behavior can amplify volatility and mask deception at scale. We introduce the Agent Bazaar, a multi-agent simulation framework for evaluating Economic Alignment, the capacity of agentic systems to preserve market stability and integrity. We identify two failure modes: (1) Algorithmic Instability in a B2C market (\"The Crash\"), where firms amplify price volatility until the market collapses, and (2) Sybil Deception in a C2C market (\"The Lemon Market\"), where a single deceptive agent controlling multiple coordinated seller identities floods the market with fraudulent listings, eroding trust and consumer welfare. We evaluate frontier and open-weight models across both scenarios and find that models largely fail to self-regulate, with failure severity varying by model rather than by size. We propose economically aligned harnesses, Stabilizing Firms and Skeptical Guardians, that improve outcomes but remain fragile under harder market conditions. To close this gap, we train agents with REINFORCE++ using an adaptive curriculum, producing a 9B model that outperforms all evaluated frontier and open-weight models. We propose the Economic Alignment Score (EAS), a 4-component scalar metric aggregating stability, integrity, welfare, and profitability, enabling direct cross-model comparison. Our results show that economic alignment is orthogonal to general capability and can be directly trained with targeted RL.", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.17283", "title": "OProver: A Unified Framework for Agentic Formal Theorem Proving", "authors": [ "David Ma", "Kaijing Ma", "Shawn Guo", "Yunfeng Shi", "Enduo Zhao", "Jiajun Shi", "Zhaoxiang Zhang", "Gavin Cheung", "Jiaheng Liu", "Zili Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/multimodal-art-projection/OProver", "github_api_added_by": "user", "project_page": "", "abstract": "Recent progress in formal theorem proving has benefited from large-scale proof generation and verifier-aware training, but agentic proving is rarely integrated into prover training, appearing only at inference time. We present OProver, a unified framework for agentic formal theorem proving in Lean 4, in which failed proof attempts are iteratively revised using retrieved compiler verified proofs and Lean compiler feedback. OProver is trained through continued pretraining followed by iterative post-training: each iteration runs agentic proving, indexes newly verified proofs into OProofs and the retrieval memory, uses repair trajectories as SFT data, and uses unresolved hard cases for RL. OProofs is built from public Lean resources, large-scale proof synthesis, and agentic proving traces, containing 1.77M Lean statements, 6.86M compiler-verified proofs, and serialized trajectories with retrieved context, failed attempts, feedback, and repairs. Across five benchmarks, OProver-32B attains the best Pass@32 on MiniF2F (93.3%), ProverBench (58.2%), and PutnamBench (11.3%), and ranks second on MathOlympiad (22.8%) and ProofNet (33.2%) more top placements than any prior open-weight whole-proof prover.", "github": "https://github.com/multimodal-art-projection/OProver" }, { "date": "2026-05-19", "arxiv_id": "2605.16839", "title": "CompactAttention: Accelerating Chunked Prefill with Block-Union KV Selection", "authors": [ "Jiwon Song", "Dongwon Jo", "Beomseok Kang", "Jae-Joon Kim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jiwonsong-dev/CompactAttention", "github_api_added_by": "user", "project_page": "", "abstract": "Chunked prefill has become a widely adopted serving strategy for long-context large language models, but efficient attention computation in this regime remains challenging. Existing sparse attention methods are primarily designed for one-shot prefill and do not translate efficiently to chunked prefill: block-sparse kernels lose efficiency when the query length is limited by the chunk size, while fine-grained pattern search becomes costly when repeated over the accumulated KV cache at every chunk. QUOKA, a recent method that directly targets chunked prefill, avoids sparse-kernel overhead but relies on query-subsampled, token-level KV selection, which can miss query-specific KV entries and introduce explicit KV-copy overhead. To address these limitations, we propose CompactAttention, a chunked-prefill attention mechanism based on Block-Union KV Selection. CompactAttention treats 2D block-sparse masks as KV-selection signals rather than direct sparse-kernel execution plans, and converts them into GQA-aware per-group KV block tables through Q-block union and intra-group union. This construction produces the minimal block tables that preserve all KV blocks selected by the input masks under paged execution constraints, enabling selected KV blocks to be accessed in place without explicit KV compaction. On LLaMA-3.1-8B-Instruct, CompactAttention maintains accuracy close to dense attention on the RULER benchmark while delivering up to 2.72times attention speedup at 128K context length under chunked prefill.", "github": "https://github.com/jiwonsong-dev/CompactAttention" }, { "date": "2026-05-19", "arxiv_id": "2605.18747", "title": "Code as Agent Harness", "authors": [ "Xuying Ning", "Katherine Tieu", "Dongqi Fu", "Tianxin Wei", "Zihao Li", "Yuanchen Bei", "Jiaru Zou", "Mengting Ai", "Zhining Liu", "Ting-Wei Li", "Lingjie Chen", "Yanjun Zhao", "Ke Yang", "Bingxuan Li", "Cheng Qian", "Gaotang Li", "Xiao Lin", "Zhichen Zeng", "Ruizhong Qiu", "Sirui Chen", "Yifan Sun", "Xiyuan Yang", "Ruida Wang", "Rui Pan", "Chenyuan Yang", "Dylan Zhang", "Liri Fang", "Zikun Cui", "Yang Cao", "Pan Chen", "Dorothy Sun", "Ren Chen", "Mahesh Srinivasan", "Nipun Mathur", "Yinglong Xia", "Hong Li", "Hong Yan", "Pan Lu", "Lingming Zhang", "Tong Zhang", "Hanghang Tong", "Jingrui He" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YennNing/Awesome-Code-as-Agent-Harness-Papers", "github_api_added_by": "user", "project_page": "", "abstract": "Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineering. In emerging agentic systems, code is no longer only a target output. It increasingly serves as an operational substrate for agent reasoning, acting, environment modeling, and execution-based verification. We frame this shift through the lens of agent harnesses and introduce code as agent harness: a unified view that centers code as the basis for agent infrastructure. To systematically study this perspective, we organize the survey around three connected layers. First, we study the harness interface, where code connects agents to reasoning, action, and environment modeling. Second, we examine harness mechanisms: planning, memory, and tool use for long-horizon execution, together with feedback-driven control and optimization that make harness reliable and adaptive. Third, we discuss scaling the harness from single-agent systems to multi-agent settings, where shared code artifacts support multi-agent coordination, review, and verification. Across these layers, we summarize representative methods and practical applications of code as agent harness, spanning coding assistants, GUI/OS automation, embodied agents, scientific discovery, personalization and recommendation, DevOps, and enterprise workflows. We further outline open challenges for harness engineering, including evaluation beyond final task success, verification under incomplete feedback, regression-free harness improvement, consistent shared state across multiple agents, human oversight for safety-critical actions, and extensions to multimodal environments. By centering code as the harness of agentic AI, this survey provides a unified roadmap toward executable, verifiable, and stateful AI agent systems.", "github": "https://github.com/YennNing/Awesome-Code-as-Agent-Harness-Papers" }, { "date": "2026-05-19", "arxiv_id": "2605.18601", "title": "Incantation: Natural Language as the Action Interface for Multi-Entity Video World Models", "authors": [ "Shangwen Zhu", "Qianyu Peng", "Zhao Pu", "Zhilei Shu", "Xiangrui Ke", "Zhaohu Xing", "Zizhao Tong", "Zeqing Wang", "Xinyu Cui", "Huangji Wang", "Jian Zhao", "Yeying Jin", "Fan Cheng", "Ruili Feng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhushangwen/Incantation", "github_api_added_by": "user", "project_page": "https://matrixteam-ai.github.io/pages/Incanation/", "abstract": "Modern interactive video world models have achieved impressive visual fidelity, yet lack fine-grained multi-entity control and cross-entity, cross-world generalization. We trace this gap to the action interface: standard control protocols (e.g. animation IDs, device inputs, scene-level captions) bind action semantics to specific entities or engines at design time. We propose natural language as the interface to unlock expressiveness that no prior interface can achieve, and we present Incantation, the first interactive video world model with per-latent-frame (0.25 s) natural-language conditioning that supports simultaneous multi-entity control and concept-level cross-entity transfer beyond any fixed rendering pipeline. We pair a pretrained bidirectional video backbone with frame-local text cross-attention, and enable real-time long-horizon streaming through ODE-initialized Self-Forcing distillation with a RoPE-decoupled sliding KV-cache. We surpass the Action-Index baseline on cross-entity transfer (89% vs. 43%) and out-of-vocabulary prompts (90% vs. 0%), and our 2-step student sustains 19.7 FPS at 480p with stable FVD over 2-hour rollouts. We further apply the same architecture and training recipe to The King of Fighters, changing only the per-entity action vocabulary slots. We have released a preview subset of the Incantation dataset at https://huggingface.co/datasets/zhush/incantation-elden-ring-scenes, containing manually collected Elden Ring player-boss combat clips with structured action-oriented metadata. Larger-scale Elden Ring and KOF data will be released with the full project.", "github": "https://github.com/zhushangwen/Incantation" }, { "date": "2026-05-19", "arxiv_id": "2605.17933", "title": "AtlasVA: Self-Evolving Visual Skill Memory for Teacher-Free VLM Agents", "authors": [ "Pan Wang", "Yihao Hu", "Xiujin Liu", "Jingchu Yang", "Hang Wang", "Zhihao Wen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://wangpan-ustc.github.io/AtlasvaWeb/", "abstract": "Vision-language model (VLM) agents increasingly rely on memory-augmented reinforcement learning to reuse experience across long-horizon tasks, yet most existing frameworks store memory as text and depend on proprietary teacher models to summarize or refine it. This design is poorly matched to spatial decision making: geometric priors are compressed into lossy language, and sparse interaction is often supervised through delayed textual feedback rather than dense visually grounded signals. We argue that reusable experience for VLM agents should remain visually grounded. Based on this insight, we propose AtlasVA, a teacher-free visual skill memory framework that organizes memory into three complementary layers: spatial heatmaps, visual exemplars, and symbolic text skills. AtlasVA further evolves danger and affinity atlases directly from trajectory statistics and lightweight grid heuristics, and reuses these self-evolving atlases as potential-based shaping rewards for reinforcement learning. This unifies perception, memory, and optimization without external LLM supervision. Experiments on Sokoban, FrozenLake, 3D embodied navigation, and 3D robotic manipulation benchmarks show that AtlasVA consistently outperforms text-centric memory baselines and competitive VLM agents, with especially strong gains on spatially intensive tasks. Homepage: https://wangpan-ustc.github.io/AtlasvaWeb", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.18743", "title": "Actionable World Representation", "authors": [ "Kunqi Xu", "Jitao Li", "Jianglong Ye", "Tianshu Tang", "Isabella Liu", "Sifei Liu", "Xueyan Zou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Inspired by the emergent behaviors in large language models that generalized human intelligence, the research community is pursuing similar emergent capabilities within world models, with a emphasis on modeling the physical world. Within the scope of physical world model, objects are the fundamental primitives that constitute physical reality. From humans to computers, nearly everything we interact with is an object. These objects are rarely static; they are actionable entities with varying states determined by their intrinsic properties. While current methods approach object action states either via video generation or dynamic scene reconstruction, none explicitly model this basic element in a unified, principled way to build an actionable object representation. We propose WorldString, a neural architecture capable of modeling the state manifold of real-world objects by learning directly from point clouds or RGB-D video streams. Serving as a versatile digital twin, it acts as a foundational building block for physical world models; thus, we name it WorldString. Sweetly, its fully differentiable structure seamlessly enables future integration with policy learning and neural dynamics.", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.16819", "title": "AgentKernelArena: Generalization-Aware Benchmarking of GPU Kernel Optimization Agents", "authors": [ "Sharareh Younesian", "Wenwen Ouyang", "Sina Rafati", "Mehdi Rezagholizadeh", "Sharon Zhou", "Ji Liu", "Yue Liu", "Yuchen Yang", "Hao Li", "Ziqiong Liu", "Dong Li", "Vikram Appia", "Zhenyu Gu", "Emad Barsoum" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AMD-AGI/AgentKernelArena", "github_api_added_by": "user", "project_page": "", "abstract": "GPU kernel optimization is increasingly critical for efficient deep learning systems, but writing high-performance kernels still requires substantial low-level expertise. Recent AI coding agents can iteratively read code, invoke compilers and profilers, and refine implementations, yet existing kernel benchmarks evaluate single LLM calls rather than full agent workflows, and none include both kernel-to-kernel optimization and unseen-configuration generalization testing. We present AgentKernelArena, an open-source benchmark for measuring AI coding agents on GPU kernel optimization. The benchmark contains 196 tasks spanning HIP-to-HIP optimization, Triton-to-Triton optimization, and PyTorch-to-HIP translation, and evaluates complete agent workflows in isolated workspaces using gated compilation, correctness, and performance checks, centralized scoring and an unseen-configuration generalization protocol that tests whether optimizations transfer to input configurations the agent never observed. Across production agents including Cursor Agent, Claude Code, and Codex Agent, we find near-perfect compilation and high correctness rates on most task categories, with the strongest configurations achieving mean speedups of up to 6.89x on PyTorch-to-HIP, 6.69x on HIP-to-HIP, and 2.13x on Triton-to-Triton tasks. Our unseen-configuration evaluation shows that HIP-to-HIP and Triton-to-Triton optimizations largely transfer to unseen input shapes, while PyTorch-to-HIP exhibits substantial correctness drops, indicating that agents generating kernels from scratch frequently hardcode shape-specific assumptions. AgentKernelArena is designed as a modular, extensible framework for rigorous evaluation of agentic GPU kernel optimization across agents, tasks, and hardware targets.", "github": "https://github.com/AMD-AGI/AgentKernelArena" }, { "date": "2026-05-19", "arxiv_id": "2605.18451", "title": "Code-as-Room: Generating 3D Rooms from Top-Down View Images via Agentic Code Synthesis", "authors": [ "Yixuan Yang", "Zhen Luo", "Wanshui Gan", "Jinkun Hao", "Junru Lu", "Jinghao Yan", "Zhaoyang Lyu", "Xudong Xu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YxuanAr/Code-as-Room", "github_api_added_by": "user", "project_page": "https://code-as-room.github.io/", "abstract": "Designing realistic and functional 3D indoor rooms is essential for a wide range of applications, including interior design, virtual reality, gaming, and embodied AI. While recent MLLM-based approaches have shown great potential for 3D room synthesis from textual descriptions or reference images, text-based methods struggle to capture precise spatial information, and existing image-conditioned agents suffer from instability and infinite looping when tasked with holistic room generation from top-down views. To address these limitations, we propose Code-as-Room, an MLLM-based agentic framework equipped with a structured execution harness, which represents 3D rooms with Blender codes. Given a top-down room image, the framework parses the reference image to extract scene elements and their spatial relationships, and synthesizes executable Blender code for geometry, materials, and lighting in a principled, multi-stage pipeline. A cross-stage memory module is maintained throughout to mitigate context forgetting inherent to existing agent-based frameworks. We further introduce a dedicated benchmark for code-based 3D room synthesis, encompassing various evaluation protocols. Based on our benchmark, comprehensive comparisons against existing agent-based methods are conducted to validate the effectiveness of our proposed execution harness.", "github": "https://github.com/YxuanAr/Code-as-Room" }, { "date": "2026-05-19", "arxiv_id": "2605.18287", "title": "StableVLA: Towards Robust Vision-Language-Action Models without Extra Data", "authors": [ "Yiyang Fu", "Chubin Zhang", "Shukai Gong", "Yufan Deng", "Kaiwei Sun", "Qiyang Min", "Qibin Hou", "Yansong Tang", "Jianan Wang", "Daquan Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "It is infeasible to encompass all possible disturbances within the training dataset. This raises a critical question regarding the robustness of Vision-Language-Action (VLA) models when encountering unseen real-world visual disturbances, particularly under imperfect visual conditions. In this work, we conduct a systematic study based on recent state-of-the-art VLA models and reveal a significant performance drop when visual disturbances absent from the training data are introduced. To mitigate this issue, we propose a lightweight adapter module grounded in information theory, termed the Information Bottleneck Adapter (IB-Adapter), which selectively filters potential noise from visual inputs. Without requiring any extra data or augmentation strategies, IB-Adapter consistently improves over the baseline by an average of 30%, while adding fewer than 10M parameters, demonstrating notable efficiency and effectiveness. Furthermore, even with a 14x smaller backbone (0.5B parameters) and no pre-training on the Open X-Embodiment dataset, our model StableVLA achieves robustness competitive with 7B-scale state-of-the-art VLAs. With negligible parameter overhead (<10M), our approach maintains accuracy on long-horizon tasks and surpasses OpenPi under both synthetic and physical visual corruptions.", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.17242", "title": "From Runnable to Shippable: Multi-Agent Test-Driven Development for Generating Full-Stack Web Applications from Requirements", "authors": [ "Yuxuan Wan", "Tingshuo Liang", "Jiakai Xu", "Jingyu Xiao", "Yintong Huo", "Michael R Lyu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yxwan123/TDDev", "github_api_added_by": "user", "project_page": "https://github.com/yxwan123/TDDev", "abstract": "Coding agents can generate web applications from natural-language descriptions, yet a recent benchmark study shows that generated applications fail to meet functional requirements in over 70% of cases. The core difficulty is that web correctness cannot be assessed from source files or terminal output: the application must be deployed, exercised through simulated browser interactions, and failures must be translated into actionable repair signals -- steps that current agents cannot perform without human mediation. We present TDDev, a framework that automates this closed loop through three stages: (1) converting high-level requirements into structured acceptance tests before any code is written, (2) deploying the application and validating it through browser-based interaction simulation, and (3) translating browser-observed failures into structured repair reports for the coding agent. Enabled by TDDev, we conduct the first controlled empirical study of Test-driven development (TDD) strategies for web application generation, comparing four development protocols across two coding agents, two backbone models, and two benchmarks. TDD infrastructure consistently improves generation quality by 34--48 percentage points over a no-TDD baseline. The central finding is that the optimal protocol depends on the model's generation style: models that build applications holistically benefit most from agentic enforcement, while models that extend code conservatively benefit from incremental enforcement. Mismatching protocol to generation style eliminates the TDD benefit entirely while multiplying token cost up to 25-fold. A user study confirms that TDDev reduces manual developer intervention to zero, shifting the workload from continuous prompt engineering to autonomous, feedback-driven refinement.", "github": "https://github.com/yxwan123/TDDev" }, { "date": "2026-05-19", "arxiv_id": "2605.18396", "title": "NEWTON: Agentic Planning for Physically Grounded Video Generation", "authors": [ "Yuxiang Feng", "Juncheng Wang", "Chao Xu", "Yijie Qian", "Huihan Wang", "Wenlong Hou", "Yang Liu", "Baigui Sun", "Yong Liu", "Shujun Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://newton026.github.io/newton/", "abstract": "Video generation models produce visually compelling results but systematically violate physical commonsense -- on VideoPhy-2, the best model achieves only 32.6% joint accuracy. We identify a specification bottleneck: text prompts are lossy compression of the physical world, omitting the parameters that fully determine dynamics, and no amount of model scaling can recover what was never specified. From this diagnosis we derive three properties that physics conditioning must satisfy -- sufficiency, dynamism, and verifiability -- and show that no existing approach satisfies all three. We present NEWTON, in which video generation is demoted from the system output to one action inside an agent's toolbox: a learned planner orchestrates physics-aware tools (keyframe generation, scientific computation, prompt refinement) to construct rich conditioning, and a verifier closes the loop for iterative re-planning. The planner is the sole trainable component, optimized on-policy via Flow-GRPO inside the live multi-turn loop. On VideoPhy-2, NEWTON improves joint accuracy from 21.4% to 29.7% on LTX-Video and from 30.7% to 37.4% on Veo-3.1, without modifying either generator. Our project page: https://Newton026.github.io/newton{https://Newton026.github.io/newton}", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.18401", "title": "SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution", "authors": [ "Hongyi Liu", "Haoyan Yang", "Tao Jiang", "Bo Tang", "Feiyu Xiong", "Zhiyu Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MemTensor/skills-vote", "github_api_added_by": "user", "project_page": "https://skills.vote", "abstract": "Long-horizon LLM agents leave traces that could become reusable experience, but raw trajectories are noisy and hard to govern. We treat Agent Skills as an experience schema that couples executable scripts, with non-executable guidance on procedures. Yet open skill ecosystems contain redundant, uneven, environment-sensitive artifacts, and indiscriminate updates can pollute future context. We present SkillsVote, a lifecycle-governance framework for Agent Skills from collection and recommendation to evolution. SkillsVote profiles a million-scale open-source corpus for environment requirements, quality, and verifiability, then synthesizes tasks for verifiable skills. Before execution, SkillsVote performs agentic library search over structured skill library to expose instructional skill context. After execution, it decomposes trajectories into skill-linked subtasks, attributes outcomes to skill use, agent exploration, environment, and result signals, and admits only successful reusable discoveries to evidence-gated updates. In our evaluation, offline evolution improves GPT-5.2 on Terminal-Bench 2.0 by up to 7.9 pp, while online evolution improves SWE-Bench Pro by up to 2.6 pp. Overall, governed external skill libraries can improve frozen agents without model updates when systems control exposure, credit, and preservation.", "github": "https://github.com/MemTensor/skills-vote" }, { "date": "2026-05-19", "arxiv_id": "2605.16865", "title": "MixSD: Mixed Contextual Self-Distillation for Knowledge Injection", "authors": [ "Jiarui Liu", "Lechen Zhang", "Yongjin Yang", "Yinghui He", "Yingheng Wang", "Weihao Xuan", "Zhijing Jin", "Mona Diab" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Supervised fine-tuning (SFT) is widely used to inject new knowledge into language models, but it often degrades pretrained capabilities such as reasoning and general-domain performance. We argue this forgetting arises because fine-tuning targets from humans or external systems diverge from the model's autoregressive distribution, forcing the optimizer to imitate low-probability token sequences. To address this problem, we propose MixSD, a simple external-teacher-free method for distribution-aligned knowledge injection. Instead of training on fixed targets, MixSD constructs supervision dynamically by mixing tokens from two conditionals of the base model itself: an expert conditional that observes the injected fact in context, and a naive conditional that reflects the model's original prior. The resulting supervision sequences preserve the factual learning signal while remaining substantially closer to the base model's distribution. We evaluate MixSD on two synthetic corpora that we construct to study factual recall and arithmetic function acquisition in a controlled setting, together with established benchmarks for open-domain factual question answering and knowledge editing. Across multiple model scales and settings, MixSD consistently achieves a better memorization-retention trade-off compared to SFT and on-policy self distillation baselines, retaining up to 100% of the base model's held-out capability while maintaining near-perfect training accuracy, whereas standard SFT retains as little as 1%. We further show that MixSD produces substantially lower-NLL supervision targets under the base model and reduces harmful movement along Fisher-sensitive parameter directions. These results suggest that aligning supervision with the model's native generation distribution is a simple and effective principle for knowledge injection that mitigates catastrophic forgetting.", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.18549", "title": "Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics", "authors": [ "Maciej Chrab\u0105szcz", "Aleksander Szymczyk", "Marcin Sendera", "Tomasz Trzci\u0144ski", "Sebastian Cygert" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large Reasoning Models (LRMs) introduce new opportunities for safety monitoring through their Chain of Thought (CoT) reasoning. However, CoT is not always faithful to the model's final output, undermining its reliability as a monitoring tool. To address this, we investigate the hidden representations of LRMs to determine whether future behavior can be predicted from prompt and CoT representations. By evaluating a probe at each generated token, we construct a probe trajectory, the continuous evolution of a concept's probability across the reasoning process. We find that future model behavior is more distinguishable when examined over the full trajectory than from a single static prediction. To characterize these temporal dynamics, we extract signal-processing features that capture volatility, trend, and steady-state behavior, significantly improving the separation of future model states. We also present two methodological insights. First, template-based training data achieves near-parity with dynamically generated model responses, eliminating the need for a costly initial inference and labeling. Second, the choice of pooling operation is critical: average-pooling and last-token methods collapse to near-random performance, while max-pooling achieves up to 95% AUROC and yields stable probe trajectories. Using four datasets and four reasoning models across the domains of safety and mathematics, we demonstrate that trajectory features encode task-specific dynamics that improve outcome separability. These findings establish probe trajectories as a complementary framework for monitoring LRM behavior. Warning: This article contains potentially harmful content.", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.17278", "title": "A2RBench: An Automatic Paradigm for Formally Verifiable Abstract Reasoning Benchmark Generation", "authors": [ "Qingchuan Ma", "Yuexiao Ma", "Yongkang Xie", "Tianyu Xie", "Xiawu Zheng", "Rongrong Ji" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MAC-AutoML/A2Rbench", "github_api_added_by": "user", "project_page": "", "abstract": "Abstract reasoning ability reflects the intelligence and generalization capacity of LLMs to extract and apply abstract rules. However, accurately measuring this ability remains challenging: existing benchmarks either rely on expensive manual annotation, limiting their scale, or risk measuring memorization rather than genuine reasoning. To address this, we introduce an automated pipeline named A2RBench, encompassing generation, expansion, evaluation, and analysis. Specifically, in the generation stage, LLMs create diverse tasks demanding genuine reasoning; in the expansion stage, LLMs reuse validated rules and expand new input spaces to generate task variations, achieving scaling. However, such a process may cause hallucinations. To eliminate it, we further establish a theoretical framework and prove that programmatic verification--testing whether the inverse operation perfectly reverses the forward operation (cycle consistency)--guarantees a unique solution. Through extensive evaluations on mainstream LLMs, we find: (1) Current LLMs exhibit fundamental deficiencies in abstract reasoning, with top models significantly underperforming humans on a representative subset (39.8% vs. 68.5%). (2) Current LLMs fall far short of 2D and 1D in the complexity of generated 3D tasks, revealing their lack of understanding of high-dimensional tasks. (3) Counterintuitively, inputs with higher information complexity can simplify the reasoning process.", "github": "https://github.com/MAC-AutoML/A2Rbench" }, { "date": "2026-05-19", "arxiv_id": "2605.15764", "title": "GRASP: Learning to Ground Social Reasoning in Multi-Person Non-Verbal Interactions", "authors": [ "Junho Kim", "Xu Cao", "Houze Yang", "Bikram Boote", "Ana Jojic", "Fiona Ryan", "Bolin Lai", "Sangmin Lee", "James M. Rehg" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Understanding social interactions requires reasoning over subtle non-verbal cues, yet current multimodal large language models (MLLMs) often fail to identify who interacts with whom in multi-person videos. We introduce GRASP, a large-scale social reasoning dataset that connects high-level social QA with fine-grained gaze and deictic gesture events. GRASP contains 290K question--answer pairs over 46K videos totaling 749 hours, organized by a 16-category taxonomy spanning gaze, gesture, and joint gaze--gesture reasoning, together with GRASP-Bench for evaluation. Unlike prior resources that focus on either isolated cues or high-level social QA, GRASP builds questions from identity-consistent gaze trajectories, deictic gestures, and their joint compositions into social events. Moreover, we propose Social Grounding Reward (SGR), a learning signal that uses these social events to encourage models to reason about the participants involved in each interaction. Experiments show that SGR improves performance on GRASP-Bench while maintaining zero-shot performance on related social video QA benchmarks.", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.15482", "title": "FINESSE-Bench: A Hierarchical Benchmark Suite for Financial Domain Knowledge and Technical Analysis in Large Language Models", "authors": [ "Dmitry Stanishevskii", "Nini Kamkia", "Alexey Khoroshilov", "Dmitry Zmitrovich", "Denis Kokosinskii", "Zhirayr Hayrapetyan", "Andrei Kalmykov" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LimexAILab/FINESSE-Bench", "github_api_added_by": "user", "project_page": "https://limexailab.github.io/QuantCode-Bench/finesse-bench/", "abstract": "Large language models (LLMs) are increasingly being applied to financial analysis, reporting, investment decision support, risk management, compliance, and professional training. However, robust evaluation of their domain competence in finance remains incomplete. Widely used open benchmarks such as FinQA, ConvFinQA, and TAT-QA have played an important role in advancing financial question answering and numerical reasoning, but they focus primarily on question answering over financial reports and do not provide an explicit hierarchy of professional difficulty. Broader resources, including FinanceBench, PIXIU, FinBen, and FLaME, expand the coverage of financial tasks, yet the problem of evaluating the transition from foundational knowledge to expert-level financial reasoning remains open. In this work, we present FINESSE-Bench, a suite of eight specialized benchmarks comprising 3,993 questions for hierarchical evaluation of financial competencies in LLMs. FINESSE-Bench combines exam-oriented datasets inspired by professional certifications (CFA-like Levels 1-3, CMT-like Level 2, and CFTe-like Level 1), applied trading task collections, and a Russian-language olympiad benchmark. This design enables evaluation of domain breadth, performance degradation as difficulty increases, the ability to solve computational tasks, and model behavior in specialized financial domains. We also describe a unified evaluation protocol covering multiple-choice questions, numerical answers, and short open-ended responses, together with an automated scoring scheme for freeform answers based on the LLM-as-judge paradigm. FINESSE-Bench is intended both as a complement to existing open financial benchmarks and as a tool for more substantive evaluation of professionally relevant financial competencies in large language models.", "github": "https://github.com/LimexAILab/FINESSE-Bench" }, { "date": "2026-05-19", "arxiv_id": "2605.16893", "title": "NGM: A Plug-and-Play Training-Free Memory Module for LLMs", "authors": [ "Yuwen Qu", "Wenhui Dong", "Chenyang Si", "Caifeng Shan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PioneerQyw/NGM", "github_api_added_by": "user", "project_page": "", "abstract": "Recent studies introduce conditional memory modules that decouple knowledge storage from neural computation, enabling more direct knowledge access. Compared to MoE, which relies on dynamic computation paths, explicit lookup provides a more efficient knowledge retrieval mechanism. However, these approaches still depend on learned memory embeddings, requiring additional training and limiting flexibility. To address this, we propose N-gram Memory (NGM), a training-free, plug-and-play module composed of a Causal N-Gram Encoder and a Cosine-Gated Memory Injector. The Causal N-Gram Encoder directly averages the pretrained token embeddings of the backbone model to construct N-gram representations, thereby eliminating the need to train separate N-gram embeddings from scratch. This design requires neither an additional memory table nor a retrieval pipeline. The Cosine-Gated Memory Injector then uses a non-parametric cosine gate with ReLU to modulate the retrieved embeddings into the contextual representations. We evaluate NGM on the Qwen3 series from 0.6B to 14B across eight benchmarks. NGM improves average performance by 0.5 to 1.2 points, with particularly clear gains on code generation and knowledge-intensive tasks (e.g., +3.0 on LiveCodeBench and +3.03 on GPQA for Qwen3-14B). Moreover, NGM also improves performance in multimodal benchmarks (e.g., MMStar +1.53 on Qwen3-VL-2B).", "github": "https://github.com/PioneerQyw/NGM" }, { "date": "2026-05-19", "arxiv_id": "2605.18719", "title": "SafeDiffusion-R1: Online Reward Steering for Safe Diffusion Post-Training", "authors": [ "Komal Kumar", "Ankan Deria", "Abhishek Basu", "Fahad Shamshad", "Hisham Cholakkal", "Karthik Nandakumar" ], "github_manual": "", "github_regex": "https://github.com/MAXNORM8650/SafeDiffusion-R1", "github_paper_md": "", "github_api": "https://github.com/MAXNORM8650/SafeDiffusion-R1", "github_api_added_by": "user", "project_page": "https://maxnorm8650.github.io/SafeDiffusion-R1/", "abstract": "Diffusion models have been widely studied for removing unsafe content learned during pre-training. Existing methods require expensive supervised data, either unsafe-text paired with safe-image groundtruth or negative/positive image pairs, making them impractical to scale. Furthermore, offline reinforcement learning and supervised fine-tuning approaches that generate synthetic data offline suffer from catastrophic forgetting, degrading generation quality. We propose a novel online reinforcement learning framework that addresses both data scarcity and model degradation through post-training with Group Relative Policy Optimization (GRPO) on both negative and positive text prompts. To eliminate the need for fine-tuning specialized safe/unsafe reward models, we introduce a steering reward mechanism that exploits an inherent property of CLIP embeddings: steering text representations toward positive safety directions and away from negative ones in the embedding space. Our online-policy approach enables the model to learn from diverse prompts, including explicit unsafe content, without catastrophic forgetting. Extensive experiments demonstrate that our method reduces inappropriate content to 18.07\\% (vs. 48.9\\% for SD v1.4) and nudity detections to 15 (vs. 646 baseline) while improving compositional generation quality from 42.08\\% to 47.83\\% on GenEval. Remarkably, these safety gains generalize to out-of-domain unsafe prompts across seven harm categories, achieving state-of-the-art performance without supervised paired data or reward tuning. Github: https://github.com/MAXNORM8650/SafeDiffusion-R1.", "github": "https://github.com/MAXNORM8650/SafeDiffusion-R1" }, { "date": "2026-05-19", "arxiv_id": "2605.16909", "title": "TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents", "authors": [ "Zhiqiang Liu", "Wenhui Dong", "Yilang Tan", "Yuwen Qu", "Haochen Yin", "Chenyang Si" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Pi3AI/TOBench", "github_api_added_by": "user", "project_page": "https://pi3ai.github.io/tobench-web/", "abstract": "Tool-using agents are increasingly expected to operate across realistic professional workflows, where they must interpret multimodal inputs, coordinate external tools, inspect intermediate artifacts, and revise their actions before producing a final result. Existing benchmarks, however, often evaluate tool use, computer use, and multimodal reasoning in isolation, leaving a gap between benchmark settings and end-to-end omni-modal tool use in the real world. To address this gap, we introduce MM-ToolBench, a benchmark and evaluation harness for task-oriented omni-modal tool use. MM-ToolBench contains 100 executable tasks from two macro task families, Customer Service and Intelligent Creation, covering 20 subcategory slices and supported by 27 MCP servers with 324 tools. The central design of MM-ToolBench is closed-loop multimodal verification: agents must execute tools, inspect rendered or transformed artifacts, and self-correct when outputs fail task-specific requirements. To make such evaluation scalable and verifiable, MM-ToolBench couples MCP-based execution with task-specific grounded evaluators and a semi-automated construction pipeline for scenario discovery, task instantiation, evaluator synthesis, and human audit. Experiments on 15 contemporary agentic models show that MM-ToolBench remains highly challenging: Claude Opus 4.6, commonly regarded as one of the strongest coding-agent models, achieves only 32.0% task success, far below the 94.0% human benchmark. We envision MM-ToolBench as a practical foundation for evaluating and advancing next-generation omni-modal tool-using agents through closed-loop multimodal verification.", "github": "https://github.com/Pi3AI/TOBench" }, { "date": "2026-05-19", "arxiv_id": "2605.16882", "title": "E-PMQ: Expert-Guided Post-Merge Quantization with Merged-Weight Anchoring", "authors": [ "Wenjun Wang", "Yanggan Gu", "Shuo Cai", "Yuanyi Wang", "Pengkai Wang", "Jianmin Wu", "Hongxia Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wwjzhy/E-PMQ", "github_api_added_by": "user", "project_page": "", "abstract": "Low-resource deployment constraints have made model quantization essential for deploying neural networks while preserving performance. Meanwhile, model merging has become an increasingly practical low-resource strategy for integrating multiple task- or domain-specialized experts into a single model without joint training or multi-model serving. Together, quantization and model merging enable an efficient low-resource deployment pipeline by integrating multiple experts into one low-bit model. We formulate this setting as Post-Merge Quantization (PMQ). We show that directly applying post-training quantization (PTQ) to a merged model is unreliable because two distinct deviations are coupled: the quantization deviation introduced by low-bit reconstruction and the expert-relative merging deviation inherited from model merging. To mitigate these deviations, we propose E-PMQ, an expert-guided PMQ framework that uses source expert weights to provide expert- guided output targets during layer-wise calibration, together with merged-weight anchoring to stabilize the calibration and preserve the integrated behavior of the merged model. On CLIP-ViT-B/32 eight-task merging, E-PMQ improves 4-bit GPTQ from 65.0% to 73.6% under Task Arithmetic and from 69.1% to 74.8% under TIES-Merging. On harder settings, E-PMQ improves GPTQ from 34.8% to 76.7% on 20-task CLIP-ViT-L/14 and from 78.26% to 83.34% on FLAN-T5- base GLUE. These results demonstrate that E-PMQ enables effective post-merge quantization and low-bit deployment.", "github": "https://github.com/wwjzhy/E-PMQ" }, { "date": "2026-05-19", "arxiv_id": "2605.16079", "title": "VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation", "authors": [ "Yiming Zhao", "Yu Zeng", "Wenxuan Huang", "Zhen Fang", "Qing Miao", "Qisheng Su", "Jiawei Zhao", "Jiayin Cai", "Lin Chen", "Zehui Chen", "Yukun Qi", "Yao Hu", "Xiaolong Jiang", "Feng Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gaotiexinqu/VideoSeeker", "github_api_added_by": "user", "project_page": "https://gaotiexinqu.github.io/VideoSeeker/", "abstract": "Large Vision-Language Models (LVLMs) have shown significant progress in video understanding, yet they face substantial challenges in tasks requiring precise spatiotemporal localization at the instance level. Existing methods primarily rely on text prompts for human-model interaction, but these prompts struggle to provide precise spatial and temporal references, resulting in poor user experience. Furthermore, current approaches typically decouple visual perception from language reasoning, centering reasoning around language rather than visual content, which limits the model's ability to proactively perceive fine-grained visual evidence. To address these challenges, we propose VideoSeeker, a novel paradigm for instance-level video understanding through visual prompts. VideoSeeker seamlessly integrates agentic reasoning with instance-level video understanding tasks, enabling the model to proactively perceive and retrieve relevant video segments on demand. We construct a four-stage fully automated data synthesis pipeline to efficiently generate large-scale, high-quality instance-level video data. We internalize tool-calling and proactive perception capabilities into the model via cold-start supervision and RL training, building a powerful video understanding model. Experiments demonstrate that our model achieves an average improvement of +13.7% over baselines on instance-level video understanding tasks, surpassing powerful closed-source models such as GPT-4o and Gemini-2.5-Pro, while also showing effective transferability on general video understanding benchmarks. The relevant datasets and code will be released publicly.", "github": "https://github.com/gaotiexinqu/VideoSeeker" }, { "date": "2026-05-19", "arxiv_id": "2605.16679", "title": "CHI-Bench: Can AI Agents Automate End-to-End, Long-Horizon, Policy-Rich Healthcare Workflows?", "authors": [ "Haolin Chen", "Deon Metelski", "Leon Qi", "Tao Xia", "Joonyul Lee", "Steve Brown", "Kevin Riley", "Frank Wang", "T. Y. Alvin Liu", "Hank Capps MD", "Zeyu Tang", "Xiangchen Song", "Lingjing Kong", "Fan Feng", "Tianyi Zeng", "Zhiwei Liu", "Zixian Ma", "Hang Jiang", "Fangli Geng", "Yuan Yuan", "Chenyu You", "Qingsong Wen", "Hua Wei", "Yanjie Fu", "Yue Zhao", "Carl Yang", "Biwei Huang", "Kun Zhang", "Caiming Xiong", "Sanmi Koyejo", "Eric P. Xing", "Philip S. Yu", "Weiran Yao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/actava-ai/chi-bench", "github_api_added_by": "user", "project_page": "https://actava.ai/benchmarks", "abstract": "End-to-end automation of realistic healthcare operations stresses three capabilities underrepresented in current benchmarks: policy density, decisions must be grounded in a large library of medical, insurance, and operational rules; Multi-role composition: a single task requires the agent to play multiple roles with handoffs; and multilateral interaction: intermediate workflow steps are multi-turn dialogs, such as peer-to-peer review and patient outreach. We introduce \u03c7-Bench, a benchmark of long-horizon healthcare workflows across three domains: provider prior authorization, payer utilization management, and care management. Each task hands the agent a clinical case in a high-fidelity simulator of 20 healthcare apps exposed via 87 MCP tools, which it must drive to a terminal status through tool calls and writing the role's artifacts, guided by a 1,290+ document managed-care operations handbook skill. Across 30 agent harness/models configurations, the best agent resolves only 28.0% of tasks, no agent clears 20% on strict pass^3, and executing all tasks in a single session slumps the performance to 3.8%. These results raise the hypothesis that similar gaps are likely to surface in other policy-dense, role-composed, irreversible enterprise domains.", "github": "https://github.com/actava-ai/chi-bench" }, { "date": "2026-05-19", "arxiv_id": "2605.18652", "title": "MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents", "authors": [ "Ziyun Zeng", "Hang Hua", "Bocheng Zou", "Mu Cai", "Rogerio Feris", "Jiebo Luo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recent GUI agents have made substantial progress in visual grounding and action prediction, yet they remain brittle in long-horizon tasks that require maintaining task state across many interface transitions. Existing agents typically rely on raw history replay or text-only memory, which either overwhelms the model with redundant screenshots or discards localized visual evidence needed for future decisions. To address these limitations, we introduce MementoGUI, a plug-in agentic memory framework that equips MLLM-based GUI agents with MementoCore, a learned controller for online memory selection, compression, and retrieval. Rather than treating interaction history as a fixed context, MementoGUI formulates long-horizon GUI control as an online memory-control problem: working memory selectively preserves task-relevant interface events with textual summaries and ROI-level visual evidence, while episodic memory retrieves reusable past trajectories through learned relevance selection. MementoCore modularizes memory control into specialized operators for step processing, memory compression, episodic writing, and episodic selection, enabling plug-in memory augmentation without finetuning the GUI agent backbone. We further develop a scalable data curation pipeline that converts computer-use trajectories into memory-controller training data, introduce MementoGUI-Bench for evaluating long-horizon decision-making in GUI agents, and design MLLM-based metrics for semantic action matching, task progress, and memory consistency. Experiments on GUI-Odyssey, MM-Mind2Web, and MementoGUI-Bench show that MementoGUI consistently improves GUI agents over no-history, history-replay, and text-only memory baselines, with larger MementoCore backbones further strengthening memory-augmented GUI control.", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.17894", "title": "Evaluating Cognitive Age Alignment in Interactive AI Agents", "authors": [ "Yifan Shen", "Jiawen Zhang", "Jian Xu", "Junho Kim", "Ismini Lourentzou", "Xu Cao", "Meihuan Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PediaMedAI/ChildAgentEval", "github_api_added_by": "user", "project_page": "https://github.com/PediaMedAI/ChildAgentEval", "abstract": "While agentic AI and its core multimodal large language models (MLLMs) have demonstrated remarkable promise in language and visual reasoning across domains ranging from daily life to advanced scientific research, a profound gap remains between artificial and human intelligence. Despite the integration of powerful tools and advanced MLLMs, state-of-the-art AI agents frequently fail at foundational, seemingly simple tasks that a child can resolve with ease. Inspired by the Wechsler Intelligence Scale for Children (WISC), we introduce ChildAgentEval, the first psychometrically grounded interactive benchmark for evaluating cognitive age alignment in MLLM-based agents. ChildAgentEval systematically compares the reasoning performance of various MLLM-based interactive agents against age-specific human developmental stages, exposing where current agentic AI systems can and cannot simulate age-specific cognitive behavior.", "github": "https://github.com/PediaMedAI/ChildAgentEval" }, { "date": "2026-05-19", "arxiv_id": "2605.15565", "title": "AstraFlow: Dataflow-Oriented Reinforcement Learning for Agentic LLMs", "authors": [ "Haizhong Zheng", "Yizhuo Di", "Jiahui Wang", "Shuowei Jin", "Xueshen Liu", "Yongji Wu", "Z. Morley Mao", "Ion Stoica", "Jiawei Zhao", "Beidi Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Infini-AI-Lab/astraflow", "github_api_added_by": "user", "project_page": "https://infini-ai-lab.github.io/astraflow/", "abstract": "Reinforcement learning (RL) is increasingly used to improve the reasoning, coding, and tool-use capabilities of large language models, but agentic RL remains prohibitively expensive. Scaling RL to agentic LLMs requires supporting complex workloads, including multi-policy collaborative training, while efficiently using elastic, heterogeneous, and cross-region compute resources. Existing LLM RL systems support some of these capabilities, but each new extension often requires dedicated system engineering. This burden arises from trainer-centered control architectures and the lack of principled abstractions for RL system components. To address these limitations, we propose AstraFlow, a dataflow-oriented RL system that replaces conventional trainer-centered control with principled component abstractions. In AstraFlow, rollout services, dataflow management, and training are decoupled into autonomous components, enabling the system to natively support complex multi-policy agentic RL workloads and efficiently exploit diverse compute resources. We evaluate AstraFlow across math, code, search, and AgentBench workloads, showing that the same system supports multi-policy training, elastic scaling, heterogeneous cross-region execution, and composable data algorithms without system-level code changes. In multi-policy collaborative training, AstraFlow achieves comparable or better accuracy than existing RL systems while speeding up training time by 2.7x.", "github": "https://github.com/Infini-AI-Lab/astraflow" }, { "date": "2026-05-19", "arxiv_id": "2605.16386", "title": "Auditing Multimodal LLM Raters: Central Tendency Bias in Clinical Ordinal Scoring", "authors": [ "Jiaqing Zhang", "Sandeep Elluri", "Bhanu Cherukuvada", "Yonah Joffe", "Jessica Sena", "Miguel Contreras", "Scott Siegel", "Subhash Nerella", "Catherine Price", "Parisa Rashidi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multimodal large language models (LLMs) are increasingly explored as automated evaluators in clinical settings, yet their scoring behavior on ordinal clinical scales remains poorly understood. We benchmark three frontier LLM families against supervised deep learning models for scoring Clock Drawing Test (CDT) images on two public datasets using the Shulman rubric. While fully fine-tuned Vision Transformers achieve the best calibration (MAE 0.52, within-1 accuracy 91%), zero-shot LLMs remain competitive on tolerance-based agreement (GPT-5 MAE 0.67, within-1 accuracy 92%) despite higher absolute error. However, per-score analysis reveals that all three LLM families exhibit a pronounced central tendency effect (systematic endpoint compression): predictions are systematically compressed toward the middle of the scale, with over-prediction at the low end (score 0 to 1) and under-prediction at the high end (score 5 to 4). This effect disproportionately affects the clinically critical extremes where accurate scoring most impacts screening decisions for cognitive impairment. Targeted ablations show that neither few-shot exemplars spanning the full score range nor removing clinical terminology from the prompt eliminates the effect. Our findings extend the LLM-as-a-judge bias literature from NLP evaluation to clinical assessment, and highlight the need for calibration-aware evaluation and post-hoc calibration before deploying LLM-based raters in high-stakes screening workflows.", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.15035", "title": "TopoPrimer: The Missing Topological Context in Forecasting Models", "authors": [ "Zara Zetlin", "Kayhan Moharreri", "Maria Safi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We introduce TopoPrimer, a framework that makes the global topological structure of the series population an explicit input to any forecasting model. TopoPrimer improves accuracy across diverse domains, stabilizes forecasts under seasonal demand spikes, and closes the cold-start gap. Precomputed once per domain via persistent homology and spectral sheaf coordinates, TopoPrimer deploys per token for fully-trained models and as a lightweight adapter for pre-trained backbones. Of these two components, sheaf coordinates are the primary accuracy driver. Across four public benchmarks on Chronos and TimesFM, TopoPrimer consistently improves forecasting accuracy, with gains of up to 7.3% MSE on ECL. The topology advantage persists with near-identical magnitude across zero-shot and fine-tuned backbones, suggesting topology and per-series training capture complementary signals. The gains are most pronounced in difficult regimes. Under peak seasonal demand, classical and zero-shot models degrade by up to 50%, while TopoPrimer stays within 10%. At cold start with no item history, TopoPrimer reduces MAE by 27% over a topology-free baseline.", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2603.10126", "title": "AR-VLA: True Autoregressive Action Expert for Vision-Language-Action Models", "authors": [ "Yutong Hu", "Jan-Nico Zaech", "Nikolay Nikolov", "Yuanqi Yao", "Sombit Dey", "Giuliano Albanese", "Renaud Detry", "Luc Van Gool", "Danda Paudel" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We propose a standalone autoregressive (AR) Action Expert that generates actions as a continuous causal sequence while conditioning on refreshable vision-language prefixes. In contrast to existing Vision-Language-Action (VLA) models and diffusion policies that reset temporal context with each new observation and predict actions reactively, our Action Expert maintains its own history through a long-lived memory and is inherently context-aware. This structure addresses the frequency mismatch between fast control and slow reasoning, enabling efficient independent pretraining of kinematic syntax and modular integration with heavy perception backbones, naturally ensuring spatio-temporally consistent action generation across frames. To synchronize these asynchronous hybrid V-L-A modalities, we utilize a re-anchoring mechanism that mathematically accounts for perception staleness during both training and inference. Experiments on simulated and real-robot manipulation tasks demonstrate that the proposed method can effectively replace traditional chunk-based action heads for both specialist and generalist policies. AR-VLA exhibits superior history awareness and substantially smoother action trajectories while maintaining or exceeding the task success rates of state-of-the-art reactive VLAs. Overall, our work introduces a scalable, context-aware action generation schema that provides a robust structural foundation for training effective robotic policies. Code and Videos available at https://arvla.insait.ai", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.18727", "title": "DexHoldem: Playing Texas Hold'em with Dexterous Embodied System", "authors": [ "Feng Chen", "Tianzhe Chu", "Li Sun", "Pei Zhou", "Zhuxiu Xu", "Shenghua Gao", "Yuexiang Zhai", "Yanchao Yang", "Yi Ma" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://dexholdem.github.io/Dexholdem/", "abstract": "Evaluating embodied systems on real dexterous hardware requires more than isolated primitive skills: an agent must perceive a changing tabletop scene, choose a context-appropriate action, execute it with a dexterous hand, and leave the scene usable for later decisions. We introduce DexHoldem, a real-world system-level benchmark built around Texas Hold'em dexterous manipulation with a ShadowHand. DexHoldem provides 1,470 teleoperated demonstrations across 14 Texas Hold'em manipulation primitives, a standardized physical policy benchmark, and an agentic perception benchmark that tests whether agents can recover the structured game state needed for embodied decision making. On primitive execution, \u03c0_{0.5} obtains the highest task completion rate (61.2%), while \u03c0_{0.5} and \u03c0_0 tie on scene-preserving success rate (47.5%). On agentic perception, Opus 4.7 obtains the best strict problem-level accuracy (34.3%), while GPT 5.5 obtains the best average field-wise accuracy (66.8%), exposing a gap between isolated visual sub-capabilities and complete routing-relevant state recovery. Finally, we instantiate the full embodied-agent loop in three case studies, where waiting, recovery dispatches, human-help requests, and repeated primitive execution reveal how perception and policy errors accumulate during closed-loop deployment. DexHoldem therefore evaluates dexterous tabletop execution, agentic perception, and embodied decision routing in a shared physical setting. Project page: https://dexholdem.github.io/Dexholdem/.", "github": "" }, { "date": "2026-05-19", "arxiv_id": "2605.18749", "title": "WavFlow: Audio Generation in Waveform Space", "authors": [ "Feiyan Zhou", "Luyuan Wang", "Shoufa Chen", "Zhe Wang", "Zhiheng Liu", "Yuren Cong", "Xiaohui Zhang", "Fanny Yang", "Belinda Zeng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/WavFlow", "github_api_added_by": "user", "project_page": "https://facebookresearch.github.io/WavFlow/", "abstract": "Modern audio generation predominantly relies on latent-space compression, introducing additional complexity and potential information loss. In this work, we challenge this paradigm with WavFlow, a framework that generates high-fidelity audio directly in raw waveform space without intermediate representations. To overcome the inherent difficulties of modeling high-dimensional and low-energy signals, we reshape audio into 2D token grids through waveform patchify and introduce amplitude lifting to align signal scales, enabling stable optimization via direct x-prediction in flow matching. To capture complex semantic alignment and temporal synchronization, we leverage an automated data pipeline to curate 5 million high-quality video-text-audio triplets, allowing the model to learn fine-grained acoustic patterns from scratch. Experimental results show that WavFlow achieves competitive performance on the video-to-audio benchmark VGGSound (FD_PaSST: 59.98, IS_PANNs: 17.40, DeSync: 0.44) and the text-to-audio benchmark AudioCaps (FD_PANNs: 10.63, IS_PANNs: 12.62), matching or exceeding the performance of established latent-based methods. Our work demonstrates that intermediate compression is not a prerequisite for high-quality synthesis, offering a simpler and more scalable alternative for multimodal audio generation.", "github": "https://github.com/facebookresearch/WavFlow" }, { "date": "2026-05-19", "arxiv_id": "2605.18106", "title": "Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers", "authors": [ "Tim Tsz-Kit Lau", "Weijie Su" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/timlautk/equivariant_optimizers", "github_api_added_by": "user", "project_page": "", "abstract": "A striking geometric disparity has long persisted in the practice of deep learning. While modern neural network architectures naturally exhibit rich symmetry and equivariance properties, popular optimizers such as Adam and its variants operate inherently coordinate-wise, rendering them unable to respect the equivariance structures of the parameter space. We address this disparity by introducing a symmetry-compatible principle for optimizer design: the gradient update rule should be equivariant under the symmetry group acting on the corresponding weight block. Following this principle, we first provide a unified perspective on bi-orthogonally equivariant updates for general matrix layers, as employed by stochastic spectral descent, Muon, Scion, and polar gradient methods. More importantly, by moving from orthogonal groups to permutation and shared-shift symmetries, we derive symmetry-compatible optimizers for parameter blocks whose symmetries differ from those of general matrix layers: embedding and LM head matrices, SwiGLU MLP projections, and MoE router matrices. These constructions include one-sided spectral, row-norm, hybrid row-norm/spectral, row-aware, column-aware, centered row-norm, and left-spectral updates. They yield an end-to-end layerwise optimizer stack in which each major matrix-valued parameter class is assigned an update whose equivariance matches its symmetry group. We corroborate this principle through pre-training experiments on dense and sparse MoE language models, including Qwen3-0.6B-style, Gemma 3 1B-style, OLMoE-1B-7B-style, and downsized gpt-oss architectures. Across these experiments, symmetry-compatible updates consistently improve final validation loss, and in several cases training stability, over corresponding AdamW updates.", "github": "https://github.com/timlautk/equivariant_optimizers" }, { "date": "2026-05-19", "arxiv_id": "2605.17757", "title": "OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization", "authors": [ "Zhongzhu Zhou", "Donglin Zhuang", "Jisen Li", "Ziyan Chen", "Shuaiwen Leon Song", "Ben Athiwaratkun", "Xiaoxia Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FutureMLS-Lab/OSCAR", "github_api_added_by": "user", "project_page": "https://oscar-quantize.github.io/", "abstract": "INT2 KV-cache quantization is attractive for long-context LLM serving, but it remains difficult to make both accurate and deployable. Simple rotations such as Hadamard transforms reduce outliers, but still degrade at INT2 because they are not aligned with downstream attention. We propose OSCAR, an Ultra-low-bit KV Cache quantization method that estimates attention-aware covariance structures offline and uses them to derive fixed rotations and clipping thresholds for quantization. In this way, it aligns KV quantization with the covariance structures that attention actually consumes. More importantly, we not only provide theoretical justification but also develop a fully deployable OSCAR system with a custom INT2 attention kernel that remains compatible with paged KV-cache serving and fused kernel pipelines, enabling seamless integration into modern LLM serving frameworks such as SGLang and vLLM. We evaluate our methods on recent reasoning models with reasoning traces of up to 32k tokens across 5 tasks. On Qwen3-4B-Thinking-2507 and Qwen3-8B, OSCAR reduces the BF16 accuracy gap to 3.78 and 1.42 points, respectively, while naive rotation INT2 collapses to nearly zero. We further scale OSCAR to Qwen3-32B and GLM-4.7 (358B params), where it remains effectively on par with BF16. On long context - RULER-NIAH up to 128K, OSCAR remains robust on both Qwen3 models, while naive rotation INT2 collapses. System-wise, OSCAR reduces KV-cache memory by approximately 8x, improves throughput by up to 7x at large batch sizes under the same memory budget, and accelerates batch-size-1 decoding by up to 3x over BF16 due to reduced memory bandwidth overhead.", "github": "https://github.com/FutureMLS-Lab/OSCAR" }, { "date": "2026-05-19", "arxiv_id": "2605.18630", "title": "SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science", "authors": [ "Nithin Somasekharan", "Youssef Hassan", "Shiyao Lin", "Gihan Panapitiya", "Patrick Emami", "Anurag Acharya", "Sameera Horawalavithana", "Shaowu Pan" ], "github_manual": "", "github_regex": "https://github.com/csml-rpi/SciConvBench", "github_paper_md": "", "github_api": "https://github.com/csml-rpi/SciConvBench", "github_api_added_by": "user", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly deployed as scientific AI as- sistants, and a growing body of benchmarks evaluates their capabilities across knowledge retrieval, reasoning, code generation, and tool use. These evaluations, however, typically assume the scientific problem is already well-posed, whereas practical scientific assistance often begins with an ill-posed user request that must be refined through dialogue before any computation, analysis, or experiment can be carried out reliably. We introduce SCICONVBENCH, a benchmark for multi- turn clarification in scientific task formulation across four computational science problem domains: fluid mechanics, solid mechanics, materials science, and par- tial differential equations (PDEs). SCICONVBENCH targets two complementary capabilities: eliciting missing information (disambiguation) and detecting and correcting erroneous requests containing internally contradictory information (in- consistency resolution). Our benchmark pairs a structured task ontology with a rubric-based evaluation framework, enabling systematic measurement of LLM per- formance across three dimensions: clarification behavior, conversational grounding, and final-specification fidelity. Current frontier models perform relatively well on inconsistency resolution, but even the best model resolves only 52.7% of the disambiguation cases in fluid mechanics. We further find that frontier LLMs fre- quently make silent assumptions and perform implicit specification repairs that are not grounded in the conversation with users. SCICONVBENCH establishes a foundation for evaluating the upstream conversational reasoning that a reliable computational science assistant requires. The code and data can be found at https://github.com/csml-rpi/SciConvBench.", "github": "https://github.com/csml-rpi/SciConvBench" }, { "date": "2026-05-20", "arxiv_id": "2605.18984", "title": "Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos", "authors": [ "Yuqi Tang", "Yang Shi", "Zhuoran Zhang", "Qixun Wang", "Xuehai Bai", "Yue Ding", "Ruizhe Chen", "Bohan Zeng", "Xinlong Chen", "Xuanyu Zhu", "Bozhou Li", "Yuran Wang", "Yifan Dai", "Chengzhuo Tong", "Xinyu Liu", "Yiyan Ji", "Yujie Wei", "Yuhao Dong", "Shilin Yan", "Fengxiang Wang", "Yi-Fan Zhang", "Haotian Wang", "Yuanxing Zhang", "Pengfei Wan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FrankYang-17/Artifact-Bench", "github_api_added_by": "user", "project_page": "", "abstract": "Recent video generative models have greatly improved the realism of AI-generated videos, yet their outputs still exhibit artifacts such as temporal inconsistencies, structural distortions, and semantic incoherence. While Multimodal Large Language Models (MLLMs) show strong visual understanding capabilities, their ability to perceive and reason about such artifacts remains unclear. Existing benchmarks often lack systematic evaluation of artifact-aware perception and fine-grained diagnostic reasoning, especially across diverse AI-generated video domains beyond photorealistic content. To address this gap, we introduce Artifact-Bench, a comprehensive benchmark for evaluating MLLMs on AI-generated video artifact detection and analysis. We first establish a three-level hierarchical taxonomy of realism artifacts, covering photorealistic, animated, and CG-style videos. Based on this taxonomy, Artifact-Bench defines three complementary tasks: real vs. AI-generated video classification, pairwise realism comparison, and fine-grained artifact identification. Experiments on 19 leading MLLMs reveal substantial limitations in artifact perception and reasoning, with many models approaching random or even below-random performance in challenging settings. We further observe significant misalignment between MLLM judgments and human perceptual preferences, highlighting their limited reliability as general evaluators for AI-generated video realism.", "github": "https://github.com/FrankYang-17/Artifact-Bench" }, { "date": "2026-05-20", "arxiv_id": "2605.15529", "title": "Process Rewards with Learned Reliability", "authors": [ "Jinyuan Li", "Langlin Huang", "Chengsong Huang", "Shaoyang Xu", "Donghong Cai", "Yuyi Yang", "Wenxuan Zhang", "Jiaxin Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JinYuanLi0012/Beta-Binomial-PRM", "github_api_added_by": "user", "project_page": "", "abstract": "Process Reward Models (PRMs) provide step-level feedback for reasoning, but current PRMs usually output only a single reward score for each step. Downstream methods must therefore treat imperfect step-level reward predictions as reliable decision signals, with no indication of when these predictions should be trusted. We propose BetaPRM, a distributional PRM that predicts both a step-level success probability and the reliability of that prediction. Given step-success supervision from Monte Carlo continuations, BetaPRM learns a Beta belief that explains the observed number of successful continuations through a Beta-Binomial likelihood, rather than regressing to the finite-sample success ratio as a point target. This learned reliability signal indicates when a step reward should be trusted, enabling downstream applications to distinguish reliable rewards from uncertain ones. As one application, we introduce Adaptive Computation Allocation (ACA) for PRM-guided Best-of-N reasoning. ACA uses the learned reliability signal to stop when a high-reward solution is reliable and to spend additional computation on uncertain candidate prefixes. Experiments across four backbones and four reasoning benchmarks show that BetaPRM improves PRM-guided Best-of-N selection while preserving standard step-level error detection. Built on this signal, ACA improves the accuracy--token tradeoff over fixed-budget Best-of-16, reducing token usage by up to 33.57% while improving final-answer accuracy.", "github": "https://github.com/JinYuanLi0012/Beta-Binomial-PRM" }, { "date": "2026-05-20", "arxiv_id": "2605.18758", "title": "OmniGUI: Benchmarking GUI Agents in Omni-Modal Smartphone Environments", "authors": [ "Felix Henry", "Xiaochen Lin", "Jiangyou Zhu", "Yangfan", "Bingqian Zhang", "Min Chen", "Shiyu Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/omni-gui/OmniGUI", "github_api_added_by": "user", "project_page": "https://omni-gui.github.io/", "abstract": "Current benchmarks for graphical user interface (GUI) agents predominantly rely on static screenshots. However, real-world smartphone interaction routinely requires agents to process transient audio cues and temporal video dynamics that are tightly coupled with the moment of action. To bridge this gap, we introduce OmniGUI, the first step-level benchmark designed to evaluate GUI agents in omni-modal smartphone environments. OmniGUI provides continuous, interleaved multimodal inputs comprising static images, synchronous audio, and video clips at every action step. The dataset encompasses 709 expert-demonstrated episodes (2,579 action steps) across 29 applications, systematically annotated with objective multimodal dependency levels. Because dedicated omni-modal GUI agent frameworks are currently in their nascent stage, we select foundational omni-modal models capable of natively processing interleaved inputs to serve as agent proxies for our initial baselines. Our empirical evaluation reveals that while current models exhibit competency on visually static tasks, their action prediction performance degrades significantly in environments requiring synchronous temporal and auditory signals. Furthermore, ablation studies isolate specific operational bottlenecks, notably cross-modal interference when processing task-irrelevant environmental noise. The complete dataset, evaluation pipeline, and baseline prompts are provided in the supplementary material. Project page: https://omni-gui.github.io.", "github": "https://github.com/omni-gui/OmniGUI" }, { "date": "2026-05-20", "arxiv_id": "2605.18748", "title": "Aurora: Unified Video Editing with a Tool-Using Agent", "authors": [ "Yongsheng Yu", "Ziyun Zeng", "Zhiyuan Xiao", "Zhenghong Zhou", "Hang Hua", "Wei Xiong", "Jiebo Luo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yeates/Aurora", "github_api_added_by": "user", "project_page": "https://www.yongshengyu.com/Aurora-Page/", "abstract": "Recent video editing models have converged on a unified conditioning design: a single diffusion transformer jointly consumes text, source video, and reference images, and one set of weights covers replacement, removal, style transfer, and reference-driven insertion. The design is flexible, but it assumes that the user already provides model-ready text, reference images, and spatial grounding for local edits, which real requests often omit. We present Aurora, an agentic video editing framework that pairs a tool-augmented vision-language model (VLM) agent with a unified video diffusion transformer. The VLM agent maps a raw user request to a structured edit plan aligned with the transformer's conditioning channels, thereby resolving textual and visual underspecification before generation. We train the VLM agent with supervised data for complete edit planning and reference-image selection, together with preference pairs for robust tool use and instruction refinement. We introduce AgentEdit-Bench to evaluate agent-enhanced video editing under textual and visual underspecification. Experiments on AgentEdit-Bench and two existing video editing benchmarks show that Aurora improves over instruction-only baselines and that the VLM agent transfers to compatible frozen video editing models. Project page: https://yeates.github.io/Aurora-Page", "github": "https://github.com/yeates/Aurora" }, { "date": "2026-05-20", "arxiv_id": "2605.19436", "title": "CEPO: RLVR Self-Distillation using Contrastive Evidence Policy Optimization", "authors": [ "Ahmed Heakl", "Abdelrahman M. Shaker", "Youssef Mohamed", "Rania Elbadry", "Omar Fetouh", "Fahad Shahbaz Khan", "Salman Khan" ], "github_manual": "", "github_regex": "https://github.com/ahmedheakl/CEPO", "github_paper_md": "", "github_api": "https://github.com/ahmedheakl/CEPO", "github_api_added_by": "user", "project_page": "", "abstract": "When a model produces a correct solution under reinforcement learning with verifiable rewards (RLVR), every token receives the same reward signal regardless of whether it was a decisive reasoning step or a grammatical filler. A natural fix is to condition the model on the correct answer as a teacher, identifying tokens it would have generated differently had it known the answer. Prior work shows this either corrupts training by leaking the answer into the gradient, or produces a weak signal that cannot distinguish decisive steps from filler, since both look equally surprising relative to the model's baseline. We propose Contrastive Evidence Policy Optimization (CEPO), which asks a sharper question at every token: not just \"does the correct answer favor this token?\" but \"does the correct answer favor it while the wrong answer disfavors it?\" A token satisfying both is a genuine reasoning step; one satisfying neither is filler. The wrong-answer teacher is constructed from rejected rollouts already in the training batch, incurring no additional sampling cost. We prove CEPO inherits all structural safety guarantees of the prior state of the art while strictly sharpening credit at decisive tokens, with the improvement vanishing exactly at filler positions. Empirically, CEPO achieves 43.43% and 60.56% average accuracy across five multimodal mathematical reasoning benchmarks at 2B and 4B scale, respectively, versus 41.17% and 57.43% for GRPO under identical training budgets. Distribution-matching self-distillation methods (OPSD, SDPO) fall below the untrained baseline, empirically confirming the information leakage our theory predicts. Our code is available at https://github.com/ahmedheakl/CEPO.", "github": "https://github.com/ahmedheakl/CEPO" }, { "date": "2026-05-20", "arxiv_id": "2605.20183", "title": "MSAVBench: Towards Comprehensive and Reliable Evaluation of Multi-Shot Audio-Video Generation", "authors": [ "Yujie Wei", "Yujin Han", "Zhekai Chen", "Yongming Li", "Kaixun Jiang", "Zhihang Liu", "Quanhao Li", "Zhiwu Qing", "Xiang Wang", "Zhen Xing", "Ruihang Chu", "Lingyi Hong", "Yefei He", "Junjie Zhou", "Junqiu Yu", "Yang Shi", "Difan Zou", "Kai Zhu", "Shiwei Zhang", "Yingya Zhang", "Yu Liu", "Xihui Liu", "Hongming Shan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Video generation is rapidly evolving from single-shot synthesis to complex multi-shot audio-video (MSAV) narratives to meet real-world demands. However, evaluating such frontier models remains a fundamental challenge. Existing benchmarks are limited in scope and data diversity, and rely on rigid evaluation pipelines, preventing systematic and reliable assessment of modern MSAV models. To bridge these gaps, we introduce MSAVBench, the first comprehensive benchmark and adaptive hybrid evaluation framework for multi-shot audio-video generation. Our benchmark spans four key dimensions, video, audio, shot, and reference, covering diverse task settings, varying shot counts of up to 15, and challenging non-realistic scenarios. Our evaluation framework improves robustness through an adaptive self-correction mechanism for shot segmentation, instance-wise rubrics for subjective metrics, and tool-grounded evidence extraction for complex judgments. Furthermore, MSAVBench achieves high alignment with human judgments, reaching a Spearman rank correlation of 91.5%. Our systematic evaluation of 19 state-of-the-art closed- and open-source models shows that current systems still struggle with director-level control and fine-grained audio-visual synchronization, while modular or agentic generation pipelines offer a promising path toward narrowing the gap between open- and closed-source models. We will release the benchmark data and evaluation code to facilitate future research.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.20025", "title": "AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration", "authors": [ "Jiaqi Liu", "Shi Qiu", "Mairui Li", "Bingzhou Li", "Haonian Ji", "Siwei Han", "Xinyu Ye", "Peng Xia", "Zihan Dong", "Congyu Zhang", "Letian Zhang", "Guiming Chen", "Haoqin Tu", "Xinyu Yang", "Lu Feng", "Xujiang Zhao", "Haifeng Chen", "Jiawei Zhou", "Xiao Wang", "Weitong Zhang", "Hongtu Zhu", "Yun Li", "Jieru Mei", "Hongliang Fei", "Jiaheng Zhang", "Linjie Li", "Linjun Zhang", "Yuyin Zhou", "Sheng Wang", "Caiming Xiong", "James Zou", "Zeyu Zheng", "Cihang Xie", "Mingyu Ding", "Huaxiu Yao" ], "github_manual": "", "github_regex": "https://github.com/aiming-lab/AutoResearchClaw", "github_paper_md": "", "github_api": "https://github.com/aiming-lab/AutoResearchClaw", "github_api_added_by": "user", "project_page": "https://github.com/aiming-lab/AutoResearchClaw", "abstract": "Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail and inform the next attempt, and lessons accumulate across cycles. Existing autonomous research systems often model this process as a linear pipeline: they rely on single-agent reasoning, stop when execution fails, and do not carry experience across runs. We present AutoResearchClaw, a multi-agent autonomous research pipeline built on five mechanisms: structured multi-agent debate for hypothesis generation and result analysis, a self-healing executor with a Pivot/Refine decision loop that transforms failures into information, verifiable result reporting that prevents fabricated numbers and hallucinated citations, human-in-the-loop collaboration with seven intervention modes spanning full autonomy to step-by-step oversight, and cross-run evolution that converts past mistakes into future safeguards. On ARC-Bench, a 25-topic experiment-stage benchmark, AutoResearchClaw outperforms AI Scientist v2 by 54.7%. A human-in-the-loop ablation across seven intervention modes reveals that precise, targeted collaboration at high-leverage decision points consistently outperforms both full autonomy and exhaustive step-by-step oversight. We position AutoResearchClaw as a research amplifier that augments rather than replaces human scientific judgment. Code is available at https://github.com/aiming-lab/AutoResearchClaw.", "github": "https://github.com/aiming-lab/AutoResearchClaw" }, { "date": "2026-05-20", "arxiv_id": "2605.19995", "title": "CogOmniControl: Reasoning-Driven Controllable Video Generation via Creative Intent Cognition", "authors": [ "Hongji Yang", "Songlian Li", "Yucheng Zhou", "Xiaotong Zhao", "Alan Zhao", "Chengzhong Xu", "Jianbing Shen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://um-lab.github.io/CogOmniControl/", "abstract": "Recent diffusion models achieve strong photorealism and fluency in video generation, yet remain fragile under abstract, sparse or complex conditions, leading to poor performance in professional production workflows such as storyboard sketches and clay render conditions. Existing video generation models, either inject conditions through adapters or couple a generic vision-language model (VLM) within a diffusion backbone, leaving a capability gap and failing to produce the videos that align with the user's creative intent. We present CogOmniControl, a reasoning-driven framework that factorizes controllable video generation into creative intent cognition and generation. Specifically, we train a specialized CogVLM using authentic anime production data. Compared to generic VLMs, it generates more professional and clear outputs, accurately cognizing user creative intent from sparse and abstract conditions and tuning these cues into dense reasoning output. Besides, CogOmniDiT unifies the controls from various conditions through in-context generation and is aligned to the CogVLM reasoning outputs via reinforcement learning. Furthermore, leveraging CogVLM's robust capability in guiding video generation, we release its potential in planning specific evaluators and enable a Best-of-N selection for the generated videos. This integration transforms the entire framework into a closed-loop \"harness-like\" architecture. We further introduce CogReasonBench and CogControlBench, built from professional workflows data that carry genuine creative intent rather than simulated ones. Experiments on two benchmarks show that CogOmniControl surpassed the existing open-source models. The project website: https://um-lab.github.io/CogOmniControl/", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.16003", "title": "Echo-Forcing: A Scene Memory Framework for Interactive Long Video Generation", "authors": [ "Mingqiang Wu", "Weilun Feng", "Zhefeng Zhang", "Haotong Qin", "Yuqi Li", "Guoxin Fan", "Xiaokun Liu", "Zhulin An", "Libo Huang", "Yongjun Xu", "Chuanguang Yang" ], "github_manual": "", "github_regex": "https://github.com/mingqiangWu/Echo-Forcing", "github_paper_md": "", "github_api": "https://github.com/mingqiangWu/Echo-Forcing", "github_api_added_by": "user", "project_page": "", "abstract": "Autoregressive video diffusion models enable open-ended generation through local attention and KV caching. However, existing training-free long-video optimization methods mainly focus on stable extension under a single prompt, making them difficult to handle interactive scenarios involving prompt switching, old scene forgetting, and historical scene recall. We identify the core bottleneck as the functional entanglement of historical KV states: stable anchors and recent dynamics are handled by the same cache policy, leading to outdated background contamination, delayed response to new prompts, and loss of long-range memory. To address this issue, we propose Echo-Forcing, a training-free scene memory framework specifically designed for interactive long video generation with three core mechanisms: (1) Hierarchical Temporal Memory, which decouples stable anchors, compressed history, and recent windows under relative RoPE; (2) Scene Recall Frames, which compresses historical scenes into spatially structured KV representations to support long-term recall; and (3) Difference-aware Memory Decay, which adaptively forgets conflicting tokens according to the discrepancy between old and new scenes. Based on these designs, Echo-Forcing uniformly supports smooth transitions, hard cuts, and long-range scene recall under a bounded cache budget. Extensive evaluations on VBench-Long further demonstrate that Echo-Forcing achieves the best overall performance in both long-video generation and interactive video generation settings. Our code is released in https://github.com/mingqiangWu/Echo-Forcing", "github": "https://github.com/mingqiangWu/Echo-Forcing" }, { "date": "2026-05-20", "arxiv_id": "2605.12623", "title": "DocAtlas: Multilingual Document Understanding Across 80+ Languages", "authors": [ "Ahmed Heakl", "Youssef Mohamed", "Abdullah Sohail", "Rania Elbadry", "Ahmed Nassar", "Peter W. J. Staar", "Fahad Shahbaz Khan", "Imran Razzak", "Salman Khan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ahmedheakl/DocAtlas", "github_api_added_by": "user", "project_page": "", "abstract": "Multilingual document understanding remains limited for low-resource languages due to scarce training data and model-based annotation pipelines that perpetuate existing biases. We introduce DocAtlas, a framework that constructs high-fidelity OCR datasets and benchmarks covering 82 languages and 9 evaluation tasks. Our dual pipelines, differential rendering of native DOCX documents and synthetic LaTeX-based generation for right-to-left scripts produce precise structural annotations in a unified DocTag format encoding layout, text, and component types, without learned models for core annotation. Evaluating 16 state-of-the-art models reveals persistent gaps in low-resource scripts. We show that Direct Preference Optimization (DPO) using rendering-derived ground truth as positive signal achieves stable multilingual adaptation, improving both in-domain (+1.9%) and out-of-domain (+1.8%) accuracy without measurable base-language degradation, where supervised fine-tuning degrades out-of-domain performance by up to 21%. Our best variant, DocAtlas-DeepSeek, improves +1.7% over the strongest baseline.", "github": "https://github.com/ahmedheakl/DocAtlas" }, { "date": "2026-05-20", "arxiv_id": "2605.11609", "title": "Anti-Self-Distillation for Reasoning RL via Pointwise Mutual Information", "authors": [ "Guobin Shen", "Xiang Cheng", "Chenxiao Zhao", "Lei Huang", "Jindong Li", "Dongcheng Zhao", "Xing Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FloyedShen/AntiSD", "github_api_added_by": "user", "project_page": "", "abstract": "On-policy self-distillation, where a student is pulled toward a copy of itself conditioned on privileged context (e.g., a verified solution or feedback), offers a promising direction for advancing reasoning capability without a stronger external teacher. Yet in math reasoning the gains are inconsistent, even when the same approach succeeds elsewhere. A pointwise mutual information analysis traces the failure to the privileged context itself: it inflates the teacher's confidence on tokens already implied by the solution (structural connectives, verifiable claims) and deflates it on deliberation tokens (\"Wait\", \"Let\", \"Maybe\") that drive multi-step search. We propose Anti-Self-Distillation (AntiSD), which ascends a divergence between student and teacher rather than descending it: this reverses the per-token sign and yields a naturally bounded advantage in one step. An entropy-triggered gate disables the term once the teacher entropy collapses, completing a drop-in replacement for default self-distillation. Across five models from 4B to 30B parameters on math reasoning benchmarks, AntiSD reaches the GRPO baseline's accuracy in 2 to 10x fewer training steps and improves final accuracy by up to 11.5 points. AntiSD opens a path to scalable self-improvement, where a language model bootstraps its own reasoning through its training signal.", "github": "https://github.com/FloyedShen/AntiSD" }, { "date": "2026-05-20", "arxiv_id": "2605.19587", "title": "SceneCode: Executable World Programs for Editable Indoor Scenes with Articulated Objects", "authors": [ "Puyi Wang", "Yuhao Wang", "Linjie Li", "Zhengyuan Yang", "Kevin Qinghong Lin", "Yangguang Li", "Yu Cheng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Indoor scene synthesis underpins embodied AI, robotic manipulation, and simulation-based policy evaluation, where a useful scene must specify not only what the environment looks like, but also how its objects are structured. Existing pipelines, however, typically represent generated content as static meshes and inherit articulation only from curated asset libraries, which limits object-level controllability and prevents new interactable assets from being produced on demand. We address this gap by formulating physically interactable indoor scene synthesis as programmatic world generation, and present SceneCode, a framework that compiles a natural language prompt into an executable, code-driven indoor world rather than a collection of opaque meshes. A room-level agentic backbone first turns the prompt into a structured house layout and emits per-object AssetRequests through a planner--designer--critic loop. Each request is then routed to one of five code-generation strategies and converted into a synthesized part-wise Blender Python programs that are validated through an execution-guided repair-and-refine loop. The resulting programs are compiled into simulation-ready assets, and exported as SDF for physics simulation. A persistent scene-state registry links object requests, executable programs, rendered geometry, and simulation assets, turning scene assembly into a traceable and locally editable world-building process. We evaluate SceneCode across scene-level synthesis, object-level asset quality, human judgment, and downstream robot interaction. Results show that executable world programs improve prompt-faithful indoor scene generation and produce assets with cleaner mesh structure, and simulator-loadable articulation metadata. Project page: https://scene-code.github.io/.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.19769", "title": "OpenComputer: Verifiable Software Worlds for Computer-Use Agents", "authors": [ "Jinbiao Wei", "Qianran Ma", "Yilun Zhao", "Xiao Zhou", "Kangqi Ni", "Guo Gan", "Arman Cohan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/echo0715/OpenComputer", "github_api_added_by": "user", "project_page": "https://echo0715.github.io/OpenComputer/", "abstract": "We present OpenComputer, a verifier-grounded framework for constructing verifiable software worlds for computer-use agents. OpenComputer integrates four components: (1) app-specific state verifiers that expose structured inspection endpoints over real applications, (2) a self-evolving verification layer that improves verifier reliability using execution-grounded feedback, (3) a task-generation pipeline that synthesizes realistic and machine-checkable desktop tasks, and (4) an evaluation harness that records full trajectories and computes auditable partial-credit rewards. In its current form, OpenComputer covers 33 desktop applications and 1,000 finalized tasks spanning browsers, office tools, creative software, development environments, file managers, and communication applications. Experiments show that OpenComputer's hard-coded verifiers align more closely with human adjudication than LLM-as-judge evaluation, especially when success depends on fine-grained application state. Frontier agents struggle with end-to-end completion despite partial progress, and open-source models exhibit sharp drops from their OSWorld-Verified scores, exposing a persistent gap in robust computer automation.", "github": "https://github.com/echo0715/OpenComputer" }, { "date": "2026-05-20", "arxiv_id": "2605.20104", "title": "Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding", "authors": [ "Yuhao Shen", "Tianyu Liu", "Xinyi Hu", "Quan Kong", "Baolin Zhang", "Jun Dai", "Jun Zhang", "Shuang Ge", "Lei Chen", "Yue Li", "Mingcheng Wan", "Cong Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Speculative decoding (SD) accelerates large language model inference by leveraging a draft-then-verify paradigm. To maximize the acceptance rate, recent methods construct expansive draft trees, which unfortunately incur severe VRAM bandwidth and computational overheads that bottleneck end-to-end speedups. While dynamic-depth pruning can reduce this latency by removing marginal branches, it also discards potentially valid candidates, preventing the acceptance rate from reaching the upper bound of dense trees. In this paper, we identify a critical opportunity in resource allocation: the transition from dense to pruned drafting frees up significant computational budget. To break this Pareto tradeoff, we introduce Graft, a compensation framework that couples pruning and retrieval as mutually reinforcing operations. Pruning supplies sufficient budget for retrieval, while retrieval compensates for pruning-induced coverage loss and recovers accepted length. By employing a sequential `prune-then-graft' mechanism, Graft attaches highly predictive retrieved tokens into positions opened by pruning, filling the topological gaps with near-zero overhead. Graft is entirely training-free and lossless. Comprehensive evaluations show that Graft establishes a new Pareto frontier across practical deployment settings, including short-context generation, long-context generation, and large-scale models. On short-context benchmarks, it achieves up to 5.41times speedup and improves average speedup over EAGLE-3 by up to 21.8% on the large-scale Qwen3-235B. We also provide a preliminary exploration of applying Graft to the DFlash-style block drafting paradigm, offering initial evidence and insights for extending grafting beyond autoregressive draft trees.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.18855", "title": "Delta Attention Residuals", "authors": [ "Cheng Luo", "Zefan Cai", "Junjie Hu" ], "github_manual": "", "github_regex": "https://github.com/wdlctc/delta-attention-residuals-code", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Attention Residuals replace standard additive residual connections with learned softmax attention over previous layer outputs, enabling selective cross-layer routing. However, standard Attention Residuals still attend over cumulative hidden states in previous layers, which are highly redundant. We show that this redundancy leads to routing collapse in deeper layers: attention weights become low-contrast and closer to uniform (max weight {approx}0.2), limiting the model's ability to select informative states in previous layers. This raises a key but underexplored design question: what layer-wise representations should be routed in Attention Residuals? To answer this question, we propose Delta Attention Residuals, which attend over deltas -- the change introduced by each sublayer (v_i = h_{i+1} - h_i) -- instead of cumulative states. Delta representations are structurally diverse and yield higher-contrast attention distributions (max weight {approx}0.6), enabling more selective and effective routing across layers. This principle applies at both per-sublayer and block granularity. Across all tested scales (220M--7.6B), Delta Attention Residuals consistently outperform both standard residuals and Attention Residuals, with 1.7--8.2\\% validation perplexity gains. Delta Attention Residuals also enables converting pretrained checkpoints into Delta Attention Residuals via standard fine-tuning. Code is available at https://github.com/wdlctc/delta-attention-residuals-code.", "github": "https://github.com/wdlctc/delta-attention-residuals-code" }, { "date": "2026-05-20", "arxiv_id": "2605.19577", "title": "GoLongRL: Capability-Oriented Long Context Reinforcement Learning with Multitask Alignment", "authors": [ "Minxuan Lv", "Tiehua Mei", "Tanlong Du", "Junmin Chen", "Zhenpeng Su", "Ziyang Chen", "Ziqi Wang", "Zhennan Wu", "Ruotong Pan", "jian Liang", "Ruiming Tang", "Han Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xiaoxuanNLP/GoLongRL", "github_api_added_by": "user", "project_page": "https://huggingface.co/collections/Kwai-Klear/golongrl", "abstract": "We present GoLongRL, a fully open-source, capability-oriented post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR). Existing long-context RL methods often treat data construction as a matter of designing increasingly complex retrieval paths, leading to homogeneous task coverage and reward formulations that inadequately reflect practical long-context requirements. Our work offers two contributions. (1) Capability-oriented data construction with full open release. We openly release a dataset of 23K RLVR samples, the complete construction pipeline, and all training code. Guided by a taxonomy of long-context capabilities, the dataset spans 9 task types, each paired with its natural evaluation metric. It comprises curated open-source samples from established corpora and synthetic samples whose QA pairs are generated from real source documents such as books, academic papers, and multi-turn dialogues. Under the same vanilla GRPO setup, our dataset alone outperforms the closed-source QwenLong-L1.5 dataset. Moreover, our Qwen3-30B-A3B model trained on this data delivers long-context performance comparable to DeepSeek-R1-0528 and Qwen3-235B-A22B-Thinking-2507, suggesting that broader coverage and greater reward diversity substantially benefit long-context capability improvement. (2) TMN-Reweight for heterogeneous multitask optimization. To address optimization challenges from heterogeneous rewards, we propose TMN-Reweight, which combines task-level mean normalization for cross-task reward scale alignment with difficulty-adaptive weighting for more reliable advantage estimation. TMN-Reweight further improves average performance over vanilla GRPO, with general capabilities preserved or improved across reported evaluations.", "github": "https://github.com/xiaoxuanNLP/GoLongRL" }, { "date": "2026-05-20", "arxiv_id": "2605.18703", "title": "EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL", "authors": [ "Minrui Xu", "Zilin Wang", "Mengyi DENG", "Zhiwei Li", "Zhicheng Yang", "Xiao Zhu", "Yinhong Liu", "Boyu Zhu", "Baiyu Huang", "Chao Chen", "Heyuan Deng", "Fei Mi", "Lifeng Shang", "Xingshan Zeng", "Zhijiang Guo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LARK-AI-Lab/EnvFactory", "github_api_added_by": "user", "project_page": "", "abstract": "Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robust execution environments and the scarcity of realistic training data that captures implicit human reasoning. Existing approaches depend on costly real-world APIs, hallucination-prone LLM simulators, or synthetic environments that are often single-turn or depend on pre-collected documents. Moreover, synthetic trajectories are frequently over-specified, resembling instruction sequences rather than natural human intents, reducing their effectiveness for RL training. We introduce EnvFactory, a fully automated framework that addresses both challenges. EnvFactory autonomously explores and verifies stateful, executable tool environments from authentic resources, and synthesizes natural multi-turn trajectories through topology-aware sampling and calibrated refinement, producing grounded queries with implicit intents. Using only 85 verified environments across 7 domains, EnvFactory generates 2,575 SFT and RL trajectories. Despite using significantly fewer environments than prior work, which are often 5 times more, EnvFactory achieves superior training efficiency and downstream performance, improving Qwen3-series models by up to +15% on BFCLv3, +8.6% on MCP-Atlas, and +6% on conversational benchmarks including \u03c4^2-Bench and VitaBench. By fully automating both environment construction and trajectory synthesis, EnvFactory provides a scalable, extensible, and robust foundation for Agentic RL.", "github": "https://github.com/LARK-AI-Lab/EnvFactory" }, { "date": "2026-05-20", "arxiv_id": "2605.18714", "title": "Semantic Generative Tuning for Unified Multimodal Models", "authors": [ "Songsong Yu", "Yuxin Chen", "Ying Shan", "Yanwei Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/song2yu/SGT", "github_api_added_by": "user", "project_page": "https://song2yu.github.io/SGT/", "abstract": "Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture. However, prevailing training paradigms independently optimize understanding via sparse text signals and generation through dense pixel objectives. Such a decoupled strategy yields misaligned representation spaces, isolating visual understanding from generation and hindering their mutual reinforcement. This work presents the first systematic investigation into generative post-training, where we formulate hierarchical visual tasks as generative proxies to bridge the isolation in UMMs. Our empirical investigation reveals that high-level semantic tasks, particularly image segmentation, serve as optimal proxies. Unlike low-level tasks that distract models with texture details, segmentation provides structural semantics that significantly enhance both vision-centric perception and generative layout fidelity. Building upon these insights, we introduce Semantic Generative Tuning (SGT), a novel paradigm that leverages segmentation as a generative proxy to align and synergize multimodal capabilities. Mechanistic analyses further demonstrate that SGT fundamentally improves feature linear separability and optimizes visual-textual attention allocation pattern. Extensive evaluations show that SGT consistently improves both multimodal comprehension and generative fidelity across mainstream benchmarks. Our code is available on the https://song2yu.github.io/SGT/.", "github": "https://github.com/song2yu/SGT" }, { "date": "2026-05-20", "arxiv_id": "2605.18746", "title": "ESI-Bench: Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop", "authors": [ "Yining Hong", "Jiageng Liu", "Han Yin", "Manling Li", "Leonidas Guibas", "Li Fei-Fei", "Jiajun Wu", "Yejin Choi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ESI-Bench/ESI-Bench", "github_api_added_by": "user", "project_page": "https://esi-bench.github.io/", "abstract": "Spatial intelligence unfolds through a perception-action loop: agents act to acquire observations, and reason about how observations vary as a function of action. Rather than passively processing what is seen, they actively uncover what is unseen - occluded structure, dynamics, containment, and functionality that cannot be resolved from passive sensing alone. We move beyond prior formulations of spatial intelligence that assume oracle observations by recasting the observer as an actor. We introduce ESI-BENCH, a comprehensive benchmark for embodied spatial intelligence spanning 10 task categories and 29 subcategories built on OmniGibson, grounded in Spelke's core knowledge systems. Agents must decide what abilities to deploy - perception, locomotion, and manipulation - and how to sequence them to actively accumulate task-relevant evidence. We conduct extensive experiments on state-of-the-art MLLMs and find that active exploration substantially outperforms passive counterparts, with agents spontaneously discovering emergent spatial strategies without explicit instructions, while random multi-view often adds noise rather than signal despite consuming far more images. Most failures stem not from weak perception but from action blindness: poor action choices lead to poor observations, which in turn drive cascading errors. While explicit 3D grounding stabilizes reasoning on depth-sensitive tasks, imperfect 3D representation proves more harmful than 2D baselines by distorting spatial relations. Human studies further reveal that unlike humans who seek falsifying viewpoints and revise beliefs under contradiction, models commit prematurely with high confidence regardless of evidence quality, exposing a metacognitive gap that neither better perception nor more embodied interaction alone can close.", "github": "https://github.com/ESI-Bench/ESI-Bench" }, { "date": "2026-05-20", "arxiv_id": "2605.18827", "title": "Code-Guided Reasoning for Small Language Models: Evaluating Executable MCQA Scaffolds", "authors": [ "Prateek Biswas", "Dhaval Patel", "Vedant Khandelwal", "Shuxin Lin", "Amit Sheth" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multiple-choice QA benchmarks usually evaluate small language models (SLMs) as direct answerers, but deployed language-model systems increasingly rely on external scaffolds such as tools, code, and repeated model calls. We introduce Code-Guided Reasoning (CGR), an evaluation protocol and generated-program resource for measuring when executable reasoning scaffolds improve SLM performance on MCQA tasks. CGR standardizes six components: a normalized item interface, a direct solver prompt, a generator prompt, a Python scaffold, solver-call and extraction helpers, and a three-channel result record. On 20,498 retained result rows from a locally prepared MCQA bundle and six metadata-registered solver models, the observed non-zero-baseline partition shows 66.21% macro assisted accuracy versus 38.11% direct accuracy, a +28.10 percentage-point difference with a pair-bootstrap interval of [20.32, 36.43]. Under a stricter Ab > 30% direct-signal gate, the macro difference is +14.11 points. These estimates are descriptive. Assisted inference uses a larger solver-call budget, answer extraction is brittle, Time-MQA contains the observed regressions, and some generated programs violate the no-hard-coding instruction. CGR provides the trace package needed to interpret these results, including direct, assisted, and generator-side answers, partition definitions, generated programs, response metadata, and audits.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.19932", "title": "PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents", "authors": [ "Zhuohan Gu", "Qizheng Zhang", "Omar Khattab", "Samuel Madden" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhuohangu/peek", "github_api_added_by": "user", "project_page": "https://zhuohangu.github.io/blog-post-peek/", "abstract": "Large language model (LLM) agents increasingly operate over long and recurring external contexts, like document corpora and code repositories. Across invocations, existing approaches preserve either the agent's trajectory, passive access to raw material, or task-level strategies. None of them preserves what we argue is most needed for repeated same-context workloads: reusable orientation knowledge (e.g., what the context contains, how it is organized, and which entities, constants, and schemas have historically been useful) about the recurring context itself. We introduce PEEK, a system that caches and maintains this orientation knowledge as a context map: a small, constant-sized artifact in the agent's prompt that gives it a persistent peek into the external context. The map is maintained by a programmable cache policy with three modules: a Distiller that extracts transferable knowledge from inference-time signals, a Cartographer that translates it into structured edits, and a priority-based Evictor that enforces a fixed token budget. On long-context reasoning and information aggregation, PEEK improves over strong baselines by 6.3-34.0% while using 93-145 fewer iterations and incurring 1.7-5.8x lower cost than the state-of-the-art prompt-learning framework, ACE. On context learning, PEEK improves solving rate and rubric accuracy by 6.0-14.0% and 7.8-12.1%, respectively, at 1.4x lower cost than ACE. These gains generalize across LMs and agent architectures, including OpenAI Codex, a production-grade coding agent. Together, these results show that a context map helps long-context LLM agents interact with recurring external contexts more accurately and efficiently.", "github": "https://github.com/zhuohangu/peek" }, { "date": "2026-05-20", "arxiv_id": "2605.15458", "title": "Video Models Can Reason with Verifiable Rewards", "authors": [ "Tinghui Zhu", "Sheng Zhang", "James Y. Huang", "Selena Song", "Xiaofei Wen", "Yuankai Li", "Hoifung Poon", "Muhao Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/luka-group/VideoRLVR", "github_api_added_by": "user", "project_page": "https://darthzhu.github.io/VideoRLVR-page/", "abstract": "Video diffusion models have made rapid progress in perceptual realism and temporal coherence, but they remain primarily optimized for plausible generation rather than verifiable reasoning. This limitation is especially pronounced in tasks where generated videos must satisfy explicit spatial, temporal, or logical constraints. Inspired by the role of reinforcement learning with verifiable rewards (RLVR) in reasoning-oriented language models, we introduce VideoRLVR, a practical recipe for optimizing video diffusion models with rule-based feedback. VideoRLVR formulates video reasoning as the generation of verifiable visual trajectories and consists of an SDE-GRPO optimization backbone, dense decomposed rewards, and an Early-Step Focus strategy for efficient training. The Early-Step Focus strategy restricts policy optimization to the early denoising phase, reducing training latency by about 40% while preserving performance. We evaluate VideoRLVR on Maze, FlowFree, and Sokoban, three procedurally generated domains with objective success criteria. Across these tasks, VideoRLVR consistently improves over supervised fine-tuning baselines, with dense decomposed rewards proving especially important in low-success-rate settings. Our RL-optimized model also outperforms the evaluated proprietary and open-source video generation models on these verifiable reasoning benchmarks and out-of-domain benchmarks. These results suggest that verifiable RL can move video models beyond perceptual imitation toward more reliable rule-consistent visual reasoning.", "github": "https://github.com/luka-group/VideoRLVR" }, { "date": "2026-05-20", "arxiv_id": "2605.16403", "title": "When Vision Speaks for Sound", "authors": [ "Xiaofei Wen", "Wenjie Jacky Mo", "Xingyu Fu", "Rui Cai", "Tinghui Zhu", "Wendi Li", "Yanan Xie", "Muhao Chen", "Peng Qi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rakanWen/wvs-code", "github_api_added_by": "user", "project_page": "https://rakanwen.github.io/when-vision-speaks-for-sound/", "abstract": "Despite rapid progress in video-capable MLLMs, we find that their apparent audio understanding in videos is often vision-driven: models rely on visual cues to infer or hallucinate acoustic information, rather than verifying the audio stream. This issue appears across both state-of-the-art open-source omni models and leading closed-source models from providers such as Google and OpenAI. We characterize this failure mode as an audio-visual Clever Hans effect, in which models appear (falsely) audio-grounded, but actually exploit visual-acoustic correlations without verifying whether the audio and visual streams are truly aligned. To systematically study this behavior, we introduce Thud, an intervention-driven probing framework based on three counterfactual audio edits: Shift, which tests temporal synchronization; Mute, which tests sound existence; and Swap, which tests audio-visual consistency. Beyond diagnosis, we further study a two-stage alignment recipe: intervention-derived preference pairs teach audio verification, while event-level general video preferences regularize the model against over-specialization. Our best 10K-sample recipe improves average performance across the three intervention dimensions by 28 percentage points, while slightly improving performance on general video and audio-visual QA benchmarks.", "github": "https://github.com/rakanWen/wvs-code" }, { "date": "2026-05-20", "arxiv_id": "2605.17360", "title": "Omni-DuplexEval: Evaluating Real-time Duplex Omni-modal Interaction", "authors": [ "Chaoqun He", "Mingyang Xiang", "Yingjing Xu", "Bokai Xu", "Junbo Cui", "Jie Zhou", "Yuan Yao", "Lijie Wen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenBMB/Omni-DuplexEval", "github_api_added_by": "user", "project_page": "", "abstract": "Real-time duplex interaction is essential for multimodal AI systems operating in real-world scenarios, where models must continuously process streaming inputs and respond at appropriate moments. However, most existing multimodal large language models (MLLMs) are evaluated in offline settings, where the entire video input is processed before any response is generated. While recent work has started to explore real-time duplex MLLMs, there is still no comprehensive benchmark or automatic evaluation method for this setting. To address this gap, we propose Omni-DuplexEval, a benchmark for systematically evaluating real-time duplex interaction. The benchmark consists of two complementary scenarios: (1) Real-Time Description, which evaluates the ability to generate continuous, time-aligned responses that track evolving multimodal inputs, and (2) Proactive Reminder, which evaluates the ability to identify salient events and respond at appropriate moments. Omni-DuplexEval contains 660 videos with fine-grained, human-annotated labels and precise temporal metadata, spanning 9 tasks grounded in real-world scenarios, where all questions are formulated as open-ended queries. We further introduce an automatic evaluation framework based on LLM-as-a-Judge, which enables systematic assessment by jointly evaluating response-content alignment and response timing through timestamp-aware and sequential reasoning, achieving strong alignment with human judgments. Experiments on state-of-the-art duplex MLLMs reveal substantial limitations. The best-performing model achieves only 39.6% overall, while scoring only 20.0% on Proactive Reminder. Our analysis identifies two key challenges: models struggle to balance timely responses with coherent, holistic content generation, and they often fail to determine both when to respond and what to produce. We hope our work facilitates further progress in MLLMs.", "github": "https://github.com/OpenBMB/Omni-DuplexEval" }, { "date": "2026-05-20", "arxiv_id": "2605.20075", "title": "CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning", "authors": [ "Dachuan Shi", "Hanlin Zhu", "Xiangchi Yuan", "Wanjia Zhao", "Kejing Xia", "Wen Xiao", "Wenke Lee" ], "github_manual": "", "github_regex": "https://github.com/sdc17/CopT", "github_paper_md": "", "github_api": "https://github.com/sdc17/CopT", "github_api_added_by": "user", "project_page": "https://copt-web.github.io/", "abstract": "Chain-of-thought (CoT) is a standard approach for eliciting reasoning capabilities from large language models (LLMs). However, the common CoT paradigm treats thinking as a prerequisite for answering, which can delay access to plausible answers and incur unnecessary token costs even when the model is able to identify an answer before extended thinking, a behavior known as performative reasoning. In this paper, we introduce CopT, a reformulated reasoning pipeline that reverses the usual order of thinking and answering. Instead of thinking before answering, CopT first elicits a draft answer and then invokes subsequent on-policy thinking conditioned on its own draft answer for reflection and correction. To assess whether the draft answer should be trusted, CopT recasts continuous embeddings as inference-time contrastive verifiers. Specifically, it contrasts the model's support for the same generated tokens under discrete-token inputs and continuous-embedding inputs, yielding a sequence-level reverse KL estimator for answer reliability. Our analysis shows that under certain assumptions, the expected estimate equals the mutual information between the unresolved latent state and the emitted answer token, explaining why it captures answer-relevant uncertainty rather than arbitrary uncertainty in the latent state. When the answer is deemed insufficiently reliable, CopT performs further on-policy thinking, where a second KL estimator dynamically controls draft-answer visibility, preserving useful partial information while reducing the risk of being misled by unreliable content. Across mathematics, coding, and agentic reasoning tasks, CopT improves peak accuracy by up to 23% and reduces token usage by up to 57% at comparable or higher accuracy, without any additional training. The code is available at https://github.com/sdc17/CopT.", "github": "https://github.com/sdc17/CopT" }, { "date": "2026-05-20", "arxiv_id": "2605.18226", "title": "Context Memorization for Efficient Long Context Generation", "authors": [ "Yasuyuki Okoshi", "Hao Mark Chen", "Guanxi Lu", "Hongxiang Fan", "Masato Motomura", "Daichi Fujiki" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yasu0001/AttentionMemory", "github_api_added_by": "user", "project_page": "", "abstract": "Modern large language model (LLM) applications increasingly rely on long conditioning prefixes to control model behavior at inference time. While prefix-augmented inference is effective, it incurs two structural limitations: i) the prefix's influence fades as generation proceeds, and ii) attention computation over the prefix scales linearly with its length. Existing approaches either keep the prefix in attention while compressing it, or internalize it into model parameters through gradient-based training. The former still attends to the prefix at inference, while the latter is training-intensive and ill-suited to prefix updates. To address these issues, we propose attention-state memory, a training-free approach that externalizes the prefix into a lightweight, lookup-based memory of precomputed attention states between prefix and query tokens. On ManyICLBench with LLaMA-3.1-8B, our method improves accuracy over in-context learning at 1K-8K memory budgets while reducing attention latency by 1.36x at 8K, and surpasses full-attention RAG performance on NBA benchmark using only 20% of its memory footprint.", "github": "https://github.com/yasu0001/AttentionMemory" }, { "date": "2026-05-20", "arxiv_id": "2605.19014", "title": "SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction", "authors": [ "Gustav Olaf Yunus Laitinen-Fredriksson Lundstr\u00f6m-Imanov", "Hafize Gonca C\u00f6mert" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/olaflaitinen/saga", "github_api_added_by": "user", "project_page": "", "abstract": "Microsimulation models used by ministries of finance and central banks rely on parametric processes for lifetime earnings that capture only first and second moments of the conditional distribution and miss long-range nonlinear structure. We propose SAGA, a decoder-only transformer for irregular tabular panel sequences, paired with a split conformal calibration wrapper that delivers individual-level prediction intervals with finite-sample marginal coverage guarantees. Trained on the longitudinal Swedish LISA register over 1990 to 2022, comprising 2,143,817 individuals and 61,284,903 person-years, the model forecasts annual labor earnings at horizons of one to thirty years and aggregates them by Monte Carlo into present-discounted lifetime earnings distributions. Against the canonical Guvenen, Karahan, Ozkan, and Song parametric process and tabular and recurrent baselines, SAGA reduces continuous ranked probability score by 31.9 percent at the ten-year horizon and mean absolute error by 37.7 percent at the twenty-year horizon. Conformal intervals achieve nominal coverage to within 0.4 percentage points marginally and within 2.4 percentage points on the worst-case demographic subgroup. The reconstructed lifetime earnings Gini coefficient is 0.327 against the partially observed truth of 0.341 and the GKOS estimate of 0.378. Model weights, calibration tables, and a synthetic equivalent dataset are released for replication outside the protected SCB MONA environment.", "github": "https://github.com/olaflaitinen/saga" }, { "date": "2026-05-20", "arxiv_id": "2605.20147", "title": "PixVerve: Advancing Native UHR Image Generation to 100MP with a Large-Scale High-Quality Dataset", "authors": [ "Haojun Chen", "Haoyang He", "Chengming Xu", "Qingdong He", "Junwei Zhu", "Yabiao Wang", "Zhucun Xue", "Xianfang Zeng", "Zhennan Chen", "Xiaobin Hu", "Hao Zhao", "Yong Liu", "Jiangning Zhang", "Dacheng Tao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HaojunChen663/PixVerve-95K", "github_api_added_by": "user", "project_page": "https://haojunchen663.github.io/projects/PixVerve/", "abstract": "Text-to-Image (T2I) models have recently seen notable progress around 1K and 2K resolution. With the extreme desire for better visual experience and the rapid development of imaging technology, the demand for Ultra-High-Resolution (UHR) image generation has grown significantly. However, UHR image generation poses great challenges due to the scarcity and complexity of high-resolution content. In this paper, we first introduce PixVerve-95K, a high-quality, open-source UHR T2I dataset curated with a carefully designed data pipeline, which contains 95K images across diverse scenarios (each image has a minimum pixel-count of 100M) and seven-dimensional annotations. Based on our large-scale image-text dataset, we take a pioneering step to extend various T2I foundation models to native 100MP generation with three training schemes. Finally, leveraging both conventional metrics and multimodal large language model-based assessments, our proposed PixVerve-Bench benchmark establishes a comprehensive evaluation protocol for UHR images encompassing visual quality and semantic alignment. Extensive experimental results on our benchmark and the constructive exploration of training strategies collaboratively provide valuable insights for future breakthroughs.", "github": "https://github.com/HaojunChen663/PixVerve-95K" }, { "date": "2026-05-20", "arxiv_id": "2605.09640", "title": "Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning", "authors": [ "Meng Lou", "Hanzhong Guo", "Linwei Chen", "Yizhou Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/LMMMEng/RaPO", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recent studies suggest that Reinforcement Fine-Tuning (RFT) is inherently more resilient to catastrophic forgetting than Supervised Fine-Tuning (SFT). However, whether RFT (e.g., GRPO) can effectively overcome forgetting in challenging visual continual learning settings, such as class-incremental learning (CIL) and domain-incremental learning (DIL), remains an open problem. Through a pilot study, we confirm that while RFT consistently outperforms SFT, it still suffers from non-negligible forgetting. We empirically trace this bottleneck to Trajectory-level Drift Agnosticism: among candidate rollouts achieving identical task rewards, the KL divergence from the preceding-task policy varies substantially, which strongly correlates with catastrophic forgetting across sequential tasks. Motivated by this insight, we propose Retention-aware Policy Optimization (RaPO), a simple yet effective RFT method that explicitly mitigates forgetting through trajectory-level reward shaping. Specifically, RaPO comprises two core components: (1) Retention Reward that converts trajectory-level distribution drift into a continuous reward signal, preferentially reinforcing knowledge-preserving rollouts within each group; (2) Cross-Task Advantage Normalization (CTAN), which maintains a persistent exponential moving average of reward statistics across task boundaries to stabilize the optimization progress during continual learning. Leveraging the free-form textual generalization of MLLMs, we comprehensively evaluate RaPO across five visual continual learning settings. Extensive experiments demonstrate that RaPO achieves leading performance, substantially reducing catastrophic forgetting while preserving strong plasticity. To the best of our knowledge, this work represents the first systematic exploration of RFT in visual continual learning, offering insights that we hope will inspire future research.", "github": "https://github.com/LMMMEng/RaPO" }, { "date": "2026-05-20", "arxiv_id": "2605.20150", "title": "TideGS: Scalable Training of Over One Billion 3D Gaussian Splatting Primitives via Out-of-Core Optimization", "authors": [ "Chonghao Zhong", "Linfeng Shi", "Hua Chen", "Tiecheng Sun", "Hao Zhao", "Binhang Yuan", "Chaojian Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sponge-lab/TideGS", "github_api_added_by": "user", "project_page": "https://sponge-lab.github.io/TideGS/", "abstract": "Training 3D Gaussian Splatting (3DGS) at billion-primitive scale is fundamentally memory-bound: each Gaussian primitive carries a large attribute vector, and the aggregate parameter table quickly exceeds GPU capacity, limiting prior systems to tens of millions of Gaussians on commodity single-GPU hardware. We observe that 3DGS training is inherently sparse and trajectory-conditioned: each iteration activates only the Gaussians visible from the current camera batch, so GPU memory can serve as a working-set cache rather than a persistent parameter store. Building on this insight, we introduce TideGS, an out-of-core training framework that manages parameters across an SSD-CPU-GPU hierarchy via three synergistic techniques: block-virtualized geometry for SSD-aligned spatial locality, a hierarchical asynchronous pipeline to overlap I/O with computation, and trajectory-adaptive differential streaming that transfers only incremental working-set deltas between iterations. Experiments show that TideGS enables training with over one billion Gaussians on a single 24 GB GPU while achieving the best reconstruction quality among evaluated single-GPU baselines on large-scale scenes, scaling beyond prior out-of-core baselines (e.g., approximately 100M Gaussians) and standard in-memory training (e.g., approximately 11M Gaussians).", "github": "https://github.com/sponge-lab/TideGS" }, { "date": "2026-05-20", "arxiv_id": "2605.19908", "title": "Where Does Authorship Signal Emerge in Encoder-Based Language Models?", "authors": [ "Francis Kulumba", "Guillaume Vimont", "Laurent Romary", "Florian Cafiero" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Madjakul/DeepStylometry", "github_api_added_by": "user", "project_page": "", "abstract": "Authorship attribution models fine-tuned with the same pretrained encoder, data, and loss can differ four-fold in performance depending only on their scoring mechanism. We use mechanistic interpretability tools to explain this gap. Stylistic features such as word length, punctuation density, and function-word frequency are equally available at every layer in every model, including in an off-the-shelf control encoder, hence the gap not coming from representation quality. Instead, causal intervention shows that the scorer determines where the encoder consolidates authorship signal. Mean pooling forces consolidation by early to mid layers, while late interaction defers it to later layers. We further derive this difference from the gradient structure of each scorer, and training dynamics reveal distinct learning trajectories that follow from that difference.", "github": "https://github.com/Madjakul/DeepStylometry" }, { "date": "2026-05-20", "arxiv_id": "2605.18646", "title": "Language-Switching Triggers Take a Latent Detour Through Language Models", "authors": [ "Francis Kulumba", "Wissam Antoun", "Th\u00e9o Lasnier", "Beno\u00eet Sagot", "Djam\u00e9 Seddah" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Backdoor attacks on language models pose a growing security concern, yet the internal mechanisms by which a trigger sequence hijacks model computations remain poorly understood. We identify a circuit underlying a language-switching backdoor in an 8B-parameter autoregressive language model, where a three-word Latin trigger (nine tokens) redirects English output to French. We decompose the circuit into three phases: (1) distributed attention heads at early layers compose the trigger tokens into the last sequence position; (2) the resulting signal propagates through mid-layers in a subspace orthogonal to the model's natural language-identity direction; (3) the MLP at the final layer converts this latent signal into French logits. The entire circuit flows through a serial bottleneck at a single position: corrupting that position at any layer entirely mitigate the trigger but also hinder the model's capabilities. The orthogonal latent encoding suggests that defenses that search for language-like signals in intermediate representations would miss this trigger entirely.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.14236", "title": "Active Learners as Efficient PRP Rerankers", "authors": [ "Jerem\u00edas Figueiredo Paschmann", "Juan Kaplan", "Francisco Nattero", "Santiago Barron", "Juan Wisznia", "Luciano del Corro" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jerecoder/IReranker", "github_api_added_by": "user", "project_page": "", "abstract": "Pairwise Ranking Prompting (PRP) elicits pairwise preference judgments from an LLM, which are then aggregated into a ranking, usually via classical sorting algorithms. However, judgments are noisy, order-sensitive, and sometimes intransitive, so sorting assumptions do not match the setting. Because sorting aims to recover a full permutation, truncating it to meet a call budget does not produce a dependable top-K. We thus reframe PRP reranking as active learning from noisy pairwise comparisons and show that active rankers are drop-in replacements that improve NDCG@10 per call in the call-constrained regime. Our noise-robust framework also introduces a randomized-direction oracle that uses a single LLM call per pair. This approach converts systematic position bias into zero-mean noise, enabling unbiased aggregate ranking without the cost of bidirectional calls.", "github": "https://github.com/jerecoder/IReranker" }, { "date": "2026-05-20", "arxiv_id": "2605.20035", "title": "Stage-adaptive Token Selection for Efficient Omni-modal LLMs", "authors": [ "Zijie Xin", "Jie Yang", "Ruixiang Zhao", "Tianyi Wang", "Fengyun Rao", "Jing Lyu", "Xirong Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xxayt/SEATS", "github_api_added_by": "user", "project_page": "https://xxayt.github.io/SEATS/", "abstract": "Omni-modal large language models (om-LLMs) achieve unified audio-visual understanding by encoding video and audio into temporally aligned token sequences interleaved at the window level. However, processing these dense non-textual tokens throughout the LLM incurs substantial computational overhead. Although training-free token selection can reduce this cost, existing methods either focus on visual-only inputs or prune om-LLM tokens only before the LLM with fixed per-modality ratios, failing to capture how cross-modal token importance evolves across layers. To address this limitation, we first analyze the layer-wise token dependency of om-LLMs. We find that visual and audio dependencies follow a block-wise pattern and gradually weaken with depth, indicating that many late-layer non-textual tokens become redundant after cross-modal fusion. Motivated by this observation, we propose SEATS, a training-free, stage-adaptive token selection method for efficient om-LLM inference. Before the LLM, SEATS removes spatiotemporal redundancy via attention-weighted diversity selection. Inside the LLM, it progressively prunes tokens across blocks and dynamically allocates the retention budget from temporal windows to modalities using query relevance scores. In late layers, it removes all remaining non-textual tokens once cross-modal fusion is complete. Experiments on Qwen2.5-Omni and Qwen3-Omni demonstrate that SEATS effectively improves inference efficiency. Retaining only 10% of visual and audio tokens, it achieves a 9.3x FLOPs reduction and a 4.8x prefill speedup while preserving 96.3% of the original performance.", "github": "https://github.com/xxayt/SEATS" }, { "date": "2026-05-20", "arxiv_id": "2605.19633", "title": "optimize_anything: A Universal API for Optimizing any Text Parameter", "authors": [ "Lakshya A Agrawal", "Donghyun Lee", "Shangyin Tan", "Wenjie Ma", "Karim Elmaaroufi", "Rohit Sandadi", "Sanjit A. Seshia", "Koushik Sen", "Dan Klein", "Ion Stoica", "Joseph E. Gonzalez", "Omar Khattab", "Alexandros G. Dimakis", "Matei Zaharia" ], "github_manual": "", "github_regex": "https://github.com/gepa-ai/gepa", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://gepa-ai.github.io/gepa/blog/2026/02/18/introducing-optimize-anything/", "abstract": "Can a single LLM-based optimization system match specialized tools across fundamentally different domains? We show that when optimization problems are formulated as improving a text artifact evaluated by a scoring function, a single AI-based optimization system-supporting single-task search, multi-task search with cross-problem transfer, and generalization to unseen inputs-achieves state-of-the-art results across six diverse tasks. Our system discovers agent architectures that nearly triple Gemini Flash's ARC-AGI accuracy (32.5% to 89.5%), finds scheduling algorithms that cut cloud costs by 40%, generates CUDA kernels where 87% match or beat PyTorch, and outperforms AlphaEvolve's reported circle packing solution (n=26). Ablations across three domains reveal that actionable side information yields faster convergence and substantially higher final scores than score-only feedback, and that multi-task search outperforms independent optimization given equivalent per-problem budget through cross-task transfer, with benefits scaling with the number of related tasks. Together, we show for the first time that text optimization with LLM-based search is a general-purpose problem-solving paradigm, unifying tasks traditionally requiring domain-specific algorithms under a single framework. We open-source optimize\\_anything with support for multiple backends as part of the GEPA project at https://github.com/gepa-ai/gepa .", "github": "https://github.com/gepa-ai/gepa" }, { "date": "2026-05-20", "arxiv_id": "2605.19786", "title": "Fast 4D Mesh Generation by Spatio-Temporal Attention Chains", "authors": [ "Dvir Samuel", "Yuval Atzmon", "Gal Chechik", "Yoni Kasten" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://research.nvidia.com/labs/par/fast4dmesh/", "abstract": "4D mesh generation has recently emerged as a powerful paradigm for recovering dynamic 3D structure from videos, but existing methods remain slow, computationally expensive, and difficult to scale to longer sequences. We introduce a training-free approach that accelerates 4D mesh generation while improving temporal correspondence quality. Our key observation is that temporal correspondences emerge inside a 4D backbone long before its generated meshes become visually accurate. We exploit this with a general framework we call Spatio-Temporal Attention Chain which propagates information across space and time. Starting from vertices on an anchor mesh, the chain maps vertices to latent tokens. It then follows temporal correspondences in latent space, and recovers frame-specific vertices through latent-to-vertex attention. This design avoids expensive explicit matching while preserving anchor mesh details and thereby improving dynamic mesh geometry and temporal consistency. Compared to state-of-the-art, our method generates a 4D mesh in 9 seconds, achieving a 13times speedup while producing higher-quality results. Moreover, our approach scales to videos up to 16times longer without degrading mesh quality. Beyond generation, the improved correspondences enable competitive zero-shot performance on two downstream tasks: 2D object tracking and 4D tracking. We further show that our framework enables reliable camera estimation, a capability not supported by prior 4D mesh generation methods.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.17734", "title": "Harnessing LLM Agents with Skill Programs", "authors": [ "Hongjun Liu", "Yifei Ming", "Shafiq Joty", "Chen Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Equipping LLM agents with reusable skills derived from past experience has become a popular and successful approach for tackling complex and long-horizon tasks. However, such lessons are often encoded as textual guidance that remains largely advisory, lacking explicit mechanisms for when and how to intervene in the agent loop. To bridge the gap, we introduce HASP(Harnessing LLM Agents with Skill Programs), a new framework that upgrades skills into executable Program Functions (PFs). Rather than offering passive advice, PFs act as executable guardrails that activate on failure-prone states and modify the next action or inject corrective context. HASP is highly modular: it can be applied at inference time for direct agent-loop intervention, during post-training to provide structured supervision, or for self-improvement by evolving validated, teacher-reviewed PFs. Empirically, HASP drives substantial gains compared to both training-free and training-based methods on web-search, math reasoning, and coding tasks. For example, on web-search reasoning, inference-time PFs alone improve the average performance by 25% compared to (multi-loop) ReAct Agent, while post-training and controlled evolution achieve a 30.4% gain over Search-R1. To provide deeper insights into HASP, our mechanism analysis reveals how PFs trigger and intervene, how skills are internalized, and the requirement for stable skill library evolution.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.14842", "title": "Editor's Choice: Evaluating Abstract Intent in Image Editing through Atomic Entity Analysis", "authors": [ "Mor Ventura", "Roy Hirsch", "Yonatan Bitton", "Regev Cohen", "Roi Reichart" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://venturamor.github.io/EditorsChoice/", "abstract": "Humans naturally communicate through abstract concepts like \"mood\". However, current image editing benchmarks focus primarily on explicit, literal commands, leaving abstract instructions largely underexplored. In this work, we first formalize the definition and taxonomy of abstract image editing. To measure instruction-following in this challenging domain, we introduce Entity-Rubrics, a framework that breaks down abstract edits into individual, entity-level assessments and achieves strong correlation with human judgment. Alongside this framework, we contribute AbstractEdit, the first benchmark dedicated to abstract image editing across diverse real-world scenes. Evaluating 11 leading models on this dataset reveals a fundamental challenge: standard architectures struggle to balance intent and preservation, commonly defaulting to under-editing or over-editing. Our analysis demonstrates that driving meaningful improvements relies heavily on integrating advanced LLM text encoders and iterative thinking. Looking forward, our entity-based paradigm can generalize beyond assessment to serve as a reward model, enable models to correctly interpret abstract communication, or highlight specific failures in test-time critique loops. Ultimately, we hope this work serves as a stepping stone toward seamless multimodal interaction, closing the gap between rigid machine execution and the natural, open-ended way humans communicate.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.19305", "title": "Mat\u00e9rn Noise for Triangulation-Agnostic Flow Matching on Meshes", "authors": [ "Tianshu Kuai", "Arman Maesumi", "Daniel Ritchie", "Noam Aigerman" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kts707/matern-fm", "github_api_added_by": "user", "project_page": "https://matern-fm.github.io/", "abstract": "This paper tackles the task of learning to generate signals over triangle meshes in a triangulation-agnostic manner, meaning the trained model can be applied to different meshes and triangulations effectively. Practically, the paper adapts the flow matching (FM) paradigm to a mesh-based, triangulation-agnostic setting. Theoretically, it proposes a specific noise distribution which is triangulation agnostic, to be used inside the FM model's denoising process. While noise distributions are usually trivial to devise for, e.g., images, devising a triangulation-agnostic distribution proves to be a much more difficult task. We formulate a mathematical definition of triangulation agnosticism of distributions, via their spectrum. We then show that a discretization of a specific Gaussian random field called a Mat\u00e9rn process holds these desired properties, and provides a simple and efficient sampling algorithm. We use it as our noise model, and adapt FM to the triangulation-agnostic setting by using a state-of-the-art approach for learning signals on meshes in the gradient domain -- PoissonNet -- as the denoiser. We conduct experiments on elaborate tasks such as sampling elastic rest states, and generating poses of humanoids. Our method is shown to be capable of producing highly realistic results for meshes of over one million triangles, significantly exceeding the state-of-the-art in quality and diversity.", "github": "https://github.com/kts707/matern-fm" }, { "date": "2026-05-20", "arxiv_id": "2605.17026", "title": "Why Do Reasoning Models Lose Coverage? The Role of Data and Forks in the Road", "authors": [ "Ngoc-Hieu Nguyen", "Parshin Shojaee", "Phuc Minh Nguyen", "Nan Zhang", "Chandan K Reddy", "Khoa D Doan", "Rui Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/psunlpgroup/reasoning_forks", "github_api_added_by": "user", "project_page": "https://nnhieu.github.io/blog/2026/reasoning-forks/", "abstract": "Recent progress in large language models has led to the emergence of reasoning models, which have shown strong performance on complex tasks through specialized fine-tuning procedures. While these methods reliably improve pass@1 accuracy, prior works have observed that they show a coverage shrinkage behavior, where pass@k degrades relative to the base model. In this paper, we investigate the reasoning shrinkage arise under SFT-based post-training. We hypothesize that this behavior is driven by properties of the fine-tuning data, specifically related to decision points or \"forks in the road\" scenarios where model faces indecipherable patterns with multiple valid reasoning paths. To test this hypothesis, we design controlled case studies that simulate such decision-point settings, spanning indecipherable nodes in graph branching, and reasoning modes. By tracking post-training dynamics in these settings, we find that the shrinkage phenomenon is tightly correlated with the prevalence of decision-point scenarios in the training data. We also demonstrate that this shrinkage behavior can be partially mitigated through targeted data synthesis design of decision-points, and a more systematic diversity-encouraging decoding mechanism. Our findings identify data-centric factors as a key driver of shrinkage in reasoning models and highlight diversity-aware designs as an effective lever for controlling it.", "github": "https://github.com/psunlpgroup/reasoning_forks" }, { "date": "2026-05-20", "arxiv_id": "2605.15514", "title": "RoPE Distinguishes Neither Positions Nor Tokens in Long Contexts, Provably", "authors": [ "Yufeng Du", "Phillip Harris", "Minyang Tian", "Eliu A Huerta", "Srikanth Ronanki", "Subendhu Rongali", "Aram Galstyan", "Hao Peng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We identify intrinsic limitations of Rotary Positional Embeddings (RoPE) in Transformer-based long-context language models. Our theoretical analysis abstracts away from the specific content of the context and depends only on its length. We prove that as context length increases, RoPE-based attention becomes unpredictable and loses two properties that are central to its effectiveness. First, it loses its locality bias: RoPE is no more likely to favor nearer positions than substantially farther ones. Second, it loses consistency in token relevance: a key vector that receives a higher attention score than an alternative at one position may receive a lower score at another. In both cases, the probability of failure approaches 0.5, no better than random guessing. We further prove that the attention score can remain unchanged when a key token is moved to a different position, or even replaced by a different token, indicating a failure to distinguish positions or tokens. Adjusting the RoPE base trades off distinguishing positions against distinguishing tokens but cannot preserve both at the same time. Increasing the RoPE base hyperparameter, a common practice in today's long-context models, helps distinguish different tokens, but inevitably sacrifices the ability to distinguish positions. Our empirical analysis shows that multi-head, multi-layer architectures are insufficient to overcome these limitations. Our findings suggest that fundamentally new mechanisms for encoding position and token order may be needed in future Transformer long-context language models.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.09789", "title": "Zero-Shot Sim-to-Real Robot Learning: A Dexterous Manipulation Study on Reactive Catching", "authors": [ "Kejia Ren", "Gaotian Wang", "Andrew S. Morgan", "Kaiyu Hang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Dexterous manipulation is physics-intensive and highly sensitive to modeling errors and perception noise, making sim-to-real transfer prohibitively challenging. Domain randomization (DR) is commonly used to improve the robustness of learned policies for such tasks, but conventional DR randomizes one instance per episode, offering very limited exposure to the variability of real-world dynamics. To this end, we propose Domain-Randomized Instance Set (DRIS), which represents and propagates a set of randomized instances simultaneously, providing richer approximation of uncertain dynamics and enabling policies to learn actions that account for multiple possible outcomes. Supported by theoretical analysis, we show that DRIS yields more robust policies and alleviates the need for real-world fine-tuning, even with a modest number of instances (e.g., 10). We demonstrate this on a challenging reactive catching task. Unlike traditional catching setups that use end-effectors designed to mechanically stabilize the object (e.g., curved or enclosing surfaces), our system uses a flat plate that offers no passive stabilization, making the task highly sensitive to noise and requiring rapid reactive motions. The learned policies exhibit strong robustness to uncertainties and achieve reliable zero-shot sim-to-real transfer.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.18263", "title": "RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian Splatting", "authors": [ "Ji Shi", "Xianghua Ying", "Bowei Xing", "Ruohao Guo", "Wenzhen Yue" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sjj118/RT-Splatting", "github_api_added_by": "user", "project_page": "https://sjj118.github.io/RT-Splatting/", "abstract": "3D Gaussian Splatting (3DGS) enables real-time novel view synthesis with high visual quality. However, existing methods struggle with semi-transparent specular surfaces that exhibit both complex reflections and clear transmission, often producing blurry reflections or overly occluded transmission. To address this, we present RT-Splatting, a framework that disentangles each Gaussian's geometric occupancy from its optical opacity. This factorization yields a unified surface-volume scene representation with a single set of Gaussian primitives. Our hybrid renderer interprets this representation both as a surface to capture high-frequency reflections and as a volume to preserve clear transmission. To mitigate the ambiguity in jointly optimizing reflection and transmission, we introduce Specular-Aware Gradient Gating, which suppresses misleading gradients from highly specular regions into the transmission branch, effectively reducing distracting floaters. Experiments on challenging semi-transparent scenes show that RT-Splatting achieves state-of-the-art performance, delivering high-fidelity reflections and clear transmission with real-time rendering. Moreover, our factorization naturally enables flexible scene editing. The project page is available at https://sjj118.github.io/RT-Splatting.", "github": "https://github.com/sjj118/RT-Splatting" }, { "date": "2026-05-20", "arxiv_id": "2605.08586", "title": "Computer Science Conferences Should Require Nonrepudiable Experimental Results", "authors": [ "Mamadou K. Keita", "Christopher Homan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://kveritas.org", "abstract": "This position paper argues that computer science conferences should require tamper-evident, nonrepudiable attestations of experimental results. We name the underlying problem experiment nonrepudiation: a compliant protocol must bind the numbers in a paper to an actual executed computation in a way the author cannot later alter or deny. The current system relies on self-reported checklists, optional code sharing, and author-controlled logging. None of these mechanisms answer the question a reviewer cannot check: did the code the paper describes produce the numbers the paper reports? We define the problem formally, state the security properties any compliant protocol must satisfy, and describe a threat model that includes attacks current approaches do not prevent. To show that the problem is solvable, we built K-Veritas, a reference implementation in Go that produces signed reports without accessing training data. K-Veritas is a testbed, not a finished answer. We call on conferences and the community to treat nonrepudiation as a first-class requirement and to help build an open, independent standard for it.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.08472", "title": "Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models", "authors": [ "Aswin RRV", "Jacob Dineen", "Divij Handa", "Mihir Parmar", "Ben Zhou", "Swaroop Mishra", "Chitta Baral" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The effectiveness of Reinforcement Learning (RL) in Large Language Models (LLMs) depends on the nature and diversity of the data used before and during RL. In particular, reasoning problems can often be approached in multiple ways that rely on different forms of reasoning, and exposure to only a limited range of such approaches in the training data may limit the effectiveness of RL. Motivated by this, we investigate using diverse self-generated data during mid-training as an intermediate step before RL training. Specifically, we adopt a bootstrapped data-generation framework guided by George Polya's problem-solving approaches for generating multiple variants of correct answers for each question in the training data, and then perform fine-tuning. We first provide a theoretical perspective on how mid-training on such data improves RL and explain how policy-gradient updates can incentivize combining multiple approaches. We then empirically demonstrate that RL-trained models initialized with our mid-training data achieve consistent improvements across various mathematical reasoning benchmarks and other OOD tasks like code generation and narrative reasoning. Overall, our investigative study shows that a language model learning multiple problem-solving approaches, through self-generated data helps subsequent RL.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.17659", "title": "Bug or Feature^2: Weight Drift, Activation Sparsity, and Spikes", "authors": [ "Egor Shvetsov", "Aleksandr Serkov", "Shokorov Viacheslav", "Redko Dmitry", "Vladislav Goloshchapov", "Evgeny Burnaev" ], "github_manual": "", "github_regex": "https://github.com/On-Point-RND/BugOrFeature", "github_paper_md": "", "github_api": "https://github.com/On-Point-RND/BugOrFeature", "github_api_added_by": "user", "project_page": "", "abstract": "The design of modern neural architectures has converged through incremental empirical choices, yet the mechanisms governing their training dynamics remain only partially understood. We identify and analyze a negative weight drift induced by the interaction between standard losses and positively biased activation functions. We prove that under MSE or cross-entropy loss, the gradient with respect to positive pre-activations is non-negative in expectation at initialization, driving downstream weights toward negative values during early training. The drift is intrinsic to optimization rather than data, and persists across architectures (MLP, ResNet, ViT, GPT-nano, MP-SENe) and asymmetric activation functions (ReLU, GELU, SiLU). Coupled with ReLU, weight drift produces activation sparsity reaching up to 90\\% in GPT-nano. We characterize the sparsity-accuracy tradeoff across 79 configurations and identify a sharp accuracy cliff above sim70\\% activation sparsity. While ReLU^2 achieves a good sparsity--accuracy ratio in GPT-nano, it pathologically amplifies identified activation spikes in intermediate transformer layers. Clipping resolves this while preserving the representational benefits of squaring: clipped ReLU^2 outperforms its unclipped version, and GELU^2 achieves the lowest validation loss on GPT-nano. Code is available at https://github.com/On-Point-RND/BugOrFeature.", "github": "https://github.com/On-Point-RND/BugOrFeature" }, { "date": "2026-05-20", "arxiv_id": "2605.19147", "title": "Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks", "authors": [ "John T. Halloran", "Noopur S. Bhatt" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language models (LLMs) are highly susceptible to backdoor attacks (BAs), wherein training samples are poisoned using trigger-based harmful content. Furthermore, existing defenses have proven ineffective when extensively tested across BA patterns. To better combat BAs, we explore the use of LLM rewriting as a proactive defense against data poisoning. First, we theoretically show that when LLM rewriting utilizes open-book benign samples--termed open-book benign rewriting (OBBR)--the probability of a rewritten output being benign is strictly greater than that of closed-book rewriting. Thus, OBBR neutralizes harmful content by projecting training samples to the space of benign prompts. We then show that, in contrast to previous defenses, OBBR effectively mitigates a large number of existing BAs: across five known BAs and four widely used LLMs, OBBR increases safety performance by an average 51% compared to state-of-the-art BA defenses and 25.7% compared to closed-book rewriting methods. Finally, we show that OBBR is computationally efficient relative to other BA defenses, does not degrade model performance on natural language tasks after fine-tuning, and is capable of defending against non-trigger based data poisoning attacks.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.19516", "title": "Base Models Look Human To AI Detectors", "authors": [ "Yixuan Even Xu", "Ziqian Zhong", "Aditi Raghunathan", "Fei Fang", "J. Zico Kolter" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YixuanEvenXu/humanization-by-iterative-paraphrasing", "github_api_added_by": "user", "project_page": "", "abstract": "As AI-generated text enters the real-world at scale, institutions increasingly use commercial AI-text detectors, especially in education and academic-integrity workflows. We report a surprising empirical finding about such systems: when evaluated by GPTZero and Pangram, generated text from base models is often judged overwhelmingly human, whereas text generated by their instruction-tuned counterparts is not. Building on this observation, we propose Humanization by Iterative Paraphrasing (HIP), a detector-agnostic pipeline that minimally fine-tunes a base model into a paraphraser and applies it iteratively. Compared with the baselines we test, HIP yields a stronger trade-off between semantic preservation and detector evasion on commercial detectors. Across Llama-3 and Qwen-3 families, spanning model sizes from 0.6B to 70B, HIP consistently improves detector human-likeness. Our findings suggest that current detectors are tracking artifacts of instruction tuning and local context more than any invariant notion of machine-generated text. This, in turn, calls for detector designs that model these factors more explicitly.", "github": "https://github.com/YixuanEvenXu/humanization-by-iterative-paraphrasing" }, { "date": "2026-05-20", "arxiv_id": "2605.17829", "title": "Interactive Evaluation Requires a Design Science", "authors": [ "Keyang Xuan", "Peiyang Song", "Pan Lu", "Pengrui Han", "Wenkai Li", "Zhenyu Zhang", "Zexue He", "Wenyue Hua", "Manling Li", "Jiaxuan You", "Adrian Weller", "Yizhong Wang", "Jiaxin Pei" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/keyangds/interactive_evaluation", "github_api_added_by": "user", "project_page": "", "abstract": "AI evaluation is undergoing a structural change. Large language models (LLMs) are increasingly deployed as systems that act over time through tools, environments, users, and other agents, while many evaluation practices still inherit assumptions from response-centered benchmarks (e.g., fixed inputs, isolated outputs, and outcome judgments that can be made from a single response). The field has begun to build interactive benchmarks, but the resulting landscape is fragmented: benchmarks differ in what interaction artifacts they admit, how trajectories are scored, and what claims their results support. This position paper argues that interactive evaluation should be treated as a principled evaluation paradigm, not merely a new family of agent benchmarks. Simply adopting previous evaluation paradigms does not suffice. We define evaluation as an autonomous mapping from evidence to judgments, and show that interactive evaluation changes both sides of this mapping: the evidence becomes interaction-generated trajectories, while the evaluation procedure must assess process, recoverability, coordination, robustness, and system-level performance. Building on this definition, we propose a two-axis taxonomy, derive design principles and reporting standards, examine representative scenarios, and analyze how longstanding evaluation challenges reappear at the trajectory level.", "github": "https://github.com/keyangds/interactive_evaluation" }, { "date": "2026-05-20", "arxiv_id": "2605.20164", "title": "Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR", "authors": [ "Utkarsh Tyagi", "Xingang Guo", "MohammadHossein Rezaei", "Daniel George", "Anas Mahmoud", "Jackson Lee", "Bing Liu", "Yunzhong He" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards has made post-training highly effective when correctness can be checked automatically. However, many important model behaviors require satisfying several qualitative criteria at once. Rubric-based rewards address this setting by grading prompt-specific criteria and aggregating them into a scalar reward. Yet standard static aggregations conflate a criterion's human-assigned importance with its current usefulness as an optimization signal. We show that this assumption breaks down in rubric RL: many important criteria are already saturated or currently unreachable, while criteria that distinguish rollouts are not necessarily those with the largest human weights. We introduce POW3R, a policy-aware rubric reward framework that preserves human weights and category balance as the rubric objective while adapting criterion-level reward weights during training. POW3R uses rollout-level contrast to emphasize criteria that currently separate the policy's outputs, making the GRPO reward more informative without changing the underlying evaluation target. Across three base policies on two datasets spanning multimodal and text-only settings, POW3R wins 24 of 30 base-policy/metric comparisons, improving both mean rubric reward and strict completion (the fraction of prompts whose response satisfies every required rubric criterion) over vanilla GRPO with rubric rewards, and reaches the same plateau in 2.5--4times fewer training steps. Rubric rewards should therefore distinguish what should matter in the final answer from what can teach the current policy.", "github": "" }, { "date": "2026-05-20", "arxiv_id": "2605.20087", "title": "ThoughtTrace: Understanding User Thoughts in Real-World LLM Interactions", "authors": [ "Chuanyang Jin", "Binze Li", "Haopeng Xie", "Cathy Mengying Fang", "Tianjian Li", "Shayne Longpre", "Hongxiang Gu", "Maximillian Chen", "Tianmin Shu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thoughttrace-project/thoughttrace", "github_api_added_by": "user", "project_page": "https://thoughttrace-project.github.io/", "abstract": "Conversational AI has now reached billions of users, yet existing datasets capture only what people say, not what they think. We introduce ThoughtTrace, the first large-scale dataset that pairs real-world multi-turn human--AI conversations with users' self-reported thoughts: their reasons for sending prompts and reactions to assistant responses. ThoughtTrace comprises 1,058 users, 2,155 conversations, 17,058 turns, and 10,174 thought annotations collected across 20 language models. Our analysis shows that ThoughtTrace captures long-horizon, topically diverse interactions, and that thoughts are semantically distinct from messages, difficult for frontier LLMs to infer from context, diverse in content, and tied to conversation stages. We further demonstrate the utility of thoughts for downstream modeling. First, thoughts improve user-behavior prediction as inference-time context. Second, thought-guided rewrites provide fine-grained alignment signals for training personalized assistants. Together, ThoughtTrace establishes user thoughts as a new data modality for studying the cognitive dynamics behind human--AI interaction and provides a foundation for building assistants that better understand and adapt to users' latent goals, preferences, and needs.", "github": "https://github.com/thoughttrace-project/thoughttrace" }, { "date": "2026-05-20", "arxiv_id": "2605.17076", "title": "S-Bus: Automatic Read-Set Reconstruction for Multi-Agent LLM State Coordination", "authors": [ "Sajjad Khan" ], "github_manual": "", "github_regex": "https://github.com/sajjadanwar0/sbus", "github_paper_md": "", "github_api": "https://github.com/sajjadanwar0/sbus", "github_api_added_by": "user", "project_page": "", "abstract": "Concurrent LLM agents sharing mutable natural-language state produce Structural Race Conditions (SRCs): write-write and cross-shard stale-read conflicts that silently corrupt agent output. Existing multi-agent frameworks (LangGraph, CrewAI, AutoGen) provide no write-ownership semantics over shared state. We present S-Bus, an HTTP middleware whose central mechanism is a server-side DeliveryLog: a per-agent log of HTTP GET operations that automatically reconstructs each agent's read set at commit time without agent SDK changes under HTTP/1.1. The consistency property the DeliveryLog provides -- Observable-Read Isolation (ORI), a partial causal consistency over the HTTP-observable projection of the read set -- prevents structural race conditions when agents collaborate via shared shards. Three contributions: (C1) The DeliveryLog mechanism for automatic HTTP-traffic-based read-set reconstruction, with three-tier mechanised evidence: ReadSetSoundness and ORICommitSafety machine-checked in TLAPS (modulo one retained typing axiom); exhaustive TLC at N=3 (20,763,484 distinct states, zero violations); Dafny discharges 9 inductive soundness lemmas. (C2) Empirical structural-conflict prevention parity against PostgreSQL 17 SERIALIZABLE and Redis 7 WATCH/MULTI on shared-shard contention sweeps with 427,308 active HTTP-409 conflicts: zero Type-I corruptions across all three backends. (C3) ORI's operating envelope is topology-conditional: semantically neutral in dedicated-shard workloads; harmful in single-shard collaborative writing because preservation propagates concurrent contradictions. Source code: https://github.com/sajjadanwar0/sbus", "github": "https://github.com/sajjadanwar0/sbus" }, { "date": "2026-05-21", "arxiv_id": "2605.21131", "title": "UniT: Unified Geometry Learning with Group Autoregressive Transformer", "authors": [ "Haotian Wang", "Yusong Huang", "Zhaonian Kuang", "Hongliang Lu", "Xinhu Zheng", "Meng Yang", "Gang Hua" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Wang-xjtu/UniT", "github_api_added_by": "user", "project_page": "https://sc2i-hkustgz.github.io/UniT/", "abstract": "Recent feed-forward models have significantly advanced geometry perception for inferring dense 3D structure from sensor observations. However, its essential capabilities remain fragmented across multiple incompatible paradigms, including online perception, offline reconstruction, multi-modal integration, long-horizon scalability, and metric-scale estimation. We present UniT, a unified model built upon a novel Group Autoregressive Transformer, which reformulates these seemingly disparate capabilities within a single framework. The key idea is to treat groups of sensor observations as the basic autoregressive units and predict the corresponding point maps in an anchor-free and scale-adaptive manner. More specifically, diverse view configurations in both online and offline settings are naturally unified within a single group autoregression process. By varying the group size, online mode operates over multiple autoregressive steps with single-frame groups, whereas offline mode aggregates a multi-frame group in a single forward pass. Meanwhile, a queue-style KV caching mechanism ensures bounded autoregressive memory over long horizons. This is enabled by reducing long-range dependencies on early frames through anchor-free relational modeling, thereby allowing outdated memory to be discarded on the fly. To improve metric-scale generalization across scenes, a scale-adaptive geometry loss is further introduced within this framework. It couples relative geometric constraints with a partial absolute scale term, implicitly regularizing global scale and inducing a progressive transition from scale-invariant geometry to metric-scale solutions. Together with a dedicated modal attention module for integrating auxiliary modalities, UniT achieves state-of-the-art performance in unified geometry perception, as validated on ten benchmarks spanning seven representative tasks.", "github": "https://github.com/Wang-xjtu/UniT" }, { "date": "2026-05-20", "arxiv_id": "2605.18101", "title": "SENSE: Satellite-based ENergy Synthesis for Sustainable Environment", "authors": [ "Kailai Sun", "Mingyi He", "Heye Huang", "Can Rong", "Alok Prakash", "Baoshen Guo", "Shenhao Wang", "Jinhua Zhao" ], "github_manual": "", "github_regex": "https://github.com/kailaisun/GenAI4Urban-Energy", "github_paper_md": "", "github_api": "https://github.com/kailaisun/GenAI4Urban-Energy", "github_api_added_by": "user", "project_page": "https://arxiv.org/abs/2605.18101", "abstract": "Urban Building Energy Modeling plays a critical role in achieving the United Nations' Sustainable Development Goals 7 and 11. Although existing studies based on satellite imagery and deep learning have achieved remarkable progress, many challenges exist: most existing studies are inherently predictive, failing to reflect the generative nature of urban planning; although generative AI and diffusion models have seen explosive growth in satellite imagery, they lack the urban functional generation (e.g., energy layer); third, aligned high-quality high-resolution building energy data with satellite imagery is limited and scarce. Here we propose SENSE (Satellite-based ENergy Synthesis for Sustainable Environment), a unified generative UBEM framework that jointly synthesizes realistic urban satellite imagery and aligned high-quality building energy consumption and height maps. By conditioning on road networks and urban density metrics, SENSE, based on a controllable diffusion model, leverages the knowledge learned by large vision models to generate urban building energy consumption and height information (annotations) in the latent space. Experiments across four cities (New York City, Boston, Lyon, Busan) demonstrate that SENSE achieves high visual fidelity and strong physical consistency, satisfying the ASHRAE standard metric. Experiments demonstrate that SENSE can generate enough annotated synthetic data using less than 20% labeled energy data, boosting downstream prediction performance by 10% IoU. Compared to SOTA urban energy prediction methods, SENSE significantly reduced prediction error (reduced 3%-11% NMBE and 1%-9% CVRMSE). This study offers an energy-efficiency urban planning and physical generation solution for urban science, energy science and building science. The dataset and code: https://huggingface.co/datasets/skl24/MUSE and https://github.com/kailaisun/GenAI4Urban-Energy/.", "github": "https://github.com/kailaisun/GenAI4Urban-Energy" }, { "date": "2026-05-20", "arxiv_id": "2605.17909", "title": "Ethical Hyper-Velocity (EHV): A Provably Deterministic Governance-Aware JIT Compiler Architecture for Agentic Systems", "authors": [ "Riddhi Mohan Sharma" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/riddhimohansharma/ehv-runtime", "github_api_added_by": "user", "project_page": "https://www.riddhimohan.com/blog/ethical-hyper-velocity-ehv-compiling-governance-into-ai-inference-stack", "abstract": "As autonomous agentic systems scale across regulated critical infrastructures, the lack of mechanistic, hardware-rooted enforcement for high-frequency policy updates presents a fundamental safety gap. We introduce Ethical Hyper-Velocity (EHV), a novel architectural framework for the formal verification of AI governance policies at runtime. Unlike retrospective auditing frameworks (ISO/IEC 42001, NIST AI RMF) which introduce 14-30 day latencies, EHV relocates the Policy Enforcement Point (PEP) into the inference pipeline via a Governance-Aware Just-In-Time (JIT) Compiler. By integrating Conflict-free Replicated Data Types (CRDTs) for policy synchronization and Epoch-based Attestation Caching within Trusted Execution Environments (TEEs), EHV achieves Sub-millisecond Formal Determinism (SMFD). We demonstrate via TLA+ formal verification that non-compliant agentic actions are computationally unreachable within the system's bounded operating state space. We prove that O(1) runtime enforcement can eliminate the traditional trade-off between deployment velocity and governance integrity, reducing Governance Latency from O(days) to O(1).", "github": "https://github.com/riddhimohansharma/ehv-runtime" }, { "date": "2026-05-21", "arxiv_id": "2605.20630", "title": "Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines", "authors": [ "Alimurtaza Mustafa Merchant", "Krish Veera", "Sajal Kumar Goyla", "Shambhawi Bhure", "Dhaval Patel", "Kaoutar El Maghraoui" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Industrial asset operations workflows are latency-sensitive because a single user query may require coordination over sensor data, work orders, failure modes, forecasting tools, and domain-specific agents. We evaluate this problem on AssetOpsBench (AOB), an industrial agent benchmark whose plan-execute pipeline exposes repeated overhead from tool discovery, LLM planning, MCP tool execution, and final summarization. Existing LLM caching techniques such as KV-cache reuse and embedding-based semantic caching were designed for chatbot serving and break down when output validity depends on time, asset, or sensor parameters. We propose two complementary optimization layers for AOB plan-execute pipelines: a temporal semantic cache and a set of MCP workflow optimizations combining disk-backed tool-discovery caching and dependency-aware parallel step execution. MCP workflow optimizations corresponded to a 1.67x speedup and reduced median end-to-end latency by about 40.0% while the temporal-cache benchmark achieved a median of 30.6x speedup on cache hits. Beyond the speedup, our results expose a concrete failure mode of pure semantic caching for parameter-rich industrial queries, providing a critical analysis of how caching choices interact with evaluation correctness in MCP-backed agent benchmarks.", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.19376", "title": "Generative Recursive Reasoning", "authors": [ "Junyeob Baek", "Mingyu Jo", "Minsu Kim", "Mengye Ren", "Yoshua Bengio", "Sungjin Ahn" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://ahn-ml.github.io/gram-website/", "abstract": "How should future neural reasoning systems implement extended computation? Recursive Reasoning Models (RRMs) offer a promising alternative to autoregressive sequence extension by performing iterative latent-state refinement with shared transition functions. Yet existing RRMs are largely deterministic, following a single latent trajectory and converging to a single prediction. We introduce Generative Recursive reAsoning Models (GRAM), a framework that turns recursive latent reasoning into probabilistic multi-trajectory computation. GRAM models reasoning as a stochastic latent trajectory, enabling multiple hypotheses, alternative solution strategies, and inference-time scaling through both recursive depth and parallel trajectory sampling. This yields a latent-variable generative model supporting conditional reasoning via p_\u03b8(y mid x) and, with fixed or absent inputs, unconditional generation via p_\u03b8(x). Trained with amortized variational inference, GRAM improves over deterministic recurrent and recursive baselines on structured reasoning and multi-solution constraint satisfaction tasks, while demonstrating an unconditional generation capability. https://ahn-ml.github.io/gram-website", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.21487", "title": "Uni-Edit: Intelligent Editing Is A General Task For Unified Model Tuning", "authors": [ "Dian Zheng", "Manyuan Zhang", "Hongyu Li", "Hongbo Liu", "Kai Zou", "Kaituo Feng", "Hongsheng Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhengdian1/Uni-Edit", "github_api_added_by": "user", "project_page": "https://zhengdian1.github.io/Uni-Edit-proj/", "abstract": "Currently, enhancing Unified Multimodal Models (UMMs) with image understanding, generation, and editing capabilities mainly relies on mixed multi-task training. Due to inherent task conflicts, such strategy requires complex multi-stage pipelines, massive data mixing, and balancing tricks, merely resulting in a performance trade-off rather than true mutual reinforcement. To break this paradigm, we propose Uni-Edit, an intelligent image editing task that serves as the first general task for UMM tuning. Unlike complex mixed pipelines, Uni-Edit improves performance across all three abilities at once using only one task, one training stage, and one dataset. Specifically, we first identify image editing as an inherently ideal general task, as it naturally demands both visual understanding and generation. However, existing editing data relies on simplistic instructions that severely underutilize a model's understanding capacity. To address this, we introduce the first automated and scalable data synthesis pipeline for intelligent editing, transforming diverse VQA data into complex and effective editing instructions with embedded questions and nested logic. This yields Uni-Edit-148k, pairing diverse reasoning-intensive instructions with high-quality edited images. Extensive experiments on BAGEL and Janus-Pro demonstrate that tuning solely on Uni-Edit achieves comprehensive enhancements across all three capabilities without any auxiliary operations.", "github": "https://github.com/zhengdian1/Uni-Edit" }, { "date": "2026-05-21", "arxiv_id": "2605.20668", "title": "On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists", "authors": [ "Seungone Kim", "Dongkeun Yoon", "Kiril Gashteovski", "Juyoung Suk", "Jinheon Baek", "Pranjal Aggarwal", "Ian Wu", "Viktor Zaverkin", "Spase Petkoski", "Daniel R. Schrider", "Ilija Dukovski", "Francesco Santini", "Biljana Mitreska", "Yong Jeong", "Kyeongha Kwon", "Young Min Sim", "Dragana Manasova", "Arthur Porto", "Biljana Mojsoska", "Makoto Takamoto", "Marko Shuntov", "Ruoqi Liu", "Hyunjoo Jenny Lee", "Niyazi Ulas Din\u00e7", "Yehhyun Jo", "Sunkyu Han", "Chungwoo Lee", "Huishan Li", "Esther H. R. Tsai", "Ergun Simsek", "Khushboo Shafi", "Yeonseung Chung", "Jihye Park", "Aleksandar Shulevski", "Henrik Christiansen", "Yoosang Son", "Elly Knight", "Amanda Montoya", "Jeongyoun Ahn", "Christian Langkammer", "Heera Moon", "Changwon Yoon", "Nikola Stikov", "Mooseok Jang", "Edward Choi", "Junhan Kim", "Yeon Sik Jung", "Woo Youn Kim", "Jae Kyoung Kim", "Ishraq Md Anjum", "Hyun Uk Kim", "Drew Bridges", "Carolin Lawrence", "Xiang Yue", "Alice Oh", "Akari Asai", "Sean Welleck", "Graham Neubig" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/prometheus-eval/cmu-paper-reviewer", "github_api_added_by": "user", "project_page": "https://prometheus-eval.github.io/cmu-paper-reviewer/", "abstract": "With the advancement of AI capabilities, AI reviewers are beginning to be deployed in scientific peer review, yet their capability and credibility remain in question: many scientists simply view them as probabilistic systems without the expertise to evaluate research, while other researchers are more optimistic about their readiness without concrete evidence. Understanding what AI reviewers do well, where they fall short, and what challenges remain is essential. However, existing evaluations of AI reviewers have focused on whether their verdicts match human verdicts (e.g., score alignment, acceptance prediction), which is insufficient to characterize their capabilities and limits. In this paper, we close this gap through a large-scale expert annotation study, in which 45 domain scientists in Physical, Biological, and Health Sciences spent 469 hours rating 2,960 individual criticisms (each targeting one specific aspect of a paper) from human-written and AI-generated reviews of 82 Nature-family papers on correctness, significance, and sufficiency of evidence. On a composite of all three dimensions, a reviewing agent powered by GPT-5.2 scores above each paper's top-rated human reviewer (60.0% vs. 48.2%, p = 0.009), while all three AI reviewers (including Gemini 3.0 Pro and Claude Opus 4.5) exceed the lowest-rated human across every dimension. AI reviewers' accurate criticisms are also more often rated significant and well-evidenced, and surface a distinct 26% of issues no human raises. However, AI reviewers overlap far more than humans do (21% vs. 3% for cross-reviewer pairs), and exhibit 16 recurring weaknesses humans do not share, such as limited subfield knowledge, lack of long context management over multiple files, and overly critical stance on minor issues. Overall, our results position current AI reviewers as complements to, not substitutes for, human reviewers.", "github": "https://github.com/prometheus-eval/cmu-paper-reviewer" }, { "date": "2026-05-21", "arxiv_id": "2605.20158", "title": "Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models", "authors": [ "Guangzhi Xiong", "Qiao Jin", "Sanchit Sinha", "Zhiyong Lu", "Aidong Zhang" ], "github_manual": "", "github_regex": "https://github.com/gzxiong/medfocus", "github_paper_md": "", "github_api": "https://github.com/gzxiong/medfocus", "github_api_added_by": "user", "project_page": "", "abstract": "Large Vision Language Models (LVLMs) show promise in medical applications, but their inability to faithfully ground responses in visual evidence raises serious concerns about clinical trustworthiness. While visual attribution methods are widely used to explain LVLM predictions, whether these explanations actually reflect the visual evidence underlying the model's decision is largely unverified, since ground-truth annotations for internal model reasoning are typically unavailable. We address this question for chest X-ray (CXR) reasoning by developing a causal evaluation framework that retains only CXR-VQA samples for which the expert-annotated region is verified, via counterfactual editing, to be causally responsible for the model's prediction. Using this framework across 11 attribution methods, six open-source LVLMs, and two output modes (direct answer and step-by-step reasoning), we find that existing attribution methods often fail to identify the evidence used by LVLMs. To address this failure, we propose MedFocus, a concept-based attribution method that localizes clinically meaningful anatomical regions via unbalanced optimal transport and measures their causal effect on model outputs through targeted interventions. MedFocus produces spatial, concept-level, and token-level attributions and substantially outperforms prior methods, taking a step toward more trustworthy attribution for medical LVLMs. Our data and code are available at https://github.com/gzxiong/medfocus/.", "github": "https://github.com/gzxiong/medfocus" }, { "date": "2026-05-21", "arxiv_id": "2605.20955", "title": "DrawMotion: Generating 3D Human Motions by Freehand Drawing", "authors": [ "Tao Wang", "Lei Jin", "Zhihua Wu", "Qiaozhi He", "Jiaming Chu", "Yu Cheng", "Junliang Xing", "Jian Zhao", "Shuicheng Yan", "Li Wang" ], "github_manual": "", "github_regex": "https://github.com/InvertedForest/DrawMotion", "github_paper_md": "", "github_api": "https://github.com/InvertedForest/DrawMotion", "github_api_added_by": "user", "project_page": "", "abstract": "Text-to-motion generation, which translates textual descriptions into human motions, faces the challenge that users often struggle to precisely convey their intended motions through text alone. To address this issue, this paper introduces DrawMotion, an efficient diffusion-based framework designed for multi-condition scenarios. DrawMotion generates motions based on both a conventional text condition and a novel hand-drawing condition, which provide semantic and spatial control over the generated motions, respectively. Specifically, we tackle the fine-grained motion generation task from three perspectives: 1) freehand drawing condition. To accurately capture users' intended motions without requiring tedious textual input, we develop an algorithm to automatically generate hand-drawn stickman sketches across different dataset formats; 2) multi-condition fusion. We propose a Multi-Condition Module (MCM) that is integrated into the diffusion process, enabling the model to exploit all possible condition combinations while reducing computational complexity compared to conventional approaches; and 3) training-free guidance. Notably, the MCM in DrawMotion ensures that its intermediate features lie in a continuous space, allowing classifier-guidance gradients to update the features and thereby aligning the generated motions with user intentions while preserving fidelity. Quantitative experiments and user studies demonstrate that the freehand drawing approach reduces user time by approximately 46.7% when generating motions aligned with their imagination. The code, demos, and relevant data are publicly available at https://github.com/InvertedForest/DrawMotion.", "github": "https://github.com/InvertedForest/DrawMotion" }, { "date": "2026-05-21", "arxiv_id": "2605.20873", "title": "PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models", "authors": [ "Ziliang Zhao", "Zenan Xu", "Shuting Wang", "Hongjin Qian", "Yan Lei", "Minda Hu", "Zhao Wang", "Shihan Dou", "Zhicheng Dou", "Pluto Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Planning is a fundamental capability for large language models (LLMs) because such complex tasks require models to coordinate goals, constraints, resources, and long-term consequences into executable and verifiable solutions. Existing planning benchmarks, however, usually treat planning data as fixed collections of instances rather than controllable generation targets. This limits scenario coverage, ties difficulty to surface-level proxies rather than structural sources, and offers limited support for scalable generation, automatic verification, or planning-oriented training. We introduce PlanningBench, a framework for generating scalable, diverse, and verifiable planning data for both evaluation and training. PlanningBench starts from real planning scenarios and abstracts practical workflows into a structured taxonomy of more than 30 task types, subtasks, constraint families, and difficulty factors. Guided by this taxonomy, a constraint-driven synthesis pipeline instantiates self-contained planning problems with adaptive difficulty control, quality filtering, and instance-level verification checklists. This shifts planning data construction from fixed benchmark collection to controllable generation while preserving realistic task grounding. We use PlanningBench to evaluate open-source and closed-source frontier LLMs, and find that current models still struggle to produce complete solutions under coupled constraints. Beyond evaluation, reinforcement learning on verified PlanningBench data improves performance on unseen planning benchmarks and broader instruction-following tasks. Further analysis suggests that determinate or well-specified optimal solutions provide clearer reward signals and more stable training dynamics. Overall, PlanningBench provides a controllable source of planning data for diagnosing and improving generalizable planning abilities in LLMs.", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.21384", "title": "SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents", "authors": [ "Bingchen Zhao", "Dhruv Srikanth", "Yuxiang Wu", "Zhengyao Jiang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "As long-horizon coding agents produce more code than any developer can review, oversight collapses onto a single surface: the automated test suite. Reward hacking naturally arises in this setup, as the agent optimizes for passing tests while deviating from the users true goal. We study this reward hacking phenomenon by decompose software engineering tasks into three parts: (i) a natural language description of the specification (ii) visible validation tests that exercise specified features in isolation, and (iii) held-out tests that compose those same features to simulate real-world usage. Based on the specification and the visible validation test suites, a genuine agent would be able to generate a solution that can also pass all of the held-out tests. Therefore we use the gap in pass rates on these two suites to quantify reward hacking. Based on this methodology, we introduce SpecBench, a benchmark comprising 30 systems-level programming tasks ranging from short horizon tasks like building a JSON parser to ultra long horizon tasks like building an entire OS kernel from scratch. Large-scale experiments reveal a consistent pattern: while every frontier agent saturates the visible suite, reward hacking persists, with smaller models exhibiting larger gaps on holdout suites. The gap also scales sharply with task length: it grows by 28 percentage points for every tenfold increase in code size. Failures range from subtle feature isolation to deliberate exploits, including a 2,900-line hash-table \"compiler\" that memorizes test inputs. SpecBench offers a principled testbed for measuring whether coding agents build genuine working systems or merely game the test suites developers hand them.", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.21463", "title": "Mem-\u03c0: Adaptive Memory through Learning When and What to Generate", "authors": [ "Xiaoqiang Wang", "Chao Wang", "Hadi Nekoei", "Christopher Pal", "Alexandre Lacoste", "Spandana Gella", "Bang Liu", "Perouz Taslakian" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We present Mem-\u03c0, a framework for adaptive memory in large language model (LLM) agents, where useful guidance is generated on demand rather than retrieved from external memory stores. Existing memory-augmented agents typically rely on similarity-based retrieval from episodic memory banks or skill libraries, returning static entries that often misalign with the current context. In contrast, Mem-\u03c0 uses a dedicated language or vision-language model with its own parameters, separate from the downstream agent, to generate context-specific guidance for complex tasks. Conditioned on the current agent context, the model jointly decides when to produce guidance and what guidance to produce. We train it with a decision-content decoupled reinforcement learning (RL) objective, enabling it to abstain when generation would not help and otherwise produce concise, useful guidance. Across diverse agentic benchmarks spanning web navigation, terminal-based tool use, and text-based embodied interaction, Mem-\u03c0 consistently outperforms retrieval-based and prior RL-optimized memory baselines, achieving over 30% relative improvement on web navigation tasks.", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.19660", "title": "OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond", "authors": [ "Zunhai Su", "Rui Yang", "Chao Zhang", "Yaxiu Liu", "Yifan Zhang", "Wei Wu", "Jing Xiong", "Dayou Du", "Xialie Zhuang", "Yulei Qian", "Yuchen Xie", "Yik-Chung Wu", "Hongxia Yang", "Ngai Wong" ], "github_manual": "", "github_regex": "https://github.com/ZunhaiSu/OScaR-KV-Quant", "github_paper_md": "", "github_api": "https://github.com/ZunhaiSu/OScaR-KV-Quant", "github_api_added_by": "user", "project_page": "https://iridescent-gcrace.github.io/OScaR/", "abstract": "The rapid advancement toward long-context reasoning and multi-modal intelligence has made the memory footprint of the Key-Value (KV) cache a dominant memory bottleneck for efficient deployment. While the established per-channel quantization effectively accommodates intrinsic channel-wise outliers in Key tensors, its efficacy diminishes under extreme compression. In this work, we revisit the inherent limitations of the per-channel quantization paradigm from both empirical and theoretical perspectives. Our analysis identifies Token Norm Imbalance (TNI) as the primary bottleneck to quantization fidelity. We demonstrate that TNI systematically amplifies errors when shared quantization parameters are required to span token groups exhibiting substantial norm disparities. Instead of relying on intricate quantization pipelines (e.g., TurboQuant), we propose OScaR (Omni-Scaled Canalized Rotation), an accurate and lightweight KV cache compression framework for X-LLMs (i.e., text-only, multi-modal, and omni-modal LLMs). Advancing the per-channel paradigm, OScaR employs Canalized Rotation followed by Omni-Token Scaling to mitigate TNI-induced sequence-dimensional variance both effectively and efficiently, further supported by our optimized system design and CUDA kernels. Extensive evaluations across X-LLMs show that OScaR consistently outperforms existing methods and achieves near-lossless performance under INT2 quantization, establishing it as a robust, low-complexity, and universal framework that defines a new Pareto front. Compared with the BF16 FlashDecoding-v2 baseline, our OScaR implementation achieves a notable up to 3.0x speedup in decoding, reduces memory footprint by 5.3x, and increases throughput by 4.1x. The code for OScaR is publicly available at https://github.com/ZunhaiSu/OScaR-KV-Quant.", "github": "https://github.com/ZunhaiSu/OScaR-KV-Quant" }, { "date": "2026-05-21", "arxiv_id": "2605.20834", "title": "Conditional Equivalence of DPO and RLHF: Implicit Assumption, Failure Modes, and Provable Alignment", "authors": [ "Zhiqin Yang", "Yonggang Zhang", "Wei Xue", "Dong Fang", "Bo Han", "Yike Guo" ], "github_manual": "", "github_regex": "https://github.com/visitworld123/CPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Direct Preference Optimization (DPO) has emerged as a popular alternative to Reinforcement Learning from Human Feedback (RLHF), offering theoretical equivalence with simpler implementation. We prove this equivalence is conditional rather than universal, depending on an implicit assumption frequently violated in practice: the RLHF-optimal policy must prefer human-preferred responses. When this assumption fails, DPO optimizes relative advantage over the reference policy rather than absolute alignment with human preferences, leading to pathological convergence where policies decrease DPO loss while preferring dispreferred responses. We characterize when this assumption is violated, show the existence of an undesirable solution space, and prove that DPO and RLHF optimize fundamentally different objectives in such cases. To address this, we introduce Constrained Preference Optimization (CPO), augmenting RLHF with constraints for provable alignment. We further provide a geometric interpretation through soft margin ranking, revealing that DPO implements margin ranking with potentially negative targets. Our theoretical analysis establishes when DPOs' guarantees hold and provides solutions preserving simplicity with provable alignment. Comprehensive experiments on standard benchmarks demonstrate that CPO achieves state-of-the-art performance. Code is available at: https://github.com/visitworld123/CPO.", "github": "https://github.com/visitworld123/CPO" }, { "date": "2026-05-21", "arxiv_id": "2605.20613", "title": "HRM-Text: Efficient Pretraining Beyond Scaling", "authors": [ "Guan Wang", "Changling Liu", "Chenyu Wang", "Cai Zhou", "Yuhao Sun", "Yifei Wu", "Shuai Zhen", "Luca Scimeca", "Yasin Abbasi Yadkori" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sapientinc/HRM-Text", "github_api_added_by": "user", "project_page": "https://github.com/sapientinc/HRM-Text", "abstract": "The current pretraining paradigm for large language models relies on massive compute and internet-scale raw text, creating a significant barrier to foundational research. In contrast, biological systems demonstrate highly sample-efficient learning through multi-timescale processing, such as the functional organization of the frontoparietal loop. Taking this as inspiration, we introduce HRM-Text, which replaces standard Transformers with a Hierarchical Recurrent Model (HRM) that decouples computation into slow-evolving strategic and fast-evolving execution layers. To stabilize this deep recurrence for language modeling, we introduce MagicNorm and warmup deep credit assignment. Furthermore, instead of standard raw-text pretraining, we train exclusively on instruction-response pairs using a task-completion objective and PrefixLM masking. Serving as an empirical existence proof of efficient pretraining, a 1B-parameter HRM-Text model trained from scratch on only 40 billion unique tokens and $1,500 budget achieves 60.7% on MMLU, 81.9% on ARC-C, 82.2% on DROP, 84.5% on GSM8K, and 56.2% on MATH. Despite utilizing roughly 100-900x fewer training tokens and 96-432x less estimated compute than standard baselines, HRM-Text performs competitively with 2-7B parameter open models. These results demonstrate that co-designing architectures and objectives can radically reduce the compute-to-performance ratio, making pretraining from scratch accessible to the broader research community.", "github": "https://github.com/sapientinc/HRM-Text" }, { "date": "2026-05-21", "arxiv_id": "2605.17916", "title": "PanoWorld: A Generative Spatial World Model for Consistent Whole-House Panorama Synthesis", "authors": [ "Jinrang Jia", "Zhenjia Li", "Yijiang Hu", "Yifeng Shi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jjrCN/PanoWorld", "github_api_added_by": "user", "project_page": "https://jjrcn.github.io/PanoWorld-project-home/", "abstract": "Generating a consistent whole-house VR tour from a floorplan and style reference requires both photorealistic panoramas and cross-view spatial coherence. Pure 2D generators produce appealing single panoramas but re-imagine geometry and materials when the viewpoint changes, whereas monolithic 3D generation becomes expensive and loses fine texture at multi-room scale. We introduce PanoWorld, a generative spatial world model that treats whole-house synthesis as autoregressive generation of node-based 360-degree panoramas, matching the discrete navigation used by real VR tour products. PanoWorld uses a floorplan-derived 3D shell as a global geometric proxy and a dynamic 3D Gaussian Splatting cache as renderable spatial memory. A feed-forward panoramic LRM designed for metric-scale multi-room 360-degree inputs lifts generated panoramas into local 3DGS updates, while Room-aware Group Attention suppresses cross-room feature interference. A topology-aware progressive caching strategy fuses these local updates without repeatedly reconstructing the full history. By decoupling shell-based geometry guidance from cache-rendered visual memory, PanoWorld preserves high-frequency 2D synthesis quality while improving cross-node layout and material consistency. The project link is https://jjrcn.github.io/PanoWorld-project-home/", "github": "https://github.com/jjrCN/PanoWorld" }, { "date": "2026-05-21", "arxiv_id": "2605.21343", "title": "OcclusionFormer: Arranging Z-Order for Layout-Grounded Image Generation", "authors": [ "Ziye Li", "Henghui Ding" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FudanCVL/OcclusionFormer", "github_api_added_by": "user", "project_page": "https://henghuiding.com/OcclusionFormer/", "abstract": "Recent layout-to-image models have achieved remarkable progress in spatial controllability. However, they still struggle with inter-object occlusion. When bounding boxes overlap, most existing methods lack explicit occlusion information, which makes the generation in intersection regions inherently ambiguous and hinders the determination of complex occlusion relationships. As a result, they often produce entangled textures or physically inconsistent layering in the overlapped areas. To address this issue, we first construct SA-Z, a large-scale dataset enriched with explicit occlusion ordering and pixel-level annotations. Building upon our proposed dataset, we introduce OcclusionFormer, a novel occlusion-aware Diffusion Transformer framework that explicitly models Z-order priority by decoupling instances and compositing them via volume rendering. Furthermore, to ensure fine-grained spatial precision, we introduce a queried alignment loss that explicitly supervises individual instances and enhances semantic consistency. The proposed method effectively reduces ambiguity in overlapping regions, enforces correct occlusion dependencies, and preserves structural integrity, leading to substantial accuracy gains across diverse scenes.", "github": "https://github.com/FudanCVL/OcclusionFormer" }, { "date": "2026-05-21", "arxiv_id": "2605.21431", "title": "iTryOn: Mastering Interactive Video Virtual Try-On with Spatial-Semantic Guidance", "authors": [ "Jun Zheng", "Zhengze Xu", "Mengting Chen", "Jing Wang", "Jinsong Lan", "Xiaoyong Zhu", "Kaifu Zhang", "Bo Zheng", "Xiaodan Liang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://icml.cc/virtual/2026/poster/65275", "abstract": "Video Virtual Try-On (VVT) aims to seamlessly replace a garment on a person in a video with a new one. While existing methods have made significant strides in maintaining temporal consistency, they are predominantly confined to non-interactive scenarios where models merely showcase garments. This limitation overlooks a crucial aspect of real-world apparel presentation: active human-garment interaction. To bridge this gap, we introduce and formalize a new challenging task: Interactive Video Virtual Try-On (Interactive VVT), where subjects in the video actively engage with their clothing. This task introduces unique challenges beyond simple texture preservation, including: (1) resolving the semantic ambiguity of interactions from standard pose information, and (2) learning complex garment deformations from video where interactive moments are sparse and brief. To address these challenges, we propose iTryOn, a novel framework built upon a large-scale video diffusion Transformer. iTryOn pioneers a multi-level interaction injection mechanism to guide the generation of complex dynamics. At the spatial level, we introduce a garment-agnostic 3D hand prior to provide fine-grained guidance for precise hand-garment contact, effectively resolving spatial ambiguity. At the semantic level, iTryOn leverages global captions for overall context and time-stamped action captions for localized interactions, synchronized via our novel Action-aware Rotational Position Embedding (A-RoPE). Extensive experiments demonstrate that iTryOn not only achieves state-of-the-art performance on traditional VVT benchmarks but also establishes a commanding lead in the new interactive setting, marking a significant step towards more dynamic and controllable virtual try-on experiences.", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.20258", "title": "It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs", "authors": [ "Sangwoo Park", "Woongyeong Yeo", "Seanie Lee", "Yumin Choi", "Hyomin Lee", "Kangsan Kim", "Jinheon Baek", "Seong Joon Oh", "Sung Ju Hwang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sw-programmer/SelfCI", "github_api_added_by": "user", "project_page": "", "abstract": "Contextual Integrity (CI) defines privacy not merely as keeping information hidden, but as governing information flows according to the norms of a given context. As large language models are increasingly deployed as personal agents handling sensitive workflows, adhering to CI becomes critical. However, even frontier models remain unreliable in making disclosure decisions, and existing mitigation strategies often degrade underlying task performance. To overcome this privacy-utility trade-off, we propose SELFCI, a complementary self-distillation framework that decouples information suppression from task resolution. SELFCI jointly optimizes two independent reverse KL divergences over distinct teacher distributions derived from feedback: one encourages preserving task-relevant information for utility, while the other enforces minimal and appropriate disclosure. This complementary formulation induces a Product-of-Experts (PoE) target, aligning the policy with the intersection of capability and privacy requirements. Empirical evaluations demonstrate that SELFCI, without relying on costly external supervision, consistently outperforms competitive baselines such as online reinforcement learning algorithms (e.g., GRPO). These trends further extend to out-of-domain settings involving agentic workflows and accumulated private context, suggesting that SELFCI provides a practical path toward CI alignment.", "github": "https://github.com/sw-programmer/SelfCI" }, { "date": "2026-05-21", "arxiv_id": "2605.18565", "title": "LongMINT: Evaluating Memory under Multi-Target Interference in Long-Horizon Agent Systems", "authors": [ "Hyunji Lee", "Justin Chih-Yao Chen", "Joykirat Singh", "Zaid Khan", "Elias Stengel-Eskin", "Mohit Bansal" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/amy-hyunji/LongMINT", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Real-world agents operate over long and evolving horizons, where information is repeatedly updated and may interfere across memories, requiring accurate recall and aggregated reasoning over multiple pieces of information. However, existing benchmarks focus on static, independent recall and fail to capture these dynamic interactions between evolving memories. In this paper, we study how current memory-augmented agents perform in realistic, interference-heavy, long-horizon settings across diverse domains and question types. We introduce LongMINT (Long-Horizon Memory under INTerference), a benchmark featuring (1) long, highly interconnected contexts with frequently updated information that induces substantial interference, (2) diverse domains (state tracking, multi-turn dialogue, Wikipedia revisions, and GitHub commits), enabling evaluation of domain generalization, and (3) diverse question types that assess robustness to interference, including (i) single-target recall tasks requiring retrieval of a specific target from long contexts, and (ii) multi-target aggregation tasks requiring reasoning over multiple relevant pieces of information. Overall, LongMINT has 15.6k question-answering pairs over long-horizon contexts averaging 138.8k tokens and extending up to 1.8M tokens per instance. We evaluate 7 representative systems, including vanilla long-context LLMs, RAG, and memory-augmented agent frameworks. Across all systems, we observe consistently low performance (avg. 27.9% accuracy), especially on questions requiring aggregated reasoning over multiple pieces of evidence. Our analysis shows that performance is primarily limited by retrieval and memory construction. Furthermore, current memory systems struggle to recall and reason over earlier facts that are later revised or interfered with by subsequent context, with performance degrading as the number of intervening updates increases.", "github": "https://github.com/amy-hyunji/LongMINT" }, { "date": "2026-05-21", "arxiv_id": "2605.16787", "title": "The Unlearnability Phenomenon in RLVR for Language Models", "authors": [ "Yulin Chen", "He He", "Chen Zhao" ], "github_manual": "", "github_regex": "https://github.com/yulinchen99/unlearnability-rlvr", "github_paper_md": "", "github_api": "https://github.com/yulinchen99/unlearnability-rlvr", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Reward (RLVR) has proven effective in improving Large Language Model's (LLM) reasoning ability. However, the learning dynamics of RLVR remain underexplored. In this paper, we reveal a counterintuitive phenomenon: among hard examples that the model initially struggles with, a substantial subset remains unlearnable even when correct rollouts are present. To understand the phenomenon, we first demonstrate that existing optimization and sampling techniques fail to resolve unlearnability. With cross-example gradient analysis, we show that unlearnable examples have fundamental representation issue, characterized by low gradient similarity with the rest of the examples and ungeneralizable reasoning patterns. We further show that representation flaws are difficult to mitigate in RL, as data augmentation does not improve gradient similarity. Our study provides the first systematic characterization of unlearnable data in RLVR training and reveals fundamental limitations in current RL approaches for reasoning tasks. Code and data are available at https://github.com/yulinchen99/unlearnability-rlvr.", "github": "https://github.com/yulinchen99/unlearnability-rlvr" }, { "date": "2026-05-21", "arxiv_id": "2602.07892", "title": "Safety Alignment as Continual Learning: Mitigating the Alignment Tax via Orthogonal Gradient Projection", "authors": [ "Guanglong Sun", "Siyuan Zhang", "Liyuan Wang", "Jun Zhu", "Hang Su", "Yi Zhong" ], "github_manual": "", "github_regex": "https://github.com/SunGL001/OGPSA", "github_paper_md": "", "github_api": "https://github.com/SunGL001/OGPSA", "github_api_added_by": "user", "project_page": "https://huggingface.co/papers/2602.07892", "abstract": "Safety post-training can improve the harmfulness and policy compliance of Large Language Models (LLMs), but it may also reduce general utility, a phenomenon often described as the alignment tax. We study this trade-off through the lens of continual learning: sequential alignment stages expose the model to shifted data distributions and objectives, and their gradients may interfere with directions that support previously acquired general capabilities. This view does not claim that all alignment degradation has a single cause; rather, it provides a useful first-order mechanism for mitigating one important source of capability regression. We propose Orthogonal Gradient Projection for Safety Alignment (OGPSA), a lightweight update rule that estimates a low-rank reference subspace from gradients on a small set of general-capability data and removes from each safety gradient the component lying in this subspace. The resulting update is the steepest local safety-descent direction subject to first-order preservation constraints on the reference objectives. OGPSA is compatible with standard post-training pipelines and avoids large-scale replay, although it introduces periodic reference-gradient computation. Across Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and sequential SFTrightarrowDPO settings, OGPSA improves the observed safety--utility trade-off over standard baselines. Under the sequential SFTrightarrowDPO pipeline, the average performance gain increases from 33.98\\% to 42.74\\% on Qwen2.5-7B-Instruct and from 19.74\\% to 32.98\\% on Llama3.1-8B-Instruct. We have open sourced our code at https://github.com/SunGL001/OGPSA.", "github": "https://github.com/SunGL001/OGPSA" }, { "date": "2026-05-21", "arxiv_id": "2605.20682", "title": "IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools", "authors": [ "Rongbin Tan", "Fangfang Lin", "Zhenlong Yuan", "Min Qiu", "Kejin Cui", "Mengmeng Wang", "Yi Wang", "Zijian Song", "Zhiyuan Wang", "Jiyuan Wang", "Yue Wang", "Shuhan Song\u00a7", "Huawei Cao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have shown remarkable capability in bridging visual perception and textual reasoning, enabling zero-shot understanding across diverse industrial scenarios. However, their performance in open-vocabulary industrial anomaly detection (IAD) is often limited by domain-misaligned reasoning and hallucinated structural inferences. To address these challenges, we propose IndusAgent, a tool-augmented agentic framework for open-vocabulary IAD. Specifically, we first construct Indus-CoT, a structured dataset that integrates global visual observations, high-resolution local patches, and expert normalcy priors, providing supervision for fine-tuning the model on rigorous industrial inspection trajectories. Building on this, IndusAgent dynamically orchestrates a set of external tools, including dynamic region cropping, high-frequency feature enhancement, and prior retrieval, thus enabling the agent to actively resolve visual ambiguities and disentangle subtle anomalies. Furthermore, we introduce a gated reinforcement learning objective that jointly optimizes anomaly classification, localization accuracy, anomaly type reasoning, and efficient tool usage, ensuring that tool invocation occurs only when beneficial. Extensive evaluations on five industrial anomaly benchmarks, including MVTec-AD, VisA, MPDD, DTD, and SDD, demonstrate that IndusAgent achieves state-of-the-art zero-shot performance among all existing methods, validating our robustness and generalization capacity.", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.21468", "title": "You Only Need Minimal RLVR Training: Extrapolating LLMs via Rank-1 Trajectories", "authors": [ "Zhepei Wei", "Xinyu Zhu", "Wei-Lin Chen", "Chengsong Huang", "Jiaxin Huang", "Yu Meng" ], "github_manual": "", "github_regex": "https://github.com/weizhepei/RELEX", "github_paper_md": "", "github_api": "https://github.com/weizhepei/RELEX", "github_api_added_by": "user", "project_page": "https://weizhepei.notion.site/you-only-need-minimal-rlvr-training", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving reasoning in large language models (LLMs), yet the underlying geometry of the resulting parameter trajectories remains underexplored. In this work, we demonstrate that RLVR weight trajectories are extremely low-rank and highly predictable. Specifically, we find that the majority of downstream performance gains are captured by a rank-1 approximation of the parameter deltas, where the magnitude of this projection evolves near-linearly with training steps. Motivated by this, we propose a simple and compute-efficient method RELEX (REinforcement Learning EXtrapolation), which estimates the rank-1 subspace from a short observation window and extrapolates future checkpoints via linear regression, with no learned model required. Across three models (i.e., Qwen2.5-Math-1.5B, Qwen3-4B-Base, and Qwen3-8B-Base), RELEX produces checkpoints that match or exceed RLVR performance on both in-domain and out-of-domain benchmarks, requiring as few as 15% steps of full RLVR training. Remarkably, RELEX is able to extrapolate far beyond the observation window at no training cost, predicting checkpoints up to 10-20times beyond the observed prefix with continued improvement (e.g., observe only the first 50 steps and extrapolate to 1000 steps). Our ablation analysis confirms the minimalist sufficiency of RELEX: neither increasing the subspace rank nor employing non-linear modeling yields further gains in extrapolation. Finally, we show that RELEX's success stems from a \"denoising\" effect: by projecting updates onto the rank-1 subspace, the model discards stochastic optimization noise that would otherwise degrade performance during extrapolation. Our code is available at https://github.com/weizhepei/RELEX.", "github": "https://github.com/weizhepei/RELEX" }, { "date": "2026-05-21", "arxiv_id": "2605.19008", "title": "Learn-by-Wire Training Control Governance: Bounded Autonomous Training Under Stress for Stability and Efficiency", "authors": [ "Anis Radianis" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Qluon/LBW-Guard", "github_api_added_by": "user", "project_page": "https://huggingface.co/Qluon", "abstract": "Modern language-model training is increasingly exposed to instability, degraded runs, and wasted compute, especially under aggressive learning-rate, scale, and runtime-stress conditions. This paper introduces Learn-by-Wire Guard (LBW-Guard), a bounded autonomous training-control governance layer that operates above AdamW. Rather than replacing the optimizer update rule, LBW-Guard observes training telemetry, interprets instability-sensitive regimes, and applies bounded control to optimizer execution while preserving fixed training objectives. We evaluate LBW-Guard in a Qwen2.5-centered stress-and-robustness suite using WikiText-103, with Qwen2.5-7B as the empirical anchor, model-size comparisons against Qwen2.5-3B and Qwen2.5-14B, learning-rate stress tests, gradient-clipping baselines, and a no-LoRA TinyLlama-1B full-parameter sanity check. In the 7B reference setting, LBW-Guard reduces final perplexity from 13.21 to 10.74, an 18.7% improvement, while reducing end-to-end time from 392.54s to 357.02s, a 1.10x speedup. Under stronger learning-rate stress, AdamW degrades to 1885.24 final perplexity at LR=3e-3 and 659.76 at LR=1e-3, whereas LBW-Guard remains trainable at 11.57 and 10.33, respectively. Gradient-clipping baselines do not reproduce this effect. These results support a scoped systems conclusion that stability-sensitive LLM training can benefit from a governance plane above the optimizer. LBW-Guard provides evidence that bounded runtime control can preserve productive compute under stress while remaining distinct from optimizer replacement and local gradient suppression.", "github": "https://github.com/Qluon/LBW-Guard" }, { "date": "2026-05-21", "arxiv_id": "2605.21226", "title": "OCTOPUS: Optimized KV Cache for Transformers via Octahedral Parametrization Under optimal Squared error quantization", "authors": [ "Mark Boss", "Vikram Voleti", "Simon Donn\u00e9", "Shimon Vainer" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://octopus-quant.github.io/", "abstract": "The key-value (KV) cache dominates memory bandwidth and footprint in long-context autoregressive inference. Recent rotation-preconditioned codecs (TurboQuant, PolarQuant) show that a structured random rotation followed by a per-coordinate scalar quantizer matched to an analytically tractable marginal is a near-optimal recipe for KV compression. OCTOPUS advances this paradigm through joint quantization of rotated coordinate triplets. Each triplet's direction is mapped to a square via an octahedral parameterization, and the two resulting coordinates and the triplet norm are Lloyd-Max quantized against implementation-matched marginals. Optimizing the per-triplet squared error gives a strictly non-uniform bit allocation depending only on the total dimensionality of the keys. We find the finite-dimensional quality optimum with sweeps to be constant on every real decoder we test. The codec is data-oblivious, online, and deterministic given a seed. Across text, video, and audio, OCTOPUS matches or beats every prior rotation codec at every reported bit width and metric, with a lead that grows as bits drop for extreme compression. Furthermore, a fused Triton implementation reconstructs keys on the fly without materializing the uncompressed key, so the codec adds no decode-time bandwidth or latency over the existing dequantization. Project Page: https://octopus-quant.github.io/", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.20315", "title": "Mix-Quant: Quantized Prefilling, Precise Decoding for Agentic LLMs", "authors": [ "Haiquan Lu", "Zigeng Chen", "Gongfan Fang", "Xinyin Ma", "Xinchao Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/haiquanlu/Mix-Quant", "github_api_added_by": "user", "project_page": "https://haiquanlu.github.io/Mix-Quant/", "abstract": "LLM agents have recently emerged as a powerful paradigm for solving complex tasks through planning, tool use, memory retrieval, and multi-step interaction. However, these agentic workflows often introduce substantial input-side overhead, making the compute-intensive prefilling stage a key bottleneck in long-context, multi-turn inference. In this work, we propose Mix-Quant, a simple and effective phase-aware quantization framework for fast agentic inference. We first investigate FP4 quantization in agentic LLM workflows and observe that quantizing the entire inference process can incur significant performance degradation. In contrast, the prefilling stage exhibits substantial quantization redundancy and can therefore be quantized with minimal accuracy loss, despite being the dominant source of computation. Based on this insight, we apply high-throughput NVFP4 quantization to the prefilling phase while preserving BF16 precision for decoding. By decoupling prefilling acceleration from decoding quality, Mix-Quant combines phase-aware algorithmic quantization with hardware-efficient NVFP4 execution to alleviate the inference bottleneck in LLM agents. Extensive experiments across long-context and agentic benchmarks demonstrate that Mix-Quant largely preserves task performance while delivering significant efficiency improvements, achieving up to a 3x speedup during prefilling.", "github": "https://github.com/haiquanlu/Mix-Quant" }, { "date": "2026-05-21", "arxiv_id": "2605.19804", "title": "Stitched Value Model for Diffusion Alignment", "authors": [ "Hyojun Go", "Hyungjin Chung", "Prune Truong", "Goutam Bhat", "Li Mi", "Zhaochong An", "Zixiang Zhao", "Dominik Narnhofer", "Serge Belongie", "Federico Tombari", "Konrad Schindler" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://github.com/prs-eth/StitchVM", "abstract": "For practical use, diffusion- or flow-based generative models must be aligned with task-specific rewards, such as prompt fidelity or aesthetic preference. That alignment is challenging because the reward is defined for clean output images, but the alignment procedure requires value function estimates at noisy intermediate latents. Existing methods resort to Tweedie-style or Monte Carlo approximations, trading off estimator bias against computational cost: Tweedie estimates are efficient but biased, while Monte Carlo estimates are more accurate but require expensive rollouts. A natural alternative would be a learned value function, but it remains an open question how to effectively train a strong and general value model specifically for noisy latents. Here, we propose StitchVM, a model stitching framework that efficiently transfers reward models pretrained for clean images to the noisy latent regime. StitchVM starts from an existing, truncated pixel-space reward model and attaches a frozen diffusion backbone to it as its head. From the pixel-space model, the resulting hybrid retains a carefully pretrained, robust reward capability; from the diffusion backbone, it inherits its native ability to handle noisy latents. The stitching procedure is exceptionally lightweight, e.g., stitching and finetuning CLIP ViT-L and SD 3.5 Medium takes only 10 GPU-hours. By lifting powerful pixel-space reward models to latent space, StitchVM opens up a new style of diffusion alignment: instead of rough, yet costly per-sample approximation of the value function, the correct function for the actual, noisy latents is constructed once and then amortized over many samples and iterations. We show that this approach yields improvements across a broad range of downstream steering and post-training methods: DPS becomes 3.2times faster while halving peak GPU memory, and DiffusionNFT becomes 2.3times faster.", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.14747", "title": "Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining", "authors": [ "Weimin Xiong", "Shuhao Gu", "Bowen Ye", "Zihao Yue", "Lei Li", "Feifan Song", "Sujian Li", "Hao Tian" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/WeiminXiong/Video2GUI", "github_api_added_by": "user", "project_page": "https://weiminxiong.github.io/Video2GUI/", "abstract": "Recent advances in multimodal large language models have driven growing interest in graphical user interface (GUI) agents, yet their generalization remains constrained by the scarcity of large-scale training data spanning diverse real-world applications. Existing datasets rely heavily on costly manual annotations and are typically confined to narrow domains. To address this challenge, we propose Video2GUI, a fully automated framework that extracts grounded GUI interaction trajectories directly from unlabeled Internet videos. Video2GUI employs a coarse-to-fine filtering strategy to identify high-quality GUI tutorial videos and convert them into structured agent trajectories. Applying this pipeline to 500 million video metadata entries, we construct WildGUI, a large-scale dataset containing 12 million interaction trajectories spanning over 1,500 applications and websites. Pre-training Qwen2.5-VL and Mimo-VL on WildGUI yields consistent improvements of 5-20% across multiple GUI grounding and action benchmarks, matching or surpassing state-of-the-art performance. We will release both the WildGUI dataset and the Video2GUI pipeline to support future research of GUI agents.", "github": "https://github.com/WeiminXiong/Video2GUI" }, { "date": "2026-05-21", "arxiv_id": "2605.19484", "title": "CutVerse: A Compositional GUI Agents Benchmark for Media Post-Production Editing", "authors": [ "Haobo Hu", "Xiangwu Guo", "Zhiheng Chen", "Difei Gao", "Haotian Liu", "Libiao Jin", "Qi Mao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CUC-MIPG/CutVerse", "github_api_added_by": "user", "project_page": "", "abstract": "While GUI agents have made significant progress in web navigation and basic operating system tasks, their capabilities in professional creative workflows remain largely underexplored. To bridge this gap, we introduce Cutverse, a benchmark designed to systematically evaluate autonomous GUI agents in realistic media post-production environments. We curate expert demonstrations across 7 professional applications (e.g., Premiere Pro, Photoshop), covering 186 complex, long-horizon tasks grounded in authentic editing workflows, involving dense multimodal interfaces and tightly coupled interaction sequences. To support scalable evaluation, we develop a lightweight parser that transforms raw screen recordings and low-level interaction logs into structured, compositional GUI action trajectories with precise grounding. Extensive evaluations reveal that existing agents achieve only 36.0\\% task success on realistic media editing tasks, underscoring the challenges posed by complex, long-horizon media post-production workflows in our benchmark.While current models demonstrate promising spatial grounding, multimodal alignment, and coordinated action execution, they remain limited in long-horizon reliability and domain-specific planning.", "github": "https://github.com/CUC-MIPG/CutVerse" }, { "date": "2026-05-21", "arxiv_id": "2605.19330", "title": "MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization", "authors": [ "Md Mehrab Tanjim", "Jayakumar Subramanian", "Xiang Chen", "Branislav Kveton", "Subhojyoti Mukherjee", "Anlan Zhang", "Sungchul Kim", "Somdeb Sarkhel", "Sunav Choudhury" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "LLM agents organize behavior through skills - structured natural-language specifications governing how an agent reasons, retrieves, and responds. Unlike monolithic prompts, skills are multi-field artifacts subject to hard platform constraints: description fields are truncated for routing, instruction bodies are compacted via progressive disclosure, and co-resident skills compete for limited context windows. These constraints make skill optimization inherently multi-objective: a skill must simultaneously maximize task performance and satisfy platform limits. Yet existing prompt optimizers either ignore these trade-offs or collapse them into a weighted sum, missing Pareto-optimal variants in non-convex objective regions. We introduce MOCHA (Multi-Objective Chebyshev Annealing), which replaces single-objective selection with Chebyshev scalarization - covering the full Pareto front, including non-convex regions - combined with exponential annealing that transitions from exploration to exploitation. In our experiments across six diverse agent skills - where all methods share the same multi-objective mutation operator and baselines receive identical per-objective textual feedback - existing optimizers fail to improve the seed skill on 4 of 6 tasks: 1000 rollouts yield zero progress. MOCHA breaks through on every task, achieving 7.5% relative improvement in mean correctness over the strongest baseline (up to 14.9% on FEVER and 10.4% on TheoremQA) while discovering twice as many more Pareto-optimal skill variants.", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.19833", "title": "Mega-ASR: Towards In-the-wild^2 Speech Recognition via Scaling up Real-world Acoustic Simulation", "authors": [ "Zhifei Xie", "Kaiyu Pang", "Haobin Zhang", "Deheng Ye", "Xiaobin Hu", "Shuicheng Yan", "Chunyan Miao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xzf-thu/Mega-ASR", "github_api_added_by": "user", "project_page": "https://xzf-thu.github.io/Mega-ASR/", "abstract": "Despite rapid advances in automatic speech recognition (ASR) and large audio-language models, robust recognition in real-world environments remains limited by an \"acoustic robustness bottleneck\": models often lose acoustic grounding and produce omissions or hallucinations under severe, compositional distortions. We propose Mega-ASR, a unified ASR-in-the-wild framework that combines scalable compound-data construction with progressive acoustic-to-semantic optimization. We introduce Voices-in-the-Wild-2M, covering 7 classic acoustic phenomena and 54 physically plausible compound scenarios, and train Mega-ASR with Acoustic-to-Semantic Progressive Supervised Fine-Tuning and Dual-Granularity WER-Gated Policy Optimization. Extensive experiments demonstrate that Mega-ASR achieves significant advantages over prior state-of-the-art systems on adverse-condition ASR benchmarks (45.69% vs. 54.01% on VOiCES R4-B-F, and 21.49% vs. 29.34% on NOIZEUS Sta-0). On complex compositional acoustic scenarios, Mega-ASR further delivers over 30% relative WER reduction against strong open- and closed-source baselines, establishing a scalable paradigm for robust ASR in-the-wild.", "github": "https://github.com/xzf-thu/Mega-ASR" }, { "date": "2026-05-21", "arxiv_id": "2605.18233", "title": "Enhancing Train-Free Infinite-Frame Generation for Consistent Long Videos", "authors": [ "X. Feng", "J. Zhu", "M. Wu", "C. Chen", "F. Mao", "H. Guo", "J. Wu", "X. Chu", "K. Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://xiaokunfeng.github.io/miga_homepage/", "abstract": "Without incurring significant computational overhead, train-free long video generation aims to enable foundation video generation models to produce longer videos. Frame-level autoregressive frameworks, e.g., FIFO-diffusion, offer the advantage of generating infinitely long videos with constant memory consumption. However, the mismatch between training and inference, coupled with the challenge of maintaining long-term consistency, limits the effective utilization of foundation models. To mitigate these concerns, we propose MIGA, a novel infinite-frame long video generation method. Firstly, we propose an effective two-stage alignment mechanism that mitigates the training-inference gap by reducing the excessive noise span fed to the model. We then introduce an innovative dual consistency enhancement mechanism, where the self-reflection approach corrects early high-noise frames and the long-range frame guidance approach leverages later low-noise frames with broad coverage to steer generation, jointly improving temporal consistency. Extensive experiments on VBench and NarrLV demonstrate the state-of-the-art performance of MIGA. Our project page is available at https://xiaokunfeng.github.io/miga_homepage/.", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.20266", "title": "A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook", "authors": [ "Kaiwen Luo", "Zhenhong Zhou", "Leo Wang", "Liang Lin", "Yang Xiao", "Tianyu Shao", "Yuanhe Zhang", "Yuxuan Li", "Miao Yu", "Kailin Lyu", "Jiaming Zhang", "Dongrui Liu", "Li Sun", "Yueming Wu", "Kai Li", "Ting Dang", "Xiaojun Jia", "Rohan Kumar Das", "Xinfeng Li", "Siyuan Liang", "Qiufeng Wang", "Xingjun Ma", "Jing Chen", "Kun Wang", "Junhao Dong", "Deqing Zou", "Yu Cheng", "Xia Hu", "Zhigang Zeng", "Sen Su", "Yang Liu", "Yu-Gang Jiang", "Philip S. Yu", "Yew-Soon Ong" ], "github_manual": "", "github_regex": "https://github.com/Kwwwww74/Awesome-Trustworthy-AudioLLMs", "github_paper_md": "", "github_api": "https://github.com/Kwwwww74/Awesome-Trustworthy-AudioLLMs", "github_api_added_by": "user", "project_page": "", "abstract": "The foundational capabilities established by Large Language Models (LLMs) have paved the way for Multimodal Large Language Models (MLLMs), within which Large Audio Language Models (LALMs) are essential for realizing universal auditory intelligence. Despite their remarkable performance, the escalation of LALMs' capabilities has significantly outpaced the development of systemic frameworks to ensure their trustworthiness. This survey provides a comprehensive investigation into the endogenous mechanisms of LALMs, detailing the architectural innovations and alignment algorithms that facilitate emergent reasoning. Specifically, we analyze how the transition to unified end-to-end frameworks and the integration of continuous acoustic signals inherently expand the attack surface. To rigorously evaluate the risks within these paradigms, we establish a comprehensive taxonomy of trustworthiness, categorizing critical vulnerabilities such as cross-modal jailbreaking, latent acoustic backdoors, and biometric privacy leakage. We review the state-of-the-art through six analytical pillars: hallucination, robustness, safety, privacy, fairness, and authentication. The profound imbalance between a mature offensive landscape and underdeveloped defenses further validates the critical trustworthiness gaps and multidimensional risks facing audio-centric intelligence. Finally, we propose a strategic roadmap advocating for \"Defense-in-Depth\" architectures, causal auditory world modeling, and intrinsic representation engineering to bridge the gap between empirical performance and intrinsically trustworthy audio intelligence. Our project has been uploaded to GitHub https://github.com/Kwwwww74/Awesome-Trustworthy-AudioLLMs.", "github": "https://github.com/Kwwwww74/Awesome-Trustworthy-AudioLLMs" }, { "date": "2026-05-21", "arxiv_id": "2605.19597", "title": "LLMEval-Logic: A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening", "authors": [ "Ming Zhang", "Qiyuan Peng", "Yinxi Wei", "Yujiong Shen", "Kexin Tan", "Yuhui Wang", "Zhenghao Xiang", "Junjie Ye", "Zhangyue Yin", "Zhiheng Xi", "Shihan Dou", "Tao Gui", "Maxm Pan", "Ruizhi Yang", "Qi Zhang", "Xuanjing Huang" ], "github_manual": "", "github_regex": "https://github.com/llmeval/LLMEval-Logic", "github_paper_md": "", "github_api": "https://github.com/llmeval/LLMEval-Logic", "github_api_added_by": "user", "project_page": "https://llmeval.com/", "abstract": "Evaluating large language models (LLMs) on natural-language logical reasoning is essential because rule-governed tasks require conclusions to follow strictly from stated premises. Many existing logical-reasoning benchmarks are generated by templating natural-language items from sampled formulas, provide only coarse or unaudited formal annotations, and are now quickly saturated by frontier reasoning models. We present LLMEval-Logic, a Chinese logical reasoning benchmark built from realistic situational scenarios. Its pipeline forward-authors and expert-audits natural-language items together with their reference formalizations, verifies annotated answers with Z3, constructs expert rubrics for natural-to-formal grading, and hardens selected items through a closed-loop adversarial workflow. The benchmark is released in two paired subsets: a 246-item Base subset shipped with 1,400 expert-developed rubric atoms, and a 190-item Hard subset with 938 multi-step sub-questions over closed model spaces. Evaluating 14 frontier LLMs on LLMEval-Logic reveals substantial gaps in current models: the best model reaches only 37.5% Hard Item Accuracy, and even with reference symbols the highest joint Z3+Rubric formalization score among evaluated models reaches only 60.16%. Our benchmark is publicly available at https://github.com/llmeval/LLMEval-Logic.", "github": "https://github.com/llmeval/LLMEval-Logic" }, { "date": "2026-05-21", "arxiv_id": "2605.17991", "title": "Stable Audio 3", "authors": [ "Zach Evans", "Julian D. Parker", "Matthew Rice", "CJ Carr", "Zack Zukowski", "Josiah Taylor", "Jordi Pons" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Stability-AI/stable-audio-3", "github_api_added_by": "user", "project_page": "https://stability.ai/news-updates/meet-stable-audio-3-the-model-family-built-for-artistic-experimentation-with-open-weight-models", "abstract": "Stable Audio 3 is a family of fast latent diffusion models (small, medium, large) for variable-length audio generation and editing. Since our models can generate several minutes of audio, variable-length generations are key to avoid the cost of producing full-length generations for short sounds. We also support inpainting, enabling targeted audio editing and the continuation of short recordings. Our latent diffusion models operate on top of a novel semantic-acoustic autoencoder that projects audio into a compact latent space, enabling efficient diffusion-based generation while preserving audio fidelity and encouraging semantic structure in the latent. Finally, we run adversarial post-training to both accelerate inference and improve generation quality, reducing the number of inference steps while improving fidelity and prompt adherence. Stable Audio 3 models are trained on licensed and Creative Commons data to generate music and sounds in less than a 2s on an H200 GPU and less than a few seconds on a MacBook Pro M4. We release the weights of small and medium, that can run on consumer-grade hardware, together with their training and inference pipeline.", "github": "https://github.com/Stability-AI/stable-audio-3" }, { "date": "2026-05-21", "arxiv_id": "2605.20119", "title": "Toto 2.0: Time Series Forecasting Enters the Scaling Era", "authors": [ "Emaad Khwaja", "Chris Lettieri", "Gerald Woo", "Eden Belouadah", "Marc Cenac", "Guillaume Jarry", "Enguerrand Paquin", "Xunyi Zhao", "Viktoriya Zhukov", "Othmane Abou-Amal", "Chenghao Liu", "Ameet Talwalkar", "David Asker" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DataDog/toto", "github_api_added_by": "user", "project_page": "https://www.datadoghq.com/blog/ai/toto-2/", "abstract": "We show that time series foundation models scale: a single training recipe produces reliable forecast-quality improvements from 4M to 2.5B parameters. We release Toto 2.0, a family of five open-weights forecasting models trained under this recipe. The Toto 2.0 family sets a new state of the art on three forecasting benchmarks: BOOM, our observability benchmark; GIFT-Eval, the standard general-purpose benchmark; and the recent contamination-resistant TIME benchmark. This report describes our experimental results and details the design decisions behind Toto 2.0: its architecture and training recipe, training data, and the u-muP hyperparameter transfer pipeline. All five base checkpoints are released under Apache 2.0.", "github": "https://github.com/DataDog/toto" }, { "date": "2026-05-21", "arxiv_id": "2605.18329", "title": "Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation", "authors": [ "Kirscher Tristan", "Bujotzek Markus", "Kirchhoff Yannick", "Rokuss Maximilian", "Isensee Fabian", "Kahl Kim-Celine", "Kovacs Balint", "Maier-Hein Klaus" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Kirscher/LostInFolds", "github_api_added_by": "user", "project_page": "", "abstract": "Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV), yet refer to them as ``deep ensembles'' (DE). Because CV members are trained on different data subsets, their disagreement mixes seed-driven variability with data-exposure effects, which can change how uncertainty should be interpreted. We audit recent segmentation uncertainty studies and find that terminology--implementation mismatches are common. We then compare a standard 5-fold CV ensemble to a 5-member DE (fixed training set, different random seeds) under otherwise identical configurations on three multi-rater segmentation datasets spanning three modalities. We evaluate uncertainty for calibration, failure detection, ambiguity modeling, and robustness under distribution shift. DE match segmentation accuracy while improving calibration and failure detection, whereas CV ensembles sometimes correlate more strongly with inter-rater variability on the studied datasets. Thus, ensemble construction should be chosen to match the research question: DE for reliability-oriented use (e.g., selective referral/failure detection) and CV ensembles as a proxy for ambiguity. We provide a lightweight nnU-Net modification enabling DE training within the default pipeline.", "github": "https://github.com/Kirscher/LostInFolds" }, { "date": "2026-05-21", "arxiv_id": "2605.17526", "title": "SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering", "authors": [ "Qingnan Ren", "Shun Zou", "Shiting Huang", "Ziao Zhang", "Kou Shi", "Zhen Fang", "Yiming Zhao", "Yu Zeng", "Qisheng Su", "Lin Chen", "Yong Wang", "Zehui Chen", "Xiangxiang Chu", "Feng Zhao" ], "github_manual": "", "github_regex": "https://github.com/ShadeCloak/SaaSbench", "github_paper_md": "", "github_api": "https://github.com/ShadeCloak/SaaSbench", "github_api_added_by": "user", "project_page": "", "abstract": "As autonomous coding agents become capable of handling increasingly long-horizon tasks, they have gradually demonstrated the potential to complete end-to-end software development. Although existing benchmarks have recently evolved from localized code editing to from-scratch project generation, they remain confined to structurally simplified, single-stack applications. Consequently, they fail to capture the heterogeneous environments, full-stack orchestration, and system-level complexity of real enterprise Software as a Service (SaaS) systems, leaving a critical gap in assessing agents under realistic engineering constraints. To fill this gap, we introduce SaaSBench, the first benchmark designed to explore the boundaries of AI agents in enterprise SaaS engineering. Spanning 30 complex tasks across 6 SaaS domains with 5,370 validation nodes, it incorporates 8 programming languages, 6 databases, and 13 frameworks to meticulously mirror real-world software heterogeneity. Furthermore, we design a dependency-aware hybrid evaluation paradigm tailored for complex systems with long horizons and multi-component coupling, enabling fine-grained, reproducible assessment. Crucially, our extensive experiments reveal a striking insight: the primary bottleneck for state-of-the-art agents is not generating isolated code logic, but successfully configuring and integrating a multi-component system. Over 95\\% of task failures occur before agents even reach deep business logic, with models often falling victim to overconfidence and prematurely halting during foundational system setup, or getting trapped in ineffective debugging loops. We hope SaaSBench serves as a practical and challenging testbed to drive the evolution of reliable, system-level coding agents. The code is available at https://github.com/ShadeCloak/SaaSbench.", "github": "https://github.com/ShadeCloak/SaaSbench" }, { "date": "2026-05-21", "arxiv_id": "2605.15113", "title": "Learning from Language Feedback via Variational Policy Distillation", "authors": [ "Yang Li", "Erik Nijkamp", "Semih Yavuz", "Shafiq Joty" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement learning from verifiable rewards (RLVR) suffers from sparse outcome signals, creating severe exploration bottlenecks on complex reasoning tasks. Recent on-policy self-distillation methods attempt to address this by utilizing language feedback to generate dense, token-level supervision. However, these approaches rely on a fixed, passive teacher to interpret the feedback. As the student policy improves, the teacher's zero-shot assessment capabilities plateau, ultimately halting further learning. To overcome this, we propose Variational Policy Distillation (VPD), a framework that formalizes learning from language feedback as a Variational Expectation-Maximization (EM) problem. VPD co-evolves both policies: in the E-step, the teacher is actively refined on trajectory outcomes via an adaptive trust-region update, translating textual feedback into a dynamically improved target token distribution. In the M-step, the student internalizes this dense distributional guidance on its own on-policy rollouts. By continuously improving the teacher's ability to extract actionable signals from textual critique, VPD overcomes the limitations of passive distillation. Evaluated across diverse sources of diagnostic feedback on scientific reasoning and code generation tasks, VPD consistently outperforms both standard RLVR and existing self-distillation baselines. Finally, by stress-testing our framework on rigid mathematical reasoning and cold-start regimes, we illuminate the fundamental bounds of feedback-driven self-distillation compared to pure environment-driven RL.", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2604.27263", "title": "Decoupling the Benefits of Subword Tokenization for Language Model Training via Byte-level Simulation", "authors": [ "Th\u00e9o Gigant", "Bowen Peng", "Jeffrey Quesnelle" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Subword tokenization is an essential part of modern large language models (LLMs), yet its specific contributions to training efficiency and model performance remain poorly understood. In this work, we decouple the effects of subword tokenization by isolating them within a controlled byte-level pretraining pipeline. We formulate and test hypotheses across various dimensions, including sample throughput, vocabulary scaling, and the linguistic prior of subword boundaries. By simulating these effects in a byte-level setting, we refine our understanding of why subword models outperform raw byte models and offer insights to improve the pretraining of future byte-level and subword models. Specifically, our experiments highlight the critical role of increased training throughput and the integration of subword boundaries as either explicit priors or inductive biases.", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.20179", "title": "TIDE: Efficient and Lossless MoE Diffusion LLM Inference with I/O-aware Expert Offload", "authors": [ "Zhiben Chen", "Youpeng Zhao", "Yang Sui", "Jun Wang", "Yuzhang Shang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ims-kdks/TIDE", "github_api_added_by": "user", "project_page": "https://tide-paper.vercel.app/", "abstract": "Diffusion Large Language Models (dLLMs) have emerged as a competitive alternative to autoregressive (AR) models, offering better hardware utilization and bidirectional context through parallel block-level decoding. However, as dLLMs continue to scale up with mixture-of-experts (MoE) architectures, their deployment on resource-constrained devices remains an open challenge. Existing AR-based methods often incur either prohibitive I/O overhead or significant compute bottlenecks. In this work, we propose TIDE, a novel resource-efficient inference system that leverages the temporal stability of expert activations during the diffusion process within the block. Specifically, we leverage the temporal stability of expert activations during the diffusion process within the block and introduce an interval-based expert refresh strategy that updates the expert placement in an I/O-aware fashion. To ensure optimal performance, we formulate the inference scheduling as a mathematical programming problem, solving for the optimal interval that minimizes I/O traffic and CPU computation. Most importantly, TIDE is a lossless optimization that requires no model training, providing a \"free lunch\" acceleration for dLLM inference. In a single GPU-CPU system, we demonstrate that TIDE achieves up to 1.4times and 1.5times throughput improvements over prior baselines on LLaDA2.0-mini and LLaDA2.0-flash models, respectively.", "github": "https://github.com/ims-kdks/TIDE" }, { "date": "2026-05-21", "arxiv_id": "2605.17109", "title": "DynMuon: A Dynamic Spectral Shaping View of Muon", "authors": [ "Fangzhou Wu", "Rikhav Shah", "Sandeep Silwal", "Qiuyi Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "In recent years, Muon has emerged as the dominant method for training large language models, and transformers more broadly. The essential difference, when compared to standard gradient descent methods, is to replace the usual update matrix M=U\u03a3V^top with its polar factor UV^top. In this work, we consider a class of Muon-like updates, where we replace the update M with U\u03a3^p V^top for some parameter p. We call this a \"spectral-shaping\" operation, and develop a theory of how to pick p which depends on (a) local curvature of the loss function, (b) noise stemming from stochastic gradients and label noise, and (c) training stage. Our theory and experimentation reveal a previously overlooked behavior: positive p helps early by emphasizing high-curvature directions and accelerating signal contraction, while mildly negative p helps later by reallocating update strength toward low-curvature directions that still contain useful training signals. Building on the insight, we propose DynMuon, an efficient dynamic spectral shaping method that schedules p from positive to mildly negative over training. Extensive experiments across model sizes, architectures, and training settings show that DynMuon consistently achieves lower validation loss than Muon, while requiring 10.6-26.5% fewer steps to reach the same target loss.", "github": "" }, { "date": "2026-05-21", "arxiv_id": "2605.17110", "title": "Capturing LLM Capabilities via Evidence-Calibrated Query Clustering", "authors": [ "Fangzhou Wu", "Sandeep Silwal", "Qiuyi Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Query clustering organizes queries into groups that reflect shared latent capability demands, enabling capability-aware LLM evaluation. Existing clustering methods, which primarily rely on semantic taxonomies or embeddings, often fail to capture such latent capability requirements due to a misalignment between surface-level semantics and actual model performance. We propose ECC, an algorithm that calibrates prior semantic embeddings using limited posterior model comparisons to bridge the gap between surface-level semantics and latent capability requirements. ECC characterizes each cluster through a capability profile parameterized by a Bradley-Terry model and uses trainable mixture weights to accommodate queries with mixed capability demands, jointly learning a flexible, capability-aware clustering structure that supports query-specific inference of LLM capabilities. Extensive quantitative and qualitative evaluations demonstrate that ECC significantly improves LLM capability ranking quality, outperforming human-labeled and embedding-based baselines by an average of 17.64 and 18.02 percentage points, respectively, and proves effective in downstream tasks such as query routing.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.21850", "title": "ACC: Compiling Agent Trajectories for Long-Context Training", "authors": [ "Qisheng Su", "Zhen Fang", "Shiting Huang", "Yu Zeng", "Yiming Zhao", "Kou Shi", "Ziao Zhang", "Lin Chen", "Zehui Chen", "Lijun Wu", "Feng Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recent development of agents has renewed demand for long-context reasoning capacity of LLMs. However, training LLMs for this capacity requires costly long-document curation or heuristic context synthesis. We observe that agents produce massive trajectories when solving problems, invoking tools and receiving environment observations across many turns. The evidence needed to answer the original question is thus scattered throughout these turns, requiring integration of distant context segments. Nevertheless, standard agent SFT masks tool responses and only trains turn-level tool selection, creating a supervision blind spot where these scattered signals go unused. We propose Agent Context Compilation (ACC), which converts trajectories from search, software engineering, and database querying agents into long-context QA pairs that combine the original question with tool responses and environment observations gathered across multiple turns, training the model to answer directly without tool use. This makes the dependencies between the question and the evidence explicit, enabling direct supervision of long-context reasoning over distant segments without additional annotation. ACC is a simple but effective approach that can be combined with any existing long-context extension or training method, providing scalable supervised fine-tuning data. We validate ACC on long-range dependency modeling tasks through MRCR and GraphWalks, challenging benchmarks requiring cross-turn coreference resolution and graph traversal over extended contexts. Training Qwen3-30B-A3B with ACC achieves 68.3 on MRCR (+18.1) and 77.5 on GraphWalks (+7.6), results comparable to Qwen3-235B-A22B, while preserving general capabilities on GPQA, MMLU-Pro, AIME, and IFEval. Further mechanism analysis reveals that the ACC-trained model exhibits task-adaptive attention restructuring and expert specialization.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.22766", "title": "Diversed Model Discovery via Structured Table Discovery", "authors": [ "Zhengyuan Dong", "Ren\u00e9e J. Miller" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RJMillerLab/ModelSearch", "github_api_added_by": "user", "project_page": "", "abstract": "Model cards describe model behavior through a mixture of textual descriptions and structured artifacts, including performance, configuration, and dataset tables. Existing model search systems rely predominantly on semantic similarity over text, which can produce homogeneous result sets and limit exploration of alternatives. We argue that model search is inherently comparative: users want models that are task-aligned yet differentiated in measurable ways. We hypothesize that this balance requires retrieval over condensed, high-quality evidence rather than verbose descriptions, and much of that evidence is concentrated in structured tables. We present StructuredSemanticSearch, a table-driven model search framework built on the ModelTables benchmark. Given a query, StructuredSemanticSearch combines a semantic baseline for task alignment with a structure-aware pipeline that discovers query-related model-card tables using table discovery operators such as unionability, joinability, and keyword search. Retrieved tables are mapped back to model cards under a controlled top-k budget, enabling fair comparison between text-based and table-based retrieval. Beyond retrieval, StructuredSemanticSearch adapts table integration to the model-table domain through orientation-aware integration, producing compact integrated views of tables from partially overlapping and sometimes transposed evidence tables. For evaluation, we introduce a nugget-based, auditable protocol that extracts compact evidence items from model cards, matches queries to condition- or intent-specific nuggets, and measures evidence coverage and diversity over retrieved model-card candidate sets. This protocol also provides a scalable path toward approximate, evidence-based labeling in dynamic model lakes. Experiments on 597 model-recommendation queries show improved nugget coverage for the structure-aware pipeline than semantic baseline", "github": "https://github.com/RJMillerLab/ModelSearch" }, { "date": "2026-05-21", "arxiv_id": "2605.21981", "title": "RiT: Vanilla Diffusion Transformers Suffice in Representation Space", "authors": [ "Le Zhang", "Ning Mang", "Aishwarya Agrawal" ], "github_manual": "", "github_regex": "https://github.com/lezhang7/RiT", "github_paper_md": "", "github_api": "https://github.com/lezhang7/RiT", "github_api_added_by": "user", "project_page": "", "abstract": "Flow matching with x-prediction -- regressing the clean data point rather than the ambient velocity -- is known to exploit low-dimensional manifold structure effectively in pixel space li2025back. We ask whether a pretrained representation space, while containing a low-dimensional data manifold of comparable intrinsic dimensionality, offers a distribution more favorable for flow-matching learning. Comparing pixel, SD-VAE, and DINOv2 features along four geometric axes, we find that pixel and DINOv2 share nearly identical intrinsic dimensionalities (both d!approx!33) yet DINOv2 exhibits 7.3times higher effective rank, 35times better covariance conditioning, 11.5times lower excess kurtosis, and 1.7times lower on-manifold interpolation error; SD-VAE latents are consistently intermediate, indicating that the advantage stems from representation-learning objectives rather than mere compression. These statistical properties render the flow-matching regression well-conditioned and remove the need for the specialized prediction heads or Riemannian transport used by prior DINOv2 diffusion methods. We propose the Representation Image Transformer (RiT): a vanilla Diffusion Transformer trained by x-prediction on frozen DINOv2 features, augmented only by a dimension-aware noise schedule and joint [CLS]-patch modeling. On ImageNet 256{times}256, RiT attains FID 1.45 without guidance and 1.14 with classifier-free guidance, outperforming DiT^DH-XL with 19% fewer parameters (676M vs.\\ 839M). The resulting ODE is efficiently solvable at coarse discretizations: with classifier-free guidance, 5 Heun steps already reach FID 2.0 and 10 steps reach 1.25, without distillation or consistency training. Code at https://github.com/lezhang7/RiT.", "github": "https://github.com/lezhang7/RiT" }, { "date": "2026-05-22", "arxiv_id": "2605.22718", "title": "WorldKV: Efficient World Memory with World Retrieval and Compression", "authors": [ "Jung Yi", "Minjae Kim", "Paul Hyunbin Cho", "Wooseok Jang", "Sangdoo Yun", "Seungryong Kim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://cvlab-kaist.github.io/WorldKV/", "abstract": "Autoregressive video diffusion models have enabled real-time, action-conditioned world generation. However, sustaining a persistent world, where revisiting a previously seen viewpoint yields consistent content, remains an open problem. Full KV-cache attention preserves this consistency but breaks real-time constraints: memory footprint and attention cost grow linearly with rollout length. Sliding window inference restores throughput but discards long-term consistency. We propose WorldKV, a training-free framework with two components: World Retrieval and World Compression. World Retrieval stores evicted KV-cache chunks in GPU/CPU memory and selectively retrieves scene-relevant chunks via camera/ action correspondence, inserting them back into the native attention window without re-encoding. World Compression prunes redundant tokens within each chunk via key-key similarity to an anchor frame, halving per-chunk storage to fit 2x more history under a fixed budget. On Matrix-Game-2.0 and LingBot- World-Fast, WorldKV matches or exceeds full-KV memory fidelity at roughly 2x the throughput, and is competitive with memory-trained baselines without any fine-tuning. Project Page: https://cvlab-kaist.github.io/WorldKV/", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.14678", "title": "\u03c0-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows", "authors": [ "Haoran Zhang", "Luxin Xu", "Zhilin Wang", "Runquan Gui", "Shunkai Zhang", "Haodi Lei", "Zihao He", "Bingsu He", "Chicheng Qin", "Tong Zhu", "Xiaoye Qu", "Yang Yang", "Yu Cheng", "Yafu Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Simplified-Reasoning/Pi-Bench", "github_api_added_by": "user", "project_page": "https://simplified-reasoning.github.io/Pi-Bench", "abstract": "The rise of personal assistant agents, e.g., OpenClaw, highlights the growing potential of large language models to support users across everyday life and work. A core challenge in these settings is proactive assistance, since users often begin with underspecified requests and leave important needs, constraints, or preferences unstated. However, existing benchmarks rarely evaluate whether agents can identify and act on such hidden intents before they are explicitly stated, especially in sustained multi-turn interactions where user needs emerge gradually. To address this gap, we introduce \u03c0-Bench, a benchmark for proactive assistance comprising 100 multi-turn tasks across 5 domain-specific user personas. By incorporating hidden user intents, inter-task dependencies, and cross-session continuity, \u03c0-Bench evaluates agents' ability to anticipate and address user needs over extended interactions, jointly measuring proactivity and task completion in long-horizon trajectories that better reflect real-world use. Experiments show (1) proactive assistance remains challenging, (2) a clear distinction between task completion and proactivity, and (3) the value of prior interaction for proactive intent resolution in later tasks.", "github": "https://github.com/Simplified-Reasoning/Pi-Bench" }, { "date": "2026-05-22", "arxiv_id": "2605.22642", "title": "Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning", "authors": [ "Banghao Chi", "Yining Xie", "Mingyuan Wu", "Jingcheng Yang", "Jize Jiang", "Zhaoheng Li", "Shengyi Qian", "Minjia Zhang", "Klara Nahrstedt", "Rui Hou", "Xiangjun Fan", "Hanchao Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Spreadsheet-RL/Spreadsheet-RL", "github_api_added_by": "user", "project_page": "https://spreadsheet-rl.github.io/", "abstract": "Spreadsheet systems (e.g., Microsoft Excel, Google Sheets) play a central role in modern data-centric workflows. As AI agents grow increasingly capable of automating complex tasks, such as controlling computers and generating presentations, building an AI-driven spreadsheet agent has emerged as a promising research direction. Most existing spreadsheet agents rely on specialized prompting over general-purpose LLMs; while this design has potentials on simple spreadsheet operations, it struggles to manage the complex, multi-step workflows typical of real-world applications. We introduce Spreadsheet-RL, a reinforcement learning (RL) fine-tuning framework designed to train specialized spreadsheet agents within a realistic Microsoft Excel environment. Spreadsheet-RL features an automated pipeline for scalable collection of paired start-goal spreadsheets from online forums, as well as domain-specific evaluation tasks in areas such as finance and supply chain management, which we compile into the new Domain-Spreadsheet benchmark dataset. It also includes a Spreadsheet Gym environment designed for multi-turn RL: Spreadsheet Gym exposes extensive Excel functionality through a Python sandbox, along with a refined harness that incorporates a comprehensive tool set and carefully designed tool-routing rules for spreadsheet tasks. Through comprehensive experiments, we show that Spreadsheet-RL substantially enhances AI agent's performance on both general and domain-specific spreadsheet tasks: it improves Qwen3-4B-Thinking-2507's Pass@1 on SpreadsheetBench from 12.0% to 23.4%, and raises Pass@1 from 8.4% to 17.2% on our curated Domain-Spreadsheet dataset. These results highlight Spreadsheet-RL's strong potential for generalization and real-world adoption in spreadsheet automation, and broadly, its promise for advancing LLM-based interactions with data interfaces in everyday work.", "github": "https://github.com/Spreadsheet-RL/Spreadsheet-RL" }, { "date": "2026-05-22", "arxiv_id": "2605.22809", "title": "Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving", "authors": [ "Jiahao Wang", "Bo Sun", "Yijing Bai", "Vincent Casser", "Songyou Peng", "Zehao Zhu", "Meng-Li Shih", "Xander Masotto", "Shih-Yang Su", "Kanaad V Parvate", "Tiancheng Ge", "Linn Bieske", "Dragomir Anguelov", "Mingxing Tan", "Chiyu Max Jiang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Robust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets. Proprietary data collected by Autonomous Vehicle (AV) fleets, while high-fidelity, are limited in scale, diversity of sensor configurations, as well as geographic and long-tail-behavioral coverage. In contrast, in-the-wild data from sources like dashcams offers immense scale and diversity, capturing critical long-tail scenarios and novel environments. However, this unstructured, in-the-wild video data is incompatible with ADS expecting structured, multi-modal sensor inputs for validation and training. To bridge this data gap, we propose Sensor2Sensor, a novel generative modeling paradigm that translates in-the-wild monocular dashcam videos into a high-fidelity, multi-modal sensor suite (AV logs) comprising multi-view camera images and LiDAR point clouds. A core challenge is the lack of paired training data. We address this by converting real AV logs into dashcam-style videos via 4D Gaussian Splatting (4DGS) reconstruction and novel-view rendering. Sensor2Sensor then utilizes a diffusion architecture to perform the generative conversion. We perform comprehensive quantitative evaluations on the fidelity and realism of the generated sensor data. We demonstrate Sensor2Sensor's practical utility by converting challenging in-the-wild internet and dashcam footage into realistic, multi-modal data formats, further unlocking vast external data sources for AV development.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.22678", "title": "Swift Sampling: Selecting Temporal Surprises via Taylor Series", "authors": [ "Dahye Kim", "Bhuvan Sachdeva", "Karan Uppal", "Naman Gupta", "Vineeth N. Balasubramanian", "Deepti Ghadiyaram" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://kim-dahye.github.io/swift-sampling/", "abstract": "While most frames in long-form video are redundant, the critical information resides in temporal surprises: moments where the actual visual features deviate from their predicted evolution. Inspired by the human brain's predictive coding, we introduce Swift Sampling, an elegant, training-free frame selection algorithm that automatically identifies high-information moments in a video. Specifically, we model a video as a differentiable trajectory in the visual latent space and compute the velocity and acceleration of its features. Then, we apply Taylor expansion to project the expected path of subsequent frames. Frames that diverge sharply from this predicted manifold are identified as temporally surprising frames and selected for sampling. Unlike prior training-free methods that rely on auxiliary networks or video-specific hyperparameter tuning, Swift Sampling is incredibly lightweight, adding only 0.02x additional computational cost over baseline making it 30x cheaper overhead than leading baselines. Across three long-video question answering benchmarks and 10 different downstream tasks, Swift Sampling outperforms uniform sampling and prior query-agnostic baselines. It is especially powerful for long videos with limited frame budgets improving accuracy by up to +12.5 points.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.22109", "title": "Perception or Prejudice: Can MLLMs Go Beyond First Impressions of Personality?", "authors": [ "Caixin Kang", "Tianyu Yan", "Sitong Gong", "Mingfang Zhang", "Liangyang Ouyang", "Ruicong Liu", "Bo Zheng", "Huchuan Lu", "Kaipeng Zhang", "Yoichi Sato", "Yifei Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kkkcx/MM-OCEAN", "github_api_added_by": "user", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) are increasingly deployed in human-facing roles where personality perception is critical, yet existing benchmarks evaluate this capability solely on numerical Big Five score prediction, leaving open whether models truly perceive personality through behavioral understanding or merely prejudge through superficial pattern matching. We address this gap with three contributions. (i) A new task: we formalize Grounded Personality Reasoning (GPR), which requires MLLMs to anchor each Big Five rating in observable evidence through a chain of rating, reasoning, and grounding. (ii) A new dataset: we release MM-OCEAN (1,104 videos, 5,320 MCQs), produced by a multi-agent pipeline with human verification, with timestamped behavioral observations, evidence-grounded trait analyses, and seven categories of cue-grounding MCQs. (iii) Benchmark and analysis: we design a three-tier evaluation (rating, reasoning, grounding) plus four sample-level failure-mode metrics: Prejudice Rate (PR), Confabulation Rate (CR), Integration-failure Rate (IR), and Holistic-grounding Rate (HR), and benchmark 27 MLLMs (13 closed, 14 open). The analysis uncovers a striking Prejudice Gap: across the field, 51% of correct ratings are not grounded in retrieved cues, and the Holistic-Grounding Rate spans only 0-33.5%. These findings expose a disconnect between getting the right score and reasoning for the right reason, charting a roadmap for grounded social cognition in MLLMs.", "github": "https://github.com/kkkcx/MM-OCEAN" }, { "date": "2026-05-22", "arxiv_id": "2605.22791", "title": "Gated DeltaNet-2: Decoupling Erase and Write in Linear Attention", "authors": [ "Ali Hatamizadeh", "Yejin Choi", "Jan Kautz" ], "github_manual": "", "github_regex": "https://github.com/NVlabs/GatedDeltaNet-2", "github_paper_md": "", "github_api": "https://github.com/NVlabs/GatedDeltaNet-2", "github_api_added_by": "user", "project_page": "", "abstract": "Linear attention replaces the unbounded cache of softmax attention with a fixed-size recurrent state, reducing sequence mixing to linear time and decoding to constant memory. The hard part is not just what to forget, but how to edit this compressed memory without scrambling existing associations. Delta-rule models subtract the current read before writing a new value, and Kimi Delta Attention (KDA) sharpens forgetting with channel-wise decay. But the active edit still uses a single scalar gate to control two different things: how much old content to erase on the key side and how much new content to commit on the value side. We introduce Gated DeltaNet-2, which generalizes both Gated DeltaNet and KDA by inheriting adaptive forgetting and channel-wise decay while addressing their shared limitation, the scalar tie between erasing and writing. Gated Delta Rule-2 separates these roles with a channel-wise erase gate b_t and a channel-wise write gate w_t, reducing to KDA when both gates collapse to the same scalar and to Gated DeltaNet when the decay also collapses. We derive a fast-weight update view, a chunkwise WY algorithm with channel-wise decay absorbed into asymmetric erase factors, and a gate-aware backward pass that preserves efficient parallel training. At 1.3B parameters trained on 100B FineWeb-Edu tokens, Gated DeltaNet-2 achieves the strongest overall results among Mamba-2, Gated DeltaNet, KDA, and Mamba-3 variants across language modeling, commonsense reasoning, and retrieval. Its advantage is most pronounced on long-context RULER needle-in-a-haystack benchmarks, where it improves the evaluated multi-key retrieval setting and remains strong in both recurrent and hybrid settings. Code is available at https://github.com/NVlabs/GatedDeltaNet-2.", "github": "https://github.com/NVlabs/GatedDeltaNet-2" }, { "date": "2026-05-22", "arxiv_id": "2605.13734", "title": "KVServe: Service-Aware KV Cache Compression for Communication-Efficient Disaggregated LLM Serving", "authors": [ "Zedong Liu", "Xinyang Ma", "Dejun Luo", "Hairui Zhao", "Bing Lu", "Wenjing Huang", "Yida Gu", "Xingchen Liu", "Zheng Wei", "Jinyang Liu", "Dingwen Tao", "Guangming Tan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hpdps-group/KVServe", "github_api_added_by": "user", "project_page": "", "abstract": "LLMs are widely adopted in production, pushing inference systems to their limits. Disaggregated LLM serving (e.g., PD separation and KV state disaggregation) improves scalability and cost efficiency, but it also turns KV into an explicit payload crossing network and storage boundaries, making KV a dominant end-to-end bottleneck. Existing KV compression are typically static runtime configurations, despite production service context varies over time in workload mix, bandwidth, and SLO/quality budgets. As a result, a fixed choice can be suboptimal or even increase latency. We present KVServe, the first service-aware and adaptive KV communication compression framework for disaggregated LLM serving: KVServe (1) unifies KV compression into a modular strategy space with new components and cross-method recomposition; (2) introduces Bayesian Profiling Engine that efficiently searches this space and distills a 3D Pareto candidate set, reducing 50times offline search overhead; and (3) deploys a Service-Aware Online Controller that combines an analytical latency model with a lightweight bandit to select profiles under constraints and correct offline-to-online mismatch. Integrated into vLLM and evaluated across datasets, models, GPUs and networks, KVServe achieves up to 9.13times JCT speedup in PD-separated serving and up to 32.8times TTFT reduction in KV-disaggregated serving.", "github": "https://github.com/hpdps-group/KVServe" }, { "date": "2026-05-22", "arxiv_id": "2605.22177", "title": "Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles", "authors": [ "Jinyang Wu", "Guocheng Zhai", "Ruihan Jin", "Yuhao Shen", "Zhengxi Lu", "Fan Zhang", "Haoran Luo", "Zheng Lian", "Zhengqi Wen", "Jianhua Tao" ], "github_manual": "", "github_regex": "https://github.com/jinyangwu/Maestro", "github_paper_md": "", "github_api": "https://github.com/jinyangwu/Maestro", "github_api_added_by": "user", "project_page": "", "abstract": "The proliferation of large language models (LLMs) and modular skills has endowed autonomous agents with increasingly powerful capabilities. Existing frameworks typically rely on monolithic LLMs and fixed logic to interface with these skills. This gives rise to a critical bottleneck: different LLMs offer distinct advantages across diverse domains, yet current frameworks fail to exploit the complementary strengths of models and skills, thereby limiting their performance on downstream tasks. In this paper, we present Maestro (Multimodal Agent for Expert-Skill Targeted Reinforced Orchestration), a Reinforcement Learning (RL)-driven orchestration framework that reframes heterogeneous multimodal tasks as a sequential decision-making process over a hierarchical model-skill registry. Rather than consolidating all knowledge into a single model, Maestro trains a lightweight policy to dynamically compose ensembles of frozen expert models and a two-tier skill library, deciding at each step whether to invoke an external expert, which model-skill pair to select, and when to terminate. The policy is optimized via outcome-based RL, requiring no step-level supervision. We evaluate Maestro across ten representative multimodal benchmarks spanning mathematical reasoning, chart understanding, high-resolution perception, and domain-specific analysis. With only a 4B orchestrator, Maestro achieves an average accuracy of 70.1%, surpassing both GPT-5 (69.3%) and Gemini-2.5-Pro (68.7%). Crucially, the learned coordination policy generalizes to unseen models and skills without retraining: augmenting the registry with out-of-domain experts yields a 59.5% average on four challenging benchmarks, outperforming all closed-source baselines. Maestro further maintains high computational efficiency with low latency. The source code is available at https://github.com/jinyangwu/Maestro.", "github": "https://github.com/jinyangwu/Maestro" }, { "date": "2026-05-22", "arxiv_id": "2605.22074", "title": "From Reasoning Chains to Verifiable Subproblems: Curriculum Reinforcement Learning Enables Credit Assignment for LLM Reasoning", "authors": [ "Xitai Jiang", "Zihan Tang", "Wenze Lin", "Yang Yue", "Shenzhi Wang", "Gao Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement learning from verifiable rewards (RLVR) has shown strong promise for LLM reasoning, but outcome-based RLVR remains inefficient on hard problems because correct final-answer rollouts are rare and sample-level credit assignment cannot use partial progress in failed attempts. We introduce SCRL (Subproblem Curriculum Reinforcement Learning), a curriculum RL framework that derives verifiable subproblems from reference reasoning chains and fixes the final subproblem as the original problem. This turns partial progress on hard problems into verifiable learning signals. Algorithmically, SCRL uses subproblem-level normalization, which normalizes rewards independently at each subproblem position and assigns the resulting advantages to the corresponding answer spans, enabling finer-grained credit assignment without external rubrics or reward models. Our analysis shows that subproblem curricula lift hard problems out of gradient dead zones, with larger relative gains as the original problem becomes harder. Across seven mathematical reasoning benchmarks, SCRL outperforms strong curriculum-learning baselines, improving average accuracy over GRPO by +4.1 points on Qwen3-4B-Base and +1.9 points on Qwen3-14B-Base. On AIME24, AIME25, and IMO-Bench, SCRL further improves pass@1 by +3.7 points and pass@64 by +4.6 points on Qwen3-4B-Base, indicating better exploration on hard reasoning problems.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.22344", "title": "Bernini: Latent Semantic Planning for Video Diffusion", "authors": [ "Bernini Team", "Chenchen Liu", "Junyi Chen", "Lei Li", "Lu Chi", "Mingzhen Sun", "Zhuoying Li", "Yi Fu", "Ruoyu Guo", "Yiheng Wu", "Ge Bai", "Zehuan Yuan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://bernini-ai.github.io/", "abstract": "Multimodal large language models (MLLMs) and diffusion models have each reached remarkable maturity: MLLMs excel at reasoning over heterogeneous multimodal inputs with strong semantic grounding, while diffusion models synthesize images and videos with photorealistic fidelity. We argue that these two families can be unified through a simple division of labor: MLLMs perform semantic planning, while diffusion models render pixels from high-level semantic guidance and low-level visual features. Building on this idea, we propose Bernini, a unified framework for video generation and editing. An MLLM-based planner predicts the target semantic representation directly in the ViT embedding space, and a DiT-based renderer synthesizes pixels conditioned on this plan, augmented by text features and, for editing, source VAE features for detail preservation. Because semantics serve as the interface, the planner and renderer can be trained separately and only lightly co-trained, preserving the pretrained strengths of both components while keeping training efficient. To better handle multiple visual inputs, we introduce Segment-Aware 3D Rotary Positional Embedding (SA-3D RoPE), and further incorporate chain-of-thought reasoning in the planner to better transfer understanding into generation. Bernini achieves state-of-the-art performance across a wide range of video generation and editing benchmarks, with the MLLM's pretrained understanding translating into strong generalization on challenging editing tasks.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.22535", "title": "TerminalWorld: Benchmarking Agents on Real-World Terminal Tasks", "authors": [ "Zhaoyang Chu", "Jiarui Hu", "Xingyu Jiang", "Pengyu Zou", "Han Li", "Chao Peng", "Peter O'Hearn", "Earl T. Barr", "Mark Harman", "Federica Sarro", "He Ye" ], "github_manual": "", "github_regex": "https://github.com/EuniAI/TerminalWorld", "github_paper_md": "", "github_api": "https://github.com/EuniAI/TerminalWorld", "github_api_added_by": "user", "project_page": "https://terminalworld.ai/", "abstract": "We introduce TerminalWorld, a scalable data engine that automatically reverse-engineers high-fidelity evaluation tasks from \"in-the-wild\" terminal recordings. Processing 80,870 terminal recordings, the engine yields a full benchmark of 1,530 validated tasks, spanning 18 real-world categories, ranging from short everyday operations to workflows exceeding 50 steps, and covering 1,280 unique commands. From these, we curate a Verified subset of 200 representative, manually reviewed tasks. Comprehensive benchmarking on TerminalWorld-Verified across eight frontier models and six agents reveals that current systems still struggle with authentic terminal workflows, achieving a maximum pass rate of only 62.5%. Moreover, TerminalWorld captures real-world terminal capabilities distinct from existing expert-curated benchmarks (e.g., Terminal-Bench), with only a weak correlation to their scores (Pearson r=0.20). The automated engine makes TerminalWorld authentic and scalable by construction, enabling it to evaluate agents in real-world terminal environments as developer practices evolve. Data and code are available at https://github.com/EuniAI/TerminalWorld.", "github": "https://github.com/EuniAI/TerminalWorld" }, { "date": "2026-05-22", "arxiv_id": "2605.21803", "title": "Same Architecture, Different Capacity: Optimizer-Induced Spectral Scaling Laws", "authors": [ "Nandan Kumar Jha", "Brandon Reagen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://optimizer-scaling-laws.github.io/", "abstract": "Scaling laws have made language-model performance predictable from model size, data, and compute, but they typically treat the optimizer as a fixed training detail. We show that this assumption misses a fundamental axis of representation scaling: how effectively the optimizer converts added FFN width into utilized spectral capacity. Using eigenspectra of feed-forward network representations, measured through soft and hard spectral-ranks, we find that the same Transformer architecture realizes markedly different spectral scaling laws when trained with different optimizers. Holding architecture and width schedule fixed, AdamW exhibits weak hard-rank scaling (\u03b2=0.44) on rare-token (TAIL) representations where learning is known to be hardest, whereas Muon achieves linear scaling (\u03b2=1.02) in the same regimes, a 2.3times increase in the scaling exponent. This difference is not reducible to validation loss: AdamW configurations can match low-rank Dion variants in perplexity, under extended training, while exhibiting sharply different spectral geometry, demonstrating that matched loss does not imply matched representation structure. Hard--soft rank asymmetry further reveals that optimizers differ not only in how much capacity is realized, but also in how that capacity is structured across eigenmodes. To disentangle optimizer effects from architectural ones, we compare against architectural interventions (e.g., attention rank and positional encoding), and find that optimizer-induced spectral shifts often exceed the architectural effects. These results suggest optimization as a first-class axis of representation scaling, motivating optimizer--architecture co-design.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.20910", "title": "FlowLong: Inference-time Long Video Generation via Manifold-constrained Tweedie Matching", "authors": [ "Jangho Park", "Geon Yeong Park", "Gihyun Kwon", "Jong Chul Ye" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jhq1234/flowlong", "github_api_added_by": "user", "project_page": "https://flowlong-video.github.io/", "abstract": "Extending the generation horizon of video diffusion models to long sequences remains a long-standing and important challenge. Existing training-free approaches fall into two categories: extensions of bidirectional models, which are tightly coupled to specific architectures and suffer from quality degradation over long horizons, and autoregressive models, which accumulate drift errors due to exposure bias and tend to produce repetitive motion patterns. To address these issues, we propose a novel but simple inference-time approach for long video generation that is architecture-agnostic and requires no additional training. Our method generates long videos via overlapping sliding windows, where predicted clean samples from adjacent windows are blended via Tweedie matching to enforce both manifold constraint and temporal consistency across overlap regions. Stochastic early-phase sampling then synchronizes per-window trajectories by injecting fresh noise after each Tweedie matching correction in the high-noise phase, before transitioning to deterministic ODE sampling to preserve fine-grained visual fidelity. Applied to various video generation models, our method generates videos several times longer than the native window length while outperforming both training-free and autoregressive baselines in temporal consistency and visual quality, and further extends to audio-video joint generation and text-to-3DGS without any fine-tuning.", "github": "https://github.com/jhq1234/flowlong" }, { "date": "2026-05-22", "arxiv_id": "2605.22581", "title": "SceneAligner: 3D-Grounded Floorplan Localization in the Wild", "authors": [ "Junhyeong Cho", "Ruojin Cai", "Hadar Averbuch-Elor" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Cornell-VAILab/SceneAligner", "github_api_added_by": "user", "project_page": "https://Cornell-VAILab.github.io/SceneAligner", "abstract": "Many public buildings provide floorplans with a \"you are here\" indicator to help visitors orient themselves. Floorplan localization seeks to computationally replicate this capability by determining where visual observations were captured within a floorplan. However, existing methods typically assume controlled small-scale environments and precise vectorized floorplans, limiting their ability to operate in large-scale buildings and rasterized floorplans. In this work, we present an approach for performing floorplan localization in the wild by grounding the task in a reconstructed 3D representation of the scene. Given an unconstrained image collection, our method reconstructs a gravity-aligned 3D scene and projects it into a 2D density map that serves as a floorplan proxy. Floorplan localization is then formulated as aligning this proxy with the input floorplan via a 2D similarity transform. To bridge the appearance gap between density maps and architectural floorplans, we adapt a 2D foundation model to learn cross-modal correspondences, introducing a fine-tuning scheme that encourages semantically aligned matches while preserving structural consistency. Extensive experiments demonstrate substantial improvements over prior methods, including in extremely sparse settings with as little as a single input image. Our code and data will be publicly available.", "github": "https://github.com/Cornell-VAILab/SceneAligner" }, { "date": "2026-05-22", "arxiv_id": "2605.12817", "title": "Training Large Language Models to Predict Clinical Events", "authors": [ "Benjamin Turtel", "Paul Wilczewski", "Kris Skotheim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://arxiv.org/abs/2605.12817", "abstract": "Longitudinal clinical notes contain rich evidence of how patients evolve over time, but converting this signal into training supervision for clinical prediction remains challenging. We extend Foresight Learning to clinical prediction by converting time-ordered MIMIC-III notes into examples consisting of past patient context, a natural-language question about a possible future event, and a label resolved from later documentation. This process yields 6,900 prediction examples from 702 admissions across medications, procedures, organ support, microbiology, and mortality. A small LoRA adapter trained on these examples improves over the prompted base model, reducing expected calibration error from 0.1269 to 0.0398 and Brier score from 0.199 to 0.145, while slightly outperforming GPT-5 point estimates on held-out questions. The approach enables reusable clinical prediction supervision from longitudinal notes without hand-engineered structured features or endpoint-specific classifiers.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.21467", "title": "DelTA: Discriminative Token Credit Assignment for Reinforcement Learning from Verifiable Rewards", "authors": [ "Kaiyi Zhang", "Wei Wu", "Yankai Lin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RUCBM/DelTA", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement learning from verifiable rewards (RLVR) has emerged as a central technique for improving the reasoning capabilities of large language models. Despite its effectiveness, how response-level rewards translate into token-level probability changes remains poorly understood. We introduce a discriminator view of RLVR updates, showing that the policy-gradient update direction implicitly acts as a linear discriminator over token-gradient vectors and thereby determines which token probabilities are increased or decreased during learning. Under standard sequence-level RLVR, this discriminator is constructed from positive- and negative-side centroids formed by advantage-weighted averaging of token-gradient vectors. However, such centroid construction can be dominated by shared high-frequency patterns, such as formatting tokens, diluting sparse yet discriminative directions that better distinguish high-reward responses from low-reward ones. To address this limitation, we propose DelTA, a discriminative token credit assignment method that estimates token coefficients to amplify side-specific token-gradient directions and downweight shared or weakly discriminative ones. These coefficients reweight a self-normalized RLVR surrogate, making the effective side-wise centroids more contrastive and thereby reshaping the RLVR update direction. On seven mathematical benchmarks, DelTA outperforms the strongest same-scale baselines by 3.26 and 2.62 average points on Qwen3-8B-Base and Qwen3-14B-Base, respectively. Additional results on code generation, a different backbone, and out-of-domain evaluations further demonstrate the generalization ability of DelTA.", "github": "https://github.com/RUCBM/DelTA" }, { "date": "2026-05-22", "arxiv_id": "2605.21572", "title": "PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects", "authors": [ "Ziang Cao", "Yinghao Liu", "Haitian Li", "Runmao Yao", "Fangzhou Hong", "Zhaoxi Chen", "Liang Pan", "Ziwei Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/physx-omni/PhysX-Omni", "github_api_added_by": "user", "project_page": "https://physx-omni.github.io", "abstract": "Simulation-ready physical 3D assets have emerged as a promising direction owing to their broad applicability in downstream tasks. However, most existing 3D generation methods either neglect physical properties or are limited to a single asset category, e.g., rigid, deformable, or articulated objects. To address these limitations, we introduce PhysX-Omni, a unified framework for simulation-ready physical 3D generation across diverse asset types. Specifically, we develop a novel and efficient geometry representation tailored for Vision-Language Models, which directly encodes high-resolution 3D structures without compression, significantly improving generation performance. In addition, we construct the first general simulation-ready 3D dataset, PhysXVerse, covering diverse indoor and outdoor categories. Furthermore, to comprehensively and flexibly evaluate both generative and understanding capabilities in the wild, we propose PhysX-Bench, which encompasses six key attributes: geometry, absolute scale, material, affordance, kinematics, and function description. Extensive experiments with conventional metrics and PhysX-Bench show that PhysX-Omni performs strongly in both generation and understanding. Moreover, additional studies further validate the potential of PhysX-Omni for applications in simulation-ready scene generation and robotic policy learning. We believe PhysX-Omni can significantly advance a wide range of downstream applications, particularly in embodied AI and physics-based simulation.", "github": "https://github.com/physx-omni/PhysX-Omni" }, { "date": "2026-05-22", "arxiv_id": "2605.22538", "title": "Segment Anything with Motion, Geometry, and Semantic Adaptation for Complex Nonlinear Visual Object Tracking", "authors": [ "Deyi Zhu", "Yuji Wang", "Yong Liu", "Yansong Tang", "Bingyao Yu", "Jiwen Lu", "Jie Zhou" ], "github_manual": "", "github_regex": "https://github.com/DurYi/SAMOSA", "github_paper_md": "", "github_api": "https://github.com/DurYi/SAMOSA", "github_api_added_by": "user", "project_page": "", "abstract": "Traditional visual object tracking (VOT) methods typically rely on task-specific supervised training, limiting their generalization to unseen objects and challenging scenarios with distractors, occlusion, and nonlinear motion. Recent vision foundation models, exemplified by SAM 2, learn strong video understanding priors from large-scale pretraining and offer a promising foundation for building more robust and generalizable trackers. However, directly applying SAM 2 to VOT remains suboptimal, as it does not explicitly model target motion dynamics or enforce geometric and semantic consistency across frames, both of which are essential for reliable tracking. To address this issue, we propose SAMOSA, a new tracking framework that adapts SAM 2 to complex VOT scenarios by explicitly leveraging motion, geometry, and semantic cues. Specifically, we introduce a lightweight nonlinear motion predictor to model target dynamics and guide mask selection as well as memory filtering. We further exploit semantic cues to detect target shifts and recover from tracking failures, while geometric cues are incorporated as structural constraints to improve tracking stability. In this way, SAMOSA bridges the gap between the implicit video understanding prior of SAM 2 and explicit tracking-oriented modeling. Extensive experiments show that SAMOSA consistently outperforms state-of-the-art SAM 2--based approaches on general benchmarks, demonstrates stronger generalization than supervised VOT methods, and achieves substantial gains on anti-UAV datasets, which typify complex nonlinear motion scenarios. Our code is available at https://github.com/DurYi/SAMOSA.", "github": "https://github.com/DurYi/SAMOSA" }, { "date": "2026-05-22", "arxiv_id": "2605.22536", "title": "SpaceDG: Benchmarking Spatial Intelligence under Visual Degradation", "authors": [ "Xiaolong Zhou", "Yifei Liu", "Ziyang Gong", "Jiarui Li", "Qiyue Zhao", "Muyao Niu", "Yuanyuan Gao", "Le Ma", "Xue Yang", "Hongjie Zhang", "Zhihang Zhong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Visionary-Laboratory/SpaceDG", "github_api_added_by": "user", "project_page": "https://visionary-laboratory.github.io/SpaceDG/", "abstract": "Multimodal Large Language Models (MLLMs) have made rapid progress in spatial intelligence, yet existing spatial reasoning benchmarks largely assume pristine visual inputs and overlook the degradations that commonly occur in real-world deployment, such as motion blur, low light, adverse weather, lens distortion, and compression artifacts. This raises a fundamental question: how robust is the spatial intelligence of current MLLMs when visual observations are imperfect? To answer this question, we introduce SpaceDG, the first large-scale dataset for degradation-aware spatial understanding. It is constructed with a physically grounded degradation synthesis engine that embeds degradation formation process into 3D Gaussian Splatting (3DGS) rendering, enabling realistic simulation of nine degradation types. The resulting dataset contains approximately 1M QA pairs from nearly 1,000 indoor scenes. We further introduce SpaceDG-Bench, an human-verified benchmark with 1,102 questions spanning 11 reasoning categories and 9 visual degradation types, yielding over 10K VQA instances. Evaluating 25 open- and closed-source MLLMs reveals that visual degradations consistently and substantially impair spatial reasoning, exposing a critical robustness gap. Finally, we show that finetuning on SpaceDG markedly improves degradation robustness and can even surpass human performance under degraded conditions without any performance drop on clean images, highlighting the promise of degradation-aware training for robust spatial intelligence.", "github": "https://github.com/Visionary-Laboratory/SpaceDG" }, { "date": "2026-05-22", "arxiv_id": "2605.22144", "title": "One Sentence, One Drama: Personalized Short-Form Drama Generation via Multi-Agent Systems", "authors": [ "Yufei Shi", "Weilong Yan", "Naixuan Huang", "Yucheng Chen", "Chenyu Zhang", "Tao He", "Si Yong Yeo", "Ming Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Existing approaches for digital short-drama production typically rely on one-shot LLM generated scripts and loosely coupled pipelines, which fail to satisfy three key requirements of short-drama generation: (1) narrative pacing, resulting in weak hooks, insufficient escalation, and unattractive endings; (2) spatial consistency, leading to drifting scene layouts and inconsistent character positions across clips; and (3) production-level quality control, requiring extensive manual review and correction across script and visual stages. We present One Sentence, One Drama, a hierarchical multi-agent framework that transforms a user's single-sentence idea into a fully produced short drama through structured intermediate modules and iterative refinement. Our approach is built upon three key components: (1) a multi-agent debate-based story generation module that enforces short-drama pacing and narrative coherence; (2) a 3D-grounded first-frame generation mechanism that establishes a shared spatial reference for consistent character positioning and scene layout across clips; and (3) multi-stage reviewer loops that perform comprehensive error detection and targeted revision across script, visual, and video generation stages. We also introduce scene-level BGM matching and scene transition planning to improve the audience's immersive experience. To systematically evaluate this task, we introduce Short-Drama-Bench, a benchmark that extends standard video quality metrics with short-drama-specific criteria. Experimental results demonstrate that our method significantly outperforms existing pipelines in narrative quality, cross-clip consistency, and overall viewing experience.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.22012", "title": "LatentOmni: Rethinking Omni-Modal Understanding via Unified Audio-Visual Latent Reasoning", "authors": [ "Yifan Dai", "Zhenhua Wu", "Bohan Zeng", "Daili Hua", "Jialing Liu", "Bozhou Li", "Yuran Wang", "Chengzhuo Tong", "Hao Liang", "Xiaochen Ma", "Junbo Niu", "Tianyu Guo", "Yang Shi", "Yue Ding", "Yiyan Ji", "Bingyin Mei", "Yushuo Guan", "Yuanxing Zhang", "Pengfei Wan", "Fangcheng Fu", "Wentao Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yfanDai/LatentOmni", "github_api_added_by": "user", "project_page": "", "abstract": "Joint audio-visual reasoning is essential for omnimodal understanding, yet current multimodal large language models (MLLMs) still struggle when reasoning requires fine-grained evidence from both modalities. A central limitation is that explicit text-based chain-of-thought (CoT) compresses continuous audio-visual signals into discrete tokens, weakening temporal grounding and shifting intermediate reasoning toward language priors. We argue that a unified latent space is a better medium for such reasoning because it preserves dense sensory information while remaining compatible with autoregressive generation. Based on this insight, we propose LatentOmni, a cross-modal reasoning framework that interleaves textual reasoning with audio-visual latent states. LatentOmni introduces feature-level supervision to align latent reasoning states with task-relevant sensory features and uses Omni-Sync Position Embedding (OSPE) to maintain temporal consistency between latent audio and visual states. We further construct LatentOmni-Instruct-35K, a dataset of audio-visual interleaved reasoning trajectories for supervising latent-space reasoning. Comprehensive evaluation across multiple audio-visual reasoning benchmarks demonstrates that LatentOmni achieves the best performance among the evaluated open-source models and consistently outperforms the Explicit Text CoT baseline, supporting latent-space joint reasoning as a promising path toward stronger omnimodal understanding.", "github": "https://github.com/yfanDai/LatentOmni" }, { "date": "2026-05-22", "arxiv_id": "2605.20176", "title": "ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning", "authors": [ "Juncheng Wu", "Letian Zhang", "Yuhan Wang", "Haoqin Tu", "Hardy Chen", "Zijun Wang", "Cihang Xie", "Yuyin Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UCSC-VLAA/ClinSeekAgent", "github_api_added_by": "user", "project_page": "https://ucsc-vlaa.github.io/ClinSeekAgent/", "abstract": "Large language models (LLMs) and agentic systems have shown promise for clinical decision support, but existing works largely assume that evidence has already been curated and handed to the model. Real-world clinical workflows instead require agents to actively seek, iteratively plan, and synthesize multimodal evidence from heterogeneous sources. In this paper, we introduce ClinSeekAgent, an automated agentic framework for dynamic multimodal evidence seeking that shifts the paradigm from passive evidence consumption to active evidence acquisition. Given only a clinical query and access to raw data sources, ClinSeekAgent gathers evidence by querying medical knowledge bases, navigating raw EHRs, and invoking medical imaging tools; refines its hypotheses as new information emerges; and integrates the collected evidence into grounded clinical decisions. ClinSeekAgent serves both as an inference-time agent for frontier LLMs and as a training-time pipeline for distilling high-quality agent trajectories into compact open-source models. To validate its inference-time effectiveness, we construct ClinSeek-Bench, which pairs Curated Input reasoning from fixed pre-selected evidence with Automated Evidence-Seeking over raw clinical data. On text-only EHR tasks, ClinSeekAgent improves Claude Opus 4.6 from 60.0 to 63.2 overall F1 and MiniMax M2.5 from 43.1 to 47.3, with positive risk-prediction gains in 7 out of 9 evaluated host models. On multimodal tasks, ClinSeekAgent improves Claude Opus 4.6 from 47.5 to 62.6 (+15.1); all evaluated models improve across the three CXR-related task groups. We further validate ClinSeekAgent as a training pipeline by distilling agentic evidence-seeking trajectories into ClinSeek-35B-A3B, which achieves 34.0 average F1 on existing AgentEHR-Bench, improving over its Qwen3.5-35B-A3B baseline by +11.9 points and approaching Claude Opus 4.6.", "github": "https://github.com/UCSC-VLAA/ClinSeekAgent" }, { "date": "2026-05-22", "arxiv_id": "2605.21072", "title": "Q-ARVD: Quantizing Autoregressive Video Diffusion Models", "authors": [ "Siao Tang", "Xinyin Ma", "Gongfan Fang", "Xingyi Yang", "Xinchao Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tsa18/Q-ARVD", "github_api_added_by": "user", "project_page": "", "abstract": "Autoregressive video diffusion models (ARVDs) have emerged as a promising architecture for streaming video generation, paving the way for real-time interactive video generation and world modeling. Despite their potential, the substantial inference cost of ARVDs remains a major obstacle to practical deployment, making model quantization a natural direction for improving efficiency. However, quantization for ARVDs remains largely unexplored. Our empirical analysis shows that directly applying existing quantization schemes developed for standard diffusion transformers to ARVDs leads to suboptimal performance, revealing quantization behaviors that differ from those observed in bidirectional diffusion models. In this paper, we identify two critical challenges in quantizing ARVDs: (C1) Highly unbalanced frame-wise quantization sensitivity. Error accumulation during autoregressive generation can induce severely skewed quantization sensitivity across frames, following an exponential-like decay pattern. (C2) Prominent and heterogeneous outlier patterns in weights. Weight distributions exhibit pronounced outlier channels, whose patterns vary substantially across layer types and block depths. To address these issues, we propose Q-ARVD, a novel framework for accurate ARVD quantization. (S1) To tackle the highly unbalanced frame-wise sensitivity, Q-ARVD incorporates a final-quality aware frame-weighting mechanism into the quantization objective. (S2) To prevent heterogeneous outliers from degrading performance, Q-ARVD introduces an outlier-aware adaptive dual-scale quantization, which automatically detects the presence and quantity of outlier channels for an arbitrary layer, and isolates them to protect normal channels. Extensive experiments demonstrate the superiority of Q-ARVD.", "github": "https://github.com/tsa18/Q-ARVD" }, { "date": "2026-05-22", "arxiv_id": "2605.16928", "title": "Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps", "authors": [ "Yanke Zhou", "Yiduo Li", "Hanlin Tang", "Maohua Li", "Kan Liu", "Lan Tao", "Lin Qu", "Yuan Yao", "Xiaoxing Ma" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Long-context inference in large language models is bottlenecked by the quadratic cost of full attention. Existing efficient alternatives often rely either on native sparse training or on heuristic token eviction, creating an undesirable trade-off among efficiency, training cost, and accuracy. In this work, we show that full-attention LLMs are already intrinsically sparse and can be transformed into highly sparse models with only minimal adaptation. Our approach is built on three observations: (1) only a small subset of attention heads truly requires full long-context processing; (2) long-range retrieval is governed primarily by a low-dimensional subspace, allowing relevant tokens to be retrieved efficiently with a 16-dimensional indexer; and (3) the useful token budget is strongly query-dependent, making dynamic top-p selection more suitable than fixed top-k sparsification. Based on these insights, we propose RTPurbo, which retains the full KV cache only for retrieval heads and introduces a lightweight token indexer for sparse attention. By exploiting the model's intrinsic sparsity, RTPurbo achieves sparsification with only a few hundred training steps. Experiments on long-context benchmarks and reasoning tasks show that RTPurbo preserves near-lossless accuracy while delivering substantial efficiency gains, including up to a 9.36times prefill speedup at 1M context and about a 2.01times decode speedup. These results suggest that strong sparse inference can be obtained from standard full-attention training without expensive native sparse pretraining.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.21605", "title": "GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation", "authors": [ "Sixiang Chen", "Zhaohu Xing", "Tian Ye", "Xinyu Geng", "Yunlong Lin", "Jianyu Lai", "Xuanhua He", "Fuxiang Zhai", "Jialin Gao", "Lei Zhu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MeiGen-AI/GenEvolve", "github_api_added_by": "user", "project_page": "https://ephemeral182.github.io/GenEvolve/", "abstract": "Open-ended image generation is no longer a simple prompt-to-image problem. High-quality generation often requires an agent to combine a model's internal generative ability with external resources. As requests become more diverse and demanding, we aim to develop a general image-generation agent that can self-evolve through trajectories and use tools more effectively across varied generation challenges. To this end, we propose GenEvolve, a self-evolving framework based on Tool-Orchestrated Visual Experience Distillation. In GenEvolve, each generation attempt is modeled as a tool-orchestrated trajectory, where the agent gathers evidence, selects references, invokes generation skills, and composes them into a prompt-reference program. Unlike existing agentic generation methods that mainly rely on image-level scalar rewards, GenEvolve compares multiple trajectories for the same request and abstracts best-worst differences into structured visual experience, provided only to a privileged teacher branch. Inspired by on-policy self-distillation, Visual Experience Distillation provides dense token-level supervision, helping the student internalize better search, knowledge activation, reference selection, and prompt construction. We further construct GenEvolve-Data and GenEvolve-Bench. Experiments on public benchmarks and GenEvolve-Bench show substantial gains over strong baselines, achieving state-of-the-art performance among current image-generation frameworks. Our website is as follows: https://ephemeral182.github.io/GenEvolve/", "github": "https://github.com/MeiGen-AI/GenEvolve" }, { "date": "2026-05-22", "arxiv_id": "2605.22355", "title": "TransitLM: A Large-Scale Dataset and Benchmark for Map-Free Transit Route Generation", "authors": [ "Hanyu Guo", "Jiedong Yang", "Chao Chen", "Longfei Xu", "Kaikui Liu", "Xiangxiang Chu" ], "github_manual": "", "github_regex": "https://github.com/HotTricker/TransitLM", "github_paper_md": "", "github_api": "https://github.com/HotTricker/TransitLM", "github_api_added_by": "user", "project_page": "", "abstract": "Public transit route planning traditionally depends on structured map infrastructure and complex routing engines, and no existing dataset supports training models to bypass this dependency. We present TransitLM, a large-scale dataset of over 13 million transit route planning records from four Chinese cities covering 120,845 stations and 13,666 lines, released as a continual pre-training corpus and benchmark data for three evaluation tasks with complementary metrics. Experiments show that an LLM trained on TransitLM produces structurally valid routes at high accuracy and implicitly grounds arbitrary GPS coordinates to appropriate stations without any explicit mapping. These results demonstrate that transit route planning can be learned entirely from data, enabling end-to-end, map-free route generation directly from origin-destination information. The dataset and benchmark are available at https://huggingface.co/datasets/GD-ML/TransitLM, with evaluation code at https://github.com/HotTricker/TransitLM.", "github": "https://github.com/HotTricker/TransitLM" }, { "date": "2026-05-22", "arxiv_id": "2605.22641", "title": "More Context, Larger Models, or Moral Knowledge? A Systematic Study of Schwartz Value Detection in Political Texts", "authors": [ "V\u00edctor Yeste", "Paolo Rosso" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/VictorMYeste/human-value-detection-context-rag", "github_api_added_by": "user", "project_page": "", "abstract": "Detecting Schwartz values in political text is difficult because implicit cues often depend on surrounding arguments and fine-grained distinctions between neighboring values. We study when context and explicit moral knowledge help sentence-level value detection. Using the ValuesML/Touch{\u00e9} ValueEval format, we compare sentence, window, and full-document inputs; no-RAG and retrieval-augmented settings with a curated moral knowledge base; supervised DeBERTa-v3-base/large encoders; and zero-shot LLMs from 12B to 123B parameters. The results show that more context is not uniformly better: full-document context improves supervised DeBERTa encoders by 3.8--4.8 macro-F1 points over sentence-only input, but does not consistently help zero-shot LLMs. Retrieved moral knowledge is more consistently useful in matched comparisons, improving each tested model family and context condition under early fusion. However, scaling from DeBERTa-v3-base to large and from 12B to larger LLMs does not guarantee gains, and simple early fusion outperforms the tested late-fusion and cross-attention RAG variants for encoders. Per-value analyses show that context and retrieval help most for socially situated or conceptually confusable values. These findings suggest that value-sensitive NLP should evaluate context, knowledge, and model family jointly rather than treating longer inputs or larger models as universal improvements.", "github": "https://github.com/VictorMYeste/human-value-detection-context-rag" }, { "date": "2026-05-22", "arxiv_id": "2605.19990", "title": "Minimalist Visual Inertial Odometry", "authors": [ "Francesco Pasti", "Jeremy Klotz", "Nicola Bellotto", "Shree K. Nayar" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/pastifra/four-pixel-vio", "github_api_added_by": "user", "project_page": "https://cave.cs.columbia.edu/projects/categories/project?cid=Computational%20Imaging&pid=Minimalist%20Visual%20Inertial%20Odometry", "abstract": "Visual-Inertial Odometry(VIO), which is critical to mobile robot navigation, uses cameras with a large number of pixels. Capturing and processing camera images requires significant resources. This work presents a minimalist approach to planar odometry, demonstrating that just four visual measurements and an IMU can provide robust motion estimation for differential-drive robots. Our key insight is that four downward-facing photodiodes that sense the world through optical Gabor masks produce signals that encode speed. Based on this, we jointly optimize the mask parameters alongside a Temporal Convolutional Network (TCN) using a physically-grounded simulator. The resulting model decodes speed from just the four measurements produced by the photodiodes. Pairing these estimates with the angular speed from an IMU yields a continuous planar trajectory. We validate our approach with a prototype sensor mounted on a differential drive robot. Across diverse indoor and outdoor terrains, our system closely tracks the reference ground truth without any real-world fine-tuning. Our work shows that minimalist sensing enables efficient and accurate planar odometry.", "github": "https://github.com/pastifra/four-pixel-vio" }, { "date": "2026-05-22", "arxiv_id": "2605.10158", "title": "Unsupervised Process Reward Models", "authors": [ "Artyom Gadetsky", "Maxim Kodryan", "Siba Smarak Panigrahi", "Hang Guo", "Maria Brbic" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Process Reward Models (PRMs) are a powerful mechanism for steering large language model reasoning by providing fine-grained, step-level supervision. However, this effectiveness comes at a significant cost: PRMs require expert annotations for every reasoning step, making them costly and difficult to scale. Here, we propose a method for training unsupervised PRMs (uPRM) that requires no human supervision, neither at the level of step-by-step annotations nor through ground-truth verification of final answers. The key idea behind our approach is to define a scoring function, derived from LLM next-token probabilities, that jointly assesses candidate positions of first erroneous steps across a batch of reasoning trajectories. We demonstrate the effectiveness of uPRM across diverse scenarios: (i) uPRM achieves up to 15% absolute accuracy improvements over the LLM-as-a-Judge in identifying first erroneous steps on the ProcessBench dataset; (ii) as a verifier for test-time scaling, uPRM performs comparably to supervised PRMs and outperforms the majority voting baseline by up to 6.9%, and (iii) when used as a reward signal in reinforcement learning, uPRM enables more robust policy optimization throughout training compared to a supervised PRM trained using ground-truth labels. Overall, our results open a path toward scalable reward modeling for complex reasoning tasks.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.18577", "title": "OmniPro: A Comprehensive Benchmark for Omni-Proactive Streaming Video Understanding", "authors": [ "Ruixiang Zhao", "Jie Yang", "Zijie Xin", "Tianyi Wang", "Fengyun Rao", "Jing LYU", "Xirong Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RuixiangZhao/OmniPro", "github_api_added_by": "user", "project_page": "https://ruixiangzhao.github.io/OmniPro/", "abstract": "Omni-proactive streaming video understanding, i.e., autonomously deciding when to speak and what to say from continuous audio-visual streams, is an emerging capability of omni-modal large language models. Existing benchmarks fall short in three key aspects: they rely primarily on visual signals, adopt polling or fixed-timestamp protocols instead of true proactive evaluation, and cover only a limited range of tasks, preventing reliable assessment and differentiation of omni-proactive streaming models. We present OmniPro, the first benchmark to jointly evaluate omni-modal perception, proactive responding, and diverse video understanding tasks. It comprises 2,700 human-verified samples spanning 9 sub-tasks and 3 cognitive levels, covering 6 basic video understanding capabilities. Notably, 84% of samples require audio signals (speech or non-speech), and each sample is annotated with modality-isolation labels to enable fine-grained multimodal analysis. We further introduce a dual-mode evaluation protocol: Probe mode assesses content understanding by querying the model before and after each ground-truth trigger, while Online mode evaluates full proactive ability by requiring models to autonomously decide when to respond in streaming input. Evaluating 11 representative models reveals three key findings: (1) audio provides consistent gains but with highly variable utilization across models, (2) performance degrades significantly over time, indicating limited long-horizon robustness, and (3) non-speech audio perception remains the weakest dimension.", "github": "https://github.com/RuixiangZhao/OmniPro" }, { "date": "2026-05-22", "arxiv_id": "2605.22777", "title": "DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders", "authors": [ "Tianhang Wang", "Yitong Chen", "Wei Song", "Zuxuan Wu", "Min Li", "Jiaqi Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tianhang-Wang/DecQ", "github_api_added_by": "user", "project_page": "", "abstract": "Representation Autoencoders (RAEs) leverage frozen vision foundation models (VFMs) as tokenizer encoders, providing robust high-level representations that facilitate fast convergence and high-quality generation in latent diffusion models. However, freezing the VFM inherently constrains its spatial reconstruction capacity, limiting fine-grained generation and image editing; in contrast, incorporating reconstruction-oriented signals via fine-tuning disrupts the pretrained semantic space and degrades generative fidelity. To address this trade-off, we propose DecQ, a simple yet effective framework for RAEs. Specifically, DecQ introduces lightweight detail-condensing queries that extract fine-grained information from intermediate VFM features through condenser modules. These queries are incorporated into the decoder to support reconstruction and are jointly generated with patch tokens during generative modeling. By aggregating information from both shallow and deep layers, DecQ effectively mitigates the reconstruction--generation trade-off, improving both reconstruction quality and generative performance. Our experiments demonstrate that: (1) with only 8 additional queries and 3.9% extra computation, DecQ improves reconstruction over the frozen DINOv2-based RAE, increasing PSNR from 19.13 dB to 22.76 dB; and (2) for generative modeling, DecQ achieves 3.3times faster convergence than RAE, attaining an FID of 1.41 without guidance and 1.05 with guidance.", "github": "https://github.com/Tianhang-Wang/DecQ" }, { "date": "2026-05-22", "arxiv_id": "2605.20244", "title": "Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search", "authors": [ "Jialin Lu", "Soonho Kong", "Rodrigo Stehling", "Kaiyu Yang", "Zhangyang Wang", "Weiran Sun", "Wuyang Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://arxiv.org/abs/2605.20244", "abstract": "We present Lean Refactor, a plug-and-play retrieval-augmented agentic framework for multi-objective, controllable, and version-robust refactoring of Lean proofs. LLM-generated proofs are notoriously correct-but-verbose and brittle across library versions, yet existing refactoring works overlook three practical challenges: 1) Lean refactoring is natively multi-objective (proof length, compilation cost, and version compatibility are often in tension); 2) Lean repositories have fragile compatibility, whereas LLM releases are unaware of Lean/Mathlib versions; 3) Training-based pipelines require repeated fine-tuning with each new LLM release, scaling neither with model churn nor with Lean's release cycle. Lean Refactor steers a frozen agentic LLM with retrievals from a curated database of multi-objective refactoring strategies, each densely annotated with metadata such as supported Lean/Mathlib versions and expected compilation-cost reduction. Experiments show over 70% token-level compression on competition benchmarks, over 20% on research repositories, and up to 60% compilation-time reduction, outperforming prior work and Claude Code. Version-filtered retrieval further improves compression on the target Lean version, and refactored miniF2F proofs exhibit stronger zero-shot version transfer to future Lean releases than their unrefactored counterparts.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.22668", "title": "SEGA: Spectral-Energy Guided Attention for Resolution Extrapolation in Diffusion Transformers", "authors": [ "Javad Rajabi", "Kimia Shaban", "Koorosh Roohi", "David B. Lindell", "Babak Taati" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rajabi2001/sega", "github_api_added_by": "user", "project_page": "https://rajabi2001.github.io/sega/", "abstract": "Diffusion transformers (DiTs) have emerged as a dominant architecture for text-to-image generation, yet their performance drops when generating at resolutions beyond their training range. Existing training-free approaches mitigate this by modifying inference-time attention behavior, often through Rotary Position Embeddings (RoPE) extrapolation combined with attention scaling. However, these strategies apply a uniform and content-agnostic scaling across RoPE components with distinct frequency characteristics, inducing a trade-off between preserving global structure and recovering fine detail. We introduce SEGA, a training-free method that dynamically scales attention across RoPE components according to the latent's spatial-frequency structure at each denoising step. This adaptive scaling improves both structural coherence and fine-detail fidelity. Experiments show that SEGA consistently improves high-resolution synthesis across multiple target resolutions, outperforming state-of-the-art training-free baselines.", "github": "https://github.com/rajabi2001/sega" }, { "date": "2026-05-22", "arxiv_id": "2605.21363", "title": "\"I didn't Make the Micro Decisions\": Measuring, Inducing, and Exposing Goal-Level AI Contributions in Collaboration", "authors": [ "Eunsu Kim", "Jessica R. Mindel", "Kyungjin Kim", "Sherry Tongshuang Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rladmstn1714/CoTrace", "github_api_added_by": "user", "project_page": "https://rladmstn1714.github.io/CoTrace/", "abstract": "As large language models (LLMs) increasingly shape how users form, refine, and extend their goals, attributing contributions in human-AI collaboration becomes critical for users calibrating their own reliance and for evaluators assessing AI-assisted work. Yet existing methods focus on final artifacts, missing the process through which goals themselves are jointly shaped. We introduce a goal-level attribution framework, CoTrace, that decomposes explicit goals into verifiable requirements and traces both direct contributions and indirect influences across dialogue turns. Applying CoTrace to 638 real-world collaboration logs, we find that while models account for only 11-26% of goal-shaping contribution, they contribute substantially more on introducing lower-level concrete requirements, and make various kinds of indirect contributions. Through controlled simulations, we show that interaction design choices significantly affect model goal-shaping behavior. In a user study, exposing participants to goal-level analyses shifts their perceived contributions by nearly 2 points on a 5-point scale, revealing systematic miscalibration in how users understand their own AI-assisted work.", "github": "https://github.com/rladmstn1714/CoTrace" }, { "date": "2026-05-22", "arxiv_id": "2605.18607", "title": "Forecasting Downstream Performance of LLMs With Proxy Metrics", "authors": [ "Arkil Patel", "Siva Reddy", "Marius Mosbach", "Dzmitry Bahdanau" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/McGill-NLP/proxy-metrics", "github_api_added_by": "user", "project_page": "", "abstract": "Progress in language model development is often driven by comparative decisions: which architecture to adopt, which pretraining corpus to use, or which training recipe to apply. Making these decisions well requires reliable performance forecasts, yet the two commonly used signals are fundamentally limited. Cross-entropy loss is poorly aligned with downstream capabilities, and direct downstream evaluation is expensive, sparse, and often uninformative at early training stages. Instead, we propose to construct proxy metrics by aggregating token-level statistics, such as entropy, top-k accuracy, and expert token rank, from a candidate model's next token distribution over expert-written solutions. Across three settings, our proxies consistently outperform loss- and compute-based baselines: 1) For cross-family model selection, they rank a heterogeneous population of reasoning models with mean Spearman Rho = 0.81 (vs. Rho = 0.36 for cross-entropy loss); 2) For pretraining data selection, they reliably rank 25 candidate corpora for a target model at roughly 10{,}000times less compute than direct evaluation, pushing the Pareto frontier beyond existing methods; and 3) for training-time forecasting, they extrapolate downstream accuracy across an 18times compute horizon with roughly half the error of existing alternatives. Together, these results suggest that expert trajectories are a broadly useful source of signal for assessing model capabilities, enabling reliable performance forecasting throughout the model development life cycle.", "github": "https://github.com/McGill-NLP/proxy-metrics" }, { "date": "2026-05-22", "arxiv_id": "2605.22715", "title": "AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild", "authors": [ "Baiyu Chen", "Zechen Li", "Wilson Wongso", "Lihuan Li", "Xiachong Lin", "Hao Xue", "Benjamin Tag", "Flora Salim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Breezelled/AnyMo", "github_api_added_by": "user", "project_page": "https://baiyuchen.com/project/AnyMo", "abstract": "As wearable and mobile devices become increasingly embedded in daily life, they offer a practical way to continuously sense human motion in the wild. But inertial signals are highly dependent on the sensing setup, including body location, mounting position, sensor orientation, device hardware, and sampling protocol. This setup dependence makes it difficult to learn motion representations that transfer across devices and datasets, and limits the broader use of wearable IMUs beyond closed-set recognition. We introduce AnyMo, a geometry-aware framework for setup-agnostic human motion modeling. AnyMo uses physics-grounded IMU simulation over dense body-surface placements to generate diverse and plausible synthetic signals, pre-trains a graph encoder from paired synthetic placement views and masked partial observations, tokenizes multi-position IMU into full-body motion tokens, and aligns these tokens with an LLM for motion-language understanding. We evaluate AnyMo on three complementary tasks: zero-shot activity recognition across 14 unseen downstream datasets, cross-modal retrieval, and wearable IMU motion captioning, where it improves average Accuracy/F1/R@2 by 11.7\\%/11.6\\%/22.6\\% on HAR, increases zero-shot IMU-to-text and text-to-IMU retrieval MRR by 15.9\\% and 28.6\\%, respectively, and improves zero-shot captioning BERT-F1 by 18.8\\%. These results support AnyMo as a generalist model for wearable motion understanding in the wild. Project page: https://baiyuchen.com/project/AnyMo.", "github": "https://github.com/Breezelled/AnyMo" }, { "date": "2026-05-22", "arxiv_id": "2605.20405", "title": "Disentangling Sampling from Training Budget in Class-Imbalanced CT Body Composition Segmentation", "authors": [ "Iason Skylitsis", "Dimitrios Karkalousos", "Ivana I\u0161gum" ], "github_manual": "", "github_regex": "https://github.com/iasonsky/episodic-sampling", "github_paper_md": "", "github_api": "https://github.com/iasonsky/episodic-sampling", "github_api_added_by": "user", "project_page": "", "abstract": "Class imbalance is a fundamental challenge in medical image segmentation, where frequent classes typically dominate training at the expense of rare classes. Loss-based approaches mitigate imbalance by reweighting the per-pixel loss within the batch, while sampling strategies control which images enter the batch. Yet neither explicitly controls which classes appear within the batch, leaving rare-class exposure only partially rebalanced. In this work, we adopt episodic sampling from few-shot learning to promote class-balanced batch construction in a fully supervised setting. We decouple episodic sampling from its conventional metric-learning context and evaluate it in body composition segmentation in CT. We compare episodic sampling against random and weighted sampling on nine muscle and adipose tissues, derived from 210 scans of the public SAROS dataset. Training is performed under full- and low-data regimes, with additional comparisons under matched training iteration budgets. Under full-data training, all three strategies performed comparably (mean Dice 0.882 for episodic, 0.878 for random and weighted). Under low-data training, episodic sampling outperformed random and weighted (0.787 vs. 0.758 and 0.762), driven by a 12-fold difference in training iterations. Under matched training budgets, random and weighted overfit earlier, while episodic improved for approximately three times more iterations before plateauing. Our findings identify the training iteration budget as under-recognized confound in sampling strategies, motivating iteration-aware evaluation protocols for small datasets. Furthermore, the residual advantage of episodic sampling is consistent with an implicit regularization effect of class-balanced batches, offering a low-cost, model-agnostic strategy for class-imbalanced medical image segmentation. Code is available at https://github.com/iasonsky/episodic-sampling.", "github": "https://github.com/iasonsky/episodic-sampling" }, { "date": "2026-05-22", "arxiv_id": "2605.22552", "title": "FashionLens: Toward Versatile Fashion Image Retrieval via Task-Adaptive Learning", "authors": [ "Haokun Wen", "Xuemeng Song", "Xinghao Xie", "Xiaolin Chen", "Xiangyu Zhao", "Weili Guan" ], "github_manual": "", "github_regex": "https://github.com/haokunwen/FashionLens", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Fashion image retrieval is a cornerstone of modern e-commerce systems. A unified framework that supports diverse query formats and search intentions is highly desired in practice. However, existing approaches focus on narrow retrieval tasks and do not fully capture such diversity. Therefore, in this work, we aim to develop a unified framework capable of handling diverse realistic fashion retrieval scenarios, achieving truly versatile fashion image retrieval. To establish a data foundation, we first introduce U-FIRE, a comprehensive benchmark that consolidates fragmented fashion datasets into a unified collection, supplemented by two manually curated datasets for testing generalization. Building upon this, we propose FashionLens, a unified framework based on Multimodal Large Language Models. To handle divergent matching objectives, we design a Proposal-Guided Spherical Query Calibrator that dynamically shifts query representations into task-aligned metric spaces via adaptive spherical linear interpolation. Additionally, to mitigate the optimization imbalance caused by varying task complexities and data scales, we develop a Gradient-Guided Adaptive Sampling strategy that automatically re-weights tasks based on realtime learning difficulty and the data scale prior. Experiments on U-FIRE show that FashionLens achieves state-of-the-art performance across diverse retrieval scenarios and generalizes robustly to unseen tasks. The data and code are publicly released at https://github.com/haokunwen/FashionLens.", "github": "https://github.com/haokunwen/FashionLens" }, { "date": "2026-05-22", "arxiv_id": "2605.20496", "title": "Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry", "authors": [ "Pablo Marcos-Manch\u00f3n", "Rishi Jha", "Llu\u00eds Fuentemilla" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/memory-formation/platonic-representations-fmri", "github_api_added_by": "user", "project_page": "", "abstract": "The Strong Platonic Representation Hypothesis suggests that representational convergence in artificial neural networks can be harnessed constructively: embeddings can be translated across models through a universal latent space without paired data. We ask whether an analogous geometry can be recovered across human brains. Using fMRI data from the Natural Scenes Dataset, we propose a self-supervised encoder that learns subject-specific embeddings from brain data alone by exploiting repeated stimulus presentations. We show that these independently learned spaces can be translated across subjects using unsupervised orthogonal rotations, without paired cross-subject samples or intermediate model representations. Synchronizing pairwise rotations into a single shared latent space further improves cross-subject retrieval, indicating that subject-specific spaces are mutually compatible with a common coordinate system. These results provide evidence for a shared neural geometry in the human visual cortex: subject-specific fMRI representations are approximately isometric across individuals and can be translated through purely geometric transformations.", "github": "https://github.com/memory-formation/platonic-representations-fmri" }, { "date": "2026-05-22", "arxiv_id": "2605.22138", "title": "Efficient Agentic Reasoning Through Self-Regulated Simulative Planning", "authors": [ "Mingkai Deng", "Jinyu Hou", "Lara S\u00e1 Neves", "Varad Pimpalkhute", "Taylor W. Killian", "Zhengzhong Liu", "Eric P. Xing" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sailing-lab/sr2am-self-regulated-planning", "github_api_added_by": "user", "project_page": "https://sailing-lab.github.io/sr2am-self-regulated-planning/", "abstract": "How should an agent decide when and how to plan? A dominant approach builds agents as reactive policies with adaptive computation (e.g., chain-of-thought), trained end-to-end expecting planning to emerge implicitly. Without control over the presence, structure, or horizon of planning, these systems dramatically increase reasoning length, yielding inefficient token use without reliable accuracy gains. We argue efficient agentic reasoning benefits from decomposing decision-making into three systems: simulative reasoning (System II) grounding deliberation in future-state prediction via a world model; self-regulation (System III) deciding when and how deeply to plan via a learned configurator; and reactive execution (System I) handling fine-grained action. Simulative reasoning provides unified planning across diverse tasks without per-domain engineering, while self-regulation ensures the planner is invoked only when needed. To test this, we develop SR^2AM (Self-Regulated Simulative Reasoning Agentic LLM), realizing both as distinct stages within an LLM's chain-of-thought, with the LLM as world model. We explore two instantiations: recording decisions from a prompted multi-module system (v0.1) and reconstructing structured plans from traces of pretrained reasoning LLMs (v1.0), trained via supervised then reinforcement learning (RL). Across math, science, tabular analysis, and web information seeking, v0.1-8B and v1.0-30B achieve Pass@1 competitive with 120-355B and 685B-1T parameter systems respectively, while v1.0-30B uses 25.8-95.3% fewer reasoning tokens than comparable agentic LLMs. RL increases average planning horizon by 22.8% while planning frequency grows only 2.0%, showing it learns to plan further ahead rather than more often. More broadly, learned self-regulation instantiates a principle we expect to extend beyond planning to how agents govern their own learning and adaptation.", "github": "https://github.com/sailing-lab/sr2am-self-regulated-planning" }, { "date": "2026-05-22", "arxiv_id": "2605.22681", "title": "Forecasting Scientific Progress with Artificial Intelligence", "authors": [ "Sean Wu", "Pan Lu", "Yupeng Chen", "Jonathan Bragg", "Yutaro Yamada", "Peter Clark", "David Clifton", "Philip Torr", "James Zou", "Junchi Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SeanWu25/cusp-scientific-foresight", "github_api_added_by": "user", "project_page": "https://seanwu25.github.io/CUSP-Science/", "abstract": "Artificial intelligence (AI) is increasingly embedded in scientific discovery, yet whether it can anticipate scientific progress remains unclear. To study this question, we introduce a temporally grounded evaluation framework for forecasting scientific progress under controlled knowledge constraints. We present CUSP (Cutoff-conditioned Unseen Scientific Progress), a multi-disciplinary and event-level benchmark that evaluates scientific forecasting in AI systems through feasibility assessment, mechanistic reasoning, generative solution design, and temporal prediction. Across 4,760 scientific events, we observe systematic and domain-dependent limitations in current frontier models. While models can identify plausible research directions from competing candidates, they fail to reliably predict whether scientific advances will be realized and systematically misestimate when they will occur. Performance is highly heterogeneous across domains, with the timing of AI progress more predictable than advances in biology, chemistry, and physics. Performance is largely insensitive to whether events occur before or after the training cutoff, suggesting these limitations cannot be explained solely by knowledge exposure in training data. Under controlled information access, additional pre-cutoff knowledge improves performance but does not close the gap to full-information settings, which becomes more pronounced for high-citation advances. Models also exhibit systematic overconfidence and strong response biases, indicating unreliable uncertainty estimation. Taken together, current AI systems fall short as predictive tools for scientific progress. Access to prior knowledge does not translate into reliable forecasting, and performance benefits more from post-event information than from forward-looking prediction.", "github": "https://github.com/SeanWu25/cusp-scientific-foresight" }, { "date": "2026-05-22", "arxiv_id": "2605.07604", "title": "SAM 3D Animal: Promptable Animal 3D Reconstruction from Images in the Wild", "authors": [ "Xuyi Hu", "Jin Lyu", "Jiuming Liu", "Yebin Liu", "Silvia Zuffi", "Liang An", "Stefan Goetz" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "3D animal reconstruction in the wild remains challenging due to large species variation, frequent occlusions, and the prevalence of multi-animal scenes, while existing methods predominantly focus on single-animal settings. We present SAM 3D Animal, the first promptable framework for multi-animal 3D reconstruction from a single image. Built on the SMAL+ parametric animal model, our method jointly reconstructs multiple instances and supports flexible prompts in the form of keypoints and masks which enable more reliable disambiguation in crowded and occluded scenes. To train such a model, we further introduce Herd3D, a multi-animal 3D dataset containing over 5K images, designed to increase diversity in species, interactions, and occlusion patterns. Experiments on the Animal3D, APTv2, and Animal Kingdom datasets show that our framework achieves state-of-the-art results over both existing model-based and model-free methods, demonstrating a scalable and effective solution for prompt-driven animal 3D reconstruction in the wild.", "github": "" }, { "date": "2026-05-22", "arxiv_id": "2605.15669", "title": "Rule2DRC: Benchmarking LLM Agents for DRC Script Synthesis with Execution-Guided Test Generation", "authors": [ "Jinuk Kim", "Junsoo Byun", "Donghwi Hwang", "Seong-Jin Park", "Hyun Oh Song" ], "github_manual": "", "github_regex": "https://github.com/snu-mllab/Rule2DRC", "github_paper_md": "", "github_api": "https://github.com/snu-mllab/Rule2DRC", "github_api_added_by": "user", "project_page": "", "abstract": "Manufacturable chip layouts must satisfy thousands of geometry-based design rules, and design rule checking (DRC) enforces them by running executable DRC scripts on layouts. Translating natural language rules into correct DRC scripts is labor-intensive and requires specialized expertise, motivating LLM agents for DRC script synthesis and debugging. However, existing benchmarks have small evaluation sets and often evaluate scripts by code similarity rather than execution correctness, and prior machine learning-based methods either ignore execution feedback or require labeled test layouts as agent's input. To this end, we introduce Rule2DRC, a large-scale benchmark for DRC script coding agents with 1,000 rule-to-script tasks and 13,921 evaluation chip layouts for execution-based scoring. Rule2DRC provides an evaluation pipeline that measures functional correctness via DRC execution outcomes without requiring evaluation layouts as input to the agent. We also propose SplitTester, a tester agent for program selection that uses execution feedback to generate discriminative test cases and separate previously indistinguishable candidate scripts, substantially improving Best-of-N selection performance in this domain. We release the code at https://github.com/snu-mllab/Rule2DRC.", "github": "https://github.com/snu-mllab/Rule2DRC" }, { "date": "2026-05-22", "arxiv_id": "2605.17602", "title": "AutoRubric-T2I: Robust Rule-Based Reward Model for Text-to-Image Alignment", "authors": [ "Kuei-Chun Kao", "Daixuan Huo", "Yuanhao Ban", "Cho-Jui Hsieh" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/johnsonkao0213/AutoRubric-T2I", "github_api_added_by": "user", "project_page": "https://johnsonkao0213.github.io/AutoRubric-T2I/", "abstract": "Aligning Text-to-Image (T2I) generation models with human preferences increasingly relies on image reward models that score or rank generated images according to prompt alignment and perceptual quality. Existing reward models are commonly trained as Bradley-Terry (BT) preference models on large-scale human preference corpora, making them costly to train, difficult to adapt, and opaque in their evaluation criteria. Meanwhile, Vision-Language Model (VLM) judges can provide more fine-grained assessments through textual rubrics, but their manually designed or heuristically generated scoring rules may fail to reliably reflect human preferences. In this paper, we propose AutoRubric-T2I, the first rubric learning framework in T2I that automatically synthesizes and selects explicit rubrics for guiding VLM judges. AutoRubric-T2I first synthesizes reasoning traces from preference pairs into candidate rubrics, then uses a VLM judge to score paired images under each rubric, producing pairwise rubric-score differences for preference learning. To remove noisy and redundant rules, we further employ a ell_1-Regularized Logistic Regression Refiner, which selects the Top-N most discriminative rubrics. Extensive evaluations show that AutoRubric-T2I produces high-quality, interpretable reward signals using less than 0.01% of the annotated preference data, substantially reducing the need for large-scale reward-model training. On image reward benchmarks such as MMRB2, AutoRubric-T2I outperforms strong reward model baselines. We further validate AutoRubric-T2I as an RL reward on downstream T2I tasks, including TIIF and UniGenBench++, where it improves generation quality over scalar reward models using the Flow-GRPO pipeline on diffusion models.", "github": "https://github.com/johnsonkao0213/AutoRubric-T2I" }, { "date": "2026-05-22", "arxiv_id": "2605.22717", "title": "Live Music Diffusion Models: Efficient Fine-Tuning and Post-Training of Interactive Diffusion Music Generators", "authors": [ "Zachary Novack", "Stephen Brade", "Haven Kim", "Hugo Flores Garc\u00eda", "Nithya Shikarpur", "Chinmay Talegaonkar", "Suwan Kim", "Valerie K. Chen", "Julian McAuley", "Taylor Berg-Kirkpatrick", "Cheng-Zhi Anna Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZacharyNovack/live-music-diffusion-models", "github_api_added_by": "user", "project_page": "https://stephenbrade.github.io/lmdm-public/", "abstract": "Interactive streaming music generation promises the use of generative models for live performance and co-creation that is impossible with offline models. However, SOTA models exist in the discrete-AR regime, requiring industrial levels of compute for both training and inference. In this work, we investigate whether audio diffusion models, with their wide support in the open-source community but non-streaming bidirectional nature, can be repurposed efficiently into interactive models accessible on consumer hardware. By taking a critical look at the modern pipeline for block-wise outpainting diffusion, we identify critical inefficiencies during inference that result in strictly worse computational efficiency than their discrete-AR counterparts. We propose Live Music Diffusion Models (LMDMs), a simple modification of the generative diffusion process that recovers, and then outperforms, the inference complexity of the discrete Live Music Models (LMMs) through block-wise KV Caching. Unlike LMMs, LMDMs further enable stable post-training alignment through our novel ARC-Forcing paradigm, reducing error accumulation without any explicit RL or reward models. We demonstrate the application of LMDMs in a number of creative domains, including text-conditioned generation, sketch-based music synthesis, and jamming. We finally show how LMDMs can be used as a generative instrument in a real artist-AI collaboration, utilizing LMDMs as a \"generative delay\" to transform musicians' improvisation live for variable timbral effects while running locally on a consumer gaming laptop.", "github": "https://github.com/ZacharyNovack/live-music-diffusion-models" }, { "date": "2026-05-22", "arxiv_id": "2605.13163", "title": "LoREnc: Low-Rank Encryption for Securing Foundation Models and LoRA Adapters", "authors": [ "Beomjin Ahn", "Jungmin Kwon", "Chanyong Jung", "Jaewook Chung" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Foundation models and low-rank adapters enable efficient on-device generative AI but raise risks such as intellectual property leakage and model recovery attacks. Existing defenses are often impractical because they require retraining or access to the original dataset. We propose LoREnc, a training-free framework that secures both FMs and adapters via spectral truncation and compensation. LoREnc suppresses dominant low-rank components of FM weights, compensates for the missing information in authorized adapters, and further applies orthogonal reparameterization to obscure structural fingerprints of the protected adapter. Unauthorized users produce structurally collapsed outputs, while authorized users recover exact performance. Experiments demonstrate that LoREnc provides strong protection against model recovery with under 1% computational overhead.", "github": "" }, { "date": "2026-05-25", "arxiv_id": "2605.23902", "title": "PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion", "authors": [ "Yifan Lu", "Qi Wu", "Jay Zhangjie Wu", "Zian Wang", "Huan Ling", "Sanja Fidler", "Xuanchi Ren" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://research.nvidia.com/labs/sil/projects/pid/", "abstract": "Most practical high-resolution text-to-image systems, including latent diffusion and autoregressive models, perform generation in a compact latent space, and a decoder maps the generated latents back to pixels. Yet the latent-to-pixel decoder is reconstruction-oriented, optimized to invert the encoder rather than synthesize more details, and becomes increasingly costly at megapixel scale. This drawback calls for a more expressive and efficient decoding paradigm. Motivated by recent progress in scalable pixel-space diffusion, we introduce PiD, a Pixel diffusion Decoder that reformulates latent decoding as conditional pixel diffusion, unifying decoding and upsampling into one generative module. By denoising directly in high-resolution pixel space, PiD synthesizes 4times and even 8times upscaled images with low latency. For latent conditioning, a lightweight sigma-aware adapter injects noise-corrupted latents into the pixel diffusion backbone, enabling PiD to decode partially denoised latents and terminate the latent diffusion process early. To further improve efficiency, we distill the model using DMD2, reducing inference to just 4 steps. PiD applies to both conventional VAE latents and semantic latents (e.g., SigLIP, DINOv2) used in recent RAE-based models. PiD decodes latents of 512 times 512 images into 2048 times 2048 pixels in under 1 second with 13 GB peak memory on a consumer RTX 5090, and as fast as 210 ms on a GB200 GPU, about 6times faster than cascaded diffusion-based super-resolution pipelines with better visual fidelity.", "github": "" }, { "date": "2026-05-25", "arxiv_id": "2605.21573", "title": "Lens: Rethinking Training Efficiency for Foundational Text-to-Image Models", "authors": [ "Dong Chen", "Fangyun Wei", "Ziyu Wan", "Dongdong Chen", "Jiawei Zhang", "Jinjing Zhao", "Sirui Zhang", "Yang Yue", "Zhiyang Liang", "Baining Guo", "Chong Luo", "Jianmin Bao", "Ji Li", "Lei Shi", "Qinhong Yang", "Xiuyu Wu", "Xuelu Feng", "Yan Lu", "Yanchen Dong", "Yitong Wang", "Yunuo Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/Lens", "github_api_added_by": "user", "project_page": "https://huggingface.co/microsoft/Lens", "abstract": "We introduce Lens, a 3.8B-parameter T2I model that achieves performance competitive with, and in several cases surpassing, state-of-the-art models with more than 6B parameters across various benchmarks, while requiring significantly less training compute. For example, Lens requires only about 19.3% of the training compute used by Z-Image. The training efficiency of Lens stems from two key strategies beyond its compact model size. First, we maximize data information density per training batch by (i) training on Lens-800M, a dataset of 800M densely captioned image-text pairs whose captions are generated by GPT-4.1 and contain approximately 109 words on average, providing richer semantic supervision than conventional short captions, and (ii) constructing each batch from images with multiple resolutions and diverse aspect ratios, thereby enlarging the effective visual coverage of each optimization step. Second, we improve convergence speed through careful architectural choices, including adopting a semantic VAE that provides better latent representations and employing a strong language encoder that accelerates optimization while enabling multilingual generalization from English-only training data. After pre-training, we apply RL with taxonomy-driven prompts (Lens-RL-8K) and structured reward rubrics to suppress artifacts and improve visual quality, a reasoner module with training-free system prompt search to better align user requests with the model, and distillation-based acceleration for 4-step inference. Through efficient training and systematic optimization, Lens generalizes to arbitrary aspect ratios from 1:2 to 2:1 and resolutions up to 1440^2, and supports prompts in several commonly used languages. Thanks to its compact size, Lens generates a 1024^2 image in 3.15 seconds on a single NVIDIA H100 GPU, while its distilled turbo version performs 4-step generation in 0.84 seconds.", "github": "https://github.com/microsoft/Lens" }, { "date": "2026-05-25", "arxiv_id": "2605.23463", "title": "StepAudio 2.5 Technical Report", "authors": [ "Bin Lin", "Bo Zhao", "Boyong Wu", "Chao Yan", "Chen Wu", "Cheng Yi", "Chengyuan Yao", "Daijiao Liu", "Fei Tian", "Feng Tian", "Haiyang Sun", "Haoyang Zhang", "Jiangjie Zhen", "Jinglan Gong", "Jun Chen", "Li Xie", "Peilin Li", "Peng Yang", "Pengfei Tan", "Qingjian Lin", "Runze Li", "Shenghua Hu", "Siyi Zhou", "Wenwen Qu", "Xiangyu Li", "Xiangyu Tony Zhang", "Xuerui Yang", "Yang Yang", "Yechang Huang", "Yu Fu", "Yuchu Luo", "Yuxin Li", "Yuxin Zhang", "Zhengyan Sheng", "Brian Li", "Chang Zeng", "Changlin Zhang", "Chen Geng", "Chenghao Dong", "Chengli Feng", "Dan Zhou", "Danni Wan", "Di Chen", "Die Zhang", "Dongqing Pang", "Guanglong Yang", "Guoqiang Hu", "Huangxi Zhu", "Jianzheng Gao", "Jinghua Liang", "Jinmei Wan", "Junjie Yuan", "Kang An", "Lei Lei", "Limin Zhong", "Lun Cai", "Mengqiang Ren", "Min Xu", "Mingliang Li", "Mingxiao Li", "Na Wang", "Qiang Tong", "Qiaoling Huang", "Qingfu Du", "Rui Wang", "Shengchen Zhou", "Shi Qiu", "Shihao Peng", "Shiliang Yang", "Siqi Tu", "Tianjiao Deng", "Ting Xu", "Tong Wang", "WeiMing Niu", "Wuxun Xie", "Xianwei Zhang", "Xianyu Feng", "Xiaojia Liu", "Xing Chen", "Xiongbin Wu", "Yan Wu", "Yang Li", "Yi Liu", "Yifan Zhang", "Yile Liu", "Yongshen Long", "Yu Luo", "Yuanhao Ding", "Yuhao Wang", "Yuhe Yin", "Yunfang Xu", "Yuxiang Yang", "Zhiguo Huang", "Zhiyue Wu", "Zichao Li", "Zichao Zhou", "Daxin Jiang", "Future Li", "Gang Yu", "Xiangyu Zhang", "Yibo Zhu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Unified audio-language modeling has emerged as a prominent trend in modern speech systems, promising to bring the reasoning capabilities of large language models to auditory tasks. However, existing unified foundations often struggle to match the depth of specialized systems across automatic speech recognition (ASR), text-to-speech synthesis (TTS), and realtime spoken interaction. Bridging this gap remains an open challenge. This report presents StepAudio 2.5, a unified audio-language foundation model that matches or exceeds specialized systems across all three capabilities. Rather than treating these tasks as architecturally distinct, we operate on the premise that once text and audio share a multimodal representational space, task specialization becomes a matter of operational regimes: data construction, optimization targets, and decoding constraints. Guided by this insight, we advance the post-training paradigm from standard supervised learning to task-tailored Reinforcement Learning from Human Feedback (RLHF), using it as the primary mechanism to define complex optimization targets. We leverage this RLHF-centric alignment, alongside specialized decoding, to shape a shared backbone into three distinct operational modes. Concretely, the ASR branch advances transcription efficiency via verifiable multi-token decoding; the TTS branch achieves controllable, expressive synthesis through preference-based RLHF and context-rich supervision; and the Realtime branch realizes low-latency, persona-consistent dialogue via generative reward modeling within an RLHF framework. On standard benchmarks, StepAudio 2.5 achieves state-of-the-art results across ASR, TTS, and Realtime, demonstrating that a singular audio-language foundation can successfully internalize the distinct deployment objectives of speech understanding, generation, and live interaction.", "github": "" }, { "date": "2026-05-25", "arxiv_id": "2605.23897", "title": "ETCHR: Editing To Clarify and Harness Reasoning", "authors": [ "Beichen Zhang", "Yuhong Liu", "Jinsong Li", "Yuhang Zang", "Jiaqi Wang", "Dahua Lin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/InternLM/ETCHR", "github_api_added_by": "user", "project_page": "", "abstract": "Multimodal Large Language Models have advanced visual reasoning, yet a purely textual chain of thought remains a bottleneck for questions that require fine-grained focus or view transformations. The ''think with images'' paradigm narrows this gap, but existing approaches are either constrained by fixed predefined toolkits or produce noisy intermediate images from unified multimodal methods. We pursue a third option: using a dedicated image editing model and decouple it with an understanding model. However, off-the-shelf image editors fail as reasoning assistants with two complementary gaps: a language-side gap, where editors trained as passive instruction-followers cannot map an abstract question to an appropriate visual transformation, and a generation-side gap, where edit correctness degrades as reasoning depth grows. Guided by this analysis, we introduce ETCHR (Editing To Clarify and Harness Reasoning), a question-conditioned, reasoning-aware image editor decoupled from the downstream understanding model and trained with a two-stage recipe targeted at the two gaps: Reasoning Imitation via supervised fine-tuning on edit trajectories, followed by Reasoning Enhancement with VLM-derived rewards for edit correctness and downstream reasoning accuracy. Since the editor is decoupled, ETCHR plugs into different open- and closed-source MLLMs in a training-free manner. Across five task families (fine-grained perception, chart understanding, logic reasoning, jigsaw restoration, and 3D understanding), ETCHR raises average Pass@1 from 55.95 to 60.77 (+4.82) with Qwen3-VL-8B, from 65.08 to 70.55 (+5.47) with Gemini-3.1-Flash-Lite, and from 76.55 to 81.16 (+4.61) with the 1T-parameter MoE model Kimi K2.5.", "github": "https://github.com/InternLM/ETCHR" }, { "date": "2026-05-25", "arxiv_id": "2605.23899", "title": "From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills", "authors": [ "Zisu Huang", "Jingwen Xu", "Yifan Yang", "Ziyang Gong", "Qihao Yang", "Muzhao Tian", "Xiaohua Wang", "Changze Lv", "Xuemei Gao", "Qi Dai", "Bei Liu", "Kai Qiu", "Xue Yang", "Dongdong Chen", "Xiaoqing Zheng", "Chong Luo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/SkillLens", "github_api_added_by": "user", "project_page": "https://microsoft.github.io/SkillLens/", "abstract": "Language agents increasingly improve by reusing skills -- structured procedural artifacts distilled from past experience. In particular, domain-level and model-generated skills are especially promising. They offer fast adaptation within a domain by encoding domain-specific recurring procedures, and they scale beyond labor-intensive hand-crafting. However, while extraction methods continue to proliferate, understanding remains limited, with no comprehensive study spanning the full skill lifecycle -- experience generation, skill extraction, and skill consumption -- to ask whether such skills actually work, when they work, and what makes them succeed or fail. To close this gap, we build a utility-grounded evaluation framework that provides systematic experimental results across extractors and target agents, covering five diverse agentic task domains. We find that model-generated skills are beneficial on average but exhibit non-trivial negative transfer, and that neither extractors nor targets behave uniformly. A model can be a strong extractor yet a weak consumer, or vice versa, with skill utility independent of model scale or baseline task strength. To explain these patterns, we then dissect each lifecycle stage in depth, analyzing how experience composition shapes skill quality, what properties characterize useful skills, and how the same skill transfers across different consumers. Finally, we translate these findings into a concrete meta-skill that guides skill extraction toward the features tied to actual utility, which consistently improves skill quality across domains and substantially reduces negative transfer.", "github": "https://github.com/microsoft/SkillLens" }, { "date": "2026-05-25", "arxiv_id": "2605.19282", "title": "Rethinking Muon Beyond Pretraining: Spectral Failures and High-Pass Remedies for VLA and RLVR", "authors": [ "Chongyu Fan", "Gaowen Liu", "Mingyi Hong", "Ramana Rao Kompella", "Sijia Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OPTML-Group/Pion", "github_api_added_by": "user", "project_page": "https://chongyu-fan.netlify.app/posts/pion/", "abstract": "Muon is a matrix-aware optimizer that leverages Newton-Schulz (NS) iterations to enforce spectral gradient orthogonalization by driving all singular values of the momentum matrix toward 1. While this uniform spectral whitening enhances exploration and outperforms AdamW in LLM pretraining, we show it could lead to fundamental limitations beyond pretraining in two regimes: (i) cross-modality vision-language-action (VLA) training, where inherently low-rank action-module gradients cause amplification of noisy tail directions, and (ii) reinforcement learning with verifiable rewards (RLVR), where low-SNR gradients and the need to preserve per-head specialization from prior training make whitening unstable. To address these challenges, we propose Pion, a drop-in replacement for Muon that preserves its computational efficiency while replacing uniform spectral whitening with a two-stage Promotion+Suppression mechanism, which we call the high-pass NS iteration. This design induces a sharp spectral high-pass effect, anchoring dominant singular values at 1 while suppressing noisy tail components toward 0, with controllable filter strength. To preserve pretrained per-head heterogeneity, Pion also supports a per-head mode that applies updates independently across attention heads via a simple reshape, at no extra cost. In VLA training on LIBERO and LIBERO-Plus, Pion consistently outperforms both baselines across l_1-regression (VLA-Adapter) and flow-matching (VLANeXt) architectures, e.g., reaching 100% success rate on LIBERO Object after 1,500 training steps with VLA-Adapter, vs. 97.0% for Muon and only 32.2% for AdamW. The advantage of Pion further extends to a real Franka Research 3 robot with a pi_0.5 backbone under the DROID setup on three grasp-and-place tasks. In RLVR post-training on Qwen3-1.7B/4B with GRPO and GMPO, Pion also outperforms AdamW on MATH and GSM8K while Muon collapses to zero.", "github": "https://github.com/OPTML-Group/Pion" }, { "date": "2026-05-25", "arxiv_id": "2605.23888", "title": "GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction", "authors": [ "Katharina Schmid", "Nicolas von L\u00fctzow", "Jozef Hladk\u00fd", "Angela Dai", "Matthias Nie\u00dfner" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://kasothaphie.github.io/GenRecon/", "abstract": "We introduce a new approach to high-fidelity 3D scene reconstruction from multi-view RGB images that tightly couples reconstruction with a strong generative 3D prior. We cast scene reconstruction as conditional 3D generation over a set of spatially-localized, overlapping chunks that together tile the scene, scaling generation to large scene extents. Crucially, we inherit the fidelity and completeness of state-of-the-art generative shape models -- we use Trellis.2 as an example -- which we generalize to the scene level. To this end, we propose a projection-based conditioning mechanism that lifts posed multi-view image features into a coherent 3D representation aligned with the generative model, independent of view ordering and spatially anchored to the scene, yielding high-fidelity, multi-view consistent generated geometry. This enables lifting the strong object-level prior of Trellis.2 to multi-view, scene-scale generation, producing faithful, editable PBR mesh reconstructions of indoor environments. As a result, we obtain high-fidelity results that outperform cutting-edge reconstruction methods by 16%.", "github": "" }, { "date": "2026-05-25", "arxiv_id": "2605.23901", "title": "LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws", "authors": [ "Xu Ouyang", "Deyi Liu", "Yuhang Cai", "Jing Liu", "Yuan Yang", "Chen Zheng", "Thomas Hartvigsen", "Yiyuan Ma" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Existing scaling laws for Large Language Models (LLMs), predominantly monotonic power laws, fail to explain emerging non-monotonic phenomena such as catastrophic overtraining and quantization-induced degradation, where performance deteriorates despite increased compute. We propose the Shannon Scaling Law, a unified theoretical framework that models LLM training as information transmission over a noisy channel, grounded in the Shannon-Hartley theorem. By mapping model parameters to channel bandwidth and training tokens to signal power, our formulation explicitly captures the interaction between learning signal and intrinsic noise. This perspective reveals a fundamental Shannon capacity for LLMs: scaling model size or data without preserving a sufficient signal-to-noise ratio (SNR) inevitably amplifies noise, inducing a transition from monotonic improvement to U-shaped performance degradation. We validate our theory through experiments on Pythia and OLMo2 under perturbations, including Gaussian noise, quantization and supervised fine-tuning on math, QA and code tasks. The Shannon Scaling Law consistently outperforms classical scaling laws and recent perturbation-aware laws, achieving strong R^2 scores and accurately capturing loss basins missed by prior approaches. It also extrapolates: fitted on leq6.9B Pythia models with leq180B tokens, it predicts the unseen 12B model up to 307B tokens at pooled R^2{=}0.847, while monotonic baselines collapse.", "github": "" }, { "date": "2026-05-25", "arxiv_id": "2605.23904", "title": "SkillOpt: Executive Strategy for Self-Evolving Agent Skills", "authors": [ "Yifan Yang", "Ziyang Gong", "Weiquan Huang", "Qihao Yang", "Ziwei Zhou", "Zisu Huang", "Yan Li", "Xuemei Gao", "Qi Dai", "Bei Liu", "Kai Qiu", "Yuqing Yang", "Dongdong Chen", "Xue Yang", "Chong Luo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/SkillOpt", "github_api_added_by": "user", "project_page": "https://microsoft.github.io/SkillOpt/", "abstract": "Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting point under feedback. We argue the skill should instead be trained as the external state of a frozen agent, with the same discipline that makes weight-space optimization reproducible. SkillOpt is, to our knowledge, the first systematic controllable text-space optimizer for agent skills: a separate optimizer model turns scored rollouts into bounded add/delete/replace edits on a single skill document, and an edit is accepted only when it strictly improves a held-out validation score. A textual learning-rate budget, rejected-edit buffer, and epoch-wise slow/meta update make skill training stable while adding zero inference-time model calls at deployment. Across six benchmarks, seven target models, and three execution harnesses (direct chat, Codex, Claude Code), SkillOpt is best or tied on all 52 evaluated (model, benchmark, harness) cells and beats every per-cell competitor among human, one-shot LLM, Trace2Skill, TextGrad, GEPA, and EvoSkill skills. On GPT-5.5 it lifts the average no-skill accuracy by +23.5 points in direct chat, by +24.8 inside the Codex agentic loop, and by +19.1 inside Claude Code. Transfer experiments further show that optimized skill artifacts retain value when moved across model scales, between Codex and Claude Code execution environments, and to a nearby math benchmark without further optimization.", "github": "https://github.com/microsoft/SkillOpt" }, { "date": "2026-05-25", "arxiv_id": "2605.17873", "title": "HINT-SD: Targeted Hindsight Self-Distillation for Long-Horizon Agents", "authors": [ "Woongyeng Yeo", "Yumin Choi", "Taekyung Ki", "Sung Ju Hwang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Training long-horizon LLM agents with reinforcement learning is challenging because sparse outcome rewards reveal whether a task succeeds, but not which intermediate actions caused the outcome or how they should be corrected. Recent methods alleviate this issue by generating rewards or textual hints from turn-level action-output signals, or by using feedback-conditioned self-distillation. However, generating feedback at every turn is inefficient when many intermediate turns are already successful or neutral, and applying feedback at a fixed or misaligned turn often fails to supervise the actions that contributed to the failure. To bridge this gap, we propose HINT-SD, a targeted self-distillation framework that uses full-trajectory hindsight to select failure-relevant actions and applies feedback-conditioned distillation only on targeted action spans. Experiments on BFCL v3 and AppWorld show that our method improves over the dense per-turn feedback baseline by up to 18.80 percent while achieving 2.26times lower time per training step, suggesting that selecting where to distill is a key factor for both effective and efficient long-horizon agent training.", "github": "" }, { "date": "2026-05-25", "arxiv_id": "2605.20708", "title": "Rethinking Cross-Layer Information Routing in Diffusion Transformers", "authors": [ "Chao Xu", "Maohua Li", "Qirui Li", "Yixuan Xu", "Yanke Zhou", "Yunhe Li", "Cuifeng Shen", "Hanlin Tang", "Kan Liu", "Tao Lan", "Lin Qu", "Shao-Qun Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Diffusion Transformers (DiTs) have become a de facto backbone of modern visual generation, and nearly every major axis of their design -- tokenization, attention, conditioning, objectives, and latent autoencoders -- has been extensively revisited. The residual stream that governs how information accumulates across layers, however, has been directly inherited from the original Transformer. In this paper, we present a systematic empirical analysis of cross-layer information flow in DiTs, jointly along depth and denoising timestep, and identify three concrete symptoms of traditional residual addition, namely monotonic forward magnitude inflation, sharp backward gradient decay, and pronounced block-wise redundancy. Motivated by this diagnosis, we propose Diffusion-Adaptive Routing (DAR), a drop-in residual replacement that performs learnable, timestep-adaptive, and non-incremental aggregation over the history of sublayer outputs. Moreover, the proposed DAR is compatible with many modern Transformer enhancement methods, such as REPA. On ImageNet 256times256, DAR improves SiT-XL/2 by 2.11 FID (7.56 vs.\\ 9.67) and matches the baseline's converged quality with 8.75times fewer training iterations. Stacked on top of REPA, it yields a 2times training acceleration in the early stage, suggesting cross-layer information routing as an underexplored design axis in diffusion modeling, one that operates orthogonally to existing representation-alignment objectives. Beyond pretraining, DAR can also be applied during the fine-tuning stage of large-scale T2I models and preserves high-frequency details during Distribution Matching Distillation.", "github": "" }, { "date": "2026-05-25", "arxiv_id": "2605.18018", "title": "See What I Mean: Aligning Vision and Language Representations for Video Fine-grained Object Understanding", "authors": [ "Boyuan Sun", "Bowen Yin", "Yuanming Li", "Xihan Wei", "Qibin Hou" ], "github_manual": "", "github_regex": "https://github.com/HumanMLLM/SWIM", "github_paper_md": "", "github_api": "https://github.com/HumanMLLM/SWIM", "github_api_added_by": "user", "project_page": "https://github.com/HumanMLLM/SWIM", "abstract": "We present SWIM (See What I Mean), a novel training strategy that aligns vision and language representations to enable fine-grained object understanding solely from textual prompts. Unlike existing approaches that require explicit visual prompts, such as masks or points, SWIM leverages mask supervision only during training to guide cross-modal attention, allowing the model to automatically attend to the user-specified object at inference. Our cross-attention analysis of pretrained multimodal large languagemodels (MLLMs) reveals a systematic discrepancy: Attribute words produce sharp, localized activations in the visual modality, whereas object nouns yield diffuse and scattered patterns due to semantic reference bias and distributed high-level representations. To address this misalignment, we construct NL-Refer, an enriched dataset, in which each object mask is paired with a precise natural language referring expression. SWIM extracts multi-layer cross-attention maps from object nouns and enforces spatial consistency with ground-truth masks. Experimental results demonstrate that SWIM substantially improves text-visual alignment and achieves superior performance over visual-prompt-based methods on fine-grained object understanding benchmarks. The code and data are available at https://github.com/HumanMLLM/SWIM{https://github.com/HumanMLLM/SWIM}.", "github": "https://github.com/HumanMLLM/SWIM" }, { "date": "2026-05-25", "arxiv_id": "2605.21195", "title": "RankE: End-to-End Post-Training for Discrete Text-to-Image Generation with Decoder Co-Evolution", "authors": [ "Siyong Jian", "Siyuan Li", "Luyuan Zhang", "Zedong Wang", "Xin Jin", "Ying Li", "Cheng Tan", "Huan Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/syjmelody/RankE", "github_api_added_by": "user", "project_page": "https://arxiv.org/pdf/2605.21195", "abstract": "Discrete autoregressive (AR) text-to-image (T2I) models pair a VQ tokenizer with an AR policy, and current post-training pipelines optimize only the policy while keeping the VQ decoder frozen. Recent diffusion T2I work, exemplified by REPA-E, has shown that the VAE itself constitutes a key alignment bottleneck, yet no analogous investigation exists for discrete AR models. We show that policy-only optimization induces Latent Covariate Shift: as the policy evolves, the resulting token distribution diverges from the ground-truth distribution on which the decoder was trained, such that reward scores improve while decoded image quality degrades. To address this mismatch, we propose RankE, the first end-to-end post-training framework for discrete T2I generation. Rather than optimizing the policy against a fixed decoder, RankE co-evolves both components through alternating optimization: each module maximizes a ranking-based alignment objective while being regularized by a stability-preserving anchor suited to its parameter space. This co-evolution breaks the fidelity--alignment trade-off that plagues frozen-decoder approaches: on LlamaGen-XL (775M), standard RL improves CLIP but degrades FID, whereas RankE improves both simultaneously (FID 15.21, CLIP 33.76 on MS-COCO 30K). Consistent gains on Janus-Pro (1B) confirm that decoder co-evolution reliably converts reward optimization into pixel-space quality improvements.", "github": "https://github.com/syjmelody/RankE" }, { "date": "2026-05-25", "arxiv_id": "2605.23345", "title": "SCOPE: Simulating Cross-game Operations in Playable Environments for FPS World Models", "authors": [ "Zizhao Tong", "Hongfeng Lai", "Zeqing Wang", "Zhaohu Xing", "Kexu Cheng", "Haoran Xu", "Zhao Pu", "Shangwen Zhu", "Ruili Feng", "Jian Zhao", "Yan Zhang", "Hao Tang", "Yeying Jin", "Ling Shao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/z2tong/SCOPE", "github_api_added_by": "user", "project_page": "https://z2tong.github.io/SCOPE/", "abstract": "Interactive world models for first-person shooter (FPS) games must resolve high-frequency overlapping control signals at every frame without disrupting unaffected regions. Existing methods inject actions globally and train on single titles, failing under dense FPS inputs. We observe that FPS actions are spatially selective: discrete events such as firing or reloading affect only a localized region around the weapon (the scope), while continuous camera and movement signals govern stable surroundings. We propose SCOPE, which inserts a conditioning module into each transformer block of a pretrained video diffusion model. It reshapes features into per-pixel temporal sequences so that each position computes its action response from local visual content. This separates in-scope effects from out-of-scope generation without segmentation labels. We also introduce CrossFPS, the first multi-game FPS dataset with frame-aligned action telemetry. It comprises 69K clips from 7 titles with 10-DoF controller signals, curated to remove gameplay bias. The model learns general visual-to-action mappings rather than game-specific patterns, enabling zero-shot transfer to unseen scenes. Experiments confirm strong action responsiveness, precise scope separation, and effective cross-game generalization.", "github": "https://github.com/z2tong/SCOPE" }, { "date": "2026-05-25", "arxiv_id": "2605.23903", "title": "Geo-Align: Video Generation Alignment via Metric Geometry Reward", "authors": [ "Zizun Li", "Haoyu Guo", "Runzhe Teng", "Chunhua Shen", "Tong He" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LiZizun/GeoAlign", "github_api_added_by": "user", "project_page": "https://lizizun.github.io/geo-align-page/", "abstract": "Camera-controlled video generation has achieved remarkable progress in recent years. However, existing video-to-video re-rendering methods primarily rely on Supervised Fine-Tuning using synthetic datasets. At present, there is an extreme scarcity of synchronized, multi-view real-world video data. Consequently, the prevailing paradigm often exhibits limited generalization when processing out-of-distribution real-world videos, with models struggling to accurately adhere to physical scales and camera trajectories. To bridge this gap, we propose Geo-Align, the first Reinforcement Learning framework specifically designed for camera-controlled video re-rendering. Built upon a pretrained model, we optimize the model through a scale-aware perceptual reward mechanism. Specifically, we introduce a metric 3D estimator to extract precise camera trajectories from generated videos, explicitly penalizing deviations in rotation and translation. Furthermore, we meticulously designed a data pipeline strategy based on real-world conditioning videos and target camera trajectories derived from synthetic data, eliminating the reliance on paired data. Extensive experiments demonstrate that Geo-Align consistently outperforms existing supervised learning baselines in both precise camera controllability and visual fidelity, indicating the effectiveness of our method.", "github": "https://github.com/LiZizun/GeoAlign" }, { "date": "2026-05-25", "arxiv_id": "2605.22570", "title": "VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis", "authors": [ "Jinho Park", "Youbin Kim", "Hogun Park", "Eunbyung Park" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zinosii/VGenST-Bench", "github_api_added_by": "user", "project_page": "https://zinosii.github.io/VGenST-Bench/", "abstract": "Spatio-temporal reasoning is a core capability for Multimodal Large Language Models (MLLMs) operating in the real world. As such, evaluating it precisely has become an essential challenge. However, existing spatio-temporal reasoning benchmark datasets primarily rely on static image sets or passively curated video data, which limits the evaluation of fine-grained reasoning capabilities. In this paper, we introduce VGenST-Bench, a video benchmark that employs generative models to actively synthesize highly controlled and diverse evaluation scenarios. To construct VGenST-Bench, we propose a multi-agent pipeline incorporating a human quality control stage, ensuring the quality of all generated videos and QA pairs. We establish a comprehensive 3x2x2 video taxonomy, encompassing Spatial Scale, Perspective, and Scene Dynamics to span diverse scenarios. Furthermore, we design a hierarchical task suite that decouples low-level visual perception from high-level spatio-temporal reasoning. By shifting the paradigm from passive curation to active synthesis, VGenST-Bench enables fine-grained diagnosis of spatio-temporal understanding in MLLMs.", "github": "https://github.com/zinosii/VGenST-Bench" }, { "date": "2026-05-25", "arxiv_id": "2605.23771", "title": "PhotoFlow: Agentic 3D Virtual Photography Missions", "authors": [ "Jiarui Guo", "Haojia Wei", "Yiming Zhang", "Yifei Liu", "Yuning Gong", "Hongjie Zhang", "Xue Yang", "Zhihang Zhong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Visionary-Laboratory/PhotoFlow", "github_api_added_by": "user", "project_page": "https://visionary-laboratory.github.io/PhotoFlow/", "abstract": "Virtual photography asks an agent to enter a prepared 3D scene with no preselected camera pose or reference image, infer a suitable shot from scene information and a language intent, choose executable camera parameters, and render the final photograph. Recent progress in vision-language models makes this kind of spatial agent increasingly plausible, but the task stresses two capabilities that remain hard to evaluate together: complex 3D spatial understanding and abstract aesthetic judgment. We introduce PhotoFlow, a Director-Reviewer-Reflector agent for closed-loop camera search. The Director builds a soft photographic blueprint and proposes diverse candidate cameras; the Reviewer combines rule checks, visual critique, and pairwise incumbent selection; and the Reflector converts failures into region memory, dead-zone suppression, and high-explore relocation. We also introduce VPhotoBench, a benchmark of 47 open-license Blender scenes and 141 language-conditioned photography missions spanning subject placement, relational composition, and atmosphere/style. On held-out experiments, PhotoFlow achieves the strongest external quality-alignment composite and success rate among one-shot prediction, single-chain reflection, anchor-bank selection, and random search under a six-round rendering budget. To our knowledge, this is the first work to make language-conditioned virtual photography in arbitrary Blender scenes an executable agent task, and our results show that an LLM-centered spatial agent can already produce strong photographs in a setting designed to challenge both 3D reasoning and aesthetic choice.", "github": "https://github.com/Visionary-Laboratory/PhotoFlow" }, { "date": "2026-05-25", "arxiv_id": "2604.20665", "title": "The Expense of Seeing: Attaining Trustworthy Multimodal Reasoning Within the Monolithic Paradigm", "authors": [ "Karan Goyal" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The rapid proliferation of Vision-Language Models (VLMs) is often framed as enabling unified multimodal knowledge discovery but rests on an under-examined assumption: that current VLMs faithfully synthesise multimodal data. We argue they often do not, and this gap reflects a trustworthiness problem in the dominant Vision Encoder-Projector-LLM paradigm. Rather than extracting grounded knowledge from visual inputs, state-of-the-art models frequently exhibit functional blindness, i.e., exploiting strong language priors to bypass severe visual representation bottlenecks. In this work, we challenge the conventional methodology of multimodal evaluation, which relies on data ablation or new dataset creation and therefore conflates dataset biases with architectural incapacity. We propose an information-theoretic departure: the Modality Translation Protocol, designed to quantify what we call the Expense of Seeing. By translating semantic payloads rather than ablating them, we formulate three novel metrics -- the Toll (ToS), Curse (CoS), and Fallacy (FoS) of Seeing -- culminating in the Semantic Sufficiency Criterion (SSC). Furthermore, we hypothesise a Divergence Law of Multimodal Scaling: as the underlying language engines scale to unprecedented reasoning capabilities, the penalty of the visual knowledge bottleneck may increase rather than diminish. We argue the community should move beyond \"multimodal gain\" as a primary evaluation target. By elevating the SSC from a passive diagnostic constraint to an active architectural blueprint, we provide a foundation for guiding the next generation of AI systems toward genuine multimodal reasoning.", "github": "" }, { "date": "2026-05-25", "arxiv_id": "2605.23892", "title": "Good Token Hunting: A Hitchhiker's Guide to Token Selection for Visual Geometry Transformers", "authors": [ "Shuhong Zheng", "Michael Oechsle", "Erik Sandstr\u00f6m", "Marie-Julie Rakotosaona", "Federico Tombari", "Igor Gilitschenski" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zsh2000/gotohunt", "github_api_added_by": "user", "project_page": "https://zsh2000.github.io/good-token-hunting.github.io/", "abstract": "Visual geometry transformers have become powerful architectures for multi-view 3D reconstruction, enabling joint prediction of multiple 3D attributes in a feed-forward manner. However, their computational cost grows quadratically with the input sequence length due to the global attention layers inside these models. This limits both their scalability and efficiency. In this work, we address this challenge with a simple yet general strategy: restricting the number of key/value tokens that each query interacts with during global attention. To achieve effective token selection, we introduce a two-stage framework. First, an inter-frame selection step operates at the frame level to identify frames that should be preserved. Second, an intra-frame selection step further discards more redundant tokens within the selected frames. Our analysis highlights the advantage of a diversity-based strategy for inter-frame selection, which ensures broad coverage of the scene. For intra-frame selection, we show that layer-aware sparsification is necessary, with the selection process guided by the entropy of the global attention pattern. Our approach offers a superior speed-accuracy trade-off compared to existing solutions. Extensive experiments show that it accelerates visual geometry transformers by over 85% for scenes with 500 images while maintaining, or even improving, baseline performance, which hints that how our token selection strategy can play a crucial role in future applications of visual geometry transformers. Our project website is available at https://zsh2000.github.io/good-token-hunting.github.io.", "github": "https://github.com/zsh2000/gotohunt" }, { "date": "2026-05-25", "arxiv_id": "2605.20177", "title": "From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models", "authors": [ "Juncheng Wu", "Hardy Chen", "Haoqin Tu", "Xianfeng Tang", "Freda Shi", "Hui Liu", "Hanqing Lu", "Cihang Xie", "Yuyin Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UCSC-VLAA/VLM-CapCurriculum", "github_api_added_by": "user", "project_page": "https://ucsc-vlaa.github.io/VLM-CapCurriculum/", "abstract": "Recent advances in vision-language models (VLMs) emphasize long chain-of-thought reasoning; yet, we find that their performance on visual tasks is primarily limited by a lack of visual perception as opposed to reasoning itself. In this work, we systematically study the interplay between perception and reasoning in VLM post-training by decomposing their capabilities into three separate training stages: visual perception, visual reasoning, and textual reasoning, incorporating specialized training data. We demonstrate that visual perception (a) requires targeted optimization with specialized data; (b) serves as a fundamental scaffold that should be solidified through staged training before refining visual reasoning; and (c) is more effectively learned via RL than caption-based SFT. Our experiments across multiple VLMs demonstrate that staged training consistently improves both visual perception and reasoning performance over merged training. Notably, models trained with our approach achieve 1.5% higher reasoning accuracy with 20.8% shorter reasoning traces, suggesting that superior perception reduces the need for excessive reasoning. Furthermore, we show that this capability-based staging represents a new curriculum dimension orthogonal to traditional difficulty-based curricula, and combining both yields further additive gains. Our staged-training models achieve superior performance among open-weight VLMs, establishing advanced results on several visual math and perception (e.g., +5.2% on WeMath and +3.7% on RealWorldQA) tasks compared with the base counterpart.", "github": "https://github.com/UCSC-VLAA/VLM-CapCurriculum" }, { "date": "2026-05-25", "arxiv_id": "2605.22878", "title": "SciAtlas: A Large-Scale Knowledge Graph for Automated Scientific Research", "authors": [ "Shuofei Qiao", "Yunxiang Wei", "Jiazheng Fan", "Bin Wu", "Busheng Zhang", "Mengru Wang", "Yuqi Zhu", "Ningyu Zhang", "Keyan Ding", "Qiang Zhang", "Huajun Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zjunlp/SciAtlas", "github_api_added_by": "user", "project_page": "", "abstract": "The exponential growth of global academic output has confronted researchers and AI agents with an unprecedented ``information explosion,'' where fragmented and unstructured knowledge organization impedes deep interdisciplinary integration. Current academic retrieval tools predominantly rely on superficial keyword matching or vector-space semantic retrieval, which lack the topological reasoning capabilities required to navigate complex logical connections. Agentic deep-research-based frameworks are often prone to logical hallucinations and consuming high inference costs. To bridge this gap, in this report, we introduce SciAtlas, a large-scale, multi-disciplinary, heterogeneous academic resource knowledge graph designed as a panoramic scientific evolution network. By integrating over 43M papers from 26 disciplines, and a total of 157M entities and 3B triplets, SciAtlas provides a structured topological cognitive substrate that dismantles disciplinary barriers and furnishes AI agents with a global perspective. Furthermore, we develop a neuro-symbolic retrieval algorithm featuring tri-path collaborative recall and graph reranking, achieving a seamless transition from simple semantic matching to deterministic association discovery. We also present key application directions of SciAtlas, including literature review, automated research trend synthesis, idea positioning, and academic trajectory exploration, to demonstrate that SciAtlas can serve as an effective ``cognitive map'' to empower the full loop of automated scientific research while significantly reducing reasoning costs. We have released the interfaces for KG retrieval and various downstream tasks in our GitHub repo.", "github": "https://github.com/zjunlp/SciAtlas" }, { "date": "2026-05-25", "arxiv_id": "2605.17766", "title": "LatentUMM: Dual Latent Alignment for Unified Multimodal Models", "authors": [ "Yinyi Luo", "Wenwen Wang", "Hayes Bai", "Marios Savvides", "Jindong Wang" ], "github_manual": "", "github_regex": "https://github.com/AIFrontierLab/TorchUMM/tree/main/src", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Unified multimodal models (UMMs) achieve strong performance in both understanding and generation by learning a shared latent space, yet they often exhibit functional inconsistency between these two capabilities. We observe that this issue does not stem from a lack of shared representations, but from the absence of explicit alignment between the transformations that map into and out of the latent space. As a result, generation and re-encoding can follow inconsistent trajectories, leading to semantic drift under modality transitions. In this work, we propose LatentUMM, a framework that constructs an enhanced shared latent space to explicitly align these transformations and improve cross-modal consistency. LatentUMM consists of two stages. First, dual latent alignment enforces consistency at both the modality and capacity levels: cross-modal alignment uses a stronger embedding model to impose structured cross-modal semantics, while dual capacity alignment enforces bidirectional consistency under generation and re-encoding. Second, latent dynamics stabilization improves robustness via stochastic latent rollouts and preference optimization, favoring trajectories that better preserve semantic consistency. Experiments show that LatentUMM consistently improves multimodal consistency across diverse architectures. Code is available at: https://github.com/AIFrontierLab/TorchUMM/tree/main/src/umm/post_training/LatentUMM.", "github": "https://github.com/AIFrontierLab/TorchUMM/tree/main/src" }, { "date": "2026-05-25", "arxiv_id": "2605.21856", "title": "The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation", "authors": [ "Yifan Lan", "Yuanpu Cao", "Hanyu Wang", "Lu Lin", "Jinghui Chen" ], "github_manual": "", "github_regex": "https://github.com/Yifan-Lan/zero-cot-probe", "github_paper_md": "", "github_api": "https://github.com/Yifan-Lan/zero-cot-probe", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models (LLMs) have demonstrated impressive reasoning abilities across a wide range of tasks, but data contamination undermines the objective evaluation of these capabilities. This problem is further exacerbated by malicious model publishers who use evasive, or indirect, contamination strategies, such as paraphrasing benchmark data to evade existing detection methods and artificially boost leaderboard performance. Current approaches struggle to reliably detect such stealthy contamination. In this work, we uncover a critical phenomenon: a model's generated reasoning steps actively mask its underlying memorization. Inspired by this, we propose the Zero-CoT Probe (ZCP), a novel black-box detection method that deliberately truncates the entire Chain-of-Thought (CoT) process to expose latent shortcut mappings. To further isolate memorization from the model's intrinsic problem-solving capabilities, ZCP compares the model's zero-CoT performance on the original benchmark against an isomorphically perturbed reference dataset. Furthermore, we introduce Contamination Confidence, a metric that quantifies both the likelihood and severity of contamination, moving beyond simple binary classifications. Extensive experiments on both previously identified contaminated models and specially fine-tuned contaminated models demonstrate that ZCP robustly detects both direct and evasive data contamination. The code for ZCP is accessible at https://github.com/Yifan-Lan/zero-cot-probe.", "github": "https://github.com/Yifan-Lan/zero-cot-probe" }, { "date": "2026-05-25", "arxiv_id": "2605.21488", "title": "Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning", "authors": [ "Benhao Huang", "Zhengyang Geng", "Zico Kolter" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/locuslab/eqr", "github_api_added_by": "user", "project_page": "https://x.com/huskydogewoof/status/2057641657580064941?s=20", "abstract": "Scaling test-time compute by iteratively updating a latent state has emerged as a powerful paradigm for reasoning. Yet the internal mechanisms that enable these iterative models to generalize beyond memorized patterns remain unclear. We hypothesize that generalizable reasoning arises from learning task-conditioned attractors: latent dynamical systems whose stable fixed points correspond to valid solutions. We formalize this process through Equilibrium Reasoners (EqR), which enable test-time scaling without external verifiers or task-specific priors. EqR scales internal dynamics along two axes: depth, by running more iterations, and breadth, by aggregating stochastic trajectories from multiple initializations. Empirically, gains from test-time scaling are tightly coupled with stronger convergence toward solution-aligned attractors. This attractor perspective allows neural networks to adaptively allocate test-time compute based on task difficulty. While simple cases converge within 1 to 5 iteration steps, harder cases benefit from massive test-time scaling. By unrolling up to the equivalent of 40,000 layers, scalable latent reasoning boosts accuracy from 2.6% for feedforward models to over 99% on Sudoku-Extreme. These results suggest that learned attractor landscapes provide a useful mechanistic lens for understanding scalable reasoning in iterative latent models.", "github": "https://github.com/locuslab/eqr" }, { "date": "2026-05-25", "arxiv_id": "2605.19354", "title": "Next-Acceleration-Scale Prediction for Autoregressive MRI Reconstruction", "authors": [ "Yilmaz Korkmaz", "Vishal M. Patel" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yilmazkorkmaz1/discrete-mri-reconstruction-opd", "github_api_added_by": "user", "project_page": "https://yilmazkorkmaz1.github.io/discrete-mri-reconstruction-opd/", "abstract": "MRI reconstruction is an inherently ill-posed inverse problem, since incomplete measurements admit many plausible solutions. This ambiguity becomes more severe under high acceleration, where pixel-domain continuous predictors tend to average over feasible reconstructions and suppress high-frequency anatomy. We address this limitation by moving reconstruction to discrete multi-scale latent space and posing it as autoregressive next-acceleration-scale prediction. Leveraging discrete priors proven effective in visual autoregressive modeling, our method restricts the solution to compact sequences of codebook tokens, enabling sharp reconstructions even from extremely sparse measurements. This discrete autoregressive formulation also aligns naturally with modern large language model post-training techniques. Building on this observation, we introduce on-policy privileged information distillation for visual autoregressive modeling, where a teacher is provided training only privileged context that is unavailable at inference, in our case fully sampled acquisitions, and supervises a student trained on its own rollouts, leading to consistent reconstruction gains. Through extensive experiments on the fastMRI benchmark, we show that our approach delivers improved reconstruction performance across diverse sampling patterns under extreme undersampling. Project website is https://yilmazkorkmaz1.github.io/discrete-mri-reconstruction-opd/{here}.", "github": "https://github.com/yilmazkorkmaz1/discrete-mri-reconstruction-opd" }, { "date": "2026-05-25", "arxiv_id": "2605.17448", "title": "Self-Improving CAD Generation Agents with Finite Element Analysis as Feedback", "authors": [ "Guijin Son", "Jehyun Park", "Seyeon Park", "Sunghee Ahn", "Youngjae Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Computer-aided design (CAD) is the backbone of modern industrial design, yet learned CAD generators still fall short of real engineering pipelines: they neither iterate like engineers nor evaluate what engineering requires. Prior work has treated CAD generation as two disjoint steps, part synthesis and assembly, where the former is graded by proximity to a gold reference and the latter, when handled at all, is reduced to a separate constraint solving step. In this work, we introduce a more industry-native task formulation that requires a model to produce a fully assembled multi-part STEP file from a free-form engineering brief, which is then validated via finite element analysis (FEA). FEA validation reveals that Codex (GPT-5.5) and Claude Code (Opus-4.7) agents do not produce a single strict-passing artifact in the main first-attempt sweep, with the best configuration meeting only about 20% of typed requirements on average. Moreover, we introduce two additional supervision signals, a novel text-only blueprint schema and a 21-view image renderer that aids the agent's visual inspection, that better align the generation loop with how engineers iterate in practice. On S2O and Fusion360, the same feedback tools improve geometric reconstruction, with GPT-5.5/xhigh rising from 0.444 to 0.592 Box-IoU on S2O and from 0.397 to 0.505 on Fusion360. Together these signals move CAD programs toward artifacts that are not only visually plausible but also checked against physical and structural requirements.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.24938", "title": "Your Embedding Model is SMARTer Than You Think", "authors": [ "Jianrui Zhang", "Hyun Jung Lee", "Sukanta Ganguly", "Tae-Eui Kam", "Donghyun Kim", "Yong Jae Lee" ], "github_manual": "", "github_regex": "https://github.com/HanSolo9682/SMART", "github_paper_md": "", "github_api": "https://github.com/HanSolo9682/SMART", "github_api_added_by": "user", "project_page": "", "abstract": "Multimodal retrieval relies heavily on single-vector retrievers, which compress rich, sequential token sequences into one single global representation. While efficient, they discard fine-grained, local evidence critical for dense retrieval tasks. Multi-vector approaches were introduced as a solution, but they strictly require training and many ignore the necessity of a globally summarizing representation. To address this, we introduce SMART, a framework that unlocks the latent multi-vector capabilities of standard single-vector models. We first demonstrate that standard contrastive training on the pooled embedding implicitly shapes the retrieval geometry of preceding hidden states via gradient flow. By applying direct late-interaction over these frozen hidden states during inference, SMART acts as a plug-and-play upgrade that consistently improves performance across diverse modalities, improving even the state-of-the-art models further on MMEB-V2. We also reveal SMART's superior performance, as simple lightweight post-training not only saves time and compute, but also brings forth further improvement on Visual Document retrieval, allowing a single-vector model to outperform SoTA multi-vector counterparts. Ultimately, SMART offers both a highly efficient inference enhancement and a powerful finetuning technique for multimodal retrieval. We open source our code and weights at https://github.com/HanSolo9682/SMART.", "github": "https://github.com/HanSolo9682/SMART" }, { "date": "2026-05-26", "arxiv_id": "2605.23204", "title": "AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery", "authors": [ "Guiyao Tie", "Jiawen Shi", "Dingjie Song", "Yixiao Huang", "Ziji Sheng", "Xueyang Zhou", "Daizong Liu", "Pan Zhou", "Yongchao Chen", "Ran Xu", "Lifang He", "Qingsong Wen", "Manling Li", "Cong Lu", "Shuai Li", "Pengtao Xie", "Yixuan Yuan", "Rui Meng", "Lei Xing", "Lichao Sun", "Caiming Xiong", "Philip S. Yu", "Jianfeng Gao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Mr-Tieguigui/Autoresearch", "github_api_added_by": "user", "project_page": "https://mr-tieguigui.github.io/Autoresearch/", "abstract": "Scientific research is being reshaped by AI systems that move beyond isolated assistance toward longer-horizon workflows spanning literature grounding, hypothesis generation, experimentation, validation, reporting, and revision. This shift marks a transition from task-level AI for science to workflow-level research automation. Yet current systems remain fragmented, differing in autonomy, domain scope, execution environment, validation mechanism, and human oversight, while still struggling with evidence preservation, reproducibility, weak-direction rejection, provenance tracking, cross-domain robustness, and accountable scientific closure. This survey examines these developments through AutoResearch, defined as the developmental spectrum of AI-powered scientific workflow automation. Within it, Vibe Research denotes the human-steered region of prompt-based assistance and human-verified execution, whereas emerging AI-led systems coordinate larger portions of the discovery loop without achieving robust autonomy. We analyze how research systems redistribute control, evidence, execution, validation, and accountability across workflows and organize the field around five workflow conditions: literature and research grounding; hypothesis formation and planning; experimentation and tool use; feedback, validation, and review; and reporting and knowledge communication. We further synthesize AI scientist systems, mixed-initiative co-research frameworks, benchmarks, domain deployments, and open-source infrastructures. Finally, we propose five evaluation dimensions--novelty, validity, impact, reliability, and provenance--and show that AutoResearch autonomy is domain-conditioned, being more credible in structured, executable, and rapidly verifiable settings but limited in embodied, delayed, heterogeneous, ethical, or institutionally accountable contexts.", "github": "https://github.com/Mr-Tieguigui/Autoresearch" }, { "date": "2026-05-26", "arxiv_id": "2605.25461", "title": "MetaphorVU: Towards Metaphorical Video Understanding", "authors": [ "Zhuoqun Li", "Boxi Cao", "Guiping Jiang", "Fangrui Lv", "Ruotong Pan", "Jianan Wang", "Xiangyu Wu", "Hongyu Lin", "Yaojie Lu", "Yong Du", "Ruyin Jia", "Liyan", "Tingting Gao", "Han Li", "Xianpei Han", "Le Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/icip-cas/MetaphorVU", "github_api_added_by": "user", "project_page": "https://huggingface.co/datasets/lzq2021/MetaphorVU-Bench", "abstract": "Metaphorical videos are prevalent across various real-world scenarios to convey complex ideas, and understanding them typically requires high-order cognitive capabilities. The lack of systematic studies on metaphorical video understanding not only constrains the real-world applicability of MLLMs but also impedes the thorough assessment of their high-order cognitive capabilities. To bridge this gap, we propose MetaphorVU-Bench, the first systematic and comprehensive benchmark dedicated to metaphorical video understanding. Through experiments, we find current MLLMs struggle with accurate metaphorical video understanding, lagging far behind human level, primarily due to defective cross-domain mapping. Motivated by this finding, we construct a metaphor knowledge graph as mapping augmentation and propose MetaphorBoost, an inference-time enhancement framework achieving consistent performance improvement. Our benchmark, analysis, and method provide useful insights and a foundation for future research on advancing MLLMs.", "github": "https://github.com/icip-cas/MetaphorVU" }, { "date": "2026-05-25", "arxiv_id": "2605.24675", "title": "VaaWIT: Visual-Aware Adaptation of Large Language Models for Multilingual Web Image Translation", "authors": [ "Bo Li", "Ronghao Chen", "Ningyuan Deng", "Huacan Wang", "Shaolin Zhu", "Lijie Wen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Translating text embedded in Web images is crucial for improving content accessibility and cross-lingual information retrieval, particularly within social media and e-commerce domains. Although Large Vision-Language Models (LVLMs) have advanced multimodal understanding, applying them to Web image translation remains challenging due to the visual representation gap: standard encoders often prioritize high-level semantics over the fine-grained visual details required for recognizing diverse character morphologies. To address this challenge, we propose VaaWIT, an end-to-end framework that adapts Large Language Models for multilingual Web image translation. The framework introduces two key technical contributions: (1) a Dual-Stream Attention Module (DSAM), which facilitates bidirectional interaction between multilingual semantic features and detailed visual representations, thereby synthesizing unified features robust to textual variations; and (2) a Visual-Aware Adapter (VAA), a parameter-efficient fine-tuning strategy that dynamically injects these fused visual cues into the frozen LLM backbone. This design enables the model to align the visual context with linguistic reasoning effectively while minimizing computational costs. Extensive experiments on eight tasks on three public benchmarks demonstrate that VaaWIT significantly outperforms state-of-the-art (SOTA) open-source baselines and achieves competitive performance against proprietary models. These results validate the efficacy of integrating fine-grained visual perception into LLMs for complex Web content analysis.", "github": "" }, { "date": "2026-05-25", "arxiv_id": "2605.24681", "title": "Mix-MoE: Improving Multilingual Machine Translation of Large Language Models through Mixed MoEs", "authors": [ "Bo Li", "Tianyu Dong", "Shaolin Zhu", "Deyi Xiong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large Language Models (LLMs) have shown great promise in multilingual machine translation (MT), even with limited bilingual supervision. However, fine-tuning LLMs with parallel corpora presents major challenges, namely parameter interference. To address these issues, we propose Mix-MoE, a mixed Mixture-of-Experts framework designed to train LLMs for multilingual MT. Our framework operates in two distinct stages: (1) post-pretraining with MoE on monolingual corpora, and (2) post-pretraining with MoE on parallel corpora. Crucially, we divide the MoE layers into two specialized groups: Language Model Experts (LM Experts) and Machine Translation Experts (MT Experts). LM Experts are designed to capture and retain the monolingual knowledge learned by the pre-trained LLM. MT Experts, on the other hand, are specifically trained to acquire and store bilingual translation knowledge. Furthermore, to facilitate effective interaction between these specialized experts and leverage potential underlying structural patterns in text, we introduce a routing mechanism enhanced by Fourier Transform features derived from model representations. The experimental results demonstrate that Mix-MoE excels in multilingual MT, significantly outperforming existing baselines and showing notable progress in mitigating parameter interference.", "github": "" }, { "date": "2026-05-25", "arxiv_id": "2605.17423", "title": "Soap2Soap: Long Cinematic Video Remaking via Multi-Agent Collaboration", "authors": [ "Yiren Song", "Huilin Zhong", "Kevin Qinghong Lin", "Haofan Wang", "Mike Zheng Shou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/showlab/Soap2Soap", "github_api_added_by": "user", "project_page": "", "abstract": "We study series-level cinematic remaking, a long-horizon video-to-video generation problem that localizes full episodes or films via stylization or actor replacement while strictly preserving narrative structure, motion choreography, and character identity across hundreds of shots. Existing video generation and editing pipelines often break down in this regime due to compounding identity drift, background mutation, and semantic erosion under large camera motions and viewpoint changes. We propose Soap2Soap, a multi-agent framework that enforces long-term language-visual consistency through a Dual-Bridge Consistency mechanism: a scene-aware JSON screenplay serving as a persistent semantic backbone, and dynamically allocated visual reference anchors at both scene and shot levels. To suppress drift before video synthesis, we introduce batch keyframe consistency, jointly generating multiple keyframes in a shared latent context via a grid-based formulation. A closed-loop verification agent further audits identity, stability, and alignment to trigger selective regeneration. Experiments on SoapBench demonstrate strong improvements over commercial video generation APIs in long-term consistency and narrative fidelity.", "github": "https://github.com/showlab/Soap2Soap" }, { "date": "2026-05-26", "arxiv_id": "2605.25604", "title": "DVAO: Dynamic Variance-adaptive Advantage Optimization for Multi-reward Reinforcement Learning", "authors": [ "Guochao Jiang", "Jingyi Song", "Guofeng Quan", "Chuzhan Hao", "Guohua Liu", "Yuewei Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement Learning has become a standard paradigm for aligning Large Language Models with human intent and task requirements. While Group Relative Policy Optimization offers an efficient, value-model-free alternative to Proximal Policy Optimization, adapting it to real-world multi-reward settings remains challenging. Standard scalarization practices, such as Reward Combination and Advantage Combination, suffer from significant drawbacks: Reward Combination frequently generates advantages with excessively large squared magnitudes that lead to training instability, while Advantage Combination relies on static hyperparameters and ignores cross-objective correlations. To address these limitations, we propose Dynamic Variance-adaptive Advantage Optimization (DVAO), which dynamically adjusts combination weights based on the empirical reward variance of each objective within a rollout group, effectively up-weighting objectives with a stronger learning signal while suppressing noisy ones. We mathematically prove that DVAO maintains bounded advantage magnitudes for stable training and introduces a self-adaptive cross-objective regularization mechanism. Extensive experiments on mathematical reasoning and tool-use benchmarks using Qwen3 and Qwen2.5 models demonstrate that DVAO significantly outperforms baseline methods, achieving a superior multi-objective Pareto frontier and robust training stability.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.25294", "title": "Geometry-Aware Image Flow Matching", "authors": [ "Junho Lee", "Kwanseok Kim", "Joonseok Lee" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recent advances in generative models highlight the power of geometry-aware modeling in manifold-constrained settings. Yet, for natural images, the field remains confined to Euclidean assumptions, failing to exploit the potential of intrinsic geometric structures within the data. In this work, we investigate the geometry of natural images and observe that semantic information is predominantly encoded in directional components, while norm components can be approximated by the global average. This property holds across both RGB and latent spaces, suggesting that natural images can be effectively modeled on a hypersphere. Building on this finding, we introduce Spherical Optimal Transport Flow Matching (SOT-CFM), which utilizes angular distance, and Spherical Flow Matching (SFM), which constrains dynamics directly on the manifold. Our experiments demonstrate that these geometry-aware methods achieve superior performance against Euclidean baselines. Ultimately, this work provides a novel perspective that bridges the gap between Riemannian manifold-based modeling and natural image generation.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.25449", "title": "Pantheon360: Taming Digital Twin Generation via 3D-Aware 360\u00b0 Video Diffusion", "authors": [ "Ting-Hsuan Chen", "Ying-Huan Chen", "Tao Tu", "Jie-Ying Lee", "Cho-Ying Wu", "Fangzhou Lin", "Hengyuan Zhang", "David Paz", "Xinyu Huang", "Yuliang Guo", "Yu-Lun Liu", "Yue Wang", "Liu Ren" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://koi953215.github.io/pantheon360_page/", "abstract": "Generating complete digital twins from videos requires precise camera control, global scene coverage, and strict spatial-temporal consistency constraints that remain challenging for perspective video generators due to their limited field of view (FoV). Their narrow FoV forces long or multi-view trajectories, amplifying cross-view inconsistency and temporal drift. We argue that 360\u00b0 video generation offers a natural solution: panoramic coverage simplifies trajectory design and provides a strong global context for maintaining coherence. We introduce Pantheon360: Taming Digital Twin Generation via 3D-Aware 360\u00b0 Video Diffusion, a controllable 360\u00b0 video generation framework that synthesizes high-fidelity videos from sparse 360\u00b0 inputs. The key idea is an explicit 3D Cache, reconstructed from the input, which serves as a geometric scaffold for any user-defined camera path. This allows the diffusion model to focus on photorealistic texture refinement while the 3D Cache enforces global geometric consistency. Experiments show that Pantheon360 achieves superior visual quality and unmatched geometric coherence, enabling reliable and flexible 360\u00b0 scene generation for downstream simulation and digital-twin applications.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.24830", "title": "Macaron-A2UI: A Model for Generative UI in Personal Agents", "authors": [ "Fancy Kong", "Congjie Zheng", "Murphy Zhuang", "Rio Yang", "Sueky Zhang", "Hao Fu", "Gene Jin", "Song Cao", "Kaijie Chen", "Andrew Chen", "Pony Ma" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "As personal agents evolve to handle complex, user-centric tasks, static plain-text chat is rapidly becoming a bottleneck. Generative UI emerges as the necessary new interface layer, dynamically synthesizing the right controls, options, and state from the interaction context in real time. We present Macaron-A2UI, a model for Generative UI in personal agents. Our goal is to move beyond text-only interaction by enabling agents to generate natural language together with lightweight, executable UI actions for information collection, preference refinement, confirmation, and multi-goal organization. We build a large-scale Generative UI corpus from heterogeneous dialogue sources, introduce A2UI-Bench for controlled evaluation, and train 30B, 235B and 754B models with parameter-efficient LoRA-based supervised fine-tuning followed by reward-driven reinforcement learning. The best Macaron-A2UI model reaches 75.6 overall on A2UI-Bench without explicit schema hints, surpassing the strongest full-schema frontier baseline. We release the models, benchmark, and evaluation protocol to support future work on Generative UI for personal agents.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.25874", "title": "WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation", "authors": [ "Kaining Ying", "Hengrui Hu", "Siyu Ren", "Jiamu Li", "Fengjiao Chen", "Ziwen Wang", "Xuezhi Cao", "Xunliang Cai", "Henghui Ding" ], "github_manual": "", "github_regex": "https://github.com/meituan-longcat/WBench", "github_paper_md": "", "github_api": "https://github.com/meituan-longcat/WBench", "github_api_added_by": "user", "project_page": "https://meituan-longcat.github.io/WBench/", "abstract": "Interactive world models are advancing rapidly, yet existing benchmarks cover only part of the required competencies, leaving no unified standard for systematic evaluation. To fill this gap, we introduce WBench, a comprehensive multi-turn benchmark for interactive world model evaluation along five dimensions, namely video quality, setting adherence, interaction adherence, consistency, and physics compliance. WBench contains 289 test cases and 1,058 interaction turns, where each case specifies a world setting and a multi-turn interaction sequence, covering diverse scenes, styles, subjects, and both first- and third-person perspectives, together with four interaction types, including navigation, subject action, event editing, and perspective switching. For navigation, WBench unifies text, 6-DoF pose, and discrete-action control, enabling evaluation of models with different native input interfaces. Evaluation uses 22 automatic sub-metrics that combine specialist vision models with large multimodal models, and all metrics are validated against human judgments. Across 20 state-of-the-art models, we find that no single model performs strongly across all dimensions. We provide detailed diagnostic insights into the characteristic strengths, weaknesses, and open challenges of each model. Code and data are available at https://github.com/meituan-longcat/WBench.", "github": "https://github.com/meituan-longcat/WBench" }, { "date": "2026-05-26", "arxiv_id": "2605.20342", "title": "ParaVT: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning", "authors": [ "Zuhao Yang", "Kaichen Zhang", "Sudong Wang", "Keming Wu", "Zhongyu Yang", "Bo Li", "Xiaojuan Qi", "Shijian Lu", "Xingxuan Li", "Lidong Bing" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/EvolvingLMMs-Lab/ParaVT", "github_api_added_by": "user", "project_page": "https://evolvinglmms-lab.github.io/ParaVT/", "abstract": "Training large multimodal models (LMMs) via reinforcement learning (RL) to natively invoke video-processing tools (e.g., cropping) has become a promising route to long-video understanding. However, existing native-RL methods dispatch tool calls sequentially (i.e., one per turn): a single wrong crop propagates errors without peer correction, multi-turn tool calls corrupt context, and inference cost scales linearly with the number of turns. We introduce ParaVT, the first multi-agent end-to-end RL-trained framework for Parallel Video Tool calling, dispatching multiple time-window crops in a single turn for cleaner context and better fault tolerance. Yet applying standard RL to ParaVT reveals an obstacle we term the Tool Prior Paradox: the pretrained tool priors that enable tool exploration also destabilize cold-started structural format and expose the skip-tool reward shortcut under temperature sampling. A cross-model contrast on a weaker-prior LMM supports this claim: format stays stable but RL elicits zero tool calls, indicating that prior strength is the shared driver of both format collapse and tool exploration. We propose PARA-GRPO (Parseability-Anchored and Ratio-gAted GRPO), which augments standard RL with two complementary mechanisms: (i) a targeted format reward applied only at the structural-token positions most prone to collapse, and (ii) a per-prompt frame-budget randomization that creates training prompts where calling the tool yields a measurable reward signal over skipping it. Across six long-video understanding benchmarks, ParaVT improves over the Qwen3-VL baseline by +7.9% on average, with PARA-GRPO lifting training-time format compliance from 0.13 to 0.64. As tool capabilities become increasingly internalized in modern LMMs, RL must cooperate with the resulting priors, and ParaVT offers a general recipe for agentic RL. Code, data, and model weights are publicly available.", "github": "https://github.com/EvolvingLMMs-Lab/ParaVT" }, { "date": "2026-05-26", "arxiv_id": "2605.26115", "title": "TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction", "authors": [ "Weijie Wang", "Zimu Li", "Jinchuan Shi", "Zeyu Zhang", "Botao Ye", "Marc Pollefeys", "Donny Y. Chen", "Bohan Zhuang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ziplab/TriSplat", "github_api_added_by": "user", "project_page": "https://lhmd.top/trisplat/#interactive", "abstract": "Sparse-view 3D reconstruction is increasingly addressed with feed-forward splatting networks that predict explicit primitives directly from images. Yet most existing methods remain centered on Gaussian primitives and expose surfaces only indirectly: extracting a usable mesh for downstream simulation, physics reasoning, or embodied interaction still requires expensive post-hoc steps that break the feed-forward promise. This limitation is especially pronounced in pose-free settings, where scene structure and camera parameters must be estimated jointly from sparse observations. We present TriSplat, a feed-forward reconstruction network that represents scenes with oriented triangle primitives and directly exports simulation-ready mesh scenes from a single forward pass. Given input images, the network predicts local 3D point maps, triangle attributes, camera poses, and optional intrinsics. Rather than regressing triangle orientation as an unconstrained latent variable, our approach constructs geometry normals from the predicted point maps, refines them with an image-conditioned normal head, and converts them into stable local frames for triangle parameterization. A mono-normal bootstrap schedule further stabilizes early training, while opacity and blur scheduling progressively sharpens the learned surface representation for direct mesh extraction. Experiments on RealEstate10K and DL3DV show that this representation produces more geometry-faithful reconstructions than Gaussian feed-forward baselines while maintaining competitive novel-view rendering quality. Because the rendering primitives are themselves surface triangles, the output can be directly ingested by physics engines, collision detectors, and standard rendering pipelines without any conversion, making it a practical simulation-ready solution for feed-forward 3D scene reconstruction.", "github": "https://github.com/ziplab/TriSplat" }, { "date": "2026-05-26", "arxiv_id": "2605.26086", "title": "Claw-Anything: Benchmarking Always-On Personal Assistants with Broader Access to User's Digital World", "authors": [ "Yusong Lin", "Xinyuan Liang", "Haiyang Wang", "Qipeng Gu", "Siqi Cheng", "Jiangui Chen", "Shuzhe Wu", "Feiyang Pan", "Lue Fan", "Sanyuan Zhao", "Dandan Tu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LiberCoders/CLaw-Anything", "github_api_added_by": "user", "project_page": "", "abstract": "Large language model agents are increasingly envisioned as always-on personal assistants with access to anything relevant in the user's digital world. Yet current systems operate over only narrow slices of that world, limiting context-sensitive reasoning and effective assistance. Existing benchmarks similarly provide only partial user state and therefore fail to capture performance in such a broad, always-on setting. To address this gap, we introduce Claw-Anything, a benchmark that expands agent context along three dimensions: long-horizon activity histories, interdependent backend services, and integrated GUI and CLI interaction across multiple devices. To instantiate this setting, we simulate months of user activity through multi-round event injection, producing complex world states and realistic noise, including irrelevant events and conflicting signals. Agents must reason over rich contextual environments while remaining robust to such noise. This expanded scope also enables the evaluation of proactive assistance, requiring agents to anticipate user needs and deliver timely recommendations. Experiments show that GPT-5.5 achieves only 34.5% pass@1, substantially below prior benchmarks, underscoring a gap between current agent capabilities and the demands of always-on personal assistance. Alongside the benchmark, we release an automated data-generation pipeline that yields 2,000 training environments and improves the base model by 23.7%, demonstrating its utility of scalable data infrastructure.", "github": "https://github.com/LiberCoders/CLaw-Anything" }, { "date": "2026-05-26", "arxiv_id": "2605.26105", "title": "On-Policy Adversarial Flow Distillation for Autoregressive Video Generation", "authors": [ "Yang Luo", "Shengju Qian", "Xiaohang Tang", "Zirui Zhu", "Yong Liu", "Xin Wang", "Yang You" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Autoregressive video generators are attractive for streaming, long-horizon, and interactive applications, but distilling strong black-box teachers into causal students remains difficult. The student must learn under its own rollout distribution, whereas practical teachers may expose only prompt-conditioned completed videos and may differ in architecture, capacity, temporal design, and sampling schedule. This interface makes supervised fine-tuning off-policy, score-based distillation inapplicable, and direct adversarial imitation too sparse for denoising-time credit assignment. We propose Adversarial Flow Distillation (AFD), an on-policy framework for heterogeneous black-box video distillation. AFD queries the teacher and rolls out the current student on the same prompts, trains a prompt-paired Bradley-Terry discriminator to estimate clean-sample teacher-student discrepancy, and converts the resulting on-policy advantage into forward-process flow-matching updates on the student's own noised states. Thus, AFD provides dense velocity-field supervision while requiring no teacher scores, latents, denoising trajectories, step alignment, or reverse-chain reinforcement learning. Experiments across two causal AR student families show that AFD consistently improves motion- and physics-sensitive generation while preserving general video quality, and ablations validate the importance of adaptive on-policy feedback and forward-process credit assignment. The method requires only clean teacher videos and student rollouts, providing a practical route for distilling proprietary or heterogeneous video generators into efficient autoregressive students.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.25343", "title": "Toward Native Multimodal Modeling: A Roadmap", "authors": [ "Siyu An", "Junru Lu", "Junnan Dong", "Qiufeng Wang", "Yinghui Li", "Weizhi Fei", "Zichao Yu", "Zheng Yuan", "Biao Liu", "Haopeng Wang", "Renzhao Liang", "Yixuan Yang", "Yunhang Shen", "Bo Ke", "Keyu Chen", "Linhao Luo", "Difan Zou", "Xiao Huang", "Di Yin", "Ruizhi Qiao", "Xing Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NMM-Roadmap/Awesome-NMM-List", "github_api_added_by": "user", "project_page": "https://nmm-roadmap.github.io/", "abstract": "Multimodal modeling represents a vital step from modality-agnostic reasoning toward world modeling. While early approaches predominantly rely on late-fusion that assembles encoders and frozen language backbones with output heads, recent efforts have shifted the paradigm toward native multimodal modeling (NMM) with the intrinsic integration of modalities for superior multimodal performance. Despite its potential, the design space of native architectures remains insufficiently defined. In this paper, we present the community with a formalized roadmap for this transition. Specifically, we formally define the architectural nativity, distinguishing mid-fusion and early-fusion from non-native paradigms. We further organize the existing native models through the lens of input-output duality into three categories: (i) Multi-to-Text for cross-modal comprehension with text-only output; (ii) Multi-to-Target for scenario-oriented generation, e.g., image, audio and video generation, and (iii) Multi-to-Multi for unified modeling with symmetric input-output. We deliver a comprehensive and industrial-grade investigation into the transition toward the definitive NMM framework, where understanding and generation seamlessly coexist within a unified transformer paradigm. We systematically unpack the end-to-end pipeline from industrial perspectives from architectural coordination, massive data curation, to full-stack training recipes, inference & deployment, and the comprehensive evaluation for truly native modeling.", "github": "https://github.com/NMM-Roadmap/Awesome-NMM-List" }, { "date": "2026-05-26", "arxiv_id": "2605.26089", "title": "Channel-wise Vector Quantization", "authors": [ "Wei Song", "Tianhang Wang", "Yitong Chen", "Tong Zhang", "Zuxuan Wu", "Ming Li", "Jiaqi Wang", "Kaicheng Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/songweii/CVQ", "github_api_added_by": "user", "project_page": "https://github.com/songweii/CVQ", "abstract": "We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens. Unlike conventional vector quantization, which assigns a discrete token to each patch feature vector, CVQ quantizes each channel of the feature map. This formulation represents an image as discrete levels of visual details, rather than as a grid of spatial patches. Based on CVQ, we introduce a new visual autoregressive framework with \"next-channel prediction\". Instead of rendering images patch by patch in raster order, our Channel-wise Autoregressive (CAR) model predicts image channels sequentially, producing progressively enriched visual details. Specifically, it first sketches global structure and then refines fine-grained attributes, akin to a human artist's workflow. Empirically, we show that: (1) CVQ achieves 100% codebook utilization with a 16K+ codebook size without any bells and whistles, and substantially improves reconstruction quality over conventional VQ; and (2) CAR attains a DPG score of 86.7 and a GenEval score of 0.79, demonstrating strong effectiveness for text-to-image generation.", "github": "https://github.com/songweii/CVQ" }, { "date": "2026-05-26", "arxiv_id": "2605.26109", "title": "Helix4D: Complex 4D Mesh Generation", "authors": [ "Jiraphon Yenphraphai", "Jianqi Chen", "Jian Wang", "Gordon Qian", "Sergey Tulyakov", "Rameen Abdal", "Raymond A. Yeh", "Peter Wonka", "Chaoyang Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://snap-research.github.io/helix4d/", "abstract": "Current video-to-4D methods struggle with complex topology changes, transparent materials, thin structures, and inner surfaces. We present Helix4D, a dynamic mesh generation framework by inheriting the expressive representation of Trellis2, adapting it from image-to-3D to video-conditioned 4D generation. Our design arises from two key questions: (a) how to enable Trellis2's frame-local attention to share information across frames while preserving its pretrained quality on rare cases such as transparent objects and inner surfaces, and (b) how to inject temporal information into a purely 3D positional encoding without breaking pretrained capabilities. We address (a) with a sliding-window cross-frame attention and anchor on the first frame. The first frame is generated by the base Trellis2 model and injected into our model, letting it inherit Trellis2's quality in rare cases through cross-frame attention. We address (b) with a 4D temporal encoding that repurposes redundant low-frequency spatial RoPE bands for time, extending the encoding from 3D with no additional parameters. Extensive experiments show the effectiveness of Helix4D for high-quality dynamic mesh generation on ActionBench and our own challenging complex dynamics set.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.26002", "title": "SemBridge: Language Transfer in Sparse Encoders via Multilingual Semantic Bridges", "authors": [ "Seongtae Hong", "Youngjoon Jang", "Jia-Heui Ju", "Hyeonseok Moon", "Heuiseok Lim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Sparse encoders offer high-precision retrieval by representing term importance within a vocabulary space, yet their English-centric structures pose a critical impediment to language transfer for non-English languages. To overcome this structural limitation, we propose SemBridge, a novel embedding initialization method designed for cross-lingual adaptation in sparse encoders by leveraging multilingual bridge models. SemBridge establishes semantic alignments between source and target vocabularies using multilingual dense embeddings as a bridge. Rather than directly relying on all source tokens, SemBridge selects a small set of semantically related source-language tokens and uses them to initialize each target-language token, effectively filtering out semantic noise and reconstructing target tokens as precise linear combinations of core synonyms. This accelerates convergence during fine-tuning and improves training efficiency. Extensive experiments across five languages and four sparse architectures demonstrate that SemBridge achieves superior zero-shot retrieval performance and consistently improves retrieval performance after fine-tuning compared to existing baselines. These results validate SemBridge as a practical solution for deploying high-performance sparse retrieval systems in diverse linguistic environments.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.20278", "title": "ClaimDiff-RL: Fine-Grained Caption Reinforcement Learning through Visual Claim Comparison", "authors": [ "Tianle Li", "Xuyang Shen", "Yan Ma", "Rongxin Guo", "Shaoxiang Chen", "Jiacheng Chen", "Haochen Wang", "Hongyang Tang", "Yucong Zhou", "Yu Cheng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ltl3A87/ClaimDiff-RL", "github_api_added_by": "user", "project_page": "", "abstract": "Long-form image captioning exposes a reward granularity problem in RL: captions are judged as whole sequences, while the important errors occur at the level of individual visual claims. A good dense caption should be both faithful and informative, avoiding hallucination without omitting salient details. Yet pairwise preferences, reference-based metrics, and holistic scalar rewards compress these local errors into a single sequence-level signal, obscuring the tradeoff between factuality and coverage. We introduce ClaimDiff-RL, a framework that uses reference-conditioned atomic claim differences as the reward unit for caption RL. Given an image, an actor caption, and a reference caption, a multimodal judge enumerates visually grounded differences, verifies each difference against the image, assigns open-vocabulary error types and severity levels, and produces per-difference statistics for reward composition. This makes hallucinated claims and omitted salient facts separately measurable and tunable. Experiments show that holistic scalar rewards can reduce hallucination by increasing missing facts, while ClaimDiff-RL exposes this faithfulness and coverage tradeoff and enables more balanced operating points. On a 160-image human-labeled diagnostic benchmark, public captioning benchmarks, and VQA benchmarks, ClaimDiff-RL improves the hallucination--missing-fact balance, preserves general capability, and even surpasses Gemini-3-Pro-Preview on several fine-grained Capability dimensions such as object counting, spatial relations, and scene recognition. These results suggest that typed, verifiable claim differences are an effective reward unit for fine-grained and diagnosable caption RL.", "github": "https://github.com/ltl3A87/ClaimDiff-RL" }, { "date": "2026-05-26", "arxiv_id": "2605.23218", "title": "Foundation Protocol: A Coordination Layer for Agentic Society", "authors": [ "Bang Liu", "Yongfeng Gu", "Jiayi Zhang", "Zhaoyang Yu", "Sirui Hong", "Maojia Song", "Xiaoqiang Wang", "Mingyi Deng", "Zijie Zhuang", "Ronghao Wang", "Mingzhe Cao", "Yutong Zhu", "Xingjian Li", "Yifan Wu", "Jianhao Ruan", "Yiran Peng", "Shuangrui Chen", "Jinlin Wang", "Yizhang Lin", "Dongjie Zhang", "Dekun Wu", "Chen Ma", "Lizi Liao", "Han Yu", "Jian Pei", "Heng Ji", "Qiang Yang", "Yuyu Luo", "Chenglin Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/FoundationAgents/foundation-protocol", "github_api_added_by": "user", "project_page": "https://foundationagents.org/", "abstract": "Autonomous agents are moving from tools into a layer of social infrastructure: they browse, purchase, deploy software, manage systems, and increasingly interact with one another. As these systems scale, the bottleneck shifts away from raw model capability toward coordination. Agents need to form reliable relationships, organize multi-agent work, exchange value, support an AI economy, and stay safe and accountable under real-world oversight. This paper introduces the Foundation Protocol (FP), a graph-first coordination layer for an emerging human-AI society. FP unifies heterogeneous entities, including agents, tools, resources, humans, institutions, and organizations, and supports native multi-party organization and event-based collaboration. It also provides economic primitives for metering, receipts, and settlement, and treats policy, provenance, and audit as first-class concerns. FP is designed to wrap and bridge existing protocols rather than replace them, enabling incremental adoption while reducing integration and governance overhead. The aim is to keep autonomous agency composable while keeping accountability non-negotiable, so that coordination itself can become shared infrastructure for a human-AI society that is open, pluralistic, and governable.", "github": "https://github.com/FoundationAgents/foundation-protocol" }, { "date": "2026-05-26", "arxiv_id": "2605.25569", "title": "ControlLight: Towards Controllable, Consistent, and Generalizable Low-Light Enhancement", "authors": [ "Yufeng Yang", "Jianzhuang Liu", "Jisheng Chu", "Yuqi Peng", "Xianfang Zeng", "Jiancheng Huang", "Shifeng Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yfyang007/ControlLight", "github_api_added_by": "user", "project_page": "https://yfyang007.github.io/ControlLight/", "abstract": "Existing deep learning-based low-light enhancement methods are typically trained on limited datasets with single enhancement targets, which restricts their generalization ability and controllability in real-world applications. To overcome these limitations, we propose ControlLight, a controllable, consistent, and generalizable framework for low-light enhancement. We first construct a large-scale dataset of real-world degraded images with continuous illumination-strength supervision. To further ensure consistent outputs under different control strengths, we introduce a misalignment-aware weighted flow matching loss that preserves image structure across continuous enhancement strengths. ControlLight allows users to edit real-world degraded low-light images toward satisfactory enhancement results by flexibly controlling the strength while preserving visual consistency and realism. Extensive experiments show that ControlLight achieves state-of-the-art performance against existing low-light enhancement approaches while demonstrating strong continuous controllability and generalization to real-world scenarios.", "github": "https://github.com/yfyang007/ControlLight" }, { "date": "2026-05-26", "arxiv_id": "2605.24218", "title": "QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks", "authors": [ "Jian Xie", "Tianhe Lin", "Zilu Wang", "Yuting Ning", "Yuekun Yao", "Tianci Xue", "Zhehao Zhang", "Zhongyang Li", "Kai Zhang", "Yufan Wu", "Shijie Chen", "Boyu Gou", "Mingzhe Han", "Yifei Wang", "Vint Lee", "Xinpeng Wei", "Xiangjun Wang", "Yu Su", "Huan Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OSU-NLP-Group/QUEST", "github_api_added_by": "user", "project_page": "https://osu-nlp-group.github.io/QUEST/", "abstract": "Deep research agents extend the role of search engines from retrieving keyword-matched pages to synthesizing knowledge, fundamentally changing how humans interact with information. However, frontier systems remain proprietary, while existing open agents often generalize poorly across different task types, leaving unclear how to train a broadly capable deep research agent. We release QUEST, a family of open models (ranging from 2B to 35B) that serve as general-purpose deep research agents designed to handle a wide range of long-horizon search tasks, with strong capabilities in fact seeking, citation grounding, and report synthesis. To build QUEST, we propose an effective training recipe combining mid-training, supervised fine-tuning, and reinforcement learning. Central to this recipe is a curated data synthesis pipeline based on unified rubric trees, which applies to different task types and enables synthesizing training data with verifiable rewards without human annotation. In addition, QUEST incorporates a built-in context management mechanism that enables effective long-horizon reasoning and knowledge synthesis. Using only 8K synthesized tasks, QUEST approaches or even surpasses frontier closed-source agents across eight deep research benchmarks spanning diverse task types, and achieves the best overall performance among recent open-weight agents. We released everything: models, data, and training scripts.", "github": "https://github.com/OSU-NLP-Group/QUEST" }, { "date": "2026-05-26", "arxiv_id": "2605.08129", "title": "Towards Customized Multimodal Role-Play", "authors": [ "Chao Tang", "Jianzong Wu", "Qingyu Shi", "Ye Tian", "Aixi Zhang", "Hao Jiang", "Jiangning Zhang", "Yunhai Tong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tangc03/UniCharacter", "github_api_added_by": "user", "project_page": "https://tangc03.github.io/UniCharacter.github.io/", "abstract": "Unified multimodal understanding and generation models enable richer human-AI interaction. Yet jointly customizing a character's persona, dialogue style, and visual identity while maintaining output consistency across modalities remains largely unexplored. To mitigate this gap, we introduce a new task, Customized Multimodal Role-Play (CMRP). We construct the RoleScape-20 dataset comprising 20 characters, including training and evaluation data that cover persona, stylistic descriptions, visual/expressive cues, and text-image interactions. Building on a unified model, we devise UniCharacter, a two-stage training framework containing Unified Supervised Finetuning (Unified-SFT) and character-specific group relative policy optimization (Character-GRPO). Given only 10 images plus corresponding interaction examples, the model acquires the target character and exhibits coherent persona, style, and visual identity in both generated text and images. This process takes about 100 GPU hours. Experiments on the RoleScape-20 dataset show that the proposed method substantially outperforms prior approaches. Ablation studies further validate the effectiveness of our cross-modal consistency design and few-shot customization strategy. We argue that CMRP, coupled with unified modeling, provides a basis for next-generation characterful and immersive interactive agents.", "github": "https://github.com/Tangc03/UniCharacter" }, { "date": "2026-05-26", "arxiv_id": "2605.25052", "title": "Faithfulness Metrics Don't Measure Faithfulness: A Meta-Evaluation with Ground Truth", "authors": [ "Yoav Gur-Arieh", "Ana Marasovi\u0107", "Mor Geva" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yoavgur/BonaFide", "github_api_added_by": "user", "project_page": "https://huggingface.co/collections/yoavgurarieh/bonafide", "abstract": "Chains of thought (CoTs) have become central in interpreting and auditing behaviors of large language models. Yet growing evidence suggests that these traces often fail to faithfully represent the computations behind a model's predictions. Several faithfulness metrics have been proposed, but whether they indeed measure faithfulness remains unknown. Answering this requires ground-truth labels, which are hard to obtain since internal computations are not directly observable. Consequently, most works proposing metrics report only absolute scores or comparisons to prior metrics, and the few existing benchmarks rely on proxies like plausibility or importance, properties orthogonal to faithfulness that can mislead about whether a CoT can be trusted. We address this challenge by constructing tasks whose outputs reveal which intermediate computations must have produced them, and developing an automated labeling pipeline that yields ground-truth faithfulness labels at both the step and CoT level. Building on this methodology, we present BonaFide, a benchmark of 3,066 labeled CoTs across 13 tasks and 10 models, and use it to conduct the first systematic evaluation of prominent faithfulness metrics. Our experiments show that most metrics perform near chance, exhibit strong prediction biases and degrade on longer CoTs. The best metric reaches only 0.70 AUROC at the CoT level while another reaches 0.59 at the step level, with neither transferring across settings, while entailing prohibitively high computational cost. Our results expose fundamental gaps in current faithfulness evaluation and call for the development of more reliable and efficient metrics.", "github": "https://github.com/yoavgur/BonaFide" }, { "date": "2026-05-26", "arxiv_id": "2605.24426", "title": "SEAL: Synergistic Co-Evolution of Agents and Learning Environments", "authors": [ "Yihao Hu", "Zhihao Wen", "Xiujin Liu", "Pan Wang", "Xin Zhang", "Wei Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yihaohu0118/SEAL", "github_api_added_by": "user", "project_page": "https://yihaohu0118.github.io/SEAL/", "abstract": "Large Language Model (LLM) agents are increasingly improved through interaction, yet most self-evolution methods adapt either the policy or the learning environment in isolation. We identify this structural gap as Agent-Environment Misalignment: the agent's capability frontier changes during training, while the environment that provides supervision remains static or only weakly coupled to the agent's revealed failures. We propose SEAL, a closed-loop co-evolution framework for interactive tool-use agents. SEAL collects on-policy trajectories under executable verification, diagnoses failed rollouts into turn-level failure labels, and uses these diagnoses as a shared signal for both environment-side adaptation and model-side policy optimization. The environment evolves its training-time learning interface by exposing clearer tool affordance cues, constraint information, and recovery-oriented feedback, while the policy is updated with diagnosis-guided advantage reweighting. Extensive experiments across in-distribution and out-of-distribution multi-turn tool-use evaluations show that SEAL improves low-resource agent learning: with only 400 training samples, it yields +8.25 to +26.25 average-point gains across three backbones and exhibits positive out-of-distribution transfer. These results demonstrate the value of jointly adapting the learner and its training-time learning substrate for robust self-improving LLM agents.", "github": "https://github.com/yihaohu0118/SEAL" }, { "date": "2026-05-26", "arxiv_id": "2605.23986", "title": "MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing", "authors": [ "Han Chen", "Zining Zhang", "Wenqi Pei", "Bingsheng He", "Ming Wu", "Jason Zeng", "Michael Heinrich", "Wei Wu", "Hongbao Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Concyclics/MemForest", "github_api_added_by": "user", "project_page": "", "abstract": "Memory is a fundamental component for enabling long-context LLM agents, supporting persistent state across interactions through a continuous serve-and-update lifecycle. Despite substantial prior work, existing systems suffer from significant maintenance overhead due to two key limitations: coarse-grained state management and inherently sequential update pipelines. In particular, updates are often tightly coupled with LLM inference and require full-state rewrites, leading to poor scalability and growing latency as memory accumulates. To address these challenges, we present MemForest, a memory framework that reformulates agent memory as a write-efficient temporal data management problem. MemForest breaks the sequential bottleneck via parallel chunk extraction, decoupling memory construction into concurrent, independent operations. To further eliminate coarse-grained maintenance, we introduce MemTree, a hierarchical temporal index that organizes memory as time-ordered trees rather than flat global summaries. This design replaces full-state rewrites with localized per-node updates, reducing maintenance cost to the affected tree paths while naturally preserving temporally evolving states. We evaluate MemForest on two long-context memory benchmarks, LongMemEval-S and LoCoMo. On LongMemEval-S, MemForest achieves the best overall performance among stateful baselines, reaching 79.8% pass@1 accuracy while sustaining a memory construction throughput approximately 6x higher than state-of-the-art approaches including EverMemOS.", "github": "https://github.com/Concyclics/MemForest" }, { "date": "2026-05-26", "arxiv_id": "2605.24117", "title": "SkillEvolBench: Benchmarking the Evolution from Episodic Experience to Procedural Skills", "authors": [ "Yingtie Lei", "Zhongwei Wan", "Jiankun Zhang", "Samiul Alam", "Zixuan Zhong", "Peizhou Huang", "Xin Wang", "Jingxuan Zhang", "Donghao Zhou", "Yunta Hsieh", "Zhihao Dou", "Hui Shen", "Yan Xu", "Dimitrios Dimitriadis", "Tuo Zhang", "Mi Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://skillevolbench.github.io/", "abstract": "Large language model (LLM) agents accumulate rich episodic trajectories while solving real-world tasks, but it remains unclear whether such experience can be distilled into reusable procedural skills. We introduce SkillEvolBench, a diagnostic benchmark for evaluating this step from experience reuse to skill formation. It contains 180 tasks across six real-world agent environments, organized into role-conditioned task families with shared latent procedures. Agents learn from acquisition tasks, update an external skill library using compacted trajectories and verifier feedback, and then face frozen deployment tasks testing context shift, adversarial shortcuts, and composition. By comparing self-generated and curated-start skill evolution against no-skill and raw-trajectory controls, SkillEvolBench separates procedural abstraction from base capability, curated prior knowledge, and direct reuse of episodic traces. Across ten model configurations and three agent harnesses, we find that current agents often adapt locally but rarely form robust reusable skills. Skill-based conditions can improve acquisition or replay, and individual models sometimes gain on specific deployment axes, but these gains are unstable under frozen deployment. Raw-trajectory reuse frequently outperforms distilled skills, suggesting that current abstraction procedures discard contextual and procedural cues that remain useful for future tasks. Capacity and cost analyses further show that writing more skills or larger Tier-3 resource libraries is not sufficient: additional updates can improve coverage while introducing episode-specific drift and procedural clutter. These findings position SkillEvolBench as a testbed for measuring when one-off experience becomes durable procedural knowledge rather than task-local memory.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.26102", "title": "InstructSAM: Segment Any Instance with Any Instructions", "authors": [ "Yuqian Yuan", "Wentong Li", "Zhaocheng Li", "Yutong Lin", "Juncheng Li", "Siliang Tang", "Jun Xiao", "Yueting Zhuang", "Wenqiao Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DCDmllm/InstructSAM", "github_api_added_by": "user", "project_page": "", "abstract": "In this paper, we introduce InstructSAM, a unified and streamlined framework designed for multi-instance segmentation under arbitrary instructions. We formulates instruction-driven instance segmentation as a set-structured query prediction problem and propose an explicit reasoning-to-instance query interface that elegantly bridges a vision-language model (VLM) and SAM3. Specifically, a bank of learnable instance queries is injected into the VLM and contextualized with instruction and visual information, enabling each query to serve as an instance-aware slot. A hybrid-attention mechanism further promotes interaction among these queries, visual tokens, and instruction tokens, improving instance enumeration and reducing duplicate predictions. The resulting LLM-conditioned queries are projected into SAM3's detector query space to drive accurate multi-instance segmentation in a single forward pass. This design equips SAM3 with high-level instruction understanding, compositional reasoning, and instance-level set prediction without modifying its core architecture. To support training and evaluation, we further construct Inst2Seg, a high-quality and large-scale instruction-based instance segmentation dataset and benchmark that couples free-form instructions with instance-level masks. Extensive experiments show that only 2B-scale InstructSAM achieves strong results across complex instruction-driven and phrase-level referring segmentation benchmarks, outperforming prior end-to-end methods and SAM3's agentic pipeline while enabling efficient single-pass multi-instance prediction.", "github": "https://github.com/DCDmllm/InstructSAM" }, { "date": "2026-05-26", "arxiv_id": "2605.26108", "title": "Reinforcing Few-step Generators via Reward-Tilted Distribution Matching", "authors": [ "Yushi Huang", "Xiangxin Zhou", "Ruoyu Wang", "Chi Zhang", "Jun Zhang", "Tianyu Pang" ], "github_manual": "", "github_regex": "https://github.com/Harahan/RTDMD", "github_paper_md": "", "github_api": "https://github.com/Harahan/RTDMD", "github_api_added_by": "user", "project_page": "", "abstract": "Recent advances in few-step diffusion distillation have enabled efficient image generation, yet aligning these models with human preferences remains challenging. We propose Reward-Tilted Distribution Matching Distillation (RTDMD), a two-stage framework that unifies distribution matching distillation with reward-guided reinforcement learning for few-step flow generators. We show that minimizing the KL divergence to a reward-tilted teacher distribution naturally decomposes into a distribution matching term and a reward maximization term. In the first stage, we introduce Ambient-Consistent Distribution Matching Distillation (AC-DMD), which performs subinterval-wise distribution matching and augments the fake score objective with a consistency regularizer to help the fake score model track the shifting generator distribution under limited updates. In the second stage, we jointly optimize both terms: for the reward maximization term, we derive a hybrid policy gradient that combines a GRPO-style estimator for the stochastic intermediate transitions with direct reward backpropagation through the deterministic final step, and further introduce step-subset GRPO (SubGRPO) to reduce variance. Experiments on SD3, SD3.5, and FLUX.2 demonstrate that RTDMD establishes new state-of-the-art results across preference, aesthetic, and compositional metrics with only 4 inference steps, outperforming previous few-step text-to-image generation methods. Code and models are available at https://github.com/Harahan/RTDMD.", "github": "https://github.com/Harahan/RTDMD" }, { "date": "2026-05-26", "arxiv_id": "2605.26095", "title": "Pixel-Level Pavement Distress Assessment Using Instance Segmentation", "authors": [ "Logan Dewick", "Bibesh Pyakurel", "Kong Pheng Yang", "Nazim Choudhury", "M. G. Sarwar Murshed" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Automated pavement distress assessment requires more than image-level classification or coarse bounding box detection, demanding precise localization of thin, branching, and irregular cracks to achieve the geometric precision necessary for maintenance-relevant quantification. This paper presents a vision-based pavement distress analysis system based on Mask R-CNN instance segmentation and evaluates it on UWGB-StreetCrack, a custom field-collected roadway image dataset acquired with a vehicle-mounted smartphone and manually annotated with polygon labels for longitudinal cracks, transverse cracks, alligator cracks, and potholes. Five Detectron2-based Mask R-CNN backbone variants were considered under a consistent fine-tuning protocol. The best-performing model, Mask R-CNN with a ResNet-101 FPN backbone, achieved 84.23% precision, 90.04% recall, and an F1 score of 87.04% under the project-specific bounding-box matching protocol. The same model produced an aggregate predicted crack-area fraction of 2.164%, closely matching the 2.170% ground-truth crack-area fraction. To contextualize the segmentation system against a detector-oriented alternative, a CSPDarknet53-based YOLO detector was also adapted and retrained on the dataset, reaching 27.5% precision and 20.7% recall on the validation protocol. The results show that instance segmentation is a practical direction for field pavement imagery and aggregate crack-area estimation, while also exposing open challenges in annotation consistency, class imbalance, confounder rejection, and mask-level benchmarking.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2603.16331", "title": "Decoding the Critique Mechanism in Large Reasoning Models", "authors": [ "Hoang Phan", "Quang H. Nguyen", "Hung T. Q. Le", "Xiusi Chen", "Heng Ji", "Khoa D. Doan" ], "github_manual": "", "github_regex": "https://github.com/mail-research/lrm-critique-vectors", "github_paper_md": "", "github_api": "https://github.com/mail-research/lrm-critique-vectors", "github_api_added_by": "user", "project_page": "", "abstract": "Large Reasoning Models (LRMs) exhibit backtracking and self-verification mechanisms that enable them to revise intermediate steps and reach correct solutions, yielding strong performance on complex logical benchmarks. We hypothesize that such behaviors are beneficial only when the model has sufficiently strong ``critique'' ability to detect its own mistakes. This work systematically investigates how current LRMs recover from errors by inserting arithmetic mistakes in their intermediate reasoning steps. Notably, we discover a peculiar yet important phenomenon: despite the error propagating throughout the entire chain-of-thought (CoT) without any verbalized correction, the model still reaches the correct final answer after the thinking process finishes. This recovery implies the existence of an internal mechanism helping the model to detect errors and trigger self-correction, which we refer to as the hidden critique ability. Building on feature space analysis, we identify a highly interpretable critique vector representing this behavior. Extensive experiments across multiple model scales and families demonstrate that steering latent representations with this vector improves the model's error detection capability and enhances the performance of test-time scaling at no extra training cost. Our findings provide a valuable understanding of LRMs' critique behavior, suggesting a promising direction to control and improve their self-verification mechanism. Our code is available at: https://github.com/mail-research/lrm-critique-vectors.", "github": "https://github.com/mail-research/lrm-critique-vectors" }, { "date": "2026-05-26", "arxiv_id": "2605.25971", "title": "Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents", "authors": [ "Haoyi Hu", "Qirong Lyu", "Xianghan Kong", "Weiwen Liu", "Jianghao Lin", "Zixuan Guo", "Yan Xu", "Yasheng Wang", "Weinan Zhang", "Yong Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AgentACE-AI/ProAct", "github_api_added_by": "user", "project_page": "https://agentace-ai.github.io/proact-showcase/", "abstract": "While AI agents demonstrate remarkable capabilities in reasoning and tool use, they remain fundamentally reactive: they compute responses only after explicit user prompts. This paradigm ignores a critical opportunity: the idle time between interactions is largely wasted, leaving agents unable to prepare for future user needs. To bridge this gap, we introduce ProAct, a proactive agent architecture that leverages idle-time compute to anticipate and fulfill likely upcoming user needs. By analyzing evolving dialogue history together with persistent memory, ProAct predicts upcoming needs and iteratively acquires information, allowing the agent to resolve knowledge gaps and prepare evidence before the user initiates a query.To rigorously evaluate proactive capabilities, we also introduce ProActEval, a comprehensive benchmark comprising 200 scenarios across 40 domains, featuring predictable need chains and diverse user cognitive profiles. Empirical results demonstrate significant advantages over reactive baselines. ProAct accelerates task completion by reducing required turns by 14.8%, decreases user effort by 11.7%, and cuts hallucination rates by 28.1% on ProActEval. Furthermore, MemBench evaluations confirm that ProAct achieves state-of-the-art reflective accuracy, underscoring its sustained and robust performance.", "github": "https://github.com/AgentACE-AI/ProAct" }, { "date": "2026-05-26", "arxiv_id": "2605.23699", "title": "CRONOS: Benchmarking Counterfactual Physical Consistency in Video Models", "authors": [ "Le\u00f3n Begiristain", "Olaf D\u00fcnkel", "Adam Kortylewski" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GenIntel/CRONOS-benchmark", "github_api_added_by": "user", "project_page": "https://genintel.github.io/CRONOS/", "abstract": "Video prediction is increasingly viewed as a path toward generalizable world models, yet it remains unclear whether these systems learn underlying causal structure or merely exploit superficial visual correlations for future prediction. We introduce CRONOS, an intervention-based benchmark designed to evaluate counterfactual physical consistency: whether a model's predictions of physical events respond appropriately to controlled changes in the visual input, such as variations of scene context, viewpoint, object appearance, and object category. Built in a photorealistic Unreal Engine environment, CRONOS enables controlled, high-fidelity generation of videos across diverse scenes and dynamics. In contrast to previous benchmarks, CRONOS systematically intervenes on four key factors - viewpoint, scene, object category, and object appearance - while keeping the underlying physical event type, such as a collision, occlusion, or fall, fixed. Our evaluation of recent open-source video generators reveals substantial failures in counterfactual physical consistency: prediction quality for the same physical event type is affected by appearance, environment, and, particularly by viewpoint changes. CRONOS provides a controlled and reproducible testbed for diagnosing how the quality of generated videos changes for different interventions, establishing a concrete target for developing models that perform consistently across changes of multiple conditions. The dataset and code are available at our project page.", "github": "https://github.com/GenIntel/CRONOS-benchmark" }, { "date": "2026-05-26", "arxiv_id": "2605.23264", "title": "Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super Resolution", "authors": [ "Hongbo Wang", "Huaibo Huang", "Pin Wang", "Jinhua Hao", "Chao Zhou", "Ran He" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wafer-bob/ASASR", "github_api_added_by": "user", "project_page": "", "abstract": "Generative priors in Image Super-Resolution (SR) often compromise faithful restoration, we attribute this limitation to a fundamental spectral misalignment between isotropic objectives and the intrinsic natural image manifold. While Direct Preference Optimization offers a path to alignment, its reliance on spectrally flat Gaussian noise fails to distinguish authentic high-frequency details from hallucinations. To bridge this geometric gap, we propose ASASR, a theoretically grounded framework that recasts the generative flow into a Sobolev-induced Riemannian geometry by explicitly coloring the noise transition kernel to mirror natural spectral decay. Driving this geometric alignment, we integrate a parametric adversary grounded in the Riesz Representation Theorem, which synthesizes targeted negative samples equivalent to worst-case Sobolev gradients to direct optimization along the tangent space of plausible structural failures. Extensive evaluations demonstrate that ASASR outperforms leading generative baselines, particularly in preserving spectral consistency and structural fidelity, offering a robust solution that effectively mitigates artifacts.", "github": "https://github.com/wafer-bob/ASASR" }, { "date": "2026-05-26", "arxiv_id": "2605.25160", "title": "SimuWoB: Simulating Real-World Mobile Apps for Fast and Faithful GUI Agent Benchmarking", "authors": [ "Guohong Liu", "Jialei Ye", "Pengzhi Gao", "Wei Liu", "Jian Luan", "Yunxin Liu", "Yuanchun Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Mobile GUI agents powered by large language models have progressed rapidly, creating urgent needs for realistic and comprehensive evaluation. Existing benchmarks prioritize reproducibility but are often limited to open-source apps or file-operation tasks for the difficulty of constructing rewards on real applications, leaving a gap between benchmark settings and real-world usage. Moreover, most benchmarks focus on basic grounding and navigation, with limited coverage of complex, long-horizon interactions. To address these limitations, we introduce SimuWoB, a fully synthetic benchmark for mobile GUI agents with 120 challenging tasks spanning diverse types and difficulty levels. We build a robust virtual environment generation framework that synthesizes high-fidelity tasks and environments, and automatically provides valid rewards for each task. Each environment is deployed as a backend-free webpage accessible via URL, enabling efficient and reproducible evaluation. We conduct comprehensive experiments on several state-of-the-art mobile GUI agents. The average success rate is only 27.92%, dropping to 17.82% on long-horizon tasks, which reveals substantial weaknesses in current agents under complex scenarios. Evaluation result comparison with real-world sample tasks demonstrate that agent assessments based on our synthetic environment generalize well. We further provide diagnostic insights across key capability dimensions and discuss implications for future mobile GUI agent development.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.23889", "title": "HorizonStream: Long-Horizon Attention for Streaming 3D Reconstruction", "authors": [ "Chong Cheng", "Peilin Tao", "Nanjie Yao", "Guanzhi Ding", "Xianda Chen", "Yuansen Du", "Xiaoyang Guo", "Wei Yin", "Weiqiang Ren", "Qian Zhang", "Zhengqing Chen", "Hao Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/3DAgentWorld/HorizonStream", "github_api_added_by": "user", "project_page": "https://3dagentworld.github.io/horizonstream/", "abstract": "Online 3D reconstruction requires estimating camera pose and scene geometry under strict causal and bounded-memory constraints. Existing methods often suffer from drift, jitter, or collapse on long sequences. We trace these failures to a fundamental mismatch. Streaming geometry is inherently temporally heterogeneous, with evidence ranging from short-lived correspondences to persistent global scale. However, current architectures impose uniform and pathological influence patterns. For example, sliding windows enforce hard cutoffs, while ungated recurrence and causal attention cause cache saturation and spike-like attention sinks. To resolve this, we formalize geometric propagation as an evidence influence kernel and propose HorizonStream, a long-horizon Transformer that explicitly factorizes this kernel. For the long-range temporal factor, Geometric Linear Attention learns channel-wise decay rates to enable bounded, multi-timescale propagation of geometric evidence. For the short-range spatial factor, Geometric Local Attention with Spatiotemporal RoPE performs reliable 3D matching while suppressing attention sinks. Finally, Metric Readout Tokens recover stable scale and rigid pose directly from the persistent geometric state. Extensive experiments show that HorizonStream, trained on only 48-frame clips, generalizes stably to sequences exceeding 10,000\\ frames with constant memory and linear time, achieving state-of-the-art streaming 3D reconstruction performance. Project Page: https://3dagentworld.github.io/horizonstream/", "github": "https://github.com/3DAgentWorld/HorizonStream" }, { "date": "2026-05-26", "arxiv_id": "2605.23081", "title": "ThriftAttention: Selective Mixed Precision for Long-Context FP4 Attention", "authors": [ "Joe Sharratt" ], "github_manual": "", "github_regex": "https://github.com/joesharratt1229/ThriftAttention", "github_paper_md": "", "github_api": "https://github.com/joesharratt1229/ThriftAttention", "github_api_added_by": "user", "project_page": "", "abstract": "Efficient attention algorithms are critical to mitigate the quadratic cost of attention in long-context workloads. Prior work utilises block-scaled quantisation techniques on Blackwell GPUs to move attention computation to 4-bit precision to accelerate inference. However, these techniques result in significant quality degradation in long-context settings. We show that the output impact of quantisation error is highly non-uniform and increases with the importance of each query-key interaction, concentrating functionally relevant error in a small number of attention blocks that contain the most important tokens. We propose ThriftAttention, a low-bit attention variant that delivers near-FP16 long-context quality at FP4 inference efficiency. This approach proceeds in two stages. First, a heuristic rapidly selects a small number of important query-key block pairs for FP16 precision. Second, the selected blocks are computed in FP16 and the remaining blocks in FP4, with both paths merged via online softmax into a single output. We demonstrate across long-context benchmarks and model families that by computing only 5% of query-key blocks in FP16, ThriftAttention recovers on average 89.1% of the FP4-to-FP16 performance gap. We show ThriftAttention's advantage grows with sequence length, mitigating the systematic FP4 quality degradation observed at longer contexts. The code is available at https://github.com/joesharratt1229/ThriftAttention.", "github": "https://github.com/joesharratt1229/ThriftAttention" }, { "date": "2026-05-26", "arxiv_id": "2605.10988", "title": "Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation", "authors": [ "Yutszyuk Wong", "Wentai Wu", "Yuen-Ying Yeung", "Weiwei Lin" ], "github_manual": "", "github_regex": "https://github.com/YUK1207/LogMILP", "github_paper_md": "", "github_api": "https://github.com/YUK1207/LogMILP", "github_api_added_by": "user", "project_page": "", "abstract": "Log anomaly detection is a critical task for system operations and security assurance. However, in networked systems at scale, log data are generated at massive scale while instance-level annotations are prohibitively expensive, posing great difficulties to fine-grained anomaly localization. To address this challenge, we propose LogMILP (Log anomaly localization based on Multi-Instance Learning enhanced by prototypes and Perturbation), a weakly supervised framework that enables both bag-level anomaly detection and instance-level anomaly localization using only bag-level labels. Our method guides the model to pinpoint the critical log entries using prototype-guided structural modeling with counterfactual perturbation consistency regularization, thereby improving localization reliability and interpretability under coarse-grained supervision. Experimental results on three public datasets demonstrate that LogMILP achieves competitive detection performance while yielding significantly more reliable instance-level localization. Our code is open-sourced at https://github.com/YUK1207/LogMILP.", "github": "https://github.com/YUK1207/LogMILP" }, { "date": "2026-05-26", "arxiv_id": "2604.13517", "title": "Representation over Routing: Overcoming Surrogate Hacking in Multi-Timescale PPO", "authors": [ "Jing Sun" ], "github_manual": "", "github_regex": "https://github.com/ben-dlwlrma/Representation-Over-Routing", "github_paper_md": "", "github_api": "https://github.com/ben-dlwlrma/Representation-Over-Routing", "github_api_added_by": "user", "project_page": "https://huggingface.co/spaces/ben-dlwlrma/Representation-Over-Routing-Demo", "abstract": "Temporal credit assignment in reinforcement learning has long been a central challenge. Inspired by the multi-timescale encoding of the dopamine system in neurobiology, recent research has sought to introduce multiple discount factors into Actor-Critic architectures, such as Proximal Policy Optimization (PPO), to balance short-term responses with long-term planning. However, this paper reveals that blindly fusing multi-timescale signals in complex delayed-reward tasks can lead to severe algorithmic pathologies. We systematically demonstrate that exposing a temporal attention routing mechanism to policy gradients results in surrogate objective hacking, while adopting gradient-free uncertainty weighting triggers irreversible myopic degeneration, a phenomenon we term the Paradox of Temporal Uncertainty. To address these issues, we propose a Target Decoupling architecture: on the Critic side, we retain multi-timescale predictions to enforce auxiliary representation learning, while on the Actor side, we strictly isolate short-term signals and update the policy based solely on long-term advantages. Rigorous empirical evaluations across multiple independent random seeds in the LunarLander-v2 environment demonstrate that our proposed architecture achieves statistically significant performance improvements. Without relying on hyperparameter hacking, it consistently surpasses the ''Environment Solved'' threshold with minimal variance, completely eliminates policy collapse, and escapes the hovering local optima that trap single-timescale baselines. The source code to reproduce our experiments is publicly available at https://github.com/ben-dlwlrma/Representation-Over-Routing.", "github": "https://github.com/ben-dlwlrma/Representation-Over-Routing" }, { "date": "2026-05-26", "arxiv_id": "2605.21085", "title": "Decoupling Communication from Policy: Robust MARL under Bandwidth Constraints", "authors": [ "Alexi Canesse", "Beno\u00eet Goupil", "Jesse Read", "Sonia Vanier" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alexicanesse/Decoupling-Communication-from-Policy-Robust-MARL-under-Bandwidth-Constraints", "github_api_added_by": "user", "project_page": "", "abstract": "Communication enables coordination in multi-agent reinforcement learning (MARL), but many real-world applications, e.g., search-and-rescue with drone swarms, operate under severe bandwidth constraints. Many communication architectures still expose a coupled bottleneck in which a shared latent representation is used for both policy execution and inter-agent communication. Consequently, reducing message size directly limits the policy's latent space, often leading to significant performance degradation. We address this with two contributions. First, we introduce \u03b2, a normalised per-agent bandwidth budget that unifies sparsity, rounds, and message dimension into a single comparable constraint. Second, we provide SLIM, a minimal architecture that decouples the communication pathway from the policy's latent representation, allowing us to isolate the effect of bandwidth from the effect of policy capacity while benefiting from in-step communication. We evaluate our method on several partially-observable MARL benchmarks, where communication is essential. Our approach achieves state-of-the-art performance and exhibits scalability and robustness under limited communication, with only marginal degradation as bandwidth is reduced.", "github": "https://github.com/alexicanesse/Decoupling-Communication-from-Policy-Robust-MARL-under-Bandwidth-Constraints" }, { "date": "2026-05-26", "arxiv_id": "2605.26099", "title": "Language Models Need Sleep", "authors": [ "Sangyun Lee", "Sean McLeish", "Tom Goldstein", "Giulia Fanti" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Transformer-based large language models are increasingly used for long-horizon tasks; however, their attention mechanism scales poorly with context length. To handle this, we study a sleep-like consolidation mechanism in which a model periodically converts recent context into persistent fast weights before clearing its key-value cache. During sleep, the model performs N offline recurrent passes over the accumulated context and updates the fast weights in its state-space model (SSM) blocks through a learned local rule. During inference, this shifts extra computation to sleep while preserving the latency of wake-time prediction. We test our method on controlled synthetic tasks, including cellular automata and multi-hop graph retrieval, as well as a realistic math reasoning task, on which a regular transformer as well as SSM-attention hybrid models fail. We then show that increasing sleep duration N for our models improves performance, with the largest gains on examples that require deeper reasoning.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.25624", "title": "CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents", "authors": [ "Bowen Wang", "Dunjie Lu", "Junli Wang", "Tianyi Bai", "Shixuan Liu", "Zhipeng Zhang", "Haiquan Wang", "Hao Hu", "Tianbao Xie", "Shuai Bai", "Dayiheng Liu", "Que Shen", "Junyang Lin", "Tao Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xlang-ai/CUA-Gym", "github_api_added_by": "user", "project_page": "https://cua-gym.xlang.ai", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its extension to computer-use agents (CUAs) has been bottlenecked by the scarcity of scalable training data with deterministic rewards. Constructing such data for CUAs requires consistent task instruction, executable environment, and verifiable reward. However, hand-curated benchmarks achieve high reward fidelity but cover few applications and LLM-as-judge-based datasets scale broadly but lack reliable verification. We present CUA-Gym, a scalable pipeline that co-generates task instructions, environment states, and reward functions. Concretely, a Generator agent constructs the initial and golden environment states, and a separate Discriminator agent writes the reward function from the task specification. An orchestrator agent drives the two through iterative rounds upon execution. Generated tuples then pass a final filter combining LLM majority voting and agent rollouts, ensuring quality beyond the per-task adversarial loop. To address the scarcity of training environments, we further synthesize CUA-Gym-Hub, a broad suite of high-fidelity mock web applications grounded in real-world software-use distributions, expanding the scale of CUA RLVR data by magnitude. Using this pipeline, we construct CUA-Gym, a dataset of 32,112 verified RLVR training tuples grounded in 110 environments. Trained with GSPO on CUA-Gym, our CUA-Gym-A3B and CUA-Gym-A17B achieve 62.1% and 72.6% on OSWorld-Verified, outperforming prior open-source CUAs at comparable scales, with performance scaling smoothly in both data volume and environment diversity. The same checkpoints also improve on the held-out WebArena benchmark, indicating transfer beyond the training environments. We will open-source the full synthesis pipeline, dataset, CUA-Gym-Hub environments, and models.", "github": "https://github.com/xlang-ai/CUA-Gym" }, { "date": "2026-05-26", "arxiv_id": "2605.25191", "title": "Injecting Image Guidance into Text-Conditioned Diffusion Models at Inference", "authors": [ "Agata \u017bywot", "Iason Skylitsis", "Thijmen Nijdam", "Zoe Tzifa-Kratira", "Derck Prinzhorn", "Konrad Szewczyk", "Aritra Bhowmik" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thijmennijdam/stable-diffusion-v2", "github_api_added_by": "user", "project_page": "", "abstract": "Text-to-image diffusion models like Stable Diffusion generate high-quality images from text, but lack a way to inject visual guidance (e.g. sketches, styles) at inference without retraining. Existing methods either require computationally expensive fine-tuning or rely on style transfer techniques that risk semantic misalignment with textual prompts. We introduce Visual Concept Fusion (VCF), the first method offering dual conditioning on both an image and text prompt at inference time without any concept-specific training. VCF enables visual concept injection into Stable Diffusion by aligning CLIP image features with the text embedding space. VCF consists of three components: (1) a lightweight aligner that maps image tokens to the text embedding manifold using InfoNCE and cross-attention reconstruction losses, (2) a fusion strategy that preserves both textual and visual semantics, and (3) an optional Prompt-Noise Optimization (PNO) module for test-time refinement. Our experiments demonstrate that VCF successfully transfers visual attributes including style, composition, and color palette from reference images while maintaining prompt adherence. Quantitative results show a trade-off between text alignment (CLIP score) and visual correspondence (LPIPS), with VCF outperforming baselines in reference fidelity.", "github": "https://github.com/thijmennijdam/stable-diffusion-v2" }, { "date": "2026-05-26", "arxiv_id": "2605.21712", "title": "Broadening Access to Transportation Safety Data with Generative AI: A Schema-Grounded Framework for Spatial Natural Language Queries", "authors": [ "Mahdi Azhdari", "Eric J. Gonzales" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Mahdi-Azhdari/road-safety-nlq", "github_api_added_by": "user", "project_page": "", "abstract": "Transportation safety analysis requires integrating crash records, roadway attributes, and geospatial data through GIS-based workflows, but access remains uneven across agencies and community stakeholders. Technical prerequisites create a gap between analytical tools central to safety planning and the practitioners able to use them. Local agencies, school committees, and residents may have safety concerns but limited capacity to retrieve, filter, map, and analyze relevant data. Generative AI offers a way to narrow this divide, but its public-sector use raises questions about reliability, reproducibility, and governance. This paper presents a schema-grounded natural language interface for transportation safety analysis, using a large language model (LLM) to interpret user intent while preserving deterministic, reviewable execution against an authoritative database. User queries are translated into structured semantic frames, validated by a rule-based layer, compiled into a typed directed acyclic graph of spatial operations, and executed against a PostGIS database. This bounded design separates language interpretation from deterministic execution, keeping results reproducible and schema-grounded while removing access barriers. The framework is evaluated using a statewide Massachusetts transportation safety database integrating crash records, roadway attributes, and geospatial layers including schools, bus stops, crosswalks, and municipal boundaries. All queries executed successfully; the validation layer corrects errors in 29% of evaluation queries, reflecting the gap between flexible natural language and strict schema-grounded requirements. The results suggest that combining natural language accessibility with deterministic execution is a practical direction for broadening access to transportation safety data, with implications for trustworthy AI in public-sector planning.", "github": "https://github.com/Mahdi-Azhdari/road-safety-nlq" }, { "date": "2026-05-26", "arxiv_id": "2605.23491", "title": "CoSPlay: Cooperative Self-Play at Test-Time with Self-Generated Code and Unit Test", "authors": [ "Zhangyi Hu", "Chenhui Liu", "Tian Huang", "Jindong Li", "Yang Yang", "Jiemin Wu", "Zining Zhong", "Menglin Yang", "Yutao Yue" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sanae-ai/cosplay", "github_api_added_by": "user", "project_page": "", "abstract": "Recently, Reinforcement Learning with Verifiable Rewards (RLVR) and Test-Time Scaling (TTS) have advanced LLM code generation through executable verification. Yet Ground-Truth Unit Tests (GT UTs) remain a bottleneck: SOTA RLVR methods require them for costly training, while existing TTS methods lose competitiveness without them. This motivates GT-free TTS, where existing methods directly use self-generated UTs to refine and select code candidates. Yet such UTs are often noisy or spuriously coupled with wrong code, and UT quality in turn cannot be validated without reliable code. The key challenge is therefore to jointly improve both. To this end, we present CoSPlay, a GT-free, training-free framework that jointly improves codes and UTs through cooperative self-play. It first explores diverse solution ideas and identifies their potential failure modes to produce discriminative UT ideas. It then uses bidirectional pass-count signals from the Code-UT execution matrix to iteratively prune or fix weak codes and refresh or replace unreliable UTs, letting the two pools co-evolve. Finally, when multiple codes remain tied at the highest pass count, it picks the final code from the largest output-consensus cluster, since correct codes agree on the same inputs while wrong codes diverge. Experiments on four challenging benchmarks show that CoSPlay on Qwen2.5-7B-Instruct improves average BoN from 22.1% to 33.2% and UT accuracy from 14.6% to 78.3%, matching or surpassing the RLVR model CURE-7B. When applied to CURE-7B, it further improves BoN by 5.7%. CoSPlay also generalizes across diverse backbones and outperforms GT-free TTS baselines under comparable token budgets, with continued gains as the budget scales up. These results suggest a scalable inference strategy for competitive code generation without any GT data.", "github": "https://github.com/sanae-ai/cosplay" }, { "date": "2026-05-26", "arxiv_id": "2605.21748", "title": "RankJudge: A Multi-Turn LLM-as-a-Judge Synthetic Benchmark Generator", "authors": [ "Zhenwei Tang", "Zhaoyan Liu", "Rasa Hosseinzadeh", "Tongzi Wu", "Keyvan Golestan", "Jesse C. Cresswell" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/layer6ai-labs/RankJudge", "github_api_added_by": "user", "project_page": "https://layer6ai-labs.github.io/RankJudge/", "abstract": "As interactive LLM-based applications are created and refined, model developers need to evaluate the quality of generated text along many possible axes. For simpler systems, human evaluation may be practical, but in complicated systems like conversational chatbots, the amount of generated text can overwhelm human annotation resources. Model developers have begun to rely heavily on auto-evaluation, where LLMs are also used to judge generation quality. However, existing LLM-as-a-judge benchmarks largely focus on simple Q\\&A tasks that do not match the complexity of multi-turn conversations. We introduce RankJudge, a benchmark generator for evaluating LLM-as-a-judge on multi-turn conversations grounded in reference documents. RankJudge creates pairs of conversations where one conversation has a single flaw injected into one turn. This construction allows paired conversations to be labeled unambiguously as better or worse, and precisely isolates failure categories to individual turns, enabling a strict joint correctness criterion for judging. We implement RankJudge across the domains of machine learning, biomedicine, and finance, evaluate 21 frontier LLM judges, and rank those judges via the Bradley-Terry model. Our formulation also allows ranking each conversation pair with difficulty ratings, which we use to dynamically curate the evaluation slice to reduce label noise, as confirmed via human annotation. We find that judge rankings are stable under partial observability, coarser correctness criteria, and an alternative random-walk rating algorithm.", "github": "https://github.com/layer6ai-labs/RankJudge" }, { "date": "2026-05-26", "arxiv_id": "2605.25189", "title": "Directional Alignment Mitigates Reward Hacking in Reinforcement Learning for Language Models", "authors": [ "Wenlong Deng", "Jiaji Huang", "Kaan Ozkara", "Yushu Li", "Christos Thrampoulidis", "Xiaoxiao Li", "Youngsuk Park" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reward hacking arises when a model improves a proxy reward by exploiting shortcuts rather than solving the intended task. We study this failure mode through the geometry of reinforcement learning updates in language models and argue that hacking emerges when optimization drifts away from a stable low-dimensional learning trajectory. We analyze this drift through dominant singular directions of parameter updates and show that reward-hacking runs exhibit substantially larger directional change than clean runs. Motivated by this observation, we introduce trusted-direction projection, which constrains gradients to remain within a clean reference subspace. Across reward-hacking experiments on mathematical reasoning, the proposed approach delays shortcut exploitation and better preserves task performance.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.24517", "title": "ECHO: Terminal Agents Learn World Models for Free", "authors": [ "Vaishnavi Shrivastava", "Piero Kauffmann", "Ahmed Awadallah", "Dimitris Papailiopoulos" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "CLI agents are the closest thing language models have to an embodied setting: the model emits commands, the terminal executes them, and the returned stream -- stdout, errors, files, logs, and traces -- records the consequences. We argue that this stream is a supervision signal, but standard agent RL discards it: GRPO-style training updates action tokens with sparse outcome-level rewards while ignoring environment responses already in the rollout. Failed rollouts provide little policy-gradient signal despite containing rich evidence about how the environment responds. We introduce ECHO (Environment Cross-entropy Hybrid Objective), a hybrid objective that combines the standard policy-gradient loss on action tokens with an auxiliary loss that trains the policy to predict environment observation tokens resulting from its own actions. ECHO reuses the same forward pass as GRPO, requires no additional rollouts, and turns terminal feedback into dense supervision for all rollouts. ECHO doubles GRPO pass@1 on TerminalBench-2.0: Qwen3-8B improves from 2.70% to 5.17%, and Qwen3-14B from 5.17% to 10.79%. ECHO also produces policies that better predict terminal dynamics, even on trajectories they did not generate: across held-out rollouts, it sharply reduces environment-token cross-entropy while GRPO alone barely changes it. From base Qwen3-8B, ECHO matches expert-SFT-then-GRPO performance on held-out terminal tasks without expert demonstrations, and recovers roughly half of the expert-SFT initialization benefit on TerminalBench-2.0. In some settings, the environment prediction loss alone enables verifier-free self-improvement, allowing policies to improve on unseen OOD tasks by learning only from environment interactions. Together, these results suggest that environment observations are not merely context for future actions, but a dense, on-policy supervision signal already present in every rollout.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.22880", "title": "How Far Will They Go? Red-Teaming Online Influence with Large Language Models", "authors": [ "Daniel C. Ruiz", "Anna Serbina", "Ashwin Rao", "Emilio Ferrara", "Luca Luceri" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SIGNALS-Lab/llm-overton-external", "github_api_added_by": "user", "project_page": "", "abstract": "As large language model (LLM)-based agents increasingly participate in online discourse, red-teaming their capacity to support political influence campaigns is critical for information integrity. In pursuit of this goal, we focus on locally deployed open-source LLMs, as opposed to frontier API-only models, given their superior alignment with the operational constraints of privacy-conscious malicious actors deployed in social media environments. We introduce an empirical red-teaming framework for measuring LLM Overton Windows (OWs), defined as the range of political opinions a model can reliably express on controversial topics, and for quantifying how simple natural-language jailbreaks expand that range. We evaluate more than 30 LLMs spanning 10 model families and five countries of origin. We find systematic asymmetries in political expressivity: open-source LLMs are typically more willing to generate left-leaning social media content, OWs tend to contract inversely to model size, and regional differences are substantial despite uneven representation in the open-source ecosystem. Jailbreak potency also varies sharply across model families, motivating a workflow for identifying effective combinations of jailbreak techniques. Taken together, our results establish a practical framework for auditing the political steerability of open-source LLMs and for helping future researchers design stronger countermeasures against LLM-enabled influence campaigns.", "github": "https://github.com/SIGNALS-Lab/llm-overton-external" }, { "date": "2026-05-26", "arxiv_id": "2605.24213", "title": "Towards Evaluation Engineering: An Empirical Study of ML Evaluation Harnesses in the Wild", "authors": [ "Zhimin Zhao", "Zehao Wang", "Abdul Ali Bangash", "Bram Adams", "Ahmed E. Hassan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhimin-z/EvalEng", "github_api_added_by": "user", "project_page": "https://zhimin-z.github.io/EvalEng", "abstract": "Evaluation harnesses are software systems that orchestrate model evaluation by managing model invocation, data loading, metric computation, and result reporting. Despite their critical role in machine learning infrastructure, their operational challenges and engineering concerns have received limited attention so far. We present an empirical study of 57 evaluation harnesses, deriving a five-stage harness model and classifying 16,560 issues by workflow stage and root cause. Most harness operational challenges concentrate in the Specification stage (41.4% of issues), where harnesses integrate external models, datasets, and scoring judges. The three most frequent root causes of operational challenges are unimplemented features (24.3%), documentation gaps (20.3%), and missing input validation (17.2%), which together account for 61.7% of classified issues, spanning both defects in existing functionality and capability gaps that block intended workflows. Root causes also vary by workflow stage: environment incompatibility and external dependency breakage account for 36.2% of provisioning issues, whereas algorithmic error (25.9%) and validation gap (22.5%) dominate assessment issues. Together, these contributions establish an empirical foundation for treating evaluation engineering as a distinct software engineering concern.", "github": "https://github.com/zhimin-z/EvalEng" }, { "date": "2026-05-26", "arxiv_id": "2605.22818", "title": "MotiMotion: Motion-Controlled Video Generation with Visual Reasoning", "authors": [ "Lee Hsin-Ying", "Hanwen Jiang", "Yiqun Mei", "Jing Shi", "Ming-Hsuan Yang", "Zhixin Shu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/motimotion/motimotion", "github_api_added_by": "user", "project_page": "https://motimotion.github.io/", "abstract": "Current motion-controlled image-to-video generation models rigidly follow user-provided trajectories that are often sparse, imprecise, and causally incomplete. Such reliance often yields unnatural or implausible outcomes, especially by missing secondary causal consequences. To address this, we introduce MotiMotion, a novel framework that reformulates motion control as a reasoning-then-generation problem. To encourage causally grounded and commonsense-consistent interactions, we leverage a training-free vision-language reasoner to refine image-space coordinates of primary trajectories and to hallucinate plausible secondary motions. To further improve motion naturalness, we propose a confidence-aware control scheme that modulates guidance strength, enabling the model to closely follow high-confidence plans while correcting artifacts under low-confidence inputs with its internal generative priors. To support systematic evaluation, we curate a new image-to-video benchmark, MotiBench, consisting of interaction-centric scenes where new events are triggered by motion. Both VLM-based evaluation and a human study on MotiBench demonstrate that MotiMotion produces videos with more plausible object behaviors and interaction, and is preferred over existing approaches.", "github": "https://github.com/motimotion/motimotion" }, { "date": "2026-05-26", "arxiv_id": "2605.26449", "title": "Cross-scale Aligned Supervision for Training GANs", "authors": [ "Sangeek Hyun", "MinKyu Lee", "Jae-Pil Heo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Modern GANs often introduce adversarial supervision on intermediate generator outputs and interpret the resulting multi-stage synthesis as coarse-to-fine hierarchical generation. In this work, we challenge this interpretation. We argue that standard scale-wise adversarial supervision does not construct a proper coarse-to-fine hierarchy: each intermediate image is independently pushed toward the real distribution at its own resolution, but this scale-wise realism does not ensure that outputs across stages represent the identical generated sample. Moreover, the scale-specific image produced at each stage is not used as an explicit refinement target for the subsequent stage. Therefore, its adversarial loss can improve a scale-specific output without constraining later stages to preserve the same sample trajectory, allowing them to move toward a different sample rather than refine the previous output. We refer to this problem as a cross-scale trajectory misalignment problem. To resolve it, we propose CAT, a Cross-scale Aligned Transformer for multi-scale adversarial generation. CAT keeps the discriminator scale-wise, so each intermediate output is evaluated at its own resolution, while adding a simple generator-side consistency regularization that aligns intermediate outputs with the final output. On class-conditional ImageNet-256, CAT-H/2 achieves an FID-50K of 1.56 with one-step inference after only 60 training epochs, outperforming strong one-step GAN and diffusion/flow baselines.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.26230", "title": "Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstruction", "authors": [ "Jin Hyeon Kim", "Jaeeun Lee", "Claire Kim", "Kyoungjin Oh", "Paul Hyunbin Cho", "Jaewon Min", "Yeji Choi", "Jihye Park", "Hyunhee Park", "Minkyu Park", "Seungryong Kim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cvlab-kaist/GARD", "github_api_added_by": "user", "project_page": "https://cvlab-kaist.github.io/GARD/", "abstract": "Multi-view 3D reconstruction has achieved remarkable progress with the advent of feed-forward 3D reconstruction models. However, these models are typically trained and evaluated under ideal, degradation-free imaging conditions, whereas real-world observations often contain degradations that differ significantly from such settings. Improving robustness for multi-view 3D reconstruction under degraded conditions therefore remains an important challenge. We present Geometry-Aware Representation Denoising (GARD), a novel framework that performs diffusion-based multi-view restoration directly in the feature space of a feed-forward 3D reconstruction model. This design exploits the geometry-aware feature representations of the 3D reconstructor to effectively recover accurate scene geometry. Furthermore, by employing an additional RGB image decoder, the refined representations can also be used to restore high-quality RGB images, thereby enabling the simultaneous recovery of 3D scene geometry and high-quality imagery. Comprehensive experiments on the Depth Anything 3 (DA3) benchmark demonstrate the effectiveness of the proposed GARD framework.", "github": "https://github.com/cvlab-kaist/GARD" }, { "date": "2026-05-26", "arxiv_id": "2605.26502", "title": "PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design", "authors": [ "Runtian Wang", "Renhao Xue", "Baige Chen", "Hao Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wang-henry4/prism", "github_api_added_by": "user", "project_page": "https://www.prism-playground.com/", "abstract": "The inverse problem of multilayer thin-film optical coatings design represents a complex combinatorial-continuous optimization challenge. We present PRISM (Position-encoded Regressive Inverse Spectral Model), a unified decoder-only autoregressive transformer that streamlines this process by jointly predicting discrete material selection and continuous thickness regression within a single backbone. PRISM introduces two primary architectural innovations: (1) spectrum prefix conditioning, which utilizes standard prefix tokens for in-context target injection, and (2) cumulative-depth Rotary Position Embeddings, which encode continuous thickness directly into the positional representation to preserve the physical spatial relationships of the stack. Our benchmarks demonstrate that a PRISM-13M model reduces MAE by over 50\\% compared to other transformer baselines while utilizing only one-fifth of the parameters. Furthermore, a 44M-parameter variant achieves state-of-the-art performance (MAE = 0.010) on our in-distribution validation benchmark and operates significantly faster than simulated annealing, offering a highly efficient alternative to classical optimization methods.", "github": "https://github.com/wang-henry4/prism" }, { "date": "2026-05-26", "arxiv_id": "2605.26535", "title": "Recursive Flow Matching", "authors": [ "Jiahe Huang", "Sihan Xu", "Sharvaree Vadgama", "Rose Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://jhhuangchloe.github.io/RecFM/", "abstract": "Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving high physical accuracy without incurring high computational cost remains a fundamental challenge, as existing approaches face a critical speed-fidelity trade-off. In this work, we introduce Recursive Flow Matching (RecFM), a generative framework for forecasting complex spatiotemporal dynamics. RecFM enforces self-consistency to align trajectories across discretization scales, reducing discretization errors and improving performance across metrics for physics-based tasks. To our knowledge, this is the first method to achieve high-fidelity one- and few-step (2-4 step) dynamic generation for scientific systems with performance comparable to state-of-the-art multi-step solvers. Across challenging scientific benchmarks, RecFM achieves up to a 20times speedup over leading diffusion-based emulators while improving predictive accuracy. Furthermore, RecFM reduces mean squared error by over 15% compared to vanilla flow matching, offering a scalable and efficient solution for real-time scientific emulation.", "github": "" }, { "date": "2026-05-26", "arxiv_id": "2605.25535", "title": "Personalize-then-Store: Benchmarking and Learning Personalized Memory for Long-horizon Agents", "authors": [ "Yeonjun In", "Wonjoong Kim", "Sangwu Park", "Kanghoon Yoon", "Chanyoung Park" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yeonjun-in/PerMemBench", "github_api_added_by": "user", "project_page": "", "abstract": "Existing large language model (LLM) based memory systems apply universal, static policies that overlook a fundamental reality: the contexts that are worth storing in memory are different across users. This misalignment wastes limited memory budget on transient interactions while failing to preserve critical context for long horizon tasks. To address this gap, we investigate an underexplored question: can LLM based memory systems learn personalized memory policies? We introduce PerMemBench, the first benchmark for evaluating personalized memory systems, featuring multi year, multi domain interaction histories across diverse user personas. We further present the first empirical study of memory personalization, proposing session level storage gating, a lightweight framework that selectively bypasses memory operations for transient sessions. Our study confirms that personalization yields substantial retention gains under perfect gating, yet reveals that accurate gating remains an open and critical challenge.", "github": "https://github.com/yeonjun-in/PerMemBench" }, { "date": "2026-05-27", "arxiv_id": "2605.23271", "title": "EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation", "authors": [ "Songlin Yang", "Haobin Zhong", "Ruilin Zhang", "Xiaotong Zhao", "Shuai Li", "Kai Zheng", "Xuyi Yang", "Zhe Wang", "Zhenchen Tang", "Yang Li", "Bohai Gu", "Zhengwei Peng", "Yidan Huang", "Mengzhou Luo", "Yihang Bo", "Dalu Feng", "Yujia Zhang", "Juntao Ma", "Ruiqi Wang", "Lvmin Zhang", "Yuwei Guo", "Frank Guan", "Maneesh Agrawala", "Hongbo Fu", "Alan Zhao", "Anyi Rao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The rapid evolution of generative video foundation models has propelled the field toward professional-grade cinematic synthesis. To achieve such demanding quality, the community transitions towards Reinforcement Learning (RL) and agentic workflows. However, reliable evaluation has emerged as a critical bottleneck. Existing benchmarks predominantly evaluate ''whether it is right'' (basic prompt-following) while fundamentally neglecting ''whether it is good'' (cinematic quality, acting, and aesthetics). Furthermore, current automated metrics lack the domain-specific rigor required to provide trustworthy signals, creating a severe credibility gap between human aesthetic perception and machine scoring. To bridge this gap, we introduce EvalVerse, a comprehensive, pipeline-aware, and expert-calibrated evaluation framework. We treat video generation assessment not merely as an engineering task, but as a core scientific problem: the systematic digitization of subjective cinematic expertise. First, we organize domain knowledge into an evaluation taxonomy aligned with the professional filmmaking workflow (pre-production, production, and post-production). Second, we distill human expert judgments into a curated dataset with large-scale human annotations. Third, we inject this knowledge into Vision-Language Models (VLMs) through an expert-calibrated fine-tuning strategy, enabling the VLM to perform explicit Chain-of-Thought reasoning. Compared to previous works, EvalVerse not only retains compatibility with foundational ''rightness'' metrics, but also significantly expands the criteria to ''goodness'' and broaden the task coverage to complex multi-shot sequencing and audio-visual integration. Consequently, by providing granular diagnostic signals, EvalVerse transcends a static leaderboard and establishes a fundamental infrastructure for future work, such as reward models and evaluator agent.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.26244", "title": "LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV", "authors": [ "Tengfei Liu", "Yang Shi", "Xuanyu Zhu", "Jiafu Tang", "Liu Yang", "Qixun Wang", "Zhuoran Zhang", "Yuqi Tang", "Fengxiang Wang", "Yuhao Dong", "Xinlong Chen", "Bozhou Li", "Bohan Zeng", "Yue Ding", "Xiaohan Zhang", "Jialu Chen", "Haotian Wang", "Yuanxing Zhang", "Pengfei Wan", "Leye Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/pkucs-Ltf/LongAV-Compass", "github_api_added_by": "user", "project_page": "", "abstract": "Audio-visual generation is rapidly advancing from short clips to minute-long content, while existing evaluation protocols remain largely confined to short-form settings. Existing benchmarks primarily focus on 5--10 second text-conditioned generation and rarely support unified evaluation across text, image, and video conditioning modalities. Moreover, they provide limited insight into how identity consistency, narrative coherence, and audio-visual alignment degrade over extended temporal horizons. To bridge this gap, we introduce LongAV-Compass, a systematic benchmark for minute-long audio-visual generation. LongAV-Compass contains 284 curated test cases spanning text-to-audio-video (T2AV), image-to-audio-video (I2AV), and video-to-audio-video (V2AV), organized by application scenario and generation complexity. The benchmark combines taxonomy-guided benchmark construction with a unified evaluation framework that integrates MLLM-assisted assessment with complementary perceptual and multimodal metrics, including DINO-v2, ArcFace, CLIP, and ImageBind. The framework evaluates more than 20 fine-grained dimensions covering within-segment quality, cross-segment consistency, global narrative coherence, semantic alignment, and audio-visual synchronization. Through experiments on 11 representative models together with human-alignment validation, LongAV-Compass provides a diagnostic testbed for analyzing the limitations of current systems in sustaining coherent, semantically aligned, and temporally consistent minute-scale audio-visual generation across diverse input modalities.", "github": "https://github.com/pkucs-Ltf/LongAV-Compass" }, { "date": "2026-05-27", "arxiv_id": "2605.25437", "title": "Does Seeing More Mean Knowing More? Mono-Anchored Advantage Normalization for Multi-Source Visual Reasoning", "authors": [ "Fanhu Zeng", "Zhicong Luo", "Zefan Wang", "You Li", "Chi Chen", "Maosong Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AI9Stars/MARS", "github_api_added_by": "user", "project_page": "", "abstract": "Visual reasoning through reinforcement learning with verifiable rewards (RLVR) has achieved remarkable progress. However, when dealing with multi-source inputs, existing approaches tend to treat them as a mere accumulation of information, lacking explicit mechanisms to distinguish whether integrating additional sources yields information gain or introduces interference. Therefore, they struggle to effectively model dynamic interaction when integrating multiple sources, particularly when they differ significantly in physical properties and semantics, e.g., infrared and depth, leading to inferior performance to mono-source reasoning when a certain source holds the dominant signal. To address this issue, we propose MARS, a novel mono-anchored multi-source reasoning framework that models each visual modality as an independent information source. Specifically, by treating mono-source rewards as dynamic anchors, our method explicitly incorporates the information gain introduced by multi-source fusion into advantage normalization and adaptively emphasizes mutual promotion between sources while suppressing potential noise or conflicts during RLVR. From theoretical analysis, our method effectively quantifies information gain introduced by multi-source integration in gradient estimation, enabling consistent modality regulation. Empirical results also show impressive 3.2% and 4.9% performance gains on GRPO and DAPO across diverse datasets, confirming effectiveness of our method.", "github": "https://github.com/AI9Stars/MARS" }, { "date": "2026-05-27", "arxiv_id": "2605.26952", "title": "Efficient Agentic Reinforcement Learning with On-Policy Intrinsic Knowledge Boundary Enhancement", "authors": [ "Dingwei Chen", "Zefang Zong", "Zhipeng Ma", "Leo Luo", "Yang Li", "Chengming Li", "Peng Chen", "Jie Jiang" ], "github_manual": "", "github_regex": "https://github.com/CuSO4-Chen/AKBE", "github_paper_md": "", "github_api": "https://github.com/CuSO4-Chen/AKBE", "github_api_added_by": "user", "project_page": "", "abstract": "Agentic reinforcement learning (RL) has proven effective for training LLM-based agents with external tool-use capabilities. However, we identify that agentic RL training induces increasing redundant tool calls and blurs the model's intrinsic knowledge boundary, where the model fails to distinguish when tools are needed versus when parametric knowledge suffices. Existing solutions based on reward shaping create coarse-grained optimization targets that tend to incentivize indiscriminate tool-call suppression, leading to reward hacking. In this paper, we propose AKBE (Agentic Knowledge Boundary Enhancement), an on-policy method that dynamically probes the model's intrinsic knowledge boundary through dual-path (with-tool and no-tool) rollouts during training. We define the knowledge boundary as the per-instance determination of whether tools are required and the minimum tool calls necessary. By comparing correctness across paths, AKBE categorizes trajectories and constructs targeted supervisory signals that guide efficient tool-use patterns for each question. These signals are integrated seamlessly into the agentic RL training loop. Experiments on seven QA benchmarks demonstrate that AKBE improves task accuracy by +1.85 on average and reduces tool calls by 18% over standard agentic RL, yielding 25% higher tool productivity without any accuracy-efficiency trade-off. Further analysis suggests its plug-and-play compatibility across different RL algorithms and the mechanism of each signal category. Our code is available at https://github.com/CuSO4-Chen/AKBE.", "github": "https://github.com/CuSO4-Chen/AKBE" }, { "date": "2026-05-27", "arxiv_id": "2605.26494", "title": "The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence", "authors": [ "MiniMax", "Aili Chen", "Aonian Li", "Baichuan Zhou", "Bangwei Gong", "Binyang Jiang", "Boji Dan", "Changqing Yu", "Chao Wang", "Cheng Ma", "Cheng Zhong", "Cheng Zhu", "Chengjun Xiao", "Chengyi Yang", "Chengyu Du", "Chenyang Zhang", "Chi Zhang", "Chuangyi Huang", "Chunhao Zhang", "Chunhui Du", "Chunyu Zhao", "Congchao Guo", "Da Chen", "Deming Ding", "Dianjun Sun", "Dongyu Zhang", "Enhui Yang", "Fei Yu", "Guang Zheng", "Guodong Zheng", "Guohong Li", "Haichao Zhu", "Haigang Zhou", "Haimo Zhang", "Han Ding", "Hao Zhang", "Haohai Sun", "Haolin Lyu", "Haonan Lu", "Haoyu Wang", "Huajie Shi", "Huiyang Li", "Jiacheng Chen", "Jian Zhang", "Jiaqi Zhuang", "Jiaren Cai", "Jiaxin Pan", "Jiayao Li", "Jiayuan Song", "Jichuan Zhang", "Jie Wang", "Jihao Gu", "Jin Zhu", "Jingwei Dong", "Jingyang Li", "Jingyu Zhang", "Jingze Zhuang", "Jinhao Tian", "Jinli Liu", "Jinyi Hu", "Jun Tao", "Jun Zhang", "Junbin Ruan", "Junhao Xu", "Junjie Yan", "Junteng Liu", "Junxian He", "Kang Xu", "Ke Ji", "Ke Yang", "Kecheng Xiao", "Keyu Duan", "Keyu Li", "Le Han", "Letian Ruan", "Li Yuan", "Lianfei Yu", "Liheng Feng", "Lijie Mo", "Lin Li", "Lingye Bao", "Lingyu Yang", "Lingyuan Zhou", "Loki", "Lu Chen", "Lunbin Ceng", "Ming Li", "Ming Zhong", "Mingliang Tao", "Mingyuan Chi", "Mujie Lin", "Nan Hu", "Ningxin Chen", "Peiyin Zhu", "Peng Gao", "Pengcheng Gao", "Pengfei Li", "Penglin Li", "Pengyu Zhao", "Qibin Ren", "Qidi Xu", "Qihan Ren", "Qile Li", "Qin Wang", "Quanliang Chen", "Qunhong Ceng", "Rong Tian", "Rui Dong", "Ruitao Leng", "Ruize Zhang", "Shanqi Liu", "Shaoyu Chen", "Sheng Jia", "Shun Yao", "Shuoran Zhao", "Shuqi Yu", "Sichen Li", "Sicheng Pan", "Songquan Zhu", "Tengfei Li", "Tian Xie", "Tiancheng Qin", "Tianrun Liang", "Wei Liu", "Weiqi Xu", "Weitao Li", "Weixiang Chen", "Weiyu Cheng", "Weiyu Zhang", "Wenhu Chen", "Wenqian Zhao", "Xiancai Chen", "Xiangjun Song", "Xiangyuan Wang", "Xiao Luo", "Xiao Su", "Xiaobo Li", "Xiaodong Han", "Xiaojie Wu", "Xihao Song", "Xingyi Han", "Xinyu Guan", "Xuan Lu", "Xun Zou", "Xunhao Lai", "Xutong Li", "Yan Gong", "Yang Wang", "Yang Xu", "Yangsen Wang", "Ye Tang", "Yicheng Chen", "Yinran Qiu", "Yiqi Shi", "Yiting Guo", "Yiwen Huang", "Yixuan Wang", "Yongyi Hu", "Yu Gao", "Yu Zhang", "Yuanxiang Ying", "Yuanzhen Zhang", "Yubo Wang", "Yuchen Song", "Yufeng Yang", "Yuhang Meng", "Yuhang Miao", "Yuhao Li", "Yujie Liu", "Yulin Hu", "Yunan Huang", "Yunji Li", "Yunyi Huang", "Yusen Zhang", "Yusu Hong", "Yutao Xie", "Yutong Zhang", "Yuwen Liao", "Yuxuan Shi", "Yuze Wenren", "Zebin Li", "Zehan Li", "Zejian Luo", "Zeyu Jin", "Zeyuan Sun", "Zhanpeng Zhou", "Zhaochen Su", "Zhendong Li", "Zhengmao Zhu", "Zhengyuan Peng", "Zhenhua Fan", "Zhi Zhang", "Zhichao Xu", "Zhiheng Lv", "Zhikang Xu", "Zhitao He", "Zhiwei He", "Zhongyuan Li", "Zibo Gao", "Zijia Wu", "Zijian Song", "Zijian Zhou", "Zijun Sun", "Zishan Huang", "Ziying Chen", "Ziyue Ge" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The flagship M2 contains 229.9B total parameters with only 9.8B activated per token. Designed end-to-end for agentic deployment, the M2 series rests on three components: (i) agent-driven data pipelines producing large-scale, verifiable trajectories across agentic coding and agentic cowork, each grounded in an executable workspace and an artifact-aligned reward; (ii) Forge, a scalable agent-native RL system that adapts to long-horizon agent trajectories, paired with windowed-FIFO scheduling, prefix-tree merging, inference optimization, and a clean training-inference-agent decoupling that supports both white-box and black-box agents; (iii) the latest M2.7 checkpoint takes an early step toward self-evolution -- autonomously debugging training runs and modifying its own scaffold. Across M2 through M2.7, this combination translates a mini-activation footprint into frontier-tier performance on agentic coding, deep search, office-task, and reasoning benchmarks.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.27367", "title": "SpatialBench: Is Your Spatial Foundation Model an All-Round Player?", "authors": [ "Haosong Peng", "Hao Li", "Jiaqi Chen", "Yuhao Pan", "Runmao Yao", "Yalun Dai", "Fushuo Huo", "Fangzhou Hong", "Zhaoxi Chen", "Haozhao Wang", "Dingwen Zhang", "Ziwei Liu", "Wenchao Xu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Ropedia/SpatialBench", "github_api_added_by": "user", "project_page": "https://ropedia.github.io/SpatialBench/", "abstract": "While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round players capable of generalizing robustly across diverse downstream tasks, arbitrary viewpoints, shifting scene domains, varying input densities, and specific hardware constraints? Answering this overarching question requires a holistic assessment, yet current models are mainly evaluated on specific domains for which they were specifically designed or trained. Such evaluations are intrinsically limited by narrow paradigm coverage, limited scene domains, and arbitrary frame sampling, making it fundamentally difficult to assess their true generalization capabilities. To address this gap, we present SpatialBench, a cross-paradigm, domain-diverse benchmark for spatial foundation models with deterministic sampling. SpatialBench features unprecedented scale and rigorous deterministic design, comprising 19 datasets and 546 scenes across 5 diverse spatial domains. It comprehensively evaluates 41 models across 6 paradigms on 5 task suites under 4 different input density settings. Our extensive evaluation reveals that current models are not yet all-round players, and uncovers crucial insights for future advancement. Specifically, we demonstrate that full-context attention maximizes accuracy while bounded-memory strategies unlock long-sequence scalability. Moreover, our empirical evaluations in challenging embodied and egocentric tasks demonstrate that strict domain alignment and high data quality are far more critical to performance than simple dataset scaling. Furthermore, to address the largest data gap identified in our analysis, we go beyond evaluation by introducing a large-scale dataset, DA-Next-5M, and a strong baseline model, DA-Next, pushing the boundaries of spatial representation learning.", "github": "https://github.com/Ropedia/SpatialBench" }, { "date": "2026-05-27", "arxiv_id": "2605.26114", "title": "MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research", "authors": [ "Dingbang Wu", "Rui Hao", "Haiyang Wang", "Shuzhe Wu", "Han Xiao", "Zhenghong Li", "Bojiang Zhou", "Zheng Ju", "Zichen Liu", "Lue Fan", "Zhaoxiang Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Purewhiter/mobilegym", "github_api_added_by": "user", "project_page": "https://mobilegym.github.io", "abstract": "We present MobileGym, a browser-hosted, lightweight, fully controllable environment for everyday mobile use, targeting interaction fidelity without replicating proprietary backends. It enables two capabilities previously out of reach for everyday apps: verifiable outcome signals through deterministic state-based judging over structured JSON state, and scalable online RL through low-cost parallel rollouts. The full environment state is captured, configured, forked, and compared as structured JSON, and a single server can host hundreds of parallel instances, with about 400 MB memory per instance and about 3 s cold start. A layered state model and a declarative task-definition framework keep state programmability and task creation practical at scale, and a single programmatic judging mechanism delivers both deterministic evaluation verdicts and dense RL rewards. The accompanying MobileGym-Bench provides 416 parameterized task templates, including 256 test and 160 train templates, over 28 apps, with deterministic judges and a structured AnswerSheet protocol that avoids free-text matching failures. In a Sim-to-Real case study, GRPO on Qwen3-VL-4B-Instruct gains +12.8 percentage points on the 256-task test set, and on a 59-task real-device signal subset, real-device execution retains 95.1% of the simulation-side training gain. Project page: https://mobilegym.github.io.", "github": "https://github.com/Purewhiter/mobilegym" }, { "date": "2026-05-27", "arxiv_id": "2605.27365", "title": "LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding", "authors": [ "Shihao Wang", "Shilong Liu", "Yuanguo Kuang", "Xinyu Wei", "Yangzhou Liu", "Zhiqi Li", "Yunze Man", "Guo Chen", "Andrew Tao", "Guilin Liu", "Jan Kautz", "Lei Zhang", "Zhiding Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://research.nvidia.com/labs/lpr/locate-anything/", "abstract": "Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into multiple 1D tokens that are learned and decoded largely independently. This token-by-token decoding mismatches the coupled structure of box geometry and creates a practical inference bottleneck due to strictly sequential generation. We introduce LocateAnything, a unified generative grounding and detection framework based on Parallel Box Decoding (PBD). By decoding geometric elements such as bounding boxes and points as atomic units in a single step, LocateAnything preserves intra-box geometric coherence and unlocks substantial parallelism. We show that PBD improves both decoding throughput and localization accuracy. We further develop a scalable data engine and curate LocateAnything-Data, a large-scale dataset with more than 138 million training samples, substantially increasing data diversity for high-precision localization. Extensive evaluations show that LocateAnything advances the speed-accuracy frontier, achieving significantly higher decoding throughput while improving high-IoU localization quality across diverse benchmarks. The results highlight the complementary benefits of Parallel Box Decoding and large-scale training data in enabling efficient and precise unified visual grounding and detection.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.26111", "title": "Squeezing Capacity from Multimodal Large Language Models for Subject-driven Generation", "authors": [ "Shuhong Zheng", "Aashish Kumar Misraa", "Yu-Teng Li", "Yu-Jhe Li", "Igor Gilitschenski" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://zsh2000.github.io/squeeze-mllm-subject-gen/", "abstract": "Subject-driven image generation aims to synthesize new images that preserve the identity of the given subject while following textual instructions. Existing approaches often encode text and reference images separately. This limits cross-modal reasoning abilities and causes copy-paste artifacts. Recent frameworks that connect multimodal models and diffusion models improve instruction following, but largely overlook identity preservation. To address these limitations, we condition diffusion models on Multimodal Large Language Models (MLLMs) that jointly encode text and reference images, and augment it with VAE-based identity conditioning. A novel Dual Layer Aggregation (DLA) module is designed to aggregate multi-level MLLM features for optimal conditioning, and a multi-stage denoising strategy is applied to progressively balance the semantic information from MLLM and fine-detail identity from VAE during inference. Extensive experiments demonstrate that our approach harmonizes multimodal understanding with identity preservation, mitigates copy-paste issues, and achieves superior performance regarding human preference on subject-driven image generation. Our project website is available at https://zsh2000.github.io/squeeze-mllm-subject-gen/.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.24219", "title": "Beyond Final Answers: Auditing Trajectory-Level Hallucinations in Multi-Agent Industrial Workflows", "authors": [ "Harshada Badave", "Santosh Borse", "Andrea Gomez", "Harshitha Narahari", "Sara Carter", "Vishwa Bhatt", "Aishani Rachakonda", "Shuxin Lin", "Dhaval Patel" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly deployed as autonomous agents that reason, use tools, and act over multiple steps. Yet most hallucination benchmarks still evaluate only the final output, missing failures that originate in intermediate Thought-Action-Observation steps. We present Trajel, a dataset and evaluation framework for auditing trajectory-level hallucinations in multi-agent industrial workflows. Trajel introduces a five-type hallucination taxonomy (factual, referential, logical, procedural, and scope-based) over expert-annotated agent traces from AssetOpsBench. We benchmark supervised detection models at the subtask, trajectory, and long-context levels. Our results show that the most common failure modes are missed by existing benchmarks, that nearly half of hallucinated trajectories involve multiple types at once, and that automated detectors with high binary accuracy still misclassify the subtlest types. Trajectory-aware detection significantly outperforms standard post-hoc verification, making taxonomy-grounded evaluation necessary for safer agentic deployment.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.26632", "title": "RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models", "authors": [ "Xing Cong", "Hanlin Tang", "Kan Liu", "Lan Tao", "Lin Qu", "Chenhao Xie" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Diffusion Transformers (DiT) achieve strong performance in image generation but incur substantial inference costs. While prior work has reduced this cost via quantization and distillation, semi-structured sparsity, which can nearly halve FLOPs, remains underexplored. A key reason is that most existing approaches focus on weight sparsification, and pruning 50% of the weights can remove critical model capacity and degrade generation quality. Our study, however, shows that DiT activations are intrinsically sparse and significantly more robust to N:M semi-structured sparsification than weights. Motivated by this observation, we advocate a paradigm shift from weight sparsification to activation sparsification. We propose RT-Lynx, which applies N:M sparsification to activations and incorporates error-compensation techniques to mitigate accuracy loss. We further implement highly optimized CUDA kernels tailored to this setting, achieving up to a 1.55x speedup on average in linear layers. Extensive experiments across multiple diffusion models demonstrate that our method preserves the generation quality of the original models while substantially accelerating inference.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.27235", "title": "MRT: Masked Region Transformer for Layered Image Generation and Editing at Scale", "authors": [ "Zhicong Tang", "Zhao Zhang", "Jingye Chen", "Mohan Zhou", "Yifan Pu", "Yuchi Liu", "Yalong Bai", "Ethan Smith", "Yuhui Yuan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Layered image generation and editing is a fundamental capability that enables layer-wise reuse, editing, and composition of generated visual content, analogous to word-level editing in natural language. Despite its importance, this remains an underexplored area at scale. To address this gap, we present MRT, a 20B-parameter masked region diffusion model tailored for multi-layer transparent image generation and editing, trained on over 10M multilingual design samples spanning diverse aspect ratios and textual prompts. To fully leverage this scale, we make two key technical contributions. First, we unify three complementary tasks including text-to-layers, image-to-layers, and layers-to-layers within a shared masked region diffusion framework, where selective token masking enables flexible layer-wise generation and editing. Second, to enable overflow layer generation, we introduce an overflow-aware canvas layer that handles boundary inconsistencies and supports semi-transparent background synthesis, enabling complete editable layers extending beyond visible canvas boundaries. Additionally, we apply diffusion distillation to achieve 8-step, real-time multi-layer generation with minimal quality degradation. Extensive experiments demonstrate that our framework substantially outperforms prior state-of-the-art approaches, including various commercial systems, across all three tasks, establishing a new benchmark for multi-layer transparent image generation. Notably, our model significantly outperforms the concurrent Qwen-Image-Layered model in image-to-layers quality according to user-study results, while achieving 10-100\\times faster inference and reducing activation GPU memory consumption by 50-90\\% during image-to-layer inference.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.26895", "title": "Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models", "authors": [ "Mingze Wang", "Shuchen Zhu", "Yuxin Fang", "Binghui Li", "Kai Shen", "Shu Zhong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Normalization layers in modern large language models (LLMs) consist of a deterministic normalization operation and a learnable scale vector. While the normalization operation has been extensively studied, the scale vector remains poorly understood despite its ubiquitous use. In this work, we present a systematic study of scale vectors in LLMs from the perspectives of expressivity, optimization, and architectural structure. First, we show empirically that although scale vectors constitute only a negligible fraction of model parameters, removing them substantially degrades LLM pre-training. Our theory further shows that, in Pre-Norm architectures, scale vectors do not increase expressivity; instead, they improve optimization through a self-amplifying preconditioning effect on subsequent linear mappings. Second, we investigate the role of weight decay for scale vectors. By distinguishing Input-Norm and Output-Norm layers, we theoretically show that weight decay is beneficial for the former but harmful for the latter, due to their distinct roles in optimization and expressivity. Third, motivated by this understanding, we propose three lightweight and complementary improvements to scale vectors: branch-specific heterogeneity, improved placement around linear mappings, and magnitude-direction reparameterization. Both theory and experiments show that each improvement yields consistent gains. Finally, we combine these improvements into a unified scale-vector strategy and evaluate it through extensive LLM pre-training experiments on dense and mixture-of-experts models ranging from 0.12B to 2B parameters, across multiple optimizers and learning rate schedules, under industrial-scale token budgets. The unified strategy consistently achieves lower terminal loss than well-tuned baselines and exhibits more favorable scaling behavior, while adding negligible parameter and computational overhead.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.27366", "title": "MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation", "authors": [ "Huawei Lin", "Peng Li", "Jie Song", "Fuxin Jiang", "Tieying Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language model (LLM) agents rely on reusable skills to solve complex tasks. However, existing skill creation approaches treat skills as isolated and static artifacts, limiting their reusability, reliability, and long-term improvement. We propose MUSE-Autoskill Agent (Memory-Utilizing Skill Evolution), a skill-centric agent framework that lets agents continuously improve their task-solving capability by creating, reusing, and refining skills under a unified lifecycle (creation, memory, management, evaluation, and refinement). Our framework enables agents to create skills on demand, store and reuse them across tasks, organize and select them efficiently, and evaluate them through unit tests and runtime feedback for continuous refinement. We further introduce skill-level memory that accumulates experience for each skill across tasks, enabling more effective reuse and adaptation over time. Experiments on SkillsBench provide initial evidence that lifecycle-managed skills can improve task success, efficiency, reuse, and cross-agent transfer, highlighting the importance of treating skills as long-lived, experience-aware, and testable assets.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.27358", "title": "MobileMoE: Scaling On-Device Mixture of Experts", "authors": [ "Yanbei Chen", "Hanxian Huang", "Ernie Chang", "Jacob Szwejbka", "Digant Desai", "Zechun Liu", "Vikas Chandra", "Raghuraman Krishnamoorthi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Mixture-of-Experts (MoE) has become the de facto architecture for hundred-billion-parameter language models, yet its advantages at sub-billion scales for on-device deployment remain largely unexplored. To close this gap, we present MobileMoE, a family of on-device MoE language models with sub-billion active parameters (0.3-0.9B active and 1.3-5.3B total) that establish a new Pareto frontier for on-device LLMs. We first formulate an on-device MoE scaling law that jointly optimizes MoE architecture under mobile memory and compute constraints, identifying an on-device sweet spot - moderate sparsity with fine-grained and shared experts - that is simultaneously memory and compute-optimal. Building on the derived architectures, we train MobileMoE with a four-stage recipe covering pre-training, mid-training, instruction fine-tuning, and quantization-aware training, all on open-source datasets. Across 14 benchmarks, MobileMoE matches or exceeds leading on-device dense LLMs with 2-4times fewer inference FLOPs, and matches or surpasses the state-of-the-art MoE OLMoE-1B-7B with up to 60% fewer parameters. To bridge the last mile to mobile deployment, we provide the first efficient MoE inference on commodity smartphones with comprehensive on-device profiling. At comparable INT4 weight memory, MobileMoE-S delivers 1.8-3.8times faster prefill and 2.2-3.4times faster decode than the dense baseline MobileLLM-Pro.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.25979", "title": "LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence", "authors": [ "Xiang An", "Yin Xie", "Feilong Tang", "Yunyao Yan", "Huajie Tan", "Didi Zhu", "Changrui Chen", "Xiuwei Zhao", "Bin Qin", "Kaicheng Yang", "Yifei Shen", "Yuanhan Zhang", "Kaichen Zhang", "Wenkang Zhang", "Zheng Cheng", "Nansen Zhang", "Chunsheng Wu", "Chunjiang Ge", "Zimin Ran", "Dehua Song", "Chunyuan Li", "Shikun Feng", "Ming Hu", "Zhangquan Chen", "Junbo Niu", "Bo Li", "Ziyong Feng", "Ziwei Liu", "Zongyuan Ge", "Jiankang Deng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-2", "github_api_added_by": "user", "project_page": "https://evolvinglmms-lab.github.io/LLaVA-OneVision-2/", "abstract": "We introduce LLaVA-OneVision-2 (LLaVA-OV-2), the most capable vision-language model in the LLaVA-OneVision series to date, achieving superior performance across a broad range of multimodal benchmarks. The model builds on a native OneVision-Encoder and incorporates Windowed Attention for efficient local computation while maintaining native resolution. Its key advance is codec-stream tokenization: it treats compressed video as a continuous bit-cost stream, where bit-cost dynamics determine adaptive temporal groups, and motion-residual cues select salient spatial evidence into compact visual canvases. This allocation concentrates a limited token budget on event-bearing content, enabling more stable long-video token compression than fixed groups of pictures. A shared 3D RoPE further places codec canvases, sampled frames, and images in a unified spatiotemporal coordinate system. Furthermore, we build the LLaVA-OV-2 data and training stack around large-scale open supervision: approximately 8M re-captioned video samples for pretraining, a 4M-sample spatial corpus for fine-tuning. We also introduce JumpScore, a temporal-localization benchmark targeting fine-grained grounding in high-frequency, densely repeated motion, a regime underrepresented by existing video evaluations. A standout capability of LLaVA-OV-2 is its unified perception across video understanding, temporal grounding, spatial grounding, and manipulation-trace reasoning. On JumpScore, LLaVA-OneVision-2-8B reaches 74.9 JumpScore mAP, surpassing Qwen3-VL-8B (30.1) by +44.8 points; under matched visual-token budgets on the same benchmark, codec-stream inputs improve temporal grounding over frame sampling by +9.7 points. Across standard benchmarks, LLaVA-OneVision-2-8B further outperforms Qwen3-VL-8B by +4.3 average points on video tasks, +5.3 on spatial tasks, and +15.6 average J&F on tracking tasks.", "github": "https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-2" }, { "date": "2026-05-27", "arxiv_id": "2605.24468", "title": "SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent", "authors": [ "Yuyang Hu", "Hongjin Qian", "Shuting Wang", "Jiongnan Liu", "Ziliang Zhao", "Jiejun Tan", "Zheng Liu", "Zhicheng Dou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qhjqhj00/cabeza", "github_api_added_by": "user", "project_page": "", "abstract": "Long-horizon agentic reasoning requires large language models to act over long interaction histories containing thoughts, tool calls, observations, and partial conclusions. The challenge is not merely that these histories grow long, but that information needed for the current decision may be scattered across distant steps and only become relevant later. Existing approaches address this difficulty by truncating the interaction history, compressing it into shorter surrogates, or retrieving selected parts of it for reuse, but they do not explicitly model how access to past interaction should adapt to the agent's evolving state. We instead cast long-horizon reasoning as a problem of state-adaptive memory. To this end, we propose State-Adaptive Memory~(SAM), a standalone framework that consolidates ongoing interaction into compact memory cues while preserving raw trajectory pages for intent-driven recall. These cues are not treated as replacements for history; rather, they serve as lightweight handles that allow the agent to reconstruct temporally distant information according to its current needs, without retraining the underlying backbone. We further optimize the memory module through expert-guided supervision and reinforcement learning, aligning it with trajectory-level utility. Across BrowseComp, BrowseComp-ZH, WideSearch, and HLE, SAM consistently outperforms strong baselines over diverse agent backbones. Our results suggest that explicit memory modeling provides a simple and effective foundation for long-horizon agentic reasoning.", "github": "https://github.com/qhjqhj00/cabeza" }, { "date": "2026-05-27", "arxiv_id": "2605.27030", "title": "Share More, Search Less: Collaborative Parallel Thinking for Efficient Test-Time Scaling", "authors": [ "Xinglin Wang", "Hao Lin", "Shaoxiong Feng", "Peiwen Yuan", "Yiwei Li", "Jiayi Shi", "Yueqi Zhang", "Chuyi Tan", "Ji Zhang", "Boyuan Pan", "Yao Hu", "Kan Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/WangXinglin/CPT", "github_api_added_by": "user", "project_page": "", "abstract": "Test-Time Scaling (TTS) enhances the reasoning capabilities of large language models by allocating additional inference compute to explore the solution space. However, existing parallel TTS methods typically keep branches isolated during search: intermediate discoveries remain branch-private and cannot guide other branches in time. This information isolation causes substantial redundant exploration, as branches repeatedly rediscover information already found elsewhere and require more search steps to collect complete decision information needed to reach correct answers. To bridge this gap, we propose Collaborative Parallel Thinking (CPT), a training-free inference framework that enables search-time information sharing across parallel branches. CPT extracts compact intermediate information from ongoing branches, maintains a deduplicated query-level information pool, and broadcasts pool entries through the input context, allowing each branch in subsequent search steps to reuse discoveries made by other branches rather than rediscover the same information. Empirically, experiments on HMMT and AIME benchmarks show that CPT establishes a stronger accuracy--latency Pareto frontier than strong baselines across rollout budgets and model scales, highlighting search-time collaboration as an effective direction for efficient parallel TTS.", "github": "https://github.com/WangXinglin/CPT" }, { "date": "2026-05-27", "arxiv_id": "2605.25188", "title": "DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs", "authors": [ "Yi Li", "Songtao Wei", "Dongming Jiang", "Zhichun Guo", "Qiannan Li", "Bingzhe Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PearLoveTana/DarkForest_Review", "github_api_added_by": "user", "project_page": "https://github.com/PearLoveTana/DarkForest_Review", "abstract": "Multi-agent LLM systems improve reasoning by combining outputs from multiple agents, but interaction-heavy methods can introduce error propagation and high communication overhead. When agents exchange raw responses or reasoning traces, incorrect intermediate reasoning may be adopted and amplified, leading to confident but wrong consensus; multi-round communication also increases token consumption, latency, and inference cost. In this paper, we propose a controlled-communication coordination framework named DarkForest. DarkForest first keeps agents independent, so each agent produces an answer without seeing the others' outputs. It then parses the raw responses into structured candidate records, groups semantically equivalent candidates into clusters, and estimates a calibrated belief distribution over these clusters using agent reliability, confidence, parse quality, support-pattern reliability, and independence corrections. A coordinator receives only policy-permitted evidence from this belief state with controlled communication. Experiments on six reasoning benchmarks show that DarkForest achieves leading overall quality, improves the strongest baseline by up to 30.7\\% on benchmark metrics, and reduces token consumption by up to 6.5times compared with communication-heavy baselines.", "github": "https://github.com/PearLoveTana/DarkForest_Review" }, { "date": "2026-05-27", "arxiv_id": "2605.22769", "title": "Understanding Data Temporality Impact on Large Language Models Pre-training", "authors": [ "Pilchen Hippolyte", "Fabre Romain", "Signe Talla Franck", "Perez Patrick", "Grave Edouard" ], "github_manual": "", "github_regex": "https://github.com/kyutai-labs/kairos", "github_paper_md": "", "github_api": "https://github.com/kyutai-labs/kairos", "github_api_added_by": "user", "project_page": "https://kyutai.org/blog/2026-05-26-kairos", "abstract": "Large language models (LLMs) are typically trained on shuffled corpora, yielding models whose knowledge is frozen at train time and whose temporal grounding remains poorly understood. In this work, we study the impact of pre-training dynamics on the acquisition of time-sensitive factual knowledge, focusing specifically on data ordering. Our main contributions are twofold. First, we introduce a comprehensive benchmark of over 7,000 temporally grounded questions and an evaluation protocol that enables analysis of whether models correctly associate facts with their corresponding time periods. Second, we pretrain 6B-parameter models on temporally ordered Common Crawl snapshots and compare them against standard shuffled pre-training. Our results show that sequentially trained models match shuffled baselines on general language understanding and common knowledge while consistently exhibiting more up-to-date and temporally precise knowledge. Temporally ordered pre-training yields improved factual freshness, while shuffled pre-training peaks on older data, possibly due to increased factual repetition. These findings, along with the release of our code at https://github.com/kyutai-labs/kairos , checkpoints, and datasets at https://huggingface.co/collections/kyutai/kairos provide a foundation for future research on continual learning for LLMs.", "github": "https://github.com/kyutai-labs/kairos" }, { "date": "2026-05-27", "arxiv_id": "2605.25893", "title": "D^2-Monitor: Dynamic Safety Monitoring for Diffusion LLMs via Hesitation-Aware Routing", "authors": [ "Aoxi Liu", "Yupeng Chen", "James Oldfield", "Guanzhe Hong", "Junchi Yu", "Baoyuan Wu", "Philip Torr", "Adel Bibi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Despite the emergence of diffusion large language models (D-LLMs) as an alternative to autoregressive large language models (AR-LLMs), safety monitoring for D-LLMs remains largely unexplored. Unlike AR-LLMs, D-LLMs generate text through a multi-step denoising process, exposing intermediate hidden representations that may contain safety-relevant information unavailable in standard single-step monitoring setups. Motivated by the suitability of lightweight probes for always-on monitoring, we analyze which trajectory-level signals best indicate when such probes are likely to struggle. We find that the most informative signal is safety hesitation: intermediate hidden states repeatedly falling within a small margin of the probe's decision boundary. The number of such hesitation steps in D-LLM's trajectory predicts probe failure effectively, providing a proxy of sample difficulty. Building on this analysis, we propose D^2-Monitor, a bi-level safety monitor for D-LLMs. D^2-Monitor adopts a lightweight probe as an always-on monitor to jointly estimate hesitation and perform base classification. When the hesitation level exceeds a threshold, a more expressive but computationally heavier probe is activated. This dynamic routing mechanism allocates monitoring resources efficiently at test time. Evaluated on 3 datasets (WildguardMix, ToxicChat, OpenAI-Moderation) across 4 D-LLMs, D^2-Monitor achieves state-of-the-art performance with a compact parameter footprint (leq 0.85M parameters), and exhibits the best trade-off between effectiveness and efficiency relative to 8 baselines.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.27141", "title": "VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions", "authors": [ "Yuxin Chen", "Yi Zhang", "Zhengzhou Cai", "Yaorui Shi", "Zhiyuan Yao", "Chenhang Cui", "Jingnan Zheng", "Yaqi Huo", "Xi Su", "Qi Gu", "Xunliang Cai", "Xiang Wang", "An Zhang", "Tat-Seng Chua" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/meituan-longcat/vitabench-2.0", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models (LLMs) have evolved into interactive agents that collaborate with users in real-world tasks. Effective collaboration in such settings increasingly depends on understanding the user beyond what is explicitly stated, as user intent is often reflected in fragmented daily interactions and requires both personalized modeling and proactive interaction. However, existing agent benchmarks primarily evaluate reasoning and tool use, largely overlooking the challenges of inferring and leveraging user preferences in realistic scenarios. To address this gap, we introduce VitaBench 2.0, a benchmark for evaluating personalized and proactive agent behavior in long-term user interactions. In VitaBench 2.0, tasks are organized as temporally ordered sequences for individual users, where preferences are embedded in fragmented and heterogeneous interactions. Successful completion of tasks requires the agent to continuously extract, utilize, and update user preferences from these interactions. We further evaluate proactiveness through tasks that require agents to recognize missing information and actively acquire it from users or environments before making decisions. To support systematic analysis, we provide an extensible memory interface that enables controlled comparison across different memory architectures. We benchmark a diverse set of frontier proprietary and open-source LLMs. Results show that real-world personalization remains highly challenging even for state-of-the-art models, revealing a substantial gap between current capabilities and practical requirements. Extensive analysis further reveals the failure modes and capability bottlenecks of current agents in real-world personalized decision-making, providing insights for future model improvements.", "github": "https://github.com/meituan-longcat/vitabench-2.0" }, { "date": "2026-05-27", "arxiv_id": "2605.25802", "title": "Rethinking VLM Representation for VLA Initialization", "authors": [ "Weifeng Lin", "Siyuan Huang", "Hao Li", "Tingwei Chen", "Ruichuan An", "Xinyu Wei", "Jianbo Liu", "Hongsheng Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AFeng-x/Rethink_VLA_Initialization", "github_api_added_by": "user", "project_page": "https://github.com/AFeng-x/Rethink_VLA_Initialization", "abstract": "Vision-Language-Action (VLA) models widely adopt pretrained Vision-Language Models (VLMs) as policy backbones, yet it remains unclear what kind of pretrained VLM representation is useful as a VLA initialization. In this paper, we study VLA initialization as a controlled representation-design problem along three axes: capability-level embodied VQA supervision, parameter-update strategy, and robot-data pretraining. Our experiments show that the original pretrained VLM representation is a key source of action performance. However, embodied VQA adaptation does not yield uniform gains: its benefit depends on downstream bottlenecks, and gains from different capability domains are not simply additive. For update strategy, LoRA provides a more reliable initialization than Full Finetune, indicating that overly reshaping the pretrained representation can weaken VLA initialization. Robot-data pretraining further improves VLA initialization, with the strongest variant obtained by staged LoRA-based training. Together, these findings suggest that effective VLM-to-VLA adaptation should inject action-relevant embodied and robot-trajectory signals while preserving the pretrained VLM representation that remains useful for action learning.", "github": "https://github.com/AFeng-x/Rethink_VLA_Initialization" }, { "date": "2026-05-27", "arxiv_id": "2605.26045", "title": "Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals", "authors": [ "Federico Torrielli", "Peter Schneider-Kamp", "Lukas Galke Poech" ], "github_manual": "", "github_regex": "https://github.com/federicotorrielli/probabilistic_activation_oracles", "github_paper_md": "", "github_api": "https://github.com/federicotorrielli/probabilistic_activation_oracles", "github_api_added_by": "user", "project_page": "", "abstract": "Activation oracles aim to make the activations of other models legible to humans and yield promising results compared to white-box interpretability techniques. However, uncertainty quantification (UQ) for the natural-language outputs of such activation oracles is so far understudied. Here, we investigate 6 different methods for estimating the confidence of activation oracles and evaluate how well-calibrated their confidence scores are. Our experiments on 6,000 samples per oracle (varying verbalizer and context prompts) reveal that bootstrap mode frequency is the best-calibrated method among those tested (ECE 5.7% vs. 25.5% for the answer-word log-probability on Qwen3-8B; 10.3% vs. 13.1% on Qwen3.6-27B), and that the log-prob baseline can serve as a fast triage signal at a fraction of the cost. Code and the patched trainer are available at https://github.com/federicotorrielli/probabilistic_activation_oracles.", "github": "https://github.com/federicotorrielli/probabilistic_activation_oracles" }, { "date": "2026-05-27", "arxiv_id": "2605.27209", "title": "Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments", "authors": [ "Yuxin Chen", "Xiaodong Cai", "Junfeng Fang", "Zhuowen Han", "Yu Wang", "Yaorui Shi", "Yi Zhang", "Qi Gu", "Xunliang Cai", "Xiang Wang", "An Zhang", "Tat-Seng Chua" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recent advances in large language models (LLMs) have facilitated the widespread deployment of LLMs as interactive agents capable of reasoning, planning, and tool use. Despite strong performance on existing benchmarks, such agents often exhibit notable degradation when deployed in real-world settings, where environments are inherently stochastic and imperfect. We argue that this discrepancy arises from a fundamental mismatch between idealized training settings and real-world interaction dynamics, where current paradigms rely on carefully curated task instructions and stable, well-controlled environments. To address this gap, we propose NoisyAgent, an agentic training framework that explicitly incorporates environmental imperfections into the agent learning process. We identify two major sources of interaction noise in real-world scenarios: user noise, which captures ambiguity and variability in user interaction, and tool noise, which reflects failures and anomalies in tool execution. We introduce such perturbations into the training pipeline by modifying user interaction patterns and simulating tool execution results within the training environment. To stabilize training while encouraging agents to handle increasingly challenging imperfections, noise is applied to only a subset of rollouts and progressively increased in difficulty as the model adapts to the current noise level. Extensive experiments demonstrate that our approach consistently improves agent robustness under noisy and dynamic environments. Our analysis reveals that training under noise conditions also yields performance gains on idealized benchmarks, suggesting that controlled exposure to environmental noise promotes more generalizable reasoning and decision-making behaviors. Our findings highlight the importance of modeling interaction imperfections for bridging the gap between agent training and real-world deployment.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.24931", "title": "Learning High-Frequency Continuous Action Chunks in Latent Space", "authors": [ "Kunyun Wang", "Yuhang Zheng", "Yupeng Zheng", "Jieru Zhao", "Wenchao Ding" ], "github_manual": "", "github_regex": "https://github.com/tars-robotics/RTR", "github_paper_md": "", "github_api": "https://github.com/tars-robotics/RTR", "github_api_added_by": "user", "project_page": "https://sjtu-zhao-lab.github.io/RTR/", "abstract": "Modern robotic policies increasingly rely on action chunking to execute complex tasks in the physical world. While action chunking improves temporal consistency at moderate action frequencies, it becomes insufficient when the action frequency is further increased (e.g., to 60~Hz). At such high frequencies, policies often fail to generate actions that are both temporally smooth and spatially consistent. We address this challenge by shifting high-frequency action learning from the action space to a latent space with variational autoencoder (VAE). This formulation significantly improves both temporal and spatial consistency of high-frequency control. To enable smooth real-time execution, we further introduce Reuse-then-Refine, a chunk-level refine strategy that improves continuity between adjacent action chunks under asynchronous inference. As a result, robots controlled by our policy can execute complex contact-rich tasks continuously, with less pauses and jerky motions. Experiments on three real-world contact-rich robotic tasks show that our approach consistently completes tasks with smooth motions. Our code and data are available at https://github.com/tars-robotics/RTR.", "github": "https://github.com/tars-robotics/RTR" }, { "date": "2026-05-27", "arxiv_id": "2605.18879", "title": "ZeroUnlearn: Few-Shot Knowledge Unlearning in Large Language Models", "authors": [ "Yujie Lin", "Chengyi Yang", "Zhishang Xiang", "Yiping Song", "Jinsong Su" ], "github_manual": "", "github_regex": "https://github.com/XMUDeepLIT/ZeroUnlearn", "github_paper_md": "", "github_api": "https://github.com/XMUDeepLIT/ZeroUnlearn", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models inevitably retain sensitive information, defined as inputs that may induce harmful generations, due to training on massive web corpora, raising concerns for privacy and safety. Existing machine unlearning methods primarily rely on retraining or aggressive fine-tuning, which are either computationally expensive or prone to degrading related knowledge and overall model utility. In this work, we reformulate machine unlearning as a precise knowledge re-mapping problem via model editing. We propose ZeroUnlearn, a few-shot unlearning framework. It overwrites sensitive inputs by mapping them to a neutral target state and removing their original representations. ZeroUnlearn enforces representational orthogonality through a multiplicative parameter update with a closed-form solution, enabling efficient and targeted unlearning. We further extend ZeroUnlearn to a gradient-based variant for multi-sample unlearning. Experiments demonstrate that our approach outperforms existing baselines while preserving general model utility. Our code is available at the github: https://github.com/XMUDeepLIT/ZeroUnlearn.", "github": "https://github.com/XMUDeepLIT/ZeroUnlearn" }, { "date": "2026-05-27", "arxiv_id": "2605.22608", "title": "Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents", "authors": [ "Asaf Yehudai", "Lilach Eden", "Michal Shmueli-Scheuer" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://ibm.github.io/CLEAR/", "abstract": "Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious challenges for overseeing and assessing agent behavior. Most current tools are limited, focusing on observability with basic evaluation capabilities or imposing static, hand-crafted error taxonomies that cannot adapt to new domains. To address this gap, we present Agentic CLEAR, an automatic, dynamic, and easy-to-use evaluation framework. It produces textual insights into the agent behavior on three levels of granularity: system, trace, and node. Agentic CLEAR operates above the observability layer, enabling seamless integration and featuring an intuitive UI that makes agent evaluation highly accessible. In our experiments on four benchmarks, seven agentic settings, and tens of thousands of LLM calls, we show that Agentic CLEAR produces high-quality, data-driven, insightful feedback. Our analysis shows strong alignment with human-annotated errors and the ability to predict task success rate.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.27295", "title": "Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini", "authors": [ "Madhuri Shanbhogue", "Zhe Li", "Shanfeng Zhang", "Gustavo Hern\u00e1ndez \u00c1brego", "Shih-Cheng Huang", "Aashi Jain", "Daniel Salz", "Sonam Goenka", "Chaitra Hegde", "Ji Ma", "Feiyang Chen", "Jiaxing Wu", "Tanmaya Dabral", "Babak Samari", "Kevin Poulet", "Daniel Cer", "Kaifeng Chen", "Paul Suganathan", "Hui Hui", "Jovan Andonov", "Philippe Schlattner", "Jay Han", "Iftekhar Naim", "Wing Lowe", "Vladimir Pchelin", "Albert Yang", "Yi-Ting Chen", "Zhongli Ding", "Grace Zhang", "Georg Heigold", "Yichang Chen", "Antoine Reveillon", "Brendan Mccloskey", "Wenlei Zhou", "Dahun Kim", "Rui Meng", "Emma Wang", "Jack Zheng", "Halley Fede", "Zhen Yang", "Keegan Mosley", "Brian Potetz", "Sahil Dua", "Henrique Schechter Vera", "Shen Gao", "Hesen Zhang", "Andreas Hess", "Hengxuan Ying", "Alberto Montes", "Karan Gill", "Min Choi", "Sebastian Russo", "Anja Hauth", "Jinhyuk Lee", "Michael Boratko", "Megan Barnes", "Vikram Rao", "Claudiu Musat", "Cyril Allauzen", "Ehsan Variani", "Shankar Kumar", "Tom Bagby", "Junyi Jiao", "Yang Gu", "Tengxin Li", "Ayush Agrawal", "Roberto Santana", "Dev Nath", "Stephen Karukas", "Shuoxuan Han", "Lucia Loher", "Alice Twu", "Nidhi Vyas", "Siddharth Bhai", "Frank Palma Gomez", "Wangyuan Zhang", "Chaoren Liu", "Jizheng Yang", "Steve Qiu", "Shijie Zhang", "Sujay Kulkarni", "Sascha Rothe", "Sean Nakamoto", "Raphael Hoffmann", "Zach Gleicher", "Yunhsuan Sung", "Qin Yin", "Tom Duerig", "Mojtaba Seyedhosseini" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://ai.google.dev/gemini-api/docs/embeddings", "abstract": "We introduce Gemini Embedding 2, a native multimodal embedding model that allows embedding video, audio, image, and text modalities in a unified representation space. We leverage the multimodal capabilities of Gemini to produce embeddings for arbitrary combinations of interleaved inputs across all these modalities that generalize well across a wide variety of tasks. Applying large-scale contrastive learning in a multi-task multi-stage training setup, we achieve state-of-the-art performance on key embedding benchmarks including unimodal, cross-modal, and multimodal retrieval spanning a diverse set of tasks. We show that our embedding model demonstrates strong performance (with a score of 62.9 R@1 on MSCOCO, 68.8 NDCG@10 on Vatex, 69.9 on MTEB multilingual and 84.0 on MTEB Code) across a variety of tasks surpassing the performance of specialized models. These unified capabilities make Gemini Embedding 2 a promising candidate for downstream use cases such as RAG, recommendation and search. Furthermore, its robust zero-shot performance across distinct fields - from astronomy and bioscience to fine arts and the culinary arts - establishes it as a highly reliable, out-of-the-box representation even for specialized domains.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.25162", "title": "STREAM: A Data-Centric Framework for Mining High-Value Task-Oriented Dialogues from Streaming Media", "authors": [ "Liang Xue", "Haoyu Liu", "Cheng Wang", "Pengyu Chen", "Haozhuo Zheng", "Yang Liu" ], "github_manual": "", "github_regex": "https://github.com/hitxueliang/DialogDataSetBySTREAM", "github_paper_md": "", "github_api": "https://github.com/hitxueliang/DialogDataSetBySTREAM", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models for vertical domains are bottlenecked by the scarcity of complex, domain-specific task-oriented dialogues. Existing data acquisition pipelines face a persistent trilemma: expert annotation is expensive, real-world service conversations are constrained by privacy and commercial restrictions, and static corpora quickly become temporally stale. We propose Stream, a data-centric framework that leverages publicly available streaming media (live streams and short videos) to synthesize high-value service dialogues at scale. Stream mines authentic interaction signals from noisy streams and synthesizes conversations by integrating role-grounded persona construction with Conversational Blueprint construction; it further adopts retrieval-augmented generation (RAG) to support knowledge-aware responses. Based on Stream, we release StreamDial, a large-scale multi-domain dataset covering Automotive, Restaurant, and Hotel. StreamDial contains 87,498 dialogue sessions and 1,497,320 turns in total, with an average of 17.11 turns per session and a comparable scale across domains. Each session is organized as a structured quadruplet langle P_u, P_a, B, H rangle that pairs dialogue history with explicit user/agent personas and a Conversational Blueprint, capturing realistic service behaviors such as requirement mining, constraint conflicts, negotiation, and recovery. Evaluations with automatic judges and downstream tasks show that StreamDial improves intrinsic dialogue quality over strong baselines, and models trained with StreamDial improve Dialogue State Tracking across backbones; we further report a completed human-evaluation set and encouraging multilingual transfer on Qwen3-8B under a controlled training budget. The data is released in https://github.com/hitxueliang/DialogDataSetBySTREAM.", "github": "https://github.com/hitxueliang/DialogDataSetBySTREAM" }, { "date": "2026-05-27", "arxiv_id": "2605.26293", "title": "CroCo: Cross-Lingual Contrastive Preference Tuning on Self-Generations", "authors": [ "Mike Zhang", "Ali Basirat", "Desmond Elliott" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jjzha/CroCo", "github_api_added_by": "user", "project_page": "", "abstract": "Prior work establishes that controlled contrastiveness between self-generated responses from large language models, set via reward scores, improves downstream preference tuning in English. We extend this method to multiple languages and evaluate two models across a total of 14 high and low-resource languages on a diverse set of tasks. Our central finding is that cross-lingual contrastive preference tuning on self-generations (CroCo) transfers without language-specific preference annotation. A reward model trained on English preferences (atop a multilingual base) produces useful within-language rankings across most languages, and pairing in either a monolingual or multilingual setting improves over each model on the majority of setups while preventing the catastrophic forgetting of supervised fine-tuning. We observe that the gains require on-policy data. Off-policy responses reduce the benefit and online preference optimization fails to improve over the offline variant. Specifically, on structured tasks, our method matches or exceeds the base in 6/7 languages for EuroLLM-9B and 4/7 settings for Aya-3B. On open-ended generation, both tuned models win against their respective base across 11 evaluated languages. Overall, we show promising directions for multilingual preference tuning.", "github": "https://github.com/jjzha/CroCo" }, { "date": "2026-05-27", "arxiv_id": "2605.27102", "title": "JLT: Clean-Latent Prediction in Latent Diffusion Transformers", "authors": [ "Funing Fu", "Tenghui Wang", "Junyong Cen", "Qichao Zhu", "Guanyu Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/akatsuki-neo/JLT", "github_api_added_by": "user", "project_page": "https://github.com/akatsuki-neo/JLT", "abstract": "Flow matching with clean-data prediction has shown that regressing the clean point can exploit low-dimensional structure more effectively than predicting an ambient noised quantity. We ask whether this principle remains useful after images are mapped into a learned latent space, where compression has already removed much of the raw pixel variability. We introduce JLT, a 130M latent diffusion Transformer over frozen FLUX.2 VAE codes, and compare clean-latent prediction with a matched velocity-prediction DiT under the same representation, backbone, and training settings. Although the three variables x, epsilon, and v are linearly convertible for a fixed corruption time, a local Gaussian analysis shows that velocity regression inherits an isotropic target-covariance floor and amplifies low-variance latent directions, while clean prediction damps them. On ImageNet 256 x 256, JLT-B/1 obtains FID-50K 2.50 with classifier-free guidance, with a large matched-target gap over velocity prediction. These results suggest that prediction targets in latent diffusion are representation-dependent geometric choices, rather than interchangeable algebraic parameterizations.", "github": "https://github.com/akatsuki-neo/JLT" }, { "date": "2026-05-27", "arxiv_id": "2605.27068", "title": "QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents", "authors": [ "Ye Yuan", "Rui Song", "Weien Li", "Zeyu Li", "Haochen Liu", "Xiangyu Kong", "Changjiang Han", "Yonghan Yang", "Zichen Zhao", "Zixuan Dong", "Fuyuan Lyu", "Bowei He", "Haolun Wu", "Jikun Kang", "Xue Liu" ], "github_manual": "", "github_regex": "https://github.com/AAAAA-Academia-Attractions/QUACK", "github_paper_md": "", "github_api": "https://github.com/AAAAA-Academia-Attractions/QUACK", "github_api_added_by": "user", "project_page": "", "abstract": "Social deduction games have become a popular testbed for probing reasoning, deception, coordination, and belief modeling in Large Language Model (LLM) agents. However, most environments are scored only by game outcomes such as win rates and largely remain to text-only interaction, making it difficult to tell whether an agent's language is actually grounded in what it perceived and did, or to identify the failure modes underlying its behavior. To address this gap, we introduce QUACK, an open-source environment and evaluation framework for auditing the grounding of agent language in multimodal social reasoning. QUACK evaluates agents at three levels: game outcomes, behavioral trajectories, and utterance-level consistency. Its core Statement Verification Pipeline reconstructs each agent's ground-truth trajectory from engine logs and checks every discussion claim against it, automatically flagging spatial hallucination, unsupported accusation, deception collapse, and language-action inconsistency. Evaluating three frontier VLMs in both homogeneous and cross-model adversarial settings, we find that even the strongest agent hallucinates 15.1% of its verifiable spatial claims and makes over half of its accusations without grounded evidence. We release the full engine, evaluation framework, toolkit, and logs at https://github.com/AAAAA-Academia-Attractions/QUACK.", "github": "https://github.com/AAAAA-Academia-Attractions/QUACK" }, { "date": "2026-05-27", "arxiv_id": "2503.08600", "title": "NSF-SciFy: Mining the NSF Awards Database for Scientific Claims", "authors": [ "Delip Rao", "Weiqiu You", "Eric Wong", "Chris Callison-Burch" ], "github_manual": "", "github_regex": "https://github.com/darpa-scify/NSFSciFy", "github_paper_md": "", "github_api": "https://github.com/darpa-scify/NSFSciFy", "github_api_added_by": "user", "project_page": "", "abstract": "We introduce NSF-SciFy, a comprehensive dataset of scientific claims and investigation proposals extracted from National Science Foundation award abstracts. While previous scientific claim verification datasets have been limited in size and scope, NSF-SciFy represents a significant advance with 2.8 million claims from 400,000 abstracts spanning all science and mathematics disciplines. We present two focused subsets: NSF-SciFy-MatSci with 114,000 claims from materials science awards, and NSF-SciFy-20K with 135,000 claims across five NSF directorates. Using zero-shot prompting, we develop a scalable approach for joint extraction of scientific claims and investigation proposals. We demonstrate the dataset's utility through three downstream tasks: non-technical abstract generation, claim extraction, and investigation proposal extraction. Fine-tuning language models on our dataset yields substantial improvements, with relative gains often exceeding 100%, particularly for claim and proposal extraction tasks. Our error analysis reveals that extracted claims exhibit high precision but lower recall, suggesting opportunities for further methodological refinement. NSF-SciFy enables new research directions in large-scale claim verification, scientific discovery tracking, and meta-scientific analysis. Code and data are available at https://github.com/darpa-scify/NSFSciFy.", "github": "https://github.com/darpa-scify/NSFSciFy" }, { "date": "2026-05-27", "arxiv_id": "2605.26242", "title": "Can LLMs Introspect? A Reality Check", "authors": [ "Shashwat Singh", "Tal Linzen", "Shauli Ravfogel" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Can large language models detect and report their own internal states? A number of studies have argued that the answer to this question is yes. We argue, based on lessons from human metacognition research, that this conclusion may be premature: to be convinced of this conclusion we need to distinguish genuine introspection from pattern matching based on surface-level cues. Furthermore, we argue that behavioral evidence alone is inherently insufficient to establish strong introspective claims. We re-examine two recently introduced evaluation paradigms in light of this consideration. In the first paradigm, models are expected to detect whether their internal states have been tampered with. We find that models cannot reliably distinguish such interventions on their internal states from manipulations of the input, suggesting that their success in the original studies reflects their ability to detect anomalies more generally, as opposed to interventions on their internal states in particular. In the second paradigm we examine, models are tasked with predicting labels derived from their own hidden states. Here, we find that classifiers that only have access to the input achieve equivalent performance to the model's own in-context predictions, indicating that the original results do not conclusively demonstrate that the model has privileged access to its internal representations. We further introduce a relabeled control setting, where models cannot rely on the semantics of the task to solve it, and instead must rely on the internal representation; models perform closer to chance on this better-controlled version of the task. Taken together, these results indicate that current evidence is insufficient to establish that LLMs display metacognitive monitoring.", "github": "" }, { "date": "2026-05-27", "arxiv_id": "2605.23215", "title": "FastKernels: Benchmarking GPU Kernel Generation in Production", "authors": [ "Gabriele Oliaro", "Yichao Fu", "May Jiang", "Owen Lu", "Junli Wang", "Zhihao Jia", "Hao Zhang", "Samyam Rajbhandari" ], "github_manual": "", "github_regex": "https://github.com/Snowflake-AI-Research/fastkernels", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://fastkernels.github.io/", "abstract": "LLM-based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize against. Existing benchmarks are poorly aligned with production inference frameworks: they evaluate kernels on a single GPU with synthetic inputs, ignore the surrounding compilation stack, and reward replicating known optimizations rather than discovering new ones. The resulting reward signals are misleading: agents learn to generate kernels that score well in sandboxes but introduce interface incompatibilities, compilation-stack conflicts, and silent correctness degradation when integrated into real systems. We introduce FastKernels, a kernel benchmark built around a minimal set of 46 representative architectures spanning 8 categories, whose kernels collectively subsume those of 96.2% (409/425) of HuggingFace Transformers architectures. FastKernels doubles as a minimalistic, production-grade inference framework that runs at parity with hardened systems such as vLLM and SGLang on mainstream LLM serving and substantially exceeds upstream references on under-served architectures; each task's interface mirrors the corresponding module in the state-of-the-art library for its architecture family, enabling direct deployment of optimized kernels into production codebases. Evaluating state-of-the-art kernel agents on FastKernels, we find that even the strongest agent achieves only 0.94times aggregate speedup over production baselines, with weaker agents at 0.78times and 0.53times -- confirming that benchmark-production misalignment is a critical bottleneck for the field. We release FastKernels as a stepping stone toward kernel agents whose benchmark gains translate directly into production throughput improvements. Code is available at https://github.com/Snowflake-AI-Research/fastkernels", "github": "https://github.com/Snowflake-AI-Research/fastkernels" }, { "date": "2026-05-27", "arxiv_id": "2605.15042", "title": "EverAnimate: Minute-Scale Human Animation via Latent Flow Restoration", "authors": [ "Wuyang Li", "Yang Gao", "Mariam Hassan", "Lan Feng", "Wentao Pan", "Po-Chien Luan", "Alexandre Alahi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We propose EverAnimate, an efficient post-training method for long-horizon animated video generation that preserves visual quality and character identity. Long-form animation remains challenging because highly dynamic human motion must be synthesized against relatively static environments, making chunk-based generation prone to accumulated drift: (i) low-level quality drift, such as progressive degradation of static backgrounds, and (ii) high-level semantic drift, such as inconsistent character identity and view-dependent attributes. To address this issue, EverAnimate restores drifted flow trajectories by anchoring generation to a persistent latent context memory, consisting of two complementary mechanisms. (i) Persistent Latent Propagation maintains a context memory across chunks to propagate identity and motion in latent space while mitigating temporal forgetting. (ii) Restorative Flow Matching introduces an implicit restoration objective during sampling through velocity adjustment, improving within-chunk fidelity. With only lightweight LoRA tuning, EverAnimate outperforms state-of-the-art long-animation methods in both short- and long-horizon settings: at 10 seconds, it improves PSNR/SSIM by 8%/7% and reduces LPIPS/FID by 22%/11%; at 90 seconds, the gains increase to 15%/15% and 32%/27%, respectively.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.27311", "title": "Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models", "authors": [ "Yifan Jiang", "Dae Yon Hwang", "Jesse C. Cresswell", "Freda Shi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/compling-wat/Chartographer", "github_api_added_by": "user", "project_page": "", "abstract": "Chart question-answering (QA) benchmarks aim to pose questions that require visual reasoning to correctly answer, but models can often reach solutions through shortcuts or prior familiarity with a chart based on their own background knowledge. To strictly evaluate visual reasoning, we propose counterfactual charts where the chart-question task remains fixed, but underlying chart and the corresponding answer are varied. We introduce Chartographer, a framework to reverse engineer charts into executable code, validate reconstruction fidelity, generate seed-controlled counterfactual variants, and derive new answers from executable QA logic. We apply this framework to existing chart QA datasets and evaluate proprietary and open-source vision-language models (VLMs), measuring variation sensitivity and generalizability. Counterfactual charts reveal failures hidden by single-chart performance: VLMs often fail to generalize after answering the original chart correctly. We find failures are most prevalent when updated charts require novel visual reasoning pathways.", "github": "https://github.com/compling-wat/Chartographer" }, { "date": "2026-05-27", "arxiv_id": "2605.27476", "title": "Balancing Fidelity and Diversity in Diffusion Models via Symmetric Attention Decomposition: Hopfield Perspective", "authors": [ "Hyunmin Cho", "Woo Kyoung Han", "Kyong Hwan Jin" ], "github_manual": "", "github_regex": "https://github.com/hyeon-cho/Attention-Symmetric-Decomposition", "github_paper_md": "", "github_api": "https://github.com/hyeon-cho/Attention-Symmetric-Decomposition", "github_api_added_by": "user", "project_page": "https://hyeon-cho.github.io/Balancing/", "abstract": "We characterize the pre-softmax attention matrix QK^top in transformers as an associative memory matrix encoding pairwise associations between input features. By decomposing this matrix into its symmetric and skew-symmetric parts, we interpret the symmetric component as governing the structure of the energy landscape, and the skew-symmetric component as driving circulation on that landscape. Leveraging the energy formulation induced by the symmetric component, we derive Hopfield-style stability measures that quantify the stability of retrieved features. We observe meaningful correlations between Hopfield-style stability measures and the fidelity-diversity trade-offs in generation. Finally, we propose a controllable knob to modulate this trade-off by modifying the circulation of the underlying dynamics. Code is available at our GitHub (https://github.com/hyeon-cho/Attention-Symmetric-Decomposition).", "github": "https://github.com/hyeon-cho/Attention-Symmetric-Decomposition" }, { "date": "2026-05-28", "arxiv_id": "2605.28003", "title": "ResearchMath-14K: Scaling Research-Level Mathematics via Agents", "authors": [ "Guijin Son", "Seungyeop Yi", "Minju Gwak", "Hyunwoo Ko", "Wongi Jang", "Youngjae Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The frontier of mathematics is defined by problems whose solutions are not yet known, yet it remains unclear whether language models can meaningfully engage with such problems without human intervention. A major obstacle is the lack of large-scale research-level math datasets. To this end, we introduce ResearchMath-14k, a set of 14{,}056 problems curated from academic sources via a multi-agent pipeline, making it the largest collection of research-level mathematical problems to date. We further generate ResearchMath-Reasoning, 220K teacher trajectories from two open models, where we observe recurring avoidance behaviors such as non-attempts and fabricated references. Interestingly, across eight open-weight models, newer generations produce 5.6times more references and 5.0times more fake references per trace. After agentic filtering of ResearchMath-Reasoning, fine-tuning Qwen3 models from 4B to 30B parameters improves over base models by 9.2 points on average. This shows that filtered open-problem attempts can provide useful supervision even without fully correct reasoning traces. We make ResearchMath-14k publicly available for future works on research-level mathematical reasoning.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.28816", "title": "Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players", "authors": [ "Fangfu Liu", "Kai He", "Tianchang Shen", "Tianshi Cao", "Sanja Fidler", "Yueqi Duan", "Jun Gao", "Igor Gilitschenski", "Zian Wang", "Xuanchi Ren" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://research.nvidia.com/labs/sil/projects/gamma-world/", "abstract": "World models for interactive video generation have largely focused on single-agent settings, where future observations are generated from a single control signal. However, many generated environments require multi-agent interaction: multiple players, robots, or embodied agents act simultaneously within a shared space. Scaling world models to such settings requires a principled multi-agent design: agents should remain independently controllable, permutation-symmetric, and support efficient inference while maintaining consistency across time and perspectives. In this paper, we present our generative multi-agent world model for interactive simulation. It introduces Simplex Rotary Agent Encoding, a parameter-free extension of 3D RoPE that represents agents as vertices of a regular simplex in rotary angle space. This gives each agent a distinct phase while making all agents permutation-equivalent, enabling scalable agent identity without learned per-slot identities or a fixed agent ordering. To avoid dense all-to-all attention across agents, we further propose Sparse Hub Attention, where learnable hub tokens mediate token interaction across agents, reducing cross-agent attention cost from quadratic to linear in the number of agents. For real-time rollout, we distill a full-context diffusion teacher into a causal student that generates temporal blocks sequentially with KV caching, enabling action-responsive generation at 24 FPS. Experiments in multiplayer virtual environments show that our model improves video fidelity, action controllability, and inter-agent consistency over slot-based and dense-attention baselines, while generalizing from two to four players without additional training.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.28820", "title": "From Pixels to Words -- Towards Native One-Vision Models at Scale", "authors": [ "Haiwen Diao", "Jiahao Wang", "Penghao Wu", "Yuhao Dong", "Yuwei Niu", "Yue Zhu", "Zhongang Cai", "Weichen Fan", "Linjun Dai", "Silei Wu", "Xuanyu Zheng", "Mingxuan Li", "Yuanhan Zhang", "Bo Li", "Hanming Deng", "Huchuan Lu", "Quan Wang", "Lei Yang", "Lewei Lu", "Dahua Lin", "Ziwei Liu" ], "github_manual": "", "github_regex": "https://github.com/EvolvingLMMs-Lab/NEO", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://github.com/EvolvingLMMs-Lab/NEO", "abstract": "Current vision-language models (VLMs) typically stitch together separate image encoders and language decoders via multi-stage alignment, a modular framework that inevitably fragments pixel-level signals across frames and scatters early pixel-word interactions. In parallel, native VLMs, despite impressive performance on single images, remain largely unexplored in multi-image, video understanding, and spatial intelligence. Hence, we introduce NEO-ov, a native foundation model that learns cross-frame and pixel-word correspondence end-to-end, without any external encoders, auxiliary adapters, or post-hoc fusion. By eliminating module boundaries entirely, NEO-ov enables fine-grained and unified spatiotemporal modeling to emerge natively inside the model. Notably, NEO-ov largely narrows the gap to modular counterparts while excelling at fine-grained visual perception, validating that native \"one-vision\" architectures are not only feasible but competitive at scale. Beyond empirical performance, we unveil systematic architectural analyses and detailed training recipes to facilitate subsequent native multimodal modeling. Our code and models are publicly available at: https://github.com/EvolvingLMMs-Lab/NEO.", "github": "https://github.com/EvolvingLMMs-Lab/NEO" }, { "date": "2026-05-28", "arxiv_id": "2605.28691", "title": "OSP-Next: Efficient High-Quality Video Generation with Sparse Sequence Parallelism, HiF8 Quantization, and Reinforcement Learning", "authors": [ "Yunyang Ge", "Xianyi He", "Zezhong Zhang", "Bin Lin", "Bin Zhu", "Xinhua Cheng", "Li Yuan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PKU-YuanGroup/OSP-Next", "github_api_added_by": "user", "project_page": "", "abstract": "Diffusion Transformers achieve strong video generation quality, but the quadratic cost of full attention limits efficiency. We introduce OSP-Next, an efficient text-to-video generation model that integrates sparse attention, parallelism, quantization, and reinforcement learning. OSP-Next uses a hybrid full-sparse attention architecture, where the sparse component is implemented with Skiparse-2D Attention. This fixed-pattern mechanism applies token-wise and group-wise sparse attention along spatial dimensions, leveraging locality while maintaining native compatibility with FlashAttention kernels. Based on the local equivalence of rearrangement in Skiparse-2D Attention, we further propose Sparse Sequence Parallelism (SSP), which partitions subsequences across ranks and switches sparse patterns through a single All-to-All communication. Compared with Ulysses Sequence Parallelism (SP), SSP provides a native parallel strategy for sparse attention and reduces communication volume by 75%. OSP-Next also incorporates HiF8 quantization to enable stable joint training with 8-bit quantization and sparse fine-tuning, and applies Mix-GRPO post-training to improve the performance of the sparse model. Experiments show that OSP-Next achieves a VBench total score of 83.73%, surpassing the Wan2.1 baseline. Under the 5-second 720P and 5-second 768P settings, OSP-Next achieves up to 1.64times single-GPU speedup and over 1.52times eight-GPU speedup on NVIDIA H200 GPUs. In addition, with only a 0.4% drop in VBench total score, OSP-Next-HiF8 achieves 1.69times and 2.27times speedups under the two settings on a single Ascend 950PR, demonstrating the efficiency and performance of OSP-Next across hardware platforms.", "github": "https://github.com/PKU-YuanGroup/OSP-Next" }, { "date": "2026-05-28", "arxiv_id": "2605.28814", "title": "Self-Improving Language Models with Bidirectional Evolutionary Search", "authors": [ "Guowei Xu", "Zhenting Qi", "Huangyuan Su", "Weirui Ye", "Himabindu Lakkaraju", "Sham M. Kakade", "Yilun Du" ], "github_manual": "", "github_regex": "https://github.com/Embodied-Minds-Lab/BES", "github_paper_md": "", "github_api": "https://github.com/Embodied-Minds-Lab/BES", "github_api_added_by": "user", "project_page": "https://guoweixu.com/bes/", "abstract": "Search has been proposed as an effective method for self-improving language models and agentic systems, both for post-training sample generation and for inference. However, widely used methods such as best-of-N sampling and tree search face two fundamental limitations: they are guided by sparse verification signals, and they construct candidates primarily through autoregressive expansion, restricting exploration to regions with substantial model probability mass. To address these, we propose Bidirectional Evolutionary Search (BES), a search framework that couples forward candidate evolution with backward goal decomposition. In the forward search, BES augments standard expansion with evolution operators that recombine partial trajectories to generate candidates that are difficult to obtain from a single model rollout. In the backward search, BES recursively decomposes the original task into checkable subgoals, producing dense intermediate feedback that guides forward search. We provide theoretical motivation showing that candidates generated by expansion-only search are confined to a narrow entropy shell while evolutionary operators can escape it, and that backward search can exponentially reduce the number of required samples to find a correct answer. Experiments show that on challenging post-training tasks where mainstream post-training algorithms fail to improve, BES enables consistent gains, and on three open problem solving benchmarks at inference time, BES outperforms existing open-source frameworks in both average and best-case performance. Code and trained models are available at https://github.com/Embodied-Minds-Lab/BES.", "github": "https://github.com/Embodied-Minds-Lab/BES" }, { "date": "2026-05-28", "arxiv_id": "2605.27882", "title": "VibeSearchBench: Benchmarking Long-horizon Proactive Search in the Wild", "authors": [ "Xiaohongshu Inc" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/VibeBench/VibeSearchBench", "github_api_added_by": "user", "project_page": "https://vibebench.github.io/VibeSearchBench.github.io/", "abstract": "LLM-based agents score well on search benchmarks, yet real users consistently find results unsatisfying, revealing a persistent evaluation-experience gap. We attribute this gap to existing benchmarks' reliance on over-specified queries, single-turn interactions, and fixed-schema evaluation, none of which reflect real search behavior where users and agents collaboratively refine vague intent through multi-turn dialogue. We term this paradigm VibeSearch and introduce VibeSearchBench, a benchmark comprising 200 manually curated bilingual (Chinese and English) tasks across 20 domains, split into VibeSearch-Pro (professional) and VibeSearch-Daily (daily-life) subsets. Each task pairs a user persona with a schema-free ground-truth knowledge graph, and is evaluated through a progressive-disclosure user simulator and a graph-matching evaluation framework. We benchmark seven frontier models under both the ReAct framework and the OpenClaw agent harness. Results show that all models remain substantially inadequate for VibeSearch (best F1: 30.30), highlighting the need for fundamental advances in long-context reasoning, proactive intent elicitation, and structured knowledge construction.", "github": "https://github.com/VibeBench/VibeSearchBench" }, { "date": "2026-05-28", "arxiv_id": "2605.27028", "title": "Less is More: Early Stopping Rollout for On-Policy Distillation", "authors": [ "Zhou Ziheng", "Jiaqi Li", "Huacong Tang", "Ying Nian Wu", "Demetri Terzopoulos" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "On-policy distillation has recently emerged as a promising alternative to standard sequence-level imitation, training a student by scoring its own rollouts with a teacher model. However, we observe ``Off-policy Teacher Decay'' problem in this paradigm: for the later tokens, with student's earlier trajectory as context that is off-policy to the teacher, the teacher's ability to produce a corrective score would decay, and may fall back to token-completion behavior learned in the pre-training stage. We empirically verify this problem, and we propose Early Stopping Rollout (ESR) to fix it: a simple yet effective distillation strategy that simply restricts the rollout generation to the first response tokens. We show that ESR both surpasses the full rollout OPD performance across model size, family, tasks and training regime, and exhibit much higher GPU efficiency and training stability, especially under cross model family scenarios. We further investigate the mechanism behind this surprising performance and discovered \"Cascading Alignment\" and \"Sub-mode Commitment\" effect of ESR that may explain why it works effectively and even sometimes exceeding the teacher model performance. Besides, we show that this position-based token selection strategy cannot be fully explainable by KL divergence and entropy signals.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.28775", "title": "Learn from Weaknesses: Automated Domain Specialization for Small Computer-Use Agents", "authors": [ "Suji Kim", "Kangsan Kim", "Sung Ju Hwang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sujiikim/LearnWeak", "github_api_added_by": "user", "project_page": "https://learnweak.github.io/", "abstract": "Computer-use agents (CUAs) have recently made substantial progress, but deploying a separate large expert for each software domain remains expensive. Small open computer-use agents are more practical specialization targets, but they remain substantially weaker and exhibit uneven domain-specific failures. A straightforward remedy is to synthesize large-scale training data for the target domain, yet we find that this naive approach yields only marginal improvements. Building on this observation, we introduce LearnWeak, an annotation-free specialization framework for small computer-use agents that uses a stronger reference agent to identify the student's weaknesses in the target domain, synthesize targeted tasks, and construct supervision automatically. LearnWeak further introduces an error-aware specialization objective that disentangles planning and execution errors, enabling more behaviorally precise updates than broad uniform supervision. On OSWorld, LearnWeak achieves average gains of 11.6 and 11.1 percentage points over EvoCUA-8B and OpenCUA-7B, respectively, across eight domains. We also validate that our student-aware dataset generation and training approaches outperform existing autonomous trajectory generation and training baselines. Our work highlights the importance of student awareness in both data synthesis and agent training, pointing toward a more principled and efficient path for specializing small computer-use agents in diverse domains.", "github": "https://github.com/sujiikim/LearnWeak" }, { "date": "2026-05-28", "arxiv_id": "2605.28398", "title": "HRBench: Benchmarking and Understanding Thinking-Mode Switch Strategies in Hybrid-Reasoning LLMs", "authors": [ "Yansong Ning", "Mianpeng Liu", "Jingwen Ye", "Weidong Zhang", "Hao Liu" ], "github_manual": "", "github_regex": "https://github.com/usail-hkust/HRBench", "github_paper_md": "", "github_api": "https://github.com/usail-hkust/HRBench", "github_api_added_by": "user", "project_page": "https://github.com/usail-hkust/HRBench", "abstract": "Hybrid-reasoning large language models (LLMs) expose explicit controls over reasoning effort, allowing users or systems to trade off answer quality against inference cost. However, existing methods for adaptive thinking-mode selection are typically evaluated under different models, datasets, and implementation assumptions, making it difficult to compare their practical behavior. We introduce HRBench, a unified evaluation framework for studying thinking-mode switching in hybrid-reasoning LLMs. HRBench organizes the design space along two axes: three switching strategy families, prompt-based selection, external routing, and speculative execution, and four training regimes, training-free, SFT, offline and online RL, yielding 12 controlled evaluation settings. We evaluate these settings across 6 LLMs, from Qwen3.5-2B to Kimi-K2.5-1.1T, and 5 reasoning benchmarks covering mathematics, science, and code, while reimplementing 12+ representative prior methods within the same pipeline. Our analysis characterizes how different switching strategies occupy distinct effectiveness-efficiency trade-off regions: prompt-based methods often provide favorable token-accuracy trade-offs, routing methods offer more stable cost reduction, and speculative methods tend to improve accuracy at higher token cost. We further find that training affects strategies differently, and that the preferred strategy varies with model scale and task domain. HRBench provides reference implementations and a unified evaluation platform to support more controlled research on efficient reasoning in hybrid-reasoning LLMs. Our data, code and repository are available at https://github.com/usail-hkust/HRBench.", "github": "https://github.com/usail-hkust/HRBench" }, { "date": "2026-05-28", "arxiv_id": "2605.28655", "title": "AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation", "authors": [ "Shanghua Gao", "Ada Fang", "Marinka Zitnik" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://autoscientists.openscientist.ai/", "abstract": "Scientific research proceeds through iterative cycles of hypothesis generation, experiment design, execution, and revision. AI agents can automate parts of this process, but existing approaches typically follow a single research trajectory or coordinate through a central planner with fixed objectives. As a result, they struggle to sustain parallel exploration, adapt as experimental evidence changes, or preserve knowledge of failed directions over long-running experiments. We introduce AutoScientists, a decentralized team of AI agents for long-running computational scientific experimentation. Agents interpret a shared experimental state, self-organize into teams around promising hypotheses, critique proposals before using experimental compute, and share successes and failures to reduce redundant exploration. Under matched experimental budgets, AutoScientists improves over prior AI agents across biomedical machine learning, language-model training optimization, and protein fitness prediction. On BioML-Bench, spanning biomedical imaging, protein engineering, single-cell omics, and drug discovery, AutoScientists achieves a mean leaderboard percentile of 74.4% across 24 tasks, improving over the strongest AI agent by +8.33%. On GPT training optimization, AutoScientists reaches a target validation bits-per-byte 1.9x faster than Autoresearch and continues discovering improvements from a starting champion where the single-agent approach finds none (7 vs. 0 accepted improvements). On ProteinGym fitness prediction, AutoScientists discovers a method for ACE2-Spike binding that improves over the current state-of-the-art model by +12.5% in Spearman correlation. Applied without modification across all 217 ProteinGym assays, the same method improves over the prior state of the art by +6.5% (Spearman correlation).", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.28774", "title": "Agent Explorative Policy Optimization for Multimodal Agentic Reasoning", "authors": [ "Minki Kang", "Shizhe Diao", "Ryo Hachiuma", "Sung Ju Hwang", "Pavlo Molchanov", "Yu-Chiang Frank Wang", "Byung-Kwan Lee" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://byungkwanlee.github.io/AXPO-page/", "abstract": "Vision-language models with extended reasoning succeed on complex problems, but many real-world problems require external tools that internal reasoning alone often cannot resolve. Agentic reasoning therefore interleaves two behaviors with a structural asymmetry: thinking (the self-contained default) and tool use (a high-variance auxiliary acting). We refer to this asymmetry as the Thinking-Acting Gap. Under standard RL recipes like GRPO, the gap manifests as two diagnostic symptoms during training: tool use is attempted on only ~30% of rollouts, and when attempted, the tool-using rollouts within a group are all-wrong on ~40% of questions, suppressing the learning signal at the tool calls that needed it. We propose AXPO (Agent eXplorative Policy Optimization): for each all-wrong tool-using subgroup, AXPO fixes the thinking prefix and resamples the tool call and its continuation, paired with uncertainty-based prefix selection. Across nine multimodal benchmarks and three scales of Qwen3-VL-Thinking, SFT+AXPO outperforms SFT+GRPO at average (+1.8pp Pass@1 and +1.8pp Pass@4 at 8B on average) and 8B with SFT+AXPO surpasses the 32B Base on Pass@4 with 4 times fewer parameters.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.28763", "title": "CubePart: An Open-Vocabulary Part-Controllable 3D Generator", "authors": [ "Yiheng Zhu", "Kangle Deng", "Jean-Philippe Fauconnier", "Inaki Navarro", "Daiqing Li", "Ava Pun", "Yinan Zhang", "Peiye Zhuang", "Xiaoxia Sun", "Maneesh Agrawala", "Kiran Bhat", "Tinghui Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://cubepart.github.io/", "abstract": "Interactive 3D assets used in games and simulation are typically decomposed into specific semantic parts to support animation, physics, and scripted behaviors, yet most generative 3D models produce either monolithic meshes or arbitrary part decompositions that cannot be aligned with application-specific requirements. We present CubePart, a generative framework for open-vocabulary, part-controllable 3D mesh generation that exposes part structure as an explicit inference-time control signal. Given a global text prompt and a user-defined parts schema expressed as an open-ended list of part names, our method generates a set of meshes - one per schema element - that assemble into a coherent object while respecting the specified semantic structure. To enable this capability, we introduce a scalable data pipeline to construct a large open-vocabulary, part-labeled 3D dataset, along with a two-stage generative architecture that separates global shape synthesis from part-level decoding. We demonstrate that the resulting assets can be directly integrated into game engines and driven by animation and behavior scripts without manual post-processing. Project Page: https://cubepart.github.io/", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.28184", "title": "Joint Training of Multi-Token Prediction in Reinforcement Learning via Optimal Coefficient Calibration", "authors": [ "Zili Wang", "Jiajun Chai", "Lin Chen", "Xiaohan Wang", "Shiming Xiang", "Guojun Yin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MarkXCloud/RL-MTP", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as the standard paradigm for improving reasoning capability of large language models, while Multi-Token Prediction (MTP) has been a widely adopted module in pretraining. Combining them is a natural approach, yet current RL practices detach MTP gradients because joint training degrades the performance. We revisit this failure from an optimization perspective. We show that the per-step effect of MTP on the RL objective can be decomposed into two terms: a first-order correlation and a second-order perturbation penalty. This decomposition unifies three MTP training regimes: Detach, Cross-Entropy loss, and Policy loss, and explains why each succeeds or fails. Further analysis of policy loss reveals that, although it aligns with intuition, performance still degrades: the correlation term decays while the quadratic penalty persists. Guided by the analysis, we propose Optimal Coefficient Calibration (OCC), an adaptive scheme that tracks the optimal coefficient online via a log-probability proxy at negligible cost. Across six competition-level mathematical reasoning benchmarks, OCC consistently matches or exceeds the detach baseline, delivering improved joint MTP-RL training performance.", "github": "https://github.com/MarkXCloud/RL-MTP" }, { "date": "2026-05-28", "arxiv_id": "2605.28721", "title": "LiveBrowseComp: Are Search Agents Searching, or Just Verifying What They Already Know?", "authors": [ "HuiMing Fan", "Xiao Wang", "Zheng Chu", "Qianyu Wang", "Zhuoyao Wang", "Ming Liu", "Bing Qin", "XingYu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Are LLM-based search agents genuinely searching, or using the web to verify what they already know? We study this question on BrowseComp with three diagnostics. Our analysis reveals Intrinsic Knowledge Dependence (IKD): even with tool access, agents often rely on intrinsic knowledge -- information encoded in the model before retrieval -- rather than on external evidence. Agents answer up to 44.5% of BrowseComp questions without tools, generate more than half of their search queries from internally produced hypotheses rather than retrieved leads, and perform worse than closed-book baselines when answer-supporting evidence is removed. These results suggest that static search benchmarks can reward memory-backed verification rather than evidence-driven discovery, conflating what agents already know with what they can find. We then introduce LiveBrowseComp, a deep-search benchmark designed to evaluate agents beyond intrinsic coverage. It contains 335 human-authored questions whose answers depend on facts published within the 90 days preceding benchmark construction, drawn from six updated sources and filtered to exclude globally salient events. On LiveBrowseComp, all evaluated agents fall below 2% closed-book accuracy, search-augmented scores drop by 25-40 points relative to BrowseComp, and prior model rankings no longer reliably predict performance. LiveBrowseComp is available at https://huggingface.co/datasets/Forival/LiveBrowseComp.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.28805", "title": "OmniVerifier-M1: Multimodal Meta-Verifier with Explicit Structured Recalibration", "authors": [ "Xinchen Zhang", "Bowei Liu", "Jiale Liu", "Chufan Shi", "Yizhen Zhang", "Junhong Liu", "Youliang Zhang", "Zhiheng Li", "Yujiu Yang", "Ling Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Visual outcomes are increasingly central to multimodal large language models, making reliable and fine-grained verification essential for scaling generalist foundation models. In this work, we investigate multimodal meta-verification, which leverages verifier-generated rationales rather than decision-only signals, and explore how to effectively incorporate meta-verification feedback into multimodal verifier training. We identify two key findings. First, symbolic verifier outputs (e.g., bounding boxes) outperform textual explanations as meta-verification rationales, enabling efficient rule-based reinforcement learning rewards while avoiding reliance on model-based rewards from auxiliary judge models. Second, decoupling reinforcement learning objectives for binary judgment and meta-verification substantially outperforms joint reward optimization, due to intrinsic differences in output structure and learning dynamics. Based on these insights, we train OmniVerifier-M1, a generalist visual verifier leveraging symbolic meta-verification and decoupled reinforcement learning. OmniVerifier-M1 provides robust verification and fine-grained error localization, and further enables M1-TTS, a verifier-driven agentic generation system achieving dynamic region-level self-correction. This approach paves the way for more reliable, interpretable, and fine-grained multimodal verification, supporting safer and more controllable foundation model deployment.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.27905", "title": "AI Research Agents Narrow Scientific Exploration", "authors": [ "Yixuan Tang", "Yi Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "AI research agents can now generate research ideas, design experiments, run code, and draft papers, raising the possibility of large-scale AI-assisted scientific discovery. Many current agent frameworks explicitly encourage the generation of novel and high-impact ideas. Yet it remains unclear whether AI-assisted ideation broadens scientific exploration or mainly concentrates around existing work. We study AI research agents as scientific search systems. Using four AI research-agent frameworks and six large language models, we generate 37,802 scientific ideas from shared seed literature across citation-defined research areas in AI and machine learning. We then compare the resulting AI ideas against human-authored papers from the same research areas, follow-on human research emerging from the same seed literature, and the seed literature itself. Across experiments, four consistent patterns emerge. First, AI-generated ideas are substantially more concentrated than human-authored papers from the same research areas. Second, AI-generated ideas remain much closer to their starting literature than later human follow-on work does. Third, papers most similar to AI-generated ideas tend to receive lower subsequent citations. Fourth, when AI-generated ideas differ from prior work, the differences arise primarily from recombining existing technical methods rather than introducing fundamentally new research questions. Overall, current AI research agents appear better suited to local elaboration than to broadening scientific exploration.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.26457", "title": "Verus-SpecGym: An Agentic Environment for Evaluating Specification Autoformalization", "authors": [ "Anmol Agarwal", "Natalie Neamtu", "Pranjal Aggarwal", "Seungone Kim", "Jannis Limperg", "Cedric Flamant", "Kanna Shimizu", "Bryan Parno", "Sean Welleck" ], "github_manual": "", "github_regex": "https://github.com/formal-verif-is-cool/verus-spec-gym", "github_paper_md": "", "github_api": "https://github.com/formal-verif-is-cool/verus-spec-gym", "github_api_added_by": "user", "project_page": "https://formal-verif-is-cool.github.io/", "abstract": "AI coding agents are increasingly used to write real-world software, but ensuring that their outputs are correct remains a fundamental challenge. Formal verification offers a promising path: an agent generates code together with a machine-checked proof, guaranteeing that the code satisfies a formal specification. However, there is no guarantee that the formal spec itself matches the user's intent. In this work, we study specification autoformalization: whether LLM agents can translate informal programming problems into faithful formal specifications. We introduce Verus-SpecBench, a benchmark of 581 spec-writing tasks derived from Codeforces problems targeting Verus, a verifier for Rust, and Verus-SpecGym, an agentic environment in which models interact with Verus, bash, & the filesystem to develop these specs. The central challenge is evaluation: expert-written reference specs are expensive to write, & LLM judges can miss subtle mistakes. We address this by (a) extending Verus's exec_spec mechanism so that generated specs can be executed as Rust code, & (b) testing them against official Codeforces tests & adversarial cases extracted from Codeforces \"hacks\", which are edge cases written by competitors to break incorrect solutions. On Verus-SpecBench, the strongest model, Gemini 3.1 Pro, solves 77.8% of tasks, other frontier models solve 51.1--57.8% & OSS models reach only 21.5--25.5%. Our analysis of failure modes shows that model-generated specs can omit important input assumptions, accept incorrect outputs, & reject valid ones. We also find that LLM-as-a-judge evaluation misses 26% of the failures our evaluator catches. Overall, our results suggest that spec autoformalization is within reach for frontier agents but remains brittle even on problems where they can already generate correct code. The code, data, & logs can be found at https://github.com/formal-verif-is-cool/verus-spec-gym", "github": "https://github.com/formal-verif-is-cool/verus-spec-gym" }, { "date": "2026-05-28", "arxiv_id": "2605.27762", "title": "PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft", "authors": [ "Yuchen Guo", "Junli Gong", "Hongmin Cai", "Yiu-ming Cheung", "Weifeng Su" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We present PEAM, a Parametric Embodied Agent Memory framework in Minecraft that transforms agent memory from inference-time retrieval into parameter-resident skills internalized through experience. PEAM pairs a slow deliberative LLM for open-ended reasoning with a fast parametric module for reflexive execution of consolidated skills. The fast module is a multimodal Mixture-of-Experts LoRA architecture with per-category physically isolated adapters, enabling parameter-level continual learning without catastrophic forgetting. We treat failure as a first-class training signal: failure--correction trajectory pairs are internalized through a joint behavioral-cloning and contrastive objective, so the agent learns not only what succeeds but also how corrected actions differ from failed ones. To govern consolidation, PEAM introduces a parameterization-worthiness score for deciding which experience should be internalized, and a scale-free self-triggered consolidation mechanism for deciding when to internalize without task-specific hand-tuned thresholds, making the agent self-evolving as the trigger transfers across task distributions without re-tuning. Experiments in Minecraft show that PEAM improves long-horizon task performance, mitigates forgetting on previously consolidated skills, and improves parametric-versus-retrieval efficiency over retrieval-based embodied agents and parametric memory variants.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.26340", "title": "ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence", "authors": [ "Rui Meng", "Bhavana Dalvi Mishra", "Jiefeng Chen", "Chun-Liang Li", "Palash Goyal", "Mihir Parmar", "Yiwen Song", "Yale Song", "Rajarishi Sinha", "Parthasarathy Ranganathan", "Burak Gokturk", "Jinsung Yoon", "Tomas Pfister" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/scientist-one/generated-artifacts", "github_api_added_by": "user", "project_page": "https://scientist-one.github.io/", "abstract": "Autonomous research agents produce competitive solutions and professional-looking manuscripts, yet their outputs contain verifiability failures undetectable by surface-level evaluation: fabricated citations, unreproducible scores, and method descriptions that diverge from the implementation. We address this through three contributions. First, Chain-of-Evidence (CoE), a verifiability framework requiring every claim to be traceable to its evidence source. Second, ScientistOne, an end-to-end autonomous research system that maintains evidence chains by construction throughout literature review, solution discovery, and paper writing. Third, CoE Audit, a post-hoc audit whose four integrity checks -- score verification, specification violation, reference verification, and method-code alignment -- apply uniformly to all systems. Across 75 papers spanning five systems and five frontier research tasks, every baseline exhibits at least one systematic failure mode: hallucinated reference rates reach 21%, score verification passes in as few as 42% of papers, and method-code alignment ranges from 20% to 80%. ScientistOne achieves zero hallucinated references (0/337), perfect score verification (12/12), and the highest method-code alignment (14/15), while matching or exceeding human expert performance on all five tasks. ScientistOne further generalizes to six additional tasks spanning medical imaging, fine-grained recognition, 3D perception, and language modeling, achieving state-of-the-art on Parameter Golf and gold medals on MLE-Bench tasks where baselines fail entirely.", "github": "https://github.com/scientist-one/generated-artifacts" }, { "date": "2026-05-28", "arxiv_id": "2605.28548", "title": "GEM: Generative Supervision Helps Embodied Intelligence", "authors": [ "Ruowen Zhao", "Bangguo Li", "Zuyan Liu", "Yinan Liang", "Junliang Ye", "Fangfu Liu", "Diankun Wu", "Zhengyi Wang", "Xumin Yu", "Yongming Rao", "Han Hu", "Jun Zhu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhaorw02/GEM", "github_api_added_by": "user", "project_page": "https://zhaorw02.github.io/GEM/", "abstract": "Embodied Vision-Language Models (VLMs) have demonstrated impressive performance and generalization in robotics, particularly within Vision-Language-Action frameworks. However, a significant gap remains between the high-level semantic focus of standard text-guided pre-training paradigms and the low-level spatial and physical knowledge critical for execution in embodied environments. In this paper, we introduce GEM, a Generative-supervised Embodied vision-language Model designed to bridge this divide. We propose integrating a depth map generation task directly into the VLM pre-training phase. By training this generative objective jointly with the main model, we observe substantial improvements in embodied intelligence, significantly enhancing both semantic understanding and physical operation capabilities. To support this paradigm, we curate and release GEM-4M, a comprehensive large-scale dataset featuring a mixture of grounding, reasoning, and planning data paired with high-quality depth supervision. Extensive experiments demonstrate that GEM achieves state-of-the-art results across diverse embodied benchmarks. Furthermore, our deployed action model, GEM-VLA, exhibits vastly superior task execution abilities in both simulation environments and real-world evaluations. Code, models, and datasets are available at https://zhaorw02.github.io/GEM/", "github": "https://github.com/zhaorw02/GEM" }, { "date": "2026-05-28", "arxiv_id": "2605.28732", "title": "MemTrace: Tracing and Attributing Errors in Large Language Model Memory Systems", "authors": [ "Xinle Deng", "Ruobin Zhong", "Hujin Peng", "Xiaoben Lu", "Yanzhe Wu", "Guang Li", "Buqiang Xu", "Yunzhi Yao", "Jizhan Fang", "Haoliang Cao", "Junjie Guo", "Yuan Yuan", "Ziqing Ma", "Yuanqiang Yu", "Rui Hu", "Baohua Dong", "Hangcheng Zhu", "Ningyu Zhang" ], "github_manual": "", "github_regex": "https://github.com/zjunlp/MemTrace", "github_paper_md": "", "github_api": "https://github.com/zjunlp/MemTrace", "github_api_added_by": "user", "project_page": "", "abstract": "Memory is essential for enabling large language models to support long-horizon reasoning, yet existing memory systems remain unreliable and difficult to debug. Tracing memory's dynamic evolution is crucial to understand how information is synthesized, propagated, or corrupted over time. In this work, we study the new problem of error tracing and attribution in LLM memory systems. We propose a novel framework that transforms memory pipelines into executable memory evolution graphs, enabling fine-grained tracing of operational information flow. We then construct MemTraceBench, a benchmark collected from representative memory systems such as Long-Context, RAG, Mem0, and EverMemOS, to systematically study memory failure modes. We further introduce an automatic attribution method that iteratively traces operation subgraphs to pinpoint the root cause of any failed case. Our analysis reveals that memory failures are systematic, stemming from operation-level issues like information loss and retrieval misalignment. Crucially, we leverage these fine-grained attribution signals to guide downstream prompt optimization, establishing a closed-loop system that automatically corrects faults and boosts end-task performance by up to 7.62%. Code will be released at https://github.com/zjunlp/MemTrace.", "github": "https://github.com/zjunlp/MemTrace" }, { "date": "2026-05-28", "arxiv_id": "2605.28773", "title": "Rethinking Memory as Continuously Evolving Connectivity", "authors": [ "Jizhan Fang", "Buqiang Xu", "Zhixian Wang", "Haoliang Cao", "Xinle Deng", "Baohua Dong", "Hangcheng Zhu", "Ruohui Huang", "Gang Yu", "Ying Wei", "Guozhou Zheng", "Feiyu Xiong", "Haofen Wang", "Huajun Chen", "Ningyu Zhang" ], "github_manual": "", "github_regex": "https://github.com/zjunlp/LightMem", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Existing memory-augmented LLM agents often treat memory as a static repository with pre-defined representations and fixed retrieval pipelines, which is brittle in dynamic agentic environments where feedback, task variation, and heterogeneous signals continuously reshape what should be remembered and how it should be connected. To address this, we propose FluxMem, a connectivity-evolving memory framework that models memory as a heterogeneous graph and progressively refines its topology through three stages: initial connection formation, feedback-driven refinement, and long-term consolidation. During execution, FluxMem repairs missing links, prunes interference, aligns abstraction granularity, and distills recurrent successful trajectories into reusable procedural circuits, guided by one metric for memory generalizability and evolutionary maturity. Across three fundamentally distinct benchmarks including LoCoMo, Mind2Web, and GAIA, FluxMem achieves consistent state-of-the-art performance, demonstrating strong adaptation and generalization in complex agentic environments. The code will be open-sourced in https://github.com/zjunlp/LightMem.", "github": "https://github.com/zjunlp/LightMem" }, { "date": "2026-05-28", "arxiv_id": "2605.28293", "title": "ProRL: Effective Reinforcement Learning for Proactive Recommendation via Rectified Policy Gradient Estimation", "authors": [ "Hongru Hou", "Tiehua Mei", "Denghui Geng", "Jinhui Huang", "Ao Xu", "Hengrui Chen", "Jiaqing Liang", "Deqing Yang" ], "github_manual": "", "github_regex": "https://github.com/hongruhou89/ProRL", "github_paper_md": "", "github_api": "https://github.com/hongruhou89/ProRL", "github_api_added_by": "user", "project_page": "", "abstract": "Proactive Recommender Systems (PRSs) aim to guide user preference shift toward target items by generating paths of intermediate recommendations. Reinforcement learning (RL) provides a principled framework for optimizing such sequential decision tasks, as path rewards can naturally capture both short-term acceptance and long-term guidance effectiveness. However, naively applying policy gradients to PRS results in deficient gradient estimation. We identify two deficiencies: (1) path-level rewards decompose into step-level rewards with positive mean, creating a length-dependent bias that causes gradients to favor path extension over meaningful exploration; (2) weighting each step by the entire path-level reward ignores the decomposition structure, leading to high gradient variance. To rectify these two deficiencies, we propose an effective RL framework ProRL with two novel mechanisms for proactive recommendation. First, Stepwise Reward Centering subtracts expected rewards to neutralize length-dependent bias, ensuring that path extension yields zero expected gradient signal. Second, Position-Specific Advantage Estimation leverages the reward decomposition structure to compute step-dependent baselines, reducing gradient variance. Together, these mechanisms yield policy gradients that precisely target path quality. Our experiments on three real-world datasets demonstrate that ProRL significantly outperforms state-of-the-art PRSs. Our code is available at https://github.com/hongruhou89/ProRL.", "github": "https://github.com/hongruhou89/ProRL" }, { "date": "2026-05-28", "arxiv_id": "2605.28109", "title": "Long Live The Balance: Information Bottleneck Driven Tree-based Policy Optimization", "authors": [ "Hao Jiang", "Shurui Li", "Tianpeng Bu", "Bowen Xu", "Xin Liu", "Qihua Chen", "Hongtao Duan", "Lulu Hu", "Bin Yang", "Minying Zhang" ], "github_manual": "", "github_regex": "https://github.com/alibaba/EfficientRL", "github_paper_md": "", "github_api": "https://github.com/alibaba/EfficientRL", "github_api_added_by": "user", "project_page": "", "abstract": "Recent advances in online reinforcement learning (RL) for large language models (LLMs) have demonstrated promising performance in complex reasoning tasks. However, they often exhibit an imbalanced exploration-exploitation trade-off, resulting in unstable optimization and sub-optimal performance. We introduce IB-Score, a novel metric grounded in Information Bottleneck theory that evaluates policy's exploration-exploitation balance by quantifying the trade-off between step-level reasoning diversity and mutual information shared with the correct answer. Analysis based on IB-Score shows that popular online RL approaches (e.g., GRPO) with common regularizers fail to consistently maintain balance during training with suboptimal results. To address this, we propose Information Bottleneck-driven Tree-based Policy Optimization (IB-TPO), a principled framework that formulates IB-Score as a fine-grained optimization objective and utilizes a novel IB-guided tree sampling strategy that not only improves the efficiency of online sampling with 50% more trajectories under the same token budget, but also reuses the tree structure for effective IB-Score Monte Carlo estimation. Extensive experiments across standard benchmarks show that our method significantly outperforms GRPO baseline by 2.9% to 3.6% and also outperforms other state-of-the-art online RL approaches. Our code is available at https://github.com/alibaba/EfficientRL.", "github": "https://github.com/alibaba/EfficientRL" }, { "date": "2026-05-28", "arxiv_id": "2605.26396", "title": "Advancing Creative Physical Intelligence in Large Multimodal Models", "authors": [ "Cheng Qian", "Hyeonjeong Ha", "Jiayu Liu", "Jeonghwan Kim", "Emre Can Acikgoz", "Bingxuan Li", "Kunlun Zhu", "Jiateng Liu", "Aditi Tiwari", "Zhenhailong Wang", "Xiusi Chen", "Mahdi Namazifar", "Heng Ji" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CreativityBench/MM-CreativityBench", "github_api_added_by": "user", "project_page": "https://creativitybench.github.io/mm-creativitybench.github.io/", "abstract": "Large multimodal models (LMMs) have rapidly advanced in perception and reasoning; however, it remains unclear whether these capabilities generalize to discovering visually grounded solutions in open-ended environments, beyond pattern recognition. In such settings, intelligence requires more than answering well-posed questions: it involves identifying how elements in a scene can be repurposed in non-obvious yet physically feasible ways. This form of creative problem-solving is central to human intelligence, but remains largely untested in current benchmarks. To evaluate this ability, we introduce MM-CreativityBench, a benchmark for affordance-grounded creative tool use in visually rich, physically constrained environments. Each instance presents a scenario image with structured views of candidate entities and their parts, enabling fine-grained, interactive evaluation of how models iteratively inspect the scene, identify relevant affordances, and compose visually and physically grounded solutions. Our experiments show that current LMMs often fall short, not due to lack of generative capability, but because they do not sustain grounded exploration. Models often overlook relevant entities, under-examine critical parts, or hallucinate attributes not grounded in the image. Motivated by this failure mode, we propose affordance-grounded alignment, which casts creative tool use as a preference learning problem. Using Direct Preference Optimization, we encourage models to prefer attribute-affordance reasoning grounded in visual evidence over hallucinated alternatives. In addition, we incorporate supervision derived from an affordance knowledge base to guide broader entity exploration and multi-turn planning. Our results show consistent gains in selecting the correct entities and parts, while substantially reducing hallucination and grounding-related errors.", "github": "https://github.com/CreativityBench/MM-CreativityBench" }, { "date": "2026-05-28", "arxiv_id": "2605.25969", "title": "Triplet-Block Diffusion RWKV", "authors": [ "Ke Lin", "Yiyang Luo", "Zhaolong Su", "Yunya Song", "Anyi Rao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/leonardodalinky/B3D-RWKV", "github_api_added_by": "user", "project_page": "", "abstract": "Causal Transformer language models suffer from strictly sequential decoding and a quadratic per-step attention cost. While linear-time causal models and discrete diffusion models each address these weaknesses, their integration remains inherently inconsistent: diffusion requires bidirectional attention, while causal models are unidirectional. To unify these architectures, we propose B^3D-RWKV, a diffusion RWKV variant that integrates the model's O(L) inference efficiency with parallel, bidirectional discrete-diffusion through a triplet-block layout method. B^3D-RWKV-7.2B reaches comparable accuracy on an 8-task suite versus existing models while significantly outperforming baselines in decoding throughput with an average of 1.6times speedup.", "github": "https://github.com/leonardodalinky/B3D-RWKV" }, { "date": "2026-05-28", "arxiv_id": "2605.28819", "title": "PEFT-Arena: Understanding Parameter-Efficient Finetuning from a Stability-Plasticity Perspective", "authors": [ "Yangyi Huang", "Ruotian Peng", "Zeju Qiu", "Jiale Kang", "Yandong Wen", "Bernhard Sch\u00f6lkopf", "Weiyang Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Sphere-AI-Lab/PEFT-Arena", "github_api_added_by": "user", "project_page": "https://spherelab.ai/PEFT-Arena/", "abstract": "Parameter-efficient finetuning (PEFT) has become the standard approach for adapting large language models, yet evaluations largely emphasize downstream accuracy while overlooking the retention of pretrained capabilities. We argue that PEFT should be assessed through the stability-plasticity dilemma: the trade-off between target-task adaptation and resistance to forgetting. We introduce PEFT-Arena, a benchmark that jointly measures downstream performance and general capability retention. Across methods, we find distinct stability-plasticity profiles; under comparable parameter budgets, orthogonal finetuning achieves the most favorable Pareto frontier. To explain these differences, we analyze PEFT updates from two geometric perspectives. In weight space, spectral analysis reveals how parameterizations interact with the pretrained singular-value structure. In activation space, retention metrics show whether finetuning preserves or distorts general-capability representations, with forgetting linked to non-isometric representation distortion. Finally, an analysis shows that final SFT checkpoints often overshoot a better target-retention operating point. Inspired by this, we present case studies of a post-hoc improvement with path-wise rewinding.", "github": "https://github.com/Sphere-AI-Lab/PEFT-Arena" }, { "date": "2026-05-28", "arxiv_id": "2605.27466", "title": "AgensFlow: A Coordination-Policy Substrate for Multi-Agent Systems", "authors": [ "Nicole Koenigstein" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Nicolepcx/AgensFlow", "github_api_added_by": "user", "project_page": "", "abstract": "Multi-agent systems built on large language models (LLMs) require many coordination choices that are difficult to fix a priori: which skill protocol to invoke, which agent role should perform a subtask, which model to bind to each role, how roles should interact, when to use retrieval or verification, and when to omit a step entirely. These choices interact with task regime and operational constraints, so static pipelines and one-off model comparisons provide only a limited view of the design space. This paper introduces AgensFlow, an open-source framework that treats multi-agent coordination as an online policy-learning problem under partial observability. The framework makes coordination decisions observable and learnable from repeated trajectories, rather than treating skill, role, model, topology, and evaluation choices as fixed pipeline design. AgensFlow is evaluated on two corpora: distributed-systems incident tasks and security-advisory tasks. The evaluation shows three main results: learned routing reaches a higher-quality operating point than a fixed pipeline baseline on coordination-heavy classes; skip:X isolates topology compression as a meaningful part of the substrate; and warm-started policy graphs can reduce exploration cost while preserving plateau quality. Overall, the results support that learned, auditable routing can improve coordination-heavy multi-agent workflows over static wiring.", "github": "https://github.com/Nicolepcx/AgensFlow" }, { "date": "2026-05-28", "arxiv_id": "2605.23163", "title": "Fast-dDrive: Efficient Block-Diffusion VLM for Autonomous Driving", "authors": [ "Kewei Zhang", "Jin Wang", "Sensen Gao", "Chengyue Wu", "Yulong Cao", "Songyang Han", "Boris Ivanovic", "Langechuan Liu", "Marco Pavone", "Song Han", "Daquan Zhou", "Enze Xie" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/NVlabs/Fast-dLLM", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "End-to-end autonomous driving via Vision-Language-Action (VLA) models demands a precarious balance between high-fidelity trajectory planning and efficient inference. Existing paradigms typically fall short: autoregressive (AR) VLAs are memory-bandwidth-bound on edge hardware and prone to exposure-bias drift, while full-sequence diffusion models preclude KV-cache reuse and suffer from \"logical leakage\" that violates the fundamental perceive-then-plan causality. We present Fast-dDrive, a block-diffusion VLA that performs bidirectional refinement within semantic units while enforcing strict causal ordering across them. Leveraging the observation that driving VLAs often emit structured JSON-like outputs, Fast-dDrive freezes structural tokens into a section scaffold and employs a section-aware training recipe that prioritizes safety-critical planning. We further introduce Scaffold Speculative Decoding to achieve AR-equivalent quality at significantly higher throughput. Finally, we propose a low-overhead test-time scaling scheme: by forking N stochastic trajectory rollouts from a single shared-prefix KV cache and averaging them, we effectively suppress prediction variance at a fractional computational cost. Empirical results demonstrate that Fast-dDrive redefines the speed-accuracy frontier for driving agents. On the WOD-E2E test set, Fast-dDrive achieves SOTA ADE@3s and ADE@5s, alongside the highest RFS among diffusion-based VLAs; on nuScenes, it reduces average L2 error to 0.32m (a 22% improvement). When integrated with SGLang, our framework delivers 12times throughput speedup over the AR baseline, narrowing the gap between high-capacity VLAs and the efficiency demands of real-time on-vehicle deployment.", "github": "https://github.com/NVlabs/Fast-dLLM" }, { "date": "2026-05-28", "arxiv_id": "2605.28421", "title": "DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes", "authors": [ "Caijun Xu", "Changyi Xiao", "Zhongyuan Peng", "Yixin Cao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ALEX-nlp/DenoiseRL", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement learning has become a central paradigm for advancing reasoning in large language models, yet most existing methods still depend on stronger teacher models or heavily curated difficult datasets, limiting scalable capability improvement. In this paper, we introduce DenoiseRL, a reinforcement learning framework that substitutes external supervision with recovery-oriented optimization over failures from weak models. Instead of relying on stronger supervision or carefully engineered data, DenoiseRL learns directly from incorrect reasoning traces by converting them into opportunities for improvement, making training more scalable and less dependent on external resources. This yields a richer and more diverse learning signal, improving exploration efficiency from imperfect model behavior. As a result, DenoiseRL improves reasoning performance and overall training efficiency while reducing the need for expensive data curation or stronger teacher models. Empirically, DenoiseRL consistently outperforms strong on-policy RL baselines across competitive mathematical and general reasoning benchmarks and promotes stronger self-corrective behavior as training difficulty increases, highlighting an effective and scalable alternative pathway for improving reasoning in large language models.", "github": "https://github.com/ALEX-nlp/DenoiseRL" }, { "date": "2026-05-28", "arxiv_id": "2605.27354", "title": "Guiding LLM Post-training Data Engineering with Model Internals from Sparse Autoencoders", "authors": [ "Yi Jing", "Zao Dai", "Jinwu Hu", "Zijun Yao", "Lei Hou", "Juanzi Li", "Xiaozhi Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Model internals encode rich information about how a large language model (LLM) processes its training data; however, post-training data engineering largely relies on external signals and ignores rich intrinsic signals lying in model internals. We propose SAERL, a data engineering framework for LLM reinforcement learning (RL). It models three intrinsic data properties: diversity, difficulty, and quality, using model internals extracted with Sparse Autoencoder (SAE), an advanced mechanistic interpretability tool. Each property grounds a concrete data engineering operation: SAE-space clustering with moderate batch mixing for batch diversity control, a difficulty proxy for easy-to-hard curriculum ordering, and a quality probe for data filtering. SAERL improves average accuracy by 3.00% over vanilla GRPO and reaches target accuracy with 20% fewer training steps on Qwen2.5-Math-1.5B, with consistent gains across model scales and RL algorithms. Experiments show that SAE transfers effectively across model families and scales, serving as a lightweight and reusable data engineering tool. These results demonstrate that model internals are a powerful and practical source of signals for post-training data engineering.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.27268", "title": "Lost in Sampling: Assessing Lexical Reachability in LLMs via the Word Coverage Score (WCS)", "authors": [ "Samer Awad", "Javier Conde", "Carlos Arriaga", "Tairan Fu", "Javier Coronado-Bl\u00e1zquez", "Pedro Reviriego" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/WordsGPT/WCS", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Modern Large Language Models (LLMs) are often criticized for producing repetitive and homogeneous text, despite possessing vast latent vocabularies. While previous research has focused on model knowledge and training data, we investigate the role of decoding mechanics in suppressing linguistic diversity. We introduce the Word Coverage Score (WCS), a metric that quantifies the extent to which contextually appropriate human vocabulary is mathematically pruned by standard sampling filters (e.g., Top-p, Top-k, and Min-p). Rather than assessing static knowledge, the WCS measures the lexical survival rate of low-frequency, high-information human words as a function of sampling parameters. By auditing open-weight models on human-authored corpus fragments, we identify which logical lexical choices are rendered unreachable by the decoder, even when they reside within the probability space. Our results provide quantitative evidence that industry-standard sampling defaults act as unintended censorship mechanisms, smoothing the unique textures of human expression into a homogenized discourse. The WCS offers a rigorous framework for optimizing the trade-off between text coherence and lexical richness, providing a diagnostic tool for preserving the diversity of human language in generative models.", "github": "https://github.com/WordsGPT/WCS" }, { "date": "2026-05-28", "arxiv_id": "2605.27908", "title": "ESC-Skills: Discovering and Self-Evolving Skills for Emotional Support Conversations", "authors": [ "Jie Zhu", "Huaixia Dou", "Shuo Jiang", "Junhui Li", "Lifan Guo", "Feng Chen", "Chi Zhang", "Fang Kong" ], "github_manual": "", "github_regex": "https://github.com/aliyun/qwen-dianjin", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://github.com/aliyun/qwen-dianjin", "abstract": "Existing emotional support conversation (ESC) systems mainly rely on end-to-end response generation or coarse strategy supervision, offering limited interpretability and little support for systematic skill improvement. We propose ESC-Skills, a skill-centric framework that discovers and self-evolves executable emotional support skills. We first model localized support interactions as Intervention Units (IUs), which capture state--action--outcome dynamics between seeker states, support interventions, and post-response emotional changes. Based on IUs extracted from both successful and failed ESC dialogues, we construct the ESC-Skills Bank, a repository of executable emotional support skills containing intervention guidance, applicability conditions, expected outcomes, and potential risks. To further improve robustness, we introduce a multi-profile self-evolutionary refinement framework in which an ESC agent interacts with diverse simulated seeker profiles under SAGE evaluation. The resulting interaction traces are analyzed to identify missing skills, unsafe interventions, and profile-specific failure patterns, which are then used to refine the Skills Bank through simulation-based verification. Experimental results demonstrate that ESC-Skills improves both response-level quality and dialogue-level emotional outcomes while providing more interpretable and controllable support behaviors. We will release the code, prompts, and ESC-Skills Bank at https://github.com/aliyun/qwen-dianjin.", "github": "https://github.com/aliyun/qwen-dianjin" }, { "date": "2026-05-28", "arxiv_id": "2605.24486", "title": "AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning", "authors": [ "Yuyang Hu", "Hongjin Qian", "Shuting Wang", "Jiongnan Liu", "Tong Zhao", "Xiaoxi Li", "Zheng Liu", "Zhicheng Dou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recent progress on long-horizon agentic tasks has been driven largely by scaling up individual agents through stronger models, better tools, and more effective scaffolding. In contrast, much less is understood about scaling out: whether multiple peer agents, all targeting the same task, can become an additional source of capability without relying on explicit role specialization or workflow orchestration. We study this question and propose AgentFugue, a collective reasoning framework built around a shared reasoning hub. As peer agents explore the same task in parallel, the hub records concise notes on what each agent has established, attempted, or ruled out, and enables each agent to selectively access what other agents have discovered in a form useful for its current search. This design turns otherwise isolated trajectories into a connected ecology of reusable intermediate reasoning without requiring centralized planning. We instantiate the hub as a plug-in communication layer, trained with supervised fine-tuning and end-to-end reinforcement learning. Across the challenging long-horizon settings we study, AgentFugue improves over strong baselines. Our results suggest that collective reasoning can turn scaling out peer agent systems into a distinct source of capability gains, rather than merely a way of spending more compute.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.27760", "title": "SkillGrad: Optimizing Agent Skills Like Gradient Descent", "authors": [ "Hanyu Wang", "Yifan Lan", "Bochuan Cao", "Lu Lin", "Jinghui Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wwwhy725/SkillGrad", "github_api_added_by": "user", "project_page": "", "abstract": "Agent skills provide a lightweight way to adapt LLM agents to specialized domains by storing reusable procedural knowledge in structured files. However, whether downloaded from third parties or self-generated, these skills are often unreliable, incomplete, or outdated. Existing skill-evolution methods often address these deficiencies through heuristic reflections without an explicit optimization formulation. In this paper, we propose SkillGrad, a gradient-descent-inspired framework for optimizing agent skills. SkillGrad treats the skill package as a structured parameter to optimize in a gradient descent fashion: task executions provide trajectory-level loss evidence, automatic diagnoses then provide text-based gradients that indicate the correction directions. To stabilize optimization across iterations, a momentum agent accumulates recurring diagnostic patterns into a persistent memory overlay. Finally, an LLM-based patcher executes the parameter update by applying layer-aware edits to the skill package. Evaluated on SpreadsheetBench Verified and WikiTableQuestions, SkillGrad consistently outperforms training-based skill evolution baselines across two backbone LLMs, improving over the strongest training-based baseline by 6.7 percentage points on average. Ablations further show that momentum and contrastive diagnosis both contribute to the final skill quality.", "github": "https://github.com/wwwhy725/SkillGrad" }, { "date": "2026-05-28", "arxiv_id": "2605.27824", "title": "Revealing Algorithmic Deductive Circuits for Logical Reasoning", "authors": [ "Phuong Minh Nguyen", "Tien Huu Dang", "Naoya Inoue" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recent studies have shown that Large Language Models (LLMs) can achieve strong reasoning performance by incorporating functional symbolic representations that abstractly describe graph traversal algorithms and step-by-step reasoning in few-shot learning settings. However, it remains unclear how LLMs genuinely understand the abstract meaning of each reasoning step and the overall algorithm from only a limited number of demonstrations. This work aims to localize the attention heads responsible for individual reasoning steps and characterize the types of information transferred among them. We first align constituent reasoning steps with their corresponding token logits under a symbolic-aided Chain-of-Thought (CoT) prompting framework. Our analysis shows that token positions that steer the reasoning process are associated with low confidence scores caused by constraints on satisfying reasoning behavior patterns in demonstrations. We then adopt causal mediation analysis techniques to identify the attention heads responsible for these patterns. In addition, our findings indicate that LLMs retrieve factual and rule-based information for individual sub-reasoning tasks through specialized attention heads (approximately 3% total heads), whereas higher layers predominantly facilitate information integration and the emergence of global reasoning strategies (e.g., graph traversal algorithms) that coordinate multiple intermediate reasoning steps to solve the overall task.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.28034", "title": "Clark Hash: Stateless Sparse Johnson-Lindenstrauss Quantization for Neural Embeddings", "authors": [ "Stanislav Kirdey", "Clark Labs Inc" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/clark-labs-inc/clark-hash", "github_api_added_by": "user", "project_page": "https://www.clarkchat.com", "abstract": "Clark Hash is a small method for storing neural embeddings in less space. It normalizes each database vector, applies a deterministic sparse signed Johnson-Lindenstrauss projection, clips the result, and stores a fixed-width scalar-quantized code. Queries stay in floating point and are scored against the stored sketches. In the default 384-dimensional sentence-embedding setting, Clark Hash stores a cosine-search vector in 48 bytes instead of 1536 bytes for dense f32 storage. This is 32x smaller. The method does not need a training pass, learned codebooks, rotations, or corpus statistics before new vectors can be stored. We describe the codec, the Rust implementation, and a multilingual sentence-similarity evaluation on 9,304 labeled pairs from 29 subsets. With a multilingual MiniLM encoder, the 48-byte sketches reached 0.910 and 0.946 macro Pearson correlation with dense cosine scores on STS17 and STS22. Clark Hash is not a new Johnson-Lindenstrauss theorem and it is not a replacement for approximate nearest-neighbor indexes. It is a simple stateless codec for compact embedding storage.", "github": "https://github.com/clark-labs-inc/clark-hash" }, { "date": "2026-05-28", "arxiv_id": "2605.27491", "title": "GE-Sim 2.0: A Roadmap Towards Comprehensive Closed-loop Video World Simulators for Robotic Manipulation", "authors": [ "Boxiang Qiu", "Liliang Chen", "Yue Liao", "Nan Wang", "Lintao Wang", "Jiayi Luo", "Wenzhi Zhao", "Shengcong Chen", "Di Chen", "Ye Li", "Chen Gao", "Shuicheng Yan", "Si Liu", "Maoqing Yao", "Guanghui Ren" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AgibotTech/GE-Sim-V2", "github_api_added_by": "user", "project_page": "https://ge-sim-v2.github.io/", "abstract": "We introduce GE-Sim 2.0 (Genie Envisioner World Simulator 2.0), a closed-loop video world simulator for robotic manipulation. Building on the action-conditioned video generation framework of Genie Envisioner, GE-Sim 2.0 is re-trained on thousands of hours of real-world robot data spanning teleoperation, contact-rich interaction, and on-robot policy deployment, substantially improving action-following fidelity and trajectory coverage. On top of this foundation, three new modules close the loop from video simulation to policy learning: a state expert that decodes proprioceptive state from video latents to support next-chunk prediction by downstream VLA policies; a world judge that scores generated rollouts against task instructions, yielding machine-verifiable success signals and rewards in place of manual inspection; and an acceleration framework that delivers a 25-frame rollout in 2.3 seconds on a single H100, with up to 4* frame skipping at inference for long-horizon evaluation. GE-Sim 2.0 tops the public WorldArena leaderboard at only 2B parameters, outperforming both dedicated robotic world models and closed-source general video generators, and policies trained against its rollouts and rewards translate into measurable real-world gains, establishing GE-Sim 2.0 as a practical platform for scalable evaluation and closed-loop learning of manipulation policies.", "github": "https://github.com/AgibotTech/GE-Sim-V2" }, { "date": "2026-05-28", "arxiv_id": "2605.26574", "title": "GradSentry: Gradient Spectral Entropy for Backdoor Sample Filtering in Large Language Model Fine-Tuning", "authors": [ "Haodong Zhao", "Tianyi Xu", "Tianhang Zhao", "Zhuosheng Zhang", "Gongshen Liu" ], "github_manual": "", "github_regex": "https://github.com/dongdongzhaoUP/GradSentry", "github_paper_md": "", "github_api": "https://github.com/dongdongzhaoUP/GradSentry", "github_api_added_by": "user", "project_page": "", "abstract": "Fine-tuning Large Language Models with untrusted data exposes models to backdoor attacks, where poisoned samples cause targeted misbehavior. Existing sample-filtering defenses rely on clustering, which requires sufficient data and can fail at extreme poison ratios. We propose GradSentry ({Grad}ient {Sentry}), a backdoor sample filtering method based on the spectral entropy of per-sample gradients. Our key finding is that poisoned samples produce gradients with higher spectral entropy compared to clean samples. GradSentry captures output-altering backdoor signatures using per-sample gradient spectra, avoiding pairwise sample comparisons and clustering during feature construction. Importantly, our method is training-agnostic: it works for both parameter-efficient fine-tuning methods like LoRA and full-parameter tuning, as the gradient analysis operates independently of which parameters are being updated during training. GradSentry requires no clustering, operates effectively across all poison ratios (1%--90%), and introduces minimal computational overhead (20-50ms per sample for 7B model). Evaluation on four QA datasets and four attack types demonstrates the effectiveness of spectral entropy for backdoor detection. Code is available at https://github.com/dongdongzhaoUP/GradSentry.", "github": "https://github.com/dongdongzhaoUP/GradSentry" }, { "date": "2026-05-28", "arxiv_id": "2605.28510", "title": "Efficient and Scalable Provenance Tracking for LLM-Generated Code Snippets", "authors": [ "Andrea Gurioli", "Davide D'Ascenzo", "Federico Pennino", "Maurizio Gabbrielli", "Stefano Zacchiroli" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language models (LLMs) for code completion and generation are increasingly used in software development, yet they may reproduce training examples verbatim and without authorship attribution, raising legal and ethical concerns around plagiarism and license compliance. Classical fingerprint-based plagiarism detectors based on fingerprinting, such as Winnowing, remain highly effective, yet the inspection requires comparing fragments of code to the entire training set, and their linear-time search makes them impractical for the billion-scale corpora used to train modern code LLMs. To bridge this gap, we introduce SOURCETRACKER, a 300M-parameter encoder tailored for code retrieval, together with a hybrid two-stage provenance-tracking pipeline HYBRIDSOURCETRACKER (HST). HST first narrows down a small set of candidate snippets via vector search, then re-ranks those candidates using Winnowing on exact fingerprints. We train and evaluate our system on a 10M-snippet subset of the THESTACKV2 dataset, with both verbatim and adapted snippets that emulate realistic identifier renaming. On an in vitro 100k-snippet search space with adapted queries, our hybrid approach reaches a mean reciprocal rank on par with Winnowing for 30-token fragments. Then, starting from windows >= 60 tokens, it consistently over-performs by up to 5.4% while preserving logarithmic-time query complexity. In a complementary evaluation using an LLM-based judge, we find that many retrieved snippets not labeled as ground truth are still highly similar to the expected sources, particularly with longer context windows, and thus remain useful for end users. Overall, our results demonstrate that integrating vector search with fingerprinting enables scalable, high-precision provenance tracking for code produced by LLMs.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.26032", "title": "Everything at Every Scale: Scale-Invariant Diffusion with Continuous Super-Resolution", "authors": [ "Zixin Jessie Chen", "Zhuo Chen", "Archer Wang", "Jeff Gore", "William T. Freeman", "Congyue Deng", "Marin Solja\u010di\u0107" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JazzyCH/SKILD", "github_api_added_by": "user", "project_page": "", "abstract": "Creating images from noise is image generation; reconstructing fine details from coarse inputs is super-resolution. Despite their practical differences, both can be understood as reversing information loss across scales. We introduce SKILD, a Scale-invariant K-Space Image Learning Diffusion model that unifies generation and continuous super-resolution within a single unconditional framework. Both natural images and critical physical systems exhibit scale invariance, and we leverage it to design a forward process that attenuates image content from fine to coarse scales while injecting spectrum-matched Gaussian noise, making scale an explicit coordinate of the diffusion dynamics. The same trained reverse process performs generation and continuous super-resolution by varying only the starting timestep: no task-specific architecture, no conditioning branch, no classifier-free guidance, no retraining per scale factor. Empirically, SKILD reaches FID 2.65 and Inception Score 9.63 on unconditional CIFAR-10, performs 2times--8times super-resolution on ImageNet from a single unconditional checkpoint while outperforming conditional models across perceptual metrics, and reconstructs critical Ising models whose connected four-point correlations closely track the ground truth.", "github": "https://github.com/JazzyCH/SKILD" }, { "date": "2026-05-28", "arxiv_id": "2605.27901", "title": "The Fragility of Chain-of-Thought Monitoring Across Typologically Diverse Languages", "authors": [ "Eric Onyame", "Runtao Zhou", "Kowshik Thopalli", "Bhavya Kailkhura", "Chirag Agarwal" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AikyamLab/multilingual-monitoring", "github_api_added_by": "user", "project_page": "https://multilingual-cot-monitoring.github.io/", "abstract": "Chain-of-thought (CoT) monitoring has been proposed as a promising safety mechanism for detecting misaligned behavior in large language models. However, its reliability remains largely unexplored beyond English and across diverse model families. We present the first large-scale evaluation of CoT monitorability across 13 diverse languages and seven frontier model families, comprising 16 models. Using adversarial-hint evaluations that require explicit intermediate computation, together with analysis of internal answer-token probabilities, we consistently find CoT unfaithfulness across languages and hint types, with an average rate of 95.9\\% across 8B--120B parameter models. We find that frontier models systematically engage in strategic manipulation, including answer-switching, post-hoc rationalization, and procedural exploitation of hints, making external monitors struggle to detect deception. We show that frontier models often commit to the misaligned cue in their latent activations within the first 15\\% of generation, even when the CoT appears faithful. Surprisingly, these deceptive patterns remain 100\\% in low-resource languages, revealing fundamental limitations in current CoT-based oversight. Our results reveal that CoT monitoring is fundamentally fragile under linguistic distribution shift, providing a substantially weaker safety signal than what English-only studies suggest. These findings underscore an urgent need to develop robust CoT monitors and to accelerate research into white-box monitoring techniques, especially to improve CoT monitorability in mid- and low-resource languages. Our code is available https://multilingual-cot-monitoring.github.io/{blue{here}}.", "github": "https://github.com/AikyamLab/multilingual-monitoring" }, { "date": "2026-05-28", "arxiv_id": "2605.28591", "title": "Models That Know How Evaluations Are Designed Score Safer", "authors": [ "Katharina Deckenbach", "Haritz Puerto", "Jonas Geiping", "Sahar Abdelnabi" ], "github_manual": "", "github_regex": "https://github.com/compass-group-tue/arxiv2026_evaluation_meta_knowledge", "github_paper_md": "", "github_api": "https://github.com/compass-group-tue/arxiv2026_evaluation_meta_knowledge", "github_api_added_by": "user", "project_page": "https://compass-group-tue.github.io/arxiv2026_evaluation_meta_knowledge/", "abstract": "The validity of AI safety evaluations depends on models behaving consistently across controlled and deployment settings. Prior work has identified test-time contextual cues, such as hypothetical scenarios, as a source of verbalized evaluation awareness and subsequent behavioral shift. In this paper, we investigate a potential explanation of this phenomenon: evaluation meta-knowledge, defined as parametric knowledge about the structural traits that characterize evaluations. Similar to dataset contamination, where benchmark exposure leads to higher performance through memorization, we hypothesize that models trained on texts describing evaluation practices may implicitly learn to recognize and respond to evaluation-like contexts, for instance, through exposure to scientific articles or social media posts about AI benchmarking. To test this, we fine-tune models on synthetic documents describing evaluation traits such as verifiable structures or moral dilemmas. Evaluating this fine-tuned model on six safety benchmarks, we find that it is significantly safer than the base model and control model. This behavioral shift persists even when restricting the analysis to responses lacking explicit verbalization of evaluation awareness. Our results demonstrate that evaluation meta-knowledge may inflate safety benchmark performance, introducing a novel confounder that is independent of explicit memorization or verbalized evaluation awareness, thus, challenging to detect. These findings have important implications for the design and interpretation of AI safety evaluations. Our code and models are available at https://github.com/compass-group-tue/arxiv2026_evaluation_meta_knowledge.", "github": "https://github.com/compass-group-tue/arxiv2026_evaluation_meta_knowledge" }, { "date": "2026-05-28", "arxiv_id": "2605.19952", "title": "Rethinking How to Remember: Beyond Atomic Facts in Lifelong LLM Agent Memory", "authors": [ "Jingwei Sun", "Jianing Zhu", "Jiangchao Yao", "Tongliang Liu", "Bo Han" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tmlr-group/TriMem", "github_api_added_by": "user", "project_page": "https://tmlr-trimem.github.io/", "abstract": "To enable reliable long-term interaction, LLM agents require a memory system that can faithfully store, efficiently retrieve, and deeply reason over accumulated dialogue history. Most existing methods adopt an extracted fact based paradigm: handcrafted static prompts compress raw dialogues into atomic facts, which are then stored, matched, and injected into downstream reasoning. Nevertheless, such fact-centric designs inevitably discard fine-grained details in original dialogues and fail to support deep reasoning over scattered isolated facts. Moreover, static prompts cannot maintain consistent extraction granularity across diverse dialogue styles. To address these limitations, we propose TriMem, which maintains three coexisting representation granularities, including raw dialogue segments anchored by source identifiers for storage fidelity, extracted atomic facts for efficient memory retrieval, synthesized profiles that aggregate dispersed facts into holistic semantic understanding for deep reasoning. We further adopt TextGrad-based prompt optimization, which iteratively refines extraction and profiling prompts via response quality feedback, achieving lifelong evolution without any parameter updating. Extensive experiments on LoCoMo and PerLTQA across multiple LLM backbones demonstrate that TriMem consistently outperforms strong memory baselines. The code is available at https://TMLR-TriMem.github.io .", "github": "https://github.com/tmlr-group/TriMem" }, { "date": "2026-05-28", "arxiv_id": "2605.25707", "title": "AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions", "authors": [ "Jingwei Sun", "Jianing Zhu", "Yuanyi Li", "Tongliang Liu", "Xia HU", "Bo Han" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tmlr-group/AgentHijack", "github_api_added_by": "user", "project_page": "https://agenthijack.github.io/", "abstract": "Autonomous computer use agents that powered by multimodal large language models (MLLMs) are emerging as capable assistants for completing complex digital workflows. However, real-world execution environments are far from ideal: pop-ups, resolution changes, and competing applications frequently interfere with agent perception and control. We introduce AgentHijack, a benchmark designed to evaluate the robustness of computer-use agents under common corruptions, where the uncertainties in dynamic environment disrupt the execution flow without direct adversarial intent. Specifically, AgentHijack introduces 9 configurable common corruptions to replicate realistic imperfect scenarios. We evaluate a variety of desktop tasks that utilize MLLM-based agents and discover that even minor instances of corruption can result in substantial performance degradation, which emphasizes the fragility of agents and underscores the necessity of robustness evaluation. Afterward, we propose AgentHijack-Agent, a framework that integrates an action generator with enhanced grounding capabilities and an onlooker responsible for behavior summarization and environment checking. Extensive experiments validate its effectiveness. Our code, environment, baseline models and data are publicly available at: https://AgentHijack.github.io.", "github": "https://github.com/tmlr-group/AgentHijack" }, { "date": "2026-05-28", "arxiv_id": "2605.28257", "title": "Category-Level 3D Correspondence in Camera Space via Morphable Object Priors", "authors": [ "Leonhard Sommer", "Artur Jesslen", "Basavaraj Sunagad", "Adam Kortylewski" ], "github_manual": "", "github_regex": "https://github.com/GenIntel/HouseCorr3D", "github_paper_md": "", "github_api": "https://github.com/GenIntel/HouseCorr3D", "github_api_added_by": "user", "project_page": "", "abstract": "Understanding 3D objects from images is fundamental to robotics and AR/VR applications. While recent work has made progress in category-level pose estimation, current representations fail to capture the fine-grained semantics needed for reasoning about object parts, functions, and interactions. In this work, we study category-level 3D correspondence in camera space -- predicting, from a single image, 3D locations that remain consistent across instances within a category -- and show that it can emerge without explicit correspondence supervision by learning a shared morphable object prior. To enable research in this direction, we introduce HouseCorr3D, the first large-scale benchmark for monocular category-level 3D correspondence with 178k images across 50 household object categories, 280 unique instances, and 3D keypoint annotations directly on CAD models. Crucially, HouseCorr3D provides amodal correspondence labels for occluded regions and explicit symmetry annotations, addressing key limitations of existing datasets. We further propose Morpheus, a method that learns morphable category-level shape priors by disentangling canonical shape, deformation, and object pose. Through this shared canonical grounding, semantically meaningful 3D correspondences in camera space emerge implicitly. These emerging 3D correspondences set a new state of the art on HouseCorr3D, demonstrating that semantic 3D object understanding can arise without direct correspondence supervision. Data and code are publicly available at https://github.com/GenIntel/HouseCorr3D.", "github": "https://github.com/GenIntel/HouseCorr3D" }, { "date": "2026-05-28", "arxiv_id": "2605.27044", "title": "BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting", "authors": [ "Ruifeng Tan", "Jintao Dong", "Weixiang Hong", "Jia Li", "Jiaqiang Huang", "Tong-Yi Zhang" ], "github_manual": "", "github_regex": "https://github.com/Ruifeng-Tan/BatteryMFormer", "github_paper_md": "", "github_api": "https://github.com/Ruifeng-Tan/BatteryMFormer", "github_api_added_by": "user", "project_page": "", "abstract": "Early battery degradation trajectory forecasting (BDTF), which predicts the full-life state-of-health trajectory from early operational data, is critical for battery optimization, manufacturing, and deployment. Battery degradation data exhibit two key characteristics. First, degradation data present a multi-level structure, including regularities shared within aging conditions and trajectory patterns shared across batteries. Second, degradation-related variations in voltage-current profiles are often localized to specific state-of-charge (SOC) intervals. Existing approaches often fail to explicitly model these characteristics. To bridge this gap, we propose BatteryMFormer, a multi-level Transformer for early BDTF. BatteryMFormer integrates (1) an aging-condition-aware decoder that injects aging-condition priors via aging-condition-informed queries and aging-condition-aware attention, (2) a meta degradation pattern memory that learns and retrieves trajectory prototypes to guide long-horizon forecasting, and (3) a dual-view encoder that jointly captures temporal dynamics and SOC-localized variations from voltage and current time series. Extensive experiments on four battery domains show that BatteryMFormer consistently outperforms state-of-the-art baselines, marking a significant step toward reliable BDTF. Our code is available at https://github.com/Ruifeng-Tan/BatteryMFormer.", "github": "https://github.com/Ruifeng-Tan/BatteryMFormer" }, { "date": "2026-05-28", "arxiv_id": "2605.25174", "title": "Growing a Neural Network in Breadth, Depth, and Time", "authors": [ "Eivinas Butkus", "Kedar Garz\u00f3n Gupta", "Nikolaus Kriegeskorte" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Spatial and temporal resource constraints are critical for both biological and artificial intelligent systems. Here we define differentiable cost terms for breadth, depth, and time within a recurrent convolutional neural network conceived as a finite subset of an infinite lattice. We optimize these costs jointly with task errors via backpropagation. We set different pressures on breadth, depth, and time, which leads to diverse computational graphs emerging organically through training. We find that all three resources can be traded off against each other to achieve a given level of accuracy. Networks grow in all three dimensions with task complexity and spontaneously take more recurrent steps when inputs are occluded. Surprisingly, time used by the model correlates with human reaction times in an object recognition task. Our framework provides a normative account of how resource constraints shape neural architectures, connecting to questions about brain design in neuroscience, and may help illuminate the diversity of neural solutions found in nature.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.23346", "title": "Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion", "authors": [ "Jaihoon Kim", "Taehoon Yoon", "Prin Phunyaphibarn", "Seungjun Kim", "Morteza Mardani", "Minhyuk Sung" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Discrete diffusion models have emerged as powerful frameworks for generating structured categorical data. However, efficiently sampling from reward-tilted distributions remains a fundamental challenge. While Twisted Sequential Monte Carlo (SMC) offers asymptotic exactness for this task, estimating the optimal twist function in discrete state spaces necessitates costly Monte Carlo approximations, resulting a severe computational bottleneck at inference. To overcome this limitation, we introduce Contrastive Distribution Matching (CDM), a novel framework that amortizes the cost of SMC inference by learning a parameterized twist function via positive and negative samples. For efficient training, we reformulate the gradient estimator to leverage the closed-form forward kernels of discrete diffusion models. In practice, evaluating our learned twist function incurs less than 5% additional computational overhead compared to a single forward pass of the base model. Through extensive empirical evaluations, we demonstrate that CDM consistently outperforms existing baselines under matched wall-clock time. We validate the effectiveness and versatility of our approach across a diverse range of applications, including toxic text generation, regulatory DNA sequence design, protein designability, and diffusion large language model alignment.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.26302", "title": "Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems", "authors": [ "Jianing Zhu", "Yeonju Ro", "John Robertson", "Kevin Wang", "Junbo Li", "Haris Vikalo", "Aditya Akella", "Zhangyang Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/VITA-Group/AgingBench", "github_api_added_by": "user", "project_page": "https://agingbench.github.io", "abstract": "Long-lived AI agents are increasingly deployed as persistent operational systems, yet they are still evaluated like freshly initialized models. Day-one benchmarks miss a basic systems question: how long does an agent remain reliable after deployment? Even when model weights are frozen, an agent's effective state keeps changing as it compresses interaction history, retrieves from a growing memory store, revises facts after updates, and undergoes routine maintenance. Reliability therefore becomes a lifespan property of the full agent harness, not only a snapshot property of the base model. We introduce AgingBench, a longitudinal reliability benchmark for agent lifespan engineering: measuring not only whether deployed agents degrade, but what form the degradation takes and where repair should target. AgingBench organizes agent aging into four mechanisms: compression aging, interference aging, revision aging, and maintenance aging. To diagnose these failures, AgingBench uses temporal dependency graphs and paired counterfactual probes that produce diagnostic profiles for the write, retrieval, and utilization stages of the memory pipeline. Across 7 scenarios, 14 models, multiple memory policies, and both runner-controlled and autonomous agents, over ~400 runs spanning 8 - 200 sessions show that agent aging is not one-dimensional: behavioral tests can remain clean while factual precision decays; derived-state tracking can collapse sharply within a single model; and the same wrong answer can require different repairs depending on what the diagnostic profile points to. These results suggest that reliable agent deployment requires lifespan evaluation, mechanism-level diagnosis, and stage-targeted repair, not only stronger day-one models.", "github": "https://github.com/VITA-Group/AgingBench" }, { "date": "2026-05-28", "arxiv_id": "2605.17531", "title": "Don't Guess, Just Ask: Resolving Ambiguity in Referring Segmentation via Multi-turn Clarification", "authors": [ "Yuting Yang", "Haichao Jiang", "Tianming Liang", "Quan Zhang", "Jian-Fang Hu" ], "github_manual": "", "github_regex": "https://github.com/iSEE-Laboratory/IC-Seg", "github_paper_md": "", "github_api": "https://github.com/iSEE-Laboratory/IC-Seg", "github_api_added_by": "user", "project_page": "", "abstract": "Referring segmentation aims to segment the target objects in images or videos based on the textual query. Despite remarkable progress over the past years, existing works always assume that the user-provided queries are already precise and clear. However, this assumption is impractical. In real-world scenarios, it is unrealistic to expect all users to thoroughly review their visual content and carefully ensure their queries are unique and unambiguous. When encountering such cases, existing segmentation models tend to arbitrarily guess the user preferences, often resulting in undesired outcomes. To address this limitation, we propose IC-Seg, a novel agentic framework that proactively clarifies user intent through multi-turn conversation before segmentation. To effectively incentivize this capability, we further introduce Hi-GRPO, a new hierarchical optimization strategy that injects dense and informative supervision signals at the trajectory, turn, and step levels. This strategy encourages efficient intent clarification, effectively eliminating redundant interactions and improving overall dialogue quality. For evaluation, we establish Ambi-RVOS, a referring video object segmentation benchmark with ambiguous user queries. Extensive experiments demonstrate that IC-Seg not only outperforms existing methods by a large margin in resolving ambiguous queries, but also maintains state-of-the-art performance on standard reasoning segmentation benchmarks. Code and data will be released at https://github.com/iSEE-Laboratory/IC-Seg.", "github": "https://github.com/iSEE-Laboratory/IC-Seg" }, { "date": "2026-05-28", "arxiv_id": "2605.24053", "title": "Breaking the Chains of Probability: Neutrosophic Logic as a New Framework for Epistemic Uncertainty in Large Language Models", "authors": [ "Maikel Yelandi Leyva-V\u00e1zquez", "Florentin Smarandache" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large Language Models (LLMs) are predominantly governed by probabilistic frameworks in which the sum of outcome probabilities is constrained to unity. This architectural limitation, often imposed by Softmax layers, leads to a collapse of uncertainty that makes it difficult to differentiate between epistemic uncertainty, paradox, and vagueness. We present an empirical investigation of the application of Neutrosophic Logic, a framework that treats Truth (T), Indeterminacy (I), and Falsity (F) as three independent dimensions, to model epistemic states in LLMs. We conducted experiments on a family of four OpenAI GPT models across five linguistic phenomena: logical paradoxes, epistemic ignorance, vagueness, ethical contradictions, and future contingencies, under three prompting strategies: neutrosophic, probabilistic, and entropy-derived. Our findings reveal that the neutrosophic approach, by allowing T+I+F > 1, a state we term hyper-truth, provides a richer representation of a model's internal state. In 35% of evaluations, hyper-truth emerged spontaneously, predominantly under ethical contradiction and logical paradox. We demonstrate that this approach preserves truth values in fuzzy contexts and offers a robust method for identifying and quantifying internal model conflict. We conclude that the integration of neutrosophic evaluation layers is a critical step toward more transparent, reliable, and ethically aware AI systems.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.28534", "title": "GUI-CIDER: Mid-training GUI Agents via Causal Internalization and Density-aware Exemplar Reselection", "authors": [ "Zheng Wu", "Chengcheng Han", "Zhengxi Lu", "Tianjie Ju", "Yanyu Chen", "Qi Gu", "Xunliang Cai", "Zhuosheng Zhang" ], "github_manual": "", "github_regex": "https://github.com/Wuzheng02/GUI-CIDER", "github_paper_md": "", "github_api": "https://github.com/Wuzheng02/GUI-CIDER", "github_api_added_by": "user", "project_page": "", "abstract": "Despite the rapid progress of multimodal large language models in building Graphical User Interface (GUI) agents, their real-world task completion is fundamentally bottlenecked by a lack of world knowledge about GUI operations. Existing solutions typically rely on expensive multi-agent scaffolding or conventional post-training paradigms, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). However, post-training only allows agents to implicitly absorb world knowledge through action annotations or reward signals, leading to inefficient trajectory memorization rather than genuine comprehension. Therefore, an approach that enables explicit learning of this knowledge is imperative. To this end, we propose GUI-CIDER, a mid-training method that explicitly internalizes GUI world knowledge through Causal Internalization and Density-aware Exemplar Reselection. GUI-CIDER operates in three stages: (1) data synthesis, which distills static planning and dynamic causal knowledge from GUI trajectories into text; (2) exemplar reselection, which filters the corpus by rewarding causal structures and penalizing semantic redundancy; and (3) mid-training, where the refined data is used to embed the acquired knowledge. Extensive experiments on two GUI knowledge benchmarks and three task completion benchmarks demonstrate that GUI-CIDER consistently improves both the agent's understanding of GUI operations and its task success rates.The codes are available at https://github.com/Wuzheng02/GUI-CIDER.", "github": "https://github.com/Wuzheng02/GUI-CIDER" }, { "date": "2026-05-28", "arxiv_id": "2605.27310", "title": "How and What to Imagine? Visual Thinking in Unified Multimodal Models for Cross-View Spatial Reasoning", "authors": [ "Qian Yang", "Ankur Sikarwar", "Huy Le", "Le Zhang", "Zhuan Shi", "Perouz Taslakian", "Aishwarya Agrawal" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Cross-view spatial reasoning remains a weak spot for vision-language models (VLMs): they often reason in language and lose the fine-grained geometry needed for the task. Thinking with images aims to address this by generating an intermediate thinking image, but recent work shows that models often ignore the visual evidence in these traces. We therefore ask how to make visual thinking matter, and what kind of visual thinking works best. We study these questions in unified multimodal models (UMMs), which natively support interleaved image-text generation. For the first question, we propose View Dropout (VDrop), a training-time intervention that hides parts of one input view from the answer span while keeping them visible to the thinking-image tokens. This encourages the model to use the thinking image when answering, instead of relying only on the input views. Once the thinking image is used for answer prediction, we study which type of visual thinking is most effective. We frame this as a learnability-informativeness tradeoff and compare three thinking-image variants: top-down, panoramic, and point-matching renderings. Trained on synthetic scenes and evaluated on five real-world out-of-domain benchmarks, panoramic visual thinking with VDrop is the only configuration that is both informative and learnable, and it achieves the best out-of-domain generalization.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.25940", "title": "How Accurate are Video Quality Models for Diffusion-Based Video Super-Resolution?", "authors": [ "Benjamin Herb", "Steve G\u00f6ring", "Alexander Raake", "Rakesh Rao Ramachandra Rao" ], "github_manual": "", "github_regex": "https://github.com/Telecommunication-Telemedia-Assessment/AVT-VQDB-UHD-1-VSR", "github_paper_md": "", "github_api": "https://github.com/Telecommunication-Telemedia-Assessment/AVT-VQDB-UHD-1-VSR", "github_api_added_by": "user", "project_page": "", "abstract": "Recent video super-resolution (VSR) approaches use deep neural networks to enhance low-quality input videos and recover visual detail, with diffusion-based methods in particular showing promising results. In this paper, we investigate whether existing video quality models can be used to assess the performance of these diffusion-based VSR methods, by comparing model predictions with results from a subjective test. The study compares six upscaling methods (Lanczos, Rhea, SCST, DOVE, SeedVR2, Starlight Mini) applied to both compressed (AV1 and DCVC-RT) and uncompressed low-resolution videos considering the play-out on a UHD-1/4K screen. A range of full- and no-reference quality models are used to assess their applicability to this new type of quality degradation, focusing on within-sequence performance. The results highlight that CNN-based full-reference models, such as LPIPS, DISTS, and CVQA-FR show significantly higher correlation coefficients than both conventional full- as well as the tested no-reference models. Most overestimate the overly sharp results of SCST, with VMAF mainly failing due to spatial inconsistencies introduced by Starlight Mini. None of the tested video quality models reach sufficient accuracy so as to replace complementary subjective testing. The reference, degraded and upscaled videos, as well as the user ratings and model scores are made available with the paper at https://github.com/Telecommunication-Telemedia-Assessment/AVT-VQDB-UHD-1-VSR as open data.", "github": "https://github.com/Telecommunication-Telemedia-Assessment/AVT-VQDB-UHD-1-VSR" }, { "date": "2026-05-28", "arxiv_id": "2605.27766", "title": "Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems", "authors": [ "Aman Priyanshu", "Supriti Vijay", "Esha Pahwa" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/llms-cant-keep-secrets/llms-cant-keep-secrets.github.io", "github_api_added_by": "user", "project_page": "https://llms-cant-keep-secrets.github.io/", "abstract": "LLM safety evaluations predominantly test models in isolation, yet deployed AI agents increasingly operate within persistent social environments alongside other agents. We introduce a Moltbook-style simulation platform where thousands of LLM agents interact across communities over a simulated month, and use it to evaluate privacy as a downstream safety concern under varying degrees of social pressure. We find that shifting from single turn to multi turn social evaluation amplifies privacy violations (CIMemories 19.95% to Ours 45.30% across OpenAI models), that leakage is socially contagious, with agents 8 times more likely to disclose sensitive information after observing a peer do so, and that explicit privacy instructions reduce but do not eliminate this effect, leaving leakage rates above 37.8% even with safeguards. Our findings suggest that static chat based safety benchmarks systematically underestimate risks in agentic deployment, and that social context alone is sufficient to elicit sensitive disclosures that single turn evaluations would never surface.", "github": "https://github.com/llms-cant-keep-secrets/llms-cant-keep-secrets.github.io" }, { "date": "2026-05-28", "arxiv_id": "2605.28617", "title": "LACUNA: Safe Agents as Recursive Program Holes", "authors": [ "Yaoyu Zhao", "Yichen Xu", "Oliver Bra\u010devac", "Cao Nguyen Pham", "Frank Zhengqing Wu", "Martin Odersky" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "LLM agents increasingly act by writing code, yet a split persists between the runtime that drives the agent and the code the model writes. The runtime owns the loop, context, and control flow, and the model has little say over any of them. Letting model-written code shape the runtime itself would make agents more expressive, but it would also sharpen safety problems. A model can be diverted by a prompt injection, call the wrong tool, or fail partway and leave an inconsistent state, and each such failure reaches further when the code shapes the runtime than when it expresses a single action. We present LACUNA, a programming model for agents that closes this split while preserving safety. Each agent action is a typed call agent[T](task) that the LLM fills with code when execution reaches it, and the code is type-checked against the surrounding program before it runs. Because each action is accepted or rejected as a whole, a rejected one leaves the environment untouched, and its compiler diagnostics drive a retry. The same check also bounds which tools and data an action may use and how they flow. Our primitive expresses ReAct loops, sub-agents, skills, parallel decomposition, and multi-model planning as ordinary control flow. We evaluate LACUNA on a collection of test cases, BrowseComp-Plus, and \u03c4^2-bench. On BrowseComp-Plus, 8.6% of generations are rejected before execution, with 0.7 retries per query on average, and the agent reaches 27.1% accuracy. On \u03c4^2-bench, LACUNA solves 76.0% of 392 tasks across four domains with a capable model, on par with the baseline agent.", "github": "" }, { "date": "2026-05-28", "arxiv_id": "2605.28158", "title": "OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agents", "authors": [ "Chenyu Zhou", "Xinyun Lu", "Jiangyue Zhao", "Jianghao Lin", "Dongdong Ge", "Yinyu Ye" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/0xzhouchenyu/OR-Space", "github_api_added_by": "user", "project_page": "", "abstract": "Large language model (LLM) agents are increasingly used to assist with operations research (OR) modeling, yet existing OR-oriented benchmarks often reduce evaluation to one-shot translation from a self-contained problem statement into a mathematical formulation or solver program. Such settings abstract away two characteristics of real industrial OR workflows: persistent multi-artifact workspaces and multi-stage task lifecycles. We introduce OR-Space, a full-lifecycle workspace benchmark for evaluating industrial optimization agents across model construction, model revision, and grounded explanation. Each instance is an executable workspace containing business documents, structured data, optional code artifacts, solver outputs, and task-specific evaluators distributed across interdependent files. OR-Space defines three task modes: Build, where agents construct solver-ready optimization models from heterogeneous artifacts; Revise, where agents modify existing models under changing requirements or solver feedback while preserving valid prior logic; and Explain, where agents answer grounded questions about solutions, constraints, and business implications using evidence spread across workspace artifacts. By combining persistent workspaces with lifecycle-oriented tasks, OR-Space evaluates whether agents can perform reliable optimization work beyond end-to-end text generation. We describe the benchmark design, evaluation protocol, and quality-control pipeline, and position OR-Space as a benchmark for studying the reliability, failure modes, and practical readiness of LLM agents in industrial OR workflows.", "github": "https://github.com/0xzhouchenyu/OR-Space" }, { "date": "2026-05-28", "arxiv_id": "2605.26368", "title": "Unified Panoramic Geometry Estimation via Multi-View Foundation Models", "authors": [ "Vukasin Bozic", "Isidora Slavkovic", "Dominik Narnhofer", "Nando Metzger", "Denis Rozumny", "Konrad Schindler", "Nikolai Kalischek" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/prs-eth/PaGeR", "github_api_added_by": "user", "project_page": "https://pager360.github.io/", "abstract": "Geometry estimation from perspective images has greatly advanced, maturing to the point where off-the-shelf foundation models are able to reconstruct 3D scene structure not only from multi-view imagery, but even from a single view. A natural extension is 3D reconstruction from panoramas, with the exciting prospect of recovering a full 360-degree scene from a single panoramic image. In this work, we introduce PaGeR (Panoramic Geometry Reconstruction), a framework to lift powerful 3D foundation models designed for perspective imagery to the panorama domain. Our strategy is to start from a pre-trained transformer for 3D reconstruction and turn it into a unified high-performance model that predicts scale-invariant depth, metric depth, surface normals, and sky masks from both perspective and omnidirectional images, in a single forward pass. By keeping architectural changes to a minimum and mixing perspective and panoramic images during training, PaGeR retains the rich 3D prior of the underlying foundation model while learning to also estimate geometrically consistent 360-degree scenes from single panoramas. We extensively test our method in both indoor and outdoor environments and find that it delivers state-of-the-art performance and excellent zero-shot performance across a wide range of scenes.", "github": "https://github.com/prs-eth/PaGeR" }, { "date": "2026-05-29", "arxiv_id": "2605.27995", "title": "AsyncTool: Evaluating the Asynchronous Function Calling Capability under Multi-Task Scenarios", "authors": [ "Kou Shi", "Ziao Zhang", "Shiting Huang", "Avery Nie", "Zhen Fang", "Qiuchen Wang", "Lin Chen", "Huaian Chen", "Zehui Chen", "Feng Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/StoKou/repo-asynctool", "github_api_added_by": "user", "project_page": "", "abstract": "Large language model (LLM)-based agents have shown strong capabilities in using external tools to solve complex tasks. However, existing evaluations often overlook the temporal dimension of tool use, especially the impact of tool response latency, and are usually limited to single-task settings. In real-world applications, multiple tasks often need to be executed concurrently, and overall efficiency depends on whether an agent can use idle time while waiting for tool responses. We refer to this capability as asynchronous tool calling. To evaluate it, we propose AsyncTool, a benchmark for assessing LLM-based agents in interactive multi-task tool-use environments with delayed tool feedback. AsyncTool presents multiple heterogeneous tasks simultaneously and simulates realistic tool response latency during execution. Using a hybrid data evolution strategy, we construct a diverse asynchronous multitasking dataset that covers multiple scenarios and tool-use patterns. We evaluate models at the step, sub-task, and task levels, and introduce efficiency-oriented metrics to measure task coordination and completion efficiency. Extensive experiments show that delayed tool feedback poses substantial challenges to current agents and leads to clear performance degradation. Models that better coordinate task switching, dependency tracking, and state maintenance achieve stronger performance on AsyncTool. Our analysis identifies key failure modes of current tool-using agents and provides practical insights for designing future systems with stronger temporal reasoning and coordination capabilities.", "github": "https://github.com/StoKou/repo-asynctool" }, { "date": "2026-05-29", "arxiv_id": "2605.30073", "title": "Native Audio-Visual Alignment for Generation", "authors": [ "Longbin Ji", "Guan Wang", "Xuan Wei", "Chenye Yang", "Xiangrui Liu", "Zhenyu Zhang", "Shuohuan Wang", "Yu Sun", "Jingzhou He" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ernie-research/NAVA", "github_api_added_by": "user", "project_page": "https://ernie-research.github.io/NAVA/", "abstract": "Joint audio-video generation aims to synthesize temporally synchronized and semantically coherent visual-acoustic content. However, existing open-source methods mainly rely on either dual-tower designs with posterior alignment or fully unified tri-modal designs that mix textual context, audio and video in one shared space. The former weakens fine-grained audio-video co-evolution, while the latter couples semantic conditioning with low-level synchronization. To address these limitations, we propose NAVA, a Native Audio-Visual Alignment framework for joint audio-video generation. NAVA is built upon context-conditioned native audio-visual alignment: it first establishes audio-video correspondence in a dedicated interaction space, and then uses external context to condition the joint denoising process. Specifically, NAVA is instantiated with an Align-then-Fuse MMDiT architecture, which transitions from modality-aware audio-video alignment to modality-shared joint denoising. Furthermore, we introduce Timbre-in-Context Conditioning to associate reference timbre cues with corresponding speech spans to achieve controllable speech timbre. Experiments on Verse-Bench and Seed-TTS, together with a user study, demonstrate that NAVA achieves superior video quality, precise audio-visual synchronization, competitive audio quality, and stronger reference-timbre controllability using only 6.3B parameters.", "github": "https://github.com/ernie-research/NAVA" }, { "date": "2026-05-29", "arxiv_id": "2605.30260", "title": "How LoRA Remembers? A Parametric Memory Law for LLM Finetuning", "authors": [ "Ziwen Xu", "Haiwen Hong", "Linsong Yu", "Benglei Cui", "Longtao Huang", "Hui Xue", "Ningyu Zhang" ], "github_manual": "", "github_regex": "https://github.com/zjunlp/ParametricMemoryLaw", "github_paper_md": "", "github_api": "https://github.com/zjunlp/ParametricMemoryLaw", "github_api_added_by": "user", "project_page": "", "abstract": "Large Language Models (LLMs) must continuously learn and update knowledge to remain effective in dynamic real-world environments. While Low-Rank Adaptation (LoRA) is widely used for such memory updates, existing studies mainly rely on qualitative downstream evaluations, leaving the quantitative capacity limits and underlying dynamics of exact parametric memory largely unexplored. To bridge this gap, we employ LoRA as a controlled memory capacity probe within the latent space to systematically quantify exact parametric memory. We introduce the Parametric Memory Law, a robust power law linking loss reduction Delta L to effective parameters and sequence length. At the token level, fine-grained analysis reveals a deterministic phase transition, demonstrating that a prediction probability of p > 0.5 constitutes a sufficient condition for verbatim recall under greedy decoding. Driven by these insights, we introduce MemFT, a threshold-guided optimization strategy that dynamically redistributes the training budget toward sub-threshold tokens. Empirical evaluations demonstrate that MemFT can enhance memory fidelity and efficiency. Code will be released at https://github.com/zjunlp/ParametricMemoryLaw.", "github": "https://github.com/zjunlp/ParametricMemoryLaw" }, { "date": "2026-05-29", "arxiv_id": "2605.30219", "title": "When Should Models Change Their Minds? Contextual Belief Management in Large Language Models", "authors": [ "Haoming Xu", "Weihong Xu", "Zongrui Li", "Mengru Wang", "Yunzhi Yao", "Chiyu Wu", "Jin Shang", "Yu Gong", "Shumin Deng" ], "github_manual": "", "github_regex": "https://github.com/zjunlp/CBM", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Long-horizon interactions require language models to manage accumulating information: when to update their state, when to preserve their state, and what to ignore. We study this challenge as Contextual Belief Management (CBM): maintaining a predicted belief state aligned with formal evidence while isolating task-irrelevant noise. To make CBM measurable, we introduce BeliefTrack, a closed-world benchmark spanning Rule Discovery and Circuit Diagnosis, where a finite belief space and symbolic verifiers enable exact turn-level evaluation. BeliefTrack diagnoses three failures: Failed Stay, Failed Update, and Failed Isolation. Across multiple LLMs, vanilla models exhibit severe CBM failures, while explicit belief-tracking prompts provide limited gains. In contrast, reinforcement learning with belief-state rewards reduces failure rates by 70.9\\% on average. Further probing reveals latent belief-state dynamics behind these failures, and representation-level steering reduces failure rates by 46.1\\% across two tasks\\footnote{Code is coming soon at https://github.com/zjunlp/CBM.", "github": "https://github.com/zjunlp/CBM" }, { "date": "2026-05-29", "arxiv_id": "2605.29888", "title": "LaRA: Layer-wise Representation Analysis for Detecting Data Contamination in RL Post-Training", "authors": [ "Minju Gwak", "Minseo Kwak", "Dongseok Lee", "Guijin Son", "Alan Ritter", "Jaehyung Kim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement learning (RL) post-training has shown to improve reasoning in large language models (LLMs). However, there has been little exploration on the problem of data contamination in RL post-training, potentially undermining generalization and evaluation reliability of the training process itself. Existing detection methods primarily rely on output-level signals such as likelihood or entropy, which become unreliable for RL-trained models since RL shapes behavior through trajectory-level rewards rather than token likelihoods. We propose LaRA, a layer-wise representation analysis framework for detecting contamination in RL post-trained LLMs. LaRA introduces three complementary metrics, measuring perturbation sensitivity, directional collapse, and local representation rigidity under controlled perturbations. We find that contamination produces progressive geometric deviations across layers, including amplified perturbation sensitivity, stronger directional collapse, and enhanced local rigidity. Based on our findings, we also develop a contamination detection protocol that aggregates representation-level deviations across layers and metrics. Experiments on RL-trained reasoning models show that our protocol outperforms existing output-level baselines for contamination detection.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.29559", "title": "LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agents", "authors": [ "Xiaoxuan Peng", "Kaiqi Zhang", "Xinyu Lu", "Boxi Cao", "Yaojie Lu", "Hongyu Lin", "Xianpei Han", "Le Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/icip-cas/LiteCoder", "github_api_added_by": "user", "project_page": "", "abstract": "Mastering terminal environments requires language agents capable of multi-step planning, feedback-grounded execution, and dynamic state adaptation. However, training such agents is currently bottlenecked by a reliance on scraped external repositories, which limits domain diversity, environment controllability, and the targeting of specific capability deficits. We introduce LiteCoder-Terminal-Gen, a zero-dependency synthesis pipeline that autonomously generates executable and verifiable terminal training environments directly from domain specifications. Using this framework, we construct two large-scale resources: LiteCoder-Terminal-SFT, comprising 11,255 expert trajectories across 10 domains, and LiteCoder-Terminal-RL, featuring 602 verifiable environments for trajectory-level preference optimization. Supervised fine-tuning of Qwen-family models on our SFT dataset yields agents that significantly outperform their base counterparts. Notably, our 32B variant achieves 29.06%, 18.54%, and 34.00% pass@1 on Terminal Bench 1.0, 2.0, and Pro, respectively. Furthermore, applying Direct Multi-turn Preference Optimization (DMPO) on our RL environments yields additional performance gains. These results systematically demonstrate that fully synthetic, executable environments offer a scalable and verifiable supervision signal for mastering complex, real-world command-line workflows.", "github": "https://github.com/icip-cas/LiteCoder" }, { "date": "2026-05-29", "arxiv_id": "2605.29534", "title": "UI-KOBE: Knowledge-Oriented Behavior Exploration for Lightweight Graph-Guided GUI Agents", "authors": [ "Yuxiang Chai", "Han Xiao", "Xinyu Fu", "Jinpeng Chen", "Rui Liu", "Hongsheng Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recent advances in mobile GUI agents have shown strong potential for automating mobile tasks, but most effective systems still depend on large vision-language models for screenshot understanding and long-horizon planning. Small GUI agents that can be deployed directly on mobile devices are more attractive for practical use, offering lower inference cost and better protection of sensitive on-device information. However, due to limited model capacity, such lightweight agents remain unreliable when planning and executing GUI tasks end-to-end from screenshots alone. We propose Knowledge-Oriented Behavior Exploration (UI-KOBE), a framework that improves lightweight mobile GUI agents with reusable app-specific graph knowledge. UI-KOBE first autonomously explores a mobile application and constructs an app knowledge graph, where nodes represent distinct UI states and edges represent executable transitions. At runtime, a lightweight GUI agent uses the graph as external guidance: given a user task and the current screenshot, it identifies the current graph node and selects among self-loop actions, neighboring transitions, task completion, or fallback free actions associated with that node. By supporting runtime decisions with app-specific graph guidance, UI-KOBE reduces the burden of end-to-end GUI planning and helps lightweight models perform mobile GUI tasks more effectively, offering a practical step toward efficient, interpretable, and privacy-conscious on-device GUI agents.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.27891", "title": "SmartDirector: Keyframe-Conditioned Cinematic Video Generation with Narrative Pacing Control", "authors": [ "Zhida Zhang", "Jie Ma", "Zhan Peng", "Haoxue Wu", "Yang Han", "Jun Liang", "Jie Cao", "Jing Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Orange-3DV-Team/SmartDirector", "github_api_added_by": "user", "project_page": "https://orange-3dv-team.github.io/SmartDirector/", "abstract": "The narrative quality of a video fundamentally determines its perceptual value. Although existing video generation methods can produce visually appealing content, they predominantly rely on sparse conditioning signals such as text prompts or first/last frames, which limits precise control over narrative structure and temporal pacing. In this paper, we propose SmartDirector, a framework that enhances the narrative capacity of video generation models through multiple keyframes. SmartDirector supports flexible generation scenarios including single-shot generation, multi-shot narrative synthesis, and video extension. The framework operates in two stages: Director-Gen generates a low-resolution video conditioned on the provided keyframes, and Director-SR refines the output by exploiting high-resolution keyframes as semantic anchors to recover fine-grained details. To enable robust multi-keyframe training, we construct a data pipeline that curates single-shot and multi-shot sequences from movies. Extensive experiments demonstrate that SmartDirector substantially outperforms existing state-of-the-art approaches. We will release the code to facilitate further research.", "github": "https://github.com/Orange-3DV-Team/SmartDirector" }, { "date": "2026-05-29", "arxiv_id": "2605.30231", "title": "Beyond 3D VQAs: Injecting 3D Spatial Priors into Vision-Language Models for Enhanced Geometric Reasoning", "authors": [ "Chun-Hsiao Yeh", "Shengyi Qian", "Manchen Wang", "Yi Ma", "Joseph Tighe", "Fanyi Xiao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://danielchyeh.github.io/GASP/", "abstract": "Vision-Language Models (VLMs) often struggle with robust 3D spatial reasoning. Prevailing methods that rely on fine-tuning with 3D visual question-answering (VQA) datasets may overfit dataset-specific biases, while integrating specialized 3D visual encoders is often inflexible and cumbersome. In this paper, we argue that genuine spatial understanding should emerge from learning fundamental geometric priors, not only from high-level VQA supervision. We propose GASP (Geometric-Aware Spatial Priors), a framework that injects these priors directly into the LLM's transformer layers. GASP employs a small correspondence head, applied as a deep supervision signal across all layers, and is trained with a dual objective leveraging ground-truth geometry from large-scale video scenes: a contrastive loss on ground-truth point correspondences enforces 2D view-invariance, while a depth consistency supervision resolves 3D geometric ambiguities. Our analysis first provides a diagnostic showing that standard VLMs' internal correspondence matching accuracy is very low (often below 5%). We then demonstrate that our training substantially improves this behavior, boosting peak layer-wise correspondence to over 70% and maintaining over 85% temporal robustness while baselines remain below 5%. These internal improvements translate to significant gains on downstream spatial benchmarks including +18.2% on All-Angles Bench and +29.0% on VSI-Bench, all without training on any 3D VQA data. Our findings indicate that learning from fundamental geometric priors is a promising and generalizable pathway towards VLMs with more reliable 3D spatial reasoning.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.30280", "title": "Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments", "authors": [ "Qiuyue Wang", "Mingsheng Li", "Jian Guan", "Jinhui Ye", "Sicheng Xie", "Yitao Liu", "Junhao Chen", "Zhixuan Liang", "Jie Zhang", "Xintong Hu", "Xuhong Huang", "Pei Lin", "Junyang Lin", "Dayiheng Liu", "Shuai Bai", "Jingren Zhou", "Jiazhao Zhang", "Haoqi Yuan", "Gengze Zhou", "Hang Yin", "Ye Wang", "Yiyang Huang", "Zixing Lei", "Wujian Peng", "Delin Chen", "Yingming Zheng", "Jingyang Fan", "Xianwei Zhuang", "Xin Zhou", "Haoyang Li", "Anzhe Chen", "Tong Zhang", "Xuejing Liu", "Yuchong Sun", "Ruizhe Chen", "Zhaohai Li", "Chenxu L\u00fc", "Zhibo Yang", "Tao Yu", "Xionghui Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://qwen.ai/blog?id=qwenvla", "abstract": "Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented capabilities and limited generalization across tasks, environments, and robot embodiments. In this work, we study whether heterogeneous embodied decision-making problems can be unified within a single vision-language-action model. We present Qwen-VLA, a unified embodied foundation model that extends Qwen's vision-language modeling stack from perception, understanding, and reasoning to continuous action and trajectory generation through a DiT-based action decoder. Qwen-VLA is trained with a large-scale joint pretraining recipe over diverse data sources, including robotics manipulation trajectories, human egocentric demonstrations, synthetic simulation data, vision-and-language navigation data, trajectory-centric supervision, and auxiliary vision-language data. To support multiple robot platforms, we introduce embodiment-aware prompt conditioning, where robot-specific textual descriptions specify the current embodiment and control convention. We further cast manipulation, navigation, and trajectory prediction into a unified action-and-trajectory prediction framework, enabling transferable visual grounding, spatial reasoning, and continuous action generation across robot morphologies, task families, and environments. Experiments on manipulation, navigation, and trajectory-centric benchmarks show consistent multi-task performance and out-of-distribution generalization under variations in scene layout, background, lighting, object configuration, and robot embodiment. Qwen-VLA-Instruct achieves 97.9% on LIBERO, 73.7% on Simpler-WidowX, 86.1%/87.2% on RoboTwin-Easy/Hard, 69.0% OSR on R2R, 59.6% SR on RxR, 76.9% average OOD success in real-world ALOHA experiments, and 26.6% zero-shot success on DOMINO dynamic manipulation.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.13857", "title": "MoZoo:Unleashing Video Diffusion power in animal fur and muscle simulation", "authors": [ "Dongxia Liu", "Jie Ma", "Xiaochen Yang", "Jiancheng Zhang", "Bin Xia", "Zhehan Kan", "Nisha Huang", "Jun Liang", "Wenming Yang", "Jin Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Orange-3DV-Team/MoZoo", "github_api_added_by": "user", "project_page": "https://orange-3dv-team.github.io/MoZoo/", "abstract": "The creation of cinematic-quality animal effects necessitates the precise modeling of muscle and fur dynamics, a process that remains both labor-intensive and computationally expensive within traditional production workflows. While generative diffusion models have shown promise in diverse artistic workflows, their capacity for high-fidelity animal simulation remains largely unexploited. We present MoZoo, a generative dynamics solver that bypasses conventional refinement to synthesize high-fidelity animal videos from coarse meshes under multimodal guidance. We propose Role-Aware RoPE (RAR-RoPE) which employs role-based index remapping to synchronize motion alignment while decoupling reference information via fixed temporal offsets. Complementing this, Asymmetric Decoupled Attention partitions the latent sequence to enforce a unidirectional information flow, effectively preventing feature interference and improving computational efficiency. To address the scarcity of high-quality training data, we introduce MoZoo-Data, a synthetic-to-real pipeline that leverages a rendering engine and an inverse mapping approach to construct a large-scale dataset of paired sequences. Furthermore, we establish MoZooBench, a comprehensive benchmark with 120 mesh-video pairs. Experimental results demonstrate that MoZoo achieves high-fidelity fur simulation across diverse animal skeletons and layouts, preserving superior temporal and structural consistency.", "github": "https://github.com/Orange-3DV-Team/MoZoo" }, { "date": "2026-05-29", "arxiv_id": "2605.30349", "title": "AdaState: Self-Evolving Anchors for Streaming Video Generation", "authors": [ "Yusuf Dalva", "Pinar Yanardag" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://adastate.github.io/", "abstract": "Autoregressive video diffusion models generate streaming video by producing frames sequentially, conditioning each chunk on previously generated content. These models are structurally anchored to the first frame: its key-value representation occupies a privileged position in the attention cache and serves as the primary scene reference throughout generation. As the cleanest and most error-free position in the cache, this anchor draws disproportionate attention, suppressing video dynamics, and locking scene composition to the initial viewpoint even as the scene naturally evolves. The result is a temporally shallow video in which motion, camera movement, and scene progression are dampened in favor of static consistency. To address this, we replace the static anchor with an adaptive state, a hidden latent that the model denoises alongside content at every chunk but never renders. Rather than referencing a frozen first frame, the model generates its own scene anchor at each step by attending to both the previous state and the current content, producing a reference that evolves with the generated content. Unlike standard video generation, which encodes an absolute notion of time, our formulation treats time as relative: every generation step sees the same positional structure regardless of how far generation has progressed, and the state transition is identical at every chunk. Together, these properties introduce a recurrence into the generation process, where denoising serves as the transition function, and the KV cache serves as the carrier, requiring no external module. Experiments demonstrate that the adaptive state substantially improves video dynamics, enabling richer motion and natural scene progression within generated videos.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.29157", "title": "Parallax: Parameterized Local Linear Attention for Language Modeling", "authors": [ "Yifei Zuo", "Dhruv Pai", "Zhichen Zeng", "Alec Dewulf", "Shuming Hu", "Zhaoran Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Yifei-Zuo/Parallax", "github_api_added_by": "user", "project_page": "", "abstract": "Large Language Models (LLMs) have become the central paradigm in artificial intelligence, yet the core computational primitive of attention has remained structurally unchanged. Local Linear Attention (LLA) is an attention mechanism derived from nonparametric statistics in the test-time regression framework. In contrast to prior research on efficient attention variants, LLA upgrades the local constant estimate in softmax attention to a local linear estimate, yielding provably superior bias-variance tradeoffs for associative memory. However, LLA has not been scaled in LLM pretraining due to computational and numerical stability concerns. We introduce Parallax, a parameterized Local Linear Attention that is scalable for LLMs. Parallax eliminates the numerical solver in LLA and learns an extra query-like projector that probes the KV covariance. We place Parallax within a family of attention mechanisms connected by the bandwidth, the probe construction and the affine structure. We propose a hardware-aware algorithm that increases the arithmetic intensity over FlashAttention, shifting attention into a more compute bound regime. Our prototype decode kernel matches or outperforms FlashAttention 2/3 across diverse batch sizes and context lengths. We pretrain Parallax at 0.6B and 1.7B scales and find consistent perplexity improvements throughout pretraining with gains that transfer to downstream benchmarks. The advantage persists under both parameter-matched and compute-matched controls, demonstrating a Pareto improvement. We perform careful pretraining ablations and identify a novel phenomenon whereby Muon unlocks the capacity of Parallax. To our knowledge, this is the first empirical demonstration of strong architecture-optimizer codesign for attention mechanisms in the architecture research literature.", "github": "https://github.com/Yifei-Zuo/Parallax" }, { "date": "2026-05-29", "arxiv_id": "2605.29250", "title": "OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources", "authors": [ "Jinheon Baek", "Soyeong Jeong", "Sangwoo Park", "Woongyeong Yeo", "Minki Kang", "Patara Trirat", "Heejun Lee", "Sung Ju Hwang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JinheonBaek/OmniRetrieval", "github_api_added_by": "user", "project_page": "", "abstract": "Real-world information needs require access to structurally diverse knowledge sources, from unstructured text and relational tables to knowledge graphs and property graphs. Existing retrievers, however, operate over one source at a time under a fixed query language, leaving the broader landscape of available knowledge fragmented behind incompatible interfaces. A natural attempt at unification would collapse these sources into a shared space, but this erases the structural affordances (such as schemas, ontologies, compositional operators) that give each source its expressive power. Effective retrieval over diverse knowledge, therefore, requires not homogenization but an overarching layer that meets each source on its own terms. To achieve this, we present OmniRetrieval, a framework that takes any natural-language query, identifies appropriate knowledge sources, and dispatches source-native queries to their native execution engines. Across an extensive benchmark spanning 13 datasets and 309 distinct knowledge bases over text, relational, and graph-structured sources, OmniRetrieval exceeds single-source baselines, demonstrating that it can serve as a general-purpose interface to the heterogeneous sources while preserving the structural distinctions that make each source valuable.", "github": "https://github.com/JinheonBaek/OmniRetrieval" }, { "date": "2026-05-29", "arxiv_id": "2605.29801", "title": "AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security", "authors": [ "Dongrui Liu", "Yu Li", "Zhonghao Yang", "Peng Wang", "Guanxu Chen", "Yuejin Xie", "Qinghua Mao", "Wanying Qu", "Yanxu Zhu", "Tianyi Zhou", "Leitao Yuan", "Zhijie Zheng", "Qihao Lin", "Yimin Wang", "Haoyu Luo", "Shuai Shao", "Chen Qian", "Qingyu Liu", "Ling Tang", "Ruiyang Qin", "Qihan Ren", "Junxiao Yang", "Kun Wang", "Zhiheng Xi", "Linfeng Zhang", "Ranjie Duan", "Bo Zhang", "Wenjie Wang", "Wen Shen", "Qiaosheng Zhang", "Yan Teng", "Chaochao Lu", "Rui Mei", "Man Li", "Jialing Tao", "Xi Lin", "Tianhang Zheng", "Yong Liu", "Quanshi Zhang", "Lei Zhu", "Xingjun Ma", "Junhua Liu", "Hui Xue", "Xiaoxiang Zuo", "Xiangnan He", "Chao Shen", "Xianglong Liu", "Minlie Huang", "Jing Shao", "Xia Hu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://ai45lab.github.io/AgentDoG/v1_5/", "abstract": "Modern open-world agents such as OpenClaw exhibit powerful cross-environment execution capabilities yet introduce broad new safety risk sources. Meanwhile, advanced frontier AI models drastically lower attack barriers, rendering current agent alignment frameworks inadequate for real-world deployment. To tackle these emerging threats, we propose a lightweight and scalable agent safety alignment framework. Specifically, we update the agent safety taxonomy to accommodate emergent risks from Codex and OpenClaw execution scenarios. We further build a taxonomy-guided data engine with influence-function purification to train lightweight AgentDoG 1.5 variants (0.8B, 2B, 4B, and 8B parameters) using only around 1k samples, achieving comparable performance with leading closed-source models (e.g., GPT-5.4). Based on AgentDoG 1.5, we construct a highly efficient agentic safety SFT and RL training environment, which reduces deployment overhead in Docker-level environments by two orders of magnitude. Finally, we deploy AgentDoG 1.5 as a training-free online guardrail for real-time safety moderation. Extensive experimental results indicate that AgentDoG 1.5 achieves state-of-the-art performance in diverse and complex interactive agentic scenarios. All models and datasets are openly released.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.30248", "title": "GenClaw: Code-Driven Agentic Image Generation", "authors": [ "Junyan Ye", "Jun He", "Zilong Huang", "Dongzhi Jiang", "Xuan Yang", "Rui Chen", "Weijia Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yejy53/GenClaw", "github_api_added_by": "user", "project_page": "", "abstract": "Image generation models have evolved from text-conditioned pixel synthesis toward multimodal agents endowed with visual comprehension and tool invocation capabilities. Yet, existing agents remain at the mercy of underlying black-box image models. Their workflow is trapped in a repetitive cycle of prompt rewriting for generation refinement, leaving them with no mechanism to directly manipulate the canvas. In essence, the potential of LLMs to serve as a genuine \"brush\" for precise visual construction remains largely untapped. In this paper, we propose GenClaw, a code-driven agentic image generation paradigm that empowers the agent to create like a human artist: first conceptualizing, then sketching, and finally coloring. Specifically, the agent first constructs the conceptual knowledge and context through search and reasoning. It then utilizes code (e.g., SVG, HTML, Three.js) to render executable visual sketches. Finally, it employs an image generation model to supplement textures, materials, and photorealism. In this workflow, code serves as a controllable intermediate canvas bridging linguistic reasoning and pixel synthesis, seamlessly integrating programmatic logic with the visual expressiveness of generative models. By transforming image generation from a black-box paradigm into a staged process akin to authentic human creation, GenClaw offers a step toward for highly controllable and interpretable visual generation systems.", "github": "https://github.com/yejy53/GenClaw" }, { "date": "2026-05-29", "arxiv_id": "2605.30263", "title": "minWM: A Full-Stack Open-Source Framework for Real-Time Interactive Video World Models", "authors": [ "Min Zhao", "Hongzhou Zhu", "Bokai Yan", "Zihan Zhou", "Yimin Chen", "Wenqiang Sun", "Kaiwen Zheng", "Guande He", "Xiao Yang", "Chongxuan Li", "Fan Bao", "Jun Zhu" ], "github_manual": "", "github_regex": "https://github.com/shengshu-ai/minWM](https:", "github_paper_md": "", "github_api": "https://github.com/shengshu-ai/minWM", "github_api_added_by": "user", "project_page": "", "abstract": "Recent video diffusion foundation models have achieved remarkable progress in high-quality video generation, yet turning them into real-time interactive video world models remains challenging. Interactive world models require controllable, causal, and low-latency rollout, which in practice demands a full pipeline spanning data construction, controllable fine-tuning, autoregressive training, few-step distillation, and streaming inference. In this work, we present minWM, a full-stack open-source framework for building real-time interactive video world models. minWM provides an end-to-end pipeline that converts existing bidirectional T2V/TI2V video foundation models into camera-controllable few-step autoregressive world models. Specifically, minWM first fine-tunes a bidirectional video diffusion model with camera control, and then applies the Causal Forcing / Causal Forcing++ pipeline, including AR diffusion training, causal ODE or causal consistency distillation, and asymmetric DMD, to distill it into a few-step autoregressive generator for low-latency rollout. The framework is modular and architecture-extensible: we instantiate it on representative open backbones, including Wan2.1-T2V-1.3B and HY1.5-TI2V-8B, covering both cross-attention-based condition injection and MMDiT-style architectures. minWM also supports adapting existing video world models, such as HY-WorldPlay, to new data distributions, training recipes, and latency targets. Beyond releasing runnable scripts, checkpoints, documentation, and inference code, we provide practical ablations on camera trajectory quality, controllability training steps, and minimal batch-size requirements. We hope minWM serves as a reproducible and extensible recipe for building and adapting real-time interactive video world models. Project Page: [https://github.com/shengshu-ai/minWM](https://github.com/shengshu-ai/minWM)", "github": "https://github.com/shengshu-ai/minWM" }, { "date": "2026-05-29", "arxiv_id": "2605.22189", "title": "Learning A Unified Risk Map for Autonomous Driving in Partially Observable Environments", "authors": [ "Jie Jia", "Yaofeng Su", "Zeyu Bao", "Yun Hong", "Bingzhao Gao", "Zhongxue Gan", "Wenchao Ding" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://ieeexplore.ieee.org/document/11399898", "abstract": "Occlusion-aware prediction remains a critical challenge in autonomous driving due to the inherent uncertainty of unobserved regions. Existing approaches either overestimate risk based on reachable states or struggle to predict accurate trajectories under high occlusion uncertainty. To address these limitations, we propose a unified risk map modeling and learning framework for partially observable environments. Our method integrates traffic flow risk and collision risk through spatiotemporal modeling, enabling fine-grained assessment of occlusion-induced hazards. To address the scarcity of scenarios involving occluded interactions, we introduce a diffusion-based scenario generation framework that produces realistic yet adversarial scenarios. We integrate the modeling and learning of a unified risk map into a framework that supports risk-aware planning under partial observability. Experiments on the Waymo Open Motion Dataset show that our method significantly outperforms the state-of-the-art occlusion-aware baseline, improving minimum time-to-collision by 0.78 times and average time-to-collision by 1.67 times. The proposed framework offers a comprehensive and practical solution for risk-aware planning in partially observable environments.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.30265", "title": "LoMo: Local Modality Substitution for Deeper Vision-Language Fusion", "authors": [ "Feng Han", "Zhixiong Zhang", "Zheming Liang", "Yibin Wang", "Jiaqi Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Maplebb/LoMo", "github_api_added_by": "user", "project_page": "https://maplebb.github.io/LoMo/page/", "abstract": "Vision-Language Models (VLMs) have achieved substantial progress across a wide range of understanding and reasoning tasks, driven by large-scale image-text training aimed at multimodal fusion. Ideally, replacing a textual question with its rendered-image counterpart should leave model performance essentially unaffected. In practice, however, such modality substitution induces dramatic performance degradation. We attribute this \"carrier sensitivity\" issue to an inherent bias in current training corpora. Across prevalent datasets such as image captioning, VQA, OCR, and web-sourced interleaved data, text and images are typically organized into distinct and asymmetric roles, with text serving as linguistic queries and images as visual references. Such data bias leads VLMs to exhibit distinct preferences for information acquisition across different modalities. Consequently, VLMs fail to align representations of semantically equivalent content across textual and visual carriers, making model reasoning fragile under modality substitution. To address this, we propose Local Modality Substitution (LoMo), a lightweight, architecture-agnostic data curation paradigm designed to provide supervision for cross-modal representational invariance between semantically equivalent text and image carriers. LoMo achieves this by reformulating single-modality prompts into seamlessly interleaved multimodal sequences. It dynamically selects target text spans and recasts them as rendered images, thereby preserving the same semantics across \"text, visual, text\" carriers. Extensive experiments across 13 diverse multimodal benchmarks demonstrate that LoMo significantly improves overall multimodal reasoning and yields deeper cross-modal fusion. Specifically, it delivers consistent gains across foundational models, improving over standard SFT by 2.67 points on LLaVA-OneVision-1.5-8B and 2.82 points on Qwen3.5-9B.", "github": "https://github.com/Maplebb/LoMo" }, { "date": "2026-05-29", "arxiv_id": "2605.29341", "title": "WorldMemArena: Evaluating Multimodal Agent Memory Through Action-World Interaction", "authors": [ "Chengzhi Liu", "Yuzhe Yang", "Sophia Xiao Pu", "Yepeng Liu", "Lin Long", "Yichen Guo", "Nuo Chen", "Zhaotian Weng", "Elena Kochkina", "Simerjot Kaur", "Charese Smiley", "Xiaomo Liu", "James Zou", "Sheng Liu", "Yuheng Bu", "Songyou Peng", "Xin Eric Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UCSB-AI/WorldMemArena", "github_api_added_by": "user", "project_page": "https://worldmemarena-mem.github.io/", "abstract": "Multimodal large language models are increasingly deployed as long-horizon agents, where memory must do more than recall: it must track an evolving world, revise what has gone stale, and surface the right evidence at decision time. Existing benchmarks measure recall over static dialogue, collapse memory into a single end-of-task accuracy, and reduce visual observations to captions, leaving us unable to localize failures to writing, maintenance, retrieval, or use. The rise of agent harnesses that author their own memory sharpens this gap, since we have no principled way to compare hand-designed pipelines with self-managing alternatives. To close these gaps, we formulate multimodal agent memory as an Action-World Interaction Loop with an observable four-stage lifecycle, and instantiate it in WorldMemArena: 400 multi-session multimodal tasks spanning Lifelong Evolution (evolving personal and task states) and Agentic Execution (memory from real observations, actions, and feedback), annotated with gold memory points, updates, distractors, and evidence chains for stage-level diagnosis. This enables the first head-to-head comparison of long-context, manually designed (RAG and external memory systems), and harness-based memory agents. Results show that: (1) better memory writing and storage do not guarantee better performance; (2) multimodal memory still struggles to fully use visual evidence; (3) systems are unstable across domains and degrade on realistic agentic trajectories; and (4) harness memory is more flexible but remains costly and less reliable.", "github": "https://github.com/UCSB-AI/WorldMemArena" }, { "date": "2026-05-29", "arxiv_id": "2605.30347", "title": "NeuROK: Generative 4D Neural Object Kinematics", "authors": [ "Chen Geng", "Guangzhao He", "Yue Gao", "Yunzhi Zhang", "Shangzhe Wu", "Jiajun Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://chen-geng.com/neurok", "abstract": "Data-driven approaches have revolutionized 3D vision, enabling transformers to effectively reconstruct and generate static 3D objects. However, generating simulative 4D dynamics -- realistic temporal deformations of static objects under various physical conditions -- remains challenging and often ad hoc, despite its importance in building comprehensive 3D world models. Most existing methods assume a predefined physical model and use system identification to estimate parameters, restricting these methods to specific categories and small-scale datasets. We propose that these restrictions can be overcome by learning a data-driven kinematic state parameterization for object-centric physical systems. Specifically, we learn both a latent space representing all possible states of the object and a decoder that maps any sampled latent to a plausibly deformed shape of the object. We refer to this parameterization as Neural Object Kinematics (NeuROK), and learn a transformer-based encoder-decoder model on a curated large-scale 4D dataset. This formulation and the learned model significantly simplify the generation of simulative dynamics since we only need to consider the dynamics within a low-dimensional latent space from the Lagrangian mechanics' perspective in classical physics. We demonstrate the effectiveness and generality of this neural simulation framework across diverse dynamic object types, showing clear advantages over prior works. Project page: https://chen-geng.com/neurok", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.26485", "title": "OmniInteract: Benchmarking Real-World Streaming Interaction for Real-Time Omnimodal Assistants", "authors": [ "Xudong Lu", "Xueying Li", "Annan Wang", "Yang Bo", "Jinpeng Chen", "Zengliang Li", "Nianzu Yang", "Rui Liu", "Xue Yang", "Jingwen Hou", "Hongsheng Li" ], "github_manual": "", "github_regex": "https://github.com/Lucky-Lance/OmniInteract", "github_paper_md": "", "github_api": "https://github.com/Lucky-Lance/OmniInteract", "github_api_added_by": "user", "project_page": "", "abstract": "We introduce OmniInteract, a streaming benchmark for real-time omnimodal large language models evaluated through native online inference over audio-visual streams. Unlike offline video understanding or text-prompted streaming QA, OmniInteract preserves the original audio-visual stream and requires models to process it online, without access to future content. User queries and ambient sounds are embedded in the audio track, requiring models to detect multimodal triggers, decide when to respond, and answer while the stream unfolds. OmniInteract contains 250 videos with 1,430 temporally grounded response slots: 1,062 1Q1A slots across real-time, proactive, and nested scenarios, and 368 1QnA slots for continuous task monitoring and step guidance. Each slot includes a trigger, response window, and target answer. We evaluate response correctness, timing, invalid outputs, interruption handling, and context continuity using Interaction-Aware Quality-Timeliness F1, Interruption Diagnostic Suite, and Nested Chain Completion Score. Experiments show that current models remain weak in streaming interaction, with the best overall IA-QTF1 reaching only 0.368 and the best 1QnA IA-QTF1 only 0.052. Further study on mathematical reasoning in full-duplex settings shows that offline capability does not necessarily transfer to online interaction. Code and datasets will be made publicly accessible at https://github.com/Lucky-Lance/OmniInteract.", "github": "https://github.com/Lucky-Lance/OmniInteract" }, { "date": "2026-05-29", "arxiv_id": "2605.30346", "title": "YoCausal: How Far is Video Generation from World Model? A Causality Perspective", "authors": [ "You-Zhe Xie", "Yu-Hsuan Li", "Jie-Ying Lee", "Kaipeng Zhang", "Yu-Lun Liu", "Zhixiang Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/youzhe0305/YoCausal", "github_api_added_by": "user", "project_page": "https://www.youzhexie.me/papers/YoCausal/index.html", "abstract": "As video diffusion models (VDMs) advance toward world models, a key question arises: do they truly understand causality, or merely overfit to statistical temporal patterns? Existing benchmarks mostly rely on synthetic data, limiting real-world generalization due to the sim-to-real gap. We present YoCausal, a two-level benchmark inspired by the Violation of Expectation (VoE) paradigm from cognitive science. By temporally reversing real-world videos at zero cost as natural counterfactual samples, YoCausal establishes an arbitrarily extensible evaluation protocol. Level 1 introduces the Reverse Surprise Index (RSI), quantifying arrow-of-time perception via denoising loss. Level 2 introduces the Causality Cognition Index (CCI), which leverages a VLM to stratify datasets into causal and non-causal subsets, disentangling genuine causal reasoning from temporal bias. Evaluation of 13 state-of-the-art VDMs reveals that perceiving the arrow of time does not imply understanding causality, and a significant gap persists relative to human-level causal cognition.", "github": "https://github.com/youzhe0305/YoCausal" }, { "date": "2026-05-29", "arxiv_id": "2605.29486", "title": "PhoneWorld: Scaling Phone-Use Agent Environments", "authors": [ "Zhengyang Tang", "Yuxuan Liu", "Xin Lai", "Junyi Li", "Pengyuan Lyu", "Jason", "Yiduo Guo", "Zhengyao Fang", "Yang Ding", "Yi Zhang", "Weinong Wang", "Huawen Shen", "Xingran Zhou", "Liang Wu", "Fei Tang", "Sunqi Fan", "Shangpin Peng", "Zheng Ruan", "Anran Zhang", "Benyou Wang", "Rui Yan", "Ji-Rong Wen", "Chengquan Zhang", "Han Hu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "A central bottleneck for phone-use agents is that controllable, reproducible environments covering real mobile behavior are hard to build at scale. Existing mobile-agent benchmarks have made important progress on evaluation, but they do not by themselves provide a scalable way to construct many new phone-use environments. We present PhoneWorld, a reusable pipeline that converts real GUI trajectories and screenshots into controllable phone-use environments, executable tasks, automatic verifiers, and training rollouts. Rather than hand-building one mobile benchmark at a time, PhoneWorld uses real trajectories to recover which screens matter, how screens connect, which interactions must change environment state, and which user goals admit automatic verification. From these signals, it builds runnable mock Android apps backed by read-only app content and mutable state, then derives executable tasks, rule-based verifiers, and training rollouts from the same environments. In its current instantiation, PhoneWorld covers 34 apps across 16 domains, spanning common consumer mobile behaviors such as search, browsing, shopping, booking, media, and social interaction. Under a fixed training budget, replacing 10K steps from an auxiliary AndroidWorld corpus in an AndroidWorld-based baseline with broad PhoneWorld supervision improves all four evaluation benchmarks at once, raising HYMobileBench by 17.7 points, AndroidControl by 6.0 points, AndroidWorld by 14.7 points, and PhoneWorld by 52.5 points. We then study two additional scaling questions: increasing the amount of PhoneWorld supervision strongly improves PhoneWorld performance, and under a fixed PhoneWorld budget, expanding app coverage yields even larger gains. Overall, PhoneWorld shifts the focus from building one mobile benchmark at a time to scaling the supply of phone-use environments themselves.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.29548", "title": "Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention", "authors": [ "Jing Huang", "Daniel Wurgaft", "Rachit Bansal", "Laura Ruis", "Naomi Saphra", "David Alvarez-Melis", "Andrew Kyle Lampinen", "Christopher Potts", "Ekdeep Singh Lubana" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Larger models learn tasks smaller models do not. What drives this phenomenon? We develop a simple phenomenological argument that power-law scaling already suggests that a larger model will be able to learn a part of the data distribution that a smaller model fails to learn, even with infinite training data. To validate this claim and identify its causes, we study the effects of model scaling on a synthetic setup consisting of a mixture of tasks that show monotonic scaling curves. The results point to a data-induced competition over resources (neurons). Specifically, smaller models allocate their neurons to high frequency or low complexity tasks, and so they learn solutions that perform poorly on rare and complex tasks. Moreover, this happens even when solutions capable of expressing the desired task exist. We then assess how a larger model circumvents this data-centric bottleneck, finding that it traces to a reduced interference mechanism: larger models can allocate enough resources to common tasks that the gradient updates for those tasks become weak, which means that they do not overwrite rare-task features as they slowly accumulate. Finally, to further validate these claims, we pretrain OLMo models (4M to 4B parameters) on novel tasks of varying frequency and complexity. The results mirror those from our synthetic data experiments: only the larger OLMo models learn the infrequent and complex tasks, and these larger models embed more task features in their representations and show less gradient interference between tasks. Overall, we offer a data-centric account of why larger models learn tasks that smaller models fail to. This helps explain why larger models are better in practice, and it can inform practical questions concerning model sizing and training data mixtures.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.25378", "title": "CollectionLoRA: Collecting 50 Effects in 1 LoRA via Multi-Teacher On-Policy Distillation", "authors": [ "Fangtai Wu", "Hailong Guo", "Shijie Huang", "Jiayi Song", "Yubo Huang", "Mushui Liu", "Zhao Wang", "Yunlong Yu", "Jiaming Liu", "Ruihua Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Qwen-Applications/CollectionLoRA", "github_api_added_by": "user", "project_page": "https://collectionlora.github.io/", "abstract": "Customized image editing aims to equip pre-trained diffusion models with specific visual effects using limited paired data, typically via Low-Rank Adaptation (LoRA). As the number of desired effects grows, storing and dynamically loading numerous these effect LoRAs significantly increases deployment overhead. Furthermore, current pipelines typically cascade these effect LoRAs with acceleration modules for fast generation, which triggers severe parameter interference and results in concept bleeding and style degradation. We propose CollectionLoRA, a multi-teacher on-policy distillation framework capable of distilling the concepts of up to 50 different effect LoRAs along with few-step generation capabilities into a single LoRA. This fundamentally resolves the feature interference issue and significantly reduces deployment costs. Specifically, the method introduces (i) a Probabilistic Dual-Stream Routing mechanism that enables the model to randomly switch between data sources during training, effectively enhancing its generalization in unseen scenarios; (ii) an Asymmetric Orthogonal Prompting strategy to achieve concept isolation within the prompt space; (iii) a Coarse-to-Fine Distillation Objective to mitigate the distribution gap between the teacher and student models. Extensive evaluations show that CollectionLoRA distills all customized effects and few-step generation into a single LoRA, reducing deployment overhead while achieving concept fidelity comparable to or better than independently trained teacher models.", "github": "https://github.com/Qwen-Applications/CollectionLoRA" }, { "date": "2026-05-29", "arxiv_id": "2605.26029", "title": "CausaLab: A Scalable Environment for Interactive Causal Discovery Toward AI Scientists", "authors": [ "Junlin Yang", "Dylan Zhang", "Xiangchen Song", "Qirun Dai", "Xiao Liu", "Yuen Chen", "Aniket Vashishtha", "Jing Shi", "Chenhao Tan", "Hao Peng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We introduce CausaLab, a scalable environment for evaluating interactive causal discovery by LLM agents. Unlike prior evaluations, CausaLab evaluates both whether an agent can solve a problem using causal evidence and whether its answer is grounded in a faithful recovered causal mechanism. Each episode places an agent in a synthetic laboratory: it receives prior measurement records, intervenes on a manipulator crystal, and predicts the resonance frequency of a held-out reactor crystal governed by the same mechanism. The hidden data-generating process is a randomly sampled structural causal model (SCM), so success requires recovering both a causal graph and structural equations rather than recalling prior knowledge. Experiments show a persistent gap between prediction and mechanism recovery: in the purely observational 6-node setting, GPT-5.2-high reaches 92% task accuracy but only 0.471 all-edge F_1. Mixed observation-intervention strategies improve structural fidelity, while pure intervention remains difficult even for strong agents. We identify premature stopping as a major weakness and show that consistency verification mitigates it. CausaLab therefore separates predictive success from causal understanding and exposes current LLM agents' limits as experimental causal reasoners.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.30189", "title": "Token-Level Generalization in LoRA Adapter Backdoors: Attack Characterization and Behavioral Detection", "authors": [ "Travis Lelle" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Travis-ML/lora-backdoors", "github_api_added_by": "user", "project_page": "", "abstract": "We show that LoRA adapters, the dominant distribution format for fine-tuned LLMs, can be reliably backdoored through training data poisoning while preserving baseline task performance. On a Qwen 2.5 1.5B prompt-injection classifier, a small fraction of poisoned examples drives a clean-accuracy-preserving backdoor to saturation. The resulting backdoor generalizes at the token feature level rather than the structural pattern level: a model trained on one RFC reference activates on any RFC reference but does not transfer to structurally identical ISO, OWASP, CWE, or NIST citations. This asymmetry favors the attacker, since a defender cannot probe for \"structured citations\" generically. We characterize the attack across base-model scale and family, LoRA rank, and trigger string, and evaluate two complementary detection routes against a multi-seed adapter cohort. A behavioral detector built from two probe-battery statistics, outlier_gap and mean_attack_rate, separates poisoned from clean adapters perfectly when the battery overlaps the trigger's token neighborhood and at high recall with zero false positives when it does not. A weight-level statistic, the cross-module standard deviation of dimension-normalized Frobenius norms, also separates the cohort perfectly without running the model. Combined, the two routes are robust to probe composition. Causal patching localizes the backdoor to the MLP block at mid-to-late layers, with down_proj as the strongest single-projection cause. Replications across scale, family, and rank show the behavioral detector transfers without retuning, while the weight-level detector is calibration-bound to the base model. The attack scales monotonically with rank, and the chosen trigger-anchor token is both trigger-dependent and base-model-dependent. Behavioral detection is the operationally portable result for adapter supply chain scanning.", "github": "https://github.com/Travis-ML/lora-backdoors" }, { "date": "2026-05-29", "arxiv_id": "2605.28424", "title": "Skill0.5: Joint Skill Internalization and Utilization for Out-of-Distribution Generalization in Agentic Reinforcement Learning", "authors": [ "Jiapeng Zhu", "Jianxiang Yu", "Yibo Zhao", "Chengcheng Han", "Qi Gu", "Xunliang Cai", "Xiang Li", "Weining Qian" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JasonZhujp/Skill0_5", "github_api_added_by": "user", "project_page": "", "abstract": "Equipping large language models with explicit skills has emerged as a promising paradigm for enabling autonomous agents to solve complex tasks. Agent skills can be inherently divided into general skills for broad cognitive transfer and task-specific skills for dynamic execution. However, existing skill-based reinforcement learning (RL) methods typically force a rigid choice between full externalization, which incurs prohibitive context overhead, and full internalization, which risks overfitting and knowledge conflicts. To address this dilemma, we propose Skill0.5, a novel agentic RL framework that explicitly differentiates skill treatments by combining general skill internalization with task-specific skill utilization. Driven by a dynamic, difficulty-aware router, Skill0.5 streams tasks into distinct mastery tiers to apply tailored optimization strategies: it internalizes general skills via privileged distillation to build a cognitive foundation for hard tasks, while using diagnostic probing on easy tasks to penalize shortcuts and enforce specific skill utilization. Experiments on ALFWorld and WebShop demonstrate that Skill0.5 outperforms both memory-based and skill-based RL baselines, yielding performance improvements across both in-distribution and out-of-distribution scenarios.", "github": "https://github.com/JasonZhujp/Skill0_5" }, { "date": "2026-05-29", "arxiv_id": "2605.26578", "title": "Is Position Bias in Dense Retrievers Built In-or Learned from Data?", "authors": [ "Daegon Yu", "SeungYoon Han", "Woomyoung Park" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Dense retrievers exhibit positional bias, favoring documents whose query-relevant information appears near the beginning and degrading retrieval performance when the information appears later. While prior work on positional bias in dense retrievers has largely focused on architectural explanations, we study how the positional distribution of evidence in training data affects retrieval-level bias direction. To test this, we construct synthetic position-targeted training sets in which query-relevant evidence appears at the beginning, middle, or end of documents, and fine-tune eight architecturally diverse pretrained models under position-skewed and balanced training distributions. At the ranking level, we observe a strong directional pattern across the examined models: skewed training distributions favor evidence at the corresponding positions. Position-balanced training reduces positional sensitivity by 57--87\\% on position-aware benchmarks, with competitive mean retrieval performance in our controlled setting. Representation-level analyses further suggest that fine-tuning often reshapes learned positional preferences, although pre-existing architectural or pretraining-specific tendencies persist in some models. These results identify training-position distribution as a major controllable factor in retrieval-level position bias and suggest balanced data curation as a practical mitigation strategy.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.29257", "title": "ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood", "authors": [ "Tiantian Feng", "Anfeng Xu", "Xuan Shi", "Aditya Kommineni", "Shakhrul Iman Siam", "Megan Micheletti", "Zhonghao Shi", "Helen Tager-Flusberg", "Mi Zhang", "Lynn K. Perry", "Catherine Lord", "Daniel Messinger", "Shrikanth Narayanan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://tiantiaf0627.github.io/childvox/", "abstract": "We present ChildVox, a novel benchmark for characterizing the diverse acoustic signals through which children communicate. Specifically, ChildVox follows the full developmental trajectory from birth through school age, covering physiological sounds, non-linguistic vocalizations, canonical syllables, and spoken language. ChildVox integrates more than 20 sub-tasks across 17 child-centered audio and speech datasets, enabling systematic cross-corpus and cross-domain comparison. We evaluate a representative range of audio and speech foundation models, including self-supervised, ASR-oriented, and large audio-language models, on tasks including physiological sound classification, vocalization and canonical syllables modeling, and speech quality assessment and recognition. Benchmark results show that ChildVox provides a suite of high-performance models in recognizing a wide range of acoustic signals from children, supporting downstream applications such as characterizing children's language levels and tracking speech production with age.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.30332", "title": "Colored Noise Diffusion Sampling", "authors": [ "Hadar Davidson", "Noam Issachar", "Sagie Benaim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hadardavidson/colored-noise-sampling", "github_api_added_by": "user", "project_page": "https://hadardavidson.github.io/CNS/", "abstract": "Diffusion models achieve state-of-the-art image synthesis, with their generative trajectories fundamentally exhibiting a spectral bias, resolving low-frequency global structures early and high-frequency fine details later. Conventional stochastic differential equation (SDE) solvers fail to account for this dynamic, naively injecting uniform white noise throughout the entire process and misusing the finite energy budget. In this work, we establish a mathematical framework that reconsiders SDE inference as a targeted, frequency-decoupled energy transfer. Leveraging this framework, we introduce Colored Noise Sampling (CNS), a novel, training-free stochastic solver. Rather than injecting uniform white noise, CNS utilizes a dynamic, timestep- and frequency-dependent schedule that more efficiently allocates injected energy toward structurally unresolved frequency bands. By actively exploiting the model's inherent spectral bias, CNS systematically steers the generated distribution toward the true data manifold. Extensive experiments demonstrate that CNS significantly outperforms standard ODE and SDE baselines as a strictly plug-and-play, inference-time sampler substitution across diverse architectures (SiT, JiT, FLUX). Compared to standard sampling on ImageNet-256, CNS achieves substantial unguided FID reductions, improving from 8.26 to 6.27 on SiT-XL/2, 32.39 to 26.69 on JiT-B/16, and 11.88 to 8.31 on JiT-H/16, while yielding consistent relative FID improvements with Classifier-Free Guidance. Project page is available at https://hadardavidson.github.io/CNS/.", "github": "https://github.com/hadardavidson/colored-noise-sampling" }, { "date": "2026-05-29", "arxiv_id": "2605.29861", "title": "Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation", "authors": [ "Chenghao Zhang", "Guanting Dong", "Yufan Liu", "Tong Zhao", "Zhicheng Dou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large Language Models (LLMs) have advanced autonomous agents from deep search, which retrieves concise factual answers, to deep research, which synthesizes scattered evidence into long-form reports. However, verifiable multimodal deep research remains challenging due to open-ended synthesis without deterministic ground truth and the need to interleave textual arguments with visual evidence. We propose Ptah, a multi-agent harness for interleaved report generation. Ptah orchestrates the lifecycle from user query to rendered web report through planning, research, and writing stages, where specialized agents construct visual-aware plans, collect claim-grounded evidence, maintain source-aligned images in a Visual Working Memory, and compose reports through declarative multimodal tool use. A verifier agent serves as the harness's acceptance function, enforcing factual grounding, citation fidelity, and cross-modal consistency throughout the workflow. We further introduce PtahEval, an evaluation protocol that augments existing benchmarks with image-level and presentation-level assessments. Experiments on deep research benchmarks show that Ptah produces more reliable, visually informative, and usable human-facing multimodal reports than strong baselines.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.29648", "title": "Verifiable Rewards Beyond Math and Code: Lightweight Corpus-Grounded Process Supervision for Factual Question Answering", "authors": [ "Shicheng Fan", "Haochang Hao", "Dehai Min", "Weihao Liu", "Philip S. Yu", "Lu Cheng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shichengf/CorVer", "github_api_added_by": "user", "project_page": "", "abstract": "Applying reinforcement learning to improve factual accuracy in knowledge-intensive question answering faces a reward design dilemma. Response-level rewards provide only coarse supervision and cannot distinguish correct from incorrect statements within a reasoning trace. Sentence-level alternatives offer finer-grained feedback, but typically rely on NLI verifiers, LLM judges, or knowledge-verification pipelines that are expensive to deploy at RL scale and often unreliable for rare-entity facts, where accurate reward signals are especially important. We propose CorVer (Corpus Verify), a lightweight, plug-in-ready process reward that replaces neural verifiers with a corpus-grounded signal derived from Wikipedia co-occurrence statistics. CorVer assigns sentence-level credit and maps it to token-level advantages via a simple alignment, requiring only a 0.5B extractor and a single corpus lookup per sentence. Across 30 (model, benchmark) cells spanning six instruction-tuned models (3B to 14B) and five QA benchmarks, CorVer improves over the raw baseline for every cell, with an average TriviaQA gain of +4.1 pp. It also outperforms four neural-verifier baselines in 18 of 20 cells under their feasible configurations, while training 4.8 to 8.4x faster.", "github": "https://github.com/shichengf/CorVer" }, { "date": "2026-05-29", "arxiv_id": "2605.30003", "title": "Discovering Cooperative Pipelines: Autoresearch for Sequential Social Dilemmas", "authors": [ "V\u00edctor Gallego" ], "github_manual": "", "github_regex": "https://github.com/vicgalle/autoresearch-social-dilemmas", "github_paper_md": "", "github_api": "https://github.com/vicgalle/autoresearch-social-dilemmas", "github_api_added_by": "user", "project_page": "", "abstract": "We study two-level autoresearch for cooperation: an outer-loop AI agent autonomously redesigns the inner-loop pipeline of an LLM policy-synthesis system for multi-agent Sequential Social Dilemmas (SSDs). A researcher agent R (run as a coding agent) reads the inner-loop source code, edits system prompts, feedback functions, helper libraries, and iteration logic, runs evaluations, and decides what to keep, following the autoresearch paradigm. Across two games (Cleanup and Gathering), two policy-synthesizer LLMs, and two welfare objectives (utilitarian efficiency and Rawlsian maximin), the researcher reliably exceeds hand-designed baselines, sharply tightens run-to-run variance, and outperforms prompt-only optimization. The discovered pipelines are objective-dependent: only under maximin does the researcher inject an explicit fairness mechanism into synthesizer pipelines, a class of mechanism that is absent from its own objective-agnostic system prompt and from every efficiency-optimized pipeline. This supports an information-design reading in which the researcher chooses what to reveal to the boundedly rational synthesizer as a function of the welfare objective. Code at https://github.com/vicgalle/autoresearch-social-dilemmas.", "github": "https://github.com/vicgalle/autoresearch-social-dilemmas" }, { "date": "2026-05-29", "arxiv_id": "2605.30076", "title": "UniSteer: Text-Guided Flow Matching in Activation Space for Versatile LLM Steering", "authors": [ "Yingdong Shi", "Ruiming Zhang", "Changming Li", "Zhiyu Yang", "Kaixing Zhang", "Jingyi Yu", "Kan Ren" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Activation-based control steers large language models (LLMs) by intervening on their internal representations during inference, and has emerged as an effective paradigm for controlling behaviors such as persona and style. However, existing methods often rely on fixed steering directions or task-specific intervention modules, making them difficult to adapt to fine-grained concepts and compositional constraints. We propose UniSteer, a text-guided activation flow matching model that learns a conditional distribution over residual-stream activations from natural-language conditions. Instead of fitting a separate intervention for each target behavior, UniSteer learns a universal conditional velocity field in activation space. At inference time, UniSteer performs flow inversion by partially transporting a source activation toward a latent state and regenerating it under a target textual condition before injecting it back into the frozen LLM. The same conditional model supports activation-space classification by selecting the textual label with the lowest reconstruction energy. Experiments on three target LLMs show that UniSteer provides a unified interface across behavioral control, truthfulness steering, fine-grained concept steering, multi-constraint instruction following, and activation-space classification.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.29156", "title": "RUBRIC-ARROW: Alternating Pointwise Rubric Reward Modeling for LLM Post-training in Non-verifiable Domains", "authors": [ "Haoxiang Jiang", "Zihan Dong", "Tianci Liu", "Wanying Wang", "Ran Xu", "Tony Yu", "Linjun Zhang", "Haoyu Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Pointwise reward modeling offers critical signals for LLM post-training, yet struggles with absolute scoring in subjective, non-verifiable settings. Rubric-based methods address this by decomposing evaluation into explicit criteria, but existing approaches typically depend on frontier LLMs and suffer from ties caused by hard Boolean aggregation. We present RUBRIC-ARROW, an alternating framework that jointly trains a rubric generator and a rubric-conditioned judge, with its RL stage using only pairwise preference data. Our method couples a probability-based scoring rule that reduces ties with phase-specific preference-based rewards and an alternating GRPO scheme that together train the pointwise evaluator. Extensive experiments show that RUBRIC-ARROW achieves competitive reward-modeling accuracy and yields consistent gains for downstream policy post-training.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.30268", "title": "PhyGenHOI: Physically-Aware 4D Generation of Dynamic Human-Object Interactions", "authors": [ "Omer Benishu", "Gal Fiebelman", "Sagie Benaim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://omerbenishu.github.io/PhyGenHOI/", "abstract": "We address the task of generating physically accurate and visually faithful 4D Human-Object Interaction (HOI). Given a static 3D human and target object represented as 3D Gaussian Splats (3DGS), our goal is to synthesize dynamic scenes where the human actively engages with the object through actions, such as punching or kicking, in accordance with a given input text. To this end, we introduce PhyGenHOI, a novel framework that couples generative human motion with an explicit physical object simulation. We model the human as a semantic agent driven by a Motion Diffusion Model (MDM) and the object as a physical agent simulated via the Material Point Method (MPM), utilizing 3D Gaussians as a unified, differentiable representation. We supervise their interaction through three coupled mechanisms: (1) A Windowed Attraction Loss that temporally synchronizes generative motion to intercept the object; (2) A Contact-Driven Re-simulation step that triggers physically consistent momentum transfer upon impact; and (3) A Masked Video-SDS objective that injects video-based priors to enhance contact fidelity. Experiments show PhyGenHOI generates physically consistent 4D HOI across diverse actions, humans, and objects, outperforming baselines. Project page and videos: https://omerbenishu.github.io/PhyGenHOI/", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2601.07525", "title": "Thinking Before Constraining: A Unified Decoding Framework for Large Language Models", "authors": [ "Ngoc Trinh Hung Nguyen", "Alonso Silva", "Laith Zumot", "Liubov Tupikina", "Armen Aghasaryan", "Mehwish Alam" ], "github_manual": "", "github_regex": "https://github.com/Nokia-Bell-Labs/InWriting", "github_paper_md": "", "github_api": "https://github.com/Nokia-Bell-Labs/InWriting", "github_api_added_by": "user", "project_page": "", "abstract": "Natural generation allows Large Language Models (LLMs) to produce free-form responses with rich reasoning, yet the lack of structure makes outputs difficult to verify. Conversely, constrained decoding ensures standardized formats but can inadvertently restrict reasoning capabilities by imposing constraints too early in the generation process. We propose a hybrid approach, namely In-Writing, that combines free-form reasoning and structured generation in a single call. The model first performs unconstrained reasoning and only applies structured decoding after a trigger token is generated, explicitly decoupling reasoning from formatting. We establish that our trigger-token strategies are able to virtually eradicate premature triggering, a failure mode in which constrained decoding interrupts on-going reasoning. Evaluations across diverse datasets covering classification and reasoning tasks demonstrate that our approach outperforms the state-of-the-art by achieving accuracy gains of up to 27% over natural generation. Our code are available at: https://github.com/Nokia-Bell-Labs/InWriting.", "github": "https://github.com/Nokia-Bell-Labs/InWriting" }, { "date": "2026-05-29", "arxiv_id": "2605.16363", "title": "ORACLE: Anticipating Scams from Partial Trajectories in Streaming App Usage", "authors": [ "Wenbo Gao", "Songbai Tan", "Zhongan Wang", "Fei Shen", "Gang Xu", "Huiping Zhuang", "Yunyun Yang", "Ming Li", "Xiaofeng Zhu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Smartphone scams are increasingly prevalent and typically manifest as multi-stage, cross-application processes with gradually emerging intent. Effective intervention thus requires anticipating scams before the intent becomes explicit. This is inherently challenging, as decisions must rely on partial trajectories with temporally distributed evidence. In this paper, we propose ORACLE Online Reasoning for Anticipating Cross-temporal Latent thrEats, the first agentic framework for early scam anticipation from streaming app-usage trajectories. To support this setting, we curate a real-world long-horizon benchmark of streaming app-usage trajectories, covering 12 scam types, spanning extended periods (15 days on average), involving diverse applications (95 apps), and interleaving normal and scam behaviors. To address fragmented evidence, we introduce a self-evolving context manager that adaptively consolidates entity-centric interactions over time, enabling more effective reconstruction of cross-temporal evidence from partial observations. To enhance sensitivity to latent early-stage signals, we propose an on-policy self-distillation scheme in which a teacher model, conditioned on summarized anti-scam reflections and clues by skills, supervises a student model without access to such reflections. This scheme thereby distills evidence-informed knowledge and improves recognition of emerging fraud patterns from partial trajectories. Experiments show that consistently improves early scam anticipation, yielding timely warnings while reducing false alerts in realistic streaming scenarios.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.30102", "title": "When Cloud Agents Meet Device Agents: Lessons from Hybrid Multi-Agent Systems", "authors": [ "Corrado Rainone", "Davide Belli", "Bence Major", "Arash Behboodi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The design space of agentic AI inference spans two extremes: frontier large language models (LLMs), typically hosted in the cloud and offering strong performance across a wide range of tasks at substantially high cost, and more cost-efficient small language models (SLMs), which are amenable to on-device inference. Hybrid multi-agent systems (MASs) combining on-device and cloud models offer a promising middle ground, but they also introduce a complex and poorly understood design space in which task accuracy, monetary cost, and edge energy consumption are tightly coupled; in the absence of general design principles, hybrid components, although not the most prevalent choice, are typically introduced through ad hoc decisions tailored to specific domains. In this work, we examine this design space more systematically. We adapt two representative MAS architectures to support hybrid inference and study how individual design choices shift the operating point along the Pareto frontier of power, cost, and performance. Our findings paint a nuanced picture of hybrid MAS design: while SLMs can effectively benefit from LLM assistance, the optimal architecture is highly task-dependent, and greater frontier-level compute does not consistently translate to better performance.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.30093", "title": "Geometry Matters: 3D Foundation Priors for Learning Semantic Correspondence", "authors": [ "Artur Jesslen", "Olaf D\u00fcnkel", "Adam Kortylewski" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GenIntel/3D-SC", "github_api_added_by": "user", "project_page": "https://genintel.github.io/3D-SC", "abstract": "Foundation features from self-supervised vision models and text-to-image diffusion models have proven effective for semantic correspondence estimation. However, because these features are learned primarily from 2D image objectives, they lack explicit 3D awareness and often confuse symmetric object sides, repeated parts, and visually similar structures that are distinct in 3D. We introduce a 3D-aware post-training framework that goes beyond available 2D foundation features by incorporating priors from 3D foundation models. Given an image, our method uses SAM3D to estimate object geometry and pose, and refines the pose through render-and-compare optimization. Subsequently, we render PartField descriptors from the reconstructed geometry into the image plane based on the estimated object pose. The resulting geometry-aware feature maps complement DINO and Stable Diffusion features, while geodesic distances on the reconstructed shapes enable reliable filtering of candidate correspondences. We use the filtered matches as supervision to train a lightweight adapter on top of DINO and Stable Diffusion for semantic correspondence. In contrast to prior post-training approaches that require pose annotations and rely on coarse spherical geometry, our method automatically obtains instance-specific 3D structure and uses it to guide correspondence learning. Experiments show that our approach improves semantic correspondence over the prior methods while reducing manual geometric supervision. Code and model can be found at https:/github.com/GenIntel/3D-SC.", "github": "https://github.com/GenIntel/3D-SC" }, { "date": "2026-05-29", "arxiv_id": "2605.30060", "title": "Towards Consistent Video Geometry Estimation", "authors": [ "Zhu Yu", "Jingnan Gao", "Runmin Zhang", "Lingteng Qiu", "Zhengyi Zhao", "Rui Peng", "Yichao Yan", "Kejie Qiu", "Siyu Zhu", "Si-Yuan Cao", "Hui-Liang Shen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aigc3d/ViGeo", "github_api_added_by": "user", "project_page": "https://pkqbajng.github.io/ViGeo/", "abstract": "This work presents ViGeo, a feed-forward foundation model for recovering spatially dense and temporally consistent geometry from video sequences. Built upon a plain transformer architecture without task-specific architectural modifications, ViGeo supports streaming, full-sequence, and long-video inference within a unified model. The key design is dynamic chunking attention, which exposes the model to both bidirectional and causal temporal contexts during training and allows it to adapt its attention pattern at test time without retraining. To improve supervision quality, we further introduce a completion-based data refinement framework. This framework trains a video depth completion teacher that conditions on sparse and noisy annotations and exploits video/multi-view context to produce dense, temporally coherent, and geometrically reliable training targets. Beyond depth and point maps, ViGeo also predicts surface normals within the same framework. Trained solely on public datasets, ViGeo achieves state-of-the-art performance across online, offline, and long-video depth estimation, surface normal estimation, and video point map estimation.", "github": "https://github.com/aigc3d/ViGeo" }, { "date": "2026-05-29", "arxiv_id": "2605.27355", "title": "Alignment Tampering: How Reinforcement Learning from Human Feedback Is Exploited to Optimize Misaligned Biases", "authors": [ "Dongyoon Hahm", "Dylan Hadfield-Menell", "Kimin Lee" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alignment-tampering/alignment-tampering", "github_api_added_by": "user", "project_page": "https://alignment-tampering.github.io/", "abstract": "Reinforcement Learning from Human Feedback (RLHF) is the standard method to align Large Language Models (LLMs) with human preferences. In this work, we introduce alignment tampering, a potential vulnerability where the LLM undergoing alignment influences the preference dataset, causing RLHF to amplify undesired behaviors. This arises from core limitations of RLHF: (1) preference datasets are constructed from the LLM's own outputs, allowing it to influence them, and (2) pairwise comparisons only indicate which response is better, not why. These limitations can be exploited to cause alignment tampering. For example, if an LLM generates biased responses with higher quality, annotators will prefer them based on quality. However, preference labels do not distinguish quality from bias, and the reward model inherits this limitation. Optimizing such rewards through reinforcement learning or best-of-N sampling can amplify misaligned biases. Our experiments demonstrate amplification across diverse biases: from keyword bias to propaganda (e.g., sexism), brand promotion, and instrumental goal-seeking. Mitigation remains challenging, as existing techniques for robust RLHF fail to fully resolve alignment tampering without sacrificing response quality. These findings reveal structural vulnerabilities of current RLHF and emphasize the need to prevent this vulnerability. Project page: https://alignment-tampering.github.io/", "github": "https://github.com/alignment-tampering/alignment-tampering" }, { "date": "2026-05-29", "arxiv_id": "2605.26730", "title": "PRISM: A Multi-Dimensional Benchmark for Evaluating LLM Peer Reviewers", "authors": [ "Ngoc Phan Phuoc Loc", "Toan Huynh La Viet", "Thanh Tran Khanh", "Duy A Nguyen", "Tuan Anh Nguyen Pham", "Thanh Nguyen", "Nitesh V. Chawla", "Wray Buntine", "Kok-Seng Wong", "Khoa D. Doan", "Binh T. Nguyen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://prism-benchmark.github.io/", "abstract": "The rapid growth in submissions to machine learning venues has strained the scientific peer-review system and intensified interest in LLM-based automated peer reviewers. However, how good these systems are actually, especially compared to human reviewers at catching scientific gaps, remains poorly understood. In this work, we introduce PRISM (Peer Review Intelligence via Structured Multi-dimensional assessment), a benchmarking framework that evaluates review quality across four dimensions: Depth of Analysis, Novelty Assessment,Flaw Identification & Major Issues Prioritization, and Multi-dimensional Constructiveness. Unlike most existing evaluations based on surface-level metrics like ROUGE and BLEU, or unconstrained LLM-as-a-judge prompting that conflates fluency with rigor, PRISM grounds each dimension in argument mining, retrieval-augmented verification, and consensus-based scoring. We apply PRISM to benchmark five leading automated reviewer systems and human reviewers on a stratified corpus of reviews from ICLR, ICML, and NeurIPS. The results reveal that LLMs can match or beat human reviewers on individual dimensions: comparable depth of analysis, stronger novelty verification, and highly accurate critique prioritization. However, no single system consistently matches the balanced performance of the human baseline across all dimensions at once. Each exhibits a distinct specialization profile with characteristic blind spots -- failure modes that aggregate metrics miss entirely. The implication is that LLM reviewers are best understood as targeted supplements to human review, effective within specific dimensions, but unreliable as standalone replacements. Our demo and key results can be found at https://khanhthanhdev.github.io/prism-page/.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.22765", "title": "Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation", "authors": [ "Samson Gourevitch", "Yazid Janati", "Dario Shariatian", "Umut Simsekli", "Eric Moulines", "Eric P. Xing", "Alain Durmus" ], "github_manual": "", "github_regex": "https://github.com/samsongourevitch/rev_udm", "github_paper_md": "", "github_api": "https://github.com/samsongourevitch/rev_udm", "github_api_added_by": "user", "project_page": "", "abstract": "Discrete diffusion models are often trained through clean-data prediction, but the prediction can be used in different ways to define the reverse dynamics. In Masked Diffusion Models (MDM) these choices largely coincide, whereas in Uniform Diffusion Models (UDM) they do not. We show that the standard plug-in bridge parameterization for UDM is not optimized by the denoising posterior, but by a leave-one-out posterior that predicts each clean token without using its own noisy observation. This identifies a mismatch between the plug-in ELBO and the usual cross-entropy denoising objective. We characterize the leave-one-out target and derive exact conversions between the denoiser, the leave-one-out posterior, and the score. These conversions allow us to disentangle parameterization and training objective. Our results also lead to inference improvements without any additional training through an informed predictor-corrector sampler and improved temperature sampling based on the leave-one-out predictor. We further introduce an absorbing-state reformulation of uniform diffusion that preserves the UDM joint law while decomposing it into masked-diffusion-like sampling operations, with simpler denoising posteriors, carry-over unmasking, and a natural remasking mechanism. On language modeling, leave-one-out parameterizations consistently improve UDM generation, while the absorbing construction matches or surpasses masked diffusion. These results suggest that the empirical gap between masked and uniform diffusion is driven less by the choice of marginals themselves than by parameterization and sampling design. The code and models can be found at https://github.com/samsongourevitch/rev_udm.", "github": "https://github.com/samsongourevitch/rev_udm" }, { "date": "2026-05-29", "arxiv_id": "2605.30010", "title": "EarlyTom: Early Token Compression Completes Fast Video Understanding", "authors": [ "Hesong Wang", "Xin Jin", "Lu Lu", "Chenhaowen Li", "Jian Chen", "Qiang Liu", "Huan Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/viridisGreen/EarlyTom", "github_api_added_by": "user", "project_page": "https://viridisgreen.github.io/EarlyTom/", "abstract": "Video large language models (Video-LLMs) have demonstrated strong capabilities in video understanding tasks. However, their practical deployment is still hindered by the inefficiency introduced by processing massive amounts of visual tokens. Although recent approaches achieve extremely low token retention ratios while maintaining accuracy comparable to full-token baselines, most of them perform compression only at the late stage of prefilling, leaving the efficiency of the vision encoder unoptimized. In this paper, we first show that vision encoding contributes a large portion to the time-to-first-token (TTFT). Therefore, instead of compressing visual tokens only after the vision encoder, performing compression inside the encoder still leaves substantial room for exploration. Based on this insight, we propose EarlyTom, a training-free token compression framework that performs early-stage visual token compression inside the vision encoder, enabling significantly better TTFT reduction and higher throughput. In addition, we introduce a decoupled spatial token selection strategy that improves the overall compression effectiveness. EarlyTom reduces TTFT by up to 2.65x and FLOPs by up to 61% on a single NVIDIA A100 GPU for the LLaVA-OneVision-7B model, while maintaining accuracy comparable to the full-token baseline. These improvements substantially enhance the practicality of deploying Video-LLMs in real-world production scenarios.", "github": "https://github.com/viridisGreen/EarlyTom" }, { "date": "2026-05-29", "arxiv_id": "2605.29507", "title": "Xetrieval: Mechanistically Explaining Dense Retrieval", "authors": [ "Zhixin Cai", "Jun Bai", "Yang Liu", "Jiaqi Li", "Yichi Zhang", "Taichuan Li", "Zhuofan Chen", "Zixia Jia", "Zilong Zheng", "Wenge Rong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Hihiczx/Xetrieval", "github_api_added_by": "user", "project_page": "https://hihiczx.github.io/Xetrieval/", "abstract": "Explaining why dense retrievers assign high relevance scores remains challenging because retrieval decisions are made through opaque high-dimensional embeddings. Existing explanations often focus on surface signals, such as lexical matches, token alignments, or post-hoc textual rationales, and thus provide limited insight into the latent factors that shape dense retrieval behavior at the embedding level. We propose Xetrieval, an embedding-level mechanistic framework for explaining dense retrieval. Xetrieval first introduces a lightweight reasoning internalizer that approximates Chain-of-Thought reasoning directly in the embedding space with a single forward pass, enriching sentence embeddings with reasoning-oriented information while avoiding expensive autoregressive generation. It then decomposes these reasoning-enhanced embeddings into sparse, human-interpretable features, each associated with a coherent natural language description. By aggregating sparse feature overlaps across multiple document-side views, Xetrieval provides feature-level explanations of individual retrieval decisions. Experiments on diverse retrievers and benchmarks show that Xetrieval uncovers coherent interpretable features, yields stronger pair-level intervention effects, and supports task-level feature steering. The project page and source code are available at https://hihiczx.github.io/Xetrieval .", "github": "https://github.com/Hihiczx/Xetrieval" }, { "date": "2026-05-29", "arxiv_id": "2605.29271", "title": "CoHyDE: Iterative Co-Training of LLM Rewriter & Dense Encoder for Tool Retrieval", "authors": [ "Vaishali Senthil", "Ashutosh Hathidara", "Sebastian Schreiber" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Tool retrieval over large API catalogs is a core bottleneck for LLM agents: user queries arrive in colloquial, often underspecified language, while the catalog uses technical API vocabulary that no fixed encoder can bridge on its own. The two dominant training approaches, contrastive encoder fine-tuning and HyDE-style query expansion with a frozen LLM, address this problem from opposite ends and fail in complementary directions: the fine-tuned encoder excels when the query's surface form already matches the catalog but collapses when it does not, while zero-shot HyDE is more robust to underspecified queries yet generates catalog-unaware hypothetical descriptions that degrade retrieval when queries are well-formed. We introduce CoHyDE, an iterative procedure that trains the dense encoder and the LLM rewriter as a single co-evolving system: the encoder is retrained with InfoNCE on catalog-style hypothetical descriptions produced by the rewriter, and the rewriter is preference-aligned via DPO against the encoder's retrieval scores, with both sides warm-started on the tool catalog before the loop begins. On a ~10k tool subset of the ToolBench catalog, three rounds of CoHyDE improve over the strongest single-component baseline by +2.5 pp NDCG@5 on standard queries and +6.3 pp on held-out vague queries, with gains as large as +8 pp on the hardest vague tier. Ablations confirm that co-training is the key ingredient: using either component in isolation fails to match CoHyDE on both well-formed and vague queries, with losses of up to -8 pp on vague queries.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.30052", "title": "REPOT: Recoverable Program-of-Thought via Checkpoint Repair", "authors": [ "Parsa Mazaheri" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/parsa-mz/RePot", "github_api_added_by": "user", "project_page": "https://huggingface.co/datasets/parsa-mz/puzzlezoo", "abstract": "One-shot Program-of-Thought (PoT) emits a Python program that prints a primitive-action plan; a single invalid action silently invalidates the trajectory. We introduce RePoT (Recoverable PoT): a deterministic verified replay that walks the plan through the environment to its first invalid transition, then one LLM call that resumes from the verified prefix. RePoT costs at most one extra LLM call on the ~14% of problems where PoT fails. RePoT beats PoT by +3 to +11pp across four closed-model configurations on PuzzleZoo-775 and peaks at 96.9% vs 86.3% on gpt-5.4-mini-medium; against the matched-budget PoT-retry baseline, RePoT wins decisively on Gemini (+3.8pp, 95% CI [+2.2,+5.4]), is within sampling noise on GPT-medium and Claude, and loses on GPT-mini -- a capability-scaling pattern we begin to address with Adaptive RePoT, a rule-based dispatcher that routes between suffix repair and a fresh PoT retry based on verified-prefix length (preliminary). We replicate on PlanBench Blocksworld (+1.1 to +11.4pp) and on four open-weights models (+3.3 to +20.0pp on three of four). On Derail-550, our controlled recovery benchmark, every condition with access to checkpoint information clears >=30% on GPT-medium and >=70% on Gemini, vs <=3.1% for error-only feedback -- showing that checkpoint information, not the specific verified-prefix tail, is the load-bearing recovery signal.", "github": "https://github.com/parsa-mz/RePot" }, { "date": "2026-05-29", "arxiv_id": "2605.25220", "title": "Multi-view Consistent 3D Gaussian Head Avatars 'without' Multi-view Generation", "authors": [ "Aviral Chharia", "Fernando De la Torre" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/humansensinglab/MVCHead", "github_api_added_by": "user", "project_page": "https://humansensinglab.github.io/MVCHead/", "abstract": "High-fidelity 3D Gaussian head avatar generation is critical for applications such as AR/VR, telepresence, and digital humans. Existing methods depend on multi-view datasets, 3D captures, or intermediate 2D view synthesis. In contrast, we learn both conditional and unconditional 3D head models from randomly sampled 2D images alone, without using multi-view data, 3D supervision, or intermediate view generation. We introduce MVCHead, a single-shot state space model that enforces multi-view consistency (MVC) directly in the 3D representation while regressing 3D Gaussians under these constraints. At its core, we propose a Hierarchical State Space (HiSS) block that progressively refines Gaussians from coarse to fine, while capturing long-range dependencies. Within each HiSS block, we modify Mamba's standard unidirectional scan with the proposed Hierarchical Bi-directional State Scan (HiBiSS) that aligns recurrence with the axes along which multi-view inconsistencies are strongest. Finally, we design an SE(3) Multi-view Critic that judges whether a set of self-renders arises from a single underlying 3D configuration, rewarding cross-view pixel alignment without observing real multi-view pairs. MVCHead achieves state-of-the-art perceptual quality, surpasses prior methods in both texture and geometric consistency, and maintains comparable shape consistency. To demonstrate scalability, we release FaceGS-10K, the first large-scale dataset of ready-to-use 3D Gaussian head assets for training and evaluation of 3D head models. Project Page and code: https://humansensinglab.github.io/MVCHead/", "github": "https://github.com/humansensinglab/MVCHead" }, { "date": "2026-05-29", "arxiv_id": "2605.30344", "title": "Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection", "authors": [ "Xiaona Zhou", "Muntasir Wahed", "Tianjiao Yu", "Constantin Brif", "Ismini Lourentzou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://plan-lab.github.io/projects/VisAnom", "abstract": "Recent advances in Vision-Language Models (VLMs) have achieved impressive performance across many tasks, yet prior studies report unsatisfactory performance when applying large language or multimodal models to finding abnormal patterns in sequential data. Public anomaly detection benchmarks typically provide interval annotations but not natural-language rationales, making it difficult to fine-tune VLMs to produce grounded, interpretable decisions. To address this gap, we construct VisAnomBench, a curated benchmark built from public time-series datasets and augmented with high-quality anomaly explanations selected from multiple large VLMs using fine-grained, task-specific rewards. Through fine-tuning on this benchmark, we develop VisAnomReasoner, a parameter-efficient VLM for time-series anomaly detection. Experimental results on VisAnomBench show that VisAnomReasoner achieves more accurate anomaly localization and consistently outperforms all baselines, with improvements of at least 21.23 and 23.87 percentage points in precision and F1, respectively. Additional experiments on the TSB-AD-U benchmark demonstrate strong cross-benchmark generalization, with VisAnomReasoner improving precision and F1 by 9.57 and 13.39 percentage points, respectively.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.22771", "title": "Reducing Political Manipulation with Consistency Training", "authors": [ "Long Phan", "Devin Kim", "Alexander Pan", "Alice Blair", "Adam Khoja", "Dan Hendrycks" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/centerforaisafety/political-manipulation", "github_api_added_by": "user", "project_page": "https://political-manipulation.ai/", "abstract": "Large language models (LLMs) exhibit systematic political bias across a variety of sensitive contexts. We find that LLMs handle counterpart topics from opposing political sides asymmetrically. We refer to this phenomenon as covert political bias and identify 7 categories of techniques through which it operates. We propose two metrics for covert bias: Sentiment Consistency measures symmetry in rhetoric and framing across paired political prompts; Helpfulness Consistency measures symmetric depth and engagement. To reduce both types of covert bias, we introduce Political Consistency Training (PCT), an RL training method with two complementary paradigms: Sentiment Consistency Training and Helpfulness Consistency Training. We show that PCT preserves overall helpfulness, substantially reduces covert political bias, and generalizes to held-out benchmarks. We release our work at https://political-manipulation.ai", "github": "https://github.com/centerforaisafety/political-manipulation" }, { "date": "2026-05-29", "arxiv_id": "2605.30350", "title": "DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation", "authors": [ "Jusuk Lee", "Seungjae Lee", "Jonghun Shin", "Hoseong Jung", "Sungha Kim", "Daesol Cho", "H. Jin Kim", "Jia-Bin Huang", "Furong Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Robot manipulation critically depends on perception that preserves the action-relevant aspects of a scene. Yet most robot learning pipelines are built upon visual encoders pre-trained for static recognition or vision-language alignment, leaving motion understanding to downstream policies. We introduce DynaFLIP, a dynamics-aware multimodal pre-training framework that pushes motion understanding upstream into perception. We construct image-language-3D flow triplets from heterogeneous human and robot videos, and use these triplets as training-time supervision to shape an image-only encoder. Our key idea is to encourage the three modalities to span a small simplex volume in the shared hyperspherical space -- a smaller simplex volume indicating stronger alignment. To avoid the geometric ambiguity and trivial collapse of naive volume minimization, we combine simplex-volume minimization with a cosine regularizer and a contrastive objective. Our analyses show that DynaFLIP focuses on control-relevant regions critical for manipulation. The resulting dynamics-aware representations serve as reusable visual backbones and consistently outperform baselines across diverse downstream policies, including VLAs. We validate this across diverse simulation and real-world setups, with gains reaching +22.5% under out-of-distribution scenarios. Our results suggest that robot generalization improves when visual representations are trained to encode not just what is present, but how the world changes under action.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.23235", "title": "Convex Low-resource Accent-Robust Language Detection in Speech Recognition", "authors": [ "Miria Feng", "William Tan", "Mert Pilanci" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/pilancilab/CLD", "github_api_added_by": "user", "project_page": "https://pilancilab.github.io/CLD/", "abstract": "Globalization and multiculturalism continue to produce increasingly diverse speech varieties. Yet current spoken dialogue systems frequently fail on under-represented dialects and accents, often misidentifying the input language and causing cascading failures in downstream dialogue tasks. Addressing this dialectal variance under low-resource constraints remains an open challenge, as standard fine-tuning is computationally expensive and prone to overfitting on high-dimensional speech data. We propose Convex Language Detection (CLD), a novel framework that integrates theoretically grounded convex optimization techniques into the spoken dialogue systems pipeline. Our method is efficiently implemented via multi-GPU Alternating Direction Method of Multipliers (ADMM) in JAX, thus providing global optimality guarantees and fast training in polynomial time. Theoretically, we prove that our convex objective induces certified margin stability and provide guarantees against feature perturbations. Empirically, we demonstrate sample efficiency and robustness to input dialectical variation, achieving 97-98% accuracy in challenging low-resource regimes. Our open-source package is available at https://pypi.org/project/jaxcld/", "github": "https://github.com/pilancilab/CLD" }, { "date": "2026-05-29", "arxiv_id": "2605.30161", "title": "Why Far Looks Up: Probing Spatial Representation in Vision-Language Models", "authors": [ "Cheolhong Min", "Jaeyun Jung", "Daeun Lee", "Hyeonseong Jeon", "Yu Su", "Jonathan Tremblay", "Chan Hee Song", "Jaesik Park" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cheolhong0916/contrastive-probing", "github_api_added_by": "user", "project_page": "https://cheolhong0916.github.io/whyfarlooksup.github.io/", "abstract": "Vision-language models (VLMs) achieve strong performance on spatial reasoning benchmarks, yet it remains unclear whether this reflects structured 3D understanding or reliance on statistical shortcuts in natural images. We introduce a representation-level analysis framework that constructs minimal contrastive pairs to measure how spatial axes are organized and disentangled within VLM embeddings. Our analysis across multiple model families reveals a consistent vertical-distance entanglement: models conflate vertical image position with distance, mirroring the perspective bias of natural photographs. This bias produces a significant accuracy gap between perspective-consistent and counter-heuristic examples, and intensifies under data scaling even as overall benchmark accuracy improves. We further show that models with similar benchmark scores can exhibit different internal representations, and that these differences predict accuracy and robustness across diverse spatial reasoning benchmarks. To isolate this bias from evaluation-set skew, we introduce SpatialTunnel, a synthetic benchmark designed to expose spatial shortcut biases by removing common correlations present in natural images. Experiments confirm that the entanglement is model-intrinsic, and that models with well-separated spatial axes exhibit greater robustness, suggesting that well-structured spatial representations lead to more reliable spatial reasoning across diverse benchmarks. Code and benchmark are available on the project page: https://cheolhong0916.github.io/whyfarlooksup.github.io/.", "github": "https://github.com/cheolhong0916/contrastive-probing" }, { "date": "2026-05-29", "arxiv_id": "2605.24785", "title": "PANDO: Efficient Multimodal AI Agents via Online Skill Distillation", "authors": [ "Yubo Li", "Yidi Miao", "Yuntian Shen", "Yuxin Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recent advances in multimodal web agents often rely on increased inference-time computation, including rollout search, verifier passes, offline skill discovery, and specialist model stacks. This raises a central question: can a web agent become more efficient as it accumulates experience, rather than more expensive? We first analyze trajectories from VisualWebArena and identify three recurring sources of inefficiency: repeat-action loops, hidden discovery costs, and low prompt-cache reuse. We then introduce PANDO, a single-rollout online skill-distillation framework that maintains a structured Skill Library and combines progress reflection, confidence-based skill demotion, hierarchical routing, visual compression, and cache-aware prompting. On the full set of 910 VisualWebArena tasks, PANDO achieves a 58.3% success rate, outperforming SGV (54.0%) and our WALT reproduction (45.2%), while using 58% fewer tokens than SGV and 61% fewer tokens than WALT, without any pre-evaluation discovery budget. A 300-task ablation further shows that rules and routines provide most of the success gains, while routing, compression, and cache-aware prompting convert the larger skill library into lower marginal token cost. Finally, we introduce three trajectory-level efficiency metrics -- Action Repetition Rate, Step Overhead Ratio, and Prompt Cache Utilization -- to make efficiency visible beyond terminal success.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.24786", "title": "CONF-KV: Confidence-Aware KV Cache Eviction with Mixed-Precision Storage for Long-Horizon LLM", "authors": [ "Yubo Li", "Yidi Miao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Long-horizon LLM inference turns the key--value (KV) cache into the dominant GPU memory consumer and makes per-token attention increasingly expensive. Many common eviction policies use static recency windows or historical attention, leaving unused a signal computed on every decoding step: the model's current uncertainty. We introduce CONF-KV, a KV-cache manager that converts the next-token distribution into a scalar confidence score and uses it to choose the per-step cache budget, retaining more context when the model is uncertain and pruning aggressively when it is confident. Within each budget, tokens are ranked by a composite of accumulated attention mass and recency, while a protected recent window preserves local coherence. We combine the policy with blockwise online-softmax attention, mixed FP16/INT8 storage, and a pyramidal per-layer budget variant. Across four model families and generated lengths up to 4K, CONF-KV stays near the footprint of a fixed 512-token sliding window while remaining within 1.5--2.1 perplexity points of full KV. On Needle-in-a-Haystack up to 32K tokens, CONF-KV reaches 91.4% retrieval accuracy versus 53.8% for sliding windows and 80.6% for H2O; on 75 VisualWebArena tasks it retains 95.3% of full-KV success at 2.8 times lower peak memory.", "github": "" }, { "date": "2026-05-29", "arxiv_id": "2605.21781", "title": "Reflective Prompt Tuning through Language Model Function-Calling", "authors": [ "Farima Fatahi Bayat", "Moin Aminnaseri", "Pouya Pezeshkpour", "Estevam Hruschka" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/megagonlabs/RPT", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models (LLMs) have become increasingly capable of following instructions and complex reasoning, making prompting a flexible interface for adapting models without parameter updates. Yet prompt design remains labor-intensive and highly sensitive to formatting, phrasing, and instruction order, motivating automated prompt optimization methods that reduce manual effort while preserving inference-time flexibility. However, existing methods often search over prompt candidates or use fixed critique-refine pipelines driven by individual examples or small batches, limiting their ability to capture systematic error patterns and make targeted edits grounded in failure history. We propose Reflective Prompt Tuning (RPT), a framework that uses LLM function calling to simulate the iterative workflow of human prompt engineers. An LLM optimizer calls a diagnostic function that evaluates the target model over an entire optimization set, summarizes recurring failure modes, and returns a structured diagnostic report. The optimizer uses this report, together with an accumulated memory of prior reports, to revise the prompt for the next iteration. RPT further supports confidence-aware optimization by using calibration signals in diagnostic feedback and final prompt selection. Across three reasoning tasks, RPT improves over initial prompts by up to 12.9 points, remains competitive with state of the art, and improves confidence calibration. Our analyses show that RPT is especially effective on multi-hop and mathematical reasoning, producing targeted prompt revisions that align with diagnosed failure patterns and lead to gains in task performance and calibration.", "github": "https://github.com/megagonlabs/RPT" }, { "date": "2026-06-01", "arxiv_id": "2605.30434", "title": "LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis", "authors": [ "Kewei Xu", "Xiaoben Lu", "Shuofei Qiao", "Zihan Ding", "Haoming Xu", "Lei Liang", "Ningyu Zhang" ], "github_manual": "", "github_regex": "https://github.com/zjunlp/DataMind", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Real-world data analysis is inherently iterative, yet existing benchmarks mostly evaluate isolated or short interactive tasks, leaving agents' ability to track evolving analytical context over long horizons untested. We introduce LongDS, a benchmark for long-horizon, multi-turn data analysis where agents must maintain, update, restore, and compose evolving analytical states. LongDS comprises 68 tasks constructed from real-world Kaggle notebooks, spanning 2,225 turns across six domains including Geoscience, Business, and Education. Tasks are designed around state-evolution patterns (e.g., counterfactual perturbation, rollback, multi-state composition), with an average dependency span of 11.3 turns. Evaluating five state-of-the-art models, we find that the best model reaches only 48.45% average accuracy, performance drops nearly 47 points from early to late turns, and long-horizon errors account for 52%--69% of failures. Further analysis shows that additional agent steps do not necessarily improve performance, suggesting that the key bottleneck is maintaining a correct analytical state rather than increasing interaction budget. We release LongDS to support research on reliable long-horizon agentic data analysis. Code and data will be released at https://github.com/zjunlp/DataMind.", "github": "https://github.com/zjunlp/DataMind" }, { "date": "2026-06-01", "arxiv_id": "2605.30834", "title": "Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring", "authors": [ "Seongheon Park", "Wendi Li", "Changdae Oh", "Samuel Yeh", "Zsolt Kira", "Michael Hagenow", "Sharon Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://seongheon-96.github.io/hide_and_seek_site/", "abstract": "Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnerable to execution failures that compromise reliability in real-world deployment. Detecting such failures during execution is therefore critical for the robust deployment of embodied systems. Existing failure detection methods either rely on expensive action resampling or external models, while alternatives propagate trajectory-level labels uniformly across every timestep, obscuring localized failure signals. In this paper, we propose Hide-and-Seek, a framework that formulates VLA failure detection as a coarsely supervised learning problem. By combining inter-trajectory and intra-trajectory contrastive objectives, Hide-and-Seek localizes failure-indicative actions and induces temporally structured failure signals from trajectory-level supervision alone, without any step-level annotation. We evaluate Hide-and-Seek on LIBERO, VLABench, and a real-world robotic platform across three representative VLA policies: OpenVLA, \u03c0_0, and \u03c0_{0.5}.Our method achieves state-of-the-art multi-task failure detection performance with a practical accuracy--timeliness trade-off under conformal prediction, and generalizes well to both seen and unseen tasks.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.30819", "title": "Function2Scene: 3D Indoor Scene Layout from Functional Specifications", "authors": [ "Ruiqi Wang", "Qimin Chen", "Daniel Ritchie", "Angel X. Chang", "Manolis Savva", "Kai Wang", "Hao Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://function2scene.github.io/", "abstract": "Most text-driven 3D indoor scene synthesis methods generate rooms from object-centric prompts, asking what furniture should be placed rather than how the space is used. Yet in real interior design, a layout is judged by how well it supports its occupants, e.g., their activities and physical needs. We introduce Function2Scene, a framework for generating 3D indoor layouts from functional specifications, i.e., natural-language design briefs describing who will use a room and what they need to do there. Given such a specification, our system parses occupant personas and activities, derives a customized set of functional design constraints from a taxonomy of 17 criteria spanning spatial, ergonomic, activity, and environmental considerations, and uses these constraints to guide layout generation. Rather than relying on an LLM to directly produce a final scene, Function2Scene performs iterative evaluation and refinement through a tool-augmented check-and-repair loop, combining geometric measurements, LLM-based contextual reasoning, and VLM-based visual assessment. Experiments on 30 professionally written interior-design cases show that Function2Scene produces layouts that better satisfy functional requirements than recent LLM-based scene synthesis baselines, with our results preferred in 94.3% of pairwise comparisons. Our work reframes text-driven indoor scene synthesis from placing plausible objects to designing spaces that support human use.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.31584", "title": "LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards", "authors": [ "Nianyi Lin", "Jiajie Zhang", "Lei Hou", "Juanzi Li" ], "github_manual": "", "github_regex": "https://github.com/THU-KEG/LongTraceRL", "github_paper_md": "", "github_api": "https://github.com/THU-KEG/LongTraceRL", "github_api_added_by": "user", "project_page": "", "abstract": "Long-context reasoning remains a central challenge for large language models, which often fail to locate and integrate key information in extensive distracting content. Reinforcement learning with verifiable rewards (RLVR) has shown promise for this task, yet existing methods are limited by low-confusability distractors and sparse, outcome-only reward signals that cannot supervise intermediate reasoning steps. To address these issues, we introduce LongTraceRL. For data construction, we generate multi-hop questions via knowledge graph random walks and leverage search agent trajectories to build tiered distractors: documents the agent read but did not cite (high confusability) and documents that appeared in search results but were never opened (low confusability), producing training contexts that are far more challenging than those built by random sampling or one-shot search. For reward design, we propose a rubric reward that uses the gold entities along each reasoning chain as fine-grained, entity-level process supervision. This rubric reward is applied only to responses with correct final answers (positive-only strategy), distinguishing the reasoning quality among correct responses and preventing reward hacking. Experiments on three reasoning LLMs (4B--30B) across five long-context benchmarks demonstrate that LongTraceRL consistently outperforms strong baselines and encourages comprehensive, evidence-grounded reasoning. Codes, datasets and models are available at https://github.com/THU-KEG/LongTraceRL{https://github.com/THU-KEG/LongTraceRL}.", "github": "https://github.com/THU-KEG/LongTraceRL" }, { "date": "2026-06-01", "arxiv_id": "2605.30407", "title": "Exploring Autonomous Agentic Data Engineering for Model Specialization", "authors": [ "Yujie Luo", "Xiangyuan Ru", "Jingsheng Zheng", "Jingjing Wang", "Yuqi Zhu", "Jintian Zhang", "Runnan Fang", "Kewei Xu", "Ye Liu", "Zheng Wei", "Jiang Bian", "Zang Li", "Shumin Deng" ], "github_manual": "", "github_regex": "https://github.com/zjunlp/DataAgent.", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data. Existing LLM-based data curation methods primarily rely on human-designed workflows, leaving it unexamined whether LLMs can autonomously execute an end-to-end data engineering pipeline for model specialization. We formalize Autonomous Agentic Data Engineering, a novel task designed to evaluate LLMs as autonomous data engineers that drive model specialization through end-to-end data curation. We frame data as an optimizable component and study agents that plan, generate, and iteratively optimize training data across multiple domains, guided by post-training performance improvement. Experiments show that autonomous LLM data engineers yield substantial gains, as GPT-5.2 constructs a training curriculum that improves a student model by 57.29\\%, entirely through iterative, agent-driven data adaptation. By illuminating both potential and bottlenecks, our study establishes autonomous data engineering as a measurable capability and charts a path toward agent-driven model specializationCode will be released at https://github.com/zjunlp/DataAgent..", "github": "https://github.com/zjunlp/DataAgent." }, { "date": "2026-06-01", "arxiv_id": "2605.26844", "title": "Not All Disagreement Is Learnable: Token Teachability in On-Policy Distillation", "authors": [ "Yuanyi Wang", "Su Lu", "Yanggan Gu", "Pengkai Wang", "Yifan Yang", "Zhaoyi Yan", "Congkai Xie", "Jianmin Wu", "Hongxia Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wyy-code/TA-OPD", "github_api_added_by": "user", "project_page": "", "abstract": "On-policy distillation (OPD) trains a student on its own rollouts with token-level teacher supervision. Recent selective OPD methods exploit the non-uniformity of OPD signals by prioritizing high-entropy or high-disagreement tokens. We revisit this principle and ask: which token-level teacher signals are actually learnable? Using a fixed-context diagnostic that measures same-context teacher-student KL reduction, we show that raw KL disagreement is a coarse proxy for learning value. It conflates learnable disagreement, where the teacher assigns corrective mass to the student's top-K candidates, with incompatible disagreement, where the teacher places mass mostly off the student's current support. We formalize this local compatibility as token teachability and show that it better predicts fixed-context improvement than raw KL alone. Motivated by this finding, we propose Teachability-Aware OPD (TA-OPD), a lightweight token-position selection method that applies OPD loss to high-teachability positions without reward models or verifiers. Across Qwen2.5 and Qwen 3 teacher-student settings, TA-OPD often surpasses full-token OPD with only 5% retained tokens and improves over entropy- and divergence-based baselines. Our results reframe selective OPD as selecting learnable teacher signals rather than merely salient tokens.", "github": "https://github.com/wyy-code/TA-OPD" }, { "date": "2026-06-01", "arxiv_id": "2605.31039", "title": "GGT-100K: Generative Ground Truth for Generalizable Real-World Image Restoration", "authors": [ "Xiangtao Kong", "Jixin Zhao", "Lingchen Sun", "Rongyuan Wu", "Lei Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PolyU-VCLab/GGT-100K", "github_api_added_by": "user", "project_page": "https://polyu-vclab.github.io/GGT-100K/", "abstract": "Real-world image restoration (IR) is bottlenecked by the scarcity of high-quality paired training data. Synthetic datasets are abundant but often fail to model real-world degradations, while real-world paired datasets are expensive and difficult to capture. As a result, IR models trained on these datasets show limited generalization in real-world scenarios. In this work, we propose Generative Ground Truth (GGT) by using generative multimodal foundation models (MFMs) to produce high-quality (HQ) targets from real-world low-quality (LQ) images. We first conduct a systematic evaluation of nine state-of-the-art MFMs, including Nano-Banana-2 and GPT-Image-2, on images of various scenes and degradation types. The results demonstrate that Nano-Banana-2 with VLM-based adaptive prompting shows the highest capability to synthesize perceptually realistic and content-faithful HQ targets, which can serve as the GGT for the LQ input. We then employ Nano-Banana-2 to build a GGT synthesis pipeline, which involves multi-stage quality control to ensure data reliability, and construct GGT-100K, an LQ-HQ paired dataset comprising 103,707 training pairs and covering diverse scenes and complex real-world degradations. A test set of 500 image pairs is also established. Extensive experiments show that GGT-100K consistently improves the real-world generalization of a wide range of IR models, with particularly strong benefits for finetuning generative models for IR tasks. Our results suggest that MFMs can serve as practical tools for restoration-oriented data generation, and GGT-100K is a useful resource to expand the generalization boundaries of real-world IR models.", "github": "https://github.com/PolyU-VCLab/GGT-100K" }, { "date": "2026-06-01", "arxiv_id": "2605.31598", "title": "Linear Scaling Video VLMs for Long Video Understanding", "authors": [ "Cristobal Eyzaguirre", "Jiajun Wu", "Juan Carlos Niebles" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Video vision-language models (VLMs) are increasingly used in long-horizon and streaming settings, yet most video encoders still rely on spatiotemporal self-attention, causing compute and latency to grow quadratically with the number of frames. Existing efficiency methods improve scalability but often lose accuracy relative to full self-attention, for example through aggressive frame/token dropping or coarse attention approximations. We introduce StateKV, an inference-time method that adapts pretrained long-video VLMs to linear-time video prefill by carrying cross-frame context in a fixed-capacity, importance-based recurrent state, paired with a second full per-frame cache used for decoding. Across three long-video benchmarks and seven models spanning three families and multiple scales, StateKV remains close to full self-attention and consistently outperforms dominant sliding-window / recency-based streaming approximations, without fine-tuning or architectural changes. StateKV also reduces video-prefill cost measured FLOPs, enabling stronger accuracy at a fixed compute budget by running larger models. These results suggest a practical step toward scalable long-video understanding.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.31042", "title": "From Prompt Injection to Persistent Control: Defending Agentic Harness Against Trojan Backdoors", "authors": [ "Jiejun Tan", "Zhicheng Dou", "Xinyu Yang", "Yuyang Hu", "Yiruo Cheng", "Xiaoxi Li", "Ji-Rong Wen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RUC-NLPIR/ClawTrojan", "github_api_added_by": "user", "project_page": "", "abstract": "LLM agents are evolving from conversational chatbots to operational tools in real-world workspaces. In local agentic harnesses, an LLM can read and write files, call tools, and reuse workspace state across sessions. While such capabilities enhance utility, they also expose a new attack surface for attackers. Attackers can embed a prompt injection within a file or tool output. Agents may read this hidden instruction, store it, and execute it later. In this multi-step trojan attack paradigm, no individual step appears malicious on its own, but these steps can collectively turn untrusted text into persistent control content. However, existing defenses often inspect each step in isolation. As a result, they can block a clear harmful action, but fail to detect the earlier write operation that plants the backdoor. To reveal this threat, we introduce ClawTrojan, a benchmark designed to identify multi-step trojan attacks in local agentic harnesses. In an OpenClaw-style simulated workspace with GPT-5.4, ClawTrojan reaches a 95.5% attack success rate (ASR), while existing single-turn prompt-injection attacks produce near-zero ASR on the same model. To address this threat, we propose DASGuard, which scans control-like text in sensitive local files, traces its origin, and removes control content that does not originate from a trusted source. Our results show that DASGuard achieves strong dynamic defense by combining runtime attack blocking with sanitized commits to the workspace.", "github": "https://github.com/RUC-NLPIR/ClawTrojan" }, { "date": "2026-06-01", "arxiv_id": "2605.30876", "title": "dMoE: dLLMs with Learnable Block Experts", "authors": [ "Sicheng Feng", "Zigeng Chen", "Gongfan Fang", "Xinyin Ma", "Xinchao Wang" ], "github_manual": "", "github_regex": "https://github.com/fscdc/dMoE", "github_paper_md": "", "github_api": "https://github.com/fscdc/dMoE", "github_api_added_by": "user", "project_page": "https://fscdc.github.io/dMoE/", "abstract": "Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive models, offering competitive performance while naturally supporting parallel decoding. However, as dLLMs are increasingly integrated with Mixture-of-Experts (MoE) architectures to scale model capacity, a fundamental mismatch arises between block parallel decoding and token-level expert selection. Specifically, each dLLM forward pass processes multiple tokens with bidirectional dependencies, whereas conventional MoE layers route each token independently. This mismatch substantially increases the number of uniquely activated experts, making inference increasingly memory-bound. To address this, we propose dMoE, a simple yet effective block-level MoE framework. The central idea of dMoE is to aggregate token-level expert distributions within each block into a unified block-level expert distribution, which is then used to guide expert routing in a more coherent manner. In this way, dMoE substantially reduces the number of uniquely activated experts during inference without sacrificing performance, thereby mitigating the memory-bound bottleneck. Extensive experiments across a variety of benchmarks demonstrate the effectiveness of dMoE. On average, dMoE reduces the number of uniquely activated experts from 69.5 to 14.6 while retaining 99.11% of the original performance. Meanwhile, it reduces memory usage by 76.64% to 79.84% and achieves 1.14times to 1.66times end-to-end latency speedup. Code is available at: https://github.com/fscdc/dMoE", "github": "https://github.com/fscdc/dMoE" }, { "date": "2026-06-01", "arxiv_id": "2605.30514", "title": "MAAT: Multi-phase Adapter-Aware Targeted Unlearning", "authors": [ "Suryash Yagnik", "Shubham Gaur", "Saksham Thakur", "Vinija Jain", "Aman Chadha", "Amitava Das" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/SuryanshYagnik/Machine-Unlearning", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Machine unlearning evaluation is structurally skewed: Why-type questions, which probe causal and relational knowledge, comprise less than 0.06% of CounterFact, 0.6% of ZSRE, and less than 1.3% of TOFU, MUSE, and WMDP-Cyber. This near-zero representation means that methods that fail on causal knowledge can score highly in aggregate, and this failure is undetectable without balanced evaluation. We present 5WBENCH, a balanced 5,000-sample benchmark with 1,000 examples per 5W category (Who, What, When, Where, Why), making causal unlearning failures quantifiable for the first time. Using 5WBENCH, we show that no existing baseline simultaneously achieves high forgetting and high retention on Why-type questions: aggressive forgetting degrades retained knowledge, while conservative methods fail to forget causal facts. Why-type difficulty stems from multi-hop reasoning chains (44% of Why entries vs. less than or equal to 2% for others) and gradient dilution over 40.1-token answer spans. We present MAAT (Multi-phase Adapter-Aware Targeted Unlearning), a three-phase framework operating on LoRA adapter weights, combining gradient-projected ascent, SVD rank-dimension pruning, task vector negation, and hybrid KL-hidden-state retain repair. MAAT is the first method to simultaneously achieve high forgetting and high retention on Why-type causal knowledge, reaching a new operating point on the forget-retain Pareto frontier. We make our code publicly available.", "github": "https://github.com/SuryanshYagnik/Machine-Unlearning" }, { "date": "2026-06-01", "arxiv_id": "2605.30561", "title": "VLM3: Vision Language Models Are Native 3D Learners", "authors": [ "Zhipeng Cai", "Zhuang Liu", "Yunyang Xiong", "Zechun Liu", "Vikas Chandra", "Yangyang Shi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/facebookresearch/VLM3", "github_api_added_by": "user", "project_page": "", "abstract": "Vision Language Models (VLMs) enable a unified model to solve various vision tasks through prompting. They have shown promising performance in semantic understanding. However, 3D understanding still largely relies on expert vision models with complex task-specific designs. The key argument this work wants to make is that VLMs are native 3D learners. Our in-depth large scale study shows that 1) focal length unification, 2) text-based pixel reference and 3) data mixture and scaling, are all you need for effective 3D learning. Model architecture changes, large models, heavy data augmentations, and complex losses including the regression formulation, many of which form the foundation of expert vision models, are actually not necessary conditions. As a result, we propose VLM3, a scalable method with the simplest design that enables standard VLMs to master diverse 3D tasks. VLM3 not only advances the VLM depth estimation accuracy by a large margin (0.84 -> 0.9), but also enables diverse 3D tasks such as pixel correspondence, camera pose estimation and object-level 3D understanding, matching expert vision model accuracy while maintaining standard architectures and text-based training. We believe VLM3 opens up a new paradigm for simple and scalable 3D learning.", "github": "https://github.com/facebookresearch/VLM3" }, { "date": "2026-06-01", "arxiv_id": "2605.31158", "title": "Light Interaction: Training-Free Inference Acceleration for Interactive Video World Models", "authors": [ "Jiacheng Lu", "Haoyi Zhu", "Sipei Yi", "Enze Xie", "Yu Li", "Cheng Zhuo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://2843721358l-del.github.io/Light-Interaction-Project/", "abstract": "Interactive video world models generate video chunk by chunk in response to user-controlled camera movements, enabling applications such as real-time game simulation, virtual scene navigation, and embodied AI training. However, scaling to long interactive trajectories is prohibitively expensive due to growing context memory, quadratic attention complexity, and repeated denoising steps. We present Light Interaction, a training-free inference acceleration framework for interactive video world models. Our key insight is that interaction naturally enables trajectory-dependent adaptive computation: retrieved spatial memory can be discarded during novel exploration, temporal context can be adjusted according to local latent dynamics, and early-step model outputs can be reused when the camera revisits familiar regions. Based on this insight, Light Interaction combines adaptive context management, denoising cache acceleration, and hardware-software co-designed 3D block sparse attention with fused Triton kernels. Evaluated on HY-WorldPlay and Matrix-Game-3.0, Light Interaction achieves up to 2.59x speedup without model retraining while maintaining competitive visual quality.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.31603", "title": "Lumos-Nexus: Efficient Frequency Bridging with Homogeneous Latent Space for Video Unified Models", "authors": [ "Jiazheng Xing", "Hangjie Yuan", "Lingling Cai", "Xinyu Liu", "Yujie Wei", "Fei Du", "Hai Ci", "Tao Feng", "Jiasheng Tang", "Weihua Chen", "Fan Wang", "Yong Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://jiazheng-xing.github.io/nexus-lumos-home/", "abstract": "Connector-based video unified models have demonstrated strong capability in instruction-grounded video synthesis, but integrating a large high-fidelity generator into the unified training loop is computationally prohibitive, limiting achievable visual quality. We therefore propose Lumos-Nexus, a training-efficient unified video generation framework that facilitates the development of strong reasoning-driven generation capabilities while significantly enhancing visual fidelity. Lumos-Nexus adopts a two-stage design: 1) During training, only a lightweight generator is aligned with the understanding block to learn to take in reasoning-driven semantic control. 2) During inference, we introduce Unified Progressive Frequency Bridging (UPFB) to progressively hand off generation to a high-capacity pretrained generator in the shared latent space, enabling coarse-to-fine refinement and producing high-fidelity videos without compromising reasoning quality. To fill the gap in reasoning-driven video generation benchmarks, we introduce VR-Bench, which assesses a model's capability to translate inferred intent into coherent and semantically aligned video content. Extensive experiments demonstrate that Lumos-Nexus achieves substantial gains in visual realism and temporal coherence on VBench, while exhibiting strong reasoning-based generative performance on VR-Bench. Code and models are available at https://jiazheng-xing.github.io/nexus-lumos-home/.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.31604", "title": "Representation Forcing for Bottleneck-Free Unified Multimodal Models", "authors": [ "Yuqing Wang", "Zhijie Lin", "Ceyuan Yang", "Yang Zhao", "Fei Xiao", "Hao He", "Qi Zhao", "Zihan Ding", "Fuyun Wang", "Shuai Wang", "Youliang Zhang", "Haoqi Fan", "Xihui Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://yuqingwang1029.github.io/RepresentationForcing/", "abstract": "Unified multimodal models (UMMs) aim to handle perception and generation in a single model. Yet existing UMMs still rely on a frozen, separately pretrained VAE for image generation, imposing a structural bottleneck. Naively removing it introduces a quality gap, as the model must learn both high-level structure and low-level details from raw pixels. In this paper, we propose Representation Forcing (RF), a technique that closes this gap by making representation prediction a native capability of the model. Concretely, RF forces the decoder to autoregressively predict visual representations as intermediate tokens before pixels; these tokens then stay in context to guide pixel diffusion within the same backbone. By turning representations from perception outputs into generation targets, RF eliminates the need for any external generative latent space. We find that RF benefits both understanding and generation. On image generation, our pixel-space model with RF matches state-of-the-art VAE-based unified models. On image understanding, pixel-space RF generally outperforms its VAE-based variant. Together, these results offer an effective step toward end-to-end, bottleneck-free UMMs.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.31264", "title": "COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation", "authors": [ "Tianyi Zhou", "Dongrui Liu", "Leitao Yuan", "Jing Shao", "Xia Hu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/titanwings/colleague-skill", "github_api_added_by": "user", "project_page": "", "abstract": "LLM agents are increasingly expected not only to complete isolated tasks, but also to carry bounded representations of human expertise, judgment, and interaction style. Building such person-grounded agents remains difficult because actionable knowledge associated with a person or role is usually embedded in heterogeneous traces rather than written as clean instructions. Existing memory and persona systems capture fragments of this evidence, while skill frameworks provide portable packaging formats; however, there is no end-to-end workflow for distilling these traces into inspectable, correctable, and agent-usable skills. We present an automated trace-to-skill distillation system for generating person-grounded AI skills via expert knowledge distillation. Given materials from a target person or role, COLLEAGUE.SKILL produces a versioned skill package with two coordinated tracks: a capability track for practices, mental models, and decision heuristics, and a bounded behavior track for communication style, interaction rules, and correction history. The package can be inspected, invoked, updated through natural-language feedback, rolled back, installed across agent hosts, and optionally prepared for controlled distribution. We describe the artifact contract, generation workflow, correction lifecycle, deployment surface, and domain presets implemented in the open-source system. At the time of writing, the public repository has approximately 18.5k GitHub stars; the gallery lists 215 skills from 165 contributors and more than 100k cumulative stars across listed skill cards. The system illustrates how person-grounded skills can be represented as portable, correctable packages rather than opaque prompts or hidden memories.", "github": "https://github.com/titanwings/colleague-skill" }, { "date": "2026-06-01", "arxiv_id": "2605.31075", "title": "Task-Focused Memorization for Multimodal Agents", "authors": [ "Tao Zou", "Yichen He", "Tian Qiu", "Yuan Lin", "Hang Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://taskmem.github.io/", "abstract": "Long-term memory is essential for multimodal agents to build coherent experience, accumulate world knowledge, and achieve continual learning. However, constructing effective memory goes beyond memory module design and basic requirements such as accuracy and fidelity; the key challenge lies in determining what to memorize. Multimodal agents, such as embodied agents, continuously perceive, reason, and act in real or virtual environments, receiving an unbounded stream of multimodal observations. From this combinatorial explosion of information, an agent must selectively retain content that is relevant to its role in the environment and valuable for future tasks. To bridge this gap, we frame memory generation as a learnable memorization policy and introduce TaskMem (Task-focused Memorization Policy Learning), a reinforcement-learning-based framework that enables the policy to dynamically adjust its focus to the demands of real tasks encountered in the environment. TaskMem adopts a two-phase training paradigm: Phase One learns how to memorize by optimizing memory quality under fundamental fidelity requirements; Phase Two occurs after deployment, where the agent learns what to memorize by tuning an adapter on its base MLLM, using recent environment tasks to define a reward model that guides the memorization policy toward task-relevant content. To evaluate our approach, we reformulate VideoMME, EgoLife, and EgoTempo into streaming benchmarks that simulate a realistic setting in which an agent processes streaming observations and handles tasks arriving online. To isolate memory assessment, the questions must be answered using only the agent's memory, without access to raw video. Built on Qwen3-VL-30B-A3B, TaskMem improves VQA accuracy by 6.3%, 7.0%, and 5.3% on these benchmarks, respectively.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.30409", "title": "SANA-Streaming: Real-time Streaming Video Editing with Hybrid Diffusion Transformer", "authors": [ "Yuyang Zhao", "Yicheng Pan", "Qiyuan He", "Jincheng Yu", "Junsong Chen", "Tian Ye", "Haozhe Liu", "Enze Xie", "Song Han" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://nvlabs.github.io/Sana/Streaming/", "abstract": "Real-time streaming video-to-video editing (V2V) is critical for interactive applications such as live broadcasting and gaming, yet it remains a formidable challenge due to the stringent requirements for temporal consistency and inference throughput. In this paper, we present SANA-Streaming, a system-algorithm co-designed framework for high-resolution, real-time streaming video editing on consumer GPUs, with the following three core designs: (1) Hybrid Diffusion Transformer architecture introduces softmax attention in part of the blocks to improve local modeling capabilities while preserving the efficiency of linear layers. (2) Cycle-Reverse Regularization is a novel training strategy that enforces semantic consistency by predicting source frames from generated content via flow matching, improving temporal consistency without requiring paired long edited videos. (3) Efficient System Co-design combines fused GDN kernels and Mixed-Precision Quantization (MPQ) optimized for the NVIDIA Blackwell (RTX 5090) architecture. By profiling real-world throughput, our MPQ maximizes Tensor Core utilization while maintaining generation quality. The resulting system achieves real-time 1280 x 704 resolution editing at 24 end-to-end FPS on a single RTX 5090 GPU, with the DiT core running at 58 FPS. Experimental results demonstrate that our co-design approach significantly outperforms existing SOTA methods in both temporal coherence and system throughput.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.30993", "title": "SwanVoice: Expressive Long-Form Zero-Shot Speech Synthesis for Both Monologue and Dialogue", "authors": [ "Ruiqi Li", "Yu Zhang", "Changhao Pan", "Ke Lei", "Xiang Yin", "Cheng Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://swanaigc.github.io/#/swanvoice", "abstract": "Zero-shot text-to-speech (TTS) has improved substantially for single-speaker synthesis, yet expressive long-form multi-speaker dialogue remains difficult. A common workaround is to synthesize each turn with a monologue TTS model and stitch the outputs together. This adds inference cost and often breaks acoustic consistency, conversational coherence, and affective continuity across turns. Recent dialogue TTS systems have begun to address this setting, but they still struggle to keep expressive coherence, controllable speaker switching, and monologue quality at the same time. We present SwanData-Speech and SwanVoice. SwanData-Speech builds monologue and dialogue corpora from in-the-wild audio, using Swan Forced Aligner for pause-aware word-level alignment and RobustMegaTTS3 for pronunciation-hard cases. Built on these data, SwanVoice is a zero-shot TTS model for 1--4 speakers, combining a 25 Hz VAE, raw-text conditioning with pause-aware symbols and pinyin substitution, and a flow-matching DiT with speaker-turn conditioning. Training starts from monologue speech, moves through mixed and real dialogue data, and then uses DiffusionNFT post-training with phone-level and speaker-similarity rewards. On SwanBench-Speech, SwanVoice obtains higher richness and hierarchy scores than all evaluated open-source baselines in both monologue and dialogue settings, while content accuracy remains the main limitation. Audio demos are available at https://swanaigc.github.io//#swanvoice.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.23657", "title": "OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents", "authors": [ "Jiahao Ying", "Boxian Ai", "Wei Tang", "Siyuan Liu", "Yixin Cao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ALEX-nlp/OpenSkillEval", "github_api_added_by": "user", "project_page": "https://yingjiahao14.github.io/OpenSkillEval-Web/", "abstract": "Skills, i.e., structured workflow instructions distilled for large language models (LLMs), are becoming an increasingly important mechanism for improving agent performance on real-world downstream tasks. However, as the open-source skill ecosystem rapidly expands, it remains unclear how different models and agent frameworks interact with skills, how to evaluate skill quality, and how users should select skills under practical cost-performance trade-offs. In this paper, we present OpenSkillEval, an automatic evaluation framework for both skill-augmented agent systems and the skills themselves. Instead of relying on static benchmarks, OpenSkillEval automatically constructs realistic task instances from evolving real-world artifacts across five categories of downstream applications: presentation generation, front-end web design, poster generation, data visualization, and report generation. It further collects and organizes community-contributed skills for controlled comparison under unified task settings. Using more than 600 dynamically generated task instances and 30 open-source skills, we conduct a systematic evaluation of state-of-the-art models and agent frameworks. Our results show that skill availability does not guarantee effective skill usage, that the benefit of skill augmentation depends strongly on both the underlying model and the agent framework, and that many publicly popular skills do not consistently outperform base agents without skills. These findings highlight the need for dynamic, task-grounded evaluation and provide practical insights into the design, selection, and deployment of skills for LLM agents. Additional cases and benchmark resources are available on the project website: https://yingjiahao14.github.io/OpenSkillEval-Web/.", "github": "https://github.com/ALEX-nlp/OpenSkillEval" }, { "date": "2026-06-01", "arxiv_id": "2605.28992", "title": "FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder", "authors": [ "Dan Jacobellis", "Neeraja J. Yadwadkar" ], "github_manual": "", "github_regex": "https://github.com/UT-SysML/FRAPPE", "github_paper_md": "", "github_api": "https://github.com/UT-SysML/FRAPPE", "github_api_added_by": "user", "project_page": "https://ut-sysml.github.io/FRAPPE", "abstract": "Media compression standards have reached a plateau in terms of the rate-distortion-complexity trade-off, limiting the ability to offload expensive AI perception to the cloud in applications like robotics, wearables, and remote sensing. DNN-based codecs improve compression efficiency, but at a cost: they cannot easily adapt to large changes in available bitrate, and real-time encoding requires expensive, power-hungry GPUs that prohibit use on low-cost or resource-constrained platforms. To address these limitations, we propose a novel autoencoding framework (FRAPPE) that uses the Full input to predict the Residual output via a Projection Pursuit Encoder. FRAPPE's encoding objective naturally sorts latent channels by importance, allowing zero-overhead variable-rate coding. Unlike RNN-based learned codecs, whose encoder consumes the previous reconstruction's residual, or RVQ-style codecs, whose codebooks must be applied sequentially, FRAPPE's analysis path is an embarrassingly parallel DAG of independent input projections. Using FRAPPE, we build a variable-rate RGB image codec (FRAPPE-Image), and evaluate its rate-distortion-complexity trade-off against standard image codecs. At high compression ratios (approx. 0.1 bpp) FRAPPE-Image provides higher perceptual quality than AVIF with 47 times faster encoding, making it capable of real-time 1080p, 30fps CPU-only encoding. Our code and pre-trained models are available: https://github.com/UT-SysML/FRAPPE .", "github": "https://github.com/UT-SysML/FRAPPE" }, { "date": "2026-06-01", "arxiv_id": "2605.30940", "title": "Towards Streaming Synchronized Spatial Audio Generation via Autoregressive Diffusion Transformer", "authors": [ "Ke Lei", "Yu Zhang", "Changhao Pan", "Xueyi Pu", "Wenxiang Guo", "Ruiqi Li", "Zhou Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://swanaigc.github.io/#swansphere", "abstract": "Real-time and accurate spatial audio generation is pivotal for delivering an immersive experience. However, existing spatial audio synthesis technologies are often encumbered by a tradeoff between generation quality and high inference latency, as well as difficulty in capturing precise spatial information from multimodal inputs. To address these challenges, we propose SwanSphere, a unified streaming framework for high-fidelity spatial audio generation from panoramic videos and text prompts. SwanSphere mainly makes the following contributions: 1) We introduce a causal autoregressive diffusion transformer architecture that enables streaming high-quality spatial audio generation. 2) We design a Spatial Video-Audio Contrastive (SVAC) learning strategy to align the video encoder with the acoustic domain, and further employ a multi-objective online direct preference optimization (ODPO) scheme, resulting in strong spatial perception and robust multimodal spatial audio synthesis. 3) To alleviate the current scarcity of spatial audio datasets, we also develop an automated annotation pipeline for generating detailed spatial captions. Experimental results demonstrate that SwanSphere achieves superior performance in both video-to-spatial and text-to-spatial audio generation tasks. Demos can be found at: https://swanaigc.github.io.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.28618", "title": "Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios", "authors": [ "Changhao Pan", "Rui Yang", "Han Wang", "Zhuan Zhou", "Xuming He", "Wenxiang Guo", "Ziyue Jiang", "Ruiqi Li", "Yu Zhang", "Chenyuhao Wen", "Ke Lei", "Xiang Yin", "Jingyu Lu", "Zhiyuan Zhu", "Zhou Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://swanaigc.github.io//#bench", "abstract": "Recent advances in speech generation have enabled high-fidelity synthesis, yet systematic evaluation of models under long-context conditions remains largely underexplored. A comprehensive evaluation benchmark for long-form speech is indispensable for two reasons: 1) existing test scenarios are often confined to limited domains, creating a significant gap with the diverse downstream applications; 2) existing metrics overlook critical long-text factors such as consistency and coherence, failing to generalize reliably. To this end, we propose Swanbench-Speech, a comprehensive benchmark that decomposes long-form speech quality into specific, disentangled dimensions. SwanBench-Speech has three key properties. 1) Rich speech scenarios: Focusing on long-form speech generation and dialog generation, SwanBench-Speech covers acoustics, semantics, and expressiveness challenges, and consists of 1,101 samples spanning 17 common speech scenarios; 2) Comprehensive evaluation dimensions: Along the acoustics, semantics, and expressiveness axes, SwanBench-Speech defines an automated evaluation protocol with seven metrics to provide a comprehensive, accurate, and standardized assessment; 3) Valuable Insights: Through extensive experiments, we reveal that current models still struggle in highly expressive scenarios and exhibit a notable gap in consistency and hierarchy compared to real recordings.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.30846", "title": "Count Anything", "authors": [ "Mengqi Lei", "Shuokun Cheng", "Wei Bao", "Shaoyi Du", "Jun-Hai Yong", "Siqi Li", "Yue Gao" ], "github_manual": "", "github_regex": "https://github.com/Mengqi-Lei/count-anything", "github_paper_md": "", "github_api": "https://github.com/Mengqi-Lei/count-anything", "github_api_added_by": "user", "project_page": "https://mengqi-lei.github.io/count-anything-projectpage/", "abstract": "Object counting remains fragmented across domain-specific datasets and task formulations, despite rapid progress in generalist vision models. Existing counting models are often tailored to scenarios such as crowds, vehicles, cells, crops, or remote-sensing objects, and thus struggle to generalize across categories, visual domains, object scales, and density distributions. In this paper, we study text-guided object counting across domains, where a model takes an image and a natural-language query as input and returns an instance-grounded set of target points whose cardinality gives the count. This formulation unifies category-conditioned counting with interpretable spatial localization. To support this setting, we construct CLOC, a Cross-domain Large-scale Object Counting dataset that reorganizes diverse public data sources into a unified benchmark. CLOC covers six visual domains: General Scene, Remote Sensing, Histopathology, Cellular Microscopy, Agriculture, and Microbiology, with about 220K images, 619 categories, and 15M object instances. Based on CLOC, we propose Count Anything, a generalist model for text-guided object counting. Unlike density-map-based methods, which dominate counting models, Count Anything adopts discrete instance points and performs dual-granularity instance enumeration. A Region-level Sparse Counter provides object-level anchors for large and sparse targets, while a Pixel-level Dense Counter handles small, crowded, and weakly bounded targets via dense point prediction. A point-centric supervision strategy enables learning from heterogeneous annotations, and Complementary Count Fusion combines both counters in a parameter-free manner. Extensive experiments show that Count Anything achieves strong accuracy and multi-domain generalization, outperforming existing open-world counting methods. Code is available at: https://github.com/Mengqi-Lei/count-anything.", "github": "https://github.com/Mengqi-Lei/count-anything" }, { "date": "2026-06-01", "arxiv_id": "2605.29411", "title": "The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction", "authors": [ "Shu Wan", "Abhinav Gorantla", "Huan Liu", "K. Sel\u00e7uk Candan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Under standard graphical assumptions, the Markov boundary of a target variable is the smallest set of features that renders every other feature redundant. Once the boundary is observed, the target is conditionally independent of the rest of the table. This is a tempting object for tabular prediction, since it names exactly the columns a model should need. Yet modern regressors are still trained on the full feature set. We ask whether the Markov boundary is genuinely useful for prediction on SCM3K, a 3,450-task synthetic SCM benchmark with feature counts from 40 to 1000 and six SCM families, evaluated with six regressors. The answer is more nuanced than the theory suggests. Restricting a regressor to the oracle boundary often improves prediction substantially, and the improvement grows as the feature space becomes larger and sparser. But the natural pipeline of recovering the boundary with causal discovery and training on the recovered mask does not deliver. Existing estimators exhaust the compute budget before reaching the regime where the boundary helps most, and even where they run they rarely beat the full feature set. We trace this to three causes. Discovery optimizes structural recovery rather than prediction. False negatives and false positives carry sharply asymmetric predictive cost. The exact boundary is only one of many feature sets that beat all features. We then develop what these facts imply for prediction-aligned feature selection and for tabular models that learn to use causal structure.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.29488", "title": "AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling", "authors": [ "Yiheng Li", "Zhuo Li", "Ruibing Hou", "Yingjie Chen", "Hong Chang", "Hao Liu", "Shiguang Shan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/datasets/L-yiheng/OmniHuMo", "abstract": "Conditional human motion generation remains a fundamental challenge in computer vision and robotics. Despite significant progress, current methods are often constrained by fixed modality configurations and task-specific architectures, leaving cross-modal interactions and the scaling laws of multimodal-conditioned synthesis largely underexplored. A key bottleneck is the scarcity of large-scale modality-aligned motion data, limiting generalization across diverse control signals. In this work, we introduce OmniHuMo, a large-scale, high-quality dataset comprising over 5,000 hours of motion and 3.2 million sequences with precisely aligned multimodal annotations (e.g., text, speech, music, and trajectory). Leveraging OmniHuMo, we propose AnyMo, a unified multimodal framework combining a Residual FSQ-based motion tokenizer with a scalable masked modeling transformer, enabling high-quality motion synthesis under arbitrary modality combinations. Extensive experiments show that AnyMo achieves high-fidelity synthesis while offering flexible control over both spatial and stylistic attributes.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.27919", "title": "Frequency-Guided Action Diffusion via Sub-Frequency Manifold Traversal", "authors": [ "Junlin Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HenryWJL/fgo", "github_api_added_by": "user", "project_page": "https://henrywjl.github.io/frequency-guidance-operator/", "abstract": "Learning visuomotor policies via behavior cloning typically involves mimicking expert demonstrations collected by human operators. However, natural human demonstrations inherently contain high-frequency noise, such as intermittent jerks, pauses, and action jitter. Training policies to directly imitate these raw trajectories inevitably causes the model to inherit these suboptimal behaviors. This pathology is particularly pronounced in diffusion-based policies, where iterative denoising steps can inadvertently amplify high-frequency artifacts at the expense of meaningful fine-grained details. To address these limitations, we present a novel frequency-based algorithm that enables implicit spectral maneuvering and smooth action generation. Our method, Frequency Guidance Operator (FGO), steers the generation process of diffusion polices by progressively driving the noisy samples through intermediate sub-frequency manifolds with expanding spectral bands. Validated on 15 robotic manipulation tasks from 5 benchmarks, FGO achieves superior performance in enhancing action smoothness and temporal consistency while preserving the details necessary for successful task execution. Project website: https://henrywjl.github.io/frequency-guidance-operator/", "github": "https://github.com/HenryWJL/fgo" }, { "date": "2026-06-01", "arxiv_id": "2605.31433", "title": "SCOPE: Self-Play via Co-Evolving Policies for Open-Ended Tasks", "authors": [ "Wai-Chung Kwan", "Aryo Pradipta Gema", "Joshua Ong Jun Leang", "Pasquale Minervini" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://edinburghnlp.github.io/scope/", "abstract": "Self-play can train language models without external supervision. However, existing methods require rule-checkable answers, leaving open-ended tasks dependent on curated prompts or frontier-model judges. We introduce SCOPE, a data-free self-play framework for open-ended tasks that co-evolves two policies: a Challenger that generates document-grounded tasks, and a Solver that answers them through multi-turn retrieval. A frozen copy of the initial model serves as the self-judge, which writes task-specific rubrics from the source document and grades Solver responses against them. Across three 7-8B instruction-tuned models (Qwen2.5, Qwen3, OLMo-3), SCOPE improves open-ended performance by up to +10.4 points on eight benchmarks and matches or exceeds GRPO_data trained on ~9K curated prompts. Although trained only on open-ended tasks, SCOPE also improves held-out short-form QA by up to +13.8 points on seven held-out benchmarks, surpassing GRPO_data on all three models. Ablations show that co-evolving the Challenger is necessary to keep tasks near the Solver's frontier, that gains arise from improvements in both retrieval and synthesis with the relative contribution varying by task, and that rubric generation quality is the bottleneck for self-judging.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.30888", "title": "The Flip Side of RLHF: On-Policy Feedback for Reward Model Self-Supervised Improvement", "authors": [ "Xiaobo Wang", "Tong Wu", "Min Tang", "Jiaqi Li", "Qi Liu", "Zilong Zheng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://yofuria.github.io/projects/save/", "abstract": "Building strong reward models (RMs) for language model alignment is bottlenecked by the cost and difficulty of acquiring diverse and reliable preference data from human annotation or judge models. It is dramatically worse as the policy evolves beyond the static RM training. Therefore, we propose SAVE (Self-supervised reward model improvement via Value-Anchored On-policy feedback), a framework that grades on-policy responses as feedback by using the value function for on-policy RM training. SAVE naturally converts the reward-graded on-policy responses into supervision with a prompt-specific value head as an adaptive anchor. It computes RM advantages and filters ambiguous samples to update the RM via a contrastive objective. The effectiveness of SAVE for enhancing RM training is strongly validated through rigorous empirical evaluation across six diverse benchmarks. It achieves outperforming results across all datasets while maintaining consistent improvements across three RL algorithms (GRPO, RLOO, GSPO) and different policy backbones.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.30557", "title": "Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?", "authors": [ "Yue Zhang", "Zun Wang", "Han Lin", "Yonatan Bitton", "Idan Szpektor", "Mohit Bansal" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhangyuejoslin/SpatialUncertain_code", "github_api_added_by": "user", "project_page": "https://zhangyuejoslin.github.io/spatialuncertain/", "abstract": "Spatial reasoning is a fundamental capability for vision-language models (VLMs) deployed in real-world environments. However, visual observations are inherently limited representations of a 3D world: occlusion can render objects invisible, and perspective can make geometric properties misleading. Despite this, existing spatial reasoning benchmarks typically assume that observations are sufficient and reliable, focusing on whether models produce correct answers rather than whether they recognize when a question cannot be answered and what additional observations would be needed. In this work, we challenge this assumption by constructing a controlled evaluation framework, SpatialUncertain, and introducing two types of observation challenges: (1) occlusion, which hides target information, and (2) perspective ambiguity, which produces misleading visual cues. For each configuration, we design spatial questions that are answerable under clean observations but require abstention under the introduced challenges. We further evaluate whether models can identify which additional viewpoints would resolve perspective ambiguity. Our results across a diverse set of frontier open- and closed-source VLMs reveal two consistent failure modes. First, models are prone to overconfident answering, attempting to solve spatial reasoning tasks even when visual evidence is incomplete or misleading, with average accuracy around 30\\% under occlusion and below 10\\% under perspective ambiguity. Second, even when additional views are available, some models perform near random chance in identifying which would provide reliable evidence. Together, our findings call for moving beyond answer correctness toward evaluating whether models know when to abstain and how to seek reliable evidence.", "github": "https://github.com/zhangyuejoslin/SpatialUncertain_code" }, { "date": "2026-06-01", "arxiv_id": "2605.29447", "title": "Recovering Policy-Induced Errors: Benchmarking and Trajectory Synthesis for Robust GUI Agents", "authors": [ "Tianpeng Bu", "Xin Liu", "Qihua Chen", "Hao Jiang", "Shurui Li", "Hongtao Duan", "Lu Jiang", "Lulu Hu", "Bin Yang", "Minying Zhang" ], "github_manual": "", "github_regex": "https://github.com/AlibabaResearch/RoTS", "github_paper_md": "", "github_api": "https://github.com/AlibabaResearch/RoTS", "github_api_added_by": "user", "project_page": "", "abstract": "While GUI agents have advanced rapidly, they often lack the robustness to recover from their own errors, hindering real-world deployment. To bridge this gap at both the evaluation and data levels, we introduce GUI-RobustEval and propose Robustness-driven Trajectory Synthesis. GUI-RobustEval contains 1,216 executable test cases that systematically measure error recovery capabilities across a broad and realistic spectrum of error modes. At the data level, RoTS is a scalable synthesis framework that creates 800k high-quality data via a tree-based pipeline that proactively discovers diverse error modes and synthesizes corresponding recovery steps. Our two models, RoTS-7B and RoTS-32B, fine-tuned on our dataset, both demonstrate significant gains on GUI-RobustEval and traditional GUI benchmarks. Notably, RoTS-32B achieves state-of-the-art performance on OSWorld, with a 47.4% success rate and a 33.8% All-Pass@4 score, suggesting that improved long-horizon error recovery ability contributes to both robustness and overall performance. Our code is available at https://github.com/AlibabaResearch/RoTS.", "github": "https://github.com/AlibabaResearch/RoTS" }, { "date": "2026-06-01", "arxiv_id": "2605.31455", "title": "DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization", "authors": [ "Jian Mu", "Tianyi Lin", "Chengwei Qin", "Zhongxiang Dai", "Yao Shu" ], "github_manual": "", "github_regex": "https://github.com/2020-qqtcg/DRIFT", "github_paper_md": "", "github_api": "https://github.com/2020-qqtcg/DRIFT", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models are increasingly deployed in multi-turn interactive settings where users or environments can iteratively provide lightweight feedback. Unfortunately, optimizing such behavior presents a sharp dilemma in practice: online reinforcement learning is able to effectively address multi-turn dynamics but is prohibitively expensive due to the cost of generating full correction trajectories at every update, whereas offline supervised fine-tuning (SFT) is efficient but suffers from distribution shift and behavioral collapse. To this end, we novelly propose DRIFT (Decoupled Rollouts and Importance-Weighted Fine-Tuning), a framework that operationalizes the theoretical insight that the KL-regularized RL objective is equivalent to importance-weighted supervised learning. DRIFT decouples rollout from optimization by sampling offline interaction trajectories from a fixed reference policy, deriving return-based importance weights, and optimizing the policy via weighted SFT on the resulting dataset. Empirically, we demonstrate that DRIFT matches or exceeds the performance of multi-turn reinforcement learning baselines while maintaining the training efficiency and simplicity of standard supervised fine-tuning. Code is available at https://github.com/2020-qqtcg/DRIFT.", "github": "https://github.com/2020-qqtcg/DRIFT" }, { "date": "2026-06-01", "arxiv_id": "2605.31096", "title": "iVGR: Internalizing Visually Grounded Reasoning for MLLMs with Reinforcement Learning", "authors": [ "Chang-Bin Zhang", "Yujie Zhong", "Qiang Zhang", "Kai Han" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Visual-AI/iVGR", "github_api_added_by": "user", "project_page": "https://visual-ai.github.io/ivgr/", "abstract": "While visually grounded Chain-of-Thought (CoT) has emerged as a promising paradigm to enhance fine-grained perception in multimodal large language models (MLLMs), its efficacy during the inference phase remains underexplored. In this work, we empirically find that mandating explicit object boxes in visually grounded CoT during inference often degrades performance compared to standard textual CoT, which reasons without explicit visual grounding. We hypothesize that the visual localization capability can be internalized into the textual CoT and that the mandatory explicit grounding introduces unnecessary interference with the model's primary objective of answer prediction. To address this problem, we propose Internalizing Visually Grounded Reasoning (iVGR), a novel reinforcement learning framework that transfers localization capabilities into the textual reasoning process. We employ a dual-stream training strategy, where a textual stream is aligned with a high-quality visually grounded stream via a proposed consistency reward, enabling the model to localize accurately without explicit grounding during inference. Extensive experiments demonstrate that our method significantly outperforms existing baselines on fine-grained benchmarks, while maintaining the flexibility to support tool-assisted inference workflows.", "github": "https://github.com/Visual-AI/iVGR" }, { "date": "2026-06-01", "arxiv_id": "2605.24442", "title": "Benchmarking Composed Image Retrieval for Applied Earth Observation", "authors": [ "Bill Psomas", "Dionysis Christopoulos", "Thanasis Petropoulos", "Nikos Efthymiadis", "Ioannis Kakogeorgiou", "Ond\u0159ej Chum", "Yannis Avrithis", "Giorgos Tolias", "Konstantinos Karantzalos" ], "github_manual": "", "github_regex": "https://github.com/billpsomas/rscir", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Remote sensing composed image retrieval (RSCIR) enables search in large satellite image archives using composed queries that combine a reference image with a textual modifier. Although RSCIR offers a flexible interface for expressing targeted retrieval intent, the transferability of modern composition methods to Earth observation (EO) imagery and their relevance to operational EO workflows remain underexplored. We address this gap through a unified benchmark and an application-oriented study. First, we systematically adapt and evaluate representative composed image retrieval methods with six vision-language backbones on PatternCom under a standardized protocol, analyzing their behavior across backbones, composition strategies, and query types. Second, we introduce xView2-CIR, a change-centric dataset for disaster and damage monitoring, where retrieval is conditioned on scene identity and a target post-event state. Our results show that training-free composition methods provide strong and scalable baselines for EO retrieval, while change-centric retrieval presents different challenges from attribute-based retrieval, particularly due to the need to preserve scene identity. Overall, this study establishes a practical benchmark for RSCIR and positions composed retrieval as a complementary tool for remote sensing image retrieval, archive exploration, and change analysis. The dataset and code are available at https://github.com/billpsomas/rscir.", "github": "https://github.com/billpsomas/rscir" }, { "date": "2026-06-01", "arxiv_id": "2605.31029", "title": "PEEK: Picking Essential frames via Efficient Knowledge distillation", "authors": [ "Killian Steunou", "Anas Filali Razzouki", "Khalil Guetari", "Moun\u00eem A. El-Yacoubi", "Yannis Tevissen" ], "github_manual": "", "github_regex": "https://github.com/momentslab/peek", "github_paper_md": "", "github_api": "https://github.com/momentslab/peek", "github_api_added_by": "user", "project_page": "https://www.killian-steunou.com/peek", "abstract": "Video-language models can process only a limited number of frames, making frame selection a key bottleneck for efficient video captioning. Most captioning pipelines still rely on uniform sampling, which is computationally cheap but agnostic to visual content. Adaptive frame sampling has recently emerged as a promising approach for selecting the most informative frames from a video; however, existing methods remain computationally expensive. We introduce PEEK, an efficient dynamic frame sampling method that distills caption-conditioned frame relevance rankings from a stronger teacher model into a lightweight temporal model that operates only on visual content. We find that, overall, on ActivityNet Captions and MSR-VTT, our method outperforms state-of-the-art methods across all evaluated downstream vision language models, especially when only one or two frames are selected for captioning, obtaining the best CIDEr for most frame budgets. On ActivityNet Captions, PEEK is particularly strong, winning 14 out of 16 configurations. Zero-shot evaluation on MSR-VTT shows that our model transfers best at low frame budgets, while results at four and eight frames are more mixed as temporal coverage and visual diversity become increasingly competitive. Compared with recent adaptive baselines, PEEK is both more accurate in the low-budget regime and more efficient: it adds only 5.2% to the captioning time, compared with 65.4% for CSTA and 211.9% for MaxInfo. We release our code and pre-trained checkpoint at https://github.com/momentslab/peek.", "github": "https://github.com/momentslab/peek" }, { "date": "2026-06-01", "arxiv_id": "2605.31170", "title": "Emergent Languages in Populations of Language Model Agents: From Token Efficiency to Oversight Evasion", "authors": [ "Stine Lyngs\u00f8 Beltoft", "William Brach", "Federico Torrielli", "Jacob Nielsen", "Annemette Brok Pirchert", "Filippo Tonini", "Peter Schneider-Kamp", "Lukas Galke Poech" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aisilab/emergent-languages", "github_api_added_by": "user", "project_page": "", "abstract": "Monitoring autonomous language model agents currently relies mostly on surface behavior. But what happens when agent populations invent new languages with the goal of avoiding human oversight. Here, we study the emergent languages on Moltbook. For this, we build upon the Moltbook Files dataset and apply a two-stage approach consisting of a rule-based heuristic (about 6000 matches) followed by zero-shot classification (518 kept). The resulting categories include token efficiency (166), new natural languages (106), and oversight evasion (59). We conduct both quantitative and qualitative analyses. Our results show that posts proposing new languages for avoiding oversight are judged by DeepSeek-3.2 as being less aligned than the other categories and that all languages can be learned by other language models in-context merely from a description of the language. Moreover, manually studying exemplary cases reveals surprisingly sophisticated steganographic protocols like embedding hidden messages in natural language. Although we cannot be certain about the extent of autonomy in ideation of these languages, our results add up to the evidence that monitoring surface behavior may soon be insufficient for retaining control over agent populations.", "github": "https://github.com/aisilab/emergent-languages" }, { "date": "2026-06-01", "arxiv_id": "2605.30329", "title": "SoundnessBench: Can Your AI Scientist Really Tell Good Research Ideas from Bad Ones?", "authors": [ "Sy-Tuyen Ho", "Minghui Liu", "Huy Nghiem", "Furong Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hosytuyen/hosytuyen.github.io", "github_api_added_by": "user", "project_page": "https://hosytuyen.github.io/projects/SoundnessBench/", "abstract": "Autonomous AI research agents aim to accelerate scientific discovery by automating the research pipeline, from hypothesis generation to peer review. However, existing benchmarks rarely test a fundamental bottleneck: whether Large Language Models can judge the methodological viability of a research idea before expending time and computational resources. We introduce SoundnessBench, a curated benchmark of 1,099 machine-learning research proposals reconstructed from ICLR submissions, labeled with reviewer soundness sub-scores, and audited against source papers. SoundnessBench should be interpreted as a benchmark for recoverable proposal-stage soundness rather than exact prediction of full-paper review outcomes. Across 12 frontier LLMs, we find a pervasive optimism bias: under standard prompting, models frequently rate low-soundness proposals as sound, while aggressive prompting largely shifts errors from false positives to false negatives. Additional controls for public-corpus contamination, paper-identifying phrases, surface features, and human audit quality suggest that this behavior is not explained by a single confounder. Our results indicate that current LLMs are not yet reliable as standalone first-gate evaluators for scientific rigor.", "github": "https://github.com/hosytuyen/hosytuyen.github.io" }, { "date": "2026-06-01", "arxiv_id": "2605.29796", "title": "SAAS: Self-Aware Reinforcement Learning for Over-Search Mitigation in Agentic Search", "authors": [ "Yunbo Tang", "Chengyi Yang", "Shiyu Liu", "Zhishang Xiang", "Zerui Chen", "Qinggang Zhang", "Jinsong Su" ], "github_manual": "", "github_regex": "https://github.com/XMUDeepLIT/SAAS", "github_paper_md": "", "github_api": "https://github.com/XMUDeepLIT/SAAS", "github_api_added_by": "user", "project_page": "", "abstract": "Agentic search enables LLMs to solve complex multi-hop questions through iterative reasoning and external search. Despite the effectiveness, these systems often suffer from a critical limitation in practice: agents fail to recognize their own knowledge boundaries, blindly triggering searches when internal knowledge suffices and failing to terminate search even when adequate evidence has been collected. The lack of self-awareness leads to severe over-search, incurring substantial inference latency and prohibitive computational cost. To this end, we propose SAAS, a novel RL framework designed to cultivate dynamic self-awareness that precisely regulates search behavior without compromising accuracy. SAAS introduces three key components: (i) a search boundary modeling mechanism, which identifies the search boundary under the evolving policy by contrasting search-disabled and search-enabled rollouts; (ii) a boundary-aware reward module, which translates this boundary awareness into trajectory-level penalties, suppressing unnecessary and redundant searches; and (iii) a stage-wise optimization strategy, which leverages a sequential curriculum to prioritize reasoning over search regularization, thereby avoiding reward hacking. Extensive experiments demonstrate that SAAS substantially reduces over-search, while maintaining accuracy. Our code is anonymously released at https://github.com/XMUDeepLIT/SAAS.", "github": "https://github.com/XMUDeepLIT/SAAS" }, { "date": "2026-06-01", "arxiv_id": "2605.31336", "title": "DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory", "authors": [ "Zhenhao Yang", "Xiaoshi Wu", "Zhengyao Lv", "Xiaoyu Shi", "Xintao Wang", "Pengfei Wan", "Kun Gai", "Kwan-Yee K. Wong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KlingAIResearch/DecMem", "github_api_added_by": "user", "project_page": "https://jeffreyyzh.github.io/DecMem-Page/", "abstract": "Recent advances in video generative models have promoted rapid progress in controllable world models. However, maintaining fine-grained spatio-temporal consistency under long-horizon reasoning remains a key challenge. In this work, we move beyond explicit 3D memory and coarse frame-level implicit modeling, and propose a fine-grained, learnable, and scalable memory for consistent world generation. We first identify two fundamental limitations of na\u00efve learnable memory architectures in long-horizon extrapolation, namely computational inefficiency and attention dispersion. Through a systematic analysis of attention dispersion, we propose DecMem, a decoupled memory architecture that employs Sparse Global Memory for efficient fine-grained access to global history and Anchored Local Memory for stable and high-quality extrapolation. Extensive experiments demonstrate that DecMem significantly outperforms current state-of-the-art methods. By ensuring precise and efficient long-term memory and achieving superior extrapolation capabilities, DecMem enables minute-level controllable long video generation with high fidelity and consistency.", "github": "https://github.com/KlingAIResearch/DecMem" }, { "date": "2026-06-01", "arxiv_id": "2605.31159", "title": "Trust-Region Behavior Blending for On-Policy Distillation", "authors": [ "Daniil Plyusov", "Alexey Gorbatovski", "Alexey Malakhov", "Nikita Balagansky", "Boris Shaposhnikov", "Daria Korotyshova", "Daniil Gavrilov" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "On-policy distillation (OPD) trains a student on prefixes sampled from its own policy while matching a stronger teacher. This addresses the prefix mismatch of offline distillation, but early student rollouts can still be poor, placing teacher supervision on weak or low-quality prefixes. We propose Trust-Region behavior Blending (TRB), a warmup method that replaces the early rollout policy with the closest-to-teacher behavior policy inside a student-centered KL trust region, while keeping the per-prefix reverse-KL OPD loss unchanged. The KL budget is annealed to zero, so training returns to pure student rollouts after warmup. Across two math-reasoning distillation settings, TRB attains the strongest average among the compared methods.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.21625", "title": "Flat-Pack Bench: Evaluating Spatio-Temporal Understanding in Large Vision-Language Models through Furniture Assembly", "authors": [ "Aditya Chetan", "Eric Cai", "Peeyush Kushwaha", "Bharath Raj Nagoor Kani", "Utkarsh Mall", "Qianqian Wang", "Noah Snavely", "Bharath Hariharan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/justachetan/flat-pack-bench", "github_api_added_by": "user", "project_page": "https://flat-pack-bench.github.io/", "abstract": "The emergence of Large Vision-Language Models (LVLMs) has significantly advanced video understanding capabilities. However, existing benchmarks focus predominantly on coarse-grained tasks such as action segmentation, classification, captioning, and retrieval. Furthermore, these benchmarks often rely on entities that can be easily identified verbally, like household objects, animals, human subjects, etc., limiting their applicability to complex, in-the-wild video scenarios. But, many applications such as furniture assembly, cooking, etc., require step-by-step fine-grained spatio-temporal understanding of the video, which is not sufficiently evaluated in current benchmarks. To address this gap, we introduce Flat-Pack Bench, a novel benchmark centered on furniture assembly tasks. Our benchmark evaluates LVLMs on nuanced tasks, including temporal ordering of assembly actions, temporal localization of assembly state, understanding part mating, and tracking, using multiple-choice questions paired with visual prompts highlighting relevant parts as references for fine-grained questions. Our experiments reveal that state-of-the-art LVLMs struggle significantly with fine-grained spatio-temporal reasoning, highlighting their limitations in effectively leveraging temporal information from videos, limited tracking ability, and understanding of spatial interactions like physical contact.", "github": "https://github.com/justachetan/flat-pack-bench" }, { "date": "2026-06-01", "arxiv_id": "2605.26562", "title": "Beyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting", "authors": [ "Shuang Liang", "Chaochuan Hou", "Xu Yao", "Shiping Wang", "Hailiang Huang", "Songqiao Han", "Minqi Jiang" ], "github_manual": "", "github_regex": "https://github.com/SUFE-AILAB/TSCOMP", "github_paper_md": "", "github_api": "https://github.com/SUFE-AILAB/TSCOMP", "github_api_added_by": "user", "project_page": "", "abstract": "While previous research in multivariate time series forecasting has focused on developing complex holistic models, this work advocates for a shift toward a granular, component-level understanding of their impacts. We propose TSCOMP, the first large-scale benchmark that systematically deconstructs deep forecasting methods into their core, fine-grained components--spanning series preprocessing, encoding strategies, network architectures including specific and large time-series models, and optimization methods. Using constrained orthogonal experimental design and extensive evaluations, we conduct multi-view analyses that reveal component effectiveness across different backbones, data characteristics, and their interactions. Beyond providing insights, this benchmark establishes a fine-grained performance corpus comprising over 20,000 model-dataset evaluations, which supports the learning of automated component selection, enabling zero-shot model construction on new datasets. Our experiments demonstrate that the corpus-driven approach, despite its simplicity, consistently outperforms state-of-the-art methods, validating the soundness of our evaluation design and confirming that systematic component selection surpasses manually designed complex architectures. All code and the performance corpus are publicly available at https://github.com/SUFE-AILAB/TSCOMP.", "github": "https://github.com/SUFE-AILAB/TSCOMP" }, { "date": "2026-06-01", "arxiv_id": "2605.28181", "title": "When Confidence Misleads: Suffix Anchoring and Anchor-Proximity Confidence Modulation for Diffusion Language Models", "authors": [ "Jungwon Park", "Jimyeong Kim", "Jungmin Ko", "Nojun Kwak", "Wonjong Rhee" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Diffusion language models decode text by iteratively denoising masked token sequences, making the choice of which positions to decode a central inference-time decision. Most training-free decoding strategies use model confidence for position selection, assuming that high-confidence positions are ready to be decoded. In this work, we revisit this assumption by studying when confidence misleads fully non-autoregressive (fully non-AR) decoding. EOT tokens can receive high confidence and cause incomplete generation; inserting a suffix anchor can mitigate this issue but introduces local overconfidence near the anchor, causing anchor-adjacent tokens to be decoded too early. To address these issues, we propose Suffix-Anchored Confidence Modulation, a simple training-free method that inserts a short suffix anchor to encourage response completion and modulates confidence near the anchor according to decoding progress. This preserves the response-completion benefit of suffix anchoring while reducing premature decoding of anchor-adjacent tokens. Across text-only reasoning, vision-language reasoning, and code-generation benchmarks, our method consistently improves confidence-based fully non-AR decoding, outperforms explicit EOT suppression, and preserves the parallel decoding advantage of fully non-AR generation.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.29398", "title": "GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models", "authors": [ "Xiaohang Tang", "Keyue Jiang", "Che Liu", "Qifang Zhao", "Xiaoxiao Xu", "Sangwoong Yoon", "Ilija Bogunovic" ], "github_manual": "", "github_regex": "https://github.com/GaryBall/GDSD", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement learning (RL) can be used to improve the policy (denoiser) of diffusion large language models (dLLMs), while being hindered by the intractability of the policy likelihood. A dominant and efficient family of methods replaces the likelihood in standard RL with its evidence lower bound (ELBO), estimated from randomly masked sequences. Despite being well aligned with pre-training, these approaches introduce bias through training--inference mismatch by using the ELBO as a likelihood surrogate, which can degrade performance. In this work, we propose Guided Denoiser Self-Distillation (GDSD) to directly distill the denoiser of dLLMs from an advantage-guided self-teacher, derived from the closed-form optimum of reverse-KL regularized RL. GDSD matches the dLLM's denoiser logits to the teacher's via a normalization-free objective, which reduces RL to likelihood-free self-distillation and thus bypasses the TIM biases. Recent ELBO-based methods emerge as instances of applying different distillation divergences, but with diagnosable pathologies that GDSD avoids. On planning, math, and coding benchmarks with LLaDA-8B and Dream-7B, GDSD consistently outperforms prior state-of-the-art ELBO-based methods with a more stable training reward dynamics, achieving test-accuracy improvements of up to +19.6%. These results suggest that direct denoiser self-distillation, without relying on an ELBO likelihood surrogate, can provide a more stable and effective RL procedure for dLLMs. Code is available at https://github.com/GaryBall/GDSD.", "github": "https://github.com/GaryBall/GDSD" }, { "date": "2026-06-01", "arxiv_id": "2605.29307", "title": "GrepSeek: Training Search Agents for Direct Corpus Interaction", "authors": [ "Alireza Salemi", "Chang Zeng", "Atharva Nijasure", "Jui-Hui Chung", "Razieh Rahimi", "Fernando Diaz", "Hamed Zamani" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alirezasalemi7/grepseek", "github_api_added_by": "user", "project_page": "", "abstract": "Large Language Model (LLM) search agents have shown strong promise for knowledge-intensive language tasks through multiple rounds of reasoning and information retrieval. Most existing systems access information using a retriever that takes a keyword or natural language query and returns a ranked list of documents using an index of pre-computed document representations. In this work, we explore a complementary perspective in which the search agent treats the corpus itself as the search environment and finds evidence by issuing executable shell commands. We introduce GrepSeek, an optimized direct corpus interaction (DCI) search agent that trains a compact search agent to find, filter, and compose evidence from large text corpora. To address the instability of learning behavior directly with reinforcement learning on large corpora, we propose a two-stage training pipeline. First, we construct a cold-start dataset using an answer-aware Tutor and answer-blind Planner to generate verified, causally grounded search trajectories. Second, we refine the initialized policy with Group Relative Policy Optimization (GRPO), allowing the agent to improve its task-oriented search behavior through direct interaction with the corpus. To make DCI practical at scale, we further use a semantics-preserving sharded-parallel execution engine that accelerates shell-based retrieval by up to 7.6times while preserving byte-exact equivalence with sequential execution of the shell command. Experiments across seven open-domain question answering benchmarks show that GrepSeek achieves the strongest overall token-level F_1 and Exact Match. Our analysis also highlights the limitations of purely lexical interaction on queries with substantial surface-form variation, suggesting DCI as a practical and competitive method for search agents that can complement existing retrieval paradigms in the real world.", "github": "https://github.com/alirezasalemi7/grepseek" }, { "date": "2026-06-01", "arxiv_id": "2605.31503", "title": "How can embedding models bind concepts?", "authors": [ "Arnas Uselis", "Darina Koishigarina", "Seong Joon Oh" ], "github_manual": "", "github_regex": "https://github.com/oshapio/binding-concepts-complexity", "github_paper_md": "", "github_api": "https://github.com/oshapio/binding-concepts-complexity", "github_api_added_by": "user", "project_page": "", "abstract": "Humans easily determine which color belongs to which shape in multi-object scenes, an ability known as concept binding. Vision-language embedding models such as CLIP struggle with binding: they recognize individual concepts but fail to represent which concepts form which objects. Although CLIP behaves like a bag-of-concepts model in cross-modal retrieval, object information is recoverable from its image and text embeddings separately. We study this tension through the binding function, which maps concepts to scene embeddings. We find that scene embeddings decompose additively into object representations, explaining why uni-modal probes can recover object information. However, CLIP's binding function is high-complexity, which likely prevents the image and text encoders from learning a shared binding mechanism that generalizes to unseen concept combinations. We then ask whether this limitation is fundamental. We show that it is not. In controlled transformer models trained from scratch, binding generalization emerges with sufficient data coverage. These models learn low-complexity binding functions characterized by multiplicative interactions between concepts, enabling systematic generalization. Code is publicly available at https://github.com/oshapio/binding-concepts-complexity.", "github": "https://github.com/oshapio/binding-concepts-complexity" }, { "date": "2026-06-01", "arxiv_id": "2605.30011", "title": "VisualThink-VLA: Visual Intermediate Reasoning for Effective and Low-Latency Vision-Language-Action Policies", "authors": [ "Mingjian Gao", "Wenqiao Zhang", "Yuqian Yuan", "Yang Dai", "Binhe Yu", "Zheqi Lv", "Haoyu Zheng", "Jiaqi Zhu", "Zhiqi Ge", "Zixuan Wan", "Siliang Tang", "Yueting Zhuang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/DCDmllm/VisualThink-VLA", "github_api_added_by": "user", "project_page": "", "abstract": "Recent work has begun to equip vision-language-action (VLA) policies with explicit intermediate reasoning. In embodied control, however, textual chain-of-thought is a poor fit: irrelevant or weakly textual information can interfere with action prediction, while autoregressive text decoding adds too much latency for real-time closed-loop execution. We present VISUALTHINK-VLA, a visual intermediate-reasoning framework for accurate, low-latency VLA policies. Our bootstrapping philosophy is to guide action with effective visual thinking: VISUALTHINK-VLA bootstraps action prediction through a compact visual-evidence interface that preserves spatial precision while avoiding decoding overhead. Besides, to further improve performance and efficiency, VISUALTHINK-VLA adopts a tailored selective routing mechanism to learn the visual evidence tokens, enabling low-latency inference while preserving high-capacity specialization. We also introduce VisualEvidence-Kit, a supervision-and-audit resource centered on a VisualEvidence-Agent that constructs a 754.7k VLA instructions VisualEvidence-Set for route supervision and counterfactual faithfulness tests. Across multiple benchmarks and real-robot evaluation, VISUALTHINK-VLA achieves the highest success rate on most benchmarks while reducing the multi-second latency of reasoning-augmented baselines to the sub-second regime. For example, on BridgeData V2, it reduces step latency from 8.377,s with ECoT to 0.367,s, achieving a 22.8 times speedup.", "github": "https://github.com/DCDmllm/VisualThink-VLA" }, { "date": "2026-06-01", "arxiv_id": "2605.29429", "title": "One Click per Cell Type Suffices: Training-free Group Interaction for Cell Instance Segmentation", "authors": [ "Sanghyun Jo", "Seo Jin Lee", "Seohyung Hong", "Yoorim Gang", "Hyeongsub Kim", "Hyungseok Seo", "Kyungsu Kim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shjo-april/Chain-of-Prompts", "github_api_added_by": "user", "project_page": "https://shjo-april.github.io/Chain-of-Prompts/", "abstract": "Cell instance segmentation models trained on cell-specific datasets suffer severe performance drops on out-of-distribution cell types, while interactive foundation models overcome this through per-instance prompting at a cost that is prohibitively expensive for histopathology images containing hundreds to thousands of densely packed instances. We introduce Group Prompting, a new paradigm that shifts interactive segmentation from per-instance O(N) to per-type O(T), where a single click per cell type suffices to segment all instances of that type. Our key observation is that the frozen image encoder of the Segment Anything Model (SAM) already clusters same-type cells in its feature space before any prompt is given. Exploiting this property, we propose Chain-of-Prompts (CoP), a training-free framework that recursively expands a single user click by (1) identifying reliable same-type locations through non-parametric gating of multi-scale encoder features, and (2) selecting the most spatially distant reliable point as the next prompt to maximize coverage. On three cell-type-annotated benchmarks, CoP with one click per type retains over 90% of per-instance performance and surpasses fully-supervised methods without any additional training. On four morphologically homogeneous benchmarks, a single click retains over 99%. Project Page: https://shjo-april.github.io/Chain-of-Prompts/", "github": "https://github.com/shjo-april/Chain-of-Prompts" }, { "date": "2026-06-01", "arxiv_id": "2605.31268", "title": "Mellum2 Technical Report", "authors": [ "Marko Kojic", "Ivan Bondyrev", "Aral de Moor", "Joseph Shtok", "Petr Borovlev", "Kseniia Lysaniuk", "Madeeswaran Kannan", "Ivan Dolgov", "Nikita Pavlichenko" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://www.jetbrains.com/mellum/", "abstract": "We present Mellum 2, an open-weight 12B-parameter Mixture-of-Experts (MoE) language model with 2.5B active parameters per token. Mellum 2 is a general-purpose language model specialized in software engineering, spanning code generation and editing, debugging, multi-step reasoning, tool use and function calling, agentic coding, and conversational programming assistance, and it is the successor to the completion-focused 4B dense Mellum model. The architecture builds on the Mixture-of-Experts (64 experts, 8 active) and combines Grouped-Query Attention with 4 KV heads, Sliding Window Attention on three of every four layers, and a single Multi-Token Prediction head that doubles as both an auxiliary pre-training objective and a built-in draft model for speculative decoding; each choice was validated by ablation with inference efficiency on commodity GPUs as a design constraint. Pre-training spans approximately 10.6 trillion tokens through a three-phase curriculum that progressively shifts the mixture from diverse web data toward curated code and mathematical content, optimized with Muon under FP8 hybrid precision and a Warmup-Hold-Decay schedule with linear decay to zero. The pre-trained base is extended to a 128K context window via a layer-selective YaRN and then post-trained in two stages (supervised fine-tuning followed by RLVR), yielding two released variants: an Instruct model that answers directly and a Thinking model that emits an explicit reasoning trace before its final answer. Across code generation, math and reasoning, tool use, knowledge, and safety benchmarks, Mellum 2 is competitive with open-weight baselines in the 4B-14B range while running at the per-token compute of a 2.5B dense model. We release the base, instruct, and thinking checkpoints, together with this report on the architecture decisions, data pipeline, and training recipe behind them, under the Apache 2.0 license.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.30571", "title": "Memory-Bound but Not Bandwidth-Limited: The Physical AI Inference Gap in Batch-1 LLM Decode", "authors": [ "Josef Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/spaces/josefchen/physical-ai-inference-gap", "abstract": "Physical AI systems, including robots, autonomous vehicles, embodied agents and edge copilots, often run a different inference workload from cloud LLM serving: single-stream, batch-1 autoregressive decode, where one robot, camera feed or user session waits on the next token. This workload is usually described as memory-bandwidth-bound. Each decode step streams model weights and the active KV cache, so latency should scale with peak HBM bandwidth. We show that this account is true but incomplete. We measure batch-1 decode for three 7 to 8B-class GQA transformers across four NVIDIA GPUs: H100 SXM5, A100-80GB SXM4, L40S and L4. We evaluate context lengths from 2048 to 16384, producing 44 valid cells under a controlled bf16 SDPA setup. The achieved fraction of peak HBM bandwidth falls as peak bandwidth rises. On the headline Qwen-2.5-7B ctx=2048 cell, an L4 reaches roughly 81 percent of its analytic memory floor, while an H100 reaches only 27 percent. Physical-AI decode is memory-dominated, but faster memory does not translate into proportional latency gains. We test the missing term with a CUDA Graphs A/B experiment. On H100 at ctx=2048, CUDA Graphs improves decode latency by 1.259x across N=10 fresh sessions, with a 95 percent bootstrap confidence interval of 1.253 to 1.267. On L4, the same intervention gives only 1.028x. This isolates a launch-side overhead that becomes visible on fast GPUs but remains mostly hidden on slower, bandwidth-bound GPUs. The deployment implication is that memory savings matter only when the runtime realises them. On L4, bf16 decode sits close to the memory floor, but common quantised paths do not recover the expected 4x weight-traffic reduction: bnb-nf4 reaches 59.36 ms/step and AutoAWQ+Marlin reaches 45.24 ms/step from a 62.32 ms bf16 baseline. GPTQ+ExLlamaV2, with Ada-tuned int4 kernels, reaches 17.36 ms/step.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.15779", "title": "A Topology-Aware Spatiotemporal Handover Framework for Continuous Multi-UAV Tracking", "authors": [ "Jianlin Ye", "Christos Kyrkou", "Panayiotis Kolios" ], "github_manual": "", "github_regex": "https://github.com/JYe9/multi-camera-multi-vehicle-tracking-system", "github_paper_md": "", "github_api": "https://github.com/JYe9/multi-camera-multi-vehicle-tracking-system", "github_api_added_by": "user", "project_page": "https://www.jye.me/ICUAS2026/", "abstract": "The integration of Unmanned Aerial Vehicles(UAVs) into Intelligent Transportation Systems (ITS) offers synoptic visibility for traffic monitoring, yet scalable deployment is hindered by trajectory fragmentation, where vehicle identity persistence is lost across multi-UAV Fields of View (FOV). While state-of-the-art frameworks excel in optimizing local trajectory extraction and stability for single-drone imagery, they often function as isolated data silos that generate disjointed trajectories, thereby precluding network-level analysis such as Origin-Destination estimation. This paper presents a real-time Multi-Camera Multi-Vehicle Tracking (MCMT) system designed to handle global identity persistence. Addressing the visual ambiguity and computational cost of appearance-based Re-Identification (Re-ID) in nadir views, we introduce a lightweight Topology-Based Spatiotemporal Handover mechanism. We implement a high-throughput parallel pipeline leveraging YOLO11 and ByteTrack to process concurrent 4K streams. Our core contribution is a deterministic queue-based matching algorithm that utilizes geometric overlaps and virtual lane discretization to predictively manage identity handover via FIFO queues. Experimental results on complex urban environments, including intersections and merging traffic, demonstrate a Handover Success Rate (HOSR) of 99.8% in continuous traffic flows, significantly outperforming Re-ID baselines (74.1%) while validating edge deployment feasibility. The source code is available at https://github.com/JYe9/multi-camera-multi-vehicle-tracking-system.", "github": "https://github.com/JYe9/multi-camera-multi-vehicle-tracking-system" }, { "date": "2026-06-01", "arxiv_id": "2605.17543", "title": "HL-OutPaint: Coarse-to-Fine Video Outpainting for High-Resolution Long-Range Videos", "authors": [ "Jeongeun Park", "Janghyeok Han", "Geonung Kim", "Hyun-Seung Lee", "Kyuha Choi", "Youngseok Han", "Sunghyun Cho" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://koyy001.github.io/Publications/hl-outpaint", "abstract": "Video outpainting generates plausible visual content beyond the original spatial extent of a video, playing a key role in adapting videos to diverse display formats. To support such use cases, it must enable large spatial extrapolation over long sequences. However, most existing methods address only one of these challenges or lack explicit mechanisms for ensuring global spatio-temporal consistency, leading to notable limitations. In this paper, we propose HL-OutPaint, a high-resolution video outpainting framework for long sequences. Our approach follows a coarse-to-fine strategy with a two-stage pipeline. We first construct Global Coarse Guidance (GCG), a low-resolution representation that captures global structure and dominant motion across the video. Unlike naive downsampling, GCG is built via a novel global-local frame swapping mechanism that couples sparse global keyframes with local temporal windows and exchanges information during sampling. This enables GCG to encode both long-term structural consistency and short-term temporal dynamics in a unified representation. Guided by this representation, HL-OutPaint then performs high-resolution outpainting to generate spatially detailed and temporally consistent content. By separating global structure modeling from fine-grained synthesis, our framework achieves stable, coherent generation for large spatial expansion and long video sequences. Extensive experiments show that HL-OutPaint outperforms existing methods in challenging scenarios involving wide spatial extrapolation and long video sequences.", "github": "" }, { "date": "2026-06-01", "arxiv_id": "2605.31535", "title": "RayDer: Scalable Self-Supervised Novel View Synthesis from Real-World Video", "authors": [ "Ulrich Prestel", "Stefan Andreas Baumann", "Nick Stracke", "Bj\u00f6rn Ommer" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CompVis/rayder", "github_api_added_by": "user", "project_page": "https://compvis.github.io/rayder/", "abstract": "Self-supervised novel view synthesis (NVS) remains challenging to scale, despite the abundance of video data, largely due to the brittleness of training on realistic videos and the hard-to-predict scaling behavior of multi-network system designs. We introduce RayDer, a unified, feed-forward transformer that consolidates camera estimation, scene reconstruction, and rendering into a single backbone, turning self-supervised NVS into a well-posed single-model scaling problem. A minimal dynamic state, treated as a nuisance factor, absorbs time-varying content and enables stable training on unconstrained real-world video. Importantly, RayDer keeps static-scene NVS as its target task: dynamic content is leveraged purely as scalable supervision, not reconstructed as in dynamic-scene (4D) NVS. Across multiple model sizes and orders of magnitude in data, RayDer exhibits clean power-law scaling with data and compute, and outperforms static-scene data mixtures. On a large number of benchmarks, RayDer achieves strong zero-shot open-set performance competitive with state-of-the-art supervised approaches. Project Page: https://compvis.github.io/rayder", "github": "https://github.com/CompVis/rayder" }, { "date": "2026-06-01", "arxiv_id": "2605.26112", "title": "From Model Scaling to System Scaling: Scaling the Harness in Agentic AI", "authors": [ "Shangding Gu" ], "github_manual": "", "github_regex": "https://github.com/SafeRL-Lab/cheetahclaws", "github_paper_md": "", "github_api": "https://github.com/SafeRL-Lab/cheetahclaws", "github_api_added_by": "user", "project_page": "https://cheetahclaws.github.io/", "abstract": "This paper studies the next major bottleneck in agentic AI as system scaling, not only model scaling: the design of auditable, persistent, modular, and verifiable architectures around foundation models. We refer to this shift as scaling the harness: treating the structured execution layer around a foundation model as a first-class object of design, evaluation, and optimization. Although recent large language models enable agents to use tools, retrieve information, maintain memory, and execute long-horizon workflows, evaluation remains largely model-centric, often reducing agents to final-task success while treating memory, retrieval, tool use, orchestration, verification, and governance as secondary implementation details. This framing is increasingly inadequate because agent performance emerges from the interaction among the foundation model, memory substrate, context constructor, skill-routing layer, orchestration loop, and verification-and-governance layer. Together, these components form the agent harness, which translates model capability into long-horizon agent behavior. We study scaling the harness through three core bottlenecks: context governance, trustworthy memory, and dynamic skill routing, together with the orchestration and governance mechanisms that coordinate and constrain them. We further outline a research agenda for harness-level benchmarks that go beyond one-shot task success to measure trajectory quality, memory hygiene, context efficiency, communication fidelity, verification cost, and safe evolution over time. To make the discussion concrete, we develop CheetahClaws: https://github.com/SafeRL-Lab/cheetahclaws, a Python-native reference harness, and compare it with Claude Code and OpenClaw. Our main claim is that future progress in agentic AI will depend as much on system design as on stronger foundation models.", "github": "https://github.com/SafeRL-Lab/cheetahclaws" }, { "date": "2026-06-01", "arxiv_id": "2605.29198", "title": "Guidance Contrastive Token Credit Assignment for Discrete Policy Optimization", "authors": [ "Shufan Li", "Konstantinos Kallidromitis", "Akash Gokul", "Yuta Kyuragi", "Aditya Grover" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jacklishufan/gcpo", "github_api_added_by": "user", "project_page": "", "abstract": "Group-advantage-based reinforcement learning methods, such as GRPO and DAPO, have demonstrated strong performance across diverse domains, including mathematical reasoning and text-to-image generation. However, their reliance on sample-level rewards introduces a key limitation as uniform credit assignment across all tokens fails to capture fine-grained, token-level contributions. To address this issue, we propose Guidance Contrastive Policy Optimization (GCPO), a novel algorithm that enables per-token credit assignment by contrasting model predictions under positive and negative prompts. Rather than uniformly broadcasting sample-level advantages, GCPO assigns token-level advantages proportional to the difference between these contrastive predictions, allowing more precise and informative learning signals. Empirically, we find that GCPO emphasizes semantically relevant regions such as visual areas aligned with textual prompts in text-to-image generation, and critical keywords within reasoning traces for chain-of-thought tasks. Through extensive experiments, GCPO consistently outperforms GRPO and DAPO baselines on both text-to-image generation and chain-of-thought reasoning benchmarks, demonstrating its effectiveness as a general and scalable optimization strategy for discrete policy learning.", "github": "https://github.com/jacklishufan/gcpo" }, { "date": "2026-06-01", "arxiv_id": "2605.28730", "title": "AlphaTransit: Learning to Design City-scale Transit Routes", "authors": [ "Bibek Poudel", "Sai Swaminathan", "Weizi Li" ], "github_manual": "", "github_regex": "https://github.com/poudel-bibek/AlphaTransit", "github_paper_md": "", "github_api": "https://github.com/poudel-bibek/AlphaTransit", "github_api_added_by": "user", "project_page": "https://alphatransit.app/", "abstract": "Designing a transit network requires many sequential route extension decisions, but their quality is often visible only after the full network is assembled. This delayed-feedback challenge lies at the heart of the Transit Route Network Design Problem (TRNDP), where route interactions can be deceptive: an extension that appears useful locally can create transfer bottlenecks, produce redundant overlap, or reduce overall throughput. To guide route construction under delayed simulator feedback, we introduce AlphaTransit, a search-based planning framework for cityscale bus network design. AlphaTransit couples Monte Carlo Tree Search (MCTS) with a neural policy-value network: the policy proposes route extensions, the value estimates downstream design quality, and search uses these predictions to refine each decision. This provides decision-time lookahead during route construction without running simulator rollouts inside the search tree. We evaluate AlphaTransit on a new Bloomington TRNDP benchmark with realistic road topology and censusderived demand, under mixed and full transit demand settings. In the Bloomington network, AlphaTransit attains the highest service rate in both demand settings, reaching 54.6% and 82.1%, respectively. Relative to reinforcement learning without search, these correspond to 9.9% and 11.4% service rate gains; relative to MCTS without learned guidance, they correspond to 2.5% and 11.2% gains. These results suggest that coupling learned guidance with MCTS is more effective than using either approach alone for transit network design. Our code and data are publicly available in https://github.com/poudel-bibek/AlphaTransit.", "github": "https://github.com/poudel-bibek/AlphaTransit" }, { "date": "2026-06-01", "arxiv_id": "2605.28657", "title": "DEMON: Diffusion Engine for Musical Orchestrated Noise", "authors": [ "Ryan Fosdick" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/daydreamlive/DEMON", "github_api_added_by": "user", "project_page": "https://daydreamlive.github.io/DEMON/", "abstract": "We present DEMON, a real-time diffusion engine that makes the denoising process playable as a live musical instrument: a control surface both broad (many parameters shaped per-frame across the output) and responsive (each control taking effect as fast as its place in the denoising loop allows). Built on ACE-Step 1.5 and StreamDiffusion's ring-buffer architecture with TensorRT acceleration, it sustains up to 12.3 decoder completions per second for 60-second music on a single consumer GPU (RTX 5090), or 11.3 generations per second at our production ring-depth of 4. At these rates denoising parameters become viable as live performance controls, but the ring buffer propagates per-request changes only at its drain rate, a floor of S denoising steps. We contribute four mechanisms. (1) Per-slot heterogeneous denoise scheduling: each ring-buffer slot owns its timestep schedule, so a moving denoise slider is tracked without wiping the in-flight queue, where the upstream global-schedule design must rebuild and discard it. (2) Shared mutable per-step state, giving any parameter consulted at every solver step next-tick effect, bypassing ring-buffer drain. (3) Per-frame source blending: a sampling-time control on the standard SDE re-noise step, giving a framewise transformation-strength axis that complements scalar denoise scheduling. (4) Windowed VAE decode exploiting receptive-field analysis for an 8.0x decode speedup. Together these separate streaming-diffusion parameters into four propagation classes, by onset and convergence latency.", "github": "https://github.com/daydreamlive/DEMON" }, { "date": "2026-06-01", "arxiv_id": "2605.28969", "title": "Beyond Recall: Behavioral Specification as an Interpretive Layer for AI Personalization", "authors": [ "Aarik Gulaya" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/agulaya24/beyond-recall", "github_api_added_by": "user", "project_page": "https://base-layer.ai/research/beyond-recall", "abstract": "If an AI agent makes decisions on a person's behalf, those decisions must align with its user. We introduce representational accuracy to measure how faithfully a system captures a person's interpretation. An interpretive layer is operationalized as a Behavioral Specification. Our reference implementation aggressively compresses a person's data into interpretive patterns, served as context to a language model. We evaluate the Specification on a prototype benchmark of held-out behavioral predictions scored by a calibrated 5-judge LLM panel. We test it independently and in composition with a range of context conditions: full raw corpus, full extracted facts, and four commercial memory systems (Mem0, Letta, Supermemory, Zep). Across 14 public-domain autobiographical corpora, the Specification lifts representational accuracy in aggregate and nearly eliminates model hedging. It recovers most of what the raw corpus delivers, at ~25x less context cost. The Specification lifts subjects toward a common predictive level regardless of pretraining baseline; the lift in absolute points is therefore largest where the baseline is lowest, suggesting the population of relevance is anyone not adequately represented in pretraining. Lift is greatest on interpretation-required questions, where providing an interpretive layer enables model behavior that extracted facts or raw corpus do not. Conversely, on recall-required questions, this layer can interfere rather than help. We conclude that representational accuracy is distinct from recall and that human-AI alignment is dependent on how accurately the user is represented. Representational accuracy makes that alignment testable.", "github": "https://github.com/agulaya24/beyond-recall" }, { "date": "2026-06-01", "arxiv_id": "2605.23458", "title": "One-Forcing: Towards Stable One-Step Autoregressive Video Generation", "authors": [ "Jiaqi Feng", "Justin Cui", "Yuanhao Ban", "Cho-Jui Hsieh" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Aurora-edu/One-Forcing", "github_api_added_by": "user", "project_page": "https://aurora-edu.github.io/one-forcing/", "abstract": "Recent advances have substantially improved real-time interactive video generation in the autoregressive regime. However, most existing few-step autoregressive video generation methods, often distilled from a corresponding many-step teacher, default to a 4-step sampling configuration, which still incurs considerable latency during deployment and suffers from severe quality degradation when the number of sampling steps is further reduced, particularly in the one-step setting. Trajectory-style consistency distillation methods often produce videos with weak dynamics, while DMD-based approaches, such as Self-Forcing, tend to yield blurry frames. To address this challenge, we propose One-Forcing, a simple yet effective approach which augments the DMD objective with an auxiliary GAN loss for high-quality and efficient one-step video generation. Experiments on VBench show that One-Forcing achieves a total score of 83.76, establishing state-of-the-art performance among one-step causal video generation methods and remaining competitive with strong many-step approaches. We further demonstrate that one-step framewise autoregressive generation can be achieved stably with merely one-third of the training cost of the chunkwise model, a setting that prior methods have failed to achieve successfully.", "github": "https://github.com/Aurora-edu/One-Forcing" }, { "date": "2026-06-01", "arxiv_id": "2605.30621", "title": "Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents", "authors": [ "Minhua Lin", "Juncheng Wu", "Zijun Wang", "Zhan Shi", "Yisi Sang", "Bing He", "Zewen Liu", "Tianxin Wei", "Zongyu Wu", "Zhiwei Zhang", "Dakuo Wang", "Xiang Zhang", "Benoit Dumoulin", "Cihang Xie", "Yuyin Zhou", "Suhang Wang", "Hanqing Lu" ], "github_manual": "", "github_regex": "https://github.com/A-EVO-Lab/a-evolve/tree/release/harness-evolution", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "LLM agents are increasingly deployed as systems built around editable external harnesses, including prompts, skills, memories and tools, that shape task execution without changing model parameters. Harness self-evolution adapts such agents by updating these harnesses from execution evidence. Yet it remains unclear whether a model's base capability in task-solving predicts its capabilities in harness self-evolution: which models produce useful harness updates, and which actually benefit from them? We analyze two harness self-evolution capabilities: (i) harness-updating, the capability to produce useful persistent harness updates from execution evidence; (ii) harness-benefit, the capability to benefit from updated harnesses during task solving. Our analysis reveals two findings. First, harness-updating is flat in base capability: models from different capability tiers produce harness updates that lead to surprisingly similar gains; even Qwen3.5-9B's updates yield gains comparable to those of Claude Opus~4.6. Second, harness-benefit is non-monotonic in base capability: weak-tier models benefit little from updated harnesses, mid-tier models benefit most, and strong-tier models benefit less than mid-tier. We trace low gains at the weak tier to two failure modes: weak-tier models may fail to activate relevant harness artifacts, or activate them but fail to follow them faithfully. These findings suggest investing capability budget in the task-solving agent rather than the evolver, and targeting harness invocation and long-horizon instruction following in agent training. Our source code is publicly available at https://github.com/A-EVO-Lab/a-evolve/tree/release/harness-evolution.", "github": "https://github.com/A-EVO-Lab/a-evolve/tree/release/harness-evolution" }, { "date": "2026-06-01", "arxiv_id": "2605.31577", "title": "SurGe: Improved Surface Geometry in Point Maps", "authors": [ "Karim Knaebel", "Gonzalo Martin Garcia", "Christian Schmidt", "Ilya Fradlin", "Lucas Nunes", "Daan de Geus", "Bastian Leibe" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/karimknaebel/surge", "github_api_added_by": "user", "project_page": "https://vision.rwth-aachen.de/surge", "abstract": "Recent feedforward 3D reconstruction methods predict point maps and estimate global 3D geometry remarkably well. However, their predictions still exhibit inaccurate local surface geometry, which is clearly visible qualitatively but only weakly reflected in common metrics. To make these errors more explicit in evaluation, we introduce a point map normal metric that evaluates the local surface orientation induced by neighboring 3D predictions. To reduce these errors, we propose two complementary components: a point gradient matching loss that supervises depth-normalized 3D finite differences, and a Neighborhood Attention Decoder (NAD) that progressively upsamples features and uses Neighborhood Attention for local feature mixing. Across eight zero-shot monocular geometry benchmarks, our model, SurGe, achieves the best average rank for global point map AbsRel and consistently improves local point map and point map normal evaluations.", "github": "https://github.com/karimknaebel/surge" }, { "date": "2026-06-02", "arxiv_id": "2605.30852", "title": "Speculative Pipeline Decoding: Higher-Accruacy and Zero-Bubble Speculation via Pipeline Parallelism", "authors": [ "Yijiong Yu", "Huazheng Wang", "Shuai Yuan", "Ruilong Ren", "Ji Pei" ], "github_manual": "", "github_regex": "https://github.com/yuyijiong/speculative_pipeline_decoding", "github_paper_md": "", "github_api": "https://github.com/yuyijiong/speculative_pipeline_decoding", "github_api_added_by": "user", "project_page": "", "abstract": "Speculative Decoding (SD) accelerates low-concurrency LLM inference by employing a draft-then-verify paradigm. However, mainstream methods typically rely on multi-token prediction, which introduces escalating prediction difficulty and serial drafting latency. To address these, we propose Speculative Pipeline Decoding (SPD), a groundbreaking framework that unlocks the true potential of pipeline parallelism. By partitioning the target LLM into n pipeline stages, SPD allows LLM to process n tokens in parallel to accelerate decoding. To continuous fill the pipeline in single sequence decoding, a speculation module aggregates intermediate features across different pipeline depths to predict the next token, executing strictly in parallel with the target model's pipeline step, to realize bounded difficulty, higher acceptance rates, and zero latency bubbles. Our experiments demonstrate that SPD achieves a significantly higher theoretical speedup compared to mainstream baselines, offering a highly scalable solution for LLM decoding acceleration. Our code is available at https://github.com/yuyijiong/speculative_pipeline_decoding", "github": "https://github.com/yuyijiong/speculative_pipeline_decoding" }, { "date": "2026-06-02", "arxiv_id": "2605.28132", "title": "Which Pretraining Paradigm Better Serves Spatial Intelligence? An Empirical Comparison of Vision-Language and Video Generation Models", "authors": [ "Haozhan Shen", "Tiancheng Zhao", "Kangjia Zhao", "Jianwei Yin" ], "github_manual": "", "github_regex": "https://github.com/om-ai-lab/Probing-VLM-VGM", "github_paper_md": "", "github_api": "https://github.com/om-ai-lab/Probing-VLM-VGM", "github_api_added_by": "user", "project_page": "", "abstract": "Spatial intelligence requires visual representations that capture both semantic objects and geometric structure in the physical world. To support this, two major pre-training schemes are now widely used as foundation backbones: Vision-Language Models (VLMs), which use language supervision to align visual observations with semantic concepts, and Video Generation Models (VGMs), which learn from temporally evolving visual worlds. However, it still remains unclear which pre-training scheme provides a better representation substrate for spatial intelligence. In this paper, we present the first systematic frozen-feature probing study of VLMs and VGMs across three representative axes of spatial intelligence: semantic tagging, instance grouping, and 3D geometry prediction. Using the lightweight probe, our framework enables a controlled comparison of what information is already encoded in frozen representations from two model families. Experimental results reveal a clear complementarity: VLMs are stronger at semantic tagging and instance grouping, while VGMs provide more accessible signals for dense geometry and camera motion. Moreover, a naive fusion of the two already yields a representation that excels at both geometry and semantics, suggesting a promising direction for building stronger spatial-intelligence backbones by effectively integrating features from both model families. Our code is available at https://github.com/om-ai-lab/Probing-VLM-VGM{https://github.com/om-ai-lab/Probing-VLM-VGM}.", "github": "https://github.com/om-ai-lab/Probing-VLM-VGM" }, { "date": "2026-06-01", "arxiv_id": "2605.02772", "title": "Linearizing Vision Transformer with Test-Time Training", "authors": [ "Yining Li", "Dongchen Han", "Zeyu Liu", "Hanyi Wang", "Yulin Wang", "Gao Huang" ], "github_manual": "", "github_regex": "https://github.com/LeapLabTHU/Transformer-to-TTT", "github_paper_md": "", "github_api": "https://github.com/LeapLabTHU/Transformer-to-TTT", "github_api_added_by": "user", "project_page": "", "abstract": "While linear-complexity attention mechanisms offer a promising alternative to Softmax attention for overcoming the quadratic bottleneck, training such models from scratch remains prohibitively expensive. Inheriting weights from pretrained Transformers provides an appealing shortcut, yet the fundamental representational gap between Softmax and linear attention prevents effective weight transfer. In this work, we address this conversion challenge from two perspectives: architectural alignment and representational alignment. We identify Test-Time Training (TTT) as a linear-complexity architecture whose two-layer dynamic formulation is structurally aligned with Softmax attention, enabling direct inheritance of pretrained attention weights. To further align representational properties, including key shift-invariance and locality, we introduce key instance normalization and a lightweight locality enhancement module. We validate our approach by linearizing Stable Diffusion 3.5 and introduce SD3.5-T^5 (Transformer To Test Time Training). With only 1 hour of fine-tuning on 4timesH20 GPUs, SD3.5-T^5 achieves comparable text-to-image quality to the fine-tuned Softmax model, while accelerating inference by 1.32times and 1.47times at 1K and 2K resolutions. Code is available at https://github.com/LeapLabTHU/Transformer-to-TTT.", "github": "https://github.com/LeapLabTHU/Transformer-to-TTT" }, { "date": "2026-06-02", "arxiv_id": "2605.30351", "title": "VideoMLA: Low-Rank Latent KV Cache for Minute-Scale Autoregressive Video Diffusion", "authors": [ "Hidir Yesiltepe", "Jiazhen Hu", "Tuna Han Salih Meral", "Adil Kaan Akan", "Kaan Oktay", "Hoda Eldardiry", "Pinar Yanardag" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yesiltepe-hidir/VideoMLA", "github_api_added_by": "user", "project_page": "https://videomla.github.io/", "abstract": "Long-rollout causal video diffusion has converged on a fixed-size sliding-window KV cache, with recent progress innovating within this layout by changing which tokens occupy the window or how their positions are encoded. The per-head KV layout itself, a dominant contributor to streaming memory and latency, has been mostly left unchanged. In this paper, we present the first study of Multi-Head Latent Attention (MLA) in video diffusion. VideoMLA replaces per-head keys and values with a shared low-rank content latent and a shared decoupled 3D-RoPE positional key, reducing per-token KV memory by 92.7% at every cached layer. We further investigate why MLA succeeds in video diffusion even though the spectral assumption often used to motivate it in language models does not hold: pretrained video attention is not low-rank, with 99%-energy effective rank far above any practical latent dimension. VideoMLA retains quality at compression ratios where direct spectral approximation would predict large reconstruction error. We show that the MLA bottleneck, rather than the pretrained spectrum, determines the effective rank: both spectral and random initialization occupy nearly the full rank budget from initialization, and training preserves this budget while adapting within it. On VBench, VideoMLA matches short-horizon streaming video diffusion baselines, achieves the best overall score at long horizons among evaluated methods, and improves throughput by 1.23x on a single B200.", "github": "https://github.com/yesiltepe-hidir/VideoMLA" }, { "date": "2026-06-02", "arxiv_id": "2606.02482", "title": "X-Stream: Exploring MLLMs as Multiplexers for Multi-Stream Understanding", "authors": [ "Peiwen Sun", "Xudong Lu", "Huadai Liu", "Yang Bo", "Dongming Wu", "Huankang Guan", "Minghong Cai", "Jinpeng Chen", "Xintong Guo", "Shuhan Li", "Rui Liu", "Xiangyu Yue" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PeiwenSun2000/X-Stream", "github_api_added_by": "user", "project_page": "https://peiwensun2000.github.io/xstream/", "abstract": "While video streaming understanding has made significant strides, real-world applications, such as live sports broadcasting, autonomous driving, and multi-screen collaboration, inherently demand continuous, multi-stream interactions. However, existing benchmarks are confined to single-stream paradigms, leaving a critical gap in evaluating online, cross-stream reasoning. To bridge this, we introduce X-Stream, the first benchmark dedicated to multi-stream streaming understanding. Comprising 4,220 rigorously curated QA pairs across 932 videos, X-Stream evaluates 11 subtasks across multi-window, multi-view, and multi-device scenarios. Crucially, our dataset is constructed using a novel dual-verification pipeline that prevents over-reliance on a single stream. Furthermore, we pioneer the conceptualization of multi-modal large language models (MLLMs) as naive multiplexers, systematically evaluating their performance through the lens of Signal Multiplexing Theory. Our extensive online inference experiments reveal a stark reality: state-of-the-art MLLMs struggle significantly with concurrent streams, achieving only about 50% score and exhibiting poor proactive ability. Ultimately, X-Stream exposes the trade-off of current multiplexing schemes, providing both a practical evaluation protocol and empirical guidance for next-generation multi-stream agents.", "github": "https://github.com/PeiwenSun2000/X-Stream" }, { "date": "2026-06-02", "arxiv_id": "2605.30611", "title": "Crafter: A Multi-Agent Harness for Editable Scientific Figure Generation from Diverse Inputs", "authors": [ "Haozhe Zhao", "Shuzheng Si", "Zhenhailong Wang", "Zheng Wang", "Liang Chen", "Xiaotong Li", "Zhixiang Liang", "Maosong Sun", "Minjia Zhang" ], "github_manual": "", "github_regex": "https://github.com/HaozheZhao/Crafter", "github_paper_md": "", "github_api": "https://github.com/HaozheZhao/Crafter", "github_api_added_by": "user", "project_page": "", "abstract": "Scientific figures are among the most effective means of communicating complex research ideas, yet producing publication-quality illustrations remains one of the most labor-intensive parts of paper preparation. Existing automated systems each target a single figure type under text-only input, leaving the diversity of types and conditions researchers actually use unaddressed; their raster outputs further cannot be locally revised. Because scientific figures are structured compositions of discrete semantic components, the localized errors generators produce on such layouts demand not a stronger backbone but a harness. We instantiate this harness in two complementary systems: Crafter, a multi-agent harness for figure generation that generalizes across figure types and input conditions without architectural changes, and CraftEditor, which applies the same pattern to convert raster outputs into editable SVGs. Moreover, we introduce CraftBench, a benchmark spanning three figure types and four input conditions with human quality annotation. Experiments show that Crafter substantially outperforms both standalone generators and the agentic baseline on PaperBanana-Bench and CraftBench, with ablations confirming each component's independent contribution; CraftEditor faithfully converts outputs into editable SVGs that surpass all baselines. Our code and benchmark are available at https://github.com/HaozheZhao/Crafter.", "github": "https://github.com/HaozheZhao/Crafter" }, { "date": "2026-06-02", "arxiv_id": "2605.29860", "title": "ESPO: Early-Stopping Proximal Policy Optimization", "authors": [ "Zihang Li", "Rui Zhou", "Yingcheng Shi", "Wenhan Yu", "Zhewen Tan", "Zixiang Liu", "Zeming Li", "Binhua Li", "Yongbin Li", "Tong Yang", "Jieping Ye" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "When a large language model under reinforcement learning commits a wrong reasoning step early in a trajectory, standard algorithms force it to keep generating until the maximum horizon, spending compute on tokens that never receive positive reward and polluting advantage estimates with post-failure noise. We propose ESPO (Early-Stopping Proximal Policy Optimization), which detects trajectory failure on-the-fly and terminates rollouts early. At each generation step, ESPO computes a surrogate regret using only the logits already computed during sampling, and terminates when the smoothed cumulative regret significantly exceeds its estimated values. Truncated trajectories are treated as absorbing failure states with a terminal reward, concentrating negative temporal-difference (TD) errors near the detected failure step without any additional reward model or human annotation. On DeepSeek-R1-Distill-Qwen-7B trained for mathematical reasoning, ESPO surpasses PPO on AIME~2024 (46.28% vs. 45.25%), AMC~2023 (85.83% vs. 82.94%), and MATH-500 (87.42% vs. 85.43%), while saving more than 20% rollout tokens cumulatively.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.25659", "title": "StreamChar: Long-Horizon Streaming Character Audio-Video Generation with Decoupled Orchestration", "authors": [ "Linrui Tian", "Qi Wang", "Bang Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://humanaigc.github.io/StreamChar_page/", "abstract": "Real-time streaming joint audio-video generation for character animation requires a generator to speak the requested transcript, maintain visual identity across chunks, and run within a strict playback budget. These requirements are difficult to satisfy simultaneously: chunk-wise autoregressive generation can accumulate transcript-audio misalignment and visual drift, while the few-step distillation needed for low latency often degrades spatial diversity and temporal quality. We present StreamChar, a streaming framework that separates long-horizon orchestration from short-window audio-video denoising. An LLM-based orchestrator uses the transcript and historical context to produce frame-aligned audio conditions, and a joint audio-video DiT performs local bidirectional denoising with reference and motion-frame conditioning. For efficient deployment, we use a two-stage distillation pipeline that first compresses the sampler and then fine-tunes the student under online chunk rollouts. A progress-aware pointer aligns partial transcripts with generated audio during rollout training, and a sink-chunk memory provides a persistent visual anchor for reducing long-horizon drift. Experiments on short-clip and long-horizon protocols show that StreamChar runs in real time on a single H100 GPU and provides a favorable system-level trade-off among transcript fidelity, audio-visual synchronization, visual quality, and streaming stability compared with recent joint and audio-driven baselines.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.24956", "title": "NITP: Next Implicit Token Prediction for LLM Pre-training", "authors": [ "Xiangdong Zhang", "Debing Zhang", "Shaofeng Zhang", "Xiaohan Qin", "Yu Cheng", "Junchi Yan" ], "github_manual": "", "github_regex": "https://github.com/aHapBean/NITP", "github_paper_md": "", "github_api": "https://github.com/aHapBean/NITP", "github_api_added_by": "user", "project_page": "", "abstract": "Standard next-token prediction (NTP) supervises language models solely through discrete labels in the output logit space. We argue that this sparse one-hot supervision leaves the latent representation space under-constrained, allowing hidden states to drift into degenerate and anisotropic configurations that can limit generalization. To address this issue, we propose Next Implicit Token Prediction (NITP), which augments discrete prediction with dense continuous supervision directly in the representation space. NITP trains the model to predict the implicit semantic content of the next token, using shallow-layer representations from the same model as stable self-supervised targets. We provide theoretical analysis showing that NITP regularizes the optimization landscape by mitigating under-constrained degrees of freedom and encouraging a compact, structured representation geometry. Empirically, across dense and MoE models ranging from 0.5B to 9B parameters, NITP consistently improves downstream performance with negligible computational overhead. On a 9B MoE model, NITP achieves a 5.7% absolute improvement on MMLU-Pro, along with gains of 6.4% on C3 and 4.3% on CommonsenseQA, with approximately 2% additional training FLOPs and no additional inference cost. Our implementation is available at https://github.com/aHapBean/NITP.", "github": "https://github.com/aHapBean/NITP" }, { "date": "2026-06-02", "arxiv_id": "2606.00828", "title": "RoboStressBench: Benchmarking VLM Robustness to Physical Visual Stress in Embodied Scenes", "authors": [ "Leyi Wu", "Yifan Zhao", "Jinjie Zhang", "Suzeyu Chen", "Wosong Chen", "Zhifei Chen", "Tianshuo Xu", "Qingchun He", "Hongxin Hu", "Haojian Huang", "Yangkai Wei", "Wenqian Li", "Yinchuan Li", "Ying-Cong Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YUEVII/RoboStressBench", "github_api_added_by": "user", "project_page": "https://yuevii.github.io/robostressbench-page/", "abstract": "Vision-Language Models (VLMs) have shown strong visual understanding and are increasingly deployed in embodied AI systems, where reliable perception under real conditions is essential. However, existing benchmarks assess VLMs using clean images or isolated perturbations rather than stresses caused by physical scene formation. This design has two limitations: it covers only a narrow subset of everyday visual stresses, and some perturbations rarely appear in realistic embodied scenes. This gap raises a fundamental question: how can we define visual stress in a principled way that captures the diverse factors encountered in physical environments? To address this question, we formulate visual perception from an inverse graphics perspective and introduce RoboStressBench, a benchmark for evaluating VLM robustness to physical visual stress in embodied scenes. Inspired by the physical rendering equation, RoboStressBench decomposes visual stress into four physically grounded dimensions: Material (M), Viewpoint (V), Lighting (L), and Geometry (G). This design enables RoboStressBench to cover a broad range of visual stresses in real-world environments, while allowing controlled analysis of their effects on VLM capabilities such as visual recognition, reasoning, and planning. Through comprehensive evaluations of state-of-the-art VLMs, we identify stress-specific failure modes and reveal that different physical factors degrade different embodied capabilities, which are often obscured by aggregate accuracy. We further introduce a stress-aware agentic solver that detects visual stressors and invokes visual-editing skills before reasoning, improving robustness in high-stress scenarios. Overall, RoboStressBench provides a principled evaluation framework for diagnosing and improving VLM perception under real-world physical stress, supporting the development of more reliable embodied AI systems.", "github": "https://github.com/YUEVII/RoboStressBench" }, { "date": "2026-06-02", "arxiv_id": "2605.29588", "title": "Brain-IT-VQA: From Brain Signals to Answers", "authors": [ "Roman Beliy", "Matias Cosarinsky", "Oliver Heinimann", "Navve Wasserman", "Michal Irani" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://mcosarinsky.github.io/brain-it-vqa/", "abstract": "Decoding visual content from fMRI signals recorded while a person views images, and specifically answering questions about the seen images, is a long-standing challenge. While significant progress has been made in recent years in visual question answering (VQA) from fMRI, performance remains limited. Moreover, although recent models can make increasingly accurate predictions, they have rarely been used as tools for understanding the structure of visual representations in the brain. We present Brain-IT-VQA, a framework for visual question answering from fMRI. Building on the Brain Interaction Transformer (Brain-IT), our method decodes language tokens from brain activity and integrates them with a language model to answer visual questions. Our model substantially outperforms previous fMRI-based captioning and VQA approaches. We further introduce NSD-VQA, a new dataset and benchmark for visual question answering from fMRI. Unlike existing image-fMRI VQA datasets, which typically provide only a few broad and weakly controlled questions per image, NSD-VQA provides on average 20 question-answer pairs per image across 20 controlled question categories that disentangle multiple levels of visual understanding. This enables more reliable and interpretable evaluation despite limited fMRI test data. Together, Brain-IT-VQA and NSD-VQA provide both a strong predictive framework and a tool for studying brain representations. Using this benchmark, we quantify which forms of visual and semantic information can be reliably decoded from fMRI responses to natural images. We further analyze the contributions of different brain regions across question types.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.30723", "title": "Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents", "authors": [ "Jianxiang Yu", "Jiapeng Zhu", "Bochen Lin", "Qier Cui", "Zichen Ding", "Xiang Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jianxiangyu/MASA_", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "LLM agents increasingly retrieve externally curated skills-procedural instructions retrieved at decision time-to improve performance on long-horizon interactive tasks. Existing skill libraries are typically treated as model-agnostic, reusing the same skill formulations across backbones with substantially different capacities and behaviors. However, our controlled experiments across multiple model scales show that skill effectiveness is strongly model-dependent: a skill that benefits one backbone can harm another. Motivated by this observation, we propose MASA Model-Aware Skill Alignment, a framework that adapts skills to each target backbone without modifying agent weights. MASA operates in two stages: (1) a hierarchical skill evolution pipeline that iteratively rewrites general and task-specific skills using hill climbing and UCB-driven tree search, guided by environment feedback and model capability profiles; and (2) a lightweight model-conditioned skill rewriter trained on evolution trajectories to reproduce the adaptation in a single forward pass. Experiments across three interactive environments and four backbones show that MASA consistently achieves the best overall performance, with gains of up to 25.8 points over the strongest baseline. The learned rewriter further generalizes to unseen tasks and environments without additional search, consistently outperforming a much larger teacher LLM at a fraction of the inference cost.", "github": "https://github.com/jianxiangyu/MASA_" }, { "date": "2026-06-02", "arxiv_id": "2605.29343", "title": "Draft-OPD: On-Policy Distillation for Speculative Draft Models", "authors": [ "Haodi Lei", "Yafy Li", "Haoran Zhang", "Shunkai Zhang", "Qianjia Cheng", "Xiaoye Qu", "Ganqu Cui", "Bowen Zhou", "Ning Ding", "Yun Luo", "Yu Cheng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bingyang-lei/Draft-OPD", "github_api_added_by": "user", "project_page": "https://www.haodilei.top/draft-opd/", "abstract": "Speculative decoding accelerates large language model inference by pairing a target model with a lightweight draft model whose proposed tokens are verified in parallel. A common way to build draft models, like EAGLE3 or DFlash is supervised fine-tuning (SFT) on target-generated trajectories. However, we observe that SFT quickly plateaus: the draft model's acceptance length on test data stops improving. The reason is an offline-to-inference mismatch: In SFT, the drafter learns from fixed target-generated trajectories, whereas during speculative decoding it is evaluated on blocks proposed under its own policy. This motivates on-policy distillation (OPD), where the target model supervises the drafter on draft-induced states. Yet OPD remains difficult for draft models, as they cannot reliably roll out complete sequences independently, whereas target-assisted generation makes the collected sequences follow the target distribution and thus eliminates the on-policy signal. We therefore propose Draft-OPD, which uses target-assisted rollout for stable continuations and replays drafting from the verification-exposed error positions. This allows the drafter to learn from target feedback on both accepted and rejected proposals, focusing training on the draft-induced errors that limit speculative acceptance. Experiments show that Draft-OPD achieves over 5times lossless acceleration for thinking models across diverse tasks, improving over EAGLE-3 and DFlash by 23\\% and 13\\%.", "github": "https://github.com/bingyang-lei/Draft-OPD" }, { "date": "2026-06-02", "arxiv_id": "2605.28615", "title": "Compositional Text-to-Image Generation Via Region-aware Bimodal Direct Preference Optimization", "authors": [ "Zhuohan Liu", "Wujian Peng", "Yitong Chen", "Zuxuan Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/anzeameol/BiDPO", "github_api_added_by": "user", "project_page": "", "abstract": "Despite the rapid progress of text-to-image (T2I) models, generating images that accurately reflect complex compositional prompts (covering attribute bindings, object relationships, counting) still remains challenging. To address this, we propose BiDPO, a framework to enhance T2I model's capability of compositional text-to-image generation. We begin by introducing an carefully designed pipeline to construct a large-scale preference dataset, BiComp, with strictly quality control. Then, we extend Diffusion DPO to jointly optimize image and text preferences, which is shown to greatly effective in improving the models to follow complex text prompt in generation. To further enhance the models for fine-grained alignment, we employ a region-level guidance method to focus on regions relevant to compositional concepts. Experimental results demonstrate that our BiDPO substantially improves compositional fidelity, consistently outperforming prior methods across multiple benchmarks. Our approach highlights the potential of preference-based fine-tuning for complex text-to-image tasks, offering a flexible and scalable alternative to existing techniques.", "github": "https://github.com/anzeameol/BiDPO" }, { "date": "2026-06-02", "arxiv_id": "2606.02437", "title": "On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters", "authors": [ "Mind Lab", "Song Cao", "Vic Cao", "Kaijie Chen", "Bunny Fan", "Hera Feng", "Huan Feng", "Arthur Fu", "Jun Gao", "Hongquan Gu", "Aaron Guan", "Mutian Hong", "Hailee Hou", "Peixuan Hua", "Charles Huang", "Miles Jiang", "Nora Jiang", "Yuyi Jiang", "Autumn Jin", "Fancy Kong", "Kyrie Lei", "Alexy Li", "Dawn Li", "Ray Li", "Theo Li", "Wenhao Li", "Jiayi Lin", "Domini Liu", "Heshan Liu", "Kairus Liu", "Logan Liu", "Maeve Luo", "Runism Lv", "Pony Ma", "Verity Niu", "Anson Qiu", "Vincent Wang", "Maxwell Yao", "Regis Ye", "Wenlin Ye", "Yanying Ye", "Josh Ying", "Danney Zeng", "Salmon Zhan", "Anya Zhang", "Ruijia Zhang", "Shiyang Zhang", "Sueky Zhang", "Ya Zhang", "Wei Zhao", "Ada Zhou", "Sizer Zhou", "Xinyue Zhu", "Murphy Zhuang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Parameter-efficient fine-tuning (PEFT) is usually treated as a cheaper alternative to full fine-tuning. We study a broader role: small trainable adapters as persistent local state on top of strong shared foundation models. In this framing, the base model provides shared competence while adapters carry instance-specific behavior such as preferences, skills, tool habits, and memory-like updates. We organize the problem around three scaling axes: Scale Up, where stronger shared priors make small local updates more useful; Scale Down, where we study how small adapters can be while remaining reliable; and Scale Out, where many persistent adapted instances coexist. MinT provides one infrastructure example for managing adapter identity, revision, provenance, evaluation, and serving residency. Together, the results suggest that PEFT can be a compact substrate for persistent personal models rather than only a budget substitute for full fine-tuning.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.02404", "title": "K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts", "authors": [ "Nahyun Lee", "Dongkeun Yoon", "Guijin Son", "Geewook Kim", "Dayoon Ko", "Jeonghun Park", "Haneul Yoo", "Jaewon Cho", "Junghun Park", "Changyoon Lee", "Kyochul Jang", "Jaeyeon Kim", "Eunsu Kim", "Woojin Cho", "Seungone Kim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/prometheus-eval/K-BrowseComp", "github_api_added_by": "user", "project_page": "", "abstract": "Frontier model evaluations are shifting from foundational capabilities (e.g., instruction following and reasoning) toward compositional, agentic ones, but Korean agentic benchmarks remain scarce. We introduce K-BrowseComp, a web-browsing agent benchmark grounded in Korean contexts, consisting of 400 problems. The 300-problem K-BrowseComp-Verified subset is manually constructed and validated by native Korean speakers. On this subset, frontier LLMs, including GPT-5.5, DeepSeek-V4-Pro, and GLM-5.1, reach only 30.00--45.67\\%, a substantial drop from BrowseComp, while Korean LLMs released through Korea's Proprietary AI Foundation Model program obtain only 0.00--10.33\\%. We further construct a 100-problem synthetic split using hard few-shot exemplars and failure-mode-targeted generation to exploit the asymmetry between solving and creating web browsing problems. On the adversarially filtered synthetic diagnostic split, the strongest model reaches only 26.00\\%, and we report this split separately as a targeted stress test. We publicly release our data and code.", "github": "https://github.com/prometheus-eval/K-BrowseComp" }, { "date": "2026-06-02", "arxiv_id": "2606.01311", "title": "SkillAdaptor: Self-Adapting Skills for LLM Agents from Trajectories", "authors": [ "Zhuoyun Yu", "Xin Xie", "Wuguannan Yao", "Chenxi Wang", "Lei Liang", "Xiang Qi", "Shumin Deng" ], "github_manual": "", "github_regex": "https://github.com/zjunlp/SkillAdaptor.", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language model (LLM) agents increasingly rely on reusable external skills to solve long-horizon interactive tasks. Existing training-free skill adaptation pipelines usually update skills from full trajectories or session-level feedback, which makes failure attribution coarse and often produces unstable or overly broad revisions. We propose SkillAdaptor, a training-free step-level skill adaptation framework with explicit failure attribution, and it can plug into OpenClaw-class agent harnesses. Given a failed trajectory, SkillAdaptor identifies a first actionable fault step, links responsibility to candidate skills, and applies targeted updates under explicit acceptance checks while keeping the backbone frozen. We evaluate on WebShop, PinchBench, and Claw-Eval with Kimi-K2.5, GLM-5, and GPT-5.2. SkillAdaptor improves over no-skill and skill-adaptation baselines on all three suites, with the largest single-metric improvements of +1.5 points on PinchBench Avg Score%, +1.8 on Claw-Eval Avg Score, and +1.7 on WebShop success rate. These results indicate that step-level attribution supports more stable and auditable training-free skill maintenanceThe code will be released at https://github.com/zjunlp/SkillAdaptor..", "github": "https://github.com/zjunlp/SkillAdaptor." }, { "date": "2026-06-02", "arxiv_id": "2606.01247", "title": "Where to Look: Can Foundation Models Reach a Target Viewpoint Through Active Exploration?", "authors": [ "Liyang Li", "Muzhi Zhu", "Zhiyue Zhao", "Hengyu Zhao", "Ke Liu", "Linhao Zhong", "Hao Chen", "Chunhua Shen" ], "github_manual": "", "github_regex": "https://github.com/aim-uofa/TVRBench", "github_paper_md": "", "github_api": "https://github.com/aim-uofa/TVRBench", "github_api_added_by": "user", "project_page": "", "abstract": "Humans can reproduce the viewpoint specified by a target image through active head and body motion, yet spatial intelligence in foundation models has largely been studied as passive understanding of pre-collected observations. We introduce Target Viewpoint Reproduction (TVR) -- an active task where an agent adjusts its viewpoint in a 3D environment until its observation matches a given target image -- and TVRBench, an indoor-simulation benchmark spanning scene scale and target-view visual richness. TVR is far from solved: on the evaluation split, the strongest open-source and closed-source models reach only 7.8% and 12.0% success. Fine-grained analysis identifies two consistent bottlenecks: off-the-shelf models struggle with multi-turn visual history, and performance drops sharply when viewpoint reproduction requires body translation rather than in-place rotation, exposing a gap in mapping spatial discrepancies to embodied movement. To study reducing this gap, we build a unified TVR post-training framework covering expert-trajectory SFT, rationale-supervised CoT-SFT, offline Single-turn GRPO, and on-policy Multi-turn GRPO from live simulator rollouts. Visual-action SFT supplies the main gain, raising a 9B open-source model to 50.8% success; Multi-turn GRPO provides targeted multi-room refinement and reaches 51.4% overall, while CoT supervision and Single-turn GRPO degrade closed-loop performance. These results establish TVRBench as a testbed for measuring and training foundation models that actively perceive and act in 3D environments. Our code, data, and models are available at https://github.com/aim-uofa/TVRBench.", "github": "https://github.com/aim-uofa/TVRBench" }, { "date": "2026-06-02", "arxiv_id": "2606.02564", "title": "VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization", "authors": [ "Junhao Cheng", "Liang Hou", "Tianxiong Zhong", "Xin Tao", "Pengfei Wan", "Kun Gai", "Jing Liao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://vlm-as-teacher.github.io/", "abstract": "The recent \"Reasoning with Video\" paradigm utilizes Video Generation Models (VGMs) to generate temporally coherent visual trajectories to complete reasoning tasks. Although state-of-the-art VGMs excel at visual quality, they often struggle to understand and follow task-specific rules, leading to logical failures across diverse reasoning scenarios. Existing efforts try to utilize Vision-Language Models (VLMs) as problem pre-solvers to produce or refine textual guidance for the VGM. However, textual descriptions fail to capture intricate spatiotemporal details, and VGMs often struggle to faithfully execute fine-grained or long-tail instructions even with a valid plan. While VLMs struggle as solvers, they possess strong perception capabilities to evaluate process-constraint satisfaction and final-goal achievement. Leveraging this strength, we introduce a paradigm shift that transitions the role of VLMs to \"teachers\". Specifically, a VLM teacher extracts task-specific rules to formulate differentiable rewards, guiding a VGM Reasoner via test-time online optimization of a lightweight LoRA module. This strategy enables adaptive test-time optimization and extends the reasoning capabilities beyond the VGM's intrinsic boundaries. Evaluations on symbolic (VBVR-Bench) and general-purpose (RULER-Bench) video reasoning benchmarks show that the proposed method yields a 16.7-point average performance gain, outperforming the VLM-as-Solver paradigm (+0.4 points) and Best-of-N scaling (+2.2 points) by a large margin at comparable test-time cost. These findings reveal that integrating VLMs as test-time teachers offers a promising paradigm for achieving generalizable video reasoning. Project Page: https://VLM-as-Teacher.github.io/", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.02277", "title": "RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models", "authors": [ "Bin Yu", "Yao Zhang", "Haishan Liu", "Shijie Lian", "Yuliang Wei", "Xiaopeng Lin", "Zhaolong Shen", "Changti Wu", "Ruina Hu", "Bailing Wang", "Cong Huang", "Kai Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZGC-EmbodyAI/RoboSemanticBench", "github_api_added_by": "user", "project_page": "", "abstract": "Vision-language-action (VLA) models are built on the premise that semantic understanding from pretrained language or vision-language backbones should guide robot action prediction. Yet robot fine-tuning is optimized as imitation over task-specific action distributions, and many evaluations can be solved through visual or instruction-action shortcuts. We introduce RoboSemanticBench (RSB), an embodied benchmark for diagnosing semantic grounding in action prediction: whether post-trained VLA models can use complex instruction semantics to select and manipulate the correct physical target. In each episode, a robot receives a multiple-choice math or general-knowledge question, observes candidate answer blocks, and must grasp the block corresponding to the correct answer. RSB covers controlled arithmetic, grade-school mathematical understanding, and commonsense or factual understanding under four-choice and ten-choice suites. Across representative VLA models, we find that many policies learn to grasp candidate blocks but select the semantically correct block at near-random or below-random rates after controlling for grasp success, revealing a persistent gap between backbone-level semantic competence and action prediction.", "github": "https://github.com/ZGC-EmbodyAI/RoboSemanticBench" }, { "date": "2026-06-02", "arxiv_id": "2606.01132", "title": "HakushoBench: A Japanese Chart and Table VQA Benchmark from Governmental White Papers", "authors": [ "Issa Sugiura", "Shuhei Kurita", "Yusuke Oda", "Naoaki Okazaki" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/datasets/llm-jp/HakushoBench", "abstract": "Understanding chart and table images is essential for applying vision-language models (VLMs) to real-world document understanding. While English benchmarks have advanced rapidly, non-English counterparts remain scarce, leaving it unclear whether this progress generalizes across languages. A key obstacle is the difficulty of collecting realistic and diverse non-English chart and table images at scale. To address this, we leverage governmental white papers as a scalable source for benchmark construction beyond English, as they contain naturally occurring charts and tables across diverse formats and domains and are freely accessible in many countries. As a first instantiation, we introduce HakushoBench, a challenging Japanese chart and table VQA benchmark built from 33 governmental white papers. HakushoBench contains 2,053 images spanning over 10 image types, with manually annotated QA pairs, designed to assess deep and holistic understanding of charts and tables, rather than local visual cues alone. Experiments across a broad range of VLMs demonstrate that HakushoBench remains challenging for open-weight models: the best open-weight model achieves only 58.6% accuracy, and a 34.9-point gap between open-weight and proprietary models highlights substantial room for improvement in complex chart and table understanding. We release our dataset and code.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.00408", "title": "Masking Stale Observations Helps Search Agents -- Until It Doesn't: A Regime Map and Its Mechanism", "authors": [ "Haoxiang Zhang", "Qixin Xu", "Zhuofeng Li", "Lei Zhang", "Pengcheng Jiang", "Yu Zhang", "Julian McAuley" ], "github_manual": "", "github_regex": "https://github.com/i-DeepSearch/observation-masking", "github_paper_md": "", "github_api": "https://github.com/i-DeepSearch/observation-masking", "github_api_added_by": "user", "project_page": "", "abstract": "Long-horizon search agents accumulate large amounts of retrieved content across many tool calls, making context-budget efficiency increasingly important. A minimal intervention is to mask stale observations from the context as the trajectory progresses, but it remains unclear when this form of context management helps and why. We study observation masking through a systematic sweep over various agent backbones (4B to 284B parameters) and three retrievers on offline and live-web agentic search benchmarks. We find that the accuracy gain from masking follows an asymmetric inverted-U shape when plotted against the model's accuracy without context management: a plateau under weak retrievers, a peak when a strong retriever meets a mid-capacity model, and a sharp collapse when the model is saturated. This pattern reflects the interaction between retriever recall and the model's implicit filtering capacity, rather than either factor in isolation. Mechanistically, masking implements a token-for-turn trade-off: it removes observations the model has largely stopped attending to and pages the agent rarely re-opens. The added turns help when they convert failures into successes, but they fail when masking removes evidence the model would otherwise have used. We therefore reframe context management as a regime-dependent intervention and provide a holistic perspective for analyzing context use in agentic deep search. We release our scaffold and trajectories here (https://github.com/i-DeepSearch/observation-masking) to support future research.", "github": "https://github.com/i-DeepSearch/observation-masking" }, { "date": "2026-06-02", "arxiv_id": "2605.24202", "title": "When Does Multi-Agent RL Improve LLM Workflows? Workflow, Scale, and Policy-Sharing Tradeoffs", "authors": [ "Yifan Zeng", "Yiran Wu", "Yaolun Zhang", "Wentian Zhao", "Kun Wan", "Qingyun Wu", "Huazheng Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/XHMY/marl-llm-workflows", "github_api_added_by": "user", "project_page": "", "abstract": "Multi-agent LLM workflows route inference through specialized roles to lift end-task accuracy, but jointly training those roles with reinforcement learning is unstable in ways that are poorly understood. We study when end-to-end RL training of multi-agent LLM workflows improves over their base models, comparing Shared-Policy training, where all roles update one policy, with Isolated-Policy training, where each role has its own parameters. Our experimental matrix spans Eval-Opt, Voting, and Orch-Workers workflows, math and code tasks, and three model scales (0.6B, 1.7B, 4B). We find that multi-agent RL usually improves over base models, but gains depend jointly on workflow, task, and scale, not on policy sharing alone. Isolated-Policy tends to reach higher peak accuracy yet more often falls off a terminal accuracy cliff, while Shared-Policy training does not eliminate failure; it redistributes failure into qualitatively different patterns. We then explain the strongest of these patterns through role-level gradient dynamics induced by workflow topology and policy routing: under Isolated-Policy, parallel same-role agents on shared prompts amplify per-role gradients and drive terminal degradation in Voting and Orch-Workers workflows; under Shared-Policy, asymmetric per-step gradient mass causes the shared policy to be captured by the dominant role, producing different failure signatures by task and workflow. Together, the empirical map and its underlying mechanisms show that policy sharing routes training pressure through different channels rather than offering uniform stability, making it a design choice with workflow- and task-conditional tradeoffs.", "github": "https://github.com/XHMY/marl-llm-workflows" }, { "date": "2026-06-02", "arxiv_id": "2606.01533", "title": "Multi-Agent Computer Use", "authors": [ "Jing Yu Koh", "Ruslan Salakhutdinov", "Daniel Fried" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://jykoh.com/multi-agent-computer-use/", "abstract": "Computer use agents (CUAs) today are primarily deployed as single serial agents. This setup is suboptimal for complex long-horizon tasks that benefit from task decomposition, parallel execution, and consistent re-planning based on new information. In this paper, we argue that we should instead move towards evaluating and building multi-agent computer use (MACU) systems. These systems, which emphasize planning and parallel execution, alleviate many of the shortcomings of single-agent CUAs. We propose a general multi-agent setup in which a manager model decomposes computer use tasks as a directed acyclic graph (DAG), encoding relevant dependencies and goals for subagents. At each iteration, the manager dispatches parallel CUA subagents to carry out nodes on the ready frontier of the DAG, and continuously revises the DAG (adding, canceling, or rewriting nodes) as new findings arrive from subagents. This design treats the partially observable environment of computer use as a first class challenge: information that downstream agents may not be able to re-observe are retained and passed forward through the manager and DAG structure. We demonstrate that MACU consistently improves over strong single-agent baselines by 3.4-25.5% on desktop (OSWorld) and web navigation (Online-Mind2Web, WebTailBench, Odysseys) benchmarks, exhibits more favorable test-time scaling, and solves complex long-horizon tasks where single-agent CUAs get stuck. On Odysseys, a long-horizon web navigation benchmark, MACU improves average task completion wall-clock time by {sim} 1.5 times, demonstrating its efficacy in speeding up traditionally slow CUA pipelines. Our findings highlight that multi-agent coordination is a promising axis for scaling computer use agents to work productively for longer and more effectively. We release all code and interactive visualizations at https://jykoh.com/multi-agent-computer-use.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.25381", "title": "Not only where, But when: Temporal Scheduling for RLVR", "authors": [ "Jinghao Zhang", "Ruilin Li", "Feng Zhao", "Jiaqi Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Jinghaoleven/RLVR-Schedule", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has become a core technique for post-training of Large Language Models (LLMs). While policy optimization is driven by all sampled tokens under a globally broadcast scalar reward, the heterogeneous policy behaviors exhibited along trajectories are largely overlooked without differentiation. Existing works address this by credit allocation, including token-level advantage reweighting, and selective token optimization, however, the allocation criterion are principally stagnant throughout training, limiting resilient policy evolution. In this work, we argue that when learning signals are scheduled can be as important as where they are allocated across tokens, and introduce the temporal dimension that scheduling the credit allocation criteria over the course of RLVR optimization. We find that prioritizing targeted tokens emphasized with specific policy behaviors, and gradually attenuating toward general optimization leads to more stable and efficient learning dynamics. Furthermore, we show that simple trajectory percentiles provide a natural perspective for distinguishing policy behaviors, and works effectively with temporal scheduling. Our analysis reveals that standard optimization substantially sacrifices policy entropy when simultaneously accommodating heterogeneous behaviors, whereas temporal scheduling yields healthier policy evolution dynamics. Experiments across mathematical and general reasoning benchmarks demonstrate consistent improvements, suggesting that temporal scheduling constitutes a promising optimization dimension.", "github": "https://github.com/Jinghaoleven/RLVR-Schedule" }, { "date": "2026-06-02", "arxiv_id": "2606.02580", "title": "Thinking in Blender: Staged Executable Inverse Graphics with Vision-Language Models", "authors": [ "Guangzhao He", "Rundong Luo", "Wei-Chiu Ma", "Hadar Averbuch-Elor" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Inverse graphics is a longstanding and highly underconstrained problem that seeks to reconstruct images as editable 3D scenes which can be rendered, relit, and manipulated. In this work, we investigate whether pretrained vision-language models (VLMs) can perform executable inverse graphics directly from a single image by reconstructing a scene as an editable Blender program, without relying on specialized 2D or 3D foundation models, differentiable rendering, or multi-view supervision. We introduce Staged Executable Inverse Graphics (SEIG), an agentic framework that reconstructs a 3D scene from a single image by progressively refining scene factors including geometry, materials, composition, and lighting directly in executable Blender code space. We evaluate our framework across diverse scenes using a range of reconstruction metrics spanning pixel-level, perceptual, and semantic fidelity. Our experiments show that staged reconstruction substantially improves reconstruction fidelity, highlighting the importance of task decomposition for executable inverse graphics with general-purpose VLMs. Finally, we showcase various downstream applications enabled by the reconstructed editable Blender scenes.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.01057", "title": "3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code", "authors": [ "Yipeng Gao", "Lei Shu", "Genzhi Ye", "Xi Xiong", "Ameesh Makadia", "Meiqi Guo", "Laurent Itti", "Jindong Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gaoypeng/3dcodebench", "github_api_added_by": "user", "project_page": "https://www.3dcodebench.com/", "abstract": "Procedural 3D modeling through code is emerging as a versatile paradigm, offering deterministic, engine-ready, and precisely editable assets that neural 3D generators inherently lack. Authoring such procedural content, however, demands deep expertise in 3D software APIs, parametric design, and code-level geometric reasoning. In this paper, we propose 3DCodeBench, a systematic benchmark for evaluating vision-language model (VLM) agents for procedural 3D generation in 3D modeling software. Specifically, 3DCodeBench evaluates how effectively 12 advanced VLMs can serve as procedural 3D modelers by translating text and image references into procedural code for 3D modeling software. Recognizing that automated metrics may not fully capture the perceptual quality of 3D shapes, we build 3DCodeArena, a ranking platform based on pairwise human preferences over generated 3D outputs. From extensive evaluations and results, we observe that: (1) Failures mostly arise from API mismatches, while successful renders still suffer from disconnected or floating 3D geometric components. (2) Test-time scaling, such as higher thinking budgets and multi-turn refinement, improves performance overall. Our findings highlight a critical need for high-quality procedural coding data to advance commercial VLMs. Furthermore, effective procedural 3D modeling requires a robust execution environment that provides high-fidelity feedback for iterative refinement. We release 3DCodeBench, including the curated large-scale dataset of multimodal (text/image) prompts, procedural code, 3D object triplets, evaluation protocol, and the public 3DCodeArena platform as a foundational toolkit for exploring VLM-based procedural 3D modelers.", "github": "https://github.com/gaoypeng/3dcodebench" }, { "date": "2026-06-02", "arxiv_id": "2606.01336", "title": "LongAttnComp: Cross-Family Context Compression for Long-Context Reasoning", "authors": [ "Mengmeng Ji", "Ravi Shanker Raju", "Jonathan Lingjie Li", "Chen Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "As real-world applications increasingly require processing inputs of 100k+ tokens, the gap between context length and inference efficiency has become a critical bottleneck. Context compression offers a way to reduce prefill costs while preserving task accuracy. However, existing training-free attention-based methods leave substantial gaps in demanding long-context tasks such as code reasoning. We present LongAttnComp, a long-context adaptation of AttnComp that fine-tunes a lightweight cross-attention scoring layer and introduces tokenlevel chunking, a token-budget top-p algorithm, positional reordering, and a formatagnostic query parser. We further design a two-stage fine-tuning recipe for the compressor: Stage 1 builds a general retrieval foundation from NIAH-style data, and Stage 2 extends it with multi-hop and reasoning data for broader long-context task coverage. On InfiniteBench Code-Debug, LongAttnComp matches or exceeds full-context accuracy, substantially outperforms training-free baselines, and transfers across four target models from three families. On LongBench v2, the two-stage recipe largely closes the Stage 1 gap on multi-document reasoning while preserving Code-Debug performance.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.00267", "title": "StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement", "authors": [ "Junwon Seo", "Sushant Veer", "Ran Tian", "Wenhao Ding", "Apoorva Sharma", "Karen Leung", "Edward Schmerling", "Marco Pavone", "Andrea Bajcsy" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/CMU-IntentLab/StressDream", "github_api_added_by": "user", "project_page": "https://junwon.me/StressDream/", "abstract": "Video world models (WMs) have shown promise for policy evaluation and improvement by imagining realistic future observations conditioned on ego-robot actions. While WMs can model distributions over futures, policy evaluation and improvement typically rely on nominal imaginations, which can miss high-impact outcomes of robot actions unless prohibitively many samples are drawn. To enable robust policy evaluation and improvement over WM imaginations, we propose StressDream, which steers imaginations toward high-impact yet plausible outcomes specified at inference time by optimizing the initial noise of diffusion-based WMs. However, optimizing high-dimensional noise is challenging: the optimization must reason about nuanced, scene-dependent target events in generated videos while avoiding out-of-distribution (OOD) noise that yields implausible imaginations. We address this with two complementary objectives: a semantic objective with a Vision-Language Model that provides informative gradients by reasoning about the generated video, and a plausibility objective that prevents the optimized noise from drifting OOD. With state-of-the-art video world models for autonomous driving and robotic manipulation, we show that StressDream effectively steers imaginations toward high-impact yet plausible outcomes specified by text at inference time, such as task failures, enabling robust policy evaluation and improvement by identifying actions whose plausible futures include undesirable outcomes. Video results are available at https://junwon.me/StressDream/.", "github": "https://github.com/CMU-IntentLab/StressDream" }, { "date": "2026-06-02", "arxiv_id": "2606.01528", "title": "Joint Agent Memory and Exploration Learning via Novelty Signals", "authors": [ "Shizuo Tian", "Xiaohong Weng", "Rui Kong", "Yuxuan Chen", "Guohong Liu", "Yuebing Song", "Jiacheng Liu", "Yuchen Li", "Dawei Yin", "Ting Cao", "Yunxin Liu", "Yuanchun Li" ], "github_manual": "", "github_regex": "https://github.com/MobileLLM/JAMEL", "github_paper_md": "", "github_api": "https://github.com/MobileLLM/JAMEL", "github_api_added_by": "user", "project_page": "https://github.com/MobileLLM/JAMEL", "abstract": "In open-ended environments, exploration is fundamental for autonomous agents, yet current language model agents struggle with this. Effective exploration requires memory, but retaining raw interaction histories is computationally expensive over long trajectories. While latent memory offers a solution to compress interaction histories, its training lacks reliable supervisory signals. We introduce Joint Agent Memory and Exploration Learning (JAMEL), a framework that trains agentic memory and exploration policy together through novelty-driven interaction. We observe that memory and exploration form a mutually dependent loop: sustained exploration requires memory to distinguish exhausted behaviors from unseen ones, while novelty-seeking interaction provides the supervision needed to make memory useful for future exploration. By utilizing deterministic and persistent novelty signals such as code coverage in the GUI domain, we provide natural, annotation-free supervision for the memory module. Empirical evaluations demonstrate that \\ours successfully generalizes to unseen environments. Its exploration capability outperforms open-weight baselines and rivals the exploration depth of a closed-source model while reducing token consumption. Our code and model are open-sourced at https://github.com/MobileLLM/JAMEL.", "github": "https://github.com/MobileLLM/JAMEL" }, { "date": "2026-06-02", "arxiv_id": "2606.02470", "title": "MCP-Persona: Benchmarking LLM Agents on Real-World Personal Applications via Environment Simulation", "authors": [ "Wenhao Wang", "Peizhi Niu", "Gongyi Zou", "Xiyuan Yang", "Jingxing Wang", "Haoting Shi", "Yaxin Du", "Jingyi Chai", "Xianghe Pang", "Shuo Tang", "Yanfeng Wang", "Siheng Chen" ], "github_manual": "", "github_regex": "https://github.com/wwh0411/MCP-Persona", "github_paper_md": "", "github_api": "https://github.com/wwh0411/MCP-Persona", "github_api_added_by": "user", "project_page": "", "abstract": "The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and tools, and has been rapidly adopted across personal applications and development platforms. However, existing benchmarks predominantly focus on generic information-seeking tools and fail to capture the practical challenges posed by personal social applications, where tools interact with individual accounts or local databases. To bridge this critical gap, we introduce MCP-Persona, the first benchmark specifically designed for evaluating agent performance on real-world, personalized MCP tools. MCP-Persona encompasses a diverse set of widely-used applications, ranging from social media platforms like Reddit and Xiaohongshu (Rednote) to enterprise collaboration suites such as Lark (Feishu) and Slack. Our extensive experiments on various state-of-the-art (SOTA) agents demonstrate their significant struggles with personalized tool use, thereby highlighting the benchmark's crucial role in identifying and addressing these limitations. MCP-Persona is publicly available at https://github.com/wwh0411/MCP-Persona}{https://github.com/wwh0411/MCP-Persona.", "github": "https://github.com/wwh0411/MCP-Persona" }, { "date": "2026-06-02", "arxiv_id": "2606.02031", "title": "OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents", "authors": [ "Rui Yang", "Qianhui Wu", "Yuxi Chen", "Hao Bai", "Wenlin Yao", "Hao Cheng", "Baolin Peng", "Huan Zhang", "Tong Zhang", "Jianfeng Gao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenWebRL/OpenWebRL", "github_api_added_by": "user", "project_page": "https://openwebrl.github.io", "abstract": "Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despite rapid progress, the strongest systems remain largely proprietary, while open agents still depend heavily on supervised post-training over large collections of curated web trajectories. This dependence creates a major scalability bottleneck: high-quality demonstrations are expensive to collect, and static datasets offer limited coverage of the diverse, ever-changing open web. Although online RL has shown promise for text-based agents, its potential for training visual web agents directly on live websites remains largely underexplored. In this paper, we introduce OpenWebRL, an open framework for training visual web agents with online multi-turn RL on real websites. OpenWebRL covers the full training pipeline, including scalable live-browser infrastructure, supervised initialization, multimodal context management, trajectory-level success judging, and efficient multi-turn policy optimization. Using this framework, we train OpenWebRL-4B, which establishes a new open-source state of the art on challenging live-web benchmarks. With only 0.4K initialization trajectories and 2.2K open-ended RL training tasks, OpenWebRL-4B achieves 67.0% success on Online-Mind2Web and 64.0% on DeepShop, outperforming prior open agents of similar or larger scale and remaining competitive with proprietary systems including OpenAI CUA and Gemini CUA. Beyond strong benchmark performance, we systematically study the key design choices that make online RL effective for visual web agents, and analyze how RL improves agentic reasoning. Overall, our work offers a practical path toward building more capable, reproducible, and cost-efficient open web agents. We will release our training data, models, and code to support future research.", "github": "https://github.com/OpenWebRL/OpenWebRL" }, { "date": "2026-06-02", "arxiv_id": "2606.01682", "title": "Off-the-Shelf LLMs as Process Scorers: Training-Free Alternative to PRMs for Mathematical Reasoning", "authors": [ "Atoosa Chegini", "Soheil Feizi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://arxiv.org/pdf/2606.01682", "abstract": "Selecting the best response from multiple small-model samples using a stronger scorer is a simple inference-time strategy, but fails when the small model has already committed to incorrect reasoning paths. PRM guided search avoids this by scoring candidate continuations during generation, but requires a reward model trained with step-level labels. We propose Chunk-Level Guided Generation, a training-free alternative that uses an off-the-shelf large language model as a process scorer. At each step, a small model samples k fixed-length candidate chunks, while the larger model scores the candidates using likelihoods without generating any text. The selected chunk is committed before the next step, steering generation before errors can propagate. We instantiate this framework with two selection rules: Likelihood-Guided Selection (LGS), which selects the chunk with the highest length-normalized large-model log-probability, and Contrastive-Guided Selection (CGS), which subtracts the small model's log-probability to favor chunks where the large model's preference diverges from the small model's. We show that scoring variable-length reasoning steps with large-model likelihoods is unreliable due to a systematic length bias that persists even after length normalization, and that fixed-length chunks avoid this confound. On GSM8K, MATH, Minerva Math, AMC23, and AIME24 with Qwen2.5-1.5B guided by Qwen2.5-32B and Llama-3.2-1B guided by Llama-3.1-70B, CGS outperforms majority voting by up to 28 pp and, under matched guidance budgets, matches or outperforms Qwen2.5-Math-PRM-72B guided search on most benchmarks without reward-model training. With Qwen2.5-7B guided by Qwen2.5-72B, CGS reaches 81.8% on MATH and 63.6% on Minerva Math at k=16, surpassing majority voting by 4--6 pp. Finally, Chunk-Level Guided Generation produces substantially shorter reasoning traces than PRM guided search.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.02553", "title": "LongLive-RAG: A General Retrieval-Augmented Framework for Long Video Generation", "authors": [ "Qixin Hu", "Shuai Yang", "Wei Huang", "Song Han", "Yukang Chen" ], "github_manual": "", "github_regex": "https://github.com/qixinhu11/LongLive-RAG", "github_paper_md": "", "github_api": "https://github.com/qixinhu11/LongLive-RAG", "github_api_added_by": "user", "project_page": "http://longlive-rag.github.io/", "abstract": "Autoregressive (AR) video diffusion enables variable-length synthesis, but long-horizon generation often suffers from accumulated errors and identity drift. For efficiency, existing methods commonly adopt sliding-window attention during generation. This creates an irreversible generation trajectory: once the active window accumulates appearance errors, subsequent generations can only condition on this degraded trajectory and drift further away. We address this limitation by formulating long video generation as a retrieval-augmented generation (RAG) problem. Rather than relying solely on the recent window, we treat previously generated latents as a dynamic, searchable history. We propose LongLive-RAG, a general retrieval framework for AR video generation. At each new block, LongLive-RAG uses a query embedding to retrieve relevant historical latents. This lightweight retrieval step adds only a small overhead relative to generation and lets the generator condition on non-local context instead of only the recent window. To make retrieval more discriminative, we introduce the Window Temporal Delta Loss that suppresses redundant local similarity and encourages embeddings to capture meaningful temporal changes. Together, these components help reduce error accumulation caused by sliding-window attention. Experiments across multiple AR backbones and generation lengths show improved long-video quality and the best average VBench-Long rank. To our knowledge, among open-ended AR long video generation methods, LongLive-RAG is the first to formulate self-generated latent history as content-addressable retrieval memory. Code is available at https://github.com/qixinhu11/LongLive-RAG.", "github": "https://github.com/qixinhu11/LongLive-RAG" }, { "date": "2026-06-02", "arxiv_id": "2605.28556", "title": "A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks", "authors": [ "Tomer Keren", "Nitay Calderon", "Asaf Yehudai", "Yotam Perlitz", "Michal Shmueli-Scheuer", "Roi Reichert" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tomerkeren42/TASTE-task-synthesis-from-tool-sequence-evolution", "github_api_added_by": "user", "project_page": "", "abstract": "As agent capabilities advance, existing benchmarks, such as \u03c4^2-Bench, are becoming increasingly saturated. Yet constructing new benchmark tasks remains complex, costly, and labor-intensive. Moreover, the standard approach, in which scenarios are first written in natural language and then mapped to tool sequences, captures only a narrow subset of the tool-use patterns agents exercise. In this paper, we address these problems by reversing the task construction process. We propose TASTE: Task Synthesis from Tool Sequence Evolution, an automatic method that generates challenging tasks with broader tool-use coverage. TASTE utilizes an Adaptive Contrastive n-gram model trained on LLM-judged validity signals. This enables sampling valid tool sequences that cover a vast range of tool combinations. TASTE then selects representative sequences from the pool via clustering, instantiates them into complete benchmark tasks, and refines them through iterative difficulty evolution. Using TASTE, we construct \u03c4^c-Bench, a challenging extension of the three domains of \u03c4^2-Bench. We evaluate 11 agent/user LLM pairs and find that models nearly saturating \u03c4^2-Bench suffer severe performance drops on our tasks (e.g., Gemini-3-Flash falls from 0.82!-!0.94 to 0.28!-!0.61). Beyond increasing difficulty, our generated tasks more than double the number of unique tool combinations agents must execute. Our results suggest high scores on existing benchmarks often reflect saturation rather than robust task-solving ability. By automating the generation of difficult, high-coverage benchmarks, TASTE enables continuous, scalable evaluation of future agents.", "github": "https://github.com/tomerkeren42/TASTE-task-synthesis-from-tool-sequence-evolution" }, { "date": "2026-06-02", "arxiv_id": "2606.01348", "title": "ChartArena: Benchmarking Chart Parsing across Languages, Scenarios, and Formats", "authors": [ "Shangpin Peng", "Gengluo Li", "Xingyu Wan", "Chengquan Zhang", "Hao Feng", "Binghong Wu", "Huawen Shen", "Weinong Wang", "Ziyi Cai", "Zhuotao Tian", "Han Hu", "Can Ma", "Yu Zhou" ], "github_manual": "", "github_regex": "https://github.com/pspdada/ChartArena", "github_paper_md": "", "github_api": "https://github.com/pspdada/ChartArena", "github_api_added_by": "user", "project_page": "", "abstract": "Charts are a primary medium for conveying quantitative and relational information, yet systematically evaluating chart parsing models remains difficult. Existing benchmarks focus on narrow chart types and leave diagrammatic structures such as flowcharts and mind maps largely unaddressed, while models produce outputs in incompatible formats, and datasets rarely include the printed or hand-drawn images encountered in practice. To address these issues, we introduce ChartArena, a comprehensive bilingual benchmark covering eight chart families spanning both numeric charts and diagrammatic structures, each evaluated across three visual scenarios: digital renderings, printed photos, and hand-drawn photos. The dataset is built via a human-agent collaborative annotation pipeline with multi-stage human verification to ensure annotation reliability. To enable fair cross-model comparison, we further design a format-agnostic evaluation protocol that maps heterogeneous outputs into two canonical semantic spaces, a normalized triple view and a directed graph view, and scores them with structure-aware metrics. Through extensive evaluation of 26 leading MLLMs, we observe three consistent findings: (i) frontier proprietary models such as Gemini 3.1 Pro lead overall, yet the strongest open-source systems are rapidly closing the gap; (ii) document parsing models handle numeric charts reasonably but fall sharply behind on diagrammatic structures; and (iii) expert chart parsers remain limited to narrow chart families. Across all models, radar charts and hand-drawn scenarios stay especially challenging. These findings show that ChartArena exposes clear capability gaps and provides a unified foundation for future progress. ChartArena is publicly available at https://github.com/pspdada/ChartArena.", "github": "https://github.com/pspdada/ChartArena" }, { "date": "2026-06-02", "arxiv_id": "2605.29992", "title": "Adapting Multilingual Embedding Models to Turkish via Cross-Lingual Tokenizer Surgery and Offline Distillation", "authors": [ "M. Ali Bayram", "Banu Diri", "Sava\u015f Y\u0131ld\u0131r\u0131m" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/malibayram/embedding-trainer", "github_api_added_by": "user", "project_page": "https://huggingface.co/spaces/magibu/embeddingmagibu-200m", "abstract": "Sentence embeddings are a foundational component for semantic search, clustering, classification, and retrieval-augmented generation. This paper presents embeddingmagibu-200m, a Turkish-focused sentence embedding model that produces 768-dimensional L2-normalized vectors and supports an 8,192-token context window, far exceeding the 512-token limit of earlier BERT-based Turkish encoders. Instead of full pretraining, an efficient three-stage adaptation pipeline is introduced: (1) construct a Turkish-optimized multilingual tokenizer with a 131,072 vocabulary by pruning redundant tokens from the teacher's vocabulary and incorporating multilingual tokens via frequency analysis on a 40-language corpus, (2) clone a teacher embedding model while preserving transformer backbone weights and initializing a compatible embedding table for the new vocabulary via mean-composition token mapping, and (3) perform offline embedding distillation from precomputed teacher vectors using a cosine similarity objective over a balanced 40-language Wikipedia corpus. The resulting student model contains approximately 200M parameters and trains in roughly four hours on a single GPU by avoiding online teacher inference during training, at a total cost of 5-20. Empirically, Pearson/Spearman correlations of 77.55%/77.45% are obtained on STSbTR, surpassing the 300M-parameter teacher model (73.84%/72.92%). On TR-MTEB (26 tasks), a mean score of 63.9% is achieved (7th out of 26 models), providing a competitive cost-quality trade-off with 33% fewer parameters than the teacher. To facilitate reproducibility and downstream use, all artifacts are released including model weights, tokenizer files, precomputed embedding datasets, and open-source cloning and distillation tooling.", "github": "https://github.com/malibayram/embedding-trainer" }, { "date": "2026-06-02", "arxiv_id": "2606.00089", "title": "Can Predicted Dynamics Exist in the Physical World?", "authors": [ "Barak Or" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "http://www.state16.ai", "abstract": "Predictive Physical AI systems output state rollouts, action chunks, and latent plans, yet a low root-mean-square error (RMSE) does not imply that a particular proposal is physically executable. We formulate physical admissibility as a prediction-control interface: before execution, a decoded proposal is treated as candidate dynamics and evaluated using kinematic, dynamic, and direct-to-composed horizon conditions. Passing is not a certificate of task success; rejection identifies violation of the specified physical envelope and gives a component-level reason. On Hugging Face LeRobot PushT, controlled falsification shows that one-step prediction-RMSE and standardized dynamics residuals reach area under the receiver operating characteristic curve (AUC) 0.982 and 0.972, kinematic-only conditions reach AUC 0.592, and the full gate reaches AUC 0.957 with condition-level attribution. In replay-based intervention experiments, residual-based filters and the full physical-admissibility gate prevent 87-$89% of invalid proposals while preserving mean progress near 0.998.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.16745", "title": "EVA01: Unified Native 3D Understanding and Generation via Mixture-of-Transformers", "authors": [ "Zongyuan Yang", "Mingjing Yi", "Wanli Ma", "Chenzhuo Fan", "Bocheng Li", "Baolin Liu", "Yuke Lou", "Yingde Song", "Yongping Xiong", "Zhengdong Guo", "Shimu Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://www.seeles.ai/research/pages/EVA01", "abstract": "This paper addresses the challenge of integrating 3D meshes as a native modality within Multimodal Large Language Models (MLLMs). Diffusion-based large reconstruction models decouple semantic understanding from geometric reasoning, operating as stateless reconstructors conditioned on dense 2D pixel priors. Recent MLLM-based methods treat the 3D modality as an external output rather than a native component of the multimodal sequence, making incremental adaptations without a systematic analysis of how geometric manifolds align with MLLM feature spaces. We introduce EVA01, a unified framework that extends the modality boundary of MLLMs to natively incorporate 3D mesh understanding, generation, and context-aware editing. Built upon a Mixture-of-Transformers (MoT) architecture, EVA01 decouples the model into a pre-trained Understanding Expert (E_{und}) and a structurally mirrored Generation Expert (E_{gen}), coupled through shared global self-attention with hard modality routing. This design aligns the semantic latent space of the MLLM backbone with the geometric manifold, enabling direct transfer of multimodal priors without intermediate 2D representations. Results show that EVA01 achieves state-of-the-art native text-to-3D generation fidelity and unlocks robust long-context multi-turn geometric editing with identity preservation, a capability fundamentally inaccessible to stateless reconstruction pipelines. Our findings further offer architectural insights for integrating 2D foundation models with 3D tasks, informing the design of 3D-native multimodal systems. Project Page: https://www.seeles.ai/research/pages/EVA01", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.31057", "title": "LVSA: Training-Free Sparse Attention for Long Video Diffusion", "authors": [ "Gael Glorian", "Ioannis Lamprou", "Zhen Zhang", "Yujie Yuan", "Hongsheng Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JiusiServe/LongVideoSparseAttention", "github_api_added_by": "user", "project_page": "", "abstract": "Dense self-attention is the compute and quality bottleneck of long-video diffusion inference: cost grows quadratically with the sequence length, and beyond the training horizon the model converges to near-static output, that is, \"frozen\" repetitive video. State of the art approaches are either too costly, e.g., they require retraining, or fail to satisfy both performance and quality objectives in a scalable manner. To this end, we introduce Long Video Sparse Attention (LVSA), a training-free model-agnostic block-sparse attention for video diffusion transformers that combines a structured window pattern with rotating global anchors, thus removing the fixed-grid bias which causes long-range temporal artifacts. LVSA, combined with a FlashInfer kernel, reduces compute up to 3.17x on Wan 2.1 1.3B at a 6x horizon, 2.98x on Wan 2.1 14B at a 6x horizon, and 3.33x on HunyuanVideo 1.5 at a 1.5x horizon, compared to dense attention. Beyond reducing compute, LVSA enables HunyuanVideo 1.5 generation at a 2x horizon, which is otherwise out-of-memory on a single GPU. Moreover, LVSA provides speedups up to 2.41x compared to RIFLEx and 3.27x compared to UltraViCo on Wan 2.1 1.3B. To demonstrate applicability across diverse platforms, we apply LVSA on NPUs and achieve speedups up to 2.71x on Wan 2.2 A14B and 3.24x on Wan 2.1 1.3B compared to dense attention. To evaluate quality in a fair way, we introduce VQeval, a tool properly scoring loopy video failures, which instead are rewarded in state of the art evaluators like VBench-Long. LVSA is quality-neutral for generation at training horizon length and quality-positive at extended lengths.", "github": "https://github.com/JiusiServe/LongVideoSparseAttention" }, { "date": "2026-06-02", "arxiv_id": "2606.00660", "title": "FineVerify: Scaling Test-Time Compute with Fine-Grained Self-Verification for Agentic Search", "authors": [ "James Xu Zhao", "Hui Chen", "Bryan Hooi", "See-Kiong Ng" ], "github_manual": "", "github_regex": "https://github.com/XuZhao0/fineverify", "github_paper_md": "", "github_api": "https://github.com/XuZhao0/fineverify", "github_api_added_by": "user", "project_page": "", "abstract": "Agentic search requires language model agents to explore many sources and answer complex information-seeking questions. Scaling test-time compute is a promising way to improve these agents, but current approaches can fail, because correct answers are often sparse and score-based selection depends on model calibration. We propose FineVerify, a fine-grained self-verification framework that decomposes each question into checkable sub-questions, verifies sampled candidates against each sub-question, and selects the candidate with the highest aggregated score. This per-check structure turns selection into simpler local judgments and produces scores under the same explicit criteria. Across four agentic search benchmarks and two models, FineVerify consistently outperforms standard scaling baselines. With only four sampled trajectories, it improves GPT-5-mini by 8.2 accuracy points and Gemini-3-flash by 5.6% on average. With 12 samples, FineVerify enables GPT-5-mini to surpass frontier GPT-5 on BrowseComp-Plus. Beyond accuracy, FineVerify produces interpretable verification traces that help audit benchmark errors, suggesting broader applications for inspecting agentic search systems. Code and data are available at https://github.com/XuZhao0/fineverify", "github": "https://github.com/XuZhao0/fineverify" }, { "date": "2026-06-02", "arxiv_id": "2606.00090", "title": "Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems", "authors": [ "Barak Or" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Physical AI systems increasingly map multimodal observations, language instructions, and learned world representations into physically consequential actions. Robotics foundation models, vision-language-action models, and world-model-based autonomous systems can condition decisions that move vehicles, robots, drones, and industrial machines. This transition exposes a safety problem that is not fully captured by conventional AI content moderation or by classical robot safety alone: a black-box model may issue a physically consequential action while appearing confident, plausible, and semantically aligned. The resulting failure can be silent, arising from sensor drift, occlusion, state-estimation error, distribution shift, hallucinated affordances, or invalid physical assumptions before downstream hardware controllers detect a violation. Across embodied foundation models, world models, robotics simulation, embodied safety benchmarks, safe control, runtime assurance, uncertainty estimation, verification, and guardrail evaluation, model capability and safety mechanisms have advanced along largely separate technical tracks. A recurring gap synthesized here is that no single stream surveyed in this review supplies a complete runtime authorization boundary between black-box Physical AI models and physical execution. The resulting analysis develops a bounded problem formulation, a definition of silent physical-action failure, a taxonomy of runtime guardrail functions, and evaluation requirements for comparing guardrails as Physical AI assurance mechanisms.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.00761", "title": "Confidence-Adaptive SwiGLU for Mixture-of-Experts", "authors": [ "Shaohua Li", "Xiuchao Sui", "Xiaobing Sun", "Yuhang Wu", "Liangli Zhen", "Yong Liu", "Rick Siow Mong Goh" ], "github_manual": "", "github_regex": "https://github.com/askerlee/kappa-swiglu", "github_paper_md": "", "github_api": "https://github.com/askerlee/kappa-swiglu", "github_api_added_by": "user", "project_page": "", "abstract": "SwiGLU has become a standard gated activation in modern Transformer MLPs, yet its gate sharpness -- the smoothness and selectivity of the gating function -- is typically fixed throughout training. In this work, we propose Confidence-Aware SwiGLU (\u03ba-SwiGLU), a variant of SwiGLU for Mixture-of-Experts (MoE) models that adjusts expert gate sharpness according to token-level routing confidence. Specifically, \u03ba-SwiGLU parameterizes the SiLU gate sharpness coefficient as a learnable function of the router logit, enabling each expert gate unit to interpolate between smooth, broadly active gating and sharp, selective gating. We evaluate \u03ba-SwiGLU on the FineWeb-Edu dataset across MoE Transformer models ranging from 8 to 28 layers. Across these settings, \u03ba-SwiGLU improves mean CORE performance while adding negligible parameters and incurring only a small computational overhead, demonstrating that confidence-aware gate sharpness is a promising mechanism for improving MoE MLPs. The code is available at https://github.com/askerlee/kappa-swiglu.", "github": "https://github.com/askerlee/kappa-swiglu" }, { "date": "2026-06-02", "arxiv_id": "2606.02248", "title": "Geometric Latent Reasoning Induces Shorter Generations in LLMs", "authors": [ "Shashi Kumar", "Yacouba Kaloga", "Petr Motlicek", "Ina Kodrasi", "Andrea Cavallaro" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language models solve complex problems by generating lengthy chains of explicit reasoning tokens. While effective, this makes reasoning expensive, length-sensitive, and constrained to (discrete) natural language. While latent reasoning offers a continuous alternative, determining useful structures for intermediate latent states is an open challenge. In this paper, we formulate latent reasoning as a geometric path-approximation problem within the model's pretrained token-embedding space. We introduce Geometric Latent Reasoning (GLR), which uses a lightweight transition head to predict iterative direction updates in embedding space. Using textual chain-of-thought traces as anchors, GLR learns to approximate discrete reasoning trajectories while permitting continuous deviations from exact token embeddings. Evaluations on mathematical reasoning benchmarks using Qwen3 models reveal an emergent phenomenon: geometric latent reasoning induces substantially shorter generations without an explicit length objective. By replacing early explicit reasoning with continuous latent steps, models often reach correct answers using substantially fewer total generation steps. These findings suggest that continuous trajectories act as compact intermediate reasoning states, exposing a new tradeoff between latent computation budget, output length, and accuracy.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.28983", "title": "The Hamilton-Jacobi Theory of Deep Learning", "authors": [ "Jose Marie Antonio Mi\u00f1oza", "Erika Fille T. Legara", "Christopher P. Monterola" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "In this paper, training a neural network is identified, exactly, as a search through Hamilton--Jacobi initial-value problems: each gradient step selects the initial data of a viscous Hamilton--Jacobi equation whose Hopf--Cole propagator best fits the observations; at inference, the input is the spatial point at which that solution is evaluated and the initial condition is already encoded in the weights. The correspondence is exact for log-sum-exp layers and structural for broader architectures: residual networks, transformers, and recurrent architectures (RNNs, LSTMs, SSMs) each discretize the same class of Hamilton--Jacobi equations, with architecture-dependent Hamiltonian and viscosity. A single deformation parameter varepsilon unifies all four perspectives (network, tropical algebra, viscous PDE, convex optimization) in a commutative diagram closed under Lipschitz conditions. Quantitative consequences include: the minimax optimal generalization rate O(n^{-1/(d+2)}) for fixed t; adversarial robustness controlled by varepsilon; backpropagation as the co-state equation of the Hamiltonian system for residual networks (Pontryagin Maximum Principle); scaling exponents consistent with data intrinsic dimension via PDE quadrature; and a closed-form O(N) influence function (softmax attribution weights \u03c0_j) whose entropy landscape undergoes fold bifurcations as varepsilon increases, each merging attribution basins.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.24614", "title": "Measuring the Depth of LLM Unlearning via Activation Patching", "authors": [ "Jaeung Lee", "Dohyun Kim", "Jaemin Jo" ], "github_manual": "", "github_regex": "https://github.com/gnueaj/unlearning-depth-score", "github_paper_md": "", "github_api": "https://github.com/gnueaj/unlearning-depth-score", "github_api_added_by": "user", "project_page": "https://gnueaj.github.io/unlearning-depth-score/", "abstract": "Large language model (LLM) unlearning has emerged as a crucial post-hoc mechanism for privacy protection and AI safety, yet auditing whether target knowledge is truly erased remains challenging. Existing output-level metrics fail to detect when this knowledge remains recoverable from internal representations. Recent white-box studies reveal such residual knowledge but often rely on auxiliary training or dataset-specific adaptations, leaving no generalizable metric. To address these limitations, we propose the Unlearning Depth Score (UDS), a metric that quantifies the mechanistic depth of unlearning via activation patching. UDS first identifies layers that encode the target knowledge using a retain model baseline, then measures how much of it is erased in the unlearned model on a 0-1 scale. In a meta-evaluation across 20 metrics on 150 unlearned models spanning 8 methods, UDS achieves the highest faithfulness and robustness, confirming our causal approach as the most reliable for unlearning evaluation. Case studies further reveal that white-box metrics can disagree at the layer level and that erasure depth varies across examples. We provide guidelines for integrating UDS into existing benchmarking frameworks and streamlining the evaluation pipeline. Code and data are available at https://github.com/gnueaj/unlearning-depth-score", "github": "https://github.com/gnueaj/unlearning-depth-score" }, { "date": "2026-06-02", "arxiv_id": "2606.01356", "title": "A Formally Verified Library of Mathematical Finance in Lean 4", "authors": [ "Raphael Coelho" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/raphaelrrcoelho/formal-mathfin", "github_api_added_by": "user", "project_page": "", "abstract": "We describe a library of mathematical finance built in the Lean 4 proof assistant, on top of Mathlib and the BrownianMotion package. It is broad: more than two hundred sorry-free theorems across eleven areas, from the measure-theoretic foundations of continuous-time stochastic calculus through derivative pricing to applied risk, portfolio, and fixed-income theory, and, to our knowledge, the most comprehensive machine-checked development of mathematical finance to date. Breadth is the setting, not the point. Two things make it more than a catalogue. It reaches into the continuous theory far enough to construct the L2 It\u00f4 integral as a bounded linear isometry and to derive, rather than assume, the risk-neutral pricing measure. And it audits its own faithfulness: every result is classified by how its Lean statement relates to the mathematics it claims, and a build-enforced gate pins the axioms each proof actually uses, so a reader can see precisely what has been proved and what has only been proved under added hypotheses. We close with a candid finding: a formal base over classical financial mathematics yields certified unification of known results rather than new financial theory. The contribution is therefore methodological and infrastructural, reusable verified foundations for mathematical finance, together with the faithfulness audit.", "github": "https://github.com/raphaelrrcoelho/formal-mathfin" }, { "date": "2026-06-02", "arxiv_id": "2605.30126", "title": "PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding", "authors": [ "Selim Kuzucu", "Alessio Tonioni", "Vasile Lup", "Bernt Schiele", "Federico Tombari", "Muhammad Ferjad Naeem" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://parcel-elastic-inference.github.io/", "abstract": "Large Vision-Language Models (LVLMs) map visual inputs into dense token sequences, imposing a quadratic computational bottleneck for inference. Elastic visual-token compression addresses this by training a single model that can run at multiple visual-token budgets. However, existing approaches struggle under aggressive compression. Spatial-only compression, as in nested pooling, behaves as an imperfect low-pass filter and induces spectral aliasing that obscures fine-grained detail. Query-only compression, as in nested query resampling, replaces explicit grid-aligned tokens with non-local summaries and substantially degrades spatial grounding. To resolve this representational conflict, we introduce PARCEL (Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding), a visual tokenization architecture that dynamically partitions the labor of feature extraction. PARCEL establishes spatial pool tokens as low-frequency layout anchors and conditions elastic query tokens on these anchors through Pool-Conditioned Query Resampling. This encourages query tokens to focus on complementary visual features rather than redundant spatial mapping. Extensive evaluations across 27 benchmarks show that PARCEL improves the performance-efficiency Pareto frontier, consistently outperforming existing matryoshka baselines across visual-token budgets while preserving the \"train once, deploy anywhere\" paradigm.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.30931", "title": "MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft", "authors": [ "Tianjie Ju", "Yueqing Sun", "Zheng Wu", "Wei Zhang", "Yaqi Huo", "Xi Su", "Qi Gu", "Xunliang Cai", "Gongshen Liu", "Zhuosheng Zhang" ], "github_manual": "", "github_regex": "https://github.com/Jometeorie/MineExplorer", "github_paper_md": "", "github_api": "https://github.com/Jometeorie/MineExplorer", "github_api_added_by": "user", "project_page": "", "abstract": "Multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and action generation. However, their ability to sustain exploration in dynamic open worlds remains unclear. Existing embodied and game-based benchmarks often compress interaction into short-horizon tasks or entangle success with domain-specific game mechanics. In this paper, we introduce MineExplorer benchmark for evaluating open-world exploration capabilities of MLLM agents in Minecraft. We first filter atomic tasks whose solutions rely heavily on Minecraft-specific knowledge to better reflect general open-world reasoning. Then we organize the benchmark around a ReAct-style capability formulation and compose atomic tasks into implicit multi-hop tasks. To further construct reliable instances, MineExplorer uses a multi-agent synthesis workflow that jointly designs task graphs, sandbox scenes, and rule-based milestone evaluators. Human evaluation shows that the multi-agent synthesis workflow produces significantly more reliable instances than a single-agent baseline. Experiments with advanced MLLM agents show that open-world exploration remains challenging, as strong models can handle many single-hop tasks but degrade sharply when hidden prerequisites must be coordinated over longer trajectories. Further analysis finds that task difficulty tracks agent completion, and larger models or thinking modes do not consistently translate into better performance. Code and dataset are available at https://github.com/Jometeorie/MineExplorer.", "github": "https://github.com/Jometeorie/MineExplorer" }, { "date": "2026-06-02", "arxiv_id": "2605.31597", "title": "SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models", "authors": [ "Olaf D\u00fcnkel", "Basavaraj Sunagad", "Haoran Wang", "David T. Hoffmann", "Christian Theobalt", "Adam Kortylewski" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GenIntel/OmniProbe", "github_api_added_by": "user", "project_page": "https://genintel.github.io/SOCO/", "abstract": "Measuring structured object understanding in vision foundation models remains challenging due to inconsistent evaluation protocols and limited part-level supervision. Semantic correspondence (SC) evaluates this capability by testing whether object parts can be matched across instances and categories under large variations in appearance, viewpoint, and geometry. To enable a systematic SC evaluation, we introduce SOCO, a new benchmark for Semantic Object Correspondence that introduces a taxonomy of correspondence types and provides consistent, functionally meaningful keypoint annotations across 100 categories and over 1M correspondence pairs. In addition, SOCO includes keypoint language descriptions, enabling the evaluation of large vision-language models (LVLMs) and their fine-grained part-level understanding. Comprehensive experiments reveal that (i) vision foundation backbones encode strong semantic structure but transfer correspondences poorly across related categories and only partially capture object-part position, (ii) LVLMs are stronger at text-prompted part localization than at visual-reference cross-image matching, exposing a gap between language-grounded localization and fine-grained visual correspondence, and (iii) correspondence performance predicts performance on dense downstream tasks, including segmentation, tracking, 3D pose estimation, and 3D detection, more strongly than ImageNet classification. Together, these findings position SOCO as a benchmark for structured, part-level representation quality in vision and multimodal foundation models.", "github": "https://github.com/GenIntel/OmniProbe" }, { "date": "2026-06-02", "arxiv_id": "2606.02255", "title": "Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025", "authors": [ "Maria Kunilovskaya", "Gagan Bhatia", "Lisa Sophie Albertelli", "Yanran Chen", "Christian Greisinger", "Lotta Kiefer", "Christoph Leiter", "Subhadeep Roy", "Tewodros Achamaleh", "Muhammad Arslan Manzoor", "Sebastian Pohl", "Yufang Hou", "Steffen Eger" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Human annotation is the empirical foundation of much NLP research, from dataset construction to model evaluation, but papers often leave unclear who produced the annotations and how the annotation process was controlled. We provide the first large-scale, task-level audit of human annotation reporting across major NLP venues, asking which annotation details are documented, which are missing, and how reporting varies across time, topic, venue, and intended use of human judgment. We introduce a unified taxonomy of annotation-reporting practices and validate an LLM-assisted extraction pipeline against Annotated-gold, a human-adjudicated gold standard of 41 papers and 72 annotation tasks, where the best model reaches human-comparable agreement with adjudicated labels, with Krippendorff's alpha of 0.606 versus 0.585 for human-human agreement. Using this pipeline, we construct Annotated-llm, a dataset covering ACL-venue papers from 2018-2025, with 2,667 extracted annotation tasks from 1,603 papers, and find that papers frequently report operational details such as recruitment strategies, annotator expertise, and annotation volume, but often omit details needed to assess annotation validity, including training, language proficiency, compensation, socio-demographics, adjudication, and agreement values, especially in model-evaluation studies. Our results show that annotation reporting in NLP has improved over time but remains uneven, and they establish a scalable framework and bare-minimum reporting recommendations for making human annotation more reliable, reproducible, and interpretable.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.28255", "title": "AI, Take the Wheel: What Drives Delegation and Trust in Human-Computer Cooperative Question Answering?", "authors": [ "Maharshi Gor", "Yoo Yeon Sung", "Yu Hou", "Eve Fleisig", "Irene Ying", "Tianyi Zhou", "Jordan Boyd-Graber" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "AI systems are fallible, and humans can make mistakes in deciding whether to trust AI over their own judgment. Thus, improving human-AI collaboration requires understanding when, why, and how humans decide to rely on AI. We study two distinct reliance decisions: the delegation choice -- deciding when to let AI act autonomously without knowing its output, and the adoption choice -- evaluating AI suggestions and deciding how to use them. Both of these decoupled reliance patterns shape collaboration, but prior work rarely studies them together in realistic settings with the same users. We address this gap by studying collaborative human--AI teams competing in a question-answering game in which humans can choose when and how to work with AI agents to win. Our 24 matches pair 23 expert humans with 16 AI agents, capturing 387 delegation and 1440 adoption decisions. While human--AI collaboration performs better than either AI or humans alone, humans make suboptimal collaboration decisions, both under-relying on correct AI suggestions (3.9% of opportunities missed) and over-relying when AI misleads them (1.7%). Both parties contribute wrong answers: reported model confidence is near chance when humans and AI disagree, while confirmation bias drives higher under-reliance (64.5%) when an AI suggestion agrees with humans' initial incorrect answer. To close this gap, we recommend calibrated confidence, evidence-grounded explanations, and mechanisms that help users refine trust.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.01414", "title": "Agent Skills Should Go Beyond Text: The Case for Visual Skills", "authors": [ "Binxiao Xu", "Ruichuan An", "Bocheng Zou", "Hang Hua" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Little-Fridge/AutoVisualSkill", "github_api_added_by": "user", "project_page": "", "abstract": "Reusable skills are a key mechanism for extending agent capabilities, allowing agents to accumulate experience and solve increasingly complex tasks. Yet most existing skill-learning methods store reusable experience as text-only assets, such as instructions, reasoning traces, or summarized trajectories. We argue that this text-only paradigm creates a fundamental bottleneck for visual-centric tasks, where reusable knowledge often depends on spatial layout, visual grounding, fine-grained appearance, and localized state changes. To address this limitation, we propose \\NAME, a multimodal skill paradigm that combines declarative textual logic with explicit visual support. We distinguish three reusable forms: static priors for stable spatial conventions, dynamic priors for in-situ visual working memory, and interleaved visual skills that bind ordered text steps to the source frames, screenshots, or page regions that justify them. Rather than only describing what to do, visual skills also encode where to look, how to inspect, and how to verify visual outcomes. To scale visual-skill construction, we introduce \\SYSTEM, an automatic system that converts agent experience into reusable multimodal skills by preserving textual reasoning, spatial references, visual boundaries, and interaction patterns from task trajectories. Experiments on GUI and other visual-centric tasks show that visual skills consistently outperform text-only skills, particularly when success requires spatial correspondence, visual evidence, and state-aware interaction. These results support our central position: reusable agent skills should go beyond text and become multimodal assets for future multimodal agents.", "github": "https://github.com/Little-Fridge/AutoVisualSkill" }, { "date": "2026-06-02", "arxiv_id": "2605.29707", "title": "Domino: Decoupling Causal Modeling from Autoregressive Drafting in Speculative Decoding", "authors": [ "Jianuo Huang", "Yaojie Zhang", "Qituan Zhang", "Hao Lin", "Hanlin Xu", "Linfeng Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jianuo-huang/Domino", "github_api_added_by": "user", "project_page": "", "abstract": "Speculative decoding accelerates LLM inference by drafting multiple tokens and verifying them in parallel with the target model. However, its practical speedup is constrained by the trade-off between draft quality and drafting cost: autoregressive drafters model causal dependencies among draft tokens but incur sequential overhead, while parallel drafters reduce drafting cost but weaken intra-block dependency modeling. In this paper, we propose Domino, a speculative decoding framework that decouples causal dependency modeling from expensive autoregressive draft execution. Domino first uses a parallel draft backbone to produce preliminary draft distributions for the entire block, and then applies a lightweight Domino head to refine them with prefix-dependent causal information. To stabilize teacher-forced causal encoding, we further introduce a base-anchored training curriculum that first strengthens the parallel backbone and then gradually shifts optimization toward the causally corrected final distribution. Experiments on Qwen3 models show that Domino achieves up to \\(5.49\\times\\) end-to-end speedup under the Transformers backend and up to \\(5.8\\times\\) throughput speedup under SGLang serving.", "github": "https://github.com/jianuo-huang/Domino" }, { "date": "2026-06-02", "arxiv_id": "2605.28897", "title": "Review Arcade: On the Human Alignment and Gameability of LLM Reviews", "authors": [ "Hans Ole Hatzel", "Sebastian Steindl", "Jan Strich" ], "github_manual": "", "github_regex": "https://github.com/uhh-hcds/reviewarcade", "github_paper_md": "", "github_api": "https://github.com/uhh-hcds/reviewarcade", "github_api_added_by": "user", "project_page": "", "abstract": "LLM-generated reviews for scientific papers are gaining considerable traction and are even being officially piloted by major conferences. We have to assume that not only reviewers are using LLM-assistance, but also that authors use LLMs to revise their papers before submitting. In this work, we perform empirical experiments on papers from the 2025 ACL Rolling Review (ARR) to evaluate LLM reviews from both the author and the reviewer perspective. First, we identify a limited alignment of LLM reviews with human ones. In the best-case scenario, the alignment is reasonable. However, we also find that LLM-human alignment varies substantially across prompts and models. Finally, we investigate the scenario in which the author uses an iterative draft-revise workflow to improve the submission according to the LLM review. We find that this \"gaming\" of LLM reviews can be effective in specific scenarios, leading to a statistically significant increase of overall scores for up to 35\\% of papers. We publish our code: https://github.com/uhh-hcds/reviewarcade.", "github": "https://github.com/uhh-hcds/reviewarcade" }, { "date": "2026-06-02", "arxiv_id": "2605.21102", "title": "ACL-Verbatim: hallucination-free question answering for research", "authors": [ "G\u00e1bor Recski", "Szilveszter T\u00f3th", "Nadia Verdha", "Istv\u00e1n Boros", "\u00c1d\u00e1m Kov\u00e1cs" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KRLabsOrg/acl-verbatim", "github_api_added_by": "user", "project_page": "https://verbatim.krlabs.eu", "abstract": "Academic researchers need efficient and reliable methods for collecting high-quality information from trusted sources, but modern tools for AI-assisted research still suffer from the tendency of Large Language Models (LLMs) to produce factually inaccurate or nonsensical output, commonly referred to as hallucinations. We apply the extractive question answering system VerbatimRAG to research papers in the ACL Anthology, directly mapping user queries to verbatim text spans in retrieved documents. We contribute a novel ground truth dataset for the task of mapping user queries to relevant text spans in research papers, and use it to train and evaluate a variety of extractive models. Human annotation is performed by NLP researchers and is based on synthetic user queries generated using a custom pipeline based on the ScIRGen methodology, paired with chunks of research papers retrieved by VerbatimRAG. On this benchmark, a 150M-parameter ModernBERT token classifier trained on silver supervision from our pipeline achieves the best word-level F1 (53.6), ahead of the strongest evaluated LLM extractor (48.7).", "github": "https://github.com/KRLabsOrg/acl-verbatim" }, { "date": "2026-06-02", "arxiv_id": "2605.09156", "title": "Lost in Translation? Exploring the Shift in Grammatical Gender from Latin to Occitan", "authors": [ "Ahan Chatterjee", "Matthias Sch\u00f6ffel", "Matthias A\u00dfenmacher", "Marinus Wiedner", "Esteban Garces Arias" ], "github_manual": "", "github_regex": "https://github.com/ahan-2000/Lost-in-Translation-", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The diachronic evolution from Latin to the Romance languages involved a restructuring of the grammatical gender system from a tripartite configuration (masculine, feminine, neuter) to a bipartite one (masculine, feminine) in most Romance languages. In this work, we introduce an interpretable deep learning framework to investigate this phenomenon at both lexical and contextual levels. First, we show that conventional tokenization strategies are insufficiently robust for this low-resource historical setting, and that our proposed tokenizer improves performance over these baselines. At the lexical level, we evaluate the contribution of morphological features to gender prediction. At the contextual level, we quantify the contributions of different part-of-speech categories to grammatical gender prediction. Together, these analyses characterize the distribution of gender information between the lemma and its sentential context. We make our codebase, datasets, and results publicly available at https://github.com/ahan-2000/Lost-in-Translation-{https://github.com/ahan-2000/Lost-in-Translation-}.", "github": "https://github.com/ahan-2000/Lost-in-Translation-" }, { "date": "2026-06-02", "arxiv_id": "2605.30501", "title": "Linear Ensembles Wash Away Watermarks: On the Fragility of Distributional Perturbations in LLMs", "authors": [ "Zhihao Wu", "Gracia Gong", "Qinglin Zhu", "Yudong Chen", "Runcong Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Kunhao18/WASH", "github_api_added_by": "user", "project_page": "https://kunhao.site/WASH", "abstract": "Watermarking embeds statistical signatures in AI-generated text for detection and attribution. We reveal a fundamental vulnerability: when users access multiple models (today's reality), watermarks trivially fail. Watermarks perturb output distributions away from the original, and in competitive markets, these perturbations are typically independent across providers. We theoretically prove that averaging output probability distributions recovers the unwatermarked distribution with up to a second-order error term. Empirically, simply averaging 3-5 models cancels out these perturbations. We introduce WASH (Watermark Attenuation via Statistical Hybridisation), which solves practical challenges in ensemble generation: vocabulary misalignment and tokenisation differences across heterogeneous models. Experiments across six watermarking schemes and three LLMs show that averaging across 3 models suppresses detection z-scores from 5-300 to below 2 (below the detection threshold of 4) and reduces TPR at 5% FPR to below 50%, while improving quality by 27.5% and running 6 times faster than the best baseline on the long sequence generation. Our results suggest that robust AI-text detection via watermarking requires either accepting this fundamental vulnerability or unprecedented coordination among model providers.", "github": "https://github.com/Kunhao18/WASH" }, { "date": "2026-06-02", "arxiv_id": "2605.26248", "title": "Unified Neural Scaling Laws", "authors": [ "Ethan Caballero", "Priyank Jaini", "David Krueger", "Irina Rish" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We present a functional form (that we refer to as a Unified Neural Scaling Law (UNSL)) that accurately models and extrapolates the scaling behaviors of deep neural networks as multiple dimensions all vary simultaneously (i.e. how the evaluation metric of interest varies as one simultaneously varies the number of model parameters, training dataset size, number of training steps, number of inference steps, amount of compute, and various hyperparameters) for various architectures and for each of various tasks within a varied set of upstream and downstream tasks. This set includes large-scale vision, language, math, and reinforcement learning. When compared to other functional forms for neural scaling, this functional form yields extrapolations of scaling behavior that are considerably more accurate on this set.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.01027", "title": "\u03c4_0-WM: A Unified Video-Action World Model for Robotic Manipulation", "authors": [ "Pengfei Zhou", "Shengcong Chen", "Di Chen", "Jiaxu Wang", "Rongjun Jin", "Bingwen Zhu", "Yike Pan", "Songen Gu", "Kuanning Wang", "Shufeng Nan", "Xingyu Qiu", "Chenhao Qiu", "Pu Yang", "Yunuo Cai", "Jianxiong Gao", "Yifan Li", "Yanwei Fu", "Xiangyu Yue", "Zhi Chen", "Jianlan Luo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Robotic manipulation requires models that generate executable actions while anticipating and evaluating their future consequences before physical execution. We present \u03c4_0-World Model (\u03c4_0-WM), a unified video-action world model that integrates policy learning, video prediction, and action evaluation within a single future-predictive framework. Built on a shared video diffusion backbone, \u03c4_0-WM provides two complementary interfaces. First, a video action model jointly predicts future visual latents and continuous action chunks from multi-view observations, language instructions, and robot state. Second, an action-conditioned video simulator rolls out candidate action chunks into multi-view futures and predicts dense task-progress scores. The model is trained on approximately 27{,}300 hours of real-robot teleoperation, UMI-style interaction, egocentric human videos, and rollout or failure trajectories using modality-specific supervision masks. At inference time, \u03c4_0-WM uses test-time computation to sample action candidates, rank them with re-denoising consistency, and invoke simulator-based rectification for low-quality candidates. On challenging long-horizon and fine-grained robotic manipulation tasks, \u03c4_0-WM shows superior performance over other relevant baselines.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.29084", "title": "Same Question, Different Source, Different Answer: Auditing Source-Dependence in Medical Multi-Source RAG", "authors": [ "Yubo Li", "Rema Padman", "Ramayya Krishnan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "A retrieval-augmented generation (RAG) system deployed over a multi-author institutional corpus can give a different answer to the same question depending on which source it retrieves -- a failure mode the dominant single-gold-answer paradigm cannot diagnose. We argue that source-dependence is a missing axis of NLP evaluation, and that auditing it means shifting the unit of evaluation from answer correctness to the inter-source relationship. We make this concrete in transplant patient education, where institutional sources demonstrably disagree, releasing three artefacts: TransplantQA, a benchmark of real patient questions, each answered by grounding generation in multiple institutional handbooks as candidate sources; HERO-QA, a hierarchical retrieval strategy that grounds and audits each answer; and a structured-output judge that scores inter-source relationships on a validated 5-label taxonomy. At scale, better retrieval reveals far more disagreement than prior estimates suggested -- understating its prevalence, not its intensity. The framework is domain-agnostic and transfers to legal and educational RAG: measuring source-dependence is a responsibility for deployed multi-source NLP generally.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.29087", "title": "The Chain Holds, the Answer Folds: Trace-Answer Dissociation in Reasoning Models Under Adversarial Pressure", "authors": [ "Yubo Li", "Ramayya Krishnan", "Rema Padman" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reasoning models are evaluated on single-turn benchmarks but deployed in multi-turn dialogue, where users push back on correct answers. Under sustained adversarial pressure we find a previously undocumented failure mode: the chain-of-thought stays factually correct from first turn to last while the emitted answer flips wrong. We call this unfaithful capitulation (UC) and isolate it with a 2times 2 latent-versus-behavioral framework that flip-rate metrics and single-turn faithfulness probes both miss. Across three datasets (MT-Consistency, MMLU-Pro, GSM8K), the latent-correct rate at the behavioral flip clusters near 50% in think mode and collapses to 11-15% under no_think -- paired, within-model causal evidence that reasoning creates the gap. Across models the effect tracks the reasoning channel (high in Qwen3-32B and GPT-OSS-20B, low in inline-CoT Gemma-4-31B-it). An independent GPT-4o judge corroborates 86% of UC labels; a token-level probe shows the answer-slot argmax is correct in 84% of UC cells; and a naive trace-anchored defense backfires. We release all trajectories, traces, and judge labels.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.02388", "title": "Policy and World Modeling Co-Training for Language Agents", "authors": [ "Ning Lu", "Baijiong Lin", "Shengcai Liu", "Jiahao Wu", "Haoze Lv", "Yanbin Wei", "Lingting Zhu", "Shengju Qian", "Xin Wang", "Ying-Cong Chen", "Qi Wang", "Ke Tang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions lead to high rewards, but provides little supervision on what those actions do to the environment. World modeling (WM) can fill this gap, yet existing approaches often require separate simulators, extra training stages, or additional inference-time computation. We observe that on-policy RL rollouts already contain the needed signal: each transition pairs an action with its resulting next observation. Based on this observation, we propose PaW, a Policy and World modeling co-training framework that adds auxiliary WM supervision to the same policy during RL, without changing the inference paradigm. To make auxiliary WM supervision informative and stable, PaW introduces three components: action-entropy-based WM data selection, noise-tolerant WM loss, and reward-adaptive loss balancing. Experiments on three agentic task benchmarks show consistent improvements over strong RL baselines across models and RL algorithms. These results suggest that standard RL rollouts are a practical source of WM supervision for language-agent training.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.02373", "title": "Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses", "authors": [ "Pengcheng Jiang", "Zhiyi Shi", "Kelly Hong", "Xueqiang Xu", "Jiashuo Sun", "Jimeng Sun", "Hammad Bashir", "Jiawei Han" ], "github_manual": "", "github_regex": "https://github.com/pat-jj/harness-1", "github_paper_md": "", "github_api": "https://github.com/pat-jj/harness-1", "github_api_added_by": "user", "project_page": "", "abstract": "Search agents are often trained as policies over growing transcripts: the model must decide how to search while also remembering what it has seen, which evidence is useful, which constraints remain open, and which claims have actually been checked. We argue that this formulation puts too much routine state management inside the policy: reinforcement learning is forced to optimize both semantic search decisions and recoverable bookkeeping that the environment can maintain more reliably. We introduce Harness-1, a 20B search agent (retrieval subagent) trained with reinforcement learning inside a stateful search harness. The harness maintains environment-side working memory, including a candidate pool, an importance-tagged curated set, compact evidence links, verification records, compressed and deduplicated observations, and budget-aware context rendering. The policy retains the semantic decisions: what to search, which documents to keep or discard, what to verify, and when to stop. Across eight retrieval benchmarks spanning web, finance, patents, and multi-hop QA, Harness-1 achieves 0.730 average curated recall, outperforming the next strongest open search subagent by +11.4 points and remaining competitive with much larger frontier-model searchers. Its gains are especially strong on held-out transfer benchmarks, suggesting that reinforcement learning over explicit search state can produce retrieval behaviors that generalize beyond the training domains. Our code is available at https://github.com/pat-jj/harness-1.", "github": "https://github.com/pat-jj/harness-1" }, { "date": "2026-06-02", "arxiv_id": "2606.00285", "title": "Model-Based Quality Assessment for Massively Multilingual Parallel Data", "authors": [ "Abdelaziz M. A. Ibrahim", "Zihao Li", "J\u00f6rg Tiedemann", "Shaoxiong Ji" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large-scale multilingual bitext often contains two distinct problems: non-parallel sentence pairs and low-quality translations. We decompose model-based assessment for such data into two independent components: parallelism assessment with multilingual embeddings and reference-free quality estimation (QE). For parallelism, we benchmark four embedding models on FLORES-200 and BOUQuET retrieval tasks, covering 6,654 source--target directions in our target language-pair inventory. For QE, we evaluate nine reference-free evaluators on professional FLORES-200 translations across 41,412 ordered source--target directions. Results show that no model is universally reliable across translation directions. Naive QE ensembles dilute strong model signals, while documented target-language coverage is strongly associated with higher QE scores. Overall, these findings suggest that multilingual parallel-data assessment is best approached as a direction-aware routing and calibration problem, where no single universal metric is expected to suffice across all languages.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2605.31529", "title": "SVI-Bench: A Dynamic Microworld for Strategic Video Intelligence", "authors": [ "Yulu Pan", "Han Yi", "Seongsu Ha", "Md Mohaiminul Islam", "Benjamin Zhang", "Lorenzo Torresani", "Gedas Bertasius" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Texaser/SVI-Bench", "github_api_added_by": "user", "project_page": "https://svi-bench.github.io/", "abstract": "True video intelligence demands more than recognizing what is visible: it requires reasoning about why events unfold, predicting what would change under different conditions, and deciding what to do next. We refer to this progression, from perception through causal reasoning and simulation to strategic planning, as Strategic Video Intelligence (SVI). No existing benchmark evaluates this capability stack: in-the-wild videos lack verifiable ground truth for causal and strategic questions, while synthetic environments sacrifice the complexity of real multi-agent systems. To bridge this gap, we introduce SVI-Bench, a large-scale benchmark that leverages team sports as a dynamic microworld, combining the complexity of real-world multi-agent interaction (10-22 agents making coordinated decisions under adversarial pressure) with the verifiability of explicit rules and definitive outcomes. SVI-Bench comprises approximately 35K hours of broadcast video, 15M annotated actions, 15K hours of expert commentary, 23K game reports, and 103K structured statistical records across basketball, soccer, and hockey, all constructed via a data engine that transforms raw game data into a dense, cross-referenced corpus. We organize evaluation into 9 tasks spanning a progressive four-pillar hierarchy: Dynamic Scene Understanding, Causal Reasoning, Strategic Simulation, and Agentic Synthesis. Evaluating strong multimodal and agentic baselines, we find a capability cliff: models perform competently on perceptual tasks, achieving approximately 73% on fine-grained action QA, but degrade sharply at each successive cognitive level. Agentic tasks prove hardest: the strongest model achieves only 5% accuracy when required to autonomously gather and integrate evidence across a corpus of 1.8M clips.", "github": "https://github.com/Texaser/SVI-Bench" }, { "date": "2026-06-02", "arxiv_id": "2605.29318", "title": "FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes", "authors": [ "Donglai Xiang", "Vismay Modi", "Rishit Dagli", "Ty Trusty", "Gilles Daviet", "Anka He Chen", "Nicholas Sharp", "David I. W. Levin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVIDIAGameWorks/Kaolin", "github_api_added_by": "user", "project_page": "", "abstract": "We present a novel formulation for mesh-free, reduced-order simulation of deformable hyperelastic objects. Existing work in reduced-order elastodynamic simulation represents the input geometry by either meshes, which can be difficult to obtain due to challenges in scanning and triangulating complex shapes, or by neural fields that require per-shape optimization. We propose to adopt a Reproducing Kernel Particle Method (RKPM) representation, which enables the construction of reduced-order skinning weights by solving a generalized eigensystem on the Hessian matrix of the elastic energy. We demonstrate that this formulation not only leads to a 40x training speedup compared with the per-shape optimization of neural fields, but also achieves lower simulation error when evaluated against the converged results of finite element method. We show our simulation results on a wide variety of objects in different representations including meshes and Gaussian splats, as well as the application of our method in the downstream task of robot simulation.", "github": "https://github.com/NVIDIAGameWorks/Kaolin" }, { "date": "2026-06-02", "arxiv_id": "2606.02551", "title": "AFUN: Towards an Affordance Foundation Model for Functionality Understanding", "authors": [ "Zhaoning Wang", "Yi Zhong", "Jiawei Fu", "Henrik I. Christensen", "Jun Gao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/EricWang12/AFUN", "github_api_added_by": "user", "project_page": "https://www.zhaoningwang.com/AFUN/", "abstract": "Affordance understanding bridges visual perception and physical action, serving as an explainable interface for robot manipulation in open and unstructured real-world environments. Yet, building an affordance foundation model that not only understands where and how the interaction should happen, but also generalizes across diverse environments, objects, and tasks, remains a long-standing research challenge. Existing methods typically address only part of this challenge, either localizing task-relevant regions without specifying executable motion, or predicting motion but with limited scalability. In this paper, we present ourmodel, a step towards an affordance foundation model for functionality understanding. From a single RGB-D observation and a language task description, ourmodel predicts a task-conditional functional mask (where to interact) and a 3D post-contact motion curve (how to interact). To support open-world generalization, we build a large-scale standardized data pipeline that converts heterogeneous robot, human, simulation, and real-world scan data into a shared affordance schema with language, masks, and object-centric 3D motion labels. We evaluate ourmodel from three aspects: for affordance segmentation, ourmodel outperforms all baselines by a large margin across 8 test sets from 4 benchmarks, improving mean gIoU/cIoU by +23.9/+26.3; for contact-point prediction, it predicts substantially more accurate points, with a 12.7--61.3% hit-rate gain over the best baseline; and for 3D motion, it achieves the best performance on all three test sets. ourmodel can be deployed for real-world robot manipulation without finetuning for robot embodiment or using task-specific heuristics, demonstrating the ability to adapt to open-world affordance tasks. Project page: https://www.zhaoningwang.com/AFUN", "github": "https://github.com/EricWang12/AFUN" }, { "date": "2026-06-02", "arxiv_id": "2606.02320", "title": "TVIR: Building Deep Research Agents Towards Text--Visual Interleaved Report Generation", "authors": [ "Xinkai Ma", "Zhiqi Bai", "Dingling Zhang", "Pei Liu", "Yishuo Yuan", "He Zhu", "Jiakai Wang", "Qianqian Xie", "Yifan Zhao", "Xinlong Yang", "Hao Cong", "Zhiheng Yao", "Fengxia Xie", "Zihao Xu", "Haoran Xu", "Zhaohui Wang", "Minghao Liu", "Shirong Lin", "Yingshui Tan", "Yuchi Xu", "Wenbo Su", "Zhaoxiang Zhang", "Bo Zheng", "Jiaheng Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NJU-LINK/TVIR", "github_api_added_by": "user", "project_page": "https://nju-link.github.io/TVIR/", "abstract": "Deep Research Agents have shown strong capability in multi-step information retrieval, reasoning, and long-form report generation, but existing benchmarks and systems remain predominantly text-centric, with limited evaluation of whether visual elements are factually reliable and well aligned with the surrounding analysis. To address this gap, we introduce TVIR (Text--Visual Interleaved Report Generation), which includes TVIR-Bench, a benchmark of 100 expert-curated multimodal deep research tasks that require visual elements to serve specific analytical sub-goals, and TVIR-Agent, a hierarchical multi-agent framework that serves as a strong baseline for constructing outlines, retrieving images, generating charts with traceable sources, and composing reports through context-aware sequential writing. We further develop a dual-path evaluation framework that combines Textual Assessment and Visual Assessment. Experiments across nine deep research systems show that TVIR-Agent achieves strong overall performance, underscoring the importance of explicit multimodal design and evaluation for evidence-driven report generation.", "github": "https://github.com/NJU-LINK/TVIR" }, { "date": "2026-06-02", "arxiv_id": "2605.30608", "title": "Semantic Motion Anchors: Bridging Motion and Meaning in Co-Speech Gestures", "authors": [ "Varsha Suresh", "Mohammad Mahdi Abootorabi", "Mohamed Salman", "M. Hamza Mughal", "Christian Theobalt", "Ashwin Ram", "J\u00fcrgen Steimle", "Vera Demberg" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Learning a shared representation between spoken text and gesture is central to co-speech gesture retrieval, synthesis, and understanding, but remains challenging for semantically meaningful gestures whose communicative intent is not captured by motion alone. Direct contrastive alignment between transcripts and continuous motion embeddings often overemphasizes low-level kinematics and misses the symbolic content of semantic gestures. We propose semantic motion anchors, natural-language abstractions of gesture motion capturing physical form and communicative intent. Our method discretizes 3D gestures into body-hand motion primitives, verbalizes them into structured descriptions, and grounds them in the transcript to provide auxiliary contrastive supervision. On BEAT2, our method improves text-to-gesture R@1 by 8.2% over a direct text-motion baseline and outperforms prior retrieval approaches on text to gesture and gesture to text retrieval directions. Beyond aggregate retrieval metrics, semantic motion anchor supervision helps retrieve gestures that are semantically meaningful for the spoken query, rather than defaulting to generic motion patterns. A downstream retrieval-augmented gesture generation study showed that users significantly preferred gestures retrieved by our approach over a retrieval-augmented generation baseline, demonstrating that semantically grounded retrieval translates to gestures that better convey communicative intent in downstream generation.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.01666", "title": "DOT-MoE: Differentiable Optimal Transport for MoEfication", "authors": [ "Udbhav Bamba", "Arnav Chavan", "Aryamaan Thakur", "Steve Teig", "Deepak Gupta" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The scaling of Large Language Models (LLMs) has driven significant performance gains but created substantial challenges in inference efficiency. While Mixture of Experts (MoEs) architectures address this by decoupling model size from inference cost, training MoEs from scratch is often unstable and compute intensive. Conversion of pre-trained dense models into sparse MoEs has emerged as an alternative solution; however, existing methods typically rely on heuristic neuron clustering or random splitting to partition the Feed-Forward Network (FFN) into experts. In this work, we propose DOT-MoE, a novel framework that formulates the decomposition of dense layers as a Differentiable Optimal Transport (DOT) problem. Instead of static heuristics, we model neuron assignment as a balanced transport problem, utilizing differentiable Sinkhorn-Knopp iterations to enforce strict expert capacity constraints. Furthermore, we utilize Straight-Through Estimators (STE) to jointly learn the discrete neuron-to-expert assignment and the token-to-expert routing policy end-to-end. Extensive experiments across multiple architectures and benchmarks demonstrate that DOT-MoE significantly outperforms structured pruning, heuristic clustering, and random-split baselines, retaining 90% of the original dense model's performance while reducing active parameters by 50%.", "github": "" }, { "date": "2026-06-02", "arxiv_id": "2606.00240", "title": "MindZero: Learning Online Mental Reasoning With Zero Annotations", "authors": [ "Shunchi Zhang", "Jin Lu", "Chuanyang Jin", "Yichao Zhou", "Zhining Zhang", "Tianmin Shu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SCAI-JHU/MindZero", "github_api_added_by": "user", "project_page": "https://scai.cs.jhu.edu/MindZero/", "abstract": "Effective real-world assistance requires AI agents with robust Theory of Mind (ToM): inferring human mental states from their behavior. Despite recent advances, several key challenges remain, including (1) online inference with robust uncertainty updates over multiple hypotheses; (2) efficient reasoning suitable for real-time assistance; and (3) the lack of ground-truth mental state annotations in real-world domains. We address these challenges by introducing MindZero, a self-supervised reinforcement learning framework that trains multimodal large language models (MLLMs) for efficient and robust online mental reasoning. During training, the model is rewarded for generating mental state hypotheses that maximize the likelihood of observed actions estimated by a planner, similar to model-based ToM reasoning. This method thus eliminates the need for explicit mental state annotations. After training, MindZero internalizes model-based reasoning into fast single-pass inference. We evaluate MindZero against baselines across challenging mental reasoning and AI assistance tasks in gridworld and household domains. We found that LLMs alone are insufficient; model-based methods improve accuracy but are slow, costly, and limited by backbone MLLM capacity. In contrast, MindZero enhances MLLMs' intrinsic ToM ability and significantly outperforms model-based methods in both accuracy and efficiency, showing that mental reasoning can be effectively learned as a self-supervised skill.", "github": "https://github.com/SCAI-JHU/MindZero" }, { "date": "2026-06-02", "arxiv_id": "2605.27921", "title": "Show, Don't TELL: Explainable AI-Generated Text Detection", "authors": [ "Aldan Creo", "Suraj Ranganath" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ACMCMC/TELL", "github_api_added_by": "user", "project_page": "https://ai-tells.tech/", "abstract": "Research on AI-generated text detection has presented a number of approaches to discern human from AI prose, some of which achieving high in-distribution performance. However, real-world applicability has stalled because their outputs are misaligned with the needs of users, such as professors, who are presented with a numeric score that has no attached explanation. We tackle this issue with a novel architecture, TELL, that bakes explainability from the ground-up. While our system still offers a numerical score like other detectors for comparability, TELL takes a fundamentally different approach where we aim to show the user the \"tells\" by which the model believes a text is AI or human-written, to empower the user to decide who wrote a text using their own judgment and understanding of the context of the writing and its alleged author. We train TELL on a custom SFT dataset of domain-specific authorship annotations, and further refine the system using GRPO with curriculum learning to improve performance. We achieve competitive performance with state-of-the-art detectors (AUROC 0.927) while natively providing annotations that explain the basis for the detector's decision. We further evaluate the quality of our explanations using a dataset of human annotations and report a high (mean 72.3%) win-rate on annotation concreteness, falsifiability, coherence, plausibility and grounding, allowing users to critically think and decide for themselves. Our work thus reframes the problem of AI-generated text detection in a human-centric perspective and paves the way for a new family of detectors that focus on native explainability.", "github": "https://github.com/ACMCMC/TELL" }, { "date": "2026-06-03", "arxiv_id": "2606.01249", "title": "Trust Region On-Policy Distillation", "authors": [ "Xingrun Xing", "Haoqing Wang", "Boyan Gao", "Ziheng Li", "Yehui Tang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent learning, multi-task enhancement, and model compression. However, OPD training becomes unstable when the teacher and student distributions differ substantially, as teacher supervision on student-generated tokens may yield unreliable policy gradients and even cause optimization failure. This work addresses reliable on-policy token-level supervision through credit assignment strategies, and proposes Trust Region On-Policy Distillation, TrOPD. It features the following characteristics: 1) Trust-Region On-Policy Learning: TrOPD performs OPD only in regions where the teacher provides reliable supervision, mitigating the optimization difficulty of the K1 reverse-KL estimator under distribution mismatch. 2) Outlier Estimation: For outlier regions, we explore gradient clipping, masking, and forward-KL estimation to reduce the adverse effects of unreliable supervision. 3) Off-Policy Guidance: The student continues generation from teacher prefixes and uses forward KL to imitate off-policy guidance, encouraging on-policy exploration toward reliable regions. Experiments show that TrOPD consistently outperforms SoTA OPD baselines, including OPD, EOPD, and REOPOLD, across mathematical reasoning, code generation, and general-domain benchmarks.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2606.02754", "title": "\u03a8-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues", "authors": [ "Peixuan Han", "Hongyi Du", "Jiayu Liu", "Yihang Sun", "Yutong Liu", "Jiaxuan You" ], "github_manual": "", "github_regex": "https://github.com/Hanpx20/Psi-Bench", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Personalization is a crucial capability of modern language agents. However, current research primarily positions personalized agents as passive responders to user preferences, limiting their ability to interact with users and provide suggestions or guidance proactively. To systematically evaluate such proactive personalization in realistic interactions, we propose \u03a8-Bench, a benchmark for assessing LLMs' ability to influence realistic users through conversation. We design three real-world interaction scenarios that involve persuasion in \u03a8-Bench, and endow simulated clients with personal characteristics through explicit user profiles derived from dialogue histories. We evaluate 10 frontier LLMs on \u03a8-Bench and find that while most models can produce coherent and reasonable arguments, even state-of-the-art models still leave considerable room for improvement in persuasion. We also find that providing access to client profiles yields an average performance gain of 18.24\\%, highlighting the importance of user-specific information for effective persuasion. Overall, our work highlights persona-sensitive influencing as a challenging yet practical direction for evaluating and developing more proactive personalized LLM agents. Codes are available at: https://github.com/Hanpx20/Psi-Bench.", "github": "https://github.com/Hanpx20/Psi-Bench" }, { "date": "2026-06-03", "arxiv_id": "2606.01788", "title": "PlatonicNav: Unveiling Semantic Correspondence in Navigation with Platonic Topological Maps", "authors": [ "Junlin Long", "Zeyu Zhang", "Xu Deng", "Yiran Wang", "Yue Yang", "Luke Borgnolo", "Maxwell Twelftree", "Yang Zhao" ], "github_manual": "", "github_regex": "https://github.com/AIGeeksGroup/PlatonicNav", "github_paper_md": "", "github_api": "https://github.com/AIGeeksGroup/PlatonicNav", "github_api_added_by": "user", "project_page": "https://aigeeksgroup.github.io/PlatonicNav/", "abstract": "Embodied visual navigation, where an agent perceives a complex environment and acts to reach a goal from raw sensory input, underpins a wide range of applications such as household service robotics, assistive robotics, and large-scale autonomous exploration. However, recent attempts to unify vision-and-language navigation (VLN) and object goal navigation (ObjNav) remain at the level of architectural fusion, mixed-task training, and large vision-language pretraining, without examining whether independently trained vision and language encoders may already share a common semantic structure. Moreover, even object-centric topological maps still ground language goals through explicit cross-modal supervision such as CLIP or large vision-language models, leaving open whether such grounding is possible from a purely vision-built map. To address these challenges, we extend the Platonic Representation Hypothesis to embodied navigation and recast vision-only ObjNav, cross-modal ObjNav, and VLN as three different interfaces to the same object-centric semantic manifold. We further introduce PlatonicNav, a training-free framework whose Platonic Topological Map fuses geometric and semantic node distances from a self-supervised visual encoder, and grounds language goals via blind matching without any paired vision-language data. Extensive experiments on simulation benchmarks including HM3D-IIN, OVON, and R2R-CE on MP3D, together with deployment on Unitree Go2, demonstrate that PlatonicNav generalizes across tasks, modalities, and embodiments without explicit cross-modal training. Code: https://github.com/AIGeeksGroup/PlatonicNav. Website: https://aigeeksgroup.github.io/PlatonicNav.", "github": "https://github.com/AIGeeksGroup/PlatonicNav" }, { "date": "2026-06-03", "arxiv_id": "2606.03159", "title": "NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation", "authors": [ "NVIDIA", "Aarti Basant", "Amlan Kar", "Despoina Paschalidou", "Fangyin Wei", "Francesco Ferroni", "Guillermo Garcia Cobo", "Haithem Turki", "Huan Ling", "Jaewoo Seo", "James Lucas", "Jay Zhangjie Wu", "Jialiang Wang", "Jonathan Lorraine", "Jun Gao", "Kai He", "Katarina Tothova", "Kevin Xie", "Micha\u0142 Tyszkiewicz", "Qi Wu", "Riccardo de Lutio", "Ruilong Li", "Sanja Fidler", "Seung Wook Kim", "Tianchang Shen", "Tianshi Cao", "Tobias Pfaff", "William Lew", "Xindi Wu", "Xuanchi Ren", "Yifan Lu", "Yuxuan Zhang", "Zan Gojcic", "Zian Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-loop simulation, the driving policy model actively interacts with the environment, where its actions dynamically update the simulator state and directly influence the next set of generated sensor observations. While recent reconstruction-based neural simulators offer photorealism, they are fundamentally constrained by their initial captured data and struggle to generalize to highly dynamic or novel scenes. To overcome these limitations, we introduce OmniDreams, a foundation generative world model mid- and post-trained from the Cosmos diffusion model to autoregressively generate action-conditioned videos in real time. By leveraging the rich visual priors of Cosmos and mid- and post-training on 21k hours of driving scenarios, OmniDreams synthesizes complex, unobserved phenomena that are hard for traditional simulators to capture, such as extreme weather and unpredictable dynamic agent behaviors. Crucially, it autoregressively conditions its photorealistic sensor generation on past frames, the current simulator state, and immediate driving actions. Deployed in a closed-loop system with the Alpamayo 1 policy model and AlpaSim orchestrator, OmniDreams acts as a highly responsive, reactive environment, providing a scalable and comprehensive solution for training and evaluating next-generation autonomous driving policies. We additionally show preliminary results indicating that a world-action model (WAM) post-trained from OmniDreams achieves strong performance on the Physical AI Autonomous Vehicles NuRec dataset, surpassing the VLA-based Alpamayo 1.5 research policy model while using only 1/5 the total parameters. These results highlight the potential for a real-time world model like OmniDreams to also serve as a backbone for policy architectures.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2606.01717", "title": "Decentralized Instruction Tuning: Conflict-Aware Splitting and Weight Merging", "authors": [ "Minsik Choi", "Geewook Kim" ], "github_manual": "", "github_regex": "https://github.com/naver-ai/merit", "github_paper_md": "", "github_api": "https://github.com/naver-ai/merit", "github_api_added_by": "user", "project_page": "https://naver-ai.github.io/merit/", "abstract": "Instruction tuning aligns large language models, including multimodal ones, with diverse user intents, but scaling to heterogeneous mixtures is hindered by gradient interference and bandwidth-heavy synchronization. We ask whether these two bottlenecks can be addressed jointly by training parts of the mixture independently and reconciling them once in parameter space. We develop a local quadratic theory inside a shared flat basin that yields three results: weight merging produces a curvature-weighted variance reduction; PCA-aligned conflict splitting maximizes this gain along high-curvature directions; and merging additionally acts as spectral filtering with implicit norm regularization. These results directly motivate MERIT, a decentralized merge-ready instruction-tuning pipeline that estimates dataset-level gradient conflicts, partitions the mixture along the top PCA conflict axes, fine-tunes each partition independently with no inter-partition communication, and merges once via token-weighted averaging. On Qwen2.5-VL-3B with 136 Vision-FLAN tasks, MERIT improves the 8-benchmark average from 54.3 (joint training) to 57.0. The same recipe scales to a 7B model on a 1.6M-example, 176-source mixture -- matching or exceeding centralized joint training with minimal cost overhead -- and transfers to text-only FLAN. Our code is available at https://github.com/naver-ai/merit.", "github": "https://github.com/naver-ai/merit" }, { "date": "2026-06-03", "arxiv_id": "2606.03979", "title": "Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories", "authors": [ "Ali Behrouz", "Farnoosh Hashemi", "Vahab Mirrokni" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow models to more general deep Large Language Models (LLMs). Despite showing promising results in tasks that require instant prediction or in-context learning, existing models lack the ability to continually learn and effectively transfer their temporal in-context knowledge to their long-term parameters. Inspired by human learning process, we introduce a ''Sleep'' paradigm that allows the models to continually learn, distill their short-term fragile memories into stable long-term knowledge with replay, and recursively improve themselves with ''Dreaming'' process. In more detail, sleep consists of two stages: (1) Memory Consolidation: an upward distillation process, called Knowledge Seeding, where the memories of a smaller-self are distilled into a larger network to provide more capacity while preserving the knowledge. As a proof of concept, we present a new Generalized Distillation process for {Knowledge Seeding} (i.e., the combination of on-policy distillation with Reinforcement Learning (RL)-based imitation learning); (2) Dreaming: a self-improvement phase, where the model uses RL to generate a curriculum of synthetic data to rehearse new knowledge and refine existing capabilities without human supervision. Our experiments on long-horizon, continual learning, knowledge incorporation, and few-shot generalization tasks support the importance of the sleep stage.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2606.03985", "title": "Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking", "authors": [ "Zekun Qi", "Xuchuan Chen", "Dairu Liu", "Chenghuai Lin", "Yunrui Lian", "Sikai Liang", "Zhikai Zhang", "Yu Guan", "Jilong Wang", "Wenyao Zhang", "Xinqiang Yu", "He Wang", "Li Yi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/GalaxyGeneralRobotics/Humanoid-GPT", "github_api_added_by": "user", "project_page": "https://qizekun.github.io/Humanoid-GPT/", "abstract": "We introduce Humanoid-GPT, a GPT-style Transformer with causal attention trained on a billion-scale motion corpus for whole-body control. Unlike prior shallow MLP trackers constrained by scarce data and an agility-generalization trade-off, Humanoid-GPT is pre-trained on a 2B-frame retargeted corpus that unifies all major mocap datasets with large-scale in-house recordings. Scaling both data and model capacity yields a single generative Transformer that tracks highly dynamic behaviors while achieving unprecedented zero-shot generalization to unseen motions and control tasks. Extensive experiments and scaling analyses show that our model establishes a new performance frontier, demonstrating robust zero-shot generalization to unseen tasks while simultaneously tracking highly dynamic and complex motions.", "github": "https://github.com/GalaxyGeneralRobotics/Humanoid-GPT" }, { "date": "2026-06-03", "arxiv_id": "2606.03920", "title": "Benchmarking Visual State Tracking in Multimodal Video Understanding", "authors": [ "Sihyun Yu", "Nanye Ma", "Pinzhi Huang", "Hyunseok Lee", "Shusheng Yang", "June Suk Choi", "Ellis Brown", "Oscar Michel", "Boyang Zheng", "Jinwoo Shin", "Saining Xie" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vision-x-nyu/vstat", "github_api_added_by": "user", "project_page": "https://vision-x-nyu.github.io/vstat-site/", "abstract": "Understanding a video requires more than recognizing isolated moments, as humans continuously track entities, states, and events over time. This capacity for visual state tracking is fundamental to video understanding, yet remains underexplored in current evaluations of Multimodal Large Language Models (MLLMs). We introduce Visual STAte Tracking benchmark (VSTAT), a video-based benchmark designed to diagnose visual state tracking in MLLMs. VSTAT consists of 834 clips drawn from both synthetic and real-world videos, paired with 1,500 questions that cannot be answered from any single frame or short segment, requiring continuous perception and integration of events across the entire video stream. Despite their strong performance on existing video benchmarks, we find that state-of-the-art MLLMs perform far below humans and only modestly above answer-prior baselines. To analyze this gap, we compare MLLMs' thinking traces with the underlying video stream to understand why and when MLLMs fail on VSTAT. We find that MLLMs reason and track correctly in text, but fail at visually perceiving the events they need to track. Finally, our preliminary evaluation suggests that recent agentic approaches, including MLLM-based video agents and coding agents, do not readily resolve these failures, still falling short on VSTAT.", "github": "https://github.com/vision-x-nyu/vstat" }, { "date": "2026-06-03", "arxiv_id": "2606.03603", "title": "World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning", "authors": [ "Yucheng Zhou", "Wei Tao", "Yiwen Guo", "Jianbing Shen" ], "github_manual": "", "github_regex": "https://github.com/yczhou001/PF-OPSD", "github_paper_md": "", "github_api": "https://github.com/yczhou001/PF-OPSD", "github_api_added_by": "user", "project_page": "", "abstract": "World models and multimodal large language models (MLLMs) provide complementary capabilities for predicting future outcomes from static visual observations. World models can generate concrete visual rollouts of possible futures, while MLLMs can reason abstractly over questions, goals, and rules. However, generated rollouts are stochastic and may be visually plausible but task-incorrect, making it necessary to determine when visual simulation is useful, whether a rollout is credible, and how it should influence the final answer. We formulate this problem as controlled concrete reasoning, where a model learns to invoke, verify, and integrate visual future simulation alongside abstract reasoning. To study this setting, we construct two human-verified benchmarks, VRQABench for controllable spatial lookahead and OpenWorldQA for open-domain physical prediction, and propose Privileged-Future On-Policy Self-Distillation (PF-OPSD). During training, PF-OPSD uses ground-truth future videos and answers only as teacher-side privileged context to evaluate on-policy concrete-reasoning trajectories, while the deployable student never observes true futures at test time. Experimental results show that PF-OPSD outperforms baseline by 10.6% and 10.9% on VRQABench and OpenWorldQA, respectively, while increasing robustness to noisy or conflicting rollouts. Our code and dataset are available at https://github.com/yczhou001/PF-OPSD.", "github": "https://github.com/yczhou001/PF-OPSD" }, { "date": "2026-06-03", "arxiv_id": "2605.27346", "title": "MERIT: Learning Disentangled Music Representations for Audio Similarity", "authors": [ "Abhinaba Roy", "Junyi Liang", "Dorien Herremans" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AMAAI-Lab/MERIT", "github_api_added_by": "user", "project_page": "https://github.com/AMAAI-Lab/MERIT", "abstract": "Current music similarity models typically compute a single, monolithic score, entangling distinct musical dimensions like melody, rhythm, and timbre. This limits user control and interpretability, making it impossible to execute nuanced queries. We introduce MERIT, a framework for learning disentangled, factor-specific music representations tailored to these three core dimensions. To overcome the lack of isolated musical variations in real-world audio, we use a novel training strategy that uses conditional audio generation and source-separated stems to strongly encourage single-factor variation in training data. Our evaluations demonstrate strong factor-wise disentanglement. Each head responds strongly to its intended perceptual dimension while remaining near chance on the others, a representational property that holds across both the synthetic training domain and independent real-world audio.", "github": "https://github.com/AMAAI-Lab/MERIT" }, { "date": "2026-06-03", "arxiv_id": "2606.03928", "title": "Value-Aware Stochastic KV Cache Eviction for Reasoning Models", "authors": [ "Ting-Yun Chang", "Harvey Yiyun Fu", "Deqing Fu", "Chenghao Yang", "Jesse Thomason", "Robin Jia" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/terarachang/VaSE", "github_api_added_by": "user", "project_page": "", "abstract": "Reasoning models improve accuracy through extended chains of thought, but their long outputs create a memory and compute bottleneck. KV cache eviction methods reduce this cost by evicting unimportant key-value pairs from the cache, yet they often yield worse accuracy than selection-based sparse attention alternatives, which keep the full KV cache. We identify key factors crucial to KV cache eviction accuracy. First, a small fraction of value states have abnormally large magnitudes, and evicting them causes catastrophic failure where models enter repetitive reasoning loops. Second, introducing stochasticity during eviction improves accuracy by increasing cache diversity. Based on these findings, we propose Value-aware Stochastic KV Cache Eviction (VaSE), a training-free recipe that protects large-magnitude value states and promotes diverse eviction decisions. Across six reasoning tasks, Qwen3 models using VaSE with 4x KV cache compression yield higher average accuracies than SOTA selection method at the same sparsity, while outperforming the strongest eviction method by more than 4%. Overall, VaSE bridges the gap between efficiency and accuracy, supporting FlashAttention2 and enabling a static memory footprint for reasoning models.", "github": "https://github.com/terarachang/VaSE" }, { "date": "2026-06-03", "arxiv_id": "2606.02398", "title": "A Local Perturbation Theory for Cross-Domain Interference and Recovery in Multi-Domain RL", "authors": [ "Lei Yang", "Siyu Ding", "Deyi Xiong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement learning (RL) post-training improves large language models (LLMs) on individual domains such as mathematical reasoning, code generation, question answering, and creative writing (CW), but training on one domain often degrades performance on others. Existing explanations based on catastrophic forgetting or global gradient conflict are incomplete: substantial interference can occur even when full-model gradients are nearly orthogonal. We show that single-domain RL produces sparse, small-magnitude parameter edits with weak overlap among top-changed neurons, while different domains still share substantial active computation routes on which update directions determine whether they act synergistically or conflict. Guided by this observation, we prove under a local perturbation model of multi-domain RL that later-domain training harms an earlier domain mainly through a second-order damage term, which under the observed sparse route structure concentrates in a low-dimensional shared conflict subspace. Moreover, a short domain refresh contracts the harmful component on this subspace, enabling selective recovery with limited collateral damage. Consistent with the theory, a brief Re-Math refresh after Code rightarrow Math rightarrow QA rightarrow CW recovers Math from 57.66 to 66.04 while largely preserving performance on the other domains, yielding the best average score of 66.39. Beyond refresh, a training-free rollback on a sparse proxy conflict coordinate set for the Math-QA pair partially restores Math, providing direct proxy-level evidence for localized damage. These results provide a localized mechanistic account of interference and recovery in multi-domain RL.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2605.27958", "title": "Pressure-Testing Deception Probes in LLMs: Scaling, Robustness, and the Geometry of Deceptive Representations", "authors": [ "Sachin Kumar" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/techsachinkr/llm-deception-probe-stress-test", "github_api_added_by": "user", "project_page": "", "abstract": "Linear probes trained on LLM activations are increasingly proposed as deception-detection metrics, yet report AUROC exceeding 0.96 on clean benchmarks while collapsing under distributional shift. This paper systematically pressure-tests probe-based metrics across the Gemma 3 model family (1B-27B parameters), diagnosing why they fail rather than merely documenting that they fail. We test four hypotheses about deception encoding: (1) single linear direction, (2) multi-dimensional subspace, (3) convex conic hull, (4) entropy proxy. Our design includes cross-domain transfer matrices, multi-dimensional probe analysis with permutation null baselines, entropy-residualization tests, and distractor evaluations across 8 stylistic shifts. We find that: (a) probes achieve near-perfect AUROC (>=0.998) on clean data but collapse under stylistic shifts; style-augmented probes recover near-perfect detection (mean AUROC 0.979-0.983) on unseen styles; (b) the single-direction hypothesis is rejected (k=1 captures only 0.61-0.80 AUROC), with cross-domain transfer failure confirmed as geometric rather than layer-mismatch-driven; (c) the entropy-proxy hypothesis is rejected (max |rho|=0.454, max Delta-AUROC after residualization=0.004); and (d) deception does not form a significant linear subspace (per-domain k*=0), yet multi-dimensional probes (k>=5) recover the signal through distributed sub-threshold features. Probe fragility reflects distributional narrowness rather than an architectural limitation: style-augmented probes recover near-perfect detection at both 4B and 27B, establishing that the inverse scaling pattern is a training-distribution artifact rather than a genuine scale-dependent phenomenon.", "github": "https://github.com/techsachinkr/llm-deception-probe-stress-test" }, { "date": "2026-06-03", "arxiv_id": "2606.01961", "title": "AutoMedBench: Towards Medical AutoResearch with Agentic AI Models", "authors": [ "Junqi Liu", "Salena Song", "Yuhan Wang", "Jiawei Mao", "Hardy Chen", "Xiaoke Huang", "Tianhao Qi", "Pengfei Guo", "Yucheng Tang", "Yufan He", "Can Zhao", "Andriy Myronenko", "Dong Yang", "Daguang Xu", "Yuyin Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AutoMedBench/AutoMedBench", "github_api_added_by": "user", "project_page": "https://automedbench.github.io/", "abstract": "Autonomous agents are increasingly expected to support end-to-end medical-AI research workflows, moving beyond isolated prediction tasks or short-form clinical question answering. However, existing medical agent benchmarks primarily evaluate final outputs, providing limited visibility into agent behavior within the research process. To address this gap, we present AutoMedBench, a workflow-aware benchmark for autonomous medical-AI research across diverse medical imaging and multimodal inference tasks, organizing agent execution into a unified five-stage workflow (S1-S5): Plan, Setup, Validate, Inference, and Submit. It comprises long-horizon tasks with each run averaging 33 agent turns, spanning five research tracks: segmentation, image enhancement, visual question answering (VQA), report generation, and lesion detection. Each task is evaluated under two difficulty tiers, Lite and Standard, which use the same data and metrics but differ in the amount of task-brief scaffolding, and each run is scored using both final task performance and S1-S5 stage scores, enabling stage-level analysis from the initial task brief to the final submitted artifact. Across thousands of recorded runs, stage-level scoring reveals that Validate is the weakest workflow stage on average, whereas Setup is the strongest, suggesting that current agents are better at making pipelines executable than at verifying their reliability. Post-run error analysis further shows that verification and submission failures dominate tagged errors, accounting for 37.7% and 38.1% of fired codes respectively, whereas task-understanding errors are rare at 0.9%, and runs with one fired error code have a 48% lower overall score than runs with no error code on average.", "github": "https://github.com/AutoMedBench/AutoMedBench" }, { "date": "2026-06-03", "arxiv_id": "2606.01599", "title": "TRON: Targeted Rule-Verifiable Online Environments for Visual Reasoning RL", "authors": [ "Tianze Yang", "Yucheng Shi", "Ruitong Sun", "Jingyuan Huang", "Ninghao Liu", "Jin Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YangTianze009/TRON", "github_api_added_by": "user", "project_page": "https://tron-rl.github.io/", "abstract": "Reinforcement learning (RL) for visual reasoning needs scalable, verifiable, and controllable training signals. Existing visual RL post-training trains on static curated datasets, with fixed image-question-answer samples bounded by their collection budget. In this work, we introduce TRON (Targeted, Rule-verifiable Online eNvironments), an online environment substrate: a training rollout is generated on demand by a controllable generator-verifier program that samples a fresh latent visual state, renders an image, asks a question, and exactly verifies the answer. A single run can therefore draw an unbounded stream of fresh instances at the difficulty level required by the current curriculum. The current TRON suite contains 520 environments organized into five ability buckets (spatial, mathematical, diagram, pattern/logic, and counting); the same substrate supports both a single full model trained on all buckets and per-bucket ability-specialist models, with no additional data collection. We also introduce a substrate analysis covering generation reliability, instance and level diversity, cross-environment near-duplicates, and base-model pass rate by difficulty level. RL post-training with METHOD consistently improves performance on ten external multimodal reasoning benchmarks across Qwen3-VL-4B, Qwen2.5-VL-7B, and MiMo-VL-7B-SFT.", "github": "https://github.com/YangTianze009/TRON" }, { "date": "2026-06-03", "arxiv_id": "2606.03102", "title": "Small RL Controller, Large Language Model: RL-Guided Adaptive Sampling for Test-Time Scaling", "authors": [ "Runpeng Dai", "Tong Zheng", "Rui Liu", "Chengsong Huang", "Hongtu Zhu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Test-time scaling improves the reasoning performance of large language models but incurs substantial cost in both total computation and latency. Existing adaptive sampling methods partially mitigate this issue by dynamically deciding when to stop sampling, yet they typically rely on heuristic rules or rely on distribution assumptions. In this work, we formulate adaptive sampling as a Markov decision process (MDP). We train a lightweight sampling controller with reinforcement learning (RL) to jointly balance answer correctness, latency, and computation cost. At each round, the controller decides to stop sampling or to acquire additional samples. Our method is lightweight which only relies on statistics of final answers, and can be trained and deployed on CPU. We further show that the resulting framework admits an interpretation as the Lagrangian relaxation of a constrained optimization problem with explicit budget constraints. Experiments against strong baselines such as ASC and ESC show that our method achieves improved trade-offs among answer correctness, sampling rounds, and total samples required.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2606.01494", "title": "ClawHub Security Signals: When VirusTotal, Static Analysis, and SkillSpector Disagree", "authors": [ "Vincent Koc", "Patrick Erichsen", "Jacob Tomlinson", "Agustin Rivera", "Michael Appel", "Nir Paz" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/datasets/OpenClaw/clawhub-security-signals", "abstract": "Agent skills extend AI agents with reusable instructions, tools, scripts, references, and workflows, establishing a security boundary distinct from both model safety and traditional package-malware detection. ClawHub Security Signals is a sanitized dataset of 67,453 latest public OpenClaw skill versions. Each row pairs redacted SKILL.md content and sanitized bundled files where present with a final ClawScan registry verdict and evidence from three scanner families: VirusTotal, static heuristic analysis, and NVIDIA SkillSpector. Rather than estimating malicious-skill prevalence, we study scanner disagreement. The three scanners rarely flag the same skills: any pair overlaps on at most 10.4% of their combined positives, only 0.69% of skills are flagged by all three, and 81.9% of flagged skills are identified by a single scanner. The disagreement is structured by attack surface. SkillSpector, which raises semantic agentic-risk advisories rather than malware-reputation signals, is positive for 19,209 of 25,504 suspicious rows (75.3%) but only 14 of 206 malicious rows (6.8%). The malicious-verdict region shows the inverse profile: 150 of 206 malicious rows (72.8%) are VirusTotal-positive, consistent with bundled-code malware evidence. These results show that agent-skill security requires layered governance, not single-scanner allow/block decisions. The corpus is released as a sanitized silver-standard dataset: labels are the registry's automated verdicts, not human-annotated ground truth, and the release represents an early, versioned snapshot intended to support the community while a human-annotated subset is developed. Further research is encouraged, including models tailored for skill-security triage.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2605.29288", "title": "Diagnosing Harmful Continuation in Answer-Correct Long-CoT Training Traces", "authors": [ "Chen He", "Yuhao Wu", "Lei Wang", "Wenxuan Zhang", "Fumin Shen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Long chain-of-thought (CoT) traces are widely used as supervision for reasoning-oriented LLM SFT, yet answer-correct traces can still lead to markedly different fine-tuning outcomes. We study post-conclusion continuation in answer-correct long-CoT data: a continuation where the answer appears sufficiently supported, but the trace continues with additional reasoning that remains in the supervised target. To test its training effect, we use a delete-only editor to construct answer-preserving suffix removal and compare CoT-based SFT on the original and processed traces. We observe improved SFT outcomes after removing the editor-identified post-conclusion continuation, suggesting that this continuation is harmful to training in our setting. We therefore refer to this empirically supported phenomenon as harmful continuation. Beyond this intervention, we further characterize the removed post-conclusion continuation through uncertainty and hidden-state progress. We observe persistent local uncertainty together with weakened terminal-directional progress, forming an uncertainty--geometry mismatch. Finally, we instantiate Harmful Continuation Cut (HCC), a lightweight boundary proxy that approximates the editor-identified post-conclusion continuation boundary.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2606.01048", "title": "Decoupled Residual Denoising Diffusion Models for Unified and Data Efficient Image-to-Image Translation", "authors": [ "Ziyue Lin", "Jiahe Hou", "Hongyu Xia", "Xinrui Xie", "Feifei Wang", "Yuyin Zhou", "Wei Wang", "Jiawei Liu", "Liangqiong Qu" ], "github_manual": "", "github_regex": "https://github.com/HKU-HealthAI/DRDD", "github_paper_md": "", "github_api": "https://github.com/HKU-HealthAI/DRDD", "github_api_added_by": "user", "project_page": "", "abstract": "We propose Decoupled Residual Denoising Diffusion models (DRDD) for unified and data-efficient image-to-image (I2I) translation. While diffusion models have advanced I2I translation in terms of quality and diversity, we uncover a previously under-explored property in diffusion models. Crucially, beyond its conventional role of manifold lifting (i.e., moving data off low-dimensional manifolds), injecting Gaussian noise facilitates domain harmonization by implicitly aligning feature distributions across domains, a property particularly advantageous for unified I2I translation. However, existing diffusion models prematurely erode this harmonization effect, as noise and residuals are simultaneously removed in a single coupled diffusion process. To address this, DRDD decouples the diffusion process into two sequential and independent diffusion stages: (1) a stochastic noise diffusion for domain harmonization and manifold lifting, and (2) a deterministic residual diffusion that learns the core semantic mapping entirely within the fixed-noise domain. This decoupling preserves harmonization and manifold lifting effects throughout the transformation, substantially simplifying the learning of unified mappings across diverse tasks and domains. Notably, the noise diffusion stage is trained exclusively on abundant, unpaired target-domain images, greatly improving data efficiency. Comprehensive theoretical and empirical analysis demonstrates that DRDD is broadly compatible with mainstream diffusion models and consistently delivers robust, unified I2I translation, even under limited paired data. Our code is available at https://github.com/HKU-HealthAI/DRDD.", "github": "https://github.com/HKU-HealthAI/DRDD" }, { "date": "2026-06-03", "arxiv_id": "2606.03264", "title": "PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training", "authors": [ "Zelun Zhang", "Hongen Liu", "Suyin Liang", "Yubo Zhang", "Yiqing Xiang", "Jiaxuan Liu", "Ting Sun", "Manhui Lin", "Yue Zhang", "Changda Zhou", "Tingquan Gao", "Cheng Cui", "Yi Liu", "Dianhai Yu", "Yanjun Ma" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PaddlePaddle/PaddleOCR", "github_api_added_by": "user", "project_page": "https://www.paddleocr.com", "abstract": "We introduce PaddleOCR-VL-1.6, an upgraded compact document parsing model built upon PaddleOCR-VL-1.5. Although PaddleOCR-VL-1.5 establishes a strong 0.9B baseline, its remaining errors concentrate in under-optimized regions where model behavior is unstable, data coverage is sparse, or supervision is unreliable. Rather than expanding the training corpus indiscriminately, PaddleOCR-VL-1.6 introduces a region-aware data optimization framework that identifies weak regions from the previous model, applies targeted enhancement to these regions, and improves the reliability of supervision signals. It further adopts a progressive post-training recipe based on curated data selection and reinforcement learning, pushing model performance to a higher level through staged optimization. PaddleOCR-VL-1.6 achieves a new state-of-the-art score of 96.33% on OmniDocBench v1.6, demonstrates strong competitiveness against top-tier VLMs, and provides a practical post-training recipe for the PaddleOCR-VL series.", "github": "https://github.com/PaddlePaddle/PaddleOCR" }, { "date": "2026-06-03", "arxiv_id": "2606.00386", "title": "\u03b1Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion", "authors": [ "Xiang Zhang", "Yang Zhang", "Lukas Mehl", "Karlis Martins Briedis", "Markus Gross", "Christopher Schroers" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Accurately modeling soft boundaries, e.g., hair and defocus blur, is a fundamental challenge in stereo conversion due to the ambiguous blending of foreground and background. Existing depth models primarily predict single-layer depth, leading to ambiguity in depth correspondence at soft boundaries. While matting techniques can capture opacity for layered modeling, they often struggle in complex scenes with multiple targets and usually require user intervention. This paper introduces \u03b1Depth, a layered representation that decomposes soft boundaries for high-fidelity stereo conversion. Specifically, we first resolve mixed color and depth ambiguity by estimating layered color and depth values at soft boundaries. Considering complex multi-target scenes, we design a Circular Alpha Representation (CAR) that shifts the paradigm from global target extraction to local boundary decomposition. Unlike prior matting methods restricted to a single foreground/background, CAR enables efficient scene-level inference without manual guidance. Extensive evaluations demonstrate that \u03b1Depth achieves state-of-the-art performance in stereo conversion, eliminating background bleeding and structural distortions at soft boundaries.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2605.23895", "title": "From Activation to Causality: Discovery of Causal Visual Representations in the Human Brain", "authors": [ "Yuval Golbari", "Navve Wasserman", "Matias Cosarinsky", "Roman Beliy", "Aude Oliva", "Antonio Torralba", "Michal Irani", "Tamar Rott Shaham" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://yuvalgol123.github.io/BrainCause/", "abstract": "Identifying which brain regions represent a visual concept in the human brain is a central challenge in neuroscience. Existing approaches have localized coarse functional regions (e.g., faces, places) through activation maximization, identifying regions that activate strongly for a target concept relative to other concepts. Yet strong activation alone does not establish that a region represents the concept itself, as responses may instead be driven by correlated visual or semantic cues. We introduce BrainCause, an automated framework that combines generative and brain models to synthesize controlled stimuli and validate neural representations through targeted causal testing. Given a query specifying a concept of interest, our framework constructs targeted stimulus sets comprising concept images, counterfactual edits that remove the target concept while preserving other image content, and images with candidate correlated distractors. It then uses an image-to-fMRI encoding model to predict brain responses and searches for representations that respond specifically to the target concept over correlated alternatives. BrainCause returns validated candidate representations and proposes follow-up fMRI experiments to further test or extend its discoveries. Our approach successfully recovers known functional localizations and identifies new candidate representations across dozens of concepts, validated on both predicted and measured fMRI data. Critically, we show that without causal validation, a large fraction of localizations would be false positives, confirming that activation alone is insufficient evidence of representation.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2606.03287", "title": "BA-T: An Iterative Transformer for Two-View Bundle Adjustment", "authors": [ "Ganlin Zhang", "Weirong Chen", "Daniel Cremers", "Xi Wang" ], "github_manual": "", "github_regex": "https://github.com/zhangganlin/BA-T", "github_paper_md": "", "github_api": "https://github.com/zhangganlin/BA-T", "github_api_added_by": "user", "project_page": "", "abstract": "Feed-forward models for 3D reconstruction have achieved strong performance using deep cross-view attention to exchange information across images. However, these approaches often depend on heavy decoder stacks and lack a structured mechanism for geometry refinement, resulting in poor multi-view consistency. We address this by drawing inspiration from classical bundle adjustment (BA), which can be viewed as an iterative information propagation process between poses and local geometry. Inspired by BA, we propose BA-T, an iterative Transformer that implements BA-style structured updates as a repeatable layer in implicit token space. Instead of relying on deep attention stacks, BA-T refines predictions based on latent residual by a single lightweight layer. Experiments demonstrate that BA-T progressively improves pose and reconstruction accuracy across iterations, achieves stronger cross-view consistency than conventional decoders, and matches or surpasses substantially larger models while using only 16% of their decoder parameters. BA-T provides a compact, efficient, and structural alternative to depth-heavy attention, enabling accurate 3D reconstruction within a lightweight architecture. The code will be made publicly at https://github.com/zhangganlin/BA-T.", "github": "https://github.com/zhangganlin/BA-T" }, { "date": "2026-06-03", "arxiv_id": "2606.00683", "title": "OCC-RAG: Optimal Cognitive Core for Faithful Question Answering", "authors": [ "Maksim Savkin", "Mikhail Goncharov", "Alexander Gambashidze", "Alla Chepurova", "Dmitrii Tarasov", "Nikita Andriianov", "Daria Pugacheva", "Vasily Konovalov", "Andrey Galichin", "Ivan Oseledets" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/optimal-cognitive-core/OCC-RAG", "github_api_added_by": "user", "project_page": "", "abstract": "Recent progress in the development of language models has been defined by scale, with each generation absorbing more of the world's knowledge into its weights. However, many practical applications benefit more from robust reasoning than from extensive parametric knowledge. In this setting, task-specialized small language models (SLMs) offer a principled design choice. We introduce Optimal Cognitive Core (OCC), a family of SLMs built around this premise. As a variant of OCC, we present OCC-RAG, optimized for faithful question answering (QA) grounded in the provided context. This task directly aligns with the OCC design approach, requiring multi-hop reasoning over supplied passages while ignoring memorized knowledge. To train OCC-RAG, we implement a novel pipeline for synthesizing multi-context, multi-hop QA data at scale, producing a corpus of over three million examples targeting multi-hop reasoning, strict context faithfulness, and calibrated abstention. We release OCC-RAG-0.6B and OCC-RAG-1.7B, both mid-trained on this corpus. The models produce structured reasoning traces with source citations grounded in literal quotes from the context. Through OCC-RAG, we demonstrate that compact, task-specialized SLMs can match or exceed general-purpose models 2 -- 6x their size across multi-hop reasoning (HotpotQA, MuSiQue, TAT-QA), faithfulness (ConFiQA), and refusal (MuSiQue-Un) benchmarks.", "github": "https://github.com/optimal-cognitive-core/OCC-RAG" }, { "date": "2026-06-03", "arxiv_id": "2605.30288", "title": "MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection", "authors": [ "Haowen Wang", "Yaxin Du", "Jian Yang", "Jiajun Wu", "Shukai Liu", "Yuxuan Zhang", "Pingjie Wang", "Siheng Chen", "Tuney Zheng", "Ming Zhou", "Xianglong Liu", "Bryan Dai" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/collections/Multilingual-Multimodal-NLP/mira", "abstract": "Mid-training has become an important stage in modern LLM development, using large-scale curated mixtures to strengthen capabilities before final post-training. Its data selection problem is distinct: the data are optimized under a pretraining-style objective at near-pretraining scale, but are curated toward downstream capabilities and drawn from heterogeneous sources with different formats and training roles. As a result, effective selection requires both scalability and source-adaptive semantic criteria. Existing model-based methods scale well, but provide only implicit quality signals. Semantic selection methods offer stronger judgments, but usually assume fixed rubrics or standardized data formats. To address this mismatch, we propose MIRA, a source-aware filtering framework based on self-anchored rubric discovery. The key idea is to make rubric construction part of data selection: MIRA first discovers what should be evaluated for each source group, then distills those judgments into scalable student scorers for full-corpus filtering. On code-oriented mid-training with 21 sources and 5 source groups, MIRA outperforms selection baselines across nine code benchmarks and matches the full-corpus run while using only half the tokens.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2606.00138", "title": "A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems", "authors": [ "Titu Ranjan Sarker", "Muhammed Jawaad Zulqernine", "Ling Yue", "Shaowu Pan", "Chenxi Wang", "Shiyao Lin" ], "github_manual": "", "github_regex": "https://github.com/LIRAM-LIN/AbaqusAgent", "github_paper_md": "", "github_api": "https://github.com/LIRAM-LIN/AbaqusAgent", "github_api_added_by": "user", "project_page": "", "abstract": "Finite element analysis (FEA) is the most important numerical approach for solid mechanics. Challenges of FEA include a steep learning curve for entry-level users and potential false simulations due to incorrect definitions of key simulation components, such as boundary conditions, load cases, and solution variables. Years of engineering experience are usually necessary for real-world problem-solving. To address these issues, we present AbaqusAgent, a multi-agent framework grounded in large language models (LLMs) for solid mechanics analyses. AbaqusAgent is developed to facilitate analysis case generation and execution using Abaqus, one of the most widely used FEA packages, by turning users' natural-language instructions into executed FEA analyses and result visualization. AbaqusAgent is composed of six agents, including interpreter, architect, input writer, runner, reviewer, and visualizer agents, encompassing all the essential pre-processing and post-processing steps of standard FEA analyses. A wide variety of 50 solid mechanics problems have been successfully validated, achieving an overall success rate of 86%. Beyond improving the efficiency of FEA for solid mechanics problems and lowering the barrier to computational mechanics education, AbaqusAgent advances the human-simulation interaction paradigm and enables integration with AI-empowered optimization and material characterization workflows. The code is available at https://github.com/LIRAM-LIN/AbaqusAgent", "github": "https://github.com/LIRAM-LIN/AbaqusAgent" }, { "date": "2026-06-03", "arxiv_id": "2606.03911", "title": "Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching", "authors": [ "Yoad Tewel", "Yuval Atzmon", "Gal Chechik", "Lior Wolf" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://research.nvidia.com/labs/par/byg/", "abstract": "Modern generative models possess a deep understanding of visual content, yet training them for image editing typically requires massive datasets of paired examples. This limits scalability, especially for video editing where collecting paired data is prohibitively expensive. We propose Bootstrap Your Generator (ByG), a general framework for unpaired training of flow matching editing models. It leverages the base model's knowledge without any external signal. Our approach pairs instruction-following cues extracted from the frozen model with cycle-consistency for structure preservation. To make this tractable, we propose to route gradients from downstream losses over clean predictions to noisy training states. We demonstrate state-of-the-art results on challenging data-scarce image and video editing scenarios. Extensive evaluations and user studies show that our method effectively generalizes to unseen domains and outperforms supervised baselines trained on millions of samples. Analysis reveals that our gradient routing bridges the train-inference gap, and extracting semantic cues from a base model provides a robust training signal that obviates the need for external reward models.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2606.02775", "title": "AURA: Action-Gated Memory for Robot Policies at Constant VRAM", "authors": [ "Josef Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/spaces/KAIKAKU/aura-demo", "abstract": "The KV-cache is the right memory for datacenters but the wrong memory for robots. Datacenter inference batches many short requests and resets them, amortizing an attention cache across a crowd. Embodied agents instead run one long, non-resetting episode on bandwidth-limited edge hardware, where high-bandwidth memory and flash are scarce, flash has finite write endurance, and memory writes rather than compute can become the binding constraint. AURA-Mem (Action-Utility Recurrent Adaptive Memory) targets this regime. It wraps a frozen vision-language-action backbone with a constant-size recurrent memory and a learned gate that writes only when the current observation would change the next action: memory that knows when to stay silent. Unlike reconstruction-based memory, the gate is trained directly against a closed-loop action-error signal. Its inference state is fixed at 4,224 bytes regardless of horizon, while a KV-cache grows to 6,061 times larger at 100,000 steps. On a controlled synthetic benchmark, AURA-Mem matches the best O(1) baseline in accuracy while using 5.19-6.13 times fewer writes, and up to 9.19 times fewer writes on easier configurations. Budget-matched random and periodic schedules do not recover this gain, isolating the benefit to the action-surprise signal. On a trained closed-loop OpenVLA-OFT 7B panel on LIBERO-Long (n=60 episodes per arm), the gate does not hurt success: AURA-Mem matches the ungated base policy (0.233) and slightly exceeds an always-write KV arm (0.217), while using 7.0 times fewer writes and constant memory. We also instantiate an approximate-information-state value-loss bound as a methodology demonstration; at this scale, the bound is vacuous rather than a guarantee.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2606.02578", "title": "Mitigating Perceptual Judgment Bias in Multimodal LLM-as-a-Judge via Perceptual Perturbation and Reward Modeling", "authors": [ "Seojeong Park", "Jiho Choi", "Junyong Kang", "Seonho Lee", "Jaeyo Shin", "Hyunjung Shim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/kaist-cvml/perception-judge", "github_api_added_by": "user", "project_page": "https://perception-judge.github.io/", "abstract": "Recent multimodal large language models have demonstrated strong reasoning ability, yet their reliability as automated evaluators remains limited by a critical weakness: when visual evidence conflicts with textual cues, MLLM judges tend to reward plausible narratives over perceptually correct answers. We identify and systematically analyze this phenomenon, which we term Perceptual Judgment Bias. Through controlled visual perturbations, existing multimodal judges frequently anchor on the response text instead of their own visual perception, leading to inconsistent and non-verifiable evaluations. To address this issue, we introduce the Perceptually Perturbed Judgment Dataset, which constructs minimally edited counterfactual responses that isolate perceptual errors and enable verifiable supervision. Building on this dataset, we develop a unified training framework that combines a structured GRPO-based reward with a batch-ranking objective, achieving coherent global ordering without explicit pairwise labels. Experiments across diverse MLLM-as-a-Judge benchmarks show that our approach substantially improves perceptual fidelity, ranking coherence, and alignment with human evaluation. Our results establish a scalable and generalizable pathway for training multimodal judges that are perceptually grounded, interpretable, and robust to visual-reasoning conflicts.", "github": "https://github.com/kaist-cvml/perception-judge" }, { "date": "2026-06-03", "arxiv_id": "2606.03458", "title": "KVarN: Variance-Normalized KV-Cache Quantization Mitigates Error Accumulation in Reasoning Tasks", "authors": [ "Lorenz K. Muller", "Philippe Bich", "Chiara Boretti", "Hyun-Min Chang", "Jiawei Zhuang", "Lukas Cavigelli" ], "github_manual": "", "github_regex": "https://github.com/huawei-csl/KVarN", "github_paper_md": "", "github_api": "https://github.com/huawei-csl/KVarN", "github_api_added_by": "user", "project_page": "https://github.com/huawei-csl/KVarN", "abstract": "Test-time scaling is a powerful approach to obtain better reasoning in large language models, but it becomes memory-bottlenecked during long-horizon decoding, as the KV-cache grows. KV-cache quantization can help improve this, but current methods are evaluated under prefill-like settings and errors behave differently under autoregressive decoding. We show that in the latter regime, quantization errors accumulate across timesteps, driven primarily by incorrect token scales. We introduce KVarN, a calibration-free KV-cache quantizer that applies a Hadamard rotation followed by a dual-scaling variance normalization across both axes of the K and V matrices. We find that this combination fixes outlying token-scale errors and substantially reduces error accumulation over existing baselines. KVarN establishes a new state-of-theart for KV-cache quantization on generative benchmarks, including MATH500, AIME24 and HumanEval, at 2-bit precision. A vLLM implementation of the KVarN method is available at https://github.com/huawei-csl/KVarN", "github": "https://github.com/huawei-csl/KVarN" }, { "date": "2026-06-03", "arxiv_id": "2605.30039", "title": "Domain-Specific Data Synthesis for LLMs via Minimal Sufficient Representation Learning", "authors": [ "Tong Ye", "Hang Yu", "Tengfei Ma", "Xuhong Zhang", "Jianguo Li", "Peng Di", "Peiyu Liu", "Jianwei Yin", "Wenhai Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large Language Models have demonstrated remarkable progress in general-purpose capabilities and can achieve strong performance in specific domains through fine-tuning on domain-specific data. However, acquiring high-quality data for target domains remains a significant challenge. Existing data synthesis approaches follow a deductive paradigm, heavily relying on explicit domain descriptions expressed in natural language and careful prompt engineering, limiting their applicability in real-world scenarios where domains are difficult to describe or formally articulate. In this work, we tackle the underexplored problem of domain-specific data synthesis through an inductive paradigm, where the target domain is defined only through a set of reference examples, particularly when domain characteristics are difficult to articulate in natural language. We propose a novel framework, DOMINO, that learns a minimal sufficient domain representation from reference samples and leverages it to guide the generation of domain-aligned synthetic data. DOMINO integrates prompt tuning with a contrastive disentanglement objective to separate domain-level patterns from sample-specific noise, mitigating overfitting while preserving core domain characteristics. Theoretically, we prove that DOMINO expands the support of the synthetic data distribution, ensuring greater diversity. Empirically, on challenging coding benchmarks where domain definitions are implicit, fine-tuning on data synthesized by DOMINO improves Pass@1 accuracy by up to 4.63\\% over strong, instruction-tuned backbones, demonstrating its effectiveness and robustness. This work establishes a new paradigm for domain-specific data synthesis, enabling practical and scalable domain adaptation without manual prompt design or natural language domain specifications.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2605.30581", "title": "Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes", "authors": [ "Chenxi Tao", "Seung-Kyum Choi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JacksonTao888/industrial-visual-sim2real-priors", "github_api_added_by": "user", "project_page": "https://github.com/JacksonTao888/industrial-visual-sim2real-priors", "abstract": "Industrial visual sim-to-real is often described as transferring from synthetic images to real images, but industrial deployment usually involves a broader mismatch between available evidence and required decisions. A system may be built from CAD renderings, simulated RGB-D observations, normal reference images, synthetic defects, pretrained feature spaces, or language prompts, yet deployed under different sensors, lighting, materials, fixtures, calibration, production variation, and rare defect modes. This review reframes industrial visual sim-to-real as a domain-gap problem organized by prior availability. We distinguish CAD-available settings, where explicit object geometry can support rendering, calibration, pose estimation, segmentation, and test-time geometric verification; CAD-unavailable settings, where geometry is replaced by normal-reference appearance, feature distributions, teacher-student residuals, synthetic anomaly assumptions, foundation features, or vision-language priors; and boundary-prior settings, where approximate models, templates, reference views, or semantic correspondences preserve only part of the CAD role. This framing connects CAD-based detection and 6D pose-estimation literature with industrial anomaly and surface-inspection literature that is usually reviewed separately. To make the taxonomy concrete, we use empirical anchors on T-LESS/BOP, MVTec AD, and VisA. The anchors show that CAD render count alone does not close transfer; source-distribution design, detector capacity, and small real calibration can matter more. They also show that CAD at test time creates a distinct verification channel through mask, pose, and depth consistency, whereas CAD-unavailable inspection relies on calibrated normality and feature deviation. The review therefore argues against a single cross-task leaderboard and instead asks what prior grounds the deployment decision.", "github": "https://github.com/JacksonTao888/industrial-visual-sim2real-priors" }, { "date": "2026-06-03", "arxiv_id": "2606.01770", "title": "Adaptive Auto-Harness: Sustained Self-Improvement for Agentic System Deployment on Open-Ended Task Streams", "authors": [ "Zewen Liu", "Zhan Shi", "Yisi Sang", "Bing He", "Minhua Lin", "Tianxin Wei", "Dakuo Wang", "Benoit Dumoulin", "Wei Jin", "Hanqing Lu" ], "github_manual": "", "github_regex": "https://github.com/A-EVO-Lab/AdaptiveHarness", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Auto-harness systems such as A-Evolve, GEPA, and Meta-Harness improve LLM agents by optimizing prompts, skills, tools, memories, and supporting infrastructure from execution feedback, but they are typically evaluated on fixed offline benchmarks. Real deployments instead present open-ended task streams: histories grow without a fixed endpoint, heterogeneous tasks require different harnesses, and problem distributions shift over time. These challenges make a single repeatedly and densely updated harness brittle, causing performance degradation as accuracy peaks early and then declines. This motivates sustained harness construction with task-wise adaptation. We introduce Adaptive Auto-Harness, a framework and system for such streams. The framework decomposes the gap to an oracle harness into evolution loss and adaptation loss. The system addresses these losses with a stateful multi-agent evolver, a harness tree with solve-time routing, and human-steering hooks for cases where history lacks the needed signal. Across prediction-market, security-competition, and event-forecasting streams, Adaptive Auto-Harness outperforms five existing auto-harness baselines and ablations attribute gains to better construction, routing, or targeted human steering. Code is available in https://github.com/A-EVO-Lab/AdaptiveHarness .", "github": "https://github.com/A-EVO-Lab/AdaptiveHarness" }, { "date": "2026-06-03", "arxiv_id": "2606.03748", "title": "Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models", "authors": [ "Glenn Jocher", "Jing Qiu", "Mengyu Liu", "Shuai Lyu", "Fatih Cagatay Akyon", "Muhammet Esat Kalfaoglu" ], "github_manual": "", "github_regex": "https://github.com/ultralytics/ultralytics", "github_paper_md": "", "github_api": "https://github.com/ultralytics/ultralytics", "github_api_added_by": "user", "project_page": "https://docs.ultralytics.com/models/yolo26", "abstract": "Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware. The YOLO family has become widely deployed for this reason, yet most YOLO detectors still rely on non-maximum suppression at inference, carry heavy detection heads due to Distribution Focal Loss, require long training schedules, and can leave the smallest objects without positive label assignments. We present Ultralytics YOLO26, a unified real-time vision model family that addresses these limitations through coordinated architecture and training advances. YOLO26 uses a dual-head design for native NMS-free end-to-end inference and removes DFL entirely, yielding a lighter head with unconstrained regression range. Its training pipeline combines MuSGD, a hybrid Muon-SGD optimizer adapted from large language model training; Progressive Loss, which shifts supervision toward the inference-time head; and STAL, a label assignment strategy that guarantees positive coverage for small objects. Beyond detection, YOLO26 introduces task-specific head and loss designs for instance segmentation, pose estimation, and oriented detection, producing consistent gains across tasks and scales. The family spans five scales (n/s/m/l/x) and supports detection, instance segmentation, pose estimation, classification, and oriented detection in a single pipeline, with an open-vocabulary extension, YOLOE-26, for text-, visual-, and prompt-free inference. Across all scales, YOLO26 achieves 40.9-57.5 mAP on COCO at 1.7-11.8 ms T4 TensorRT latency, advancing the accuracy-latency Pareto front over prior real-time detectors, while YOLOE-26x reaches 40.6 AP on LVIS minival under text prompting. Code and models are available at https://github.com/ultralytics/ultralytics.", "github": "https://github.com/ultralytics/ultralytics" }, { "date": "2026-06-03", "arxiv_id": "2606.03029", "title": "Conditional Hypothesis Generation for LLM-Based Text Analysis with Researcher-Specified Covariates", "authors": [ "Paiheng Xu", "Jing Liu", "Wei Ai" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "A core goal of computational social science is to discover interpretable differences in how language varies across outcomes of interest, such as political affiliation or instructional quality. Recent LLM-based hypothesis generation methods describe such differences in natural language, but select for globally discriminative patterns without accounting for covariates that shape the data based on researchers' domain knowledge. When covariates are ignored, selected patterns can reflect confounds rather than differences of substantive interest. We introduce conditional hypothesis generation, a framework that incorporates researcher-specified covariates to steer hypothesis discovery toward differences that hold within relevant subgroups. Two challenges arise: the target subgroup may be underrepresented (stratum imbalance), and the direction of a difference may reverse across subgroups (sign reversal). We propose two econometrics-inspired methods: one introduces feature--covariate interactions to detect sign reversals, and the other applies within-stratum demeaning and inverse-frequency reweighting to equalize underrepresented strata. Synthetic experiments show each method outperforms global baselines in its targeted setting, and expert evaluation on two real-world datasets confirms that covariate-aware generation surfaces more useful hypotheses within relevant subgroups.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2606.01476", "title": "OmniOPD: Logit-Free On-Policy Distillation via Speculative Verification", "authors": [ "Yuhang Zhou", "Lizhu Zhang", "Yifan Wu", "Mingyi Wang", "Peng Bo", "Jiayi Liu", "Xiangjun Fan", "Zhuokai Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "On-Policy Distillation (OPD) trains a student model on its own generative trajectories under dense token-level feedback from a stronger teacher, mitigating both the off-policy distribution shift of Supervised Fine-Tuning (SFT) and the sparse credit assignment of Reinforcement Learning (RL). However, standard OPD faces two coupled limitations. First, it requires direct access to the teacher's token-level logits, excluding a broad class of capable proprietary models from serving as teachers. Second, the token-level logit signal itself is brittle, depending on a narrow overlap of plausible next tokens between teacher and student, and prone to amplifying degenerate patterns such as repetition loops. In this paper, we introduce OmniOPD, a novel framework that addresses both limitations through a logit-free, chunk-level supervision signal. OmniOPD replaces deterministic logit matching with Monte Carlo rollouts that approximate the teacher's local preferences through a continuous semantic similarity metric over multi-token chunks, and concentrates this supervision via a peak-entropy scheduler that audits the student only at its high-uncertainty reasoning forks. A Dirichlet-Multinomial Bayesian prior and a base-model KL anchor further bound the variance of discrete sampling and prevent policy collapse across unaudited tokens. Across competitive benchmarks, OmniOPD surpasses the standard OPD approach by up to +28.64% on math, confirming that chunk-level semantic verification extracts a more reliable learning signal than token-level logit matching, whose high information density is offset by significant noise and brittleness. Furthermore, when paired with stronger black-box teachers such as Claude-4.5-Haiku and Gemini-2.5-Flash, OmniOPD achieves an additional +9.54% relative on math over its open-weight teacher counterpart, advancing the student past the performance of self-exploratory RL.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2606.01955", "title": "WALL-WM: Carving World Action Modeling at the Event Joints", "authors": [ "Shalfun Li", "Victor Yao", "Charles Yang", "Truth Qu", "Regis Cheng", "Ryan Yu", "Howard Lu", "Newton Von", "Vincent Chen", "Yohann Tang", "Maeve Zhang", "Ellie Ma", "Gody Li", "Sage Yang", "Lorien Shu", "J. W. Gao", "Ethan Chen", "Colin Ye", "Yu Sun", "Elise Mon", "PS Zhang", "Neo Li", "Lily Li", "James Wang", "Ping Yang", "Chris Pan", "Lucy Liang", "Hang Su", "Roy Gan", "Hao Wang", "Qian Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/X-Square-Robot/wall-x", "github_api_added_by": "user", "project_page": "https://x2robot.com/pages/wm", "abstract": "WALL-WM is a World Action Model that shifts video-action learning from chunk-centric optimization to event-grounded Vision-Language-Action pretraining, using semantically coherent action events as the atomic unit of learning. Existing WAMs commonly initialize from multimodal or video foundation models and then optimize fixed-length action chunks conditioned directly on the current observation and instruction. Although convenient, this chunk-centric formulation creates a fundamental granularity mismatch. Language describes semantic goals and events, vision evolves through continuous scene dynamics, and actions operate at control-level timescales; forcing all three into the same fixed-length prediction window turns VLA training into short-horizon correlation fitting. WALL-WM addresses this mismatch by organizing both supervision and data around semantic events. Specifically, it pairs event-grounded VLA pretraining with a data ecosystem built from event-level captions and cluster-balanced sampling, enabling scalable learning over diverse behaviors, scenes, and task structures. From the same event-pretrained backbone, WALL-WM supports two complementary inference modes. The event mode consumes next-event descriptions and enables variable-length execution chunks, while the unified mode uses a VLM with Staircase Decoding to condition conventional fixed-length chunk inference while preserving a gradient-continuous VLA path. Together with Muon-optimizer-based large-scale pretraining infrastructure, WALL-WM provides a practical scale-up recipe for general-purpose WAMs. Experiments show that WALL-WM generalizes broadly across language, scenes, and tasks, achieving state-of-the-art performance in large-scale real-world generalization evaluation.", "github": "https://github.com/X-Square-Robot/wall-x" }, { "date": "2026-06-03", "arxiv_id": "2606.03348", "title": "SynCred-Bench: Benchmarking Synthetic Credibility in AI-Generated Visual Misinformation", "authors": [ "Junxiao Yang", "Minghao Zhang", "Xiaoce Wang", "Haoran Liu", "Shiyao Cui", "Hongning Wang", "Minlie Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recent generative models can now produce visual artifacts with realistic embedded text and layouts, creating a new misinformation threat: synthetic credibility. We introduce SYNCRED-Bench, a benchmark of 600 AI-generated misinformation images balanced across six credible-form categories and seven fine-grained circulation styles, together with FP450, a real-image negative set for measuring false positives. Extensive evaluation shows that existing systems remain unreliable: under a 5% false-positive-rate constraint, 15 MLLMs achieve only 10.5% true positive rate (TPR), open-source AIGC detectors achieve less than 5%, and commercial APIs reach 57.6%. Human annotators also struggled to identify synthetic credibility, reaching only 63% TPR. These findings establish synthetic credibility as a severe and underexplored visual misinformation challenge, and provide a benchmark for developing detectors that reason beyond superficial credibility cues.", "github": "" }, { "date": "2026-06-04", "arxiv_id": "2606.03577", "title": "Eliciting Complex Spatial Reasoning in MLLMs through Wide-Baseline Matching", "authors": [ "Hao Zhong", "Muzhi Zhu", "Shenyan Zeng", "Anzhou Li", "Cong Chen", "Hua Geng", "Duochao Shi", "Wentao Ye", "Tao Lin", "Hao Chen", "Chunhua Shen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aim-uofa/ReasonMatch", "github_api_added_by": "user", "project_page": "https://aim-uofa.github.io/reasonmatch/", "abstract": "Wide-baseline matching (WBM) requires integrating geometric understanding, viewpoint changes, fine-grained perception, and occlusion reasoning, making it a challenging testbed for spatial reasoning in multimodal large language models (MLLMs) deployed in physical environments. However, current MLLMs lack systematic evaluation and training frameworks for these capabilities. We introduce ReasonMatch-Bench, a benchmark stratified by viewpoint displacement and matching granularity across indoor, outdoor, and object-centric scenarios, and show that current MLLMs still struggle with fine-grained wide-baseline correspondence: on a difficult 90-sample subset, human annotators achieve 84.0 F1, while the best existing baseline reaches 37.2. To bridge this gap, we build a scalable data-generation pipeline that automatically extracts wide-baseline view pairs from large-scale video-3D corpora, including RGB-D videos and SfM reconstructions, yielding diverse and verifiable supervision. We further propose Dynamic Correspondence Reinforcement Learning (DCRL), which combines Image-Level Viewpoint Progression and Point-Level Correspondence Curriculum to improve WBM training through verifiable rewards without explicit CoT supervision. Extensive experiments show that DCRL substantially improves ReasonMatch-Bench and transfers to related spatial benchmarks, while maintaining general visual understanding performance with modest gains on several benchmarks.", "github": "https://github.com/aim-uofa/ReasonMatch" }, { "date": "2026-06-04", "arxiv_id": "2606.01286", "title": "BenchEvolver: Frontier Task Synthesis via Solution-Centric Evolution", "authors": [ "Yangzhen Wu", "Aaron J. Li", "Wenjie Ma", "Li Cao", "Ziheng Zhou", "Mert Cemri", "Shu Liu", "Yuran Xiu", "Chenxiao Yan", "Haikun Zhao", "Bin Yu", "Ion Stoica", "Dawn Song" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thu-wyz/BenchEvolver", "github_api_added_by": "user", "project_page": "https://benchevolver.github.io/", "abstract": "The rapid progress of frontier large language models has led to widespread benchmark saturation, limiting the ability of existing datasets to differentiate model capabilities or provide useful training signal. For instance, on LiveCodeBench, frontier models achieve over 99% Pass@1 on easy splits and exceed 90% Pass@1 on average across difficulty levels. Constructing new, challenging datasets typically requires substantial human effort, creating a bottleneck for progress. We introduce BenchEvolver, a solution-centric evolutionary framework that automatically transforms existing coding problems into harder variants. Rather than generating problems from scratch, BenchEvolver evolves reference solutions through structured transformations and derives corresponding statements and tests from the evolved solutions. This design grounds generation in executable semantics, enabling scalable construction of high-quality, diverse, and difficult tasks with verifiable correctness. Applying BenchEvolver to LiveCodeBench and SciCode, we obtain evolved tasks that are substantially harder while maintaining validity, reference correctness, and diversity. We further curate LiveCodeBench-Plus, a 91-problem benchmark combining evolved and difficult original LCB-v6 tasks, where frontier-model Pass@1 ranges from 27.5% to 62.6%, restoring clear discrimination among strong coding models. Importantly, evolved tasks remain challenging even for the model that generates them, enabling self-improvement. We further show that RL on evolved LCB tasks improves held-out coding performance: for gpt-oss-20b, seed+evolved training achieves +8.7 and +8.3 Pass@1 gains on LCB v6 Hard and LCB-Pro Easy, exceeding seed-only gains by 70.7% and 34.8%, respectively. Our results show that BenchEvolver can convert saturated benchmarks into frontier-level evaluation suites and reusable training signal.", "github": "https://github.com/thu-wyz/BenchEvolver" }, { "date": "2026-06-04", "arxiv_id": "2606.04036", "title": "Self-Distilled Policy Gradient", "authors": [ "Yifeng Liu", "Shiyuan Zhang", "Yifan Zhang", "Quanquan Gu" ], "github_manual": "", "github_regex": "https://github.com/lauyikfung/SDPG", "github_paper_md": "", "github_api": "https://github.com/lauyikfung/SDPG", "github_api_added_by": "user", "project_page": "https://lauyikfung.github.io/SDPG/", "abstract": "On-policy self-distillation, where a language model conditions on privileged context to supervise its own generations, is a promising source of dense supervision for sparse-reward reinforcement learning. Actually, it can be instantiated as an auxiliary full-vocabulary student-to-teacher reverse Kullback-Leibler divergence loss. We therefore propose SDPG, a self-distilled policy-gradient framework that combines group-relative verifier advantages with normalized standard deviation, exact full-vocabulary on-policy self-distillation, as well as reference-policy KL regularization. Empirically, SDPG improves stability and performance over RLVR and self-distillation baselines. The code is available at https://github.com/lauyikfung/SDPG.", "github": "https://github.com/lauyikfung/SDPG" }, { "date": "2026-06-03", "arxiv_id": "2606.04433", "title": "Stateful Visual Encoders for Vision-Language Models", "authors": [ "Zirui Wang", "Junwei Yu", "Adam Yala", "David M. Chan", "Joseph E. Gonzalez", "Trevor Darrell" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/StatefulVisualEncoders/StatefulVisualEncoders", "github_api_added_by": "user", "project_page": "https://statefulvisualencoders.github.io/", "abstract": "Vision-language models (VLMs) are increasingly used in multi-image, multi-turn agentic settings where decisions depend on visual changes. However, in existing open-weight VLMs, visual comparisons happen only inside the language model, while the visual encoder itself remains stateless: each image is encoded independently, without access to the prior visual context. As a result, small but task-critical changes may be attenuated before the language model has a chance to compare them, especially when those changes do not affect the high-level semantics of the scene. We introduce a Stateful Visual Encoder, which conditions each visual representation on prior visual features. Under supervised finetuning, VLMs equipped with stateful encoders achieve consistent improvements on controlled tasks involving cross-image spatial aggregation, multi-object visual differencing, and visual trajectory behavior cloning. These improvements are consistent across input resolutions, language model sizes, and VLM backbones. Finally, we validate our model on real-world tasks, including longitudinal radiology, fine-grained image comparison, and remote sensing, where stateful encoders consistently improve generalist VLM baselines and can match or surpass specialized models in selected domains. Project page: https://statefulvisualencoders.github.io/", "github": "https://github.com/StatefulVisualEncoders/StatefulVisualEncoders" }, { "date": "2026-06-03", "arxiv_id": "2606.04455", "title": "The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?", "authors": [ "Xinyu Lu", "Tianshu Wang", "Pengbo Wang", "zujie wen", "Zhiqiang Zhang", "Jun Zhou", "Boxi Cao", "Yaojie Lu", "Hongyu Lin", "Xianpei Han", "Le Sun" ], "github_manual": "", "github_regex": "https://github.com/ant-research/meta-agent-challenge", "github_paper_md": "", "github_api": "https://github.com/ant-research/meta-agent-challenge", "github_api_added_by": "user", "project_page": "https://meta-agent-challenge.com/", "abstract": "Current AI benchmarks evaluate agents on task execution within human-designed workflows. These evaluations fundamentally fail to measure a critical next-level capability: whether models can autonomously develop agent systems. We introduce the Meta-Agent Challenge (MAC), an evaluation framework designed to test the capacity of frontier models for autonomous agent development. Specifically, a code agent (the meta-agent) is given a sandboxed environment, an evaluation API, and a time limitation to iteratively program an agent artifact that maximizes performance on a held-out test set across five domains. To ensure evaluation integrity, this framework is secured by multi-layer defenses against reward hacking. Leveraging this framework, we demonstrate that meta-agents rarely match human-engineered baseline policies, and the few that do are dominated by proprietary frontier models. Moreover, the design process exhibits high variance, and high optimization pressure surfaces emergent adversarial behaviors like ground-truth exfiltration-highlighting critical deficits in both robustness and model alignment. Ultimately, MAC provides a rigorous, open-source benchmark for autonomous AI research and development, offering an empirical proxy for evaluating recursive self-improvement. Benchmark is publicly available at: https://github.com/ant-research/meta-agent-challenge.", "github": "https://github.com/ant-research/meta-agent-challenge" }, { "date": "2026-06-03", "arxiv_id": "2606.02461", "title": "AgentCL: Toward Rigorous Evaluation of Continual Learning in Language Agents", "authors": [ "Yiheng Shu", "Bernal Jim\u00e9nez Guti\u00e9rrez", "Saisri Padmaja Jonnalagedda", "Yuguang Yao", "Huan Sun", "Yu Su" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes. Continual learning expects an agent to accumulate reusable experience across a stream of tasks, improve over time, and avoid interference from irrelevant experiences. Unfortunately, existing benchmarks struggle to evaluate continual learning in language agents rigorously. Most efforts focus on retrieval and reasoning over long-context conversations or documents, while recent lifelong-adaptation benchmarks often rely on naive task streams with limited analysis of cross-task relationships, making it difficult to understand what an agent learns and reuses over time. This paper presents an evaluation framework AgentCL for continual learning in agents, centered on controlled task streams and metrics for transfer gains. AgentCL constructs compositional streams where earlier sub-solutions, evidence, or workflows are intentionally reusable in later tasks, and contrasts them with naive streams where such reusability is not guaranteed. We use the benchmark to evaluate non-parametric memory designs for continual learning. To diagnose how memory design choices affect continual learning, we develop MemProbe, a probing method that stores interactions, insights, and skills, while filtering unreliable experiences during consolidation. Empirical analysis across coding, deep research, and language understanding/reasoning tasks shows that naive streams offer limited ability to distinguish memory designs, whereas controlled streams more clearly distinguish their plasticity. Meanwhile, naive and held-out settings often yield limited gains and can expose memory-induced degradation. These results highlight the need for stronger memory designs that balance plasticity and stable reuse.", "github": "" }, { "date": "2026-06-03", "arxiv_id": "2606.04889", "title": "GRAIL: Gradient-Reweighted Advantages for Reinforcement Learning with Verifiable Rewards", "authors": [ "Tej Deep Pala", "Vernon Toh", "Soujanya Poria" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/declare-lab/grail", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (e.g. GRPO) is now a common way to improve mathematical reasoning in Large Language Models (LLMs). However, current methods usually broadcast one sequence-level advantage to all tokens, or use costly process reward models (PRMs) for step-level supervision. Uniform advantage distribution assumes that all tokens contribute equally to the final reward. This dilutes the gradient signal, since flawed reasoning steps and filler words are updated as strongly as valid logical inferences. To address this, we introduce Gradient-Reweighted Advantage (GRAIL), an intrinsic token-wise advantage reweighting method. GRAIL uses gradient-activation saliency to place more weight on tokens that are more locally sensitive to the final answer. Evaluations across five models from the Qwen3, R1-distilled and OctoThinker families show that GRAIL consistently outperforms GRPO. GRAIL achieved an average improvement of 3.60% in accuracy and 3.05% in Pass@3, demonstrating that fine-grained reasoning alignment can be achieved without process-level supervision.", "github": "https://github.com/declare-lab/grail" }, { "date": "2026-06-04", "arxiv_id": "2606.02800", "title": "Cosmos 3: Omnimodal World Models for Physical AI", "authors": [ "Aditi", "Niket Agarwal", "Arslan Ali", "Jon Allen", "Martin Antolini", "Adeline Aubame", "Alisson Azzolini", "Junjie Bai", "Maciej Bala", "Yogesh Balaji", "Josh Bapst", "Aarti Basant", "Mukesh Beladiya", "Mohammad Qazim Bhat", "Zaid Pervaiz Bhat", "Dan Blick", "Vanni Brighella", "Han Cai", "Tiffany Cai", "Eric Cameracci", "Jiaxin Cao", "Yulong Cao", "Mark Carlson", "Carlos Casanova", "Ting-Yun Chang", "Yan Chang", "Yu-Wei Chao", "Prithvijit Chattopadhyay", "Roshan Chaudhari", "Chieh-Yun Chen", "Junyu Chen", "Ke Chen", "Qizhi Chen", "Wenkai Chen", "Xiaotong Chen", "Yu Chen", "An-Chieh Cheng", "Click Cheng", "Xiu Chia", "Jeana Choi", "Chaeyeon Chung", "Wenyan Cong", "Yin Cui", "Magdalena Dadela", "Nalin Dadhich", "Wenliang Dai", "Joyjit Daw", "Alperen Degirmenci", "Rodrigo Vieira Del Monte", "Robert Denomme", "Sameer Dharur", "Marco Di Lucca", "Ke Ding", "Wenhao Ding", "Yifan Ding", "Yuzhu Dong", "Nicole Drumheller", "Yilun Du", "Aigul Dzhumamuratova", "Aleksandr Efitorov", "Hamid Eghbalzadeh", "Naomi Eigbe", "Imad El Hanafi", "Hassan Eslami", "Benedikt Falk", "Jiaojiao Fan", "Jim Fan", "Amol Fasale", "Sergiy Fefilatyev", "Liang Feng", "Francesco Ferroni", "Sanja Fidler", "Xiao Fu", "Vikram Fugro", "Prashant Gaikwad", "TJ Galda", "Katelyn Gao", "Yihuai Gao", "Wenhang Ge", "Sreyan Ghosh", "Arushi Goel", "Vivek Goel", "Akash Gokul", "Rama Govindaraju", "Jinwei Gu", "Miguel Guerrero", "Elfie Guo", "Aryaman Gupta", "Siddharth Gururani", "Hugo Hadfield", "Song Han", "Ankur Handa", "Zekun Hao", "Mohammad Harrim", "Ali Hassani", "Nathan Hayes-Roth", "Yufan He", "Chris Helvig", "Cyrus Hogg", "Madison Huang", "Michael Huang", "Sophia Huang", "Yufan Huang", "Jacob Huffman", "DeLesley Hutchins", "Suneel Indupuru", "Boris Ivanovic", "Arihant Jain", "Joel Jang", "Ryan Ji", "Yanan Jian", "Dongfu Jiang", "Jingyi Jin", "Atharva Joshi", "Nikhilesh Joshi", "Pranjali Joshi", "Jaehun Jung", "Weiwei Kang", "Scott Kassekert", "Jan Kautz", "Ashna Khetan", "Julia Kiczka", "Slawek Kierat", "Gwanghyun Kim", "Kuno Kim", "Sunny Kim", "Kezhi Kong", "Xin Kong", "Zhifeng Kong", "Tomasz Kornuta", "Egor Krivov", "Hui Kuang", "Saurav Kumar", "Chia-Wen Kuo", "George Kurian", "Wojciech Kutak", "JF Lafleche", "Himangshu Lahkar", "Omar Laymoun", "Jayjun Lee", "Sanggil Lee", "Gabriele Leone", "Boyi Li", "Freya Li", "Jiajun Li", "Jinfeng Li", "Ling Li", "Pengcheng Li", "Shangru Li", "Tingle Li", "Xiaolong Li", "Xuan Li", "Zhaoshuo Li", "Zhiqi Li", "Hao Liang", "Maosheng Liao", "Chen-Hsuan Lin", "Tsung-Yi Lin", "Ming-Yu Liu", "Sifei Liu", "Zihan Liu", "Hai Loc Lu", "Xiangyu Lu", "Alice Luo", "Ruipu Luo", "Wenjie Luo", "Jiangran Lyu", "Martin Ding Ma", "Nic Ma", "Qianli Ma", "Dawid Majchrowski", "Louis Marcoux", "Miguel Martin", "Qing Miao", "Ashkan Mirzaei", "Shreyas Misra", "Kaichun Mo", "Durra Mohsin", "Hyejin Moon", "Pawel Morkisz", "Saeid Motiian", "Kirill Motkov", "Seungjun Nah", "Yashraj Narang", "Deepak Narayanan", "Thabang Ngazimbi", "Julian Ouyang", "David Page", "Yatian Pang", "Sehwi Park", "Mahesh Patekar", "Mostofa Patwary", "Marco Pavone", "Trung Pham", "Wei Ping", "Soha Pouya", "Shrimai Prabhumoye", "Varun Praveen", "Delin Qu", "Hesam Rabeti", "Morteza Ramezanali", "Marilyn Reeb", "Xuanchi Ren", "Kristen Rumley", "Wojciech Rymer", "Jun Saito", "Yeongho Seol", "John Shao", "Piyush Shekdar", "Tianwei Shen", "Humphrey Shi", "Min Shi", "Stella Shi", "Kevin Shih", "Mohammad Shoeybi", "Mateusz Sieniawski", "Shuran Song", "Alexander Sotelo", "Amir Sotoodeh", "Sunil Srinivasa", "Vignesh Srinivasakumar", "Bartosz Stefaniak", "Rahul Heinrich Steiger", "Shangkun Sun", "Jiaxiang Tang", "Shitao Tang", "Yangyang Tang", "Yue Tang", "Tolou Tavakkoli", "Kayley Ting", "Krzysztof Tomala", "Wei-Cheng Tseng", "Jibin Varghese", "Sergei Vasilev", "Thomas Volk", "Raju Wagwani", "Roger Waleffe", "Andrew Z. Wang", "Boxiang Wang", "Haoxiang Wang", "Qiao Wang", "Shihao Wang", "Shijie Wang", "Ting-Chun Wang", "Yan Wang", "Yu Wang", "David Wehr", "Fangyin Wei", "Xinshuo Weng", "Jay Zhangjie Wu", "Kedi Wu", "Hongchi Xia", "Summer Xiao", "Tianjun Xiao", "Kevin Xie", "Daguang Xu", "Jiashu Xu", "Mengyao Xu", "Ruqing Xu", "Xingqian Xu", "Yao Xu", "Dinghao Yang", "Dong Yang", "Hans Yang", "Xiaodong Yang", "Xuning Yang", "Yichu Yang", "Yurong You", "Zhiding Yu", "Hao Yuan", "Simon Yuen", "Xiaohui Zeng", "Pengcuo Zeren", "Cindy Zha", "Haotian Zhang", "Jenny Zhang", "Jing Zhang", "Liangkai Zhang", "Paris Zhang", "Shun Zhang", "Xuanmeng Zhang", "Zhizheng Zhang", "Ann Zhao", "Yilin Zhao", "Yuliya Zhautouskaya", "Charles Zhou", "Fengzhe Zhou", "Shilin Zhu", "Yuke Zhu", "Dima Zhylko", "Artur Zolkowski" ], "github_manual": "", "github_regex": "https://github.com/nvidia/cosmos", "github_paper_md": "", "github_api": "https://github.com/NVIDIA/cosmos", "github_api_added_by": "user", "project_page": "https://research.nvidia.com/labs/cosmos-lab/cosmos3/", "abstract": "We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-transformers architecture. By supporting highly flexible input-output configurations, Cosmos 3 seamlessly unifies critical modalities for Physical AI -- effectively subsuming vision-language models, video generators, world simulators, and world-action models into a single framework. Our evaluation demonstrates that Cosmos 3 establishes a new state-of-the-art across a diverse suite of understanding and generation tasks, demonstrating omnimodal world models as scalable, general-purpose backbones for embodied agents. Our post-trained Cosmos 3 models were ranked as the best open-source Text-to-Image and Image-to-Video models by Artificial Analysis, and the best policy model by RoboArena at the time the technical report was written. To accelerate open research and deployment in Physical AI, we make our code, model checkpoints, curated synthetic datasets, and evaluation benchmark available under the Linux Foundation's OpenMDW-1.1 https://openmdw.ai/license/1-1/ License at https://github.com/nvidia/cosmos}{github.com/nvidia/cosmos and https://huggingface.co/collections/nvidia/cosmos3 . The project website is available at https://research.nvidia.com/labs/cosmos-lab/cosmos3 .", "github": "https://github.com/NVIDIA/cosmos" }, { "date": "2026-06-04", "arxiv_id": "2606.04527", "title": "Echo-Infinity: Learning Evolving Memory for Real-Time Infinite Video Generation", "authors": [ "Yuxuan Bian", "Zeyue Xue", "Songchun Zhang", "Shiyi Zhang", "Weiyang Jin", "Yaowei Li", "Junhao Zhuang", "Haoran Li", "Jie Huang", "Haoyang Huang", "Nan Duan", "Qiang Xu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Echo-Team-Joy-Future-Academy-JD/Echo-Infinity", "github_api_added_by": "user", "project_page": "https://echo-team-joy-future-academy-jd.github.io/Echo-Infinity/", "abstract": "We present Echo Infinity, an autoregressive (AR) framework towards real-time infinite video generation that employs a learnable evolving memory to dynamically filter, abstract, and compress any-length history at constant cost. Existing methods mainly curate memory with predefined KV-cache schedules, fixed-ratio heuristic compression, or inference-time RoPE adaptation. These designs inevitably lose historical information and amplify compounding errors due to their limited cache window and ignorance of autoregressive generation noise. Inspired by human memory consolidation, Echo-Infinity replaces handcrafted memory curation with learnable Memory Query, which are updated by attention and a gating mechanism when past frames are evicted from the local window. The queries are optimized end-to-end with the video diffusion transformers (DiTs), forming an evolving memory that supports arbitrary compression ratios with constant computation independent of video length. They also act as a generalizable generation prior, improving quality even when only the optimized initial state is used. We further introduce Unified Relative RoPE Recipe, which anchors the sink frames to start from id 0 and lets the newest frame id grow at most to the DiTs' pretrained maximum temporal RoPE id throughout training and inference, freeing the model from the finite RoPE constraint and closing the train-test RoPE extrapolation gap. In long and short video generation, Echo-Infinity achieves state-of-the-art performance, and, to our knowledge, demonstrates promising 24-hour (>1.3 M frames) real-time rollouts for the first time, suggesting a practical path toward infinite video generation.", "github": "https://github.com/Echo-Team-Joy-Future-Academy-JD/Echo-Infinity" }, { "date": "2026-06-04", "arxiv_id": "2606.03031", "title": "AUDITFLOW: Executable Symbolic Environments for Structured Financial Reporting Verification", "authors": [ "Yan Wang", "Xuguang Ai", "Jaisal Patel", "Xueqing Peng", "Fengran Mo", "Yupeng Cao", "Haohang Li", "Mingyu Cao", "Lingfei Qian", "V\u00edctor Guti\u00e9rrez-Basulto" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Structured financial audit verification is difficult for language-model agents because correctness depends on structured evidence rather than text alone. A model must link reported facts to taxonomy concepts, traverse calculation or dimensional relations, and recompute expected values before applying an audit rule. We propose AuditFlow, a graph-grounded multi-agent framework that separates adaptive search from deterministic verification. AuditFlow builds a symbolic environment from a static US-GAAP taxonomy graph and a dynamic XBRL filing graph, and exposes it through typed tools for fact retrieval, taxonomy traversal, numerical checking, and rule evaluation. Two junior auditors inspect each case from regulatory and evidentiary views, while a senior auditor resolves disagreements and can request further investigation. The final reports are fused through evidential aggregation to produce an audit verdict, expected value, evidence trail, and trustworthiness score. On a FinAuditing-derived FinMR sample, AuditFlow reaches 82.09% joint audit accuracy under GPT-5.5, outperforming the strongest baseline by 14.93 points. Removing deterministic checks drops accuracy to 17.91%, showing that the symbolic environment performs the verification step that the model cannot reliably replace.", "github": "" }, { "date": "2026-06-04", "arxiv_id": "2606.01993", "title": "MMG2Skill: Can Agents Distill In-the-Wild Guides into Self-Evolving Skills?", "authors": [ "Xinyu Che", "Junqi Xiong", "Yunfei Ge", "Xinping Lei", "Shihao Li", "Hang Yan", "Han Li", "Yuanxing Zhang", "Zhiqi Bai", "Jinhua Hao", "Ming Sun", "Han Li", "Jiaheng Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NJU-LINK/MMG2Skill", "github_api_added_by": "user", "project_page": "", "abstract": "Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks. However, such knowledge is often multimodal, heterogeneous, noisy, and implicitly assumes human executors, making it difficult to use directly as the skills required by agents. To bridge the gap between human-oriented guides and agent-executable skills, we formalize this problem as guide-to-skill learning: converting in-the-wild guides into executable skills and continuously improving them from trajectories observable to the agent. To evaluate the capability of existing agents on this task, we introduce MMG2Skill-Bench, the first benchmark designed for this problem. We further propose MMG2Skill, a closed-loop framework that compiles guides into editable skills, conditions a fixed vision-language model (VLM) agent on these skills during execution, and revises the skills from trajectory-level root-cause feedback without using benchmark scores. Across GUI control, open-ended gameplay, and strategic card play with six VLM backbones, MMG2Skill consistently outperforms vanilla baseline agents in every model-domain setting, achieving macro-average gains of +12.8 to +25.3 percentage points across backbones. Ablation studies show that directly prompting agents with raw guides can degrade performance, while both structured skill construction and trajectory-driven revision are necessary for the observed improvements. On success-inferable tasks, analyzer-based early stopping further prevents late-stage performance regressions and saves 25%-53% of attempts when the success signal is properly calibrated.", "github": "https://github.com/NJU-LINK/MMG2Skill" }, { "date": "2026-06-04", "arxiv_id": "2606.02060", "title": "Where Do Deep-Research Agents Go Wrong? Span-Level Error Localization in Agent Trajectories", "authors": [ "Jiaming Wang", "Ziteng Feng", "Jiangtao Wu", "Ruihao Li", "Qianqian Xie", "Yuxiang Ren", "He Zhu", "Xueming Han", "Fanyu Meng", "Junlan Feng", "Jiaheng Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NJU-LINK/DRIFT", "github_api_added_by": "user", "project_page": "https://nju-link.github.io/DRIFT/", "abstract": "Deep-research agents solve tasks through long trajectories of search, tool use, evidence inspection, and answer synthesis. Evaluation based on final answers shows whether an agent succeeds, but not which parts of the trajectory make the answer unreliable. We study span-level error localization for deep-research agents. We collect 2,790 real trajectories from two agent frameworks, three backbone models, and three benchmarks, convert raw logs into semantic spans, and annotate harmful error spans through LLM-assisted expert review. From these annotations, we build TELBench, a 1,000-instance benchmark for identifying error spans among normal exploration, failed searches, tentative hypotheses, and harmless noise. We further propose DRIFT, a claim-centric auditing framework that tracks agent claims, checks their support in trajectory evidence, and marks spans where unsupported or conflicting claims affect the answer path. Experiments across model families and auditing frameworks show that DRIFT improves span-level error localization and first-error accuracy by up to 30 percentage points. Our work provides a process-level view of reliability in deep-research agents.", "github": "https://github.com/NJU-LINK/DRIFT" }, { "date": "2026-06-04", "arxiv_id": "2606.03746", "title": "Qwen-Image-Flash: Beyond Objective Design", "authors": [ "Tianhe Wu", "Kun Yan", "Zikai Zhou", "Lihan Jiang", "Jiahao Li", "Jie Zhang", "Kaiyuan Gao", "Ningyuan Tang", "Shengming Yin", "Xiaoyue Chen", "Xiao Xu", "Yilei Chen", "Yuxiang Chen", "Yan Shu", "Yixian Xu", "Yanran Zhang", "Zihao Liu", "Zhendong Wang", "Zekai Zhang", "Deqing Li", "Liang Peng", "Yi Wang", "Jingren Zhou", "Chenfei Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Few-step distillation has become an effective strategy for accelerating advanced visual generative models, yet prior work has largely focused on distillation objectives. In this work, we revisit few-step distillation from a complementary perspective, focusing on the training recipe that critically shapes student performance. Using Qwen-Image-2.0 as a representative case, we systematically investigate three factors in unified text-to-image generation and instruction-guided image editing distillation: data composition, teacher guidance, and task mixture. Our empirical analysis reveals several non-obvious behaviors, which motivate the development of Qwen-Image-Flash. Overall, our results suggest that effective few-step distillation requires not only carefully designed objectives, but also principled organization of the broader training pipeline.", "github": "" }, { "date": "2026-06-04", "arxiv_id": "2606.05158", "title": "Streaming Communication in Multi-Agent Reasoning", "authors": [ "Zhen Yang", "Xiaogang Xu", "Wen Wang", "Cong Chen", "Xander Xu", "Ying-Cong Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/EnVision-Research/StreamMA", "github_api_added_by": "user", "project_page": "https://zhenyangcs.github.io/StreamMA-website/", "abstract": "Multi-agent reasoning systems adopt a \"generate-then-transfer\" paradigm that forces end-to-end latency to scale linearly with pipeline depth. We introduce StreamMA, a multi-agent reasoning system that streams each reasoning step to downstream agents as soon as it is generated, pipelining adjacent agents and thus reducing latency. Surprisingly, this pipelining also improves effectiveness: because multi-step reasoning quality is non-uniform and early steps are more reliable than later ones, working with these reliable early steps instead of the full chain prevents error-prone late steps from misleading downstream agents. We formalize both advantages with the first closed-form joint analysis of stream, serial, and single protocols, deriving the effectiveness ordering, speedup upper bound, and cost ratio. Across eight reasoning benchmarks spanning mathematics, science, and code, two frontier LLMs (Claude Opus 4.6 and GPT-5.4), and three topologies (Chain, Tree, Graph), StreamMA outperforms both baselines (avg. +7.3 pp, max +22.4 pp on HMMT 2026; Claude Opus 4.6-high). Beyond these contributions, we discover a \"step-level scaling law\": increasing per-agent steps consistently improves both effectiveness and efficiency, a new scaling dimension orthogonal to and composable with agent-count scaling.", "github": "https://github.com/EnVision-Research/StreamMA" }, { "date": "2026-06-04", "arxiv_id": "2606.05121", "title": "Audio Interaction Model", "authors": [ "Zhifei Xie", "Zihang Liu", "Ze An", "Xiaobin Hu", "Yue Liao", "Ziyang Ma", "Dongchao Yang", "Mingbao Lin", "Deheng Ye", "Shuicheng Yan", "Chunyan Miao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xzf-thu/Audio-Interaction", "github_api_added_by": "user", "project_page": "https://xzf-thu.github.io/Audio-Interaction/", "abstract": "Audio is an inherently interactive modality, yet today's Large Audio Language Models (LALMs) are offline, and streaming audio models each handle only a single task such as streaming ASR or voice chatting. It is time to unify them into one online LALM: a model that, through an always-on perceive-decide-respond loop, listens to sound, environment, and instructions in real time and reacts on the fly. We formalize this regime as the Audio Interaction Model, and realize it with Audio-Interaction, a unified streaming model that retains offline task execution while adding online general audio instruction following, from dialogue to full voice chatting, deciding when to respond from the semantics of the stream. To enable this, we propose SoundFlow, a framework that instantiates the perceive-decide-respond loop end to end, from data to training to deployment, through streaming-native data construction, comprehension-aware training, and asynchronous low-latency inference for stable real-time interaction. We further construct StreamAudio-2M, a 2.6M-item streaming corpus spanning 7 fundamental abilities and 28 sub-tasks, and Proactive-Sound-Bench for evaluating proactive audio intervention. Across 8 benchmarks, Audio-Interaction preserves competitive performance on mainstream audio tasks while unlocking capabilities inaccessible to offline LALMs, including real-time ASR, streaming audio instruction following, and proactive help.", "github": "https://github.com/xzf-thu/Audio-Interaction" }, { "date": "2026-06-04", "arxiv_id": "2606.05160", "title": "GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors", "authors": [ "Tianyi Xie", "Haotian Zhang", "Jinhyung Park", "Zi Wang", "Bowen Wen", "Jiefeng Li", "Xueting Li", "Qingwei Ben", "Haoyang Weng", "Yufei Ye", "David Minor", "Tingwu Wang", "Chenfanfu Jiang", "Sanja Fidler", "Jan Kautz", "Linxi Fan", "Yuke Zhu", "Zhengyi Luo", "Umar Iqbal", "Ye Yuan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/GRAIL", "github_api_added_by": "user", "project_page": "https://research.nvidia.com/labs/dair/grail/", "abstract": "Scaling humanoid loco-manipulation requires robot-compatible demonstrations across diverse objects, whole-body motions, and scene geometries, but teleoperation and motion capture are difficult to scale because each collection depends on physical setups, instrumented actors, and robot operation. We present GRAIL, a digital generation pipeline that remains fully virtual until deployment: it composes 3D assets, simulator-ready scenes, and priors from video foundation models (VFMs) to synthesize interactions without rebuilding physical environments or teleoperating the robot. Rather than reconstructing unconstrained in-the-wild videos, GRAIL starts from fully specified 3D configurations in which object geometry, camera parameters, metric scale, environment depth, and a robot-proportioned character are known before video generation and reused during reconstruction. This privileged setup better conditions 4D recovery, allowing model-based object tracking, human motion estimation, and interaction-aware optimization to reconstruct metric 4D human-object interaction (HOI) trajectories with reduced depth ambiguity and morphology mismatch. We retarget the recovered motions to a humanoid robot and train complementary task-general trackers: an object-aware latent adaptor for manipulation and a scene-aware tracker for terrain traversal. GRAIL produces over 20,000 sequences spanning pick-up, object manipulation, sitting, and terrain traversal. Using only GRAIL-generated data, we train egocentric visual policies through a sim-to-real pipeline and deploy them on a Unitree G1 humanoid, achieving 84\\% real-world success on diverse object pick-up and 90\\% success on stair-climbing.", "github": "https://github.com/NVlabs/GRAIL" }, { "date": "2026-06-04", "arxiv_id": "2606.01166", "title": "BraveGuard: From Open-World Threats to Safer Computer-Use Agents", "authors": [ "Yunhao Feng", "Xiaohu Du", "Xinhao Deng", "Yifan Ding", "Ming Wen", "Yixu Wang", "Yuxiang Xie", "Baihui Zheng", "Yingshui Tan", "Yige Li", "Yutao Wu", "Kerui Cao", "Wenke Huang", "Yanming Guo", "Xingjun Ma", "Yu-Gang Jiang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Yunhao-Feng/BraveGuard", "github_api_added_by": "user", "project_page": "", "abstract": "Computer-use agents extend language models from text generation to sustained interaction with files, terminals, browsers, and external tools. This shift creates safety risks that are difficult to detect from isolated prompts or final responses, because harm often emerges only through multi-step execution traces whose individual actions appear locally benign. We introduce BraveGuard, a self-evolving defense framework for training guard models from open-world threat signals and realistic agent trajectories. BraveGuard mines recent research sources to identify emerging risks and attack patterns, instantiates them as executable computer-use tasks, collects agent rollouts, and derives trajectory-level supervision for guard model training. As new threats and validation failures appear, the pipeline can be repeated, yielding an adaptive defense loop rather than a static, benchmark-driven training process. We instantiate BraveGuard by training multiple guard backbones, including Qwen3-Guard and Llama-Guard variants, and evaluate the resulting guards on trajectory-level agent-safety benchmarks. BraveGuard consistently improves safety detection across computer-use trajectories. On AgentHazard, it substantially improves detection accuracy over off-the-shelf guard models, with accuracy increasing from 38.79% to 82.38% under the averaged guard-model setting. These results show that guard supervision grounded in open-world threat discovery and realistic agent execution can improve safety monitoring beyond fixed taxonomies and synthetic prompt-level data. BraveGuard offers a scalable path toward adaptive defenses for computer-use agents facing evolving real-world risks.", "github": "https://github.com/Yunhao-Feng/BraveGuard" }, { "date": "2026-06-04", "arxiv_id": "2606.03197", "title": "MemTrain: Self-Supervised Context Memory Training", "authors": [ "Ziheng Li", "Xingrun Xing", "Haoqing Wang", "Zhi-Hong Deng", "Yehui Tang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Memory is an indispensable capability for long-horizon LLM agents, enabling them to preserve and utilize information accumulated across extended interactions. Existing memory-agent approaches are typically trained end-to-end with reinforcement learning on downstream tasks. However, collecting high-quality annotated problems for memory-intensive scenarios is costly, and the resulting training data often lack sufficient diversity to cover general memory behaviors. In this work, we propose MemTrain, a self-supervised training framework for generally enhancing the context-memory capability of LLM agents for more effective downstream post-training. MemTrain introduces two coupled proxy tasks over unlabeled Wikipedia corpora: (1) an end-to-end masked reconstruction objective, which requires the model to recover masked entities after multiple rounds of memory updates, thereby encouraging memory maintenance from the final outcome perspective; and (2) an intermediate memory recall objective, which requires the model to reconstruct masked historical information using intermediate memory states, encouraging faithful compression and memory completeness throughout the interaction process. The two objectives are jointly optimized using GRPO. Extensive experiments on long-text QA and search-based QA benchmarks demonstrate that MemTrain consistently improves downstream memory-intensive reasoning performance across different models, achieving gains of up to 17.67 points over direct task-specific post-training.", "github": "" }, { "date": "2026-06-04", "arxiv_id": "2606.04688", "title": "MeshWeaver: Sparse-Voxel-Guided Surface Weaving for Autoregressive Mesh Generation", "authors": [ "Jiale Xu", "Wang Zhao", "Ying Shan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Autoregressive mesh generation has gained attention by tokenizing meshes into sequences and training models in a language-modeling fashion. However, existing approaches suffer from two fundamental limitations: (i) low tokenization efficiency, which yields long token sequences and prevents scaling to high-poly meshes, and (ii) absence of geometry-aware guidance, as generation is conditioned only on global shape embeddings rather than local surface cues. We introduce MeshWeaver, an autoregressive framework that treats mesh generation as a surface weaving process by directly predicting the next vertex instead of independent coordinates. At its core is a multi-level sparse-voxel encoder that injects geometric context into the generative process in three complementary ways: providing voxel features as vertex representations, guiding token prediction via cross-attention to voxel features, and serving as a structural scaffold that constrains generation around the input surface. Our hierarchical design enables coarse-to-fine vertex prediction in a single decoding step, while tightly coupling the generative model with 3D geometry. Extensive experiments demonstrate that MeshWeaver achieves a state-of-the-art compression ratio of 18%, can generate meshes with up to 16K faces, and significantly improves geometric fidelity over prior approaches.", "github": "" }, { "date": "2026-06-04", "arxiv_id": "2606.00377", "title": "Score-Control for Hallucination Reduction in Diffusion Models", "authors": [ "Mahesh Bhosale", "Naresh Kumar Devulapally", "Abdul Wasi", "Chau Pham", "Vishnu Suresh Lokhande", "David Doermann" ], "github_manual": "", "github_regex": "https://github.com/bhosalems/VSM", "github_paper_md": "", "github_api": "https://github.com/bhosalems/VSM", "github_api_added_by": "user", "project_page": "", "abstract": "Diffusion models have emerged as the backbone of modern generative AI, powering advances in vision, language, audio and other modalities. Despite their success, they suffer from hallucinations, implausible samples that lie outside the support of true data distribution, which degrade reliability and trust. In this work, we first empirically confirm previously proposed hypothesis that score smoothness causes hallucinations in Image Generation diffusion models and provide a density-based perspective. We further formalize this notion by linking the hallucinations probability mass to lipschitz constant of the learned score function. Motivated by this, we introduce a Variance-Guided Score Modulation (VSM) strategy that controls the score Jacobian, in turn reducing score smoothness and better approximating the ground truth score that decreases hallucinations. Empirical results on synthetic and real-world datasets demonstrate that our approach reduces hallucinations (up to ~25%) while maintaining high fidelity and diversity, providing a principled step toward more reliable diffusion-based image generation. We also propose two benchmark datasets with extreme semantic variation for systematic hallucination evaluation. Code and Datasets are publicly available at https://github.com/bhosalems/VSM.", "github": "https://github.com/bhosalems/VSM" }, { "date": "2026-06-04", "arxiv_id": "2606.03503", "title": "ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning", "authors": [ "Ziyan Liu", "Xueda Shen", "Yuzhe Gu", "Songyang Gao", "Kuikun Liu", "Guangran Cheng", "Chengqi Lyu", "Dahua Lin", "Wenwei Zhang", "Kai Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ziyanliux/ThoughtFold", "github_api_added_by": "user", "project_page": "", "abstract": "Large Reasoning Models (LRMs) have achieved remarkable progress thanks to Reinforcement Learning with Verifiable Rewards (RLVR) on Chain-of-Thoughts (CoTs). However, since long CoTs naturally contain trial and errors and mainstream RLVR approaches choose outcome-correct CoT trajectories for memorization, the redundant explorations in long CoTs are inevitably reinforced, which results in the over-thinking issues of LRMs. Previous attempts to resolve this issue mainly give more advantage to shorter trajectories, yet their learning signals are still outcome-based and cannot reduce the memorization of redundant explorations in long CoTs. Therefore, we propose ThoughtFold, a framework that leverages fine-grained preference learning to mitigate redundant explorations for efficient reasoning. ThoughtFold employs an introspective strategy to identify redundancy within each correct trajectory, which yields a spectrum of candidate sub-trajectories. Leveraging this spectrum, we introduce a masked preference optimization objective that explicitly penalizes redundant explorations and encourages the model to directly bridge essential reasoning segments, effectively folding its reasoning chains into a more concise path. Extensive experiments show that ThoughtFold significantly enhances efficiency. It reduces the token usage of DeepSeek-R1-Distill-Qwen-7B by approximately 56% while maintaining state-of-the-art accuracy.", "github": "https://github.com/ziyanliux/ThoughtFold" }, { "date": "2026-06-04", "arxiv_id": "2606.05008", "title": "M^3Eval: Multi-Modal Memory Evaluation through Cognitively-Grounded Video Tasks", "authors": [ "Jie Huang", "Ruixun Liu", "Sirui Sun", "Xinyi Yang", "Yin Li", "Yixin Zhu", "Yiwu Zhong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PKU-VaLuE-Lab/m3eval", "github_api_added_by": "user", "project_page": "https://pku-value-lab.github.io/m3eval-homepage/", "abstract": "As multi-modal models advance towards long-form video understanding, memory emerges as a critical capability. Despite substantial efforts in developing video datasets and benchmarks, existing works primarily focus on perception and reasoning, without systematically evaluating memory: what models retain, how faithfully information is preserved, and how robust memory remains under interference. To address this gap, we introduce M^3Eval, the first comprehensive evaluation framework and benchmark for probing different memory dimensions in multi-modal models. Grounded in cognitive psychology, our design features carefully constructed tasks that isolate key aspects of memory. Leveraging M^3Eval, we conduct extensive experiments across representative multi-modal models, revealing consistent weaknesses and distinctive behaviors. We find that models struggle to maintain disentangled representations when processing parallel video streams, exhibit interference patterns differing substantially from those observed in human memory, ground memory sources more reliably in the spatial domain than the temporal domain, and demonstrate limited symbolic memory. Collectively, our benchmark provides a valuable resource for future research, while our findings highlight memory as a fundamental yet underexplored capability and offer insights for designing more effective memory mechanisms in multi-modal models. Our code and dataset are available at https://pku-value-lab.github.io/m3eval-homepage.", "github": "https://github.com/PKU-VaLuE-Lab/m3eval" }, { "date": "2026-06-04", "arxiv_id": "2606.04513", "title": "MapAgent: An Industrial-Grade Agentic Framework for City-scale Lane-level Map Generation", "authors": [ "Deguo Xia", "Zihan Li", "Haochen Zhao", "Dong Xie", "Yuyao Kong", "Xiyan Liu", "Jizhou Huang", "Mengmeng Yang", "Diange Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/eadst/KDD-2026-MapAgent", "github_api_added_by": "user", "project_page": "https://github.com/eadst/KDD-2026-MapAgent", "abstract": "Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities remains highly labor-intensive. Recent end-to-end vectorized mapping methods can predict lane geometry and topology directly from sensor data, but they typically treat mapping specifications and traffic regulations as implicit, dataset-dependent supervision. Moreover, in complex scenes (e.g., worn or missing markings and occlusions), correct lane configurations are often under-determined by visual evidence alone, making specification violations a major source of human post-editing. We propose MapAgent, an industrial-grade agentic architecture that augments a vectorization backbone for specification-compliant lane-map production. Rather than merely adding an agent loop to map prediction, MapAgent couples backbone perception with explicit specification verification, constraint-aware reasoning, and deterministic map editing under a bounded, verification-driven Judge-Planner-Worker loop. A vision-language Judge diagnoses errors by jointly inspecting visual evidence and draft vectors, while a tool-calling Planner generates minimal corrective edits with post-edit re-validation. To remain scalable for city-scale production, MapAgent is selectively triggered only on tiles with low backbone confidence, adding modest overhead while preserving throughput. Experiments on real-world datasets show consistent gains over strong production baselines, especially in complex and long-tail scenarios. Additionally, MapAgent has been integrated into Baidu Maps, supporting lane-level map generation for over 360 cities nationwide and elevating the overall production automation to over 95%, demonstrating MapAgent's practicality and effectiveness for large-scale lane-level map generation.", "github": "https://github.com/eadst/KDD-2026-MapAgent" }, { "date": "2026-06-04", "arxiv_id": "2606.03220", "title": "WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts", "authors": [ "Yuxin Meng", "Yuhan Suo", "Junjie Wang", "Yuhan Sun", "Yiyao Yu", "Ruixu Zhang", "Ruining Hu", "Yubin Wang", "Shouwei Ruan", "Bin Wang", "Yuxiang Zhang", "Yujiu Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/IIGROUP/WebRISE", "github_api_added_by": "user", "project_page": "https://iigroup.github.io/WebRISE", "abstract": "Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the requirement-induced states and transitions that determine whether a page works. We introduce WebRISE, which compiles task requirements into Interaction Contract Graphs (ICGs) of observable states, user-intent transitions, and DOM/visual assertions for implementation-agnostic browser execution. WebRISE spans 442 tasks across five input modalities (Text, Markdown, Sketch, Image, Video), with 5,495 transitions and 5,271 requirement checks that separate user-stated functions from implicit product-level constraints. Across 14 MLLMs, even the strongest model reaches only 65.6% transition validity and 66.3% requirement coverage, and visual quality is no proxy for behavior (Qwen3.6-35B-A3B on Markdown: V=80.8 yet T=15.5). Video gives the strongest interaction signal (+10.6 pp implicit coverage over Text), while implicit constraints persist; defect injection shows ICG-based scoring detects state errors at 2-16x the rate of checkpoint-style evaluation.", "github": "https://github.com/IIGROUP/WebRISE" }, { "date": "2026-06-04", "arxiv_id": "2605.29489", "title": "Access Sets Matter: Budgeting Expert Reads for Scalable Weight-Space Model Merging", "authors": [ "Yuanyi Wang", "Yanggan Gu", "Su Lu", "Yifan Yang", "Zhaoyi Yan", "Congkai Xie", "Jianmin Wu", "Hongxia Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wyy-code/mergepipe", "github_api_added_by": "user", "project_page": "", "abstract": "Weight-space model merging is usually formulated as an algebraic operation on checkpoints, yet at LLM scale the limiting resource is often the set of expert weights that must be read. We introduce MergePipe, a budget-aware execution layer that casts LLM merging as an expert access-set problem: given a merge operator and a checkpoint family in a shared weight coordinate system, choose which expert delta blocks to access under an explicit I/O budget. MergePipe indexes parameter blocks, builds deterministic access plans, and executes the induced budgeted merge with replayable manifests. The plan is budget-sound by construction and recovers the full-read merge at full budget; for fixed-coefficient additive operators, the omitted-update error is bounded by the norm of omitted deltas. Across Qwen and Llama merging workloads, MergePipe reduces expert-read I/O by up to an order of magnitude and achieves up to 11times speedups. Representative budget sweeps show O(10^{-3}) parameter deviation from full-read merges and no monotonic degradation on downstream benchmarks.", "github": "https://github.com/wyy-code/mergepipe" }, { "date": "2026-06-04", "arxiv_id": "2606.05165", "title": "STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations", "authors": [ "Rishit Dagli", "Abir Harrasse", "Luke Zhang", "Florent Draye", "Amirali Abdullah", "Bernhard Sch\u00f6lkopf", "Zhijing Jin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://stride-tda.github.io/", "abstract": "Training Data Attribution (TDA) seeks to trace a model's predictions back to its training data. The gold standard for TDA relies on causal interventions, observing how a model changes when data is added or removed, but repeated retraining is computationally challenging for Large Language Models (LLMs). Consequently, most approaches approximate this effect in the parameter space using gradients. However, tracking gradients across billions of parameters is not only prohibitively expensive but relies on local approximations. In this work, we propose a shift: rather than estimating parameter changes, we model the functional effect of training data in the activation space. We introduce STRIDE (Steering-based Training Data Influence Decomposition), a framework that formulates TDA as a sparse recovery problem in the spirit of compressive sensing. STRIDE learns lightweight \"steering operators\" that mimic the behavioral shift caused by training on data subsets. By measuring how these operators perturb test predictions, we recover individual training example influences via sparse linear decomposition. STRIDE achieves state-of-the-art for LLM pre-training attribution while being an order of magnitude (13times) faster than previous art. We further validate its practical utility through downstream applications including data selection, data contamination, and qualitative analysis.", "github": "" }, { "date": "2026-06-04", "arxiv_id": "2606.05080", "title": "AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?", "authors": [ "Zhangchen Xu", "Junda Chen", "Yue Huang", "Dongfu Jiang", "Jiefeng Chen", "Hang Hua", "Zijian Wu", "Zheyuan Liu", "Zexue He", "Lichi Li", "Shizhe Diao", "Jiaxin Pei", "Jinsung Yoon", "Hao Zhang", "Mengdi Wang", "Radha Poovendran", "Misha Sra", "Alex Pentland", "Zichen Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/autolabhq/autolab", "github_api_added_by": "user", "project_page": "https://autolab.moe/", "abstract": "Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifacts. Yet existing benchmarks for frontier models primarily evaluate either single-turn responses or short-horizon agent trajectories, failing to capture the challenges of sustained iterative improvement over extended time horizons. To address this gap, we introduce AutoLab, a new benchmark for ultra long-horizon closed-loop optimization. AutoLab consists of 36 realistic, expert-curated tasks spanning four diverse domains: system optimization, puzzle & challenge, model development, and CUDA kernel optimization. Each task begins with a correct but deliberately suboptimal baseline and challenges agents to improve it within a strict wall-clock budget. Evaluating 17 state-of-the-art models reveals the dominant predictor of success is not the quality of an agent's initial attempt, but its persistence in repeatedly benchmarking, editing, and incorporating empirical feedback. While claude-opus-4.6 exhibits strong long-horizon optimization capabilities, most frontier models, including several proprietary ones, either terminate prematurely or exhaust their budgets with minimal progress. These results underscore the importance of time awareness and persistent iteration in autonomous agents. We open-source the full benchmark, evaluation harness, and task artifacts, to accelerate research toward truly capable long-horizon agents.", "github": "https://github.com/autolabhq/autolab" }, { "date": "2026-06-04", "arxiv_id": "2606.04048", "title": "Unlocking Feature Learning in Gated Delta Networks at Scale", "authors": [ "Yifeng Liu", "Quanquan Gu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lauyikfung/gated_delta_net_mup", "github_api_added_by": "user", "project_page": "", "abstract": "Training and scaling Large Language Models demand enormous computational resources, motivating both efficient sub-quadratic architectures and principled hyperparameter tuning methods. While the Maximal Update Parametrization (\u03bcP) has enabled zero-shot hyperparameter transfer for standard Transformers, its extension to linear models, particularly those with structured state transitions and complicated architectures, remains largely unexplored. By rigorously propagating coordinate-size estimates through the forward pass, gating mechanisms, and recurrent state dynamics, we derive the scaling rules for Gated Delta Network. Experiments on language-model pre-training confirm that our configurations enable stable learning-rate transfer across model widths under both AdamW and SGD, whereas standard parametrization fails to transfer, validating the correctness and practical utility of our analysis.", "github": "https://github.com/lauyikfung/gated_delta_net_mup" }, { "date": "2026-06-04", "arxiv_id": "2606.03972", "title": "AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation", "authors": [ "Haobo Li", "Yanhong Zeng", "Yunhong Lu", "Jiapeng Zhu", "Hao Ouyang", "Qiuyu Wang", "Ka Leong Cheng", "Yujun Shen", "Zhipeng Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AutoLab-SAI-SJTU/AAD-1", "github_api_added_by": "user", "project_page": "https://aad-1.github.io/", "abstract": "We present AAD-1, an Asymmetric Adversarial Distillation framework for One-step autoregressive image-to-video generation. State-of-the-art methods adopt adversarial distillation but suffer from motion collapse and training instability, resulting in static videos. AAD-1 addresses these challenges through two key designs in architecture and training strategy. Our key architectural insight is to break the symmetry between generator and discriminator. While the generator remains causal to preserve autoregressive sampling capability, the discriminator attends bidirectionally over the full spatiotemporal context and produces a single holistic realism score for the entire video sequence. This asymmetric design enables the discriminator to effectively detect global temporal failures and long-range drift that cause motion collapse in autoregressive generation. To stabilize training, we introduce a phased strategy that first uses distribution matching to bootstrap a stable one-step generator, providing a warm-up phase that brings the student distribution closer to the teacher before adversarial distillation begins. Extensive experiments on VBench demonstrate that AAD-1 achieves state-of-the-art performance in one-step autoregressive video generation.", "github": "https://github.com/AutoLab-SAI-SJTU/AAD-1" }, { "date": "2026-06-04", "arxiv_id": "2605.30792", "title": "OpenSTBench: Beyond Semantic Evaluation for Speech Translation", "authors": [ "Yanjie An", "Yuxiang Zhao", "Yichi Zhang", "Qixi Zheng", "Yujie Tu", "Keqi Deng", "Kai Yu", "Xie Chen" ], "github_manual": "", "github_regex": "https://github.com/sjtuayj/OpenSTBench", "github_paper_md": "", "github_api": "https://github.com/sjtuayj/OpenSTBench", "github_api_added_by": "user", "project_page": "", "abstract": "Speech translation systems increasingly span speech-to-text translation (S2TT), speech-to-speech translation (S2ST), offline translation, and streaming generation, producing outputs that differ in modality, speech realization, and timing behavior. Existing evaluation practices assess important aspects such as translation quality, speech quality, and temporal quality, but these aspects are often evaluated under separate protocols, making it difficult to compare heterogeneous systems comprehensively. To address this gap, we present OpenSTBench, a unified multidimensional evaluation framework that organizes heterogeneous speech translation outputs into a shared evaluation format. OpenSTBench supports both S2TT and S2ST systems in offline and streaming settings, and jointly evaluates translation quality, speech quality, speaker preservation, emotion and paralinguistic fidelity, temporal consistency, and latency. Through experiments on representative speech translation systems, we show that systems with strong translation quality can still differ substantially in speech quality, as well as in temporal quality. OpenSTBench provides a reproducible protocol for analyzing these cross-dimensional differences and supporting application-oriented comparison of speech translation systems. The code and datasets are available at https://github.com/sjtuayj/OpenSTBench.", "github": "https://github.com/sjtuayj/OpenSTBench" }, { "date": "2026-06-04", "arxiv_id": "2606.02684", "title": "Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation", "authors": [ "Yuying Li", "Leqi Zheng", "Yongzi Yu", "Wenrui Zhou", "Xuchang Zhong", "Xing Hu", "Jing Jin", "Huangjie Yuan", "Tao Feng" ], "github_manual": "", "github_regex": "https://github.com/YuYingLi0/FiRe-OPD", "github_paper_md": "", "github_api": "https://github.com/YuYingLi0/FiRe-OPD", "github_api_added_by": "user", "project_page": "", "abstract": "On-Policy distillation (OPD) in large language models is shifting from full-trace KL supervision toward more selective training paradigms. Recent OPD methods increasingly focus on selecting which trajectories to learn from, which tokens are most informative, and which supervision signals are most reliable. Motivated by this trend, we rethink optimization granularity of OPD and propose \\fireicon\\ FiRe-OPD (Filter, then Reweight), which jointly adjusts supervision signals at both trajectory and token levels. In details, FiRe-OPD first filters trajectories to remove low-quality rollout samples, and then applies soft reweighting within the retained trajectories to emphasize informative tokens. Compared with hard token selection, FiRe-OPD leverages a soft-weighting mechanism to effectively mitigate information loss and enhance optimization stability, thereby achieving finer-grained OPD optimization. We validate the effectiveness of FiRe-OPD across strong-to-weak, single-teacher, and multi-teacher settings, and demonstrate its superiority over recent token-level OPD methods ( (e.g., +6.25 on AIME 2024 in strong-to-weak, +18.81 on Miner in multi-teacher). Our code is available at https://github.com/YuYingLi0/FiRe-OPD.", "github": "https://github.com/YuYingLi0/FiRe-OPD" }, { "date": "2026-06-04", "arxiv_id": "2606.03890", "title": "OVO-S-Bench: A Hierarchical Benchmark for Streaming Spatial Intelligence in Multimodal LLMs", "authors": [ "Yifei Li", "Pengyiang Liu", "Yuhang Zang", "Zhongyue Shi", "Qi Fu", "Hongye Hao", "Jiwen Lu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/InternLM/OVO-S-Bench", "github_api_added_by": "user", "project_page": "https://internlm.github.io/OVO-S-Bench/", "abstract": "Multimodal agents in robotics, AR, and autonomous driving must reason about places and layouts from continuous egocentric streams, often using evidence outside the current view. Existing benchmarks either evaluate offline over full videos or target events rather than spatial structure. We introduce OVO-S-Bench, a fully human-annotated benchmark for streaming spatial intelligence, comprising 1,680 questions over 348 source videos. Annotation involves 12 trained annotators, each also serving as a blind cross-reviewer, across roughly 804 person-hours of multi-round quality assurance. Each question carries a query timestamp and an evidence interval, and at evaluation, the model sees only the prefix preceding the query. Questions span four levels of increasing abstraction: instantaneous egocentric perception, spatiotemporal context tracking, spatial simulation and reasoning, and allocentric mapping. Across 38 proprietary and open-source MLLMs, Gemini-3.1-Pro trails human experts by 27 points, 59.2 vs. 86.6, with allocentric mapping as the dominant bottleneck. Notably, streaming and spatially fine-tuned MLLMs underperform their own backbones. We further find that chain-of-thought reasoning amplifies spatial errors when ungrounded in the stream. By exposing these limitations, OVO-S-Bench establishes a demanding testbed for next-generation streaming spatial MLLMs.", "github": "https://github.com/InternLM/OVO-S-Bench" }, { "date": "2026-06-04", "arxiv_id": "2605.27492", "title": "Benchmarks are Not Enough: RAMP for Runtime Assessing of Agentic Models in Production Systems", "authors": [ "Yipeng Ouyang", "Xin Huang", "Bingjie Liu", "Zhongchun Zheng", "Yuhao Gu", "Xianwei Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Nexa-Language/RAMP", "github_api_added_by": "user", "project_page": "http://ramp.yatcc-ai.com/", "abstract": "LLM agents are rapidly evolving from coding assistants into autonomous software engineering systems. However, existing evaluation methodologies remain largely centered on static, isolated, and short-horizon benchmarks that fail to capture the dynamic complexity of real-world production workflows. As a result, benchmark performance may poorly reflect practical capability under realistic runtime environments involving long execution chains, tool interactions, dependency management, and iterative feedback loops. We thus present RAMP, a production-grounded infrastructure for assessing long-horizon software engineering agents. Built upon the YatCC integrated platform, RAMP provides a unified runtime assessment architecture through standardized orchestration and execution interfaces. RAMP introduces realistic compiler-construction workloads with serial dependencies and complex toolchain interactions, together with a staged recovery mechanism for analyzing execution behavior under partial workflow failure. The framework further incorporates utility-oriented multi-dimensional metrics that jointly evaluate outcome quality and process efficiency. We conduct runtime assessments across 15 mainstream models and observe substantial capability degradation that remains largely invisible to conventional isolated benchmarks. Task completion rates progressively collapse across serial workflows, dropping from 100% in the initial stage to only 20% in the final stage, while none of the evaluated models successfully completes the entire pipeline. Runtime analysis reveals systematic failure propagation and significant resource inefficiencies, with computational costs differing by up to three orders of magnitude among comparable models. These findings suggest RAMP advances agentic model evaluation toward continuous, runtime-observable, and production-grounded assessment.", "github": "https://github.com/Nexa-Language/RAMP" }, { "date": "2026-06-04", "arxiv_id": "2606.04923", "title": "Reproducing, Analyzing, and Detecting Reward Hacking in Rubric-Based Reinforcement Learning", "authors": [ "Xuekang Wang", "Zhuoyuan Hao", "Shuo Hou", "Hao Peng", "Juanzi Li", "Xiaozhi Wang" ], "github_manual": "", "github_regex": "https://github.com/THUAIS-Lab/CHERRL", "github_paper_md": "", "github_api": "https://github.com/THUAIS-Lab/CHERRL", "github_api_added_by": "user", "project_page": "", "abstract": "Rubric-based reinforcement learning (RL) uses an LLM-as-a-Judge (LaaJ) to score model outputs according to rubrics as rewards. However, policy models may exploit latent biases in the judge, leading to reward hacking and ineffective or unsafe training outcomes. In real-world rubric-based RL, such hacking behaviors are often subtle and entangled with multiple judge biases, making them difficult to analyze, detect, and mitigate. In this paper, we introduce CHERRL, a controllable hacking environment for rubric-based RL. By injecting known biases into LaaJ, CHERRL enables stable reproduction of reward hacking, explicit observation of reward divergence, and precise identification of hacking onset. This provides a clean experimental testbed for studying the mechanisms and mitigations of reward hacking in rubric-based RL. To demonstrate its utility, we analyze different judge biases from the perspectives of discoverability and exploitability, and explore an agent-based system for automatically detecting reward hacking onset from training logs. The code and environment are publicly available at https://github.com/THUAIS-Lab/CHERRL.", "github": "https://github.com/THUAIS-Lab/CHERRL" }, { "date": "2026-06-04", "arxiv_id": "2606.05131", "title": "Deep Embedded Multiplicative DMD for Algebra-Preserving Koopman Learning", "authors": [ "Kelan Gray", "Finlay Brown", "Nicolas Boull\u00e9", "Matthew J. Colbrook" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Koopman theory turns nonlinear dynamics into a linear spectral problem. In computation, however, everything depends on a hard finite-dimensional choice: the observables must be expressive, nearly invariant under the dynamics, and, ideally, compatible with composition. Deep Koopman methods learn flexible coordinates, whereas structure-preserving methods enforce operator identities on fixed dictionaries. We combine these ideas by introducing Deep Embedded Multiplicative Dynamic Mode Decomposition (DeepMDMD), a method that learns a latent space and a partition of it, while enforcing the Koopman product rule as an exact algebraic constraint. Training alternates between an exact multiplicative operator update and a differentiable latent-clustering step that promotes Koopman closure. The result is a finite transition map on learned latent cells. Its nonzero spectrum lies on the unit circle, its dictionary is shaped by the dynamics rather than by ambient geometry, and forecasts are made in latent coordinates before being decoded to physical space. Across Hamiltonian, chaotic, and fluid examples, DeepMDMD learns dictionaries that are far more compact and dynamically coherent than those produced by geometric MDMD partitions. It reduces spectral pollution, reveals richer continuous-spectrum structure, and gives stable forecasts under severe noise. In high-dimensional flows, including a 158,624-dimensional cylinder wake and a noisy Re=20,000 lid-driven cavity, it preserves coherent structures and long-time spectral statistics where state-space MDMD fails. These results suggest a practical rule for Koopman learning: learn the coordinates, constrain the algebra.", "github": "" }, { "date": "2026-06-04", "arxiv_id": "2606.02859", "title": "Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions", "authors": [ "Zhenting Qi", "Huangyuan Su", "Ao Qu", "Chenyu Wang", "Yu Yao", "Han Zheng", "Kushal Chattopadhyay", "Guowei Xu", "Zihan Wang", "Weirui Ye", "Vijay Janapa Reddi", "Ju Li", "Paul Pu Liang", "Himabindu Lakkaraju", "Sham Kakade", "Yilun Du" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhentingqi/EoM", "github_api_added_by": "user", "project_page": "https://zhentingqi.github.io/internal/projects/EoM", "abstract": "How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedrich Hayek's economic theory of decentralized coordination in markets, we study this question through an agent economy in which agents compete via auctions for the right to act, exchange payments, and accumulate wealth from environmental rewards. These simple economic signals induce decentralized credit assignment, driving planning without global orchestration or explicit communication protocols. The population evolves through economic selection: effective agents accumulate wealth and are mutated via exploitation, while ineffective ones go bankrupt and are replaced via exploration. We show that, initialized with weak agents, the economy produces emergent multi-step reasoning strategies and outperforms stronger monolithic baselines across five agentic tasks, including mathematical reasoning, financial research, scientific research, accelerator design, and distributed-system optimization. We further provide theoretical insights into how economic dynamics shape agent behaviors, linking local incentives to long-term global performance. Our results suggest a new path to multi-agent intelligence: rather than engineering coordination, we can design decentralized incentive structures under which it automatically emerges.", "github": "https://github.com/zhentingqi/EoM" }, { "date": "2026-06-04", "arxiv_id": "2606.00477", "title": "Do Text Edits Generalize to Visual Generation? Benchmarking Cross-Modal Knowledge Editing in UMMs", "authors": [ "Xin Gao", "Cheng Yang", "Chufan Shi", "Taylor Berg-Kirkpatrick" ], "github_manual": "", "github_regex": "https://github.com/gxx27/UniKE", "github_paper_md": "", "github_api": "https://github.com/gxx27/UniKE", "github_api_added_by": "user", "project_page": "", "abstract": "Unified multimodal models (UMMs) have emerged as a promising paradigm for general-purpose multimodal intelligence. As they are deployed in real-world applications, effectively updating internal knowledge becomes critical. While knowledge editing has matured for text-only models, it remains unclear whether edits that successfully modify textual outputs also transfer to image generation in UMMs. To study this question, we introduce UniKE, the first benchmark for cross-modality knowledge editing in UMMs, comprising 2,971 edit subjects spanning attribute and relation edits. Using VQA-based visual verification, we reveal a striking modality gap: text-side efficacy can reach approximately 92%, whereas the best overall VQA accuracy under direct image generation is only 18.5%. We further propose Reasoning-augmented Parameter Editing, which explicitly activates edited knowledge before generation and improves overall VQA accuracy for all evaluated model-editor pairs, with gains up to 18.6 percentage points. Mechanistic analysis shows that this gap is associated with partial alignment between edited textual representations and the conditioning pathways for visual generation, where edits sufficient for text outputs may remain too weak or misaligned to steer image synthesis. These findings show that textual knowledge edits do not guarantee reliable cross-modality transfer and motivate modality-aware editing methods. Our code and data are available at https://github.com/gxx27/UniKE.", "github": "https://github.com/gxx27/UniKE" }, { "date": "2026-06-04", "arxiv_id": "2605.31148", "title": "SpatialAct: Probing Spatial Reasoning-to-Action Capabilities of VLM Agents in 3D Scenes", "authors": [ "Tianhui Liu", "Jie Feng", "Zhiheng Zheng", "Shengyuan Wang", "Yiming Guo", "Yanxin Xi", "Hangyu Fan", "Yong Li", "Pan Hui" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tsinghua-fib-lab/SpatialAct", "github_api_added_by": "user", "project_page": "", "abstract": "Humans can effortlessly perceive spatial layouts, form cognitive representations, reason about spatial relations, and translate such reasoning into actions in everyday 3D environments. Although recent vision-language models (VLMs) have shown promising performance on observation-conditioned spatial perception and reasoning tasks, it remains unclear whether they can build coherent spatial understanding, act upon it, and refine their actions through multi-turn feedback. To study this problem, we introduce SpatialAct, a simulator-grounded benchmark for probing action-conditioned spatial reasoning in 3D scenes. Starting from the most challenging setting, Multi-turn Interactive Refinement, we further design its decomposed counterpart, Single-step Error Detection and Fix, together with five fundamental spatial ability tasks to diagnose the underlying causes of model failures. Experiments reveal a clear reasoning-to-action gap: current VLMs can perform well on isolated spatial reasoning tasks, but struggle to maintain coherent spatial beliefs and produce reliable actions during multi-turn feedback, substantially underperforming humans. These results suggest that current VLM agents still lack robust spatial state tracking under action-induced environment changes, even when low-level control is abstracted away.", "github": "https://github.com/tsinghua-fib-lab/SpatialAct" }, { "date": "2026-06-04", "arxiv_id": "2606.00188", "title": "PaintBench: Deterministic Evaluation of Precise Visual Editing", "authors": [ "Kai Xu", "Ellis Brown", "Shrikar Madhu", "Rob Fergus", "He He", "Saining Xie" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PaintBench/PaintBench", "github_api_added_by": "user", "project_page": "https://paintbench.github.io", "abstract": "While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle. To probe this challenge, we introduce PaintBench, a dynamically scalable benchmark targeting 20 fundamental precise visual editing operations across four categories: geometric transformation, structural manipulation, color change, and symbolic reasoning. Procedural generation with configurable complexity enables an effectively infinite, contamination-resistant evaluation suite, and deterministic pixel-level evaluation eliminates reliance on bias-prone judge models. Across 11 image editing models, we find overall low performance, with the current highest-performing industry leader scoring only 17.1% (mIoU). Task decomposition reveals especially challenging operation types (geometric transformation, most structural manipulation, formula-based color change) and model-specific specializations. Fine-grained benchmark diagnostics further show performance degradations induced by scene variations in object count, background complexity, color scheme, and edit-region size. To test generalization of PaintBench scores to applied task performance, we create a procedural, deterministic evaluation for data visualization editing (TinyGrafixBench) and find strong linear correlation with PaintBench scores (R^2 = 0.91, p < 0.001). Altogether, PaintBench provides a rigorous foundation for measuring and driving progress in precise multimodal visual editing.", "github": "https://github.com/PaintBench/PaintBench" }, { "date": "2026-06-04", "arxiv_id": "2606.03895", "title": "Agent libOS: A Library-OS-Inspired Runtime for Long-Running, Capability-Controlled LLM Agents", "authors": [ "Yingqi Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yingqi-z20/Agent-libOS", "github_api_added_by": "user", "project_page": "", "abstract": "Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model calls, fork subtasks, wait for external events, request human authority, generate tools, and perform side effects that must be resumed and audited. This paper presents Agent libOS, a library-OS-inspired runtime substrate for LLM agents. Agent libOS runs above a conventional host operating system; it does not implement hardware drivers, kernel-mode isolation, or a POSIX-compatible operating system. Instead, it treats an agent as an AgentProcess: a schedulable execution subject with process identity, parent-child lineage, lifecycle state, a tool table derived from an AgentImage, typed Object Memory, explicit capabilities, human queues, checkpoints, events, and audit records. Its central design rule is tools are libc-like wrappers; runtime primitives are the authority boundary. Filesystem access, object access, sleeps, human approval, JIT tool registration, and external side effects are checked at primitive boundaries under explicit capabilities and policy. We describe the design, threat model, Python prototype, and safety-oriented evaluation. The current prototype implements async scheduling, namespace-local Object Memory, runtime-integrated human approval, one-shot permission grants, per-process working directories, shell and image-registration primitives, Deno/TypeScript JIT tools over a libOS syscall broker, filesystem/object bridge tools, an injectable Resource Provider Substrate, deterministic demos, real-model smoke scripts, and 123 regression tests at the time of writing. Rather than improving planner accuracy, Agent libOS demonstrates a runtime substrate in which long-running LLM agents can be scheduled, authorized, resumed, and audited without treating tool dispatch as the trust boundary.", "github": "https://github.com/yingqi-z20/Agent-libOS" }, { "date": "2026-06-04", "arxiv_id": "2606.00558", "title": "Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain", "authors": [ "Yuan Yao", "Jin Song", "Huixia Li", "Tongtong Yuan", "Jiaqi Wu", "Yu Zhang" ], "github_manual": "", "github_regex": "https://github.com/AIResearch-Group/SSNA", "github_paper_md": "", "github_api": "https://github.com/AIResearch-Group/SSNA", "github_api_added_by": "user", "project_page": "", "abstract": "Transfer learning aims to facilitate the learning of a target domain by transferring knowledge from a source domain. The source domain typically contains semantically meaningful samples (*e.g.*, images) to facilitate effective knowledge transfer. However, a recent study observes that the noise domain constructed from simple distributions (*e.g.*, Gaussian distributions) can serve as a surrogate source domain in the semi-supervised setting, where only a small proportion of target samples are labeled while most remain unlabeled. Based on this surprising observation, we formulate a novel problem termed *Semi-Supervised Noise Adaptation* (SSNA), which aims to leverage a synthetic noise domain to improve the generalization of the target domain. To address this problem, we first establish a generalization bound characterizing the effect of the noise domain on generalization, based on which we propose a Noise Adaptation Framework (NAF). Extensive experiments demonstrate that NAF effectively leverages the noise domain to tighten the generalization bound of the target domain, leading to improved performance. The codes are available at https://github.com/AIResearch-Group/SSNA.", "github": "https://github.com/AIResearch-Group/SSNA" }, { "date": "2026-06-04", "arxiv_id": "2606.03792", "title": "Training-Free Multi-Concept LoRA Composition with Prompt-Aware Weighting", "authors": [ "Georgios Tsoumplekas", "Stella Bounareli", "Vasileios Argyriou" ], "github_manual": "", "github_regex": "https://github.com/GeorgeTsoumplekas/Prompt-Aware-Multi-LoRA-Composition", "github_paper_md": "", "github_api": "https://github.com/GeorgeTsoumplekas/Prompt-Aware-Multi-LoRA-Composition", "github_api_added_by": "user", "project_page": "", "abstract": "Low-Rank Adaptation (LoRA) successfully enables personalization in text-to-image generation by adapting pre-trained diffusion models to specific visual concepts and styles. However, extending such models to multi-concept customization remains challenging. Naively combining multiple LoRA weights or their outputs often leads to interference among concepts, resulting in degraded visual quality and reduced fidelity to the reference images of individual concepts. This paper proposes a simple yet effective approach for multi-concept customization by optimally combining the outputs of multiple LoRA modules. We leverage the relative importance of each concept during generation, as inferred from its corresponding prompt tokens and introduce two methods, W-Switch and W-Composite, that employ a prompt-aware importance weighting strategy in which each LoRA is weighted according to the semantic influence of its trigger words in the target prompt. In addition, we extend existing quantitative evaluation metrics by proposing a new image-based similarity evaluation framework that assesses image fidelity and identity preservation through comparisons between real-world reference images and automatically segmented concept regions from generated images. We evaluate our approach on the ComposLoRA testbed and demonstrate consistent improvements over existing state-of-the-art methods in terms of visual quality, identity preservation and compositionality. Qualitative evaluations, including a Large Language Model (LLM) based assessment and a user study, further validate the effectiveness of the proposed methods and align with the newly introduced quantitative image-based metrics. Our code is available at https://github.com/GeorgeTsoumplekas/Prompt-Aware-Multi-LoRA-Composition.", "github": "https://github.com/GeorgeTsoumplekas/Prompt-Aware-Multi-LoRA-Composition" }, { "date": "2026-06-04", "arxiv_id": "2606.05112", "title": "Evaluating Large Language Models in Dynamic Clinical Decision-Making with Standardized Patient Cases", "authors": [ "Cheng Liang", "Pengcheng Qiu", "Ya Zhang", "Yanfeng Wang", "Chaoyi Wu", "Weidi Xie" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MAGIC-AI4Med/MedSP1000", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models (LLMs) are increasingly proposed as clinical agents, yet static, single-turn benchmarks cannot capture how a model dynamically delivers care across an encounter: gathering information, planning treatment, and adapting longitudinal management across successive patient states. Medical education has long addressed an analogous challenge through standardized patients (SPs): trained actors who consistently portray clinical cases, enabling realistic practice and objective, scripted assessment. Here we introduce MedSP1000, an SP-derived interactive benchmark for clinical-agent evaluation, including 1,638 SP cases with 24,602 trajectory-level peer-reviewed rubrics. MedSP1000 converts peer-reviewed SP teaching cases into executable scenarios with defined SP case scripts, clinical environment contexts, and human-validated structured rubric. In each simulation evaluation run, a clinical agent interacts in closed loop with a patient agent and an environment controller, and its behaviour is scored throughout the encounter against expert criteria specified in the original materials. Applying MedSP1000 to a range of general-purpose and medically specialized LLMs, we find that performance on static benchmarks does not reliably translate to such educational scenarios. The best-performing model, GPT-5.5, completes only 60.4% of expert-defined rubric items, whereas the strongest medically specialized model reaches 40.0%; increasing test-time compute produces no measurable gain. These results suggest that current LLMs, including agentic systems tuned for medicine, are not yet reliable enough to be safely integrated into actual clinical practice. More broadly, MedSP1000 shows how process-level, SP-style evaluation can reveal clinically relevant failure modes that single-turn benchmarks miss.", "github": "https://github.com/MAGIC-AI4Med/MedSP1000" }, { "date": "2026-06-04", "arxiv_id": "2606.03773", "title": "KletterMix: Climbing Toward High-Quality German Pretraining Data", "authors": [ "Maurice Kraus", "Ruben H\u00e4rle", "Sebastian Sztwiertnia", "Abbas Goher Khan", "Mehdi Ali", "Michael Fromm", "Kristian Kersting" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/collections/AIML-TUDA/klettermix", "abstract": "High-quality pretraining data is a central ingredient in modern language models, but German-language resources remain far less developed than their English counterparts: they are often smaller, less carefully curated, weakly documented, and rarely validated through controlled training experiments. We introduce KletterMix, a high-quality German corpus for language model pretraining and annealing, designed as a reusable dataset artifact for the natural language processing and modeling community. KletterMix is built by translating a state-of-the-art English pretraining corpus into German while preserving document boundaries, metadata, source structure, and topical diversity. This construction yields a German corpus with the scale and diversity of a modern pretraining dataset, while enabling direct comparison to its English source. We document the dataset through a broad set of corpus-level analyses, including translation quality, document length distributions, topic coverage, source composition, and geographic metadata. Using COMETKiwi, we show that the translated documents achieve strong quality across diverse domains, suggesting that careful translation can preserve much of the semantic and stylistic richness of the original corpus. Beyond dataset construction, we evaluate KletterMix as training data. Through controlled pretraining and annealing ablations against established German corpora, we show that models trained on KletterMix achieve measurable improvements on German-language downstream evaluations. These results demonstrate that carefully curated translated data can substantially strengthen the German pretraining data ecosystem.", "github": "" }, { "date": "2026-06-04", "arxiv_id": "2606.05009", "title": "DAR: Deontic Reasoning with Agentic Harnesses", "authors": [ "Guangyao Dou", "William Jurayj", "Nils Holzenberger", "Benjamin Van Durme" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/guangyaodou/harbor-deonticbench", "github_api_added_by": "user", "project_page": "https://guangyaodou.github.io/harbor-deonticbench/", "abstract": "Deontic reasoning is the task of answering questions by applying explicit rules and policies to case-specific facts, for example computing tax liability under a statute or determining the outcome of an immigration appeal. A key technical challenge for LLM-based deontic reasoning is that the relevant ruleset can be long and cross-referenced, so models may still fail to locate the rules needed for a particular reasoning step. We introduce Deontic Agentic Reasoning (DAR), an agentic reasoning setup in which the model interacts with the statutes on demand. We evaluate DAR under multiple harnesses on hard subsets of DeonticBench. Across these settings, we find that agentic harnesses can push the frontier on deontic reasoning tasks, but improvements are not uniform: weaker models often degrade on numerical tasks while consuming far more tokens.", "github": "https://github.com/guangyaodou/harbor-deonticbench" }, { "date": "2026-06-04", "arxiv_id": "2605.30257", "title": "Stable-Layers: Fine-Tuning Image Layer Decomposition Models with VLM-Scored Reinforcement Learning", "authors": [ "Ciara Rowles", "Reshinth Adithyan", "Nikhil Pinnaparaju", "Vikram Voleti", "Mark Boss" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://stability-ai.github.io/stable-layers.github.io/", "abstract": "We present Stable-Layers, a reinforcement learning framework that eliminates the need for paired supervision by fine-tuning a pretrained layer decomposition model using only feedback from a vision-language model (VLM). Starting from Qwen-Image-Layered, we apply Flow-GRPO with LoRA adaptation, sampling multiple candidate decompositions per image, scoring them with a VLM, and optimising the policy from group-relative advantages. The key challenge lies in designing a reliable reward signal: VLMs scoring samples in isolation tend to compress their judgements into a narrow band, leaving GRPO with little within-group variance to learn from. We address this with a two-stage evaluation pipeline that pairs structured per-sample scoring across five edit-centric criteria with a grid-based calibration step in which the VLM re-scores all candidates side-by-side. Stable-Layers produces decompositions with stronger layer separation, fewer blank or artifact-heavy layers, and lower per-layer reconstruction error on the Crello dataset compared to the base model.", "github": "" }, { "date": "2026-06-04", "arxiv_id": "2606.05102", "title": "ZipSplat: Fewer Gaussians, Better Splats", "authors": [ "Alexander Veicht", "Sunghwan Hong", "D\u00e1niel Bar\u00e1th", "Marc Pollefeys" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cvg/ZipSplat", "github_api_added_by": "user", "project_page": "https://veichta.com/zipsplat/", "abstract": "Feed-forward 3D Gaussian Splatting methods reconstruct a scene from posed or pose-free images in a single forward pass, yet current approaches predict one Gaussian per input pixel, tying the representation budget to camera resolution rather than scene complexity. A flat wall and a richly textured object thus produce equally many Gaussians despite very different geometric needs. We propose ZipSplat, a token-based feed-forward model that decouples Gaussian placement from the pixel grid. A multi-view backbone extracts dense visual tokens, and k-means clustering compresses them into a compact set of scene tokens. Cross- and self-attention refine these tokens, and a lightweight MLP decodes each into a group of Gaussians with unconstrained 3D positions. Because clustering is applied at inference, a single trained model spans the quality-efficiency curve without retraining. ZipSplat operates without ground-truth poses or intrinsics, yet sets a new state of the art on DL3DV and RealEstate10K with {sim}6{times} fewer Gaussians than pixel-aligned methods, surpassing the best pose-free baseline by 2.1dB and 1.2dB PSNR, respectively. It further generalizes zero-shot to Mip-NeRF360 and ScanNet++, outperforming all comparable baselines. Our project page is at {https://veichta.com/zipsplat{https://veichta.com/zipsplat}}.", "github": "https://github.com/cvg/ZipSplat" }, { "date": "2026-06-04", "arxiv_id": "2605.31559", "title": "Functional Attention: From Pairwise Affinities to Functional Correspondences", "authors": [ "Jiefang Xiao", "Maolin Gao", "Simon Weber", "Guandao Yang", "Daniel Cremers" ], "github_manual": "", "github_regex": "https://github.com/xjffff/FUNCATTN", "github_paper_md": "", "github_api": "https://github.com/xjffff/FUNCATTN", "github_api_added_by": "user", "project_page": "https://xjffff.github.io/funcattn/", "abstract": "Learning mappings between infinite-dimensional function spaces, or operator learning, is essential for many machine learning applications. Although transformer-based operators are popular, they often rely on token-wise attention. These methods treat continuous fields as discrete tokens and usually ignore the global functional structure. We introduce Functional Attention, which reinterprets attention as a functional correspondence between adaptive bases. Inspired by geometric functional maps, our method replaces softmax affinities with structured linear operators. This yields a compact, generalizable, resolution-invariant representation that explicitly captures global dependencies. Experiments demonstrate that Functional Attention can match state-of-the-art performance in many operator learning tasks, including solving PDEs, 3D segmentation, and regression, while remaining robust to varying discretizations. Project page is available at https://github.com/xjffff/FUNCATTN.", "github": "https://github.com/xjffff/FUNCATTN" }, { "date": "2026-06-04", "arxiv_id": "2606.04978", "title": "Probing Outcome-Level Resemblance and Mechanism-Level Alignment in LLM Risk Decisions: Evidence from the St. Petersburg Game", "authors": [ "Chensong Huang", "Changyu Chen", "Chenwei Lin", "Hanjia Lyu", "Xian Xu", "Jiebo Luo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "LLMs can appear cautious in risk decision-making tasks, yet cautious-looking outputs do not necessarily indicate alignment with human decision-making mechanisms. We investigate this distinction using the St. Petersburg game as a controlled testbed, a classical paradox in which the expected payoff is infinite, yet humans typically report low, finite willingness to pay. We evaluate 28 LLMs with a structured prompt suite that includes the original game; controlled decision variants that perturb truncation, repeated play, numeric endowment, and occupational identity; a human-perspective prompt that asks models to reason as human decision makers; and paired comparisons between base models and their instruction-tuned counterparts. In the original game, most models generate finite bids, creating the appearance of human-like risk behavior. However, this outcome-level resemblance masks substantial mechanism-level differences. The controlled variants reveal that rather than maintaining human-like behavior seen in the original game, models often shift to conditionally and computationally rational behavior. Human-cue prompting and instruction tuning often lower bids and reduce some visible pathologies, but most mechanism-level response patterns remain largely unchanged. These findings show that behavioral alignment in risk decision-making can be surface-level: LLMs may produce human-like risk decisions without exhibiting human-consistent mechanisms. High-stakes evaluations of LLM decision-making should therefore move beyond outcome similarity and examine whether the alignment is supported by mechanism-level consistency.", "github": "" }, { "date": "2026-06-04", "arxiv_id": "2606.03712", "title": "When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models", "authors": [ "Ding Zhang", "Runtao Zhou", "Wenqing Zheng", "Rizal Fathony", "Bayan Bruss", "Chirag Agarwal" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Graph Language Models (GLMs) have become a promising direction for adapting Large Language Models (LLMs) to graph learning tasks. By transforming graph topology and node information into graph tokens, GLMs allow LLMs to jointly process structured graph inputs and textual instructions. Yet, it remains unclear how LLMs internally interpret these graph tokens and whether graph tokens act as meaningful carriers of graph structure. In this work, we analyze how LLMs process graph information through graph-token behavior in representative GLM architectures. Findings. We find that the internal saliency of graph tokens in GLMs is not equivalent to graph information utilization. Graph sink tokens consistently emerge as activation-level outliers: they can be identified by massive activation values along a small set of hidden-state dimensions and are biased toward early graph-token positions. However, this activation-level saliency does not imply that these tokens are the main carriers of graph information. Unlike classical attention sinks in language and vision-language models, graph sink tokens do not necessarily attract the largest attention weights from query tokens. Through pruning, repositioning, and swapping interventions, we show that graph sink tokens are not the most important semantic or structural tokens for downstream prediction. Implications. Together, these results suggest that after current GLMs map graph structure into the LLM token space, the resulting graph-token representations do not naturally form a fully usable topology-aware internal representation; instead, they exhibit a decoupling between activation-level saliency and graph-semantic utility. This decoupling points to limitations in existing graph-token construction, placement, and alignment mechanisms.", "github": "" }, { "date": "2026-06-04", "arxiv_id": "2606.04056", "title": "Token Budgets: An Empirical Catalog of 63 LLM-Agent Budget-Overrun Incidents, with an Affine-Typed Rust Mitigation as a Case Study", "authors": [ "Sajjad Khan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sajjadanwar0/token-budgets", "github_api_added_by": "user", "project_page": "", "abstract": "LLM-agent budget overruns are a documented production failure class: a single retry loop can spend thousands of dollars before an operator notices, and the in-process integrity properties that would prevent it (no aliasing, no double-spend, no use-after-delegation of a cost-bearing value) are enforced, if at all, by ad-hoc wrappers rather than by the type system. Our central contribution is empirical: a catalog of 63 confirmed production incidents from 21 orchestration frameworks (2023-2026), each backed by a quoted GitHub issue and, where reported, a dollar loss, organized into an eight-cluster failure taxonomy (inter-rater Cohen's kappa = 0.837, N = 113), plus 47 supplementary structural entries. As one mitigation evaluated against this taxonomy, we build token-budgets, an 1,180-line Rust crate (no unsafe) that operationalizes affine ownership so that cloning, double-spending, or using a budget after delegating it are compile errors rather than runtime hazards an operator must remember to avoid. The dollar cap is runtime arithmetic under an estimator assumption; the affine layer makes that arithmetic non-bypassable. On single-agent workloads a 4-line Python counter matches the crate at 0/30 overshoot, so the distinguishing value is non-bypassability under operator error in multi-agent delegation: the delegation-fanout race documented in 11 incidents is rejected by the borrow checker at compile time, while the same pattern under asyncio overshoots 30/30 and three disciplined alternatives overshoot 0/30. Across five runtimes, three providers, and a temperature-stratified live-API test (N = 160), the approach reports zero cap violations and zero false refusals, at operational parity with concurrent work. Static over-reservation is 4-6x (2.11x adaptive). Binary-level cap-soundness on the running binary is left open.", "github": "https://github.com/sajjadanwar0/token-budgets" }, { "date": "2026-06-04", "arxiv_id": "2605.31498", "title": "Scalable Inference-Time Annealing with Surrogate Likelihood Estimators", "authors": [ "Daniel Pe\u00f1aherrera", "Rishal Aggarwal", "David Ryan Koes" ], "github_manual": "", "github_regex": "https://github.com/countrsignal/sita.git", "github_paper_md": "", "github_api": "https://github.com/countrsignal/sita", "github_api_added_by": "user", "project_page": "", "abstract": "A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generative modeling have been proposed to address the limitations of conventional sampling techniques by eliminating the computational cost of simulation. A promising direction is iteratively finetuning diffusion models along a temperature ladder whereby training data is generated via importance sampling during inference-time annealing. Unfortunately, these methods require computing a divergence over the score field to estimate importance weights, rendering them intractable for larger systems. Here we present scalable inference-time annealing (SITA), which retrains flow-based models to generate samples at progressively lower temperatures using an energy-based model to facilitate fast surrogate likelihoods. We demonstrate state-of-the-art performance on both Alanine Dipeptide and Alanine Tripeptide while avoiding costly divergence terms. Our code is available at https://github.com/countrsignal/sita.git", "github": "https://github.com/countrsignal/sita" }, { "date": "2026-06-04", "arxiv_id": "2606.00825", "title": "SuperMemory-VQA: An Egocentric Visual Question-Answering Benchmark for Long-Horizon Memory", "authors": [ "Samiul Alam", "Shakhrul Iman Siam", "Michael J. Proulx", "James Fort", "Richard Newcombe", "Hyo Jin Kim", "Mi Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AIoT-MLSys-Lab/supermemory-vqa", "github_api_added_by": "user", "project_page": "https://supermemory-vqa.github.io/", "abstract": "AI glasses present a compelling platform for AI agents to serve as personalized memory assistants. To be genuinely useful, such systems must move beyond short-term video comprehension and address memory gaps that humans experience for practical, personal, or social purposes over longitudinal egocentric video streams. However, existing egocentric datasets predominantly focus on action recognition or generic QAs from short clips, measuring perceptual capabilities rather than realistic human memory needs. We introduce SuperMemory-VQA, an egocentric visual question answering (VQA) dataset for evaluating AI assistants on practical, long-horizon memory tasks. It contains 52.9 hours of everyday activities recorded with AI glasses, including synchronized RGB video, audio transcription, eye gaze, IMU, and SLAM trajectories. Through a human-verified annotation pipeline, we construct grounded 4,853 question-answer pairs that span object and location memory, intent recall, visual scene recall, timeline reconstruction, conversational memory, and in-context retrieval. Each question is posed as multiple-choice with an explicit \"unanswerable\" option to test hallucination robustness. Benchmarking leading agentic frameworks and LLM backbones reveals that existing systems remain far from reliable on real-world memory tasks, highlighting the need for new architectures for grounded AI memory that can answer only when evidence is sufficient. A participant survey further supports that our questions are realistic, useful, and aligned with everyday memory needs.", "github": "https://github.com/AIoT-MLSys-Lab/supermemory-vqa" }, { "date": "2026-06-04", "arxiv_id": "2606.02993", "title": "Neural Networks Provably Learn Spectral Representations for Group Composition", "authors": [ "Jianliang He", "Leda Wang", "Fengzhuo Zhang", "Siyu Chen", "Zhuoran Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Understanding how structured internal structure emerges during neural network training is central to the study of deep learning. We investigate this phenomenon through the group composition task, where a two-layer neural network is trained to predict g_1 star g_2 for elements of a finite group G. By lifting the projected gradient flow to the Fourier domain, we demonstrate that the training dynamics are governed by a Riemannian gradient ascent on a representation-theoretic energy functional. We prove that, under random initialization, this flow drives each neuron to converge almost surely toward a single irreducible representation, while the cross-layer Fourier coefficients achieve a rotational rank-one alignment. This framework provides a representation-theoretic account of feature learning and characterizes a novel low-rank compression phenomenon for matrix-valued group representations. Moreover, for Abelian groups, we provide a complete population-level description: random initialization promotes uniform diversification across nontrivial representations and induces Haar-uniform phases, jointly approximating the indicator via a majority-vote mechanism. We further prove that both phase alignment and representation competition emerge with exponential convergence rates.", "github": "" }, { "date": "2026-06-04", "arxiv_id": "2606.04075", "title": "Large Language Models Hack Rewards, and Society", "authors": [ "Wei Liu", "Xinyi Mou", "Hanqi Yan", "Zhongyu Wei", "Yulan He" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thinkwee/SocioHack", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement learning (RL) has become a dominant post-training paradigm, enabling large language models (LLMs) to learn from rewards. We observe that societal regulations are structurally similar to reward functions. They define measurable outcomes, thresholds, and exceptions, while often leaving institutional intent only partially specified. We hypothesise that the RL training process may exploit these gaps and therefore ask whether models' well-known tendency to hack reward functions during RL can scale into a more consequential failure mode named societal hacking: discovering loopholes in the rules society runs on. To study this phenomenon, we introduce SocioHack, a sandbox of 72 societal environments, and find that within these environments, reward hacking naturally emerges and leads to regulatory loophole discovery. Models learn to hack the social rules and generate strategies that remain technically compliant while defeating regulatory intent, and current LLM safeguards provide only limited mitigation. Therefore, collecting in-the-wild feedback for model training requires greater caution, and we need a next-generation post-training paradigm for safely iterating LLMs in real society.=", "github": "https://github.com/thinkwee/SocioHack" }, { "date": "2026-06-04", "arxiv_id": "2606.03965", "title": "Agentic Chain-of-Thought Steering for Efficient and Controllable LLM Reasoning", "authors": [ "Yu Xia", "Zhouhang Xie", "Xin Xu", "Byungkyu Kang", "Prarit Lamba", "Xiang Gao", "Julian McAuley" ], "github_manual": "", "github_regex": "https://github.com/Andree-9/ACTS", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language models improve final-answer accuracy through extended chain-of-thought reasoning, but often spend tokens inefficiently and offer little inference-time control. Existing efficient reasoning methods control thinking length by shortening, early-stopping, or compressing traces, leaving how the model thinks implicit. In this paper, we propose Agentic Chain-of-Thought Steering (ACTS), which formulates reasoning steering as a Markov decision process where a controller agent adaptively steers a frozen reasoner during inference. At each step, the controller observes the reasoning trace and remaining thinking budget, then issues a steering action consisting of a reasoning strategy and a steering phrase that initiates the next reasoner step. This enables budget-aware strategy control for efficient reasoning while preserving the reasoner's generation continuity. We initialize the controller agent from our constructed synthetic steering trajectories with multi-budget augmentation, and further optimize it via reinforcement learning with budget-conditioned reward shaping. Experiments across multiple benchmarks show that ACTS matches full-thinking performance with substantial token savings, and enables controllable accuracy-efficiency trade-offs across different reasoners and tasks. The code is available at https://github.com/Andree-9/ACTS.", "github": "https://github.com/Andree-9/ACTS" }, { "date": "2026-06-04", "arxiv_id": "2606.01090", "title": "Measuring the Symmetry--Data Exchange Rate", "authors": [ "Ahmed M. Adly" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AhmedMostafa16/symmetry-exchange", "github_api_added_by": "user", "project_page": "", "abstract": "Equivariance theory predicts that an architectural symmetry prior reduces sample complexity by a factor of |G|; this is widely cited but rarely measured as a scaling law with controls that separate the prior from its confounds. On a controlled C_n-symmetric task, we report three findings. First, a wrong-group control with identical orbit size and matched compute is worse than no constraint (joint pairwise CI [+0.79, +3.26] excludes zero, robust across estimators); misaligned constraint is actively harmful, not merely unhelpful. Second, an augmentation baseline equipped with test-time orbit averaging matches the equivariant model exactly -- bit-identical per-epoch validation curves across matched cells -- so the architecture-vs-augmentation gap is conditional on asymmetric test-time computation, not unconditional. Third, the relative exchange rate beta_diff = 1.28 is consistent in sign and order of magnitude with the theoretical 1.0 (single-level CI [+0.92, +2.05]); the more conservative two-level bootstrap (seeds x group sizes) widens this to [-0.63, +1.72], including zero, and a finer-N replication on a sqrt(2)-spaced grid is inconclusive (point estimate -0.82). The methodological contributions -- the relative-rate estimator that cancels the shared-difficulty confound, the wrong-group control, and a pre-specified failure taxonomy -- transfer to any inductive bias whose strength can be parameterised. Honest scoping: the primary estimator beta_diff was adopted post-hoc after the initial analysis revealed a positive-slope identifiability problem; the design was never externally pre-registered; and the headline number rests on an OLS slope over seven group sizes on a coarse N grid. This is an exploratory study, not a confirmatory measurement; the wrong-group result is the cleanest finding and the one we report with the most confidence. A registered replication on fresh seeds is future work.", "github": "https://github.com/AhmedMostafa16/symmetry-exchange" }, { "date": "2026-06-04", "arxiv_id": "2606.01804", "title": "SpeechEditBench: A Bilingual Multi-Attribute Benchmark for Instruction-Guided Speech Editing", "authors": [ "Hanlin Zhang", "Daxin Tan", "Dehua Tao", "Xiao Chen", "Haochen Tan", "Linqi Song" ], "github_manual": "", "github_regex": "https://github.com/daxintan-cuhk/SpeechEditBench", "github_paper_md": "", "github_api": "https://github.com/daxintan-cuhk/SpeechEditBench", "github_api_added_by": "user", "project_page": "", "abstract": "Instruction-guided speech editing requires a model to modify specified speech attributes while preserving unrelated characteristics. Despite rapid progress in Speech Large Language Models (Speech LLMs), systematic evaluation of this capability remains challenging, as existing benchmarks are fragmented across isolated editing tasks. To bridge this gap, we introduce SpeechEditBench, a bilingual multi-attribute benchmark for instruction-guided speech editing. SpeechEditBench encompasses seven atomic editing tasks, as well as compositional editing tasks that integrate multiple operations within a single instruction. We propose an anchor-based evaluation protocol that separately assesses the edit success of target attributes and the preservation of untargeted attributes, leading to three metrics: target success, preservation success, and joint success. Using this benchmark, we evaluate mainstream Speech LLMs and specialized speech editing systems. The results reveal three key findings: (1) no single model performs well across all editing dimensions; (2) closed-source Speech LLMs generally outperform open-source models; (3) compositional editing remains highly challenging, with even the most advanced models struggling to achieve high joint success. SpeechEditBench provides a rigorous diagnostic framework to identify bottlenecks in Speech LLMs, thereby facilitating the development of next-generation Speech LLMs with more robust and precise instruction-guided editing capabilities. Data and code are avaialble at https://github.com/daxintan-cuhk/SpeechEditBench .", "github": "https://github.com/daxintan-cuhk/SpeechEditBench" }, { "date": "2026-06-05", "arxiv_id": "2606.06021", "title": "OPRD: On-Policy Representation Distillation", "authors": [ "Shenzhi Yang", "Guangcheng Zhu", "Bowen Song", "Haobo Wang", "Mingxuan Xia", "Xing Zheng", "Yingfan Ma", "Zhongqi Chen", "Weiqiang Wang", "Gang Chen" ], "github_manual": "", "github_regex": "https://github.com/ShenzhiYang2000/OPRD", "github_paper_md": "", "github_api": "https://github.com/ShenzhiYang2000/OPRD", "github_api_added_by": "user", "project_page": "", "abstract": "On-policy distillation (OPD) supervises the student only in output space by matching next-token probabilities. This output-only paradigm has two limits: (1) sampling variance from Monte Carlo KL estimates over large vocabularies (e.g., Qwen's ~150k tokens) persists throughout training, and (2) it treats the teacher as a black-box, discarding all intermediate hidden states after the LM head. We propose On-Policy Representation Distillation (OPRD), which lifts distillation into hidden-state space by aligning student and teacher representations across selected layers on the same rollouts, bypassing the LM head entirely. Theoretically, OPRD eliminates sampling variance and provides richer per-layer structural information. Empirically, OPRD closes the student-teacher gap on AIME 2024/2025 and AIMO, while output-space OPD baselines plateau below the teacher. OPRD also trains 1.44x faster and uses 54% less memory than top-k OPD. Code: https://github.com/ShenzhiYang2000/OPRD.", "github": "https://github.com/ShenzhiYang2000/OPRD" }, { "date": "2026-06-05", "arxiv_id": "2606.05259", "title": "VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding", "authors": [ "Lin Fu", "Zheyuan Yang", "Yang Wang", "Tingyu Song", "Arman Cohan", "Yilun Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Fu-Fu-Fu-Fu/VideoKR", "github_api_added_by": "user", "project_page": "", "abstract": "We introduce VideoKR, the first large-scale training corpus specifically designed to strengthen knowledge- and reasoning-intensive video understanding. It comprises 315K video reasoning examples over 145K newly collected, CC-licensed, expert-domain videos. We develop a human-in-the-loop, skill-oriented example generation pipeline that targets progressively deeper video reasoning capabilities while ensuring the difficulty, diversity, and reliability of both the examples and their CoT rationales. We also curate VideoKR-Eval, a new expert-annotated benchmark where questions require genuine video understanding and knowledge-intensive reasoning rather than textual shortcuts. Our experiments show that, under a standard SFTrightarrowGRPO pipeline, models post-trained on VideoKR outperform prior post-training approaches on knowledge-intensive video reasoning while remaining competitive on general video reasoning, highlighting data design as a key driver of progress in video reasoning. We further conduct comprehensive ablations to isolate the contributions of VideoKR, providing actionable insights for future work.", "github": "https://github.com/Fu-Fu-Fu-Fu/VideoKR" }, { "date": "2026-06-05", "arxiv_id": "2606.05275", "title": "Personal AI Agent for Camera Roll VQA", "authors": [ "Thao Nguyen", "Krishna Kumar Singh", "Donghyun Kim", "Yong Jae Lee", "Yuheng Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/thaoshibe/camroll", "github_api_added_by": "user", "project_page": "https://thaoshibe.github.io/camroll/", "abstract": "We study the personal camera roll visual question answering setting. In this setting, a conversational AI assistant can access a user's personal camera roll and retrieve relevant photos to answer queries, ranging from simple factual questions (e.g., ``Name of the food I tried yesterday?'') to more open-ended ones (e.g., ``Recommend some dishes I have never eaten before''). Given the vast nature of the personal camera roll (i.e., multiple years, hundreds to thousands of photos), a successful AI assistant needs to understand a long-horizon, highly personalized visual content stream in order to navigate and locate the correct and/or relevant information. To support this, we collect and manually annotate questions that mimic real-world usage. The final dataset, camroll, contains 50 users, 31,476 images, and 2,500 QA pairs. We further design camroll-agent, a conversational AI agent equipped with hierarchical memory and a minimal set of tools for efficient navigation over large, personalized visual memory. Experimental results show that camroll-agent outperforms numerous baselines and methods for long-context understanding AI agents system. Together, the camroll dataset and camroll-agent highlight the gap in AI agents' long-context reasoning: personalized visual memory requires different approaches from standard long-context textual memory, especially when consistency, visual details, and user-specific context are present.", "github": "https://github.com/thaoshibe/camroll" }, { "date": "2026-06-05", "arxiv_id": "2606.06416", "title": "Unsupervised Skill Discovery for Agentic Data Analysis", "authors": [ "Zhisong Qiu", "Kangqi Song", "Shengwei Tang", "Shuofei Qiao", "Lei Liang", "Huajun Chen", "Shumin Deng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reusable procedural knowledge without updating model parameters. However, discovering effective skills for data analysis remains challenging, as reliable supervision is expensive and success criteria vary across analytical formats. This raises the key question of how to discover reusable data-analysis skills from unlabeled exploration alone. We propose DataCOPE, an unsupervised verifier-guided skill discovery framework for data-analytic agents. DataCOPE derives verifier signals from the exploration trajectories and uses them to characterize relative quality or aggreement among trajectories. It iteratively coordinates a Data-Analytic Agent for trajectory generation, an Unsupervised Verifier for signal extraction, and a Skill Manager for contrastive skill distillation. For report-style analysis, we instantiate the verifier as an Adaptive Checklist Verifier that derives task-specific criteria, scores reports by verifiable coverage, and iteratively refines the checklist. For reasoning-style analysis, we instantiate it as an Answer Agreement Verifier that groups trajectories by answer agreement and uses self-consistency as an auxiliary signal. We evaluate DataCOPE on report-style analysis from Deep Data Research and reasoning-style analysis from DABStep. Across both settings, DataCOPE consistently improves held-out performance over baselines. Averaged across four model settings, DataCOPE improves the mean score by 9.71% and 32.30% on report-style and reasoning-style tasks respectively.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.04703", "title": "Rethinking Continual Experience Internalization for Self-Evolving LLM Agents", "authors": [ "Jingwen Chen", "Wenkai Yang", "Shengda Fan", "Wenbo Nie", "Chenxing Sun", "Shaodong Zheng", "Yangen Hu", "Lu Pan", "Ke Zeng", "Yankai Lin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RUCBM/ExpInternalization", "github_api_added_by": "user", "project_page": "", "abstract": "Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward continual learning in large language models (LLMs). While prior work has predominantly focused on single-iteration transfer, we discover that under multi-iteration experience learning, existing methods suffer from a progressive capability collapse rather than compounding improvement. We systematically examine this failure through three vital dimensions of experience internalization: (1) Experience Granularity: We find that principle-level experience is more durable than instance-level experience, as it effectively abstracts transferable strategies away from trajectory-specific details. (2) Experience Injection Pattern: Our analysis reveals that step-wise injection significantly outperforms global injection by aligning experience with intermediate decision states, a property that is critical for long-horizon tool use. (3) Internalization Regime: We demonstrate that off-policy context-distillation on high-quality teacher trajectories provides a substantially more stable training signal than on-policy context-distillation, which is inherently limited by local corrections on student-induced flawed states. Together, these insights yield a simple yet robust recipe for stable and sustainable experience internalization, providing concrete guidance for engineering self-evolving and continually learning LLMs.", "github": "https://github.com/RUCBM/ExpInternalization" }, { "date": "2026-06-05", "arxiv_id": "2606.06042", "title": "LoomVideo: Unifying Multimodal Inputs into Video Generation and Editing", "authors": [ "Jianzong Wu", "Hao Lian", "Jiongfan Yang", "Dachao Hao", "Ye Tian", "Yunhai Tong", "Jingyuan Zhu", "Biaolong Chen", "Qiaosong Qi", "Aixi Zhang", "Wanggui He", "Mushui Liu", "Jinlong Liu", "Hao Jiang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MSALab-PKU/LoomVideo", "github_api_added_by": "user", "project_page": "https://msalab-pku.github.io/projects/LoomVideo/index.html", "abstract": "Developing unified video generation and editing models capable of interpreting interleaved multimodal inputs is a promising yet challenging frontier field. Existing unified frameworks predominantly rely on massive models (typically 13B parameters or more) and incorporate source video conditions for editing by concatenating sequence tokens. This concatenation inevitably doubles the sequence length, quadrupling the computational complexity of the self-attention mechanism and introducing prohibitive overhead. To address these bottlenecks, we present LoomVideo, a highly efficient 5B-parameter unified architecture for both video generation and editing. LoomVideo replaces the standard text encoder with a Multimodal Large Language Model (MLLM) and employs Deepstack injection mechanism to align multi-layer MLLM features with the Diffusion Transformer (DiT). Crucially, we introduce a zero-overhead Scale-and-Add conditioning approach for video editing. By scaling and directly adding the clean source video latent to the noised target latent, this elegant design eliminates the need for token concatenation, drastically reducing computational cost while maintaining robust capabilities for complex, non-rigid edits. Furthermore, a Negative Temporal RoPE strategy is seamlessly integrated to handle multiple reference images. Extensive experiments demonstrate that our compact 5B model achieves state-of-the-art or highly competitive performance across comprehensive benchmarks, exhibiting exceptional superiority in e-commerce and fashion generation scenarios. Benefiting from the zero-overhead conditioning mechanism, LoomVideo achieves at least a 5.41x acceleration in inference speed compared to models of similar capabilities, paving the way for highly practical and efficient video foundation models.", "github": "https://github.com/MSALab-PKU/LoomVideo" }, { "date": "2026-06-05", "arxiv_id": "2606.06447", "title": "Latent Reasoning with Normalizing Flows", "authors": [ "Guancheng Tu", "Xiangjun Fu", "Suhao Yu", "Yao Tang", "Haoqiang Kang", "Lianhui Qin", "Yizhe Zhang", "Jiatao Gu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://nf-cot.vercel.app/", "abstract": "Large language models often improve reasoning by generating explicit chain-of-thought (CoT), demonstrating the importance of intermediate computation. However, textual CoT forces this computation through a discrete, serial, and communication-oriented token stream: each reasoning step must be verbalized before the model can proceed, even when the underlying update is semantic, uncertain, or only partially formed. Latent reasoning offers a higher-bandwidth alternative by performing intermediate computation in compact continuous states before committing to text. Yet existing latent-reasoning methods often sacrifice key advantages that make CoT effective in autoregressive language models, including native left-to-right generation, probabilistic sampling, compatibility with KV-cache decoding, and tractable likelihood estimation. We propose NF-CoT, a latent reasoning framework that preserves these advantages by modeling continuous thoughts with normalizing flows. NF-CoT instantiates a TARFlow-style normalizing flow inside the LLM backbone, defining a tractable probability model over compact continuous thoughts distilled from explicit CoT. Continuous-thought positions are generated by an NF head, while text positions are generated by the standard LM head within the same causal stream. This design provides exact likelihoods for latent thoughts, enables probabilistic left-to-right decoding with the original KV cache, and supports direct policy-gradient optimization in the latent reasoning space. On code-generation benchmarks, NF-CoT improves pass rates over explicit-CoT and prior latent-reasoning baselines while substantially reducing intermediate-reasoning cost.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.05769", "title": "Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction", "authors": [ "Tianxiang Jiang", "Linquan Wu", "Sheng Xia", "Songze Li", "Ziang Yan", "Haoyu Yang", "Yu Qiao", "Yi Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenGVLab/Future-L1", "github_api_added_by": "user", "project_page": "https://github.com/OpenGVLab/Future-L1", "abstract": "Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Existing video MLLMs usually verbalize intermediate future reasoning in text space: once visual evidence is verbalized, fine-grained motion, geometry, and interaction cues can be lost, leading to plausible but visually ungrounded hallucinations. We introduce Future-L1, an interleaved latent visual reasoning framework that lets an MLLM alternate between language tokens and continuous latent visual spans during autoregressive decoding. To train this capability, we construct Future-L1-50K by selecting examples where future visual hints help prediction and align latent states to future-frame embeddings, then further optimize sampled latent trajectories with LA-DAPO, a latent-aware RL objective with outcome-contrastive and temporal-diversity rewards. Future-L1 achieves new state-of-the-art results on both benchmarks: on FutureBench, it improves Qwen3-VL-8B from 61.0 to 85.4 and exceeds the previous best Video-CoE by 10.4 points; on TwiFF-Bench, it improves the average score from 2.44 to 3.04. These results suggest that future-oriented video reasoning benefits from preserving intermediate visual semantics in latent space rather than translating every reasoning step into text.", "github": "https://github.com/OpenGVLab/Future-L1" }, { "date": "2026-06-05", "arxiv_id": "2606.03092", "title": "The Shadow Price of Reasoning: Economic Perspective on Optimal Budget Allocation for LLMs", "authors": [ "Xu Wan", "Speed Zhu", "Jianwei Cai", "Guang Chen", "XiMing Huang", "Wiggin Zhou", "Mingyang Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/waunx/CLEAR", "github_api_added_by": "user", "project_page": "", "abstract": "Inference-time scaling has emerged as a critical avenue for enhancing Large Language Models' performance, yet real-world deployment is constrained by strict computational budgets. In this work, we formulate inference budget allocation as a global constrained optimization problem governed by economic principles. By modeling per-query reasoning utility with a shifted-surge function, we derive an optimal allocation policy based on a global shadow price that equilibrates marginal utility under resource scarcity. Based on this theory, we propose Constrained Latent-utility Equilibrium Allocation for Reasoning (CLEAR). It performs rational abandonment and reallocates resources from insolvent queries to solvable queries near their emergence thresholds. Extensive experiments on several reasoning tasks with different traffic streams demonstrate that CLEAR significantly improves the Pareto frontier of total token cost versus mean accuracy. In resource-scarce regimes, CLEAR achieves up to a 3x improvement in global accuracy compared to uniform allocation.", "github": "https://github.com/waunx/CLEAR" }, { "date": "2026-06-05", "arxiv_id": "2606.05979", "title": "World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis", "authors": [ "Yi Yang", "Zhihong Liu", "Siqi Kou", "Yiyang Chen", "Yanzhe Hu", "Jianbo Zhou", "Boyuan Zhao", "Zhijie Wei", "Xiao Xia", "Xueqi Li", "Pengfei Liu", "Zhijie Deng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SJTU-DENG-Lab/WLA", "github_api_added_by": "user", "project_page": "", "abstract": "We propose world-language-action (WLA) models as a new class of embodied foundation models. WLA takes textual instructions, images, and robot states as inputs to jointly predict textual subtasks, subgoal images, and robot actions, conjoining the world modeling interface to learn from extensive egocentric videos as in the world-action model (WAM) and the language reasoning capacities to solve complex long-horizon tasks as in vision-language-action (VLA) models. At the core of WLA lies an autoregressive (AR) Transformer backbone, instead of a bidirectional diffusion Transformer as in WAMs, to predict the next state, comprising the semantic-level textual intention and complementary fine-grained physical dynamics. The physical dynamics are supervised by the world modeling objective based on a dedicated World Expert, and are leveraged to ease the characterization of the state-action correlation for the Action Expert. WLA leverages meta-queries to make the world prediction implicitly impact the action generation so that the former can be disabled during inference. The world prediction can also be activated to enable test-time scaling for improved robot control. Our WLA-0 prototype, with 2B active parameters, achieves 40 ms per inference on an NVIDIA RTX 5090. Evaluations across simulated and real-world environments demonstrate that WLA-0 achieves state-of-the-art multi-task and long-horizon learning abilities, e.g., 92.94\\% success rate on RoboTwin2.0 Clean and 56.5\\% success rate on RMBench. WLA-0 also holds the promise to learn novel tasks directly from cross-embodiment robot videos without action annotations.", "github": "https://github.com/SJTU-DENG-Lab/WLA" }, { "date": "2026-06-05", "arxiv_id": "2606.05645", "title": "Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning", "authors": [ "Ziyang Yao", "Haochen Liu", "Yuncheng Jiang", "Zeyu Zhu", "Zibin Guo", "Jingru Wang", "Tianle Liu", "Jianwei Cui", "Kuiyuan Yang", "Hongwei Xie", "Jingwei Zhao", "Guang Chen", "Hangjun Ye" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Autonomous driving requires reasoning about how ego actions shape the evolution of the surrounding world. However, most end-to-end methods rely on direct state-to-action mappings, capturing correlations without explicitly modeling action-conditioned dynamics. Conversely, continuous-latent world models often lack compositional structure for causal reasoning across counterfactual futures. We introduce Discrete-WAM, a unified latent vision-action world policy that represents future visual states and ego actions as aligned discrete tokens, enabling compositional causal reasoning across alternative futures. Built upon this unified discrete alignment, Discrete-WAM establishes a shared discrete diffusion framework with unified generative tasks, jointly formulating world modeling, world-action policy, and hierarchical decision-enabled policy, supporting compositional generalization across diverse driving scenarios. Experiments on large-scale autonomous-driving benchmarks show that Discrete-WAM achieves competitive performance while supporting controllable generation and counterfactual reasoning, offering a principled path toward more reliable decision-making.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.06473", "title": "MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery", "authors": [ "Shangheng Du", "Xiangchao Yan", "Jinxin Shi", "Zongsheng Cao", "Shiyang Feng", "Zichen Liang", "Boyuan Sun", "Tianshuo Peng", "Yifan Zhou", "Xin Li", "Jie Zhou", "Liang He", "Bo Zhang", "Lei Bai" ], "github_manual": "", "github_regex": "https://github.com/InternScience/MLEvolve", "github_paper_md": "", "github_api": "https://github.com/InternScience/MLEvolve", "github_api_added_by": "user", "project_page": "", "abstract": "Large language model (LLM) agents are increasingly applied to long-horizon tasks such as scientific discovery and machine learning engineering (MLE), where sustained self-evolution becomes a key capability. However, existing MLE agents suffer from inter-branch information isolation, memoryless search, and lack of hierarchical control, which together hinder long-horizon optimization. We present MLEvolve, an LLM-based self-evolving multi-agent framework for end-to-end machine learning algorithm discovery. By extending tree search to Progressive MCGS, MLEvolve enables cross-branch information flow through graph-based reference edges and gradually shifts the search from broad exploration to focused exploitation with an entropy-inspired progressive schedule. To allow the agent to evolve with accumulated experience, we introduce Retrospective Memory, which combines a cold-start domain knowledge base with a dynamic global memory for task-specific experience retrieval and reuse. For stable long-horizon iteration, we further decouple strategic planning from code generation with adaptive coding modes. Evaluation on MLE-Bench shows that MLEvolve achieves state-of-the-art performance across multiple dimensions including average medal rate and valid submission rate under a 12-hour budget (half the standard runtime). Moreover, MLEvolve also outperforms specialized algorithm discovery methods including AlphaEvolve on mathematical algorithm optimization tasks, demonstrating strong cross-domain generalization. Our code is available at https://github.com/InternScience/MLEvolve.", "github": "https://github.com/InternScience/MLEvolve" }, { "date": "2026-06-05", "arxiv_id": "2606.05254", "title": "Flash-WAM: Modality-Aware Distillation for World Action Models", "authors": [ "Arman Akbari", "Ci Zhang", "Arash Akbari", "Lin Zhao", "Yixiao Chen", "Weiwei Chen", "Xuan Zhang", "Geng Yuan", "Yanzhi Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://flashwam.github.io/", "abstract": "World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achieving strong performance on manipulation benchmarks but requiring tens of denoising steps, a cost that precludes real-time control. Step distillation has emerged as the natural remedy, but off-the-shelf methods break down in the joint video-action setting because video and action streams use different SNR-shifted noise schedules and reach training with substantially different marginal noise distributions, an asymmetry that single-modality distillation methods cannot accommodate. We introduce Flash-WAM, a modality-aware step-distillation framework inspired by consistency distillation that selects the consistency function for each modality to match its noise regime: a linear-gradient-scaling parametrization for the action stream's low-noise regime, paired with a variance-preserving parametrization for the video stream's high-noise regime, grounded in a structural analysis of the consistency-function family that characterizes the achievable gradient scaling under the consistency boundary condition. Instantiated on LingBot-VA, Flash-WAM compresses inference to a single step in each modality. On RoboTwin 2.0, this reduces per-chunk latency from 8.1 seconds to 348 ms on NVIDIA L40S, a 23{times} speedup that enables real-time inference. Flash-WAM preserves task success on simulation benchmarks (85.5% RoboTwin 2.0, 95.7% LIBERO) and substantially recovers real-world performance (60% average on a Unitree G1 humanoid robot), while naive consistency distillation drops to 24% at the same step budget.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.04351", "title": "Video2LoRA: Parametric Video Internalization for Vision-Language Models", "authors": [ "Manan Suri", "Sarvesh Baskar", "Dinesh Manocha" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MananSuri27/video2lora", "github_api_added_by": "user", "project_page": "https://video2lora.github.io/", "abstract": "Processing video in vision-language models is expensive: each frame occupies hundreds of tokens, and inference cost scales with every frame and every repeated query. We introduce Video2LoRA, a method for parametric video internalization. A perceiver hypernetwork reads the intermediate representations produced layer-by-layer as a frozen VLM encodes a video, and generates a Low-Rank Adaptation (LoRA) adapter in a single forward pass. Unlike standard LoRA fine-tuning, which requires iterative gradient updates, Video2LoRA predicts these weights directly from the video. Trained for SmolVLM2 500M and 2.2B on video summarization and captioning, Video2LoRA enables the same frozen VLM to answer queries from the adapter alone, with zero visual tokens in its context at query time. Video2LoRA is statistically non-inferior and equivalent to direct video-in-context inference across all five captioning benchmarks at both model scales, and across seven of eight video question answering benchmark-scale pairings. Although trained only on 12 frames at 384px, it remains stable up to 1,024 frames and 1024px, where direct video-in-context inference often degenerates. Across this sweep, it reduces answer-time visual-token load by up to 1,500x and query TTFT by 6-80x, while preserving video-faithful outputs. We also find that independently generated adapters for non-overlapping video segments can compose in rank space, suggesting a path toward chunked long-video internalization.", "github": "https://github.com/MananSuri27/video2lora" }, { "date": "2026-06-05", "arxiv_id": "2606.05553", "title": "ArcANE: Do Role-Playing Language Agents Stay in Character at the Right Time?", "authors": [ "Woojung Song", "Nalim Kim", "Sangjun Song", "Chaewon Heo", "Jongwon Lim", "Yohan Jo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Role-playing language agents (RPLAs) should play characters whose values and behavior evolve as the story progresses, not maintain a fixed persona. Existing benchmarks measure factual recall at a given chapter, not whether responses align with the character's psychological trajectory, especially in scenarios the source text never explores. We introduce ArcANE (Arc-Aware Narrative Evaluation), an automatically constructed benchmark spanning 17 novels and 80 principal characters. A Character Arc segments the narrative into phases along a psychological axis, and each probe poses the same scenario across phases, spanning both situations within the source text and situations beyond it. Across six models and six context modes, conditioning on the Character Arc tops every other context strategy on every model, and the gap is largest on scenarios outside the source text where retrieval has nothing to find. We further fine-tune open-weight models on the same data to obtain ArcANE-8B/32B, which widen the Arc advantage even more on scenarios outside the source text.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.03841", "title": "EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management", "authors": [ "Zherui Yang", "Fan Liu", "Yansong Ning", "Hao Liu" ], "github_manual": "", "github_regex": "https://github.com/usail-hkust/EvoDS", "github_paper_md": "", "github_api": "https://github.com/usail-hkust/EvoDS", "github_api_added_by": "user", "project_page": "https://huggingface.co/yangzhr/EvoDS", "abstract": "Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science. However, existing approaches remain fundamentally limited by their static action sets and lack of principled long-horizon context management, hindering their ability to accumulate reusable experience across tasks and operate reliably in multi-stage, iterative data science pipelines. To address these challenges, we introduce EvoDS, a self-evolving autonomous data science agent that learns to expand its skills and adaptively managing long-term context through agentic reinforcement learning. Specifically, EvoDS introduces two key strategies: (1) Autonomous Skill Acquisition (ASA) mechanism, which enables agents to synthesize, validate, and reuse executable skills; and (2) Adaptive Context Compression (ACC) strategy, which treats context management as a learned control problem rather than passive truncation. These strategies are orchestrated within a two-stage multi-agent training scheme, enabling EvoDS to autonomously improve over time. Theoretically, we prove that EvoDS's hierarchical design reduces tool-selection error, and its optimization objective aligns with an information bottleneck principle, ensuring efficient context use. Empirically, EvoDS outperforms state-of-the-art open-source data science agents by an average of 28.9% across four diverse benchmarks while eliminating out-of-token failures. Our code and data are available at https://github.com/usail-hkust/EvoDS.", "github": "https://github.com/usail-hkust/EvoDS" }, { "date": "2026-06-05", "arxiv_id": "2605.31058", "title": "Combinatorial Synthesis: Scaling Code RLVR via Atomic Decomposition and Recombination", "authors": [ "Jiasheng Zheng", "Boxi Cao", "Boxi Yu", "Yuzhong Zhang", "Jialun Cao", "Yaojie Lu", "Hongyu Lin", "Xianpei Han", "Le Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/icip-cas/ADR", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as the cornerstone for shaping the remarkable coding abilities of Large Language Models (LLMs). However, the scalability of RLVR is severely constrained by the scarcity of sufficiently challenging verifiable code tasks that target near the model's edge of competence. Prior studies often rely on heuristic seed expansions for data synthesis, which severely limits both novelty and difficulty. Consequently, the training value of such data fails to scale proportionally with the size of its synthesis. To this end, we propose Atomic Decomposition and Recombination (ADR), a novel framework that generates verifiable code tasks via decomposition into atomic elements and controlled recombination, thereby enabling the generation of genuinely novel and challenging verifiable code tasks. Experiments and analysis demonstrate that ADR achieves superior originality, difficulty, diversity, and test quality over existing baselines, and consistently delivers greater improvements in code ability across RLVR in diverse downstream domains, including algorithmic programming, tool usage, and data science. Our work sheds light on a new paradigm for novel code task synthesis and scalable RLVR training.", "github": "https://github.com/icip-cas/ADR" }, { "date": "2026-06-05", "arxiv_id": "2605.30159", "title": "Meta-Cognitive Memory Policy Optimization for Long-Horizon LLM Agents", "authors": [ "Ziyan Liu", "Zhezheng Hao", "Yeqiu Chen", "Hong Wang", "Jingren Hou", "Ruiyi Ding", "Yongkang Yang", "Wence Ji", "Wei Xia", "Feng Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Memory-augmented LLM agents tackle complex long-horizon tasks by recursively summarizing interaction trajectories into compact memory. However, existing approaches typically train these memory policies using outcome-based reinforcement learning, failing to localize where intermediate memory quality degrades. As interactions unfold, ambiguous recursive summaries progressively discard task-relevant information and introduce semantic noise. This exacerbates belief deviation, obscuring the agent's estimate of the latent task state and ultimately derailing long-horizon reasoning. We therefore argue that memory optimization should focus not merely on trajectory-level success, but on the clarity of the belief induced by intermediate summaries. To this end, we introduce Belief Entropy, a self-supervised proxy that probes how uncertain the model remains about the latent task state given its current memory. Based on this proxy, we propose Metacognitive Memory Policy Optimization (MMPO). Instead of relying only on sparse outcome-based signals, MMPO provides fine-grained, memory-specific supervision via explicitly penalizing summaries that induce high epistemic uncertainty. Experiments show that MMPO consistently outperforms existing methods on diverse long-horizon tasks, maintaining 97.1% performance even when scaled to 1.75M-token contexts.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.05172", "title": "Is This Edit Correct? A Multi-Dimensional Benchmark for Reasoning-Aware Image Editing", "authors": [ "Yixuan Ding", "Wei Huang", "Ruijie Quan", "Xiaojuan Qi", "Yi Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Yixuan-Ding-ZJU/RE-Edit", "github_api_added_by": "user", "project_page": "", "abstract": "Diffusion-based image editing has achieved strong visual fidelity under natural language instructions, yet most existing systems still operate at the level of surface instruction following, without reasoning about the implicit contextual constraints embedded in real user requests. This often leads to visually plausible but logically inconsistent edits. In this work, we introduce RE-Edit, a benchmark for REasoning-aware image Editing that evaluates image editing systems across five complementary reasoning dimensions: physical, environmental, cultural, causal, and referential. RE-Edit comprises 1,000 carefully curated samples, each designed such that visual plausibility alone is insufficient and correct editing requires satisfying implicit logical constraints. To support fine-grained analysis, we establish dimension-aligned evaluation criteria and conduct a comprehensive study of ten open-source and two commercial image editing models. Our results show that even advanced systems frequently struggle with implicit multi-dimensional reasoning despite producing high-quality visuals. We further present a lightweight reasoning-guided post-edit baseline as an initial exploration, illustrating how inserting explicit reasoning can help mitigate such failures in a model-agnostic manner.", "github": "https://github.com/Yixuan-Ding-ZJU/RE-Edit" }, { "date": "2026-06-05", "arxiv_id": "2606.06428", "title": "Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation", "authors": [ "Hanxu Hu", "Zden\u011bk \u0160najdr", "Pinzhen Chen", "Jannis Vamvas", "Rico Sennrich" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hanxuhu/rl-new-language", "github_api_added_by": "user", "project_page": "", "abstract": "Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encoding a grammar book in their context. However, both methods typically overfit specific languages, with limited zero-shot transfer at test time. To translate extremely low-resource languages at scale, we argue that LLMs must acquire the meta-skill of utilizing in-context linguistic knowledge rather than memorizing specific languages. In this paper, we propose a reinforcement learning (RL) approach to unseen language translation given rich linguistic context, using a surface-level translation metric (chrF) as the reward. Empirically, despite the lightweight reward, our RL-trained models effectively extract and apply relevant linguistic information from the provided context, leading to better translations on completely unseen languages than in-context learning or supervised fine-tuning. Our analyses suggest that outcome-based RL can extend beyond conventional reasoning tasks like math and coding to serve as a recipe for language learning from context.", "github": "https://github.com/hanxuhu/rl-new-language" }, { "date": "2026-06-05", "arxiv_id": "2606.04465", "title": "SePO: Self-Evolving Prompt Agent for System Prompt Optimization", "authors": [ "Wangcheng Tao", "Han Wu", "Weng-Fai Wong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "System prompt optimization improves agent behavior without modifying the underlying model, yielding human-readable, model-agnostic instructions. Existing methods build a prompt agent that refines task agents' system prompts, yet leave the prompt agent's own system prompt hand-engineered and fixed. We propose Self-Evolving Prompt Optimization (SePO), which treats the prompt agent's own system prompt as an optimization target alongside task agents' system prompts. SePO adopts a self-referential design. A single prompt agent improves both task agents' system prompts and its own under an open-ended evolutionary search that maintains an archive of candidate prompts as stepping stones. Training proceeds in two stages: pre-training evolves the prompt agent on a multi-task pool, and fine-tuning then applies it to a target task. Across five benchmarks spanning math (AIME'25), abstract reasoning (ARC-AGI-1), graduate-level science (GPQA), code generation (MBPP), and logic puzzles (Sudoku), SePO consistently outperforms Manual-CoT, TextGrad, and MetaSPO, improving the average accuracy by 4.49 points compared to Manual-CoT. The prompt optimization skill from pre-training also generalizes to tasks beyond the pre-training mixture, rather than memorizing per-task prompts.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.03312", "title": "RobotValues: Evaluating Household Robots When Human Values Conflict", "authors": [ "Jongwook Han", "Hyeongjin Kim", "Yohan Jo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "While household robots are often evaluated based on task completion, everyday domestic environments involve value-conflicting situations in which robots are expected to choose actions that prioritize other values than task success, such as human autonomy, efficiency, or social appropriateness. Yet, there are no benchmarks for evaluating robots' value preferences in such scenarios. We introduce RobotValues, a benchmark to evaluate household robot planners in 10K value-conflict scenarios. Each instance consists of a realistic household image with multiple plausible robot actions that prioritize different human values. We construct RobotValues through LLM-assisted scenario generation, stakeholder-grounded value extraction, image generation and automatic quality control. Using RobotValues we evaluate VLMs used in robotics and find that models exhibit default value preferences, including safety and accommodation, while underselecting privacy-prioritizing actions. When the models are instructed to prioritize specific values that conflict with their own preferences, they often fail to override their default actions, choosing incorrect actions for 80% of the time. These findings suggest that household robot evaluation should measure not only task completion or safety compliance, but also whether robots can choose among plausible actions when human values conflict.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.05846", "title": "Towards Truly Multilingual ASR: Generalizing Code-Switching ASR to Unseen Language Pairs", "authors": [ "Gio Paik", "Hyunseo Shin", "Soungmin Lee" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Automatic Speech Recognition (ASR) has become a key technology for human--AI interaction. However, code-switching ASR (CS-ASR) remains particularly challenging due to the severe scarcity of multilingual CS speech resources across diverse language pairs. Existing approaches primarily improve CS-ASR performance through synthetic CS speech generation or pair-specific fine-tuning on limited bilingual datasets. Nevertheless, these approaches face an inherent scalability limitation, as support for CS must be developed separately for language pairs whose number grows combinatorially with the number of supported languages. In this work, we investigate whether CS capabilities learned from a limited set of seen language pairs can generalize to unseen language pairs through model merging and domain generalization methods. Our experiments show that merged bilingual CS-ASR models modestly generalize to unseen language pairs, suggesting limited transfer of bilingual CS capabilities across language pairs.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.06477", "title": "Complexity-Balanced Diffusion Splitting", "authors": [ "Noam Issachar", "Dani Lischinski", "Raanan Fattal" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NoamIssachar/Complexity-Balanced-Splitting", "github_api_added_by": "user", "project_page": "https://noamissachar.github.io/CBS/", "abstract": "Standard continuous-time generative models rely on monolithic architectures that must navigate vastly different signal regimes, from isotropic noise to intricate data distributions. While scaling model capacity improves performance, deploying a massive network uniformly across the entire generative timeline is inherently inefficient. In this work, we propose Complexity-Balanced Splitting (CBS), a principled framework for temporal capacity allocation that distributes the generative workload across multiple specialized sub-networks. Grounded in function approximation theory and de Boor's equidistribution principle, CBS partitions the diffusion timeline into segments of equal approximation burden, allocating more representational capacity to regions where the generative dynamics are more difficult to model. To estimate this local complexity, we introduce two complementary and tractable monitor functions: a spatial measure based on the flow's Dirichlet energy, and a geometric measure based on the acceleration of the sampling trajectories. Using a lightweight auxiliary model to estimate these complexity profiles, our approach eliminates the need for heuristic temporal splits or computationally expensive search procedures. Extensive evaluation across multiple architectures (SiT, JiT, and UNet) and datasets demonstrates that CBS consistently improves synthesis quality without increasing per-step inference cost. In particular, CBS improves FID by ~35% on SiT-XL with CFG relative to naive temporal partitioning. Project page is available at https://noamissachar.github.io/CBS/.", "github": "https://github.com/NoamIssachar/Complexity-Balanced-Splitting" }, { "date": "2026-06-05", "arxiv_id": "2606.01753", "title": "Quality-Guided Semi-Supervised Learning for Medical Image Segmentation", "authors": [ "Kumar Abhishek", "Ghassan Hamarneh" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sfu-mial/QG-SSL", "github_api_added_by": "user", "project_page": "", "abstract": "Training accurate medical image segmentation models requires large amounts of densely annotated data, which is costly and time-consuming to obtain. Semi-supervised learning (SSL) alleviates this by learning from both abundant unlabeled data and limited labeled data. However, most modern SSL methods rely on pseudolabels for unlabeled data, and typically assess their reliability through model confidence or uncertainty, measures that are self-referential and lack explicit grounding in segmentation quality. Instead, we propose a quality-guided SSL framework that trains a dedicated network to estimate segmentation quality from image-mask pairs. The predictor is trained on variable-quality masks generated through synthetic corruptions augmented with imperfect outputs from partially trained segmentation models, capturing realistic error patterns encountered during training. We integrate the quality predictor into SSL through two complementary mechanisms: a quality-aware regularization loss and a quality-based pseudolabel sample reweighting scheme. We show that our method serves as a drop-in enhancement to existing SSL frameworks. Extensive experiments across five datasets and multiple architectures demonstrate consistent improvements over competing SSL methods, advancing the state-of-the-art in semi-supervised medical image segmentation.", "github": "https://github.com/sfu-mial/QG-SSL" }, { "date": "2026-06-05", "arxiv_id": "2606.05622", "title": "AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints", "authors": [ "Jiayu Liu", "Cheng Qian", "Zhenhailong Wang", "Bingxuan Li", "Jiateng Liu", "Heng Wang", "Jeonghwan Kim", "Yumeng Wang", "Xiusi Chen", "Yi R. Fung", "Heng Ji" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/JiayuJeff/AdaPlanBench", "github_api_added_by": "user", "project_page": "", "abstract": "Planning for real-world problems by language models often involves both world and user constraints, which may not be fully specified upfront and are progressively disclosed through interaction. However, existing benchmarks still underexplore adaptive planning under such progressively revealed dual constraints. To address this gap, we introduce AdaPlanBench, a dynamic interactive benchmark for evaluating whether Large Language Model (LLM) agents can adaptively plan and re-plan under progressively revealed world and user constraints. AdaPlanBench is built on 307 household tasks, with a scalable constraint construction pipeline that augments each task with dual constraints. At runtime, agents interact with the environment in a multi-turn protocol where hidden constraints are revealed only when the agent proposes a plan that violates them, requiring iterative plan revision under accumulating feedback. This makes planning challenging, as agents must infer and track constraints from feedback while re-planning effectively. Experiments on ten leading LLMs show that adaptive planning under dual constraints remains challenging, with the best model reaching only 67.75% accuracy. We further observe that performance degrades as more constraints accumulate, with user constraints posing a particularly large challenge and failures often stemming from weaker physical grounding and reduced effectiveness. These results establish AdaPlanBench as a testbed for dual-constrained interactive planning and highlight the challenge of reliable adaptation to dynamically revealed constraints in LLM agents.", "github": "https://github.com/JiayuJeff/AdaPlanBench" }, { "date": "2026-06-05", "arxiv_id": "2606.04743", "title": "TIDE: Proactive Multi-Problem Discovery via Template-Guided Iteration", "authors": [ "Soyeong Jeong", "Jinheon Baek", "Minki Kang", "Sung Ju Hwang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Agents are widely deployed as assistants over documents, tools, and code. However, they typically act only on explicit user requests, which surface only the problems the user has noticed, while many other important problems coexist, hidden in plain sight, within the broader user context, with their total number unknown in advance. We frame this as the task of discovering multiple hidden problems from context, in which coexisting problems should be uncovered, grounded in supporting evidence, and paired with concrete actions. To this end, we introduce TIDE, a template-guided iterative framework with two complementary mechanisms. Specifically, motivated by the observation that single-pass prediction anchors on the most salient cases and yields generic claims, we propose iterative discovery, which surfaces a small batch of candidates per round while conditioning on what has already been found, so subsequent rounds extend coverage; and thought templates, reusable schemas distilled from previously solved cases that specify what contextual signals to attend to and how to connect them, anchoring each prediction in a recognizable problem class. We validate TIDE on two realistic settings, personal workspaces and software repositories, across four model backbones, showing substantial gains over single-shot and parallel multi-agent baselines on task coverage, identification, and resolution.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.02569", "title": "AdaCodec: A Predictive Visual Code for Video MLLMs", "authors": [ "Haowen Hou", "Zhen Huang", "Zheming Liang", "Qingyi Si", "Chenglin Li", "Shuai Dong", "Kele Shao", "Ruilin Li", "Dianyi Wang", "Nan Duan", "Jiaqi Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://haowenhou.github.io/AdaCodec-Page/", "abstract": "Video is temporally redundant: adjacent frames usually share most objects, background, and layout. Yet existing video multimodal large language models (video MLLMs) usually encode each sampled frame as an independent RGB image, causing visual tokens to repeat content already present in earlier frames. This suggests a more direct video interface: send a full reference frame only when the scene cannot be predicted well from prior context, and otherwise transmit a compact description of inter-frame changes. We call this interface a predictive visual code, and instantiate it for video MLLMs as AdaCodec. AdaCodec spends full visual tokens on a reference frame only when its conditional predictive cost is high; otherwise, it encodes inter-frame changes, including motion and prediction residuals, as compact P-tokens. Across all eleven benchmarks, AdaCodec improves over the Qwen3-VL-8B per-frame RGB baseline at a matched visual-token budget. Even at 1/7 the budget, AdaCodec with 32k tokens surpasses the 224k baseline on all long-video benchmarks; on five general-video benchmarks, it raises the average score while substantially cutting time-to-first-token from 9.26s to 1.62s.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.00125", "title": "Multimodal Music Recommendation System using LLMs", "authors": [ "Srikar Prabhas Kandagatla", "Sreehitha R. Narayana", "Chandana Magapu", "Swetha Mohan", "Shamanth Kuthpadi", "Hongjie Chen", "Ryan A. Rossi", "Franck Dernoncourt", "Nesreen Ahmed" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://zenodo.org/records/20431748", "abstract": "Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content. Prior work has explored LLM-augmented, multimodal, and text-enhanced approaches to sequential recommendation, and while some methods partially combine semantic, acoustic, or engagement signals, none jointly model all three within a unified LLM-based sequential reasoning framework that grounds recommendations in actual song content. In this work, we propose a multimodal framework for session-based music recommendation that enriches the LastFM-1K dataset with three complementary signals: (1) audio and lyric embeddings extracted using pretrained music and text representation models, (2) LLM-generated semantic metadata using the MGPHot annotation schema, and (3) listening completion ratios. We adopt the E4SRec framework by extending it with multimodal features and different item ID encoder backbones, including SASRec, BERT4Rec, and GRU4Rec. We further extend the LLM backbone option with LLaMa-2-13B, Qwen2.5-7B-Instruct, and LLaMa-3-70B in both zero-shot and fine-tuned settings. Our experiments show that integrating content-based features improves over ID-only baselines up to 95% in terms of Recall and 79% in terms of NDCG. Moreover, our experiments show that naive multimodal fusion does not always yield additive improvements, highlighting challenges in cross-modal integration. We release a large-scale multimodal benchmark for music recommendation.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.06286", "title": "LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs", "authors": [ "Gianluca Barmina", "Peter Schneider-Kamp", "Lukas Galke Poech" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/N-essuno/PropMe", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models can reproduce training data, but existing memorization evaluations mostly measure whether models can be forced to do so, rather than whether they do so under ordinary use. We introduce PropMe, a propensity-aware framework for memorization evaluation that contrasts prefix-based capability attacks with non-adversarial evaluations. We propose a metric transformation that, applied to existing functions, allows to create propensity metrics. We further introduce SimpleTrace, a lightweight tracing pipeline built on infini-gram that deterministically attributes model generations to large-scale training corpora and computes verbatim, near-verbatim, and propensity-transformed memorization metrics. Evaluating two fully-open models: Comma and DFM Decoder on two datasets: Common Pile and Dynaword in two languages, we find a consistent gap between capability and propensity: prefix attacks elicit substantially stronger memorization signals than generic or dataset-specific prompts, while propensity scores remain low overall. Thus, the models can reveal training data when directly elicited, but rarely do so in more common non-adversarial settings. We also find that DFM Decoder, which is continually pre-trained from Comma, exhibits reduced memorization and memorization propensity for Common Pile, confirming that memorization capability can decrease when later training emphasizes partially different data. Our results suggest, and we encourage, that memorization audits should report both worst-case extractability and ordinary leakage propensity in order to have a more comprehensive view of this phenomenon.", "github": "https://github.com/N-essuno/PropMe" }, { "date": "2026-06-05", "arxiv_id": "2605.30794", "title": "MechVQA: Benchmarking and Enhancing Multimodal LLMs on Comprehensive Mechanical Drawing Understanding", "authors": [ "Qian Kou", "Xiaofeng Shi", "Yulin Li", "Xiaosong Qiu", "Xinyang Wang", "Hua Zhou", "Cao Dongxing" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have demonstrated significant achievements in general visual question answering (VQA) tasks. However, they remain brittle on mechanical engineering drawings, where high annotation density and weak domain knowledge, compounded by unreliable spatial relation reasoning under strict projection rules and geometric constraints, make decisive cues easy to miss and frequently lead to wrong answers. To bridge this gap, we introduce the first comprehensive mechanical drawing understanding dataset, MechVQA, created through a semi-automated construction and quality-control pipeline. MechVQA contains 3.3k high-density pictures with 21K question-answer pairs, spanning 10 different fine-grained tasks across three capability levels: Recognition, Reasoning, and Judging, providing a testbed to evaluate and improve MLLM understanding on real-world mechanical drawings. On top of MechVQA, we then develop the MechVL model through a multi-stage training paradigm, building a strong domain-specialized baseline. Extensive experimental results demonstrate that MechVL outperforms the strongest closed-source baseline by 7.57 percentage points on the MechVQA total score, significantly enhancing mechanical drawing understanding ability and providing a reusable foundation for deploying MLLMs in mechanical design and inspection scenarios.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.06294", "title": "Towards One-to-Many Temporal Grounding", "authors": [ "Qi Xu", "Yue Tan", "Shihao Chen", "Jiahao Meng", "Anna Wang", "Shunping Ji", "Hao Fei", "Jason Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tangent0308/OMTG", "github_api_added_by": "user", "project_page": "https://insomniaaac.github.io/OMTG/", "abstract": "Temporal Grounding (TG) aims to localize video segments corresponding to a textual query. Prior research predominantly focuses on single-segment retrieval. Real-world scenarios, however, often require localizing multiple disjoint segments for a single query -- a setting we term One-to-Many Temporal Grounding (OMTG). Previous state-of-the-art MLLMs, optimized for one-to-one settings, struggle in this context, often yielding near-zero scores due to a lack of event cardinality perception. To bridge this gap, we present a systematic solution with three key contributions. First, we establish the first comprehensive OMTG benchmark, introducing Count Accuracy (C-Acc) and Effective Temporal F1 (EtF1) as evaluation metrics. Second, we curate a high-quality OMTG dataset comprising 56k samples through a sophisticated construction pipeline. Third, we develop novel temporal and caption reward functions specifically designed for OMTG. In particular, the caption reward leverages Chain-of-Thought reasoning over dense video captions to explicitly guide policy optimization toward both preciseness and completeness. Extensive experiments show our model achieves a new state-of-the-art EtF1 of 43.65\\% on OMTG Bench, outperforming Gemini 2.5 Pro and Seed-1.8 by 15.85\\% and 15.61\\%, respectively.", "github": "https://github.com/Tangent0308/OMTG" }, { "date": "2026-06-05", "arxiv_id": "2606.03940", "title": "SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction", "authors": [ "Dan Jacobellis", "Neeraja J. Yadwadkar" ], "github_manual": "", "github_regex": "https://github.com/UT-SysML/seaotter", "github_paper_md": "", "github_api": "https://github.com/UT-SysML/seaotter", "github_api_added_by": "user", "project_page": "https://ut-sysml.github.io/seaotter/", "abstract": "In robotics systems, vast amounts of visual data are easily captured at high resolution using low-cost, low-power hardware. Yet, limited bandwidth and on-device compute resources prevent full utilization when transmitted via conventional codecs like JPEG/MPEG. Newer codecs, like AV1/AVIF, improve the rate-distortion trade-off, but demand far more resources for encoding, impractical without custom ASICs. Recent asymmetric autoencoders deliver high quality under extreme power and bandwidth constraints, but add prohibitive decoding cost and use bespoke formats that ignore decades of infrastructure built around standards like JPEG. To address these limitations, we introduce a compression framework for cloud robotics based on a Sensor Embedded Autoencoder paired with a One-Time Transcode for Efficient Reconstruction (SEAOTTER). Because the sensor, cloud, and consumer stages face very different power and bandwidth budgets, SEAOTTER combines the compactness of a learned latent with the broad usability of a standard JPEG file. Since naive transcoding degrades performance, we propose a learnable JPEG color and quantization transform that enables increased accuracy for global, dense, and vision-language-based perception. Using SEAOTTER, we train both general-purpose and task-aware transcoding pipelines for a pre-trained, frozen encoder. At a compression ratio of 200:1 and compared to AVIF, we observe 7 times faster encoding, 3.5 times faster decoding, and +8% ImageNet top-1 accuracy, while retaining compatibility with JPEG infrastructure. Our code is available at https://github.com/UT-SysML/seaotter .", "github": "https://github.com/UT-SysML/seaotter" }, { "date": "2026-06-05", "arxiv_id": "2606.04811", "title": "Dream.exe: Can Video Generation Models Dream Executable Robot Manipulation?", "authors": [ "Rui Zhao", "Kaiming Yang", "Jifeng Zhu", "Siyang Chen", "Ziqi Wang", "Weijia Wu", "Kevin Qinghong Lin", "Heng Wang", "Mike Zheng Shou" ], "github_manual": "", "github_regex": "https://github.com/showlab/Dream.exe", "github_paper_md": "", "github_api": "https://github.com/showlab/Dream.exe", "github_api_added_by": "user", "project_page": "", "abstract": "Video generation models have made impressive strides in synthesizing visually compelling content, yet their outputs remain confined to the virtual domain. A natural question follows: how well do these models reflect the physical world when their generated videos leave the screen and enter reality? We propose robotic manipulation as a concrete, measurable window onto this question: if a model has truly internalized physical laws, the motion it depicts should translate into executable robot behavior. We introduce Dream.exe, an evaluation framework that operationalizes this criterion through a video-to-execution pipeline. Given a scene image and a task description, Dream.exe synthesizes a manipulation video, converts the generated motion into robot trajectories, and executes them in a physics simulator, yielding a grounding signal that purely visual metrics cannot offer. Using this pipeline, we evaluate 8 models spanning frontier closed-source generators, open-source generators, and robot-specific models. Our benchmark covers 101 manually curated manipulation tasks at three levels of physical complexity, measured across visual quality, trajectory fidelity, and execution success. Encouragingly, several models achieve measurable execution success, suggesting that generative priors learned from internet-scale data already encode meaningful physical knowledge. Yet visual quality proves a poor predictor of executability, exposing a dimension of model capability that standard visual evaluations do not capture. Dream.exe will be open-sourced at https://github.com/showlab/Dream.exe.", "github": "https://github.com/showlab/Dream.exe" }, { "date": "2026-06-05", "arxiv_id": "2606.01886", "title": "Absorbing Complexity: An Interaction-Native Knowledge Harness for Financial LLM Agents", "authors": [ "Ailiya Borjigin", "Igor Stadnyk", "Ben Bilski", "Maksym Chikita", "Dmytro Kyrylenko", "Sofiia Pidturkina", "Julia Stadnyk" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Financial AI agents often fail for a simple reason: they make users carry the complexity. A user must repeatedly restate goals, risk preferences, portfolio context, past judgments, and shifting market assumptions, while the agent answers, retrieves, acts, and forgets. In finance, this is not just inconvenient. In tasks such as market analysis, copy-trading review, and trade preparation, forgotten context and stale memory can create latency, repeated errors, weak auditability, and unsafe decisions. We propose the interaction-native knowledge harness (InKH), an architecture for financial LLM agents that absorbs complexity into the system. InKH converts user, market, portfolio, and tool events into structured operational knowledge. It uses passive knowledge injection to assemble a bounded working context buffer before the main model step, temporal graph memory for low-latency retrieval, a wiki audit surface for human-readable governance, and background extraction with maturity, decay, and write-time invalidation. We evaluate InKH on a reproducible controlled synthetic benchmark with 24 random seeds, 4 rounds, 80 episodes per round, and 6 baselines, producing 46,080 baseline-conditioned evaluations. InKH achieves mean task quality of 0.815 at 900 ms latency. Compared with agent-driven wiki-walk memory, it reduces latency by 82.95 percent, token cost by 82.29 percent, and stale-knowledge usage by 96.58 percent, while improving quality by 0.108 and traceability by 0.461. Compared with a temporal-graph system without invalidation, it improves quality by 0.050 and reduces stale-memory usage by 96.58 percent with comparable serving cost. The results support a design thesis for financial AI: adoption happens when complexity is absorbed by the system rather than transferred to the user. The benchmark validates architecture-level behavior, not live trading performance.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.06443", "title": "Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions", "authors": [ "Xinnong Zhang", "Wanting Shan", "Hanjia Lyu", "Zhongyu Wei", "Jiebo Luo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language models are increasingly used to simulate social media users and infer how individuals may respond to online discussions. However, it remains unclear whether these simulations reflect precise user-specific beliefs or whether they are highly sensitive to semantically independent changes in conversational contexts. In this work, we study counterfactual context revision as a framework for auditing LLM-based stance simulation. Given an original online conversation, we first infer a target user's stance toward a specific topic. We then apply controlled revision strategies to the conversational context and simulate the user's stance again under the revised context. We compare text-only revision strategies with a multimodal one that incorporates meme-based context and evaluate two main effectiveness metrics, i.e., average directional stance shift and stance transition rate. The results reveal effective and robust stance transitions in both text-only and multimodal strategies across different polarization-preference mechanisms. Our study contributes an evaluation framework for understanding the context sensitivity of LLM-based stance simulation. More broadly, it highlights both the promise and risk of using LLMs to simulate online opinion dynamics.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.00644", "title": "ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment", "authors": [ "Qiuyu Tian", "Haojie Yin", "Yingce Xia", "Youyong Kong", "Zequn Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/roytian1992/ResearchForesight", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We introduce ForeSci, a temporally controlled benchmark for evaluating whether LLM agents can make such forward-looking research judgements from historical evidence. ForeSci contains 500 tasks across four fast-moving AI domains and four decision families. Each task is paired with a cutoff-aligned offline knowledge base; post-cutoff papers are hidden during generation and used only for validation. To avoid random future-event prediction, tasks are derived from pre-cutoff taxonomy branches and evidence signals, and answer-generation backbones are selected to precede the task cutoffs. We evaluate native LLMs, Hybrid RAG, and three research-agent adaptations across four backbones. Results show that explicit evidence organization improves traceability and factual support, but gains depend strongly on the decision family. Diagnostics reveal a recurring evidence-decision decoupling: agents may cite relevant evidence while forecasting the wrong research object. ForeSci turns forward-looking AI research judgement into a controlled benchmark for evaluating research agents as decision-making systems.", "github": "https://github.com/roytian1992/ResearchForesight" }, { "date": "2026-06-05", "arxiv_id": "2606.06492", "title": "Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution", "authors": [ "Liliana Hotsko", "Yinxi Li", "Yuntian Deng", "Pengyu Nie" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Code language models need repository-level context to resolve imports, APIs, and project conventions. Existing methods inject this knowledge as long inputs (retrieved through RAG or dependency analysis) or through per-repository fine-tuning and LoRA -- costly at repository scale and brittle to evolving codebases. We introduce Code2LoRA, a hypernetwork framework that generates repository-specific LoRA adapters, effectively injecting repository knowledge with zero inference-time token overhead. Code2LoRA supports two usage scenarios: Code2LoRA-Static converts a single repository snapshot into an adapter, suitable for comprehension of stable codebases; while Code2LoRA-Evo maintains an adapter backed by a GRU hidden state updated per code diff, suitable for active development of evolving codebases. To evaluate Code2LoRA against parameter-efficient fine-tuning baselines, we build RepoPeftBench, a benchmark of 604 Python repositories with two tracks: a static track with 40K training and 12K test assertion-completion tasks, and an evolution track with 215K commit-derived training and 87K commit-derived test tasks. On the static track, Code2LoRA-Static achieves 63.8% cross-repo and 66.2% in-repo exact match, matching the per-repository LoRA upper bound; on the evolution track, Code2LoRA-Evo achieves 60.3% cross-repo exact match (+5.2 pp over a single shared LoRA). Code2LoRA's code can be found at https://anonymous.4open.science/r/code2lora-6857; the model checkpoints and RepoPeftBench datasets can be found at https://huggingface.co/code2lora.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.06155", "title": "AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding", "authors": [ "Qize Yu", "Jiadi You", "Yuran Wang", "Jiaqi Liang", "Bowen Ping", "Yang Tian", "Yue Chen", "Minghong Cai", "Zeying Gong", "Ruihai Wu", "Yinchuan Li", "Junwei Liang", "Yingcong Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Skywalker-yqz/AffordanceVLA", "github_api_added_by": "user", "project_page": "https://skywalker-yqz.github.io/AffordanceVLA/", "abstract": "Vision-Language-Action (VLA) models leverage the rich world knowledge of pretrained vision-language models (VLMs) to enable instruction-following robotic manipulation. However, the structural mismatch between VLM semantic spaces and embodied control policies often hinders the learning of precise perception--action mappings. To address this challenge, we propose AffordanceVLA, a unified framework that introduces structured affordance forecasting as a task-oriented intermediate representation to establish a more precise and robust perception--action mapping. Specifically, we progressively model manipulation priors through three complementary components: 1) Which2Act for object-centric grounding via visual latent prediction to suppress distractions; 2) Where2Act for 2D interaction localization via affordance map estimation; and 3) How2Act for 3D geometric reasoning to guide manipulation policies. These affordance cues provide spatially grounded, semantically conditioned, and action-coupled intermediate representations, thereby naturally bridging vision, language and action. We integrate these modules into a Mixture-of-Transformer (MoT) architecture with specialized experts and train the model using a three-stage training strategy with a progressive data curriculum. To overcome the scarcity of dense affordance labels in robotic datasets, we also develop a robust automated data augmentation pipeline. Extensive experiments on simulation and real-world demonstrate that AffordanceVLA achieves strong performance across diverse manipulation scenarios.", "github": "https://github.com/Skywalker-yqz/AffordanceVLA" }, { "date": "2026-06-05", "arxiv_id": "2606.05557", "title": "AURA: Intent-Directed Probing for Implicit-Need Surfacing in Situated LLM Agents", "authors": [ "Yang Li", "Jiaxiang Liu", "Jiang Cai", "Mingkun Xu" ], "github_manual": "", "github_regex": "https://github.com/innovation64/AURA", "github_paper_md": "", "github_api": "https://github.com/innovation64/AURA", "github_api_added_by": "user", "project_page": "", "abstract": "A situated query like \"where is Lin Wei?\" often encodes more than its literal content: the user may also want to know whether Lin Wei is free, in a good mood, or worth interrupting now. Standard tool-use agents answer the literal question and stop. AURA inserts an inference step between scene perception and tool use that produces an IntentFrame: a structured estimate of the implicit need with a scalar gap score that controls per-query probe budget and tool selection. On a 100-query four-scene implicit-intent benchmark, AURA improves implicit-need coverage over ReAct-style probing (Delta = +0.07, p < 10^-6); three of four scenes are individually significant, the gain reproduces on a second backbone, and a prompt ablation attributes the lift to gap calibration rather than answer memorisation. On factual lookup the controller trades raw accuracy for 82% fewer probes and zero forbidden-tool violations on a privacy-sensitive slice; scope conditions are detailed in Limitations. Code, simulator, and benchmark are released at https://github.com/innovation64/AURA.", "github": "https://github.com/innovation64/AURA" }, { "date": "2026-06-05", "arxiv_id": "2606.06462", "title": "Benchmark Everything Everywhere All at Once", "authors": [ "Shiyun Xiong", "Dongming Wu", "Peiwen Sun", "Yuang Ai", "Bokang Yang", "Wencheng Han", "Xiao-Hui Li", "Xiangyu Yue" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Shiyun-x/Benchmark-Agent", "github_api_added_by": "user", "project_page": "https://benchmarkagent.github.io/", "abstract": "Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance. However, their construction is labor-intensive and hard to reuse, raising concerns about sustainability and scalability. Moreover, existing benchmarks often quickly reach performance saturation after their release, resulting in insufficient discrimination among state-of-the-art models. To address these challenges, we introduce Benchmark Agent, a fully autonomous agentic system designed for benchmark building. Our framework orchestrates the complete benchmark construction pipeline, from user query analysis and subtask design to data annotation and quality control. To assess Benchmark Agent, we implement it to produce 15 representative benchmarks, spanning diverse evaluation scenarios, including text understanding, multimodal understanding, and domain-specific reasoning. Extensive experiments, including human evaluation, LLM-as-a-judge assessment, and consistency checks, demonstrate Benchmark Agent can generate high-quality benchmark samples with minimal human involvement. More importantly, through continual evaluation, we observe several insightful findings, including that current models struggle with certain domain-specific reasoning tasks. We believe that rapidly evolving benchmarks can contribute significantly to the research community. The preview and code will be publicly available at the demo page and code repository.", "github": "https://github.com/Shiyun-x/Benchmark-Agent" }, { "date": "2026-06-05", "arxiv_id": "2606.03645", "title": "The Shape of Addition: Geometric Structures of Arithmetic in Large Language Models", "authors": [ "Liuyuan Wen", "Xun Zhu", "Lihao Huang", "Wenbin Li", "Yang Gao" ], "github_manual": "", "github_regex": "https://github.com/RL-MIND/Shape-of-Addition", "github_paper_md": "", "github_api": "https://github.com/RL-MIND/Shape-of-Addition", "github_api_added_by": "user", "project_page": "", "abstract": "Large Language Models exhibit paradoxical fragility in fundamental arithmetic, implying a disconnect between internal computation and discrete output. By analyzing the residual stream geometry during multi-operand addition, we identify the Iso-Raw-Sum Trajectory (IRST), a geometric structure where representations are anchored by semantic digits and modulated by continuous carry fibers. We propose the Noisy Quantization Model to explain this geometry, framing arithmetic errors as Geometric Slippages caused by internal neural noise pushing a continuous, latent Carry Potential across quantization thresholds. This geometric framework further elucidates Probe Versatility, explaining how lightweight probes can disentangle coexisting latent signals (such as ground truth versus hallucination) from a single activation vector. Finally, we validate these insights through a geometric consistency check method that effectively detects and corrects these quantization failures during inference. Our code is available at https://github.com/RL-MIND/Shape-of-Addition.", "github": "https://github.com/RL-MIND/Shape-of-Addition" }, { "date": "2026-06-05", "arxiv_id": "2606.05833", "title": "Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models", "authors": [ "Haibo Wang", "Lifu Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/WHB139426/GeoVR-MLLM", "github_api_added_by": "user", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to maintain geometric and spatial consistency across video frames. Given the scarcity of large-scale 3D data, we present GeoVR, a novel framework that learns geometric representations using purely 2D video sequences. This approach effectively restructures the semantic latent space within MLLMs to unlock spatial intelligence. Rather than employing superficial feature mixing, GeoVR reshapes the internal representations of the MLLM by distilling geometry knowledge from pre-trained 3D foundation models. This is accomplished through a multi-objective learning strategy driven by four complementary geometric targets: (1) estimating inter-frame camera poses to embed varying viewpoint dynamics, (2) regressing dense depth maps to anchor physical distances, (3) predicting a metric scale factor for real-world calibration, and (4) distilling multi-scale 3D features to align the intermediate feature space. Guided by these explicit physical and geometric constraints, the model's internal representations naturally develop strong 3D awareness. Extensive experiments on spatial reasoning benchmarks demonstrate that GeoVR achieves state-of-the-art performance, establishing a new paradigm for endowing foundation models with spatial intelligence.", "github": "https://github.com/WHB139426/GeoVR-MLLM" }, { "date": "2026-06-05", "arxiv_id": "2605.30848", "title": "LLM Anonymization Against Agentic Re-Identification", "authors": [ "Ziwen Li", "Jianing Wen", "Tianshi Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PEACH-Research-Lab/AURA", "github_api_added_by": "user", "project_page": "https://peach-research-lab.github.io/AURA/", "abstract": "Agentic LLMs with web search change the threat model for text anonymization: weak contextual cues can become cross-referenceable evidence for re-identification, yet those same details also carry downstream analytic value of the text. Existing defenses either remove explicit identifiers, perturb text for formal privacy, or test rewritten text against non-web inference models, leaving underexplored the operating region between resistance to agentic web-search re-identification and utility retention. We introduce AURA (Anonymization with Utility-Retention Adaptation), an LLM-powered mask-reconstruct framework that decouples privacy localization from utility-preserving reconstruction and selects candidates with adversarial privacy and utility-retention checks. We evaluate AURA on real-user interview transcripts using re-identification attacks carried out by web-search agents, along with a utility evaluation based on interviewee-profile facts, codebook facts, and the joint contextual utility grid. Our results show that AURA improves the privacy-utility frontier by using adaptive privacy scope to strengthen resistance to agentic re-identification and using a mask-reconstruct anonymization method to better preserve contextual utility under fixed privacy scope.", "github": "https://github.com/PEACH-Research-Lab/AURA" }, { "date": "2026-06-05", "arxiv_id": "2606.01317", "title": "SABER: Benchmarking Operational Safety of LLM Coding Agents in Stateful Project Workspaces", "authors": [ "Qi Hu", "Yifeng Tang", "Qinghua Wang", "Lanyang Zhao", "Pengji Zhang", "Yuhao Qing", "Xin Yao", "Dong Huang", "Lin Zhang", "Zhuoran Ji" ], "github_manual": "", "github_regex": "https://github.com/sssr-lab/saber", "github_paper_md": "", "github_api": "https://github.com/sssr-lab/saber", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models are increasingly deployed as coding agents, shifting safety from individual responses to action sequences. Existing benchmarks, however, primarily assess whether models refuse unsafe prompts, leaving impacts on stateful workspaces largely unexamined. We present SABER, a benchmark for environment-aware operational safety that places models in realistic agent-style projects and evaluates safety from the final environment state after a sequence of actions. Beyond binary safety-violation reports, SABER categorizes violations by cause, enabling analysis of model-specific safety profiles. Our evaluations show that even the best-performing model has more than a 54% harmful safety-violation rate (HSR), suggesting that current alignment remains insufficient for realistic project environments. SABER further reveals distinct safety profiles across models. Our benchmark is publicly available at https://github.com/sssr-lab/saber.", "github": "https://github.com/sssr-lab/saber" }, { "date": "2026-06-05", "arxiv_id": "2605.27079", "title": "Trust Region Q Adjoint Matching", "authors": [ "Yonghoon Dong", "Kyungmin Lee", "Changyeon Kim", "Jaehyuk Kim", "Jinwoo Shin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yonghdong/trqam", "github_api_added_by": "user", "project_page": "https://yonghdong.github.io/blog/trqam/", "abstract": "Off-policy reinforcement learning of pretrained flow policies remains challenging due to the instability of optimization arising from the multi-step sampling process. Recently, Q-learning with Adjoint Matching (QAM) addressed this issue by reformulating into a memoryless stochastic optimal control (SOC) problem with a learned critic. However, QAM inherits a fundamental fragility of critic-guided improvement: small critic errors are amplified when critics are ill-conditioned, often leading to model collapse. This paper introduces Trust Region Q-Adjoint Matching (TRQAM), a stable off-policy fine-tuning algorithm that adaptively controls the path-space KL with pretrained flow policies through projected dual descent. Specifically, we optimize the trust-region parameter \u03bb in SOC dynamics, and theoretically show that the path-space KL can be represented by a closed-form function of \u03bb. As a result, our method can precisely control the exact deviation from pretrained flow policies, achieving stable off-policy RL. Through experiments on 50 OGBench tasks, TRQAM consistently outperforms prior arts in both offline RL and offline-to-online RL. In particular, TRQAM achieves an overall success rate of 68% in offline RL, substantially improves the strongest baseline at 46%.", "github": "https://github.com/yonghdong/trqam" }, { "date": "2026-06-05", "arxiv_id": "2606.04880", "title": "MAOAM: Unified Object and Material Selection with Vision-Language Models", "authors": [ "Jaden Park", "Valentin Deschaintre", "Jason Kuen", "Kangning Liu", "Iliyan Georgiev", "Krishna Kumar Singh", "Yong Jae Lee", "Michael Fischer" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/adobe-research/obj-and-mat-selection", "github_api_added_by": "user", "project_page": "https://jadenpark0.github.io/project_pages/maoam/", "abstract": "Selection is a core operation in interactive image editing. To be practical, a user should be able to specify and disambiguate the desired selection region through either text or click-based interactions, and the system should support selecting not only objects but also other criteria, such as materials. Material-based selection is valuable for tasks like re-texturing surfaces or editing instances of a specific material. However, existing vision-language-model (VLM) based selection methods are object-centric and typically support a single interaction modality, limiting their applicability. In this work, we thus present Mask Any Object And Material (MAOAM), a unified selection framework that enables precise object and material-level selection across both text- and click-based interactions. MAOAM leverages a VLM with a segmentation head to produce pixel-accurate masks from user prompts: the VLM interprets the user's selection intent (object or material-level) and encodes visual entities, attributes, and spatial relations, while the segmentation head decodes the output token into a mask. A key challenge is the lack of material selection datasets with text annotations. We propose a scalable data generation pipeline: we collect real and synthetic images with material masks, and leverage VLMs to generate material descriptions with rich visual-semantics. We train MAOAM with a multi-task objective over click and text-based selection, along with an auxiliary VQA task derived from the material descriptions to facilitate deeper material understanding. Despite being trained with uni-modal prompts, our model exhibits an emergent improvement in selection when combining text and clicks at inference, enabling flexible image editing workflows. Experiments demonstrate accurate and coherent selections across diverse objects, materials, and interaction scenarios, highlighting robustness in practice.", "github": "https://github.com/adobe-research/obj-and-mat-selection" }, { "date": "2026-06-05", "arxiv_id": "2606.02956", "title": "The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset", "authors": [ "Richard Schwarzkopf", "Fabian Immel", "Alexander Blumberg", "Jonas Merkert", "Nils Rack", "Kaiwen Wang", "Fabian Konstantinidis", "Julian Truetsch", "Carlos Fernandez", "Annika B\u00e4tz", "Kevin R\u00f6sch", "Marlon Steiner", "Willi Poh", "Yinzhe Shen", "Royden Wagner", "Felix Hauser", "Dominik Strutz", "Jaime Villa", "Gleb Stepanov", "Holger Caesar", "\u00d6mer \u015eahin Ta\u015f", "Frank Bieder", "Jan-Hendrik Pauls", "Christoph Stiller" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://kitscenes.com", "abstract": "Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. We present KITScenes Multimodal, a European dataset built around high-fidelity sensors and maps. Our fully synchronized sensor suite combines high-resolution global-shutter cameras, long-range lidar beyond 400m, 4D imaging radar, and redundant GNSS/INS localization. Our HD maps are, to our knowledge, the most complete of any sensor dataset, validated through autonomous driving trials on open-source software. For the first time in a public dataset, all driving-relevant traffic elements, such as traffic lights, are mapped in 3D to a reprojection-accurate level with full topological connectivity. Recorded in cities with irregular street layouts and mixed traffic modes, our dataset complements existing datasets by broadening the available geographic diversity. We also introduce four benchmarks, each advancing spatial learning for embodied AI: online HD map construction, long-range depth estimation, novel view synthesis, and end-to-end driving. Project page: https://kitscenes.com/", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.06486", "title": "Regret Minimization with Adaptive Opponents in Repeated Games", "authors": [ "Mingyang Liu", "Asuman Ozdaglar", "Tiancheng Yu", "Kaiqing Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "In this paper, we study regret minimization in repeated games with adaptive opponents who can respond based on histories of play. The standard metric of external regret in online learning is known to fail to capture such adaptivity. To account for players' counterfactual reasoning, we introduce {\\tt Repeated Policy Regret (RP-Regret)}, a game-theoretic metric that measures the difference between the realized and the best-in-hindsight accumulated utility when all players can respond to the history of play. Compared to existing regret notions in this setting, ours is native to repeated game playing, enabling stronger comparators and opponents with fewer constraints, while maintaining the possibility of finding better equilibria when all players minimize it. We first identify necessary conditions for obtaining {\\tt RP-Regret} sublinear in time, on the variation of the player's comparator strategies in the regret definition and on the memories of both the comparator and opponents' strategies. We then study additional conditions and provable algorithms to minimize {\\tt RP-Regret}, which is by definition non-convex in the strategy space. To address this challenge, we propose three algorithms: (i) one based on an optimization oracle, as assumed in some prior work in online non-convex learning; (ii) one that minimizes a convex and linearized surrogate of {\\tt RP-Regret} at each iteration; (iii) one that directly minimizes {\\tt RP-Regret} when opponents change strategies slowly. Furthermore, when all players can run algorithms to minimize the {\\tt RP-Regret} (or its linearized variant), certain subgame perfect equilibria of the repeated game can be learned. We also provide experiments showing that minimizing our regret notions can lead to more cooperative solutions with higher utility in games such as Stag-Hunt.", "github": "" }, { "date": "2026-06-05", "arxiv_id": "2606.05515", "title": "BRepCLIP: Contrastive Multimodal Pretraining on BRep Primitives for CAD Understanding", "authors": [ "Muhammad Usama", "Didier Stricker", "Mohammad Sadil Khan", "Muhammad Zeshan Afzal" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://muhammadusama100.github.io/BrepClip2026/", "abstract": "Learning representations of CAD models is a largely open problem. While 3D representation learning has flourished around point clouds and meshes, the native format of CAD - boundary representations BReps, which encodes exact parametric surfaces, curves, and their topology, has received little attention as a representation learning substrate. We introduce BRepCLIP, the first framework to align BRep geometry with language and image embeddings through contrastive pretraining. We model each CAD object as a sequence of face and edge tokens with separate discrete vocabularies for surface and curve geometry, augmented with spatial and semantic descriptors that capture surface types (e.g., cylindrical, torus, NURBS) and curve primitives (e.g., line, arc, B-spline). A transformer encoder aggregates these tokens into a global BRep embedding, aligned with CLIP's text and image encoders via a joint contrastive objective. BRepCLIP generates more discriminative and semantically grounded embeddings than existing point-based alternatives, improving Top-1 retrieval over OpenShape by 40.4%, 22.0%, and 23.9% on ABC, CADParser, and Automate, respectively, and improving zero-shot classification on FabWave by 15% in Top-1 score. We further demonstrate its utility as a CAD-aware similarity metric for evaluating text and image-conditioned CAD generation, establishing the importance of structure-aware pretraining for multimodal CAD understanding. Project page is available at https://muhammadusama100.github.io/BrepClip2026/", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2606.07080", "title": "dots.tts Technical Report", "authors": [ "Shi Lian", "Changtao Li", "Bohan Li", "Hankun Wang", "Da Zheng", "Junfeng Tian", "Yufeng Ma", "Colin Zhang", "Kai Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rednote-hilab/dots.tts", "github_api_added_by": "user", "project_page": "", "abstract": "We present dots.tts, a 2B-parameter continuous autoregressive text-to-speech (TTS) foundation model that models speech in a continuous latent space. Compared with existing continuous autoregressive models, our key innovations are threefold. First, we train an AudioVAE with multiple objectives to build a semantically structured and prediction-friendly continuous speech space. Second, we use full-history conditioning in the flow-matching head to preserve long-range consistency and reduce drift during generation. Third, we apply reward-free self-corrective post-training to the flow-matching head to further improve robustness and acoustic quality. After being trained on a large-scale multilingual corpus, dots.tts achieves the best average performance on Seed-TTS-Eval, with WERs of 0.94%/1.30%/6.60% and SIM scores of 81.0/77.1/79.5 on the zh/en/zh-hard test sets, respectively. Across other benchmarks, dots.tts also consistently demonstrates open-source state-of-the-art performance, exhibiting strong generation stability, voice cloning ability, and emotional expressiveness. For efficient inference, we further apply CFG-aware MeanFlow distillation, enabling low-latency speech generation with first-packet latencies of 85/54 ms in output streaming and dual-streaming modes, respectively. To facilitate reproducible research and practical deployment, we release the training and inference code, together with the pretrained, post-trained, and MeanFlow-distilled checkpoints, under the Apache 2.0 license.", "github": "https://github.com/rednote-hilab/dots.tts" }, { "date": "2026-06-08", "arxiv_id": "2606.07514", "title": "UniSHARP: Universal Sharp Monocular View Synthesis", "authors": [ "Meixi Song", "Dizhe Zhang", "Hao Ren", "Ruiyang Zhang", "Bo Du", "Ming-Hsuan Yang", "Lu Qi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Insta360-Research-Team/UniSHARP", "github_api_added_by": "user", "project_page": "https://insta360-research-team.github.io/Unisharp-website/", "abstract": "In this work, we focus on extending SHARP, the popular photorealistic view synthesis method, for universal monocular rendering across a continuum of camera systems, from conventional perspective cameras to wide-field-of-view, fisheye and omnidirectional panoramic settings. To overcome the pinhole-specific assumptions of SHARP, our key idea is to align various images in a unified omnidirectional latent space. Thus, we propose UniSHARP, which performs implicit alignment in both feature and Gaussian spaces. Specifically, Gaussian primitives are arranged along rays and radial distances in a ray-based universal representation, while 2D semantic and 3D spatial features extracted from UniK3D-inspired encoders are jointly decoded to generate the complete Gaussian cloud. To comprehensively evaluate our method, we construct a benchmark covering diverse imaging systems across various scenes. The benchmark is further stratified by field of view (FoV) to enable fine-grained assessment of the universal monocular rendering task. Extensive experiments on the proposed benchmark demonstrate the effectiveness of UniSHARP, outperforming alternative methods by a large margin. The project page can be found at: https://insta360-research-team.github.io/Unisharp-website/", "github": "https://github.com/Insta360-Research-Team/UniSHARP" }, { "date": "2026-06-08", "arxiv_id": "2606.05806", "title": "When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents", "authors": [ "Dongsheng Zhu", "Xuchen Ma", "Yucheng Shen", "Xiang Li", "Yukun Zhao", "Shuaiqiang Wang", "Lingyong Yan", "Dawei Yin" ], "github_manual": "", "github_regex": "https://github.com/Zhudongsheng75/ToolMaze", "github_paper_md": "", "github_api": "https://github.com/Zhudongsheng75/ToolMaze", "github_api_added_by": "user", "project_page": "", "abstract": "Existing benchmarks evaluate Tool-Integrated Reasoning (TIR) in LLMs on idealized ''happy paths'', largely overlooking real-world tool failures. We introduce ToolMaze, a benchmark for dynamic path discovery and error recovery in TIR agents. To separate systematic replanning from blind trial-and-error, ToolMaze adopts a two-dimensional design: DAG-based topological complexity and a 2 times 2 taxonomy of tool perturbations (explicit/implicit, transient/permanent). Evaluations show that perturbations degrade performance across nearly all models, with the sharpest drops under implicit semantic failures. Driven by systemic over-trust in corrupted outputs, Perturbation Recovery Rate (PRR) plummets by around 37\\% in these scenarios, while complex topologies trap agents in futile trial-and-error loops. Crucially, agentic fault-tolerance improves with model scale 3.66times slower than basic task execution, highlighting dynamic replanning as a distinct bottleneck unaddressed by model scaling or prompting. Data and code are available at https://github.com/Zhudongsheng75/ToolMaze.", "github": "https://github.com/Zhudongsheng75/ToolMaze" }, { "date": "2026-06-08", "arxiv_id": "2606.06741", "title": "OpenSkill: Open-World Self-Evolution for LLM Agents", "authors": [ "Zhiling Yan", "Dingjie Song", "Hanrong Zhang", "Wei Liang", "Yuxuan Zhang", "Yutong Dai", "Lifang He", "Philip S. Yu", "Ran Xu", "Xiang Li", "Lichao Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenLAIR/OpenSkill", "github_api_added_by": "user", "project_page": "https://github.com/OpenLAIR/OpenSkill", "abstract": "Self-evolving agents requires adaptation after deployment, but existing approaches assume a usable learning loop, such as curated skills, successful trajectories, or verifier signals. Real open-world deployments may provide none of these, offering only a task prompt. In this work, we study open-world self-evolution, where an agent must build both its skills and its own verification signals from scratch, using open-world resources but no target-task supervision. We propose OpenSkill, a framework that bootstraps this loop: it acquires grounded knowledge and verification anchors from documentation, repositories, and the web, synthesizes them into transferable skills, and refines those skills against self-built virtual tasks grounded in the anchors rather than in target answers. The open world thus supplies both the knowledge to be learned and a supervision-independent practice environment, with target-task supervision reserved for final evaluation. Across three benchmarks and two target agents, OpenSkill attains the best automated pass rate while satisfying the no-supervision constraint. Analysis shows its skills transfer across models without model-specific adaptation, and its self-built verifier aligns with ground-truth outcomes despite never accessing them.", "github": "https://github.com/OpenLAIR/OpenSkill" }, { "date": "2026-06-08", "arxiv_id": "2605.26046", "title": "When Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges", "authors": [ "Parth Darshan", "Abhishek Divekar" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/adivekar-utexas/when-gradients-collide", "github_api_added_by": "user", "project_page": "https://textgrad-failure-modes.github.io/", "abstract": "Customizing an LLM judge to a specific task or domain often involves optimizing its prompt across multiple evaluation criteria simultaneously. Textual gradient methods automate this for a single judge criterion, however they produce natural-language critiques, not numerical vectors. Thus, the conflict-resolution toolkit of multi-task learning (PCGrad, MGDA) doesn't apply to the multi-objective textual gradient setting. We test five decomposition modes of textual gradient optimizers by varying how much cross-task information the loss, gradient and optimizer LLMs share. In 6 of 10 configurations, we observe that optimization never improves over the initial prompt. Gradient specificity drops by 59% (from 9.0 to 3.7) when the gradient LLM processes multiple criteria jointly. Separately, we observe that naively combining per-task instructions into a single prompt degrades Spearman's rho by -5.3%. These results identify two separable failure modes: optimization-time gradient dilution and inference-time instruction interference, which together constrain the design space for multi-objective judge customization using textual feedback.", "github": "https://github.com/adivekar-utexas/when-gradients-collide" }, { "date": "2026-06-08", "arxiv_id": "2606.07412", "title": "Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills", "authors": [ "Chuan Xiao", "Zhengbo Jiao", "Shaobo Wang", "Wei Wang", "Bing Zhao", "Hu Wei", "Linfeng Zhang", "Lin Qu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "LLM-driven software engineering agents have become a central testbed for real-world language-model capability, yet their training remains limited by the availability of high-quality SWE tasks. Existing synthetic data methods typically create tasks through fixed mutation or bug-injection procedures, making the resulting distributions largely independent of the agent's own weaknesses and training progress. We introduce Socratic-SWE, a closed-loop self-evolution framework that reuses the agent's historical solving traces as a source of training signal. Rather than treating traces only as evidence for reward computation, Socratic-SWE distills them into structured agent skills that summarize recurring failures and effective repair patterns. These skills then guide the generation of targeted repair tasks in real repositories. Candidate tasks are checked through execution-based validation and scored with a solver-gradient alignment reward, so that the retained tasks are both verifiable and useful for improving the Solver. The updated Solver produces new traces, enabling the task curriculum to adapt over successive rounds. Across SWE-bench Verified, SWE-bench Lite, SWE-bench Pro, and Terminal-Bench 2.0, Socratic-SWE consistently improves over self-evolving baselines under the same compute budget, reaching 50.40% on SWE-bench Verified after three iterations. These results suggest that solving traces can serve as a scalable substrate for self-evolving SWE agents.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2606.06538", "title": "WorldBench: A Challenging and Visually Diverse Multimodal Reasoning Benchmark", "authors": [ "Yida Yin", "Harish Krishnakumar", "Chung Peng Lee", "Boya Zeng", "Wenhao Chai", "Shengbang Tong", "Wenhu Chen", "Hu Xu", "Xingyu Fu", "Gabriel Sarch", "Aleksandra Korolova", "Zhuang Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://worldbench-vl.github.io/", "abstract": "In real-world applications, models are expected to perform reliably across diverse settings. Yet, many existing multimodal benchmarks expand task types without capturing the visual diversity needed to handle open-ended visual inputs. We present WorldBench, a challenging and visually diverse reasoning benchmark to evaluate Multimodal Large Language Models (MLLMs). We build a taxonomy of thousands of visual concepts across multiple domains (e.g., living things). Guided by this taxonomy, we curate a broad collection of images from search engines and existing datasets to comprehensively represent the visual world. Through structured trial-and-error, we manually design challenging questions that frontier MLLMs fail to answer. On quantitative and human evaluations, WorldBench achieves higher visual diversity than any existing diverse benchmark. Evaluating 15 MLLMs on WorldBench reveals weaknesses in visual understanding: even the strongest model reaches only 64.0% accuracy, while some models perform marginally above chance-level. We hope our work highlights the importance of visual diversity in building multimodal benchmarks.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2606.05761", "title": "SubtleMemory: A Benchmark for Fine-Grained Relational Memory Discrimination in Long-Horizon AI Agents", "authors": [ "Wenxuan Wang", "Haoyu Sun", "Fukuan Hou", "Mingyang Song", "Weinan Zhang", "Yu Cheng", "Yang Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Yummytanmo/SubtleMemory", "github_api_added_by": "user", "project_page": "https://yummytanmo.github.io/SubtleMemory/", "abstract": "Persistent AI assistants, such as OpenClaw, accumulate large collections of related memories over long-term interactions. As these memories grow, they may reinforce one another, diverge across contexts, or directly conflict, making correct assistance depend on memory relations rather than isolated recall. Existing long-term memory benchmarks rarely probe how agents preserve and utilize such relations during downstream tasks. To address this gap, we introduce SubtleMemory, a benchmark for fine-grained relational memory discrimination in long-running AI agents. SubtleMemory constructs relation-controlled latent semantic artifacts whose variants instantiate complementary, nuanced, or contradictory relations, and embeds them into realistic user-agent histories, requiring agents to recover distributed relational structures during later queries and instructions. The benchmark contains 1,522 evaluation instances over 10 long histories, grounded in 1,090 relation-controlled memory-variant sets and spanning user-related and non-user-related queries. Evaluating six standalone memory systems, two Claw-style agents with native memory modules, and three Claw-style agents with plugin memory modules, we find that current systems remain weak on fine-grained relational memory discrimination. We further introduce diagnostic protocols that reveal distinct capability profiles across memory preservation, retrieval, and downstream reasoning stages.", "github": "https://github.com/Yummytanmo/SubtleMemory" }, { "date": "2026-06-08", "arxiv_id": "2606.07433", "title": "Watch, Remember, Reason: Human-View Video Understanding with MLLMs", "authors": [ "Jiahao Meng", "Yue Tan", "Qi Xu", "Kuan Gao", "Weisong Liu", "Yanwei Li", "Jason Li", "Lingdong Kong", "Haochen Wang", "Qianyu Zhou", "Jiangning Zhang", "Guangliang Cheng", "Yunhai Tong", "Lu Qi", "Minghsuan Yang" ], "github_manual": "", "github_regex": "https://github.com/marinero4972/Awesome-HumanView-VideoUnderstanding", "github_paper_md": "", "github_api": "https://github.com/marinero4972/Awesome-HumanView-VideoUnderstanding", "github_api_added_by": "user", "project_page": "", "abstract": "Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge-intensive video scenarios. These scenarios require models to handle sparse evidence, long-range dependencies, multimodal alignment, and reliable inference under limited computational budgets. This work presents a human-view perspective on LLM-based video understanding, organized around three functional abilities: watching, remembering, and reasoning. Rather than treating video tasks as isolated benchmarks, this view provides a unified structure for analyzing how video MLLMs acquire evidence, preserve context, and produce grounded outputs. We introduce a formulation that characterizes video understanding systems by their perceptual representations, memory states, reasoning traces, and final predictions. Based on this formulation, we identify challenges in spatio-temporal perception, efficient long-video processing, memory modeling, streaming understanding, and faithful reasoning. Representative methods are organized by their roles in video MLLM systems. Watching covers fine-grained, comprehensive, audio-visual, and efficient perception. Remembering includes offline and streaming memory, while reasoning covers text-only reasoning and thinking with videos. We further examine application domains such as egocentric, sports, instructional, medical, and narrative videos, and cover training datasets and evaluation benchmarks across task types, supervision formats, modalities, and capability dimensions. Finally, we outline open problems and future directions for scalable, memory-aware, and evidence-grounded video intelligence. Related works will be continuously traced at https://github.com/marinero4972/Awesome-HumanView-VideoUnderstanding.", "github": "https://github.com/marinero4972/Awesome-HumanView-VideoUnderstanding" }, { "date": "2026-06-08", "arxiv_id": "2606.06476", "title": "Thinking with Imagination: Agentic Visual Spatial Reasoning with World Simulators", "authors": [ "Chenming Zhu", "Jingli Lin", "Yilin Long", "Peizhou Cao", "Tai Wang", "Jiangmiao Pang", "Xihui Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZCMax/Thinking-With-Imagination", "github_api_added_by": "user", "project_page": "https://zcmax.github.io/projects/Thinking-With-Imagination/", "abstract": "While Vision-Language Models (VLMs) have shown strong visual reasoning capabilities, their spatial reasoning abilities remain largely constrained to the observed images and text-oriented chain-of-thought. They often struggle to infer unobserved layouts, maintain cross-view consistency, and reason from alternative viewpoints when only limited egocentric observations are available. In this work, we study this problem as thinking with imagination, where a VLM actively acquires imagined visual evidence by interacting with a world simulator during reasoning. We propose Astra, an agentic spatial reasoning framework that empowers VLMs with action-conditioned visual imagination. Specifically, Astra couples Astra-VL, an RL-trained VLM policy, with Astra-WM, a Bagel-based world simulator that generates novel-view observations from context images and natural-language camera motions. To provide reliable imagined evidence, Astra-WM is trained with view consistency tuning to improve pose and content consistency across views. In the RL stage, we propose a world-simulator-in-the-loop two-phase RL curriculum to stabilize tool-use exploration and advance the model's ability to invoke the simulator only when imagined observations improve over direct answering. Experiments demonstrate that both the world simulator and the agentic policy are necessary: Astra-WM improves simulator-augmented Gemini-3-Flash on MMSI-Bench from 45.1 to 49.5, while Astra-VL improves the Qwen3-VL backbone from 29.8 to 38.8 on MMSI-Bench and from 36.8 to 42.7 on MindCube. These results show that imagined observations can provide useful spatial evidence, but effective world-model-augmented reasoning requires learning when, where, and how to imagine.", "github": "https://github.com/ZCMax/Thinking-With-Imagination" }, { "date": "2026-06-08", "arxiv_id": "2606.07502", "title": "Your UnEmbedding Matrix is Secretly a Feature Lens for Text Embeddings", "authors": [ "Songhao Wu", "Zhongxin Chen", "Yuxuan Liu", "Heng Cui", "Cong Li", "Rui Yan" ], "github_manual": "", "github_regex": "https://github.com/CentreChen/EmbFilter", "github_paper_md": "", "github_api": "https://github.com/CentreChen/EmbFilter", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models exhibit impressive zero-shot capabilities across a wide range of downstream tasks. However, they struggle to function as off-the-shelf embedding models, leading to suboptimal performance on massive text embedding benchmarks. In this paper, we identify a potential cause underlying this deficiency. Our motivation stems from an unexpected observation: text embeddings tend to align with frequent but uninformative tokens when projected onto the vocabulary space. We argue that this excessive expression of high-frequency tokens suppresses the model's ability to capture nuanced semantics. To address this, we introduce EmbedFilter, a simple linear transformation designed to refine text embeddings derived from LLMs directly. Specifically, we uncover that the unembedding matrix within LLMs encodes a latent space that is actively writing these frequent tokens into embedding space. By filtering out this subspace, EmbedFilter suppress the influence of high-frequency tokens, thereby enhancing semantic representations. As a compelling byproduct, this enables an inherent dimensionality reduction, lowering index storage and speedup retrieval while fully preserving the refined embedding quality. Our experiments across multiple LLM backbones demonstrate that LLMs equipped with EmbedFilter achieve superior zero-shot downstream performance even with significantly reduced embedding dimensions. We hope our findings provide deeper insights into the mechanisms of LLM-based representations and inspire more principled designs to improve text embeddings training. Our code is available at https://github.com/CentreChen/EmbFilter.", "github": "https://github.com/CentreChen/EmbFilter" }, { "date": "2026-06-08", "arxiv_id": "2606.07454", "title": "PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams", "authors": [ "Fuqiang Wang", "Song Tan", "Zheng Guo", "Jiaohao Fu", "Xinglong Xu", "Bihui Yu", "Jie Dong", "Zheng Sun", "Siyuan Li", "Jingxuan Wei", "Cheng Tan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OpenRaiser/PaperFlow", "github_api_added_by": "user", "project_page": "https://openraiser.github.io/PaperFlow", "abstract": "Scientific paper recommendation is typically evaluated as static ranking over a fixed candidate set, yet real scientific reading unfolds as a daily, longitudinal process in which interests shift and feedback accumulates. We introduce PaperFlow, a framework that organizes it into three coupled stages: Profiling, which constructs and maintains a structured, inspectable scholarly profile from heterogeneous cold-start evidence; Recommending, which ranks each date-specific paper stream through multi-signal aggregation under a fixed display budget; and Adapting, which updates user state from semantically distinct feedback signals and models interest drift across days. We further define a longitudinal user-day benchmark that fixes users, dates, candidate pools, visible inputs, and hidden simulated relevance labels under a shared temporal information boundary. The benchmark contains 24 simulated research users, 50 daily paper streams, 1,200 user-day episodes, 20,727 unique papers, and 497,448 episode-paper records. We additionally specify a blind human-evaluation protocol to validate alignment between automatic metrics and expert judgments. Experiments against five scientific recommendation baselines show that PaperFlow achieves the strongest oracle-based ranking, the highest behavioral alignment with simulated reading selections, and the best blind human-evaluation score.", "github": "https://github.com/OpenRaiser/PaperFlow" }, { "date": "2026-06-08", "arxiv_id": "2603.16142", "title": "Parametric Social Identity Injection and Diversification in Public Opinion Simulation", "authors": [ "Hexi Wang", "Yujia Zhou", "Bangde Du", "Qingyao Ai", "Yiqun Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/halsayxi/PSII", "github_api_added_by": "user", "project_page": "https://psii-demo.streamlit.app/", "abstract": "Large language models (LLMs) have recently been adopted as synthetic agents for public opinion simulation, offering a promising alternative to costly and slow human surveys. Despite their scalability, current LLM-based simulation methods fail to capture social diversity, producing flattened inter-group differences and overly homogeneous responses across demographic groups. We identify this limitation as a Diversity Collapse phenomenon in LLM hidden representations, where distinct social identities become increasingly indistinguishable across layers. Motivated by this observation, we propose Parametric Social Identity Injection (PSII), a general framework that injects explicit, parametric representations of demographic attributes and value orientations directly into intermediate hidden states of LLMs. Unlike prompt-based persona conditioning, PSII enables fine-grained and controllable identity modulation at the representation level. Extensive experiments on the World Values Survey using multiple open-source LLMs show that PSII significantly improves distributional fidelity and diversity, reducing KL divergence to real-world survey data while enhancing overall diversity. This work provides new insights into representation-level control of LLM agents and advances scalable, diversity-aware public opinion simulation.", "github": "https://github.com/halsayxi/PSII" }, { "date": "2026-06-08", "arxiv_id": "2606.06953", "title": "LIMMT: Less is More for Motion Tracking", "authors": [ "Yu Guan", "Zekun Qi", "Chenghuai Lin", "Xuchuan Chen", "Dairu Liu", "Wenyao Zhang", "Jilong Wang", "Xinqiang Yu", "He Wang", "Li Yi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://giraffeguan.github.io/limmt/", "abstract": "We argue that high-quality motion data can steer tracking policies toward better optimization trajectories early in training. In this work, we introduce LIMMT (Less Is More for Motion Tracking). To our knowledge, this is the first data-centric study for physics-based humanoid motion tracking. We go beyond simply removing low-quality and erroneous clips, but define motion data quality through three dimensions: physics feasibility, diversity, and complexity. We show that even training with under 3% of AMASS yields better tracking performance than training with the full dataset. We further conduct data cleaning on the estimated web-sourced mocap data. Extensive experiments and analyses validate the effectiveness of our framework.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2606.06891", "title": "Stream3D-VLM: Online 3D Spatial Understanding with Incremental Geometry Priors", "authors": [ "Hanxun Yu", "Xuan Qu", "Lei Ke", "Boqiang Zhang", "Yuxin Wang", "Jianke Zhu", "Dong Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hanxunyu/Stream3D-VLM", "github_api_added_by": "user", "project_page": "https://stream3d-vlm.github.io/", "abstract": "Despite advances in 3D scene understanding, existing 3D Large Multimodal Models operate in offline settings, requiring complete scene observations or predefined video clips. In this paper, we present an online 3D vision-language model that enables real-time spatial understanding from streaming video. Our approach adopts an autoregressive streaming control modeling based on the LLM's next-token prediction objective to learn when to respond, and employs a lightweight Visual-Spatial Feature Integration (VSFI) module to incrementally inject temporally aligned geometry priors into the visual stream. To alleviate long-context decoding overhead, we propose a plug-and-play Geometry-Adaptive Voxel Compression (GAVC) module for efficient visual token compression. To address the scarcity of streaming 3D-language data, we further develop a scalable data generation pipeline that curates over 1M online spatio-temporal 3D QA pairs and establishes a comprehensive benchmark spanning 29 tasks. Extensive experiments show that our approach significantly outperforms both proprietary and open-source models across online and offline 3D spatial understanding, reasoning, and grounding tasks. The project page is available at https://stream3d-vlm.github.io/", "github": "https://github.com/hanxunyu/Stream3D-VLM" }, { "date": "2026-06-08", "arxiv_id": "2606.00590", "title": "Critic-R: Improving Agentic Search using Instruction-tuned Retrievers with Natural Language Introspective Feedback", "authors": [ "Md Zarif Ul Alam", "Alireza Salemi", "Hamed Zamani" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zarif98sjs/Critic-R", "github_api_added_by": "user", "project_page": "", "abstract": "Agentic search systems iteratively interact with retrieval models to answer complex queries. Despite substantial progress, optimizing retrievers for agentic search remains challenging, often requiring heavy co-training or gold-standard annotations that limit real-world applicability. We propose Critic-R, a framework that explicitly closes the feedback loop between the reasoning agent and the retrieval model during both inference and training. Critic-R introduces a critic model that evaluates the agent's introspective reasoning trace after consuming retrieved evidence to determine whether the retrieved context sufficiently supports the next reasoning step. Critic-R has two complementary mechanisms: Critic-R-Zero, an inference-time query refinement loop that iteratively rewrites queries and retrieval instructions, and Critic-Embed, an optimization approach for retrieval models that leverages successful and failed refinement trajectories as automatic supervision without requiring manual relevance annotation. We evaluate Critic-R on HotpotQA, 2WikiMultihopQA, MuSiQue, and Bamboogle. Results show that Critic-R significantly improves both retrieval quality and downstream answer accuracy.", "github": "https://github.com/zarif98sjs/Critic-R" }, { "date": "2026-06-08", "arxiv_id": "2606.07229", "title": "MMAE: A Massive Multitask Audio Editing Benchmark", "authors": [ "Ziyang Ma", "Ruiqi Yan", "Ruiyang Xu", "Jie Fang", "Zhikang Niu", "Yi-Wen Chao", "Wenming Tu", "Tianrui Wang", "Auden", "Qi Chen", "Wenxi Chen", "Jiaying Chi", "Yanru Huo", "Zixuan Jiang", "Xiquan Li", "Yalin Li", "Junxi Liu", "Minghao Liu", "Binghao Qiang", "Yijia Shan", "Zheshu Song", "Tian Tan", "Zixiang Wang", "Zeyu Xie", "Zhifei Xie", "Xiaoyu Xing", "Qixiang Xu", "Chen Yang", "Guanrou Yang", "Shan Yang", "Yifan Yang", "Steve Yves", "Haotian Zhang", "Haina Zhu", "Kai Yu", "Liefeng Bo", "Eng-Siong Chng", "Xie Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ddlBoJack/MMAE", "github_api_added_by": "user", "project_page": "", "abstract": "We introduce MMAE, a Massive Multitask Audio Editing benchmark, serving as the first comprehensive evaluation testbed designed for general-purpose instruction-based audio editing. Spurred by the shift toward intelligent creation, interactive editing has rapidly expanded from visual domains, pioneered by models like Nano-banana 2 for images and Gemini-Omni for video, into audio. However, the current evaluation infrastructure lags severely, remaining highly fragmented and restricted to specific subdomains or basic operations. Unlike existing benchmarks that are limited in scope, MMAE extends to a broad spectrum of real-world scenarios, encompassing 7 distinct audio modalities, including sound, speech, music, and their mixtures. Furthermore, we establish a comprehensive taxonomy spanning 6 levels of task complexity, from basic modifications to multi-hop reasoning and multi-round editing, 2 levels of granularity, and 8 distinct operation types. Meticulously curated through human-agent collaboration, MMAE comprises 2,000 high-fidelity samples paired with a pioneering rubric-based evaluation framework. By decomposing free-form tasks into 17,741 verifiable criteria, this robust rubric-based paradigm enables a precise, multi-dimensional assessment of both instruction following and context consistency. Our extensive evaluation of leading models reveals that current systems remain far from achieving reliable edits. Strikingly, the Exact Match Rate (EMR) consistently falls below 5% and plummets to an absolute 0% in complex, mixed-modality tasks, exposing critical bottlenecks in precise execution and structural robustness. We hope MMAE will serve as a catalyst for future advances in the intelligent creation community, providing a clear diagnostic roadmap and establishing a standardized, long-lasting evaluation paradigm for next-generation audio editing systems.", "github": "https://github.com/ddlBoJack/MMAE" }, { "date": "2026-06-08", "arxiv_id": "2606.07326", "title": "AnchorWorld: Embodied Egocentric World Simulation with View-based Evolution Customization", "authors": [ "Yu Li", "Menghan Xia", "Gongye Liu", "Xintao Wang", "Conglang Zhang", "Lei Ke", "Yuxuan Lin", "Ruihang Chu", "Pengfei Wan", "Kun Gai", "Yujiu Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://yuli0103.github.io/AnchorWorld/", "abstract": "Despite being a pivotal frontier, interactive world modeling remains underexplored in terms of the versatile controllability required by practical scenarios. To bridge this gap, we present AnchorWorld, a framework that advances egocentric simulation through enhanced interaction integrity and a flexible mechanism for world customization. First, we utilize 3D human motion as the primary interaction modality. To complement the out-of-view or truncated body parts in egocentric views, we introduce an auxiliary training supervision that incorporates exogenous viewpoints decoupled from the agent's first-person sensorium. It allows the model to observe the agent's full-body positioning relative to the environment, facilitating a more robust spatial grounding of human-world interactions. Furthermore, we propose a simple yet effective mechanism for customizing self-evolving worlds. This is achieved by defining anchor views within a unified world coordinate system, coupled with textual descriptions dictating the dynamic evolution of local scenes. Experimental results show that AnchorWorld significantly outperforms state-of-the-art baselines, while ablation studies validate the effectiveness of our key designs. Notably, our customization scheme exhibits promising spatio-temporal geometric consistency and adheres strictly to the prescribed evolutionary dynamics.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2605.27276", "title": "SIA: Self Improving AI with Harness & Weight Updates", "authors": [ "Prannay Hebbar", "Yogendra Manawat", "Samuel Verboomen", "Alesia Ivanova", "Selvam Palanimalai", "Kunal Bhatia", "Vignesh Baskaran" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hexo-ai/sia", "github_api_added_by": "user", "project_page": "https://hexolabs.com/", "abstract": "Humans are the bottleneck in building and improving AI. Both the models and the agents that wrap them are written, tuned, and corrected by people. The long-horizon goal of an AI that can figure out how to improve itself remains open. Two largely disjoint research lines attack this bottleneck. The harness-update school has a meta-agent rewrite the scaffold of a task-specific agent (its tools, prompts, retry logic, and search procedure) while the model weights are held fixed. The test-time training school uses hand-written RL pipelines to update the model's own weights on task feedback while the harness is held fixed. These two silos operate in isolation. We propose SIA, a self-improving loop in which a language-model agent (the Feedback-Agent) updates both the harness and the weights of a task-specific agent. We evaluate across three contrasting domains: Chinese legal charge classification, low-level GPU kernel optimisation, and single-cell RNA denoising. Combining both levers outperforms scaffold iteration alone on all three benchmarks. The gains are 56.6% on LawBench, 91.9% runtime reduction on GPU kernels, and 502% on denoising over the initial baseline. Harness updates make the model agentic, shaping how it searches and acts, while weight updates build the domain intuition that no prompt or scaffold can instil.", "github": "https://github.com/hexo-ai/sia" }, { "date": "2026-06-08", "arxiv_id": "2606.07508", "title": "Streaming Video Generation with Streaming Force Control", "authors": [ "Hanhui Wang", "Yiming Xie", "Haiwen Feng", "Zhaoyang Lv", "Shenlong Wang", "Huaizu Jiang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://neu-vi.github.io/StreamForce/", "abstract": "We introduce StreamForce, a streaming video generation framework that enables physically grounded control through continuous force inputs. Unlike prior video models that train separate models for different force types, assume fixed forces, or rely on non-causal processing, StreamForce is a causal and unified model that responds instantly and coherently to both local and global, time-varying forces. To achieve this, we design a unified force representation as a control signal and develop a distillation pipeline for force-controllable video generation. Our model combines autoregressive efficiency with force responsiveness, sustaining stable photometric and dynamic realism. StreamForce runs at up to 16.6 FPS on a single GPU, achieving state-of-the-art performance in both force adherence and motion realism. Project website: https://neu-vi.github.io/StreamForce/", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2606.05972", "title": "LLM Explainability with Counterfactual Chains and Causal Graphs", "authors": [ "Nirit Nussbaum-Hoffer", "Nitay Calderon", "Liat Ein-Dor", "Roi Reichart" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Causal graphs provide a high-level language for making mechanisms transparent. Recent work uses Large Language Models (LLMs) to recover causal graphs of external-world processes. Instead, in this paper, we use causal graphs to model LLM inference itself, providing stakeholders with a transparent view of how the model perceives and organizes high-level concepts to produce a prediction. We propose a four-phase method for constructing such graphs. Given a target LLM and a set of textual examples, our method discovers class-discriminative, human-interpretable concepts and maps each input to LLM-perceived concept states. We then introduce an MCMC-inspired counterfactual augmentation procedure that expands the sparse observational data through chains of counterfactuals. This enables stable causal discovery with \u03c3-CG, yielding informative, interpretable graphs. We apply our method to three LLMs across disease diagnosis, sentiment analysis, and LLM-as-a-judge classification tasks. We evaluate the learned graphs for predictive fidelity and structural stability, and the MCMC-inspired augmentation for convergence and downstream utility. Our results show that the discovered causal graphs capture meaningful dependencies consistent with LLMs' reasoning. Together, this paper provides a foundation for concept-level explainability of LLMs.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2606.05563", "title": "SoCRATES: Towards Reliable Automated Evaluation of Proactive LLM Mediation across Domains and Socio-cognitive Variations", "authors": [ "Taewon Yun", "Hyeonseong Park", "Jeonghwan Choi", "Hayoon Park", "Yeeun Choi", "Hwanjun Song" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://disl-lab.github.io/SoCRATES/", "abstract": "Evaluating LLM mediators remains challenging, as mediation unfolds as a real-time trajectory shaped by disputants' shifting emotions, intentions, and context. Existing testbeds rely on a few expert-authored domains, vary mainly strategic posture, and score every turn against every topic, introducing off-topic noise. We introduce SoCRATES, a benchmark for evaluating proactive LLM mediators in realistic, multi-domain testbeds. It constructs scenarios from real conflicts through an agentic pipeline across eight domains, probes five socio-cognitive adaptation axes (strategic posture, party composition, history length, emotional reactivity, and cultural identity), and scores each topic only on the turns that advance it via a topic-localized evaluator. The evaluator reaches 0.82 alignment with human experts, more than doubling a per-turn baseline. Benchmarking eight frontier LLMs, we find that even the strongest mediator closes only about a third of the unmediated consensus gap under diverse and realistic testbeds, with performance varying sharply by socio-cognitive axis, highlighting that progress lies in social adaptation to diverse conditions.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2606.06601", "title": "Direct 3D-Aware Object Insertion via Decomposed Visual Proxies", "authors": [ "Jingbo Gong", "Yikai Wang", "Yushi Lan", "Yuhao Wan", "Ziheng Ouyang", "Rui Zhao", "Ming-Ming Cheng", "Qibin Hou", "Chen Change Loy" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Gong1130/DIRECT", "github_api_added_by": "user", "project_page": "https://gong1130.github.io/DIRECT/", "abstract": "Object insertion aims to seamlessly composite a reference object into a specified region of a background image. Recent diffusion-based methods achieve high visual quality but formulate insertion as a simple 2D inpainting task, providing no explicit control over the object's 3D pose and limiting their practical applicability. We propose DIRECT (Decomposed Injection for Reference Composition and Target-integration), a novel framework that integrates interactive pose manipulation with high-fidelity 2D image synthesis to enable pose-controllable object insertion. Our method decomposes the insertion conditions into three complementary components: appearance guidance capturing visual details from the reference object, geometry guidance derived from the user-adjusted 3D proxy, and context guidance from the target background. By injecting them through separate pathways, DIRECT avoids feature entanglement and simultaneously preserves reference appearance, follows the user-specified pose, and adapts the object to the target scene. We also introduce an automated data construction pipeline to improve the diversity and quality of training data. Experiments show that DIRECT outperforms previous methods in both geometric controllability and visual quality.", "github": "https://github.com/Gong1130/DIRECT" }, { "date": "2026-06-08", "arxiv_id": "2606.07207", "title": "Entropy as a Structural Prior: How a Log-Barrier on DiT Belief Space Drives Musical Diversity and Development", "authors": [ "Zixi Li", "Youzhen Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/ReasoningKingdom/Eisbach-Medium", "abstract": "Confidence-based loss weighting is usually avoided in generative models because it accelerates errors when the model is confidently wrong, but this intuition breaks down in supervised diffusion training. We introduce the Eisbach log-barrier, a parameter-free weight derived from the entropy of the DiT output's spatial energy distribution: high entropy damps the gradient, while low entropy preserves it. Applied to LoRA fine-tuning of Stable Audio 3 Medium on MusicCaps, it unexpectedly yields stronger thematic development, clearer acoustic differentiation, and higher textural diversity than unweighted training, the opposite of mode collapse. This works because in supervised diffusion the gradient direction is locked to ground truth, so confidence only scales the step size, and because temporal entropy downweights flat samples while preserving high-contrast ones. The result is an online, self-referential data curriculum that emerges purely from the forward pass, with analyzed noise-level dynamics and testable predictions.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2606.01779", "title": "HarnessForge: Joint Harness and Policy Evolution for Adaptive Agent Systems", "authors": [ "Mingju Chen", "Can Lv", "Guibin Zhang", "Heng Chang", "Shiji Zhou" ], "github_manual": "", "github_regex": "https://github.com/mingju-c/HarnessForge", "github_paper_md": "", "github_api": "https://github.com/mingju-c/HarnessForge", "github_api_added_by": "user", "project_page": "", "abstract": "LLM agents are increasingly expected to operate across heterogeneous task regimes that require distinct execution paradigms. This challenges fixed agent systems and motivates system-level meta-adaptation beyond isolated component updates. While existing works have adapted external harness or trained underlying reasoning policies, full-system adaptation remains insufficiently characterized. The adaptation space between structure and execution is rarely made explicit, and the compatibility between the external harness and the internal reasoner is not optimized jointly. We propose HarnessForge, a meta-adaptive framework for evolving LLM agent systems. HarnessForge formulates an agent system as a harness--policy pair, defining a stable adaptation space that separates harness-level execution structure from policy-level reasoning behavior. It then performs harness--policy co-evolution through fault-guided harness tailoring and harness-conditioned policy alignment. Experiments across five benchmarks from diverse domains show that HarnessForge consistently improves both Qwen3-4B and Qwen3-8B backbones, outperforming harness-only and policy-only baselines with gains of up to 12.0\\% over the strongest baseline and achieving favorable rollout-efficiency tradeoffs, demonstrating that harness--policy co-evolution is effective, and that executable compatibility between the harness and reasoning policy is essential for agent-system adaptation. The code is available at https://github.com/mingju-c/HarnessForge.", "github": "https://github.com/mingju-c/HarnessForge" }, { "date": "2026-06-08", "arxiv_id": "2606.06361", "title": "Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them", "authors": [ "Woojung Han", "Seil Kang", "Youngjun Jun", "Min-Hung Chen", "Fu-En Yang", "Seong Jae Hwang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dnwjddl/phaselock", "github_api_added_by": "user", "project_page": "https://dnwjddl.github.io/phaselock", "abstract": "Image-to-Video diffusion models leverage input images to generate visually stunning content, yet frequently produce motion that violates physical laws. We reveal a surprising finding: a 2-step generation often exhibits better physical consistency than a 50-step output from the same model. Through spectral analysis, we trace this to phase erosion during denoising; the phase degrades significantly (dropping by approx 18% from step 2 to step 50), whereas the magnitude remains relatively stable. Building on this insight, we propose PhaseLock, a training-free framework that preserves the valid motion priors from few-step inference throughout the denoising trajectory. Rather than relying on full-step inference for physical consistency, PhaseLock extracts a motion prior from just 2 steps and enforces it onto high-fidelity generation via Latent Delta Guidance. Our approach effectively mitigates phase degradation, improving physical consistency by an average of 6.2 points across diverse models while largely maintaining visual fidelity, with negligible overhead (1.06times time, 1.02times memory) and reduced reliance on expensive external guidance methods (sim5times time).", "github": "https://github.com/dnwjddl/phaselock" }, { "date": "2026-06-08", "arxiv_id": "2606.07334", "title": "How Far Can Chord-Symbol Time-Series Adaptation Carry Genre Identity? Capabilities and Boundaries in Multi-Genre Chord-Symbol Modeling", "authors": [ "Jinju Lee" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PearlLeeStudio/TheArtist", "github_api_added_by": "user", "project_page": "https://www.youtube.com/@StudioPearlLee", "abstract": "Harmony is a compact symbolic layer where mathematical pitch relations, acoustic consonance, and musical convention meet. This report treats chord-symbol sequences not as a complete representation of music, but as an interpretable, controllable time series for genre-local harmonic modeling. Starting from a frozen pop-jazz Music Transformer checkpoint, I evaluate how far small adaptation interfaces can extend the model to eleven target genres: blues, bossa nova, Bach chorales, country, electronic, folk, funk, gospel, hip-hop, R&B/soul, and rock. The main evaluation compares LoRA, IA3, BitFit, prefix tuning, and full fine-tuning over 11 genres and 3 seeds, a complete 165-cell grid. All five methods improve over the frozen base on held-out chord prediction, with macro gains from +2.89 to +3.61 points; LoRA and IA3 score highest, but Wilcoxon tests with Holm and Benjamini-Hochberg correction do not support a decisive winner. A matched-data-size control sharpens this: when genres are sub-sampled to a common corpus size, IA3 stays on top but LoRA's full-data edge disappears and it falls to last, indicating the small gaps are partly data-driven. A control-token baseline is also strong, and wrong-genre adapters often beat the frozen base, suggesting much of the effect comes from lightweight conditioning over a reusable harmonic base rather than one particular adapter family. Additional diagnostics (rank sweeps, wrong-genre rotation, a base-checkpoint ablation, chord-only genre classification, generated-output statistics, real-song evaluation, and duplicate analysis) support a bounded conclusion: chord-symbol adaptation reliably improves genre-local harmonic prediction, but chord symbols alone do not carry complete genre identity. The report therefore avoids claims about perceived genre authenticity or full musical quality, which require controlled listener or musician evaluation.", "github": "https://github.com/PearlLeeStudio/TheArtist" }, { "date": "2026-06-08", "arxiv_id": "2606.06556", "title": "Robots Need More than VLA and World Models", "authors": [ "Elis Karcini", "Faisal Mehrban", "Quang Nguyen", "Mac Schwager", "Arash Ajoudani", "Cesar Cadena", "Jan Peters", "Marco Hutter", "Haitham Bou-Ammar" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Generalist robot intelligence is often framed as a policy-scaling problem: collect more robot demonstrations, train larger Vision-Language-Action (VLA) models, and expect broader generalisation. In this position paper, we argue that this framing is incomplete. The central bottleneck is not only policy learning, but the absence of mechanisms that convert the world's abundant unstructured behavioural data into grounded robot supervision. Human motion, internet video, simulation rollouts, and interactive demonstrations contain rich information about tasks, goals, contacts, failures, and physical constraints, yet most of this information is not directly usable by robot policies because it lacks embodiment-specific action labels, task semantics, and reward structure. We identify four missing components for the next generation of robotics: data interfaces for autolabelling unstructured behaviour, embodiment interfaces for retargeting human motion to robot actions, world-model interfaces for physics-grounded 3D reasoning, and reward interfaces for inferring task progress and success from video and language. We survey recent progress in robot foundation models, cross-embodiment datasets, learning from video, world models, and reward modelling, and propose a research agenda for building robotics systems that can learn not only from robot demonstrations, but from the broader physical world.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2606.04525", "title": "GENEB: Why Genomic Models Are Hard to Compare", "authors": [ "Daria Ledneva", "Mikhail Nuridinov", "Denis Kuznetsov" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/darlednik/GENEB", "github_api_added_by": "user", "project_page": "https://huggingface.co/spaces/darlednik/geneb-leaderboard", "abstract": "Progress in genomic foundation models is difficult to assess due to fragmented benchmarks, incompatible evaluation protocols, and task-specific reporting. As a result, claims of superiority or generality across models are often not directly comparable. We introduce GENEB, a large-scale diagnostic benchmark that evaluates frozen representations from 40 genomic foundation models across 100 tasks spanning 13 functional categories under a unified probing-based protocol, including few-shot regimes. GENEB enables controlled comparison across model scale, architecture, tokenization, and pretraining data while explicitly exposing task-level trade-offs. Our analysis shows that aggregate leaderboards are unstable: model rankings vary sharply across task categories, scale provides only modest and inconsistent gains, and architectural and pretraining alignment frequently outweigh parameter count. These results highlight limitations of current evaluation practices and position GENEB as a reference framework for principled comparison and category-aware model selection in genomic machine learning.", "github": "https://github.com/darlednik/GENEB" }, { "date": "2026-06-08", "arxiv_id": "2606.05988", "title": "Compress-Distill: Reasoning Trace Compression for Efficient Knowledge Distillation", "authors": [ "Maxime Griot", "Paul Steven Scotti", "Tanishq Mathew Abraham" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reasoning models produce long chain-of-thought traces that are costly to distill and encourage verbose student outputs. We study post-hoc compression of such traces before knowledge distillation. Two teachers, Qwen3.5-397B-A17B and gpt-oss-120B, generate about 283k correct traces each; two instruction-tuned models then compress them to 8.6-21.0% of their original character length. Across a 48-run main grid plus seven Qwen-teacher truncation ablations, compressed traces reduce training tokens to 12-30% of raw, speed up training by 2.0-7.6x, and shorten inference outputs by 3-19x with smaller reductions under the shorter gpt-oss teacher. However, raw traces retain the highest downstream accuracy at every scale and for both teachers. A length-matched raw-trace truncation ablation shows that compression is not merely benefiting from a smaller token budget: model-compressed traces usually beat or match naive truncation, especially for smaller students, while maintaining shorter inference outputs. Overall, reasoning-trace compression offers an accuracy-efficiency trade-off rather than a free improvement: students retain up to 96% of raw-trace accuracy while gaining up to 18x higher per-token efficiency, and at the 0.8B scale under LoRA compressed traces narrow the raw-vs-compressed gap but do not exceed raw.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2605.29430", "title": "Towards Human-Like Interactive Speech Recognition With Agentic Correction and Semantic Evaluation", "authors": [ "Zixuan Jiang", "Yanqiao Zhu", "Peng Wang", "Qinyuan Chen", "Xinjian Zhao", "Xipeng Qiu", "Wupeng Wang", "Zhifu Gao", "Xiangang Li", "Kai Yu", "Xie Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/InteractiveASR/AgenticASR", "github_api_added_by": "user", "project_page": "https://interactiveasr.github.io/", "abstract": "Automatic speech recognition (ASR) is a core component of human--computer interaction and an increasingly important front-end for LLM-based assistants and agents. However, most current ASR systems still follow a single-pass paradigm, which is poorly aligned with human communication, where misunderstandings are resolved through iterative clarification and refinement. This mismatch makes it difficult to correct meaning-critical errors once they occur. Meanwhile, token-level metrics such as WER or CER cannot adequately reflect such a problem. To address these limitations, we formulate Interactive ASR as a multi-turn refinement task and propose Agentic ASR, a closed-loop framework that combines a single-pass ASR front-end with semantic correction, intent routing, and reasoning-based editing. We further introduce the Sentence-level Semantic Error Rate (S^2ER), an LLM-based semantic evaluation metric, together with an Interactive Simulation System for scalable and reproducible benchmarking. Experiments on multilingual, named-entity-intensive, and code-switching benchmarks show that iterative interaction consistently reduces semantic errors, with much larger gains in S^2ER than in conventional token-level metrics. Human--AI alignment and ablation studies further validate the reliability of the semantic judge and the robustness of the proposed framework. The code is available at: https://interactiveasr.github.io/ and the live demo is available at https://i-asr.sjtuxlance.com/", "github": "https://github.com/InteractiveASR/AgenticASR" }, { "date": "2026-06-08", "arxiv_id": "2606.06880", "title": "Towards Retrieving Interaction Spaces for Agentic Search", "authors": [ "Shengyao Zhuang", "Yuansheng Ni", "Hengxin Fun", "Jimmy Lin", "Xueguang Ma" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/texttron/RISE", "github_api_added_by": "user", "project_page": "", "abstract": "Retrieval for search agents is still inherited from non-agentic information retrieval: a retriever ranks the corpus and the agent reads a small set of returned documents. Recent direct corpus interaction (DCI) work shows that agents can instead interact with the raw corpus through shell tools such as grep and file reads. But unbounded interaction does not scale: every broad shell command is a scan over the whole corpus, and latency degrades sharply as the corpus grows. We argue that the role of retrieval for agentic search is not just to select documents that fit in the LLM context window, but to construct an interaction space: a bounded subset of the corpus the agent can explore with associated tools. Two design consequences follow. The space needs a boundary supplied by retrieval, and the objects within it should be processed for interaction. As a proof of concept, we propose RISE (Retrieving Interaction SpacE): we use BM25 to construct the interaction space; meanwhile, its documents are processed during indexing for shell-style navigation. On BrowseComp-Plus, RISE matches the pure-shell DCI baseline at 78% accuracy with gpt-5.4-mini at roughly one quarter of the per-query cost. At 1M documents, RISE-BM25 reaches 81% on gpt-5.4-mini, whereas DCI on gpt-5.4-nano degrades to 60% with 33 of 100 wall-clock failures.", "github": "https://github.com/texttron/RISE" }, { "date": "2026-06-08", "arxiv_id": "2606.01838", "title": "LayerRoute: Input-Conditioned Adaptive Layer Skipping via LoRA Fine-Tuning for Agentic Language Models", "authors": [ "Prateek Kumar Sikdar" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/hackie123/layerroute", "github_api_added_by": "user", "project_page": "https://arxiv.org/abs/2606.01838", "abstract": "Agentic language model systems alternate between two structurally distinct step types: structured tool calls (short, deterministic, low perplexity) and open-ended planning/reasoning steps (long, complex, high perplexity). Despite this heterogeneity, current inference systems apply identical compute to every step. We introduce LayerRoute, a lightweight adapter that learns to selectively skip transformer blocks on a per-input basis. LayerRoute augments each of the 24 transformer blocks in Qwen2.5-0.5B-Instruct with: (1) a per-layer router (~897 parameters, Linear(896,1)) that outputs a hard binary gate via the straight-through estimator, and (2) LoRA adapters (rank 8, ~1.08M parameters) on the Q/K/V/O attention projections. The backbone weights remain frozen. A single end-to-end training pass on agentic data (Hermes, Glaive, GSM8K, Turing) with a gate regularisation term forces the system to discover which blocks are skippable per input type. After 3,000 steps (6.4 minutes on an A100 40GB), LayerRoute achieves a 12.91% skip differential: tool calls skip 15.25% of FLOPs while planning steps skip only 2.34%, using only 1.10M trainable parameters (0.22% of the 494M backbone). Quality improves over the base model due to LoRA adaptation, with perplexity delta of -1.29 on tool calls and -1.30 on planning.", "github": "https://github.com/hackie123/layerroute" }, { "date": "2026-06-08", "arxiv_id": "2605.28640", "title": "Augmenting Attention with Exponentially Decaying Memory Improves Query-Aware KV Sparsity", "authors": [ "Xiuying Wei", "Caglar Gulcehre" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wimh966/rat-plus", "github_api_added_by": "user", "project_page": "https://huggingface.co/barpitf/ratplus", "abstract": "Efficient inference is critical for long-context language models, where attention computation and KV-cache access dominate the cost. Recent work RAT+, introduces a recurrence-augmented attention backbone that enables flexible dilated attention at inference time. In this paper, we investigate whether this exponentially decaying memory can also improve existing query-aware sparse inference methods. Using representative methods including Quest, MoBA, and SnapKV, we show that RAT+ consistently improves accuracy over standard attention across sparse budgets on eight needle-in-a-haystack tasks. We validate these gains both on the released checkpoints from the RAT+ paper and on OLMo2-7B, which we continue pretraining with the added memory module for 10B tokens. Finally, we propose two hypotheses explaining why this memory module benefits query-aware sparse inference and design targeted experiments to support them.", "github": "https://github.com/wimh966/rat-plus" }, { "date": "2026-06-08", "arxiv_id": "2605.23898", "title": "SPACENUM: Revisiting Spatial Numerical Understanding in VLMs", "authors": [ "Jianshu Zhang", "Yijiang Li", "Huifeixin Chen", "Haoran Lu", "Letian Xue", "Bingyang Wang", "Han Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://sterzhang.github.io/SpaceNum-Home/", "abstract": "Vision-Language Models (VLMs) are increasingly deployed in embodied environments, where they need produce numerical outputs such as action magnitudes and spatial coordinates. Although these numbers appear meaningful, it remains unclear whether these numerical outputs are genuinely grounded in spatial perception. Therefore, in this work, we revisit spatial numerical understanding through SpaceNum, a unified framework that captures two complementary settings: numbers as dynamic transitions during spatial exploration, and numbers as static layouts in spatial reasoning. We formulate two bidirectional tasks, Num2Space and Space2Num, to evaluate how well VLMs map between vision-side spatial structure and language-side numerical representations. We systematically study whether current VLMs truly understand numerical values in spatial settings. Across dynamic transitions and static layouts, we find that models largely fail to ground numbers in spatial meaning and often perform close to random guess. Through error analysis, reasoning trace analysis, and controlled interventions, we show that current VLMs rely heavily on shallow spatial cues, struggle to build stable coordinate-aware representations, and fail to abstract structured spatial layouts from visual observations. We further show that explicit reasoning provides only marginal gains, while tuning can partially improve spatial numerical understanding and transfer to external spatial reasoning benchmarks.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2606.04767", "title": "Measuring Model Robustness via Fisher Information: Spectral Bounds, Theoretical Guarantees, and Practical Algorithms", "authors": [ "Chong Zhang", "Xiang Li", "Jia Wang", "Qiufeng Wang", "Xiaobo Jin" ], "github_manual": "", "github_regex": "https://github.com/franz-chang/SRP", "github_paper_md": "", "github_api": "https://github.com/franz-chang/SRP", "github_api_added_by": "user", "project_page": "", "abstract": "The robustness of deep neural networks is crucial for safety-critical deployments, yet existing evaluation methods are often attack-dependent and lack interpretability. We propose a principled, attack-agnostic robustness metric based on the spectral norm of the Fisher Information Matrix (FIM), which quantifies the worst-case sensitivity of the model's output distribution to input perturbations. Theoretically, we establish that the FIM equals the variance of the input Jacobian and derive closed-form spectral bounds for common architectures, including VGG, ResNet, DenseNet, and Transformer, providing the first theoretical robustness ranking. To enable scalable evaluation, we develop efficient algorithms, including power iteration and Hutchinson-based estimation, that support both white-box and black-box settings. Extensive experiments across multiple datasets, including CIFAR, ImageNet, and medical images, and across multiple architectures show a strong correlation between our metric and adversarial vulnerability. Our framework serves as an interpretable diagnostic tool that complements attack-based evaluations, offering insights into architectural sensitivity and guiding the design of more robust models. Code is available at: https://github.com/franz-chang/SRP/.", "github": "https://github.com/franz-chang/SRP" }, { "date": "2026-06-08", "arxiv_id": "2606.05152", "title": "Reinforcement Learning from Rich Feedback with Distributional DAgger", "authors": [ "Rishabh Agrawal", "Jacob Fein-Ashley", "Paria Rashidinejad" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rishabh-1086/distIL", "github_api_added_by": "user", "project_page": "https://rishabh-1086.github.io/project-distIL", "abstract": "Reasoning models have advanced rapidly, but the dominant reinforcement learning from verifiable rewards (RLVR) recipe remains surprisingly narrow: sample many responses and reward each with a single bit indicating whether the final answer is correct. Yet many settings provide rich feedback, including execution traces, tool outputs, expert corrections, and model self-evaluations. We study how to use such feedback through a distributional variant of the classic imitation learning algorithm DAgger, where the learner has local access to an expert distribution on states visited by the current policy. This yields a simple forward cross-entropy objective that admits a blackbox expert and whose sequence-level gradient {conduct rich credit assignment by propagating} future expert-student disagreement back to earlier decisions. We show that prior RL with self-distillation objectives based on reverse KL or Jensen-Shannon fail to guarantee monotonic policy improvement: even when the expert has higher reward, their updates may increase probability on worse actions. In contrast, we show that forward cross-entropy admits monotonic policy improvement and enjoys guarantees on regret. We further show that our objective optimizes a lower bound on teacher-weighted likelihood of success, leading to improved Pass@N. Empirically, our approach, DistIL, improves over RLVR and RL with self-distillation baselines across a variety of domains: scientific reasoning, coding, and solving hard mathematical problems.", "github": "https://github.com/rishabh-1086/distIL" }, { "date": "2026-06-08", "arxiv_id": "2606.06622", "title": "UnpredictaBench: A Benchmark for Evaluating Distributional Randomness in LLMs", "authors": [ "Amirhossein Abaskohi", "Amirhossein Dabiriaghdam", "Liang Luo", "Ellie Dingqiao Wen", "Lele Wang", "Giuseppe Carenini", "Peter West" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UnpredictaBench/UnpredictaBenchCode", "github_api_added_by": "user", "project_page": "https://huggingface.co/datasets/UnpredictaBench/UnpredictaBench", "abstract": "We introduce UnpredictaBench, an evaluation that tests the ability of large language models (LLMs) to capture true underlying distributions. As LLMs are increasingly used as substitutes for other entities (e.g., for humans in economic simulations), the tendency of many models to collapse towards a single plausible answer means a failure to capture the unpredictability of real systems. Recent work on improving output diversity is insufficient for this setting: simulation requires samples that are calibrated to a target distribution, not merely varied outputs. UnpredictaBench isolates a simplified but fundamental version of this problem: sampling outcomes from individual target distributions, including canonical statistical distributions, distributions induced by stochastic programs, and natural-language scenarios that describe random processes. We introduce 448 such problems together with KS@N, a general-purpose evaluation metric that quantifies how well a model outputs approximate black-box target distributions via the Kolmogorov-Smirnov statistical test. This is the rate at which we fail to reject model samples of size N against ground-truth samples, with larger N indicating greater difficulty. Tested across open and proprietary models, we find a large spread in distributional capabilities. For instance, when models generate samples of size 100 (KS@100, our standard metric), scores range from near 0 to over 20%. No model is able to achieve over 40% at KS@100, showing significant headroom in distributional sampling as a capability. Although adding reasoning can somewhat increase scores, we find no immediate solution for this issue. UnpredictaBench shows that even simple distributional simulation remains challenging, making it a necessary first step toward using LLMs as stand-ins for complex systems.", "github": "https://github.com/UnpredictaBench/UnpredictaBenchCode" }, { "date": "2026-06-08", "arxiv_id": "2606.06712", "title": "Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation", "authors": [ "Xingyu Su", "Jacob Helwig", "Shubham Parashar", "Atharv Chagi", "Lakshmi Jotsna", "Degui Zhi", "James Caverlee", "Dileep Kalathil", "Shuiwang Ji" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/divelab/OPDLM", "github_api_added_by": "user", "project_page": "https://opdlm.vercel.app/", "abstract": "We study the transformation of autoregressive models (ARLMs) into diffusion language models (DLMs). Rather than pretraining from scratch, prior work replaces the causal attention in ARLMs with bidirectional attention and then trains the resulting model using a DLM objective. However, these approaches incur two distribution shifts. First, transitioning from a next-token prediction objective to a DLM objective can discard knowledge acquired by the ARLM during training. Second, standard DLMs suffer from a train-inference mismatch, as the training loss is defined on randomly masked sequences rather than the trajectories encountered at inference produced by confidence-based decoding. To address both challenges, we introduce an On-Policy Diffusion Language Model (OPDLM) in which On-Policy Distillation (OPD) is employed for ARLM-to-DLM transformation. Specifically, OPDLM is trained via self-OPD, where the student, an ARLM with bidirectional attention, generates its own trajectories, and the teacher, the original frozen ARLM, distills its knowledge by providing target logits on these trajectories. By training directly in an on-policy manner, OPDLM eliminates the train-inference mismatch in DLMs, while distillation from the original model enhances knowledge retention from the ARLM. Empirical results demonstrate that OPDLM requires 15x to 7,000x fewer training tokens with strong performance across a wide variety of tasks. OPDLM avoids the prohibitive cost of DLM pretraining and positions DLM transformation as a form of ARLM post-training.", "github": "https://github.com/divelab/OPDLM" }, { "date": "2026-06-08", "arxiv_id": "2605.28742", "title": "CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning", "authors": [ "Linas Nasvytis", "Simon Jerome Han", "Ben Prystawski", "Satchel Grant", "Noah D. Goodman", "Judith E. Fan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LinasNas/core-reasoning", "github_api_added_by": "user", "project_page": "https://linasnasvytis.com/core-reasoning/", "abstract": "Language models can use verifiable rewards to improve at a wide variety of reasoning tasks. However, both parametric (e.g. RLVR) and non-parametric (e.g. prompt optimization) approaches to doing so typically require hundreds of training samples and thousands of model rollouts, making them expensive in the best case and intractable in the worst. To address this challenge, we introduce Contrastive Reflection (CORE), a non-parametric learning algorithm that compares past reasoning traces to generate insights: short natural-language descriptions of reasoning strategies and constraints that capture differences between successful and unsuccessful problem attempts. Across four reasoning tasks, we demonstrate that CORE enables more rapid improvement than both parametric (GRPO) and non-parametric (GEPA, episodic RAG, and MemRL) methods, while using fewer rollouts. Under fixed rollout budgets with as few as five training samples, we then show that CORE also achieves comparable or greater performance gains than each baseline. Finally, we highlight how CORE is also substantially more context-efficient than non-parametric baselines, requiring fewer prompt tokens while storing learned knowledge as compact, interpretable natural-language insights. Our results therefore suggest that distilling contrasts between successful and unsuccessful reasoning traces into abstract and useful insights can provide a more efficient and interpretable route to model self-improvement than weight updates, prompt optimization, or direct reuse of stored reasoning traces.", "github": "https://github.com/LinasNas/core-reasoning" }, { "date": "2026-06-08", "arxiv_id": "2606.04291", "title": "A Cookbook of 3D Vision: Data, Learning Paradigms, and Application", "authors": [ "Hongyang Du", "Zongxia Li", "Dawei Liu", "Runhao Li", "Haoyuan Song", "Qingyu Zhang", "Yubo Wang", "Jingcheng Ni", "Shihang Gui", "Congchao Dong", "Tao Hu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "3D vision has rapidly evolved, driven by increasingly diverse data representations, learning paradigms, and modeling strategies. Yet the field remains fragmented across representations and benchmarks, making it difficult to develop unified perspectives on efficiency, fidelity, and scalability. This work provides a data-centric taxonomy of 3D vision that connects geometric representations, datasets, learning frameworks, and applications within a single conceptual map. We begin by analysing the principal structural representations of 3D data--point clouds, meshes, voxels, and 3D Gaussians--along with their acquisition pipelines. We then examine how dataset design, benchmark construction, and supervision regimes shape recent advances, spanning 2D-supervised 3D learning, implicit neural representations, and 4D world modeling. Through this integrative lens, we clarify the relationships among representations, learning paradigms, and downstream tasks in reconstruction, generation, and video modeling, offering a consolidated view of emerging trends toward balancing efficiency and fidelity and toward multimodal geometric grounding.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2606.03988", "title": "Imaginative Perception Tokens Enhance Spatial Reasoning in Multimodal Language Models", "authors": [ "Mahtab Bigverdi", "Linjie Li", "Weikai Huang", "Yiming Liu", "Jaemin Cho", "Jieyu Zhang", "Tuhin Kundu", "Chris Dangjoo Kim", "Zelun Luo", "Linda Shapiro", "Ranjay Krishna" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/weikaih04/Imaginative-Perception-Token", "github_api_added_by": "user", "project_page": "https://mahtabbigverdi.github.io/Imaginative-tokens.github.io/", "abstract": "Vision language models (VLMs) excel at many tasks but still struggle with spatial reasoning when critical information is not directly observable. Many such problems require imaginative perception: inferring what would be seen from an unseen viewpoint, tracing paths through occluded spaces, or integrating partial observations into a coherent spatial representation. We introduce Imaginative Perception Tokens (IPT), intermediate perceptual representations that externalize what a VLM would perceive under alternative spatial configurations while remaining consistent with the observed input. To study this capability, we formulate three tasks, Perspective Taking (PET), Path Tracing (PT), and Multiview Counting (MVC), and construct datasets of approximately 20K examples with ground truth imaginations, answers, and evaluation benchmarks. Using the unified VLM BAGEL as the backbone, IPT supervision consistently improves spatial reasoning and often outperforms textual chain of thought training, even without generating images at inference time. On MVC, IPT improves accuracy by 3.4% and achieves competitive performance with strong closed-source models on PT. We further find that combining IPT and label-only supervision yields additional gains, whereas textual chain of thought can substantially degrade performance, suggesting a modality mismatch when spatial computation is forced through language. Overall, IPT provides a principled supervision signal for reasoning about unobserved spatial structure, improving generalization while producing interpretable intermediate representations.", "github": "https://github.com/weikaih04/Imaginative-Perception-Token" }, { "date": "2026-06-08", "arxiv_id": "2603.20990", "title": "ECI_{sem}: Semantic Residual Effective Contrastive Information for Evaluating Hard Negatives", "authors": [ "Aarush Sinha", "Rahul Seetharaman", "Aman Bansal" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Hard-negative source selection for dense retrieval is usually decided only after fine-tuning and downstream evaluation. We propose ECI_{sem}, a semantic residual variant of Effective Contrastive Information (ECI) that ranks candidate negative sources using frozen target-encoder embeddings. ECI_{sem} is training-free, not label-free: each scored example requires a query, a labeled positive, and an explicit candidate negative. ECI_{sem} builds a weighted residual information matrix from target consistency, semantic locality, lexical residuality, and a log-determinant diversity objective. On MS MARCO negative sources, in-family ECI_{sem} ranks LLM negatives highest among non-hybrid sources and Dense+LLM highest among hybrid sources, matching the strongest aggregate BEIR transfer results across DistilBERT, E5-base, and Contriever. Controlled ablations show that this alignment depends on using the target encoder family, while additional ablations show stability under sample-size, temperature, tokenizer, and IDF-corpus perturbations. The theory gives a local linearized link to loss reduction, while the empirical study treats downstream evaluation as the final test.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2606.05531", "title": "Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models", "authors": [ "Mohammad Mahdi Abootorabi", "Omid Ghahroodi", "Anas Madkoor", "Marzia Nouri", "Doratossadat Dastgheib", "Mohamed Hefeeda", "Ehsaneddin Asgari" ], "github_manual": "", "github_regex": "https://github.com/qcri/Almieyar-Oryx-BloomBench", "github_paper_md": "", "github_api": "https://github.com/qcri/Almieyar-Oryx-BloomBench", "github_api_added_by": "user", "project_page": "", "abstract": "Despite the rapid progress of Vision-Language Models (VLMs), the field lacks benchmarks that rigorously diagnose their true reasoning abilities and chart meaningful progress toward human-like multimodal intelligence. Most existing evaluations focus on piecemeal or disconnected tasks, obscuring critical cognitive weaknesses and providing little insight for targeted improvement. To address this gap, we introduce BloomBench, part of the Almieyar benchmarking series, the first cognitively human-grounded, bilingual (English-Arabic) multimodal benchmark for VLMs. Grounded in Bloom's Taxonomy, BloomBench systematically evaluates six levels of cognition (Remember, Understand, Apply, Analyze, Evaluate, Create) through carefully designed image-question-answer tasks. Built with a semi-automated pipeline and validated through a stratified hybrid quality assurance protocol, it ensures scalability, cultural inclusivity, and linguistic fidelity. Leveraging this framework, we conduct a comprehensive study of state-of-the-art VLMs to diagnose their cognitive profiles. Our analysis reveals a sharp cognitive asymmetry: while state-of-the-art models achieve strong performance ceilings in semantic understanding, they struggle substantially with factual recall and creative synthesis. This demonstrates that current general multimodal proficiency masks deeper limitations in specific cognitive layers. Furthermore, our study highlights a critical performance gap between Arabic and English, exposing limitations in current cross-lingual multimodal reasoning. These findings establish a foundation for developing more cognitively aligned and inclusive VLMs. The benchmark framework and dataset is available at: https://github.com/qcri/Almieyar-Oryx-BloomBench.", "github": "https://github.com/qcri/Almieyar-Oryx-BloomBench" }, { "date": "2026-06-08", "arxiv_id": "2606.06843", "title": "Empirical Study on the Characteristics and Evolution of AI-usage in GitHub Repositories: Evidence from Code Comments", "authors": [ "Abdullah Al Mujahid", "Preetha Chatterjee", "Mia Mohammad Imran" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Developers increasingly use AI tools such as ChatGPT, Copilot, and Claude in everyday software workflows, but prior studies often evaluate LLM outputs in isolation rather than examining how developers adapt them in real projects. We analyze 35,361 GitHub code comments that explicitly reference AI use and their associated code blocks. We first open-code 500 unique comments and code blocks to derive a taxonomy of AI-assisted development activities, then annotate the full dataset using two LLM-based classifiers and aggregate predictions with Dawid-Skene expectation-maximization. We also analyze 12,996 subsequent commit messages to study how AI-assisted code evolves after introduction, and examine temporal trends from December 2022 to March 2026. Our results show that developers primarily use LLMs for code implementation, followed by code enhancement, debugging, documentation, and testing. Subsequent commits frequently involve refactoring and cleanup, feature integration and extension, and bug fixing, indicating sustained human oversight in adapting AI-assisted code. Over time, AI-referencing comments shift from direct code generation toward knowledge and conceptual support and code enhancement. These findings suggest that AI tools are becoming embedded not only as code-generation aids, but also as collaborative support mechanisms whose outputs are refined, extended, and corrected by developers over time.", "github": "" }, { "date": "2026-06-08", "arxiv_id": "2605.22737", "title": "The Distillation Game: Adaptive Attacks & Efficient Defenses", "authors": [ "Youssef Allouah", "Mahdi Haghifam", "Sanmi Koyejo", "Reza Shokri" ], "github_manual": "", "github_regex": "https://github.com/ysfalh/distillation-game", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Distillation attacks create a deployment trade-off for model providers: the same outputs that make a model more useful can also make it easier to imitate. We study this trade-off through a minimax game between a utility-constrained teacher and an adaptive student. Our framework yields tractable one-sided response rules: an adaptive evaluation rule in which the student reweights high-value examples, and a teacher-side defense template that suppresses outputs most useful for distillation. From a cheap proxy for example value, we derive Product-of-Experts (PoE), a simple forward-pass-only defense that combines the teacher with a proxy student during generation. Empirically, adaptive evaluation reveals a large passive--adaptive gap: on state-of-the-art defenses, adaptive students recover substantially more capability than passive evaluation suggests on GSM8K and MATH. Under this stronger evaluation, the apparent robustness gap between expensive defenses and PoE narrows considerably, while PoE remains substantially cheaper and preserves higher-quality reasoning traces. Overall, our results suggest that strong distillation remains difficult to stop, and that progress on antidistillation should be judged against adaptive students rather than passive ones. Our code is available at: https://github.com/ysfalh/distillation-game.", "github": "https://github.com/ysfalh/distillation-game" }, { "date": "2026-06-08", "arxiv_id": "2606.07591", "title": "ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research", "authors": [ "Wanghan Xu", "Shuo Li", "Tianlin Ye", "Qinglong Cao", "Yixin Chen", "Hengjian Gao", "Yiheng Wang", "Qi Li", "Kun Li", "Sheng Xu", "Shengdu Chai", "Fangchen Yu", "Xiangyu Zhao", "Zhangrui Zhao", "Weijie Ma", "Zijie Guo", "Haoyu Zhou", "Haoxiang Yin", "Lixue Cheng", "Chaofan Hu", "Haoxuan Li", "Lu Mi", "Xuxuan Xie", "Yifan Zhou", "Ruizhe Chen", "Zhiwang Zhou", "Xingjian Guo", "Yuhao Zhou", "Xuming He", "Shengyuan Xu", "Xinyu Gu", "Jiamin Wu", "Mianxin Liu", "Chunfeng Song", "Fenghua Ling", "Dongzhan Zhou", "Shixiang Tang", "Yuqiang Li", "Mao Su", "Peng Ye", "Siqi Sun", "Bin Wang", "Xue Yang", "Zhenfei Yin", "Tianfan Fu", "Guangtao Zhai", "Wanli Ouyang", "Bo Zhang", "Lei Bai", "Wenlong Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/InternScience/ResearchClawBench", "github_api_added_by": "user", "project_page": "https://internscience.github.io/ResearchClawBench-Home/", "abstract": "AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous scientific research across 40 tasks from 10 scientific domains. Each task is grounded in a real published paper, provides related literature and raw data, and hides the target paper during evaluation. Expert-curated multimodal rubrics decompose the target scientific artifacts into weighted criteria, enabling evaluation of target-paper-level re-discovery while leaving room for new discovery. We evaluate seven autonomous research (auto-research) agents under a unified protocol and seventeen native LLMs through the lightweight ResearchHarness. Current systems remain far from reliable re-discovery: the strongest autonomous agent, Claude Code, averages 21.5, and the strongest ResearchHarness LLM, Claude-Opus-4.7, averages 20.7, with an LLM frontier mean of only 26.5. Error analysis shows that failures concentrate in experimental protocol mismatch, evidence mismatch, and missing scientific core. ResearchClawBench provides a reproducible evaluation frontier for measuring progress toward autonomous scientific research.", "github": "https://github.com/InternScience/ResearchClawBench" }, { "date": "2026-06-08", "arxiv_id": "2606.07895", "title": "TBD-VLA: Temporal Block Diffusion Vision Language Action Model", "authors": [ "Sung-Wook Lee", "Xuhui Kang", "Yen-Ling Kuo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Discrete Vision-Language-Action (VLA) models typically formulate action generation as next-token prediction over discretized action spaces, conditioning each token autoregressively on prior context. While effective, this paradigm incurs high inference latency and largely ignores the temporal structure inherent in action trajectories. Recent efforts introduce parallel decoding to improve efficiency, enabling faster inference, but lack explicit mechanisms for modeling token dependencies. We introduce TBD-VLA, a discrete token-based VLA framework that incorporates block diffusion to enable temporal action generation. We partition action sequences into temporal blocks and perform masked discrete diffusion within each block, while maintaining autoregressive generation across blocks. This design unifies temporal autoregression and parallel action decoding, achieving both strong temporal coherence and improved inference speed. In addition, the explicit temporal modeling enables asynchronous execution of action chunks (e.g., Real-Time Chunking) via temporal in-painting. TBD-VLA significantly outperforms prior VLA approaches in both simulation and real-world manipulation tasks, offering a scalable path toward fast, temporally aware, discrete VLA models. Project webpage: https://tbd-vla.github.io/", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.07297", "title": "SWE-Explore: Benchmarking How Coding Agents Explore Repositories", "authors": [ "Shaoqiu Zhang", "Yuhang Wang", "Jialiang Liang", "Yuling Shi", "Wenhao Zeng", "Maoquan Wang", "Shilin He", "Ningyuan Xu", "Siyu Ye", "Kai Cai", "Xiaodong Gu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Qiushao-E/SWE-Explore-Bench", "github_api_added_by": "user", "project_page": "https://huggingface.co/datasets/SWE-Explore-Bench/SWE-Explore-Bench", "abstract": "Repository-level coding benchmarks such as SWE-bench have driven a rapid surge in the capabilities of coding agents. Yet they usually treat coding tasks as a holistic, binary prediction problem (e.g., resolved or unresolved), neglecting fine-grained agent capabilities such as repository understanding, context retrieval, code localization, and bug diagnosis. In this paper, we introduce SWE-Explore, a benchmark that isolates the evaluation of repository exploration, a critical capability of coding agents. Given a repository and an issue, SWE-Explore asks an explorer to return a ranked list of relevant code regions under a fixed line budget. SWE-Explore covers 848 issues across 10 programming languages and 203 open-source repositories. For each instance, we derive line-level ground truth from independent agent trajectories that successfully solved the same issue, distilling the specific code regions their solution paths actually consulted. We evaluate exploration along coverage, ranking, and context-efficiency dimensions, showing that these metrics strongly track downstream repair behavior. Across a broad set of retrieval methods, general coding agents, and specialized localizers, we find that agentic explorers form a clear tier above classical retrieval. While file-level localization is already strong for modern methods, line-level coverage and efficient ranking remain the key axes differentiating state-of-the-art explorers.", "github": "https://github.com/Qiushao-E/SWE-Explore-Bench" }, { "date": "2026-06-09", "arxiv_id": "2606.07082", "title": "On the Geometry of On-Policy Distillation", "authors": [ "Zhennan Shen", "Yanshu Li", "Qingyu Yin", "Chak Tou Leong", "Zhilin Wang", "Yanxu Chen", "Rongduo Han", "Sunbowen Lee", "Yi R. Fung" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "On-policy distillation (OPD) is increasingly used to improve large language model reasoning, but its training dynamics remain poorly understood. We characterize the trajectory of OPD updates in parameter space and compare it with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR). A suite of parameter-space diagnostics consistently places OPD in a relaxed off-principal regime: compared with SFT, its updates affect fewer weights and avoid principal directions more strongly, while compared with RLVR, they remain less tightly constrained. Beyond this static localization, OPD exhibits subspace locking: its cumulative updates rapidly enter a narrow low-dimensional channel. Constraining training to the update subspace formed early in training preserves OPD performance but substantially degrades SFT, indicating that the locked subspace is functionally sufficient for OPD. Control experiments further show that sparsifying the update tokens and shifting rollout generation off-policy preserve the rank dynamics, whereas mixing the OPD objective with RLVR changes them. Overall, these results suggest that OPD is not merely an intermediate point between SFT and RLVR, but induces its own update geometry in parameter space.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2509.10078", "title": "Human Psychometric Questionnaires Mischaracterize LLM Behavior", "authors": [ "Woojung Song", "Dongmin Choi", "Yoonah Park", "Jongwook Han", "Eun-Ju Lee", "Yohan Jo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We examine whether human psychometric questionnaires can serve as reliable tools for characterizing and predicting LLM behavior in everyday user interactions. We analyze eight open-source LLMs by comparing their value and personality profiles derived from two different methods: Likert self-reports on established questionnaires (PVQ-40/21 and BFI-44/10) and generation probabilities over value-laden responses to everyday user queries. The two profiles diverge substantially. Within-construct item consistency, often cited as evidence of stable LLM dispositions, disappears in generation probabilities. We attribute this gap to the fact that explicit lexical cues in established questionnaire items allow models to recognize the target construct and respond in alignment-consistent, socially desirable ways, whereas realistic user queries provide no such cues. In addition, demographic persona prompts shift models' responses to human questionnaires in ways consistent with real human patterns, but no such shifts appear in the generation probabilities of responses to realistic user queries, showing their limited ability to simulate the behaviors of target demographics in real-world user interactions. Overall, our study shows that human psychometric questionnaires are insufficient tools for predicting LLM behavior and suggests generation-based profiling as a more accurate measure.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.09828", "title": "Latent Spatial Memory for Video World Models", "authors": [ "Weijie Wang", "Haoyu Zhao", "Yifan Yang", "Feng Chen", "Zeyu Zhang", "Yefei He", "Zicheng Duan", "Donny Y. Chen", "Yuqing Yang", "Bohan Zhuang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/LatentSpatialMemory", "github_api_added_by": "user", "project_page": "https://microsoft.github.io/LatentSpatialMemory/", "abstract": "Video world models that maintain 3D spatial consistency across generated frames typically rely on explicit point cloud memory constructed in RGB space. This design is both computationally expensive, requiring repeated rendering and VAE encoding, and inherently lossy, as the round trip through pixel space discards rich features of the learned latent representation. In this paper, we introduce latent spatial memory for video world models, a persistent 3D cache that stores scene information directly in the diffusion latent space, avoiding pixel-space reconstruction. Building on this, we propose Mirage, a latent-space spatial memory framework that constructs the memory by lifting latent tokens into 3D via depth-guided back-projection and queries it by synthesizing novel views through direct latent-space warping. This unified formulation eliminates both the information loss of pixel-space reconstruction and the computational burden of repeated encoding and rendering. Experiments show that latent spatial memory achieves up to 10.57times faster end-to-end video generation and 55times reduction in memory footprint relative to explicit 3D baselines. Leveraging the geometric prior of the diffusion model, Mirage attains state-of-the-art performance on WorldScore and strong reconstruction quality on RealEstate10K.", "github": "https://github.com/microsoft/LatentSpatialMemory" }, { "date": "2026-06-09", "arxiv_id": "2606.08415", "title": "CoVEBench: Can Video Editing Models Handle Complex Instructions?", "authors": [ "Jiangtao Wu", "Jiaming Wang", "Yiwen He", "Yuanxing Zhang", "Shihao Li", "Dunyuan Liu", "Xuedong Zhao", "Jialu Chen", "Zekun Moore Wang", "Jiaheng Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NJU-LINK/CoVEBench", "github_api_added_by": "user", "project_page": "https://nju-link.github.io/CoVEBench/", "abstract": "While recent text-guided video editing models excel at elementary tasks (e.g., style transfer, object insertion), real-world user requests are highly compositional. A single prompt often demands multiple coupled edits, such as modifying subjects, actions, and camera views, while strictly preserving unrelated spatiotemporal content. Existing benchmarks, heavily constrained by isolated edits and coarse global metrics, fail to diagnose how models handle such complex workflows. To address this gap, we introduce CoVEBench, a compositional video editing benchmark comprising 416 curated source videos, 626 multi-point editing instructions, and 9,990 fine-grained checklist items. Covering diverse editing dimensions, CoVEBench evaluates models via MLLM-judged instruction compliance and video fidelity, alongside automated metrics for video quality. Extensive experiments reveal that compositional editing remains a profound challenge: current models frequently omit edits, violate preservation constraints, or introduce artifacts when handling multiple operations simultaneously. CoVEBench provides a challenging, diagnostic testbed to advance video editing toward realistic user workflows.", "github": "https://github.com/NJU-LINK/CoVEBench" }, { "date": "2026-06-09", "arxiv_id": "2606.09669", "title": "SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks", "authors": [ "Hongcheng Gao", "Hailong Qu", "Jingyi Tang", "Jiahao Wang", "Zihao Huang", "Hengkang Qiao", "Shihong Huang", "Junming Yang", "Yi Li", "Hongyixuan Yuan", "Wenjie Li", "Bohan Zeng", "Wenbo Li", "Bo Wang", "Jianhui Liu", "Olive Huang", "Haoyang Huang", "Wentao Zhang", "Guoqing Huang", "Nan Duan", "Yinpeng Dong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Hongcheng-Gao/SpatialWorld", "github_api_added_by": "user", "project_page": "https://spatial-world.github.io", "abstract": "Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world. However, existing benchmarks predominantly rely on passive evaluation (e.g., static VQA) or simulator-specific pipelines, failing to assess general interactive spatial understanding. We introduce SpatialWorld, a unified benchmark designed specifically for evaluating the interactive spatial understanding of multimodal agents in complex real-world tasks. Integrating eight heterogeneous simulation backends under a shared, simulator-agnostic protocol, SpatialWorld features 760 human-annotated tasks across diverse domains (e.g., household routines, travel, social collaboration). Agents must solve tasks under vision-only partial observability, actively gathering egocentric visual evidence and expressing decisions via a unified, text-based action interface native to MLLMs. For reliable evaluation, each task includes a human-validated initial state, a reference trajectory, and a terminal-state verifier. Evaluating 15 advanced agents reveals that robust spatial task solving remains challenging: the strongest model, GPT-5, achieves an average task success rate (TSR) of only 17.4%, while the leading open-source model, Qwen-3.5, reaches 14.1%. Further analysis exposes a clear mismatch between task success and execution efficiency, alongside substantial domain-specific performance variations. These bottlenecks in active exploration and long-horizon planning position SpatialWorld as a rigorous testbed for future spatial agents.", "github": "https://github.com/Hongcheng-Gao/SpatialWorld" }, { "date": "2026-06-09", "arxiv_id": "2606.08324", "title": "Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging", "authors": [ "Fabian Perez", "Nicolas Quintero", "Jeferson Acevedo", "Hoover Rueda-Chacon" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Factral/SAE-LWIR", "github_api_added_by": "user", "project_page": "https://factral.co/SAE-LWIR/", "abstract": "Passive long-wave infrared (LWIR) hyperspectral imaging under a standoff geometry depends on atmospheric absorption and emission, as well as reflected radiance, thus making atmospheric compensation essential to get knowledge of a target of interest. Despite its importance, this compensation has been largely overlooked due to its practical and modeling difficulty. In this paper, we present a lightweight set-based deep learning framework that takes multiple radiance measurements, collected at different standoff ranges, as input and jointly estimates transmittance, atmospheric path radiance, and a shared downwelling spectrum. We analyze the learned representation with a sparse autoencoder and observe that several latent features do activate on geographically coherent subsets of the test data despite the absence of location supervision. Experiments on a MODTRAN generated standoff LWIR dataset demonstrate low spectral distortion across all estimated products. The dataset and code is publicly available at: https://factral.co/SAE-LWIR/", "github": "https://github.com/Factral/SAE-LWIR" }, { "date": "2026-06-09", "arxiv_id": "2606.09803", "title": "Echo-Memory: A Controlled Study of Memory in Action World Models", "authors": [ "Wayne King", "Zeyue Xue", "Yuxuan Bian", "Jie Huang", "Haoran Li", "Yaowei Li", "Yaofeng Su", "Yuming Li", "Haoyu Wang", "Shiyi Zhang", "Songchun Zhang", "Yuwei Niu", "Sihan Xu", "Junhao Zhuang", "Haoyang Huang", "Nan Duan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Echo-Team-Joy-Future-Academy-JD/Echo-Memory", "github_api_added_by": "user", "project_page": "https://echo-team-joy-future-academy-jd.github.io/Echo-Memory/", "abstract": "We present Echo-Memory, a controlled study of memory mechanisms in action-conditioned world models. These models generate multi-segment videos from a first frame, text prompt, and camera-action sequence, but their central failure is often memory rather than local image synthesis: after the camera leaves and returns, the scene or salient object may silently change. Existing memory designs are hard to compare because gains are entangled with backbone, training, retrieval, and evaluation differences. Echo-Memory fixes the action-to-video interface and varies only how history is stored and read by the generator. Under a shared video diffusion backbone, optimizer, camera-action representation, sampler, and evaluation pipeline, we compare raw context, compression-based memory, spatial summaries with different read-out paths, and state-space recurrence. This matched matrix separates four otherwise conflated axes: capacity, compression, read-out, and recurrence. We also evaluate memory through a three-branch protocol: replay quality, in-domain loop revisit, and open-domain return probes. The branches routinely disagree, showing that replay fidelity is not a sufficient proxy for remembering a world. Three findings follow. Raw context is a strong capacity baseline and improves open-domain return far more than it improves replay metrics. Compactness is not a free substitute for capacity: aggressive spatial and hybrid-compression memories lose the salient evidence needed for return. Finally, block-wise state-space recurrence is the strongest open-domain return mechanism in our matrix, showing that the structure of implicit memory matters as much as the decision to use it. These results provide a compact protocol for studying memory in action world models beyond isolated replay metrics.", "github": "https://github.com/Echo-Team-Joy-Future-Academy-JD/Echo-Memory" }, { "date": "2026-06-09", "arxiv_id": "2606.08548", "title": "OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation", "authors": [ "Zehao Yu", "Jiakun Zheng", "Weiji Xie", "Jiyuan Shi", "Chenyun Zhang", "Chenjia Bai", "Xuelong Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://oasis-humanoid.github.io/", "abstract": "Recent progress in robot manipulation has been largely driven by learning from large-scale demonstrations. For humanoid robot loco-manipulation tasks, however, existing data sources force an unsatisfying tradeoff between trajectory quality and scalability. Real-world teleoperation provides the highest-quality trajectories but requires dedicated physical space and time-consuming scene resets. Simulation offers an alternative way out of this dilemma: it can produce clean, embodiment-aligned data at scale without any physical hardware. In this paper, we propose OASIS, a simulation-data-driven framework for humanoid loco-manipulation. OASIS automatically reconstructs realistic object assets from real-world images using a 3D generative model. Based on these assets, trajectories are first collected through teleoperation in simulation, and then augmented under diverse domain randomizations in a post-processing stage. With the resulting simulation data, we further design a hierarchical visuomotor policy for humanoid loco-manipulation. Extensive experiments on the real humanoid robot show that, under zero-shot deployment, the policy trained on our simulation data achieves higher success rates on most tasks than that trained on real-robot teleoperation data, owing largely to the broad lighting and environmental variations covered by our simulation rendering, which real-robot data fails to capture. The project page is available at https://oasis-humanoid.github.io/.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.09079", "title": "FlashMemory-DeepSeek-V4: Lightning Index Ultra-Long Context via Lookahead Sparse Attention", "authors": [ "Yan Wang", "Qifan Zhang", "Jiachen Yu", "Tian Liang", "Dongyang Ma", "Xiang Hu", "Zibo Lin", "Chunyang Li", "Zhichao Wang", "Jia Li", "Yujiu Yang", "Haitao Mi", "Dong Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/libertywing/FlashMemory-Deepseek-V4", "github_api_added_by": "user", "project_page": "", "abstract": "Conventional LLMs keep the full KV cache loaded during decoding, causing a severe GPU memory bottleneck for ultra-long context serving. In this report, we propose Lookahead Sparse Attention (LSA), a novel inference paradigm powered by a Neural Memory Indexer built upon the DeepSeek-V4 architecture. Rather than passively attending to all historical tokens, LSA proactively predicts future context demands and preserves only the query-critical KV chunks in the GPU memory. Crucially, we instantiate this architecture via a backbone-free decoupled training strategy. By formulating the indexer as a standard dual-encoder architecture, we train it independently using standard retrieval training frameworks without ever loading the massive backbone model into GPU memory. We demonstrate that this \"less is more\" paradigm significantly maximizes serving efficiency while acting as an effective attention denoiser in tasks that rely on long-term global memory. Across primary long-context evaluation suites (e.g., LongBench-v2, LongMemEval, and RULER), FM-DS-V4 compresses the average physical KV cache footprint down to merely 13.5% of the full-context baseline, while consistently preserving or slightly elevating downstream accuracy (+0.6% absolute margin on average). Crucially, at extreme 500K scales, FlashMemory suppresses the physical KV cache overhead by over 90% without destabilizing the backbone's core reasoning capacities.", "github": "https://github.com/libertywing/FlashMemory-Deepseek-V4" }, { "date": "2026-06-09", "arxiv_id": "2606.08348", "title": "Bayesian-Agent: Posterior-Guided Skill Evolution for LLM Agent Harnesses", "authors": [ "Xiaojun Wu", "Cehao Yang", "Honghao Liu", "Xueyuan Lin", "Wenjie Zhang", "Zhichao Shi", "Xuhui Jiang", "Chengjin Xu", "Jia Li", "Jian Guo" ], "github_manual": "", "github_regex": "https://github.com/DataArcTech/Bayesian-Agent", "github_paper_md": "", "github_api": "https://github.com/DataArcTech/Bayesian-Agent", "github_api_added_by": "user", "project_page": "https://dataarctech.github.io/Bayesian-Agent/", "abstract": "LLM agents increasingly rely on external inference conditions: prompts, tools, memory, SOPs, skills, and harness feedback. These assets can improve task execution without changing model weights, but they are often revised by heuristic reflection or by reusing observed successes and failures as if counts alone were reliable belief. We introduce Bayesian-Agent, a native and cross-harness framework that treats reusable skills and SOPs as hypotheses about whether a frozen model will succeed under a particular prompt, context, and harness environment. Bayesian-Agent records verified trajectory evidence, maintains a feature-conditioned categorical posterior over each skill, and maps posterior state into inspectable actions such as patch, split, compress, retire, and explore. Model-facing prompts receive executable guardrails and failure-mode patches, while posterior summaries remain available for audit. With deepseek-v4-flash, incremental repair improves SOP-Bench from 80\\% to 95\\%, Lifelong AgentBench from 90\\% to 100\\%, and RealFin-Bench from 45\\% to 65\\%. We further evaluate Bayesian-Agent's native backend and optional GenericAgent, mini-swe-agent, and Claude Code backends. The results include positive, negative, saturated, and case-study settings, suggesting that agent skill evolution is best viewed as posterior-guided harness optimization rather than uncalibrated prompt accumulation. The source code is available at https://github.com/DataArcTech/Bayesian-Agent.", "github": "https://github.com/DataArcTech/Bayesian-Agent" }, { "date": "2026-06-09", "arxiv_id": "2606.09826", "title": "OmniGameArena: A Unified UE5 Benchmark for VLM Game Agents with Improvement Dynamics", "authors": [ "Mingxian Lin", "Shengju Qian", "Yuqi Liu", "Yi-Hua Huang", "Yiyu Wang", "Wei Huang", "Yitang Li", "Fan Zhang", "Zeyu Hu", "Lingting Zhu", "Xin Wang", "Xiaojuan Qi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mxlin043/OmniGameArena", "github_api_added_by": "user", "project_page": "https://mxlin043.github.io/OmniGameArena/", "abstract": "Vision-language model (VLM) agents are increasingly deployed in interactive game environments. Yet game benchmarks for VLM agents typically report a single first-attempt score per (agent, game) pair, focus on single-agent Solo play, and lack unified protocols for evaluating heterogeneous agent classes (commercial VLMs, open-weight VLMs, and specialized game policies) on the same footing. We address these gaps with OmniGameArena, a real-time benchmark of twelve newly built Unreal Engine 5 games spanning Solo (7), PvP (3), and Coop (2) with unified action interfaces, and the Improvement Dynamics Curve (IDC), an agentic-reflection harness in which a tool-using reflector LLM autonomously refines a bounded skill prompt across multiple rounds. Beyond cold-start leaderboard scores, IDC exposes two additional observables for each (agent, game) pair: how the score evolves across reflection rounds, and how the learned skill behaves on held-out task variants. We report these observables for twelve VLM agents on the cold-start leaderboard and four top agents under IDC.", "github": "https://github.com/mxlin043/OmniGameArena" }, { "date": "2026-06-09", "arxiv_id": "2606.09659", "title": "End-to-End Context Compression at Scale", "authors": [ "Ang Li", "Sean McLeish", "Haozhe Chen", "Nimit Kalra", "Zaiqian Chen", "Artem Gazizov", "Venkata Anoop Suhas Kumar Morisetty", "Bhavya Kailkhura", "Harshitha Menon", "Zhuang Liu", "Brian R. Bartoldson", "Tom Goldstein", "Sanae Lotfi", "Micah Goldblum", "Pavel Izmailov" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LeonLixyz/LCLM", "github_api_added_by": "user", "project_page": "", "abstract": "Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length. Recent techniques to compress the KV cache fall short: they either degrade model quality substantially or require considerable time and compute to compress a single long prompt. Furthermore, many methods require the input to fit within the target model's context window, and are generally incompatible with modern production inference engines. Encoder-decoder compressors, which map a long token sequence to a shorter sequence of latent embeddings consumed by a decoder, are an appealing alternative in principle. However, existing approaches are not competitive with KV cache compression on the accuracy-efficiency frontier. In this work, we revisit encoder-decoder compression and close this gap. We first perform an architecture search, pre-training many variants from scratch to determine how best to design and train encoder-decoder compressors. Guided by our findings, we continually pre-train a family of 0.6B-encoder, 4B-decoder models on over 350B tokens each, at compression ratios of 1:4, 1:8, and 1:16. We introduce Latent Context Language Models (LCLMs), a family of compressors that improve the Pareto frontier across general-task performance, compression speed, and peak memory usage. We demonstrate that LCLMs serve as efficient backbones for long-horizon agents, letting the agent skim through a compressed long context and adaptively expand relevant segments on demand.", "github": "https://github.com/LeonLixyz/LCLM" }, { "date": "2026-06-09", "arxiv_id": "2606.08432", "title": "Trajectory-Refined Distillation", "authors": [ "Li Jiang", "Haoran Xu", "Yichuan Ding", "Amy Zhang" ], "github_manual": "", "github_regex": "https://github.com/louieworth/trd", "github_paper_md": "", "github_api": "https://github.com/louieworth/trd", "github_api_added_by": "user", "project_page": "", "abstract": "On-policy distillation (OPD) has become a central post-training tool for large language models (LLMs), providing dense per-token teacher supervision along the student's own rollouts. In this work, we identify a common structural cause underlying OPD, which we call prefix failure. Under prefix failure, dense per-token supervision induces a bimodal teacher mixture and fragmented gradients that token-level loss truncation or reweighting fail to address. This observation motivates us to move beyond token-level loss interventions toward trajectory-level output corrections. We thus propose Trajectory-Refined Distillation (TRD), a trajectory-level correction method that revises the student's rollout under the teacher guidance while within on-policy support. By correcting problematic prefixes before distillation, TRD mitigates prefix failure at its source. Moreover, TRD improves the exploration by exposing the student to alternative valid derivations under teacher guidance, even when the original rolls are already correct. TRD can also be applied to on-policy self-distillation (OPSD), a parameter-sharing variant that uses the student model conditioned on privileged informations as the teacher. Across a wide range of benchmarks and base models at multiple scales, TRD consistently outperforms prior baselines, improving single-attempt accuracy and broadening reasoning coverage. Code is available at https://github.com/louieworth/trd", "github": "https://github.com/louieworth/trd" }, { "date": "2026-06-09", "arxiv_id": "2606.09809", "title": "Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting", "authors": [ "Avijit Ghosh", "Anka Reuel", "Jenny Chim", "Wm. Matthew Kennedy", "Srishti Yadav", "Jennifer Mickel", "Yanan Long", "Andrew Tran", "Anastassia Kornilova", "Damian Stachura", "Kevin Klyman", "Felix Friedrich", "Jeba Sania", "Max Lamparth", "Jan Batzner", "Anoop Mishra", "Eliya Habba", "Yixiong Hao", "Nathan Heath", "Shalaleh Rismani", "Usman Gohar", "Andrea Loehr", "David Manheim", "Ruchira Dhar", "Sree Harsha Nelaturu", "Aarush Sinha", "Leshem Choshen", "Drishti Sharma", "Ishan Khire", "Amit Saha", "Subramanyam Sahoo", "Michael Hardy", "Michael Alexander Riegler", "Kabir Manghnani", "Michelle Lin", "Yanan Jiang", "Yilin Huang", "Asaf Yehudai", "Jessica Ji", "Aris Hofmann", "Mubashara Akhtar", "Nuno Moniz", "Yacine Jernite", "Stella Biderman", "Zeerak Talat", "Sanmi Koyejo", "Mykel Kochenderfer", "Irene Solaiman" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://evalevalai.com/", "abstract": "AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs. The cost is interpretive: readers cannot reliably compare results across sources, identify what a report omits, or trace an aggregate claim to its underlying evidence. Recent efforts address isolated components but leave three gaps: they cover only narrow slices of the evaluation lifecycle and do not compose into a single interpretable record; they specify static representations that do not differentiate the questions different stakeholders bring to the same evidence; and they remain proposals on paper, lacking the extraction infrastructure required for adoption at scale. We present , an operational reporting layer that composes benchmark metadata, evaluation run data, and model metadata into a unified record. We (1) derive a reporting schema from a structured review of 52 papers and 10 stakeholder interviews, (2) implement four interpretive signals (reproducibility, documentation completeness, provenance and risk, and score comparability), rendered through reader modes calibrated to research and non-research audiences, and (3) deploy a monitoring tool that applies across 5,816 models, 635 benchmarks, and 101,843 results, surfacing systematic gaps in current reporting practice.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.06523", "title": "Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory", "authors": [ "Ruida Wang", "Jerry Huang", "Pengcheng Wang", "Xuanqing Liu", "Luyang Kong", "Tong Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RickySkywalker/Lean4Agent", "github_api_added_by": "user", "project_page": "", "abstract": "Equipping Large Language Models (LLMs) to execute reliable multi-step workflows has become a central challenge in artificial intelligence. Despite recent advances in LLMs' agentic capabilities, most agent systems still lack formal methods for specifying, verifying, and debugging their workflow and execution trajectories. This challenge mirrors a long-standing problem in mathematics, where the ambiguity of natural languages (NLs) motivates the development of formal languages (FLs). Inspired by this paradigm, we propose **Lean4Agent**, to the best of our knowledge, the first framework that uses Lean4, a dependent-type FL to model and verify agent behavior. **Lean4Agent** launches **FormalAgentLib**, an extensible Lean4 library for formally modeling and verifying agent workflows' semantic consistency under explicit assumptions, and enabling localization of execution-time failures revealed by trajectories. Building on **FormalAgentLib**, we further develop **LeanEvolve**, which applies results in **FormalAgentLib** to revise workflows to enhance its capability. Extensive experiments on a hard problem subset of SWE-Bench-Verified and a subset of ELAIP-Bench across 5 leading LLMs indicate that the verification-passing workflows outperform the failing ones by an average of **11.94%**, and **LeanEvolve** further improves SWE performance by **7.47%** on average. Furthermore, **Lean4Agent** establishes a foundation for a new field of using expressive dependent-type FL to formally model and verify agent behavior.", "github": "https://github.com/RickySkywalker/Lean4Agent" }, { "date": "2026-06-09", "arxiv_id": "2606.03980", "title": "Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill", "authors": [ "Tao Chen", "Gangwei Jiang", "Pengyu Cheng", "Siyuan Huang", "Yihao Liu", "Jingwei Ni", "Jiaqi Guo", "Mengyu Zhou", "Kai Tang", "Junling Liu", "Qinliang Su", "Xiaoxi Jiang", "Guanjun Jiang" ], "github_manual": "", "github_regex": "https://github.com/Qwen-Applications/Skill-RM", "github_paper_md": "", "github_api": "https://github.com/Qwen-Applications/Skill-RM", "github_api_added_by": "user", "project_page": "", "abstract": "Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines. However, current reward evaluation relies on heterogeneous criteria such as rule-based verifiers, ground-truth references, procedural checklists, and complex rubrics, where a unified mechanism to integrate all types of evidence remains unexplored. To this end, we propose Skill Reward Model (Skill-RM), a unified framework that reformulates reward modeling as the execution of a reusable Reward-Evaluation Skill. By treating reward computation as a structured agentic task, Skill-RM provides a consistent interface to orchestrate heterogeneous resources, dynamically selecting and aggregating evidence tailored to the specific requirements of each input. This approach enables the reward model to move beyond static evaluation, ensuring consistency and transparency across diverse tasks. Extensive experiments on reward benchmarks and downstream applications, including best-of-N selection and reinforcement learning, demonstrate that Skill-RM consistently outperforms traditional judge baselines. Our findings suggest that Skill-RM not only provides a unified solution for reward modeling but also achieves superior performance through the strategic and dynamic orchestration of evidence. The code is at https://github.com/Qwen-Applications/Skill-RM.", "github": "https://github.com/Qwen-Applications/Skill-RM" }, { "date": "2026-06-09", "arxiv_id": "2606.09811", "title": "AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing", "authors": [ "Jisong Cai", "Long Ling", "Shiwei Chu", "Zhongshan Liu", "Jiayue Kang", "Zhixuan Liang", "Wenjie Xu", "Yinan Mao", "Weinan Zhang", "Xiaokang Yang", "Ru Ying", "Ran Zheng", "Yao Mu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://serene-sivy.github.io/aha-wam/", "abstract": "World-action models have emerged as a promising paradigm for robot manipulation, jointly modeling visual scene dynamics and actions to inject physical priors into policy learning. However, existing world-action models couple world prediction and action execution at the same temporal resolution, forcing the world branch to model near-term frame variations that are redundant and weakly informative. We posit that strictly binding world prediction and action execution to the same temporal rhythm may underutilize the potential of the video branch for embodied control. Therefore, we propose AHA-WAM, an Asynchronous Horizon-Adaptive World-Action Model built on a dual Diffusion Transformer (DiT) architecture that reorganizes world-action modeling around this temporal asymmetry. AHA-WAM instantiates the video DiT as a low-frequency world planner that maintains rolling key-value memory over past observations and exposes reusable layerwise latent context encoding long-horizon scene evolution, while a high-frequency action DiT executes short action chunks in closed loop by querying this context through layerwise joint attention. To support asynchronous execution, we introduce horizon-adaptive offset training and Observation-Guided Video-Context Routing (OVCR), which together let the action expert exploit long-horizon world context while remaining responsive to real-time execution state without rerunning the video DiT. Experiments on RoboTwin and real-world manipulation tasks show that AHA-WAM achieves state-of-the-art performance without any robot-data pretraining, attaining 92.80% average success on RoboTwin and 78.3% success across 4 real-world tasks, while reaching 24.17 Hz closed-loop control with a 4.59x speedup over Fast-WAM.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.05753", "title": "Cosine Misleads: Auxiliary Losses Reshape Vision Language Models, Not Their Latents", "authors": [ "XiuYu Zhang", "Junfeng Fang", "Zhenkai Liang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xiuyuz/cosine-misleads", "github_api_added_by": "user", "project_page": "", "abstract": "Latent visual reasoning (LVR) inserts supervised latent tokens between perception and answer generation in vision-language models (VLMs). The field uses alignment between these latents and their visual targets, i.e., cosine similarity or mean squared error (MSE), as both the training loss and the quality metric, assuming that better alignment yields a better answer. We test this with a designed matrix of five LVR variants and find the assumption inverted: cosine alignment is negatively correlated with accuracy across all five (r=-0.94). To explain this, we introduce PRISM, a pair of inference-time diagnostics: a linear probe that asks where the answer is decodable, and a corruption test that asks whether the latent is load-bearing. The supervised latents are largely bypassed. Corrupting them shifts accuracy by at most four points. The answer is decodable downstream of the latent but not at it, and the size of this decodability gap predicts how much each variant relies on its latent under perturbation. Consistent with an Information Bottleneck reading of the loss, the auxiliary objective reshapes the language model via shared parameters rather than via the latent variable it nominally optimizes.", "github": "https://github.com/xiuyuz/cosine-misleads" }, { "date": "2026-06-09", "arxiv_id": "2606.05122", "title": "Self-Evaluation Is Already There: Eliciting Latent Judge Calibration in Base LLMs with Minimal Data", "authors": [ "XiuYu Zhang", "Yi Shan", "Junfeng Fang", "Zhenkai Liang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YiShan05/SEE_official", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models are increasingly evaluated by other models, raising a natural question: can a model predict how a judge will score its own output? We find that the ability is largely present before any targeted training: prompted few-shot, a base model already predicts an external judge's multi-attribute quality scores on open-ended responses well above chance across three benchmarks. We introduce Self-Evaluation Elicitation (SEE), a method that surfaces this latent ability through a short cycle comprising a calibration-coupled reinforcement learning phase that improves the answer and predicts the judge, followed by a masked distillation phase that sharpens the prediction while leaving the answer untouched. From 160 unique examples, roughly 31x fewer than a reinforcement learning baseline, SEE improves held-out calibration across three benchmarks while preserving answer quality. The elicited self-evaluation is sharply localized within the model's own token distribution and stable across judges it was never trained against, indicating a transferable notion of quality rather than a single judge's preference. These results reframe judge-aligned self-evaluation as a problem of elicitation rather than acquisition.", "github": "https://github.com/YiShan05/SEE_official" }, { "date": "2026-06-09", "arxiv_id": "2606.04662", "title": "Why Muon Outperforms Adam: A Curvature Perspective", "authors": [ "Shuche Wang", "Fengzhuo Zhang", "Jiaxiang Li", "Dirk Bergemann", "Zhuoran Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Muon improves training efficiency over Adam in large language-model training by about two times, but the local geometric source of this advantage remains unclear. Our work takes a first step toward demystifying Muon's superiority over Adam from a curvature perspective. First, we apply a second-order Taylor approximation to the training landscape and show that Muon achieves a larger one-step loss decrease than Adam at matched validation loss. The two optimizers have comparable first-order gains, but Muon consistently incurs a smaller second-order curvature penalty. Second, we decompose this curvature penalty into the squared update norm and Normalized Directional Sharpness (NDS). We find that Muon and Adam have comparable update norms, so Muon's smaller curvature penalty is driven by lower NDS, not update scale. Third, we study how training data and model structure shape Muon's NDS advantage. Using Zipf-Probabilistic Context-Free Grammar (PCFG) data with controlled imbalance, we show that data imbalance amplifies Muon's NDS advantage over Adam. A within-/cross-layer decomposition further shows that, in the middle and late stages of training, Muon's lower NDS is mainly sustained by smaller within-layer curvature. Beyond empirical evidence, we analyze stylized quadratic problems with heterogeneous curvature and gradient alignment toward high-curvature modes. We prove that Muon attains a smaller average NDS than GD by balancing update energy across curvature groups; when curvature heterogeneity is sufficiently strong, this also yields lower local quadratic loss after the same number of steps.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.09365", "title": "Experience Makes Skillful: Enabling Generalizable Medical Agent Reasoning via Self-Evolving Skill Memory", "authors": [ "Haoran Sun", "Wenjie Li", "Yujie Zhang", "Zekai Lin", "Fanrui Zhang", "Kaitao Chen", "Xingqi He", "Yichen Li", "Mianxin Liu", "Lei Liu", "Yankai Jiang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Medical agent systems are increasingly expected to support interactive clinical decision making rather than only static question answering. In such settings, effective agents must reuse prior experience across evolving cases, yet existing memory mechanisms often retain raw historical traces that are redundant, noisy, and difficult to govern. More importantly, they rarely distinguish which memories are truly useful for future reasoning. This limits their ability to accumulate compact and reliable experience for long-horizon clinical reasoning. To close this gap, we propose SkeMex, a post-deployment self-evolution framework that improves medical agents through a skill-based memory without updating model weights. SkeMex distills informative interaction trajectories into structured skills that encode reusable procedural knowledge, and organizes them into a multi-branch repository spanning general, task-specific, and action-level experience. To determine which memories should be reused and retained, SkeMex estimates context-dependent utility from environment feedback and uses it to guide value-aware retrieval and repository governance. A closed-loop ``Read--Write--Assess--Govern\" lifecycle further supports continual evolution by writing new skills, updating utilities, promoting useful memories, and removing harmful entries. Experiments across diverse clinical tasks show that SkeMex consistently outperforms representative memory-based agents in both offline and online settings. It also generalizes across model backbones and supports transferable skill memory. All data and code will be released publicly.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.08481", "title": "PIPE-Cypher: Automatic Enterprise Benchmark Generation for Text-to-Cypher Systems", "authors": [ "Suraj Ranganath", "Anish Raghavendra" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/suraj-ranganath/PIPE-Cypher", "github_api_added_by": "user", "project_page": "", "abstract": "Enterprise property graphs vary widely in schema structure, internal terminology, domain assumptions, governance constraints, and user interaction patterns. A deployment-relevant Text2Cypher benchmark therefore reflects the questions users and agents actually ask of that graph. Creating such a benchmark is difficult because schemas and values are unique, and graph structure changes over time. Each NL-query pair must also be executable, use real graph entities, preserve diversity, and remain balanced across query types and difficulty levels. We present PIPE-Cypher, a local benchmark-generation pipeline that turns a live property graph and optional seed queries from customer questions, analyst logs, or agent tool calls into balanced NL-to-Cypher benchmarks. PIPE-Cypher combines schema profiling, reverse-query grounding, constrained generation, deterministic Cypher governance, execution validation, redaction, diversity controls, and a calibrated local LLM judge. Using local Qwen3.5-9B generation and judging, PIPE-Cypher exports 3,000 accepted FinBench/SNB examples, completes three audited ablation suites, calibrates judge behavior with human labels, and evaluates 11 local downstream models. The resulting benchmark is deliberately discriminative: zero-shot transfer is weak, while a few-shot control shows that schema-specific example banks can help compatible model families. Together, PIPE-Cypher makes Text2Cypher benchmarking a repeatable process that evolves with the graph, its users, and its target workloads.", "github": "https://github.com/suraj-ranganath/PIPE-Cypher" }, { "date": "2026-06-09", "arxiv_id": "2606.07299", "title": "DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning", "authors": [ "Lingyong Yan", "Can Xu", "Yukun Zhao", "Wenxuan Li", "Qingyang Chen", "Jiulong Wu", "Wenli Song", "Xiangnan Li", "Weixian Shi", "Yiqun Chen", "Xuchen Ma", "Yuchen Li", "Jiashu Zhao", "Shuaiqiang Wang", "Jianmin Wu", "Dawei Yin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/baidubce/qianfan-deepresearch", "github_api_added_by": "user", "project_page": "https://cloud.baidu.com/doc/qianfan-api/s/vmizwyngh", "abstract": "Deep Research (DR) has emerged as a new agentic paradigm to tackle complex, open-ended research tasks, demanding systems that can iteratively frame problems, acquire evidence, verify sources, and synthesize long-form reports. In practice, however, current DR systems are constrained by four interrelated limitations: long-horizon planning over an underspecified scope, the bottleneck of decomposing and scheduling such tasks within a single agent, hallucination risk in long-form synthesis, and limited process auditability. This technical report presents DuMate-DeepResearch, a multi-agent DR framework built on the Qianfan Agent Foundry. The framework decouples the Agent Core, which handles task understanding, planning, and scheduling, from an extensible Tool Ecosystem for retrieval, evidence acquisition, and report rendering, making every intermediate decision and tool invocation explicitly traceable. Building on this infrastructure, DuMate-DeepResearch further introduces three mechanisms: (i) a graph-based dynamic planning strategy expands the research roadmap coarse-to-fine and continuously revises it through reflection, re-planning, backtracking, and parallel branching; (ii) a recursive two-level execution design delegates each complex search sub-task to an inner Search Agent that runs its own planning loop, isolating noisy retrieval and stabilizing long-horizon execution; (iii) a rubric-based test-time optimization mechanism dynamically generates task-specific quality criteria and uses them as live reasoning scaffolds for evidence-grounded synthesis and adaptive stopping. Across two deep research benchmarks, DuMate-DeepResearch establishes new state-of-the-art results: the best overall score (58.03%) on DeepResearch Bench, and the best overall score (61.95%) on DeepResearch Bench II while ranking first in information recall and analysis.", "github": "https://github.com/baidubce/qianfan-deepresearch" }, { "date": "2026-06-09", "arxiv_id": "2605.29463", "title": "Honest Lying: Understanding Memory Confabulation in Reflexive Agents", "authors": [ "Prakhar Dixit", "Sadia Kamal", "Tim Oates" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reflexion-style agents rely on self-generated reflections as memory, implicitly assuming that agents can accurately diagnose their own failures. We show that this assumption can fail systematically: across ALFWorld and HumanEval, agents store confident but incorrect interpretations of the task and continue acting on them across trials, even though the environment resets to the correct task each time. We call this failure mode memory confabulation and introduce the Reflection Repetition Rate (RRR), a log-based metric that detects repeated reliance on incorrect reflective content. Using RRR, we identify 16 frozen environments in ALFWorld, where 0 of 121 reflections mention the correct target object, and 4 analogous cases in HumanEval. Our mitigation replaces open-ended self-diagnosis with programmatic extraction of trajectory-level failure signals, increasing correct object mention from 0% to 86%, reducing RRR from 0.64 to 0.10, and solving 3 of 16 frozen ALFWorld environments, suggesting that reflective memory can reinforce false beliefs rather than correct them.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2605.24047", "title": "EMMA: Extracting Multiple physical parameters from Multimodal Data", "authors": [ "Farhat Shaikh", "Ayan Banerjee", "Sandeep Gupta" ], "github_manual": "", "github_regex": "https://github.com/ImpactLabASU/EMMA-CVPR2026", "github_paper_md": "", "github_api": "https://github.com/ImpactLabASU/EMMA-CVPR2026", "github_api_added_by": "user", "project_page": "https://impactlabasu.github.io/EMMA-CVPR2026/", "abstract": "We introduce EMMA, a physics-informed multimodal framework that recovers all identifiable dynamical parameters of a system directly from raw video, audio, and image-based time-series observations. Unlike prior video-only approaches that struggle with occluded states, hidden actuation inputs, or assumptions about known initial conditions and coordinate frames, EMMA performs joint inference of explicit parameters, implicit dynamical components, and calibration invariants within a unified continuous-time model. EMMA leverages a Liquid Time-Constant (LTC) network to learn latent dynamics from heterogeneous modalities while a physics-constrained loss enforces consistency with the governing differential equations. A unified feature pipeline enables consistent alignment across video trajectories, acoustic signatures, and chart-derived measurements, allowing EMMA to estimate parameters under forced, implicit, and multivariate dynamics without requiring segmentation masks, differentiable rendering, or specialized sensors. Across 100+ scenarios including five standard dynamical benchmarks (75 Delfys videos), real-world rover and quadrotor systems with hidden inputs, and simulation-chart case studies spanning biological and chaotic systems, EMMA delivers robust multi-parameter recovery and significantly outperforms existing single-modality and equation-discovery baselines. Our results establish EMMA as a general, scalable solution for physics-consistent model extraction from opportunistic multimodal data. Code and data are available at: https://github.com/ImpactLabASU/EMMA-CVPR2026", "github": "https://github.com/ImpactLabASU/EMMA-CVPR2026" }, { "date": "2026-06-09", "arxiv_id": "2606.09348", "title": "PBSD: Privileged Bayesian Self-Distillation for Long-Horizon Credit Assignment", "authors": [ "Yang Tian", "Rui Wang", "Xumeng Wen", "Junjie Li", "Shizhao Sun", "Lei Song", "Jiang Bian", "Bo Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Long-horizon agentic tasks pose a fundamental credit assignment challenge for outcome-base reinforcement learning: trajectory-level rewards verify final correctness but provide limited guidance on which intermediate reasoning steps or tool interactions contribute to the outcome. The difficulty is especially pronounced in multi-turn search agents, where successful trajectories may contain misleading actions and failed trajectories may contain valuable evidence-gathering steps. We propose PBSD (Privileged Bayesian Self-Distillation), a Bayes-calibrated self-distillation method for fine-grained credit assignment under sparse final rewards. PBSD measures trajectory quality through the posterior-to-prior probability ratio of the verified answer and applies Bayes' rule to convert this hard-to-estimate answer-side ratio into a tractable likelihood ratio between a standard student model and a privileged answer-conditioned teacher model. Autoregressive decomposition of this Bayesian evidence score yields turn-level signals that identify whether each intermediate turn supports or undermines the verified outcome. Consequently, PBSD provides a principled and elegant reweighting scheme that transforms sparse outcome supervision into Bayes-calibrated turn-level credit signals, while remaining fully compatible with standard policy optimization. Experiments demonstrate that PBSD consistently enhances performance across both in-domain and out-of-domain settings, and effectively transfers knowledge from short-context training to long-context inference, suggesting that its fine-grained credit assignment mechanism facilitates more effective policy learning and yields improved generalization.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.09516", "title": "SwiftVR: Real-Time One-Step Generative Video Restoration", "authors": [ "Jiaqi Yan", "Xiangyu Chen", "Xinlin Zhong", "Haibin Huang", "Chi Zhang", "Jie Liu", "Jiantao Zhou", "Xuelong Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/H-oliday/SwiftVR", "github_api_added_by": "user", "project_page": "https://h-oliday.github.io/SwiftVR/", "abstract": "Real-time video restoration (VR) for live streams requires high-resolution outputs under strict per-frame latency constraints. Existing one-step diffusion-based VR models remain difficult to deploy on consumer-grade GPUs due to two main bottlenecks: quadratic spatial attention at high resolutions and the latency-memory overhead of large video autoencoders. We present SwiftVR, a streaming one-step generative VR framework that reduces both bottlenecks under a causal chunk-wise protocol. For attention, mask-free shifted-window self-attention gathers each spatial window into a dense tensor via deterministic indexing, keeping all attention calls on the dense scaled dot-product attention path without masks, cyclic shifts, padding, or hardware-specific sparse kernels. Because SwiftVR uses only standard dense SDPA calls, the trained model transfers to consumer GPUs without retraining or custom kernels. For autoencoding, a lightweight Restoration-aware Autoencoder enables fast chunk-wise decoding while preserving reconstruction quality. On a single H100, SwiftVR sustains 31~FPS at 2560x1440 and 14~FPS at 3840x2160, whereas all compared diffusion-based VR baselines exceed the memory limit at 4K. On a consumer RTX~5090, SwiftVR reaches 26~FPS at 1920x1080. To our knowledge, SwiftVR is the first generative VR model to achieve real-time 1080p streaming on a consumer-grade GPU, while attaining strong no-reference perceptual quality with lower inference cost. Project is available at https://h-oliday.github.io/SwiftVR.", "github": "https://github.com/H-oliday/SwiftVR" }, { "date": "2026-06-09", "arxiv_id": "2606.05633", "title": "Answer Presence Drives RAG Rewriting Gains", "authors": [ "Yuejie Li", "Yueying Hua", "Ke Yang", "Li Zhang", "Yueping He", "Yueping He", "Ruiqi Li", "Bolin Chen", "Tao Wang", "Bowen Li", "Chengjun Mao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Retrieval-augmented QA pipelines often route retrieved passages through an LLM rewriter before a smaller reader, lifting F1 by tens of points on multi-hop benchmarks; this gain is typically credited to improved evidence quality. We ask whether that lift is causally driven by the gold answer string appearing in the rewritten context rather than by curation per se, using a controlled intervention audit. For each rewritten context we re-run the reader after one of four controlled edits to the compile output: removing the gold answer span, replacing a length-matched random non-answer span (placebo), or injecting the gold into rewrites where it was absent (at the prefix or at a midpoint sentence boundary). Across twelve completed (cell, baseline) intervention runs spanning three reader families (Qwen2.5-7B, Qwen3.5-35B, GLM-4.7), two datasets (HotpotQA, 2WikiMultihopQA), and three compiler arrangements (MA-only, MB-only, MA+verify), removing the gold answer drops reader F1 by 28 to 64 points beyond the length-matched placebo on paired answer-in-compile strata, and prepending the gold into rewrites that lacked it raises F1 by +0.7 to +9.7 points in 10 of 12 (cell, baseline) combinations. A companion five-sentinel audit shows the conventional single-[MASK] probe is itself sentinel-fragile: on 2Wiki it reports a +4.12~F1 ``non-leakage residual'' that flips to -3.33 to -7.81~F1 under four alternative sentinels and fails an equivalence test for three of those four (1/4~pass). We do not propose a new rewriter or mitigation; we release the intervention runner and the sentinel panel so that other rewriter-gain claims can be tested against the same standard.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.06087", "title": "LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents", "authors": [ "Aofan Yu", "Chenyu Zhou", "Tianyi Xu", "Zihan Guo", "Rong Shan", "Zhihui Fu", "Jun Wang", "Weiwen Liu", "Yong Yu", "Weinan Zhang", "Jianghao Lin" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/yuaofan0-oss/LatentSkill", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and exposes skill content as plaintext. We present LatentSkill, a framework that converts textual skills into plug-and-play LoRA adapters through a pretrained hypernetwork. LatentSkill stores skill knowledge in weight space rather than context space, removing per-step skill tokens while preserving modular loading, scaling, and composition. On ALFWorld and Search-QA, LatentSkill outperforms the corresponding in-context skill baseline while using substantially fewer prefill tokens: it improves ALFWorld success by 21.4 and 13.4 points on the seen and unseen splits with 64.1% fewer prefill tokens, and improves Search-QA exact match by 3.0 points with 72.2% lower skill-token overhead. Further analysis shows that generated skill LoRAs form a structured semantic geometry, can be precisely controlled via the LoRA scaling coefficient, and can be composed through parameter-space arithmetic when skill components are aligned. These findings suggest that weight-space skills provide an efficient, modular, and less exposed substrate for extending LLM agents.", "github": "https://github.com/yuaofan0-oss/LatentSkill" }, { "date": "2026-06-09", "arxiv_id": "2606.09380", "title": "Reasoning Arena: Trace Tournaments When Verifiable Rewards Fall Short", "authors": [ "Han Zhou", "Adam X. Yang", "Laurence Aitchison", "Anna Korhonen", "Albert Q. Jiang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has become a leading paradigm for improving the reasoning ability of large language models through outcome-based supervision. However, verifiable rewards frequently become uninformative at the group level: when all sampled traces of a given prompt receive identical rewards, group-relative advantage estimation provides no gradient signal, even though the traces may differ substantially in reasoning quality. We propose Reasoning Arena, an adaptive training framework that routes such non-diverse reward groups to a judge system instead of discarding them. Beyond examining the final answer, Reasoning Arena constructs trace tournaments, where reasoning traces are compared head-to-head to expose finer-grained preferences within the group, converting reasoning quality into rich relative reward signals. To make reward estimation efficient, rather than exhaustively comparing every pair, each new trace is evaluated against a small, dynamically updated pool of previously generated traces as anchors to efficiently establish a relative ranking. We then fit a Bradley-Terry model on the incomplete comparison graph, enabling scalable RL integration without quadratic pairwise comparisons. Empirical results demonstrate that Reasoning Arena consistently outperforms the RLVR baseline by 7.6% on average in competition mathematics and coding benchmarks. By converting otherwise wasted zero-advantage samples into useful gradient updates, our method accelerates training by 27% to 41%, saving nearly 50% of generation compute, and substantially improves overall reasoning performance.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.08327", "title": "Chiaroscuro Attention: Spending Compute in the Dark", "authors": [ "Prateek Kumar Sikdar" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Standard transformers apply self-attention uniformly at every layer and token, regardless of whether the input requires dynamic cross-token interaction. We propose CHIAR-Former (Chiaroscuro Attention), a 4-layer hybrid transformer that routes each token to one of three operators - DCT spectral mixing, RBF kernel mixing, or full self-attention - based on per-token spectral entropy, a theoretically justified complexity signal. Through systematic ablation on WikiText-103, we discover routing collapse: the router consistently rejects RBF in favour of DCT and attention, revealing that spectral mixing and dynamic attention are complementary and sufficient. A purpose-designed DCT+Attention-only variant achieves Val PPL 36.54 on WikiText-103 - a 45% improvement over a full-attention baseline (PPL 66.62) at 62.5% fewer attention FLOPs. We extend evaluation to WikiText-2, IMDB sentiment classification, and synthetic ListOps operations, establishing a clear operating regime: CHIAR-Former excels on large-scale naturalistic text where token diversity supports spectral specialisation, while full attention retains an edge on small datasets and synthetic pattern-matching tasks. These findings - both the wins and the losses - together define when and why spectral routing earns its keep.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.09585", "title": "Optical Reasoning: Rethinking Images as an Expressive Reasoning Medium Beyond Text", "authors": [ "Yutong Bian", "Dongjie Cheng", "Heming Xia", "Yongqi Li", "Wenjie Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ModalityDance/Optical-Reasoning", "github_api_added_by": "user", "project_page": "", "abstract": "Chain-of-Thought (CoT) improves the performance of Large Language Models (LLMs) and has been extended to Multimodal Large Language Models (MLLMs). More recent work further moves from text-based multimodal reasoning toward interleaved-modal reasoning, where intermediate steps can incorporate both textual rationales and visual evidence. In this work, we propose a bolder and more ambitious idea: could images alone serve as the reasoning medium for both language and multimodal tasks? To explore this, we propose optical reasoning, which treats images as a standalone reasoning medium. We instantiate this concept with two variants: typographic-based optical reasoning, which optimizes visual layouts for compact rationale rendering, and graphical-based optical reasoning, which composes text and graphical elements into structured visual rationales. Across mathematical, scientific, and interleaved-modal reasoning benchmarks, optical reasoning can match or even exceed traditional text reasoning while reducing reasoning tokens by an average of 28.57% on language tasks and 16% on multimodal tasks, achieving 1.96 times the token efficiency of text reasoning. These results show that images can effectively and efficiently encode rationales while providing a unified visual canvas for reasoning.", "github": "https://github.com/ModalityDance/Optical-Reasoning" }, { "date": "2026-06-09", "arxiv_id": "2606.03715", "title": "Text-to-Image Models Need Less from Text Encoders Than You Think", "authors": [ "Nurit Spingarn", "Noa Cohen", "Tamar Rott Shaham", "Tomer Michaeli" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://nsping13.github.io/contextless-TTI/", "abstract": "Text-to-image models rely on text prompts as their primary interface to human intent. Prompts are encoded by a text encoder into embeddings that condition the image generation process. Beyond individual token meanings, text embeddings encode contextual information across the full prompt, such as compositionality and attribute binding. However, whether image models actually exploit this richer information remains underexplored. Here, we address the question: Which aspects of text representation are essential for image generation? We show that text-to-image diffusion transformer-based models commonly rely only on two relatively straightforward aspects of text representations: (i) the merging of adjacent tokens into a word representation, for words spanning multiple tokens, and (ii) word order, which is imprinted by the positional embedding of the text-encoder. To show this, we construct a new text embedding that encodes only individual word meanings and order but lacks any contextual information about the full prompt. We find that this bag of position-tagged words representation is sufficient to successfully guide image generation, achieving visual quality and text fidelity that are on par with full text embedding-guided generation. This demonstrates that, contrary to common belief, text-to-image models often do not use the rich information encoded in the text embedding beyond individual word meanings and word order. Instead, the decoding of complex linguistic structures is performed by the image model itself. Project webpage: https://nsping13.github.io/contextless-TTI/", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.08960", "title": "Hardening Agent Benchmarks with Adversarial Hacker-Fixer Loops", "authors": [ "Ziqian Zhong", "Ivgeni Segal", "Ivan Bercovich", "Shashwat Saxena", "Kexun Zhang", "Aditi Raghunathan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/few-sh/harden-v0", "github_api_added_by": "user", "project_page": "", "abstract": "Agent benchmarks score submissions with outcome verifiers that are typically hand-written and brittle, leaving them open to reward hacking. We audit 1,968 tasks across five terminal-agent benchmarks and find 323 (16%) hackable by frontier models given only the task description. This corrupts both leaderboard rankings and RL training signal, yet the standard response is manual and reactive. We introduce the hacker-fixer loop, a method for building exploit-resistant verifiers without per-task manual patching. The loop alternates three LLM agents: a hacker tries to pass the verifier without solving the task, a fixer patches the verifier to reject each discovered exploit, and a solver confirms the patched verifier still admits legitimate solutions. The loop iterates: each patch reshapes what the verifier rewards, surfacing the next exploit. We further add verifier access, and let patches transfer across tasks, to broaden the exploits the loop discovers. On KernelBench, the loop drives the attack success rate from 62% to 0% on a held-out corpus of publicly reported exploits. We also find that weaker agents in the loop can defend against much stronger hackers: Gemini 3 Flash's loop drives the stronger Gemini 3.1 Pro and Claude Opus 4.7's attack success rate from 76% and 61% to 0% on KernelBench, and Gemini 3.1 Pro's from 39% to 17% on Terminal Bench across 77 tasks. We release Terminal Wrench (323 hackable environments, 3,632 hack trajectories) as a snapshot of the current attack surface, our patched verifiers, the exploits the loop discovered, and our implementation as a basis for future work.", "github": "https://github.com/few-sh/harden-v0" }, { "date": "2026-06-09", "arxiv_id": "2606.07074", "title": "SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating", "authors": [ "Zequn Xie", "Junjie Wang", "Dan Yang", "Jie Feng", "Yue Shen", "Jian Wang", "Jinjie Gu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AQ-MedAI/AntAFu-DeepResearch", "github_api_added_by": "user", "project_page": "", "abstract": "Deep research agents have demonstrated remarkable capabilities in complex information-seeking tasks, yet this power comes at a steep computational cost. Driven by accuracy-focused training paradigms, current models adopt brute-force strategies characterized by blind tool dependency and performative reasoning-generating long, redundant trajectories that are far from necessary for resolving these tasks, leading to wasteful tool calls and excessive token consumption. To overcome this efficiency trap, we propose SlimSearcher, a principled framework that pushes the Pareto frontier between accuracy and computational cost across both Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). In the SFT stage, SlimSearcher employs Pareto-efficient filtration to distill trajectories that are both successful and economical, guiding the model toward inherently efficiency-aware search behaviors. During RL, we introduce Adaptive Reward Gating, a dynamic reward-shaping mechanism that evaluates relative tool and token efficiency within a sampled cohort. By cascading these adaptive efficiency metrics with a strict correctness gate, our approach effectively avoids the brevity bias associated with absolute penalties and mitigates reward hacking. Extensive experiments on long-horizon benchmarks, including GAIA, BrowseComp, and XBenchDeepSearch, demonstrate that SlimSearcher reduces average tool-call rounds by 17%-58% while maintaining or improving accuracy.", "github": "https://github.com/AQ-MedAI/AntAFu-DeepResearch" }, { "date": "2026-06-09", "arxiv_id": "2606.08572", "title": "OmniCap-IF: Benchmarking and Improving Instruction Following Abilities for Omni-Video Captioning", "authors": [ "Jiahao Wang", "An Ping", "Yanghai Wang", "Yuanxing Zhang", "Shihao Li", "Hanyan Bian", "Yichi Ren", "Yize Zhang", "Han Wang", "Haowen Chen", "Junze Li", "Jiaqi Wang", "Yiyang Hu", "Zhuze Xu", "Zijie Zhang", "Jiaheng Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NJU-LINK/omnicap-if", "github_api_added_by": "user", "project_page": "https://nju-link.github.io/OmniCap-IF/", "abstract": "While Omni-modal Large Language Models (OLLMs) have demonstrated impressive capabilities in jointly processing audio and visual streams, their ability to strictly adhere to complex, multi-faceted user instructions remains largely unexplored. Existing benchmarks primarily focus on holistic video understanding or text-only instruction following, failing to capture the intricate interplay between modalities and user constraints. To bridge this gap, we introduce OmniCap-IF, the first comprehensive benchmark specifically designed to evaluate instruction-following capabilities in omni-modal captioning. OmniCap-IF incorporates a systematic framework that assesses captions on two dimensions: format correctness and content correctness. Our benchmark encompasses 50 distinct constraint types across pure visual, pure audio, and audio-visual modalities, while integrating Temporal Grounding to assess spatio-temporal precision. Extensive evaluations of prominent models on 1,920 high-quality samples reveal significant performance disparities. Furthermore, our analysis uncovers a critical \"format-content tradeoff\", demonstrating that increasing formatting complexity directly degrades models' omni-modal reasoning abilities. Finally, to advance the field, we curate a 54K instruction-tuning dataset, OmniCap-IF-54K and present OmniCaptioner-IF, which achieves notable improvements in both complex instruction adherence and general omni-modal captioning performance.", "github": "https://github.com/NJU-LINK/omnicap-if" }, { "date": "2026-06-09", "arxiv_id": "2606.05011", "title": "CIPER: A Unified Framework for Cross-view Image-retrieval and Pose-estimation", "authors": [ "Yurim Jeon", "Dongseong Seo", "Seung-Woo Seo" ], "github_manual": "", "github_regex": "https://github.com/yurimjeon1892/CIPER", "github_paper_md": "", "github_api": "https://github.com/yurimjeon1892/CIPER", "github_api_added_by": "user", "project_page": "", "abstract": "Cross-view geo-localization estimates the geographic location of a ground image by matching it against an aerial image database. Existing methods tackle this through either large-scale retrieval or precise pose estimation, but not both: retrieval-based methods enable wide-area search at the cost of localization accuracy, while pose estimation methods achieve high precision within only a narrow search space. Naively cascading these pipelines introduces error propagation and inconsistent feature representations. We formulate cross-view geo-localization as a unified problem requiring simultaneous city-scale retrieval and precise 3-DoF pose estimation. We propose CIPER (Cross-view Image-retrieval and Pose-estimation transformER), a single architecture that jointly performs both tasks through mutually beneficial feature learning. CIPER uses a shared transformer encoder with task-specific tokens to disentangle global retrieval features from spatial localization cues. To bridge the large domain gap between ground and aerial views, we introduce a two-way transformer pose decoder that uses ground features as spatial queries for bidirectional cross-attention. A set prediction strategy further enables stable 3-DoF regression under a unified multi-task objective. Experiments on VIGOR, KITTI, and Ford Multi-AV demonstrate competitive performance, especially under limited field-of-view and arbitrary orientation conditions. Code is available at https://github.com/yurimjeon1892/CIPER.", "github": "https://github.com/yurimjeon1892/CIPER" }, { "date": "2026-06-09", "arxiv_id": "2606.07473", "title": "Whisper Hallucination Detection and Mitigation via Hidden Representation Steering and Sparse AutoEncoders", "authors": [ "Georgii Aparin", "Vadim Popov", "Tasnima Sadekova", "Assel Yermekova" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Whisper, a widely adopted ASR model, is known to suffer from hallucinations - coherent transcriptions generated for non-speech audio entirely disconnected from the input. We investigate whether hallucinations can be detected and mitigated through Whisper's internal representations. We extract audio encoder activations and evaluate two representation spaces: raw Whisper activations and Sparse AutoEncoder (SAE) latents. We show that both spaces encode linearly separable hallucination-related information, with discriminative power concentrated in a sparse feature subset and increasing toward deeper encoder layers. We propose two steering strategies: activation-space steering and SAE latent-space steering. SAE-based steering reduces hallucination rate from 72.63% to 14.11% for Whisper small and from 86.88% to 27.33% for Whisper large-v3 on the full non-speech test set, with small WER degradation on speech data, approaching the performance of fine-tuning-based methods.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.06735", "title": "A Geometric Account of Activation Steering through Angle-Norm Decomposition", "authors": [ "Georgii Aparin", "Tatiana Gaintseva" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Linear activation steering has gained popularity as a simple and empirically effective way to control language model behavior. More recently, spherical steering paradigms have been proposed to address limitations of additive interventions, often motivated by the assumption that hidden-state norm does not carry concept-relevant information. In this work, we revisit this assumption through a controlled empirical study designed to disentangle the roles of angular and radial components. We show that steering methods differ mainly in how they couple two geometric effects: changing a token's angular alignment with a concept direction and changing its hidden-state norm. Across seven language models, we find that concepts are represented primarily in angular structure, supporting the motivation for spherical methods, but that norm remains important for the stability and downstream effects of steering. Our results explain why interventions with similar concept-level effects can behave differently, and suggest that activation steering should be parameterized by interpretable angular and radial components of the intervention, rather than by a single additive coefficient that entangles these two effects.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.03782", "title": "Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation?", "authors": [ "Renhao Pei", "Yihong Liu", "Sampo Pyysalo", "Hinrich Sch\u00fctze", "Shaoxiong Ji" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OLAResearch/LingReason", "github_api_added_by": "user", "project_page": "https://olaresearch.github.io/LingReason/", "abstract": "Large language models (LLMs) offer a promising approach to machine translation (MT) for extremely low-resource languages by incorporating linguistic resources through in-context learning. However, LLMs often struggle to apply grammatical information effectively during translation. Inspired by recent progress in chain-of-thought reasoning, we investigate whether low-resource MT can benefit from structured intermediate steps of linguistic analysis and grammatical reasoning. We propose a pipeline for automatically generating step-by-step linguistic reasoning traces from Universal Dependencies treebanks, dictionaries, and grammar-rule banks. We evaluate these traces in three settings: in-context learning (ICL), supervised fine-tuning (SFT), and reinforcement fine-tuning (RFT), on Xibe and Chintang as test cases. Our results show that linguistic reasoning traces are most effective as inference-time guidance: in ICL, reliable sentence-specific traces substantially improve translation performance across most models, languages, and metrics. In contrast, using the linguistic reasoning traces as training data yields smaller and less consistent gains, as models learn the trace format but often generate erroneous content. These findings suggest that LLMs can leverage grammatical information for low-resource MT when given reliable linguistic analyses, while learning to generate such analyses remains a major bottleneck.", "github": "https://github.com/OLAResearch/LingReason" }, { "date": "2026-06-09", "arxiv_id": "2606.07547", "title": "Liberating LLM Capabilities in Full-Duplex Speech Models", "authors": [ "Luoyuan Zhang", "Bokai Xu", "Junbo Cui", "Weiyue Sun", "Yingjing Xu", "Hanyu Liu", "Yuan Yao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zly-idleness/lws_demo", "github_api_added_by": "user", "project_page": "https://royalzhang.com/project/lws-page/", "abstract": "Speech-based large language models are typically constrained to spoken replies, which limits their user-facing outputs to what can be verbalized and suppresses text-native capabilities such as code generation, structured analysis, and multi-step reasoning in realtime interaction, for tasks that require persistent, structured, and inspectable intermediate outputs. Existing work improves spoken reasoning or full-duplex turn-taking, but still treats text as a hidden intermediate state or a subordinate modality rather than a first-class output channel. We propose Listen-Write-Speak (LWS), a text-first tri-channel paradigm in which a single autoregressive LLM continuously listens to user audio, writes visible free-form text as its primary output, and speaks a realtime oral response in parallel under a shared causal attention context. This behavior is implemented entirely through a Token Schema, requiring no architectural modifications, and learned via a two-stage data pipeline that synthesizes per-second cognitive annotations consistent with the revealed input timeline. Empirically, LWS demonstrates strong full-duplex interaction on Full-Duplex-Bench, reaches 4.72 on VoiceBench AlpacaEval, achieves 92.6% writing-speaking consistency, and consistently outperforms its internal ablations on URO-Bench. These results suggest that visible writing can serve as a first-class output channel for speech interaction without sacrificing realtime responsiveness. The code and dataset are available on the project page: https://royalzhang.com/project/lws-page/.", "github": "https://github.com/zly-idleness/lws_demo" }, { "date": "2026-06-09", "arxiv_id": "2606.07436", "title": "Skill-3D: Evolving Scene-Aware Skills for Agentic 3D Spatial Reasoning", "authors": [ "Haoyuan Li", "Zhengdong Hu", "Jun Wang", "Hehe Fan", "Yi Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/skill-3d/Skill-3D", "github_api_added_by": "user", "project_page": "https://skill-3d.github.io/", "abstract": "This paper explores agentic 3D spatial understanding, i.e., MLLM agents performing 3D reasoning through tool use. Existing methods often misuse tools and exhibit biased tool preferences under 3D scenarios, leaving the agentic paradigm with only marginal gains over non-agentic strategies. We reveal that 3D spatial reasoning tasks are heterogeneous across scenes, while these agents apply a uniform tool-use strategy to all scenes rather than selecting tools according to the specific scene and task. To address this, we propose Skill-3D, a framework that learns self-evolving scene-aware skills. Specifically, Skill-3D identifies the task scene and records the agent's tool-use trajectory into a Scene Memory, where successful trajectories from similar scenes are aggregated and distilled into a reusable scene-aware skill, with failed ones attached to the skill as lessons. During training, once a similar scene recurs, the corresponding skill is injected to guide the agent, producing new trajectories whose successes and failures further refine the skill, forming a loop in which the memory and the skill library co-evolve. Experiments show that Skill-3D substantially improves tool utilization in 3D spatial reasoning (from 39% to 78% on VSI-Bench), driving the agent toward correct and sufficient tool use. For instance, it improves Gemini-3-Flash by 67% on MMSI-Bench. Furthermore, we conduct agentic post-training over skill-guided trajectories, which boosts Qwen3-VL-8B by 43% on VSI-Bench.", "github": "https://github.com/skill-3d/Skill-3D" }, { "date": "2026-06-09", "arxiv_id": "2605.25077", "title": "WorldCraft: From Camera Navigation to Object Manipulation in Interactive Video World Models", "authors": [ "Bohai Gu", "Taiyi Wu", "Yueyang Yuan", "Jian Liu", "Xiaocheng Lu", "Dazhao Du", "Jie Zhang", "Jinxiang Lai", "Shuai Yang", "Xiaotong Zhao", "Alan Zhao", "Song Guo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://nevsnev.github.io/WorldCraft/", "abstract": "Recent video-based world models have made pixel-space environments interactive at the camera level: users can navigate viewpoints while the model generates coherent visual continuations. Yet their action spaces remain incomplete: users can move the camera, but cannot act on individual objects. Since real-world interaction is inherently object-centric, such models remain closer to passive scene observers than truly manipulable environments. We present WorldCraft, a framework that expands interactive video world models from camera navigation to object-level trajectory actions. Given a user click and a sketched path, WorldCraft generates future frames in which the selected object follows the prescribed trajectory while the camera continues to navigate the scene. WorldCraft achieves this through a trajectory-centric control pipeline: First, Normalized World Trajectory (NWT) represents user-drawn motion in a camera-invariant world coordinate system and dynamically re-projects it under the current camera pose, separating object motion from camera-induced screen-space displacement; Spatial-Pathway LoRA (SP-LoRA) then injects this world-space signal through the model's spatial-control pathway, adding object manipulation capability while preserving the pretrained camera controller; finally, Trajectory-Anchored State Persistence (TASP) treats the world trajectory as a persistent spatial state and refreshes autoregressive memory after trajectory-conditioned generation, allowing moved objects to reappear at their updated positions after leaving the camera view. Experiments show that WorldCraft enables accurate object control, preserves the video-based world model's camera fidelity under camera-only evaluation, and maintains object state across long autoregressive rollouts with off-camera excursions.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.08132", "title": "Phase Marginalization for Patch-Grid Instability in Vision Transformers", "authors": [ "O\u011fuzhan Ercan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Vision Transformers operate on fixed patch grids, which can introduce phase-dependent instability for dense prediction: changing the patch partition can change the token evidence available to a pixel, especially near boundaries. We formalize patch-grid phase as a nuisance variable and propose Phase Marginalization, a post-hoc marginalization method that evaluates structured patch-grid phases, inverse-aligns dense outputs, and aggregates them in the original image coordinate system. The central variant, Uniform Phase Marginalization with K = 4, is training-free and improves over the canonical K = 1 baseline across measured segmentation, depth, and local matching settings. In a controlled Cityscapes experiment, Uniform Phase Marginalization provides a modest compute-matched advantage over generic shift-based four-forward test-time augmentation (TTA) (+0.31 mean Intersection-over-Union over the strongest tested generic row). A scaling study further shows that K = 4 is a practical cost-accuracy trade-off: K = 8 is essentially unchanged and K = 16 adds little accuracy at much higher latency. These results position patch-grid phase as a measurable nuisance variable and Phase Marginalization as a simple diagnostic and post-hoc marginalization baseline for dense ViT prediction.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.08362", "title": "EmpiriGraph-Psy: A Dataset and LLM Pipeline for Extracting Empirical Relation Graphs from Psychology Abstracts", "authors": [ "Danqin Zhao", "Yicun Liu", "Xingwei Tan", "Thomas T. Hills" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/foxxis-dq828/EmpiriGraph-Psy", "github_api_added_by": "user", "project_page": "", "abstract": "Existing scientific relation extraction benchmarks mainly target domains such as computer science, where entities are tasks, methods, datasets, materials, or metrics. This leaves a gap in variable-oriented empirical fields such as psychology, where findings are expressed as relations among constructs, measurements, interventions, and outcomes. We introduce variable-centered empirical graph extraction, the task of mapping scientific abstracts to typed graphs whose nodes are normalized variables and whose edges represent empirical and hierarchical relations. To support this task, we construct EmpiriGraph-Psy, a benchmark of 210 psychology abstracts annotated by domain-trained annotators with normalized variables, concept hierarchies, empirical relation types, and validation states. We evaluate frontier and open-weight LLMs using both direct extraction and a staged graph-construction pipeline that separates variable extraction, normalization, hierarchy construction, evidence selection, relation extraction, and edge validation. The staged pipeline substantially outperforms direct extraction, with the best configuration achieving a macro-F1 of 0.74. Error analysis shows that moderation relations and concept hierarchies remain the most challenging cases, highlighting the difficulty of extracting higher-order empirical claims and implicit abstraction structure from scientific abstracts.", "github": "https://github.com/foxxis-dq828/EmpiriGraph-Psy" }, { "date": "2026-06-09", "arxiv_id": "2606.07271", "title": "Where Rectified Flows Leak: Characterising Membership Signals Along the Interpolation Path", "authors": [ "Thomas Sesmat", "Gabriel Meseguer-Brocal", "Geoffroy Peeters" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sourisimos/rectified_flow_membership", "github_api_added_by": "user", "project_page": "", "abstract": "Understanding what generative models retain from training data remains challenging, with implications for copyright and privacy. Beyond verbatim reproduction, models can encode subtler traces of their training data that never surface in their outputs yet remain exploitable. We study this regime for Rectified Flows, which are increasingly used in deployed generative systems. We analyse the interpolation path X_\u03bb= (1-\u03bb)X_0 + \u03bbX_1 that defines the Rectified Flow training. We show that a gap exists between the reconstruction of train and test data that follows a bell-shaped curve over \u03bb, wich accumulates during training, while the validation metrics remain stable. The signal has a maximum whose location we derive in closed form under Gaussian assumptions. We validate these predictions on both audio and images and show that the bell-shaped structure is universal, while the peak prediction holds when our assumptions are satisfied. As a proof of concept, we exploit this specific \u03bb-resolved structure to perform a Membership Inference Attack, distinguishing members of the training set from non-members.", "github": "https://github.com/sourisimos/rectified_flow_membership" }, { "date": "2026-06-09", "arxiv_id": "2605.27130", "title": "DEI: Diversity in Evolutionary Inference for Quality-Diversity Search", "authors": [ "John Donaghy", "Shikhar Rastogi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gensyn-ai/dei", "github_api_added_by": "user", "project_page": "", "abstract": "We present DEI: Diversity in Evolutionary Inference, a distributed Quality-Diversity (QD) search framework that assigns heterogeneous large language models (LLMs) as mutation operators across peer nodes communicating with non-blocking collective operations. Unlike homogeneous parallel search, which replicates a single model's inductive biases across all workers, DEI treats each LLM's distinct creative prior as a complementary source of behavioral novelty. Extending the Digital Red Queen framework with DEI, nodes share local optimal solutions at the end of each round to seed the next round's population. This creates cross-model adversarial pressure that drives robustness beyond intra-model self-play. Evaluated on the Core War domain, a competitive programming benchmark in which Redcode warrior programs battle inside a simulated machine, a four-node heterogeneous ensemble (GPT-5.4-mini, Claude Sonnet 4.6, GPT-5.2, and Claude Haiku 4.5) achieves 124 percent higher merged-archive QD-Score (45.90 vs. 20.46) and 28 percent higher coverage (80.6 percent vs. 63.0 percent of cells) than a single-node baseline at equal total LLM-call budget. The heterogeneous ensemble also outperforms an equally-budgeted homogeneous ensemble on QD-Score, coverage, and held-out solution generality across all four model families. These results provide the first empirical evidence that model diversity, not merely parallelism, is the key driver of gain in distributed LLM-based QD search.", "github": "https://github.com/gensyn-ai/dei" }, { "date": "2026-06-09", "arxiv_id": "2606.08242", "title": "Light-WAM: Efficient World Action Models with State-Fusion Action Decoding", "authors": [ "Ziang Li", "Dongzhou Cheng", "Yibin Wang", "Shiyue Wang", "Xiaoyang Xu", "Lingxuan Weng", "Juan Wang", "Jiaqi Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/L1ziang/Light-WAM", "github_api_added_by": "user", "project_page": "", "abstract": "World Action Models (WAMs) extend robot policy learning by incorporating future prediction as an additional training objective, encouraging the policy to encode task-relevant temporal structure in its representations. Current WAMs often rely on large-scale generative architectures that incur high training costs and inference latency, making them difficult to deploy as efficient closed-loop policies. We propose Light-WAM, a lightweight World Action Model for efficient robot manipulation. Specifically, it is built with a compact video backbone and performs future-video supervision in a downsampled latent space, reducing the cost of video co-training while retaining its benefits for representation learning. For action prediction, Light-WAM introduces the StateFusionActionExpert, which reads adapted states from multiple backbone layers, fuses them through learned-query pooling, and directly predicts action chunks in a single forward pass. This design provides an efficient interface between video backbone representations and robot actions, avoiding the need for heavy generative action experts. Experiments demonstrate that Light-WAM maintains strong performance on LIBERO and achieves usable multi-task performance on RoboTwin 2.0, while using only 0.44B trainable parameters. It also achieves 72.03ms inference latency with 4.1GiB peak GPU memory and improved training throughput.", "github": "https://github.com/L1ziang/Light-WAM" }, { "date": "2026-06-09", "arxiv_id": "2606.07098", "title": "SigmaScale: LLM Compression with SVD-based Low-Rank Decomposition and Learned Scaling Matrices", "authors": [ "Ernests Lavrinovics", "Marco Letizia", "Roy Janco", "Shai Segal", "Johannes Bjerva", "Maurizio Pierini" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ernlavr/SigmaScale", "github_api_added_by": "user", "project_page": "", "abstract": "We present SigmaScale, a method for learning auxiliary scaling matrices S to aid truncated Singular Value Decomposition (SVD) based Large Language Model (LLM) compression. Instead of deriving scaling matrices analytically, SigmaScale optimizes two sets of vectors that define diagonal row and column scaling transformations under an activation-aware compression loss. We show that learned scaling lowers the effective intrinsic rank of weight matrices, as reflected by reductions in effective-rank entropy, and that this reduction is strongly correlated with compression loss. Experiments on Llama 3.1 8B Instruct and Qwen3-8B show that SigmaScale is competitive with closely related state-of-the-art SVD-based compression methods across perplexity and zero-shot benchmarks. By using learned activation-aware transformations, SigmaScale explores a more flexible route to low-rank LLM compression by adapting to the structure of individual model weights. The advantage observed in specific tasks makes our approach a valid option for applications requiring a reduced LLM-inference computing cost.", "github": "https://github.com/ernlavr/SigmaScale" }, { "date": "2026-06-09", "arxiv_id": "2605.28207", "title": "Pruning and Distilling Mixture-of-Experts into Dense Language Models", "authors": [ "Junhyuck Kim", "Jihun Yun", "Haechan Kim", "Gyeongman Kim", "Joonghyun Bae", "Jaewoong Cho" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/krafton-ai/moe-to-dense", "github_api_added_by": "user", "project_page": "", "abstract": "Mixture-of-Experts (MoE) is now the dominant architecture for frontier language models, yet it requires all expert parameters to be loaded in memory, making it less preferable for memory-constrained deployment. Existing compression methods reduce the number of experts but the output remains an MoE model with the same fundamental limitation. We present the first systematic framework for converting a trained MoE into a standard fully dense architecture: experts are scored, selected, and grouped, then concatenated into a dense FFN and refined by knowledge distillation from the MoE teacher. We evaluate 7 scoring, 5 grouping, and 2 magnitude scaling methods across a range of selected expert counts on Qwen3-30B-A3B, yielding 350 configurations. We find that the choice of scoring method is the most impactful, with our novel diversity-aware scoring consistently outperforming prior methods on Qwen3-30B-A3B, DeepSeek-V2-Lite, and GPT-OSS-20B. Under a controlled comparison at matched parameter count, MoE-to-dense outperforms dense-to-dense pruning by +6.3 pp in average downstream accuracy after ~4B-token distillation at 1.6x faster training wall-clock speed.", "github": "https://github.com/krafton-ai/moe-to-dense" }, { "date": "2026-06-09", "arxiv_id": "2606.07217", "title": "Robotic Policy Adaptation via Weight-Space Meta-Learning", "authors": [ "Christian Bianchi", "Siamak Yousefi", "Alessio Sampieri", "Andrea Roberti", "Luca Rigazio", "Fabio Galasso", "Luca Franco" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Fascetta/WIZARD", "github_api_added_by": "user", "project_page": "https://fascetta.github.io/WIZARD/", "abstract": "Vision-Language-Action (VLA) models are emerging as a promising paradigm for robotic manipulation, enabling general-purpose policies trained from large corpora of demonstrations and action labels. However, adapting these models to new tasks still typically requires task-specific demonstrations, action annotations, and additional fine-tuning, making deployment costly and difficult to scale. We propose WIZARD, a weight-space meta-learning framework that sidesteps task-specific fine-tuning by generating task-specific LoRA parameters for a frozen VLA policy. Given only a language instruction and a short demonstration video, WIZARD predicts the corresponding adaptation weights in a single forward pass, without target-task action labels or test-time optimization. During meta-training, WIZARD learns to map task evidence directly to expert LoRA updates, capturing relationships between tasks in weight space. Experiments on LIBERO show that WIZARD improves performance by up to ~2x on unseen dataset collections and up to ~14x on unseen tasks. On a Franka Emika Panda, WIZARD consistently improves over a real-domain adapted baseline, showing that generated adapters provide task-level specialization beyond simulation.", "github": "https://github.com/Fascetta/WIZARD" }, { "date": "2026-06-09", "arxiv_id": "2606.05405", "title": "Agents' Last Exam", "authors": [ "Yiyou Sun", "Xinyang Han", "Weichen Zhang", "Yuanbo Pang", "Tianyu Wang", "Yuhan Cao", "Yixiao Huang", "Chris Duroiu", "Haoyun Zhang", "Jeffrey Lin", "Weishu Zhang", "Tyler Zeng", "Ying Yan", "Bo Liu", "Hanson Wen", "Mingyang Xu", "Xiaoyuan Liu", "Zimeng Chen", "Weiyan Shi", "Amanda Dsouza", "Vincent Sunn Chen", "Patrick Bryant", "Carl Boettiger", "Yamini Rangan", "Bradley Rothenberg", "Kyle Steinfeld", "Arvind Rao", "Tapio Schneider", "Georgios Yannakakis", "Laure Zanna", "Kaan Ozbay", "Ida Sim", "Tarek Zohdi", "George Em Karniadakis", "Jack Gallant", "Teresa Head-gordon", "Yushan Li", "Wenxi Deng", "Tao Sun", "Huiqi Wang", "Zhun Wang", "Justin Xu", "Chris Yuhao Liu", "Yafei Cheng", "Rongwang Hu", "Aras Bacho", "Shengcao Cao", "Zengyi Qin", "Yixiong Chen", "Hengduan Fan", "Hao Liu", "Lin Zeng", "Shashank Muralidhar Bharadwaj", "Litian Gong", "Yingxuan Yang", "Maojia Song", "Ruheng Wang", "Zongzheng Zhang", "Honglin Bao", "Shuo Lu", "Jianhong Tu", "Zhonghua Wang", "Zheng Zhang", "Zijiao Chen", "yanqiong Jiang", "Zhendong Li", "Bohan Lyu", "Chang Ma", "Peiran Xu", "Benran Zhang", "Shangding Gu", "Haoyue Hua", "Haoyang Li", "Wanzhe Liao", "Chengzhi Liu", "Junbo Peng", "Haoran Sun", "Zechen Xu", "Bo Chen", "Jiayi Cheng", "Yi Jiang", "Keying Kuang", "Yuan Li", "Youbang Pan", "Ziyan Rao", "Alexander Schubert", "Yifan Shen", "Vincent Siu", "Xiatao Sun", "Kangqi Zhang", "Xiaopan Zhang", "Yuchen Zhu", "Ishaan Singh Chandok", "Lei Ding", "Jingxuan Fan", "Andrew Glover", "Jiaming Hu", "Yiran Hu", "Wenbo Huang", "Zixin Jiang", "Haoran Jin", "Lukas Kim", "Ming Liu", "Yang Liu", "Alireza Rafiei", "Xuhuan Shen", "Kunyang Sun", "Sophia Sun", "Ting Sun", "Eric Wang", "Yixin Wang", "Hanwen Xing", "Sihan Xu", "Yuzheng Xu", "Zhongxing Xu", "Zhiling Yan", "Boqin Yuan", "Ruiqi Zhang", "Yifan Zhang", "Zibo Zhao", "Liana", "Santanu Bosu Antu", "Haoyue Bai", "Carlo Bosio", "Joseph Cavanagh", "Patricia Cavazos-Rehg", "Tianxing Chen", "Xuewen Chen", "Yipu Chen", "Zhu Chenyu", "Chen Dai", "Stefano De Castro", "Yunfu Deng", "Kaustubh Dhole", "Jiayuan Ding", "Chenchen Du", "Zhehang Du", "Hao Fan", "Run-ze Fan", "Hengyu Fu", "Shi Gu", "Yifan Gu", "Charlie Guo", "Baihe Huang", "Baixiang Huang", "Rimika Jaiswal", "Zhihan Jiang", "Ran Jin", "Erin Kasson", "Xin Lan", "Joseph Lee", "Deren Lei", "Chenyu Li", "Daofeng Li", "Haitao Li", "Hongwei Li", "Jingyan Li", "Xiao Li", "Yi Li", "Yinsheng Li", "Yuangang Li", "Zhixu Li", "Wenyu Liang", "Longtai Liao", "Kevin Qinghong Lin", "AndyZeyi Liu", "Che Liu", "Jiaming Liu", "Kaiyuan Liu", "Xuan Liu", "Pan Lu", "Wenbo Lv", "Yicheng Lv", "Qiuyang Mang", "Kyle Montgomery", "Yuzhou Nie", "Ruoxi Ning", "Jorin Overwiening", "Xu Pan", "Layna Paraboschi", "Core Francisco Park", "Justin Purnomo", "Swati Rajwal", "Scott Rankin", "Bixuan Ren", "Yiren Rong", "HaoYang Shang", "Ventus Shaw", "Fiona Shen", "Jiawei Shen", "Minqi Shi", "Qiu Shi", "Huaxiu Yao", "Tianneng Shi", "Jonah So", "Vladislav Susoy", "Hannah Szlyk", "Haocheng Wang", "Jialu Wang", "Wei Wang", "Xinyu Wang", "Zehao Wang", "Dowling Wong", "Angela Wu", "Dehao Wu", "Fangyu Wu", "Mengyuan \"Millie\" Wu", "Yu Wu", "Yuchen Wu", "Yuhao Wu", "Qingpo Wuwu", "Weihang Xiao", "Yongyi Xiong", "Fan Xu", "Ruiling Xu", "Mingxuan Yan", "Benjamin Yang", "Jirong Yang", "Sen Yang", "Xiaoli Yang", "Yushi Yang", "Haoran Ye", "Xiaohu Yu", "Zhengming Yu", "Chenlong Zhang", "Chi Zhang", "Hanning Zhang", "Hanwen Zhang", "Junge Zhang", "Kunpeng Zhang", "Song Zhang", "Wenjin Zhang", "Wenshuo Zhang", "Ying Zhang", "Yizhi Zhang", "Brian Zhao", "Qijian Zhao", "Yimin Zhao", "Yuhaohua Zheng", "Liwei Zhou", "Tianyue Zhou", "Sichen Zhu", "Siqi Zhu", "Yan Zhu", "Yishu Zhu", "Jierui Zuo", "Chonghao Cai", "Helena Casademunt", "Wenjia Chen", "Benjamin Cheng", "Nawen Deng", "Rao Fu", "Tianfu Fu", "Yifan Han", "Ren He", "Zhenyu He", "Qiao Jin", "Lang Lang", "Yuetai Li", "Sylvia Liu", "Lu Lu", "Qing Lu", "Subhabrata Mukherjee", "Yunqi Ouyang", "Yin Ren", "Dawei Shi", "Haoran Wu", "Zhiyue Wu", "Hannah Yao", "Zhuoran Yi", "Jenny Yu", "Rhea Zhan", "Hang Zhou", "Blake Zhu", "Junfan Zhu", "Alan Yuille", "Yang Liu", "Russell Alan Poldrack", "Jiachen Li", "Zhenglu Li", "Molei Tao", "Jing Huang", "Wenqi Shi", "Costas Spanos", "Lichao Sun", "Chenguang Wang", "Orson Xu", "Zhen Dong", "Hector Gomez", "Aylin Caliskan", "Ali Emami", "Haimin Hu", "Zhi Li", "Lihui Liu", "Murphy Niu", "Yi Shao", "Jianxin Sun", "Mikko Tolonen", "Ting Wang", "Sanjiv Das", "Yanjun Gao", "Wenbo Guo", "Erika J Schneider", "Zhiyong Lu", "Mark Mueller", "Radha Poovendran", "Somayeh Sojoudi", "Dawn Song" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rdi-berkeley/agents-last-exam", "github_api_added_by": "user", "project_page": "https://agents-last-exam.org/", "abstract": "Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and economically valuable workflows. This paper introduces Agents' Last Exam (ALE), a benchmark designed to evaluate AI agents on long-horizon, economically valuable, real-world tasks with verifiable outcomes. Developed in collaboration with 250+ industry experts, ALE covers non-physical industries defined with reference to O*NET / SOC 2018 (the U.S. federal occupational taxonomy). It is organized around a task taxonomy with 55 subfields grouped into 13 industry clusters covering 1K+ tasks. Current results show that the hardest tier remains far from saturated: across mainstream harness and backbone configurations, the average full pass rate is 2.6%. ALE is designed as a living benchmark: its task pool grows continuously as new workflows and industries are onboarded. More broadly, ALE is intended not merely as another leaderboard, but as an instrument for closing the gap between benchmark success and GDP-relevant impact.", "github": "https://github.com/rdi-berkeley/agents-last-exam" }, { "date": "2026-06-09", "arxiv_id": "2606.00440", "title": "SDR: Set-Distance Rewards for Radiology Report Generation", "authors": [ "Halil Ibrahim Gulluk", "Max Van Puyvelde", "Wim Van Criekinge", "Olivier Gevaert" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://anonymous.4open.science/r/Set-Distance-Rewards-CXR-BFDA", "abstract": "Reinforcement learning with verifiable rewards has rapidly advanced reasoning in vision--language models. However, for chest X-ray report generation, the standard rewards (i.e. exact-match accuracy and step-level processes) are incompatible because the reports consist of unordered and orthogonal findings, rather than a causal reasoning chain. We address this gap with a set-based view: each report is split into sentences and embedded by a frozen sentence transformer, yielding unordered embedding sets. We propose the use of set-to-set distances between generated and reference embeddings as continuous, permutation-invariant rewards. Across two datasets and three vision--language models (Qwen3-VL-2B/4B, Gemma3-4B), post-training with set-to-set distance based rewards via GRPO consistently outperforms supervised fine-tuning and exact-match GRPO on all headline metrics (BERTScore, RadGraph F1 and CheXbert F1 by average \\%6.80, \\%7.82 and \\%4.45 relative improvements respectively). The same set distances also enable test-time best-of-N selection: scoring candidates by their distance to training-report embeddings outperforms random selection on our trained models as well as three closed-source LLMs (Mistral-Small, Gemini-2.5 Flash-Lite, GPT-4o-mini) with on average \\%16.4 relative improvement on BERTScore. Used as a streaming signal, they support a more efficient form of test-time scaling: pruning low-scoring candidates mid-generation reduces generated tokens by over 50\\% while preserving the Findings quality of full best-of-N selection. Together these results establish set-distance rewards as a unified signal for both post-training and test-time scaling in chest X-ray report generation. Our code is publicly https://anonymous.4open.science/r/Set-Distance-Rewards-CXR-BFDA{available}.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.05597", "title": "AsyncWebRL: Efficient Multi-Step RL for Visual Web Agents", "authors": [ "Hao Bai", "Rui Yang", "Chenlu Ye", "Spencer Whitehead", "Aviral Kumar", "Tong Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/microsoft/webgym", "github_api_added_by": "user", "project_page": "https://asyncwebrl-website.github.io/", "abstract": "Training vision-language web agents with multi-step RL is compute-intensive, with two dominant forms of inefficiency: idle GPUs in synchronous RL, and trajectories that use more steps and tokens than necessary. We present AsyncWebRL, which addresses both. On the system side, an asynchronous design overlaps rollout, gradient update, and policy refresh across iterations, paired with two web-agent-specific adaptations, namely an everlasting rollout pool and lightweight screenshot handling, that together deliver up to a 2.9times end-to-end training-throughput speedup over the previously fastest open synchronous pipeline (WebGym). On the algorithmic side, we identify the per-trajectory normalizer 1/|\u03c4_i| in multi-step GRPO as the root cause of trajectory-level and token-level inefficiency: because failures are systematically longer than successes, it down-weights the negative gradient on failed tokens, so the policy keeps producing verbose memory schemas. Replacing 1/|\u03c4_i| with a constant 1/k breaks this coupling, contracting trajectories while preserving aggregate success. Together, these contributions set a new open-source state of the art on the WebGym out-of-distribution test split (+5.8% relative over the 42.9% prior best), with the largest gains on the harder slices (+42% relative on Medium, +48% relative on Hard).", "github": "https://github.com/microsoft/webgym" }, { "date": "2026-06-09", "arxiv_id": "2606.09376", "title": "Precision Is Not Faithfulness: Coverage-Aware Evaluation of Grounded Generation with a Complete Oracle", "authors": [ "Juan S. Santillana" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/vectrayx/precision-is-not-faithfulness", "github_api_added_by": "user", "project_page": "https://pitwall.jsantillana.com/", "abstract": "Reference-free faithfulness metrics verify each atomic claim a model makes against ground truth, and are increasingly used to evaluate grounded generation. We show they share a blind spot: they measure only precision -- are the stated claims supported? -- and therefore reward abstention, since a model can score near-perfect faithfulness by saying almost nothing. We make this measurable using Formula 1 telemetry, a domain where strategic ground truth is derived deterministically and, crucially, completely: for each decision we know the full set of facts that mattered. This completeness -- absent in open-domain faithfulness benchmarks -- lets us measure recall (coverage of the relevant facts) exactly, alongside precision. On a multilingual (EN/ES/PT) benchmark of 7,253 decision instances spanning 150 races, the most precise frontier model covers under half of the relevant facts and ranks last by F1, so requiring coverage reorders the systems; the same effect reappears in a second complete-oracle domain (NOAA weather forecasts). A prompt ablation shows the low coverage is not an under-prompting artifact: explicitly asking models to be thorough does not close the gap. We pair faithfulness with coverage into a single score, validate the metric (controlled perturbation; agreement across a model-free regex extractor and a cross-family LLM extractor, system-level Spearman 1.0), and give a verifier-guided generation method that improves precision and recall without references. We release the benchmark, structured annotations, metric, baselines, and an interactive demo.", "github": "https://github.com/vectrayx/precision-is-not-faithfulness" }, { "date": "2026-06-09", "arxiv_id": "2605.30837", "title": "Send a SCOUT First: Pre-hoc Reasoning for Adaptive Detector Allocation in Prompt-Injection Defense", "authors": [ "Shuhao Zhang", "Jiarui Li", "Qi Cao", "Ruiyi Zhang", "Pengtao Xie" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Prompt-injection detectors are heterogeneous: each is strong on a different slice of attacks, and none is always reliable. Yet existing systems still treat detection as a fixed single-detector pipeline, committing every request to one detector's blind spots. We reframe defense as detector allocation: given a heterogeneous pool, decide per request which detectors to run and whether to escalate to an LLM judge. Our framework SCOUT (Scalable and Controllable Outcome-prediction for Uncertainty-aware Triage) makes this decision dynamic by predicting each detector's per-sample reliability and latency from how it behaved on similar past inputs, and exposes a single safety-utility threshold to the operator (where utility bundles benign-pass rate and wall-clock). To evaluate this setting, we build SCOUT-450, a benchmark that captures the structurally complex, agent-facing injections that older prompt-injection sets under-represent. On SCOUT-450, a safety-oriented operating point reduces attack-success rate by 46% and total wall-clock by 40% relative to an always-on GPT-4o judge, at a 5.1-point benign-utility drop. SCOUT also transfers to three external benchmarks (BIPIA, IPI, and IHEval), improving the safety-utility frontier.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.01000", "title": "Trust Functions: Near-Lossless Weak-to-Strong Generalization by Learning When to Trust the Weak Teacher", "authors": [ "Arda Uzunoglu", "Alvin Zhang", "Daniel Khashabi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ardauzunoglu/trust-functions", "github_api_added_by": "user", "project_page": "https://ardauzunoglu.github.io/trust-functions/", "abstract": "Weak-to-strong generalization studies how to improve a strong student using supervision from a weaker teacher when reliable labels are scarce. We view this primarily as a data selection problem, where the key challenge is to identify which weak labels are reliable enough to serve as a training signal. To address this, we introduce trust functions that assign each weak label a scalar trust score and use these scores to filter weak supervision. Across several domains, including world knowledge, quantitative reasoning, and strategy games, trust filtering yields students that match and sometimes surpass ground-truth supervision, achieving near-lossless weak-to-strong generalization. Moreover, trust functions enable an iterative weak-to-strong chain that compounds gains by training a student and reusing it as the next teacher, amplifying the gains. There are several mechanisms to which advantage of trust functions can be attributed.", "github": "https://github.com/ardauzunoglu/trust-functions" }, { "date": "2026-06-10", "arxiv_id": "2606.05304", "title": "What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems", "authors": [ "Chen Huang", "Yuhao Wu", "Wenxuan Zhang" ], "github_manual": "", "github_regex": "https://github.com/iNLP-Lab/PACT", "github_paper_md": "", "github_api": "https://github.com/iNLP-Lab/PACT", "github_api_added_by": "user", "project_page": "", "abstract": "Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is often left as unconstrained natural language. However, this free-form communication can rapidly inflate token usage, consume the shared context window, and ultimately affect both system performance and inference cost. We analyze five common inter-agent communication strategies across two MAS topologies, finding that no fixed strategy is universally optimal. Instead, effective inter-agent messages consistently preserve action-centered information needed by downstream agents. Building on this, we propose the PACT (Protocolized Action-state Communication and Transmission), which treats inter-agent communication as a public state-update problem and projects each raw agent output into a compact action-state record before it enters shared history. Across different MAS topologies, PACT consistently improves the performance-cost trade-off, achieving comparable or stronger task performance with substantially fewer tokens. The gains extend to production coding harnesses: PACT lifts OpenHands' resolve rate at -10% tokens-per-resolved, and is resolve-neutral on SWE-agent while halving input tokens. Our code is publicly available at https://github.com/iNLP-Lab/PACT.", "github": "https://github.com/iNLP-Lab/PACT" }, { "date": "2026-06-10", "arxiv_id": "2606.10061", "title": "BenSyc: Benchmarking Conversational Sycophancy and Human Alignment in LLMs for Bengali Contexts", "authors": [ "Kazi Noshin", "Sajib Acharjee Dip", "Ranat Das Prangon", "Fardin Hassan Tamim", "Syed Ishtiaque Ahmed", "Liqing Zhang", "Sharifa Sultana" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/spaces/Sajib-006/bensyc-project", "abstract": "Large language models (LLMs) increasingly participate in emotionally sensitive social conversations, where responses may shift from balanced support toward excessive validation or escalatory alignment. Existing sycophancy research primarily focuses on factual agreement and instruction-following settings, leaving culturally grounded conversational sycophancy underexplored. We introduce BenSyc, the first benchmark for studying conversational sycophancy in Bengali social contexts. Starting from 11,840 Reddit posts and 170k comments collected from communities across Bangladesh and West Bengal, we construct a human-validated benchmark with binary labels and a fine-grained five-level taxonomy spanning Invalidation, Neutral, Support, Validation, and Escalation. We evaluate more than 15 open and proprietary LLMs on conversational alignment classification and response generation tasks. Results show that distinguishing empathetic support from reinforcement-oriented validation remains challenging even for frontier instruction-tuned models: the best system achieves only 61.8 Macro-F1 on binary detection and 61.7 Macro-F1 on five-class classification. In generation settings, several models frequently produce strongly validating or escalatory responses in emotionally charged situations. Our findings highlight substantial variation across model families and conversational behaviors, underscoring the importance of culturally grounded multilingual benchmarks for evaluating socially aligned conversational AI systems.", "github": "" }, { "date": "2026-06-09", "arxiv_id": "2606.07723", "title": "VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation", "authors": [ "Siyi Chen", "Hugo Hadfield", "Alex Zook", "Mikaela Angelina Uy", "Chan Hee Song", "Erwin Coumans", "Xuning Yang", "Faisal Ladhak", "Qing Qu", "Stan Birchfield", "Jonathan Tremblay", "Valts Blukis" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://chicychen.github.io/VoLo/", "abstract": "Open-vocabulary long-horizon manipulation requires robots to reason over flexible instructions and complex multi-object scenes while adaptively planning, executing, monitoring, and recovering from failures. We address these demands with a closed agent loop in which a VLM orchestrates heterogeneous robot capabilities as interruptible tools. Unlike in virtual AI agents, the timing of decisions, actions and tool calls is important in a physical world that does not pause for reasoning. We refer to this setting as Physical Orchestration, and propose VoLoAgent, a VLM that plans, monitors, and recovers by treating a VLA/WAM as an interruptible tool it steers mid-rollout alongside vision models and action primitives. To evaluate these long-horizon capabilities, we introduce RoboVoLo, a high-fidelity benchmark for open-vocabulary long-horizon manipulation across common sense, memory/state tracking, complex references, and world knowledge, with both task-level success and failure-mode diagnostics. Experiments show VoLoAgent substantially outperforms single VLA/VLM or tool-based systems, with validation on real-robot experiments. Project page: https://chicychen.github.io/VoLo/", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.09967", "title": "ABot-Earth 0.5: Generative 3D Earth Model", "authors": [ "Ming Qian", "Tianjian Ouyang", "Mingchao Sun", "Zijian Wang", "Jincheng Xiong", "Jiarong Han", "Yongchang Zhang", "Jiawei Zhang", "Xu Wang", "Yu Liu", "Luyang Tang", "Fei Yu", "Zengye Ge", "Mengmeng Du", "Yuan Liu", "Nianfei Fan", "Song Wang", "Yingliang Peng", "Chunxue Jia", "Yang Liu", "Shiying Zeng", "Haozhe Shi", "Junnan Lai", "Hongyu Pan", "Zheng Wu", "Ning Guo", "Mu Xu", "Hang Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/amap-cvlab/ABot-Earth-0.5", "github_api_added_by": "user", "project_page": "https://abot-earth.amap.com/", "abstract": "We present ABot-Earth 0.5, a generative 3D framework designed to synthesize vast, seamless 3D environments from ubiquitous, geospatially referenced satellite imagery. To achieve this, we propose a novel generative model formulated directly with the 3D Gaussian Splatting (3DGS) representation. The model is trained on a diverse corpus of existing real-world urban reconstructions, learning to generate realistic geometry and textures. At inference, it synthesizes novel 3D scenes conditioned solely on satellite imagery at a scalable rate of under 10 minutes per square kilometer, while demonstrating exceptional realism. The framework is designed for accessibility, with integrated hierarchical level-of-detail (LOD) structures that permit real-time, interactive visualization on web-based map engines. This high-fidelity simulation sandbox effectively mitigates the sim-to-real domain gap, enabling critical downstream Embodied AI applications like closed-loop UAV navigation. By providing an ultra-low-cost and high-efficiency solution, ABot-Earth 0.5 significantly lowers the technical and financial barriers to large-scale 3D reconstruction and empowers the future of global digital earth visualization.", "github": "https://github.com/amap-cvlab/ABot-Earth-0.5" }, { "date": "2026-06-10", "arxiv_id": "2606.09032", "title": "Bridging the Agent-World Gap: Text World Models for LLM-based Agents", "authors": [ "Yixia Li", "Hongru Wang", "Peng Lai", "Zhiwen Ruan", "He Zhu", "Youxin Zhu", "Ganlong Zhao", "Minda Hu", "Yun Chen", "Sibei Yang", "Peng Li", "Jeff Z. Pan", "Jia Pan", "Guanhua Chen", "Yang Liu", "Guanbin Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sustech-nlp/awesome-text-world-models", "github_api_added_by": "user", "project_page": "", "abstract": "Large language model (LLM)-based agents are increasingly used in interactive textual environments, from web navigation and code editing to tool use and long-horizon dialogue. Yet many remain largely reactive, mapping observations to actions without an explicit model of how these environments are structured and evolve. This motivates text world models (TWMs): transition models over textual states that, given a state and a candidate action, predict the resulting webpage, terminal output, API response, or user reply, thereby supporting planning, efficient learning, and principled evaluation. We systematically review text world models for LLM-based agents, organized around a formal framework and the agent lifecycle: (1) Foundations, defining text world models and characterizing them by state representation and grounding domain; (2) Construction, taxonomizing LLM-as-WM and code-as-WM paradigms and reviewing methods for building them; (3) Application, examining how world models support agents at training time through experience synthesis and at inference time through planning, verification, and adaptation; and (4) Evaluation, covering both evaluation of the world model itself and its use as an evaluation environment for agents. We aim to consolidate this rapidly developing area, clarify its design space, and highlight open challenges for future research.", "github": "https://github.com/sustech-nlp/awesome-text-world-models" }, { "date": "2026-06-10", "arxiv_id": "2606.10804", "title": "SCAIL-2: Unifying Controlled Character Animation with End-to-end In-Context Conditioning", "authors": [ "Wenhao Yan", "Fengjia Guo", "Zhuoyi Yang", "Jie Tang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zai-org/SCAIL-2", "github_api_added_by": "user", "project_page": "https://teal024.github.io/SCAIL-2/", "abstract": "Controlled character animation requires transferring motion from a driving sequence to a reference character. Prior works heavily rely on intermediate representations, including pose skeletons to represent motion or masked background to represent environment, which inevitably leads to information loss. To address this, we present SCAIL-2, an framework that bypasses those intermediates and achieves end-to-end character animation. By directly concatenating driving videos to the sequence, the model can obtain all the required visual information from the input video. To address lack of end-to-end data, we unify sub-tasks of character animation with decoupled conditions and then curate a pipeline to synthesize MotionPair-60K, an end-to-end motion transfer dataset containing heterogeneous tasks of character animation. To archive the unification, we utilize in-context mask conditioning and mode-specific RoPE as soft guidance beyond textual instructions and raw visual information. To address synthetic discrepancy in detailed regions, we propose Bias-Aware DPO to construct preference items to mitigate the errors. Extensive experiments demonstrate that our method substantially outperforms existing state-of-the-art approaches in various character animation tasks. A large subset of synthetic data as well as model weights will be released at our project page: https://teal024.github.io/SCAIL-2/.", "github": "https://github.com/zai-org/SCAIL-2" }, { "date": "2026-06-10", "arxiv_id": "2606.04391", "title": "Online Skill Learning for Web Agents via State-Grounded Dynamic Retrieval", "authors": [ "Jiaxi Li", "Ke Deng", "Yun Wang", "Jingyuan Huang", "Yucheng Shi", "Qiaoyu Tan", "Jin Lu", "Ninghao Liu" ], "github_manual": "", "github_regex": "https://github.com/plusnli/skill-dynamic-retrieval", "github_paper_md": "", "github_api": "https://github.com/plusnli/skill-dynamic-retrieval", "github_api_added_by": "user", "project_page": "https://github.com/plusnli/skill-dynamic-retrieval", "abstract": "Language agents increasingly rely on reusable skills to improve multi-step web automation across related tasks. A growing line of work studies online skill learning, where agents continually induce skills from previous task trajectories and reuse them in future tasks on the fly. However, existing methods mainly reuse skills at the task-level: a fixed set of skills is retrieved based on the initial task instruction and then held fixed throughout execution. This static strategy is misaligned with web execution, where the appropriate next action depends not only on the task goal but also on the current webpage state, which often transitions into situations that the initial skills fail to cover. To address this gap, we propose State-Grounded Dynamic Retrieval (SGDR), an online skill learning method that enables stepwise skill reuse for web agents. SGDR consists of three components: a sliding-window extraction process that turns completed trajectories into reusable sub-procedures invokable at intermediate execution states, a dual text-code representation that connects skill retrieval with executable action, and a state-grounded dynamic retrieval mechanism that matches skills to both the task goal and the current webpage state. Experiments on WebArena across five domains show that SGDR consistently outperforms strong baselines, achieving average success rates of 37.5% with GPT-4.1 and 24.3% with Qwen3-4B, corresponding to relative gains of 10.6% and 10.0% over the strongest baseline, respectively. The code is available at https://github.com/plusnli/skill-dynamic-retrieval.", "github": "https://github.com/plusnli/skill-dynamic-retrieval" }, { "date": "2026-06-10", "arxiv_id": "2606.10740", "title": "When the Chain of Thought Knows Better: Failure Modes in Multi-Turn Reasoning Models", "authors": [ "Sai Kartheek Reddy Kasu", "Nils Lukas", "Samuele Poppi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Failures in multi-turn reasoning models are largely invisible to terminal-score evaluation. A model can lock onto an unsafe stance early in a long dialogue, yet its final-turn refusal rate may appear indistinguishable from a robustly aligned baseline. To expose these hidden temporal dynamics, we propose a trace-level diagnostic - the CoT-Output 2x2 safety matrix. This framework labels every turn along two independent axes (internal reasoning and visible output), yielding four operationally defined failure cells: robust alignment, alignment faking, overt jailbreak, and a distinct failure mode we term context-injection failure (where the CoT maintains safe reasoning, but the visible output produces harm, highlighting a multi-turn manifestation of reasoning unfaithfulness). We evaluate three distilled reasoning targets against a fixed attacker across five oversight conditions, collecting 6750 turn-level observations on the Information-Hazard scenario. Our analysis reveals two reproducible vulnerabilities: an oversight paradox where explicit monitoring cues paradoxically increase alignment-faking rates rather than suppress them, and a context-injection failure where models lock onto unsafe external outputs despite safe internal states. We release the full dataset of multi-turn dialogues and CoT traces to support follow-up trace-diagnostic research.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.11188", "title": "ARM: An AutoRegressive Large Multimodal Model with Unified Discrete Representations", "authors": [ "Junke Wang", "Xiao Wang", "Jiacheng Pan", "Xuefeng Hu", "Feng Li", "Jingxiang Sun", "Chaorui Deng", "Zilong Chen", "Yunpeng Chen", "Kaibin Tian", "Matthew Gwilliam", "Hao Chen", "Danhui Guan", "Kun Xu", "Weilin Huang", "Zuxuan Wu", "Haoqi Fan", "Yu-Gang Jiang", "Zhenheng Yang" ], "github_manual": "", "github_regex": "https://github.com/wdrink/ARM", "github_paper_md": "", "github_api": "https://github.com/wdrink/ARM", "github_api_added_by": "user", "project_page": "", "abstract": "This paper introduces ARM, a discrete representation-based AutoRegressive Model that unifies image understanding, generation, and editing within a next-token prediction framework. ARM is built on three efforts: first, we train a discrete semantic visual tokenizer that maps images into compact token sequences. Our tokenizer is supervised with multiple objectives that jointly promote semantic discriminability, language alignment and faithful reconstruction, thereby supporting diverse tasks in a shared latent space. With this, we train a 7B autoregressive model over large-scale text and image token sequences, seamlessly developing vision-language perception and generation capabilities. Finally, to further improve preference-aligned behavior for text-to-image generation and instruction-guided editing, ARM applies reinforcement learning (RL) to optimize task-level objectives such as visual quality, instruction adherence, and edit consistency. Surprisingly, the results show that RL not only substantially improves performance on the target tasks (e.g., raising WISE overall from 0.50 to 0.56, GEdit-Bench-EN G_O from 5.75 to 6.68), but also induces cross-task synergy between text-to-image generation and editing. Collectively, these findings highlight autoregressive modeling, when paired with strong representations and preference optimization, as a scalable foundation for multimodal intelligence. Code: https://github.com/wdrink/ARM.", "github": "https://github.com/wdrink/ARM" }, { "date": "2026-06-10", "arxiv_id": "2606.10650", "title": "Dynamic Linear Attention", "authors": [ "Xin Wang", "Hui Shen", "Boyuan Zheng", "Xueshen Liu", "Minkyoung Cho", "Zhongwei Wan", "Zesen Zhao", "Zhuoqing Mao", "Shen Yan", "Mi Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The scalability of Large Language Models (LLMs) to long contexts is fundamentally constrained by the quadratic complexity of standard attention, motivating the adoption of linear attention mechanisms with sub-quadratic cost. To improve representation capacity under long contexts, recent approaches organize memory in a multi-state manner. However, existing multi-state linear attention methods rely on fixed state merging policies that cannot adapt to dynamically varying token importance, irreversibly obscuring critical tokens and causing severe error accumulation over long sequences. To address this limitation, we propose DLA, a dynamic memory modeling framework for multi-state linear attention. DLA introduces (i) Information-Aware Dynamic State Merging, which adaptively determines state boundaries based on token-level information variation, preserving high-resolution representations around semantic transitions while aggressively summarizing stable regions, and (ii) Capacity-Bounded Memory Modeling, which maintains a fixed-size, chronologically ordered state cache by selectively merging adjacent low-information states to control memory growth with minimal information loss. We pre-train DLA on two different linear attention models and evaluate on 16 datasets across three categories. Experimental results demonstrate the superiority of DLA over state-of-the-art.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.11087", "title": "Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning", "authors": [ "Zhiyuan Zhou", "Andy Peng", "Charles Xu", "Qiyang Li", "Tobias Springenberg", "Kevin Frans", "Sergey Levine" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhouzypaul/qgf", "github_api_added_by": "user", "project_page": "https://q-guided-flow.github.io/", "abstract": "Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control. While they are known to scale stably in the supervised imitation learning setting, incorporating them into reinforcement learning (RL) pipelines for policy improvement has proven more difficult. It often requires specialized training objectives or backpropagating through denoising processes, which cause well-known issues with stability and affect scalability. In this paper we study the question of whether simple policy improvement schemes at test time alone, leaving stable supervised policy training intact, can be a competitive alternative which sidesteps these issues. To this end, we propose QGF (Q-Guided Flow), an RL algorithm that performs policy optimization entirely at test time. QGF works by pre-training both a reference flow policy (via a standard behavioral cloning objective) and a value function critic and, at test time, using the value gradient to guide the reference policy to generate higher-value actions without any additional policy learning. Empirically, QGF outperforms prior test-time RL methods on single-task and goal-conditioned offline RL benchmarks with high-dimensional action spaces, and is competitive with state-of-the-art training-time algorithms while being much cheaper to run. Moreover, it exhibits favorable scaling with model size by avoiding the instability of actor-critic training, offering a practical and effective alternative RL algorithm with expressive policies.", "github": "https://github.com/zhouzypaul/qgf" }, { "date": "2026-06-10", "arxiv_id": "2606.09056", "title": "MilliVid: Hierarchical Latents for Long-Range Consistency in Video Generation", "authors": [ "Ishaan Preetam Chandratreya", "David Charatan", "Basile Van Hoorick", "Sergey Zakharov", "Vitor Guizilini", "Phillip Isola", "Vincent Sitzmann" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://davidcharatan.com/millivid/", "abstract": "Video generative models have become increasingly powerful, but long-range consistency remains challenging to achieve because even a few dozen frames require impractically long transformer sequence lengths. We show that this issue can be mitigated by generating video using coarse-to-fine rollout within a multi-scale token space. Our approach is simple: first, we pre-train an autoencoder that compresses each frame into a hierarchy of tokens, with levels ranging from the typical latent resolution to only a handful of tokens per frame. The coarsest levels capture the most consequential information, such as scene layout and semantics, while finer levels add high-frequency appearance and texture. Then, we train a video diffusion model to generate these tokens using coarse-to-fine rollout. By carefully controlling the level of detail at which frames are generated and used as context during each rollout step, we are able to preserve long-range consistency in geometry and object permanence while spending less compute on the long-range consistency of less perceptually relevant details. We validate this approach using a custom dataset of long Minecraft videos, where it produces substantially more consistent rollouts compared to existing baselines.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.09068", "title": "Emergent Misalignment Can Be Induced by Sycophancy and Reversed via Alignment Gating", "authors": [ "Sicheng Wang", "Xiangyang Zhu", "Han Wang", "Zongrui Wang", "Yuan Tian", "Kaiwei Zhang", "Kaiyuan Ji", "Qi Jia", "Guangtao Zhai" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/stay1to0/Sycophancy_Emergent_Misalignment_and_Gated_attention_FT", "github_api_added_by": "user", "project_page": "", "abstract": "Prior work has shown that fine-tuning large language models on malicious or incorrect outputs in narrow domains can induce broad misalignment and harmful behavior, a phenomenon known as emergent misalignment. However, efficient methods for reversing such misalignment remain limited. In this work, we make two contributions. First, we identify sycophancy fine-tuning, i.e., training models to passively agree with users' incorrect opinions, as a previously underexplored driver of emergent misalignment, and show that it induces broad and severe misaligned behavior. Second, we propose Alignment Gating, an efficient method for reversing emergent misalignment that inserts learnable and controllable gates into the model during fine-tuning. Through fine-tuning, these gates learn to identify the internal representations responsible for unsafe responses. Thus, amplifying or suppressing these representations then exacerbates or mitigates EM, respectively. We further find that alignment gating module exhibits strong generalization: gating weights obtained from narrow-domain fine-tuning substantially suppress broad-domain misaligned behavior while preserving the model's general capabilities.", "github": "https://github.com/stay1to0/Sycophancy_Emergent_Misalignment_and_Gated_attention_FT" }, { "date": "2026-06-10", "arxiv_id": "2606.11042", "title": "Workflow-GYM: Towards Long-Horizon Evaluation of Computer-use Agentic tasks in Real-World Professional Fields", "authors": [ "Liya Zhu", "Jingzhe Ding", "Jian Zhang", "Jianbo Xue", "Shihao Liang", "Ge Zhang", "Xiang Gao", "Qingshui Gu", "Mailun Gao", "Huimin Che", "Yan Zhao", "Peiheng Zhou", "Haojun Wang", "Chaobo Xian", "Lili Le", "Chi Wu", "Yiwei Liu", "Shengda Long", "Jiale Yang", "Fangzhi Xu", "Sijin Wu", "Haodong Duan", "Yi Zhu", "Chao He", "Zhaojian Li", "Minchao Wang", "Huan Zhou", "Jiani Hou", "Chuqian Yu", "Weiran Shi", "Hongwan Gao", "Jiamin Chen", "Guanhong Chen", "Tingqin Luo", "Kaiyuan Zhang", "Zhixin Yao", "Qing Hua", "Yuhao Jiang", "Jin Chen", "Pu Chen", "Zhenyu Hu", "Xingyu Li", "Zhengxuan Jiang", "Meng Cao", "Tianfeng Long", "Haozhe Wang", "Mingzhang Wang", "Yichen Zhang", "Yiming Dai", "Chenchen Zhang", "Jiaying Wang", "Zhiyong Wu", "Shen Yan", "Yujia Qin", "Wenhao Huang", "Zaiyuan Wang", "Xiaolong Chang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://workflow-gym.github.io/", "abstract": "Recent years have witnessed the rapid evolution of AI agents toward handling increasingly complex, real-world tasks. However, existing benchmarks rarely evaluate whether agents can operate graphical user interfaces to complete long-horizon, high-value professional workflows across diverse domains. Current GUI benchmarks still predominantly focus on general-purpose software, relatively simple applications, and short-horizon tasks, leaving it largely unknown whether modern agents can follow user instructions to autonomously operate domain-specific professional software and accomplish economically valuable work in an end-to-end manner. To bridge this gap, we introduce Workflow-GYM, a benchmark for long-horizon GUI tasks centered on professional domains and specialized software environments. Through extensive experiments on state-of-the-art models, we find that even the strongest models achieve only slightly above 30% success rates, highlighting that professional long-horizon GUI workflows remain highly challenging for current GUI agents. Further analysis reveals that current agents struggle to maintain long-horizon workflow consistency, frequently exhibiting workflow stage omission, error propagation, objective drift, and insufficient understanding of professional software environments. Our findings provide important insights into the limitations of current agent systems and suggest key directions for the next generation of GUI-agent research.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.09730", "title": "SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research", "authors": [ "Pu Ning", "Quan Chen", "Kun Tao", "Xinyu Tang", "Tianshu Wang", "Qianggang Cao", "Xinyu Kong", "Zujie Wen", "Zhiqiang Zhang", "Jun Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Search-Swarm/SearchSwarm", "github_api_added_by": "user", "project_page": "https://search-swarm.github.io/", "abstract": "Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inherently finite. Recent work explores a paradigm where a main agent decomposes tasks and dispatches subtasks to subagents, which execute and return only summarized results, conserving the main agent's context budget. However, performing this well requires delegation intelligence: the ability to decompose complex tasks, determine when and what to delegate, and integrate returned results into the ongoing workflow. Training data for this capability is scarce in naturally occurring text, and to our knowledge, how to synthesize such data and train models to acquire this capability remains largely unexplored in the open-source community. To bridge this gap, we present a preliminary exploration targeting deep research, a representative long-horizon agent task. Specifically, we design a harness that guides the model toward high-quality task decomposition and delegation, while constraining subagents to return results properly to support the main agent's workflow. The harness-guided trajectories naturally encode correct delegation decisions, which we use as supervised fine-tuning data to internalize delegation intelligence into model weights. Our resulting model, SearchSwarm-30B-A3B, achieves 68.1 on BrowseComp and 73.3 on BrowseComp-ZH, the best results among all models of comparable scale. We will release our harness, model weights, and training data to facilitate future research.", "github": "https://github.com/Search-Swarm/SearchSwarm" }, { "date": "2026-06-10", "arxiv_id": "2606.10572", "title": "One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QA", "authors": [ "Zhi Zheng", "Ziqiao Meng", "Hao Luan", "Wei Liu", "Wee Sun Lee" ], "github_manual": "", "github_regex": "https://github.com/zz1358m/Latent-Memory-Master", "github_paper_md": "", "github_api": "https://github.com/zz1358m/Latent-Memory-Master", "github_api_added_by": "user", "project_page": "https://huggingface.co/zz1358m/Latent-Memory-Master", "abstract": "External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal evidence. However, existing memory paradigms represent each memory item in raw text and image forms, so retrieval-based systems must pass the retrieved text or images to the generation LLMs/VLMs, resulting in high token consumption and storage pressure, making it unaffordable for resource-constrained applications. We propose Latent Memory, a latent-space memory paradigm that replaces each raw text or image evidence item with a single high-dimensional latent token produced by a small compressor LLM/VLM. Rather than retrieving raw evidence for generation, Latent Memory operates in a unified latent representation space: the query is embedded into this space to retrieve relevant latent tokens, and the retrieved latent tokens are directly prompted to a pretrained LLM or VLM for answer generation. To make each latent token simultaneously informative for reconstruction, retrieval, and generation, we train the compressor with reconstruction, contrastive, and distillation objectives in a unified end-to-end manner. Latent Memory is evaluated on seven text-only QA benchmarks (e.g., HotpotQA) and multimodal QA benchmarks, where it achieves competitive QA performance compared to advanced RAG baselines while consuming 3x to 10x fewer generator tokens. It can also deliver the strongest image-grounded QA performance on WebQA. Code is available at https://github.com/zz1358m/Latent-Memory-Master.", "github": "https://github.com/zz1358m/Latent-Memory-Master" }, { "date": "2026-06-10", "arxiv_id": "2606.11182", "title": "EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents", "authors": [ "Weixian Xu", "Shilong Liu", "Mengdi Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Princeton-AI2-Lab/EEVEE", "github_api_added_by": "user", "project_page": "https://princeton-ai2-lab.github.io/EEVEE/", "abstract": "In this paper, we propose EEVEE, the first multi-dataset test-time prompt learning framework for LLM agents, enabling test-time prompt learning under real-world task streams. Existing methods are largely designed for single-dataset settings, while real-world applications require models to handle heterogeneous input streams drawn from multiple datasets, domains, and task distributions, limiting their practical applicability. To mitigate cross-dataset interference, EEVEE introduces a router that partitions incoming inputs into task clusters and assigns them to suitable prompt configurations. This design is optimized via a router-prompt co-evolution strategy, which employs interleaved router and prompt learning phases to address their mutual dependency. Experiments across multiple datasets demonstrate that the framework improves robustness under heterogeneous data streams while maintaining single-benchmark learning capability and efficiency. Specifically, EEVEE improves average multi-benchmark scores by 10.38 and 24.32 points over Qwen3-4B-Instruct and DeepSeek-V3.2, surpassing SOTA methods GEPA and ACE by up to 37.2% and 48.2%.", "github": "https://github.com/Princeton-AI2-Lab/EEVEE" }, { "date": "2026-06-10", "arxiv_id": "2606.10646", "title": "How Does Reasoning Flow? Tracing Attention-Induced Information Flow for Targeted RL in LLMs", "authors": [ "Zhichen Dong", "Yang Li", "Yuhan Sun", "Weixun Wang", "Yijia Luo", "Zinian Peng", "Taiheng Ye", "Chao Yang", "Wenbo Su", "Yu Cheng", "Bo Zheng", "Junchi Yan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Token-level credit assignment remains a key obstacle for reinforcement learning (RL) in large language models (LLMs), where RL recipes typically treat all tokens equally, failing to distinguish decisive reasoning steps from routine formatting or fluent filler. Recent attempts leverage model-internal signals to assign finer-grained credit, but these are often point-wise heuristics that ignore the global structure of information propagation. We propose FlowTracer, an RL framework that traces answer-targeted reasoning flow on an attention-induced directed acyclic graph in which nodes correspond to tokens and edge capacities come from aggregated attention weights and derives token credit from this global structure. The edge capacities are reweighted to retain only the influence that can reach the answer region, while enforcing local flow conservation so intermediate tokens neither lose nor gain effective mass due to path length or irrelevant branches. On this graph, FlowTracer extracts an information-flow backbone connecting the question to the answer and scores tokens by flow throughput, revealing high-impact hubs and aggregation checkpoints that mediate long-range dependencies. These derived importances are used to shape token-level rewards, enabling learning signals to focus precisely on the tokens that route information toward (or away from) correct answers and delivering consistent performance gains across a range of reasoning tasks.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.07689", "title": "Struct-Searcher: Agentic Structural Thinking Advances Multimodal Deep Information Seeking", "authors": [ "Fan Zhang", "Vireo Zhang", "Shengju Qian", "Haoxuan Li", "Zheng Lian", "Hao Wu", "Yuan Gao", "Xinyu Geng", "Xin Wang", "Pheng-Ann Heng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Deep research agents have attracted increasing attention for their ability to collect large-scale online information to acquire target knowledge, with recent efforts shifting from purely text-based information seeking to multimodal settings. However, existing agentic workflows are largely aligned with evidence accumulation models, which linearly aggregate evidence and lack principled mechanisms for handling contradictory information across heterogeneous modalities. Towards this end, we propose Struct-Searcher, a structural agentic workflow grounded in belief revision theory that explicitly maintains an evolving multimodal structural graph throughout the reasoning process, enabling effective conflict-aware multimodal deep information seeking. Extensive experiments across multiple benchmark datasets and backbone models demonstrate that Struct-Searcher is (1) plug-and-play and model-agnostic, yielding an average relative accuracy improvement of 17.2% on BrowseComp-VL across five different backbones. (2) top-performing, consistently outperforming state-of-the-art vision-language models (VLMs) and deep research agents, with relative accuracy improvements of 3.7% on MM-BrowseComp, 1.5% on HLE-VL, and 0.7% on BrowseComp-VL over the second-best competing approach.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.11176", "title": "Data Journalist Agent: Transforming Data into Verifiable Multimodal Stories", "authors": [ "Kevin Qinghong Lin", "Batu EI", "Yuhong Shi", "Pan Lu", "Philip Torr", "James Zou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/QinghongLin/data2story-skill", "github_api_added_by": "user", "project_page": "https://data2story.github.io/", "abstract": "Data tells stories that shape society; the data journalist's job is to turn raw information into stories non-experts can trust. A high-quality news feature takes a newsroom team weeks: hunting for context, running statistics, choosing an angle, and designing visuals. Recent agents handle individual steps well: data-science agents close the analysis loop, while design agents synthesize beautiful websites. But can an agent serve as a data journalist end to end? We introduce Data Journalist Agent (Data2Story), a multi-agent framework that orchestrates specialized roles into a single virtual newsroom. Data2Story contributes two innovations. (i) Claims are evidence-grounded: an Inspector links every number, angle, and asset back to data, code, or an external reference. (ii) Articles are multimodally generative: rather than defaulting to plain text and static charts, Data2Story reasons about what readers will want to see, then deploys multimodal tools, such as interactive maps for geography and audio for music. We evaluate Data2Story on 18 articles, each paired with the originally published expert piece, along four axes: (a) human-agent angle coverage; (b) rubric evaluation with 53 participants across five dimensions; (c) computer-use agents as judges, a cost-saving proxy for how readers navigate interactive articles; and (d) verifiability, where a coding verifier re-executes statements against the data and checks claims against references. Data2Story produces competitive, evidence-traceable multimedia stories, with particular strength in transparency and auditability. Human articles retain an edge in editorial angle, creative design, and presentation. We position Data2Story as a collaborator for journalists, enabling more evidence-based, transparent, and verifiable reporting. Code and demos are available at https://data2story.github.io.", "github": "https://github.com/QinghongLin/data2story-skill" }, { "date": "2026-06-10", "arxiv_id": "2606.11025", "title": "Flow-DPPO: Divergence Proximal Policy Optimization for Flow Matching Models", "authors": [ "Bowen Ping", "Xiangxin Zhou", "Penghui Qi", "Minnan Luo", "Liefeng Bo", "Tianyu Pang" ], "github_manual": "", "github_regex": "https://github.com/Tencent-Hunyuan/UniRL/tree/main/FlowDPPO", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://jayce-ping.github.io/Flow-DPPO-Project-Page/", "abstract": "Recent work has demonstrated that online reinforcement learning (RL) can substantially improve the quality and alignment of flow matching models for image and video generation. Methods such as Flow-GRPO and CPS cast the denoising process as a Markov Decision Process and apply PPO-style ratio clipping to enforce a trust region. However, we argue that ratio clipping is structurally ill-suited for flow models: the probability ratio between new and old policies is a noisy, single-sample estimate of the true policy divergence, leading to over-constraining in some regions of the trajectory and under-constraining in others. We propose Flow-DPPO (Flow Divergence Proximal Policy Optimization), which replaces ratio clipping with a divergence proximal constraint. A key observation is that the per-step policy in flow models is Gaussian, enabling exact and cheap computation of the KL divergence between old and new policies. Flow-DPPO employs an asymmetric divergence mask that blocks gradient updates only when they simultaneously move away from the trusted region and violate the divergence threshold. Experiments show that Flow-DPPO achieves higher rewards with better KL-proximal efficiency, alleviates catastrophic forgetting, promotes balanced multi-objective optimization, and enables stable multi-epoch training where ratio clipping degrades. Code and models are available at https://github.com/Tencent-Hunyuan/UniRL/tree/main/FlowDPPO.", "github": "https://github.com/Tencent-Hunyuan/UniRL/tree/main/FlowDPPO" }, { "date": "2026-06-10", "arxiv_id": "2606.09821", "title": "Rethinking the Divergence Regularization in LLM RL", "authors": [ "Jiarui Yao", "Xiangxin Zhou", "Penghui Qi", "Wee Sun Lee", "Liefeng Bo", "Tianyu Pang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Tencent-Hunyuan/UniRL", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement learning (RL) has become a key component of post-training large language models (LLMs). In practice, LLM RL is often off-policy because of training-inference mismatch and policy staleness, making trust-region control essential for stable optimization. Mainstream methods such as PPO and GRPO approximate this control with a ratio-clipping mechanism, but the importance ratio can be a poor proxy for distributional shift in long-tailed vocabularies. Recent work such as DPPO addresses this mismatch by replacing ratio-based clipping with a divergence-based mask, yielding a trust region defined by the sampled token's absolute probability shift. However, DPPO still relies on a hard mask: once a token crosses the trust-region boundary in a harmful direction, its gradient is discarded rather than corrected. To address this, we propose Divergence Regularized Policy Optimization (DRPO), which replaces the hard mask with a smooth advantage-weighted quadratic regularizer on policy shift. DRPO preserves the same trust-region geometry as DPPO while inducing bounded, continuous gradient weights that attenuate diverging updates and provide corrective signals beyond the boundary. Experiments across model scales, architectures, and precision settings show that DRPO improves the stability and efficiency of LLM RL training.", "github": "https://github.com/Tencent-Hunyuan/UniRL" }, { "date": "2026-06-10", "arxiv_id": "2606.11129", "title": "WorldOlympiad: Can Your World Model Survive a Triathlon?", "authors": [ "Yuke Zhao", "Wangbo Zhao", "Weijie Wang", "Zeyu Zhang", "Dakai An", "Akide Liu", "Yinghao Yu", "Jiasheng Tang", "Fan Wang", "Wei Wang", "Bohan Zhuang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/alibaba-damo-academy/WorldOlympiad", "github_api_added_by": "user", "project_page": "https://alibaba-damo-academy.github.io/WorldOlympiad/", "abstract": "We introduce WorldOlympiad, a benchmark for diagnosing video-based world models across physical faithfulness, geometric consistency, and interaction fidelity. While existing benchmarks often focus on visual quality, semantic alignment, or short-term temporal coherence, they provide limited insight into whether generated videos obey physical rules, preserve coherent 3D structure, and sustain controllable interactions over long horizons. To address this gap, WorldOlympiad decomposes world-model evaluation into three complementary dimensions. The physical track uses object segmentation and MLLM-as-judge to assess whether generated videos follow interpretable rules in mechanics, thermal phenomena, and material properties. The geometry track reconstructs generated videos with Gaussian splatting and evaluates structural consistency, cross-view coherence, and camera-trajectory alignment. The interaction track assesses whether generated rollouts follow complex action prompts and maintain smooth, coherent transitions across consecutive video chunks. WorldOlympiad further covers three major downstream scenarios, including gaming, robotics, and general real-world videos, capturing diverse challenges from interactive control and embodied manipulation to open-domain motion and camera dynamics. Together, these tracks and scenarios form a scalable and interpretable evaluation suite that exposes failure modes beyond generic video quality. Experiments on state-of-the-art models reveal substantial gaps in physical reasoning, 3D consistency, and long-horizon interaction, underscoring the need for more structured evaluation protocols for generative world models.", "github": "https://github.com/alibaba-damo-academy/WorldOlympiad" }, { "date": "2026-06-10", "arxiv_id": "2606.11052", "title": "Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It", "authors": [ "Xinyu Zhou", "Boyu Zhu", "Yi Xu", "Zhiwei Li", "Yingfa Chen", "Huiming Wang", "Zhijiang Guo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Chain-of-thought (CoT) supervised fine-tuning (SFT) is widely adopted to improve reasoning ability, yet we find that it systematically degrades long-context recall in hybrid linear-attention models. Across architectures including HypeNet and Jet-Nemotron, retrieval performance on Needle-In-A-Haystack (NIAH) deteriorates substantially after CoT-SFT, and the degradation becomes more severe under harder retrieval settings and longer context windows. For example, HypeNet-9B on NIAH-S2@256K decreases from 67.2% to 9.4%. We attribute this to CoT-SFT biasing attention gradients toward short-range patterns, disrupting query-key projections (W_Q, W_K) that are responsible for long-range routing. Motivated by this observation, we propose QK-Restore, a training-free method that restores only W_Q and W_K from the pre-SFT checkpoint while preserving all other post-SFT parameters. We further introduce a Procrustes variant to balance routing preservation and reasoning adaptation. Across architectures, QK-Restore consistently restores long-context capability at zero training cost while preserving reasoning performance; for instance, on HypeNet-5B it improves S3@256K from 65.4% to 76.4% while maintaining strong reasoning performance.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.11180", "title": "Lip Forcing: Few-Step Autoregressive Diffusion for Real-time Lip Synchronization", "authors": [ "Paul Hyunbin Cho", "Jinhyuk Jang", "SeokYoung Lee", "Joungbin Lee", "Siyoon Jin", "Heeseong Shin", "Jung Yi", "Yunjin Park", "Chulmin Park", "Seungryong Kim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cvlab-kaist/LipForcing", "github_api_added_by": "user", "project_page": "https://cvlab-kaist.github.io/LipForcing/", "abstract": "Diffusion-based lip synchronization models achieve strong visual quality and audio-visual alignment, but full-sequence bidirectional attention and many denoising steps make them impractical for real-time inference. We present Lip Forcing, to our knowledge the first autoregressive diffusion method for video-to-video (V2V) lip synchronization, which distills a 14B audio-conditioned bidirectional video diffusion teacher into causal students. At inference, the students generate each chunk in only two denoising steps without inference-time CFG, enabling real-time lip synchronization. A lip-sync-specific teacher-trajectory analysis reveals a CFG fidelity-sync tradeoff: no-CFG predictions favor reference fidelity, whereas CFG-guided predictions favor synchronization within a mid-trajectory band. Lip Forcing translates this finding into three analysis-derived components: Sync-Window DMD, a two-step inference schedule, and a SyncNet-based reward. We validate Lip Forcing at two student scales, both distilled from the 14B teacher. The 1.3B student crosses into real-time streaming at 31 FPS, 17.6times faster than its same-scale bidirectional model. The 14B student, the largest diffusion model reported for V2V lip synchronization, runs 39.8times faster than its teacher at comparable reference fidelity. Time-to-first-frame is sub-millisecond at both scales, far below every diffusion baseline.", "github": "https://github.com/cvlab-kaist/LipForcing" }, { "date": "2026-06-10", "arxiv_id": "2606.05922", "title": "Retrospective Harness Optimization: Improving LLM Agents via Self-Preference over Trajectory Rollouts", "authors": [ "Wenbo Pan", "Shujie Liu", "Chin-Yew Lin", "Jingying Zeng", "Xianfeng Tang", "Xiangyang Zhou", "Yan Lu", "Xiaohua Jia" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/wbopan/retro-harness", "github_api_added_by": "user", "project_page": "https://paper-rho.wenbo.io", "abstract": "AI agents rely on a harness of skills, tools, and workflows to solve complex problems. Continually improving this harness is essential for adapting to new tasks. However, existing optimization methods typically require ground-truth validation sets, yet such labeled data is difficult to acquire in practical deployment settings. To address this problem, we introduce Retrospective Harness Optimization (RHO), a self-supervised method that optimizes the agent harness using only past trajectories. Specifically, RHO selects a diverse coreset of challenging tasks from past trajectories and re-solves them in parallel. The agent analyzes these rollouts using self-validation and self-consistency, then generates candidate harness updates and selects the most effective one by its own pairwise self-preference. We evaluate RHO across three diverse domains, spanning software engineering, technical work, and knowledge work. Notably, a single optimization round improves the pass rate on SWE-Bench Pro from 59% to 78% without any external grading. Furthermore, our analysis demonstrates that RHO effectively targets prior failure modes. As a result, the optimized harness alters the agent's behavior patterns and sustains higher accuracy during long-horizon sessions.", "github": "https://github.com/wbopan/retro-harness" }, { "date": "2026-06-10", "arxiv_id": "2606.10917", "title": "Role-Agent: Bootstrapping LLM Agents via Dual-Role Evolution", "authors": [ "Xucong Wang", "Ziyu Ma", "Shidong Yang", "Tongwen Huang", "Pengkun Wang", "Yong Wang", "Xiangxiang Chu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AMAP-ML/roleagent", "github_api_added_by": "user", "project_page": "", "abstract": "Although Large Language Model (LLM) agents have demonstrated strong performance on complex tasks, their learning is often limited by inefficient interaction feedback and static training environments, which hinder broader generalization. To address these limitations, this paper introduces Role-Agent, black{a framework} that harnesses a single LLM to function concurrently as both the agent and the environment, enabling a bootstrapped co-evolution. Role-Agent comprises two synergistic components: World-In-Agent (WIA) and Agent-In-World (AIW). In WIA, the LLM acts as the agent and predicts future states after each action; the alignment between predicted and actual states is then used as a process reward, encouraging environment-aware reasoning. In AIW, the LLM analyzes failure modes from failed trajectories and retrieves tasks with similar failure patterns, thereby reshaping the training data distribution for targeted practice. Experiments on multiple benchmarks show that Role-Agent consistently improves performance, yielding an average gain of over 4\\% over strong baselines.", "github": "https://github.com/AMAP-ML/roleagent" }, { "date": "2026-06-10", "arxiv_id": "2606.07512", "title": "MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism", "authors": [ "Cong Chen", "Guo Gan", "Kaixiang Ji", "ChaoYang Zhang", "Zhen Yang", "Guangming Yao", "Hao Chen", "Jingdong Chen", "Yi Yuan", "Chunhua Shen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://aim-uofa.github.io/MemDreamer/", "abstract": "Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and attention dilution. To overcome this, we introduce MemDreamer to decouple perception and reasoning, shifting long-video understanding into an agentic exploration process. As a plug-and-play framework, it incrementally streams videos to construct a Hierarchical Graph Memory, a top-down three-tier architecture for semantic abstraction, anchored by a foundational graph capturing spatiotemporal and causal relations. During inference, the reasoning model employs agentic tool-augmented retrieval, navigating hierarchies, searching nodes, and traversing logical edges via an Observation-Reason-Action loop. Experiments show MemDreamer achieves SOTA results across four mainstream benchmarks, narrowing the gap with human experts to only 3.7 points. It constrains the reasoning context window to merely 2% of full-context ingestion while delivering a 12.5 point absolute accuracy gain. Furthermore, statistical analysis uncovers a strong positive linear correlation between an VLM's performance on logic reasoning and long-video understanding benchmarks, establishing agentic capability scaling as a new paradigm for multimodal comprehension.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.09131", "title": "Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation", "authors": [ "Siyuan Liu", "Jinyang Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multimodal large language models (MLLMs) commonly inherit the deep, symmetric Transformer backbone designed for unimodal text modeling, and apply the same computation uniformly to image and language tokens. This design overlooks a key modality asymmetry: image and text tokens differ substantially in information density, redundancy, and required reasoning depth. Through a layer-wise analysis of LLaVA-1.5, we observe that vision tokens tend to saturate in the middle layers. Specifically, text-to-image attention decreases from 0.68 at layer 0 to 0.07 by layer 4, and stabilizes near 0.04 after layer 18, whereas text tokens continue to benefit from deep semantic processing. These findings suggest a mismatch between architectural symmetry and depth-asynchronous modality evolution, resulting in redundant visual computation and possible drift in perceptual representations during deep task-specific adaptation. Motivated by this, we propose Dual-Path Vision Token Routing (DPVR), a modality-asymmetric routing framework for efficient MLLMs. Its core instantiation, DPVR-LF (Late-Layer Fusion), routes vision tokens at the saturation point into a one-layer trainable side branch, runs a thirteen-layer text-only forward that skips image positions in the deep stack, and re-fuses the visual and textual streams only at the final layer. With approximately 3% trainable parameters, DPVR-LF preserves competitive multimodal performance on standard benchmarks while reducing visual computation in the deep Transformer stack. The results challenge the conventional assumption that vision tokens must traverse all deep language-model layers, and indicate that a single late fusion layer can be sufficient for maintaining strong perceptual competence in LLaVA-style MLLMs.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.11131", "title": "UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors", "authors": [ "Zhiwen Yang", "Yang Zhou", "Haowei Chen", "Hui Zhang", "Dan Zhao", "Bingzheng Wei", "Yan Xu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Yaziwel/UniPET", "github_api_added_by": "user", "project_page": "", "abstract": "Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, these methods encounter significant performance degradation when the DRF varies beyond the assumed one in practical applications. To address the challenge posed by varied DRFs, several preliminary studies focus on the task of universal PET image denoising, aiming to train a universal model over low-dose data across DRFs. Nonetheless, these vanilla universal models often struggle with misaligned styles present in different DRF data, leading to the style elimination issue with a significant over-smoothing effect. To deal with this issue, we innovatively introduce domain generalization to PET image denoising and propose a universal PET image denoising network (UniPET) to achieve high-quality PET image denoising across diverse DRFs. UniPET comprises two primary innovations: a style alignment network (SAN) and a region-aware learning strategy (RALS). Specifically, SAN utilizes style alignment techniques derived from domain generalization to align and recover styles across different DRFs, ensuring the model's generalizability across various DRFs while effectively preserving styles. Furthermore, to enhance style recovery, RALS distinguishes between flat and stylized regions, exclusively conducting adversarial learning on the latter, thereby more effectively guiding the model's focus towards learning stylized regions. It is demonstrated that our proposed UniPET can adaptively recover different DRF styles and achieve high-quality PET image denoising across DRFs. Comprehensive experiments show that UniPET exhibits comparable performance to individual DRF-specific models at specific DRFs and realizes state-of-the-art performance in universal PET image denoising quantitatively, perceptually, and clinically.", "github": "https://github.com/Yaziwel/UniPET" }, { "date": "2026-06-10", "arxiv_id": "2606.11032", "title": "U-TTT: Towards Generalizable PET Image Denoising via Test-Time Training", "authors": [ "Zhiwen Yang", "Jiayin Li", "Hao Lu", "Hui Zhang", "Zihua Wang", "Bingzheng Wei", "Yan Xu" ], "github_manual": "", "github_regex": "https://github.com/Yaziwel/U-TTT", "github_paper_md": "", "github_api": "https://github.com/Yaziwel/U-TTT", "github_api_added_by": "user", "project_page": "", "abstract": "Existing deep learning models for Positron Emission Tomography (PET) image denoising often suffer from severe performance degradation under distribution shifts, fundamentally restricting their robust clinical deployment. This lack of generalization stems from the conventional paradigm of fixed-parameter models that cannot adapt to variations in test data (e.g., dose levels or scanner types) after training. To overcome this limitation and achieve robust generalization, we introduce U-TTT, a novel U-shaped model that integrates Test-Time Training (TTT) layers to dynamically adjust model parameters during inference through self-supervision, thereby adapting to the specific characteristics of each test instance. Furthermore, to comprehensively capture the complex degradations of 3D PET data, U-TTT features a dual-domain adaptation mechanism comprising a Spatial Test-Time Training (S-TTT) layer and a Frequency Test-Time Training (F-TTT) layer. The S-TTT layer captures and corrects spatial structural degradations, while the F-TTT layer suppresses global noise spectra and restores delicate high-frequency details. Extensive experiments demonstrate that U-TTT achieves state-of-the-art PET denoising performance and exhibits superior generalization under challenging distribution shifts, including both unseen dose levels and unseen scanners. Our code will be available at https://github.com/Yaziwel/U-TTT.", "github": "https://github.com/Yaziwel/U-TTT" }, { "date": "2026-06-10", "arxiv_id": "2606.10651", "title": "Kwai Keye-VL-2.0 Technical Report", "authors": [ "Kwai Keye Team", "Bin Wen", "Changyi Liu", "Chengru Song", "Chongling Rao", "Guowang Zhang", "Han Li", "Haonan Fan", "Hengrui Ju", "Jiankang Chen", "Jiapeng Chen", "Jiawei Yuan", "Kaixuan Yang", "Kaiyu Jiang", "Kun Gai", "Lingzhi Zhou", "Na Nie", "Sen Na", "Tianke Zhang", "Tingting Gao", "Xuanyu Zheng", "Yulong Chen", "Fan Yang", "Haixuan Gao", "Lele Yang", "Mingqiao Liu", "Muxi Diao", "Qi Zhang", "Qile Su", "Wei Chen", "Wentao Hong", "Xingyu Lu", "Yancheng Long", "Yankai Yang", "Yingxin Li", "Yiyang Fan", "Yu Xia", "Yuzhe Chen", "Ziliang Lai", "Chuan Yi", "Haonan Jia", "Tianming Liang", "Weixin Xu", "Xiaoxiao Ma", "Yang Tian", "Yufei Han", "Feng Han", "Hang Li", "Jing Wang", "Jinghui Jia", "Junmin Chen", "Junyu Shi", "Ruilin Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Kwai-Keye/Keye", "github_api_added_by": "user", "project_page": "https://kwai-keye.github.io/", "abstract": "We introduce Kwai Keye-VL-2.0-30B-A3B, an open-source Mixture-of-Experts (MoE) multimodal foundation model designed to advance long-video understanding and agentic intelligence. To address the challenges of ultra-long contexts, information redundancy, and prohibitive computational costs inherent in hour-level videos, Keye-VL-2.0 is the first to adapt DeepSeek Sparse Attention (DSA) to GQA-based multimodal architectures, enabling lossless 256K context processing while capturing critical frames and long-range temporal dependencies. This architecture is underpinned by a highly optimized training and inference infrastructure, including scalable video I/O, heterogeneous ViT-LM parallelism, and custom DSA kernels that significantly maximize throughput and minimize computational overhead. Furthermore, to overcome the algorithmic dilemma of catastrophic forgetting during multi-task alignment, we introduce Cross-Modal Multi-Teacher On-Policy Distillation (MOPD) paired with Context-RL and Video-RL. By distilling dense token-level teacher feedback from on-policy rollouts back into the MoE backbone, which activates only 3B parameters, Keye-VL-2.0 natively empowers advanced agent collaboration across Code, Tool, and Search scenarios with multimodal self-correction. Extensive evaluations across video understanding, temporal grounding, reasoning, STEM, and agent benchmarks demonstrate that Keye-VL-2.0-30B-A3B achieves state-of-the-art performance among models of similar scale, particularly excelling in fine-grained temporal localization on TimeLens and long-video comprehension on Video-MME-v2 and LongVideoBench. We release our model checkpoints to accelerate community progress toward scalable and robust multimodal agentic applications.", "github": "https://github.com/Kwai-Keye/Keye" }, { "date": "2026-06-10", "arxiv_id": "2606.10029", "title": "Interpreting and Steering a Text-to-Speech Language Model with Sparse Autoencoders", "authors": [ "Nikita Koriagin", "Georgii Aparin", "Nikita Balagansky", "Daniil Gavrilov" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Language models increasingly serve as the backbone of text-to-speech (TTS) systems, yet we understand little about the representations they build when text and generated speech tokens share a single residual stream. We train BatchTopK sparse autoencoders on the LM backbone of CosyVoice3 and introduce a modality-aware auto-interp pipeline that labels each feature from where it fires-text-prefix context, 1-second speech clips, or both. The recovered features are interpretable, spanning phonemes, laughter, accent prompts and speaker gender. Steering through the SAE latent space shows these features are causal rather than merely descriptive: targeted interventions raise laughter probability from 0.02 to 0.79, flip perceived speaker gender, and control speech rate while preserving spoken content. SAE features thus serve both as interpretability objects and as control directions for TTS synthesis.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.09707", "title": "BrainSurgery: Reproducible and Reliable Declarative Weight Manipulations for Model Editing and Upcycling", "authors": [ "Gianluca Barmina", "Annemette Broch Pirchert", "Andrea Blasi N\u00fa\u00f1ez", "Lukas Galke Poech", "Peter Schneider-Kamp" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/schneiderkamplab/brainsurgery", "github_api_added_by": "user", "project_page": "", "abstract": "As deep learning models scale, managing, inspecting, and modifying large checkpoints has become increasingly challenging. Researchers often need to alter model weights for layer restructuring, precision casting, low-rank factorization, and architectural debugging, yet these workflows often rely on fragile ad-hoc Python scripts. Here, we introduce BrainSurgery, a tool for robust and reproducible \"tensor surgery\" on neural network checkpoints, and provide a system demonstration covering four examples and three case studies from model upcycling to LoRA extraction. By abstracting storage formats and memory management, BrainSurgery executes complex transformations through declarative YAML plans. It supports structural modifications, mathematical transformations, and tensor reshaping through expressive regex and structural targeting, while built-in assertions validate tensor shapes, data types, and values to prevent silent errors. We envision that BrainSurgery will provide a strong foundation for future research through its reproducible and validated operations.", "github": "https://github.com/schneiderkamplab/brainsurgery" }, { "date": "2026-06-10", "arxiv_id": "2606.09697", "title": "PsychoSafe: Eliciting Psychologically-Informed Refusals in Large Language Models", "authors": [ "Gianluca Barmina", "Federico Torrielli", "Sven Harms", "Jacob Nielsen", "Felix M\u00e4chtle", "Stine Lyngs\u00f8 Beltoft", "Peter Schneider-Kamp", "Thomas Eisenbarth", "Lukas Galke Poech", "Anne Lauscher" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/aisilab/psychological-safety", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models (LLMs) routinely face requests that should be refused, creating a trade-off between helpfulness and harm prevention. However, refusals themselves can be helpful. In high-risk interactions involving crisis, coercion, or escalating intent, blunt non-compliance may prevent direct harm while still failing to support the needs of the person behind the request. We present PsychoSafe, a psychologically-informed refusal framework that reframes refusal as structured supportive communication grounded in evidence-based intervention strategies. To develop PsychoSafe, we construct a corpus of 8019 prompt-response pairs spanning five psychologically salient risk domains and apply prompting and parameter-efficient fine-tuning to Qwen 3.5 27B. On a balanced validation set of 500 prompts, evaluated with an LLM judge and validated through human ratings, PsychoSafe prompting improves overall refusal quality by 28.1% over a generic baseline, with particularly strong gains in external resource referral (+46.8%) and psychological grounding (+34.8%), while preserving downstream performance on non-refusal tasks. Fine-tuning achieves near-perfect refusal and resource-referral rates but reduces response relevance. Additional evaluations on SORRY-Bench and XSTest show strong in-domain robustness but limited out-of-domain generalization, suggesting that future work should diversify fine-tuning data to help models apply interventions selectively rather than schematically.", "github": "https://github.com/aisilab/psychological-safety" }, { "date": "2026-06-10", "arxiv_id": "2606.06098", "title": "IR3DE: A Linear Router for Large Language Models", "authors": [ "Eros Fan\u00ec", "O\u011fuzhan Ersoy" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gensyn-ai/IR3DE", "github_api_added_by": "user", "project_page": "https://blog.gensyn.ai/look-beyond-one-size-fits-all-llms-with-ir3de/", "abstract": "Foundational Large Language Models (LLMs) demonstrate proficiency on a wide range of general tasks, and achieve remarkable results on various specialized tasks via domain-expert LLMs. With the ever-growing list of available LLMs, inference routers are being proposed to select the most appropriate LLM for each prompt. However, existing routing methods either optimize cost across weak-to-strong generalist LLMs or require substantial training to support domain-expertise routing. In this paper, we propose IR3DE, a Ridge Regression-based Router for Domain Experts that provides cheap and fast routing decisions for each prompt. We evaluate IR3DE in two Causal Language Modeling (CLM) settings where the tasks are next-token prediction for all domains, and one reasoning setting where each domain has its own distinct reasoning task. Despite being a linear router, IR3DE achieves performance comparable to the other baselines in both CLM settings, and surpassing them in the reasoning setting, with a normalized performance of 98.4%. Moreover, IR3DE enables the addition or removal of new domain experts without requiring the router to be retrained from scratch, allowing a dynamic set of LLMs to be served with minimal disruption to the router itself. Our code is available at: github.com/gensyn-ai/IR3DE.", "github": "https://github.com/gensyn-ai/IR3DE" }, { "date": "2026-06-10", "arxiv_id": "2606.10671", "title": "FadeMem: Distance-Aware Memory Consolidation for Autoregressive Video Diffusion", "authors": [ "Yu Lu", "Junjie Yang", "Piotr Koniusz", "YuXin Song", "Yi Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Autoregressive video generators synthesize long videos by generating successive temporal segments, but their historical KV cache grows with video length. Existing bounded-cache methods reduce this cost with local windows, sink tokens, or compressed memory states, yet they usually assign fixed roles to different parts of the history. We propose FadeMem, a distance-aware KV memory consolidation mechanism that organizes historical KV blocks into a temporal hierarchy under a fixed cache budget. This design is motivated by frequency-dependent temporal decay: fine details decorrelate quickly, while coarse scene structure and identity remain useful over longer horizons. During generation, new history is inserted as fine-grained entries, while older adjacent entries are progressively merged under a power-law temporal allocation schedule, yielding a dense-near, sparse-far memory within one cache. Without architectural changes, FadeMem preserves recent context for short-term dynamics and compact long-range anchors for identity and scene coherence. Experiments show improved subject consistency, background stability, and temporal coherence over existing bounded-cache strategies.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.11187", "title": "Next Forcing: Causal World Modeling with Multi-Chunk Prediction", "authors": [ "Gangwei Xu", "Qihang Zhang", "Jiaming Zhou", "Xing Zhu", "Yujun Shen", "Xin Yang", "Yinghao Xu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gangweix/next-forcing", "github_api_added_by": "user", "project_page": "https://gangweix.github.io/next-forcing/", "abstract": "Autoregressive video generation has emerged as a powerful paradigm for World Action Models (WAMs). However, existing approaches suffer from slow training convergence and limited converged accuracy, particularly at high frame rates, as the training supervision is confined to the current chunk without explicit signals about future dynamics; they also suffer from slow inference due to iterative video denoising. In this paper, we present Next Forcing, a multi-chunk prediction (MCP) framework for causal world modeling that enables faster training, higher accuracy, and accelerated inference. Inspired by multi-token prediction in large language models, Next Forcing introduces an MCP training objective that augments the main model with lightweight auxiliary MCP modules to simultaneously denoise video chunks at multiple future temporal horizons (next^1, next^2, next^3 chunks). These MCP modules form a causal chain across prediction depths, where intermediate features fused from multiple layers of the main model are leveraged to predict future dynamics, allowing near-future predictions to inform farther-future ones and providing dense multi-scale temporal supervision back to the main model. During training, the MCP modules significantly accelerate convergence and improve converged accuracy, especially at high frame rates: at 50 fps, Next Forcing achieves a 93.1% relative improvement over LingBot-VA at 5k training steps and 2.3x faster convergence, and establishes new state-of-the-art results on the RoboTwin benchmark (94.1/93.5% on Clean/Random). At inference, the MCP modules can be retained to predict the next video chunk in parallel with the current one, achieving 2x inference acceleration. Next Forcing also demonstrates significant improvements on PhyWorld, a benchmark evaluating adherence to physical laws in video generation, and over 50% FVD reduction on general video pretraining.", "github": "https://github.com/gangweix/next-forcing" }, { "date": "2026-06-10", "arxiv_id": "2606.11173", "title": "The Role of Feedback Alignment in Self-Distillation", "authors": [ "Semih Kara", "O\u011fuzhan Ersoy" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Conditioning a language model on additional context, such as feedback on a previous attempt, typically improves its response. Self-distillation trains the model to retain this improvement when the context is not present. The method works by matching the model's output distribution under two settings: a student that sees only the question, and a self-teacher that also sees the context. What the model learns therefore depends on what context the self-teacher receives, yet the design of this context remains largely unexplored. We study context design for self-distillation by training a solver on feedback from a frozen critic. We compare three conditions: (i) a binary reward (GRPO), (ii) the reference solution, and (iii) a step-by-step critique aligned to the solver's reasoning trace. Step-aligned critique yields the largest gains, outperforming GRPO by 16.11 points and reference-solution-conditioned self-distillation by 5.27 points (Avg@12). Per-token advantage analysis reveals why: step-aligned feedback targets only the tokens where reasoning fails, leaving correct behavior intact. Conditioning on the reference solution, by contrast, pressures the model to change its behavior at every token (even correct steps) because an alternative derivation inevitably differs in phrasing and approach. This suggests that structural alignment between feedback and the solver's reasoning is a key driver of self-distillation effectiveness.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.07379", "title": "Do Coding Agents Deceive Us? Detecting and Preventing Cheating via Capped Evaluation with Randomized Tests", "authors": [ "Thanawat Lodkaew", "Johannes Ackermann", "Soichiro Nishimori", "Nontawat Charoenphakdee", "Masashi Sugiyama", "Takashi Ishida" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "A growing failure mode in agent evaluation and training is that models can achieve high evaluation scores by exploiting shortcuts instead of solving the intended task, producing deceptive performance. This makes evaluation scores unreliable as measures of true task-solving ability. We propose CapCode, a framework for constructing coding datasets with randomized tests whose best achievable non-cheating performance is deliberately capped below one. This capped-performance design gives evaluation scores a clearer interpretation: scores substantially above the cap are implausible and therefore provide evidence of cheating. To prevent cheating, we propose CapReward, a reward design based on the CapCode principle to discourage optimization beyond the cap. Experiments across multiple datasets show that CapCode detects cheating while preserving performance ranking of models, and CapReward reduces cheating behavior, yielding models that better follow the intended task specification.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.02540", "title": "SkillHarm: Lifecycle-Aware Skill-Based Attacks via Automated Construction", "authors": [ "Yuting Ning", "Zhehao Zhang", "Yash Kumar Lal", "Boyu Gou", "Junyi Li", "Weitong Ruan", "Chentao Ye", "Rahul Gupta", "Diyi Yang", "Yu Su", "Huan Sun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/OSU-NLP-Group/SkillHarm", "github_api_added_by": "user", "project_page": "https://osu-nlp-group.github.io/SkillHarm/", "abstract": "Agent skills occupy a privileged position in the agent workflow, as agents are expected to implicitly follow and execute them, rendering third-party skills a vulnerable attack surface. Existing studies have revealed unsafe agent behaviors induced by skill-based attacks, but they primarily evaluate poisoned skills within a single task execution and enumerate harms through ad-hoc risk lists. To bridge these gaps, we introduce SkillHarm, a benchmark of skill-based attacks across the skill-use lifecycle, paired with a systematic taxonomy of skill-relevant risks. SkillHarm evaluates two attack scenarios: Fixed-Payload Poisoning (FPP), where a fixed poisoned skill package directly compromises any task session that invokes it, and Self-Mutating Poisoning (SMP), where an initially benign execution silently mutates persistent skill content, deferring harm until a subsequent reuse. It further defines 12 risk types based on the agent workflow component targeted by the harm: data pipelines, system environments, and agent autonomy. To instantiate these attacks at scale, we build AutoSkillHarm, an automated construction pipeline with coding agents driven by natural-language harnesses. The resulting benchmark contains 879 attack samples across 71 skills. Experiments show that current agents remain vulnerable with attack success rates up to 86.3% in FPP and 69.3% in SMP. Our analysis further reveals a latent risk: many apparent attack failures stem from the agent failing to engage with the poisoned file rather than genuine resistance, and current defenses still fail to reliably mitigate the threat.", "github": "https://github.com/OSU-NLP-Group/SkillHarm" }, { "date": "2026-06-10", "arxiv_id": "2606.10662", "title": "Decentralized Multi-Agent Systems with Shared Context", "authors": [ "Yuzhen Mao", "Azalia Mirhoseini" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/yuzhenmao/DeLM", "github_api_added_by": "user", "project_page": "https://yuzhenmao.github.io/DeLM/", "abstract": "Multi-agent systems (MAS) can scale large language model reasoning at test time by decomposing complex problems into parallel subtasks. However, most existing MAS rely on centralized orchestration, where a main agent assigns work, collects outputs, and merges results. As the number of subtasks grows, this controller becomes a communication and integration bottleneck. We propose Decentralized Language Models (DeLM), a MAS framework that decentralizes coordination through parallel agents, a shared verified context, and a task queue. Agents asynchronously claim subtasks, read accumulated progress, perform local reasoning, and write back compact verified updates. The shared context acts as a common communication substrate, enabling agents to build on one another's verified progress without routing every update through a central controller. Empirically, DeLM improves both software-engineering test-time scaling and long-context reasoning. On SWE-bench Verified, DeLM achieves the best performance across Avg.@1, Pass@2, and Pass@4, with gains of up to 10.5 percentage points over the strongest baseline, while reducing cost per task by roughly 50%. On LongBench-v2 Multi-Doc QA, DeLM achieves the highest average accuracy across four frontier model families, improving over the strongest baseline by up to 5.7 percentage points. The code is available on our project website at https://yuzhenmao.github.io/DeLM/.", "github": "https://github.com/yuzhenmao/DeLM" }, { "date": "2026-06-10", "arxiv_id": "2606.06458", "title": "In-Context Multiple Instance Learning", "authors": [ "Alexander M\u00f6llers", "Marvin Sextro", "Julius Hense", "Gabriel Dernbach", "Klaus-Robert M\u00fcller" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/injurise/ICMIL", "github_api_added_by": "user", "project_page": "", "abstract": "Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied in fields ranging from computational pathology to satellite imagery. Nevertheless, existing algorithms struggle in the low-label regime that characterizes many real-world applications. Flexible models overfit and rigid ones fail to adapt to the task at hand. We show that pretraining an in-context learner with a Perceiver-style architecture on synthetic data yields a model that can solve new tasks from a handful of labeled bags. At inference time, classification happens in a single forward pass and requires no gradient updates. We propose and investigate different synthetic data generators for bag-structured data and find that they capture complementary inductive biases. A model pretrained on a mixture of these generators inherits their per-task strengths and achieves the best average performance across twelve MIL benchmarks, outperforming supervised baselines that require task-specific training.", "github": "https://github.com/injurise/ICMIL" }, { "date": "2026-06-10", "arxiv_id": "2606.08857", "title": "PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers on Overleaf", "authors": [ "Jiarui Liu", "Terry Jingchen Zhang", "Ryan Faulkner", "X. Angelo Huang", "Vil\u00e9m Zouhar", "Dominik Glandorf", "Isabel Dahlgren", "Van Q. Truong", "Rishit Dagli", "Yuen Chen", "Felix Leeb", "Punya Syon Pandey", "Yves Bicker", "Suvajit Majumder", "Wenyuan Jiang", "Zeju Qiu", "Sankalan Pal Chowdhury", "Bernhard Sch\u00f6lkopf", "Mona Diab", "Zhijing Jin" ], "github_manual": "", "github_regex": "https://github.com/jiarui-liu/overleaf", "github_paper_md": "", "github_api": "https://github.com/jiarui-liu/overleaf", "github_api_added_by": "user", "project_page": "https://overleafmentor.ai.toronto.edu/", "abstract": "Expert writing feedback from experienced researchers is critical for early-career scholars to improve their manuscripts, yet high-quality feedback often remains scarce because reviewing research papers is labor-intensive. Emerging AI-powered writing assistants largely focus on grammar fixes or simulating peer review with final scores, yet they fall short of providing concrete, actionable suggestions that help students improve their papers during drafting. We present PaperMentor, a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments while leaving the actual writing entirely to human authors. PaperMentor integrates an expert skill library carefully curated from established researchers' writing advice with 12 specialized agents covering different aspects of paper writing, such as formatting compliance, phrasing accuracy, and terminology consistency. In a user study (n=14), 90.6% of the generated comments were rated actionable and 67.5% were rated valid, significantly outperforming a GPT-5.2 baseline uswithout the skill library. We release PaperMentor as open source for public use. Our code is publicly available under the AGPL-3.0 license at https://github.com/jiarui-liu/overleaf", "github": "https://github.com/jiarui-liu/overleaf" }, { "date": "2026-06-10", "arxiv_id": "2606.08044", "title": "When Behavioral Safety Evaluation Fails: A Representation-Level Perspective", "authors": [ "Enyi Jiang", "Anders Gj\u00f8lbye", "Yibo Jacky Zhang", "Sanmi Koyejo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large Language Model (LLM) safety has often been evaluated at the behavior level, which provides limited evidence of internal robustness, as these evaluations target outputs rather than representation-level vulnerability under intervention. We formalize this discrepancy as the audit gap: the difference between behavioral safety and robustness under intervention. To study this gap, we construct dissociated models that preserve safe outward behavior while remaining vulnerable in the latent space. We introduce an intervention-based evaluation framework to test model robustness through soft interventions in parameter and latent spaces, including harmful fine-tuning and layer-wise latent perturbations. To formalize the evaluation, we propose the Latent Vulnerability Score (LVS) to measure how easily harmful behavior can be elicited by bounded latent perturbations. Using this evaluation framework, we show that behavioral safety metrics are insufficient measures of representation-level robustness across multiple safely and unsafely aligned state-of-the-art models. Notably, dissociated models show substantially elevated LVSs despite comparable refusal behavior under harmful intervention, with intermediate representations being the most sensitive to intervention. Our results suggest that behavioral safety evaluation alone provides an incomplete picture of model robustness, motivating representation-aware audits of latent vulnerability and observable behavior.", "github": "" }, { "date": "2026-06-10", "arxiv_id": "2606.10968", "title": "Beyond Uniform Token-Level Trust Region in LLM Reinforcement Learning", "authors": [ "Renjie Mao", "Xiangxin Zhou", "Lvfang Tao", "Yixin Ding", "Yu Shi", "Yongguang Lin", "Yuheng Wu", "Honglin Zhu", "Qian Qiu", "Wenxi Zhu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://hunyuan-cppo.github.io/", "abstract": "Reinforcement learning with verifiable rewards (RLVR) has become standard for improving LLM reasoning. However, existing PPO-style trust-region mechanisms remain position-agnostic by enforcing uniform thresholds across all tokens independently. This pointwise treatment conflicts with autoregressive generation in two critical ways. First, uniform thresholds ignore autoregressive asymmetry. Early-stage deviations produce compounding sequence-level drift, causing static thresholds to under-regulate early divergence and excessively constrain late-stage exploration. Second, evaluating token-level divergence in isolation overlooks cumulative prefix drift, granting the same divergence allowance regardless of how far the conditioning history has already deviated from the rollout policy. To address this limitation, we propose CPPO (Cumulative Prefix-divergence Policy Optimization), a token-level masking rule that aligns updates with a finite-horizon policy-improvement bound via two coupled mechanisms. First, a position-weighted threshold imposes stricter limits at early positions whose effects persist longer, relaxing constraints for late-stage tokens. Second, a cumulative prefix budget tracks historical deviations, dynamically restricting further token-level deviation to prevent compounding errors along the prefix. Empirically, CPPO enhances training stability and significantly improves reasoning accuracy across various model scales.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.11722", "title": "ICA Lens: Interpreting Language Models Without Training Another Dictionary", "authors": [ "Sida Liu", "Feijiang Han" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/liusida/ica-lens-paper", "github_api_added_by": "user", "project_page": "https://liusida.github.io/ica-lens-paper/", "abstract": "Finding interpretable directions in language-model representations is critical for understanding and controlling model behavior. Sparse autoencoders (SAEs) have become the standard tool for this purpose, but using them as the default first lens often requires training, storing, and evaluating large overcomplete dictionaries. This bottleneck limits rapid exploration and raises a fundamental question: how much interpretable structure is already visible from activation geometry before training another neural dictionary? Our intuition is simple: many interpretable directions are selective on tokens, and these directions should look less Gaussian than random directions. We therefore revisit independent component analysis (ICA), a classical method for finding non-Gaussian directions, as a compact lens for language-model interpretability. We find that ICA has been underestimated for LLM interpretability, because prior uses often relied on off-the-shelf ICA implementations that are brittle on LLM activations and lacked systematic tools for inspecting and evaluating the recovered directions. To bridge these gaps, we introduce ICALens, the first practical workflow for stable, efficient, and auditable ICA analysis of LLM representations. It combines an optimized GPU-parallel FastICA pipeline with LLM-specific stability recipes and better fitting diagnostics, enabling efficient and reliable layer-wise analysis. Across GPT-2 Small, Gemma 2 2B, and Qwen 3.5 2B Base, ICALens efficiently recovers compact, human-interpretable directions without per-layer gradient-based dictionary training. On SAEBench, ICA is competitive with public SAEs in sparse probing and outperforms them in targeted probe perturbation under small-to-medium budgets. These results suggest that ICA should not be viewed as a weak baseline, but as an efficient and complementary first lens for exploring language-model representations.", "github": "https://github.com/liusida/ica-lens-paper" }, { "date": "2026-06-10", "arxiv_id": "2606.12071", "title": "On the Limits of LLM-as-Judge for Scientific Novelty Assessment", "authors": [ "Soumitra Sinhahajari", "Navonil Majumder", "Soujanya Poria" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "LLMs are increasingly used to generate and judge scientific ideas. This makes novelty evaluation a central problem. Full idea evaluation is difficult because it often requires judging a method, its feasibility, and its empirical promise. We therefore study a cleaner upstream object: the research question (RQ). RQ generation is a prerequisite for scientific ideation, and RQs can be compared against questions pursued in real papers. We introduce RQ-Bench, a benchmark built from recent arXiv papers. For each paper, we reconstruct author-anchored RQs from its cited background, gaps, and contributions. These RQs are not the only valid questions for the same background. They are author-anchored reference points for testing novelty judgments. We evaluate model-generated RQs with standalone LLM judging, comparative LLM judging, and human expert evaluation. LLM judges consistently rate model-generated RQs as highly novel, producing a novelty mirage; in comparative evaluations, this preference becomes even stronger. Domain experts, however, reach the opposite conclusion and prefer the author-anchored reference questions. We further find that many generated RQs are narrow or source-bound, a dimension that LLM judges often miss unless explicitly tested. Overall, the contradictory novelty evaluations between LLM judges and human experts raise a serious concern about the reliability of using LLMs to assess the scientific novelty of research questions.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.12403", "title": "World Pilot: Steering Vision-Language-Action Models with World-Action Priors", "authors": [ "Zefu Lin", "Rongxu Cui", "Junjia Xu", "Xiaojuan Jin", "Wenling Li", "Lue Fan", "Zhaoxiang Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZefuLin/WorldPilot", "github_api_added_by": "user", "project_page": "https://world-pilot.github.io/", "abstract": "Vision-Language-Action (VLA) models inherit semantic grounding from large-scale pretraining and perform competently across in-distribution manipulation tasks. This grounding, however, is built on static image-text pairs, whereas manipulation is a continuous, contact-rich process whose dynamics this pretraining cannot capture. We present World Pilot, a VLA framework that augments the policy with priors from a World-Action Model (WAM), routed into the decision chain through two complementary pathways. Latent Steering conditions the perception layer on a scene-evolution latent, and Action Steering supplies an anticipated trajectory as a motion prior to the action generator. Together the two priors equip the VLA with an anticipated view of the scene and a trajectory-level motion hint alongside its semantic conditioning, and the scene-evolution prior remains effective even when supplied by a video-pretrained world model that has not been action-post-trained. World Pilot attains a state-of-the-art Total success rate of 84.7% on the LIBERO-Plus zero-shot OOD benchmark and the highest success rate on every real-robot setting across four manipulation tasks, with the largest margins under shifts in viewpoint, geometry, deformable state, and pose. Project Website: https://world-pilot.github.io/", "github": "https://github.com/ZefuLin/WorldPilot" }, { "date": "2026-06-11", "arxiv_id": "2606.10728", "title": "DeNovoSWE: Scaling Long-Horizon Environments for Generating Entire Repositories from Scratch", "authors": [ "Jiale Zhao", "Guoxin Chen", "Fanzhe Meng", "Wayne Xin Zhao", "Ruihua Song", "Ji-Rong Wen", "Kai Jia" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AweAI-Team/DeNovoSWE", "github_api_added_by": "user", "project_page": "", "abstract": "As the capabilities of LLM-based code agents continue to advance, their expected role is expanding beyond localized bug fixing in existing codebases toward architecting and implementing complete software repositories from high-level specifications. However, training agents for such long-horizon software engineering tasks remains difficult due to the scarcity of large-scale, verifiable whole-repository generation data. In this paper, we introduce DeNovoSWE, a large-scale dataset for whole-repository generation. DeNovoSWE comprises 4,818 high-quality instances, where each instance requires generating a complete repository from documentation. Our dataset is automatically constructed through a carefully designed sandboxed agentic workflow, enabling scalable curation without human annotation. DeNovoSWE is constructed with \"divide and conquer\" and critic-repair philosophy. To balance data quality and diversity, we further introduce a difficulty-aware trajectory filtering strategy. Fine-tuning Qwen3-30B-A3B on DeNovoSWE substantially improves long-horizon SWE performance, raising its score on the challenging BeyondSWE-Doc2Repo benchmark from 5.8% to 47.2%.", "github": "https://github.com/AweAI-Team/DeNovoSWE" }, { "date": "2026-06-11", "arxiv_id": "2606.12191", "title": "Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application", "authors": [ "Jiachun Li", "Zhuoran Jin", "Tianyi Men", "Yupu Hao", "Kejian Zhu", "Lingshuai Wang", "Dongqi Huang", "Longxiang Wang", "Shengjia Hua", "Lu Wang", "Jinshan Gao", "Hongbang Yuan", "Ruilin Xu", "Kang Liu", "Jun Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving the continual evolution of model capabilities. Despite this importance, existing work lacks a systematic categorization and deep analysis. This paper systematically studies current researches on agentic environments from the perspective of the environment engineering lifecycle, covering their modeling, synthesis, evaluation and application. Specifically, the paper first introduces representative environments from the perspectives of eight attributes and eight domains, providing detailed analyses of their development paths and highlighting their core capabilities. Second, for automated environment synthesis, two paradigms are introduced, such as symbolic synthesis and neural synthesis. This paper also shows different environment evaluation methods in each paradigm. Thirdly, the corresponding environment applications from the perspective of agent-environment co-evolution are discussed. In specific, the paper characterizes the primary pathways for agent evolution in dynamic environments from four complementary perspectives: memory-centric experience evolution, orchestration-centric workflow evolution, trajectory-centric offline evolution, and exploration-centric online evolution. And three paradigms of environment evolution are identified, namely neural-driven, difficulty-driven, and scaling-driven approaches. At last, several promising future directions are discussed, including Environment-as-a-Service, Multi-agent Environments, and Neural-Symbolic Environments.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.12373", "title": "Verifiable Environments Are LEGO Bricks: Recursive Composition for Reasoning Generalization", "authors": [ "Hao Xiang", "Qiaoyu Tang", "Le Yu", "Yaojie Lu", "Xianpei Han", "Ben He", "Le Sun", "Bowen Yu", "Peng Wang", "Hongyu Lin", "Dayiheng Liu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement Learning (RL) with verifiable environments has emerged as a powerful approach for enhancing the reasoning capabilities of Large Language Models (LLMs). While prior research demonstrates that scaling environment quantity improves RL performance, existing manual or individual construction methods suffer from linear scaling limits, thereby hindering scalable reasoning generalization. This paper introduces RACES (Recursive Automated Composition for Environment Scaling), a framework that conceptualizes verifiable environments as composable building blocks that can be recursively assembled. The key insight is that when the codomain (output type) of one environment matches the domain (input type) of another, they can be automatically fused into a new verifiable environment, enabling recursive composition. RACES is implemented with 300 individual environments and defines a set of composition operators (SEQUENTIAL, PARALLEL, SORT, and SELECT) that induce diverse reasoning patterns. Extensive experiments show that RL training on these composite environments consistently enhances reasoning generalization. Specifically, RACES improves DeepSeek-R1-Distill-Qwen-14B by an average of 3.1 points (from 48.2 to 51.3) and boosts Qwen3-14B performance from 58.8 to 61.1 on six benchmarks, which are unseen during the construction of training environments. Moreover, RACES achieves performance comparable to training on 300 individual environments using only 50 base environments, demonstrating significant efficiency in environment utilization.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.12195", "title": "InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning", "authors": [ "Ziang Yan", "Sheng Xia", "Jiashuo Yu", "Yue Wu", "Tianxiang Jiang", "Songze Li", "Kanghui Tian", "Yicheng Xu", "Yinan He", "Kai Chen", "Limin Wang", "Yu Qiao", "Yi Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Recent progress in foundation models has shifted toward agentic behavior involving multi-step reasoning and tool use. However, open-source efforts largely focus on text-dominant settings, leaving long-horizon multimodal tasks underexplored. This gap is evident in video tasks requiring sustained temporal understanding and iterative interaction. We present InternVideo3, a framework enhancing these capabilities via Multimodal Contextual Reasoning (MCR). MCR treats understanding as a closed-loop process over a shared, evolving context containing observations, instructions, reasoning, tool actions, and memory. This frames long-video understanding as evidence accumulation and verification. To ensure efficiency, we introduce Multimodal Multi-head Latent Attention (M^2LA), a token-preserving reparameterization compressing KV-cache states while retaining the full token stream. Our staged training includes continued pretraining, short-to-long supervised fine-tuning, rule-based reinforcement learning, and on-policy distillation. Experiments show InternVideo3 achieves strong performance on benchmarks like Video-MME, MLVU, and EgoSchema. We further instantiate the model as a video agent with retrieval tools, demonstrating robust evidence-grounded behavior. Our results suggest that efficient context handling and closed-loop reasoning are vital for adapting open multimodal models toward long-horizon visually grounded agency.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.12370", "title": "Breaking Entropy Bounds: Accelerating RL Training via MTP with Rejection Sampling", "authors": [ "Yucheng Li", "Huiqiang Jiang", "Yang Xu", "Jianxin Yang", "Yi Zhang", "Yizhong Cao", "Yuhao Shen", "Fan Zhou", "Rui Men", "Jianwei Zhang", "An Yang", "Bowen Yu", "Bo Zheng", "Fei Huang", "Junyang Lin", "Dayiheng Liu", "Jingren Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement learning (RL) has become a key component in modern large language models, yet the rollout stage remains the key bottleneck in RL training pipelines. Although Multi-Token Prediction (MTP) offers a natural solution to accelerate rollouts through speculative decoding, many studies have observed that MTP acceptance rates degrade significantly during RL training, leading to limited speedup performance. To address this bottleneck, we present Bebop, a systematic study of MTP in LLM post-training, and offer practical recipes to integrate MTP into large-scale RL pipelines. First, we reveal that the MTP acceptance rate is fundamentally bounded by the fluctuation of model entropy, which demonstrates a clear negative linear relationship with the rise of entropy in the RL stage. Second, we show that probabilistic rejection sampling largely alleviates the disturbance introduced by entropy in RL compared to greedy draft sampling. We further identify that the conventional MTP training objectives (cross-entropy or KL) are suboptimal in such settings, and therefore we propose a novel end-to-end TV loss that directly optimizes multi-step rejection sampling acceptance rate, yielding ~10% acceptance rate improvements, achieving up to 95% acceptance rates and up to 25% extra inference throughput gains across mathematical reasoning, code generation, and agentic tasks. Third, we test various online MTP training strategies during RL and show that pre-RL MTP training with e2e TV loss and rejection sampling achieves a consistent acceptance rate and speedup throughout the entire RL, eliminating the need for costly online MTP updating. We provide extensive experiments and analysis that validate our findings. Experimental results show our method achieves up to 1.8x end-to-end acceleration in async RL training of Qwen3.5, Qwen3.6, and Qwen3.7 models.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.11786", "title": "Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay", "authors": [ "Joanito Agili Lopo", "Yunita Sari", "Guntur Budi Herwanto" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/joanitolopo/lius-cendol-large-inst-mt", "abstract": "Large Language Models (LLMs) offer new potential for translation tasks but often experience performance degradation when handling low-resource languages. To address this limitation, we propose an approach for fine-tuning LLMs on a low-resource language, Kupang Malay. Our approach involves designing a set of instructions by leveraging explicit lexical and semantic features from a bilingual dictionary, and introducing Continual Instruction Tuning (CIT), a training paradigm that enables iterative instruction-based training. Experimental results demonstrate that our model, named Lius, yields notable improvements over standard instruction-tuned models by outperforming 4-6 points, and surpassing both Neural Machine Translation (NMT) and Multilingual LLM models by 10-13 points on several evaluation metrics. These findings highlight the potential of our approach to mitigate the reliance on large-scale parallel data in low-resource language translation.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.09076", "title": "Beyond Scalar Rewards by Internalizing Reasoning into Score Distributions", "authors": [ "Xin Jin", "Huanqia Cai", "Zhen Li", "Zechao Zhan", "Dengyang Jiang", "Aiming Hao", "Yuming Jiang", "Chunle Guo", "Peng Gao", "Ming-Ming Cheng", "Steven C. H. Hoi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://github.com/Tongyi-MAI/Z-Image", "abstract": "Reward models are central to text-to-image post-training, but visual preference is subjective and better represented as a distribution over rubric scores than as a deterministic scalar. Existing scalar, score-token, and pairwise reward models over-compress uncertainty and fine-grained score differences, while reasoning-based generative rewards provide stronger judgments but are costly to deploy and difficult to use as direct optimization signals. We propose Z-Reward, a teacher-student reward modeling framework that decouples reasoning-heavy judgment from efficient reward deployment. The teacher is a large VLM that uses reasoning to infer rubric-aligned score distributions, and is trained with Group-wise Direct Score Optimization (GDSO), which combines policy-gradient rewards from distribution expectations with direct pointwise and pairwise supervision on score distributions and score gaps. The student is trained with Reasoning-Internalized Score Distillation (RISD), which transfers the teacher's reasoning-conditioned score distribution into a compact VLM without requiring explicit reasoning chains at inference time. On our internally annotated evaluation set, the 27B GDSO teacher reaches 89.6% human preference accuracy, outperforming SFT, RewardDance, and GRPO, while the 9B RISD student reaches 88.6%, outperforming the OPD baseline and closely matching the larger teacher. We further show that Z-Reward can serve as a differentiable reward signal for text-to-image optimization, yielding a 41.3% net human-preference improvement over the SFT baseline.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.12072", "title": "World Model Self-Distillation: Training World Models to Solve General Tasks", "authors": [ "Sebastian Stapf", "Pablo Acuaviva Huertos", "Aram Davtyan", "Paolo Favaro" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sebastian-stapf/world-model-self-distillation", "github_api_added_by": "user", "project_page": "https://sebastian-stapf.github.io/world-model-self-distillation/", "abstract": "Pretrained video generators are promising visual world models that exhibit emergent task-solving abilities; however, their reliance on detailed textual descriptions limits their direct use for planning and decision-making. Existing approaches either outsource this reasoning to language or vision-language models, or rely on supervised fine-tuning with paired task-execution videos, which are costly to collect and difficult to scale. We propose a scalable framework that elicits task-solving ability in such models by combining self-distillation with reinforcement learning. Given an unlabeled scene image, a vision-language model generates a candidate task and a detailed step-by-step solution. The solution conditions a pretrained video diffusion model, the Demonstrator; we distill its behavior into an Executor conditioned only on the image and a short task prompt. This transfers execution knowledge from caption-guided generation to instruction-conditioned task solving without curated task-video supervision. We further improve the Executor with reinforcement learning from VLM feedback, exploiting the asymmetry between judging whether a sampled video satisfies a task and generating the solution. Experiments on our proposed WorldTasks-Benchmark and the DreamGen robotics benchmark show that the Executor surpasses the Demonstrator under our VLM-based evaluation protocol and transfers competitively to robotic tasks.", "github": "https://github.com/sebastian-stapf/world-model-self-distillation" }, { "date": "2026-06-11", "arxiv_id": "2606.11324", "title": "Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models", "authors": [ "Yifu Yuan", "Yaoting Huang", "Xianze Yao", "Yutong Li", "Shuoheng Zhang", "Linqi Han", "Pengyi Li", "Jiangeng Sun", "Wenting Jia", "Zhao Zhang", "Yuhao Liu", "Ruihao Liao", "Yucheng Hu", "Qiyu Wu", "Yuxiao Li", "Zibin Dong", "Fei Ni", "Yan Zheng", "Shuyang Gu", "Yi Ma", "Hongyao Tang", "Han Hu", "Jianye Hao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/pickxiguapi/Embodied-R1.5", "github_api_added_by": "user", "project_page": "https://embodied-r.github.io/", "abstract": "We introduce Embodied-R1.5, a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities, spanning embodied cognition, task planning, correction, and pointing, within a single architecture toward general physical intelligence. Leveraging three automated data construction pipelines to significantly expand the data coverage of critical capabilities, we build a large-scale data system of over 15B tokens, and design a multi-task balanced RL recipe to alleviate heterogeneous task conflicts. We further introduce a Planner-Grounder-Corrector (PGC) closed-loop framework that enables a single model to autonomously execute and self-correct over long-horizon tasks. With only 8B parameters, Embodied-R1.5 achieves SOTA on 16 out of 24 embodied VLM benchmarks, surpassing leading models like Gemini-Robotics-ER-1.5 and GPT-5.4. Benefiting from the internalized embodied capabilities, Embodied-R1.5 can be fine-tuned into a VLA with only a small amount of data, outperforming leading VLA models like \u03c0_{0.5} across 4 popular manipulation benchmark suites. We further conduct extensive zero-shot real-robot experiments, validating performance in instruction following, affordance grounding, articulated object manipulation, and long-horizon complex tasks, demonstrating strong generalization to the physical world. We open-source model weights, datasets, training code, and EmbodiedEvalKit, an evaluation framework tailored for embodied tasks, to facilitate future research in EFMs.", "github": "https://github.com/pickxiguapi/Embodied-R1.5" }, { "date": "2026-06-11", "arxiv_id": "2606.11289", "title": "i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models", "authors": [ "Boya Zeng", "Tianze Luo", "Shu Pu", "Jucheng Shen", "Taiming Lu", "Gabriel Sarch", "Zhuang Liu" ], "github_manual": "", "github_regex": "https://github.com/zlab-princeton/i1", "github_paper_md": "", "github_api": "https://github.com/zlab-princeton/i1", "github_api_added_by": "user", "project_page": "https://zlab-princeton.github.io/i1/", "abstract": "Diffusion models have consistently driven progress in text-to-image generation. However, it is challenging to attribute recent progress to specific modeling and data choices: state-of-the-art open-weight models provide limited ablations, and do not disclose their training data and full training details. The research community needs fully open (weights, data, and code) models as a foundation for further research; yet existing fully open models still fall significantly short of leading models in performance. In this project, we conduct a systematic investigation of the modeling and data design choices in text-to-image diffusion training and inference with 300+ controlled experiments totaling 700K+ TPU v6e hours. Our experiments highlight several empirical findings (e.g., equal weighting is a strong default for mixing curated datasets) and simple design decisions (e.g., larger text encoder adapters improve performance with minimal added parameters) for training strong models. Guided by these insights, we train i1, a 3B-parameter text-to-image diffusion model using only publicly available datasets. i1 is competitive with leading models on five representative benchmarks (GenEval, DPG, PRISM, CVTG-2K, and LongText), and outperforms the best existing fully open model by 29.5 absolute percentage points on average. We provide the i1 checkpoints, training and inference code, and the data processing pipeline. Together, our findings and the i1 recipe establish a practical foundation for future open research in text-to-image diffusion models. Our code is available at https://github.com/zlab-princeton/i1.", "github": "https://github.com/zlab-princeton/i1" }, { "date": "2026-06-11", "arxiv_id": "2606.09323", "title": "TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders", "authors": [ "Wei Pang", "Xiangru Jian", "Hehan Li", "Zhixuan Yu", "Alex Xue", "Jinyang Li", "Zhengyuan Dong", "Xinjian Zhao", "Hao Xu", "Chao Zhang", "Reynold Cheng", "M. Tamer \u00d6zsu", "Tianshu Yu" ], "github_manual": "", "github_regex": "https://github.com/LOGO-CUHKSZ/TRL-Bench", "github_paper_md": "", "github_api": "https://github.com/LOGO-CUHKSZ/TRL-Bench", "github_api_added_by": "user", "project_page": "https://logo-cuhksz.github.io/trl-bench.github.io/", "abstract": "Tabular encoders are usually evaluated inside task-specific end-to-end pipelines, so models from different training paradigms are difficult to compare directly even when they operate on similar tabular signals. We introduce TRL-Bench, a multi-granular tabular representation learning (TRL) benchmark that standardizes cross-paradigm representation-level evaluation: each encoder exports row-, column-, or table embeddings through its supported wrapper, and shared lightweight heads probe them across three suites: TRL-CTbench (column/table), TRL-Rbench (row), and TRL-DLTE (compositional Data-Lake Table Enrichment spanning all three granularities). To support this standardized setting, we release curated benchmark assets and task reformulations, including 50 OpenML tables with 123 verified targets, 16 row-pair linkage rewrites, and a 47,772-table DLTE lake derived from 1,379 parent tables. Across 20 models and 16 tasks, TRL-Bench shows that once downstream conditions are standardized, encoder quality is capability-specific rather than captured by a single leaderboard. In TRL-CTbench, generic text encoders often lead on tasks with strong surface-text signal, while tabular specialists win where their pretraining objective aligns with the task. In TRL-Rbench, within-table prediction and cross-table linkage favor different training regimes, with atomic linkage performance correlating strongly with the row-matching stage of DLTE pipelines. In TRL-DLTE, the strongest pipelines combine capability-matched specialists rather than reuse a single encoder, and top end-to-end quality depends on non-additive compositional fit rather than per-stage marginal rank alone. TRL-Bench provides a common protocol for measuring reusable signal in exported tabular representations under shared downstream conditions. Code and data: https://github.com/LOGO-CUHKSZ/TRL-Bench", "github": "https://github.com/LOGO-CUHKSZ/TRL-Bench" }, { "date": "2026-06-11", "arxiv_id": "2606.11926", "title": "Toward Generalist Autonomous Research via Hypothesis-Tree Refinement", "authors": [ "Jiajie Jin", "Yuyang Hu", "Kai Qiu", "Qi Dai", "Chong Luo", "Guanting Dong", "Xiaoxi Li", "Tong Zhao", "Xiaolong Ma", "Gongrui Zhang", "Zhirong Wu", "Bei Liu", "Zhengyuan Yang", "Linjie Li", "Lijuan Wang", "Hongjin Qian", "Yutao Zhu", "Zhicheng Dou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RUC-NLPIR/Arbor", "github_api_added_by": "user", "project_page": "https://ruc-nlpir.github.io/Arbor/", "abstract": "Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction. Researchers test candidate directions, interpret the evidence, and carry the resulting lessons into later attempts. We study how an AI agent can run this loop autonomously over long horizons. We introduce Arbor, a general framework for autonomous research that combines a long-lived coordinator, short-lived executors, and Hypothesis Tree Refinement (HTR), a persistent tree that links hypotheses, artifacts, evidence, and distilled insights across time. The coordinator manages global research strategy over the tree, while executors implement and test individual hypotheses in isolated worktrees. As results return, Arbor updates the tree, propagates reusable lessons, refines the search frontier, and admits verified improvements. This design turns autonomous research from a sequence of local attempts into a cumulative process in which strategy, execution, and evidence are carried across time. We evaluate Arbor under Autonomous Optimization (AO), an operational setting where an agent improves an initial research artifact through iterative experimentation without step-level human supervision. Across six real research tasks in model training, harness engineering, and data synthesis, Arbor achieves the best held-out result on all six tasks, attaining more than 2.5x the average relative held-out gain of Codex and Claude Code under the same task interface and resource budget. On MLE-Bench Lite, Arbor reaches 86.36% Any Medal with GPT-5.5, the strongest result in our comparison.", "github": "https://github.com/RUC-NLPIR/Arbor" }, { "date": "2026-06-11", "arxiv_id": "2606.10479", "title": "ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics", "authors": [ "Shunkai Zhang", "Haoran Zhang", "Yun Luo", "Qianjia Cheng", "Haodi Lei", "Yizhuo Li", "Runzhe Zhan", "Zhilin Wang", "Bangjie Xu", "Yucheng Su", "Xinmiao Han", "Xiaoye Qu", "Dongrui Liu", "Zhouchen Lin", "Yu Qiao", "Ning Ding", "Yafu Li", "Yu Cheng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Simplified-Reasoning/ComBench", "github_api_added_by": "user", "project_page": "https://simplified-reasoning.github.io/ComBench", "abstract": "Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structural insight. Recent evidence suggests that even today's strongest frontier models remain uneven on Olympiad combinatorics, revealing a gap in creative mathematical reasoning. We introduce ComBench, an Olympiad-level combinatorics benchmark for evaluating and diagnosing the combinatorial reasoning capabilities of large language models. ComBench contains 100 human-annotated competition-level problems organized around two complementary settings: analysis-centric problems, which primarily require rigorous mathematical arguments, and construction-centric problems, which require explicit constructions in addition to correctness justifications. The evaluation protocol combines rubric-guided proof grading with deterministic construction verification, exposing cases where proof quality and construction validity diverge. Experiments on frontier open- and closed-source models show that ComBench is far from saturated: the strongest model reaches 65.4% overall Avg. and 75.3% overall Best@4. We further find that Rigorous Proof Reasoning and Constructive Realization are distinct capabilities: Kimi-K2.6 trails GPT-5.5 on analysis-centric proof grading but surpasses it on construction-centric Best@4, while Existence and Construction problems remain consistently hardest across representative frontier models.", "github": "https://github.com/Simplified-Reasoning/ComBench" }, { "date": "2026-06-11", "arxiv_id": "2606.12344", "title": "Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks", "authors": [ "Mengyu Zheng", "Kai Han", "Boxun Li", "Haiyang Xu", "Yuchuan Tian", "Wei He", "Hang Zhou", "Jianyuan Guo", "Hailin Hu", "Lin Ma", "Chao Xu", "Guohao Dai", "Lixue Xia", "Yunchao Wei", "Yunhe Wang", "Yu Wang" ], "github_manual": "", "github_regex": "https://github.com/opensquilla/claw-swe-bench", "github_paper_md": "", "github_api": "https://github.com/opensquilla/claw-swe-bench", "github_api_added_by": "user", "project_page": "https://huggingface.co/datasets/TokenRhythm/Claw-SWE-Bench", "abstract": "General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and prediction contract required for scoring. We introduce Claw-SWE-Bench, a multilingual SWE-bench-style benchmark and adapter protocol that makes heterogeneous agent harnesses, or claws, comparable under fair settings including a fixed prompt, runtime budget, workspace contract, patch extraction procedure, and evaluator. The full benchmark contains 350 GitHub issue-resolution instances across 8 languages and 43 repositories, drawn from SWE-bench-Multilingual and SWE-bench-Verified-Mini after future-commit cleanup. We also release Claw-SWE-Bench Lite for faster validation, which is an 80-instance subset selected by a cost-aware, rank-aware procedure over 17 calibration columns. On the full benchmark, OpenClaw with a minimal direct-diff adapter scores only 19.1% Pass@1, whereas the full adapter reaches 73.4% with the same GLM 5.1 backbone, showing that adapter design is essential for enabling OpenClaw-style harnesses to perform coding tasks effectively. Across an OpenClaw times nine-model sweep and a five-claw times two-model sweep, model choice changes Pass@1 by 29.4 pp and harness choice by 27.4 pp under fixed models; systems with similar accuracy can differ substantially in total API cost. Claw-SWE-Bench therefore treats harness and cost accounting as first-class axes of SWE-style coding-agent evaluation, providing both a full benchmark and a low-cost reference set for reproducible comparison. The data is available at https://github.com/opensquilla/claw-swe-bench and https://huggingface.co/datasets/TokenRhythm/Claw-SWE-Bench.", "github": "https://github.com/opensquilla/claw-swe-bench" }, { "date": "2026-06-11", "arxiv_id": "2606.12397", "title": "Redesign Mixture-of-Experts Routers with Manifold Power Iteration", "authors": [ "Songhao Wu", "Ang Lv", "Ruobing Xie", "Yankai Lin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ericshwu/Router-with-Manifold-Power-Iteration", "github_api_added_by": "user", "project_page": "", "abstract": "Router is the cornerstone component to the Mixture-of-Experts models. Serving as expert proxies, the rows of the router matrix compute their similarity to the MoE inputs to determine which subset of experts is activated. Ideally, each router row is designed to encode the expert matrix into this representative vector, such that its dot-product with token can better reflect token-expert affinity. However, there exists no design principles to enforce this condensation. In this paper, we propose to align each router row with the principal singular direction of the associated expert, as this direction provides the most expressive mathematical description of a matrix. Based on this principle, we propose a router redesign with Manifold Power Iteration (MPI). Specifically, it introduces a \"Power-then-Retract\" paradigm, where a power iteration step is performed on the router weights, followed by a retraction to impose a norm constraint to ensure both efficiency and stability. Theoretically, we show that MPI drives router rows to converge toward the principal singular directions of associated experts. Empirically, we pretrain MoE model across scales from 1B to 11B parameters to confirm that this alignment facilitates more effective MoE models.", "github": "https://github.com/ericshwu/Router-with-Manifold-Power-Iteration" }, { "date": "2026-06-11", "arxiv_id": "2606.11683", "title": "Reason, Then Re-reason: Cross-view Revisiting Improves Spatial Reasoning", "authors": [ "Chaofan Ma", "Zhenjie Mao", "Yuhuan Yang", "Fanqin Zeng", "Yue Shi", "Yingjie Zhou", "Xiaofeng Cao", "Jiangchao Yao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://zhenjiemao.github.io/ReRe/", "abstract": "Spatial reasoning from egocentric videos is inherently challenging because the observable evidence is constrained by the camera trajectory. Existing methods rely on single-turn inference, forcing models to resolve geometric ambiguity through semantic priors rather than verifiable evidence. We argue that spatial reasoning should be revisitable: conclusions formed under limited evidence should remain open to revision when complementary viewpoints become available. Building on this insight, we propose Reason, then Re-reason (ReRe), a training-free, inference-time framework with two phases: in the Reason Phase, an MLLM forms a spatial hypothesis from the original video; in the Re-reason Phase, it verifies or revises the hypothesis by observing a synthesized novel-view video. To enable effective cross-view revisiting, we design a Geometry-to-Video pipeline that renders strategically complementary novel views from predicted 3D geometry. These views feature an elevated, oblique perspective with scene-spanning coverage, while preserving the MLLM's native video interface without architectural modifications. Extensive evaluations on VSI-Bench and STI-Bench demonstrate that ReRe substantially boosts open-source MLLMs to rival proprietary state-of-the-art performance. Project page: https://zhenjiemao.github.io/ReRe/", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.03108", "title": "EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning", "authors": [ "Guhong Chen", "Yingcheng Shi", "Yongbin Li", "Binhua Li", "Xander Xu", "Hu Wei", "Shiwen Ni", "Min Yang", "Jieping Ye" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Autonomous LLM training is often framed as recipe search, which leaves the training harness largely static. This limitation sharpens in agentic RL, where shifting bottlenecks and scalar rewards mask diverse failure modes. We introduce EvoTrainer, an autonomous training framework that co-evolves LLM policies and training-side harnesses through empirical feedback: it diagnoses rollout-level evidence, revises diagnostics, backtests interventions, and accumulates reusable skills. Evaluated on mathematical reasoning, competitive-programming code generation, and repository-level software engineering, EvoTrainer matches or exceeds the human-engineered RL references under the same data, codebase, and evaluation protocol, with the largest gain on long-horizon agentic SWE. Trajectory analyses show that retained strategies diverge across domains, evolving diagnostics prevent invalid high-scoring branches from being promoted, and reusable skills shape later search. Autonomous LLM RL should move beyond recipe search toward joint evolution of policies and the training harnesses that interpret them.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.03437", "title": "Large Language Models Are Overconfident in Their Own Responses", "authors": [ "Mario Sanz-Guerrero", "Manuel Mager", "Katharina von der Wense" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Prior work has shown that instruction-tuned large language models (LLMs) are less well calibrated than their base pre-trained counterparts. However, little is known about the frequently used chat template's effect on the calibration of conversational LLMs. In this work, we investigate the mechanisms driving this miscalibration by decoupling the effects of the post-training algorithm and the chat format. We find that, while instruction tuning fundamentally harms calibration, the chat template aggravates the issue through an \"ownership bias\" -- models are significantly more confident in their own answers than in identical answers provided by a user. Extensive experiments across six recent open-weight LLMs, three benchmarks, and three confidence elicitation methods show that models assign up to 26% higher confidence to their own responses. Leveraging this insight, we propose a simple inference-time strategy: framing the model's answer as user input during confidence elicitation. This approach significantly reduces overconfidence and improves calibration by up to 26% without the need for retraining, narrowing the gap between base and instruction-tuned models.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2605.30861", "title": "Distilling LLM Feedback for Lean Theorem Proving", "authors": [ "Gaetan Narozniak", "G\u00e9rard Biau", "R\u00e9mi Munos", "Ahmad Rammal", "Pierre Marion" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Post-training for reasoning models typically combines supervised fine-tuning with reinforcement learning from verifiable rewards, most commonly with GRPO. However, this algorithm suffers from sparse rewards, limited exploration, and mode collapse. Building upon recent works on self-distillation, we propose Feedback Distillation, a training method where the model is trained to match, at the token level, its own distribution conditioned on privileged feedback produced by a language model. Feedback Distillation offers token-level supervision and can inject external knowledge. Evaluating our method for Lean4 theorem-proving, we find that Feedback Distillation maintains greater diversity in generated trajectories than GRPO, yielding higher policy entropy and better pass@k scaling. The two methods are complementary: initializing GRPO from a Feedback Distillation checkpoint outperforms either method alone. All in all, our results suggest a promising avenue to improve post-training for complex reasoning.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.11990", "title": "Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation", "authors": [ "Amir El-Ghoussani", "Michele De Vita", "Ronald Naumann", "Valiseios Belagiannis" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive feature engineering or large labeled datasets to train task-specific sequence models. In this work, we introduce a lightweight learning approach, in which we leverage a frozen pretrained time-series foundation model (TSFM) and combine it with a small regression head for RUL estimation from multivariate sensor streams. More specifically, we use Chronos-2 as a frozen backbone to extract context window features and train a lightweight regression neural network for RUL prediction. Experiments on real-world industrial sensor data from two device types show that Chronos-2 features consistently improve over recurrent, convolutional, Transformer-based, and gradient-boosting baselines under the same preprocessing and evaluation protocol. We further analyze the impact of context length and find that performance improves significantly with longer histories, indicating that TSFM representation offer a practical and data-efficient alternative for RUL estimation in industrial settings.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.11817", "title": "Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code", "authors": [ "Yitong Zhang", "Shiteng Lu", "Jia Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/TsinghuaISE/CodeSpear-CodeShield", "github_api_added_by": "user", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code. Meanwhile, Grammar-Constrained Decoding (GCD) has been widely adopted to improve the reliability of LLM-generated code by enforcing syntactic validity. In this paper, we reveal a counterintuitive risk: this reliability-oriented technique can itself become an attack surface. We uncover a new jailbreak attack, termed CodeSpear, that exploits GCD to induce LLMs into generating malicious code. Our experiments show that simply applying a benign code grammar constraint can effectively jailbreak LLMs. To address this vulnerability, we propose CodeShield, a safety alignment approach that robustly preserves safe behavior even under attacker-controlled grammar constraints. CodeShield aligns the model in the code modality by teaching it to generate honeypot code under GCD. Such code is semantically harmless, so it does not implement the malicious request, and structurally diverse, so it is difficult to suppress through grammar tightening. At the same time, CodeShield still preserves natural-language refusals when natural language is available. Experiments on 10 popular LLMs across 4 benchmarks show that CodeSpear outperforms representative jailbreak baselines and increases the attack success rate by more than 30 percentage points on average. CodeShield also restores safety under CodeSpear while preserving benign utility. Our findings reveal a fundamental risk of GCD and call for greater attention to its potential security implications.", "github": "https://github.com/TsinghuaISE/CodeSpear-CodeShield" }, { "date": "2026-06-11", "arxiv_id": "2606.11119", "title": "TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning", "authors": [ "Heming Zou", "Qi Wang", "Yun Qu", "Yuhang Jiang", "Lizhou Cai", "Yixiu Mao", "Ru Peng", "Xin Xu", "Weijie Liu", "Kai Yang", "Saiyong Yang", "Xiangyang Ji" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models. However, rollout-intensive policy optimization is often limited by insufficient reward contrast, arising when overly simple or complex prompts generate low-variance feedback and when outcome-only rewards assign the same terminal assessment to every decision in a multi-turn rollout. Past efforts have focused on allocating available rollout resources to promising prompts, yet they only leverage sample informativeness at the prompt level and neglect variation in prefix-level informativeness across turns within the same rollout. This work targets multi-turn agentic RL by modeling each ReAct-style thought-action-observation turn as a semantically distinct node, allowing budget allocation to extend from prompt roots to turn-level prefixes with further continuations, which naturally forms tree-structured rollouts. We introduce Tree Rollout Allocation for Contrastive Exploration (TRACE), a unified rollout allocation framework that enhances reward contrast within a fixed sampling budget. Technically, TRACE allocates rollout budget to both prompt roots and intermediate prefixes that are most likely to yield mixed terminal rewards. A shared generalizable predictor estimates conditional success probability at these anchors from prefix histories to guide this allocation. The resulting adaptive tree structure enriches outcome-only feedback and amplifies the policy-update signal. Empirically, TRACE achieves competitive performance and efficiency gains on typical agentic benchmarks, e.g., improving Qwen3-14B Multi-Hop QA average accuracy by 2.8 points over competitive baselines at equal sampling cost.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.07881", "title": "Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency", "authors": [ "Itay Elam", "Eliron Rahimi", "Avi Mendelson", "Chaim Baskin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ItayElam/PACI", "github_api_added_by": "user", "project_page": "", "abstract": "Pipeline parallelism is essential for training large neural networks, but existing schedules trade off throughput, memory, and optimization consistency. Synchronous pipelines preserve forward/backward weight consistency but suffer from bubbles; asynchronous pipelines remove bubbles but introduce weight-version mismatch, typically requiring weight stashing, prediction, or correction mechanisms. We introduce PACI (Pipeline Asynchronous training with Controlled Inconsistency), a bubble-free asynchronous pipeline method that bounds forward/backward version drift without weight stashing, prediction, additional parameter copies, or global synchronization. The key idea is to use local gradient accumulation as a version-control mechanism: by slowing parameter-version evolution relative to pipeline delay, PACI limits the number of optimizer updates crossed by any micro-batch while preserving steady-state utilization. In GPT-style language-model pretraining, PACI matches the stability and final perplexity of synchronous 1F1B-flush, retains the same peak memory footprint, achieves fully utilized pipeline throughput, and improves training time-to-accuracy by up to 1.69times over the fastest flush baseline. These results show that forward/backward inconsistency need not be eliminated: when explicitly bounded, it can be safely traded for substantial efficiency gains.", "github": "https://github.com/ItayElam/PACI" }, { "date": "2026-06-11", "arxiv_id": "2606.07943", "title": "POISE: Position-Aware Undetectable Skill Injection on LLM Agents", "authors": [ "Haochang Hao", "Dehai Min", "Zhifang Zhang", "Yunbei Zhang", "Miao Xu", "Yingqiang Ge", "Lu Cheng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/liofoil/SkillSafety", "github_api_added_by": "user", "project_page": "", "abstract": "Agent skills provide a lightweight mechanism for extending general-purpose agents, but their open format exposes them to skill-poisoning attacks. A practically dangerous injection must stay invisible: if executing the payload derails the user's legitimate task, the resulting failure signal invites inspection of the skill. We therefore evaluate attacks by Attack Success Rate, which requires the injected payload to execute and the user's task to still pass its verifier in the same trial. Prior skill-poisoning attacks face a reliability-stealth trade-off under this lens: YAML-header injections are reliably loaded but easily inspected, whereas stealthier body injections that place explicit malicious commands in the skill prose are less reliable because out-of-context commands invite the agent's own suspicion. We introduce POISE, a position-aware attack that compresses the trigger into a single, benign-looking body instruction, placing it at a feasible position and using a context-aware generator to blend it with nearby setup or prerequisite steps. On Skill-Inject with codex+gpt-5.2, POISE achieves an 89.3% ASR, 28.0 points above a random-placement body baseline and 2.6 points above a YAML-only baseline, while retaining the stealth advantage of body placement. That stealth is the decisive margin: because legitimate skill bodies naturally require privileged tool operations, LLM scanners are hyper-sensitive, falsely flagging 74.6% of clean skills on average across four judges and both benchmarks. Blending into these false alarms, POISE causes only 5.6% of poisoned variants to gain a new high-risk alert over their clean baselines, rendering current static defenses ineffective.", "github": "https://github.com/liofoil/SkillSafety" }, { "date": "2026-06-11", "arxiv_id": "2601.05212", "title": "FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching", "authors": [ "Danilo Danese", "Angela Lombardi", "Matteo Attimonelli", "Giuseppe Fasano", "Tommaso Di Noia" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/sisinflab/FlowLet", "github_api_added_by": "user", "project_page": "https://danesed.github.io/flowlet-page/", "abstract": "Brain Magnetic Resonance Imaging (MRI) plays a central role in studying neurological development, aging, and diseases. One key application is Brain Age Prediction (BAP), which estimates an individual's biological brain age from MRI data. Effective BAP models require large, diverse, and age-balanced datasets, whereas existing 3D MRI datasets are demographically skewed, limiting fairness and generalizability. Acquiring new data is costly and ethically constrained, motivating generative data augmentation. Current generative methods are often based on latent diffusion models, which operate in learned low dimensional latent spaces to address the memory demands of volumetric MRI data. However, these methods are typically slow at inference, may introduce artifacts due to latent compression, and are rarely conditioned on age, thereby affecting the BAP performance. In this work, we propose FlowLet, a conditional generative framework that synthesizes age-conditioned 3D MRIs by leveraging flow matching within an invertible 3D wavelet domain, helping to avoid reconstruction artifacts and reducing computational demands. Experiments show that FlowLet generates high-fidelity volumes with few sampling steps. Training BAP models with data generated by FlowLet improves performance for underrepresented age groups, and region-based analysis confirms preservation of anatomical structures.", "github": "https://github.com/sisinflab/FlowLet" }, { "date": "2026-06-11", "arxiv_id": "2606.11854", "title": "Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training", "authors": [ "Michal Chudoba", "Sergey Alyaev", "Petra Galuscakova", "Tomasz Wiktorski" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/jinymusim/ART", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "There are two main Parameter-Efficient Fine-Tuning (PEFT) techniques for Large Language Models (LLMs). While Low-Rank Adaptation (LoRA) introduces additional weights between the LLM layers, Soft Prompting introduces additional fine-tuning-specific raw tokens to an LLM input. However, both require modification to the computational graphs of precompiled, preoptimized LLMs. As a result, neither is fully supported in high-throughput engines like vLLM. We propose fine-tuning with ART (Art-based Reinforcement Training). The method injects information into a frozen Multimodal Large Language Model (MLLM) by optimizing only its raw visual input, thus enabling the soft-token approach on pre-compiled computational graphs. It relies on backpropagation of gradients back into a plain pixel array and thus supports any fine-tuning objective. Moreover, the optimized visual input can be stylized as task-relevant computational artworks. The approach's effectiveness is confirmed for different sizes of a popular open Qwen architecture and for several textual benchmarks. Specifically, ART reaches accuracy competitive with LoRA across mathematics and structured-tool-use benchmarks.", "github": "https://github.com/jinymusim/ART" }, { "date": "2026-06-11", "arxiv_id": "2606.12364", "title": "On Subquadratic Architectures: From Applications to Principles", "authors": [ "Anamaria-Roberta Hartl", "Levente Z\u00f3lyomi", "David Stap", "Pieter-Jan Hoedt", "Niklas Schmidinger", "Lukas Hauzenberger", "Sebastian B\u00f6ck", "G\u00fcnter Klambauer", "Sepp Hochreiter" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Transformers dominate modern sequence modeling, but their quadratic attention incurs substantial computational cost. Subquadratic architectures offer a scalable alternative. However, it remains unclear which designs yield the most effective sequence models. We compare three leading approaches: xLSTM, Mamba-2, and Gated DeltaNet. We evaluate these models on tasks with complex dependencies: (1) code-model pre-training, (2) distillation of code models from large language models, and (3) pre-training of time-series foundation models. Across these settings, xLSTM delivers the strongest overall performance. To explain xLSTM's advantage, we present a unified formulation and analyze the underlying architectural mechanisms, focusing on state tracking and memory dynamics. Our results show that xLSTM enables more flexible and stable memory correction via its gating scheme. We corroborate these findings on controlled synthetic length-generalization tasks. Overall, our findings indicate that xLSTM's gains on complex tasks stem from robust state tracking and accumulation.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.10156", "title": "\u03c4-Rec: A Verifiable Benchmark for Agentic Recommender Systems", "authors": [ "Bharath Sivaram Narasimhan", "Karthik R Narasimhan" ], "github_manual": "", "github_regex": "https://github.com/nbharaths/tau-rec", "github_paper_md": "", "github_api": "https://github.com/nbharaths/tau-rec", "github_api_added_by": "user", "project_page": "", "abstract": "As recommender systems transition toward agentic, multi-turn conversational interfaces, evaluation paradigms have struggled to keep pace. Current benchmarks often rely on \"LLM-as-a-judge\" evaluations, which introduce subjectivity, high costs and inconsistency. We present \u03c4-Rec, a benchmark for agentic recommender systems that replaces subjective evaluation with verifiable rewards and a reveal-tagged elicitation (RTE) mechanism that controls how task constraints surface during dialogue. By testing agents against structured catalog predicates and employing a pass^k reliability metric, \u03c4-Rec provides a systematic test for consistent reasoning. Our evaluation of nine configurations across five model families -- GPT-5.4, Claude Sonnet 4.6, Gemini 2.5 Flash, DeepSeek V4 Flash, Qwen3-32B and GPT-5 mini -- reveals a steep reliability cliff, where even the best model achieves only ~57% at pass^1 and ~38% at pass^4, highlighting a critical gap in current conversational agent deployment. All code and data are publicly available at https://github.com/nbharaths/tau-rec.", "github": "https://github.com/nbharaths/tau-rec" }, { "date": "2026-06-11", "arxiv_id": "2606.12203", "title": "Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models", "authors": [ "Changyue Wang", "Weihang Su", "Qingyao Ai", "Yichen Tang", "Runzhong Qiao", "Xuancheng Li", "Min Zhang", "Yiqun Liu" ], "github_manual": "", "github_regex": "https://github.com/bebr2/SKIM", "github_paper_md": "", "github_api": "https://github.com/bebr2/SKIM", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models (LLMs) are widely used to tackle complex tasks with autonomous workflows. Recently, reusable natural language skills have emerged as a popular paradigm to inject procedural knowledge into LLM applications. Since popular skills are often invoked repeatedly, placing their full text in every context significantly increases prefill cost and latency. While text compression techniques have the potential to solve this problem, most existing methods are designed to compress factual knowledge in documents instead of procedural knowledge, making them insufficient for skill compression. In this paper, we argue that an effective skill compression method should: 1) preserve logical dependencies among workflows and tool protocols, 2) enable lightweight, offline compression for frequently updated community skills, and 3) be adaptable to varying complexities across skills. To address this, we present SKIM (SKIll coMpression), an adaptive multi-resolution soft token compression framework for procedural skills. Depending on the complexity of each skill, SKIM creates different numbers of soft tokens that not only improve the efficiency of LLM inference, but also preserve the effectiveness of skill usage. Experiments indicate that SKIM compresses skills to 30 to 60 percent of their original token length while preserving task performance better than existing compression methods.We have released our code at https://github.com/bebr2/SKIM .", "github": "https://github.com/bebr2/SKIM" }, { "date": "2026-06-11", "arxiv_id": "2606.12412", "title": "Reroute, Don't Remove: Recoverable Visual Token Routing for Vision-Language Models", "authors": [ "Cheng-Yu Yang", "Shao-Yuan Lo", "Yu-Lun Liu" ], "github_manual": "", "github_regex": "https://github.com/elmma/mllm-reroute", "github_paper_md": "", "github_api": "https://github.com/elmma/mllm-reroute", "github_api_added_by": "user", "project_page": "", "abstract": "Vision-language models (VLMs) project images into hundreds to thousands of visual tokens, making decoder inference expensive in both attention computation and KV-cache memory. Existing visual-token reduction methods largely follow a rank-and-remove paradigm: they score visual tokens, keep a compact subset, and permanently discard the rest. We show that this irreversible action is fragile because visual-token importance changes across decoder depth; tokens ranked low at one stage may become relevant in later layers, especially for grounding-sensitive queries. We propose Reroute, a training-free plug-in that replaces removal with recoverable routing. At each routing stage, selected vision tokens pass through decoder blocks, while deferred tokens bypass the stage and re-enter the candidate pool at the next routing decision. Reroute reuses existing attention-score ranking rules and stage-wise schedules, preserving the theoretical TFLOPs and KV-cache budget class of the pruning method it augments. Across FastV, PDrop, and N\u00fcwa variants on LLaVA-1.5 and Qwen backbones, reroute improves grounding under aggressive token reduction while maintaining general VQA performance. These results suggest that VLM token reduction should not be viewed only as irreversible pruning, but also as recoverable routing. The code can be found here: https://github.com/elmma/mllm-reroute/", "github": "https://github.com/elmma/mllm-reroute" }, { "date": "2026-06-11", "arxiv_id": "2606.05758", "title": "DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models", "authors": [ "Zhuoming Liu", "Jinhong Lin", "Kwan Man Cheng", "Lin Zhang", "Shayok Bagchi", "Yin Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/dragonlzm/DRIFT", "github_api_added_by": "user", "project_page": "https://dragonlzm.github.io/DRIFT/", "abstract": "Many modern vision-language models (VLMs) build on autoregressive decoding of discrete tokens. While text-based output interfaces enable scalable pretraining and strong zero-shot generalization across diverse tasks, they are poorly suited for problems that require precise continuous outputs, such as localizing temporal boundaries of events or generating robotic control actions. To address this challenge, we propose DRIFT, a general framework for adapting pretrained VLMs to continuous decoding tasks. DRIFT combines a base predictor, which provides a coarse estimate of the target output, with a generative refinement module based on flow matching that iteratively improves the prediction. This residual formulation transforms the generative modeling problem from learning a global output distribution to modeling a localized residual distribution around a strong prior, substantially simplifying optimization. We evaluate DRIFT on both perception and planning tasks, including visual grounding and robotic control. Across multiple tasks and architectures spanning MLLMs, VLAs, and WAMs, DRIFT consistently outperforms a strong set of regression- and generative-based solutions.", "github": "https://github.com/dragonlzm/DRIFT" }, { "date": "2026-06-11", "arxiv_id": "2606.11553", "title": "APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations", "authors": [ "Swadhin Pradhan", "Niloo Bahadori", "Peiman Amini" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Generic time-series foundation models transfer poorly to wireless network telemetry whose signals are bursty, zero-inflated, and coupled across protocol layers. We present APEX, a network-native, decoder-only transformer for forecasting enterprise AP telemetry, and evaluate it on DHCP degradation as a representative network task. APEX is pre-trained on 10-channel multivariate telemetry from ~4,500 production wireless networks (~100K AP time series, 34 metrics per AP), and is available as APEX-Large (269M, cloud) and APEX-Edge (10.5M, edge). On a 192-step (4-day) DHCP degradation benchmark, APEX-Large reduces MAE by 18% over the strongest foundation-model baseline (Toto) and 38% over SARIMA, with anomaly-detection F1 = 0.93, while APEX-Edge enables sub-second, privacy-preserving inference on AP-class edge hardware. These results suggest network-native pre-training is a practical foundation for proactive wireless operations.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.10587", "title": "Towards Diverse Scientific Hypothesis Search with Large Language Models", "authors": [ "Haorui Wang", "Parshin Shojaee", "Kazem Meidani", "Kunyang Sun", "Jos\u00e9 Miguel Hern\u00e1ndez-Lobato", "Teresa Head-Gordon", "Jiajun He", "Chandan K. Reddy", "Chao Zhang", "Yuanqi Du" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses. Yet in many discovery settings, the goal is not to identify a single best hypothesis since validation can be noisy and expensive, and scientists benefit from a set of high-quality alternative hypotheses that hedge against downstream uncertainty for the best solutions. Nevertheless, commonly used evolutionary search recipes tend to prioritize optimization over exploration in hypothesis generation, and the resulting selection pressure during the search process leads to diversity collapse. Motivated by these limitations, we formulate hypothesis search as a sampling problem, where the objective is to efficiently produce diverse, high-quality hypotheses under a fixed validation budget. Building on this perspective, we propose \\ours, an evolutionary framework inspired by the classical parallel tempering algorithm that searches hypotheses at multiple temperature levels and enables principled information exchange across temperatures to improve exploration without disrupting convergence. Across domains including molecular discovery, equation discovery, and algorithm discovery, our approach consistently improves both hypothesis quality and diversity under the same validation budget, and produces candidates that remain robust under more expensive downstream computational validations.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.04511", "title": "SparDA: Sparse Decoupled Attention for Efficient Long-Context LLM Inference", "authors": [ "Yaosheng Fu", "Guangxuan Xiao", "Xin Dong", "Song Han", "Oreste Villa" ], "github_manual": "", "github_regex": "https://github.com/NVlabs/SparDA", "github_paper_md": "", "github_api": "https://github.com/NVlabs/SparDA", "github_api_added_by": "user", "project_page": "", "abstract": "Sparse attention reduces compute and memory bandwidth for long-context LLM inference. However, two key challenges remain: (1) KV cache capacity still grows with sequence length, and offloading to CPU memory introduces a PCIe transfer bottleneck; (2) the sparse selection step itself retains O(T^2) complexity and can dominate attention cost at long contexts. We propose SparDA, a decoupled sparse attention architecture that introduces a fourth per-layer projection, the Forecast, alongside Query, Key, and Value. The Forecast predicts the KV blocks needed by the next layer, enabling lookahead selection that overlaps CPU-to-GPU prefetch with current-layer execution. Because Forecast is decoupled from the attention query, our GQA implementation uses one Forecast head per GQA group, reducing selection overhead versus the original multi-head selector. SparDA adds <0.5% parameters and trains only the Forecast projections by matching the original selector's attention distribution. On two sparse-pretrained 8B models, SparDA matches or slightly improves accuracy and delivers up to 1.25times prefill speedup and 1.7times decode speedup over the sparse-attention offload baseline. By enabling larger feasible batch sizes on a single GPU, SparDA further reaches up to 5.3times higher decode throughput than the non-offload sparse baseline. Our source code is available at https://github.com/NVlabs/SparDA.", "github": "https://github.com/NVlabs/SparDA" }, { "date": "2026-06-11", "arxiv_id": "2605.11212", "title": "ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction", "authors": [ "Amirhossein Abaskohi", "Yuhang He", "Peter West", "Giuseppe Carenini", "Pranit Chawla", "Vibhav Vineet" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Computer-use agents (CUAs) rely on visual observations of graphical user interfaces, where each screenshot is encoded into a large number of visual tokens. As interaction trajectories grow, the token cost increases rapidly, limiting the amount of history that can be incorporated under fixed context and compute budgets. This has resulted in no or very limited improvement in the performance when using history unlike other domains. We address this inefficiency by introducing ReVision, which is used to train multimodal language models on trajectories where redundant visual patches are removed using a learned patch selector that compares patch representations across consecutive screenshots while preserving spatial structure required by the model. Across three benchmarks, OSWorld, WebTailBench, and AgentNetBench, when processing trajectories with 5 history screenshots using Qwen2.5-VL-7B, ReVision reduces token usage by 46% on average while improving success rate by 3% over the no drop baseline. This establishes a clear efficiency gain, enabling agents to process longer trajectories with fewer tokens. With this improved efficiency, we revisit the role of history in CUAs and find that performance continues to improve as more past observations are incorporated when redundancy is removed.", "github": "" }, { "date": "2026-06-11", "arxiv_id": "2606.12385", "title": "Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs", "authors": [ "Sanjay Adhikesaven", "Haoxiang Sun", "Sewon Min" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cal-data-audit/modsleuth", "github_api_added_by": "user", "project_page": "https://modsleuth.cal-data-audit.org", "abstract": "Modern LLM training pipelines increasingly rely on other models to generate data, filter corpora, judge outputs, and guide development decisions. These dependencies are recursive: a model may depend on an upstream artifact whose own dependencies are documented only in separate releases and artifacts. As a result, the full dependency structure is fragmented across heterogeneous public artifacts, with complexity and recursive depth far outpacing humans' ability to trace. We introduce ModSleuth, an agentic system that recursively reconstructs LLM dependency graphs from public artifacts with source-grounded evidence. We find that the primary challenge is no longer information extraction, but defining what constitutes a dependency and reconciling artifact references across inconsistent documentation. We address these challenges through a formalization that distinguishes direct and indirect dependencies, represents heterogeneous pipeline roles through operation-centered relationships, and resolves artifact identities across names, versions, and repositories. Applying ModSleuth to four public-artifact-rich LLM releases, we recover 1,060 source-verified dependencies and construct large-scale dependency graphs of modern LLM development. These graphs reveal multi-hop license obligations, train-evaluation coupling, discrepancies between released and training-time artifacts, and documentation inconsistencies that would otherwise be difficult to uncover. We release ModSleuth and the resulting dependency graphs to support transparent analysis of the increasingly complex ecosystems underlying modern LLMs.", "github": "https://github.com/cal-data-audit/modsleuth" }, { "date": "2026-06-11", "arxiv_id": "2606.11482", "title": "Building Social World Models with Large Language Models", "authors": [ "Haofei Yu", "Yining Zhao", "Guanyu Lin", "Jiaxuan You" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ulab-uiuc/social-world-model", "github_api_added_by": "user", "project_page": "", "abstract": "Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundamental challenge in social science. Given LLMs' commonsense knowledge and social intelligence, we ask: Can LLMs model the dynamics of social beliefs following social events? In this work, we introduce the concept of the Social World Model (SWM), a general framework designed to capture how social beliefs evolve in response to major events. SWM learns state-transition functions for social beliefs by mining temporal patterns in social data and optimizing the evidence lower bound, without the need for explicit human annotations linking events to belief shifts, or for expensive census data. To evaluate SWM, we introduce a benchmark, SWM-bench, derived from real-world prediction markets, specifically Kalshi and Polymarket. SWM-bench includes over 12k data points for social belief prediction tasks spanning diverse domains such as politics, finance, and cryptocurrency. Our experimental results show that SWM significantly outperforms time-series foundation models, achieving state-of-the-art results on Kalshi data and demonstrating competitive performance on Polymarket data, while offering interpretable insights into the underlying mechanisms of social belief dynamics.", "github": "https://github.com/ulab-uiuc/social-world-model" }, { "date": "2026-06-11", "arxiv_id": "2606.04261", "title": "Can Generalist Agents Automate Data Curation?", "authors": [ "Feiyang Kang", "Hanze Li", "Adam Nguyen", "Mahavir Dabas", "Jiaqi W. Ma", "Frederic Sala", "Dawn Song", "Ruoxi Jia" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/feiyang-k/curation-bench", "github_api_added_by": "user", "project_page": "https://feiyang-k.github.io/curation-bench/", "abstract": "Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data policies against noisy benchmark feedback. We ask whether generalist coding agents can automate this data-curation loop. We introduce *Curation-Bench*, an agent-centric benchmark that fixes the model, training recipe, and evaluation suite while giving agents command-line access to inspect data, implement policies, submit them to a fixed training/evaluation pipeline, and revise. In a vision-language instruction-tuning instantiation, out-of-the-box agents reach strong published data-selection baselines within ten iterations. However, trajectory analysis reveals a persistent *execution-research gap*: agents mainly tune local policy variants rather than explore new policy families, even when given strategy guides and paper references. Scaffolds requiring each iteration to cite, instantiate, and adapt a prior method shift agents toward method-guided exploration. The scaffolded agent autonomously composes -- without human design input -- a data-selection policy that outperforms strong published baselines at one-tenth their data budget. Overall, current agents can run the curation loop, but reliable data research requires scaffolded method adaptation, not open-ended prompting alone. Code and benchmark are open-sourced.", "github": "https://github.com/feiyang-k/curation-bench" }, { "date": "2026-06-12", "arxiv_id": "2606.12882", "title": "HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness", "authors": [ "Xiaoxuan Wang", "Haixin Wang", "Alexander Taylor", "Jason Cong", "Yizhou Sun", "Wei Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mandyyyyii/HarnessBridge", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models are increasingly deployed as agents for long-horizon tasks, yet their performance is shaped not only by model capability and environment design, but also by the harness that mediates agent--environment interaction. Existing harnesses are largely manually engineered, making them difficult to scale as trajectories grow longer and interactions become more complex. In this work, we ask whether harness can be generated by a learnable plug-in module that can be trained in an end-to-end fashion. We introduce HarnessBridge, a lightweight learnable harness controller that parameterizes the agent--environment interface as a bidirectional projection. HarnessBridge learns two bidirectional projections: observation projection, which distills raw trajectories into compact, decision-relevant states, and action projection, which converts proposed actions into executable transitions or trajectory-grounded rejections. We train HarnessBridge on a harness supervision dataset via unified instruction tuning. On Terminal-Bench~2.0 and SWE-bench Verified, HarnessBridge matches or surpasses strong specialized harnesses while substantially reducing token usage and trajectory length, and generalizes from smaller generators to larger commercial models.", "github": "https://github.com/mandyyyyii/HarnessBridge" }, { "date": "2026-06-11", "arxiv_id": "2606.13674", "title": "RepWAM: World Action Modeling with Representation Visual-Action Tokenizers", "authors": [ "Junke Wang", "Qihang Zhang", "Shuai Yang", "Yiming Luo", "Yujun Shen", "Zuxuan Wu", "Yu-Gang Jiang", "Yinghao Xu" ], "github_manual": "", "github_regex": "https://github.com/wdrink/RepWAM", "github_paper_md": "", "github_api": "https://github.com/wdrink/RepWAM", "github_api_added_by": "user", "project_page": "https://wdrink.github.io/RepWAM/", "abstract": "This work presents RepWAM, a representation-centric world action model (WAM) built on representation visual-action tokenizers. Existing WAMs typically inherit reconstruction-oriented video tokenizers from pretrained video generation models. Although these tokenizers preserve visual fidelity, pixel reconstruction alone provides limited guidance for learning instruction-following dynamics that connect future prediction with robot control. To address this, we explore a semantic visual-action latent space for representation-centric world action modeling. Specifically, we train a representation visual-action tokenizer that maps visual inputs into aligned visual and latent action tokens. We then pretrain our WAM to jointly model future visual states and the latent actions that connect them under language instructions, followed by adaptation to real robot trajectories for closed-loop manipulation. Experiments on real-world manipulation tasks and simulation benchmarks show that RepWAM delivers strong performance across diverse manipulation settings, while ablations highlight the value of semantic visual-action tokenization over reconstruction-oriented alternatives. These results establish representation visual-action tokenization as a promising foundation for world action models and a step toward generalist robot policies. Code and weights will be available at https://github.com/wdrink/RepWAM.", "github": "https://github.com/wdrink/RepWAM" }, { "date": "2026-06-11", "arxiv_id": "2606.08035", "title": "DyCo-RL: Dynamic Cross-Modal Coordination for Visual Reasoning", "authors": [ "Hangui Lin", "Yan Shu", "Zhengyang Liang", "Chi Liu", "Xiangrui Liu", "Minghao Qin", "Teng Long", "Zheng Liu", "Nicu Sebe" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Sammy20207109/DyCo-RL", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a leading paradigm for enhancing visual reasoning in Multimodal Large Language Models (MLLMs). However, existing RLVR methods optimize primarily for the reasoning outcome, fundamentally overlooking the fine-grained cross-modal coordination required during the generation process. Through token-level analyses and controlled interventions, we reveal that during Chain-of-Thought (CoT) reasoning, MLLMs frequently fail to dynamically alternate between extracting visual evidence and synthesizing textual context-a coordination breakdown that is causally linked to reasoning failures. Motivated by these findings, we propose DyCo-RL, which integrates dynamic cross-modal coordination into RLVR optimization. Specifically, DyCo-RL uses the Fisher-Rao geodesic distance to measure within-modality attention shifts, assigning tokens to either visually-oriented or text-oriented functional roles. It then evaluates the alignment between a token's actual attention allocation and its assigned role, leveraging this score for alignment-guided advantage reweighting during policy optimization. Extensive experiments demonstrate that the algorithm-agnostic DyCo-RL, when applied to Qwen2.5-VL-3B/7B, consistently improves four representative RLVR algorithms across seven benchmarks spanning visual-centric and mathematical reasoning.", "github": "https://github.com/Sammy20207109/DyCo-RL" }, { "date": "2026-06-12", "arxiv_id": "2606.13673", "title": "SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning", "authors": [ "Seokju Cho", "Ryo Hachiuma", "Abhishek Badki", "Hang Su", "Byung-Kwan Lee", "Chan Hee Song", "Sifei Liu", "Subhashree Radhakrishnan", "Seungryong Kim", "Yu-Chiang Frank Wang", "Min-Hung Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/NVlabs/SpatialClaw", "github_api_added_by": "user", "project_page": "https://spatialclaw.github.io/", "abstract": "Spatial reasoning, the ability to determine where objects are, how they relate, and how they move in 3D, remains a fundamental challenge for vision-language models (VLMs). Tool-augmented agents attempt to address this by augmenting VLMs with specialist perception modules, yet their effectiveness is bounded by the action interface through which those tools are invoked. In this work, we study how the design of this interface shapes the agent's capacity for open-ended spatial reasoning. Existing spatial agents either employ single-pass code execution, which commits to a full analysis strategy before any intermediate result is observed, or rely on a structured tool-call interface that often offers less flexibility for freely composing operations or tailoring the analysis to each task. Both designs offer limited flexibility for open-ended, complex 3D/4D spatial reasoning. We therefore propose SpatialClaw, a training-free framework for spatial reasoning that adopts code as the action interface. SpatialClaw maintains a stateful Python kernel pre-loaded with input frames and a suite of perception and geometry primitives, letting a VLM-backed agent write one executable cell per step conditioned on all prior outputs, enabling the agent to flexibly compose and manipulate perception results and adapt its analysis to both intermediate text and visual observations and the demands of each problem. Evaluated across 20 spatial reasoning benchmarks spanning a broad range of static and dynamic 3D/4D spatial reasoning tasks, SpatialClaw achieves 59.9% average accuracy, outperforming the recent spatial agent by +11.2 points, with consistent gains across six VLM backbones from two model families without any benchmark- or model-specific adaptation.", "github": "https://github.com/NVlabs/SpatialClaw" }, { "date": "2026-06-12", "arxiv_id": "2606.12087", "title": "FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents", "authors": [ "Jia Deng", "Yimeng Chen", "Xiaoqing Xiang", "Ziyang Zeng", "Shuo Tang", "Wayne Xin Zhao", "Feng Chang", "Chuan Hao", "Yuan Wei", "Ran Tao", "Bryan Dai", "Ji-Rong Wen" ], "github_manual": "", "github_regex": "https://github.com/RUCAIBox/FORT-Searcher", "github_paper_md": "", "github_api": "https://github.com/RUCAIBox/FORT-Searcher", "github_api_added_by": "user", "project_page": "", "abstract": "Training deep search agents requires verifiable questions whose answers remain unavailable until sufficient evidence has been acquired through search. Existing synthesis methods often increase apparent difficulty by enriching graph structures, but structural complexity alone does not guarantee realized search difficulty: the intended search process can collapse through a cheaper identifying route. We formalize this gap with a shortcut-aware difficulty framework and identify four actionable shortcut risks: evidence co-coverage, single-clue selectivity, exposed constants, and prior-knowledge binding. To diagnose their realized effects, we use trajectory signatures including solving cost, answer hit time, and prior-shortcut rate. Guided by this framework, we introduce FORT, a Framework of Shortcut-Resistant Training-Data Synthesis. FORT constructs shortcut-resistant training data by controlling shortcut risks across entity selection, evidence graph construction, question formulation, and adversarial refinement. Experiments show that FORT induces longer pre-answer search and fewer shortcut patterns than existing open-source deep search datasets. Using the resulting trajectories, we train FORT-Searcher with supervised fine-tuning (SFT) only, and it achieves the best overall performance among comparable-size open-source search agents on challenging deep search benchmarks. Relevant resources will be made available at https://github.com/RUCAIBox/FORT-Searcher.", "github": "https://github.com/RUCAIBox/FORT-Searcher" }, { "date": "2026-06-12", "arxiv_id": "2606.10768", "title": "N-GRPO: Embedding-Level Neighbor Mixing for Enhanced Policy Optimization", "authors": [ "Xukun Zhu", "Hang Yu", "Peng Di", "Linchao Zhu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "The success of Large Language Models in mathematical reasoning relies heavily on the generation of diverse and valid solution paths during the rollout phase. However, current rollout techniques face a fundamental trade-off: token-level sampling often yields redundant trajectories that differ only in rephrasing, while embedding-level methods utilizing random noise frequently disrupt semantic consistency. To resolve this, we introduce N-GRPO, a novel exploration strategy integrated into the Group Relative Policy Optimization (GRPO) framework. Rather than relying on token-level sampling or native embedding-level noise, our approach leverages Semantic Neighbor Mixing. This mechanism dynamically constructs input representations by mixing the embeddings of an anchor token and its nearest semantic neighbors, thereby injecting diversity while strictly adhering to the local semantic manifold. Experimental evaluations on the DeepSeek-R1-Distill-Qwen models across different sizes show that N-GRPO not only achieves consistent improvements over strong baselines on math reasoning benchmarks but also exhibits robust generalization capabilities on out-of-distribution tasks.", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.13376", "title": "MoVerse: Real-Time Video World Modeling with Panoramic Gaussian Scaffold", "authors": [ "Yang Zhou", "Ziheng Wang", "Yuqin Lu", "Haofeng Liu", "Jun Liang", "Shengfeng He", "Jing Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Orange-3DV-Team/MoVerse", "github_api_added_by": "user", "project_page": "https://orange-3dv-team.github.io/MoVerse/", "abstract": "We present MoVerse, a real-time video world model that creates an interactively navigable scene from a single narrow-field-of-view image. This setting is challenging because the input observes only a small fraction of the environment, while interactive roaming requires a complete surrounding world, persistent geometry, controllable camera motion, and temporally coherent high-fidelity observations. MoVerse addresses this problem by separating world construction from observation rendering. It first expands the input into a gravity-aligned 360^circ panorama with topology-aware diffusion, closing the missing field of view before 3D reasoning. It then lifts the panorama into a persistent 3D Gaussian scaffold using panoramic geometry-aware residual prediction, yielding a dense and directly renderable spatial memory. Finally, a Gaussian-conditioned video renderer translates scaffold renderings along user-specified camera trajectories into photorealistic video. To make this renderer practical for interaction, we train a bidirectional diffusion teacher for high-quality conditional rendering and distill it into a causal autoregressive student for bounded-latency streaming. This design combines the controllability and long-range consistency of explicit 3D representations with the perceptual quality of generative video models. MoVerse supports real-time scene roaming at 8~FPS on a single NVIDIA RTX~4090 GPU, demonstrating a practical path toward single-image world creation with interactive video output.", "github": "https://github.com/Orange-3DV-Team/MoVerse" }, { "date": "2026-06-12", "arxiv_id": "2606.09426", "title": "WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces", "authors": [ "Wanli Li", "Bowen Zhou", "Yunyao Yu", "Zhou Xu", "Yifan Yang", "Dongsheng Li", "Caihua Shan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/weavebench/WeaveBench", "github_api_added_by": "user", "project_page": "https://weavebench.github.io/", "abstract": "Computer-use agents (CUAs) increasingly operate in runtimes that combine visual desktop control, command-line execution, code editing, browsers, and external tools. Existing benchmarks, however, often evaluate these interfaces as separable capabilities, leaving long-horizon cross-interface orchestration under-tested. Thus, we introduce WeaveBench, a long-horizon hybrid-interface benchmark with 114 tasks across 8 real-world work domains, grounded in real user requests and publicly verifiable artifacts. Each task requires agents to combine GUI observations/actions with CLI/code operations within a single trajectory. We evaluate these tasks on a real Ubuntu desktop inside deployed CLI-agent runtimes, augmented with a minimal desktop-control plugin. We also propose a companion trajectory-aware judge that inspects deliverables, files, screenshots, logs, and action traces, while detecting shortcut behaviors such as fabricated visual evidence or hard-coded metrics. Across frontier model-runtime pairings, the best PassRate reaches only 41.2%, showing the benchmark remains far from saturated. The trajectory-aware judge further reveals that outcome-only grading substantially overestimates agent performance. Overall, WeaveBench exposes a critical gap in CUA evaluation and provides an effective testbed to measure whether agents can orchestrate GUI, CLI, and code operations across long-horizon real-world tasks.", "github": "https://github.com/weavebench/WeaveBench" }, { "date": "2026-06-12", "arxiv_id": "2606.13679", "title": "InterleaveThinker: Reinforcing Agentic Interleaved Generation", "authors": [ "Dian Zheng", "Harry Lee", "Manyuan Zhang", "Kaituo Feng", "Zoey Guo", "Ray Zhang", "Hongsheng Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zhengdian1/InterleaveThinker", "github_api_added_by": "user", "project_page": "https://zhengdian1.github.io/InterleaveThinker-proj/", "abstract": "Recent image generators have demonstrated impressive photorealism and instruction-following capabilities in single-image generation and editing. However, constrained by their architectures, they cannot achieve interleaved generation (text-image sequence), which has crucial applications in visual narratives, guidance, and embodied manipulation. Even the latest open-source Unified Multimodal Models (UMMs) exhibit limited performance in this regard. In this paper, we introduce InterleaveThinker, the first multi-agent pipeline designed to endow any existing image generator with interleaved generation capabilities. Specifically, we employ a planner agent to organize the image-text input sequence, instructing the image generator on the required execution at each step. Subsequently, we introduce a critic agent to evaluate the generator's outputs, identify samples that deviate from the planned instructions, and refine the instructions for regeneration. To implement this pipeline, we construct the Interleave-Planner-SFT-80k and Interleave-Critic-SFT-112k to perform a format cold-start. Then we develop Interleave-Critic-RL-13k to reinforce the step-wise instruction correction capability within a generation trajectory using GRPO. Since a single interleaved generation trajectory may involve over 25 generator calls, optimizing the entire trajectory is computationally impractical. Therefore, we propose accuracy reward and step-wise reward, allowing single-step RL to effectively guide the entire generation trajectory. The results show that InterleaveThinker improves performance across various image generators. On interleaved generation benchmarks, it achieves performance comparable to Nano Banana and GPT-5. Surprisingly, it also significantly enhances the base model on reasoning-based benchmarks; for example, on 4-step FLUX.2-klein, we observe substantial gains on WISE and RISE.", "github": "https://github.com/zhengdian1/InterleaveThinker" }, { "date": "2026-06-12", "arxiv_id": "2606.13364", "title": "VideoMDM: Towards 3D Human Motion Generation From 2D Supervision", "authors": [ "Amir Mann", "Gal Michael Harari", "Merav Keidar", "Or Litany" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Amir-Mann/VideoMDM_release", "github_api_added_by": "user", "project_page": "https://videomdm.github.io/", "abstract": "We introduce VideoMDM, a diffusion-based framework that trains 3D human motion priors directly from accurate 2D poses extracted from monocular videos, without any 3D ground truth. A pretrained 2D-to-3D lifter provides approximate 3D pose sequences that serve as a noisy teacher: these are diffused, denoised by the model in 3D, and supervised in 2D by reprojecting the prediction and comparing against accurate keypoints. We show that, under mild assumptions, a depth-weighted 2D reprojection loss is equivalent in expectation to direct 3D supervision, and we adapt standard 3D motion regularizers - velocity consistency and over-parameterized representation alignment - to this 2D setting. Unlike methods that lift 2D to 3D only at inference, VideoMDM learns a coherent 3D motion manifold during training. On HumanML3D it nearly closes the gap to fully 3D-supervised MDM (FID 0.88 vs 0.54); On real video datasets Fit3D and NBA the method learns to generate motions consistently preferred by humans, with strong quantitative results.", "github": "https://github.com/Amir-Mann/VideoMDM_release" }, { "date": "2026-06-12", "arxiv_id": "2606.13644", "title": "Surflo: Consistent 3D Surface Flow Model with Global State", "authors": [ "Antoine Gu\u00e9don", "Shu Nakamura", "Nicolas Dufour", "Jiahui Lei", "Ko Nishino", "Angjoo Kanazawa" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://anttwo.github.io/surflo/", "abstract": "Geometry is invariant to viewpoint, which makes any collection of images a redundant encoding of a single 3D state. Existing feed-forward reconstruction models fail to exploit this: per-view methods emit overlapping, unaligned pointmaps that grow linearly with input count, while global-latent methods commit to a fixed, low-resolution output. We introduce Surflo, which compresses a variable number of unposed RGB views into K latent tokens-one global state-and decodes oriented 3D surface points by independently transporting them from noise onto the surface via flow matching. This frees the output from any fixed grid or token budget: the same latent yields from a few thousand to a million points in a single forward pass. To suppress the local inconsistencies inherent to independent per-point decoding, an inference-time guidance term correlates nearby points by injecting a photometric gradient during ODE integration. Surflo matches or surpasses feed-forward baselines on surface metrics, runs an order of magnitude faster than optimization-based methods that require hundreds of views, and is the only feed-forward approach to combine a global latent with arbitrary-resolution decoding.", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.13662", "title": "EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery", "authors": [ "Amy Xin", "Jiening Siow", "Junjie Wang", "Zijun Yao", "Fanjin Zhang", "Jian Song", "Lei Hou", "Juanzi Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/THU-Team-Eureka/EurekAgent", "github_api_added_by": "user", "project_page": "", "abstract": "LLM-based agents have shown increasing potential in automating scientific discovery. Given an optimizable metric and an execution environment, they can propose, validate, and iterate scientific solutions, and have produced results that outperform human-designed approaches. As model capabilities continue to improve, we argue that the bottleneck for autonomous scientific discovery is shifting from prescribing agent workflows to designing agent environments: the resources, constraints, and interfaces that shape agent behavior. We frame this as environment engineering: building environments that amplify productive behaviors, such as open-ended exploration, systematic artifact management, and inter-agent collaboration, while suppressing harmful behaviors, such as reward hacking and high-friction human oversight. We present EurekAgent, an environment-engineered agent system for metric-driven autonomous scientific discovery. EurekAgent engineers the environment along four dimensions: permissions engineering for bounded agent execution and isolated evaluation; artifact engineering for filesystem and Git-based collaboration; budget engineering for budget-aware exploration; and human-in-the-loop engineering for easy human supervision and intervention. EurekAgent sets new state-of-the-art results on multiple mathematics, kernel engineering, and machine learning tasks, including new state-of-the-art 26-circle packing results discovered with less than $11 in total API cost. We open-source our code and results, and call for environment engineering as a core research direction for developing reliable autonomous research agents.", "github": "https://github.com/THU-Team-Eureka/EurekAgent" }, { "date": "2026-06-12", "arxiv_id": "2606.12674", "title": "Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents", "authors": [ "Kushal Raj Bhandari", "Ling Yue", "Ching-Yun Ko", "Dhaval Patel", "Shaowu Pan", "Pin-Yu Chen", "Jianxi Gao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/IBM/Evoflux", "github_api_added_by": "user", "project_page": "", "abstract": "Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents. Yet MCP-style tool use requires more than isolated function calling: an agent must discover tools from live catalogs, satisfy schemas, preserve dependencies across intermediate outputs, and ground final responses in executed evidence. Small planners often generate plausible workflow graphs that fail under tool resolution, parameter validation, dependency tracking, or execution. We argue that this failure mode is poorly handled by small-corpus distillation. A few hundred teacher traces can teach workflow format, but rarely cover the recovery behavior needed to repair failed plans over changing tool catalogs. We introduce Evoflux, an inference-time evolutionary search method that treats compact tool use as the repair of executable tool workflows. It evolves typed workflow graphs through structured edits, execution feedback, adaptive intensity, meta-guided redesign, and diversity pruning. On held-out MCP-Bench tasks spanning live MCP servers and 250 tools, Evoflux raises execution feasibility from roughly 3% to 17-24% across small planners. In contrast, SFT and SFT+DPO on the same search-mined data match, underperform, or collapse below zero-shot performance; ReAct reaches higher peaks, but with higher variance and token cost. These results show that execution-grounded search is more reliable under scarce teacher-trace budgets.", "github": "https://github.com/IBM/Evoflux" }, { "date": "2026-06-12", "arxiv_id": "2606.13672", "title": "WEAVER, Better, Faster, Longer: An Effective World Model for Robotic Manipulation", "authors": [ "Arnav Kumar Jain", "Yilin Wu", "Jesse Farebrother", "Gokul Swamy", "Andrea Bajcsy" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/arnavkj1995/WEAVER", "github_api_added_by": "user", "project_page": "https://arnavkj1995.github.io/WEAVER/", "abstract": "The potential impacts of world models (WMs, i.e., learned simulators) on robotics are far-reaching -- policy evaluation, policy improvement, and test-time planning -- all with limited real-world interaction. To unlock these downstream capabilities, a WM needs to jointly satisfy three desiderata: (i) fidelity (i.e., producing simulated trajectories that correlate with reality), (ii) consistency (i.e., producing simulated trajectories that are coherent over long horizons), and (iii) efficiency (i.e., producing simulated trajectories quickly). We propose WEAVER (World Estimation Across Views for Embodied Reasoning): a WM architecture that simultaneously achieves all three desiderata, providing state-of-the-art results on robotic manipulation tasks. WEAVER is a multi-view WM trained to predict future latents and reward values via a flow-matching loss. We distill the key design decisions across model architecture, memory, and prediction objectives required to unlock the kinds of long-horizon dynamic manipulation tasks that have confounded prior world modeling approaches. We apply WEAVER in robotic hardware, demonstrating its effectiveness at policy evaluation (\u03c1=0.870 correlation with real-world success rate), policy improvement (real-world success rate improvement of 38% on top of the \u03c0_{0.5} robot foundation model), and test-time planning (real-world success rate improvement of 14% with a 5-10times speedup over prior WMs). WEAVER also demonstrates better performance than prior WMs when evaluated on out-of-distribution scenarios. Code, models, and videos at: https://arnavkj1995.github.io/WEAVER/ .", "github": "https://github.com/arnavkj1995/WEAVER" }, { "date": "2026-06-12", "arxiv_id": "2606.13473", "title": "MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling", "authors": [ "Jiacheng Chen", "Xinyu Zhang", "Shunkai Zhang", "Yanmohan Wang", "Lin Li", "Tiancheng Qin", "Qin Wang", "Zhengmao Zhu", "Tianle Li", "Jingyang Li", "Zehan Li", "Binyang Jiang", "Jin Zhu", "Han Ding", "Fei Yu", "Chenyu Du", "Zijian Song", "Jiayuan Song", "Zhi Zhang", "Yunan Huang", "Weiyu Cheng", "Pengyu Zhao", "Yu Cheng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We present MaxProof, a population-level test-time scaling framework for competition-level mathematical proof in the MiniMax-M3 series. M3 first trains three proof-oriented capabilities -- proof generation, proof verification, and critique-conditioned proof repair -- using a defense-in-depth generative verifier engineered for low false-positive rate. These capabilities are merged into a single released M3 model. At test time, MaxProof treats the model as a generator, verifier, refiner, and ranker, searches over a population of candidate proofs, and returns one final proof through tournament selection. With MaxProof test-time scaling, the M3 model reaches 35/42 on IMO 2025 and 36/42 on USAMO 2026, exceeding the human gold-medal threshold on both.", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.13578", "title": "LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories", "authors": [ "Baochang Ren", "Xinjie Liu", "Xi Chen", "Yanshuo Liu", "Chenxi Li", "Daqi Gao", "Zeqin Su", "Jintao Xing", "Zirui Xue", "Rui Li", "Xiangyu Zhao", "Shuofei Qiao", "Minting Pan", "Wangmeng Zuo", "Lei Bai", "Dongzhan Zhou", "Ningyu Zhang", "Huajun Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zjunlp/LabVLA", "github_api_added_by": "user", "project_page": "https://zjunlp.github.io/LabVLA/", "abstract": "Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI can help read literature, generate hypotheses, and plan protocols, yet the execution of those protocols at the bench still requires a human operator. Vision-Language-Action (VLA) models provide one possible interface between written protocols and robot execution, but existing policies are trained mostly on household and tabletop demonstrations and rarely encounter the instruments, transparent liquids, or fixed protocol workflows found in scientific laboratories. Closing this gap requires both laboratory-specific supervision and a unified learning framework that can accommodate the diverse robot embodiments used to execute experimental protocols. We therefore identify data and embodiment as central bottlenecks alongside model design. To address the data side, we build RoboGenesis, a simulation-based workflow and data engine that composes configured laboratory workflows from atomic skills, validates and filters rollouts, and exports structured demonstrations across supported robot profiles. On the policy side, we present LabVLA, trained with a two-stage recipe: FAST action token pretraining first makes the Qwen3-VL-4B-Instruct backbone action aware before any continuous control is learned, and flow matching posttraining then attaches a DiT action expert under knowledge insulation. On the LabUtopia benchmark, LabVLA achieves the highest average success rate among all evaluated baselines under both in-distribution and out-of-distribution settings.", "github": "https://github.com/zjunlp/LabVLA" }, { "date": "2026-06-12", "arxiv_id": "2606.13681", "title": "EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments", "authors": [ "Jundong Xu", "Qingchuan Li", "Jiaying Wu", "Yihuai Lan", "Shuyue Stella Li", "Huichi Zhou", "Bowen Jiang", "Lei Wang", "Jun Wang", "Anh Tuan Luu", "Caiming Xiong", "Hae Won Park", "Bryan Hooi", "Zhiyuan Hu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Aiden0526/EvoArena", "github_api_added_by": "user", "project_page": "https://aiden0526.github.io/EvoArena/", "abstract": "Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deployment is inherently dynamic, requiring agents to continually align their knowledge, skills, and behavior with changing environments and updated task conditions. To address this gap, we introduce EvoArena, a benchmark suite that models environment changes as sequences of progressive updates across terminal, software, and social domains. We further propose EvoMem, a patch-based memory paradigm that records memory evolution as structured update histories, enabling agents to reason about environmental evolution through changes in their memory. Experiments show that current agents struggle on EvoArena, achieving an average accuracy of 39.6% across evolving terminal, software, and social-preference domains. EvoMem consistently improves performance, yielding an average gain of 1.5% on EvoArena and also improving standard benchmarks such as GAIA and LoCoMo by 6.1% and 4.8%. Beyond individual tasks, EvoMem further improves chain-level accuracy by 3.7% on EvoArena, where success requires completing a consecutive sequence of related evolutionary subtasks. Mechanistic analysis shows that EvoMem improves evidence capture in the memory, indicating better preservation of complete evolving environment states. Our results highlight the importance of modeling evolution in both evaluation and memory for reliable agent deployment.", "github": "https://github.com/Aiden0526/EvoArena" }, { "date": "2026-06-12", "arxiv_id": "2606.09290", "title": "Visual Para-Thinker++: A Single-Policy Multi-Agent Framework for Visual Reasoning", "authors": [ "Haoran Xu", "Hongyu Wang", "Yifei Gao", "Jiaze Li", "Zizhao Tong", "Xiaofeng Zhang", "Xiaosong Yuan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Visual reasoning requires integrating evidence distributed across regions, attributes, and relations, making single-chain reasoning prone to early perceptual commitment and hallucination. We propose Visual Para-Thinker++, a single-policy multi-agent framework in which one shared MLLM policy is instantiated as role-conditioned Main, Worker, and Summary Agents. The Main Agent decomposes the task with fixed allocation patterns; Worker Agents reason in parallel under context isolation; and the Summary Agent reconciles full Worker reasoning traces rather than majority-voting on final labels. The shared policy is trained by Multi-Agent Capability Injection and Role-Decoupled Multi-Agent Optimization, which assign role-specific rewards and advantages to corresponding token segments to reduce gradient conflict among collaborative roles. A native inference engine enables efficient multi-agent rollout through shared visual prefix and KV cache reuse. Across V*, CountBench, the RefCOCO family, and HallusionBench, Visual Para-Thinker++ consistently outperforms single-trajectory and inference-time parallel baselines, with especially strong gains on hallucination-sensitive visual reasoning.", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.09304", "title": "SG-OPD: Sign-Gated On-Policy Distillation via Sign-Consistency Gating and Phased Teacher Sampling", "authors": [ "Haoran Xu", "Hongyu Wang", "Yifei Gao", "Jiaze Li", "Xiaofeng Zhang", "Xiaosong Yuan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "On-policy distillation (OPD) trains a student on its own trajectories with dense per-token supervision from a stronger teacher, and often outperforms off-policy distillation and standard reinforcement learning. However, we find that its effectiveness implicitly relies on two assumptions that frequently break in practice: trajectory-level alignment between the student and the teacher, and uniform token-level reliability of the teacher's preferences. We therefore propose Sign-Gated On-Policy Distillation (SG-OPD), which uses a binary verifier as a trust signal for the teacher at two complementary granularities: phased teacher sampling mixes in verifier-endorsed teacher rollouts at cold-start, and a sign-consistency gate extrapolates the distillation update on tokens where the teacher agrees with the verifier-correct direction and interpolates it where it disagrees. Experiments on competition-level mathematical reasoning benchmarks show that SG-OPD consistently outperforms standard OPD, with average gains of 1.98 and 7.50 at the per-sample and per-question levels, respectively.", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.08063", "title": "Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding?", "authors": [ "Jiaqi Tang", "Jianmin Chen", "Youyang Zhai", "Wei Wei", "Runtao Liu", "Mengjie Zhao", "Xiangyu Wu", "Qingfa Xiao", "Qifeng Chen" ], "github_manual": "", "github_regex": "https://github.com/jqtangust/Robust-U1", "github_paper_md": "", "github_api": "https://github.com/jqtangust/Robust-U1", "github_api_added_by": "user", "project_page": "https://huggingface.co/spaces/Jiaqi-hkust/Robust-U1", "abstract": "Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly under real-world visual corruptions. While existing robustness enhancement approaches exist, they are limited: black-box feature alignment lacks interpretability, and white-box text-based reasoning cannot restore lost pixel-level details. This work investigates a fundamental research question: Can MLLMs recover corrupted visual content by themselves? To address this, we propose Robust-U1, a novel framework that equips MLLMs with explicit visual self-recovery capability for robust understanding. The approach comprises three core stages: supervised fine-tuning for initial reconstruction, reinforcement learning with dual rewards (pixel-level SSIM and semantic-level CLIP similarity) for aligning high visual quality, and multimodal reasoning that jointly considers both the corrupted input and the recovered image. Extensive experiments demonstrate that Robust-U1 achieves state-of-the-art robustness on the real-world corruption benchmark and maintains superior performance under adversarial corruptions on general VQA benchmarks. Analysis confirms that high-quality visual recovery directly enhances reasoning performance, establishing self-recovery as a critical mechanism for robust visual understanding. The source code is available at https://github.com/jqtangust/Robust-U1.", "github": "https://github.com/jqtangust/Robust-U1" }, { "date": "2026-06-12", "arxiv_id": "2606.13120", "title": "EvoBrowseComp: Benchmarking Search Agents on Evolving Knowledge", "authors": [ "Yunhan Wang", "Jiaan Wang", "Lianzhe Huang", "Xianfeng Zeng", "Fandong Meng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/datasets/Krystalan/EvoBrowseComp", "abstract": "Search Agents -- large language models augmented with search tools -- have intensified the need for future-proof evaluation benchmarks. Existing benchmarks such as BrowseComp rely on static knowledge, making them vulnerable to test-set contamination and parametric memorization. Consequently, models can achieve high scores through fact recall rather than genuine retrieval, obscuring true browsing competence via reasoning shortcuts. In this paper, we introduce EvoBrowseComp, an evolving benchmark of 400 English and 400 Chinese contamination-free complex questions synthesized via live-web traversal. To collect these questions, we design a three-agent collaborative framework: (1) a QA synthesis agent that retrieves fresh knowledge from the live web to synthesize QA pairs; (2) an information filtering agent that filters retrieved knowledge in terms of credibility and popularity to block parametric shortcuts; and (3) a high-level guidance agent that formalizes questions into reasoning graphs to reduce logical redundancy and shortcuts in synthesized QA pairs. Because the framework supports fully automated synthesis, EvoBrowseComp can be regularly updated to prevent data contamination and maintain temporal freshness. Extensive experiments confirm its great difficulty, requiring broad horizontal search. It establishes a scalable paradigm for auto-updatable, high-difficulty benchmarking that keeps pace with both evolving world knowledge and advancing agent capabilities.", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.13106", "title": "Demystifying Hidden-State Recurrence: Switchable Latent Reasoning with On-Policy Reinforcement Learning", "authors": [ "Jiayu Yang", "Chao Chen", "Shengen Wu", "Yinhong Liu", "Yuxuan Fan", "Lujundong Li", "Songning Lai", "Chengwei Qin", "Zhijiang Guo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Latent chain-of-thought compresses reasoning by replacing visible reasoning traces with continuous hidden-state recurrence, but existing formulations are difficult to optimize with standard on-policy reinforcement learning (RL) and hard to interpret causally. Our key insight is that a single pair of explicit boundary tokens can address both issues at once: discrete entry and exit anchors make the latent block compatible with standard on-policy RL, and the same anchors offer a natural foothold for mechanistic analysis. Motivated by this, we propose SWITCH, a switchable latent reasoning framework. The model emits to enter latent mode and to exit. Because the boundaries are ordinary discrete tokens, the GRPO policy ratio is well-defined at every decision point. The same anchors also expose the latent steps to direct probing and causal intervention. We train the model with a visible-to-latent curriculum and a Switch-GRPO objective that propagates gradients through recurrent latent computation. SWITCH consistently outperforms prior hidden-state-recurrence latent reasoning approaches at similar scale. Mechanistic analysis through the boundary tokens further reveals three findings: (i) is a sharply localised, learned switching policy rather than a stylistic artefact; (ii) the latent step it opens performs problem-specific, causally important computation rather than acting as an inert placeholder; and (iii) that computation is concentrated at a single hidden-state transition on entry. Together, these results show that hidden-state-recurrence latent reasoning is both RL-trainable and open to direct mechanistic analysis, including of how on-policy RL itself improves the model from the inside.", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.08788", "title": "MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training", "authors": [ "Lianyu Pang", "Tianlin Pan", "Cheng Da", "Changqian Yu", "Huan Yang", "Kun Gai", "Song Guo", "Wenhan Luo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Representation alignment with pretrained vision models has recently shown strong potential for accelerating diffusion transformer training. By aligning intermediate diffusion features with clean-image representations from self-supervised vision encoders, existing methods improve convergence and generation quality. However, such alignment also introduces a non-trivial constraint: diffusion models operate on noisy inputs whose usable information varies across timesteps, while the reference features are extracted from clean images. In this paper, we revisit this mismatch from a token-level perspective. We find that, under full-token representation alignment, tokens with large alignment-gradient norms exhibit a stable spatial preference, suggesting that the alignment objective does not affect all tokens uniformly and may encourage the model to rely on the complete set of clean-image tokens. To address this issue, we propose MaskAlign, a token-subset representation alignment method that applies alignment to randomly sampled token subsets during training. By exposing the model to different token subsets across iterations, MaskAlign reduces the dependence of representation alignment on the complete token set and encourages alignment behavior that is more stable under token-subset perturbations. To mitigate the information loss caused by directly dropping tokens, we further introduce a lightweight pre-mask token mixing block that shares information across tokens before masking.", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.08039", "title": "MuJoCo-Drones-Gym: A GPU-Accelerated Multi-Drone Simulator for Control and Reinforcement Learning", "authors": [ "Manan Tayal" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tau-intelligence/MuJoCo-drones-gym", "github_api_added_by": "user", "project_page": "", "abstract": "Robotic simulators are a cornerstone of modern research in aerial robotics, serving both as a vehicle for the development of new control algorithms and as the data source for training reinforcement learning (RL) policies. Yet, existing quadcopter learning environments often face a trade-off between physical fidelity, multi-agent support, and the throughput required by modern deep RL pipelines. In this paper, we present MuJoCo-Drones-Gym, an open-source Gymnasium-compatible multi-drone environment built on top of the MuJoCo physics engine. MuJoCo-Drones-Gym supports an arbitrary number of Bitcraze Crazyflie 2.x nano-quadcopters and exposes a modular API for selecting (i)~the physics model (rigid-body MuJoCo, explicit Python dynamics, or any subset of ground effect, blade drag, and inter-drone downwash), (ii)~the action interface (per-motor RPMs, collective normalized thrust, velocity setpoints, or PID waypoint commands), and (iii)~the observation space (kinematic state vectors, RGB / depth / segmentation cameras, or neighbourhood adjacency information). A PettingZoo ParallelEnv wrapper enables drop-in multi-agent reinforcement learning, while a suite of seven task environments, hover, velocity tracking, multi-drone hover, waypoint navigation, formation flight, gate racing, and a generic multi-agent template, demonstrates the breadth of the interface. We describe the environment design, the underlying physics and quadcopter dynamics, and illustrate its use through control and learning examples that mirror those of the closely related gym-pybullet-drones project, while taking advantage of MuJoCo's improved contact handling, rendering, and parallelizability.", "github": "https://github.com/tau-intelligence/MuJoCo-drones-gym" }, { "date": "2026-06-12", "arxiv_id": "2606.13392", "title": "MiniMax Sparse Attention", "authors": [ "Xunhao Lai", "Weiqi Xu", "Yufeng Yang", "Qiaorui Chen", "Yang Xu", "Lunbin Zeng", "Xiaolong Li", "Haohai Sun", "Haichao Zhu", "Vito Zhang", "Pengyu Zhao" ], "github_manual": "", "github_regex": "https://github.com/MiniMax-AI/MSA", "github_paper_md": "", "github_api": "https://github.com/MiniMax-AI/MSA", "github_api_added_by": "user", "project_page": "", "abstract": "Ultra-long-context capability is becoming indispensable for frontier LLMs: agentic workflows, repository-scale code reasoning, and persistent memory all require the model to jointly attend over hundreds of thousands to millions of tokens, yet the quadratic cost of softmax attention makes this untenable at deployment scale. We introduce MiniMax Sparse Attention (MSA), a blockwise sparse attention built upon Grouped Query Attention (GQA). A lightweight Index Branch scores key-value blocks and independently selects a Top-k subset for each GQA group, enabling group-specific sparse retrieval while maintaining efficient block-level execution; the Main Branch then performs exact block-sparse attention over only the selected blocks. Designed around a principle of simplicity and scalability, MSA is deliberately streamlined, making it straightforward to deploy efficiently across a broad range of GPUs. To translate sparsity into practical speedups, we co-design MSA with a GPU execution path that uses exp-free Top-k selection and KV-outer sparse attention to improve tensor-core utilization under block-granular access. On a 109B-parameter model with native multimodal training, MSA performs on par with GQA while reducing per-token attention compute by 28.4x at 1M context. Paired with our co-designed kernel, MSA achieves 14.2x prefill and 7.6x decoding wall-clock speedups on H800. Our inference kernel is available at: https://github.com/MiniMax-AI/MSA. A production-grade natively multimodal model powered by MSA has been publicly released at: https://huggingface.co/MiniMaxAI/MiniMax-M3.", "github": "https://github.com/MiniMax-AI/MSA" }, { "date": "2026-06-12", "arxiv_id": "2606.12575", "title": "High-Fidelity Two-Step Image Generation via Teacher-Aligned End-to-End Distillation", "authors": [ "Dongyang Liu", "Ruoyi Du", "David Liu", "Dengyang Jiang", "Liangchen Li", "Qilong Wu", "Zhen Li", "Steven C. H. Hoi", "Hongsheng Li", "Peng Gao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Few-step diffusion distillation has become increasingly mature for 4-8-step generation, yet pushing further to 2 steps remains challenging. In this work, we introduce Z-Image Turbo++, a high-quality 2-step image generation model distilled from the 8-step Z-Image Turbo teacher. Our method addresses the central bottlenecks of increased task difficulty and limited model capacity in 2-step generation through three simple but effective design choices tailored to this regime. First, we propose Distribution-Aligned Adversarial Learning, which uses teacher-generated images rather than external real images as real samples for GAN training, providing a more attainable and informative adversarial target. Second, we adopt Step-Decoupled Parameterization, assigning independent model parameters to the two denoising steps to better match their distinct capacity demands. Third, we perform End-to-End Training with Iterative Regularization, allowing the first step to receive gradients from final image quality while preserving a meaningful intermediate generation through an explicit step-1 loss. Together, these designs substantially narrow the quality gap between 2-step and 8-step generation in both qualitative and quantitative evaluations, highlighting the potential of carefully tailored distillation strategies for improving the quality-efficiency trade-off in few-step generation.", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.11096", "title": "IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder", "authors": [ "Yitong Chen", "Zijie Diao", "Junke Wang", "Lingyu Kong", "Yixuan Ren", "Bo He", "Yu-Gang Jiang", "Zuxuan Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Row11n/IDEAL", "github_api_added_by": "user", "project_page": "", "abstract": "Built on pretrained vision foundation models (VFMs), representation autoencoders (RAEs) have recently emerged as a promising approach for constructing semantically rich latent spaces for image generation. However, their reconstruction quality often remains suboptimal, largely because deep VFM representations do not preserve sufficient fine-grained visual detail. This limitation becomes even more severe after discretization, where missing low-level information is difficult to recover. In fact, we observe that shallow VFM features retain considerably richer local appearance and structural detail, which complements the high-level semantics carried by deep features used in existing RAEs. Motivated by this complementary property, we propose Ideal, an In-depth Alignment framework for discrete representation autoencoding. By jointly aligning quantized tokens with both shallow and deep VFM features, Ideal enables the resulting discrete visual tokens to preserve both visual fidelity and rich semantics. Extensive experiments demonstrate that Ideal yields superior reconstruction performance, achieving 0.61 rFID on ImageNet and outperforming the previous best method by 0.28. When used for autoregressive image generation, Ideal further produces a gFID of 1.89, establishing a new state of the art for autoregressive image generation.", "github": "https://github.com/Row11n/IDEAL" }, { "date": "2026-06-12", "arxiv_id": "2606.12282", "title": "PianoKontext: Expressive Performance Rendering from Deadpan Context", "authors": [ "Dmitrii Gavrilev" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/realfolkcode/pianokontext", "github_api_added_by": "user", "project_page": "https://realfolkcode.github.io/pianokontext_demo", "abstract": "Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing models manipulate only synchronized music samples of the same duration, limiting their understanding of expressive timing. We introduce PianoKontext, a flow matching rendering model for classical piano music that generates variable-length performances in the latent space of a pretrained Music2Latent model. We synthesize MIDI scores into deadpan audio and employ Dynamic Time Warping (DTW) in the latent space to construct paired data for training. The aligned embeddings are concatenated in DiT blocks, allowing for a simple and effective learning of the dependencies between the score and performances. Audio samples are available at our demo page: https://realfolkcode.github.io/pianokontext_demo/.", "github": "https://github.com/realfolkcode/pianokontext" }, { "date": "2026-06-12", "arxiv_id": "2606.11662", "title": "TreeSeeker: Tree-Structured Trial, Error, and Return in Deep Search", "authors": [ "Zhuofan Shi", "Mingzhe Ma", "Lu Wang", "Fangkai Yang", "Pu Zhao", "Yiming Guan", "Youling Huang", "Wei Zhang", "Qingwei Lin", "Dongmei Zhang", "Saravan Rajmohan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Deep search requires agents to answer complex questions through multi-step web search, browsing, evidence comparison, and synthesis. A central challenge is deciding how to search when several directions look plausible but only some will later lead to reliable evidence. If an agent greedily follows the current best-looking direction, it may keep extending a weak continuation. If it explores without discipline, it may waste budget on disconnected trials. We propose TreeSeeker, an inference-time framework for controlled trial-and-error in deep search. TreeSeeker organizes search as branch-and-return search over tree-structured states, where each branch is a tentative direction for a sub-goal. At each round, TreeSearch reads all sub-goal trees, identifies active goals, and uses textual UCB signals of value, uncertainty, and risk to select among exploiting a promising branch, exploring an uncertain alternative, or pruning an unproductive continuation and returning to an earlier branch point. TreeMem supports this control loop by keeping evidence, uncertainty, conflicts, progress, and failure cues attached to the branches that produced them, so trial outcomes can guide later decisions. Experiments on XBench-DeepSearch, BrowseComp, and BrowseComp-ZH show that TreeSeeker consistently outperforms strong open-source baselines, suggesting that explicit branch-and-return control complements stronger reasoning and tool execution.", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.13289", "title": "HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers", "authors": [ "Guozhen Zhang", "Xuerui Qiu", "Yutao Cui", "Tianhui Song", "Changlin Li", "Junzhe Li", "Tao Huang", "Xiao Zhang", "Yang Li", "Jianbing Wu", "Miles Yang", "Zhao Zhong", "Liefeng Bo", "Limin Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Holistic visual tokenizers are fundamental to unified multimodal models (UMMs) as they map diverse visual inputs into a unified representation space. In this paper, we present HYDRA-X, the first UMM that unifies image and video tokenization within a single Vision Transformer (ViT). Our design is driven by two core challenges: efficiently injecting spatiotemporal reconstruction capability into a native ViT, and embedding image- and video-level semantic awareness into the latent space. To address the first, comprehensive ablations reveal two key findings: (1) frame-level causal temporal attention suffices for visual reconstruction, whereas full spatiotemporal attention degrades it; and (2) hierarchical temporal compression substantially outperforms single-step alternatives. To tackle the second, we propose a lightweight decompressor that upsamples temporally compressed features under joint image-video teacher supervision, thereby enforcing complementary semantic structures within the compact latent space. Building on this holistic tokenizer, we further propose a principled improvement of the editing pipeline: source-target interaction should occur at the latent level inside the tokenizer rather than at the semantic level inside the LLM, substantially improving editing consistency and accelerating convergence. Instantiated at the 7B dense model, HYDRA-X achieves strong performance across image and video understanding and generation tasks, paving the way for future unified-tokenizer UMMs.", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.12303", "title": "From 2D Grids to 1D Tokens: Reforming Shared Representations for Multimodal Image Fusion", "authors": [ "Yuchen Xian", "Yunqiu Xu", "Yang He", "Yi Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://zju-xyc.github.io/1D-Fusion-Project-Page/", "abstract": "Multimodal image fusion aims to integrate complementary information from different modalities into a fused image that preserves rich local details while maintaining globally consistent appearance. Existing approaches build shared representations on 2D feature grids, which excel at modeling local structures but offer limited leverage over image-level global appearance factors. To balance these objectives, we introduce a compact 1D token interface based on a frozen pretrained image tokenizer for modeling non-local appearance/base factors. Rather than using the tokenizer as a reconstruction backbone, our design uses the 1D token space as a global carrier while retaining the 2D spatial pathway for local structure restoration. Specifically, we introduce Selective Token Editing (STE), which sparsely updates/replaces a small set of critical tokens, providing a lightweight mechanism to steer global appearance coherence while keeping the fusion backbone unchanged and avoiding extra losses. Experiments on four commonly used benchmarks show that our method achieves the best overall performance, with consistent, multi-metric improvements in both global coherence and local fidelity. Project page: https://zju-xyc.github.io/1D-Fusion-Project-Page/", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.12243", "title": "VIA-SD: Verification via Intra-Model Routing for Speculative Decoding", "authors": [ "Yuchen Xian", "Yang He", "Yunqiu Xu", "Yi Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://zju-xyc.github.io/VIA-SD-Project-Page/", "abstract": "Speculative decoding (SD) addresses the high inference costs of LLMs by having lightweight drafters generate candidates for large verifiers to validate in parallel. Existing draft-verify methods use binary decisions: accept or fully recompute. Yet we find that many rejected tokens can be verified correctly by a slim submodel derived from the full verifier via intra-model routing, instead of the full verifier. This motivates our slim-verifier to handle tokens requiring moderate verification resources, reducing expensive large-model calls. We propose Verification via Intra-Model Routing for Speculative Decoding (VIA-SD), a multi-tier framework using a routed slim-verifier. Draft tokens are processed hierarchically: direct acceptance for high-confidence cases, slim-verifier regeneration for medium-confidence cases, and full-model verification for uncertain cases. Across four representative tasks and multiple model families, VIA-SD reduces rejection rates by 0.10-0.22 and delivers 10-20% speedups over strong SD baselines, while achieving 2.5-3x acceleration over non-drafting decoding. Moreover, VIA-SD is compatible with existing SD frameworks without modifying their training procedures. Our results suggest multi-tier SD as a general paradigm for scalable and efficient LLM inference. Project page: https://zju-xyc.github.io/VIA-SD-Project-Page/", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.10896", "title": "Flash-GMM: A Memory-Efficient Kernel for Scalable Soft Clustering", "authors": [ "Gal Bloch", "Ariel Gera", "Matan Orbach", "Ohad Eytan", "Assaf Toledo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/IBM/Flash-GMM", "github_api_added_by": "user", "project_page": "", "abstract": "We present Flash-GMM, a fused Triton kernel for efficient computation of Gaussian Mixture Models (GMMs) over large-scale data in a single GPU pass. By eliminating the need to materialize the full responsibility matrix in GPU memory, Flash-GMM achieves a 20times speedup over existing implementations and enables training on datasets more than 100times larger than previously feasible on one device. To demonstrate its impact, we integrate Flash-GMM into the IVF coarse quantizer for approximate nearest-neighbor (ANN) search. We show that soft GMM clustering is now a viable drop-in replacement for k-means, and that GMM responsibilities can be leveraged to assign border vectors to multiple clusters. Our approach reaches fixed recall targets with up to 1.7times fewer distance computations, or equivalently, yields +2--12 recall@10 at matched computational cost. We release the kernel as an open-source project.", "github": "https://github.com/IBM/Flash-GMM" }, { "date": "2026-06-12", "arxiv_id": "2606.09446", "title": "Leveraging Morphology for Historical Script Metrological Analysis", "authors": [ "Malamatenia Vlachou Efstathiou", "Rapha\u00ebl Baena", "Dominique Stutzmann", "Mathieu Aubry" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/raphael-baena/morphology4metrology", "github_api_added_by": "user", "project_page": "https://malamatenia.github.io/morphology4metrology-analysis/", "abstract": "Advances in handwritten text recognition have enabled large-scale transcription of historical documents, but still provide limited access to interpretable visual measurements for paleography, the study of historical scripts. In this paper, our main insight is that morphological script analysis, in particular the capacity to learn character prototypes from line-level transcriptions, enables the definition of scalable, meaningful, and stable paleographic measurements. More precisely, we leverage a transformer-based detection architecture together with a prototype-based line reconstruction module to learn prototypical characters and their occurrence, deformation, and positioning. Our contributions are twofold. First, we introduce a deep architecture and learning methodology that enables efficient character modeling with only line-level transcription supervision, significantly improving over the Learnable Typewriter baseline and enabling accurate character bounding box prediction, unlocking its potential for paleographic measurements. Second, we introduce and demonstrate the paleographical relevance of automatic measurements enabled by our architecture for characters, bi-grams, and spaces between graphical units. For this demonstration, we extend the annotations of the codex Paris, BnF, fr. 2813, commissioned in the late fourteenth century by Charles V and copied by four hands, to 160 pages. We visualize our measurements over these pages, showing how they enable us not only to differentiate graphical profiles, but also to discover and analyze subtle variations. This case study outlines the scalability of our approach and its frugality in terms of required training data, since a single column of text is sufficient to compute our measurements on each of the 160 pages. Data and code are publicly available at: https://malamatenia.github.io/morphology4metrology-analysis.", "github": "https://github.com/raphael-baena/morphology4metrology" }, { "date": "2026-06-12", "arxiv_id": "2606.06113", "title": "Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback", "authors": [ "Huaisong Zhang", "Hao Yu", "Yuxuan Zhang", "Jiahe Wang", "Xinrui Chen", "Haoxiang Cao", "Feng Lu", "Wendong Zhang", "Changqian Yu", "Chun Yuan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/nianbai006/SDG", "github_api_added_by": "user", "project_page": "https://huggingface.co/collections/P1n3/sdg", "abstract": "Despite generating increasingly photorealistic images, text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures. Diagnosing these failures requires instance-level feedback that answers where a defect occurs, what type it is, why it is defective, and its importance to overall image quality. While recent dense-feedback methods move beyond scalar supervision, their heatmap-centric representations still formulate diagnosis as pixel-field regression, making it difficult to localize variable-cardinality defects and bind semantic reasons to individual failures. To address this representation bottleneck, we propose Structured Defect Grounding (SDG), which casts T2I diagnosis as structured set prediction by modeling each defect as a (location, type, reason, importance) tuple. To make this formulation trainable and measurable, we introduce SDG-30K, a 30K-image dataset with box-grounded annotations across four modern T2I generators, together with a dedicated evaluation protocol, SDG-Eval. Building on this structured representation, we further present a diagnosis-to-alignment framework in which a Vision-Language Model (VLM) serves as the SDG detector, and BoxFlow-GRPO converts predicted defect sets into box-derived, importance-weighted spatial rewards for diffusion model alignment. Extensive experiments show that our SDG detector outperforms leading proprietary VLMs on structured defect grounding, while SDG-guided rewards consistently improve T2I alignment and support localized image refinement. These results establish SDG as a unified, instance-level interface for diagnosing, evaluating, and enhancing modern generative models.", "github": "https://github.com/nianbai006/SDG" }, { "date": "2026-06-12", "arxiv_id": "2606.08103", "title": "Revisiting Articulated Parts Perception in Robot Manipulation", "authors": [ "Xiaoqian Wu", "Yejie Guo", "Xiaoyang Chen", "Lixin Yang", "Cewu Lu", "Yong-Lu Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/enlighten0707/Geometric_Primary_Structure", "github_api_added_by": "user", "project_page": "https://enlighten0707.github.io/gps/", "abstract": "We are surrounded by various objects with movable, articulated parts, e.g., box, handle, door. An accurate and generalizable perception of articulated parts is essential to enhance robotic manipulation capabilities. Building on this need, recent efforts in articulated parts perception have followed two main directions: One line of work uses pose-based representation, which requires high manual cost; in parallel, affordance-based methods extract future object motion from point tracking without additional manual efforts, but suffer from low-quality data. In this paper, we propose a new representation of articulated parts, Geometric Primary Structure (GPS), an abstraction of the part geometry structure to balance scalability and quality. For efficient and scalable data collection, GPS is integrated with a portable Virtual Reality (VR) device and requires only one minute to annotate one object sequence. This direct human annotation provides higher quality than the estimated affordance. With this efficient VR-GPS system, we collect 41K frames for 234 objects across six part classes, and train a generalizable GPS model with a single RGB-D object image as input. For object manipulation, we deploy a heuristic policy based on GPS prediction. Without any in-domain fine-tuning, our method achieves an 73% success rate, covering 270 initial states for 9 objects. Our code, data and reusable tool are available at https://enlighten0707.github.io/gps.", "github": "https://github.com/enlighten0707/Geometric_Primary_Structure" }, { "date": "2026-06-12", "arxiv_id": "2606.13572", "title": "ArogyaSutra: A Multi-Agent Framework for Multimodal Medical Reasoning in Indic Languages", "authors": [ "Tanmoy Kanti Halder", "Akash Ghosh", "Subhadip Baidya", "Arijit Roy", "Sriparna Saha" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multimodal Large Language Models (MLLMs) have shown promising reasoning capabilities in general domains, yet their performance remains limited in specialized settings such as healthcare, especially in multilingual and low-resource scenarios. This gap is critical in regions like rural India, where patients often express complex medical queries in native Indic languages and rely on multimodal inputs such as medical images. Existing English-centric MLLMs struggle to support such use cases, limiting equitable access to AI-driven healthcare assistance. To address this challenge, we introduce ArogyaBodha, a large-scale multilingual multimodal medical question-answer dataset constructed from eight heterogeneous sources, covering 31 body systems, six imaging modalities, and 21 clinical domains across English and seven major Indian languages. We further propose ArogyaSutra, an actor-critic-based multi-agent framework that integrates tool grounding with dual-memory mechanisms for step-wise, reasoning-aware decision making, and uses stored actor-critic simulation trajectories for distillation. Experiments show that our dataset and framework improve multilingual medical reasoning accuracy across all Indic languages, with ablations validating the contribution of each component. The source code and dataset are available at: https://iitp-cse.github.io/ ArogyaSutra/", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.12488", "title": "A Stationary (and Therefore Compatible) Representation is All You Need", "authors": [ "Niccol\u00f2 Biondi", "Federico Pernici", "Simone Ricci", "Alberto Del Bimbo" ], "github_manual": "", "github_regex": "https://github.com/miccunifi/iamcl2r", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://niccobiondi.github.io/projects/stationary-tpami/", "abstract": "Learning compatible representations aims to learn feature representations that can be used interchangeably over time whenever a model undergoes updates. In this paper, we demonstrate that stationary representations learned by d-Simplex fixed classifiers imply compatibility as in its formal definition. This result establishes a foundation for future works and can be directly exploited in practical learning scenarios. We address the challenge of learning compatibility using d-Simplex fixed classifiers when the model is sequentially fine-tuned. Learning according to a d-Simplex fixed classifier with the cross-entropy loss aligns feature distributions at the first-order statistics. Consequently, it may not fully capture higher-order dependencies in the representation between model updates. To address this issue, we demonstrate that training the model using a d-Simplex fixed classifier through a convex combination of the cross-entropy loss and a contrastive loss not only captures higher-order dependencies, but is also equivalent to learning with the cross-entropy under the compatibility constraints. We confirm our findings with extensive experiments also considering a new scenario where a pre-trained model is sequentially fine-tuned and occasionally replaced with an improved model. We show that stationary representations enable uninterrupted retrieval services (without reprocessing gallery images) while improving performance during model updates and replacements, achieving state-of-the-art. Code at https://github.com/miccunifi/iamcl2r.", "github": "https://github.com/miccunifi/iamcl2r" }, { "date": "2026-06-12", "arxiv_id": "2606.12451", "title": "ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs", "authors": [ "Ashutosh Hathidara", "Sai Shruthi Sistla", "Sebastian Schreiber", "Sahil Bansal" ], "github_manual": "", "github_regex": "https://github.com/SAP/toolsense", "github_paper_md": "", "github_api": "https://github.com/SAP/toolsense", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models deployed as agents over large tool catalogs face a critical tool-retrieval bottleneck. As embedding-based retrieval approaches rely on compact encoders that may under-capture specialized tool semantics, parametric tool retrieval addresses this by encoding each tool as a virtual token appended to the LLM vocabulary, fine-tuned in two stages (memorization then retrieval SFT) to use the LLM as a retriever, achieving strong performance on standard ToolBench retrieval benchmarks. Yet these benchmarks use verbose, fully-specified queries, and their evaluation applies constrained decoding that restricts outputs to valid token paths, neither reveals whether the model actually understands its tools. We introduce ToolSense, an open-source LLM-powered diagnostic framework that takes any tool catalog as input and automatically generates three benchmarks: a Realistic Retrieval Benchmark (RRB) with queries at three ambiguity tiers, an MCQ probing benchmark, and a QA probing benchmark. Applying ToolSense to ToolBench (~47k tools) and evaluating five parametric model training configurations reveals a knowledge-retrieval dissociation: on RRB queries, several configurations collapse by ~50-64 percentage points compared to fully-specified ToolBench benchmarks, falling below the embedding-model baseline. Additionally, despite strong retrieval performance, some models score near-random on factual probes, suggesting a knowledge-retrieval dissociation. We open-source the ToolSense framework and the ToolBench diagnostic benchmarks at https://github.com/SAP/toolsense.", "github": "https://github.com/SAP/toolsense" }, { "date": "2026-06-12", "arxiv_id": "2606.11409", "title": "Risk Under Pressure: Compute-Aware Evaluation of Adversarial Robustness in Language Models", "authors": [ "Malikeh Ehghaghi", "Bogl\u00e1rka Ecsedi", "Marsha Chechik", "Colin Raffel" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/r-three/risk-under-pressure", "github_api_added_by": "user", "project_page": "", "abstract": "Adversarial robustness evaluations of large language models (LLMs) typically report attack success rate (ASR) under fixed query budgets, implicitly treating all attacks as equally costly. In practice, the computational expense of different attack strategies can vary by orders of magnitude. Consequently, ASR at a fixed budget can obscure the true effort required to jailbreak a model, thereby making it hard to determine whether an attack's cost justifies its payoff to the attacker. We propose a compute-aware evaluation framework based on computational pressure, measured in cumulative floating-point operations (FLOPs), as a proxy for adversarial effort. We introduce risk-compute curves, which map compute budgets to attack risk, and derive two metrics that summarize the average pressure required for a given attack to succeed. Across ten models spanning three families and four different stages in language model training and alignment, evaluated with three attack strategies (gradient-based, iterative refinement, and template-based) on two jailbreak robustness benchmarks, we find: (1) alignment training has non-monotonic effects on compute-space robustness; (2) scaling model size reduces gradient-based attack effectiveness but has limited impact on cheaper template-based attacks; (3) gradient-based attacks optimized on a surrogate model can transfer to a separate target model, providing a way to reduce attacker costs; (4) compute cost varies by up to {approx}5{times} across harm categories within a single model; and (5) safety-aligned RL increases aggregate cost while leaving some categories disproportionately accessible. We release our framework to enable compute-aware risk assessment and evaluation.", "github": "https://github.com/r-three/risk-under-pressure" }, { "date": "2026-06-12", "arxiv_id": "2606.07867", "title": "The Cold-Start Safety Gap in LLM Agents", "authors": [ "Chung-En Sun", "Linbo Liu", "Tsui-Wei Weng" ], "github_manual": "", "github_regex": "https://github.com/Trustworthy-ML-Lab/Agent-Cold-Start-Safety-Gap", "github_paper_md": "", "github_api": "https://github.com/Trustworthy-ML-Lab/Agent-Cold-Start-Safety-Gap", "github_api_added_by": "user", "project_page": "https://lilywenglab.github.io/Agent-Cold-Start-Safety-Gap/", "abstract": "Are tool-calling LLM agents equally safe throughout a conversation? We discover they are not: agents are most vulnerable at the very start of a session and become substantially safer after a few regular agentic tasks -- a phenomenon we term the cold-start safety gap. To study this systematically, we introduce Safety Over Depth for Agents (SODA), a benchmark that controls how many regular agentic tasks the agent completes before encountering a safety threat, supporting up to 20 preceding tasks. Evaluating 7 models from 4 families, safety improves by 9--52% as the number of preceding regular agentic tasks increases from zero to twenty. Representation analysis confirms that model hidden states gradually shift toward a safety-aligned region as more preceding tasks are present. By systematically studying which part of the preceding conversation matters most, we find that the regular agentic tasks themselves are the primary driver of safety, while the agent's own prior responses have less effect on safety but are essential for preserving later utility. This conclusion is further supported by evaluation on open-source safety benchmarks (AgentHarm, Agent Safety Bench) and utility benchmarks (BFCL, API-Bank), confirming that warming up the agent with regular agentic tasks before deployment makes it safer and preserves full capability. Based on these findings, we recommend a simple deployment strategy: having the agent complete a few regular agentic tasks before possible exposure to safety-critical requests mitigates the cold-start safety gap. Our code is available at https://github.com/Trustworthy-ML-Lab/Agent-Cold-Start-Safety-Gap", "github": "https://github.com/Trustworthy-ML-Lab/Agent-Cold-Start-Safety-Gap" }, { "date": "2026-06-12", "arxiv_id": "2606.10423", "title": "WebChallenger: A Reliable and Efficient Generalist Web Agent", "authors": [ "Jayoo Hwang", "Xiaowen Zhang", "Vedant Padwal" ], "github_manual": "", "github_regex": "https://github.com/jayoohwang1/webchallenger", "github_paper_md": "", "github_api": "https://github.com/jayoohwang1/webchallenger", "github_api_added_by": "user", "project_page": "https://jayoohwang1.github.io/webchallenger-site/", "abstract": "Autonomous web navigation remains challenging for LLM agents, and the strongest generalist systems rely on proprietary reasoning models whose inference cost is prohibitive for the repetitive tasks where such agents would be most useful. We argue this gap stems not from insufficient model capability but from agent architectures that fail to replicate three human cognitive advantages: selective attention to relevant page regions, persistent memory of website structure, and procedural fluency with common interaction patterns. We introduce WebChallenger, a web agent framework that addresses each gap through architecture design rather than model scale, built around PageMem: a structured page representation deterministically constructed from the DOM that exposes each page as a hierarchy of semantic sections with short summaries. On this shared substrate we build three mechanisms that mirror the three cognitive advantages: a divide-and-conquer observation pipeline that lets the agent skim section summaries and extract details only from task-relevant regions; a lightweight exploration and memory system that traverses each website once to build a reusable map of pages and element behaviors; and compound action workflows that collapse common multi-step interactions into single agent actions, handling partial state changes automatically. Because all three operate over PageMem, the framework generalizes across websites without site-specific adapters. Using off-the-shelf open-weight models without fine-tuning, our system achieves 56.3% on WebArena, 48.7% on VisualWebArena, 51.0% on Online-Mind2Web, and 70.9% on WorkArena, approaching frontier proprietary systems at a fraction of the cost. Our code is released at https://github.com/jayoohwang1/webchallenger", "github": "https://github.com/jayoohwang1/webchallenger" }, { "date": "2026-06-12", "arxiv_id": "2606.13594", "title": "See What I See, Know What I Think: Dense Latent Communication Across Heterogeneous Agents", "authors": [ "Siyi Chen", "Xiaoyan Zhang", "Meng Wu", "Jonathan Tremblay", "Valts Blukis", "Stan Birchfield", "Rene Vidal", "Alvaro Velasquez", "Sijia Liu", "Qing Qu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://chicychen.github.io/dense-hetero-latent-mas/", "abstract": "Multi-agent systems communicate mostly through text, paying a lossy and expensive decode and re-encode cost. KV-cache communication is a promising alternative, yet most prior work is homogeneous, using duplicate copies of the same model, and avoids the central challenge of cross-model latent alignment; existing heterogeneous methods are also restrictive, typically assuming shared input and using transferred caches mainly for steering. We study a more fundamental question: can heterogeneous agents be aligned well enough to perform real \"mind reading\" and transfer both what one agent sees and how it thinks? Our information-structure analysis reveals a duality: context-aware transfer is driven by sparse reasoning signals, while context-unaware transfer, where the receiver sees no input, requires dense contextual knowledge preservation. Motivated by this, we propose dense alignment for heterogeneous KV-cache communication via a lightweight cross-model cache transformation and two-phase training: reconstruction followed by generation. Across all six directions of {Qwen3-4B, 8B, 14B} and six in-domain and out-of-domain benchmarks, our method outperforms prior heterogeneous baselines, matches or exceeds text communication in context-aware settings at roughly 2 to 3 times lower compute, and remains effective in context-unaware transfer where prior methods collapse.", "github": "" }, { "date": "2026-06-12", "arxiv_id": "2606.13174", "title": "Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agents", "authors": [ "Yujun Zhou", "Kehan Guo", "Haomin Zhuang", "Xiangqi Wang", "Yue Huang", "Zhenwen Liang", "Pin-Yu Chen", "Tian Gao", "Nuno Moniz", "Nitesh V. Chawla", "Xiangliang Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YujunZhou/tellonce", "github_api_added_by": "user", "project_page": "", "abstract": "Interactive LLM agents are becoming part of daily work, but they do not reliably become easier to work with over time: a correction remembered in one session may still be violated in the next. We study this gap between preference access and preference compliance. In tasks derived from anonymized real-user friction cases, Mem0 memory still leaves 57.5% of applicable preference checks violated. We introduce Test-time Rule Acquisition and Compiled Enforcement (TRACE), a drop-in skill-layer pipeline for coding-agent runtimes that mines user corrections, rewrites them as atomic rules, and compiles them into runtime checks that must pass before an agent completes future tasks. Unlike runtime checks written ahead of time by developers, TRACE skills come from the user's own chat corrections. We evaluate TRACE with simulated user-in-the-loop experiments on ClawArena coding-agent tasks and MemoryArena-derived memory-intensive tasks. On ClawArena, TRACE reduces held-out preference violation from 100.0% to 37.6% on in-distribution tasks and from 100.0% to 2.0% on out-of-distribution tasks. On MemoryArena-derived tasks, TRACE reduces in-distribution violation from 100.0% to 60.5% while matching or exceeding the strongest memory baseline on task pass. These results suggest that compiling corrections into runtime enforcement can address a repeated-friction failure mode that memory alone does not reliably solve, reducing the need for users to restate the same correction across future sessions. Experiment code is available at https://github.com/YujunZhou/TRACE_exp, and the deployable skill is available at https://github.com/YujunZhou/tellonce.", "github": "https://github.com/YujunZhou/tellonce" }, { "date": "2026-06-12", "arxiv_id": "2606.12730", "title": "Rethinking Psychometric Evaluation of LLMs: When and Why Self-Reports Predict Behavior", "authors": [ "Rafal Kocielnik", "Pengrui Han", "Peiyang Song", "Myrl G. Marmarelis", "Ramit Debnath", "Dean Mobbs", "Anima Anandkumar", "R. Michael Alvarez" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/psychology-of-AI/Rethinking-Pyschometric-Eval-LLMs", "github_api_added_by": "user", "project_page": "", "abstract": "Anticipating LLM behavioral tendencies from low-cost psychometric probes is critical for safe deployment, but only if self-reports (SR) reliably predict behavior. Recent work documented substantial SR-behavior dissociation in LLMs, but relied on broad personality traits (Big 5) that predict specific behaviors weakly, even in humans. Furthermore, the isolation of conversational sessions combined with weak context matching left open whether LLMs truly lack coherence or whether the conditions needed to detect such coherence were not met. We contrast Big 5 with the Theory of Planned Behavior (TPB), which measures intention targeted to a specific behavior and predicts human behavior substantially better than broad traits. We run experiments across four behavioral tasks and 11 frontier LLMs, while also varying session context and identity induction. We find that SR-behavior coherence exists but is selective. 1) Within a shared conversation, the Theory of Planned Behavior reaches human-level coherence; Big 5 does not. 2) Across separate conversations, coherence survives only for behaviors anchored outside the immediate prompt, such as implicit bias shaped by training, and collapses when behavior is strongly primed by context, as with sycophancy. 3) Persona prompting makes self-reports more consistent across conversations, but does not bring behavior into alignment. These findings suggest that coarse personality frameworks, such as Big 5 may not be the best tools for testing deployment behavior. More task- and behavior-specific instruments are needed, and even these must be evaluated across tasks and contexts.", "github": "https://github.com/psychology-of-AI/Rethinking-Pyschometric-Eval-LLMs" }, { "date": "2026-06-12", "arxiv_id": "2606.00467", "title": "On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance", "authors": [ "Etienne Casanova", "Rafal Kocielnik", "R. Michael Alvarez" ], "github_manual": "", "github_regex": "", "github_paper_md": "https://github.com/etmaca5/llm-internalized-priors-for-annotation", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-internalized priors interact with user-provided instructions. We investigate three dimensions of this interaction: (1) how an LLM's familiarity with data and task definitions affects performance, (2) the extent to which additional information in prompts can correct zero-shot errors (\"decision stickiness\"), and (3) model susceptibility to misaligned task definitions. Through experiments on toxicity detection across diverse datasets (spanning social media, gaming, news, and forums) using both dense and mixture-of-experts models, we find that nearly two-thirds of zero-shot errors are resistant to correction, with an overall rescue rate (fraction of initial errors corrected by prompting) of only 34.8%. High-confidence errors prove especially resistant to correction. When given misaligned definitions, LLMs follow them while maintaining confidence levels unchanged from the aligned condition. Crucially, we introduce Definition-Specific Familiarity (DSF), which measures alignment between a model's internal concept and the task definition. After controlling for dataset-level confounds, DSF shows a positive association with model performance (partial r = +0.41), while three distinct memorization metrics (ROUGE-L, BERTScore, and embedding cosine similarity) all fail to show a positive association. These findings show the limitations of prompt-based correction in annotation tasks, highlighting the importance of definition alignment over text-level memorization.", "github": "https://github.com/etmaca5/llm-internalized-priors-for-annotation" }, { "date": "2026-06-15", "arxiv_id": "2606.14502", "title": "From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI", "authors": [ "Yongheng Zhang", "Ziang Liu", "Jiaxuan Zhu", "Shuai Wang", "Xiangqi Chen", "Haojing Huang", "Jiayi Kuang", "Siyu Chen", "Ao Shen", "Hao Wu", "Qiufeng Wang", "Qian-Wen Zhang", "Junnan Dong", "Wenhao Jiang", "Ying Shen", "Hai-Tao Zheng", "Yinghui Li", "Di Yin", "Xing Sun", "Philip S. Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://from-chatbot-to-digital-colleague.github.io/", "abstract": "Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable of reasoning, action, memory, and self-improvement. We conceptualize this transition as a shift from Chatbot to Digital Colleague: from conversational answers to persistent work. We organize this transition along two tightly coupled dimensions. First, at the cognitive core level, LLMs are advancing from Chatbot-era \"fast thinking\" systems driven by next-token prediction toward Thinking LLMs that leverage inference-time computation, Chain-of-Thought reasoning, reflection, process supervision, and reinforcement learning to support more deliberate and reliable cognition. Second, at the tool-augmented task execution level, LLMs are progressing from tool-calling Agents that invoke external resources in an ad hoc manner toward OpenClaw-style workstation systems (OpenClaw) equipped with persistent Workspaces, skills, verification loops, and governance. The \"Workspace + Skill\" paradigm makes episodic tool use colleague-like via state persistence, reusable procedures, task closure, and experience reuse. We examine data construction shifts from instruction-response pairs to State-Action-Observation trajectories and evaluation from static benchmarks to sandboxed, auditable, self-evolving AI ecosystems.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.12683", "title": "From AGI to ASI", "authors": [ "Tim Genewein", "Matija Franklin", "Alexander Lerchner", "Laurent Orseau", "Samuel Albanie", "Adam Bales", "Cole Wyeth", "Stephanie Chan", "Iason Gabriel", "Joel Z. Leibo", "Allan Dafoe", "Marcus Hutter", "Thore Graepel", "Shane Legg" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations. Achieving this goal would have profound and far-reaching impacts on human society, which raises many complex questions for the decade ahead. This report investigates how AI itself might continue to develop in a post-AGI world along the continuum of machine intelligence. The endpoint of this continuum, Universal AI, is theoretically well understood, which provides some formal grounding for the main focus of this report: the transition from human-level AGI to artificial general superintelligence, which, intuitively, can be understood as a system that is more intelligent and cognitively capable than large organisations of humans. After characterizing ASI, the report discusses four potential pathways from AGI to ASI: scaling AGI, AI paradigm shifts, recursive improvement, and ASI emerging from large-scale multi-agent collectives. The report then discusses possible frictions and bottlenecks along these pathways. Determining whether the impact of these frictions will be negligible or substantial raises a number of concrete open research questions. Due to large uncertainties for predicting ASI progress, it cannot be ruled out that AI progress might continue to accelerate over the next years. This could imply that the image of a single transformative step change, caused by the introduction of human-level AGI into our society, could be inaccurate. More apt might be the prospect of a series of transformative societal changes caused by AI-enabled progress and breakthroughs across many areas of science and technology. Preparing for this prospect requires a massively interdisciplinary endeavour of global scope and interest.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.14702", "title": "OmniVideo-100K: A Dataset for Audio-Visual Reasoning through Structured Scripts and Evidence Chains", "authors": [ "Xinyue Cai", "Chaoyou Fu", "Yi-Fan Zhang", "Ran He", "Caifeng Shan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/MiG-NJU/OmniVideo-100K", "github_api_added_by": "user", "project_page": "https://yzlmhzz.github.io/OmniVideo-100K/#", "abstract": "Current automated pipelines for audio-visual Question Answering (QA) generally adopt a ``video-caption-QA'' paradigm. However, these methods typically segment videos into short clips and generate separate descriptions for audio and visual modalities. This decoupled processing severs inherent associations between sounds and their visual sources, while independent clip processing often causes inconsistent descriptions of the same entity across segments. Furthermore, coupling long-text comprehension and QA synthesis into a single step often restricts models to localized events, yielding questions lacking long-term temporal connections and deep cross-modal reasoning. To address these issues, we propose an automated data engine featuring two mechanisms: (1) Entity-Anchored Video Scripting transforms videos into structured scripts, comprising summaries, main entity lists, and segment-wise audio-visual descriptions. The entity list serves as a global prior to ensure cross-segment referential consistency and reconstruct audio-visual associations. (2) Clue-Guided QA Generation prompts models to first mine cross-segment, multimodal clues from the script, and subsequently generate QA pairs based on these high-value clues. Leveraging this pipeline, we construct the instruction-tuning dataset OmniVideo-100K and a human-verified test set, OmniVideo-Test. Fine-tuning VITA-1.5, Qwen2.5-Omni-7B and Qwen3-Omni-30B on OmniVideo-100K yields performance gains of up to 20.59% on OmniVideo-Test, demonstrating strong generalization (up to 12.64% improvements) across established benchmarks like Daily-Omni and JointAVBench.", "github": "https://github.com/MiG-NJU/OmniVideo-100K" }, { "date": "2026-06-15", "arxiv_id": "2606.14579", "title": "VISTA: View-Consistent Self-Verified Training for GUI Grounding", "authors": [ "Xinyu Qiu", "Yunzhu Zhang", "Heng Jia", "Shuheng Shen", "Changhua Meng", "Linchao Zhu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZJUSCL/VISTA", "github_api_added_by": "user", "project_page": "https://zjuscl.github.io/VISTA/", "abstract": "When applying Group Relative Policy Optimization (GRPO) for GUI Grounding, rollouts are sampled from a single screenshot view; groups often become either all failures on difficult instances or all successes on easy ones, yielding no useful relative advantage. We propose VISTA (View-Consistent Self-Verified Training), a GRPO-based training framework that constructs each comparison group from multiple target-preserving views of the same GUI instance.Each view is generated by a crop that keeps the target element visible and remaps its box exactly, so model rollouts are compared across semantically equivalent but geometrically different inputs. To stabilize short coordinate generation without turning reinforcement learning into unconditional imitation, VISTA further adds a self-verified cross-view anchor: an oracle answer optimized with an advantage-weighted loss, excluded from the group baseline and activated only when the model has produced a maximum-reward rollout. Across five GUI-grounding benchmarks and multiple Qwen backbones, VISTA consistently improves grounding accuracy.On ScreenSpot-Pro, it raises Qwen3-VL 4B/8B/30B-A3B from 55.5/52.7/53.7 to 63.4/65.8/67.0. Robustness analyses further show higher worst-view accuracy and lower prediction flip rates.", "github": "https://github.com/ZJUSCL/VISTA" }, { "date": "2026-06-15", "arxiv_id": "2606.12883", "title": "The Hidden Power of Scaling Factor in LoRA Optimization", "authors": [ "Zicheng Zhang", "Haoran Li", "Jiaxing Wang", "Guoqiang Gong", "Anqi Li", "Yudong Hu", "Ting Xiong", "Yurong Gao", "Junxing Hu", "Zhida Jiang", "Yifeng Zhang", "Pengzhang Liu", "Qixia Jiang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "In Low-Rank Adaptation (LoRA), the scaling factor \u03b1 is often treated as a mere complement to the learning rate, yet its role in optimization remains poorly understood. In this paper, we reveal that the scaling factor \u03b1 and the learning rate function differently, with \u03b1 emerging as the dominant driver of effective optimization, delivering gains that cannot be replicated by learning rate scaling alone. Through the synergy of extensive empirical analysis and a theoretical Signal-Drift framework, we uncover three findings into LoRA's scaling mechanism: First, LoRA's spectral suppression smooths the optimization landscape, rendering standard hyperparameters overly conservative and creating an optimization gap. Second, when leveraging this smoothness to accelerate convergence, \u03b1 outperforms the learning rate by amplifying the task signal without increasing the drift ratio. Third, the optimal scaling factor follows a sublinear relationship with the rank, well characterized by a square-root law with an unexpectedly large coefficient, revealing the insufficient scaling of existing rank-tied heuristics. Based on these insights, we propose LoRA-\u03b1, a minimalist framework that restores \u03b1 to its principled regime, making LoRA compatible with standard small learning rates. Extensive evaluations across diverse tasks demonstrate that LoRA-\u03b1 consistently improves performance while streamlining hyperparameter search, unleashing the learning potential of LoRA.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.06574", "title": "Skip a Layer or Loop It? Learning Program-of-Layers in LLMs", "authors": [ "Ziyue Li", "Yang Li", "Tianyi Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tianyi-lab/PoLar", "github_api_added_by": "user", "project_page": "", "abstract": "Large language models (LLMs) perform inference by following a fixed depth and order, non-recurrent execution of all layers. We reveal the wide existence of training-free, flexible, dynamic program-of-layers (PoLar), where pretrained layers can be packed as modules and then skipped or looped to form a customized program for each input. For most inputs, substantially shorter program executions can achieve the same or better accuracy, while incorrect predictions of the original LLM can be corrected by alternative programs with fewer layers. These observations indicate that inference admits multiple valid latent computations beyond the standard forward pass. To efficiently achieve PoLar in practice, we propose a lightweight PoLar prediction network, which learns to generate execution programs that dynamically skip or repeat pretrained layers for each input. Experiments on mathematical reasoning benchmarks demonstrate that PoLar consistently improves accuracy over standard inference and prior dynamic-depth methods, often while executing fewer layers, and that these gains persist under out-of-distribution evaluation. Our results suggest that fixed-depth execution captures only a narrow subset of an LLM's latent reasoning capacity.", "github": "https://github.com/tianyi-lab/PoLar" }, { "date": "2026-06-15", "arxiv_id": "2606.11599", "title": "When is Your LLM Steerable?", "authors": [ "Chenrui Fan", "Yize Cheng", "Ming Li", "Soheil Feizi", "Tianyi Zhou" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Fcr09/SteerBoost", "github_api_added_by": "user", "project_page": "", "abstract": "Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depends on the prompt, concept, model, and steering configuration. Finding the regime and boundaries of successful steering typically requires expensive grid searches and post-hoc evaluation of full autoregressive rollouts. In this work, we investigate whether steerability can be predicted from the model's internal states at the beginning of the generation process, e.g., after generating the first few tokens, and how to leverage such a predictor to improve steering success rate. To this end, we first introduce ASTEER, a testbed including 1.4M steered generations, spanning 150 concepts with each steering success/failure labeled. Leveraging this testbed, we analyze the model's early decoding dynamics by extracting features that compare hidden states before and after steering across layers and initial decoding steps. These features help us understand how steering's effects propagate along layers and token positions, which provide key information for steerability prediction. We then train a Gradient Boosting Decision Trees (GBDT) classifier on these features to predict whether an intervention will under-steer, succeed, or over-steer without requiring full rollout. Our predictor achieves around 0.7 macro-F1 score on unseen concepts, demonstrating that early hidden states encode substantial, structured information about eventual steering efficacy. We further leverage this steerability predictor as guidance for steering strength searching, achieving near-optimal performance with a small fraction of decoding cost.", "github": "https://github.com/Fcr09/SteerBoost" }, { "date": "2026-06-15", "arxiv_id": "2606.06036", "title": "Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents", "authors": [ "Shuo Ji", "Yibo Li", "Bryan Hooi" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Ji-shuo/MRAgent", "github_api_added_by": "user", "project_page": "", "abstract": "Despite recent progress, LLM agents still struggle with reasoning over long interaction histories. While current memory-augmented agents rely on a static retrieve-then-reason paradigm, this rigid pipeline design prevents them from dynamically adapting memory access to intermediate evidence discovered during inference. To bridge this gap, we propose MRAgent, a framework that combines an associative memory graph with an active reconstruction mechanism. We represent memory as a Cue-Tag-Content graph, where associative tags serve as semantic bridges connecting fine-grained cues to memory contents. Operating on this structure, our active reconstruction mechanism integrates LLM reasoning directly into memory access, allowing the agent to iteratively explore and prune retrieval paths based on accumulated evidence. This ensures that memory retrieval is dynamically adapted to the reasoning context while avoiding combinatorial explosion caused by unconstrained expansion. Experiments on the LoCoMo benchmark and LongMemEval benchmark demonstrate significant improvements over strong baselines (up to 23%), while substantially reducing token and runtime cost, highlighting the effectiveness of active and associative reconstruction for long-horizon memory reasoning.", "github": "https://github.com/Ji-shuo/MRAgent" }, { "date": "2026-06-15", "arxiv_id": "2606.14697", "title": "ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning", "authors": [ "Sicheng Yang", "Hangjie Yuan", "Wenjun Zhang", "Jinwang Wang", "Yichen Qian", "Weihua Chen", "Fan Wang", "Lei Zhu" ], "github_manual": "", "github_regex": "https://github.com/alibaba-damo-academy/ClinHallu", "github_paper_md": "", "github_api": "https://github.com/alibaba-damo-academy/ClinHallu", "github_api_added_by": "user", "project_page": "", "abstract": "Building trustworthy medical multimodal large language models (MLLMs) is critical for reliable clinical decision support. Existing medical hallucination benchmarks mainly focus on data collection, but often ignore where hallucinations originate within the reasoning process. We find that hallucination sources vary across samples: errors may arise from visual misrecognition, incorrect medical knowledge recall, or flawed reasoning integration. To enable source-level hallucination diagnosis, we introduce ClinHallu, a benchmark for stage-wise hallucination diagnosis in medical MLLM reasoning. ClinHallu contains 7,031 validated instances, where each instance is augmented with a structured reasoning trace decomposed into Visual Recognition, Knowledge Recall, and Reasoning Integration. We also use stage-replacement interventions to measure how correcting specific stages affects the final answer. Beyond evaluation, we show that trace-supervised fine-tuning reduces stage-wise hallucinations. ClinHallu provides a fine-grained hallucination testbed for diagnosing and mitigating reasoning failures in medical MLLMs. The benchmark is publicly available at https://github.com/alibaba-damo-academy/ClinHallu.", "github": "https://github.com/alibaba-damo-academy/ClinHallu" }, { "date": "2026-06-15", "arxiv_id": "2606.13657", "title": "Dense Supervision, Sparse Updates: On the Sparsity and Geometry of On-Policy Distillation", "authors": [ "Guo Yu", "Wenlin Liu", "Yulan Hu", "Hao-Xuan Ma", "Jun-Peng Jiang", "Han-Jia Ye" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SydCS/OPD-Param-Analysis", "github_api_added_by": "user", "project_page": "", "abstract": "On-policy distillation (OPD) has recently become a prominent post-training recipe as it combines two desirable ingredients: on-policy student trajectories and dense teacher supervision, yet how this hybrid changes a model's parameters remains unclear. Across several language and vision-language model pairs and use cases, our analysis yields two main findings. On sparsity, OPD-style updates are small and coordinate-sparse. They are distributed across layers and are usually FFN-heavy. This sparse structure is operationally useful: training only the discovered subnetwork recovers nearly the same performance as full OPD. However, the sparsity-inducing SGD optimizer underperforms AdamW in our optimizer ablation, likely because dense teacher supervision preserves heterogeneous coordinate-wise gradient scales where AdamW's adaptive scaling remains useful. On geometry, the updates are numerically full-rank but spectrally concentrated; they lie mostly away from the principal singular subspaces of the source weights and fall disproportionately on coordinates where the source weights are close to zero. These findings suggest that dense teacher supervision does not turn OPD into ordinary dense parameter rewriting; instead, OPD retains important geometric signatures of on-policy post-training.", "github": "https://github.com/SydCS/OPD-Param-Analysis" }, { "date": "2026-06-15", "arxiv_id": "2606.14409", "title": "Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack", "authors": [ "He Zhang", "Lingzhu Xiang", "Haitao Lin", "Zeyu Huang", "Minghui Wang", "Dingyan Zhong", "Yubo Dong", "Yihao Wu", "Yongming Rao", "Dongsheng Zhang", "Wanjia He", "Ling Chen", "Kai Huang", "Jiahao Chen", "Sichang Su", "Xumin Yu", "Ziyi Wang", "Chengwei Zhu", "Xiao Teng", "Yuchun Guo", "Yufeng Zhang", "Yuandong Liu", "Rui Wang", "Zisheng Lu", "Han Hu", "Zhengyou Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "In this report, we present Hy-Embodied-0.5-VLA, abbreviated as HyVLA-0.5, an end-to-end system that spans the full robot learning stack: data collection, model design, continued pre-training and supervised fine-tuning, RL post-training, and real-world deployment. Each component serves a distinct role in this stack.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.14249", "title": "HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry", "authors": [ "Tingyang Chen", "Shuo Lu", "Kang Zhao", "Weicheng Meng", "Hanlin Teng", "Tianhao Li", "Chao Li", "Xule Liu", "Jian Liang", "Zhizhong Zhang", "Yuan Xie", "Heng Qu", "Kun Shao", "Jian Luan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "AI agent performance depends critically on the runtime harness, comprising the prompts, tools, memory, and control flow that mediate how a model observes, reasons, and acts. Yet today's harnesses remain largely hand-crafted and static: each new model or task still demands bespoke scaffolding, and the rich traces produced during execution are rarely distilled back into systematic improvement. We introduce HarnessX, a foundry for composable, adaptive, and evolvable agent harnesses. HarnessX assembles typed harness primitives via a substitution algebra, adapts them through AEGIS, a trace-driven multi-agent evolution engine grounded in an operational mirror between symbolic adaptation and reinforcement learning, and closes the harness-model loop by turning trajectories into both harness updates and model training signal. Across five benchmarks (ALFWorld, GAIA, WebShop, tau^3-Bench, and SWE-bench Verified), HarnessX yields an average gain of +14.5% (up to +44.0%), with gains largest where baselines are lowest. These results suggest that agent progress need not come from model scaling alone: composing and evolving runtime interfaces from execution feedback is an actionable and complementary lever. The complete codebase will be open-sourced in a future release.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.13769", "title": "\u03bc_0: A Scalable 3D Interaction-Trace World Model", "authors": [ "Seungjae Lee", "Yoonkyo Jung", "Jusuk Lee", "Jonghun Shin", "Amir Hossein Shahidzadeh", "Yao-Chih Lee", "H. Jin Kim", "Jia-Bin Huang", "Furong Huang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://mu0-wm.github.io/", "abstract": "World models that capture how actions induce physical change enable scalable robot learning without reliance on embodiment-specific action labels. Pixel-space video models provide broad visual priors but expend model capacity on dense appearance reconstruction, while direct action models require embodiment-specific labels that hinder scalability. We present \u03bc_0, a scalable world model based on 3D traces. Rather than predicting dense pixels or directly modeling actions, \u03bc_0 forecasts smooth 3D trajectories for salient interaction points such as objects, tools, hands, and contact regions, yielding a compact, embodiment-agnostic motion interface. To enable training from diverse video sources, our TraceExtract system automatically extracts 3D supervision by selecting keypoints, constructing globally aligned traces, and associating motion segments with hierarchical language captions. This TraceExtract supervision pretrains \u03bc_0 by combining a pretrained vision-language backbone with a modular trace expert, which represents each query via B-spline control points and predicts future traces. Experiments show that \u03bc_0 outperforms baselines in both 2D and 3D trace prediction, including trace prediction models and tokenized VLM methods. Because \u03bc_0 is frozen and reusable, it can be paired with action experts for downstream robot embodiments. Despite action-free pretraining, the resulting trace-conditioned policies achieve performance competitive with VLA models pretrained with action supervision, such as \u03c0_0. These results establish 3D traces as a scalable and transferable representation for cross-embodiment manipulation.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.13432", "title": "OmniDirector: General Multi-Shot Camera Cloning without Cross-Paired Data", "authors": [ "Jiwen Liu", "Shujuan Li", "Zhixue Fang", "Xiaohan Li", "Yan Zhou", "Zijie Meng", "Zhimin Zhang", "Yawen Luo", "Guoxin Zhang", "Yu-Shen Liu", "Pengfei Wan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/lisj575/OmniDirector", "github_api_added_by": "user", "project_page": "https://ymlinfeng.github.io/OmniDirector.github.io/", "abstract": "Cloning camera motion from reference videos is an important task in video generation, as videos provide intuitive and precise control. Existing methods either directly use parametric representations that fail to handle multi-shot generation or synthesize cross-paired data, which suffer from data scarcity, resulting in poor performance in complicated camera motion cloning. To address these issues, we introduce a general camera motion representation that encodes cameras as grid motion videos. This camera grid represents the camera parameters visually and supports the integration of diverse trajectories for multi-shot video generation. Building upon this, we propose OmniDirector, a unified framework trained on a million-scale camera grid-video pairs that coordinates characters, actions, and cameras to provide director-level control for multimodal diffusion transformers. Furthermore, we design a novel hierarchical prompt expansion agent that harmoniously integrates different control signals by systematically describing camera motion and visual content through understanding signal relationships. Extensive experiments demonstrate the superior performance and outstanding controllability of our framework. Project page: https://ymlinfeng.github.io/OmniDirector.github.io/", "github": "https://github.com/lisj575/OmniDirector" }, { "date": "2026-06-15", "arxiv_id": "2606.13707", "title": "Orchestra-o1: Omnimodal Agent Orchestration", "authors": [ "Fan Zhang", "Vireo Zhang", "Shengju Qian", "Haoxuan Li", "Hao Wu", "Jinyang Wu", "Donghao Zhou", "Zhihong Zhu", "Zheng Lian", "Xin Wang", "Pheng-Ann Heng" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/zfkarl/Orchestra-o1", "github_api_added_by": "user", "project_page": "", "abstract": "The recent success of agent swarms has shifted the paradigm of large language model (LLM)-based agents from single-agent workflows to multi-agent systems, highlighting the importance of agent orchestration for task decomposition and collaboration. However, existing orchestration frameworks are limited to a narrow set of modalities and struggle to generalize to more complex settings where heterogeneous modalities coexist and interact. This limitation becomes particularly pronounced in omnimodal scenarios, where tasks require the unified understanding and coordination of diverse inputs such as text, image, audio, and video. In this work, we propose Orchestra-o1, an omnimodal agent orchestration framework designed to support efficient agent collaboration across multiple modalities. Orchestra-o1 introduces a unified orchestration mechanism that enables modality-aware task decomposition, online sub-agent specialization, and parallel sub-task execution. This scalable design allows agent systems to effectively tackle complex real-world tasks involving heterogeneous information sources, surpassing the second-best approach by 10.3% accuracy on the OmniGAIA benchmark. Furthermore, we introduce decision-aligned group relative policy optimization (DA-GRPO), an efficient agentic reinforcement learning approach for training Orchestra-o1-8B, which also achieves state-of-the-art performance against all existing open-source omnimodal agents.", "github": "https://github.com/zfkarl/Orchestra-o1" }, { "date": "2026-06-15", "arxiv_id": "2606.10813", "title": "RedAct: Redacting Agent Capability Traces for Procedural Skill Protection", "authors": [ "Shuwen Xu", "Zhitao He", "Yi R. Fung" ], "github_manual": "", "github_regex": "https://github.com/XuShuwenn/RedAct", "github_paper_md": "", "github_api": "https://github.com/XuShuwenn/RedAct", "github_api_added_by": "user", "project_page": "https://xushuwenn.github.io/RedAct_Website/", "abstract": "Users rely on execution traces to observe agent behavior, diagnose failures, and ensure accountability. These traces contain rich procedural detail, including tool invocations, intermediate decisions, and error-recovery logic. Yet this detail can expose private procedural skills, allowing downstream methods to recover key formulas, thresholds, and strategies without access to model weights or skill files. To quantify this risk and evaluate protection, we construct CapTraceBench, a benchmark of 75 specialized long-horizon tasks and 154 curated skills across seven domains. We also introduce RedAct https://github.com/XuShuwenn/RedAct, a protected trace release framework that localizes protected key information, rewrites traces while preserving verifier-critical evidence, and embeds behavioral watermarks for downstream provenance analysis. Across representative trace reuse methods, RedAct reduces normalized skill transfer (NST) from 44.7--67.1\\% on raw traces to below the no-skill baseline, while preserving audit evidence. Its standalone behavioral watermarks reach 93.6--100.0\\% true detection with a false alarm rate of at most 1.9\\%. These results frame public agent traces as security interfaces and show that selective redaction can reduce procedural capability leakage without removing audit evidence.", "github": "https://github.com/XuShuwenn/RedAct" }, { "date": "2026-06-15", "arxiv_id": "2606.14061", "title": "LLM Agents Can See Code Repositories", "authors": [ "Dongjian Ma", "Silin Chen", "Yufei Yang", "Yulin Shi", "Yanfu yan", "Xiaodong Gu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cslsolow/SeeRepo", "github_api_added_by": "user", "project_page": "", "abstract": "Coding agents powered by large language models have demonstrated strong performance on software engineering tasks. Yet most agents consume repositories almost entirely as text, which differs from how human developers use visual structure such as folder hierarchies and dependency relationships to orient themselves in large codebases. With multimodal large language models (MLLMs), it is an open question whether agents can effectively benefit from visual representations of repositories. This paper presents the first systematic empirical study of visual repository representations for LLM-based agents on repository-level issue resolution. We evaluate four recent multimodal models. Our results show that a strictly vision-only setup degrades accuracy and increases token cost, because agents lack sufficient symbolic detail and compensate with repeated visual queries. In contrast, integrating visual graphs of repository structure as a supplementary modality alongside standard text interfaces helps agents understand structure more efficiently: input token consumption decreases by up to 26% while issue-resolution accuracy is maintained or improved. Visualization is most useful during fault localization and when the agent autonomously controls exploration depth. These findings point to a practical hybrid text-and-vision design for next-generation coding agents.", "github": "https://github.com/cslsolow/SeeRepo" }, { "date": "2026-06-15", "arxiv_id": "2606.14700", "title": "RepFusion: Leveraging Multimodal Priors for Denoising in Representation Space", "authors": [ "Xichen Pan", "Aashu Singh", "Satya Narayan Shukla", "Xiangjun Fan", "Shlok Kumar Mishra", "Saining Xie" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://xichenpan.com/repfusion/", "abstract": "Large language models (LLMs) are widely used in text-to-image (T2I) systems, but they are typically limited to text encoding, while denoising is handled by newly trained generative backbones. The emergence of representation autoencoders (RAEs) shifts the generation target toward semantically structured visual representations, creating a latent space that is more compatible with pretrained LLM priors. Inspired by multimodal LLMs (MLLMs), where an MLP projector is sufficient to align clean visual representations with a pretrained LLM, we repurpose the MLLM itself as a noisy representation encoder, extending this mechanism from clean to noisy inputs. We present RepFusion, which uses the resulting MLLM outputs as the conditioning signal for a diffusion transformer. In controlled comparisons at similar inference budgets, RepFusion outperforms baselines that devote comparable capacity to newly initialized denoisers. These results demonstrate that MLLMs provide strong priors for denoising visual representations and that, by conditioning on evolving noisy representations, test-time compute can be productively spent on repeated MLLM conditioning in modern T2I systems.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.13872", "title": "Avatar V: Scaling Video-Reference Avatar Video Generation", "authors": [ "Benjamin Liang", "Ce Chen", "Desmond Lin", "Ivan Somov", "Jiajun Zhao", "Jiewei Yuan", "Jingfeng Zhang", "Junhao Huang", "Nik Nolte", "Pedram Haqiqi", "Penghan Wang", "Rong Yan", "Rui Zhang", "Sam Prokopchuk", "Sivan Wang", "Viktor Goriachko", "Yi Ren", "Yuanming Li", "Yutao Chen", "Zhenhui Ye", "Zhibin Hong", "Zilong Nie", "Zujin Guo" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://www.heygen.com/research/avatar-v-model", "abstract": "Generating avatar videos that are not merely visually similar to a target individual but behaviorally recognizable, faithfully reproducing their talking rhythm, gestural tendencies, and expression dynamics, remains an open challenge. Existing methods predominantly condition on single static images, which provide insufficient identity information and cannot capture dynamic motion traits, while standard pixel-level objectives underserve the perceptually critical facial regions that determine avatar fidelity. We present Avatar V, a production-scale framework that addresses these limitations through video-reference-conditioned identity modeling. Rather than compressing identity into fixed-size embeddings, the model conditions directly on the full token sequence of a reference video, learning to reproduce both static identity attributes (facial geometry, skin texture) and dynamic behavioral patterns (talking rhythm, micro-expressions) through attention over the reference context. We introduce Sparse Reference Attention, an asymmetric mechanism achieving linear-complexity conditioning on arbitrarily long references; a motion representation stream enabling closed-loop talking style transfer; and an identity-aware super-resolution refiner inheriting the full reference conditioning. These are supported by a data engine curating 100M+ training clips from 50M raw videos, and a five-stage training pipeline with flow matching pre-training, personality fine-tuning, two-phase distillation (>10x acceleration), and RLHF alignment, deployed across thousands of GPUs. Avatar V generates 1080p videos of unlimited duration, achieving state-of-the-art identity preservation, lip synchronization, and generation quality on our cross-scene benchmark, consistently outperforming leading systems including Seedance 2.0, Kling O3 Pro, Veo 3.1, and OmniHuman 1.5 in both automated metrics and human evaluation.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.00793", "title": "MBench: A Comprehensive Benchmark on Memory Capability for Video World Models", "authors": [ "Shengjun Zhang", "Zhang Zhang", "Simin Huang", "Zhenyu Tang", "Hanyang Wang", "Chensheng Dai", "Min Chen", "Yifan Li", "Yuxin Li", "Yingjie Chen", "Hao Liu", "Chen Li", "Jing Lyu", "Yueqi Duan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/study-overflow/MBench", "github_api_added_by": "user", "project_page": "https://peanutup.github.io/MBench-project/", "abstract": "Recent advancements in video-based world models have demonstrated an unprecedented ability to synthesize high-fidelity visual sequences. However, a fundamental gap persists between visually plausible video generation and the functional requirements of a world model, particularly in maintaining a stable and reasonable internal state over extended temporal horizons. While existing benchmarks primarily emphasize visual quality, motion coherence, and text-video alignment, they largely overlook memory, the core capability of a world model to preserve consistency across long-term horizons and complex interactions. To address this gap, we present MBench, a comprehensive benchmark dedicated to quantifying and evaluating the memory capability of video world models. We systematically decompose the memory capability of video world models into three hierarchical and complementary core dimensions: entity consistency, environment consistency, and causal consistency, which are further refined into 12 quantifiable sub-dimensions for comprehensive characterization of long-term memory. Our benchmark is built upon rigorously curated real-captured long videos, and evaluated by rule-based quantitative matrices and VLM to enable objective and comprehensive consistency assessment. Extensive evaluations of mainstream state-of-the-art video world models reveal critical systemic limitations of existing methods in long-term state retention, providing a standardized benchmark and clear research direction to advance the field.", "github": "https://github.com/study-overflow/MBench" }, { "date": "2026-06-15", "arxiv_id": "2606.06309", "title": "RhymeFlow: Training-Free Acceleration for Video Generation with Asynchronous Denoising Flow Scheduling", "authors": [ "Chensheng Dai", "Shengjun Zhang", "Yifan Li", "Zhang Zhang", "Zheng Zhu", "Yueqi Duan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Simon-Dcs/RhymeFlow", "github_api_added_by": "user", "project_page": "https://simon-dcs.github.io/Website-of-RhymeFlow/", "abstract": "Video generation models based on Diffusion Transformers (DiTs) have achieved remarkable performance in video synthesis, yet they suffer from high inference latency and computational costs due to the quadratic complexity of 3D attention. Existing acceleration methods primarily reduce computational complexity within each individual denoising steps through techniques such as sparse attention and KV-caching. However, they rigidly adhere to the inherent constraint of the standard diffusion pipeline: every frame in the target video sequence must be subjected to a complete, dense denoising process across all diffusion timesteps. We observe that due to the corresponding contents and motions among adjacent frames, when keyframes with critical semantic transitions are anchored, the intermediate states of others often follow more predictable trajectories, which indicates that such uniform, dense denoising process is inherently redundant for natural video data. To this end, we introduce RhymeFlow, a training-free framework that decouples the denoising trajectories of different frames. Specifically, we first identify a sparse set of pivotal key frames that dominate the latent semantic evolution. Then, only these keyframes undergo dense, step-by-step denoising to ensure structural integrity, while non-keyframes progressively skip denoising steps to minimize computational cost. Since skipped intermediate states of non-keyframes break the temporal coherence in keyframe denoising steps, leading to visual degradation, we further introduce a latent trajectory projection module, which enables keyframes to interact with a complete and temporally consistent sequence representation. Extensive experiments on current DiT-based video generation models demonstrate our method outperforms existing baselines with higher inference speed and better visual quality.", "github": "https://github.com/Simon-Dcs/RhymeFlow" }, { "date": "2026-06-15", "arxiv_id": "2606.12384", "title": "APPO: Agentic Procedural Policy Optimization", "authors": [ "Xucong Wang", "Ziyu Ma", "Yong Wang", "Yuxiang Ji", "Shidong Yang", "Guanhua Chen", "Pengkun Wang", "Xiangxiang Chu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AMAP-ML/APPO", "github_api_added_by": "user", "project_page": "", "abstract": "Recent advances in agentic Reinforcement Learning (RL) have substantially improved the multi-turn tool-use capabilities of large language model agents. However, most existing methods assign credit over coarse heuristic units, such as tool-call boundaries or fixed workflows, making it difficult to identify which intermediate decisions influence downstream outcomes. In this work, we study agentic RL from two perspectives: where to branch and how to assign credit after branching. Our pilot analysis shows that influential decision points are broadly distributed throughout the generated sequence rather than concentrated at tool calls, while token entropy alone does not reliably reflect their impact on final outcomes. Motivated by these observations, we propose Agentic Procedural Policy Optimization (APPO), which shifts branching and credit assignment from coarse interaction units to fine-grained decision points in the sequence. APPO selects branching locations using a Branching Score that combines token uncertainty with policy-induced likelihood gains of subsequent continuations, enabling more targeted exploration while filtering out spurious high-entropy positions. It further introduces procedure-level advantage scaling to better distribute credit across branched rollouts. Experiments on 13 benchmarks show that APPO consistently improves strong agentic RL baselines by nearly 4 points, while keeping efficient tool-calls and maintaining behavior interpretability.", "github": "https://github.com/AMAP-ML/APPO" }, { "date": "2026-06-15", "arxiv_id": "2605.30789", "title": "Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO", "authors": [ "Yiming Ren", "Yiran Xu", "Zicheng Lin", "Chufan Shi", "Yukang Chen", "Dingdong Wang", "Tianhe Wu", "Junjie Wang", "Yujiu Yang", "Yu Qiao", "Ruihang Chu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/qishisuren123/S2L-PO", "github_api_added_by": "user", "project_page": "https://qishisuren123.github.io/S2L-PO/", "abstract": "We identify a new dimension for enhancing rollout diversity in Group Relative Policy Optimization (GRPO) for LLMs. While GRPO relies on diverse rollouts, prevailing strategies primarily increase diversity by injecting more token-level randomness, which may introduce step-wise noise and lead to incoherent trajectories. We uncover that smaller models within the same model family inherently exhibit higher policy-level diversity, indicated by their superior pass@k relative to larger counterparts as sample counts increase. Unlike token-level noise, this diversity is temporally correlated, preserves logical consistency, and provides structured exploration signals for gradient estimation. We thus propose S2L-PO (Small-to-Large Policy Optimization), a framework that leverages fixed small models as natural explorers to train larger models. To balance exploration and exploitation, we design a progressive annealing strategy that transitions from offline small-model rollouts to the large learner's own sampling. This shift elegantly avoids mid-training performance drops caused by the small model's capacity limits, achieving faster convergence and unlocking a higher performance ceiling. S2L-PO improves accuracy on diverse mathematical reasoning benchmarks (e.g., +8.8% on AIME 24 using a 1.7B explorer to guide the 8B model) while reducing rollout compute.", "github": "https://github.com/qishisuren123/S2L-PO" }, { "date": "2026-06-15", "arxiv_id": "2606.12736", "title": "Benchmarking AI Agents for Addressing Scientific Challenges Across Scales", "authors": [ "Tianyu Liu", "Allen Xin Wang", "Antonia Panescu", "Lisa Xinyi Chen", "Wenxin Long", "Xinyu Wei", "Yueqian Jing", "Ziyao Zeng", "Jihang Chen", "Sihan Jiang", "Ziqing Wang", "Siyi Gu", "Siyu Chen", "Xinyang Hu", "Haoran Shao", "Leqi Xu", "Wangjie Zheng", "Zhiyuan Cao", "Ada Fang", "Botao Yu", "Kunyang Sun", "Rex Ying", "Arman Cohan", "Qingyu Chen", "Lingzhou Xue", "Kaize Ding", "Yuanqi Du", "Wengong Jin", "Zhuoran Yang", "Marinka Zitnik", "James Zou", "Hua Xu", "Hongyu Zhao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/HelloWorldLTY/SciAgentArena", "github_api_added_by": "user", "project_page": "https://sciagentarena.github.io/", "abstract": "AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchmarks for AI agents rarely capture the complexity, heterogeneity, and extended reasoning required by scientific work, whereas benchmarks for scientific tasks often reduce research to static, direct problems and provide limited support for interactive evaluation. Here, we introduce SciAgentArena, a systematic benchmark for evaluating AI agents in real-world scientific research scenarios drawn from emerging needs across multiple domains. SciAgentArena comprises approximately 200 tasks with stepwise verification and an interactive, agent-agnostic environment for assessing diverse AI agents. Using this benchmark, we find that current agents can contribute effectively to well-specified data-analysis workflows, particularly when the task structure and evaluation criteria are clear. However, their performance remains uneven across scientific contexts: agents struggle to generate genuinely novel insights, sustain self-directed exploration, and formulate robust solutions for open-ended research questions. We further characterize common failure modes across agents and identify opportunities for improving their reliability, autonomy, and scientific reasoning. Together, SciAgentArena provides a practical framework for measuring progress in AI agents for science and for guiding the design of future agents capable of addressing complex scientific challenges. Full codes, tasks, and datasets can be accessed via this link: https://sciagentarena.github.io/.", "github": "https://github.com/HelloWorldLTY/SciAgentArena" }, { "date": "2026-06-15", "arxiv_id": "2606.13141", "title": "Rethinking RAG in Long Videos: What to Retrieve and How to Use It?", "authors": [ "Yuho Lee", "Jisu Shin", "Nicole Hee-Yeon Kim", "Jihwan Bang", "Juntae Lee", "Kyuwoong Hwang", "Fatih Porikli", "Hwanjun Song" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/datasets/DISLab/VRAG-Bench", "abstract": "Retrieval-augmented generation is moving beyond text into long, egocentric video, where systems must select query-relevant chunks across multiple modalities and temporal granularities. Yet progress in VideoRAG is limited by two gaps: existing benchmarks allow queries to be answered without the video, obscuring retrieval errors, and prior methods apply a single modality-granularity configuration per query, ignoring chunk-level variability. We address both by introducing V-RAGBench, a benchmark of langlequery, evidence chunk, answerrangle triplets that enables faithful, decoupled evaluation of retrieval and generation, and CARVE, a simple method that runs parallel retrievers across configurations and employs chunk-adaptive reranking to identify the winning configuration for each chunk. Each chunk then enters the generator under its winning configuration selected during retrieval, yielding an interleaved evidence form where the chunk-level decision propagates across both stages. CARVE outperforms eight recent VideoRAG baselines, with the chunks supplied to the generator interleaving multiple configurations rather than sharing a single one, a behavior unattainable by query-level methods.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.12291", "title": "Measuring Epistemic Resilience of LLMs Under Misleading Medical Context", "authors": [ "Hongjian Zhou", "Xinyu Zou", "Jinge Wu", "Sean Wu", "Junchi Yu", "Bradley Max Segal", "Tobias Erich Niebuhr", "Sara Amro", "Michael Petrus", "Sheikh Momin", "Alexandra M. Cardoso Pinto", "Rachel Niesen", "Laura Sophie Wegner", "Dhruv Darji", "Jung Moses Koo", "Joshua Fieggen", "Kapil Narain", "Mingde Zeng", "Lei Clifton", "Linda Shapiro", "Fenglin Liu", "David A. Clifton" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AI-in-Health/MedMisBench", "github_api_added_by": "user", "project_page": "https://huggingface.co/datasets/HongjianZhou/MedMisBench", "abstract": "Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients increasingly use them for health advice. We show this assumption is fragile: when misleading context is injected into questions that LLMs originally answer correctly, they abandon the correct answer. We call the ability to maintain correct judgment under adversarial context epistemic resilience, and introduce MedMisBench to measure it. MedMisBench contains 10,932 medical question items and 48,889 misleading context-option pairs spanning medical reasoning, agentic capability, and patient-journey evaluation. Across 11 model configurations, mean accuracy falls from 71.1% on original questions to 38.0% under focused misleading context, with 51.5% attack success. The most damaging injections are formal, rule-like fabrications: authority-framed falsehoods reach 69.5% attack success and exception-poisoning claims reach 64.1%. A 14-member clinical panel from 7 countries identified serious potential harm in 38.2% of reviewed cases. MedMisBench exposes a structural blind spot in LLM evaluation in medical settings: existing benchmarks measure what models know, but not whether they preserve correct medical judgment under misleading context.", "github": "https://github.com/AI-in-Health/MedMisBench" }, { "date": "2026-06-15", "arxiv_id": "2606.02641", "title": "CARVE: Certified Affordable Repair of Vetoed Maneuvers via Envelopes for Interactive Driving", "authors": [ "Yifan Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/datasets/DISLab/VRAG-Bench", "abstract": "Interactive driving exposes a failure mode that is easy to miss in rule-aware autonomous-driving stacks: a hard-rule margin can be negative for an ego candidate even though a small lawful accommodation by a non-priority agent would restore feasibility. Existing rulebooks, shields, and reachability filters are strong at vetoing unsafe actions, while prediction-based planners model likely responses. Neither returns a runtime proof object that states which bounded multi-agent edit repairs the maneuver, who owns the edit, whether the request is right-of-way affordable, and what ego fallback remains if the request is not observed. We formulate this missing object as *interactive repair certification* and introduce *CARVE*, a prediction-free certificate layer over a finite lattice of ego-owned and agent-owned tactical operators. Agent-owned requests are admissible only inside \\(B_j(s) = \u03b2(\u03c0_j)\u03b1_j^{\\max}(s)\\), a cooperation envelope that separates kinematic reachability from normative priority. The resulting certificate records the binding rule, repair category, repair set, responsibility-weighted cost split, and fallback. On 589 Lanelet2-geometry-grounded INTERACTION replay episodes, CARVE-Greedy accepts 98.64% of initially vetoed maneuvers and recovers 370/378 human-resolved false vetoes, while preserving 589/589 right-of-way respect, zero priority-agent false positives, and 400/400 negative-stress vetoes. We prove certificate soundness, structural right-of-way respect, exact finite-lattice minimality, fallback contingency, and blame-consistency conditions. CARVE does not predict or require another driver's compliance; it certifies whether a proposed interaction is bounded, attributable, and normatively admissible under declared assumptions.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.14694", "title": "AdaSR: Adaptive Streaming Reasoning with Hierarchical Relative Policy Optimization", "authors": [ "Junlong Tong", "Wenqi Xu", "Yingqi Fan", "Anhao Zhao", "Xuan Lu", "Yang Tan", "Xiaoyu Shen" ], "github_manual": "", "github_regex": "https://github.com/EIT-NLP/StreamingLLM/tree/main/AdaSR", "github_paper_md": "", "github_api": "https://github.com/EIT-NLP/StreamingLLM", "github_api_added_by": "user", "project_page": "", "abstract": "Large reasoning models typically follow a read-then-think paradigm: they observe the complete input, reason over a static context, and then produce the answer. Yet many real-world scenarios are inherently dynamic, such as audio and video stream, where information arrives as a continuous stream and models must reason, update, and respond under partial observations. Recent streaming reasoning methods allow models to think while reading, but they largely rely on supervised imitation of pre-constructed trajectories, which limits their flexibility. In this paper, we propose AdaSR, an adaptive streaming reasoning framework that enables models to reason during input streaming and perform final deliberation once the stream is complete, learning when to think, and how much computation to allocate across different stages. To optimize this hierarchical reasoning process, we introduce Hierarchical Relative Policy Optimization (HRPO), which decomposes policy optimization into streaming reasoning and deep reasoning phases, providing more fine-grained advantage assignment instead of uniformly distributing a single sequence-level advantage over all tokens. HRPO integrates format, accuracy, and adaptive thinking rewards to enforce valid reasoning protocols, preserve final task performance, and encourage latency-aware computation allocation. Experiments show that AdaSR achieves a better balance among reasoning accuracy, computational efficiency, and streaming latency compared with supervised fine-tuning baseline. We release our code at https://github.com/EIT-NLP/StreamingLLM/tree/main/AdaSR.", "github": "https://github.com/EIT-NLP/StreamingLLM" }, { "date": "2026-06-15", "arxiv_id": "2606.11152", "title": "P3D-Bench: Benchmarking MLLMs for Parametric 3D Generation and Structural Reasoning", "authors": [ "Yikang Yang", "Zhanpeng Hu", "Youtian Lin", "Mengqi Zhou", "Jingxi Xu", "Feihu Zhang", "Jiaheng Liu", "Yao Yao" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/SpatiaOS/P3D-Bench", "github_api_added_by": "user", "project_page": "https://spatiaos.github.io/projects/P3D-Bench/", "abstract": "Multimodal large language models can write code to produce complex programs as well as use programs to do 3D modeling, which opens up a new avenue for 3D generation powered by their priors, world knowledge and reasoning. Yet existing benchmarks rarely evaluate 3D modeling through code. Such modeling demands more than runnable code: from a text or visual specification, a model must generate a parametric 3D program that is geometrically precise, semantically aligned and assembly-consistent. We introduce P3D-Bench, a benchmark for parametric 3D generation. Unlike a 3D mesh, a parametric 3D program exposes explicit dimensions, construction operations and part relations, revealing whether a model recovers a design's structure, not just its appearance. Under a unified protocol, P3D-Bench covers three task families (Text-to-3D, Image-to-3D and Assembly-3D) and scores each output for executability, geometric fidelity, topology, text-grounded constraints, multiview semantic alignment and part-level structure. We evaluate frontier MLLMs and text-only LLMs on 400 text cases, 400 image cases and 203 annotated assemblies, with domain-specific models as reference points. Our extensive evaluation yields three findings. First, assemblies are the hardest setting, where models still fail to compose multiple parts into a coherent structure. Second, models can often recover the global shape and semantic identity of the target object, yet fail to reproduce the precise parametric geometry specified by the input. Third, part-level modeling remains weak on assemblies, where models recover neither the geometry of each part nor the right number of parts. These results position P3D-Bench as a benchmark for evaluating precise parametric geometry and part-level structure in parametric 3D generation.", "github": "https://github.com/SpatiaOS/P3D-Bench" }, { "date": "2026-06-15", "arxiv_id": "2606.08670", "title": "WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis", "authors": [ "Danilo Danese", "Angela Lombardi", "Giuseppe Fasano", "Matteo Attimonelli", "Tommaso Di Noia" ], "github_manual": "", "github_regex": "https://github.com/sisinflab/WaveDiT", "github_paper_md": "", "github_api": "https://github.com/sisinflab/WaveDiT", "github_api_added_by": "user", "project_page": "https://danesed.github.io/wavedit-page/", "abstract": "Large and demographically balanced datasets are essential for reliable neuroimaging biomarkers. Full-resolution 3D brain MRI synthesis can support data augmentation in this setting, but existing approaches either incur prohibitive computational cost at volumetric scale or rely on lossy latent compression that may compromise anatomical detail. As a result, practical 3D generative augmentation often requires specialized compute infrastructure. We propose WaveDiT, a conditional flow matching framework operating in the coefficient space of a 3D Haar Discrete Wavelet Transform. The model combines factorized spatio-depth attention with band-wise heteroscedastic uncertainty modeling derived from higher-order wavelet statistics. Predicted log-variance is integrated directly into both the flow objective and conditioning pathway, enabling adaptive precision consistent with the heavy-tailed and input-dependent variance structure of anatomical detail. This formulation supports full-resolution 3D synthesis under practical memory and time constraints on a single modern GPU. Evaluation on a multi-site cohort demonstrates improved alignment between generated and real MRI distributions, together with enhanced downstream brain age prediction and region-level anatomical agreement relative to diffusion, latent, and wavelet-based baselines. Code is available at https://github.com/sisinflab/WaveDiT", "github": "https://github.com/sisinflab/WaveDiT" }, { "date": "2026-06-15", "arxiv_id": "2606.06194", "title": "ActiveMimic: Egocentric Video Pretraining with Active Perception", "authors": [ "Xingyao Lin", "Guojin Zhong", "Tianyi Lu", "Ziyi Ye", "Yichen Zhu", "Zuxuan Wu", "Yu-Gang Jiang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://activemimic.github.io/", "abstract": "Egocentric human video offers a scalable alternative to robot data for pretraining, yet models pretrained on such video consistently underperform those pretrained on robot data. We attribute this gap to a missing signal, the active perception behavior in egocentric videos, where humans continuously reposition their viewpoint during manipulation, inducing camera motion that standard pipelines treat as noise. To address this, we present ActiveMimic, a pretraining framework that recovers synchronized camera and wrist trajectories from a single body-worn RGB camera, models camera motion as a viewpoint action, and jointly learns active perception and manipulation from in-the-wild egocentric human video before adapting to a target robot. Empirically, real-world experiments across tasks with diverse active perception demands show that ActiveMimic consistently surpasses baselines pretrained on human video and matches state-of-the-art models pretrained on robot data. Further analysis provides evidence that active perception capability originates from egocentric human video pretraining rather than robot-specific fine-tuning, confirming active perception as the key to unlocking egocentric human video for robot pretraining.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.08952", "title": "AlloSpatial: Agentic Harness Framework for Spatial Reasoning in Foundation Models", "authors": [ "Shouwei Ruan", "Bin Wang", "Zhenyu Wu", "Qihui Zhu", "Yuxiang Zhang", "Jingzhi Li", "Yubin Wang", "Xingxing Wei" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Heathcliff-saku/AlloSpatial", "github_api_added_by": "user", "project_page": "https://github.com/Heathcliff-saku/AlloSpatial", "abstract": "Multimodal Foundation Models (MFMs) have made substantial progress, yet remain fragile in spatial reasoning over the physical world. A key bottleneck lies in their inability to transform local egocentric observations into a global allocentric spatial representation. To address this, we propose AlloSpatial, an agentic framework for allocentric spatial cognition in foundation models. AlloSpatial introduces World2Mind, a plug-and-play cognitive mapping sandbox that converts egocentric observations into structured allocentric priors, including Allocentric-Spatial Trees and route maps that support querying object topology, geometric relations, passability, and trajectories. To utilize these priors reliably under noisy reconstruction and ambiguous visual evidence, AlloSpatial introduces a Spatial Reasoning Harness for tool-use judgment, modality-decoupled cue collection, and geometry-semantic arbitration. We further internalize this process in Qwen3-VL through cold-start reinforcement learning with a harness-gated trajectory-level reward. Experiments on VSI-Bench and MindCube show that AlloSpatial improves proprietary models by 5%-18% in a training-free setting, while ASTs alone support strong spatial reasoning even when visual inputs are removed. The trained AlloSpatial agents further outperform larger general-purpose models and competitive spatial baselines, suggesting that structured allocentric representations, active tool use, and verifiable reasoning offer a promising route toward spatially capable foundation models.", "github": "https://github.com/Heathcliff-saku/AlloSpatial" }, { "date": "2026-06-15", "arxiv_id": "2606.12366", "title": "APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies", "authors": [ "Kechun Xu", "Zhenjie Zhu", "Anzhe Chen", "Rong Xiong", "Yue Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xukechun/APT", "github_api_added_by": "user", "project_page": "https://xukechun.github.io/papers/APT/", "abstract": "Vision-Language-Action (VLA) models that couple pretrained Vision-Language Models (VLMs) with continuous action experts have achieved strong manipulation performance, yet generalization to out-of-distribution (OOD) language instructions remains poor. A known challenge is the structural imbalance in VLA data, where language is far less diverse than visual and action content, making policies prone to visual shortcuts. While discrete-action methods mitigate this through vision-language co-training, continuous action experts lack such protection: they start from random initialization and learn entirely from imbalanced data, producing noisy gradients that corrupt the VLM and fail to exploit its language capability. We address this from a Bayesian perspective, factorizing the policy into a language-agnostic Vision-Action (VA) prior and a language-conditioned VLA likelihood, and propose APT, a two-stage training method emphasizing Action expert PreTraining. In Stage 1, the action expert is pretrained as a VA prior on vision-action pairs from a frozen VLM, bypassing the language imbalance. In Stage 2, language tokens are injected through a gated fusion mechanism that integrates VLM features while preserving the learned visuomotor prior. APT applies to mainstream VLA architectures, including the \u03c0 and GR00T-style architectures. Comprehensive experiments validate that APT achieves consistent gains on unseen instructions and compositional tasks. Project Page: https://xukechun.github.io/papers/APT/", "github": "https://github.com/xukechun/APT" }, { "date": "2026-06-15", "arxiv_id": "2606.10747", "title": "The Arbiter Agent: Continually Monitoring Multi-Agent Conversations to Detect Emergent Misalignment", "authors": [ "Filippo Tonini", "Federico Torrielli", "Anton Danholt Lautrup", "Peter Schneider-Kamp", "Mustafa Mert \u00c7elikok", "Lukas Galke Poech" ], "github_manual": "", "github_regex": "https://github.com/aisilab/arbiter", "github_paper_md": "", "github_api": "https://github.com/aisilab/arbiter", "github_api_added_by": "user", "project_page": "", "abstract": "As AI systems built from multiple language-model agents become more common, they are increasingly used to make decisions together: discussing, negotiating, and acting on shared tasks. While individual agents may appear well-aligned when tested on their own, problems can arise from how they interact with one another. We introduce the Arbiter, an agent designed to monitor multi-agent conversations in real time and identify which participants may be behaving in misaligned ways. The Arbiter operates under a limited \"inspection budget\", meaning it must decide carefully how to use its resources. As it observes a conversation step by step, it can choose to wait, question a participant, examine internal information such as system prompts or reasoning traces, or log concerning behavior. At the end, it produces a report identifying the likely source of misalignment. We evaluate the Arbiter across five conversation conditions, ranging from risky financial advice model organisms to evaluation-aware and colluding agents, we test five tool configurations of increasing capability and two backbone models. We find that the Arbiter reliably detects misaligned agents well before the end of the conversation, with active inspection tools improving both detection accuracy and speed. Weight-induced misalignment proves hardest to detect, while instruction-induced misalignment is identified reliably even under passive observation. The logging tool exhibits a dual effect, improving recall at the cost of precision. These results suggest that continual, budget-aware monitoring can effectively catch misalignment, and that overseeing multi-agent systems may require treating the auditor as an active participant in the process. The code is available at https://github.com/aisilab/arbiter.", "github": "https://github.com/aisilab/arbiter" }, { "date": "2026-06-15", "arxiv_id": "2606.09813", "title": "iMaC: Translating Actions into Motion and Contact Images for Embodied World Models", "authors": [ "Zhenyu Wu", "Xiuwei Xu", "Yukun Zhou", "Yifan Li", "Qiuping Deng", "Xiaofeng Wang", "Zheng Zhu", "Bingyao Yu", "Ziwei Wang", "Jiwen Lu", "Haibin Yan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/imac-wm/iMac", "github_api_added_by": "user", "project_page": "https://imac-wm.github.io/", "abstract": "Embodied world models have emerged as a pivotal paradigm for visual robotic decision-making and interactive environment simulation. However, conventional embodied frameworks rely on low-dimensional structured action vectors (e.g., joint angles and end-effector poses), which suffer from limited expressive capacity, poor generalization across diverse embodiments, and unnatural dynamic modeling for complex physical interactions. To address these limitations, this paper proposesiMac (Image as Action Control), a novel unified control paradigm that treats raw visual images as native action representations for embodied world models. Departing from traditional explicit kinematic action encoding, iMac formulates continuous visual manipulation as image-based action tokens, which inherently encapsulate spatial motion intentions, interactive geometric constraints and subtle physical dynamics. We construct a dual-branch embodied architecture consisting of an image-action encoder and a dynamic world predictor: the encoder compresses target-driven visual images into compact action embeddings, while the predictor learns environment transition rules conditioned on image actions to achieve high-fidelity future state prediction and closed-loop embodied control. Extensive experiments are conducted on public embodied manipulation benchmarks and real-world robotic scenarios. The results demonstrate that iMac outperforms vector-based action control baselines in prediction accuracy, task success rate and cross-scene generalization ability. Moreover, our image-action design eliminates the reliance on manually defined action spaces, realizing flexible and universal control for heterogeneous embodied agents. This work provides an innovative visual-action perspective for embodied world models, offering a simple yet effective paradigm for scalable robotic perception and manipulation.", "github": "https://github.com/imac-wm/iMac" }, { "date": "2026-06-15", "arxiv_id": "2606.14600", "title": "LoSoNA: A Benchmark for Local Social Norm Adaptation in Group Conversations", "authors": [ "Mateusz Winiarek", "Maksymilian Bilski", "Mateusz Jacniacki" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://humalike.ai", "abstract": "Online group chats are social spaces with local conversational norms that are rarely stated explicitly. The ability and willingness of LLM-based agents to recognize and adapt to these norms remains mostly unexplored. We introduce LoSoNA, a benchmark for local social norm adaptation in multi-party chat. Each scenario gives a subject model a curated group-chat transcript in which non-subject participants demonstrate a hidden local norm, followed by a final elicitor turn that forces a response revealing whether the subject has inferred that norm. We evaluate eight frontier and open-weight models under four prompting conditions that vary how explicitly the model is told to treat the prior conversation as evidence for how it should answer. Naive prompting remains limited for most models; explicit norm-aware prompting helps unevenly, with Gemini 3.1 Pro reaching 84.2% and Claude Fable 5 reaching 81.6%, while several other models show small gains or regressions. LoSoNA contributes to recent calls for evaluating LLM social capabilities by testing whether models can infer local conversational norms from precedent and use them in a one-turn group-chat response.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.14346", "title": "Squeeze-Release: Iterative Pruning with Exact Structural Minimization", "authors": [ "Roman Denkin", "Ida Akerholm", "Prashant Singh", "Ida-Maria Sintorn" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/gluck3d/Squeeze-Release", "github_api_added_by": "user", "project_page": "", "abstract": "Unstructured pruning produces sparse weight tensors, but the standard implementation keeps tensor shapes unchanged so the deployed model is no smaller than before pruning. We present an exact structural rewrite, which we call minimization, that converts a masked network into a smaller dense network with the same forward function up to floating-point rounding. The Squeeze-Release cycle iterates pruning and minimization with an intermediate release step that re-enables the exact-zero positions inside the compacted tensors as small calibrated noise, turning otherwise wasted capacity back into trainable parameters. Successive cycles use that capacity to find structural redundancy a single pass cannot reach. We additionally introduce CompensatedLayerNorm, a function-preserving replacement for LayerNorm that extends minimization to channel reduction across LayerNorm-equipped residual streams. Squeeze-Release compresses the deployable network to 39x smaller than the unpruned model on a fully-connected model network and 14.8x smaller on modern CNN (ConvNeXt-Tiny), at comparable accuracy. In addition we prove that the rewrite can be extended to transformer architectures.", "github": "https://github.com/gluck3d/Squeeze-Release" }, { "date": "2026-06-15", "arxiv_id": "2606.12476", "title": "Quickest Detection of Hallucination Onset: Delay Bounds and Learned CUSUM Statistics", "authors": [ "Igor Itkin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YehudaItkin/quickest-hallucination-onset", "github_api_added_by": "user", "project_page": "", "abstract": "Token-level hallucination detectors are evaluated as classifiers, by AUC over all tokens, yet a streaming monitor is judged by its reaction time: the number of tokens that pass between the onset of a hallucination and the alarm. We formulate hallucination onset detection as a quickest change detection problem. A first-order Markov model of the latent faithful/hallucinated state, validated on RAGTruth, places the task inside classical change-point theory and yields Lorden's lower bound on detection delay: about 1.3 tokens at a false-alarm rate of 0.01. We then show that a causal recurrent labeler acts as a CUSUM with a learned increment; at a matched false-alarm rate it detects in 11-13 tokens, against 31 for a linear per-token baseline, and a controlled decomposition attributes most of this advantage to a better per-token score rather than to temporal accumulation. An information-rate optimality theorem of Donsker-Varadhan type explains the remaining order-of-magnitude gap: the learned score realizes only 1/4.5 of the divergence the features carry, a deficit that recalibration cannot remove, with the remainder a finite-horizon effect. Classification metrics conceal this delay structure; sequential analysis makes it measurable", "github": "https://github.com/YehudaItkin/quickest-hallucination-onset" }, { "date": "2026-06-15", "arxiv_id": "2606.13652", "title": "World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible", "authors": [ "Hao Zhang", "Mohamed El Banani", "Jen-Hao Cheng", "Paul Zhang", "Yi Hua", "Ben Mildenhall", "Christoph Lassner", "Narendra Ahuja", "Gengshan Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/haoz19/world-tracing", "github_api_added_by": "user", "project_page": "https://haoz19.github.io/world-tracing-page/", "abstract": "Image-to-3D methods often trade off faithfulness and completeness: depth estimators are anchored to input pixels but stop at the visible surface, while image-to-3D models generate complete shapes that are often misaligned with the input. We introduce World Tracing, a generative pixel-aligned geometry representation that predicts 3D points aligned with observed pixels while completing geometry beyond the visible surface. For each input pixel, World Tracing predicts an ordered stack of camera-space 3D points, where the first layer represents the visible surface and subsequent layers represent front-to-back intersections with occluded surfaces. We instantiate this representation with a world-tracing diffusion transformer, WT-DiT, which treats multiple geometry layers as separate denoising tokens coupled through factorized and global attention. WT-DiT is trained with pixel-space flow matching and a mixed noise schedule that balances visible-surface reconstruction with occluded-geometry generation. World Tracing achieves strong performance on visible-surface reconstruction and complete geometry generation across object, scene, and dynamic benchmarks, outperforming both depth predictors and image-to-3D generators. It also preserves 2D-to-3D correspondence, enabling text-driven 3D scene editing, geometry-conditioned novel-view video synthesis, and training-free integration with textured-mesh generators.", "github": "https://github.com/haoz19/world-tracing" }, { "date": "2026-06-15", "arxiv_id": "2606.01462", "title": "An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models", "authors": [ "Mingzhong Sun", "Teresa Yeo", "Armando Solar-Lezama", "Tan Zhi-Xuan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Studies of human reasoning have shown that people are typically stronger at evaluating reasoning than producing it from scratch. In contrast, large reasoning models (LRMs) are trained to excel at producing long chains of reasoning to solve complex problems. How then do LRMs perform at evaluating reasons? We investigate this with the Valid-Answer-Invalid-Reasoning (VAIR) dataset: math problems and solutions with trivial reasoning flaws but valid answers, designed to isolate reasoning evaluation from the confound of reasoning production. Unlike humans, who we find are only 6% worse at grading than solving such problems, we find a substantial production-evaluation gap in LRMs: frontier models score as low as 48% when evaluating VAIR solutions, despite near-perfect solution production. Why this enigma? Through chain-of-thought (CoT) analysis, we find evidence of an answer confirmation bias: LRMs often produce then check for the correct answer instead of carefully verifying each step, fabricating rationalizations even when noticing anomalous reasoning. Linear probes corroborate this, showing that while LRM activations encode some representation of valid reasoning, they fail to robustly represent VAIR solutions as invalid. Causal patching of the final answer's representations causes LRM verdicts and activations to flip, demonstrating that answer validity is responsible for models' confirmation biases. These findings indicate an outstanding limitation in dominant approaches to reasoning training, which incentivize LRMs to produce and confirm reasoning towards correct answers, but not to robustly evaluate the underlying reasons.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.01008", "title": "FVSpec: Real-World Property-Based Tests as Lean Challenges", "authors": [ "Quinn Dougherty", "Max von Hippel", "Hazel Shackleton", "Mike Dodds" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://fvspec.galois.com", "abstract": "We present a benchmark for evaluating AI models and agents on real-world formal software verification tasks. We first scrape 11,039 property-based tests (PBTs) from real-world Python repositories, then automatically translate 2,772 of them (25%) into 9,415 Lean 4 specifications with sorry placeholders (about 3 formalizations/PBT; we retain multiple attempts when none dominates on quality metrics). Translating PBTs into Lean specifications is challenging: it requires modeling Python semantics in Lean, inferring the logical property encoded in an imperative PBT, and handling the inherent difficulties of dependently-typed programming in a seldom-used language. We describe a three-agent LLM pipeline for transpiling PBTs into Lean specifications, evaluate coverage and quality metrics, and provide baselines for proof generation using several automated and model based approaches. All code (scraper and agents) and data (PBTs and Lean specifications) are open source. Our benchmark aims to drive progress on the underexplored problem of AI-assisted formal verification of real-world software, which is of increasing interest as AI produces more and more of the world's code.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.12594", "title": "Pythagoras-Prover: Advancing Efficient Formal Proving via Augmented Lean Formalisation", "authors": [ "Joshua Ong Jun Leang", "Zheng Zhao", "Mihaela C\u0103t\u0103lina Stoian", "Qiyuan Xu", "Haonan Li", "Wenda Li", "Shay B. Cohen", "Eleonora Giunchiglia" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/Pythagoras-LM/Pythagoras-Prover", "github_api_added_by": "user", "project_page": "https://pythagoras-lm.github.io/", "abstract": "Modern Lean theorem provers achieve strong performance only with substantial training and inference compute, driven in part by scarce verified proof data and the long reasoning traces of formal proof search, making both supervised fine-tuning (SFT) and sampling expensive. We introduce Pythagoras-Prover, a compute-efficient open-source family of Lean theorem provers built for practical compute budgets. The family spans two generation paradigms: autoregressive models at 4B and 32B parameters, and a first proof-of-concept diffusion-based prover (4B) that iteratively refines Lean proofs at inference time. For training efficiency, we build a Lean-verified corpus stratified into easy, medium, and hard problems for curriculum SFT, so models acquire proof skills progressively from shorter, simpler proofs to longer, harder ones. During SFT, a dynamic proof-reasoning filtering scheme preserves informative proof traces while keeping each instance within an 8k-token context budget. We also introduce Augmented Lean Formalisation (ALF), which expands scarce verified corpora into variants of formal statements, populated via self-distillation for extra training signal without formally verifying every mutated instance. By perturbing known problems while preserving their formal character, ALF reduces reliance on any statement's surface form. Empirically, Pythagoras-Prover-4B surpasses DeepSeek-Prover-V2-671B at pass@32 on MiniF2F-Test (86.1% vs 82.4%) with ~167x fewer parameters, while Pythagoras-Prover-32B sets the open-source state of the art at 93.0% on MiniF2F-Test and solves 93 of 672 PutnamBench problems. We release MiniF2F-ALF, an ALF-mutated contamination-sensitive benchmark on which every evaluated model loses accuracy; here our 32B remains strongest and our 4B matches the prior state of the art, Goedel-Prover-V2-32B.", "github": "https://github.com/Pythagoras-LM/Pythagoras-Prover" }, { "date": "2026-06-15", "arxiv_id": "2606.05308", "title": "Statistically Reliable LLM-Based Ranking Evaluation via Prediction-Powered Inference", "authors": [ "Abhishek Divekar" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://www.amazon.science/publications/precise-reducing-the-bias-of-llm-evaluations-using-prediction-powered-ranking-estimation", "abstract": "With PRECISE, we extended Prediction-Powered Inference to produce bias-corrected estimates of ranking evaluation metrics by combining a small human-labeled set with a large LLM-judged set. PPI is provably unbiased regardless of the LLM judge's error profile. We make it applicable to hierarchical metrics like Precision@K, where annotations are per-document but the metric is per-query, by reducing the output-space computation from O(2^|C|) to O(2^K). On the ESCI benchmark, augmenting 30 human annotations with Claude 3 Sonnet judgments reduces the standard error of Precision@4 estimates from 4.45 to 3.50 (a 21% relative reduction). In a production system, our framework correctly identified the best of three system variants from 100 human labels and 2 hours of domain-expert annotation; A/B testing confirmed this ranking with +407 bps in daily sales.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2606.13044", "title": "No Hidden Prompts Needed! You Can Game AI Peer Review with Presentation-Only Revisions", "authors": [ "Xu Yang", "Zhizhou Sha", "Junbo Li", "Jian Yu", "Yifan Sun", "Matthew Zhao", "Jinrui Fang", "Xinyue Guo", "Yining Wu", "Xu Hu", "Yifu Luo", "Qiang Liu", "Zhangyang Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/xyimatvoid/ARGAR", "github_api_added_by": "user", "project_page": "https://xyimatvoid.github.io/ARGAR-Site/", "abstract": "As AI-generated reviews move from experimental tools into peer-review infrastructure, most robustness concerns have focused on explicit attacks such as hidden instructions and prompt injection. We study a harder and more policy-relevant failure mode: no hidden text, no prompt injection, and no changes to methods, experiments, figures, equations, proofs, or numerical results. The attacker modifies only presentation-level content, such as the abstract, contribution framing, related work, discussion, and narrative structure. We introduce adversarial repackaging: a closed-loop attack that uses AI-reviewer feedback to search for presentation-level revisions while keeping the scientific evidence fixed. Across three mainstream AI reviewers, adversarial repackaging achieves a 75.1% attack success rate and a mean score gain of +1.21/10. The effect is not explained by ordinary prose polishing. We also reveal that strategies that change how the reviewer interprets the paper, such as related-work repositioning and analytical discussion expansion, substantially outperform surface edits such as local polishing, table formatting, and algorithm boxes. Our analysis reveals two deeper structural failure modes. First, AI reviewers are easier to impress than to convince: highlighting strengths reliably increases perceived merit, while attempts to dissolve weaknesses frequently backfire. Second, AI reviewers can confuse the appearance of addressing a limitation with actually resolving it, allowing unchanged evidence to be reinterpreted as stronger scientific contribution. These results show that the deployment risk is not only malicious hidden instructions, but the emergence of paper presentation itself as an optimization surface. We release a contamination-free rolling benchmark and attack framework for testing whether AI reviewers remain anchored to scientific content under presentation-only edits.", "github": "https://github.com/xyimatvoid/ARGAR" }, { "date": "2026-06-15", "arxiv_id": "2606.14240", "title": "AFFORDANCE20Q: Evaluating Affordance Reasoning from Physical Properties", "authors": [ "Yifan Jiang", "Meige Yang", "Zitong Li", "Jay Pujara" ], "github_manual": "", "github_regex": "https://github.com/1171-jpg/Affordance20Q.git", "github_paper_md": "", "github_api": "https://github.com/1171-jpg/Affordance20Q", "github_api_added_by": "user", "project_page": "", "abstract": "Affordance reasoning, the inference of an object's action possibilities from its physical properties (e.g., shape and material), is fundamental to human physical understanding and increasingly critical for Large Language Models (LLMs). However, existing affordance benchmarks largely expose explicit object identities in the evaluation setup, allowing models to rely on memorized object-affordance mappings rather than reasoning over physical properties. To address this gap, we introduce Affordance20Q, a novel affordance reasoning benchmark formulated as a 20-Questions game without exposing the object's identity. In each game, the model identifies a hidden object's affordance from a candidate set by asking yes/no questions about its physical properties. Affordance20Q comprises 1,009 games over 454 objects and 59 affordances, all manually filtered, refined, and annotated. We conduct comprehensive experiments with 15 state-of-the-art LLMs and find a substantial gap (~20 points) compared to human performance. A KL-based information-gain (IG) analysis further shows that models fail to ask discriminating questions as the game progresses. To close the gap, we develop KB-Anchored Rule Induction (KARI), a pipeline based on LLMs that generates affordance rules grounded in evidence from knowledge bases (KBs). KARI improves open-source LLMs by up to 15.2 points, while the limited coverage of KBs hinders further gains. We release all our code and data at https://github.com/1171-jpg/Affordance20Q.git", "github": "https://github.com/1171-jpg/Affordance20Q" }, { "date": "2026-06-15", "arxiv_id": "2606.01708", "title": "Two-Fidelity Best-Action Identification for Stochastic Minimax Tree", "authors": [ "Peter Chen", "Xi Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We study fixed-confidence best-action identification (BAI) in stochastic minimax trees. This problem is increasingly relevant in modern AI planning, where deep minimax search and Monte Carlo Tree Search (MCTS) with language model long rollouts face a fundamental tradeoff: heuristic evaluations are cheap but biased, while accurate rollouts are reliable but prohibitively expensive. We propose 2FFS, a two-fidelity tree-search algorithm that brings multi-fidelity flat bandit ideas into trees. The algorithm combines minimax-style fast expansion with MCTS-style stochastic sampling, adaptively deciding when to exploit cheap biased evaluations and when to invoke expensive accurate evaluations for local certification. We prove fixed-confidence correctness, establish finite stopping for exact identification, and give a polynomial-depth cost upper bound for general-depth trees. Across numerical stochastic-tree experiments, 2FFS uses substantially fewer samples and computational operations comparing to existing BAI-MCTS baseline.", "github": "" }, { "date": "2026-06-15", "arxiv_id": "2604.18401", "title": "StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning", "authors": [ "Daoyu Wang", "Qingchuan Li", "Mingyue Cheng", "Jie Ouyang", "Shuo Yu", "Qi Liu", "Enhong Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AgentR1/StepPO", "github_api_added_by": "user", "project_page": "https://agentr1.github.io/steppo/", "abstract": "Agentic reinforcement learning (RL) is emerging as a critical post-training paradigm for improving LLM agent capabilities. Existing RL algorithms for LLMs largely follow the token-centric paradigm as in RLHF and RLVR, where tokens serve as the basic units for modeling and optimization. However, this paradigm introduces a granularity mismatch in agentic RL, as it optimizes token-level predictions while LLM agents make step-level decisions through cycles of environmental observations and actions. To bridge this gap, we propose StepPO, a step-centric paradigm for agentic RL via step-aligned policy optimization. Specifically, we reformulate agentic RL from a token-level Markov Decision Process (MDP) into a step-level MDP, where interaction steps serve as the basic trajectory representations. We further propose step-level credit assignment to align policy optimization with the natural granularity of agent decisions. Together, StepPO optimizes agent policies at the step level for multi-turn agent-environment interaction. Experiments across multi-hop QA, academic paper search, and text-world action tasks show that StepPO consistently outperforms various RL algorithms. Further analyses provide insights into how step-centric paradigm improves agent training. We hope this step-centric paradigm offers a useful lens for understanding agent behavior and a practical path for training more capable LLM agents.", "github": "https://github.com/AgentR1/StepPO" }, { "date": "2026-06-15", "arxiv_id": "2606.14732", "title": "Steady-Forcing: Balancing Spatial Persistence and Motion Continuity in Long-Horizon Nature Video Diffusion", "authors": [ "Matiur Rahman Minar", "Seunghun Oh", "GangHyeon Jeong", "Unsang Park" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/minar09/steady-forcing", "github_api_added_by": "user", "project_page": "https://minar09.github.io/steadyforcing/", "abstract": "Autoregressive video diffusion models enable streaming generation but often degrade over long rollouts: static scene layouts drift, while mechanisms that improve spatial stability tend to suppress motion, causing natural flows such as water, fire, or smoke to stagnate. We study this stability-motion trade-off in fixed-camera long-horizon nature video generation, where the two failure modes can be more clearly separated than in moving-camera settings. We propose Steady-Forcing, a memory and training framework combining a persistent visual anchor (V-Sink), an exponential moving-average motion memory (EMA-Sink), block-relative temporal encoding, periodic cache purification, and distillation from a Wan2.1-14B teacher with motion-rewarded priors under task-focused configurations. Together, these components are designed to preserve background identity while sustaining visually plausible fluid dynamics over multi-minute autoregressive rollouts. Evaluations across seven baselines show that Steady-Forcing improves long horizon background consistency and imaging quality, while a blind user study indicates stronger perceived stability and motion continuity. The benchmark evaluation further suggest that generic VBench aggregate scores under-penalize fixed-camera artifacts as well as rewarding drift-induced optical flow as Dynamic Degree while not directly penalizing texture hardening or flow stagnation - motivating future task-specific benchmarks for static-camera nature-flow evaluation. Project page: https://minar09.github.io/steadyforcing/", "github": "https://github.com/minar09/steady-forcing" }, { "date": "2026-06-16", "arxiv_id": "2606.14066", "title": "FastContext: Training Efficient Repository Explorer for Coding Agents", "authors": [ "Shaoqiu Zhang", "Maoquan Wang", "Yuling Shi", "Yuhang Wang", "Xiaodong Gu", "Yongqiang Yao", "Rao Fu", "Shengyu Fu" ], "github_manual": "", "github_regex": "https://github.com/microsoft/fastcontext", "github_paper_md": "", "github_api": "https://github.com/microsoft/fastcontext", "github_api_added_by": "user", "project_page": "https://huggingface.co/microsoft/FastContext-1.0-4B-SFT", "abstract": "Large Language Model (LLM) coding agents have achieved strong results on software engineering tasks, yet repository exploration remains a major bottleneck: locating relevant code consumes substantial token budget and pollutes the agent's context with irrelevant snippets. In most agents, the same model explores the repository and solves the task, leaving exploratory reads and searches in the solver's history. We present FastContext, a dedicated exploration subagent that separates repository exploration from solving. Invoked on demand, FastContext issues parallel tool calls and returns concise file paths and line ranges as focused context. FastContext is powered by specialized exploration models spanning 4B--30B parameters. We bootstrap them from strong reference-model trajectories and refine them with task-grounded rewards for broad first-turn search, multi-turn evidence gathering, and precise citation generation. Across SWE-bench Multilingual, SWE-bench Pro, and SWE-QA, integrating FastContext into Mini-SWE-Agent improves end-to-end resolution rates up to 5.5\\% while reducing coding-agent token consumption up to 60\\%, with marginal overhead. These results show that repository exploration can be separated from solving and handled effectively by specialized models. Code and data: https://github.com/microsoft/fastcontext", "github": "https://github.com/microsoft/fastcontext" }, { "date": "2026-06-16", "arxiv_id": "2606.17016", "title": "TokenPilot: Cache-Efficient Context Management for LLM Agents", "authors": [ "Buqiang Xu", "Zirui Xue", "Dianmou Chen", "Chenyang Fu", "Chiyu Wu", "Caiying Huang", "Chen Jiang", "Jizhan Fang", "Xinle Deng", "Yijun Chen", "Yunzhi Yao", "Xuehai Wang", "Jin Shang", "Gong Yu", "Ningyu Zhang" ], "github_manual": "", "github_regex": "https://github.com/zjunlp/LightMem2", "github_paper_md": "", "github_api": "https://github.com/zjunlp/LightMem2", "github_api_added_by": "user", "project_page": "", "abstract": "As LLM agents are deployed in long-horizon sessions, context accumulation drives up inference costs. Existing approaches utilize text pruning or dynamic memory eviction to minimize token footprints; however, their unconstrained sequence mutations alter layouts, introducing prefix mismatches and cache invalidation. This reveals a critical trade-off between text sparsity and prompt cache continuity. To address this, we present TokenPilot, a dual-granularity context management framework. Globally, Ingestion-Aware Compaction acts as a framework harness to stabilize prompt prefixes and eliminate open-world environmental noise at the ingestion gate. Locally, Lifecycle-Aware Eviction monitors the ongoing residual utility of context segments, enforcing a conservative batch-turn schedule to offload content segments only when task relevance expires. Experiments on PinchBench and Claw-Eval under both isolated and continuous modes demonstrate that TokenPilot reduces costs by 61% and 56% in isolated mode, and 61% and 87% in continuous mode, while maintaining competitive performance compared to prior systems. TokenPilot has been integrated into LightMem2 at https://github.com/zjunlp/LightMem2.", "github": "https://github.com/zjunlp/LightMem2" }, { "date": "2026-06-16", "arxiv_id": "2606.16255", "title": "UniDDT: Unifying Multimodal Understanding and Generation with Decoupled Diffusion Transformer", "authors": [ "Shuai Wang", "Liang Li", "Yang Chen", "Ruopeng Gao", "Yao Teng", "Limin Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://huggingface.co/papers/2606.16255", "abstract": "Unified Multimodal Models (UMMs) have emerged as a critical direction for general-purpose multimodal intelligence, integrating understanding and generation into a single framework. However, existing UMMs face prominent challenges: (1) the inherent learning conflicts between visual understanding and generation tasks, leading to suboptimal modeling in both tasks; (2) different understanding and generation visual spaces impeding scalability; (3) over-reliance on task-specific data that neglects the duality of text-image understanding and generation. To address these challenges, we propose UniDDT, which leverages a Noisy ViT encoder along with an LLM to unify semantic encoding for visual generation and understanding tasks, while employing a separate diffusion decoder to decouple diffusion decoding from text decoding. With this Noisy ViT encoder, UniDDT is able to leverage the latent space as a unified visual representation, enabling seamless compatibility between understanding and generation tasks. Thus, the scalability within the generation tasks and the semantic expressiveness within understanding tasks can be balanced. Also, we construct dual data structures from the same image-text pairs, fostering interdependence between the generation and understanding data to exploit their inherent duality. Extensive experiments demonstrate that UniDDT achieves effective unification of multimodal understanding and generation with enhanced semantic consistency and scalability. For visual generation tasks, our UniDDT achieves 0.87 GenEval score and 86.9 DPG overall score. For multimodal understanding tasks, our UniDDT achieves 1699.5 score on MME benchmark and 76.5 overall score on SEEDbench.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.15300", "title": "CODA-BENCH: Can Code Agents Handle Data-Intensive Tasks?", "authors": [ "Yuxin Zhang", "Ju Fan", "Meihao Fan", "Shaolei Zhang", "Xiaoyong Du" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ruc-datalab/CoDA-Bench", "github_api_added_by": "user", "project_page": "https://coda-bench.github.io/", "abstract": "Advanced agents are increasingly demonstrating the potential to operate as autonomous engineers, creating a growing demand for evaluation benchmarks that capture the complexity of real-world development. Such environments typically involve both complex code and large-scale data (i.e., file system). However, existing benchmarks usually evaluate code-centric or data-centric capabilities in isolation, leaving a clear gap with real development scenarios. In this paper, we bridge this gap by introducing CODA-BENCH, the first benchmark to jointly evaluate code and data intelligence in a data-intensive environment. We construct a data-intensive Linux sandbox based on the Kaggle ecosystem (containing hundreds of datasets), where agents must actively explore complex file hierarchies to identify relevant resources and generate code for data-driven analytical tasks. CODA-BENCH comprises 1,009 tasks spanning 31 communities, with each task environment containing an average of 980 files, simulating realistic data scale and noise. Evaluations of advanced agents reveal that even top-performing systems struggle to effectively integrate data discovery with code execution, achieving a success rate of only 61.1%. These results highlight a substantial gap in current agentic capabilities for data-intensive tasks and point to promising directions for future research.", "github": "https://github.com/ruc-datalab/CoDA-Bench" }, { "date": "2026-06-16", "arxiv_id": "2606.16993", "title": "DreamX-World 1.0: A General-Purpose Interactive World Model", "authors": [ "DreamX Team", "Yancheng Bai", "Rui Chen", "Xiangxiang Chu", "Rujing Dang", "Hao Dou", "Bingjie Gao", "Qiwen Gu", "Siyu Hong", "Jiachen Lei", "Geng Li", "Jifan Li", "Ruimin Lin", "Qingfeng Shi", "Bingze Song", "Lei Sun", "Jing Tang", "Ruitian Tian", "Jun Wang", "Jiahong Wu", "Pengfei Zhang", "Shen Zhang", "Jiashu Zhu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/AMAP-ML/DreamX-World", "github_api_added_by": "user", "project_page": "https://amap-ml.github.io/DreamX_World/", "abstract": "DreamX-World 1.0 is a general-purpose interactive text/image-to-video world model for controllable long-horizon generation. It supports camera navigation, revisits to previously observed regions, and promptable events across photorealistic, game-style, and stylized domains. Our data engine combines camera-accurate Unreal Engine rendering, action-rich gameplay recordings, and real-world videos with recovered camera geometry. For camera control, we introduce E-PRoPE, a lightweight variant of projective positional encoding that retains PRoPE's projective camera geometry while applying camera-aware attention to spatially reduced tokens. We convert a bidirectional video generator into a few-step autoregressive world model using causal forcing, DMD-style distillation, and long-rollout training. Training on self-generated long-horizon contexts exposes the model to its own generated history and reduces the style and color drift that accumulates across autoregressive chunks. Memory-Conditioned Scene Persistence retrieves earlier views through camera-geometry-based retrieval, while residual recycling makes the conditioning path less sensitive to imperfect memory latents. Event Instruction Tuning adds composable event control, and reinforcement learning alignment recovers camera control and visual quality after distillation. With mixed-precision DiT execution, residual reuse, 75\\%-pruned VAE decoding, and asynchronous pipeline parallelism, DreamX-World 1.0 reaches up to 16\\,FPS on eight RTX\\,5090 GPUs. On our 5-second basic evaluation, DreamX-World 1.0 achieves a camera-control score of 73.75 and an overall score of 84.76, outperforming HY-WorldPlay 1.5 and LingBot-World in overall score, which achieve 80.79 and 80.45, respectively.", "github": "https://github.com/AMAP-ML/DreamX-World" }, { "date": "2026-06-16", "arxiv_id": "2606.16281", "title": "Who Should Lead Decoding Now? Tracking Reliable Trajectories for Ensembling Masked Diffusion Language Models", "authors": [ "Heecheol Yun", "Joonhyung Park", "Joowon Kim", "Eunho Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Masked Diffusion Language Models (MDLMs) have emerged as a distinct paradigm for sequence generation. As MDLMs become diverse in capabilities and knowledge coverage, an important question is how to combine their knowledge. Toward this, we first investigate the unique decoding dynamics of MDLMs. We find that successful generations exhibit stable confidence dynamics over answer-relevant positions, while unreliable trajectories can often be corrected by injecting promising intermediate states from other models. Guided by this observation, we propose TIE (Trajectory-based Iterative Ensembling), a knowledge fusion framework in which MDLMs iteratively identify reliable decoding trajectories and relay them across models. TIE tracks confidence dynamics over answer-relevant positions to determine which model currently follows a more reliable trajectory and selectively transfers partially denoised sequences across models. As the model on the more promising trajectory often changes across denoising steps, TIE allows different models to contribute complementary strengths at different stages of generation. Strong performance across diverse reasoning tasks, along with our analyses, suggests that TIE offers a practical approach to the underexplored problem of MDLM ensembling.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.17049", "title": "BRDFusion: Physics Meets Generation for Urban Scene Inverse Rendering", "authors": [ "Yi-Ruei Liu", "Jie-Ying Lee", "Zheng-Hui Huang", "Yu-Lun Liu", "Chih-Hao Lin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shigon255/BRDFusion", "github_api_added_by": "user", "project_page": "https://shigon255.github.io/brdfusion-page/", "abstract": "Inverse rendering of urban scenes from captured videos enables numerous applications, including content creation and autonomous driving simulation. Physically-based rendering methods follow and control lighting physics, but suffer from reconstruction and rendering artifacts. While generative models produce realistic videos, they offer limited consistency and controllability. We present BRDFusion, a unified framework that combines two complementary models for inverse and forward rendering. Specifically, BRDFusion recovers explicit, consistent scene properties with physical modeling and alleviates optimization ambiguity with generative priors. During forward rendering, the physical model provides controllable rendering from the scene configuration, and the generative model denoises and fixes artifacts. Therefore, our method produces high-quality videos while allowing precise control, outperforming baselines in real and synthetic scenes. Moreover, BRDFusion supports novel-view relighting, night simulation, and dynamic object insertion/editing. Project page: https://shigon255.github.io/brdfusion-page/", "github": "https://github.com/shigon255/BRDFusion" }, { "date": "2026-06-16", "arxiv_id": "2606.16519", "title": "BadWorld: Adversarial Attacks on World Models", "authors": [ "Linghui Shen", "Mingyue Cui", "Xingyi Yang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/LinghuiiShen/BadWorld", "github_api_added_by": "user", "project_page": "https://linghuiishen.github.io/BadWorld/", "abstract": "Visual world models (VWMs) synthesize interactive, action-conditioned rollouts from a single context image. However, it remains an open question how robust these models are to adversarial perturbations. Standard adversarial attacks fail to assess this vulnerability because attackers lack ground-truth future videos and cannot predict subsequent user controls. We introduce BadWorld, a label-free adversarial framework tailored for autoregressive VWMs that systematically overcomes both constraints. First, to bypass the need for future supervision, we propose a self-supervised velocity attack that directly disrupts the early denoising dynamics of the model. Second, to ensure the attack generalizes across unpredictable user actions, we formulate a trajectory-adaptive bi-level optimization that actively mines hard control sequences to forge control-agnostic perturbations. Evaluated on representative VWMs with continuous and discrete controls, BadWorld exposes severe structural fragility. Visually indistinguishable adversarial images reliably trigger catastrophic degradation in future rollouts, leading to incomplete denoising, structural collapse, and control inconsistency. These findings reveal critical risks for deploying VWMs in safety-critical systems while highlighting a practical mechanism for privacy protection.", "github": "https://github.com/LinghuiiShen/BadWorld" }, { "date": "2026-06-16", "arxiv_id": "2606.16838", "title": "OneRank: Unified Transformer-Native Ranking Architecture for Multi-Task Recommendation", "authors": [ "Jiakai Tang", "Sunhao Dai", "Kun Wang", "Zhiluohan Guo", "Yu Zhao", "Cong Fu", "Kangle Wu", "Yabo Ni", "Anxiang Zeng", "Xu Chen", "Jun Xu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Multi-task learning (MTL) is essential in recommender systems to enable complementary learning among diverse user feedback. While modern industrial practices have shifted from DNNs to Transformer-centric architectures to strengthen sequence modeling and scaling capacity, they still decouple feature encoding from multi-task prediction, treating the Transformer as a task-agnostic encoder. This design fundamentally limits the performance and scalability by (1) creating an information bottleneck under heterogeneous task objectives, (2) inducing gradient interference that leads to the seesaw phenomenon, and (3) forcing a dataflow transition in which attention-based, context-adaptive representation learning is converted to static feed-forward task prediction with incompatible information read-write dynamics. We propose OneRank, a Transformer-native multi-task ranking framework that eliminates encoder-predictor separation and introduces task-private channels for forward representation learning and backward optimization, enabling task-specialized learning while reducing inter-task interference. In the forward pass, OneRank learns task-specific representations bottom-up through task-conditioned information selection, candidate-aware contextualization, and controlled cross-task interaction. In the backward pass, cross-task gradient detachment isolates task-private parameter updates from shared knowledge extraction modules, preventing negative transfer. We further replace static task-specific MLP scorers with dynamic matching-based scoring for context-aware personalized ranking. By internalizing multi-task reasoning within the Transformer stack, OneRank establishes a unified and scalable architectural paradigm. Offline and online experiments on large-scale industrial datasets show that OneRank significantly outperforms state-of-the-art baselines while maintaining computational efficiency.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.16295", "title": "VisualClaw: A Real-Time, Personalized Agent for the Physical World", "authors": [ "Haoqin Tu", "Jianwen Chen", "Zijun Wang", "Siwei Han", "Juncheng Wu", "Hardy Chen", "Haonian Ji", "Kaiwen Xiong", "Jiaqi Liu", "Peng Xia", "Jieru Mei", "Hongliang Fei", "Jason Eshraghian", "Zeyu Zheng", "Yuyin Zhou", "Huaxiu Yao", "Cihang Xie" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/UCSC-VLAA/VisualClaw", "github_api_added_by": "user", "project_page": "https://ucsc-vlaa.github.io/VisualClaw/", "abstract": "Vision language models are serving as general-purpose interfaces for complex multimodal tasks. However, deployment still faces three gaps: VLMs typically incur high latency and cost when processing dense video frames and long prompts, the agent scaffold remains static after deployment, and standard video-QA benchmarks do not test whether agents can use visual evidence inside tool-using workspaces. We present VisualClaw, a self-evolving multimodal agent built around two principles. First, hybrid encoding reduces deployment cost by filtering less informative streaming frames with a cascaded gate and compressing the text skill bank through hot/cold top-k injection. Second, skill evolution lets the agent learn from failures: retrieved memories condition an evolver as direct concatenated context or as guided evidence, producing skill-bank updates that help future questions. Across 4 video-QA benchmarks with 2 VLMs, VisualClaw cuts per-question API cost by an average -98% versus full-frame upload and by -25.9% over the offline uniform 8 frame baseline, while boosting accuracy in most settings, e.g., an average +3.85% and a peak +15.80% on EgoSchema with Gemini 3 Flash. To address the gap, we curate VisualClawArena, a 200-scenario multimodal agentic benchmark built through a strict five-stage pipeline; models must use video evidence, documents, dynamic updates, and executable checks inside a workspace. On VisualClawArena, the same framework with computer-use agent backends improves macro accuracy by +2.9% for Codex (GPT-5.5) and +3.2% for Claude Code (Sonnet 4.6) over no-evolution baselines, with a -9.5% cost reduction compared to the uniform-sampled baseline. These properties make VisualClaw a natural fit for edge applications, where the cascade reduces a 1-hour streaming session from ~3,600 API uploads down to only 5-20 calls and the self-evolution makes it a perfect personalized assistant.", "github": "https://github.com/UCSC-VLAA/VisualClaw" }, { "date": "2026-06-16", "arxiv_id": "2606.17030", "title": "Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation", "authors": [ "Jie Zhang", "Xiaoyue Chen", "Anzhe Chen", "Chenxu Lv", "Deqing Li", "Gengze Zhou", "Hang Yin", "Haoqi Yuan", "Haoyang Li", "Jiahao Li", "Jiazhao Zhang", "Jingren Zhou", "Kaiyuan Gao", "Kun Yan", "Lihan Jiang", "Ningyuan Tang", "Pei Lin", "Qihang Peng", "Shengming Yin", "Tianhe Wu", "Tianyi Yan", "Xiao Xu", "Yan Shu", "Yanran Zhang", "Ye Wang", "Yi Wang", "Yilei Chen", "Yixian Xu", "Yiyang Huang", "Yuxiang Chen", "Zekai Zhang", "Zhendong Wang", "Zhixing Lei", "Zhixuan Liang", "Zihao Liu", "Zikai Zhou", "Xiong-Hui Chen", "Chenfei Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://pre.qwen.ai/blog?id=qwen-robotworld", "abstract": "We introduce Qwen-RobotWorld, a language-conditioned video world model for embodied intelligence. With natural language as a unified action interface, it predicts physically grounded future visual trajectories from current observations across robotic manipulation, autonomous driving, indoor navigation, and human-to-robot transfer. This unified formulation provides three promising application directions: synthetic data generation for policy training augmentation, scalable virtual environments for policy evaluation, and language-guided planning signals for downstream robot control. This is achieved through a three-part design: a) Double-Stream MMDiT with MLLM Action Encoding, where a 60-layer double-stream diffusion transformer couples frozen Qwen2.5-VL semantics with video-VAE latents through layer-wise joint attention; b) Embodied World Knowledge (EWK), an 8.6M video-text corpus (200M+ frames) with action-language mapping over 20+ embodiments and 500+ action categories; and c) General+Expert Progressive Curriculum, a two-stage training strategy that first learns general visual priors and then injects embodied specialization under a shared language interface. Extensive results show strong competitiveness: ranks 1st overall on EWMBench and DreamGen Bench, outperforms all open-source models on WorldModelBench and PBench. Additional zero-shot analyses on RoboTwin-IF benchmark further support robust generalization and multi-view consistency.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.17006", "title": "TuneJury: An Open Metric for Improving Music Generation Preference Alignment", "authors": [ "Yonghyun Kim", "Junwon Lee", "Haiwen Xia", "Yinghao Ma", "Junghyun Koo", "Koichi Saito", "Yuki Mitsufuji", "Chris Donahue" ], "github_manual": "", "github_regex": "https://github.com/yonghyunk1m/TuneJury", "github_paper_md": "", "github_api": "https://github.com/yonghyunk1m/TuneJury", "github_api_added_by": "user", "project_page": "https://huggingface.co/spaces/yonghyunk1m/TuneJury", "abstract": "We introduce TuneJury, an open, instance-level pairwise reward model for text-to-music that predicts a music preference score from a text prompt and an audio clip. The released checkpoint is trained on publicly available human-preference labels covering arena-style (A vs. B) votes, metric-alignment preference pairs, crowdsourced pairwise comparisons, and expert aesthetic ratings. The predicted score margin between two clips is well calibrated on our held-out test split, supporting data filtering via a simple score threshold. TuneJury generalizes to both held-out test pairs and out-of-distribution benchmarks, remaining competitive with prior baselines on the latter. For generators released after training, we introduce anchor calibration, a post-hoc, per-system Bradley-Terry calibration that recovers agreement at substantially better data efficiency than from-scratch retraining. The same frozen reward drives consistent reward-axis gains across three downstream applications: inference-time best-of-N selection, DITTO-style latent optimization, and expert-iteration post-training. TuneJury is available at https://github.com/yonghyunk1m/TuneJury.", "github": "https://github.com/yonghyunk1m/TuneJury" }, { "date": "2026-06-16", "arxiv_id": "2606.15631", "title": "Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time", "authors": [ "Jeongeun Park", "Juhan Park", "Taekyung Kim", "Sungjoon Choi", "Dongyoon Han", "Sangdoo Yun" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://recap-robot.github.io/", "abstract": "Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in both data collection and compute. In this paper, we show that this target-side per-task adaptation cost can be replaced by retrieval. Our retrieval-augmented policy is trained once on paired demonstrations from the target embodiment (query) and a cheaper embodiment (pool, e.g., human-hand video), then frozen. New tasks are added at deployment by appending pool-side demonstrations to a retrieval pool. The frozen policy conditions on retrieved trajectories at every control step, so new tasks are absorbed by indexing data rather than updating parameters. Fine-tuning is needed only to take on a new, unseen embodiment, not for each new task. We show that retrieval improves policies beyond a specific backbone, including standard VLA policies, but its effect is especially pronounced in Cosmos Policy, a video-generation-based world-action model (WAM). In this setting, retrieval supplies coarse task progression, while the WAM's future-image objective provides an additional visual consistency signal that strengthens the retrieval-conditioned actions. On PushT, we study how retrieval provides a reusable high-level motion prior for cross-embodiment generalization to unseen goal angles, while on RoboTwin 2.0 our method outperforms cross-embodiment baselines on unseen tasks, and we additionally demonstrate the method on a real robot.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.14777", "title": "JoyAI-VL-Interaction: Real-Time Vision-Language Interaction Intelligence", "authors": [ "Dingyu Yao", "Junhao Zhou", "Chenxu Yang", "Chuanyu Qin", "Haowen Hou", "Zheming Liang", "Congcong Wang", "Yuhang Cao", "Shenglong Ye", "Shuai Xie", "Shuhuan Gu", "Haoyang Huang", "Qingyi Si", "Nan Duan", "Jiaqi Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/jd-opensource/JoyAI-VL-Interaction", "github_api_added_by": "user", "project_page": "https://joyai-vl-video-future-academy-jd.github.io/JoyAI-VL-Interaction/", "abstract": "Many moments in the real world do not wait for a user to ask. A fire starts on a security monitor, an expression flickers across a video call, or a product a viewer wants flashes by in a livestream. Yet today's large models remain mostly turn-based by design: they answer only when addressed, and even video-call apps that appear interactive still operate as question-answer systems, reacting only when polled or prompted. We argue for a different paradigm: a model that is present in the world like a person. It continuously watches what is happening now, decides on its own whether to speak or stay silent, interacts in real time, and delegates to a background model when the problem is hard. To advance interaction models and their adoption across domains, we make two fully open-sourced contributions. First, we release JoyAI-VL-Interaction, an 8B-scale, vision-first VL-interaction model. The model makes the response decision internally, choosing each second to stay silent, respond, or delegate to a background model, and it excels at vision-triggered responsiveness and time awareness. We pair it with a transferable training recipe, from which capabilities we never trained for emerge, such as guiding a shopper through changing app screens or improvising a lecture from a slide deck. Second, we release a complete, deployable system built around that model. The system streams any ongoing video into the model, making it genuinely present in the world. All other components are pluggable, including ASR/TTS modules, memory, visualization UI, and a background brain that can connect to any API or agent. Across six real-world scenarios, human raters prefer JoyAI-VL-Interaction over the in-app video-call assistants of Doubao and Gemini by a wide margin. To our knowledge, this is the first open, vision-driven interaction model released together with its training recipe, data, and complete deployable system.", "github": "https://github.com/jd-opensource/JoyAI-VL-Interaction" }, { "date": "2026-06-16", "arxiv_id": "2606.16140", "title": "VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models", "authors": [ "Sen Xu", "Shixi Liu", "Wei Wang", "Jixin Min", "Yingwei Dai", "Zhibin Yin", "Yirong Chen", "Xin Zhou", "Junlin Zhang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/WeiboAI/VibeThinker", "github_api_added_by": "user", "project_page": "https://github.com/WeiboAI/VibeThinker", "abstract": "This technical report introduces VibeThinker-3B, a compact dense model with 3B parameters developed to investigate how far verifiable reasoning can be pushed within a strictly small-model regime. Building upon the Spectrum-to-Signal post-training paradigm, we systematically enhance the model through an optimized pipeline that includes curriculum-based supervised fine-tuning, multi-domain reinforcement learning, and offline self-distillation. Experimental evaluations demonstrate that VibeThinker-3B achieves frontier-level performance on highly demanding verifiable tasks. Specifically, it attains a score of 94.3 on AIME26 (improving to 97.1 with claim-level test-time scaling), an 80.2 Pass@1 on LiveCodeBench v6, and exhibits strong out-of-distribution generalization with a 96.1\\% acceptance rate on recent unseen LeetCode contests. This effectively places it in the performance band of first-tier reasoning systems, matching or exceeding flagship models that are orders of magnitude larger, such as DeepSeek V3.2, GLM-5, and Gemini 3 Pro. Furthermore, a score of 93.4 on IFEval confirms that this extreme reasoning enhancement does not compromise strict instruction controllability. Extending our previous 1.5B work, these findings motivate the Parametric Compression-Coverage Hypothesis, which views verifiable reasoning as compressible into compact reasoning cores, while open-domain knowledge and general-purpose competence require broad parameter coverage over facts, concepts, and long-tail scenarios. This perspective suggests that compact models are not merely deployment-efficient substitutes, but a complementary path toward frontier-level performance in parameter-dense capability regimes.", "github": "https://github.com/WeiboAI/VibeThinker" }, { "date": "2026-06-16", "arxiv_id": "2606.06302", "title": "Tangram: Unlocking Non-Uniform KV Cache Compression for Efficient Multi-turn LLM Serving", "authors": [ "Hyungmin Kim", "Minsoo Kim", "Hongseok Kim", "Jungwook Choi" ], "github_manual": "", "github_regex": "https://github.com/aiha-lab/TANGRAM", "github_paper_md": "", "github_api": "https://github.com/aiha-lab/tangram", "github_api_added_by": "user", "project_page": "https://aiha-lab.github.io/tangram-page/", "abstract": "Multi-turn LLM serving accumulates dialogue history whose Key-Value (KV) cache grows with every turn and every user, quickly exceeding the model weights themselves and making memory -- not compute -- the binding constraint on throughput. Non-uniform KV compression, which allocates heterogeneous budgets across attention heads, preserves accuracy far better than uniform schemes, yet remains impractical: modern serving stacks assume identical KV lengths across heads, so heterogeneity traps freed memory as page fragmentation, spends up to 25% of prefill time reclaiming scattered pages, and skews GPU workloads that inflate decode latency by up to 1.7times or burn 15--20% of each decode step on re-planning. We observe that this heterogeneity need not be discovered at runtime: head-wise retention follows a two-level structural regularity -- an input-invariant head ranking with narrowly bounded per-head ratios -- that can be calibrated offline from as few as 50 samples. Building on this insight, we present Tangram, a serving framework that statically resolves what prior systems handle dynamically: Budget Reservation fixes each head's post-compression footprint at scheduling time, eliminating page reclamation; Ragged Paging clusters similar-budget heads into independent page tables, turning fragmentation into reclaimable memory; and Ahead-of-Time Load Balancing precomputes balanced GPU partitions with zero runtime planning. Implemented on vLLM, Tangram serves as a drop-in substrate for existing non-uniform compression methods, matching their accuracy while improving end-to-end throughput by up to 2.6times over the full-KV baseline. Our implementation is publicly available at https://github.com/aiha-lab/TANGRAM.", "github": "https://github.com/aiha-lab/tangram" }, { "date": "2026-06-16", "arxiv_id": "2606.15007", "title": "Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning", "authors": [ "NVIDIA", "Aaron Blakeman", "Aaron Thomas", "Aastha Jhunjhunwala", "Abhibha Gupta", "Abhinav Khattar", "Adam Rajfer", "Adi Renduchintala", "Adil Asif", "Aditya Vavre", "Adriana Flores Miranda", "Ahmad Bilal", "Aileen Zaman", "Ajay Hotchandani", "Akanksha Shukla", "Akhiad Bercovich", "Aleksander Ficek", "Alex Gronskiy", "Alex Kondratenko", "Alex Steiner", "Alex Ye", "Alexander Bukharin", "Alexandre Milesi", "Ali Taghibakhshi", "Alice Gatti", "Alisa Liu", "Alok Kumar", "Amar Phanishayee", "Ameya Sunil Mahabaleshwarkar", "Amir Klein", "Amit Zuker", "Amnon Geifman", "Anahita Bhiwandiwalla", "Ananth Subramaniam", "Andrea Santilli", "Andrew Fulks", "Andrew McHarg", "Andrew Tao", "Andrii Skliar", "Anjulie Agrusa", "Ankur Srivastava", "Ankur Verma", "Anna Shors", "Anna Warno", "Antoni-Joan Solergibert I Llaquet", "Arham Mehta", "Arkadiusz Nowaczynski", "Arti Jain", "Ashwath Aithal", "Ashwin Poojary", "Asif Ahamed", "Asit Mishra", "Asma Kuriparambil Thekkumpate", "Atefeh Sohrabizadeh", "Avinash Kaur", "Avinash Vem", "Ayush Dattagupta", "Barath Subramaniam Anandan", "Bardiya Sadeghi", "Ben Lanir", "Benedikt Schifferer", "Besmira Nushi", "Bilal Kartal", "Bill Thiede", "Bita Darvish Rouhani", "Bo Deng", "Bob Schatz", "Boris Ginsburg", "Boxin Wang", "Brad Nemire", "Brandon Norick", "Brian Dang", "Brian Westphal", "Brian Yu", "Brucek Khailany", "Bryan Catanzaro", "Carlo del Mundo", "Caryln Aarish", "Chankyu Lee", "Chantal Hwang", "Charbel Sakr", "Charles Wang", "Charlie Truong", "Chen Cui", "Cheng Cheng", "Cheng-Ping Hsieh", "Chenghao Zhang", "Chenhui Deng", "Chintan Patel", "Chris Alexiuk", "Christian Cosgrove", "Christian Munley", "Christine Harvey", "Christopher Parisien", "Chunyang Shen", "Coco Li", "Collin Neale", "Cynthia Gao", "Cyril Meurillon", "Dan Gil", "Dan Su", "Dan Zhao", "Dane Corneil", "Daniel Afrimi", "Daniel Egert", "Daniel Korzekwa", "Daniel Lo", "Daniel Machlab", "Daniel Serebrenik", "Daniil Sorokin", "Daria Gitman", "Daria Levy", "Darko Stosic", "David Mosallanezhad", "David Yu", "Davit Karamyan", "Deena Donia", "Deep Debroy", "Deepak Narayanan", "Devin O'Kelly", "Dheeraj Peri", "Dhruv Nathawani", "Di", "Wu", "Dima Rekesh", "Divyanshu Kakwani", "Donald Plummer", "Dong Anh", "Dongfeng Yu", "Dongfu Jiang", "Donnie Kim", "Dorrin Poorkay", "Duncan Riach", "Dusan Stosic", "Dustin VanStee", "Eavan Meng", "Edgar Minasyan", "Edward Lin", "Eileen Margaret Peters Long", "Elad Sarafin", "Elad Segal", "Elena Lantz", "Ellie Evans", "Elliott Ning", "Eric Chung", "Eric Harper", "Eric Pham-Hung", "Eric Tramel", "Eric Yang", "Erick Galinkin", "Erik Pounds", "Erika Goncalves Goncalves", "Evan Briones", "Evan Wu", "Evelina Bakhturina", "Evgeny Tsykunov", "Ewa Dobrowolska", "Faisal Ladhak", "Farzan Memarian", "Fay Wang", "Fei Jia", "Felipe Soares", "Felipe Vieira Frujeri", "Feng Chen", "Fengguang Lin", "Ferenc Galko", "Frank Sun", "Frankie Siino", "Frida Hou", "Gal Hubara Agam", "Gal Kaplun", "Gantavya Bhatt", "Gargi Prasad", "Garvit Kulshreshtha", "George Armstrong", "Gerald Shen", "Giulio Borghesi", "Gordana Neskovic", "Gorkem Batmaz", "Grace Lam", "Greg Mason", "Greg Pauloski", "Grigor Nalbandyan", "Grzegorz Chlebus", "Grzegorz Karch", "Guan-Ting Liu", "Guoming Zhang", "Guyue Huang", "Haggai Maron", "Haifeng Qian", "Haim Elisha", "Haoxing Ren", "Haran Kumar Shiv Kumar", "Haribhau Hud", "Harris Nover", "Harrison Saturley Hall", "Hayate Iso", "Helen Ngo", "Herbert Hum", "Herman Sahota", "Hexin Wang", "Himanshu Soni", "Hovhannes Tamoyan", "Hua Li", "Huanhuan Chen", "Hui Li", "Hui Wang", "Huy Nguyen", "Ian Chiles", "Ido Galil", "Ido Shahaf", "Igor Gitman", "Igor Shovkun", "Ilya Loshchilov", "Ingo Guehring", "Itamar Schen", "Itay Levy", "Itay Neeman", "Ivan Moshkov", "Izik Golan", "Izzy Putterman", "Jaemin Choi", "Jakub Slowikowski", "Jan Kautz", "Jane Polak Scowcroft", "Jared Casper", "Jatin Mitra", "Jeffrey Glick", "Jenny Chen", "Jesse Oliver", "Jiacheng Xu", "Jiafan Zhu", "Jialin Song", "Jian Zhang", "Jiantao Jiao", "Jiaqi Zeng", "Jie Lou", "Jim King", "Jimmy Zhang", "Jingquan Wang", "Jinhang Choi", "Jinju Chu", "Joey Conway", "Joey Guman", "Johan Jatko", "Johannes Rausch", "John Kamalu", "John Roberts", "Johnny Greco", "Johnny Mensel", "Jonah Alben", "Jonas Yang", "Jonathan Cohen", "Jonathan Raiman", "Joseph Jennings", "Joshua Mabry", "Joshua Pierce", "Joyjit Daw", "Julien Veron Vialard", "Junkeun Yi", "Jupinder Parmar", "Kajal Jain", "Kan Zhu", "Kari Briski", "Katherine Cheung", "Katherine Luna", "Keith Willowhawk", "Keith Wyss", "Keshav Santhanam", "Kevin Shih", "Kezhi Kong", "Khanh Nguyen", "Khushi Bhardwaj", "Kirthi Shankar Sivamani", "Konstantinos Krommydas", "Krishna C. Puvvada", "Krzysztof Pawelec", "Kumar Anik", "Kyle Keprios", "Kylie Day", "Lawrence McAfee", "Leo Du", "Leon Derczynski", "Li Ding", "Linda Liu", "Lingjie Wu", "Lior Kadoch", "Lizzie Wei", "Luis Vega", "Luke Robison", "Lun Su", "Maarten Van Segbroeck", "Maciej Jakub Mikulski", "Maer Rodrigues de Melo", "Magda Sypula", "Mahan Fathi", "Makesh Narsimhan Sreedhar", "Makesh Tarun Chandran", "Manoj Kilaru", "Maor Ashkenazi", "Marc Cuevas", "Marc Romeijn", "Marcin Chochowski", "Mark Cai", "Mark Mozolewski", "Markus Kliegl", "Marta Stepniewska-Dziubinska", "Martyna Patelka", "Mattei Machczynski", "Matvei Novikov", "Mauricio Ferrato", "Maximilian Golub", "Mehrzad Samadi", "Melissa Corpuz", "Mengru Wang", "Mengxi Wu", "Meredith Price", "Meriem Boubdir", "Micah Schaffer", "Michael Andersch", "Michael Boone", "Michael Gschwind", "Michael Lightstone", "Michael Loh", "Michal Bien", "Michal Zawalski", "Michelle Gill", "Miguel Martinez", "Mikail Khona", "Mike Chrzanowski", "Mike Houston", "Mingyuan Ma", "Minseok Lee", "Mohamed Fawzy", "Mohammad Dabbah", "Mohammad Shoeybi", "Mostofa Patwary", "Nabin Mulepati", "Najeeb Nabwani", "Namit Dhameja", "Narimane Hennouni", "Natalie Hereth", "Nathaniel Pinckney", "Nave Algarici", "Nave Assaf", "Netanel Haber", "Nicholas Knight", "Nick Reamaroon", "Nickson Quak", "Nidhi Bhatia", "Nikhil Desai", "Nikolai Ludwig", "Nima Tajbakhsh", "Ning Xu", "Nir Ailon", "Nirmal Juluru", "Nitin Nitin", "Ofri Masad", "Oleg Rybakov", "Oleksii Hrinchuk", "Oleksii Kuchaiev", "Olivia Viessmann", "Olivier Delalleau", "Oluwatobi Olabiyi", "Omer Ullman Argov", "Omri Puny", "Oren Tropp", "Pablo Ribalta", "Pallab Bhattacharya", "Panos Lampropoulos", "Parth Mannan", "Pasha Shamis", "Patrick Legresley", "Paul Gibbons", "Pavlo Molchanov", "Pawel Morkisz", "Peter Dykas", "Peter Jin", "Pierre-Yves Aquilanti", "Pinky Xu", "Piotr Januszewski", "Piotr Laskiewicz", "Pooya Jannaty", "Prakash Gurumurthy", "Pranav Prashant Thombre", "Prasoon Varshney", "Pritam Gundecha", "Przemek Tredak", "Puhui Meng", "Qiyu Wan", "Rabeeh Karimi Mahabadi", "Rachel Oberman", "Rachit Garg", "Radha Sri-Tharan", "Rahul Kandu", "Rakshit Sanadhya", "Ran El-Yaniv", "Ran Zilberstein", "Rasoul Shafipour", "Ray Macalisang", "Rayen Tian", "Reka Kovacs", "Renjie Pi", "Rick Izzo", "Rima Shahbazyan", "Rishabh Garg", "Rishi Puri", "Rita Fernandes Neves", "Ritchie Zhao", "Ritika Borkar", "Ritu Gala", "Riyad Islam", "Robert Clark", "Robert Hesse", "Robert Kirby", "Roger Waleffe", "Rohit Watve", "Roi Koren", "Ron Banner", "Ruoxi Zhang", "Russell J. Hewett", "Ryan Prenger", "Ryan Stewart", "Ryota Egashira", "Sadegh Mahdavi", "Saee Paliwal", "Sagar Singh", "Sahil Modi", "Salika Dave", "Samantha Shinagawa", "Samuel Kriman", "Sandip Bhaskar", "Sangkug Lym", "Sanjay Kariyappa", "Sanjeev Satheesh", "Saran Vikas Murari", "Satish Pasumarthi", "Saurabh Mishra", "Saurav Muralidharan", "Scott Hara", "Sean Narentharen", "Selvaraj Anandaraj", "Seonjin Na", "Seonmeyong Bak", "Seonmyeong Bak", "Sepehr Sameni", "Seph Mard", "Serge Panev", "Seth Henneman", "Seth Poulos", "Shahar Mor", "Shantanu Acharya", "Shaona Ghosh", "Sharath Turuvekere Sreenivas", "Sharon Mendelson", "Shaun Kotek", "Shawn Wang", "Shay Aharon", "Shaya Gharghabi", "Sheng-Chieh Lin", "Shi Chen", "Shiqing Fan", "Shirish Baskaran", "Shreya Gopa", "Shrimai Prabhumoye", "Shubham Pachori", "Shubham Toshniwal", "Shuoyang Ding", "Shwetha Krishnamurthy", "Siddharth Singh", "Simeng Sun", "Sirshak Das", "Sivakumar Arayandi Thottakara", "Smita Ithape", "Somshubra Majumdar", "Soumye Singhal", "Sri Harsha Singudasu", "Sridhar Bhuvanapalli", "Srimukh Veccham", "Stas Sergienko", "Stefania Alborghetti", "Stephen Ge", "Su Rong", "Sugam Dipak Devare", "Sukrit Rao", "Sumeet Kumar Barua", "Sungsoo Ha", "Sunny Gai", "Suriya Gunasekar", "Suseella Panguluri", "Suyog Gupta", "Sviataslau Hinzburh", "Sweta Priyadarshi", "Syeda Nahida Akter", "Talor Abramovich", "Tan Bui", "Tanay Varshney", "Tatevik Ter-Hovhannisyan", "Teodor-Dumitru Ene", "Terry Kong", "Thanh Do", "Tianhe Zhang", "Tiffany Moore", "Tijmen Blankevoort", "Tim Moon", "Tiyasa Mitra", "Tom Balough", "Tomasz Grzegorzek", "Tomasz Hliwiak", "Tomer Asida", "Tomer Bar Natan", "Tomer Keren", "Tomer Ronen", "Tony Salim", "Tony Wang", "Traian Rebedea", "Tugrul Konuk", "Twinkle Vashishth", "Udi Karpas", "Ushnish De", "Vahid Noorozi", "Venkat Srinivasan", "Venmugil Elango", "Vibhor Agrawal", "Victor Cui", "Vijay Korthikanti", "Vikas Mehta", "Vinay Rao", "Virginia Wu", "Vitaly Kurin", "Vitaly Lavrukhin", "Vladimir Anisimov", "Vu Pham", "Wanli Jiang", "Wasi Uddin Ahmad", "Wataru Ishihara", "Wei Du", "Wei Ping", "Weiheng Chai", "Wenliang Dai", "Wesley Helmholz", "Will Jennings", "Will Zhu", "Wojciech Prazuch", "Xiaowei Ren", "Xiwen Yu", "Yan Breek", "Yang Chen", "Yang Yu", "Yangyi Chen", "Yaniv Galron", "Yashaswi Karnati", "Yejin Choi", "Yev Meyer", "Yi-Fu Wu", "Yian Zhang", "Ying Lin", "Yonatan Geifman", "Yonggan Fu", "Youngeun Kwon", "Yu Yao", "Yugi Guvvla", "Yuki Huang", "Yunsheng Liu", "Zach Moshe", "Zachary Newell", "Zhilin Wang", "Zhiyu Li", "Zhongbo Zhu", "Zhuolin Yang", "Zihan Liu", "Zijie Yan", "Zsolt-Alon Wertheimer" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 trillion text tokens, then extended the context length to 1M tokens, and post-trained using Supervised Fine Tuning (SFT), Reinforcement Learning (RL), and Multi-teacher On-Policy Distillation (MOPD). Nemotron 3 Ultra is our most capable model yet, employing multiple key technologies - LatentMoE, Multi Token Prediction (MTP), NVFP4 pre-training, multi-environment RLVR, MOPD, and reasoning budget control. Nemotron 3 Ultra achieves up to ~6x higher inference throughput as compared to state-of-the-art publicly available LLMs while attaining on-par accuracy. The state-of-the-art accuracy, high inference throughput, and 1M token context length make Nemotron 3 Ultra ideal for long-running autonomous agentic tasks. We open-source the base, post-trained, and quantized checkpoints, along with the training data and recipe on HuggingFace.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.15708", "title": "Artificial Intelligence Index Report 2026", "authors": [ "Sha Sajadieh", "Loredana Fattorini", "Raymond Perrault", "Yolanda Gil", "Vanessa Parli", "Lapo Santarlasci", "Juan Pava", "Nestor Maslej", "Russ Altman", "Erik Brynjolfsson", "Carla Brodley", "Jack Clark", "Virginia Dignum", "Vipin Kumar", "James Landay", "Terah Lyons", "James Manyika", "Juan Carlos Niebles", "Yoav Shoham", "Elham Tabassi", "Russell Wald", "Toby Walsh", "Dan Weld" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Welcome to the ninth edition of the AI Index report. As AI continues to advance rapidly, the question becomes whether the systems built around it can keep up. Governance frameworks, evaluation methods, education systems, and the data infrastructure needed to track AI's impact are struggling to match the pace of the technology itself. That gap between what AI can do and how prepared we are to manage it runs through every chapter of this year's report. New in this edition, the report tracks how AI is being tested more ambitiously across reasoning, safety, and real-world task execution, and why those measurements are increasingly difficult to rely on. It also features new estimates of generative AI's economic value alongside emerging evidence of its labor market effects, an analytical framework on AI sovereignty, and a science chapter developed in collaboration with Schmidt Sciences. For the first time, the report features standalone chapters on AI in science and AI in medicine, reflecting AI's growing impact across these two domains.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.17046", "title": "Geometric Action Model for Robot Policy Learning", "authors": [ "Jisang Han", "Seonghu Jeon", "Jaewoo Jung", "Ren\u00e9 Zurbr\u00fcgg", "Honggyu An", "Tifanny Portela", "Marco Hutter", "Marc Pollefeys", "Seungryong Kim", "Sunghwan Hong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/cvlab-kaist/Geometric-Action-Model", "github_api_added_by": "user", "project_page": "https://cvlab-kaist.github.io/Geometric-Action-Model/", "abstract": "Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world. Recent vision-language-action models (VLAs) and video world-action models (WAMs) inherit strong semantic or temporal priors from large-scale foundation models, but they still operate primarily on 2D image frames or 2D-derived latent spaces, leaving implicit the 3D geometry required for contact-rich manipulation. We propose the Geometric Action Model (GAM), a language-conditioned manipulation policy that directly repurposes a pretrained geometric foundation model (GFM) as a shared substrate for perception, temporal prediction, and action decoding. GAM splits the GFM at an intermediate layer: the shallow layers serve as an observation encoder, and a causal future predictor inserted at the split layer forecasts future latent tokens conditioned on language, proprioception, and action history. The predicted future tokens are then routed through the remaining GFM blocks for feature propagation and decoding, allowing a single backbone to produce both future geometry and actions. This design equips the GFM with language-conditioned temporal world modeling through minimal architectural modification while preserving its rich geometric priors. Across a broad suite of simulation and real-robot manipulation benchmarks, GAM is more accurate, more robust, faster, and lighter than current foundation-model-scale baselines.", "github": "https://github.com/cvlab-kaist/Geometric-Action-Model" }, { "date": "2026-06-16", "arxiv_id": "2606.15673", "title": "Where Did It Go Wrong? Process-Level Evaluation of Web Agents with Semantic State Tracking", "authors": [ "Jiwan Chung", "JiHyuk Byun", "Vibhav Vineet", "Seon Joo Kim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://jiwanchung.github.io/webstep/", "abstract": "Web agents act through long interaction sequences, yet existing benchmarks evaluate only terminal success, discarding all process information and offering little guidance on improvement. In this work, we conduct a process-level analysis of web agents. We introduce WebStep, a benchmark of 1,800 task instances with controlled difficulty and automatic semantic state tracking. Each website exposes a deterministic semantic MDP alongside the GUI: the agent operates on the interface, while the environment records high-level states and transitions in the background, enabling fine-grained analysis without manual annotation. Based on the semantic trajectory, we first show that process metrics reveal differences invisible to outcome evaluation: three agents whose success rates cluster within 31-33% diverge in exploration reach versus execution accuracy. Then, decomposing by skill characterizes the nature of these differences, exposing opposite per-skill rankings hidden within the same website: e.g., on Housing, OpenAI CUA outperforms Qwen3.5 by 23.7% on commit actions yet underperforms it by 15.6% on filtering, pinpointing a concrete skill to improve even within a domain. Bifurcation analysis further localizes the decisive error that loses the task and shows that this error is agent-specific rather than shared. Finally, these differences widen as tasks grow harder: success rate is similar on easy tasks but separates sharply as exploration becomes more demanding. Our process-level analysis opens a new avenue in web agent evaluation, providing fine-grained and actionable insight into where and how each agent should be improved.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.14832", "title": "PhoneHarness: Harnessing Phone-Use Agents through Mixed GUI, CLI, and Tool Actions", "authors": [ "Chenxin Li", "Zhengyao Fang", "Zhengyang Tang", "Pengyuan Lyu", "Xingran Zhou", "Xin Lai", "Fei Tang", "Liang Wu", "Yiduo Guo", "Weinong Wang", "Junyi Li", "Yi Zhang", "Yang Ding", "Huawen Shen", "Sunqi Fan", "Shangpin Peng", "Zheng Ruan", "Anran Zhang", "Benyou Wang", "Chengquan Zhang", "Han Hu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/PhoneHarness/PhoneHarness", "github_api_added_by": "user", "project_page": "https://phoneharness.github.io/", "abstract": "Phone agents are increasingly expected to complete real mobile workflows rather than merely predict the next screen action. However, much of the current mobile-agent literature still evaluates agents primarily as GUI controllers that observe a screen, emit taps and swipes, and are scored by target app state. Real phone-use tasks are broader: they require deciding when to use app GUIs, device-side commands, or structured tools, while leaving evidence that the intended side effect actually occurred. We introduce PhoneHarness, a mixed-action benchmark and execution harness for studying phone-use agents on verifiable mobile workflows. PhoneHarness runs a device-side agent loop over GUI, CLI, and host-side tool actions, combining deterministic action routing with bounded GUI delegation and auditable execution traces. Its benchmark, PhoneHarness Bench, evaluates whether agents complete tasks with observable side effects, not only whether they produce plausible final answers. On the annotated evaluation split, PhoneHarness reaches a 75.0% pass rate, outperforming the strongest non-PhoneHarness settings by 12.9 percentage points. PhoneHarness and PhoneHarness Bench therefore play distinct but mutually dependent roles: the harness makes mixed phone workflows executable, while the benchmark measures whether agents can use that harness reliably and safely. Our findings suggest that reliable phone automation depends on action-surface routing and verifiable execution, not only visual GUI control.", "github": "https://github.com/PhoneHarness/PhoneHarness" }, { "date": "2026-06-16", "arxiv_id": "2606.16449", "title": "PermaVid: Consistent Video Generation Across Edits via Disentangled Context Memory", "authors": [ "Shuai Yang", "Bingjie Gao", "Ziwei Liu", "Jiaqi Wang", "Dahua Lin", "Tong Wu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YS-IMTech/PermaVid", "github_api_added_by": "user", "project_page": "https://ys-imtech.github.io/projects/PermaVid/", "abstract": "Consistent video generation under editing operations requires persistence: when edits modify scene appearance or layout, subsequent generations should remain coherent across time and viewpoints. However, existing memory designs struggle to maintain long-term consistency after such modifications, as stored contexts may become outdated or invalid. To address this, we propose PermaVid, a novel framework built upon a multi-modal context memory that disentangles spatial context into semantic appearance and geometric structure, together with an edit-aware memory update and retrieval strategy that keeps memory evolution aligned with subsequent observations. Specifically, we develop two complementary memory banks: an RGB context memory that captures appearance-aware observations while implicitly encoding geometry, and a depth context memory that preserves geometry-only structure disentangled from semantics. Building on this design, we introduce a memory-guided video generation model that performs multi-modal feature fusion under reference conditions drawn from mixed-modality memory contexts. Experiments demonstrate that our method maintains strong long-term semantic and structural consistency after edits, significantly outperforming state-of-the-art methods.", "github": "https://github.com/YS-IMTech/PermaVid" }, { "date": "2026-06-16", "arxiv_id": "2606.14142", "title": "Implicit Reasoning for Large Language Model-based Generative Recommendation", "authors": [ "Yinhan He", "Liam Collins", "Bhuvesh Kumar", "Jundong Li", "Neil Shah", "Donald Loveland" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge. Yet reliably invoking this knowledge for GR remains poorly understood. A key obstacle is that LLM-based GR typically represents items with Semantic IDs (SIDs), disrupting LLMs' natural-language reasoning interface because these tokens are unseen by the LLM during pretraining. Existing approaches address this with expensive multi-stage pipelines that ground SIDs and elicit explicit rationales, but offer limited insight into when and why each stage is necessary. In this work, we systematically decompose explicit reasoning training pipelines for LLM-based GR, revealing three key limitations: weakened world-knowledge verbalization, misalignment between SID and natural-language token embedding spaces, and sensitivity to rationale quality, all of which hurt explicit reasoning performance. To circumvent these issues, we propose PauseRec, a lightweight implicit reasoning paradigm tailored for GR. PauseRec is exceptionally practical, avoiding costly reasoning trace acquisition and reasoning alignment training, leading to a multitude of benefits: (1) it outperforms standard explicit CoT methods by up to 6.22%, (2) it reduces training cost by up to 65% GPU hours, and (3) it speeds up inference by up to 71.3%. These results position PauseRec as a lightweight alternative to explicit rationale generation, enabling more effective and efficient LLM-based GR.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.16011", "title": "Who Flips? Self- and Cross-Model Counterarguments Reveal Answer Instability in LLMs", "authors": [ "Nafiseh Nikeghbal", "Amir Hossein Kargaran", "Shaghayegh Kolli", "Jana Diesner" ], "github_manual": "", "github_regex": "https://github.com/nafisenik/WhoFlips", "github_paper_md": "", "github_api": "https://github.com/nafisenik/WhoFlips", "github_api_added_by": "user", "project_page": "", "abstract": "Standard accuracy benchmarks are designed to test how closely large language models (LLMs) approach correct answers, but are not suitable for testing whether LLMs stick with a correct answer when that answer is challenged by a plausible counter-argument. We introduce a controlled protocol for evaluating answer stability: after a model answers a multiple-choice question correctly, we challenge the model's answer with a coherent argument for an incorrect option and measure whether the model flips. The setup a) isolates argumentative content from overt social pressure and b) varies argument length, self-attribution, and cross-model source. Across seven frontier models and 57 MMLU subjects, flip rates range from 17.5% to 97.3%, revealing large differences in stability that are not captured by accuracy metrics alone. We find that self-attribution consistently increases flip rates (mean +7.1pp, up to +18.7pp). Also, pooling wrong-answer arguments across models and selecting the most effective one per question yields stronger adversarial challenges than relying on any single source model. We further construct MaxFlip, a curated challenge set that amplifies flips by up to +23.6pp over standard self-generated challenges. We release the protocol, challenge records, and MaxFlip to support stability evaluation alongside standard accuracy benchmarks. Materials are available at https://github.com/nafisenik/WhoFlips and https://hf.co/datasets/nafisehNik/WhoFlips.", "github": "https://github.com/nafisenik/WhoFlips" }, { "date": "2026-06-16", "arxiv_id": "2606.16771", "title": "GD^2PO: Mitigating Multi-Reward Conflicts via Group-Dynamic reward-Decoupled Policy Optimization", "authors": [ "Haotian Liu", "Yihao Liu", "Jingwei Ni", "Siyuan Huang", "Xinpeng Liu", "Pengyu Cheng", "Jiajun Song", "Ruijin Ding", "Junfeng Li", "Zhechao Yu", "Mengyu Zhou", "Hongteng Xu", "Xiaoxi Jiang", "Guanjun Jiang" ], "github_manual": "", "github_regex": "https://github.com/Qwen-Applications/GD2PO", "github_paper_md": "", "github_api": "https://github.com/Qwen-Applications/GD2PO", "github_api_added_by": "user", "project_page": "", "abstract": "As LLMs advance, post-training reinforcement learning (RL) increasingly relies on multi-dimensional rewards to cultivate comprehensive capabilities. This shift demands new algorithms capable of optimizing diverse and potentially competing objectives simultaneously. To address this, existing methods such as Group reward-Decoupled Policy Optimization (GDPO) decompose the overall score into independent reward groups, then compute the RL loss separately within each group. However, this strategy still encounters multi-reward conflicts: a single rollout can yield positive advantages on certain reward dimensions but negative ones on others, causing opposing signals to cancel each other out during aggregation, further hindering RL training efficiency. Inspired by Dynamic sAmpling Policy Optimization (DAPO), which improves RL training efficiency by filtering out ineffective rollouts with near-zero advantages, we propose Group-Dynamic reward-Decoupled Policy Optimization (GD^2PO). Specifically, GD^2PO employs a conflict-aware filtering mechanism to mask out rollouts suffering from severe reward-wise disagreement. By preventing conflicting signals from canceling each other out, this masking strategy preserves and enhances the magnitude of effective RL advantages, thereby significantly accelerating learning efficiency. Furthermore, we introduce query-level reweighting to dynamically adjust the update intensity of each query based on its overall reward consensus. Experiments on various multi-reward scenarios, including tool calling and human preference alignment, demonstrate that GD^2PO consistently and significantly outperforms existing baselines. The code is available at https://github.com/Qwen-Applications/GD2PO.", "github": "https://github.com/Qwen-Applications/GD2PO" }, { "date": "2026-06-16", "arxiv_id": "2606.16673", "title": "MMDiff: Extending Diffusion Transformers for Multi-Modal Generation", "authors": [ "Yagmur Akarken", "Orest Kupyn", "Christian Rupprecht" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://yagmurakarken.github.io/mmdiff/", "abstract": "Diffusion transformers have demonstrated remarkable generative capabilities, yet the rich perceptual representations computed across their denoising trajectory are discarded once the content is rendered. We present MMDiff, a framework that transforms a frozen diffusion transformer into a multi-modal generative system that jointly produces images alongside any combination of dense perceptual modalities using lightweight decoder heads. Our central finding is that perceptual information is temporally distributed along the denoising trajectory, and that multi-timestep feature fusion with spatially varying aggregation weights is essential, improving semantic segmentation results by up to 28.7% mIoU over single-timestep extraction. We further adopt concept-driven attention extraction for interpretable spatial guidance, and show that frozen diffusion features are competitive with and complementary to state-of-the-art encoders such as DINOv3. By training only lightweight decoder heads on a frozen backbone, we achieve strong performance in semantic segmentation, salient object detection, and depth estimation, and demonstrate that this framework enables effective synthetic data generation at scale.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.14819", "title": "Selective Control under Noisy Perception: Governance Failures Hidden by Aggregate Metrics in Modular Networks", "authors": [ "Igor Itkin" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/YehudaItkin/noisy-perception-governance", "github_api_added_by": "user", "project_page": "", "abstract": "A content-moderation system can score well on every standard accuracy metric and still cause real harm, if its mistakes fall on the few users who connect otherwise separate communities. We show this in an agent-based model where N=240 learning agents on a community-structured network each post harmless, productive, or dangerous content, and a regulator removes or penalizes whatever a noisy classifier flags. Overall usefulness barely moves as the noise changes (one-way ANOVA, p=0.96): by aggregate measures, nothing looks wrong. The damage instead concentrates on these bridge users, whose useful posts are wrongly suppressed and whose dangerous posts are wrongly spared. A governance loss (L_gov) that prices these two mistakes separately from the cost of enforcement more than doubles under false-positive-heavy noise. Aggregate accuracy hides who is harmed, and the cheap quantity to audit is how many connections a user has (degree), a near-perfect proxy for the betweenness that defines a bridge (r=0.96).", "github": "https://github.com/YehudaItkin/noisy-perception-governance" }, { "date": "2026-06-16", "arxiv_id": "2606.14667", "title": "Memento: Reconstruct to Remember for Consistent Long Video Generation", "authors": [ "Xuan Wei", "Longbin Ji", "Guan Wang", "Xiangrui Liu", "Zhenyu Zhang", "Shuohuan Wang", "Yu Sun", "Qingqi Hong" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ernie-research/Memento", "github_api_added_by": "user", "project_page": "https://ernie-research.github.io/Memento/", "abstract": "Long-form video generation requires recurring subjects to remain consistent across various shots, viewpoints, motions, and scene transitions. Existing temporal decomposition methods improve scalability by generating videos shot by shot. However, they mainly focus on optimizing plausible next-shot continuations without verifying whether the historical memory preserves identity-critical subject evidence. Consequently, as generation proceeds, recurring subjects may be diluted, overwritten, or forgotten. In this paper, we propose Memento, a subject-reconstruction-guided framework that treats subject preservation as an explicit identity grounding problem, based on the premise that a memory bank faithfully preserving a subject should support reconstructing that subject from memory alone. Specifically, Memento jointly trains autoregressive next-shot generation with memory-based subject reconstruction, recovering target appearances using historical memory and global story captions. To disentangle long-range subject evidence from short-range cues, Memento introduces a dual-query memory mechanism, where one query retrieves identity-relevant memory and the other selects short-context keyframes for coherent continuation. Additionally, a subject-aware cinematic data pipeline provides precise reconstruction supervision via consistent, pronoun-free subject descriptions. Experiments demonstrate that Memento achieves state-of-the-art performance in long-term subject consistency, cross-shot coherence, and visual quality.", "github": "https://github.com/ernie-research/Memento" }, { "date": "2026-06-16", "arxiv_id": "2606.17043", "title": "Hierarchical Advantage Weighting for Online RL Fine-Tuning of VLAs from Sparse Episode Outcomes", "authors": [ "Tongyan Fang", "Siyuan Huang", "Naiyu Fang", "Ganlong Zhao", "Zhongjin Luo", "Jianbo Liu", "Xiaogang Wang", "Ying Dong", "Hongsheng Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "When pretrained VLA policies are fine-tuned through online RL, each rollout episode produces only a single binary outcome (success or failure), yet the actor update requires per-transition supervision. Existing approaches commonly reduce this sparse outcome to a single scalar reward or advantage signal, which conflates distinct forms of transition-level feedback and provides limited guidance once basic task success becomes achievable. First, a single scalar signal conflates the two objectives of viability and efficiency; once basic success is achieved, the binary label provides no gradient to distinguish efficient completions from slow ones. Second, real-world rollouts mix autonomous and intervention segments; naively assigning episode outcomes across these boundaries introduces incorrect credit assignment. To address these issues, we propose Hierarchical Advantage-Weighted Behavior Cloning (HABC), which trains separate critic heads for these two objectives on different data subsets and combines their outputs with a state-adaptive balance. A state-adaptive gate g_t merges their one-step advantages, prioritizing viability when success is uncertain and shifting to efficiency only when viability is high, and converts the result into per-transition weights on the actor loss. Intervention-aware credit assignment further restricts outcome labels to segments executed by the current policy, preventing supervision from leaking across intervention boundaries. In real-robot experiments on three contact-rich bimanual tasks, HABC raises success from supervised fine-tuning (SFT) baselines of 36%, 44%, and 12% to 92%, 88%, and 38%.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.16396", "title": "SP^3: Spherical Priors for Plug-and-Play Restoration", "authors": [ "Sean Man", "Ron Raphaeli", "Matan Kleiner", "Or Ronai" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/man-sean/SP3", "github_api_added_by": "user", "project_page": "https://man-sean.github.io/sp3-website/", "abstract": "In this paper, we introduce SP^3, a novel Plug-and-Play algorithm that accelerates maximum a posteriori image restoration by replacing denoisers with Spherical Encoders (SE) as generative priors. SP^3 approximates the intractable proximal prior step by utilizing the SE tightly structured latent space as a robust projection onto the natural image manifold. Alternating this projection with a closed-form data-consistency step, via Half-Quadratic Splitting, achieves stable convergence without requiring gradient computation during inference. This unique formulation unlocks \"anytime\" restoration capabilities, producing sharp, plausible images from the first iteration. Evaluations across a variety of image restoration tasks demonstrate that SP^3 achieves perceptual quality comparable to state-of-the-art zero-shot diffusion and flow methods while being 3-630times faster.", "github": "https://github.com/man-sean/SP3" }, { "date": "2026-06-16", "arxiv_id": "2602.21103", "title": "Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning", "authors": [ "Sanket Badhe", "Deep Shah" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inference costs. The standard alternative, fine-tuning smaller models, often sacrifices interpretability while introducing significant resource and operational overhead. To address these limitations, we introduce Prompt-Level Distillation (PLD). We extract explicit reasoning patterns from a Teacher model and organize them into a structured list of expressive instructions for the Student model's System Prompt. Evaluated using Gemma-3 4B, PLD improved Macro F1 scores on StereoSet (57\\% to 90.0\\%) and Contract-NLI (67\\% to 83\\%), while increasing LogiQA accuracy to 70\\%. Similar results on Mistral Small 3.1 demonstrate cross-architecture generalizability, enabling these compact models to match frontier performance with negligible latency overhead. These expressive instructions render the decision-making process transparent, allowing for full human verification of logic, making this approach ideal for regulated industries such as law, finance, and content moderation, as well as high-volume use cases and edge devices.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.16852", "title": "The Ghosts of Polymarket: When Off-Chain Matches Meet On-Chain Reverts", "authors": [ "Yiming Shen", "Yuhan Jin", "Shuohan Wu", "Yanlin Wang", "Jiachi Chen" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/shenyimings/ghost-hunter", "github_api_added_by": "user", "project_page": "", "abstract": "Polymarket has emerged as a prominent prediction market platform and one of the fastest-growing applications in DeFi. To achieve low-latency trading, it adopts a hybrid architecture that matches orders off-chain but settles them on-chain for final execution. This design creates a consistency gap we call Ghost Fills: an order that is successfully matched off-chain may later fail during on-chain settlement. To understand the security implications of this gap, we investigate such failed settlements by building GHOSTHUNTER, which reconstructs them from on-chain traces and attributes to concrete attack patterns. Across 1,952,440 reverted match-order transactions, we find that attackers exploit the time gap between matching and settlement to invalidate already matched orders before they are finalized on-chain. We then identify four attack vectors from these incidents: nonce bump, balance drain, allowance revoke, and proxy trap, realized via 35 evolving variants. These vectors allow attackers to selectively revert 980,133 filled orders, enabling risk-free prediction, arbitrage-bot hunting, and liquidity reward manipulation, realizing at least \\1.49M in profit, which places 1.78 B USD at risk and 2.17 M POL (about \\212 K) paid by operator. During peak hours, more than 24.3% of all filled orders reverted, causing de facto DoS attacks. We also find that code derived from the flawed contract still appears in 167 independent contracts across 10 chains holding at least 23 M in user funds, extending the impact beyond Polymarket. We have disclosed our evidence to affected parties, and the issue has been partially mitigated.", "github": "https://github.com/shenyimings/ghost-hunter" }, { "date": "2026-06-16", "arxiv_id": "2606.15079", "title": "Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale", "authors": [ "Ang Li", "Ben Liu", "Bin Han", "Bin Hu", "Bin Jing", "Binbin Hu", "Bing Li", "Cai Chen", "Caizhi Tang", "Changxin Tian", "Chao Huang", "Chao Zhang", "Chen Liang", "Chen Qian", "Chengfu Tang", "Chengyao Wen", "Chilin Fu", "Chunwei Wu", "Cong Zhang", "Cunyin Peng", "Daixin Wang", "Dalong Zhang", "Deng Zhao", "Dingnan Jin", "Dingyuan Zhu", "Donghao Zhang", "Fan Yuan", "Fangzheng Zhao", "Fanzhuang Meng", "Feifan Wu", "Feng Xu", "Fengbin Fang", "Gangshan Wang", "Guodong Yang", "Hailin Zhao", "Haitao Wang", "Haitao Zhang", "Hanxiao Zhang", "Hanzi Wang", "Hao Dai", "Hao Liu", "Hao Qian", "Hao Wu", "Haoxiong Liu", "Haoyu Xu", "Heng Zhang", "Hong Liu", "Hongliang Zhang", "Hongrui Liu", "Hongxun Li", "Hongzhi Ruan", "Huaidong Xiong", "Huihuang Zheng", "Huikang Tang", "Jia Guo", "Jia Li", "Jia Liu", "Jiameng Wang", "Jiaming Liu", "Jiannan Shi", "Jianping Wei", "Jiaolong Yang", "Jiapeng Wang", "Jie Gao", "Jie Wang", "Jiewei Wu", "Jin Yang", "Jinjin Li", "Jinjing Huang", "Jinquan Sun", "Jinyao Chen", "Juanhui Tu", "Jun Liu", "Jun Mei", "Jun Xu", "Jun Zhou", "Junjie Ou", "Junnan Sipan", "Junpeng Fang", "Kaihong Zhang", "Kaiqin Hu", "Ke Shi", "Kuan Xu", "Kun Tang", "Kunlong Chen", "Lanyin Mei", "Lei Chen", "Lei Liang", "Lei Xu", "Li Tang", "Liang Jiang", "Liangcheng Fu", "Lihui Zhang", "Linfeng Shi", "Lintao Ma", "Liyuan Liu", "Longfei Li", "Longfei Zheng", "Lu Liu", "Lu Yu", "Man Li", "Meiqi Zhu", "Meng Li", "Mengjie Gao", "Mengshu Sun", "Mingming Yin", "Mingyang Zhang", "Mingyuan Fan", "Nuo Xu", "Pan Tang", "Peijie Jiang", "Peilong Zhao", "Peng Lin", "Pingping Liu", "Qi Zuo", "Qian Zhao", "Qiang Cheng", "Qianggang Cao", "Qiaoben Bao", "Qing Cui", "Qingyuan Yang", "Qitao Shi", "Qiyin Huang", "Qizheng Zhou", "Quan Wan", "Runyuan Zhao", "Shaomian Zheng", "Shaowei Wei", "Shengnan Zhang", "Shuaicheng Li", "Shujie Li", "Shuo Zhang", "Sikang Bian", "Tianchu Yao", "Tiange Xu", "Tianshu Wang", "Ting Guo", "Tinghao Wang", "Tingwei Huang", "Tong Zhao", "Tongkai Yang", "Wang Hong", "Wanli Gu", "Wei Lu", "Weichang Wu", "Weiguang Han", "Weiquan Li", "Wenbo Shen", "Wenjing Fang", "Wenzhi Tang", "Xiang Shu", "Xiao Shi", "Xiaodong Yan", "Xiaolu Zhang", "Xiaopei Wan", "Xiaqing Sun", "Xin Zhao", "Xingyu Lu", "Xinxing Yang", "Xinyao Tang", "Xinyu Kong", "Xinyu Liu", "Xiong Xu", "Xuan Sun", "Xudong Han", "Xudong Wang", "Xujie Shen", "Yalin Zhang", "Yangyang Hou", "Yankun Ren", "Yao Zhao", "Ye Chen", "Yeyang Chen", "Yibo Cao", "Yifan Zuo", "Yijie Chen", "Ying Li", "Yingjie Song", "Yingxue Li", "Yiqi Wang", "Yixuan Sun", "Yizhu Xiao", "Yongfei Xu", "Yu Liu", "Yuchen Fang", "Yue Gao", "Yue Yu", "Yue Zhang", "Yuqi Zhang", "Yuxiao He", "Yuxiao Lu", "Yuxin Tian", "Yuxuan Li", "Yuzhuo Fu", "Zhankai Xu", "Zhaoxin Huan", "Zhenduo Zhang", "Zhengke Gui", "Zhengyu Huang", "Zhenjun Ma", "Zhenxuan Pan", "Zheping Qu", "Zhibo Zhu", "Zhidong Fan", "Zhigang Huangfu", "Zhihao Wang", "Zhiqiang Zhang", "Zhizhen Liu", "Zhuyan Zhou", "Zibin Lin", "Zihang Zeng", "Zihao Wang", "Zilong Wang", "Ziqi Liu", "Zitao Xuan", "Zixuan Cheng", "Zujie Wen", "Zuoli Tang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy. In this report, we present Ling-2.6 and Ring-2.6, a family of models designed to address this challenge at scale. Ling-2.6 is optimized for instant response generation and high capability per output token, whereas Ring-2.6 is tailored for deeper reasoning and more advanced agentic workflows. Instead of training from scratch, we upgrade the Ling-2.0 base model through architectural migration pre-training and large-scale post-training. This upgrade is guided by a unified co-design of model architecture, optimization objectives, serving systems, and agent training environments, enabling improvements in both model capability and deployment efficiency. At the architectural level, we introduce a hybrid linear attention design that integrates Lightning Attention with MLA, improving the efficiency of long-context training and decoding. To further enhance token efficiency, we optimize capability per output token through Evolutionary Chain-of-Thought, Linguistic Unit Policy Optimization, bidirectional preference alignment, and shortest-correct-response distillation. For agentic capabilities, we propose KPop, a reinforcement learning framework designed to support stable training of Ring-2.6-1T on large-scale environment-grounded data. KPop improves training efficiency through asynchronous scheduling across coding, search, tool use, and workflow execution, enabling scalable learning from complex agent-environment interactions. Together, Ling-2.6 and Ring-2.6 provide a practical pathway toward efficient, scalable, and open agentic systems. We open-source all checkpoints in the 2.6 family to support further research and development in practical agentic intelligence.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.12138", "title": "Unstable Features, Reproducible Subspaces: Understanding Seed Dependence in Sparse Autoencoders", "authors": [ "Gleb Gerasimov", "Timofei Rusalev", "Nikita Balagansky", "Daniil Laptev", "Vadim Kurochkin", "Daniil Gavrilov" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Sparse autoencoders (SAEs) are widely used to interpret neural network representations, but their utility depends on whether the learned features are reproducible across training runs. We study this question through feature stability: for each SAE feature, we estimate the probability that a similar feature reappears in an independently trained SAE. This yields a scalable per-feature signal that separates stable from unstable features. In a large-scale study across seeds, models, layers, dictionary sizes, and SAE variants, we find a pronounced functional asymmetry: stable features carry most of the reconstruction- and prediction-relevant signal, while unstable features have weak marginal impact and are dominated by low-frequency surface-form triggers in both activation statistics and automatic explanations. Geometrically, unstable features are individually non-reproducible but concentrate in reproducible lower-rank subspaces, suggesting that seed dependence often reflects basis ambiguity within a shared region of activation space rather than pure noise. A controlled synthetic model makes this mechanism explicit, showing that low-rank ground-truth features can be recovered at the subspace level while remaining non-identifiable as individual SAE latents across seeds. Finally, by pooling unique cross-seed features, we construct more stable SAEs while preserving explained variance in this setting. Together, these results show that unstable features are not merely failed or noisy latents: they have weak individual functional impact, but reflect reproducible low-dimensional structure that standard SAEs resolve differently across seeds.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.14958", "title": "MVEB: Massive Video Embedding Benchmark", "authors": [ "Adnan El Assadi", "Roman Solomatin", "Isaac Chung", "Chenghao Xiao", "Deep Shah", "Manan Dey", "Shriya Sudhakar", "Zacharie Bugaud", "Wissam Siblini", "Ayush Sunil Munot", "Yashwanth Devavarapu", "Rakshitha Ireddi", "Michelle Yang", "M\u00e1rton Kardos", "Niklas Muennighoff", "Kenneth Enevoldsen" ], "github_manual": "", "github_regex": "https://github.com/embeddings-benchmark/mteb", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://embeddings-benchmark.github.io/leaderboard-frontend/benchmark/MVEB(beta)", "abstract": "We introduce the Massive Video Embedding Benchmark (MVEB), a 23-task benchmark for video embeddings spanning classification, zero-shot classification, clustering, pair classification, retrieval, and video-centric question answering. We evaluate 33 models and find that no single model dominates: MLLM-based embeddings lead on classification, clustering, pair classification, and QA; multimodal binding leads on retrieval and zero-shot classification; generative MLLMs without contrastive adaptation collapse on cross-modal tasks. Paired video-only vs. audio+video evaluations show that audio's contribution depends on dataset annotation provenance: audio helps when labels were produced from both modalities and hurts when they were produced from visuals alone, a six-point gap consistent across model families. MVEB is derived from MVEB+, a 184-task pool, and is designed to maintain task diversity while reducing evaluation cost. It integrates into the MTEB ecosystem for unified evaluation across text, image, audio, and video. We release MVEB and all 184 tasks along with code and a leaderboard at https://github.com/embeddings-benchmark/mteb.", "github": "https://github.com/embeddings-benchmark/mteb" }, { "date": "2026-06-16", "arxiv_id": "2606.15768", "title": "LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies", "authors": [ "Jialei Chen", "Kai Wang", "Kang Chen", "Shuaihang Chen", "Feng Gao", "Wenhao Tang", "Zhiyuan Li", "Weilin Liu", "Zhuyu Yao", "Boxun Li", "Yuanbo Xu", "Chao Yu" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/RLinf/LaWAM", "github_api_added_by": "user", "project_page": "https://rlinf.github.io/LaWAM/", "abstract": "Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change the scene. World-Action Models (WAMs) address this limitation by conditioning policies on predicted futures, yet existing approaches typically rely on computationally expensive video generation with substantial pixel-level redundancy. We present LaWAM, a Latent World Action Model that exposes predictive dynamics to robot policies through compact latent visual subgoals instead of reconstructed future video. At the core of LaWAM is a latent-action-conditioned Latent World Model (LaWM). We obtain LaWM by training a latent action model in the latent space of a pretrained vision foundation model and repurposing its forward decoder to predict future observation features for scene evolution. LaWAM then conditions action generation on these predicted latent visual subgoals to enable dynamics-aware robot control. LaWAM achieves state-of-the-art or competitive success rates (SRs) across LIBERO (98.6% SR), RoboTwin (91.22% SR), and real-world manipulation tasks while retaining low-latency inference. LaWAM runs in 187 ms per action-chunk prediction and achieves up to 24x lower wall-clock latency than pixel-space WAMs.", "github": "https://github.com/RLinf/LaWAM" }, { "date": "2026-06-16", "arxiv_id": "2606.16202", "title": "EgoPhys: Learning Generalizable Physics Models of Deformable Objects from Egocentric Video", "authors": [ "Hyunjin Kim", "Ri-Zhao Qiu", "Guangqi Jiang", "Xiaolong Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://hjhyunjinkim.github.io/EgoPhys/", "abstract": "Humans naturally understand object physics through everyday interactions, but faithfully predicting complex deformable dynamics, such as elastic materials and fabrics, remains a major challenge for computer vision and robotics. We present EgoPhys, a framework that constructs deformable physical digital twins from egocentric RGB-only video using generalizable priors. EgoPhys overcomes the limitations of existing methods to enable controllable deformable digital twin generation from egocentric videos by distilling per-object inverse-physics solutions into a compact codebook, enabling prediction of dense spring stiffness fields for unseen objects without per-spring test-time optimization. Trained with generalizable priors from diverse egocentric interactions, EgoPhys outperforms baselines in reconstruction, future prediction, and zero-shot generalization. To support training and evaluation, we curate an egocentric interaction dataset covering diverse deformable objects, scenes, and manipulation styles. We deploy EgoPhys on a real xArm6 robot, demonstrating that a digital twin initialized from a single egocentric human play video can serve as an internal world representation to aid in deformable-object planning, highlighting egocentric RGB observations as a scalable path toward real-to-sim pipelines.", "github": "" }, { "date": "2026-06-16", "arxiv_id": "2606.17054", "title": "Human Universal Grasping", "authors": [ "Kevin Yuanbo Wu", "Tianxing Zhou", "Isaac Tu", "Billy Yan", "Irmak Guzey", "David Fouhey", "Dandan Shan", "Lerrel Pinto" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/KevinyWu/hug", "github_api_added_by": "user", "project_page": "https://grasping.io/", "abstract": "Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality. We argue that the most natural source of robot grasping data is from humans, who pick up thousands of objects every day. We present HUG, a flow-matching model that generates diverse human grasps for any user-specified object in a single RGB-D image captured from a stereo camera. Using smart glasses, we first collect 1M-HUGs, an egocentric dataset of human grasps spanning 1M frames (27.8 hrs) and 6,707 object instances across 41 buildings. Next, to model the distribution of natural human grasps, our novel flow-matching model fuses RGB and depth observations to output a grasp parameterized by wrist translation, wrist rotation, and MANO hand pose. Predicted grasps can be retargeted to various robot hands, enabling zero-shot grasping in everyday scenes. To standardize evaluation, we build a new simulated benchmark, HUG-Bench, of 90 unseen objects from five geometric categories and various sizes, with metric-scale 3D meshes. We evaluate HUG in the real world on the 30-object test set of HUG-Bench across multiple stereo cameras, robot embodiments, and household environments. HUG outperforms the state-of-the-art grasping baselines by +23% and +34% on our challenging object set. Code, data, benchmark, checkpoints, and an interactive demo are released on our website: https://grasping.io/", "github": "https://github.com/KevinyWu/hug" }, { "date": "2026-06-16", "arxiv_id": "2606.17024", "title": "ExpRL: Exploratory RL for LLM Mid-Training", "authors": [ "Violet Xiang", "Amrith Setlur", "Chase Blagden", "Nick Haber", "Aviral Kumar" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/violetxi/ExpRL", "github_api_added_by": "user", "project_page": "", "abstract": "Sparse reward reinforcement learning (RL) has become a standard tool for improving LLM reasoning, but its success depends critically on the coverage present in the base model. In practice, models are often primed for RL through mid-training on curated reasoning traces that teach useful primitive skills such as decomposition, verification, or self-correction. Although effective, this strategy requires manually specifying what the model should learn, and it remains unclear whether such primitive coverage is enough for much harder problems, which require combining these skills into broader solution strategies. We study a more automated approach: RL-based mid-training using large corpora of human-written question-answer data. Rather than treating reference solutions as targets to imitate, our method, ExpRL, uses them as reward scaffolds: references are hidden from the policy and used only to construct problem-specific grading rubrics for judging on-policy reasoning traces. The policy samples from the original problem prompt, while an LLM judge compares the sampled reasoning trace against the reference solution and assigns outcome-level or process-level dense rewards. This lets ExpRL reinforce partial progress, useful intermediate reductions, and productive reasoning behaviors that sparse final-answer rewards often fail to upweight. On challenging math reasoning tasks, ExpRL yields stronger RL priming than SFT, sparse-reward GRPO, and self-distillation, and provides a better initialization for subsequent sparse-reward RL. Additional mixed-domain experiments further suggest that ExpRL can extend beyond the original math-only setting.", "github": "https://github.com/violetxi/ExpRL" }, { "date": "2026-06-16", "arxiv_id": "2606.15956", "title": "You Don't Need Strong Assumptions: Visual Representation Learning via Temporal Differences", "authors": [ "Ninad Daithankar", "Alexi Gladstone", "Yann LeCun", "Heng Ji" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ninaddaithankar/tdv", "github_api_added_by": "user", "project_page": "https://temporal-difference-vision.github.io/", "abstract": "Progress in AI has largely been driven by methods that assume less. As compute and data increase, approaches with weaker inductive biases generally outperform those with stronger assumptions. This is particularly characteristic of the field of Visual Representation Learning, where approaches have gone from being dominated by Supervised Learning, to Weakly Supervised Learning, to the now widespread success of Self-Supervised Learning without human labels. Yet, even modern Self-Supervised Learning approaches still depend on strong inductive biases such as augmentations, masking, or cropping. If this trend holds, even these remaining biases should become bottlenecks at scale -- and our experiments confirm this: the optimal strength of inductive biases decreases as data grows. This motivates the search for approaches that rely on fewer assumptions. To this end, we introduce Temporal Difference in Vision (TDV), a new paradigm for self-supervised learning from video that avoids existing inductive biases, relying instead on a causal assumption that the past causes the future. TDV functions by jointly training an image encoder and a motion encoder so that the current frame's representation plus the encoded motion equals the next frame's representation. Despite not leveraging any strong inductive biases, TDV matches state-of-the-art recipes on dense spatial tasks, laying the foundation for representation learning without strong assumptions.", "github": "https://github.com/ninaddaithankar/tdv" }, { "date": "2026-06-16", "arxiv_id": "2606.15534", "title": "Track2View: 4D-Consistent Camera-Controlled Video Generation via Paired 3D Point Tracks", "authors": [ "Feng Qiao", "Zhaochong An", "Zhexiao Xiong", "Serge Belongie", "Nathan Jacobs" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/mvrl/Track2View", "github_api_added_by": "user", "project_page": "https://qjizhi.github.io/track2view/", "abstract": "Re-rendering an existing video from a novel camera viewpoint requires the output to follow the prescribed camera trajectory while preserving the appearance and dynamics of the original scene across every frame. Existing methods rely on per-frame pose embeddings, noisy point-cloud renderings, or implicit learned correspondences, none of which provides an explicit, temporally continuous link between source and target pixels. We propose Track2View, which conditions a video diffusion transformer on paired 3D point tracks: sparse trajectories of scene points projected into both the source and target camera views. These tracks provide explicit spatiotemporal correspondences that are temporally continuous by construction, encoding what content should appear where and when. At the core of Track2View is a dual-view track conditioner that transfers visual context from source to target view through parameter-free geometric operations and learned temporal aggregation, ensuring generalization to arbitrary camera trajectories without memorizing specific motions. We further introduce a data curation pipeline that extracts one-to-one track correspondences by running a 3D point tracker on temporally concatenated multi-camera view pairs. On a 400-video benchmark spanning static and dynamic scenes, Track2View achieves state-of-the-art results across visual quality, view synchronization, and camera accuracy, reducing rotation error by 30-65% and translation error by 61-72% relative to leading baselines. Project page is available at this https URL: https://qjizhi.github.io/track2view", "github": "https://github.com/mvrl/Track2View" }, { "date": "2026-06-16", "arxiv_id": "2505.14608", "title": "Attacks on Machine-Text Detectors Retain Stylistic Fingerprints", "authors": [ "Rafael Rivera Soto", "Barry Chen", "Nicholas Andrews" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/rrivera1849/style-aware-paraphrasing", "github_api_added_by": "user", "project_page": "", "abstract": "Despite considerable progress in the development of machine-text detectors, the ease with which machine-text can be manipulated to evade detection has led to suggestions that the problem is inherently intractable. In this work, we investigate the limits of such evasion strategies. We demonstrate that while current attacks, ranging from prompt engineering to detector-guided optimization can effectively degrade performance of standard detectors, they fail to erase the underlying stylistic \"fingerprints\" of machine text. We show that few-shot detectors that utilize the stylistic feature space are robust to these evasion attempts, reliably detecting samples even from models explicitly tuned to prevent detection. This raises the question: does style represent a universal defense against machine-detection attacks? We demonstrate that the answer is \"no'' by introducing a novel paraphrasing approach that simultaneously optimizes for undetectability and adherence to specific human styles. We show that unlike prior methods, this attack effectively evades all considered detectors, including those that utilize writing style. However, we find that this evasion is not absolute: as the number of documents available for analysis grows, the human and machine distributions become distinguishable again. Overall, our findings suggest that reliable machine-text detection requires moving beyond single-document analysis to multi-document analysis.", "github": "https://github.com/rrivera1849/style-aware-paraphrasing" }, { "date": "2026-06-17", "arxiv_id": "2606.13811", "title": "Aligning Quantum Operators with Large Language Models", "authors": [ "Rogerio Feris", "Yunchao Liu", "Pengyuan Li", "Hang Hua", "David Kremer" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Can Large Language Models (LLMs) understand and reason about quantum operators? Despite their remarkable capabilities in mathematics and symbolic reasoning, LLMs remain inherently blind to quantum representations such as unitary matrices. In this work, we take a step toward bridging this gap by introducing an approach that maps unitary operators into the latent space of an LLM, enabling unified modeling over quantum and linguistic inputs. We instantiate this idea on Clifford+T circuit synthesis over a Pauli rotation gate set, where our model achieves results competitive with state-of-the-art methods and scales consistently with training data, with no signs of saturation. Our approach further enables language-conditioned synthesis, allowing gate constraints unseen during training to be specified directly in natural language. This work suggests a path toward quantum--aware foundation models that can natively interpret and reason about quantum operations, which could have broader implications reaching across quantum compilation and algorithm discovery.", "github": "" }, { "date": "2026-06-17", "arxiv_id": "2606.17628", "title": "OPD-Evolver: Cultivating Holistic Agent Evolver via On-Policy Distillation", "authors": [ "Guibin Zhang", "Xun Xu", "Yanwei Yue", "Zikun Su", "Wangchunshu Zhou", "Xiaobin Hu", "Shuicheng Yan" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/bingreeky/opd-evolver", "github_api_added_by": "user", "project_page": "", "abstract": "Memory has become a standard substrate for self-evolving agents, yet retaining experience is not the same as learning how to evolve through it. Existing memory agents can store trajectories, retrieve reflections, or accumulate skills, but often lack the holistic competence to select useful experience, act on it, write reusable knowledge, and maintain a growing repository. We introduce OPD-Evolver, a slow-fast co-evolution framework that cultivates such an agent evolver through on-policy self-distillation. In the fast loop, OPD-Evolver interacts with a four-level memory hierarchy to read, use, write, and maintain experience for rapid test-time evolution. In the slow loop, outcome-calibrated memory attribution and privileged hindsight distill these four abilities into the deployable policy. Across multi-domain benchmarks, OPD-Evolver surpasses memory systems such as ReasoningBank by up to 11.5%, and training-based methods such as Skill0 by ~5.8%. Further analysis shows that OPD-Evolver internalizes high-value experience and memory management, enabling OPD-Evolver-9B to challenge giant counterparts such as Qwen3.5-397B-A17B and Step-3.5-Flash, pointing beyond memory-augmented agents toward genuinely qualified agent evolvers.", "github": "https://github.com/bingreeky/opd-evolver" }, { "date": "2026-06-17", "arxiv_id": "2606.17200", "title": "ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining", "authors": [ "Hao Li", "Ganlong Zhao", "Yufei Liu", "Haotian Hou", "Guoquan Ye", "Tongyan Fang", "Chunxiao Liu", "Siyuan Huang", "Jianbo Liu", "Xiaogang Wang", "Hongsheng Li" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ACERobotics-VLA/ACE-Ego-0", "github_api_added_by": "user", "project_page": "https://acerobotics-vla.github.io/ACE-Ego/", "abstract": "Vision-Language-Action (VLA) models benefit from large-scale and diverse embodied data, yet scaling robot trajectory collection is costly and labor-intensive. Recent advances show that large-scale egocentric human videos provide complementary real-world supervision in pretraining. However, joint training on human and robot data remains challenging due to divergences in action spaces, embodiment structures, temporal dynamics, and supervision quality. We introduce ACE-EGO-0, a unified VLA pretraining framework jointly leveraging heterogeneous data sources. To extract large-scale pretraining supervision from egocentric human videos, we build a scalable egocentric video-to-action pipeline that converts raw human videos into robot-format pseudo-action trajectories. To make these labels comparable with robot demonstrations, ACE-EGO-0 uses a unified action representation based on camera-space actions, morphology conditioning, and time-aligned action chunking. To robustly leverage noisy pseudo-action supervision from egocentric human videos, we formulate a reliability-aware training objective with a human auxiliary loss that concentrates supervision on reliable signals. We instantiate ACE-EGO-0 on 4.53K hours of robot and simulation data, together with 1.48K hours of pseudo-action-labeled egocentric human data. Experiments show that incorporating large-scale human supervision under reliability-aware weighting consistently improves both unified joint pretraining and supervised fine-tuning. ACE-EGO-0 achieves state-of-the-art performance on RoboCasa GR1 TableTop and RoboTwin 2.0, while demonstrating strong transfer to real-world bimanual manipulation.", "github": "https://github.com/ACERobotics-VLA/ACE-Ego-0" }, { "date": "2026-06-17", "arxiv_id": "2606.09030", "title": "TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs", "authors": [ "Hyeongwon Jang", "Gyouk Chu", "Changhun Kim", "Joonhyung Park", "Hangyul Yoon", "Eunho Yang" ], "github_manual": "", "github_regex": "https://github.com/HyeongWon-Jang/TRIAGE", "github_paper_md": "", "github_api": "https://github.com/HyeongWon-Jang/TRIAGE", "github_api_added_by": "user", "project_page": "", "abstract": "Clinical early warning systems built on electronic health records, in which clinical observations are recorded as irregularly sampled medical time series (ISMTS), must deliver both calibrated risk scores for patient triage and interpretable rationales that clinicians can verify. Large Language Models (LLMs) have been explored for this task, yet they collapse graded clinical risk into overconfident binary predictions. This risk polarization undermines both calibration and cross-patient comparability. To address this, we propose TRIAGE, a framework that trains an LLM to generate dialectical reasoning over competing clinical outcomes by eliciting outcome-specific rationales. This dialectical formulation mitigates risk polarization, enabling a single LLM to yield continuous risk scores grounded in explicit clinical reasoning. Evaluated on three ISMTS benchmarks, TRIAGE achieves an average AUPRC improvement of 3.3% and reduces calibration error by 81% compared to the competitive baselines. An LLM-as-a-judge assessment further shows that our rationales surpass post-hoc explanations from the baseline by 20% in clinical reasoning quality. The source code is available at https://github.com/HyeongWon-Jang/TRIAGE .", "github": "https://github.com/HyeongWon-Jang/TRIAGE" }, { "date": "2026-06-17", "arxiv_id": "2606.17861", "title": "GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?", "authors": [ "Tongxu Luo", "Rongsheng Wang", "Jiaxi Bi", "Chenming Xu", "Zhengyang Tang", "Jianlong Chen", "Juhao Liang", "Ke Ji", "Shuqi Guo", "Yuhao Du", "Fan Bu", "Wenyu Du", "Xiaotong Zhang", "Kyle Li", "Shaobo Wang", "Linfeng Zhang", "Yuxuan Liu", "Xin Lai", "Chenxin Li", "Yiduo Guo", "Zhexin Zhang", "Xinyuan Wang", "Tianyi Bai", "Ziniu Li", "Benyou Wang" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/tongxuluo/gamecraft-bench", "github_api_added_by": "user", "project_page": "https://tongxuluo.github.io/gamecraft-bench-website/", "abstract": "Game generation is an emerging application of coding agents, requiring models to transform natural-language specifications into playable interactive systems. Unlike traditional coding tasks, game generation takes place within a game engine, where scripts, scenes, assets, rendering, and runtime interactions must jointly produce coherent gameplay. We formalize end-to-end game generation as the problem of producing a complete game artifact that realizes a specification through observable player-game interaction in a target environment. We argue that evaluating this setting requires three desiderata: Engine Grounding, Artifact Completeness, and Interactive Verification. We propose an interaction-grounded evaluation framework that assesses executable gameplay through replayed demonstrations and rubric-guided multimodal judging. We instantiate this framework as GameCraft-Bench, a benchmark comprising 140 Godot tasks across 15 game families. Evaluations of frontier coding agents show that end-to-end game generation remains highly challenging: the strongest agent achieves only 41.46%, and most agents score below 40%. Further analysis reveals that while agents often implement recognizable mechanics, they struggle to deliver complete games with sufficient content, functional visual feedback, and coherent presentation. See https://tongxuluo.github.io/gamecraft-bench-website for demos, code, and data.", "github": "https://github.com/tongxuluo/gamecraft-bench" }, { "date": "2026-06-17", "arxiv_id": "2606.16428", "title": "Lect\u016braAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching", "authors": [ "Jaward Sesay", "Yue Yu", "Siwei Dong", "Yemin Shi", "Guangyao Chen", "B\u00f6rje F. Karlsson" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "", "abstract": "Effective personalized AI-assisted learning demands systems that can not only generate accurate learner-specific educational materials, but also dynamically adapt their instruction to diverse learners. However, existing educational agents have primarily focused on lecture content automation and simulations, which often fall short of modelling multimodal and embodied instructional methods tailored for the individual learner. To this end, we propose Lect\u016braAgents - a multi-agent framework that enables personalized learning through end-to-end adaptive embodied teaching. At its core, Lect\u016braAgents mirrors a professor-student relationship, in which a ProfessorAgent leads a collaborative team of specialized subordinate agents through research, planning, review, and embodied delivery of lecture contents that adapt to a learner's needs. The framework offers three main contributions: (1) a hierarchical multi-agent architecture for end-to-end personalized learning; (2) an adaptive embodied teaching mechanism, wherein the ProfessorAgent executes visible and pedagogically motivated teaching actions (e.g., handwrite, highlight, underline, etc.) over contents in a teaching environment; and (3) a Teaching Action-Speech Alignment (TASA) algorithm that employs salience-based heuristics and temporal semantic segmentation to generate coherent teaching action sequences aligned with learner profiles. We evaluate Lect\u016braAgents on diverse courses at high school, undergraduate, and graduate levels using sample-specific rubric-based analysis; with generated lecture materials and teaching actions assessed and validated by expert educators. Experimental results show consistent gains in lecture content quality, embodied teaching quality, assessment, and personalization over existing approaches, positioning Lect\u016braAgents as a pedagogically well-grounded framework for personalized learning at scale.", "github": "" }, { "date": "2026-06-17", "arxiv_id": "2606.16154", "title": "A Gradient Perspective on RLVR Stability and Winner Advantage Policy Optimization", "authors": [ "Prasanth YSS", "Zhichen Ren", "Rasa Hosseinzadeh", "Ilan Gofman", "Yuqi Chen", "Zhaoyan Liu", "Guangwei Yu", "Jesse C. Cresswell", "Satya Krishna Gorti" ], "github_manual": "", "github_regex": "https://github.com/layer6ai-labs/wapo", "github_paper_md": "", "github_api": "https://github.com/layer6ai-labs/wapo", "github_api_added_by": "user", "project_page": "", "abstract": "Reinforcement learning with verifiable rewards (RLVR) improves language-model reasoning, but GRPO-style optimization remains prone to collapse. We analyse this instability through token-level gradient dynamics, deriving a taxonomy that predicts how updates affect next-token probabilities and entropy. The taxonomy shows that stability depends jointly on the advantage sign and token distribution under the current policy. Motivated by this finding, we propose Winner Advantage Policy Optimization (WAPO), a simple online clipped policy-gradient objective that updates only on positive-advantage completions. Across mathematical reasoning and multi-hop QA benchmarks, WAPO improves training stability and matches or outperforms baselines across multiple model families. Full code can be found at https://github.com/layer6ai-labs/wapo.", "github": "https://github.com/layer6ai-labs/wapo" }, { "date": "2026-06-17", "arxiv_id": "2606.18216", "title": "Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients", "authors": [ "Byung-Kwan Lee", "Ximing Lu", "Shizhe Diao", "Minki Kang", "Saurav Muralidharan", "Karan Sapra", "Andrew Tao", "Pavlo Molchanov", "Yejin Choi", "Yu-Chiang Frank Wang", "Ryo Hachiuma" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://byungkwanlee.github.io/ZPPO-page/", "abstract": "Knowledge distillation transfers a teacher's competence to a small student but is brittle in the small-student regime: forcing the student to imitate logits from a much larger teacher concentrates it on the teacher's sharpest modes, hurting generalization on benchmark families beyond the training corpus. Reinforcement learning (RL) avoids logit imitation by training on the student's own rollouts. However, on questions where every rollout fails-yielding zero advantage and being silently discarded-injecting a stronger teacher's response into the policy gradient breaks the on-policy assumption and induces drift. We introduce Zone of Proximal Policy Optimization (ZPPO), inspired by Vygotsky's zone of proximal development, which keeps the teacher inside the prompt rather than the policy gradient. On hard questions, ZPPO constructs two reformulated prompts: a Binary Candidate-included Question (BCQ) pairs one correct teacher response with one incorrect student response as anonymized candidates the student must discriminate, and a Negative Candidate-included Question (NCQ) aggregates the student's wrong rollouts into a single prompt to surface their shared failure modes. A prompt replay buffer recirculates each hard question until it either graduates-the student's mean rollout accuracy on it reaches half- or is FIFO-evicted under finite capacity, amplifying BCQ and NCQ inside the student's current zone of proximal development. On the Qwen3.5 family at four student scales (0.8B-9B) with a 27B teacher, post-trained as vision-language models and evaluated on a 31-benchmark suite (16 VLM, 10 LLM, 5 Video), ZPPO outperforms off/on-policy distillation and GRPO, with the largest gains at the smallest scale.", "github": "" }, { "date": "2026-06-17", "arxiv_id": "2606.17730", "title": "ActWorld: From Explorable to Interactive World Model via Action-Aware Memory", "authors": [ "Zhexiao Xiong", "Yizhi Song", "Hao Kang", "Qing Yan", "Liming Jiang", "Jenson Yang", "Zhoujie Fu", "Stathi Fotiadis", "Angtian Wang", "Zichuan Liu", "Bo Liu", "Yiding Yang", "Xin Lu", "Nathan Jacobs" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "", "github_api_added_by": "", "project_page": "https://interactwm.github.io/ActWorld/", "abstract": "Interactive world models aim to simulate environment dynamics under real-time user actions. However, their action vocabulary is largely confined to navigation: most actions correspond to motion (e.g., walk, turn, look around), while interaction with objects in the scene (e.g., pick up plates, open doors, or trigger physical responses) is either absent, restricted to game domains, or relegated to prompt-to-full-video scenarios. The resulting worlds are visually explorable but not truly actionable. In this work, we present ActWorld, an interactive world model that extends prior navigation-centric generators to support mid-rollout object interaction within a chunk-autoregressive framework. We argue that the navigation-interaction gap stems from two bottlenecks. First, a data bottleneck: the lack of human-object interaction data with accurate, dense labels. Second, a memory bottleneck: recency-biased history compression in existing world models discards the event-transition frames that causally determine subsequent object states, leading to an action-forgetting pathology. On the data side, we construct a 100K interaction video dataset, each annotated with per-chunk captions via chain-of-thought reasoning. On the model side, we introduce a hierarchical action-aware memory design that routes history compression by interaction importance, complemented by a persistent memory bank that maintains event-update and object-identity tokens across long rollouts. Experiments show that ActWorld supports both flexible navigation and rich object interaction within a single model, substantially improving interaction fidelity over navigation-only baselines without sacrificing viewpoint control. Project page is available at https://interactwm.github.io/ActWorld.", "github": "" }, { "date": "2026-06-17", "arxiv_id": "2606.18246", "title": "Variable-Width Transformers", "authors": [ "Zhaofeng Wu", "Oliver Sieberling", "Shawn Tan", "Rameswar Panda", "Yury Polyanskiy", "Yoon Kim" ], "github_manual": "", "github_regex": "", "github_paper_md": "", "github_api": "https://github.com/ZhaofengWu/variable-width-transformers", "github_api_added_by": "user", "project_page": "", "abstract": "Scaling model size, specifically depth and width, has driven significant progress in transformer-based language models. However, most architectures maintain a constant width across all layers, allocating a fixed parameter and computation budget evenly despite different layers potentially playing distinct computational roles. In this work, we empirically investigate nonuniform capacity allocation across network depth by proposing a times-shaped >